diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/device_host_transport/nvshmem_constants.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/device_host_transport/nvshmem_constants.h new file mode 100644 index 0000000000000000000000000000000000000000..67799ff862c44047f6228db1606920e975f417d5 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/device_host_transport/nvshmem_constants.h @@ -0,0 +1,85 @@ +/* + * Copyright (c) 2018-2025, NVIDIA CORPORATION. All rights reserved. + * + * NVIDIA CORPORATION and its licensors retain all intellectual property + * and proprietary rights in and to this software, related documentation + * and any modifications thereto. Any use, reproduction, disclosure or + * distribution of this software and related documentation without an express + * license agreement from NVIDIA CORPORATION is strictly prohibited. + * + * See License.txt for license information + */ + +#ifndef _NVSHMEM_CONSTANTS_H_ +#define _NVSHMEM_CONSTANTS_H_ + +#if !defined __CUDACC_RTC__ +#include +#else +#include +#endif +#include "non_abi/nvshmem_version.h" + +#define CHANNEL_BUF_SIZE (1 << CHANNEL_BUF_SIZE_LOG) +#define CHANNEL_BUF_SIZE_LOG 22 +#define CHANNEL_ENTRY_BYTES 8 + +/* This is not the NVSHMEM release version, it is the supported OpenSHMEM spec version. */ +#define NVSHMEM_MAJOR_VERSION 1 +#define NVSHMEM_MINOR_VERSION 3 + +#define NVSHMEM_VENDOR_VERSION \ + ((NVSHMEM_VENDOR_MAJOR_VERSION)*10000 + (NVSHMEM_VENDOR_MINOR_VERSION)*100 + \ + (NVSHMEM_VENDOR_PATCH_VERSION)) + +#define NVSHMEMI_SUBST_AND_STRINGIFY_HELPER(S) #S +#define NVSHMEMI_SUBST_AND_STRINGIFY(S) NVSHMEMI_SUBST_AND_STRINGIFY_HELPER(S) + +#define NVSHMEM_VENDOR_STRING \ + "NVSHMEM v" \ + NVSHMEMI_SUBST_AND_STRINGIFY(NVSHMEM_VENDOR_MAJOR_VERSION) "." \ + NVSHMEMI_SUBST_AND_STRINGIFY(NVSHMEM_VENDOR_MINOR_VERSION) "." \ + NVSHMEMI_SUBST_AND_STRINGIFY(NVSHMEM_VENDOR_PATCH_VERSION) + +#define NVSHMEM_MAX_NAME_LEN 256 + +typedef enum nvshmemi_cmp_type { + NVSHMEM_CMP_EQ = 0, + NVSHMEM_CMP_NE, + NVSHMEM_CMP_GT, + NVSHMEM_CMP_LE, + NVSHMEM_CMP_LT, + NVSHMEM_CMP_GE, + NVSHMEM_CMP_SENTINEL = INT_MAX, +} nvshmemx_cmp_type_t; + +enum nvshmemi_thread_support { + NVSHMEM_THREAD_SINGLE = 0, + NVSHMEM_THREAD_FUNNELED, + NVSHMEM_THREAD_SERIALIZED, + NVSHMEM_THREAD_MULTIPLE, + NVSHMEM_THREAD_TYPE_SENTINEL = INT_MAX, +}; + +enum { + PROXY_GLOBAL_EXIT_NOT_REQUESTED = 0, + PROXY_GLOBAL_EXIT_INIT, + PROXY_GLOBAL_EXIT_REQUESTED, + PROXY_GLOBAL_EXIT_FINISHED, + PROXY_GLOBAL_EXIT_MAX_STATE = INT_MAX +}; + +#define PROXY_DMA_REQ_BYTES 32 +#define PROXY_AMO_REQ_BYTES 40 +#define PROXY_INLINE_REQ_BYTES 24 + +typedef enum { + NVSHMEM_STATUS_NOT_INITIALIZED = 0, + NVSHMEM_STATUS_IS_BOOTSTRAPPED, + NVSHMEM_STATUS_IS_INITIALIZED, + NVSHMEM_STATUS_LIMITED_MPG, + NVSHMEM_STATUS_FULL_MPG, + NVSHMEM_STATUS_INVALID = INT_MAX, +} nvshmemx_init_status_t; + +#endif diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/host/nvshmem_api.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/host/nvshmem_api.h new file mode 100644 index 0000000000000000000000000000000000000000..87ecaeae35061d7d0e36baa017dcf457d5cce32f --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/host/nvshmem_api.h @@ -0,0 +1,489 @@ +/* + * Copyright (c) 2018-2025, NVIDIA CORPORATION. All rights reserved. + * + * NVIDIA CORPORATION and its licensors retain all intellectual property + * and proprietary rights in and to this software, related documentation + * and any modifications thereto. Any use, reproduction, disclosure or + * distribution of this software and related documentation without an express + * license agreement from NVIDIA CORPORATION is strictly prohibited. + * + * See License.txt for license information + */ + +#ifndef _NVSHMEM_API_H_ +#define _NVSHMEM_API_H_ + +#include +#include +#include +#include +#include +#include +#include +#include "device_host/nvshmem_common.cuh" +#include "device_host_transport/nvshmem_constants.h" +#include "host/nvshmem_macros.h" +#include "non_abi/nvshmem_version.h" + +int nvshmemi_init_thread(int requested_thread_support, int *provided_thread_support, + unsigned int bootstrap_flags, nvshmemx_init_attr_t *bootstrap_attr, + nvshmemi_version_t); + +#ifdef __cplusplus +extern "C" { +#endif + +#define NVSHMEMI_UNUSED_ARG(ARG) (void)(ARG) + +// Library initialization +#define NONZERO_EXIT(status, ...) \ + do { \ + if (status != 0) { \ + fprintf(stderr, "%s:%d: non-zero status: %d: %s, exiting... ", __FILE__, __LINE__, \ + status, strerror(errno)); \ + fprintf(stderr, __VA_ARGS__); \ + exit(-1); \ + } \ + } while (0) + +int nvshmemx_init_status(); + +static inline void nvshmem_init() { + int status = 0, requested = NVSHMEM_THREAD_SERIALIZED, provided; + nvshmemi_version_t app_nvshmem_version = { + NVSHMEM_VENDOR_MAJOR_VERSION, NVSHMEM_VENDOR_MINOR_VERSION, NVSHMEM_VENDOR_PATCH_VERSION}; + status = nvshmemi_init_thread(requested, &provided, 0, NULL, app_nvshmem_version); + NONZERO_EXIT(status, "aborting due to error in nvshmemi_init_thread \n"); +} + +static inline int nvshmem_init_thread(int requested, int *provided) { + int status = 0; + nvshmemi_version_t app_nvshmem_version = { + NVSHMEM_VENDOR_MAJOR_VERSION, NVSHMEM_VENDOR_MINOR_VERSION, NVSHMEM_VENDOR_PATCH_VERSION}; + status = nvshmemi_init_thread(requested, provided, 0, NULL, app_nvshmem_version); + NONZERO_EXIT(status, "aborting due to error in nvshmemi_init_thread \n"); + return status; +} + +void nvshmem_query_thread(int *provided); +NVSHMEMI_HOSTDEVICE_PREFIX void nvshmem_global_exit(int status); +void nvshmemi_finalize(); +static inline void nvshmem_finalize() { nvshmemi_finalize(); } + +// PE info query +NVSHMEMI_HOSTDEVICE_PREFIX int nvshmem_my_pe(); +NVSHMEMI_HOSTDEVICE_PREFIX int nvshmem_n_pes(); +NVSHMEMI_HOSTDEVICE_PREFIX void nvshmem_info_get_version(int *major, int *minor); +NVSHMEMI_HOSTDEVICE_PREFIX void nvshmem_info_get_name(char *name); +NVSHMEMI_HOSTDEVICE_PREFIX void nvshmemx_vendor_get_version_info(int *major, int *minor, + int *patch); + +// Heap management +void *nvshmem_malloc(size_t size); +void *nvshmem_calloc(size_t, size_t); +void *nvshmem_align(size_t, size_t); + +void nvshmem_free(void *ptr); +void *nvshmem_realloc(void *ptr, size_t size); +NVSHMEMI_HOSTDEVICE_PREFIX void *nvshmem_ptr(const void *ptr, int pe); +NVSHMEMI_HOSTDEVICE_PREFIX void *nvshmemx_mc_ptr(nvshmem_team_t team, const void *ptr); + +//////////////////// OpenSHMEM 1.3 Atomics //////////////////// + +#define NVSHMEMI_REPT_OPGROUP_FOR_BITWISE_AMO(OPGRPNAME, opname) \ + NVSHMEMI_DECL_TYPE_##OPGRPNAME(uint, unsigned int, opname) \ + NVSHMEMI_DECL_TYPE_##OPGRPNAME(ulong, unsigned long, opname) \ + NVSHMEMI_DECL_TYPE_##OPGRPNAME(ulonglong, unsigned long long, opname) \ + NVSHMEMI_DECL_TYPE_##OPGRPNAME(int32, int32_t, opname) \ + NVSHMEMI_DECL_TYPE_##OPGRPNAME(uint32, uint32_t, opname) \ + NVSHMEMI_DECL_TYPE_##OPGRPNAME(int64, int64_t, opname) \ + NVSHMEMI_DECL_TYPE_##OPGRPNAME(uint64, uint64_t, opname) + +#define NVSHMEMI_REPT_OPGROUP_FOR_STANDARD_AMO(OPGRPNAME, opname) \ + NVSHMEMI_DECL_TYPE_##OPGRPNAME(int, int, opname) \ + NVSHMEMI_DECL_TYPE_##OPGRPNAME(long, long, opname) \ + NVSHMEMI_DECL_TYPE_##OPGRPNAME(longlong, long long, opname) \ + NVSHMEMI_DECL_TYPE_##OPGRPNAME(size, size_t, opname) \ + NVSHMEMI_DECL_TYPE_##OPGRPNAME(ptrdiff, ptrdiff_t, opname) + +#define NVSHMEMI_REPT_OPGROUP_FOR_EXTENDED_AMO(OPGRPNAME, opname) \ + NVSHMEMI_DECL_TYPE_##OPGRPNAME(float, float, opname) \ + NVSHMEMI_DECL_TYPE_##OPGRPNAME(double, double, opname) + +/* inc */ +#define NVSHMEMI_DECL_TYPE_INC(type, TYPE, opname) \ + NVSHMEMI_HOSTDEVICE_PREFIX void nvshmem_##type##_atomic_##opname(TYPE *dest, int pe); + +/* finc */ +#define NVSHMEMI_DECL_TYPE_FINC(type, TYPE, opname) \ + NVSHMEMI_HOSTDEVICE_PREFIX TYPE nvshmem_##type##_atomic_##opname(TYPE *dest, int pe); + +/* fetch */ +#define NVSHMEMI_DECL_TYPE_FETCH(type, TYPE, opname) \ + NVSHMEMI_HOSTDEVICE_PREFIX TYPE nvshmem_##type##_atomic_##opname(const TYPE *dest, int pe); + +/* add, set */ +#define NVSHMEMI_DECL_TYPE_ADD_SET(type, TYPE, opname) \ + NVSHMEMI_HOSTDEVICE_PREFIX void nvshmem_##type##_atomic_##opname(TYPE *dest, TYPE value, \ + int pe); + +/* fadd, swap */ +#define NVSHMEMI_DECL_TYPE_FADD_SWAP(type, TYPE, opname) \ + NVSHMEMI_HOSTDEVICE_PREFIX TYPE nvshmem_##type##_atomic_##opname(TYPE *dest, TYPE value, \ + int pe); + +/* cswap */ +#define NVSHMEMI_DECL_TYPE_CSWAP(type, TYPE, opname) \ + NVSHMEMI_HOSTDEVICE_PREFIX TYPE nvshmem_##type##_atomic_##opname(TYPE *dest, TYPE cond, \ + TYPE value, int pe); + +NVSHMEMI_REPT_OPGROUP_FOR_BITWISE_AMO(INC, inc) +NVSHMEMI_REPT_OPGROUP_FOR_STANDARD_AMO(INC, inc) + +NVSHMEMI_REPT_OPGROUP_FOR_BITWISE_AMO(FINC, fetch_inc) +NVSHMEMI_REPT_OPGROUP_FOR_STANDARD_AMO(FINC, fetch_inc) + +NVSHMEMI_REPT_OPGROUP_FOR_BITWISE_AMO(FETCH, fetch) +NVSHMEMI_REPT_OPGROUP_FOR_STANDARD_AMO(FETCH, fetch) +NVSHMEMI_REPT_OPGROUP_FOR_EXTENDED_AMO(FETCH, fetch) + +NVSHMEMI_REPT_OPGROUP_FOR_BITWISE_AMO(ADD_SET, add) +NVSHMEMI_REPT_OPGROUP_FOR_STANDARD_AMO(ADD_SET, add) + +NVSHMEMI_REPT_OPGROUP_FOR_BITWISE_AMO(ADD_SET, set) +NVSHMEMI_REPT_OPGROUP_FOR_STANDARD_AMO(ADD_SET, set) +NVSHMEMI_REPT_OPGROUP_FOR_EXTENDED_AMO(ADD_SET, set) + +NVSHMEMI_REPT_OPGROUP_FOR_BITWISE_AMO(FADD_SWAP, fetch_add) +NVSHMEMI_REPT_OPGROUP_FOR_STANDARD_AMO(FADD_SWAP, fetch_add) + +NVSHMEMI_REPT_OPGROUP_FOR_BITWISE_AMO(FADD_SWAP, swap) +NVSHMEMI_REPT_OPGROUP_FOR_STANDARD_AMO(FADD_SWAP, swap) +NVSHMEMI_REPT_OPGROUP_FOR_EXTENDED_AMO(FADD_SWAP, swap) + +NVSHMEMI_REPT_OPGROUP_FOR_BITWISE_AMO(CSWAP, compare_swap) +NVSHMEMI_REPT_OPGROUP_FOR_STANDARD_AMO(CSWAP, compare_swap) + +#undef NVSHMEMI_DECL_TYPE_INC +#undef NVSHMEMI_DECL_TYPE_FINC +#undef NVSHMEMI_DECL_TYPE_FETCH +#undef NVSHMEMI_DECL_TYPE_ADD_SET +#undef NVSHMEMI_DECL_TYPE_FADD_SWAP +#undef NVSHMEMI_DECL_TYPE_CSWAP + +//////////////////// OpenSHMEM 1.4 Atomics //////////////////// + +/* and, or, xor */ +#define NVSHMEMI_DECL_TYPE_AND_OR_XOR(type, TYPE, opname) \ + NVSHMEMI_HOSTDEVICE_PREFIX void nvshmem_##type##_atomic_##opname(TYPE *dest, TYPE value, \ + int pe); + +/* fand, for, fxor */ +#define NVSHMEMI_DECL_TYPE_FAND_FOR_FXOR(type, TYPE, opname) \ + NVSHMEMI_HOSTDEVICE_PREFIX TYPE nvshmem_##type##_atomic_fetch_##opname(TYPE *dest, TYPE value, \ + int pe); + +NVSHMEMI_REPT_OPGROUP_FOR_BITWISE_AMO(AND_OR_XOR, and) +NVSHMEMI_REPT_OPGROUP_FOR_BITWISE_AMO(AND_OR_XOR, or) +NVSHMEMI_REPT_OPGROUP_FOR_BITWISE_AMO(AND_OR_XOR, xor) + +NVSHMEMI_REPT_OPGROUP_FOR_BITWISE_AMO(FAND_FOR_FXOR, and) +NVSHMEMI_REPT_OPGROUP_FOR_BITWISE_AMO(FAND_FOR_FXOR, or) +NVSHMEMI_REPT_OPGROUP_FOR_BITWISE_AMO(FAND_FOR_FXOR, xor) + +#undef NVSHMEMI_DECL_TYPE_AND_OR_XOR +#undef NVSHMEMI_DECL_TYPE_FAND_FOR_FXOR + +//////////////////// Put //////////////////// + +#define NVSHMEMI_DECL_TYPE_P(NAME, TYPE) \ + NVSHMEMI_HOSTDEVICE_PREFIX void nvshmem_##NAME##_p(TYPE *dest, const TYPE value, int pe); + +NVSHMEMI_REPT_FOR_STANDARD_RMA_TYPES(NVSHMEMI_DECL_TYPE_P) +#undef NVSHMEMI_DECL_TYPE_P + +#define NVSHMEMI_DECL_TYPE_PUT(NAME, TYPE) \ + NVSHMEMI_HOSTDEVICE_PREFIX void nvshmem_##NAME##_put(TYPE *dest, const TYPE *source, \ + size_t nelems, int pe); + +NVSHMEMI_REPT_FOR_STANDARD_RMA_TYPES(NVSHMEMI_DECL_TYPE_PUT) +#undef NVSHMEMI_DECL_TYPE_PUT + +#define NVSHMEMI_DECL_TYPE_PUT(NAME, TYPE) \ + NVSHMEMI_HOSTDEVICE_PREFIX void nvshmem_##NAME##_put(TYPE *dest, const TYPE *source, \ + size_t nelems, int pe); + +NVSHMEMI_REPT_FOR_STANDARD_RMA_TYPES(NVSHMEMI_DECL_TYPE_PUT) +#undef NVSHMEMI_DECL_TYPE_PUT + +#define NVSHMEMI_DECL_SIZE_PUT(NAME) \ + NVSHMEMI_HOSTDEVICE_PREFIX void nvshmem_put##NAME(void *dest, const void *source, \ + size_t nelems, int pe); + +NVSHMEMI_REPT_FOR_SIZES(NVSHMEMI_DECL_SIZE_PUT) +#undef NVSHMEMI_DECL_SIZE_PUT + +NVSHMEMI_HOSTDEVICE_PREFIX void nvshmem_putmem(void *dest, const void *source, size_t bytes, + int pe); + +#define NVSHMEMI_DECL_TYPE_IPUT(NAME, TYPE) \ + NVSHMEMI_HOSTDEVICE_PREFIX void nvshmem_##NAME##_iput( \ + TYPE *dest, const TYPE *source, ptrdiff_t dst, ptrdiff_t sst, size_t nelems, int pe); + +NVSHMEMI_REPT_FOR_STANDARD_RMA_TYPES(NVSHMEMI_DECL_TYPE_IPUT) +#undef NVSHMEMI_DECL_TYPE_IPUT + +#define NVSHMEMI_DECL_SIZE_IPUT(NAME) \ + NVSHMEMI_HOSTDEVICE_PREFIX void nvshmem_iput##NAME( \ + void *dest, const void *source, ptrdiff_t dst, ptrdiff_t sst, size_t nelems, int pe); + +NVSHMEMI_REPT_FOR_SIZES(NVSHMEMI_DECL_SIZE_IPUT) +#undef NVSHMEMI_DECL_SIZE_IPUT + +#define NVSHMEMI_DECL_TYPE_PUT_NBI(NAME, TYPE) \ + NVSHMEMI_HOSTDEVICE_PREFIX void nvshmem_##NAME##_put_nbi(TYPE *dest, const TYPE *source, \ + size_t nelems, int pe); + +NVSHMEMI_REPT_FOR_STANDARD_RMA_TYPES(NVSHMEMI_DECL_TYPE_PUT_NBI) +#undef NVSHMEM_DECL_TYPE_PUT_NBI + +#define NVSHMEMI_DECL_SIZE_PUT_NBI(NAME) \ + NVSHMEMI_HOSTDEVICE_PREFIX void nvshmem_put##NAME##_nbi(void *dest, const void *source, \ + size_t nelems, int pe); + +NVSHMEMI_REPT_FOR_SIZES(NVSHMEMI_DECL_SIZE_PUT_NBI) +#undef NVSHMEMI_DECL_SIZE_PUT_NBI + +NVSHMEMI_HOSTDEVICE_PREFIX void nvshmem_putmem_nbi(void *dest, const void *source, size_t bytes, + int pe); + +//////////////////// Get //////////////////// + +#define NVSHMEMI_DECL_TYPE_G(NAME, TYPE) \ + NVSHMEMI_HOSTDEVICE_PREFIX TYPE nvshmem_##NAME##_g(const TYPE *src, int pe); + +NVSHMEMI_REPT_FOR_STANDARD_RMA_TYPES(NVSHMEMI_DECL_TYPE_G) +#undef NVSHMEMI_DECL_TYPE_G + +#define NVSHMEMI_DECL_TYPE_GET(NAME, TYPE) \ + NVSHMEMI_HOSTDEVICE_PREFIX void nvshmem_##NAME##_get(TYPE *dest, const TYPE *source, \ + size_t nelems, int pe); + +NVSHMEMI_REPT_FOR_STANDARD_RMA_TYPES(NVSHMEMI_DECL_TYPE_GET) +#undef NVSHMEMI_DECL_TYPE_GET + +#define NVSHMEMI_DECL_SIZE_GET(NAME) \ + NVSHMEMI_HOSTDEVICE_PREFIX void nvshmem_get##NAME(void *dest, const void *source, \ + size_t nelems, int pe); + +NVSHMEMI_REPT_FOR_SIZES(NVSHMEMI_DECL_SIZE_GET) +#undef NVSHMEMI_DECL_SIZE_GET + +NVSHMEMI_HOSTDEVICE_PREFIX void nvshmem_getmem(void *dest, const void *source, size_t bytes, + int pe); + +#define NVSHMEMI_DECL_TYPE_IGET(NAME, TYPE) \ + NVSHMEMI_HOSTDEVICE_PREFIX void nvshmem_##NAME##_iget( \ + TYPE *dest, const TYPE *source, ptrdiff_t dst, ptrdiff_t sst, size_t nelems, int pe); + +NVSHMEMI_REPT_FOR_STANDARD_RMA_TYPES(NVSHMEMI_DECL_TYPE_IGET) +#undef NVSHMEMI_DECL_TYPE_IGET + +#define NVSHMEMI_DECL_SIZE_IGET(NAME) \ + NVSHMEMI_HOSTDEVICE_PREFIX void nvshmem_iget##NAME( \ + void *dest, const void *source, ptrdiff_t dst, ptrdiff_t sst, size_t nelems, int pe); + +NVSHMEMI_REPT_FOR_SIZES(NVSHMEMI_DECL_SIZE_IGET) +#undef NVSHMEMI_DECL_SIZE_IGET + +#define NVSHMEMI_DECL_TYPE_GET_NBI(NAME, TYPE) \ + NVSHMEMI_HOSTDEVICE_PREFIX void nvshmem_##NAME##_get_nbi(TYPE *dest, const TYPE *source, \ + size_t nelems, int pe); + +NVSHMEMI_REPT_FOR_STANDARD_RMA_TYPES(NVSHMEMI_DECL_TYPE_GET_NBI) +#undef NVSHMEMI_DECL_TYPE_GET_NBI + +#define NVSHMEMI_DECL_SIZE_GET_NBI(NAME) \ + NVSHMEMI_HOSTDEVICE_PREFIX void nvshmem_get##NAME##_nbi(void *dest, const void *source, \ + size_t nelems, int pe); + +NVSHMEMI_REPT_FOR_SIZES(NVSHMEMI_DECL_SIZE_GET_NBI) +#undef NVSHMEMI_DECL_SIZE_GET_NBI + +NVSHMEMI_HOSTDEVICE_PREFIX void nvshmem_getmem_nbi(void *dest, const void *source, size_t bytes, + int pe); + +#ifdef __CUDACC__ +/* Signal API */ +#define NVSHMEMI_DECL_PUT_SIGNAL(TYPENAME, TYPE) \ + __device__ void nvshmem_##TYPENAME##_put_signal(TYPE *dest, const TYPE *source, size_t nelems, \ + uint64_t *sig_addr, uint64_t signal, \ + int sig_op, int pe); +NVSHMEMI_REPT_FOR_STANDARD_RMA_TYPES(NVSHMEMI_DECL_PUT_SIGNAL) +#undef NVSHMEMI_DECL_PUT_SIGNAL + +#define NVSHMEMI_DECL_PUT_SIGNAL_NBI(TYPENAME, TYPE) \ + __device__ void nvshmem_##TYPENAME##_put_signal_nbi(TYPE *dest, const TYPE *source, \ + size_t nelems, uint64_t *sig_addr, \ + uint64_t signal, int sig_op, int pe); +NVSHMEMI_REPT_FOR_STANDARD_RMA_TYPES(NVSHMEMI_DECL_PUT_SIGNAL_NBI) +#undef NVSHMEMI_DECL_PUT_SIGNAL_NBI +#endif + +#ifdef __CUDACC__ +#define NVSHMEMI_DECL_SIZE_PUT_SIGNAL(BITS) \ + __device__ void nvshmem_put##BITS##_signal(void *dest, const void *source, size_t nelems, \ + uint64_t *sig_addr, uint64_t signal, int sig_op, \ + int pe); +NVSHMEMI_REPT_FOR_SIZES(NVSHMEMI_DECL_SIZE_PUT_SIGNAL) +#undef NVSHMEMI_DECL_SIZE_PUT_SIGNAL + +#define NVSHMEMI_DECL_SIZE_PUT_SIGNAL_NBI(BITS) \ + __device__ void nvshmem_put##BITS##_signal_nbi(void *dest, const void *source, size_t nelems, \ + uint64_t *sig_addr, uint64_t signal, \ + int sig_op, int pe); +NVSHMEMI_REPT_FOR_SIZES(NVSHMEMI_DECL_SIZE_PUT_SIGNAL_NBI) +#undef NVSHMEMI_DECL_SIZE_PUT_SIGNAL_NBI + +__device__ void nvshmem_putmem_signal(void *dest, const void *source, size_t bytes, + uint64_t *sig_addr, uint64_t signal, int sig_op, int pe); + +__device__ void nvshmem_putmem_signal_nbi(void *dest, const void *source, size_t bytes, + uint64_t *sig_addr, uint64_t signal, int sig_op, int pe); +#endif + +NVSHMEMI_HOSTDEVICE_PREFIX uint64_t nvshmem_signal_fetch(uint64_t *sig_addr); + +//////////////////// Point-to-Point Synchronization //////////////////// + +NVSHMEMI_HOSTDEVICE_PREFIX void nvshmem_quiet(); +NVSHMEMI_HOSTDEVICE_PREFIX void nvshmem_fence(); +#ifdef __CUDACC__ +__device__ uint64_t nvshmem_signal_wait_until(uint64_t *sig_addr, int cmp, uint64_t cmp_val); + +#define NVSHMEMI_DECL_WAIT_UNTIL(NAME, TYPE) \ + __device__ void nvshmem_##NAME##_wait_until(TYPE *ivar, int cmp, TYPE cmp_value); + +NVSHMEMI_REPT_FOR_WAIT_TYPES(NVSHMEMI_DECL_WAIT_UNTIL) +#undef NVSHMEMI_DECL_WAIT_UNTIL + +#define NVSHMEMI_DECL_WAIT_UNTIL_ALL(NAME, TYPE) \ + __device__ void nvshmem_##NAME##_wait_until_all(TYPE *ivar, size_t nelems, const int *status, \ + int cmp, TYPE cmp_value); + +NVSHMEMI_REPT_FOR_WAIT_TYPES(NVSHMEMI_DECL_WAIT_UNTIL_ALL) +#undef NVSHMEMI_DECL_WAIT_UNTIL_ALL + +#define NVSHMEMI_DECL_WAIT_UNTIL_ANY(NAME, TYPE) \ + __device__ size_t nvshmem_##NAME##_wait_until_any(TYPE *ivar, size_t nelems, \ + const int *status, int cmp, TYPE cmp_value); + +NVSHMEMI_REPT_FOR_WAIT_TYPES(NVSHMEMI_DECL_WAIT_UNTIL_ANY) +#undef NVSHMEMI_DECL_WAIT_UNTIL_ANY + +#define NVSHMEMI_DECL_WAIT_UNTIL_SOME(NAME, TYPE) \ + __device__ size_t nvshmem_##NAME##_wait_until_some( \ + TYPE *ivar, size_t nelems, size_t *indices, const int *status, int cmp, TYPE cmp_value); + +NVSHMEMI_REPT_FOR_WAIT_TYPES(NVSHMEMI_DECL_WAIT_UNTIL_SOME) +#undef NVSHMEMI_DECL_WAIT_UNTIL_SOME + +#define NVSHMEMI_DECL_WAIT_UNTIL_ALL_VECTOR(NAME, TYPE) \ + __device__ void nvshmem_##NAME##_wait_until_all_vector( \ + TYPE *ivars, size_t nelems, const int *status, int cmp, TYPE *cmp_values); + +NVSHMEMI_REPT_FOR_WAIT_TYPES(NVSHMEMI_DECL_WAIT_UNTIL_ALL_VECTOR) +#undef NVSHMEMI_DECL_WAIT_UNTIL_ALL_VECTOR + +#define NVSHMEMI_DECL_WAIT_UNTIL_ANY_VECTOR(NAME, TYPE) \ + __device__ size_t nvshmem_##NAME##_wait_until_any_vector( \ + TYPE *ivars, size_t nelems, const int *status, int cmp, TYPE *cmp_values); + +NVSHMEMI_REPT_FOR_WAIT_TYPES(NVSHMEMI_DECL_WAIT_UNTIL_ANY_VECTOR) +#undef NVSHMEMI_DECL_WAIT_UNTIL_ANY_VECTOR + +#define NVSHMEMI_DECL_WAIT_UNTIL_SOME_VECTOR(NAME, TYPE) \ + __device__ size_t nvshmem_##NAME##_wait_until_some_vector(TYPE *ivars, size_t nelems, \ + size_t *indices, const int *status, \ + int cmp, TYPE *cmp_values); + +NVSHMEMI_REPT_FOR_WAIT_TYPES(NVSHMEMI_DECL_WAIT_UNTIL_SOME_VECTOR) +#undef NVSHMEMI_DECL_WAIT_UNTIL_SOME_VECTOR + +#define NVSHMEMI_DECL_TEST(NAME, TYPE) \ + __device__ int nvshmem_##NAME##_test(TYPE *ivar, int cmp, TYPE cmp_value); + +NVSHMEMI_REPT_FOR_WAIT_TYPES(NVSHMEMI_DECL_TEST) +#undef NVSHMEMI_DECL_TEST + +#define NVSHMEMI_DECL_TEST_ALL(Name, Type) \ + __device__ int nvshmem_##Name##_test_all(Type *ivars, size_t nelems, const int *status, \ + int cmp, Type cmp_value); + +NVSHMEMI_REPT_FOR_WAIT_TYPES(NVSHMEMI_DECL_TEST_ALL) +#undef NVSHMEMI_DECL_TEST_ALL + +#define NVSHMEMI_DECL_TEST_ANY(Name, Type) \ + __device__ size_t nvshmem_##Name##_test_any(Type *ivars, size_t nelems, const int *status, \ + int cmp, Type cmp_value); + +NVSHMEMI_REPT_FOR_WAIT_TYPES(NVSHMEMI_DECL_TEST_ANY) +#undef NVSHMEMI_DECL_TEST_ANY + +#define NVSHMEMI_DECL_TEST_SOME(Name, Type) \ + __device__ size_t nvshmem_##Name##_test_some(Type *ivars, size_t nelems, size_t *indices, \ + const int *status, int cmp, Type cmp_value); + +NVSHMEMI_REPT_FOR_WAIT_TYPES(NVSHMEMI_DECL_TEST_SOME) +#undef NVSHMEMI_DECL_TEST_SOME + +#define NVSHMEMI_DECL_TEST_ALL_VECTOR(NAME, TYPE) \ + __device__ int nvshmem_##NAME##_test_all_vector(TYPE *ivars, size_t nelems, const int *status, \ + int cmp, TYPE *cmp_values); + +NVSHMEMI_REPT_FOR_WAIT_TYPES(NVSHMEMI_DECL_TEST_ALL_VECTOR) +#undef NVSHMEMI_DECL_TEST_ALL_VECTOR + +#define NVSHMEMI_DECL_TEST_ANY_VECTOR(NAME, TYPE) \ + __device__ size_t nvshmem_##NAME##_test_any_vector( \ + TYPE *ivars, size_t nelems, const int *status, int cmp, TYPE *cmp_values); + +NVSHMEMI_REPT_FOR_WAIT_TYPES(NVSHMEMI_DECL_TEST_ANY_VECTOR) +#undef NVSHMEMI_DECL_TEST_ANY_VECTOR + +#define NVSHMEMI_DECL_TEST_SOME_VECTOR(NAME, TYPE) \ + __device__ size_t nvshmem_##NAME##_test_some_vector(TYPE *ivars, size_t nelems, \ + size_t *indices, const int *status, \ + int cmp, TYPE *cmp_values); + +NVSHMEMI_REPT_FOR_WAIT_TYPES(NVSHMEMI_DECL_TEST_SOME_VECTOR) +#undef NVSHMEMI_DECL_TEST_SOME_VECTOR +#endif /* __CUDACC__ */ + +//////////////////// Teams API //////////////////// + +NVSHMEMI_HOSTDEVICE_PREFIX int nvshmem_team_my_pe(nvshmem_team_t team); +NVSHMEMI_HOSTDEVICE_PREFIX int nvshmem_team_n_pes(nvshmem_team_t team); + +void nvshmem_team_get_config(nvshmem_team_t team, nvshmem_team_config_t *config); +NVSHMEMI_HOSTDEVICE_PREFIX int nvshmem_team_translate_pe(nvshmem_team_t src_team, int src_pe, + nvshmem_team_t dest_team); +int nvshmem_team_split_strided(nvshmem_team_t parent_team, int PE_start, int PE_stride, int PE_size, + const nvshmem_team_config_t *config, long config_mask, + nvshmem_team_t *new_team); + +int nvshmemx_team_get_uniqueid(nvshmemx_team_uniqueid_t *uniqueid); +int nvshmemx_team_init(nvshmem_team_t *team, nvshmem_team_config_t *config, long config_mask, + int npes, int pe_idx_in_team); +int nvshmem_team_split_2d(nvshmem_team_t parent_team, int xrange, + const nvshmem_team_config_t *xaxis_config, long xaxis_mask, + nvshmem_team_t *xaxis_team, const nvshmem_team_config_t *yaxis_config, + long yaxis_mask, nvshmem_team_t *yaxis_team); +void nvshmem_team_destroy(nvshmem_team_t team); + +#ifdef __cplusplus +} +#endif + +#include "host/nvshmem_coll_api.h" +#endif diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/host/nvshmem_coll_api.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/host/nvshmem_coll_api.h new file mode 100644 index 0000000000000000000000000000000000000000..11eee1782ad1e7a1e7437b2f88028f82a3f52f53 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/host/nvshmem_coll_api.h @@ -0,0 +1,111 @@ +/* + * Copyright (c) 2018-2025, NVIDIA CORPORATION. All rights reserved. + * + * NVIDIA CORPORATION and its licensors retain all intellectual property + * and proprietary rights in and to this software, related documentation + * and any modifications thereto. Any use, reproduction, disclosure or + * distribution of this software and related documentation without an express + * license agreement from NVIDIA CORPORATION is strictly prohibited. + * + * See License.txt for license information + */ + +#ifndef _NVSHMEM_COLL_API_H_ +#define _NVSHMEM_COLL_API_H_ +#include "device_host/nvshmem_types.h" +#include "device_host/nvshmem_common.cuh" +#include "host/nvshmem_macros.h" + +#ifdef __cplusplus +extern "C" { +#endif +//=============================== +// standard nvshmem collective calls +//=============================== + +// alltoall(s) collectives +#define DECL_NVSHMEM_TYPENAME_ALLTOALL(TYPENAME, TYPE) \ + NVSHMEMI_HOSTDEVICE_PREFIX int nvshmem_##TYPENAME##_alltoall(nvshmem_team_t team, TYPE *dest, \ + const TYPE *src, size_t nelems); + +NVSHMEMI_REPT_FOR_STANDARD_RMA_TYPES(DECL_NVSHMEM_TYPENAME_ALLTOALL) +#undef DECL_NVSHMEM_TYPENAME_ALLTOALL + +#define DECL_NVSHMEM_TYPENAME_ALLTOALLS(TYPENAME, TYPE) \ + NVSHMEMI_HOSTDEVICE_PREFIX int nvshmem_##TYPENAME##_alltoalls(nvshmem_team_t team, TYPE *dest, \ + const TYPE *src, ptrdiff_t dst, \ + ptrdiff_t sst, size_t nelems); + +NVSHMEMI_REPT_FOR_STANDARD_RMA_TYPES(DECL_NVSHMEM_TYPENAME_ALLTOALLS) +#undef DECL_NVSHMEM_TYPENAME_ALLTOALLS + +NVSHMEMI_HOSTDEVICE_PREFIX int nvshmem_alltoallmem(nvshmem_team_t team, void *dest, const void *src, + size_t nelems); + +// barrier collectives +NVSHMEMI_HOSTDEVICE_PREFIX int nvshmem_barrier(nvshmem_team_t team); +NVSHMEMI_HOSTDEVICE_PREFIX void nvshmem_barrier_all(); + +// sync collectives +NVSHMEMI_HOSTDEVICE_PREFIX int nvshmem_team_sync(nvshmem_team_t team); +NVSHMEMI_HOSTDEVICE_PREFIX void nvshmem_sync_all(); +#define nvshmem_sync nvshmem_team_sync + +// broadcast collectives +#define DECL_NVSHMEM_TYPENAME_BROADCAST(TYPENAME, TYPE) \ + NVSHMEMI_HOSTDEVICE_PREFIX int nvshmem_##TYPENAME##_broadcast( \ + nvshmem_team_t team, TYPE *dest, const TYPE *src, size_t nelem, int PE_root); +NVSHMEMI_REPT_FOR_STANDARD_RMA_TYPES(DECL_NVSHMEM_TYPENAME_BROADCAST) +#undef DECL_NVSHMEM_TYPENAME_BROADCAST + +NVSHMEMI_HOSTDEVICE_PREFIX int nvshmem_broadcastmem(nvshmem_team_t team, void *dest, + const void *src, size_t nelems, int PE_root); + +// fcollect collective +#define DECL_NVSHMEM_TYPENAME_FCOLLECT(TYPENAME, TYPE) \ + NVSHMEMI_HOSTDEVICE_PREFIX int nvshmem_##TYPENAME##_fcollect(nvshmem_team_t team, TYPE *dest, \ + const TYPE *src, size_t nelem); +NVSHMEMI_REPT_FOR_STANDARD_RMA_TYPES(DECL_NVSHMEM_TYPENAME_FCOLLECT) +#undef DECL_NVSHMEM_TYPENAME_FCOLLECT + +NVSHMEMI_HOSTDEVICE_PREFIX int nvshmem_fcollectmem(nvshmem_team_t team, void *dest, const void *src, + size_t nelems); +// reduction collectives +#define NVSHMEMI_DECL_TEAM_REDUCE(NAME, TYPE, OP) \ + NVSHMEMI_HOSTDEVICE_PREFIX int nvshmem_##NAME##_##OP##_reduce( \ + nvshmem_team_t team, TYPE *dest, const TYPE *src, size_t nreduce); + +NVSHMEMI_REPT_FOR_BITWISE_REDUCE_TYPES(NVSHMEMI_DECL_TEAM_REDUCE, and) +NVSHMEMI_REPT_FOR_BITWISE_REDUCE_TYPES(NVSHMEMI_DECL_TEAM_REDUCE, or) +NVSHMEMI_REPT_FOR_BITWISE_REDUCE_TYPES(NVSHMEMI_DECL_TEAM_REDUCE, xor) + +NVSHMEMI_REPT_FOR_STANDARD_REDUCE_TYPES(NVSHMEMI_DECL_TEAM_REDUCE, max) +NVSHMEMI_REPT_FOR_STANDARD_REDUCE_TYPES(NVSHMEMI_DECL_TEAM_REDUCE, min) + +NVSHMEMI_REPT_FOR_ARITH_REDUCE_TYPES(NVSHMEMI_DECL_TEAM_REDUCE, sum) +NVSHMEMI_REPT_FOR_ARITH_REDUCE_TYPES(NVSHMEMI_DECL_TEAM_REDUCE, prod) + +#undef NVSHMEMI_DECL_TEAM_REDUCE + +// reducescatter collectives +#define NVSHMEMI_DECL_TEAM_REDUCESCATTER(NAME, TYPE, OP) \ + NVSHMEMI_HOSTDEVICE_PREFIX int nvshmem_##NAME##_##OP##_reducescatter( \ + nvshmem_team_t team, TYPE *dest, const TYPE *src, size_t nreduce); + +NVSHMEMI_REPT_FOR_BITWISE_REDUCE_TYPES(NVSHMEMI_DECL_TEAM_REDUCESCATTER, and) +NVSHMEMI_REPT_FOR_BITWISE_REDUCE_TYPES(NVSHMEMI_DECL_TEAM_REDUCESCATTER, or) +NVSHMEMI_REPT_FOR_BITWISE_REDUCE_TYPES(NVSHMEMI_DECL_TEAM_REDUCESCATTER, xor) + +NVSHMEMI_REPT_FOR_STANDARD_REDUCE_TYPES(NVSHMEMI_DECL_TEAM_REDUCESCATTER, max) +NVSHMEMI_REPT_FOR_STANDARD_REDUCE_TYPES(NVSHMEMI_DECL_TEAM_REDUCESCATTER, min) + +NVSHMEMI_REPT_FOR_ARITH_REDUCE_TYPES(NVSHMEMI_DECL_TEAM_REDUCESCATTER, sum) +NVSHMEMI_REPT_FOR_ARITH_REDUCE_TYPES(NVSHMEMI_DECL_TEAM_REDUCESCATTER, prod) + +#undef NVSHMEMI_DECL_TEAM_REDUCESCATTER + +#ifdef __cplusplus +} +#endif + +#endif /* NVSHMEM_COLL_H */ diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/host/nvshmem_macros.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/host/nvshmem_macros.h new file mode 100644 index 0000000000000000000000000000000000000000..de5b72e8cc3f189633105aaad1558c332725f7ba --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/host/nvshmem_macros.h @@ -0,0 +1,23 @@ + +#ifndef _NVSHMEM_MACROS_H_ +#define _NVSHMEM_MACROS_H_ + +#include +#include "non_abi/nvshmem_build_options.h" + +#ifdef __CUDA_ARCH__ +#ifdef NVSHMEMI_HOST_ONLY +#define NVSHMEMI_HOSTDEVICE_PREFIX __host__ +#else +#define NVSHMEMI_HOSTDEVICE_PREFIX __host__ __device__ +#endif +#else +#define NVSHMEMI_HOSTDEVICE_PREFIX +#endif + +#if defined NVSHMEM_HOSTLIB_ONLY +#undef NVSHMEMI_HOSTDEVICE_PREFIX +#define NVSHMEMI_HOSTDEVICE_PREFIX __host__ __device__ __attribute__((always_inline)) +#endif + +#endif diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/host/nvshmemx_api.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/host/nvshmemx_api.h new file mode 100644 index 0000000000000000000000000000000000000000..11f0882867b8e095bcfc01e495ec962240cec965 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/host/nvshmemx_api.h @@ -0,0 +1,470 @@ +/* + * Copyright (c) 2018-2025, NVIDIA CORPORATION. All rights reserved. + * + * NVIDIA CORPORATION and its licensors retain all intellectual property + * and proprietary rights in and to this software, related documentation + * and any modifications thereto. Any use, reproduction, disclosure or + * distribution of this software and related documentation without an express + * license agreement from NVIDIA CORPORATION is strictly prohibited. + * + * See License.txt for license information + */ + +#ifndef _NVSHMEMX_API_H_ +#define _NVSHMEMX_API_H_ + +#include +#include +#include +#include +#include "device_host_transport/nvshmem_constants.h" +#include "device_host/nvshmem_common.cuh" +#include "non_abi/nvshmem_version.h" +#include "host/nvshmemx_coll_api.h" +#include "host/nvshmem_macros.h" +#include "non_abi/nvshmemx_error.h" +#include "host/nvshmem_api.h" + +int nvshmemi_collective_launch(const void *func, dim3 gridDims, dim3 blockDims, void **args, + size_t sharedMem, cudaStream_t stream); + +int nvshmemi_collective_launch_query_gridsize(const void *func, dim3 blockDims, void **args, + size_t sharedMem, int *gridsize); + +#ifdef __cplusplus +extern "C" { +#endif + +enum flags { + NVSHMEMX_INIT_THREAD_PES = 1, + NVSHMEMX_INIT_WITH_MPI_COMM = 1 << 1, + NVSHMEMX_INIT_WITH_SHMEM = 1 << 2, + NVSHMEMX_INIT_WITH_UNIQUEID = 1 << 3, + NVSHMEMX_INIT_MAX = 1 << 31 +}; + +// Heap management extensions +int nvshmemx_buffer_register(void *addr, size_t length); +int nvshmemx_buffer_unregister(void *addr); +void nvshmemx_buffer_unregister_all(); + +// Initialization & Finalization extensions +int nvshmemx_hostlib_init_attr(unsigned int flags, nvshmemx_init_attr_t *attr); +void nvshmemx_hostlib_finalize(); + +static inline int nvshmemx_init_attr(unsigned int flags, nvshmemx_init_attr_t *attributes) { + int status = 0, requested = NVSHMEM_THREAD_SERIALIZED, provided; + nvshmemi_version_t app_nvshmem_version = { + NVSHMEM_VENDOR_MAJOR_VERSION, NVSHMEM_VENDOR_MINOR_VERSION, NVSHMEM_VENDOR_PATCH_VERSION}; + if (attributes != NULL) { + nvshmemx_init_init_attr_ver_only((*attributes)); + } + status = nvshmemi_init_thread(requested, &provided, flags, attributes, app_nvshmem_version); + NONZERO_EXIT(status, "aborting due to error in nvshmemi_init_thread \n"); + return status; +} + +int nvshmemx_set_attr_uniqueid_args(const int myrank, const int nranks, + const nvshmemx_uniqueid_t *uniqueid, + nvshmemx_init_attr_t *attr); +int nvshmemx_set_attr_mpi_comm_args(void *mpi_comm, nvshmemx_init_attr_t *nvshmem_attr); +int nvshmemx_get_uniqueid(nvshmemx_uniqueid_t *uniqueid); + +int nvshmemx_cumodule_init(CUmodule module); +int nvshmemx_cumodule_finalize(CUmodule module); + +void *nvshmemx_buffer_register_symmetric(void *buf_ptr, size_t size, int flags); +int nvshmemx_buffer_unregister_symmetric(void *mmap_ptr, size_t size); +#if defined(CUDA_VERSION) && CUDART_VERSION < 12000 +/** + * CULibrary is a CUDA 12 type + * This is handled "correctly" by wrappers in `cudawrap.h` + * But that's not #include-able from here since it's in `internal/` + * This type needs to match the one in `cudawrap.h` + */ +typedef void *CUlibrary; +#endif + +int nvshmemx_culibrary_init(CUlibrary library); +int nvshmemx_culibrary_finalize(CUlibrary library); + +//////////////////// Put On Stream //////////////////// + +#define NVSHMEMX_DECL_TYPE_P_ON_STREAM(NAME, TYPE) \ + void nvshmemx_##NAME##_p_on_stream(TYPE *dest, const TYPE value, int pe, cudaStream_t cstrm); + +NVSHMEMI_REPT_FOR_STANDARD_RMA_TYPES(NVSHMEMX_DECL_TYPE_P_ON_STREAM) +#undef NVSHMEMX_DECL_TYPE_P_ON_STREAM + +#define NVSHMEMX_DECL_TYPE_PUT_ON_STREAM(NAME, TYPE) \ + void nvshmemx_##NAME##_put_on_stream(TYPE *dest, const TYPE *source, size_t nelems, int pe, \ + cudaStream_t cstrm); + +NVSHMEMI_REPT_FOR_STANDARD_RMA_TYPES(NVSHMEMX_DECL_TYPE_PUT_ON_STREAM) +#undef NVSHMEMX_DECL_TYPE_PUT_ON_STREAM + +#define NVSHMEMX_DECL_SIZE_PUT_ON_STREAM(NAME) \ + void nvshmemx_put##NAME##_on_stream(void *dest, const void *source, size_t nelems, int pe, \ + cudaStream_t cstrm); + +NVSHMEMI_REPT_FOR_SIZES(NVSHMEMX_DECL_SIZE_PUT_ON_STREAM) +#undef NVSHMEMX_DECL_SIZE_PUT_ON_STREAM + +void nvshmemx_putmem_on_stream(void *dest, const void *source, size_t bytes, int pe, + cudaStream_t cstrm); + +#define NVSHMEMX_DECL_TYPE_PUT_SIGNAL_ON_STREAM(NAME, TYPE) \ + void nvshmemx_##NAME##_put_signal_on_stream(TYPE *dest, const TYPE *source, size_t nelems, \ + uint64_t *sig_addr, uint64_t signal, int sig_op, \ + int pe, cudaStream_t cstrm); + +NVSHMEMI_REPT_FOR_STANDARD_RMA_TYPES(NVSHMEMX_DECL_TYPE_PUT_SIGNAL_ON_STREAM) +#undef NVSHMEMX_DECL_TYPE_PUT_SIGNAL_ON_STREAM + +#define NVSHMEMX_DECL_SIZE_PUT_SIGNAL_ON_STREAM(NAME) \ + void nvshmemx_put##NAME##_signal_on_stream(void *dest, const void *source, size_t nelems, \ + uint64_t *sig_addr, uint64_t signal, int sig_op, \ + int pe, cudaStream_t cstrm); + +NVSHMEMI_REPT_FOR_SIZES(NVSHMEMX_DECL_SIZE_PUT_SIGNAL_ON_STREAM) +#undef NVSHMEMX_DECL_SIZE_PUT_SIGNAL_ON_STREAM + +void nvshmemx_putmem_signal_on_stream(void *dest, const void *source, size_t bytes, + uint64_t *sig_addr, uint64_t signal, int sig_op, int pe, + cudaStream_t cstrm); + +#define NVSHMEMX_DECL_TYPE_PUT_SIGNAL_NBI_ON_STREAM(NAME, TYPE) \ + void nvshmemx_##NAME##_put_signal_nbi_on_stream(TYPE *dest, const TYPE *source, size_t nelems, \ + uint64_t *sig_addr, uint64_t signal, \ + int sig_op, int pe, cudaStream_t cstrm); + +NVSHMEMI_REPT_FOR_STANDARD_RMA_TYPES(NVSHMEMX_DECL_TYPE_PUT_SIGNAL_NBI_ON_STREAM) +#undef NVSHMEMX_DECL_TYPE_PUT_SIGNAL_NBI_ON_STREAM + +#define NVSHMEMX_DECL_SIZE_PUT_SIGNAL_NBI_ON_STREAM(NAME) \ + void nvshmemx_put##NAME##_signal_nbi_on_stream(void *dest, const void *source, size_t nelems, \ + uint64_t *sig_addr, uint64_t signal, \ + int sig_op, int pe, cudaStream_t cstrm); + +NVSHMEMI_REPT_FOR_SIZES(NVSHMEMX_DECL_SIZE_PUT_SIGNAL_NBI_ON_STREAM) +#undef NVSHMEMX_DECL_SIZE_PUT_SIGNAL_NBI_ON_STREAM + +void nvshmemx_putmem_signal_nbi_on_stream(void *dest, const void *source, size_t bytes, + uint64_t *sig_addr, uint64_t signal, int sig_op, int pe, + cudaStream_t cstrm); + +#define NVSHMEMX_DECL_TYPE_IPUT_ON_STREAM(NAME, TYPE) \ + void nvshmemx_##NAME##_iput_on_stream(TYPE *dest, const TYPE *source, ptrdiff_t dst, \ + ptrdiff_t sst, size_t nelems, int pe, \ + cudaStream_t cstrm); + +NVSHMEMI_REPT_FOR_STANDARD_RMA_TYPES(NVSHMEMX_DECL_TYPE_IPUT_ON_STREAM) +#undef NVSHMEMX_DECL_TYPE_IPUT_ON_STREAM + +#define NVSHMEMX_DECL_SIZE_IPUT_ON_STREAM(NAME) \ + void nvshmemx_iput##NAME##_on_stream(void *dest, const void *source, ptrdiff_t dst, \ + ptrdiff_t sst, size_t nelems, int pe, \ + cudaStream_t cstrm); + +NVSHMEMI_REPT_FOR_SIZES(NVSHMEMX_DECL_SIZE_IPUT_ON_STREAM) +#undef NVSHMEMX_DECL_SIZE_IPUT_ON_STREAM + +#define NVSHMEMX_DECL_TYPE_PUT_NBI_ON_STREAM(NAME, TYPE) \ + void nvshmemx_##NAME##_put_nbi_on_stream(TYPE *dest, const TYPE *source, size_t nelems, \ + int pe, cudaStream_t cstrm); + +NVSHMEMI_REPT_FOR_STANDARD_RMA_TYPES(NVSHMEMX_DECL_TYPE_PUT_NBI_ON_STREAM) +#undef NVSHMEMX_DECL_TYPE_PUT_NBI_ON_STREAM + +#define NVSHMEMX_DECL_SIZE_PUT_NBI_ON_STREAM(NAME) \ + void nvshmemx_put##NAME##_nbi_on_stream(void *dest, const void *source, size_t nelems, int pe, \ + cudaStream_t cstrm); + +NVSHMEMI_REPT_FOR_SIZES(NVSHMEMX_DECL_SIZE_PUT_NBI_ON_STREAM) +#undef NVSHMEMX_DECL_SIZE_PUT_NBI_ON_STREAM + +void nvshmemx_putmem_nbi_on_stream(void *dest, const void *source, size_t bytes, int pe, + cudaStream_t cstrm); + +//////////////////// Get On Stream //////////////////// + +#define NVSHMEMX_DECL_TYPE_G_ON_STREAM(NAME, TYPE) \ + TYPE nvshmemx_##NAME##_g_on_stream(const TYPE *src, int pe, cudaStream_t cstrm); + +NVSHMEMI_REPT_FOR_STANDARD_RMA_TYPES(NVSHMEMX_DECL_TYPE_G_ON_STREAM) +#undef NVSHMEMX_DECL_TYPE_G_ON_STREAM + +#define NVSHMEMX_DECL_TYPE_GET_ON_STREAM(NAME, TYPE) \ + void nvshmemx_##NAME##_get_on_stream(TYPE *dest, const TYPE *source, size_t nelems, int pe, \ + cudaStream_t cstrm); + +NVSHMEMI_REPT_FOR_STANDARD_RMA_TYPES(NVSHMEMX_DECL_TYPE_GET_ON_STREAM) +#undef NVSHMEMX_DECL_TYPE_GET_ON_STREAM + +#define NVSHMEMX_DECL_TYPE_IGET_ON_STREAM(NAME, TYPE) \ + void nvshmemx_##NAME##_iget_on_stream(TYPE *dest, const TYPE *source, ptrdiff_t dst, \ + ptrdiff_t sst, size_t nelems, int pe, \ + cudaStream_t cstrm); + +NVSHMEMI_REPT_FOR_STANDARD_RMA_TYPES(NVSHMEMX_DECL_TYPE_IGET_ON_STREAM) +#undef NVSHMEMX_DECL_TYPE_IGET_ON_STREAM + +#define NVSHMEMX_DECL_SIZE_GET_ON_STREAM(NAME) \ + void nvshmemx_get##NAME##_on_stream(void *dest, const void *source, size_t nelems, int pe, \ + cudaStream_t cstrm); + +NVSHMEMI_REPT_FOR_SIZES(NVSHMEMX_DECL_SIZE_GET_ON_STREAM) +#undef NVSHMEMX_DECL_SIZE_GET_ON_STREAM + +void nvshmemx_getmem_on_stream(void *dest, const void *source, size_t bytes, int pe, + cudaStream_t cstrm); + +#define NVSHMEMX_DECL_SIZE_IGET_ON_STREAM(NAME) \ + void nvshmemx_iget##NAME##_on_stream(void *dest, const void *source, ptrdiff_t dst, \ + ptrdiff_t sst, size_t nelems, int pe, \ + cudaStream_t cstrm); + +NVSHMEMI_REPT_FOR_SIZES(NVSHMEMX_DECL_SIZE_IGET_ON_STREAM) +#undef NVSHMEMX_DECL_SIZE_IGET_ON_STREAM + +#define NVSHMEMX_DECL_TYPE_GET_NBI_ON_STREAM(NAME, TYPE) \ + void nvshmemx_##NAME##_get_nbi_on_stream(TYPE *dest, const TYPE *source, size_t nelems, \ + int pe, cudaStream_t cstrm); + +NVSHMEMI_REPT_FOR_STANDARD_RMA_TYPES(NVSHMEMX_DECL_TYPE_GET_NBI_ON_STREAM) +#undef NVSHMEMX_DECL_TYPE_GET_NBI_ON_STREAM + +#define NVSHMEMX_DECL_SIZE_GET_NBI_ON_STREAM(NAME) \ + void nvshmemx_get##NAME##_nbi_on_stream(void *dest, const void *source, size_t nelems, int pe, \ + cudaStream_t cstrm); + +NVSHMEMI_REPT_FOR_SIZES(NVSHMEMX_DECL_SIZE_GET_NBI_ON_STREAM) +#undef NVSHMEMX_DECL_SIZE_GET_NBI_ON_STREAM + +void nvshmemx_getmem_nbi_on_stream(void *dest, const void *source, size_t bytes, int pe, + cudaStream_t cstrm); + +//////////////////// Synchronization On Stream //////////////////// + +void nvshmemx_quiet_on_stream(cudaStream_t cstrm); + +void nvshmemx_signal_op_on_stream(uint64_t *sig_addr, uint64_t signal, int sig_op, int pe, + cudaStream_t cstrm); + +#define NVSHMEMX_DECL_WAIT_UNTIL_ON_STREAM(NAME, Type) \ + void nvshmemx_##NAME##_wait_until_on_stream(Type *ivar, int cmp, Type cmp_value, \ + cudaStream_t cstream); +NVSHMEMI_REPT_FOR_WAIT_TYPES(NVSHMEMX_DECL_WAIT_UNTIL_ON_STREAM) +#undef NVSHMEMX_DECL_WAIT_UNTIL_ON_STREAM + +#define NVSHMEMX_DECL_WAIT_UNTIL_ALL_ON_STREAM(NAME, Type) \ + void nvshmemx_##NAME##_wait_until_all_on_stream(Type *ivars, size_t nelems, const int *status, \ + int cmp, Type cmp_value, \ + cudaStream_t cstream); +NVSHMEMI_REPT_FOR_WAIT_TYPES(NVSHMEMX_DECL_WAIT_UNTIL_ALL_ON_STREAM) +#undef NVSHMEMX_DECL_WAIT_UNTIL_ALL_ON_STREAM + +#define NVSHMEMX_DECL_WAIT_UNTIL_ALL_VECTOR_ON_STREAM(NAME, Type) \ + void nvshmemx_##NAME##_wait_until_all_vector_on_stream(Type *ivars, size_t nelems, \ + const int *status, int cmp, \ + Type *cmp_value, cudaStream_t cstream); +NVSHMEMI_REPT_FOR_WAIT_TYPES(NVSHMEMX_DECL_WAIT_UNTIL_ALL_VECTOR_ON_STREAM) +#undef NVSHMEMX_DECL_WAIT_UNTIL_ALL_VECTOR_ON_STREAM + +void nvshmemx_signal_wait_until_on_stream(uint64_t *sig_addr, int cmp, uint64_t cmp_value, + cudaStream_t cstream); +//////////////////// Put on Thread Group //////////////////// + +#define NVSHMEMX_DECL_TYPE_PUT_THREADGROUP(NAME, TYPE) \ + __device__ void nvshmemx_##NAME##_put_warp(TYPE *dest, const TYPE *source, size_t nelems, \ + int pe); \ + __device__ void nvshmemx_##NAME##_put_block(TYPE *dest, const TYPE *source, size_t nelems, \ + int pe); + +NVSHMEMI_REPT_FOR_STANDARD_RMA_TYPES(NVSHMEMX_DECL_TYPE_PUT_THREADGROUP) +#undef NVSHMEMX_DECL_TYPE_PUT_THREADGROUP + +#define NVSHMEMX_DECL_SIZE_PUT_THREADGROUP(NAME) \ + __device__ void nvshmemx_put##NAME##_warp(void *dest, const void *source, size_t nelems, \ + int pe); \ + __device__ void nvshmemx_put##NAME##_block(void *dest, const void *source, size_t nelems, \ + int pe); + +NVSHMEMI_REPT_FOR_SIZES(NVSHMEMX_DECL_SIZE_PUT_THREADGROUP) +#undef NVSHMEMX_DECL_SIZE_PUT_THREADGROUP + +__device__ void nvshmemx_putmem_warp(void *dest, const void *source, size_t bytes, int pe); +__device__ void nvshmemx_putmem_block(void *dest, const void *source, size_t bytes, int pe); + +/* __device__ nvshmem__put_signal_scope */ +#define NVSHMEMI_TYPENAME_PUT_SIGNAL_SCOPE_DECL(SCOPE, SC_SUFFIX, SC_PREFIX, TYPENAME, TYPE) \ + __device__ void nvshmemx_##TYPENAME##_put_signal##SC_SUFFIX( \ + TYPE *dest, const TYPE *source, size_t nelems, uint64_t *sig_addr, uint64_t signal, \ + int sig_op, int pe); +NVSHMEMI_REPT_FOR_STANDARD_RMA_TYPES_WITH_SCOPE2(NVSHMEMI_TYPENAME_PUT_SIGNAL_SCOPE_DECL, warp, + _warp, x) +NVSHMEMI_REPT_FOR_STANDARD_RMA_TYPES_WITH_SCOPE2(NVSHMEMI_TYPENAME_PUT_SIGNAL_SCOPE_DECL, block, + _block, x) +#undef NVSHMEMI_TYPENAME_PUT_SIGNAL_SCOPE_DECL + +/* __device__ nvshmem_putmem_signal_scope */ +#define NVSHMEMI_PUTMEM_SIGNAL_SCOPE_DECL(SCOPE, SC_SUFFIX, SC_PREFIX) \ + __device__ void nvshmemx_putmem_signal##SC_SUFFIX(void *dest, const void *source, \ + size_t nelems, uint64_t *sig_addr, \ + uint64_t signal, int sig_op, int pe); + +NVSHMEMI_PUTMEM_SIGNAL_SCOPE_DECL(warp, _warp, x) +NVSHMEMI_PUTMEM_SIGNAL_SCOPE_DECL(block, _block, x) +#undef NVSHMEMI_PUTMEM_SIGNAL_SCOPE_DECL + +/* __device__ nvshmem_putsize_signal_scope */ +#define NVSHMEMI_PUTSIZE_SIGNAL_SCOPE_DECL(SCOPE, SC_SUFFIX, SC_PREFIX, BITS) \ + __device__ void nvshmemx_put##BITS##_signal##SC_SUFFIX(void *dest, const void *source, \ + size_t nelems, uint64_t *sig_addr, \ + uint64_t signal, int sig_op, int pe); + +NVSHMEMI_REPT_FOR_SIZES_WITH_SCOPE2(NVSHMEMI_PUTSIZE_SIGNAL_SCOPE_DECL, warp, _warp, x) +NVSHMEMI_REPT_FOR_SIZES_WITH_SCOPE2(NVSHMEMI_PUTSIZE_SIGNAL_SCOPE_DECL, block, _block, x) +#undef NVSHMEMI_REPT_PUTSIZE_SIGNAL_FOR_SCOPE + +/* __device__ nvshmem__put_signal_nbi_scope */ +#define NVSHMEMI_TYPENAME_PUT_SIGNAL_NBI_SCOPE_DECL(SCOPE, SC_SUFFIX, SC_PREFIX, TYPENAME, TYPE) \ + __device__ void nvshmemx_##TYPENAME##_put_signal_nbi##SC_SUFFIX( \ + TYPE *dest, const TYPE *source, size_t nelems, uint64_t *sig_addr, uint64_t signal, \ + int sig_op, int pe); +NVSHMEMI_REPT_FOR_STANDARD_RMA_TYPES_WITH_SCOPE2(NVSHMEMI_TYPENAME_PUT_SIGNAL_NBI_SCOPE_DECL, warp, + _warp, x) +NVSHMEMI_REPT_FOR_STANDARD_RMA_TYPES_WITH_SCOPE2(NVSHMEMI_TYPENAME_PUT_SIGNAL_NBI_SCOPE_DECL, block, + _block, x) +#undef NVSHMEMI_TYPENAME_PUT_SIGNAL_NBI_SCOPE_DECL + +/* __device__ nvshmem_putmem_signal_nbi_scope */ +#define NVSHMEMI_PUTMEM_SIGNAL_NBI_SCOPE_DECL(SCOPE, SC_SUFFIX, SC_PREFIX) \ + __device__ void nvshmemx_putmem_signal_nbi##SC_SUFFIX(void *dest, const void *source, \ + size_t nelems, uint64_t *sig_addr, \ + uint64_t signal, int sig_op, int pe); + +NVSHMEMI_PUTMEM_SIGNAL_NBI_SCOPE_DECL(warp, _warp, x) +NVSHMEMI_PUTMEM_SIGNAL_NBI_SCOPE_DECL(block, _block, x) +#undef NVSHMEMI_PUTMEM_SIGNAL_NBI_SCOPE_DECL + +/* __device__ nvshmem_putsize_signal_nbi_scope */ +#define NVSHMEMI_PUTSIZE_SIGNAL_NBI_SCOPE_DECL(SCOPE, SC_SUFFIX, SC_PREFIX, BITS) \ + __device__ void nvshmemx_put##BITS##_signal_nbi##SC_SUFFIX( \ + void *dest, const void *source, size_t nelems, uint64_t *sig_addr, uint64_t signal, \ + int sig_op, int pe); + +NVSHMEMI_REPT_FOR_SIZES_WITH_SCOPE2(NVSHMEMI_PUTSIZE_SIGNAL_NBI_SCOPE_DECL, warp, _warp, x) +NVSHMEMI_REPT_FOR_SIZES_WITH_SCOPE2(NVSHMEMI_PUTSIZE_SIGNAL_NBI_SCOPE_DECL, block, _block, x) +#undef NVSHMEMI_REPT_PUTSIZE_SIGNAL_NBI_FOR_SCOPE + +#define NVSHMEMX_DECL_TYPE_IPUT_THREADGROUP(NAME, TYPE) \ + __device__ void nvshmemx_##NAME##_iput_warp(TYPE *dest, const TYPE *source, ptrdiff_t dst, \ + ptrdiff_t sst, size_t nelems, int pe); \ + __device__ void nvshmemx_##NAME##_iput_block(TYPE *dest, const TYPE *source, ptrdiff_t dst, \ + ptrdiff_t sst, size_t nelems, int pe); + +NVSHMEMI_REPT_FOR_STANDARD_RMA_TYPES(NVSHMEMX_DECL_TYPE_IPUT_THREADGROUP) +#undef NVSHMEMX_DECL_TYPE_IPUT_THREADGROUP + +#define NVSHMEMX_DECL_SIZE_IPUT_THREADGROUP(NAME) \ + __device__ void nvshmemx_iput##NAME##_warp(void *dest, const void *source, ptrdiff_t dst, \ + ptrdiff_t sst, size_t nelems, int pe); \ + __device__ void nvshmemx_iput##NAME##_block(void *dest, const void *source, ptrdiff_t dst, \ + ptrdiff_t sst, size_t nelems, int pe); + +NVSHMEMI_REPT_FOR_SIZES(NVSHMEMX_DECL_SIZE_IPUT_THREADGROUP) +#undef NVSHMEMX_DECL_SIZE_IPUT_THREADGROUP + +#define NVSHMEMX_DECL_TYPE_PUT_NBI_THREADGROUP(NAME, TYPE) \ + __device__ void nvshmemx_##NAME##_put_nbi_warp(TYPE *dest, const TYPE *source, size_t nelems, \ + int pe); \ + __device__ void nvshmemx_##NAME##_put_nbi_block(TYPE *dest, const TYPE *source, size_t nelems, \ + int pe); + +NVSHMEMI_REPT_FOR_STANDARD_RMA_TYPES(NVSHMEMX_DECL_TYPE_PUT_NBI_THREADGROUP) +#undef NVSHMEMX_DECL_TYPE_PUT_NBI_THREADGROUP + +#define NVSHMEMX_DECL_SIZE_PUT_NBI_THREADGROUP(NAME) \ + __device__ void nvshmemx_put##NAME##_nbi_warp(void *dest, const void *source, size_t nelems, \ + int pe); \ + __device__ void nvshmemx_put##NAME##_nbi_block(void *dest, const void *source, size_t nelems, \ + int pe); + +NVSHMEMI_REPT_FOR_SIZES(NVSHMEMX_DECL_SIZE_PUT_NBI_THREADGROUP) +#undef NVSHMEMX_DECL_SIZE_PUT_NBI_THREADGROUP + +__device__ void nvshmemx_putmem_nbi_warp(void *dest, const void *source, size_t bytes, int pe); +__device__ void nvshmemx_putmem_nbi_block(void *dest, const void *source, size_t bytes, int pe); + +//////////////////// Get on Thread Group //////////////////// + +#define NVSHMEMX_DECL_TYPE_GET_THREADGROUP(NAME, TYPE) \ + __device__ void nvshmemx_##NAME##_get_warp(TYPE *dest, const TYPE *source, size_t nelems, \ + int pe); \ + __device__ void nvshmemx_##NAME##_get_block(TYPE *dest, const TYPE *source, size_t nelems, \ + int pe); + +NVSHMEMI_REPT_FOR_STANDARD_RMA_TYPES(NVSHMEMX_DECL_TYPE_GET_THREADGROUP) +#undef NVSHMEMX_DECL_TYPE_GET_THREADGROUP + +#define NVSHMEMX_DECL_TYPE_IGET_THREADGROUP(NAME, TYPE) \ + __device__ void nvshmemx_##NAME##_iget_warp(TYPE *dest, const TYPE *source, ptrdiff_t dst, \ + ptrdiff_t sst, size_t nelems, int pe); \ + __device__ void nvshmemx_##NAME##_iget_block(TYPE *dest, const TYPE *source, ptrdiff_t dst, \ + ptrdiff_t sst, size_t nelems, int pe); + +NVSHMEMI_REPT_FOR_STANDARD_RMA_TYPES(NVSHMEMX_DECL_TYPE_IGET_THREADGROUP) +#undef NVSHMEMX_DECL_TYPE_IGET_THREADGROUP + +#define NVSHMEMX_DECL_SIZE_GET_THREADGROUP(NAME) \ + __device__ void nvshmemx_get##NAME##_warp(void *dest, const void *source, size_t nelems, \ + int pe); \ + __device__ void nvshmemx_get##NAME##_block(void *dest, const void *source, size_t nelems, \ + int pe); +NVSHMEMI_REPT_FOR_SIZES(NVSHMEMX_DECL_SIZE_GET_THREADGROUP) +#undef NVSHMEMX_DECL_SIZE_GET_THREADGROUP + +__device__ void nvshmemx_getmem_warp(void *dest, const void *source, size_t bytes, int pe); +__device__ void nvshmemx_getmem_block(void *dest, const void *source, size_t bytes, int pe); + +#define NVSHMEMX_DECL_SIZE_IGET_THREADGROUP(NAME) \ + __device__ void nvshmemx_iget##NAME##_warp(void *dest, const void *source, ptrdiff_t dst, \ + ptrdiff_t sst, size_t nelems, int pe); \ + __device__ void nvshmemx_iget##NAME##_block(void *dest, const void *source, ptrdiff_t dst, \ + ptrdiff_t sst, size_t nelems, int pe); + +NVSHMEMI_REPT_FOR_SIZES(NVSHMEMX_DECL_SIZE_IGET_THREADGROUP) +#undef NVSHMEMX_DECL_SIZE_IGET_THREADGROUP + +#define NVSHMEMX_DECL_TYPE_GET_NBI_THREADGROUP(NAME, TYPE) \ + __device__ void nvshmemx_##NAME##_get_nbi_warp(TYPE *dest, const TYPE *source, size_t nelems, \ + int pe); \ + __device__ void nvshmemx_##NAME##_get_nbi_block(TYPE *dest, const TYPE *source, size_t nelems, \ + int pe); + +NVSHMEMI_REPT_FOR_STANDARD_RMA_TYPES(NVSHMEMX_DECL_TYPE_GET_NBI_THREADGROUP) +#undef NVSHMEMX_DECL_TYPE_GET_NBI_THREADGROUP + +#define NVSHMEMX_DECL_SIZE_GET_NBI_THREADGROUP(NAME) \ + __device__ void nvshmemx_get##NAME##_nbi_warp(void *dest, const void *source, size_t nelems, \ + int pe); \ + __device__ void nvshmemx_get##NAME##_nbi_block(void *dest, const void *source, size_t nelems, \ + int pe); + +NVSHMEMI_REPT_FOR_SIZES(NVSHMEMX_DECL_SIZE_GET_NBI_THREADGROUP) +#undef NVSHMEMX_DECL_SIZE_GET_NBI_THREADGROUP + +__device__ void nvshmemx_getmem_nbi_warp(void *dest, const void *source, size_t bytes, int pe); +__device__ void nvshmemx_getmem_nbi_block(void *dest, const void *source, size_t bytes, int pe); + +//////////////////// Signal //////////////////// + +NVSHMEMI_HOSTDEVICE_PREFIX void nvshmemx_signal_op(uint64_t *sig_addr, uint64_t signal, int sig_op, + int pe); + +#ifdef __cplusplus +} +#endif + +#endif /* _NVSHMEMX_API_H_ */ diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/host/nvshmemx_coll_api.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/host/nvshmemx_coll_api.h new file mode 100644 index 0000000000000000000000000000000000000000..5ed2c209736f0d1f1ed334567bf2c14b35d1dcd9 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/host/nvshmemx_coll_api.h @@ -0,0 +1,214 @@ +/* + * Copyright (c) 2018-2025, NVIDIA CORPORATION. All rights reserved. + * + * NVIDIA CORPORATION and its licensors retain all intellectual property + * and proprietary rights in and to this software, related documentation + * and any modifications thereto. Any use, reproduction, disclosure or + * distribution of this software and related documentation without an express + * license agreement from NVIDIA CORPORATION is strictly prohibited. + * + * See License.txt for license information + */ + +#ifndef _NVSHMEMX_COLL_API_H_ +#define _NVSHMEMX_COLL_API_H_ +#include +#include "host/nvshmem_macros.h" + +#ifdef __cplusplus +extern "C" { +#endif +//========================================== +// nvshmem collective calls with stream param +//========================================== + +// alltoall(s) collectives +#define DECL_NVSHMEMX_TYPENAME_ALLTOALL_ON_STREAM(TYPENAME, TYPE) \ + int nvshmemx_##TYPENAME##_alltoall_on_stream(nvshmem_team_t team, TYPE *dest, const TYPE *src, \ + size_t nelem, cudaStream_t stream); +NVSHMEMI_REPT_FOR_STANDARD_RMA_TYPES(DECL_NVSHMEMX_TYPENAME_ALLTOALL_ON_STREAM) +#undef DECL_NVSHMEMX_TYPENAME_ALLTOALL_ON_STREAM + +int nvshmemx_alltoallmem_on_stream(nvshmem_team_t team, void *dest, const void *src, size_t nelem, + cudaStream_t stream); + +// barrier collectives +int nvshmemx_barrier_on_stream(nvshmem_team_t team, cudaStream_t stream); +void nvshmemx_barrier_all_on_stream(cudaStream_t); + +// sync collectives +int nvshmemx_team_sync_on_stream(nvshmem_team_t team, cudaStream_t stream); +void nvshmemx_sync_all_on_stream(cudaStream_t); + +// broadcast collectives +#define DECL_NVSHMEMX_TYPENAME_BROADCAST_ON_STREAM(TYPENAME, TYPE) \ + int nvshmemx_##TYPENAME##_broadcast_on_stream(nvshmem_team_t team, TYPE *dest, \ + const TYPE *src, size_t nelem, int PE_root, \ + cudaStream_t stream); +NVSHMEMI_REPT_FOR_STANDARD_RMA_TYPES(DECL_NVSHMEMX_TYPENAME_BROADCAST_ON_STREAM) +#undef DECL_NVSHMEMX_TYPENAME_BROADCAST_ON_STREAM + +int nvshmemx_broadcastmem_on_stream(nvshmem_team_t team, void *dest, const void *src, size_t nelem, + int PE_root, cudaStream_t stream); + +// fcollect collectives +#define DECL_NVSHMEMX_TYPENAME_FCOLLECT_ON_STREAM(TYPENAME, TYPE) \ + int nvshmemx_##TYPENAME##_fcollect_on_stream(nvshmem_team_t team, TYPE *dest, const TYPE *src, \ + size_t nelem, cudaStream_t stream); +NVSHMEMI_REPT_FOR_STANDARD_RMA_TYPES(DECL_NVSHMEMX_TYPENAME_FCOLLECT_ON_STREAM) +#undef DECL_NVSHMEMX_TYPENAME_FCOLLECT_ON_STREAM + +int nvshmemx_fcollectmem_on_stream(nvshmem_team_t team, void *dest, const void *src, size_t nelem, + cudaStream_t stream); + +// reduction collectives +#define NVSHMEMI_DECL_REDUCE_ONSTREAM(NAME, TYPE, OP) \ + NVSHMEMI_HOSTDEVICE_PREFIX int nvshmemx_##NAME##_##OP##_reduce_on_stream( \ + nvshmem_team_t team, TYPE *dest, const TYPE *src, size_t nreduce, cudaStream_t stream); + +NVSHMEMI_REPT_FOR_BITWISE_REDUCE_TYPES(NVSHMEMI_DECL_REDUCE_ONSTREAM, and) +NVSHMEMI_REPT_FOR_BITWISE_REDUCE_TYPES(NVSHMEMI_DECL_REDUCE_ONSTREAM, or) +NVSHMEMI_REPT_FOR_BITWISE_REDUCE_TYPES(NVSHMEMI_DECL_REDUCE_ONSTREAM, xor) + +NVSHMEMI_REPT_FOR_STANDARD_REDUCE_TYPES(NVSHMEMI_DECL_REDUCE_ONSTREAM, max) +NVSHMEMI_REPT_FOR_STANDARD_REDUCE_TYPES(NVSHMEMI_DECL_REDUCE_ONSTREAM, min) + +NVSHMEMI_REPT_FOR_ARITH_REDUCE_TYPES(NVSHMEMI_DECL_REDUCE_ONSTREAM, sum) +NVSHMEMI_REPT_FOR_ARITH_REDUCE_TYPES(NVSHMEMI_DECL_REDUCE_ONSTREAM, prod) + +#undef NVSHMEMI_DECL_REDUCE_ONSTREAM + +#define NVSHMEMI_DECL_REDUCESCATTER_ONSTREAM(NAME, TYPE, OP) \ + NVSHMEMI_HOSTDEVICE_PREFIX int nvshmemx_##NAME##_##OP##_reducescatter_on_stream( \ + nvshmem_team_t team, TYPE *dest, const TYPE *src, size_t nreduce, cudaStream_t stream); + +NVSHMEMI_REPT_FOR_BITWISE_REDUCE_TYPES(NVSHMEMI_DECL_REDUCESCATTER_ONSTREAM, and) +NVSHMEMI_REPT_FOR_BITWISE_REDUCE_TYPES(NVSHMEMI_DECL_REDUCESCATTER_ONSTREAM, or) +NVSHMEMI_REPT_FOR_BITWISE_REDUCE_TYPES(NVSHMEMI_DECL_REDUCESCATTER_ONSTREAM, xor) + +NVSHMEMI_REPT_FOR_STANDARD_REDUCE_TYPES(NVSHMEMI_DECL_REDUCESCATTER_ONSTREAM, max) +NVSHMEMI_REPT_FOR_STANDARD_REDUCE_TYPES(NVSHMEMI_DECL_REDUCESCATTER_ONSTREAM, min) + +NVSHMEMI_REPT_FOR_ARITH_REDUCE_TYPES(NVSHMEMI_DECL_REDUCESCATTER_ONSTREAM, sum) +NVSHMEMI_REPT_FOR_ARITH_REDUCE_TYPES(NVSHMEMI_DECL_REDUCESCATTER_ONSTREAM, prod) + +#undef NVSHMEMI_DECL_REDUCESCATTER_ONSTREAM + +//========================================== +// nvshmem collective calls on threadgroup +//========================================== + +// alltoall(s) collectives +#define DECL_NVSHMEMX_TYPENAME_ALLTOALL_SCOPE(SCOPE, TYPENAME, TYPE) \ + __device__ int nvshmemx_##TYPENAME##_alltoall_##SCOPE(nvshmem_team_t team, TYPE *dest, \ + const TYPE *src, size_t nelem); + +NVSHMEMI_REPT_FOR_STANDARD_RMA_TYPES_WITH_SCOPE(DECL_NVSHMEMX_TYPENAME_ALLTOALL_SCOPE, warp) +NVSHMEMI_REPT_FOR_STANDARD_RMA_TYPES_WITH_SCOPE(DECL_NVSHMEMX_TYPENAME_ALLTOALL_SCOPE, block) +#undef DECL_NVSHMEMX_TYPENAME_ALLTOALL_SCOPE + +__device__ int nvshmemx_alltoallmem_warp(nvshmem_team_t team, void *dest, const void *src, + size_t nelem); +__device__ int nvshmemx_alltoallmem_block(nvshmem_team_t team, void *dest, const void *src, + size_t nelem); + +// barrier collectives +__device__ int nvshmemx_barrier_warp(nvshmem_team_t team); +__device__ void nvshmemx_barrier_all_warp(); +__device__ int nvshmemx_barrier_warpgroup(nvshmem_team_t team); +__device__ int nvshmemx_barrier_block(nvshmem_team_t team); +__device__ void nvshmemx_barrier_all_block(); + +// sync collectives +__device__ int nvshmemx_team_sync_warp(nvshmem_team_t team); +__device__ void nvshmemx_sync_all_warp(); +__device__ int nvshmemx_team_sync_block(nvshmem_team_t team); +__device__ void nvshmemx_sync_all_block(); + +// broadcast collectives +#define DECL_NVSHMEMX_TYPENAME_BROADCAST_SCOPE(SCOPE, TYPENAME, TYPE) \ + __device__ int nvshmemx_##TYPENAME##_broadcast_##SCOPE( \ + nvshmem_team_t team, TYPE *dest, const TYPE *src, size_t nelem, int PE_root); +NVSHMEMI_REPT_FOR_STANDARD_RMA_TYPES_WITH_SCOPE(DECL_NVSHMEMX_TYPENAME_BROADCAST_SCOPE, warp) +NVSHMEMI_REPT_FOR_STANDARD_RMA_TYPES_WITH_SCOPE(DECL_NVSHMEMX_TYPENAME_BROADCAST_SCOPE, block) +#undef DECL_NVSHMEMX_TYPENAME_BROADCAST_SCOPE + +__device__ int nvshmemx_broadcastmem_warp(nvshmem_team_t team, void *dest, const void *src, + size_t nelem); +__device__ int nvshmemx_broadcastmem_block(nvshmem_team_t team, void *dest, const void *src, + size_t nelem); + +// fcollect collectives +#define DECL_NVSHMEMX_TYPENAME_FCOLLECT_SCOPE(SCOPE, TYPENAME, TYPE) \ + __device__ int nvshmemx_##TYPENAME##_fcollect_##SCOPE(nvshmem_team_t team, TYPE *dest, \ + const TYPE *src, size_t nelem); +NVSHMEMI_REPT_FOR_STANDARD_RMA_TYPES_WITH_SCOPE(DECL_NVSHMEMX_TYPENAME_FCOLLECT_SCOPE, warp) +NVSHMEMI_REPT_FOR_STANDARD_RMA_TYPES_WITH_SCOPE(DECL_NVSHMEMX_TYPENAME_FCOLLECT_SCOPE, block) +#undef DECL_NVSHMEMX_TYPENAME_FCOLLECT_SCOPE + +__device__ int nvshmemx_fcollectmem_warp(nvshmem_team_t team, void *dest, const void *src, + size_t nelem); +__device__ int nvshmemx_fcollectmem_block(nvshmem_team_t team, void *dest, const void *src, + size_t nelem); + +// reduction collectives +#define DECL_NVSHMEMX_TYPENAME_OP_REDUCE_THREADGROUP(SCOPE, TYPENAME, TYPE, OP) \ + NVSHMEMI_HOSTDEVICE_PREFIX int nvshmemx_##TYPENAME##_##OP##_reduce_##SCOPE( \ + nvshmem_team_t team, TYPE *dest, const TYPE *src, size_t nreduce); + +#define DECL_NVSHMEMX_TYPENAME_OP_REDUCE(SC) \ + NVSHMEMI_REPT_FOR_BITWISE_REDUCE_TYPES_WITH_SCOPE( \ + DECL_NVSHMEMX_TYPENAME_OP_REDUCE_THREADGROUP, SC, and) \ + NVSHMEMI_REPT_FOR_BITWISE_REDUCE_TYPES_WITH_SCOPE( \ + DECL_NVSHMEMX_TYPENAME_OP_REDUCE_THREADGROUP, SC, or) \ + NVSHMEMI_REPT_FOR_BITWISE_REDUCE_TYPES_WITH_SCOPE( \ + DECL_NVSHMEMX_TYPENAME_OP_REDUCE_THREADGROUP, SC, xor) \ + \ + NVSHMEMI_REPT_FOR_STANDARD_REDUCE_TYPES_WITH_SCOPE( \ + DECL_NVSHMEMX_TYPENAME_OP_REDUCE_THREADGROUP, SC, max) \ + NVSHMEMI_REPT_FOR_STANDARD_REDUCE_TYPES_WITH_SCOPE( \ + DECL_NVSHMEMX_TYPENAME_OP_REDUCE_THREADGROUP, SC, min) \ + \ + NVSHMEMI_REPT_FOR_ARITH_REDUCE_TYPES_WITH_SCOPE(DECL_NVSHMEMX_TYPENAME_OP_REDUCE_THREADGROUP, \ + SC, sum) \ + NVSHMEMI_REPT_FOR_ARITH_REDUCE_TYPES_WITH_SCOPE(DECL_NVSHMEMX_TYPENAME_OP_REDUCE_THREADGROUP, \ + SC, prod) + +DECL_NVSHMEMX_TYPENAME_OP_REDUCE(warp); +DECL_NVSHMEMX_TYPENAME_OP_REDUCE(block); + +NVSHMEMI_HOSTDEVICE_PREFIX int nvshmemx_double2_maxloc_reduce_block(nvshmem_team_t team, + double2 *dest, + const double2 *src, + size_t nreduce); + +#define DECL_NVSHMEMX_TYPENAME_OP_REDUCESCATTER_THREADGROUP(SCOPE, TYPENAME, TYPE, OP) \ + NVSHMEMI_HOSTDEVICE_PREFIX int nvshmemx_##TYPENAME##_##OP##_reducescatter_##SCOPE( \ + nvshmem_team_t team, TYPE *dest, const TYPE *src, size_t nreduce); + +#define DECL_NVSHMEMX_TYPENAME_OP_REDUCESCATTER(SC) \ + NVSHMEMI_REPT_FOR_BITWISE_REDUCE_TYPES_WITH_SCOPE( \ + DECL_NVSHMEMX_TYPENAME_OP_REDUCESCATTER_THREADGROUP, SC, and) \ + NVSHMEMI_REPT_FOR_BITWISE_REDUCE_TYPES_WITH_SCOPE( \ + DECL_NVSHMEMX_TYPENAME_OP_REDUCESCATTER_THREADGROUP, SC, or) \ + NVSHMEMI_REPT_FOR_BITWISE_REDUCE_TYPES_WITH_SCOPE( \ + DECL_NVSHMEMX_TYPENAME_OP_REDUCESCATTER_THREADGROUP, SC, xor) \ + \ + NVSHMEMI_REPT_FOR_STANDARD_REDUCE_TYPES_WITH_SCOPE( \ + DECL_NVSHMEMX_TYPENAME_OP_REDUCESCATTER_THREADGROUP, SC, max) \ + NVSHMEMI_REPT_FOR_STANDARD_REDUCE_TYPES_WITH_SCOPE( \ + DECL_NVSHMEMX_TYPENAME_OP_REDUCESCATTER_THREADGROUP, SC, min) \ + \ + NVSHMEMI_REPT_FOR_ARITH_REDUCE_TYPES_WITH_SCOPE( \ + DECL_NVSHMEMX_TYPENAME_OP_REDUCESCATTER_THREADGROUP, SC, sum) \ + NVSHMEMI_REPT_FOR_ARITH_REDUCE_TYPES_WITH_SCOPE( \ + DECL_NVSHMEMX_TYPENAME_OP_REDUCESCATTER_THREADGROUP, SC, prod) + +DECL_NVSHMEMX_TYPENAME_OP_REDUCESCATTER(warp); +DECL_NVSHMEMX_TYPENAME_OP_REDUCESCATTER(block); + +#ifdef __cplusplus +} +#endif + +#endif /* NVSHMEMX_COLL_H */ diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/device/coll/alltoall.cuh b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/device/coll/alltoall.cuh new file mode 100644 index 0000000000000000000000000000000000000000..617305050047696c4cf410bedc6a32cf50c95799 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/device/coll/alltoall.cuh @@ -0,0 +1,157 @@ +/* + * Copyright (c) 2017-2025, NVIDIA CORPORATION. All rights reserved. + * + * See License.txt for license information + */ + +#ifndef ALLTOALL_DEVICE_CUH +#define ALLTOALL_DEVICE_CUH +#include +#include "non_abi/device/wait/nvshmemi_wait_until_apis.cuh" +#include "non_abi/device/threadgroup/nvshmemi_common_device_defines.cuh" +#include "non_abi/device/common/nvshmemi_common_device.cuh" +#include "barrier.cuh" + +#ifdef __CUDA_ARCH__ + +#define NVSHMEMI_ALLTOALL_SMALL_MSGSIZE 16 +#define NVSHMEMI_ALLTOALL_MEDIUM_MSGSIZE 16384 + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_alltoall_allpush_threadgroup( + nvshmem_team_t team, T *dest, const T *source, size_t nelems) { + nvshmemi_team_t *teami = nvshmemi_device_state_d.team_pool[team]; + int PE_size = teami->size; + int next_rank, src_offset, dst_offset; + const int mype = nvshmemi_device_state_d.mype; + int my_idx_in_active_set = teami->my_pe; + int myIdx = nvshmemi_thread_id_in_threadgroup(); + int groupSize = nvshmemi_threadgroup_size(); + uint64_t *psync = (uint64_t *)nvshmemi_team_get_psync(teami, ALLTOALL); + uint64_t *pwrk = &teami->alltoall_pwrk[teami->alltoall_count % 2]; + const size_t msgsize = nelems * sizeof(T); + const int first_unused_warp = (PE_size + (warpSize - 1)) / warpSize; + const int my_warp_idx = myIdx / warpSize; + const int num_warps = groupSize / warpSize; + + dst_offset = nelems * my_idx_in_active_set; + + /* Do remote ops and local ops < 16 bytes from a single thread */ + /* TODO: Find a more optimal transfer point than 16 bytes */ + for (int i = myIdx; i < PE_size; i += groupSize) { + next_rank = nvshmemi_team_translate_pe_to_team_world_wrap(teami, my_idx_in_active_set + i); + void *peer_base_addr = (void *)__ldg( + (const long long unsigned *)nvshmemi_device_state_d.peer_heap_base_p2p + next_rank); + src_offset = nelems * ((i + my_idx_in_active_set) % PE_size); + if (!peer_base_addr) { + /* We are breaking rank with the rest of the group here so send the RMA with thread + * scope. */ + nvshmemi_transfer_put_signal( + (void *)(dest + dst_offset), (void *)(source + src_offset), msgsize, + (void *)(psync + mype), 1ULL, NVSHMEMI_AMO_SIGNAL_ADD, next_rank, true); + } else if (msgsize <= NVSHMEMI_ALLTOALL_SMALL_MSGSIZE) { + nvshmemi_put_nbi_threadgroup( + dest + dst_offset, source + src_offset, nelems, next_rank); + } + } + + if (SCOPE == NVSHMEMI_THREADGROUP_BLOCK && PE_size < groupSize && + num_warps > first_unused_warp && msgsize > NVSHMEMI_ALLTOALL_SMALL_MSGSIZE && + msgsize <= NVSHMEMI_ALLTOALL_MEDIUM_MSGSIZE) { + if (my_warp_idx >= first_unused_warp) { + for (int ii = my_warp_idx - first_unused_warp; ii < PE_size; + ii += (num_warps - first_unused_warp)) { + next_rank = + nvshmemi_team_translate_pe_to_team_world_wrap(teami, my_idx_in_active_set + ii); + src_offset = nelems * ((my_idx_in_active_set + ii) % PE_size); + void *peer_base_addr = (void *)__ldg( + (const long long unsigned *)nvshmemi_device_state_d.peer_heap_base_p2p + + next_rank); + if (peer_base_addr) { + nvshmemi_put_nbi_threadgroup( + dest + dst_offset, source + src_offset, nelems, next_rank); + } + } + } + } else if (msgsize > NVSHMEMI_ALLTOALL_SMALL_MSGSIZE) { + for (int ii = 0; ii < PE_size; ii++) { + next_rank = + nvshmemi_team_translate_pe_to_team_world_wrap(teami, my_idx_in_active_set + ii); + src_offset = nelems * ((my_idx_in_active_set + ii) % PE_size); + void *peer_base_addr = (void *)__ldg( + (const long long unsigned *)nvshmemi_device_state_d.peer_heap_base_p2p + next_rank); + if (peer_base_addr) { + nvshmemi_put_nbi_threadgroup(dest + dst_offset, source + src_offset, + nelems, next_rank); + } + } + } + + nvshmemi_threadgroup_sync(); + /* A fence and signal is required - note that we can skip any size check here because it's + * inherent in the boolean. */ + if (myIdx == 0) { + atomicAdd((unsigned long long *)pwrk, 1ULL); + __threadfence_system(); + } + nvshmemi_threadgroup_sync(); + for (int i = myIdx; i < PE_size; i += groupSize) { + next_rank = nvshmemi_team_translate_pe_to_team_world_wrap(teami, my_idx_in_active_set + i); + void *peer_base_addr = (void *)__ldg( + (const long long unsigned *)nvshmemi_device_state_d.peer_heap_base_p2p + next_rank); + if (peer_base_addr) { + nvshmemi_signal_op((psync + mype), 1ULL, NVSHMEMI_AMO_SIGNAL_ADD, next_rank); + } + } + + nvshmemi_threadgroup_sync(); + for (int i = myIdx; i < PE_size; i += groupSize) { + next_rank = nvshmemi_team_translate_pe_to_team_world_wrap(teami, my_idx_in_active_set + i); + nvshmemi_wait_until_greater_than_equals((psync + next_rank), *pwrk, + NVSHMEMI_CALL_SITE_SIGNAL_WAIT_UNTIL_GE); + } + if (SCOPE == NVSHMEMI_THREADGROUP_BLOCK && PE_size < groupSize) { + if (my_warp_idx == first_unused_warp) + nvshmemi_transfer_quiet(false); + } else + nvshmemi_transfer_quiet(false); + nvshmemi_threadgroup_sync(); + if (myIdx == 0) teami->alltoall_count++; +} + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_alltoall_p2p_allpush_threadgroup( + nvshmem_team_t team, T *dest, const T *source, size_t nelems) { + nvshmemi_team_t *teami = nvshmemi_device_state_d.team_pool[team]; + int PE_size = teami->size; + int next_rank; + int src_offset; + int dst_offset; + int my_idx_in_active_set = teami->my_pe; + T *dst_ptr; + int groupSize = nvshmemi_threadgroup_size(); + + for (int ii = 0; ii < PE_size; ii++) { + next_rank = nvshmemi_team_translate_pe_to_team_world_wrap(teami, my_idx_in_active_set + ii); + src_offset = nelems * ((my_idx_in_active_set + ii) % PE_size); + dst_offset = nelems * teami->my_pe; + dst_ptr = (T *)nvshmemi_ptr((void *)(dest + dst_offset), next_rank); + nvshmemi_memcpy_threadgroup(dst_ptr, source + src_offset, nelems * sizeof(T)); + } + + nvshmemi_barrier_threadgroup(team); +} + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_alltoall_threadgroup(nvshmem_team_t team, + T *dest, + const T *source, + size_t nelems) { + if (nvshmemi_device_state_d.job_connectivity <= NVSHMEMI_JOB_GPU_LDST_REMOTE_ATOMICS) + nvshmemi_alltoall_p2p_allpush_threadgroup(team, dest, source, nelems); + else + nvshmemi_alltoall_allpush_threadgroup(team, dest, source, nelems); +} + +#endif /* __CUDA_ARCH__ */ +#endif /* ALLTOALL_DEVICE_CUH */ diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/device/coll/barrier.cuh b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/device/coll/barrier.cuh new file mode 100644 index 0000000000000000000000000000000000000000..6fc740a04b0ee88462787d4c38b52d26ede92f1a --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/device/coll/barrier.cuh @@ -0,0 +1,281 @@ +/* + * Copyright (c) 2017-2025, NVIDIA CORPORATION. All rights reserved. + * + * See License.txt for license information + */ +#ifndef BARRIER_DEVICE_CUH +#define BARRIER_DEVICE_CUH +#include +#include "non_abi/device/team/nvshmemi_team_defines.cuh" +#include "non_abi/nvshmem_build_options.h" +#if defined(NVSHMEM_ENABLE_ALL_DEVICE_INLINING) || defined(__NVSHMEM_NUMBA_SUPPORT__) +#include "non_abi/device/pt-to-pt/transfer_device.cuh" +#else +#include "non_abi/device/pt-to-pt/nvshmemi_transfer_api.cuh" +#endif +#include "non_abi/device/wait/nvshmemi_wait_until_apis.cuh" +#include "device_host/nvshmem_tensor.h" +#include "non_abi/device/threadgroup/nvshmemi_common_device_defines.cuh" +#include "non_abi/device/common/nvshmemi_common_device.cuh" +#include "utils.cuh" + +#ifdef __CUDA_ARCH__ + +template +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE void sync_dissem_pow2_threadgroup( + nvshmem_team_t team) { + nvshmemi_team_t *teami = nvshmemi_device_state_d.team_pool[team]; + int size = teami->size; + volatile long *pSync = (volatile long *)nvshmemi_team_get_psync(teami, SYNC); + volatile long *sync_counter = (volatile long *)nvshmemi_team_get_sync_counter(teami); + volatile long *sync_arr = NULL; + int shift; + int to_nbr_idx, to_nbr; + int from_nbr_idx, from_nbr; + int temp = size - 1; /* used to calculate number of phases */ + int myIdx = nvshmemi_thread_id_in_threadgroup(); + int groupSize = nvshmemi_threadgroup_size(); + sync_arr = (volatile long *)pSync; + int pow_k = 1; + int phase_num = 0; + volatile long *counter = sync_counter; + while (temp) { + /* notify neighbors */ + for (int j = myIdx + 1; j <= k - 1; j += groupSize) { + shift = j << phase_num; + if (shift >= size) break; + + to_nbr_idx = teami->my_pe + shift; + to_nbr = nvshmemi_team_translate_pe_to_team_world_wrap(teami, to_nbr_idx); + nvshmemi_signal_for_barrier(((long *)sync_arr + nvshmemi_device_state_d.mype), + counter[0], to_nbr); + } + + /* wait for neighbors notification */ + for (int j = myIdx + 1; j <= k - 1; j += groupSize) { + shift = j << phase_num; + if (shift >= size) break; + + from_nbr_idx = teami->my_pe - shift; + if (from_nbr_idx < 0) from_nbr_idx = size + from_nbr_idx; + from_nbr = nvshmemi_team_translate_pe_to_team_world_wrap(teami, from_nbr_idx); + nvshmemi_wait_until_greater_than_equals(sync_arr + from_nbr, counter[0], + NVSHMEMI_CALL_SITE_BARRIER_WARP); + } + pow_k <<= logk; + temp >>= logk; + phase_num++; + nvshmemi_threadgroup_sync(); + } + if (!myIdx) sync_counter[0] += 1; + nvshmemi_threadgroup_sync(); +} + +template +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE void sync_dissem_threadgroup_non_strided( + nvshmem_team_t team) { + nvshmemi_team_t *teami = nvshmemi_device_state_d.team_pool[team]; + + volatile long *pSync = (volatile long *)nvshmemi_team_get_psync(teami, SYNC); + volatile long *sync_counter = (volatile long *)nvshmemi_team_get_sync_counter(teami); + int num_phases = 0; + + int myIdx = nvshmemi_thread_id_in_threadgroup(); + int groupSize = nvshmemi_threadgroup_size(); + int my_idx_in_active_set = teami->my_pe; + int shift, to_nbr_idx, to_nbr, from_nbr_idx, from_nbr; + int size = teami->size; + int temp = size - 1; /* used to calculate number of phases */ + /* radix for the dissemination algorithm */ + int k = min(nvshmemi_device_state_d.gpu_coll_env_params_var.barrier_tg_dissem_kval, size); + int pow_k = 1; + while (temp) { + num_phases++; + temp /= k; + } + + volatile long *counter = sync_counter; + for (int i = 0; i < num_phases; i++) { + /* notify neighbors */ + for (int j = myIdx + 1; j <= k - 1; j += groupSize) { + shift = j * pow_k; + if (shift >= size) break; + to_nbr_idx = (my_idx_in_active_set + shift) % size; + to_nbr = teami->pe_mapping[to_nbr_idx]; + nvshmemi_signal_for_barrier(((long *)pSync + nvshmemi_device_state_d.mype), + counter[0], to_nbr); + } + + /* wait for neighbors notification */ + for (int j = myIdx + 1; j <= k - 1; j += groupSize) { + shift = j * pow_k; + if (shift >= size) break; + + from_nbr_idx = my_idx_in_active_set - shift; + if (from_nbr_idx < 0) from_nbr_idx = size + from_nbr_idx; + from_nbr = teami->pe_mapping[from_nbr_idx]; + nvshmemi_wait_until_greater_than_equals(pSync + from_nbr, counter[0], + NVSHMEMI_CALL_SITE_BARRIER_WARP); + } + pow_k *= k; + nvshmemi_threadgroup_sync(); + } + if (!myIdx) sync_counter[0] += 1; + nvshmemi_threadgroup_sync(); +} + +template +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE void sync_dissem_threadgroup_2( + int start, int stride, int size, volatile long *pSync, volatile long *sync_counter) { + int num_phases = 0; + int k = + min(nvshmemi_device_state_d.gpu_coll_env_params_var.barrier_tg_dissem_kval, size); /* radix + for the dissemination algorithm */ + int my_idx_in_active_set = (nvshmemi_device_state_d.mype - start) / stride; + volatile long *sync_arr = NULL; + int shift; + int to_nbr_idx, to_nbr; + int from_nbr_idx, from_nbr; + int temp = size - 1; /* used to calculate number of phases */ + while (temp) { + num_phases++; + temp /= k; + } + int myIdx = nvshmemi_thread_id_in_threadgroup(); + int groupSize = nvshmemi_threadgroup_size(); + sync_arr = (volatile long *)pSync; + int pow_k = 1; + volatile long *counter = sync_counter; + for (int i = 0; i < num_phases; i++) { + /* notify neighbors */ + for (int j = myIdx + 1; j <= k - 1; j += groupSize) { + shift = j * pow_k; + if (shift >= size) break; + to_nbr_idx = (my_idx_in_active_set + shift) % size; + to_nbr = start + to_nbr_idx * stride; + nvshmemi_signal_for_barrier(((long *)sync_arr + nvshmemi_device_state_d.mype), + counter[0], to_nbr); + } + + /* wait for neighbors notification */ + for (int j = myIdx + 1; j <= k - 1; j += groupSize) { + shift = j * pow_k; + if (shift >= size) break; + + from_nbr_idx = my_idx_in_active_set - shift; + if (from_nbr_idx < 0) from_nbr_idx = size + from_nbr_idx; + from_nbr = start + from_nbr_idx * stride; + nvshmemi_wait_until_greater_than_equals(sync_arr + from_nbr, counter[0], + NVSHMEMI_CALL_SITE_BARRIER_WARP); + } + pow_k *= k; + nvshmemi_threadgroup_sync(); + } + if (!myIdx) sync_counter[0] += 1; + nvshmemi_threadgroup_sync(); +} + +template +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE void sync_dissem_threadgroup( + nvshmem_team_t team) { + nvshmemi_team_t *teami = nvshmemi_device_state_d.team_pool[team]; + int start = teami->start; + int stride = teami->stride; + int size = teami->size; + volatile long *pSync = (volatile long *)nvshmemi_team_get_psync(teami, SYNC); + volatile long *sync_counter = (volatile long *)nvshmemi_team_get_sync_counter(teami); + + if (stride > 0) { + sync_dissem_threadgroup_2(start, stride, size, pSync, sync_counter); + } else { + sync_dissem_threadgroup_non_strided(team); + } +} + +template +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_sync_algo_threadgroup( + nvshmem_team_t team) { + nvshmemi_team_t *teami = nvshmemi_device_state_d.team_pool[team]; + int size = teami->size; + int k = min(nvshmemi_device_state_d.gpu_coll_env_params_var.barrier_tg_dissem_kval, size); + k = max(k, 2); + /* only p2p connected team for BLOCK scoped collectives should use larger radix of dissem + * algorithm */ + if (teami->are_gpus_p2p_connected && SCOPE == NVSHMEMI_THREADGROUP_BLOCK) k = teami->size; + switch (k) { + case 2: + sync_dissem_pow2_threadgroup<2, 1, SCOPE>(team); + break; + case 4: + sync_dissem_pow2_threadgroup<4, 2, SCOPE>(team); + break; + case 8: + sync_dissem_pow2_threadgroup<8, 3, SCOPE>(team); + break; + case 16: + sync_dissem_pow2_threadgroup<16, 4, SCOPE>(team); + break; + case 32: + sync_dissem_pow2_threadgroup<32, 5, SCOPE>(team); + break; + default: + sync_dissem_threadgroup(team); + break; + } +} + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_sync_threadgroup(nvshmem_team_t team) { + nvshmemi_threadgroup_sync(); + nvshmemi_sync_algo_threadgroup(team); +} + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_barrier_threadgroup(nvshmem_team_t team) { + int myIdx = nvshmemi_thread_id_in_threadgroup(); + nvshmemi_threadgroup_sync(); + if ((nvshmemi_device_state_d.job_connectivity > NVSHMEMI_JOB_GPU_LDST)) { + nvshmemi_transfer_quiet(true); + } else if (!myIdx) { + __threadfence_system(); + } + nvshmemi_threadgroup_sync(); + + nvshmemi_sync_algo_threadgroup(team); + + if (!myIdx) { + if (nvshmemi_device_state_d.job_connectivity > NVSHMEMI_JOB_GPU_PROXY) + nvshmemi_transfer_enforce_consistency_at_target(false); + } + nvshmemi_threadgroup_sync(); +} + +/******** tile APIs **************/ +template +__device__ inline int nvshmemi_tile_collective_wait(nvshmem_team_t team, uint64_t flag) { + static_assert((algo == nvshmemx::tile_coll_algo_t::NVLS_ONE_SHOT_PULL_NBI) || + (algo == nvshmemx::tile_coll_algo_t::NVLS_ONE_SHOT_PUSH_NBI) || + (algo == nvshmemx::tile_coll_algo_t::NVLS_TWO_SHOT_PUSH_NBI), + "Unsupported tile algorithm"); + + assert(!flag && "Currently non-zero flag value is unsupported"); + + if constexpr ((algo == nvshmemx::tile_coll_algo_t::NVLS_TWO_SHOT_PUSH_NBI) || + (algo == nvshmemx::tile_coll_algo_t::NVLS_ONE_SHOT_PUSH_NBI)) { + nvshmemi_threadgroup_sync(); + if (!nvshmemi_thread_id_in_threadgroup()) { + __threadfence_system(); + } + nvshmemi_sync_algo_threadgroup(team); + return 0; + } else { + // PULL variants store only to local HBM mem + __threadfence(); // ensure stores are visible in local GPU mem + nvshmemi_threadgroup_sync(); + return 0; + } +} + +#endif /* __CUDA_ARCH__ */ + +#endif /* BARRIER_DEVICE_CUH */ diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/device/coll/broadcast.cuh b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/device/coll/broadcast.cuh new file mode 100644 index 0000000000000000000000000000000000000000..d49f2fc632fc21d26fd8e73beea8e7c02b8da3e0 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/device/coll/broadcast.cuh @@ -0,0 +1,412 @@ +/* + * Copyright (c) 2017-2025, NVIDIA CORPORATION. All rights reserved. + * + * See License.txt for license information + */ + +#ifndef BROADCAST_DEVICE_CUH +#define BROADCAST_DEVICE_CUH + +#include +#include "device_host_transport/nvshmem_common_transport.h" // for NVSHMEMI_OP_PUT +#include "non_abi/nvshmem_build_options.h" +#if defined(NVSHMEM_ENABLE_ALL_DEVICE_INLINING) || defined(__NVSHMEM_NUMBA_SUPPORT__) +#include "non_abi/device/pt-to-pt/transfer_device.cuh" +#else +#include "non_abi/device/pt-to-pt/nvshmemi_transfer_api.cuh" +#endif +#include "non_abi/device/wait/nvshmemi_wait_until_apis.cuh" +#include "non_abi/device/threadgroup/nvshmemi_common_device_defines.cuh" +#include "non_abi/device/common/nvshmemi_common_device.cuh" +#include "non_abi/device/team/nvshmemi_team_defines.cuh" + +#ifdef __CUDA_ARCH__ +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_bcast_intranode_tree_threadgroup( + nvshmem_team_t team, T *dest, const T *source, size_t nelems, int PE_root, int k) { + nvshmemi_team_t *teami = nvshmemi_device_state_d.team_pool[team]; + const int myIdx = nvshmemi_thread_id_in_threadgroup(); + const int groupSize = nvshmemi_threadgroup_size(); + if (!myIdx) { /* Only one thread should increment */ + teami->ll_flag++; + teami->bcast_count++; + if (teami->bcast_sync_offset + nelems * sizeof(T) * 2 > + sizeof(long) * NVSHMEMI_BCAST_SYNC_SIZE) { + nvshmemi_barrier_threadgroup(team); + teami->bcast_sync_offset = 0; + } + } + nvshmemi_threadgroup_sync(); + const uint32_t ll_flag = teami->ll_flag; + char *pWrk = (char *)nvshmemi_team_get_psync(teami, BCAST); + size_t recv_offset = teami->bcast_sync_offset; + const int my_pe_in_team = nvshmemi_team_my_pe(team); + + if (PE_root != my_pe_in_team) { + nvshmemi_recvLL(dest, (uint64_t *)(pWrk + recv_offset), nelems, ll_flag); + } else { + nvshmemi_packLL_naive((uint64_t *)(pWrk + recv_offset), source, nelems, ll_flag); + } + + /*if (SCOPE == NVSHMEMI_THREADGROUP_BLOCK) { + nvshmemi_threadgroup_sync(); // wait for block scoped recvLL/packLL to complete + int warp_id = myIdx / 32; + int num_warps = (groupSize + 31) / 32; + size_t size_per_peer = nelems * sizeof(T) * 2; + int num_peers = k; + size_t total_size = size_per_peer * num_peers; + int size_per_warp = min (nelems * sizeof(T) * 2, 512l); + for (size_t start = warp_id * size_per_warp; start < total_size; start += num_warps * + size_per_warp) { int peer_id = start / size_per_peer; size_t offset = start % size_per_peer; int + child_in_team = ((my_pe_in_team + (teami->size - PE_root)) % teami->size) * k + peer_id + 1; if + (child_in_team >= teami->size) break; child_in_team = (child_in_team + PE_root) % teami->size; + int child = + nvshmemi_team_translate_pe(teami, child_in_team, + nvshmemi_device_state_d.team_pool[NVSHMEM_TEAM_WORLD_INDEX]); + //printf("sending data %d to %d at offset %llu, peer_id: %d, size_per_peer: %llu, + nelems: %llu\n", size_per_warp, child, offset, peer_id, size_per_peer, nelems); + //printf("myIdx: %d, start: %lld, num_warps: %d\n", myIdx, start, num_warps); + nvshmemi_put_nbi_threadgroup(pWrk + recv_offset + + offset, pWrk + recv_offset + offset, size_per_warp, child); + } + } else */ + { + nvshmemi_threadgroup_sync(); + for (int i = 0; i < k; i++) { + int child_in_team = + ((my_pe_in_team + (teami->size - PE_root)) % teami->size) * k + i + 1; + if (child_in_team >= teami->size) break; + child_in_team = (child_in_team + PE_root) % teami->size; + int child = nvshmemi_team_translate_pe(team, child_in_team, NVSHMEM_TEAM_WORLD_INDEX); + + nvshmemii_put_nbi_threadgroup( + (uint64_t *)(pWrk + recv_offset), (uint64_t *)(pWrk + recv_offset), + nelems * sizeof(T) / sizeof(uint32_t), child); + } + } + if (PE_root == my_pe_in_team && dest != source) + nvshmemi_memcpy_threadgroup(dest, source, nelems * sizeof(T)); + if (!myIdx) { /* Only one thread should increment */ + teami->bcast_sync_offset += sizeof(T) * nelems * 2; + } + nvshmemi_threadgroup_sync(); +} + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_bcast_internode_tree_threadgroup( + nvshmem_team_t team, T *dest, const T *source, size_t nelems, int PE_root, int k) { + nvshmemi_team_t *teami = nvshmemi_device_state_d.team_pool[team]; + const int myIdx = nvshmemi_thread_id_in_threadgroup(); + const int groupSize = nvshmemi_threadgroup_size(); + if (!myIdx) { /* Only one thread should increment */ + teami->ll_flag++; + teami->bcast_count++; + if ((teami->bcast_sync_offset + nelems * sizeof(T) * 2) > + (sizeof(long) * NVSHMEMI_BCAST_SYNC_SIZE)) { + nvshmemi_barrier_threadgroup(team); + teami->bcast_sync_offset = 0; + } + } + nvshmemi_threadgroup_sync(); + const uint32_t ll_flag = teami->ll_flag; + char *pWrk = (char *)nvshmemi_team_get_psync(teami, BCAST); + size_t recv_offset = teami->bcast_sync_offset; + const int my_pe_in_team = nvshmemi_team_my_pe(team); + + if (PE_root != my_pe_in_team) { + nvshmemi_recvLL(dest, (uint64_t *)(pWrk + recv_offset), nelems, ll_flag); + } else { + nvshmemi_packLL_naive((uint64_t *)(pWrk + recv_offset), source, nelems, ll_flag); + } + nvshmemi_threadgroup_sync(); + for (int i = myIdx; i < k; i += groupSize) { + int child_in_team = ((my_pe_in_team + (teami->size - PE_root)) % teami->size) * k + i + 1; + if (child_in_team >= teami->size) break; + child_in_team = (child_in_team + PE_root) % teami->size; + int child = nvshmemi_team_translate_pe(team, child_in_team, NVSHMEM_TEAM_WORLD_INDEX); + + nvshmemi_put_nbi_threadgroup( + (uint64_t *)(pWrk + recv_offset), (uint64_t *)(pWrk + recv_offset), + nelems * sizeof(T) / sizeof(uint32_t), child); + } + if (PE_root == my_pe_in_team && dest != source) + nvshmemi_memcpy_threadgroup(dest, source, nelems * sizeof(T)); + if (!myIdx) { /* Only one thread should increment */ + teami->bcast_sync_offset += sizeof(T) * nelems * 2; + } + nvshmemi_threadgroup_sync(); +} + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_bcast_tree_threadgroup( + nvshmem_team_t team, T *dest, const T *source, size_t nelems, int PE_root, int k) { + nvshmemi_team_t *teami = nvshmemi_device_state_d.team_pool[team]; + const int myIdx = nvshmemi_thread_id_in_threadgroup(); + const int groupSize = nvshmemi_threadgroup_size(); + if (!myIdx) { /* Only one thread should increment */ + teami->ll_flag++; + teami->bcast_count++; + if ((teami->bcast_sync_offset + nelems * sizeof(T) * 2) > + (sizeof(long) * NVSHMEMI_BCAST_SYNC_SIZE)) { + nvshmemi_barrier_threadgroup(team); + teami->bcast_sync_offset = 0; + } + } + nvshmemi_threadgroup_sync(); + const uint32_t ll_flag = teami->ll_flag; + char *pWrk = (char *)nvshmemi_team_get_psync(teami, BCAST); + size_t recv_offset = teami->bcast_sync_offset; + const int my_pe_in_team = nvshmemi_team_my_pe(team); + + if (PE_root != my_pe_in_team) { + nvshmemi_recvLL(dest, (uint64_t *)(pWrk + recv_offset), nelems, ll_flag); + } else { + nvshmemi_packLL_naive((uint64_t *)(pWrk + recv_offset), source, nelems, ll_flag); + } + nvshmemi_threadgroup_sync(); + /* Do remote transfers first */ + for (int i = myIdx; i < k; i += groupSize) { + int child_in_team = ((my_pe_in_team + (teami->size - PE_root)) % teami->size) * k + i + 1; + if (child_in_team >= teami->size) break; + child_in_team = (child_in_team + PE_root) % teami->size; + int child = nvshmemi_team_translate_pe(team, child_in_team, NVSHMEM_TEAM_WORLD_INDEX); + bool is_remote = (nvshmemi_ptr(pWrk, child) == NULL) ? true : false; + if (is_remote) + nvshmemi_put_nbi_threadgroup( + (uint64_t *)(pWrk + recv_offset), (uint64_t *)(pWrk + recv_offset), + nelems * sizeof(T) / sizeof(uint32_t), child); + } + + /* Do P2P transfers */ + for (int i = 0; i < k; i++) { + int child_in_team = ((my_pe_in_team + (teami->size - PE_root)) % teami->size) * k + i + 1; + if (child_in_team >= teami->size) break; + child_in_team = (child_in_team + PE_root) % teami->size; + int child = nvshmemi_team_translate_pe(team, child_in_team, NVSHMEM_TEAM_WORLD_INDEX); + + bool is_remote = (nvshmemi_ptr(pWrk, child) == NULL) ? true : false; + if (!is_remote) + nvshmemii_put_nbi_threadgroup( + (uint64_t *)(pWrk + recv_offset), (uint64_t *)(pWrk + recv_offset), + nelems * sizeof(T) / sizeof(uint32_t), child); + } + + if (PE_root == my_pe_in_team && dest != source) + nvshmemi_memcpy_threadgroup(dest, source, nelems * sizeof(T)); + if (!myIdx) { /* Only one thread should increment */ + teami->bcast_sync_offset += sizeof(T) * nelems * 2; + } + nvshmemi_threadgroup_sync(); +} + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_bcast_nonLL_tree_threadgroup( + nvshmem_team_t team, T *dest, const T *source, size_t nelems, int PE_root, int k) { + nvshmemi_team_t *teami = nvshmemi_device_state_d.team_pool[team]; + const int myIdx = nvshmemi_thread_id_in_threadgroup(); + const int groupSize = nvshmemi_threadgroup_size(); + if (!myIdx) { /* Only one thread should increment */ + teami->ll_flag++; + teami->bcast_count++; + if ((teami->bcast_sync_offset + sizeof(uint64_t)) > + (sizeof(long) * NVSHMEMI_BCAST_SYNC_SIZE)) { + nvshmemi_barrier_threadgroup(team); + teami->bcast_sync_offset = 0; + } + } + nvshmemi_threadgroup_sync(); + const uint32_t ll_flag = teami->ll_flag; + char *pWrk = (char *)nvshmemi_team_get_psync(teami, BCAST); + size_t recv_offset = teami->bcast_sync_offset; + const int my_pe_in_team = nvshmemi_team_my_pe(team); + + if (PE_root != my_pe_in_team) { + nvshmemi_wait_until((uint64_t *)(pWrk + recv_offset), NVSHMEM_CMP_EQ, ll_flag); + } + nvshmemi_threadgroup_sync(); + /* Do remote transfers first */ + for (int i = myIdx; i < k; i += groupSize) { + int child_in_team = ((my_pe_in_team + (teami->size - PE_root)) % teami->size) * k + i + 1; + if (child_in_team >= teami->size) break; + child_in_team = (child_in_team + PE_root) % teami->size; + int child = nvshmemi_team_translate_pe(team, child_in_team, NVSHMEM_TEAM_WORLD_INDEX); + bool is_remote = (nvshmemi_ptr(pWrk, child) == NULL) ? true : false; + if (is_remote) + nvshmemi_put_signal_threadgroup( + dest, (PE_root == my_pe_in_team) ? source : dest, nelems, + (uint64_t *)(pWrk + recv_offset), ll_flag, NVSHMEMI_AMO_SIGNAL_SET, child, 1); + } + + /* Do P2P transfers */ + for (int i = 0; i < k; i++) { + int child_in_team = ((my_pe_in_team + (teami->size - PE_root)) % teami->size) * k + i + 1; + if (child_in_team >= teami->size) break; + child_in_team = (child_in_team + PE_root) % teami->size; + int child = nvshmemi_team_translate_pe(team, child_in_team, NVSHMEM_TEAM_WORLD_INDEX); + + bool is_remote = (nvshmemi_ptr(pWrk, child) == NULL) ? true : false; + if (!is_remote) + nvshmemii_put_signal_threadgroup( + dest, (PE_root == my_pe_in_team) ? source : dest, nelems, + (uint64_t *)(pWrk + recv_offset), ll_flag, NVSHMEMI_AMO_SIGNAL_SET, child, 1); + } + + if (PE_root == my_pe_in_team && dest != source) + nvshmemi_memcpy_threadgroup(dest, source, nelems * sizeof(T)); + if (!myIdx) { /* Only one thread should increment */ + nvshmemi_quiet(); + teami->bcast_sync_offset += + 2 * sizeof(uint64_t); /* incrementing minimally by 16 bytes because this buffer is used + by packLL and packLL does 16byte writes */ + } + nvshmemi_threadgroup_sync(); +} + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_bcast_put2all_threadgroup( + nvshmem_team_t team, T *dest, const T *source, size_t nelems, int PE_root) { + nvshmemi_team_t *teami = nvshmemi_device_state_d.team_pool[team]; + int i; + int root = nvshmemi_team_translate_pe(team, PE_root, NVSHMEM_TEAM_WORLD_INDEX); + if (root == nvshmemi_device_state_d.mype) { + for (i = 0; i < teami->size; i++) { + int pe = nvshmemi_team_translate_pe_to_team_world_wrap(teami, i); + nvshmemi_put_nbi_threadgroup(dest, source, nelems, pe); + } + } + nvshmemi_barrier_threadgroup(team); +} + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_bcast_put2all_direct_threadgroup( + nvshmem_team_t team, T *dest, const T *source, size_t nelems, int PE_root) { + int myIdx = nvshmemi_thread_id_in_threadgroup(); + nvshmemi_team_t *teami = nvshmemi_device_state_d.team_pool[team]; + int i; + int root = nvshmemi_team_translate_pe(team, PE_root, NVSHMEM_TEAM_WORLD_INDEX); + T *dst_ptr; + if (root == nvshmemi_device_state_d.mype) { + for (i = 0; i < teami->size; i++) { + int pe = nvshmemi_team_translate_pe_to_team_world_wrap(teami, i); + dst_ptr = (T *)nvshmemi_ptr(dest, pe); + nvshmemi_memcpy_threadgroup(dst_ptr, source, nelems * sizeof(T)); + } + } + + nvshmemi_barrier_threadgroup(team); +} + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_bcast_hierarchical_threadgroup( + nvshmem_team_t team, T *dest, const T *source, size_t nelems, int PE_root) { + nvshmemi_team_t *teami = nvshmemi_device_state_d.team_pool[team]; + if (teami->is_team_same_mype_node) { + nvshmemi_bcast_internode_tree_threadgroup( + team, dest, source, nelems, PE_root, + nvshmemi_device_state_d.gpu_coll_env_params_var.bcast_tree_kval); + } else if (teami->is_team_node) { + nvshmemi_bcast_intranode_tree_threadgroup( + team, dest, source, nelems, PE_root, + nvshmemi_device_state_d.gpu_coll_env_params_var.bcast_tree_kval); + } else { + int team_npes_node = nvshmemi_team_n_pes(teami->team_node); + int PE_root_idx_in_team_node = PE_root % team_npes_node; + int my_idx_in_team_node = nvshmemi_team_my_pe(team) % team_npes_node; + if (PE_root_idx_in_team_node == my_idx_in_team_node) { + nvshmemi_bcast_internode_tree_threadgroup( + teami->team_same_mype_node, dest, source, nelems, PE_root / team_npes_node, + nvshmemi_device_state_d.gpu_coll_env_params_var.bcast_tree_kval); + } + nvshmemi_bcast_intranode_tree_threadgroup( + teami->team_node, dest, dest, nelems, PE_root_idx_in_team_node, + nvshmemi_device_state_d.gpu_coll_env_params_var.bcast_tree_kval); + } +} + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_broadcast_threadgroup( + nvshmem_team_t team, T *dest, const T *source, size_t nelems, int PE_root) { + int bcast_algo = nvshmemi_device_state_d.gpu_coll_env_params_var.bcast_algo; + switch (bcast_algo) { + case 0: + if (NVSHMEMI_BCAST_SYNC_SIZE * sizeof(long) >= (nelems * sizeof(T) * 2) && + sizeof(T) >= sizeof(uint32_t) && nelems % 2 == 0 && + nelems * sizeof(T) <= 16384) { /* LL algos */ + if (nvshmemi_team_n_pes(team) > 32 && + nvshmemi_device_state_d.pe_dist == + NVSHMEMI_PE_DIST_BLOCK) { /* hierarchical topo-aware */ + bcast_algo = 2; + } else + bcast_algo = 3; + } else /* non-LL algorithm */ + bcast_algo = 4; + break; + case 1: /* Brutefoce algorithm: send one to all followed by barrier */ + break; + case 2: /* Topology aware - two level hierarchical algorithm with LL approach */ + if (NVSHMEMI_BCAST_SYNC_SIZE * sizeof(long) >= (nelems * sizeof(T) * 2) && + sizeof(T) >= sizeof(uint32_t) && nelems % 2 == 0 && + nvshmemi_device_state_d.pe_dist == NVSHMEMI_PE_DIST_BLOCK) { + } else { + /*printf("User selected algo: %d, but it is not supported with currect config, \ + using default algo selection strategy..\n", \ + nvshmemi_device_state_d.gpu_coll_env_params_var.bcast_algo);*/ + bcast_algo = 1; + } + break; + case 3: /* Topology unaware tree algrithm with LL approach*/ + if (NVSHMEMI_BCAST_SYNC_SIZE * sizeof(long) >= (nelems * sizeof(T) * 2) && + sizeof(T) >= sizeof(uint32_t) && nelems % 2 == 0) { + } else { + /*printf("User selected algo: %d, but it is not supported with currect config, \ + using default algo selection strategy..\n", + nvshmemi_device_state_d.gpu_coll_env_params_var.bcast_algo);*/ + bcast_algo = 1; + } + break; + case 4: /* Topology unaware flat tree algrithm with LL approach*/ + if (NVSHMEMI_BCAST_SYNC_SIZE * sizeof(long) >= (nelems * sizeof(T) * 2) && + sizeof(T) >= sizeof(uint32_t) && nelems % 2 == 0) { + } else { + /* printf("User selected algo: %d, but it is not supported with currect config, \ + using default algo selection strategy..\n", + nvshmemi_device_state_d.gpu_coll_env_params_var.bcast_algo);*/ + bcast_algo = 1; + } + break; + default: + printf("Specified bcast algo:%d not supported, aborting...\n", bcast_algo); + assert(0); + break; + } + + switch (bcast_algo) { + case 1: /* Brutefoce algorithm: send one to all followed by barrier */ + if (nvshmemi_device_state_d.job_connectivity <= NVSHMEMI_JOB_GPU_LDST_REMOTE_ATOMICS) { + nvshmemi_bcast_put2all_direct_threadgroup(team, dest, source, nelems, + PE_root); + } else { + nvshmemi_bcast_put2all_threadgroup(team, dest, source, nelems, PE_root); + } + break; + case 2: /* Topology aware - two level hierarchical algorithm with LL approach */ + nvshmemi_bcast_hierarchical_threadgroup(team, dest, source, nelems, PE_root); + break; + case 3: /* Topology unaware tree algrithm with LL approach*/ + nvshmemi_bcast_tree_threadgroup( + team, dest, source, nelems, PE_root, + nvshmemi_device_state_d.gpu_coll_env_params_var.bcast_tree_kval); + break; + case 4: /* Topology unaware flat tree algrithm with LL approach*/ + nvshmemi_bcast_nonLL_tree_threadgroup(team, dest, source, nelems, PE_root, + nvshmemi_team_n_pes(team) - 1); + break; + default: + assert(0); + break; + } +} + +#endif /* __CUDA_ARCH__ */ + +#endif diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/device/coll/defines.cuh b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/device/coll/defines.cuh new file mode 100644 index 0000000000000000000000000000000000000000..f7d58983f509eea4e9ca3e9641eeeef0c61b2a6b --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/device/coll/defines.cuh @@ -0,0 +1,19 @@ +/* + * Copyright (c) 2018-2025, NVIDIA CORPORATION. All rights reserved. + * + * See License.txt for license information + */ + +#ifndef _NVSHMEMI_DEVICE_COLL_DEFINES_H_ +#define _NVSHMEMI_DEVICE_COLL_DEFINES_H_ + +#include "alltoall.cuh" +#include "barrier.cuh" +#include "broadcast.cuh" +#include "fcollect.cuh" +#include "reduce.cuh" +#include "reducescatter.cuh" +#include "broadcast.cuh" +#include "fcollect.cuh" + +#endif /* NVSHMEMI_DEVICE_COLL_DEFINES_H */ diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/device/coll/fcollect.cuh b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/device/coll/fcollect.cuh new file mode 100644 index 0000000000000000000000000000000000000000..75cd4b1ced3eb10782bb913fde1d51f41394d38b --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/device/coll/fcollect.cuh @@ -0,0 +1,1080 @@ +/* + * Copyright (c) 2017-2025, NVIDIA CORPORATION. All rights reserved. + * + * See License.txt for license information + */ +#ifndef FCOLLECT_DEVICE_CUH +#define FCOLLECT_DEVICE_CUH + +#if !defined __CUDACC_RTC__ +#include +#include +#else +#include "cuda/std/cstdint" +#include +#endif + +#include +#include "device_host/nvshmem_common.cuh" +#include "non_abi/device/common/nvshmemi_common_device.cuh" +#include "non_abi/device/common/nvshmemi_tile_utils.cuh" +#include "non_abi/nvshmem_build_options.h" +#include "device_host/nvshmem_tensor.h" +#if defined(NVSHMEM_ENABLE_ALL_DEVICE_INLINING) || defined(__NVSHMEM_NUMBA_SUPPORT__) +#include "non_abi/device/pt-to-pt/transfer_device.cuh" +#else +#include "non_abi/device/pt-to-pt/nvshmemi_transfer_api.cuh" +#endif +#include "non_abi/device/threadgroup/nvshmemi_common_device_defines.cuh" +#include "non_abi/device/common/nvshmemi_common_device.cuh" +#include "non_abi/device/team/nvshmemi_team_defines.cuh" + +#define _FCOLLECT_LL8_PSYNC_SCALE_FACTOR 2 + +#define _FCOLLECT_MAX(x, y) ((x) > (y) ? (x) : (y)) +#define _FCOLLECT_MIN(x, y) ((x) < (y) ? (x) : (y)) + +typedef enum { LL8 = 0, LL128 } ll_version_t; + +#ifdef __CUDA_ARCH__ + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_FORCE_INLINE void nvshmemi_fcollect_nvls_ll_threadgroup( + nvshmem_team_t team, T *dest, const T *source, size_t nelems) { +#if defined __clang_llvm_bitcode_lib__ + if (__nvvm_reflect("__CUDA_ARCH") >= 900) { + nvshmemi_team_t *teami = nvshmemi_device_state_d.team_pool[team]; + const size_t fcollect_ll_threshold = + nvshmemi_device_state_d.gpu_coll_env_params_var.fcollect_ll_threshold; + const size_t fcollect_count = teami->fcollect_count; + const uint32_t ll_flag = teami->fcollect_count; + char *pWrk = (char *)nvshmemi_team_get_psync(teami, FCOLLECT) + + (2 * teami->size * fcollect_ll_threshold * + (fcollect_count % 2)); /* same for NVLS in terms of size */ + const size_t pack_offset = (nvshmemi_team_my_pe(team) * nelems * sizeof(T)) / + sizeof(uint32_t); /* offset in pSync space */ + /* Find the multicast ptr for pWrk + pack_offset and do a store to remote pSync */ + void *mcast_pWrk = nvshmemi_mc_ptr(teami, (void *)((uint64_t *)pWrk + pack_offset)); + nvshmemi_mcast_packLL((uint64_t *)mcast_pWrk, source, nelems, ll_flag); + for (int ii = 0; ii < teami->size; ii += 1) { + size_t prev_offset = (nelems * ii * sizeof(T)) / sizeof(uint32_t); + nvshmemi_mcast_recvLL(dest + (ii * nelems), (uint64_t *)pWrk + prev_offset, + nelems, ll_flag); + } + + nvshmemi_threadgroup_sync(); + } else { + assert(0 && "NVLink SHARP is not supported on this platform"); + } +#else +#if __CUDA_ARCH__ >= 900 && CUDART_VERSION >= 12010 + nvshmemi_team_t *teami = nvshmemi_device_state_d.team_pool[team]; + const size_t fcollect_ll_threshold = + nvshmemi_device_state_d.gpu_coll_env_params_var.fcollect_ll_threshold; + const size_t fcollect_count = teami->fcollect_count; + const uint32_t ll_flag = teami->fcollect_count; + char *pWrk = (char *)nvshmemi_team_get_psync(teami, FCOLLECT) + + (2 * teami->size * fcollect_ll_threshold * + (fcollect_count % 2)); /* same for NVLS in terms of size */ + const size_t pack_offset = (nvshmemi_team_my_pe(team) * nelems * sizeof(T)) / + sizeof(uint32_t); /* offset in pSync space */ + /* Find the multicast ptr for pWrk + pack_offset and do a store to remote pSync */ + void *mcast_pWrk = nvshmemi_mc_ptr(teami, (void *)((uint64_t *)pWrk + pack_offset)); + nvshmemi_mcast_packLL((uint64_t *)mcast_pWrk, source, nelems, ll_flag); + for (int ii = 0; ii < teami->size; ii += 1) { + size_t prev_offset = (nelems * ii * sizeof(T)) / sizeof(uint32_t); + nvshmemi_mcast_recvLL(dest + (ii * nelems), (uint64_t *)pWrk + prev_offset, + nelems, ll_flag); + } + + nvshmemi_threadgroup_sync(); +#else + assert(0 && "NVLink SHARP is not supported on this platform"); +#endif +#endif +} + +/* This function must not ever call a block-scoped synchronization API. + * See call-site in nvshmemi_fcollect_threadgroup + */ +template +__device__ NVSHMEMI_DEVICE_ALWAYS_FORCE_INLINE void nvshmemi_fcollect_allpush_ll_threadgroup( + nvshmem_team_t team, T *dest, const T *source, size_t nelems) { + nvshmemi_team_t *teami = nvshmemi_device_state_d.team_pool[team]; + nvshmemi_team_t *teami_node = NULL; + const size_t fcollect_count = teami->fcollect_count; + const uint32_t ll_flag = teami->fcollect_count; + const int my_pe_in_team = teami->my_pe; + const int myIdx = nvshmemi_thread_id_in_threadgroup(); + const int groupSize = nvshmemi_threadgroup_size(); + const int myWarpIdx = nvshmemi_thread_id_in_threadgroup(); + size_t data_element_offset; + size_t psync_element_offset; + size_t psync_remote_write_elements; + T *peer_addr; + + T *pWrk; + size_t pack_offset; + size_t max_data_elems_per_warp; + size_t max_psync_elems_per_warp; + int next_pe, start_pe; + int num_pes_per_group, remaining_pes; + int num_warp_groups, num_warps_per_group; + int warp_id, warp_count, warp_group_id; + + if (NODE_SAFE) { + /* + * Really we mean team shared here, but that is not a team that exists today. + * TODO: Replace team_node with team_shared. + */ + if (teami->are_gpus_p2p_connected) { + teami_node = teami; + } else if (teami->team_node != NVSHMEM_TEAM_INVALID && + nvshmemi_device_state_d.team_pool[NVSHMEM_TEAM_SHARED]->is_team_node) { + teami_node = nvshmemi_device_state_d.team_pool[teami->team_node]; + } + } + + warp_count = nvshmemi_threadgroup_size() / 32; + warp_id = myIdx / 32; + + if (LL_VERSION == LL8) { + const size_t fcollect_ll_threshold = + nvshmemi_device_state_d.gpu_coll_env_params_var.fcollect_ll_threshold / sizeof(T); + pWrk = (T *)nvshmemi_team_get_psync(teami, FCOLLECT) + + (_FCOLLECT_LL8_PSYNC_SCALE_FACTOR * teami->size * fcollect_ll_threshold * + (fcollect_count % 2)); + /* round up to 16 bytes*/ + psync_remote_write_elements = + NVSHMEMI_TEAM_ROUND_UP((nelems * _FCOLLECT_LL8_PSYNC_SCALE_FACTOR), 16 / sizeof(T)); + pack_offset = my_pe_in_team * psync_remote_write_elements; + if (!NODE_SAFE || !teami->is_team_node) { + nvshmemi_packLL((T *)(pWrk + pack_offset), source, nelems, ll_flag, teami, + 1, my_pe_in_team); + } + } else { + const size_t fcollect_ll_threshold = + nvshmemi_device_state_d.gpu_coll_env_params_var.fcollect_ll128_threshold / sizeof(T); + pWrk = (T *)nvshmemi_team_get_psync(teami, FCOLLECT_128) + + (teami->size * NVSHMEMI_FCOLLECT_LL128_CALC_PSYNC_SIZE(fcollect_ll_threshold, T) * + (fcollect_count % 2)); + psync_remote_write_elements = NVSHMEMI_FCOLLECT_LL128_CALC_PSYNC_SIZE(nelems, T); + pack_offset = my_pe_in_team * psync_remote_write_elements; + if (!NODE_SAFE || !teami->is_team_node) { + nvshmemi_packLL128(pWrk + pack_offset, source, nelems, ll_flag, teami, 1, + my_pe_in_team, 0); + } + } + + /* send out non blocking puts for all remote PEs */ + if (teami_node != teami) { + for (uint32_t ii = myIdx + 1 + my_pe_in_team; ii < teami->size + my_pe_in_team; + ii += groupSize) { + next_pe = nvshmemi_team_translate_pe_to_team_world_wrap(teami, ii); + if (NODE_SAFE) { + if (nvshmemi_ptr(pWrk, next_pe) == NULL) { + nvshmemi_put_nbi_threadgroup( + pWrk + pack_offset, pWrk + pack_offset, psync_remote_write_elements, + next_pe); + } + } else { + nvshmemi_put_nbi_threadgroup( + pWrk + pack_offset, pWrk + pack_offset, psync_remote_write_elements, next_pe); + } + } + nvshmemi_threadgroup_sync(); + } + + if (LL_VERSION == LL8) { + max_data_elems_per_warp = _LL_8_DATA_BYTES_PER_WARP * _LL_MAX_UNROLL / sizeof(T); + max_psync_elems_per_warp = _LL_8_PSYNC_BYTES_PER_WARP * _LL_MAX_UNROLL / sizeof(T); + if (nelems < max_data_elems_per_warp) { + if (teami_node) { + for (uint32_t ii = warp_id + my_pe_in_team; ii < teami_node->size + my_pe_in_team; + ii += warp_count) { + next_pe = nvshmemi_team_translate_pe_to_team_world_wrap(teami_node, ii); + peer_addr = (T *)nvshmemi_ptr(pWrk, next_pe) + pack_offset; + nvshmemi_packLL_naive((uint64_t *)peer_addr, + source, nelems, ll_flag); + } + } + + for (uint32_t ii = warp_id; ii < teami->size; ii += warp_count) { + next_pe = ii; + pack_offset = + next_pe * NVSHMEMI_TEAM_ROUND_UP((nelems * _FCOLLECT_LL8_PSYNC_SCALE_FACTOR), + 16 / sizeof(T)); + nvshmemi_recvLL( + dest + (next_pe * nelems), (uint64_t *)(pWrk + pack_offset), nelems, ll_flag); + } + return; + } + } else { + max_data_elems_per_warp = _LL_128_DATA_BYTES_PER_WARP * _LL_MAX_UNROLL / sizeof(T); + max_psync_elems_per_warp = _LL_128_PSYNC_BYTES_PER_WARP * _LL_MAX_UNROLL / sizeof(T); + } + + num_warp_groups = + _FCOLLECT_MAX(1, warp_count / _FCOLLECT_MAX(1, nelems / max_data_elems_per_warp)); + num_warps_per_group = warp_count / num_warp_groups; + warp_group_id = warp_id / num_warps_per_group; + + if (teami_node != NULL) { + /* first n ggroups take on an extra PE in the case of remainder */ + num_pes_per_group = teami_node->size / num_warp_groups; + remaining_pes = teami_node->size % num_warp_groups; + num_pes_per_group += warp_group_id < remaining_pes ? 1 : 0; + + start_pe = num_pes_per_group * warp_group_id; + start_pe += warp_group_id >= remaining_pes ? remaining_pes : 0; + + data_element_offset = warp_id % num_warps_per_group * max_data_elems_per_warp; + psync_element_offset = warp_id % num_warps_per_group * max_psync_elems_per_warp; + /* All warps except final one per-pe should be full. */ + for (; data_element_offset + max_data_elems_per_warp < nelems; + data_element_offset += num_warps_per_group * max_data_elems_per_warp, + psync_element_offset += num_warps_per_group * max_psync_elems_per_warp) { + if (LL_VERSION == LL8) { + nvshmemi_packLL( + pWrk + pack_offset + psync_element_offset, source + data_element_offset, + max_data_elems_per_warp, ll_flag, teami_node, num_pes_per_group, start_pe); + } else { + nvshmemi_packLL128( + pWrk + pack_offset + psync_element_offset, source + data_element_offset, + max_data_elems_per_warp, ll_flag, teami_node, num_pes_per_group, start_pe, + warp_id % num_warps_per_group); + } + } + + if (nelems > data_element_offset) { + if (LL_VERSION == LL8) { + nvshmemi_packLL( + pWrk + pack_offset + psync_element_offset, source + data_element_offset, + nelems - data_element_offset, ll_flag, teami_node, num_pes_per_group, start_pe); + } else { + nvshmemi_packLL128( + pWrk + pack_offset + psync_element_offset, source + data_element_offset, + nelems - data_element_offset, ll_flag, teami_node, num_pes_per_group, start_pe, + warp_id % num_warps_per_group); + ; + } + } + } + + /* todo: also try unrolling in recvLL */ + num_pes_per_group = teami->size / num_warp_groups; + remaining_pes = teami->size % num_warp_groups; + /* first n ggroups take on an extra PE in the case of remainder */ + num_pes_per_group += warp_group_id < remaining_pes ? 1 : 0; + + start_pe = num_pes_per_group * warp_group_id; + start_pe += warp_group_id >= remaining_pes ? remaining_pes : 0; + data_element_offset = warp_id % num_warps_per_group * max_data_elems_per_warp; + psync_element_offset = warp_id % num_warps_per_group * max_psync_elems_per_warp; + for (; data_element_offset + max_data_elems_per_warp < nelems; + data_element_offset += num_warps_per_group * max_data_elems_per_warp, + psync_element_offset += num_warps_per_group * max_psync_elems_per_warp) { + for (next_pe = start_pe; next_pe < start_pe + num_pes_per_group; next_pe++) { + if (LL_VERSION == LL8) { + pack_offset = + next_pe * NVSHMEMI_TEAM_ROUND_UP((nelems * _FCOLLECT_LL8_PSYNC_SCALE_FACTOR), + 16 / sizeof(T)) + + psync_element_offset; + nvshmemi_recvLL( + dest + (next_pe * nelems) + data_element_offset, + (uint64_t *)(pWrk + pack_offset), max_data_elems_per_warp, ll_flag); + } else { + pack_offset = next_pe * NVSHMEMI_FCOLLECT_LL128_CALC_PSYNC_SIZE(nelems, T) + + psync_element_offset; + nvshmemi_recvLL128( + dest + (next_pe * nelems) + data_element_offset, pWrk + pack_offset, + max_data_elems_per_warp, ll_flag); + } + } + } + + if (nelems > data_element_offset) { + for (next_pe = start_pe; next_pe < start_pe + num_pes_per_group; next_pe++) { + if (LL_VERSION == LL8) { + pack_offset = + next_pe * NVSHMEMI_TEAM_ROUND_UP((nelems * _FCOLLECT_LL8_PSYNC_SCALE_FACTOR), + 16 / sizeof(T)) + + psync_element_offset; + nvshmemi_recvLL( + dest + (next_pe * nelems) + data_element_offset, + (uint64_t *)(pWrk + pack_offset), nelems - data_element_offset, ll_flag); + } else { + pack_offset = next_pe * NVSHMEMI_FCOLLECT_LL128_CALC_PSYNC_SIZE(nelems, T) + + psync_element_offset; + nvshmemi_recvLL128(dest + (next_pe * nelems) + data_element_offset, + pWrk + pack_offset, nelems - data_element_offset, ll_flag); + } + } + } +} + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_fcollect_allpush_threadgroup( + nvshmem_team_t team, T *dest, const T *source, int dest_offset, size_t nelems) { + nvshmemi_team_t *teami = nvshmemi_device_state_d.team_pool[team]; + int next_rank; + int myIdx = nvshmemi_thread_id_in_threadgroup(); + + // nvshmemi_threadgroup_sync(); + for (int ii = teami->my_pe; ii < teami->size + teami->my_pe; ii++) { + next_rank = nvshmemi_team_translate_pe_to_team_world_wrap(teami, ii); + nvshmemi_put_nbi_threadgroup(dest + dest_offset, source, nelems, next_rank); + } + nvshmemi_barrier_threadgroup(team); +} + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_fcollect_p2p_allpush_threadgroup( + nvshmem_team_t team, T *dest, const T *source, int dest_offset, size_t nelems) { + nvshmemi_team_t *teami = nvshmemi_device_state_d.team_pool[team]; + int next_rank; + T *dst_ptr; + nvshmemi_threadgroup_sync(); + for (int ii = teami->my_pe; ii < teami->size + teami->my_pe; ii++) { + next_rank = nvshmemi_team_translate_pe_to_team_world_wrap(teami, ii); + dst_ptr = (T *)nvshmemi_ptr((void *)(dest + dest_offset), next_rank); + nvshmemi_memcpy_threadgroup(dst_ptr, source, nelems * sizeof(T)); + } + nvshmemi_barrier_threadgroup(team); +} + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_fcollect_nvls_allpush_threadgroup( + nvshmem_team_t team, T *dest, const T *source, int dest_offset, size_t nelems) { +#if defined __clang_llvm_bitcode_lib__ + if (__nvvm_reflect("__CUDA_ARCH") >= 900) { + nvshmemi_team_t *teami = nvshmemi_device_state_d.team_pool[team]; + nvshmemi_threadgroup_sync(); + T *dst_ptr = (T *)nvshmemi_mc_ptr(teami, (void *)(dest + dest_offset)); + nvshmemi_mcast_memcpy_threadgroup(dst_ptr, source, nelems * sizeof(T)); + nvshmemi_barrier_threadgroup(team); + } else { + assert(0 && "NVLS is not supported on this platform"); + } +#else +#if __CUDA_ARCH__ >= 900 && CUDART_VERSION >= 12010 + nvshmemi_team_t *teami = nvshmemi_device_state_d.team_pool[team]; + nvshmemi_threadgroup_sync(); + T *dst_ptr = (T *)nvshmemi_mc_ptr(teami, (void *)(dest + dest_offset)); + nvshmemi_mcast_memcpy_threadgroup(dst_ptr, source, nelems * sizeof(T)); + nvshmemi_barrier_threadgroup(team); +#else + assert(0 && "NVLS is not supported on this platform"); +#endif +#endif +} + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_FORCE_INLINE void nvshmemi_fcollect_threadgroup( + nvshmem_team_t team, T *dest, const T *source, int dest_offset, size_t nelems) { + int myIdx = nvshmemi_thread_id_in_threadgroup(); + int nthreads = nvshmemi_threadgroup_size(); + if (!myIdx) /* Only one thread should increment fcollect_count */ + nvshmemi_device_state_d.team_pool[team]->fcollect_count += 1; + nvshmemi_threadgroup_sync(); + constexpr bool is_half_prec = + is_half::value || is_bfloat::value || is_uint16::value || is_int16::value; + int fcollect_algo = nvshmemi_device_state_d.gpu_coll_env_params_var.fcollect_algo; + int p2p_direct = + (nvshmemi_device_state_d.job_connectivity <= NVSHMEMI_JOB_GPU_LDST_REMOTE_ATOMICS); + const size_t fcollect_ll_threshold = + nvshmemi_device_state_d.gpu_coll_env_params_var.fcollect_ll_threshold; + const size_t fcollect_ll128_threshold = + nvshmemi_device_state_d.gpu_coll_env_params_var.fcollect_ll128_threshold; + /* NVLS LL performs better with block scoped than thread/warp scoped operations + due to better efficiency of distributing cvt/pack/unpack ops across threads across GPUs */ + const uint8_t prefer_nvls_ll = (SCOPE == NVSHMEMI_THREADGROUP_BLOCK); + bool valid_ll_configuration = (SCOPE != NVSHMEMI_THREADGROUP_THREAD && + ((sizeof(T) >= sizeof(uint32_t) && (nelems % 2 == 0)) || + (is_half_prec && (nelems % 4 == 0) && + (nvshmemi_device_state_d.team_pool[team]->size % 2 == 0)))); + /* DISABLE non NVLS LL for hybrid MNNVL configurations. */ + valid_ll_configuration &= + (nvshmemi_device_state_d.team_pool[NVSHMEM_TEAM_WORLD_INDEX]->are_gpus_p2p_connected || + nvshmemi_device_state_d.team_pool[NVSHMEM_TEAM_SHARED]->is_team_node || + !nvshmemi_device_state_d.team_pool[NVSHMEM_TEAM_NODE_INDEX]->are_gpus_p2p_connected); + /* This 2-level selection logic is implemented to reduce code duplication of calling leaf + * functions on the device code */ + switch (fcollect_algo) { + case 0: /* default selection */ { + if (valid_ll_configuration && fcollect_ll_threshold >= (nelems * sizeof(T))) { + fcollect_algo = FCOLLECT_LL8; /* LL algorithm */ + } else if (valid_ll_configuration && (nelems * sizeof(T)) < fcollect_ll128_threshold) { + fcollect_algo = FCOLLECT_LL128; + } else if (sizeof(T) >= sizeof(uint32_t) && (nelems % 2 == 0) && + nvshmemi_device_state_d.gpu_coll_env_params_var.fcollect_ll_threshold >= + (nelems * sizeof(T)) && + nvshmemi_device_state_d.team_pool[team]->nvls_rsc_base_ptr != NULL && + prefer_nvls_ll) { + fcollect_algo = FCOLLECT_NVLS_LL; /* NVLS LL algorithm */ + } else if (nvshmemi_device_state_d.team_pool[team]->nvls_rsc_base_ptr != NULL && + (nelems * sizeof(T)) % 4 == 0) { + fcollect_algo = FCOLLECT_NVLS; /* NVLS One shot algorithm */ + } else { + fcollect_algo = FCOLLECT_ONESHOT; /* P2P One shot algorithm */ + } + } break; + case FCOLLECT_LL8: /* LL algorithm */ + if (!valid_ll_configuration) { + fcollect_algo = FCOLLECT_ONESHOT; + } + break; + case FCOLLECT_ONESHOT: /* One shot */ + break; + case FCOLLECT_NVLS: + if (nvshmemi_device_state_d.team_pool[team]->nvls_rsc_base_ptr != NULL && + (nelems * sizeof(T)) % 4 == 0) { + /* NVLS simple */ + break; + } else { + fcollect_algo = FCOLLECT_ONESHOT; /* One shot */ + break; + } + case FCOLLECT_NVLS_LL: + if (sizeof(T) >= sizeof(uint32_t) && (nelems % 2 == 0) && + nvshmemi_device_state_d.gpu_coll_env_params_var.fcollect_ll_threshold >= + (nelems * sizeof(T)) && + nvshmemi_device_state_d.team_pool[team]->nvls_rsc_base_ptr != NULL) { + fcollect_algo = FCOLLECT_NVLS_LL; /* Use NVLS LL */ + } else if (nvshmemi_device_state_d.team_pool[team]->nvls_rsc_base_ptr != NULL && + (nelems * sizeof(T)) % 4 == 0) { + fcollect_algo = FCOLLECT_NVLS; /* Switch to NVLS simple */ + } else { + fcollect_algo = FCOLLECT_ONESHOT; /* One shot */ + } + break; + case FCOLLECT_LL128: /* LL 128 */ + if (!valid_ll_configuration) { + fcollect_algo = FCOLLECT_ONESHOT; + } + break; + default: + assert(0 && "Unsupported fcollect algo"); + break; + } + + switch (fcollect_algo) { + case FCOLLECT_LL8: + if (myIdx < nthreads / 32 * 32) { + if (nvshmemi_device_state_d.team_pool[NVSHMEM_TEAM_NODE_INDEX] + ->are_gpus_p2p_connected) { + nvshmemi_fcollect_allpush_ll_threadgroup(team, dest, + source, nelems); + } else { + nvshmemi_fcollect_allpush_ll_threadgroup(team, dest, + source, nelems); + } + } + nvshmemi_threadgroup_sync(); + break; + case FCOLLECT_ONESHOT: + if (p2p_direct) + nvshmemi_fcollect_p2p_allpush_threadgroup(team, dest, source, dest_offset, + nelems); + else + nvshmemi_fcollect_allpush_threadgroup(team, dest, source, dest_offset, + nelems); + break; + case FCOLLECT_NVLS: + nvshmemi_fcollect_nvls_allpush_threadgroup(team, dest, source, dest_offset, + nelems); + break; + case FCOLLECT_LL128: + if (myIdx < nthreads / 32 * 32) { + if (nvshmemi_device_state_d.team_pool[NVSHMEM_TEAM_NODE_INDEX] + ->are_gpus_p2p_connected) { + nvshmemi_fcollect_allpush_ll_threadgroup(team, dest, + source, nelems); + } else { + nvshmemi_fcollect_allpush_ll_threadgroup( + team, dest, source, nelems); + } + } + nvshmemi_threadgroup_sync(); + break; + case FCOLLECT_NVLS_LL: + nvshmemi_fcollect_nvls_ll_threadgroup(team, dest, source, nelems); + break; + default: + assert(0); + break; + } +} + +// ************** Tile allgather **************/ + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_tile_allgather_mcast_threadgroup_v4( + int4 *dest, const int4 *source, const int nelem_major_dim, const int nelem_minor_dim, + const int src_stride_minor_dim, const int dst_stride_minor_dim, const int src_stride_major_dim, + const int dst_stride_major_dim, tuple_t start_coord, tuple_t boundary) { + /*src_stride_major_dim == 0 && dst_stride_major_dim == 0 for vectorized implementation*/ + int myIdx = nvshmemi_thread_id_in_threadgroup(); + int groupSize = nvshmemi_threadgroup_size(); + int nelems = nelem_major_dim * nelem_minor_dim; /* # vec elems*/ + if constexpr (std::is_empty::value) { + /* If no predicate, we vectorize the operation */ + for (size_t j = myIdx; j < nelems; j += groupSize) { + uint32_t u4[4]; + asm("ld.global.v4.b32 {%0, %1, %2, %3}, [%4];" + : "=r"(u4[0]), "=r"(u4[1]), "=r"(u4[2]), "=r"(u4[3]) + : "l"(source + ((j) % nelem_major_dim) + + ((j) / nelem_major_dim) * src_stride_minor_dim)); + + asm("multimem.st.global.v4.f32 [%0], {%1, %2, %3, %4};" ::"l"( + dest + ((j) % nelem_major_dim) + + (((j) / nelem_major_dim) * dst_stride_minor_dim)), + "r"(u4[0]), "r"(u4[1]), "r"(u4[2]), "r"(u4[3]) + : "memory"); + } + } else { + using vtype = int4; + using cxx_type = uint32_t; + for (size_t j = myIdx; j < nelems; j += groupSize) { + uint32_t u4[4]; + /* nelem_major_dim is in vector units*/ + uint32_t elem_coord_major = (j % nelem_major_dim) * (sizeof(vtype) / sizeof(elemType)); + uint32_t elem_coord_minor = (j / nelem_major_dim); + + /* start_coord, boundary are in elemType units */ + /* Check if entire vector is within boundary */ + /* start_coord_major_dim + elem_coord_major_dim + vector len (in elements) <= + * boundary_major_dim */ + if (is_less_than( + start_coord, create_coord_tuple(elem_coord_major, elem_coord_minor), + boundary, (sizeof(vtype) / sizeof(elemType)))) { + asm("ld.global.v4.b32 {%0, %1, %2, %3}, [%4];" + : "=r"(u4[0]), "=r"(u4[1]), "=r"(u4[2]), "=r"(u4[3]) + : "l"(source + ((j) % nelem_major_dim) + + ((j) / nelem_major_dim) * src_stride_minor_dim)); + + asm("multimem.st.global.v4.f32 [%0], {%1, %2, %3, %4};" ::"l"( + dest + ((j) % nelem_major_dim) + + (((j) / nelem_major_dim) * dst_stride_minor_dim)), + "r"(u4[0]), "r"(u4[1]), "r"(u4[2]), "r"(u4[3]) + : "memory"); + + } else { /* not all pred elems in vector are 1 */ + /* perform operations one elem at a time */ + /* if elem type is < 4B (e.g., f16, bf16), we check at granularity of 4B */ + + /* convert elem_coord_major from elemType to cxx_type units */ + /* no change to elem_coord_minor */ + elem_coord_major = (elem_coord_major * sizeof(elemType)) / sizeof(cxx_type); + + /* vector is partially within boundary, check each element */ + cxx_type val; + for (int u = 0; u < sizeof(vtype) / sizeof(cxx_type); ++u) { + /* check if elem is within boundary, use u & elem_coord_major in elemType units + */ + if (is_less_than( + start_coord, + create_coord_tuple( + ((elem_coord_major + u) * sizeof(cxx_type) / sizeof(elemType)), + elem_coord_minor), + boundary)) { + /* convert strides from vector to cxx_type units */ + asm("ld.global.b32 %0, [%1];" + : "=r"(val) + : "l"(reinterpret_cast(source) + + (elem_coord_major + u) + + (elem_coord_minor * src_stride_minor_dim * + (sizeof(vtype) / sizeof(cxx_type))))); + + asm("multimem.st.global.u32 [%0], %1;" ::"l"( + reinterpret_cast(dest) + (elem_coord_major + u) + + (elem_coord_minor * dst_stride_minor_dim * + (sizeof(vtype) / sizeof(cxx_type)))), + "r"(val) + : "memory"); + } + } + } + } + } /*end of if else*/ +} + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_tile_allgather_mcast_threadgroup_v2( + uint64_t *dest, const uint64_t *source, const int nelem_major_dim, const int nelem_minor_dim, + const int src_stride_minor_dim, const int dst_stride_minor_dim, const int src_stride_major_dim, + const int dst_stride_major_dim, tuple_t start_coord, tuple_t boundary) { + /* src_stride_major_dim == 0 && dst_stride_major_dim == 0 for vectorized implementation */ + int myIdx = nvshmemi_thread_id_in_threadgroup(); + int groupSize = nvshmemi_threadgroup_size(); + int nelems = nelem_major_dim * nelem_minor_dim; /* # vec elems*/ + + if constexpr (std::is_empty::value) { + /* If no predicate, we vectorize the operation */ + for (size_t j = myIdx; j < nelems; j += groupSize) { + uint64_t val1; + asm("ld.global.b64 %0, [%1];" + : "=l"(val1) + : "l"(source + ((j) % nelem_major_dim) + + ((j) / nelem_major_dim) * src_stride_minor_dim)); + + asm("multimem.st.global.u64 [%0], %1;" ::"l"( + dest + ((j) % nelem_major_dim) + + (((j) / nelem_major_dim) * dst_stride_minor_dim)), + "l"(val1) + : "memory"); + } + } else { + using vtype = uint64_t; + using cxx_type = uint32_t; + for (size_t j = myIdx; j < nelems; j += groupSize) { + uint64_t val1; + /* nelem_major_dim is in vector units*/ + /* compute elem_coord_major in elemType units*/ + uint32_t elem_coord_major = (j % nelem_major_dim) * (sizeof(vtype) / sizeof(elemType)); + uint32_t elem_coord_minor = (j / nelem_major_dim); + + /* start_coord, boundary are in elemType units */ + /* Check if entire vector is within boundary */ + /* start_coord_major_dim + elem_coord_major_dim + vector len (in elements) <= + * boundary_major_dim */ + if (is_less_than( + start_coord, create_coord_tuple(elem_coord_major, elem_coord_minor), + boundary, (sizeof(vtype) / sizeof(elemType)))) { + asm("ld.global.b64 %0, [%1];" + : "=l"(val1) + : "l"(source + ((j) % nelem_major_dim) + + ((j) / nelem_major_dim) * src_stride_minor_dim)); + + asm("multimem.st.global.u64 [%0], %1;" ::"l"( + dest + ((j) % nelem_major_dim) + + (((j) / nelem_major_dim) * dst_stride_minor_dim)), + "l"(val1) + : "memory"); + + } else { /* not all pred elems in vector are 1 */ + /* perform operations one elem at a time */ + /* if elem type is < 4B (e.g., f16, bf16), we check at granularity of 4B */ + + /* convert elem_coord_major from elemType to cxx_type units */ + /* no change to elem_coord_minor */ + elem_coord_major = (elem_coord_major * sizeof(elemType)) / sizeof(cxx_type); + + /* vector is partially within boundary, check each element */ + cxx_type val; + for (int u = 0; u < sizeof(vtype) / sizeof(cxx_type); ++u) { + /* check if elem is within boundary, use u and elem_coord_major in elemType + * units */ + if (is_less_than( + start_coord, + create_coord_tuple( + ((elem_coord_major + u) * sizeof(cxx_type) / sizeof(elemType)), + elem_coord_minor), + boundary)) { + /* convert strides from vector to cxx_type units */ + asm("ld.global.b32 %0, [%1];" + : "=r"(val) + : "l"(reinterpret_cast(source) + + (elem_coord_major + u) + + (elem_coord_minor * src_stride_minor_dim * + (sizeof(vtype) / sizeof(cxx_type))))); + + asm("multimem.st.global.u32 [%0], %1;" ::"l"( + reinterpret_cast(dest) + (elem_coord_major + u) + + (elem_coord_minor * dst_stride_minor_dim * + (sizeof(vtype) / sizeof(cxx_type)))), + "r"(val) + : "memory"); + } + } + } + } + } /*end of if else*/ +} + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_tile_allgather_mcast_threadgroup_v1( + uint32_t *dest, const uint32_t *source, const int nelem_major_dim, const int nelem_minor_dim, + const int src_stride_minor_dim, const int dst_stride_minor_dim, const int src_stride_major_dim, + const int dst_stride_major_dim, tuple_t start_coord, tuple_t boundary) { + int myIdx = nvshmemi_thread_id_in_threadgroup(); + int groupSize = nvshmemi_threadgroup_size(); + int nelems = nelem_major_dim * nelem_minor_dim; /* # vec elems*/ + using vtype = uint32_t; + using cxx_type = uint32_t; + if constexpr (std::is_empty::value) { + cxx_type val; + for (size_t j = myIdx; j < nelems; j += groupSize) { + asm("ld.global.b32 %0, [%1];" + : "=r"(val) + : "l"(source + ((j % nelem_major_dim) * src_stride_major_dim) + + ((j / nelem_major_dim) * src_stride_minor_dim))); + + asm("multimem.st.global.u32 [%0], %1;" ::"l"( + dest + ((j % nelem_major_dim) * dst_stride_major_dim) + + ((j / nelem_major_dim) * dst_stride_minor_dim)), + "r"(val) + : "memory"); + } + } else { + for (size_t j = myIdx; j < nelems; j += groupSize) { + /* nelem_major_dim is in vector units*/ + /* compute elem_coord_major in elemType units*/ + uint32_t elem_coord_major = (j % nelem_major_dim) * (sizeof(vtype) / sizeof(elemType)); + uint32_t elem_coord_minor = (j / nelem_major_dim); + cxx_type val; + + /* convert elem_coord_major from elemType to cxx_type units */ + /* no change to elem_coord_minor */ + elem_coord_major = (elem_coord_major * sizeof(elemType)) / sizeof(cxx_type); + + for (int u = 0; u < sizeof(vtype) / sizeof(cxx_type); ++u) { + /* check if elem is within boundary, use u and elem_coord_major in elemType units */ + if (is_less_than( + start_coord, + create_coord_tuple( + ((elem_coord_major + u) * sizeof(cxx_type) / sizeof(elemType)), + elem_coord_minor), + boundary)) { + /* convert strides from vector to cxx_type units */ + asm("ld.global.b32 %0, [%1];" + : "=r"(val) + : "l"(reinterpret_cast(source) + (elem_coord_major + u) + + (elem_coord_minor * src_stride_minor_dim * + (sizeof(vtype) / sizeof(cxx_type))))); + + asm("multimem.st.global.u32 [%0], %1;" ::"l"( + reinterpret_cast(dest) + (elem_coord_major + u) + + (elem_coord_minor * dst_stride_minor_dim * + (sizeof(vtype) / sizeof(cxx_type)))), + "r"(val) + : "memory"); + } + } + } + } /*end of if else*/ +} + +// Select implementation based on the operation, datatype +template +__device__ inline void nvshmemi_tile_allgather_nvls_threadgroup_vec( + nvshmem_team_t team, T *src, T *dst, + const int size_major_dim, // size along the major dimension in elements + const int size_minor_dim, // size along the minor dimension in elements + const int src_stride_minor_dim, // src stride along minor dimension in elements + const int dst_stride_minor_dim, // dst stride along minor dimension in elements + const int src_stride_major_dim, // src stride along major dimension in elements + const int dst_stride_major_dim, // dst stride along major dimension in elements + tuple_t start_coord, tuple_t boundary) { + // src is local, dst is multicast address + vtype *src_v = reinterpret_cast(src); + vtype *dst_v = reinterpret_cast(nvshmemx_mc_ptr(team, dst)); + assert((dst_v != nullptr) && "Failed to get multicast ptr for destination"); + + int src_stride_minor_dim_v = src_stride_minor_dim; + if (src_stride_minor_dim > 1) { + src_stride_minor_dim_v = (src_stride_minor_dim * sizeof(T)) / sizeof(vtype); + } + int dst_stride_minor_dim_v = dst_stride_minor_dim; + if (dst_stride_minor_dim > 1) { + dst_stride_minor_dim_v = (dst_stride_minor_dim * sizeof(T)) / sizeof(vtype); + } + int src_stride_major_dim_v = src_stride_major_dim; // keep stride as is if ==1 + if (src_stride_major_dim > 1) { + src_stride_major_dim_v = (src_stride_major_dim * sizeof(T)) / sizeof(vtype); + } + int dst_stride_major_dim_v = dst_stride_major_dim; + if (dst_stride_major_dim > 1) { + dst_stride_major_dim_v = (dst_stride_major_dim * sizeof(T)) / sizeof(vtype); + } + + int nelem_major_dim = (size_major_dim * sizeof(T)) / sizeof(vtype); + int nelem_minor_dim = size_minor_dim; + + if constexpr (std::is_same::value) { + nvshmemi_tile_allgather_mcast_threadgroup_v4( + dst_v, src_v, nelem_major_dim, nelem_minor_dim, src_stride_minor_dim_v, + dst_stride_minor_dim_v, src_stride_major_dim_v, dst_stride_major_dim_v, start_coord, + boundary); + + } else if constexpr (std::is_same::value) { + nvshmemi_tile_allgather_mcast_threadgroup_v2( + dst_v, src_v, nelem_major_dim, nelem_minor_dim, src_stride_minor_dim_v, + dst_stride_minor_dim_v, src_stride_major_dim_v, dst_stride_major_dim_v, start_coord, + boundary); + + } else if constexpr (std::is_same::value) { + nvshmemi_tile_allgather_mcast_threadgroup_v1( + dst_v, src_v, nelem_major_dim, nelem_minor_dim, src_stride_minor_dim_v, + dst_stride_minor_dim_v, src_stride_major_dim_v, dst_stride_major_dim_v, start_coord, + boundary); + + } else { + if (std::is_same::value) { + nvshmemi_tile_allgather_mcast_threadgroup_v4( + dst_v, src_v, nelem_major_dim, nelem_minor_dim, src_stride_minor_dim_v, + dst_stride_minor_dim_v, src_stride_major_dim_v, dst_stride_major_dim_v, start_coord, + boundary); + + } else if (std::is_same::value) { + nvshmemi_tile_allgather_mcast_threadgroup_v2( + dst_v, src_v, nelem_major_dim, nelem_minor_dim, src_stride_minor_dim_v, + dst_stride_minor_dim_v, src_stride_major_dim_v, dst_stride_major_dim_v, start_coord, + boundary); + + } else if (std::is_same::value) { + nvshmemi_tile_allgather_mcast_threadgroup_v1( + dst_v, src_v, nelem_major_dim, nelem_minor_dim, src_stride_minor_dim_v, + dst_stride_minor_dim_v, src_stride_major_dim_v, dst_stride_major_dim_v, start_coord, + boundary); + } else { + assert(0 && "unsupported vector type"); + } + } +} + +template +__device__ inline void nvshmemi_tile_allgather_nvls_dim(nvshmem_team_t team, + src_tensor_t src_tensor, + dst_tensor_t dst_tensor, + tuple_t start_coord, tuple_t boundary) { + using T = typename src_tensor_t::value_type; + int mype = nvshmem_team_my_pe(team); + + /* The destination tensor is npes * src_tensor size + * Assume 4 PEs, with src_tensor as shown below + * __ __ __ __ + * |__| |__| |__| |__| // src_tensor of 4 PEs + * + * dest_tensor is 4 x src_tensor + * __ __ + * |__|__| // dest_tensor of a PE (4xsrc_tensor) + * |__|__| + * + * Other shapes are possible for dst_tensor + * __ __ __ __ __ + * |__|__|__|__| |__| + * |__| + * |__| + * |__| + * + * For every src_tensor from a given PE, we need to find the starting offset within + * dest_tensor. + */ + + // Compute the offset within dst tile specific for the PE + T *dst = + (dst_tensor.data() + + (((mype * get_shape_element(src_tensor)) % + get_shape_element(dst_tensor)) * + get_stride_element(dst_tensor)) + + (((mype * get_shape_element(src_tensor)) / + get_shape_element(dst_tensor)) * + (get_shape_element(src_tensor) * get_stride_element(dst_tensor)))); + + // Since it is PUSH based implementation, number of elements being copied is same as + // src_tensor so, elements in each dimension is based on src_tensor but stride is based on + // dst_tensor + + // check for vector len == 4 + // Conditions: ptr must be aligned to int4, shape must be a multiple of 16, stride must be a + // multiple of 16 + if (((size_t)src_tensor.data() % sizeof(int4) == 0) && + ((size_t)dst_tensor.data() % sizeof(int4) == 0) && + (((get_tuple_val(src_tensor.shape()) * sizeof(T)) % sizeof(int4)) == 0) && + (((get_stride_element(src_tensor) * sizeof(T)) % sizeof(int4)) == 0) && + (((get_stride_element(dst_tensor) * sizeof(T)) % sizeof(int4)) == 0)) { + nvshmemi_tile_allgather_nvls_threadgroup_vec( + team, src_tensor.data(), dst, + get_shape_element(src_tensor), // contiguous size + get_shape_element(src_tensor), // strided size + get_stride_element(src_tensor), // src stride minor_dim + get_stride_element(dst_tensor), // dst stride minor_dim + get_stride_element(src_tensor), // src stride major_dim; equal to 1 + get_stride_element(dst_tensor), // dst stride major_dim; equal to 1 + start_coord, boundary); + + } else if (((size_t)src_tensor.data() % sizeof(uint64_t) == 0) && + ((size_t)dst_tensor.data() % sizeof(uint64_t) == 0) && + (((get_tuple_val(src_tensor.shape()) * sizeof(T)) % sizeof(uint64_t)) == + 0) && + (((get_stride_element(src_tensor) * sizeof(T)) % sizeof(uint64_t)) == + 0) && + (((get_stride_element(dst_tensor) * sizeof(T)) % sizeof(uint64_t)) == + 0)) { + nvshmemi_tile_allgather_nvls_threadgroup_vec( + team, src_tensor.data(), dst, + get_shape_element(src_tensor), // contiguous size + get_shape_element(src_tensor), // strided size + get_stride_element(src_tensor), // src stride minor_dim + get_stride_element(dst_tensor), // dst stride minor_dim + get_stride_element(src_tensor), // src stride major_dim; equal to 1 + get_stride_element(dst_tensor), // dst stride major_dim; equal to 1 + start_coord, boundary); + + } else { // vector len 1 + nvshmemi_tile_allgather_nvls_threadgroup_vec( + team, src_tensor.data(), dst, + get_shape_element(src_tensor), // contiguous size + get_shape_element(src_tensor), // strided size + get_stride_element(src_tensor), // src stride minor_dim + get_stride_element(dst_tensor), // dst stride minor_dim + get_stride_element(src_tensor), // src stride major_dim; equal to 1 + get_stride_element(dst_tensor), // dst stride major_dim; equal to 1 + start_coord, boundary); + } +} +// specialize for the vectorization +template +__device__ inline void nvshmemi_tile_allgather_nvls_threadgroup(nvshmem_team_t team, + src_tensor_t src_tensor, + dst_tensor_t dst_tensor, + tuple_t start_coord, + tuple_t boundary) { + using T = typename src_tensor_t::value_type; + if constexpr ((get_constant(safe_get<0>(decltype(src_tensor.stride()){})) == 1) && + (get_constant(safe_get<0>(decltype(dst_tensor.stride()){})) == 1)) { + // dim 0 major + constexpr int major_dim = 0; + constexpr int minor_dim = 1; + + if constexpr (sizeof(T) < 4) { + // Shape along major dimension should be divisible by 2, because we operate at fp16x2 + assert(((get_shape_element(src_tensor) % 2) == 0) && + ((get_shape_element(dst_tensor) % 2) == 0) && + "Currently for 16B datatypes, we only support tensors which are 32b aligned " + "along their continuous dimension"); + } + + nvshmemi_tile_allgather_nvls_dim(team, src_tensor, dst_tensor, start_coord, + boundary); + } else if constexpr ((get_constant(safe_get<1>(decltype(src_tensor.stride()){})) == 1) && + (get_constant(safe_get<1>(decltype(dst_tensor.stride()){})) == 1)) { + // dim 1 major + constexpr int major_dim = 1; + constexpr int minor_dim = 0; + + if constexpr (sizeof(T) < 4) { + // Shape along major dimension should be divisible by 2, because we operate at fp16x2 + assert(((get_shape_element(src_tensor) % 2) == 0) && + ((get_shape_element(dst_tensor) % 2) == 0) && + "Currently for 16B datatypes, we only support tensors which are 32b aligned " + "along their continuous dimension"); + } + + nvshmemi_tile_allgather_nvls_dim(team, src_tensor, dst_tensor, start_coord, + boundary); + } else { + // No contiguous dimension found at compile time + // TODO support when major dimension for src and tensor are different + if ((get_stride_element<1>(src_tensor) == 1) && (get_stride_element<1>(dst_tensor) == 1)) { + constexpr int major_dim = 1; + constexpr int minor_dim = 0; + + if constexpr (sizeof(T) < 4) { + // Shape along major dimension should be divisible by 2, because we operate at + // fp16x2 + assert(((get_shape_element(src_tensor) % 2) == 0) && + ((get_shape_element(dst_tensor) % 2) == 0) && + "Currently for 16B datatypes, we only support tensors which are 32b aligned " + "along their continuous dimension"); + } + + nvshmemi_tile_allgather_nvls_dim(team, src_tensor, dst_tensor, start_coord, + boundary); + } else { + // setting major_dim to 0, minor_dim to 1 + constexpr int major_dim = 0; + constexpr int minor_dim = 1; + + if constexpr (sizeof(T) < 4) { + // Shape along major dimension should be divisible by 2, because we operate at + // fp16x2 + assert(((get_shape_element(src_tensor) % 2) == 0) && + ((get_shape_element(dst_tensor) % 2) == 0) && + "Currently for 16B datatypes, we only support tensors which are 32b aligned " + "along their continuous dimension"); + } + nvshmemi_tile_allgather_nvls_dim(team, src_tensor, dst_tensor, start_coord, + boundary); + } + } +} + +// Tile allgather entrypoint +// Call underlying function based on scope and algo +template +__device__ inline int nvshmemi_tile_allgather(nvshmem_team_t team, src_tensor_t src_tensor, + dst_tensor_t dst_tensor, tuple_t start_coord, + tuple_t boundary, uint64_t flag) { +#if defined(__cplusplus) && __cplusplus < 201703L + assert(0 && "Tile-granular APIs need C++ 17"); +#endif + using T = typename src_tensor_t::value_type; + + static_assert( + std::is_same::value, + "Source and destination tensors must have the same type"); + + static_assert((algo == nvshmemx::tile_coll_algo_t::NVLS_ONE_SHOT_PUSH_NBI), + "Unsupported tile AllGather algorithm. " + "Currently NVLS_ONE_SHOT_PUSH_NBI is supported for tile allgather"); + + static_assert((scope == NVSHMEMI_THREADGROUP_THREAD) || (scope == NVSHMEMI_THREADGROUP_WARP) || + (scope == NVSHMEMI_THREADGROUP_WARPGROUP) || + (scope == NVSHMEMI_THREADGROUP_BLOCK), + "Unsupported scope"); + + assert((src_tensor.data() != nullptr) && (dst_tensor.data() != nullptr) && + "Null pointers passed"); + + // check shape + assert((get_shape_element<0>(src_tensor) * get_shape_element<1>(src_tensor) * + nvshmem_team_n_pes(team)) && + (get_shape_element<0>(dst_tensor) * get_shape_element<1>(dst_tensor))); + + // TODO add other data types + static_assert(((is_half::value) || (is_bfloat::value) || (is_float::value)), + "Unsupported datatype"); + + // check if both src and dst have same continuous dimension + // TODO relax this constraint + assert( + (((get_stride_element<0>(src_tensor) == 1) && (get_stride_element<0>(dst_tensor) == 1)) || + ((get_stride_element<1>(src_tensor) == 1) && (get_stride_element<1>(dst_tensor) == 1))) && + "Currently we only support cases where source and destination tile are continuous " + "along one dimension"); + + assert(!flag && "Currently non-zero flag value is unsupported"); + + // NVLS Gather only has one-shot push support currently + if constexpr (algo == nvshmemx::tile_coll_algo_t::NVLS_ONE_SHOT_PUSH_NBI) { + // check for NVLS support in hardware +#if __CUDA_ARCH__ >= 900 && CUDART_VERSION >= 12010 + + // NVLS ONE_SHOT AllGather is PUSH based algo, so we can directly start communicating + // User should ensure src data is ready + + nvshmemi_tile_allgather_nvls_threadgroup( + team, src_tensor, dst_tensor, start_coord, boundary); +#else + assert(__CUDA_ARCH__ >= 900 && CUDART_VERSION >= 12010 && + "Unsupported NVLS on this platform"); +#endif + return 0; + } else { + // Extend as other algorithms are added + return 0; + } +} +#endif /* __CUDA_ARCH__ */ +#endif /* FCOLLECT_DEVICE_CUH */ diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/device/coll/reduce.cuh b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/device/coll/reduce.cuh new file mode 100644 index 0000000000000000000000000000000000000000..17eee2e33d0daf5cdca8769cf3b80bfe695fd4e8 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/device/coll/reduce.cuh @@ -0,0 +1,2208 @@ +/* + * Copyright (c) 2017-2025, NVIDIA CORPORATION. All rights reserved. + * + * See License.txt for license information + */ + +#ifndef REDUCE_DEVICE_CUH +#define REDUCE_DEVICE_CUH + +#include +#include "non_abi/device/pt-to-pt/proxy_device.cuh" +#include "non_abi/device/threadgroup/nvshmemi_common_device_defines.cuh" +#include "non_abi/device/common/nvshmemi_common_device.cuh" +#include "non_abi/device/team/nvshmemi_team_defines.cuh" +#include "non_abi/device/common/nvshmemi_tile_utils.cuh" +#include "device_host/nvshmem_tensor.h" +#include "non_abi/nvshmem_build_options.h" +#if defined(NVSHMEM_ENABLE_ALL_DEVICE_INLINING) || defined(__NVSHMEM_NUMBA_SUPPORT__) +#include "non_abi/device/pt-to-pt/transfer_device.cuh" +#else +#include "non_abi/device/pt-to-pt/nvshmemi_transfer_api.cuh" +#endif +#include "non_abi/device/wait/nvshmemi_wait_until_apis.cuh" +#include "utils.cuh" +#include "fcollect.cuh" +#include "broadcast.cuh" + +#ifdef __CUDACC__ +#include +#include +namespace cg = cooperative_groups; +#endif + +#ifdef __CUDA_ARCH__ + +#define GPU_BITS_COPY_THREADGROUP_DIRECT(TYPENAME, TYPE, dest, src, nelems, myIdx, groupSize) \ + do { \ + int i; \ + for (i = myIdx; i < nelems; i += groupSize) { \ + *((TYPE *)dest + i) = *((TYPE *)src + i); \ + } \ + } while (0) + +template +#if !defined __CUDACC_RTC__ +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE + typename std::enable_if::value, T>::type + perform_gpu_rdxn(T op1, T op2) { +#else +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE + typename cuda::std::enable_if::value, T>::type + perform_gpu_rdxn(T op1, T op2) { +#endif + switch (op) { + case RDXN_OPS_SUM: + return op1 + op2; + case RDXN_OPS_PROD: + return op1 * op2; + case RDXN_OPS_AND: + return op1 & op2; + case RDXN_OPS_OR: + return op1 | op2; + case RDXN_OPS_XOR: + return op1 ^ op2; + case RDXN_OPS_MIN: + return (op1 < op2) ? op1 : op2; + case RDXN_OPS_MAX: + return (op1 > op2) ? op1 : op2; + default: + printf("Unsupported rdxn op\n"); + assert(0); + return T(); + } +} + +template +#if !defined __CUDACC_RTC__ +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE + typename std::enable_if::value, T>::type + perform_gpu_rdxn(T op1, T op2) { +#else +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE + typename cuda::std::enable_if::value, T>::type + perform_gpu_rdxn(T op1, T op2) { +#endif + switch (op) { + case RDXN_OPS_SUM: + return op1 + op2; + case RDXN_OPS_PROD: + return op1 * op2; + case RDXN_OPS_MIN: + return (op1 < op2) ? op1 : op2; + case RDXN_OPS_MAX: + return (op1 > op2) ? op1 : op2; + default: + printf("Unsupported rdxn op\n"); + assert(0); + return T(); + } +} + +template <> +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE double2 +perform_gpu_rdxn(double2 op1, double2 op2) { + return (op1.x > op2.x) ? op1 : op2; +} + +template +NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE __device__ void gpu_linear_reduce_threadgroup( + TYPE *x, TYPE *y, TYPE *z, size_t nelems) { + int myIdx = nvshmemi_thread_id_in_threadgroup(); + int groupSize = nvshmemi_threadgroup_size(); + int i; + for (i = myIdx; i < nelems; i += groupSize) { + z[i] = perform_gpu_rdxn(x[i], y[i]); + } +} + +#define NVSHMEMI_MCAST_PTX_REG_TYPE_u32 "r" +#define NVSHMEMI_MCAST_PTX_REG_TYPE_b32 "r" +#define NVSHMEMI_MCAST_PTX_REG_TYPE_s32 "r" +#define NVSHMEMI_MCAST_PTX_REG_TYPE_f32 "f" +#define NVSHMEMI_MCAST_PTX_REG_TYPE_u64 "l" +#define NVSHMEMI_MCAST_PTX_REG_TYPE_b64 "l" +#define NVSHMEMI_MCAST_PTX_REG_TYPE_s64 "l" +#define NVSHMEMI_MCAST_PTX_REG_TYPE_f64 "d" +#define NVSHMEMI_MCAST_PTX_REG_TYPE_f16x2 "r" +#define NVSHMEMI_MCAST_PTX_REG_TYPE_bf16x2 "r" + +#define NVSHMEMI_MCAST_ADD_MIXOP_f16x2 "add.acc::f32" +#define NVSHMEMI_MCAST_ADD_MIXOP_bf16x2 "add.acc::f32" +#define NVSHMEMI_MCAST_ADD_MIXOP_f32 "add" +#define NVSHMEMI_MCAST_MIN_MIXOP_f16x2 "min.acc::f32" +#define NVSHMEMI_MCAST_MIN_MIXOP_bf16x2 "min.acc::f32" +#define NVSHMEMI_MCAST_MIN_MIXOP_f32 "min" +#define NVSHMEMI_MCAST_MAX_MIXOP_f16x2 "max.acc::f32" +#define NVSHMEMI_MCAST_MAX_MIXOP_bf16x2 "max.acc::f32" +#define NVSHMEMI_MCAST_MAX_MIXOP_f32 "max" + +// mcast ldreduce+multimem.st of 16B +// The requirement to use these primitives is that nelems % UNROLL == 0 +#define NVSHMEMI_MCAST16_REDUCE_THREADGROUP_SUM_V4(PTX_TYPE) \ + template \ + __device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void \ + nvshmemi_##PTX_TYPE##_add_reduce_mcast16_v4_threadgroup(int4 *dest, const int4 *source, \ + size_t nelems) { \ + int myIdx = nvshmemi_thread_id_in_threadgroup(); \ + int groupSize = nvshmemi_threadgroup_size(); \ + for (size_t j = myIdx * UNROLL; j < nelems; j += groupSize * UNROLL) { \ + uint32_t u4[4 * UNROLL]; \ + _Pragma("unroll UNROLL") for (int u = 0; u < UNROLL; u++) { \ + asm("multimem.ld_reduce.global." NVSHMEMI_MCAST_ADD_MIXOP_##PTX_TYPE \ + ".v4." #PTX_TYPE " {%0, %1, %2, %3}, [%4];" \ + : "=r"(u4[4 * u]), "=r"(u4[4 * u + 1]), "=r"(u4[4 * u + 2]), \ + "=r"(u4[4 * u + 3]) \ + : "l"(source + j + u)); \ + } \ + _Pragma("unroll UNROLL") for (int u = 0; u < UNROLL; u++) { \ + if (ONESHOT) { \ + asm("st.global.v4.b32 [%0], {%1, %2, %3, %4};" ::"l"(dest + j + u), \ + "r"(u4[4 * u]), "r"(u4[4 * u + 1]), "r"(u4[4 * u + 2]), \ + "r"(u4[4 * u + 3])); \ + } else { \ + asm("multimem.st.global.v4." #PTX_TYPE \ + " [%0], {%1, %2, %3, %4};" ::"l"(dest + j + u), \ + "r"(u4[4 * u]), "r"(u4[4 * u + 1]), "r"(u4[4 * u + 2]), \ + "r"(u4[4 * u + 3])); \ + } \ + } \ + } \ + } +// mcast ldreduce+multimem.st of 8B +#define NVSHMEMI_MCAST8_REDUCE_THREADGROUP_SUM_V2(CXX_TYPE, PTX_TYPE) \ + template \ + __device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void \ + nvshmemi_##PTX_TYPE##_add_reduce_mcast8_v2_threadgroup( \ + uint64_t *dest, const uint64_t *source, size_t nelems) { \ + int myIdx = nvshmemi_thread_id_in_threadgroup(); \ + int groupSize = nvshmemi_threadgroup_size(); \ + for (size_t j = myIdx; j < nelems; j += groupSize) { \ + CXX_TYPE val1[2]; \ + asm("multimem.ld_reduce.global." NVSHMEMI_MCAST_ADD_MIXOP_##PTX_TYPE \ + ".v2." #PTX_TYPE " {%0, %1}, [%2];" \ + : "=" NVSHMEMI_MCAST_PTX_REG_TYPE_##PTX_TYPE(val1[0]), \ + "=" NVSHMEMI_MCAST_PTX_REG_TYPE_##PTX_TYPE(val1[1]) \ + : "l"(source + j)); \ + if (ONESHOT) \ + asm("st.global.v2.b32 [%0], {%1, %2};" ::"l"(dest + j), \ + NVSHMEMI_MCAST_PTX_REG_TYPE_##PTX_TYPE(val1[0]), \ + NVSHMEMI_MCAST_PTX_REG_TYPE_##PTX_TYPE(val1[1])); \ + else \ + asm("multimem.st.global.v2." #PTX_TYPE " [%0], {%1, %2};" ::"l"(dest + j), \ + NVSHMEMI_MCAST_PTX_REG_TYPE_##PTX_TYPE(val1[0]), \ + NVSHMEMI_MCAST_PTX_REG_TYPE_##PTX_TYPE(val1[1])); \ + } \ + } + +#define NVSHMEMI_MCAST4_REDUCE_THREADGROUP(OP, CXX_TYPE, PTX_TYPE) \ + template \ + __device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void \ + nvshmemi_##PTX_TYPE##_##OP##_reduce_mcast4_threadgroup( \ + uint32_t *dest, const uint32_t *source, size_t nelems) { \ + int myIdx = nvshmemi_thread_id_in_threadgroup(); \ + int groupSize = nvshmemi_threadgroup_size(); \ + for (size_t j = myIdx; j < nelems; j += groupSize) { \ + CXX_TYPE val1; \ + asm("multimem.ld_reduce.global." NVSHMEMI_MCAST_ADD_MIXOP_##PTX_TYPE "." #PTX_TYPE \ + " %0, [%1];" \ + : "=" NVSHMEMI_MCAST_PTX_REG_TYPE_##PTX_TYPE(val1) \ + : "l"(source + j)); \ + if (ONESHOT) \ + asm("st.global.b32 [%0], %1;" ::"l"(dest + j), \ + NVSHMEMI_MCAST_PTX_REG_TYPE_##PTX_TYPE(val1)); \ + else \ + asm("multimem.st.global." #PTX_TYPE " [%0], %1;" ::"l"(dest + j), \ + NVSHMEMI_MCAST_PTX_REG_TYPE_##PTX_TYPE(val1)); \ + } \ + } + +/* nvshmemi__add_reduce_mcast16_v4_threadgroup(int4 *dest,const int4 *source, size_t + * nelems) distributes contiguous "nelems" elements across the threadgroup. For tile collective, + * input is a tile (often strided along a dimension), calling the above function along the contigous + * dimension repeatedly will underutilize the threads so using a dedicated function + */ +#define NVSHMEMI_MCAST_TILE_ALLREDUCE_THREADGROUP(CXX_TYPE, PTX_TYPE, OP_TYPE) \ + template \ + __device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void \ + nvshmemi_##PTX_TYPE##_tile_allreduce##OP_TYPE##_mcast_threadgroup_v4( \ + int4 *dest, const int4 *source, const int nelem_major_dim, const int nelem_minor_dim, \ + const int src_stride_minor_dim, const int dst_stride_minor_dim, \ + const int src_stride_major_dim, const int dst_stride_major_dim, tuple_t start_coord, \ + tuple_t boundary) { \ + /*src_stride_major_dim == 1 && dst_stride_major_dim == 1 for vectorized implementation*/ \ + int myIdx = nvshmemi_thread_id_in_threadgroup(); \ + int groupSize = nvshmemi_threadgroup_size(); \ + int nelems = nelem_major_dim * nelem_minor_dim; /* # vec elems*/ \ + using vtype = int4; \ + if constexpr (cuda::std::is_empty::value) { \ + /* If no predicate, we vectorize the operation */ \ + for (size_t j = myIdx * UNROLL; j < nelems; j += groupSize * UNROLL) { \ + uint32_t u4[4 * UNROLL]; \ + _Pragma("unroll UNROLL") for (int u = 0; u < UNROLL; u++) { \ + asm("multimem.ld_reduce.global." NVSHMEMI_MCAST_##OP_TYPE##_MIXOP_##PTX_TYPE \ + ".v4." #PTX_TYPE " {%0, %1, %2, %3}, [%4];" \ + : "=r"(u4[4 * u]), "=r"(u4[4 * u + 1]), "=r"(u4[4 * u + 2]), \ + "=r"(u4[4 * u + 3]) \ + : "l"(source + ((j + u) % nelem_major_dim) + \ + ((j + u) / nelem_major_dim) * src_stride_minor_dim)); \ + } \ + _Pragma("unroll UNROLL") for (int u = 0; u < UNROLL; u++) { \ + if (ONESHOT) { \ + asm("st.global.v4.b32 [%0], {%1, %2, %3, %4};" ::"l"( \ + dest + ((j + u) % nelem_major_dim) + \ + (((j + u) / nelem_major_dim) * dst_stride_minor_dim)), \ + "r"(u4[4 * u]), "r"(u4[4 * u + 1]), "r"(u4[4 * u + 2]), \ + "r"(u4[4 * u + 3])); \ + } else { \ + asm("multimem.st.global.v4." #PTX_TYPE " [%0], {%1, %2, %3, %4};" ::"l"( \ + dest + ((j + u) % nelem_major_dim) + \ + (((j + u) / nelem_major_dim) * dst_stride_minor_dim)), \ + "r"(u4[4 * u]), "r"(u4[4 * u + 1]), "r"(u4[4 * u + 2]), \ + "r"(u4[4 * u + 3])); \ + } \ + } \ + } \ + } else { \ + /* if predicate is provided, we use UNROLL == 1 to prevent repeated computation of \ + * pred*/ \ + for (size_t j = myIdx; j < nelems; j += groupSize) { \ + uint32_t u4[4]; \ + /* nelem_major_dim is in vector units*/ \ + uint32_t elem_coord_major = \ + (j % nelem_major_dim) * (sizeof(vtype) / sizeof(elemType)); \ + uint32_t elem_coord_minor = (j / nelem_major_dim); \ + \ + /* start_coord, boundary are in elemType units */ \ + /* Check if entire vector is within boundary */ \ + /* start_coord_major_dim + elem_coord_major_dim + vector len (in elements) <= \ + * boundary_major_dim */ \ + if (is_less_than( \ + start_coord, \ + create_coord_tuple(elem_coord_major, elem_coord_minor), \ + boundary, (sizeof(vtype) / sizeof(elemType)))) { \ + /* entire vector is within boundary*/ \ + asm("multimem.ld_reduce.global." NVSHMEMI_MCAST_##OP_TYPE##_MIXOP_##PTX_TYPE \ + ".v4." #PTX_TYPE " {%0, %1, %2, %3}, [%4];" \ + : "=r"(u4[0]), "=r"(u4[1]), "=r"(u4[2]), "=r"(u4[3]) \ + : "l"(source + ((j) % nelem_major_dim) + \ + ((j) / nelem_major_dim) * src_stride_minor_dim)); \ + \ + if (ONESHOT) { \ + asm("st.global.v4.b32 [%0], {%1, %2, %3, %4};" ::"l"( \ + dest + (j % nelem_major_dim) + \ + ((j / nelem_major_dim) * dst_stride_minor_dim)), \ + "r"(u4[0]), "r"(u4[1]), "r"(u4[2]), "r"(u4[3])); \ + } else { \ + asm("multimem.st.global.v4." #PTX_TYPE " [%0], {%1, %2, %3, %4};" ::"l"( \ + dest + (j % nelem_major_dim) + \ + ((j / nelem_major_dim) * dst_stride_minor_dim)), \ + "r"(u4[0]), "r"(u4[1]), "r"(u4[2]), "r"(u4[3])); \ + } \ + } else { \ + /* vector is partially within boundary, check each element */ \ + CXX_TYPE val; \ + /* convert elem_coord_major from elemType to CXX_TYPE units */ \ + /* no change to elem_coord_minor */ \ + elem_coord_major = (elem_coord_major * sizeof(elemType)) / sizeof(CXX_TYPE); \ + for (int u = 0; u < sizeof(vtype) / sizeof(CXX_TYPE); ++u) { \ + /* check if elem is within boundary, use u & elem_coord_major in elemType \ + * units */ \ + if (is_less_than( \ + start_coord, \ + create_coord_tuple( \ + ((elem_coord_major + u) * sizeof(CXX_TYPE) / \ + sizeof(elemType)), \ + elem_coord_minor), \ + boundary)) { \ + /* convert strides from vector to CXX_TYPE units */ \ + asm("multimem.ld_reduce." \ + "global." NVSHMEMI_MCAST_##OP_TYPE##_MIXOP_##PTX_TYPE \ + "." #PTX_TYPE " %0, [%1];" \ + : "=" NVSHMEMI_MCAST_PTX_REG_TYPE_##PTX_TYPE(val) \ + : "l"(reinterpret_cast(source) + \ + (elem_coord_major + u) + \ + (elem_coord_minor * src_stride_minor_dim * \ + (sizeof(vtype) / sizeof(CXX_TYPE))))); \ + if (ONESHOT) \ + asm("st.global.b32 [%0], %1;" ::"l"( \ + reinterpret_cast(dest) + \ + +(elem_coord_major + u) + \ + +(elem_coord_minor * dst_stride_minor_dim * \ + (sizeof(vtype) / sizeof(CXX_TYPE)))), \ + NVSHMEMI_MCAST_PTX_REG_TYPE_##PTX_TYPE(val)); \ + else \ + asm("multimem.st.global." #PTX_TYPE \ + " [%0], %1;" ::"l"(reinterpret_cast(dest) + \ + (elem_coord_major + u) + \ + (elem_coord_minor * dst_stride_minor_dim * \ + (sizeof(vtype) / sizeof(CXX_TYPE)))), \ + NVSHMEMI_MCAST_PTX_REG_TYPE_##PTX_TYPE(val)); \ + } \ + } \ + } \ + } \ + } /*end of if else*/ \ + } \ + \ + /* ************** Vector len = 2 specialization ************** */ \ + template \ + __device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void \ + nvshmemi_##PTX_TYPE##_tile_allreduce##OP_TYPE##_mcast_threadgroup_v2( \ + uint64_t *dest, const uint64_t *source, const int nelem_major_dim, \ + const int nelem_minor_dim, const int src_stride_minor_dim, \ + const int dst_stride_minor_dim, const int src_stride_major_dim, \ + const int dst_stride_major_dim, tuple_t start_coord, tuple_t boundary) { \ + using vtype = uint64_t; \ + /*src_stride_major_dim == 0 && dst_stride_major_dim == 0 for vectorized implementation*/ \ + int myIdx = nvshmemi_thread_id_in_threadgroup(); \ + int groupSize = nvshmemi_threadgroup_size(); \ + int nelems = nelem_major_dim * nelem_minor_dim; \ + \ + if constexpr (cuda::std::is_empty::value) { \ + /* If no predicate, we vectorize the operation */ \ + for (size_t j = myIdx; j < nelems; j += groupSize) { \ + CXX_TYPE val1[2]; \ + asm("multimem.ld_reduce.global." NVSHMEMI_MCAST_##OP_TYPE##_MIXOP_##PTX_TYPE \ + ".v2." #PTX_TYPE " {%0, %1}, [%2];" \ + : "=" NVSHMEMI_MCAST_PTX_REG_TYPE_##PTX_TYPE(val1[0]), \ + "=" NVSHMEMI_MCAST_PTX_REG_TYPE_##PTX_TYPE(val1[1]) \ + : "l"(source + (j % nelem_major_dim) + \ + ((j / nelem_major_dim) * src_stride_minor_dim))); \ + if (ONESHOT) \ + asm("st.global.v2.b32 [%0], {%1, %2};" ::"l"( \ + dest + (j % nelem_major_dim) + \ + ((j / nelem_major_dim) * dst_stride_minor_dim)), \ + NVSHMEMI_MCAST_PTX_REG_TYPE_##PTX_TYPE(val1[0]), \ + NVSHMEMI_MCAST_PTX_REG_TYPE_##PTX_TYPE(val1[1])); \ + else \ + asm("multimem.st.global.v2." #PTX_TYPE \ + " [%0], {%1, %2};" ::"l"(dest + (j % nelem_major_dim) + \ + ((j / nelem_major_dim) * dst_stride_minor_dim)), \ + NVSHMEMI_MCAST_PTX_REG_TYPE_##PTX_TYPE(val1[0]), \ + NVSHMEMI_MCAST_PTX_REG_TYPE_##PTX_TYPE(val1[1])); \ + } \ + } else { \ + for (size_t j = myIdx; j < nelems; j += groupSize) { \ + CXX_TYPE val1[2]; \ + /* nelem_major_dim is in vector units, convert to elemType units*/ \ + uint32_t elem_coord_major = \ + (j % nelem_major_dim) * (sizeof(vtype) / sizeof(elemType)); \ + uint32_t elem_coord_minor = (j / nelem_major_dim); \ + \ + /* start_coord, boundary are in elemType units */ \ + /* Check if entire vector is within boundary */ \ + /* start_coord_major_dim + elem_coord_major_dim + vector len (in elements) <= \ + * boundary_major_dim */ \ + if (is_less_than( \ + start_coord, \ + create_coord_tuple(elem_coord_major, elem_coord_minor), \ + boundary, (sizeof(vtype) / sizeof(elemType)))) { \ + asm("multimem.ld_reduce.global." NVSHMEMI_MCAST_##OP_TYPE##_MIXOP_##PTX_TYPE \ + ".v2." #PTX_TYPE " {%0, %1}, [%2];" \ + : "=" NVSHMEMI_MCAST_PTX_REG_TYPE_##PTX_TYPE(val1[0]), \ + "=" NVSHMEMI_MCAST_PTX_REG_TYPE_##PTX_TYPE(val1[1]) \ + : "l"(source + (j % nelem_major_dim) + \ + ((j / nelem_major_dim) * src_stride_minor_dim))); \ + if (ONESHOT) \ + asm("st.global.v2.b32 [%0], {%1, %2};" ::"l"( \ + dest + (j % nelem_major_dim) + \ + ((j / nelem_major_dim) * dst_stride_minor_dim)), \ + NVSHMEMI_MCAST_PTX_REG_TYPE_##PTX_TYPE(val1[0]), \ + NVSHMEMI_MCAST_PTX_REG_TYPE_##PTX_TYPE(val1[1])); \ + else \ + asm("multimem.st.global.v2." #PTX_TYPE " [%0], {%1, %2};" ::"l"( \ + dest + (j % nelem_major_dim) + \ + ((j / nelem_major_dim) * dst_stride_minor_dim)), \ + NVSHMEMI_MCAST_PTX_REG_TYPE_##PTX_TYPE(val1[0]), \ + NVSHMEMI_MCAST_PTX_REG_TYPE_##PTX_TYPE(val1[1])); \ + } else { \ + /* convert elem_coord_major from elemType to CXX_TYPE units */ \ + /* no change to elem_coord_minor */ \ + elem_coord_major = (elem_coord_major * sizeof(elemType)) / sizeof(CXX_TYPE); \ + /* vector is partially within boundary, check each element */ \ + for (int u = 0; u < sizeof(vtype) / sizeof(CXX_TYPE); ++u) { \ + /* check if elem is within boundary, use u & elem_coord_major in elemType \ + * units */ \ + if (is_less_than( \ + start_coord, \ + create_coord_tuple( \ + ((elem_coord_major + u) * sizeof(CXX_TYPE) / \ + sizeof(elemType)), \ + elem_coord_minor), \ + boundary)) { \ + /* convert strides from vector to CXX_TYPE units */ \ + asm("multimem.ld_reduce." \ + "global." NVSHMEMI_MCAST_##OP_TYPE##_MIXOP_##PTX_TYPE \ + "." #PTX_TYPE " %0, [%1];" \ + : "=" NVSHMEMI_MCAST_PTX_REG_TYPE_##PTX_TYPE(val1[0]) \ + : "l"(reinterpret_cast(source) + \ + (elem_coord_major + u) + \ + (elem_coord_minor * src_stride_minor_dim * \ + (sizeof(vtype) / sizeof(CXX_TYPE))))); \ + if (ONESHOT) \ + asm("st.global.b32 [%0], %1;" ::"l"( \ + reinterpret_cast(dest) + \ + +(elem_coord_major + u) + \ + +(elem_coord_minor * dst_stride_minor_dim * \ + (sizeof(vtype) / sizeof(CXX_TYPE)))), \ + NVSHMEMI_MCAST_PTX_REG_TYPE_##PTX_TYPE(val1[0])); \ + else \ + asm("multimem.st.global." #PTX_TYPE \ + " [%0], %1;" ::"l"(reinterpret_cast(dest) + \ + (elem_coord_major + u) + \ + (elem_coord_minor * dst_stride_minor_dim * \ + (sizeof(vtype) / sizeof(CXX_TYPE)))), \ + NVSHMEMI_MCAST_PTX_REG_TYPE_##PTX_TYPE(val1[0])); \ + } \ + } \ + } \ + } \ + } \ + } \ + /* ************** Vector len = 1 specialization ************** */ \ + /* TODO: for majorStride != 1, this will cause error as we do f32 reduce and not f16 */ \ + template \ + __device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void \ + nvshmemi_##PTX_TYPE##_tile_allreduce##OP_TYPE##_mcast_threadgroup_v1( \ + uint32_t *dest, const uint32_t *source, const int nelem_major_dim, \ + const int nelem_minor_dim, const int src_stride_minor_dim, \ + const int dst_stride_minor_dim, const int src_stride_major_dim, \ + const int dst_stride_major_dim, tuple_t start_coord, tuple_t boundary) { \ + using vtype = uint32_t; \ + /* This variant supports strides along both major and minor dimensions */ \ + int myIdx = nvshmemi_thread_id_in_threadgroup(); \ + int groupSize = nvshmemi_threadgroup_size(); \ + int nelems = nelem_major_dim * nelem_minor_dim; \ + if constexpr (cuda::std::is_empty::value) { \ + /* Case: no predicate */ \ + for (size_t j = myIdx; j < nelems; j += groupSize) { \ + CXX_TYPE val1; \ + asm("multimem.ld_reduce.global." NVSHMEMI_MCAST_##OP_TYPE##_MIXOP_##PTX_TYPE \ + "." #PTX_TYPE " %0, [%1];" \ + : "=" NVSHMEMI_MCAST_PTX_REG_TYPE_##PTX_TYPE(val1) \ + : "l"(source + ((j % nelem_major_dim) * src_stride_major_dim) + \ + ((j / nelem_major_dim) * src_stride_minor_dim))); \ + if (ONESHOT) \ + asm("st.global.b32 [%0], %1;" ::"l"( \ + dest + ((j % nelem_major_dim) * dst_stride_major_dim) + \ + ((j / nelem_major_dim) * dst_stride_minor_dim)), \ + NVSHMEMI_MCAST_PTX_REG_TYPE_##PTX_TYPE(val1)); \ + else \ + asm("multimem.st.global." #PTX_TYPE \ + " [%0], %1;" ::"l"(dest + ((j % nelem_major_dim) * dst_stride_major_dim) + \ + ((j / nelem_major_dim) * dst_stride_minor_dim)), \ + NVSHMEMI_MCAST_PTX_REG_TYPE_##PTX_TYPE(val1)); \ + } \ + } else { \ + for (size_t j = myIdx; j < nelems; j += groupSize) { \ + uint32_t elem_coord_major = \ + (j % nelem_major_dim) * (sizeof(vtype) / sizeof(elemType)); \ + uint32_t elem_coord_minor = (j / nelem_major_dim); \ + CXX_TYPE val1; \ + /* check if elem is within boundary, convert u to elemType units */ \ + /* for major stride > 1, we can't do f32 for half/bfloat */ \ + if (is_less_than( \ + start_coord, \ + create_coord_tuple(elem_coord_major, elem_coord_minor), \ + boundary)) { \ + /* convert elem_coord_major from elemType to CXX_TYPE units */ \ + /* no change to elem_coord_minor */ \ + elem_coord_major = (elem_coord_major * sizeof(elemType)) / sizeof(CXX_TYPE); \ + \ + /* convert strides from vector to CXX_TYPE units */ \ + asm("multimem.ld_reduce.global." NVSHMEMI_MCAST_##OP_TYPE##_MIXOP_##PTX_TYPE \ + "." #PTX_TYPE " %0, [%1];" \ + : "=" NVSHMEMI_MCAST_PTX_REG_TYPE_##PTX_TYPE(val1) \ + : "l"(reinterpret_cast(source) + \ + (elem_coord_major * src_stride_major_dim) + \ + (elem_coord_minor * src_stride_minor_dim * \ + (sizeof(vtype) / sizeof(CXX_TYPE))))); \ + if (ONESHOT) \ + asm("st.global.b32 [%0], %1;" ::"l"( \ + reinterpret_cast(dest) + \ + +(elem_coord_major * dst_stride_major_dim) + \ + +(elem_coord_minor * dst_stride_minor_dim * \ + (sizeof(vtype) / sizeof(CXX_TYPE)))), \ + NVSHMEMI_MCAST_PTX_REG_TYPE_##PTX_TYPE(val1)); \ + else \ + asm("multimem.st.global." #PTX_TYPE \ + " [%0], %1;" ::"l"(reinterpret_cast(dest) + \ + (elem_coord_major * dst_stride_major_dim) + \ + (elem_coord_minor * dst_stride_minor_dim * \ + (sizeof(vtype) / sizeof(CXX_TYPE)))), \ + NVSHMEMI_MCAST_PTX_REG_TYPE_##PTX_TYPE(val1)); \ + } \ + } \ + } \ + } \ + template \ + __device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void \ + nvshmemi_##PTX_TYPE##_tile_allreduce##OP_TYPE##_mcast_threadgroup( \ + vtype *dest, const vtype *source, const int nelem_major_dim, \ + const int nelem_minor_dim, const int src_stride_minor_dim, \ + const int dst_stride_minor_dim, const int src_stride_major_dim, \ + const int dst_stride_major_dim, tuple_t start_coord, tuple_t boundary) { \ + if constexpr (cuda::std::is_same::value) { \ + nvshmemi_##PTX_TYPE##_tile_allreduce##OP_TYPE##_mcast_threadgroup_v4< \ + elemType, SCOPE, tuple_t, 1, ONESHOT, major_dim, minor_dim>( \ + dest, source, nelem_major_dim, nelem_minor_dim, src_stride_minor_dim, \ + dst_stride_minor_dim, src_stride_major_dim, dst_stride_major_dim, start_coord, \ + boundary); \ + } else if constexpr (cuda::std::is_same::value) { \ + nvshmemi_##PTX_TYPE##_tile_allreduce##OP_TYPE##_mcast_threadgroup_v2< \ + elemType, SCOPE, tuple_t, ONESHOT, major_dim, minor_dim>( \ + dest, source, nelem_major_dim, nelem_minor_dim, src_stride_minor_dim, \ + dst_stride_minor_dim, src_stride_major_dim, dst_stride_major_dim, start_coord, \ + boundary); \ + } else if constexpr (cuda::std::is_same::value) { \ + nvshmemi_##PTX_TYPE##_tile_allreduce##OP_TYPE##_mcast_threadgroup_v1< \ + elemType, SCOPE, tuple_t, ONESHOT, major_dim, minor_dim>( \ + dest, source, nelem_major_dim, nelem_minor_dim, src_stride_minor_dim, \ + dst_stride_minor_dim, src_stride_major_dim, dst_stride_major_dim, start_coord, \ + boundary); \ + } else { \ + if (cuda::std::is_same::value) { \ + nvshmemi_##PTX_TYPE##_tile_allreduce##OP_TYPE##_mcast_threadgroup_v4< \ + elemType, SCOPE, tuple_t, 1, ONESHOT, major_dim, minor_dim>( \ + dest, source, nelem_major_dim, nelem_minor_dim, src_stride_minor_dim, \ + dst_stride_minor_dim, src_stride_major_dim, dst_stride_major_dim, start_coord, \ + boundary); \ + } else if (cuda::std::is_same::value) { \ + nvshmemi_##PTX_TYPE##_tile_allreduce##OP_TYPE##_mcast_threadgroup_v2< \ + elemType, SCOPE, tuple_t, ONESHOT, major_dim, minor_dim>( \ + dest, source, nelem_major_dim, nelem_minor_dim, src_stride_minor_dim, \ + dst_stride_minor_dim, src_stride_major_dim, dst_stride_major_dim, start_coord, \ + boundary); \ + } else if (cuda::std::is_same::value) { \ + nvshmemi_##PTX_TYPE##_tile_allreduce##OP_TYPE##_mcast_threadgroup_v1< \ + elemType, SCOPE, tuple_t, ONESHOT, major_dim, minor_dim>( \ + dest, source, nelem_major_dim, nelem_minor_dim, src_stride_minor_dim, \ + dst_stride_minor_dim, src_stride_major_dim, dst_stride_major_dim, start_coord, \ + boundary); \ + } else { \ + printf("Unsupported vector len %lu\n", sizeof(vtype)); \ + assert(0 && "Unsupported vtype"); \ + } \ + } \ + } + +// mcast ldreduce+st of 16B +// The requirement to use these primitives is that nelems % UNROLL == 0 +#define NVSHMEMI_MCAST16_LOCAL_REDUCE_THREADGROUP_SUM_V4(PTX_TYPE) \ + template \ + __device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void \ + nvshmemi_##PTX_TYPE##_add_local_reduce_mcast16_v4_threadgroup( \ + int4 *dest, const int4 *source, size_t nelems) { \ + int myIdx = nvshmemi_thread_id_in_threadgroup(); \ + int groupSize = nvshmemi_threadgroup_size(); \ + for (size_t j = myIdx * UNROLL; j < nelems; j += groupSize * UNROLL) { \ + uint32_t u4[4 * UNROLL]; \ + _Pragma("unroll UNROLL") for (int u = 0; u < UNROLL; u++) { \ + asm("multimem.ld_reduce.global." NVSHMEMI_MCAST_ADD_MIXOP_##PTX_TYPE \ + ".v4." #PTX_TYPE " {%0, %1, %2, %3}, [%4];" \ + : "=r"(u4[4 * u]), "=r"(u4[4 * u + 1]), "=r"(u4[4 * u + 2]), \ + "=r"(u4[4 * u + 3]) \ + : "l"(source + j + u)); \ + } \ + _Pragma("unroll UNROLL") for (int u = 0; u < UNROLL; u++) { \ + asm("st.global.v4.b32 [%0], {%1, %2, %3, %4};" ::"l"(dest + j + u), \ + "r"(u4[4 * u]), "r"(u4[4 * u + 1]), "r"(u4[4 * u + 2]), "r"(u4[4 * u + 3])); \ + } \ + } \ + } + +// mcast ldreduce+st of 8B +#define NVSHMEMI_MCAST8_LOCAL_REDUCE_THREADGROUP_MINMAX(OP, CXX_TYPE, PTX_TYPE) \ + template \ + __device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void \ + nvshmemi_##PTX_TYPE##_##OP##_local_reduce_mcast8_threadgroup( \ + uint64_t *dest, const uint64_t *source, size_t nelems) { \ + int myIdx = nvshmemi_thread_id_in_threadgroup(); \ + int groupSize = nvshmemi_threadgroup_size(); \ + for (size_t j = myIdx; j < nelems; j += groupSize) { \ + CXX_TYPE val1; \ + asm("multimem.ld_reduce.global." #OP "." #PTX_TYPE " %0, [%1];" \ + : "=" NVSHMEMI_MCAST_PTX_REG_TYPE_##PTX_TYPE(val1) \ + : "l"(source + j)); \ + asm("st.global.b64 [%0], %1;" ::"l"(dest + j), \ + NVSHMEMI_MCAST_PTX_REG_TYPE_##PTX_TYPE(val1)); \ + } \ + } + +#define NVSHMEMI_MCAST8_LOCAL_REDUCE_THREADGROUP_SUM(CXX_TYPE, PTX_TYPE) \ + template \ + __device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void \ + nvshmemi_##PTX_TYPE##_add_local_reduce_mcast8_threadgroup( \ + uint64_t *dest, const uint64_t *source, size_t nelems) { \ + int myIdx = nvshmemi_thread_id_in_threadgroup(); \ + int groupSize = nvshmemi_threadgroup_size(); \ + for (size_t j = myIdx; j < nelems; j += groupSize) { \ + CXX_TYPE val1; \ + asm("multimem.ld_reduce.global.add." #PTX_TYPE " %0, [%1];" \ + : "=" NVSHMEMI_MCAST_PTX_REG_TYPE_##PTX_TYPE(val1) \ + : "l"(source + j)); \ + asm("st.global.b64 [%0], %1;" ::"l"(dest + j), \ + NVSHMEMI_MCAST_PTX_REG_TYPE_##PTX_TYPE(val1)); \ + } \ + } + +#define NVSHMEMI_MCAST8_LOCAL_REDUCE_THREADGROUP(OP, CXX_TYPE, PTX_TYPE) \ + template \ + __device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void \ + nvshmemi_##PTX_TYPE##_##OP##_local_reduce_mcast8_threadgroup( \ + uint64_t *dest, const uint64_t *source, size_t nelems) { \ + int myIdx = nvshmemi_thread_id_in_threadgroup(); \ + int groupSize = nvshmemi_threadgroup_size(); \ + for (size_t j = myIdx; j < nelems; j += groupSize) { \ + CXX_TYPE val1; \ + asm("multimem.ld_reduce.global." #OP "." #PTX_TYPE " %0, [%1];" \ + : "=" NVSHMEMI_MCAST_PTX_REG_TYPE_##PTX_TYPE(val1) \ + : "l"(source + j)); \ + asm("st.global.b64 [%0], %1;" ::"l"(dest + j), \ + NVSHMEMI_MCAST_PTX_REG_TYPE_##PTX_TYPE(val1)); \ + } \ + } + +#define NVSHMEMI_MCAST8_LOCAL_REDUCE_THREADGROUP_SUM_V2(CXX_TYPE, PTX_TYPE) \ + template \ + __device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void \ + nvshmemi_##PTX_TYPE##_add_local_reduce_mcast8_v2_threadgroup( \ + uint64_t *dest, const uint64_t *source, size_t nelems) { \ + int myIdx = nvshmemi_thread_id_in_threadgroup(); \ + int groupSize = nvshmemi_threadgroup_size(); \ + for (size_t j = myIdx; j < nelems; j += groupSize) { \ + CXX_TYPE val1[2]; \ + asm("multimem.ld_reduce.global." NVSHMEMI_MCAST_ADD_MIXOP_##PTX_TYPE \ + ".v2." #PTX_TYPE " {%0, %1}, [%2];" \ + : "=" NVSHMEMI_MCAST_PTX_REG_TYPE_##PTX_TYPE(val1[0]), \ + "=" NVSHMEMI_MCAST_PTX_REG_TYPE_##PTX_TYPE(val1[1]) \ + : "l"(source + j)); \ + asm("st.global.v2.b32 [%0], {%1, %2};" ::"l"(dest + j), \ + NVSHMEMI_MCAST_PTX_REG_TYPE_##PTX_TYPE(val1[0]), \ + NVSHMEMI_MCAST_PTX_REG_TYPE_##PTX_TYPE(val1[1])); \ + } \ + } + +// mcast ldreduce+st of 4B +#define NVSHMEMI_MCAST4_LOCAL_REDUCE_THREADGROUP_SUM(CXX_TYPE, PTX_TYPE) \ + template \ + __device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void \ + nvshmemi_##PTX_TYPE##_add_local_reduce_mcast4_threadgroup( \ + uint32_t *dest, const uint32_t *source, size_t nelems) { \ + int myIdx = nvshmemi_thread_id_in_threadgroup(); \ + int groupSize = nvshmemi_threadgroup_size(); \ + for (size_t j = myIdx; j < nelems; j += groupSize) { \ + CXX_TYPE val1; \ + asm("multimem.ld_reduce.global." NVSHMEMI_MCAST_ADD_MIXOP_##PTX_TYPE "." #PTX_TYPE \ + " %0, [%1];" \ + : "=" NVSHMEMI_MCAST_PTX_REG_TYPE_##PTX_TYPE(val1) \ + : "l"(source + j)); \ + asm("st.global.b32 [%0], %1;" ::"l"(dest + j), \ + NVSHMEMI_MCAST_PTX_REG_TYPE_##PTX_TYPE(val1)); \ + } \ + } + +#define NVSHMEMI_MCAST4_LOCAL_REDUCE_THREADGROUP(OP, CXX_TYPE, PTX_TYPE) \ + template \ + __device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void \ + nvshmemi_##PTX_TYPE##_##OP##_local_reduce_mcast4_threadgroup( \ + uint32_t *dest, const uint32_t *source, size_t nelems) { \ + int myIdx = nvshmemi_thread_id_in_threadgroup(); \ + int groupSize = nvshmemi_threadgroup_size(); \ + for (size_t j = myIdx; j < nelems; j += groupSize) { \ + CXX_TYPE val1; \ + asm("multimem.ld_reduce.global." #OP "." #PTX_TYPE " %0, [%1];" \ + : "=" NVSHMEMI_MCAST_PTX_REG_TYPE_##PTX_TYPE(val1) \ + : "l"(source + j)); \ + asm("st.global.b32 [%0], %1;" ::"l"(dest + j), \ + NVSHMEMI_MCAST_PTX_REG_TYPE_##PTX_TYPE(val1)); \ + } \ + } + +#define NVSHMEMI_MCAST_RDXN_OP_IS_CAP(OP) \ + (OP != RDXN_OPS_PROD && OP != RDXN_OPS_MAXLOC && OP != RDXN_OPS_sentinel) +#define NVSHMEMI_MCAST_RDXN_OP_IS_CAP_8B(OP) (NVSHMEMI_MCAST_RDXN_OP_IS_CAP(OP)) +#define NVSHMEMI_MCAST_RDXN_OP_IS_CAP_16B(OP) (OP == RDXN_OPS_SUM) +#define NVSHMEMI_MCAST_RDXN_OP_IS_CAP_UNTYPED(OP) \ + (OP == RDXN_OPS_SUM || OP == RDXN_OPS_AND || OP == RDXN_OPS_OR || OP == RDXN_OPS_XOR) + +/* Supported matrix here: + * https://docs.nvidia.com/cuda/parallel-thread-execution/#data-movement-and-conversion-instructions-multimem-ld-reduce-multimem-st-multimem-red + */ + +/* 4B local reduce primitives */ +NVSHMEMI_MCAST4_LOCAL_REDUCE_THREADGROUP(add, uint32_t, u32) +NVSHMEMI_MCAST4_LOCAL_REDUCE_THREADGROUP(add, int32_t, s32) +NVSHMEMI_MCAST4_LOCAL_REDUCE_THREADGROUP_SUM(float, f32) +NVSHMEMI_MCAST4_LOCAL_REDUCE_THREADGROUP_SUM(uint32_t, f16x2) +NVSHMEMI_MCAST4_LOCAL_REDUCE_THREADGROUP_SUM(uint32_t, bf16x2) +NVSHMEMI_MCAST4_LOCAL_REDUCE_THREADGROUP(min, uint32_t, u32) +NVSHMEMI_MCAST4_LOCAL_REDUCE_THREADGROUP(min, int32_t, s32) +NVSHMEMI_MCAST4_LOCAL_REDUCE_THREADGROUP(max, uint32_t, u32) +NVSHMEMI_MCAST4_LOCAL_REDUCE_THREADGROUP(max, int32_t, s32) +NVSHMEMI_MCAST4_LOCAL_REDUCE_THREADGROUP(and, uint32_t, b32) +NVSHMEMI_MCAST4_LOCAL_REDUCE_THREADGROUP(xor, uint32_t, b32) +NVSHMEMI_MCAST4_LOCAL_REDUCE_THREADGROUP(or, uint32_t, b32) + +/* 8B local reduce primitives */ +NVSHMEMI_MCAST8_LOCAL_REDUCE_THREADGROUP_SUM(uint64_t, u64) +NVSHMEMI_MCAST8_LOCAL_REDUCE_THREADGROUP_SUM(double, f64) +NVSHMEMI_MCAST8_LOCAL_REDUCE_THREADGROUP_SUM_V2(float, f32) +NVSHMEMI_MCAST8_LOCAL_REDUCE_THREADGROUP_SUM_V2(uint32_t, f16x2) +NVSHMEMI_MCAST8_LOCAL_REDUCE_THREADGROUP_SUM_V2(uint32_t, bf16x2) +NVSHMEMI_MCAST8_LOCAL_REDUCE_THREADGROUP_MINMAX(min, uint64_t, u64) +NVSHMEMI_MCAST8_LOCAL_REDUCE_THREADGROUP_MINMAX(min, int64_t, s64) +NVSHMEMI_MCAST8_LOCAL_REDUCE_THREADGROUP_MINMAX(max, uint64_t, u64) +NVSHMEMI_MCAST8_LOCAL_REDUCE_THREADGROUP_MINMAX(max, int64_t, s64) +NVSHMEMI_MCAST8_LOCAL_REDUCE_THREADGROUP(and, uint64_t, b64) +NVSHMEMI_MCAST8_LOCAL_REDUCE_THREADGROUP(xor, uint64_t, b64) +NVSHMEMI_MCAST8_LOCAL_REDUCE_THREADGROUP(or, uint64_t, b64) + +/* 16B local reduce primitives */ +NVSHMEMI_MCAST16_LOCAL_REDUCE_THREADGROUP_SUM_V4(f32) +NVSHMEMI_MCAST16_LOCAL_REDUCE_THREADGROUP_SUM_V4(f16x2) +NVSHMEMI_MCAST16_LOCAL_REDUCE_THREADGROUP_SUM_V4(bf16x2) + +/* 4B-16B reduce (SUM) primitives */ +NVSHMEMI_MCAST8_REDUCE_THREADGROUP_SUM_V2(float, f32) +NVSHMEMI_MCAST8_REDUCE_THREADGROUP_SUM_V2(uint32_t, f16x2) +NVSHMEMI_MCAST8_REDUCE_THREADGROUP_SUM_V2(uint32_t, bf16x2) +NVSHMEMI_MCAST4_REDUCE_THREADGROUP(add, float, f32) +NVSHMEMI_MCAST4_REDUCE_THREADGROUP(add, uint32_t, f16x2) +NVSHMEMI_MCAST4_REDUCE_THREADGROUP(add, uint32_t, bf16x2) +NVSHMEMI_MCAST16_REDUCE_THREADGROUP_SUM_V4(f32) +NVSHMEMI_MCAST16_REDUCE_THREADGROUP_SUM_V4(f16x2) +NVSHMEMI_MCAST16_REDUCE_THREADGROUP_SUM_V4(bf16x2) + +// ld_reduce errors on using .acc for min, max +#undef NVSHMEMI_MCAST_MIN_MIXOP_f16x2 +#undef NVSHMEMI_MCAST_MIN_MIXOP_bf16x2 +#undef NVSHMEMI_MCAST_MAX_MIXOP_f16x2 +#undef NVSHMEMI_MCAST_MAX_MIXOP_bf16x2 +#define NVSHMEMI_MCAST_MIN_MIXOP_f16x2 "min" +#define NVSHMEMI_MCAST_MIN_MIXOP_bf16x2 "min" +#define NVSHMEMI_MCAST_MAX_MIXOP_f16x2 "max" +#define NVSHMEMI_MCAST_MAX_MIXOP_bf16x2 "max" +NVSHMEMI_MCAST_TILE_ALLREDUCE_THREADGROUP(float, f32, ADD) +NVSHMEMI_MCAST_TILE_ALLREDUCE_THREADGROUP(uint32_t, f16x2, ADD) +NVSHMEMI_MCAST_TILE_ALLREDUCE_THREADGROUP(uint32_t, bf16x2, ADD) +NVSHMEMI_MCAST_TILE_ALLREDUCE_THREADGROUP(uint32_t, f16x2, MIN) +NVSHMEMI_MCAST_TILE_ALLREDUCE_THREADGROUP(uint32_t, bf16x2, MIN) +NVSHMEMI_MCAST_TILE_ALLREDUCE_THREADGROUP(uint32_t, f16x2, MAX) +NVSHMEMI_MCAST_TILE_ALLREDUCE_THREADGROUP(uint32_t, bf16x2, MAX) +// resetting defines to ensure rest code can use it as is +#undef NVSHMEMI_MCAST_MIN_MIXOP_f16x2 +#undef NVSHMEMI_MCAST_MIN_MIXOP_bf16x2 +#undef NVSHMEMI_MCAST_MAX_MIXOP_f16x2 +#undef NVSHMEMI_MCAST_MAX_MIXOP_bf16x2 +#define NVSHMEMI_MCAST_MIN_MIXOP_f16x2 "min.acc::f32" +#define NVSHMEMI_MCAST_MIN_MIXOP_bf16x2 "min.acc::f32" +#define NVSHMEMI_MCAST_MAX_MIXOP_f16x2 "max.acc::f32" +#define NVSHMEMI_MCAST_MAX_MIXOP_bf16x2 "max.acc::f32" + +#if defined(__cplusplus) && __cplusplus >= 201703L +#define IF_CONSTEXPR(expression) if constexpr (expression) +#define ELSE_IF_CONSTEXPR(expression) else if constexpr (expression) +#else +#define IF_CONSTEXPR(expression) if (expression) +#define ELSE_IF_CONSTEXPR(expression) else if (expression) +#endif + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE int nvshmemi_local_reduce_mcast_threadgroup( + TYPE *__restrict__ dest, const TYPE *__restrict__ src, size_t nreduce) { + constexpr bool is_unsigned = std::is_integral::value && std::is_unsigned::value; + constexpr bool is_signed = std::is_integral::value && std::is_signed::value; + constexpr bool is_float_v = is_float::value; + constexpr bool is_double_v = is_double::value; + constexpr bool is_half_v = is_half::value; + constexpr bool is_bfloat_v = is_bfloat::value; + size_t len = nreduce * sizeof(TYPE); + + if ((uintptr_t)dest % sizeof(int4) == 0 && (uintptr_t)src % sizeof(int4) == 0 && + len >= sizeof(int4) && NVSHMEMI_MCAST_RDXN_OP_IS_CAP_16B(OP) && !is_double_v) { + const size_t nelems = len / sizeof(int4); + int4 *__restrict__ dst_p = (int4 *)dest; + const int4 *__restrict__ src_p = (const int4 *)src; + + IF_CONSTEXPR(is_unsigned || is_signed || is_float_v) { + if (len >= 192 && len % 192 == 0) + nvshmemi_f32_add_local_reduce_mcast16_v4_threadgroup(dst_p, src_p, + nelems); + else + nvshmemi_f32_add_local_reduce_mcast16_v4_threadgroup(dst_p, src_p, + nelems); + } + ELSE_IF_CONSTEXPR(is_half_v) { + if (len >= 192 && len % 192 == 0) + nvshmemi_f16x2_add_local_reduce_mcast16_v4_threadgroup(dst_p, src_p, + nelems); + else + nvshmemi_f16x2_add_local_reduce_mcast16_v4_threadgroup(dst_p, src_p, + nelems); + } + ELSE_IF_CONSTEXPR(is_bfloat_v) { + if (len >= 192 && len % 192 == 0) + nvshmemi_bf16x2_add_local_reduce_mcast16_v4_threadgroup(dst_p, src_p, + nelems); + else + nvshmemi_bf16x2_add_local_reduce_mcast16_v4_threadgroup(dst_p, src_p, + nelems); + } + len -= nelems * sizeof(int4); + if (0 == len) return 0; + dest = (TYPE *)(dst_p + nelems); + src = (TYPE *)(src_p + nelems); + } + + if ((uintptr_t)dest % sizeof(uint64_t) == 0 && (uintptr_t)src % sizeof(uint64_t) == 0 && + len >= sizeof(uint64_t) && NVSHMEMI_MCAST_RDXN_OP_IS_CAP_8B(OP)) { + const size_t nelems = len / sizeof(uint64_t); + uint64_t *__restrict__ dst_p = (uint64_t *)dest; + const uint64_t *__restrict__ src_p = (const uint64_t *)src; + switch (OP) { + case RDXN_OPS_SUM: + if (is_unsigned || is_signed) + nvshmemi_u64_add_local_reduce_mcast8_threadgroup(dst_p, src_p, + nelems); + else if (is_float_v) + nvshmemi_f32_add_local_reduce_mcast8_v2_threadgroup(dst_p, src_p, + nelems); + else if (is_double_v) + nvshmemi_f64_add_local_reduce_mcast8_threadgroup(dst_p, src_p, + nelems); + else if (is_half_v) + nvshmemi_f16x2_add_local_reduce_mcast8_v2_threadgroup(dst_p, src_p, + nelems); + else if (is_bfloat_v) + nvshmemi_bf16x2_add_local_reduce_mcast8_v2_threadgroup( + dst_p, src_p, nelems); + break; + case RDXN_OPS_MIN: + if (is_unsigned) + nvshmemi_u64_min_local_reduce_mcast8_threadgroup(dst_p, src_p, + nelems); + else if (is_signed) + nvshmemi_s64_min_local_reduce_mcast8_threadgroup(dst_p, src_p, + nelems); + break; + case RDXN_OPS_MAX: + if (is_unsigned) + nvshmemi_u64_max_local_reduce_mcast8_threadgroup(dst_p, src_p, + nelems); + else if (is_signed) + nvshmemi_s64_max_local_reduce_mcast8_threadgroup(dst_p, src_p, + nelems); + break; + case RDXN_OPS_AND: + nvshmemi_b64_and_local_reduce_mcast8_threadgroup(dst_p, src_p, nelems); + break; + case RDXN_OPS_XOR: + nvshmemi_b64_xor_local_reduce_mcast8_threadgroup(dst_p, src_p, nelems); + break; + case RDXN_OPS_OR: + nvshmemi_b64_or_local_reduce_mcast8_threadgroup(dst_p, src_p, nelems); + break; + default: + break; + } + + len -= nelems * sizeof(uint64_t); + if (0 == len) return 0; + dest = (TYPE *)(dst_p + nelems); + src = (TYPE *)(src_p + nelems); + } + + if ((uintptr_t)dest % sizeof(uint32_t) == 0 && (uintptr_t)src % sizeof(uint32_t) == 0 && + len >= sizeof(uint32_t)) { + const size_t nelems = len / sizeof(uint32_t); + uint32_t *__restrict__ dst_p = (uint32_t *)dest; + const uint32_t *__restrict__ src_p = (const uint32_t *)src; + switch (OP) { + case RDXN_OPS_SUM: + if (is_unsigned) + nvshmemi_u32_add_local_reduce_mcast4_threadgroup(dst_p, src_p, + nelems); + else if (is_signed) + nvshmemi_s32_add_local_reduce_mcast4_threadgroup(dst_p, src_p, + nelems); + else if (is_float_v) + nvshmemi_f32_add_local_reduce_mcast4_threadgroup(dst_p, src_p, + nelems); + else if (is_half_v) + nvshmemi_f16x2_add_local_reduce_mcast4_threadgroup(dst_p, src_p, + nelems); + else if (is_bfloat_v) + nvshmemi_bf16x2_add_local_reduce_mcast4_threadgroup(dst_p, src_p, + nelems); + break; + case RDXN_OPS_MIN: + if (is_unsigned) + nvshmemi_u32_min_local_reduce_mcast4_threadgroup(dst_p, src_p, + nelems); + else if (is_signed) + nvshmemi_s32_min_local_reduce_mcast4_threadgroup(dst_p, src_p, + nelems); + break; + case RDXN_OPS_MAX: + if (is_unsigned) + nvshmemi_u32_max_local_reduce_mcast4_threadgroup(dst_p, src_p, + nelems); + else if (is_signed) + nvshmemi_s32_max_local_reduce_mcast4_threadgroup(dst_p, src_p, + nelems); + break; + case RDXN_OPS_XOR: + nvshmemi_b32_xor_local_reduce_mcast4_threadgroup(dst_p, src_p, nelems); + break; + case RDXN_OPS_AND: + nvshmemi_b32_and_local_reduce_mcast4_threadgroup(dst_p, src_p, nelems); + break; + case RDXN_OPS_OR: + nvshmemi_b32_or_local_reduce_mcast4_threadgroup(dst_p, src_p, nelems); + break; + default: + break; + } + + len -= nelems * sizeof(uint32_t); + if (0 == len) return 0; + } + + /* Return the remainder length, incase the caller wants to retry with unicast */ + return (len); +} + +template +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE void gpu_rdxn_on_demand_2( + int start, int stride, int size, TYPE *dest, const TYPE *source, size_t nelems, TYPE *pWrk, + volatile long *pSync, volatile long *sync_counter) { + int next_rank = -1; + TYPE *op1 = NULL, *op2 = NULL; + size_t i; + volatile TYPE *tmp_operand; + int my_active_set_pe = ((nvshmemi_device_state_d.mype - start) / stride); + tmp_operand = (TYPE *)pWrk; + nvshmemi_put_threadgroup(dest, source, nelems, + nvshmemi_device_state_d.mype); + for (i = 1; i < size; i++) { + next_rank = start + ((my_active_set_pe + i) % size) * stride; + nvshmemi_put_nbi_threadgroup((TYPE *)tmp_operand, source, + nelems, next_rank); + nvshmemi_quiet(); + sync_dissem_threadgroup_2(start, stride, size, pSync, + sync_counter); + op1 = (TYPE *)dest; + op2 = (TYPE *)tmp_operand; + gpu_linear_reduce_threadgroup(op1, op2, op1, nelems); + sync_dissem_threadgroup_2(start, stride, size, pSync, + sync_counter); + } +} + +template +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE void gpu_rdxn_on_demand( + nvshmem_team_t team, TYPE *dest, const TYPE *source, size_t nelems) { + int next_rank = -1; + TYPE *op1 = NULL, *op2 = NULL; + nvshmemi_team_t *teami = nvshmemi_device_state_d.team_pool[team]; + size_t i; + volatile TYPE *tmp_operand; + int my_active_set_pe = teami->my_pe; + tmp_operand = (volatile TYPE *)nvshmemi_team_get_psync(teami, REDUCE); + nvshmemi_put_threadgroup(dest, source, nelems, + nvshmemi_device_state_d.mype); + for (i = 1 + my_active_set_pe; i < teami->size + my_active_set_pe; i++) { + int next_rank = nvshmemi_team_translate_pe_to_team_world_wrap(teami, i); + nvshmemi_put_nbi_threadgroup((TYPE *)tmp_operand, source, + nelems, next_rank); + nvshmemi_quiet(); + sync_dissem_threadgroup(team); + op1 = (TYPE *)dest; + op2 = (TYPE *)tmp_operand; + gpu_linear_reduce_threadgroup(op1, op2, op1, nelems); + sync_dissem_threadgroup(team); + } +} + +/* pWrk usage - (k - 1) * nreduce for step 1 + - k * step2_nphases * nreduce for receiving step 2 data + - step2_nphases * nreduce for sending data of each phase */ +template +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE void gpu_rdxn_recexch_threadgroup( + nvshmem_team_t team, TYPE *dst, const TYPE *source, size_t nreduce) { + nvshmemi_team_t *teami = nvshmemi_device_state_d.team_pool[team]; + TYPE *pWrk = (TYPE *)nvshmemi_team_get_psync(teami, REDUCE); + volatile long *pSync = (volatile long *)nvshmemi_team_get_psync(teami, SYNC); + volatile long *sync_counter = (volatile long *)nvshmemi_team_get_sync_counter(teami); + const int step1_sendto = teami->reduce_recexch.step1_sendto; + const int step1_nrecvs = teami->reduce_recexch.step1_nrecvs; + const int *step1_recvfrom = teami->reduce_recexch.step1_recvfrom; + const int step2_nphases = teami->reduce_recexch.step2_nphases; + int **step2_nbrs = teami->reduce_recexch.step2_nbrs; + const int rank = nvshmemi_device_state_d.mype; + const int k = nvshmemi_device_state_d.gpu_coll_env_params_var.reduce_recexch_kval; + + int myIdx = nvshmemi_thread_id_in_threadgroup(); + int groupSize = nvshmemi_threadgroup_size(); + int in_step2 = (step1_sendto == -1); /* whether this rank participates in Step 2 */ + + if (in_step2 == 1) { + for (size_t i = myIdx; i < nreduce; i += groupSize) { + dst[i] = source[i]; + } + nvshmemi_threadgroup_sync(); + } + + if (in_step2 == 0) { + size_t offset = (step1_sendto - rank - 1) * nreduce; + nvshmemi_put_nbi_threadgroup(pWrk + offset, source, nreduce, step1_sendto); + if (!myIdx) { + nvshmemi_fence(); + nvshmemi_signal_for_barrier((long *)(pSync + rank), sync_counter[0], + step1_sendto); + } + } else if (step1_nrecvs != 0) { + for (int i = 0; i < step1_nrecvs; i += 1) { + nvshmemi_wait_until((long *)pSync + step1_recvfrom[i], NVSHMEM_CMP_GE, + sync_counter[0]); + size_t offset = (rank - step1_recvfrom[i] - 1) * nreduce; + gpu_linear_reduce_threadgroup(dst, (pWrk + offset), dst, nreduce); + } + nvshmemi_threadgroup_sync(); + } + + /* Step 2 */ + if (in_step2) { + size_t send_offset = (k - 1) * nreduce + k * step2_nphases * nreduce; + size_t recv_offset = (k - 1) * nreduce; + for (int phase = 0; phase < step2_nphases; phase++) { + int num_small = k - 1; + for (int i = 0; i < k - 1; i++) { + if (step2_nbrs[phase][i] > rank) { + num_small = i; + break; + } + } + /* copy the data to end of pWrk that can be used as source for puts + while we use dst for reduction */ + for (size_t i = myIdx; i < nreduce; i += groupSize) { + pWrk[send_offset + phase * nreduce + i] = dst[i]; + } + nvshmemi_threadgroup_sync(); + for (int i = 0; i < k - 1; i++) { + size_t offset = recv_offset + k * phase * nreduce + num_small * nreduce; + nvshmemi_put_nbi_threadgroup(pWrk + offset, + pWrk + send_offset + phase * nreduce, + nreduce, step2_nbrs[phase][i]); + } + if (!myIdx) nvshmemi_fence(); + nvshmemi_threadgroup_sync(); + for (int i = myIdx; i < k - 1; i += groupSize) { + nvshmemi_signal_for_barrier((long *)(pSync + rank), sync_counter[0], + step2_nbrs[phase][i]); + } + + for (int i = 0; i < k - 1; i += 1) { + nvshmemi_wait_until((uint64_t *)(pSync + step2_nbrs[phase][i]), + NVSHMEM_CMP_GE, sync_counter[0]); + int offset = recv_offset + k * phase * nreduce; + if (step2_nbrs[phase][i] < rank) + offset += i * nreduce; + else + offset += (i + 1) * nreduce; + gpu_linear_reduce_threadgroup(dst, (pWrk + offset), dst, nreduce); + } + /*nvshmem_quiet(); */ /*wait for my puts to complete */ + } + } + + /* Step 3 */ + if (step1_nrecvs > 0) { + for (int i = 0; i < step1_nrecvs; i++) { + nvshmemi_put_nbi_threadgroup(dst, dst, nreduce, step1_recvfrom[i]); + } + if (!myIdx) nvshmemi_fence(); + nvshmemi_threadgroup_sync(); + for (int i = myIdx; i < step1_nrecvs; i += groupSize) { + nvshmemi_signal_for_barrier((long *)(pSync + rank), sync_counter[0], + step1_recvfrom[i]); + } + } else if (step1_sendto != -1) { + if (!myIdx) + nvshmemi_wait_until((uint64_t *)(pSync + step1_sendto), NVSHMEM_CMP_GE, + sync_counter[0]); + } + nvshmemi_threadgroup_sync(); + if (!myIdx) sync_counter[0] += 1; + nvshmemi_threadgroup_sync(); +} + +template +NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE __device__ void +nvshmemi_gpu_rdxn_threadgroup_zcopy_get_bar_direct(nvshmem_team_t team, TYPE *dest, + const TYPE *source, size_t nreduce) { + int next_rank = -1; + int src_offset = -1; + int next_offset = -1; + char *base = NULL; + char *peer_base = NULL; + char *peer_source = NULL; + nvshmemi_team_t *teami = nvshmemi_device_state_d.team_pool[team]; + int myIdx = nvshmemi_thread_id_in_threadgroup(); + int groupSize = nvshmemi_threadgroup_size(); + TYPE *pWrk = (TYPE *)nvshmemi_team_get_psync(teami, REDUCE); + int i; + int my_active_set_pe = teami->my_pe; + + base = (char *)((void *)__ldg( + (const long long unsigned *)nvshmemi_device_state_d.peer_heap_base_p2p + + nvshmemi_device_state_d.mype)); + src_offset = ((char *)source - base); + + next_rank = nvshmemi_team_translate_pe_to_team_world_wrap(teami, my_active_set_pe + 1); + next_offset = src_offset; + peer_base = (char *)((void *)__ldg( + (const long long unsigned *)nvshmemi_device_state_d.peer_heap_base_p2p + next_rank)); + peer_source = peer_base + next_offset; + gpu_linear_reduce_threadgroup((void *)source, peer_source, dest, nreduce); + + for (i = 2; i < teami->size; i++) { + next_rank = nvshmemi_team_translate_pe_to_team_world_wrap(teami, my_active_set_pe + i); + next_offset = src_offset; + peer_base = (char *)((void *)__ldg( + (const long long unsigned *)nvshmemi_device_state_d.peer_heap_base_p2p + next_rank)); + peer_source = peer_base + next_offset; + gpu_linear_reduce_threadgroup(dest, peer_source, dest, nreduce); + } + nvshmemi_barrier_threadgroup(team); +} + +template +NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE __device__ void gpu_rdxn_segment_threadgroup( + nvshmem_team_t team, TYPE *dest, const TYPE *source, size_t nelems) { + int type_size = sizeof(TYPE); + size_t msg_len = nelems * type_size; + int next_rank = -1; + nvshmemi_team_t *teami = nvshmemi_device_state_d.team_pool[team]; + int myIdx = nvshmemi_thread_id_in_threadgroup(); + int groupSize = nvshmemi_threadgroup_size(); + TYPE *pWrk = (TYPE *)nvshmemi_team_get_psync(teami, REDUCE); + TYPE *op1 = NULL, *op2 = NULL; + int i; + size_t j; + volatile TYPE *tmp_operand; + size_t remainder = 0; + size_t rnds_floor = 0; + size_t offset = 0; + int pe_offset = 0; + int pes_per_round = 0; + int round = 0; + size_t exchange_size = 0; + int my_active_set_pe = teami->my_pe; + size_t nvshm_gpu_rdxn_seg_size = + (nvshmemi_device_state_d.gpu_coll_env_params_var.reduce_scratch_size / 2) / sizeof(long); + + tmp_operand = (TYPE *)pWrk; + nvshmemi_put_nbi_threadgroup((TYPE *)dest, (const TYPE *)source, nelems, + nvshmemi_device_state_d.mype); + + rnds_floor = msg_len / nvshm_gpu_rdxn_seg_size; + remainder = msg_len % nvshm_gpu_rdxn_seg_size; + + for (j = 0; j < rnds_floor; j++) { + exchange_size = nvshm_gpu_rdxn_seg_size; + for (i = 1; i < teami->size; i++) { + next_rank = nvshmemi_team_translate_pe_to_team_world_wrap(teami, my_active_set_pe + i); + nvshmemi_put_nbi_threadgroup((TYPE *)tmp_operand, + (const TYPE *)source + offset, + (exchange_size / sizeof(TYPE)), next_rank); + nvshmemi_barrier_threadgroup(team); + op1 = (TYPE *)dest + offset; + op2 = (TYPE *)tmp_operand; + gpu_linear_reduce_threadgroup(op1, op2, op1, + (exchange_size / sizeof(TYPE))); + nvshmemi_sync_threadgroup(team); + } + offset += (exchange_size / sizeof(TYPE)); + } + if (remainder != 0) { + exchange_size = remainder; + pes_per_round = nvshm_gpu_rdxn_seg_size / remainder; + pe_offset = 1; + do { + round = 0; + for (i = pe_offset; ((round < pes_per_round) && (i < teami->size)); i++) { + next_rank = + nvshmemi_team_translate_pe_to_team_world_wrap(teami, my_active_set_pe + i); + nvshmemi_put_nbi_threadgroup( + (TYPE *)((TYPE *)tmp_operand + (round * (exchange_size / sizeof(TYPE)))), + (TYPE *)source + offset, (exchange_size / sizeof(TYPE)), next_rank); + round++; + pe_offset++; + } + nvshmemi_barrier_threadgroup(team); + for (i = 0; i < round; i++) { + op1 = (TYPE *)dest + offset; + op2 = (TYPE *)((TYPE *)tmp_operand + (i * (exchange_size / sizeof(TYPE)))); + gpu_linear_reduce_threadgroup(op1, op2, op1, + (exchange_size / sizeof(TYPE))); + } + nvshmemi_sync_threadgroup(team); + } while (pe_offset < teami->size); + } +} + +template +NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_FORCE_INLINE __device__ void +nvshmemi_gpu_rdxn_hierarchical_fcollect_threadgroup(nvshmem_team_t team, TYPE *dest, + const TYPE *source, size_t nreduce) { + nvshmemi_team_t *teami_node = nvshmemi_device_state_d.team_pool[NVSHMEM_TEAM_NODE_INDEX]; + nvshmemi_team_t *teami_same_mype_node = + nvshmemi_device_state_d.team_pool[NVSHMEMX_TEAM_SAME_MYPE_NODE]; + int myIdx = nvshmemi_thread_id_in_threadgroup(); + int groupSize = nvshmemi_threadgroup_size(); + + if (!myIdx) { /* Only one thread should increment rdxn_count */ + teami_node->rdxn_count++; + teami_same_mype_node->rdxn_count++; + } + nvshmemi_threadgroup_sync(); + + TYPE *pWrk = (TYPE *)nvshmemi_team_get_psync(teami_node, REDUCE); + if (teami_node->size >= 2) + nvshmemi_fcollect_threadgroup( + NVSHMEMX_TEAM_NODE, pWrk, source, nvshmemi_team_my_pe(NVSHMEMX_TEAM_NODE) * nreduce, + nreduce); + else + nvshmemi_memcpy_threadgroup(dest, source, nreduce * sizeof(TYPE)); + + if (teami_node->size >= 2) { + for (int j = myIdx; j < nreduce; j += groupSize) { + gpu_linear_reduce_threadgroup( + pWrk + j, pWrk + nreduce + j, dest + j, 1); + for (int i = 2; i < teami_node->size; i++) { + gpu_linear_reduce_threadgroup( + pWrk + i * nreduce + j, dest + j, dest + j, 1); + } + } + } + nvshmemi_threadgroup_sync(); + + if (teami_same_mype_node->size >= 2) { + pWrk = (TYPE *)nvshmemi_team_get_psync(teami_same_mype_node, REDUCE); + nvshmemi_fcollect_threadgroup( + NVSHMEMX_TEAM_SAME_MYPE_NODE, pWrk, dest, + nvshmemi_team_my_pe(NVSHMEMX_TEAM_SAME_MYPE_NODE) * nreduce, nreduce); +#if CUDART_VERSION >= 12000 && defined(__cplusplus) && __cplusplus >= 201703L + if constexpr (SCOPE == NVSHMEMI_THREADGROUP_BLOCK && OP == RDXN_OPS_SUM && + sizeof(TYPE) >= 4 && sizeof(TYPE) <= 8) { + for (int i = myIdx; i < nreduce; i += groupSize) *(dest + i) = 0; + nvshmemi_threadgroup_sync(); + auto block = cg::this_thread_block(); + auto tile = cg::tiled_partition<32>(block); + for (int j = 0; j < nreduce; j++) { + cg::reduce_update_async( + tile, cuda::atomic_ref(dest[j]), + (myIdx < teami_same_mype_node->size) ? *((TYPE *)pWrk + myIdx * nreduce + j) + : (TYPE)0, + cg::plus()); + } + } else +#endif + { + for (int j = myIdx; j < nreduce; j += groupSize) { + gpu_linear_reduce_threadgroup( + (TYPE *)pWrk + j, (TYPE *)pWrk + nreduce + j, dest + j, 1); + for (int i = 2; i < teami_same_mype_node->size; i++) { + gpu_linear_reduce_threadgroup( + (TYPE *)pWrk + i * nreduce + j, dest + j, dest + j, 1); + } + } + } + nvshmemi_threadgroup_sync(); + } +} + +template +NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE __device__ void nvshmemi_gpu_rdxn_threadgroup( + nvshmem_team_t team, TYPE *dest, const TYPE *source, size_t nreduce) { +#ifdef NVSHMEM_GPU_COLL_USE_LDST + nvshmemi_gpu_rdxn_threadgroup_zcopy_get_bar_direct(team, dest, source, + nreduce); +#else + nvshmemi_team_t *teami = nvshmemi_device_state_d.team_pool[team]; + bool is_team_world = + nvshmemi_team_is_identical(teami, nvshmemi_device_state_d.team_pool[NVSHMEM_TEAM_WORLD]); + int k = nvshmemi_device_state_d.gpu_coll_env_params_var.reduce_recexch_kval; + if (is_team_world && sizeof(TYPE) >= 4 && nreduce % 2 == 0 && + nvshmemi_device_state_d.gpu_coll_env_params_var.reduce_scratch_size / 2 >= + teami->size * nreduce * sizeof(TYPE) && + nvshmemi_device_state_d.gpu_coll_env_params_var.fcollect_ll_threshold >= + nreduce * sizeof(TYPE) && + SCOPE == NVSHMEMI_THREADGROUP_BLOCK) + nvshmemi_gpu_rdxn_hierarchical_fcollect_threadgroup(team, dest, source, + nreduce); + else if (is_team_world && + ((nvshmemi_device_state_d.gpu_coll_env_params_var.reduce_scratch_size / 2) / + sizeof(long)) >= + ((k - 1) * nreduce + k * teami->reduce_recexch.step2_nphases * nreduce + + teami->reduce_recexch.step2_nphases * nreduce)) { + gpu_rdxn_recexch_threadgroup(team, dest, source, nreduce); + } else { + gpu_rdxn_segment_threadgroup(team, dest, source, nreduce); + } +#endif +} + +#define ALIGNED_UNROLLED_LEN 192 /* 16B (v4.b32) * UNROLL=12 */ +#define NVSHMEMI_HALF_ADD_REDUCE_MCAST16_THREADGROUP_UNROLLED(SCOPE, ONESHOT, dst, src, nelems) \ + nvshmemi_f16x2_add_reduce_mcast16_v4_threadgroup(dst, src, nelems) +#define NVSHMEMI_BFLOAT_ADD_REDUCE_MCAST16_THREADGROUP_UNROLLED(SCOPE, ONESHOT, dst, src, nelems) \ + nvshmemi_bf16x2_add_reduce_mcast16_v4_threadgroup(dst, src, nelems) +#define NVSHMEMI_FLOAT_ADD_REDUCE_MCAST16_THREADGROUP_UNROLLED(SCOPE, ONESHOT, dst, src, nelems) \ + nvshmemi_f32_add_reduce_mcast16_v4_threadgroup(dst, src, nelems) + +/* Works for inplace and out-of-place reduction for ONESHOT == 0 + * Works for out-of-place reduction for ONESHOT == 1 + */ +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_add_reduce_mcast_threadroup( + nvshmemi_team_t *teami, TYPE *__restrict__ dst_ptr, const TYPE *__restrict__ src_ptr, + int nreduce) { + TYPE *src = (TYPE *)nvshmemi_mc_ptr(teami, src_ptr); + TYPE *dest; + if (ONESHOT) + dest = dst_ptr; + else + dest = (TYPE *)nvshmemi_mc_ptr(teami, dst_ptr); + nvshmemi_threadgroup_sync(); + size_t len = nreduce * sizeof(TYPE); + constexpr bool is_half_v = is_half::value; + constexpr bool is_bfloat_v = is_bfloat::value; + + if ((uintptr_t)dest % sizeof(int4) == 0 && (uintptr_t)src % sizeof(int4) == 0 && + len >= sizeof(int4)) { + const size_t nelems = len / sizeof(int4); + int4 *__restrict__ dst_p = (int4 *)dest; + const int4 *__restrict__ src_p = (const int4 *)src; + IF_CONSTEXPR(is_half_v) { + if (len >= ALIGNED_UNROLLED_LEN && len % ALIGNED_UNROLLED_LEN == 0) + NVSHMEMI_HALF_ADD_REDUCE_MCAST16_THREADGROUP_UNROLLED(SCOPE, ONESHOT, dst_p, src_p, + nelems); + else + nvshmemi_f16x2_add_reduce_mcast16_v4_threadgroup(dst_p, src_p, + nelems); + } + ELSE_IF_CONSTEXPR(is_bfloat_v) { + if (len >= ALIGNED_UNROLLED_LEN && len % ALIGNED_UNROLLED_LEN == 0) + NVSHMEMI_BFLOAT_ADD_REDUCE_MCAST16_THREADGROUP_UNROLLED(SCOPE, ONESHOT, dst_p, + src_p, nelems); + else + nvshmemi_bf16x2_add_reduce_mcast16_v4_threadgroup(dst_p, src_p, + nelems); + } + else { + if (len >= ALIGNED_UNROLLED_LEN && len % ALIGNED_UNROLLED_LEN == 0) + NVSHMEMI_FLOAT_ADD_REDUCE_MCAST16_THREADGROUP_UNROLLED(SCOPE, ONESHOT, dst_p, src_p, + nelems); + else + nvshmemi_f32_add_reduce_mcast16_v4_threadgroup(dst_p, src_p, + nelems); + } + len -= nelems * sizeof(int4); + if (0 == len) return; + dest = (TYPE *)(dst_p + nelems); + src = (TYPE *)(src_p + nelems); + } + + if ((uintptr_t)dest % sizeof(uint64_t) == 0 && (uintptr_t)src % sizeof(uint64_t) == 0 && + len >= sizeof(uint64_t)) { + const size_t nelems = len / sizeof(uint64_t); + uint64_t *__restrict__ dst_p = (uint64_t *)dest; + const uint64_t *__restrict__ src_p = (const uint64_t *)src; + IF_CONSTEXPR(is_half_v) { + nvshmemi_f16x2_add_reduce_mcast8_v2_threadgroup(dst_p, src_p, nelems); + } + ELSE_IF_CONSTEXPR(is_bfloat_v) { + nvshmemi_bf16x2_add_reduce_mcast8_v2_threadgroup(dst_p, src_p, nelems); + } + else { + nvshmemi_f32_add_reduce_mcast8_v2_threadgroup(dst_p, src_p, nelems); + } + len -= nelems * sizeof(uint64_t); + if (0 == len) return; + dest = (TYPE *)(dst_p + nelems); + src = (TYPE *)(src_p + nelems); + } + + if ((uintptr_t)dest % sizeof(uint32_t) == 0 && (uintptr_t)src % sizeof(uint32_t) == 0 && + len >= sizeof(uint32_t)) { + const size_t nelems = len / sizeof(uint32_t); + uint32_t *__restrict__ dst_p = (uint32_t *)dest; + const uint32_t *__restrict__ src_p = (const uint32_t *)src; + IF_CONSTEXPR(is_half_v) { + nvshmemi_f16x2_add_reduce_mcast4_threadgroup(dst_p, src_p, nelems); + } + ELSE_IF_CONSTEXPR(is_bfloat_v) { + nvshmemi_bf16x2_add_reduce_mcast4_threadgroup(dst_p, src_p, nelems); + } + else { + nvshmemi_f32_add_reduce_mcast4_threadgroup(dst_p, src_p, nelems); + } + len -= nelems * sizeof(uint32_t); + if (0 == len) return; + } + + return; +} + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_add_reduce_nvls_twoshot_threadgroup( + nvshmem_team_t team, TYPE *dest, const TYPE *source, size_t nreduce) { +#if defined __clang_llvm_bitcode_lib__ + if (__nvvm_reflect("__CUDA_ARCH") >= 900) { + nvshmemi_team_t *teami = nvshmemi_device_state_d.team_pool[team]; + int myIdx = nvshmemi_thread_id_in_threadgroup(); + int my_idx_in_active_set = teami->my_pe; + /* Divide nreduce by team size and handle for the 3 cases */ + int elems_per_pe = nreduce / teami->size; + int elems_remain = nreduce % teami->size; + // Case 1: elems_per_pe == 0 => GPU [size-1] does the work on nreduce + // Case 2: elems_per_pe != 0 and elems_remain != 0 => GPU [0-size-2] does elems_per_pe, + // GPU[size-1] does elems_per_pe + elems_remain Case 3: elems_per_pe != 0 and elems_remain + // == 0 + // => all GPUs do work for elems_per_pe + int my_nelems = elems_per_pe; + if (my_idx_in_active_set == (teami->size - 1)) { + my_nelems = elems_per_pe + elems_remain; + } + + if (my_nelems > 0) { + nvshmemi_add_reduce_mcast_threadroup( + teami, dest + elems_per_pe * my_idx_in_active_set, + source + elems_per_pe * my_idx_in_active_set, my_nelems); + } + + nvshmemi_barrier_threadgroup(team); + } else { + assert(0 && "Unsupported NVLS on this platform\n"); + } +#else +#if __CUDA_ARCH__ >= 900 && CUDART_VERSION >= 12010 + nvshmemi_team_t *teami = nvshmemi_device_state_d.team_pool[team]; + int my_idx_in_active_set = teami->my_pe; + int myIdx = nvshmemi_thread_id_in_threadgroup(); + /* Divide nreduce by team size and handle for the 3 cases */ + int elems_per_pe = nreduce / teami->size; + int elems_remain = nreduce % teami->size; + // Case 1: elems_per_pe == 0 => GPU [size-1] does the work on nreduce + // Case 2: elems_per_pe != 0 and elems_remain != 0 => GPU [0-size-2] does elems_per_pe, + // GPU[size-1] does elems_per_pe + elems_remain Case 3: elems_per_pe != 0 and elems_remain == 0 + // => all GPUs do work for elems_per_pe + int my_nelems = elems_per_pe; + if (my_idx_in_active_set == (teami->size - 1)) { + my_nelems = elems_per_pe + elems_remain; + } + + if (my_nelems > 0) { + nvshmemi_add_reduce_mcast_threadroup( + teami, dest + elems_per_pe * my_idx_in_active_set, + source + elems_per_pe * my_idx_in_active_set, my_nelems); + } + + nvshmemi_barrier_threadgroup(team); +#else + assert(0 && "Unsupported NVLS on this platform\n"); +#endif +#endif +} + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_add_reduce_nvls_oneshot_threadgroup( + nvshmem_team_t team, TYPE *dest, const TYPE *source, size_t nreduce) { +#if defined __clang_llvm_bitcode_lib__ + if (__nvvm_reflect("__CUDA_ARCH") >= 900) { + nvshmemi_team_t *teami = nvshmemi_device_state_d.team_pool[team]; + /* Assign nreduce for all PEs. It may lead to duplicate reduction, but avoid AG stage to + * communicate partial results as compared to two-shot */ + int elems_per_pe = nreduce; + // Case 1: elems_per_pe == 0 => no GPUs do any work. + // Case 2: elems_per_pe != 0 => all GPUs do work for elems_per_pe + if (elems_per_pe > 0) { + nvshmemi_add_reduce_mcast_threadroup(teami, dest, source, elems_per_pe); + } + + /** + * Using __threadfence_system() is an overkill since we store to local vidmem buffers at the + * end of ONESHOT add_reducast_mcast The only requirement is not reorder store with sync. + * Since this code is inlined, this requirement is important (non-inlined function call + * would automatically guarantee this). Since we use PTX for store, compiler should + * typically not reorder PTX. So opportunistically, we don't introduce membar.cta PTX here. + */ + nvshmemi_sync_algo_threadgroup(team); + } else { + assert(0 && "Unsupported NVLS on this platform\n"); + } +#else +#if __CUDA_ARCH__ >= 900 && CUDART_VERSION >= 12010 + nvshmemi_team_t *teami = nvshmemi_device_state_d.team_pool[team]; + /* Assign nreduce for all PEs. It may lead to duplicate reduction, but avoid AG stage to + * communicate partial results as compared to two-shot */ + int elems_per_pe = nreduce; + // Case 1: elems_per_pe == 0 => no GPUs do any work. + // Case 2: elems_per_pe != 0 => all GPUs do work for elems_per_pe + if (elems_per_pe > 0) { + nvshmemi_add_reduce_mcast_threadroup(teami, dest, source, elems_per_pe); + } + + /** + * Using __threadfence_system() is an overkill since we store to local vidmem buffers at the end + * of ONESHOT add_reducast_mcast The only requirement is not reorder store with sync. Since this + * code is inlined, this requirement is important (non-inlined function call would automatically + * guarantee this). Since we use PTX for store, compiler should typically not reorder PTX. So + * opportunistically, we don't introduce membar.cta PTX here. + */ + nvshmemi_sync_algo_threadgroup(team); +#else + assert(0 && "Unsupported NVLS on this platform\n"); +#endif +#endif +} + +/* This is the entry function for any rdxn collective op - host, on-stream, device + There is only one exception - nvshmemi_reduce_kernel that is directly calling + a specific reduction algorithm. That is a special need for team creation */ +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_reduce_threadgroup(nvshmem_team_t team, + TYPE *dest, + const TYPE *source, + size_t nreduce) { + int myIdx = nvshmemi_thread_id_in_threadgroup(); + if (!myIdx) /* Only one thread should increment rdxn_count */ + nvshmemi_device_state_d.team_pool[team]->rdxn_count += 1; + nvshmemi_threadgroup_sync(); + + constexpr bool is_rdxn_sum = (OP == RDXN_OPS_SUM); + constexpr bool is_float_v = + is_float::value || is_half::value || is_bfloat::value; + + constexpr bool is_half_prec = is_half::value || is_bfloat::value; + int reduce_algo = nvshmemi_device_state_d.gpu_coll_env_params_var.reduce_algo; + + bool is_nvls_algo_supported = + is_rdxn_sum && is_float_v && + nvshmemi_device_state_d.team_pool[team]->nvls_rsc_base_ptr != NULL && + (nreduce * sizeof(TYPE)) % 4 == 0; + bool is_inplace_op = (source == dest); // exact overlap + // inplace ops cannot use 1-shot AR as there can be a race condition due to execution of + // ld_reduce on a given MC offset on the switch and a posted stg to the same MC offset on any of + // the GPUs, causing incorrect result. + bool is_one_shot_supported = + (is_nvls_algo_supported) && !(is_half_prec && nreduce == 1) && !(is_inplace_op); + bool is_two_shot_supported = + (is_nvls_algo_supported) && + !(is_half_prec && (nreduce <= nvshmemi_device_state_d.team_pool[team]->size || + (nreduce % nvshmemi_device_state_d.team_pool[team]->size > 0))); + + /* When adding new algorithms in 3.x, start at 5. This is for backward compatibility reasons, + * see cpu_coll.cpp. + */ + switch (reduce_algo) { + case 0: /* NVLS Two Shot or One Shot Allreduce for REDUCE_SUM and float/bfloat/half dtype */ + case 1: + case 2: + if (nvshmemi_device_state_d.gpu_coll_env_params_var.reduce_nvls_threshold >= nreduce && + is_one_shot_supported) + reduce_algo = 4; + else if (is_two_shot_supported) + reduce_algo = 3; + else { + reduce_algo = 0; + } + break; + case 3: + /* When forcing NVLS algos, legalize for unsupported case and switch back to non-NVLS + * algos multimem PTX don't support BF16/FP16 single element reduction + */ + if (!is_two_shot_supported) { + reduce_algo = 0; + } + + break; + case 4: + /* When forcing NVLS algos, legalize for unsupported case and switch back to non-NVLS + * algos multimem PTX don't support BF16/FP16 single element reduction + */ + if (!is_one_shot_supported) { + reduce_algo = 0; + } + + break; + default: + break; + } + + switch (reduce_algo) { + case 3: /* NVLS Two Shot Allreduce (RS + AG) */ + nvshmemi_add_reduce_nvls_twoshot_threadgroup(team, dest, source, nreduce); + break; + case 4: /* NVLS One Shot Allreduce (AR) */ + nvshmemi_add_reduce_nvls_oneshot_threadgroup(team, dest, source, nreduce); + break; + default: + nvshmemi_gpu_rdxn_threadgroup(team, dest, source, nreduce); + + break; + } +} + +NVSHMEMI_STATIC __device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void +nvshmemi_double2_maxloc_reduce_alltoall_block(nvshmem_team_t team, double2 *dest, + const double2 *source) { +#define SCOPE NVSHMEMI_THREADGROUP_BLOCK + nvshmemi_team_t *teami = nvshmemi_device_state_d.team_pool[team]; + const int myIdx = nvshmemi_thread_id_in_threadgroup(); + const int groupSize = nvshmemi_threadgroup_size(); + if (!myIdx) { /* Only one thread should increment */ + teami->ll_flag++; + teami->rdxn_count += 1; + } + nvshmemi_threadgroup_sync(); + const uint32_t ll_flag = teami->ll_flag; + char *pWrk = (char *)nvshmemi_team_get_psync(teami, REDUCE); + + nvshmemi_packLL_naive((uint64_t *)pWrk, source, 1, ll_flag); + nvshmemi_threadgroup_sync(); + const int my_pe = nvshmemi_team_my_pe(team); + const int n_pes = nvshmemi_team_n_pes(team); + for (int i = myIdx + 1; i < n_pes; i += groupSize) { + int peer = (my_pe + i) % n_pes; + size_t offset = 2 * sizeof(double2) * (my_pe + 2); + nvshmemi_put_nbi_threadgroup( + (uint64_t *)(pWrk + offset), (uint64_t *)(pWrk), sizeof(double2) / sizeof(uint32_t), + nvshmemi_team_translate_pe(team, peer, NVSHMEM_TEAM_WORLD)); + } + + if (!myIdx) { + dest[0] = source[0]; + for (int i = 1; i < n_pes; i++) { + int peer = (my_pe + i) % n_pes; + size_t offset = 2 * sizeof(double2) * (peer + 2); + nvshmemi_recvLL( + (double2 *)(pWrk + 2 * sizeof(double2)), (uint64_t *)(pWrk + offset), 1, ll_flag); + dest[0] = perform_gpu_rdxn( + dest[0], *((double2 *)(pWrk + 2 * sizeof(double2)))); + } + } + nvshmemi_threadgroup_sync(); +#undef SCOPE +} + +NVSHMEMI_STATIC __device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void +nvshmemi_double2_maxloc_rooted_reduce_flat_block(nvshmem_team_t team, double2 *dest, + const double2 *source) { +#define SCOPE NVSHMEMI_THREADGROUP_BLOCK + nvshmemi_team_t *teami = nvshmemi_device_state_d.team_pool[team]; + const int myIdx = nvshmemi_thread_id_in_threadgroup(); + const int groupSize = nvshmemi_threadgroup_size(); + if (!myIdx) { /* Only one thread should increment */ + teami->ll_flag++; + teami->rdxn_count += 1; + } + nvshmemi_threadgroup_sync(); + const uint32_t ll_flag = teami->ll_flag; + char *pWrk = (char *)nvshmemi_team_get_psync(teami, REDUCE); + + if (nvshmemi_team_my_pe(team) != 0) { + nvshmemi_packLL_naive((uint64_t *)pWrk, source, 1, ll_flag); + size_t offset = 2 * sizeof(double2) * nvshmemi_team_my_pe(team); + nvshmemi_put_nbi_threadgroup( + (uint64_t *)(pWrk + offset), (uint64_t *)(pWrk), sizeof(double2) / sizeof(uint32_t), + nvshmemi_team_translate_pe(team, 0, NVSHMEM_TEAM_WORLD)); + } else { + dest[0] = source[0]; + if (!myIdx) { + for (int i = 1; i < teami->size; i += 1) { + size_t offset = 2 * sizeof(double2) * i; + nvshmemi_recvLL( + (double2 *)pWrk, (uint64_t *)(pWrk + offset), 1, ll_flag); + dest[0] = perform_gpu_rdxn(dest[0], *(double2 *)pWrk); + } + } + nvshmemi_threadgroup_sync(); + } +#undef SCOPE +} + +NVSHMEMI_STATIC __device__ NVSHMEMI_DEVICE_ALWAYS_INLINE int nvshmemi_double2_maxloc_reduce_block( + nvshmem_team_t team, double2 *dest, const double2 *source, size_t nreduce) { +#ifdef NVSHMEM_DEBUG + assert(nreduce == 1); +#endif +#define SCOPE NVSHMEMI_THREADGROUP_BLOCK + nvshmemi_team_t *teami = nvshmemi_device_state_d.team_pool[team]; + switch (nvshmemi_device_state_d.gpu_coll_env_params_var.reduce_maxloc_algo) { + case 1: /* Alltoall algorithm */ + nvshmemi_double2_maxloc_reduce_alltoall_block(team, dest, source); + break; + case 2: /* Topo-unaware: Flat reduce + Flat bcast */ + nvshmemi_double2_maxloc_rooted_reduce_flat_block(team, dest, source); + nvshmemi_bcast_internode_tree_threadgroup( + team, dest, dest, 1, 0, nvshmemi_team_n_pes(team) - 1); + break; + case 3: /* Topo aware two-level flat reduce + Topo aware two-level tree bcast */ + if (teami->is_team_node || teami->is_team_same_mype_node) { + nvshmemi_double2_maxloc_rooted_reduce_flat_block(team, dest, source); + } else { + nvshmemi_double2_maxloc_rooted_reduce_flat_block(teami->team_node, dest, source); + if (nvshmemi_team_my_pe(teami->team_node) == 0) { + nvshmemi_double2_maxloc_rooted_reduce_flat_block(teami->team_same_mype_node, + dest, dest); + } + } + nvshmemi_bcast_hierarchical_threadgroup(team, dest, dest, 1, 0); + break; + case 4: /* Topo aware two-level flat reduce + Topo aware two-level tree bcast */ + if (teami->is_team_node || teami->is_team_same_mype_node) { + nvshmemi_double2_maxloc_reduce_alltoall_block(team, dest, source); + } else { + nvshmemi_double2_maxloc_reduce_alltoall_block(teami->team_node, dest, source); + nvshmemi_double2_maxloc_reduce_alltoall_block(teami->team_same_mype_node, dest, + dest); + } + break; + default: + assert(0); + break; + } +#undef SCOPE + return 0; +} + +/******* Tile collective functions ********/ + +// Select implementation based on the operation, datatype +template +__device__ inline void nvshmemi_tile_allreduce_nvls_thread_vec( + nvshmem_team_t team, T *src, T *dst, + const int size_major_dim, // size along the major dimension in elements + const int size_minor_dim, // size along the minor dimension in elements + const int src_stride_minor_dim, // src stride along minor dimension in elements + const int dst_stride_minor_dim, // dst stride along minor dimension in elements + const int src_stride_major_dim, // src stride along major dimension in elements + const int dst_stride_major_dim, // dst stride along major dimension in elements + tuple_t start_coord, tuple_t boundary) { + vtype *src_v = reinterpret_cast(nvshmemx_mc_ptr(team, src)); + vtype *dst_v = + reinterpret_cast(dst); // one-shot does only local stores to destination + if (ONESHOT == 0) { + dst_v = reinterpret_cast(nvshmemx_mc_ptr(team, dst)); // two-shot + assert((dst_v != nullptr) && "Failed to get multicast ptr for destination"); + } + + int src_stride_minor_dim_v = src_stride_minor_dim; + if (src_stride_minor_dim > 1) { + src_stride_minor_dim_v = (src_stride_minor_dim * sizeof(T)) / sizeof(vtype); + } + int dst_stride_minor_dim_v = dst_stride_minor_dim; + if (dst_stride_minor_dim > 1) { + dst_stride_minor_dim_v = (dst_stride_minor_dim * sizeof(T)) / sizeof(vtype); + } + int src_stride_major_dim_v = src_stride_major_dim; // keep stride as is if ==1 + if (src_stride_major_dim > 1) { + src_stride_major_dim_v = (src_stride_major_dim * sizeof(T)) / sizeof(vtype); + } + int dst_stride_major_dim_v = dst_stride_major_dim; + if (dst_stride_major_dim > 1) { + dst_stride_major_dim_v = (dst_stride_major_dim * sizeof(T)) / sizeof(vtype); + } + int nelem_major_dim = (size_major_dim * sizeof(T)) / sizeof(vtype); + const int nelem_minor_dim = size_minor_dim; + + if constexpr (is_half::value) { + if constexpr (op == RDXN_OPS_SUM) { + nvshmemi_f16x2_tile_allreduceADD_mcast_threadgroup( + dst_v, src_v, nelem_major_dim, nelem_minor_dim, src_stride_minor_dim_v, + dst_stride_minor_dim_v, src_stride_major_dim_v, dst_stride_major_dim_v, start_coord, + boundary); + } else if constexpr (op == RDXN_OPS_MIN) { + nvshmemi_f16x2_tile_allreduceMIN_mcast_threadgroup( + dst_v, src_v, nelem_major_dim, nelem_minor_dim, src_stride_minor_dim_v, + dst_stride_minor_dim_v, src_stride_major_dim_v, dst_stride_major_dim_v, start_coord, + boundary); + } else if constexpr (op == RDXN_OPS_MAX) { + nvshmemi_f16x2_tile_allreduceMAX_mcast_threadgroup( + dst_v, src_v, nelem_major_dim, nelem_minor_dim, src_stride_minor_dim_v, + dst_stride_minor_dim_v, src_stride_major_dim_v, dst_stride_major_dim_v, start_coord, + boundary); + } + } else if constexpr (is_bfloat::value) { + if constexpr (op == RDXN_OPS_SUM) { + nvshmemi_bf16x2_tile_allreduceADD_mcast_threadgroup( + dst_v, src_v, nelem_major_dim, nelem_minor_dim, src_stride_minor_dim_v, + dst_stride_minor_dim_v, src_stride_major_dim_v, dst_stride_major_dim_v, start_coord, + boundary); + } else if constexpr (op == RDXN_OPS_MIN) { + nvshmemi_bf16x2_tile_allreduceMIN_mcast_threadgroup( + dst_v, src_v, nelem_major_dim, nelem_minor_dim, src_stride_minor_dim_v, + dst_stride_minor_dim_v, src_stride_major_dim_v, dst_stride_major_dim_v, start_coord, + boundary); + } else if constexpr (op == RDXN_OPS_MAX) { + nvshmemi_bf16x2_tile_allreduceMAX_mcast_threadgroup( + dst_v, src_v, nelem_major_dim, nelem_minor_dim, src_stride_minor_dim_v, + dst_stride_minor_dim_v, src_stride_major_dim_v, dst_stride_major_dim_v, start_coord, + boundary); + } + } else if constexpr (is_float::value) { + static_assert((op != RDXN_OPS_MIN) && (op != RDXN_OPS_MAX), + "NVLS allreduce/reduce min/max not supported for float datatype"); + if constexpr (op == RDXN_OPS_SUM) { + nvshmemi_f32_tile_allreduceADD_mcast_threadgroup( + dst_v, src_v, nelem_major_dim, nelem_minor_dim, src_stride_minor_dim_v, + dst_stride_minor_dim_v, src_stride_major_dim_v, dst_stride_major_dim_v, start_coord, + boundary); + } else { + assert(0 && "unsupported reduce operation"); + } + } +} + +template +__device__ inline void nvshmemi_tile_allreduce_nvls_dim(nvshmem_team_t team, + src_tensor_t src_tensor, + dst_tensor_t dst_tensor, + tuple_t start_coord, tuple_t boundary) { + using T = typename src_tensor_t::value_type; + + // check for vector len == 4 + // Conditions: ptr must be aligned to int4, shape must be a multiple of 16, stride must be a + // multiple of 16 + if (((size_t)src_tensor.data() % sizeof(int4) == 0) && + ((size_t)dst_tensor.data() % sizeof(int4) == 0) && + (((get_tuple_val(src_tensor.shape()) * sizeof(T)) % sizeof(int4)) == 0) && + (((get_stride_element(src_tensor) * sizeof(T)) % sizeof(int4)) == 0) && + (((get_stride_element(dst_tensor) * sizeof(T)) % sizeof(int4)) == 0)) { + nvshmemi_tile_allreduce_nvls_thread_vec( + team, src_tensor.data(), dst_tensor.data(), + get_shape_element(src_tensor), // contiguous size + get_shape_element(src_tensor), // strided size + get_stride_element(src_tensor), // src stride minor_dim + get_stride_element(dst_tensor), // dst stride minor_dim + get_stride_element(src_tensor), // src stride major_dim; equal to 1 + get_stride_element(dst_tensor), // dst stride major_dim; equal to 1 + start_coord, boundary); + + } else if (((size_t)src_tensor.data() % sizeof(uint64_t) == 0) && + ((size_t)dst_tensor.data() % sizeof(uint64_t) == 0) && + (((get_tuple_val(src_tensor.shape()) * sizeof(T)) % sizeof(uint64_t)) == + 0) && + (((get_stride_element(src_tensor) * sizeof(T)) % sizeof(uint64_t)) == + 0) && + (((get_stride_element(dst_tensor) * sizeof(T)) % sizeof(uint64_t)) == + 0)) { + // Vector length == 2 + nvshmemi_tile_allreduce_nvls_thread_vec( + team, src_tensor.data(), dst_tensor.data(), + get_shape_element(src_tensor), // contiguous size + get_shape_element(src_tensor), // strided size + get_stride_element(src_tensor), // src stride minor_dim + get_stride_element(dst_tensor), // dst stride minor_dim + get_stride_element(src_tensor), // src stride major_dim; equal to 1 + get_stride_element(dst_tensor), // dst stride major_dim; equal to 1 + start_coord, boundary); + + } else { // vector len 1 + nvshmemi_tile_allreduce_nvls_thread_vec( + team, src_tensor.data(), dst_tensor.data(), + get_shape_element(src_tensor), // contiguous size + get_shape_element(src_tensor), // strided size + get_stride_element(src_tensor), // src stride minor_dim + get_stride_element(dst_tensor), // dst stride minor_dim + get_stride_element(src_tensor), // src stride major_dim; equal to 1 + get_stride_element(dst_tensor), // dst stride major_dim; equal to 1 + start_coord, boundary); + } +} +// specialize for the vectorization +template +__device__ inline void nvshmemi_tile_allreduce_nvls_thread(nvshmem_team_t team, + src_tensor_t src_tensor, + dst_tensor_t dst_tensor, + tuple_t start_coord, tuple_t boundary) { + using T = typename src_tensor_t::value_type; + + if constexpr ((get_constant(safe_get<0>(decltype(src_tensor.stride()){})) == 1) && + (get_constant(safe_get<0>(decltype(dst_tensor.stride()){})) == 1)) { + // dim 0 major + constexpr int major_dim = 0; + constexpr int minor_dim = 1; + + if constexpr (sizeof(T) < 4) { + // Shape along major dimension should be divisible by 2, because we operate at fp16x2 + assert(((get_shape_element(src_tensor) % 2) == 0) && + ((get_shape_element(dst_tensor) % 2) == 0) && + "Currently for 16B datatypes, we only support tensors which are 32b aligned " + "along their continuous dimension"); + } + + nvshmemi_tile_allreduce_nvls_dim(team, src_tensor, dst_tensor, + start_coord, boundary); + + } else if constexpr ((get_constant(safe_get<1>(decltype(src_tensor.stride()){})) == 1) && + (get_constant(safe_get<1>(decltype(dst_tensor.stride()){})) == 1)) { + // dim 1 major + constexpr int major_dim = 1; + constexpr int minor_dim = 0; + + if constexpr (sizeof(T) < 4) { + // Shape along major dimension should be divisible by 2, because we operate at fp16x2 + assert(((get_shape_element(src_tensor) % 2) == 0) && + ((get_shape_element(dst_tensor) % 2) == 0) && + "Currently for 16B datatypes, we only support tensors which are 32b aligned " + "along their continuous dimension"); + } + + nvshmemi_tile_allreduce_nvls_dim(team, src_tensor, dst_tensor, + start_coord, boundary); + } else { + // No contiguous dimension found at compile time + // TODO support when major dimension for src and dst tensors are different + if ((get_stride_element<1>(src_tensor) == 1) && (get_stride_element<1>(dst_tensor) == 1)) { + constexpr int major_dim = 1; + constexpr int minor_dim = 0; + + if constexpr (sizeof(T) < 4) { + // Shape along major dimension should be divisible by 2, because we operate at + // fp16x2 + assert(((get_shape_element(src_tensor) % 2) == 0) && + ((get_shape_element(dst_tensor) % 2) == 0) && + "Currently for 16B datatypes, we only support tensors which are 32b aligned " + "along their continuous dimension"); + } + + nvshmemi_tile_allreduce_nvls_dim( + team, src_tensor, dst_tensor, start_coord, boundary); + } else { + // setting major_dim to 0, minor_dim to 1 + constexpr int major_dim = 0; + constexpr int minor_dim = 1; + + if constexpr (sizeof(T) < 4) { + // Shape along major dimension should be divisible by 2, because we operate at + // fp16x2 + assert(((get_shape_element(src_tensor) % 2) == 0) && + ((get_shape_element(dst_tensor) % 2) == 0) && + "Currently for 16B datatypes, we only support tensors which are 32b aligned " + "along their continuous dimension"); + } + nvshmemi_tile_allreduce_nvls_dim( + team, src_tensor, dst_tensor, start_coord, boundary); + } + } +} + +// Tile allreduce entrypoint +// Call underlying function based on scope and algo +template +__device__ inline int nvshmemi_tile_allreduce(nvshmem_team_t team, src_tensor_t src_tensor, + dst_tensor_t dst_tensor, tuple_t start_coord, + tuple_t boundary, int root, uint64_t flag) { + using T = typename src_tensor_t::value_type; +#if defined(__cplusplus) && __cplusplus < 201703L + assert(0 && "Tile-granular APIs need C++ 17"); +#endif + + static_assert(cuda::std::is_same::value, + "Source and destination tensors must have the same type"); + + static_assert(cuda::std::is_same().shape()), + decltype(cuda::std::declval().shape())>::value, + "Source and destination tensors must have same shape"); + + static_assert((algo == nvshmemx::tile_coll_algo_t::NVLS_ONE_SHOT_PULL_NBI) || + (algo == nvshmemx::tile_coll_algo_t::NVLS_TWO_SHOT_PUSH_NBI), + "Unsupported tile AllReduce algorithm. " + "Currently NVLS_TILE_ONE_SHOT_PULL_NBI and NVLS_TILE_TWO_SHOT_PUSH_NBI " + "are supported for tile allreduce"); + + static_assert((scope == NVSHMEMI_THREADGROUP_THREAD) || (scope == NVSHMEMI_THREADGROUP_WARP) || + (scope == NVSHMEMI_THREADGROUP_WARPGROUP) || + (scope == NVSHMEMI_THREADGROUP_BLOCK), + "Unsupported scope"); + + static_assert((op == RDXN_OPS_SUM) || (op == RDXN_OPS_MIN) || (op == RDXN_OPS_MAX), + "Unsupported operation"); + + static_assert(((is_half::value) || (is_bfloat::value) || (is_float::value)), + "Unsupported datatype"); + + assert((src_tensor.data() != nullptr) && (dst_tensor.data() != nullptr) && + "Null pointers passed"); + + // check if both src and dst have same continuous dimension + // TODO relax this constraint + assert( + (((get_stride_element<0>(src_tensor) == 1) && (get_stride_element<0>(dst_tensor) == 1)) || + ((get_stride_element<1>(src_tensor) == 1) && (get_stride_element<1>(dst_tensor) == 1))) && + "Currently we only support cases where source and destination tile are continuous " + "along one dimension"); + + assert(!flag && "Currently non-zero flag value is unsupported"); + + if constexpr (algo == nvshmemx::tile_coll_algo_t::NVLS_TWO_SHOT_PUSH_NBI) { + // check for NVLS support in hardware +#if __CUDA_ARCH__ >= 900 && CUDART_VERSION >= 12010 + assert(__CUDA_ARCH__ >= 900 && CUDART_VERSION >= 12010); + + // As this algo PULLs data from other PEs, we need to ensure src data is ready + // Ensure all PEs have reached this point and pushed their data to local mem + + __threadfence(); // ensure data is visible in local GPU mem + nvshmemi_sync_algo_threadgroup(team); + + // Only root will perform all reduce for two-shot + if (root == -1) { + assert(0 && "Root must be specified for NVLS two-shot tile allreduce"); + return 0; + } else if (root != nvshmem_team_my_pe(team)) { + return 0; + } + + nvshmemi_tile_allreduce_nvls_thread( + team, src_tensor, dst_tensor, start_coord, boundary); +#else + assert(__CUDA_ARCH__ >= 900 && CUDART_VERSION >= 12010 && + "Unsupported NVLS on this platform"); +#endif + return 0; + } else { + // check for NVLS support in hardware +#if __CUDA_ARCH__ >= 900 && CUDART_VERSION >= 12010 + + // As this algo PULLs data from other PEs, we need to ensure src data is ready + // Ensure all PEs have reached this point and pushed their data to local mem + + __threadfence(); // ensure data is visible in local GPU mem + nvshmemi_sync_algo_threadgroup(team); + + // root is not used in one-shot allreduce + // One-shot allreduce + nvshmemi_tile_allreduce_nvls_thread( + team, src_tensor, dst_tensor, start_coord, boundary); +#else + assert(__CUDA_ARCH__ >= 900 && CUDART_VERSION >= 12010 && + "Unsupported NVLS on this platform"); +#endif + return 0; + } +} + +// Tile reduce entrypoint +// Call underlying function based on scope and algo +template +__device__ inline int nvshmemi_tile_reduce(nvshmem_team_t team, src_tensor_t src_tensor, + dst_tensor_t dst_tensor, tuple_t start_coord, + tuple_t boundary, int root, uint64_t flag) { + using T = typename src_tensor_t::value_type; + +#if defined(__cplusplus) && __cplusplus < 201703L + assert(0 && "Tile-granular APIs need C++ 17"); +#endif + + static_assert(cuda::std::is_same::value, + "Source and destination tensors must have the same type"); + + static_assert(cuda::std::is_same().shape()), + decltype(cuda::std::declval().shape())>::value, + "Source and destination tensors must have same shape"); + + static_assert((algo == nvshmemx::tile_coll_algo_t::NVLS_ONE_SHOT_PULL_NBI), + "Unsupported tile Reduce algorithm. " + "Currently NVLS_ONE_SHOT_PULL_NBI is supported for tile reduce"); + + static_assert((scope == NVSHMEMI_THREADGROUP_THREAD) || (scope == NVSHMEMI_THREADGROUP_WARP) || + (scope == NVSHMEMI_THREADGROUP_WARPGROUP) || + (scope == NVSHMEMI_THREADGROUP_BLOCK), + "Unsupported scope"); + + static_assert((op == RDXN_OPS_SUM) || (op == RDXN_OPS_MIN) || (op == RDXN_OPS_MAX), + "Unsupported operation"); + + static_assert(((is_half::value) || (is_bfloat::value) || (is_float::value)), + "Unsupported datatype"); + + assert((src_tensor.data() != nullptr) && (dst_tensor.data() != nullptr) && + "Null pointers passed"); + + // check if both src and dst have same continuous dimension + // TODO relax this constraint + assert( + (((get_stride_element<0>(src_tensor) == 1) && (get_stride_element<0>(dst_tensor) == 1)) || + ((get_stride_element<1>(src_tensor) == 1) && (get_stride_element<1>(dst_tensor) == 1))) && + "Currently we only support cases where source and destination tile are continuous " + "along one dimension"); + + assert(!flag && "Currently non-zero flag value is unsupported"); + + // NVLS Reduce only has one-shot + if constexpr (algo == nvshmemx::tile_coll_algo_t::NVLS_ONE_SHOT_PULL_NBI) { + // check for NVLS support in hardware +#if __CUDA_ARCH__ >= 900 && CUDART_VERSION >= 12010 + // As this algo PULLs data from other PEs, we need to ensure src data is ready + // Ensure all PEs have reached this point and pushed their data to local mem + + __threadfence(); // ensure data is visible in local GPU mem + nvshmemi_sync_algo_threadgroup(team); + + // Reduce is implemented as one-shot AllReduce with a root + + // Only root will perform reduce + if (root == -1) { + assert(0 && "Root must be specified for NVLS tile reduce"); + return 0; + } else if (root != nvshmem_team_my_pe(team)) { + return 0; + } + + nvshmemi_tile_allreduce_nvls_thread( + team, src_tensor, dst_tensor, start_coord, boundary); +#else + assert(__CUDA_ARCH__ >= 900 && CUDART_VERSION >= 12010 && + "Unsupported NVLS on this platform"); +#endif + return 0; + } else { + // Extend as other algorithms are added + return 0; + } +} + +#endif /* __CUDA_ARCH__ */ +#endif /* REDUCE_DEVICE_CUH */ diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/device/coll/reducescatter.cuh b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/device/coll/reducescatter.cuh new file mode 100644 index 0000000000000000000000000000000000000000..1307730ad0e82fe06404ae29ad67e82f4b864abf --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/device/coll/reducescatter.cuh @@ -0,0 +1,203 @@ +/* + * Copyright (c) 2017-2025, NVIDIA CORPORATION. All rights reserved. + * + * See License.txt for license information + */ + +#ifndef REDUCESCATTER_DEVICE_CUH +#define REDUCESCATTER_DEVICE_CUH + +#include +#include "non_abi/device/pt-to-pt/proxy_device.cuh" +#include "non_abi/device/threadgroup/nvshmemi_common_device_defines.cuh" +#include "non_abi/device/common/nvshmemi_common_device.cuh" +#include "non_abi/device/team/nvshmemi_team_defines.cuh" +#include "non_abi/nvshmem_build_options.h" +#if defined(NVSHMEM_ENABLE_ALL_DEVICE_INLINING) || defined(__NVSHMEM_NUMBA_SUPPORT__) +#include "non_abi/device/pt-to-pt/transfer_device.cuh" +#else +#include "non_abi/device/pt-to-pt/nvshmemi_transfer_api.cuh" +#endif +#include "non_abi/device/wait/nvshmemi_wait_until_apis.cuh" +#include "utils.cuh" +#include "fcollect.cuh" +#include "broadcast.cuh" +#include "reduce.cuh" + +#ifdef __CUDACC__ +#include +#include +namespace cg = cooperative_groups; +#endif + +#ifdef __CUDA_ARCH__ + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_reducescatter_allpush_threadgroup( + nvshmem_team_t team, TYPE *dest, const TYPE *source, size_t nreduce) { + nvshmemi_team_t *teami = nvshmemi_device_state_d.team_pool[team]; + TYPE *pWrk = (TYPE *)nvshmemi_team_get_psync(teami, REDUCE); + + if (teami->size == 1) { + nvshmemi_memcpy_threadgroup((char *)dest, (char *)source, sizeof(TYPE) * nreduce); + nvshmemi_threadgroup_sync(); + return; + } + + if (nreduce * sizeof(TYPE) * teami->size > + nvshmemi_device_state_d.gpu_coll_env_params_var.reduce_scratch_size * 0.5) { + assert(0 && + "Not enough space to perform reducescatter. Increase value of " + "NVSHMEM_REDUCE_SCRATCH_SIZE"); + } + + /* pWrk: pe0 data array, pe1 data array, pe2 data array, .... */ + for (int i = 1; i < teami->size; i++) { // don't send to self + int peer_pe_idx = (teami->my_pe + i) % teami->size; + int peer_pe = nvshmemi_team_translate_pe(team, peer_pe_idx, NVSHMEM_TEAM_WORLD); + nvshmemii_put_nbi_threadgroup( + (TYPE *)((char *)pWrk + teami->my_pe * nreduce * sizeof(TYPE)), + source + peer_pe_idx * nreduce, nreduce, peer_pe); + } + nvshmemi_barrier_threadgroup(team); + + TYPE *op1, *op2; + TYPE *op3; + for (int pe = 1; pe < teami->size; pe++) { // Need to perform npes - 1 reductions + op3 = dest; + if (pe == 1) { // this is the first reduction + if (nvshmemi_team_my_pe(team) == 0) { + op1 = (TYPE *)source; + op2 = (TYPE *)((char *)pWrk + nreduce * sizeof(TYPE)); + } else if (nvshmemi_team_my_pe(team) == 1) { + op1 = (TYPE *)(char *)pWrk; + op2 = (TYPE *)source + nreduce; + } else { + op1 = (TYPE *)(char *)pWrk; + op2 = (TYPE *)((char *)pWrk + nreduce * sizeof(TYPE)); + } + } else { + op1 = dest; + if (pe == nvshmemi_team_my_pe(team)) + op2 = (TYPE *)source + pe * nreduce; + else + op2 = (TYPE *)((char *)pWrk + pe * nreduce * sizeof(TYPE)); + } + gpu_linear_reduce_threadgroup(op1, op2, op3, nreduce); + } + nvshmemi_threadgroup_sync(); +} + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_reducescatter_nvls_allpush_threadgroup( + nvshmem_team_t team, TYPE *dest, const TYPE *source, int source_offset, size_t nreduce) { +#if defined __clang_llvm_bitcode_lib__ + if (__nvvm_reflect("__CUDA_ARCH") >= 900) { + nvshmemi_team_t *teami = nvshmemi_device_state_d.team_pool[team]; + TYPE *src_ptr = (TYPE *)nvshmemi_mc_ptr(teami, (void *)(source + source_offset)); + nvshmemi_threadgroup_sync(); + nvshmemi_local_reduce_mcast_threadgroup(dest, src_ptr, nreduce); + /* Since ld.red is done atomically on the NVSwitch, the value obtained into local dest + * ref for a given PE would be ready, right away. We can still have a case that after + * returning from this kernel, source buffer can be mutated on one PE, while another PE is + * still performing ld.red, causing data correctness issue. We don't however need to add + * threadfence_system for ordering since the subsequent load to source buffer will be + * ordered already to prior ld.reduce (RAR) by HW. + */ + nvshmemi_sync_threadgroup(team); + } else { + assert(0 && "Unsupported NVLS algo on this platform"); + } +#else +#if __CUDA_ARCH__ >= 900 && CUDART_VERSION >= 12010 + nvshmemi_team_t *teami = nvshmemi_device_state_d.team_pool[team]; + TYPE *src_ptr = (TYPE *)nvshmemi_mc_ptr(teami, (void *)(source + source_offset)); + nvshmemi_threadgroup_sync(); + nvshmemi_local_reduce_mcast_threadgroup(dest, src_ptr, nreduce); + /* Since ld.red is done atomically on the NVSwitch, the value obtained into local dest + * ref for a given PE would be ready, right away. We can still have a case that after returning + * from this kernel, source buffer can be mutated on one PE, while another PE is still + * performing ld.red, causing data correctness issue. We don't however need to add + * threadfence_system for ordering since the subsequent load to source buffer will be ordered + * already to prior ld.reduce (RAR) by HW. + */ + nvshmemi_sync_threadgroup(team); +#else + assert(0 && "Unsupported NVLS algo on this platform"); +#endif +#endif +} + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_reducescatter_threadgroup( + nvshmem_team_t team, TYPE *dest, const TYPE *source, int source_offset, size_t nreduce) { + int myIdx = nvshmemi_thread_id_in_threadgroup(); + int groupSize = nvshmemi_threadgroup_size(); + + if (!myIdx) /* Only one thread should increment rdxn_count */ + nvshmemi_device_state_d.team_pool[team]->rdxn_count += 1; + nvshmemi_threadgroup_sync(); + + constexpr bool is_float_v = is_float::value; + constexpr bool is_double_v = is_double::value; + constexpr bool is_half_prec = is_half::value || is_bfloat::value; + /* For SUM/AND/XOR/OR, support is untyped */ + /* For MIN/MAX, support is type specific */ + constexpr bool is_mcast_red_op = + NVSHMEMI_MCAST_RDXN_OP_IS_CAP_UNTYPED(OP) || + ((OP == RDXN_OPS_MIN || OP == RDXN_OPS_MAX) && (!is_float_v && !is_double_v)); + constexpr bool is_mcast_canoncial_type = (sizeof(TYPE) >= sizeof(uint32_t)); + bool is_half_precondition = + is_half_prec && nreduce >= 2 && (nvshmemi_device_state_d.team_pool[team]->size % 2 == 0); + bool is_nvls_algo_supported = + ((is_mcast_red_op && is_mcast_canoncial_type) || + (OP == RDXN_OPS_SUM && is_half_precondition)) && + (nvshmemi_device_state_d.team_pool[team]->nvls_rsc_base_ptr != NULL) && + ((nreduce * sizeof(TYPE)) % 4 == 0); + + int reducescatter_algo = nvshmemi_device_state_d.gpu_coll_env_params_var.reducescatter_algo; + /* This 2-level selection logic is implemented to reduce code duplication of calling leaf + * functions on the device code */ + switch (reducescatter_algo) { + case 0: /* default selection */ + if (is_nvls_algo_supported) { + reducescatter_algo = 3; /* NVLS One Shot */ + } else { + reducescatter_algo = 2; /* P2P One Shot */ + } + break; + case 2: /* One Shot */ + break; + case 3: + if (is_nvls_algo_supported) { + /* NVLS one shot */ + break; + } else { + reducescatter_algo = 2; /* One shot */ + break; + } + default: + assert(0 && "Specified reducescatter algo not supported, aborting...\n"); + break; + } + + switch (reducescatter_algo) { + case 2: + nvshmemi_reducescatter_allpush_threadgroup(team, dest, source, + nreduce); + break; + case 3: + nvshmemi_reducescatter_nvls_allpush_threadgroup( + team, dest, source, source_offset, nreduce); + break; + default: + assert(0); + break; + } + + /* since both allpush and nvls one-shot algos don't store data remotely at the end of the + * collective, threadfence_system when cuda_graph is enabled is not required */ +} + +#endif /* __CUDA_ARCH__ */ +#endif /* REDUCESCATTER_DEVICE_CUH */ diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/device/coll/utils.cuh b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/device/coll/utils.cuh new file mode 100644 index 0000000000000000000000000000000000000000..5ea821516a3b4642109a46c080ced0099710f8b6 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/device/coll/utils.cuh @@ -0,0 +1,54 @@ +/* + * Copyright (c) 2017-2025, NVIDIA CORPORATION. All rights reserved. + * + * See License.txt for license information + */ + +#ifndef _NVSHMEMI_DEVICE_COLL_UTILS_H_ +#define _NVSHMEMI_DEVICE_COLL_UTILS_H_ + +#include +#if !defined __CUDACC_RTC__ +#include +#else +#include +#endif +#include "non_abi/nvshmem_build_options.h" +#include "device/nvshmem_device_macros.h" +#if defined(NVSHMEM_ENABLE_ALL_DEVICE_INLINING) || defined(__NVSHMEM_NUMBA_SUPPORT__) +#include "non_abi/device/pt-to-pt/transfer_device.cuh" +#else +#include "non_abi/device/pt-to-pt/nvshmemi_transfer_api.cuh" +#endif +#include "non_abi/device/team/nvshmemi_team_defines.cuh" + +/* This is signaling function used in barrier algorithm. +nvshmem__signal function cannot be used in barrier because it uses a +combination of P2P path and IB path depending on how the peer GPU is +connected. In contrast to that, this fuction uses either P2P path (when all GPUs +are NVLink connected) or IB path (when any of the GPU is not NVLink connected). + +Using this function in barrier is necessary to ensure any previous RMA +operations are visible. When combination of P2P and IB path are used +as in nvshmem__signal function, it can lead to race conditions. +For example NVLink writes (of data and signal) can overtake IB writes. +And hence the data may not be visible after the barrier operation. +*/ +#ifdef __CUDA_ARCH__ +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_signal_for_barrier(T *dest, const T value, + int pe) { + const void *peer_base_addr = + (void *)__ldg((const long long unsigned *)nvshmemi_device_state_d.peer_heap_base_p2p + pe); + if (nvshmemi_device_state_d.job_connectivity <= NVSHMEMI_JOB_GPU_LDST) { + volatile T *dest_actual = + (volatile T *)((char *)(peer_base_addr) + + ((char *)dest - (char *)(nvshmemi_device_state_d.heap_base))); + *dest_actual = value; + } else { + nvshmemi_transfer_amo_nonfetch((void *)dest, value, pe, NVSHMEMI_AMO_SIGNAL); + } +} +#endif /* __CUDA_ARCH__ */ + +#endif /* NVSHMEMI_DEVICE_COLL_UTILS_H */ diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/device/common/nvshmemi_common_device.cuh b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/device/common/nvshmemi_common_device.cuh new file mode 100644 index 0000000000000000000000000000000000000000..d2c80371ab68a1338d30848845bea65abcc59492 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/device/common/nvshmemi_common_device.cuh @@ -0,0 +1,1037 @@ +/* + * Copyright (c) 2016-2025, NVIDIA CORPORATION. All rights reserved. + * + * See License.txt for license information + */ +#ifndef _NVSHMEM_COMMON_DEVICE_CUH_ +#define _NVSHMEM_COMMON_DEVICE_CUH_ + +#include +#if !defined __CUDACC_RTC__ +#include +#include +#include +#else +#include +#include +#include +#endif +#include "non_abi/nvshmem_build_options.h" +#include "device_host/nvshmem_common.cuh" +#include "device_host_transport/nvshmem_common_transport.h" +#include "non_abi/device/threadgroup/nvshmemi_common_device_defines.cuh" +#if defined(NVSHMEM_ENABLE_ALL_DEVICE_INLINING) || defined(__NVSHMEM_NUMBA_SUPPORT__) +#include "non_abi/device/pt-to-pt/transfer_device.cuh" +#else +#include "non_abi/device/pt-to-pt/nvshmemi_transfer_api.cuh" +#endif +#include "non_abi/device/pt-to-pt/proxy_device.cuh" +#include "non_abi/device/team/nvshmemi_team_defines.cuh" + +#define _LL_MAX_UNROLL 4 + +#define _LL_128_FLAG_THREAD 8 +#define _LL_128_FLAG_SIZE 8 +#define _LL_128_STORE_SIZE 16 +#define _LL_128_PACKETS_PER_WARP 4 +#define _LL_128_PACKET_DATA_SIZE 120 +#define _LL_128_PACKET_PSYNC_SIZE 128 +#define _LL_128_NUM_DATA_ELEMS_PER_PACKET(T) (_LL_128_PACKET_DATA_SIZE / sizeof(T)) +#define _LL_128_NUM_PSYNC_ELEMS_PER_PACKET(T) (_LL_128_PACKET_PSYNC_SIZE / sizeof(T)) +#define _LL_128_NUM_ELEMS_PER_STORE(T) (_LL_128_STORE_SIZE / sizeof(T)) +#define _LL_128_NUM_ELEMS_PER_FLAG(T) (_LL_128_FLAG_SIZE / sizeof(T)) +#define _LL_128_NUM_DATA_ELEMS_PER_WARP(UNROLL, T) \ + (UNROLL * _LL_128_PACKETS_PER_WARP * _LL_128_NUM_DATA_ELEMS_PER_PACKET(T)) +#define _LL_128_NUM_PSYNC_ELEMS_PER_WARP(UNROLL, T) \ + (UNROLL * _LL_128_PACKETS_PER_WARP * _LL_128_NUM_PSYNC_ELEMS_PER_PACKET(T)) + +#define _LL_8_DATA_BYTES_PER_WARP 256 +#define _LL_8_PSYNC_BYTES_PER_WARP 512 +#define _LL_128_DATA_BYTES_PER_WARP 480 +#define _LL_128_PSYNC_BYTES_PER_WARP 512 + +typedef enum { _LL_PSYNC_NON_VOLATILE = 0, _LL_PSYNC_VOLATILE } nvshmemi_psync_volatile_t; + +#ifdef __CUDA_ARCH__ + +/* This is used to create custom floating type traits such fp16, fp8, and native float types etc + */ +#if !defined __CUDACC_RTC__ +template +struct is_float : std::false_type {}; +template <> +struct is_float : std::true_type {}; + +template +struct is_half : std::false_type {}; +template <> +struct is_half : std::true_type {}; + +template +struct is_bfloat : std::false_type {}; +template <> +struct is_bfloat<__nv_bfloat16> : std::true_type {}; + +template +struct is_double : std::false_type {}; +template <> +struct is_double : std::true_type {}; + +template +struct is_uint16 : std::false_type {}; +template <> +struct is_uint16 : std::true_type {}; + +template +struct is_int16 : std::false_type {}; +template <> +struct is_int16 : std::true_type {}; + +#else + +template +struct is_float : cuda::std::false_type {}; +template <> +struct is_float : cuda::std::true_type {}; + +template +struct is_double : cuda::std::false_type {}; +template <> +struct is_double : cuda::std::true_type {}; + +template +struct is_half : cuda::std::false_type {}; +template <> +struct is_half : cuda::std::true_type {}; + +template +struct is_bfloat : cuda::std::false_type {}; +template <> +struct is_bfloat<__nv_bfloat16> : cuda::std::true_type {}; + +template +struct is_uint16 : cuda::std::false_type {}; +template <> +struct is_uint16 : cuda::std::true_type {}; + +template +struct is_int16 : cuda::std::false_type {}; +template <> +struct is_int16 : cuda::std::true_type {}; + +#endif + +__device__ int nvshmemi_team_translate_pe(nvshmemi_team_t *src_team, int src_pe, + nvshmemi_team_t *dest_team); +__device__ long *nvshmemi_team_get_psync(nvshmemi_team_t *team, nvshmemi_team_op_t op); +__device__ long *nvshmemi_team_get_sync_counter(nvshmemi_team_t *team); + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_quiet() { + int myIdx = nvshmemi_thread_id_in_threadgroup(); + if ((nvshmemi_device_state_d.job_connectivity > NVSHMEMI_JOB_GPU_LDST)) { + nvshmemi_transfer_quiet(true); + } else { + if (!myIdx) + __threadfence_system(); /* Use __threadfence_system instead of __threadfence + for data visibility in case of intra-node GPU transfers */ + nvshmemi_threadgroup_sync(); + } +} + +template __device__ void nvshmemi_quiet(); +template __device__ void nvshmemi_quiet(); +template __device__ void nvshmemi_quiet(); + +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_fence() { + if (nvshmemi_device_state_d.job_connectivity > NVSHMEMI_JOB_GPU_LDST) { + nvshmemi_transfer_fence(); + } + __threadfence_system(); /* Use __threadfence_system instead of __threadfence + for data visibility in case of intra-node GPU transfers */ +} + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE int nvshmemi_test(volatile T *ivar, int cmp, T cmp_value) { + int return_value = 0; + if (NVSHMEM_CMP_GE == cmp) { + if (*ivar >= cmp_value) return_value = 1; + } else if (NVSHMEM_CMP_EQ == cmp) { + if (*ivar == cmp_value) return_value = 1; + } else if (NVSHMEM_CMP_NE == cmp) { + if (*ivar != cmp_value) return_value = 1; + } else if (NVSHMEM_CMP_GT == cmp) { + if (*ivar > cmp_value) return_value = 1; + } else if (NVSHMEM_CMP_LT == cmp) { + if (*ivar < cmp_value) return_value = 1; + } else if (NVSHMEM_CMP_LE == cmp) { + if (*ivar <= cmp_value) return_value = 1; + } + return return_value; +} + +#if !defined __CUDACC_RTC__ +#define TYPE_IS_FLOAT(T) std::is_floating_point::value +#else +#define TYPE_IS_FLOAT(T) cuda::std::is_floating_point::value +#endif + +// mcast store of 16B +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_mcast16_store_threadgroup(int4 *dest, + const int4 *source, + size_t len) { + int myIdx = nvshmemi_thread_id_in_threadgroup(); + int groupSize = nvshmemi_threadgroup_size(); + for (size_t j = myIdx * 4; j < (len / sizeof(uint32_t)); j += groupSize * 4) { + uint32_t u4[4]; + asm("ld.global.v4.b32 {%0, %1, %2, %3}, [%4]; " + : "=r"(u4[0]), "=r"(u4[1]), "=r"(u4[2]), "=r"(u4[3]) + : "l"(source + j / 4)); + asm("multimem.st.global.v4.f32 [%0], {%1, %2, %3, %4};" ::"l"(dest + j / 4), "r"(u4[0]), + "r"(u4[1]), "r"(u4[2]), "r"(u4[3]) + : "memory"); + } +} + +// mcast store of 8B +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_mcast8_store_threadgroup( + uint64_t *dest, const uint64_t *source, size_t len) { + int myIdx = nvshmemi_thread_id_in_threadgroup(); + int groupSize = nvshmemi_threadgroup_size(); + for (size_t j = myIdx; j < (len / sizeof(uint64_t)); j += groupSize) { + uint64_t val1; + asm("ld.global.b64 %0, [%1];" : "=l"(val1) : "l"(source + j)); + asm("multimem.st.global.u64 [%0], %1;" ::"l"(dest + j), "l"(val1) : "memory"); + } +} + +// mcast store of 4B +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_mcast4_store_threadgroup( + uint32_t *dest, const uint32_t *source, size_t len) { + int myIdx = nvshmemi_thread_id_in_threadgroup(); + int groupSize = nvshmemi_threadgroup_size(); + for (size_t j = myIdx; j < (len / sizeof(uint32_t)); j += groupSize) { + uint32_t val1; + asm("ld.global.b32 %0, [%1];" : "=r"(val1) : "l"(source + j)); + asm("multimem.st.global.u32 [%0], %1;" ::"l"(dest + j), "r"(val1) : "memory"); + } +} + +/** + * This function returns non-zero unaligned bytes that require a unicast store + */ +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE size_t +nvshmemi_mcast_memcpy_threadgroup(T *__restrict__ dst, const T *__restrict__ src, size_t len) { + /* + * If src and dst are 16B aligned copy as much as possible using 16B chunks + */ + if ((uintptr_t)dst % 16 == 0 && (uintptr_t)src % 16 == 0 && len >= 16) { + int4 *__restrict__ dst_p = (int4 *)dst; + const int4 *__restrict__ src_p = (const int4 *)src; + const size_t nelems = len / 16; + nvshmemi_mcast16_store_threadgroup(dst_p, src_p, len); + len -= nelems * 16; + + if (0 == len) return 0; + dst = (T *)(dst_p + nelems); + src = (T *)(src_p + nelems); + } + + /* + * If src and dst are 8B aligned copy as much as possible using 8B chunks + */ + if ((uintptr_t)dst % 8 == 0 && (uintptr_t)src % 8 == 0 && len >= 8) { + const size_t nelems = len / 8; + uint64_t *__restrict__ dst_p = (uint64_t *)dst; + const uint64_t *__restrict__ src_p = (const uint64_t *)src; + nvshmemi_mcast8_store_threadgroup(dst_p, src_p, len); + len -= nelems * 8; + + if (0 == len) return 0; + dst = (T *)(dst_p + nelems); + src = (T *)(src_p + nelems); + } + + /* + * If src and dst are 4B aligned copy as much as possible using 4B chunks + */ + if ((uintptr_t)dst % 4 == 0 && (uintptr_t)src % 4 == 0 && len >= 4) { + const size_t nelems = len / 4; + uint32_t *__restrict__ dst_p = (uint32_t *)dst; + const uint32_t *__restrict__ src_p = (const uint32_t *)src; + nvshmemi_mcast4_store_threadgroup(dst_p, src_p, len); + len -= nelems * 4; + + if (0 == len) return 0; + dst = (T *)(dst_p + nelems); + src = (T *)(src_p + nelems); + } + + /* if len is non-zero, caller will retry with unicast stores */ + return (len); +} + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_memcpy_threadgroup( + void *__restrict__ dst, const void *__restrict__ src, size_t len) { + int myIdx = nvshmemi_thread_id_in_threadgroup(); + int groupSize = nvshmemi_threadgroup_size(); + /* + * If src and dst are 16B aligned copy as much as possible using 16B chunks + */ + if ((uintptr_t)dst % 16 == 0 && (uintptr_t)src % 16 == 0) { + const size_t nelems = len / 16; + +#ifdef __clang_llvm_bitcode_lib__ + uint32_t *__restrict__ dst_p = (uint32_t *)dst; + const uint32_t *__restrict__ src_p = (const uint32_t *)src; + for (size_t i = myIdx * 4; i < nelems * 4; i += groupSize * 4) { + asm volatile("st.volatile.global.v4.u32 [%0], {%1,%2,%3,%4};" ::"l"((dst_p + i)), + "r"(*(src_p + i)), "r"(*(src_p + i + 1)), "r"(*(src_p + i + 2)), + "r"(*(src_p + i + 3))); + } +#else + int4 *__restrict__ dst_p = (int4 *)dst; + const int4 *__restrict__ src_p = (const int4 *)src; + for (size_t i = myIdx; i < nelems; i += groupSize) dst_p[i] = src_p[i]; +#endif + len -= nelems * 16; + + if (0 == len) return; + +#ifdef __clang_llvm_bitcode_lib__ + dst = (void *)(dst_p + nelems * 4); + src = (void *)(src_p + nelems * 4); +#else + dst = (void *)(dst_p + nelems); + src = (void *)(src_p + nelems); +#endif + } + + /* + * If src and dst are 8B aligned copy as much as possible using 8B chunks + */ + if ((uintptr_t)dst % 8 == 0 && (uintptr_t)src % 8 == 0) { + uint64_t *__restrict__ dst_p = (uint64_t *)dst; + const uint64_t *__restrict__ src_p = (const uint64_t *)src; + const size_t nelems = len / 8; + + for (size_t i = myIdx; i < nelems; i += groupSize) dst_p[i] = src_p[i]; + + len -= nelems * 8; + + if (0 == len) return; + + dst = (void *)(dst_p + nelems); + src = (void *)(src_p + nelems); + } + + /* + * If src and dst are 4B aligned copy as much as possible using 4B chunks + */ + if ((uintptr_t)dst % 4 == 0 && (uintptr_t)src % 4 == 0) { + uint32_t *__restrict__ dst_p = (uint32_t *)dst; + const uint32_t *__restrict__ src_p = (const uint32_t *)src; + const size_t nelems = len / 4; + + for (size_t i = myIdx; i < nelems; i += groupSize) dst_p[i] = src_p[i]; + + len -= nelems * 4; + + if (0 == len) return; + + dst = (void *)(dst_p + nelems); + src = (void *)(src_p + nelems); + } + + /* + * If src and dst are 2B aligned copy as much as possible using 2B chunks + */ + if ((uintptr_t)dst % 2 == 0 && (uintptr_t)src % 2 == 0) { + uint16_t *__restrict__ dst_p = (uint16_t *)dst; + const uint16_t *__restrict__ src_p = (const uint16_t *)src; + const size_t nelems = len / 2; + + for (size_t i = myIdx; i < nelems; i += groupSize) dst_p[i] = src_p[i]; + + len -= nelems * 2; + + if (0 == len) return; + + dst = (void *)(dst_p + nelems); + src = (void *)(src_p + nelems); + } + + unsigned char *__restrict__ dst_c = (unsigned char *)dst; + const unsigned char *__restrict__ src_c = (const unsigned char *)src; + + for (size_t i = myIdx; i < len; i += groupSize) dst_c[i] = src_c[i]; +} + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_p(T *dest, const T value, int pe) { + const void *peer_base_addr = + (void *)__ldg((const long long unsigned *)nvshmemi_device_state_d.peer_heap_base_p2p + pe); + if (peer_base_addr) { + T *dest_actual = (T *)((char *)(peer_base_addr) + + ((char *)dest - (char *)(nvshmemi_device_state_d.heap_base))); + *dest_actual = value; + } else { + nvshmemi_transfer_rma_p((void *)dest, value, pe); + } +} + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE T nvshmemi_g(const T *source, int pe) { + const void *peer_base_addr = + (void *)__ldg((const long long unsigned *)nvshmemi_device_state_d.peer_heap_base_p2p + pe); + if (peer_base_addr) { + T *source_actual = (T *)((char *)(peer_base_addr) + + ((char *)source - (char *)(nvshmemi_device_state_d.heap_base))); + return *source_actual; + } else { + return nvshmemi_transfer_rma_g((void *)source, pe); + } +} + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_put_threadgroup(T *dest, const T *source, + size_t nelems, int pe) { + nvshmemi_threadgroup_sync(); + void *peer_base_addr = + (void *)__ldg((const long long unsigned *)nvshmemi_device_state_d.peer_heap_base_p2p + pe); + if (peer_base_addr) { + char *dest_actual = + (char *)(peer_base_addr) + ((char *)dest - (char *)(nvshmemi_device_state_d.heap_base)); + nvshmemi_memcpy_threadgroup((void *)dest_actual, (const void *)source, + nelems * sizeof(T)); + } else { + nvshmemi_transfer_rma((void *)dest, (void *)source, + nelems * sizeof(T), pe); + } + nvshmemi_threadgroup_sync(); +} + +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_signal_op(uint64_t *sig_addr, + uint64_t signal, int sig_op, + int pe) { + const void *peer_base_addr = + (void *)__ldg((const long long unsigned *)nvshmemi_device_state_d.peer_heap_base_p2p + pe); + if (sig_op == NVSHMEMI_AMO_SIGNAL_SET && peer_base_addr != NULL) { + volatile uint64_t *dest_actual = + (volatile uint64_t *)((char *)(peer_base_addr) + + ((char *)sig_addr - (char *)(nvshmemi_device_state_d.heap_base))); + *dest_actual = signal; + } else if (nvshmemi_device_state_d.job_connectivity <= NVSHMEMI_JOB_GPU_LDST) { + volatile uint64_t *dest_actual = + (volatile uint64_t *)((char *)(peer_base_addr) + + ((char *)sig_addr - (char *)(nvshmemi_device_state_d.heap_base))); + /* sig_op == NVSHMEM_SIGNAL_ADD */ + atomicAdd_system((unsigned long long *)dest_actual, signal); + } else { + nvshmemi_transfer_amo_nonfetch((void *)sig_addr, signal, pe, + (nvshmemi_amo_t)sig_op); + } +} + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemii_put_signal_threadgroup( + T *dest, const T *source, size_t nelems, uint64_t *sig_addr, uint64_t signal, int sig_op, + int pe, bool is_nbi) { + int myIdx = nvshmemi_thread_id_in_threadgroup(); + void *peer_base_addr = + (void *)__ldg((const long long unsigned *)nvshmemi_device_state_d.peer_heap_base_p2p + pe); + if (peer_base_addr) { + char *dest_actual = + (char *)(peer_base_addr) + ((char *)dest - (char *)(nvshmemi_device_state_d.heap_base)); + nvshmemi_memcpy_threadgroup((void *)dest_actual, (const void *)source, + nelems * sizeof(T)); + nvshmemi_threadgroup_sync(); + if (!myIdx) { + __threadfence_system(); + nvshmemi_signal_op(sig_addr, signal, sig_op, pe); + } + } else { + nvshmemi_transfer_put_signal((void *)dest, (void *)source, nelems * sizeof(T), + (void *)sig_addr, signal, (nvshmemi_amo_t)sig_op, pe, + is_nbi); + } +} + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_put_signal_threadgroup( + T *dest, const T *source, size_t nelems, uint64_t *sig_addr, uint64_t signal, int sig_op, + int pe, bool is_nbi) { + nvshmemi_threadgroup_sync(); + nvshmemii_put_signal_threadgroup(dest, source, nelems, sig_addr, signal, sig_op, pe, + is_nbi); + nvshmemi_threadgroup_sync(); +} + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_get_threadgroup(T *dest, const T *source, + size_t nelems, int pe) { + nvshmemi_threadgroup_sync(); + void *peer_base_addr = + (void *)__ldg((const long long unsigned *)nvshmemi_device_state_d.peer_heap_base_p2p + pe); + if (peer_base_addr) { + char *source_actual = (char *)(peer_base_addr) + + ((char *)source - (char *)(nvshmemi_device_state_d.heap_base)); + nvshmemi_memcpy_threadgroup((void *)dest, (const void *)source_actual, + nelems * sizeof(T)); + } else { + nvshmemi_transfer_rma((void *)source, (void *)dest, + nelems * sizeof(T), pe); + } + nvshmemi_threadgroup_sync(); +} + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemii_put_nbi_threadgroup(T *dest, + const T *source, + size_t nelems, int pe) { + void *peer_base_addr = + (void *)__ldg((const long long unsigned *)nvshmemi_device_state_d.peer_heap_base_p2p + pe); + if (peer_base_addr) { + char *dest_actual = + (char *)(peer_base_addr) + ((char *)dest - (char *)(nvshmemi_device_state_d.heap_base)); + nvshmemi_memcpy_threadgroup((void *)dest_actual, (const void *)source, + nelems * sizeof(T)); + } else { + nvshmemi_transfer_rma_nbi((void *)dest, (void *)source, + nelems * sizeof(T), pe); + } +} + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_put_nbi_threadgroup(T *dest, const T *source, + size_t nelems, int pe) { + nvshmemi_threadgroup_sync(); + nvshmemii_put_nbi_threadgroup(dest, source, nelems, pe); + nvshmemi_threadgroup_sync(); +} + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_get_nbi_threadgroup(T *dest, const T *source, + size_t nelems, int pe) { + nvshmemi_threadgroup_sync(); + void *peer_base_addr = + (void *)__ldg((const long long unsigned *)nvshmemi_device_state_d.peer_heap_base_p2p + pe); + if (peer_base_addr) { + char *source_actual = (char *)(peer_base_addr) + + ((char *)source - (char *)(nvshmemi_device_state_d.heap_base)); + nvshmemi_memcpy_threadgroup((void *)dest, (const void *)source_actual, + nelems * sizeof(T)); + } else { + nvshmemi_transfer_rma_nbi((void *)source, (void *)dest, + nelems * sizeof(T), pe); + } + nvshmemi_threadgroup_sync(); +} + +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void *nvshmemi_mc_ptr(nvshmemi_team_t *team, + const void *ptr) { + ptrdiff_t offset = (char *)ptr - (char *)nvshmemi_device_state_d.heap_base; + if (ptr >= nvshmemi_device_state_d.heap_base && offset < nvshmemi_device_state_d.heap_size && + team->nvls_rsc_base_ptr != NULL) { + void *mc_addr = (void *)__ldg((const long long unsigned *)team->nvls_rsc_base_ptr); + if (mc_addr != NULL) mc_addr = (void *)((char *)mc_addr + offset); + return mc_addr; + } else + return NULL; +} + +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void *nvshmemi_ptr(const void *ptr, int pe) { + ptrdiff_t offset = (char *)ptr - (char *)nvshmemi_device_state_d.heap_base; + + if (ptr >= nvshmemi_device_state_d.heap_base && offset < nvshmemi_device_state_d.heap_size) { + void *peer_addr = (void *)__ldg( + (const long long unsigned *)nvshmemi_device_state_d.peer_heap_base_p2p + pe); + if (peer_addr != NULL) peer_addr = (void *)((char *)peer_addr + offset); + return peer_addr; + } else + return NULL; +} + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_store_128b_register( + uint64_t dest_regs[2 * UNROLL], uint64_t ll_flag, uint64_t ll_flag_mask, T *src, + uint8_t unroll_stride) { + union { + uint64_t regs8[2]; + uint32_t regs4[4]; + }; + int myIdx = nvshmemi_thread_id_in_threadgroup(); + + /* in both 16 and 8 byte cases, the flag thread needs to write two 8 byte chunks. */ + +#pragma unroll + for (int unroll = 0; unroll < UNROLL; unroll++) { + if (sizeof(T) >= 8) { + uint64_t *src8 = (uint64_t *)(src + unroll * unroll_stride); + asm("ld.b64 %0, [%1];" : "=l"(regs8[0]) : "l"(src8)); + asm("ld.b64 %0, [%1];" : "=l"(regs8[1]) : "l"(src8 + 1)); + } else if (sizeof(T) == 4) { + uint32_t *src4 = (uint32_t *)(src + unroll * unroll_stride); + asm("ld.b32 %0, [%1];" : "=r"(regs4[0]) : "l"(src4)); + asm("ld.b32 %0, [%1];" : "=r"(regs4[1]) : "l"(src4 + 1)); + asm("ld.b32 %0, [%1];" : "=r"(regs4[2]) : "l"(src4 + 2)); + asm("ld.b32 %0, [%1];" : "=r"(regs4[3]) : "l"(src4 + 3)); + } else { + uint32_t lower, upper; + /* funnelshift_r will use shift to select the + * proper four bytes depending on the alignment. + * 0x10 = 2, 0x11 = 3, 0x01 = 1 + */ + uint8_t shift = (uintptr_t)src % 4; + /* We need to align the buffer to the nearest 4 bytes + * Shift down 2 bytes in the case of 2B alignment down + * 1B in the case of 1B alignment. Shifting down is always + * appropriate due to 256 B alignment of cudaMalloc(). + */ + uint32_t *src4 = + (uint32_t *)((uintptr_t)(src + unroll * unroll_stride) & -(uintptr_t)4); + /* 2 Byte aligned - 2B in lower, 2B in upper */ + /* 1 Byte aligned (0x01) - 1B in upper, 3B in lower */ + /* 1 Byte aligned (0x11) - 3B in upper, 1B in lower */ + asm("ld.b32 %0, [%1];" : "=r"(lower) : "l"(src4)); + asm("ld.b32 %0, [%1];" : "=r"(upper) : "l"(src4 + 1)); + regs4[0] = __funnelshift_r(lower, upper, 8 * shift); + asm("ld.b32 %0, [%1];" : "=r"(lower) : "l"(src4 + 1)); + asm("ld.b32 %0, [%1];" : "=r"(upper) : "l"(src4 + 2)); + regs4[1] = __funnelshift_r(lower, upper, 8 * shift); + asm("ld.b32 %0, [%1];" : "=r"(lower) : "l"(src4 + 2)); + asm("ld.b32 %0, [%1];" : "=r"(upper) : "l"(src4 + 3)); + regs4[2] = __funnelshift_r(lower, upper, 8 * shift); + asm("ld.b32 %0, [%1];" : "=r"(lower) : "l"(src4 + 4)); + asm("ld.b32 %0, [%1];" : "=r"(upper) : "l"(src4 + 5)); + regs4[3] = __funnelshift_r(lower, upper, 8 * shift); + } + dest_regs[unroll * 2] = regs8[0]; + dest_regs[unroll * 2 + 1] = (regs8[1] & ll_flag) | ll_flag_mask; + } +} + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_store_varlen_register( + uint64_t dest_regs[2], uint64_t ll_flag, uint64_t ll_flag_mask, void *src, uint8_t nelems) { + uint8_t i; + union { + uint64_t regs8[2]; + uint32_t regs4[4]; + }; + + /* in both 16 and 8 byte cases, the flag thread needs to write two 8 byte chunks. */ + if (sizeof(T) >= 8) { + uint64_t *src8 = (uint64_t *)src; + for (i = 0; i < nelems; i++) { + asm("ld.b64 %0, [%1];" : "=l"(regs8[i]) : "l"(src8 + i)); + } + } else if (sizeof(T) == 4) { + uint32_t *src4 = (uint32_t *)src; + for (i = 0; i < nelems; i++) { + asm("ld.b32 %0, [%1];" : "=r"(regs4[i]) : "l"(src4 + i)); + } + } else { + uint32_t lower, upper; + /* funnelshift_r will use shift to select the + * proper four bytes depending on the alignment. + * 0x10 = 2, 0x11 = 3, 0x01 = 1 + */ + uint8_t shift = (uintptr_t)src % 4; + /* We need to align the buffer to the nearest 4 bytes + * Shift down 2 bytes in the case of 2B alignment down + * 1B in the case of 1B alignment. Shifting down is always + * appropriate due to 256 B alignment of cudaMalloc(). + */ + uint32_t *src4 = (uint32_t *)((uintptr_t)src & -(uintptr_t)4); + /* 2 Byte aligned - 2B in lower, 2B in upper */ + /* 1 Byte aligned (0x01) - 1B in upper, 3B in lower */ + /* 1 Byte aligned (0x11) - 3B in upper, 1B in lower */ + for (i = 0; i < nelems; i++) { + asm("ld.b32 %0, [%1];" : "=r"(lower) : "l"(src4 + i)); + asm("ld.b32 %0, [%1];" : "=r"(upper) : "l"(src4 + i + 1)); + regs4[i] = __funnelshift_r(lower, upper, 8 * shift); + } + } + + dest_regs[0] = regs8[0]; + dest_regs[1] = (regs8[1] & ll_flag) | ll_flag_mask; +} + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_packLL128(T *psync, const T *source, + size_t nelems, uint64_t ll_flag, + nvshmemi_team_t *teami, + int pe_count, int team_offset, + int pe_group_offset) { + const int myIdx = nvshmemi_thread_id_in_threadgroup(); + const uint32_t warp_mask = 0x000000FF << (8 * (myIdx / 8)); + const int num_warps = nvshmemi_threadgroup_size() / NVSHMEMI_WARP_SIZE; + /* max 127 */ + const uint8_t remain = nelems % _LL_128_NUM_DATA_ELEMS_PER_PACKET(T); + /* max ~ 2048 * 16 * 4*/ + const uint32_t source_offset = + (myIdx / _LL_128_FLAG_THREAD) * UNROLL * _LL_128_NUM_DATA_ELEMS_PER_PACKET(T) + + myIdx % _LL_128_FLAG_THREAD * _LL_128_NUM_ELEMS_PER_STORE(T); + const uint32_t psync_offset = + (myIdx / _LL_128_FLAG_THREAD) * UNROLL * _LL_128_NUM_PSYNC_ELEMS_PER_PACKET(T) + + myIdx % _LL_128_FLAG_THREAD * _LL_128_NUM_ELEMS_PER_STORE(T); + const uint32_t source_stride = num_warps * _LL_128_NUM_DATA_ELEMS_PER_WARP(UNROLL, T); + const uint32_t psync_stride = num_warps * _LL_128_NUM_PSYNC_ELEMS_PER_WARP(UNROLL, T); + int current_global_pe_index; + const bool is_flag_thread = !((myIdx + 1) % _LL_128_FLAG_THREAD); + + /* We will always have a SCOPES worth of elements when doing unrolling */ + if (UNROLL > 1) { + assert(remain == 0); + assert(nelems % _LL_128_NUM_DATA_ELEMS_PER_WARP(UNROLL, T) == 0); + } + + /* Both source_end and psync_end remove the last < 128 bytes. + * These will be handled after the loop. */ + T *source_end = (T *)(source + nelems - remain); + + uint64_t regs[2 * UNROLL]; + T *source_ptr; + T *psync_ptr; + T *remote_psync_ptr; + uint64_t ll_flag_mask; + + assert(nvshmemi_threadgroup_size() % NVSHMEMI_WARP_SIZE == 0); + assert((uintptr_t)psync % 16 == 0); + assert(sizeof(T) <= 8); + + if (!is_flag_thread) { + ll_flag = UINT64_MAX; + ll_flag_mask = 0x0ULL; + } else { + ll_flag_mask = ll_flag; + } + __syncwarp(); + + /* Initial offset by thread */ + source_ptr = (T *)source + source_offset; + psync_ptr = (T *)psync + psync_offset; + + /* Only gate on source_end. psync_end is not important here. */ + for (; source_ptr < source_end; source_ptr += source_stride, psync_ptr += psync_stride) { + nvshmemi_store_128b_register(regs, ll_flag, ll_flag_mask, source_ptr, + _LL_128_NUM_DATA_ELEMS_PER_PACKET(T)); + + /* definitely possible we are not synchronized before loads to psync. Significant perf + * overhead? */ + for (int pe = 0; pe < pe_count; pe++) { + current_global_pe_index = nvshmemi_team_translate_pe_to_team_world_wrap( + teami, team_offset + (pe_group_offset + pe) % pe_count); + /* if (VOLATILE && current_global_pe_index == my_pe_idx) { + continue; + } */ + remote_psync_ptr = (T *)nvshmemi_ptr(psync_ptr, current_global_pe_index); +#pragma unroll + for (int unroll = 0; unroll < UNROLL * 2; unroll += 2) { + asm volatile( + "st.volatile.global.v2.b64 [%0], {%1,%2};" ::"l"((int4 *)remote_psync_ptr), + "l"(regs[unroll]), "l"(regs[unroll + 1])); + remote_psync_ptr += _LL_128_NUM_PSYNC_ELEMS_PER_PACKET(T); + } + } + } + + if (UNROLL == 1 && remain) { + uint8_t num_elems_per_store; + T *psync_end = + psync + (nelems - remain) + + (nelems / _LL_128_NUM_DATA_ELEMS_PER_PACKET(T)) * _LL_128_NUM_ELEMS_PER_FLAG(T); + /* Last packet will contain < 128 bytes. the variable remain is usually > 0. */ + /* only need 1/4th of a warp */ + if (myIdx < 8) { + source_ptr = source_end + source_offset; + psync_ptr = psync_end + psync_offset; + if (source_offset + _LL_128_NUM_ELEMS_PER_STORE(T) <= remain) { + nvshmemi_store_128b_register(regs, UINT64_MAX, ll_flag_mask, source_ptr, + _LL_128_NUM_DATA_ELEMS_PER_PACKET(T)); + } else { + num_elems_per_store = source_offset > remain ? 0 : remain - source_offset; + nvshmemi_store_varlen_register(regs, ll_flag, ll_flag_mask, source_ptr, + num_elems_per_store); + } + __syncwarp(warp_mask); + for (int pe = 0; pe < pe_count; pe++) { + current_global_pe_index = nvshmemi_team_translate_pe_to_team_world_wrap( + teami, team_offset + (pe_group_offset + pe) % pe_count); + /* if (VOLATILE && current_global_pe_index == my_pe_idx) { + continue; + } */ + remote_psync_ptr = (T *)nvshmemi_ptr(psync_ptr, current_global_pe_index); + asm volatile( + "st.volatile.global.v2.b64 [%0], {%1,%2};" ::"l"((int4 *)remote_psync_ptr), + "l"(regs[0]), "l"(regs[1])); + } + } + } + nvshmemi_threadgroup_sync(); +} + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_ll128_load_psync(uint64_t reg[UNROLL * 2], + uint64_t *src, + uint64_t ll_flag, + uint32_t warp_mask, + bool is_flag_thread) { + uint64_t *offset_src; + int i; + bool flag_arrived; + do { + flag_arrived = true; +#pragma unroll + for (i = 0; i < UNROLL * 2; i += 2) { + offset_src = src + _LL_128_NUM_PSYNC_ELEMS_PER_PACKET(uint64_t) * (i / 2); + asm volatile("ld.volatile.global.v2.b64 {%0,%1}, [%2];" + : "=l"(reg[i]), "=l"(reg[i + 1]) + : "l"((int4 *)offset_src)); + flag_arrived &= (reg[i + 1] == ll_flag); + flag_arrived |= !is_flag_thread; + } + flag_arrived = __all_sync(warp_mask, flag_arrived != false); + } while (!flag_arrived); +} + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_recvLL128(T *dest, const T *psync, + size_t nelems, uint64_t flag) { + const int myIdx = nvshmemi_thread_id_in_threadgroup(); + const uint32_t warp_mask = 0x000000FF << (8 * (myIdx / 8)); + /* max 127 */ + const uint8_t remain = nelems % _LL_128_NUM_DATA_ELEMS_PER_PACKET(T); + /* max ~ 2048 * 16 * 4*/ + const uint32_t dest_offset = + (myIdx / _LL_128_FLAG_THREAD) * UNROLL * _LL_128_NUM_DATA_ELEMS_PER_PACKET(T) + + myIdx % _LL_128_FLAG_THREAD * _LL_128_NUM_ELEMS_PER_STORE(T); + const uint32_t psync_offset = + (myIdx / _LL_128_FLAG_THREAD) * UNROLL * _LL_128_NUM_PSYNC_ELEMS_PER_PACKET(T) + + myIdx % _LL_128_FLAG_THREAD * _LL_128_NUM_ELEMS_PER_STORE(T); + const bool is_flag_thread = !((myIdx + 1) % _LL_128_FLAG_THREAD); + uint8_t source_copy_len = 16 >> is_flag_thread; + + /* We will always have a SCOPES worth of elements when doing unrolling */ + if (UNROLL > 1) { + assert(remain == 0); + assert(nelems % _LL_128_NUM_DATA_ELEMS_PER_WARP(UNROLL, T) == 0); + } + + /* Both dest_end and psync_end remove the last < 128 bytes. + * These will be handled after the loop. */ + T *dest_end = (T *)(dest + nelems - remain); + + uint64_t regs[2 * UNROLL]; + T *dest_ptr; + T *cur_dest_ptr; + T *psync_ptr; + + assert((uintptr_t)psync % 16 == 0); + assert(sizeof(T) <= 8); + + /* Initial offset by thread */ + dest_ptr = (T *)dest + dest_offset; + psync_ptr = (T *)psync + psync_offset; + for (; dest_ptr < dest_end; dest_ptr += _LL_128_NUM_DATA_ELEMS_PER_WARP(UNROLL, T), + psync_ptr += _LL_128_NUM_PSYNC_ELEMS_PER_WARP(UNROLL, T)) { + if (UNROLL > 1) { + nvshmemi_ll128_load_psync(regs, (uint64_t *)psync_ptr, flag, 0xFFFFFFFF, + is_flag_thread); + } else { + nvshmemi_ll128_load_psync(regs, (uint64_t *)psync_ptr, flag, warp_mask, + is_flag_thread); + } + + cur_dest_ptr = dest_ptr; +#pragma unroll + for (int i = 0; i < UNROLL * 2; i += 2) { + asm("st.global.b64 [%0], %1;" ::"l"((uint64_t *)cur_dest_ptr), "l"(regs[i])); + if (!is_flag_thread) { + asm("st.global.b64 [%0], %1;" ::"l"((uint64_t *)cur_dest_ptr + 1), + "l"(regs[i + 1])); + } + if (UNROLL > 1) { + __syncwarp(); + } else { + __syncwarp(warp_mask); + } + cur_dest_ptr += _LL_128_NUM_DATA_ELEMS_PER_PACKET(T); + } + } + + if (UNROLL == 1 && remain) { + T *psync_end = + (T *)psync + (nelems - remain) + + (nelems / _LL_128_NUM_DATA_ELEMS_PER_PACKET(T)) * _LL_128_NUM_ELEMS_PER_FLAG(T); + if (remain && myIdx < 8) { + dest_ptr = dest_end + dest_offset; + psync_ptr = psync_end + psync_offset; + nvshmemi_ll128_load_psync<1>(regs, (uint64_t *)psync_ptr, flag, warp_mask, + is_flag_thread); + if (dest_offset < remain) { + source_copy_len = dest_offset + _LL_128_NUM_ELEMS_PER_STORE(T) < remain + ? _LL_128_STORE_SIZE + : ((remain - dest_offset) * sizeof(T)); + nvshmemi_memcpy_threadgroup(dest_ptr, regs, + source_copy_len); + } + } + } + __syncwarp(); +} + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_mcast_recvLL(T *dest, const uint64_t *src, + size_t nelems, uint32_t flag) { + int myIdx = nvshmemi_thread_id_in_threadgroup(); + int groupSize = nvshmemi_threadgroup_size(); + size_t num_subelems = (nelems * sizeof(T)) / sizeof(uint32_t); + if (TYPE_IS_FLOAT(T)) { + for (int i = myIdx; i < num_subelems; i += groupSize) { + float data1, flag1; + volatile uint32_t flagu32; + do { + asm volatile("ld.volatile.global.v2.f32 {%0, %1}, [%2];" + : "=f"(data1), "=f"(flag1) + : "l"(&src[i])); + asm("cvt.rni.u32.f32 %0, %1;" : "=r"(flagu32) : "f"(flag1)); + } while ((flagu32 != flag)); + *(float *)((char *)dest + i * sizeof(float)) = data1; + } + } else { + for (int i = 2 * myIdx; i < num_subelems; i += 2 * groupSize) { + uint32_t flag1, flag2, data1, data2; + do { + asm volatile("ld.volatile.global.v4.u32 {%0,%1,%2,%3}, [%4];" + : "=r"(data1), "=r"(flag1), "=r"(data2), "=r"(flag2) + : "l"(&src[i])); + } while ((flag1 != flag) || (flag2 != flag)); + *(uint32_t *)((char *)dest + i * sizeof(uint32_t)) = data1; + *(uint32_t *)((char *)dest + (i + 1) * sizeof(uint32_t)) = data2; + } + } +} + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_mcast_packLL(uint64_t *dest, const T *source, + size_t nelems, + uint32_t ll_flag) { + int myIdx = nvshmemi_thread_id_in_threadgroup(); + int groupSize = nvshmemi_threadgroup_size(); + const size_t num_subelems = (nelems * sizeof(T)) / sizeof(float); + float flagf32; + if (TYPE_IS_FLOAT(T)) asm("cvt.rn.f32.u32 %0, %1;" : "=f"(flagf32) : "r"(ll_flag)); + for (int i = 2 * myIdx; i < num_subelems; i += 2 * groupSize) { + if (TYPE_IS_FLOAT(T)) { + float val1 = *(float *)((char *)source + i * sizeof(float)); + float val2 = *(float *)((char *)source + (i + 1) * sizeof(float)); + asm("multimem.st.global.v4.f32 [%0], {%1, %2, %3, %4};" ::"l"(&dest[i]), "f"(val1), + "f"(flagf32), "f"(val2), "f"(flagf32) + : "memory"); + } else { + uint32_t val1 = *(uint32_t *)((char *)source + i * sizeof(uint32_t)); + uint32_t val2 = *(uint32_t *)((char *)source + (i + 1) * sizeof(uint32_t)); + asm("multimem.st.global.v4.f32 [%0], {%1, %2, %3, %4};" ::"l"(&dest[i]), "r"(val1), + "r"(ll_flag), "r"(val2), "r"(ll_flag) + : "memory"); + } + } +} + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_recvLL(T *dest, const uint64_t *src, + size_t nelems, uint32_t flag) { + int myIdx = nvshmemi_thread_id_in_threadgroup(); + int groupSize = nvshmemi_threadgroup_size(); + size_t num_subelems = (nelems * sizeof(T)) / sizeof(uint32_t); + for (int i = 2 * myIdx; i < num_subelems; i += 2 * groupSize) { + uint32_t flag1, flag2, data1, data2; + do { + asm volatile("ld.volatile.global.v4.u32 {%0,%1,%2,%3}, [%4];" + : "=r"(data1), "=r"(flag1), "=r"(data2), "=r"(flag2) + : "l"(&src[i])); + + } while ((flag1 != flag) || (flag2 != flag)); + *(uint32_t *)((char *)dest + i * sizeof(uint32_t)) = data1; + *(uint32_t *)((char *)dest + (i + 1) * sizeof(uint32_t)) = data2; + } +} + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_packLL_naive(uint64_t *dest, const T *source, + size_t nelems, + uint32_t ll_flag) { + int myIdx = nvshmemi_thread_id_in_threadgroup(); + int groupSize = nvshmemi_threadgroup_size(); + size_t num_subelems = (nelems * sizeof(T)) / sizeof(uint32_t); + for (int i = myIdx * 2; i < num_subelems; i += groupSize * 2) { + size_t dst_offset = 2 * i * sizeof(uint32_t); + size_t src_offset = i * sizeof(uint32_t); + asm volatile("st.volatile.global.v4.u32 [%0], {%1,%2,%3,%4};" ::"l"( + (uint32_t *)((char *)dest + dst_offset)), + "r"(*(uint32_t *)((char *)source + src_offset)), "r"(ll_flag), + "r"(*(uint32_t *)((char *)source + src_offset + sizeof(uint32_t))), + "r"(ll_flag)); + } +} + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_packLL(T *psync, const T *source, + size_t nelems, uint32_t ll_flag, + nvshmemi_team_t *teami, int pe_count, + int team_offset) { + const size_t num_subelems = (nelems * sizeof(T)) / sizeof(uint32_t); + const int myIdx = nvshmemi_thread_id_in_threadgroup(); + const int groupSize = nvshmemi_threadgroup_size(); + /* each thread will write 2 subelements (8 bytes) */ + const size_t element_start = myIdx * UNROLL * 2; + const size_t element_stride = groupSize * UNROLL * 2; + size_t element_offset; + uint32_t regs[2 * UNROLL]; + + int current_global_pe_index; + uint32_t *current_dest_address; + + /* We need to be sure to fill each unrolled loop */ + assert(num_subelems % UNROLL * 2 == 0); + assert((uintptr_t)psync % 16 == 0); + + for (element_offset = element_start; element_offset < num_subelems; + element_offset += element_stride) { +#pragma unroll + for (int unroll = 0; unroll < UNROLL * 2; unroll += 2) { + if (sizeof(T) == sizeof(uint64_t)) { + asm("ld.global.v2.u32 {%0,%1}, [%2];" + : "=r"(regs[unroll]), "=r"(regs[unroll + 1]) + : "l"((uint32_t *)source + element_offset + unroll)); + } else { + regs[unroll] = *((uint32_t *)source + element_offset + unroll); + regs[unroll + 1] = *((uint32_t *)source + element_offset + unroll + 1); + } + } + for (int pe = 0; pe < pe_count; pe++) { + current_global_pe_index = + nvshmemi_team_translate_pe_to_team_world_wrap(teami, team_offset + pe); + current_dest_address = + (uint32_t *)nvshmemi_ptr(psync, current_global_pe_index) + element_offset * 2; + +#pragma unroll + for (int unroll = 0; unroll < UNROLL * 2; unroll += 2) { + asm volatile("st.volatile.global.v4.u32 [%0], {%1,%2,%3,%4};" ::"l"( + (int4 *)current_dest_address), + "r"(regs[unroll]), "r"(ll_flag), "r"(regs[unroll + 1]), "r"(ll_flag)); + current_dest_address += sizeof(int4) / sizeof(uint32_t); + } + } + } +} + +#endif /* __CUDA__ARCH__ */ +#endif /* _NVSHMEM_COMMON_DEVICE_CUH_ */ diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/device/common/nvshmemi_tile_utils.cuh b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/device/common/nvshmemi_tile_utils.cuh new file mode 100644 index 0000000000000000000000000000000000000000..4d7e4e08a06f3f618588af11b18dee179f3062b7 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/device/common/nvshmemi_tile_utils.cuh @@ -0,0 +1,288 @@ +/* + * Copyright (c) 2016-2025, NVIDIA CORPORATION. All rights reserved. + * + * See License.txt for license information + */ +#ifndef _NVSHMEM_TILE_UTILS_CUH_ +#define _NVSHMEM_TILE_UTILS_CUH_ + +#include +#include "cuda/std/tuple" +#include "cuda/std/type_traits" +#include "cuda/std/utility" +#include "host/nvshmem_macros.h" +#include "device_host/nvshmem_tensor.h" + +using tuple5Int_t = cuda::std::tuple; + +/**** Functions to get constant values at compile time ****/ + +template +struct is_integral_constant { + static const bool value = false; +}; + +// Specialization for integral_constant types +template +struct is_integral_constant< + T, typename cuda::std::enable_if, T>::value>::type> { + static const bool value = true; +}; + +// Check if T is an integral constant with specific value +template +struct is_integral_constant_value { + static const bool value = false; +}; + +template +struct is_integral_constant_value< + T, N, typename cuda::std::enable_if::value>::type> { + static const bool value = (T::value == N); +}; + +// Helper function to get integral constant value at compile time +template +struct get_constant_value { + __host__ __device__ __forceinline__ static constexpr int value() { return 0; } // default case +}; + +template +struct get_constant_value::value>::type> { + __host__ __device__ __forceinline__ static constexpr int value() { return T::value; } +}; + +// Convenience wrapper function +template +__host__ __device__ __forceinline__ constexpr int get_constant() { + return get_constant_value::value(); +} + +template +__host__ __device__ __forceinline__ constexpr int get_constant(T val) { + return get_constant_value::value(); +} +/**** End of Functions to get constant values at compile time ****/ + +/***** Helper functions for tuples ******/ +// Traits to get tuple size at compile time +template +struct tuple_size_traits { + static constexpr size_t value = cuda::std::tuple_size::value; +}; + +// Check if index is in bounds +template +struct is_index_in_bounds { + static constexpr bool value = (I >= 0 && I < tuple_size_traits::value); +}; +/**** End of Helper functions for tuples ******/ + +/*** Function to return the size of a tuple at compile time ****/ +// TODO: overload for cute::tuple +template +NVSHMEMI_HOSTDEVICE_PREFIX constexpr size_t get_tuple_size( + cuda::std::tuple const& /*tuple*/) { // The instance isn't needed + return cuda::std::tuple_size>::value; +} + +template +/*NVSHMEMI_HOSTDEVICE_PREFIX constexpr*/ +__host__ __device__ __forceinline__ constexpr decltype( + cuda::std::get(cuda::std::declval&>())) +get_tuple_val(cuda::std::tuple const& tuple) { + return cuda::std::get(tuple); +} + +/*** Accessor functions for shape ***/ +/*** if index used is out of bounds, return 1 ***/ +// Primary template for shape access (out-of-bounds) +template +struct tensor_shape_element_impl { + NVSHMEMI_HOSTDEVICE_PREFIX static constexpr int get(const nvshmemx::Tensor& tensor) { + return 1; // Out of bounds, return 1 + } +}; + +// Specialization for in-bounds shape access +template +struct tensor_shape_element_impl { + NVSHMEMI_HOSTDEVICE_PREFIX static constexpr auto get(const nvshmemx::Tensor& tensor) + -> decltype(cuda::std::get(tensor.shape())) { + return cuda::std::get(tensor.shape()); + } +}; + +// Public interface for shape +template +NVSHMEMI_HOSTDEVICE_PREFIX constexpr auto get_shape_element( + const nvshmemx::Tensor& tensor) + -> decltype( + tensor_shape_element_impl< + I, T, Layout, is_index_in_bounds::value>::get(tensor)) { + return tensor_shape_element_impl< + I, T, Layout, is_index_in_bounds::value>::get(tensor); +} + +/*** Accessor functions for stride ***/ +/*** if index used is out of bounds, return 0 ***/ + +// Primary template for stride access (out-of-bounds) +template +struct tensor_stride_element_impl { + NVSHMEMI_HOSTDEVICE_PREFIX static constexpr int get(const nvshmemx::Tensor& tensor) { + return 0; // Out of bounds, return 0 for stride + } +}; + +// Specialization for in-bounds stride access +template +struct tensor_stride_element_impl { + NVSHMEMI_HOSTDEVICE_PREFIX static constexpr auto get(const nvshmemx::Tensor& tensor) + -> decltype(cuda::std::get(tensor.stride())) { + return cuda::std::get(tensor.stride()); + } +}; + +// Public interface for stride +template +NVSHMEMI_HOSTDEVICE_PREFIX constexpr auto get_stride_element( + const nvshmemx::Tensor& tensor) + -> decltype( + tensor_stride_element_impl< + I, T, Layout, is_index_in_bounds::value>::get(tensor)) { + return tensor_stride_element_impl< + I, T, Layout, is_index_in_bounds::value>::get(tensor); +} + +/**** Safe access functions for tuples ******/ +/*** Returns 0 for out-of-bounds access ***/ + +// Primary template for safe tuple access (out-of-bounds) +template +struct safe_get_impl { + // Out of bounds access returns int + NVSHMEMI_HOSTDEVICE_PREFIX + static constexpr int get(const Tuple&) { + return 0; // Default to 0 for out-of-bounds + } +}; + +// Specialization for in-bounds access +template +struct safe_get_impl { + // Define the return type for in-bounds access + typedef decltype(cuda::std::get(cuda::std::declval())) return_type; + + NVSHMEMI_HOSTDEVICE_PREFIX + static constexpr return_type get(const Tuple& tuple) { return cuda::std::get(tuple); } +}; + +// Public interface for safe tuple access +template +NVSHMEMI_HOSTDEVICE_PREFIX constexpr + typename cuda::std::conditional<(I < tuple_size_traits::value), + decltype(cuda::std::get(cuda::std::declval())), + int>::type + safe_get(const Tuple& tuple) { + return safe_get_impl::value)>::get(tuple); +} + +/**** Function to tuple of size 2 accounting for the major dimension */ +// TODO: expand this to support more than 2 dimensions +template +NVSHMEMI_HOSTDEVICE_PREFIX __forceinline__ tuple5Int_t create_coord_tuple(int coord_major, + int coord_minor) { + if (major_dim == 0) { + return cuda::std::make_tuple(coord_major, coord_minor, 0, 0, 0); + } else { + return cuda::std::make_tuple(coord_minor, coord_major, 0, 0, 0); + } +} + +/* + * Functionto compare elements of tuples + */ +// TODO make a cute variant of this +// Helper to check bounds for each dimension +// Helper to check if a dimension is valid +template +NVSHMEMI_HOSTDEVICE_PREFIX __forceinline__ bool check_dimension_valid(const tuple_t& start_coord, + const tuple5Int_t& elem_coord, + const tuple_t& boundary, + int dim, int major_dim, + int vlen) { + if (dim == major_dim) { + return (get_tuple_val<0>(start_coord) + get_tuple_val<0>(elem_coord) + (vlen - 1) < + get_tuple_val<0>(boundary)); + } else { + return (get_tuple_val<0>(start_coord) + get_tuple_val<0>(elem_coord) < + get_tuple_val<0>(boundary)); + } +} + +// Primary template for dimension checking +template +struct dimension_checker { + NVSHMEMI_HOSTDEVICE_PREFIX __forceinline__ static bool check(const tuple_t& start_coord, + const tuple5Int_t& elem_coord, + const tuple_t& boundary, + int major_dim, int vlen) { + bool current_valid; + + if (Dim == major_dim) { + current_valid = + (get_tuple_val(start_coord) + get_tuple_val(elem_coord) + (vlen - 1) < + get_tuple_val(boundary)); + } else { + current_valid = (get_tuple_val(start_coord) + get_tuple_val(elem_coord) < + get_tuple_val(boundary)); + } + + return current_valid && dimension_checker::check( + start_coord, elem_coord, boundary, major_dim, vlen); + } +}; + +// Base case: When Dim == MaxDim +template +struct dimension_checker { + NVSHMEMI_HOSTDEVICE_PREFIX __forceinline__ static bool check(const tuple_t&, const tuple5Int_t&, + const tuple_t&, int, int) { + return true; // End of recursion + } +}; + +// Special case for 1D tuples +template +NVSHMEMI_HOSTDEVICE_PREFIX __forceinline__ + typename cuda::std::enable_if<(tuple_size_traits::value == 1), bool>::type + is_less_than_impl(const tuple_t& start_coord, const tuple5Int_t& elem_coord, + const tuple_t& boundary, int vlen) { + return check_dimension_valid(start_coord, elem_coord, boundary, 0, major_dim, vlen); +} + +// Special case for 2D tuples +template +NVSHMEMI_HOSTDEVICE_PREFIX __forceinline__ + typename cuda::std::enable_if<(tuple_size_traits::value == 2), bool>::type + is_less_than_impl(const tuple_t& start_coord, const tuple5Int_t& elem_coord, + const tuple_t& boundary, int vlen) { + return dimension_checker<0, 2, tuple_t>::check(start_coord, elem_coord, boundary, major_dim, + vlen); +} + +// Main function with runtime dispatching based on tuple size +template +NVSHMEMI_HOSTDEVICE_PREFIX __forceinline__ bool is_less_than(const tuple_t& start_coord, + const tuple5Int_t& elem_coord, + const tuple_t& boundary, + int vlen = 1) { + static_assert(tuple_size_traits::value <= 2, + "Tuples with more than 2 dimensions are not supported"); + + return is_less_than_impl(start_coord, elem_coord, boundary, vlen); +} + +#endif diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/device/pt-to-pt/ibgda_device.cuh b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/device/pt-to-pt/ibgda_device.cuh new file mode 100644 index 0000000000000000000000000000000000000000..8fa3dec887b970384078c724bd1de6a081fd4110 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/device/pt-to-pt/ibgda_device.cuh @@ -0,0 +1,3512 @@ +/* + * Copyright (c) 2016-2025, NVIDIA CORPORATION. All rights reserved. + * + * See License.txt for license information + */ + +#ifndef _NVSHMEMI_IBGDA_DEVICE_H_ +#define _NVSHMEMI_IBGDA_DEVICE_H_ + +#include +#if !defined __CUDACC_RTC__ +#include +#else +#include +#endif +#include "infiniband/mlx5dv.h" + +#include "non_abi/device/threadgroup/nvshmemi_common_device_defines.cuh" +#include "device_host_transport/nvshmem_common_ibgda.h" +#include "non_abi/nvshmem_build_options.h" +#include "utils_device.h" + +#include + +//#define NVSHMEM_IBGDA_DEBUG +//#define NVSHMEM_TIMEOUT_DEVICE_POLLING + +#define NVSHMEMI_MIN(x, y) ((x) < (y) ? (x) : (y)) +#define NVSHMEMI_MAX(x, y) ((x) > (y) ? (x) : (y)) + +#define NVSHMEMI_IBGDA_PTX_OPTIMIZATION_MFENCE + +#ifdef NVSHMEM_IBGDA_SUPPORT_GPUMEM_ONLY +// These PTX optimizations are for GPU memory access only. +// Both data and NIC control objects must be in GPU memory. +#define NVSHMEMI_IBGDA_PTX_OPTIMIZATION_ATOMIC_READ_SET +#define NVSHMEMI_IBGDA_PTX_OPTIMIZATION_STORE_RELEASE +#endif + +#define IBGDA_FULL_WARP 0xffffffffU +#define IBGDA_POLL_TIMEOUT 4000000000LLU + +/* When we exceed a specific number of threads doing quiet + * we end up with cache thrashing which causes a significant + * perf hit. TODO: Tune this number for each supported arch. + */ +#define IBGDA_MAX_THREADS_PER_QUIET 32 + +// MLX5 accepts up to 2 GiB per command +#define IBGDA_MAX_TRANSFER_SIZE 2147483648LLU + +#ifndef likely +#define likely(x) (__builtin_expect(!!(x), 1)) +#endif + +#ifndef unlikely +#define unlikely(x) (__builtin_expect(!!(x), 0)) +#endif + +#ifndef ACCESS_ONCE +#define ACCESS_ONCE(x) (*(volatile typeof(x) *)&(x)) +#endif + +/** + * DO NOT use BSWAP(READ_ONCE(x)) as it could create a bug. + * BSWAP is a pre-processor function. It will be unrolled to many READ_ONCE. + */ +#ifndef READ_ONCE +#define READ_ONCE(x) ACCESS_ONCE(x) +#endif + +#ifndef WRITE_ONCE +#define WRITE_ONCE(x, v) (ACCESS_ONCE(x) = (v)) +#endif + +#ifdef NVSHMEM_IBGDA_DEBUG +struct mlx5_err_cqe_ex { + uint8_t rsvd0[32]; + __be32 srqn; + uint8_t rsvd1[16]; + uint8_t hw_err_synd; + uint8_t hw_synd_type; + uint8_t vendor_err_synd; + uint8_t syndrome; + __be32 s_wqe_opcode_qpn; + __be16 wqe_counter; + uint8_t signature; + uint8_t op_own; +}; +typedef struct mlx5_err_cqe_ex ibgda_mlx5_err_cqe_t; +#else +typedef struct mlx5_err_cqe ibgda_mlx5_err_cqe_t; +#endif + +#define IBGDA_4_BYTE_EXT_AMO_OPMOD 0x08000000 +#define IBGDA_8_BYTE_EXT_AMO_OPMOD 0x09000000 + +typedef enum ibgda_mlx5_fm { + IBGDA_MLX5_FM_NO_FENCE = 0, + IBGDA_MLX5_FM_INITIATOR_SMALL_FENCE = 1 << 5, + IBGDA_MLX5_FM_FENCE = 2 << 5, + IBGDA_MLX5_FM_STRONG_ORDERING = 3 << 5, + IBGDA_MLX5_FM_FENCE_AND_INITIATOR_SMALL_FENCE = 4 << 5, + OBGDA_MLX5_FM_OP_MAX = INT_MAX, +} ibgda_mlx5_fm_t; + +enum { + IBGDA_MLX5_OPCODE_DUMP = 0x23, + IBGDA_MLX5_OPCODE_SENTINEL = INT_MAX + +}; + +typedef struct mlx5_wqe_ctrl_seg __attribute__((__aligned__(8))) ibgda_ctrl_seg_t; + +// The ext flag (in dqp_dct) must be set to disable. +typedef struct { + __be64 dc_key; + __be32 dqp_dct; + uint8_t stat_rate_sl; + uint8_t fl_mlid; + __be16 rlid; +} __attribute__((__packed__)) __attribute__((__aligned__(4))) ibgda_half_av_seg_t; +#if __cplusplus >= 201103L +static_assert(sizeof(ibgda_half_av_seg_t) == 16, "sizeof(ibgda_half_av_seg_t) == 16 failed."); +#endif + +typedef struct { + uint32_t add_data; + uint32_t field_boundary; + uint64_t reserved; +} __attribute__((__packed__)) ibgda_atomic_32_masked_fa_seg_t; +#if __cplusplus >= 201103L +static_assert(sizeof(ibgda_atomic_32_masked_fa_seg_t) == 16, + "sizeof(ibgda_atomic_32_masked_fa_seg_t) == 16 failed."); +#endif + +typedef struct { + uint64_t add_data; + uint64_t field_boundary; +} __attribute__((__packed__)) ibgda_atomic_64_masked_fa_seg_t; +#if __cplusplus >= 201103L +static_assert(sizeof(ibgda_atomic_64_masked_fa_seg_t) == 16, + "sizeof(ibgda_atomic_64_masked_fa_seg_t) == 16 failed."); +#endif + +typedef struct { + uint32_t swap_data; + uint32_t compare_data; + uint32_t swap_mask; + uint32_t compare_mask; +} __attribute__((__packed__)) ibgda_atomic_32_masked_cs_seg_t; +#if __cplusplus >= 201103L +static_assert(sizeof(ibgda_atomic_32_masked_cs_seg_t) == 16, + "sizeof(ibgda_atomic_32_masked_cs_seg_t) == 16 failed."); +#endif + +typedef struct { + uint64_t swap; + uint64_t compare; +} __attribute__((__packed__)) ibgda_atomic_64_masked_cs_seg_t; +#if __cplusplus >= 201103L +static_assert(sizeof(ibgda_atomic_64_masked_cs_seg_t) == 16, + "sizeof(ibgda_atomic_64_masked_cs_seg_t) == 16 failed."); +#endif + +#ifdef __CUDA_ARCH__ + +#ifdef NVSHMEM_TIMEOUT_DEVICE_POLLING +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE uint64_t ibgda_query_globaltimer() { + uint64_t ret; + asm volatile("mov.u64 %0, %%globaltimer;" : "=l"(ret)::"memory"); + return ret; +} +#endif /* NVSHMEM_TIMEOUT_DEVICE_POLLING */ + +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE nvshmemi_ibgda_device_state_t * +ibgda_get_state() { + return &nvshmemi_ibgda_device_state_d; +} + +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE bool ibgda_is_rc_enabled() { + return ibgda_get_state()->num_rc_per_pe > 0; +} + +// Prevent code reordering from both compiler and GPU +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE void IBGDA_MFENCE() { +#ifdef NVSHMEMI_IBGDA_PTX_OPTIMIZATION_MFENCE + asm volatile("fence.acq_rel.cta;" ::: "memory"); +#else + __threadfence_block(); +#endif /* NVSHMEMI_IBGDA_PTX_OPTIMIZATION_MFENCE */ +} + +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE void IBGDA_MEMBAR_NO_OPTIMIZATION() { +#ifdef NVSHMEM_IBGDA_SUPPORT_GPUMEM_ONLY + __threadfence(); +#else + if (likely(ibgda_get_state()->nic_buf_on_gpumem)) + __threadfence(); + else + __threadfence_system(); +#endif /* NVSHMEM_IBGDA_SUPPORT_GPUMEM_ONLY */ +} + +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE void IBGDA_MEMBAR() { +// st.release automatically adds membar in SASS. +#ifndef NVSHMEMI_IBGDA_PTX_OPTIMIZATION_STORE_RELEASE + +#ifdef NVSHMEM_IBGDA_SUPPORT_GPUMEM_ONLY + __threadfence(); +#else + if (likely(ibgda_get_state()->nic_buf_on_gpumem)) + __threadfence(); + else + __threadfence_system(); +#endif /* NVSHMEM_IBGDA_SUPPORT_GPUMEM_ONLY */ + +#endif /* NVSHMEMI_IBGDA_PTX_OPTIMIZATION_STORE_RELEASE */ +} + +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE int nvshmemi_thread_id_in_warp() { + int myIdx; + asm volatile("mov.u32 %0, %%laneid;" : "=r"(myIdx)); + return myIdx; +} + +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE int nvshmemi_warp_size() { + return ((blockDim.x * blockDim.y * blockDim.z) < warpSize) + ? (blockDim.x * blockDim.y * blockDim.z) + : warpSize; +} + +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_warp_sync() { __syncwarp(); } + +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE int nvshmemi_thread_id_in_block() { + return (threadIdx.x + threadIdx.y * blockDim.x + threadIdx.z * blockDim.x * blockDim.y); +} + +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE int nvshmemi_block_size() { + return (blockDim.x * blockDim.y * blockDim.z); +} + +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE uint32_t ibgda_get_smid() { + uint32_t smid; + asm("mov.u32 %0, %%smid;" : "=r"(smid)); + return smid; +} + +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE uint32_t ibgda_get_ctaid() { + return (blockIdx.x + blockIdx.y * gridDim.x + blockIdx.z * gridDim.x * gridDim.y); +} + +template +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE void ibgda_store_relaxed(T *ptr, T val) { + WRITE_ONCE(*ptr, val); +} + +template <> +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void ibgda_store_relaxed(uint8_t *ptr, uint8_t val) { +#ifdef NVSHMEMI_IBGDA_PTX_OPTIMIZATION_STORE_RELEASE + uint16_t _val = val; + asm volatile("st.relaxed.gpu.global.L1::no_allocate.b8 [%0], %1;" : : "l"(ptr), "h"(_val)); +#else + WRITE_ONCE(*ptr, val); +#endif +} + +template <> +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void ibgda_store_relaxed(uint16_t *ptr, uint16_t val) { +#ifdef NVSHMEMI_IBGDA_PTX_OPTIMIZATION_STORE_RELEASE + asm volatile("st.relaxed.gpu.global.L1::no_allocate.b16 [%0], %1;" : : "l"(ptr), "h"(val)); +#else + WRITE_ONCE(*ptr, val); +#endif +} + +template <> +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void ibgda_store_relaxed(uint32_t *ptr, uint32_t val) { +#ifdef NVSHMEMI_IBGDA_PTX_OPTIMIZATION_STORE_RELEASE + asm volatile("st.relaxed.gpu.global.L1::no_allocate.b32 [%0], %1;" : : "l"(ptr), "r"(val)); +#else + WRITE_ONCE(*ptr, val); +#endif +} + +template <> +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void ibgda_store_relaxed(uint64_t *ptr, uint64_t val) { +#ifdef NVSHMEMI_IBGDA_PTX_OPTIMIZATION_STORE_RELEASE + asm volatile("st.relaxed.gpu.global.L1::no_allocate.b64 [%0], %1;" : : "l"(ptr), "l"(val)); +#else + WRITE_ONCE(*ptr, val); +#endif +} + +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE void ibgda_store_release(uint32_t *ptr, + uint32_t val) { +#ifdef NVSHMEMI_IBGDA_PTX_OPTIMIZATION_STORE_RELEASE + asm volatile("st.release.gpu.global.L1::no_allocate.b32 [%0], %1;" : : "l"(ptr), "r"(val)); +#else + WRITE_ONCE(*ptr, val); +#endif +} + +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE void ibgda_store_release(uint64_t *ptr, + uint64_t val) { +#ifdef NVSHMEMI_IBGDA_PTX_OPTIMIZATION_STORE_RELEASE + asm volatile("st.release.gpu.global.L1::no_allocate.b64 [%0], %1;" : : "l"(ptr), "l"(val)); +#else + WRITE_ONCE(*ptr, val); +#endif +} + +/** + * DO NOT use BSWAP(ibgda_atomic_read(x)) as it could create a bug. + * See the comment near READ_ONCE. + */ +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE uint8_t ibgda_atomic_read(uint8_t *ptr) { +#ifdef NVSHMEMI_IBGDA_PTX_OPTIMIZATION_ATOMIC_READ_SET + uint16_t ret; + asm volatile("ld.relaxed.gpu.global.L1::no_allocate.b8 %0, [%1];" : "=h"(ret) : "l"(ptr)); + return (uint8_t)ret; +#else + return READ_ONCE(*ptr); +#endif +} + +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE uint16_t ibgda_atomic_read(uint16_t *ptr) { +#ifdef NVSHMEMI_IBGDA_PTX_OPTIMIZATION_ATOMIC_READ_SET + uint16_t ret; + asm volatile("ld.relaxed.gpu.global.L1::no_allocate.b16 %0, [%1];" : "=h"(ret) : "l"(ptr)); + return ret; +#else + return READ_ONCE(*ptr); +#endif +} + +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE uint32_t ibgda_atomic_read(uint32_t *ptr) { +#ifdef NVSHMEMI_IBGDA_PTX_OPTIMIZATION_ATOMIC_READ_SET + uint32_t ret; + asm volatile("ld.relaxed.gpu.global.L1::no_allocate.b32 %0, [%1];" : "=r"(ret) : "l"(ptr)); + return ret; +#else + return READ_ONCE(*ptr); +#endif +} + +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE uint64_t ibgda_atomic_read(uint64_t *ptr) { +#ifdef NVSHMEMI_IBGDA_PTX_OPTIMIZATION_ATOMIC_READ_SET + uint64_t ret; + asm volatile("ld.relaxed.gpu.global.L1::no_allocate.b64 %0, [%1];" : "=l"(ret) : "l"(ptr)); + return ret; +#else + return READ_ONCE(*ptr); +#endif +} + +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE void ibgda_atomic_set(int *ptr, int val) { +#ifdef NVSHMEMI_IBGDA_PTX_OPTIMIZATION_ATOMIC_READ_SET + asm volatile("st.relaxed.gpu.global.L1::no_allocate.b32 [%0], %1;" : : "l"(ptr), "r"(val)); +#else + WRITE_ONCE(*ptr, val); +#endif +} + +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE size_t +ibgda_cal_transfer_size(size_t req_size, size_t lchunk_size, size_t rchunk_size) { + return NVSHMEMI_MIN(IBGDA_MAX_TRANSFER_SIZE, + NVSHMEMI_MIN(req_size, NVSHMEMI_MIN(rchunk_size, lchunk_size))); +} + +template +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE void ibgda_lock_acquire(int *lock) { + if (nvshmemi_thread_id_in_threadgroup() == 0) + while (atomicCAS(lock, 0, 1) == 1) + ; // Wait until we get the lock. + + if (SCOPE == NVSHMEMI_THREADGROUP_THREAD) + IBGDA_MFENCE(); // Prevent reordering before lock is acquired. + + // For other scopes, __syncwarp / __syncthreads guarantee the ordering + nvshmemi_threadgroup_sync(); +} + +template +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE void ibgda_lock_release(int *lock) { + // For other scopes, __syncwarp / __syncthreads guarantee the ordering + nvshmemi_threadgroup_sync(); + + if (SCOPE == NVSHMEMI_THREADGROUP_THREAD) + IBGDA_MFENCE(); // Prevent reordering before lock is released. + + if (nvshmemi_thread_id_in_threadgroup() == 0) ibgda_atomic_set(lock, 0); +} + +// Multiple threads may update get_head concurrently. +// Only the latest one w.r.t. wqe_idx is important. +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE void ibgda_update_get_head( + nvshmemi_ibgda_device_qp_t *qp, uint64_t new_get_head) { + nvshmemi_ibgda_device_qp_management_t *mvars = &qp->mvars; + atomicMax((unsigned long long int *)&mvars->tx_wq.get_head, + (unsigned long long int)new_get_head); +} + +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE void ibgda_update_get_tail( + nvshmemi_ibgda_device_qp_t *qp, uint64_t new_get_tail) { + nvshmemi_ibgda_device_qp_management_t *mvars = &qp->mvars; + atomicMax((unsigned long long int *)&mvars->tx_wq.get_tail, + (unsigned long long int)new_get_tail); +} + +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE void *ibgda_get_wqe_ptr( + nvshmemi_ibgda_device_qp_t *qp, uint16_t wqe_idx) { + uint16_t cnt = qp->tx_wq.nwqes; + uint16_t idx = wqe_idx & (cnt - 1); + return (void *)((uintptr_t)qp->tx_wq.wqe + (idx << MLX5_SEND_WQE_SHIFT)); +} + +#ifdef NVSHMEM_TIMEOUT_DEVICE_POLLING +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE int ibgda_check_poll_timeout( + nvshmemi_ibgda_device_cq_t *cq, uint64_t now, uint64_t start, uint64_t idx, int *error) { + int status = 0; + + struct mlx5_cqe64 *cqe64 = (struct mlx5_cqe64 *)cq->cqe; + uint8_t opown; + uint8_t opcode; + uint16_t wqe_counter; + + if (unlikely(now - start > IBGDA_POLL_TIMEOUT)) { + *error = -ETIME; + + opown = ibgda_atomic_read(&cqe64->op_own); + opcode = opown >> 4; + + wqe_counter = ibgda_atomic_read(&cqe64->wqe_counter); + wqe_counter = BSWAP16(wqe_counter); + + printf( + "[%d] ibgda_poll_cq timeout:\n" + " cons_idx=%#lx, prod_idx=%#lx, cqn=%#x, qpn=%#x, opcode=%#x\n" + " wqe_counter=%#x, resv_head=%#lx, ready_head=%#lx\n" + " while waiting for idx=%#lx.\n", + nvshmemi_device_state_d.mype, ibgda_atomic_read(cq->cons_idx), + ibgda_atomic_read(cq->prod_idx), cq->cqn, cq->qpn, opcode, wqe_counter, + ibgda_atomic_read(cq->resv_head), ibgda_atomic_read(cq->ready_head), idx); + status = -1; + } + return status; +} +#endif + +#if __cplusplus >= 201103L +static_assert(NVSHMEMI_IBGDA_MAX_QP_DEPTH <= 32768, + "static_assert(NVSHMEMI_IBGDA_MAX_QP_DEPTH <= 32768) failed"); +#endif +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE int ibgda_poll_cq( + nvshmemi_ibgda_device_cq_t *cq, uint64_t idx, int *error) { + int status = 0; + struct mlx5_cqe64 *cqe64 = (struct mlx5_cqe64 *)cq->cqe; + nvshmemi_ibgda_device_state_t *state = ibgda_get_state(); + const uint32_t ncqes = cq->ncqes; + + uint8_t opown; + uint8_t opcode; + uint16_t wqe_counter; + uint16_t new_wqe_counter; + +#ifdef NVSHMEM_TIMEOUT_DEVICE_POLLING + uint64_t start = ibgda_query_globaltimer(); + uint64_t now; +#endif + + uint64_t cons_idx = ibgda_atomic_read(cq->cons_idx); + uint64_t new_cons_idx; + + assert(likely(cq->qp_type == NVSHMEMI_IBGDA_DEVICE_QP_TYPE_DCI || + cq->qp_type == NVSHMEMI_IBGDA_DEVICE_QP_TYPE_RC)); + + if (unlikely(cons_idx >= idx)) goto out; + +#ifdef NVSHMEM_IBGDA_DEBUG + // We can skip opcode == MLX5_CQE_INVALID check because we have already + // initialized the CQ buffer to 0xff. With the QP depth range we enforce, + // cons_idx cannot progress unless wqe_counter read from the CQ buffer is + // a valid value. + do { + opown = ibgda_atomic_read(&cqe64->op_own); + opcode = opown >> 4; + +#ifdef NVSHMEM_TIMEOUT_DEVICE_POLLING + // TODO: Integrate timeout handler with the core NVSHMEM + now = ibgda_query_globaltimer(); + status = ibgda_check_poll_timeout(cq, now, start, idx, error); + if (status != 0) goto check_opcode; +#endif /* NVSHMEM_TIMEOUT_DEVICE_POLLING */ + } while (unlikely(opcode == MLX5_CQE_INVALID)); + + // Prevent reordering of the opcode wait above + IBGDA_MFENCE(); +#endif /* NVSHMEM_IBGDA_DEBUG */ + +#ifdef NVSHMEM_TIMEOUT_DEVICE_POLLING + start = ibgda_query_globaltimer(); +#endif + + // If idx is a lot greater than cons_idx, we might get incorrect result due + // to wqe_counter wraparound. We need to check prod_idx to be sure that idx + // has already been submitted. + while (unlikely(ibgda_atomic_read(cq->prod_idx) < idx)) + ; + IBGDA_MFENCE(); + + do { + new_wqe_counter = ibgda_atomic_read(&cqe64->wqe_counter); + new_wqe_counter = BSWAP16(new_wqe_counter); +#ifdef NVSHMEM_TIMEOUT_DEVICE_POLLING + now = ibgda_query_globaltimer(); + status = ibgda_check_poll_timeout(cq, now, start, idx, error); + if (status != 0) goto check_opcode; + + // Observe progress. Reset the timer. + if (new_wqe_counter != wqe_counter) start = now; +#endif + wqe_counter = new_wqe_counter; + + // Another thread may have updated cons_idx. + cons_idx = ibgda_atomic_read(cq->cons_idx); + if (likely(cons_idx >= idx)) goto out; + } + // NOTE: This while loop is part of do while above. + // wqe_counter is the HW consumer index. However, we always maintain index + // + 1 in SW. To be able to compare with idx, we need to use wqe_counter + + // 1. Because wqe_counter is uint16_t, it may wraparound. Still we know for + // sure that if idx - wqe_counter - 1 < ncqes, wqe_counter + 1 is less than + // idx, and thus we need to wait. We don't need to wait when idx == + // wqe_counter + 1. That's why we use - (uint16_t)2 here to make this case + // wraparound. + while (unlikely(((uint16_t)((uint16_t)idx - wqe_counter - (uint16_t)2) < ncqes))); + + // new_cons_idx is uint64_t but wqe_counter is uint16_t. Thus, we get the + // MSB from idx. We also need to take care of wraparound. + ++wqe_counter; + new_cons_idx = + (idx & ~(0xffffULL) | wqe_counter) + (((uint16_t)idx > wqe_counter) ? 0x10000ULL : 0x0); + atomicMax((unsigned long long int *)cq->cons_idx, (unsigned long long int)new_cons_idx); + +#ifdef NVSHMEM_TIMEOUT_DEVICE_POLLING +check_opcode: +#endif + + // NVSHMEM always treats CQE errors as fatal. + // Even if this error doesn't belong to the CQE in cons_idx, + // we will just report and terminate the process. + opown = ibgda_atomic_read(&cqe64->op_own); + opcode = opown >> 4; + + if (unlikely(opcode == MLX5_CQE_REQ_ERR)) { + ibgda_mlx5_err_cqe_t *cqe_err = (ibgda_mlx5_err_cqe_t *)cqe64; + *error = cqe_err->syndrome; +#ifdef NVSHMEM_IBGDA_DEBUG + __be16 wqe_counter = ibgda_atomic_read(&cqe_err->wqe_counter); + __be32 s_wqe_opcode_qpn = ibgda_atomic_read(&cqe_err->s_wqe_opcode_qpn); + printf( + "[%d] got completion with err:\n" + " syndrome=%#x, vendor_err_synd=%#x, hw_err_synd=%#x, hw_synd_type=%#x,\n" + " wqe_counter=%#x, s_wqe_opcode_qpn=%#x,\n" + " cqn=%#x, cons_idx=%#lx, prod_idx=%#lx, idx=%#lx\n", + nvshmemi_device_state_d.mype, cqe_err->syndrome, cqe_err->vendor_err_synd, + cqe_err->hw_err_synd, cqe_err->hw_synd_type, BSWAP16(wqe_counter), + BSWAP32(s_wqe_opcode_qpn), cq->cqn, cons_idx, ibgda_atomic_read(cq->prod_idx), idx); +#endif /* NVSHMEM_IBGDA_DEBUG */ + status = -1; + } + +out: + // Prevent reordering of this function and subsequent instructions + IBGDA_MFENCE(); + + return status; +} + +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE void ibgda_write_nop_wqe( + nvshmemi_ibgda_device_qp_t *qp, uint16_t wqe_idx, void **out_wqes) { + ibgda_ctrl_seg_t ctrl_seg; + + ibgda_ctrl_seg_t *ctrl_seg_ptr = (ibgda_ctrl_seg_t *)out_wqes[0]; + + ctrl_seg = { + 0, + }; + ctrl_seg.qpn_ds = HTOBE32((qp->qpn << 8) | 2); + ctrl_seg.fm_ce_se = MLX5_WQE_CTRL_CQ_UPDATE; + ctrl_seg.opmod_idx_opcode = HTOBE32((wqe_idx << 8) | MLX5_OPCODE_NOP); + + // wqe_ptr will not be consumed by GPU. + // WRITE_ONCE ensures that compiler will not removed this code. + uint32_t *dst = (uint32_t *)ctrl_seg_ptr; + uint32_t *src = (uint32_t *)&ctrl_seg; + for (int i = 0; i < sizeof(*ctrl_seg_ptr) / sizeof(uint32_t); ++i) + ibgda_store_relaxed(&dst[i], src[i]); +} + +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE void ibgda_write_dump_wqe( + nvshmemi_ibgda_device_qp_t *qp, uint64_t laddr, __be32 lkey, uint32_t bytes, uint16_t wqe_idx, + ibgda_mlx5_fm_t fm, void **out_wqes) { + ibgda_ctrl_seg_t ctrl_seg; + struct mlx5_wqe_data_seg data_seg; + + ibgda_ctrl_seg_t *ctrl_seg_ptr = (ibgda_ctrl_seg_t *)out_wqes[0]; + struct mlx5_wqe_data_seg *data_seg_ptr = + (struct mlx5_wqe_data_seg *)((uintptr_t)out_wqes[0] + sizeof(*ctrl_seg_ptr)); + + data_seg.byte_count = HTOBE32(bytes); + data_seg.lkey = lkey; + data_seg.addr = HTOBE64(laddr); + + ctrl_seg = { + 0, + }; + ctrl_seg.qpn_ds = HTOBE32((qp->qpn << 8) | 2); + ctrl_seg.fm_ce_se = MLX5_WQE_CTRL_CQ_UPDATE | fm; + ctrl_seg.opmod_idx_opcode = HTOBE32((wqe_idx << 8) | IBGDA_MLX5_OPCODE_DUMP); + + // wqe_ptr will not be consumed by GPU. + // WRITE_ONCE ensures that compiler will not removed this code. + uint32_t *dst = (uint32_t *)ctrl_seg_ptr; + uint32_t *src = (uint32_t *)&ctrl_seg; + for (int i = 0; i < sizeof(*ctrl_seg_ptr) / sizeof(uint32_t); ++i) + ibgda_store_relaxed(&dst[i], src[i]); + + dst = (uint32_t *)data_seg_ptr; + src = (uint32_t *)&data_seg; + for (int i = 0; i < sizeof(*data_seg_ptr) / sizeof(uint32_t); ++i) + ibgda_store_relaxed(&dst[i], src[i]); +} + +template +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE void ibgda_write_rdma_write_wqe( + nvshmemi_ibgda_device_qp_t *qp, nvshmemi_ibgda_device_dct_t *dct, uint64_t laddr, __be32 lkey, + uint64_t raddr, __be32 rkey, uint32_t bytes, uint16_t wqe_idx, uint8_t fm_ce_se, + void **out_wqes) { + ibgda_ctrl_seg_t ctrl_seg; + struct mlx5_wqe_raddr_seg raddr_seg; + struct mlx5_wqe_data_seg data_seg; + + ibgda_ctrl_seg_t *ctrl_seg_ptr = (ibgda_ctrl_seg_t *)out_wqes[0]; + void *av_seg_ptr = (void *)((uintptr_t)ctrl_seg_ptr + sizeof(*ctrl_seg_ptr)); + struct mlx5_wqe_raddr_seg *raddr_seg_ptr; + struct mlx5_wqe_data_seg *data_seg_ptr; + + size_t av_seg_size; + int ds; + + if (qp->qp_type == NVSHMEMI_IBGDA_DEVICE_QP_TYPE_DCI) { + if (support_half_av_seg) { + ds = 4; + av_seg_size = sizeof(ibgda_half_av_seg_t); + raddr_seg_ptr = (struct mlx5_wqe_raddr_seg *)((uintptr_t)av_seg_ptr + av_seg_size); + } else { + ds = 6; + av_seg_size = sizeof(struct mlx5_wqe_av); + raddr_seg_ptr = (struct mlx5_wqe_raddr_seg *)out_wqes[1]; + } + } else { + ds = 3; + av_seg_size = 0; + raddr_seg_ptr = (struct mlx5_wqe_raddr_seg *)((uintptr_t)av_seg_ptr + av_seg_size); + } + data_seg_ptr = (struct mlx5_wqe_data_seg *)((uintptr_t)raddr_seg_ptr + sizeof(*raddr_seg_ptr)); + + raddr_seg.raddr = HTOBE64(raddr); + raddr_seg.rkey = rkey; + raddr_seg.reserved = 0; + + data_seg.byte_count = HTOBE32(bytes); + data_seg.lkey = lkey; + data_seg.addr = HTOBE64(laddr); + + ctrl_seg = { + 0, + }; + ctrl_seg.qpn_ds = HTOBE32((qp->qpn << 8) | ds); + ctrl_seg.fm_ce_se = fm_ce_se; + ctrl_seg.opmod_idx_opcode = HTOBE32((wqe_idx << 8) | MLX5_OPCODE_RDMA_WRITE); + + uint32_t *dst = (uint32_t *)ctrl_seg_ptr; + uint32_t *src = (uint32_t *)&ctrl_seg; + for (int i = 0; i < sizeof(*ctrl_seg_ptr) / sizeof(uint32_t); ++i) + ibgda_store_relaxed(&dst[i], src[i]); + + if (av_seg_size > 0) { + dst = (uint32_t *)av_seg_ptr; + src = (uint32_t *)dct; + for (int i = 0; i < av_seg_size / sizeof(uint32_t); ++i) + ibgda_store_relaxed(&dst[i], src[i]); + } + + dst = (uint32_t *)raddr_seg_ptr; + src = (uint32_t *)&raddr_seg; + for (int i = 0; i < sizeof(*raddr_seg_ptr) / sizeof(uint32_t); ++i) + ibgda_store_relaxed(&dst[i], src[i]); + + dst = (uint32_t *)data_seg_ptr; + src = (uint32_t *)&data_seg; + for (int i = 0; i < sizeof(*data_seg_ptr) / sizeof(uint32_t); ++i) + ibgda_store_relaxed(&dst[i], src[i]); +} + +template +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE void ibgda_write_rdma_write_inl_wqe( + nvshmemi_ibgda_device_qp_t *qp, nvshmemi_ibgda_device_dct_t *dct, const void *val, + uint64_t raddr, __be32 rkey, uint32_t bytes, uint16_t wqe_idx, uint8_t fm_ce_se, + void **out_wqes) { + ibgda_ctrl_seg_t ctrl_seg; + struct mlx5_wqe_raddr_seg raddr_seg; + struct mlx5_wqe_inl_data_seg inl_seg; + + ibgda_ctrl_seg_t *ctrl_seg_ptr = (ibgda_ctrl_seg_t *)out_wqes[0]; + void *av_seg_ptr = (void *)((uintptr_t)ctrl_seg_ptr + sizeof(*ctrl_seg_ptr)); + struct mlx5_wqe_raddr_seg *raddr_seg_ptr; + struct mlx5_wqe_inl_data_seg *inl_seg_ptr; + void *wqe_data_ptr; + + size_t av_seg_size; + int ds; + + // Allow up to 12 bytes + assert(likely(bytes <= 12)); + + if (qp->qp_type == NVSHMEMI_IBGDA_DEVICE_QP_TYPE_DCI) { + if (support_half_av_seg) { + ds = 4; + av_seg_size = sizeof(ibgda_half_av_seg_t); + raddr_seg_ptr = (struct mlx5_wqe_raddr_seg *)((uintptr_t)av_seg_ptr + av_seg_size); + } else { + ds = 6; + av_seg_size = sizeof(struct mlx5_wqe_av); + raddr_seg_ptr = (struct mlx5_wqe_raddr_seg *)out_wqes[1]; + } + } else { + ds = 3; + av_seg_size = 0; + raddr_seg_ptr = (struct mlx5_wqe_raddr_seg *)av_seg_ptr; + } + inl_seg_ptr = + (struct mlx5_wqe_inl_data_seg *)((uintptr_t)raddr_seg_ptr + sizeof(*raddr_seg_ptr)); + wqe_data_ptr = (void *)((uintptr_t)inl_seg_ptr + sizeof(*inl_seg_ptr)); + + raddr_seg.raddr = HTOBE64(raddr); + raddr_seg.rkey = rkey; + raddr_seg.reserved = 0; + + inl_seg.byte_count = HTOBE32(bytes | MLX5_INLINE_SEG); + + ctrl_seg = { + 0, + }; + ctrl_seg.qpn_ds = HTOBE32((qp->qpn << 8) | ds); + ctrl_seg.fm_ce_se = fm_ce_se; + ctrl_seg.opmod_idx_opcode = HTOBE32((wqe_idx << 8) | MLX5_OPCODE_RDMA_WRITE); + + uint32_t *dst = (uint32_t *)ctrl_seg_ptr; + uint32_t *src = (uint32_t *)&ctrl_seg; + for (int i = 0; i < sizeof(*ctrl_seg_ptr) / sizeof(uint32_t); ++i) + ibgda_store_relaxed(&dst[i], src[i]); + + if (av_seg_size > 0) { + dst = (uint32_t *)av_seg_ptr; + src = (uint32_t *)dct; + for (int i = 0; i < av_seg_size / sizeof(uint32_t); ++i) + ibgda_store_relaxed(&dst[i], src[i]); + } + + dst = (uint32_t *)raddr_seg_ptr; + src = (uint32_t *)&raddr_seg; + for (int i = 0; i < sizeof(*raddr_seg_ptr) / sizeof(uint32_t); ++i) + ibgda_store_relaxed(&dst[i], src[i]); + + dst = (uint32_t *)inl_seg_ptr; + src = (uint32_t *)&inl_seg; + for (int i = 0; i < sizeof(*inl_seg_ptr) / sizeof(uint32_t); ++i) + ibgda_store_relaxed(&dst[i], src[i]); + + switch (bytes) { + case 1: + ibgda_store_relaxed((uint8_t *)wqe_data_ptr, *((uint8_t *)val)); + break; + case 2: + ibgda_store_relaxed((uint16_t *)wqe_data_ptr, *((uint16_t *)val)); + break; + case 4: + ibgda_store_relaxed((uint32_t *)wqe_data_ptr, *((uint32_t *)val)); + break; + case 8: + // wqe_data_ptr is aligned at 4B. We cannot use uint64_t here. + ibgda_store_relaxed(&(((uint32_t *)wqe_data_ptr)[0]), ((uint32_t *)val)[0]); + ibgda_store_relaxed(&(((uint32_t *)wqe_data_ptr)[1]), ((uint32_t *)val)[1]); + break; + default: + memcpy(wqe_data_ptr, val, bytes); + } +} + +/** + * For DC, support only half av seg. + * The header already consumes 1 wqebb and leaves 12 bytes for NVSHMEMI_DEVICE_ALWAYS_INLINE data. + * The last wqebb is no-op. + * One wqebb is 64 bytes. + * Pre-calculate as it is faster to do lookup. + * Formula: ceil(((sizeof(T) * 32) - 12) / 64) + 2 + * + * For RC + * The header already consumes 1 wqebb and leaves 12 + 16 bytes for NVSHMEMI_DEVICE_ALWAYS_INLINE + * data. The last wqebb is no-op. One wqebb is 64 bytes. Pre-calculate as it is faster to do lookup. + * Formula: ceil(((sizeof(T) * 32) - (12 + 16)) / 64) + 2 + */ +template +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE uint32_t +ibgda_get_num_wqes_in_inl_combine_warp() { + if (qp_type == NVSHMEMI_IBGDA_DEVICE_QP_TYPE_DCI) { + // DC supports up to 16 DS WQE + switch (sizeof(T)) { + case 1: + case 2: + return 3; + case 4: + return 4; + default: +#ifdef NVSHMEM_IBGDA_DEBUG + printf("Unsupported type.\n"); +#endif + assert(0); + return 0; + } + } else { + // RC supports up to 64 DS WQE + switch (sizeof(T)) { + case 1: + case 2: + return 3; + case 4: + return 4; + case 8: + return 6; + default: +#ifdef NVSHMEM_IBGDA_DEBUG + printf("Unsupported type.\n"); +#endif + assert(0); + return 0; + } + } +} + +/** + * For DC, support only half av seg. + * The header already consumes 4 ds and leaves 12 bytes for NVSHMEMI_DEVICE_ALWAYS_INLINE data. + * One ds is 16 bytes. + * Pre-calculate as it is faster to do lookup. + * Formula: ceil(((sizeof(T) * 32) - 12) / 16) + 4 + * + * For RC + * The header already consumes 3 ds and leaves 12 bytes for NVSHMEMI_DEVICE_ALWAYS_INLINE data. + * One ds is 16 bytes. + * Pre-calculate as it is faster to do lookup. + * Formula: ceil(((sizeof(T) * 32) - 12) / 16) + 3 + */ +template +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE uint32_t +ibgda_get_ds_in_inl_combine_warp() { + if (qp_type == NVSHMEMI_IBGDA_DEVICE_QP_TYPE_DCI) { + // DC supports up to 16 DS WQE + switch (sizeof(T)) { + case 1: + return 6; + case 2: + return 8; + case 4: + return 12; + default: +#ifdef NVSHMEM_IBGDA_DEBUG + printf("Unsupported type.\n"); +#endif + assert(0); + return 0; + } + } else { + // DC supports up to 16 DS WQE + switch (sizeof(T)) { + case 1: + return 5; + case 2: + return 7; + case 4: + return 11; + case 8: + return 19; + default: +#ifdef NVSHMEM_IBGDA_DEBUG + printf("Unsupported type.\n"); +#endif + assert(0); + return 0; + } + } +} + +template +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE void +ibgda_write_rdma_write_inl_wqe_combine_warp(nvshmemi_ibgda_device_qp_t *qp, + nvshmemi_ibgda_device_dct_t *dct, const T val, + uint64_t _raddr, __be32 rkey, uint16_t wqe_idx, + int my_tid, void **out_wqes) { + ibgda_ctrl_seg_t ctrl_seg; + struct mlx5_wqe_raddr_seg raddr_seg; + struct mlx5_wqe_inl_data_seg inl_seg; + + ibgda_ctrl_seg_t *ctrl_seg_ptr = (ibgda_ctrl_seg_t *)out_wqes[0]; + void *av_seg_ptr = (void *)((uintptr_t)ctrl_seg_ptr + sizeof(*ctrl_seg_ptr)); + struct mlx5_wqe_raddr_seg *raddr_seg_ptr; + struct mlx5_wqe_inl_data_seg *inl_seg_ptr; + + size_t av_seg_size; + int ds; + + uint32_t bytes = sizeof(T); + uint64_t raddr = _raddr - (my_tid * bytes); + + int remaining_size_for_data_in_first_wqebb; + uint32_t nop_relative_wqe_idx; + + if (qp->qp_type == NVSHMEMI_IBGDA_DEVICE_QP_TYPE_DCI) { + ds = ibgda_get_ds_in_inl_combine_warp(); + av_seg_size = sizeof(ibgda_half_av_seg_t); + remaining_size_for_data_in_first_wqebb = 12; + nop_relative_wqe_idx = + ibgda_get_num_wqes_in_inl_combine_warp() - 1; + } else { + ds = ibgda_get_ds_in_inl_combine_warp(); + av_seg_size = 0; + remaining_size_for_data_in_first_wqebb = 28; + nop_relative_wqe_idx = + ibgda_get_num_wqes_in_inl_combine_warp() - 1; + } + + raddr_seg_ptr = (struct mlx5_wqe_raddr_seg *)((uintptr_t)av_seg_ptr + av_seg_size); + inl_seg_ptr = + (struct mlx5_wqe_inl_data_seg *)((uintptr_t)raddr_seg_ptr + sizeof(*raddr_seg_ptr)); + + raddr_seg.raddr = HTOBE64(raddr); + raddr_seg.rkey = rkey; + raddr_seg.reserved = 0; + + inl_seg.byte_count = HTOBE32((bytes * warpSize) | MLX5_INLINE_SEG); + + ctrl_seg = { + 0, + }; + ctrl_seg.qpn_ds = HTOBE32((qp->qpn << 8) | ds); + // ctrl_seg.fm_ce_se = MLX5_WQE_CTRL_CQ_UPDATE; + // This RDMA WRITE wqe will not get CQ update to avoid dynamic size calculation in poll_cq. + // Instead, the NO-OP wqe (last one) will get CQ update because it is always 1 WQEBB. + ctrl_seg.fm_ce_se = 0; + ctrl_seg.opmod_idx_opcode = HTOBE32((wqe_idx << 8) | MLX5_OPCODE_RDMA_WRITE); + + uint32_t *dst = (uint32_t *)ctrl_seg_ptr; + uint32_t *src = (uint32_t *)&ctrl_seg; + for (int i = 0; i < sizeof(*ctrl_seg_ptr) / sizeof(uint32_t); ++i) + ibgda_store_relaxed(&dst[i], src[i]); + + if (av_seg_size > 0) { + dst = (uint32_t *)av_seg_ptr; + src = (uint32_t *)dct; + for (int i = 0; i < av_seg_size / sizeof(uint32_t); ++i) + ibgda_store_relaxed(&dst[i], src[i]); + } + + dst = (uint32_t *)raddr_seg_ptr; + src = (uint32_t *)&raddr_seg; + for (int i = 0; i < sizeof(*raddr_seg_ptr) / sizeof(uint32_t); ++i) + ibgda_store_relaxed(&dst[i], src[i]); + + dst = (uint32_t *)inl_seg_ptr; + src = (uint32_t *)&inl_seg; + for (int i = 0; i < sizeof(*inl_seg_ptr) / sizeof(uint32_t); ++i) + ibgda_store_relaxed(&dst[i], src[i]); + + uint32_t my_base_data_idx = my_tid * bytes; + if (bytes <= 4) { + T *wqe_data_ptr; + if (my_base_data_idx < remaining_size_for_data_in_first_wqebb) + wqe_data_ptr = (T *)((uintptr_t)inl_seg_ptr + sizeof(*inl_seg_ptr) + my_base_data_idx); + else { + uint32_t my_data_idx = my_base_data_idx - remaining_size_for_data_in_first_wqebb; + int my_data_in_wqe_idx = my_data_idx / 64 + 1; + my_data_idx &= (64 - 1); // my_data_idx % 64 + wqe_data_ptr = (T *)((uintptr_t)out_wqes[my_data_in_wqe_idx] + my_data_idx); + } + ibgda_store_relaxed(wqe_data_ptr, val); + } else { + // wqe_data_ptr is 4-byte aligned but not 8-byte aligned. + assert(likely(bytes == 8 && qp->qp_type == NVSHMEMI_IBGDA_DEVICE_QP_TYPE_RC)); + uint32_t *wqe_data_ptr; +#pragma unroll + for (int i = 0; i < 2; ++i) { + uint32_t my_data_idx = my_base_data_idx + (i * 4); + if (my_data_idx < remaining_size_for_data_in_first_wqebb) + wqe_data_ptr = + (uint32_t *)((uintptr_t)inl_seg_ptr + sizeof(*inl_seg_ptr) + my_data_idx); + else { + uint32_t my_idx = my_data_idx - remaining_size_for_data_in_first_wqebb; + int my_data_in_wqe_idx = my_idx / 64 + 1; + my_idx &= (64 - 1); // my_idx % 64 + wqe_data_ptr = (uint32_t *)((uintptr_t)out_wqes[my_data_in_wqe_idx] + my_idx); + } + ibgda_store_relaxed(wqe_data_ptr, *((uint32_t *)&val + i)); + } + } + + wqe_idx += nop_relative_wqe_idx; + ctrl_seg = { + 0, + }; + ctrl_seg.qpn_ds = HTOBE32((qp->qpn << 8) | 1); + ctrl_seg.fm_ce_se = MLX5_WQE_CTRL_CQ_UPDATE; + ctrl_seg.opmod_idx_opcode = HTOBE32((wqe_idx << 8) | MLX5_OPCODE_NOP); + + ctrl_seg_ptr = (ibgda_ctrl_seg_t *)out_wqes[nop_relative_wqe_idx]; + + dst = (uint32_t *)ctrl_seg_ptr; + src = (uint32_t *)&ctrl_seg; + for (int i = 0; i < sizeof(*ctrl_seg_ptr) / sizeof(uint32_t); ++i) + ibgda_store_relaxed(&dst[i], src[i]); +} + +/** + * For DCI with sizeof(T) == 8 only. + * DC supports up to 16 DS WQE. + * For sizeof(T) == 8, we split to two WQEs of NVSHMEMI_DEVICE_ALWAYS_INLINE size 8 * 16 + */ +template +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE void +ibgda_write_rdma_write_inl_wqe_combine_warp_for_dci_8B(nvshmemi_ibgda_device_qp_t *dci, + nvshmemi_ibgda_device_dct_t *dct, + const T val, uint64_t _raddr, __be32 rkey, + uint16_t _wqe_idx, int my_tid, + void **out_wqes) { + assert(likely(sizeof(T) == 8 && dci->qp_type == NVSHMEMI_IBGDA_DEVICE_QP_TYPE_DCI)); + + // base_tid = my_tid >= 16 ? 16 : 0; + int base_tid = my_tid & (~0xF); + + // base_wqe_idx = base_tid / 4; + int base_out_wqe_idx = base_tid >> 2; + + uint16_t wqe_idx = _wqe_idx + base_out_wqe_idx; + + ibgda_ctrl_seg_t ctrl_seg; + struct mlx5_wqe_raddr_seg raddr_seg; + struct mlx5_wqe_inl_data_seg inl_seg; + + ibgda_ctrl_seg_t *ctrl_seg_ptr = (ibgda_ctrl_seg_t *)out_wqes[base_out_wqe_idx]; + void *av_seg_ptr = (void *)((uintptr_t)ctrl_seg_ptr + sizeof(*ctrl_seg_ptr)); + struct mlx5_wqe_raddr_seg *raddr_seg_ptr; + struct mlx5_wqe_inl_data_seg *inl_seg_ptr; + uint32_t *wqe_data_ptr; + + size_t av_seg_size; + int ds = ibgda_get_ds_in_inl_combine_warp(); + + uint64_t raddr = _raddr - ((my_tid - base_tid) * 8); + + av_seg_size = sizeof(ibgda_half_av_seg_t); + raddr_seg_ptr = (struct mlx5_wqe_raddr_seg *)((uintptr_t)av_seg_ptr + av_seg_size); + inl_seg_ptr = + (struct mlx5_wqe_inl_data_seg *)((uintptr_t)raddr_seg_ptr + sizeof(*raddr_seg_ptr)); + + raddr_seg.raddr = HTOBE64(raddr); + raddr_seg.rkey = rkey; + raddr_seg.reserved = 0; + + inl_seg.byte_count = HTOBE32((8 * warpSize / 2) | MLX5_INLINE_SEG); + + ctrl_seg = { + 0, + }; + ctrl_seg.qpn_ds = HTOBE32((dci->qpn << 8) | ds); + // ctrl_seg.fm_ce_se = MLX5_WQE_CTRL_CQ_UPDATE; + // This RDMA WRITE wqe will not get CQ update to avoid dynamic size calculation in poll_cq. + // Instead, the NO-OP wqe (last one) will get CQ update because it is always 1 WQEBB. + ctrl_seg.fm_ce_se = 0; + ctrl_seg.opmod_idx_opcode = HTOBE32((wqe_idx << 8) | MLX5_OPCODE_RDMA_WRITE); + + uint32_t *dst = (uint32_t *)ctrl_seg_ptr; + uint32_t *src = (uint32_t *)&ctrl_seg; + for (int i = 0; i < sizeof(*ctrl_seg_ptr) / sizeof(uint32_t); ++i) + ibgda_store_relaxed(&dst[i], src[i]); + + dst = (uint32_t *)av_seg_ptr; + src = (uint32_t *)dct; + for (int i = 0; i < av_seg_size / sizeof(uint32_t); ++i) ibgda_store_relaxed(&dst[i], src[i]); + + dst = (uint32_t *)raddr_seg_ptr; + src = (uint32_t *)&raddr_seg; + for (int i = 0; i < sizeof(*raddr_seg_ptr) / sizeof(uint32_t); ++i) + ibgda_store_relaxed(&dst[i], src[i]); + + dst = (uint32_t *)inl_seg_ptr; + src = (uint32_t *)&inl_seg; + for (int i = 0; i < sizeof(*inl_seg_ptr) / sizeof(uint32_t); ++i) + ibgda_store_relaxed(&dst[i], src[i]); + + for (int i = 0; i < 2; ++i) { + uint32_t my_data_idx = ((my_tid - base_tid) * 2 + i) * 4; + if (my_data_idx < 12) + wqe_data_ptr = + (uint32_t *)((uintptr_t)inl_seg_ptr + sizeof(*inl_seg_ptr) + my_data_idx); + else { + my_data_idx -= 12; + int my_data_in_wqe_idx = my_data_idx / 64 + 1; + my_data_idx &= (64 - 1); // my_data_idx % 64 + wqe_data_ptr = (uint32_t *)((uintptr_t)out_wqes[my_data_in_wqe_idx + base_out_wqe_idx] + + my_data_idx); + } + + ibgda_store_relaxed(wqe_data_ptr, ((uint32_t *)&val)[i]); + } + + uint32_t nop_relative_wqe_idx = + ibgda_get_num_wqes_in_inl_combine_warp() - 1; + + wqe_idx += nop_relative_wqe_idx; + ctrl_seg = { + 0, + }; + ctrl_seg.qpn_ds = HTOBE32((dci->qpn << 8) | 1); + ctrl_seg.fm_ce_se = MLX5_WQE_CTRL_CQ_UPDATE; + ctrl_seg.opmod_idx_opcode = HTOBE32((wqe_idx << 8) | MLX5_OPCODE_NOP); + + ctrl_seg_ptr = (ibgda_ctrl_seg_t *)out_wqes[nop_relative_wqe_idx + base_out_wqe_idx]; + + dst = (uint32_t *)ctrl_seg_ptr; + src = (uint32_t *)&ctrl_seg; + for (int i = 0; i < sizeof(*ctrl_seg_ptr) / sizeof(uint32_t); ++i) + ibgda_store_relaxed(&dst[i], src[i]); +} + +template +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE void ibgda_write_rdma_read_wqe( + nvshmemi_ibgda_device_qp_t *qp, nvshmemi_ibgda_device_dct_t *dct, uint64_t laddr, __be32 lkey, + uint64_t raddr, __be32 rkey, uint32_t bytes, uint16_t wqe_idx, uint8_t fm_ce_se, + void **out_wqes) { + ibgda_ctrl_seg_t ctrl_seg; + struct mlx5_wqe_raddr_seg raddr_seg; + struct mlx5_wqe_data_seg data_seg; + + ibgda_ctrl_seg_t *ctrl_seg_ptr = (ibgda_ctrl_seg_t *)out_wqes[0]; + void *av_seg_ptr = (void *)((uintptr_t)ctrl_seg_ptr + sizeof(*ctrl_seg_ptr)); + struct mlx5_wqe_raddr_seg *raddr_seg_ptr; + struct mlx5_wqe_data_seg *data_seg_ptr; + + size_t av_seg_size; + int ds; + + if (qp->qp_type == NVSHMEMI_IBGDA_DEVICE_QP_TYPE_DCI) { + if (support_half_av_seg) { + ds = 4; + av_seg_size = sizeof(ibgda_half_av_seg_t); + raddr_seg_ptr = (struct mlx5_wqe_raddr_seg *)((uintptr_t)av_seg_ptr + av_seg_size); + } else { + ds = 6; + av_seg_size = sizeof(struct mlx5_wqe_av); + raddr_seg_ptr = (struct mlx5_wqe_raddr_seg *)out_wqes[1]; + } + } else { + ds = 3; + av_seg_size = 0; + raddr_seg_ptr = (struct mlx5_wqe_raddr_seg *)((uintptr_t)av_seg_ptr + av_seg_size); + } + data_seg_ptr = (struct mlx5_wqe_data_seg *)((uintptr_t)raddr_seg_ptr + sizeof(*raddr_seg_ptr)); + + raddr_seg.raddr = HTOBE64(raddr); + raddr_seg.rkey = rkey; + raddr_seg.reserved = 0; + + data_seg.byte_count = HTOBE32(bytes); + data_seg.lkey = lkey; + data_seg.addr = HTOBE64(laddr); + + ctrl_seg = { + 0, + }; + ctrl_seg.qpn_ds = HTOBE32((qp->qpn << 8) | ds); + ctrl_seg.fm_ce_se = fm_ce_se; + ctrl_seg.opmod_idx_opcode = HTOBE32((wqe_idx << 8) | MLX5_OPCODE_RDMA_READ); + + uint32_t *dst = (uint32_t *)ctrl_seg_ptr; + uint32_t *src = (uint32_t *)&ctrl_seg; + for (int i = 0; i < sizeof(*ctrl_seg_ptr) / sizeof(uint32_t); ++i) + ibgda_store_relaxed(&dst[i], src[i]); + + if (av_seg_size > 0) { + dst = (uint32_t *)av_seg_ptr; + src = (uint32_t *)dct; + for (int i = 0; i < av_seg_size / sizeof(uint32_t); ++i) + ibgda_store_relaxed(&dst[i], src[i]); + } + + dst = (uint32_t *)raddr_seg_ptr; + src = (uint32_t *)&raddr_seg; + for (int i = 0; i < sizeof(*raddr_seg_ptr) / sizeof(uint32_t); ++i) + ibgda_store_relaxed(&dst[i], src[i]); + + dst = (uint32_t *)data_seg_ptr; + src = (uint32_t *)&data_seg; + for (int i = 0; i < sizeof(*data_seg_ptr) / sizeof(uint32_t); ++i) + ibgda_store_relaxed(&dst[i], src[i]); +} + +template +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE uint32_t +ibgda_get_num_wqes_in_atomic(nvshmemi_amo_t amo_op, nvshmemi_ibgda_device_qp_type_t qp_type) { + if (qp_type == NVSHMEMI_IBGDA_DEVICE_QP_TYPE_DCI) + return 2; + else if (sizeof(T) == 8) { + // RC + switch (amo_op) { + case NVSHMEMI_AMO_SIGNAL: + case NVSHMEMI_AMO_SIGNAL_SET: + case NVSHMEMI_AMO_SWAP: + case NVSHMEMI_AMO_SET: + case NVSHMEMI_AMO_FETCH_AND: + case NVSHMEMI_AMO_AND: + case NVSHMEMI_AMO_FETCH_OR: + case NVSHMEMI_AMO_OR: + return 2; + } + } + return 1; +} + +template +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE void ibgda_write_atomic_wqe( + nvshmemi_ibgda_device_qp_t *qp, nvshmemi_ibgda_device_dct_t *dct, const void *val_1, + const void *val_2, uint64_t laddr, __be32 lkey, uint64_t raddr, __be32 rkey, uint32_t bytes, + uint16_t wqe_idx, nvshmemi_amo_t amo_op, uint8_t fm_ce_se, void **out_wqes) { + ibgda_ctrl_seg_t ctrl_seg; + struct mlx5_wqe_raddr_seg raddr_seg; + struct mlx5_wqe_atomic_seg atomic_seg_1; + struct mlx5_wqe_atomic_seg atomic_seg_2; + struct mlx5_wqe_data_seg data_seg; + + ibgda_ctrl_seg_t *ctrl_seg_ptr = (ibgda_ctrl_seg_t *)out_wqes[0]; + void *av_seg_ptr = (void *)((uintptr_t)ctrl_seg_ptr + sizeof(*ctrl_seg_ptr)); + struct mlx5_wqe_raddr_seg *raddr_seg_ptr; + struct mlx5_wqe_atomic_seg *atomic_seg_1_ptr; + struct mlx5_wqe_atomic_seg *atomic_seg_2_ptr; + struct mlx5_wqe_data_seg *data_seg_ptr; + + size_t av_seg_size; + int ds; + + if (qp->qp_type == NVSHMEMI_IBGDA_DEVICE_QP_TYPE_DCI) { + if (support_half_av_seg) { + ds = 5; + av_seg_size = sizeof(ibgda_half_av_seg_t); + raddr_seg_ptr = (struct mlx5_wqe_raddr_seg *)((uintptr_t)av_seg_ptr + av_seg_size); + atomic_seg_1_ptr = + (struct mlx5_wqe_atomic_seg *)((uintptr_t)raddr_seg_ptr + sizeof(*raddr_seg_ptr)); + atomic_seg_2_ptr = (struct mlx5_wqe_atomic_seg *)out_wqes[1]; + } else { + ds = 7; + av_seg_size = sizeof(struct mlx5_wqe_av); + raddr_seg_ptr = (struct mlx5_wqe_raddr_seg *)out_wqes[1]; + atomic_seg_1_ptr = + (struct mlx5_wqe_atomic_seg *)((uintptr_t)raddr_seg_ptr + sizeof(*raddr_seg_ptr)); + atomic_seg_2_ptr = (struct mlx5_wqe_atomic_seg *)((uintptr_t)atomic_seg_1_ptr + + sizeof(*atomic_seg_1_ptr)); + } + } else { + ds = 4; + av_seg_size = 0; + raddr_seg_ptr = (struct mlx5_wqe_raddr_seg *)((uintptr_t)av_seg_ptr + av_seg_size); + atomic_seg_1_ptr = + (struct mlx5_wqe_atomic_seg *)((uintptr_t)raddr_seg_ptr + sizeof(*raddr_seg_ptr)); + atomic_seg_2_ptr = + (struct mlx5_wqe_atomic_seg *)((uintptr_t)atomic_seg_1_ptr + sizeof(*atomic_seg_1_ptr)); + } + data_seg_ptr = (struct mlx5_wqe_data_seg *)atomic_seg_2_ptr; + + raddr_seg.raddr = HTOBE64(raddr); + raddr_seg.rkey = rkey; + raddr_seg.reserved = 0; + + ctrl_seg = { + 0, + }; + + assert(likely(bytes == 4 || bytes == 8)); + switch (amo_op) { + case NVSHMEMI_AMO_FETCH_INC: + case NVSHMEMI_AMO_INC: { + if (bytes == 4) { + ctrl_seg.opmod_idx_opcode = HTOBE32(MLX5_OPCODE_ATOMIC_MASKED_FA | (wqe_idx << 8) | + IBGDA_4_BYTE_EXT_AMO_OPMOD); + + ibgda_atomic_32_masked_fa_seg_t *atomic_32_masked_fa_seg = + (ibgda_atomic_32_masked_fa_seg_t *)&atomic_seg_1; + atomic_32_masked_fa_seg->add_data = HTOBE32((uint32_t)1); + atomic_32_masked_fa_seg->field_boundary = 0; + } else { + ctrl_seg.opmod_idx_opcode = HTOBE32(MLX5_OPCODE_ATOMIC_MASKED_FA | (wqe_idx << 8) | + IBGDA_8_BYTE_EXT_AMO_OPMOD); + + ibgda_atomic_64_masked_fa_seg_t *atomic_64_masked_fa_seg = + (ibgda_atomic_64_masked_fa_seg_t *)&atomic_seg_1; + atomic_64_masked_fa_seg->add_data = HTOBE64((uint64_t)1); + atomic_64_masked_fa_seg->field_boundary = 0; + } + break; + } + case NVSHMEMI_AMO_SIGNAL: + case NVSHMEMI_AMO_SIGNAL_SET: + case NVSHMEMI_AMO_SWAP: + case NVSHMEMI_AMO_SET: { + if (bytes == 4) { + ctrl_seg.opmod_idx_opcode = HTOBE32(MLX5_OPCODE_ATOMIC_MASKED_CS | (wqe_idx << 8) | + IBGDA_4_BYTE_EXT_AMO_OPMOD); + + ibgda_atomic_32_masked_cs_seg_t *atomic_32_masked_cs_seg = + (ibgda_atomic_32_masked_cs_seg_t *)&atomic_seg_1; + atomic_32_masked_cs_seg->swap_data = HTOBE32(*(uint32_t *)val_1); + atomic_32_masked_cs_seg->compare_data = 0; + atomic_32_masked_cs_seg->compare_mask = 0; + atomic_32_masked_cs_seg->swap_mask = UINT32_MAX; + } else { + ++ds; + ctrl_seg.opmod_idx_opcode = HTOBE32(MLX5_OPCODE_ATOMIC_MASKED_CS | (wqe_idx << 8) | + IBGDA_8_BYTE_EXT_AMO_OPMOD); + + ibgda_atomic_64_masked_cs_seg_t *atomic_64_masked_cs_data_seg = + (ibgda_atomic_64_masked_cs_seg_t *)&atomic_seg_1; + atomic_64_masked_cs_data_seg->swap = HTOBE64(*(uint64_t *)val_1); + atomic_64_masked_cs_data_seg->compare = 0; + + ibgda_atomic_64_masked_cs_seg_t *atomic_64_masked_cs_mask_seg = + (ibgda_atomic_64_masked_cs_seg_t *)&atomic_seg_2; + atomic_64_masked_cs_mask_seg->swap = UINT64_MAX; + atomic_64_masked_cs_mask_seg->compare = 0; + + if (qp->qp_type == NVSHMEMI_IBGDA_DEVICE_QP_TYPE_DCI) + data_seg_ptr = + (struct mlx5_wqe_data_seg *)((uintptr_t)atomic_seg_2_ptr + + sizeof(*atomic_64_masked_cs_mask_seg)); + else + data_seg_ptr = (struct mlx5_wqe_data_seg *)out_wqes[1]; + } + break; + } + case NVSHMEMI_AMO_SIGNAL_ADD: + case NVSHMEMI_AMO_ADD: { + if (bytes == 4) { + ctrl_seg.opmod_idx_opcode = HTOBE32(MLX5_OPCODE_ATOMIC_MASKED_FA | (wqe_idx << 8) | + IBGDA_4_BYTE_EXT_AMO_OPMOD); + + ibgda_atomic_32_masked_fa_seg_t *atomic_32_masked_fa_seg = + (ibgda_atomic_32_masked_fa_seg_t *)&atomic_seg_1; + atomic_32_masked_fa_seg->add_data = HTOBE32(*(uint32_t *)val_1); + atomic_32_masked_fa_seg->field_boundary = 0; + } else { + ctrl_seg.opmod_idx_opcode = HTOBE32(MLX5_OPCODE_ATOMIC_MASKED_FA | (wqe_idx << 8) | + IBGDA_8_BYTE_EXT_AMO_OPMOD); + + ibgda_atomic_64_masked_fa_seg_t *atomic_64_masked_fa_seg = + (ibgda_atomic_64_masked_fa_seg_t *)&atomic_seg_1; + atomic_64_masked_fa_seg->add_data = HTOBE64(*(uint64_t *)val_1); + atomic_64_masked_fa_seg->field_boundary = 0; + } + break; + } + case NVSHMEMI_AMO_FETCH_AND: + case NVSHMEMI_AMO_AND: { + if (bytes == 4) { + ctrl_seg.opmod_idx_opcode = HTOBE32(MLX5_OPCODE_ATOMIC_MASKED_CS | (wqe_idx << 8) | + IBGDA_4_BYTE_EXT_AMO_OPMOD); + + ibgda_atomic_32_masked_cs_seg_t *atomic_32_masked_cs_seg = + (ibgda_atomic_32_masked_cs_seg_t *)&atomic_seg_1; + atomic_32_masked_cs_seg->swap_data = HTOBE32(*(uint32_t *)val_1); + atomic_32_masked_cs_seg->compare_data = 0; + atomic_32_masked_cs_seg->compare_mask = 0; + atomic_32_masked_cs_seg->swap_mask = HTOBE32(~(*(uint32_t *)val_1)); + } else { + ++ds; + ctrl_seg.opmod_idx_opcode = HTOBE32(MLX5_OPCODE_ATOMIC_MASKED_CS | (wqe_idx << 8) | + IBGDA_8_BYTE_EXT_AMO_OPMOD); + + ibgda_atomic_64_masked_cs_seg_t *atomic_64_masked_cs_data_seg = + (ibgda_atomic_64_masked_cs_seg_t *)&atomic_seg_1; + atomic_64_masked_cs_data_seg->swap = HTOBE64(*(uint64_t *)val_1); + atomic_64_masked_cs_data_seg->compare = 0; + + ibgda_atomic_64_masked_cs_seg_t *atomic_64_masked_cs_mask_seg = + (ibgda_atomic_64_masked_cs_seg_t *)&atomic_seg_2; + atomic_64_masked_cs_mask_seg->swap = HTOBE64(~(*(uint64_t *)val_1)); + atomic_64_masked_cs_mask_seg->compare = 0; + + if (qp->qp_type == NVSHMEMI_IBGDA_DEVICE_QP_TYPE_DCI) + data_seg_ptr = + (struct mlx5_wqe_data_seg *)((uintptr_t)atomic_seg_2_ptr + + sizeof(*atomic_64_masked_cs_mask_seg)); + else + data_seg_ptr = (struct mlx5_wqe_data_seg *)out_wqes[1]; + } + break; + } + case NVSHMEMI_AMO_FETCH_OR: + case NVSHMEMI_AMO_OR: { + if (bytes == 4) { + ctrl_seg.opmod_idx_opcode = HTOBE32(MLX5_OPCODE_ATOMIC_MASKED_CS | (wqe_idx << 8) | + IBGDA_4_BYTE_EXT_AMO_OPMOD); + + ibgda_atomic_32_masked_cs_seg_t *atomic_32_masked_cs_seg = + (ibgda_atomic_32_masked_cs_seg_t *)&atomic_seg_1; + atomic_32_masked_cs_seg->swap_data = HTOBE32(*(uint32_t *)val_1); + atomic_32_masked_cs_seg->compare_data = 0; + atomic_32_masked_cs_seg->compare_mask = 0; + atomic_32_masked_cs_seg->swap_mask = HTOBE32(*(uint32_t *)val_1); + } else { + ++ds; + ctrl_seg.opmod_idx_opcode = HTOBE32(MLX5_OPCODE_ATOMIC_MASKED_CS | (wqe_idx << 8) | + IBGDA_8_BYTE_EXT_AMO_OPMOD); + + ibgda_atomic_64_masked_cs_seg_t *atomic_64_masked_cs_data_seg = + (ibgda_atomic_64_masked_cs_seg_t *)&atomic_seg_1; + atomic_64_masked_cs_data_seg->swap = HTOBE64(*(uint64_t *)val_1); + atomic_64_masked_cs_data_seg->compare = 0; + + ibgda_atomic_64_masked_cs_seg_t *atomic_64_masked_cs_mask_seg = + (ibgda_atomic_64_masked_cs_seg_t *)&atomic_seg_2; + atomic_64_masked_cs_mask_seg->swap = HTOBE64(*(uint64_t *)val_1); + atomic_64_masked_cs_mask_seg->compare = 0; + + if (qp->qp_type == NVSHMEMI_IBGDA_DEVICE_QP_TYPE_DCI) + data_seg_ptr = + (struct mlx5_wqe_data_seg *)((uintptr_t)atomic_seg_2_ptr + + sizeof(*atomic_64_masked_cs_mask_seg)); + else + data_seg_ptr = (struct mlx5_wqe_data_seg *)out_wqes[1]; + } + break; + } + case NVSHMEMI_AMO_FETCH_XOR: + case NVSHMEMI_AMO_XOR: { + if (bytes == 4) { + ctrl_seg.opmod_idx_opcode = HTOBE32(MLX5_OPCODE_ATOMIC_MASKED_FA | (wqe_idx << 8) | + IBGDA_4_BYTE_EXT_AMO_OPMOD); + + ibgda_atomic_32_masked_fa_seg_t *atomic_32_masked_fa_seg = + (ibgda_atomic_32_masked_fa_seg_t *)&atomic_seg_1; + atomic_32_masked_fa_seg->add_data = HTOBE32(*(uint32_t *)val_1); + atomic_32_masked_fa_seg->field_boundary = UINT32_MAX; + } else { + ctrl_seg.opmod_idx_opcode = HTOBE32(MLX5_OPCODE_ATOMIC_MASKED_FA | (wqe_idx << 8) | + IBGDA_8_BYTE_EXT_AMO_OPMOD); + + ibgda_atomic_64_masked_fa_seg_t *atomic_64_masked_fa_seg = + (ibgda_atomic_64_masked_fa_seg_t *)&atomic_seg_1; + atomic_64_masked_fa_seg->add_data = HTOBE64(*(uint64_t *)val_1); + atomic_64_masked_fa_seg->field_boundary = UINT64_MAX; + } + break; + } + case NVSHMEMI_AMO_FETCH: { + if (bytes == 4) { + ctrl_seg.opmod_idx_opcode = HTOBE32(MLX5_OPCODE_ATOMIC_MASKED_FA | (wqe_idx << 8) | + IBGDA_4_BYTE_EXT_AMO_OPMOD); + + ibgda_atomic_32_masked_fa_seg_t *atomic_32_masked_fa_seg = + (ibgda_atomic_32_masked_fa_seg_t *)&atomic_seg_1; + atomic_32_masked_fa_seg->add_data = 0; + atomic_32_masked_fa_seg->field_boundary = 0; + } else { + ctrl_seg.opmod_idx_opcode = HTOBE32(MLX5_OPCODE_ATOMIC_MASKED_FA | (wqe_idx << 8) | + IBGDA_8_BYTE_EXT_AMO_OPMOD); + + ibgda_atomic_64_masked_fa_seg_t *atomic_64_masked_fa_seg = + (ibgda_atomic_64_masked_fa_seg_t *)&atomic_seg_1; + atomic_64_masked_fa_seg->add_data = 0; + atomic_64_masked_fa_seg->field_boundary = 0; + } + break; + } + case NVSHMEMI_AMO_FETCH_ADD: { + if (bytes == 4) { + ctrl_seg.opmod_idx_opcode = HTOBE32(MLX5_OPCODE_ATOMIC_MASKED_FA | (wqe_idx << 8) | + IBGDA_4_BYTE_EXT_AMO_OPMOD); + + ibgda_atomic_32_masked_fa_seg_t *atomic_32_masked_fa_seg = + (ibgda_atomic_32_masked_fa_seg_t *)&atomic_seg_1; + atomic_32_masked_fa_seg->add_data = HTOBE32(*(uint32_t *)val_1); + atomic_32_masked_fa_seg->field_boundary = 0; + } else { + ctrl_seg.opmod_idx_opcode = HTOBE32(MLX5_OPCODE_ATOMIC_FA | (wqe_idx << 8)); + atomic_seg_1.swap_add = HTOBE64(*(uint64_t *)val_1); + } + break; + } + case NVSHMEMI_AMO_COMPARE_SWAP: { + if (bytes == 4) { + ctrl_seg.opmod_idx_opcode = HTOBE32(MLX5_OPCODE_ATOMIC_MASKED_CS | (wqe_idx << 8) | + IBGDA_4_BYTE_EXT_AMO_OPMOD); + + ibgda_atomic_32_masked_cs_seg_t *atomic_32_masked_cs_seg = + (ibgda_atomic_32_masked_cs_seg_t *)&atomic_seg_1; + atomic_32_masked_cs_seg->swap_data = HTOBE32(*(uint32_t *)val_1); + atomic_32_masked_cs_seg->compare_data = HTOBE32(*(uint32_t *)val_2); + atomic_32_masked_cs_seg->compare_mask = UINT32_MAX; + atomic_32_masked_cs_seg->swap_mask = UINT32_MAX; + } else { + ctrl_seg.opmod_idx_opcode = HTOBE32(MLX5_OPCODE_ATOMIC_CS | (wqe_idx << 8)); + atomic_seg_1.swap_add = HTOBE64(*(uint64_t *)val_1); + atomic_seg_1.compare = HTOBE64(*(uint64_t *)val_2); + } + break; + } + default: { assert(0); } + } + + ctrl_seg.qpn_ds = HTOBE32((qp->qpn << 8) | ds); + + data_seg.byte_count = HTOBE32(bytes); + data_seg.lkey = lkey; + data_seg.addr = HTOBE64(laddr); + + ctrl_seg.fm_ce_se = fm_ce_se; + + uint32_t *dst = (uint32_t *)ctrl_seg_ptr; + uint32_t *src = (uint32_t *)&ctrl_seg; + for (int i = 0; i < sizeof(*ctrl_seg_ptr) / sizeof(uint32_t); ++i) + ibgda_store_relaxed(&dst[i], src[i]); + + if (av_seg_size > 0) { + dst = (uint32_t *)av_seg_ptr; + src = (uint32_t *)dct; + for (int i = 0; i < av_seg_size / sizeof(uint32_t); ++i) + ibgda_store_relaxed(&dst[i], src[i]); + } + + dst = (uint32_t *)raddr_seg_ptr; + src = (uint32_t *)&raddr_seg; + for (int i = 0; i < sizeof(*raddr_seg_ptr) / sizeof(uint32_t); ++i) + ibgda_store_relaxed(&dst[i], src[i]); + + dst = (uint32_t *)atomic_seg_1_ptr; + src = (uint32_t *)&atomic_seg_1; + for (int i = 0; i < sizeof(*atomic_seg_1_ptr) / sizeof(uint32_t); ++i) + ibgda_store_relaxed(&dst[i], src[i]); + + dst = (uint32_t *)atomic_seg_2_ptr; + src = (uint32_t *)&atomic_seg_2; + for (int i = 0; i < sizeof(*atomic_seg_2_ptr) / sizeof(uint32_t); ++i) + ibgda_store_relaxed(&dst[i], src[i]); + + dst = (uint32_t *)data_seg_ptr; + src = (uint32_t *)&data_seg; + for (int i = 0; i < sizeof(*data_seg_ptr) / sizeof(uint32_t); ++i) + ibgda_store_relaxed(&dst[i], src[i]); +} + +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE void ibgda_update_dbr( + nvshmemi_ibgda_device_qp_t *qp, uint32_t dbrec_head) { + // DBREC contains the index of the next empty WQEBB. + __be32 dbrec_val; + __be32 *dbrec_ptr = qp->tx_wq.dbrec; + + // This is equivalent to + // WRITE_ONCE(dbrec_ptr, HTOBE32(dbrec_head & 0xffff)); + asm volatile( + "{\n\t" + ".reg .b32 mask1;\n\t" + ".reg .b32 dbrec_head_16b;\n\t" + ".reg .b32 ign;\n\t" + ".reg .b32 mask2;\n\t" + "mov.b32 mask1, 0xffff;\n\t" + "mov.b32 mask2, 0x123;\n\t" + "and.b32 dbrec_head_16b, %1, mask1;\n\t" + "prmt.b32 %0, dbrec_head_16b, ign, mask2;\n\t" + "}" + : "=r"(dbrec_val) + : "r"(dbrec_head)); + ibgda_store_release(dbrec_ptr, dbrec_val); +} + +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE void ibgda_ring_db( + nvshmemi_ibgda_device_qp_t *qp, uint16_t prod_idx) { + uint64_t *bf_ptr = (uint64_t *)qp->tx_wq.bf; + ibgda_ctrl_seg_t ctrl_seg = {.opmod_idx_opcode = HTOBE32(prod_idx << 8), + .qpn_ds = HTOBE32(qp->qpn << 8)}; + + ibgda_store_release(bf_ptr, *((uint64_t *)&ctrl_seg)); +} + +template +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE void ibgda_post_send( + nvshmemi_ibgda_device_qp_t *qp, uint64_t new_prod_idx) { + nvshmemi_ibgda_device_qp_management_t *mvars = &qp->mvars; + uint64_t old_prod_idx; + + // Update prod_idx before ringing the db so that we know which index is needed in quiet/fence. + ibgda_lock_acquire(&mvars->post_send_lock); + + if (need_strong_flush) + old_prod_idx = atomicMax((unsigned long long int *)&mvars->tx_wq.prod_idx, + (unsigned long long int)new_prod_idx); + else + old_prod_idx = atomicMax_block((unsigned long long int *)&mvars->tx_wq.prod_idx, + (unsigned long long int)new_prod_idx); + + if (likely(new_prod_idx > old_prod_idx)) { + IBGDA_MEMBAR(); + ibgda_update_dbr(qp, new_prod_idx); + IBGDA_MEMBAR(); + ibgda_ring_db(qp, new_prod_idx); + } + + ibgda_lock_release(&mvars->post_send_lock); +} + +template +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE void ibgda_proxy_post_send( + nvshmemi_ibgda_device_qp_t *qp, uint64_t new_prod_idx) { + nvshmemi_ibgda_device_qp_management_t *mvars = &qp->mvars; + uint64_t old_prod_idx; + + if (need_strong_flush) { + old_prod_idx = atomicMax((unsigned long long int *)&mvars->tx_wq.prod_idx, + (unsigned long long int)new_prod_idx); + } else { + old_prod_idx = atomicMax_block((unsigned long long int *)&mvars->tx_wq.prod_idx, + (unsigned long long int)new_prod_idx); + } + + if (likely(new_prod_idx > old_prod_idx)) { + atomicMax_system((unsigned long long int *)qp->tx_wq.bf, + (unsigned long long int)new_prod_idx); + } +} + +// If `qp` is shared among CTAs, need_strong_flush must be set to true because +// we must push prior writes from this CTA to L2 before coalescing DB. +template +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE void ibgda_submit_requests( + nvshmemi_ibgda_device_qp_t *qp, uint64_t base_wqe_idx, uint16_t num_wqes) { + nvshmemi_ibgda_device_state_t *state = ibgda_get_state(); + nvshmemi_ibgda_device_qp_management_t *mvars = &qp->mvars; + uint64_t mask = ~((uint64_t)(state->num_requests_in_batch - 1)); + + uint64_t new_wqe_idx = base_wqe_idx + num_wqes; + + unsigned long long int *ready_idx = (unsigned long long int *)(&mvars->tx_wq.ready_head); + + // Wait for prior WQE slots to be filled first. + // They might not be post-sent yet. + if (need_strong_flush) { + // membar from a different CTA does not push prior writes of this CTA. + // We must push them out first because a different CTA might post-send for us. + IBGDA_MEMBAR_NO_OPTIMIZATION(); + while (atomicCAS(ready_idx, (unsigned long long int)base_wqe_idx, + (unsigned long long int)new_wqe_idx) != base_wqe_idx) + ; + IBGDA_MFENCE(); + } else { + // It is ok for those wqes to not be visible to the GPU scope yet. + // ibgda_post_send will take care of that (if we choose to call it). + IBGDA_MFENCE(); + while (atomicCAS_block(ready_idx, (unsigned long long int)base_wqe_idx, + (unsigned long long int)new_wqe_idx) != base_wqe_idx) + ; + IBGDA_MFENCE(); + } + + bool do_post_send = + (new_wqe_idx == ibgda_atomic_read(&mvars->tx_wq.resv_head)) // No concurrent submissions + || ((base_wqe_idx & mask) != + (new_wqe_idx & mask)) // Num of not-yet-posted wqes is beyond the threshold. + || (num_wqes >= state->num_requests_in_batch); // The number of wqes in this submission + // reaches the threshold. + + if (do_post_send) { + if (!state->use_async_postsend) + ibgda_post_send(qp, new_wqe_idx); + else { + ibgda_proxy_post_send(qp, new_wqe_idx); + } + } +} + +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE uint64_t +ibgda_quiet(nvshmemi_ibgda_device_qp_t *qp) { + uint64_t prod_idx = ibgda_atomic_read(&qp->mvars.tx_wq.ready_head); + nvshmemi_ibgda_device_cq_t cq = *qp->tx_wq.cq; + + int err = 0; + int status = ibgda_poll_cq(&cq, prod_idx, &err); + // TODO: Integrate the error handler with the core NVSHMEM +#ifdef NVSHMEM_IBGDA_DEBUG + if (status) { + printf("ibgda_poll_cq failed with error=%d.\n", err); + } +#endif + assert(likely(status == 0)); + return prod_idx; +} + +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE void ibgda_wait_for_slot_availability( + nvshmemi_ibgda_device_qp_t *qp, uint64_t wqe_idx) { + int status = 0; + int err = 0; + uint16_t nwqes = qp->tx_wq.nwqes; + + // We don't want wqe_idx - nwqes to wraparound. + if (likely(wqe_idx >= nwqes)) { + nvshmemi_ibgda_device_cq_t cq = *qp->tx_wq.cq; + status = ibgda_poll_cq(&cq, wqe_idx - nwqes, &err); + // TODO: Integrate the error handler with the core NVSHMEM + if (status) { + printf("ibgda_poll_cq failed with error=%d.\n", err); + } + assert(likely(status == 0)); + } + IBGDA_MFENCE(); +} + +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE int ibgda_get_proxy_pe(int pe) { + if (nvshmemi_device_state_d.enable_rail_opt == 1) + return (pe / nvshmemi_device_state_d.node_npes) * nvshmemi_device_state_d.node_npes + + nvshmemi_device_state_d.node_mype; + return pe; +} + +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE uint32_t ibgda_get_dct_id(int pe, + int dev_idx) { + nvshmemi_ibgda_device_state_t *state = ibgda_get_state(); + uint32_t id = ibgda_get_ctaid(); + /* There are ndcts_per_pe * state->num_devices_initialized per pe. */ + uint32_t dct_id = (pe * state->ndcts_per_pe * state->num_devices_initialized) + + (((id % state->ndcts_per_pe) * state->num_devices_initialized) + dev_idx); + return dct_id; +} + +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE nvshmemi_ibgda_device_dct_t *ibgda_get_dct( + int pe, int dev_idx) { + nvshmemi_ibgda_device_state_t *state = ibgda_get_state(); + uint32_t dct_idx = ibgda_get_dct_id(pe, dev_idx); + + if (dct_idx < NVSHMEMI_IBGDA_MAX_CONST_DCTS) return &state->constmem.dcts[dct_idx]; + + return &state->globalmem.dcts[dct_idx - NVSHMEMI_IBGDA_MAX_CONST_DCTS]; +} + +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE nvshmemi_ibgda_device_qp_t *ibgda_get_dci( + int pe, bool *out_shared_among_ctas) { + nvshmemi_ibgda_device_state_t *state = ibgda_get_state(); + uint32_t id; + uint32_t dev_offset; + bool shared_among_ctas = false; + uint32_t warpid = nvshmemi_thread_id_in_block() / nvshmemi_warp_size(); + + switch (state->dci_map_type) { + case NVSHMEMI_IBGDA_DEVICE_QP_MAP_TYPE_CTA: + id = ibgda_get_ctaid(); + break; + case NVSHMEMI_IBGDA_DEVICE_QP_MAP_TYPE_SM: + id = ibgda_get_smid(); + shared_among_ctas = true; + break; + case NVSHMEMI_IBGDA_DEVICE_QP_MAP_TYPE_WARP: + id = ibgda_get_ctaid() * nvshmemi_block_size() / nvshmemi_warp_size() + warpid; + break; + case NVSHMEMI_IBGDA_DEVICE_QP_MAP_TYPE_DCT: { + uint32_t dct_id; + uint32_t group_id = + ibgda_get_ctaid() * nvshmemi_block_size() / nvshmemi_warp_size() + warpid; + + dct_id = ibgda_get_dct_id(pe, 0); + id = (group_id % state->num_dct_groups) * state->ndcts_per_pe * + nvshmemi_device_state_d.npes * state->num_devices_initialized + + dct_id * state->num_devices_initialized; + shared_among_ctas = true; + break; + } + default: + assert(0); + break; + } + dev_offset = ++state->globalmem.qp_group_switches[id % state->num_qp_groups]; + + /* round down */ + id = id / state->num_devices_initialized; + /* add dev index */ + id = (id * state->num_devices_initialized) + (dev_offset % state->num_devices_initialized); + + uint32_t idx; + if (id < state->num_exclusive_dcis) + idx = id; + else { + idx = state->num_exclusive_dcis + (id % state->num_shared_dcis); + shared_among_ctas = true; + } + + *out_shared_among_ctas = shared_among_ctas; + return &state->globalmem.dcis[idx]; +} + +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE nvshmemi_ibgda_device_qp_t *ibgda_get_rc( + int pe, bool *out_shared_among_ctas) { + nvshmemi_ibgda_device_state_t *state = ibgda_get_state(); + uint32_t id; + uint32_t idx; + uint32_t dev_offset; + uint32_t warpid = nvshmemi_thread_id_in_block() / nvshmemi_warp_size(); + + assert(pe != nvshmemi_device_state_d.mype); + + switch (state->rc_map_type) { + case NVSHMEMI_IBGDA_DEVICE_QP_MAP_TYPE_CTA: + id = ibgda_get_ctaid(); + break; + case NVSHMEMI_IBGDA_DEVICE_QP_MAP_TYPE_SM: + id = ibgda_get_smid(); + break; + case NVSHMEMI_IBGDA_DEVICE_QP_MAP_TYPE_WARP: + id = ibgda_get_ctaid() * nvshmemi_block_size() / nvshmemi_warp_size() + warpid; + break; + default: + assert(0); + break; + } + + dev_offset = ++state->globalmem.qp_group_switches[id % state->num_qp_groups]; + + /* round down */ + id = id / state->num_devices_initialized; + id = (id * state->num_devices_initialized) + (dev_offset % state->num_devices_initialized); + + idx = (pe * state->num_rc_per_pe * state->num_devices_initialized) + + (id % (state->num_rc_per_pe * state->num_devices_initialized)); + + *out_shared_among_ctas = true; + return &state->globalmem.rcs[idx]; +} + +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE nvshmemi_ibgda_device_qp_t *ibgda_get_qp( + int pe, bool *out_shared_among_ctas) { + nvshmemi_ibgda_device_state_t *state = ibgda_get_state(); + + if (ibgda_is_rc_enabled() && pe != nvshmemi_device_state_d.mype) + return ibgda_get_rc(pe, out_shared_among_ctas); + else + return ibgda_get_dci(pe, out_shared_among_ctas); +} + +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE void ibgda_get_lkey( + uint64_t addr, __be32 *lkey, size_t *chunk_size, bool *is_sysmem_scope, uint32_t dev_idx) { + nvshmemi_ibgda_device_state_t *state = ibgda_get_state(); + uint64_t heap_start = (uint64_t)nvshmemi_device_state_d.heap_base; + uint64_t heap_end = heap_start + nvshmemi_device_state_d.heap_size - 1; + size_t max_len = 1ULL << 30; + if (heap_start <= addr && addr <= heap_end) { + // addr in the symmetric heap + uint64_t idx = ((addr - heap_start) >> state->log2_cumem_granularity) * + state->num_devices_initialized + + dev_idx; + nvshmemi_ibgda_device_key_t device_key; + + if (idx < NVSHMEMI_IBGDA_MAX_CONST_LKEYS) + device_key = state->constmem.lkeys[idx]; + else + device_key = state->globalmem.lkeys[idx - NVSHMEMI_IBGDA_MAX_CONST_LKEYS]; + + assert(addr < device_key.next_addr); + + *lkey = device_key.key; + *chunk_size = device_key.next_addr - addr; + *chunk_size = *chunk_size < max_len ? *chunk_size : max_len; + *is_sysmem_scope = (nvshmemi_device_state_d.symmetric_heap_kind == 1); + return; + } else { + // local-only addr + nvshmemi_ibgda_device_local_only_mhandle_t *mhandle = + state->globalmem.local_only_mhandle_head; + + while (mhandle) { + if (mhandle->start <= addr && addr <= mhandle->end) { + *lkey = mhandle->lkeys[dev_idx]; + *chunk_size = mhandle->end - addr + 1; + *chunk_size = *chunk_size < max_len ? *chunk_size : max_len; + *is_sysmem_scope = mhandle->is_sysmem_scope; + return; + } + mhandle = mhandle->next; + } + } + + // lkey is not found. + assert(0); +} + +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE void ibgda_get_raddr_rkey( + uint64_t addr, int dst_pe, int proxy_pe, uint64_t *out_raddr, __be32 *out_rkey, + size_t *out_chunk_size, uint32_t dev_idx) { + nvshmemi_ibgda_device_state_t *state = ibgda_get_state(); + uint64_t heap_start = (uint64_t)nvshmemi_device_state_d.heap_base; + uint64_t roffset = addr - heap_start; + int npes; + // nvcc from CUDA12.0 - 12.2 seems to have a bug. It causes + // nvshmemi_device_state_d.npes to become 0 in this function. + // WAR: Force reload of nvshmemi_device_state_d.npes. We may reload from L1 + // most of the time, so the performance hit is minimal. + asm volatile("ld.b32 %0, [%1];" : "=r"(npes) : "l"(&nvshmemi_device_state_d.npes)); + + uint64_t idx = + ((roffset >> state->log2_cumem_granularity) * npes * state->num_devices_initialized) + + (proxy_pe * state->num_devices_initialized) + dev_idx; + nvshmemi_ibgda_device_key_t device_key; + uint64_t raddr; + + if (idx < NVSHMEMI_IBGDA_MAX_CONST_RKEYS) + device_key = state->constmem.rkeys[idx]; + else + device_key = state->globalmem.rkeys[idx - NVSHMEMI_IBGDA_MAX_CONST_RKEYS]; + + assert(roffset < device_key.next_addr); + + raddr = (uint64_t)nvshmemi_device_state_d.peer_heap_base_remote[proxy_pe] + roffset; + if (dst_pe != proxy_pe) + raddr += (dst_pe % nvshmemi_device_state_d.node_npes - nvshmemi_device_state_d.node_mype) * + nvshmemi_device_state_d.heap_size; + + *out_raddr = raddr; + *out_rkey = device_key.key; + *out_chunk_size = device_key.next_addr - roffset; +} + +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE uint64_t ibgda_reserve_wqe_slots( + nvshmemi_ibgda_device_qp_t *qp, unsigned long long int num_wqes, bool is_qp_shared_among_ctas) { + nvshmemi_ibgda_device_qp_management_t *mvars = &qp->mvars; + uint64_t wqe_idx; + +// OK to keep this conditional since we only support one build per major verion. +#if CUDART_VERSION >= 12000 + if (is_qp_shared_among_ctas) + wqe_idx = atomicAdd((unsigned long long int *)&mvars->tx_wq.resv_head, num_wqes); + else + wqe_idx = atomicAdd_block((unsigned long long int *)&mvars->tx_wq.resv_head, num_wqes); +#else + // WAR NVBUG 3749055. The fix is in nvcc of CUDA 12.0 and later. + if (is_qp_shared_among_ctas) + asm volatile("atom.relaxed.gpu.global.add.u64 %0, [%1], %2;" + : "=l"(wqe_idx) + : "l"(&mvars->tx_wq.resv_head), "l"(num_wqes)); + else + asm volatile("atom.relaxed.cta.global.add.u64 %0, [%1], %2;" + : "=l"(wqe_idx) + : "l"(&mvars->tx_wq.resv_head), "l"(num_wqes)); +#endif + // If last slot is available, all prior slots are also available. + ibgda_wait_for_slot_availability(qp, wqe_idx + num_wqes); + return wqe_idx; +} + +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE uint64_t +ibgda_reserve_ibuf_slots(nvshmemi_ibgda_device_qp_t *qp, unsigned long long int num_slots) { + nvshmemi_ibgda_device_qp_management_t *mvars = &qp->mvars; + uint32_t nslots = qp->ibuf.nslots; + uint64_t base_idx = atomicAdd((unsigned long long int *)&mvars->ibuf.head, num_slots); + uint64_t idx = base_idx + num_slots; + + // Wait until the slots become available. + while (idx - ibgda_atomic_read(&mvars->ibuf.tail) > nslots) + ; + + // Prevent the reordering of the above wait loop. + IBGDA_MFENCE(); + + return base_idx; +} + +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE void ibgda_release_ibuf( + nvshmemi_ibgda_device_qp_t *qp, unsigned long long int base_idx, + unsigned long long int num_slots) { + nvshmemi_ibgda_device_qp_management_t *mvars = &qp->mvars; + unsigned long long int new_idx = base_idx + num_slots; + IBGDA_MFENCE(); + // Wait here. + while (atomicCAS((unsigned long long int *)&mvars->ibuf.tail, (unsigned long long int)base_idx, + new_idx) != base_idx) + ; + IBGDA_MFENCE(); +} + +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE uint64_t +ibgda_get_ibuf_addr(nvshmemi_ibgda_device_qp_t *qp, uint64_t idx) { + idx = idx & (qp->ibuf.nslots - 1); + + // buf[0] is reserved for non-fetch operations + return (uint64_t)qp->ibuf.buf + NVSHMEMI_IBGDA_IBUF_SLOT_SIZE * (idx + 1); +} + +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE bool ibgda_can_coalesce_warp( + unsigned int amask, nvshmemi_ibgda_device_qp_t *qp) { + int pred_same_qp; + + if (amask != IBGDA_FULL_WARP) return false; + + __match_all_sync(amask, qp->qpn, &pred_same_qp); + if (!pred_same_qp) return false; + + return true; +} + +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE bool ibgda_can_coalesce_warp_pe( + unsigned int amask, int pe) { + int pred_same_pe; + + if (amask != IBGDA_FULL_WARP) return false; + + __match_all_sync(amask, pe, &pred_same_pe); + if (!pred_same_pe) return false; + + return true; +} + +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE uint64_t +ibgda_cst(nvshmemi_ibgda_device_qp_t *dci, bool is_dci_shared_among_ctas) { + assert(likely(dci->qp_type == NVSHMEMI_IBGDA_DEVICE_QP_TYPE_DCI)); + + nvshmemi_ibgda_device_dct_t *dct = ibgda_get_dct(nvshmemi_device_state_d.mype, dci->dev_idx); + + uint64_t laddr = (uint64_t)dci->ibuf.buf; + __be32 lkey = dci->ibuf.lkey; + + const int num_wqes = 1; + + uint64_t base_wqe_idx = ibgda_reserve_wqe_slots(dci, num_wqes, is_dci_shared_among_ctas); + + void *wqe_ptrs[1]; + wqe_ptrs[0] = ibgda_get_wqe_ptr(dci, base_wqe_idx); + + // DUMP OP causes the NIC to read laddr, which is always on GPU memory. + // For CST, it is cheaper than RDMA READ. + ibgda_write_dump_wqe(dci, laddr, lkey, sizeof(char), base_wqe_idx, IBGDA_MLX5_FM_NO_FENCE, + wqe_ptrs); + + // Don't update get_head here because this is internal cst + if (is_dci_shared_among_ctas) + ibgda_submit_requests(dci, base_wqe_idx, num_wqes); + else + ibgda_submit_requests(dci, base_wqe_idx, num_wqes); + + return ibgda_quiet(dci); +} + +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE uint64_t +ibgda_quiet_with_cst(nvshmemi_ibgda_device_qp_t *qp, bool is_qp_shared_among_ctas) { + nvshmemi_ibgda_device_state_t *state = ibgda_get_state(); + nvshmemi_ibgda_device_qp_management_t *mvars = &qp->mvars; + + uint64_t get_head; + uint64_t ticket; + uint64_t get_tail; + + if (state->may_skip_cst) { + ticket = ibgda_quiet(qp); + } else { + // We want to read get_head before calling ibgda_quiet. Thus, ticket = + // ibgda_quiet(qp) cannot be combined. + get_head = ibgda_atomic_read(&mvars->tx_wq.get_head); + ticket = ibgda_quiet(qp); + get_tail = ibgda_atomic_read(&mvars->tx_wq.get_tail); + + // TODO: Change to WAIT + DUMP + // In that case, we don't have to do quiet first + if (get_tail < get_head) { + if (qp->qp_type == NVSHMEMI_IBGDA_DEVICE_QP_TYPE_DCI) { + ticket = ibgda_cst(qp, is_qp_shared_among_ctas); + ibgda_update_get_tail(qp, ticket); + } else { + // We don't have RC loopback to self. + // So, we grab a DCI for CST. + bool is_dci_shared_among_ctas; + nvshmemi_ibgda_device_qp_t *dci = + ibgda_get_dci(nvshmemi_device_state_d.mype, &is_dci_shared_among_ctas); + uint64_t cst_ticket = ibgda_cst(dci, is_dci_shared_among_ctas); + ibgda_update_get_tail(dci, cst_ticket); + ibgda_update_get_tail(qp, ticket); + } + } + } + + return ticket; +} + +template +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE void ibgda_rma_thread( + uint64_t rptr, uint64_t lptr, size_t remaining_size, int dst_pe, int proxy_pe) { + nvshmemi_ibgda_device_state_t *state = ibgda_get_state(); + unsigned int amask = __activemask(); + bool can_coalesce_warp = ibgda_can_coalesce_warp_pe(amask, proxy_pe); + int my_tid; + int tg_size; + + const bool need_cst = (channel_op == NVSHMEMI_OP_GET) && !state->may_skip_cst; + const bool need_immediate_cst = !nbi && need_cst; + + int is_qp_shared_among_ctas; + nvshmemi_ibgda_device_qp_t *qp; + nvshmemi_ibgda_device_dct_t *dct; + + if (can_coalesce_warp) { + my_tid = nvshmemi_thread_id_in_threadgroup(); + tg_size = nvshmemi_threadgroup_size(); + if (my_tid == 0) { + qp = ibgda_get_qp(proxy_pe, (bool *)&is_qp_shared_among_ctas); + } + qp = (nvshmemi_ibgda_device_qp_t *)__shfl_sync(IBGDA_FULL_WARP, (uintptr_t)qp, 0); + is_qp_shared_among_ctas = __shfl_sync(IBGDA_FULL_WARP, is_qp_shared_among_ctas, 0); + } else { + qp = ibgda_get_qp(proxy_pe, (bool *)&is_qp_shared_among_ctas); + my_tid = nvshmemi_thread_id_in_threadgroup(); + tg_size = nvshmemi_threadgroup_size(); + } + dct = ibgda_get_dct(proxy_pe, qp->dev_idx); + + const bool need_additional_wqe = + need_immediate_cst || + ((qp->qp_type == NVSHMEMI_IBGDA_DEVICE_QP_TYPE_DCI) && !support_half_av_seg); + int num_wqes_per_cmd = + (qp->qp_type == NVSHMEMI_IBGDA_DEVICE_QP_TYPE_DCI) ? (support_half_av_seg ? 1 : 2) : 1; + + bool did_quiet = false; + + if (unlikely(remaining_size == 0)) return; + + while (remaining_size > 0) { + amask = __activemask(); + + bool is_data_buf_in_sysmem; + + __be32 lkey; + size_t lchunk_size; + ibgda_get_lkey(lptr, &lkey, &lchunk_size, &is_data_buf_in_sysmem, qp->dev_idx); + + __be32 rkey; + uint64_t raddr; + size_t rchunk_size; + ibgda_get_raddr_rkey(rptr, dst_pe, proxy_pe, &raddr, &rkey, &rchunk_size, qp->dev_idx); + + size_t transfer_size = ibgda_cal_transfer_size(remaining_size, lchunk_size, rchunk_size); + + can_coalesce_warp = ibgda_can_coalesce_warp(amask, qp); + if (can_coalesce_warp) { + my_tid = nvshmemi_thread_id_in_threadgroup(); + tg_size = nvshmemi_threadgroup_size(); + } else { + my_tid = nvshmemi_thread_id_in_threadgroup(); + tg_size = nvshmemi_threadgroup_size(); + } + + int num_wqes = num_wqes_per_cmd * tg_size + (need_additional_wqe ? 1 : 0); + + uint64_t base_wqe_idx; + + if (my_tid == 0) { + base_wqe_idx = ibgda_reserve_wqe_slots(qp, num_wqes, is_qp_shared_among_ctas); + } + + if (can_coalesce_warp) { + base_wqe_idx = __shfl_sync(amask, base_wqe_idx, 0); + } + + uint64_t my_wqe_idx = base_wqe_idx + (my_tid * num_wqes_per_cmd); + + void *wqe_ptrs[2]; + wqe_ptrs[0] = ibgda_get_wqe_ptr(qp, my_wqe_idx); + wqe_ptrs[1] = ibgda_get_wqe_ptr(qp, my_wqe_idx + 1); + + // Generate CQE only if we create the last WQE in the group. + uint8_t fm_ce_se = + (!need_additional_wqe && (my_tid == tg_size - 1)) ? MLX5_WQE_CTRL_CQ_UPDATE : 0; + + switch (channel_op) { + case NVSHMEMI_OP_PUT: + ibgda_write_rdma_write_wqe(qp, dct, lptr, lkey, raddr, rkey, + transfer_size, my_wqe_idx, fm_ce_se, + wqe_ptrs); + break; + case NVSHMEMI_OP_GET: + ibgda_write_rdma_read_wqe(qp, dct, lptr, lkey, raddr, rkey, + transfer_size, my_wqe_idx, fm_ce_se, + wqe_ptrs); + break; + default: +#ifdef NVSHMEM_IBGDA_DEBUG + printf("Unsupported channel_op.\n"); +#endif + assert(0); + } + + if (can_coalesce_warp) { + nvshmemi_warp_sync(); + } + + if (my_tid == tg_size - 1) { + if (need_immediate_cst) { + // Enqueue CST op in the QP. This command has NIC Fence, which + // waits for all prior READ/ATOMIC to finish before issuing this + // DUMP. + my_wqe_idx += num_wqes_per_cmd; + wqe_ptrs[0] = ibgda_get_wqe_ptr(qp, my_wqe_idx); + ibgda_write_dump_wqe(qp, (uint64_t)qp->ibuf.buf, qp->ibuf.lkey, sizeof(char), + my_wqe_idx, IBGDA_MLX5_FM_FENCE, wqe_ptrs); + } else { + if (need_additional_wqe) { + my_wqe_idx += num_wqes_per_cmd; + wqe_ptrs[0] = ibgda_get_wqe_ptr(qp, my_wqe_idx); + ibgda_write_nop_wqe(qp, my_wqe_idx, wqe_ptrs); + } + + if (need_cst) { + // For nbi, we will do CST in QUIET. + // GET index must be visible before the new cons index. + ibgda_update_get_head(qp, base_wqe_idx + num_wqes); + } + } + + // Require membar.sys to push data buffer to the point of consistency. + if (channel_op == NVSHMEMI_OP_PUT && is_data_buf_in_sysmem) __threadfence_system(); + + if (is_qp_shared_among_ctas) + ibgda_submit_requests(qp, base_wqe_idx, num_wqes); + else + ibgda_submit_requests(qp, base_wqe_idx, num_wqes); + } + + remaining_size -= transfer_size; + + rptr += transfer_size; + lptr += transfer_size; + + if (can_coalesce_warp) { + if (!nbi) { + bool do_coalesce_quiet = __all_sync(amask, remaining_size == 0); + if (do_coalesce_quiet && my_tid == tg_size - 1) { + // CST, if required, has already been enqueued. We simply need to + // do ibgda_quiet here. + ibgda_quiet(qp); + } + did_quiet |= do_coalesce_quiet; + } + nvshmemi_warp_sync(); + } + } + + if (!nbi && !did_quiet) { + // CST, if required, has already been enqueued. We simply need to + // do ibgda_quiet here. + ibgda_quiet(qp); + } +} + +#if __cplusplus >= 201103L +static_assert(NVSHMEMI_IBGDA_MIN_QP_DEPTH >= 64, + "static_assert(NVSHMEMI_IBGDA_MIN_QP_DEPTH >= 64) failed"); +#endif +template +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE void ibgda_rma(uint64_t req_rptr, + uint64_t req_lptr, + size_t bytes, int dst_pe, + int proxy_pe) { + assert(SCOPE == NVSHMEMI_THREADGROUP_WARP || SCOPE == NVSHMEMI_THREADGROUP_BLOCK); + + // Use only warp 0 + int my_tid = nvshmemi_thread_id_in_threadgroup(); + int tg_size = nvshmemi_threadgroup_size(); + nvshmemi_ibgda_device_state_t *state = ibgda_get_state(); + + const bool need_cst = (channel_op == NVSHMEMI_OP_GET) && !state->may_skip_cst; + const bool need_immediate_cst = !nbi && need_cst; + bool need_additional_wqe; + + int is_qp_shared_among_ctas = 0; + nvshmemi_ibgda_device_qp_t *qp; + nvshmemi_ibgda_device_dct_t *dct; + + int num_wqes; + int num_wqes_per_cmd; + + uint64_t base_wqe_idx; + uint64_t my_wqe_idx; + + void *wqe_ptrs[2]; + + size_t remaining_size = bytes; + + size_t transfer_size; + size_t my_transfer_size = 0; + + uint64_t rptr = req_rptr; + uint64_t lptr = req_lptr; + + __be32 lkey; + __be32 my_lkey = 0; + uint64_t my_laddr; + size_t lchunk_size; + + __be32 rkey; + __be32 my_rkey = 0; + uint64_t raddr; + uint64_t my_raddr; + size_t rchunk_size; + + int chunk_idx = 0; + + bool is_data_buf_in_sysmem; + + uint8_t fm_ce_se; + + if (unlikely(remaining_size == 0)) goto out; + + // Not warp 0, wait at the exit. + if (my_tid >= tg_size) { + goto out; + } + my_tid = nvshmemi_thread_id_in_threadgroup(); + + if (my_tid == 0) { + qp = ibgda_get_qp(proxy_pe, (bool *)&is_qp_shared_among_ctas); + } + qp = (nvshmemi_ibgda_device_qp_t *)__shfl_sync(IBGDA_FULL_WARP, (uintptr_t)qp, 0); + is_qp_shared_among_ctas = __shfl_sync(IBGDA_FULL_WARP, is_qp_shared_among_ctas, 0); + dct = ibgda_get_dct(proxy_pe, qp->dev_idx); + + need_additional_wqe = + need_immediate_cst || + ((qp->qp_type == NVSHMEMI_IBGDA_DEVICE_QP_TYPE_DCI) && !support_half_av_seg); + + num_wqes_per_cmd = + (qp->qp_type == NVSHMEMI_IBGDA_DEVICE_QP_TYPE_DCI) ? (support_half_av_seg ? 1 : 2) : 1; + + // Calculate how many chunks we need to send. + while (remaining_size > 0) { + ibgda_get_lkey(lptr, &lkey, &lchunk_size, &is_data_buf_in_sysmem, qp->dev_idx); + ibgda_get_raddr_rkey(rptr, dst_pe, proxy_pe, &raddr, &rkey, &rchunk_size, qp->dev_idx); + transfer_size = ibgda_cal_transfer_size(remaining_size, lchunk_size, rchunk_size); + if (my_tid == chunk_idx) { + my_lkey = lkey; + my_laddr = lptr; + my_rkey = rkey; + my_raddr = raddr; + my_transfer_size = transfer_size; + } + + remaining_size -= transfer_size; + rptr += transfer_size; + lptr += transfer_size; + + ++chunk_idx; + } + + // Too many chunks. Use ibgda_rma_thread to handle it instead. + if (unlikely(chunk_idx > tg_size)) { + if (my_tid == 0) { + ibgda_rma_thread(req_rptr, req_lptr, bytes, + dst_pe, proxy_pe); + } + goto out; + } + + num_wqes = num_wqes_per_cmd * chunk_idx + (need_additional_wqe ? 1 : 0); + + if (my_tid == 0) { + base_wqe_idx = ibgda_reserve_wqe_slots(qp, num_wqes, is_qp_shared_among_ctas); + } + + base_wqe_idx = __shfl_sync(IBGDA_FULL_WARP, base_wqe_idx, 0); + my_wqe_idx = base_wqe_idx + (my_tid * num_wqes_per_cmd); + + // Generate CQE only if we create the last WQE in the group. + fm_ce_se = (!need_additional_wqe && (my_tid == chunk_idx - 1)) ? MLX5_WQE_CTRL_CQ_UPDATE : 0; + + if (my_tid < chunk_idx) { + wqe_ptrs[0] = ibgda_get_wqe_ptr(qp, my_wqe_idx); + wqe_ptrs[1] = ibgda_get_wqe_ptr(qp, my_wqe_idx + 1); + + switch (channel_op) { + case NVSHMEMI_OP_PUT: + ibgda_write_rdma_write_wqe(qp, dct, my_laddr, my_lkey, + my_raddr, my_rkey, my_transfer_size, + my_wqe_idx, fm_ce_se, wqe_ptrs); + break; + case NVSHMEMI_OP_GET: + ibgda_write_rdma_read_wqe(qp, dct, my_laddr, my_lkey, my_raddr, + my_rkey, my_transfer_size, + my_wqe_idx, fm_ce_se, wqe_ptrs); + break; + default: +#ifdef NVSHMEM_IBGDA_DEBUG + printf("Unsupported channel_op.\n"); +#endif + assert(0); + } + } + + nvshmemi_warp_sync(); + + if (my_tid == chunk_idx - 1) { + if (need_immediate_cst) { + my_wqe_idx += num_wqes_per_cmd; + wqe_ptrs[0] = ibgda_get_wqe_ptr(qp, my_wqe_idx); + // Enqueue CST op in the QP. This command has NIC Fence, which + // waits for all prior READ/ATOMIC to finish before issuing this + // DUMP. + ibgda_write_dump_wqe(qp, (uint64_t)qp->ibuf.buf, qp->ibuf.lkey, sizeof(char), + my_wqe_idx, IBGDA_MLX5_FM_FENCE, wqe_ptrs); + } else { + if (need_additional_wqe) { + my_wqe_idx += num_wqes_per_cmd; + wqe_ptrs[0] = ibgda_get_wqe_ptr(qp, my_wqe_idx); + ibgda_write_nop_wqe(qp, my_wqe_idx, wqe_ptrs); + } + + if (need_cst) { + // For nbi, we will do CST in QUIET. + // GET index must be visible before the new cons index. + // ibgda_submit_requests has fence, which guarantees the ordering. + ibgda_update_get_head(qp, base_wqe_idx + num_wqes); + } + } + + // Require membar.sys to push data buffer to the point of consistency. + if (channel_op == NVSHMEMI_OP_PUT && is_data_buf_in_sysmem) __threadfence_system(); + + if (is_qp_shared_among_ctas) + ibgda_submit_requests(qp, base_wqe_idx, num_wqes); + else + ibgda_submit_requests(qp, base_wqe_idx, num_wqes); + + if (!nbi) { + // CST, if required, has already been enqueued. We simply need to + // do ibgda_quiet here. + ibgda_quiet(qp); + } + } + +out: + nvshmemi_threadgroup_sync(); +} + +/** + * RMA P base + */ +#if __cplusplus >= 201103L +static_assert(NVSHMEMI_IBGDA_MIN_QP_DEPTH >= 64, + "static_assert(NVSHMEMI_IBGDA_MIN_QP_DEPTH >= 64) failed"); +#endif +template +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_ibgda_rma_p_impl( + void *rptr, const T value, int dst_pe) { + static_assert((can_combine_data && is_full_warp) || (!can_combine_data), + "can_combine_data check 1 failed.\n"); + static_assert((can_combine_data && support_half_av_seg) || (!can_combine_data), + "can_combine_data check 2 failed.\n"); + + int my_tid; + int tg_size; + int proxy_pe = ibgda_get_proxy_pe(dst_pe); + int is_qp_shared_among_ctas; + nvshmemi_ibgda_device_qp_t *qp; + nvshmemi_ibgda_device_dct_t *dct; + + nvshmemi_ibgda_device_state_t *state = ibgda_get_state(); + + if (is_full_warp) { + my_tid = nvshmemi_thread_id_in_threadgroup(); + tg_size = nvshmemi_threadgroup_size(); + if (my_tid == 0) { + qp = ibgda_get_qp(proxy_pe, (bool *)&is_qp_shared_among_ctas); + } + qp = (nvshmemi_ibgda_device_qp_t *)__shfl_sync(IBGDA_FULL_WARP, (uintptr_t)qp, 0); + is_qp_shared_among_ctas = __shfl_sync(IBGDA_FULL_WARP, is_qp_shared_among_ctas, 0); + } else { + qp = ibgda_get_qp(proxy_pe, (bool *)&is_qp_shared_among_ctas); + my_tid = nvshmemi_thread_id_in_threadgroup(); + tg_size = nvshmemi_threadgroup_size(); + } + dct = ibgda_get_dct(proxy_pe, qp->dev_idx); + + __be32 rkey; + uint64_t raddr; + size_t rchunk_size; + ibgda_get_raddr_rkey((uint64_t)rptr, dst_pe, proxy_pe, &raddr, &rkey, &rchunk_size, + qp->dev_idx); + + // With proper alignment (requirement of NVSHMEM), one element cannot span multiple chunks. + assert(rchunk_size >= sizeof(T)); + + int num_wqes_per_cmd; + int num_wqes; + + bool need_additional_wqe = false; + + if (can_combine_data) { + if (qp->qp_type == NVSHMEMI_IBGDA_DEVICE_QP_TYPE_RC) { + num_wqes_per_cmd = + ibgda_get_num_wqes_in_inl_combine_warp(); + } else if (sizeof(T) == 8) { + num_wqes_per_cmd = + 2 * ibgda_get_num_wqes_in_inl_combine_warp(); + } else { + num_wqes_per_cmd = + ibgda_get_num_wqes_in_inl_combine_warp(); + } + num_wqes = num_wqes_per_cmd; + } else { + num_wqes_per_cmd = + (qp->qp_type == NVSHMEMI_IBGDA_DEVICE_QP_TYPE_DCI) ? (support_half_av_seg ? 1 : 2) : 1; + num_wqes = num_wqes_per_cmd * tg_size; + } + + if (!can_combine_data && num_wqes_per_cmd > 1) { + ++num_wqes; + need_additional_wqe = true; + } + + uint64_t base_wqe_idx; + + if (my_tid == 0) { + base_wqe_idx = ibgda_reserve_wqe_slots(qp, num_wqes, is_qp_shared_among_ctas); + } + + if (is_full_warp) { + base_wqe_idx = __shfl_sync(IBGDA_FULL_WARP, base_wqe_idx, 0); + } + + // Generate CQE only if we create the last WQE in the group. + uint8_t fm_ce_se = + (!need_additional_wqe && (my_tid == tg_size - 1)) ? MLX5_WQE_CTRL_CQ_UPDATE : 0; + + uint64_t my_wqe_idx = + can_combine_data ? base_wqe_idx : base_wqe_idx + (my_tid * num_wqes_per_cmd); + + void *wqe_ptrs[8]; +#pragma unroll + for (int i = 0; i < 8; ++i) { + wqe_ptrs[i] = ibgda_get_wqe_ptr(qp, my_wqe_idx + i); + } + + if (can_combine_data && sizeof(T) == 8 && qp->qp_type == NVSHMEMI_IBGDA_DEVICE_QP_TYPE_DCI) + ibgda_write_rdma_write_inl_wqe_combine_warp_for_dci_8B(qp, dct, value, raddr, rkey, + my_wqe_idx, my_tid, wqe_ptrs); + else if (can_combine_data) + ibgda_write_rdma_write_inl_wqe_combine_warp(qp, dct, value, raddr, rkey, my_wqe_idx, + my_tid, wqe_ptrs); + else + ibgda_write_rdma_write_inl_wqe(qp, dct, &value, raddr, rkey, sizeof(T), + my_wqe_idx, fm_ce_se, wqe_ptrs); + + if (is_full_warp) nvshmemi_warp_sync(); + + if (my_tid == tg_size - 1) { + if (need_additional_wqe) { + my_wqe_idx += num_wqes_per_cmd; + wqe_ptrs[0] = ibgda_get_wqe_ptr(qp, my_wqe_idx); + ibgda_write_nop_wqe(qp, my_wqe_idx, wqe_ptrs); + } + + if (is_qp_shared_among_ctas) + ibgda_submit_requests(qp, base_wqe_idx, num_wqes); + else + ibgda_submit_requests(qp, base_wqe_idx, num_wqes); + } + + if (is_full_warp) nvshmemi_warp_sync(); +} + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_ibgda_rma_p(void *rptr, const T value, + int dst_pe) { + unsigned int amask = __activemask(); + bool can_combine_data = false; + int pred_pe = 0; + int pred_contiguous = 0; + int pred_rkey = 0; + int my_tid; + + nvshmemi_ibgda_device_state_t *state = ibgda_get_state(); + + if (amask == IBGDA_FULL_WARP) { + /* TODO: Adding multi-dev support could have caused a regression with coalescing. */ + nvshmemi_ibgda_device_state_t *state = ibgda_get_state(); + __be32 rkey; + uint64_t raddr; + size_t rchunk_size; + int proxy_pe = ibgda_get_proxy_pe(dst_pe); + ibgda_get_raddr_rkey((uint64_t)rptr, dst_pe, proxy_pe, &raddr, &rkey, &rchunk_size, 0); + my_tid = nvshmemi_thread_id_in_threadgroup(); + __match_all_sync(IBGDA_FULL_WARP, dst_pe, &pred_pe); + __match_all_sync(IBGDA_FULL_WARP, (uintptr_t)(rptr) - (my_tid * sizeof(T)), + &pred_contiguous); + __match_all_sync(IBGDA_FULL_WARP, rkey, &pred_rkey); + can_combine_data = (pred_pe && pred_contiguous && pred_rkey && state->support_half_av_seg); + + if (can_combine_data) + nvshmemi_ibgda_rma_p_impl(rptr, value, dst_pe); + else if (state->support_half_av_seg) + nvshmemi_ibgda_rma_p_impl(rptr, value, dst_pe); + else + nvshmemi_ibgda_rma_p_impl(rptr, value, dst_pe); + } else if (state->support_half_av_seg) + nvshmemi_ibgda_rma_p_impl(rptr, value, dst_pe); + else + nvshmemi_ibgda_rma_p_impl(rptr, value, dst_pe); +} + +/** + * RMA G base + */ +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE T nvshmemi_ibgda_rma_g_impl(void *rptr, int dst_pe, + int proxy_pe) { + unsigned int amask = __activemask(); + int my_tid; + int tg_size; + + nvshmemi_ibgda_device_state_t *state = ibgda_get_state(); + const bool need_cst = !state->may_skip_cst; + + uint64_t base_wqe_idx; + uint64_t base_ibuf_idx; + + T ret; + + int is_qp_shared_among_ctas; + nvshmemi_ibgda_device_dct_t *dct; + nvshmemi_ibgda_device_qp_t *qp; + + __be32 rkey; + uint64_t raddr; + size_t rchunk_size; + + bool can_coalesce_warp = ibgda_can_coalesce_warp_pe(amask, proxy_pe); + bool can_combine_data = false; + int pred_contiguous = 0; + int pred_rkey = 0; + + if (can_coalesce_warp) { + my_tid = nvshmemi_thread_id_in_threadgroup(); + tg_size = nvshmemi_threadgroup_size(); + if (my_tid == 0) { + qp = ibgda_get_qp(proxy_pe, (bool *)&is_qp_shared_among_ctas); + } + qp = (nvshmemi_ibgda_device_qp_t *)__shfl_sync(IBGDA_FULL_WARP, (uintptr_t)qp, 0); + is_qp_shared_among_ctas = __shfl_sync(IBGDA_FULL_WARP, is_qp_shared_among_ctas, 0); + ibgda_get_raddr_rkey((uint64_t)rptr, dst_pe, proxy_pe, &raddr, &rkey, &rchunk_size, + qp->dev_idx); + + __match_all_sync(IBGDA_FULL_WARP, (uintptr_t)(rptr) - (my_tid * sizeof(T)), + &pred_contiguous); + __match_all_sync(IBGDA_FULL_WARP, rkey, &pred_rkey); + can_combine_data = (pred_contiguous && pred_rkey); + } else { + my_tid = nvshmemi_thread_id_in_threadgroup(); + tg_size = nvshmemi_threadgroup_size(); + qp = ibgda_get_qp(proxy_pe, (bool *)&is_qp_shared_among_ctas); + ibgda_get_raddr_rkey((uint64_t)rptr, dst_pe, proxy_pe, &raddr, &rkey, &rchunk_size, + qp->dev_idx); + } + dct = ibgda_get_dct(proxy_pe, qp->dev_idx); + + const bool need_additional_wqe = + need_cst || ((qp->qp_type == NVSHMEMI_IBGDA_DEVICE_QP_TYPE_DCI) && !support_half_av_seg); + + int num_wqes_per_cmd = + (qp->qp_type == NVSHMEMI_IBGDA_DEVICE_QP_TYPE_DCI) ? (support_half_av_seg ? 1 : 2) : 1; + + int num_wqes = (can_combine_data ? num_wqes_per_cmd : num_wqes_per_cmd * tg_size) + + (need_additional_wqe ? 1 : 0); + + int num_ibuf_slots = can_coalesce_warp ? 1 : tg_size; + + if (my_tid == 0) { + base_ibuf_idx = ibgda_reserve_ibuf_slots(qp, num_ibuf_slots); + base_wqe_idx = ibgda_reserve_wqe_slots(qp, num_wqes, is_qp_shared_among_ctas); + } + + if (can_coalesce_warp) { + base_wqe_idx = __shfl_sync(amask, base_wqe_idx, 0); + base_ibuf_idx = __shfl_sync(amask, base_ibuf_idx, 0); + } + + uint64_t my_wqe_idx = + can_combine_data ? base_wqe_idx : base_wqe_idx + (my_tid * num_wqes_per_cmd); + uint64_t my_ibuf_idx = can_coalesce_warp ? base_ibuf_idx : base_ibuf_idx + my_tid; + + void *wqe_ptrs[2]; + wqe_ptrs[0] = ibgda_get_wqe_ptr(qp, my_wqe_idx); + wqe_ptrs[1] = ibgda_get_wqe_ptr(qp, my_wqe_idx + 1); + + uint64_t laddr = + ibgda_get_ibuf_addr(qp, my_ibuf_idx) + (can_coalesce_warp ? my_tid * sizeof(T) : 0); + __be32 lkey = qp->ibuf.lkey; + + // Generate CQE only if we create the last WQE in the group. + uint8_t fm_ce_se = (!need_additional_wqe && ((can_combine_data && (my_tid == 0)) || + (!can_combine_data && (my_tid == tg_size - 1)))) + ? MLX5_WQE_CTRL_CQ_UPDATE + : 0; + + if (!can_combine_data) { + ibgda_write_rdma_read_wqe(qp, dct, laddr, lkey, raddr, rkey, sizeof(T), + my_wqe_idx, fm_ce_se, wqe_ptrs); + + } else if (my_tid == 0) { + ibgda_write_rdma_read_wqe( + qp, dct, laddr, lkey, raddr, rkey, sizeof(T) * tg_size, my_wqe_idx, fm_ce_se, wqe_ptrs); + } + + if (can_coalesce_warp) nvshmemi_warp_sync(); + + if (need_additional_wqe && (my_tid == (tg_size - 1))) { + my_wqe_idx += num_wqes_per_cmd; + wqe_ptrs[0] = ibgda_get_wqe_ptr(qp, my_wqe_idx); + fm_ce_se = MLX5_WQE_CTRL_CQ_UPDATE; + + if (need_cst) + // Enqueue CST op in the QP. This command has NIC Fence, which + // waits for all prior READ/ATOMIC to finish before issuing this + // DUMP. + ibgda_write_dump_wqe(qp, (uint64_t)qp->ibuf.buf, qp->ibuf.lkey, sizeof(char), + my_wqe_idx, IBGDA_MLX5_FM_FENCE, wqe_ptrs); + else + ibgda_write_nop_wqe(qp, my_wqe_idx, wqe_ptrs); + } + if (fm_ce_se > 0) { + if (is_qp_shared_among_ctas) + ibgda_submit_requests(qp, base_wqe_idx, num_wqes); + else + ibgda_submit_requests(qp, base_wqe_idx, num_wqes); + + ibgda_quiet(qp); + } + + if (can_coalesce_warp) nvshmemi_warp_sync(); + + ret = READ_ONCE(*(T *)laddr); + + if (can_coalesce_warp) nvshmemi_warp_sync(); + + if (my_tid == tg_size - 1) ibgda_release_ibuf(qp, base_ibuf_idx, num_ibuf_slots); + + if (can_coalesce_warp) nvshmemi_warp_sync(); + + return ret; +} + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE T nvshmemi_ibgda_rma_g(void *rptr, int dst_pe) { + T ret; + nvshmemi_ibgda_device_state_t *state = ibgda_get_state(); + + int proxy_pe = ibgda_get_proxy_pe(dst_pe); + + if (state->support_half_av_seg) + ret = nvshmemi_ibgda_rma_g_impl(rptr, dst_pe, proxy_pe); + else + ret = nvshmemi_ibgda_rma_g_impl(rptr, dst_pe, proxy_pe); + return ret; +} + +/** + * RMA NBI base + */ +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_ibgda_rma_nbi(void *rptr, void *lptr, + size_t bytes, int dst_pe) { + nvshmemi_ibgda_device_state_t *state = ibgda_get_state(); + int proxy_pe = ibgda_get_proxy_pe(dst_pe); + if (SCOPE == NVSHMEMI_THREADGROUP_THREAD) { + if (state->support_half_av_seg) { + ibgda_rma_thread((uint64_t)rptr, (uint64_t)lptr, bytes, dst_pe, + proxy_pe); + } else { + ibgda_rma_thread((uint64_t)rptr, (uint64_t)lptr, bytes, dst_pe, + proxy_pe); + } + } else { + if (state->support_half_av_seg) { + ibgda_rma((uint64_t)rptr, (uint64_t)lptr, bytes, dst_pe, + proxy_pe); + } else { + ibgda_rma((uint64_t)rptr, (uint64_t)lptr, bytes, dst_pe, + proxy_pe); + } + } +} + +/** + * RMA (blocking) base + */ +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_ibgda_rma(void *rptr, void *lptr, + size_t bytes, int dst_pe) { + nvshmemi_ibgda_device_state_t *state = ibgda_get_state(); + int proxy_pe = ibgda_get_proxy_pe(dst_pe); + if (SCOPE == NVSHMEMI_THREADGROUP_THREAD) { + if (state->support_half_av_seg) { + ibgda_rma_thread((uint64_t)rptr, (uint64_t)lptr, bytes, dst_pe, + proxy_pe); + } else { + ibgda_rma_thread((uint64_t)rptr, (uint64_t)lptr, bytes, + dst_pe, proxy_pe); + } + } else { + if (state->support_half_av_seg) { + ibgda_rma((uint64_t)rptr, (uint64_t)lptr, bytes, dst_pe, + proxy_pe); + } else { + ibgda_rma((uint64_t)rptr, (uint64_t)lptr, bytes, + dst_pe, proxy_pe); + } + } +} + +/** + * AMO non-fetch base + */ +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_ibgda_amo_nonfetch_impl(void *rptr, + const T value, + int pe, + nvshmemi_amo_t op) { + unsigned int amask = __activemask(); + int my_tid; + int tg_size; + + int is_qp_shared_among_ctas; + nvshmemi_ibgda_device_qp_t *qp; + nvshmemi_ibgda_device_dct_t *dct; + + __be32 rkey; + uint64_t raddr; + size_t rchunk_size; + + bool can_coalesce_warp = ibgda_can_coalesce_warp_pe(amask, pe); + + if (can_coalesce_warp) { + my_tid = nvshmemi_thread_id_in_threadgroup(); + tg_size = nvshmemi_threadgroup_size(); + if (my_tid == 0) { + qp = ibgda_get_qp(pe, (bool *)&is_qp_shared_among_ctas); + } + qp = (nvshmemi_ibgda_device_qp_t *)__shfl_sync(IBGDA_FULL_WARP, (uintptr_t)qp, 0); + is_qp_shared_among_ctas = __shfl_sync(IBGDA_FULL_WARP, is_qp_shared_among_ctas, 0); + } else { + my_tid = nvshmemi_thread_id_in_threadgroup(); + tg_size = nvshmemi_threadgroup_size(); + qp = ibgda_get_qp(pe, (bool *)&is_qp_shared_among_ctas); + } + dct = ibgda_get_dct(pe, qp->dev_idx); + ibgda_get_raddr_rkey((uint64_t)rptr, pe, pe, &raddr, &rkey, &rchunk_size, qp->dev_idx); + + int num_wqes_per_cmd = ibgda_get_num_wqes_in_atomic(op, qp->qp_type); + + const bool need_additional_wqe = (num_wqes_per_cmd > 1); + + int num_wqes = num_wqes_per_cmd * tg_size + (need_additional_wqe ? 1 : 0); + + uint64_t base_wqe_idx; + + if (my_tid == 0) base_wqe_idx = ibgda_reserve_wqe_slots(qp, num_wqes, is_qp_shared_among_ctas); + + if (can_coalesce_warp) base_wqe_idx = __shfl_sync(amask, base_wqe_idx, 0); + + uint64_t my_wqe_idx = base_wqe_idx + (my_tid * num_wqes_per_cmd); + + void *wqe_ptrs[2]; + wqe_ptrs[0] = ibgda_get_wqe_ptr(qp, my_wqe_idx); + wqe_ptrs[1] = ibgda_get_wqe_ptr(qp, my_wqe_idx + 1); + + uint8_t fm_ce_se = + (!need_additional_wqe && (my_tid == tg_size - 1)) ? MLX5_WQE_CTRL_CQ_UPDATE : 0; + + ibgda_write_atomic_wqe(qp, dct, &value, NULL, (uint64_t)qp->ibuf.buf, + qp->ibuf.lkey, raddr, rkey, sizeof(T), my_wqe_idx, + op, fm_ce_se, wqe_ptrs); + + if (can_coalesce_warp) nvshmemi_warp_sync(); + + if (my_tid == tg_size - 1) { + if (need_additional_wqe) { + my_wqe_idx += num_wqes_per_cmd; + wqe_ptrs[0] = ibgda_get_wqe_ptr(qp, my_wqe_idx); + ibgda_write_nop_wqe(qp, my_wqe_idx, wqe_ptrs); + } + + if (is_qp_shared_among_ctas) + ibgda_submit_requests(qp, base_wqe_idx, num_wqes); + else + ibgda_submit_requests(qp, base_wqe_idx, num_wqes); + } + + if (can_coalesce_warp) nvshmemi_warp_sync(); +} + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_ibgda_amo_nonfetch(void *rptr, const T value, + int pe, + nvshmemi_amo_t op) { + nvshmemi_ibgda_device_state_t *state = ibgda_get_state(); + + if (state->support_half_av_seg) + nvshmemi_ibgda_amo_nonfetch_impl(rptr, value, pe, op); + else + nvshmemi_ibgda_amo_nonfetch_impl(rptr, value, pe, op); +} + +/** + * AMO fetch base + */ +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE T nvshmemi_ibgda_amo_fetch_impl(void *rptr, const T value, + const T compare, int pe, + nvshmemi_amo_t op) { + unsigned int amask = __activemask(); + int my_tid; + int tg_size; + + nvshmemi_ibgda_device_state_t *state = ibgda_get_state(); + const bool need_cst = !state->may_skip_cst; + + T ret; + + int is_qp_shared_among_ctas; + nvshmemi_ibgda_device_qp_t *qp; + nvshmemi_ibgda_device_dct_t *dct; + + __be32 rkey; + uint64_t raddr; + size_t rchunk_size; + + bool can_coalesce_warp = ibgda_can_coalesce_warp_pe(amask, pe); + + if (can_coalesce_warp) { + my_tid = nvshmemi_thread_id_in_threadgroup(); + tg_size = nvshmemi_threadgroup_size(); + if (my_tid == 0) { + qp = ibgda_get_qp(pe, (bool *)&is_qp_shared_among_ctas); + } + qp = (nvshmemi_ibgda_device_qp_t *)__shfl_sync(IBGDA_FULL_WARP, (uintptr_t)qp, 0); + is_qp_shared_among_ctas = __shfl_sync(IBGDA_FULL_WARP, is_qp_shared_among_ctas, 0); + } else { + my_tid = nvshmemi_thread_id_in_threadgroup(); + tg_size = nvshmemi_threadgroup_size(); + qp = ibgda_get_qp(pe, (bool *)&is_qp_shared_among_ctas); + } + dct = ibgda_get_dct(pe, qp->dev_idx); + ibgda_get_raddr_rkey((uint64_t)rptr, pe, pe, &raddr, &rkey, &rchunk_size, qp->dev_idx); + + int num_wqes_per_cmd = ibgda_get_num_wqes_in_atomic(op, qp->qp_type); + + const bool need_additional_wqe = (num_wqes_per_cmd > 1) || need_cst; + + int num_wqes = num_wqes_per_cmd * tg_size + (need_additional_wqe ? 1 : 0); + + uint64_t base_wqe_idx; + uint64_t base_ibuf_idx; + + if (my_tid == 0) { + base_ibuf_idx = ibgda_reserve_ibuf_slots(qp, tg_size); + base_wqe_idx = ibgda_reserve_wqe_slots(qp, num_wqes, is_qp_shared_among_ctas); + } + + if (can_coalesce_warp) { + base_wqe_idx = __shfl_sync(amask, base_wqe_idx, 0); + base_ibuf_idx = __shfl_sync(amask, base_ibuf_idx, 0); + } + + uint64_t my_wqe_idx = base_wqe_idx + (my_tid * num_wqes_per_cmd); + uint64_t my_ibuf_idx = base_ibuf_idx + my_tid; + + uint64_t laddr = ibgda_get_ibuf_addr(qp, my_ibuf_idx); + __be32 lkey = qp->ibuf.lkey; + + void *wqe_ptrs[2]; + wqe_ptrs[0] = ibgda_get_wqe_ptr(qp, my_wqe_idx); + wqe_ptrs[1] = ibgda_get_wqe_ptr(qp, my_wqe_idx + 1); + + uint8_t fm_ce_se = + (!need_additional_wqe && (my_tid == tg_size - 1)) ? MLX5_WQE_CTRL_CQ_UPDATE : 0; + + ibgda_write_atomic_wqe(qp, dct, &value, &compare, laddr, lkey, raddr, rkey, + sizeof(T), my_wqe_idx, op, fm_ce_se, wqe_ptrs); + + if (can_coalesce_warp) nvshmemi_warp_sync(); + + if (my_tid == tg_size - 1) { + if (need_additional_wqe) { + my_wqe_idx += num_wqes_per_cmd; + wqe_ptrs[0] = ibgda_get_wqe_ptr(qp, my_wqe_idx); + + if (need_cst) + // Enqueue CST op in the QP. This command has NIC Fence, which + // waits for all prior READ/ATOMIC to finish before issuing this + // DUMP. + ibgda_write_dump_wqe(qp, (uint64_t)qp->ibuf.buf, qp->ibuf.lkey, sizeof(char), + my_wqe_idx, IBGDA_MLX5_FM_FENCE, wqe_ptrs); + else + ibgda_write_nop_wqe(qp, my_wqe_idx, wqe_ptrs); + } + + if (is_qp_shared_among_ctas) + ibgda_submit_requests(qp, base_wqe_idx, num_wqes); + else + ibgda_submit_requests(qp, base_wqe_idx, num_wqes); + + ibgda_quiet(qp); + } + + if (can_coalesce_warp) nvshmemi_warp_sync(); + + ret = READ_ONCE(*(T *)laddr); + if (sizeof(T) == 4) ret = BSWAP32((uint32_t)ret); + + if (can_coalesce_warp) nvshmemi_warp_sync(); + + if (my_tid == tg_size - 1) ibgda_release_ibuf(qp, base_ibuf_idx, tg_size); + + if (can_coalesce_warp) nvshmemi_warp_sync(); + + return ret; +} + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE T nvshmemi_ibgda_amo_fetch(void *rptr, const T value, + const T compare, int pe, + nvshmemi_amo_t op) { + T ret; + nvshmemi_ibgda_device_state_t *state = ibgda_get_state(); + + if (state->support_half_av_seg) + ret = nvshmemi_ibgda_amo_fetch_impl(rptr, value, compare, pe, op); + else + ret = nvshmemi_ibgda_amo_fetch_impl(rptr, value, compare, pe, op); + return ret; +} + +#if __cplusplus >= 201103L +static_assert(NVSHMEMI_IBGDA_MIN_QP_DEPTH >= 128, + "static_assert(NVSHMEMI_IBGDA_MIN_QP_DEPTH >= 128) failed"); +#endif +template +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_ibgda_put_signal_thread_impl( + void *rptr, void *lptr, size_t bytes, void *sig_rptr, uint64_t signal, nvshmemi_amo_t sig_op, + int pe) { + nvshmemi_ibgda_device_state_t *state = ibgda_get_state(); + nvshmemi_ibgda_device_qp_t *qp; + nvshmemi_ibgda_device_dct_t *dct; + size_t lchunk_size; + size_t rchunk_size; + size_t sig_rchunk_size; + uint64_t sig_raddr; + uint64_t raddr; + + unsigned int amask = __activemask(); + int my_tid; + int tg_size; + __be32 lkey; + __be32 rkey; + __be32 sig_rkey; + + bool can_coalesce_warp = ibgda_can_coalesce_warp_pe(amask, pe); + int is_qp_shared_among_ctas; + bool is_data_buf_in_sysmem; + + if (can_coalesce_warp) { + my_tid = nvshmemi_thread_id_in_threadgroup(); + tg_size = nvshmemi_threadgroup_size(); + if (my_tid == 0) { + qp = ibgda_get_qp(pe, (bool *)&is_qp_shared_among_ctas); + } + qp = (nvshmemi_ibgda_device_qp_t *)__shfl_sync(IBGDA_FULL_WARP, (uintptr_t)qp, 0); + is_qp_shared_among_ctas = __shfl_sync(IBGDA_FULL_WARP, is_qp_shared_among_ctas, 0); + } else { + my_tid = nvshmemi_thread_id_in_threadgroup(); + tg_size = nvshmemi_threadgroup_size(); + qp = ibgda_get_qp(pe, (bool *)&is_qp_shared_among_ctas); + } + dct = ibgda_get_dct(pe, qp->dev_idx); + ibgda_get_lkey((uint64_t)lptr, &lkey, &lchunk_size, &is_data_buf_in_sysmem, qp->dev_idx); + ibgda_get_raddr_rkey((uint64_t)rptr, pe, pe, &raddr, &rkey, &rchunk_size, qp->dev_idx); + ibgda_get_raddr_rkey((uint64_t)sig_rptr, pe, pe, &sig_raddr, &sig_rkey, &sig_rchunk_size, + qp->dev_idx); + + const int num_atomic_wqes_per_cmd = ibgda_get_num_wqes_in_atomic(sig_op, qp->qp_type); + const bool need_additional_wqe = (num_atomic_wqes_per_cmd > 1); + int num_wqes; + uint8_t fm_ce_se; + + size_t transfer_size = ibgda_cal_transfer_size(bytes, lchunk_size, rchunk_size); + uint64_t base_wqe_idx; + uint64_t my_wqe_idx; + + if (transfer_size == bytes) { + amask = __activemask(); + can_coalesce_warp = ibgda_can_coalesce_warp(amask, qp); + if (can_coalesce_warp) { + my_tid = nvshmemi_thread_id_in_threadgroup(); + tg_size = nvshmemi_threadgroup_size(); + } else { + my_tid = nvshmemi_thread_id_in_threadgroup(); + tg_size = nvshmemi_threadgroup_size(); + } + + int num_rdma_write_wqes_per_cmd = + (qp->qp_type == NVSHMEMI_IBGDA_DEVICE_QP_TYPE_DCI) ? (support_half_av_seg ? 1 : 2) : 1; + + int num_wqes_per_cmd = num_rdma_write_wqes_per_cmd + num_atomic_wqes_per_cmd; + num_wqes = num_wqes_per_cmd * tg_size + (need_additional_wqe ? 1 : 0); + + if (my_tid == 0) { + base_wqe_idx = ibgda_reserve_wqe_slots(qp, num_wqes, is_qp_shared_among_ctas); + } + + if (can_coalesce_warp) { + base_wqe_idx = __shfl_sync(amask, base_wqe_idx, 0); + } + + my_wqe_idx = base_wqe_idx + (my_tid * num_wqes_per_cmd); + + void *wqe_ptrs[4]; + wqe_ptrs[0] = ibgda_get_wqe_ptr(qp, my_wqe_idx); + wqe_ptrs[1] = ibgda_get_wqe_ptr(qp, my_wqe_idx + 1); + wqe_ptrs[2] = ibgda_get_wqe_ptr(qp, my_wqe_idx + 2); + wqe_ptrs[3] = ibgda_get_wqe_ptr(qp, my_wqe_idx + 3); + + ibgda_write_rdma_write_wqe(qp, dct, (uint64_t)lptr, lkey, raddr, rkey, + bytes, my_wqe_idx, 0, wqe_ptrs); + + fm_ce_se = (!need_additional_wqe && (my_tid == tg_size - 1)) ? MLX5_WQE_CTRL_CQ_UPDATE : 0; + + ibgda_write_atomic_wqe( + qp, dct, &signal, NULL, (uint64_t)qp->ibuf.buf, qp->ibuf.lkey, sig_raddr, sig_rkey, + sizeof(signal), my_wqe_idx + num_rdma_write_wqes_per_cmd, sig_op, fm_ce_se, + &wqe_ptrs[num_rdma_write_wqes_per_cmd]); + + if (can_coalesce_warp) { + nvshmemi_warp_sync(); + } + + if (my_tid == tg_size - 1) { + if (need_additional_wqe) { + my_wqe_idx += num_wqes_per_cmd; + wqe_ptrs[0] = ibgda_get_wqe_ptr(qp, my_wqe_idx); + ibgda_write_nop_wqe(qp, my_wqe_idx, wqe_ptrs); + } + + // Require membar.sys to push data buffer to the point of consistency. + if (is_data_buf_in_sysmem) __threadfence_system(); + if (is_qp_shared_among_ctas) + ibgda_submit_requests(qp, base_wqe_idx, num_wqes); + else + ibgda_submit_requests(qp, base_wqe_idx, num_wqes); + + if (!is_nbi) { + ibgda_quiet(qp); + } + } + + if (can_coalesce_warp) { + nvshmemi_warp_sync(); + } + } else { + ibgda_rma_thread( + (uintptr_t)rptr, (uintptr_t)lptr, bytes, pe, pe); + + num_wqes = num_atomic_wqes_per_cmd + (need_additional_wqe ? 1 : 0); + + base_wqe_idx = ibgda_reserve_wqe_slots(qp, num_wqes, is_qp_shared_among_ctas); + my_wqe_idx = base_wqe_idx; + + void *wqe_ptrs[2]; + wqe_ptrs[0] = ibgda_get_wqe_ptr(qp, my_wqe_idx); + wqe_ptrs[1] = ibgda_get_wqe_ptr(qp, my_wqe_idx + 1); + + fm_ce_se = (!need_additional_wqe) ? MLX5_WQE_CTRL_CQ_UPDATE : 0; + + ibgda_write_atomic_wqe( + qp, dct, &signal, NULL, (uint64_t)qp->ibuf.buf, qp->ibuf.lkey, sig_raddr, sig_rkey, + sizeof(signal), my_wqe_idx, sig_op, fm_ce_se, wqe_ptrs); + + if (need_additional_wqe) { + my_wqe_idx += num_atomic_wqes_per_cmd; + wqe_ptrs[0] = ibgda_get_wqe_ptr(qp, my_wqe_idx); + ibgda_write_nop_wqe(qp, my_wqe_idx, wqe_ptrs); + } + + if (is_qp_shared_among_ctas) + ibgda_submit_requests(qp, base_wqe_idx, num_wqes); + else + ibgda_submit_requests(qp, base_wqe_idx, num_wqes); + + if (!is_nbi) { + ibgda_quiet(qp); + } + } +} + +/** + * PUT SIGNAL base + */ +#if __cplusplus >= 201103L +static_assert(NVSHMEMI_IBGDA_MIN_QP_DEPTH >= 64, + "static_assert(NVSHMEMI_IBGDA_MIN_QP_DEPTH >= 64) failed"); +#endif +template +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_ibgda_put_signal_impl( + void *req_rptr, void *req_lptr, size_t bytes, void *sig_rptr, uint64_t signal, + nvshmemi_amo_t sig_op, int pe) { + assert(SCOPE == NVSHMEMI_THREADGROUP_WARP || SCOPE == NVSHMEMI_THREADGROUP_BLOCK); + + // Use only wrap 0 + int my_tid = nvshmemi_thread_id_in_threadgroup(); + int tg_size = nvshmemi_threadgroup_size(); + nvshmemi_ibgda_device_state_t *state = ibgda_get_state(); + + int is_qp_shared_among_ctas; + nvshmemi_ibgda_device_qp_t *qp; + nvshmemi_ibgda_device_dct_t *dct; + + int num_rdma_write_wqes_per_cmd; + int num_atomic_wqes_per_cmd; + bool need_additional_wqe; + + int num_wqes; + + uint64_t base_wqe_idx; + uint64_t my_wqe_idx; + + void *wqe_ptrs[2]; + + size_t remaining_size = bytes; + + size_t transfer_size; + size_t my_transfer_size = 0; + + uint64_t rptr = (uint64_t)req_rptr; + uint64_t lptr = (uint64_t)req_lptr; + + __be32 lkey; + __be32 my_lkey = 0; + uint64_t my_laddr; + size_t lchunk_size; + + __be32 rkey; + __be32 my_rkey = 0; + uint64_t raddr; + uint64_t my_raddr; + size_t rchunk_size; + + int chunk_idx = 0; + + bool is_data_buf_in_sysmem; + + // Not warp 0, wait at the exit. + if (my_tid >= tg_size) { + goto out; + } + + my_tid = nvshmemi_thread_id_in_threadgroup(); + + if (my_tid == 0) { + qp = ibgda_get_qp(pe, (bool *)&is_qp_shared_among_ctas); + } + qp = (nvshmemi_ibgda_device_qp_t *)__shfl_sync(IBGDA_FULL_WARP, (uintptr_t)qp, 0); + is_qp_shared_among_ctas = __shfl_sync(IBGDA_FULL_WARP, is_qp_shared_among_ctas, 0); + dct = ibgda_get_dct(pe, qp->dev_idx); + + num_rdma_write_wqes_per_cmd = + (qp->qp_type == NVSHMEMI_IBGDA_DEVICE_QP_TYPE_DCI) ? (support_half_av_seg ? 1 : 2) : 1; + + num_atomic_wqes_per_cmd = ibgda_get_num_wqes_in_atomic(sig_op, qp->qp_type); + + need_additional_wqe = (num_atomic_wqes_per_cmd > 1); + + // Calculate how many chunks we need to send. + while (remaining_size > 0) { + ibgda_get_lkey(lptr, &lkey, &lchunk_size, &is_data_buf_in_sysmem, qp->dev_idx); + ibgda_get_raddr_rkey(rptr, pe, pe, &raddr, &rkey, &rchunk_size, qp->dev_idx); + transfer_size = ibgda_cal_transfer_size(remaining_size, lchunk_size, rchunk_size); + if (my_tid == chunk_idx) { + my_lkey = lkey; + my_laddr = lptr; + my_rkey = rkey; + my_raddr = raddr; + my_transfer_size = transfer_size; + } + + remaining_size -= transfer_size; + rptr += transfer_size; + lptr += transfer_size; + + ++chunk_idx; + } + + // Too many chunks. Use nvshmemi_ibgda_put_signal_thread_impl to handle it instead. + // Note that we need one thread to handle amo. + if (unlikely(chunk_idx > tg_size - 1)) { + if (my_tid == 0) { + nvshmemi_ibgda_put_signal_thread_impl( + req_rptr, req_lptr, bytes, sig_rptr, signal, sig_op, pe); + } + goto out; + } + + num_wqes = num_rdma_write_wqes_per_cmd * chunk_idx + num_atomic_wqes_per_cmd + + (need_additional_wqe ? 1 : 0); + + if (my_tid == 0) { + base_wqe_idx = ibgda_reserve_wqe_slots(qp, num_wqes, is_qp_shared_among_ctas); + } + + base_wqe_idx = __shfl_sync(IBGDA_FULL_WARP, base_wqe_idx, 0); + my_wqe_idx = base_wqe_idx + (my_tid * num_rdma_write_wqes_per_cmd); + + wqe_ptrs[0] = ibgda_get_wqe_ptr(qp, my_wqe_idx); + wqe_ptrs[1] = ibgda_get_wqe_ptr(qp, my_wqe_idx + 1); + + if (my_tid < chunk_idx) { + ibgda_write_rdma_write_wqe(qp, dct, my_laddr, my_lkey, my_raddr, + my_rkey, my_transfer_size, my_wqe_idx, 0, + wqe_ptrs); + } else if (my_tid == chunk_idx) { + __be32 sig_rkey; + uint64_t sig_raddr; + size_t sig_rchunk_size; + ibgda_get_raddr_rkey((uint64_t)sig_rptr, pe, pe, &sig_raddr, &sig_rkey, &sig_rchunk_size, + qp->dev_idx); + + uint8_t fm_ce_se = (!need_additional_wqe) ? MLX5_WQE_CTRL_CQ_UPDATE : 0; + + ibgda_write_atomic_wqe( + qp, dct, &signal, NULL, (uint64_t)qp->ibuf.buf, qp->ibuf.lkey, sig_raddr, sig_rkey, + sizeof(signal), my_wqe_idx, sig_op, fm_ce_se, wqe_ptrs); + + if (need_additional_wqe) { + my_wqe_idx += num_atomic_wqes_per_cmd; + wqe_ptrs[0] = ibgda_get_wqe_ptr(qp, my_wqe_idx); + ibgda_write_nop_wqe(qp, my_wqe_idx, wqe_ptrs); + } + } + + nvshmemi_warp_sync(); + + if (my_tid == chunk_idx) { + // Require membar.sys to push data buffer to the point of consistency. + if (is_data_buf_in_sysmem) __threadfence_system(); + + if (is_qp_shared_among_ctas) + ibgda_submit_requests(qp, base_wqe_idx, num_wqes); + else + ibgda_submit_requests(qp, base_wqe_idx, num_wqes); + + if (!is_nbi) { + ibgda_quiet(qp); + } + } + +out: + nvshmemi_threadgroup_sync(); +} + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_ibgda_put_signal( + void *rptr, void *lptr, size_t bytes, void *sig_rptr, uint64_t signal, nvshmemi_amo_t sig_op, + int pe, bool is_nbi) { + nvshmemi_ibgda_device_state_t *state = ibgda_get_state(); + if (SCOPE == NVSHMEMI_THREADGROUP_THREAD) { + if (is_nbi && state->support_half_av_seg) + nvshmemi_ibgda_put_signal_thread_impl(rptr, lptr, bytes, sig_rptr, signal, + sig_op, pe); + else if (is_nbi && !state->support_half_av_seg) + nvshmemi_ibgda_put_signal_thread_impl(rptr, lptr, bytes, sig_rptr, signal, + sig_op, pe); + else if (!is_nbi && state->support_half_av_seg) + nvshmemi_ibgda_put_signal_thread_impl(rptr, lptr, bytes, sig_rptr, signal, + sig_op, pe); + else + nvshmemi_ibgda_put_signal_thread_impl(rptr, lptr, bytes, sig_rptr, signal, + sig_op, pe); + } else { + if (is_nbi && state->support_half_av_seg) + nvshmemi_ibgda_put_signal_impl(rptr, lptr, bytes, sig_rptr, signal, + sig_op, pe); + else if (is_nbi && !state->support_half_av_seg) + nvshmemi_ibgda_put_signal_impl(rptr, lptr, bytes, sig_rptr, signal, + sig_op, pe); + else if (!is_nbi && state->support_half_av_seg) + nvshmemi_ibgda_put_signal_impl(rptr, lptr, bytes, sig_rptr, signal, + sig_op, pe); + else + nvshmemi_ibgda_put_signal_impl(rptr, lptr, bytes, sig_rptr, signal, + sig_op, pe); + } +} + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_ibgda_quiet() { + nvshmemi_ibgda_device_state_t *state = ibgda_get_state(); + nvshmemi_ibgda_device_qp_t *qp; + uint32_t ndcis = state->num_shared_dcis + state->num_exclusive_dcis; + uint32_t nrcs = + state->num_rc_per_pe * nvshmemi_device_state_d.npes * state->num_devices_initialized; + uint32_t index_in_scope = nvshmemi_thread_id_in_threadgroup(); + uint32_t scope_size = nvshmemi_threadgroup_size(); + + scope_size = + scope_size > IBGDA_MAX_THREADS_PER_QUIET ? IBGDA_MAX_THREADS_PER_QUIET : scope_size; + + if (index_in_scope < scope_size) { + for (uint32_t i = index_in_scope; i < ndcis; i += scope_size) { + qp = &state->globalmem.dcis[i]; + ibgda_quiet_with_cst(qp, true); + } + + for (uint32_t i = index_in_scope; i < nrcs; i += scope_size) { + if (i / (state->num_rc_per_pe * state->num_devices_initialized) == + nvshmemi_device_state_d.mype) { + continue; + } + qp = &state->globalmem.rcs[i]; + ibgda_quiet_with_cst(qp, true); + } + } +} + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_ibgda_fence() { + // Multiple QPs may target the same PE before fence. + // We need to quiet those QPs. + // TODO: Make it more efficient. + nvshmemi_ibgda_device_state_t *state = ibgda_get_state(); + uint32_t ndcis = state->num_shared_dcis + state->num_exclusive_dcis; + uint32_t index_in_scope = nvshmemi_thread_id_in_threadgroup(); + uint32_t scope_size = nvshmemi_threadgroup_size(); + uint32_t nrcs = state->num_rc_per_pe * nvshmemi_device_state_d.npes; + nvshmemi_ibgda_device_qp_t *qp; + + // As all WQEs always go to the same QP, FENCE is naturally guaranteed. + if (unlikely(ndcis + nrcs <= 1)) return; + + scope_size = + scope_size > IBGDA_MAX_THREADS_PER_QUIET ? IBGDA_MAX_THREADS_PER_QUIET : scope_size; + + // Fence does not guarantee the completion of prior operations. + // It is ok for GET to finish without data arrival. + // Use ibgda_quiet here instead of ibgda_quiet_with_cst since it is cheaper. + if (index_in_scope < scope_size) { + for (uint32_t i = index_in_scope; i < ndcis; i += scope_size) { + qp = &state->globalmem.dcis[i]; + ibgda_quiet(qp); + } + + for (uint32_t i = index_in_scope; i < nrcs; i += scope_size) { + if (i / state->num_rc_per_pe == nvshmemi_device_state_d.mype) continue; + qp = &state->globalmem.rcs[i]; + ibgda_quiet(qp); + } + } + + nvshmemi_threadgroup_sync(); +} + +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_ibgda_enforce_consistency_at_target( + bool use_membar) { + nvshmemi_ibgda_device_state_t *state = ibgda_get_state(); + + if (!state->may_skip_cst) { + bool is_dci_shared_among_ctas; + // We don't have RC loopback to self. + // So, DCI is always used here. + nvshmemi_ibgda_device_qp_t *dci; + + /* We must run the cst op on all devices */ + for (int i = 0; i < state->num_devices_initialized; i++) { + dci = ibgda_get_dci(nvshmemi_device_state_d.mype, &is_dci_shared_among_ctas); + ibgda_cst(dci, is_dci_shared_among_ctas); + } + } + + // TODO: This fence is from the design of Proxy. + // Review if we still need it when we fully move to IBGDA -- especially for on-stream API. + if (use_membar) { + __threadfence_system(); // XXX: prevents store to issue_d reordered to before load from + // cst_ack_d (breaks cst -> rma) + } +} + +#endif /* __CUDA_ARCH__ */ + +#endif /* _NVSHMEMI_IBGDA_DEVICE_H_ */ diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/device/pt-to-pt/nvshmemi_transfer_api.cuh b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/device/pt-to-pt/nvshmemi_transfer_api.cuh new file mode 100644 index 0000000000000000000000000000000000000000..d88c5d0143664362ef36755fae2178fc95cc3901 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/device/pt-to-pt/nvshmemi_transfer_api.cuh @@ -0,0 +1,42 @@ +/* + * Copyright (c) 2016-2025, NVIDIA CORPORATION. All rights reserved. + * + * See License.txt for license information + */ + +#include +#include "non_abi/device/threadgroup/nvshmemi_common_device_defines.cuh" + +#ifndef _NVSHMEMI_TRANSFER_H_ +#define _NVSHMEMI_TRANSFER_H_ +template +__device__ void nvshmemi_transfer_rma_p(void *rptr, const T value, int pe); + +template +__device__ T nvshmemi_transfer_rma_g(void *rptr, int pe); + +template +__device__ void nvshmemi_transfer_rma(void *rptr, void *lptr, size_t bytes, int pe); + +template +__device__ void nvshmemi_transfer_put_signal(void *rptr, void *lptr, size_t bytes, void *sig_addr, + uint64_t signal, nvshmemi_amo_t sig_op, int pe, + bool is_nbi); + +template +__device__ void nvshmemi_transfer_rma_nbi(void *rptr, void *lptr, size_t bytes, int pe); + +template +__device__ T nvshmemi_transfer_amo_fetch(void *rptr, T value, T compare, int pe, nvshmemi_amo_t op); + +template +__device__ void nvshmemi_transfer_amo_nonfetch(void *rptr, T value, int pe, nvshmemi_amo_t op); + +template +__device__ void nvshmemi_transfer_quiet(bool use_membar); + +template +__device__ void nvshmemi_transfer_fence(); +__device__ void nvshmemi_transfer_enforce_consistency_at_target(bool use_membar); +__device__ void nvshmemi_transfer_syncapi_update_mem(); +#endif /* _NVSHMEMI_TRANSFER_H_ */ diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/device/pt-to-pt/proxy_device.cuh b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/device/pt-to-pt/proxy_device.cuh new file mode 100644 index 0000000000000000000000000000000000000000..184dac5bac2eb98d9cc7ff13526a8fdf0ffb6aa7 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/device/pt-to-pt/proxy_device.cuh @@ -0,0 +1,572 @@ +/**** + * Copyright (c) 2014, NVIDIA Corporation. All rights reserved. + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are met: + * + * * Redistributions of source code must retain the above copyright notice, + * this list of conditions and the following disclaimer. + * * Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * * Neither the name of the NVIDIA Corporation nor the names of its + * contributors may be used to endorse or promote products derived from + * this software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE + * ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE + * LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + * CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF + * SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS + * INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN + * CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) + * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF + * THE POSSIBILITY OF SUCH DAMAGE. + * + * + * The U.S. Department of Energy funded the development of this software + * under subcontract 7078610 with Lawrence Berkeley National Laboratory. + * + ****/ + +#ifndef PROXY_DEVICE_CUH +#define PROXY_DEVICE_CUH + +#include +#include "utils_device.h" +#include "non_abi/device/wait/nvshmemi_wait_until_apis.cuh" +/* this file does not directly use the definitions from device_host/nvshmem_proxy_channel.h */ +/* But the way the requests are filled in directly represents those structures. */ +#include "device_host/nvshmem_proxy_channel.h" // IWYU pragma: keep + +#ifdef __CUDA_ARCH__ +NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_FORCE_INLINE __device__ void check_channel_availability( + uint64_t tail_idx) { + uint64_t complete; + complete = *((volatile uint64_t *)nvshmemi_device_state_d.proxy_channels_complete_local_ptr); + if ((complete + nvshmemi_device_state_d.proxy_channel_buf_size - 1) < tail_idx) { + nvshmemi_wait_until_greater_than_equals_add( + nvshmemi_device_state_d.proxy_channels_complete, + nvshmemi_device_state_d.proxy_channel_buf_size - 1, tail_idx, + NVSHMEMI_CALL_SITE_PROXY_CHECK_CHANNEL_AVAILABILITY); + atomicMax( + (unsigned long long int *)nvshmemi_device_state_d.proxy_channels_complete_local_ptr, + *nvshmemi_device_state_d.proxy_channels_complete); + __threadfence_system(); // XXX: prevents store to buf_d reordered to before load from + // complete_d (breaks rma) + } +} + +NVSHMEMI_STATIC __device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_proxy_quiet( + bool use_membar) { + uint64_t quiet_issue; + quiet_issue = (*(volatile uint64_t *)nvshmemi_device_state_d.proxy_channels_issue); + atomicMax((unsigned long long int *)nvshmemi_device_state_d.proxy_channels_quiet_issue, + quiet_issue); + + nvshmemi_wait_until_greater_than_equals( + nvshmemi_device_state_d.proxy_channels_quiet_ack, quiet_issue, + NVSHMEMI_CALL_SITE_PROXY_QUIET); + if (use_membar) { + __threadfence_system(); // XXX: prevents store to issue_d reordered to before load from + // quiet_ack_d (breaks quiet -> rma) + } +} + +NVSHMEMI_STATIC __device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_proxy_global_exit( + int status) { + int rc; + + rc = atomicCAS(nvshmemi_device_state_d.global_exit_request_state, + PROXY_GLOBAL_EXIT_NOT_REQUESTED, PROXY_GLOBAL_EXIT_INIT); + if (rc == PROXY_GLOBAL_EXIT_NOT_REQUESTED) { + *nvshmemi_device_state_d.global_exit_code = status; + __threadfence_system(); + rc = atomicCAS(nvshmemi_device_state_d.global_exit_request_state, PROXY_GLOBAL_EXIT_INIT, + PROXY_GLOBAL_EXIT_REQUESTED); + assert(rc == PROXY_GLOBAL_EXIT_INIT); + } + /* Note, this will block indefinitely, but that is fine as nvshmemi_global_exit should never + * return. */ + long long int now, later; + asm volatile("mov.u64 %0, %%globaltimer;" : "=l"(now)); + do { + asm volatile("mov.u64 %0, %%globaltimer;" : "=l"(later)); + } while (later >= now); +} + +NVSHMEMI_STATIC __device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void +nvshmemi_proxy_enforce_consistency_at_target(bool use_membar) { + uint64_t cst_issue; + cst_issue = (*(volatile uint64_t *)nvshmemi_device_state_d.proxy_channels_issue); + atomicMax((unsigned long long int *)nvshmemi_device_state_d.proxy_channels_cst_issue, + cst_issue); + + nvshmemi_wait_until_greater_than_equals( + nvshmemi_device_state_d.proxy_channels_cst_ack, cst_issue, + NVSHMEMI_CALL_SITE_PROXY_ENFORCE_CONSISTENCY_AT_TARGET); + if (use_membar) { + __threadfence_system(); // XXX: prevents store to issue_d reordered to before load from + // cst_ack_d (breaks cst -> rma) + } +} + +NVSHMEMI_STATIC __device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void copy_to_channel(void *ptr, + uint32_t size) { + channel_bounce_buffer_t bounce; + uint64_t idx, tail_idx; + volatile uint64_t *channel_ptr; + char *src_ptr = (char *)ptr; + uint32_t size_with_flags; + uint32_t size_remaining; + + /* idx is an every increasing counter. Since it is 64 bit integer, practically + it will not overflow */ + size_with_flags = (size * 8) / 7; + if (size_with_flags % 8) { + size_with_flags += (8 - (size_with_flags % 8)); + } + idx = atomicAdd((unsigned long long int *)nvshmemi_device_state_d.proxy_channels_issue, + size_with_flags); + tail_idx = idx + (size_with_flags - 1); + + // flow-control + check_channel_availability(tail_idx); + + for (size_remaining = size; size_remaining > 7; idx += sizeof(uint64_t), size_remaining -= 7) { + channel_ptr = (volatile uint64_t *)((uint64_t)nvshmemi_device_state_d.proxy_channels_buf + + (idx & (CHANNEL_BUF_SIZE - 1))); + memcpy(&bounce.bytes[1], src_ptr, 7); + /* Note, the second to last bit being set denotes a continuation of the same request to the + * other side. */ + bounce.bytes[0] = + (char)(!((idx >> nvshmemi_device_state_d.proxy_channel_buf_logsize) & 1) | 0x10); + *channel_ptr = bounce.whole_buffer; + src_ptr += 7; + } + + channel_ptr = (volatile uint64_t *)((uint64_t)nvshmemi_device_state_d.proxy_channels_buf + + (idx & (CHANNEL_BUF_SIZE - 1))); + memcpy(&bounce.bytes[1], src_ptr, size_remaining); + bounce.bytes[0] = (char)!((idx >> nvshmemi_device_state_d.proxy_channel_buf_logsize) & 1); + *channel_ptr = bounce.whole_buffer; +} + +NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_FORCE_INLINE __device__ void transfer_dma( + void *rptr, void *lptr, size_t bytes, int pe, int channel_op) { + uint64_t idx, tail_idx, *req; + int size = PROXY_DMA_REQ_BYTES; + int group_size = 1; + void *buf_ptr = nvshmemi_device_state_d.proxy_channels_buf; + void *base_ptr = nvshmemi_device_state_d.heap_base; + const uint64_t mask_lowest_byte = 0xFFFFFFFFFFFFFF00u; + const uint64_t mask_upper_7_bytes = 0x00000000000000FFu; + + __threadfence(); + + /* idx is an every increasing counter. Since it is 64 bit integer, practically + it will not overflow */ + idx = atomicAdd((unsigned long long int *)nvshmemi_device_state_d.proxy_channels_issue, size); + tail_idx = idx + (size - 1); + + // flow-control + check_channel_availability(tail_idx); + + req = (uint64_t *)((uint8_t *)buf_ptr + (idx & (CHANNEL_BUF_SIZE - 1))); + uint64_t curr_flag = !((idx >> nvshmemi_device_state_d.proxy_channel_buf_logsize) & 1); + /* curr_flag is either 0 or 1. Starting at idx = 0 to idx = + * nvshmemi_device_state_d.proxy_channel_buf_size - 1, it will be 1, then for next + * nvshmemi_device_state_d.proxy_channel_buf_size idx values it will be 0, and so + * on. + */ + uint64_t roffset = (uint64_t)((char *)rptr - (char *)base_ptr); + uint64_t laddr = (uint64_t)lptr; + uint64_t op = channel_op; + uint16_t pe_u16 = pe; + uint64_t size_u64 = bytes; + + /* base_request_t + * 32 | 8 | 8 | 8 | 8 + * roffset_high | roffset_low | op | group_size | flag */ + *((volatile uint64_t *)req) = + (uint64_t)((roffset << 24) | (op << 16) | (group_size << 8) | curr_flag); + + /* put_dma_request_0 + * 56 | 8 + * laddr_high | flag */ + idx += CHANNEL_ENTRY_BYTES; + uint64_t laddr_high = laddr & mask_lowest_byte; + req = (uint64_t *)((uint8_t *)buf_ptr + (idx & (CHANNEL_BUF_SIZE - 1))); + curr_flag = !((idx >> nvshmemi_device_state_d.proxy_channel_buf_logsize) & 1); + *((volatile uint64_t *)req) = laddr_high | curr_flag; + + /* put_dma_request_1 + * 32 | 16 | 8 | 8 + * size_high | size_low | laddr_low | flag */ + idx += CHANNEL_ENTRY_BYTES; + uint64_t laddr_low = laddr & mask_upper_7_bytes; + req = (uint64_t *)((uint8_t *)buf_ptr + + (idx & (nvshmemi_device_state_d.proxy_channel_buf_size - 1))); + curr_flag = !((idx >> nvshmemi_device_state_d.proxy_channel_buf_logsize) & 1); + *((volatile uint64_t *)req) = (uint64_t)(size_u64 << 16 | laddr_low << 8 | curr_flag); + + /* put_dma_request_2 + * 32 | 16 | 8 | 8 + * resv2 | pe | resv1 | flag */ + idx += CHANNEL_ENTRY_BYTES; + req = (uint64_t *)((uint8_t *)buf_ptr + (idx & (CHANNEL_BUF_SIZE - 1))); + curr_flag = !((idx >> nvshmemi_device_state_d.proxy_channel_buf_logsize) & 1); + *((volatile uint64_t *)req) = (uint64_t)((pe_u16 << 16) | curr_flag); +} + +/*XXX : Only no const version is used*/ +template +NVSHMEMI_STATIC __device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_proxy_rma(void *rptr, + void *lptr, + size_t bytes, + int pe) { + assert(0); + /*XXX:to be used for 1) NVSHMEMI_DEVICE_ALWAYS_INLINE 2) DMA with ack 3) DMA by staging in + * another buffer*/ +} + +NVSHMEMI_STATIC __device__ NVSHMEMI_DEVICE_ALWAYS_FORCE_INLINE void nvshmemi_proxy_rma_nbi( + void *rptr, void *lptr, size_t bytes, int pe, nvshmemi_op_t op) { + if (!bytes) return; + transfer_dma(rptr, lptr, bytes, pe, op); +} + +template +NVSHMEMI_STATIC __device__ NVSHMEMI_DEVICE_ALWAYS_INLINE T nvshmemi_proxy_rma_g(void *source, + int pe) { +// check for CC >= 7.0 because the code depends on __match_all_sync +#if __CUDA_ARCH__ >= 700 + constexpr unsigned int full_warp = 0xffffffffu; + const unsigned int amask = __activemask(); + unsigned int laneId; + asm("mov.u32 %0, %%laneid;" : "=r"(laneId)); + int pred_pe = 0; + int pred_contigous = 0; + + // check if warp is coalesced, if all lanes put to the same PE and if buffer is contigous + if (full_warp == amask && full_warp == __match_all_sync(full_warp, pe, &pred_pe) && + full_warp == __match_all_sync(full_warp, + reinterpret_cast(reinterpret_cast(source) - + (laneId * sizeof(T))), + &pred_contigous)) { + assert(pred_pe && pred_contigous); + + g_elem_t *elem = nullptr; + int coalescing_buf_byte_offset = -1; + if (0 == laneId) { + uint64_t counter = atomicAdd( + (unsigned long long int *)nvshmemi_device_state_d.proxy_channel_g_buf_head_ptr, 1); + uint64_t idx = counter * sizeof(g_elem_t); + uint64_t idx_in_buf = idx & (nvshmemi_device_state_d.proxy_channel_g_buf_size - 1); + elem = (g_elem_t *)(nvshmemi_device_state_d.proxy_channel_g_buf + idx_in_buf); + coalescing_buf_byte_offset = + (idx_in_buf / sizeof(g_elem_t)) * NVSHMEMI_WARP_SIZE * sizeof(uint64_t); + // Using a g_elem_t as to for a coalescing buffer at the same index + uint64_t flag = (idx >> nvshmemi_device_state_d.proxy_channel_g_buf_log_size) * 2; + + /* wait until element can be used */ + nvshmemi_wait_until_greater_than_equals((volatile uint64_t *)&(elem->flag), + flag, NVSHMEMI_CALL_SITE_G_WAIT_FLAG); + static_assert(sizeof(T) <= sizeof(uint64_t), "sizeof(T) exceeds sizeof(uint64_t)"); + nvshmemi_proxy_rma_nbi(source, + (void *)(nvshmemi_device_state_d.proxy_channel_g_coalescing_buf + + coalescing_buf_byte_offset), + NVSHMEMI_WARP_SIZE * sizeof(T), pe, NVSHMEMI_OP_G); + nvshmemi_proxy_quiet(false); + } + coalescing_buf_byte_offset = __shfl_sync(amask, coalescing_buf_byte_offset, 0); + T *__restrict__ coalescing_buf = reinterpret_cast( + nvshmemi_device_state_d.proxy_channel_g_coalescing_buf + coalescing_buf_byte_offset); + T return_val = coalescing_buf[laneId]; + __syncwarp(amask); + if (0 == laneId) { + __threadfence(); + /* release the element for the next thread */ + elem->flag += 2; + } + + return return_val; + } else +#endif //__CUDA_ARCH__ >= 700 + { + uint64_t counter = atomicAdd( + (unsigned long long int *)nvshmemi_device_state_d.proxy_channel_g_buf_head_ptr, 1); + uint64_t idx = counter * sizeof(g_elem_t); + uint64_t idx_in_buf = idx & (nvshmemi_device_state_d.proxy_channel_g_buf_size - 1); + g_elem_t *elem = (g_elem_t *)(nvshmemi_device_state_d.proxy_channel_g_buf + idx_in_buf); + uint64_t flag = (idx >> nvshmemi_device_state_d.proxy_channel_g_buf_log_size) * 2; + + /* wait until element can be used */ + nvshmemi_wait_until_greater_than_equals((volatile uint64_t *)&(elem->flag), flag, + NVSHMEMI_CALL_SITE_G_WAIT_FLAG); + + nvshmemi_proxy_rma_nbi(source, (void *)elem, sizeof(T), pe, NVSHMEMI_OP_G); + nvshmemi_proxy_quiet(false); + + __threadfence(); + T return_val = *(T *)(&(elem->data)); + __threadfence(); + /* release the element for the next thread */ + elem->flag += 2; + + return return_val; + } +} + +NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_FORCE_INLINE __device__ void convert_val_to_uint64( + uint64_t *dest, void *value, size_t size) { + uint8_t byte_buffer_1; + uint16_t byte_buffer_2; + uint32_t byte_buffer_4; + uint64_t byte_buffer_8; + + switch (size) { + case 1: + memcpy(&byte_buffer_1, value, size); + *dest = byte_buffer_1; + break; + case 2: + memcpy(&byte_buffer_2, value, size); + *dest = byte_buffer_2; + break; + case 4: + memcpy(&byte_buffer_4, value, size); + *dest = byte_buffer_4; + break; + case 8: + memcpy(&byte_buffer_8, value, size); + *dest = byte_buffer_8; + break; + default: + printf("Invalid size value provided to convert_val_to_uint64 in proxy_device.cu.\n"); + } +} + +template +NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_FORCE_INLINE __device__ void transfer_inline( + void *rptr, T value, int pe, nvshmemi_op_t optype) { + uint64_t idx, tail_idx, *req; + int size = PROXY_INLINE_REQ_BYTES; + int group_size = 1; + void *buf_ptr = nvshmemi_device_state_d.proxy_channels_buf; + void *base_ptr = nvshmemi_device_state_d.heap_base; + + idx = atomicAdd((unsigned long long int *)nvshmemi_device_state_d.proxy_channels_issue, size); + tail_idx = idx + (size - 1); + + // flow-control + check_channel_availability(tail_idx); + + req = (uint64_t *)((uint8_t *)buf_ptr + (idx & (CHANNEL_BUF_SIZE - 1))); + uint64_t curr_flag = !((idx >> nvshmemi_device_state_d.proxy_channel_buf_logsize) & 1); + uint64_t roffset = (uint64_t)((char *)rptr - (char *)base_ptr); + uint64_t op = optype; + uint16_t pe_u16 = pe; + uint64_t size_u64 = sizeof(T); + + uint64_t lvalue_buffer = 0; + const uint64_t mask_4_bytes = 0x00000000ffffffff; + + convert_val_to_uint64(&lvalue_buffer, &value, sizeof(T)); + + /* base_request_t + * 32 | 8 | 8 | 8 | 8 + * roffset_high | roffset_low | op | group_size | flag */ + *((volatile uint64_t *)req) = + (uint64_t)((roffset << 24) | (op << 16) | (group_size << 8) | curr_flag); + + /* put_inline_request_0 + * 32 | 16 | 8 | 8 + * lvalue (low) | pe | resv | flag */ + idx += CHANNEL_ENTRY_BYTES; + req = (uint64_t *)((uint8_t *)buf_ptr + (idx & (CHANNEL_BUF_SIZE - 1))); + curr_flag = !((idx >> nvshmemi_device_state_d.proxy_channel_buf_logsize) & 1); + /* mask lower four bytes. */ + const uint64_t lvalue_low = lvalue_buffer & mask_4_bytes; + *((volatile uint64_t *)req) = (lvalue_low << 32 | ((uint64_t)pe_u16 << 16) | curr_flag); + + /* put_inline_request_1 + * 32 | 16 | 8 | 8 + * lvalue(high) | size | resv | flag */ + idx += CHANNEL_ENTRY_BYTES; + req = (uint64_t *)((uint8_t *)buf_ptr + (idx & (CHANNEL_BUF_SIZE - 1))); + curr_flag = !((idx >> nvshmemi_device_state_d.proxy_channel_buf_logsize) & 1); + const uint64_t lvalue_high = lvalue_buffer & ~mask_4_bytes; + *((volatile uint64_t *)req) = (lvalue_high | size_u64 << 16 | curr_flag); +} + +template +NVSHMEMI_STATIC __device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_proxy_rma_p(void *rptr, + const T value, + int pe) { + transfer_inline(rptr, value, pe, NVSHMEMI_OP_P); +} + +template +NVSHMEMI_STATIC __device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void amo( + void *rptr, uint64_t g_buf_counter /* used only for fetch atomics */, T swap_add, T compare, + int pe, nvshmemi_amo_t amo_op) { + uint64_t idx, tail_idx, *req; + int size = PROXY_AMO_REQ_BYTES; + int group_size = 1; + void *buf_ptr = nvshmemi_device_state_d.proxy_channels_buf; + void *base_ptr = nvshmemi_device_state_d.heap_base; + + idx = atomicAdd((unsigned long long int *)nvshmemi_device_state_d.proxy_channels_issue, size); + tail_idx = idx + (size - 1); + + // flow-control + check_channel_availability(tail_idx); + + req = (uint64_t *)((uint8_t *)buf_ptr + (idx & (CHANNEL_BUF_SIZE - 1))); + uint64_t curr_flag = !((idx >> nvshmemi_device_state_d.proxy_channel_buf_logsize) & 1); + uint64_t roffset = (uint64_t)((char *)rptr - (char *)base_ptr); + uint64_t op = NVSHMEMI_OP_AMO; + uint64_t amo = amo_op; + uint16_t pe_u16 = pe; + uint64_t size_u64 = sizeof(T); + uint64_t swap_add_buffer; + uint64_t compare_buffer; + const uint64_t mask_1_byte = 0x00FFFFFFFFFFFFFF; + const uint64_t mask_4_bytes = 0x00000000FFFFFFFF; + const uint64_t mask_7_bytes = 0x00000000000000FF; + + convert_val_to_uint64(&swap_add_buffer, &swap_add, sizeof(T)); + convert_val_to_uint64(&compare_buffer, &compare, sizeof(T)); + + /* base_request_t + * 32 | 8 | 8 | 8 | 8 + * roffset_high | roffset_low | op | group_size | flag */ + *((volatile uint64_t *)req) = + (uint64_t)((roffset << 24) | (op << 16) | (group_size << 8) | curr_flag); + + /* amo_request_0 + * 32 | 16 | 8 | 8 + * swap_add_low | pe | amo | flag */ + idx += CHANNEL_ENTRY_BYTES; + req = (uint64_t *)((uint8_t *)buf_ptr + (idx & (CHANNEL_BUF_SIZE - 1))); + curr_flag = !((idx >> nvshmemi_device_state_d.proxy_channel_buf_logsize) & 1); + const uint64_t swap_add_low = swap_add_buffer & mask_4_bytes; + *((volatile uint64_t *)req) = + (swap_add_low << 32 | ((uint64_t)pe_u16 << 16) | (amo << 8) | curr_flag); + + /* amo_request_1 + * 32 | 16 | 8 | 8 + * swap_add_high | size | compare_low | flag */ + idx += CHANNEL_ENTRY_BYTES; + req = (uint64_t *)((uint8_t *)buf_ptr + (idx & (CHANNEL_BUF_SIZE - 1))); + curr_flag = !((idx >> nvshmemi_device_state_d.proxy_channel_buf_logsize) & 1); + const uint64_t swap_add_high = swap_add_buffer & ~mask_4_bytes; + const uint64_t compare_low = compare_buffer & mask_7_bytes; + *((volatile uint64_t *)req) = (swap_add_high | size_u64 << 16 | compare_low << 8 | curr_flag); + + /* amo_request_2 + * 32 | 16 | 8 | 8 + * comapare_high | flag */ + idx += CHANNEL_ENTRY_BYTES; + req = (uint64_t *)((uint8_t *)buf_ptr + (idx & (CHANNEL_BUF_SIZE - 1))); + curr_flag = !((idx >> nvshmemi_device_state_d.proxy_channel_buf_logsize) & 1); + /* only mask the low byte for */ + const uint64_t compare_high = compare_buffer & ~mask_7_bytes; + *((volatile uint64_t *)req) = (compare_high | curr_flag); + + /* amo_request_3 + * 32 | 16 | 8 | 8 + * g_buf_counter_low | flag */ + idx += CHANNEL_ENTRY_BYTES; + req = (uint64_t *)((uint8_t *)buf_ptr + (idx & (CHANNEL_BUF_SIZE - 1))); + curr_flag = !((idx >> nvshmemi_device_state_d.proxy_channel_buf_logsize) & 1); + /* assumes g_buf_counter <= (1 << 56) */ + const uint64_t g_buf_counter_low = g_buf_counter & mask_1_byte; + *((volatile uint64_t *)req) = (g_buf_counter_low << 8 | curr_flag); +} + +template +NVSHMEMI_STATIC __device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_proxy_amo_nonfetch( + void *rptr, T swap_add, int pe, nvshmemi_amo_t op) { + amo(rptr, 0 /* dummy value */, swap_add, 0, pe, op); +} + +template +NVSHMEMI_STATIC __device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_proxy_amo_fetch( + void *rptr, void *lptr, T swap_add, T compare, int pe, nvshmemi_amo_t op) { + uint64_t counter = atomicAdd( + (unsigned long long int *)nvshmemi_device_state_d.proxy_channel_g_buf_head_ptr, 1); + uint64_t idx = counter * sizeof(g_elem_t); + uint64_t idx_in_buf = idx & (nvshmemi_device_state_d.proxy_channel_g_buf_size - 1); + g_elem_t *elem = (g_elem_t *)(nvshmemi_device_state_d.proxy_channel_g_buf + idx_in_buf); + uint64_t flag = (idx >> nvshmemi_device_state_d.proxy_channel_g_buf_log_size) * 2; + size_t atomic_size = sizeof(T); + + /* wait until element can be used */ + nvshmemi_wait_until_greater_than_equals((volatile uint64_t *)&(elem->flag), flag, + NVSHMEMI_CALL_SITE_AMO_FETCH_WAIT_FLAG); + __threadfence(); + + amo(rptr, counter, swap_add, compare, pe, op); + + /* The IBDEVX transport doesn't rely on an active message from the receiver for atomics. */ + if (nvshmemi_device_state_d.atomics_complete_on_quiet) { + nvshmemi_proxy_quiet(false); + elem->flag += 1; + /* MLNX NICs will typically return 8 byte atomics in host-endian and 4 byte atomics in + * big-enian. This behavior is controlled by flags read from the NIC at runtime. + */ + if (atomic_size < nvshmemi_device_state_d.atomics_le_min_size) { + if (atomic_size == 8) { + NTOH64(&elem->data); + } else if (atomic_size == 4) { + NTOH32(&elem->data); + } else { + /* Our APIs currently only support 4 and 8-byte atomics. Any other size is a fatal + * error. */ + assert(false); + } + } + } else { + nvshmemi_wait_until_greater_than_equals( + (volatile uint64_t *)&(elem->flag), flag + 1, NVSHMEMI_CALL_SITE_AMO_FETCH_WAIT_DATA); + } + __threadfence(); + T return_val = *(T *)(&(elem->data)); + __threadfence(); + + /* release the element for the next thread */ + elem->flag += 1; + + *((T *)lptr) = return_val; +} + +NVSHMEMI_STATIC __device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_proxy_fence() { + // making it a no-op as it is a no-op for IB RC, the only transport + uint64_t idx, tail_idx, *req; + int size = sizeof(uint64_t); + + idx = atomicAdd((unsigned long long int *)nvshmemi_device_state_d.proxy_channels_issue, size); + tail_idx = idx + (size - 1); + + // flow-control + check_channel_availability(tail_idx); + + req = (uint64_t *)((uint64_t)nvshmemi_device_state_d.proxy_channels_buf + + (idx & (nvshmemi_device_state_d.proxy_channel_buf_size - 1))); + uint64_t curr_flag = !((idx >> nvshmemi_device_state_d.proxy_channel_buf_logsize) & 1); + uint64_t op = NVSHMEMI_OP_FENCE; + + /* base_request_t + * 32 | 8 | 8 | 8 | 8 + * resv | resv | op | resv | flag */ + *((volatile uint64_t *)req) = (uint64_t)((op << 16) | curr_flag); + + return; +} + +#endif /* __CUDA_ARCH__ */ +#endif /* PROXY_DEVICE_CUH */ diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/device/pt-to-pt/transfer_device.cuh b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/device/pt-to-pt/transfer_device.cuh new file mode 100644 index 0000000000000000000000000000000000000000..54f5d596ba3e70b3e50b00a54edd7cd1c6f5212f --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/device/pt-to-pt/transfer_device.cuh @@ -0,0 +1,302 @@ +/* + * Copyright (c) 2016-2025, NVIDIA CORPORATION. All rights reserved. + * + * See License.txt for license information + */ + +#ifndef TRANSFER_DEVICE_CUH +#define TRANSFER_DEVICE_CUH + +#include +#include "non_abi/nvshmem_build_options.h" +#include "non_abi/device/threadgroup/nvshmemi_common_device_defines.cuh" +#include "device_host/nvshmem_common.cuh" +#include "device/nvshmem_device_macros.h" +#include "non_abi/device/pt-to-pt/proxy_device.cuh" + +#ifdef NVSHMEM_IBGDA_SUPPORT +#include "non_abi/device/pt-to-pt/ibgda_device.cuh" +#endif + +#if defined __clang_llvm_bitcode_lib__ +#define NVSHMEMI_TRANSFER_INLINE \ + __attribute__((noinline, section(".text.compute"), not_tail_called)) +#define NVSHMEMI_TRANSFER_STATIC +#elif defined NVSHMEM_ENABLE_ALL_DEVICE_INLINING +#define NVSHMEMI_TRANSFER_INLINE inline +#define NVSHMEMI_TRANSFER_STATIC static +#else +#define NVSHMEMI_TRANSFER_INLINE __noinline__ +#define NVSHMEMI_TRANSFER_STATIC +#endif + +#define TRANSFER_REPT_FOR_STANDARD_RMA_TYPES(FN_TEMPLATE) \ + FN_TEMPLATE(char) \ + FN_TEMPLATE(unsigned char) \ + FN_TEMPLATE(short) \ + FN_TEMPLATE(unsigned short) \ + FN_TEMPLATE(int) \ + FN_TEMPLATE(unsigned int) \ + FN_TEMPLATE(long) \ + FN_TEMPLATE(unsigned long) \ + FN_TEMPLATE(long long) \ + FN_TEMPLATE(unsigned long long) \ + FN_TEMPLATE(float) \ + FN_TEMPLATE(double) + +#define TRANSFER_REPT_FOR_STANDARD_AMO_TYPES(FN_TEMPLATE) \ + FN_TEMPLATE(short) \ + FN_TEMPLATE(unsigned short) \ + FN_TEMPLATE(int) \ + FN_TEMPLATE(unsigned int) \ + FN_TEMPLATE(long) \ + FN_TEMPLATE(unsigned long) \ + FN_TEMPLATE(long long) \ + FN_TEMPLATE(unsigned long long) + +#define TRANSFER_REPT_FOR_EXTENDED_AMO_TYPES(FN_TEMPLATE) \ + FN_TEMPLATE(float) \ + FN_TEMPLATE(double) + +#define TRANSFER_REPT_FOR_ALL_SCOPES(FN_TEMPLATE) \ + FN_TEMPLATE(NVSHMEMI_THREADGROUP_THREAD) \ + FN_TEMPLATE(NVSHMEMI_THREADGROUP_WARP) \ + FN_TEMPLATE(NVSHMEMI_THREADGROUP_BLOCK) + +#ifdef __CUDA_ARCH__ +__device__ NVSHMEMI_TRANSFER_INLINE void nvshmemi_transfer_syncapi_update_mem() { + __threadfence(); /* 1. Ensures consitency op is not called before the prior test/wait condition + has been met + 2. Needed to prevent reorder of instructions after sync api (when the + following if condition is false) */ + if (nvshmemi_device_state_d.job_connectivity > NVSHMEMI_JOB_GPU_PROXY) { + nvshmemi_proxy_enforce_consistency_at_target(true); + } +} + +template +NVSHMEMI_TRANSFER_STATIC __device__ NVSHMEMI_TRANSFER_INLINE void nvshmemi_transfer_rma_p( + void *rptr, const T value, int pe) { +#ifdef NVSHMEM_IBGDA_SUPPORT + if (nvshmemi_device_state_d.ibgda_is_initialized) { + nvshmemi_ibgda_rma_p(rptr, value, pe); + } else +#endif + { + nvshmemi_proxy_rma_p(rptr, value, pe); + } +} + +#define TRANSFER_DECL_RMA_P(Type) \ + template __device__ void nvshmemi_transfer_rma_p(void *rptr, const Type value, int pe); + +TRANSFER_REPT_FOR_STANDARD_RMA_TYPES(TRANSFER_DECL_RMA_P) + +template +NVSHMEMI_TRANSFER_STATIC __device__ NVSHMEMI_TRANSFER_INLINE T nvshmemi_transfer_rma_g(void *rptr, + int pe) { +#ifdef NVSHMEM_IBGDA_SUPPORT + if (nvshmemi_device_state_d.ibgda_is_initialized) { + return nvshmemi_ibgda_rma_g(rptr, pe); + } else +#endif + { + return nvshmemi_proxy_rma_g(rptr, pe); + } +} + +#define TRANSFER_DECL_RMA_G(Type) \ + template __device__ Type nvshmemi_transfer_rma_g(void *rptr, int pe); + +TRANSFER_REPT_FOR_STANDARD_RMA_TYPES(TRANSFER_DECL_RMA_G) + +template +NVSHMEMI_TRANSFER_STATIC __device__ NVSHMEMI_TRANSFER_INLINE void nvshmemi_transfer_rma( + void *rptr, void *lptr, size_t bytes, int pe) { +#ifdef NVSHMEM_IBGDA_SUPPORT + if (nvshmemi_device_state_d.ibgda_is_initialized) { + nvshmemi_ibgda_rma(rptr, lptr, bytes, pe); + } else +#endif + { + int myIdx = nvshmemi_thread_id_in_threadgroup(); + if (!myIdx) { + nvshmemi_proxy_rma_nbi(rptr, lptr, bytes, pe, channel_op); + nvshmemi_proxy_quiet(false); + if (SCOPE == nvshmemi_threadgroup_thread) + __threadfence_block(); /* to prevent reuse of src buffer before quiet completion; + for warp/block scope, following sync op will accomplish that */ + } + } +} + +#define TRANSFER_DECL_RMA(SCOPE) \ + template __device__ void nvshmemi_transfer_rma(void *rptr, void *lptr, \ + size_t bytes, int pe); \ + template __device__ void nvshmemi_transfer_rma(void *rptr, void *lptr, \ + size_t bytes, int pe); + +TRANSFER_REPT_FOR_ALL_SCOPES(TRANSFER_DECL_RMA) + +template +NVSHMEMI_TRANSFER_STATIC __device__ NVSHMEMI_TRANSFER_INLINE void nvshmemi_transfer_put_signal( + void *rptr, void *lptr, size_t bytes, void *sig_addr, uint64_t signal, nvshmemi_amo_t sig_op, + int pe, bool is_nbi) { +#ifdef NVSHMEM_IBGDA_SUPPORT + if (nvshmemi_device_state_d.ibgda_is_initialized) { + nvshmemi_ibgda_put_signal(rptr, lptr, bytes, sig_addr, signal, sig_op, pe, is_nbi); + } else +#endif + { + int myIdx = nvshmemi_thread_id_in_threadgroup(); + if (!myIdx) { + nvshmemi_proxy_rma_nbi(rptr, lptr, bytes, pe, NVSHMEMI_OP_PUT); + nvshmemi_proxy_fence(); + nvshmemi_proxy_amo_nonfetch(sig_addr, signal, pe, sig_op); + if (is_nbi == 0) { + nvshmemi_proxy_quiet(false); + if (SCOPE == nvshmemi_threadgroup_thread) + __threadfence_block(); /* to prevent reuse of src buffer before quiet completion + for warp/block scope, following sync op will accomplish that + */ + } + } + } +} + +#define TRANSFER_DECL_PUT_SIGNAL(SCOPE) \ + template __device__ void nvshmemi_transfer_put_signal( \ + void *rptr, void *lptr, size_t bytes, void *sig_rptr, uint64_t signal, \ + nvshmemi_amo_t sig_op, int pe, bool is_nbi); + +TRANSFER_REPT_FOR_ALL_SCOPES(TRANSFER_DECL_PUT_SIGNAL) + +template +NVSHMEMI_TRANSFER_STATIC __device__ NVSHMEMI_TRANSFER_INLINE void nvshmemi_transfer_rma_nbi( + void *rptr, void *lptr, size_t bytes, int pe) { +#ifdef NVSHMEM_IBGDA_SUPPORT + if (nvshmemi_device_state_d.ibgda_is_initialized) { + nvshmemi_ibgda_rma_nbi(rptr, lptr, bytes, pe); + } else +#endif + { + int myIdx = nvshmemi_thread_id_in_threadgroup(); + if (!myIdx) nvshmemi_proxy_rma_nbi(rptr, lptr, bytes, pe, channel_op); + } +} + +#define TRANSFER_DECL_RMA_NBI(SCOPE) \ + template __device__ void nvshmemi_transfer_rma_nbi( \ + void *rptr, void *lptr, size_t bytes, int pe); \ + template __device__ void nvshmemi_transfer_rma_nbi( \ + void *rptr, void *lptr, size_t bytes, int pe); + +TRANSFER_REPT_FOR_ALL_SCOPES(TRANSFER_DECL_RMA_NBI) + +template +NVSHMEMI_TRANSFER_STATIC __device__ NVSHMEMI_TRANSFER_INLINE T +nvshmemi_transfer_amo_fetch(void *rptr, T value, T compare, int pe, nvshmemi_amo_t op) { +#ifdef NVSHMEM_IBGDA_SUPPORT + if (nvshmemi_device_state_d.ibgda_is_initialized) { + return nvshmemi_ibgda_amo_fetch(rptr, value, compare, pe, op); + } else +#endif + { + T retval; + nvshmemi_proxy_amo_fetch(rptr, (void *)&retval, value, compare, pe, op); + return retval; + } +} + +#define TRANSFER_DECL_AMO_FETCH(Type) \ + template __device__ Type nvshmemi_transfer_amo_fetch( \ + void *rptr, const Type value, const Type compare, int pe, nvshmemi_amo_t op); + +TRANSFER_REPT_FOR_STANDARD_AMO_TYPES(TRANSFER_DECL_AMO_FETCH); +TRANSFER_REPT_FOR_EXTENDED_AMO_TYPES(TRANSFER_DECL_AMO_FETCH); + +template +NVSHMEMI_TRANSFER_STATIC __device__ NVSHMEMI_TRANSFER_INLINE void nvshmemi_transfer_amo_nonfetch( + void *rptr, T value, int pe, nvshmemi_amo_t op) { +#ifdef NVSHMEM_IBGDA_SUPPORT + if (nvshmemi_device_state_d.ibgda_is_initialized) { + nvshmemi_ibgda_amo_nonfetch(rptr, value, pe, op); + } else +#endif + { + nvshmemi_proxy_amo_nonfetch(rptr, value, pe, op); + } +} + +#define TRANSFER_DECL_AMO_NONFETCH(Type) \ + template __device__ void nvshmemi_transfer_amo_nonfetch(void *rptr, const Type value, \ + int pe, nvshmemi_amo_t op); + +TRANSFER_REPT_FOR_STANDARD_AMO_TYPES(TRANSFER_DECL_AMO_NONFETCH); +TRANSFER_REPT_FOR_EXTENDED_AMO_TYPES(TRANSFER_DECL_AMO_NONFETCH); + +template +NVSHMEMI_TRANSFER_STATIC __device__ NVSHMEMI_TRANSFER_INLINE void nvshmemi_transfer_quiet( + bool use_membar) { + // quiet_on_stream also shares this code path. + int myIdx = nvshmemi_thread_id_in_threadgroup(); +#ifdef NVSHMEM_IBGDA_SUPPORT + if (nvshmemi_device_state_d.ibgda_is_initialized) { + nvshmemi_ibgda_quiet(); + if (use_membar) { + if (!myIdx) { + __threadfence_system(); + } + } + } else +#endif + if (nvshmemi_device_state_d.proxy == NVSHMEMI_PROXY_FULL) { + if (!myIdx) { + nvshmemi_proxy_quiet(use_membar); + } + } + nvshmemi_threadgroup_sync(); +} + +#define TRANSFER_DECL_QUIET(SCOPE) \ + template __device__ void nvshmemi_transfer_quiet(bool use_membar); + +TRANSFER_REPT_FOR_ALL_SCOPES(TRANSFER_DECL_QUIET); +#undef TRANSFER_DECL_QUIET + +template +NVSHMEMI_TRANSFER_STATIC __device__ NVSHMEMI_TRANSFER_INLINE void nvshmemi_transfer_fence() { +#ifdef NVSHMEM_IBGDA_SUPPORT + if (nvshmemi_device_state_d.ibgda_is_initialized) { + nvshmemi_ibgda_fence(); + } else +#endif + if ((nvshmemi_device_state_d.proxy == NVSHMEMI_PROXY_FULL) && + !nvshmemi_device_state_d.proxy_ops_are_ordered) { + int myIdx = nvshmemi_thread_id_in_threadgroup(); + if (!myIdx) { + nvshmemi_proxy_fence(); + } + } + nvshmemi_threadgroup_sync(); +} + +#define TRANSFER_DECL_FENCE(SCOPE) template __device__ void nvshmemi_transfer_fence(); + +TRANSFER_REPT_FOR_ALL_SCOPES(TRANSFER_DECL_FENCE); +#undef TRANSFER_DECL_FENCE + +NVSHMEMI_TRANSFER_STATIC __device__ NVSHMEMI_TRANSFER_INLINE void +nvshmemi_transfer_enforce_consistency_at_target(bool use_membar) { +#ifdef NVSHMEM_IBGDA_SUPPORT + if (nvshmemi_device_state_d.ibgda_is_initialized) { + nvshmemi_ibgda_enforce_consistency_at_target(use_membar); + } else +#endif + { + nvshmemi_proxy_enforce_consistency_at_target(use_membar); + } +} +#endif /* __CUDA_ARCH__ */ + +#endif /* TRANSFER_DEVICE_CUH */ diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/device/pt-to-pt/utils_device.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/device/pt-to-pt/utils_device.h new file mode 100644 index 0000000000000000000000000000000000000000..c7d40b2378b7451e881ff64a487bbeac63c1b132 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/device/pt-to-pt/utils_device.h @@ -0,0 +1,111 @@ +/* + * Copyright (c) 2016-2025, NVIDIA CORPORATION. All rights reserved. + * + * See License.txt for license information + */ + +#ifndef _COMM_DEVICE_UTILS_H +#define _COMM_DEVICE_UTILS_H + +#if !defined __CUDACC_RTC__ +#include +#else +#include "cuda/std/cstdint" +#endif + +#include + +#if defined __CUDACC__ +#define NVSHMEMI_COMM_DEVICE_UTILS_USE_PTX +#endif +#define NTOH64(x) \ + *x = ((*(x)&0xFF00000000000000) >> 56 | (*(x)&0x00FF000000000000) >> 40 | \ + (*(x)&0x0000FF0000000000) >> 24 | (*(x)&0x000000FF00000000) >> 8 | \ + (*(x)&0x00000000FF000000) << 8 | (*(x)&0x0000000000FF0000) << 24 | \ + (*(x)&0x000000000000FF00) << 40 | (*(x)&0x00000000000000FF) << 56) + +#define NTOH32(x) \ + *x = ((*(x)&0xFF000000) >> 24 | (*(x)&0x00FF0000) >> 8 | (*(x)&0x0000FF00) << 8 | \ + (*(x)&0x000000FF) << 24) + +#ifdef NVSHMEMI_COMM_DEVICE_UTILS_USE_PTX + +#if defined __CUDACC__ + +__device__ static inline uint64_t BSWAP64(uint64_t x) { + uint64_t ret; + asm volatile( + "{\n\t" + ".reg .b32 mask;\n\t" + ".reg .b32 ign;\n\t" + ".reg .b32 lo;\n\t" + ".reg .b32 hi;\n\t" + ".reg .b32 new_lo;\n\t" + ".reg .b32 new_hi;\n\t" + "mov.b32 mask, 0x0123;\n\t" + "mov.b64 {lo,hi}, %1;\n\t" + "prmt.b32 new_hi, lo, ign, mask;\n\t" + "prmt.b32 new_lo, hi, ign, mask;\n\t" + "mov.b64 %0, {new_lo,new_hi};\n\t" + "}" + : "=l"(ret) + : "l"(x)); + return ret; +} + +__device__ static inline uint32_t BSWAP32(uint32_t x) { + uint32_t ret; + asm volatile( + "{\n\t" + ".reg .b32 mask;\n\t" + ".reg .b32 ign;\n\t" + "mov.b32 mask, 0x0123;\n\t" + "prmt.b32 %0, %1, ign, mask;\n\t" + "}" + : "=r"(ret) + : "r"(x)); + return ret; +} + +__device__ static inline uint16_t BSWAP16(uint16_t x) { + uint16_t ret; + + uint32_t a = (uint32_t)x; + uint32_t d; + asm volatile( + "{\n\t" + ".reg .b32 mask;\n\t" + ".reg .b32 ign;\n\t" + "mov.b32 mask, 0x4401;\n\t" + "mov.b32 ign, 0x0;\n\t" + "prmt.b32 %0, %1, ign, mask;\n\t" + "}" + : "=r"(d) + : "r"(a)); + ret = (uint16_t)d; + return ret; +} + +#endif /*! __CUDACC__ */ + +#else /* NVSHMEMI_COMM_DEVICE_UTILS_USE_PTX */ + +#define BSWAP64(x) \ + ((((x)&0xff00000000000000ull) >> 56) | (((x)&0x00ff000000000000ull) >> 40) | \ + (((x)&0x0000ff0000000000ull) >> 24) | (((x)&0x000000ff00000000ull) >> 8) | \ + (((x)&0x00000000ff000000ull) << 8) | (((x)&0x0000000000ff0000ull) << 24) | \ + (((x)&0x000000000000ff00ull) << 40) | (((x)&0x00000000000000ffull) << 56)) + +#define BSWAP32(x) \ + ((((x)&0xff000000) >> 24) | (((x)&0x00ff0000) >> 8) | (((x)&0x0000ff00) << 8) | \ + (((x)&0x000000ff) << 24)) + +#define BSWAP16(x) ((((x)&0xff00) >> 8) | (((x)&0x00ff) << 8)) + +#endif /* NVSHMEMI_COMM_DEVICE_UTILS_USE_PTX */ + +#define HTOBE64(x) BSWAP64(x) +#define HTOBE32(x) BSWAP32(x) +#define HTOBE16(x) BSWAP16(x) + +#endif /* _COMM_DEVICE_UTILS_H */ diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/device/team/nvshmemi_team_defines.cuh b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/device/team/nvshmemi_team_defines.cuh new file mode 100644 index 0000000000000000000000000000000000000000..c5c744c847e61c35d99b002f420685fc0b11e771 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/device/team/nvshmemi_team_defines.cuh @@ -0,0 +1,174 @@ +#ifndef _NVSHMEMI_TEAM_DEFINES_CUH_ +#define _NVSHMEMI_TEAM_DEFINES_CUH_ + +#include "device_host/nvshmem_common.cuh" +#include +#if !defined __CUDACC_RTC__ +#include +#else +#include +#endif + +#ifdef __CUDA_ARCH__ + +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE int nvshmemi_team_my_pe( + nvshmem_team_t team) { + if (team == NVSHMEM_TEAM_INVALID) + return -1; + else if (team == NVSHMEM_TEAM_WORLD) + return nvshmemi_device_state_d.mype; + else if (team == NVSHMEMX_TEAM_NODE) + return nvshmemi_device_state_d.node_mype; + else + return nvshmemi_device_state_d.team_pool[team]->my_pe; +} + +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE int nvshmemi_team_n_pes(nvshmem_team_t team) { + if (team == NVSHMEM_TEAM_INVALID) + return -1; + else if (team == NVSHMEM_TEAM_WORLD) + return nvshmemi_device_state_d.npes; + else if (team == NVSHMEMX_TEAM_NODE) + return nvshmemi_device_state_d.node_npes; + else + return nvshmemi_device_state_d.team_pool[team]->size; +} + +NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE __device__ size_t get_fcollect_psync_len_per_team() { + size_t fcollect_ll_threshold = + nvshmemi_device_state_d.gpu_coll_env_params_var.fcollect_ll_threshold; + size_t fcollect_sync_size = + (2 * 2 * nvshmemi_device_state_d.npes * fcollect_ll_threshold) / sizeof(long); + + return fcollect_sync_size; +} + +NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE __device__ size_t +get_fcollect_ll128_psync_len_per_team() { + size_t fcollect_ll128_threshold = + nvshmemi_device_state_d.gpu_coll_env_params_var.fcollect_ll128_threshold; + size_t fcollect_ll128_sync_size = + NVSHMEMI_FCOLLECT_LL128_CALC_PSYNC_SIZE(fcollect_ll128_threshold, char); + + /* scale for npes and two separate psyncs */ + fcollect_ll128_sync_size = + fcollect_ll128_sync_size * 2 * nvshmemi_device_state_d.npes / sizeof(long); + + return fcollect_ll128_sync_size; +} + +NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE __device__ size_t get_psync_len_per_team() { + size_t fcollect_sync_size = get_fcollect_psync_len_per_team(); + size_t fcollect_ll128_sync_size = get_fcollect_ll128_psync_len_per_team(); + /* sync: Two buffers are used - one for sync/barrier collective ops, the second one during team + split operation reduce: Two pWrk's are used alternatively across consecutive reduce calls, + this is to avoid having to put a barrier in between bcast: The buffer is split to do multiple + consecutive broadcast, when all buffers are used, a barrier is called and then again we begin + from the start of the buffer fcollect: Two sets of buffer are used to alternate between - + same way as in reduce. The other fator of 2 is because when using LL double the space is + needed to fuse flag with data */ + + return (2 * NVSHMEMI_SYNC_SIZE + + nvshmemi_device_state_d.gpu_coll_env_params_var.reduce_scratch_size / sizeof(long) + + NVSHMEMI_BCAST_SYNC_SIZE + fcollect_sync_size + 2 * NVSHMEMI_ALLTOALL_SYNC_SIZE + + fcollect_ll128_sync_size); +} + +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE int +nvshmemi_team_translate_pe_to_team_world_wrap(nvshmemi_team_t *src_team, int src_pe) { + return src_team->pe_mapping[src_pe % src_team->size]; +} + +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE int +nvshmemi_team_translate_pe_from_team_world(nvshmemi_team_t *dest_team, int src_pe) { + return dest_team->pe_mapping[dest_team->size + src_pe]; +} + +__device__ NVSHMEMI_STATIC NVSHMEMI_DEVICE_ALWAYS_INLINE bool nvshmemi_team_is_identical( + nvshmemi_team_t *t1, nvshmemi_team_t *t2) { + if (t1->start != t2->start || t1->size != t2->size) { + return false; + } + + /* shortcut for teams with linear stride */ + if (t1->stride > 0 && t2->stride == t1->stride) { + return true; + } + + for (int i = 0; i < t1->size; i++) { + if (t1->pe_mapping[i] != t2->pe_mapping[i]) { + return false; + } + } + + return true; +} + +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE int nvshmemi_team_translate_pe(nvshmem_team_t src_team, + int src_pe, + nvshmem_team_t dest_team) { + if (src_team == NVSHMEM_TEAM_INVALID || dest_team == NVSHMEM_TEAM_INVALID) return -1; + nvshmemi_team_t *src_teami, *dest_teami; + + src_teami = nvshmemi_device_state_d.team_pool[src_team]; + dest_teami = nvshmemi_device_state_d.team_pool[dest_team]; + int src_pe_world, dest_pe = -1; + + if (src_pe > src_teami->size) return -1; + + src_pe_world = src_teami->pe_mapping[src_pe]; + assert(src_pe_world >= src_teami->start && src_pe_world < nvshmemi_device_state_d.npes); + dest_pe = dest_teami->pe_mapping[dest_teami->size + src_pe_world]; + + return dest_pe; +} + +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE long *nvshmemi_team_get_psync(nvshmemi_team_t *team, + nvshmemi_team_op_t op) { + long *team_psync; + size_t psync_fcollect_len; + psync_fcollect_len = get_fcollect_psync_len_per_team(); + team_psync = &nvshmemi_device_state_d.psync_pool[team->team_idx * get_psync_len_per_team()]; + switch (op) { + case SYNC: + return team_psync; + case REDUCE: + return &team_psync + [2 * NVSHMEMI_SYNC_SIZE + + (((nvshmemi_device_state_d.gpu_coll_env_params_var.reduce_scratch_size / 2) / + sizeof(long)) * + (team->rdxn_count % 2))]; + case BCAST: + return &team_psync[2 * NVSHMEMI_SYNC_SIZE + + nvshmemi_device_state_d.gpu_coll_env_params_var.reduce_scratch_size / + sizeof(long)]; + case FCOLLECT: + return &team_psync[2 * NVSHMEMI_SYNC_SIZE + + nvshmemi_device_state_d.gpu_coll_env_params_var.reduce_scratch_size / + sizeof(long) + + NVSHMEMI_BCAST_SYNC_SIZE]; + case ALLTOALL: + return &team_psync[2 * NVSHMEMI_SYNC_SIZE + + nvshmemi_device_state_d.gpu_coll_env_params_var.reduce_scratch_size / + sizeof(long) + + NVSHMEMI_BCAST_SYNC_SIZE + psync_fcollect_len + + (NVSHMEMI_ALLTOALL_SYNC_SIZE * (team->alltoall_count % 2))]; + case FCOLLECT_128: + return &team_psync[2 * NVSHMEMI_SYNC_SIZE + + nvshmemi_device_state_d.gpu_coll_env_params_var.reduce_scratch_size / + sizeof(long) + + NVSHMEMI_BCAST_SYNC_SIZE + psync_fcollect_len + + 2 * NVSHMEMI_ALLTOALL_SYNC_SIZE]; + default: + printf("Incorrect argument to nvshmemi_team_get_psync\n"); + return NULL; + } +} + +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE long *nvshmemi_team_get_sync_counter( + nvshmemi_team_t *team) { + return &nvshmemi_device_state_d.sync_counter[2 * team->team_idx]; +} +#endif + +#endif diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/device/threadgroup/nvshmemi_common_device_defines.cuh b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/device/threadgroup/nvshmemi_common_device_defines.cuh new file mode 100644 index 0000000000000000000000000000000000000000..8f3910f798ee0c8096e8444b7be28cf75020322a --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/device/threadgroup/nvshmemi_common_device_defines.cuh @@ -0,0 +1,117 @@ +/* + * Copyright (c) 2016-2025, NVIDIA CORPORATION. All rights reserved. + * + * See License.txt for license information + */ +#ifndef _NVSHMEM_COMMON_DEVICE_DEFINES_CUH_ +#define _NVSHMEM_COMMON_DEVICE_DEFINES_CUH_ +#include +#include "device_host/nvshmem_common.cuh" + +#if defined(__CUDACC_RDC__) && !defined(__NVSHMEM_NUMBA_SUPPORT__) +#define EXTERN_CONSTANT extern __constant__ +#elif defined(__clang__) +#define EXTERN_CONSTANT extern __constant__ __attribute__((address_space(4))) +#else +#define EXTERN_CONSTANT static __constant__ +#endif +EXTERN_CONSTANT nvshmemi_device_host_state_t nvshmemi_device_state_d; +#undef EXTERN_CONSTANT + +#ifdef __NVSHMEM_NUMBA_SUPPORT__ +static __constant__ nvshmemi_version_t nvshmemi_device_lib_version_d = { + NVSHMEM_VENDOR_MAJOR_VERSION, NVSHMEM_VENDOR_MINOR_VERSION, NVSHMEM_VENDOR_PATCH_VERSION}; +#endif + +typedef enum { + nvshmemi_threadgroup_thread = 0, + NVSHMEMI_THREADGROUP_THREAD = 0, + nvshmemi_threadgroup_warp = 1, + NVSHMEMI_THREADGROUP_WARP = 1, + nvshmemi_threadgroup_warpgroup = 2, + NVSHMEMI_THREADGROUP_WARPGROUP = 2, + nvshmemi_threadgroup_block = 3, + NVSHMEMI_THREADGROUP_BLOCK = 3 +} threadgroup_t; + +#ifdef __CUDA_ARCH__ + +template +__device__ __forceinline__ int nvshmemi_thread_id_in_threadgroup() { + switch (scope) { + case NVSHMEMI_THREADGROUP_THREAD: + return 0; + case NVSHMEMI_THREADGROUP_WARP: + int myIdx; + asm volatile("mov.u32 %0, %%laneid;" : "=r"(myIdx)); + return myIdx; + case NVSHMEMI_THREADGROUP_WARPGROUP: + return ( + (threadIdx.x + threadIdx.y * blockDim.x + threadIdx.z * blockDim.x * blockDim.y) % + (4 * warpSize)); + case NVSHMEMI_THREADGROUP_BLOCK: + return (threadIdx.x + threadIdx.y * blockDim.x + threadIdx.z * blockDim.x * blockDim.y); + default: + printf("unrecognized threadscope passed\n"); + assert(0); + return -1; + } +} + +template +__device__ inline int nvshmemi_threadgroup_size() { + switch (scope) { + case NVSHMEMI_THREADGROUP_THREAD: + return 1; + case NVSHMEMI_THREADGROUP_WARP: + return ((blockDim.x * blockDim.y * blockDim.z) < warpSize) + ? (blockDim.x * blockDim.y * blockDim.z) + : warpSize; + case NVSHMEMI_THREADGROUP_WARPGROUP: + // warpgroup = 4 warps + return ((blockDim.x * blockDim.y * blockDim.z) < (4 * warpSize)) + ? (blockDim.x * blockDim.y * blockDim.z) + : (4 * warpSize); + case NVSHMEMI_THREADGROUP_BLOCK: + return (blockDim.x * blockDim.y * blockDim.z); + default: + printf("unrecognized threadscope passed\n"); + assert(0); + return -1; + } +} + +template +__device__ inline void nvshmemi_threadgroup_sync() { + uint32_t tid, barrierId; + switch (scope) { + case NVSHMEMI_THREADGROUP_THREAD: + return; + case NVSHMEMI_THREADGROUP_WARP: + __syncwarp(); + break; + case NVSHMEMI_THREADGROUP_WARPGROUP: + // break; + tid = + threadIdx.x + (threadIdx.y * blockDim.x) + (threadIdx.z * blockDim.x * blockDim.y); + + // each warpgroup has a unique barrier id per CTA + barrierId = (tid / (4 * warpSize)); + // Ensure blocksize is a multiple of warpgroup size + assert((blockDim.x * blockDim.y * blockDim.z) % (4 * warpSize) == 0); + // Hardware limit on named barriers (max 16) + assert((barrierId < 16) && "Reduce blocksize"); + asm volatile("bar.sync %0, %1;" : : "r"(barrierId), "r"(4 * warpSize)); + break; + case NVSHMEMI_THREADGROUP_BLOCK: + __syncthreads(); + break; + default: + printf("unrecognized threadscope passed\n"); + assert(0); + break; + } +} +#endif + +#endif diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/device/wait/nvshmemi_wait_until_apis.cuh b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/device/wait/nvshmemi_wait_until_apis.cuh new file mode 100644 index 0000000000000000000000000000000000000000..ea66499835bf690746d978f9784a50a399b3d03b --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/device/wait/nvshmemi_wait_until_apis.cuh @@ -0,0 +1,158 @@ +/* + * Copyright (c) 2016-2025, NVIDIA CORPORATION. All rights reserved. + * + * See License.txt for license information + */ + +#ifndef _NVSHMEM_WAIT_UNTIL_APIS_CUH_ +#define _NVSHMEM_WAIT_UNTIL_APIS_CUH_ + +#ifdef __CUDA_ARCH__ + +#include +#include "device_host_transport/nvshmem_constants.h" +#include "device/nvshmem_device_macros.h" +#include "non_abi/device/threadgroup/nvshmemi_common_device_defines.cuh" +#include "device_host/nvshmem_common.cuh" +#include "non_abi/nvshmem_build_options.h" + +#define TIMEOUT_NCYCLES 1e10 + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_check_timeout_and_log( + long long int start, int caller, uintptr_t signal_addr, T signal_val_found, + T signal_val_expected) { + long long int now; + asm volatile("mov.u64 %0, %%globaltimer;" : "=l"(now)); + if ((now - start) > TIMEOUT_NCYCLES) { + nvshmemi_timeout_t *timeout_d = nvshmemi_device_state_d.timeout; + timeout_d->caller = caller; + timeout_d->signal_addr = signal_addr; + *(T *)(&timeout_d->signal_val_found) = signal_val_found; + *(T *)(&timeout_d->signal_val_expected) = signal_val_expected; + *((volatile uint64_t *)(&timeout_d->signal)) = 1; + } +} + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_wait_until_greater_than(volatile T *addr, + T val, int caller) { +#ifdef NVSHMEM_TIMEOUT_DEVICE_POLLING + long long int start; + asm volatile("mov.u64 %0, %%globaltimer;" : "=l"(start)); +#endif + while (*addr <= val) { +#ifdef NVSHMEM_TIMEOUT_DEVICE_POLLING + nvshmemi_check_timeout_and_log(start, caller, (uintptr_t)addr, *addr, val); +#endif + } +} + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_wait_until_greater_than_equals( + volatile T *addr, T val, int caller) { +#ifdef NVSHMEM_TIMEOUT_DEVICE_POLLING + long long int start; + asm volatile("mov.u64 %0, %%globaltimer;" : "=l"(start)); +#endif + while (*addr < val) { +#ifdef NVSHMEM_TIMEOUT_DEVICE_POLLING + nvshmemi_check_timeout_and_log(start, caller, (uintptr_t)addr, *addr, val); +#endif + } +} + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_wait_until_lesser_than(volatile T *addr, + T val, int caller) { +#ifdef NVSHMEM_TIMEOUT_DEVICE_POLLING + long long int start; + asm volatile("mov.u64 %0, %%globaltimer;" : "=l"(start)); +#endif + while (*addr >= val) { +#ifdef NVSHMEM_TIMEOUT_DEVICE_POLLING + nvshmemi_check_timeout_and_log(start, caller, (uintptr_t)addr, *addr, val); +#endif + } +} + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_wait_until_lesser_than_equals( + volatile T *addr, T val, int caller) { +#ifdef NVSHMEM_TIMEOUT_DEVICE_POLLING + long long int start; + asm volatile("mov.u64 %0, %%globaltimer;" : "=l"(start)); +#endif + while (*addr > val) { +#ifdef NVSHMEM_TIMEOUT_DEVICE_POLLING + nvshmemi_check_timeout_and_log(start, caller, (uintptr_t)addr, *addr, val); +#endif + } +} + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_wait_until_equals(volatile T *addr, T val, + int caller) { +#ifdef NVSHMEM_TIMEOUT_DEVICE_POLLING + long long int start; + asm volatile("mov.u64 %0, %%globaltimer;" : "=l"(start)); +#endif + while (*addr != val) { +#ifdef NVSHMEM_TIMEOUT_DEVICE_POLLING + nvshmemi_check_timeout_and_log(start, caller, (uintptr_t)addr, *addr, val); +#endif + } +} + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_wait_until_not_equals(volatile T *addr, + T val, int caller) { +#ifdef NVSHMEM_TIMEOUT_DEVICE_POLLING + long long int start; + asm volatile("mov.u64 %0, %%globaltimer;" : "=l"(start)); +#endif + while (*addr == val) { +#ifdef NVSHMEM_TIMEOUT_DEVICE_POLLING + nvshmemi_check_timeout_and_log(start, caller, (uintptr_t)addr, *addr, val); +#endif + } +} + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_wait_until_greater_than_equals_add( + volatile T *addr, uint64_t toadd, T val, int caller) { +#ifdef NVSHMEM_TIMEOUT_DEVICE_POLLING + long long int start; + asm volatile("mov.u64 %0, %%globaltimer;" : "=l"(start)); +#endif + T valataddr; + do { + valataddr = *addr; +#ifdef NVSHMEM_TIMEOUT_DEVICE_POLLING + nvshmemi_check_timeout_and_log(start, caller, (uintptr_t)addr, valataddr + toadd, val); +#endif + } while (valataddr + toadd < val); +} + +template +__device__ NVSHMEMI_DEVICE_ALWAYS_INLINE void nvshmemi_wait_until(volatile T *ivar, int cmp, + T cmp_value) { + if (NVSHMEM_CMP_GE == cmp) { + nvshmemi_wait_until_greater_than_equals(ivar, cmp_value, + NVSHMEMI_CALL_SITE_WAIT_UNTIL_GE); + } else if (NVSHMEM_CMP_EQ == cmp) { + nvshmemi_wait_until_equals(ivar, cmp_value, NVSHMEMI_CALL_SITE_WAIT_UNTIL_EQ); + } else if (NVSHMEM_CMP_NE == cmp) { + nvshmemi_wait_until_not_equals(ivar, cmp_value, NVSHMEMI_CALL_SITE_WAIT_UNTIL_NE); + } else if (NVSHMEM_CMP_GT == cmp) { + nvshmemi_wait_until_greater_than(ivar, cmp_value, NVSHMEMI_CALL_SITE_WAIT_UNTIL_GT); + } else if (NVSHMEM_CMP_LT == cmp) { + nvshmemi_wait_until_lesser_than(ivar, cmp_value, NVSHMEMI_CALL_SITE_WAIT_UNTIL_LT); + } else if (NVSHMEM_CMP_LE == cmp) { + nvshmemi_wait_until_lesser_than_equals(ivar, cmp_value, + NVSHMEMI_CALL_SITE_WAIT_UNTIL_LE); + } +} + +#endif +#endif diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/nvshmem_build_options.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/nvshmem_build_options.h new file mode 100644 index 0000000000000000000000000000000000000000..e180dc994e2a2d8c4c600b7098d9307b12976720 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/nvshmem_build_options.h @@ -0,0 +1,37 @@ +#pragma once + +/* #undef NVSHMEM_COMPLEX_SUPPORT */ +/* #undef NVSHMEM_DEBUG */ +/* #undef NVSHMEM_DEVEL */ +/* #undef NVSHMEM_TRACE */ +/* #undef NVSHMEM_DEFAULT_PMI2 */ +/* #undef NVSHMEM_DEFAULT_PMIX */ +/* #undef NVSHMEM_DEFAULT_UCX */ +/* #undef NVSHMEM_GPU_COLL_USE_LDST */ +#define NVSHMEM_IBDEVX_SUPPORT +#define NVSHMEM_IBRC_SUPPORT +#define NVSHMEM_LIBFABRIC_SUPPORT +#define NVSHMEM_MPI_SUPPORT +#define NVSHMEM_NVTX +#define NVSHMEM_PMIX_SUPPORT +#define NVSHMEM_SHMEM_SUPPORT +/* #undef NVSHMEM_TEST_STATIC_LIB */ +/* #undef NVSHMEM_TIMEOUT_DEVICE_POLLING */ +#define NVSHMEM_UCX_SUPPORT +/* #undef NVSHMEM_USE_DLMALLOC */ +#define NVSHMEM_USE_NCCL +#define NVSHMEM_USE_GDRCOPY +#define NVSHMEM_USE_MLX5DV +/* #undef NVSHMEM_VERBOSE */ +#define NVSHMEM_BUILD_TESTS +#define NVSHMEM_BUILD_EXAMPLES +#define NVSHMEM_IBGDA_SUPPORT +/* #undef NVSHMEM_IBGDA_SUPPORT_GPUMEM_ONLY */ +/* #undef NVSHMEM_ENABLE_ALL_DEVICE_INLINING */ +/* #undef NVSHMEM_HOSTLIB_ONLY */ + +#if defined __clang_llvm_bitcode_lib__ || defined NVSHMEM_HOSTLIB_ONLY +#undef NVSHMEM_IBGDA_SUPPORT +#undef NVSHMEM_IBGDA_SUPPORT_GPUMEM_ONLY +#define NVSHMEM_ENABLE_ALL_DEVICE_INLINING +#endif diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/nvshmem_version.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/nvshmem_version.h new file mode 100644 index 0000000000000000000000000000000000000000..dadcd2c15bd7d72402561e2598e6e9db6cc12d43 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/nvshmem_version.h @@ -0,0 +1,19 @@ +/* Note - For packaging reasons, do not move this file from src/include/non_abi */ + +#pragma once + +// clang-format off + +#define NVSHMEM_VENDOR_MAJOR_VERSION 3 +#define NVSHMEM_VENDOR_MINOR_VERSION 4 +#define NVSHMEM_VENDOR_PATCH_VERSION 5 +#define NVSHMEM_VENDOR_PACKAGE_VERSION 0 +#define NVSHMEM_TRANSPORT_PLUGIN_MAJOR_VERSION 3 +#define NVSHMEM_TRANSPORT_PLUGIN_MINOR_VERSION 0 +#define NVSHMEM_TRANSPORT_PLUGIN_PATCH_VERSION 0 +#define NVSHMEM_BOOTSTRAP_PLUGIN_MAJOR_VERSION 3 +#define NVSHMEM_BOOTSTRAP_PLUGIN_MINOR_VERSION 0 +#define NVSHMEM_BOOTSTRAP_PLUGIN_PATCH_VERSION 0 +#define NVSHMEM_BUILD_VARS "NVSHMEM_DEBUG=OFF NVSHMEM_DEVEL=OFF NVSHMEM_DEFAULT_PMI2=OFF NVSHMEM_DEFAULT_PMIX=OFF NVSHMEM_DEFAULT_UCX=OFF NVSHMEM_ENABLE_ALL_DEVICE_INLINING=OFF NVSHMEM_GPU_COLL_USE_LDST=OFF NVSHMEM_IBGDA_SUPPORT=ON NVSHMEM_IBGDA_SUPPORT_GPUMEM_ONLY=OFF NVSHMEM_IBDEVX_SUPPORT=ON NVSHMEM_IBRC_SUPPORT=ON NVSHMEM_LIBFABRIC_SUPPORT=ON NVSHMEM_MPI_SUPPORT=ON NVSHMEM_NVTX=ON NVSHMEM_PMIX_SUPPORT=ON NVSHMEM_SHMEM_SUPPORT=ON NVSHMEM_TEST_STATIC_LIB=OFF NVSHMEM_TIMEOUT_DEVICE_POLLING=OFF NVSHMEM_TRACE=OFF NVSHMEM_UCX_SUPPORT=ON NVSHMEM_USE_DLMALLOC=OFF NVSHMEM_USE_NCCL=ON NVSHMEM_USE_GDRCOPY=ON NVSHMEM_VERBOSE=OFF CUDA_HOME=/dvs/p4/build/sw/rel/gpgpu/toolkit/r12.9/build/cuda GDRCOPY_HOME=/dvs/p4/build/sw/rel/gpgpu/toolkit/r12.9/gdrcopy_build LIBFABRIC_HOME=/data/tmp/libfabric_install MPI_HOME=/data/tmp/openmpi_install NCCL_HOME=/dvs/p4/build/sw/rel/gpgpu/toolkit/r12.9/nccl/build NVSHMEM_PREFIX=/dvs/p4/build/sw/rel/gpgpu/toolkit/r12.9/main_nvshmem/install PMIX_HOME=/data/tmp/pmix_install SHMEM_HOME=/data/tmp/openmpi_install UCX_HOME=/dvs/p4/build/sw/gpgpu/openucx/build" + +// clang-format on diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/nvshmemx_error.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/nvshmemx_error.h new file mode 100644 index 0000000000000000000000000000000000000000..67992224776cef168e0952d0ce64704d0f4d8106 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/include/non_abi/nvshmemx_error.h @@ -0,0 +1,161 @@ +/* + * Copyright (c) 2018-2025, NVIDIA CORPORATION. All rights reserved. + * + * NVIDIA CORPORATION and its licensors retain all intellectual property + * and proprietary rights in and to this software, related documentation + * and any modifications thereto. Any use, reproduction, disclosure or + * distribution of this software and related documentation without an express + * license agreement from NVIDIA CORPORATION is strictly prohibited. + * + * See License.txt for license information + */ + +#ifndef _NVSHMEMX_ERROR_H_ +#define _NVSHMEMX_ERROR_H_ +#if !defined __CUDACC_RTC__ +#include // for errno +#include +#include // for stderr, stdout, NULL +#include // IWYU pragma: keep for strerror +#include // IWYU pragma: keep for exit +#else +#define fprintf(...) +#define exit(...) +#include +#include +#endif + +/* The !! idiom is used to convert non-boolean types to booleans. + * Doing so in this case allows us to ensure that __builtin_expect + * will be given a clean boolean value for the comparison. */ +#define nvshmemxi_error_unlikely(x) __builtin_expect(!!(x), 0) + +#if defined __cplusplus || defined __clang_llvm_bitcode_lib__ +extern "C" { +#endif + +enum nvshmemx_status { + NVSHMEMX_SUCCESS = 0, + NVSHMEMX_ERROR_INVALID_VALUE, + NVSHMEMX_ERROR_OUT_OF_MEMORY, + NVSHMEMX_ERROR_NOT_SUPPORTED, + NVSHMEMX_ERROR_SYMMETRY, + NVSHMEMX_ERROR_GPU_NOT_SELECTED, + NVSHMEMX_ERROR_COLLECTIVE_LAUNCH_FAILED, + NVSHMEMX_ERROR_INTERNAL, + NVSHMEMX_ERROR_SENTINEL = INT_MAX +}; + +#define NVSHMEMI_ERROR_EXIT(...) \ + do { \ + fprintf(stderr, "%s:%s:%d: ", __FILE__, __FUNCTION__, __LINE__); \ + fprintf(stderr, __VA_ARGS__); \ + fprintf(stderr, "\n"); \ + exit(-1); \ + } while (0) + +#define NVSHMEMI_ERROR_PRINT(...) \ + do { \ + fprintf(stderr, "%s:%s:%d: ", __FILE__, __FUNCTION__, __LINE__); \ + fprintf(stderr, __VA_ARGS__); \ + fprintf(stderr, "\n"); \ + } while (0) + +#define NVSHMEMI_WARN_PRINT(...) \ + do { \ + fprintf(stdout, "WARN: "); \ + fprintf(stdout, __VA_ARGS__); \ + fprintf(stdout, "\n"); \ + } while (0) + +#define NVSHMEMI_ERROR_JMP(status, err, label, ...) \ + do { \ + fprintf(stderr, "%s:%d: non-zero status: %d ", __FILE__, __LINE__, err); \ + fprintf(stderr, __VA_ARGS__); \ + fprintf(stderr, "\n"); \ + status = err; \ + goto label; \ + } while (0) + +#define NVSHMEMI_NULL_ERROR_JMP(var, status, err, label, ...) \ + do { \ + if (nvshmemxi_error_unlikely(var == NULL)) { \ + fprintf(stderr, "%s:%d: NULL value ", __FILE__, __LINE__); \ + fprintf(stderr, __VA_ARGS__); \ + fprintf(stderr, "\n"); \ + status = err; \ + goto label; \ + } \ + } while (0) + +#define NVSHMEMI_EQ_ERROR_JMP(status, expected, err, label, ...) \ + do { \ + if (nvshmemxi_error_unlikely(status == expected)) { \ + fprintf(stderr, "%s:%d: error status: %d ", __FILE__, __LINE__, status); \ + fprintf(stderr, __VA_ARGS__); \ + fprintf(stderr, "\n"); \ + status = err; \ + goto label; \ + } \ + } while (0) + +#define NVSHMEMI_NE_ERROR_JMP(status, expected, err, label, ...) \ + do { \ + if (nvshmemxi_error_unlikely(status != expected)) { \ + fprintf(stderr, "%s:%d: non-zero status: %d ", __FILE__, __LINE__, status); \ + fprintf(stderr, __VA_ARGS__); \ + fprintf(stderr, "\n"); \ + status = err; \ + goto label; \ + } \ + } while (0) + +#define NVSHMEMI_NZ_ERROR_JMP(status, err, label, ...) \ + do { \ + if (nvshmemxi_error_unlikely(status != 0)) { \ + fprintf(stderr, "%s:%d: non-zero status: %d ", __FILE__, __LINE__, status); \ + fprintf(stderr, __VA_ARGS__); \ + fprintf(stderr, "\n"); \ + status = err; \ + goto label; \ + } \ + } while (0) + +#define NVSHMEMI_CHECK_ERROR_JMP(statement, status, err, label, ...) \ + do { \ + if (nvshmemxi_error_unlikely(statement)) { \ + fprintf(stderr, "%s:%d: ", __FILE__, __LINE__); \ + fprintf(stderr, __VA_ARGS__); \ + fprintf(stderr, "\n"); \ + status = err; \ + goto label; \ + } \ + } while (0) + +#define NVSHMEMI_NZ_EXIT(status, ...) \ + do { \ + if (nvshmemxi_error_unlikely(status != 0)) { \ + fprintf(stderr, "%s:%d: non-zero status: %d: %s, exiting... ", __FILE__, __LINE__, \ + status, strerror(errno)); \ + fprintf(stderr, __VA_ARGS__); \ + fprintf(stderr, "\n"); \ + exit(-1); \ + } \ + } while (0) + +#define NVSHMEMI_NZ_SYSCHECK_EXIT(sys_status, ...) \ + do { \ + if (nvshmemxi_error_unlikely((sys_status) != 0)) { \ + fprintf(stderr, "%s:%d: non-zero status: %d: %s, exiting... ", __FILE__, __LINE__, \ + (sys_status), strerror(sys_status)); \ + fprintf(stderr, __VA_ARGS__); \ + fprintf(stderr, "\n"); \ + exit(-1); \ + } \ + } while (0) + +#if defined __cplusplus || defined __clang_llvm_bitcode_lib__ +} +#endif + +#endif diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/lib/nvshmem_bootstrap_mpi.so.3 b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/lib/nvshmem_bootstrap_mpi.so.3 new file mode 100644 index 0000000000000000000000000000000000000000..9293c789d0f3efbe397f873bea86f6835258b6c1 Binary files /dev/null and b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/lib/nvshmem_bootstrap_mpi.so.3 differ diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/lib/nvshmem_bootstrap_pmi.so.3 b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/lib/nvshmem_bootstrap_pmi.so.3 new file mode 100644 index 0000000000000000000000000000000000000000..623f1b7a2b76feb6cc7b69c23e6baf6db4daa1b8 Binary files /dev/null and b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/lib/nvshmem_bootstrap_pmi.so.3 differ diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/lib/nvshmem_bootstrap_pmi2.so.3 b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/lib/nvshmem_bootstrap_pmi2.so.3 new file mode 100644 index 0000000000000000000000000000000000000000..4292e02f0c1b7527c75ac49541dd8ff8d5ee49b7 Binary files /dev/null and b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/lib/nvshmem_bootstrap_pmi2.so.3 differ diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/lib/nvshmem_bootstrap_pmix.so.3 b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/lib/nvshmem_bootstrap_pmix.so.3 new file mode 100644 index 0000000000000000000000000000000000000000..2d3ba69bc58e16d66b6942c54c183fa5f34d1d4d Binary files /dev/null and b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/lib/nvshmem_bootstrap_pmix.so.3 differ diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/lib/nvshmem_bootstrap_shmem.so.3 b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/lib/nvshmem_bootstrap_shmem.so.3 new file mode 100644 index 0000000000000000000000000000000000000000..24a115101dcc71bf66f3028c6e20a083d01f5a4a Binary files /dev/null and b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/lib/nvshmem_bootstrap_shmem.so.3 differ diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/lib/nvshmem_bootstrap_uid.so.3 b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/lib/nvshmem_bootstrap_uid.so.3 new file mode 100644 index 0000000000000000000000000000000000000000..fb51d30e197d7a894c03ba0338c2362d3b81bcb7 Binary files /dev/null and b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/lib/nvshmem_bootstrap_uid.so.3 differ diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/lib/nvshmem_transport_ucx.so.3 b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/lib/nvshmem_transport_ucx.so.3 new file mode 100644 index 0000000000000000000000000000000000000000..e516ca1d45110f47fdfdff86f68973af79d49284 Binary files /dev/null and b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvshmem/lib/nvshmem_transport_ucx.so.3 differ diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvToolsExt.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvToolsExt.h new file mode 100644 index 0000000000000000000000000000000000000000..2bd2a0ada91cb3552c52a3f4890ecd445925df5c --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvToolsExt.h @@ -0,0 +1,1578 @@ +/* +* Copyright 2009-2024 NVIDIA Corporation. All rights reserved. +* +* NOTICE TO USER: +* +* This source code is subject to NVIDIA ownership rights under U.S. and +* international Copyright laws. +* +* This software and the information contained herein is PROPRIETARY and +* CONFIDENTIAL to NVIDIA and is being provided under the terms and conditions +* of a form of NVIDIA software license agreement. +* +* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE +* CODE FOR ANY PURPOSE. IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR +* IMPLIED WARRANTY OF ANY KIND. NVIDIA DISCLAIMS ALL WARRANTIES WITH +* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF +* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE. +* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL, +* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS +* OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE +* OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE +* OR PERFORMANCE OF THIS SOURCE CODE. +* +* U.S. Government End Users. This source code is a "commercial item" as +* that term is defined at 48 C.F.R. 2.101 (OCT 1995), consisting of +* "commercial computer software" and "commercial computer software +* documentation" as such terms are used in 48 C.F.R. 12.212 (SEPT 1995) +* and is provided to the U.S. Government only as a commercial end item. +* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through +* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the +* source code with only those rights set forth herein. +* +* Any use of this source code in individual and commercial software must +* include, in the user documentation and internal comments to the code, +* the above Disclaimer and U.S. Government End Users Notice. +*/ + +/** \file nvToolsExt.h + */ + +/* +* Deprecation and upcoming removal warning for NVTX version 2 +* This warning can be suppressed by defining NVTX_SUPPRESS_V2_DEPRECATION_WARNING +*/ +#ifndef NVTX_SUPPRESS_V2_DEPRECATION_WARNING +#ifndef NVTX_V2_DEPRECATION_WARNING_MESSAGE +#define NVTX_V2_DEPRECATION_WARNING_MESSAGE "DEPRECATION NOTICE: NVTX 2 is deprecated and will be removed from toolkits that distribute it in their next major version (CUDA 12.9). Please use NVTX 3 instead. All NVTX 2 API functions are supported in version 3. The implementation is now header-only, so you no longer need to link with and distribute a dynamic library to use NVTX. Ensure you are explicitly using NVTX 3 by writing #include instead of #include . Remove nvToolsExt from your link command and linker search paths, and stop deploying libnvToolsExt.so or nvToolsExt64_1.dll -- these binary libraries will not be shipped with NVTX 3 for C/C++ usage. A new library called 'libnvtx3interop.so' or 'nvtx3interop.dll' will be provided for interop with other languages. This message can be suppressed by defining NVTX_SUPPRESS_V2_DEPRECATION_WARNING before including the NVTX 2 headers." +#ifdef _MSC_VER +#define NVTX_V2_WARNING_HELPER2(x) #x +#define NVTX_V2_WARNING_HELPER(x) NVTX_V2_WARNING_HELPER2(x) +#pragma message(__FILE__ ":" NVTX_V2_WARNING_HELPER(__LINE__) ": note: \"" NVTX_V2_DEPRECATION_WARNING_MESSAGE "\"\n") +#else +#pragma message NVTX_V2_DEPRECATION_WARNING_MESSAGE +#endif +#endif /* NVTX_V2_DEPRECATION_WARNING_MESSAGE */ +#endif /* NVTX_SUPPRESS_V2_DEPRECATION_WARNING */ + +/* ========================================================================= */ +/** \mainpage + * \tableofcontents + * \section INTRODUCTION Introduction + * + * The NVIDIA Tools Extension library is a set of functions that a + * developer can use to provide additional information to tools. + * The additional information is used by the tool to improve + * analysis and visualization of data. + * + * The library introduces close to zero overhead if no tool is + * attached to the application. The overhead when a tool is + * attached is specific to the tool. + * + * \section INITIALIZATION_SECTION Initialization + * + * Typically the tool's library that plugs into NVTX is indirectly + * loaded via enviromental properties that are platform specific. + * For some platform or special cases, the user may be required + * to instead explicity initialize instead though. This can also + * be helpful to control when the API loads a tool's library instead + * of what would typically be the first function call to emit info. + * For these rare case, see \ref INITIALIZATION for additional information. + * + * \section MARKERS_AND_RANGES Markers and Ranges + * + * Markers and ranges are used to describe events at a specific time (markers) + * or over a time span (ranges) during the execution of the application + * respectively. + * + * \subsection MARKERS Markers + * + * Markers denote specific moments in time. + * + * + * See \ref DOMAINS and \ref EVENT_ATTRIBUTES for additional information on + * how to specify the domain. + * + * \subsection THREAD_RANGES Thread Ranges + * + * Thread ranges denote nested time ranges. Nesting is maintained per thread + * per domain and does not require any additional correlation mechanism. The + * duration of a thread range is defined by the corresponding pair of + * nvtxRangePush* to nvtxRangePop API calls. + * + * See \ref DOMAINS and \ref EVENT_ATTRIBUTES for additional information on + * how to specify the domain. + * + * \subsection PROCESS_RANGES Process Ranges + * + * Process ranges denote a time span that can expose arbitrary concurrency, as + * opposed to thread ranges that only support nesting. In addition the range + * start event can happen on a different thread than the end marker. For the + * correlation of a start/end pair an unique correlation ID is used that is + * returned from the start API call and needs to be passed into the end API + * call. + * + * \subsection EVENT_ATTRIBUTES Event Attributes + * + * \ref MARKERS_AND_RANGES can be annotated with various attributes to provide + * additional information for an event or to guide the tool's visualization of + * the data. Each of the attributes is optional and if left unused the + * attributes fall back to a default value. The attributes include: + * - color + * - category + * + * To specify any attribute other than the text message, the \ref + * EVENT_ATTRIBUTE_STRUCTURE "Event Attribute Structure" must be used. + * + * \section DOMAINS Domains + * + * Domains enable developers to scope annotations. By default all events and + * annotations are in the default domain. Additional domains can be registered. + * This allows developers to scope markers, ranges, and resources names to + * avoid conflicts. + * + * The function ::nvtxDomainCreateA or ::nvtxDomainCreateW is used to create + * a named domain. + * + * Each domain maintains its own + * - categories + * - thread range stacks + * - registered strings + * + * The function ::nvtxDomainDestroy marks the end of the domain. Destroying + * a domain unregisters and destroys all objects associated with it such as + * registered strings, resource objects, named categories, and started ranges. + * + * \section RESOURCE_NAMING Resource Naming + * + * This section covers calls that allow to annotate objects with user-provided + * names in order to allow for a better analysis of complex trace data. All of + * the functions take the handle or the ID of the object to name and the name. + * The functions can be called multiple times during the execution of an + * application, however, in that case it is implementation dependent which + * name will be reported by the tool. + * + * \subsection CATEGORY_NAMING Category Naming + * + * Some function in this library support associating an integer category + * to enable filtering and sorting. The category naming functions allow + * the application to associate a user friendly name with the integer + * category. Support for domains have been added in NVTX_VERSION_2 to + * avoid collisions when domains are developed independantly. + * + * \subsection RESOURCE_OBJECTS Resource Objects + * + * Resource objects are a generic mechanism for attaching data to an application + * resource. The identifier field makes the association to a pointer or handle, + * while the type field helps provide deeper understanding of the identifier as + * well as enabling differentiation in cases where handles generated by different + * APIs may collide. The resource object may also have an associated message to + * associate with the application resource, enabling further annotation of this + * object and how it is used. + * + * The resource object was introduced in NVTX_VERSION_2 to supersede existing naming + * functions and allow the application resource identified by those functions to be + * associated to a domain. The other naming functions are still supported for backward + * compatibility but will be associated only to the default domain. + * + * \subsection RESOURCE_NAMING_OS Resource Naming + * + * Some operating system resources creation APIs do not support providing a user friendly + * name, such as some OS thread creation APIs. This API support resource naming though + * both through resource objects and functions following the pattern + * nvtxName[RESOURCE_TYPE][A|W](identifier, name). Resource objects introduced in NVTX_VERSION 2 + * supersede the other functions with a a more general method of assigning names to OS resources, + * along with associating them to domains too. The older nvtxName* functions are only associated + * with the default domain. + * \section EXTENSIONS Optional Extensions + * Optional extensions will either appear within the existing sections the extend or appear + * in the "Related Pages" when they introduce new concepts. + */ + +#ifndef NVTOOLSEXT_H_ +#define NVTOOLSEXT_H_ + +#if defined(_MSC_VER) + #ifdef NVTX_EXPORTS + #define NVTX_DECLSPEC + #else + #define NVTX_DECLSPEC __declspec(dllimport) + #endif /* NVTX_EXPORTS */ + #define NVTX_API __stdcall + #define NVTX_INLINE_STATIC __inline static +#else /*defined(__GNUC__)*/ + #define NVTX_DECLSPEC + #define NVTX_API + #define NVTX_INLINE_STATIC inline static +#endif /* Platform */ + +/** + * The nvToolsExt library depends on stdint.h. If the build tool chain in use + * does not include stdint.h then define NVTX_STDINT_TYPES_ALREADY_DEFINED + * and define the following types: + *
    + *
  • uint8_t + *
  • int8_t + *
  • uint16_t + *
  • int16_t + *
  • uint32_t + *
  • int32_t + *
  • uint64_t + *
  • int64_t + *
  • uintptr_t + *
  • intptr_t + *
+ #define NVTX_STDINT_TYPES_ALREADY_DEFINED if you are using your own header file. + */ +#ifndef NVTX_STDINT_TYPES_ALREADY_DEFINED +#include +#endif + +#include + +#ifdef __cplusplus +extern "C" { +#endif /* __cplusplus */ + +/** + * Tools Extension API version + */ +#define NVTX_VERSION 2 + +/** + * Size of the nvtxEventAttributes_t structure. + */ +#define NVTX_EVENT_ATTRIB_STRUCT_SIZE ( (uint16_t)( sizeof(nvtxEventAttributes_t) ) ) + +/** + * Size of the nvtxInitializationAttributes_t structure. + */ +#define NVTX_INITIALIZATION_ATTRIB_STRUCT_SIZE ( (uint16_t)( sizeof(nvtxInitializationAttributes_t) ) ) + +#define NVTX_NO_PUSH_POP_TRACKING ((int)-2) + +typedef uint64_t nvtxRangeId_t; + + +/* \brief String Handle Structure. +* \anchor STRING_HANDLE_STRUCTURE +* +* This structure is opaque to the user and is used as a handle to reference +* a string. The tools will return a pointer through the API for the application +* to hold on it's behalf to reference the string in the future. +* +*/ +typedef struct nvtxStringHandle* nvtxStringHandle_t; + +/* \brief Domain Handle Structure. +* \anchor DOMAIN_HANDLE_STRUCTURE +* +* This structure is opaque to the user and is used as a handle to reference +* a domain. The tools will return a pointer through the API for the application +* to hold on its behalf to reference the domain in the future. +* +*/ +typedef struct nvtxDomainHandle* nvtxDomainHandle_t; + + + + + + +/* ========================================================================= */ +/** \defgroup GENERAL General + * @{ + */ + +/** --------------------------------------------------------------------------- + * Color Types + * ------------------------------------------------------------------------- */ +typedef enum nvtxColorType_t +{ + NVTX_COLOR_UNKNOWN = 0, /**< Color attribute is unused. */ + NVTX_COLOR_ARGB = 1 /**< An ARGB color is provided. */ +} nvtxColorType_t; + +/** --------------------------------------------------------------------------- + * Message Types + * ------------------------------------------------------------------------- */ +typedef enum nvtxMessageType_t +{ + NVTX_MESSAGE_UNKNOWN = 0, /**< Message payload is unused. */ + NVTX_MESSAGE_TYPE_ASCII = 1, /**< A character sequence is used as payload. */ + NVTX_MESSAGE_TYPE_UNICODE = 2, /**< A wide character sequence is used as payload. */ + /* NVTX_VERSION_2 */ + NVTX_MESSAGE_TYPE_REGISTERED = 3 /**< A unique string handle that was registered + with \ref nvtxDomainRegisterStringA() or + \ref nvtxDomainRegisterStringW(). */ +} nvtxMessageType_t; + +typedef union nvtxMessageValue_t +{ + const char* ascii; + const wchar_t* unicode; + /* NVTX_VERSION_2 */ + nvtxStringHandle_t registered; +} nvtxMessageValue_t; + + +/** @} */ /*END defgroup*/ + +/* ========================================================================= */ +/** \defgroup INITIALIZATION Initialization +* @{ +* Typically the tool's library that plugs into NVTX is indirectly +* loaded via enviromental properties that are platform specific. +* For some platform or special cases, the user may be required +* to instead explicity initialize instead though. This can also +* be helpful to control when the API loads a tool's library instead +* of what would typically be the first function call to emit info. +*/ + +/** --------------------------------------------------------------------------- +* Initialization Modes +* ------------------------------------------------------------------------- */ +typedef enum nvtxInitializationMode_t +{ + NVTX_INITIALIZATION_MODE_UNKNOWN = 0, /**< A platform that supports indirect initialization will attempt this style, otherwise expect failure. */ + NVTX_INITIALIZATION_MODE_CALLBACK_V1 = 1, /**< A function pointer conforming to NVTX_VERSION=1 will be used. */ + NVTX_INITIALIZATION_MODE_CALLBACK_V2 = 2, /**< A function pointer conforming to NVTX_VERSION=2 will be used. */ + NVTX_INITIALIZATION_MODE_SIZE +} nvtxInitializationMode_t; + + +/** \brief Initialization Attribute Structure. +* \anchor INITIALIZATION_ATTRIBUTE_STRUCTURE +* +* This structure is used to describe the attributes used for initialization +* of the NVTX API. +* +* \par Initializing the Attributes +* +* The caller should always perform the following three tasks when using +* attributes: +*
    +*
  • Zero the structure +*
  • Set the version field +*
  • Set the size field +*
+* +* Zeroing the structure sets all the event attributes types and values +* to the default value. +* +* The version and size field are used by the Tools Extension +* implementation to handle multiple versions of the attributes structure. +* NVTX_INITIALIZATION_ATTRIB_STRUCT_SIZE may be used for the size. +* +* It is recommended that the caller use one of the following to methods +* to initialize the event attributes structure: +* +* \par Method 1: Initializing nvtxInitializationAttributes_t for future compatibility +* \code +* nvtxInitializationAttributes_t initAttribs = {0}; +* initAttribs.version = NVTX_VERSION; +* initAttribs.size = NVTX_INITIALIZATION_ATTRIB_STRUCT_SIZE; +* \endcode +* +* \par Method 2: Initializing nvtxInitializationAttributes_t for a specific version +* \code +* nvtxInitializationAttributes_t initAttribs = {0}; +* initAttribs.version =2; +* initAttribs.size = (uint16_t)(sizeof(nvtxInitializationAttributes_v2)); +* \endcode +* +* If the caller uses Method 1 it is critical that the entire binary +* layout of the structure be configured to 0 so that all fields +* are initialized to the default value. +* +* The caller should either use both NVTX_VERSION and +* NVTX_INITIALIZATION_ATTRIB_STRUCT_SIZE (Method 1) or use explicit values +* and a versioned type (Method 2). Using a mix of the two methods +* will likely cause either source level incompatibility or binary +* incompatibility in the future. +* +* \par Settings Attribute Types and Values +* +* +* \par Example: +* \code +* // Initialize +* nvtxInitializationAttributes_t initAttribs = {0}; +* initAttribs.version = NVTX_VERSION; +* initAttribs.size = NVTX_INITIALIZATION_ATTRIB_STRUCT_SIZE; +* +* // Configure the Attributes +* initAttribs.mode = NVTX_INITIALIZATION_MODE_CALLBACK_V2; +* initAttribs.fnptr = InitializeInjectionNvtx2; +* \endcode + +* \sa +* ::nvtxInitializationMode_t +* ::nvtxInitialize +*/ +typedef struct nvtxInitializationAttributes_v2 +{ + /** + * \brief Version flag of the structure. + * + * Needs to be set to NVTX_VERSION to indicate the version of NVTX APIs + * supported in this header file. This can optionally be overridden to + * another version of the tools extension library. + */ + uint16_t version; + + /** + * \brief Size of the structure. + * + * Needs to be set to the size in bytes of the event attribute + * structure used to specify the event. + */ + uint16_t size; + + /** + * \brief Mode of initialization. + * + * The mode of initialization dictates the overall behavior and which + * attributes in this struct will be used. + * + * Default Value is NVTX_INITIALIZATION_MODE_UNKNOWN = 0 + * \sa + * ::nvtxInitializationMode_t + */ + uint32_t mode; + + /** + * \brief Function pointer used for initialization if the mode requires + * + * The user has retrieved this function pointer from the tool library + * and would like to use it to initialize. The mode must be set to a + * NVTX_INITIALIZATION_MODE_CALLBACK_V# for this to be used. The mode + * will dictate the expectations for this member. The function signature + * will be cast from void(*)() to the appropriate signature for the mode. + * the expected behavior of the function will also depend on the mode + * beyond the simple function signature. + * + * Default Value is NVTX_INITIALIZATION_MODE_UNKNOWN which will either + * initialize based on external properties or fail if not supported on + * the given platform. + + * \sa + * ::nvtxInitializationMode_t + */ + void(*fnptr)(void); + +} nvtxInitializationAttributes_v2; + +typedef struct nvtxInitializationAttributes_v2 nvtxInitializationAttributes_t; + + +/* ------------------------------------------------------------------------- */ +/** \brief Force initialization (optional on most platforms) +* +* Force NVTX library to initialize. On some platform NVTX will implicit initialize +* upon the first function call into an NVTX API. +* +* \return Result codes are simplest to assume NVTX_SUCCESS or !NVTX_SUCCESS +* +* \param initAttrib - The initialization attribute structure +* +* \sa +* ::nvtxInitializationAttributes_t +* +* \version \NVTX_VERSION_2 +* @{ */ +NVTX_DECLSPEC int NVTX_API nvtxInitialize(const nvtxInitializationAttributes_t* initAttrib); +/** @} */ + + +/** @} */ /*END defgroup*/ + +/* ========================================================================= */ +/** \defgroup EVENT_ATTRIBUTES Event Attributes +* @{ +*/ + +/** --------------------------------------------------------------------------- +* Payload Types +* ------------------------------------------------------------------------- */ +typedef enum nvtxPayloadType_t +{ + NVTX_PAYLOAD_UNKNOWN = 0, /**< Color payload is unused. */ + NVTX_PAYLOAD_TYPE_UNSIGNED_INT64 = 1, /**< A 64 bit unsigned integer value is used as payload. */ + NVTX_PAYLOAD_TYPE_INT64 = 2, /**< A 64 bit signed integer value is used as payload. */ + NVTX_PAYLOAD_TYPE_DOUBLE = 3, /**< A 64 bit floating point value is used as payload. */ + /* NVTX_VERSION_2 */ + NVTX_PAYLOAD_TYPE_UNSIGNED_INT32 = 4, /**< A 32 bit floating point value is used as payload. */ + NVTX_PAYLOAD_TYPE_INT32 = 5, /**< A 32 bit floating point value is used as payload. */ + NVTX_PAYLOAD_TYPE_FLOAT = 6 /**< A 32 bit floating point value is used as payload. */ +} nvtxPayloadType_t; + +/** \brief Event Attribute Structure. + * \anchor EVENT_ATTRIBUTE_STRUCTURE + * + * This structure is used to describe the attributes of an event. The layout of + * the structure is defined by a specific version of the tools extension + * library and can change between different versions of the Tools Extension + * library. + * + * \par Initializing the Attributes + * + * The caller should always perform the following three tasks when using + * attributes: + *
    + *
  • Zero the structure + *
  • Set the version field + *
  • Set the size field + *
+ * + * Zeroing the structure sets all the event attributes types and values + * to the default value. + * + * The version and size field are used by the Tools Extension + * implementation to handle multiple versions of the attributes structure. + * + * It is recommended that the caller use one of the following to methods + * to initialize the event attributes structure: + * + * \par Method 1: Initializing nvtxEventAttributes for future compatibility + * \code + * nvtxEventAttributes_t eventAttrib = {0}; + * eventAttrib.version = NVTX_VERSION; + * eventAttrib.size = NVTX_EVENT_ATTRIB_STRUCT_SIZE; + * \endcode + * + * \par Method 2: Initializing nvtxEventAttributes for a specific version + * \code + * nvtxEventAttributes_t eventAttrib = {0}; + * eventAttrib.version = 1; + * eventAttrib.size = (uint16_t)(sizeof(nvtxEventAttributes_v1)); + * \endcode + * + * If the caller uses Method 1 it is critical that the entire binary + * layout of the structure be configured to 0 so that all fields + * are initialized to the default value. + * + * The caller should either use both NVTX_VERSION and + * NVTX_EVENT_ATTRIB_STRUCT_SIZE (Method 1) or use explicit values + * and a versioned type (Method 2). Using a mix of the two methods + * will likely cause either source level incompatibility or binary + * incompatibility in the future. + * + * \par Settings Attribute Types and Values + * + * + * \par Example: + * \code + * // Initialize + * nvtxEventAttributes_t eventAttrib = {0}; + * eventAttrib.version = NVTX_VERSION; + * eventAttrib.size = NVTX_EVENT_ATTRIB_STRUCT_SIZE; + * + * // Configure the Attributes + * eventAttrib.colorType = NVTX_COLOR_ARGB; + * eventAttrib.color = 0xFF880000; + * eventAttrib.messageType = NVTX_MESSAGE_TYPE_ASCII; + * eventAttrib.message.ascii = "Example"; + * \endcode + * + * In the example the caller does not have to set the value of + * \ref ::nvtxEventAttributes_v2::category or + * \ref ::nvtxEventAttributes_v2::payload as these fields were set to + * the default value by {0}. + * \sa + * ::nvtxDomainMarkEx + * ::nvtxDomainRangeStartEx + * ::nvtxDomainRangePushEx + */ +typedef struct nvtxEventAttributes_v2 +{ + /** + * \brief Version flag of the structure. + * + * Needs to be set to NVTX_VERSION to indicate the version of NVTX APIs + * supported in this header file. This can optionally be overridden to + * another version of the tools extension library. + */ + uint16_t version; + + /** + * \brief Size of the structure. + * + * Needs to be set to the size in bytes of the event attribute + * structure used to specify the event. + */ + uint16_t size; + + /** + * \brief ID of the category the event is assigned to. + * + * A category is a user-controlled ID that can be used to group + * events. The tool may use category IDs to improve filtering or + * enable grouping of events in the same category. The functions + * \ref ::nvtxNameCategoryA or \ref ::nvtxNameCategoryW can be used + * to name a category. + * + * Default Value is 0 + */ + uint32_t category; + + /** \brief Color type specified in this attribute structure. + * + * Defines the color format of the attribute structure's \ref COLOR_FIELD + * "color" field. + * + * Default Value is NVTX_COLOR_UNKNOWN + */ + int32_t colorType; /* nvtxColorType_t */ + + /** \brief Color assigned to this event. \anchor COLOR_FIELD + * + * The color that the tool should use to visualize the event. + */ + uint32_t color; + + /** + * \brief Payload type specified in this attribute structure. + * + * Defines the payload format of the attribute structure's \ref PAYLOAD_FIELD + * "payload" field. + * + * Default Value is NVTX_PAYLOAD_UNKNOWN + */ + int32_t payloadType; /* nvtxPayloadType_t */ + + int32_t reserved0; + + /** + * \brief Payload assigned to this event. \anchor PAYLOAD_FIELD + * + * A numerical value that can be used to annotate an event. The tool could + * use the payload data to reconstruct graphs and diagrams. + */ + union payload_t + { + uint64_t ullValue; + int64_t llValue; + double dValue; + /* NVTX_VERSION_2 */ + uint32_t uiValue; + int32_t iValue; + float fValue; + } payload; + + /** \brief Message type specified in this attribute structure. + * + * Defines the message format of the attribute structure's \ref MESSAGE_FIELD + * "message" field. + * + * Default Value is NVTX_MESSAGE_UNKNOWN + */ + int32_t messageType; /* nvtxMessageType_t */ + + /** \brief Message assigned to this attribute structure. \anchor MESSAGE_FIELD + * + * The text message that is attached to an event. + */ + nvtxMessageValue_t message; + +} nvtxEventAttributes_v2; + +typedef struct nvtxEventAttributes_v2 nvtxEventAttributes_t; + +/** @} */ /*END defgroup*/ +/* ========================================================================= */ +/** \defgroup MARKERS_AND_RANGES Markers and Ranges + * + * See \ref MARKERS_AND_RANGES for more details + * + * @{ + */ + +/** \name Marker */ + +/* ------------------------------------------------------------------------- */ +/** \brief Marks an instantaneous event in the application. +* +* A marker can contain a text message or specify additional information +* using the event attributes structure. These attributes include a text +* message, color, category, and a payload. Each of the attributes is optional +* and can only be sent out using the \ref nvtxDomainMarkEx function. +* +* nvtxDomainMarkEx(NULL, event) is equivalent to calling +* nvtxMarkEx(event). +* +* \param domain - The domain of scoping the category. +* \param eventAttrib - The event attribute structure defining the marker's +* attribute types and attribute values. +* +* \sa +* ::nvtxMarkEx +* +* \version \NVTX_VERSION_2 +* @{ */ +NVTX_DECLSPEC void NVTX_API nvtxDomainMarkEx(nvtxDomainHandle_t domain, const nvtxEventAttributes_t* eventAttrib); +/** @} */ + +/* ------------------------------------------------------------------------- */ +/** \brief Marks an instantaneous event in the application. + * + * A marker can contain a text message or specify additional information + * using the event attributes structure. These attributes include a text + * message, color, category, and a payload. Each of the attributes is optional + * and can only be sent out using the \ref nvtxMarkEx function. + * If \ref nvtxMarkA or \ref nvtxMarkW are used to specify the marker + * or if an attribute is unspecified then a default value will be used. + * + * \param eventAttrib - The event attribute structure defining the marker's + * attribute types and attribute values. + * + * \par Example: + * \code + * // zero the structure + * nvtxEventAttributes_t eventAttrib = {0}; + * // set the version and the size information + * eventAttrib.version = NVTX_VERSION; + * eventAttrib.size = NVTX_EVENT_ATTRIB_STRUCT_SIZE; + * // configure the attributes. 0 is the default for all attributes. + * eventAttrib.colorType = NVTX_COLOR_ARGB; + * eventAttrib.color = 0xFF880000; + * eventAttrib.messageType = NVTX_MESSAGE_TYPE_ASCII; + * eventAttrib.message.ascii = "Example nvtxMarkEx"; + * nvtxMarkEx(&eventAttrib); + * \endcode + * + * \sa + * ::nvtxDomainMarkEx + * + * \version \NVTX_VERSION_1 + * @{ */ +NVTX_DECLSPEC void NVTX_API nvtxMarkEx(const nvtxEventAttributes_t* eventAttrib); +/** @} */ + +/* ------------------------------------------------------------------------- */ +/** \brief Marks an instantaneous event in the application. + * + * A marker created using \ref nvtxMarkA or \ref nvtxMarkW contains only a + * text message. + * + * \param message - The message associated to this marker event. + * + * \par Example: + * \code + * nvtxMarkA("Example nvtxMarkA"); + * nvtxMarkW(L"Example nvtxMarkW"); + * \endcode + * + * \sa + * ::nvtxDomainMarkEx + * ::nvtxMarkEx + * + * \version \NVTX_VERSION_0 + * @{ */ +NVTX_DECLSPEC void NVTX_API nvtxMarkA(const char* message); +NVTX_DECLSPEC void NVTX_API nvtxMarkW(const wchar_t* message); +/** @} */ + + +/** \name Process Ranges */ + +/* ------------------------------------------------------------------------- */ +/** \brief Starts a process range in a domain. +* +* \param domain - The domain of scoping the category. +* \param eventAttrib - The event attribute structure defining the range's +* attribute types and attribute values. +* +* \return The unique ID used to correlate a pair of Start and End events. +* +* \remarks Ranges defined by Start/End can overlap. +* +* \par Example: +* \code +* nvtxDomainHandle_t domain = nvtxDomainCreateA("my domain"); +* nvtxEventAttributes_t eventAttrib = {0}; +* eventAttrib.version = NVTX_VERSION; +* eventAttrib.size = NVTX_EVENT_ATTRIB_STRUCT_SIZE; +* eventAttrib.messageType = NVTX_MESSAGE_TYPE_ASCII; +* eventAttrib.message.ascii = "my range"; +* nvtxRangeId_t rangeId = nvtxDomainRangeStartEx(&eventAttrib); +* // ... +* nvtxDomainRangeEnd(rangeId); +* \endcode +* +* \sa +* ::nvtxDomainRangeEnd +* +* \version \NVTX_VERSION_2 +* @{ */ +NVTX_DECLSPEC nvtxRangeId_t NVTX_API nvtxDomainRangeStartEx(nvtxDomainHandle_t domain, const nvtxEventAttributes_t* eventAttrib); +/** @} */ + +/* ------------------------------------------------------------------------- */ +/** \brief Starts a process range. + * + * \param eventAttrib - The event attribute structure defining the range's + * attribute types and attribute values. + * + * \return The unique ID used to correlate a pair of Start and End events. + * + * \remarks Ranges defined by Start/End can overlap. + * + * \par Example: + * \code + * nvtxEventAttributes_t eventAttrib = {0}; + * eventAttrib.version = NVTX_VERSION; + * eventAttrib.size = NVTX_EVENT_ATTRIB_STRUCT_SIZE; + * eventAttrib.category = 3; + * eventAttrib.colorType = NVTX_COLOR_ARGB; + * eventAttrib.color = 0xFF0088FF; + * eventAttrib.messageType = NVTX_MESSAGE_TYPE_ASCII; + * eventAttrib.message.ascii = "Example Range"; + * nvtxRangeId_t rangeId = nvtxRangeStartEx(&eventAttrib); + * // ... + * nvtxRangeEnd(rangeId); + * \endcode + * + * \sa + * ::nvtxRangeEnd + * ::nvtxDomainRangeStartEx + * + * \version \NVTX_VERSION_1 + * @{ */ +NVTX_DECLSPEC nvtxRangeId_t NVTX_API nvtxRangeStartEx(const nvtxEventAttributes_t* eventAttrib); +/** @} */ + +/* ------------------------------------------------------------------------- */ +/** \brief Starts a process range. + * + * \param message - The event message associated to this range event. + * + * \return The unique ID used to correlate a pair of Start and End events. + * + * \remarks Ranges defined by Start/End can overlap. + * + * \par Example: + * \code + * nvtxRangeId_t r1 = nvtxRangeStartA("Range 1"); + * nvtxRangeId_t r2 = nvtxRangeStartW(L"Range 2"); + * nvtxRangeEnd(r1); + * nvtxRangeEnd(r2); + * \endcode + * + * \sa + * ::nvtxRangeEnd + * ::nvtxRangeStartEx + * ::nvtxDomainRangeStartEx + * + * \version \NVTX_VERSION_0 + * @{ */ +NVTX_DECLSPEC nvtxRangeId_t NVTX_API nvtxRangeStartA(const char* message); +NVTX_DECLSPEC nvtxRangeId_t NVTX_API nvtxRangeStartW(const wchar_t* message); +/** @} */ + +/* ------------------------------------------------------------------------- */ +/** \brief Ends a process range. +* +* \param domain - The domain +* \param id - The correlation ID returned from a nvtxRangeStart call. +* +* \remarks This function is offered completeness but is an alias for ::nvtxRangeEnd. +* It does not need a domain param since that is associated iwth the range ID at ::nvtxDomainRangeStartEx +* +* \par Example: +* \code +* nvtxDomainHandle_t domain = nvtxDomainCreateA("my domain"); +* nvtxEventAttributes_t eventAttrib = {0}; +* eventAttrib.version = NVTX_VERSION; +* eventAttrib.size = NVTX_EVENT_ATTRIB_STRUCT_SIZE; +* eventAttrib.messageType = NVTX_MESSAGE_TYPE_ASCII; +* eventAttrib.message.ascii = "my range"; +* nvtxRangeId_t rangeId = nvtxDomainRangeStartEx(&eventAttrib); +* // ... +* nvtxDomainRangeEnd(rangeId); +* \endcode +* +* \sa +* ::nvtxDomainRangeStartEx +* +* \version \NVTX_VERSION_2 +* @{ */ +NVTX_DECLSPEC void NVTX_API nvtxDomainRangeEnd(nvtxDomainHandle_t domain, nvtxRangeId_t id); +/** @} */ + +/* ------------------------------------------------------------------------- */ +/** \brief Ends a process range. + * + * \param id - The correlation ID returned from an nvtxRangeStart call. + * + * \sa + * ::nvtxDomainRangeStartEx + * ::nvtxRangeStartEx + * ::nvtxRangeStartA + * ::nvtxRangeStartW + * + * \version \NVTX_VERSION_0 + * @{ */ +NVTX_DECLSPEC void NVTX_API nvtxRangeEnd(nvtxRangeId_t id); +/** @} */ + +/** \name Thread Ranges */ + +/* ------------------------------------------------------------------------- */ +/** \brief Starts a nested thread range. +* +* \param domain - The domain of scoping. +* \param eventAttrib - The event attribute structure defining the range's +* attribute types and attribute values. +* +* \return The 0 based level of range being started. This value is scoped to the domain. +* If an error occurs, a negative value is returned. +* +* \par Example: +* \code +* nvtxDomainHandle_t domain = nvtxDomainCreateA("example domain"); +* nvtxEventAttributes_t eventAttrib = {0}; +* eventAttrib.version = NVTX_VERSION; +* eventAttrib.size = NVTX_EVENT_ATTRIB_STRUCT_SIZE; +* eventAttrib.colorType = NVTX_COLOR_ARGB; +* eventAttrib.color = 0xFFFF0000; +* eventAttrib.messageType = NVTX_MESSAGE_TYPE_ASCII; +* eventAttrib.message.ascii = "Level 0"; +* nvtxDomainRangePushEx(domain, &eventAttrib); +* +* // Re-use eventAttrib +* eventAttrib.messageType = NVTX_MESSAGE_TYPE_UNICODE; +* eventAttrib.message.unicode = L"Level 1"; +* nvtxDomainRangePushEx(domain, &eventAttrib); +* +* nvtxDomainRangePop(domain); //level 1 +* nvtxDomainRangePop(domain); //level 0 +* \endcode +* +* \sa +* ::nvtxDomainRangePop +* +* \version \NVTX_VERSION_2 +* @{ */ +NVTX_DECLSPEC int NVTX_API nvtxDomainRangePushEx(nvtxDomainHandle_t domain, const nvtxEventAttributes_t* eventAttrib); +/** @} */ + +/* ------------------------------------------------------------------------- */ +/** \brief Starts a nested thread range. + * + * \param eventAttrib - The event attribute structure defining the range's + * attribute types and attribute values. + * + * \return The 0 based level of range being started. This level is per domain. + * If an error occurs a negative value is returned. + * + * \par Example: + * \code + * nvtxEventAttributes_t eventAttrib = {0}; + * eventAttrib.version = NVTX_VERSION; + * eventAttrib.size = NVTX_EVENT_ATTRIB_STRUCT_SIZE; + * eventAttrib.colorType = NVTX_COLOR_ARGB; + * eventAttrib.color = 0xFFFF0000; + * eventAttrib.messageType = NVTX_MESSAGE_TYPE_ASCII; + * eventAttrib.message.ascii = "Level 0"; + * nvtxRangePushEx(&eventAttrib); + * + * // Re-use eventAttrib + * eventAttrib.messageType = NVTX_MESSAGE_TYPE_UNICODE; + * eventAttrib.message.unicode = L"Level 1"; + * nvtxRangePushEx(&eventAttrib); + * + * nvtxRangePop(); + * nvtxRangePop(); + * \endcode + * + * \sa + * ::nvtxDomainRangePushEx + * ::nvtxRangePop + * + * \version \NVTX_VERSION_1 + * @{ */ +NVTX_DECLSPEC int NVTX_API nvtxRangePushEx(const nvtxEventAttributes_t* eventAttrib); +/** @} */ + +/* ------------------------------------------------------------------------- */ +/** \brief Starts a nested thread range. + * + * \param message - The event message associated to this range event. + * + * \return The 0 based level of range being started. If an error occurs a + * negative value is returned. + * + * \par Example: + * \code + * nvtxRangePushA("Level 0"); + * nvtxRangePushW(L"Level 1"); + * nvtxRangePop(); + * nvtxRangePop(); + * \endcode + * + * \sa + * ::nvtxDomainRangePushEx + * ::nvtxRangePop + * + * \version \NVTX_VERSION_0 + * @{ */ +NVTX_DECLSPEC int NVTX_API nvtxRangePushA(const char* message); +NVTX_DECLSPEC int NVTX_API nvtxRangePushW(const wchar_t* message); +/** @} */ + + +/* ------------------------------------------------------------------------- */ +/** \brief Ends a nested thread range. +* +* \return The level of the range being ended. If an error occurs a negative +* value is returned on the current thread. +* +* \par Example: +* \code +* nvtxDomainHandle_t domain = nvtxDomainCreate("example library"); +* nvtxDomainRangePushA(domain, "Level 0"); +* nvtxDomainRangePushW(domain, L"Level 1"); +* nvtxDomainRangePop(domain); +* nvtxDomainRangePop(domain); +* \endcode +* +* \sa +* ::nvtxRangePushEx +* ::nvtxRangePushA +* ::nvtxRangePushW +* +* \version \NVTX_VERSION_2 +* @{ */ +NVTX_DECLSPEC int NVTX_API nvtxDomainRangePop(nvtxDomainHandle_t domain); +/** @} */ + +/* ------------------------------------------------------------------------- */ +/** \brief Ends a nested thread range. + * + * \return The level of the range being ended. If an error occurs a negative + * value is returned on the current thread. + * + * \par Example: + * \code + * nvtxRangePushA("Level 0"); + * nvtxRangePushW(L"Level 1"); + * nvtxRangePop(); + * nvtxRangePop(); + * \endcode + * + * \sa + * ::nvtxRangePushEx + * ::nvtxRangePushA + * ::nvtxRangePushW + * + * \version \NVTX_VERSION_0 + * @{ */ +NVTX_DECLSPEC int NVTX_API nvtxRangePop(void); +/** @} */ + + +/** @} */ /*END defgroup*/ +/* ========================================================================= */ +/** \defgroup RESOURCE_NAMING Resource Naming + * + * See \ref RESOURCE_NAMING for more details + * + * @{ + */ + + +/* ------------------------------------------------------------------------- */ +/** \name Functions for Generic Resource Naming*/ +/* ------------------------------------------------------------------------- */ + +/* ------------------------------------------------------------------------- */ +/** \cond SHOW_HIDDEN +* \brief Resource typing helpers. +* +* Classes are used to make it easy to create a series of resource types +* per API without collisions +*/ +#define NVTX_RESOURCE_MAKE_TYPE(CLASS, INDEX) ((((uint32_t)(NVTX_RESOURCE_CLASS_ ## CLASS))<<16)|((uint32_t)(INDEX))) +#define NVTX_RESOURCE_CLASS_GENERIC 1 +/** \endcond */ + +/* ------------------------------------------------------------------------- */ +/** \brief Generic resource type for when a resource class is not available. +* +* \sa +* ::nvtxDomainResourceCreate +* +* \version \NVTX_VERSION_2 +*/ +typedef enum nvtxResourceGenericType_t +{ + NVTX_RESOURCE_TYPE_UNKNOWN = 0, + NVTX_RESOURCE_TYPE_GENERIC_POINTER = NVTX_RESOURCE_MAKE_TYPE(GENERIC, 1), /**< Generic pointer assumed to have no collisions with other pointers. */ + NVTX_RESOURCE_TYPE_GENERIC_HANDLE = NVTX_RESOURCE_MAKE_TYPE(GENERIC, 2), /**< Generic handle assumed to have no collisions with other handles. */ + NVTX_RESOURCE_TYPE_GENERIC_THREAD_NATIVE = NVTX_RESOURCE_MAKE_TYPE(GENERIC, 3), /**< OS native thread identifier. */ + NVTX_RESOURCE_TYPE_GENERIC_THREAD_POSIX = NVTX_RESOURCE_MAKE_TYPE(GENERIC, 4) /**< POSIX pthread identifier. */ +} nvtxResourceGenericType_t; + + + +/** \brief Resource Attribute Structure. +* \anchor RESOURCE_ATTRIBUTE_STRUCTURE +* +* This structure is used to describe the attributes of a resource. The layout of +* the structure is defined by a specific version of the tools extension +* library and can change between different versions of the Tools Extension +* library. +* +* \par Initializing the Attributes +* +* The caller should always perform the following three tasks when using +* attributes: +*
    +*
  • Zero the structure +*
  • Set the version field +*
  • Set the size field +*
+* +* Zeroing the structure sets all the resource attributes types and values +* to the default value. +* +* The version and size field are used by the Tools Extension +* implementation to handle multiple versions of the attributes structure. +* +* It is recommended that the caller use one of the following to methods +* to initialize the event attributes structure: +* +* \par Method 1: Initializing nvtxEventAttributes for future compatibility +* \code +* nvtxResourceAttributes_t attribs = {0}; +* attribs.version = NVTX_VERSION; +* attribs.size = NVTX_RESOURCE_ATTRIB_STRUCT_SIZE; +* \endcode +* +* \par Method 2: Initializing nvtxEventAttributes for a specific version +* \code +* nvtxResourceAttributes_v0 attribs = {0}; +* attribs.version = 2; +* attribs.size = (uint16_t)(sizeof(nvtxResourceAttributes_v0)); +* \endcode +* +* If the caller uses Method 1 it is critical that the entire binary +* layout of the structure be configured to 0 so that all fields +* are initialized to the default value. +* +* The caller should either use both NVTX_VERSION and +* NVTX_RESOURCE_ATTRIB_STRUCT_SIZE (Method 1) or use explicit values +* and a versioned type (Method 2). Using a mix of the two methods +* will likely cause either source level incompatibility or binary +* incompatibility in the future. +* +* \par Settings Attribute Types and Values +* +* +* \par Example: +* \code +* nvtxDomainHandle_t domain = nvtxDomainCreateA("example domain"); +* +* // Initialize +* nvtxResourceAttributes_t attribs = {0}; +* attribs.version = NVTX_VERSION; +* attribs.size = NVTX_RESOURCE_ATTRIB_STRUCT_SIZE; +* +* // Configure the Attributes +* attribs.identifierType = NVTX_RESOURCE_TYPE_GENERIC_POINTER; +* attribs.identifier.pValue = (const void*)pMutex; +* attribs.messageType = NVTX_MESSAGE_TYPE_ASCII; +* attribs.message.ascii = "Single thread access to database."; +* +* nvtxResourceHandle_t handle = nvtxDomainResourceCreate(domain, attribs); +* \endcode +* +* \sa +* ::nvtxDomainResourceCreate +*/ +typedef struct nvtxResourceAttributes_v0 +{ + /** + * \brief Version flag of the structure. + * + * Needs to be set to NVTX_VERSION to indicate the version of NVTX APIs + * supported in this header file. This can optionally be overridden to + * another version of the tools extension library. + */ + uint16_t version; + + /** + * \brief Size of the structure. + * + * Needs to be set to the size in bytes of this attribute + * structure. + */ + uint16_t size; + + /** + * \brief Identifier type specifies how to interpret the identifier field + * + * Defines the identifier format of the attribute structure's \ref RESOURCE_IDENTIFIER_FIELD + * "identifier" field. + * + * Default Value is NVTX_RESOURCE_TYPE_UNKNOWN + */ + int32_t identifierType; /* values from enums following the pattern nvtxResource[name]Type_t */ + + /** + * \brief Identifier for the resource. + * \anchor RESOURCE_IDENTIFIER_FIELD + * + * An identifier may be a pointer or a handle to an OS or middleware API object. + * The resource type will assist in avoiding collisions where handles values may collide. + */ + union identifier_t + { + const void* pValue; + uint64_t ullValue; + } identifier; + + /** \brief Message type specified in this attribute structure. + * + * Defines the message format of the attribute structure's \ref RESOURCE_MESSAGE_FIELD + * "message" field. + * + * Default Value is NVTX_MESSAGE_UNKNOWN + */ + int32_t messageType; /* nvtxMessageType_t */ + + /** \brief Message assigned to this attribute structure. \anchor RESOURCE_MESSAGE_FIELD + * + * The text message that is attached to a resource. + */ + nvtxMessageValue_t message; + +} nvtxResourceAttributes_v0; + +typedef struct nvtxResourceAttributes_v0 nvtxResourceAttributes_t; + +/* \cond SHOW_HIDDEN +* \version \NVTX_VERSION_2 +*/ +#define NVTX_RESOURCE_ATTRIB_STRUCT_SIZE ( (uint16_t)( sizeof(nvtxResourceAttributes_v0) ) ) +typedef struct nvtxResourceHandle* nvtxResourceHandle_t; +/** \endcond */ + + + +/* ------------------------------------------------------------------------- */ +/** \brief Create a resource object to track and associate data with OS and middleware objects +* +* Allows users to associate an API handle or pointer with a user-provided name. +* +* +* \param domain - Domain to own the resource object +* \param attribs - Attributes to be associated with the resource +* +* \return A handle that represents the newly created resource object. +* +* \par Example: +* \code +* nvtxDomainHandle_t domain = nvtxDomainCreateA("example domain"); +* nvtxResourceAttributes_t attribs = {0}; +* attribs.version = NVTX_VERSION; +* attribs.size = NVTX_RESOURCE_ATTRIB_STRUCT_SIZE; +* attribs.identifierType = NVTX_RESOURCE_TYPE_GENERIC_POINTER; +* attribs.identifier.pValue = (const void*)pMutex; +* attribs.messageType = NVTX_MESSAGE_TYPE_ASCII; +* attribs.message.ascii = "Single thread access to database."; +* nvtxResourceHandle_t handle = nvtxDomainResourceCreate(domain, attribs); +* \endcode +* +* \sa +* ::nvtxResourceAttributes_t +* ::nvtxDomainResourceDestroy +* +* \version \NVTX_VERSION_2 +* @{ */ +NVTX_DECLSPEC nvtxResourceHandle_t NVTX_API nvtxDomainResourceCreate(nvtxDomainHandle_t domain, nvtxResourceAttributes_t* attribs); +/** @} */ + +/* ------------------------------------------------------------------------- */ +/** \brief Destroy a resource object to track and associate data with OS and middleware objects +* +* Allows users to associate an API handle or pointer with a user-provided name. +* +* \param resource - Handle to the resource in which to operate. +* +* \par Example: +* \code +* nvtxDomainHandle_t domain = nvtxDomainCreateA("example domain"); +* nvtxResourceAttributes_t attribs = {0}; +* attribs.version = NVTX_VERSION; +* attribs.size = NVTX_RESOURCE_ATTRIB_STRUCT_SIZE; +* attribs.identifierType = NVTX_RESOURCE_TYPE_GENERIC_POINTER; +* attribs.identifier.pValue = (const void*)pMutex; +* attribs.messageType = NVTX_MESSAGE_TYPE_ASCII; +* attribs.message.ascii = "Single thread access to database."; +* nvtxResourceHandle_t handle = nvtxDomainResourceCreate(domain, attribs); +* nvtxDomainResourceDestroy(handle); +* \endcode +* +* \sa +* ::nvtxDomainResourceCreate +* +* \version \NVTX_VERSION_2 +* @{ */ +NVTX_DECLSPEC void NVTX_API nvtxDomainResourceDestroy(nvtxResourceHandle_t resource); +/** @} */ + + +/** \name Functions for NVTX Category Naming*/ + +/* ------------------------------------------------------------------------- */ +/** +* \brief Annotate an NVTX category used within a domain. +* +* Categories are used to group sets of events. Each category is identified +* through a unique ID and that ID is passed into any of the marker/range +* events to assign that event to a specific category. The nvtxDomainNameCategory +* function calls allow the user to assign a name to a category ID that is +* specific to the domain. +* +* nvtxDomainNameCategory(NULL, category, name) is equivalent to calling +* nvtxNameCategory(category, name). +* +* \param domain - The domain of scoping the category. +* \param category - The category ID to name. +* \param name - The name of the category. +* +* \remarks The category names are tracked per domain. +* +* \par Example: +* \code +* nvtxDomainHandle_t domain = nvtxDomainCreateA("example"); +* nvtxDomainNameCategoryA(domain, 1, "Memory Allocation"); +* nvtxDomainNameCategoryW(domain, 2, L"Memory Transfer"); +* \endcode +* +* \version \NVTX_VERSION_2 +* @{ */ +NVTX_DECLSPEC void NVTX_API nvtxDomainNameCategoryA(nvtxDomainHandle_t domain, uint32_t category, const char* name); +NVTX_DECLSPEC void NVTX_API nvtxDomainNameCategoryW(nvtxDomainHandle_t domain, uint32_t category, const wchar_t* name); +/** @} */ + +/** \brief Annotate an NVTX category. + * + * Categories are used to group sets of events. Each category is identified + * through a unique ID and that ID is passed into any of the marker/range + * events to assign that event to a specific category. The nvtxNameCategory + * function calls allow the user to assign a name to a category ID. + * + * \param category - The category ID to name. + * \param name - The name of the category. + * + * \remarks The category names are tracked per process. + * + * \par Example: + * \code + * nvtxNameCategory(1, "Memory Allocation"); + * nvtxNameCategory(2, "Memory Transfer"); + * nvtxNameCategory(3, "Memory Object Lifetime"); + * \endcode + * + * \version \NVTX_VERSION_1 + * @{ */ +NVTX_DECLSPEC void NVTX_API nvtxNameCategoryA(uint32_t category, const char* name); +NVTX_DECLSPEC void NVTX_API nvtxNameCategoryW(uint32_t category, const wchar_t* name); +/** @} */ + +/** \name Functions for OS Threads Naming*/ + +/* ------------------------------------------------------------------------- */ +/** \brief Annotate an OS thread. + * + * Allows the user to name an active thread of the current process. If an + * invalid thread ID is provided or a thread ID from a different process is + * used the behavior of the tool is implementation dependent. + * + * The thread name is associated to the default domain. To support domains + * use resource objects via ::nvtxDomainResourceCreate. + * + * \param threadId - The ID of the thread to name. + * \param name - The name of the thread. + * + * \par Example: + * \code + * nvtxNameOsThread(GetCurrentThreadId(), "MAIN_THREAD"); + * \endcode + * + * \version \NVTX_VERSION_1 + * @{ */ +NVTX_DECLSPEC void NVTX_API nvtxNameOsThreadA(uint32_t threadId, const char* name); +NVTX_DECLSPEC void NVTX_API nvtxNameOsThreadW(uint32_t threadId, const wchar_t* name); +/** @} */ + + +/** @} */ /*END defgroup*/ +/* ========================================================================= */ +/** \defgroup STRING_REGISTRATION String Registration +* +* Registered strings are intended to increase performance by lowering instrumentation +* overhead. String may be registered once and the handle may be passed in place of +* a string where an the APIs may allow. +* +* See \ref STRING_REGISTRATION for more details +* +* @{ +*/ + +/* ------------------------------------------------------------------------- */ +/** \brief Register a string. + +* Registers an immutable string with NVTX. Once registered the pointer used +* to register the domain name can be used in nvtxEventAttributes_t +* \ref MESSAGE_FIELD. This allows NVTX implementation to skip copying the +* contents of the message on each event invocation. +* +* String registration is an optimization. It is recommended to use string +* registration if the string will be passed to an event many times. +* +* String are not unregistered, except that by unregistering the entire domain +* +* \param domain - Domain handle. If NULL then the global domain is used. +* \param string - A unique pointer to a sequence of characters. +* +* \return A handle representing the registered string. +* +* \par Example: +* \code +* nvtxDomainCreateA("com.nvidia.nvtx.example"); +* nvtxStringHandle_t message = nvtxDomainRegisterStringA(domain, "registered string"); +* nvtxEventAttributes_t eventAttrib = {0}; +* eventAttrib.version = NVTX_VERSION; +* eventAttrib.size = NVTX_EVENT_ATTRIB_STRUCT_SIZE; +* eventAttrib.messageType = NVTX_MESSAGE_TYPE_REGISTERED; +* eventAttrib.message.registered = message; +* \endcode +* +* \version \NVTX_VERSION_2 +* @{ */ +NVTX_DECLSPEC nvtxStringHandle_t NVTX_API nvtxDomainRegisterStringA(nvtxDomainHandle_t domain, const char* string); +NVTX_DECLSPEC nvtxStringHandle_t NVTX_API nvtxDomainRegisterStringW(nvtxDomainHandle_t domain, const wchar_t* string); +/** @} */ + +/** @} */ /*END defgroup*/ +/* ========================================================================= */ +/** \defgroup DOMAINS Domains +* +* Domains are used to group events to a developer defined scope. Middleware +* vendors may also scope their own events to avoid collisions with the +* the application developer's events, so that the application developer may +* inspect both parts and easily differentiate or filter them. By default +* all events are scoped to a global domain where NULL is provided or when +* using APIs provided b versions of NVTX below v2 +* +* Domains are intended to be typically long lived objects with the intention +* of logically separating events of large modules from each other such as +* middleware libraries from each other and the main application. +* +* See \ref DOMAINS for more details +* +* @{ +*/ + +/* ------------------------------------------------------------------------- */ +/** \brief Register a NVTX domain. +* +* Domains are used to scope annotations. All NVTX_VERSION_0 and NVTX_VERSION_1 +* annotations are scoped to the global domain. The function nvtxDomainCreate +* creates a new named domain. +* +* Each domain maintains its own nvtxRangePush and nvtxRangePop stack. +* +* \param name - A unique string representing the domain. +* +* \return A handle representing the domain. +* +* \par Example: +* \code +* nvtxDomainHandle_t domain = nvtxDomainCreateA("com.nvidia.nvtx.example"); +* +* nvtxMarkA("nvtxMarkA to global domain"); +* +* nvtxEventAttributes_t eventAttrib1 = {0}; +* eventAttrib1.version = NVTX_VERSION; +* eventAttrib1.size = NVTX_EVENT_ATTRIB_STRUCT_SIZE; +* eventAttrib1.message.ascii = "nvtxDomainMarkEx to global domain"; +* nvtxDomainMarkEx(NULL, &eventAttrib1); +* +* nvtxEventAttributes_t eventAttrib2 = {0}; +* eventAttrib2.version = NVTX_VERSION; +* eventAttrib2.size = NVTX_EVENT_ATTRIB_STRUCT_SIZE; +* eventAttrib2.message.ascii = "nvtxDomainMarkEx to com.nvidia.nvtx.example"; +* nvtxDomainMarkEx(domain, &eventAttrib2); +* nvtxDomainDestroy(domain); +* \endcode +* +* \sa +* ::nvtxDomainDestroy +* +* \version \NVTX_VERSION_2 +* @{ */ +NVTX_DECLSPEC nvtxDomainHandle_t NVTX_API nvtxDomainCreateA(const char* name); +NVTX_DECLSPEC nvtxDomainHandle_t NVTX_API nvtxDomainCreateW(const wchar_t* name); +/** @} */ + +/* ------------------------------------------------------------------------- */ +/** \brief Unregister a NVTX domain. +* +* Unregisters the domain handle and frees all domain specific resources. +* +* \param domain - the domain handle +* +* \par Example: +* \code +* nvtxDomainHandle_t domain = nvtxDomainCreateA("com.nvidia.nvtx.example"); +* nvtxDomainDestroy(domain); +* \endcode +* +* \sa +* ::nvtxDomainCreateA +* ::nvtxDomainCreateW +* +* \version \NVTX_VERSION_2 +* @{ */ +NVTX_DECLSPEC void NVTX_API nvtxDomainDestroy(nvtxDomainHandle_t domain); +/** @} */ + + +/** @} */ /*END defgroup*/ +/* ========================================================================= */ +/** \cond SHOW_HIDDEN */ + +#ifdef UNICODE + #define nvtxMark nvtxMarkW + #define nvtxRangeStart nvtxRangeStartW + #define nvtxRangePush nvtxRangePushW + #define nvtxNameCategory nvtxNameCategoryW + #define nvtxNameOsThread nvtxNameOsThreadW + /* NVTX_VERSION_2 */ + #define nvtxDomainCreate nvtxDomainCreateW + #define nvtxDomainRegisterString nvtxDomainRegisterStringW + #define nvtxDomainNameCategory nvtxDomainNameCategoryW +#else + #define nvtxMark nvtxMarkA + #define nvtxRangeStart nvtxRangeStartA + #define nvtxRangePush nvtxRangePushA + #define nvtxNameCategory nvtxNameCategoryA + #define nvtxNameOsThread nvtxNameOsThreadA + /* NVTX_VERSION_2 */ + #define nvtxDomainCreate nvtxDomainCreateA + #define nvtxDomainRegisterString nvtxDomainRegisterStringA + #define nvtxDomainNameCategory nvtxDomainNameCategoryA +#endif + +/** \endcond */ + +#ifdef __cplusplus +} +#endif /* __cplusplus */ + +#endif /* NVTOOLSEXT_H_ */ diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvToolsExtCuda.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvToolsExtCuda.h new file mode 100644 index 0000000000000000000000000000000000000000..d2017d54458b7798da17029212c735d69f4b71a8 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvToolsExtCuda.h @@ -0,0 +1,164 @@ +/* +* Copyright 2009-2017 NVIDIA Corporation. All rights reserved. +* +* NOTICE TO USER: +* +* This source code is subject to NVIDIA ownership rights under U.S. and +* international Copyright laws. +* +* This software and the information contained herein is PROPRIETARY and +* CONFIDENTIAL to NVIDIA and is being provided under the terms and conditions +* of a form of NVIDIA software license agreement. +* +* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE +* CODE FOR ANY PURPOSE. IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR +* IMPLIED WARRANTY OF ANY KIND. NVIDIA DISCLAIMS ALL WARRANTIES WITH +* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF +* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE. +* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL, +* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS +* OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE +* OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE +* OR PERFORMANCE OF THIS SOURCE CODE. +* +* U.S. Government End Users. This source code is a "commercial item" as +* that term is defined at 48 C.F.R. 2.101 (OCT 1995), consisting of +* "commercial computer software" and "commercial computer software +* documentation" as such terms are used in 48 C.F.R. 12.212 (SEPT 1995) +* and is provided to the U.S. Government only as a commercial end item. +* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through +* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the +* source code with only those rights set forth herein. +* +* Any use of this source code in individual and commercial software must +* include, in the user documentation and internal comments to the code, +* the above Disclaimer and U.S. Government End Users Notice. +*/ + +#ifndef NVTOOLSEXT_CUDA_H_ +#define NVTOOLSEXT_CUDA_H_ + +#include "cuda.h" + +#include "nvToolsExt.h" + +#ifdef __cplusplus +extern "C" { +#endif /* __cplusplus */ + +/* ========================================================================= */ +/** \name Functions for CUDA Resource Naming +*/ +/** \addtogroup RESOURCE_NAMING + * \section RESOURCE_NAMING_CUDA CUDA Resource Naming + * + * This section covers the API functions that allow to annotate CUDA resources + * with user-provided names. + * + * @{ + */ + +/* ------------------------------------------------------------------------- */ +/* \cond SHOW_HIDDEN +* \brief Used to build a non-colliding value for resource types separated class +* \version \NVTX_VERSION_2 +*/ +#define NVTX_RESOURCE_CLASS_CUDA 4 +/** \endcond */ + +/* ------------------------------------------------------------------------- */ +/** \brief Resource types for CUDA +*/ +typedef enum nvtxResourceCUDAType_t +{ + NVTX_RESOURCE_TYPE_CUDA_DEVICE = NVTX_RESOURCE_MAKE_TYPE(CUDA, 1), /* CUdevice */ + NVTX_RESOURCE_TYPE_CUDA_CONTEXT = NVTX_RESOURCE_MAKE_TYPE(CUDA, 2), /* CUcontext */ + NVTX_RESOURCE_TYPE_CUDA_STREAM = NVTX_RESOURCE_MAKE_TYPE(CUDA, 3), /* CUstream */ + NVTX_RESOURCE_TYPE_CUDA_EVENT = NVTX_RESOURCE_MAKE_TYPE(CUDA, 4) /* CUevent */ +} nvtxResourceCUDAType_t; + + +/* ------------------------------------------------------------------------- */ +/** \brief Annotates a CUDA device. + * + * Allows the user to associate a CUDA device with a user-provided name. + * + * \param device - The handle of the CUDA device to name. + * \param name - The name of the CUDA device. + * + * \version \NVTX_VERSION_1 + * @{ */ +NVTX_DECLSPEC void NVTX_API nvtxNameCuDeviceA(CUdevice device, const char* name); +NVTX_DECLSPEC void NVTX_API nvtxNameCuDeviceW(CUdevice device, const wchar_t* name); +/** @} */ + +/* ------------------------------------------------------------------------- */ +/** \brief Annotates a CUDA context. + * + * Allows the user to associate a CUDA context with a user-provided name. + * + * \param context - The handle of the CUDA context to name. + * \param name - The name of the CUDA context. + * + * \par Example: + * \code + * CUresult status = cuCtxCreate( &cuContext, 0, cuDevice ); + * if ( CUDA_SUCCESS != status ) + * goto Error; + * nvtxNameCuContext(cuContext, "CTX_NAME"); + * \endcode + * + * \version \NVTX_VERSION_1 + * @{ */ +NVTX_DECLSPEC void NVTX_API nvtxNameCuContextA(CUcontext context, const char* name); +NVTX_DECLSPEC void NVTX_API nvtxNameCuContextW(CUcontext context, const wchar_t* name); +/** @} */ + +/* ------------------------------------------------------------------------- */ +/** \brief Annotates a CUDA stream. + * + * Allows the user to associate a CUDA stream with a user-provided name. + * + * \param stream - The handle of the CUDA stream to name. + * \param name - The name of the CUDA stream. + * + * \version \NVTX_VERSION_1 + * @{ */ +NVTX_DECLSPEC void NVTX_API nvtxNameCuStreamA(CUstream stream, const char* name); +NVTX_DECLSPEC void NVTX_API nvtxNameCuStreamW(CUstream stream, const wchar_t* name); +/** @} */ + +/* ------------------------------------------------------------------------- */ +/** \brief Annotates a CUDA event. + * + * Allows the user to associate a CUDA event with a user-provided name. + * + * \param event - The handle of the CUDA event to name. + * \param name - The name of the CUDA event. + * + * \version \NVTX_VERSION_1 + * @{ */ +NVTX_DECLSPEC void NVTX_API nvtxNameCuEventA(CUevent event, const char* name); +NVTX_DECLSPEC void NVTX_API nvtxNameCuEventW(CUevent event, const wchar_t* name); +/** @} */ + +/** @} */ /* END RESOURCE_NAMING */ + +/* ========================================================================= */ +#ifdef UNICODE + #define nvtxNameCuDevice nvtxNameCuDeviceW + #define nvtxNameCuContext nvtxNameCuContextW + #define nvtxNameCuStream nvtxNameCuStreamW + #define nvtxNameCuEvent nvtxNameCuEventW +#else + #define nvtxNameCuDevice nvtxNameCuDeviceA + #define nvtxNameCuContext nvtxNameCuContextA + #define nvtxNameCuStream nvtxNameCuStreamA + #define nvtxNameCuEvent nvtxNameCuEventA +#endif + +#ifdef __cplusplus +} +#endif /* __cplusplus */ + +#endif /* NVTOOLSEXT_CUDA_H_ */ diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvToolsExtCudaRt.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvToolsExtCudaRt.h new file mode 100644 index 0000000000000000000000000000000000000000..5af4cebe4e6e19968204bd82fb76bc03840506ae --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvToolsExtCudaRt.h @@ -0,0 +1,140 @@ +/* +* Copyright 2009-2017 NVIDIA Corporation. All rights reserved. +* +* NOTICE TO USER: +* +* This source code is subject to NVIDIA ownership rights under U.S. and +* international Copyright laws. +* +* This software and the information contained herein is PROPRIETARY and +* CONFIDENTIAL to NVIDIA and is being provided under the terms and conditions +* of a form of NVIDIA software license agreement. +* +* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE +* CODE FOR ANY PURPOSE. IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR +* IMPLIED WARRANTY OF ANY KIND. NVIDIA DISCLAIMS ALL WARRANTIES WITH +* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF +* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE. +* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL, +* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS +* OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE +* OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE +* OR PERFORMANCE OF THIS SOURCE CODE. +* +* U.S. Government End Users. This source code is a "commercial item" as +* that term is defined at 48 C.F.R. 2.101 (OCT 1995), consisting of +* "commercial computer software" and "commercial computer software +* documentation" as such terms are used in 48 C.F.R. 12.212 (SEPT 1995) +* and is provided to the U.S. Government only as a commercial end item. +* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through +* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the +* source code with only those rights set forth herein. +* +* Any use of this source code in individual and commercial software must +* include, in the user documentation and internal comments to the code, +* the above Disclaimer and U.S. Government End Users Notice. +*/ + +#ifndef NVTOOLSEXT_CUDART_H_ +#define NVTOOLSEXT_CUDART_H_ + +#include "cuda.h" +#include "driver_types.h" + +#include "nvToolsExt.h" + +#ifdef __cplusplus +extern "C" { +#endif /* __cplusplus */ + +/* ========================================================================= */ +/** \name Functions for CUDA Resource Naming +*/ +/** \addtogroup RESOURCE_NAMING + * \section RESOURCE_NAMING_CUDART CUDA Runtime Resource Naming + * + * This section covers the API functions that allow to annotate CUDA resources + * with user-provided names. + * + * @{ + */ + +/* ------------------------------------------------------------------------- */ +/* \cond SHOW_HIDDEN +* \brief Used to build a non-colliding value for resource types separated class +* \version \NVTX_VERSION_2 +*/ +#define NVTX_RESOURCE_CLASS_CUDART 5 +/** \endcond */ + +/* ------------------------------------------------------------------------- */ +/** \brief Resource types for CUDART +*/ +typedef enum nvtxResourceCUDARTType_t +{ + NVTX_RESOURCE_TYPE_CUDART_DEVICE = NVTX_RESOURCE_MAKE_TYPE(CUDART, 0), /* int device */ + NVTX_RESOURCE_TYPE_CUDART_STREAM = NVTX_RESOURCE_MAKE_TYPE(CUDART, 1), /* cudaStream_t */ + NVTX_RESOURCE_TYPE_CUDART_EVENT = NVTX_RESOURCE_MAKE_TYPE(CUDART, 2) /* cudaEvent_t */ +} nvtxResourceCUDARTType_t; + + +/* ------------------------------------------------------------------------- */ +/** \brief Annotates a CUDA device. + * + * Allows the user to associate a CUDA device with a user-provided name. + * + * \param device - The id of the CUDA device to name. + * \param name - The name of the CUDA device. + * + * \version \NVTX_VERSION_1 + * @{ */ +NVTX_DECLSPEC void NVTX_API nvtxNameCudaDeviceA(int device, const char* name); +NVTX_DECLSPEC void NVTX_API nvtxNameCudaDeviceW(int device, const wchar_t* name); +/** @} */ + +/* ------------------------------------------------------------------------- */ +/** \brief Annotates a CUDA stream. + * + * Allows the user to associate a CUDA stream with a user-provided name. + * + * \param stream - The handle of the CUDA stream to name. + * \param name - The name of the CUDA stream. + * + * \version \NVTX_VERSION_1 + * @{ */ +NVTX_DECLSPEC void NVTX_API nvtxNameCudaStreamA(cudaStream_t stream, const char* name); +NVTX_DECLSPEC void NVTX_API nvtxNameCudaStreamW(cudaStream_t stream, const wchar_t* name); +/** @} */ + +/* ------------------------------------------------------------------------- */ +/** \brief Annotates a CUDA event. + * + * Allows the user to associate a CUDA event with a user-provided name. + * + * \param event - The handle of the CUDA event to name. + * \param name - The name of the CUDA event. + * + * \version \NVTX_VERSION_1 + * @{ */ +NVTX_DECLSPEC void NVTX_API nvtxNameCudaEventA(cudaEvent_t event, const char* name); +NVTX_DECLSPEC void NVTX_API nvtxNameCudaEventW(cudaEvent_t event, const wchar_t* name); +/** @} */ + +/** @} */ /* END RESOURCE_NAMING */ + +/* ========================================================================= */ +#ifdef UNICODE + #define nvtxNameCudaDevice nvtxNameCudaDeviceW + #define nvtxNameCudaStream nvtxNameCudaStreamW + #define nvtxNameCudaEvent nvtxNameCudaEventW +#else + #define nvtxNameCudaDevice nvtxNameCudaDeviceA + #define nvtxNameCudaStream nvtxNameCudaStreamA + #define nvtxNameCudaEvent nvtxNameCudaEventA +#endif + +#ifdef __cplusplus +} +#endif /* __cplusplus */ + +#endif /* NVTOOLSEXT_CUDART_H_ */ diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvToolsExtOpenCL.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvToolsExtOpenCL.h new file mode 100644 index 0000000000000000000000000000000000000000..e826610a405a2c32eda5a851f7930b7b3feeb323 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvToolsExtOpenCL.h @@ -0,0 +1,214 @@ +/* +* Copyright 2009-2017 NVIDIA Corporation. All rights reserved. +* +* NOTICE TO USER: +* +* This source code is subject to NVIDIA ownership rights under U.S. and +* international Copyright laws. +* +* This software and the information contained herein is PROPRIETARY and +* CONFIDENTIAL to NVIDIA and is being provided under the terms and conditions +* of a form of NVIDIA software license agreement. +* +* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE +* CODE FOR ANY PURPOSE. IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR +* IMPLIED WARRANTY OF ANY KIND. NVIDIA DISCLAIMS ALL WARRANTIES WITH +* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF +* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE. +* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL, +* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS +* OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE +* OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE +* OR PERFORMANCE OF THIS SOURCE CODE. +* +* U.S. Government End Users. This source code is a "commercial item" as +* that term is defined at 48 C.F.R. 2.101 (OCT 1995), consisting of +* "commercial computer software" and "commercial computer software +* documentation" as such terms are used in 48 C.F.R. 12.212 (SEPT 1995) +* and is provided to the U.S. Government only as a commercial end item. +* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through +* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the +* source code with only those rights set forth herein. +* +* Any use of this source code in individual and commercial software must +* include, in the user documentation and internal comments to the code, +* the above Disclaimer and U.S. Government End Users Notice. +*/ + +#ifndef NVTOOLSEXT_OPENCL_H_ +#define NVTOOLSEXT_OPENCL_H_ + +#include + +#include "nvToolsExt.h" + +#ifdef __cplusplus +extern "C" { +#endif /* __cplusplus */ + +/* ========================================================================= */ +/** \name Functions for OpenCL Resource Naming + */ +/** \addtogroup RESOURCE_NAMING + * \section RESOURCE_NAMING_OPENCL OpenCL Resource Naming + * + * This section covers the API functions that allow to annotate OpenCL resources + * with user-provided names. + * + * @{ + */ + +/* ------------------------------------------------------------------------- */ +/* \cond SHOW_HIDDEN +* \brief Used to build a non-colliding value for resource types separated class +* \version \NVTX_VERSION_2 +*/ +#define NVTX_RESOURCE_CLASS_OPENCL 6 +/** \endcond */ + +/* ------------------------------------------------------------------------- */ +/** \brief Resource types for OpenCL +*/ +typedef enum nvtxResourceOpenCLType_t +{ + NVTX_RESOURCE_TYPE_OPENCL_DEVICE = NVTX_RESOURCE_MAKE_TYPE(OPENCL, 1), + NVTX_RESOURCE_TYPE_OPENCL_CONTEXT = NVTX_RESOURCE_MAKE_TYPE(OPENCL, 2), + NVTX_RESOURCE_TYPE_OPENCL_COMMANDQUEUE = NVTX_RESOURCE_MAKE_TYPE(OPENCL, 3), + NVTX_RESOURCE_TYPE_OPENCL_MEMOBJECT = NVTX_RESOURCE_MAKE_TYPE(OPENCL, 4), + NVTX_RESOURCE_TYPE_OPENCL_SAMPLER = NVTX_RESOURCE_MAKE_TYPE(OPENCL, 5), + NVTX_RESOURCE_TYPE_OPENCL_PROGRAM = NVTX_RESOURCE_MAKE_TYPE(OPENCL, 6), + NVTX_RESOURCE_TYPE_OPENCL_EVENT = NVTX_RESOURCE_MAKE_TYPE(OPENCL, 7) +} nvtxResourceOpenCLType_t; + + +/* ------------------------------------------------------------------------- */ +/** \brief Annotates an OpenCL device. + * + * Allows to associate an OpenCL device with a user-provided name. + * + * \param device - The handle of the OpenCL device to name. + * \param name - The name of the OpenCL device. + * + * \version \NVTX_VERSION_1 + * @{ */ +NVTX_DECLSPEC void NVTX_API nvtxNameClDeviceA(cl_device_id device, const char* name); +NVTX_DECLSPEC void NVTX_API nvtxNameClDeviceW(cl_device_id device, const wchar_t* name); +/** @} */ + +/* ------------------------------------------------------------------------- */ +/** \brief Annotates an OpenCL context. + * + * Allows to associate an OpenCL context with a user-provided name. + * + * \param context - The handle of the OpenCL context to name. + * \param name - The name of the OpenCL context. + * + * \version \NVTX_VERSION_1 + * @{ */ +NVTX_DECLSPEC void NVTX_API nvtxNameClContextA(cl_context context, const char* name); +NVTX_DECLSPEC void NVTX_API nvtxNameClContextW(cl_context context, const wchar_t* name); +/** @} */ + +/* ------------------------------------------------------------------------- */ +/** \brief Annotates an OpenCL command queue. + * + * Allows to associate an OpenCL command queue with a user-provided name. + * + * \param command_queue - The handle of the OpenCL command queue to name. + * \param name - The name of the OpenCL command queue. + * + * \version \NVTX_VERSION_1 + * @{ */ +NVTX_DECLSPEC void NVTX_API nvtxNameClCommandQueueA(cl_command_queue command_queue, const char* name); +NVTX_DECLSPEC void NVTX_API nvtxNameClCommandQueueW(cl_command_queue command_queue, const wchar_t* name); +/** @} */ + +/* ------------------------------------------------------------------------- */ +/** \brief Annotates an OpenCL memory object. + * + * Allows to associate an OpenCL memory object with a user-provided name. + * + * \param memobj - The handle of the OpenCL memory object to name. + * \param name - The name of the OpenCL memory object. + * + * \version \NVTX_VERSION_1 + * @{ */ +NVTX_DECLSPEC void NVTX_API nvtxNameClMemObjectA(cl_mem memobj, const char* name); +NVTX_DECLSPEC void NVTX_API nvtxNameClMemObjectW(cl_mem memobj, const wchar_t* name); +/** @} */ + +/* ------------------------------------------------------------------------- */ +/** \brief Annotates an OpenCL sampler. + * + * Allows to associate an OpenCL sampler with a user-provided name. + * + * \param sampler - The handle of the OpenCL sampler to name. + * \param name - The name of the OpenCL sampler. + * + * \version \NVTX_VERSION_1 + * @{ */ +NVTX_DECLSPEC void NVTX_API nvtxNameClSamplerA(cl_sampler sampler, const char* name); +NVTX_DECLSPEC void NVTX_API nvtxNameClSamplerW(cl_sampler sampler, const wchar_t* name); +/** @} */ + +/* ------------------------------------------------------------------------- */ +/** \brief Annotates an OpenCL program. + * + * Allows to associate an OpenCL program with a user-provided name. + * + * \param program - The handle of the OpenCL program to name. + * \param name - The name of the OpenCL program. + * + * \code + * cpProgram = clCreateProgramWithSource(cxGPUContext, 1, + * (const char **) &cSourceCL, &program_length, &ciErrNum); + * shrCheckErrorEX(ciErrNum, CL_SUCCESS, pCleanup); + * nvtxNameClProgram(cpProgram, L"PROGRAM_NAME"); + * \endcode + * + * \version \NVTX_VERSION_1 + * @{ */ +NVTX_DECLSPEC void NVTX_API nvtxNameClProgramA(cl_program program, const char* name); +NVTX_DECLSPEC void NVTX_API nvtxNameClProgramW(cl_program program, const wchar_t* name); +/** @} */ + +/* ------------------------------------------------------------------------- */ +/** \brief Annotates an OpenCL event. + * + * Allows to associate an OpenCL event with a user-provided name. + * + * \param evnt - The handle of the OpenCL event to name. + * \param name - The name of the OpenCL event. + * + * \version \NVTX_VERSION_1 + * @{ */ +NVTX_DECLSPEC void NVTX_API nvtxNameClEventA(cl_event evnt, const char* name); +NVTX_DECLSPEC void NVTX_API nvtxNameClEventW(cl_event evnt, const wchar_t* name); +/** @} */ + +/** @} */ /* END RESOURCE_NAMING */ + +/* ========================================================================= */ +#ifdef UNICODE + #define nvtxNameClDevice nvtxNameClDeviceW + #define nvtxNameClContext nvtxNameClContextW + #define nvtxNameClCommandQueue nvtxNameClCommandQueueW + #define nvtxNameClMemObject nvtxNameClMemObjectW + #define nvtxNameClSampler nvtxNameClSamplerW + #define nvtxNameClProgram nvtxNameClProgramW + #define nvtxNameClEvent nvtxNameClEventW +#else + #define nvtxNameClDevice nvtxNameClDeviceA + #define nvtxNameClContext nvtxNameClContextA + #define nvtxNameClCommandQueue nvtxNameClCommandQueueA + #define nvtxNameClMemObject nvtxNameClMemObjectA + #define nvtxNameClSampler nvtxNameClSamplerA + #define nvtxNameClProgram nvtxNameClProgramA + #define nvtxNameClEvent nvtxNameClEventA +#endif + +#ifdef __cplusplus +} +#endif /* __cplusplus */ + +#endif /* NVTOOLSEXT_OPENCL_H_ */ diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvToolsExtSync.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvToolsExtSync.h new file mode 100644 index 0000000000000000000000000000000000000000..933e60b6943cbdb08975b67d39764cc97658a5fd --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvToolsExtSync.h @@ -0,0 +1,406 @@ +/* +* Copyright 2009-2017 NVIDIA Corporation. All rights reserved. +* +* NOTICE TO USER: +* +* This source code is subject to NVIDIA ownership rights under U.S. and +* international Copyright laws. +* +* This software and the information contained herein is PROPRIETARY and +* CONFIDENTIAL to NVIDIA and is being provided under the terms and conditions +* of a form of NVIDIA software license agreement. +* +* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE +* CODE FOR ANY PURPOSE. IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR +* IMPLIED WARRANTY OF ANY KIND. NVIDIA DISCLAIMS ALL WARRANTIES WITH +* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF +* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE. +* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL, +* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS +* OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE +* OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE +* OR PERFORMANCE OF THIS SOURCE CODE. +* +* U.S. Government End Users. This source code is a "commercial item" as +* that term is defined at 48 C.F.R. 2.101 (OCT 1995), consisting of +* "commercial computer software" and "commercial computer software +* documentation" as such terms are used in 48 C.F.R. 12.212 (SEPT 1995) +* and is provided to the U.S. Government only as a commercial end item. +* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through +* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the +* source code with only those rights set forth herein. +* +* Any use of this source code in individual and commercial software must +* include, in the user documentation and internal comments to the code, +* the above Disclaimer and U.S. Government End Users Notice. +*/ + +#ifndef NVTOOLSEXT_SYNC_H_ +#define NVTOOLSEXT_SYNC_H_ + +#include "nvToolsExt.h" + + +#ifdef __cplusplus +extern "C" { +#endif /* __cplusplus */ + +/* \cond SHOW_HIDDEN +* \version \NVTX_VERSION_2 +*/ +#define NVTX_SYNCUSER_ATTRIB_STRUCT_SIZE ( (uint16_t)( sizeof(nvtxSyncUserAttributes_v0) ) ) +/** \endcond */ + + +/** +* \page PAGE_SYNCHRONIZATION Synchronization +* +* This section covers a subset of the API that allow users to track additional +* synchronization details of their application. Naming OS synchronization primitives +* may allow users to better understand the data collected by traced synchronization +* APIs. Additionally, a user defined synchronization object can allow the users to +* to tell the tools when the user is building their own synchronization system +* that do not rely on the OS to provide behaviors and instead use techniques like +* atomic operations and spinlocks. +* +* See module \ref SYNCHRONIZATION for details. +* +* \par Example: +* \code +* class MyMutex +* { +* volatile long bLocked; +* nvtxSyncUser_t hSync; +* public: +* MyMutex(const char* name, nvtxDomainHandle_t d){ +* bLocked = 0; +* +* nvtxSyncUserAttributes_t attribs = { 0 }; +* attribs.version = NVTX_VERSION; +* attribs.size = NVTX_SYNCUSER_ATTRIB_STRUCT_SIZE; +* attribs.messageType = NVTX_MESSAGE_TYPE_ASCII; +* attribs.message.ascii = name; +* hSync = nvtxDomainSyncUserCreate(d, &attribs); +* } +* +* ~MyMutex() { +* nvtxDomainSyncUserDestroy(hSync); +* } +* +* bool Lock() { +* nvtxDomainSyncUserAcquireStart(hSync); +* bool acquired = __sync_bool_compare_and_swap(&bLocked, 0, 1);//atomic compiler intrinsic + +* if (acquired) { +* nvtxDomainSyncUserAcquireSuccess(hSync); +* } +* else { +* nvtxDomainSyncUserAcquireFailed(hSync); +* } +* return acquired; +* } + +* void Unlock() { +* nvtxDomainSyncUserReleasing(hSync); +* bLocked = false; +* } +* }; +* \endcode +* +* \version \NVTX_VERSION_2 +*/ + +/* ------------------------------------------------------------------------- */ +/* \cond SHOW_HIDDEN +* \brief Used to build a non-colliding value for resource types separated class +* \version \NVTX_VERSION_2 +*/ +#define NVTX_RESOURCE_CLASS_SYNC_OS 2 /**< Synchronization objects that are OS specific. */ +#define NVTX_RESOURCE_CLASS_SYNC_PTHREAD 3 /**< Synchronization objects that are from the POSIX Threads API (pthread)*/ +/** \endcond */ + + +/* ------------------------------------------------------------------------- */ +/** \defgroup SYNCHRONIZATION Synchronization +* See page \ref PAGE_SYNCHRONIZATION. +* @{ +*/ + +/** \brief Resource type values for OSs with POSIX Thread API support + */ +typedef enum nvtxResourceSyncPosixThreadType_t +{ + NVTX_RESOURCE_TYPE_SYNC_PTHREAD_MUTEX = NVTX_RESOURCE_MAKE_TYPE(SYNC_PTHREAD, 1), /* pthread_mutex_t */ + NVTX_RESOURCE_TYPE_SYNC_PTHREAD_CONDITION = NVTX_RESOURCE_MAKE_TYPE(SYNC_PTHREAD, 2), /* pthread_cond_t */ + NVTX_RESOURCE_TYPE_SYNC_PTHREAD_RWLOCK = NVTX_RESOURCE_MAKE_TYPE(SYNC_PTHREAD, 3), /* pthread_rwlock_t */ + NVTX_RESOURCE_TYPE_SYNC_PTHREAD_BARRIER = NVTX_RESOURCE_MAKE_TYPE(SYNC_PTHREAD, 4), /* pthread_barrier_t */ + NVTX_RESOURCE_TYPE_SYNC_PTHREAD_SPINLOCK = NVTX_RESOURCE_MAKE_TYPE(SYNC_PTHREAD, 5), /* pthread_spinlock_t */ + NVTX_RESOURCE_TYPE_SYNC_PTHREAD_ONCE = NVTX_RESOURCE_MAKE_TYPE(SYNC_PTHREAD, 6) /* pthread_once_t */ +} nvtxResourceSyncPosixThreadType_t; + +/** \brief Resource type values for Windows OSs +*/ +typedef enum nvtxResourceSyncWindowsType_t +{ + NVTX_RESOURCE_TYPE_SYNC_WINDOWS_MUTEX = NVTX_RESOURCE_MAKE_TYPE(SYNC_OS, 1), + NVTX_RESOURCE_TYPE_SYNC_WINDOWS_SEMAPHORE = NVTX_RESOURCE_MAKE_TYPE(SYNC_OS, 2), + NVTX_RESOURCE_TYPE_SYNC_WINDOWS_EVENT = NVTX_RESOURCE_MAKE_TYPE(SYNC_OS, 3), + NVTX_RESOURCE_TYPE_SYNC_WINDOWS_CRITICAL_SECTION = NVTX_RESOURCE_MAKE_TYPE(SYNC_OS, 4), + NVTX_RESOURCE_TYPE_SYNC_WINDOWS_SRWLOCK = NVTX_RESOURCE_MAKE_TYPE(SYNC_OS, 5) +} nvtxResourceSyncWindowsType_t; + +/** \brief Resource type values for Linux and Linux derived OSs such as Android +* \sa +* ::nvtxResourceSyncPosixThreadType_t +*/ +typedef enum nvtxResourceSyncLinuxType_t +{ + NVTX_RESOURCE_TYPE_SYNC_LINUX_MUTEX = NVTX_RESOURCE_MAKE_TYPE(SYNC_OS, 1), + NVTX_RESOURCE_TYPE_SYNC_LINUX_FUTEX = NVTX_RESOURCE_MAKE_TYPE(SYNC_OS, 2), + NVTX_RESOURCE_TYPE_SYNC_LINUX_SEMAPHORE = NVTX_RESOURCE_MAKE_TYPE(SYNC_OS, 3), + NVTX_RESOURCE_TYPE_SYNC_LINUX_COMPLETION = NVTX_RESOURCE_MAKE_TYPE(SYNC_OS, 4), + NVTX_RESOURCE_TYPE_SYNC_LINUX_SPINLOCK = NVTX_RESOURCE_MAKE_TYPE(SYNC_OS, 5), + NVTX_RESOURCE_TYPE_SYNC_LINUX_SEQLOCK = NVTX_RESOURCE_MAKE_TYPE(SYNC_OS, 6), + NVTX_RESOURCE_TYPE_SYNC_LINUX_RCU = NVTX_RESOURCE_MAKE_TYPE(SYNC_OS, 7) +} nvtxResourceSyncLinuxType_t; + +/** \brief Resource type values for Android come from Linux. +* \sa +* ::nvtxResourceSyncLinuxType_t +* ::nvtxResourceSyncPosixThreadType_t +*/ +typedef enum nvtxResourceSyncLinuxType_t nvtxResourceSyncAndroidType_t; + +/** \brief User Defined Synchronization Object Handle . +* \anchor SYNCUSER_HANDLE_STRUCTURE +* +* This structure is opaque to the user and is used as a handle to reference +* a user defined syncrhonization object. The tools will return a pointer through the API for the application +* to hold on it's behalf to reference the string in the future. +* +*/ +typedef struct nvtxSyncUser* nvtxSyncUser_t; + +/** \brief User Defined Synchronization Object Attributes Structure. +* \anchor USERDEF_SYNC_ATTRIBUTES_STRUCTURE +* +* This structure is used to describe the attributes of a user defined synchronization +* object. The layout of the structure is defined by a specific version of the tools +* extension library and can change between different versions of the Tools Extension +* library. +* +* \par Initializing the Attributes +* +* The caller should always perform the following three tasks when using +* attributes: +*
    +*
  • Zero the structure +*
  • Set the version field +*
  • Set the size field +*
+* +* Zeroing the structure sets all the event attributes types and values +* to the default value. +* +* The version and size field are used by the Tools Extension +* implementation to handle multiple versions of the attributes structure. +* +* It is recommended that the caller use one of the following to methods +* to initialize the event attributes structure: +* +* \par Method 1: Initializing nvtxEventAttributes for future compatibility +* \code +* nvtxSyncUserAttributes_t attribs = {0}; +* attribs.version = NVTX_VERSION; +* attribs.size = NVTX_SYNCUSER_ATTRIB_STRUCT_SIZE; +* \endcode +* +* \par Method 2: Initializing nvtxSyncUserAttributes_t for a specific version +* \code +* nvtxSyncUserAttributes_t attribs = {0}; +* attribs.version = 1; +* attribs.size = (uint16_t)(sizeof(nvtxSyncUserAttributes_t)); +* \endcode +* +* If the caller uses Method 1 it is critical that the entire binary +* layout of the structure be configured to 0 so that all fields +* are initialized to the default value. +* +* The caller should either use both NVTX_VERSION and +* NVTX_SYNCUSER_ATTRIB_STRUCT_SIZE (Method 1) or use explicit values +* and a versioned type (Method 2). Using a mix of the two methods +* will likely cause either source level incompatibility or binary +* incompatibility in the future. +* +* \par Settings Attribute Types and Values +* +* +* \par Example: +* \code +* // Initialize +* nvtxSyncUserAttributes_t attribs = {0}; +* attribs.version = NVTX_VERSION; +* attribs.size = NVTX_SYNCUSER_ATTRIB_STRUCT_SIZE; +* +* // Configure the Attributes +* attribs.messageType = NVTX_MESSAGE_TYPE_ASCII; +* attribs.message.ascii = "Example"; +* \endcode +* +* \sa +* ::nvtxDomainSyncUserCreate +*/ +typedef struct nvtxSyncUserAttributes_v0 +{ + /** + * \brief Version flag of the structure. + * + * Needs to be set to NVTX_VERSION to indicate the version of NVTX APIs + * supported in this header file. This can optionally be overridden to + * another version of the tools extension library. + */ + uint16_t version; + + /** + * \brief Size of the structure. + * + * Needs to be set to the size in bytes of the event attribute + * structure used to specify the event. + */ + uint16_t size; + + /** \brief Message type specified in this attribute structure. + * + * Defines the message format of the attribute structure's \ref nvtxSyncUserAttributes_v0::message + * "message" field. + * + * Default Value is NVTX_MESSAGE_UNKNOWN + */ + int32_t messageType; /* nvtxMessageType_t */ + + /** \brief Message assigned to this attribute structure. + * + * The text message that is attached to an event. + */ + nvtxMessageValue_t message; + +} nvtxSyncUserAttributes_v0; + +typedef struct nvtxSyncUserAttributes_v0 nvtxSyncUserAttributes_t; + +/* ------------------------------------------------------------------------- */ +/** \brief Create a user defined synchronization object +* This is used to track non-OS synchronization working with spinlocks and atomics +* +* \param domain - Domain to own the resource +* \param attribs - A structure to assign multiple attributes to the object. +* +* \return A handle that represents the newly created user defined synchronization object. +* +* \sa +* ::nvtxDomainSyncUserCreate +* ::nvtxDomainSyncUserDestroy +* ::nvtxDomainSyncUserAcquireStart +* ::nvtxDomainSyncUserAcquireFailed +* ::nvtxDomainSyncUserAcquireSuccess +* ::nvtxDomainSyncUserReleasing +* +* \version \NVTX_VERSION_2 +*/ +NVTX_DECLSPEC nvtxSyncUser_t NVTX_API nvtxDomainSyncUserCreate(nvtxDomainHandle_t domain, const nvtxSyncUserAttributes_t* attribs); + +/* ------------------------------------------------------------------------- */ +/** \brief Destroy a user defined synchronization object +* This is used to track non-OS synchronization working with spinlocks and atomics +* +* \param handle - A handle to the object to operate on. +* +* \sa +* ::nvtxDomainSyncUserCreate +* ::nvtxDomainSyncUserDestroy +* ::nvtxDomainSyncUserAcquireStart +* ::nvtxDomainSyncUserAcquireFailed +* ::nvtxDomainSyncUserAcquireSuccess +* ::nvtxDomainSyncUserReleasing +* +* \version \NVTX_VERSION_2 +*/ +NVTX_DECLSPEC void NVTX_API nvtxDomainSyncUserDestroy(nvtxSyncUser_t handle); + +/* ------------------------------------------------------------------------- */ +/** \brief Signal to tools that an attempt to acquire a user defined synchronization object +* +* \param handle - A handle to the object to operate on. +* +* \sa +* ::nvtxDomainSyncUserCreate +* ::nvtxDomainSyncUserDestroy +* ::nvtxDomainSyncUserAcquireStart +* ::nvtxDomainSyncUserAcquireFailed +* ::nvtxDomainSyncUserAcquireSuccess +* ::nvtxDomainSyncUserReleasing +* +* \version \NVTX_VERSION_2 +*/ +NVTX_DECLSPEC void NVTX_API nvtxDomainSyncUserAcquireStart(nvtxSyncUser_t handle); + +/* ------------------------------------------------------------------------- */ +/** \brief Signal to tools of failure in acquiring a user defined synchronization object +* This should be called after \ref nvtxDomainSyncUserAcquireStart +* +* \param handle - A handle to the object to operate on. +* +* \sa +* ::nvtxDomainSyncUserCreate +* ::nvtxDomainSyncUserDestroy +* ::nvtxDomainSyncUserAcquireStart +* ::nvtxDomainSyncUserAcquireFailed +* ::nvtxDomainSyncUserAcquireSuccess +* ::nvtxDomainSyncUserReleasing +* +* \version \NVTX_VERSION_2 +*/NVTX_DECLSPEC void NVTX_API nvtxDomainSyncUserAcquireFailed(nvtxSyncUser_t handle); + +/* ------------------------------------------------------------------------- */ +/** \brief Signal to tools of success in acquiring a user defined synchronization object +* This should be called after \ref nvtxDomainSyncUserAcquireStart. +* +* \param handle - A handle to the object to operate on. +* +* \sa +* ::nvtxDomainSyncUserCreate +* ::nvtxDomainSyncUserDestroy +* ::nvtxDomainSyncUserAcquireStart +* ::nvtxDomainSyncUserAcquireFailed +* ::nvtxDomainSyncUserAcquireSuccess +* ::nvtxDomainSyncUserReleasing +* +* \version \NVTX_VERSION_2 +*/NVTX_DECLSPEC void NVTX_API nvtxDomainSyncUserAcquireSuccess(nvtxSyncUser_t handle); + +/* ------------------------------------------------------------------------- */ +/** \brief Signal to tools of releasing a reservation on user defined synchronization object +* This should be called after \ref nvtxDomainSyncUserAcquireSuccess. +* +* \param handle - A handle to the object to operate on. +* +* \sa +* ::nvtxDomainSyncUserCreate +* ::nvtxDomainSyncUserDestroy +* ::nvtxDomainSyncUserAcquireStart +* ::nvtxDomainSyncUserAcquireFailed +* ::nvtxDomainSyncUserAcquireSuccess +* ::nvtxDomainSyncUserReleasing +* +* \version \NVTX_VERSION_2 +*/ +NVTX_DECLSPEC void NVTX_API nvtxDomainSyncUserReleasing(nvtxSyncUser_t handle); + + +/** @} */ /*END defgroup*/ + +#ifdef __cplusplus +} +#endif /* __cplusplus */ + +#endif /* NVTOOLSEXT_SYNC_H_ */ diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvToolsExt.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvToolsExt.h new file mode 100644 index 0000000000000000000000000000000000000000..9fab7d874340fad50d63d80b6b68f678ec243191 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvToolsExt.h @@ -0,0 +1,1499 @@ +/* +* Copyright 2009-2016 NVIDIA Corporation. All rights reserved. +* +* NOTICE TO USER: +* +* This source code is subject to NVIDIA ownership rights under U.S. and +* international Copyright laws. +* +* This software and the information contained herein is PROPRIETARY and +* CONFIDENTIAL to NVIDIA and is being provided under the terms and conditions +* of a form of NVIDIA software license agreement. +* +* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE +* CODE FOR ANY PURPOSE. IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR +* IMPLIED WARRANTY OF ANY KIND. NVIDIA DISCLAIMS ALL WARRANTIES WITH +* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF +* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE. +* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL, +* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS +* OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE +* OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE +* OR PERFORMANCE OF THIS SOURCE CODE. +* +* U.S. Government End Users. This source code is a "commercial item" as +* that term is defined at 48 C.F.R. 2.101 (OCT 1995), consisting of +* "commercial computer software" and "commercial computer software +* documentation" as such terms are used in 48 C.F.R. 12.212 (SEPT 1995) +* and is provided to the U.S. Government only as a commercial end item. +* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through +* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the +* source code with only those rights set forth herein. +* +* Any use of this source code in individual and commercial software must +* include, in the user documentation and internal comments to the code, +* the above Disclaimer and U.S. Government End Users Notice. +*/ + +/** \file nvToolsExt.h + */ + +/* ========================================================================= */ +/** \mainpage + * \tableofcontents + * \section INTRODUCTION Introduction + * + * The NVIDIA Tools Extension library is a set of functions that a + * developer can use to provide additional information to tools. + * The additional information is used by the tool to improve + * analysis and visualization of data. + * + * The library introduces close to zero overhead if no tool is + * attached to the application. The overhead when a tool is + * attached is specific to the tool. + * + * \section INITIALIZATION_SECTION Initialization + * + * Typically the tool's library that plugs into NVTX is indirectly + * loaded via enviromental properties that are platform specific. + * For some platform or special cases, the user may be required + * to instead explicity initialize instead though. This can also + * be helpful to control when the API loads a tool's library instead + * of what would typically be the first function call to emit info. + * For these rare case, see \ref INITIALIZATION for additional information. + * + * \section MARKERS_AND_RANGES Markers and Ranges + * + * Markers and ranges are used to describe events at a specific time (markers) + * or over a time span (ranges) during the execution of the application + * respectively. + * + * \subsection MARKERS Markers + * + * Markers denote specific moments in time. + * + * + * See \ref DOMAINS and \ref EVENT_ATTRIBUTES for additional information on + * how to specify the domain. + * + * \subsection THREAD_RANGES Thread Ranges + * + * Thread ranges denote nested time ranges. Nesting is maintained per thread + * per domain and does not require any additional correlation mechanism. The + * duration of a thread range is defined by the corresponding pair of + * nvtxRangePush* to nvtxRangePop API calls. + * + * See \ref DOMAINS and \ref EVENT_ATTRIBUTES for additional information on + * how to specify the domain. + * + * \subsection PROCESS_RANGES Process Ranges + * + * Process ranges denote a time span that can expose arbitrary concurrency, as + * opposed to thread ranges that only support nesting. In addition the range + * start event can happen on a different thread than the end marker. For the + * correlation of a start/end pair an unique correlation ID is used that is + * returned from the start API call and needs to be passed into the end API + * call. + * + * \subsection EVENT_ATTRIBUTES Event Attributes + * + * \ref MARKERS_AND_RANGES can be annotated with various attributes to provide + * additional information for an event or to guide the tool's visualization of + * the data. Each of the attributes is optional and if left unused the + * attributes fall back to a default value. The attributes include: + * - color + * - category + * + * To specify any attribute other than the text message, the \ref + * EVENT_ATTRIBUTE_STRUCTURE "Event Attribute Structure" must be used. + * + * \section DOMAINS Domains + * + * Domains enable developers to scope annotations. By default all events and + * annotations are in the default domain. Additional domains can be registered. + * This allows developers to scope markers, ranges, and resources names to + * avoid conflicts. + * + * The function ::nvtxDomainCreateA or ::nvtxDomainCreateW is used to create + * a named domain. + * + * Each domain maintains its own + * - categories + * - thread range stacks + * - registered strings + * + * The function ::nvtxDomainDestroy marks the end of the domain. Destroying + * a domain unregisters and destroys all objects associated with it such as + * registered strings, resource objects, named categories, and started ranges. + * + * \section RESOURCE_NAMING Resource Naming + * + * This section covers calls that allow to annotate objects with user-provided + * names in order to allow for a better analysis of complex trace data. All of + * the functions take the handle or the ID of the object to name and the name. + * The functions can be called multiple times during the execution of an + * application, however, in that case it is implementation dependent which + * name will be reported by the tool. + * + * \subsection CATEGORY_NAMING Category Naming + * + * Some function in this library support associating an integer category + * to enable filtering and sorting. The category naming functions allow + * the application to associate a user friendly name with the integer + * category. Support for domains have been added in NVTX_VERSION_2 to + * avoid collisions when domains are developed independantly. + * + * \subsection RESOURCE_OBJECTS Resource Objects + * + * Resource objects are a generic mechanism for attaching data to an application + * resource. The identifier field makes the association to a pointer or handle, + * while the type field helps provide deeper understanding of the identifier as + * well as enabling differentiation in cases where handles generated by different + * APIs may collide. The resource object may also have an associated message to + * associate with the application resource, enabling further annotation of this + * object and how it is used. + * + * The resource object was introduced in NVTX_VERSION_2 to supersede existing naming + * functions and allow the application resource identified by those functions to be + * associated to a domain. The other naming functions are still supported for backward + * compatibility but will be associated only to the default domain. + * + * \subsection RESOURCE_NAMING_OS Resource Naming + * + * Some operating system resources creation APIs do not support providing a user friendly + * name, such as some OS thread creation APIs. This API support resource naming though + * both through resource objects and functions following the pattern + * nvtxName[RESOURCE_TYPE][A|W](identifier, name). Resource objects introduced in NVTX_VERSION 2 + * supersede the other functions with a a more general method of assigning names to OS resources, + * along with associating them to domains too. The older nvtxName* functions are only associated + * with the default domain. + * \section EXTENSIONS Optional Extensions + * Optional extensions will either appear within the existing sections the extend or appear + * in the "Related Pages" when they introduce new concepts. + */ + + /** + * Tools Extension API version + */ +#if defined(NVTX_VERSION) && NVTX_VERSION < 3 +#error "Trying to #include NVTX version 3 in a source file where an older NVTX version has already been included. If you are not directly using NVTX (the NVIDIA Tools Extension library), you are getting this error because libraries you are using have included different versions of NVTX. Suggested solutions are: (1) reorder #includes so the newest NVTX version is included first, (2) avoid using the conflicting libraries in the same .c/.cpp file, or (3) update the library using the older NVTX version to use the newer version instead." +#endif + +/* Header guard */ +#if !defined(NVTX_VERSION) +#define NVTX_VERSION 3 + +#if defined(_MSC_VER) +#define NVTX_API __stdcall +#define NVTX_INLINE_STATIC __inline static +#else /*defined(__GNUC__)*/ +#define NVTX_API +#define NVTX_INLINE_STATIC inline static +#endif /* Platform */ + +#if defined(NVTX_NO_IMPL) +/* When omitting implementation, avoid declaring functions inline */ +/* without definitions, since this causes compiler warnings. */ +#define NVTX_DECLSPEC +#elif defined(NVTX_EXPORT_API) +/* Allow overriding definition of NVTX_DECLSPEC when exporting API. */ +/* Default is empty, meaning non-inline with external linkage. */ +#if !defined(NVTX_DECLSPEC) +#define NVTX_DECLSPEC +#endif +#else +/* Normal NVTX usage defines the NVTX API inline with static */ +/* (internal) linkage. */ +#define NVTX_DECLSPEC NVTX_INLINE_STATIC +#endif + +#include "nvtxDetail/nvtxLinkOnce.h" + +#define NVTX_VERSIONED_IDENTIFIER_L3(NAME, VERSION) NAME##_v##VERSION +#define NVTX_VERSIONED_IDENTIFIER_L2(NAME, VERSION) NVTX_VERSIONED_IDENTIFIER_L3(NAME, VERSION) +#define NVTX_VERSIONED_IDENTIFIER(NAME) NVTX_VERSIONED_IDENTIFIER_L2(NAME, NVTX_VERSION) + +/** + * The nvToolsExt library depends on stdint.h. If the build tool chain in use + * does not include stdint.h then define NVTX_STDINT_TYPES_ALREADY_DEFINED + * and define the following types: + *
    + *
  • uint8_t + *
  • int8_t + *
  • uint16_t + *
  • int16_t + *
  • uint32_t + *
  • int32_t + *
  • uint64_t + *
  • int64_t + *
  • uintptr_t + *
  • intptr_t + *
+ * #define NVTX_STDINT_TYPES_ALREADY_DEFINED if you are using your own header file. + */ +#ifndef NVTX_STDINT_TYPES_ALREADY_DEFINED +#include +#endif + +#include + +#ifdef __cplusplus +extern "C" { +#endif /* __cplusplus */ + +/** +* Result Codes +*/ + +#define NVTX_SUCCESS 0 +#define NVTX_FAIL 1 +#define NVTX_ERR_INIT_LOAD_PROPERTY 2 +#define NVTX_ERR_INIT_ACCESS_LIBRARY 3 +#define NVTX_ERR_INIT_LOAD_LIBRARY 4 +#define NVTX_ERR_INIT_MISSING_LIBRARY_ENTRY_POINT 5 +#define NVTX_ERR_INIT_FAILED_LIBRARY_ENTRY_POINT 6 +#define NVTX_ERR_NO_INJECTION_LIBRARY_AVAILABLE 7 + +/** + * Size of the nvtxEventAttributes_t structure. + */ +#define NVTX_EVENT_ATTRIB_STRUCT_SIZE ( (uint16_t)( sizeof(nvtxEventAttributes_t) ) ) + +#define NVTX_NO_PUSH_POP_TRACKING ((int)-2) + +typedef uint64_t nvtxRangeId_t; + +/* Forward declaration of opaque domain registration structure */ +struct nvtxDomainRegistration_st; +typedef struct nvtxDomainRegistration_st nvtxDomainRegistration; + +/* \brief Domain Handle Structure. +* \anchor DOMAIN_HANDLE_STRUCTURE +* +* This structure is opaque to the user and is used as a handle to reference +* a domain. This type is returned from tools when using the NVTX API to +* create a domain. +* +*/ +typedef nvtxDomainRegistration* nvtxDomainHandle_t; + +/* Forward declaration of opaque string registration structure */ +struct nvtxStringRegistration_st; +typedef struct nvtxStringRegistration_st nvtxStringRegistration; + +/* \brief Registered String Handle Structure. +* \anchor REGISTERED_STRING_HANDLE_STRUCTURE +* +* This structure is opaque to the user and is used as a handle to reference +* a registered string. This type is returned from tools when using the NVTX +* API to create a registered string. +* +*/ +typedef nvtxStringRegistration* nvtxStringHandle_t; + +/* ========================================================================= */ +/** \defgroup GENERAL General + * @{ + */ + +/** --------------------------------------------------------------------------- + * Color Types + * ------------------------------------------------------------------------- */ +typedef enum nvtxColorType_t +{ + NVTX_COLOR_UNKNOWN = 0, /**< Color attribute is unused. */ + NVTX_COLOR_ARGB = 1 /**< An ARGB color is provided. */ +} nvtxColorType_t; + +/** --------------------------------------------------------------------------- + * Message Types + * ------------------------------------------------------------------------- */ +typedef enum nvtxMessageType_t +{ + NVTX_MESSAGE_UNKNOWN = 0, /**< Message payload is unused. */ + NVTX_MESSAGE_TYPE_ASCII = 1, /**< A character sequence is used as payload. */ + NVTX_MESSAGE_TYPE_UNICODE = 2, /**< A wide character sequence is used as payload. */ + /* NVTX_VERSION_2 */ + NVTX_MESSAGE_TYPE_REGISTERED = 3, /**< A unique string handle that was registered + with \ref nvtxDomainRegisterStringA() or + \ref nvtxDomainRegisterStringW(). */ +} nvtxMessageType_t; + +typedef union nvtxMessageValue_t +{ + const char* ascii; + const wchar_t* unicode; + /* NVTX_VERSION_2 */ + nvtxStringHandle_t registered; +} nvtxMessageValue_t; + + +/** @} */ /*END defgroup*/ +/* ------------------------------------------------------------------------- */ +/** \brief Force initialization (optional) +* +* Force NVTX library to initialize. The first call to any NVTX API function +* will automatically initialize the entire API. This can make the first call +* much slower than subsequent calls. In applications where the first call to +* NVTX may be in a performance-critical section, calling nvtxInitialize before +* any performance-critical sections will ensure NVTX initialization occurs at +* an acceptable time. Since nvtxInitialize takes no parameters and has no +* expected behavior besides initialization, it is convenient to add a call to +* nvtxInitialize in NVTX-instrumented applications that need to force earlier +* initialization without changing any other code. For example, if an app's +* first NVTX call is nvtxDomainCreate, and it is difficult to move that call +* earlier because the domain handle must be stored in an object only created +* at that point, adding a call to nvtxInitialize at the top of main() will +* ensure the later call to nvtxDomainCreate is as fast as possible. +* +* \version \NVTX_VERSION_3 +* +* \param reserved - must be zero or NULL. +* +* @{ */ +NVTX_DECLSPEC void NVTX_API nvtxInitialize(const void* reserved); +/** @} */ + + +/** @} */ /*END defgroup*/ + +/* ========================================================================= */ +/** \defgroup EVENT_ATTRIBUTES Event Attributes +* @{ +*/ + +/** --------------------------------------------------------------------------- +* Payload Types +* ------------------------------------------------------------------------- */ +typedef enum nvtxPayloadType_t +{ + NVTX_PAYLOAD_UNKNOWN = 0, /**< Color payload is unused. */ + NVTX_PAYLOAD_TYPE_UNSIGNED_INT64 = 1, /**< A 64 bit unsigned integer value is used as payload. */ + NVTX_PAYLOAD_TYPE_INT64 = 2, /**< A 64 bit signed integer value is used as payload. */ + NVTX_PAYLOAD_TYPE_DOUBLE = 3, /**< A 64 bit floating point value is used as payload. */ + /* NVTX_VERSION_2 */ + NVTX_PAYLOAD_TYPE_UNSIGNED_INT32 = 4, /**< A 32 bit floating point value is used as payload. */ + NVTX_PAYLOAD_TYPE_INT32 = 5, /**< A 32 bit floating point value is used as payload. */ + NVTX_PAYLOAD_TYPE_FLOAT = 6 /**< A 32 bit floating point value is used as payload. */ +} nvtxPayloadType_t; + +/** \brief Event Attribute Structure. + * \anchor EVENT_ATTRIBUTE_STRUCTURE + * + * This structure is used to describe the attributes of an event. The layout of + * the structure is defined by a specific version of the tools extension + * library and can change between different versions of the Tools Extension + * library. + * + * \par Initializing the Attributes + * + * The caller should always perform the following three tasks when using + * attributes: + *
    + *
  • Zero the structure + *
  • Set the version field + *
  • Set the size field + *
+ * + * Zeroing the structure sets all the event attributes types and values + * to the default value. + * + * The version and size field are used by the Tools Extension + * implementation to handle multiple versions of the attributes structure. + * + * It is recommended that the caller use one of the following to methods + * to initialize the event attributes structure: + * + * \par Method 1: Initializing nvtxEventAttributes for future compatibility + * \code + * nvtxEventAttributes_t eventAttrib = {0}; + * eventAttrib.version = NVTX_VERSION; + * eventAttrib.size = NVTX_EVENT_ATTRIB_STRUCT_SIZE; + * \endcode + * + * \par Method 2: Initializing nvtxEventAttributes for a specific version + * \code + * nvtxEventAttributes_t eventAttrib = {0}; + * eventAttrib.version = 1; + * eventAttrib.size = (uint16_t)(sizeof(nvtxEventAttributes_v1)); + * \endcode + * + * If the caller uses Method 1 it is critical that the entire binary + * layout of the structure be configured to 0 so that all fields + * are initialized to the default value. + * + * The caller should either use both NVTX_VERSION and + * NVTX_EVENT_ATTRIB_STRUCT_SIZE (Method 1) or use explicit values + * and a versioned type (Method 2). Using a mix of the two methods + * will likely cause either source level incompatibility or binary + * incompatibility in the future. + * + * \par Settings Attribute Types and Values + * + * + * \par Example: + * \code + * // Initialize + * nvtxEventAttributes_t eventAttrib = {0}; + * eventAttrib.version = NVTX_VERSION; + * eventAttrib.size = NVTX_EVENT_ATTRIB_STRUCT_SIZE; + * + * // Configure the Attributes + * eventAttrib.colorType = NVTX_COLOR_ARGB; + * eventAttrib.color = 0xFF880000; + * eventAttrib.messageType = NVTX_MESSAGE_TYPE_ASCII; + * eventAttrib.message.ascii = "Example"; + * \endcode + * + * In the example the caller does not have to set the value of + * \ref ::nvtxEventAttributes_v2::category or + * \ref ::nvtxEventAttributes_v2::payload as these fields were set to + * the default value by {0}. + * \sa + * ::nvtxDomainMarkEx + * ::nvtxDomainRangeStartEx + * ::nvtxDomainRangePushEx + */ +typedef struct nvtxEventAttributes_v2 +{ + /** + * \brief Version flag of the structure. + * + * Needs to be set to NVTX_VERSION to indicate the version of NVTX APIs + * supported in this header file. This can optionally be overridden to + * another version of the tools extension library. + */ + uint16_t version; + + /** + * \brief Size of the structure. + * + * Needs to be set to the size in bytes of the event attribute + * structure used to specify the event. + */ + uint16_t size; + + /** + * \brief ID of the category the event is assigned to. + * + * A category is a user-controlled ID that can be used to group + * events. The tool may use category IDs to improve filtering or + * enable grouping of events in the same category. The functions + * \ref ::nvtxNameCategoryA or \ref ::nvtxNameCategoryW can be used + * to name a category. + * + * Default Value is 0 + */ + uint32_t category; + + /** \brief Color type specified in this attribute structure. + * + * Defines the color format of the attribute structure's \ref COLOR_FIELD + * "color" field. + * + * Default Value is NVTX_COLOR_UNKNOWN + */ + int32_t colorType; /* nvtxColorType_t */ + + /** \brief Color assigned to this event. \anchor COLOR_FIELD + * + * The color that the tool should use to visualize the event. + */ + uint32_t color; + + /** + * \brief Payload type specified in this attribute structure. + * + * Defines the payload format of the attribute structure's \ref PAYLOAD_FIELD + * "payload" field. + * + * Default Value is NVTX_PAYLOAD_UNKNOWN + */ + int32_t payloadType; /* nvtxPayloadType_t */ + + int32_t reserved0; + + /** + * \brief Payload assigned to this event. \anchor PAYLOAD_FIELD + * + * A numerical value that can be used to annotate an event. The tool could + * use the payload data to reconstruct graphs and diagrams. + */ + union payload_t + { + uint64_t ullValue; + int64_t llValue; + double dValue; + /* NVTX_VERSION_2 */ + uint32_t uiValue; + int32_t iValue; + float fValue; + } payload; + + /** \brief Message type specified in this attribute structure. + * + * Defines the message format of the attribute structure's \ref MESSAGE_FIELD + * "message" field. + * + * Default Value is NVTX_MESSAGE_UNKNOWN + */ + int32_t messageType; /* nvtxMessageType_t */ + + /** \brief Message assigned to this attribute structure. \anchor MESSAGE_FIELD + * + * The text message that is attached to an event. + */ + nvtxMessageValue_t message; + +} nvtxEventAttributes_v2; + +typedef struct nvtxEventAttributes_v2 nvtxEventAttributes_t; + +/** @} */ /*END defgroup*/ +/* ========================================================================= */ +/** \defgroup MARKERS_AND_RANGES Markers and Ranges + * + * See \ref MARKERS_AND_RANGES for more details + * + * @{ + */ + +/** \name Marker */ + +/* ------------------------------------------------------------------------- */ +/** \brief Marks an instantaneous event in the application. +* +* A marker can contain a text message or specify additional information +* using the event attributes structure. These attributes include a text +* message, color, category, and a payload. Each of the attributes is optional +* and can only be sent out using the \ref nvtxDomainMarkEx function. +* +* nvtxDomainMarkEx(NULL, event) is equivalent to calling +* nvtxMarkEx(event). +* +* \param domain - The domain of scoping the category. +* \param eventAttrib - The event attribute structure defining the marker's +* attribute types and attribute values. +* +* \sa +* ::nvtxMarkEx +* +* \version \NVTX_VERSION_2 +* @{ */ +NVTX_DECLSPEC void NVTX_API nvtxDomainMarkEx(nvtxDomainHandle_t domain, const nvtxEventAttributes_t* eventAttrib); +/** @} */ + +/* ------------------------------------------------------------------------- */ +/** \brief Marks an instantaneous event in the application. + * + * A marker can contain a text message or specify additional information + * using the event attributes structure. These attributes include a text + * message, color, category, and a payload. Each of the attributes is optional + * and can only be sent out using the \ref nvtxMarkEx function. + * If \ref nvtxMarkA or \ref nvtxMarkW are used to specify the marker + * or if an attribute is unspecified then a default value will be used. + * + * \param eventAttrib - The event attribute structure defining the marker's + * attribute types and attribute values. + * + * \par Example: + * \code + * // zero the structure + * nvtxEventAttributes_t eventAttrib = {0}; + * // set the version and the size information + * eventAttrib.version = NVTX_VERSION; + * eventAttrib.size = NVTX_EVENT_ATTRIB_STRUCT_SIZE; + * // configure the attributes. 0 is the default for all attributes. + * eventAttrib.colorType = NVTX_COLOR_ARGB; + * eventAttrib.color = 0xFF880000; + * eventAttrib.messageType = NVTX_MESSAGE_TYPE_ASCII; + * eventAttrib.message.ascii = "Example nvtxMarkEx"; + * nvtxMarkEx(&eventAttrib); + * \endcode + * + * \sa + * ::nvtxDomainMarkEx + * + * \version \NVTX_VERSION_1 + * @{ */ +NVTX_DECLSPEC void NVTX_API nvtxMarkEx(const nvtxEventAttributes_t* eventAttrib); +/** @} */ + +/* ------------------------------------------------------------------------- */ +/** \brief Marks an instantaneous event in the application. + * + * A marker created using \ref nvtxMarkA or \ref nvtxMarkW contains only a + * text message. + * + * \param message - The message associated to this marker event. + * + * \par Example: + * \code + * nvtxMarkA("Example nvtxMarkA"); + * nvtxMarkW(L"Example nvtxMarkW"); + * \endcode + * + * \sa + * ::nvtxDomainMarkEx + * ::nvtxMarkEx + * + * \version \NVTX_VERSION_0 + * @{ */ +NVTX_DECLSPEC void NVTX_API nvtxMarkA(const char* message); +NVTX_DECLSPEC void NVTX_API nvtxMarkW(const wchar_t* message); +/** @} */ + + +/** \name Process Ranges */ + +/* ------------------------------------------------------------------------- */ +/** \brief Starts a process range in a domain. +* +* \param domain - The domain of scoping the category. +* \param eventAttrib - The event attribute structure defining the range's +* attribute types and attribute values. +* +* \return The unique ID used to correlate a pair of Start and End events. +* +* \remarks Ranges defined by Start/End can overlap. +* +* \par Example: +* \code +* nvtxDomainHandle_t domain = nvtxDomainCreateA("my domain"); +* nvtxEventAttributes_t eventAttrib = {0}; +* eventAttrib.version = NVTX_VERSION; +* eventAttrib.size = NVTX_EVENT_ATTRIB_STRUCT_SIZE; +* eventAttrib.messageType = NVTX_MESSAGE_TYPE_ASCII; +* eventAttrib.message.ascii = "my range"; +* nvtxRangeId_t rangeId = nvtxDomainRangeStartEx(&eventAttrib); +* // ... +* nvtxDomainRangeEnd(rangeId); +* \endcode +* +* \sa +* ::nvtxDomainRangeEnd +* +* \version \NVTX_VERSION_2 +* @{ */ +NVTX_DECLSPEC nvtxRangeId_t NVTX_API nvtxDomainRangeStartEx(nvtxDomainHandle_t domain, const nvtxEventAttributes_t* eventAttrib); +/** @} */ + +/* ------------------------------------------------------------------------- */ +/** \brief Starts a process range. + * + * \param eventAttrib - The event attribute structure defining the range's + * attribute types and attribute values. + * + * \return The unique ID used to correlate a pair of Start and End events. + * + * \remarks Ranges defined by Start/End can overlap. + * + * \par Example: + * \code + * nvtxEventAttributes_t eventAttrib = {0}; + * eventAttrib.version = NVTX_VERSION; + * eventAttrib.size = NVTX_EVENT_ATTRIB_STRUCT_SIZE; + * eventAttrib.category = 3; + * eventAttrib.colorType = NVTX_COLOR_ARGB; + * eventAttrib.color = 0xFF0088FF; + * eventAttrib.messageType = NVTX_MESSAGE_TYPE_ASCII; + * eventAttrib.message.ascii = "Example Range"; + * nvtxRangeId_t rangeId = nvtxRangeStartEx(&eventAttrib); + * // ... + * nvtxRangeEnd(rangeId); + * \endcode + * + * \sa + * ::nvtxRangeEnd + * ::nvtxDomainRangeStartEx + * + * \version \NVTX_VERSION_1 + * @{ */ +NVTX_DECLSPEC nvtxRangeId_t NVTX_API nvtxRangeStartEx(const nvtxEventAttributes_t* eventAttrib); +/** @} */ + +/* ------------------------------------------------------------------------- */ +/** \brief Starts a process range. + * + * \param message - The event message associated to this range event. + * + * \return The unique ID used to correlate a pair of Start and End events. + * + * \remarks Ranges defined by Start/End can overlap. + * + * \par Example: + * \code + * nvtxRangeId_t r1 = nvtxRangeStartA("Range 1"); + * nvtxRangeId_t r2 = nvtxRangeStartW(L"Range 2"); + * nvtxRangeEnd(r1); + * nvtxRangeEnd(r2); + * \endcode + * + * \sa + * ::nvtxRangeEnd + * ::nvtxRangeStartEx + * ::nvtxDomainRangeStartEx + * + * \version \NVTX_VERSION_0 + * @{ */ +NVTX_DECLSPEC nvtxRangeId_t NVTX_API nvtxRangeStartA(const char* message); +NVTX_DECLSPEC nvtxRangeId_t NVTX_API nvtxRangeStartW(const wchar_t* message); +/** @} */ + +/* ------------------------------------------------------------------------- */ +/** \brief Ends a process range. +* +* \param domain - The domain +* \param id - The correlation ID returned from a nvtxRangeStart call. +* +* \remarks This function is offered completeness but is an alias for ::nvtxRangeEnd. +* It does not need a domain param since that is associated iwth the range ID at ::nvtxDomainRangeStartEx +* +* \par Example: +* \code +* nvtxDomainHandle_t domain = nvtxDomainCreateA("my domain"); +* nvtxEventAttributes_t eventAttrib = {0}; +* eventAttrib.version = NVTX_VERSION; +* eventAttrib.size = NVTX_EVENT_ATTRIB_STRUCT_SIZE; +* eventAttrib.messageType = NVTX_MESSAGE_TYPE_ASCII; +* eventAttrib.message.ascii = "my range"; +* nvtxRangeId_t rangeId = nvtxDomainRangeStartEx(&eventAttrib); +* // ... +* nvtxDomainRangeEnd(rangeId); +* \endcode +* +* \sa +* ::nvtxDomainRangeStartEx +* +* \version \NVTX_VERSION_2 +* @{ */ +NVTX_DECLSPEC void NVTX_API nvtxDomainRangeEnd(nvtxDomainHandle_t domain, nvtxRangeId_t id); +/** @} */ + +/* ------------------------------------------------------------------------- */ +/** \brief Ends a process range. + * + * \param id - The correlation ID returned from an nvtxRangeStart call. + * + * \sa + * ::nvtxDomainRangeStartEx + * ::nvtxRangeStartEx + * ::nvtxRangeStartA + * ::nvtxRangeStartW + * + * \version \NVTX_VERSION_0 + * @{ */ +NVTX_DECLSPEC void NVTX_API nvtxRangeEnd(nvtxRangeId_t id); +/** @} */ + +/** \name Thread Ranges */ + +/* ------------------------------------------------------------------------- */ +/** \brief Starts a nested thread range. +* +* \param domain - The domain of scoping. +* \param eventAttrib - The event attribute structure defining the range's +* attribute types and attribute values. +* +* \return The 0 based level of range being started. This value is scoped to the domain. +* If an error occurs, a negative value is returned. +* +* \par Example: +* \code +* nvtxDomainHandle_t domain = nvtxDomainCreateA("example domain"); +* nvtxEventAttributes_t eventAttrib = {0}; +* eventAttrib.version = NVTX_VERSION; +* eventAttrib.size = NVTX_EVENT_ATTRIB_STRUCT_SIZE; +* eventAttrib.colorType = NVTX_COLOR_ARGB; +* eventAttrib.color = 0xFFFF0000; +* eventAttrib.messageType = NVTX_MESSAGE_TYPE_ASCII; +* eventAttrib.message.ascii = "Level 0"; +* nvtxDomainRangePushEx(domain, &eventAttrib); +* +* // Re-use eventAttrib +* eventAttrib.messageType = NVTX_MESSAGE_TYPE_UNICODE; +* eventAttrib.message.unicode = L"Level 1"; +* nvtxDomainRangePushEx(domain, &eventAttrib); +* +* nvtxDomainRangePop(domain); //level 1 +* nvtxDomainRangePop(domain); //level 0 +* \endcode +* +* \sa +* ::nvtxDomainRangePop +* +* \version \NVTX_VERSION_2 +* @{ */ +NVTX_DECLSPEC int NVTX_API nvtxDomainRangePushEx(nvtxDomainHandle_t domain, const nvtxEventAttributes_t* eventAttrib); +/** @} */ + +/* ------------------------------------------------------------------------- */ +/** \brief Starts a nested thread range. + * + * \param eventAttrib - The event attribute structure defining the range's + * attribute types and attribute values. + * + * \return The 0 based level of range being started. This level is per domain. + * If an error occurs a negative value is returned. + * + * \par Example: + * \code + * nvtxEventAttributes_t eventAttrib = {0}; + * eventAttrib.version = NVTX_VERSION; + * eventAttrib.size = NVTX_EVENT_ATTRIB_STRUCT_SIZE; + * eventAttrib.colorType = NVTX_COLOR_ARGB; + * eventAttrib.color = 0xFFFF0000; + * eventAttrib.messageType = NVTX_MESSAGE_TYPE_ASCII; + * eventAttrib.message.ascii = "Level 0"; + * nvtxRangePushEx(&eventAttrib); + * + * // Re-use eventAttrib + * eventAttrib.messageType = NVTX_MESSAGE_TYPE_UNICODE; + * eventAttrib.message.unicode = L"Level 1"; + * nvtxRangePushEx(&eventAttrib); + * + * nvtxRangePop(); + * nvtxRangePop(); + * \endcode + * + * \sa + * ::nvtxDomainRangePushEx + * ::nvtxRangePop + * + * \version \NVTX_VERSION_1 + * @{ */ +NVTX_DECLSPEC int NVTX_API nvtxRangePushEx(const nvtxEventAttributes_t* eventAttrib); +/** @} */ + +/* ------------------------------------------------------------------------- */ +/** \brief Starts a nested thread range. + * + * \param message - The event message associated to this range event. + * + * \return The 0 based level of range being started. If an error occurs a + * negative value is returned. + * + * \par Example: + * \code + * nvtxRangePushA("Level 0"); + * nvtxRangePushW(L"Level 1"); + * nvtxRangePop(); + * nvtxRangePop(); + * \endcode + * + * \sa + * ::nvtxDomainRangePushEx + * ::nvtxRangePop + * + * \version \NVTX_VERSION_0 + * @{ */ +NVTX_DECLSPEC int NVTX_API nvtxRangePushA(const char* message); +NVTX_DECLSPEC int NVTX_API nvtxRangePushW(const wchar_t* message); +/** @} */ + + +/* ------------------------------------------------------------------------- */ +/** \brief Ends a nested thread range. +* +* \return The level of the range being ended. If an error occurs a negative +* value is returned on the current thread. +* +* \par Example: +* \code +* nvtxDomainHandle_t domain = nvtxDomainCreate("example library"); +* nvtxDomainRangePushA(domain, "Level 0"); +* nvtxDomainRangePushW(domain, L"Level 1"); +* nvtxDomainRangePop(domain); +* nvtxDomainRangePop(domain); +* \endcode +* +* \sa +* ::nvtxRangePushEx +* ::nvtxRangePushA +* ::nvtxRangePushW +* +* \version \NVTX_VERSION_2 +* @{ */ +NVTX_DECLSPEC int NVTX_API nvtxDomainRangePop(nvtxDomainHandle_t domain); +/** @} */ + +/* ------------------------------------------------------------------------- */ +/** \brief Ends a nested thread range. + * + * \return The level of the range being ended. If an error occurs a negative + * value is returned on the current thread. + * + * \par Example: + * \code + * nvtxRangePushA("Level 0"); + * nvtxRangePushW(L"Level 1"); + * nvtxRangePop(); + * nvtxRangePop(); + * \endcode + * + * \sa + * ::nvtxRangePushEx + * ::nvtxRangePushA + * ::nvtxRangePushW + * + * \version \NVTX_VERSION_0 + * @{ */ +NVTX_DECLSPEC int NVTX_API nvtxRangePop(void); +/** @} */ + + +/** @} */ /*END defgroup*/ +/* ========================================================================= */ +/** \defgroup RESOURCE_NAMING Resource Naming + * + * See \ref RESOURCE_NAMING for more details + * + * @{ + */ + + +/* ------------------------------------------------------------------------- */ +/** \name Functions for Generic Resource Naming*/ +/* ------------------------------------------------------------------------- */ + +/* ------------------------------------------------------------------------- */ +/** \cond SHOW_HIDDEN +* \brief Resource typing helpers. +* +* Classes are used to make it easy to create a series of resource types +* per API without collisions +*/ +#define NVTX_RESOURCE_MAKE_TYPE(CLASS, INDEX) ((((uint32_t)(NVTX_RESOURCE_CLASS_ ## CLASS))<<16)|((uint32_t)(INDEX))) +#define NVTX_RESOURCE_CLASS_GENERIC 1 +/** \endcond */ + +/* ------------------------------------------------------------------------- */ +/** \brief Generic resource type for when a resource class is not available. +* +* \sa +* ::nvtxDomainResourceCreate +* +* \version \NVTX_VERSION_2 +*/ +typedef enum nvtxResourceGenericType_t +{ + NVTX_RESOURCE_TYPE_UNKNOWN = 0, + NVTX_RESOURCE_TYPE_GENERIC_POINTER = NVTX_RESOURCE_MAKE_TYPE(GENERIC, 1), /**< Generic pointer assumed to have no collisions with other pointers. */ + NVTX_RESOURCE_TYPE_GENERIC_HANDLE = NVTX_RESOURCE_MAKE_TYPE(GENERIC, 2), /**< Generic handle assumed to have no collisions with other handles. */ + NVTX_RESOURCE_TYPE_GENERIC_THREAD_NATIVE = NVTX_RESOURCE_MAKE_TYPE(GENERIC, 3), /**< OS native thread identifier. */ + NVTX_RESOURCE_TYPE_GENERIC_THREAD_POSIX = NVTX_RESOURCE_MAKE_TYPE(GENERIC, 4) /**< POSIX pthread identifier. */ +} nvtxResourceGenericType_t; + + + +/** \brief Resource Attribute Structure. +* \anchor RESOURCE_ATTRIBUTE_STRUCTURE +* +* This structure is used to describe the attributes of a resource. The layout of +* the structure is defined by a specific version of the tools extension +* library and can change between different versions of the Tools Extension +* library. +* +* \par Initializing the Attributes +* +* The caller should always perform the following three tasks when using +* attributes: +*
    +*
  • Zero the structure +*
  • Set the version field +*
  • Set the size field +*
+* +* Zeroing the structure sets all the resource attributes types and values +* to the default value. +* +* The version and size field are used by the Tools Extension +* implementation to handle multiple versions of the attributes structure. +* +* It is recommended that the caller use one of the following to methods +* to initialize the event attributes structure: +* +* \par Method 1: Initializing nvtxEventAttributes for future compatibility +* \code +* nvtxResourceAttributes_t attribs = {0}; +* attribs.version = NVTX_VERSION; +* attribs.size = NVTX_RESOURCE_ATTRIB_STRUCT_SIZE; +* \endcode +* +* \par Method 2: Initializing nvtxEventAttributes for a specific version +* \code +* nvtxResourceAttributes_v0 attribs = {0}; +* attribs.version = 2; +* attribs.size = (uint16_t)(sizeof(nvtxResourceAttributes_v0)); +* \endcode +* +* If the caller uses Method 1 it is critical that the entire binary +* layout of the structure be configured to 0 so that all fields +* are initialized to the default value. +* +* The caller should either use both NVTX_VERSION and +* NVTX_RESOURCE_ATTRIB_STRUCT_SIZE (Method 1) or use explicit values +* and a versioned type (Method 2). Using a mix of the two methods +* will likely cause either source level incompatibility or binary +* incompatibility in the future. +* +* \par Settings Attribute Types and Values +* +* +* \par Example: +* \code +* nvtxDomainHandle_t domain = nvtxDomainCreateA("example domain"); +* +* // Initialize +* nvtxResourceAttributes_t attribs = {0}; +* attribs.version = NVTX_VERSION; +* attribs.size = NVTX_RESOURCE_ATTRIB_STRUCT_SIZE; +* +* // Configure the Attributes +* attribs.identifierType = NVTX_RESOURCE_TYPE_GENERIC_POINTER; +* attribs.identifier.pValue = (const void*)pMutex; +* attribs.messageType = NVTX_MESSAGE_TYPE_ASCII; +* attribs.message.ascii = "Single thread access to database."; +* +* nvtxResourceHandle_t handle = nvtxDomainResourceCreate(domain, attribs); +* \endcode +* +* \sa +* ::nvtxDomainResourceCreate +*/ +typedef struct nvtxResourceAttributes_v0 +{ + /** + * \brief Version flag of the structure. + * + * Needs to be set to NVTX_VERSION to indicate the version of NVTX APIs + * supported in this header file. This can optionally be overridden to + * another version of the tools extension library. + */ + uint16_t version; + + /** + * \brief Size of the structure. + * + * Needs to be set to the size in bytes of this attribute + * structure. + */ + uint16_t size; + + /** + * \brief Identifier type specifies how to interpret the identifier field + * + * Defines the identifier format of the attribute structure's \ref RESOURCE_IDENTIFIER_FIELD + * "identifier" field. + * + * Default Value is NVTX_RESOURCE_TYPE_UNKNOWN + */ + int32_t identifierType; /* values from enums following the pattern nvtxResource[name]Type_t */ + + /** + * \brief Identifier for the resource. + * \anchor RESOURCE_IDENTIFIER_FIELD + * + * An identifier may be a pointer or a handle to an OS or middleware API object. + * The resource type will assist in avoiding collisions where handles values may collide. + */ + union identifier_t + { + const void* pValue; + uint64_t ullValue; + } identifier; + + /** \brief Message type specified in this attribute structure. + * + * Defines the message format of the attribute structure's \ref RESOURCE_MESSAGE_FIELD + * "message" field. + * + * Default Value is NVTX_MESSAGE_UNKNOWN + */ + int32_t messageType; /* nvtxMessageType_t */ + + /** \brief Message assigned to this attribute structure. \anchor RESOURCE_MESSAGE_FIELD + * + * The text message that is attached to a resource. + */ + nvtxMessageValue_t message; + +} nvtxResourceAttributes_v0; + +typedef struct nvtxResourceAttributes_v0 nvtxResourceAttributes_t; + +/* \cond SHOW_HIDDEN +* \version \NVTX_VERSION_2 +*/ +#define NVTX_RESOURCE_ATTRIB_STRUCT_SIZE ( (uint16_t)( sizeof(nvtxResourceAttributes_v0) ) ) +typedef struct nvtxResourceHandle* nvtxResourceHandle_t; +/** \endcond */ + + + +/* ------------------------------------------------------------------------- */ +/** \brief Create a resource object to track and associate data with OS and middleware objects +* +* Allows users to associate an API handle or pointer with a user-provided name. +* +* +* \param domain - Domain to own the resource object +* \param attribs - Attributes to be associated with the resource +* +* \return A handle that represents the newly created resource object. +* +* \par Example: +* \code +* nvtxDomainHandle_t domain = nvtxDomainCreateA("example domain"); +* nvtxResourceAttributes_t attribs = {0}; +* attribs.version = NVTX_VERSION; +* attribs.size = NVTX_RESOURCE_ATTRIB_STRUCT_SIZE; +* attribs.identifierType = NVTX_RESOURCE_TYPE_GENERIC_POINTER; +* attribs.identifier.pValue = (const void*)pMutex; +* attribs.messageType = NVTX_MESSAGE_TYPE_ASCII; +* attribs.message.ascii = "Single thread access to database."; +* nvtxResourceHandle_t handle = nvtxDomainResourceCreate(domain, attribs); +* \endcode +* +* \sa +* ::nvtxResourceAttributes_t +* ::nvtxDomainResourceDestroy +* +* \version \NVTX_VERSION_2 +* @{ */ +NVTX_DECLSPEC nvtxResourceHandle_t NVTX_API nvtxDomainResourceCreate(nvtxDomainHandle_t domain, nvtxResourceAttributes_t* attribs); +/** @} */ + +/* ------------------------------------------------------------------------- */ +/** \brief Destroy a resource object to track and associate data with OS and middleware objects +* +* Allows users to associate an API handle or pointer with a user-provided name. +* +* \param resource - Handle to the resource in which to operate. +* +* \par Example: +* \code +* nvtxDomainHandle_t domain = nvtxDomainCreateA("example domain"); +* nvtxResourceAttributes_t attribs = {0}; +* attribs.version = NVTX_VERSION; +* attribs.size = NVTX_RESOURCE_ATTRIB_STRUCT_SIZE; +* attribs.identifierType = NVTX_RESOURCE_TYPE_GENERIC_POINTER; +* attribs.identifier.pValue = (const void*)pMutex; +* attribs.messageType = NVTX_MESSAGE_TYPE_ASCII; +* attribs.message.ascii = "Single thread access to database."; +* nvtxResourceHandle_t handle = nvtxDomainResourceCreate(domain, attribs); +* nvtxDomainResourceDestroy(handle); +* \endcode +* +* \sa +* ::nvtxDomainResourceCreate +* +* \version \NVTX_VERSION_2 +* @{ */ +NVTX_DECLSPEC void NVTX_API nvtxDomainResourceDestroy(nvtxResourceHandle_t resource); +/** @} */ + + +/** \name Functions for NVTX Category Naming*/ + +/* ------------------------------------------------------------------------- */ +/** +* \brief Annotate an NVTX category used within a domain. +* +* Categories are used to group sets of events. Each category is identified +* through a unique ID and that ID is passed into any of the marker/range +* events to assign that event to a specific category. The nvtxDomainNameCategory +* function calls allow the user to assign a name to a category ID that is +* specific to the domain. +* +* nvtxDomainNameCategory(NULL, category, name) is equivalent to calling +* nvtxNameCategory(category, name). +* +* \param domain - The domain of scoping the category. +* \param category - The category ID to name. +* \param name - The name of the category. +* +* \remarks The category names are tracked per domain. +* +* \par Example: +* \code +* nvtxDomainHandle_t domain = nvtxDomainCreateA("example"); +* nvtxDomainNameCategoryA(domain, 1, "Memory Allocation"); +* nvtxDomainNameCategoryW(domain, 2, L"Memory Transfer"); +* \endcode +* +* \version \NVTX_VERSION_2 +* @{ */ +NVTX_DECLSPEC void NVTX_API nvtxDomainNameCategoryA(nvtxDomainHandle_t domain, uint32_t category, const char* name); +NVTX_DECLSPEC void NVTX_API nvtxDomainNameCategoryW(nvtxDomainHandle_t domain, uint32_t category, const wchar_t* name); +/** @} */ + +/** \brief Annotate an NVTX category. + * + * Categories are used to group sets of events. Each category is identified + * through a unique ID and that ID is passed into any of the marker/range + * events to assign that event to a specific category. The nvtxNameCategory + * function calls allow the user to assign a name to a category ID. + * + * \param category - The category ID to name. + * \param name - The name of the category. + * + * \remarks The category names are tracked per process. + * + * \par Example: + * \code + * nvtxNameCategory(1, "Memory Allocation"); + * nvtxNameCategory(2, "Memory Transfer"); + * nvtxNameCategory(3, "Memory Object Lifetime"); + * \endcode + * + * \version \NVTX_VERSION_1 + * @{ */ +NVTX_DECLSPEC void NVTX_API nvtxNameCategoryA(uint32_t category, const char* name); +NVTX_DECLSPEC void NVTX_API nvtxNameCategoryW(uint32_t category, const wchar_t* name); +/** @} */ + +/** \name Functions for OS Threads Naming*/ + +/* ------------------------------------------------------------------------- */ +/** \brief Annotate an OS thread. + * + * Allows the user to name an active thread of the current process. If an + * invalid thread ID is provided or a thread ID from a different process is + * used the behavior of the tool is implementation dependent. + * + * Tools expect thread ID to be a number that uniquely identifies the thread + * at the time of the call. Note that a thread's ID can be reused after + * it is destroyed. Tools may choose how to handle aliasing of thread IDs. + * + * POSIX pthread_t type returned by pthread_self() may not comply with these + * expectations. Please use OS-specific thread ID instead of pthread_t. + * + * The thread name is associated to the default domain. To support domains + * use resource objects via ::nvtxDomainResourceCreate. + * + * \param threadId - The ID of the thread to name. + * \param name - The name of the thread. + * + * \par Examples: + * MS Windows: + * \code + * #include + * nvtxNameOsThread(GetCurrentThreadId(), "Current thread"); + * nvtxNameOsThread(GetThreadId(SomeThreadHandle), "Other thread"); + * \endcode + * + * Android: + * \code + * #include + * nvtxNameOsThreadA(gettid(), "Current thread"); + * nvtxNameOsThreadA(getpid(), "Main thread"); + * \endcode + * + * Linux: + * \code + * #include + * nvtxNameOsThreadA(syscall(SYS_gettid), "Current thread"); + * \endcode + * \code + * #include + * nvtxNameOsThreadA(getpid(), "Main thread"); + * \endcode + * + * OS X: + * \code + * #include + * nvtxNameOsThreadA(syscall(SYS_thread_selfid), "Current thread"); + * \endcode + * \code + * #include + * __uint64_t id; + * pthread_threadid_np(pthread_self(), &id); + * nvtxNameOsThreadA(id, "Current thread"); + * pthread_threadid_np(somePThreadId, &id); + * nvtxNameOsThreadA(id, "Other thread"); + * \endcode + * + * \version \NVTX_VERSION_1 + * @{ */ +NVTX_DECLSPEC void NVTX_API nvtxNameOsThreadA(uint32_t threadId, const char* name); +NVTX_DECLSPEC void NVTX_API nvtxNameOsThreadW(uint32_t threadId, const wchar_t* name); +/** @} */ + + +/** @} */ /*END defgroup*/ +/* ========================================================================= */ +/** \defgroup STRING_REGISTRATION String Registration +* +* Registered strings are intended to increase performance by lowering instrumentation +* overhead. String may be registered once and the handle may be passed in place of +* a string where an the APIs may allow. +* +* See \ref STRING_REGISTRATION for more details +* +* @{ +*/ + +/* ------------------------------------------------------------------------- */ +/** \brief Register a string. + +* Registers an immutable string with NVTX. Once registered the pointer used +* to register the domain name can be used in nvtxEventAttributes_t +* \ref MESSAGE_FIELD. This allows NVTX implementation to skip copying the +* contents of the message on each event invocation. +* +* String registration is an optimization. It is recommended to use string +* registration if the string will be passed to an event many times. +* +* String are not unregistered, except that by unregistering the entire domain +* +* \param domain - Domain handle. If NULL then the global domain is used. +* \param string - A unique pointer to a sequence of characters. +* +* \return A handle representing the registered string. +* +* \par Example: +* \code +* nvtxDomainCreateA("com.nvidia.nvtx.example"); +* nvtxStringHandle_t message = nvtxDomainRegisterStringA(domain, "registered string"); +* nvtxEventAttributes_t eventAttrib = {0}; +* eventAttrib.version = NVTX_VERSION; +* eventAttrib.size = NVTX_EVENT_ATTRIB_STRUCT_SIZE; +* eventAttrib.messageType = NVTX_MESSAGE_TYPE_REGISTERED; +* eventAttrib.message.registered = message; +* \endcode +* +* \version \NVTX_VERSION_2 +* @{ */ +NVTX_DECLSPEC nvtxStringHandle_t NVTX_API nvtxDomainRegisterStringA(nvtxDomainHandle_t domain, const char* string); +NVTX_DECLSPEC nvtxStringHandle_t NVTX_API nvtxDomainRegisterStringW(nvtxDomainHandle_t domain, const wchar_t* string); +/** @} */ + +/** @} */ /*END defgroup*/ +/* ========================================================================= */ +/** \defgroup DOMAINS Domains +* +* Domains are used to group events to a developer defined scope. Middleware +* vendors may also scope their own events to avoid collisions with the +* the application developer's events, so that the application developer may +* inspect both parts and easily differentiate or filter them. By default +* all events are scoped to a global domain where NULL is provided or when +* using APIs provided b versions of NVTX below v2 +* +* Domains are intended to be typically long lived objects with the intention +* of logically separating events of large modules from each other such as +* middleware libraries from each other and the main application. +* +* See \ref DOMAINS for more details +* +* @{ +*/ + +/* ------------------------------------------------------------------------- */ +/** \brief Register a NVTX domain. +* +* Domains are used to scope annotations. All NVTX_VERSION_0 and NVTX_VERSION_1 +* annotations are scoped to the global domain. The function nvtxDomainCreate +* creates a new named domain. +* +* Each domain maintains its own nvtxRangePush and nvtxRangePop stack. +* +* \param name - A unique string representing the domain. +* +* \return A handle representing the domain. +* +* \par Example: +* \code +* nvtxDomainHandle_t domain = nvtxDomainCreateA("com.nvidia.nvtx.example"); +* +* nvtxMarkA("nvtxMarkA to global domain"); +* +* nvtxEventAttributes_t eventAttrib1 = {0}; +* eventAttrib1.version = NVTX_VERSION; +* eventAttrib1.size = NVTX_EVENT_ATTRIB_STRUCT_SIZE; +* eventAttrib1.message.ascii = "nvtxDomainMarkEx to global domain"; +* nvtxDomainMarkEx(NULL, &eventAttrib1); +* +* nvtxEventAttributes_t eventAttrib2 = {0}; +* eventAttrib2.version = NVTX_VERSION; +* eventAttrib2.size = NVTX_EVENT_ATTRIB_STRUCT_SIZE; +* eventAttrib2.message.ascii = "nvtxDomainMarkEx to com.nvidia.nvtx.example"; +* nvtxDomainMarkEx(domain, &eventAttrib2); +* nvtxDomainDestroy(domain); +* \endcode +* +* \sa +* ::nvtxDomainDestroy +* +* \version \NVTX_VERSION_2 +* @{ */ +NVTX_DECLSPEC nvtxDomainHandle_t NVTX_API nvtxDomainCreateA(const char* name); +NVTX_DECLSPEC nvtxDomainHandle_t NVTX_API nvtxDomainCreateW(const wchar_t* name); +/** @} */ + +/* ------------------------------------------------------------------------- */ +/** \brief Unregister a NVTX domain. +* +* Unregisters the domain handle and frees all domain specific resources. +* +* \param domain - the domain handle +* +* \par Example: +* \code +* nvtxDomainHandle_t domain = nvtxDomainCreateA("com.nvidia.nvtx.example"); +* nvtxDomainDestroy(domain); +* \endcode +* +* \sa +* ::nvtxDomainCreateA +* ::nvtxDomainCreateW +* +* \version \NVTX_VERSION_2 +* @{ */ +NVTX_DECLSPEC void NVTX_API nvtxDomainDestroy(nvtxDomainHandle_t domain); +/** @} */ + + +/** @} */ /*END defgroup*/ +/* ========================================================================= */ +/** \cond SHOW_HIDDEN */ + +#ifdef UNICODE + #define nvtxMark nvtxMarkW + #define nvtxRangeStart nvtxRangeStartW + #define nvtxRangePush nvtxRangePushW + #define nvtxNameCategory nvtxNameCategoryW + #define nvtxNameOsThread nvtxNameOsThreadW + /* NVTX_VERSION_2 */ + #define nvtxDomainCreate nvtxDomainCreateW + #define nvtxDomainRegisterString nvtxDomainRegisterStringW + #define nvtxDomainNameCategory nvtxDomainNameCategoryW +#else + #define nvtxMark nvtxMarkA + #define nvtxRangeStart nvtxRangeStartA + #define nvtxRangePush nvtxRangePushA + #define nvtxNameCategory nvtxNameCategoryA + #define nvtxNameOsThread nvtxNameOsThreadA + /* NVTX_VERSION_2 */ + #define nvtxDomainCreate nvtxDomainCreateA + #define nvtxDomainRegisterString nvtxDomainRegisterStringA + #define nvtxDomainNameCategory nvtxDomainNameCategoryA +#endif + +/** \endcond */ + +#ifdef __cplusplus +} /* extern "C" */ +#endif /* __cplusplus */ + +#define NVTX_IMPL_GUARD /* Ensure other headers cannot included directly */ + +#include "nvtxDetail/nvtxTypes.h" + +#ifndef NVTX_NO_IMPL +#include "nvtxDetail/nvtxImpl.h" +#endif /*NVTX_NO_IMPL*/ + +#undef NVTX_IMPL_GUARD + +#endif /* !defined(NVTX_VERSION) */ diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvToolsExtCuda.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvToolsExtCuda.h new file mode 100644 index 0000000000000000000000000000000000000000..8b1e71b7779089430b39e0fe3483ad536bb4bb7d --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvToolsExtCuda.h @@ -0,0 +1,170 @@ +/* +* Copyright 2009-2016 NVIDIA Corporation. All rights reserved. +* +* NOTICE TO USER: +* +* This source code is subject to NVIDIA ownership rights under U.S. and +* international Copyright laws. +* +* This software and the information contained herein is PROPRIETARY and +* CONFIDENTIAL to NVIDIA and is being provided under the terms and conditions +* of a form of NVIDIA software license agreement. +* +* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE +* CODE FOR ANY PURPOSE. IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR +* IMPLIED WARRANTY OF ANY KIND. NVIDIA DISCLAIMS ALL WARRANTIES WITH +* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF +* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE. +* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL, +* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS +* OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE +* OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE +* OR PERFORMANCE OF THIS SOURCE CODE. +* +* U.S. Government End Users. This source code is a "commercial item" as +* that term is defined at 48 C.F.R. 2.101 (OCT 1995), consisting of +* "commercial computer software" and "commercial computer software +* documentation" as such terms are used in 48 C.F.R. 12.212 (SEPT 1995) +* and is provided to the U.S. Government only as a commercial end item. +* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through +* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the +* source code with only those rights set forth herein. +* +* Any use of this source code in individual and commercial software must +* include, in the user documentation and internal comments to the code, +* the above Disclaimer and U.S. Government End Users Notice. +*/ + +#include "nvToolsExt.h" + +#include "cuda.h" + +#ifndef NVTOOLSEXT_CUDA_V3 +#define NVTOOLSEXT_CUDA_V3 + +#ifdef __cplusplus +extern "C" { +#endif /* __cplusplus */ + +/* ========================================================================= */ +/** \name Functions for CUDA Resource Naming +*/ +/** \addtogroup RESOURCE_NAMING + * \section RESOURCE_NAMING_CUDA CUDA Resource Naming + * + * This section covers the API functions that allow to annotate CUDA resources + * with user-provided names. + * + * @{ + */ + +/* ------------------------------------------------------------------------- */ +/* \cond SHOW_HIDDEN +* \brief Used to build a non-colliding value for resource types separated class +* \version \NVTX_VERSION_2 +*/ +#define NVTX_RESOURCE_CLASS_CUDA 4 +/** \endcond */ + +/* ------------------------------------------------------------------------- */ +/** \brief Resource types for CUDA +*/ +typedef enum nvtxResourceCUDAType_t +{ + NVTX_RESOURCE_TYPE_CUDA_DEVICE = NVTX_RESOURCE_MAKE_TYPE(CUDA, 1), /* CUdevice */ + NVTX_RESOURCE_TYPE_CUDA_CONTEXT = NVTX_RESOURCE_MAKE_TYPE(CUDA, 2), /* CUcontext */ + NVTX_RESOURCE_TYPE_CUDA_STREAM = NVTX_RESOURCE_MAKE_TYPE(CUDA, 3), /* CUstream */ + NVTX_RESOURCE_TYPE_CUDA_EVENT = NVTX_RESOURCE_MAKE_TYPE(CUDA, 4), /* CUevent */ +} nvtxResourceCUDAType_t; + + +/* ------------------------------------------------------------------------- */ +/** \brief Annotates a CUDA device. + * + * Allows the user to associate a CUDA device with a user-provided name. + * + * \param device - The handle of the CUDA device to name. + * \param name - The name of the CUDA device. + * + * \version \NVTX_VERSION_1 + * @{ */ +NVTX_DECLSPEC void NVTX_API nvtxNameCuDeviceA(CUdevice device, const char* name); +NVTX_DECLSPEC void NVTX_API nvtxNameCuDeviceW(CUdevice device, const wchar_t* name); +/** @} */ + +/* ------------------------------------------------------------------------- */ +/** \brief Annotates a CUDA context. + * + * Allows the user to associate a CUDA context with a user-provided name. + * + * \param context - The handle of the CUDA context to name. + * \param name - The name of the CUDA context. + * + * \par Example: + * \code + * CUresult status = cuCtxCreate( &cuContext, 0, cuDevice ); + * if ( CUDA_SUCCESS != status ) + * goto Error; + * nvtxNameCuContext(cuContext, "CTX_NAME"); + * \endcode + * + * \version \NVTX_VERSION_1 + * @{ */ +NVTX_DECLSPEC void NVTX_API nvtxNameCuContextA(CUcontext context, const char* name); +NVTX_DECLSPEC void NVTX_API nvtxNameCuContextW(CUcontext context, const wchar_t* name); +/** @} */ + +/* ------------------------------------------------------------------------- */ +/** \brief Annotates a CUDA stream. + * + * Allows the user to associate a CUDA stream with a user-provided name. + * + * \param stream - The handle of the CUDA stream to name. + * \param name - The name of the CUDA stream. + * + * \version \NVTX_VERSION_1 + * @{ */ +NVTX_DECLSPEC void NVTX_API nvtxNameCuStreamA(CUstream stream, const char* name); +NVTX_DECLSPEC void NVTX_API nvtxNameCuStreamW(CUstream stream, const wchar_t* name); +/** @} */ + +/* ------------------------------------------------------------------------- */ +/** \brief Annotates a CUDA event. + * + * Allows the user to associate a CUDA event with a user-provided name. + * + * \param event - The handle of the CUDA event to name. + * \param name - The name of the CUDA event. + * + * \version \NVTX_VERSION_1 + * @{ */ +NVTX_DECLSPEC void NVTX_API nvtxNameCuEventA(CUevent event, const char* name); +NVTX_DECLSPEC void NVTX_API nvtxNameCuEventW(CUevent event, const wchar_t* name); +/** @} */ + +/** @} */ /* END RESOURCE_NAMING */ + +/* ========================================================================= */ +#ifdef UNICODE + #define nvtxNameCuDevice nvtxNameCuDeviceW + #define nvtxNameCuContext nvtxNameCuContextW + #define nvtxNameCuStream nvtxNameCuStreamW + #define nvtxNameCuEvent nvtxNameCuEventW +#else + #define nvtxNameCuDevice nvtxNameCuDeviceA + #define nvtxNameCuContext nvtxNameCuContextA + #define nvtxNameCuStream nvtxNameCuStreamA + #define nvtxNameCuEvent nvtxNameCuEventA +#endif + +#ifdef __cplusplus +} +#endif /* __cplusplus */ + +#ifndef NVTX_NO_IMPL +#define NVTX_IMPL_GUARD_CUDA /* Ensure other headers cannot included directly */ +#include "nvtxDetail/nvtxImplCuda_v3.h" +#undef NVTX_IMPL_GUARD_CUDA +#endif /*NVTX_NO_IMPL*/ + +#endif /* NVTOOLSEXT_CUDA_V3 */ diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvToolsExtCudaRt.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvToolsExtCudaRt.h new file mode 100644 index 0000000000000000000000000000000000000000..044d3dab5c32e7ec6646f3a1f3e9d89e52d40ba2 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvToolsExtCudaRt.h @@ -0,0 +1,146 @@ +/* +* Copyright 2009-2016 NVIDIA Corporation. All rights reserved. +* +* NOTICE TO USER: +* +* This source code is subject to NVIDIA ownership rights under U.S. and +* international Copyright laws. +* +* This software and the information contained herein is PROPRIETARY and +* CONFIDENTIAL to NVIDIA and is being provided under the terms and conditions +* of a form of NVIDIA software license agreement. +* +* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE +* CODE FOR ANY PURPOSE. IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR +* IMPLIED WARRANTY OF ANY KIND. NVIDIA DISCLAIMS ALL WARRANTIES WITH +* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF +* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE. +* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL, +* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS +* OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE +* OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE +* OR PERFORMANCE OF THIS SOURCE CODE. +* +* U.S. Government End Users. This source code is a "commercial item" as +* that term is defined at 48 C.F.R. 2.101 (OCT 1995), consisting of +* "commercial computer software" and "commercial computer software +* documentation" as such terms are used in 48 C.F.R. 12.212 (SEPT 1995) +* and is provided to the U.S. Government only as a commercial end item. +* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through +* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the +* source code with only those rights set forth herein. +* +* Any use of this source code in individual and commercial software must +* include, in the user documentation and internal comments to the code, +* the above Disclaimer and U.S. Government End Users Notice. +*/ + +#include "nvToolsExt.h" + +#include "cuda.h" +#include "driver_types.h" + +#ifndef NVTOOLSEXT_CUDART_V3 +#define NVTOOLSEXT_CUDART_V3 + +#ifdef __cplusplus +extern "C" { +#endif /* __cplusplus */ + +/* ========================================================================= */ +/** \name Functions for CUDA Resource Naming +*/ +/** \addtogroup RESOURCE_NAMING + * \section RESOURCE_NAMING_CUDART CUDA Runtime Resource Naming + * + * This section covers the API functions that allow to annotate CUDA resources + * with user-provided names. + * + * @{ + */ + +/* ------------------------------------------------------------------------- */ +/* \cond SHOW_HIDDEN +* \brief Used to build a non-colliding value for resource types separated class +* \version \NVTX_VERSION_2 +*/ +#define NVTX_RESOURCE_CLASS_CUDART 5 +/** \endcond */ + +/* ------------------------------------------------------------------------- */ +/** \brief Resource types for CUDART +*/ +typedef enum nvtxResourceCUDARTType_t +{ + NVTX_RESOURCE_TYPE_CUDART_DEVICE = NVTX_RESOURCE_MAKE_TYPE(CUDART, 0), /* int device */ + NVTX_RESOURCE_TYPE_CUDART_STREAM = NVTX_RESOURCE_MAKE_TYPE(CUDART, 1), /* cudaStream_t */ + NVTX_RESOURCE_TYPE_CUDART_EVENT = NVTX_RESOURCE_MAKE_TYPE(CUDART, 2), /* cudaEvent_t */ +} nvtxResourceCUDARTType_t; + + +/* ------------------------------------------------------------------------- */ +/** \brief Annotates a CUDA device. + * + * Allows the user to associate a CUDA device with a user-provided name. + * + * \param device - The id of the CUDA device to name. + * \param name - The name of the CUDA device. + * + * \version \NVTX_VERSION_1 + * @{ */ +NVTX_DECLSPEC void NVTX_API nvtxNameCudaDeviceA(int device, const char* name); +NVTX_DECLSPEC void NVTX_API nvtxNameCudaDeviceW(int device, const wchar_t* name); +/** @} */ + +/* ------------------------------------------------------------------------- */ +/** \brief Annotates a CUDA stream. + * + * Allows the user to associate a CUDA stream with a user-provided name. + * + * \param stream - The handle of the CUDA stream to name. + * \param name - The name of the CUDA stream. + * + * \version \NVTX_VERSION_1 + * @{ */ +NVTX_DECLSPEC void NVTX_API nvtxNameCudaStreamA(cudaStream_t stream, const char* name); +NVTX_DECLSPEC void NVTX_API nvtxNameCudaStreamW(cudaStream_t stream, const wchar_t* name); +/** @} */ + +/* ------------------------------------------------------------------------- */ +/** \brief Annotates a CUDA event. + * + * Allows the user to associate a CUDA event with a user-provided name. + * + * \param event - The handle of the CUDA event to name. + * \param name - The name of the CUDA event. + * + * \version \NVTX_VERSION_1 + * @{ */ +NVTX_DECLSPEC void NVTX_API nvtxNameCudaEventA(cudaEvent_t event, const char* name); +NVTX_DECLSPEC void NVTX_API nvtxNameCudaEventW(cudaEvent_t event, const wchar_t* name); +/** @} */ + +/** @} */ /* END RESOURCE_NAMING */ + +/* ========================================================================= */ +#ifdef UNICODE + #define nvtxNameCudaDevice nvtxNameCudaDeviceW + #define nvtxNameCudaStream nvtxNameCudaStreamW + #define nvtxNameCudaEvent nvtxNameCudaEventW +#else + #define nvtxNameCudaDevice nvtxNameCudaDeviceA + #define nvtxNameCudaStream nvtxNameCudaStreamA + #define nvtxNameCudaEvent nvtxNameCudaEventA +#endif + +#ifdef __cplusplus +} +#endif /* __cplusplus */ + +#ifndef NVTX_NO_IMPL +#define NVTX_IMPL_GUARD_CUDART /* Ensure other headers cannot included directly */ +#include "nvtxDetail/nvtxImplCudaRt_v3.h" +#undef NVTX_IMPL_GUARD_CUDART +#endif /*NVTX_NO_IMPL*/ + +#endif /* NVTOOLSEXT_CUDART_V3 */ diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvToolsExtOpenCL.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvToolsExtOpenCL.h new file mode 100644 index 0000000000000000000000000000000000000000..38a9290b438d30f8450b6ea2f4e5de68d37d4a97 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvToolsExtOpenCL.h @@ -0,0 +1,220 @@ +/* +* Copyright 2009-2016 NVIDIA Corporation. All rights reserved. +* +* NOTICE TO USER: +* +* This source code is subject to NVIDIA ownership rights under U.S. and +* international Copyright laws. +* +* This software and the information contained herein is PROPRIETARY and +* CONFIDENTIAL to NVIDIA and is being provided under the terms and conditions +* of a form of NVIDIA software license agreement. +* +* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE +* CODE FOR ANY PURPOSE. IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR +* IMPLIED WARRANTY OF ANY KIND. NVIDIA DISCLAIMS ALL WARRANTIES WITH +* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF +* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE. +* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL, +* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS +* OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE +* OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE +* OR PERFORMANCE OF THIS SOURCE CODE. +* +* U.S. Government End Users. This source code is a "commercial item" as +* that term is defined at 48 C.F.R. 2.101 (OCT 1995), consisting of +* "commercial computer software" and "commercial computer software +* documentation" as such terms are used in 48 C.F.R. 12.212 (SEPT 1995) +* and is provided to the U.S. Government only as a commercial end item. +* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through +* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the +* source code with only those rights set forth herein. +* +* Any use of this source code in individual and commercial software must +* include, in the user documentation and internal comments to the code, +* the above Disclaimer and U.S. Government End Users Notice. +*/ + +#include "nvToolsExt.h" + +#include + +#ifndef NVTOOLSEXT_OPENCL_V3 +#define NVTOOLSEXT_OPENCL_V3 + +#ifdef __cplusplus +extern "C" { +#endif /* __cplusplus */ + +/* ========================================================================= */ +/** \name Functions for OpenCL Resource Naming + */ +/** \addtogroup RESOURCE_NAMING + * \section RESOURCE_NAMING_OPENCL OpenCL Resource Naming + * + * This section covers the API functions that allow to annotate OpenCL resources + * with user-provided names. + * + * @{ + */ + +/* ------------------------------------------------------------------------- */ +/* \cond SHOW_HIDDEN +* \brief Used to build a non-colliding value for resource types separated class +* \version \NVTX_VERSION_2 +*/ +#define NVTX_RESOURCE_CLASS_OPENCL 6 +/** \endcond */ + +/* ------------------------------------------------------------------------- */ +/** \brief Resource types for OpenCL +*/ +typedef enum nvtxResourceOpenCLType_t +{ + NVTX_RESOURCE_TYPE_OPENCL_DEVICE = NVTX_RESOURCE_MAKE_TYPE(OPENCL, 1), + NVTX_RESOURCE_TYPE_OPENCL_CONTEXT = NVTX_RESOURCE_MAKE_TYPE(OPENCL, 2), + NVTX_RESOURCE_TYPE_OPENCL_COMMANDQUEUE = NVTX_RESOURCE_MAKE_TYPE(OPENCL, 3), + NVTX_RESOURCE_TYPE_OPENCL_MEMOBJECT = NVTX_RESOURCE_MAKE_TYPE(OPENCL, 4), + NVTX_RESOURCE_TYPE_OPENCL_SAMPLER = NVTX_RESOURCE_MAKE_TYPE(OPENCL, 5), + NVTX_RESOURCE_TYPE_OPENCL_PROGRAM = NVTX_RESOURCE_MAKE_TYPE(OPENCL, 6), + NVTX_RESOURCE_TYPE_OPENCL_EVENT = NVTX_RESOURCE_MAKE_TYPE(OPENCL, 7), +} nvtxResourceOpenCLType_t; + + +/* ------------------------------------------------------------------------- */ +/** \brief Annotates an OpenCL device. + * + * Allows to associate an OpenCL device with a user-provided name. + * + * \param device - The handle of the OpenCL device to name. + * \param name - The name of the OpenCL device. + * + * \version \NVTX_VERSION_1 + * @{ */ +NVTX_DECLSPEC void NVTX_API nvtxNameClDeviceA(cl_device_id device, const char* name); +NVTX_DECLSPEC void NVTX_API nvtxNameClDeviceW(cl_device_id device, const wchar_t* name); +/** @} */ + +/* ------------------------------------------------------------------------- */ +/** \brief Annotates an OpenCL context. + * + * Allows to associate an OpenCL context with a user-provided name. + * + * \param context - The handle of the OpenCL context to name. + * \param name - The name of the OpenCL context. + * + * \version \NVTX_VERSION_1 + * @{ */ +NVTX_DECLSPEC void NVTX_API nvtxNameClContextA(cl_context context, const char* name); +NVTX_DECLSPEC void NVTX_API nvtxNameClContextW(cl_context context, const wchar_t* name); +/** @} */ + +/* ------------------------------------------------------------------------- */ +/** \brief Annotates an OpenCL command queue. + * + * Allows to associate an OpenCL command queue with a user-provided name. + * + * \param command_queue - The handle of the OpenCL command queue to name. + * \param name - The name of the OpenCL command queue. + * + * \version \NVTX_VERSION_1 + * @{ */ +NVTX_DECLSPEC void NVTX_API nvtxNameClCommandQueueA(cl_command_queue command_queue, const char* name); +NVTX_DECLSPEC void NVTX_API nvtxNameClCommandQueueW(cl_command_queue command_queue, const wchar_t* name); +/** @} */ + +/* ------------------------------------------------------------------------- */ +/** \brief Annotates an OpenCL memory object. + * + * Allows to associate an OpenCL memory object with a user-provided name. + * + * \param memobj - The handle of the OpenCL memory object to name. + * \param name - The name of the OpenCL memory object. + * + * \version \NVTX_VERSION_1 + * @{ */ +NVTX_DECLSPEC void NVTX_API nvtxNameClMemObjectA(cl_mem memobj, const char* name); +NVTX_DECLSPEC void NVTX_API nvtxNameClMemObjectW(cl_mem memobj, const wchar_t* name); +/** @} */ + +/* ------------------------------------------------------------------------- */ +/** \brief Annotates an OpenCL sampler. + * + * Allows to associate an OpenCL sampler with a user-provided name. + * + * \param sampler - The handle of the OpenCL sampler to name. + * \param name - The name of the OpenCL sampler. + * + * \version \NVTX_VERSION_1 + * @{ */ +NVTX_DECLSPEC void NVTX_API nvtxNameClSamplerA(cl_sampler sampler, const char* name); +NVTX_DECLSPEC void NVTX_API nvtxNameClSamplerW(cl_sampler sampler, const wchar_t* name); +/** @} */ + +/* ------------------------------------------------------------------------- */ +/** \brief Annotates an OpenCL program. + * + * Allows to associate an OpenCL program with a user-provided name. + * + * \param program - The handle of the OpenCL program to name. + * \param name - The name of the OpenCL program. + * + * \code + * cpProgram = clCreateProgramWithSource(cxGPUContext, 1, + * (const char **) &cSourceCL, &program_length, &ciErrNum); + * shrCheckErrorEX(ciErrNum, CL_SUCCESS, pCleanup); + * nvtxNameClProgram(cpProgram, L"PROGRAM_NAME"); + * \endcode + * + * \version \NVTX_VERSION_1 + * @{ */ +NVTX_DECLSPEC void NVTX_API nvtxNameClProgramA(cl_program program, const char* name); +NVTX_DECLSPEC void NVTX_API nvtxNameClProgramW(cl_program program, const wchar_t* name); +/** @} */ + +/* ------------------------------------------------------------------------- */ +/** \brief Annotates an OpenCL event. + * + * Allows to associate an OpenCL event with a user-provided name. + * + * \param evnt - The handle of the OpenCL event to name. + * \param name - The name of the OpenCL event. + * + * \version \NVTX_VERSION_1 + * @{ */ +NVTX_DECLSPEC void NVTX_API nvtxNameClEventA(cl_event evnt, const char* name); +NVTX_DECLSPEC void NVTX_API nvtxNameClEventW(cl_event evnt, const wchar_t* name); +/** @} */ + +/** @} */ /* END RESOURCE_NAMING */ + +/* ========================================================================= */ +#ifdef UNICODE + #define nvtxNameClDevice nvtxNameClDeviceW + #define nvtxNameClContext nvtxNameClContextW + #define nvtxNameClCommandQueue nvtxNameClCommandQueueW + #define nvtxNameClMemObject nvtxNameClMemObjectW + #define nvtxNameClSampler nvtxNameClSamplerW + #define nvtxNameClProgram nvtxNameClProgramW + #define nvtxNameClEvent nvtxNameClEventW +#else + #define nvtxNameClDevice nvtxNameClDeviceA + #define nvtxNameClContext nvtxNameClContextA + #define nvtxNameClCommandQueue nvtxNameClCommandQueueA + #define nvtxNameClMemObject nvtxNameClMemObjectA + #define nvtxNameClSampler nvtxNameClSamplerA + #define nvtxNameClProgram nvtxNameClProgramA + #define nvtxNameClEvent nvtxNameClEventA +#endif + +#ifdef __cplusplus +} +#endif /* __cplusplus */ + +#ifndef NVTX_NO_IMPL +#define NVTX_IMPL_GUARD_OPENCL /* Ensure other headers cannot included directly */ +#include "nvtxDetail/nvtxImplOpenCL_v3.h" +#undef NVTX_IMPL_GUARD_OPENCL +#endif /*NVTX_NO_IMPL*/ + +#endif /* NVTOOLSEXT_OPENCL_V3 */ diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvToolsExtSync.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvToolsExtSync.h new file mode 100644 index 0000000000000000000000000000000000000000..afc3db98fe1b05d8dfb309221243ae3b4c14dd9d --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvToolsExtSync.h @@ -0,0 +1,411 @@ +/* +* Copyright 2009-2016 NVIDIA Corporation. All rights reserved. +* +* NOTICE TO USER: +* +* This source code is subject to NVIDIA ownership rights under U.S. and +* international Copyright laws. +* +* This software and the information contained herein is PROPRIETARY and +* CONFIDENTIAL to NVIDIA and is being provided under the terms and conditions +* of a form of NVIDIA software license agreement. +* +* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE +* CODE FOR ANY PURPOSE. IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR +* IMPLIED WARRANTY OF ANY KIND. NVIDIA DISCLAIMS ALL WARRANTIES WITH +* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF +* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE. +* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL, +* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS +* OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE +* OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE +* OR PERFORMANCE OF THIS SOURCE CODE. +* +* U.S. Government End Users. This source code is a "commercial item" as +* that term is defined at 48 C.F.R. 2.101 (OCT 1995), consisting of +* "commercial computer software" and "commercial computer software +* documentation" as such terms are used in 48 C.F.R. 12.212 (SEPT 1995) +* and is provided to the U.S. Government only as a commercial end item. +* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through +* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the +* source code with only those rights set forth herein. +* +* Any use of this source code in individual and commercial software must +* include, in the user documentation and internal comments to the code, +* the above Disclaimer and U.S. Government End Users Notice. +*/ + +#include "nvToolsExt.h" + +#ifndef NVTOOLSEXT_SYNC_V3 +#define NVTOOLSEXT_SYNC_V3 + +#ifdef __cplusplus +extern "C" { +#endif /* __cplusplus */ + +/* \cond SHOW_HIDDEN +* \version \NVTX_VERSION_2 +*/ +#define NVTX_SYNCUSER_ATTRIB_STRUCT_SIZE ( (uint16_t)( sizeof(nvtxSyncUserAttributes_v0) ) ) +/** \endcond */ + + +/** +* \page PAGE_SYNCHRONIZATION Synchronization +* +* This section covers a subset of the API that allow users to track additional +* synchronization details of their application. Naming OS synchronization primitives +* may allow users to better understand the data collected by traced synchronization +* APIs. Additionally, a user defined synchronization object can allow the users to +* to tell the tools when the user is building their own synchronization system +* that do not rely on the OS to provide behaviors and instead use techniques like +* atomic operations and spinlocks. +* +* See module \ref SYNCHRONIZATION for details. +* +* \par Example: +* \code +* class MyMutex +* { +* volatile long bLocked; +* nvtxSyncUser_t hSync; +* public: +* MyMutex(const char* name, nvtxDomainHandle_t d){ +* bLocked = 0; +* +* nvtxSyncUserAttributes_t attribs = { 0 }; +* attribs.version = NVTX_VERSION; +* attribs.size = NVTX_SYNCUSER_ATTRIB_STRUCT_SIZE; +* attribs.messageType = NVTX_MESSAGE_TYPE_ASCII; +* attribs.message.ascii = name; +* hSync = nvtxDomainSyncUserCreate(d, &attribs); +* } +* +* ~MyMutex() { +* nvtxDomainSyncUserDestroy(hSync); +* } +* +* bool Lock() { +* nvtxDomainSyncUserAcquireStart(hSync); +* bool acquired = __sync_bool_compare_and_swap(&bLocked, 0, 1);//atomic compiler intrinsic + +* if (acquired) { +* nvtxDomainSyncUserAcquireSuccess(hSync); +* } +* else { +* nvtxDomainSyncUserAcquireFailed(hSync); +* } +* return acquired; +* } + +* void Unlock() { +* nvtxDomainSyncUserReleasing(hSync); +* bLocked = false; +* } +* }; +* \endcode +* +* \version \NVTX_VERSION_2 +*/ + +/* ------------------------------------------------------------------------- */ +/* \cond SHOW_HIDDEN +* \brief Used to build a non-colliding value for resource types separated class +* \version \NVTX_VERSION_2 +*/ +#define NVTX_RESOURCE_CLASS_SYNC_OS 2 /**< Synchronization objects that are OS specific. */ +#define NVTX_RESOURCE_CLASS_SYNC_PTHREAD 3 /**< Synchronization objects that are from the POSIX Threads API (pthread)*/ +/** \endcond */ + + +/* ------------------------------------------------------------------------- */ +/** \defgroup SYNCHRONIZATION Synchronization +* See page \ref PAGE_SYNCHRONIZATION. +* @{ +*/ + +/** \brief Resource type values for OSs with POSIX Thread API support + */ +typedef enum nvtxResourceSyncPosixThreadType_t +{ + NVTX_RESOURCE_TYPE_SYNC_PTHREAD_MUTEX = NVTX_RESOURCE_MAKE_TYPE(SYNC_PTHREAD, 1), /* pthread_mutex_t */ + NVTX_RESOURCE_TYPE_SYNC_PTHREAD_CONDITION = NVTX_RESOURCE_MAKE_TYPE(SYNC_PTHREAD, 2), /* pthread_cond_t */ + NVTX_RESOURCE_TYPE_SYNC_PTHREAD_RWLOCK = NVTX_RESOURCE_MAKE_TYPE(SYNC_PTHREAD, 3), /* pthread_rwlock_t */ + NVTX_RESOURCE_TYPE_SYNC_PTHREAD_BARRIER = NVTX_RESOURCE_MAKE_TYPE(SYNC_PTHREAD, 4), /* pthread_barrier_t */ + NVTX_RESOURCE_TYPE_SYNC_PTHREAD_SPINLOCK = NVTX_RESOURCE_MAKE_TYPE(SYNC_PTHREAD, 5), /* pthread_spinlock_t */ + NVTX_RESOURCE_TYPE_SYNC_PTHREAD_ONCE = NVTX_RESOURCE_MAKE_TYPE(SYNC_PTHREAD, 6) /* pthread_once_t */ +} nvtxResourceSyncPosixThreadType_t; + +/** \brief Resource type values for Windows OSs +*/ +typedef enum nvtxResourceSyncWindowsType_t +{ + NVTX_RESOURCE_TYPE_SYNC_WINDOWS_MUTEX = NVTX_RESOURCE_MAKE_TYPE(SYNC_OS, 1), + NVTX_RESOURCE_TYPE_SYNC_WINDOWS_SEMAPHORE = NVTX_RESOURCE_MAKE_TYPE(SYNC_OS, 2), + NVTX_RESOURCE_TYPE_SYNC_WINDOWS_EVENT = NVTX_RESOURCE_MAKE_TYPE(SYNC_OS, 3), + NVTX_RESOURCE_TYPE_SYNC_WINDOWS_CRITICAL_SECTION = NVTX_RESOURCE_MAKE_TYPE(SYNC_OS, 4), + NVTX_RESOURCE_TYPE_SYNC_WINDOWS_SRWLOCK = NVTX_RESOURCE_MAKE_TYPE(SYNC_OS, 5) +} nvtxResourceSyncWindowsType_t; + +/** \brief Resource type values for Linux and Linux derived OSs such as Android +* \sa +* ::nvtxResourceSyncPosixThreadType_t +*/ +typedef enum nvtxResourceSyncLinuxType_t +{ + NVTX_RESOURCE_TYPE_SYNC_LINUX_MUTEX = NVTX_RESOURCE_MAKE_TYPE(SYNC_OS, 1), + NVTX_RESOURCE_TYPE_SYNC_LINUX_FUTEX = NVTX_RESOURCE_MAKE_TYPE(SYNC_OS, 2), + NVTX_RESOURCE_TYPE_SYNC_LINUX_SEMAPHORE = NVTX_RESOURCE_MAKE_TYPE(SYNC_OS, 3), + NVTX_RESOURCE_TYPE_SYNC_LINUX_COMPLETION = NVTX_RESOURCE_MAKE_TYPE(SYNC_OS, 4), + NVTX_RESOURCE_TYPE_SYNC_LINUX_SPINLOCK = NVTX_RESOURCE_MAKE_TYPE(SYNC_OS, 5), + NVTX_RESOURCE_TYPE_SYNC_LINUX_SEQLOCK = NVTX_RESOURCE_MAKE_TYPE(SYNC_OS, 6), + NVTX_RESOURCE_TYPE_SYNC_LINUX_RCU = NVTX_RESOURCE_MAKE_TYPE(SYNC_OS, 7) +} nvtxResourceSyncLinuxType_t; + +/** \brief Resource type values for Android come from Linux. +* \sa +* ::nvtxResourceSyncLinuxType_t +* ::nvtxResourceSyncPosixThreadType_t +*/ +typedef enum nvtxResourceSyncLinuxType_t nvtxResourceSyncAndroidType_t; + +/** \brief User Defined Synchronization Object Handle . +* \anchor SYNCUSER_HANDLE_STRUCTURE +* +* This structure is opaque to the user and is used as a handle to reference +* a user defined syncrhonization object. The tools will return a pointer through the API for the application +* to hold on it's behalf to reference the string in the future. +* +*/ +typedef struct nvtxSyncUser* nvtxSyncUser_t; + +/** \brief User Defined Synchronization Object Attributes Structure. +* \anchor USERDEF_SYNC_ATTRIBUTES_STRUCTURE +* +* This structure is used to describe the attributes of a user defined synchronization +* object. The layout of the structure is defined by a specific version of the tools +* extension library and can change between different versions of the Tools Extension +* library. +* +* \par Initializing the Attributes +* +* The caller should always perform the following three tasks when using +* attributes: +*
    +*
  • Zero the structure +*
  • Set the version field +*
  • Set the size field +*
+* +* Zeroing the structure sets all the event attributes types and values +* to the default value. +* +* The version and size field are used by the Tools Extension +* implementation to handle multiple versions of the attributes structure. +* +* It is recommended that the caller use one of the following to methods +* to initialize the event attributes structure: +* +* \par Method 1: Initializing nvtxEventAttributes for future compatibility +* \code +* nvtxSyncUserAttributes_t attribs = {0}; +* attribs.version = NVTX_VERSION; +* attribs.size = NVTX_SYNCUSER_ATTRIB_STRUCT_SIZE; +* \endcode +* +* \par Method 2: Initializing nvtxSyncUserAttributes_t for a specific version +* \code +* nvtxSyncUserAttributes_t attribs = {0}; +* attribs.version = 1; +* attribs.size = (uint16_t)(sizeof(nvtxSyncUserAttributes_t)); +* \endcode +* +* If the caller uses Method 1 it is critical that the entire binary +* layout of the structure be configured to 0 so that all fields +* are initialized to the default value. +* +* The caller should either use both NVTX_VERSION and +* NVTX_SYNCUSER_ATTRIB_STRUCT_SIZE (Method 1) or use explicit values +* and a versioned type (Method 2). Using a mix of the two methods +* will likely cause either source level incompatibility or binary +* incompatibility in the future. +* +* \par Settings Attribute Types and Values +* +* +* \par Example: +* \code +* // Initialize +* nvtxSyncUserAttributes_t attribs = {0}; +* attribs.version = NVTX_VERSION; +* attribs.size = NVTX_SYNCUSER_ATTRIB_STRUCT_SIZE; +* +* // Configure the Attributes +* attribs.messageType = NVTX_MESSAGE_TYPE_ASCII; +* attribs.message.ascii = "Example"; +* \endcode +* +* \sa +* ::nvtxDomainSyncUserCreate +*/ +typedef struct nvtxSyncUserAttributes_v0 +{ + /** + * \brief Version flag of the structure. + * + * Needs to be set to NVTX_VERSION to indicate the version of NVTX APIs + * supported in this header file. This can optionally be overridden to + * another version of the tools extension library. + */ + uint16_t version; + + /** + * \brief Size of the structure. + * + * Needs to be set to the size in bytes of the event attribute + * structure used to specify the event. + */ + uint16_t size; + + /** \brief Message type specified in this attribute structure. + * + * Defines the message format of the attribute structure's \ref nvtxSyncUserAttributes_v0::message + * "message" field. + * + * Default Value is NVTX_MESSAGE_UNKNOWN + */ + int32_t messageType; /* nvtxMessageType_t */ + + /** \brief Message assigned to this attribute structure. + * + * The text message that is attached to an event. + */ + nvtxMessageValue_t message; + +} nvtxSyncUserAttributes_v0; + +typedef struct nvtxSyncUserAttributes_v0 nvtxSyncUserAttributes_t; + +/* ------------------------------------------------------------------------- */ +/** \brief Create a user defined synchronization object +* This is used to track non-OS synchronization working with spinlocks and atomics +* +* \param domain - Domain to own the resource +* \param attribs - A structure to assign multiple attributes to the object. +* +* \return A handle that represents the newly created user defined synchronization object. +* +* \sa +* ::nvtxDomainSyncUserCreate +* ::nvtxDomainSyncUserDestroy +* ::nvtxDomainSyncUserAcquireStart +* ::nvtxDomainSyncUserAcquireFailed +* ::nvtxDomainSyncUserAcquireSuccess +* ::nvtxDomainSyncUserReleasing +* +* \version \NVTX_VERSION_2 +*/ +NVTX_DECLSPEC nvtxSyncUser_t NVTX_API nvtxDomainSyncUserCreate(nvtxDomainHandle_t domain, const nvtxSyncUserAttributes_t* attribs); + +/* ------------------------------------------------------------------------- */ +/** \brief Destroy a user defined synchronization object +* This is used to track non-OS synchronization working with spinlocks and atomics +* +* \param handle - A handle to the object to operate on. +* +* \sa +* ::nvtxDomainSyncUserCreate +* ::nvtxDomainSyncUserDestroy +* ::nvtxDomainSyncUserAcquireStart +* ::nvtxDomainSyncUserAcquireFailed +* ::nvtxDomainSyncUserAcquireSuccess +* ::nvtxDomainSyncUserReleasing +* +* \version \NVTX_VERSION_2 +*/ +NVTX_DECLSPEC void NVTX_API nvtxDomainSyncUserDestroy(nvtxSyncUser_t handle); + +/* ------------------------------------------------------------------------- */ +/** \brief Signal to tools that an attempt to acquire a user defined synchronization object +* +* \param handle - A handle to the object to operate on. +* +* \sa +* ::nvtxDomainSyncUserCreate +* ::nvtxDomainSyncUserDestroy +* ::nvtxDomainSyncUserAcquireStart +* ::nvtxDomainSyncUserAcquireFailed +* ::nvtxDomainSyncUserAcquireSuccess +* ::nvtxDomainSyncUserReleasing +* +* \version \NVTX_VERSION_2 +*/ +NVTX_DECLSPEC void NVTX_API nvtxDomainSyncUserAcquireStart(nvtxSyncUser_t handle); + +/* ------------------------------------------------------------------------- */ +/** \brief Signal to tools of failure in acquiring a user defined synchronization object +* This should be called after \ref nvtxDomainSyncUserAcquireStart +* +* \param handle - A handle to the object to operate on. +* +* \sa +* ::nvtxDomainSyncUserCreate +* ::nvtxDomainSyncUserDestroy +* ::nvtxDomainSyncUserAcquireStart +* ::nvtxDomainSyncUserAcquireFailed +* ::nvtxDomainSyncUserAcquireSuccess +* ::nvtxDomainSyncUserReleasing +* +* \version \NVTX_VERSION_2 +*/NVTX_DECLSPEC void NVTX_API nvtxDomainSyncUserAcquireFailed(nvtxSyncUser_t handle); + +/* ------------------------------------------------------------------------- */ +/** \brief Signal to tools of success in acquiring a user defined synchronization object +* This should be called after \ref nvtxDomainSyncUserAcquireStart. +* +* \param handle - A handle to the object to operate on. +* +* \sa +* ::nvtxDomainSyncUserCreate +* ::nvtxDomainSyncUserDestroy +* ::nvtxDomainSyncUserAcquireStart +* ::nvtxDomainSyncUserAcquireFailed +* ::nvtxDomainSyncUserAcquireSuccess +* ::nvtxDomainSyncUserReleasing +* +* \version \NVTX_VERSION_2 +*/NVTX_DECLSPEC void NVTX_API nvtxDomainSyncUserAcquireSuccess(nvtxSyncUser_t handle); + +/* ------------------------------------------------------------------------- */ +/** \brief Signal to tools of releasing a reservation on user defined synchronization object +* This should be called after \ref nvtxDomainSyncUserAcquireSuccess. +* +* \param handle - A handle to the object to operate on. +* +* \sa +* ::nvtxDomainSyncUserCreate +* ::nvtxDomainSyncUserDestroy +* ::nvtxDomainSyncUserAcquireStart +* ::nvtxDomainSyncUserAcquireFailed +* ::nvtxDomainSyncUserAcquireSuccess +* ::nvtxDomainSyncUserReleasing +* +* \version \NVTX_VERSION_2 +*/ +NVTX_DECLSPEC void NVTX_API nvtxDomainSyncUserReleasing(nvtxSyncUser_t handle); + + +/** @} */ /*END defgroup*/ + +#ifdef __cplusplus +} +#endif /* __cplusplus */ + +#ifndef NVTX_NO_IMPL +#define NVTX_IMPL_GUARD_SYNC /* Ensure other headers cannot included directly */ +#include "nvtxDetail/nvtxImplSync_v3.h" +#undef NVTX_IMPL_GUARD_SYNC +#endif /*NVTX_NO_IMPL*/ + +#endif /* NVTOOLSEXT_SYNC_V3 */ diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxImpl.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxImpl.h new file mode 100644 index 0000000000000000000000000000000000000000..bdc6bd4c72d74ef0f1fe5c2c8b4c49b196927fa7 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxImpl.h @@ -0,0 +1,469 @@ +/* This file was procedurally generated! Do not modify this file by hand. */ + +/* +* Copyright 2009-2016 NVIDIA Corporation. All rights reserved. +* +* NOTICE TO USER: +* +* This source code is subject to NVIDIA ownership rights under U.S. and +* international Copyright laws. +* +* This software and the information contained herein is PROPRIETARY and +* CONFIDENTIAL to NVIDIA and is being provided under the terms and conditions +* of a form of NVIDIA software license agreement. +* +* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE +* CODE FOR ANY PURPOSE. IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR +* IMPLIED WARRANTY OF ANY KIND. NVIDIA DISCLAIMS ALL WARRANTIES WITH +* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF +* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE. +* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL, +* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS +* OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE +* OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE +* OR PERFORMANCE OF THIS SOURCE CODE. +* +* U.S. Government End Users. This source code is a "commercial item" as +* that term is defined at 48 C.F.R. 2.101 (OCT 1995), consisting of +* "commercial computer software" and "commercial computer software +* documentation" as such terms are used in 48 C.F.R. 12.212 (SEPT 1995) +* and is provided to the U.S. Government only as a commercial end item. +* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through +* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the +* source code with only those rights set forth herein. +* +* Any use of this source code in individual and commercial software must +* include, in the user documentation and internal comments to the code, +* the above Disclaimer and U.S. Government End Users Notice. +*/ + +#ifndef NVTX_IMPL_GUARD +#error Never include this file directly -- it is automatically included by nvToolsExt.h (except when NVTX_NO_IMPL is defined). +#endif + +/* ---- Include required platform headers ---- */ + +#if defined(_WIN32) + +#include + +#else +#include + +#if defined(__ANDROID__) +#include +#endif + +#if defined(__linux__) || defined(__CYGWIN__) +#include +#endif + +#include +#include +#include +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include + +#endif + +/* ---- Define macros used in this file ---- */ + +#define NVTX_INIT_STATE_FRESH 0 +#define NVTX_INIT_STATE_STARTED 1 +#define NVTX_INIT_STATE_COMPLETE 2 + +#ifdef NVTX_DEBUG_PRINT +#ifdef __ANDROID__ +#include +#define NVTX_ERR(...) __android_log_print(ANDROID_LOG_ERROR, "NVTOOLSEXT", __VA_ARGS__); +#define NVTX_INFO(...) __android_log_print(ANDROID_LOG_INFO, "NVTOOLSEXT", __VA_ARGS__); +#else +#include +#define NVTX_ERR(...) fprintf(stderr, "NVTX_ERROR: " __VA_ARGS__) +#define NVTX_INFO(...) fprintf(stderr, "NVTX_INFO: " __VA_ARGS__) +#endif +#else /* !defined(NVTX_DEBUG_PRINT) */ +#define NVTX_ERR(...) +#define NVTX_INFO(...) +#endif + +#ifdef __cplusplus +extern "C" { +#endif /* __cplusplus */ + +#ifdef __GNUC__ +#pragma GCC visibility push(hidden) +#endif + +/* ---- Forward declare all functions referenced in globals ---- */ + +NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(void); +NVTX_LINKONCE_FWDDECL_FUNCTION int NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxEtiGetModuleFunctionTable)( + NvtxCallbackModule module, + NvtxFunctionTable* out_table, + unsigned int* out_size); +NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxEtiSetInjectionNvtxVersion)( + uint32_t version); +NVTX_LINKONCE_FWDDECL_FUNCTION const void* NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxGetExportTable)( + uint32_t exportTableId); + +#include "nvtxInitDecls.h" + +/* ---- Define all globals ---- */ + +typedef struct nvtxGlobals_t +{ + volatile unsigned int initState; + NvtxExportTableCallbacks etblCallbacks; + NvtxExportTableVersionInfo etblVersionInfo; + + /* Implementation function pointers */ + nvtxMarkEx_impl_fntype nvtxMarkEx_impl_fnptr; + nvtxMarkA_impl_fntype nvtxMarkA_impl_fnptr; + nvtxMarkW_impl_fntype nvtxMarkW_impl_fnptr; + nvtxRangeStartEx_impl_fntype nvtxRangeStartEx_impl_fnptr; + nvtxRangeStartA_impl_fntype nvtxRangeStartA_impl_fnptr; + nvtxRangeStartW_impl_fntype nvtxRangeStartW_impl_fnptr; + nvtxRangeEnd_impl_fntype nvtxRangeEnd_impl_fnptr; + nvtxRangePushEx_impl_fntype nvtxRangePushEx_impl_fnptr; + nvtxRangePushA_impl_fntype nvtxRangePushA_impl_fnptr; + nvtxRangePushW_impl_fntype nvtxRangePushW_impl_fnptr; + nvtxRangePop_impl_fntype nvtxRangePop_impl_fnptr; + nvtxNameCategoryA_impl_fntype nvtxNameCategoryA_impl_fnptr; + nvtxNameCategoryW_impl_fntype nvtxNameCategoryW_impl_fnptr; + nvtxNameOsThreadA_impl_fntype nvtxNameOsThreadA_impl_fnptr; + nvtxNameOsThreadW_impl_fntype nvtxNameOsThreadW_impl_fnptr; + + nvtxNameCuDeviceA_fakeimpl_fntype nvtxNameCuDeviceA_impl_fnptr; + nvtxNameCuDeviceW_fakeimpl_fntype nvtxNameCuDeviceW_impl_fnptr; + nvtxNameCuContextA_fakeimpl_fntype nvtxNameCuContextA_impl_fnptr; + nvtxNameCuContextW_fakeimpl_fntype nvtxNameCuContextW_impl_fnptr; + nvtxNameCuStreamA_fakeimpl_fntype nvtxNameCuStreamA_impl_fnptr; + nvtxNameCuStreamW_fakeimpl_fntype nvtxNameCuStreamW_impl_fnptr; + nvtxNameCuEventA_fakeimpl_fntype nvtxNameCuEventA_impl_fnptr; + nvtxNameCuEventW_fakeimpl_fntype nvtxNameCuEventW_impl_fnptr; + + nvtxNameClDeviceA_fakeimpl_fntype nvtxNameClDeviceA_impl_fnptr; + nvtxNameClDeviceW_fakeimpl_fntype nvtxNameClDeviceW_impl_fnptr; + nvtxNameClContextA_fakeimpl_fntype nvtxNameClContextA_impl_fnptr; + nvtxNameClContextW_fakeimpl_fntype nvtxNameClContextW_impl_fnptr; + nvtxNameClCommandQueueA_fakeimpl_fntype nvtxNameClCommandQueueA_impl_fnptr; + nvtxNameClCommandQueueW_fakeimpl_fntype nvtxNameClCommandQueueW_impl_fnptr; + nvtxNameClMemObjectA_fakeimpl_fntype nvtxNameClMemObjectA_impl_fnptr; + nvtxNameClMemObjectW_fakeimpl_fntype nvtxNameClMemObjectW_impl_fnptr; + nvtxNameClSamplerA_fakeimpl_fntype nvtxNameClSamplerA_impl_fnptr; + nvtxNameClSamplerW_fakeimpl_fntype nvtxNameClSamplerW_impl_fnptr; + nvtxNameClProgramA_fakeimpl_fntype nvtxNameClProgramA_impl_fnptr; + nvtxNameClProgramW_fakeimpl_fntype nvtxNameClProgramW_impl_fnptr; + nvtxNameClEventA_fakeimpl_fntype nvtxNameClEventA_impl_fnptr; + nvtxNameClEventW_fakeimpl_fntype nvtxNameClEventW_impl_fnptr; + + nvtxNameCudaDeviceA_impl_fntype nvtxNameCudaDeviceA_impl_fnptr; + nvtxNameCudaDeviceW_impl_fntype nvtxNameCudaDeviceW_impl_fnptr; + nvtxNameCudaStreamA_fakeimpl_fntype nvtxNameCudaStreamA_impl_fnptr; + nvtxNameCudaStreamW_fakeimpl_fntype nvtxNameCudaStreamW_impl_fnptr; + nvtxNameCudaEventA_fakeimpl_fntype nvtxNameCudaEventA_impl_fnptr; + nvtxNameCudaEventW_fakeimpl_fntype nvtxNameCudaEventW_impl_fnptr; + + nvtxDomainMarkEx_impl_fntype nvtxDomainMarkEx_impl_fnptr; + nvtxDomainRangeStartEx_impl_fntype nvtxDomainRangeStartEx_impl_fnptr; + nvtxDomainRangeEnd_impl_fntype nvtxDomainRangeEnd_impl_fnptr; + nvtxDomainRangePushEx_impl_fntype nvtxDomainRangePushEx_impl_fnptr; + nvtxDomainRangePop_impl_fntype nvtxDomainRangePop_impl_fnptr; + nvtxDomainResourceCreate_impl_fntype nvtxDomainResourceCreate_impl_fnptr; + nvtxDomainResourceDestroy_impl_fntype nvtxDomainResourceDestroy_impl_fnptr; + nvtxDomainNameCategoryA_impl_fntype nvtxDomainNameCategoryA_impl_fnptr; + nvtxDomainNameCategoryW_impl_fntype nvtxDomainNameCategoryW_impl_fnptr; + nvtxDomainRegisterStringA_impl_fntype nvtxDomainRegisterStringA_impl_fnptr; + nvtxDomainRegisterStringW_impl_fntype nvtxDomainRegisterStringW_impl_fnptr; + nvtxDomainCreateA_impl_fntype nvtxDomainCreateA_impl_fnptr; + nvtxDomainCreateW_impl_fntype nvtxDomainCreateW_impl_fnptr; + nvtxDomainDestroy_impl_fntype nvtxDomainDestroy_impl_fnptr; + nvtxInitialize_impl_fntype nvtxInitialize_impl_fnptr; + + nvtxDomainSyncUserCreate_impl_fntype nvtxDomainSyncUserCreate_impl_fnptr; + nvtxDomainSyncUserDestroy_impl_fntype nvtxDomainSyncUserDestroy_impl_fnptr; + nvtxDomainSyncUserAcquireStart_impl_fntype nvtxDomainSyncUserAcquireStart_impl_fnptr; + nvtxDomainSyncUserAcquireFailed_impl_fntype nvtxDomainSyncUserAcquireFailed_impl_fnptr; + nvtxDomainSyncUserAcquireSuccess_impl_fntype nvtxDomainSyncUserAcquireSuccess_impl_fnptr; + nvtxDomainSyncUserReleasing_impl_fntype nvtxDomainSyncUserReleasing_impl_fnptr; + + /* Tables of function pointers -- Extra null added to the end to ensure + * a crash instead of silent corruption if a tool reads off the end. */ + NvtxFunctionPointer* functionTable_CORE [NVTX_CBID_CORE_SIZE + 1]; + NvtxFunctionPointer* functionTable_CUDA [NVTX_CBID_CUDA_SIZE + 1]; + NvtxFunctionPointer* functionTable_OPENCL[NVTX_CBID_OPENCL_SIZE + 1]; + NvtxFunctionPointer* functionTable_CUDART[NVTX_CBID_CUDART_SIZE + 1]; + NvtxFunctionPointer* functionTable_CORE2 [NVTX_CBID_CORE2_SIZE + 1]; + NvtxFunctionPointer* functionTable_SYNC [NVTX_CBID_SYNC_SIZE + 1]; +} nvtxGlobals_t; + +NVTX_LINKONCE_DEFINE_GLOBAL nvtxGlobals_t NVTX_VERSIONED_IDENTIFIER(nvtxGlobals) = +{ + NVTX_INIT_STATE_FRESH, + + { + sizeof(NvtxExportTableCallbacks), + NVTX_VERSIONED_IDENTIFIER(nvtxEtiGetModuleFunctionTable) + }, + { + sizeof(NvtxExportTableVersionInfo), + NVTX_VERSION, + 0, + NVTX_VERSIONED_IDENTIFIER(nvtxEtiSetInjectionNvtxVersion) + }, + + /* Implementation function pointers */ + NVTX_VERSIONED_IDENTIFIER(nvtxMarkEx_impl_init), + NVTX_VERSIONED_IDENTIFIER(nvtxMarkA_impl_init), + NVTX_VERSIONED_IDENTIFIER(nvtxMarkW_impl_init), + NVTX_VERSIONED_IDENTIFIER(nvtxRangeStartEx_impl_init), + NVTX_VERSIONED_IDENTIFIER(nvtxRangeStartA_impl_init), + NVTX_VERSIONED_IDENTIFIER(nvtxRangeStartW_impl_init), + NVTX_VERSIONED_IDENTIFIER(nvtxRangeEnd_impl_init), + NVTX_VERSIONED_IDENTIFIER(nvtxRangePushEx_impl_init), + NVTX_VERSIONED_IDENTIFIER(nvtxRangePushA_impl_init), + NVTX_VERSIONED_IDENTIFIER(nvtxRangePushW_impl_init), + NVTX_VERSIONED_IDENTIFIER(nvtxRangePop_impl_init), + NVTX_VERSIONED_IDENTIFIER(nvtxNameCategoryA_impl_init), + NVTX_VERSIONED_IDENTIFIER(nvtxNameCategoryW_impl_init), + NVTX_VERSIONED_IDENTIFIER(nvtxNameOsThreadA_impl_init), + NVTX_VERSIONED_IDENTIFIER(nvtxNameOsThreadW_impl_init), + + NVTX_VERSIONED_IDENTIFIER(nvtxNameCuDeviceA_impl_init), + NVTX_VERSIONED_IDENTIFIER(nvtxNameCuDeviceW_impl_init), + NVTX_VERSIONED_IDENTIFIER(nvtxNameCuContextA_impl_init), + NVTX_VERSIONED_IDENTIFIER(nvtxNameCuContextW_impl_init), + NVTX_VERSIONED_IDENTIFIER(nvtxNameCuStreamA_impl_init), + NVTX_VERSIONED_IDENTIFIER(nvtxNameCuStreamW_impl_init), + NVTX_VERSIONED_IDENTIFIER(nvtxNameCuEventA_impl_init), + NVTX_VERSIONED_IDENTIFIER(nvtxNameCuEventW_impl_init), + + NVTX_VERSIONED_IDENTIFIER(nvtxNameClDeviceA_impl_init), + NVTX_VERSIONED_IDENTIFIER(nvtxNameClDeviceW_impl_init), + NVTX_VERSIONED_IDENTIFIER(nvtxNameClContextA_impl_init), + NVTX_VERSIONED_IDENTIFIER(nvtxNameClContextW_impl_init), + NVTX_VERSIONED_IDENTIFIER(nvtxNameClCommandQueueA_impl_init), + NVTX_VERSIONED_IDENTIFIER(nvtxNameClCommandQueueW_impl_init), + NVTX_VERSIONED_IDENTIFIER(nvtxNameClMemObjectA_impl_init), + NVTX_VERSIONED_IDENTIFIER(nvtxNameClMemObjectW_impl_init), + NVTX_VERSIONED_IDENTIFIER(nvtxNameClSamplerA_impl_init), + NVTX_VERSIONED_IDENTIFIER(nvtxNameClSamplerW_impl_init), + NVTX_VERSIONED_IDENTIFIER(nvtxNameClProgramA_impl_init), + NVTX_VERSIONED_IDENTIFIER(nvtxNameClProgramW_impl_init), + NVTX_VERSIONED_IDENTIFIER(nvtxNameClEventA_impl_init), + NVTX_VERSIONED_IDENTIFIER(nvtxNameClEventW_impl_init), + + NVTX_VERSIONED_IDENTIFIER(nvtxNameCudaDeviceA_impl_init), + NVTX_VERSIONED_IDENTIFIER(nvtxNameCudaDeviceW_impl_init), + NVTX_VERSIONED_IDENTIFIER(nvtxNameCudaStreamA_impl_init), + NVTX_VERSIONED_IDENTIFIER(nvtxNameCudaStreamW_impl_init), + NVTX_VERSIONED_IDENTIFIER(nvtxNameCudaEventA_impl_init), + NVTX_VERSIONED_IDENTIFIER(nvtxNameCudaEventW_impl_init), + + NVTX_VERSIONED_IDENTIFIER(nvtxDomainMarkEx_impl_init), + NVTX_VERSIONED_IDENTIFIER(nvtxDomainRangeStartEx_impl_init), + NVTX_VERSIONED_IDENTIFIER(nvtxDomainRangeEnd_impl_init), + NVTX_VERSIONED_IDENTIFIER(nvtxDomainRangePushEx_impl_init), + NVTX_VERSIONED_IDENTIFIER(nvtxDomainRangePop_impl_init), + NVTX_VERSIONED_IDENTIFIER(nvtxDomainResourceCreate_impl_init), + NVTX_VERSIONED_IDENTIFIER(nvtxDomainResourceDestroy_impl_init), + NVTX_VERSIONED_IDENTIFIER(nvtxDomainNameCategoryA_impl_init), + NVTX_VERSIONED_IDENTIFIER(nvtxDomainNameCategoryW_impl_init), + NVTX_VERSIONED_IDENTIFIER(nvtxDomainRegisterStringA_impl_init), + NVTX_VERSIONED_IDENTIFIER(nvtxDomainRegisterStringW_impl_init), + NVTX_VERSIONED_IDENTIFIER(nvtxDomainCreateA_impl_init), + NVTX_VERSIONED_IDENTIFIER(nvtxDomainCreateW_impl_init), + NVTX_VERSIONED_IDENTIFIER(nvtxDomainDestroy_impl_init), + NVTX_VERSIONED_IDENTIFIER(nvtxInitialize_impl_init), + + NVTX_VERSIONED_IDENTIFIER(nvtxDomainSyncUserCreate_impl_init), + NVTX_VERSIONED_IDENTIFIER(nvtxDomainSyncUserDestroy_impl_init), + NVTX_VERSIONED_IDENTIFIER(nvtxDomainSyncUserAcquireStart_impl_init), + NVTX_VERSIONED_IDENTIFIER(nvtxDomainSyncUserAcquireFailed_impl_init), + NVTX_VERSIONED_IDENTIFIER(nvtxDomainSyncUserAcquireSuccess_impl_init), + NVTX_VERSIONED_IDENTIFIER(nvtxDomainSyncUserReleasing_impl_init), + + /* Tables of function pointers */ + { + 0, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxMarkEx_impl_fnptr, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxMarkA_impl_fnptr, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxMarkW_impl_fnptr, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangeStartEx_impl_fnptr, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangeStartA_impl_fnptr, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangeStartW_impl_fnptr, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangeEnd_impl_fnptr, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangePushEx_impl_fnptr, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangePushA_impl_fnptr, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangePushW_impl_fnptr, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangePop_impl_fnptr, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCategoryA_impl_fnptr, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCategoryW_impl_fnptr, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameOsThreadA_impl_fnptr, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameOsThreadW_impl_fnptr, + 0 + }, + { + 0, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuDeviceA_impl_fnptr, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuDeviceW_impl_fnptr, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuContextA_impl_fnptr, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuContextW_impl_fnptr, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuStreamA_impl_fnptr, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuStreamW_impl_fnptr, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuEventA_impl_fnptr, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuEventW_impl_fnptr, + 0 + }, + { + 0, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClDeviceA_impl_fnptr, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClDeviceW_impl_fnptr, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClContextA_impl_fnptr, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClContextW_impl_fnptr, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClCommandQueueA_impl_fnptr, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClCommandQueueW_impl_fnptr, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClMemObjectA_impl_fnptr, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClMemObjectW_impl_fnptr, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClSamplerA_impl_fnptr, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClSamplerW_impl_fnptr, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClProgramA_impl_fnptr, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClProgramW_impl_fnptr, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClEventA_impl_fnptr, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClEventW_impl_fnptr, + 0 + }, + { + 0, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCudaDeviceA_impl_fnptr, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCudaDeviceW_impl_fnptr, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCudaStreamA_impl_fnptr, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCudaStreamW_impl_fnptr, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCudaEventA_impl_fnptr, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCudaEventW_impl_fnptr, + 0 + }, + { + 0, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainMarkEx_impl_fnptr, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainRangeStartEx_impl_fnptr, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainRangeEnd_impl_fnptr, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainRangePushEx_impl_fnptr, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainRangePop_impl_fnptr, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainResourceCreate_impl_fnptr, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainResourceDestroy_impl_fnptr, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainNameCategoryA_impl_fnptr, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainNameCategoryW_impl_fnptr, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainRegisterStringA_impl_fnptr, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainRegisterStringW_impl_fnptr, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainCreateA_impl_fnptr, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainCreateW_impl_fnptr, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainDestroy_impl_fnptr, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxInitialize_impl_fnptr, + 0 + }, + { + 0, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainSyncUserCreate_impl_fnptr, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainSyncUserDestroy_impl_fnptr, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainSyncUserAcquireStart_impl_fnptr, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainSyncUserAcquireFailed_impl_fnptr, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainSyncUserAcquireSuccess_impl_fnptr, + (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainSyncUserReleasing_impl_fnptr, + 0 + } +}; + +/* ---- Define static inline implementations of core API functions ---- */ + +#include "nvtxImplCore.h" + +/* ---- Define implementations of export table functions ---- */ + +NVTX_LINKONCE_DEFINE_FUNCTION int NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxEtiGetModuleFunctionTable)( + NvtxCallbackModule module, + NvtxFunctionTable* out_table, + unsigned int* out_size) +{ + unsigned int bytes = 0; + NvtxFunctionTable table = (NvtxFunctionTable)0; + + switch (module) + { + case NVTX_CB_MODULE_CORE: + table = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).functionTable_CORE; + bytes = (unsigned int)sizeof(NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).functionTable_CORE); + break; + case NVTX_CB_MODULE_CUDA: + table = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).functionTable_CUDA; + bytes = (unsigned int)sizeof(NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).functionTable_CUDA); + break; + case NVTX_CB_MODULE_OPENCL: + table = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).functionTable_OPENCL; + bytes = (unsigned int)sizeof(NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).functionTable_OPENCL); + break; + case NVTX_CB_MODULE_CUDART: + table = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).functionTable_CUDART; + bytes = (unsigned int)sizeof(NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).functionTable_CUDART); + break; + case NVTX_CB_MODULE_CORE2: + table = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).functionTable_CORE2; + bytes = (unsigned int)sizeof(NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).functionTable_CORE2); + break; + case NVTX_CB_MODULE_SYNC: + table = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).functionTable_SYNC; + bytes = (unsigned int)sizeof(NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).functionTable_SYNC); + break; + default: return 0; + } + + if (out_size) + *out_size = (bytes / (unsigned int)sizeof(NvtxFunctionPointer*)) - 1; + + if (out_table) + *out_table = table; + + return 1; +} + +NVTX_LINKONCE_DEFINE_FUNCTION const void* NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxGetExportTable)(uint32_t exportTableId) +{ + switch (exportTableId) + { + case NVTX_ETID_CALLBACKS: return &NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).etblCallbacks; + case NVTX_ETID_VERSIONINFO: return &NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).etblVersionInfo; + default: return 0; + } +} + +NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxEtiSetInjectionNvtxVersion)(uint32_t version) +{ + /* Reserved for custom implementations to resolve problems with tools */ + (void)version; +} + +/* ---- Define implementations of init versions of all API functions ---- */ + +#include "nvtxInitDefs.h" + +/* ---- Define implementations of initialization functions ---- */ + +#include "nvtxInit.h" + +#ifdef __GNUC__ +#pragma GCC visibility pop +#endif + +#ifdef __cplusplus +} /* extern "C" */ +#endif /* __cplusplus */ diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxImplCore.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxImplCore.h new file mode 100644 index 0000000000000000000000000000000000000000..aee1014ecd53f8b980442109e51fdbc7672ff6d0 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxImplCore.h @@ -0,0 +1,299 @@ +NVTX_DECLSPEC void NVTX_API nvtxMarkEx(const nvtxEventAttributes_t* eventAttrib) +{ +#ifndef NVTX_DISABLE + nvtxMarkEx_impl_fntype local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxMarkEx_impl_fnptr; + if(local!=0) + (*local)(eventAttrib); +#endif /*NVTX_DISABLE*/ +} + +NVTX_DECLSPEC void NVTX_API nvtxMarkA(const char* message) +{ +#ifndef NVTX_DISABLE + nvtxMarkA_impl_fntype local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxMarkA_impl_fnptr; + if(local!=0) + (*local)(message); +#endif /*NVTX_DISABLE*/ +} + +NVTX_DECLSPEC void NVTX_API nvtxMarkW(const wchar_t* message) +{ +#ifndef NVTX_DISABLE + nvtxMarkW_impl_fntype local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxMarkW_impl_fnptr; + if(local!=0) + (*local)(message); +#endif /*NVTX_DISABLE*/ +} + +NVTX_DECLSPEC nvtxRangeId_t NVTX_API nvtxRangeStartEx(const nvtxEventAttributes_t* eventAttrib) +{ +#ifndef NVTX_DISABLE + nvtxRangeStartEx_impl_fntype local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangeStartEx_impl_fnptr; + if(local!=0) + return (*local)(eventAttrib); + else +#endif /*NVTX_DISABLE*/ + return (nvtxRangeId_t)0; +} + +NVTX_DECLSPEC nvtxRangeId_t NVTX_API nvtxRangeStartA(const char* message) +{ +#ifndef NVTX_DISABLE + nvtxRangeStartA_impl_fntype local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangeStartA_impl_fnptr; + if(local!=0) + return (*local)(message); + else +#endif /*NVTX_DISABLE*/ + return (nvtxRangeId_t)0; +} + +NVTX_DECLSPEC nvtxRangeId_t NVTX_API nvtxRangeStartW(const wchar_t* message) +{ +#ifndef NVTX_DISABLE + nvtxRangeStartW_impl_fntype local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangeStartW_impl_fnptr; + if(local!=0) + return (*local)(message); + else +#endif /*NVTX_DISABLE*/ + return (nvtxRangeId_t)0; +} + +NVTX_DECLSPEC void NVTX_API nvtxRangeEnd(nvtxRangeId_t id) +{ +#ifndef NVTX_DISABLE + nvtxRangeEnd_impl_fntype local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangeEnd_impl_fnptr; + if(local!=0) + (*local)(id); +#endif /*NVTX_DISABLE*/ +} + +NVTX_DECLSPEC int NVTX_API nvtxRangePushEx(const nvtxEventAttributes_t* eventAttrib) +{ +#ifndef NVTX_DISABLE + nvtxRangePushEx_impl_fntype local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangePushEx_impl_fnptr; + if(local!=0) + return (*local)(eventAttrib); + else +#endif /*NVTX_DISABLE*/ + return (int)NVTX_NO_PUSH_POP_TRACKING; +} + +NVTX_DECLSPEC int NVTX_API nvtxRangePushA(const char* message) +{ +#ifndef NVTX_DISABLE + nvtxRangePushA_impl_fntype local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangePushA_impl_fnptr; + if(local!=0) + return (*local)(message); + else +#endif /*NVTX_DISABLE*/ + return (int)NVTX_NO_PUSH_POP_TRACKING; +} + +NVTX_DECLSPEC int NVTX_API nvtxRangePushW(const wchar_t* message) +{ +#ifndef NVTX_DISABLE + nvtxRangePushW_impl_fntype local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangePushW_impl_fnptr; + if(local!=0) + return (*local)(message); + else +#endif /*NVTX_DISABLE*/ + return (int)NVTX_NO_PUSH_POP_TRACKING; +} + +NVTX_DECLSPEC int NVTX_API nvtxRangePop(void) +{ +#ifndef NVTX_DISABLE + nvtxRangePop_impl_fntype local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangePop_impl_fnptr; + if(local!=0) + return (*local)(); + else +#endif /*NVTX_DISABLE*/ + return (int)NVTX_NO_PUSH_POP_TRACKING; +} + +NVTX_DECLSPEC void NVTX_API nvtxNameCategoryA(uint32_t category, const char* name) +{ +#ifndef NVTX_DISABLE + nvtxNameCategoryA_impl_fntype local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCategoryA_impl_fnptr; + if(local!=0) + (*local)(category, name); +#endif /*NVTX_DISABLE*/ +} + +NVTX_DECLSPEC void NVTX_API nvtxNameCategoryW(uint32_t category, const wchar_t* name) +{ +#ifndef NVTX_DISABLE + nvtxNameCategoryW_impl_fntype local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCategoryW_impl_fnptr; + if(local!=0) + (*local)(category, name); +#endif /*NVTX_DISABLE*/ +} + +NVTX_DECLSPEC void NVTX_API nvtxNameOsThreadA(uint32_t threadId, const char* name) +{ +#ifndef NVTX_DISABLE + nvtxNameOsThreadA_impl_fntype local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameOsThreadA_impl_fnptr; + if(local!=0) + (*local)(threadId, name); +#endif /*NVTX_DISABLE*/ +} + +NVTX_DECLSPEC void NVTX_API nvtxNameOsThreadW(uint32_t threadId, const wchar_t* name) +{ +#ifndef NVTX_DISABLE + nvtxNameOsThreadW_impl_fntype local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameOsThreadW_impl_fnptr; + if(local!=0) + (*local)(threadId, name); +#endif /*NVTX_DISABLE*/ +} + +NVTX_DECLSPEC void NVTX_API nvtxDomainMarkEx(nvtxDomainHandle_t domain, const nvtxEventAttributes_t* eventAttrib) +{ +#ifndef NVTX_DISABLE + nvtxDomainMarkEx_impl_fntype local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainMarkEx_impl_fnptr; + if(local!=0) + (*local)(domain, eventAttrib); +#endif /*NVTX_DISABLE*/ +} + +NVTX_DECLSPEC nvtxRangeId_t NVTX_API nvtxDomainRangeStartEx(nvtxDomainHandle_t domain, const nvtxEventAttributes_t* eventAttrib) +{ +#ifndef NVTX_DISABLE + nvtxDomainRangeStartEx_impl_fntype local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainRangeStartEx_impl_fnptr; + if(local!=0) + return (*local)(domain, eventAttrib); + else +#endif /*NVTX_DISABLE*/ + return (nvtxRangeId_t)0; +} + +NVTX_DECLSPEC void NVTX_API nvtxDomainRangeEnd(nvtxDomainHandle_t domain, nvtxRangeId_t id) +{ +#ifndef NVTX_DISABLE + nvtxDomainRangeEnd_impl_fntype local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainRangeEnd_impl_fnptr; + if(local!=0) + (*local)(domain, id); +#endif /*NVTX_DISABLE*/ +} + +NVTX_DECLSPEC int NVTX_API nvtxDomainRangePushEx(nvtxDomainHandle_t domain, const nvtxEventAttributes_t* eventAttrib) +{ +#ifndef NVTX_DISABLE + nvtxDomainRangePushEx_impl_fntype local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainRangePushEx_impl_fnptr; + if(local!=0) + return (*local)(domain, eventAttrib); + else +#endif /*NVTX_DISABLE*/ + return (int)NVTX_NO_PUSH_POP_TRACKING; +} + +NVTX_DECLSPEC int NVTX_API nvtxDomainRangePop(nvtxDomainHandle_t domain) +{ +#ifndef NVTX_DISABLE + nvtxDomainRangePop_impl_fntype local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainRangePop_impl_fnptr; + if(local!=0) + return (*local)(domain); + else +#endif /*NVTX_DISABLE*/ + return (int)NVTX_NO_PUSH_POP_TRACKING; +} + +NVTX_DECLSPEC nvtxResourceHandle_t NVTX_API nvtxDomainResourceCreate(nvtxDomainHandle_t domain, nvtxResourceAttributes_t* attribs) +{ +#ifndef NVTX_DISABLE + nvtxDomainResourceCreate_impl_fntype local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainResourceCreate_impl_fnptr; + if(local!=0) + return (*local)(domain, attribs); + else +#endif /*NVTX_DISABLE*/ + return (nvtxResourceHandle_t)0; +} + +NVTX_DECLSPEC void NVTX_API nvtxDomainResourceDestroy(nvtxResourceHandle_t resource) +{ +#ifndef NVTX_DISABLE + nvtxDomainResourceDestroy_impl_fntype local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainResourceDestroy_impl_fnptr; + if(local!=0) + (*local)(resource); +#endif /*NVTX_DISABLE*/ +} + +NVTX_DECLSPEC void NVTX_API nvtxDomainNameCategoryA(nvtxDomainHandle_t domain, uint32_t category, const char* name) +{ +#ifndef NVTX_DISABLE + nvtxDomainNameCategoryA_impl_fntype local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainNameCategoryA_impl_fnptr; + if(local!=0) + (*local)(domain, category, name); +#endif /*NVTX_DISABLE*/ +} + +NVTX_DECLSPEC void NVTX_API nvtxDomainNameCategoryW(nvtxDomainHandle_t domain, uint32_t category, const wchar_t* name) +{ +#ifndef NVTX_DISABLE + nvtxDomainNameCategoryW_impl_fntype local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainNameCategoryW_impl_fnptr; + if(local!=0) + (*local)(domain, category, name); +#endif /*NVTX_DISABLE*/ +} + +NVTX_DECLSPEC nvtxStringHandle_t NVTX_API nvtxDomainRegisterStringA(nvtxDomainHandle_t domain, const char* string) +{ +#ifndef NVTX_DISABLE + nvtxDomainRegisterStringA_impl_fntype local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainRegisterStringA_impl_fnptr; + if(local!=0) + return (*local)(domain, string); + else +#endif /*NVTX_DISABLE*/ + return (nvtxStringHandle_t)0; +} + +NVTX_DECLSPEC nvtxStringHandle_t NVTX_API nvtxDomainRegisterStringW(nvtxDomainHandle_t domain, const wchar_t* string) +{ +#ifndef NVTX_DISABLE + nvtxDomainRegisterStringW_impl_fntype local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainRegisterStringW_impl_fnptr; + if(local!=0) + return (*local)(domain, string); + else +#endif /*NVTX_DISABLE*/ + return (nvtxStringHandle_t)0; +} + +NVTX_DECLSPEC nvtxDomainHandle_t NVTX_API nvtxDomainCreateA(const char* message) +{ +#ifndef NVTX_DISABLE + nvtxDomainCreateA_impl_fntype local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainCreateA_impl_fnptr; + if(local!=0) + return (*local)(message); + else +#endif /*NVTX_DISABLE*/ + return (nvtxDomainHandle_t)0; +} + +NVTX_DECLSPEC nvtxDomainHandle_t NVTX_API nvtxDomainCreateW(const wchar_t* message) +{ +#ifndef NVTX_DISABLE + nvtxDomainCreateW_impl_fntype local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainCreateW_impl_fnptr; + if(local!=0) + return (*local)(message); + else +#endif /*NVTX_DISABLE*/ + return (nvtxDomainHandle_t)0; +} + +NVTX_DECLSPEC void NVTX_API nvtxDomainDestroy(nvtxDomainHandle_t domain) +{ +#ifndef NVTX_DISABLE + nvtxDomainDestroy_impl_fntype local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainDestroy_impl_fnptr; + if(local!=0) + (*local)(domain); +#endif /*NVTX_DISABLE*/ +} + +NVTX_DECLSPEC void NVTX_API nvtxInitialize(const void* reserved) +{ +#ifndef NVTX_DISABLE + nvtxInitialize_impl_fntype local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxInitialize_impl_fnptr; + if(local!=0) + (*local)(reserved); +#endif /*NVTX_DISABLE*/ +} diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxImplCudaRt_v3.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxImplCudaRt_v3.h new file mode 100644 index 0000000000000000000000000000000000000000..24be8bd4722506d4ca318b07519884fc407847cd --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxImplCudaRt_v3.h @@ -0,0 +1,112 @@ +/* This file was procedurally generated! Do not modify this file by hand. */ + +/* +* Copyright 2009-2016 NVIDIA Corporation. All rights reserved. +* +* NOTICE TO USER: +* +* This source code is subject to NVIDIA ownership rights under U.S. and +* international Copyright laws. +* +* This software and the information contained herein is PROPRIETARY and +* CONFIDENTIAL to NVIDIA and is being provided under the terms and conditions +* of a form of NVIDIA software license agreement. +* +* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE +* CODE FOR ANY PURPOSE. IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR +* IMPLIED WARRANTY OF ANY KIND. NVIDIA DISCLAIMS ALL WARRANTIES WITH +* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF +* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE. +* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL, +* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS +* OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE +* OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE +* OR PERFORMANCE OF THIS SOURCE CODE. +* +* U.S. Government End Users. This source code is a "commercial item" as +* that term is defined at 48 C.F.R. 2.101 (OCT 1995), consisting of +* "commercial computer software" and "commercial computer software +* documentation" as such terms are used in 48 C.F.R. 12.212 (SEPT 1995) +* and is provided to the U.S. Government only as a commercial end item. +* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through +* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the +* source code with only those rights set forth herein. +* +* Any use of this source code in individual and commercial software must +* include, in the user documentation and internal comments to the code, +* the above Disclaimer and U.S. Government End Users Notice. +*/ + +#ifndef NVTX_IMPL_GUARD_CUDART +#error Never include this file directly -- it is automatically included by nvToolsExtCudaRt.h (except when NVTX_NO_IMPL is defined). +#endif + +#ifdef __cplusplus +extern "C" { +#endif /* __cplusplus */ + +//typedef void (NVTX_API * nvtxNameCudaDeviceA_impl_fntype)(int device, const char* name); +//typedef void (NVTX_API * nvtxNameCudaDeviceW_impl_fntype)(int device, const wchar_t* name); +typedef void (NVTX_API * nvtxNameCudaStreamA_impl_fntype)(cudaStream_t stream, const char* name); +typedef void (NVTX_API * nvtxNameCudaStreamW_impl_fntype)(cudaStream_t stream, const wchar_t* name); +typedef void (NVTX_API * nvtxNameCudaEventA_impl_fntype)(cudaEvent_t event, const char* name); +typedef void (NVTX_API * nvtxNameCudaEventW_impl_fntype)(cudaEvent_t event, const wchar_t* name); + +NVTX_DECLSPEC void NVTX_API nvtxNameCudaDeviceA(int device, const char* name) +{ +#ifndef NVTX_DISABLE + nvtxNameCudaDeviceA_impl_fntype local = (nvtxNameCudaDeviceA_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCudaDeviceA_impl_fnptr; + if(local!=0) + (*local)(device, name); +#endif /*NVTX_DISABLE*/ +} + +NVTX_DECLSPEC void NVTX_API nvtxNameCudaDeviceW(int device, const wchar_t* name) +{ +#ifndef NVTX_DISABLE + nvtxNameCudaDeviceW_impl_fntype local = (nvtxNameCudaDeviceW_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCudaDeviceW_impl_fnptr; + if(local!=0) + (*local)(device, name); +#endif /*NVTX_DISABLE*/ +} + +NVTX_DECLSPEC void NVTX_API nvtxNameCudaStreamA(cudaStream_t stream, const char* name) +{ +#ifndef NVTX_DISABLE + nvtxNameCudaStreamA_impl_fntype local = (nvtxNameCudaStreamA_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCudaStreamA_impl_fnptr; + if(local!=0) + (*local)(stream, name); +#endif /*NVTX_DISABLE*/ +} + +NVTX_DECLSPEC void NVTX_API nvtxNameCudaStreamW(cudaStream_t stream, const wchar_t* name) +{ +#ifndef NVTX_DISABLE + nvtxNameCudaStreamW_impl_fntype local = (nvtxNameCudaStreamW_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCudaStreamW_impl_fnptr; + if(local!=0) + (*local)(stream, name); +#endif /*NVTX_DISABLE*/ +} + +NVTX_DECLSPEC void NVTX_API nvtxNameCudaEventA(cudaEvent_t event, const char* name) +{ +#ifndef NVTX_DISABLE + nvtxNameCudaEventA_impl_fntype local = (nvtxNameCudaEventA_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCudaEventA_impl_fnptr; + if(local!=0) + (*local)(event, name); +#endif /*NVTX_DISABLE*/ +} + +NVTX_DECLSPEC void NVTX_API nvtxNameCudaEventW(cudaEvent_t event, const wchar_t* name) +{ +#ifndef NVTX_DISABLE + nvtxNameCudaEventW_impl_fntype local = (nvtxNameCudaEventW_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCudaEventW_impl_fnptr; + if(local!=0) + (*local)(event, name); +#endif /*NVTX_DISABLE*/ +} + +#ifdef __cplusplus +} /* extern "C" */ +#endif /* __cplusplus */ + diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxImplCuda_v3.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxImplCuda_v3.h new file mode 100644 index 0000000000000000000000000000000000000000..ee30111bd123b2c1706f23e73e3803b8c1a02927 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxImplCuda_v3.h @@ -0,0 +1,133 @@ +/* This file was procedurally generated! Do not modify this file by hand. */ + +/* +* Copyright 2009-2016 NVIDIA Corporation. All rights reserved. +* +* NOTICE TO USER: +* +* This source code is subject to NVIDIA ownership rights under U.S. and +* international Copyright laws. +* +* This software and the information contained herein is PROPRIETARY and +* CONFIDENTIAL to NVIDIA and is being provided under the terms and conditions +* of a form of NVIDIA software license agreement. +* +* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE +* CODE FOR ANY PURPOSE. IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR +* IMPLIED WARRANTY OF ANY KIND. NVIDIA DISCLAIMS ALL WARRANTIES WITH +* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF +* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE. +* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL, +* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS +* OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE +* OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE +* OR PERFORMANCE OF THIS SOURCE CODE. +* +* U.S. Government End Users. This source code is a "commercial item" as +* that term is defined at 48 C.F.R. 2.101 (OCT 1995), consisting of +* "commercial computer software" and "commercial computer software +* documentation" as such terms are used in 48 C.F.R. 12.212 (SEPT 1995) +* and is provided to the U.S. Government only as a commercial end item. +* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through +* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the +* source code with only those rights set forth herein. +* +* Any use of this source code in individual and commercial software must +* include, in the user documentation and internal comments to the code, +* the above Disclaimer and U.S. Government End Users Notice. +*/ + +#ifndef NVTX_IMPL_GUARD_CUDA +#error Never include this file directly -- it is automatically included by nvToolsExtCuda.h (except when NVTX_NO_IMPL is defined). +#endif + + +#ifdef __cplusplus +extern "C" { +#endif /* __cplusplus */ + +typedef void (NVTX_API * nvtxNameCuDeviceA_impl_fntype)(CUdevice device, const char* name); +typedef void (NVTX_API * nvtxNameCuDeviceW_impl_fntype)(CUdevice device, const wchar_t* name); +typedef void (NVTX_API * nvtxNameCuContextA_impl_fntype)(CUcontext context, const char* name); +typedef void (NVTX_API * nvtxNameCuContextW_impl_fntype)(CUcontext context, const wchar_t* name); +typedef void (NVTX_API * nvtxNameCuStreamA_impl_fntype)(CUstream stream, const char* name); +typedef void (NVTX_API * nvtxNameCuStreamW_impl_fntype)(CUstream stream, const wchar_t* name); +typedef void (NVTX_API * nvtxNameCuEventA_impl_fntype)(CUevent event, const char* name); +typedef void (NVTX_API * nvtxNameCuEventW_impl_fntype)(CUevent event, const wchar_t* name); + +NVTX_DECLSPEC void NVTX_API nvtxNameCuDeviceA(CUdevice device, const char* name) +{ +#ifndef NVTX_DISABLE + nvtxNameCuDeviceA_impl_fntype local = (nvtxNameCuDeviceA_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuDeviceA_impl_fnptr; + if(local!=0) + (*local)(device, name); +#endif /*NVTX_DISABLE*/ +} + +NVTX_DECLSPEC void NVTX_API nvtxNameCuDeviceW(CUdevice device, const wchar_t* name) +{ +#ifndef NVTX_DISABLE + nvtxNameCuDeviceW_impl_fntype local = (nvtxNameCuDeviceW_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuDeviceW_impl_fnptr; + if(local!=0) + (*local)(device, name); +#endif /*NVTX_DISABLE*/ +} + +NVTX_DECLSPEC void NVTX_API nvtxNameCuContextA(CUcontext context, const char* name) +{ +#ifndef NVTX_DISABLE + nvtxNameCuContextA_impl_fntype local = (nvtxNameCuContextA_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuContextA_impl_fnptr; + if(local!=0) + (*local)(context, name); +#endif /*NVTX_DISABLE*/ +} + +NVTX_DECLSPEC void NVTX_API nvtxNameCuContextW(CUcontext context, const wchar_t* name) +{ +#ifndef NVTX_DISABLE + nvtxNameCuContextW_impl_fntype local = (nvtxNameCuContextW_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuContextW_impl_fnptr; + if(local!=0) + (*local)(context, name); +#endif /*NVTX_DISABLE*/ +} + +NVTX_DECLSPEC void NVTX_API nvtxNameCuStreamA(CUstream stream, const char* name) +{ +#ifndef NVTX_DISABLE + nvtxNameCuStreamA_impl_fntype local = (nvtxNameCuStreamA_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuStreamA_impl_fnptr; + if(local!=0) + (*local)(stream, name); +#endif /*NVTX_DISABLE*/ +} + +NVTX_DECLSPEC void NVTX_API nvtxNameCuStreamW(CUstream stream, const wchar_t* name) +{ +#ifndef NVTX_DISABLE + nvtxNameCuStreamW_impl_fntype local = (nvtxNameCuStreamW_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuStreamW_impl_fnptr; + if(local!=0) + (*local)(stream, name); +#endif /*NVTX_DISABLE*/ +} + +NVTX_DECLSPEC void NVTX_API nvtxNameCuEventA(CUevent event, const char* name) +{ +#ifndef NVTX_DISABLE + nvtxNameCuEventA_impl_fntype local = (nvtxNameCuEventA_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuEventA_impl_fnptr; + if(local!=0) + (*local)(event, name); +#endif /*NVTX_DISABLE*/ +} + +NVTX_DECLSPEC void NVTX_API nvtxNameCuEventW(CUevent event, const wchar_t* name) +{ +#ifndef NVTX_DISABLE + nvtxNameCuEventW_impl_fntype local = (nvtxNameCuEventW_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuEventW_impl_fnptr; + if(local!=0) + (*local)(event, name); +#endif /*NVTX_DISABLE*/ +} + +#ifdef __cplusplus +} /* extern "C" */ +#endif /* __cplusplus */ + diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxImplOpenCL_v3.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxImplOpenCL_v3.h new file mode 100644 index 0000000000000000000000000000000000000000..0e73224cce26b49c8f7585be3a41dd0f428fe07e --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxImplOpenCL_v3.h @@ -0,0 +1,192 @@ +/* This file was procedurally generated! Do not modify this file by hand. */ + +/* +* Copyright 2009-2016 NVIDIA Corporation. All rights reserved. +* +* NOTICE TO USER: +* +* This source code is subject to NVIDIA ownership rights under U.S. and +* international Copyright laws. +* +* This software and the information contained herein is PROPRIETARY and +* CONFIDENTIAL to NVIDIA and is being provided under the terms and conditions +* of a form of NVIDIA software license agreement. +* +* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE +* CODE FOR ANY PURPOSE. IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR +* IMPLIED WARRANTY OF ANY KIND. NVIDIA DISCLAIMS ALL WARRANTIES WITH +* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF +* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE. +* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL, +* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS +* OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE +* OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE +* OR PERFORMANCE OF THIS SOURCE CODE. +* +* U.S. Government End Users. This source code is a "commercial item" as +* that term is defined at 48 C.F.R. 2.101 (OCT 1995), consisting of +* "commercial computer software" and "commercial computer software +* documentation" as such terms are used in 48 C.F.R. 12.212 (SEPT 1995) +* and is provided to the U.S. Government only as a commercial end item. +* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through +* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the +* source code with only those rights set forth herein. +* +* Any use of this source code in individual and commercial software must +* include, in the user documentation and internal comments to the code, +* the above Disclaimer and U.S. Government End Users Notice. +*/ + +#ifndef NVTX_IMPL_GUARD_OPENCL +#error Never include this file directly -- it is automatically included by nvToolsExtCuda.h (except when NVTX_NO_IMPL is defined). +#endif + + +#ifdef __cplusplus +extern "C" { +#endif /* __cplusplus */ + +typedef void (NVTX_API * nvtxNameClDeviceA_impl_fntype)(cl_device_id device, const char* name); +typedef void (NVTX_API * nvtxNameClDeviceW_impl_fntype)(cl_device_id device, const wchar_t* name); +typedef void (NVTX_API * nvtxNameClContextA_impl_fntype)(cl_context context, const char* name); +typedef void (NVTX_API * nvtxNameClContextW_impl_fntype)(cl_context context, const wchar_t* name); +typedef void (NVTX_API * nvtxNameClCommandQueueA_impl_fntype)(cl_command_queue command_queue, const char* name); +typedef void (NVTX_API * nvtxNameClCommandQueueW_impl_fntype)(cl_command_queue command_queue, const wchar_t* name); +typedef void (NVTX_API * nvtxNameClMemObjectA_impl_fntype)(cl_mem memobj, const char* name); +typedef void (NVTX_API * nvtxNameClMemObjectW_impl_fntype)(cl_mem memobj, const wchar_t* name); +typedef void (NVTX_API * nvtxNameClSamplerA_impl_fntype)(cl_sampler sampler, const char* name); +typedef void (NVTX_API * nvtxNameClSamplerW_impl_fntype)(cl_sampler sampler, const wchar_t* name); +typedef void (NVTX_API * nvtxNameClProgramA_impl_fntype)(cl_program program, const char* name); +typedef void (NVTX_API * nvtxNameClProgramW_impl_fntype)(cl_program program, const wchar_t* name); +typedef void (NVTX_API * nvtxNameClEventA_impl_fntype)(cl_event evnt, const char* name); +typedef void (NVTX_API * nvtxNameClEventW_impl_fntype)(cl_event evnt, const wchar_t* name); + +NVTX_DECLSPEC void NVTX_API nvtxNameClDeviceA(cl_device_id device, const char* name) +{ +#ifndef NVTX_DISABLE + nvtxNameClDeviceA_impl_fntype local = (nvtxNameClDeviceA_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClDeviceA_impl_fnptr; + if(local!=0) + (*local)(device, name); +#endif /*NVTX_DISABLE*/ +} + +NVTX_DECLSPEC void NVTX_API nvtxNameClDeviceW(cl_device_id device, const wchar_t* name) +{ +#ifndef NVTX_DISABLE + nvtxNameClDeviceW_impl_fntype local = (nvtxNameClDeviceW_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClDeviceW_impl_fnptr; + if(local!=0) + (*local)(device, name); +#endif /*NVTX_DISABLE*/ +} + +NVTX_DECLSPEC void NVTX_API nvtxNameClContextA(cl_context context, const char* name) +{ +#ifndef NVTX_DISABLE + nvtxNameClContextA_impl_fntype local = (nvtxNameClContextA_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClContextA_impl_fnptr; + if(local!=0) + (*local)(context, name); +#endif /*NVTX_DISABLE*/ +} + +NVTX_DECLSPEC void NVTX_API nvtxNameClContextW(cl_context context, const wchar_t* name) +{ +#ifndef NVTX_DISABLE + nvtxNameClContextW_impl_fntype local = (nvtxNameClContextW_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClContextW_impl_fnptr; + if(local!=0) + (*local)(context, name); +#endif /*NVTX_DISABLE*/ +} + +NVTX_DECLSPEC void NVTX_API nvtxNameClCommandQueueA(cl_command_queue command_queue, const char* name) +{ +#ifndef NVTX_DISABLE + nvtxNameClCommandQueueA_impl_fntype local = (nvtxNameClCommandQueueA_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClCommandQueueA_impl_fnptr; + if(local!=0) + (*local)(command_queue, name); +#endif /*NVTX_DISABLE*/ +} + +NVTX_DECLSPEC void NVTX_API nvtxNameClCommandQueueW(cl_command_queue command_queue, const wchar_t* name) +{ +#ifndef NVTX_DISABLE + nvtxNameClCommandQueueW_impl_fntype local = (nvtxNameClCommandQueueW_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClCommandQueueW_impl_fnptr; + if(local!=0) + (*local)(command_queue, name); +#endif /*NVTX_DISABLE*/ +} + +NVTX_DECLSPEC void NVTX_API nvtxNameClMemObjectA(cl_mem memobj, const char* name) +{ +#ifndef NVTX_DISABLE + nvtxNameClMemObjectA_impl_fntype local = (nvtxNameClMemObjectA_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClMemObjectA_impl_fnptr; + if(local!=0) + (*local)(memobj, name); +#endif /*NVTX_DISABLE*/ +} + +NVTX_DECLSPEC void NVTX_API nvtxNameClMemObjectW(cl_mem memobj, const wchar_t* name) +{ +#ifndef NVTX_DISABLE + nvtxNameClMemObjectW_impl_fntype local = (nvtxNameClMemObjectW_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClMemObjectW_impl_fnptr; + if(local!=0) + (*local)(memobj, name); +#endif /*NVTX_DISABLE*/ +} + +NVTX_DECLSPEC void NVTX_API nvtxNameClSamplerA(cl_sampler sampler, const char* name) +{ +#ifndef NVTX_DISABLE + nvtxNameClSamplerA_impl_fntype local = (nvtxNameClSamplerA_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClSamplerA_impl_fnptr; + if(local!=0) + (*local)(sampler, name); +#endif /*NVTX_DISABLE*/ +} + +NVTX_DECLSPEC void NVTX_API nvtxNameClSamplerW(cl_sampler sampler, const wchar_t* name) +{ +#ifndef NVTX_DISABLE + nvtxNameClSamplerW_impl_fntype local = (nvtxNameClSamplerW_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClSamplerW_impl_fnptr; + if(local!=0) + (*local)(sampler, name); +#endif /*NVTX_DISABLE*/ +} + +NVTX_DECLSPEC void NVTX_API nvtxNameClProgramA(cl_program program, const char* name) +{ +#ifndef NVTX_DISABLE + nvtxNameClProgramA_impl_fntype local = (nvtxNameClProgramA_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClProgramA_impl_fnptr; + if(local!=0) + (*local)(program, name); +#endif /*NVTX_DISABLE*/ +} + +NVTX_DECLSPEC void NVTX_API nvtxNameClProgramW(cl_program program, const wchar_t* name) +{ +#ifndef NVTX_DISABLE + nvtxNameClProgramW_impl_fntype local = (nvtxNameClProgramW_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClProgramW_impl_fnptr; + if(local!=0) + (*local)(program, name); +#endif /*NVTX_DISABLE*/ +} + +NVTX_DECLSPEC void NVTX_API nvtxNameClEventA(cl_event evnt, const char* name) +{ +#ifndef NVTX_DISABLE + nvtxNameClEventA_impl_fntype local = (nvtxNameClEventA_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClEventA_impl_fnptr; + if(local!=0) + (*local)(evnt, name); +#endif /*NVTX_DISABLE*/ +} + +NVTX_DECLSPEC void NVTX_API nvtxNameClEventW(cl_event evnt, const wchar_t* name) +{ +#ifndef NVTX_DISABLE + nvtxNameClEventW_impl_fntype local = (nvtxNameClEventW_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClEventW_impl_fnptr; + if(local!=0) + (*local)(evnt, name); +#endif /*NVTX_DISABLE*/ +} + +#ifdef __cplusplus +} /* extern "C" */ +#endif /* __cplusplus */ diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxImplSync_v3.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxImplSync_v3.h new file mode 100644 index 0000000000000000000000000000000000000000..accc621a3d5071438f6ed7b9e2192ea8ad56977c --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxImplSync_v3.h @@ -0,0 +1,114 @@ +/* This file was procedurally generated! Do not modify this file by hand. */ + +/* +* Copyright 2009-2016 NVIDIA Corporation. All rights reserved. +* +* NOTICE TO USER: +* +* This source code is subject to NVIDIA ownership rights under U.S. and +* international Copyright laws. +* +* This software and the information contained herein is PROPRIETARY and +* CONFIDENTIAL to NVIDIA and is being provided under the terms and conditions +* of a form of NVIDIA software license agreement. +* +* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE +* CODE FOR ANY PURPOSE. IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR +* IMPLIED WARRANTY OF ANY KIND. NVIDIA DISCLAIMS ALL WARRANTIES WITH +* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF +* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE. +* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL, +* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS +* OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE +* OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE +* OR PERFORMANCE OF THIS SOURCE CODE. +* +* U.S. Government End Users. This source code is a "commercial item" as +* that term is defined at 48 C.F.R. 2.101 (OCT 1995), consisting of +* "commercial computer software" and "commercial computer software +* documentation" as such terms are used in 48 C.F.R. 12.212 (SEPT 1995) +* and is provided to the U.S. Government only as a commercial end item. +* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through +* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the +* source code with only those rights set forth herein. +* +* Any use of this source code in individual and commercial software must +* include, in the user documentation and internal comments to the code, +* the above Disclaimer and U.S. Government End Users Notice. +*/ + +#ifndef NVTX_IMPL_GUARD_SYNC +#error Never include this file directly -- it is automatically included by nvToolsExtCuda.h (except when NVTX_NO_IMPL is defined). +#endif + + +#ifdef __cplusplus +extern "C" { +#endif /* __cplusplus */ + +typedef nvtxSyncUser_t (NVTX_API * nvtxDomainSyncUserCreate_impl_fntype)(nvtxDomainHandle_t domain, const nvtxSyncUserAttributes_t* attribs); +typedef void (NVTX_API * nvtxDomainSyncUserDestroy_impl_fntype)(nvtxSyncUser_t handle); +typedef void (NVTX_API * nvtxDomainSyncUserAcquireStart_impl_fntype)(nvtxSyncUser_t handle); +typedef void (NVTX_API * nvtxDomainSyncUserAcquireFailed_impl_fntype)(nvtxSyncUser_t handle); +typedef void (NVTX_API * nvtxDomainSyncUserAcquireSuccess_impl_fntype)(nvtxSyncUser_t handle); +typedef void (NVTX_API * nvtxDomainSyncUserReleasing_impl_fntype)(nvtxSyncUser_t handle); + +NVTX_DECLSPEC nvtxSyncUser_t NVTX_API nvtxDomainSyncUserCreate(nvtxDomainHandle_t domain, const nvtxSyncUserAttributes_t* attribs) +{ +#ifndef NVTX_DISABLE + nvtxDomainSyncUserCreate_impl_fntype local = (nvtxDomainSyncUserCreate_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainSyncUserCreate_impl_fnptr; + if(local!=0) + return (*local)(domain, attribs); + else +#endif /*NVTX_DISABLE*/ + return (nvtxSyncUser_t)0; +} + +NVTX_DECLSPEC void NVTX_API nvtxDomainSyncUserDestroy(nvtxSyncUser_t handle) +{ +#ifndef NVTX_DISABLE + nvtxDomainSyncUserDestroy_impl_fntype local = (nvtxDomainSyncUserDestroy_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainSyncUserDestroy_impl_fnptr; + if(local!=0) + (*local)(handle); +#endif /*NVTX_DISABLE*/ +} + +NVTX_DECLSPEC void NVTX_API nvtxDomainSyncUserAcquireStart(nvtxSyncUser_t handle) +{ +#ifndef NVTX_DISABLE + nvtxDomainSyncUserAcquireStart_impl_fntype local = (nvtxDomainSyncUserAcquireStart_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainSyncUserAcquireStart_impl_fnptr; + if(local!=0) + (*local)(handle); +#endif /*NVTX_DISABLE*/ +} + +NVTX_DECLSPEC void NVTX_API nvtxDomainSyncUserAcquireFailed(nvtxSyncUser_t handle) +{ +#ifndef NVTX_DISABLE + nvtxDomainSyncUserAcquireFailed_impl_fntype local = (nvtxDomainSyncUserAcquireFailed_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainSyncUserAcquireFailed_impl_fnptr; + if(local!=0) + (*local)(handle); +#endif /*NVTX_DISABLE*/ +} + +NVTX_DECLSPEC void NVTX_API nvtxDomainSyncUserAcquireSuccess(nvtxSyncUser_t handle) +{ +#ifndef NVTX_DISABLE + nvtxDomainSyncUserAcquireSuccess_impl_fntype local = (nvtxDomainSyncUserAcquireSuccess_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainSyncUserAcquireSuccess_impl_fnptr; + if(local!=0) + (*local)(handle); +#endif /*NVTX_DISABLE*/ +} + +NVTX_DECLSPEC void NVTX_API nvtxDomainSyncUserReleasing(nvtxSyncUser_t handle) +{ +#ifndef NVTX_DISABLE + nvtxDomainSyncUserReleasing_impl_fntype local = (nvtxDomainSyncUserReleasing_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainSyncUserReleasing_impl_fnptr; + if(local!=0) + (*local)(handle); +#endif /*NVTX_DISABLE*/ +} + +#ifdef __cplusplus +} /* extern "C" */ +#endif /* __cplusplus */ diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxInit.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxInit.h new file mode 100644 index 0000000000000000000000000000000000000000..f2bbd3a120da351f1ad1d81dde6bfb46244559cc --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxInit.h @@ -0,0 +1,343 @@ +/* This file was procedurally generated! Do not modify this file by hand. */ + +/* +* Copyright 2009-2016 NVIDIA Corporation. All rights reserved. +* +* NOTICE TO USER: +* +* This source code is subject to NVIDIA ownership rights under U.S. and +* international Copyright laws. +* +* This software and the information contained herein is PROPRIETARY and +* CONFIDENTIAL to NVIDIA and is being provided under the terms and conditions +* of a form of NVIDIA software license agreement. +* +* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE +* CODE FOR ANY PURPOSE. IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR +* IMPLIED WARRANTY OF ANY KIND. NVIDIA DISCLAIMS ALL WARRANTIES WITH +* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF +* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE. +* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL, +* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS +* OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE +* OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE +* OR PERFORMANCE OF THIS SOURCE CODE. +* +* U.S. Government End Users. This source code is a "commercial item" as +* that term is defined at 48 C.F.R. 2.101 (OCT 1995), consisting of +* "commercial computer software" and "commercial computer software +* documentation" as such terms are used in 48 C.F.R. 12.212 (SEPT 1995) +* and is provided to the U.S. Government only as a commercial end item. +* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through +* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the +* source code with only those rights set forth herein. +* +* Any use of this source code in individual and commercial software must +* include, in the user documentation and internal comments to the code, +* the above Disclaimer and U.S. Government End Users Notice. +*/ + +#ifndef NVTX_IMPL_GUARD +#error Never include this file directly -- it is automatically included by nvToolsExt.h (except when NVTX_NO_IMPL is defined). +#endif + +/* ---- Platform-independent helper definitions and functions ---- */ + +/* Prefer macros over inline functions to reduce symbol resolution at link time */ + +#if defined(_WIN32) +#define NVTX_PATHCHAR wchar_t +#define NVTX_STR(x) L##x +#define NVTX_GETENV _wgetenv +#define NVTX_BUFSIZE MAX_PATH +#define NVTX_DLLHANDLE HMODULE +#define NVTX_DLLOPEN(x) LoadLibraryW(x) +#define NVTX_DLLFUNC GetProcAddress +#define NVTX_DLLCLOSE FreeLibrary +#define NVTX_YIELD() SwitchToThread() +#define NVTX_MEMBAR() MemoryBarrier() +#define NVTX_ATOMIC_WRITE_32(address, value) InterlockedExchange((volatile LONG*)address, value) +#define NVTX_ATOMIC_CAS_32(old, address, exchange, comparand) old = InterlockedCompareExchange((volatile LONG*)address, exchange, comparand) +#elif defined(__GNUC__) +#define NVTX_PATHCHAR char +#define NVTX_STR(x) x +#define NVTX_GETENV getenv +#define NVTX_BUFSIZE PATH_MAX +#define NVTX_DLLHANDLE void* +#define NVTX_DLLOPEN(x) dlopen(x, RTLD_LAZY) +#define NVTX_DLLFUNC dlsym +#define NVTX_DLLCLOSE dlclose +#define NVTX_YIELD() sched_yield() +#define NVTX_MEMBAR() __sync_synchronize() +/* Ensure full memory barrier for atomics, to match Windows functions */ +#define NVTX_ATOMIC_WRITE_32(address, value) __sync_synchronize(); __sync_lock_test_and_set(address, value) +#define NVTX_ATOMIC_CAS_32(old, address, exchange, comparand) __sync_synchronize(); old = __sync_val_compare_and_swap(address, exchange, comparand) +#else +#error The library does not support your configuration! +#endif + +/* Define this to 1 for platforms that where pre-injected libraries can be discovered. */ +#if defined(_WIN32) +/* TODO */ +#define NVTX_SUPPORT_ALREADY_INJECTED_LIBRARY 0 +#else +#define NVTX_SUPPORT_ALREADY_INJECTED_LIBRARY 0 +#endif + +/* Define this to 1 for platforms that support environment variables */ +/* TODO: Detect UWP, a.k.a. Windows Store app, and set this to 0. */ +/* Try: #if defined(WINAPI_FAMILY_PARTITION) && WINAPI_FAMILY_PARTITION(WINAPI_PARTITION_APP) */ +#define NVTX_SUPPORT_ENV_VARS 1 + +/* Define this to 1 for platforms that support dynamic/shared libraries */ +#define NVTX_SUPPORT_DYNAMIC_INJECTION_LIBRARY 1 + +/* Injection libraries implementing InitializeInjectionNvtx2 may be statically linked, +* and this will override any dynamic injection. Useful for platforms where dynamic +* injection is not available. Since weak symbols not explicitly marked extern are +* guaranteed to be initialized to zero if no definitions are found by the linker, the +* dynamic injection process proceeds normally if pfnInitializeInjectionNvtx2 is 0. */ +#if defined(__GNUC__) && !defined(_WIN32) && !defined(__CYGWIN__) +#define NVTX_SUPPORT_STATIC_INJECTION_LIBRARY 1 +/* To statically inject an NVTX library, define InitializeInjectionNvtx2_fnptr as a normal +* symbol (not weak) pointing to the implementation of InitializeInjectionNvtx2 (which +* does not need to be named "InitializeInjectionNvtx2" as is necessary in a dynamic +* injection library. */ +__attribute__((weak)) NvtxInitializeInjectionNvtxFunc_t InitializeInjectionNvtx2_fnptr; +#else +#define NVTX_SUPPORT_STATIC_INJECTION_LIBRARY 0 +#endif + +/* This function tries to find or load an NVTX injection library and get the +* address of its InitializeInjection2 function. If such a function pointer +* is found, it is called, and passed the address of this NVTX instance's +* nvtxGetExportTable function, so the injection can attach to this instance. +* If the initialization fails for any reason, any dynamic library loaded will +* be freed, and all NVTX implementation functions will be set to no-ops. If +* initialization succeeds, NVTX functions not attached to the tool will be set +* to no-ops. This is implemented as one function instead of several small +* functions to minimize the number of weak symbols the linker must resolve. +* Order of search is: +* - Pre-injected library exporting InitializeInjectionNvtx2 +* - Loadable library exporting InitializeInjectionNvtx2 +* - Path specified by env var NVTX_INJECTION??_PATH (?? is 32 or 64) +* - On Android, libNvtxInjection??.so within the package (?? is 32 or 64) +* - Statically-linked injection library defining InitializeInjectionNvtx2_fnptr +*/ +NVTX_LINKONCE_FWDDECL_FUNCTION int NVTX_VERSIONED_IDENTIFIER(nvtxInitializeInjectionLibrary)(void); +NVTX_LINKONCE_DEFINE_FUNCTION int NVTX_VERSIONED_IDENTIFIER(nvtxInitializeInjectionLibrary)(void) +{ + const char* const initFuncName = "InitializeInjectionNvtx2"; + NvtxInitializeInjectionNvtxFunc_t init_fnptr = (NvtxInitializeInjectionNvtxFunc_t)0; + NVTX_DLLHANDLE injectionLibraryHandle = (NVTX_DLLHANDLE)0; + int entryPointStatus = 0; + +#if NVTX_SUPPORT_ALREADY_INJECTED_LIBRARY + /* Use POSIX global symbol chain to query for init function from any module */ + init_fnptr = (NvtxInitializeInjectionNvtxFunc_t)NVTX_DLLFUNC(0, initFuncName); +#endif + +#if NVTX_SUPPORT_DYNAMIC_INJECTION_LIBRARY + /* Try discovering dynamic injection library to load */ + if (!init_fnptr) + { +#if NVTX_SUPPORT_ENV_VARS + /* If env var NVTX_INJECTION64_PATH is set, it should contain the path + * to a 64-bit dynamic NVTX injection library (and similar for 32-bit). */ + const NVTX_PATHCHAR* const nvtxEnvVarName = (sizeof(void*) == 4) + ? NVTX_STR("NVTX_INJECTION32_PATH") + : NVTX_STR("NVTX_INJECTION64_PATH"); +#endif /* NVTX_SUPPORT_ENV_VARS */ + NVTX_PATHCHAR injectionLibraryPathBuf[NVTX_BUFSIZE]; + const NVTX_PATHCHAR* injectionLibraryPath = (const NVTX_PATHCHAR*)0; + + /* Refer to this variable explicitly in case all references to it are #if'ed out */ + (void)injectionLibraryPathBuf; + +#if NVTX_SUPPORT_ENV_VARS + /* Disable the warning for getenv & _wgetenv -- this usage is safe because + * these functions are not called again before using the returned value. */ +#if defined(_MSC_VER) +#pragma warning( push ) +#pragma warning( disable : 4996 ) +#endif + injectionLibraryPath = NVTX_GETENV(nvtxEnvVarName); +#if defined(_MSC_VER) +#pragma warning( pop ) +#endif +#endif + +#if defined(__ANDROID__) + if (!injectionLibraryPath) + { + const char *bits = (sizeof(void*) == 4) ? "32" : "64"; + char cmdlineBuf[32]; + char pkgName[PATH_MAX]; + int count; + int pid; + FILE *fp; + size_t bytesRead; + size_t pos; + + pid = (int)getpid(); + count = snprintf(cmdlineBuf, sizeof(cmdlineBuf), "/proc/%d/cmdline", pid); + if (count <= 0 || count >= (int)sizeof(cmdlineBuf)) + { + NVTX_ERR("Path buffer too small for: /proc/%d/cmdline\n", pid); + return NVTX_ERR_INIT_ACCESS_LIBRARY; + } + + fp = fopen(cmdlineBuf, "r"); + if (!fp) + { + NVTX_ERR("File couldn't be opened: %s\n", cmdlineBuf); + return NVTX_ERR_INIT_ACCESS_LIBRARY; + } + + bytesRead = fread(pkgName, 1, sizeof(pkgName) - 1, fp); + fclose(fp); + if (bytesRead == 0) + { + NVTX_ERR("Package name couldn't be read from file: %s\n", cmdlineBuf); + return NVTX_ERR_INIT_ACCESS_LIBRARY; + } + + pkgName[bytesRead] = 0; + + /* String can contain colon as a process separator. In this case the package name is before the colon. */ + pos = 0; + while (pos < bytesRead && pkgName[pos] != ':' && pkgName[pos] != '\0') + { + ++pos; + } + pkgName[pos] = 0; + + count = snprintf(injectionLibraryPathBuf, NVTX_BUFSIZE, "/data/data/%s/files/libNvtxInjection%s.so", pkgName, bits); + if (count <= 0 || count >= NVTX_BUFSIZE) + { + NVTX_ERR("Path buffer too small for: /data/data/%s/files/libNvtxInjection%s.so\n", pkgName, bits); + return NVTX_ERR_INIT_ACCESS_LIBRARY; + } + + /* On Android, verify path is accessible due to aggressive file access restrictions. */ + /* For dlopen, if the filename contains a leading slash, then it is interpreted as a */ + /* relative or absolute pathname; otherwise it will follow the rules in ld.so. */ + if (injectionLibraryPathBuf[0] == '/') + { +#if (__ANDROID_API__ < 21) + int access_err = access(injectionLibraryPathBuf, F_OK | R_OK); +#else + int access_err = faccessat(AT_FDCWD, injectionLibraryPathBuf, F_OK | R_OK, 0); +#endif + if (access_err != 0) + { + NVTX_ERR("Injection library path wasn't accessible [code=%s] [path=%s]\n", strerror(errno), injectionLibraryPathBuf); + return NVTX_ERR_INIT_ACCESS_LIBRARY; + } + } + injectionLibraryPath = injectionLibraryPathBuf; + } +#endif + + /* At this point, injectionLibraryPath is specified if a dynamic + * injection library was specified by a tool. */ + if (injectionLibraryPath) + { + /* Load the injection library */ + injectionLibraryHandle = NVTX_DLLOPEN(injectionLibraryPath); + if (!injectionLibraryHandle) + { + NVTX_ERR("Failed to load injection library\n"); + return NVTX_ERR_INIT_LOAD_LIBRARY; + } + else + { + /* Attempt to get the injection library's entry-point */ + init_fnptr = (NvtxInitializeInjectionNvtxFunc_t)NVTX_DLLFUNC(injectionLibraryHandle, initFuncName); + if (!init_fnptr) + { + NVTX_DLLCLOSE(injectionLibraryHandle); + NVTX_ERR("Failed to get address of function InitializeInjectionNvtx2 from injection library\n"); + return NVTX_ERR_INIT_MISSING_LIBRARY_ENTRY_POINT; + } + } + } + } +#endif + +#if NVTX_SUPPORT_STATIC_INJECTION_LIBRARY + if (!init_fnptr) + { + /* Check weakly-defined function pointer. A statically-linked injection can define this as + * a normal symbol and it will take precedence over a dynamic injection. */ + if (InitializeInjectionNvtx2_fnptr) + { + init_fnptr = InitializeInjectionNvtx2_fnptr; + } + } +#endif + + /* At this point, if init_fnptr is not set, then no tool has specified + * an NVTX injection library -- return non-success result so all NVTX + * API functions will be set to no-ops. */ + if (!init_fnptr) + { + return NVTX_ERR_NO_INJECTION_LIBRARY_AVAILABLE; + } + + /* Invoke injection library's initialization function. If it returns + * 0 (failure) and a dynamic injection was loaded, unload it. */ + entryPointStatus = init_fnptr(NVTX_VERSIONED_IDENTIFIER(nvtxGetExportTable)); + if (entryPointStatus == 0) + { + NVTX_ERR("Failed to initialize injection library -- initialization function returned 0\n"); + if (injectionLibraryHandle) + { + NVTX_DLLCLOSE(injectionLibraryHandle); + } + return NVTX_ERR_INIT_FAILED_LIBRARY_ENTRY_POINT; + } + + return NVTX_SUCCESS; +} + +NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(void) +{ + unsigned int old; + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).initState == NVTX_INIT_STATE_COMPLETE) + { + return; + } + + NVTX_ATOMIC_CAS_32( + old, + &NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).initState, + NVTX_INIT_STATE_STARTED, + NVTX_INIT_STATE_FRESH); + if (old == NVTX_INIT_STATE_FRESH) + { + int result; + int forceAllToNoops; + + /* Load & initialize injection library -- it will assign the function pointers */ + result = NVTX_VERSIONED_IDENTIFIER(nvtxInitializeInjectionLibrary)(); + + /* Set all pointers not assigned by the injection to null */ + forceAllToNoops = result != NVTX_SUCCESS; /* Set all to null if injection init failed */ + NVTX_VERSIONED_IDENTIFIER(nvtxSetInitFunctionsToNoops)(forceAllToNoops); + + /* Signal that initialization has finished, so now the assigned function pointers will be used */ + NVTX_ATOMIC_WRITE_32( + &NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).initState, + NVTX_INIT_STATE_COMPLETE); + } + else /* Spin-wait until initialization has finished */ + { + NVTX_MEMBAR(); + while (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).initState != NVTX_INIT_STATE_COMPLETE) + { + NVTX_YIELD(); + NVTX_MEMBAR(); + } + } +} diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxInitDecls.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxInitDecls.h new file mode 100644 index 0000000000000000000000000000000000000000..757a7296093750e75721fb93855d0aa37da64103 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxInitDecls.h @@ -0,0 +1,73 @@ +#ifndef NVTX_IMPL_GUARD +#error Never include this file directly -- it is automatically included by nvToolsExt.h (except when NVTX_NO_IMPL is defined). +#endif + +NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxMarkEx_impl_init)(const nvtxEventAttributes_t* eventAttrib); +NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxMarkA_impl_init)(const char* message); +NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxMarkW_impl_init)(const wchar_t* message); +NVTX_LINKONCE_FWDDECL_FUNCTION nvtxRangeId_t NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxRangeStartEx_impl_init)(const nvtxEventAttributes_t* eventAttrib); +NVTX_LINKONCE_FWDDECL_FUNCTION nvtxRangeId_t NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxRangeStartA_impl_init)(const char* message); +NVTX_LINKONCE_FWDDECL_FUNCTION nvtxRangeId_t NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxRangeStartW_impl_init)(const wchar_t* message); +NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxRangeEnd_impl_init)(nvtxRangeId_t id); +NVTX_LINKONCE_FWDDECL_FUNCTION int NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxRangePushEx_impl_init)(const nvtxEventAttributes_t* eventAttrib); +NVTX_LINKONCE_FWDDECL_FUNCTION int NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxRangePushA_impl_init)(const char* message); +NVTX_LINKONCE_FWDDECL_FUNCTION int NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxRangePushW_impl_init)(const wchar_t* message); +NVTX_LINKONCE_FWDDECL_FUNCTION int NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxRangePop_impl_init)(void); +NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCategoryA_impl_init)(uint32_t category, const char* name); +NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCategoryW_impl_init)(uint32_t category, const wchar_t* name); +NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameOsThreadA_impl_init)(uint32_t threadId, const char* name); +NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameOsThreadW_impl_init)(uint32_t threadId, const wchar_t* name); + +NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCuDeviceA_impl_init)(nvtx_CUdevice device, const char* name); +NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCuDeviceW_impl_init)(nvtx_CUdevice device, const wchar_t* name); +NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCuContextA_impl_init)(nvtx_CUcontext context, const char* name); +NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCuContextW_impl_init)(nvtx_CUcontext context, const wchar_t* name); +NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCuStreamA_impl_init)(nvtx_CUstream stream, const char* name); +NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCuStreamW_impl_init)(nvtx_CUstream stream, const wchar_t* name); +NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCuEventA_impl_init)(nvtx_CUevent event, const char* name); +NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCuEventW_impl_init)(nvtx_CUevent event, const wchar_t* name); + +NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameClDeviceA_impl_init)(nvtx_cl_device_id device, const char* name); +NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameClDeviceW_impl_init)(nvtx_cl_device_id device, const wchar_t* name); +NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameClContextA_impl_init)(nvtx_cl_context context, const char* name); +NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameClContextW_impl_init)(nvtx_cl_context context, const wchar_t* name); +NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameClCommandQueueA_impl_init)(nvtx_cl_command_queue command_queue, const char* name); +NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameClCommandQueueW_impl_init)(nvtx_cl_command_queue command_queue, const wchar_t* name); +NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameClMemObjectA_impl_init)(nvtx_cl_mem memobj, const char* name); +NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameClMemObjectW_impl_init)(nvtx_cl_mem memobj, const wchar_t* name); +NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameClSamplerA_impl_init)(nvtx_cl_sampler sampler, const char* name); +NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameClSamplerW_impl_init)(nvtx_cl_sampler sampler, const wchar_t* name); +NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameClProgramA_impl_init)(nvtx_cl_program program, const char* name); +NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameClProgramW_impl_init)(nvtx_cl_program program, const wchar_t* name); +NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameClEventA_impl_init)(nvtx_cl_event evnt, const char* name); +NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameClEventW_impl_init)(nvtx_cl_event evnt, const wchar_t* name); + +NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCudaDeviceA_impl_init)(int device, const char* name); +NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCudaDeviceW_impl_init)(int device, const wchar_t* name); +NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCudaStreamA_impl_init)(nvtx_cudaStream_t stream, const char* name); +NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCudaStreamW_impl_init)(nvtx_cudaStream_t stream, const wchar_t* name); +NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCudaEventA_impl_init)(nvtx_cudaEvent_t event, const char* name); +NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCudaEventW_impl_init)(nvtx_cudaEvent_t event, const wchar_t* name); + +NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainMarkEx_impl_init)(nvtxDomainHandle_t domain, const nvtxEventAttributes_t* eventAttrib); +NVTX_LINKONCE_FWDDECL_FUNCTION nvtxRangeId_t NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainRangeStartEx_impl_init)(nvtxDomainHandle_t domain, const nvtxEventAttributes_t* eventAttrib); +NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainRangeEnd_impl_init)(nvtxDomainHandle_t domain, nvtxRangeId_t id); +NVTX_LINKONCE_FWDDECL_FUNCTION int NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainRangePushEx_impl_init)(nvtxDomainHandle_t domain, const nvtxEventAttributes_t* eventAttrib); +NVTX_LINKONCE_FWDDECL_FUNCTION int NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainRangePop_impl_init)(nvtxDomainHandle_t domain); +NVTX_LINKONCE_FWDDECL_FUNCTION nvtxResourceHandle_t NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainResourceCreate_impl_init)(nvtxDomainHandle_t domain, nvtxResourceAttributes_t* attribs); +NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainResourceDestroy_impl_init)(nvtxResourceHandle_t resource); +NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainNameCategoryA_impl_init)(nvtxDomainHandle_t domain, uint32_t category, const char* name); +NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainNameCategoryW_impl_init)(nvtxDomainHandle_t domain, uint32_t category, const wchar_t* name); +NVTX_LINKONCE_FWDDECL_FUNCTION nvtxStringHandle_t NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainRegisterStringA_impl_init)(nvtxDomainHandle_t domain, const char* string); +NVTX_LINKONCE_FWDDECL_FUNCTION nvtxStringHandle_t NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainRegisterStringW_impl_init)(nvtxDomainHandle_t domain, const wchar_t* string); +NVTX_LINKONCE_FWDDECL_FUNCTION nvtxDomainHandle_t NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainCreateA_impl_init)(const char* message); +NVTX_LINKONCE_FWDDECL_FUNCTION nvtxDomainHandle_t NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainCreateW_impl_init)(const wchar_t* message); +NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainDestroy_impl_init)(nvtxDomainHandle_t domain); +NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxInitialize_impl_init)(const void* reserved); + +NVTX_LINKONCE_FWDDECL_FUNCTION nvtxSyncUser_t NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainSyncUserCreate_impl_init)(nvtxDomainHandle_t domain, const nvtxSyncUserAttributes_t* attribs); +NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainSyncUserDestroy_impl_init)(nvtxSyncUser_t handle); +NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainSyncUserAcquireStart_impl_init)(nvtxSyncUser_t handle); +NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainSyncUserAcquireFailed_impl_init)(nvtxSyncUser_t handle); +NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainSyncUserAcquireSuccess_impl_init)(nvtxSyncUser_t handle); +NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainSyncUserReleasing_impl_init)(nvtxSyncUser_t handle); diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxInitDefs.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxInitDefs.h new file mode 100644 index 0000000000000000000000000000000000000000..93d5f0e975059b00f8fb32d95ab06483cc53d80e --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxInitDefs.h @@ -0,0 +1,565 @@ +#ifndef NVTX_IMPL_GUARD +#error Never include this file directly -- it is automatically included by nvToolsExt.h (except when NVTX_NO_IMPL is defined). +#endif + +NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxMarkEx_impl_init)(const nvtxEventAttributes_t* eventAttrib){ + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + nvtxMarkEx(eventAttrib); +} + +NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxMarkA_impl_init)(const char* message){ + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + nvtxMarkA(message); +} + +NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxMarkW_impl_init)(const wchar_t* message){ + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + nvtxMarkW(message); +} + +NVTX_LINKONCE_DEFINE_FUNCTION nvtxRangeId_t NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxRangeStartEx_impl_init)(const nvtxEventAttributes_t* eventAttrib){ + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + return nvtxRangeStartEx(eventAttrib); +} + +NVTX_LINKONCE_DEFINE_FUNCTION nvtxRangeId_t NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxRangeStartA_impl_init)(const char* message){ + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + return nvtxRangeStartA(message); +} + +NVTX_LINKONCE_DEFINE_FUNCTION nvtxRangeId_t NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxRangeStartW_impl_init)(const wchar_t* message){ + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + return nvtxRangeStartW(message); +} + +NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxRangeEnd_impl_init)(nvtxRangeId_t id){ + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + nvtxRangeEnd(id); +} + +NVTX_LINKONCE_DEFINE_FUNCTION int NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxRangePushEx_impl_init)(const nvtxEventAttributes_t* eventAttrib){ + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + return nvtxRangePushEx(eventAttrib); +} + +NVTX_LINKONCE_DEFINE_FUNCTION int NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxRangePushA_impl_init)(const char* message){ + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + return nvtxRangePushA(message); +} + +NVTX_LINKONCE_DEFINE_FUNCTION int NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxRangePushW_impl_init)(const wchar_t* message){ + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + return nvtxRangePushW(message); +} + +NVTX_LINKONCE_DEFINE_FUNCTION int NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxRangePop_impl_init)(void){ + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + return nvtxRangePop(); +} + +NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCategoryA_impl_init)(uint32_t category, const char* name){ + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + nvtxNameCategoryA(category, name); +} + +NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCategoryW_impl_init)(uint32_t category, const wchar_t* name){ + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + nvtxNameCategoryW(category, name); +} + +NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameOsThreadA_impl_init)(uint32_t threadId, const char* name){ + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + nvtxNameOsThreadA(threadId, name); +} + +NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameOsThreadW_impl_init)(uint32_t threadId, const wchar_t* name){ + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + nvtxNameOsThreadW(threadId, name); +} + +NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainMarkEx_impl_init)(nvtxDomainHandle_t domain, const nvtxEventAttributes_t* eventAttrib){ + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + nvtxDomainMarkEx(domain, eventAttrib); +} + +NVTX_LINKONCE_DEFINE_FUNCTION nvtxRangeId_t NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainRangeStartEx_impl_init)(nvtxDomainHandle_t domain, const nvtxEventAttributes_t* eventAttrib){ + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + return nvtxDomainRangeStartEx(domain, eventAttrib); +} + +NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainRangeEnd_impl_init)(nvtxDomainHandle_t domain, nvtxRangeId_t id){ + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + nvtxDomainRangeEnd(domain, id); +} + +NVTX_LINKONCE_DEFINE_FUNCTION int NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainRangePushEx_impl_init)(nvtxDomainHandle_t domain, const nvtxEventAttributes_t* eventAttrib){ + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + return nvtxDomainRangePushEx(domain, eventAttrib); +} + +NVTX_LINKONCE_DEFINE_FUNCTION int NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainRangePop_impl_init)(nvtxDomainHandle_t domain){ + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + return nvtxDomainRangePop(domain); +} + +NVTX_LINKONCE_DEFINE_FUNCTION nvtxResourceHandle_t NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainResourceCreate_impl_init)(nvtxDomainHandle_t domain, nvtxResourceAttributes_t* attribs){ + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + return nvtxDomainResourceCreate(domain, attribs); +} + +NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainResourceDestroy_impl_init)(nvtxResourceHandle_t resource){ + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + nvtxDomainResourceDestroy(resource); +} + +NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainNameCategoryA_impl_init)(nvtxDomainHandle_t domain, uint32_t category, const char* name){ + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + nvtxDomainNameCategoryA(domain, category, name); +} + +NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainNameCategoryW_impl_init)(nvtxDomainHandle_t domain, uint32_t category, const wchar_t* name){ + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + nvtxDomainNameCategoryW(domain, category, name); +} + +NVTX_LINKONCE_DEFINE_FUNCTION nvtxStringHandle_t NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainRegisterStringA_impl_init)(nvtxDomainHandle_t domain, const char* string){ + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + return nvtxDomainRegisterStringA(domain, string); +} + +NVTX_LINKONCE_DEFINE_FUNCTION nvtxStringHandle_t NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainRegisterStringW_impl_init)(nvtxDomainHandle_t domain, const wchar_t* string){ + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + return nvtxDomainRegisterStringW(domain, string); +} + +NVTX_LINKONCE_DEFINE_FUNCTION nvtxDomainHandle_t NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainCreateA_impl_init)(const char* message){ + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + return nvtxDomainCreateA(message); +} + +NVTX_LINKONCE_DEFINE_FUNCTION nvtxDomainHandle_t NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainCreateW_impl_init)(const wchar_t* message){ + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + return nvtxDomainCreateW(message); +} + +NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainDestroy_impl_init)(nvtxDomainHandle_t domain){ + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + nvtxDomainDestroy(domain); +} + +NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxInitialize_impl_init)(const void* reserved){ + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + nvtxInitialize(reserved); +} + +NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCuDeviceA_impl_init)(nvtx_CUdevice device, const char* name){ + nvtxNameCuDeviceA_fakeimpl_fntype local; + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuDeviceA_impl_fnptr; + if (local) + local(device, name); +} + +NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCuDeviceW_impl_init)(nvtx_CUdevice device, const wchar_t* name){ + nvtxNameCuDeviceW_fakeimpl_fntype local; + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuDeviceW_impl_fnptr; + if (local) + local(device, name); +} + +NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCuContextA_impl_init)(nvtx_CUcontext context, const char* name){ + nvtxNameCuContextA_fakeimpl_fntype local; + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuContextA_impl_fnptr; + if (local) + local(context, name); +} + +NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCuContextW_impl_init)(nvtx_CUcontext context, const wchar_t* name){ + nvtxNameCuContextW_fakeimpl_fntype local; + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuContextW_impl_fnptr; + if (local) + local(context, name); +} + +NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCuStreamA_impl_init)(nvtx_CUstream stream, const char* name){ + nvtxNameCuStreamA_fakeimpl_fntype local; + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuStreamA_impl_fnptr; + if (local) + local(stream, name); +} + +NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCuStreamW_impl_init)(nvtx_CUstream stream, const wchar_t* name){ + nvtxNameCuStreamW_fakeimpl_fntype local; + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuStreamW_impl_fnptr; + if (local) + local(stream, name); +} + +NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCuEventA_impl_init)(nvtx_CUevent event, const char* name){ + nvtxNameCuEventA_fakeimpl_fntype local; + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuEventA_impl_fnptr; + if (local) + local(event, name); +} + +NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCuEventW_impl_init)(nvtx_CUevent event, const wchar_t* name){ + nvtxNameCuEventW_fakeimpl_fntype local; + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuEventW_impl_fnptr; + if (local) + local(event, name); +} + +NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCudaDeviceA_impl_init)(int device, const char* name){ + nvtxNameCudaDeviceA_impl_fntype local; + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCudaDeviceA_impl_fnptr; + if (local) + local(device, name); +} + +NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCudaDeviceW_impl_init)(int device, const wchar_t* name){ + nvtxNameCudaDeviceW_impl_fntype local; + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCudaDeviceW_impl_fnptr; + if (local) + local(device, name); +} + +NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCudaStreamA_impl_init)(nvtx_cudaStream_t stream, const char* name){ + nvtxNameCudaStreamA_fakeimpl_fntype local; + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCudaStreamA_impl_fnptr; + if (local) + local(stream, name); +} + +NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCudaStreamW_impl_init)(nvtx_cudaStream_t stream, const wchar_t* name){ + nvtxNameCudaStreamW_fakeimpl_fntype local; + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCudaStreamW_impl_fnptr; + if (local) + local(stream, name); +} + +NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCudaEventA_impl_init)(nvtx_cudaEvent_t event, const char* name){ + nvtxNameCudaEventA_fakeimpl_fntype local; + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCudaEventA_impl_fnptr; + if (local) + local(event, name); +} + +NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCudaEventW_impl_init)(nvtx_cudaEvent_t event, const wchar_t* name){ + nvtxNameCudaEventW_fakeimpl_fntype local; + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCudaEventW_impl_fnptr; + if (local) + local(event, name); +} + +NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameClDeviceA_impl_init)(nvtx_cl_device_id device, const char* name){ + nvtxNameClDeviceA_fakeimpl_fntype local; + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClDeviceA_impl_fnptr; + if (local) + local(device, name); +} + +NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameClDeviceW_impl_init)(nvtx_cl_device_id device, const wchar_t* name){ + nvtxNameClDeviceW_fakeimpl_fntype local; + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClDeviceW_impl_fnptr; + if (local) + local(device, name); +} + +NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameClContextA_impl_init)(nvtx_cl_context context, const char* name){ + nvtxNameClContextA_fakeimpl_fntype local; + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClContextA_impl_fnptr; + if (local) + local(context, name); +} + +NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameClContextW_impl_init)(nvtx_cl_context context, const wchar_t* name){ + nvtxNameClContextW_fakeimpl_fntype local; + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClContextW_impl_fnptr; + if (local) + local(context, name); +} + +NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameClCommandQueueA_impl_init)(nvtx_cl_command_queue command_queue, const char* name){ + nvtxNameClCommandQueueA_fakeimpl_fntype local; + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClCommandQueueA_impl_fnptr; + if (local) + local(command_queue, name); +} + +NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameClCommandQueueW_impl_init)(nvtx_cl_command_queue command_queue, const wchar_t* name){ + nvtxNameClCommandQueueW_fakeimpl_fntype local; + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClCommandQueueW_impl_fnptr; + if (local) + local(command_queue, name); +} + +NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameClMemObjectA_impl_init)(nvtx_cl_mem memobj, const char* name){ + nvtxNameClMemObjectA_fakeimpl_fntype local; + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClMemObjectA_impl_fnptr; + if (local) + local(memobj, name); +} + +NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameClMemObjectW_impl_init)(nvtx_cl_mem memobj, const wchar_t* name){ + nvtxNameClMemObjectW_fakeimpl_fntype local; + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClMemObjectW_impl_fnptr; + if (local) + local(memobj, name); +} + +NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameClSamplerA_impl_init)(nvtx_cl_sampler sampler, const char* name){ + nvtxNameClSamplerA_fakeimpl_fntype local; + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClSamplerA_impl_fnptr; + if (local) + local(sampler, name); +} + +NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameClSamplerW_impl_init)(nvtx_cl_sampler sampler, const wchar_t* name){ + nvtxNameClSamplerW_fakeimpl_fntype local; + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClSamplerW_impl_fnptr; + if (local) + local(sampler, name); +} + +NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameClProgramA_impl_init)(nvtx_cl_program program, const char* name){ + nvtxNameClProgramA_fakeimpl_fntype local; + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClProgramA_impl_fnptr; + if (local) + local(program, name); +} + +NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameClProgramW_impl_init)(nvtx_cl_program program, const wchar_t* name){ + nvtxNameClProgramW_fakeimpl_fntype local; + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClProgramW_impl_fnptr; + if (local) + local(program, name); +} + +NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameClEventA_impl_init)(nvtx_cl_event evnt, const char* name){ + nvtxNameClEventA_fakeimpl_fntype local; + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClEventA_impl_fnptr; + if (local) + local(evnt, name); +} + +NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameClEventW_impl_init)(nvtx_cl_event evnt, const wchar_t* name){ + nvtxNameClEventW_fakeimpl_fntype local; + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClEventW_impl_fnptr; + if (local) + local(evnt, name); +} + +NVTX_LINKONCE_DEFINE_FUNCTION nvtxSyncUser_t NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainSyncUserCreate_impl_init)(nvtxDomainHandle_t domain, const nvtxSyncUserAttributes_t* attribs){ + nvtxDomainSyncUserCreate_impl_fntype local; + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainSyncUserCreate_impl_fnptr; + if (local) { + return local(domain, attribs); + } + return (nvtxSyncUser_t)0; +} + +NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainSyncUserDestroy_impl_init)(nvtxSyncUser_t handle){ + nvtxDomainSyncUserDestroy_impl_fntype local; + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainSyncUserDestroy_impl_fnptr; + if (local) + local(handle); +} + +NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainSyncUserAcquireStart_impl_init)(nvtxSyncUser_t handle){ + nvtxDomainSyncUserAcquireStart_impl_fntype local; + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainSyncUserAcquireStart_impl_fnptr; + if (local) + local(handle); +} + +NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainSyncUserAcquireFailed_impl_init)(nvtxSyncUser_t handle){ + nvtxDomainSyncUserAcquireFailed_impl_fntype local; + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainSyncUserAcquireFailed_impl_fnptr; + if (local) + local(handle); +} + +NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainSyncUserAcquireSuccess_impl_init)(nvtxSyncUser_t handle){ + nvtxDomainSyncUserAcquireSuccess_impl_fntype local; + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainSyncUserAcquireSuccess_impl_fnptr; + if (local) + local(handle); +} + +NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainSyncUserReleasing_impl_init)(nvtxSyncUser_t handle){ + nvtxDomainSyncUserReleasing_impl_fntype local; + NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(); + local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainSyncUserReleasing_impl_fnptr; + if (local) + local(handle); +} + +NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_VERSIONED_IDENTIFIER(nvtxSetInitFunctionsToNoops)(int forceAllToNoops); +NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_VERSIONED_IDENTIFIER(nvtxSetInitFunctionsToNoops)(int forceAllToNoops) +{ + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxMarkEx_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxMarkEx_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxMarkEx_impl_fnptr = NULL; + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxMarkA_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxMarkA_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxMarkA_impl_fnptr = NULL; + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxMarkW_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxMarkW_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxMarkW_impl_fnptr = NULL; + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangeStartEx_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxRangeStartEx_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangeStartEx_impl_fnptr = NULL; + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangeStartA_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxRangeStartA_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangeStartA_impl_fnptr = NULL; + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangeStartW_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxRangeStartW_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangeStartW_impl_fnptr = NULL; + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangeEnd_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxRangeEnd_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangeEnd_impl_fnptr = NULL; + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangePushEx_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxRangePushEx_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangePushEx_impl_fnptr = NULL; + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangePushA_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxRangePushA_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangePushA_impl_fnptr = NULL; + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangePushW_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxRangePushW_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangePushW_impl_fnptr = NULL; + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangePop_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxRangePop_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangePop_impl_fnptr = NULL; + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCategoryA_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameCategoryA_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCategoryA_impl_fnptr = NULL; + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCategoryW_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameCategoryW_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCategoryW_impl_fnptr = NULL; + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameOsThreadA_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameOsThreadA_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameOsThreadA_impl_fnptr = NULL; + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameOsThreadW_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameOsThreadW_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameOsThreadW_impl_fnptr = NULL; + + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuDeviceA_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameCuDeviceA_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuDeviceA_impl_fnptr = NULL; + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuDeviceW_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameCuDeviceW_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuDeviceW_impl_fnptr = NULL; + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuContextA_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameCuContextA_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuContextA_impl_fnptr = NULL; + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuContextW_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameCuContextW_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuContextW_impl_fnptr = NULL; + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuStreamA_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameCuStreamA_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuStreamA_impl_fnptr = NULL; + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuStreamW_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameCuStreamW_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuStreamW_impl_fnptr = NULL; + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuEventA_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameCuEventA_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuEventA_impl_fnptr = NULL; + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuEventW_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameCuEventW_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuEventW_impl_fnptr = NULL; + + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClDeviceA_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameClDeviceA_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClDeviceA_impl_fnptr = NULL; + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClDeviceW_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameClDeviceW_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClDeviceW_impl_fnptr = NULL; + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClContextA_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameClContextA_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClContextA_impl_fnptr = NULL; + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClContextW_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameClContextW_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClContextW_impl_fnptr = NULL; + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClCommandQueueA_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameClCommandQueueA_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClCommandQueueA_impl_fnptr = NULL; + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClCommandQueueW_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameClCommandQueueW_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClCommandQueueW_impl_fnptr = NULL; + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClMemObjectA_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameClMemObjectA_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClMemObjectA_impl_fnptr = NULL; + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClMemObjectW_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameClMemObjectW_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClMemObjectW_impl_fnptr = NULL; + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClSamplerA_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameClSamplerA_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClSamplerA_impl_fnptr = NULL; + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClSamplerW_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameClSamplerW_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClSamplerW_impl_fnptr = NULL; + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClProgramA_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameClProgramA_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClProgramA_impl_fnptr = NULL; + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClProgramW_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameClProgramW_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClProgramW_impl_fnptr = NULL; + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClEventA_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameClEventA_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClEventA_impl_fnptr = NULL; + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClEventW_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameClEventW_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClEventW_impl_fnptr = NULL; + + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCudaDeviceA_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameCudaDeviceA_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCudaDeviceA_impl_fnptr = NULL; + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCudaDeviceW_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameCudaDeviceW_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCudaDeviceW_impl_fnptr = NULL; + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCudaStreamA_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameCudaStreamA_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCudaStreamA_impl_fnptr = NULL; + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCudaStreamW_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameCudaStreamW_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCudaStreamW_impl_fnptr = NULL; + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCudaEventA_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameCudaEventA_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCudaEventA_impl_fnptr = NULL; + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCudaEventW_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameCudaEventW_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCudaEventW_impl_fnptr = NULL; + + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainMarkEx_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxDomainMarkEx_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainMarkEx_impl_fnptr = NULL; + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainRangeStartEx_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxDomainRangeStartEx_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainRangeStartEx_impl_fnptr = NULL; + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainRangeEnd_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxDomainRangeEnd_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainRangeEnd_impl_fnptr = NULL; + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainRangePushEx_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxDomainRangePushEx_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainRangePushEx_impl_fnptr = NULL; + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainRangePop_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxDomainRangePop_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainRangePop_impl_fnptr = NULL; + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainResourceCreate_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxDomainResourceCreate_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainResourceCreate_impl_fnptr = NULL; + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainResourceDestroy_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxDomainResourceDestroy_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainResourceDestroy_impl_fnptr = NULL; + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainNameCategoryA_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxDomainNameCategoryA_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainNameCategoryA_impl_fnptr = NULL; + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainNameCategoryW_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxDomainNameCategoryW_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainNameCategoryW_impl_fnptr = NULL; + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainRegisterStringA_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxDomainRegisterStringA_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainRegisterStringA_impl_fnptr = NULL; + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainRegisterStringW_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxDomainRegisterStringW_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainRegisterStringW_impl_fnptr = NULL; + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainCreateA_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxDomainCreateA_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainCreateA_impl_fnptr = NULL; + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainCreateW_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxDomainCreateW_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainCreateW_impl_fnptr = NULL; + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainDestroy_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxDomainDestroy_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainDestroy_impl_fnptr = NULL; + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxInitialize_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxInitialize_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxInitialize_impl_fnptr = NULL; + + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainSyncUserCreate_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxDomainSyncUserCreate_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainSyncUserCreate_impl_fnptr = NULL; + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainSyncUserDestroy_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxDomainSyncUserDestroy_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainSyncUserDestroy_impl_fnptr = NULL; + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainSyncUserAcquireStart_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxDomainSyncUserAcquireStart_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainSyncUserAcquireStart_impl_fnptr = NULL; + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainSyncUserAcquireFailed_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxDomainSyncUserAcquireFailed_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainSyncUserAcquireFailed_impl_fnptr = NULL; + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainSyncUserAcquireSuccess_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxDomainSyncUserAcquireSuccess_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainSyncUserAcquireSuccess_impl_fnptr = NULL; + if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainSyncUserReleasing_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxDomainSyncUserReleasing_impl_init) || forceAllToNoops) + NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainSyncUserReleasing_impl_fnptr = NULL; +} diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxLinkOnce.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxLinkOnce.h new file mode 100644 index 0000000000000000000000000000000000000000..6c6eaa732464519fc7d93f98b646729dab0db8b9 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxLinkOnce.h @@ -0,0 +1,75 @@ +#ifndef __NVTX_LINKONCE_H__ +#define __NVTX_LINKONCE_H__ + +/* This header defines macros to permit making definitions of global variables + * and functions in C/C++ header files which may be included multiple times in + * a translation unit or linkage unit. It allows authoring header-only libraries + * which can be used by multiple other header-only libraries (either as the same + * copy or multiple copies), and does not require any build changes, such as + * adding another .c file, linking a static library, or deploying a dynamic + * library. Globals defined with these macros have the property that they have + * the same address, pointing to a single instance, for the entire linkage unit. + * It is expected but not guaranteed that each linkage unit will have a separate + * instance. + * + * In some situations it is desirable to declare a variable without initializing + * it, refer to it in code or other variables' initializers, and then initialize + * it later. Similarly, functions can be prototyped, have their address taken, + * and then have their body defined later. In such cases, use the FWDDECL macros + * when forward-declaring LINKONCE global variables without initializers and + * function prototypes, and then use the DEFINE macros when later defining them. + * Although in many cases the FWDDECL macro is equivalent to the DEFINE macro, + * following this pattern makes code maximally portable. + */ + +#if defined(__MINGW32__) /* MinGW */ + #define NVTX_LINKONCE_WEAK __attribute__((section(".gnu.linkonce.0."))) + #if defined(__cplusplus) + #define NVTX_LINKONCE_DEFINE_GLOBAL __declspec(selectany) + #define NVTX_LINKONCE_DEFINE_FUNCTION extern "C" inline NVTX_LINKONCE_WEAK + #else + #define NVTX_LINKONCE_DEFINE_GLOBAL __declspec(selectany) + #define NVTX_LINKONCE_DEFINE_FUNCTION NVTX_LINKONCE_WEAK + #endif +#elif defined(_MSC_VER) /* MSVC */ + #if defined(__cplusplus) + #define NVTX_LINKONCE_DEFINE_GLOBAL extern "C" __declspec(selectany) + #define NVTX_LINKONCE_DEFINE_FUNCTION extern "C" inline + #else + #define NVTX_LINKONCE_DEFINE_GLOBAL __declspec(selectany) + #define NVTX_LINKONCE_DEFINE_FUNCTION __inline + #endif +#elif defined(__CYGWIN__) && defined(__clang__) /* Clang on Cygwin */ + #define NVTX_LINKONCE_WEAK __attribute__((section(".gnu.linkonce.0."))) + #if defined(__cplusplus) + #define NVTX_LINKONCE_DEFINE_GLOBAL NVTX_LINKONCE_WEAK + #define NVTX_LINKONCE_DEFINE_FUNCTION extern "C" NVTX_LINKONCE_WEAK + #else + #define NVTX_LINKONCE_DEFINE_GLOBAL NVTX_LINKONCE_WEAK + #define NVTX_LINKONCE_DEFINE_FUNCTION NVTX_LINKONCE_WEAK + #endif +#elif defined(__CYGWIN__) /* Assume GCC or compatible */ + #define NVTX_LINKONCE_WEAK __attribute__((weak)) + #if defined(__cplusplus) + #define NVTX_LINKONCE_DEFINE_GLOBAL __declspec(selectany) + #define NVTX_LINKONCE_DEFINE_FUNCTION extern "C" inline + #else + #define NVTX_LINKONCE_DEFINE_GLOBAL NVTX_LINKONCE_WEAK + #define NVTX_LINKONCE_DEFINE_FUNCTION NVTX_LINKONCE_WEAK + #endif +#else /* All others: Assume GCC, clang, or compatible */ + #define NVTX_LINKONCE_WEAK __attribute__((weak)) + #define NVTX_LINKONCE_HIDDEN __attribute__((visibility("hidden"))) + #if defined(__cplusplus) + #define NVTX_LINKONCE_DEFINE_GLOBAL NVTX_LINKONCE_HIDDEN NVTX_LINKONCE_WEAK + #define NVTX_LINKONCE_DEFINE_FUNCTION extern "C" NVTX_LINKONCE_HIDDEN inline + #else + #define NVTX_LINKONCE_DEFINE_GLOBAL NVTX_LINKONCE_HIDDEN NVTX_LINKONCE_WEAK + #define NVTX_LINKONCE_DEFINE_FUNCTION NVTX_LINKONCE_HIDDEN NVTX_LINKONCE_WEAK + #endif +#endif + +#define NVTX_LINKONCE_FWDDECL_GLOBAL NVTX_LINKONCE_DEFINE_GLOBAL extern +#define NVTX_LINKONCE_FWDDECL_FUNCTION NVTX_LINKONCE_DEFINE_FUNCTION + +#endif /* __NVTX_LINKONCE_H__ */ diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxTypes.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxTypes.h new file mode 100644 index 0000000000000000000000000000000000000000..8ddb1a9b3baff99c78b1c9acc06ac0eb64de8d6d --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxTypes.h @@ -0,0 +1,333 @@ +/* +* Copyright 2009-2016 NVIDIA Corporation. All rights reserved. +* +* NOTICE TO USER: +* +* This source code is subject to NVIDIA ownership rights under U.S. and +* international Copyright laws. +* +* This software and the information contained herein is PROPRIETARY and +* CONFIDENTIAL to NVIDIA and is being provided under the terms and conditions +* of a form of NVIDIA software license agreement. +* +* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE +* CODE FOR ANY PURPOSE. IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR +* IMPLIED WARRANTY OF ANY KIND. NVIDIA DISCLAIMS ALL WARRANTIES WITH +* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF +* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE. +* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL, +* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS +* OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE +* OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE +* OR PERFORMANCE OF THIS SOURCE CODE. +* +* U.S. Government End Users. This source code is a "commercial item" as +* that term is defined at 48 C.F.R. 2.101 (OCT 1995), consisting of +* "commercial computer software" and "commercial computer software +* documentation" as such terms are used in 48 C.F.R. 12.212 (SEPT 1995) +* and is provided to the U.S. Government only as a commercial end item. +* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through +* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the +* source code with only those rights set forth herein. +* +* Any use of this source code in individual and commercial software must +* include, in the user documentation and internal comments to the code, +* the above Disclaimer and U.S. Government End Users Notice. +*/ + +/* This header defines types which are used by the internal implementation +* of NVTX and callback subscribers. API clients do not use these types, +* so they are defined here instead of in nvToolsExt.h to clarify they are +* not part of the NVTX client API. */ + +#ifndef NVTX_IMPL_GUARD +#error Never include this file directly -- it is automatically included by nvToolsExt.h. +#endif + +/* ------ Dependency-free types binary-compatible with real types ------- */ + +/* In order to avoid having the NVTX core API headers depend on non-NVTX +* headers like cuda.h, NVTX defines binary-compatible types to use for +* safely making the initialization versions of all NVTX functions without +* needing to have definitions for the real types. */ + +typedef int nvtx_CUdevice; +typedef void* nvtx_CUcontext; +typedef void* nvtx_CUstream; +typedef void* nvtx_CUevent; + +typedef void* nvtx_cudaStream_t; +typedef void* nvtx_cudaEvent_t; + +typedef void* nvtx_cl_platform_id; +typedef void* nvtx_cl_device_id; +typedef void* nvtx_cl_context; +typedef void* nvtx_cl_command_queue; +typedef void* nvtx_cl_mem; +typedef void* nvtx_cl_program; +typedef void* nvtx_cl_kernel; +typedef void* nvtx_cl_event; +typedef void* nvtx_cl_sampler; + +typedef struct nvtxSyncUser* nvtxSyncUser_t; +struct nvtxSyncUserAttributes_v0; +typedef struct nvtxSyncUserAttributes_v0 nvtxSyncUserAttributes_t; + +/* --------- Types for function pointers (with fake API types) ---------- */ + +typedef void (NVTX_API * nvtxMarkEx_impl_fntype)(const nvtxEventAttributes_t* eventAttrib); +typedef void (NVTX_API * nvtxMarkA_impl_fntype)(const char* message); +typedef void (NVTX_API * nvtxMarkW_impl_fntype)(const wchar_t* message); +typedef nvtxRangeId_t (NVTX_API * nvtxRangeStartEx_impl_fntype)(const nvtxEventAttributes_t* eventAttrib); +typedef nvtxRangeId_t (NVTX_API * nvtxRangeStartA_impl_fntype)(const char* message); +typedef nvtxRangeId_t (NVTX_API * nvtxRangeStartW_impl_fntype)(const wchar_t* message); +typedef void (NVTX_API * nvtxRangeEnd_impl_fntype)(nvtxRangeId_t id); +typedef int (NVTX_API * nvtxRangePushEx_impl_fntype)(const nvtxEventAttributes_t* eventAttrib); +typedef int (NVTX_API * nvtxRangePushA_impl_fntype)(const char* message); +typedef int (NVTX_API * nvtxRangePushW_impl_fntype)(const wchar_t* message); +typedef int (NVTX_API * nvtxRangePop_impl_fntype)(void); +typedef void (NVTX_API * nvtxNameCategoryA_impl_fntype)(uint32_t category, const char* name); +typedef void (NVTX_API * nvtxNameCategoryW_impl_fntype)(uint32_t category, const wchar_t* name); +typedef void (NVTX_API * nvtxNameOsThreadA_impl_fntype)(uint32_t threadId, const char* name); +typedef void (NVTX_API * nvtxNameOsThreadW_impl_fntype)(uint32_t threadId, const wchar_t* name); + +/* Real impl types are defined in nvtxImplCuda_v3.h, where CUDA headers are included */ +typedef void (NVTX_API * nvtxNameCuDeviceA_fakeimpl_fntype)(nvtx_CUdevice device, const char* name); +typedef void (NVTX_API * nvtxNameCuDeviceW_fakeimpl_fntype)(nvtx_CUdevice device, const wchar_t* name); +typedef void (NVTX_API * nvtxNameCuContextA_fakeimpl_fntype)(nvtx_CUcontext context, const char* name); +typedef void (NVTX_API * nvtxNameCuContextW_fakeimpl_fntype)(nvtx_CUcontext context, const wchar_t* name); +typedef void (NVTX_API * nvtxNameCuStreamA_fakeimpl_fntype)(nvtx_CUstream stream, const char* name); +typedef void (NVTX_API * nvtxNameCuStreamW_fakeimpl_fntype)(nvtx_CUstream stream, const wchar_t* name); +typedef void (NVTX_API * nvtxNameCuEventA_fakeimpl_fntype)(nvtx_CUevent event, const char* name); +typedef void (NVTX_API * nvtxNameCuEventW_fakeimpl_fntype)(nvtx_CUevent event, const wchar_t* name); + +/* Real impl types are defined in nvtxImplOpenCL_v3.h, where OPENCL headers are included */ +typedef void (NVTX_API * nvtxNameClDeviceA_fakeimpl_fntype)(nvtx_cl_device_id device, const char* name); +typedef void (NVTX_API * nvtxNameClDeviceW_fakeimpl_fntype)(nvtx_cl_device_id device, const wchar_t* name); +typedef void (NVTX_API * nvtxNameClContextA_fakeimpl_fntype)(nvtx_cl_context context, const char* name); +typedef void (NVTX_API * nvtxNameClContextW_fakeimpl_fntype)(nvtx_cl_context context, const wchar_t* name); +typedef void (NVTX_API * nvtxNameClCommandQueueA_fakeimpl_fntype)(nvtx_cl_command_queue command_queue, const char* name); +typedef void (NVTX_API * nvtxNameClCommandQueueW_fakeimpl_fntype)(nvtx_cl_command_queue command_queue, const wchar_t* name); +typedef void (NVTX_API * nvtxNameClMemObjectA_fakeimpl_fntype)(nvtx_cl_mem memobj, const char* name); +typedef void (NVTX_API * nvtxNameClMemObjectW_fakeimpl_fntype)(nvtx_cl_mem memobj, const wchar_t* name); +typedef void (NVTX_API * nvtxNameClSamplerA_fakeimpl_fntype)(nvtx_cl_sampler sampler, const char* name); +typedef void (NVTX_API * nvtxNameClSamplerW_fakeimpl_fntype)(nvtx_cl_sampler sampler, const wchar_t* name); +typedef void (NVTX_API * nvtxNameClProgramA_fakeimpl_fntype)(nvtx_cl_program program, const char* name); +typedef void (NVTX_API * nvtxNameClProgramW_fakeimpl_fntype)(nvtx_cl_program program, const wchar_t* name); +typedef void (NVTX_API * nvtxNameClEventA_fakeimpl_fntype)(nvtx_cl_event evnt, const char* name); +typedef void (NVTX_API * nvtxNameClEventW_fakeimpl_fntype)(nvtx_cl_event evnt, const wchar_t* name); + +/* Real impl types are defined in nvtxImplCudaRt_v3.h, where CUDART headers are included */ +typedef void (NVTX_API * nvtxNameCudaDeviceA_impl_fntype)(int device, const char* name); +typedef void (NVTX_API * nvtxNameCudaDeviceW_impl_fntype)(int device, const wchar_t* name); +typedef void (NVTX_API * nvtxNameCudaStreamA_fakeimpl_fntype)(nvtx_cudaStream_t stream, const char* name); +typedef void (NVTX_API * nvtxNameCudaStreamW_fakeimpl_fntype)(nvtx_cudaStream_t stream, const wchar_t* name); +typedef void (NVTX_API * nvtxNameCudaEventA_fakeimpl_fntype)(nvtx_cudaEvent_t event, const char* name); +typedef void (NVTX_API * nvtxNameCudaEventW_fakeimpl_fntype)(nvtx_cudaEvent_t event, const wchar_t* name); + +typedef void (NVTX_API * nvtxDomainMarkEx_impl_fntype)(nvtxDomainHandle_t domain, const nvtxEventAttributes_t* eventAttrib); +typedef nvtxRangeId_t (NVTX_API * nvtxDomainRangeStartEx_impl_fntype)(nvtxDomainHandle_t domain, const nvtxEventAttributes_t* eventAttrib); +typedef void (NVTX_API * nvtxDomainRangeEnd_impl_fntype)(nvtxDomainHandle_t domain, nvtxRangeId_t id); +typedef int (NVTX_API * nvtxDomainRangePushEx_impl_fntype)(nvtxDomainHandle_t domain, const nvtxEventAttributes_t* eventAttrib); +typedef int (NVTX_API * nvtxDomainRangePop_impl_fntype)(nvtxDomainHandle_t domain); +typedef nvtxResourceHandle_t (NVTX_API * nvtxDomainResourceCreate_impl_fntype)(nvtxDomainHandle_t domain, nvtxResourceAttributes_t* attribs); +typedef void (NVTX_API * nvtxDomainResourceDestroy_impl_fntype)(nvtxResourceHandle_t resource); +typedef void (NVTX_API * nvtxDomainNameCategoryA_impl_fntype)(nvtxDomainHandle_t domain, uint32_t category, const char* name); +typedef void (NVTX_API * nvtxDomainNameCategoryW_impl_fntype)(nvtxDomainHandle_t domain, uint32_t category, const wchar_t* name); +typedef nvtxStringHandle_t (NVTX_API * nvtxDomainRegisterStringA_impl_fntype)(nvtxDomainHandle_t domain, const char* string); +typedef nvtxStringHandle_t (NVTX_API * nvtxDomainRegisterStringW_impl_fntype)(nvtxDomainHandle_t domain, const wchar_t* string); +typedef nvtxDomainHandle_t (NVTX_API * nvtxDomainCreateA_impl_fntype)(const char* message); +typedef nvtxDomainHandle_t (NVTX_API * nvtxDomainCreateW_impl_fntype)(const wchar_t* message); +typedef void (NVTX_API * nvtxDomainDestroy_impl_fntype)(nvtxDomainHandle_t domain); +typedef void (NVTX_API * nvtxInitialize_impl_fntype)(const void* reserved); + +typedef nvtxSyncUser_t (NVTX_API * nvtxDomainSyncUserCreate_impl_fntype)(nvtxDomainHandle_t domain, const nvtxSyncUserAttributes_t* attribs); +typedef void (NVTX_API * nvtxDomainSyncUserDestroy_impl_fntype)(nvtxSyncUser_t handle); +typedef void (NVTX_API * nvtxDomainSyncUserAcquireStart_impl_fntype)(nvtxSyncUser_t handle); +typedef void (NVTX_API * nvtxDomainSyncUserAcquireFailed_impl_fntype)(nvtxSyncUser_t handle); +typedef void (NVTX_API * nvtxDomainSyncUserAcquireSuccess_impl_fntype)(nvtxSyncUser_t handle); +typedef void (NVTX_API * nvtxDomainSyncUserReleasing_impl_fntype)(nvtxSyncUser_t handle); + +/* ---------------- Types for callback subscription --------------------- */ + +typedef const void *(NVTX_API * NvtxGetExportTableFunc_t)(uint32_t exportTableId); +typedef int (NVTX_API * NvtxInitializeInjectionNvtxFunc_t)(NvtxGetExportTableFunc_t exportTable); + +typedef enum NvtxCallbackModule +{ + NVTX_CB_MODULE_INVALID = 0, + NVTX_CB_MODULE_CORE = 1, + NVTX_CB_MODULE_CUDA = 2, + NVTX_CB_MODULE_OPENCL = 3, + NVTX_CB_MODULE_CUDART = 4, + NVTX_CB_MODULE_CORE2 = 5, + NVTX_CB_MODULE_SYNC = 6, + /* --- New constants must only be added directly above this line --- */ + NVTX_CB_MODULE_SIZE, + NVTX_CB_MODULE_FORCE_INT = 0x7fffffff +} NvtxCallbackModule; + +typedef enum NvtxCallbackIdCore +{ + NVTX_CBID_CORE_INVALID = 0, + NVTX_CBID_CORE_MarkEx = 1, + NVTX_CBID_CORE_MarkA = 2, + NVTX_CBID_CORE_MarkW = 3, + NVTX_CBID_CORE_RangeStartEx = 4, + NVTX_CBID_CORE_RangeStartA = 5, + NVTX_CBID_CORE_RangeStartW = 6, + NVTX_CBID_CORE_RangeEnd = 7, + NVTX_CBID_CORE_RangePushEx = 8, + NVTX_CBID_CORE_RangePushA = 9, + NVTX_CBID_CORE_RangePushW = 10, + NVTX_CBID_CORE_RangePop = 11, + NVTX_CBID_CORE_NameCategoryA = 12, + NVTX_CBID_CORE_NameCategoryW = 13, + NVTX_CBID_CORE_NameOsThreadA = 14, + NVTX_CBID_CORE_NameOsThreadW = 15, + /* --- New constants must only be added directly above this line --- */ + NVTX_CBID_CORE_SIZE, + NVTX_CBID_CORE_FORCE_INT = 0x7fffffff +} NvtxCallbackIdCore; + +typedef enum NvtxCallbackIdCore2 +{ + NVTX_CBID_CORE2_INVALID = 0, + NVTX_CBID_CORE2_DomainMarkEx = 1, + NVTX_CBID_CORE2_DomainRangeStartEx = 2, + NVTX_CBID_CORE2_DomainRangeEnd = 3, + NVTX_CBID_CORE2_DomainRangePushEx = 4, + NVTX_CBID_CORE2_DomainRangePop = 5, + NVTX_CBID_CORE2_DomainResourceCreate = 6, + NVTX_CBID_CORE2_DomainResourceDestroy = 7, + NVTX_CBID_CORE2_DomainNameCategoryA = 8, + NVTX_CBID_CORE2_DomainNameCategoryW = 9, + NVTX_CBID_CORE2_DomainRegisterStringA = 10, + NVTX_CBID_CORE2_DomainRegisterStringW = 11, + NVTX_CBID_CORE2_DomainCreateA = 12, + NVTX_CBID_CORE2_DomainCreateW = 13, + NVTX_CBID_CORE2_DomainDestroy = 14, + NVTX_CBID_CORE2_Initialize = 15, + /* --- New constants must only be added directly above this line --- */ + NVTX_CBID_CORE2_SIZE, + NVTX_CBID_CORE2_FORCE_INT = 0x7fffffff +} NvtxCallbackIdCore2; + +typedef enum NvtxCallbackIdCuda +{ + NVTX_CBID_CUDA_INVALID = 0, + NVTX_CBID_CUDA_NameCuDeviceA = 1, + NVTX_CBID_CUDA_NameCuDeviceW = 2, + NVTX_CBID_CUDA_NameCuContextA = 3, + NVTX_CBID_CUDA_NameCuContextW = 4, + NVTX_CBID_CUDA_NameCuStreamA = 5, + NVTX_CBID_CUDA_NameCuStreamW = 6, + NVTX_CBID_CUDA_NameCuEventA = 7, + NVTX_CBID_CUDA_NameCuEventW = 8, + /* --- New constants must only be added directly above this line --- */ + NVTX_CBID_CUDA_SIZE, + NVTX_CBID_CUDA_FORCE_INT = 0x7fffffff +} NvtxCallbackIdCuda; + +typedef enum NvtxCallbackIdCudaRt +{ + NVTX_CBID_CUDART_INVALID = 0, + NVTX_CBID_CUDART_NameCudaDeviceA = 1, + NVTX_CBID_CUDART_NameCudaDeviceW = 2, + NVTX_CBID_CUDART_NameCudaStreamA = 3, + NVTX_CBID_CUDART_NameCudaStreamW = 4, + NVTX_CBID_CUDART_NameCudaEventA = 5, + NVTX_CBID_CUDART_NameCudaEventW = 6, + /* --- New constants must only be added directly above this line --- */ + NVTX_CBID_CUDART_SIZE, + NVTX_CBID_CUDART_FORCE_INT = 0x7fffffff +} NvtxCallbackIdCudaRt; + +typedef enum NvtxCallbackIdOpenCL +{ + NVTX_CBID_OPENCL_INVALID = 0, + NVTX_CBID_OPENCL_NameClDeviceA = 1, + NVTX_CBID_OPENCL_NameClDeviceW = 2, + NVTX_CBID_OPENCL_NameClContextA = 3, + NVTX_CBID_OPENCL_NameClContextW = 4, + NVTX_CBID_OPENCL_NameClCommandQueueA = 5, + NVTX_CBID_OPENCL_NameClCommandQueueW = 6, + NVTX_CBID_OPENCL_NameClMemObjectA = 7, + NVTX_CBID_OPENCL_NameClMemObjectW = 8, + NVTX_CBID_OPENCL_NameClSamplerA = 9, + NVTX_CBID_OPENCL_NameClSamplerW = 10, + NVTX_CBID_OPENCL_NameClProgramA = 11, + NVTX_CBID_OPENCL_NameClProgramW = 12, + NVTX_CBID_OPENCL_NameClEventA = 13, + NVTX_CBID_OPENCL_NameClEventW = 14, + /* --- New constants must only be added directly above this line --- */ + NVTX_CBID_OPENCL_SIZE, + NVTX_CBID_OPENCL_FORCE_INT = 0x7fffffff +} NvtxCallbackIdOpenCL; + +typedef enum NvtxCallbackIdSync +{ + NVTX_CBID_SYNC_INVALID = 0, + NVTX_CBID_SYNC_DomainSyncUserCreate = 1, + NVTX_CBID_SYNC_DomainSyncUserDestroy = 2, + NVTX_CBID_SYNC_DomainSyncUserAcquireStart = 3, + NVTX_CBID_SYNC_DomainSyncUserAcquireFailed = 4, + NVTX_CBID_SYNC_DomainSyncUserAcquireSuccess = 5, + NVTX_CBID_SYNC_DomainSyncUserReleasing = 6, + /* --- New constants must only be added directly above this line --- */ + NVTX_CBID_SYNC_SIZE, + NVTX_CBID_SYNC_FORCE_INT = 0x7fffffff +} NvtxCallbackIdSync; + +/* IDs for NVTX Export Tables */ +typedef enum NvtxExportTableID +{ + NVTX_ETID_INVALID = 0, + NVTX_ETID_CALLBACKS = 1, + NVTX_ETID_RESERVED0 = 2, + NVTX_ETID_VERSIONINFO = 3, + /* --- New constants must only be added directly above this line --- */ + NVTX_ETID_SIZE, + NVTX_ETID_FORCE_INT = 0x7fffffff +} NvtxExportTableID; + +typedef void (* NvtxFunctionPointer)(void); /* generic uncallable function pointer, must be casted to appropriate function type */ +typedef NvtxFunctionPointer** NvtxFunctionTable; /* double pointer because array(1) of pointers(2) to function pointers */ + +typedef struct NvtxExportTableCallbacks +{ + size_t struct_size; + + /* returns an array of pointer to function pointers*/ + int (NVTX_API *GetModuleFunctionTable)( + NvtxCallbackModule module, + NvtxFunctionTable* out_table, + unsigned int* out_size); +} NvtxExportTableCallbacks; + +typedef struct NvtxExportTableVersionInfo +{ + /* sizeof(NvtxExportTableVersionInfo) */ + size_t struct_size; + + /* The API version comes from the NVTX library linked to the app. The + * injection library is can use this info to make some assumptions */ + uint32_t version; + + /* Reserved for alignment, do not use */ + uint32_t reserved0; + + /* This must be set by tools when attaching to provide applications + * the ability to, in emergency situations, detect problematic tools + * versions and modify the NVTX source to prevent attaching anything + * that causes trouble in the app. Currently, this value is ignored. */ + void (NVTX_API *SetInjectionNvtxVersion)( + uint32_t version); +} NvtxExportTableVersionInfo; + + + + + + + diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/lib/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/lib/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/lib/libnvToolsExt.so.1 b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/lib/libnvToolsExt.so.1 new file mode 100644 index 0000000000000000000000000000000000000000..0759d4db37a1fdc5d11de80ac521666120cc927e Binary files /dev/null and b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia/nvtx/lib/libnvToolsExt.so.1 differ diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cublas_cu12-12.8.4.1.dist-info/INSTALLER b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cublas_cu12-12.8.4.1.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cublas_cu12-12.8.4.1.dist-info/INSTALLER @@ -0,0 +1 @@ +pip diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cublas_cu12-12.8.4.1.dist-info/License.txt b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cublas_cu12-12.8.4.1.dist-info/License.txt new file mode 100644 index 0000000000000000000000000000000000000000..b491c70e0aef319022ded661e111ddbd45b8a17f --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cublas_cu12-12.8.4.1.dist-info/License.txt @@ -0,0 +1,1568 @@ +End User License Agreement +-------------------------- + + +Preface +------- + +The Software License Agreement in Chapter 1 and the Supplement +in Chapter 2 contain license terms and conditions that govern +the use of NVIDIA software. By accepting this agreement, you +agree to comply with all the terms and conditions applicable +to the product(s) included herein. + + +NVIDIA Driver + + +Description + +This package contains the operating system driver and +fundamental system software components for NVIDIA GPUs. + + +NVIDIA CUDA Toolkit + + +Description + +The NVIDIA CUDA Toolkit provides command-line and graphical +tools for building, debugging and optimizing the performance +of applications accelerated by NVIDIA GPUs, runtime and math +libraries, and documentation including programming guides, +user manuals, and API references. + + +Default Install Location of CUDA Toolkit + +Windows platform: + +%ProgramFiles%\NVIDIA GPU Computing Toolkit\CUDA\v#.# + +Linux platform: + +/usr/local/cuda-#.# + +Mac platform: + +/Developer/NVIDIA/CUDA-#.# + + +NVIDIA CUDA Samples + + +Description + +This package includes over 100+ CUDA examples that demonstrate +various CUDA programming principles, and efficient CUDA +implementation of algorithms in specific application domains. + + +Default Install Location of CUDA Samples + +Windows platform: + +%ProgramData%\NVIDIA Corporation\CUDA Samples\v#.# + +Linux platform: + +/usr/local/cuda-#.#/samples + +and + +$HOME/NVIDIA_CUDA-#.#_Samples + +Mac platform: + +/Developer/NVIDIA/CUDA-#.#/samples + + +NVIDIA Nsight Visual Studio Edition (Windows only) + + +Description + +NVIDIA Nsight Development Platform, Visual Studio Edition is a +development environment integrated into Microsoft Visual +Studio that provides tools for debugging, profiling, analyzing +and optimizing your GPU computing and graphics applications. + + +Default Install Location of Nsight Visual Studio Edition + +Windows platform: + +%ProgramFiles(x86)%\NVIDIA Corporation\Nsight Visual Studio Edition #.# + + +1. 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CUDA Toolkit Supplement to Software License Agreement for +NVIDIA Software Development Kits +------------------------------------------------------------ + + +Release date: August 16, 2018 +----------------------------- + +The terms in this supplement govern your use of the NVIDIA +CUDA Toolkit SDK under the terms of your license agreement +(“Agreement”) as modified by this supplement. Capitalized +terms used but not defined below have the meaning assigned to +them in the Agreement. + +This supplement is an exhibit to the Agreement and is +incorporated as an integral part of the Agreement. In the +event of conflict between the terms in this supplement and the +terms in the Agreement, the terms in this supplement govern. + + +2.1. License Scope + +The SDK is licensed for you to develop applications only for +use in systems with NVIDIA GPUs. + + +2.2. Distribution + +The portions of the SDK that are distributable under the +Agreement are listed in Attachment A. + + +2.3. 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NVIDIA does not grant to you +under this Agreement any necessary patent or other rights with +respect to any audio and/or video encoders and decoders. + + +2.5. Licensing + +If the distribution terms in this Agreement are not suitable +for your organization, or for any questions regarding this +Agreement, please contact NVIDIA at +nvidia-compute-license-questions@nvidia.com. + + +2.6. Attachment A + +The following portions of the SDK are distributable under the +Agreement: + +Component + +CUDA Runtime + +Windows + +cudart.dll, cudart_static.lib, cudadevrt.lib + +Mac OSX + +libcudart.dylib, libcudart_static.a, libcudadevrt.a + +Linux + +libcudart.so, libcudart_static.a, libcudadevrt.a + +Android + +libcudart.so, libcudart_static.a, libcudadevrt.a + +Component + +CUDA FFT Library + +Windows + +cufft.dll, cufftw.dll, cufft.lib, cufftw.lib + +Mac OSX + +libcufft.dylib, libcufft_static.a, libcufftw.dylib, +libcufftw_static.a + +Linux + +libcufft.so, libcufft_static.a, libcufftw.so, +libcufftw_static.a + +Android + +libcufft.so, libcufft_static.a, libcufftw.so, +libcufftw_static.a + +Component + +CUDA BLAS Library + +Windows + +cublas.dll, cublasLt.dll + +Mac OSX + +libcublas.dylib, libcublasLt.dylib, libcublas_static.a, +libcublasLt_static.a + +Linux + +libcublas.so, libcublasLt.so, libcublas_static.a, +libcublasLt_static.a + +Android + +libcublas.so, libcublasLt.so, libcublas_static.a, +libcublasLt_static.a + +Component + +NVIDIA "Drop-in" BLAS Library + +Windows + +nvblas.dll + +Mac OSX + +libnvblas.dylib + +Linux + +libnvblas.so + +Component + +CUDA Sparse Matrix Library + +Windows + +cusparse.dll, cusparse.lib + +Mac OSX + +libcusparse.dylib, libcusparse_static.a + +Linux + +libcusparse.so, libcusparse_static.a + +Android + +libcusparse.so, libcusparse_static.a + +Component + +CUDA Linear Solver Library + +Windows + +cusolver.dll, cusolver.lib + +Mac OSX + +libcusolver.dylib, libcusolver_static.a + +Linux + +libcusolver.so, libcusolver_static.a + +Android + +libcusolver.so, libcusolver_static.a + +Component + +CUDA Random Number Generation Library + +Windows + +curand.dll, curand.lib + +Mac OSX + +libcurand.dylib, libcurand_static.a + +Linux + +libcurand.so, libcurand_static.a + +Android + +libcurand.so, libcurand_static.a + +Component + +CUDA Accelerated Graph Library + +Component + +NVIDIA Performance Primitives Library + +Windows + +nppc.dll, nppc.lib, nppial.dll, nppial.lib, nppicc.dll, +nppicc.lib, nppicom.dll, nppicom.lib, nppidei.dll, +nppidei.lib, nppif.dll, nppif.lib, nppig.dll, nppig.lib, +nppim.dll, nppim.lib, nppist.dll, nppist.lib, nppisu.dll, +nppisu.lib, nppitc.dll, nppitc.lib, npps.dll, npps.lib + +Mac OSX + +libnppc.dylib, libnppc_static.a, libnppial.dylib, +libnppial_static.a, libnppicc.dylib, libnppicc_static.a, +libnppicom.dylib, libnppicom_static.a, libnppidei.dylib, +libnppidei_static.a, libnppif.dylib, libnppif_static.a, +libnppig.dylib, libnppig_static.a, libnppim.dylib, +libnppisu_static.a, libnppitc.dylib, libnppitc_static.a, +libnpps.dylib, libnpps_static.a + +Linux + +libnppc.so, libnppc_static.a, libnppial.so, +libnppial_static.a, libnppicc.so, libnppicc_static.a, +libnppicom.so, libnppicom_static.a, libnppidei.so, +libnppidei_static.a, libnppif.so, libnppif_static.a +libnppig.so, libnppig_static.a, libnppim.so, +libnppim_static.a, libnppist.so, libnppist_static.a, +libnppisu.so, libnppisu_static.a, libnppitc.so +libnppitc_static.a, libnpps.so, libnpps_static.a + +Android + +libnppc.so, libnppc_static.a, libnppial.so, +libnppial_static.a, libnppicc.so, libnppicc_static.a, +libnppicom.so, libnppicom_static.a, libnppidei.so, +libnppidei_static.a, libnppif.so, libnppif_static.a +libnppig.so, libnppig_static.a, libnppim.so, +libnppim_static.a, libnppist.so, libnppist_static.a, +libnppisu.so, libnppisu_static.a, libnppitc.so +libnppitc_static.a, libnpps.so, libnpps_static.a + +Component + +NVIDIA JPEG Library + +Linux + +libnvjpeg.so, libnvjpeg_static.a + +Component + +Internal common library required for statically linking to +cuBLAS, cuSPARSE, cuFFT, cuRAND, nvJPEG and NPP + +Mac OSX + +libculibos.a + +Linux + +libculibos.a + +Component + +NVIDIA Runtime Compilation Library and Header + +All + +nvrtc.h + +Windows + +nvrtc.dll, nvrtc-builtins.dll + +Mac OSX + +libnvrtc.dylib, libnvrtc-builtins.dylib + +Linux + +libnvrtc.so, libnvrtc-builtins.so + +Component + +NVIDIA Optimizing Compiler Library + +Windows + +nvvm.dll + +Mac OSX + +libnvvm.dylib + +Linux + +libnvvm.so + +Component + +NVIDIA Common Device Math Functions Library + +Windows + +libdevice.10.bc + +Mac OSX + +libdevice.10.bc + +Linux + +libdevice.10.bc + +Component + +CUDA Occupancy Calculation Header Library + +All + +cuda_occupancy.h + +Component + +CUDA Half Precision Headers + +All + +cuda_fp16.h, cuda_fp16.hpp + +Component + +CUDA Profiling Tools Interface (CUPTI) Library + +Windows + +cupti.dll + +Mac OSX + +libcupti.dylib + +Linux + +libcupti.so + +Component + +NVIDIA Tools Extension Library + +Windows + +nvToolsExt.dll, nvToolsExt.lib + +Mac OSX + +libnvToolsExt.dylib + +Linux + +libnvToolsExt.so + +Component + +NVIDIA CUDA Driver Libraries + +Linux + +libcuda.so, libnvidia-fatbinaryloader.so, +libnvidia-ptxjitcompiler.so + +The NVIDIA CUDA Driver Libraries are only distributable in +applications that meet this criteria: + + 1. The application was developed starting from a NVIDIA CUDA + container obtained from Docker Hub or the NVIDIA GPU + Cloud, and + + 2. The resulting application is packaged as a Docker + container and distributed to users on Docker Hub or the + NVIDIA GPU Cloud only. + + +2.7. Attachment B + + +Additional Licensing Obligations + +The following third party components included in the SOFTWARE +are licensed to Licensee pursuant to the following terms and +conditions: + + 1. Licensee's use of the GDB third party component is + subject to the terms and conditions of GNU GPL v3: + + This product includes copyrighted third-party software licensed + under the terms of the GNU General Public License v3 ("GPL v3"). + All third-party software packages are copyright by their respective + authors. GPL v3 terms and conditions are hereby incorporated into + the Agreement by this reference: http://www.gnu.org/licenses/gpl.txt + + Consistent with these licensing requirements, the software + listed below is provided under the terms of the specified + open source software licenses. To obtain source code for + software provided under licenses that require + redistribution of source code, including the GNU General + Public License (GPL) and GNU Lesser General Public License + (LGPL), contact oss-requests@nvidia.com. This offer is + valid for a period of three (3) years from the date of the + distribution of this product by NVIDIA CORPORATION. + + Component License + CUDA-GDB GPL v3 + + 2. Licensee represents and warrants that any and all third + party licensing and/or royalty payment obligations in + connection with Licensee's use of the H.264 video codecs + are solely the responsibility of Licensee. + + 3. Licensee's use of the Thrust library is subject to the + terms and conditions of the Apache License Version 2.0. + All third-party software packages are copyright by their + respective authors. Apache License Version 2.0 terms and + conditions are hereby incorporated into the Agreement by + this reference. + http://www.apache.org/licenses/LICENSE-2.0.html + + In addition, Licensee acknowledges the following notice: + Thrust includes source code from the Boost Iterator, + Tuple, System, and Random Number libraries. + + Boost Software License - Version 1.0 - August 17th, 2003 + . . . . + + Permission is hereby granted, free of charge, to any person or + organization obtaining a copy of the software and accompanying + documentation covered by this license (the "Software") to use, + reproduce, display, distribute, execute, and transmit the Software, + and to prepare derivative works of the Software, and to permit + third-parties to whom the Software is furnished to do so, all + subject to the following: + + The copyright notices in the Software and this entire statement, + including the above license grant, this restriction and the following + disclaimer, must be included in all copies of the Software, in whole + or in part, and all derivative works of the Software, unless such + copies or derivative works are solely in the form of machine-executable + object code generated by a source language processor. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, + EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF + MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND + NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR + ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE FOR ANY DAMAGES OR + OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING + FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR + OTHER DEALINGS IN THE SOFTWARE. + + 4. Licensee's use of the LLVM third party component is + subject to the following terms and conditions: + + ====================================================== + LLVM Release License + ====================================================== + University of Illinois/NCSA + Open Source License + + Copyright (c) 2003-2010 University of Illinois at Urbana-Champaign. + All rights reserved. + + Developed by: + + LLVM Team + + University of Illinois at Urbana-Champaign + + http://llvm.org + + Permission is hereby granted, free of charge, to any person obtaining a copy + of this software and associated documentation files (the "Software"), to + deal with the Software without restriction, including without limitation the + rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + sell copies of the Software, and to permit persons to whom the Software is + furnished to do so, subject to the following conditions: + + * Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimers. + + * Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimers in the + documentation and/or other materials provided with the distribution. + + * Neither the names of the LLVM Team, University of Illinois at Urbana- + Champaign, nor the names of its contributors may be used to endorse or + promote products derived from this Software without specific prior + written permission. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL + THE CONTRIBUTORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR + OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, + ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER + DEALINGS WITH THE SOFTWARE. + + 5. Licensee's use (e.g. nvprof) of the PCRE third party + component is subject to the following terms and + conditions: + + ------------ + PCRE LICENCE + ------------ + PCRE is a library of functions to support regular expressions whose syntax + and semantics are as close as possible to those of the Perl 5 language. + Release 8 of PCRE is distributed under the terms of the "BSD" licence, as + specified below. The documentation for PCRE, supplied in the "doc" + directory, is distributed under the same terms as the software itself. The + basic library functions are written in C and are freestanding. Also + included in the distribution is a set of C++ wrapper functions, and a just- + in-time compiler that can be used to optimize pattern matching. These are + both optional features that can be omitted when the library is built. + + THE BASIC LIBRARY FUNCTIONS + --------------------------- + Written by: Philip Hazel + Email local part: ph10 + Email domain: cam.ac.uk + University of Cambridge Computing Service, + Cambridge, England. + Copyright (c) 1997-2012 University of Cambridge + All rights reserved. + + PCRE JUST-IN-TIME COMPILATION SUPPORT + ------------------------------------- + Written by: Zoltan Herczeg + Email local part: hzmester + Emain domain: freemail.hu + Copyright(c) 2010-2012 Zoltan Herczeg + All rights reserved. + + STACK-LESS JUST-IN-TIME COMPILER + -------------------------------- + Written by: Zoltan Herczeg + Email local part: hzmester + Emain domain: freemail.hu + Copyright(c) 2009-2012 Zoltan Herczeg + All rights reserved. + + THE C++ WRAPPER FUNCTIONS + ------------------------- + Contributed by: Google Inc. + Copyright (c) 2007-2012, Google Inc. + All rights reserved. + + THE "BSD" LICENCE + ----------------- + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are met: + + * Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + + * Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + * Neither the name of the University of Cambridge nor the name of Google + Inc. nor the names of their contributors may be used to endorse or + promote products derived from this software without specific prior + written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE + ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE + LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF + SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS + INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN + CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) + ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + POSSIBILITY OF SUCH DAMAGE. + + 6. Some of the cuBLAS library routines were written by or + derived from code written by Vasily Volkov and are subject + to the Modified Berkeley Software Distribution License as + follows: + + Copyright (c) 2007-2009, Regents of the University of California + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + * Neither the name of the University of California, Berkeley nor + the names of its contributors may be used to endorse or promote + products derived from this software without specific prior + written permission. + + THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR + IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED + WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE + DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, + INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES + (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR + SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) + HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, + STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING + IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + POSSIBILITY OF SUCH DAMAGE. + + 7. Some of the cuBLAS library routines were written by or + derived from code written by Davide Barbieri and are + subject to the Modified Berkeley Software Distribution + License as follows: + + Copyright (c) 2008-2009 Davide Barbieri @ University of Rome Tor Vergata. + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + * The name of the author may not be used to endorse or promote + products derived from this software without specific prior + written permission. + + THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR + IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED + WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE + DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, + INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES + (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR + SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) + HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, + STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING + IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + POSSIBILITY OF SUCH DAMAGE. + + 8. Some of the cuBLAS library routines were derived from + code developed by the University of Tennessee and are + subject to the Modified Berkeley Software Distribution + License as follows: + + Copyright (c) 2010 The University of Tennessee. + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer listed in this license in the documentation and/or + other materials provided with the distribution. + * Neither the name of the copyright holders nor the names of its + contributors may be used to endorse or promote products derived + from this software without specific prior written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + 9. Some of the cuBLAS library routines were written by or + derived from code written by Jonathan Hogg and are subject + to the Modified Berkeley Software Distribution License as + follows: + + Copyright (c) 2012, The Science and Technology Facilities Council (STFC). + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + * Neither the name of the STFC nor the names of its contributors + may be used to endorse or promote products derived from this + software without specific prior written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE STFC BE + LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF + SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR + BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, + WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE + OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN + IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + 10. Some of the cuBLAS library routines were written by or + derived from code written by Ahmad M. Abdelfattah, David + Keyes, and Hatem Ltaief, and are subject to the Apache + License, Version 2.0, as follows: + + -- (C) Copyright 2013 King Abdullah University of Science and Technology + Authors: + Ahmad Abdelfattah (ahmad.ahmad@kaust.edu.sa) + David Keyes (david.keyes@kaust.edu.sa) + Hatem Ltaief (hatem.ltaief@kaust.edu.sa) + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + * Neither the name of the King Abdullah University of Science and + Technology nor the names of its contributors may be used to endorse + or promote products derived from this software without specific prior + written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + HOLDERS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE + + 11. Some of the cuSPARSE library routines were written by or + derived from code written by Li-Wen Chang and are subject + to the NCSA Open Source License as follows: + + Copyright (c) 2012, University of Illinois. + + All rights reserved. + + Developed by: IMPACT Group, University of Illinois, http://impact.crhc.illinois.edu + + Permission is hereby granted, free of charge, to any person obtaining + a copy of this software and associated documentation files (the + "Software"), to deal with the Software without restriction, including + without limitation the rights to use, copy, modify, merge, publish, + distribute, sublicense, and/or sell copies of the Software, and to + permit persons to whom the Software is furnished to do so, subject to + the following conditions: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimers in the documentation and/or other materials provided + with the distribution. + * Neither the names of IMPACT Group, University of Illinois, nor + the names of its contributors may be used to endorse or promote + products derived from this Software without specific prior + written permission. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, + EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF + MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND + NONINFRINGEMENT. IN NO EVENT SHALL THE CONTRIBUTORS OR COPYRIGHT + HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR + IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS WITH THE + SOFTWARE. + + 12. Some of the cuRAND library routines were written by or + derived from code written by Mutsuo Saito and Makoto + Matsumoto and are subject to the following license: + + Copyright (c) 2009, 2010 Mutsuo Saito, Makoto Matsumoto and Hiroshima + University. All rights reserved. + + Copyright (c) 2011 Mutsuo Saito, Makoto Matsumoto, Hiroshima + University and University of Tokyo. All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + * Neither the name of the Hiroshima University nor the names of + its contributors may be used to endorse or promote products + derived from this software without specific prior written + permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. 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Shaw Research. + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions, and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions, and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + * Neither the name of D. E. Shaw Research nor the names of its + contributors may be used to endorse or promote products derived + from this software without specific prior written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. 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Some of the cuBLAS library routines uses code from + OpenAI, which is subject to the following license: + + License URL + https://github.com/openai/openai-gemm/blob/master/LICENSE + + License Text + The MIT License + + Copyright (c) 2016 OpenAI (http://openai.com), 2016 Google Inc. + + Permission is hereby granted, free of charge, to any person obtaining a copy + of this software and associated documentation files (the "Software"), to deal + in the Software without restriction, including without limitation the rights + to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + copies of the Software, and to permit persons to whom the Software is + furnished to do so, subject to the following conditions: + + The above copyright notice and this permission notice shall be included in + all copies or substantial portions of the Software. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. 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IN NO EVENT SHALL THE + AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + + 20. Licensee's use of linmath.h header for CPU functions for + GL vector/matrix operations from lunarG is subject to the + Apache License Version 2.0. + + 21. The DX12-CUDA sample uses the d3dx12.h header, which is + subject to the MIT license . + +----------------- diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cublas_cu12-12.8.4.1.dist-info/METADATA b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cublas_cu12-12.8.4.1.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..cb533db38cb400ef0b0e71cea45e7ce506422bfd --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cublas_cu12-12.8.4.1.dist-info/METADATA @@ -0,0 +1,44 @@ +Metadata-Version: 2.2 +Name: nvidia-cublas-cu12 +Version: 12.8.4.1 +Summary: CUBLAS native runtime libraries +Home-page: https://developer.nvidia.com/cuda-zone +Author: Nvidia CUDA Installer Team +Author-email: compute_installer@nvidia.com +License: NVIDIA Proprietary Software +Keywords: cuda,nvidia,runtime,machine learning,deep learning +Classifier: Development Status :: 4 - Beta +Classifier: 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a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cuda_cupti_cu12-12.8.90.dist-info/INSTALLER b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cuda_cupti_cu12-12.8.90.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cuda_cupti_cu12-12.8.90.dist-info/INSTALLER @@ -0,0 +1 @@ +pip diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cuda_cupti_cu12-12.8.90.dist-info/License.txt b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cuda_cupti_cu12-12.8.90.dist-info/License.txt new file mode 100644 index 0000000000000000000000000000000000000000..b491c70e0aef319022ded661e111ddbd45b8a17f --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cuda_cupti_cu12-12.8.90.dist-info/License.txt @@ -0,0 +1,1568 @@ 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(i) you fail to comply with any term of this + Agreement and the non-compliance is not fixed within + thirty (30) days following notice from NVIDIA (or + immediately if you violate NVIDIA’s intellectual + property rights); + + b. (ii) you commence or participate in any legal + proceeding against NVIDIA with respect to the SDK; or + + c. (iii) NVIDIA decides to no longer provide the SDK in + a country or, in NVIDIA’s sole discretion, the + continued use of it is no longer commercially viable. + + 4. Upon any termination of this Agreement, you agree to + promptly discontinue use of the SDK and destroy all copies + in your possession or control. Your prior distributions in + accordance with this Agreement are not affected by the + termination of this Agreement. Upon written request, you + will certify in writing that you have complied with your + commitments under this section. 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CUDA Toolkit Supplement to Software License Agreement for +NVIDIA Software Development Kits +------------------------------------------------------------ + + +Release date: August 16, 2018 +----------------------------- + +The terms in this supplement govern your use of the NVIDIA +CUDA Toolkit SDK under the terms of your license agreement +(“Agreement”) as modified by this supplement. Capitalized +terms used but not defined below have the meaning assigned to +them in the Agreement. + +This supplement is an exhibit to the Agreement and is +incorporated as an integral part of the Agreement. In the +event of conflict between the terms in this supplement and the +terms in the Agreement, the terms in this supplement govern. + + +2.1. License Scope + +The SDK is licensed for you to develop applications only for +use in systems with NVIDIA GPUs. + + +2.2. Distribution + +The portions of the SDK that are distributable under the +Agreement are listed in Attachment A. + + +2.3. Operating Systems + +Those portions of the SDK designed exclusively for use on the +Linux or FreeBSD operating systems, or other operating systems +derived from the source code to these operating systems, may +be copied and redistributed for use in accordance with this +Agreement, provided that the object code files are not +modified in any way (except for unzipping of compressed +files). + + +2.4. Audio and Video Encoders and Decoders + +You acknowledge and agree that it is your sole responsibility +to obtain any additional third-party licenses required to +make, have made, use, have used, sell, import, and offer for +sale your products or services that include or incorporate any +third-party software and content relating to audio and/or +video encoders and decoders from, including but not limited +to, Microsoft, Thomson, Fraunhofer IIS, Sisvel S.p.A., +MPEG-LA, and Coding Technologies. NVIDIA does not grant to you +under this Agreement any necessary patent or other rights with +respect to any audio and/or video encoders and decoders. + + +2.5. Licensing + +If the distribution terms in this Agreement are not suitable +for your organization, or for any questions regarding this +Agreement, please contact NVIDIA at +nvidia-compute-license-questions@nvidia.com. + + +2.6. Attachment A + +The following portions of the SDK are distributable under the +Agreement: + +Component + +CUDA Runtime + +Windows + +cudart.dll, cudart_static.lib, cudadevrt.lib + +Mac OSX + +libcudart.dylib, libcudart_static.a, libcudadevrt.a + +Linux + +libcudart.so, libcudart_static.a, libcudadevrt.a + +Android + +libcudart.so, libcudart_static.a, libcudadevrt.a + +Component + +CUDA FFT Library + +Windows + +cufft.dll, cufftw.dll, cufft.lib, cufftw.lib + +Mac OSX + +libcufft.dylib, libcufft_static.a, libcufftw.dylib, +libcufftw_static.a + +Linux + +libcufft.so, libcufft_static.a, libcufftw.so, +libcufftw_static.a + +Android + +libcufft.so, libcufft_static.a, libcufftw.so, +libcufftw_static.a + +Component + +CUDA BLAS Library + +Windows + +cublas.dll, cublasLt.dll + +Mac OSX + +libcublas.dylib, libcublasLt.dylib, libcublas_static.a, +libcublasLt_static.a + +Linux + +libcublas.so, libcublasLt.so, libcublas_static.a, +libcublasLt_static.a + +Android + +libcublas.so, libcublasLt.so, libcublas_static.a, +libcublasLt_static.a + +Component + +NVIDIA "Drop-in" BLAS Library + +Windows + +nvblas.dll + +Mac OSX + +libnvblas.dylib + +Linux + +libnvblas.so + +Component + +CUDA Sparse Matrix Library + +Windows + +cusparse.dll, cusparse.lib + +Mac OSX + +libcusparse.dylib, libcusparse_static.a + +Linux + +libcusparse.so, libcusparse_static.a + +Android + +libcusparse.so, libcusparse_static.a + +Component + +CUDA Linear Solver Library + +Windows + +cusolver.dll, cusolver.lib + +Mac OSX + +libcusolver.dylib, libcusolver_static.a + +Linux + +libcusolver.so, libcusolver_static.a + +Android + +libcusolver.so, libcusolver_static.a + +Component + +CUDA Random Number Generation Library + +Windows + +curand.dll, curand.lib + +Mac OSX + +libcurand.dylib, libcurand_static.a + +Linux + +libcurand.so, libcurand_static.a + +Android + +libcurand.so, libcurand_static.a + +Component + +CUDA Accelerated Graph Library + +Component + +NVIDIA Performance Primitives Library + +Windows + +nppc.dll, nppc.lib, nppial.dll, nppial.lib, nppicc.dll, +nppicc.lib, nppicom.dll, nppicom.lib, nppidei.dll, +nppidei.lib, nppif.dll, nppif.lib, nppig.dll, nppig.lib, +nppim.dll, nppim.lib, nppist.dll, nppist.lib, nppisu.dll, +nppisu.lib, nppitc.dll, nppitc.lib, npps.dll, npps.lib + +Mac OSX + +libnppc.dylib, libnppc_static.a, libnppial.dylib, +libnppial_static.a, libnppicc.dylib, libnppicc_static.a, +libnppicom.dylib, libnppicom_static.a, libnppidei.dylib, +libnppidei_static.a, libnppif.dylib, libnppif_static.a, +libnppig.dylib, libnppig_static.a, libnppim.dylib, +libnppisu_static.a, libnppitc.dylib, libnppitc_static.a, +libnpps.dylib, libnpps_static.a + +Linux + +libnppc.so, libnppc_static.a, libnppial.so, +libnppial_static.a, libnppicc.so, libnppicc_static.a, +libnppicom.so, libnppicom_static.a, libnppidei.so, +libnppidei_static.a, libnppif.so, libnppif_static.a +libnppig.so, libnppig_static.a, libnppim.so, +libnppim_static.a, libnppist.so, libnppist_static.a, +libnppisu.so, libnppisu_static.a, libnppitc.so +libnppitc_static.a, libnpps.so, libnpps_static.a + +Android + +libnppc.so, libnppc_static.a, libnppial.so, +libnppial_static.a, libnppicc.so, libnppicc_static.a, +libnppicom.so, libnppicom_static.a, libnppidei.so, +libnppidei_static.a, libnppif.so, libnppif_static.a +libnppig.so, libnppig_static.a, libnppim.so, +libnppim_static.a, libnppist.so, libnppist_static.a, +libnppisu.so, libnppisu_static.a, libnppitc.so +libnppitc_static.a, libnpps.so, libnpps_static.a + +Component + +NVIDIA JPEG Library + +Linux + +libnvjpeg.so, libnvjpeg_static.a + +Component + +Internal common library required for statically linking to +cuBLAS, cuSPARSE, cuFFT, cuRAND, nvJPEG and NPP + +Mac OSX + +libculibos.a + +Linux + +libculibos.a + +Component + +NVIDIA Runtime Compilation Library and Header + +All + +nvrtc.h + +Windows + +nvrtc.dll, nvrtc-builtins.dll + +Mac OSX + +libnvrtc.dylib, libnvrtc-builtins.dylib + +Linux + +libnvrtc.so, libnvrtc-builtins.so + +Component + +NVIDIA Optimizing Compiler Library + +Windows + +nvvm.dll + +Mac OSX + +libnvvm.dylib + +Linux + +libnvvm.so + +Component + +NVIDIA Common Device Math Functions Library + +Windows + +libdevice.10.bc + +Mac OSX + +libdevice.10.bc + +Linux + +libdevice.10.bc + +Component + +CUDA Occupancy Calculation Header Library + +All + +cuda_occupancy.h + +Component + +CUDA Half Precision Headers + +All + +cuda_fp16.h, cuda_fp16.hpp + +Component + +CUDA Profiling Tools Interface (CUPTI) Library + +Windows + +cupti.dll + +Mac OSX + +libcupti.dylib + +Linux + +libcupti.so + +Component + +NVIDIA Tools Extension Library + +Windows + +nvToolsExt.dll, nvToolsExt.lib + +Mac OSX + +libnvToolsExt.dylib + +Linux + +libnvToolsExt.so + +Component + +NVIDIA CUDA Driver Libraries + +Linux + +libcuda.so, libnvidia-fatbinaryloader.so, +libnvidia-ptxjitcompiler.so + +The NVIDIA CUDA Driver Libraries are only distributable in +applications that meet this criteria: + + 1. The application was developed starting from a NVIDIA CUDA + container obtained from Docker Hub or the NVIDIA GPU + Cloud, and + + 2. The resulting application is packaged as a Docker + container and distributed to users on Docker Hub or the + NVIDIA GPU Cloud only. + + +2.7. Attachment B + + +Additional Licensing Obligations + +The following third party components included in the SOFTWARE +are licensed to Licensee pursuant to the following terms and +conditions: + + 1. Licensee's use of the GDB third party component is + subject to the terms and conditions of GNU GPL v3: + + This product includes copyrighted third-party software licensed + under the terms of the GNU General Public License v3 ("GPL v3"). + All third-party software packages are copyright by their respective + authors. GPL v3 terms and conditions are hereby incorporated into + the Agreement by this reference: http://www.gnu.org/licenses/gpl.txt + + Consistent with these licensing requirements, the software + listed below is provided under the terms of the specified + open source software licenses. To obtain source code for + software provided under licenses that require + redistribution of source code, including the GNU General + Public License (GPL) and GNU Lesser General Public License + (LGPL), contact oss-requests@nvidia.com. This offer is + valid for a period of three (3) years from the date of the + distribution of this product by NVIDIA CORPORATION. + + Component License + CUDA-GDB GPL v3 + + 2. Licensee represents and warrants that any and all third + party licensing and/or royalty payment obligations in + connection with Licensee's use of the H.264 video codecs + are solely the responsibility of Licensee. + + 3. Licensee's use of the Thrust library is subject to the + terms and conditions of the Apache License Version 2.0. + All third-party software packages are copyright by their + respective authors. Apache License Version 2.0 terms and + conditions are hereby incorporated into the Agreement by + this reference. + http://www.apache.org/licenses/LICENSE-2.0.html + + In addition, Licensee acknowledges the following notice: + Thrust includes source code from the Boost Iterator, + Tuple, System, and Random Number libraries. + + Boost Software License - Version 1.0 - August 17th, 2003 + . . . . + + Permission is hereby granted, free of charge, to any person or + organization obtaining a copy of the software and accompanying + documentation covered by this license (the "Software") to use, + reproduce, display, distribute, execute, and transmit the Software, + and to prepare derivative works of the Software, and to permit + third-parties to whom the Software is furnished to do so, all + subject to the following: + + The copyright notices in the Software and this entire statement, + including the above license grant, this restriction and the following + disclaimer, must be included in all copies of the Software, in whole + or in part, and all derivative works of the Software, unless such + copies or derivative works are solely in the form of machine-executable + object code generated by a source language processor. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, + EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF + MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND + NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR + ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE FOR ANY DAMAGES OR + OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING + FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR + OTHER DEALINGS IN THE SOFTWARE. + + 4. Licensee's use of the LLVM third party component is + subject to the following terms and conditions: + + ====================================================== + LLVM Release License + ====================================================== + University of Illinois/NCSA + Open Source License + + Copyright (c) 2003-2010 University of Illinois at Urbana-Champaign. + All rights reserved. + + Developed by: + + LLVM Team + + University of Illinois at Urbana-Champaign + + http://llvm.org + + Permission is hereby granted, free of charge, to any person obtaining a copy + of this software and associated documentation files (the "Software"), to + deal with the Software without restriction, including without limitation the + rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + sell copies of the Software, and to permit persons to whom the Software is + furnished to do so, subject to the following conditions: + + * Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimers. + + * Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimers in the + documentation and/or other materials provided with the distribution. + + * Neither the names of the LLVM Team, University of Illinois at Urbana- + Champaign, nor the names of its contributors may be used to endorse or + promote products derived from this Software without specific prior + written permission. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL + THE CONTRIBUTORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR + OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, + ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER + DEALINGS WITH THE SOFTWARE. + + 5. Licensee's use (e.g. nvprof) of the PCRE third party + component is subject to the following terms and + conditions: + + ------------ + PCRE LICENCE + ------------ + PCRE is a library of functions to support regular expressions whose syntax + and semantics are as close as possible to those of the Perl 5 language. + Release 8 of PCRE is distributed under the terms of the "BSD" licence, as + specified below. The documentation for PCRE, supplied in the "doc" + directory, is distributed under the same terms as the software itself. The + basic library functions are written in C and are freestanding. Also + included in the distribution is a set of C++ wrapper functions, and a just- + in-time compiler that can be used to optimize pattern matching. These are + both optional features that can be omitted when the library is built. + + THE BASIC LIBRARY FUNCTIONS + --------------------------- + Written by: Philip Hazel + Email local part: ph10 + Email domain: cam.ac.uk + University of Cambridge Computing Service, + Cambridge, England. + Copyright (c) 1997-2012 University of Cambridge + All rights reserved. + + PCRE JUST-IN-TIME COMPILATION SUPPORT + ------------------------------------- + Written by: Zoltan Herczeg + Email local part: hzmester + Emain domain: freemail.hu + Copyright(c) 2010-2012 Zoltan Herczeg + All rights reserved. + + STACK-LESS JUST-IN-TIME COMPILER + -------------------------------- + Written by: Zoltan Herczeg + Email local part: hzmester + Emain domain: freemail.hu + Copyright(c) 2009-2012 Zoltan Herczeg + All rights reserved. + + THE C++ WRAPPER FUNCTIONS + ------------------------- + Contributed by: Google Inc. + Copyright (c) 2007-2012, Google Inc. + All rights reserved. + + THE "BSD" LICENCE + ----------------- + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are met: + + * Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + + * Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + * Neither the name of the University of Cambridge nor the name of Google + Inc. nor the names of their contributors may be used to endorse or + promote products derived from this software without specific prior + written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE + ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE + LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF + SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS + INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN + CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) + ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + POSSIBILITY OF SUCH DAMAGE. + + 6. Some of the cuBLAS library routines were written by or + derived from code written by Vasily Volkov and are subject + to the Modified Berkeley Software Distribution License as + follows: + + Copyright (c) 2007-2009, Regents of the University of California + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + * Neither the name of the University of California, Berkeley nor + the names of its contributors may be used to endorse or promote + products derived from this software without specific prior + written permission. + + THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR + IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED + WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE + DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, + INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES + (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR + SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) + HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, + STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING + IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + POSSIBILITY OF SUCH DAMAGE. + + 7. Some of the cuBLAS library routines were written by or + derived from code written by Davide Barbieri and are + subject to the Modified Berkeley Software Distribution + License as follows: + + Copyright (c) 2008-2009 Davide Barbieri @ University of Rome Tor Vergata. + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + * The name of the author may not be used to endorse or promote + products derived from this software without specific prior + written permission. + + THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR + IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED + WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE + DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, + INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES + (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR + SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) + HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, + STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING + IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + POSSIBILITY OF SUCH DAMAGE. + + 8. Some of the cuBLAS library routines were derived from + code developed by the University of Tennessee and are + subject to the Modified Berkeley Software Distribution + License as follows: + + Copyright (c) 2010 The University of Tennessee. + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer listed in this license in the documentation and/or + other materials provided with the distribution. + * Neither the name of the copyright holders nor the names of its + contributors may be used to endorse or promote products derived + from this software without specific prior written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + 9. Some of the cuBLAS library routines were written by or + derived from code written by Jonathan Hogg and are subject + to the Modified Berkeley Software Distribution License as + follows: + + Copyright (c) 2012, The Science and Technology Facilities Council (STFC). + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + * Neither the name of the STFC nor the names of its contributors + may be used to endorse or promote products derived from this + software without specific prior written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE STFC BE + LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF + SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR + BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, + WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE + OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN + IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + 10. Some of the cuBLAS library routines were written by or + derived from code written by Ahmad M. Abdelfattah, David + Keyes, and Hatem Ltaief, and are subject to the Apache + License, Version 2.0, as follows: + + -- (C) Copyright 2013 King Abdullah University of Science and Technology + Authors: + Ahmad Abdelfattah (ahmad.ahmad@kaust.edu.sa) + David Keyes (david.keyes@kaust.edu.sa) + Hatem Ltaief (hatem.ltaief@kaust.edu.sa) + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + * Neither the name of the King Abdullah University of Science and + Technology nor the names of its contributors may be used to endorse + or promote products derived from this software without specific prior + written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + HOLDERS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE + + 11. Some of the cuSPARSE library routines were written by or + derived from code written by Li-Wen Chang and are subject + to the NCSA Open Source License as follows: + + Copyright (c) 2012, University of Illinois. + + All rights reserved. + + Developed by: IMPACT Group, University of Illinois, http://impact.crhc.illinois.edu + + Permission is hereby granted, free of charge, to any person obtaining + a copy of this software and associated documentation files (the + "Software"), to deal with the Software without restriction, including + without limitation the rights to use, copy, modify, merge, publish, + distribute, sublicense, and/or sell copies of the Software, and to + permit persons to whom the Software is furnished to do so, subject to + the following conditions: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimers in the documentation and/or other materials provided + with the distribution. + * Neither the names of IMPACT Group, University of Illinois, nor + the names of its contributors may be used to endorse or promote + products derived from this Software without specific prior + written permission. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, + EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF + MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND + NONINFRINGEMENT. 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All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + * Neither the name of the Hiroshima University nor the names of + its contributors may be used to endorse or promote products + derived from this software without specific prior written + permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + 13. Some of the cuRAND library routines were derived from + code developed by D. E. Shaw Research and are subject to + the following license: + + Copyright 2010-2011, D. E. Shaw Research. + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions, and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions, and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + * Neither the name of D. E. Shaw Research nor the names of its + contributors may be used to endorse or promote products derived + from this software without specific prior written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + 14. Some of the Math library routines were written by or + derived from code developed by Norbert Juffa and are + subject to the following license: + + Copyright (c) 2015-2017, Norbert Juffa + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + 1. Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + 2. Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. 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Licensee's use of the lz4 third party component is + subject to the following terms and conditions: + + Copyright (C) 2011-2013, Yann Collet. + BSD 2-Clause License (http://www.opensource.org/licenses/bsd-license.php) + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following disclaimer + in the documentation and/or other materials provided with the + distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + 16. The NPP library uses code from the Boost Math Toolkit, + and is subject to the following license: + + Boost Software License - Version 1.0 - August 17th, 2003 + . . . . + + Permission is hereby granted, free of charge, to any person or + organization obtaining a copy of the software and accompanying + documentation covered by this license (the "Software") to use, + reproduce, display, distribute, execute, and transmit the Software, + and to prepare derivative works of the Software, and to permit + third-parties to whom the Software is furnished to do so, all + subject to the following: + + The copyright notices in the Software and this entire statement, + including the above license grant, this restriction and the following + disclaimer, must be included in all copies of the Software, in whole + or in part, and all derivative works of the Software, unless such + copies or derivative works are solely in the form of machine-executable + object code generated by a source language processor. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, + EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF + MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND + NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR + ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE FOR ANY DAMAGES OR + OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING + FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR + OTHER DEALINGS IN THE SOFTWARE. + + 17. Portions of the Nsight Eclipse Edition is subject to the + following license: + + The Eclipse Foundation makes available all content in this plug-in + ("Content"). Unless otherwise indicated below, the Content is provided + to you under the terms and conditions of the Eclipse Public License + Version 1.0 ("EPL"). A copy of the EPL is available at http:// + www.eclipse.org/legal/epl-v10.html. For purposes of the EPL, "Program" + will mean the Content. + + If you did not receive this Content directly from the Eclipse + Foundation, the Content is being redistributed by another party + ("Redistributor") and different terms and conditions may apply to your + use of any object code in the Content. Check the Redistributor's + license that was provided with the Content. If no such license exists, + contact the Redistributor. Unless otherwise indicated below, the terms + and conditions of the EPL still apply to any source code in the + Content and such source code may be obtained at http://www.eclipse.org. + + 18. Some of the cuBLAS library routines uses code from + OpenAI, which is subject to the following license: + + License URL + https://github.com/openai/openai-gemm/blob/master/LICENSE + + License Text + The MIT License + + Copyright (c) 2016 OpenAI (http://openai.com), 2016 Google Inc. + + Permission is hereby granted, free of charge, to any person obtaining a copy + of this software and associated documentation files (the "Software"), to deal + in the Software without restriction, including without limitation the rights + to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + copies of the Software, and to permit persons to whom the Software is + furnished to do so, subject to the following conditions: + + The above copyright notice and this permission notice shall be included in + all copies or substantial portions of the Software. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN + THE SOFTWARE. + + 19. Licensee's use of the Visual Studio Setup Configuration + Samples is subject to the following license: + + The MIT License (MIT) + Copyright (C) Microsoft Corporation. All rights reserved. + + Permission is hereby granted, free of charge, to any person + obtaining a copy of this software and associated documentation + files (the "Software"), to deal in the Software without restriction, + including without limitation the rights to use, copy, modify, merge, + publish, distribute, sublicense, and/or sell copies of the Software, + and to permit persons to whom the Software is furnished to do so, + subject to the following conditions: + + The above copyright notice and this permission notice shall be included + in all copies or substantial portions of the Software. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS + OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + + 20. Licensee's use of linmath.h header for CPU functions for + GL vector/matrix operations from lunarG is subject to the + Apache License Version 2.0. + + 21. The DX12-CUDA sample uses the d3dx12.h header, which is + subject to the MIT license . + +----------------- diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cuda_cupti_cu12-12.8.90.dist-info/METADATA b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cuda_cupti_cu12-12.8.90.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..c36cf7041f12162438176dfa15e5ff5deb561f11 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cuda_cupti_cu12-12.8.90.dist-info/METADATA @@ -0,0 +1,44 @@ +Metadata-Version: 2.2 +Name: nvidia-cuda-cupti-cu12 +Version: 12.8.90 +Summary: CUDA profiling tools runtime libs. +Home-page: https://developer.nvidia.com/cuda-zone +Author: Nvidia CUDA Installer Team +Author-email: compute_installer@nvidia.com +License: NVIDIA Proprietary Software +Keywords: cuda,nvidia,runtime,machine learning,deep learning +Classifier: Development Status :: 4 - Beta 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System :: Microsoft :: Windows +Classifier: Operating System :: POSIX :: Linux +Requires-Python: >=3 +License-File: License.txt +Dynamic: author +Dynamic: author-email +Dynamic: classifier +Dynamic: description +Dynamic: home-page +Dynamic: keywords +Dynamic: license +Dynamic: requires-python +Dynamic: summary + +Provides libraries to enable third party tools using GPU profiling APIs. diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cuda_cupti_cu12-12.8.90.dist-info/RECORD b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cuda_cupti_cu12-12.8.90.dist-info/RECORD new file mode 100644 index 0000000000000000000000000000000000000000..153e2ceaa3893f5d00b4b7fb9ed91d91bb8308e8 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cuda_cupti_cu12-12.8.90.dist-info/RECORD @@ -0,0 +1,56 @@ +nvidia/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0 +nvidia/__pycache__/__init__.cpython-310.pyc,, 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+End User License Agreement +-------------------------- + + +Preface +------- + +The Software License Agreement in Chapter 1 and the Supplement +in Chapter 2 contain license terms and conditions that govern +the use of NVIDIA software. By accepting this agreement, you +agree to comply with all the terms and conditions applicable +to the product(s) included herein. + + +NVIDIA Driver + + +Description + +This package contains the operating system driver and +fundamental system software components for NVIDIA GPUs. + + +NVIDIA CUDA Toolkit + + +Description + +The NVIDIA CUDA Toolkit provides command-line and graphical +tools for building, debugging and optimizing the performance +of applications accelerated by NVIDIA GPUs, runtime and math +libraries, and documentation including programming guides, +user manuals, and API references. + + +Default Install Location of CUDA Toolkit + +Windows platform: + +%ProgramFiles%\NVIDIA GPU Computing Toolkit\CUDA\v#.# + +Linux platform: + +/usr/local/cuda-#.# + +Mac platform: + +/Developer/NVIDIA/CUDA-#.# + + +NVIDIA CUDA Samples + + +Description + +This package includes over 100+ CUDA examples that demonstrate +various CUDA programming principles, and efficient CUDA +implementation of algorithms in specific application domains. + + +Default Install Location of CUDA Samples + +Windows platform: + +%ProgramData%\NVIDIA Corporation\CUDA Samples\v#.# + +Linux platform: + +/usr/local/cuda-#.#/samples + +and + +$HOME/NVIDIA_CUDA-#.#_Samples + +Mac platform: + +/Developer/NVIDIA/CUDA-#.#/samples + + +NVIDIA Nsight Visual Studio Edition (Windows only) + + +Description + +NVIDIA Nsight Development Platform, Visual Studio Edition is a +development environment integrated into Microsoft Visual +Studio that provides tools for debugging, profiling, analyzing +and optimizing your GPU computing and graphics applications. + + +Default Install Location of Nsight Visual Studio Edition + +Windows platform: + +%ProgramFiles(x86)%\NVIDIA Corporation\Nsight Visual Studio Edition #.# + + +1. License Agreement for NVIDIA Software Development Kits +--------------------------------------------------------- + + +Release Date: July 26, 2018 +--------------------------- + + +Important NoticeRead before downloading, installing, +copying or using the licensed software: +------------------------------------------------------- + +This license agreement, including exhibits attached +("Agreement”) is a legal agreement between you and NVIDIA +Corporation ("NVIDIA") and governs your use of a NVIDIA +software development kit (“SDK”). + +Each SDK has its own set of software and materials, but here +is a description of the types of items that may be included in +a SDK: source code, header files, APIs, data sets and assets +(examples include images, textures, models, scenes, videos, +native API input/output files), binary software, sample code, +libraries, utility programs, programming code and +documentation. + +This Agreement can be accepted only by an adult of legal age +of majority in the country in which the SDK is used. + +If you are entering into this Agreement on behalf of a company +or other legal entity, you represent that you have the legal +authority to bind the entity to this Agreement, in which case +“you” will mean the entity you represent. + +If you don’t have the required age or authority to accept +this Agreement, or if you don’t accept all the terms and +conditions of this Agreement, do not download, install or use +the SDK. + +You agree to use the SDK only for purposes that are permitted +by (a) this Agreement, and (b) any applicable law, regulation +or generally accepted practices or guidelines in the relevant +jurisdictions. + + +1.1. 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CUDA Toolkit Supplement to Software License Agreement for +NVIDIA Software Development Kits +------------------------------------------------------------ + + +Release date: August 16, 2018 +----------------------------- + +The terms in this supplement govern your use of the NVIDIA +CUDA Toolkit SDK under the terms of your license agreement +(“Agreement”) as modified by this supplement. Capitalized +terms used but not defined below have the meaning assigned to +them in the Agreement. + +This supplement is an exhibit to the Agreement and is +incorporated as an integral part of the Agreement. In the +event of conflict between the terms in this supplement and the +terms in the Agreement, the terms in this supplement govern. + + +2.1. License Scope + +The SDK is licensed for you to develop applications only for +use in systems with NVIDIA GPUs. + + +2.2. Distribution + +The portions of the SDK that are distributable under the +Agreement are listed in Attachment A. + + +2.3. Operating Systems + +Those portions of the SDK designed exclusively for use on the +Linux or FreeBSD operating systems, or other operating systems +derived from the source code to these operating systems, may +be copied and redistributed for use in accordance with this +Agreement, provided that the object code files are not +modified in any way (except for unzipping of compressed +files). + + +2.4. Audio and Video Encoders and Decoders + +You acknowledge and agree that it is your sole responsibility +to obtain any additional third-party licenses required to +make, have made, use, have used, sell, import, and offer for +sale your products or services that include or incorporate any +third-party software and content relating to audio and/or +video encoders and decoders from, including but not limited +to, Microsoft, Thomson, Fraunhofer IIS, Sisvel S.p.A., +MPEG-LA, and Coding Technologies. NVIDIA does not grant to you +under this Agreement any necessary patent or other rights with +respect to any audio and/or video encoders and decoders. + + +2.5. Licensing + +If the distribution terms in this Agreement are not suitable +for your organization, or for any questions regarding this +Agreement, please contact NVIDIA at +nvidia-compute-license-questions@nvidia.com. + + +2.6. Attachment A + +The following portions of the SDK are distributable under the +Agreement: + +Component + +CUDA Runtime + +Windows + +cudart.dll, cudart_static.lib, cudadevrt.lib + +Mac OSX + +libcudart.dylib, libcudart_static.a, libcudadevrt.a + +Linux + +libcudart.so, libcudart_static.a, libcudadevrt.a + +Android + +libcudart.so, libcudart_static.a, libcudadevrt.a + +Component + +CUDA FFT Library + +Windows + +cufft.dll, cufftw.dll, cufft.lib, cufftw.lib + +Mac OSX + +libcufft.dylib, libcufft_static.a, libcufftw.dylib, +libcufftw_static.a + +Linux + +libcufft.so, libcufft_static.a, libcufftw.so, +libcufftw_static.a + +Android + +libcufft.so, libcufft_static.a, libcufftw.so, +libcufftw_static.a + +Component + +CUDA BLAS Library + +Windows + +cublas.dll, cublasLt.dll + +Mac OSX + +libcublas.dylib, libcublasLt.dylib, libcublas_static.a, +libcublasLt_static.a + +Linux + +libcublas.so, libcublasLt.so, libcublas_static.a, +libcublasLt_static.a + +Android + +libcublas.so, libcublasLt.so, libcublas_static.a, +libcublasLt_static.a + +Component + +NVIDIA "Drop-in" BLAS Library + +Windows + +nvblas.dll + +Mac OSX + +libnvblas.dylib + +Linux + +libnvblas.so + +Component + +CUDA Sparse Matrix Library + +Windows + +cusparse.dll, cusparse.lib + +Mac OSX + +libcusparse.dylib, libcusparse_static.a + +Linux + +libcusparse.so, libcusparse_static.a + +Android + +libcusparse.so, libcusparse_static.a + +Component + +CUDA Linear Solver Library + +Windows + +cusolver.dll, cusolver.lib + +Mac OSX + +libcusolver.dylib, libcusolver_static.a + +Linux + +libcusolver.so, libcusolver_static.a + +Android + +libcusolver.so, libcusolver_static.a + +Component + +CUDA Random Number Generation Library + +Windows + +curand.dll, curand.lib + +Mac OSX + +libcurand.dylib, libcurand_static.a + +Linux + +libcurand.so, libcurand_static.a + +Android + +libcurand.so, libcurand_static.a + +Component + +CUDA Accelerated Graph Library + +Component + +NVIDIA Performance Primitives Library + +Windows + +nppc.dll, nppc.lib, nppial.dll, nppial.lib, nppicc.dll, +nppicc.lib, nppicom.dll, nppicom.lib, nppidei.dll, +nppidei.lib, nppif.dll, nppif.lib, nppig.dll, nppig.lib, +nppim.dll, nppim.lib, nppist.dll, nppist.lib, nppisu.dll, +nppisu.lib, nppitc.dll, nppitc.lib, npps.dll, npps.lib + +Mac OSX + +libnppc.dylib, libnppc_static.a, libnppial.dylib, +libnppial_static.a, libnppicc.dylib, libnppicc_static.a, +libnppicom.dylib, libnppicom_static.a, libnppidei.dylib, +libnppidei_static.a, libnppif.dylib, libnppif_static.a, +libnppig.dylib, libnppig_static.a, libnppim.dylib, +libnppisu_static.a, libnppitc.dylib, libnppitc_static.a, +libnpps.dylib, libnpps_static.a + +Linux + +libnppc.so, libnppc_static.a, libnppial.so, +libnppial_static.a, libnppicc.so, libnppicc_static.a, +libnppicom.so, libnppicom_static.a, libnppidei.so, +libnppidei_static.a, libnppif.so, libnppif_static.a +libnppig.so, libnppig_static.a, libnppim.so, +libnppim_static.a, libnppist.so, libnppist_static.a, +libnppisu.so, libnppisu_static.a, libnppitc.so +libnppitc_static.a, libnpps.so, libnpps_static.a + +Android + +libnppc.so, libnppc_static.a, libnppial.so, +libnppial_static.a, libnppicc.so, libnppicc_static.a, +libnppicom.so, libnppicom_static.a, libnppidei.so, +libnppidei_static.a, libnppif.so, libnppif_static.a +libnppig.so, libnppig_static.a, libnppim.so, +libnppim_static.a, libnppist.so, libnppist_static.a, +libnppisu.so, libnppisu_static.a, libnppitc.so +libnppitc_static.a, libnpps.so, libnpps_static.a + +Component + +NVIDIA JPEG Library + +Linux + +libnvjpeg.so, libnvjpeg_static.a + +Component + +Internal common library required for statically linking to +cuBLAS, cuSPARSE, cuFFT, cuRAND, nvJPEG and NPP + +Mac OSX + +libculibos.a + +Linux + +libculibos.a + +Component + +NVIDIA Runtime Compilation Library and Header + +All + +nvrtc.h + +Windows + +nvrtc.dll, nvrtc-builtins.dll + +Mac OSX + +libnvrtc.dylib, libnvrtc-builtins.dylib + +Linux + +libnvrtc.so, libnvrtc-builtins.so + +Component + +NVIDIA Optimizing Compiler Library + +Windows + +nvvm.dll + +Mac OSX + +libnvvm.dylib + +Linux + +libnvvm.so + +Component + +NVIDIA Common Device Math Functions Library + +Windows + +libdevice.10.bc + +Mac OSX + +libdevice.10.bc + +Linux + +libdevice.10.bc + +Component + +CUDA Occupancy Calculation Header Library + +All + +cuda_occupancy.h + +Component + +CUDA Half Precision Headers + +All + +cuda_fp16.h, cuda_fp16.hpp + +Component + +CUDA Profiling Tools Interface (CUPTI) Library + +Windows + +cupti.dll + +Mac OSX + +libcupti.dylib + +Linux + +libcupti.so + +Component + +NVIDIA Tools Extension Library + +Windows + +nvToolsExt.dll, nvToolsExt.lib + +Mac OSX + +libnvToolsExt.dylib + +Linux + +libnvToolsExt.so + +Component + +NVIDIA CUDA Driver Libraries + +Linux + +libcuda.so, libnvidia-fatbinaryloader.so, +libnvidia-ptxjitcompiler.so + +The NVIDIA CUDA Driver Libraries are only distributable in +applications that meet this criteria: + + 1. The application was developed starting from a NVIDIA CUDA + container obtained from Docker Hub or the NVIDIA GPU + Cloud, and + + 2. The resulting application is packaged as a Docker + container and distributed to users on Docker Hub or the + NVIDIA GPU Cloud only. + + +2.7. Attachment B + + +Additional Licensing Obligations + +The following third party components included in the SOFTWARE +are licensed to Licensee pursuant to the following terms and +conditions: + + 1. Licensee's use of the GDB third party component is + subject to the terms and conditions of GNU GPL v3: + + This product includes copyrighted third-party software licensed + under the terms of the GNU General Public License v3 ("GPL v3"). + All third-party software packages are copyright by their respective + authors. GPL v3 terms and conditions are hereby incorporated into + the Agreement by this reference: http://www.gnu.org/licenses/gpl.txt + + Consistent with these licensing requirements, the software + listed below is provided under the terms of the specified + open source software licenses. To obtain source code for + software provided under licenses that require + redistribution of source code, including the GNU General + Public License (GPL) and GNU Lesser General Public License + (LGPL), contact oss-requests@nvidia.com. This offer is + valid for a period of three (3) years from the date of the + distribution of this product by NVIDIA CORPORATION. + + Component License + CUDA-GDB GPL v3 + + 2. Licensee represents and warrants that any and all third + party licensing and/or royalty payment obligations in + connection with Licensee's use of the H.264 video codecs + are solely the responsibility of Licensee. + + 3. Licensee's use of the Thrust library is subject to the + terms and conditions of the Apache License Version 2.0. + All third-party software packages are copyright by their + respective authors. Apache License Version 2.0 terms and + conditions are hereby incorporated into the Agreement by + this reference. + http://www.apache.org/licenses/LICENSE-2.0.html + + In addition, Licensee acknowledges the following notice: + Thrust includes source code from the Boost Iterator, + Tuple, System, and Random Number libraries. + + Boost Software License - Version 1.0 - August 17th, 2003 + . . . . + + Permission is hereby granted, free of charge, to any person or + organization obtaining a copy of the software and accompanying + documentation covered by this license (the "Software") to use, + reproduce, display, distribute, execute, and transmit the Software, + and to prepare derivative works of the Software, and to permit + third-parties to whom the Software is furnished to do so, all + subject to the following: + + The copyright notices in the Software and this entire statement, + including the above license grant, this restriction and the following + disclaimer, must be included in all copies of the Software, in whole + or in part, and all derivative works of the Software, unless such + copies or derivative works are solely in the form of machine-executable + object code generated by a source language processor. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, + EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF + MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND + NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR + ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE FOR ANY DAMAGES OR + OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING + FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR + OTHER DEALINGS IN THE SOFTWARE. + + 4. Licensee's use of the LLVM third party component is + subject to the following terms and conditions: + + ====================================================== + LLVM Release License + ====================================================== + University of Illinois/NCSA + Open Source License + + Copyright (c) 2003-2010 University of Illinois at Urbana-Champaign. + All rights reserved. + + Developed by: + + LLVM Team + + University of Illinois at Urbana-Champaign + + http://llvm.org + + Permission is hereby granted, free of charge, to any person obtaining a copy + of this software and associated documentation files (the "Software"), to + deal with the Software without restriction, including without limitation the + rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + sell copies of the Software, and to permit persons to whom the Software is + furnished to do so, subject to the following conditions: + + * Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimers. + + * Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimers in the + documentation and/or other materials provided with the distribution. + + * Neither the names of the LLVM Team, University of Illinois at Urbana- + Champaign, nor the names of its contributors may be used to endorse or + promote products derived from this Software without specific prior + written permission. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL + THE CONTRIBUTORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR + OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, + ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER + DEALINGS WITH THE SOFTWARE. + + 5. Licensee's use (e.g. nvprof) of the PCRE third party + component is subject to the following terms and + conditions: + + ------------ + PCRE LICENCE + ------------ + PCRE is a library of functions to support regular expressions whose syntax + and semantics are as close as possible to those of the Perl 5 language. + Release 8 of PCRE is distributed under the terms of the "BSD" licence, as + specified below. The documentation for PCRE, supplied in the "doc" + directory, is distributed under the same terms as the software itself. The + basic library functions are written in C and are freestanding. Also + included in the distribution is a set of C++ wrapper functions, and a just- + in-time compiler that can be used to optimize pattern matching. These are + both optional features that can be omitted when the library is built. + + THE BASIC LIBRARY FUNCTIONS + --------------------------- + Written by: Philip Hazel + Email local part: ph10 + Email domain: cam.ac.uk + University of Cambridge Computing Service, + Cambridge, England. + Copyright (c) 1997-2012 University of Cambridge + All rights reserved. + + PCRE JUST-IN-TIME COMPILATION SUPPORT + ------------------------------------- + Written by: Zoltan Herczeg + Email local part: hzmester + Emain domain: freemail.hu + Copyright(c) 2010-2012 Zoltan Herczeg + All rights reserved. + + STACK-LESS JUST-IN-TIME COMPILER + -------------------------------- + Written by: Zoltan Herczeg + Email local part: hzmester + Emain domain: freemail.hu + Copyright(c) 2009-2012 Zoltan Herczeg + All rights reserved. + + THE C++ WRAPPER FUNCTIONS + ------------------------- + Contributed by: Google Inc. + Copyright (c) 2007-2012, Google Inc. + All rights reserved. + + THE "BSD" LICENCE + ----------------- + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are met: + + * Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + + * Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + * Neither the name of the University of Cambridge nor the name of Google + Inc. nor the names of their contributors may be used to endorse or + promote products derived from this software without specific prior + written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE + ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE + LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF + SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS + INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN + CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) + ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + POSSIBILITY OF SUCH DAMAGE. + + 6. Some of the cuBLAS library routines were written by or + derived from code written by Vasily Volkov and are subject + to the Modified Berkeley Software Distribution License as + follows: + + Copyright (c) 2007-2009, Regents of the University of California + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + * Neither the name of the University of California, Berkeley nor + the names of its contributors may be used to endorse or promote + products derived from this software without specific prior + written permission. + + THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR + IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED + WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE + DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, + INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES + (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR + SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) + HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, + STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING + IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + POSSIBILITY OF SUCH DAMAGE. + + 7. Some of the cuBLAS library routines were written by or + derived from code written by Davide Barbieri and are + subject to the Modified Berkeley Software Distribution + License as follows: + + Copyright (c) 2008-2009 Davide Barbieri @ University of Rome Tor Vergata. + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + * The name of the author may not be used to endorse or promote + products derived from this software without specific prior + written permission. + + THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR + IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED + WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE + DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, + INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES + (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR + SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) + HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, + STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING + IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + POSSIBILITY OF SUCH DAMAGE. + + 8. Some of the cuBLAS library routines were derived from + code developed by the University of Tennessee and are + subject to the Modified Berkeley Software Distribution + License as follows: + + Copyright (c) 2010 The University of Tennessee. + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer listed in this license in the documentation and/or + other materials provided with the distribution. + * Neither the name of the copyright holders nor the names of its + contributors may be used to endorse or promote products derived + from this software without specific prior written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + 9. Some of the cuBLAS library routines were written by or + derived from code written by Jonathan Hogg and are subject + to the Modified Berkeley Software Distribution License as + follows: + + Copyright (c) 2012, The Science and Technology Facilities Council (STFC). + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + * Neither the name of the STFC nor the names of its contributors + may be used to endorse or promote products derived from this + software without specific prior written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE STFC BE + LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF + SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR + BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, + WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE + OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN + IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + 10. Some of the cuBLAS library routines were written by or + derived from code written by Ahmad M. Abdelfattah, David + Keyes, and Hatem Ltaief, and are subject to the Apache + License, Version 2.0, as follows: + + -- (C) Copyright 2013 King Abdullah University of Science and Technology + Authors: + Ahmad Abdelfattah (ahmad.ahmad@kaust.edu.sa) + David Keyes (david.keyes@kaust.edu.sa) + Hatem Ltaief (hatem.ltaief@kaust.edu.sa) + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + * Neither the name of the King Abdullah University of Science and + Technology nor the names of its contributors may be used to endorse + or promote products derived from this software without specific prior + written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + HOLDERS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE + + 11. Some of the cuSPARSE library routines were written by or + derived from code written by Li-Wen Chang and are subject + to the NCSA Open Source License as follows: + + Copyright (c) 2012, University of Illinois. + + All rights reserved. + + Developed by: IMPACT Group, University of Illinois, http://impact.crhc.illinois.edu + + Permission is hereby granted, free of charge, to any person obtaining + a copy of this software and associated documentation files (the + "Software"), to deal with the Software without restriction, including + without limitation the rights to use, copy, modify, merge, publish, + distribute, sublicense, and/or sell copies of the Software, and to + permit persons to whom the Software is furnished to do so, subject to + the following conditions: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimers in the documentation and/or other materials provided + with the distribution. + * Neither the names of IMPACT Group, University of Illinois, nor + the names of its contributors may be used to endorse or promote + products derived from this Software without specific prior + written permission. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, + EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF + MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND + NONINFRINGEMENT. IN NO EVENT SHALL THE CONTRIBUTORS OR COPYRIGHT + HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR + IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS WITH THE + SOFTWARE. + + 12. Some of the cuRAND library routines were written by or + derived from code written by Mutsuo Saito and Makoto + Matsumoto and are subject to the following license: + + Copyright (c) 2009, 2010 Mutsuo Saito, Makoto Matsumoto and Hiroshima + University. All rights reserved. + + Copyright (c) 2011 Mutsuo Saito, Makoto Matsumoto, Hiroshima + University and University of Tokyo. All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + * Neither the name of the Hiroshima University nor the names of + its contributors may be used to endorse or promote products + derived from this software without specific prior written + permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + 13. Some of the cuRAND library routines were derived from + code developed by D. E. Shaw Research and are subject to + the following license: + + Copyright 2010-2011, D. E. Shaw Research. + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions, and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions, and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + * Neither the name of D. E. Shaw Research nor the names of its + contributors may be used to endorse or promote products derived + from this software without specific prior written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + 14. Some of the Math library routines were written by or + derived from code developed by Norbert Juffa and are + subject to the following license: + + Copyright (c) 2015-2017, Norbert Juffa + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + 1. Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + 2. Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + 15. Licensee's use of the lz4 third party component is + subject to the following terms and conditions: + + Copyright (C) 2011-2013, Yann Collet. + BSD 2-Clause License (http://www.opensource.org/licenses/bsd-license.php) + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following disclaimer + in the documentation and/or other materials provided with the + distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. 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The DX12-CUDA sample uses the d3dx12.h header, which is + subject to the MIT license . + +----------------- diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cuda_nvrtc_cu12-12.8.93.dist-info/METADATA b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cuda_nvrtc_cu12-12.8.93.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..f8e857fa2ae30c6714fae1abaa2f8196efda7ab9 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cuda_nvrtc_cu12-12.8.93.dist-info/METADATA @@ -0,0 +1,44 @@ +Metadata-Version: 2.2 +Name: nvidia-cuda-nvrtc-cu12 +Version: 12.8.93 +Summary: NVRTC native runtime libraries +Home-page: https://developer.nvidia.com/cuda-zone +Author: Nvidia CUDA Installer Team +Author-email: compute_installer@nvidia.com +License: NVIDIA Proprietary Software +Keywords: cuda,nvidia,runtime,machine learning,deep learning +Classifier: Development Status :: 4 - Beta 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b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cuda_runtime_cu12-12.8.90.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cuda_runtime_cu12-12.8.90.dist-info/INSTALLER @@ -0,0 +1 @@ +pip diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cuda_runtime_cu12-12.8.90.dist-info/License.txt b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cuda_runtime_cu12-12.8.90.dist-info/License.txt new file mode 100644 index 0000000000000000000000000000000000000000..b491c70e0aef319022ded661e111ddbd45b8a17f --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cuda_runtime_cu12-12.8.90.dist-info/License.txt @@ -0,0 +1,1568 @@ +End User License Agreement +-------------------------- + + +Preface +------- + +The Software License 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CUDA Toolkit Supplement to Software License Agreement for +NVIDIA Software Development Kits +------------------------------------------------------------ + + +Release date: August 16, 2018 +----------------------------- + +The terms in this supplement govern your use of the NVIDIA +CUDA Toolkit SDK under the terms of your license agreement +(“Agreement”) as modified by this supplement. Capitalized +terms used but not defined below have the meaning assigned to +them in the Agreement. + +This supplement is an exhibit to the Agreement and is +incorporated as an integral part of the Agreement. In the +event of conflict between the terms in this supplement and the +terms in the Agreement, the terms in this supplement govern. + + +2.1. License Scope + +The SDK is licensed for you to develop applications only for +use in systems with NVIDIA GPUs. + + +2.2. Distribution + +The portions of the SDK that are distributable under the +Agreement are listed in Attachment A. + + +2.3. 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Attachment A + +The following portions of the SDK are distributable under the +Agreement: + +Component + +CUDA Runtime + +Windows + +cudart.dll, cudart_static.lib, cudadevrt.lib + +Mac OSX + +libcudart.dylib, libcudart_static.a, libcudadevrt.a + +Linux + +libcudart.so, libcudart_static.a, libcudadevrt.a + +Android + +libcudart.so, libcudart_static.a, libcudadevrt.a + +Component + +CUDA FFT Library + +Windows + +cufft.dll, cufftw.dll, cufft.lib, cufftw.lib + +Mac OSX + +libcufft.dylib, libcufft_static.a, libcufftw.dylib, +libcufftw_static.a + +Linux + +libcufft.so, libcufft_static.a, libcufftw.so, +libcufftw_static.a + +Android + +libcufft.so, libcufft_static.a, libcufftw.so, +libcufftw_static.a + +Component + +CUDA BLAS Library + +Windows + +cublas.dll, cublasLt.dll + +Mac OSX + +libcublas.dylib, libcublasLt.dylib, libcublas_static.a, +libcublasLt_static.a + +Linux + +libcublas.so, libcublasLt.so, libcublas_static.a, +libcublasLt_static.a + +Android + +libcublas.so, libcublasLt.so, libcublas_static.a, +libcublasLt_static.a + +Component + +NVIDIA "Drop-in" BLAS Library + +Windows + +nvblas.dll + +Mac OSX + +libnvblas.dylib + +Linux + +libnvblas.so + +Component + +CUDA Sparse Matrix Library + +Windows + +cusparse.dll, cusparse.lib + +Mac OSX + +libcusparse.dylib, libcusparse_static.a + +Linux + +libcusparse.so, libcusparse_static.a + +Android + +libcusparse.so, libcusparse_static.a + +Component + +CUDA Linear Solver Library + +Windows + +cusolver.dll, cusolver.lib + +Mac OSX + +libcusolver.dylib, libcusolver_static.a + +Linux + +libcusolver.so, libcusolver_static.a + +Android + +libcusolver.so, libcusolver_static.a + +Component + +CUDA Random Number Generation Library + +Windows + +curand.dll, curand.lib + +Mac OSX + +libcurand.dylib, libcurand_static.a + +Linux + +libcurand.so, libcurand_static.a + +Android + +libcurand.so, libcurand_static.a + +Component + +CUDA Accelerated Graph Library + +Component + +NVIDIA Performance Primitives Library + +Windows + +nppc.dll, nppc.lib, nppial.dll, nppial.lib, nppicc.dll, +nppicc.lib, nppicom.dll, nppicom.lib, nppidei.dll, +nppidei.lib, nppif.dll, nppif.lib, nppig.dll, nppig.lib, +nppim.dll, nppim.lib, nppist.dll, nppist.lib, nppisu.dll, +nppisu.lib, nppitc.dll, nppitc.lib, npps.dll, npps.lib + +Mac OSX + +libnppc.dylib, libnppc_static.a, libnppial.dylib, +libnppial_static.a, libnppicc.dylib, libnppicc_static.a, +libnppicom.dylib, libnppicom_static.a, libnppidei.dylib, +libnppidei_static.a, libnppif.dylib, libnppif_static.a, +libnppig.dylib, libnppig_static.a, libnppim.dylib, +libnppisu_static.a, libnppitc.dylib, libnppitc_static.a, +libnpps.dylib, libnpps_static.a + +Linux + +libnppc.so, libnppc_static.a, libnppial.so, +libnppial_static.a, libnppicc.so, libnppicc_static.a, +libnppicom.so, libnppicom_static.a, libnppidei.so, +libnppidei_static.a, libnppif.so, libnppif_static.a +libnppig.so, libnppig_static.a, libnppim.so, +libnppim_static.a, libnppist.so, libnppist_static.a, +libnppisu.so, libnppisu_static.a, libnppitc.so +libnppitc_static.a, libnpps.so, libnpps_static.a + +Android + +libnppc.so, libnppc_static.a, libnppial.so, +libnppial_static.a, libnppicc.so, libnppicc_static.a, +libnppicom.so, libnppicom_static.a, libnppidei.so, +libnppidei_static.a, libnppif.so, libnppif_static.a +libnppig.so, libnppig_static.a, libnppim.so, +libnppim_static.a, libnppist.so, libnppist_static.a, +libnppisu.so, libnppisu_static.a, libnppitc.so +libnppitc_static.a, libnpps.so, libnpps_static.a + +Component + +NVIDIA JPEG Library + +Linux + +libnvjpeg.so, libnvjpeg_static.a + +Component + +Internal common library required for statically linking to +cuBLAS, cuSPARSE, cuFFT, cuRAND, nvJPEG and NPP + +Mac OSX + +libculibos.a + +Linux + +libculibos.a + +Component + +NVIDIA Runtime Compilation Library and Header + +All + +nvrtc.h + +Windows + +nvrtc.dll, nvrtc-builtins.dll + +Mac OSX + +libnvrtc.dylib, libnvrtc-builtins.dylib + +Linux + +libnvrtc.so, libnvrtc-builtins.so + +Component + +NVIDIA Optimizing Compiler Library + +Windows + +nvvm.dll + +Mac OSX + +libnvvm.dylib + +Linux + +libnvvm.so + +Component + +NVIDIA Common Device Math Functions Library + +Windows + +libdevice.10.bc + +Mac OSX + +libdevice.10.bc + +Linux + +libdevice.10.bc + +Component + +CUDA Occupancy Calculation Header Library + +All + +cuda_occupancy.h + +Component + +CUDA Half Precision Headers + +All + +cuda_fp16.h, cuda_fp16.hpp + +Component + +CUDA Profiling Tools Interface (CUPTI) Library + +Windows + +cupti.dll + +Mac OSX + +libcupti.dylib + +Linux + +libcupti.so + +Component + +NVIDIA Tools Extension Library + +Windows + +nvToolsExt.dll, nvToolsExt.lib + +Mac OSX + +libnvToolsExt.dylib + +Linux + +libnvToolsExt.so + +Component + +NVIDIA CUDA Driver Libraries + +Linux + +libcuda.so, libnvidia-fatbinaryloader.so, +libnvidia-ptxjitcompiler.so + +The NVIDIA CUDA Driver Libraries are only distributable in +applications that meet this criteria: + + 1. The application was developed starting from a NVIDIA CUDA + container obtained from Docker Hub or the NVIDIA GPU + Cloud, and + + 2. The resulting application is packaged as a Docker + container and distributed to users on Docker Hub or the + NVIDIA GPU Cloud only. + + +2.7. Attachment B + + +Additional Licensing Obligations + +The following third party components included in the SOFTWARE +are licensed to Licensee pursuant to the following terms and +conditions: + + 1. Licensee's use of the GDB third party component is + subject to the terms and conditions of GNU GPL v3: + + This product includes copyrighted third-party software licensed + under the terms of the GNU General Public License v3 ("GPL v3"). + All third-party software packages are copyright by their respective + authors. GPL v3 terms and conditions are hereby incorporated into + the Agreement by this reference: http://www.gnu.org/licenses/gpl.txt + + Consistent with these licensing requirements, the software + listed below is provided under the terms of the specified + open source software licenses. To obtain source code for + software provided under licenses that require + redistribution of source code, including the GNU General + Public License (GPL) and GNU Lesser General Public License + (LGPL), contact oss-requests@nvidia.com. This offer is + valid for a period of three (3) years from the date of the + distribution of this product by NVIDIA CORPORATION. + + Component License + CUDA-GDB GPL v3 + + 2. Licensee represents and warrants that any and all third + party licensing and/or royalty payment obligations in + connection with Licensee's use of the H.264 video codecs + are solely the responsibility of Licensee. + + 3. Licensee's use of the Thrust library is subject to the + terms and conditions of the Apache License Version 2.0. + All third-party software packages are copyright by their + respective authors. Apache License Version 2.0 terms and + conditions are hereby incorporated into the Agreement by + this reference. + http://www.apache.org/licenses/LICENSE-2.0.html + + In addition, Licensee acknowledges the following notice: + Thrust includes source code from the Boost Iterator, + Tuple, System, and Random Number libraries. + + Boost Software License - Version 1.0 - August 17th, 2003 + . . . . + + Permission is hereby granted, free of charge, to any person or + organization obtaining a copy of the software and accompanying + documentation covered by this license (the "Software") to use, + reproduce, display, distribute, execute, and transmit the Software, + and to prepare derivative works of the Software, and to permit + third-parties to whom the Software is furnished to do so, all + subject to the following: + + The copyright notices in the Software and this entire statement, + including the above license grant, this restriction and the following + disclaimer, must be included in all copies of the Software, in whole + or in part, and all derivative works of the Software, unless such + copies or derivative works are solely in the form of machine-executable + object code generated by a source language processor. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, + EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF + MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND + NON-INFRINGEMENT. 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Licensee's use of the LLVM third party component is + subject to the following terms and conditions: + + ====================================================== + LLVM Release License + ====================================================== + University of Illinois/NCSA + Open Source License + + Copyright (c) 2003-2010 University of Illinois at Urbana-Champaign. + All rights reserved. + + Developed by: + + LLVM Team + + University of Illinois at Urbana-Champaign + + http://llvm.org + + Permission is hereby granted, free of charge, to any person obtaining a copy + of this software and associated documentation files (the "Software"), to + deal with the Software without restriction, including without limitation the + rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + sell copies of the Software, and to permit persons to whom the Software is + furnished to do so, subject to the following conditions: + + * Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimers. + + * Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimers in the + documentation and/or other materials provided with the distribution. + + * Neither the names of the LLVM Team, University of Illinois at Urbana- + Champaign, nor the names of its contributors may be used to endorse or + promote products derived from this Software without specific prior + written permission. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL + THE CONTRIBUTORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR + OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, + ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER + DEALINGS WITH THE SOFTWARE. + + 5. Licensee's use (e.g. nvprof) of the PCRE third party + component is subject to the following terms and + conditions: + + ------------ + PCRE LICENCE + ------------ + PCRE is a library of functions to support regular expressions whose syntax + and semantics are as close as possible to those of the Perl 5 language. + Release 8 of PCRE is distributed under the terms of the "BSD" licence, as + specified below. The documentation for PCRE, supplied in the "doc" + directory, is distributed under the same terms as the software itself. The + basic library functions are written in C and are freestanding. Also + included in the distribution is a set of C++ wrapper functions, and a just- + in-time compiler that can be used to optimize pattern matching. These are + both optional features that can be omitted when the library is built. + + THE BASIC LIBRARY FUNCTIONS + --------------------------- + Written by: Philip Hazel + Email local part: ph10 + Email domain: cam.ac.uk + University of Cambridge Computing Service, + Cambridge, England. + Copyright (c) 1997-2012 University of Cambridge + All rights reserved. + + PCRE JUST-IN-TIME COMPILATION SUPPORT + ------------------------------------- + Written by: Zoltan Herczeg + Email local part: hzmester + Emain domain: freemail.hu + Copyright(c) 2010-2012 Zoltan Herczeg + All rights reserved. + + STACK-LESS JUST-IN-TIME COMPILER + -------------------------------- + Written by: Zoltan Herczeg + Email local part: hzmester + Emain domain: freemail.hu + Copyright(c) 2009-2012 Zoltan Herczeg + All rights reserved. + + THE C++ WRAPPER FUNCTIONS + ------------------------- + Contributed by: Google Inc. + Copyright (c) 2007-2012, Google Inc. + All rights reserved. + + THE "BSD" LICENCE + ----------------- + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are met: + + * Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + + * Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + * Neither the name of the University of Cambridge nor the name of Google + Inc. nor the names of their contributors may be used to endorse or + promote products derived from this software without specific prior + written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE + ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE + LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF + SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS + INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN + CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) + ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + POSSIBILITY OF SUCH DAMAGE. + + 6. Some of the cuBLAS library routines were written by or + derived from code written by Vasily Volkov and are subject + to the Modified Berkeley Software Distribution License as + follows: + + Copyright (c) 2007-2009, Regents of the University of California + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + * Neither the name of the University of California, Berkeley nor + the names of its contributors may be used to endorse or promote + products derived from this software without specific prior + written permission. + + THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR + IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED + WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE + DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, + INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES + (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR + SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) + HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, + STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING + IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + POSSIBILITY OF SUCH DAMAGE. + + 7. Some of the cuBLAS library routines were written by or + derived from code written by Davide Barbieri and are + subject to the Modified Berkeley Software Distribution + License as follows: + + Copyright (c) 2008-2009 Davide Barbieri @ University of Rome Tor Vergata. + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + * The name of the author may not be used to endorse or promote + products derived from this software without specific prior + written permission. + + THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR + IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED + WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE + DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, + INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES + (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR + SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) + HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, + STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING + IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + POSSIBILITY OF SUCH DAMAGE. + + 8. Some of the cuBLAS library routines were derived from + code developed by the University of Tennessee and are + subject to the Modified Berkeley Software Distribution + License as follows: + + Copyright (c) 2010 The University of Tennessee. + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer listed in this license in the documentation and/or + other materials provided with the distribution. + * Neither the name of the copyright holders nor the names of its + contributors may be used to endorse or promote products derived + from this software without specific prior written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + 9. Some of the cuBLAS library routines were written by or + derived from code written by Jonathan Hogg and are subject + to the Modified Berkeley Software Distribution License as + follows: + + Copyright (c) 2012, The Science and Technology Facilities Council (STFC). + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + * Neither the name of the STFC nor the names of its contributors + may be used to endorse or promote products derived from this + software without specific prior written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE STFC BE + LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF + SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR + BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, + WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE + OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN + IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + 10. Some of the cuBLAS library routines were written by or + derived from code written by Ahmad M. Abdelfattah, David + Keyes, and Hatem Ltaief, and are subject to the Apache + License, Version 2.0, as follows: + + -- (C) Copyright 2013 King Abdullah University of Science and Technology + Authors: + Ahmad Abdelfattah (ahmad.ahmad@kaust.edu.sa) + David Keyes (david.keyes@kaust.edu.sa) + Hatem Ltaief (hatem.ltaief@kaust.edu.sa) + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + * Neither the name of the King Abdullah University of Science and + Technology nor the names of its contributors may be used to endorse + or promote products derived from this software without specific prior + written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + HOLDERS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE + + 11. Some of the cuSPARSE library routines were written by or + derived from code written by Li-Wen Chang and are subject + to the NCSA Open Source License as follows: + + Copyright (c) 2012, University of Illinois. + + All rights reserved. + + Developed by: IMPACT Group, University of Illinois, http://impact.crhc.illinois.edu + + Permission is hereby granted, free of charge, to any person obtaining + a copy of this software and associated documentation files (the + "Software"), to deal with the Software without restriction, including + without limitation the rights to use, copy, modify, merge, publish, + distribute, sublicense, and/or sell copies of the Software, and to + permit persons to whom the Software is furnished to do so, subject to + the following conditions: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimers in the documentation and/or other materials provided + with the distribution. + * Neither the names of IMPACT Group, University of Illinois, nor + the names of its contributors may be used to endorse or promote + products derived from this Software without specific prior + written permission. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, + EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF + MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND + NONINFRINGEMENT. IN NO EVENT SHALL THE CONTRIBUTORS OR COPYRIGHT + HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR + IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS WITH THE + SOFTWARE. + + 12. Some of the cuRAND library routines were written by or + derived from code written by Mutsuo Saito and Makoto + Matsumoto and are subject to the following license: + + Copyright (c) 2009, 2010 Mutsuo Saito, Makoto Matsumoto and Hiroshima + University. All rights reserved. + + Copyright (c) 2011 Mutsuo Saito, Makoto Matsumoto, Hiroshima + University and University of Tokyo. All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + * Neither the name of the Hiroshima University nor the names of + its contributors may be used to endorse or promote products + derived from this software without specific prior written + permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + 13. Some of the cuRAND library routines were derived from + code developed by D. E. Shaw Research and are subject to + the following license: + + Copyright 2010-2011, D. E. 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Some of the cuBLAS library routines uses code from + OpenAI, which is subject to the following license: + + License URL + https://github.com/openai/openai-gemm/blob/master/LICENSE + + License Text + The MIT License + + Copyright (c) 2016 OpenAI (http://openai.com), 2016 Google Inc. + + Permission is hereby granted, free of charge, to any person obtaining a copy + of this software and associated documentation files (the "Software"), to deal + in the Software without restriction, including without limitation the rights + to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + copies of the Software, and to permit persons to whom the Software is + furnished to do so, subject to the following conditions: + + The above copyright notice and this permission notice shall be included in + all copies or substantial portions of the Software. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. 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The DX12-CUDA sample uses the d3dx12.h header, which is + subject to the MIT license . + +----------------- diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cuda_runtime_cu12-12.8.90.dist-info/METADATA b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cuda_runtime_cu12-12.8.90.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..14b471170862b26f1dc2b83800d568d5c38533ec --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cuda_runtime_cu12-12.8.90.dist-info/METADATA @@ -0,0 +1,44 @@ +Metadata-Version: 2.2 +Name: nvidia-cuda-runtime-cu12 +Version: 12.8.90 +Summary: CUDA Runtime native Libraries +Home-page: https://developer.nvidia.com/cuda-zone +Author: Nvidia CUDA Installer Team +Author-email: compute_installer@nvidia.com +License: NVIDIA Proprietary Software +Keywords: cuda,nvidia,runtime,machine learning,deep learning +Classifier: Development Status :: 4 - Beta 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As examples, licenses that require as a condition of use, modification, and/or distribution that the SDK be (i) disclosed or distributed in source code form; (ii) licensed for the purpose of making derivative works; or (iii) redistributable at no charge. + +2.6 Unless you have an agreement with NVIDIA for this purpose, you may not use the SDK with any system or application where the use or failure of the system or application can reasonably be expected to threaten or result in personal injury, death, or catastrophic loss. Examples include use in avionics, navigation, military, medical, life support or other life critical applications. NVIDIA does not design, test or manufacture the SDK for these critical uses and NVIDIA shall not be liable to you or any third party, in whole or in part, for any claims or damages arising from such uses. + +2.7 You agree to defend, indemnify and hold harmless NVIDIA and its affiliates, and their respective employees, contractors, agents, officers and directors, from and against any and all claims, damages, obligations, losses, liabilities, costs or debt, fines, restitutions and expenses (including but not limited to attorney’s fees and costs incident to establishing the right of indemnification) arising out of or related to your use of the SDK outside of the scope of this Agreement, or not in compliance with its terms. + +3. Ownership. + +3.1 NVIDIA or its licensors hold all rights, title and interest in and to the SDK and its modifications and derivative works, including their respective intellectual property rights, subject to your rights under Section 3.2. This SDK may include software and materials from NVIDIA’s licensors, and these licensors are intended third party beneficiaries that may enforce this Agreement with respect to their intellectual property rights. + +3.2 You hold all rights, title and interest in and to your applications and your derivative works of the sample source code delivered in the SDK, including their respective intellectual property rights, subject to NVIDIA’s rights under section 3.1. + +3.3 You may, but don’t have to, provide to NVIDIA suggestions, feature requests or other feedback regarding the SDK, including possible enhancements or modifications to the SDK. For any feedback that you voluntarily provide, you hereby grant NVIDIA and its affiliates a perpetual, non-exclusive, worldwide, irrevocable license to use, reproduce, modify, license, sublicense (through multiple tiers of sublicensees), and distribute (through multiple tiers of distributors) it without the payment of any royalties or fees to you. NVIDIA will use feedback at its choice. NVIDIA is constantly looking for ways to improve its products, so you may send feedback to NVIDIA through the developer portal at https://developer.nvidia.com. + +4. No Warranties. + +THE SDK IS PROVIDED BY NVIDIA “AS IS” AND “WITH ALL FAULTS.” TO THE MAXIMUM EXTENT PERMITTED BY LAW, NVIDIA AND ITS AFFILIATES EXPRESSLY DISCLAIM ALL WARRANTIES OF ANY KIND OR NATURE, WHETHER EXPRESS, IMPLIED OR STATUTORY, INCLUDING, BUT NOT LIMITED TO, ANY WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE, NON-INFRINGEMENT, OR THE ABSENCE OF ANY DEFECTS THEREIN, WHETHER LATENT OR PATENT. NO WARRANTY IS MADE ON THE BASIS OF TRADE USAGE, COURSE OF DEALING OR COURSE OF TRADE. + +5. Limitations of Liability. + +TO THE MAXIMUM EXTENT PERMITTED BY LAW, NVIDIA AND ITS AFFILIATES SHALL NOT BE LIABLE FOR ANY SPECIAL, INCIDENTAL, PUNITIVE OR CONSEQUENTIAL DAMAGES, OR ANY LOST PROFITS, LOSS OF USE, LOSS OF DATA OR LOSS OF GOODWILL, OR THE COSTS OF PROCURING SUBSTITUTE PRODUCTS, ARISING OUT OF OR IN CONNECTION WITH THIS AGREEMENT OR THE USE OR PERFORMANCE OF THE SDK, WHETHER SUCH LIABILITY ARISES FROM ANY CLAIM BASED UPON BREACH OF CONTRACT, BREACH OF WARRANTY, TORT (INCLUDING NEGLIGENCE), PRODUCT LIABILITY OR ANY OTHER CAUSE OF ACTION OR THEORY OF LIABILITY. IN NO EVENT WILL NVIDIA’S AND ITS AFFILIATES TOTAL CUMULATIVE LIABILITY UNDER OR ARISING OUT OF THIS AGREEMENT EXCEED US$10.00. THE NATURE OF THE LIABILITY OR THE NUMBER OF CLAIMS OR SUITS SHALL NOT ENLARGE OR EXTEND THIS LIMIT. + +These exclusions and limitations of liability shall apply regardless if NVIDIA or its affiliates have been advised of the possibility of such damages, and regardless of whether a remedy fails its essential purpose. These exclusions and limitations of liability form an essential basis of the bargain between the parties, and, absent any of these exclusions or limitations of liability, the provisions of this Agreement, including, without limitation, the economic terms, would be substantially different. + +6. Termination. + +6.1 This Agreement will continue to apply until terminated by either you or NVIDIA as described below. + +6.2 If you want to terminate this Agreement, you may do so by stopping to use the SDK. + +6.3 NVIDIA may, at any time, terminate this Agreement if: (i) you fail to comply with any term of this Agreement and the non-compliance is not fixed within thirty (30) days following notice from NVIDIA (or immediately if you violate NVIDIA’s intellectual property rights); (ii) you commence or participate in any legal proceeding against NVIDIA with respect to the SDK; or (iii) NVIDIA decides to no longer provide the SDK in a country or, in NVIDIA’s sole discretion, the continued use of it is no longer commercially viable. + +6.4 Upon any termination of this Agreement, you agree to promptly discontinue use of the SDK and destroy all copies in your possession or control. Your prior distributions in accordance with this Agreement are not affected by the termination of this Agreement. Upon written request, you will certify in writing that you have complied with your commitments under this section. Upon any termination of this Agreement all provisions survive except for the licenses granted to you. + +7. General. + +If you wish to assign this Agreement or your rights and obligations, including by merger, consolidation, dissolution or operation of law, contact NVIDIA to ask for permission. Any attempted assignment not approved by NVIDIA in writing shall be void and of no effect. NVIDIA may assign, delegate or transfer this Agreement and its rights and obligations, and if to a non-affiliate you will be notified. + +You agree to cooperate with NVIDIA and provide reasonably requested information to verify your compliance with this Agreement. + +This Agreement will be governed in all respects by the laws of the United States and of the State of Delaware as those laws are applied to contracts entered into and performed entirely within Delaware by Delaware residents, without regard to the conflicts of laws principles. The United Nations Convention on Contracts for the International Sale of Goods is specifically disclaimed. You agree to all terms of this Agreement in the English language. + +The state or federal courts residing in Santa Clara County, California shall have exclusive jurisdiction over any dispute or claim arising out of this Agreement. Notwithstanding this, you agree that NVIDIA shall still be allowed to apply for injunctive remedies or an equivalent type of urgent legal relief in any jurisdiction. + +If any court of competent jurisdiction determines that any provision of this Agreement is illegal, invalid or unenforceable, such provision will be construed as limited to the extent necessary to be consistent with and fully enforceable under the law and the remaining provisions will remain in full force and effect. Unless otherwise specified, remedies are cumulative. + +Each party acknowledges and agrees that the other is an independent contractor in the performance of this Agreement. + +The SDK has been developed entirely at private expense and is “commercial items” consisting of “commercial computer software” and “commercial computer software documentation” provided with RESTRICTED RIGHTS. Use, duplication or disclosure by the U.S. Government or a U.S. Government subcontractor is subject to the restrictions in this Agreement pursuant to DFARS 227.7202-3(a) or as set forth in subparagraphs (b)(1) and (2) of the Commercial Computer Software - Restricted Rights clause at FAR 52.227-19, as applicable. Contractor/manufacturer is NVIDIA, 2788 San Tomas Expressway, Santa Clara, CA 95051. + +The SDK is subject to United States export laws and regulations. You agree that you will not ship, transfer or export the SDK into any country, or use the SDK in any manner, prohibited by the United States Bureau of Industry and Security or economic sanctions regulations administered by the U.S. Department of Treasury’s Office of Foreign Assets Control (OFAC), or any applicable export laws, restrictions or regulations. These laws include restrictions on destinations, end users and end use. By accepting this Agreement, you confirm that you are not a resident or citizen of any country currently embargoed by the U.S. and that you are not otherwise prohibited from receiving the SDK. + +Any notice delivered by NVIDIA to you under this Agreement will be delivered via mail, email or fax. You agree that any notices that NVIDIA sends you electronically will satisfy any legal communication requirements. Please direct your legal notices or other correspondence to NVIDIA Corporation, 2788 San Tomas Expressway, Santa Clara, California 95051, United States of America, Attention: Legal Department. + +This Agreement and any exhibits incorporated into this Agreement constitute the entire agreement of the parties with respect to the subject matter of this Agreement and supersede all prior negotiations or documentation exchanged between the parties relating to this SDK license. Any additional and/or conflicting terms on documents issued by you are null, void, and invalid. Any amendment or waiver under this Agreement shall be in writing and signed by representatives of both parties. + +(v. January 28, 2020) + + +cuDNN SUPPLEMENT TO SOFTWARE LICENSE AGREEMENT FOR NVIDIA SOFTWARE DEVELOPMENT KITS + +The terms in this supplement govern your use of the NVIDIA cuDNN SDK under the terms of your license agreement (“Agreement”) as modified by this supplement. Capitalized terms used but not defined below have the meaning assigned to them in the Agreement. + +This supplement is an exhibit to the Agreement and is incorporated as an integral part of the Agreement. In the event of conflict between the terms in this supplement and the terms in the Agreement, the terms in this supplement govern. + +4.1 License Scope. The SDK is licensed for you to develop applications only for use in systems with NVIDIA GPUs. + +2. Distribution. The following portions of the SDK are distributable under the Agreement: the runtime files .so and .h, cudnn64_7.dll, and cudnn.lib. + +In addition to the rights above, for parties that are developing software intended solely for use on Jetson development kits or Jetson modules and running Linux for Tegra software the following shall apply: the SDK may be distributed in its entirety, as provided by NVIDIA and without separation of its components, for you and/or your licensees to create software development kits for use only on the Jetson platform and running Linux for Tegra software. + +3. Licensing. If the distribution terms in this Agreement are not suitable for your organization, or for any questions regarding this Agreement, please contact NVIDIA at nvidia-compute-license-questions@nvidia.com. + (v. January 28, 2020) + diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cudnn_cu12-9.10.2.21.dist-info/top_level.txt b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cudnn_cu12-9.10.2.21.dist-info/top_level.txt new file mode 100644 index 0000000000000000000000000000000000000000..862f7abf232cdfbb928609856247292e81c9decb --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cudnn_cu12-9.10.2.21.dist-info/top_level.txt @@ -0,0 +1 @@ +nvidia diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cufft_cu12-11.3.3.83.dist-info/INSTALLER b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cufft_cu12-11.3.3.83.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cufft_cu12-11.3.3.83.dist-info/INSTALLER @@ -0,0 +1 @@ +pip diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cufft_cu12-11.3.3.83.dist-info/License.txt b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cufft_cu12-11.3.3.83.dist-info/License.txt new file mode 100644 index 0000000000000000000000000000000000000000..b491c70e0aef319022ded661e111ddbd45b8a17f --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cufft_cu12-11.3.3.83.dist-info/License.txt @@ -0,0 +1,1568 @@ +End User License Agreement +-------------------------- + + +Preface +------- + +The Software License Agreement in Chapter 1 and the Supplement +in Chapter 2 contain license terms and conditions that govern +the use of NVIDIA software. By accepting this agreement, you +agree to comply with all the terms and conditions applicable +to the product(s) included herein. + + +NVIDIA Driver + + +Description + +This package contains the operating system driver and +fundamental system software components for NVIDIA GPUs. + + +NVIDIA CUDA Toolkit + + +Description + +The NVIDIA CUDA Toolkit provides command-line and graphical +tools for building, debugging and optimizing the performance +of applications accelerated by NVIDIA GPUs, runtime and math +libraries, and documentation including programming guides, +user manuals, and API references. + + +Default Install Location of CUDA Toolkit + +Windows platform: + +%ProgramFiles%\NVIDIA GPU Computing Toolkit\CUDA\v#.# + +Linux platform: + +/usr/local/cuda-#.# + +Mac platform: + +/Developer/NVIDIA/CUDA-#.# + + +NVIDIA CUDA Samples + + +Description + +This package includes over 100+ CUDA examples that demonstrate +various CUDA programming principles, and efficient CUDA +implementation of algorithms in specific application domains. + + +Default Install Location of CUDA Samples + +Windows platform: + +%ProgramData%\NVIDIA Corporation\CUDA Samples\v#.# + +Linux platform: + +/usr/local/cuda-#.#/samples + +and + +$HOME/NVIDIA_CUDA-#.#_Samples + +Mac platform: + +/Developer/NVIDIA/CUDA-#.#/samples + + +NVIDIA Nsight Visual Studio Edition (Windows only) + + +Description + +NVIDIA Nsight Development Platform, Visual Studio Edition is a +development environment integrated into Microsoft Visual +Studio that provides tools for debugging, profiling, analyzing +and optimizing your GPU computing and graphics applications. + + +Default Install Location of Nsight Visual Studio Edition + +Windows platform: + +%ProgramFiles(x86)%\NVIDIA Corporation\Nsight Visual Studio Edition #.# + + +1. License Agreement for NVIDIA Software Development Kits +--------------------------------------------------------- + + +Release Date: July 26, 2018 +--------------------------- + + +Important NoticeRead before downloading, installing, +copying or using the licensed software: +------------------------------------------------------- + +This license agreement, including exhibits attached +("Agreement”) is a legal agreement between you and NVIDIA +Corporation ("NVIDIA") and governs your use of a NVIDIA +software development kit (“SDK”). + +Each SDK has its own set of software and materials, but here +is a description of the types of items that may be included in +a SDK: source code, header files, APIs, data sets and assets +(examples include images, textures, models, scenes, videos, +native API input/output files), binary software, sample code, +libraries, utility programs, programming code and +documentation. + +This Agreement can be accepted only by an adult of legal age +of majority in the country in which the SDK is used. + +If you are entering into this Agreement on behalf of a company +or other legal entity, you represent that you have the legal +authority to bind the entity to this Agreement, in which case +“you” will mean the entity you represent. + +If you don’t have the required age or authority to accept +this Agreement, or if you don’t accept all the terms and +conditions of this Agreement, do not download, install or use +the SDK. + +You agree to use the SDK only for purposes that are permitted +by (a) this Agreement, and (b) any applicable law, regulation +or generally accepted practices or guidelines in the relevant +jurisdictions. + + +1.1. License + + +1.1.1. License Grant + +Subject to the terms of this Agreement, NVIDIA hereby grants +you a non-exclusive, non-transferable license, without the +right to sublicense (except as expressly provided in this +Agreement) to: + + 1. Install and use the SDK, + + 2. Modify and create derivative works of sample source code + delivered in the SDK, and + + 3. Distribute those portions of the SDK that are identified + in this Agreement as distributable, as incorporated in + object code format into a software application that meets + the distribution requirements indicated in this Agreement. + + +1.1.2. Distribution Requirements + +These are the distribution requirements for you to exercise +the distribution grant: + + 1. Your application must have material additional + functionality, beyond the included portions of the SDK. + + 2. The distributable portions of the SDK shall only be + accessed by your application. + + 3. The following notice shall be included in modifications + and derivative works of sample source code distributed: + “This software contains source code provided by NVIDIA + Corporation.” + + 4. Unless a developer tool is identified in this Agreement + as distributable, it is delivered for your internal use + only. + + 5. The terms under which you distribute your application + must be consistent with the terms of this Agreement, + including (without limitation) terms relating to the + license grant and license restrictions and protection of + NVIDIA’s intellectual property rights. Additionally, you + agree that you will protect the privacy, security and + legal rights of your application users. + + 6. You agree to notify NVIDIA in writing of any known or + suspected distribution or use of the SDK not in compliance + with the requirements of this Agreement, and to enforce + the terms of your agreements with respect to distributed + SDK. + + +1.1.3. Authorized Users + +You may allow employees and contractors of your entity or of +your subsidiary(ies) to access and use the SDK from your +secure network to perform work on your behalf. + +If you are an academic institution you may allow users +enrolled or employed by the academic institution to access and +use the SDK from your secure network. + +You are responsible for the compliance with the terms of this +Agreement by your authorized users. If you become aware that +your authorized users didn’t follow the terms of this +Agreement, you agree to take reasonable steps to resolve the +non-compliance and prevent new occurrences. + + +1.1.4. Pre-Release SDK + +The SDK versions identified as alpha, beta, preview or +otherwise as pre-release, may not be fully functional, may +contain errors or design flaws, and may have reduced or +different security, privacy, accessibility, availability, and +reliability standards relative to commercial versions of +NVIDIA software and materials. Use of a pre-release SDK may +result in unexpected results, loss of data, project delays or +other unpredictable damage or loss. + +You may use a pre-release SDK at your own risk, understanding +that pre-release SDKs are not intended for use in production +or business-critical systems. + +NVIDIA may choose not to make available a commercial version +of any pre-release SDK. NVIDIA may also choose to abandon +development and terminate the availability of a pre-release +SDK at any time without liability. + + +1.1.5. Updates + +NVIDIA may, at its option, make available patches, workarounds +or other updates to this SDK. Unless the updates are provided +with their separate governing terms, they are deemed part of +the SDK licensed to you as provided in this Agreement. You +agree that the form and content of the SDK that NVIDIA +provides may change without prior notice to you. While NVIDIA +generally maintains compatibility between versions, NVIDIA may +in some cases make changes that introduce incompatibilities in +future versions of the SDK. + + +1.1.6. Third Party Licenses + +The SDK may come bundled with, or otherwise include or be +distributed with, third party software licensed by a NVIDIA +supplier and/or open source software provided under an open +source license. Use of third party software is subject to the +third-party license terms, or in the absence of third party +terms, the terms of this Agreement. Copyright to third party +software is held by the copyright holders indicated in the +third-party software or license. + + +1.1.7. Reservation of Rights + +NVIDIA reserves all rights, title, and interest in and to the +SDK, not expressly granted to you under this Agreement. + + +1.2. Limitations + +The following license limitations apply to your use of the +SDK: + + 1. You may not reverse engineer, decompile or disassemble, + or remove copyright or other proprietary notices from any + portion of the SDK or copies of the SDK. + + 2. Except as expressly provided in this Agreement, you may + not copy, sell, rent, sublicense, transfer, distribute, + modify, or create derivative works of any portion of the + SDK. For clarity, you may not distribute or sublicense the + SDK as a stand-alone product. + + 3. Unless you have an agreement with NVIDIA for this + purpose, you may not indicate that an application created + with the SDK is sponsored or endorsed by NVIDIA. + + 4. You may not bypass, disable, or circumvent any + encryption, security, digital rights management or + authentication mechanism in the SDK. + + 5. You may not use the SDK in any manner that would cause it + to become subject to an open source software license. As + examples, licenses that require as a condition of use, + modification, and/or distribution that the SDK be: + + a. Disclosed or distributed in source code form; + + b. Licensed for the purpose of making derivative works; + or + + c. Redistributable at no charge. + + 6. Unless you have an agreement with NVIDIA for this + purpose, you may not use the SDK with any system or + application where the use or failure of the system or + application can reasonably be expected to threaten or + result in personal injury, death, or catastrophic loss. + Examples include use in avionics, navigation, military, + medical, life support or other life critical applications. + NVIDIA does not design, test or manufacture the SDK for + these critical uses and NVIDIA shall not be liable to you + or any third party, in whole or in part, for any claims or + damages arising from such uses. + + 7. You agree to defend, indemnify and hold harmless NVIDIA + and its affiliates, and their respective employees, + contractors, agents, officers and directors, from and + against any and all claims, damages, obligations, losses, + liabilities, costs or debt, fines, restitutions and + expenses (including but not limited to attorney’s fees + and costs incident to establishing the right of + indemnification) arising out of or related to your use of + the SDK outside of the scope of this Agreement, or not in + compliance with its terms. + + +1.3. Ownership + + 1. NVIDIA or its licensors hold all rights, title and + interest in and to the SDK and its modifications and + derivative works, including their respective intellectual + property rights, subject to your rights described in this + section. This SDK may include software and materials from + NVIDIA’s licensors, and these licensors are intended + third party beneficiaries that may enforce this Agreement + with respect to their intellectual property rights. + + 2. You hold all rights, title and interest in and to your + applications and your derivative works of the sample + source code delivered in the SDK, including their + respective intellectual property rights, subject to + NVIDIA’s rights described in this section. + + 3. You may, but don’t have to, provide to NVIDIA + suggestions, feature requests or other feedback regarding + the SDK, including possible enhancements or modifications + to the SDK. For any feedback that you voluntarily provide, + you hereby grant NVIDIA and its affiliates a perpetual, + non-exclusive, worldwide, irrevocable license to use, + reproduce, modify, license, sublicense (through multiple + tiers of sublicensees), and distribute (through multiple + tiers of distributors) it without the payment of any + royalties or fees to you. NVIDIA will use feedback at its + choice. NVIDIA is constantly looking for ways to improve + its products, so you may send feedback to NVIDIA through + the developer portal at https://developer.nvidia.com. + + +1.4. No Warranties + +THE SDK IS PROVIDED BY NVIDIA “AS IS” AND “WITH ALL +FAULTS.” TO THE MAXIMUM EXTENT PERMITTED BY LAW, NVIDIA AND +ITS AFFILIATES EXPRESSLY DISCLAIM ALL WARRANTIES OF ANY KIND +OR NATURE, WHETHER EXPRESS, IMPLIED OR STATUTORY, INCLUDING, +BUT NOT LIMITED TO, ANY WARRANTIES OF MERCHANTABILITY, FITNESS +FOR A PARTICULAR PURPOSE, TITLE, NON-INFRINGEMENT, OR THE +ABSENCE OF ANY DEFECTS THEREIN, WHETHER LATENT OR PATENT. NO +WARRANTY IS MADE ON THE BASIS OF TRADE USAGE, COURSE OF +DEALING OR COURSE OF TRADE. + + +1.5. Limitation of Liability + +TO THE MAXIMUM EXTENT PERMITTED BY LAW, NVIDIA AND ITS +AFFILIATES SHALL NOT BE LIABLE FOR ANY SPECIAL, INCIDENTAL, +PUNITIVE OR CONSEQUENTIAL DAMAGES, OR ANY LOST PROFITS, LOSS +OF USE, LOSS OF DATA OR LOSS OF GOODWILL, OR THE COSTS OF +PROCURING SUBSTITUTE PRODUCTS, ARISING OUT OF OR IN CONNECTION +WITH THIS AGREEMENT OR THE USE OR PERFORMANCE OF THE SDK, +WHETHER SUCH LIABILITY ARISES FROM ANY CLAIM BASED UPON BREACH +OF CONTRACT, BREACH OF WARRANTY, TORT (INCLUDING NEGLIGENCE), +PRODUCT LIABILITY OR ANY OTHER CAUSE OF ACTION OR THEORY OF +LIABILITY. IN NO EVENT WILL NVIDIA’S AND ITS AFFILIATES +TOTAL CUMULATIVE LIABILITY UNDER OR ARISING OUT OF THIS +AGREEMENT EXCEED US$10.00. THE NATURE OF THE LIABILITY OR THE +NUMBER OF CLAIMS OR SUITS SHALL NOT ENLARGE OR EXTEND THIS +LIMIT. + +These exclusions and limitations of liability shall apply +regardless if NVIDIA or its affiliates have been advised of +the possibility of such damages, and regardless of whether a +remedy fails its essential purpose. These exclusions and +limitations of liability form an essential basis of the +bargain between the parties, and, absent any of these +exclusions or limitations of liability, the provisions of this +Agreement, including, without limitation, the economic terms, +would be substantially different. + + +1.6. Termination + + 1. This Agreement will continue to apply until terminated by + either you or NVIDIA as described below. + + 2. If you want to terminate this Agreement, you may do so by + stopping to use the SDK. + + 3. NVIDIA may, at any time, terminate this Agreement if: + + a. (i) you fail to comply with any term of this + Agreement and the non-compliance is not fixed within + thirty (30) days following notice from NVIDIA (or + immediately if you violate NVIDIA’s intellectual + property rights); + + b. (ii) you commence or participate in any legal + proceeding against NVIDIA with respect to the SDK; or + + c. (iii) NVIDIA decides to no longer provide the SDK in + a country or, in NVIDIA’s sole discretion, the + continued use of it is no longer commercially viable. + + 4. Upon any termination of this Agreement, you agree to + promptly discontinue use of the SDK and destroy all copies + in your possession or control. Your prior distributions in + accordance with this Agreement are not affected by the + termination of this Agreement. Upon written request, you + will certify in writing that you have complied with your + commitments under this section. Upon any termination of + this Agreement all provisions survive except for the + license grant provisions. + + +1.7. General + +If you wish to assign this Agreement or your rights and +obligations, including by merger, consolidation, dissolution +or operation of law, contact NVIDIA to ask for permission. Any +attempted assignment not approved by NVIDIA in writing shall +be void and of no effect. NVIDIA may assign, delegate or +transfer this Agreement and its rights and obligations, and if +to a non-affiliate you will be notified. + +You agree to cooperate with NVIDIA and provide reasonably +requested information to verify your compliance with this +Agreement. + +This Agreement will be governed in all respects by the laws of +the United States and of the State of Delaware as those laws +are applied to contracts entered into and performed entirely +within Delaware by Delaware residents, without regard to the +conflicts of laws principles. 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Unless otherwise specified, +remedies are cumulative. + +Each party acknowledges and agrees that the other is an +independent contractor in the performance of this Agreement. + +The SDK has been developed entirely at private expense and is +“commercial items” consisting of “commercial computer +software” and “commercial computer software +documentation” provided with RESTRICTED RIGHTS. Use, +duplication or disclosure by the U.S. Government or a U.S. +Government subcontractor is subject to the restrictions in +this Agreement pursuant to DFARS 227.7202-3(a) or as set forth +in subparagraphs (c)(1) and (2) of the Commercial Computer +Software - Restricted Rights clause at FAR 52.227-19, as +applicable. Contractor/manufacturer is NVIDIA, 2788 San Tomas +Expressway, Santa Clara, CA 95051. + +The SDK is subject to United States export laws and +regulations. You agree that you will not ship, transfer or +export the SDK into any country, or use the SDK in any manner, +prohibited by the United States Bureau of Industry and +Security or economic sanctions regulations administered by the +U.S. Department of Treasury’s Office of Foreign Assets +Control (OFAC), or any applicable export laws, restrictions or +regulations. These laws include restrictions on destinations, +end users and end use. By accepting this Agreement, you +confirm that you are not a resident or citizen of any country +currently embargoed by the U.S. and that you are not otherwise +prohibited from receiving the SDK. + +Any notice delivered by NVIDIA to you under this Agreement +will be delivered via mail, email or fax. You agree that any +notices that NVIDIA sends you electronically will satisfy any +legal communication requirements. Please direct your legal +notices or other correspondence to NVIDIA Corporation, 2788 +San Tomas Expressway, Santa Clara, California 95051, United +States of America, Attention: Legal Department. + +This Agreement and any exhibits incorporated into this +Agreement constitute the entire agreement of the parties with +respect to the subject matter of this Agreement and supersede +all prior negotiations or documentation exchanged between the +parties relating to this SDK license. Any additional and/or +conflicting terms on documents issued by you are null, void, +and invalid. Any amendment or waiver under this Agreement +shall be in writing and signed by representatives of both +parties. + + +2. CUDA Toolkit Supplement to Software License Agreement for +NVIDIA Software Development Kits +------------------------------------------------------------ + + +Release date: August 16, 2018 +----------------------------- + +The terms in this supplement govern your use of the NVIDIA +CUDA Toolkit SDK under the terms of your license agreement +(“Agreement”) as modified by this supplement. Capitalized +terms used but not defined below have the meaning assigned to +them in the Agreement. + +This supplement is an exhibit to the Agreement and is +incorporated as an integral part of the Agreement. In the +event of conflict between the terms in this supplement and the +terms in the Agreement, the terms in this supplement govern. + + +2.1. License Scope + +The SDK is licensed for you to develop applications only for +use in systems with NVIDIA GPUs. + + +2.2. Distribution + +The portions of the SDK that are distributable under the +Agreement are listed in Attachment A. + + +2.3. Operating Systems + +Those portions of the SDK designed exclusively for use on the +Linux or FreeBSD operating systems, or other operating systems +derived from the source code to these operating systems, may +be copied and redistributed for use in accordance with this +Agreement, provided that the object code files are not +modified in any way (except for unzipping of compressed +files). + + +2.4. Audio and Video Encoders and Decoders + +You acknowledge and agree that it is your sole responsibility +to obtain any additional third-party licenses required to +make, have made, use, have used, sell, import, and offer for +sale your products or services that include or incorporate any +third-party software and content relating to audio and/or +video encoders and decoders from, including but not limited +to, Microsoft, Thomson, Fraunhofer IIS, Sisvel S.p.A., +MPEG-LA, and Coding Technologies. NVIDIA does not grant to you +under this Agreement any necessary patent or other rights with +respect to any audio and/or video encoders and decoders. + + +2.5. Licensing + +If the distribution terms in this Agreement are not suitable +for your organization, or for any questions regarding this +Agreement, please contact NVIDIA at +nvidia-compute-license-questions@nvidia.com. + + +2.6. Attachment A + +The following portions of the SDK are distributable under the +Agreement: + +Component + +CUDA Runtime + +Windows + +cudart.dll, cudart_static.lib, cudadevrt.lib + +Mac OSX + +libcudart.dylib, libcudart_static.a, libcudadevrt.a + +Linux + +libcudart.so, libcudart_static.a, libcudadevrt.a + +Android + +libcudart.so, libcudart_static.a, libcudadevrt.a + +Component + +CUDA FFT Library + +Windows + +cufft.dll, cufftw.dll, cufft.lib, cufftw.lib + +Mac OSX + +libcufft.dylib, libcufft_static.a, libcufftw.dylib, +libcufftw_static.a + +Linux + +libcufft.so, libcufft_static.a, libcufftw.so, +libcufftw_static.a + +Android + +libcufft.so, libcufft_static.a, libcufftw.so, +libcufftw_static.a + +Component + +CUDA BLAS Library + +Windows + +cublas.dll, cublasLt.dll + +Mac OSX + +libcublas.dylib, libcublasLt.dylib, libcublas_static.a, +libcublasLt_static.a + +Linux + +libcublas.so, libcublasLt.so, libcublas_static.a, +libcublasLt_static.a + +Android + +libcublas.so, libcublasLt.so, libcublas_static.a, +libcublasLt_static.a + +Component + +NVIDIA "Drop-in" BLAS Library + +Windows + +nvblas.dll + +Mac OSX + +libnvblas.dylib + +Linux + +libnvblas.so + +Component + +CUDA Sparse Matrix Library + +Windows + +cusparse.dll, cusparse.lib + +Mac OSX + +libcusparse.dylib, libcusparse_static.a + +Linux + +libcusparse.so, libcusparse_static.a + +Android + +libcusparse.so, libcusparse_static.a + +Component + +CUDA Linear Solver Library + +Windows + +cusolver.dll, cusolver.lib + +Mac OSX + +libcusolver.dylib, libcusolver_static.a + +Linux + +libcusolver.so, libcusolver_static.a + +Android + +libcusolver.so, libcusolver_static.a + +Component + +CUDA Random Number Generation Library + +Windows + +curand.dll, curand.lib + +Mac OSX + +libcurand.dylib, libcurand_static.a + +Linux + +libcurand.so, libcurand_static.a + +Android + +libcurand.so, libcurand_static.a + +Component + +CUDA Accelerated Graph Library + +Component + +NVIDIA Performance Primitives Library + +Windows + +nppc.dll, nppc.lib, nppial.dll, nppial.lib, nppicc.dll, +nppicc.lib, nppicom.dll, nppicom.lib, nppidei.dll, +nppidei.lib, nppif.dll, nppif.lib, nppig.dll, nppig.lib, +nppim.dll, nppim.lib, nppist.dll, nppist.lib, nppisu.dll, +nppisu.lib, nppitc.dll, nppitc.lib, npps.dll, npps.lib + +Mac OSX + +libnppc.dylib, libnppc_static.a, libnppial.dylib, +libnppial_static.a, libnppicc.dylib, libnppicc_static.a, +libnppicom.dylib, libnppicom_static.a, libnppidei.dylib, +libnppidei_static.a, libnppif.dylib, libnppif_static.a, +libnppig.dylib, libnppig_static.a, libnppim.dylib, +libnppisu_static.a, libnppitc.dylib, libnppitc_static.a, +libnpps.dylib, libnpps_static.a + +Linux + +libnppc.so, libnppc_static.a, libnppial.so, +libnppial_static.a, libnppicc.so, libnppicc_static.a, +libnppicom.so, libnppicom_static.a, libnppidei.so, +libnppidei_static.a, libnppif.so, libnppif_static.a +libnppig.so, libnppig_static.a, libnppim.so, +libnppim_static.a, libnppist.so, libnppist_static.a, +libnppisu.so, libnppisu_static.a, libnppitc.so +libnppitc_static.a, libnpps.so, libnpps_static.a + +Android + +libnppc.so, libnppc_static.a, libnppial.so, +libnppial_static.a, libnppicc.so, libnppicc_static.a, +libnppicom.so, libnppicom_static.a, libnppidei.so, +libnppidei_static.a, libnppif.so, libnppif_static.a +libnppig.so, libnppig_static.a, libnppim.so, +libnppim_static.a, libnppist.so, libnppist_static.a, +libnppisu.so, libnppisu_static.a, libnppitc.so +libnppitc_static.a, libnpps.so, libnpps_static.a + +Component + +NVIDIA JPEG Library + +Linux + +libnvjpeg.so, libnvjpeg_static.a + +Component + +Internal common library required for statically linking to +cuBLAS, cuSPARSE, cuFFT, cuRAND, nvJPEG and NPP + +Mac OSX + +libculibos.a + +Linux + +libculibos.a + +Component + +NVIDIA Runtime Compilation Library and Header + +All + +nvrtc.h + +Windows + +nvrtc.dll, nvrtc-builtins.dll + +Mac OSX + +libnvrtc.dylib, libnvrtc-builtins.dylib + +Linux + +libnvrtc.so, libnvrtc-builtins.so + +Component + +NVIDIA Optimizing Compiler Library + +Windows + +nvvm.dll + +Mac OSX + +libnvvm.dylib + +Linux + +libnvvm.so + +Component + +NVIDIA Common Device Math Functions Library + +Windows + +libdevice.10.bc + +Mac OSX + +libdevice.10.bc + +Linux + +libdevice.10.bc + +Component + +CUDA Occupancy Calculation Header Library + +All + +cuda_occupancy.h + +Component + +CUDA Half Precision Headers + +All + +cuda_fp16.h, cuda_fp16.hpp + +Component + +CUDA Profiling Tools Interface (CUPTI) Library + +Windows + +cupti.dll + +Mac OSX + +libcupti.dylib + +Linux + +libcupti.so + +Component + +NVIDIA Tools Extension Library + +Windows + +nvToolsExt.dll, nvToolsExt.lib + +Mac OSX + +libnvToolsExt.dylib + +Linux + +libnvToolsExt.so + +Component + +NVIDIA CUDA Driver Libraries + +Linux + +libcuda.so, libnvidia-fatbinaryloader.so, +libnvidia-ptxjitcompiler.so + +The NVIDIA CUDA Driver Libraries are only distributable in +applications that meet this criteria: + + 1. The application was developed starting from a NVIDIA CUDA + container obtained from Docker Hub or the NVIDIA GPU + Cloud, and + + 2. The resulting application is packaged as a Docker + container and distributed to users on Docker Hub or the + NVIDIA GPU Cloud only. + + +2.7. Attachment B + + +Additional Licensing Obligations + +The following third party components included in the SOFTWARE +are licensed to Licensee pursuant to the following terms and +conditions: + + 1. Licensee's use of the GDB third party component is + subject to the terms and conditions of GNU GPL v3: + + This product includes copyrighted third-party software licensed + under the terms of the GNU General Public License v3 ("GPL v3"). + All third-party software packages are copyright by their respective + authors. GPL v3 terms and conditions are hereby incorporated into + the Agreement by this reference: http://www.gnu.org/licenses/gpl.txt + + Consistent with these licensing requirements, the software + listed below is provided under the terms of the specified + open source software licenses. To obtain source code for + software provided under licenses that require + redistribution of source code, including the GNU General + Public License (GPL) and GNU Lesser General Public License + (LGPL), contact oss-requests@nvidia.com. This offer is + valid for a period of three (3) years from the date of the + distribution of this product by NVIDIA CORPORATION. + + Component License + CUDA-GDB GPL v3 + + 2. Licensee represents and warrants that any and all third + party licensing and/or royalty payment obligations in + connection with Licensee's use of the H.264 video codecs + are solely the responsibility of Licensee. + + 3. Licensee's use of the Thrust library is subject to the + terms and conditions of the Apache License Version 2.0. + All third-party software packages are copyright by their + respective authors. Apache License Version 2.0 terms and + conditions are hereby incorporated into the Agreement by + this reference. + http://www.apache.org/licenses/LICENSE-2.0.html + + In addition, Licensee acknowledges the following notice: + Thrust includes source code from the Boost Iterator, + Tuple, System, and Random Number libraries. + + Boost Software License - Version 1.0 - August 17th, 2003 + . . . . + + Permission is hereby granted, free of charge, to any person or + organization obtaining a copy of the software and accompanying + documentation covered by this license (the "Software") to use, + reproduce, display, distribute, execute, and transmit the Software, + and to prepare derivative works of the Software, and to permit + third-parties to whom the Software is furnished to do so, all + subject to the following: + + The copyright notices in the Software and this entire statement, + including the above license grant, this restriction and the following + disclaimer, must be included in all copies of the Software, in whole + or in part, and all derivative works of the Software, unless such + copies or derivative works are solely in the form of machine-executable + object code generated by a source language processor. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, + EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF + MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND + NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR + ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE FOR ANY DAMAGES OR + OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING + FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR + OTHER DEALINGS IN THE SOFTWARE. + + 4. Licensee's use of the LLVM third party component is + subject to the following terms and conditions: + + ====================================================== + LLVM Release License + ====================================================== + University of Illinois/NCSA + Open Source License + + Copyright (c) 2003-2010 University of Illinois at Urbana-Champaign. + All rights reserved. + + Developed by: + + LLVM Team + + University of Illinois at Urbana-Champaign + + http://llvm.org + + Permission is hereby granted, free of charge, to any person obtaining a copy + of this software and associated documentation files (the "Software"), to + deal with the Software without restriction, including without limitation the + rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + sell copies of the Software, and to permit persons to whom the Software is + furnished to do so, subject to the following conditions: + + * Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimers. + + * Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimers in the + documentation and/or other materials provided with the distribution. + + * Neither the names of the LLVM Team, University of Illinois at Urbana- + Champaign, nor the names of its contributors may be used to endorse or + promote products derived from this Software without specific prior + written permission. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL + THE CONTRIBUTORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR + OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, + ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER + DEALINGS WITH THE SOFTWARE. + + 5. Licensee's use (e.g. nvprof) of the PCRE third party + component is subject to the following terms and + conditions: + + ------------ + PCRE LICENCE + ------------ + PCRE is a library of functions to support regular expressions whose syntax + and semantics are as close as possible to those of the Perl 5 language. + Release 8 of PCRE is distributed under the terms of the "BSD" licence, as + specified below. The documentation for PCRE, supplied in the "doc" + directory, is distributed under the same terms as the software itself. The + basic library functions are written in C and are freestanding. Also + included in the distribution is a set of C++ wrapper functions, and a just- + in-time compiler that can be used to optimize pattern matching. These are + both optional features that can be omitted when the library is built. + + THE BASIC LIBRARY FUNCTIONS + --------------------------- + Written by: Philip Hazel + Email local part: ph10 + Email domain: cam.ac.uk + University of Cambridge Computing Service, + Cambridge, England. + Copyright (c) 1997-2012 University of Cambridge + All rights reserved. + + PCRE JUST-IN-TIME COMPILATION SUPPORT + ------------------------------------- + Written by: Zoltan Herczeg + Email local part: hzmester + Emain domain: freemail.hu + Copyright(c) 2010-2012 Zoltan Herczeg + All rights reserved. + + STACK-LESS JUST-IN-TIME COMPILER + -------------------------------- + Written by: Zoltan Herczeg + Email local part: hzmester + Emain domain: freemail.hu + Copyright(c) 2009-2012 Zoltan Herczeg + All rights reserved. + + THE C++ WRAPPER FUNCTIONS + ------------------------- + Contributed by: Google Inc. + Copyright (c) 2007-2012, Google Inc. + All rights reserved. + + THE "BSD" LICENCE + ----------------- + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are met: + + * Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + + * Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + * Neither the name of the University of Cambridge nor the name of Google + Inc. nor the names of their contributors may be used to endorse or + promote products derived from this software without specific prior + written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE + ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE + LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF + SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS + INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN + CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) + ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + POSSIBILITY OF SUCH DAMAGE. + + 6. Some of the cuBLAS library routines were written by or + derived from code written by Vasily Volkov and are subject + to the Modified Berkeley Software Distribution License as + follows: + + Copyright (c) 2007-2009, Regents of the University of California + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + * Neither the name of the University of California, Berkeley nor + the names of its contributors may be used to endorse or promote + products derived from this software without specific prior + written permission. + + THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR + IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED + WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE + DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, + INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES + (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR + SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) + HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, + STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING + IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + POSSIBILITY OF SUCH DAMAGE. + + 7. Some of the cuBLAS library routines were written by or + derived from code written by Davide Barbieri and are + subject to the Modified Berkeley Software Distribution + License as follows: + + Copyright (c) 2008-2009 Davide Barbieri @ University of Rome Tor Vergata. + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + * The name of the author may not be used to endorse or promote + products derived from this software without specific prior + written permission. + + THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR + IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED + WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE + DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, + INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES + (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR + SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) + HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, + STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING + IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + POSSIBILITY OF SUCH DAMAGE. + + 8. Some of the cuBLAS library routines were derived from + code developed by the University of Tennessee and are + subject to the Modified Berkeley Software Distribution + License as follows: + + Copyright (c) 2010 The University of Tennessee. + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer listed in this license in the documentation and/or + other materials provided with the distribution. + * Neither the name of the copyright holders nor the names of its + contributors may be used to endorse or promote products derived + from this software without specific prior written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + 9. Some of the cuBLAS library routines were written by or + derived from code written by Jonathan Hogg and are subject + to the Modified Berkeley Software Distribution License as + follows: + + Copyright (c) 2012, The Science and Technology Facilities Council (STFC). + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + * Neither the name of the STFC nor the names of its contributors + may be used to endorse or promote products derived from this + software without specific prior written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE STFC BE + LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF + SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR + BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, + WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE + OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN + IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + 10. Some of the cuBLAS library routines were written by or + derived from code written by Ahmad M. Abdelfattah, David + Keyes, and Hatem Ltaief, and are subject to the Apache + License, Version 2.0, as follows: + + -- (C) Copyright 2013 King Abdullah University of Science and Technology + Authors: + Ahmad Abdelfattah (ahmad.ahmad@kaust.edu.sa) + David Keyes (david.keyes@kaust.edu.sa) + Hatem Ltaief (hatem.ltaief@kaust.edu.sa) + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + * Neither the name of the King Abdullah University of Science and + Technology nor the names of its contributors may be used to endorse + or promote products derived from this software without specific prior + written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + HOLDERS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE + + 11. Some of the cuSPARSE library routines were written by or + derived from code written by Li-Wen Chang and are subject + to the NCSA Open Source License as follows: + + Copyright (c) 2012, University of Illinois. + + All rights reserved. + + Developed by: IMPACT Group, University of Illinois, http://impact.crhc.illinois.edu + + Permission is hereby granted, free of charge, to any person obtaining + a copy of this software and associated documentation files (the + "Software"), to deal with the Software without restriction, including + without limitation the rights to use, copy, modify, merge, publish, + distribute, sublicense, and/or sell copies of the Software, and to + permit persons to whom the Software is furnished to do so, subject to + the following conditions: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimers in the documentation and/or other materials provided + with the distribution. + * Neither the names of IMPACT Group, University of Illinois, nor + the names of its contributors may be used to endorse or promote + products derived from this Software without specific prior + written permission. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, + EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF + MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND + NONINFRINGEMENT. IN NO EVENT SHALL THE CONTRIBUTORS OR COPYRIGHT + HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR + IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS WITH THE + SOFTWARE. + + 12. Some of the cuRAND library routines were written by or + derived from code written by Mutsuo Saito and Makoto + Matsumoto and are subject to the following license: + + Copyright (c) 2009, 2010 Mutsuo Saito, Makoto Matsumoto and Hiroshima + University. All rights reserved. + + Copyright (c) 2011 Mutsuo Saito, Makoto Matsumoto, Hiroshima + University and University of Tokyo. All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + * Neither the name of the Hiroshima University nor the names of + its contributors may be used to endorse or promote products + derived from this software without specific prior written + permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + 13. Some of the cuRAND library routines were derived from + code developed by D. E. Shaw Research and are subject to + the following license: + + Copyright 2010-2011, D. E. Shaw Research. + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions, and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions, and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + * Neither the name of D. E. Shaw Research nor the names of its + contributors may be used to endorse or promote products derived + from this software without specific prior written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + 14. Some of the Math library routines were written by or + derived from code developed by Norbert Juffa and are + subject to the following license: + + Copyright (c) 2015-2017, Norbert Juffa + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + 1. Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + 2. Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + 15. Licensee's use of the lz4 third party component is + subject to the following terms and conditions: + + Copyright (C) 2011-2013, Yann Collet. + BSD 2-Clause License (http://www.opensource.org/licenses/bsd-license.php) + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following disclaimer + in the documentation and/or other materials provided with the + distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. 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The NPP library uses code from the Boost Math Toolkit, + and is subject to the following license: + + Boost Software License - Version 1.0 - August 17th, 2003 + . . . . + + Permission is hereby granted, free of charge, to any person or + organization obtaining a copy of the software and accompanying + documentation covered by this license (the "Software") to use, + reproduce, display, distribute, execute, and transmit the Software, + and to prepare derivative works of the Software, and to permit + third-parties to whom the Software is furnished to do so, all + subject to the following: + + The copyright notices in the Software and this entire statement, + including the above license grant, this restriction and the following + disclaimer, must be included in all copies of the Software, in whole + or in part, and all derivative works of the Software, unless such + copies or derivative works are solely in the form of machine-executable + object code generated by a source language processor. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, + EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF + MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND + NON-INFRINGEMENT. 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Some of the cuBLAS library routines uses code from + OpenAI, which is subject to the following license: + + License URL + https://github.com/openai/openai-gemm/blob/master/LICENSE + + License Text + The MIT License + + Copyright (c) 2016 OpenAI (http://openai.com), 2016 Google Inc. + + Permission is hereby granted, free of charge, to any person obtaining a copy + of this software and associated documentation files (the "Software"), to deal + in the Software without restriction, including without limitation the rights + to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + copies of the Software, and to permit persons to whom the Software is + furnished to do so, subject to the following conditions: + + The above copyright notice and this permission notice shall be included in + all copies or substantial portions of the Software. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. 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All rights reserved. + + Permission is hereby granted, free of charge, to any person + obtaining a copy of this software and associated documentation + files (the "Software"), to deal in the Software without restriction, + including without limitation the rights to use, copy, modify, merge, + publish, distribute, sublicense, and/or sell copies of the Software, + and to permit persons to whom the Software is furnished to do so, + subject to the following conditions: + + The above copyright notice and this permission notice shall be included + in all copies or substantial portions of the Software. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS + OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + + 20. Licensee's use of linmath.h header for CPU functions for + GL vector/matrix operations from lunarG is subject to the + Apache License Version 2.0. + + 21. The DX12-CUDA sample uses the d3dx12.h header, which is + subject to the MIT license . + +----------------- diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cufft_cu12-11.3.3.83.dist-info/METADATA b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cufft_cu12-11.3.3.83.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..82f85353688d5a5d26114a8ee58082974d0a0a6c --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cufft_cu12-11.3.3.83.dist-info/METADATA @@ -0,0 +1,46 @@ +Metadata-Version: 2.2 +Name: nvidia-cufft-cu12 +Version: 11.3.3.83 +Summary: CUFFT native runtime libraries +Home-page: https://developer.nvidia.com/cuda-zone +Author: Nvidia CUDA Installer Team +Author-email: compute_installer@nvidia.com +License: NVIDIA Proprietary Software +Keywords: cuda,nvidia,runtime,machine learning,deep learning +Classifier: Development Status :: 4 - Beta +Classifier: 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b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cufile_cu12-1.13.1.3.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cufile_cu12-1.13.1.3.dist-info/INSTALLER @@ -0,0 +1 @@ +pip diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cufile_cu12-1.13.1.3.dist-info/License.txt b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cufile_cu12-1.13.1.3.dist-info/License.txt new file mode 100644 index 0000000000000000000000000000000000000000..b491c70e0aef319022ded661e111ddbd45b8a17f --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cufile_cu12-1.13.1.3.dist-info/License.txt @@ -0,0 +1,1568 @@ +End User License Agreement +-------------------------- + + +Preface +------- + +The Software License Agreement in Chapter 1 and the Supplement +in Chapter 2 contain license terms and conditions that govern +the use of NVIDIA software. By accepting this agreement, you +agree to comply with all the terms and conditions applicable +to the product(s) included herein. + + +NVIDIA Driver + + +Description + +This package contains the operating system driver and +fundamental system software components for NVIDIA GPUs. + + +NVIDIA CUDA Toolkit + + +Description + +The NVIDIA CUDA Toolkit provides command-line and graphical +tools for building, debugging and optimizing the performance +of applications accelerated by NVIDIA GPUs, runtime and math +libraries, and documentation including programming guides, +user manuals, and API references. + + +Default Install Location of CUDA Toolkit + +Windows platform: + +%ProgramFiles%\NVIDIA GPU Computing Toolkit\CUDA\v#.# + +Linux platform: + +/usr/local/cuda-#.# + +Mac platform: + +/Developer/NVIDIA/CUDA-#.# + + +NVIDIA CUDA Samples + + +Description + +This package includes over 100+ CUDA examples that demonstrate +various CUDA programming principles, and efficient CUDA +implementation of algorithms in specific application domains. + + +Default Install Location of CUDA Samples + +Windows platform: + +%ProgramData%\NVIDIA Corporation\CUDA Samples\v#.# + +Linux platform: + +/usr/local/cuda-#.#/samples + +and + +$HOME/NVIDIA_CUDA-#.#_Samples + +Mac platform: + +/Developer/NVIDIA/CUDA-#.#/samples + + +NVIDIA Nsight Visual Studio Edition (Windows only) + + +Description + +NVIDIA Nsight Development Platform, Visual Studio Edition is a +development environment integrated into Microsoft Visual +Studio that provides tools for debugging, profiling, analyzing +and optimizing your GPU computing and graphics applications. + + +Default Install Location of Nsight Visual Studio Edition + +Windows platform: + +%ProgramFiles(x86)%\NVIDIA Corporation\Nsight Visual Studio Edition #.# + + +1. License Agreement for NVIDIA Software Development Kits +--------------------------------------------------------- + + +Release Date: July 26, 2018 +--------------------------- + + +Important NoticeRead before downloading, installing, +copying or using the licensed software: +------------------------------------------------------- + +This license agreement, including exhibits attached +("Agreement”) is a legal agreement between you and NVIDIA +Corporation ("NVIDIA") and governs your use of a NVIDIA +software development kit (“SDK”). + +Each SDK has its own set of software and materials, but here +is a description of the types of items that may be included in +a SDK: source code, header files, APIs, data sets and assets +(examples include images, textures, models, scenes, videos, +native API input/output files), binary software, sample code, +libraries, utility programs, programming code and +documentation. + +This Agreement can be accepted only by an adult of legal age +of majority in the country in which the SDK is used. + +If you are entering into this Agreement on behalf of a company +or other legal entity, you represent that you have the legal +authority to bind the entity to this Agreement, in which case +“you” will mean the entity you represent. + +If you don’t have the required age or authority to accept +this Agreement, or if you don’t accept all the terms and +conditions of this Agreement, do not download, install or use +the SDK. + +You agree to use the SDK only for purposes that are permitted +by (a) this Agreement, and (b) any applicable law, regulation +or generally accepted practices or guidelines in the relevant +jurisdictions. + + +1.1. 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CUDA Toolkit Supplement to Software License Agreement for +NVIDIA Software Development Kits +------------------------------------------------------------ + + +Release date: August 16, 2018 +----------------------------- + +The terms in this supplement govern your use of the NVIDIA +CUDA Toolkit SDK under the terms of your license agreement +(“Agreement”) as modified by this supplement. Capitalized +terms used but not defined below have the meaning assigned to +them in the Agreement. + +This supplement is an exhibit to the Agreement and is +incorporated as an integral part of the Agreement. In the +event of conflict between the terms in this supplement and the +terms in the Agreement, the terms in this supplement govern. + + +2.1. License Scope + +The SDK is licensed for you to develop applications only for +use in systems with NVIDIA GPUs. + + +2.2. Distribution + +The portions of the SDK that are distributable under the +Agreement are listed in Attachment A. + + +2.3. 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NVIDIA does not grant to you +under this Agreement any necessary patent or other rights with +respect to any audio and/or video encoders and decoders. + + +2.5. Licensing + +If the distribution terms in this Agreement are not suitable +for your organization, or for any questions regarding this +Agreement, please contact NVIDIA at +nvidia-compute-license-questions@nvidia.com. + + +2.6. Attachment A + +The following portions of the SDK are distributable under the +Agreement: + +Component + +CUDA Runtime + +Windows + +cudart.dll, cudart_static.lib, cudadevrt.lib + +Mac OSX + +libcudart.dylib, libcudart_static.a, libcudadevrt.a + +Linux + +libcudart.so, libcudart_static.a, libcudadevrt.a + +Android + +libcudart.so, libcudart_static.a, libcudadevrt.a + +Component + +CUDA FFT Library + +Windows + +cufft.dll, cufftw.dll, cufft.lib, cufftw.lib + +Mac OSX + +libcufft.dylib, libcufft_static.a, libcufftw.dylib, +libcufftw_static.a + +Linux + +libcufft.so, libcufft_static.a, libcufftw.so, +libcufftw_static.a + +Android + +libcufft.so, libcufft_static.a, libcufftw.so, +libcufftw_static.a + +Component + +CUDA BLAS Library + +Windows + +cublas.dll, cublasLt.dll + +Mac OSX + +libcublas.dylib, libcublasLt.dylib, libcublas_static.a, +libcublasLt_static.a + +Linux + +libcublas.so, libcublasLt.so, libcublas_static.a, +libcublasLt_static.a + +Android + +libcublas.so, libcublasLt.so, libcublas_static.a, +libcublasLt_static.a + +Component + +NVIDIA "Drop-in" BLAS Library + +Windows + +nvblas.dll + +Mac OSX + +libnvblas.dylib + +Linux + +libnvblas.so + +Component + +CUDA Sparse Matrix Library + +Windows + +cusparse.dll, cusparse.lib + +Mac OSX + +libcusparse.dylib, libcusparse_static.a + +Linux + +libcusparse.so, libcusparse_static.a + +Android + +libcusparse.so, libcusparse_static.a + +Component + +CUDA Linear Solver Library + +Windows + +cusolver.dll, cusolver.lib + +Mac OSX + +libcusolver.dylib, libcusolver_static.a + +Linux + +libcusolver.so, libcusolver_static.a + +Android + +libcusolver.so, libcusolver_static.a + +Component + +CUDA Random Number Generation Library + +Windows + +curand.dll, curand.lib + +Mac OSX + +libcurand.dylib, libcurand_static.a + +Linux + +libcurand.so, libcurand_static.a + +Android + +libcurand.so, libcurand_static.a + +Component + +CUDA Accelerated Graph Library + +Component + +NVIDIA Performance Primitives Library + +Windows + +nppc.dll, nppc.lib, nppial.dll, nppial.lib, nppicc.dll, +nppicc.lib, nppicom.dll, nppicom.lib, nppidei.dll, +nppidei.lib, nppif.dll, nppif.lib, nppig.dll, nppig.lib, +nppim.dll, nppim.lib, nppist.dll, nppist.lib, nppisu.dll, +nppisu.lib, nppitc.dll, nppitc.lib, npps.dll, npps.lib + +Mac OSX + +libnppc.dylib, libnppc_static.a, libnppial.dylib, +libnppial_static.a, libnppicc.dylib, libnppicc_static.a, +libnppicom.dylib, libnppicom_static.a, libnppidei.dylib, +libnppidei_static.a, libnppif.dylib, libnppif_static.a, +libnppig.dylib, libnppig_static.a, libnppim.dylib, +libnppisu_static.a, libnppitc.dylib, libnppitc_static.a, +libnpps.dylib, libnpps_static.a + +Linux + +libnppc.so, libnppc_static.a, libnppial.so, +libnppial_static.a, libnppicc.so, libnppicc_static.a, +libnppicom.so, libnppicom_static.a, libnppidei.so, +libnppidei_static.a, libnppif.so, libnppif_static.a +libnppig.so, libnppig_static.a, libnppim.so, +libnppim_static.a, libnppist.so, libnppist_static.a, +libnppisu.so, libnppisu_static.a, libnppitc.so +libnppitc_static.a, libnpps.so, libnpps_static.a + +Android + +libnppc.so, libnppc_static.a, libnppial.so, +libnppial_static.a, libnppicc.so, libnppicc_static.a, +libnppicom.so, libnppicom_static.a, libnppidei.so, +libnppidei_static.a, libnppif.so, libnppif_static.a +libnppig.so, libnppig_static.a, libnppim.so, +libnppim_static.a, libnppist.so, libnppist_static.a, +libnppisu.so, libnppisu_static.a, libnppitc.so +libnppitc_static.a, libnpps.so, libnpps_static.a + +Component + +NVIDIA JPEG Library + +Linux + +libnvjpeg.so, libnvjpeg_static.a + +Component + +Internal common library required for statically linking to +cuBLAS, cuSPARSE, cuFFT, cuRAND, nvJPEG and NPP + +Mac OSX + +libculibos.a + +Linux + +libculibos.a + +Component + +NVIDIA Runtime Compilation Library and Header + +All + +nvrtc.h + +Windows + +nvrtc.dll, nvrtc-builtins.dll + +Mac OSX + +libnvrtc.dylib, libnvrtc-builtins.dylib + +Linux + +libnvrtc.so, libnvrtc-builtins.so + +Component + +NVIDIA Optimizing Compiler Library + +Windows + +nvvm.dll + +Mac OSX + +libnvvm.dylib + +Linux + +libnvvm.so + +Component + +NVIDIA Common Device Math Functions Library + +Windows + +libdevice.10.bc + +Mac OSX + +libdevice.10.bc + +Linux + +libdevice.10.bc + +Component + +CUDA Occupancy Calculation Header Library + +All + +cuda_occupancy.h + +Component + +CUDA Half Precision Headers + +All + +cuda_fp16.h, cuda_fp16.hpp + +Component + +CUDA Profiling Tools Interface (CUPTI) Library + +Windows + +cupti.dll + +Mac OSX + +libcupti.dylib + +Linux + +libcupti.so + +Component + +NVIDIA Tools Extension Library + +Windows + +nvToolsExt.dll, nvToolsExt.lib + +Mac OSX + +libnvToolsExt.dylib + +Linux + +libnvToolsExt.so + +Component + +NVIDIA CUDA Driver Libraries + +Linux + +libcuda.so, libnvidia-fatbinaryloader.so, +libnvidia-ptxjitcompiler.so + +The NVIDIA CUDA Driver Libraries are only distributable in +applications that meet this criteria: + + 1. The application was developed starting from a NVIDIA CUDA + container obtained from Docker Hub or the NVIDIA GPU + Cloud, and + + 2. The resulting application is packaged as a Docker + container and distributed to users on Docker Hub or the + NVIDIA GPU Cloud only. + + +2.7. Attachment B + + +Additional Licensing Obligations + +The following third party components included in the SOFTWARE +are licensed to Licensee pursuant to the following terms and +conditions: + + 1. Licensee's use of the GDB third party component is + subject to the terms and conditions of GNU GPL v3: + + This product includes copyrighted third-party software licensed + under the terms of the GNU General Public License v3 ("GPL v3"). + All third-party software packages are copyright by their respective + authors. GPL v3 terms and conditions are hereby incorporated into + the Agreement by this reference: http://www.gnu.org/licenses/gpl.txt + + Consistent with these licensing requirements, the software + listed below is provided under the terms of the specified + open source software licenses. To obtain source code for + software provided under licenses that require + redistribution of source code, including the GNU General + Public License (GPL) and GNU Lesser General Public License + (LGPL), contact oss-requests@nvidia.com. This offer is + valid for a period of three (3) years from the date of the + distribution of this product by NVIDIA CORPORATION. + + Component License + CUDA-GDB GPL v3 + + 2. Licensee represents and warrants that any and all third + party licensing and/or royalty payment obligations in + connection with Licensee's use of the H.264 video codecs + are solely the responsibility of Licensee. + + 3. Licensee's use of the Thrust library is subject to the + terms and conditions of the Apache License Version 2.0. + All third-party software packages are copyright by their + respective authors. Apache License Version 2.0 terms and + conditions are hereby incorporated into the Agreement by + this reference. + http://www.apache.org/licenses/LICENSE-2.0.html + + In addition, Licensee acknowledges the following notice: + Thrust includes source code from the Boost Iterator, + Tuple, System, and Random Number libraries. + + Boost Software License - Version 1.0 - August 17th, 2003 + . . . . + + Permission is hereby granted, free of charge, to any person or + organization obtaining a copy of the software and accompanying + documentation covered by this license (the "Software") to use, + reproduce, display, distribute, execute, and transmit the Software, + and to prepare derivative works of the Software, and to permit + third-parties to whom the Software is furnished to do so, all + subject to the following: + + The copyright notices in the Software and this entire statement, + including the above license grant, this restriction and the following + disclaimer, must be included in all copies of the Software, in whole + or in part, and all derivative works of the Software, unless such + copies or derivative works are solely in the form of machine-executable + object code generated by a source language processor. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, + EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF + MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND + NON-INFRINGEMENT. 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Licensee's use of the LLVM third party component is + subject to the following terms and conditions: + + ====================================================== + LLVM Release License + ====================================================== + University of Illinois/NCSA + Open Source License + + Copyright (c) 2003-2010 University of Illinois at Urbana-Champaign. + All rights reserved. + + Developed by: + + LLVM Team + + University of Illinois at Urbana-Champaign + + http://llvm.org + + Permission is hereby granted, free of charge, to any person obtaining a copy + of this software and associated documentation files (the "Software"), to + deal with the Software without restriction, including without limitation the + rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + sell copies of the Software, and to permit persons to whom the Software is + furnished to do so, subject to the following conditions: + + * Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimers. + + * Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimers in the + documentation and/or other materials provided with the distribution. + + * Neither the names of the LLVM Team, University of Illinois at Urbana- + Champaign, nor the names of its contributors may be used to endorse or + promote products derived from this Software without specific prior + written permission. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL + THE CONTRIBUTORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR + OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, + ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER + DEALINGS WITH THE SOFTWARE. + + 5. Licensee's use (e.g. nvprof) of the PCRE third party + component is subject to the following terms and + conditions: + + ------------ + PCRE LICENCE + ------------ + PCRE is a library of functions to support regular expressions whose syntax + and semantics are as close as possible to those of the Perl 5 language. + Release 8 of PCRE is distributed under the terms of the "BSD" licence, as + specified below. The documentation for PCRE, supplied in the "doc" + directory, is distributed under the same terms as the software itself. The + basic library functions are written in C and are freestanding. Also + included in the distribution is a set of C++ wrapper functions, and a just- + in-time compiler that can be used to optimize pattern matching. These are + both optional features that can be omitted when the library is built. + + THE BASIC LIBRARY FUNCTIONS + --------------------------- + Written by: Philip Hazel + Email local part: ph10 + Email domain: cam.ac.uk + University of Cambridge Computing Service, + Cambridge, England. + Copyright (c) 1997-2012 University of Cambridge + All rights reserved. + + PCRE JUST-IN-TIME COMPILATION SUPPORT + ------------------------------------- + Written by: Zoltan Herczeg + Email local part: hzmester + Emain domain: freemail.hu + Copyright(c) 2010-2012 Zoltan Herczeg + All rights reserved. + + STACK-LESS JUST-IN-TIME COMPILER + -------------------------------- + Written by: Zoltan Herczeg + Email local part: hzmester + Emain domain: freemail.hu + Copyright(c) 2009-2012 Zoltan Herczeg + All rights reserved. + + THE C++ WRAPPER FUNCTIONS + ------------------------- + Contributed by: Google Inc. + Copyright (c) 2007-2012, Google Inc. + All rights reserved. + + THE "BSD" LICENCE + ----------------- + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are met: + + * Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + + * Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + * Neither the name of the University of Cambridge nor the name of Google + Inc. nor the names of their contributors may be used to endorse or + promote products derived from this software without specific prior + written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE + ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE + LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF + SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS + INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN + CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) + ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + POSSIBILITY OF SUCH DAMAGE. + + 6. Some of the cuBLAS library routines were written by or + derived from code written by Vasily Volkov and are subject + to the Modified Berkeley Software Distribution License as + follows: + + Copyright (c) 2007-2009, Regents of the University of California + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + * Neither the name of the University of California, Berkeley nor + the names of its contributors may be used to endorse or promote + products derived from this software without specific prior + written permission. + + THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR + IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED + WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE + DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, + INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES + (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR + SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) + HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, + STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING + IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + POSSIBILITY OF SUCH DAMAGE. + + 7. Some of the cuBLAS library routines were written by or + derived from code written by Davide Barbieri and are + subject to the Modified Berkeley Software Distribution + License as follows: + + Copyright (c) 2008-2009 Davide Barbieri @ University of Rome Tor Vergata. + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + * The name of the author may not be used to endorse or promote + products derived from this software without specific prior + written permission. + + THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR + IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED + WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE + DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, + INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES + (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR + SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) + HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, + STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING + IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + POSSIBILITY OF SUCH DAMAGE. + + 8. Some of the cuBLAS library routines were derived from + code developed by the University of Tennessee and are + subject to the Modified Berkeley Software Distribution + License as follows: + + Copyright (c) 2010 The University of Tennessee. + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer listed in this license in the documentation and/or + other materials provided with the distribution. + * Neither the name of the copyright holders nor the names of its + contributors may be used to endorse or promote products derived + from this software without specific prior written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + 9. Some of the cuBLAS library routines were written by or + derived from code written by Jonathan Hogg and are subject + to the Modified Berkeley Software Distribution License as + follows: + + Copyright (c) 2012, The Science and Technology Facilities Council (STFC). + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + * Neither the name of the STFC nor the names of its contributors + may be used to endorse or promote products derived from this + software without specific prior written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE STFC BE + LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF + SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR + BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, + WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE + OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN + IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + 10. Some of the cuBLAS library routines were written by or + derived from code written by Ahmad M. Abdelfattah, David + Keyes, and Hatem Ltaief, and are subject to the Apache + License, Version 2.0, as follows: + + -- (C) Copyright 2013 King Abdullah University of Science and Technology + Authors: + Ahmad Abdelfattah (ahmad.ahmad@kaust.edu.sa) + David Keyes (david.keyes@kaust.edu.sa) + Hatem Ltaief (hatem.ltaief@kaust.edu.sa) + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + * Neither the name of the King Abdullah University of Science and + Technology nor the names of its contributors may be used to endorse + or promote products derived from this software without specific prior + written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + HOLDERS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE + + 11. Some of the cuSPARSE library routines were written by or + derived from code written by Li-Wen Chang and are subject + to the NCSA Open Source License as follows: + + Copyright (c) 2012, University of Illinois. + + All rights reserved. + + Developed by: IMPACT Group, University of Illinois, http://impact.crhc.illinois.edu + + Permission is hereby granted, free of charge, to any person obtaining + a copy of this software and associated documentation files (the + "Software"), to deal with the Software without restriction, including + without limitation the rights to use, copy, modify, merge, publish, + distribute, sublicense, and/or sell copies of the Software, and to + permit persons to whom the Software is furnished to do so, subject to + the following conditions: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimers in the documentation and/or other materials provided + with the distribution. + * Neither the names of IMPACT Group, University of Illinois, nor + the names of its contributors may be used to endorse or promote + products derived from this Software without specific prior + written permission. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, + EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF + MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND + NONINFRINGEMENT. IN NO EVENT SHALL THE CONTRIBUTORS OR COPYRIGHT + HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR + IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS WITH THE + SOFTWARE. + + 12. Some of the cuRAND library routines were written by or + derived from code written by Mutsuo Saito and Makoto + Matsumoto and are subject to the following license: + + Copyright (c) 2009, 2010 Mutsuo Saito, Makoto Matsumoto and Hiroshima + University. All rights reserved. + + Copyright (c) 2011 Mutsuo Saito, Makoto Matsumoto, Hiroshima + University and University of Tokyo. All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + * Neither the name of the Hiroshima University nor the names of + its contributors may be used to endorse or promote products + derived from this software without specific prior written + permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. 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The NPP library uses code from the Boost Math Toolkit, + and is subject to the following license: + + Boost Software License - Version 1.0 - August 17th, 2003 + . . . . + + Permission is hereby granted, free of charge, to any person or + organization obtaining a copy of the software and accompanying + documentation covered by this license (the "Software") to use, + reproduce, display, distribute, execute, and transmit the Software, + and to prepare derivative works of the Software, and to permit + third-parties to whom the Software is furnished to do so, all + subject to the following: + + The copyright notices in the Software and this entire statement, + including the above license grant, this restriction and the following + disclaimer, must be included in all copies of the Software, in whole + or in part, and all derivative works of the Software, unless such + copies or derivative works are solely in the form of machine-executable + object code generated by a source language processor. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, + EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF + MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND + NON-INFRINGEMENT. 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Some of the cuBLAS library routines uses code from + OpenAI, which is subject to the following license: + + License URL + https://github.com/openai/openai-gemm/blob/master/LICENSE + + License Text + The MIT License + + Copyright (c) 2016 OpenAI (http://openai.com), 2016 Google Inc. + + Permission is hereby granted, free of charge, to any person obtaining a copy + of this software and associated documentation files (the "Software"), to deal + in the Software without restriction, including without limitation the rights + to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + copies of the Software, and to permit persons to whom the Software is + furnished to do so, subject to the following conditions: + + The above copyright notice and this permission notice shall be included in + all copies or substantial portions of the Software. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. 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IN NO EVENT SHALL THE + AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + + 20. Licensee's use of linmath.h header for CPU functions for + GL vector/matrix operations from lunarG is subject to the + Apache License Version 2.0. + + 21. The DX12-CUDA sample uses the d3dx12.h header, which is + subject to the MIT license . + +----------------- diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cufile_cu12-1.13.1.3.dist-info/METADATA b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cufile_cu12-1.13.1.3.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..4d22d2b0a860e67abb882c6ec18c0a5dc468a39f --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cufile_cu12-1.13.1.3.dist-info/METADATA @@ -0,0 +1,44 @@ +Metadata-Version: 2.2 +Name: nvidia-cufile-cu12 +Version: 1.13.1.3 +Summary: cuFile GPUDirect libraries +Home-page: https://developer.nvidia.com/cuda-zone +Author: Nvidia CUDA Installer Team +Author-email: compute_installer@nvidia.com +License: NVIDIA Proprietary Software +Keywords: cuda,nvidia,runtime,machine learning,deep learning +Classifier: Development Status :: 4 - Beta +Classifier: Intended Audience :: Developers +Classifier: Intended Audience :: Education +Classifier: Intended Audience :: Science/Research +Classifier: License :: Other/Proprietary License +Classifier: Natural Language :: English +Classifier: Programming Language :: Python :: 3 +Classifier: Programming Language :: Python :: 3.5 +Classifier: Programming Language :: Python :: 3.6 +Classifier: Programming Language :: Python :: 3.7 +Classifier: Programming Language :: Python :: 3.8 +Classifier: Programming Language :: Python :: 3.9 +Classifier: Programming Language :: Python :: 3.10 +Classifier: Programming Language :: Python :: 3.11 +Classifier: Programming Language :: Python :: 3 :: Only +Classifier: Topic :: Scientific/Engineering +Classifier: Topic :: Scientific/Engineering :: Mathematics +Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence +Classifier: Topic :: Software Development +Classifier: Topic :: Software Development :: Libraries +Classifier: Operating System :: Microsoft :: 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a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cufile_cu12-1.13.1.3.dist-info/top_level.txt b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cufile_cu12-1.13.1.3.dist-info/top_level.txt new file mode 100644 index 0000000000000000000000000000000000000000..862f7abf232cdfbb928609856247292e81c9decb --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cufile_cu12-1.13.1.3.dist-info/top_level.txt @@ -0,0 +1 @@ +nvidia diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_curand_cu12-10.3.9.90.dist-info/INSTALLER b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_curand_cu12-10.3.9.90.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_curand_cu12-10.3.9.90.dist-info/INSTALLER @@ -0,0 +1 @@ +pip diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_curand_cu12-10.3.9.90.dist-info/License.txt b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_curand_cu12-10.3.9.90.dist-info/License.txt new file mode 100644 index 0000000000000000000000000000000000000000..b491c70e0aef319022ded661e111ddbd45b8a17f --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_curand_cu12-10.3.9.90.dist-info/License.txt @@ -0,0 +1,1568 @@ +End User License Agreement +-------------------------- + + +Preface +------- + +The Software License Agreement in Chapter 1 and the Supplement +in Chapter 2 contain license terms and conditions that govern +the use of NVIDIA software. 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(i) you fail to comply with any term of this + Agreement and the non-compliance is not fixed within + thirty (30) days following notice from NVIDIA (or + immediately if you violate NVIDIA’s intellectual + property rights); + + b. (ii) you commence or participate in any legal + proceeding against NVIDIA with respect to the SDK; or + + c. (iii) NVIDIA decides to no longer provide the SDK in + a country or, in NVIDIA’s sole discretion, the + continued use of it is no longer commercially viable. + + 4. Upon any termination of this Agreement, you agree to + promptly discontinue use of the SDK and destroy all copies + in your possession or control. Your prior distributions in + accordance with this Agreement are not affected by the + termination of this Agreement. Upon written request, you + will certify in writing that you have complied with your + commitments under this section. 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CUDA Toolkit Supplement to Software License Agreement for +NVIDIA Software Development Kits +------------------------------------------------------------ + + +Release date: August 16, 2018 +----------------------------- + +The terms in this supplement govern your use of the NVIDIA +CUDA Toolkit SDK under the terms of your license agreement +(“Agreement”) as modified by this supplement. Capitalized +terms used but not defined below have the meaning assigned to +them in the Agreement. + +This supplement is an exhibit to the Agreement and is +incorporated as an integral part of the Agreement. In the +event of conflict between the terms in this supplement and the +terms in the Agreement, the terms in this supplement govern. + + +2.1. License Scope + +The SDK is licensed for you to develop applications only for +use in systems with NVIDIA GPUs. + + +2.2. Distribution + +The portions of the SDK that are distributable under the +Agreement are listed in Attachment A. + + +2.3. Operating Systems + +Those portions of the SDK designed exclusively for use on the +Linux or FreeBSD operating systems, or other operating systems +derived from the source code to these operating systems, may +be copied and redistributed for use in accordance with this +Agreement, provided that the object code files are not +modified in any way (except for unzipping of compressed +files). + + +2.4. Audio and Video Encoders and Decoders + +You acknowledge and agree that it is your sole responsibility +to obtain any additional third-party licenses required to +make, have made, use, have used, sell, import, and offer for +sale your products or services that include or incorporate any +third-party software and content relating to audio and/or +video encoders and decoders from, including but not limited +to, Microsoft, Thomson, Fraunhofer IIS, Sisvel S.p.A., +MPEG-LA, and Coding Technologies. NVIDIA does not grant to you +under this Agreement any necessary patent or other rights with +respect to any audio and/or video encoders and decoders. + + +2.5. Licensing + +If the distribution terms in this Agreement are not suitable +for your organization, or for any questions regarding this +Agreement, please contact NVIDIA at +nvidia-compute-license-questions@nvidia.com. + + +2.6. Attachment A + +The following portions of the SDK are distributable under the +Agreement: + +Component + +CUDA Runtime + +Windows + +cudart.dll, cudart_static.lib, cudadevrt.lib + +Mac OSX + +libcudart.dylib, libcudart_static.a, libcudadevrt.a + +Linux + +libcudart.so, libcudart_static.a, libcudadevrt.a + +Android + +libcudart.so, libcudart_static.a, libcudadevrt.a + +Component + +CUDA FFT Library + +Windows + +cufft.dll, cufftw.dll, cufft.lib, cufftw.lib + +Mac OSX + +libcufft.dylib, libcufft_static.a, libcufftw.dylib, +libcufftw_static.a + +Linux + +libcufft.so, libcufft_static.a, libcufftw.so, +libcufftw_static.a + +Android + +libcufft.so, libcufft_static.a, libcufftw.so, +libcufftw_static.a + +Component + +CUDA BLAS Library + +Windows + +cublas.dll, cublasLt.dll + +Mac OSX + +libcublas.dylib, libcublasLt.dylib, libcublas_static.a, +libcublasLt_static.a + +Linux + +libcublas.so, libcublasLt.so, libcublas_static.a, +libcublasLt_static.a + +Android + +libcublas.so, libcublasLt.so, libcublas_static.a, +libcublasLt_static.a + +Component + +NVIDIA "Drop-in" BLAS Library + +Windows + +nvblas.dll + +Mac OSX + +libnvblas.dylib + +Linux + +libnvblas.so + +Component + +CUDA Sparse Matrix Library + +Windows + +cusparse.dll, cusparse.lib + +Mac OSX + +libcusparse.dylib, libcusparse_static.a + +Linux + +libcusparse.so, libcusparse_static.a + +Android + +libcusparse.so, libcusparse_static.a + +Component + +CUDA Linear Solver Library + +Windows + +cusolver.dll, cusolver.lib + +Mac OSX + +libcusolver.dylib, libcusolver_static.a + +Linux + +libcusolver.so, libcusolver_static.a + +Android + +libcusolver.so, libcusolver_static.a + +Component + +CUDA Random Number Generation Library + +Windows + +curand.dll, curand.lib + +Mac OSX + +libcurand.dylib, libcurand_static.a + +Linux + +libcurand.so, libcurand_static.a + +Android + +libcurand.so, libcurand_static.a + +Component + +CUDA Accelerated Graph Library + +Component + +NVIDIA Performance Primitives Library + +Windows + +nppc.dll, nppc.lib, nppial.dll, nppial.lib, nppicc.dll, +nppicc.lib, nppicom.dll, nppicom.lib, nppidei.dll, +nppidei.lib, nppif.dll, nppif.lib, nppig.dll, nppig.lib, +nppim.dll, nppim.lib, nppist.dll, nppist.lib, nppisu.dll, +nppisu.lib, nppitc.dll, nppitc.lib, npps.dll, npps.lib + +Mac OSX + +libnppc.dylib, libnppc_static.a, libnppial.dylib, +libnppial_static.a, libnppicc.dylib, libnppicc_static.a, +libnppicom.dylib, libnppicom_static.a, libnppidei.dylib, +libnppidei_static.a, libnppif.dylib, libnppif_static.a, +libnppig.dylib, libnppig_static.a, libnppim.dylib, +libnppisu_static.a, libnppitc.dylib, libnppitc_static.a, +libnpps.dylib, libnpps_static.a + +Linux + +libnppc.so, libnppc_static.a, libnppial.so, +libnppial_static.a, libnppicc.so, libnppicc_static.a, +libnppicom.so, libnppicom_static.a, libnppidei.so, +libnppidei_static.a, libnppif.so, libnppif_static.a +libnppig.so, libnppig_static.a, libnppim.so, +libnppim_static.a, libnppist.so, libnppist_static.a, +libnppisu.so, libnppisu_static.a, libnppitc.so +libnppitc_static.a, libnpps.so, libnpps_static.a + +Android + +libnppc.so, libnppc_static.a, libnppial.so, +libnppial_static.a, libnppicc.so, libnppicc_static.a, +libnppicom.so, libnppicom_static.a, libnppidei.so, +libnppidei_static.a, libnppif.so, libnppif_static.a +libnppig.so, libnppig_static.a, libnppim.so, +libnppim_static.a, libnppist.so, libnppist_static.a, +libnppisu.so, libnppisu_static.a, libnppitc.so +libnppitc_static.a, libnpps.so, libnpps_static.a + +Component + +NVIDIA JPEG Library + +Linux + +libnvjpeg.so, libnvjpeg_static.a + +Component + +Internal common library required for statically linking to +cuBLAS, cuSPARSE, cuFFT, cuRAND, nvJPEG and NPP + +Mac OSX + +libculibos.a + +Linux + +libculibos.a + +Component + +NVIDIA Runtime Compilation Library and Header + +All + +nvrtc.h + +Windows + +nvrtc.dll, nvrtc-builtins.dll + +Mac OSX + +libnvrtc.dylib, libnvrtc-builtins.dylib + +Linux + +libnvrtc.so, libnvrtc-builtins.so + +Component + +NVIDIA Optimizing Compiler Library + +Windows + +nvvm.dll + +Mac OSX + +libnvvm.dylib + +Linux + +libnvvm.so + +Component + +NVIDIA Common Device Math Functions Library + +Windows + +libdevice.10.bc + +Mac OSX + +libdevice.10.bc + +Linux + +libdevice.10.bc + +Component + +CUDA Occupancy Calculation Header Library + +All + +cuda_occupancy.h + +Component + +CUDA Half Precision Headers + +All + +cuda_fp16.h, cuda_fp16.hpp + +Component + +CUDA Profiling Tools Interface (CUPTI) Library + +Windows + +cupti.dll + +Mac OSX + +libcupti.dylib + +Linux + +libcupti.so + +Component + +NVIDIA Tools Extension Library + +Windows + +nvToolsExt.dll, nvToolsExt.lib + +Mac OSX + +libnvToolsExt.dylib + +Linux + +libnvToolsExt.so + +Component + +NVIDIA CUDA Driver Libraries + +Linux + +libcuda.so, libnvidia-fatbinaryloader.so, +libnvidia-ptxjitcompiler.so + +The NVIDIA CUDA Driver Libraries are only distributable in +applications that meet this criteria: + + 1. The application was developed starting from a NVIDIA CUDA + container obtained from Docker Hub or the NVIDIA GPU + Cloud, and + + 2. The resulting application is packaged as a Docker + container and distributed to users on Docker Hub or the + NVIDIA GPU Cloud only. + + +2.7. Attachment B + + +Additional Licensing Obligations + +The following third party components included in the SOFTWARE +are licensed to Licensee pursuant to the following terms and +conditions: + + 1. Licensee's use of the GDB third party component is + subject to the terms and conditions of GNU GPL v3: + + This product includes copyrighted third-party software licensed + under the terms of the GNU General Public License v3 ("GPL v3"). + All third-party software packages are copyright by their respective + authors. GPL v3 terms and conditions are hereby incorporated into + the Agreement by this reference: http://www.gnu.org/licenses/gpl.txt + + Consistent with these licensing requirements, the software + listed below is provided under the terms of the specified + open source software licenses. To obtain source code for + software provided under licenses that require + redistribution of source code, including the GNU General + Public License (GPL) and GNU Lesser General Public License + (LGPL), contact oss-requests@nvidia.com. This offer is + valid for a period of three (3) years from the date of the + distribution of this product by NVIDIA CORPORATION. + + Component License + CUDA-GDB GPL v3 + + 2. Licensee represents and warrants that any and all third + party licensing and/or royalty payment obligations in + connection with Licensee's use of the H.264 video codecs + are solely the responsibility of Licensee. + + 3. Licensee's use of the Thrust library is subject to the + terms and conditions of the Apache License Version 2.0. + All third-party software packages are copyright by their + respective authors. Apache License Version 2.0 terms and + conditions are hereby incorporated into the Agreement by + this reference. + http://www.apache.org/licenses/LICENSE-2.0.html + + In addition, Licensee acknowledges the following notice: + Thrust includes source code from the Boost Iterator, + Tuple, System, and Random Number libraries. + + Boost Software License - Version 1.0 - August 17th, 2003 + . . . . + + Permission is hereby granted, free of charge, to any person or + organization obtaining a copy of the software and accompanying + documentation covered by this license (the "Software") to use, + reproduce, display, distribute, execute, and transmit the Software, + and to prepare derivative works of the Software, and to permit + third-parties to whom the Software is furnished to do so, all + subject to the following: + + The copyright notices in the Software and this entire statement, + including the above license grant, this restriction and the following + disclaimer, must be included in all copies of the Software, in whole + or in part, and all derivative works of the Software, unless such + copies or derivative works are solely in the form of machine-executable + object code generated by a source language processor. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, + EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF + MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND + NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR + ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE FOR ANY DAMAGES OR + OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING + FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR + OTHER DEALINGS IN THE SOFTWARE. + + 4. Licensee's use of the LLVM third party component is + subject to the following terms and conditions: + + ====================================================== + LLVM Release License + ====================================================== + University of Illinois/NCSA + Open Source License + + Copyright (c) 2003-2010 University of Illinois at Urbana-Champaign. + All rights reserved. + + Developed by: + + LLVM Team + + University of Illinois at Urbana-Champaign + + http://llvm.org + + Permission is hereby granted, free of charge, to any person obtaining a copy + of this software and associated documentation files (the "Software"), to + deal with the Software without restriction, including without limitation the + rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + sell copies of the Software, and to permit persons to whom the Software is + furnished to do so, subject to the following conditions: + + * Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimers. + + * Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimers in the + documentation and/or other materials provided with the distribution. + + * Neither the names of the LLVM Team, University of Illinois at Urbana- + Champaign, nor the names of its contributors may be used to endorse or + promote products derived from this Software without specific prior + written permission. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL + THE CONTRIBUTORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR + OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, + ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER + DEALINGS WITH THE SOFTWARE. + + 5. Licensee's use (e.g. nvprof) of the PCRE third party + component is subject to the following terms and + conditions: + + ------------ + PCRE LICENCE + ------------ + PCRE is a library of functions to support regular expressions whose syntax + and semantics are as close as possible to those of the Perl 5 language. + Release 8 of PCRE is distributed under the terms of the "BSD" licence, as + specified below. The documentation for PCRE, supplied in the "doc" + directory, is distributed under the same terms as the software itself. The + basic library functions are written in C and are freestanding. Also + included in the distribution is a set of C++ wrapper functions, and a just- + in-time compiler that can be used to optimize pattern matching. These are + both optional features that can be omitted when the library is built. + + THE BASIC LIBRARY FUNCTIONS + --------------------------- + Written by: Philip Hazel + Email local part: ph10 + Email domain: cam.ac.uk + University of Cambridge Computing Service, + Cambridge, England. + Copyright (c) 1997-2012 University of Cambridge + All rights reserved. + + PCRE JUST-IN-TIME COMPILATION SUPPORT + ------------------------------------- + Written by: Zoltan Herczeg + Email local part: hzmester + Emain domain: freemail.hu + Copyright(c) 2010-2012 Zoltan Herczeg + All rights reserved. + + STACK-LESS JUST-IN-TIME COMPILER + -------------------------------- + Written by: Zoltan Herczeg + Email local part: hzmester + Emain domain: freemail.hu + Copyright(c) 2009-2012 Zoltan Herczeg + All rights reserved. + + THE C++ WRAPPER FUNCTIONS + ------------------------- + Contributed by: Google Inc. + Copyright (c) 2007-2012, Google Inc. + All rights reserved. + + THE "BSD" LICENCE + ----------------- + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are met: + + * Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + + * Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + * Neither the name of the University of Cambridge nor the name of Google + Inc. nor the names of their contributors may be used to endorse or + promote products derived from this software without specific prior + written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE + ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE + LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF + SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS + INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN + CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) + ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + POSSIBILITY OF SUCH DAMAGE. + + 6. Some of the cuBLAS library routines were written by or + derived from code written by Vasily Volkov and are subject + to the Modified Berkeley Software Distribution License as + follows: + + Copyright (c) 2007-2009, Regents of the University of California + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + * Neither the name of the University of California, Berkeley nor + the names of its contributors may be used to endorse or promote + products derived from this software without specific prior + written permission. + + THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR + IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED + WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE + DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, + INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES + (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR + SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) + HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, + STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING + IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + POSSIBILITY OF SUCH DAMAGE. + + 7. Some of the cuBLAS library routines were written by or + derived from code written by Davide Barbieri and are + subject to the Modified Berkeley Software Distribution + License as follows: + + Copyright (c) 2008-2009 Davide Barbieri @ University of Rome Tor Vergata. + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + * The name of the author may not be used to endorse or promote + products derived from this software without specific prior + written permission. + + THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR + IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED + WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE + DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, + INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES + (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR + SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) + HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, + STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING + IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + POSSIBILITY OF SUCH DAMAGE. + + 8. Some of the cuBLAS library routines were derived from + code developed by the University of Tennessee and are + subject to the Modified Berkeley Software Distribution + License as follows: + + Copyright (c) 2010 The University of Tennessee. + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer listed in this license in the documentation and/or + other materials provided with the distribution. + * Neither the name of the copyright holders nor the names of its + contributors may be used to endorse or promote products derived + from this software without specific prior written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + 9. Some of the cuBLAS library routines were written by or + derived from code written by Jonathan Hogg and are subject + to the Modified Berkeley Software Distribution License as + follows: + + Copyright (c) 2012, The Science and Technology Facilities Council (STFC). + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + * Neither the name of the STFC nor the names of its contributors + may be used to endorse or promote products derived from this + software without specific prior written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE STFC BE + LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF + SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR + BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, + WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE + OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN + IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + 10. Some of the cuBLAS library routines were written by or + derived from code written by Ahmad M. Abdelfattah, David + Keyes, and Hatem Ltaief, and are subject to the Apache + License, Version 2.0, as follows: + + -- (C) Copyright 2013 King Abdullah University of Science and Technology + Authors: + Ahmad Abdelfattah (ahmad.ahmad@kaust.edu.sa) + David Keyes (david.keyes@kaust.edu.sa) + Hatem Ltaief (hatem.ltaief@kaust.edu.sa) + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + * Neither the name of the King Abdullah University of Science and + Technology nor the names of its contributors may be used to endorse + or promote products derived from this software without specific prior + written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + HOLDERS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE + + 11. Some of the cuSPARSE library routines were written by or + derived from code written by Li-Wen Chang and are subject + to the NCSA Open Source License as follows: + + Copyright (c) 2012, University of Illinois. + + All rights reserved. + + Developed by: IMPACT Group, University of Illinois, http://impact.crhc.illinois.edu + + Permission is hereby granted, free of charge, to any person obtaining + a copy of this software and associated documentation files (the + "Software"), to deal with the Software without restriction, including + without limitation the rights to use, copy, modify, merge, publish, + distribute, sublicense, and/or sell copies of the Software, and to + permit persons to whom the Software is furnished to do so, subject to + the following conditions: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimers in the documentation and/or other materials provided + with the distribution. + * Neither the names of IMPACT Group, University of Illinois, nor + the names of its contributors may be used to endorse or promote + products derived from this Software without specific prior + written permission. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, + EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF + MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND + NONINFRINGEMENT. IN NO EVENT SHALL THE CONTRIBUTORS OR COPYRIGHT + HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR + IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS WITH THE + SOFTWARE. + + 12. Some of the cuRAND library routines were written by or + derived from code written by Mutsuo Saito and Makoto + Matsumoto and are subject to the following license: + + Copyright (c) 2009, 2010 Mutsuo Saito, Makoto Matsumoto and Hiroshima + University. All rights reserved. + + Copyright (c) 2011 Mutsuo Saito, Makoto Matsumoto, Hiroshima + University and University of Tokyo. All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + * Neither the name of the Hiroshima University nor the names of + its contributors may be used to endorse or promote products + derived from this software without specific prior written + permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. 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Shaw Research. + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions, and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions, and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + * Neither the name of D. E. Shaw Research nor the names of its + contributors may be used to endorse or promote products derived + from this software without specific prior written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. 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Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + 2. Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + 15. Licensee's use of the lz4 third party component is + subject to the following terms and conditions: + + Copyright (C) 2011-2013, Yann Collet. + BSD 2-Clause License (http://www.opensource.org/licenses/bsd-license.php) + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following disclaimer + in the documentation and/or other materials provided with the + distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + 16. The NPP library uses code from the Boost Math Toolkit, + and is subject to the following license: + + Boost Software License - Version 1.0 - August 17th, 2003 + . . . . + + Permission is hereby granted, free of charge, to any person or + organization obtaining a copy of the software and accompanying + documentation covered by this license (the "Software") to use, + reproduce, display, distribute, execute, and transmit the Software, + and to prepare derivative works of the Software, and to permit + third-parties to whom the Software is furnished to do so, all + subject to the following: + + The copyright notices in the Software and this entire statement, + including the above license grant, this restriction and the following + disclaimer, must be included in all copies of the Software, in whole + or in part, and all derivative works of the Software, unless such + copies or derivative works are solely in the form of machine-executable + object code generated by a source language processor. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, + EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF + MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND + NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR + ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE FOR ANY DAMAGES OR + OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING + FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR + OTHER DEALINGS IN THE SOFTWARE. + + 17. Portions of the Nsight Eclipse Edition is subject to the + following license: + + The Eclipse Foundation makes available all content in this plug-in + ("Content"). Unless otherwise indicated below, the Content is provided + to you under the terms and conditions of the Eclipse Public License + Version 1.0 ("EPL"). A copy of the EPL is available at http:// + www.eclipse.org/legal/epl-v10.html. For purposes of the EPL, "Program" + will mean the Content. + + If you did not receive this Content directly from the Eclipse + Foundation, the Content is being redistributed by another party + ("Redistributor") and different terms and conditions may apply to your + use of any object code in the Content. 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Some of the cuBLAS library routines uses code from + OpenAI, which is subject to the following license: + + License URL + https://github.com/openai/openai-gemm/blob/master/LICENSE + + License Text + The MIT License + + Copyright (c) 2016 OpenAI (http://openai.com), 2016 Google Inc. + + Permission is hereby granted, free of charge, to any person obtaining a copy + of this software and associated documentation files (the "Software"), to deal + in the Software without restriction, including without limitation the rights + to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + copies of the Software, and to permit persons to whom the Software is + furnished to do so, subject to the following conditions: + + The above copyright notice and this permission notice shall be included in + all copies or substantial portions of the Software. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN + THE SOFTWARE. + + 19. Licensee's use of the Visual Studio Setup Configuration + Samples is subject to the following license: + + The MIT License (MIT) + Copyright (C) Microsoft Corporation. All rights reserved. + + Permission is hereby granted, free of charge, to any person + obtaining a copy of this software and associated documentation + files (the "Software"), to deal in the Software without restriction, + including without limitation the rights to use, copy, modify, merge, + publish, distribute, sublicense, and/or sell copies of the Software, + and to permit persons to whom the Software is furnished to do so, + subject to the following conditions: + + The above copyright notice and this permission notice shall be included + in all copies or substantial portions of the Software. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS + OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + + 20. Licensee's use of linmath.h header for CPU functions for + GL vector/matrix operations from lunarG is subject to the + Apache License Version 2.0. + + 21. The DX12-CUDA sample uses the d3dx12.h header, which is + subject to the MIT license . + +----------------- diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_curand_cu12-10.3.9.90.dist-info/METADATA b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_curand_cu12-10.3.9.90.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..7617880958e7c1f9e79db788e5ab17cad840dca2 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_curand_cu12-10.3.9.90.dist-info/METADATA @@ -0,0 +1,44 @@ +Metadata-Version: 2.2 +Name: nvidia-curand-cu12 +Version: 10.3.9.90 +Summary: CURAND native runtime libraries +Home-page: https://developer.nvidia.com/cuda-zone +Author: Nvidia CUDA Installer Team +Author-email: compute_installer@nvidia.com +License: NVIDIA Proprietary Software +Keywords: cuda,nvidia,runtime,machine learning,deep learning +Classifier: Development Status :: 4 - Beta +Classifier: 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a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cusolver_cu12-11.7.3.90.dist-info/INSTALLER b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cusolver_cu12-11.7.3.90.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cusolver_cu12-11.7.3.90.dist-info/INSTALLER @@ -0,0 +1 @@ +pip diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cusolver_cu12-11.7.3.90.dist-info/License.txt b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cusolver_cu12-11.7.3.90.dist-info/License.txt new file mode 100644 index 0000000000000000000000000000000000000000..b491c70e0aef319022ded661e111ddbd45b8a17f --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cusolver_cu12-11.7.3.90.dist-info/License.txt @@ -0,0 +1,1568 @@ +End User License Agreement +-------------------------- + + +Preface +------- + +The Software License Agreement in Chapter 1 and the Supplement +in Chapter 2 contain license terms and conditions that govern +the use of NVIDIA software. By accepting this agreement, you +agree to comply with all the terms and conditions applicable +to the product(s) included herein. + + +NVIDIA Driver + + +Description + +This package contains the operating system driver and +fundamental system software components for NVIDIA GPUs. + + +NVIDIA CUDA Toolkit + + +Description + +The NVIDIA CUDA Toolkit provides command-line and graphical +tools for building, debugging and optimizing the performance +of applications accelerated by NVIDIA GPUs, runtime and math +libraries, and documentation including programming guides, +user manuals, and API references. + + +Default Install Location of CUDA Toolkit + +Windows platform: + +%ProgramFiles%\NVIDIA GPU Computing Toolkit\CUDA\v#.# + +Linux platform: + +/usr/local/cuda-#.# + +Mac platform: + +/Developer/NVIDIA/CUDA-#.# + + +NVIDIA CUDA Samples + + +Description + +This package includes over 100+ CUDA examples that demonstrate +various CUDA programming principles, and efficient CUDA +implementation of algorithms in specific application domains. + + +Default Install Location of CUDA Samples + +Windows platform: + +%ProgramData%\NVIDIA Corporation\CUDA Samples\v#.# + +Linux platform: + +/usr/local/cuda-#.#/samples + +and + +$HOME/NVIDIA_CUDA-#.#_Samples + +Mac platform: + +/Developer/NVIDIA/CUDA-#.#/samples + + +NVIDIA Nsight Visual Studio Edition (Windows only) + + +Description + +NVIDIA Nsight Development Platform, Visual Studio Edition is a +development environment integrated into Microsoft Visual +Studio that provides tools for debugging, profiling, analyzing +and optimizing your GPU computing and graphics applications. + + +Default Install Location of Nsight Visual Studio Edition + +Windows platform: + +%ProgramFiles(x86)%\NVIDIA Corporation\Nsight Visual Studio Edition #.# + + +1. License Agreement for NVIDIA Software Development Kits +--------------------------------------------------------- + + +Release Date: July 26, 2018 +--------------------------- + + +Important NoticeRead before downloading, installing, +copying or using the licensed software: +------------------------------------------------------- + +This license agreement, including exhibits attached +("Agreement”) is a legal agreement between you and NVIDIA +Corporation ("NVIDIA") and governs your use of a NVIDIA +software development kit (“SDK”). + +Each SDK has its own set of software and materials, but here +is a description of the types of items that may be included in +a SDK: source code, header files, APIs, data sets and assets +(examples include images, textures, models, scenes, videos, +native API input/output files), binary software, sample code, +libraries, utility programs, programming code and +documentation. + +This Agreement can be accepted only by an adult of legal age +of majority in the country in which the SDK is used. + +If you are entering into this Agreement on behalf of a company +or other legal entity, you represent that you have the legal +authority to bind the entity to this Agreement, in which case +“you” will mean the entity you represent. + +If you don’t have the required age or authority to accept +this Agreement, or if you don’t accept all the terms and +conditions of this Agreement, do not download, install or use +the SDK. + +You agree to use the SDK only for purposes that are permitted +by (a) this Agreement, and (b) any applicable law, regulation +or generally accepted practices or guidelines in the relevant +jurisdictions. + + +1.1. 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You may not reverse engineer, decompile or disassemble, + or remove copyright or other proprietary notices from any + portion of the SDK or copies of the SDK. + + 2. Except as expressly provided in this Agreement, you may + not copy, sell, rent, sublicense, transfer, distribute, + modify, or create derivative works of any portion of the + SDK. For clarity, you may not distribute or sublicense the + SDK as a stand-alone product. + + 3. Unless you have an agreement with NVIDIA for this + purpose, you may not indicate that an application created + with the SDK is sponsored or endorsed by NVIDIA. + + 4. You may not bypass, disable, or circumvent any + encryption, security, digital rights management or + authentication mechanism in the SDK. + + 5. You may not use the SDK in any manner that would cause it + to become subject to an open source software license. As + examples, licenses that require as a condition of use, + modification, and/or distribution that the SDK be: + + a. 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This SDK may include software and materials from + NVIDIA’s licensors, and these licensors are intended + third party beneficiaries that may enforce this Agreement + with respect to their intellectual property rights. + + 2. You hold all rights, title and interest in and to your + applications and your derivative works of the sample + source code delivered in the SDK, including their + respective intellectual property rights, subject to + NVIDIA’s rights described in this section. + + 3. You may, but don’t have to, provide to NVIDIA + suggestions, feature requests or other feedback regarding + the SDK, including possible enhancements or modifications + to the SDK. For any feedback that you voluntarily provide, + you hereby grant NVIDIA and its affiliates a perpetual, + non-exclusive, worldwide, irrevocable license to use, + reproduce, modify, license, sublicense (through multiple + tiers of sublicensees), and distribute (through multiple + tiers of distributors) it without the payment of any + royalties or fees to you. NVIDIA will use feedback at its + choice. NVIDIA is constantly looking for ways to improve + its products, so you may send feedback to NVIDIA through + the developer portal at https://developer.nvidia.com. + + +1.4. No Warranties + +THE SDK IS PROVIDED BY NVIDIA “AS IS” AND “WITH ALL +FAULTS.” TO THE MAXIMUM EXTENT PERMITTED BY LAW, NVIDIA AND +ITS AFFILIATES EXPRESSLY DISCLAIM ALL WARRANTIES OF ANY KIND +OR NATURE, WHETHER EXPRESS, IMPLIED OR STATUTORY, INCLUDING, +BUT NOT LIMITED TO, ANY WARRANTIES OF MERCHANTABILITY, FITNESS +FOR A PARTICULAR PURPOSE, TITLE, NON-INFRINGEMENT, OR THE +ABSENCE OF ANY DEFECTS THEREIN, WHETHER LATENT OR PATENT. NO +WARRANTY IS MADE ON THE BASIS OF TRADE USAGE, COURSE OF +DEALING OR COURSE OF TRADE. + + +1.5. Limitation of Liability + +TO THE MAXIMUM EXTENT PERMITTED BY LAW, NVIDIA AND ITS +AFFILIATES SHALL NOT BE LIABLE FOR ANY SPECIAL, INCIDENTAL, +PUNITIVE OR CONSEQUENTIAL DAMAGES, OR ANY LOST PROFITS, LOSS +OF USE, LOSS OF DATA OR LOSS OF GOODWILL, OR THE COSTS OF +PROCURING SUBSTITUTE PRODUCTS, ARISING OUT OF OR IN CONNECTION +WITH THIS AGREEMENT OR THE USE OR PERFORMANCE OF THE SDK, +WHETHER SUCH LIABILITY ARISES FROM ANY CLAIM BASED UPON BREACH +OF CONTRACT, BREACH OF WARRANTY, TORT (INCLUDING NEGLIGENCE), +PRODUCT LIABILITY OR ANY OTHER CAUSE OF ACTION OR THEORY OF +LIABILITY. IN NO EVENT WILL NVIDIA’S AND ITS AFFILIATES +TOTAL CUMULATIVE LIABILITY UNDER OR ARISING OUT OF THIS +AGREEMENT EXCEED US$10.00. THE NATURE OF THE LIABILITY OR THE +NUMBER OF CLAIMS OR SUITS SHALL NOT ENLARGE OR EXTEND THIS +LIMIT. + +These exclusions and limitations of liability shall apply +regardless if NVIDIA or its affiliates have been advised of +the possibility of such damages, and regardless of whether a +remedy fails its essential purpose. These exclusions and +limitations of liability form an essential basis of the +bargain between the parties, and, absent any of these +exclusions or limitations of liability, the provisions of this +Agreement, including, without limitation, the economic terms, +would be substantially different. + + +1.6. Termination + + 1. This Agreement will continue to apply until terminated by + either you or NVIDIA as described below. + + 2. If you want to terminate this Agreement, you may do so by + stopping to use the SDK. + + 3. NVIDIA may, at any time, terminate this Agreement if: + + a. (i) you fail to comply with any term of this + Agreement and the non-compliance is not fixed within + thirty (30) days following notice from NVIDIA (or + immediately if you violate NVIDIA’s intellectual + property rights); + + b. (ii) you commence or participate in any legal + proceeding against NVIDIA with respect to the SDK; or + + c. (iii) NVIDIA decides to no longer provide the SDK in + a country or, in NVIDIA’s sole discretion, the + continued use of it is no longer commercially viable. + + 4. Upon any termination of this Agreement, you agree to + promptly discontinue use of the SDK and destroy all copies + in your possession or control. Your prior distributions in + accordance with this Agreement are not affected by the + termination of this Agreement. Upon written request, you + will certify in writing that you have complied with your + commitments under this section. Upon any termination of + this Agreement all provisions survive except for the + license grant provisions. + + +1.7. General + +If you wish to assign this Agreement or your rights and +obligations, including by merger, consolidation, dissolution +or operation of law, contact NVIDIA to ask for permission. Any +attempted assignment not approved by NVIDIA in writing shall +be void and of no effect. NVIDIA may assign, delegate or +transfer this Agreement and its rights and obligations, and if +to a non-affiliate you will be notified. + +You agree to cooperate with NVIDIA and provide reasonably +requested information to verify your compliance with this +Agreement. + +This Agreement will be governed in all respects by the laws of +the United States and of the State of Delaware as those laws +are applied to contracts entered into and performed entirely +within Delaware by Delaware residents, without regard to the +conflicts of laws principles. The United Nations Convention on +Contracts for the International Sale of Goods is specifically +disclaimed. You agree to all terms of this Agreement in the +English language. + +The state or federal courts residing in Santa Clara County, +California shall have exclusive jurisdiction over any dispute +or claim arising out of this Agreement. Notwithstanding this, +you agree that NVIDIA shall still be allowed to apply for +injunctive remedies or an equivalent type of urgent legal +relief in any jurisdiction. + +If any court of competent jurisdiction determines that any +provision of this Agreement is illegal, invalid or +unenforceable, such provision will be construed as limited to +the extent necessary to be consistent with and fully +enforceable under the law and the remaining provisions will +remain in full force and effect. Unless otherwise specified, +remedies are cumulative. + +Each party acknowledges and agrees that the other is an +independent contractor in the performance of this Agreement. + +The SDK has been developed entirely at private expense and is +“commercial items” consisting of “commercial computer +software” and “commercial computer software +documentation” provided with RESTRICTED RIGHTS. Use, +duplication or disclosure by the U.S. Government or a U.S. +Government subcontractor is subject to the restrictions in +this Agreement pursuant to DFARS 227.7202-3(a) or as set forth +in subparagraphs (c)(1) and (2) of the Commercial Computer +Software - Restricted Rights clause at FAR 52.227-19, as +applicable. Contractor/manufacturer is NVIDIA, 2788 San Tomas +Expressway, Santa Clara, CA 95051. + +The SDK is subject to United States export laws and +regulations. You agree that you will not ship, transfer or +export the SDK into any country, or use the SDK in any manner, +prohibited by the United States Bureau of Industry and +Security or economic sanctions regulations administered by the +U.S. Department of Treasury’s Office of Foreign Assets +Control (OFAC), or any applicable export laws, restrictions or +regulations. These laws include restrictions on destinations, +end users and end use. By accepting this Agreement, you +confirm that you are not a resident or citizen of any country +currently embargoed by the U.S. and that you are not otherwise +prohibited from receiving the SDK. + +Any notice delivered by NVIDIA to you under this Agreement +will be delivered via mail, email or fax. You agree that any +notices that NVIDIA sends you electronically will satisfy any +legal communication requirements. Please direct your legal +notices or other correspondence to NVIDIA Corporation, 2788 +San Tomas Expressway, Santa Clara, California 95051, United +States of America, Attention: Legal Department. + +This Agreement and any exhibits incorporated into this +Agreement constitute the entire agreement of the parties with +respect to the subject matter of this Agreement and supersede +all prior negotiations or documentation exchanged between the +parties relating to this SDK license. Any additional and/or +conflicting terms on documents issued by you are null, void, +and invalid. Any amendment or waiver under this Agreement +shall be in writing and signed by representatives of both +parties. + + +2. CUDA Toolkit Supplement to Software License Agreement for +NVIDIA Software Development Kits +------------------------------------------------------------ + + +Release date: August 16, 2018 +----------------------------- + +The terms in this supplement govern your use of the NVIDIA +CUDA Toolkit SDK under the terms of your license agreement +(“Agreement”) as modified by this supplement. Capitalized +terms used but not defined below have the meaning assigned to +them in the Agreement. + +This supplement is an exhibit to the Agreement and is +incorporated as an integral part of the Agreement. In the +event of conflict between the terms in this supplement and the +terms in the Agreement, the terms in this supplement govern. + + +2.1. License Scope + +The SDK is licensed for you to develop applications only for +use in systems with NVIDIA GPUs. + + +2.2. Distribution + +The portions of the SDK that are distributable under the +Agreement are listed in Attachment A. + + +2.3. Operating Systems + +Those portions of the SDK designed exclusively for use on the +Linux or FreeBSD operating systems, or other operating systems +derived from the source code to these operating systems, may +be copied and redistributed for use in accordance with this +Agreement, provided that the object code files are not +modified in any way (except for unzipping of compressed +files). + + +2.4. Audio and Video Encoders and Decoders + +You acknowledge and agree that it is your sole responsibility +to obtain any additional third-party licenses required to +make, have made, use, have used, sell, import, and offer for +sale your products or services that include or incorporate any +third-party software and content relating to audio and/or +video encoders and decoders from, including but not limited +to, Microsoft, Thomson, Fraunhofer IIS, Sisvel S.p.A., +MPEG-LA, and Coding Technologies. NVIDIA does not grant to you +under this Agreement any necessary patent or other rights with +respect to any audio and/or video encoders and decoders. + + +2.5. Licensing + +If the distribution terms in this Agreement are not suitable +for your organization, or for any questions regarding this +Agreement, please contact NVIDIA at +nvidia-compute-license-questions@nvidia.com. + + +2.6. Attachment A + +The following portions of the SDK are distributable under the +Agreement: + +Component + +CUDA Runtime + +Windows + +cudart.dll, cudart_static.lib, cudadevrt.lib + +Mac OSX + +libcudart.dylib, libcudart_static.a, libcudadevrt.a + +Linux + +libcudart.so, libcudart_static.a, libcudadevrt.a + +Android + +libcudart.so, libcudart_static.a, libcudadevrt.a + +Component + +CUDA FFT Library + +Windows + +cufft.dll, cufftw.dll, cufft.lib, cufftw.lib + +Mac OSX + +libcufft.dylib, libcufft_static.a, libcufftw.dylib, +libcufftw_static.a + +Linux + +libcufft.so, libcufft_static.a, libcufftw.so, +libcufftw_static.a + +Android + +libcufft.so, libcufft_static.a, libcufftw.so, +libcufftw_static.a + +Component + +CUDA BLAS Library + +Windows + +cublas.dll, cublasLt.dll + +Mac OSX + +libcublas.dylib, libcublasLt.dylib, libcublas_static.a, +libcublasLt_static.a + +Linux + +libcublas.so, libcublasLt.so, libcublas_static.a, +libcublasLt_static.a + +Android + +libcublas.so, libcublasLt.so, libcublas_static.a, +libcublasLt_static.a + +Component + +NVIDIA "Drop-in" BLAS Library + +Windows + +nvblas.dll + +Mac OSX + +libnvblas.dylib + +Linux + +libnvblas.so + +Component + +CUDA Sparse Matrix Library + +Windows + +cusparse.dll, cusparse.lib + +Mac OSX + +libcusparse.dylib, libcusparse_static.a + +Linux + +libcusparse.so, libcusparse_static.a + +Android + +libcusparse.so, libcusparse_static.a + +Component + +CUDA Linear Solver Library + +Windows + +cusolver.dll, cusolver.lib + +Mac OSX + +libcusolver.dylib, libcusolver_static.a + +Linux + +libcusolver.so, libcusolver_static.a + +Android + +libcusolver.so, libcusolver_static.a + +Component + +CUDA Random Number Generation Library + +Windows + +curand.dll, curand.lib + +Mac OSX + +libcurand.dylib, libcurand_static.a + +Linux + +libcurand.so, libcurand_static.a + +Android + +libcurand.so, libcurand_static.a + +Component + +CUDA Accelerated Graph Library + +Component + +NVIDIA Performance Primitives Library + +Windows + +nppc.dll, nppc.lib, nppial.dll, nppial.lib, nppicc.dll, +nppicc.lib, nppicom.dll, nppicom.lib, nppidei.dll, +nppidei.lib, nppif.dll, nppif.lib, nppig.dll, nppig.lib, +nppim.dll, nppim.lib, nppist.dll, nppist.lib, nppisu.dll, +nppisu.lib, nppitc.dll, nppitc.lib, npps.dll, npps.lib + +Mac OSX + +libnppc.dylib, libnppc_static.a, libnppial.dylib, +libnppial_static.a, libnppicc.dylib, libnppicc_static.a, +libnppicom.dylib, libnppicom_static.a, libnppidei.dylib, +libnppidei_static.a, libnppif.dylib, libnppif_static.a, +libnppig.dylib, libnppig_static.a, libnppim.dylib, +libnppisu_static.a, libnppitc.dylib, libnppitc_static.a, +libnpps.dylib, libnpps_static.a + +Linux + +libnppc.so, libnppc_static.a, libnppial.so, +libnppial_static.a, libnppicc.so, libnppicc_static.a, +libnppicom.so, libnppicom_static.a, libnppidei.so, +libnppidei_static.a, libnppif.so, libnppif_static.a +libnppig.so, libnppig_static.a, libnppim.so, +libnppim_static.a, libnppist.so, libnppist_static.a, +libnppisu.so, libnppisu_static.a, libnppitc.so +libnppitc_static.a, libnpps.so, libnpps_static.a + +Android + +libnppc.so, libnppc_static.a, libnppial.so, +libnppial_static.a, libnppicc.so, libnppicc_static.a, +libnppicom.so, libnppicom_static.a, libnppidei.so, +libnppidei_static.a, libnppif.so, libnppif_static.a +libnppig.so, libnppig_static.a, libnppim.so, +libnppim_static.a, libnppist.so, libnppist_static.a, +libnppisu.so, libnppisu_static.a, libnppitc.so +libnppitc_static.a, libnpps.so, libnpps_static.a + +Component + +NVIDIA JPEG Library + +Linux + +libnvjpeg.so, libnvjpeg_static.a + +Component + +Internal common library required for statically linking to +cuBLAS, cuSPARSE, cuFFT, cuRAND, nvJPEG and NPP + +Mac OSX + +libculibos.a + +Linux + +libculibos.a + +Component + +NVIDIA Runtime Compilation Library and Header + +All + +nvrtc.h + +Windows + +nvrtc.dll, nvrtc-builtins.dll + +Mac OSX + +libnvrtc.dylib, libnvrtc-builtins.dylib + +Linux + +libnvrtc.so, libnvrtc-builtins.so + +Component + +NVIDIA Optimizing Compiler Library + +Windows + +nvvm.dll + +Mac OSX + +libnvvm.dylib + +Linux + +libnvvm.so + +Component + +NVIDIA Common Device Math Functions Library + +Windows + +libdevice.10.bc + +Mac OSX + +libdevice.10.bc + +Linux + +libdevice.10.bc + +Component + +CUDA Occupancy Calculation Header Library + +All + +cuda_occupancy.h + +Component + +CUDA Half Precision Headers + +All + +cuda_fp16.h, cuda_fp16.hpp + +Component + +CUDA Profiling Tools Interface (CUPTI) Library + +Windows + +cupti.dll + +Mac OSX + +libcupti.dylib + +Linux + +libcupti.so + +Component + +NVIDIA Tools Extension Library + +Windows + +nvToolsExt.dll, nvToolsExt.lib + +Mac OSX + +libnvToolsExt.dylib + +Linux + +libnvToolsExt.so + +Component + +NVIDIA CUDA Driver Libraries + +Linux + +libcuda.so, libnvidia-fatbinaryloader.so, +libnvidia-ptxjitcompiler.so + +The NVIDIA CUDA Driver Libraries are only distributable in +applications that meet this criteria: + + 1. The application was developed starting from a NVIDIA CUDA + container obtained from Docker Hub or the NVIDIA GPU + Cloud, and + + 2. The resulting application is packaged as a Docker + container and distributed to users on Docker Hub or the + NVIDIA GPU Cloud only. + + +2.7. Attachment B + + +Additional Licensing Obligations + +The following third party components included in the SOFTWARE +are licensed to Licensee pursuant to the following terms and +conditions: + + 1. Licensee's use of the GDB third party component is + subject to the terms and conditions of GNU GPL v3: + + This product includes copyrighted third-party software licensed + under the terms of the GNU General Public License v3 ("GPL v3"). + All third-party software packages are copyright by their respective + authors. GPL v3 terms and conditions are hereby incorporated into + the Agreement by this reference: http://www.gnu.org/licenses/gpl.txt + + Consistent with these licensing requirements, the software + listed below is provided under the terms of the specified + open source software licenses. To obtain source code for + software provided under licenses that require + redistribution of source code, including the GNU General + Public License (GPL) and GNU Lesser General Public License + (LGPL), contact oss-requests@nvidia.com. This offer is + valid for a period of three (3) years from the date of the + distribution of this product by NVIDIA CORPORATION. + + Component License + CUDA-GDB GPL v3 + + 2. Licensee represents and warrants that any and all third + party licensing and/or royalty payment obligations in + connection with Licensee's use of the H.264 video codecs + are solely the responsibility of Licensee. + + 3. Licensee's use of the Thrust library is subject to the + terms and conditions of the Apache License Version 2.0. + All third-party software packages are copyright by their + respective authors. Apache License Version 2.0 terms and + conditions are hereby incorporated into the Agreement by + this reference. + http://www.apache.org/licenses/LICENSE-2.0.html + + In addition, Licensee acknowledges the following notice: + Thrust includes source code from the Boost Iterator, + Tuple, System, and Random Number libraries. + + Boost Software License - Version 1.0 - August 17th, 2003 + . . . . + + Permission is hereby granted, free of charge, to any person or + organization obtaining a copy of the software and accompanying + documentation covered by this license (the "Software") to use, + reproduce, display, distribute, execute, and transmit the Software, + and to prepare derivative works of the Software, and to permit + third-parties to whom the Software is furnished to do so, all + subject to the following: + + The copyright notices in the Software and this entire statement, + including the above license grant, this restriction and the following + disclaimer, must be included in all copies of the Software, in whole + or in part, and all derivative works of the Software, unless such + copies or derivative works are solely in the form of machine-executable + object code generated by a source language processor. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, + EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF + MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND + NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR + ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE FOR ANY DAMAGES OR + OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING + FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR + OTHER DEALINGS IN THE SOFTWARE. + + 4. Licensee's use of the LLVM third party component is + subject to the following terms and conditions: + + ====================================================== + LLVM Release License + ====================================================== + University of Illinois/NCSA + Open Source License + + Copyright (c) 2003-2010 University of Illinois at Urbana-Champaign. + All rights reserved. + + Developed by: + + LLVM Team + + University of Illinois at Urbana-Champaign + + http://llvm.org + + Permission is hereby granted, free of charge, to any person obtaining a copy + of this software and associated documentation files (the "Software"), to + deal with the Software without restriction, including without limitation the + rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + sell copies of the Software, and to permit persons to whom the Software is + furnished to do so, subject to the following conditions: + + * Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimers. + + * Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimers in the + documentation and/or other materials provided with the distribution. + + * Neither the names of the LLVM Team, University of Illinois at Urbana- + Champaign, nor the names of its contributors may be used to endorse or + promote products derived from this Software without specific prior + written permission. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL + THE CONTRIBUTORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR + OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, + ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER + DEALINGS WITH THE SOFTWARE. + + 5. Licensee's use (e.g. nvprof) of the PCRE third party + component is subject to the following terms and + conditions: + + ------------ + PCRE LICENCE + ------------ + PCRE is a library of functions to support regular expressions whose syntax + and semantics are as close as possible to those of the Perl 5 language. + Release 8 of PCRE is distributed under the terms of the "BSD" licence, as + specified below. The documentation for PCRE, supplied in the "doc" + directory, is distributed under the same terms as the software itself. The + basic library functions are written in C and are freestanding. Also + included in the distribution is a set of C++ wrapper functions, and a just- + in-time compiler that can be used to optimize pattern matching. These are + both optional features that can be omitted when the library is built. + + THE BASIC LIBRARY FUNCTIONS + --------------------------- + Written by: Philip Hazel + Email local part: ph10 + Email domain: cam.ac.uk + University of Cambridge Computing Service, + Cambridge, England. + Copyright (c) 1997-2012 University of Cambridge + All rights reserved. + + PCRE JUST-IN-TIME COMPILATION SUPPORT + ------------------------------------- + Written by: Zoltan Herczeg + Email local part: hzmester + Emain domain: freemail.hu + Copyright(c) 2010-2012 Zoltan Herczeg + All rights reserved. + + STACK-LESS JUST-IN-TIME COMPILER + -------------------------------- + Written by: Zoltan Herczeg + Email local part: hzmester + Emain domain: freemail.hu + Copyright(c) 2009-2012 Zoltan Herczeg + All rights reserved. + + THE C++ WRAPPER FUNCTIONS + ------------------------- + Contributed by: Google Inc. + Copyright (c) 2007-2012, Google Inc. + All rights reserved. + + THE "BSD" LICENCE + ----------------- + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are met: + + * Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + + * Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + * Neither the name of the University of Cambridge nor the name of Google + Inc. nor the names of their contributors may be used to endorse or + promote products derived from this software without specific prior + written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE + ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE + LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF + SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS + INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN + CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) + ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + POSSIBILITY OF SUCH DAMAGE. + + 6. Some of the cuBLAS library routines were written by or + derived from code written by Vasily Volkov and are subject + to the Modified Berkeley Software Distribution License as + follows: + + Copyright (c) 2007-2009, Regents of the University of California + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + * Neither the name of the University of California, Berkeley nor + the names of its contributors may be used to endorse or promote + products derived from this software without specific prior + written permission. + + THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR + IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED + WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE + DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, + INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES + (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR + SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) + HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, + STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING + IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + POSSIBILITY OF SUCH DAMAGE. + + 7. Some of the cuBLAS library routines were written by or + derived from code written by Davide Barbieri and are + subject to the Modified Berkeley Software Distribution + License as follows: + + Copyright (c) 2008-2009 Davide Barbieri @ University of Rome Tor Vergata. + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + * The name of the author may not be used to endorse or promote + products derived from this software without specific prior + written permission. + + THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR + IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED + WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE + DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, + INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES + (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR + SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) + HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, + STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING + IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + POSSIBILITY OF SUCH DAMAGE. + + 8. Some of the cuBLAS library routines were derived from + code developed by the University of Tennessee and are + subject to the Modified Berkeley Software Distribution + License as follows: + + Copyright (c) 2010 The University of Tennessee. + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer listed in this license in the documentation and/or + other materials provided with the distribution. + * Neither the name of the copyright holders nor the names of its + contributors may be used to endorse or promote products derived + from this software without specific prior written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + 9. Some of the cuBLAS library routines were written by or + derived from code written by Jonathan Hogg and are subject + to the Modified Berkeley Software Distribution License as + follows: + + Copyright (c) 2012, The Science and Technology Facilities Council (STFC). + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + * Neither the name of the STFC nor the names of its contributors + may be used to endorse or promote products derived from this + software without specific prior written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE STFC BE + LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF + SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR + BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, + WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE + OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN + IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + 10. Some of the cuBLAS library routines were written by or + derived from code written by Ahmad M. Abdelfattah, David + Keyes, and Hatem Ltaief, and are subject to the Apache + License, Version 2.0, as follows: + + -- (C) Copyright 2013 King Abdullah University of Science and Technology + Authors: + Ahmad Abdelfattah (ahmad.ahmad@kaust.edu.sa) + David Keyes (david.keyes@kaust.edu.sa) + Hatem Ltaief (hatem.ltaief@kaust.edu.sa) + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + * Neither the name of the King Abdullah University of Science and + Technology nor the names of its contributors may be used to endorse + or promote products derived from this software without specific prior + written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + HOLDERS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE + + 11. Some of the cuSPARSE library routines were written by or + derived from code written by Li-Wen Chang and are subject + to the NCSA Open Source License as follows: + + Copyright (c) 2012, University of Illinois. + + All rights reserved. + + Developed by: IMPACT Group, University of Illinois, http://impact.crhc.illinois.edu + + Permission is hereby granted, free of charge, to any person obtaining + a copy of this software and associated documentation files (the + "Software"), to deal with the Software without restriction, including + without limitation the rights to use, copy, modify, merge, publish, + distribute, sublicense, and/or sell copies of the Software, and to + permit persons to whom the Software is furnished to do so, subject to + the following conditions: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimers in the documentation and/or other materials provided + with the distribution. + * Neither the names of IMPACT Group, University of Illinois, nor + the names of its contributors may be used to endorse or promote + products derived from this Software without specific prior + written permission. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, + EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF + MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND + NONINFRINGEMENT. IN NO EVENT SHALL THE CONTRIBUTORS OR COPYRIGHT + HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR + IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS WITH THE + SOFTWARE. + + 12. Some of the cuRAND library routines were written by or + derived from code written by Mutsuo Saito and Makoto + Matsumoto and are subject to the following license: + + Copyright (c) 2009, 2010 Mutsuo Saito, Makoto Matsumoto and Hiroshima + University. All rights reserved. + + Copyright (c) 2011 Mutsuo Saito, Makoto Matsumoto, Hiroshima + University and University of Tokyo. All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + * Neither the name of the Hiroshima University nor the names of + its contributors may be used to endorse or promote products + derived from this software without specific prior written + permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + 13. Some of the cuRAND library routines were derived from + code developed by D. E. Shaw Research and are subject to + the following license: + + Copyright 2010-2011, D. E. Shaw Research. + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions, and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions, and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + * Neither the name of D. E. Shaw Research nor the names of its + contributors may be used to endorse or promote products derived + from this software without specific prior written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + 14. Some of the Math library routines were written by or + derived from code developed by Norbert Juffa and are + subject to the following license: + + Copyright (c) 2015-2017, Norbert Juffa + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + 1. Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + 2. Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + 15. Licensee's use of the lz4 third party component is + subject to the following terms and conditions: + + Copyright (C) 2011-2013, Yann Collet. + BSD 2-Clause License (http://www.opensource.org/licenses/bsd-license.php) + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following disclaimer + in the documentation and/or other materials provided with the + distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + 16. The NPP library uses code from the Boost Math Toolkit, + and is subject to the following license: + + Boost Software License - Version 1.0 - August 17th, 2003 + . . . . + + Permission is hereby granted, free of charge, to any person or + organization obtaining a copy of the software and accompanying + documentation covered by this license (the "Software") to use, + reproduce, display, distribute, execute, and transmit the Software, + and to prepare derivative works of the Software, and to permit + third-parties to whom the Software is furnished to do so, all + subject to the following: + + The copyright notices in the Software and this entire statement, + including the above license grant, this restriction and the following + disclaimer, must be included in all copies of the Software, in whole + or in part, and all derivative works of the Software, unless such + copies or derivative works are solely in the form of machine-executable + object code generated by a source language processor. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, + EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF + MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND + NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR + ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE FOR ANY DAMAGES OR + OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING + FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR + OTHER DEALINGS IN THE SOFTWARE. + + 17. Portions of the Nsight Eclipse Edition is subject to the + following license: + + The Eclipse Foundation makes available all content in this plug-in + ("Content"). Unless otherwise indicated below, the Content is provided + to you under the terms and conditions of the Eclipse Public License + Version 1.0 ("EPL"). A copy of the EPL is available at http:// + www.eclipse.org/legal/epl-v10.html. For purposes of the EPL, "Program" + will mean the Content. + + If you did not receive this Content directly from the Eclipse + Foundation, the Content is being redistributed by another party + ("Redistributor") and different terms and conditions may apply to your + use of any object code in the Content. Check the Redistributor's + license that was provided with the Content. If no such license exists, + contact the Redistributor. Unless otherwise indicated below, the terms + and conditions of the EPL still apply to any source code in the + Content and such source code may be obtained at http://www.eclipse.org. + + 18. Some of the cuBLAS library routines uses code from + OpenAI, which is subject to the following license: + + License URL + https://github.com/openai/openai-gemm/blob/master/LICENSE + + License Text + The MIT License + + Copyright (c) 2016 OpenAI (http://openai.com), 2016 Google Inc. + + Permission is hereby granted, free of charge, to any person obtaining a copy + of this software and associated documentation files (the "Software"), to deal + in the Software without restriction, including without limitation the rights + to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + copies of the Software, and to permit persons to whom the Software is + furnished to do so, subject to the following conditions: + + The above copyright notice and this permission notice shall be included in + all copies or substantial portions of the Software. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN + THE SOFTWARE. + + 19. Licensee's use of the Visual Studio Setup Configuration + Samples is subject to the following license: + + The MIT License (MIT) + Copyright (C) Microsoft Corporation. All rights reserved. + + Permission is hereby granted, free of charge, to any person + obtaining a copy of this software and associated documentation + files (the "Software"), to deal in the Software without restriction, + including without limitation the rights to use, copy, modify, merge, + publish, distribute, sublicense, and/or sell copies of the Software, + and to permit persons to whom the Software is furnished to do so, + subject to the following conditions: + + The above copyright notice and this permission notice shall be included + in all copies or substantial portions of the Software. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS + OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + + 20. Licensee's use of linmath.h header for CPU functions for + GL vector/matrix operations from lunarG is subject to the + Apache License Version 2.0. + + 21. The DX12-CUDA sample uses the d3dx12.h header, which is + subject to the MIT license . + +----------------- diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cusolver_cu12-11.7.3.90.dist-info/METADATA b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cusolver_cu12-11.7.3.90.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..6d092eb90b55dc0d052f1b28fac1dfda2f61901a --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cusolver_cu12-11.7.3.90.dist-info/METADATA @@ -0,0 +1,48 @@ +Metadata-Version: 2.2 +Name: nvidia-cusolver-cu12 +Version: 11.7.3.90 +Summary: CUDA solver native runtime libraries +Home-page: https://developer.nvidia.com/cuda-zone +Author: Nvidia CUDA Installer Team +Author-email: compute_installer@nvidia.com +License: NVIDIA Proprietary Software +Keywords: cuda,nvidia,runtime,machine learning,deep learning +Classifier: Development Status :: 4 - Beta 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mode 100644 index 0000000000000000000000000000000000000000..b491c70e0aef319022ded661e111ddbd45b8a17f --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cusparse_cu12-12.5.8.93.dist-info/License.txt @@ -0,0 +1,1568 @@ +End User License Agreement +-------------------------- + + +Preface +------- + +The Software License Agreement in Chapter 1 and the Supplement +in Chapter 2 contain license terms and conditions that govern +the use of NVIDIA software. By accepting this agreement, you +agree to comply with all the terms and conditions applicable +to the product(s) included herein. + + +NVIDIA Driver + + +Description + +This package contains the operating system driver and +fundamental system software components for NVIDIA GPUs. + + +NVIDIA CUDA Toolkit + + +Description + +The NVIDIA CUDA Toolkit provides command-line and graphical +tools for building, debugging and optimizing the performance +of applications accelerated by NVIDIA GPUs, runtime and math +libraries, and documentation including programming guides, +user manuals, and API references. + + +Default Install Location of CUDA Toolkit + +Windows platform: + +%ProgramFiles%\NVIDIA GPU Computing Toolkit\CUDA\v#.# + +Linux platform: + +/usr/local/cuda-#.# + +Mac platform: + +/Developer/NVIDIA/CUDA-#.# + + +NVIDIA CUDA Samples + + +Description + +This package includes over 100+ CUDA examples that demonstrate +various CUDA programming principles, and efficient CUDA +implementation of algorithms in specific application domains. + + +Default Install Location of CUDA Samples + +Windows platform: + +%ProgramData%\NVIDIA Corporation\CUDA Samples\v#.# + +Linux platform: + +/usr/local/cuda-#.#/samples + +and + +$HOME/NVIDIA_CUDA-#.#_Samples + +Mac platform: + +/Developer/NVIDIA/CUDA-#.#/samples + + +NVIDIA Nsight Visual Studio Edition (Windows only) + + +Description + +NVIDIA Nsight Development Platform, Visual Studio Edition is a +development environment integrated into Microsoft Visual +Studio that provides tools for debugging, profiling, analyzing +and optimizing your GPU computing and graphics applications. + + +Default Install Location of Nsight Visual Studio Edition + +Windows platform: + +%ProgramFiles(x86)%\NVIDIA Corporation\Nsight Visual Studio Edition #.# + + +1. License Agreement for NVIDIA Software Development Kits +--------------------------------------------------------- + + +Release Date: July 26, 2018 +--------------------------- + + +Important NoticeRead before downloading, installing, +copying or using the licensed software: +------------------------------------------------------- + +This license agreement, including exhibits attached +("Agreement”) is a legal agreement between you and NVIDIA +Corporation ("NVIDIA") and governs your use of a NVIDIA +software development kit (“SDK”). + +Each SDK has its own set of software and materials, but here +is a description of the types of items that may be included in +a SDK: source code, header files, APIs, data sets and assets +(examples include images, textures, models, scenes, videos, +native API input/output files), binary software, sample code, +libraries, utility programs, programming code and +documentation. + +This Agreement can be accepted only by an adult of legal age +of majority in the country in which the SDK is used. + +If you are entering into this Agreement on behalf of a company +or other legal entity, you represent that you have the legal +authority to bind the entity to this Agreement, in which case +“you” will mean the entity you represent. + +If you don’t have the required age or authority to accept +this Agreement, or if you don’t accept all the terms and +conditions of this Agreement, do not download, install or use +the SDK. + +You agree to use the SDK only for purposes that are permitted +by (a) this Agreement, and (b) any applicable law, regulation +or generally accepted practices or guidelines in the relevant +jurisdictions. + + +1.1. 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CUDA Toolkit Supplement to Software License Agreement for +NVIDIA Software Development Kits +------------------------------------------------------------ + + +Release date: August 16, 2018 +----------------------------- + +The terms in this supplement govern your use of the NVIDIA +CUDA Toolkit SDK under the terms of your license agreement +(“Agreement”) as modified by this supplement. Capitalized +terms used but not defined below have the meaning assigned to +them in the Agreement. + +This supplement is an exhibit to the Agreement and is +incorporated as an integral part of the Agreement. In the +event of conflict between the terms in this supplement and the +terms in the Agreement, the terms in this supplement govern. + + +2.1. License Scope + +The SDK is licensed for you to develop applications only for +use in systems with NVIDIA GPUs. + + +2.2. Distribution + +The portions of the SDK that are distributable under the +Agreement are listed in Attachment A. + + +2.3. Operating Systems + +Those portions of the SDK designed exclusively for use on the +Linux or FreeBSD operating systems, or other operating systems +derived from the source code to these operating systems, may +be copied and redistributed for use in accordance with this +Agreement, provided that the object code files are not +modified in any way (except for unzipping of compressed +files). + + +2.4. Audio and Video Encoders and Decoders + +You acknowledge and agree that it is your sole responsibility +to obtain any additional third-party licenses required to +make, have made, use, have used, sell, import, and offer for +sale your products or services that include or incorporate any +third-party software and content relating to audio and/or +video encoders and decoders from, including but not limited +to, Microsoft, Thomson, Fraunhofer IIS, Sisvel S.p.A., +MPEG-LA, and Coding Technologies. NVIDIA does not grant to you +under this Agreement any necessary patent or other rights with +respect to any audio and/or video encoders and decoders. + + +2.5. Licensing + +If the distribution terms in this Agreement are not suitable +for your organization, or for any questions regarding this +Agreement, please contact NVIDIA at +nvidia-compute-license-questions@nvidia.com. + + +2.6. Attachment A + +The following portions of the SDK are distributable under the +Agreement: + +Component + +CUDA Runtime + +Windows + +cudart.dll, cudart_static.lib, cudadevrt.lib + +Mac OSX + +libcudart.dylib, libcudart_static.a, libcudadevrt.a + +Linux + +libcudart.so, libcudart_static.a, libcudadevrt.a + +Android + +libcudart.so, libcudart_static.a, libcudadevrt.a + +Component + +CUDA FFT Library + +Windows + +cufft.dll, cufftw.dll, cufft.lib, cufftw.lib + +Mac OSX + +libcufft.dylib, libcufft_static.a, libcufftw.dylib, +libcufftw_static.a + +Linux + +libcufft.so, libcufft_static.a, libcufftw.so, +libcufftw_static.a + +Android + +libcufft.so, libcufft_static.a, libcufftw.so, +libcufftw_static.a + +Component + +CUDA BLAS Library + +Windows + +cublas.dll, cublasLt.dll + +Mac OSX + +libcublas.dylib, libcublasLt.dylib, libcublas_static.a, +libcublasLt_static.a + +Linux + +libcublas.so, libcublasLt.so, libcublas_static.a, +libcublasLt_static.a + +Android + +libcublas.so, libcublasLt.so, libcublas_static.a, +libcublasLt_static.a + +Component + +NVIDIA "Drop-in" BLAS Library + +Windows + +nvblas.dll + +Mac OSX + +libnvblas.dylib + +Linux + +libnvblas.so + +Component + +CUDA Sparse Matrix Library + +Windows + +cusparse.dll, cusparse.lib + +Mac OSX + +libcusparse.dylib, libcusparse_static.a + +Linux + +libcusparse.so, libcusparse_static.a + +Android + +libcusparse.so, libcusparse_static.a + +Component + +CUDA Linear Solver Library + +Windows + +cusolver.dll, cusolver.lib + +Mac OSX + +libcusolver.dylib, libcusolver_static.a + +Linux + +libcusolver.so, libcusolver_static.a + +Android + +libcusolver.so, libcusolver_static.a + +Component + +CUDA Random Number Generation Library + +Windows + +curand.dll, curand.lib + +Mac OSX + +libcurand.dylib, libcurand_static.a + +Linux + +libcurand.so, libcurand_static.a + +Android + +libcurand.so, libcurand_static.a + +Component + +CUDA Accelerated Graph Library + +Component + +NVIDIA Performance Primitives Library + +Windows + +nppc.dll, nppc.lib, nppial.dll, nppial.lib, nppicc.dll, +nppicc.lib, nppicom.dll, nppicom.lib, nppidei.dll, +nppidei.lib, nppif.dll, nppif.lib, nppig.dll, nppig.lib, +nppim.dll, nppim.lib, nppist.dll, nppist.lib, nppisu.dll, +nppisu.lib, nppitc.dll, nppitc.lib, npps.dll, npps.lib + +Mac OSX + +libnppc.dylib, libnppc_static.a, libnppial.dylib, +libnppial_static.a, libnppicc.dylib, libnppicc_static.a, +libnppicom.dylib, libnppicom_static.a, libnppidei.dylib, +libnppidei_static.a, libnppif.dylib, libnppif_static.a, +libnppig.dylib, libnppig_static.a, libnppim.dylib, +libnppisu_static.a, libnppitc.dylib, libnppitc_static.a, +libnpps.dylib, libnpps_static.a + +Linux + +libnppc.so, libnppc_static.a, libnppial.so, +libnppial_static.a, libnppicc.so, libnppicc_static.a, +libnppicom.so, libnppicom_static.a, libnppidei.so, +libnppidei_static.a, libnppif.so, libnppif_static.a +libnppig.so, libnppig_static.a, libnppim.so, +libnppim_static.a, libnppist.so, libnppist_static.a, +libnppisu.so, libnppisu_static.a, libnppitc.so +libnppitc_static.a, libnpps.so, libnpps_static.a + +Android + +libnppc.so, libnppc_static.a, libnppial.so, +libnppial_static.a, libnppicc.so, libnppicc_static.a, +libnppicom.so, libnppicom_static.a, libnppidei.so, +libnppidei_static.a, libnppif.so, libnppif_static.a +libnppig.so, libnppig_static.a, libnppim.so, +libnppim_static.a, libnppist.so, libnppist_static.a, +libnppisu.so, libnppisu_static.a, libnppitc.so +libnppitc_static.a, libnpps.so, libnpps_static.a + +Component + +NVIDIA JPEG Library + +Linux + +libnvjpeg.so, libnvjpeg_static.a + +Component + +Internal common library required for statically linking to +cuBLAS, cuSPARSE, cuFFT, cuRAND, nvJPEG and NPP + +Mac OSX + +libculibos.a + +Linux + +libculibos.a + +Component + +NVIDIA Runtime Compilation Library and Header + +All + +nvrtc.h + +Windows + +nvrtc.dll, nvrtc-builtins.dll + +Mac OSX + +libnvrtc.dylib, libnvrtc-builtins.dylib + +Linux + +libnvrtc.so, libnvrtc-builtins.so + +Component + +NVIDIA Optimizing Compiler Library + +Windows + +nvvm.dll + +Mac OSX + +libnvvm.dylib + +Linux + +libnvvm.so + +Component + +NVIDIA Common Device Math Functions Library + +Windows + +libdevice.10.bc + +Mac OSX + +libdevice.10.bc + +Linux + +libdevice.10.bc + +Component + +CUDA Occupancy Calculation Header Library + +All + +cuda_occupancy.h + +Component + +CUDA Half Precision Headers + +All + +cuda_fp16.h, cuda_fp16.hpp + +Component + +CUDA Profiling Tools Interface (CUPTI) Library + +Windows + +cupti.dll + +Mac OSX + +libcupti.dylib + +Linux + +libcupti.so + +Component + +NVIDIA Tools Extension Library + +Windows + +nvToolsExt.dll, nvToolsExt.lib + +Mac OSX + +libnvToolsExt.dylib + +Linux + +libnvToolsExt.so + +Component + +NVIDIA CUDA Driver Libraries + +Linux + +libcuda.so, libnvidia-fatbinaryloader.so, +libnvidia-ptxjitcompiler.so + +The NVIDIA CUDA Driver Libraries are only distributable in +applications that meet this criteria: + + 1. The application was developed starting from a NVIDIA CUDA + container obtained from Docker Hub or the NVIDIA GPU + Cloud, and + + 2. The resulting application is packaged as a Docker + container and distributed to users on Docker Hub or the + NVIDIA GPU Cloud only. + + +2.7. Attachment B + + +Additional Licensing Obligations + +The following third party components included in the SOFTWARE +are licensed to Licensee pursuant to the following terms and +conditions: + + 1. Licensee's use of the GDB third party component is + subject to the terms and conditions of GNU GPL v3: + + This product includes copyrighted third-party software licensed + under the terms of the GNU General Public License v3 ("GPL v3"). + All third-party software packages are copyright by their respective + authors. GPL v3 terms and conditions are hereby incorporated into + the Agreement by this reference: http://www.gnu.org/licenses/gpl.txt + + Consistent with these licensing requirements, the software + listed below is provided under the terms of the specified + open source software licenses. To obtain source code for + software provided under licenses that require + redistribution of source code, including the GNU General + Public License (GPL) and GNU Lesser General Public License + (LGPL), contact oss-requests@nvidia.com. This offer is + valid for a period of three (3) years from the date of the + distribution of this product by NVIDIA CORPORATION. + + Component License + CUDA-GDB GPL v3 + + 2. Licensee represents and warrants that any and all third + party licensing and/or royalty payment obligations in + connection with Licensee's use of the H.264 video codecs + are solely the responsibility of Licensee. + + 3. Licensee's use of the Thrust library is subject to the + terms and conditions of the Apache License Version 2.0. + All third-party software packages are copyright by their + respective authors. Apache License Version 2.0 terms and + conditions are hereby incorporated into the Agreement by + this reference. + http://www.apache.org/licenses/LICENSE-2.0.html + + In addition, Licensee acknowledges the following notice: + Thrust includes source code from the Boost Iterator, + Tuple, System, and Random Number libraries. + + Boost Software License - Version 1.0 - August 17th, 2003 + . . . . + + Permission is hereby granted, free of charge, to any person or + organization obtaining a copy of the software and accompanying + documentation covered by this license (the "Software") to use, + reproduce, display, distribute, execute, and transmit the Software, + and to prepare derivative works of the Software, and to permit + third-parties to whom the Software is furnished to do so, all + subject to the following: + + The copyright notices in the Software and this entire statement, + including the above license grant, this restriction and the following + disclaimer, must be included in all copies of the Software, in whole + or in part, and all derivative works of the Software, unless such + copies or derivative works are solely in the form of machine-executable + object code generated by a source language processor. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, + EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF + MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND + NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR + ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE FOR ANY DAMAGES OR + OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING + FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR + OTHER DEALINGS IN THE SOFTWARE. + + 4. Licensee's use of the LLVM third party component is + subject to the following terms and conditions: + + ====================================================== + LLVM Release License + ====================================================== + University of Illinois/NCSA + Open Source License + + Copyright (c) 2003-2010 University of Illinois at Urbana-Champaign. + All rights reserved. + + Developed by: + + LLVM Team + + University of Illinois at Urbana-Champaign + + http://llvm.org + + Permission is hereby granted, free of charge, to any person obtaining a copy + of this software and associated documentation files (the "Software"), to + deal with the Software without restriction, including without limitation the + rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + sell copies of the Software, and to permit persons to whom the Software is + furnished to do so, subject to the following conditions: + + * Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimers. + + * Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimers in the + documentation and/or other materials provided with the distribution. + + * Neither the names of the LLVM Team, University of Illinois at Urbana- + Champaign, nor the names of its contributors may be used to endorse or + promote products derived from this Software without specific prior + written permission. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL + THE CONTRIBUTORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR + OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, + ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER + DEALINGS WITH THE SOFTWARE. + + 5. Licensee's use (e.g. nvprof) of the PCRE third party + component is subject to the following terms and + conditions: + + ------------ + PCRE LICENCE + ------------ + PCRE is a library of functions to support regular expressions whose syntax + and semantics are as close as possible to those of the Perl 5 language. + Release 8 of PCRE is distributed under the terms of the "BSD" licence, as + specified below. The documentation for PCRE, supplied in the "doc" + directory, is distributed under the same terms as the software itself. The + basic library functions are written in C and are freestanding. Also + included in the distribution is a set of C++ wrapper functions, and a just- + in-time compiler that can be used to optimize pattern matching. These are + both optional features that can be omitted when the library is built. + + THE BASIC LIBRARY FUNCTIONS + --------------------------- + Written by: Philip Hazel + Email local part: ph10 + Email domain: cam.ac.uk + University of Cambridge Computing Service, + Cambridge, England. + Copyright (c) 1997-2012 University of Cambridge + All rights reserved. + + PCRE JUST-IN-TIME COMPILATION SUPPORT + ------------------------------------- + Written by: Zoltan Herczeg + Email local part: hzmester + Emain domain: freemail.hu + Copyright(c) 2010-2012 Zoltan Herczeg + All rights reserved. + + STACK-LESS JUST-IN-TIME COMPILER + -------------------------------- + Written by: Zoltan Herczeg + Email local part: hzmester + Emain domain: freemail.hu + Copyright(c) 2009-2012 Zoltan Herczeg + All rights reserved. + + THE C++ WRAPPER FUNCTIONS + ------------------------- + Contributed by: Google Inc. + Copyright (c) 2007-2012, Google Inc. + All rights reserved. + + THE "BSD" LICENCE + ----------------- + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are met: + + * Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + + * Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + * Neither the name of the University of Cambridge nor the name of Google + Inc. nor the names of their contributors may be used to endorse or + promote products derived from this software without specific prior + written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE + ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE + LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF + SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS + INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN + CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) + ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + POSSIBILITY OF SUCH DAMAGE. + + 6. Some of the cuBLAS library routines were written by or + derived from code written by Vasily Volkov and are subject + to the Modified Berkeley Software Distribution License as + follows: + + Copyright (c) 2007-2009, Regents of the University of California + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + * Neither the name of the University of California, Berkeley nor + the names of its contributors may be used to endorse or promote + products derived from this software without specific prior + written permission. + + THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR + IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED + WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE + DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, + INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES + (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR + SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) + HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, + STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING + IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + POSSIBILITY OF SUCH DAMAGE. + + 7. Some of the cuBLAS library routines were written by or + derived from code written by Davide Barbieri and are + subject to the Modified Berkeley Software Distribution + License as follows: + + Copyright (c) 2008-2009 Davide Barbieri @ University of Rome Tor Vergata. + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + * The name of the author may not be used to endorse or promote + products derived from this software without specific prior + written permission. + + THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR + IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED + WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE + DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, + INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES + (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR + SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) + HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, + STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING + IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + POSSIBILITY OF SUCH DAMAGE. + + 8. Some of the cuBLAS library routines were derived from + code developed by the University of Tennessee and are + subject to the Modified Berkeley Software Distribution + License as follows: + + Copyright (c) 2010 The University of Tennessee. + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer listed in this license in the documentation and/or + other materials provided with the distribution. + * Neither the name of the copyright holders nor the names of its + contributors may be used to endorse or promote products derived + from this software without specific prior written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + 9. Some of the cuBLAS library routines were written by or + derived from code written by Jonathan Hogg and are subject + to the Modified Berkeley Software Distribution License as + follows: + + Copyright (c) 2012, The Science and Technology Facilities Council (STFC). + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + * Neither the name of the STFC nor the names of its contributors + may be used to endorse or promote products derived from this + software without specific prior written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE STFC BE + LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF + SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR + BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, + WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE + OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN + IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + 10. Some of the cuBLAS library routines were written by or + derived from code written by Ahmad M. Abdelfattah, David + Keyes, and Hatem Ltaief, and are subject to the Apache + License, Version 2.0, as follows: + + -- (C) Copyright 2013 King Abdullah University of Science and Technology + Authors: + Ahmad Abdelfattah (ahmad.ahmad@kaust.edu.sa) + David Keyes (david.keyes@kaust.edu.sa) + Hatem Ltaief (hatem.ltaief@kaust.edu.sa) + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + * Neither the name of the King Abdullah University of Science and + Technology nor the names of its contributors may be used to endorse + or promote products derived from this software without specific prior + written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + HOLDERS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE + + 11. Some of the cuSPARSE library routines were written by or + derived from code written by Li-Wen Chang and are subject + to the NCSA Open Source License as follows: + + Copyright (c) 2012, University of Illinois. + + All rights reserved. + + Developed by: IMPACT Group, University of Illinois, http://impact.crhc.illinois.edu + + Permission is hereby granted, free of charge, to any person obtaining + a copy of this software and associated documentation files (the + "Software"), to deal with the Software without restriction, including + without limitation the rights to use, copy, modify, merge, publish, + distribute, sublicense, and/or sell copies of the Software, and to + permit persons to whom the Software is furnished to do so, subject to + the following conditions: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimers in the documentation and/or other materials provided + with the distribution. + * Neither the names of IMPACT Group, University of Illinois, nor + the names of its contributors may be used to endorse or promote + products derived from this Software without specific prior + written permission. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, + EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF + MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND + NONINFRINGEMENT. IN NO EVENT SHALL THE CONTRIBUTORS OR COPYRIGHT + HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR + IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS WITH THE + SOFTWARE. + + 12. Some of the cuRAND library routines were written by or + derived from code written by Mutsuo Saito and Makoto + Matsumoto and are subject to the following license: + + Copyright (c) 2009, 2010 Mutsuo Saito, Makoto Matsumoto and Hiroshima + University. All rights reserved. + + Copyright (c) 2011 Mutsuo Saito, Makoto Matsumoto, Hiroshima + University and University of Tokyo. All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + * Neither the name of the Hiroshima University nor the names of + its contributors may be used to endorse or promote products + derived from this software without specific prior written + permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + 13. Some of the cuRAND library routines were derived from + code developed by D. E. Shaw Research and are subject to + the following license: + + Copyright 2010-2011, D. E. Shaw Research. + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions, and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions, and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + * Neither the name of D. E. Shaw Research nor the names of its + contributors may be used to endorse or promote products derived + from this software without specific prior written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + 14. Some of the Math library routines were written by or + derived from code developed by Norbert Juffa and are + subject to the following license: + + Copyright (c) 2015-2017, Norbert Juffa + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + 1. Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + 2. Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + 15. Licensee's use of the lz4 third party component is + subject to the following terms and conditions: + + Copyright (C) 2011-2013, Yann Collet. + BSD 2-Clause License (http://www.opensource.org/licenses/bsd-license.php) + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following disclaimer + in the documentation and/or other materials provided with the + distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + 16. The NPP library uses code from the Boost Math Toolkit, + and is subject to the following license: + + Boost Software License - Version 1.0 - August 17th, 2003 + . . . . + + Permission is hereby granted, free of charge, to any person or + organization obtaining a copy of the software and accompanying + documentation covered by this license (the "Software") to use, + reproduce, display, distribute, execute, and transmit the Software, + and to prepare derivative works of the Software, and to permit + third-parties to whom the Software is furnished to do so, all + subject to the following: + + The copyright notices in the Software and this entire statement, + including the above license grant, this restriction and the following + disclaimer, must be included in all copies of the Software, in whole + or in part, and all derivative works of the Software, unless such + copies or derivative works are solely in the form of machine-executable + object code generated by a source language processor. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, + EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF + MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND + NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR + ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE FOR ANY DAMAGES OR + OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING + FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR + OTHER DEALINGS IN THE SOFTWARE. + + 17. Portions of the Nsight Eclipse Edition is subject to the + following license: + + The Eclipse Foundation makes available all content in this plug-in + ("Content"). Unless otherwise indicated below, the Content is provided + to you under the terms and conditions of the Eclipse Public License + Version 1.0 ("EPL"). A copy of the EPL is available at http:// + www.eclipse.org/legal/epl-v10.html. For purposes of the EPL, "Program" + will mean the Content. + + If you did not receive this Content directly from the Eclipse + Foundation, the Content is being redistributed by another party + ("Redistributor") and different terms and conditions may apply to your + use of any object code in the Content. 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Some of the cuBLAS library routines uses code from + OpenAI, which is subject to the following license: + + License URL + https://github.com/openai/openai-gemm/blob/master/LICENSE + + License Text + The MIT License + + Copyright (c) 2016 OpenAI (http://openai.com), 2016 Google Inc. + + Permission is hereby granted, free of charge, to any person obtaining a copy + of this software and associated documentation files (the "Software"), to deal + in the Software without restriction, including without limitation the rights + to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + copies of the Software, and to permit persons to whom the Software is + furnished to do so, subject to the following conditions: + + The above copyright notice and this permission notice shall be included in + all copies or substantial portions of the Software. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. 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IN NO EVENT SHALL THE + AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + + 20. Licensee's use of linmath.h header for CPU functions for + GL vector/matrix operations from lunarG is subject to the + Apache License Version 2.0. + + 21. The DX12-CUDA sample uses the d3dx12.h header, which is + subject to the MIT license . + +----------------- diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cusparse_cu12-12.5.8.93.dist-info/METADATA b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cusparse_cu12-12.5.8.93.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..6f49c69886f619189dab562063548d38dab861af --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cusparse_cu12-12.5.8.93.dist-info/METADATA @@ -0,0 +1,46 @@ +Metadata-Version: 2.2 +Name: nvidia-cusparse-cu12 +Version: 12.5.8.93 +Summary: CUSPARSE native runtime libraries +Home-page: https://developer.nvidia.com/cuda-zone +Author: Nvidia CUDA Installer Team +Author-email: compute_installer@nvidia.com +License: NVIDIA Proprietary Software +Keywords: cuda,nvidia,runtime,machine learning,deep learning +Classifier: Development Status :: 4 - Beta +Classifier: Intended Audience :: Developers +Classifier: Intended Audience :: Education +Classifier: Intended Audience :: Science/Research +Classifier: License :: Other/Proprietary License +Classifier: Natural Language :: English +Classifier: Programming Language :: Python :: 3 +Classifier: Programming Language :: Python :: 3.5 +Classifier: Programming Language :: Python :: 3.6 +Classifier: Programming Language :: Python :: 3.7 +Classifier: Programming Language :: Python :: 3.8 +Classifier: Programming Language :: Python :: 3.9 +Classifier: Programming Language :: Python :: 3.10 +Classifier: Programming Language :: Python :: 3.11 +Classifier: Programming Language :: Python :: 3 :: Only +Classifier: Topic :: Scientific/Engineering +Classifier: Topic :: Scientific/Engineering :: Mathematics +Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence +Classifier: Topic :: Software Development +Classifier: Topic :: Software Development :: Libraries +Classifier: Operating System :: Microsoft :: Windows +Classifier: Operating System :: POSIX :: Linux +Requires-Python: >=3 +License-File: License.txt +Requires-Dist: nvidia-nvjitlink-cu12 +Dynamic: author +Dynamic: author-email +Dynamic: classifier +Dynamic: description +Dynamic: home-page +Dynamic: keywords +Dynamic: license +Dynamic: requires-dist +Dynamic: requires-python +Dynamic: summary + +CUSPARSE native runtime libraries diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cusparse_cu12-12.5.8.93.dist-info/RECORD b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cusparse_cu12-12.5.8.93.dist-info/RECORD new file mode 100644 index 0000000000000000000000000000000000000000..716c6cc25f79dd0e43b5b439a01fbf5aa4c3e634 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cusparse_cu12-12.5.8.93.dist-info/RECORD @@ -0,0 +1,17 @@ +nvidia/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0 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py3-none-manylinux_2_17_x86_64 + diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cusparse_cu12-12.5.8.93.dist-info/top_level.txt b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cusparse_cu12-12.5.8.93.dist-info/top_level.txt new file mode 100644 index 0000000000000000000000000000000000000000..862f7abf232cdfbb928609856247292e81c9decb --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cusparse_cu12-12.5.8.93.dist-info/top_level.txt @@ -0,0 +1 @@ +nvidia diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cusparselt_cu12-0.7.1.dist-info/INSTALLER b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cusparselt_cu12-0.7.1.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cusparselt_cu12-0.7.1.dist-info/INSTALLER @@ -0,0 +1 @@ +pip diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cusparselt_cu12-0.7.1.dist-info/METADATA b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cusparselt_cu12-0.7.1.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..0d8d05f17dde9634f260d0bdb1dfa5e4e09ac40f --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cusparselt_cu12-0.7.1.dist-info/METADATA @@ -0,0 +1,138 @@ +Metadata-Version: 2.2 +Name: nvidia-cusparselt-cu12 +Version: 0.7.1 +Summary: NVIDIA cuSPARSELt +Home-page: https://developer.nvidia.com/cusparselt +Author: NVIDIA Corporation +Author-email: cuda_installer@nvidia.com +License: NVIDIA Proprietary Software +Keywords: cuda,nvidia,machine learning,high-performance computing +Classifier: Topic :: Scientific/Engineering +Classifier: Environment :: GPU :: NVIDIA CUDA +Classifier: Environment :: GPU :: NVIDIA CUDA :: 12 +Description-Content-Type: text/x-rst +Dynamic: author +Dynamic: author-email +Dynamic: classifier +Dynamic: description +Dynamic: description-content-type +Dynamic: home-page +Dynamic: keywords +Dynamic: license +Dynamic: summary + +################################################################################### +cuSPARSELt: A High-Performance CUDA Library for Sparse Matrix-Matrix Multiplication +################################################################################### + +**NVIDIA cuSPARSELt** is a high-performance CUDA library dedicated to general matrix-matrix operations in which at least one operand is a sparse matrix: + +.. math:: + + D = Activation(\alpha op(A) \cdot op(B) + \beta op(C) + bias) \cdot scale + +where :math:`op(A)/op(B)` refers to in-place operations such as transpose/non-transpose, and :math:`alpha, beta, scale` are scalars. + +The *cuSPARSELt APIs* allow flexibility in the algorithm/operation selection, epilogue, and matrix characteristics, including memory layout, alignment, and data types. + +**Download:** `developer.nvidia.com/cusparselt/downloads `_ + +**Provide Feedback:** `Math-Libs-Feedback@nvidia.com `_ + +**Examples**: +`cuSPARSELt Example 1 `_, +`cuSPARSELt Example 2 `_ + +**Blog post**: + +- `Exploiting NVIDIA Ampere Structured Sparsity with cuSPARSELt `_ +- `Structured Sparsity in the NVIDIA Ampere Architecture and Applications in Search Engines `__ +- `Making the Most of Structured Sparsity in the NVIDIA Ampere Architecture `__ + +================================================================================ +Key Features +================================================================================ + +* *NVIDIA Sparse MMA tensor core* support +* Mixed-precision computation support: + + +--------------+----------------+-----------------+-------------+ + | Input A/B | Input C | Output D | Compute | + +==============+================+=================+=============+ + | `FP32` | `FP32` | `FP32` | `FP32` | + +--------------+----------------+-----------------+-------------+ + | `FP16` | `FP16` | `FP16` | `FP32` | + + + + +-------------+ + | | | | `FP16` | + +--------------+----------------+-----------------+-------------+ + | `BF16` | `BF16` | `BF16` | `FP32` | + +--------------+----------------+-----------------+-------------+ + | `INT8` | `INT8` | `INT8` | `INT32` | + + +----------------+-----------------+ + + | | `INT32` | `INT32` | | + + +----------------+-----------------+ + + | | `FP16` | `FP16` | | + + +----------------+-----------------+ + + | | `BF16` | `BF16` | | + +--------------+----------------+-----------------+-------------+ + | `E4M3` | `FP16` | `E4M3` | `FP32` | + + +----------------+-----------------+ + + | | `BF16` | `E4M3` | | + + +----------------+-----------------+ + + | | `FP16` | `FP16` | | + + +----------------+-----------------+ + + | | `BF16` | `BF16` | | + + +----------------+-----------------+ + + | | `FP32` | `FP32` | | + +--------------+----------------+-----------------+-------------+ + | `E5M2` | `FP16` | `E5M2` | `FP32` | + + +----------------+-----------------+ + + | | `BF16` | `E5M2` | | + + +----------------+-----------------+ + + | | `FP16` | `FP16` | | + + +----------------+-----------------+ + + | | `BF16` | `BF16` | | + + +----------------+-----------------+ + + | | `FP32` | `FP32` | | + +--------------+----------------+-----------------+-------------+ + +* Matrix pruning and compression functionalities +* Activation functions, bias vector, and output scaling +* Batched computation (multiple matrices in a single run) +* GEMM Split-K mode +* Auto-tuning functionality (see `cusparseLtMatmulSearch()`) +* NVTX ranging and Logging functionalities + +================================================================================ +Support +================================================================================ + +* *Supported SM Architectures*: `SM 8.0`, `SM 8.6`, `SM 8.9`, `SM 9.0`, `SM 10.0`, `SM 12.0` +* *Supported CPU architectures and operating systems*: + ++------------+--------------------+ +| OS | CPU archs | ++============+====================+ +| `Windows` | `x86_64` | ++------------+--------------------+ +| `Linux` | `x86_64`, `Arm64` | ++------------+--------------------+ + + +================================================================================ +Documentation +================================================================================ + +Please refer to https://docs.nvidia.com/cuda/cusparselt/index.html for the cuSPARSELt documentation. + +================================================================================ +Installation +================================================================================ + +The cuSPARSELt wheel can be installed as follows: + +.. code-block:: bash + + pip install nvidia-cusparselt-cuXX + +where XX is the CUDA major version (currently CUDA 12 only is supported). diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cusparselt_cu12-0.7.1.dist-info/RECORD b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cusparselt_cu12-0.7.1.dist-info/RECORD new file mode 100644 index 0000000000000000000000000000000000000000..30e4efef3e08f6d1b034459ddb2d89345be567fb --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cusparselt_cu12-0.7.1.dist-info/RECORD @@ -0,0 +1,8 @@ +nvidia/cusparselt/LICENSE.txt,sha256=6NFYiFpoG5Xsem_AbdjUpSmJ83TLE4DIpMj7J_09XV4,17948 +nvidia/cusparselt/include/cusparseLt.h,sha256=dFgNMQTtWOFwjS7nRvZcKp4VV_kbHRWNLZPBWR4RjDg,17876 +nvidia/cusparselt/lib/libcusparseLt.so.0,sha256=s9haAxHq39POyGsCkcmktXacui8kip5-gWf78_-pmGM,451980513 +nvidia_cusparselt_cu12-0.7.1.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4 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a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cusparselt_cu12-0.7.1.dist-info/top_level.txt b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cusparselt_cu12-0.7.1.dist-info/top_level.txt new file mode 100644 index 0000000000000000000000000000000000000000..9b2aa4bee1c5613e5c54c1332a435b1c2db4c6cf --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_cusparselt_cu12-0.7.1.dist-info/top_level.txt @@ -0,0 +1 @@ +nvidia/cusparselt diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_nccl_cu12-2.27.5.dist-info/INSTALLER b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_nccl_cu12-2.27.5.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_nccl_cu12-2.27.5.dist-info/INSTALLER @@ -0,0 +1 @@ +pip diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_nccl_cu12-2.27.5.dist-info/METADATA b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_nccl_cu12-2.27.5.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..267439298875d4fb658a6913dc51b48c9026e3ea --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_nccl_cu12-2.27.5.dist-info/METADATA @@ -0,0 +1,45 @@ +Metadata-Version: 2.4 +Name: nvidia-nccl-cu12 +Version: 2.27.5 +Summary: NVIDIA Collective Communication Library (NCCL) Runtime +Home-page: https://developer.nvidia.com/cuda-zone +Author: Nvidia CUDA Installer Team +Author-email: compute_installer@nvidia.com +License: BSD-3-Clause +Keywords: cuda,nvidia,runtime,machine learning,deep learning +Classifier: Development Status :: 4 - Beta +Classifier: Intended Audience :: Developers +Classifier: Intended Audience :: Education +Classifier: Intended Audience :: Science/Research +Classifier: License :: Other/Proprietary License +Classifier: Natural Language :: English +Classifier: Programming Language :: Python :: 3 +Classifier: Programming Language :: Python :: 3.5 +Classifier: Programming Language :: Python :: 3.6 +Classifier: Programming Language :: Python :: 3.7 +Classifier: Programming Language :: Python :: 3.8 +Classifier: Programming Language :: Python :: 3.9 +Classifier: Programming Language :: Python :: 3.10 +Classifier: Programming Language :: Python :: 3.11 +Classifier: Programming Language :: Python :: 3 :: Only +Classifier: Topic :: Scientific/Engineering +Classifier: Topic :: Scientific/Engineering :: Mathematics +Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence +Classifier: Topic :: Software Development +Classifier: Topic :: Software Development :: Libraries +Classifier: Operating System :: Microsoft :: Windows +Classifier: Operating System :: POSIX :: Linux +Requires-Python: >=3 +License-File: License.txt +Dynamic: author +Dynamic: author-email +Dynamic: classifier +Dynamic: description +Dynamic: home-page +Dynamic: keywords +Dynamic: license +Dynamic: license-file +Dynamic: requires-python +Dynamic: summary + +NCCL (pronounced "Nickel") is a stand-alone library of standard collective communication routines for GPUs, implementing all-reduce, all-gather, reduce, broadcast, and reduce-scatter. It has been optimized to achieve high bandwidth on any platform using PCIe, NVLink, NVswitch, as well as networking using InfiniBand Verbs or TCP/IP sockets. diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_nccl_cu12-2.27.5.dist-info/RECORD b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_nccl_cu12-2.27.5.dist-info/RECORD new file mode 100644 index 0000000000000000000000000000000000000000..d5cd7999f23e3c076bf3e6003af961d30e12e933 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_nccl_cu12-2.27.5.dist-info/RECORD @@ -0,0 +1,8 @@ +nvidia/nccl/include/nccl.h,sha256=SyQKH6eYWMqM3_z1wvxArqyos-rsYXKCE3FfCQKro6g,22566 +nvidia/nccl/lib/libnccl.so.2,sha256=HZwQMi76zeaVz2QNsfKfwVhskp8Qh_DhIC0tXnD7cWg,429628328 +nvidia_nccl_cu12-2.27.5.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4 +nvidia_nccl_cu12-2.27.5.dist-info/METADATA,sha256=8QYX3uAOzTd6A-kQPrzyDDMwXQ30cZGlQEmKtF-Kxn0,2019 +nvidia_nccl_cu12-2.27.5.dist-info/RECORD,, +nvidia_nccl_cu12-2.27.5.dist-info/WHEEL,sha256=gy6FWQgpujK_dnYc155G2NL32NQjpi5ebTEXjh8SGZQ,144 +nvidia_nccl_cu12-2.27.5.dist-info/licenses/License.txt,sha256=DwF0prTgszrCY3W_cpUzB1sy9MUaW2gCo9dC19zcmnY,1895 +nvidia_nccl_cu12-2.27.5.dist-info/top_level.txt,sha256=fTkAtiFuL16nUrB9ytDDtpytz2t0B4NvYTnRzwAhO14,7 diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_nccl_cu12-2.27.5.dist-info/WHEEL b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_nccl_cu12-2.27.5.dist-info/WHEEL new file mode 100644 index 0000000000000000000000000000000000000000..29738220b339f398b2cbbc9ac8ccb38a39bdd7b2 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_nccl_cu12-2.27.5.dist-info/WHEEL @@ -0,0 +1,6 @@ +Wheel-Version: 1.0 +Generator: setuptools (80.9.0) +Root-Is-Purelib: true +Tag: py3-none-manylinux2014_x86_64 +Tag: py3-none-manylinux_2_17_x86_64 + diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_nccl_cu12-2.27.5.dist-info/licenses/License.txt b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_nccl_cu12-2.27.5.dist-info/licenses/License.txt new file mode 100644 index 0000000000000000000000000000000000000000..bcd1867a02a6a8c1e592b92e2e50f34e531f2d87 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_nccl_cu12-2.27.5.dist-info/licenses/License.txt @@ -0,0 +1,39 @@ + + Copyright (c) 2015-2020, NVIDIA CORPORATION. All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + * Neither the name of NVIDIA CORPORATION, Lawrence Berkeley National + Laboratory, the U.S. Department of Energy, nor the names of their + contributors may be used to endorse or promote products derived + from this software without specific prior written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY + EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR + PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR + CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY + OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + The U.S. Department of Energy funded the development of this software + under subcontract 7078610 with Lawrence Berkeley National Laboratory. + + +This code also includes files from the NVIDIA Tools Extension SDK project. + +See: + + https://github.com/NVIDIA/NVTX + +for more information and license details. diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_nccl_cu12-2.27.5.dist-info/top_level.txt b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_nccl_cu12-2.27.5.dist-info/top_level.txt new file mode 100644 index 0000000000000000000000000000000000000000..862f7abf232cdfbb928609856247292e81c9decb --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_nccl_cu12-2.27.5.dist-info/top_level.txt @@ -0,0 +1 @@ +nvidia diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_nvjitlink_cu12-12.8.93.dist-info/INSTALLER b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_nvjitlink_cu12-12.8.93.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_nvjitlink_cu12-12.8.93.dist-info/INSTALLER @@ -0,0 +1 @@ +pip diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_nvjitlink_cu12-12.8.93.dist-info/License.txt b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_nvjitlink_cu12-12.8.93.dist-info/License.txt new file mode 100644 index 0000000000000000000000000000000000000000..b491c70e0aef319022ded661e111ddbd45b8a17f --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_nvjitlink_cu12-12.8.93.dist-info/License.txt @@ -0,0 +1,1568 @@ +End User License Agreement +-------------------------- + + +Preface +------- + +The Software License Agreement in Chapter 1 and the Supplement +in Chapter 2 contain license terms and conditions that govern +the use of NVIDIA software. By accepting this agreement, you +agree to comply with all the terms and conditions applicable +to the product(s) included herein. + + +NVIDIA Driver + + +Description + +This package contains the operating system driver and +fundamental system software components for NVIDIA GPUs. + + +NVIDIA CUDA Toolkit + + +Description + +The NVIDIA CUDA Toolkit provides command-line and graphical +tools for building, debugging and optimizing the performance +of applications accelerated by NVIDIA GPUs, runtime and math +libraries, and documentation including programming guides, +user manuals, and API references. + + +Default Install Location of CUDA Toolkit + +Windows platform: + +%ProgramFiles%\NVIDIA GPU Computing Toolkit\CUDA\v#.# + +Linux platform: + +/usr/local/cuda-#.# + +Mac platform: + +/Developer/NVIDIA/CUDA-#.# + + +NVIDIA CUDA Samples + + +Description + +This package includes over 100+ CUDA examples that demonstrate +various CUDA programming principles, and efficient CUDA +implementation of algorithms in specific application domains. + + +Default Install Location of CUDA Samples + +Windows platform: + +%ProgramData%\NVIDIA Corporation\CUDA Samples\v#.# + +Linux platform: + +/usr/local/cuda-#.#/samples + +and + +$HOME/NVIDIA_CUDA-#.#_Samples + +Mac platform: + +/Developer/NVIDIA/CUDA-#.#/samples + + +NVIDIA Nsight Visual Studio Edition (Windows only) + + +Description + +NVIDIA Nsight Development Platform, Visual Studio Edition is a +development environment integrated into Microsoft Visual +Studio that provides tools for debugging, profiling, analyzing +and optimizing your GPU computing and graphics applications. + + +Default Install Location of Nsight Visual Studio Edition + +Windows platform: + +%ProgramFiles(x86)%\NVIDIA Corporation\Nsight Visual Studio Edition #.# + + +1. 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CUDA Toolkit Supplement to Software License Agreement for +NVIDIA Software Development Kits +------------------------------------------------------------ + + +Release date: August 16, 2018 +----------------------------- + +The terms in this supplement govern your use of the NVIDIA +CUDA Toolkit SDK under the terms of your license agreement +(“Agreement”) as modified by this supplement. Capitalized +terms used but not defined below have the meaning assigned to +them in the Agreement. + +This supplement is an exhibit to the Agreement and is +incorporated as an integral part of the Agreement. In the +event of conflict between the terms in this supplement and the +terms in the Agreement, the terms in this supplement govern. + + +2.1. License Scope + +The SDK is licensed for you to develop applications only for +use in systems with NVIDIA GPUs. + + +2.2. Distribution + +The portions of the SDK that are distributable under the +Agreement are listed in Attachment A. + + +2.3. 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Attachment A + +The following portions of the SDK are distributable under the +Agreement: + +Component + +CUDA Runtime + +Windows + +cudart.dll, cudart_static.lib, cudadevrt.lib + +Mac OSX + +libcudart.dylib, libcudart_static.a, libcudadevrt.a + +Linux + +libcudart.so, libcudart_static.a, libcudadevrt.a + +Android + +libcudart.so, libcudart_static.a, libcudadevrt.a + +Component + +CUDA FFT Library + +Windows + +cufft.dll, cufftw.dll, cufft.lib, cufftw.lib + +Mac OSX + +libcufft.dylib, libcufft_static.a, libcufftw.dylib, +libcufftw_static.a + +Linux + +libcufft.so, libcufft_static.a, libcufftw.so, +libcufftw_static.a + +Android + +libcufft.so, libcufft_static.a, libcufftw.so, +libcufftw_static.a + +Component + +CUDA BLAS Library + +Windows + +cublas.dll, cublasLt.dll + +Mac OSX + +libcublas.dylib, libcublasLt.dylib, libcublas_static.a, +libcublasLt_static.a + +Linux + +libcublas.so, libcublasLt.so, libcublas_static.a, +libcublasLt_static.a + +Android + +libcublas.so, libcublasLt.so, libcublas_static.a, +libcublasLt_static.a + +Component + +NVIDIA "Drop-in" BLAS Library + +Windows + +nvblas.dll + +Mac OSX + +libnvblas.dylib + +Linux + +libnvblas.so + +Component + +CUDA Sparse Matrix Library + +Windows + +cusparse.dll, cusparse.lib + +Mac OSX + +libcusparse.dylib, libcusparse_static.a + +Linux + +libcusparse.so, libcusparse_static.a + +Android + +libcusparse.so, libcusparse_static.a + +Component + +CUDA Linear Solver Library + +Windows + +cusolver.dll, cusolver.lib + +Mac OSX + +libcusolver.dylib, libcusolver_static.a + +Linux + +libcusolver.so, libcusolver_static.a + +Android + +libcusolver.so, libcusolver_static.a + +Component + +CUDA Random Number Generation Library + +Windows + +curand.dll, curand.lib + +Mac OSX + +libcurand.dylib, libcurand_static.a + +Linux + +libcurand.so, libcurand_static.a + +Android + +libcurand.so, libcurand_static.a + +Component + +CUDA Accelerated Graph Library + +Component + +NVIDIA Performance Primitives Library + +Windows + +nppc.dll, nppc.lib, nppial.dll, nppial.lib, nppicc.dll, +nppicc.lib, nppicom.dll, nppicom.lib, nppidei.dll, +nppidei.lib, nppif.dll, nppif.lib, nppig.dll, nppig.lib, +nppim.dll, nppim.lib, nppist.dll, nppist.lib, nppisu.dll, +nppisu.lib, nppitc.dll, nppitc.lib, npps.dll, npps.lib + +Mac OSX + +libnppc.dylib, libnppc_static.a, libnppial.dylib, +libnppial_static.a, libnppicc.dylib, libnppicc_static.a, +libnppicom.dylib, libnppicom_static.a, libnppidei.dylib, +libnppidei_static.a, libnppif.dylib, libnppif_static.a, +libnppig.dylib, libnppig_static.a, libnppim.dylib, +libnppisu_static.a, libnppitc.dylib, libnppitc_static.a, +libnpps.dylib, libnpps_static.a + +Linux + +libnppc.so, libnppc_static.a, libnppial.so, +libnppial_static.a, libnppicc.so, libnppicc_static.a, +libnppicom.so, libnppicom_static.a, libnppidei.so, +libnppidei_static.a, libnppif.so, libnppif_static.a +libnppig.so, libnppig_static.a, libnppim.so, +libnppim_static.a, libnppist.so, libnppist_static.a, +libnppisu.so, libnppisu_static.a, libnppitc.so +libnppitc_static.a, libnpps.so, libnpps_static.a + +Android + +libnppc.so, libnppc_static.a, libnppial.so, +libnppial_static.a, libnppicc.so, libnppicc_static.a, +libnppicom.so, libnppicom_static.a, libnppidei.so, +libnppidei_static.a, libnppif.so, libnppif_static.a +libnppig.so, libnppig_static.a, libnppim.so, +libnppim_static.a, libnppist.so, libnppist_static.a, +libnppisu.so, libnppisu_static.a, libnppitc.so +libnppitc_static.a, libnpps.so, libnpps_static.a + +Component + +NVIDIA JPEG Library + +Linux + +libnvjpeg.so, libnvjpeg_static.a + +Component + +Internal common library required for statically linking to +cuBLAS, cuSPARSE, cuFFT, cuRAND, nvJPEG and NPP + +Mac OSX + +libculibos.a + +Linux + +libculibos.a + +Component + +NVIDIA Runtime Compilation Library and Header + +All + +nvrtc.h + +Windows + +nvrtc.dll, nvrtc-builtins.dll + +Mac OSX + +libnvrtc.dylib, libnvrtc-builtins.dylib + +Linux + +libnvrtc.so, libnvrtc-builtins.so + +Component + +NVIDIA Optimizing Compiler Library + +Windows + +nvvm.dll + +Mac OSX + +libnvvm.dylib + +Linux + +libnvvm.so + +Component + +NVIDIA Common Device Math Functions Library + +Windows + +libdevice.10.bc + +Mac OSX + +libdevice.10.bc + +Linux + +libdevice.10.bc + +Component + +CUDA Occupancy Calculation Header Library + +All + +cuda_occupancy.h + +Component + +CUDA Half Precision Headers + +All + +cuda_fp16.h, cuda_fp16.hpp + +Component + +CUDA Profiling Tools Interface (CUPTI) Library + +Windows + +cupti.dll + +Mac OSX + +libcupti.dylib + +Linux + +libcupti.so + +Component + +NVIDIA Tools Extension Library + +Windows + +nvToolsExt.dll, nvToolsExt.lib + +Mac OSX + +libnvToolsExt.dylib + +Linux + +libnvToolsExt.so + +Component + +NVIDIA CUDA Driver Libraries + +Linux + +libcuda.so, libnvidia-fatbinaryloader.so, +libnvidia-ptxjitcompiler.so + +The NVIDIA CUDA Driver Libraries are only distributable in +applications that meet this criteria: + + 1. The application was developed starting from a NVIDIA CUDA + container obtained from Docker Hub or the NVIDIA GPU + Cloud, and + + 2. The resulting application is packaged as a Docker + container and distributed to users on Docker Hub or the + NVIDIA GPU Cloud only. + + +2.7. Attachment B + + +Additional Licensing Obligations + +The following third party components included in the SOFTWARE +are licensed to Licensee pursuant to the following terms and +conditions: + + 1. Licensee's use of the GDB third party component is + subject to the terms and conditions of GNU GPL v3: + + This product includes copyrighted third-party software licensed + under the terms of the GNU General Public License v3 ("GPL v3"). + All third-party software packages are copyright by their respective + authors. GPL v3 terms and conditions are hereby incorporated into + the Agreement by this reference: http://www.gnu.org/licenses/gpl.txt + + Consistent with these licensing requirements, the software + listed below is provided under the terms of the specified + open source software licenses. To obtain source code for + software provided under licenses that require + redistribution of source code, including the GNU General + Public License (GPL) and GNU Lesser General Public License + (LGPL), contact oss-requests@nvidia.com. This offer is + valid for a period of three (3) years from the date of the + distribution of this product by NVIDIA CORPORATION. + + Component License + CUDA-GDB GPL v3 + + 2. Licensee represents and warrants that any and all third + party licensing and/or royalty payment obligations in + connection with Licensee's use of the H.264 video codecs + are solely the responsibility of Licensee. + + 3. Licensee's use of the Thrust library is subject to the + terms and conditions of the Apache License Version 2.0. + All third-party software packages are copyright by their + respective authors. Apache License Version 2.0 terms and + conditions are hereby incorporated into the Agreement by + this reference. + http://www.apache.org/licenses/LICENSE-2.0.html + + In addition, Licensee acknowledges the following notice: + Thrust includes source code from the Boost Iterator, + Tuple, System, and Random Number libraries. + + Boost Software License - Version 1.0 - August 17th, 2003 + . . . . + + Permission is hereby granted, free of charge, to any person or + organization obtaining a copy of the software and accompanying + documentation covered by this license (the "Software") to use, + reproduce, display, distribute, execute, and transmit the Software, + and to prepare derivative works of the Software, and to permit + third-parties to whom the Software is furnished to do so, all + subject to the following: + + The copyright notices in the Software and this entire statement, + including the above license grant, this restriction and the following + disclaimer, must be included in all copies of the Software, in whole + or in part, and all derivative works of the Software, unless such + copies or derivative works are solely in the form of machine-executable + object code generated by a source language processor. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, + EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF + MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND + NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR + ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE FOR ANY DAMAGES OR + OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING + FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR + OTHER DEALINGS IN THE SOFTWARE. + + 4. Licensee's use of the LLVM third party component is + subject to the following terms and conditions: + + ====================================================== + LLVM Release License + ====================================================== + University of Illinois/NCSA + Open Source License + + Copyright (c) 2003-2010 University of Illinois at Urbana-Champaign. + All rights reserved. + + Developed by: + + LLVM Team + + University of Illinois at Urbana-Champaign + + http://llvm.org + + Permission is hereby granted, free of charge, to any person obtaining a copy + of this software and associated documentation files (the "Software"), to + deal with the Software without restriction, including without limitation the + rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + sell copies of the Software, and to permit persons to whom the Software is + furnished to do so, subject to the following conditions: + + * Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimers. + + * Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimers in the + documentation and/or other materials provided with the distribution. + + * Neither the names of the LLVM Team, University of Illinois at Urbana- + Champaign, nor the names of its contributors may be used to endorse or + promote products derived from this Software without specific prior + written permission. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL + THE CONTRIBUTORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR + OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, + ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER + DEALINGS WITH THE SOFTWARE. + + 5. Licensee's use (e.g. nvprof) of the PCRE third party + component is subject to the following terms and + conditions: + + ------------ + PCRE LICENCE + ------------ + PCRE is a library of functions to support regular expressions whose syntax + and semantics are as close as possible to those of the Perl 5 language. + Release 8 of PCRE is distributed under the terms of the "BSD" licence, as + specified below. The documentation for PCRE, supplied in the "doc" + directory, is distributed under the same terms as the software itself. The + basic library functions are written in C and are freestanding. Also + included in the distribution is a set of C++ wrapper functions, and a just- + in-time compiler that can be used to optimize pattern matching. These are + both optional features that can be omitted when the library is built. + + THE BASIC LIBRARY FUNCTIONS + --------------------------- + Written by: Philip Hazel + Email local part: ph10 + Email domain: cam.ac.uk + University of Cambridge Computing Service, + Cambridge, England. + Copyright (c) 1997-2012 University of Cambridge + All rights reserved. + + PCRE JUST-IN-TIME COMPILATION SUPPORT + ------------------------------------- + Written by: Zoltan Herczeg + Email local part: hzmester + Emain domain: freemail.hu + Copyright(c) 2010-2012 Zoltan Herczeg + All rights reserved. + + STACK-LESS JUST-IN-TIME COMPILER + -------------------------------- + Written by: Zoltan Herczeg + Email local part: hzmester + Emain domain: freemail.hu + Copyright(c) 2009-2012 Zoltan Herczeg + All rights reserved. + + THE C++ WRAPPER FUNCTIONS + ------------------------- + Contributed by: Google Inc. + Copyright (c) 2007-2012, Google Inc. + All rights reserved. + + THE "BSD" LICENCE + ----------------- + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are met: + + * Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + + * Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + * Neither the name of the University of Cambridge nor the name of Google + Inc. nor the names of their contributors may be used to endorse or + promote products derived from this software without specific prior + written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE + ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE + LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF + SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS + INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN + CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) + ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + POSSIBILITY OF SUCH DAMAGE. + + 6. Some of the cuBLAS library routines were written by or + derived from code written by Vasily Volkov and are subject + to the Modified Berkeley Software Distribution License as + follows: + + Copyright (c) 2007-2009, Regents of the University of California + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + * Neither the name of the University of California, Berkeley nor + the names of its contributors may be used to endorse or promote + products derived from this software without specific prior + written permission. + + THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR + IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED + WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE + DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, + INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES + (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR + SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) + HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, + STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING + IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + POSSIBILITY OF SUCH DAMAGE. + + 7. Some of the cuBLAS library routines were written by or + derived from code written by Davide Barbieri and are + subject to the Modified Berkeley Software Distribution + License as follows: + + Copyright (c) 2008-2009 Davide Barbieri @ University of Rome Tor Vergata. + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + * The name of the author may not be used to endorse or promote + products derived from this software without specific prior + written permission. + + THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR + IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED + WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE + DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, + INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES + (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR + SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) + HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, + STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING + IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + POSSIBILITY OF SUCH DAMAGE. + + 8. Some of the cuBLAS library routines were derived from + code developed by the University of Tennessee and are + subject to the Modified Berkeley Software Distribution + License as follows: + + Copyright (c) 2010 The University of Tennessee. + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer listed in this license in the documentation and/or + other materials provided with the distribution. + * Neither the name of the copyright holders nor the names of its + contributors may be used to endorse or promote products derived + from this software without specific prior written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + 9. Some of the cuBLAS library routines were written by or + derived from code written by Jonathan Hogg and are subject + to the Modified Berkeley Software Distribution License as + follows: + + Copyright (c) 2012, The Science and Technology Facilities Council (STFC). + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + * Neither the name of the STFC nor the names of its contributors + may be used to endorse or promote products derived from this + software without specific prior written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE STFC BE + LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF + SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR + BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, + WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE + OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN + IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + 10. Some of the cuBLAS library routines were written by or + derived from code written by Ahmad M. Abdelfattah, David + Keyes, and Hatem Ltaief, and are subject to the Apache + License, Version 2.0, as follows: + + -- (C) Copyright 2013 King Abdullah University of Science and Technology + Authors: + Ahmad Abdelfattah (ahmad.ahmad@kaust.edu.sa) + David Keyes (david.keyes@kaust.edu.sa) + Hatem Ltaief (hatem.ltaief@kaust.edu.sa) + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + * Neither the name of the King Abdullah University of Science and + Technology nor the names of its contributors may be used to endorse + or promote products derived from this software without specific prior + written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + HOLDERS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE + + 11. Some of the cuSPARSE library routines were written by or + derived from code written by Li-Wen Chang and are subject + to the NCSA Open Source License as follows: + + Copyright (c) 2012, University of Illinois. + + All rights reserved. + + Developed by: IMPACT Group, University of Illinois, http://impact.crhc.illinois.edu + + Permission is hereby granted, free of charge, to any person obtaining + a copy of this software and associated documentation files (the + "Software"), to deal with the Software without restriction, including + without limitation the rights to use, copy, modify, merge, publish, + distribute, sublicense, and/or sell copies of the Software, and to + permit persons to whom the Software is furnished to do so, subject to + the following conditions: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimers in the documentation and/or other materials provided + with the distribution. + * Neither the names of IMPACT Group, University of Illinois, nor + the names of its contributors may be used to endorse or promote + products derived from this Software without specific prior + written permission. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, + EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF + MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND + NONINFRINGEMENT. IN NO EVENT SHALL THE CONTRIBUTORS OR COPYRIGHT + HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR + IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS WITH THE + SOFTWARE. + + 12. Some of the cuRAND library routines were written by or + derived from code written by Mutsuo Saito and Makoto + Matsumoto and are subject to the following license: + + Copyright (c) 2009, 2010 Mutsuo Saito, Makoto Matsumoto and Hiroshima + University. All rights reserved. + + Copyright (c) 2011 Mutsuo Saito, Makoto Matsumoto, Hiroshima + University and University of Tokyo. All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + * Neither the name of the Hiroshima University nor the names of + its contributors may be used to endorse or promote products + derived from this software without specific prior written + permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + 13. Some of the cuRAND library routines were derived from + code developed by D. E. Shaw Research and are subject to + the following license: + + Copyright 2010-2011, D. E. Shaw Research. + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions, and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions, and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + * Neither the name of D. E. Shaw Research nor the names of its + contributors may be used to endorse or promote products derived + from this software without specific prior written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + 14. Some of the Math library routines were written by or + derived from code developed by Norbert Juffa and are + subject to the following license: + + Copyright (c) 2015-2017, Norbert Juffa + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + 1. Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + 2. Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + 15. Licensee's use of the lz4 third party component is + subject to the following terms and conditions: + + Copyright (C) 2011-2013, Yann Collet. + BSD 2-Clause License (http://www.opensource.org/licenses/bsd-license.php) + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following disclaimer + in the documentation and/or other materials provided with the + distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. 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The NPP library uses code from the Boost Math Toolkit, + and is subject to the following license: + + Boost Software License - Version 1.0 - August 17th, 2003 + . . . . + + Permission is hereby granted, free of charge, to any person or + organization obtaining a copy of the software and accompanying + documentation covered by this license (the "Software") to use, + reproduce, display, distribute, execute, and transmit the Software, + and to prepare derivative works of the Software, and to permit + third-parties to whom the Software is furnished to do so, all + subject to the following: + + The copyright notices in the Software and this entire statement, + including the above license grant, this restriction and the following + disclaimer, must be included in all copies of the Software, in whole + or in part, and all derivative works of the Software, unless such + copies or derivative works are solely in the form of machine-executable + object code generated by a source language processor. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, + EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF + MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND + NON-INFRINGEMENT. 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Some of the cuBLAS library routines uses code from + OpenAI, which is subject to the following license: + + License URL + https://github.com/openai/openai-gemm/blob/master/LICENSE + + License Text + The MIT License + + Copyright (c) 2016 OpenAI (http://openai.com), 2016 Google Inc. + + Permission is hereby granted, free of charge, to any person obtaining a copy + of this software and associated documentation files (the "Software"), to deal + in the Software without restriction, including without limitation the rights + to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + copies of the Software, and to permit persons to whom the Software is + furnished to do so, subject to the following conditions: + + The above copyright notice and this permission notice shall be included in + all copies or substantial portions of the Software. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. 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All rights reserved. + + Permission is hereby granted, free of charge, to any person + obtaining a copy of this software and associated documentation + files (the "Software"), to deal in the Software without restriction, + including without limitation the rights to use, copy, modify, merge, + publish, distribute, sublicense, and/or sell copies of the Software, + and to permit persons to whom the Software is furnished to do so, + subject to the following conditions: + + The above copyright notice and this permission notice shall be included + in all copies or substantial portions of the Software. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS + OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + + 20. Licensee's use of linmath.h header for CPU functions for + GL vector/matrix operations from lunarG is subject to the + Apache License Version 2.0. + + 21. The DX12-CUDA sample uses the d3dx12.h header, which is + subject to the MIT license . + +----------------- diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_nvjitlink_cu12-12.8.93.dist-info/METADATA b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_nvjitlink_cu12-12.8.93.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..132da4d3c2e9e8c3a1ef56f482a283a26849c2cb --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_nvjitlink_cu12-12.8.93.dist-info/METADATA @@ -0,0 +1,44 @@ +Metadata-Version: 2.2 +Name: nvidia-nvjitlink-cu12 +Version: 12.8.93 +Summary: Nvidia JIT LTO Library +Home-page: https://developer.nvidia.com/cuda-zone +Author: Nvidia CUDA Installer Team +Author-email: compute_installer@nvidia.com +License: NVIDIA Proprietary Software +Keywords: cuda,nvidia,runtime,machine learning,deep learning +Classifier: Development Status :: 4 - Beta +Classifier: Intended Audience :: Developers +Classifier: Intended Audience :: Education +Classifier: Intended Audience :: Science/Research +Classifier: License :: Other/Proprietary License +Classifier: Natural Language :: English +Classifier: Programming Language :: Python :: 3 +Classifier: Programming Language :: Python :: 3.5 +Classifier: Programming Language :: Python :: 3.6 +Classifier: Programming Language :: Python :: 3.7 +Classifier: Programming Language :: Python :: 3.8 +Classifier: Programming Language :: Python :: 3.9 +Classifier: Programming Language :: Python :: 3.10 +Classifier: Programming Language :: Python :: 3.11 +Classifier: Programming Language :: Python :: 3 :: Only +Classifier: Topic :: Scientific/Engineering +Classifier: Topic :: Scientific/Engineering :: Mathematics +Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence +Classifier: Topic :: Software Development +Classifier: Topic :: Software Development :: Libraries +Classifier: Operating System :: Microsoft :: Windows +Classifier: Operating System :: POSIX :: Linux +Requires-Python: >=3 +License-File: License.txt +Dynamic: author +Dynamic: author-email +Dynamic: classifier +Dynamic: description +Dynamic: home-page +Dynamic: keywords +Dynamic: license +Dynamic: requires-python +Dynamic: summary + +NVIDIA compiler library for JIT LTO functionality. diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_nvjitlink_cu12-12.8.93.dist-info/RECORD b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_nvjitlink_cu12-12.8.93.dist-info/RECORD new file mode 100644 index 0000000000000000000000000000000000000000..5f1396b0a3f6d19714eb27ca76669cac73c62493 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_nvjitlink_cu12-12.8.93.dist-info/RECORD @@ -0,0 +1,16 @@ +nvidia/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0 +nvidia/__pycache__/__init__.cpython-310.pyc,, 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a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_nvjitlink_cu12-12.8.93.dist-info/top_level.txt b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_nvjitlink_cu12-12.8.93.dist-info/top_level.txt new file mode 100644 index 0000000000000000000000000000000000000000..862f7abf232cdfbb928609856247292e81c9decb --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_nvjitlink_cu12-12.8.93.dist-info/top_level.txt @@ -0,0 +1 @@ +nvidia diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_nvshmem_cu12-3.4.5.dist-info/INSTALLER b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_nvshmem_cu12-3.4.5.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_nvshmem_cu12-3.4.5.dist-info/INSTALLER @@ -0,0 +1 @@ +pip diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_nvshmem_cu12-3.4.5.dist-info/METADATA b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_nvshmem_cu12-3.4.5.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..f3ada19d221682ba6ca5c6adf69b9d1051769483 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_nvshmem_cu12-3.4.5.dist-info/METADATA @@ -0,0 +1,45 @@ +Metadata-Version: 2.4 +Name: nvidia-nvshmem-cu12 +Version: 3.4.5 +Summary: NVSHMEM creates a global address space that provides efficient and scalable communication for NVIDIA GPU clusters. +Home-page: https://developer.nvidia.com/cuda-zone +Author: Nvidia CUDA Installer Team +Author-email: compute_installer@nvidia.com +License-Expression: LicenseRef-NVIDIA-Proprietary +Keywords: cuda,nvidia,runtime,machine learning,deep learning +Classifier: Development Status :: 4 - Beta +Classifier: Intended Audience :: Developers +Classifier: Intended Audience :: Education +Classifier: Intended Audience :: Science/Research +Classifier: Natural Language :: English +Classifier: Programming Language :: Python :: 3 +Classifier: Programming Language :: Python :: 3.5 +Classifier: Programming Language :: Python :: 3.6 +Classifier: Programming Language :: Python :: 3.7 +Classifier: Programming Language :: Python :: 3.8 +Classifier: Programming Language :: Python :: 3.9 +Classifier: Programming Language :: Python :: 3.10 +Classifier: Programming Language :: Python :: 3.11 +Classifier: Programming Language :: Python :: 3 :: Only +Classifier: Topic :: Scientific/Engineering +Classifier: Topic :: Scientific/Engineering :: Mathematics +Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence +Classifier: Topic :: Software Development +Classifier: Topic :: Software Development :: Libraries +Classifier: Operating System :: Microsoft :: Windows +Classifier: Operating System :: POSIX :: Linux +Requires-Python: >=3 +License-File: License.txt +Dynamic: author +Dynamic: author-email +Dynamic: classifier +Dynamic: description +Dynamic: home-page +Dynamic: keywords +Dynamic: license +Dynamic: license-file +Dynamic: license-expression +Dynamic: requires-python +Dynamic: summary + +NVSHMEM is a parallel programming interface based on OpenSHMEM that provides efficient and scalable communication for NVIDIA GPU clusters. 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By accepting this agreement, you +agree to comply with all the terms and conditions applicable +to the product(s) included herein. + + +NVIDIA Driver + + +Description + +This package contains the operating system driver and +fundamental system software components for NVIDIA GPUs. + + +NVIDIA CUDA Toolkit + + +Description + +The NVIDIA CUDA Toolkit provides command-line and graphical +tools for building, debugging and optimizing the performance +of applications accelerated by NVIDIA GPUs, runtime and math +libraries, and documentation including programming guides, +user manuals, and API references. + + +Default Install Location of CUDA Toolkit + +Windows platform: + +%ProgramFiles%\NVIDIA GPU Computing Toolkit\CUDA\v#.# + +Linux platform: + +/usr/local/cuda-#.# + +Mac platform: + +/Developer/NVIDIA/CUDA-#.# + + +NVIDIA CUDA Samples + + +Description + +This package includes over 100+ CUDA examples that demonstrate +various CUDA programming principles, and efficient CUDA +implementation of algorithms in specific application domains. + + +Default Install Location of CUDA Samples + +Windows platform: + +%ProgramData%\NVIDIA Corporation\CUDA Samples\v#.# + +Linux platform: + +/usr/local/cuda-#.#/samples + +and + +$HOME/NVIDIA_CUDA-#.#_Samples + +Mac platform: + +/Developer/NVIDIA/CUDA-#.#/samples + + +NVIDIA Nsight Visual Studio Edition (Windows only) + + +Description + +NVIDIA Nsight Development Platform, Visual Studio Edition is a +development environment integrated into Microsoft Visual +Studio that provides tools for debugging, profiling, analyzing +and optimizing your GPU computing and graphics applications. + + +Default Install Location of Nsight Visual Studio Edition + +Windows platform: + +%ProgramFiles(x86)%\NVIDIA Corporation\Nsight Visual Studio Edition #.# + + +1. License Agreement for NVIDIA Software Development Kits +--------------------------------------------------------- + + +Release Date: July 26, 2018 +--------------------------- + + +Important NoticeRead before downloading, installing, +copying or using the licensed software: +------------------------------------------------------- + +This license agreement, including exhibits attached +("Agreement”) is a legal agreement between you and NVIDIA +Corporation ("NVIDIA") and governs your use of a NVIDIA +software development kit (“SDK”). + +Each SDK has its own set of software and materials, but here +is a description of the types of items that may be included in +a SDK: source code, header files, APIs, data sets and assets +(examples include images, textures, models, scenes, videos, +native API input/output files), binary software, sample code, +libraries, utility programs, programming code and +documentation. + +This Agreement can be accepted only by an adult of legal age +of majority in the country in which the SDK is used. + +If you are entering into this Agreement on behalf of a company +or other legal entity, you represent that you have the legal +authority to bind the entity to this Agreement, in which case +“you” will mean the entity you represent. + +If you don’t have the required age or authority to accept +this Agreement, or if you don’t accept all the terms and +conditions of this Agreement, do not download, install or use +the SDK. + +You agree to use the SDK only for purposes that are permitted +by (a) this Agreement, and (b) any applicable law, regulation +or generally accepted practices or guidelines in the relevant +jurisdictions. + + +1.1. 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CUDA Toolkit Supplement to Software License Agreement for +NVIDIA Software Development Kits +------------------------------------------------------------ + + +Release date: August 16, 2018 +----------------------------- + +The terms in this supplement govern your use of the NVIDIA +CUDA Toolkit SDK under the terms of your license agreement +(“Agreement”) as modified by this supplement. Capitalized +terms used but not defined below have the meaning assigned to +them in the Agreement. + +This supplement is an exhibit to the Agreement and is +incorporated as an integral part of the Agreement. In the +event of conflict between the terms in this supplement and the +terms in the Agreement, the terms in this supplement govern. + + +2.1. License Scope + +The SDK is licensed for you to develop applications only for +use in systems with NVIDIA GPUs. + + +2.2. Distribution + +The portions of the SDK that are distributable under the +Agreement are listed in Attachment A. + + +2.3. 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NVIDIA does not grant to you +under this Agreement any necessary patent or other rights with +respect to any audio and/or video encoders and decoders. + + +2.5. Licensing + +If the distribution terms in this Agreement are not suitable +for your organization, or for any questions regarding this +Agreement, please contact NVIDIA at +nvidia-compute-license-questions@nvidia.com. + + +2.6. Attachment A + +The following portions of the SDK are distributable under the +Agreement: + +Component + +CUDA Runtime + +Windows + +cudart.dll, cudart_static.lib, cudadevrt.lib + +Mac OSX + +libcudart.dylib, libcudart_static.a, libcudadevrt.a + +Linux + +libcudart.so, libcudart_static.a, libcudadevrt.a + +Android + +libcudart.so, libcudart_static.a, libcudadevrt.a + +Component + +CUDA FFT Library + +Windows + +cufft.dll, cufftw.dll, cufft.lib, cufftw.lib + +Mac OSX + +libcufft.dylib, libcufft_static.a, libcufftw.dylib, +libcufftw_static.a + +Linux + +libcufft.so, libcufft_static.a, libcufftw.so, +libcufftw_static.a + +Android + +libcufft.so, libcufft_static.a, libcufftw.so, +libcufftw_static.a + +Component + +CUDA BLAS Library + +Windows + +cublas.dll, cublasLt.dll + +Mac OSX + +libcublas.dylib, libcublasLt.dylib, libcublas_static.a, +libcublasLt_static.a + +Linux + +libcublas.so, libcublasLt.so, libcublas_static.a, +libcublasLt_static.a + +Android + +libcublas.so, libcublasLt.so, libcublas_static.a, +libcublasLt_static.a + +Component + +NVIDIA "Drop-in" BLAS Library + +Windows + +nvblas.dll + +Mac OSX + +libnvblas.dylib + +Linux + +libnvblas.so + +Component + +CUDA Sparse Matrix Library + +Windows + +cusparse.dll, cusparse.lib + +Mac OSX + +libcusparse.dylib, libcusparse_static.a + +Linux + +libcusparse.so, libcusparse_static.a + +Android + +libcusparse.so, libcusparse_static.a + +Component + +CUDA Linear Solver Library + +Windows + +cusolver.dll, cusolver.lib + +Mac OSX + +libcusolver.dylib, libcusolver_static.a + +Linux + +libcusolver.so, libcusolver_static.a + +Android + +libcusolver.so, libcusolver_static.a + +Component + +CUDA Random Number Generation Library + +Windows + +curand.dll, curand.lib + +Mac OSX + +libcurand.dylib, libcurand_static.a + +Linux + +libcurand.so, libcurand_static.a + +Android + +libcurand.so, libcurand_static.a + +Component + +CUDA Accelerated Graph Library + +Component + +NVIDIA Performance Primitives Library + +Windows + +nppc.dll, nppc.lib, nppial.dll, nppial.lib, nppicc.dll, +nppicc.lib, nppicom.dll, nppicom.lib, nppidei.dll, +nppidei.lib, nppif.dll, nppif.lib, nppig.dll, nppig.lib, +nppim.dll, nppim.lib, nppist.dll, nppist.lib, nppisu.dll, +nppisu.lib, nppitc.dll, nppitc.lib, npps.dll, npps.lib + +Mac OSX + +libnppc.dylib, libnppc_static.a, libnppial.dylib, +libnppial_static.a, libnppicc.dylib, libnppicc_static.a, +libnppicom.dylib, libnppicom_static.a, libnppidei.dylib, +libnppidei_static.a, libnppif.dylib, libnppif_static.a, +libnppig.dylib, libnppig_static.a, libnppim.dylib, +libnppisu_static.a, libnppitc.dylib, libnppitc_static.a, +libnpps.dylib, libnpps_static.a + +Linux + +libnppc.so, libnppc_static.a, libnppial.so, +libnppial_static.a, libnppicc.so, libnppicc_static.a, +libnppicom.so, libnppicom_static.a, libnppidei.so, +libnppidei_static.a, libnppif.so, libnppif_static.a +libnppig.so, libnppig_static.a, libnppim.so, +libnppim_static.a, libnppist.so, libnppist_static.a, +libnppisu.so, libnppisu_static.a, libnppitc.so +libnppitc_static.a, libnpps.so, libnpps_static.a + +Android + +libnppc.so, libnppc_static.a, libnppial.so, +libnppial_static.a, libnppicc.so, libnppicc_static.a, +libnppicom.so, libnppicom_static.a, libnppidei.so, +libnppidei_static.a, libnppif.so, libnppif_static.a +libnppig.so, libnppig_static.a, libnppim.so, +libnppim_static.a, libnppist.so, libnppist_static.a, +libnppisu.so, libnppisu_static.a, libnppitc.so +libnppitc_static.a, libnpps.so, libnpps_static.a + +Component + +NVIDIA JPEG Library + +Linux + +libnvjpeg.so, libnvjpeg_static.a + +Component + +Internal common library required for statically linking to +cuBLAS, cuSPARSE, cuFFT, cuRAND, nvJPEG and NPP + +Mac OSX + +libculibos.a + +Linux + +libculibos.a + +Component + +NVIDIA Runtime Compilation Library and Header + +All + +nvrtc.h + +Windows + +nvrtc.dll, nvrtc-builtins.dll + +Mac OSX + +libnvrtc.dylib, libnvrtc-builtins.dylib + +Linux + +libnvrtc.so, libnvrtc-builtins.so + +Component + +NVIDIA Optimizing Compiler Library + +Windows + +nvvm.dll + +Mac OSX + +libnvvm.dylib + +Linux + +libnvvm.so + +Component + +NVIDIA Common Device Math Functions Library + +Windows + +libdevice.10.bc + +Mac OSX + +libdevice.10.bc + +Linux + +libdevice.10.bc + +Component + +CUDA Occupancy Calculation Header Library + +All + +cuda_occupancy.h + +Component + +CUDA Half Precision Headers + +All + +cuda_fp16.h, cuda_fp16.hpp + +Component + +CUDA Profiling Tools Interface (CUPTI) Library + +Windows + +cupti.dll + +Mac OSX + +libcupti.dylib + +Linux + +libcupti.so + +Component + +NVIDIA Tools Extension Library + +Windows + +nvToolsExt.dll, nvToolsExt.lib + +Mac OSX + +libnvToolsExt.dylib + +Linux + +libnvToolsExt.so + +Component + +NVIDIA CUDA Driver Libraries + +Linux + +libcuda.so, libnvidia-fatbinaryloader.so, +libnvidia-ptxjitcompiler.so + +The NVIDIA CUDA Driver Libraries are only distributable in +applications that meet this criteria: + + 1. The application was developed starting from a NVIDIA CUDA + container obtained from Docker Hub or the NVIDIA GPU + Cloud, and + + 2. The resulting application is packaged as a Docker + container and distributed to users on Docker Hub or the + NVIDIA GPU Cloud only. + + +2.7. Attachment B + + +Additional Licensing Obligations + +The following third party components included in the SOFTWARE +are licensed to Licensee pursuant to the following terms and +conditions: + + 1. Licensee's use of the GDB third party component is + subject to the terms and conditions of GNU GPL v3: + + This product includes copyrighted third-party software licensed + under the terms of the GNU General Public License v3 ("GPL v3"). + All third-party software packages are copyright by their respective + authors. GPL v3 terms and conditions are hereby incorporated into + the Agreement by this reference: http://www.gnu.org/licenses/gpl.txt + + Consistent with these licensing requirements, the software + listed below is provided under the terms of the specified + open source software licenses. To obtain source code for + software provided under licenses that require + redistribution of source code, including the GNU General + Public License (GPL) and GNU Lesser General Public License + (LGPL), contact oss-requests@nvidia.com. This offer is + valid for a period of three (3) years from the date of the + distribution of this product by NVIDIA CORPORATION. + + Component License + CUDA-GDB GPL v3 + + 2. Licensee represents and warrants that any and all third + party licensing and/or royalty payment obligations in + connection with Licensee's use of the H.264 video codecs + are solely the responsibility of Licensee. + + 3. Licensee's use of the Thrust library is subject to the + terms and conditions of the Apache License Version 2.0. + All third-party software packages are copyright by their + respective authors. Apache License Version 2.0 terms and + conditions are hereby incorporated into the Agreement by + this reference. + http://www.apache.org/licenses/LICENSE-2.0.html + + In addition, Licensee acknowledges the following notice: + Thrust includes source code from the Boost Iterator, + Tuple, System, and Random Number libraries. + + Boost Software License - Version 1.0 - August 17th, 2003 + . . . . + + Permission is hereby granted, free of charge, to any person or + organization obtaining a copy of the software and accompanying + documentation covered by this license (the "Software") to use, + reproduce, display, distribute, execute, and transmit the Software, + and to prepare derivative works of the Software, and to permit + third-parties to whom the Software is furnished to do so, all + subject to the following: + + The copyright notices in the Software and this entire statement, + including the above license grant, this restriction and the following + disclaimer, must be included in all copies of the Software, in whole + or in part, and all derivative works of the Software, unless such + copies or derivative works are solely in the form of machine-executable + object code generated by a source language processor. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, + EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF + MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND + NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR + ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE FOR ANY DAMAGES OR + OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING + FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR + OTHER DEALINGS IN THE SOFTWARE. + + 4. Licensee's use of the LLVM third party component is + subject to the following terms and conditions: + + ====================================================== + LLVM Release License + ====================================================== + University of Illinois/NCSA + Open Source License + + Copyright (c) 2003-2010 University of Illinois at Urbana-Champaign. + All rights reserved. + + Developed by: + + LLVM Team + + University of Illinois at Urbana-Champaign + + http://llvm.org + + Permission is hereby granted, free of charge, to any person obtaining a copy + of this software and associated documentation files (the "Software"), to + deal with the Software without restriction, including without limitation the + rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + sell copies of the Software, and to permit persons to whom the Software is + furnished to do so, subject to the following conditions: + + * Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimers. + + * Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimers in the + documentation and/or other materials provided with the distribution. + + * Neither the names of the LLVM Team, University of Illinois at Urbana- + Champaign, nor the names of its contributors may be used to endorse or + promote products derived from this Software without specific prior + written permission. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL + THE CONTRIBUTORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR + OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, + ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER + DEALINGS WITH THE SOFTWARE. + + 5. Licensee's use (e.g. nvprof) of the PCRE third party + component is subject to the following terms and + conditions: + + ------------ + PCRE LICENCE + ------------ + PCRE is a library of functions to support regular expressions whose syntax + and semantics are as close as possible to those of the Perl 5 language. + Release 8 of PCRE is distributed under the terms of the "BSD" licence, as + specified below. The documentation for PCRE, supplied in the "doc" + directory, is distributed under the same terms as the software itself. The + basic library functions are written in C and are freestanding. Also + included in the distribution is a set of C++ wrapper functions, and a just- + in-time compiler that can be used to optimize pattern matching. These are + both optional features that can be omitted when the library is built. + + THE BASIC LIBRARY FUNCTIONS + --------------------------- + Written by: Philip Hazel + Email local part: ph10 + Email domain: cam.ac.uk + University of Cambridge Computing Service, + Cambridge, England. + Copyright (c) 1997-2012 University of Cambridge + All rights reserved. + + PCRE JUST-IN-TIME COMPILATION SUPPORT + ------------------------------------- + Written by: Zoltan Herczeg + Email local part: hzmester + Emain domain: freemail.hu + Copyright(c) 2010-2012 Zoltan Herczeg + All rights reserved. + + STACK-LESS JUST-IN-TIME COMPILER + -------------------------------- + Written by: Zoltan Herczeg + Email local part: hzmester + Emain domain: freemail.hu + Copyright(c) 2009-2012 Zoltan Herczeg + All rights reserved. + + THE C++ WRAPPER FUNCTIONS + ------------------------- + Contributed by: Google Inc. + Copyright (c) 2007-2012, Google Inc. + All rights reserved. + + THE "BSD" LICENCE + ----------------- + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are met: + + * Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + + * Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + * Neither the name of the University of Cambridge nor the name of Google + Inc. nor the names of their contributors may be used to endorse or + promote products derived from this software without specific prior + written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE + ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE + LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF + SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS + INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN + CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) + ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + POSSIBILITY OF SUCH DAMAGE. + + 6. Some of the cuBLAS library routines were written by or + derived from code written by Vasily Volkov and are subject + to the Modified Berkeley Software Distribution License as + follows: + + Copyright (c) 2007-2009, Regents of the University of California + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + * Neither the name of the University of California, Berkeley nor + the names of its contributors may be used to endorse or promote + products derived from this software without specific prior + written permission. + + THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR + IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED + WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE + DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, + INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES + (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR + SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) + HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, + STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING + IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + POSSIBILITY OF SUCH DAMAGE. + + 7. Some of the cuBLAS library routines were written by or + derived from code written by Davide Barbieri and are + subject to the Modified Berkeley Software Distribution + License as follows: + + Copyright (c) 2008-2009 Davide Barbieri @ University of Rome Tor Vergata. + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + * The name of the author may not be used to endorse or promote + products derived from this software without specific prior + written permission. + + THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR + IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED + WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE + DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, + INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES + (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR + SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) + HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, + STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING + IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + POSSIBILITY OF SUCH DAMAGE. + + 8. Some of the cuBLAS library routines were derived from + code developed by the University of Tennessee and are + subject to the Modified Berkeley Software Distribution + License as follows: + + Copyright (c) 2010 The University of Tennessee. + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer listed in this license in the documentation and/or + other materials provided with the distribution. + * Neither the name of the copyright holders nor the names of its + contributors may be used to endorse or promote products derived + from this software without specific prior written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + 9. Some of the cuBLAS library routines were written by or + derived from code written by Jonathan Hogg and are subject + to the Modified Berkeley Software Distribution License as + follows: + + Copyright (c) 2012, The Science and Technology Facilities Council (STFC). + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + * Neither the name of the STFC nor the names of its contributors + may be used to endorse or promote products derived from this + software without specific prior written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE STFC BE + LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF + SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR + BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, + WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE + OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN + IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + 10. Some of the cuBLAS library routines were written by or + derived from code written by Ahmad M. Abdelfattah, David + Keyes, and Hatem Ltaief, and are subject to the Apache + License, Version 2.0, as follows: + + -- (C) Copyright 2013 King Abdullah University of Science and Technology + Authors: + Ahmad Abdelfattah (ahmad.ahmad@kaust.edu.sa) + David Keyes (david.keyes@kaust.edu.sa) + Hatem Ltaief (hatem.ltaief@kaust.edu.sa) + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + * Neither the name of the King Abdullah University of Science and + Technology nor the names of its contributors may be used to endorse + or promote products derived from this software without specific prior + written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + HOLDERS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE + + 11. Some of the cuSPARSE library routines were written by or + derived from code written by Li-Wen Chang and are subject + to the NCSA Open Source License as follows: + + Copyright (c) 2012, University of Illinois. + + All rights reserved. + + Developed by: IMPACT Group, University of Illinois, http://impact.crhc.illinois.edu + + Permission is hereby granted, free of charge, to any person obtaining + a copy of this software and associated documentation files (the + "Software"), to deal with the Software without restriction, including + without limitation the rights to use, copy, modify, merge, publish, + distribute, sublicense, and/or sell copies of the Software, and to + permit persons to whom the Software is furnished to do so, subject to + the following conditions: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimers in the documentation and/or other materials provided + with the distribution. + * Neither the names of IMPACT Group, University of Illinois, nor + the names of its contributors may be used to endorse or promote + products derived from this Software without specific prior + written permission. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, + EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF + MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND + NONINFRINGEMENT. IN NO EVENT SHALL THE CONTRIBUTORS OR COPYRIGHT + HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR + IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS WITH THE + SOFTWARE. + + 12. Some of the cuRAND library routines were written by or + derived from code written by Mutsuo Saito and Makoto + Matsumoto and are subject to the following license: + + Copyright (c) 2009, 2010 Mutsuo Saito, Makoto Matsumoto and Hiroshima + University. All rights reserved. + + Copyright (c) 2011 Mutsuo Saito, Makoto Matsumoto, Hiroshima + University and University of Tokyo. All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + * Neither the name of the Hiroshima University nor the names of + its contributors may be used to endorse or promote products + derived from this software without specific prior written + permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + 13. Some of the cuRAND library routines were derived from + code developed by D. E. Shaw Research and are subject to + the following license: + + Copyright 2010-2011, D. E. Shaw Research. + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions, and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions, and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + * Neither the name of D. E. Shaw Research nor the names of its + contributors may be used to endorse or promote products derived + from this software without specific prior written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + 14. Some of the Math library routines were written by or + derived from code developed by Norbert Juffa and are + subject to the following license: + + Copyright (c) 2015-2017, Norbert Juffa + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + 1. Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + 2. Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + 15. Licensee's use of the lz4 third party component is + subject to the following terms and conditions: + + Copyright (C) 2011-2013, Yann Collet. + BSD 2-Clause License (http://www.opensource.org/licenses/bsd-license.php) + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following disclaimer + in the documentation and/or other materials provided with the + distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. 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The NPP library uses code from the Boost Math Toolkit, + and is subject to the following license: + + Boost Software License - Version 1.0 - August 17th, 2003 + . . . . + + Permission is hereby granted, free of charge, to any person or + organization obtaining a copy of the software and accompanying + documentation covered by this license (the "Software") to use, + reproduce, display, distribute, execute, and transmit the Software, + and to prepare derivative works of the Software, and to permit + third-parties to whom the Software is furnished to do so, all + subject to the following: + + The copyright notices in the Software and this entire statement, + including the above license grant, this restriction and the following + disclaimer, must be included in all copies of the Software, in whole + or in part, and all derivative works of the Software, unless such + copies or derivative works are solely in the form of machine-executable + object code generated by a source language processor. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, + EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF + MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND + NON-INFRINGEMENT. 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Some of the cuBLAS library routines uses code from + OpenAI, which is subject to the following license: + + License URL + https://github.com/openai/openai-gemm/blob/master/LICENSE + + License Text + The MIT License + + Copyright (c) 2016 OpenAI (http://openai.com), 2016 Google Inc. + + Permission is hereby granted, free of charge, to any person obtaining a copy + of this software and associated documentation files (the "Software"), to deal + in the Software without restriction, including without limitation the rights + to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + copies of the Software, and to permit persons to whom the Software is + furnished to do so, subject to the following conditions: + + The above copyright notice and this permission notice shall be included in + all copies or substantial portions of the Software. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. 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The DX12-CUDA sample uses the d3dx12.h header, which is + subject to the MIT license . + +----------------- diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_nvshmem_cu12-3.4.5.dist-info/top_level.txt b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_nvshmem_cu12-3.4.5.dist-info/top_level.txt new file mode 100644 index 0000000000000000000000000000000000000000..862f7abf232cdfbb928609856247292e81c9decb --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_nvshmem_cu12-3.4.5.dist-info/top_level.txt @@ -0,0 +1 @@ +nvidia diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_nvtx_cu12-12.8.90.dist-info/INSTALLER b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_nvtx_cu12-12.8.90.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_nvtx_cu12-12.8.90.dist-info/INSTALLER @@ -0,0 +1 @@ +pip diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_nvtx_cu12-12.8.90.dist-info/License.txt b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_nvtx_cu12-12.8.90.dist-info/License.txt new file mode 100644 index 0000000000000000000000000000000000000000..bd8b243dfa02d4e7080150180520f742d2861d15 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/nvidia_nvtx_cu12-12.8.90.dist-info/License.txt @@ -0,0 +1,218 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib-3.3.1.dist-info/METADATA @@ -0,0 +1,194 @@ +Metadata-Version: 2.4 +Name: oauthlib +Version: 3.3.1 +Summary: A generic, spec-compliant, thorough implementation of the OAuth request-signing logic +Home-page: https://github.com/oauthlib/oauthlib +Author: The OAuthlib Community +Maintainer: Jonathan Huot +Maintainer-email: jonathan.huot@gmail.com +License: BSD-3-Clause +Platform: any +Classifier: Development Status :: 5 - Production/Stable +Classifier: Environment :: Web Environment +Classifier: Intended Audience :: Developers +Classifier: Operating System :: MacOS +Classifier: Operating System :: POSIX +Classifier: Operating System :: POSIX :: Linux +Classifier: Programming Language :: Python +Classifier: Programming Language :: Python :: 3 +Classifier: Programming Language :: Python :: 3.8 +Classifier: Programming Language :: Python :: 3.9 +Classifier: Programming Language :: Python :: 3.10 +Classifier: Programming Language :: Python :: 3.11 +Classifier: Programming Language :: Python :: 3.12 +Classifier: Programming Language :: Python :: 3.13 +Classifier: Programming Language :: Python :: 3 :: Only +Classifier: Programming Language :: Python :: Implementation +Classifier: Programming Language :: Python :: Implementation :: CPython +Classifier: Programming Language :: Python :: Implementation :: PyPy +Classifier: Topic :: Software Development :: Libraries :: Python Modules +Requires-Python: >=3.8 +Description-Content-Type: text/x-rst +License-File: LICENSE +Provides-Extra: rsa +Requires-Dist: cryptography>=3.0.0; extra == "rsa" +Provides-Extra: signedtoken +Requires-Dist: cryptography>=3.0.0; extra == "signedtoken" +Requires-Dist: pyjwt<3,>=2.0.0; extra == "signedtoken" +Provides-Extra: signals +Requires-Dist: blinker>=1.4.0; extra == "signals" +Dynamic: author +Dynamic: classifier +Dynamic: description +Dynamic: description-content-type +Dynamic: home-page +Dynamic: license +Dynamic: license-file +Dynamic: maintainer +Dynamic: maintainer-email +Dynamic: platform +Dynamic: provides-extra +Dynamic: requires-python +Dynamic: summary + +OAuthLib - Python Framework for OAuth1 & OAuth2 +=============================================== + +*A generic, spec-compliant, thorough implementation of the OAuth request-signing +logic for Python 3.8+* + +.. image:: https://github.com/oauthlib/oauthlib/actions/workflows/python-build.yml/badge.svg + :target: https://github.com/oauthlib/oauthlib/actions + :alt: GitHub Actions +.. image:: https://coveralls.io/repos/oauthlib/oauthlib/badge.svg?branch=master + :target: https://coveralls.io/r/oauthlib/oauthlib + :alt: Coveralls +.. image:: https://img.shields.io/pypi/pyversions/oauthlib.svg + :target: https://pypi.org/project/oauthlib/ + :alt: Download from PyPI +.. image:: https://img.shields.io/pypi/l/oauthlib.svg + :target: https://pypi.org/project/oauthlib/ + :alt: License +.. image:: https://app.fossa.io/api/projects/git%2Bgithub.com%2Foauthlib%2Foauthlib.svg?type=shield + :target: https://app.fossa.io/projects/git%2Bgithub.com%2Foauthlib%2Foauthlib?ref=badge_shield + :alt: FOSSA Status +.. image:: https://img.shields.io/readthedocs/oauthlib.svg + :target: https://oauthlib.readthedocs.io/en/latest/index.html + :alt: Read the Docs +.. image:: https://badges.gitter.im/oauthlib/oauthlib.svg + :target: https://gitter.im/oauthlib/Lobby + :alt: Chat on Gitter + + +.. image:: https://raw.githubusercontent.com/oauthlib/oauthlib/8d71b161fd145d11c40d55c9ab66ac134a303253/docs/logo/oauthlib-banner-700x192.png + :target: https://github.com/oauthlib/oauthlib/ + :alt: OAuth + Python = OAuthlib Python Framework + + +OAuth often seems complicated and difficult-to-implement. There are several +prominent libraries for handling OAuth requests, but they all suffer from one or +both of the following: + +1. They predate the `OAuth 1.0 spec`_, AKA RFC 5849. +2. They predate the `OAuth 2.0 spec`_, AKA RFC 6749. +3. They assume the usage of a specific HTTP request library. + +.. _`OAuth 1.0 spec`: https://tools.ietf.org/html/rfc5849 +.. _`OAuth 2.0 spec`: https://tools.ietf.org/html/rfc6749 + +OAuthLib is a framework which implements the logic of OAuth1 or OAuth2 without +assuming a specific HTTP request object or web framework. Use it to graft OAuth +client support onto your favorite HTTP library, or provide support onto your +favourite web framework. If you're a maintainer of such a library, write a thin +veneer on top of OAuthLib and get OAuth support for very little effort. + + +Documentation +-------------- + +Full documentation is available on `Read the Docs`_. All contributions are very +welcome! The documentation is still quite sparse, please open an issue for what +you'd like to know, or discuss it in our `Gitter community`_, or even better, send a +pull request! + +.. _`Gitter community`: https://gitter.im/oauthlib/Lobby +.. _`Read the Docs`: https://oauthlib.readthedocs.io/en/latest/index.html + +Interested in making OAuth requests? +------------------------------------ + +Then you might be more interested in using `requests`_ which has OAuthLib +powered OAuth support provided by the `requests-oauthlib`_ library. + +.. _`requests`: https://github.com/requests/requests +.. _`requests-oauthlib`: https://github.com/requests/requests-oauthlib + +Which web frameworks are supported? +----------------------------------- + +The following packages provide OAuth support using OAuthLib. + +- For Django there is: + - `django-oauth-toolkit`_, which includes `Django REST framework`_ support. + - `django-allauth`_, which includes `Django REST framework`_ as well as `Django Ninja`_ support. +- For Flask there is `flask-oauthlib`_ and `Flask-Dance`_. +- For Pyramid there is `pyramid-oauthlib`_. +- For Bottle there is `bottle-oauthlib`_. + +If you have written an OAuthLib package that supports your favorite framework, +please open a Pull Request, updating the documentation. + +.. _`django-oauth-toolkit`: https://github.com/evonove/django-oauth-toolkit +.. _`flask-oauthlib`: https://github.com/lepture/flask-oauthlib +.. _`Django REST framework`: http://django-rest-framework.org +.. _`Flask-Dance`: https://github.com/singingwolfboy/flask-dance +.. _`pyramid-oauthlib`: https://github.com/tilgovi/pyramid-oauthlib +.. _`bottle-oauthlib`: https://github.com/thomsonreuters/bottle-oauthlib +.. _`django-allauth`: https://allauth.org/ +.. _`Django Ninja`: https://django-ninja.dev/ + +Using OAuthLib? Please get in touch! +------------------------------------ +Patching OAuth support onto an http request framework? Creating an OAuth +provider extension for a web framework? Simply using OAuthLib to Get Things Done +or to learn? + +No matter which we'd love to hear from you in our `Gitter community`_ or if you have +anything in particular you would like to have, change or comment on don't +hesitate for a second to send a pull request or open an issue. We might be quite +busy and therefore slow to reply but we love feedback! + +Chances are you have run into something annoying that you wish there was +documentation for, if you wish to gain eternal fame and glory, and a drink if we +have the pleasure to run into each other, please send a docs pull request =) + +.. _`Gitter community`: https://gitter.im/oauthlib/Lobby + +License +------- + +OAuthLib is yours to use and abuse according to the terms of the BSD-3-Clause license. +Check the LICENSE file for full details. + +Credits +------- + +OAuthLib has been started and maintained several years by Idan Gazit and other +amazing `AUTHORS`_. Thanks to their wonderful work, the open-source `community`_ +creation has been possible and the project can stay active and reactive to users +requests. + + +.. _`AUTHORS`: https://github.com/oauthlib/oauthlib/blob/master/AUTHORS +.. _`community`: https://github.com/oauthlib/ + +Changelog +--------- + +*OAuthLib is in active development, with the core of both OAuth1 and OAuth2 +completed, for providers as well as clients.* See `supported features`_ for +details. + +.. _`supported features`: https://oauthlib.readthedocs.io/en/latest/feature_matrix.html + +For a full changelog see ``CHANGELOG.rst``. diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib-3.3.1.dist-info/RECORD b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib-3.3.1.dist-info/RECORD new file mode 100644 index 0000000000000000000000000000000000000000..2e1a1f5d7be535b505ef7e8af100a78bb62aaebe --- /dev/null +++ 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b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib-3.3.1.dist-info/licenses/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..ffab12676f701309c33ae08d489e7305c9883ccb --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib-3.3.1.dist-info/licenses/LICENSE @@ -0,0 +1,27 @@ +Copyright (c) The OAuthlib Community +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + + 1. Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + + 2. Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + 3. Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from this + software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib-3.3.1.dist-info/top_level.txt b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib-3.3.1.dist-info/top_level.txt new file mode 100644 index 0000000000000000000000000000000000000000..b5f3f0e3450f1ffb7a28cd40aed368a56254cacb --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib-3.3.1.dist-info/top_level.txt @@ -0,0 +1 @@ +oauthlib diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..462612f85e6723880402838e317bde491650e93c --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/__init__.py @@ -0,0 +1,34 @@ +""" + oauthlib + ~~~~~~~~ + + A generic, spec-compliant, thorough implementation of the OAuth + request-signing logic. + + :copyright: (c) The OAuthlib Community + :license: BSD-3-Clause, see LICENSE for details. +""" +import logging +from logging import NullHandler + +__author__ = 'The OAuthlib Community' +__version__ = '3.3.1' + +logging.getLogger('oauthlib').addHandler(NullHandler()) + +_DEBUG = False + +def set_debug(debug_val): + """Set value of debug flag + + :param debug_val: Value to set. Must be a bool value. + """ + global _DEBUG # noqa: PLW0603 + _DEBUG = debug_val + +def get_debug(): + """Get debug mode value. + + :return: `True` if debug mode is on, `False` otherwise + """ + return _DEBUG diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/common.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/common.py new file mode 100644 index 0000000000000000000000000000000000000000..dfa851798828b24f54e767a7f61a2180cd59b48c --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/common.py @@ -0,0 +1,433 @@ +""" +oauthlib.common +~~~~~~~~~~~~~~ + +This module provides data structures and utilities common +to all implementations of OAuth. +""" +import collections +import datetime +import logging +import re +import time +import urllib.parse as urlparse +from urllib.parse import ( + quote as _quote, unquote as _unquote, urlencode as _urlencode, +) + +from . import get_debug + +try: + from secrets import SystemRandom, randbits +except ImportError: + from random import SystemRandom, getrandbits as randbits + +UNICODE_ASCII_CHARACTER_SET = ('abcdefghijklmnopqrstuvwxyz' + 'ABCDEFGHIJKLMNOPQRSTUVWXYZ' + '0123456789') + +CLIENT_ID_CHARACTER_SET = (r' !"#$%&\'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMN' + 'OPQRSTUVWXYZ[\\]^_`abcdefghijklmnopqrstuvwxyz{|}') + +SANITIZE_PATTERN = re.compile(r'([^&;]*(?:password|token)[^=]*=)[^&;]+', re.IGNORECASE) +INVALID_HEX_PATTERN = re.compile(r'%[^0-9A-Fa-f]|%[0-9A-Fa-f][^0-9A-Fa-f]') + +always_safe = ('ABCDEFGHIJKLMNOPQRSTUVWXYZ' + 'abcdefghijklmnopqrstuvwxyz' + '0123456789_.-') + +log = logging.getLogger('oauthlib') + + +# 'safe' must be bytes (Python 2.6 requires bytes, other versions allow either) +def quote(s, safe=b'/'): + s = s.encode('utf-8') if isinstance(s, str) else s + s = _quote(s, safe) + # PY3 always returns unicode. PY2 may return either, depending on whether + # it had to modify the string. + if isinstance(s, bytes): + s = s.decode('utf-8') + return s + + +def unquote(s): + s = _unquote(s) + # PY3 always returns unicode. PY2 seems to always return what you give it, + # which differs from quote's behavior. Just to be safe, make sure it is + # unicode before we return. + if isinstance(s, bytes): + s = s.decode('utf-8') + return s + + +def urlencode(params): + utf8_params = encode_params_utf8(params) + urlencoded = _urlencode(utf8_params) + if isinstance(urlencoded, str): + return urlencoded + else: + return urlencoded.decode("utf-8") + + +def encode_params_utf8(params): + """Ensures that all parameters in a list of 2-element tuples are encoded to + bytestrings using UTF-8 + """ + encoded = [] + for k, v in params: + encoded.append(( + k.encode('utf-8') if isinstance(k, str) else k, + v.encode('utf-8') if isinstance(v, str) else v)) + return encoded + + +def decode_params_utf8(params): + """Ensures that all parameters in a list of 2-element tuples are decoded to + unicode using UTF-8. + """ + decoded = [] + for k, v in params: + decoded.append(( + k.decode('utf-8') if isinstance(k, bytes) else k, + v.decode('utf-8') if isinstance(v, bytes) else v)) + return decoded + + +urlencoded = set(always_safe) | set('=&;:%+~,*@!()/?\'$') + + +def urldecode(query): + """Decode a query string in x-www-form-urlencoded format into a sequence + of two-element tuples. + + Unlike urlparse.parse_qsl(..., strict_parsing=True) urldecode will enforce + correct formatting of the query string by validation. If validation fails + a ValueError will be raised. urllib.parse_qsl will only raise errors if + any of name-value pairs omits the equals sign. + """ + # Check if query contains invalid characters + if query and not set(query) <= urlencoded: + error = ("Error trying to decode a non urlencoded string. " + "Found invalid characters: %s " + "in the string: '%s'. " + "Please ensure the request/response body is " + "x-www-form-urlencoded.") + raise ValueError(error % (set(query) - urlencoded, query)) + + # Check for correctly hex encoded values using a regular expression + # All encoded values begin with % followed by two hex characters + # correct = %00, %A0, %0A, %FF + # invalid = %G0, %5H, %PO + if INVALID_HEX_PATTERN.search(query): + raise ValueError('Invalid hex encoding in query string.') + + # We want to allow queries such as "c2" whereas urlparse.parse_qsl + # with the strict_parsing flag will not. + params = urlparse.parse_qsl(query, keep_blank_values=True) + + # unicode all the things + return decode_params_utf8(params) + + +def extract_params(raw): + """Extract parameters and return them as a list of 2-tuples. + + Will successfully extract parameters from urlencoded query strings, + dicts, or lists of 2-tuples. Empty strings/dicts/lists will return an + empty list of parameters. Any other input will result in a return + value of None. + """ + if isinstance(raw, (bytes, str)): + try: + params = urldecode(raw) + except ValueError: + params = None + elif hasattr(raw, '__iter__'): + try: + dict(raw) + except ValueError: + params = None + except TypeError: + params = None + else: + params = list(raw.items() if isinstance(raw, dict) else raw) + params = decode_params_utf8(params) + else: + params = None + + return params + + +def generate_nonce(): + """Generate pseudorandom nonce that is unlikely to repeat. + + Per `section 3.3`_ of the OAuth 1 RFC 5849 spec. + Per `section 3.2.1`_ of the MAC Access Authentication spec. + + A random 64-bit number is appended to the epoch timestamp for both + randomness and to decrease the likelihood of collisions. + + .. _`section 3.2.1`: https://tools.ietf.org/html/draft-ietf-oauth-v2-http-mac-01#section-3.2.1 + .. _`section 3.3`: https://tools.ietf.org/html/rfc5849#section-3.3 + """ + return str(str(randbits(64)) + generate_timestamp()) + + +def generate_timestamp(): + """Get seconds since epoch (UTC). + + Per `section 3.3`_ of the OAuth 1 RFC 5849 spec. + Per `section 3.2.1`_ of the MAC Access Authentication spec. + + .. _`section 3.2.1`: https://tools.ietf.org/html/draft-ietf-oauth-v2-http-mac-01#section-3.2.1 + .. _`section 3.3`: https://tools.ietf.org/html/rfc5849#section-3.3 + """ + return str(int(time.time())) + + +def generate_token(length=30, chars=UNICODE_ASCII_CHARACTER_SET): + """Generates a non-guessable OAuth token + + OAuth (1 and 2) does not specify the format of tokens except that they + should be strings of random characters. Tokens should not be guessable + and entropy when generating the random characters is important. Which is + why SystemRandom is used instead of the default random.choice method. + """ + rand = SystemRandom() + return ''.join(rand.choice(chars) for x in range(length)) + + +def generate_signed_token(private_pem, request): + import jwt # noqa: PLC0415 + + now = datetime.datetime.utcnow() + + claims = { + 'scope': request.scope, + 'exp': now + datetime.timedelta(seconds=request.expires_in) + } + + claims.update(request.claims) + + token = jwt.encode(claims, private_pem, 'RS256') + token = to_unicode(token, "UTF-8") + + return token + + +def verify_signed_token(public_pem, token): + import jwt # noqa: PLC0415 + + return jwt.decode(token, public_pem, algorithms=['RS256']) + + +def generate_client_id(length=30, chars=CLIENT_ID_CHARACTER_SET): + """Generates an OAuth client_id + + OAuth 2 specify the format of client_id in + https://tools.ietf.org/html/rfc6749#appendix-A. + """ + return generate_token(length, chars) + + +def add_params_to_qs(query, params): + """Extend a query with a list of two-tuples.""" + if isinstance(params, dict): + params = params.items() + queryparams = urlparse.parse_qsl(query, keep_blank_values=True) + queryparams.extend(params) + return urlencode(queryparams) + + +def add_params_to_uri(uri, params, fragment=False): + """Add a list of two-tuples to the uri query components.""" + sch, net, path, par, query, fra = urlparse.urlparse(uri) + if fragment: + fra = add_params_to_qs(fra, params) + else: + query = add_params_to_qs(query, params) + return urlparse.urlunparse((sch, net, path, par, query, fra)) + + +def safe_string_equals(a, b): + """ Near-constant time string comparison. + + Used in order to avoid timing attacks on sensitive information such + as secret keys during request verification (`rootLabs`_). + + .. _`rootLabs`: http://rdist.root.org/2010/01/07/timing-independent-array-comparison/ + + """ + if len(a) != len(b): + return False + + result = 0 + for x, y in zip(a, b): + result |= ord(x) ^ ord(y) + return result == 0 + + +def to_unicode(data, encoding='UTF-8'): + """Convert a number of different types of objects to unicode.""" + if isinstance(data, str): + return data + + if isinstance(data, bytes): + return str(data, encoding=encoding) + + if hasattr(data, '__iter__'): + try: + dict(data) + except TypeError: + pass + except ValueError: + # Assume it's a one dimensional data structure + return (to_unicode(i, encoding) for i in data) + else: + # We support 2.6 which lacks dict comprehensions + if hasattr(data, 'items'): + data = data.items() + return {to_unicode(k, encoding): to_unicode(v, encoding) for k, v in data} + + return data + + +class CaseInsensitiveDict(dict): + + """Basic case insensitive dict with strings only keys.""" + + proxy = {} + + def __init__(self, data): + self.proxy = {k.lower(): k for k in data} + for k in data: + self[k] = data[k] + + def __contains__(self, k): + return k.lower() in self.proxy + + def __delitem__(self, k): + key = self.proxy[k.lower()] + super().__delitem__(key) + del self.proxy[k.lower()] + + def __getitem__(self, k): + key = self.proxy[k.lower()] + return super().__getitem__(key) + + def get(self, k, default=None): + return self[k] if k in self else default # noqa: SIM401 + + def __setitem__(self, k, v): + super().__setitem__(k, v) + self.proxy[k.lower()] = k + + def update(self, *args, **kwargs): + super().update(*args, **kwargs) + for k in dict(*args, **kwargs): + self.proxy[k.lower()] = k + + +class Request: + + """A malleable representation of a signable HTTP request. + + Body argument may contain any data, but parameters will only be decoded if + they are one of: + + * urlencoded query string + * dict + * list of 2-tuples + + Anything else will be treated as raw body data to be passed through + unmolested. + """ + + def __init__(self, uri, http_method='GET', body=None, headers=None, + encoding='utf-8'): + # Convert to unicode using encoding if given, else assume unicode + def encode(x): + return to_unicode(x, encoding) if encoding else x + + self.uri = encode(uri) + self.http_method = encode(http_method) + self.headers = CaseInsensitiveDict(encode(headers or {})) + self.body = encode(body) + self.decoded_body = extract_params(self.body) + self.oauth_params = [] + self.validator_log = {} + + self._params = { + "access_token": None, + "client": None, + "client_id": None, + "client_secret": None, + "code": None, + "code_challenge": None, + "code_challenge_method": None, + "code_verifier": None, + "extra_credentials": None, + "grant_type": None, + "redirect_uri": None, + "refresh_token": None, + "request_token": None, + "response_type": None, + "scope": None, + "scopes": None, + "state": None, + "token": None, + "user": None, + "token_type_hint": None, + + # OpenID Connect + "response_mode": None, + "nonce": None, + "display": None, + "prompt": None, + "claims": None, + "max_age": None, + "ui_locales": None, + "id_token_hint": None, + "login_hint": None, + "acr_values": None + } + self._params.update(dict(urldecode(self.uri_query))) + self._params.update(dict(self.decoded_body or [])) + + def __getattr__(self, name): + if name in self._params: + return self._params[name] + else: + raise AttributeError(name) + + def __repr__(self): + if not get_debug(): + return "" + body = self.body + headers = self.headers.copy() + if body: + body = SANITIZE_PATTERN.sub('\1', str(body)) + if 'Authorization' in headers: + headers['Authorization'] = '' + return ''.format( + self.uri, self.http_method, headers, body) + + @property + def uri_query(self): + return urlparse.urlparse(self.uri).query + + @property + def uri_query_params(self): + if not self.uri_query: + return [] + return urlparse.parse_qsl(self.uri_query, keep_blank_values=True, + strict_parsing=True) + + @property + def duplicate_params(self): + seen_keys = collections.defaultdict(int) + all_keys = (p[0] + for p in (self.decoded_body or []) + self.uri_query_params) + for k in all_keys: + seen_keys[k] += 1 + return [k for k, c in seen_keys.items() if c > 1] diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth1/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth1/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..9caf12a90d878efa4460811310818ee3572fcdd4 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth1/__init__.py @@ -0,0 +1,23 @@ +""" +oauthlib.oauth1 +~~~~~~~~~~~~~~ + +This module is a wrapper for the most recent implementation of OAuth 1.0 Client +and Server classes. +""" +from .rfc5849 import ( + SIGNATURE_HMAC, SIGNATURE_HMAC_SHA1, SIGNATURE_HMAC_SHA256, + SIGNATURE_HMAC_SHA512, SIGNATURE_PLAINTEXT, SIGNATURE_RSA, + SIGNATURE_RSA_SHA1, SIGNATURE_RSA_SHA256, SIGNATURE_RSA_SHA512, + SIGNATURE_TYPE_AUTH_HEADER, SIGNATURE_TYPE_BODY, SIGNATURE_TYPE_QUERY, + Client, +) +from .rfc5849.endpoints import ( + AccessTokenEndpoint, AuthorizationEndpoint, RequestTokenEndpoint, + ResourceEndpoint, SignatureOnlyEndpoint, WebApplicationServer, +) +from .rfc5849.errors import ( + InsecureTransportError, InvalidClientError, InvalidRequestError, + InvalidSignatureMethodError, OAuth1Error, +) +from .rfc5849.request_validator import RequestValidator diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth1/rfc5849/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth1/rfc5849/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..85e0b90b635398a3a2636ed8b5887d5ddb8c59c3 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth1/rfc5849/__init__.py @@ -0,0 +1,366 @@ +""" +oauthlib.oauth1.rfc5849 +~~~~~~~~~~~~~~ + +This module is an implementation of various logic needed +for signing and checking OAuth 1.0 RFC 5849 requests. + +It supports all three standard signature methods defined in RFC 5849: + +- HMAC-SHA1 +- RSA-SHA1 +- PLAINTEXT + +It also supports signature methods that are not defined in RFC 5849. These are +based on the standard ones but replace SHA-1 with the more secure SHA-256: + +- HMAC-SHA256 +- RSA-SHA256 + +""" +import base64 +import hashlib +import logging +import urllib.parse as urlparse + +from oauthlib.common import ( + Request, generate_nonce, generate_timestamp, to_unicode, urlencode, +) + +from . import parameters, signature + +log = logging.getLogger(__name__) + +# Available signature methods +# +# Note: SIGNATURE_HMAC and SIGNATURE_RSA are kept for backward compatibility +# with previous versions of this library, when it the only HMAC-based and +# RSA-based signature methods were HMAC-SHA1 and RSA-SHA1. But now that it +# supports other hashing algorithms besides SHA1, explicitly identifying which +# hashing algorithm is being used is recommended. +# +# Note: if additional values are defined here, don't forget to update the +# imports in "../__init__.py" so they are available outside this module. + +SIGNATURE_HMAC_SHA1 = "HMAC-SHA1" +SIGNATURE_HMAC_SHA256 = "HMAC-SHA256" +SIGNATURE_HMAC_SHA512 = "HMAC-SHA512" +SIGNATURE_HMAC = SIGNATURE_HMAC_SHA1 # deprecated variable for HMAC-SHA1 + +SIGNATURE_RSA_SHA1 = "RSA-SHA1" +SIGNATURE_RSA_SHA256 = "RSA-SHA256" +SIGNATURE_RSA_SHA512 = "RSA-SHA512" +SIGNATURE_RSA = SIGNATURE_RSA_SHA1 # deprecated variable for RSA-SHA1 + +SIGNATURE_PLAINTEXT = "PLAINTEXT" + +SIGNATURE_METHODS = ( + SIGNATURE_HMAC_SHA1, + SIGNATURE_HMAC_SHA256, + SIGNATURE_HMAC_SHA512, + SIGNATURE_RSA_SHA1, + SIGNATURE_RSA_SHA256, + SIGNATURE_RSA_SHA512, + SIGNATURE_PLAINTEXT +) + +SIGNATURE_TYPE_AUTH_HEADER = 'AUTH_HEADER' +SIGNATURE_TYPE_QUERY = 'QUERY' +SIGNATURE_TYPE_BODY = 'BODY' + +CONTENT_TYPE_FORM_URLENCODED = 'application/x-www-form-urlencoded' + + +class Client: + + """A client used to sign OAuth 1.0 RFC 5849 requests.""" + SIGNATURE_METHODS = { + SIGNATURE_HMAC_SHA1: signature.sign_hmac_sha1_with_client, + SIGNATURE_HMAC_SHA256: signature.sign_hmac_sha256_with_client, + SIGNATURE_HMAC_SHA512: signature.sign_hmac_sha512_with_client, + SIGNATURE_RSA_SHA1: signature.sign_rsa_sha1_with_client, + SIGNATURE_RSA_SHA256: signature.sign_rsa_sha256_with_client, + SIGNATURE_RSA_SHA512: signature.sign_rsa_sha512_with_client, + SIGNATURE_PLAINTEXT: signature.sign_plaintext_with_client + } + + @classmethod + def register_signature_method(cls, method_name, method_callback): + cls.SIGNATURE_METHODS[method_name] = method_callback + + def __init__(self, client_key, + client_secret=None, + resource_owner_key=None, + resource_owner_secret=None, + callback_uri=None, + signature_method=SIGNATURE_HMAC_SHA1, + signature_type=SIGNATURE_TYPE_AUTH_HEADER, + rsa_key=None, verifier=None, realm=None, + encoding='utf-8', decoding=None, + nonce=None, timestamp=None): + """Create an OAuth 1 client. + + :param client_key: Client key (consumer key), mandatory. + :param resource_owner_key: Resource owner key (oauth token). + :param resource_owner_secret: Resource owner secret (oauth token secret). + :param callback_uri: Callback used when obtaining request token. + :param signature_method: SIGNATURE_HMAC, SIGNATURE_RSA or SIGNATURE_PLAINTEXT. + :param signature_type: SIGNATURE_TYPE_AUTH_HEADER (default), + SIGNATURE_TYPE_QUERY or SIGNATURE_TYPE_BODY + depending on where you want to embed the oauth + credentials. + :param rsa_key: RSA key used with SIGNATURE_RSA. + :param verifier: Verifier used when obtaining an access token. + :param realm: Realm (scope) to which access is being requested. + :param encoding: If you provide non-unicode input you may use this + to have oauthlib automatically convert. + :param decoding: If you wish that the returned uri, headers and body + from sign be encoded back from unicode, then set + decoding to your preferred encoding, i.e. utf-8. + :param nonce: Use this nonce instead of generating one. (Mainly for testing) + :param timestamp: Use this timestamp instead of using current. (Mainly for testing) + """ + # Convert to unicode using encoding if given, else assume unicode + def encode(x): + return to_unicode(x, encoding) if encoding else x + + self.client_key = encode(client_key) + self.client_secret = encode(client_secret) + self.resource_owner_key = encode(resource_owner_key) + self.resource_owner_secret = encode(resource_owner_secret) + self.signature_method = encode(signature_method) + self.signature_type = encode(signature_type) + self.callback_uri = encode(callback_uri) + self.rsa_key = encode(rsa_key) + self.verifier = encode(verifier) + self.realm = encode(realm) + self.encoding = encode(encoding) + self.decoding = encode(decoding) + self.nonce = encode(nonce) + self.timestamp = encode(timestamp) + + def __repr__(self): + attrs = vars(self).copy() + attrs['client_secret'] = '****' if attrs['client_secret'] else None + attrs['rsa_key'] = '****' if attrs['rsa_key'] else None + attrs[ + 'resource_owner_secret'] = '****' if attrs['resource_owner_secret'] else None + attribute_str = ', '.join('{}={}'.format(k, v) for k, v in attrs.items()) + return '<{} {}>'.format(self.__class__.__name__, attribute_str) + + def get_oauth_signature(self, request): + """Get an OAuth signature to be used in signing a request + + To satisfy `section 3.4.1.2`_ item 2, if the request argument's + headers dict attribute contains a Host item, its value will + replace any netloc part of the request argument's uri attribute + value. + + .. _`section 3.4.1.2`: https://tools.ietf.org/html/rfc5849#section-3.4.1.2 + """ + if self.signature_method == SIGNATURE_PLAINTEXT: + # fast-path + return signature.sign_plaintext(self.client_secret, + self.resource_owner_secret) + + uri, headers, body = self._render(request) + + collected_params = signature.collect_parameters( + uri_query=urlparse.urlparse(uri).query, + body=body, + headers=headers) + log.debug("Collected params: {}".format(collected_params)) + + normalized_params = signature.normalize_parameters(collected_params) + normalized_uri = signature.base_string_uri(uri, headers.get('Host', None)) + log.debug("Normalized params: {}".format(normalized_params)) + log.debug("Normalized URI: {}".format(normalized_uri)) + + base_string = signature.signature_base_string(request.http_method, + normalized_uri, normalized_params) + + log.debug("Signing: signature base string: {}".format(base_string)) + + if self.signature_method not in self.SIGNATURE_METHODS: + raise ValueError('Invalid signature method.') + + sig = self.SIGNATURE_METHODS[self.signature_method](base_string, self) + + log.debug("Signature: {}".format(sig)) + return sig + + def get_oauth_params(self, request): + """Get the basic OAuth parameters to be used in generating a signature. + """ + nonce = (generate_nonce() + if self.nonce is None else self.nonce) + timestamp = (generate_timestamp() + if self.timestamp is None else self.timestamp) + params = [ + ('oauth_nonce', nonce), + ('oauth_timestamp', timestamp), + ('oauth_version', '1.0'), + ('oauth_signature_method', self.signature_method), + ('oauth_consumer_key', self.client_key), + ] + if self.resource_owner_key: + params.append(('oauth_token', self.resource_owner_key)) + if self.callback_uri: + params.append(('oauth_callback', self.callback_uri)) + if self.verifier: + params.append(('oauth_verifier', self.verifier)) + + # providing body hash for requests other than x-www-form-urlencoded + # as described in https://tools.ietf.org/html/draft-eaton-oauth-bodyhash-00#section-4.1.1 + # 4.1.1. When to include the body hash + # * [...] MUST NOT include an oauth_body_hash parameter on requests with form-encoded request bodies + # * [...] SHOULD include the oauth_body_hash parameter on all other requests. + # Note that SHA-1 is vulnerable. The spec acknowledges that in https://tools.ietf.org/html/draft-eaton-oauth-bodyhash-00#section-6.2 + # At this time, no further effort has been made to replace SHA-1 for the OAuth Request Body Hash extension. + content_type = request.headers.get('Content-Type', None) + content_type_eligible = content_type and content_type.find('application/x-www-form-urlencoded') < 0 + if request.body is not None and content_type_eligible: + params.append(('oauth_body_hash', base64.b64encode(hashlib.sha1(request.body.encode('utf-8')).digest()).decode('utf-8'))) # noqa: S324 + + return params + + def _render(self, request, formencode=False, realm=None): + """Render a signed request according to signature type + + Returns a 3-tuple containing the request URI, headers, and body. + + If the formencode argument is True and the body contains parameters, it + is escaped and returned as a valid formencoded string. + """ + # TODO what if there are body params on a header-type auth? + # TODO what if there are query params on a body-type auth? + + uri, headers, body = request.uri, request.headers, request.body + + # TODO: right now these prepare_* methods are very narrow in scope--they + # only affect their little thing. In some cases (for example, with + # header auth) it might be advantageous to allow these methods to touch + # other parts of the request, like the headers—so the prepare_headers + # method could also set the Content-Type header to x-www-form-urlencoded + # like the spec requires. This would be a fundamental change though, and + # I'm not sure how I feel about it. + if self.signature_type == SIGNATURE_TYPE_AUTH_HEADER: + headers = parameters.prepare_headers( + request.oauth_params, request.headers, realm=realm) + elif self.signature_type == SIGNATURE_TYPE_BODY and request.decoded_body is not None: + body = parameters.prepare_form_encoded_body( + request.oauth_params, request.decoded_body) + if formencode: + body = urlencode(body) + headers['Content-Type'] = 'application/x-www-form-urlencoded' + elif self.signature_type == SIGNATURE_TYPE_QUERY: + uri = parameters.prepare_request_uri_query( + request.oauth_params, request.uri) + else: + raise ValueError('Unknown signature type specified.') + + return uri, headers, body + + def sign(self, uri, http_method='GET', body=None, headers=None, realm=None): + """Sign a request + + Signs an HTTP request with the specified parts. + + Returns a 3-tuple of the signed request's URI, headers, and body. + Note that http_method is not returned as it is unaffected by the OAuth + signing process. Also worth noting is that duplicate parameters + will be included in the signature, regardless of where they are + specified (query, body). + + The body argument may be a dict, a list of 2-tuples, or a formencoded + string. The Content-Type header must be 'application/x-www-form-urlencoded' + if it is present. + + If the body argument is not one of the above, it will be returned + verbatim as it is unaffected by the OAuth signing process. Attempting to + sign a request with non-formencoded data using the OAuth body signature + type is invalid and will raise an exception. + + If the body does contain parameters, it will be returned as a properly- + formatted formencoded string. + + Body may not be included if the http_method is either GET or HEAD as + this changes the semantic meaning of the request. + + All string data MUST be unicode or be encoded with the same encoding + scheme supplied to the Client constructor, default utf-8. This includes + strings inside body dicts, for example. + """ + # normalize request data + request = Request(uri, http_method, body, headers, + encoding=self.encoding) + + # sanity check + content_type = request.headers.get('Content-Type', None) + multipart = content_type and content_type.startswith('multipart/') + should_have_params = content_type == CONTENT_TYPE_FORM_URLENCODED + has_params = request.decoded_body is not None + # 3.4.1.3.1. Parameter Sources + # [Parameters are collected from the HTTP request entity-body, but only + # if [...]: + # * The entity-body is single-part. + if multipart and has_params: + raise ValueError( + "Headers indicate a multipart body but body contains parameters.") + # * The entity-body follows the encoding requirements of the + # "application/x-www-form-urlencoded" content-type as defined by + # [W3C.REC-html40-19980424]. + elif should_have_params and not has_params: + raise ValueError( + "Headers indicate a formencoded body but body was not decodable.") + # * The HTTP request entity-header includes the "Content-Type" + # header field set to "application/x-www-form-urlencoded". + elif not should_have_params and has_params: + raise ValueError( + "Body contains parameters but Content-Type header was {} " + "instead of {}".format(content_type or "not set", + CONTENT_TYPE_FORM_URLENCODED)) + + # 3.5.2. Form-Encoded Body + # Protocol parameters can be transmitted in the HTTP request entity- + # body, but only if the following REQUIRED conditions are met: + # o The entity-body is single-part. + # o The entity-body follows the encoding requirements of the + # "application/x-www-form-urlencoded" content-type as defined by + # [W3C.REC-html40-19980424]. + # o The HTTP request entity-header includes the "Content-Type" header + # field set to "application/x-www-form-urlencoded". + elif self.signature_type == SIGNATURE_TYPE_BODY and not ( + should_have_params and has_params and not multipart): + raise ValueError( + 'Body signatures may only be used with form-urlencoded content') + + # We amend https://tools.ietf.org/html/rfc5849#section-3.4.1.3.1 + # with the clause that parameters from body should only be included + # in non GET or HEAD requests. Extracting the request body parameters + # and including them in the signature base string would give semantic + # meaning to the body, which it should not have according to the + # HTTP 1.1 spec. + elif http_method.upper() in ('GET', 'HEAD') and has_params: + raise ValueError('GET/HEAD requests should not include body.') + + # generate the basic OAuth parameters + request.oauth_params = self.get_oauth_params(request) + + # generate the signature + request.oauth_params.append( + ('oauth_signature', self.get_oauth_signature(request))) + + # render the signed request and return it + uri, headers, body = self._render(request, formencode=True, + realm=(realm or self.realm)) + + if self.decoding: + log.debug('Encoding URI, headers and body to %s.', self.decoding) + uri = uri.encode(self.decoding) + body = body.encode(self.decoding) if body else body + new_headers = {} + for k, v in headers.items(): + new_headers[k.encode(self.decoding)] = v.encode(self.decoding) + headers = new_headers + return uri, headers, body diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth1/rfc5849/endpoints/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth1/rfc5849/endpoints/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..9f30389f239929754078e3f59688de418d986e29 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth1/rfc5849/endpoints/__init__.py @@ -0,0 +1,8 @@ +from .access_token import AccessTokenEndpoint +from .authorization import AuthorizationEndpoint +from .base import BaseEndpoint +from .request_token import RequestTokenEndpoint +from .resource import ResourceEndpoint +from .signature_only import SignatureOnlyEndpoint + +from .pre_configured import WebApplicationServer # isort:skip diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth1/rfc5849/endpoints/access_token.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth1/rfc5849/endpoints/access_token.py new file mode 100644 index 0000000000000000000000000000000000000000..13665db08fda810cfc46fa45448d06fe9ec99ae9 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth1/rfc5849/endpoints/access_token.py @@ -0,0 +1,215 @@ +# -*- coding: utf-8 -*- +""" +oauthlib.oauth1.rfc5849.endpoints.access_token +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +This module is an implementation of the access token provider logic of +OAuth 1.0 RFC 5849. It validates the correctness of access token requests, +creates and persists tokens as well as create the proper response to be +returned to the client. +""" +import logging + +from oauthlib.common import urlencode + +from .. import errors +from .base import BaseEndpoint + +log = logging.getLogger(__name__) + + +class AccessTokenEndpoint(BaseEndpoint): + + """An endpoint responsible for providing OAuth 1 access tokens. + + Typical use is to instantiate with a request validator and invoke the + ``create_access_token_response`` from a view function. The tuple returned + has all information necessary (body, status, headers) to quickly form + and return a proper response. See :doc:`/oauth1/validator` for details on which + validator methods to implement for this endpoint. + """ + + def create_access_token(self, request, credentials): + """Create and save a new access token. + + Similar to OAuth 2, indication of granted scopes will be included as a + space separated list in ``oauth_authorized_realms``. + + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :returns: The token as an urlencoded string. + """ + request.realms = self.request_validator.get_realms( + request.resource_owner_key, request) + token = { + 'oauth_token': self.token_generator(), + 'oauth_token_secret': self.token_generator(), + # Backport the authorized scopes indication used in OAuth2 + 'oauth_authorized_realms': ' '.join(request.realms) + } + token.update(credentials) + self.request_validator.save_access_token(token, request) + return urlencode(token.items()) + + def create_access_token_response(self, uri, http_method='GET', body=None, + headers=None, credentials=None): + """Create an access token response, with a new request token if valid. + + :param uri: The full URI of the token request. + :param http_method: A valid HTTP verb, i.e. GET, POST, PUT, HEAD, etc. + :param body: The request body as a string. + :param headers: The request headers as a dict. + :param credentials: A list of extra credentials to include in the token. + :returns: A tuple of 3 elements. + 1. A dict of headers to set on the response. + 2. The response body as a string. + 3. The response status code as an integer. + + An example of a valid request:: + + >>> from your_validator import your_validator + >>> from oauthlib.oauth1 import AccessTokenEndpoint + >>> endpoint = AccessTokenEndpoint(your_validator) + >>> h, b, s = endpoint.create_access_token_response( + ... 'https://your.provider/access_token?foo=bar', + ... headers={ + ... 'Authorization': 'OAuth oauth_token=234lsdkf....' + ... }, + ... credentials={ + ... 'my_specific': 'argument', + ... }) + >>> h + {'Content-Type': 'application/x-www-form-urlencoded'} + >>> b + 'oauth_token=lsdkfol23w54jlksdef&oauth_token_secret=qwe089234lkjsdf&oauth_authorized_realms=movies+pics&my_specific=argument' + >>> s + 200 + + An response to invalid request would have a different body and status:: + + >>> b + 'error=invalid_request&description=missing+resource+owner+key' + >>> s + 400 + + The same goes for an an unauthorized request: + + >>> b + '' + >>> s + 401 + """ + resp_headers = {'Content-Type': 'application/x-www-form-urlencoded'} + try: + request = self._create_request(uri, http_method, body, headers) + valid, processed_request = self.validate_access_token_request( + request) + if valid: + token = self.create_access_token(request, credentials or {}) + self.request_validator.invalidate_request_token( + request.client_key, + request.resource_owner_key, + request) + return resp_headers, token, 200 + else: + return {}, None, 401 + except errors.OAuth1Error as e: + return resp_headers, e.urlencoded, e.status_code + + def validate_access_token_request(self, request): + """Validate an access token request. + + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :raises: OAuth1Error if the request is invalid. + :returns: A tuple of 2 elements. + 1. The validation result (True or False). + 2. The request object. + """ + self._check_transport_security(request) + self._check_mandatory_parameters(request) + + if not request.resource_owner_key: + raise errors.InvalidRequestError( + description='Missing resource owner.') + + if not self.request_validator.check_request_token( + request.resource_owner_key): + raise errors.InvalidRequestError( + description='Invalid resource owner key format.') + + if not request.verifier: + raise errors.InvalidRequestError( + description='Missing verifier.') + + if not self.request_validator.check_verifier(request.verifier): + raise errors.InvalidRequestError( + description='Invalid verifier format.') + + if not self.request_validator.validate_timestamp_and_nonce( + request.client_key, request.timestamp, request.nonce, request, + request_token=request.resource_owner_key): + return False, request + + # The server SHOULD return a 401 (Unauthorized) status code when + # receiving a request with invalid client credentials. + # Note: This is postponed in order to avoid timing attacks, instead + # a dummy client is assigned and used to maintain near constant + # time request verification. + # + # Note that early exit would enable client enumeration + valid_client = self.request_validator.validate_client_key( + request.client_key, request) + if not valid_client: + request.client_key = self.request_validator.dummy_client + + # The server SHOULD return a 401 (Unauthorized) status code when + # receiving a request with invalid or expired token. + # Note: This is postponed in order to avoid timing attacks, instead + # a dummy token is assigned and used to maintain near constant + # time request verification. + # + # Note that early exit would enable resource owner enumeration + valid_resource_owner = self.request_validator.validate_request_token( + request.client_key, request.resource_owner_key, request) + if not valid_resource_owner: + request.resource_owner_key = self.request_validator.dummy_request_token + + # The server MUST verify (Section 3.2) the validity of the request, + # ensure that the resource owner has authorized the provisioning of + # token credentials to the client, and ensure that the temporary + # credentials have not expired or been used before. The server MUST + # also verify the verification code received from the client. + # .. _`Section 3.2`: https://tools.ietf.org/html/rfc5849#section-3.2 + # + # Note that early exit would enable resource owner authorization + # verifier enumertion. + valid_verifier = self.request_validator.validate_verifier( + request.client_key, + request.resource_owner_key, + request.verifier, + request) + + valid_signature = self._check_signature(request, is_token_request=True) + + # log the results to the validator_log + # this lets us handle internal reporting and analysis + request.validator_log['client'] = valid_client + request.validator_log['resource_owner'] = valid_resource_owner + request.validator_log['verifier'] = valid_verifier + request.validator_log['signature'] = valid_signature + + # We delay checking validity until the very end, using dummy values for + # calculations and fetching secrets/keys to ensure the flow of every + # request remains almost identical regardless of whether valid values + # have been supplied. This ensures near constant time execution and + # prevents malicious users from guessing sensitive information + v = all((valid_client, valid_resource_owner, valid_verifier, + valid_signature)) + if not v: + log.info("[Failure] request verification failed.") + log.info("Valid client:, %s", valid_client) + log.info("Valid token:, %s", valid_resource_owner) + log.info("Valid verifier:, %s", valid_verifier) + log.info("Valid signature:, %s", valid_signature) + return v, request diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth1/rfc5849/endpoints/authorization.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth1/rfc5849/endpoints/authorization.py new file mode 100644 index 0000000000000000000000000000000000000000..00d9576b01ecd9879a4485cf083c2affa79a520c --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth1/rfc5849/endpoints/authorization.py @@ -0,0 +1,158 @@ +# -*- coding: utf-8 -*- +""" +oauthlib.oauth1.rfc5849.endpoints.authorization +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +This module is an implementation of various logic needed +for signing and checking OAuth 1.0 RFC 5849 requests. +""" +from urllib.parse import urlencode + +from oauthlib.common import add_params_to_uri + +from .. import errors +from .base import BaseEndpoint + + +class AuthorizationEndpoint(BaseEndpoint): + + """An endpoint responsible for letting authenticated users authorize access + to their protected resources to a client. + + Typical use would be to have two views, one for displaying the authorization + form and one to process said form on submission. + + The first view will want to utilize ``get_realms_and_credentials`` to fetch + requested realms and useful client credentials, such as name and + description, to be used when creating the authorization form. + + During form processing you can use ``create_authorization_response`` to + validate the request, create a verifier as well as prepare the final + redirection URI used to send the user back to the client. + + See :doc:`/oauth1/validator` for details on which validator methods to implement + for this endpoint. + """ + + def create_verifier(self, request, credentials): + """Create and save a new request token. + + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :param credentials: A dict of extra token credentials. + :returns: The verifier as a dict. + """ + verifier = { + 'oauth_token': request.resource_owner_key, + 'oauth_verifier': self.token_generator(), + } + verifier.update(credentials) + self.request_validator.save_verifier( + request.resource_owner_key, verifier, request) + return verifier + + def create_authorization_response(self, uri, http_method='GET', body=None, + headers=None, realms=None, credentials=None): + """Create an authorization response, with a new request token if valid. + + :param uri: The full URI of the token request. + :param http_method: A valid HTTP verb, i.e. GET, POST, PUT, HEAD, etc. + :param body: The request body as a string. + :param headers: The request headers as a dict. + :param credentials: A list of credentials to include in the verifier. + :returns: A tuple of 3 elements. + 1. A dict of headers to set on the response. + 2. The response body as a string. + 3. The response status code as an integer. + + If the callback URI tied to the current token is "oob", a response with + a 200 status code will be returned. In this case, it may be desirable to + modify the response to better display the verifier to the client. + + An example of an authorization request:: + + >>> from your_validator import your_validator + >>> from oauthlib.oauth1 import AuthorizationEndpoint + >>> endpoint = AuthorizationEndpoint(your_validator) + >>> h, b, s = endpoint.create_authorization_response( + ... 'https://your.provider/authorize?oauth_token=...', + ... credentials={ + ... 'extra': 'argument', + ... }) + >>> h + {'Location': 'https://the.client/callback?oauth_verifier=...&extra=argument'} + >>> b + None + >>> s + 302 + + An example of a request with an "oob" callback:: + + >>> from your_validator import your_validator + >>> from oauthlib.oauth1 import AuthorizationEndpoint + >>> endpoint = AuthorizationEndpoint(your_validator) + >>> h, b, s = endpoint.create_authorization_response( + ... 'https://your.provider/authorize?foo=bar', + ... credentials={ + ... 'extra': 'argument', + ... }) + >>> h + {'Content-Type': 'application/x-www-form-urlencoded'} + >>> b + 'oauth_verifier=...&extra=argument' + >>> s + 200 + """ + request = self._create_request(uri, http_method=http_method, body=body, + headers=headers) + + if not request.resource_owner_key: + raise errors.InvalidRequestError( + 'Missing mandatory parameter oauth_token.') + if not self.request_validator.verify_request_token( + request.resource_owner_key, request): + raise errors.InvalidClientError() + + request.realms = realms + if (request.realms and not self.request_validator.verify_realms( + request.resource_owner_key, request.realms, request)): + raise errors.InvalidRequestError( + description=('User granted access to realms outside of ' + 'what the client may request.')) + + verifier = self.create_verifier(request, credentials or {}) + redirect_uri = self.request_validator.get_redirect_uri( + request.resource_owner_key, request) + if redirect_uri == 'oob': + response_headers = { + 'Content-Type': 'application/x-www-form-urlencoded'} + response_body = urlencode(verifier) + return response_headers, response_body, 200 + else: + populated_redirect = add_params_to_uri( + redirect_uri, verifier.items()) + return {'Location': populated_redirect}, None, 302 + + def get_realms_and_credentials(self, uri, http_method='GET', body=None, + headers=None): + """Fetch realms and credentials for the presented request token. + + :param uri: The full URI of the token request. + :param http_method: A valid HTTP verb, i.e. GET, POST, PUT, HEAD, etc. + :param body: The request body as a string. + :param headers: The request headers as a dict. + :returns: A tuple of 2 elements. + 1. A list of request realms. + 2. A dict of credentials which may be useful in creating the + authorization form. + """ + request = self._create_request(uri, http_method=http_method, body=body, + headers=headers) + + if not self.request_validator.verify_request_token( + request.resource_owner_key, request): + raise errors.InvalidClientError() + + realms = self.request_validator.get_realms( + request.resource_owner_key, request) + return realms, {'resource_owner_key': request.resource_owner_key} diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth1/rfc5849/endpoints/base.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth1/rfc5849/endpoints/base.py new file mode 100644 index 0000000000000000000000000000000000000000..8d3d89c6727c430e0e216c5bdb5abb26cabebf40 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth1/rfc5849/endpoints/base.py @@ -0,0 +1,239 @@ +# -*- coding: utf-8 -*- +""" +oauthlib.oauth1.rfc5849.endpoints.base +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +This module is an implementation of various logic needed +for signing and checking OAuth 1.0 RFC 5849 requests. +""" +import time + +from oauthlib.common import CaseInsensitiveDict, Request, generate_token + +from .. import ( + CONTENT_TYPE_FORM_URLENCODED, SIGNATURE_HMAC_SHA1, SIGNATURE_HMAC_SHA256, + SIGNATURE_HMAC_SHA512, SIGNATURE_PLAINTEXT, SIGNATURE_RSA_SHA1, + SIGNATURE_RSA_SHA256, SIGNATURE_RSA_SHA512, SIGNATURE_TYPE_AUTH_HEADER, + SIGNATURE_TYPE_BODY, SIGNATURE_TYPE_QUERY, errors, signature, utils, +) + + +class BaseEndpoint: + + def __init__(self, request_validator, token_generator=None): + self.request_validator = request_validator + self.token_generator = token_generator or generate_token + + def _get_signature_type_and_params(self, request): + """Extracts parameters from query, headers and body. Signature type + is set to the source in which parameters were found. + """ + # Per RFC5849, only the Authorization header may contain the 'realm' + # optional parameter. + header_params = signature.collect_parameters(headers=request.headers, + exclude_oauth_signature=False, with_realm=True) + body_params = signature.collect_parameters(body=request.body, + exclude_oauth_signature=False) + query_params = signature.collect_parameters(uri_query=request.uri_query, + exclude_oauth_signature=False) + + params = [] + params.extend(header_params) + params.extend(body_params) + params.extend(query_params) + signature_types_with_oauth_params = list(filter(lambda s: s[2], ( + (SIGNATURE_TYPE_AUTH_HEADER, params, + utils.filter_oauth_params(header_params)), + (SIGNATURE_TYPE_BODY, params, + utils.filter_oauth_params(body_params)), + (SIGNATURE_TYPE_QUERY, params, + utils.filter_oauth_params(query_params)) + ))) + + if len(signature_types_with_oauth_params) > 1: + found_types = [s[0] for s in signature_types_with_oauth_params] + raise errors.InvalidRequestError( + description=('oauth_ params must come from only 1 signature' + 'type but were found in %s', + ', '.join(found_types))) + + try: + signature_type, params, oauth_params = signature_types_with_oauth_params[ + 0] + except IndexError: + raise errors.InvalidRequestError( + description='Missing mandatory OAuth parameters.') + + return signature_type, params, oauth_params + + def _create_request(self, uri, http_method, body, headers): + # Only include body data from x-www-form-urlencoded requests + headers = CaseInsensitiveDict(headers or {}) + if "Content-Type" in headers and CONTENT_TYPE_FORM_URLENCODED in headers["Content-Type"]: # noqa: SIM108 + request = Request(uri, http_method, body, headers) + else: + request = Request(uri, http_method, '', headers) + signature_type, params, oauth_params = ( + self._get_signature_type_and_params(request)) + + # The server SHOULD return a 400 (Bad Request) status code when + # receiving a request with duplicated protocol parameters. + if len(dict(oauth_params)) != len(oauth_params): + raise errors.InvalidRequestError( + description='Duplicate OAuth1 entries.') + + oauth_params = dict(oauth_params) + request.signature = oauth_params.get('oauth_signature') + request.client_key = oauth_params.get('oauth_consumer_key') + request.resource_owner_key = oauth_params.get('oauth_token') + request.nonce = oauth_params.get('oauth_nonce') + request.timestamp = oauth_params.get('oauth_timestamp') + request.redirect_uri = oauth_params.get('oauth_callback') + request.verifier = oauth_params.get('oauth_verifier') + request.signature_method = oauth_params.get('oauth_signature_method') + request.realm = dict(params).get('realm') + request.oauth_params = oauth_params + + # Parameters to Client depend on signature method which may vary + # for each request. Note that HMAC-SHA1 and PLAINTEXT share parameters + request.params = [(k, v) for k, v in params if k != "oauth_signature"] + + if 'realm' in request.headers.get('Authorization', ''): + request.params = [(k, v) + for k, v in request.params if k != "realm"] + + return request + + def _check_transport_security(self, request): + # TODO: move into oauthlib.common from oauth2.utils + if (self.request_validator.enforce_ssl and + not request.uri.lower().startswith("https://")): + raise errors.InsecureTransportError() + + def _check_mandatory_parameters(self, request): + # The server SHOULD return a 400 (Bad Request) status code when + # receiving a request with missing parameters. + if not all((request.signature, request.client_key, + request.nonce, request.timestamp, + request.signature_method)): + raise errors.InvalidRequestError( + description='Missing mandatory OAuth parameters.') + + # OAuth does not mandate a particular signature method, as each + # implementation can have its own unique requirements. Servers are + # free to implement and document their own custom methods. + # Recommending any particular method is beyond the scope of this + # specification. Implementers should review the Security + # Considerations section (`Section 4`_) before deciding on which + # method to support. + # .. _`Section 4`: https://tools.ietf.org/html/rfc5849#section-4 + if (request.signature_method not in self.request_validator.allowed_signature_methods): + raise errors.InvalidSignatureMethodError( + description="Invalid signature, {} not in {!r}.".format( + request.signature_method, + self.request_validator.allowed_signature_methods)) + + # Servers receiving an authenticated request MUST validate it by: + # If the "oauth_version" parameter is present, ensuring its value is + # "1.0". + if ('oauth_version' in request.oauth_params and + request.oauth_params['oauth_version'] != '1.0'): + raise errors.InvalidRequestError( + description='Invalid OAuth version.') + + # The timestamp value MUST be a positive integer. Unless otherwise + # specified by the server's documentation, the timestamp is expressed + # in the number of seconds since January 1, 1970 00:00:00 GMT. + if len(request.timestamp) != 10: + raise errors.InvalidRequestError( + description='Invalid timestamp size') + + try: + ts = int(request.timestamp) + + except ValueError: + raise errors.InvalidRequestError( + description='Timestamp must be an integer.') + + else: + # To avoid the need to retain an infinite number of nonce values for + # future checks, servers MAY choose to restrict the time period after + # which a request with an old timestamp is rejected. + if abs(time.time() - ts) > self.request_validator.timestamp_lifetime: + raise errors.InvalidRequestError( + description=('Timestamp given is invalid, differ from ' + 'allowed by over %s seconds.' % ( + self.request_validator.timestamp_lifetime))) + + # Provider specific validation of parameters, used to enforce + # restrictions such as character set and length. + if not self.request_validator.check_client_key(request.client_key): + raise errors.InvalidRequestError( + description='Invalid client key format.') + + if not self.request_validator.check_nonce(request.nonce): + raise errors.InvalidRequestError( + description='Invalid nonce format.') + + def _check_signature(self, request, is_token_request=False): + # ---- RSA Signature verification ---- + if request.signature_method in {SIGNATURE_RSA_SHA1, SIGNATURE_RSA_SHA256, SIGNATURE_RSA_SHA512}: + # RSA-based signature method + + # The server verifies the signature per `[RFC3447] section 8.2.2`_ + # .. _`[RFC3447] section 8.2.2`: https://tools.ietf.org/html/rfc3447#section-8.2.1 + + rsa_key = self.request_validator.get_rsa_key( + request.client_key, request) + + if request.signature_method == SIGNATURE_RSA_SHA1: + valid_signature = signature.verify_rsa_sha1(request, rsa_key) + elif request.signature_method == SIGNATURE_RSA_SHA256: + valid_signature = signature.verify_rsa_sha256(request, rsa_key) + elif request.signature_method == SIGNATURE_RSA_SHA512: + valid_signature = signature.verify_rsa_sha512(request, rsa_key) + else: + valid_signature = False + + # ---- HMAC or Plaintext Signature verification ---- + else: + # Non-RSA based signature method + + # Servers receiving an authenticated request MUST validate it by: + # Recalculating the request signature independently as described in + # `Section 3.4`_ and comparing it to the value received from the + # client via the "oauth_signature" parameter. + # .. _`Section 3.4`: https://tools.ietf.org/html/rfc5849#section-3.4 + + client_secret = self.request_validator.get_client_secret( + request.client_key, request) + + resource_owner_secret = None + if request.resource_owner_key: + if is_token_request: + resource_owner_secret = \ + self.request_validator.get_request_token_secret( + request.client_key, request.resource_owner_key, + request) + else: + resource_owner_secret = \ + self.request_validator.get_access_token_secret( + request.client_key, request.resource_owner_key, + request) + + if request.signature_method == SIGNATURE_HMAC_SHA1: + valid_signature = signature.verify_hmac_sha1( + request, client_secret, resource_owner_secret) + elif request.signature_method == SIGNATURE_HMAC_SHA256: + valid_signature = signature.verify_hmac_sha256( + request, client_secret, resource_owner_secret) + elif request.signature_method == SIGNATURE_HMAC_SHA512: + valid_signature = signature.verify_hmac_sha512( + request, client_secret, resource_owner_secret) + elif request.signature_method == SIGNATURE_PLAINTEXT: + valid_signature = signature.verify_plaintext( + request, client_secret, resource_owner_secret) + else: + valid_signature = False + + return valid_signature diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth1/rfc5849/endpoints/pre_configured.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth1/rfc5849/endpoints/pre_configured.py new file mode 100644 index 0000000000000000000000000000000000000000..23e3cfc84e0ee61cd461f476c435ef318945ff66 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth1/rfc5849/endpoints/pre_configured.py @@ -0,0 +1,14 @@ +from . import ( + AccessTokenEndpoint, AuthorizationEndpoint, RequestTokenEndpoint, + ResourceEndpoint, +) + + +class WebApplicationServer(RequestTokenEndpoint, AuthorizationEndpoint, + AccessTokenEndpoint, ResourceEndpoint): + + def __init__(self, request_validator): + RequestTokenEndpoint.__init__(self, request_validator) + AuthorizationEndpoint.__init__(self, request_validator) + AccessTokenEndpoint.__init__(self, request_validator) + ResourceEndpoint.__init__(self, request_validator) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth1/rfc5849/endpoints/request_token.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth1/rfc5849/endpoints/request_token.py new file mode 100644 index 0000000000000000000000000000000000000000..0323cfb845a7836eab0e181cc9229031788e957e --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth1/rfc5849/endpoints/request_token.py @@ -0,0 +1,209 @@ +# -*- coding: utf-8 -*- +""" +oauthlib.oauth1.rfc5849.endpoints.request_token +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +This module is an implementation of the request token provider logic of +OAuth 1.0 RFC 5849. It validates the correctness of request token requests, +creates and persists tokens as well as create the proper response to be +returned to the client. +""" +import logging + +from oauthlib.common import urlencode + +from .. import errors +from .base import BaseEndpoint + +log = logging.getLogger(__name__) + + +class RequestTokenEndpoint(BaseEndpoint): + + """An endpoint responsible for providing OAuth 1 request tokens. + + Typical use is to instantiate with a request validator and invoke the + ``create_request_token_response`` from a view function. The tuple returned + has all information necessary (body, status, headers) to quickly form + and return a proper response. See :doc:`/oauth1/validator` for details on which + validator methods to implement for this endpoint. + """ + + def create_request_token(self, request, credentials): + """Create and save a new request token. + + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :param credentials: A dict of extra token credentials. + :returns: The token as an urlencoded string. + """ + token = { + 'oauth_token': self.token_generator(), + 'oauth_token_secret': self.token_generator(), + 'oauth_callback_confirmed': 'true' + } + token.update(credentials) + self.request_validator.save_request_token(token, request) + return urlencode(token.items()) + + def create_request_token_response(self, uri, http_method='GET', body=None, + headers=None, credentials=None): + """Create a request token response, with a new request token if valid. + + :param uri: The full URI of the token request. + :param http_method: A valid HTTP verb, i.e. GET, POST, PUT, HEAD, etc. + :param body: The request body as a string. + :param headers: The request headers as a dict. + :param credentials: A list of extra credentials to include in the token. + :returns: A tuple of 3 elements. + 1. A dict of headers to set on the response. + 2. The response body as a string. + 3. The response status code as an integer. + + An example of a valid request:: + + >>> from your_validator import your_validator + >>> from oauthlib.oauth1 import RequestTokenEndpoint + >>> endpoint = RequestTokenEndpoint(your_validator) + >>> h, b, s = endpoint.create_request_token_response( + ... 'https://your.provider/request_token?foo=bar', + ... headers={ + ... 'Authorization': 'OAuth realm=movies user, oauth_....' + ... }, + ... credentials={ + ... 'my_specific': 'argument', + ... }) + >>> h + {'Content-Type': 'application/x-www-form-urlencoded'} + >>> b + 'oauth_token=lsdkfol23w54jlksdef&oauth_token_secret=qwe089234lkjsdf&oauth_callback_confirmed=true&my_specific=argument' + >>> s + 200 + + An response to invalid request would have a different body and status:: + + >>> b + 'error=invalid_request&description=missing+callback+uri' + >>> s + 400 + + The same goes for an an unauthorized request: + + >>> b + '' + >>> s + 401 + """ + resp_headers = {'Content-Type': 'application/x-www-form-urlencoded'} + try: + request = self._create_request(uri, http_method, body, headers) + valid, processed_request = self.validate_request_token_request( + request) + if valid: + token = self.create_request_token(request, credentials or {}) + return resp_headers, token, 200 + else: + return {}, None, 401 + except errors.OAuth1Error as e: + return resp_headers, e.urlencoded, e.status_code + + def validate_request_token_request(self, request): + """Validate a request token request. + + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :raises: OAuth1Error if the request is invalid. + :returns: A tuple of 2 elements. + 1. The validation result (True or False). + 2. The request object. + """ + self._check_transport_security(request) + self._check_mandatory_parameters(request) + + if request.realm: + request.realms = request.realm.split(' ') + else: + request.realms = self.request_validator.get_default_realms( + request.client_key, request) + if not self.request_validator.check_realms(request.realms): + raise errors.InvalidRequestError( + description='Invalid realm {}. Allowed are {!r}.'.format( + request.realms, self.request_validator.realms)) + + if not request.redirect_uri: + raise errors.InvalidRequestError( + description='Missing callback URI.') + + if not self.request_validator.validate_timestamp_and_nonce( + request.client_key, request.timestamp, request.nonce, request, + request_token=request.resource_owner_key): + return False, request + + # The server SHOULD return a 401 (Unauthorized) status code when + # receiving a request with invalid client credentials. + # Note: This is postponed in order to avoid timing attacks, instead + # a dummy client is assigned and used to maintain near constant + # time request verification. + # + # Note that early exit would enable client enumeration + valid_client = self.request_validator.validate_client_key( + request.client_key, request) + if not valid_client: + request.client_key = self.request_validator.dummy_client + + # Note that `realm`_ is only used in authorization headers and how + # it should be interpreted is not included in the OAuth spec. + # However they could be seen as a scope or realm to which the + # client has access and as such every client should be checked + # to ensure it is authorized access to that scope or realm. + # .. _`realm`: https://tools.ietf.org/html/rfc2617#section-1.2 + # + # Note that early exit would enable client realm access enumeration. + # + # The require_realm indicates this is the first step in the OAuth + # workflow where a client requests access to a specific realm. + # This first step (obtaining request token) need not require a realm + # and can then be identified by checking the require_resource_owner + # flag and absence of realm. + # + # Clients obtaining an access token will not supply a realm and it will + # not be checked. Instead the previously requested realm should be + # transferred from the request token to the access token. + # + # Access to protected resources will always validate the realm but note + # that the realm is now tied to the access token and not provided by + # the client. + valid_realm = self.request_validator.validate_requested_realms( + request.client_key, request.realms, request) + + # Callback is normally never required, except for requests for + # a Temporary Credential as described in `Section 2.1`_ + # .._`Section 2.1`: https://tools.ietf.org/html/rfc5849#section-2.1 + valid_redirect = self.request_validator.validate_redirect_uri( + request.client_key, request.redirect_uri, request) + if not request.redirect_uri: + raise NotImplementedError('Redirect URI must either be provided ' + 'or set to a default during validation.') + + valid_signature = self._check_signature(request) + + # log the results to the validator_log + # this lets us handle internal reporting and analysis + request.validator_log['client'] = valid_client + request.validator_log['realm'] = valid_realm + request.validator_log['callback'] = valid_redirect + request.validator_log['signature'] = valid_signature + + # We delay checking validity until the very end, using dummy values for + # calculations and fetching secrets/keys to ensure the flow of every + # request remains almost identical regardless of whether valid values + # have been supplied. This ensures near constant time execution and + # prevents malicious users from guessing sensitive information + v = all((valid_client, valid_realm, valid_redirect, valid_signature)) + if not v: + log.info("[Failure] request verification failed.") + log.info("Valid client: %s.", valid_client) + log.info("Valid realm: %s.", valid_realm) + log.info("Valid callback: %s.", valid_redirect) + log.info("Valid signature: %s.", valid_signature) + return v, request diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth1/rfc5849/endpoints/resource.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth1/rfc5849/endpoints/resource.py new file mode 100644 index 0000000000000000000000000000000000000000..8641152e4ee765c889ab608080ae3c6aee3e58e8 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth1/rfc5849/endpoints/resource.py @@ -0,0 +1,163 @@ +# -*- coding: utf-8 -*- +""" +oauthlib.oauth1.rfc5849.endpoints.resource +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +This module is an implementation of the resource protection provider logic of +OAuth 1.0 RFC 5849. +""" +import logging + +from .. import errors +from .base import BaseEndpoint + +log = logging.getLogger(__name__) + + +class ResourceEndpoint(BaseEndpoint): + + """An endpoint responsible for protecting resources. + + Typical use is to instantiate with a request validator and invoke the + ``validate_protected_resource_request`` in a decorator around a view + function. If the request is valid, invoke and return the response of the + view. If invalid create and return an error response directly from the + decorator. + + See :doc:`/oauth1/validator` for details on which validator methods to implement + for this endpoint. + + An example decorator:: + + from functools import wraps + from your_validator import your_validator + from oauthlib.oauth1 import ResourceEndpoint + endpoint = ResourceEndpoint(your_validator) + + def require_oauth(realms=None): + def decorator(f): + @wraps(f) + def wrapper(request, *args, **kwargs): + v, r = provider.validate_protected_resource_request( + request.url, + http_method=request.method, + body=request.data, + headers=request.headers, + realms=realms or []) + if v: + return f(*args, **kwargs) + else: + return abort(403) + """ + + def validate_protected_resource_request(self, uri, http_method='GET', + body=None, headers=None, realms=None): + """Create a request token response, with a new request token if valid. + + :param uri: The full URI of the token request. + :param http_method: A valid HTTP verb, i.e. GET, POST, PUT, HEAD, etc. + :param body: The request body as a string. + :param headers: The request headers as a dict. + :param realms: A list of realms the resource is protected under. + This will be supplied to the ``validate_realms`` + method of the request validator. + :returns: A tuple of 2 elements. + 1. True if valid, False otherwise. + 2. An oauthlib.common.Request object. + """ + try: + request = self._create_request(uri, http_method, body, headers) + except errors.OAuth1Error: + return False, None + + try: + self._check_transport_security(request) + self._check_mandatory_parameters(request) + except errors.OAuth1Error: + return False, request + + if not request.resource_owner_key: + return False, request + + if not self.request_validator.check_access_token( + request.resource_owner_key): + return False, request + + if not self.request_validator.validate_timestamp_and_nonce( + request.client_key, request.timestamp, request.nonce, request, + access_token=request.resource_owner_key): + return False, request + + # The server SHOULD return a 401 (Unauthorized) status code when + # receiving a request with invalid client credentials. + # Note: This is postponed in order to avoid timing attacks, instead + # a dummy client is assigned and used to maintain near constant + # time request verification. + # + # Note that early exit would enable client enumeration + valid_client = self.request_validator.validate_client_key( + request.client_key, request) + if not valid_client: + request.client_key = self.request_validator.dummy_client + + # The server SHOULD return a 401 (Unauthorized) status code when + # receiving a request with invalid or expired token. + # Note: This is postponed in order to avoid timing attacks, instead + # a dummy token is assigned and used to maintain near constant + # time request verification. + # + # Note that early exit would enable resource owner enumeration + valid_resource_owner = self.request_validator.validate_access_token( + request.client_key, request.resource_owner_key, request) + if not valid_resource_owner: + request.resource_owner_key = self.request_validator.dummy_access_token + + # Note that `realm`_ is only used in authorization headers and how + # it should be interpreted is not included in the OAuth spec. + # However they could be seen as a scope or realm to which the + # client has access and as such every client should be checked + # to ensure it is authorized access to that scope or realm. + # .. _`realm`: https://tools.ietf.org/html/rfc2617#section-1.2 + # + # Note that early exit would enable client realm access enumeration. + # + # The require_realm indicates this is the first step in the OAuth + # workflow where a client requests access to a specific realm. + # This first step (obtaining request token) need not require a realm + # and can then be identified by checking the require_resource_owner + # flag and absence of realm. + # + # Clients obtaining an access token will not supply a realm and it will + # not be checked. Instead the previously requested realm should be + # transferred from the request token to the access token. + # + # Access to protected resources will always validate the realm but note + # that the realm is now tied to the access token and not provided by + # the client. + valid_realm = self.request_validator.validate_realms(request.client_key, + request.resource_owner_key, request, uri=request.uri, + realms=realms) + + valid_signature = self._check_signature(request) + + # log the results to the validator_log + # this lets us handle internal reporting and analysis + request.validator_log['client'] = valid_client + request.validator_log['resource_owner'] = valid_resource_owner + request.validator_log['realm'] = valid_realm + request.validator_log['signature'] = valid_signature + + # We delay checking validity until the very end, using dummy values for + # calculations and fetching secrets/keys to ensure the flow of every + # request remains almost identical regardless of whether valid values + # have been supplied. This ensures near constant time execution and + # prevents malicious users from guessing sensitive information + v = all((valid_client, valid_resource_owner, valid_realm, + valid_signature)) + if not v: + log.info("[Failure] request verification failed.") + log.info("Valid client: %s", valid_client) + log.info("Valid token: %s", valid_resource_owner) + log.info("Valid realm: %s", valid_realm) + log.info("Valid signature: %s", valid_signature) + return v, request diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth1/rfc5849/endpoints/signature_only.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth1/rfc5849/endpoints/signature_only.py new file mode 100644 index 0000000000000000000000000000000000000000..d693ccb7f6bf368754223f822a143dc81b8232df --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth1/rfc5849/endpoints/signature_only.py @@ -0,0 +1,82 @@ +# -*- coding: utf-8 -*- +""" +oauthlib.oauth1.rfc5849.endpoints.signature_only +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +This module is an implementation of the signing logic of OAuth 1.0 RFC 5849. +""" + +import logging + +from .. import errors +from .base import BaseEndpoint + +log = logging.getLogger(__name__) + + +class SignatureOnlyEndpoint(BaseEndpoint): + + """An endpoint only responsible for verifying an oauth signature.""" + + def validate_request(self, uri, http_method='GET', + body=None, headers=None): + """Validate a signed OAuth request. + + :param uri: The full URI of the token request. + :param http_method: A valid HTTP verb, i.e. GET, POST, PUT, HEAD, etc. + :param body: The request body as a string. + :param headers: The request headers as a dict. + :returns: A tuple of 2 elements. + 1. True if valid, False otherwise. + 2. An oauthlib.common.Request object. + """ + try: + request = self._create_request(uri, http_method, body, headers) + except errors.OAuth1Error as err: + log.info( + 'Exception caught while validating request, %s.' % err) + return False, None + + try: + self._check_transport_security(request) + self._check_mandatory_parameters(request) + except errors.OAuth1Error as err: + log.info( + 'Exception caught while validating request, %s.' % err) + return False, request + + if not self.request_validator.validate_timestamp_and_nonce( + request.client_key, request.timestamp, request.nonce, request): + log.debug('[Failure] verification failed: timestamp/nonce') + return False, request + + # The server SHOULD return a 401 (Unauthorized) status code when + # receiving a request with invalid client credentials. + # Note: This is postponed in order to avoid timing attacks, instead + # a dummy client is assigned and used to maintain near constant + # time request verification. + # + # Note that early exit would enable client enumeration + valid_client = self.request_validator.validate_client_key( + request.client_key, request) + if not valid_client: + request.client_key = self.request_validator.dummy_client + + valid_signature = self._check_signature(request) + + # log the results to the validator_log + # this lets us handle internal reporting and analysis + request.validator_log['client'] = valid_client + request.validator_log['signature'] = valid_signature + + # We delay checking validity until the very end, using dummy values for + # calculations and fetching secrets/keys to ensure the flow of every + # request remains almost identical regardless of whether valid values + # have been supplied. This ensures near constant time execution and + # prevents malicious users from guessing sensitive information + v = all((valid_client, valid_signature)) + if not v: + log.info("[Failure] request verification failed.") + log.info("Valid client: %s", valid_client) + log.info("Valid signature: %s", valid_signature) + return v, request diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth1/rfc5849/errors.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth1/rfc5849/errors.py new file mode 100644 index 0000000000000000000000000000000000000000..8774d40741b4b26a3721be545c66513505aaa8d1 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth1/rfc5849/errors.py @@ -0,0 +1,76 @@ +""" +oauthlib.oauth1.rfc5849.errors +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Error used both by OAuth 1 clients and provicers to represent the spec +defined error responses for all four core grant types. +""" +from oauthlib.common import add_params_to_uri, urlencode + + +class OAuth1Error(Exception): + error = None + description = '' + + def __init__(self, description=None, uri=None, status_code=400, + request=None): + """ + description: A human-readable ASCII [USASCII] text providing + additional information, used to assist the client + developer in understanding the error that occurred. + Values for the "error_description" parameter MUST NOT + include characters outside the set + x20-21 / x23-5B / x5D-7E. + + uri: A URI identifying a human-readable web page with information + about the error, used to provide the client developer with + additional information about the error. Values for the + "error_uri" parameter MUST conform to the URI- Reference + syntax, and thus MUST NOT include characters outside the set + x21 / x23-5B / x5D-7E. + + state: A CSRF protection value received from the client. + + request: Oauthlib Request object + """ + self.description = description or self.description + message = '({}) {}'.format(self.error, self.description) + if request: + message += ' ' + repr(request) + super().__init__(message) + + self.uri = uri + self.status_code = status_code + + def in_uri(self, uri): + return add_params_to_uri(uri, self.twotuples) + + @property + def twotuples(self): + error = [('error', self.error)] + if self.description: + error.append(('error_description', self.description)) + if self.uri: + error.append(('error_uri', self.uri)) + return error + + @property + def urlencoded(self): + return urlencode(self.twotuples) + + +class InsecureTransportError(OAuth1Error): + error = 'insecure_transport_protocol' + description = 'Only HTTPS connections are permitted.' + + +class InvalidSignatureMethodError(OAuth1Error): + error = 'invalid_signature_method' + + +class InvalidRequestError(OAuth1Error): + error = 'invalid_request' + + +class InvalidClientError(OAuth1Error): + error = 'invalid_client' diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth1/rfc5849/parameters.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth1/rfc5849/parameters.py new file mode 100644 index 0000000000000000000000000000000000000000..2163772df3c7f53d539fa8cb0b73cc0e547614f2 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth1/rfc5849/parameters.py @@ -0,0 +1,133 @@ +""" +oauthlib.parameters +~~~~~~~~~~~~~~~~~~~ + +This module contains methods related to `section 3.5`_ of the OAuth 1.0a spec. + +.. _`section 3.5`: https://tools.ietf.org/html/rfc5849#section-3.5 +""" +from urllib.parse import urlparse, urlunparse + +from oauthlib.common import extract_params, urlencode + +from . import utils + + +# TODO: do we need filter_params now that oauth_params are handled by Request? +# We can easily pass in just oauth protocol params. +@utils.filter_params +def prepare_headers(oauth_params, headers=None, realm=None): + """**Prepare the Authorization header.** + Per `section 3.5.1`_ of the spec. + + Protocol parameters can be transmitted using the HTTP "Authorization" + header field as defined by `RFC2617`_ with the auth-scheme name set to + "OAuth" (case insensitive). + + For example:: + + Authorization: OAuth realm="Example", + oauth_consumer_key="0685bd9184jfhq22", + oauth_token="ad180jjd733klru7", + oauth_signature_method="HMAC-SHA1", + oauth_signature="wOJIO9A2W5mFwDgiDvZbTSMK%2FPY%3D", + oauth_timestamp="137131200", + oauth_nonce="4572616e48616d6d65724c61686176", + oauth_version="1.0" + + + .. _`section 3.5.1`: https://tools.ietf.org/html/rfc5849#section-3.5.1 + .. _`RFC2617`: https://tools.ietf.org/html/rfc2617 + """ + headers = headers or {} + + # Protocol parameters SHALL be included in the "Authorization" header + # field as follows: + authorization_header_parameters_parts = [] + for oauth_parameter_name, value in oauth_params: + # 1. Parameter names and values are encoded per Parameter Encoding + # (`Section 3.6`_) + # + # .. _`Section 3.6`: https://tools.ietf.org/html/rfc5849#section-3.6 + escaped_name = utils.escape(oauth_parameter_name) + escaped_value = utils.escape(value) + + # 2. Each parameter's name is immediately followed by an "=" character + # (ASCII code 61), a """ character (ASCII code 34), the parameter + # value (MAY be empty), and another """ character (ASCII code 34). + part = '{}="{}"'.format(escaped_name, escaped_value) + + authorization_header_parameters_parts.append(part) + + # 3. Parameters are separated by a "," character (ASCII code 44) and + # OPTIONAL linear whitespace per `RFC2617`_. + # + # .. _`RFC2617`: https://tools.ietf.org/html/rfc2617 + authorization_header_parameters = ', '.join( + authorization_header_parameters_parts) + + # 4. The OPTIONAL "realm" parameter MAY be added and interpreted per + # `RFC2617 section 1.2`_. + # + # .. _`RFC2617 section 1.2`: https://tools.ietf.org/html/rfc2617#section-1.2 + if realm: + # NOTE: realm should *not* be escaped + authorization_header_parameters = ('realm="%s", ' % realm + + authorization_header_parameters) + + # the auth-scheme name set to "OAuth" (case insensitive). + authorization_header = 'OAuth %s' % authorization_header_parameters + + # contribute the Authorization header to the given headers + full_headers = {} + full_headers.update(headers) + full_headers['Authorization'] = authorization_header + return full_headers + + +def _append_params(oauth_params, params): + """Append OAuth params to an existing set of parameters. + + Both params and oauth_params is must be lists of 2-tuples. + + Per `section 3.5.2`_ and `3.5.3`_ of the spec. + + .. _`section 3.5.2`: https://tools.ietf.org/html/rfc5849#section-3.5.2 + .. _`3.5.3`: https://tools.ietf.org/html/rfc5849#section-3.5.3 + + """ + merged = list(params) + merged.extend(oauth_params) + # The request URI / entity-body MAY include other request-specific + # parameters, in which case, the protocol parameters SHOULD be appended + # following the request-specific parameters, properly separated by an "&" + # character (ASCII code 38) + merged.sort(key=lambda i: i[0].startswith('oauth_')) + return merged + + +def prepare_form_encoded_body(oauth_params, body): + """Prepare the Form-Encoded Body. + + Per `section 3.5.2`_ of the spec. + + .. _`section 3.5.2`: https://tools.ietf.org/html/rfc5849#section-3.5.2 + + """ + # append OAuth params to the existing body + return _append_params(oauth_params, body) + + +def prepare_request_uri_query(oauth_params, uri): + """Prepare the Request URI Query. + + Per `section 3.5.3`_ of the spec. + + .. _`section 3.5.3`: https://tools.ietf.org/html/rfc5849#section-3.5.3 + + """ + # append OAuth params to the existing set of query components + sch, net, path, par, query, fra = urlparse(uri) + query = urlencode( + _append_params(oauth_params, extract_params(query) or [])) + return urlunparse((sch, net, path, par, query, fra)) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth1/rfc5849/request_validator.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth1/rfc5849/request_validator.py new file mode 100644 index 0000000000000000000000000000000000000000..e937aabf4082850d8e966e75530dde6ff0becb13 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth1/rfc5849/request_validator.py @@ -0,0 +1,849 @@ +""" +oauthlib.oauth1.rfc5849 +~~~~~~~~~~~~~~ + +This module is an implementation of various logic needed +for signing and checking OAuth 1.0 RFC 5849 requests. +""" +from . import SIGNATURE_METHODS, utils + + +class RequestValidator: + + """A validator/datastore interaction base class for OAuth 1 providers. + + OAuth providers should inherit from RequestValidator and implement the + methods and properties outlined below. Further details are provided in the + documentation for each method and property. + + Methods used to check the format of input parameters. Common tests include + length, character set, membership, range or pattern. These tests are + referred to as `whitelisting or blacklisting`_. Whitelisting is better + but blacklisting can be useful to spot malicious activity. + The following have methods a default implementation: + + - check_client_key + - check_request_token + - check_access_token + - check_nonce + - check_verifier + - check_realms + + The methods above default to whitelist input parameters, checking that they + are alphanumerical and between a minimum and maximum length. Rather than + overloading the methods a few properties can be used to configure these + methods. + + * @safe_characters -> (character set) + * @client_key_length -> (min, max) + * @request_token_length -> (min, max) + * @access_token_length -> (min, max) + * @nonce_length -> (min, max) + * @verifier_length -> (min, max) + * @realms -> [list, of, realms] + + Methods used to validate/invalidate input parameters. These checks usually + hit either persistent or temporary storage such as databases or the + filesystem. See each methods documentation for detailed usage. + The following methods must be implemented: + + - validate_client_key + - validate_request_token + - validate_access_token + - validate_timestamp_and_nonce + - validate_redirect_uri + - validate_requested_realms + - validate_realms + - validate_verifier + - invalidate_request_token + + Methods used to retrieve sensitive information from storage. + The following methods must be implemented: + + - get_client_secret + - get_request_token_secret + - get_access_token_secret + - get_rsa_key + - get_realms + - get_default_realms + - get_redirect_uri + + Methods used to save credentials. + The following methods must be implemented: + + - save_request_token + - save_verifier + - save_access_token + + Methods used to verify input parameters. This methods are used during + authorizing request token by user (AuthorizationEndpoint), to check if + parameters are valid. During token authorization request is not signed, + thus 'validation' methods can not be used. The following methods must be + implemented: + + - verify_realms + - verify_request_token + + To prevent timing attacks it is necessary to not exit early even if the + client key or resource owner key is invalid. Instead dummy values should + be used during the remaining verification process. It is very important + that the dummy client and token are valid input parameters to the methods + get_client_secret, get_rsa_key and get_(access/request)_token_secret and + that the running time of those methods when given a dummy value remain + equivalent to the running time when given a valid client/resource owner. + The following properties must be implemented: + + * @dummy_client + * @dummy_request_token + * @dummy_access_token + + Example implementations have been provided, note that the database used is + a simple dictionary and serves only an illustrative purpose. Use whichever + database suits your project and how to access it is entirely up to you. + The methods are introduced in an order which should make understanding + their use more straightforward and as such it could be worth reading what + follows in chronological order. + + .. _`whitelisting or blacklisting`: https://www.schneier.com/blog/archives/2011/01/whitelisting_vs.html + """ + + def __init__(self): + pass + + @property + def allowed_signature_methods(self): + return SIGNATURE_METHODS + + @property + def safe_characters(self): + return set(utils.UNICODE_ASCII_CHARACTER_SET) + + @property + def client_key_length(self): + return 20, 30 + + @property + def request_token_length(self): + return 20, 30 + + @property + def access_token_length(self): + return 20, 30 + + @property + def timestamp_lifetime(self): + return 600 + + @property + def nonce_length(self): + return 20, 30 + + @property + def verifier_length(self): + return 20, 30 + + @property + def realms(self): + return [] + + @property + def enforce_ssl(self): + return True + + def check_client_key(self, client_key): + """Check that the client key only contains safe characters + and is no shorter than lower and no longer than upper. + """ + lower, upper = self.client_key_length + return (set(client_key) <= self.safe_characters and + lower <= len(client_key) <= upper) + + def check_request_token(self, request_token): + """Checks that the request token contains only safe characters + and is no shorter than lower and no longer than upper. + """ + lower, upper = self.request_token_length + return (set(request_token) <= self.safe_characters and + lower <= len(request_token) <= upper) + + def check_access_token(self, request_token): + """Checks that the token contains only safe characters + and is no shorter than lower and no longer than upper. + """ + lower, upper = self.access_token_length + return (set(request_token) <= self.safe_characters and + lower <= len(request_token) <= upper) + + def check_nonce(self, nonce): + """Checks that the nonce only contains only safe characters + and is no shorter than lower and no longer than upper. + """ + lower, upper = self.nonce_length + return (set(nonce) <= self.safe_characters and + lower <= len(nonce) <= upper) + + def check_verifier(self, verifier): + """Checks that the verifier contains only safe characters + and is no shorter than lower and no longer than upper. + """ + lower, upper = self.verifier_length + return (set(verifier) <= self.safe_characters and + lower <= len(verifier) <= upper) + + def check_realms(self, realms): + """Check that the realm is one of a set allowed realms.""" + return all(r in self.realms for r in realms) + + def _subclass_must_implement(self, fn): + """ + Returns a NotImplementedError for a function that should be implemented. + :param fn: name of the function + """ + m = "Missing function implementation in {}: {}".format(type(self), fn) + return NotImplementedError(m) + + @property + def dummy_client(self): + """Dummy client used when an invalid client key is supplied. + + :returns: The dummy client key string. + + The dummy client should be associated with either a client secret, + a rsa key or both depending on which signature methods are supported. + Providers should make sure that + + get_client_secret(dummy_client) + get_rsa_key(dummy_client) + + return a valid secret or key for the dummy client. + + This method is used by + + * AccessTokenEndpoint + * RequestTokenEndpoint + * ResourceEndpoint + * SignatureOnlyEndpoint + """ + raise self._subclass_must_implement("dummy_client") + + @property + def dummy_request_token(self): + """Dummy request token used when an invalid token was supplied. + + :returns: The dummy request token string. + + The dummy request token should be associated with a request token + secret such that get_request_token_secret(.., dummy_request_token) + returns a valid secret. + + This method is used by + + * AccessTokenEndpoint + """ + raise self._subclass_must_implement("dummy_request_token") + + @property + def dummy_access_token(self): + """Dummy access token used when an invalid token was supplied. + + :returns: The dummy access token string. + + The dummy access token should be associated with an access token + secret such that get_access_token_secret(.., dummy_access_token) + returns a valid secret. + + This method is used by + + * ResourceEndpoint + """ + raise self._subclass_must_implement("dummy_access_token") + + def get_client_secret(self, client_key, request): + """Retrieves the client secret associated with the client key. + + :param client_key: The client/consumer key. + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :returns: The client secret as a string. + + This method must allow the use of a dummy client_key value. + Fetching the secret using the dummy key must take the same amount of + time as fetching a secret for a valid client:: + + # Unlikely to be near constant time as it uses two database + # lookups for a valid client, and only one for an invalid. + from your_datastore import ClientSecret + if ClientSecret.has(client_key): + return ClientSecret.get(client_key) + else: + return 'dummy' + + # Aim to mimic number of latency inducing operations no matter + # whether the client is valid or not. + from your_datastore import ClientSecret + return ClientSecret.get(client_key, 'dummy') + + Note that the returned key must be in plaintext. + + This method is used by + + * AccessTokenEndpoint + * RequestTokenEndpoint + * ResourceEndpoint + * SignatureOnlyEndpoint + """ + raise self._subclass_must_implement('get_client_secret') + + def get_request_token_secret(self, client_key, token, request): + """Retrieves the shared secret associated with the request token. + + :param client_key: The client/consumer key. + :param token: The request token string. + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :returns: The token secret as a string. + + This method must allow the use of a dummy values and the running time + must be roughly equivalent to that of the running time of valid values:: + + # Unlikely to be near constant time as it uses two database + # lookups for a valid client, and only one for an invalid. + from your_datastore import RequestTokenSecret + if RequestTokenSecret.has(client_key): + return RequestTokenSecret.get((client_key, request_token)) + else: + return 'dummy' + + # Aim to mimic number of latency inducing operations no matter + # whether the client is valid or not. + from your_datastore import RequestTokenSecret + return ClientSecret.get((client_key, request_token), 'dummy') + + Note that the returned key must be in plaintext. + + This method is used by + + * AccessTokenEndpoint + """ + raise self._subclass_must_implement('get_request_token_secret') + + def get_access_token_secret(self, client_key, token, request): + """Retrieves the shared secret associated with the access token. + + :param client_key: The client/consumer key. + :param token: The access token string. + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :returns: The token secret as a string. + + This method must allow the use of a dummy values and the running time + must be roughly equivalent to that of the running time of valid values:: + + # Unlikely to be near constant time as it uses two database + # lookups for a valid client, and only one for an invalid. + from your_datastore import AccessTokenSecret + if AccessTokenSecret.has(client_key): + return AccessTokenSecret.get((client_key, request_token)) + else: + return 'dummy' + + # Aim to mimic number of latency inducing operations no matter + # whether the client is valid or not. + from your_datastore import AccessTokenSecret + return ClientSecret.get((client_key, request_token), 'dummy') + + Note that the returned key must be in plaintext. + + This method is used by + + * ResourceEndpoint + """ + raise self._subclass_must_implement("get_access_token_secret") + + def get_default_realms(self, client_key, request): + """Get the default realms for a client. + + :param client_key: The client/consumer key. + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :returns: The list of default realms associated with the client. + + The list of default realms will be set during client registration and + is outside the scope of OAuthLib. + + This method is used by + + * RequestTokenEndpoint + """ + raise self._subclass_must_implement("get_default_realms") + + def get_realms(self, token, request): + """Get realms associated with a request token. + + :param token: The request token string. + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :returns: The list of realms associated with the request token. + + This method is used by + + * AuthorizationEndpoint + * AccessTokenEndpoint + """ + raise self._subclass_must_implement("get_realms") + + def get_redirect_uri(self, token, request): + """Get the redirect URI associated with a request token. + + :param token: The request token string. + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :returns: The redirect URI associated with the request token. + + It may be desirable to return a custom URI if the redirect is set to "oob". + In this case, the user will be redirected to the returned URI and at that + endpoint the verifier can be displayed. + + This method is used by + + * AuthorizationEndpoint + """ + raise self._subclass_must_implement("get_redirect_uri") + + def get_rsa_key(self, client_key, request): + """Retrieves a previously stored client provided RSA key. + + :param client_key: The client/consumer key. + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :returns: The rsa public key as a string. + + This method must allow the use of a dummy client_key value. Fetching + the rsa key using the dummy key must take the same amount of time + as fetching a key for a valid client. The dummy key must also be of + the same bit length as client keys. + + Note that the key must be returned in plaintext. + + This method is used by + + * AccessTokenEndpoint + * RequestTokenEndpoint + * ResourceEndpoint + * SignatureOnlyEndpoint + """ + raise self._subclass_must_implement("get_rsa_key") + + def invalidate_request_token(self, client_key, request_token, request): + """Invalidates a used request token. + + :param client_key: The client/consumer key. + :param request_token: The request token string. + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :returns: None + + Per `Section 2.3`_ of the spec: + + "The server MUST (...) ensure that the temporary + credentials have not expired or been used before." + + .. _`Section 2.3`: https://tools.ietf.org/html/rfc5849#section-2.3 + + This method should ensure that provided token won't validate anymore. + It can be simply removing RequestToken from storage or setting + specific flag that makes it invalid (note that such flag should be + also validated during request token validation). + + This method is used by + + * AccessTokenEndpoint + """ + raise self._subclass_must_implement("invalidate_request_token") + + def validate_client_key(self, client_key, request): + """Validates that supplied client key is a registered and valid client. + + :param client_key: The client/consumer key. + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :returns: True or False + + Note that if the dummy client is supplied it should validate in same + or nearly the same amount of time as a valid one. + + Ensure latency inducing tasks are mimiced even for dummy clients. + For example, use:: + + from your_datastore import Client + try: + return Client.exists(client_key, access_token) + except DoesNotExist: + return False + + Rather than:: + + from your_datastore import Client + if access_token == self.dummy_access_token: + return False + else: + return Client.exists(client_key, access_token) + + This method is used by + + * AccessTokenEndpoint + * RequestTokenEndpoint + * ResourceEndpoint + * SignatureOnlyEndpoint + """ + raise self._subclass_must_implement("validate_client_key") + + def validate_request_token(self, client_key, token, request): + """Validates that supplied request token is registered and valid. + + :param client_key: The client/consumer key. + :param token: The request token string. + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :returns: True or False + + Note that if the dummy request_token is supplied it should validate in + the same nearly the same amount of time as a valid one. + + Ensure latency inducing tasks are mimiced even for dummy clients. + For example, use:: + + from your_datastore import RequestToken + try: + return RequestToken.exists(client_key, access_token) + except DoesNotExist: + return False + + Rather than:: + + from your_datastore import RequestToken + if access_token == self.dummy_access_token: + return False + else: + return RequestToken.exists(client_key, access_token) + + This method is used by + + * AccessTokenEndpoint + """ + raise self._subclass_must_implement("validate_request_token") + + def validate_access_token(self, client_key, token, request): + """Validates that supplied access token is registered and valid. + + :param client_key: The client/consumer key. + :param token: The access token string. + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :returns: True or False + + Note that if the dummy access token is supplied it should validate in + the same or nearly the same amount of time as a valid one. + + Ensure latency inducing tasks are mimiced even for dummy clients. + For example, use:: + + from your_datastore import AccessToken + try: + return AccessToken.exists(client_key, access_token) + except DoesNotExist: + return False + + Rather than:: + + from your_datastore import AccessToken + if access_token == self.dummy_access_token: + return False + else: + return AccessToken.exists(client_key, access_token) + + This method is used by + + * ResourceEndpoint + """ + raise self._subclass_must_implement("validate_access_token") + + def validate_timestamp_and_nonce(self, client_key, timestamp, nonce, + request, request_token=None, access_token=None): + """Validates that the nonce has not been used before. + + :param client_key: The client/consumer key. + :param timestamp: The ``oauth_timestamp`` parameter. + :param nonce: The ``oauth_nonce`` parameter. + :param request_token: Request token string, if any. + :param access_token: Access token string, if any. + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :returns: True or False + + Per `Section 3.3`_ of the spec. + + "A nonce is a random string, uniquely generated by the client to allow + the server to verify that a request has never been made before and + helps prevent replay attacks when requests are made over a non-secure + channel. The nonce value MUST be unique across all requests with the + same timestamp, client credentials, and token combinations." + + .. _`Section 3.3`: https://tools.ietf.org/html/rfc5849#section-3.3 + + One of the first validation checks that will be made is for the validity + of the nonce and timestamp, which are associated with a client key and + possibly a token. If invalid then immediately fail the request + by returning False. If the nonce/timestamp pair has been used before and + you may just have detected a replay attack. Therefore it is an essential + part of OAuth security that you not allow nonce/timestamp reuse. + Note that this validation check is done before checking the validity of + the client and token.:: + + nonces_and_timestamps_database = [ + (u'foo', 1234567890, u'rannoMstrInghere', u'bar') + ] + + def validate_timestamp_and_nonce(self, client_key, timestamp, nonce, + request_token=None, access_token=None): + + return ((client_key, timestamp, nonce, request_token or access_token) + not in self.nonces_and_timestamps_database) + + This method is used by + + * AccessTokenEndpoint + * RequestTokenEndpoint + * ResourceEndpoint + * SignatureOnlyEndpoint + """ + raise self._subclass_must_implement("validate_timestamp_and_nonce") + + def validate_redirect_uri(self, client_key, redirect_uri, request): + """Validates the client supplied redirection URI. + + :param client_key: The client/consumer key. + :param redirect_uri: The URI the client which to redirect back to after + authorization is successful. + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :returns: True or False + + It is highly recommended that OAuth providers require their clients + to register all redirection URIs prior to using them in requests and + register them as absolute URIs. See `CWE-601`_ for more information + about open redirection attacks. + + By requiring registration of all redirection URIs it should be + straightforward for the provider to verify whether the supplied + redirect_uri is valid or not. + + Alternatively per `Section 2.1`_ of the spec: + + "If the client is unable to receive callbacks or a callback URI has + been established via other means, the parameter value MUST be set to + "oob" (case sensitive), to indicate an out-of-band configuration." + + .. _`CWE-601`: http://cwe.mitre.org/top25/index.html#CWE-601 + .. _`Section 2.1`: https://tools.ietf.org/html/rfc5849#section-2.1 + + This method is used by + + * RequestTokenEndpoint + """ + raise self._subclass_must_implement("validate_redirect_uri") + + def validate_requested_realms(self, client_key, realms, request): + """Validates that the client may request access to the realm. + + :param client_key: The client/consumer key. + :param realms: The list of realms that client is requesting access to. + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :returns: True or False + + This method is invoked when obtaining a request token and should + tie a realm to the request token and after user authorization + this realm restriction should transfer to the access token. + + This method is used by + + * RequestTokenEndpoint + """ + raise self._subclass_must_implement("validate_requested_realms") + + def validate_realms(self, client_key, token, request, uri=None, + realms=None): + """Validates access to the request realm. + + :param client_key: The client/consumer key. + :param token: A request token string. + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :param uri: The URI the realms is protecting. + :param realms: A list of realms that must have been granted to + the access token. + :returns: True or False + + How providers choose to use the realm parameter is outside the OAuth + specification but it is commonly used to restrict access to a subset + of protected resources such as "photos". + + realms is a convenience parameter which can be used to provide + a per view method pre-defined list of allowed realms. + + Can be as simple as:: + + from your_datastore import RequestToken + request_token = RequestToken.get(token, None) + + if not request_token: + return False + return set(request_token.realms).issuperset(set(realms)) + + This method is used by + + * ResourceEndpoint + """ + raise self._subclass_must_implement("validate_realms") + + def validate_verifier(self, client_key, token, verifier, request): + """Validates a verification code. + + :param client_key: The client/consumer key. + :param token: A request token string. + :param verifier: The authorization verifier string. + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :returns: True or False + + OAuth providers issue a verification code to clients after the + resource owner authorizes access. This code is used by the client to + obtain token credentials and the provider must verify that the + verifier is valid and associated with the client as well as the + resource owner. + + Verifier validation should be done in near constant time + (to avoid verifier enumeration). To achieve this we need a + constant time string comparison which is provided by OAuthLib + in ``oauthlib.common.safe_string_equals``:: + + from your_datastore import Verifier + correct_verifier = Verifier.get(client_key, request_token) + from oauthlib.common import safe_string_equals + return safe_string_equals(verifier, correct_verifier) + + This method is used by + + * AccessTokenEndpoint + """ + raise self._subclass_must_implement("validate_verifier") + + def verify_request_token(self, token, request): + """Verify that the given OAuth1 request token is valid. + + :param token: A request token string. + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :returns: True or False + + This method is used only in AuthorizationEndpoint to check whether the + oauth_token given in the authorization URL is valid or not. + This request is not signed and thus similar ``validate_request_token`` + method can not be used. + + This method is used by + + * AuthorizationEndpoint + """ + raise self._subclass_must_implement("verify_request_token") + + def verify_realms(self, token, realms, request): + """Verify authorized realms to see if they match those given to token. + + :param token: An access token string. + :param realms: A list of realms the client attempts to access. + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :returns: True or False + + This prevents the list of authorized realms sent by the client during + the authorization step to be altered to include realms outside what + was bound with the request token. + + Can be as simple as:: + + valid_realms = self.get_realms(token) + return all((r in valid_realms for r in realms)) + + This method is used by + + * AuthorizationEndpoint + """ + raise self._subclass_must_implement("verify_realms") + + def save_access_token(self, token, request): + """Save an OAuth1 access token. + + :param token: A dict with token credentials. + :param request: OAuthlib request. + :type request: oauthlib.common.Request + + The token dictionary will at minimum include + + * ``oauth_token`` the access token string. + * ``oauth_token_secret`` the token specific secret used in signing. + * ``oauth_authorized_realms`` a space separated list of realms. + + Client key can be obtained from ``request.client_key``. + + The list of realms (not joined string) can be obtained from + ``request.realm``. + + This method is used by + + * AccessTokenEndpoint + """ + raise self._subclass_must_implement("save_access_token") + + def save_request_token(self, token, request): + """Save an OAuth1 request token. + + :param token: A dict with token credentials. + :param request: OAuthlib request. + :type request: oauthlib.common.Request + + The token dictionary will at minimum include + + * ``oauth_token`` the request token string. + * ``oauth_token_secret`` the token specific secret used in signing. + * ``oauth_callback_confirmed`` the string ``true``. + + Client key can be obtained from ``request.client_key``. + + This method is used by + + * RequestTokenEndpoint + """ + raise self._subclass_must_implement("save_request_token") + + def save_verifier(self, token, verifier, request): + """Associate an authorization verifier with a request token. + + :param token: A request token string. + :param verifier: A dictionary containing the oauth_verifier and + oauth_token + :param request: OAuthlib request. + :type request: oauthlib.common.Request + + We need to associate verifiers with tokens for validation during the + access token request. + + Note that unlike save_x_token token here is the ``oauth_token`` token + string from the request token saved previously. + + This method is used by + + * AuthorizationEndpoint + """ + raise self._subclass_must_implement("save_verifier") diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth1/rfc5849/signature.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth1/rfc5849/signature.py new file mode 100644 index 0000000000000000000000000000000000000000..a27cb2e76fcae785b8672401458e04c96ab25cb6 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth1/rfc5849/signature.py @@ -0,0 +1,852 @@ +""" +This module is an implementation of `section 3.4`_ of RFC 5849. + +**Usage** + +Steps for signing a request: + +1. Collect parameters from the request using ``collect_parameters``. +2. Normalize those parameters using ``normalize_parameters``. +3. Create the *base string URI* using ``base_string_uri``. +4. Create the *signature base string* from the above three components + using ``signature_base_string``. +5. Pass the *signature base string* and the client credentials to one of the + sign-with-client functions. The HMAC-based signing functions needs + client credentials with secrets. The RSA-based signing functions needs + client credentials with an RSA private key. + +To verify a request, pass the request and credentials to one of the verify +functions. The HMAC-based signing functions needs the shared secrets. The +RSA-based verify functions needs the RSA public key. + +**Scope** + +All of the functions in this module should be considered internal to OAuthLib, +since they are not imported into the "oauthlib.oauth1" module. Programs using +OAuthLib should not use directly invoke any of the functions in this module. + +**Deprecated functions** + +The "sign_" methods that are not "_with_client" have been deprecated. They may +be removed in a future release. Since they are all internal functions, this +should have no impact on properly behaving programs. + +.. _`section 3.4`: https://tools.ietf.org/html/rfc5849#section-3.4 +""" + +import binascii +import hashlib +import hmac +import ipaddress +import logging +import urllib.parse as urlparse +import warnings + +from oauthlib.common import extract_params, safe_string_equals, urldecode + +from . import utils +import contextlib + +log = logging.getLogger(__name__) + + +# ==== Common functions ========================================== + +def signature_base_string( + http_method: str, + base_str_uri: str, + normalized_encoded_request_parameters: str) -> str: + """ + Construct the signature base string. + + The *signature base string* is the value that is calculated and signed by + the client. It is also independently calculated by the server to verify + the signature, and therefore must produce the exact same value at both + ends or the signature won't verify. + + The rules for calculating the *signature base string* are defined in + section 3.4.1.1`_ of RFC 5849. + + .. _`section 3.4.1.1`: https://tools.ietf.org/html/rfc5849#section-3.4.1.1 + """ + + # The signature base string is constructed by concatenating together, + # in order, the following HTTP request elements: + + # 1. The HTTP request method in uppercase. For example: "HEAD", + # "GET", "POST", etc. If the request uses a custom HTTP method, it + # MUST be encoded (`Section 3.6`_). + # + # .. _`Section 3.6`: https://tools.ietf.org/html/rfc5849#section-3.6 + base_string = utils.escape(http_method.upper()) + + # 2. An "&" character (ASCII code 38). + base_string += '&' + + # 3. The base string URI from `Section 3.4.1.2`_, after being encoded + # (`Section 3.6`_). + # + # .. _`Section 3.4.1.2`: https://tools.ietf.org/html/rfc5849#section-3.4.1.2 + # .. _`Section 3.6`: https://tools.ietf.org/html/rfc5849#section-3.6 + base_string += utils.escape(base_str_uri) + + # 4. An "&" character (ASCII code 38). + base_string += '&' + + # 5. The request parameters as normalized in `Section 3.4.1.3.2`_, after + # being encoded (`Section 3.6`). + # + # .. _`Sec 3.4.1.3.2`: https://tools.ietf.org/html/rfc5849#section-3.4.1.3.2 + # .. _`Section 3.6`: https://tools.ietf.org/html/rfc5849#section-3.6 + base_string += utils.escape(normalized_encoded_request_parameters) + + return base_string + + +def base_string_uri(uri: str, host: str = None) -> str: + """ + Calculates the _base string URI_. + + The *base string URI* is one of the components that make up the + *signature base string*. + + The ``host`` is optional. If provided, it is used to override any host and + port values in the ``uri``. The value for ``host`` is usually extracted from + the "Host" request header from the HTTP request. Its value may be just the + hostname, or the hostname followed by a colon and a TCP/IP port number + (hostname:port). If a value for the``host`` is provided but it does not + contain a port number, the default port number is used (i.e. if the ``uri`` + contained a port number, it will be discarded). + + The rules for calculating the *base string URI* are defined in + section 3.4.1.2`_ of RFC 5849. + + .. _`section 3.4.1.2`: https://tools.ietf.org/html/rfc5849#section-3.4.1.2 + + :param uri: URI + :param host: hostname with optional port number, separated by a colon + :return: base string URI + """ + + if not isinstance(uri, str): + raise ValueError('uri must be a string.') + + # FIXME: urlparse does not support unicode + output = urlparse.urlparse(uri) + scheme = output.scheme + hostname = output.hostname + port = output.port + path = output.path + params = output.params + + # The scheme, authority, and path of the request resource URI `RFC3986` + # are included by constructing an "http" or "https" URI representing + # the request resource (without the query or fragment) as follows: + # + # .. _`RFC3986`: https://tools.ietf.org/html/rfc3986 + + if not scheme: + raise ValueError('missing scheme') + + # Per `RFC 2616 section 5.1.2`_: + # + # Note that the absolute path cannot be empty; if none is present in + # the original URI, it MUST be given as "/" (the server root). + # + # .. _`RFC 2616 5.1.2`: https://tools.ietf.org/html/rfc2616#section-5.1.2 + if not path: + path = '/' + + # 1. The scheme and host MUST be in lowercase. + scheme = scheme.lower() + # Note: if ``host`` is used, it will be converted to lowercase below + if hostname is not None: + hostname = hostname.lower() + + # 2. The host and port values MUST match the content of the HTTP + # request "Host" header field. + if host is not None: + # NOTE: override value in uri with provided host + # Host argument is equal to netloc. It means it's missing scheme. + # Add it back, before parsing. + + host = host.lower() + host = f"{scheme}://{host}" + output = urlparse.urlparse(host) + hostname = output.hostname + port = output.port + + # 3. The port MUST be included if it is not the default port for the + # scheme, and MUST be excluded if it is the default. Specifically, + # the port MUST be excluded when making an HTTP request `RFC2616`_ + # to port 80 or when making an HTTPS request `RFC2818`_ to port 443. + # All other non-default port numbers MUST be included. + # + # .. _`RFC2616`: https://tools.ietf.org/html/rfc2616 + # .. _`RFC2818`: https://tools.ietf.org/html/rfc2818 + + if hostname is None: + raise ValueError('missing host') + + # NOTE: Try guessing if we're dealing with IP or hostname + with contextlib.suppress(ValueError): + hostname = ipaddress.ip_address(hostname) + + + if isinstance(hostname, ipaddress.IPv6Address): + hostname = f"[{hostname}]" + elif isinstance(hostname, ipaddress.IPv4Address): + hostname = f"{hostname}" + + if port is not None and not (0 < port <= 65535): + raise ValueError('port out of range') # 16-bit unsigned ints + if (scheme, port) in (('http', 80), ('https', 443)): + netloc = hostname # default port for scheme: exclude port num + elif port: + netloc = f"{hostname}:{port}" # use hostname:port + else: + netloc = hostname + + v = urlparse.urlunparse((scheme, netloc, path, params, '', '')) + + # RFC 5849 does not specify which characters are encoded in the + # "base string URI", nor how they are encoded - which is very bad, since + # the signatures won't match if there are any differences. Fortunately, + # most URIs only use characters that are clearly not encoded (e.g. digits + # and A-Z, a-z), so have avoided any differences between implementations. + # + # The example from its section 3.4.1.2 illustrates that spaces in + # the path are percent encoded. But it provides no guidance as to what other + # characters (if any) must be encoded (nor how); nor if characters in the + # other components are to be encoded or not. + # + # This implementation **assumes** that **only** the space is percent-encoded + # and it is done to the entire value (not just to spaces in the path). + # + # This code may need to be changed if it is discovered that other characters + # are expected to be encoded. + # + # Note: the "base string URI" returned by this function will be encoded + # again before being concatenated into the "signature base string". So any + # spaces in the URI will actually appear in the "signature base string" + # as "%2520" (the "%20" further encoded according to section 3.6). + + return v.replace(' ', '%20') + + +def collect_parameters(uri_query='', body=None, headers=None, + exclude_oauth_signature=True, with_realm=False): + """ + Gather the request parameters from all the parameter sources. + + This function is used to extract all the parameters, which are then passed + to ``normalize_parameters`` to produce one of the components that make up + the *signature base string*. + + Parameters starting with `oauth_` will be unescaped. + + Body parameters must be supplied as a dict, a list of 2-tuples, or a + form encoded query string. + + Headers must be supplied as a dict. + + The rules where the parameters must be sourced from are defined in + `section 3.4.1.3.1`_ of RFC 5849. + + .. _`Sec 3.4.1.3.1`: https://tools.ietf.org/html/rfc5849#section-3.4.1.3.1 + """ + if body is None: + body = [] + headers = headers or {} + params = [] + + # The parameters from the following sources are collected into a single + # list of name/value pairs: + + # * The query component of the HTTP request URI as defined by + # `RFC3986, Section 3.4`_. The query component is parsed into a list + # of name/value pairs by treating it as an + # "application/x-www-form-urlencoded" string, separating the names + # and values and decoding them as defined by W3C.REC-html40-19980424 + # `W3C-HTML-4.0`_, Section 17.13.4. + # + # .. _`RFC3986, Sec 3.4`: https://tools.ietf.org/html/rfc3986#section-3.4 + # .. _`W3C-HTML-4.0`: https://www.w3.org/TR/1998/REC-html40-19980424/ + if uri_query: + params.extend(urldecode(uri_query)) + + # * The OAuth HTTP "Authorization" header field (`Section 3.5.1`_) if + # present. The header's content is parsed into a list of name/value + # pairs excluding the "realm" parameter if present. The parameter + # values are decoded as defined by `Section 3.5.1`_. + # + # .. _`Section 3.5.1`: https://tools.ietf.org/html/rfc5849#section-3.5.1 + if headers: + headers_lower = {k.lower(): v for k, v in headers.items()} + authorization_header = headers_lower.get('authorization') + if authorization_header is not None: + params.extend([i for i in utils.parse_authorization_header( + authorization_header) if with_realm or i[0] != 'realm']) + + # * The HTTP request entity-body, but only if all of the following + # conditions are met: + # * The entity-body is single-part. + # + # * The entity-body follows the encoding requirements of the + # "application/x-www-form-urlencoded" content-type as defined by + # W3C.REC-html40-19980424 `W3C-HTML-4.0`_. + + # * The HTTP request entity-header includes the "Content-Type" + # header field set to "application/x-www-form-urlencoded". + # + # .. _`W3C-HTML-4.0`: https://www.w3.org/TR/1998/REC-html40-19980424/ + + # TODO: enforce header param inclusion conditions + bodyparams = extract_params(body) or [] + params.extend(bodyparams) + + # ensure all oauth params are unescaped + unescaped_params = [] + for k, v in params: + if k.startswith('oauth_'): + v = utils.unescape(v) + unescaped_params.append((k, v)) + + # The "oauth_signature" parameter MUST be excluded from the signature + # base string if present. + if exclude_oauth_signature: + unescaped_params = list(filter(lambda i: i[0] != 'oauth_signature', + unescaped_params)) + + return unescaped_params + + +def normalize_parameters(params) -> str: + """ + Calculate the normalized request parameters. + + The *normalized request parameters* is one of the components that make up + the *signature base string*. + + The rules for parameter normalization are defined in `section 3.4.1.3.2`_ of + RFC 5849. + + .. _`Sec 3.4.1.3.2`: https://tools.ietf.org/html/rfc5849#section-3.4.1.3.2 + """ + + # The parameters collected in `Section 3.4.1.3`_ are normalized into a + # single string as follows: + # + # .. _`Section 3.4.1.3`: https://tools.ietf.org/html/rfc5849#section-3.4.1.3 + + # 1. First, the name and value of each parameter are encoded + # (`Section 3.6`_). + # + # .. _`Section 3.6`: https://tools.ietf.org/html/rfc5849#section-3.6 + key_values = [(utils.escape(k), utils.escape(v)) for k, v in params] + + # 2. The parameters are sorted by name, using ascending byte value + # ordering. If two or more parameters share the same name, they + # are sorted by their value. + key_values.sort() + + # 3. The name of each parameter is concatenated to its corresponding + # value using an "=" character (ASCII code 61) as a separator, even + # if the value is empty. + parameter_parts = ['{}={}'.format(k, v) for k, v in key_values] + + # 4. The sorted name/value pairs are concatenated together into a + # single string by using an "&" character (ASCII code 38) as + # separator. + return '&'.join(parameter_parts) + + +# ==== Common functions for HMAC-based signature methods ========= + +def _sign_hmac(hash_algorithm_name: str, + sig_base_str: str, + client_secret: str, + resource_owner_secret: str): + """ + **HMAC-SHA256** + + The "HMAC-SHA256" signature method uses the HMAC-SHA256 signature + algorithm as defined in `RFC4634`_:: + + digest = HMAC-SHA256 (key, text) + + Per `section 3.4.2`_ of the spec. + + .. _`RFC4634`: https://tools.ietf.org/html/rfc4634 + .. _`section 3.4.2`: https://tools.ietf.org/html/rfc5849#section-3.4.2 + """ + + # The HMAC-SHA256 function variables are used in following way: + + # text is set to the value of the signature base string from + # `Section 3.4.1.1`_. + # + # .. _`Section 3.4.1.1`: https://tools.ietf.org/html/rfc5849#section-3.4.1.1 + text = sig_base_str + + # key is set to the concatenated values of: + # 1. The client shared-secret, after being encoded (`Section 3.6`_). + # + # .. _`Section 3.6`: https://tools.ietf.org/html/rfc5849#section-3.6 + key = utils.escape(client_secret or '') + + # 2. An "&" character (ASCII code 38), which MUST be included + # even when either secret is empty. + key += '&' + + # 3. The token shared-secret, after being encoded (`Section 3.6`_). + # + # .. _`Section 3.6`: https://tools.ietf.org/html/rfc5849#section-3.6 + key += utils.escape(resource_owner_secret or '') + + # Get the hashing algorithm to use + + m = { + 'SHA-1': hashlib.sha1, + 'SHA-256': hashlib.sha256, + 'SHA-512': hashlib.sha512, + } + hash_alg = m[hash_algorithm_name] + + # Calculate the signature + + # FIXME: HMAC does not support unicode! + key_utf8 = key.encode('utf-8') + text_utf8 = text.encode('utf-8') + signature = hmac.new(key_utf8, text_utf8, hash_alg) + + # digest is used to set the value of the "oauth_signature" protocol + # parameter, after the result octet string is base64-encoded + # per `RFC2045, Section 6.8`. + # + # .. _`RFC2045, Sec 6.8`: https://tools.ietf.org/html/rfc2045#section-6.8 + return binascii.b2a_base64(signature.digest())[:-1].decode('utf-8') + + +def _verify_hmac(hash_algorithm_name: str, + request, + client_secret=None, + resource_owner_secret=None): + """Verify a HMAC-SHA1 signature. + + Per `section 3.4`_ of the spec. + + .. _`section 3.4`: https://tools.ietf.org/html/rfc5849#section-3.4 + + To satisfy `RFC2616 section 5.2`_ item 1, the request argument's uri + attribute MUST be an absolute URI whose netloc part identifies the + origin server or gateway on which the resource resides. Any Host + item of the request argument's headers dict attribute will be + ignored. + + .. _`RFC2616 section 5.2`: https://tools.ietf.org/html/rfc2616#section-5.2 + + """ + norm_params = normalize_parameters(request.params) + bs_uri = base_string_uri(request.uri) + sig_base_str = signature_base_string(request.http_method, bs_uri, + norm_params) + signature = _sign_hmac(hash_algorithm_name, sig_base_str, + client_secret, resource_owner_secret) + match = safe_string_equals(signature, request.signature) + if not match: + log.debug('Verify HMAC failed: signature base string: %s', sig_base_str) + return match + + +# ==== HMAC-SHA1 ================================================= + +def sign_hmac_sha1_with_client(sig_base_str, client): + return _sign_hmac('SHA-1', sig_base_str, + client.client_secret, client.resource_owner_secret) + + +def verify_hmac_sha1(request, client_secret=None, resource_owner_secret=None): + return _verify_hmac('SHA-1', request, client_secret, resource_owner_secret) + + +def sign_hmac_sha1(base_string, client_secret, resource_owner_secret): + """ + Deprecated function for calculating a HMAC-SHA1 signature. + + This function has been replaced by invoking ``sign_hmac`` with "SHA-1" + as the hash algorithm name. + + This function was invoked by sign_hmac_sha1_with_client and + test_signatures.py, but does any application invoke it directly? If not, + it can be removed. + """ + warnings.warn('use sign_hmac_sha1_with_client instead of sign_hmac_sha1', + DeprecationWarning) + + # For some unknown reason, the original implementation assumed base_string + # could either be bytes or str. The signature base string calculating + # function always returned a str, so the new ``sign_rsa`` only expects that. + + base_string = base_string.decode('ascii') \ + if isinstance(base_string, bytes) else base_string + + return _sign_hmac('SHA-1', base_string, + client_secret, resource_owner_secret) + + +# ==== HMAC-SHA256 =============================================== + +def sign_hmac_sha256_with_client(sig_base_str, client): + return _sign_hmac('SHA-256', sig_base_str, + client.client_secret, client.resource_owner_secret) + + +def verify_hmac_sha256(request, client_secret=None, resource_owner_secret=None): + return _verify_hmac('SHA-256', request, + client_secret, resource_owner_secret) + + +def sign_hmac_sha256(base_string, client_secret, resource_owner_secret): + """ + Deprecated function for calculating a HMAC-SHA256 signature. + + This function has been replaced by invoking ``sign_hmac`` with "SHA-256" + as the hash algorithm name. + + This function was invoked by sign_hmac_sha256_with_client and + test_signatures.py, but does any application invoke it directly? If not, + it can be removed. + """ + warnings.warn( + 'use sign_hmac_sha256_with_client instead of sign_hmac_sha256', + DeprecationWarning) + + # For some unknown reason, the original implementation assumed base_string + # could either be bytes or str. The signature base string calculating + # function always returned a str, so the new ``sign_rsa`` only expects that. + + base_string = base_string.decode('ascii') \ + if isinstance(base_string, bytes) else base_string + + return _sign_hmac('SHA-256', base_string, + client_secret, resource_owner_secret) + + +# ==== HMAC-SHA512 =============================================== + +def sign_hmac_sha512_with_client(sig_base_str: str, + client): + return _sign_hmac('SHA-512', sig_base_str, + client.client_secret, client.resource_owner_secret) + + +def verify_hmac_sha512(request, + client_secret: str = None, + resource_owner_secret: str = None): + return _verify_hmac('SHA-512', request, + client_secret, resource_owner_secret) + + +# ==== Common functions for RSA-based signature methods ========== + +_jwt_rsa = {} # cache of RSA-hash implementations from PyJWT jwt.algorithms + + +def _get_jwt_rsa_algorithm(hash_algorithm_name: str): + """ + Obtains an RSAAlgorithm object that implements RSA with the hash algorithm. + + This method maintains the ``_jwt_rsa`` cache. + + Returns a jwt.algorithm.RSAAlgorithm. + """ + if hash_algorithm_name in _jwt_rsa: + # Found in cache: return it + return _jwt_rsa[hash_algorithm_name] + else: + # Not in cache: instantiate a new RSAAlgorithm + + # PyJWT has some nice pycrypto/cryptography abstractions + import jwt.algorithms as jwt_algorithms # noqa: PLC0415 + m = { + 'SHA-1': jwt_algorithms.hashes.SHA1, + 'SHA-256': jwt_algorithms.hashes.SHA256, + 'SHA-512': jwt_algorithms.hashes.SHA512, + } + v = jwt_algorithms.RSAAlgorithm(m[hash_algorithm_name]) + + _jwt_rsa[hash_algorithm_name] = v # populate cache + + return v + + +def _prepare_key_plus(alg, keystr): + """ + Prepare a PEM encoded key (public or private), by invoking the `prepare_key` + method on alg with the keystr. + + The keystr should be a string or bytes. If the keystr is bytes, it is + decoded as UTF-8 before being passed to prepare_key. Otherwise, it + is passed directly. + """ + if isinstance(keystr, bytes): + keystr = keystr.decode('utf-8') + return alg.prepare_key(keystr) + + +def _sign_rsa(hash_algorithm_name: str, + sig_base_str: str, + rsa_private_key: str): + """ + Calculate the signature for an RSA-based signature method. + + The ``alg`` is used to calculate the digest over the signature base string. + For the "RSA_SHA1" signature method, the alg must be SHA-1. While OAuth 1.0a + only defines the RSA-SHA1 signature method, this function can be used for + other non-standard signature methods that only differ from RSA-SHA1 by the + digest algorithm. + + Signing for the RSA-SHA1 signature method is defined in + `section 3.4.3`_ of RFC 5849. + + The RSASSA-PKCS1-v1_5 signature algorithm used defined by + `RFC3447, Section 8.2`_ (also known as PKCS#1), with the `alg` as the + hash function for EMSA-PKCS1-v1_5. To + use this method, the client MUST have established client credentials + with the server that included its RSA public key (in a manner that is + beyond the scope of this specification). + + .. _`section 3.4.3`: https://tools.ietf.org/html/rfc5849#section-3.4.3 + .. _`RFC3447, Section 8.2`: https://tools.ietf.org/html/rfc3447#section-8.2 + """ + + # Get the implementation of RSA-hash + + alg = _get_jwt_rsa_algorithm(hash_algorithm_name) + + # Check private key + + if not rsa_private_key: + raise ValueError('rsa_private_key required for RSA with ' + + alg.hash_alg.name + ' signature method') + + # Convert the "signature base string" into a sequence of bytes (M) + # + # The signature base string, by definition, only contain printable US-ASCII + # characters. So encoding it as 'ascii' will always work. It will raise a + # ``UnicodeError`` if it can't encode the value, which will never happen + # if the signature base string was created correctly. Therefore, using + # 'ascii' encoding provides an extra level of error checking. + + m = sig_base_str.encode('ascii') + + # Perform signing: S = RSASSA-PKCS1-V1_5-SIGN (K, M) + + key = _prepare_key_plus(alg, rsa_private_key) + s = alg.sign(m, key) + + # base64-encoded per RFC2045 section 6.8. + # + # 1. While b2a_base64 implements base64 defined by RFC 3548. As used here, + # it is the same as base64 defined by RFC 2045. + # 2. b2a_base64 includes a "\n" at the end of its result ([:-1] removes it) + # 3. b2a_base64 produces a binary string. Use decode to produce a str. + # It should only contain only printable US-ASCII characters. + + return binascii.b2a_base64(s)[:-1].decode('ascii') + + +def _verify_rsa(hash_algorithm_name: str, + request, + rsa_public_key: str): + """ + Verify a base64 encoded signature for a RSA-based signature method. + + The ``alg`` is used to calculate the digest over the signature base string. + For the "RSA_SHA1" signature method, the alg must be SHA-1. While OAuth 1.0a + only defines the RSA-SHA1 signature method, this function can be used for + other non-standard signature methods that only differ from RSA-SHA1 by the + digest algorithm. + + Verification for the RSA-SHA1 signature method is defined in + `section 3.4.3`_ of RFC 5849. + + .. _`section 3.4.3`: https://tools.ietf.org/html/rfc5849#section-3.4.3 + + To satisfy `RFC2616 section 5.2`_ item 1, the request argument's uri + attribute MUST be an absolute URI whose netloc part identifies the + origin server or gateway on which the resource resides. Any Host + item of the request argument's headers dict attribute will be + ignored. + + .. _`RFC2616 Sec 5.2`: https://tools.ietf.org/html/rfc2616#section-5.2 + """ + + try: + # Calculate the *signature base string* of the actual received request + + norm_params = normalize_parameters(request.params) + bs_uri = base_string_uri(request.uri) + sig_base_str = signature_base_string( + request.http_method, bs_uri, norm_params) + + # Obtain the signature that was received in the request + + sig = binascii.a2b_base64(request.signature.encode('ascii')) + + # Get the implementation of RSA-with-hash algorithm to use + + alg = _get_jwt_rsa_algorithm(hash_algorithm_name) + + # Verify the received signature was produced by the private key + # corresponding to the `rsa_public_key`, signing exact same + # *signature base string*. + # + # RSASSA-PKCS1-V1_5-VERIFY ((n, e), M, S) + + key = _prepare_key_plus(alg, rsa_public_key) + + # The signature base string only contain printable US-ASCII characters. + # The ``encode`` method with the default "strict" error handling will + # raise a ``UnicodeError`` if it can't encode the value. So using + # "ascii" will always work. + + verify_ok = alg.verify(sig_base_str.encode('ascii'), key, sig) + + if not verify_ok: + log.debug('Verify failed: RSA with ' + alg.hash_alg.name + + ': signature base string=%s' + sig_base_str) + return verify_ok + + except UnicodeError: + # A properly encoded signature will only contain printable US-ASCII + # characters. The ``encode`` method with the default "strict" error + # handling will raise a ``UnicodeError`` if it can't decode the value. + # So using "ascii" will work with all valid signatures. But an + # incorrectly or maliciously produced signature could contain other + # bytes. + # + # This implementation treats that situation as equivalent to the + # signature verification having failed. + # + # Note: simply changing the encode to use 'utf-8' will not remove this + # case, since an incorrect or malicious request can contain bytes which + # are invalid as UTF-8. + return False + + +# ==== RSA-SHA1 ================================================== + +def sign_rsa_sha1_with_client(sig_base_str, client): + # For some reason, this function originally accepts both str and bytes. + # This behaviour is preserved here. But won't be done for the newer + # sign_rsa_sha256_with_client and sign_rsa_sha512_with_client functions, + # which will only accept strings. The function to calculate a + # "signature base string" always produces a string, so it is not clear + # why support for bytes would ever be needed. + sig_base_str = sig_base_str.decode('ascii')\ + if isinstance(sig_base_str, bytes) else sig_base_str + + return _sign_rsa('SHA-1', sig_base_str, client.rsa_key) + + +def verify_rsa_sha1(request, rsa_public_key: str): + return _verify_rsa('SHA-1', request, rsa_public_key) + + +def sign_rsa_sha1(base_string, rsa_private_key): + """ + Deprecated function for calculating a RSA-SHA1 signature. + + This function has been replaced by invoking ``sign_rsa`` with "SHA-1" + as the hash algorithm name. + + This function was invoked by sign_rsa_sha1_with_client and + test_signatures.py, but does any application invoke it directly? If not, + it can be removed. + """ + warnings.warn('use _sign_rsa("SHA-1", ...) instead of sign_rsa_sha1', + DeprecationWarning) + + if isinstance(base_string, bytes): + base_string = base_string.decode('ascii') + + return _sign_rsa('SHA-1', base_string, rsa_private_key) + + +# ==== RSA-SHA256 ================================================ + +def sign_rsa_sha256_with_client(sig_base_str: str, client): + return _sign_rsa('SHA-256', sig_base_str, client.rsa_key) + + +def verify_rsa_sha256(request, rsa_public_key: str): + return _verify_rsa('SHA-256', request, rsa_public_key) + + +# ==== RSA-SHA512 ================================================ + +def sign_rsa_sha512_with_client(sig_base_str: str, client): + return _sign_rsa('SHA-512', sig_base_str, client.rsa_key) + + +def verify_rsa_sha512(request, rsa_public_key: str): + return _verify_rsa('SHA-512', request, rsa_public_key) + + +# ==== PLAINTEXT ================================================= + +def sign_plaintext_with_client(_signature_base_string, client): + # _signature_base_string is not used because the signature with PLAINTEXT + # is just the secret: it isn't a real signature. + return sign_plaintext(client.client_secret, client.resource_owner_secret) + + +def sign_plaintext(client_secret, resource_owner_secret): + """Sign a request using plaintext. + + Per `section 3.4.4`_ of the spec. + + The "PLAINTEXT" method does not employ a signature algorithm. It + MUST be used with a transport-layer mechanism such as TLS or SSL (or + sent over a secure channel with equivalent protections). It does not + utilize the signature base string or the "oauth_timestamp" and + "oauth_nonce" parameters. + + .. _`section 3.4.4`: https://tools.ietf.org/html/rfc5849#section-3.4.4 + + """ + + # The "oauth_signature" protocol parameter is set to the concatenated + # value of: + + # 1. The client shared-secret, after being encoded (`Section 3.6`_). + # + # .. _`Section 3.6`: https://tools.ietf.org/html/rfc5849#section-3.6 + signature = utils.escape(client_secret or '') + + # 2. An "&" character (ASCII code 38), which MUST be included even + # when either secret is empty. + signature += '&' + + # 3. The token shared-secret, after being encoded (`Section 3.6`_). + # + # .. _`Section 3.6`: https://tools.ietf.org/html/rfc5849#section-3.6 + signature += utils.escape(resource_owner_secret or '') + + return signature + + +def verify_plaintext(request, client_secret=None, resource_owner_secret=None): + """Verify a PLAINTEXT signature. + + Per `section 3.4`_ of the spec. + + .. _`section 3.4`: https://tools.ietf.org/html/rfc5849#section-3.4 + """ + signature = sign_plaintext(client_secret, resource_owner_secret) + match = safe_string_equals(signature, request.signature) + if not match: + log.debug('Verify PLAINTEXT failed') + return match diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth1/rfc5849/utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth1/rfc5849/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..0915105bcbd0a0d392c68ff84ee0d1cdf80119ca --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth1/rfc5849/utils.py @@ -0,0 +1,84 @@ +""" +oauthlib.utils +~~~~~~~~~~~~~~ + +This module contains utility methods used by various parts of the OAuth +spec. +""" +import urllib.request as urllib2 + +from oauthlib.common import quote, unquote + +UNICODE_ASCII_CHARACTER_SET = ('abcdefghijklmnopqrstuvwxyz' + 'ABCDEFGHIJKLMNOPQRSTUVWXYZ' + '0123456789') + + +def filter_params(target): + """Decorator which filters params to remove non-oauth_* parameters + + Assumes the decorated method takes a params dict or list of tuples as its + first argument. + """ + def wrapper(params, *args, **kwargs): + params = filter_oauth_params(params) + return target(params, *args, **kwargs) + + wrapper.__doc__ = target.__doc__ + return wrapper + + +def filter_oauth_params(params): + """Removes all non oauth parameters from a dict or a list of params.""" + def is_oauth(kv): + return kv[0].startswith('oauth_') + if isinstance(params, dict): + return list(filter(is_oauth, list(params.items()))) + else: + return list(filter(is_oauth, params)) + + +def escape(u): + """Escape a unicode string in an OAuth-compatible fashion. + + Per `section 3.6`_ of the spec. + + .. _`section 3.6`: https://tools.ietf.org/html/rfc5849#section-3.6 + + """ + if not isinstance(u, str): + raise ValueError('Only unicode objects are escapable. ' + + 'Got {!r} of type {}.'.format(u, type(u))) + # Letters, digits, and the characters '_.-' are already treated as safe + # by urllib.quote(). We need to add '~' to fully support rfc5849. + return quote(u, safe=b'~') + + +def unescape(u): + if not isinstance(u, str): + raise ValueError('Only unicode objects are unescapable.') + return unquote(u) + + +def parse_keqv_list(l): # noqa: E741 + """A unicode-safe version of urllib2.parse_keqv_list""" + # With Python 2.6, parse_http_list handles unicode fine + return urllib2.parse_keqv_list(l) + + +def parse_http_list(u): + """A unicode-safe version of urllib2.parse_http_list""" + # With Python 2.6, parse_http_list handles unicode fine + return urllib2.parse_http_list(u) + + +def parse_authorization_header(authorization_header): + """Parse an OAuth authorization header into a list of 2-tuples""" + auth_scheme = 'OAuth '.lower() + if authorization_header[:len(auth_scheme)].lower().startswith(auth_scheme): + items = parse_http_list(authorization_header[len(auth_scheme):]) + try: + return list(parse_keqv_list(items).items()) + except (IndexError, ValueError): + pass + raise ValueError('Malformed authorization header') diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..3bb51021741f2a0acc423c4a61b3061814ce7b32 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/__init__.py @@ -0,0 +1,70 @@ +""" +oauthlib.oauth2 +~~~~~~~~~~~~~~ + +This module is a wrapper for the most recent implementation of OAuth 2.0 Client +and Server classes. +""" + +from .rfc6749.clients import ( + BackendApplicationClient, + Client, + LegacyApplicationClient, + MobileApplicationClient, + ServiceApplicationClient, + WebApplicationClient, +) +from .rfc6749.endpoints import ( + AuthorizationEndpoint, + BackendApplicationServer, + IntrospectEndpoint, + LegacyApplicationServer, + MetadataEndpoint, + MobileApplicationServer, + ResourceEndpoint, + RevocationEndpoint, + Server, + TokenEndpoint, + WebApplicationServer, +) +from .rfc6749.errors import ( + AccessDeniedError, + FatalClientError, + InsecureTransportError, + InvalidClientError, + InvalidClientIdError, + InvalidGrantError, + InvalidRedirectURIError, + InvalidRequestError, + InvalidRequestFatalError, + InvalidScopeError, + MismatchingRedirectURIError, + MismatchingStateError, + MissingClientIdError, + MissingCodeError, + MissingRedirectURIError, + MissingResponseTypeError, + MissingTokenError, + MissingTokenTypeError, + OAuth2Error, + ServerError, + TemporarilyUnavailableError, + TokenExpiredError, + UnauthorizedClientError, + UnsupportedGrantTypeError, + UnsupportedResponseTypeError, + UnsupportedTokenTypeError, +) +from .rfc6749.grant_types import ( + AuthorizationCodeGrant, + ClientCredentialsGrant, + ImplicitGrant, + RefreshTokenGrant, + ResourceOwnerPasswordCredentialsGrant, +) +from .rfc6749.request_validator import RequestValidator +from .rfc6749.tokens import BearerToken, OAuth2Token +from .rfc6749.utils import is_secure_transport +from .rfc8628.clients import DeviceClient +from oauthlib.oauth2.rfc8628.endpoints import DeviceAuthorizationEndpoint, DeviceApplicationServer +from oauthlib.oauth2.rfc8628.grant_types import DeviceCodeGrant diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..4b75a8a196c6d9e3ff0815c97a7feccf95d9fcd9 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/__init__.py @@ -0,0 +1,16 @@ +""" +oauthlib.oauth2.rfc6749 +~~~~~~~~~~~~~~~~~~~~~~~ + +This module is an implementation of various logic needed +for consuming and providing OAuth 2.0 RFC6749. +""" +import functools +import logging + +from .endpoints.base import BaseEndpoint, catch_errors_and_unavailability +from .errors import ( + FatalClientError, OAuth2Error, ServerError, TemporarilyUnavailableError, +) + +log = logging.getLogger(__name__) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/clients/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/clients/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..8fc6c955a24dfecd3181f2a2e876fb587299266e --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/clients/__init__.py @@ -0,0 +1,14 @@ +# -*- coding: utf-8 -*- +""" +oauthlib.oauth2.rfc6749 +~~~~~~~~~~~~~~~~~~~~~~~ + +This module is an implementation of various logic needed +for consuming OAuth 2.0 RFC6749. +""" +from .backend_application import BackendApplicationClient +from .base import AUTH_HEADER, BODY, URI_QUERY, Client +from .legacy_application import LegacyApplicationClient +from .mobile_application import MobileApplicationClient +from .service_application import ServiceApplicationClient +from .web_application import WebApplicationClient diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/clients/backend_application.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/clients/backend_application.py new file mode 100644 index 0000000000000000000000000000000000000000..e11e8fae380fc88784c0d429488b67aac3447dd3 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/clients/backend_application.py @@ -0,0 +1,74 @@ +# -*- coding: utf-8 -*- +""" +oauthlib.oauth2.rfc6749 +~~~~~~~~~~~~~~~~~~~~~~~ + +This module is an implementation of various logic needed +for consuming and providing OAuth 2.0 RFC6749. +""" +from ..parameters import prepare_token_request +from .base import Client + + +class BackendApplicationClient(Client): + + """A public client utilizing the client credentials grant workflow. + + The client can request an access token using only its client + credentials (or other supported means of authentication) when the + client is requesting access to the protected resources under its + control, or those of another resource owner which has been previously + arranged with the authorization server (the method of which is beyond + the scope of this specification). + + The client credentials grant type MUST only be used by confidential + clients. + + Since the client authentication is used as the authorization grant, + no additional authorization request is needed. + """ + + grant_type = 'client_credentials' + + def prepare_request_body(self, body='', scope=None, + include_client_id=False, **kwargs): + """Add the client credentials to the request body. + + The client makes a request to the token endpoint by adding the + following parameters using the "application/x-www-form-urlencoded" + format per `Appendix B`_ in the HTTP request entity-body: + + :param body: Existing request body (URL encoded string) to embed parameters + into. This may contain extra parameters. Default ''. + :param scope: The scope of the access request as described by + `Section 3.3`_. + + :param include_client_id: `True` to send the `client_id` in the + body of the upstream request. This is required + if the client is not authenticating with the + authorization server as described in + `Section 3.2.1`_. False otherwise (default). + :type include_client_id: Boolean + + :param kwargs: Extra credentials to include in the token request. + + The client MUST authenticate with the authorization server as + described in `Section 3.2.1`_. + + The prepared body will include all provided credentials as well as + the ``grant_type`` parameter set to ``client_credentials``:: + + >>> from oauthlib.oauth2 import BackendApplicationClient + >>> client = BackendApplicationClient('your_id') + >>> client.prepare_request_body(scope=['hello', 'world']) + 'grant_type=client_credentials&scope=hello+world' + + .. _`Appendix B`: https://tools.ietf.org/html/rfc6749#appendix-B + .. _`Section 3.3`: https://tools.ietf.org/html/rfc6749#section-3.3 + .. _`Section 3.2.1`: https://tools.ietf.org/html/rfc6749#section-3.2.1 + """ + kwargs['client_id'] = self.client_id + kwargs['include_client_id'] = include_client_id + scope = self.scope if scope is None else scope + return prepare_token_request(self.grant_type, body=body, + scope=scope, **kwargs) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/clients/base.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/clients/base.py new file mode 100644 index 0000000000000000000000000000000000000000..17f833d236cb0f1ca7a4dd85f661b2e6be6b087e --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/clients/base.py @@ -0,0 +1,597 @@ +# -*- coding: utf-8 -*- +""" +oauthlib.oauth2.rfc6749 +~~~~~~~~~~~~~~~~~~~~~~~ + +This module is an implementation of various logic needed +for consuming OAuth 2.0 RFC6749. +""" +import base64 +import hashlib +import time +import warnings + +from oauthlib.common import UNICODE_ASCII_CHARACTER_SET, generate_token +from oauthlib.oauth2.rfc6749 import tokens +from oauthlib.oauth2.rfc6749.errors import ( + InsecureTransportError, TokenExpiredError, +) +from oauthlib.oauth2.rfc6749.parameters import ( + parse_expires, + parse_token_response, prepare_token_request, + prepare_token_revocation_request, +) +from oauthlib.oauth2.rfc6749.utils import is_secure_transport + +AUTH_HEADER = 'auth_header' +URI_QUERY = 'query' +BODY = 'body' + +FORM_ENC_HEADERS = { + 'Content-Type': 'application/x-www-form-urlencoded' +} + + +class Client: + """Base OAuth2 client responsible for access token management. + + This class also acts as a generic interface providing methods common to all + client types such as ``prepare_authorization_request`` and + ``prepare_token_revocation_request``. The ``prepare_x_request`` methods are + the recommended way of interacting with clients (as opposed to the abstract + prepare uri/body/etc methods). They are recommended over the older set + because they are easier to use (more consistent) and add a few additional + security checks, such as HTTPS and state checking. + + Some of these methods require further implementation only provided by the + specific purpose clients such as + :py:class:`oauthlib.oauth2.MobileApplicationClient` and thus you should always + seek to use the client class matching the OAuth workflow you need. For + Python, this is usually :py:class:`oauthlib.oauth2.WebApplicationClient`. + + """ + refresh_token_key = 'refresh_token' + + def __init__(self, client_id, + default_token_placement=AUTH_HEADER, + token_type='Bearer', + access_token=None, + refresh_token=None, + mac_key=None, + mac_algorithm=None, + token=None, + scope=None, + state=None, + redirect_url=None, + state_generator=generate_token, + code_verifier=None, + code_challenge=None, + code_challenge_method=None, + **kwargs): + """Initialize a client with commonly used attributes. + + :param client_id: Client identifier given by the OAuth provider upon + registration. + + :param default_token_placement: Tokens can be supplied in the Authorization + header (default), the URL query component (``query``) or the request + body (``body``). + + :param token_type: OAuth 2 token type. Defaults to Bearer. Change this + if you specify the ``access_token`` parameter and know it is of a + different token type, such as a MAC, JWT or SAML token. Can + also be supplied as ``token_type`` inside the ``token`` dict parameter. + + :param access_token: An access token (string) used to authenticate + requests to protected resources. Can also be supplied inside the + ``token`` dict parameter. + + :param refresh_token: A refresh token (string) used to refresh expired + tokens. Can also be supplied inside the ``token`` dict parameter. + + :param mac_key: Encryption key used with MAC tokens. + + :param mac_algorithm: Hashing algorithm for MAC tokens. + + :param token: A dict of token attributes such as ``access_token``, + ``token_type`` and ``expires_at``. + + :param scope: A list of default scopes to request authorization for. + + :param state: A CSRF protection string used during authorization. + + :param redirect_url: The redirection endpoint on the client side to which + the user returns after authorization. + + :param state_generator: A no argument state generation callable. Defaults + to :py:meth:`oauthlib.common.generate_token`. + + :param code_verifier: PKCE parameter. A cryptographically random string that is used to correlate the + authorization request to the token request. + + :param code_challenge: PKCE parameter. A challenge derived from the code verifier that is sent in the + authorization request, to be verified against later. + + :param code_challenge_method: PKCE parameter. A method that was used to derive code challenge. + Defaults to "plain" if not present in the request. + """ + + self.client_id = client_id + self.default_token_placement = default_token_placement + self.token_type = token_type + self.access_token = access_token + self.refresh_token = refresh_token + self.mac_key = mac_key + self.mac_algorithm = mac_algorithm + self.token = token or {} + self.scope = scope + self.state_generator = state_generator + self.state = state + self.redirect_url = redirect_url + self.code_verifier = code_verifier + self.code_challenge = code_challenge + self.code_challenge_method = code_challenge_method + self.code = None + self.expires_in = None + self._expires_at = None + self.populate_token_attributes(self.token) + + @property + def token_types(self): + """Supported token types and their respective methods + + Additional tokens can be supported by extending this dictionary. + + The Bearer token spec is stable and safe to use. + + The MAC token spec is not yet stable and support for MAC tokens + is experimental and currently matching version 00 of the spec. + """ + return { + 'Bearer': self._add_bearer_token, + 'MAC': self._add_mac_token + } + + def prepare_request_uri(self, *args, **kwargs): + """Abstract method used to create request URIs.""" + raise NotImplementedError("Must be implemented by inheriting classes.") + + def prepare_request_body(self, *args, **kwargs): + """Abstract method used to create request bodies.""" + raise NotImplementedError("Must be implemented by inheriting classes.") + + def parse_request_uri_response(self, *args, **kwargs): + """Abstract method used to parse redirection responses.""" + raise NotImplementedError("Must be implemented by inheriting classes.") + + def add_token(self, uri, http_method='GET', body=None, headers=None, + token_placement=None, **kwargs): + """Add token to the request uri, body or authorization header. + + The access token type provides the client with the information + required to successfully utilize the access token to make a protected + resource request (along with type-specific attributes). The client + MUST NOT use an access token if it does not understand the token + type. + + For example, the "bearer" token type defined in + [`I-D.ietf-oauth-v2-bearer`_] is utilized by simply including the access + token string in the request: + + .. code-block:: http + + GET /resource/1 HTTP/1.1 + Host: example.com + Authorization: Bearer mF_9.B5f-4.1JqM + + while the "mac" token type defined in [`I-D.ietf-oauth-v2-http-mac`_] is + utilized by issuing a MAC key together with the access token which is + used to sign certain components of the HTTP requests: + + .. code-block:: http + + GET /resource/1 HTTP/1.1 + Host: example.com + Authorization: MAC id="h480djs93hd8", + nonce="274312:dj83hs9s", + mac="kDZvddkndxvhGRXZhvuDjEWhGeE=" + + .. _`I-D.ietf-oauth-v2-bearer`: https://tools.ietf.org/html/rfc6749#section-12.2 + .. _`I-D.ietf-oauth-v2-http-mac`: https://tools.ietf.org/html/rfc6749#section-12.2 + """ + if not is_secure_transport(uri): + raise InsecureTransportError() + + token_placement = token_placement or self.default_token_placement + + case_insensitive_token_types = { + k.lower(): v for k, v in self.token_types.items()} + if self.token_type.lower() not in case_insensitive_token_types: + raise ValueError("Unsupported token type: %s" % self.token_type) + + if not (self.access_token or self.token.get('access_token')): + raise ValueError("Missing access token.") + + if self._expires_at and self._expires_at < time.time(): + raise TokenExpiredError() + + return case_insensitive_token_types[self.token_type.lower()](uri, http_method, body, + headers, token_placement, **kwargs) + + def prepare_authorization_request(self, authorization_url, state=None, + redirect_url=None, scope=None, **kwargs): + """Prepare the authorization request. + + This is the first step in many OAuth flows in which the user is + redirected to a certain authorization URL. This method adds + required parameters to the authorization URL. + + :param authorization_url: Provider authorization endpoint URL. + :param state: CSRF protection string. Will be automatically created if + not provided. The generated state is available via the ``state`` + attribute. Clients should verify that the state is unchanged and + present in the authorization response. This verification is done + automatically if using the ``authorization_response`` parameter + with ``prepare_token_request``. + :param redirect_url: Redirect URL to which the user will be returned + after authorization. Must be provided unless previously setup with + the provider. If provided then it must also be provided in the + token request. + :param scope: List of scopes to request. Must be equal to + or a subset of the scopes granted when obtaining the refresh + token. If none is provided, the ones provided in the constructor are + used. + :param kwargs: Additional parameters to included in the request. + :returns: The prepared request tuple with (url, headers, body). + """ + if not is_secure_transport(authorization_url): + raise InsecureTransportError() + + self.state = state or self.state_generator() + self.redirect_url = redirect_url or self.redirect_url + # do not assign scope to self automatically anymore + scope = self.scope if scope is None else scope + auth_url = self.prepare_request_uri( + authorization_url, redirect_uri=self.redirect_url, + scope=scope, state=self.state, **kwargs) + return auth_url, FORM_ENC_HEADERS, '' + + def prepare_token_request(self, token_url, authorization_response=None, + redirect_url=None, state=None, body='', **kwargs): + """Prepare a token creation request. + + Note that these requests usually require client authentication, either + by including client_id or a set of provider specific authentication + credentials. + + :param token_url: Provider token creation endpoint URL. + :param authorization_response: The full redirection URL string, i.e. + the location to which the user was redirected after successful + authorization. Used to mine credentials needed to obtain a token + in this step, such as authorization code. + :param redirect_url: The redirect_url supplied with the authorization + request (if there was one). + :param state: + :param body: Existing request body (URL encoded string) to embed parameters + into. This may contain extra parameters. Default ''. + :param kwargs: Additional parameters to included in the request. + :returns: The prepared request tuple with (url, headers, body). + """ + if not is_secure_transport(token_url): + raise InsecureTransportError() + + state = state or self.state + if authorization_response: + self.parse_request_uri_response( + authorization_response, state=state) + self.redirect_url = redirect_url or self.redirect_url + body = self.prepare_request_body(body=body, + redirect_uri=self.redirect_url, **kwargs) + + return token_url, FORM_ENC_HEADERS, body + + def prepare_refresh_token_request(self, token_url, refresh_token=None, + body='', scope=None, **kwargs): + """Prepare an access token refresh request. + + Expired access tokens can be replaced by new access tokens without + going through the OAuth dance if the client obtained a refresh token. + This refresh token and authentication credentials can be used to + obtain a new access token, and possibly a new refresh token. + + :param token_url: Provider token refresh endpoint URL. + :param refresh_token: Refresh token string. + :param body: Existing request body (URL encoded string) to embed parameters + into. This may contain extra parameters. Default ''. + :param scope: List of scopes to request. Must be equal to + or a subset of the scopes granted when obtaining the refresh + token. If none is provided, the ones provided in the constructor are + used. + :param kwargs: Additional parameters to included in the request. + :returns: The prepared request tuple with (url, headers, body). + """ + if not is_secure_transport(token_url): + raise InsecureTransportError() + + # do not assign scope to self automatically anymore + scope = self.scope if scope is None else scope + body = self.prepare_refresh_body(body=body, + refresh_token=refresh_token, scope=scope, **kwargs) + return token_url, FORM_ENC_HEADERS, body + + def prepare_token_revocation_request(self, revocation_url, token, + token_type_hint="access_token", body='', callback=None, **kwargs): + """Prepare a token revocation request. + + :param revocation_url: Provider token revocation endpoint URL. + :param token: The access or refresh token to be revoked (string). + :param token_type_hint: ``"access_token"`` (default) or + ``"refresh_token"``. This is optional and if you wish to not pass it you + must provide ``token_type_hint=None``. + :param body: + :param callback: A jsonp callback such as ``package.callback`` to be invoked + upon receiving the response. Not that it should not include a () suffix. + :param kwargs: Additional parameters to included in the request. + :returns: The prepared request tuple with (url, headers, body). + + Note that JSONP request may use GET requests as the parameters will + be added to the request URL query as opposed to the request body. + + An example of a revocation request + + .. code-block:: http + + POST /revoke HTTP/1.1 + Host: server.example.com + Content-Type: application/x-www-form-urlencoded + Authorization: Basic czZCaGRSa3F0MzpnWDFmQmF0M2JW + + token=45ghiukldjahdnhzdauz&token_type_hint=refresh_token + + An example of a jsonp revocation request + + .. code-block:: http + + GET /revoke?token=agabcdefddddafdd&callback=package.myCallback HTTP/1.1 + Host: server.example.com + Content-Type: application/x-www-form-urlencoded + Authorization: Basic czZCaGRSa3F0MzpnWDFmQmF0M2JW + + and an error response + + .. code-block:: javascript + + package.myCallback({"error":"unsupported_token_type"}); + + Note that these requests usually require client credentials, client_id in + the case for public clients and provider specific authentication + credentials for confidential clients. + """ + if not is_secure_transport(revocation_url): + raise InsecureTransportError() + + return prepare_token_revocation_request(revocation_url, token, + token_type_hint=token_type_hint, body=body, callback=callback, + **kwargs) + + def parse_request_body_response(self, body, scope=None, **kwargs): + """Parse the JSON response body. + + If the access token request is valid and authorized, the + authorization server issues an access token as described in + `Section 5.1`_. A refresh token SHOULD NOT be included. If the request + failed client authentication or is invalid, the authorization server + returns an error response as described in `Section 5.2`_. + + :param body: The response body from the token request. + :param scope: Scopes originally requested. If none is provided, the ones + provided in the constructor are used. + :return: Dictionary of token parameters. + :raises: Warning if scope has changed. :py:class:`oauthlib.oauth2.errors.OAuth2Error` + if response is invalid. + + These response are json encoded and could easily be parsed without + the assistance of OAuthLib. However, there are a few subtle issues + to be aware of regarding the response which are helpfully addressed + through the raising of various errors. + + A successful response should always contain + + **access_token** + The access token issued by the authorization server. Often + a random string. + + **token_type** + The type of the token issued as described in `Section 7.1`_. + Commonly ``Bearer``. + + While it is not mandated it is recommended that the provider include + + **expires_in** + The lifetime in seconds of the access token. For + example, the value "3600" denotes that the access token will + expire in one hour from the time the response was generated. + If omitted, the authorization server SHOULD provide the + expiration time via other means or document the default value. + + **scope** + Providers may supply this in all responses but are required to only + if it has changed since the authorization request. + + .. _`Section 5.1`: https://tools.ietf.org/html/rfc6749#section-5.1 + .. _`Section 5.2`: https://tools.ietf.org/html/rfc6749#section-5.2 + .. _`Section 7.1`: https://tools.ietf.org/html/rfc6749#section-7.1 + """ + scope = self.scope if scope is None else scope + self.token = parse_token_response(body, scope=scope) + self.populate_token_attributes(self.token) + return self.token + + def prepare_refresh_body(self, body='', refresh_token=None, scope=None, **kwargs): + """Prepare an access token request, using a refresh token. + + If the authorization server issued a refresh token to the client, the + client makes a refresh request to the token endpoint by adding the + following parameters using the `application/x-www-form-urlencoded` + format in the HTTP request entity-body: + + :param refresh_token: REQUIRED. The refresh token issued to the client. + :param scope: OPTIONAL. The scope of the access request as described by + Section 3.3. The requested scope MUST NOT include any scope + not originally granted by the resource owner, and if omitted is + treated as equal to the scope originally granted by the + resource owner. Note that if none is provided, the ones provided + in the constructor are used if any. + """ + refresh_token = refresh_token or self.refresh_token + scope = self.scope if scope is None else scope + return prepare_token_request(self.refresh_token_key, body=body, scope=scope, + refresh_token=refresh_token, **kwargs) + + def _add_bearer_token(self, uri, http_method='GET', body=None, + headers=None, token_placement=None): + """Add a bearer token to the request uri, body or authorization header.""" + if token_placement == AUTH_HEADER: + headers = tokens.prepare_bearer_headers(self.access_token, headers) + + elif token_placement == URI_QUERY: + uri = tokens.prepare_bearer_uri(self.access_token, uri) + + elif token_placement == BODY: + body = tokens.prepare_bearer_body(self.access_token, body) + + else: + raise ValueError("Invalid token placement.") + return uri, headers, body + + def create_code_verifier(self, length): + """Create PKCE **code_verifier** used in computing **code_challenge**. + See `RFC7636 Section 4.1`_ + + :param length: REQUIRED. The length of the code_verifier. + + The client first creates a code verifier, "code_verifier", for each + OAuth 2.0 [RFC6749] Authorization Request, in the following manner: + + .. code-block:: text + + code_verifier = high-entropy cryptographic random STRING using the + unreserved characters [A-Z] / [a-z] / [0-9] / "-" / "." / "_" / "~" + from Section 2.3 of [RFC3986], with a minimum length of 43 characters + and a maximum length of 128 characters. + + .. _`RFC7636 Section 4.1`: https://tools.ietf.org/html/rfc7636#section-4.1 + """ + code_verifier = None + + if not length >= 43: + raise ValueError("Length must be greater than or equal to 43") + + if not length <= 128: + raise ValueError("Length must be less than or equal to 128") + + code_verifier = generate_token(length, UNICODE_ASCII_CHARACTER_SET + "-._~") + + self.code_verifier = code_verifier + + return code_verifier + + def create_code_challenge(self, code_verifier, code_challenge_method=None): + """Create PKCE **code_challenge** derived from the **code_verifier**. + See `RFC7636 Section 4.2`_ + + :param code_verifier: REQUIRED. The **code_verifier** generated from `create_code_verifier()`. + :param code_challenge_method: OPTIONAL. The method used to derive the **code_challenge**. Acceptable values include `S256`. DEFAULT is `plain`. + + The client then creates a code challenge derived from the code + verifier by using one of the following transformations on the code + verifier:: + + plain + code_challenge = code_verifier + S256 + code_challenge = BASE64URL-ENCODE(SHA256(ASCII(code_verifier))) + + If the client is capable of using `S256`, it MUST use `S256`, as + `S256` is Mandatory To Implement (MTI) on the server. Clients are + permitted to use `plain` only if they cannot support `S256` for some + technical reason and know via out-of-band configuration that the + server supports `plain`. + + The plain transformation is for compatibility with existing + deployments and for constrained environments that can't use the S256 transformation. + + .. _`RFC7636 Section 4.2`: https://tools.ietf.org/html/rfc7636#section-4.2 + """ + code_challenge = None + + if code_verifier is None: + raise ValueError("Invalid code_verifier") + + if code_challenge_method is None: + code_challenge_method = "plain" + self.code_challenge_method = code_challenge_method + code_challenge = code_verifier + self.code_challenge = code_challenge + + if code_challenge_method == "S256": + h = hashlib.sha256() + h.update(code_verifier.encode(encoding='ascii')) + sha256_val = h.digest() + code_challenge = bytes.decode(base64.urlsafe_b64encode(sha256_val)) + # replace '+' with '-', '/' with '_', and remove trailing '=' + code_challenge = code_challenge.replace("+", "-").replace("/", "_").replace("=", "") + self.code_challenge = code_challenge + + return code_challenge + + def _add_mac_token(self, uri, http_method='GET', body=None, + headers=None, token_placement=AUTH_HEADER, ext=None, **kwargs): + """Add a MAC token to the request authorization header. + + Warning: MAC token support is experimental as the spec is not yet stable. + """ + if token_placement != AUTH_HEADER: + raise ValueError("Invalid token placement.") + + headers = tokens.prepare_mac_header(self.access_token, uri, + self.mac_key, http_method, headers=headers, body=body, ext=ext, + hash_algorithm=self.mac_algorithm, **kwargs) + return uri, headers, body + + def _populate_attributes(self, response): + warnings.warn("Please switch to the public method " + "populate_token_attributes.", DeprecationWarning) + return self.populate_token_attributes(response) + + def populate_code_attributes(self, response): + """Add attributes from an auth code response to self.""" + + if 'code' in response: + self.code = response.get('code') + + def populate_token_attributes(self, response): + """Add attributes from a token exchange response to self.""" + + if 'access_token' in response: + self.access_token = response.get('access_token') + + if 'refresh_token' in response: + self.refresh_token = response.get('refresh_token') + + if 'token_type' in response: + self.token_type = response.get('token_type') + + vin, vat, v_at = parse_expires(response) + if vin: + self.expires_in = vin + if vat: + self.expires_at = vat + if v_at: + self._expires_at = v_at + + if 'mac_key' in response: + self.mac_key = response.get('mac_key') + + if 'mac_algorithm' in response: + self.mac_algorithm = response.get('mac_algorithm') diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/clients/legacy_application.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/clients/legacy_application.py new file mode 100644 index 0000000000000000000000000000000000000000..9920981d2c601761492ac1511c3ecd7a6a6a2e51 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/clients/legacy_application.py @@ -0,0 +1,84 @@ +# -*- coding: utf-8 -*- +""" +oauthlib.oauth2.rfc6749 +~~~~~~~~~~~~~~~~~~~~~~~ + +This module is an implementation of various logic needed +for consuming and providing OAuth 2.0 RFC6749. +""" +from ..parameters import prepare_token_request +from .base import Client + + +class LegacyApplicationClient(Client): + + """A public client using the resource owner password and username directly. + + The resource owner password credentials grant type is suitable in + cases where the resource owner has a trust relationship with the + client, such as the device operating system or a highly privileged + application. The authorization server should take special care when + enabling this grant type, and only allow it when other flows are not + viable. + + The grant type is suitable for clients capable of obtaining the + resource owner's credentials (username and password, typically using + an interactive form). It is also used to migrate existing clients + using direct authentication schemes such as HTTP Basic or Digest + authentication to OAuth by converting the stored credentials to an + access token. + + The method through which the client obtains the resource owner + credentials is beyond the scope of this specification. The client + MUST discard the credentials once an access token has been obtained. + """ + + grant_type = 'password' + + def __init__(self, client_id, **kwargs): + super().__init__(client_id, **kwargs) + + def prepare_request_body(self, username, password, body='', scope=None, + include_client_id=False, **kwargs): + """Add the resource owner password and username to the request body. + + The client makes a request to the token endpoint by adding the + following parameters using the "application/x-www-form-urlencoded" + format per `Appendix B`_ in the HTTP request entity-body: + + :param username: The resource owner username. + :param password: The resource owner password. + :param body: Existing request body (URL encoded string) to embed parameters + into. This may contain extra parameters. Default ''. + :param scope: The scope of the access request as described by + `Section 3.3`_. + :param include_client_id: `True` to send the `client_id` in the + body of the upstream request. This is required + if the client is not authenticating with the + authorization server as described in + `Section 3.2.1`_. False otherwise (default). + :type include_client_id: Boolean + :param kwargs: Extra credentials to include in the token request. + + If the client type is confidential or the client was issued client + credentials (or assigned other authentication requirements), the + client MUST authenticate with the authorization server as described + in `Section 3.2.1`_. + + The prepared body will include all provided credentials as well as + the ``grant_type`` parameter set to ``password``:: + + >>> from oauthlib.oauth2 import LegacyApplicationClient + >>> client = LegacyApplicationClient('your_id') + >>> client.prepare_request_body(username='foo', password='bar', scope=['hello', 'world']) + 'grant_type=password&username=foo&scope=hello+world&password=bar' + + .. _`Appendix B`: https://tools.ietf.org/html/rfc6749#appendix-B + .. _`Section 3.3`: https://tools.ietf.org/html/rfc6749#section-3.3 + .. _`Section 3.2.1`: https://tools.ietf.org/html/rfc6749#section-3.2.1 + """ + kwargs['client_id'] = self.client_id + kwargs['include_client_id'] = include_client_id + scope = self.scope if scope is None else scope + return prepare_token_request(self.grant_type, body=body, username=username, + password=password, scope=scope, **kwargs) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/clients/mobile_application.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/clients/mobile_application.py new file mode 100644 index 0000000000000000000000000000000000000000..023cf23623ba806446eec8716f34b62d06e00cff --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/clients/mobile_application.py @@ -0,0 +1,174 @@ +# -*- coding: utf-8 -*- +""" +oauthlib.oauth2.rfc6749 +~~~~~~~~~~~~~~~~~~~~~~~ + +This module is an implementation of various logic needed +for consuming and providing OAuth 2.0 RFC6749. +""" +from ..parameters import parse_implicit_response, prepare_grant_uri +from .base import Client + + +class MobileApplicationClient(Client): + + """A public client utilizing the implicit code grant workflow. + + A user-agent-based application is a public client in which the + client code is downloaded from a web server and executes within a + user-agent (e.g. web browser) on the device used by the resource + owner. Protocol data and credentials are easily accessible (and + often visible) to the resource owner. Since such applications + reside within the user-agent, they can make seamless use of the + user-agent capabilities when requesting authorization. + + The implicit grant type is used to obtain access tokens (it does not + support the issuance of refresh tokens) and is optimized for public + clients known to operate a particular redirection URI. These clients + are typically implemented in a browser using a scripting language + such as JavaScript. + + As a redirection-based flow, the client must be capable of + interacting with the resource owner's user-agent (typically a web + browser) and capable of receiving incoming requests (via redirection) + from the authorization server. + + Unlike the authorization code grant type in which the client makes + separate requests for authorization and access token, the client + receives the access token as the result of the authorization request. + + The implicit grant type does not include client authentication, and + relies on the presence of the resource owner and the registration of + the redirection URI. Because the access token is encoded into the + redirection URI, it may be exposed to the resource owner and other + applications residing on the same device. + """ + + response_type = 'token' + + def prepare_request_uri(self, uri, redirect_uri=None, scope=None, + state=None, **kwargs): + """Prepare the implicit grant request URI. + + The client constructs the request URI by adding the following + parameters to the query component of the authorization endpoint URI + using the "application/x-www-form-urlencoded" format, per `Appendix B`_: + + :param redirect_uri: OPTIONAL. The redirect URI must be an absolute URI + and it should have been registered with the OAuth + provider prior to use. As described in `Section 3.1.2`_. + + :param scope: OPTIONAL. The scope of the access request as described by + Section 3.3`_. These may be any string but are commonly + URIs or various categories such as ``videos`` or ``documents``. + + :param state: RECOMMENDED. An opaque value used by the client to maintain + state between the request and callback. The authorization + server includes this value when redirecting the user-agent back + to the client. The parameter SHOULD be used for preventing + cross-site request forgery as described in `Section 10.12`_. + + :param kwargs: Extra arguments to include in the request URI. + + In addition to supplied parameters, OAuthLib will append the ``client_id`` + that was provided in the constructor as well as the mandatory ``response_type`` + argument, set to ``token``:: + + >>> from oauthlib.oauth2 import MobileApplicationClient + >>> client = MobileApplicationClient('your_id') + >>> client.prepare_request_uri('https://example.com') + 'https://example.com?client_id=your_id&response_type=token' + >>> client.prepare_request_uri('https://example.com', redirect_uri='https://a.b/callback') + 'https://example.com?client_id=your_id&response_type=token&redirect_uri=https%3A%2F%2Fa.b%2Fcallback' + >>> client.prepare_request_uri('https://example.com', scope=['profile', 'pictures']) + 'https://example.com?client_id=your_id&response_type=token&scope=profile+pictures' + >>> client.prepare_request_uri('https://example.com', foo='bar') + 'https://example.com?client_id=your_id&response_type=token&foo=bar' + + .. _`Appendix B`: https://tools.ietf.org/html/rfc6749#appendix-B + .. _`Section 2.2`: https://tools.ietf.org/html/rfc6749#section-2.2 + .. _`Section 3.1.2`: https://tools.ietf.org/html/rfc6749#section-3.1.2 + .. _`Section 3.3`: https://tools.ietf.org/html/rfc6749#section-3.3 + .. _`Section 10.12`: https://tools.ietf.org/html/rfc6749#section-10.12 + """ + scope = self.scope if scope is None else scope + return prepare_grant_uri(uri, self.client_id, self.response_type, + redirect_uri=redirect_uri, state=state, scope=scope, **kwargs) + + def parse_request_uri_response(self, uri, state=None, scope=None): + """Parse the response URI fragment. + + If the resource owner grants the access request, the authorization + server issues an access token and delivers it to the client by adding + the following parameters to the fragment component of the redirection + URI using the "application/x-www-form-urlencoded" format: + + :param uri: The callback URI that resulted from the user being redirected + back from the provider to you, the client. + :param state: The state provided in the authorization request. + :param scope: The scopes provided in the authorization request. + :return: Dictionary of token parameters. + :raises: OAuth2Error if response is invalid. + + A successful response should always contain + + **access_token** + The access token issued by the authorization server. Often + a random string. + + **token_type** + The type of the token issued as described in `Section 7.1`_. + Commonly ``Bearer``. + + **state** + If you provided the state parameter in the authorization phase, then + the provider is required to include that exact state value in the + response. + + While it is not mandated it is recommended that the provider include + + **expires_in** + The lifetime in seconds of the access token. For + example, the value "3600" denotes that the access token will + expire in one hour from the time the response was generated. + If omitted, the authorization server SHOULD provide the + expiration time via other means or document the default value. + + **scope** + Providers may supply this in all responses but are required to only + if it has changed since the authorization request. + + A few example responses can be seen below:: + + >>> response_uri = 'https://example.com/callback#access_token=sdlfkj452&state=ss345asyht&token_type=Bearer&scope=hello+world' + >>> from oauthlib.oauth2 import MobileApplicationClient + >>> client = MobileApplicationClient('your_id') + >>> client.parse_request_uri_response(response_uri) + { + 'access_token': 'sdlfkj452', + 'token_type': 'Bearer', + 'state': 'ss345asyht', + 'scope': [u'hello', u'world'] + } + >>> client.parse_request_uri_response(response_uri, state='other') + Traceback (most recent call last): + File "", line 1, in + File "oauthlib/oauth2/rfc6749/__init__.py", line 598, in parse_request_uri_response + **scope** + File "oauthlib/oauth2/rfc6749/parameters.py", line 197, in parse_implicit_response + raise ValueError("Mismatching or missing state in params.") + ValueError: Mismatching or missing state in params. + >>> def alert_scope_changed(message, old, new): + ... print(message, old, new) + ... + >>> oauthlib.signals.scope_changed.connect(alert_scope_changed) + >>> client.parse_request_body_response(response_body, scope=['other']) + ('Scope has changed from "other" to "hello world".', ['other'], ['hello', 'world']) + + .. _`Section 7.1`: https://tools.ietf.org/html/rfc6749#section-7.1 + .. _`Section 3.3`: https://tools.ietf.org/html/rfc6749#section-3.3 + """ + scope = self.scope if scope is None else scope + self.token = parse_implicit_response(uri, state=state, scope=scope) + self.populate_token_attributes(self.token) + return self.token diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/clients/service_application.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/clients/service_application.py new file mode 100644 index 0000000000000000000000000000000000000000..abf22d2d8e8dd52a921bda529341ba01ee59249b --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/clients/service_application.py @@ -0,0 +1,189 @@ +# -*- coding: utf-8 -*- +""" +oauthlib.oauth2.rfc6749 +~~~~~~~~~~~~~~~~~~~~~~~ + +This module is an implementation of various logic needed +for consuming and providing OAuth 2.0 RFC6749. +""" +import time + +from oauthlib.common import to_unicode + +from ..parameters import prepare_token_request +from .base import Client + + +class ServiceApplicationClient(Client): + """A public client utilizing the JWT bearer grant. + + JWT bearer tokes can be used to request an access token when a client + wishes to utilize an existing trust relationship, expressed through the + semantics of (and digital signature or keyed message digest calculated + over) the JWT, without a direct user approval step at the authorization + server. + + This grant type does not involve an authorization step. It may be + used by both public and confidential clients. + """ + + grant_type = 'urn:ietf:params:oauth:grant-type:jwt-bearer' + + def __init__(self, client_id, private_key=None, subject=None, issuer=None, + audience=None, **kwargs): + """Initialize a JWT client with defaults for implicit use later. + + :param client_id: Client identifier given by the OAuth provider upon + registration. + + :param private_key: Private key used for signing and encrypting. + Must be given as a string. + + :param subject: The principal that is the subject of the JWT, i.e. + which user is the token requested on behalf of. + For example, ``foo@example.com. + + :param issuer: The JWT MUST contain an "iss" (issuer) claim that + contains a unique identifier for the entity that issued + the JWT. For example, ``your-client@provider.com``. + + :param audience: A value identifying the authorization server as an + intended audience, e.g. + ``https://provider.com/oauth2/token``. + + :param kwargs: Additional arguments to pass to base client, such as + state and token. See ``Client.__init__.__doc__`` for + details. + """ + super().__init__(client_id, **kwargs) + self.private_key = private_key + self.subject = subject + self.issuer = issuer + self.audience = audience + + def prepare_request_body(self, + private_key=None, + subject=None, + issuer=None, + audience=None, + expires_at=None, + issued_at=None, + extra_claims=None, + body='', + scope=None, + include_client_id=False, + **kwargs): + """Create and add a JWT assertion to the request body. + + :param private_key: Private key used for signing and encrypting. + Must be given as a string. + + :param subject: (sub) The principal that is the subject of the JWT, + i.e. which user is the token requested on behalf of. + For example, ``foo@example.com. + + :param issuer: (iss) The JWT MUST contain an "iss" (issuer) claim that + contains a unique identifier for the entity that issued + the JWT. For example, ``your-client@provider.com``. + + :param audience: (aud) A value identifying the authorization server as an + intended audience, e.g. + ``https://provider.com/oauth2/token``. + + :param expires_at: A unix expiration timestamp for the JWT. Defaults + to an hour from now, i.e. ``round(time.time()) + 3600``. + + :param issued_at: A unix timestamp of when the JWT was created. + Defaults to now, i.e. ``time.time()``. + + :param extra_claims: A dict of additional claims to include in the JWT. + + :param body: Existing request body (URL encoded string) to embed parameters + into. This may contain extra parameters. Default ''. + + :param scope: The scope of the access request. + + :param include_client_id: `True` to send the `client_id` in the + body of the upstream request. This is required + if the client is not authenticating with the + authorization server as described in + `Section 3.2.1`_. False otherwise (default). + :type include_client_id: Boolean + + :param not_before: A unix timestamp after which the JWT may be used. + Not included unless provided. * + + :param jwt_id: A unique JWT token identifier. Not included unless + provided. * + + :param kwargs: Extra credentials to include in the token request. + + Parameters marked with a `*` above are not explicit arguments in the + function signature, but are specially documented arguments for items + appearing in the generic `**kwargs` keyworded input. + + The "scope" parameter may be used, as defined in the Assertion + Framework for OAuth 2.0 Client Authentication and Authorization Grants + [I-D.ietf-oauth-assertions] specification, to indicate the requested + scope. + + Authentication of the client is optional, as described in + `Section 3.2.1`_ of OAuth 2.0 [RFC6749] and consequently, the + "client_id" is only needed when a form of client authentication that + relies on the parameter is used. + + The following non-normative example demonstrates an Access Token + Request with a JWT as an authorization grant (with extra line breaks + for display purposes only): + + .. code-block: http + + POST /token.oauth2 HTTP/1.1 + Host: as.example.com + Content-Type: application/x-www-form-urlencoded + + grant_type=urn%3Aietf%3Aparams%3Aoauth%3Agrant-type%3Ajwt-bearer + &assertion=eyJhbGciOiJFUzI1NiJ9. + eyJpc3Mi[...omitted for brevity...]. + J9l-ZhwP[...omitted for brevity...] + + .. _`Section 3.2.1`: https://tools.ietf.org/html/rfc6749#section-3.2.1 + """ + import jwt # noqa: PLC0415 + + key = private_key or self.private_key + if not key: + raise ValueError('An encryption key must be supplied to make JWT' + ' token requests.') + claim = { + 'iss': issuer or self.issuer, + 'aud': audience or self.audience, + 'sub': subject or self.subject, + 'exp': int(expires_at or time.time() + 3600), + 'iat': int(issued_at or time.time()), + } + + for attr in ('iss', 'aud', 'sub'): + if claim[attr] is None: + raise ValueError( + 'Claim must include %s but none was given.' % attr) + + if 'not_before' in kwargs: + claim['nbf'] = kwargs.pop('not_before') + + if 'jwt_id' in kwargs: + claim['jti'] = kwargs.pop('jwt_id') + + claim.update(extra_claims or {}) + + assertion = jwt.encode(claim, key, 'RS256') + assertion = to_unicode(assertion) + + kwargs['client_id'] = self.client_id + kwargs['include_client_id'] = include_client_id + scope = self.scope if scope is None else scope + return prepare_token_request(self.grant_type, + body=body, + assertion=assertion, + scope=scope, + **kwargs) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/clients/web_application.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/clients/web_application.py new file mode 100644 index 0000000000000000000000000000000000000000..3bf94c4b51b30c85a94edf261394ddfce5bce2a5 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/clients/web_application.py @@ -0,0 +1,222 @@ +# -*- coding: utf-8 -*- +""" +oauthlib.oauth2.rfc6749 +~~~~~~~~~~~~~~~~~~~~~~~ + +This module is an implementation of various logic needed +for consuming and providing OAuth 2.0 RFC6749. +""" +import warnings + +from ..parameters import ( + parse_authorization_code_response, prepare_grant_uri, + prepare_token_request, +) +from .base import Client + + +class WebApplicationClient(Client): + + """A client utilizing the authorization code grant workflow. + + A web application is a confidential client running on a web + server. Resource owners access the client via an HTML user + interface rendered in a user-agent on the device used by the + resource owner. The client credentials as well as any access + token issued to the client are stored on the web server and are + not exposed to or accessible by the resource owner. + + The authorization code grant type is used to obtain both access + tokens and refresh tokens and is optimized for confidential clients. + As a redirection-based flow, the client must be capable of + interacting with the resource owner's user-agent (typically a web + browser) and capable of receiving incoming requests (via redirection) + from the authorization server. + """ + + grant_type = 'authorization_code' + + def __init__(self, client_id, code=None, **kwargs): + super().__init__(client_id, **kwargs) + self.code = code + + def prepare_request_uri(self, uri, redirect_uri=None, scope=None, + state=None, code_challenge=None, code_challenge_method='plain', **kwargs): + """Prepare the authorization code request URI + + The client constructs the request URI by adding the following + parameters to the query component of the authorization endpoint URI + using the "application/x-www-form-urlencoded" format, per `Appendix B`_: + + :param redirect_uri: OPTIONAL. The redirect URI must be an absolute URI + and it should have been registered with the OAuth + provider prior to use. As described in `Section 3.1.2`_. + + :param scope: OPTIONAL. The scope of the access request as described by + Section 3.3`_. These may be any string but are commonly + URIs or various categories such as ``videos`` or ``documents``. + + :param state: RECOMMENDED. An opaque value used by the client to maintain + state between the request and callback. The authorization + server includes this value when redirecting the user-agent back + to the client. The parameter SHOULD be used for preventing + cross-site request forgery as described in `Section 10.12`_. + + :param code_challenge: OPTIONAL. PKCE parameter. REQUIRED if PKCE is enforced. + A challenge derived from the code_verifier that is sent in the + authorization request, to be verified against later. + + :param code_challenge_method: OPTIONAL. PKCE parameter. A method that was used to derive code challenge. + Defaults to "plain" if not present in the request. + + :param kwargs: Extra arguments to include in the request URI. + + In addition to supplied parameters, OAuthLib will append the ``client_id`` + that was provided in the constructor as well as the mandatory ``response_type`` + argument, set to ``code``:: + + >>> from oauthlib.oauth2 import WebApplicationClient + >>> client = WebApplicationClient('your_id') + >>> client.prepare_request_uri('https://example.com') + 'https://example.com?client_id=your_id&response_type=code' + >>> client.prepare_request_uri('https://example.com', redirect_uri='https://a.b/callback') + 'https://example.com?client_id=your_id&response_type=code&redirect_uri=https%3A%2F%2Fa.b%2Fcallback' + >>> client.prepare_request_uri('https://example.com', scope=['profile', 'pictures']) + 'https://example.com?client_id=your_id&response_type=code&scope=profile+pictures' + >>> client.prepare_request_uri('https://example.com', code_challenge='kjasBS523KdkAILD2k78NdcJSk2k3KHG6') + 'https://example.com?client_id=your_id&response_type=code&code_challenge=kjasBS523KdkAILD2k78NdcJSk2k3KHG6' + >>> client.prepare_request_uri('https://example.com', code_challenge_method='S256') + 'https://example.com?client_id=your_id&response_type=code&code_challenge_method=S256' + >>> client.prepare_request_uri('https://example.com', foo='bar') + 'https://example.com?client_id=your_id&response_type=code&foo=bar' + + .. _`Appendix B`: https://tools.ietf.org/html/rfc6749#appendix-B + .. _`Section 2.2`: https://tools.ietf.org/html/rfc6749#section-2.2 + .. _`Section 3.1.2`: https://tools.ietf.org/html/rfc6749#section-3.1.2 + .. _`Section 3.3`: https://tools.ietf.org/html/rfc6749#section-3.3 + .. _`Section 10.12`: https://tools.ietf.org/html/rfc6749#section-10.12 + """ + scope = self.scope if scope is None else scope + return prepare_grant_uri(uri, self.client_id, 'code', + redirect_uri=redirect_uri, scope=scope, state=state, code_challenge=code_challenge, + code_challenge_method=code_challenge_method, **kwargs) + + def prepare_request_body(self, code=None, redirect_uri=None, body='', + include_client_id=True, code_verifier=None, **kwargs): + """Prepare the access token request body. + + The client makes a request to the token endpoint by adding the + following parameters using the "application/x-www-form-urlencoded" + format in the HTTP request entity-body: + + :param code: REQUIRED. The authorization code received from the + authorization server. + + :param redirect_uri: REQUIRED, if the "redirect_uri" parameter was included in the + authorization request as described in `Section 4.1.1`_, and their + values MUST be identical. + + :param body: Existing request body (URL encoded string) to embed parameters + into. This may contain extra parameters. Default ''. + + :param include_client_id: `True` (default) to send the `client_id` in the + body of the upstream request. This is required + if the client is not authenticating with the + authorization server as described in `Section 3.2.1`_. + :type include_client_id: Boolean + + :param code_verifier: OPTIONAL. A cryptographically random string that is used to correlate the + authorization request to the token request. + + :param kwargs: Extra parameters to include in the token request. + + In addition OAuthLib will add the ``grant_type`` parameter set to + ``authorization_code``. + + If the client type is confidential or the client was issued client + credentials (or assigned other authentication requirements), the + client MUST authenticate with the authorization server as described + in `Section 3.2.1`_:: + + >>> from oauthlib.oauth2 import WebApplicationClient + >>> client = WebApplicationClient('your_id') + >>> client.prepare_request_body(code='sh35ksdf09sf') + 'grant_type=authorization_code&code=sh35ksdf09sf' + >>> client.prepare_request_body(code_verifier='KB46DCKJ873NCGXK5GD682NHDKK34GR') + 'grant_type=authorization_code&code_verifier=KB46DCKJ873NCGXK5GD682NHDKK34GR' + >>> client.prepare_request_body(code='sh35ksdf09sf', foo='bar') + 'grant_type=authorization_code&code=sh35ksdf09sf&foo=bar' + + `Section 3.2.1` also states: + In the "authorization_code" "grant_type" request to the token + endpoint, an unauthenticated client MUST send its "client_id" to + prevent itself from inadvertently accepting a code intended for a + client with a different "client_id". This protects the client from + substitution of the authentication code. (It provides no additional + security for the protected resource.) + + .. _`Section 4.1.1`: https://tools.ietf.org/html/rfc6749#section-4.1.1 + .. _`Section 3.2.1`: https://tools.ietf.org/html/rfc6749#section-3.2.1 + """ + code = code or self.code + if 'client_id' in kwargs: + warnings.warn("`client_id` has been deprecated in favor of " + "`include_client_id`, a boolean value which will " + "include the already configured `self.client_id`.", + DeprecationWarning) + if kwargs['client_id'] != self.client_id: + raise ValueError("`client_id` was supplied as an argument, but " + "it does not match `self.client_id`") + + kwargs['client_id'] = self.client_id + kwargs['include_client_id'] = include_client_id + return prepare_token_request(self.grant_type, code=code, body=body, + redirect_uri=redirect_uri, code_verifier=code_verifier, **kwargs) + + def parse_request_uri_response(self, uri, state=None): + """Parse the URI query for code and state. + + If the resource owner grants the access request, the authorization + server issues an authorization code and delivers it to the client by + adding the following parameters to the query component of the + redirection URI using the "application/x-www-form-urlencoded" format: + + :param uri: The callback URI that resulted from the user being redirected + back from the provider to you, the client. + :param state: The state provided in the authorization request. + + **code** + The authorization code generated by the authorization server. + The authorization code MUST expire shortly after it is issued + to mitigate the risk of leaks. A maximum authorization code + lifetime of 10 minutes is RECOMMENDED. The client MUST NOT + use the authorization code more than once. If an authorization + code is used more than once, the authorization server MUST deny + the request and SHOULD revoke (when possible) all tokens + previously issued based on that authorization code. + The authorization code is bound to the client identifier and + redirection URI. + + **state** + If the "state" parameter was present in the authorization request. + + This method is mainly intended to enforce strict state checking with + the added benefit of easily extracting parameters from the URI:: + + >>> from oauthlib.oauth2 import WebApplicationClient + >>> client = WebApplicationClient('your_id') + >>> uri = 'https://example.com/callback?code=sdfkjh345&state=sfetw45' + >>> client.parse_request_uri_response(uri, state='sfetw45') + {'state': 'sfetw45', 'code': 'sdfkjh345'} + >>> client.parse_request_uri_response(uri, state='other') + Traceback (most recent call last): + File "", line 1, in + File "oauthlib/oauth2/rfc6749/__init__.py", line 357, in parse_request_uri_response + back from the provider to you, the client. + File "oauthlib/oauth2/rfc6749/parameters.py", line 153, in parse_authorization_code_response + raise MismatchingStateError() + oauthlib.oauth2.rfc6749.errors.MismatchingStateError + """ + response = parse_authorization_code_response(uri, state=state) + self.populate_code_attributes(response) + return response diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/endpoints/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/endpoints/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..1695b41b663eded80893849ec0b4ecc6f69c0e5c --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/endpoints/__init__.py @@ -0,0 +1,17 @@ +""" +oauthlib.oauth2.rfc6749 +~~~~~~~~~~~~~~~~~~~~~~~ + +This module is an implementation of various logic needed +for consuming and providing OAuth 2.0 RFC6749. +""" +from .authorization import AuthorizationEndpoint +from .introspect import IntrospectEndpoint +from .metadata import MetadataEndpoint +from .pre_configured import ( + BackendApplicationServer, LegacyApplicationServer, MobileApplicationServer, + Server, WebApplicationServer, +) +from .resource import ResourceEndpoint +from .revocation import RevocationEndpoint +from .token import TokenEndpoint diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/endpoints/authorization.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/endpoints/authorization.py new file mode 100644 index 0000000000000000000000000000000000000000..71967865dce4a1d6cc344036a732f7bc008dbaeb --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/endpoints/authorization.py @@ -0,0 +1,114 @@ +""" +oauthlib.oauth2.rfc6749 +~~~~~~~~~~~~~~~~~~~~~~~ + +This module is an implementation of various logic needed +for consuming and providing OAuth 2.0 RFC6749. +""" +import logging + +from oauthlib.common import Request +from oauthlib.oauth2.rfc6749 import utils + +from .base import BaseEndpoint, catch_errors_and_unavailability + +log = logging.getLogger(__name__) + + +class AuthorizationEndpoint(BaseEndpoint): + + """Authorization endpoint - used by the client to obtain authorization + from the resource owner via user-agent redirection. + + The authorization endpoint is used to interact with the resource + owner and obtain an authorization grant. The authorization server + MUST first verify the identity of the resource owner. The way in + which the authorization server authenticates the resource owner (e.g. + username and password login, session cookies) is beyond the scope of + this specification. + + The endpoint URI MAY include an "application/x-www-form-urlencoded" + formatted (per `Appendix B`_) query component, + which MUST be retained when adding additional query parameters. The + endpoint URI MUST NOT include a fragment component:: + + https://example.com/path?query=component # OK + https://example.com/path?query=component#fragment # Not OK + + Since requests to the authorization endpoint result in user + authentication and the transmission of clear-text credentials (in the + HTTP response), the authorization server MUST require the use of TLS + as described in Section 1.6 when sending requests to the + authorization endpoint:: + + # We will deny any request which URI schema is not with https + + The authorization server MUST support the use of the HTTP "GET" + method [RFC2616] for the authorization endpoint, and MAY support the + use of the "POST" method as well:: + + # HTTP method is currently not enforced + + Parameters sent without a value MUST be treated as if they were + omitted from the request. The authorization server MUST ignore + unrecognized request parameters. Request and response parameters + MUST NOT be included more than once:: + + # Enforced through the design of oauthlib.common.Request + + .. _`Appendix B`: https://tools.ietf.org/html/rfc6749#appendix-B + """ + + def __init__(self, default_response_type, default_token_type, + response_types): + BaseEndpoint.__init__(self) + self._response_types = response_types + self._default_response_type = default_response_type + self._default_token_type = default_token_type + + @property + def response_types(self): + return self._response_types + + @property + def default_response_type(self): + return self._default_response_type + + @property + def default_response_type_handler(self): + return self.response_types.get(self.default_response_type) + + @property + def default_token_type(self): + return self._default_token_type + + @catch_errors_and_unavailability + def create_authorization_response(self, uri, http_method='GET', body=None, + headers=None, scopes=None, credentials=None): + """Extract response_type and route to the designated handler.""" + request = Request( + uri, http_method=http_method, body=body, headers=headers) + request.scopes = scopes + # TODO: decide whether this should be a required argument + request.user = None # TODO: explain this in docs + for k, v in (credentials or {}).items(): + setattr(request, k, v) + response_type_handler = self.response_types.get( + request.response_type, self.default_response_type_handler) + log.debug('Dispatching response_type %s request to %r.', + request.response_type, response_type_handler) + return response_type_handler.create_authorization_response( + request, self.default_token_type) + + @catch_errors_and_unavailability + def validate_authorization_request(self, uri, http_method='GET', body=None, + headers=None): + """Extract response_type and route to the designated handler.""" + request = Request( + uri, http_method=http_method, body=body, headers=headers) + + request.scopes = utils.scope_to_list(request.scope) + + response_type_handler = self.response_types.get( + request.response_type, self.default_response_type_handler) + return response_type_handler.validate_authorization_request(request) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/endpoints/base.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/endpoints/base.py new file mode 100644 index 0000000000000000000000000000000000000000..987fac6a8fbd272ce98008e5e21d43fa7f9bcd67 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/endpoints/base.py @@ -0,0 +1,113 @@ +""" +oauthlib.oauth2.rfc6749 +~~~~~~~~~~~~~~~~~~~~~~~ + +This module is an implementation of various logic needed +for consuming and providing OAuth 2.0 RFC6749. +""" +import functools +import logging + +from ..errors import ( + FatalClientError, InvalidClientError, InvalidRequestError, OAuth2Error, + ServerError, TemporarilyUnavailableError, UnsupportedTokenTypeError, +) + +log = logging.getLogger(__name__) + + +class BaseEndpoint: + + def __init__(self): + self._available = True + self._catch_errors = False + self._valid_request_methods = None + + @property + def valid_request_methods(self): + return self._valid_request_methods + + @valid_request_methods.setter + def valid_request_methods(self, valid_request_methods): + if valid_request_methods is not None: + valid_request_methods = [x.upper() for x in valid_request_methods] + self._valid_request_methods = valid_request_methods + + + @property + def available(self): + return self._available + + @available.setter + def available(self, available): + self._available = available + + @property + def catch_errors(self): + return self._catch_errors + + @catch_errors.setter + def catch_errors(self, catch_errors): + self._catch_errors = catch_errors + + def _raise_on_missing_token(self, request): + """Raise error on missing token.""" + if not request.token: + raise InvalidRequestError(request=request, + description='Missing token parameter.') + def _raise_on_invalid_client(self, request): + """Raise on failed client authentication.""" + if self.request_validator.client_authentication_required(request): + if not self.request_validator.authenticate_client(request): + log.debug('Client authentication failed, %r.', request) + raise InvalidClientError(request=request) + elif not self.request_validator.authenticate_client_id(request.client_id, request): + log.debug('Client authentication failed, %r.', request) + raise InvalidClientError(request=request) + + def _raise_on_unsupported_token(self, request): + """Raise on unsupported tokens.""" + if (request.token_type_hint and + request.token_type_hint in self.valid_token_types and + request.token_type_hint not in self.supported_token_types): + raise UnsupportedTokenTypeError(request=request) + + def _raise_on_bad_method(self, request): + if self.valid_request_methods is None: + raise ValueError('Configure "valid_request_methods" property first') + if request.http_method.upper() not in self.valid_request_methods: + raise InvalidRequestError(request=request, + description=('Unsupported request method %s' % request.http_method.upper())) + + def _raise_on_bad_post_request(self, request): + """Raise if invalid POST request received + """ + if request.http_method.upper() == 'POST': + query_params = request.uri_query or "" + if query_params: + raise InvalidRequestError(request=request, + description=('URL query parameters are not allowed')) + +def catch_errors_and_unavailability(f): + @functools.wraps(f) + def wrapper(endpoint, uri, *args, **kwargs): + if not endpoint.available: + e = TemporarilyUnavailableError() + log.info('Endpoint unavailable, ignoring request %s.' % uri) + return {}, e.json, 503 + + if endpoint.catch_errors: + try: + return f(endpoint, uri, *args, **kwargs) + except OAuth2Error: + raise + except FatalClientError: + raise + except Exception as e: + error = ServerError() + log.warning( + 'Exception caught while processing request, %s.' % e) + return {}, error.json, 500 + else: + return f(endpoint, uri, *args, **kwargs) + return wrapper diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/endpoints/introspect.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/endpoints/introspect.py new file mode 100644 index 0000000000000000000000000000000000000000..ef73988d48db03e7fc64d54e9fb6315f32923fde --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/endpoints/introspect.py @@ -0,0 +1,120 @@ +""" +oauthlib.oauth2.rfc6749.endpoint.introspect +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +An implementation of the OAuth 2.0 `Token Introspection`. + +.. _`Token Introspection`: https://tools.ietf.org/html/rfc7662 +""" +import json +import logging + +from oauthlib.common import Request + +from ..errors import OAuth2Error +from .base import BaseEndpoint, catch_errors_and_unavailability + +log = logging.getLogger(__name__) + + +class IntrospectEndpoint(BaseEndpoint): + + """Introspect token endpoint. + + This endpoint defines a method to query an OAuth 2.0 authorization + server to determine the active state of an OAuth 2.0 token and to + determine meta-information about this token. OAuth 2.0 deployments + can use this method to convey information about the authorization + context of the token from the authorization server to the protected + resource. + + To prevent the values of access tokens from leaking into + server-side logs via query parameters, an authorization server + offering token introspection MAY disallow the use of HTTP GET on + the introspection endpoint and instead require the HTTP POST method + to be used at the introspection endpoint. + """ + + valid_token_types = ('access_token', 'refresh_token') + valid_request_methods = ('POST',) + + def __init__(self, request_validator, supported_token_types=None): + BaseEndpoint.__init__(self) + self.request_validator = request_validator + self.supported_token_types = ( + supported_token_types or self.valid_token_types) + + @catch_errors_and_unavailability + def create_introspect_response(self, uri, http_method='POST', body=None, + headers=None): + """Create introspect valid or invalid response + + If the authorization server is unable to determine the state + of the token without additional information, it SHOULD return + an introspection response indicating the token is not active + as described in Section 2.2. + """ + resp_headers = { + 'Content-Type': 'application/json', + 'Cache-Control': 'no-store', + 'Pragma': 'no-cache', + } + request = Request(uri, http_method, body, headers) + try: + self.validate_introspect_request(request) + log.debug('Token introspect valid for %r.', request) + except OAuth2Error as e: + log.debug('Client error during validation of %r. %r.', request, e) + resp_headers.update(e.headers) + return resp_headers, e.json, e.status_code + + claims = self.request_validator.introspect_token( + request.token, + request.token_type_hint, + request + ) + if claims is None: + return resp_headers, json.dumps({'active': False}), 200 + if "active" in claims: + claims.pop("active") + return resp_headers, json.dumps(dict(active=True, **claims)), 200 + + def validate_introspect_request(self, request): + """Ensure the request is valid. + + The protected resource calls the introspection endpoint using + an HTTP POST request with parameters sent as + "application/x-www-form-urlencoded". + + * token REQUIRED. The string value of the token. + * token_type_hint OPTIONAL. + + A hint about the type of the token submitted for + introspection. The protected resource MAY pass this parameter to + help the authorization server optimize the token lookup. If the + server is unable to locate the token using the given hint, it MUST + extend its search across all of its supported token types. An + authorization server MAY ignore this parameter, particularly if it + is able to detect the token type automatically. + + * access_token: An Access Token as defined in [`RFC6749`], `section 1.4`_ + * refresh_token: A Refresh Token as defined in [`RFC6749`], `section 1.5`_ + + The introspection endpoint MAY accept other OPTIONAL + parameters to provide further context to the query. For + instance, an authorization server may desire to know the IP + address of the client accessing the protected resource to + determine if the correct client is likely to be presenting the + token. The definition of this or any other parameters are + outside the scope of this specification, to be defined by + service documentation or extensions to this specification. + + .. _`section 1.4`: http://tools.ietf.org/html/rfc6749#section-1.4 + .. _`section 1.5`: http://tools.ietf.org/html/rfc6749#section-1.5 + .. _`RFC6749`: http://tools.ietf.org/html/rfc6749 + """ + self._raise_on_bad_method(request) + self._raise_on_bad_post_request(request) + self._raise_on_missing_token(request) + self._raise_on_invalid_client(request) + self._raise_on_unsupported_token(request) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/endpoints/metadata.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/endpoints/metadata.py new file mode 100644 index 0000000000000000000000000000000000000000..34274cba67baf4699d9c3f82ecc421a6af824d8e --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/endpoints/metadata.py @@ -0,0 +1,238 @@ +""" +oauthlib.oauth2.rfc6749.endpoint.metadata +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +An implementation of the `OAuth 2.0 Authorization Server Metadata`. + +.. _`OAuth 2.0 Authorization Server Metadata`: https://tools.ietf.org/html/rfc8414 +""" +import copy +import json +import logging + +from .. import grant_types, utils +from .authorization import AuthorizationEndpoint +from .base import BaseEndpoint, catch_errors_and_unavailability +from .introspect import IntrospectEndpoint +from .revocation import RevocationEndpoint +from .token import TokenEndpoint + +log = logging.getLogger(__name__) + + +class MetadataEndpoint(BaseEndpoint): + + """OAuth2.0 Authorization Server Metadata endpoint. + + This specification generalizes the metadata format defined by + `OpenID Connect Discovery 1.0` in a way that is compatible + with OpenID Connect Discovery while being applicable to a wider set + of OAuth 2.0 use cases. This is intentionally parallel to the way + that OAuth 2.0 Dynamic Client Registration Protocol [`RFC7591`_] + generalized the dynamic client registration mechanisms defined by + OpenID Connect Dynamic Client Registration 1.0 + in a way that is compatible with it. + + .. _`OpenID Connect Discovery 1.0`: https://openid.net/specs/openid-connect-discovery-1_0.html + .. _`RFC7591`: https://tools.ietf.org/html/rfc7591 + """ + + def __init__(self, endpoints, claims={}, raise_errors=True): + assert isinstance(claims, dict) # noqa: S101 + for endpoint in endpoints: + assert isinstance(endpoint, BaseEndpoint) # noqa: S101 + + BaseEndpoint.__init__(self) + self.raise_errors = raise_errors + self.endpoints = endpoints + self.initial_claims = claims + self.claims = self.validate_metadata_server() + + @catch_errors_and_unavailability + def create_metadata_response(self, uri, http_method='GET', body=None, + headers=None): + """Create metadata response + """ + headers = { + 'Content-Type': 'application/json', + 'Access-Control-Allow-Origin': '*', + } + return headers, json.dumps(self.claims), 200 + + def validate_metadata(self, array, key, is_required=False, is_list=False, is_url=False, is_issuer=False): + if not self.raise_errors: + return + + if key not in array: + if is_required: + raise ValueError("key {} is a mandatory metadata.".format(key)) + + elif is_issuer: + if not utils.is_secure_transport(array[key]): + raise ValueError("key {}: {} must be an HTTPS URL".format(key, array[key])) + if "?" in array[key] or "&" in array[key] or "#" in array[key]: + raise ValueError("key {}: {} must not contain query or fragment components".format(key, array[key])) + + elif is_url: + if not array[key].startswith("http"): + raise ValueError("key {}: {} must be an URL".format(key, array[key])) + + elif is_list: + if not isinstance(array[key], list): + raise ValueError("key {}: {} must be an Array".format(key, array[key])) + for elem in array[key]: + if not isinstance(elem, str): + raise ValueError("array {}: {} must contains only string (not {})".format(key, array[key], elem)) + + def validate_metadata_token(self, claims, endpoint): + """ + If the token endpoint is used in the grant type, the value of this + parameter MUST be the same as the value of the "grant_type" + parameter passed to the token endpoint defined in the grant type + definition. + """ + self._grant_types.extend(endpoint._grant_types.keys()) + claims.setdefault("token_endpoint_auth_methods_supported", ["client_secret_post", "client_secret_basic"]) + + self.validate_metadata(claims, "token_endpoint_auth_methods_supported", is_list=True) + self.validate_metadata(claims, "token_endpoint_auth_signing_alg_values_supported", is_list=True) + self.validate_metadata(claims, "token_endpoint", is_required=True, is_url=True) + + def validate_metadata_authorization(self, claims, endpoint): + claims.setdefault("response_types_supported", + list(filter(lambda x: x != "none", endpoint._response_types.keys()))) + claims.setdefault("response_modes_supported", ["query", "fragment"]) + + # The OAuth2.0 Implicit flow is defined as a "grant type" but it is not + # using the "token" endpoint, as such, we have to add it explicitly to + # the list of "grant_types_supported" when enabled. + if "token" in claims["response_types_supported"]: + self._grant_types.append("implicit") + + self.validate_metadata(claims, "response_types_supported", is_required=True, is_list=True) + self.validate_metadata(claims, "response_modes_supported", is_list=True) + if "code" in claims["response_types_supported"]: + code_grant = endpoint._response_types["code"] + if not isinstance(code_grant, grant_types.AuthorizationCodeGrant) and hasattr(code_grant, "default_grant"): + code_grant = code_grant.default_grant + + claims.setdefault("code_challenge_methods_supported", + list(code_grant._code_challenge_methods.keys())) + self.validate_metadata(claims, "code_challenge_methods_supported", is_list=True) + self.validate_metadata(claims, "authorization_endpoint", is_required=True, is_url=True) + + def validate_metadata_revocation(self, claims, endpoint): + claims.setdefault("revocation_endpoint_auth_methods_supported", + ["client_secret_post", "client_secret_basic"]) + + self.validate_metadata(claims, "revocation_endpoint_auth_methods_supported", is_list=True) + self.validate_metadata(claims, "revocation_endpoint_auth_signing_alg_values_supported", is_list=True) + self.validate_metadata(claims, "revocation_endpoint", is_required=True, is_url=True) + + def validate_metadata_introspection(self, claims, endpoint): + claims.setdefault("introspection_endpoint_auth_methods_supported", + ["client_secret_post", "client_secret_basic"]) + + self.validate_metadata(claims, "introspection_endpoint_auth_methods_supported", is_list=True) + self.validate_metadata(claims, "introspection_endpoint_auth_signing_alg_values_supported", is_list=True) + self.validate_metadata(claims, "introspection_endpoint", is_required=True, is_url=True) + + def validate_metadata_server(self): + """ + Authorization servers can have metadata describing their + configuration. The following authorization server metadata values + are used by this specification. More details can be found in + `RFC8414 section 2`_ : + + issuer + REQUIRED + + authorization_endpoint + URL of the authorization server's authorization endpoint + [`RFC6749#Authorization`_]. This is REQUIRED unless no grant types are supported + that use the authorization endpoint. + + token_endpoint + URL of the authorization server's token endpoint [`RFC6749#Token`_]. This + is REQUIRED unless only the implicit grant type is supported. + + scopes_supported + RECOMMENDED. + + response_types_supported + REQUIRED. + + Other OPTIONAL fields: + jwks_uri, + registration_endpoint, + response_modes_supported + + grant_types_supported + OPTIONAL. JSON array containing a list of the OAuth 2.0 grant + type values that this authorization server supports. The array + values used are the same as those used with the "grant_types" + parameter defined by "OAuth 2.0 Dynamic Client Registration + Protocol" [`RFC7591`_]. If omitted, the default value is + "["authorization_code", "implicit"]". + + token_endpoint_auth_methods_supported + + token_endpoint_auth_signing_alg_values_supported + + service_documentation + + ui_locales_supported + + op_policy_uri + + op_tos_uri + + revocation_endpoint + + revocation_endpoint_auth_methods_supported + + revocation_endpoint_auth_signing_alg_values_supported + + introspection_endpoint + + introspection_endpoint_auth_methods_supported + + introspection_endpoint_auth_signing_alg_values_supported + + code_challenge_methods_supported + + Additional authorization server metadata parameters MAY also be used. + Some are defined by other specifications, such as OpenID Connect + Discovery 1.0 [`OpenID.Discovery`_]. + + .. _`RFC8414 section 2`: https://tools.ietf.org/html/rfc8414#section-2 + .. _`RFC6749#Authorization`: https://tools.ietf.org/html/rfc6749#section-3.1 + .. _`RFC6749#Token`: https://tools.ietf.org/html/rfc6749#section-3.2 + .. _`RFC7591`: https://tools.ietf.org/html/rfc7591 + .. _`OpenID.Discovery`: https://openid.net/specs/openid-connect-discovery-1_0.html + """ + claims = copy.deepcopy(self.initial_claims) + self.validate_metadata(claims, "issuer", is_required=True, is_issuer=True) + self.validate_metadata(claims, "jwks_uri", is_url=True) + self.validate_metadata(claims, "scopes_supported", is_list=True) + self.validate_metadata(claims, "service_documentation", is_url=True) + self.validate_metadata(claims, "ui_locales_supported", is_list=True) + self.validate_metadata(claims, "op_policy_uri", is_url=True) + self.validate_metadata(claims, "op_tos_uri", is_url=True) + + self._grant_types = [] + for endpoint in self.endpoints: + if isinstance(endpoint, TokenEndpoint): + self.validate_metadata_token(claims, endpoint) + if isinstance(endpoint, AuthorizationEndpoint): + self.validate_metadata_authorization(claims, endpoint) + if isinstance(endpoint, RevocationEndpoint): + self.validate_metadata_revocation(claims, endpoint) + if isinstance(endpoint, IntrospectEndpoint): + self.validate_metadata_introspection(claims, endpoint) + + # "grant_types_supported" is a combination of all OAuth2 grant types + # allowed in the current provider implementation. + claims.setdefault("grant_types_supported", self._grant_types) + self.validate_metadata(claims, "grant_types_supported", is_list=True) + return claims diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/endpoints/pre_configured.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/endpoints/pre_configured.py new file mode 100644 index 0000000000000000000000000000000000000000..8f6aa32b7420c4447e0b7419276143955ead1e8a --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/endpoints/pre_configured.py @@ -0,0 +1,287 @@ +""" +oauthlib.oauth2.rfc6749.endpoints.pre_configured +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +This module is an implementation of various endpoints needed +for providing OAuth 2.0 RFC6749 servers. +""" + +from ..grant_types import ( + AuthorizationCodeGrant, + ClientCredentialsGrant, + ImplicitGrant, + RefreshTokenGrant, + ResourceOwnerPasswordCredentialsGrant, +) +from ..tokens import BearerToken +from .authorization import AuthorizationEndpoint +from .introspect import IntrospectEndpoint +from .resource import ResourceEndpoint +from .revocation import RevocationEndpoint +from .token import TokenEndpoint +from oauthlib.oauth2.rfc8628.grant_types import DeviceCodeGrant + + +class Server( + AuthorizationEndpoint, IntrospectEndpoint, TokenEndpoint, ResourceEndpoint, RevocationEndpoint +): + """ + An all-in-one endpoint featuring all four major grant types + and extension grants. + """ + + def __init__( + self, + request_validator, + token_expires_in=None, + token_generator=None, + refresh_token_generator=None, + *args, + **kwargs, + ): + """Construct a new all-grants-in-one server. + + :param request_validator: An implementation of + oauthlib.oauth2.RequestValidator. + :param token_expires_in: An int or a function to generate a token + expiration offset (in seconds) given a + oauthlib.common.Request object. + :param token_generator: A function to generate a token from a request. + :param refresh_token_generator: A function to generate a token from a + request for the refresh token. + :param kwargs: Extra parameters to pass to authorization-, + token-, resource-, and revocation-endpoint constructors. + """ + self.auth_grant = AuthorizationCodeGrant(request_validator) + self.implicit_grant = ImplicitGrant(request_validator) + self.password_grant = ResourceOwnerPasswordCredentialsGrant(request_validator) + self.credentials_grant = ClientCredentialsGrant(request_validator) + self.refresh_grant = RefreshTokenGrant(request_validator) + self.device_code_grant = DeviceCodeGrant(request_validator, **kwargs) + + self.bearer = BearerToken( + request_validator, token_generator, token_expires_in, refresh_token_generator + ) + + AuthorizationEndpoint.__init__( + self, + default_response_type="code", + response_types={ + "code": self.auth_grant, + "token": self.implicit_grant, + "none": self.auth_grant, + }, + default_token_type=self.bearer, + ) + + TokenEndpoint.__init__( + self, + default_grant_type="authorization_code", + grant_types={ + "authorization_code": self.auth_grant, + "password": self.password_grant, + "client_credentials": self.credentials_grant, + "refresh_token": self.refresh_grant, + "urn:ietf:params:oauth:grant-type:device_code": self.device_code_grant, + }, + default_token_type=self.bearer, + ) + ResourceEndpoint.__init__( + self, default_token="Bearer", token_types={"Bearer": self.bearer} + ) + RevocationEndpoint.__init__(self, request_validator) + IntrospectEndpoint.__init__(self, request_validator) + + +class WebApplicationServer( + AuthorizationEndpoint, IntrospectEndpoint, TokenEndpoint, ResourceEndpoint, RevocationEndpoint +): + """An all-in-one endpoint featuring Authorization code grant and Bearer tokens.""" + + def __init__( + self, + request_validator, + token_generator=None, + token_expires_in=None, + refresh_token_generator=None, + **kwargs, + ): + """Construct a new web application server. + + :param request_validator: An implementation of + oauthlib.oauth2.RequestValidator. + :param token_expires_in: An int or a function to generate a token + expiration offset (in seconds) given a + oauthlib.common.Request object. + :param token_generator: A function to generate a token from a request. + :param refresh_token_generator: A function to generate a token from a + request for the refresh token. + :param kwargs: Extra parameters to pass to authorization-, + token-, resource-, and revocation-endpoint constructors. + """ + self.auth_grant = AuthorizationCodeGrant(request_validator) + self.refresh_grant = RefreshTokenGrant(request_validator) + self.bearer = BearerToken( + request_validator, token_generator, token_expires_in, refresh_token_generator + ) + AuthorizationEndpoint.__init__( + self, + default_response_type="code", + response_types={"code": self.auth_grant}, + default_token_type=self.bearer, + ) + TokenEndpoint.__init__( + self, + default_grant_type="authorization_code", + grant_types={ + "authorization_code": self.auth_grant, + "refresh_token": self.refresh_grant, + }, + default_token_type=self.bearer, + ) + ResourceEndpoint.__init__( + self, default_token="Bearer", token_types={"Bearer": self.bearer} + ) + RevocationEndpoint.__init__(self, request_validator) + IntrospectEndpoint.__init__(self, request_validator) + + +class MobileApplicationServer( + AuthorizationEndpoint, IntrospectEndpoint, ResourceEndpoint, RevocationEndpoint +): + """An all-in-one endpoint featuring Implicit code grant and Bearer tokens.""" + + def __init__( + self, + request_validator, + token_generator=None, + token_expires_in=None, + refresh_token_generator=None, + **kwargs, + ): + """Construct a new implicit grant server. + + :param request_validator: An implementation of + oauthlib.oauth2.RequestValidator. + :param token_expires_in: An int or a function to generate a token + expiration offset (in seconds) given a + oauthlib.common.Request object. + :param token_generator: A function to generate a token from a request. + :param refresh_token_generator: A function to generate a token from a + request for the refresh token. + :param kwargs: Extra parameters to pass to authorization-, + token-, resource-, and revocation-endpoint constructors. + """ + self.implicit_grant = ImplicitGrant(request_validator) + self.bearer = BearerToken( + request_validator, token_generator, token_expires_in, refresh_token_generator + ) + AuthorizationEndpoint.__init__( + self, + default_response_type="token", + response_types={"token": self.implicit_grant}, + default_token_type=self.bearer, + ) + ResourceEndpoint.__init__( + self, default_token="Bearer", token_types={"Bearer": self.bearer} + ) + RevocationEndpoint.__init__( + self, request_validator, supported_token_types=["access_token"] + ) + IntrospectEndpoint.__init__( + self, request_validator, supported_token_types=["access_token"] + ) + + +class LegacyApplicationServer( + TokenEndpoint, IntrospectEndpoint, ResourceEndpoint, RevocationEndpoint +): + """An all-in-one endpoint featuring Resource Owner Password Credentials grant and Bearer tokens.""" + + def __init__( + self, + request_validator, + token_generator=None, + token_expires_in=None, + refresh_token_generator=None, + **kwargs, + ): + """Construct a resource owner password credentials grant server. + + :param request_validator: An implementation of + oauthlib.oauth2.RequestValidator. + :param token_expires_in: An int or a function to generate a token + expiration offset (in seconds) given a + oauthlib.common.Request object. + :param token_generator: A function to generate a token from a request. + :param refresh_token_generator: A function to generate a token from a + request for the refresh token. + :param kwargs: Extra parameters to pass to authorization-, + token-, resource-, and revocation-endpoint constructors. + """ + self.password_grant = ResourceOwnerPasswordCredentialsGrant(request_validator) + self.refresh_grant = RefreshTokenGrant(request_validator) + self.bearer = BearerToken( + request_validator, token_generator, token_expires_in, refresh_token_generator + ) + TokenEndpoint.__init__( + self, + default_grant_type="password", + grant_types={ + "password": self.password_grant, + "refresh_token": self.refresh_grant, + }, + default_token_type=self.bearer, + ) + ResourceEndpoint.__init__( + self, default_token="Bearer", token_types={"Bearer": self.bearer} + ) + RevocationEndpoint.__init__(self, request_validator) + IntrospectEndpoint.__init__(self, request_validator) + + +class BackendApplicationServer( + TokenEndpoint, IntrospectEndpoint, ResourceEndpoint, RevocationEndpoint +): + """An all-in-one endpoint featuring Client Credentials grant and Bearer tokens.""" + + def __init__( + self, + request_validator, + token_generator=None, + token_expires_in=None, + refresh_token_generator=None, + **kwargs, + ): + """Construct a client credentials grant server. + + :param request_validator: An implementation of + oauthlib.oauth2.RequestValidator. + :param token_expires_in: An int or a function to generate a token + expiration offset (in seconds) given a + oauthlib.common.Request object. + :param token_generator: A function to generate a token from a request. + :param refresh_token_generator: A function to generate a token from a + request for the refresh token. + :param kwargs: Extra parameters to pass to authorization-, + token-, resource-, and revocation-endpoint constructors. + """ + self.credentials_grant = ClientCredentialsGrant(request_validator) + self.bearer = BearerToken( + request_validator, token_generator, token_expires_in, refresh_token_generator + ) + TokenEndpoint.__init__( + self, + default_grant_type="client_credentials", + grant_types={"client_credentials": self.credentials_grant}, + default_token_type=self.bearer, + ) + ResourceEndpoint.__init__( + self, default_token="Bearer", token_types={"Bearer": self.bearer} + ) + RevocationEndpoint.__init__( + self, request_validator, supported_token_types=["access_token"] + ) + IntrospectEndpoint.__init__( + self, request_validator, supported_token_types=["access_token"] + ) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/endpoints/resource.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/endpoints/resource.py new file mode 100644 index 0000000000000000000000000000000000000000..d1ff5049d858311a88c4a4115f59437cf20f6243 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/endpoints/resource.py @@ -0,0 +1,84 @@ +""" +oauthlib.oauth2.rfc6749 +~~~~~~~~~~~~~~~~~~~~~~~ + +This module is an implementation of various logic needed +for consuming and providing OAuth 2.0 RFC6749. +""" +import logging + +from oauthlib.common import Request + +from .base import BaseEndpoint, catch_errors_and_unavailability + +log = logging.getLogger(__name__) + + +class ResourceEndpoint(BaseEndpoint): + + """Authorizes access to protected resources. + + The client accesses protected resources by presenting the access + token to the resource server. The resource server MUST validate the + access token and ensure that it has not expired and that its scope + covers the requested resource. The methods used by the resource + server to validate the access token (as well as any error responses) + are beyond the scope of this specification but generally involve an + interaction or coordination between the resource server and the + authorization server:: + + # For most cases, returning a 403 should suffice. + + The method in which the client utilizes the access token to + authenticate with the resource server depends on the type of access + token issued by the authorization server. Typically, it involves + using the HTTP "Authorization" request header field [RFC2617] with an + authentication scheme defined by the specification of the access + token type used, such as [RFC6750]:: + + # Access tokens may also be provided in query and body + https://example.com/protected?access_token=kjfch2345sdf # Query + access_token=sdf23409df # Body + """ + + def __init__(self, default_token, token_types): + BaseEndpoint.__init__(self) + self._tokens = token_types + self._default_token = default_token + + @property + def default_token(self): + return self._default_token + + @property + def default_token_type_handler(self): + return self.tokens.get(self.default_token) + + @property + def tokens(self): + return self._tokens + + @catch_errors_and_unavailability + def verify_request(self, uri, http_method='GET', body=None, headers=None, + scopes=None): + """Validate client, code etc, return body + headers""" + request = Request(uri, http_method, body, headers) + request.token_type = self.find_token_type(request) + request.scopes = scopes + token_type_handler = self.tokens.get(request.token_type, + self.default_token_type_handler) + log.debug('Dispatching token_type %s request to %r.', + request.token_type, token_type_handler) + return token_type_handler.validate_request(request), request + + def find_token_type(self, request): + """Token type identification. + + RFC 6749 does not provide a method for easily differentiating between + different token types during protected resource access. We estimate + the most likely token type (if any) by asking each known token type + to give an estimation based on the request. + """ + estimates = sorted(((t.estimate_type(request), n) + for n, t in self.tokens.items()), reverse=True) + return estimates[0][1] if estimates else None diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/endpoints/revocation.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/endpoints/revocation.py new file mode 100644 index 0000000000000000000000000000000000000000..596d0860fae67017a86b1042fcf9edb674fdf38d --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/endpoints/revocation.py @@ -0,0 +1,126 @@ +""" +oauthlib.oauth2.rfc6749.endpoint.revocation +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +An implementation of the OAuth 2 `Token Revocation`_ spec (draft 11). + +.. _`Token Revocation`: https://tools.ietf.org/html/draft-ietf-oauth-revocation-11 +""" +import logging + +from oauthlib.common import Request + +from ..errors import OAuth2Error +from .base import BaseEndpoint, catch_errors_and_unavailability + +log = logging.getLogger(__name__) + + +class RevocationEndpoint(BaseEndpoint): + + """Token revocation endpoint. + + Endpoint used by authenticated clients to revoke access and refresh tokens. + Commonly this will be part of the Authorization Endpoint. + """ + + valid_token_types = ('access_token', 'refresh_token') + valid_request_methods = ('POST',) + + def __init__(self, request_validator, supported_token_types=None, + enable_jsonp=False): + BaseEndpoint.__init__(self) + self.request_validator = request_validator + self.supported_token_types = ( + supported_token_types or self.valid_token_types) + self.enable_jsonp = enable_jsonp + + @catch_errors_and_unavailability + def create_revocation_response(self, uri, http_method='POST', body=None, + headers=None): + """Revoke supplied access or refresh token. + + + The authorization server responds with HTTP status code 200 if the + token has been revoked successfully or if the client submitted an + invalid token. + + Note: invalid tokens do not cause an error response since the client + cannot handle such an error in a reasonable way. Moreover, the purpose + of the revocation request, invalidating the particular token, is + already achieved. + + The content of the response body is ignored by the client as all + necessary information is conveyed in the response code. + + An invalid token type hint value is ignored by the authorization server + and does not influence the revocation response. + """ + resp_headers = { + 'Content-Type': 'application/json', + 'Cache-Control': 'no-store', + 'Pragma': 'no-cache', + } + request = Request( + uri, http_method=http_method, body=body, headers=headers) + try: + self.validate_revocation_request(request) + log.debug('Token revocation valid for %r.', request) + except OAuth2Error as e: + log.debug('Client error during validation of %r. %r.', request, e) + response_body = e.json + if self.enable_jsonp and request.callback: + response_body = '{}({});'.format(request.callback, response_body) + resp_headers.update(e.headers) + return resp_headers, response_body, e.status_code + + self.request_validator.revoke_token(request.token, + request.token_type_hint, request) + + response_body = '' + if self.enable_jsonp and request.callback: + response_body = request.callback + '();' + return {}, response_body, 200 + + def validate_revocation_request(self, request): + """Ensure the request is valid. + + The client constructs the request by including the following parameters + using the "application/x-www-form-urlencoded" format in the HTTP + request entity-body: + + token (REQUIRED). The token that the client wants to get revoked. + + token_type_hint (OPTIONAL). A hint about the type of the token + submitted for revocation. Clients MAY pass this parameter in order to + help the authorization server to optimize the token lookup. If the + server is unable to locate the token using the given hint, it MUST + extend its search across all of its supported token types. An + authorization server MAY ignore this parameter, particularly if it is + able to detect the token type automatically. This specification + defines two such values: + + * access_token: An Access Token as defined in [RFC6749], + `section 1.4`_ + + * refresh_token: A Refresh Token as defined in [RFC6749], + `section 1.5`_ + + Specific implementations, profiles, and extensions of this + specification MAY define other values for this parameter using + the registry defined in `Section 4.1.2`_. + + The client also includes its authentication credentials as described in + `Section 2.3`_. of [`RFC6749`_]. + + .. _`section 1.4`: https://tools.ietf.org/html/rfc6749#section-1.4 + .. _`section 1.5`: https://tools.ietf.org/html/rfc6749#section-1.5 + .. _`section 2.3`: https://tools.ietf.org/html/rfc6749#section-2.3 + .. _`Section 4.1.2`: https://tools.ietf.org/html/draft-ietf-oauth-revocation-11#section-4.1.2 + .. _`RFC6749`: https://tools.ietf.org/html/rfc6749 + """ + self._raise_on_bad_method(request) + self._raise_on_bad_post_request(request) + self._raise_on_missing_token(request) + self._raise_on_invalid_client(request) + self._raise_on_unsupported_token(request) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/endpoints/token.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/endpoints/token.py new file mode 100644 index 0000000000000000000000000000000000000000..ab9e0918b30da3271335fbbac370d5667c9b1d61 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/endpoints/token.py @@ -0,0 +1,119 @@ +""" +oauthlib.oauth2.rfc6749 +~~~~~~~~~~~~~~~~~~~~~~~ + +This module is an implementation of various logic needed +for consuming and providing OAuth 2.0 RFC6749. +""" +import logging + +from oauthlib.common import Request +from oauthlib.oauth2.rfc6749 import utils + +from .base import BaseEndpoint, catch_errors_and_unavailability + +log = logging.getLogger(__name__) + + +class TokenEndpoint(BaseEndpoint): + + """Token issuing endpoint. + + The token endpoint is used by the client to obtain an access token by + presenting its authorization grant or refresh token. The token + endpoint is used with every authorization grant except for the + implicit grant type (since an access token is issued directly). + + The means through which the client obtains the location of the token + endpoint are beyond the scope of this specification, but the location + is typically provided in the service documentation. + + The endpoint URI MAY include an "application/x-www-form-urlencoded" + formatted (per `Appendix B`_) query component, + which MUST be retained when adding additional query parameters. The + endpoint URI MUST NOT include a fragment component:: + + https://example.com/path?query=component # OK + https://example.com/path?query=component#fragment # Not OK + + Since requests to the token endpoint result in the transmission of + clear-text credentials (in the HTTP request and response), the + authorization server MUST require the use of TLS as described in + Section 1.6 when sending requests to the token endpoint:: + + # We will deny any request which URI schema is not with https + + The client MUST use the HTTP "POST" method when making access token + requests:: + + # HTTP method is currently not enforced + + Parameters sent without a value MUST be treated as if they were + omitted from the request. The authorization server MUST ignore + unrecognized request parameters. Request and response parameters + MUST NOT be included more than once:: + + # Delegated to each grant type. + + .. _`Appendix B`: https://tools.ietf.org/html/rfc6749#appendix-B + """ + + valid_request_methods = ('POST',) + + def __init__(self, default_grant_type, default_token_type, grant_types): + BaseEndpoint.__init__(self) + self._grant_types = grant_types + self._default_token_type = default_token_type + self._default_grant_type = default_grant_type + + @property + def grant_types(self): + return self._grant_types + + @property + def default_grant_type(self): + return self._default_grant_type + + @property + def default_grant_type_handler(self): + return self.grant_types.get(self.default_grant_type) + + @property + def default_token_type(self): + return self._default_token_type + + @catch_errors_and_unavailability + def create_token_response(self, uri, http_method='POST', body=None, + headers=None, credentials=None, grant_type_for_scope=None, + claims=None): + """Extract grant_type and route to the designated handler.""" + request = Request( + uri, http_method=http_method, body=body, headers=headers) + self.validate_token_request(request) + # 'scope' is an allowed Token Request param in both the "Resource Owner Password Credentials Grant" + # and "Client Credentials Grant" flows + # https://tools.ietf.org/html/rfc6749#section-4.3.2 + # https://tools.ietf.org/html/rfc6749#section-4.4.2 + request.scopes = utils.scope_to_list(request.scope) + + request.extra_credentials = credentials + if grant_type_for_scope: + request.grant_type = grant_type_for_scope + + # OpenID Connect claims, if provided. The server using oauthlib might choose + # to implement the claims parameter of the Authorization Request. In this case + # it should retrieve those claims and pass them via the claims argument here, + # as a dict. + if claims: + request.claims = claims + + grant_type_handler = self.grant_types.get(request.grant_type, + self.default_grant_type_handler) + log.debug('Dispatching grant_type %s request to %r.', + request.grant_type, grant_type_handler) + return grant_type_handler.create_token_response( + request, self.default_token_type) + + def validate_token_request(self, request): + self._raise_on_bad_method(request) + self._raise_on_bad_post_request(request) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/errors.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/errors.py new file mode 100644 index 0000000000000000000000000000000000000000..be8e7a1ec6d74c00df1509d4d7a18b6213bdbf23 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/errors.py @@ -0,0 +1,399 @@ +""" +oauthlib.oauth2.rfc6749.errors +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Error used both by OAuth 2 clients and providers to represent the spec +defined error responses for all four core grant types. +""" +import json +import inspect +import sys + +from oauthlib.common import add_params_to_uri, urlencode + + +class OAuth2Error(Exception): + error = None + status_code = 400 + description = '' + + def __init__(self, description=None, uri=None, state=None, + status_code=None, request=None): + """ + :param description: A human-readable ASCII [USASCII] text providing + additional information, used to assist the client + developer in understanding the error that occurred. + Values for the "error_description" parameter + MUST NOT include characters outside the set + x20-21 / x23-5B / x5D-7E. + + :param uri: A URI identifying a human-readable web page with information + about the error, used to provide the client developer with + additional information about the error. Values for the + "error_uri" parameter MUST conform to the URI- Reference + syntax, and thus MUST NOT include characters outside the set + x21 / x23-5B / x5D-7E. + + :param state: A CSRF protection value received from the client. + + :param status_code: + + :param request: OAuthlib request. + :type request: oauthlib.common.Request + """ + if description is not None: + self.description = description + + message = '({}) {}'.format(self.error, self.description) + if request: + message += ' ' + repr(request) + super().__init__(message) + + self.uri = uri + self.state = state + + if status_code: + self.status_code = status_code + + if request: + self.redirect_uri = request.redirect_uri + self.client_id = request.client_id + self.scopes = request.scopes + self.response_type = request.response_type + self.response_mode = request.response_mode + self.grant_type = request.grant_type + if state is None: + self.state = request.state + else: + self.redirect_uri = None + self.client_id = None + self.scopes = None + self.response_type = None + self.response_mode = None + self.grant_type = None + + def in_uri(self, uri): + fragment = self.response_mode == "fragment" + return add_params_to_uri(uri, self.twotuples, fragment) + + @property + def twotuples(self): + error = [('error', self.error)] + if self.description: + error.append(('error_description', self.description)) + if self.uri: + error.append(('error_uri', self.uri)) + if self.state: + error.append(('state', self.state)) + return error + + @property + def urlencoded(self): + return urlencode(self.twotuples) + + @property + def json(self): + return json.dumps(dict(self.twotuples)) + + @property + def headers(self): + if self.status_code == 401: + """ + https://tools.ietf.org/html/rfc6750#section-3 + + All challenges defined by this specification MUST use the auth-scheme + value "Bearer". This scheme MUST be followed by one or more + auth-param values. + """ + authvalues = ['error="{}"'.format(self.error)] + if self.description: + authvalues.append('error_description="{}"'.format(self.description)) + if self.uri: + authvalues.append('error_uri="{}"'.format(self.uri)) + return {"WWW-Authenticate": "Bearer " + ", ".join(authvalues)} + return {} + + +class TokenExpiredError(OAuth2Error): + error = 'token_expired' + + +class InsecureTransportError(OAuth2Error): + error = 'insecure_transport' + description = 'OAuth 2 MUST utilize https.' + + +class MismatchingStateError(OAuth2Error): + error = 'mismatching_state' + description = 'CSRF Warning! State not equal in request and response.' + + +class MissingCodeError(OAuth2Error): + error = 'missing_code' + + +class MissingTokenError(OAuth2Error): + error = 'missing_token' + + +class MissingTokenTypeError(OAuth2Error): + error = 'missing_token_type' + + +class FatalClientError(OAuth2Error): + """ + Errors during authorization where user should not be redirected back. + + If the request fails due to a missing, invalid, or mismatching + redirection URI, or if the client identifier is missing or invalid, + the authorization server SHOULD inform the resource owner of the + error and MUST NOT automatically redirect the user-agent to the + invalid redirection URI. + + Instead the user should be informed of the error by the provider itself. + """ + + +class InvalidRequestFatalError(FatalClientError): + """ + For fatal errors, the request is missing a required parameter, includes + an invalid parameter value, includes a parameter more than once, or is + otherwise malformed. + """ + error = 'invalid_request' + + +class InvalidRedirectURIError(InvalidRequestFatalError): + description = 'Invalid redirect URI.' + + +class MissingRedirectURIError(InvalidRequestFatalError): + description = 'Missing redirect URI.' + + +class MismatchingRedirectURIError(InvalidRequestFatalError): + description = 'Mismatching redirect URI.' + + +class InvalidClientIdError(InvalidRequestFatalError): + description = 'Invalid client_id parameter value.' + + +class MissingClientIdError(InvalidRequestFatalError): + description = 'Missing client_id parameter.' + + +class InvalidRequestError(OAuth2Error): + """ + The request is missing a required parameter, includes an invalid + parameter value, includes a parameter more than once, or is + otherwise malformed. + """ + error = 'invalid_request' + + +class MissingResponseTypeError(InvalidRequestError): + description = 'Missing response_type parameter.' + + +class MissingCodeChallengeError(InvalidRequestError): + """ + If the server requires Proof Key for Code Exchange (PKCE) by OAuth + public clients and the client does not send the "code_challenge" in + the request, the authorization endpoint MUST return the authorization + error response with the "error" value set to "invalid_request". The + "error_description" or the response of "error_uri" SHOULD explain the + nature of error, e.g., code challenge required. + """ + description = 'Code challenge required.' + + +class MissingCodeVerifierError(InvalidRequestError): + """ + The request to the token endpoint, when PKCE is enabled, has + the parameter `code_verifier` REQUIRED. + """ + description = 'Code verifier required.' + + +class AccessDeniedError(OAuth2Error): + """ + The resource owner or authorization server denied the request. + """ + error = 'access_denied' + + +class UnsupportedResponseTypeError(OAuth2Error): + """ + The authorization server does not support obtaining an authorization + code using this method. + """ + error = 'unsupported_response_type' + + +class UnsupportedCodeChallengeMethodError(InvalidRequestError): + """ + If the server supporting PKCE does not support the requested + transformation, the authorization endpoint MUST return the + authorization error response with "error" value set to + "invalid_request". The "error_description" or the response of + "error_uri" SHOULD explain the nature of error, e.g., transform + algorithm not supported. + """ + description = 'Transform algorithm not supported.' + + +class InvalidScopeError(OAuth2Error): + """ + The requested scope is invalid, unknown, or malformed, or + exceeds the scope granted by the resource owner. + + https://tools.ietf.org/html/rfc6749#section-5.2 + """ + error = 'invalid_scope' + + +class ServerError(OAuth2Error): + """ + The authorization server encountered an unexpected condition that + prevented it from fulfilling the request. (This error code is needed + because a 500 Internal Server Error HTTP status code cannot be returned + to the client via a HTTP redirect.) + """ + error = 'server_error' + + +class TemporarilyUnavailableError(OAuth2Error): + """ + The authorization server is currently unable to handle the request + due to a temporary overloading or maintenance of the server. + (This error code is needed because a 503 Service Unavailable HTTP + status code cannot be returned to the client via a HTTP redirect.) + """ + error = 'temporarily_unavailable' + + +class InvalidClientError(FatalClientError): + """ + Client authentication failed (e.g. unknown client, no client + authentication included, or unsupported authentication method). + The authorization server MAY return an HTTP 401 (Unauthorized) status + code to indicate which HTTP authentication schemes are supported. + If the client attempted to authenticate via the "Authorization" request + header field, the authorization server MUST respond with an + HTTP 401 (Unauthorized) status code, and include the "WWW-Authenticate" + response header field matching the authentication scheme used by the + client. + """ + error = 'invalid_client' + status_code = 401 + + +class InvalidGrantError(OAuth2Error): + """ + The provided authorization grant (e.g. authorization code, resource + owner credentials) or refresh token is invalid, expired, revoked, does + not match the redirection URI used in the authorization request, or was + issued to another client. + + https://tools.ietf.org/html/rfc6749#section-5.2 + """ + error = 'invalid_grant' + status_code = 400 + + +class UnauthorizedClientError(OAuth2Error): + """ + The authenticated client is not authorized to use this authorization + grant type. + """ + error = 'unauthorized_client' + + +class UnsupportedGrantTypeError(OAuth2Error): + """ + The authorization grant type is not supported by the authorization + server. + """ + error = 'unsupported_grant_type' + + +class UnsupportedTokenTypeError(OAuth2Error): + """ + The authorization server does not support the hint of the + presented token type. I.e. the client tried to revoke an access token + on a server not supporting this feature. + """ + error = 'unsupported_token_type' + + +class InvalidTokenError(OAuth2Error): + """ + The access token provided is expired, revoked, malformed, or + invalid for other reasons. The resource SHOULD respond with + the HTTP 401 (Unauthorized) status code. The client MAY + request a new access token and retry the protected resource + request. + """ + error = 'invalid_token' + status_code = 401 + description = ("The access token provided is expired, revoked, malformed, " + "or invalid for other reasons.") + + +class InsufficientScopeError(OAuth2Error): + """ + The request requires higher privileges than provided by the + access token. The resource server SHOULD respond with the HTTP + 403 (Forbidden) status code and MAY include the "scope" + attribute with the scope necessary to access the protected + resource. + """ + error = 'insufficient_scope' + status_code = 403 + description = ("The request requires higher privileges than provided by " + "the access token.") + + +class ConsentRequired(OAuth2Error): + """ + The Authorization Server requires End-User consent. + + This error MAY be returned when the prompt parameter value in the + Authentication Request is none, but the Authentication Request cannot be + completed without displaying a user interface for End-User consent. + """ + error = 'consent_required' + + +class LoginRequired(OAuth2Error): + """ + The Authorization Server requires End-User authentication. + + This error MAY be returned when the prompt parameter value in the + Authentication Request is none, but the Authentication Request cannot be + completed without displaying a user interface for End-User authentication. + """ + error = 'login_required' + + +class CustomOAuth2Error(OAuth2Error): + """ + This error is a placeholder for all custom errors not described by the RFC. + Some of the popular OAuth2 providers are using custom errors. + """ + def __init__(self, error, *args, **kwargs): + self.error = error + super().__init__(*args, **kwargs) + + +def raise_from_error(error, params=None): + kwargs = { + 'description': params.get('error_description'), + 'uri': params.get('error_uri'), + 'state': params.get('state') + } + for _, cls in inspect.getmembers(sys.modules[__name__], inspect.isclass): + if cls.error == error: + raise cls(**kwargs) + raise CustomOAuth2Error(error=error, **kwargs) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/grant_types/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/grant_types/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..eb88cfc2e9749d22780532fd75af2fc8b7c2fae2 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/grant_types/__init__.py @@ -0,0 +1,11 @@ +""" +oauthlib.oauth2.rfc6749.grant_types +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +""" +from .authorization_code import AuthorizationCodeGrant +from .client_credentials import ClientCredentialsGrant +from .implicit import ImplicitGrant +from .refresh_token import RefreshTokenGrant +from .resource_owner_password_credentials import ( + ResourceOwnerPasswordCredentialsGrant, +) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/grant_types/authorization_code.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/grant_types/authorization_code.py new file mode 100644 index 0000000000000000000000000000000000000000..09dc619988941ea1dd83e15b4b4f11689b45bb36 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/grant_types/authorization_code.py @@ -0,0 +1,547 @@ +""" +oauthlib.oauth2.rfc6749.grant_types +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +""" +import base64 +import hashlib +import json +import logging + +from oauthlib import common + +from .. import errors +from .base import GrantTypeBase + +log = logging.getLogger(__name__) + + +def code_challenge_method_s256(verifier, challenge): + """ + If the "code_challenge_method" from `Section 4.3`_ was "S256", the + received "code_verifier" is hashed by SHA-256, base64url-encoded, and + then compared to the "code_challenge", i.e.: + + BASE64URL-ENCODE(SHA256(ASCII(code_verifier))) == code_challenge + + How to implement a base64url-encoding + function without padding, based upon the standard base64-encoding + function that uses padding. + + To be concrete, example C# code implementing these functions is shown + below. Similar code could be used in other languages. + + static string base64urlencode(byte [] arg) + { + string s = Convert.ToBase64String(arg); // Regular base64 encoder + s = s.Split('=')[0]; // Remove any trailing '='s + s = s.Replace('+', '-'); // 62nd char of encoding + s = s.Replace('/', '_'); // 63rd char of encoding + return s; + } + + In python urlsafe_b64encode is already replacing '+' and '/', but preserve + the trailing '='. So we have to remove it. + + .. _`Section 4.3`: https://tools.ietf.org/html/rfc7636#section-4.3 + """ + return base64.urlsafe_b64encode( + hashlib.sha256(verifier.encode()).digest() + ).decode().rstrip('=') == challenge + + +def code_challenge_method_plain(verifier, challenge): + """ + If the "code_challenge_method" from `Section 4.3`_ was "plain", they are + compared directly, i.e.: + + code_verifier == code_challenge. + + .. _`Section 4.3`: https://tools.ietf.org/html/rfc7636#section-4.3 + """ + return verifier == challenge + + +class AuthorizationCodeGrant(GrantTypeBase): + + """`Authorization Code Grant`_ + + The authorization code grant type is used to obtain both access + tokens and refresh tokens and is optimized for confidential clients. + Since this is a redirection-based flow, the client must be capable of + interacting with the resource owner's user-agent (typically a web + browser) and capable of receiving incoming requests (via redirection) + from the authorization server:: + + +----------+ + | Resource | + | Owner | + | | + +----------+ + ^ + | + (B) + +----|-----+ Client Identifier +---------------+ + | -+----(A)-- & Redirection URI ---->| | + | User- | | Authorization | + | Agent -+----(B)-- User authenticates --->| Server | + | | | | + | -+----(C)-- Authorization Code ---<| | + +-|----|---+ +---------------+ + | | ^ v + (A) (C) | | + | | | | + ^ v | | + +---------+ | | + | |>---(D)-- Authorization Code ---------' | + | Client | & Redirection URI | + | | | + | |<---(E)----- Access Token -------------------' + +---------+ (w/ Optional Refresh Token) + + Note: The lines illustrating steps (A), (B), and (C) are broken into + two parts as they pass through the user-agent. + + Figure 3: Authorization Code Flow + + The flow illustrated in Figure 3 includes the following steps: + + (A) The client initiates the flow by directing the resource owner's + user-agent to the authorization endpoint. The client includes + its client identifier, requested scope, local state, and a + redirection URI to which the authorization server will send the + user-agent back once access is granted (or denied). + + (B) The authorization server authenticates the resource owner (via + the user-agent) and establishes whether the resource owner + grants or denies the client's access request. + + (C) Assuming the resource owner grants access, the authorization + server redirects the user-agent back to the client using the + redirection URI provided earlier (in the request or during + client registration). The redirection URI includes an + authorization code and any local state provided by the client + earlier. + + (D) The client requests an access token from the authorization + server's token endpoint by including the authorization code + received in the previous step. When making the request, the + client authenticates with the authorization server. The client + includes the redirection URI used to obtain the authorization + code for verification. + + (E) The authorization server authenticates the client, validates the + authorization code, and ensures that the redirection URI + received matches the URI used to redirect the client in + step (C). If valid, the authorization server responds back with + an access token and, optionally, a refresh token. + + OAuth 2.0 public clients utilizing the Authorization Code Grant are + susceptible to the authorization code interception attack. + + A technique to mitigate against the threat through the use of Proof Key for Code + Exchange (PKCE, pronounced "pixy") is implemented in the current oauthlib + implementation. + + .. _`Authorization Code Grant`: https://tools.ietf.org/html/rfc6749#section-4.1 + .. _`PKCE`: https://tools.ietf.org/html/rfc7636 + """ + + default_response_mode = 'query' + response_types = ['code'] + + # This dict below is private because as RFC mention it: + # "S256" is Mandatory To Implement (MTI) on the server. + # + _code_challenge_methods = { + 'plain': code_challenge_method_plain, + 'S256': code_challenge_method_s256 + } + + def create_authorization_code(self, request): + """ + Generates an authorization grant represented as a dictionary. + + :param request: OAuthlib request. + :type request: oauthlib.common.Request + """ + grant = {'code': common.generate_token()} + if hasattr(request, 'state') and request.state: + grant['state'] = request.state + log.debug('Created authorization code grant %r for request %r.', + grant, request) + return grant + + def create_authorization_response(self, request, token_handler): + """ + The client constructs the request URI by adding the following + parameters to the query component of the authorization endpoint URI + using the "application/x-www-form-urlencoded" format, per `Appendix B`_: + + response_type + REQUIRED. Value MUST be set to "code" for standard OAuth2 + authorization flow. For OpenID Connect it must be one of + "code token", "code id_token", or "code token id_token" - we + essentially test that "code" appears in the response_type. + client_id + REQUIRED. The client identifier as described in `Section 2.2`_. + redirect_uri + OPTIONAL. As described in `Section 3.1.2`_. + scope + OPTIONAL. The scope of the access request as described by + `Section 3.3`_. + state + RECOMMENDED. An opaque value used by the client to maintain + state between the request and callback. The authorization + server includes this value when redirecting the user-agent back + to the client. The parameter SHOULD be used for preventing + cross-site request forgery as described in `Section 10.12`_. + + The client directs the resource owner to the constructed URI using an + HTTP redirection response, or by other means available to it via the + user-agent. + + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :param token_handler: A token handler instance, for example of type + oauthlib.oauth2.BearerToken. + :returns: headers, body, status + :raises: FatalClientError on invalid redirect URI or client id. + + A few examples:: + + >>> from your_validator import your_validator + >>> request = Request('https://example.com/authorize?client_id=valid' + ... '&redirect_uri=http%3A%2F%2Fclient.com%2F') + >>> from oauthlib.common import Request + >>> from oauthlib.oauth2 import AuthorizationCodeGrant, BearerToken + >>> token = BearerToken(your_validator) + >>> grant = AuthorizationCodeGrant(your_validator) + >>> request.scopes = ['authorized', 'in', 'some', 'form'] + >>> grant.create_authorization_response(request, token) + (u'http://client.com/?error=invalid_request&error_description=Missing+response_type+parameter.', None, None, 400) + >>> request = Request('https://example.com/authorize?client_id=valid' + ... '&redirect_uri=http%3A%2F%2Fclient.com%2F' + ... '&response_type=code') + >>> request.scopes = ['authorized', 'in', 'some', 'form'] + >>> grant.create_authorization_response(request, token) + (u'http://client.com/?code=u3F05aEObJuP2k7DordviIgW5wl52N', None, None, 200) + >>> # If the client id or redirect uri fails validation + >>> grant.create_authorization_response(request, token) + Traceback (most recent call last): + File "", line 1, in + File "oauthlib/oauth2/rfc6749/grant_types.py", line 515, in create_authorization_response + >>> grant.create_authorization_response(request, token) + File "oauthlib/oauth2/rfc6749/grant_types.py", line 591, in validate_authorization_request + oauthlib.oauth2.rfc6749.errors.InvalidClientIdError + + .. _`Appendix B`: https://tools.ietf.org/html/rfc6749#appendix-B + .. _`Section 2.2`: https://tools.ietf.org/html/rfc6749#section-2.2 + .. _`Section 3.1.2`: https://tools.ietf.org/html/rfc6749#section-3.1.2 + .. _`Section 3.3`: https://tools.ietf.org/html/rfc6749#section-3.3 + .. _`Section 10.12`: https://tools.ietf.org/html/rfc6749#section-10.12 + """ + try: + self.validate_authorization_request(request) + log.debug('Pre resource owner authorization validation ok for %r.', + request) + + # If the request fails due to a missing, invalid, or mismatching + # redirection URI, or if the client identifier is missing or invalid, + # the authorization server SHOULD inform the resource owner of the + # error and MUST NOT automatically redirect the user-agent to the + # invalid redirection URI. + except errors.FatalClientError as e: + log.debug('Fatal client error during validation of %r. %r.', + request, e) + raise + + # If the resource owner denies the access request or if the request + # fails for reasons other than a missing or invalid redirection URI, + # the authorization server informs the client by adding the following + # parameters to the query component of the redirection URI using the + # "application/x-www-form-urlencoded" format, per Appendix B: + # https://tools.ietf.org/html/rfc6749#appendix-B + except errors.OAuth2Error as e: + log.debug('Client error during validation of %r. %r.', request, e) + request.redirect_uri = request.redirect_uri or self.error_uri + redirect_uri = common.add_params_to_uri( + request.redirect_uri, e.twotuples, + fragment=request.response_mode == "fragment") + return {'Location': redirect_uri}, None, 302 + + grant = self.create_authorization_code(request) + for modifier in self._code_modifiers: + grant = modifier(grant, token_handler, request) + if 'access_token' in grant: + self.request_validator.save_token(grant, request) + log.debug('Saving grant %r for %r.', grant, request) + self.request_validator.save_authorization_code( + request.client_id, grant, request) + return self.prepare_authorization_response( + request, grant, {}, None, 302) + + def create_token_response(self, request, token_handler): + """Validate the authorization code. + + The client MUST NOT use the authorization code more than once. If an + authorization code is used more than once, the authorization server + MUST deny the request and SHOULD revoke (when possible) all tokens + previously issued based on that authorization code. The authorization + code is bound to the client identifier and redirection URI. + + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :param token_handler: A token handler instance, for example of type + oauthlib.oauth2.BearerToken. + + """ + headers = self._get_default_headers() + try: + self.validate_token_request(request) + log.debug('Token request validation ok for %r.', request) + except errors.OAuth2Error as e: + log.debug('Client error during validation of %r. %r.', request, e) + headers.update(e.headers) + return headers, e.json, e.status_code + + token = token_handler.create_token(request, refresh_token=self.refresh_token) + + for modifier in self._token_modifiers: + token = modifier(token, token_handler, request) + + self.request_validator.save_token(token, request) + self.request_validator.invalidate_authorization_code( + request.client_id, request.code, request) + headers.update(self._create_cors_headers(request)) + return headers, json.dumps(token), 200 + + def validate_authorization_request(self, request): + """Check the authorization request for normal and fatal errors. + + A normal error could be a missing response_type parameter or the client + attempting to access scope it is not allowed to ask authorization for. + Normal errors can safely be included in the redirection URI and + sent back to the client. + + Fatal errors occur when the client_id or redirect_uri is invalid or + missing. These must be caught by the provider and handled, how this + is done is outside of the scope of OAuthLib but showing an error + page describing the issue is a good idea. + + :param request: OAuthlib request. + :type request: oauthlib.common.Request + """ + + # First check for fatal errors + + # If the request fails due to a missing, invalid, or mismatching + # redirection URI, or if the client identifier is missing or invalid, + # the authorization server SHOULD inform the resource owner of the + # error and MUST NOT automatically redirect the user-agent to the + # invalid redirection URI. + + # First check duplicate parameters + for param in ('client_id', 'response_type', 'redirect_uri', 'scope', 'state'): + try: + duplicate_params = request.duplicate_params + except ValueError: + raise errors.InvalidRequestFatalError(description='Unable to parse query string', request=request) + if param in duplicate_params: + raise errors.InvalidRequestFatalError(description='Duplicate %s parameter.' % param, request=request) + + # REQUIRED. The client identifier as described in Section 2.2. + # https://tools.ietf.org/html/rfc6749#section-2.2 + if not request.client_id: + raise errors.MissingClientIdError(request=request) + + if not self.request_validator.validate_client_id(request.client_id, request): + raise errors.InvalidClientIdError(request=request) + + # OPTIONAL. As described in Section 3.1.2. + # https://tools.ietf.org/html/rfc6749#section-3.1.2 + log.debug('Validating redirection uri %s for client %s.', + request.redirect_uri, request.client_id) + + # OPTIONAL. As described in Section 3.1.2. + # https://tools.ietf.org/html/rfc6749#section-3.1.2 + self._handle_redirects(request) + + # Then check for normal errors. + + # If the resource owner denies the access request or if the request + # fails for reasons other than a missing or invalid redirection URI, + # the authorization server informs the client by adding the following + # parameters to the query component of the redirection URI using the + # "application/x-www-form-urlencoded" format, per Appendix B. + # https://tools.ietf.org/html/rfc6749#appendix-B + + # Note that the correct parameters to be added are automatically + # populated through the use of specific exceptions. + + request_info = {} + for validator in self.custom_validators.pre_auth: + request_info.update(validator(request)) + + # REQUIRED. + if request.response_type is None: + raise errors.MissingResponseTypeError(request=request) + # Value MUST be set to "code" or one of the OpenID authorization code including + # response_types "code token", "code id_token", "code token id_token" + elif 'code' not in request.response_type and request.response_type != 'none': + raise errors.UnsupportedResponseTypeError(request=request) + + if not self.request_validator.validate_response_type(request.client_id, + request.response_type, + request.client, request): + + log.debug('Client %s is not authorized to use response_type %s.', + request.client_id, request.response_type) + raise errors.UnauthorizedClientError(request=request) + + # OPTIONAL. Validate PKCE request or reply with "error"/"invalid_request" + # https://tools.ietf.org/html/rfc6749#section-4.4.1 + if self.request_validator.is_pkce_required(request.client_id, request) is True and request.code_challenge is None: + raise errors.MissingCodeChallengeError(request=request) + + if request.code_challenge is not None: + request_info["code_challenge"] = request.code_challenge + + # OPTIONAL, defaults to "plain" if not present in the request. + if request.code_challenge_method is None: + request.code_challenge_method = "plain" + + if request.code_challenge_method not in self._code_challenge_methods: + raise errors.UnsupportedCodeChallengeMethodError(request=request) + request_info["code_challenge_method"] = request.code_challenge_method + + # OPTIONAL. The scope of the access request as described by Section 3.3 + # https://tools.ietf.org/html/rfc6749#section-3.3 + self.validate_scopes(request) + + request_info.update({ + 'client_id': request.client_id, + 'redirect_uri': request.redirect_uri, + 'response_type': request.response_type, + 'state': request.state, + 'request': request + }) + + for validator in self.custom_validators.post_auth: + request_info.update(validator(request)) + + return request.scopes, request_info + + def validate_token_request(self, request): + """ + :param request: OAuthlib request. + :type request: oauthlib.common.Request + """ + # REQUIRED. Value MUST be set to "authorization_code". + if request.grant_type not in ('authorization_code', 'openid'): + raise errors.UnsupportedGrantTypeError(request=request) + + for validator in self.custom_validators.pre_token: + validator(request) + + if request.code is None: + raise errors.InvalidRequestError( + description='Missing code parameter.', request=request) + + for param in ('client_id', 'grant_type', 'redirect_uri'): + if param in request.duplicate_params: + raise errors.InvalidRequestError(description='Duplicate %s parameter.' % param, + request=request) + + if self.request_validator.client_authentication_required(request): + # If the client type is confidential or the client was issued client + # credentials (or assigned other authentication requirements), the + # client MUST authenticate with the authorization server as described + # in Section 3.2.1. + # https://tools.ietf.org/html/rfc6749#section-3.2.1 + if not self.request_validator.authenticate_client(request): + log.debug('Client authentication failed, %r.', request) + raise errors.InvalidClientError(request=request) + elif not self.request_validator.authenticate_client_id(request.client_id, request): + # REQUIRED, if the client is not authenticating with the + # authorization server as described in Section 3.2.1. + # https://tools.ietf.org/html/rfc6749#section-3.2.1 + log.debug('Client authentication failed, %r.', request) + raise errors.InvalidClientError(request=request) + + if not hasattr(request.client, 'client_id'): + raise NotImplementedError('Authenticate client must set the ' + 'request.client.client_id attribute ' + 'in authenticate_client.') + + request.client_id = request.client_id or request.client.client_id + + # Ensure client is authorized use of this grant type + self.validate_grant_type(request) + + # REQUIRED. The authorization code received from the + # authorization server. + if not self.request_validator.validate_code(request.client_id, + request.code, request.client, request): + log.debug('Client, %r (%r), is not allowed access to scopes %r.', + request.client_id, request.client, request.scopes) + raise errors.InvalidGrantError(request=request) + + # OPTIONAL. Validate PKCE code_verifier + challenge = self.request_validator.get_code_challenge(request.code, request) + + if challenge is not None: + if request.code_verifier is None: + raise errors.MissingCodeVerifierError(request=request) + + challenge_method = self.request_validator.get_code_challenge_method(request.code, request) + if challenge_method is None: + raise errors.InvalidGrantError(request=request, description="Challenge method not found") + + if challenge_method not in self._code_challenge_methods: + raise errors.ServerError( + description="code_challenge_method {} is not supported.".format(challenge_method), + request=request + ) + + if not self.validate_code_challenge(challenge, + challenge_method, + request.code_verifier): + log.debug('request provided a invalid code_verifier.') + raise errors.InvalidGrantError(request=request) + elif self.request_validator.is_pkce_required(request.client_id, request) is True: + if request.code_verifier is None: + raise errors.MissingCodeVerifierError(request=request) + raise errors.InvalidGrantError(request=request, description="Challenge not found") + + for attr in ('user', 'scopes'): + if getattr(request, attr, None) is None: + log.debug('request.%s was not set on code validation.', attr) + + # REQUIRED, if the "redirect_uri" parameter was included in the + # authorization request as described in Section 4.1.1, and their + # values MUST be identical. + if request.redirect_uri is None: + request.using_default_redirect_uri = True + request.redirect_uri = self.request_validator.get_default_redirect_uri( + request.client_id, request) + log.debug('Using default redirect_uri %s.', request.redirect_uri) + if not request.redirect_uri: + raise errors.MissingRedirectURIError(request=request) + else: + request.using_default_redirect_uri = False + log.debug('Using provided redirect_uri %s', request.redirect_uri) + + if not self.request_validator.confirm_redirect_uri(request.client_id, request.code, + request.redirect_uri, request.client, + request): + log.debug('Redirect_uri (%r) invalid for client %r (%r).', + request.redirect_uri, request.client_id, request.client) + raise errors.MismatchingRedirectURIError(request=request) + + for validator in self.custom_validators.post_token: + validator(request) + + def validate_code_challenge(self, challenge, challenge_method, verifier): + if challenge_method in self._code_challenge_methods: + return self._code_challenge_methods[challenge_method](verifier, challenge) + raise NotImplementedError('Unknown challenge_method %s' % challenge_method) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/grant_types/base.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/grant_types/base.py new file mode 100644 index 0000000000000000000000000000000000000000..d96a2db4fae8f8a73bdda5240afe2f1080e52982 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/grant_types/base.py @@ -0,0 +1,265 @@ +""" +oauthlib.oauth2.rfc6749.grant_types +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +""" +import logging +from itertools import chain + +from oauthlib.common import add_params_to_uri +from oauthlib.oauth2.rfc6749 import errors, utils +from oauthlib.uri_validate import is_absolute_uri + +from ..request_validator import RequestValidator +from ..utils import is_secure_transport + +log = logging.getLogger(__name__) + + +class ValidatorsContainer: + """ + Container object for holding custom validator callables to be invoked + as part of the grant type `validate_authorization_request()` or + `validate_authorization_request()` methods on the various grant types. + + Authorization validators must be callables that take a request object and + return a dict, which may contain items to be added to the `request_info` + returned from the grant_type after validation. + + Token validators must be callables that take a request object and + return None. + + Both authorization validators and token validators may raise OAuth2 + exceptions if validation conditions fail. + + Authorization validators added to `pre_auth` will be run BEFORE + the standard validations (but after the critical ones that raise + fatal errors) as part of `validate_authorization_request()` + + Authorization validators added to `post_auth` will be run AFTER + the standard validations as part of `validate_authorization_request()` + + Token validators added to `pre_token` will be run BEFORE + the standard validations as part of `validate_token_request()` + + Token validators added to `post_token` will be run AFTER + the standard validations as part of `validate_token_request()` + + For example: + + >>> def my_auth_validator(request): + ... return {'myval': True} + >>> auth_code_grant = AuthorizationCodeGrant(request_validator) + >>> auth_code_grant.custom_validators.pre_auth.append(my_auth_validator) + >>> def my_token_validator(request): + ... if not request.everything_okay: + ... raise errors.OAuth2Error("uh-oh") + >>> auth_code_grant.custom_validators.post_token.append(my_token_validator) + """ + + def __init__(self, post_auth, post_token, + pre_auth, pre_token): + self.pre_auth = pre_auth + self.post_auth = post_auth + self.pre_token = pre_token + self.post_token = post_token + + @property + def all_pre(self): + return chain(self.pre_auth, self.pre_token) + + @property + def all_post(self): + return chain(self.post_auth, self.post_token) + + +class GrantTypeBase: + error_uri = None + request_validator = None + default_response_mode = 'fragment' + refresh_token = True + response_types = ['code'] + + def __init__(self, request_validator=None, **kwargs): + self.request_validator = request_validator or RequestValidator() + + # Transforms class variables into instance variables: + self.response_types = self.response_types + self.refresh_token = self.refresh_token + self._setup_custom_validators(kwargs) + self._code_modifiers = [] + self._token_modifiers = [] + + for kw, val in kwargs.items(): + setattr(self, kw, val) + + def _setup_custom_validators(self, kwargs): + post_auth = kwargs.get('post_auth', []) + post_token = kwargs.get('post_token', []) + pre_auth = kwargs.get('pre_auth', []) + pre_token = kwargs.get('pre_token', []) + if not hasattr(self, 'validate_authorization_request'): + if post_auth or pre_auth: + msg = ("{} does not support authorization validators. Use " + "token validators instead.").format(self.__class__.__name__) + raise ValueError(msg) + # Using tuples here because they can't be appended to: + post_auth, pre_auth = (), () + self.custom_validators = ValidatorsContainer(post_auth, post_token, + pre_auth, pre_token) + + def register_response_type(self, response_type): + self.response_types.append(response_type) + + def register_code_modifier(self, modifier): + self._code_modifiers.append(modifier) + + def register_token_modifier(self, modifier): + self._token_modifiers.append(modifier) + + def create_authorization_response(self, request, token_handler): + """ + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :param token_handler: A token handler instance, for example of type + oauthlib.oauth2.BearerToken. + """ + raise NotImplementedError('Subclasses must implement this method.') + + def create_token_response(self, request, token_handler): + """ + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :param token_handler: A token handler instance, for example of type + oauthlib.oauth2.BearerToken. + """ + raise NotImplementedError('Subclasses must implement this method.') + + def add_token(self, token, token_handler, request): + """ + :param token: + :param token_handler: A token handler instance, for example of type + oauthlib.oauth2.BearerToken. + :param request: OAuthlib request. + :type request: oauthlib.common.Request + """ + # Only add a hybrid access token on auth step if asked for + if request.response_type not in ["token", "code token", "id_token token", "code id_token token"]: + return token + + token.update(token_handler.create_token(request, refresh_token=False)) + return token + + def validate_grant_type(self, request): + """ + :param request: OAuthlib request. + :type request: oauthlib.common.Request + """ + client_id = getattr(request, 'client_id', None) + if not self.request_validator.validate_grant_type(client_id, + request.grant_type, request.client, request): + log.debug('Unauthorized from %r (%r) access to grant type %s.', + request.client_id, request.client, request.grant_type) + raise errors.UnauthorizedClientError(request=request) + + def validate_scopes(self, request): + """ + :param request: OAuthlib request. + :type request: oauthlib.common.Request + """ + if not request.scopes: + request.scopes = utils.scope_to_list(request.scope) or utils.scope_to_list( + self.request_validator.get_default_scopes(request.client_id, request)) + log.debug('Validating access to scopes %r for client %r (%r).', + request.scopes, request.client_id, request.client) + if not self.request_validator.validate_scopes(request.client_id, + request.scopes, request.client, request): + raise errors.InvalidScopeError(request=request) + + def prepare_authorization_response(self, request, token, headers, body, status): + """Place token according to response mode. + + Base classes can define a default response mode for their authorization + response by overriding the static `default_response_mode` member. + + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :param token: + :param headers: + :param body: + :param status: + """ + request.response_mode = request.response_mode or self.default_response_mode + + if request.response_mode not in ('query', 'fragment'): + log.debug('Overriding invalid response mode %s with %s', + request.response_mode, self.default_response_mode) + request.response_mode = self.default_response_mode + + token_items = token.items() + + if request.response_type == 'none': + state = token.get('state', None) + token_items = [('state', state)] if state else [] + + if request.response_mode == 'query': + headers['Location'] = add_params_to_uri( + request.redirect_uri, token_items, fragment=False) + return headers, body, status + + if request.response_mode == 'fragment': + headers['Location'] = add_params_to_uri( + request.redirect_uri, token_items, fragment=True) + return headers, body, status + + raise NotImplementedError( + 'Subclasses must set a valid default_response_mode') + + def _get_default_headers(self): + """Create default headers for grant responses.""" + return { + 'Content-Type': 'application/json', + 'Cache-Control': 'no-store', + 'Pragma': 'no-cache', + } + + def _handle_redirects(self, request): + if request.redirect_uri is not None: + request.using_default_redirect_uri = False + log.debug('Using provided redirect_uri %s', request.redirect_uri) + if not is_absolute_uri(request.redirect_uri): + raise errors.InvalidRedirectURIError(request=request) + + # The authorization server MUST verify that the redirection URI + # to which it will redirect the access token matches a + # redirection URI registered by the client as described in + # Section 3.1.2. + # https://tools.ietf.org/html/rfc6749#section-3.1.2 + if not self.request_validator.validate_redirect_uri( + request.client_id, request.redirect_uri, request): + raise errors.MismatchingRedirectURIError(request=request) + else: + request.redirect_uri = self.request_validator.get_default_redirect_uri( + request.client_id, request) + request.using_default_redirect_uri = True + log.debug('Using default redirect_uri %s.', request.redirect_uri) + if not request.redirect_uri: + raise errors.MissingRedirectURIError(request=request) + if not is_absolute_uri(request.redirect_uri): + raise errors.InvalidRedirectURIError(request=request) + + def _create_cors_headers(self, request): + """If CORS is allowed, create the appropriate headers.""" + if 'origin' not in request.headers: + return {} + + origin = request.headers['origin'] + if not is_secure_transport(origin): + log.debug('Origin "%s" is not HTTPS, CORS not allowed.', origin) + return {} + elif not self.request_validator.is_origin_allowed( + request.client_id, origin, request): + log.debug('Invalid origin "%s", CORS not allowed.', origin) + return {} + else: + log.debug('Valid origin "%s", injecting CORS headers.', origin) + return {'Access-Control-Allow-Origin': origin} diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/grant_types/client_credentials.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/grant_types/client_credentials.py new file mode 100644 index 0000000000000000000000000000000000000000..35c544027ec8b89cd7a5d092e293869de8dd91a5 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/grant_types/client_credentials.py @@ -0,0 +1,122 @@ +""" +oauthlib.oauth2.rfc6749.grant_types +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +""" +import json +import logging + +from .. import errors +from .base import GrantTypeBase + +log = logging.getLogger(__name__) + + +class ClientCredentialsGrant(GrantTypeBase): + + """`Client Credentials Grant`_ + + The client can request an access token using only its client + credentials (or other supported means of authentication) when the + client is requesting access to the protected resources under its + control, or those of another resource owner that have been previously + arranged with the authorization server (the method of which is beyond + the scope of this specification). + + The client credentials grant type MUST only be used by confidential + clients:: + + +---------+ +---------------+ + : : : : + : :>-- A - Client Authentication --->: Authorization : + : Client : : Server : + : :<-- B ---- Access Token ---------<: : + : : : : + +---------+ +---------------+ + + Figure 6: Client Credentials Flow + + The flow illustrated in Figure 6 includes the following steps: + + (A) The client authenticates with the authorization server and + requests an access token from the token endpoint. + + (B) The authorization server authenticates the client, and if valid, + issues an access token. + + .. _`Client Credentials Grant`: https://tools.ietf.org/html/rfc6749#section-4.4 + """ + + def create_token_response(self, request, token_handler): + """Return token or error in JSON format. + + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :param token_handler: A token handler instance, for example of type + oauthlib.oauth2.BearerToken. + + If the access token request is valid and authorized, the + authorization server issues an access token as described in + `Section 5.1`_. A refresh token SHOULD NOT be included. If the request + failed client authentication or is invalid, the authorization server + returns an error response as described in `Section 5.2`_. + + .. _`Section 5.1`: https://tools.ietf.org/html/rfc6749#section-5.1 + .. _`Section 5.2`: https://tools.ietf.org/html/rfc6749#section-5.2 + """ + headers = self._get_default_headers() + try: + log.debug('Validating access token request, %r.', request) + self.validate_token_request(request) + except errors.OAuth2Error as e: + log.debug('Client error in token request. %s.', e) + headers.update(e.headers) + return headers, e.json, e.status_code + + token = token_handler.create_token(request, refresh_token=False) + + for modifier in self._token_modifiers: + token = modifier(token) + + self.request_validator.save_token(token, request) + + log.debug('Issuing token to client id %r (%r), %r.', + request.client_id, request.client, token) + return headers, json.dumps(token), 200 + + def validate_token_request(self, request): + """ + :param request: OAuthlib request. + :type request: oauthlib.common.Request + """ + for validator in self.custom_validators.pre_token: + validator(request) + + if not getattr(request, 'grant_type', None): + raise errors.InvalidRequestError('Request is missing grant type.', + request=request) + + if not request.grant_type == 'client_credentials': + raise errors.UnsupportedGrantTypeError(request=request) + + for param in ('grant_type', 'scope'): + if param in request.duplicate_params: + raise errors.InvalidRequestError(description='Duplicate %s parameter.' % param, + request=request) + + log.debug('Authenticating client, %r.', request) + if not self.request_validator.authenticate_client(request): + log.debug('Client authentication failed, %r.', request) + raise errors.InvalidClientError(request=request) + elif not hasattr(request.client, 'client_id'): + raise NotImplementedError('Authenticate client must set the ' + 'request.client.client_id attribute ' + 'in authenticate_client.') + # Ensure client is authorized use of this grant type + self.validate_grant_type(request) + + request.client_id = request.client_id or request.client.client_id + log.debug('Authorizing access to client %r.', request.client_id) + self.validate_scopes(request) + + for validator in self.custom_validators.post_token: + validator(request) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/grant_types/implicit.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/grant_types/implicit.py new file mode 100644 index 0000000000000000000000000000000000000000..cd3bfeb643b1e8e77a04804ef7d3809e07355420 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/grant_types/implicit.py @@ -0,0 +1,373 @@ +""" +oauthlib.oauth2.rfc6749.grant_types +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +""" +import logging + +from oauthlib import common + +from .. import errors +from .base import GrantTypeBase + +log = logging.getLogger(__name__) + + +class ImplicitGrant(GrantTypeBase): + + """`Implicit Grant`_ + + The implicit grant type is used to obtain access tokens (it does not + support the issuance of refresh tokens) and is optimized for public + clients known to operate a particular redirection URI. These clients + are typically implemented in a browser using a scripting language + such as JavaScript. + + Unlike the authorization code grant type, in which the client makes + separate requests for authorization and for an access token, the + client receives the access token as the result of the authorization + request. + + The implicit grant type does not include client authentication, and + relies on the presence of the resource owner and the registration of + the redirection URI. Because the access token is encoded into the + redirection URI, it may be exposed to the resource owner and other + applications residing on the same device:: + + +----------+ + | Resource | + | Owner | + | | + +----------+ + ^ + | + (B) + +----|-----+ Client Identifier +---------------+ + | -+----(A)-- & Redirection URI --->| | + | User- | | Authorization | + | Agent -|----(B)-- User authenticates -->| Server | + | | | | + | |<---(C)--- Redirection URI ----<| | + | | with Access Token +---------------+ + | | in Fragment + | | +---------------+ + | |----(D)--- Redirection URI ---->| Web-Hosted | + | | without Fragment | Client | + | | | Resource | + | (F) |<---(E)------- Script ---------<| | + | | +---------------+ + +-|--------+ + | | + (A) (G) Access Token + | | + ^ v + +---------+ + | | + | Client | + | | + +---------+ + + Note: The lines illustrating steps (A) and (B) are broken into two + parts as they pass through the user-agent. + + Figure 4: Implicit Grant Flow + + The flow illustrated in Figure 4 includes the following steps: + + (A) The client initiates the flow by directing the resource owner's + user-agent to the authorization endpoint. The client includes + its client identifier, requested scope, local state, and a + redirection URI to which the authorization server will send the + user-agent back once access is granted (or denied). + + (B) The authorization server authenticates the resource owner (via + the user-agent) and establishes whether the resource owner + grants or denies the client's access request. + + (C) Assuming the resource owner grants access, the authorization + server redirects the user-agent back to the client using the + redirection URI provided earlier. The redirection URI includes + the access token in the URI fragment. + + (D) The user-agent follows the redirection instructions by making a + request to the web-hosted client resource (which does not + include the fragment per [RFC2616]). The user-agent retains the + fragment information locally. + + (E) The web-hosted client resource returns a web page (typically an + HTML document with an embedded script) capable of accessing the + full redirection URI including the fragment retained by the + user-agent, and extracting the access token (and other + parameters) contained in the fragment. + + (F) The user-agent executes the script provided by the web-hosted + client resource locally, which extracts the access token. + + (G) The user-agent passes the access token to the client. + + See `Section 10.3`_ and `Section 10.16`_ for important security considerations + when using the implicit grant. + + .. _`Implicit Grant`: https://tools.ietf.org/html/rfc6749#section-4.2 + .. _`Section 10.3`: https://tools.ietf.org/html/rfc6749#section-10.3 + .. _`Section 10.16`: https://tools.ietf.org/html/rfc6749#section-10.16 + """ + + response_types = ['token'] + grant_allows_refresh_token = False + + def create_authorization_response(self, request, token_handler): + """Create an authorization response. + + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :param token_handler: A token handler instance, for example of type + oauthlib.oauth2.BearerToken. + + The client constructs the request URI by adding the following + parameters to the query component of the authorization endpoint URI + using the "application/x-www-form-urlencoded" format, per `Appendix B`_: + + response_type + REQUIRED. Value MUST be set to "token" for standard OAuth2 implicit flow + or "id_token token" or just "id_token" for OIDC implicit flow + + client_id + REQUIRED. The client identifier as described in `Section 2.2`_. + + redirect_uri + OPTIONAL. As described in `Section 3.1.2`_. + + scope + OPTIONAL. The scope of the access request as described by + `Section 3.3`_. + + state + RECOMMENDED. An opaque value used by the client to maintain + state between the request and callback. The authorization + server includes this value when redirecting the user-agent back + to the client. The parameter SHOULD be used for preventing + cross-site request forgery as described in `Section 10.12`_. + + The authorization server validates the request to ensure that all + required parameters are present and valid. The authorization server + MUST verify that the redirection URI to which it will redirect the + access token matches a redirection URI registered by the client as + described in `Section 3.1.2`_. + + .. _`Section 2.2`: https://tools.ietf.org/html/rfc6749#section-2.2 + .. _`Section 3.1.2`: https://tools.ietf.org/html/rfc6749#section-3.1.2 + .. _`Section 3.3`: https://tools.ietf.org/html/rfc6749#section-3.3 + .. _`Section 10.12`: https://tools.ietf.org/html/rfc6749#section-10.12 + .. _`Appendix B`: https://tools.ietf.org/html/rfc6749#appendix-B + """ + return self.create_token_response(request, token_handler) + + def create_token_response(self, request, token_handler): + """Return token or error embedded in the URI fragment. + + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :param token_handler: A token handler instance, for example of type + oauthlib.oauth2.BearerToken. + + If the resource owner grants the access request, the authorization + server issues an access token and delivers it to the client by adding + the following parameters to the fragment component of the redirection + URI using the "application/x-www-form-urlencoded" format, per + `Appendix B`_: + + access_token + REQUIRED. The access token issued by the authorization server. + + token_type + REQUIRED. The type of the token issued as described in + `Section 7.1`_. Value is case insensitive. + + expires_in + RECOMMENDED. The lifetime in seconds of the access token. For + example, the value "3600" denotes that the access token will + expire in one hour from the time the response was generated. + If omitted, the authorization server SHOULD provide the + expiration time via other means or document the default value. + + scope + OPTIONAL, if identical to the scope requested by the client; + otherwise, REQUIRED. The scope of the access token as + described by `Section 3.3`_. + + state + REQUIRED if the "state" parameter was present in the client + authorization request. The exact value received from the + client. + + The authorization server MUST NOT issue a refresh token. + + .. _`Appendix B`: https://tools.ietf.org/html/rfc6749#appendix-B + .. _`Section 3.3`: https://tools.ietf.org/html/rfc6749#section-3.3 + .. _`Section 7.1`: https://tools.ietf.org/html/rfc6749#section-7.1 + """ + try: + self.validate_token_request(request) + + # If the request fails due to a missing, invalid, or mismatching + # redirection URI, or if the client identifier is missing or invalid, + # the authorization server SHOULD inform the resource owner of the + # error and MUST NOT automatically redirect the user-agent to the + # invalid redirection URI. + except errors.FatalClientError as e: + log.debug('Fatal client error during validation of %r. %r.', + request, e) + raise + + # If the resource owner denies the access request or if the request + # fails for reasons other than a missing or invalid redirection URI, + # the authorization server informs the client by adding the following + # parameters to the fragment component of the redirection URI using the + # "application/x-www-form-urlencoded" format, per Appendix B: + # https://tools.ietf.org/html/rfc6749#appendix-B + except errors.OAuth2Error as e: + log.debug('Client error during validation of %r. %r.', request, e) + return {'Location': common.add_params_to_uri(request.redirect_uri, e.twotuples, + fragment=True)}, None, 302 + + # In OIDC implicit flow it is possible to have a request_type that does not include the access_token! + # "id_token token" - return the access token and the id token + # "id_token" - don't return the access token + token = token_handler.create_token(request, refresh_token=False) if 'token' in request.response_type.split() else {} + + if request.state is not None: + token['state'] = request.state + + for modifier in self._token_modifiers: + token = modifier(token, token_handler, request) + + # In OIDC implicit flow it is possible to have a request_type that does + # not include the access_token! In this case there is no need to save a token. + if "token" in request.response_type.split(): + self.request_validator.save_token(token, request) + + return self.prepare_authorization_response( + request, token, {}, None, 302) + + def validate_authorization_request(self, request): + """ + :param request: OAuthlib request. + :type request: oauthlib.common.Request + """ + return self.validate_token_request(request) + + def validate_token_request(self, request): + """Check the token request for normal and fatal errors. + + :param request: OAuthlib request. + :type request: oauthlib.common.Request + + This method is very similar to validate_authorization_request in + the AuthorizationCodeGrant but differ in a few subtle areas. + + A normal error could be a missing response_type parameter or the client + attempting to access scope it is not allowed to ask authorization for. + Normal errors can safely be included in the redirection URI and + sent back to the client. + + Fatal errors occur when the client_id or redirect_uri is invalid or + missing. These must be caught by the provider and handled, how this + is done is outside of the scope of OAuthLib but showing an error + page describing the issue is a good idea. + """ + + # First check for fatal errors + + # If the request fails due to a missing, invalid, or mismatching + # redirection URI, or if the client identifier is missing or invalid, + # the authorization server SHOULD inform the resource owner of the + # error and MUST NOT automatically redirect the user-agent to the + # invalid redirection URI. + + # First check duplicate parameters + for param in ('client_id', 'response_type', 'redirect_uri', 'scope', 'state'): + try: + duplicate_params = request.duplicate_params + except ValueError: + raise errors.InvalidRequestFatalError(description='Unable to parse query string', request=request) + if param in duplicate_params: + raise errors.InvalidRequestFatalError(description='Duplicate %s parameter.' % param, request=request) + + # REQUIRED. The client identifier as described in Section 2.2. + # https://tools.ietf.org/html/rfc6749#section-2.2 + if not request.client_id: + raise errors.MissingClientIdError(request=request) + + if not self.request_validator.validate_client_id(request.client_id, request): + raise errors.InvalidClientIdError(request=request) + + # OPTIONAL. As described in Section 3.1.2. + # https://tools.ietf.org/html/rfc6749#section-3.1.2 + self._handle_redirects(request) + + # Then check for normal errors. + + request_info = self._run_custom_validators(request, + self.custom_validators.all_pre) + + # If the resource owner denies the access request or if the request + # fails for reasons other than a missing or invalid redirection URI, + # the authorization server informs the client by adding the following + # parameters to the fragment component of the redirection URI using the + # "application/x-www-form-urlencoded" format, per Appendix B. + # https://tools.ietf.org/html/rfc6749#appendix-B + + # Note that the correct parameters to be added are automatically + # populated through the use of specific exceptions + + # REQUIRED. + if request.response_type is None: + raise errors.MissingResponseTypeError(request=request) + # Value MUST be one of our registered types: "token" by default or if using OIDC "id_token" or "id_token token" + elif not set(request.response_type.split()).issubset(self.response_types): + raise errors.UnsupportedResponseTypeError(request=request) + + log.debug('Validating use of response_type token for client %r (%r).', + request.client_id, request.client) + if not self.request_validator.validate_response_type(request.client_id, + request.response_type, + request.client, request): + + log.debug('Client %s is not authorized to use response_type %s.', + request.client_id, request.response_type) + raise errors.UnauthorizedClientError(request=request) + + # OPTIONAL. The scope of the access request as described by Section 3.3 + # https://tools.ietf.org/html/rfc6749#section-3.3 + self.validate_scopes(request) + + request_info.update({ + 'client_id': request.client_id, + 'redirect_uri': request.redirect_uri, + 'response_type': request.response_type, + 'state': request.state, + 'request': request, + }) + + request_info = self._run_custom_validators( + request, + self.custom_validators.all_post, + request_info + ) + + return request.scopes, request_info + + def _run_custom_validators(self, + request, + validations, + request_info=None): + # Make a copy so we don't modify the existing request_info dict + request_info = {} if request_info is None else request_info.copy() + # For implicit grant, auth_validators and token_validators are + # basically equivalent since the token is returned from the + # authorization endpoint. + for validator in validations: + result = validator(request) + if result is not None: + request_info.update(result) + return request_info diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/grant_types/refresh_token.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/grant_types/refresh_token.py new file mode 100644 index 0000000000000000000000000000000000000000..43bf55ac908a7fe93ceff81b10c93a4418d8851c --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/grant_types/refresh_token.py @@ -0,0 +1,139 @@ +""" +oauthlib.oauth2.rfc6749.grant_types +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +""" +import json +import logging + +from .. import errors, utils +from .base import GrantTypeBase + +log = logging.getLogger(__name__) + + +class RefreshTokenGrant(GrantTypeBase): + + """`Refresh token grant`_ + + .. _`Refresh token grant`: https://tools.ietf.org/html/rfc6749#section-6 + """ + + def __init__(self, request_validator=None, + issue_new_refresh_tokens=True, + **kwargs): + super().__init__( + request_validator, + issue_new_refresh_tokens=issue_new_refresh_tokens, + **kwargs) + + def create_token_response(self, request, token_handler): + """Create a new access token from a refresh_token. + + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :param token_handler: A token handler instance, for example of type + oauthlib.oauth2.BearerToken. + + If valid and authorized, the authorization server issues an access + token as described in `Section 5.1`_. If the request failed + verification or is invalid, the authorization server returns an error + response as described in `Section 5.2`_. + + The authorization server MAY issue a new refresh token, in which case + the client MUST discard the old refresh token and replace it with the + new refresh token. The authorization server MAY revoke the old + refresh token after issuing a new refresh token to the client. If a + new refresh token is issued, the refresh token scope MUST be + identical to that of the refresh token included by the client in the + request. + + .. _`Section 5.1`: https://tools.ietf.org/html/rfc6749#section-5.1 + .. _`Section 5.2`: https://tools.ietf.org/html/rfc6749#section-5.2 + """ + headers = self._get_default_headers() + try: + log.debug('Validating refresh token request, %r.', request) + self.validate_token_request(request) + except errors.OAuth2Error as e: + log.debug('Client error in token request, %s.', e) + headers.update(e.headers) + return headers, e.json, e.status_code + + token = token_handler.create_token(request, + refresh_token=self.issue_new_refresh_tokens) + + for modifier in self._token_modifiers: + token = modifier(token, token_handler, request) + + self.request_validator.save_token(token, request) + + log.debug('Issuing new token to client id %r (%r), %r.', + request.client_id, request.client, token) + headers.update(self._create_cors_headers(request)) + return headers, json.dumps(token), 200 + + def validate_token_request(self, request): + """ + :param request: OAuthlib request. + :type request: oauthlib.common.Request + """ + # REQUIRED. Value MUST be set to "refresh_token". + if request.grant_type != 'refresh_token': + raise errors.UnsupportedGrantTypeError(request=request) + + for validator in self.custom_validators.pre_token: + validator(request) + + if request.refresh_token is None: + raise errors.InvalidRequestError( + description='Missing refresh token parameter.', + request=request) + + # Because refresh tokens are typically long-lasting credentials used to + # request additional access tokens, the refresh token is bound to the + # client to which it was issued. If the client type is confidential or + # the client was issued client credentials (or assigned other + # authentication requirements), the client MUST authenticate with the + # authorization server as described in Section 3.2.1. + # https://tools.ietf.org/html/rfc6749#section-3.2.1 + if self.request_validator.client_authentication_required(request): + log.debug('Authenticating client, %r.', request) + if not self.request_validator.authenticate_client(request): + log.debug('Invalid client (%r), denying access.', request) + raise errors.InvalidClientError(request=request) + # Ensure that request.client_id is set. + if request.client_id is None and request.client is not None: + request.client_id = request.client.client_id + elif not self.request_validator.authenticate_client_id(request.client_id, request): + log.debug('Client authentication failed, %r.', request) + raise errors.InvalidClientError(request=request) + + # Ensure client is authorized use of this grant type + self.validate_grant_type(request) + + # REQUIRED. The refresh token issued to the client. + log.debug('Validating refresh token %s for client %r.', + request.refresh_token, request.client) + if not self.request_validator.validate_refresh_token( + request.refresh_token, request.client, request): + log.debug('Invalid refresh token, %s, for client %r.', + request.refresh_token, request.client) + raise errors.InvalidGrantError(request=request) + + original_scopes = utils.scope_to_list( + self.request_validator.get_original_scopes( + request.refresh_token, request)) + + if request.scope: + request.scopes = utils.scope_to_list(request.scope) + if (not all(s in original_scopes for s in request.scopes) + and not self.request_validator.is_within_original_scope( + request.scopes, request.refresh_token, request)): + log.debug('Refresh token %s lack requested scopes, %r.', + request.refresh_token, request.scopes) + raise errors.InvalidScopeError(request=request) + else: + request.scopes = original_scopes + + for validator in self.custom_validators.post_token: + validator(request) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/grant_types/resource_owner_password_credentials.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/grant_types/resource_owner_password_credentials.py new file mode 100644 index 0000000000000000000000000000000000000000..55d928709a3b80a4a3af7b95a5854a78d312af2a --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/grant_types/resource_owner_password_credentials.py @@ -0,0 +1,198 @@ +""" +oauthlib.oauth2.rfc6749.grant_types +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +""" +import json +import logging + +from .. import errors +from .base import GrantTypeBase + +log = logging.getLogger(__name__) + + +class ResourceOwnerPasswordCredentialsGrant(GrantTypeBase): + + """`Resource Owner Password Credentials Grant`_ + + The resource owner password credentials grant type is suitable in + cases where the resource owner has a trust relationship with the + client, such as the device operating system or a highly privileged + application. The authorization server should take special care when + enabling this grant type and only allow it when other flows are not + viable. + + This grant type is suitable for clients capable of obtaining the + resource owner's credentials (username and password, typically using + an interactive form). It is also used to migrate existing clients + using direct authentication schemes such as HTTP Basic or Digest + authentication to OAuth by converting the stored credentials to an + access token:: + + +----------+ + | Resource | + | Owner | + | | + +----------+ + v + | Resource Owner + (A) Password Credentials + | + v + +---------+ +---------------+ + | |>--(B)---- Resource Owner ------->| | + | | Password Credentials | Authorization | + | Client | | Server | + | |<--(C)---- Access Token ---------<| | + | | (w/ Optional Refresh Token) | | + +---------+ +---------------+ + + Figure 5: Resource Owner Password Credentials Flow + + The flow illustrated in Figure 5 includes the following steps: + + (A) The resource owner provides the client with its username and + password. + + (B) The client requests an access token from the authorization + server's token endpoint by including the credentials received + from the resource owner. When making the request, the client + authenticates with the authorization server. + + (C) The authorization server authenticates the client and validates + the resource owner credentials, and if valid, issues an access + token. + + .. _`Resource Owner Password Credentials Grant`: https://tools.ietf.org/html/rfc6749#section-4.3 + """ + + def create_token_response(self, request, token_handler): + """Return token or error in json format. + + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :param token_handler: A token handler instance, for example of type + oauthlib.oauth2.BearerToken. + + If the access token request is valid and authorized, the + authorization server issues an access token and optional refresh + token as described in `Section 5.1`_. If the request failed client + authentication or is invalid, the authorization server returns an + error response as described in `Section 5.2`_. + + .. _`Section 5.1`: https://tools.ietf.org/html/rfc6749#section-5.1 + .. _`Section 5.2`: https://tools.ietf.org/html/rfc6749#section-5.2 + """ + headers = self._get_default_headers() + try: + if self.request_validator.client_authentication_required(request): + log.debug('Authenticating client, %r.', request) + if not self.request_validator.authenticate_client(request): + log.debug('Client authentication failed, %r.', request) + raise errors.InvalidClientError(request=request) + elif not self.request_validator.authenticate_client_id(request.client_id, request): + log.debug('Client authentication failed, %r.', request) + raise errors.InvalidClientError(request=request) + log.debug('Validating access token request, %r.', request) + self.validate_token_request(request) + except errors.OAuth2Error as e: + log.debug('Client error in token request, %s.', e) + headers.update(e.headers) + return headers, e.json, e.status_code + + token = token_handler.create_token(request, self.refresh_token) + + for modifier in self._token_modifiers: + token = modifier(token) + + self.request_validator.save_token(token, request) + + log.debug('Issuing token %r to client id %r (%r) and username %s.', + token, request.client_id, request.client, request.username) + return headers, json.dumps(token), 200 + + def validate_token_request(self, request): + """ + :param request: OAuthlib request. + :type request: oauthlib.common.Request + + The client makes a request to the token endpoint by adding the + following parameters using the "application/x-www-form-urlencoded" + format per Appendix B with a character encoding of UTF-8 in the HTTP + request entity-body: + + grant_type + REQUIRED. Value MUST be set to "password". + + username + REQUIRED. The resource owner username. + + password + REQUIRED. The resource owner password. + + scope + OPTIONAL. The scope of the access request as described by + `Section 3.3`_. + + If the client type is confidential or the client was issued client + credentials (or assigned other authentication requirements), the + client MUST authenticate with the authorization server as described + in `Section 3.2.1`_. + + The authorization server MUST: + + o require client authentication for confidential clients or for any + client that was issued client credentials (or with other + authentication requirements), + + o authenticate the client if client authentication is included, and + + o validate the resource owner password credentials using its + existing password validation algorithm. + + Since this access token request utilizes the resource owner's + password, the authorization server MUST protect the endpoint against + brute force attacks (e.g., using rate-limitation or generating + alerts). + + .. _`Section 3.3`: https://tools.ietf.org/html/rfc6749#section-3.3 + .. _`Section 3.2.1`: https://tools.ietf.org/html/rfc6749#section-3.2.1 + """ + for validator in self.custom_validators.pre_token: + validator(request) + + for param in ('grant_type', 'username', 'password'): + if not getattr(request, param, None): + raise errors.InvalidRequestError( + 'Request is missing %s parameter.' % param, request=request) + + for param in ('grant_type', 'username', 'password', 'scope'): + if param in request.duplicate_params: + raise errors.InvalidRequestError(description='Duplicate %s parameter.' % param, request=request) + + # This error should rarely (if ever) occur if requests are routed to + # grant type handlers based on the grant_type parameter. + if not request.grant_type == 'password': + raise errors.UnsupportedGrantTypeError(request=request) + + log.debug('Validating username %s.', request.username) + if not self.request_validator.validate_user(request.username, + request.password, request.client, request): + raise errors.InvalidGrantError( + 'Invalid credentials given.', request=request) + elif not hasattr(request.client, 'client_id'): + raise NotImplementedError( + 'Validate user must set the ' + 'request.client.client_id attribute ' + 'in authenticate_client.') + log.debug('Authorizing access to user %r.', request.user) + + # Ensure client is authorized use of this grant type + self.validate_grant_type(request) + + if request.client: + request.client_id = request.client_id or request.client.client_id + self.validate_scopes(request) + + for validator in self.custom_validators.post_token: + validator(request) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/parameters.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/parameters.py new file mode 100644 index 0000000000000000000000000000000000000000..8268ef927cc2d174f44a00e60cf07156fb223db7 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/parameters.py @@ -0,0 +1,528 @@ +""" +oauthlib.oauth2.rfc6749.parameters +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +This module contains methods related to `Section 4`_ of the OAuth 2 RFC. + +.. _`Section 4`: https://tools.ietf.org/html/rfc6749#section-4 +""" +import json +import os +import time +import urllib.parse as urlparse + +from oauthlib.common import add_params_to_qs, add_params_to_uri +from oauthlib.signals import scope_changed + +from .errors import ( + InsecureTransportError, MismatchingStateError, MissingCodeError, + MissingTokenError, MissingTokenTypeError, raise_from_error, +) +from .tokens import OAuth2Token +from .utils import is_secure_transport, list_to_scope, scope_to_list + + +def prepare_grant_uri(uri, client_id, response_type, redirect_uri=None, + scope=None, state=None, code_challenge=None, code_challenge_method='plain', **kwargs): + """Prepare the authorization grant request URI. + + The client constructs the request URI by adding the following + parameters to the query component of the authorization endpoint URI + using the ``application/x-www-form-urlencoded`` format as defined by + [`W3C.REC-html401-19991224`_]: + + :param uri: + :param client_id: The client identifier as described in `Section 2.2`_. + :param response_type: To indicate which OAuth 2 grant/flow is required, + "code" and "token". + :param redirect_uri: The client provided URI to redirect back to after + authorization as described in `Section 3.1.2`_. + :param scope: The scope of the access request as described by + `Section 3.3`_. + :param state: An opaque value used by the client to maintain + state between the request and callback. The authorization + server includes this value when redirecting the user-agent + back to the client. The parameter SHOULD be used for + preventing cross-site request forgery as described in + `Section 10.12`_. + :param code_challenge: PKCE parameter. A challenge derived from the + code_verifier that is sent in the authorization + request, to be verified against later. + :param code_challenge_method: PKCE parameter. A method that was used to derive the + code_challenge. Defaults to "plain" if not present in the request. + :param kwargs: Extra arguments to embed in the grant/authorization URL. + + An example of an authorization code grant authorization URL: + + .. code-block:: http + + GET /authorize?response_type=code&client_id=s6BhdRkqt3&state=xyz + &code_challenge=kjasBS523KdkAILD2k78NdcJSk2k3KHG6&code_challenge_method=S256 + &redirect_uri=https%3A%2F%2Fclient%2Eexample%2Ecom%2Fcb HTTP/1.1 + Host: server.example.com + + .. _`W3C.REC-html401-19991224`: https://tools.ietf.org/html/rfc6749#ref-W3C.REC-html401-19991224 + .. _`Section 2.2`: https://tools.ietf.org/html/rfc6749#section-2.2 + .. _`Section 3.1.2`: https://tools.ietf.org/html/rfc6749#section-3.1.2 + .. _`Section 3.3`: https://tools.ietf.org/html/rfc6749#section-3.3 + .. _`section 10.12`: https://tools.ietf.org/html/rfc6749#section-10.12 + """ + if not is_secure_transport(uri): + raise InsecureTransportError() + + params = [(('response_type', response_type)), + (('client_id', client_id))] + + if redirect_uri: + params.append(('redirect_uri', redirect_uri)) + if scope: + params.append(('scope', list_to_scope(scope))) + if state: + params.append(('state', state)) + if code_challenge is not None: + params.append(('code_challenge', code_challenge)) + params.append(('code_challenge_method', code_challenge_method)) + + for k in kwargs: + if kwargs[k]: + params.append((str(k), kwargs[k])) + + return add_params_to_uri(uri, params) + + +def prepare_token_request(grant_type, body='', include_client_id=True, code_verifier=None, **kwargs): + """Prepare the access token request. + + The client makes a request to the token endpoint by adding the + following parameters using the ``application/x-www-form-urlencoded`` + format in the HTTP request entity-body: + + :param grant_type: To indicate grant type being used, i.e. "password", + "authorization_code" or "client_credentials". + + :param body: Existing request body (URL encoded string) to embed parameters + into. This may contain extra parameters. Default ''. + + :param include_client_id: `True` (default) to send the `client_id` in the + body of the upstream request. This is required + if the client is not authenticating with the + authorization server as described in + `Section 3.2.1`_. + :type include_client_id: Boolean + + :param client_id: Unicode client identifier. Will only appear if + `include_client_id` is True. * + + :param client_secret: Unicode client secret. Will only appear if set to a + value that is not `None`. Invoking this function with + an empty string will send an empty `client_secret` + value to the server. * + + :param code: If using authorization_code grant, pass the previously + obtained authorization code as the ``code`` argument. * + + :param redirect_uri: If the "redirect_uri" parameter was included in the + authorization request as described in + `Section 4.1.1`_, and their values MUST be identical. * + + :param code_verifier: PKCE parameter. A cryptographically random string that is used to correlate the + authorization request to the token request. + + :param kwargs: Extra arguments to embed in the request body. + + Parameters marked with a `*` above are not explicit arguments in the + function signature, but are specially documented arguments for items + appearing in the generic `**kwargs` keyworded input. + + An example of an authorization code token request body: + + .. code-block:: http + + grant_type=authorization_code&code=SplxlOBeZQQYbYS6WxSbIA + &redirect_uri=https%3A%2F%2Fclient%2Eexample%2Ecom%2Fcb + + .. _`Section 4.1.1`: https://tools.ietf.org/html/rfc6749#section-4.1.1 + """ + params = [('grant_type', grant_type)] + + if 'scope' in kwargs: + kwargs['scope'] = list_to_scope(kwargs['scope']) + + # pull the `client_id` out of the kwargs. + client_id = kwargs.pop('client_id', None) + if include_client_id and client_id is not None: + params.append(('client_id', client_id)) + + # use code_verifier if code_challenge was passed in the authorization request + if code_verifier is not None: + params.append(('code_verifier', code_verifier)) + + # the kwargs iteration below only supports including boolean truth (truthy) + # values, but some servers may require an empty string for `client_secret` + client_secret = kwargs.pop('client_secret', None) + if client_secret is not None: + params.append(('client_secret', client_secret)) + + # this handles: `code`, `redirect_uri`, and other undocumented params + for k in kwargs: + if kwargs[k]: + params.append((str(k), kwargs[k])) + + return add_params_to_qs(body, params) + + +def prepare_token_revocation_request(url, token, token_type_hint="access_token", + callback=None, body='', **kwargs): + """Prepare a token revocation request. + + The client constructs the request by including the following parameters + using the ``application/x-www-form-urlencoded`` format in the HTTP request + entity-body: + + :param token: REQUIRED. The token that the client wants to get revoked. + + :param token_type_hint: OPTIONAL. A hint about the type of the token + submitted for revocation. Clients MAY pass this + parameter in order to help the authorization server + to optimize the token lookup. If the server is + unable to locate the token using the given hint, it + MUST extend its search across all of its supported + token types. An authorization server MAY ignore + this parameter, particularly if it is able to detect + the token type automatically. + + This specification defines two values for `token_type_hint`: + + * access_token: An access token as defined in [RFC6749], + `Section 1.4`_ + + * refresh_token: A refresh token as defined in [RFC6749], + `Section 1.5`_ + + Specific implementations, profiles, and extensions of this + specification MAY define other values for this parameter using the + registry defined in `Section 4.1.2`_. + + .. _`Section 1.4`: https://tools.ietf.org/html/rfc6749#section-1.4 + .. _`Section 1.5`: https://tools.ietf.org/html/rfc6749#section-1.5 + .. _`Section 4.1.2`: https://tools.ietf.org/html/rfc7009#section-4.1.2 + + """ + if not is_secure_transport(url): + raise InsecureTransportError() + + params = [('token', token)] + + if token_type_hint: + params.append(('token_type_hint', token_type_hint)) + + for k in kwargs: + if kwargs[k]: + params.append((str(k), kwargs[k])) + + headers = {'Content-Type': 'application/x-www-form-urlencoded'} + + if callback: + params.append(('callback', callback)) + return add_params_to_uri(url, params), headers, body + else: + return url, headers, add_params_to_qs(body, params) + + +def parse_authorization_code_response(uri, state=None): + """Parse authorization grant response URI into a dict. + + If the resource owner grants the access request, the authorization + server issues an authorization code and delivers it to the client by + adding the following parameters to the query component of the + redirection URI using the ``application/x-www-form-urlencoded`` format: + + **code** + REQUIRED. The authorization code generated by the + authorization server. The authorization code MUST expire + shortly after it is issued to mitigate the risk of leaks. A + maximum authorization code lifetime of 10 minutes is + RECOMMENDED. The client MUST NOT use the authorization code + more than once. If an authorization code is used more than + once, the authorization server MUST deny the request and SHOULD + revoke (when possible) all tokens previously issued based on + that authorization code. The authorization code is bound to + the client identifier and redirection URI. + + **state** + REQUIRED if the "state" parameter was present in the client + authorization request. The exact value received from the + client. + + :param uri: The full redirect URL back to the client. + :param state: The state parameter from the authorization request. + + For example, the authorization server redirects the user-agent by + sending the following HTTP response: + + .. code-block:: http + + HTTP/1.1 302 Found + Location: https://client.example.com/cb?code=SplxlOBeZQQYbYS6WxSbIA + &state=xyz + + """ + if not is_secure_transport(uri): + raise InsecureTransportError() + + query = urlparse.urlparse(uri).query + params = dict(urlparse.parse_qsl(query)) + + if state and params.get('state') != state: + raise MismatchingStateError() + + if 'error' in params: + raise_from_error(params.get('error'), params) + + if 'code' not in params: + raise MissingCodeError("Missing code parameter in response.") + + return params + + +def parse_implicit_response(uri, state=None, scope=None): + """Parse the implicit token response URI into a dict. + + If the resource owner grants the access request, the authorization + server issues an access token and delivers it to the client by adding + the following parameters to the fragment component of the redirection + URI using the ``application/x-www-form-urlencoded`` format: + + **access_token** + REQUIRED. The access token issued by the authorization server. + + **token_type** + REQUIRED. The type of the token issued as described in + Section 7.1. Value is case insensitive. + + **expires_in** + RECOMMENDED. The lifetime in seconds of the access token. For + example, the value "3600" denotes that the access token will + expire in one hour from the time the response was generated. + If omitted, the authorization server SHOULD provide the + expiration time via other means or document the default value. + + **scope** + OPTIONAL, if identical to the scope requested by the client, + otherwise REQUIRED. The scope of the access token as described + by Section 3.3. + + **state** + REQUIRED if the "state" parameter was present in the client + authorization request. The exact value received from the + client. + + :param uri: + :param state: + :param scope: + + Similar to the authorization code response, but with a full token provided + in the URL fragment: + + .. code-block:: http + + HTTP/1.1 302 Found + Location: http://example.com/cb#access_token=2YotnFZFEjr1zCsicMWpAA + &state=xyz&token_type=example&expires_in=3600 + """ + if not is_secure_transport(uri): + raise InsecureTransportError() + + fragment = urlparse.urlparse(uri).fragment + params = dict(urlparse.parse_qsl(fragment, keep_blank_values=True)) + + if 'scope' in params: + params['scope'] = scope_to_list(params['scope']) + + vin, vat, v_at = parse_expires(params) + if vin: + params['expires_in'] = vin + elif 'expires_in' in params: + params.pop('expires_in') + if vat: + params['expires_at'] = vat + elif 'expires_at' in params: + params.pop('expires_at') + + if state and params.get('state') != state: + raise ValueError("Mismatching or missing state in params.") + + params = OAuth2Token(params, old_scope=scope) + validate_token_parameters(params) + return params + + +def parse_token_response(body, scope=None): + """Parse the JSON token response body into a dict. + + The authorization server issues an access token and optional refresh + token, and constructs the response by adding the following parameters + to the entity body of the HTTP response with a 200 (OK) status code: + + access_token + REQUIRED. The access token issued by the authorization server. + token_type + REQUIRED. The type of the token issued as described in + `Section 7.1`_. Value is case insensitive. + expires_in + RECOMMENDED. The lifetime in seconds of the access token. For + example, the value "3600" denotes that the access token will + expire in one hour from the time the response was generated. + If omitted, the authorization server SHOULD provide the + expiration time via other means or document the default value. + refresh_token + OPTIONAL. The refresh token which can be used to obtain new + access tokens using the same authorization grant as described + in `Section 6`_. + scope + OPTIONAL, if identical to the scope requested by the client, + otherwise REQUIRED. The scope of the access token as described + by `Section 3.3`_. + + The parameters are included in the entity body of the HTTP response + using the "application/json" media type as defined by [`RFC4627`_]. The + parameters are serialized into a JSON structure by adding each + parameter at the highest structure level. Parameter names and string + values are included as JSON strings. Numerical values are included + as JSON numbers. The order of parameters does not matter and can + vary. + + :param body: The full json encoded response body. + :param scope: The scope requested during authorization. + + For example: + + .. code-block:: http + + HTTP/1.1 200 OK + Content-Type: application/json + Cache-Control: no-store + Pragma: no-cache + + { + "access_token":"2YotnFZFEjr1zCsicMWpAA", + "token_type":"example", + "expires_in":3600, + "refresh_token":"tGzv3JOkF0XG5Qx2TlKWIA", + "example_parameter":"example_value" + } + + .. _`Section 7.1`: https://tools.ietf.org/html/rfc6749#section-7.1 + .. _`Section 6`: https://tools.ietf.org/html/rfc6749#section-6 + .. _`Section 3.3`: https://tools.ietf.org/html/rfc6749#section-3.3 + .. _`RFC4627`: https://tools.ietf.org/html/rfc4627 + """ + try: + params = json.loads(body) + except ValueError: + + # Fall back to URL-encoded string, to support old implementations, + # including (at time of writing) Facebook. See: + # https://github.com/oauthlib/oauthlib/issues/267 + + params = dict(urlparse.parse_qsl(body)) + + if 'scope' in params: + params['scope'] = scope_to_list(params['scope']) + + vin, vat, v_at = parse_expires(params) + if vin: + params['expires_in'] = vin + elif 'expires_in' in params: + params.pop('expires_in') + if vat: + params['expires_at'] = vat + elif 'expires_at' in params: + params.pop('expires_at') + + params = OAuth2Token(params, old_scope=scope) + validate_token_parameters(params) + return params + + +def validate_token_parameters(params): + """Ensures token presence, token type, expiration and scope in params.""" + if 'error' in params: + raise_from_error(params.get('error'), params) + + if 'access_token' not in params: + raise MissingTokenError(description="Missing access token parameter.") + + if 'token_type' not in params and os.environ.get('OAUTHLIB_STRICT_TOKEN_TYPE'): + raise MissingTokenTypeError() + + # If the issued access token scope is different from the one requested by + # the client, the authorization server MUST include the "scope" response + # parameter to inform the client of the actual scope granted. + # https://tools.ietf.org/html/rfc6749#section-3.3 + if params.scope_changed: + message = 'Scope has changed from "{old}" to "{new}".'.format( + old=params.old_scope, new=params.scope, + ) + scope_changed.send(message=message, old=params.old_scopes, new=params.scopes) + if not os.environ.get('OAUTHLIB_RELAX_TOKEN_SCOPE', None): + w = Warning(message) + w.token = params + w.old_scope = params.old_scopes + w.new_scope = params.scopes + raise w + +def parse_expires(params): + """Parse `expires_in`, `expires_at` fields from params + + Parse following these rules: + - `expires_in` must be either integer, float or None. If a float, it is converted into an integer. + - `expires_at` is not in specification so it does its best to: + - convert into a int, else + - convert into a float, else + - reuse the same type as-is (usually string) + - `_expires_at` is a special internal value returned to be always an `int`, based + either on the presence of `expires_at`, or reuse the current time plus + `expires_in`. This is typically used to validate token expiry. + + :param params: Dict with expires_in and expires_at optionally set + :return: Tuple of `expires_in`, `expires_at`, and `_expires_at`. None if not set. + """ + expires_in = None + expires_at = None + _expires_at = None + + if 'expires_in' in params: + if isinstance(params.get('expires_in'), int): + expires_in = params.get('expires_in') + elif isinstance(params.get('expires_in'), float): + expires_in = int(params.get('expires_in')) + elif isinstance(params.get('expires_in'), str): + try: + # Attempt to convert to int + expires_in = int(params.get('expires_in')) + except ValueError: + raise ValueError("expires_in must be an int") + elif params.get('expires_in') is not None: + raise ValueError("expires_in must be an int") + + if 'expires_at' in params: + if isinstance(params.get('expires_at'), (float, int)): + expires_at = params.get('expires_at') + _expires_at = expires_at + elif isinstance(params.get('expires_at'), str): + try: + # Attempt to convert to int first, then float if int fails + expires_at = int(params.get('expires_at')) + _expires_at = expires_at + except ValueError: + try: + expires_at = float(params.get('expires_at')) + _expires_at = expires_at + except ValueError: + # no change from str + expires_at = params.get('expires_at') + if _expires_at is None and expires_in: + expires_at = round(time.time()) + expires_in + _expires_at = expires_at + return expires_in, expires_at, _expires_at diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/request_validator.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/request_validator.py new file mode 100644 index 0000000000000000000000000000000000000000..6d6ebaa8e983c72c235dd1a07ff9ee778c864b06 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/request_validator.py @@ -0,0 +1,680 @@ +""" +oauthlib.oauth2.rfc6749.request_validator +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +""" +import logging + +log = logging.getLogger(__name__) + + +class RequestValidator: + + def client_authentication_required(self, request, *args, **kwargs): + """Determine if client authentication is required for current request. + + According to the rfc6749, client authentication is required in the following cases: + - Resource Owner Password Credentials Grant, when Client type is Confidential or when + Client was issued client credentials or whenever Client provided client + authentication, see `Section 4.3.2`_. + - Authorization Code Grant, when Client type is Confidential or when Client was issued + client credentials or whenever Client provided client authentication, + see `Section 4.1.3`_. + - Refresh Token Grant, when Client type is Confidential or when Client was issued + client credentials or whenever Client provided client authentication, see + `Section 6`_ + + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :rtype: True or False + + Method is used by: + - Authorization Code Grant + - Resource Owner Password Credentials Grant + - Refresh Token Grant + + .. _`Section 4.3.2`: https://tools.ietf.org/html/rfc6749#section-4.3.2 + .. _`Section 4.1.3`: https://tools.ietf.org/html/rfc6749#section-4.1.3 + .. _`Section 6`: https://tools.ietf.org/html/rfc6749#section-6 + """ + return True + + def authenticate_client(self, request, *args, **kwargs): + """Authenticate client through means outside the OAuth 2 spec. + + Means of authentication is negotiated beforehand and may for example + be `HTTP Basic Authentication Scheme`_ which utilizes the Authorization + header. + + Headers may be accesses through request.headers and parameters found in + both body and query can be obtained by direct attribute access, i.e. + request.client_id for client_id in the URL query. + + The authentication process is required to contain the identification of + the client (i.e. search the database based on the client_id). In case the + client doesn't exist based on the received client_id, this method has to + return False and the HTTP response created by the library will contain + 'invalid_client' message. + + After the client identification succeeds, this method needs to set the + client on the request, i.e. request.client = client. A client object's + class must contain the 'client_id' attribute and the 'client_id' must have + a value. + + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :rtype: True or False + + Method is used by: + - Authorization Code Grant + - Resource Owner Password Credentials Grant (may be disabled) + - Client Credentials Grant + - Refresh Token Grant + + .. _`HTTP Basic Authentication Scheme`: https://tools.ietf.org/html/rfc1945#section-11.1 + """ + raise NotImplementedError('Subclasses must implement this method.') + + def authenticate_client_id(self, client_id, request, *args, **kwargs): + """Ensure client_id belong to a non-confidential client. + + A non-confidential client is one that is not required to authenticate + through other means, such as using HTTP Basic. + + Note, while not strictly necessary it can often be very convenient + to set request.client to the client object associated with the + given client_id. + + :param client_id: Unicode client identifier. + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :rtype: True or False + + Method is used by: + - Authorization Code Grant + """ + raise NotImplementedError('Subclasses must implement this method.') + + def confirm_redirect_uri(self, client_id, code, redirect_uri, client, request, + *args, **kwargs): + """Ensure that the authorization process represented by this authorization + code began with this 'redirect_uri'. + + If the client specifies a redirect_uri when obtaining code then that + redirect URI must be bound to the code and verified equal in this + method, according to RFC 6749 section 4.1.3. Do not compare against + the client's allowed redirect URIs, but against the URI used when the + code was saved. + + :param client_id: Unicode client identifier. + :param code: Unicode authorization_code. + :param redirect_uri: Unicode absolute URI. + :param client: Client object set by you, see ``.authenticate_client``. + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :rtype: True or False + + Method is used by: + - Authorization Code Grant (during token request) + """ + raise NotImplementedError('Subclasses must implement this method.') + + def get_default_redirect_uri(self, client_id, request, *args, **kwargs): + """Get the default redirect URI for the client. + + :param client_id: Unicode client identifier. + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :rtype: The default redirect URI for the client + + Method is used by: + - Authorization Code Grant + - Implicit Grant + """ + raise NotImplementedError('Subclasses must implement this method.') + + def get_default_scopes(self, client_id, request, *args, **kwargs): + """Get the default scopes for the client. + + :param client_id: Unicode client identifier. + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :rtype: List of default scopes + + Method is used by all core grant types: + - Authorization Code Grant + - Implicit Grant + - Resource Owner Password Credentials Grant + - Client Credentials grant + """ + raise NotImplementedError('Subclasses must implement this method.') + + def get_original_scopes(self, refresh_token, request, *args, **kwargs): + """Get the list of scopes associated with the refresh token. + + :param refresh_token: Unicode refresh token. + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :rtype: List of scopes. + + Method is used by: + - Refresh token grant + """ + raise NotImplementedError('Subclasses must implement this method.') + + def is_within_original_scope(self, request_scopes, refresh_token, request, *args, **kwargs): + """Check if requested scopes are within a scope of the refresh token. + + When access tokens are refreshed the scope of the new token + needs to be within the scope of the original token. This is + ensured by checking that all requested scopes strings are on + the list returned by the get_original_scopes. If this check + fails, is_within_original_scope is called. The method can be + used in situations where returning all valid scopes from the + get_original_scopes is not practical. + + :param request_scopes: A list of scopes that were requested by client. + :param refresh_token: Unicode refresh_token. + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :rtype: True or False + + Method is used by: + - Refresh token grant + """ + return False + + def introspect_token(self, token, token_type_hint, request, *args, **kwargs): + """Introspect an access or refresh token. + + Called once the introspect request is validated. This method should + verify the *token* and either return a dictionary with the list of + claims associated, or `None` in case the token is unknown. + + Below the list of registered claims you should be interested in: + + - scope : space-separated list of scopes + - client_id : client identifier + - username : human-readable identifier for the resource owner + - token_type : type of the token + - exp : integer timestamp indicating when this token will expire + - iat : integer timestamp indicating when this token was issued + - nbf : integer timestamp indicating when it can be "not-before" used + - sub : subject of the token - identifier of the resource owner + - aud : list of string identifiers representing the intended audience + - iss : string representing issuer of this token + - jti : string identifier for the token + + Note that most of them are coming directly from JWT RFC. More details + can be found in `Introspect Claims`_ or `JWT Claims`_. + + The implementation can use *token_type_hint* to improve lookup + efficiency, but must fallback to other types to be compliant with RFC. + + The dict of claims is added to request.token after this method. + + :param token: The token string. + :param token_type_hint: access_token or refresh_token. + :param request: OAuthlib request. + :type request: oauthlib.common.Request + + Method is used by: + - Introspect Endpoint (all grants are compatible) + + .. _`Introspect Claims`: https://tools.ietf.org/html/rfc7662#section-2.2 + .. _`JWT Claims`: https://tools.ietf.org/html/rfc7519#section-4 + """ + raise NotImplementedError('Subclasses must implement this method.') + + def invalidate_authorization_code(self, client_id, code, request, *args, **kwargs): + """Invalidate an authorization code after use. + + :param client_id: Unicode client identifier. + :param code: The authorization code grant (request.code). + :param request: OAuthlib request. + :type request: oauthlib.common.Request + + Method is used by: + - Authorization Code Grant + """ + raise NotImplementedError('Subclasses must implement this method.') + + def revoke_token(self, token, token_type_hint, request, *args, **kwargs): + """Revoke an access or refresh token. + + :param token: The token string. + :param token_type_hint: access_token or refresh_token. + :param request: OAuthlib request. + :type request: oauthlib.common.Request + + Method is used by: + - Revocation Endpoint + """ + raise NotImplementedError('Subclasses must implement this method.') + + def rotate_refresh_token(self, request): + """Determine whether to rotate the refresh token. Default, yes. + + When access tokens are refreshed the old refresh token can be kept + or replaced with a new one (rotated). Return True to rotate and + and False for keeping original. + + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :rtype: True or False + + Method is used by: + - Refresh Token Grant + """ + return True + + def save_authorization_code(self, client_id, code, request, *args, **kwargs): + """Persist the authorization_code. + + The code should at minimum be stored with: + - the client_id (``client_id``) + - the redirect URI used (``request.redirect_uri``) + - a resource owner / user (``request.user``) + - the authorized scopes (``request.scopes``) + + To support PKCE, you MUST associate the code with: + - Code Challenge (``request.code_challenge``) and + - Code Challenge Method (``request.code_challenge_method``) + + To support OIDC, you MUST associate the code with: + - nonce, if present (``code["nonce"]``) + + The ``code`` argument is actually a dictionary, containing at least a + ``code`` key with the actual authorization code: + + ``{'code': 'sdf345jsdf0934f'}`` + + It may also have a ``claims`` parameter which, when present, will be a dict + deserialized from JSON as described at + http://openid.net/specs/openid-connect-core-1_0.html#ClaimsParameter + This value should be saved in this method and used again in ``.validate_code``. + + :param client_id: Unicode client identifier. + :param code: A dict of the authorization code grant and, optionally, state. + :param request: OAuthlib request. + :type request: oauthlib.common.Request + + Method is used by: + - Authorization Code Grant + """ + raise NotImplementedError('Subclasses must implement this method.') + + def save_token(self, token, request, *args, **kwargs): + """Persist the token with a token type specific method. + + Currently, only save_bearer_token is supported. + + :param token: A (Bearer) token dict. + :param request: OAuthlib request. + :type request: oauthlib.common.Request + """ + return self.save_bearer_token(token, request, *args, **kwargs) + + def save_bearer_token(self, token, request, *args, **kwargs): + """Persist the Bearer token. + + The Bearer token should at minimum be associated with: + - a client and it's client_id, if available + - a resource owner / user (request.user) + - authorized scopes (request.scopes) + - an expiration time + - a refresh token, if issued + - a claims document, if present in request.claims + + The Bearer token dict may hold a number of items:: + + { + 'token_type': 'Bearer', + 'access_token': 'askfjh234as9sd8', + 'expires_in': 3600, + 'scope': 'string of space separated authorized scopes', + 'refresh_token': '23sdf876234', # if issued + 'state': 'given_by_client', # if supplied by client (implicit ONLY) + } + + Note that while "scope" is a string-separated list of authorized scopes, + the original list is still available in request.scopes. + + The token dict is passed as a reference so any changes made to the dictionary + will go back to the user. If additional information must return to the client + user, and it is only possible to get this information after writing the token + to storage, it should be added to the token dictionary. If the token + dictionary must be modified but the changes should not go back to the user, + a copy of the dictionary must be made before making the changes. + + Also note that if an Authorization Code grant request included a valid claims + parameter (for OpenID Connect) then the request.claims property will contain + the claims dict, which should be saved for later use when generating the + id_token and/or UserInfo response content. + + :param token: A Bearer token dict. + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :rtype: The default redirect URI for the client + + Method is used by all core grant types issuing Bearer tokens: + - Authorization Code Grant + - Implicit Grant + - Resource Owner Password Credentials Grant (might not associate a client) + - Client Credentials grant + """ + raise NotImplementedError('Subclasses must implement this method.') + + def validate_bearer_token(self, token, scopes, request): + """Ensure the Bearer token is valid and authorized access to scopes. + + :param token: A string of random characters. + :param scopes: A list of scopes associated with the protected resource. + :param request: OAuthlib request. + :type request: oauthlib.common.Request + + A key to OAuth 2 security and restricting impact of leaked tokens is + the short expiration time of tokens, *always ensure the token has not + expired!*. + + Two different approaches to scope validation: + + 1) all(scopes). The token must be authorized access to all scopes + associated with the resource. For example, the + token has access to ``read-only`` and ``images``, + thus the client can view images but not upload new. + Allows for fine grained access control through + combining various scopes. + + 2) any(scopes). The token must be authorized access to one of the + scopes associated with the resource. For example, + token has access to ``read-only-images``. + Allows for fine grained, although arguably less + convenient, access control. + + A powerful way to use scopes would mimic UNIX ACLs and see a scope + as a group with certain privileges. For a restful API these might + map to HTTP verbs instead of read, write and execute. + + Note, the request.user attribute can be set to the resource owner + associated with this token. Similarly the request.client and + request.scopes attribute can be set to associated client object + and authorized scopes. If you then use a decorator such as the + one provided for django these attributes will be made available + in all protected views as keyword arguments. + + :param token: Unicode Bearer token + :param scopes: List of scopes (defined by you) + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :rtype: True or False + + Method is indirectly used by all core Bearer token issuing grant types: + - Authorization Code Grant + - Implicit Grant + - Resource Owner Password Credentials Grant + - Client Credentials Grant + """ + raise NotImplementedError('Subclasses must implement this method.') + + def validate_client_id(self, client_id, request, *args, **kwargs): + """Ensure client_id belong to a valid and active client. + + Note, while not strictly necessary it can often be very convenient + to set request.client to the client object associated with the + given client_id. + + :param client_id: Unicode client identifier. + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :rtype: True or False + + Method is used by: + - Authorization Code Grant + - Implicit Grant + """ + raise NotImplementedError('Subclasses must implement this method.') + + def validate_code(self, client_id, code, client, request, *args, **kwargs): + """Verify that the authorization_code is valid and assigned to the given + client. + + Before returning true, set the following based on the information stored + with the code in 'save_authorization_code': + + - request.user + - request.scopes + - request.claims (if given) + + OBS! The request.user attribute should be set to the resource owner + associated with this authorization code. Similarly request.scopes + must also be set. + + The request.claims property, if it was given, should assigned a dict. + + If PKCE is enabled (see 'is_pkce_required' and 'save_authorization_code') + you MUST set the following based on the information stored: + + - request.code_challenge + - request.code_challenge_method + + :param client_id: Unicode client identifier. + :param code: Unicode authorization code. + :param client: Client object set by you, see ``.authenticate_client``. + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :rtype: True or False + + Method is used by: + - Authorization Code Grant + """ + raise NotImplementedError('Subclasses must implement this method.') + + def validate_grant_type(self, client_id, grant_type, client, request, *args, **kwargs): + """Ensure client is authorized to use the grant_type requested. + + :param client_id: Unicode client identifier. + :param grant_type: Unicode grant type, i.e. authorization_code, password. + :param client: Client object set by you, see ``.authenticate_client``. + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :rtype: True or False + + Method is used by: + - Authorization Code Grant + - Resource Owner Password Credentials Grant + - Client Credentials Grant + - Refresh Token Grant + """ + raise NotImplementedError('Subclasses must implement this method.') + + def validate_redirect_uri(self, client_id, redirect_uri, request, *args, **kwargs): + """Ensure client is authorized to redirect to the redirect_uri requested. + + All clients should register the absolute URIs of all URIs they intend + to redirect to. The registration is outside of the scope of oauthlib. + + :param client_id: Unicode client identifier. + :param redirect_uri: Unicode absolute URI. + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :rtype: True or False + + Method is used by: + - Authorization Code Grant + - Implicit Grant + """ + raise NotImplementedError('Subclasses must implement this method.') + + def validate_refresh_token(self, refresh_token, client, request, *args, **kwargs): + """Ensure the Bearer token is valid and authorized access to scopes. + + OBS! The request.user attribute should be set to the resource owner + associated with this refresh token. + + :param refresh_token: Unicode refresh token. + :param client: Client object set by you, see ``.authenticate_client``. + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :rtype: True or False + + Method is used by: + - Authorization Code Grant (indirectly by issuing refresh tokens) + - Resource Owner Password Credentials Grant (also indirectly) + - Refresh Token Grant + """ + raise NotImplementedError('Subclasses must implement this method.') + + def validate_response_type(self, client_id, response_type, client, request, *args, **kwargs): + """Ensure client is authorized to use the response_type requested. + + :param client_id: Unicode client identifier. + :param response_type: Unicode response type, i.e. code, token. + :param client: Client object set by you, see ``.authenticate_client``. + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :rtype: True or False + + Method is used by: + - Authorization Code Grant + - Implicit Grant + """ + raise NotImplementedError('Subclasses must implement this method.') + + def validate_scopes(self, client_id, scopes, client, request, *args, **kwargs): + """Ensure the client is authorized access to requested scopes. + + :param client_id: Unicode client identifier. + :param scopes: List of scopes (defined by you). + :param client: Client object set by you, see ``.authenticate_client``. + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :rtype: True or False + + Method is used by all core grant types: + - Authorization Code Grant + - Implicit Grant + - Resource Owner Password Credentials Grant + - Client Credentials Grant + """ + raise NotImplementedError('Subclasses must implement this method.') + + def validate_user(self, username, password, client, request, *args, **kwargs): + """Ensure the username and password is valid. + + OBS! The validation should also set the user attribute of the request + to a valid resource owner, i.e. request.user = username or similar. If + not set you will be unable to associate a token with a user in the + persistence method used (commonly, save_bearer_token). + + :param username: Unicode username. + :param password: Unicode password. + :param client: Client object set by you, see ``.authenticate_client``. + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :rtype: True or False + + Method is used by: + - Resource Owner Password Credentials Grant + """ + raise NotImplementedError('Subclasses must implement this method.') + + def is_pkce_required(self, client_id, request): + """Determine if current request requires PKCE. Default, False. + This is called for both "authorization" and "token" requests. + + Override this method by ``return True`` to enable PKCE for everyone. + You might want to enable it only for public clients. + Note that PKCE can also be used in addition of a client authentication. + + OAuth 2.0 public clients utilizing the Authorization Code Grant are + susceptible to the authorization code interception attack. This + specification describes the attack as well as a technique to mitigate + against the threat through the use of Proof Key for Code Exchange + (PKCE, pronounced "pixy"). See `RFC7636`_. + + :param client_id: Client identifier. + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :rtype: True or False + + Method is used by: + - Authorization Code Grant + + .. _`RFC7636`: https://tools.ietf.org/html/rfc7636 + """ + return False + + def get_code_challenge(self, code, request): + """Is called for every "token" requests. + + When the server issues the authorization code in the authorization + response, it MUST associate the ``code_challenge`` and + ``code_challenge_method`` values with the authorization code so it can + be verified later. + + Typically, the ``code_challenge`` and ``code_challenge_method`` values + are stored in encrypted form in the ``code`` itself but could + alternatively be stored on the server associated with the code. The + server MUST NOT include the ``code_challenge`` value in client requests + in a form that other entities can extract. + + Return the ``code_challenge`` associated to the code. + If ``None`` is returned, code is considered to not be associated to any + challenges. + + :param code: Authorization code. + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :rtype: code_challenge string + + Method is used by: + - Authorization Code Grant - when PKCE is active + + """ + return None + + def get_code_challenge_method(self, code, request): + """Is called during the "token" request processing, when a + ``code_verifier`` and a ``code_challenge`` has been provided. + + See ``.get_code_challenge``. + + Must return ``plain`` or ``S256``. You can return a custom value if you have + implemented your own ``AuthorizationCodeGrant`` class. + + :param code: Authorization code. + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :rtype: code_challenge_method string + + Method is used by: + - Authorization Code Grant - when PKCE is active + + """ + raise NotImplementedError('Subclasses must implement this method.') + + def is_origin_allowed(self, client_id, origin, request, *args, **kwargs): + """Indicate if the given origin is allowed to access the token endpoint + via Cross-Origin Resource Sharing (CORS). CORS is used by browser-based + clients, such as Single-Page Applications, to perform the Authorization + Code Grant. + + (Note: If performing Authorization Code Grant via a public client such + as a browser, you should use PKCE as well.) + + If this method returns true, the appropriate CORS headers will be added + to the response. By default this method always returns False, meaning + CORS is disabled. + + :param client_id: Unicode client identifier. + :param redirect_uri: Unicode origin. + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :rtype: bool + + Method is used by: + - Authorization Code Grant + - Refresh Token Grant + + """ + return False diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/tokens.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/tokens.py new file mode 100644 index 0000000000000000000000000000000000000000..73b8c66a9621aa2b81f3adb7a97b84fc051b4142 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/tokens.py @@ -0,0 +1,350 @@ +""" +oauthlib.oauth2.rfc6749.tokens +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +This module contains methods for adding two types of access tokens to requests. + +- Bearer https://tools.ietf.org/html/rfc6750 +- MAC https://tools.ietf.org/html/draft-ietf-oauth-v2-http-mac-01 +""" +import hashlib +import hmac +import warnings +from binascii import b2a_base64 +from urllib.parse import urlparse + +from oauthlib import common +from oauthlib.common import add_params_to_qs, add_params_to_uri + +from . import utils + + +class OAuth2Token(dict): + + def __init__(self, params, old_scope=None): + super().__init__(params) + self._new_scope = None + if params.get('scope'): + self._new_scope = set(utils.scope_to_list(params['scope'])) + if old_scope is not None: + self._old_scope = set(utils.scope_to_list(old_scope)) + if self._new_scope is None: + # the rfc says that if the scope hasn't changed, it's optional + # in params so set the new scope to the old scope + self._new_scope = self._old_scope + else: + self._old_scope = self._new_scope + + @property + def scope_changed(self): + return self._new_scope != self._old_scope + + @property + def old_scope(self): + return utils.list_to_scope(self._old_scope) + + @property + def old_scopes(self): + return list(self._old_scope) + + @property + def scope(self): + return utils.list_to_scope(self._new_scope) + + @property + def scopes(self): + return list(self._new_scope) + + @property + def missing_scopes(self): + return list(self._old_scope - self._new_scope) + + @property + def additional_scopes(self): + return list(self._new_scope - self._old_scope) + + +def prepare_mac_header(token, uri, key, http_method, + nonce=None, + headers=None, + body=None, + ext='', + hash_algorithm='hmac-sha-1', + issue_time=None, + draft=0): + """Add an `MAC Access Authentication`_ signature to headers. + + Unlike OAuth 1, this HMAC signature does not require inclusion of the + request payload/body, neither does it use a combination of client_secret + and token_secret but rather a mac_key provided together with the access + token. + + Currently two algorithms are supported, "hmac-sha-1" and "hmac-sha-256", + `extension algorithms`_ are not supported. + + Example MAC Authorization header, linebreaks added for clarity + + Authorization: MAC id="h480djs93hd8", + nonce="1336363200:dj83hs9s", + mac="bhCQXTVyfj5cmA9uKkPFx1zeOXM=" + + .. _`MAC Access Authentication`: https://tools.ietf.org/html/draft-ietf-oauth-v2-http-mac-01 + .. _`extension algorithms`: https://tools.ietf.org/html/draft-ietf-oauth-v2-http-mac-01#section-7.1 + + :param token: + :param uri: Request URI. + :param key: MAC given provided by token endpoint. + :param http_method: HTTP Request method. + :param nonce: + :param headers: Request headers as a dictionary. + :param body: + :param ext: + :param hash_algorithm: HMAC algorithm provided by token endpoint. + :param issue_time: Time when the MAC credentials were issued (datetime). + :param draft: MAC authentication specification version. + :return: headers dictionary with the authorization field added. + """ + http_method = http_method.upper() + host, port = utils.host_from_uri(uri) + + if hash_algorithm.lower() == 'hmac-sha-1': + h = hashlib.sha1 + elif hash_algorithm.lower() == 'hmac-sha-256': + h = hashlib.sha256 + else: + raise ValueError('unknown hash algorithm') + + if draft == 0: + nonce = nonce or '{}:{}'.format(utils.generate_age(issue_time), + common.generate_nonce()) + else: + ts = common.generate_timestamp() + nonce = common.generate_nonce() + + sch, net, path, par, query, fra = urlparse(uri) + + request_uri = path + '?' + query if query else path + + # Hash the body/payload + if body is not None and draft == 0: + body = body.encode('utf-8') + bodyhash = b2a_base64(h(body).digest())[:-1].decode('utf-8') + else: + bodyhash = '' + + # Create the normalized base string + base = [] + if draft == 0: + base.append(nonce) + else: + base.append(ts) + base.append(nonce) + base.append(http_method.upper()) + base.append(request_uri) + base.append(host) + base.append(port) + if draft == 0: + base.append(bodyhash) + base.append(ext or '') + base_string = '\n'.join(base) + '\n' + + # hmac struggles with unicode strings - http://bugs.python.org/issue5285 + if isinstance(key, str): + key = key.encode('utf-8') + sign = hmac.new(key, base_string.encode('utf-8'), h) + sign = b2a_base64(sign.digest())[:-1].decode('utf-8') + + header = [] + header.append('MAC id="%s"' % token) + if draft != 0: + header.append('ts="%s"' % ts) + header.append('nonce="%s"' % nonce) + if bodyhash: + header.append('bodyhash="%s"' % bodyhash) + if ext: + header.append('ext="%s"' % ext) + header.append('mac="%s"' % sign) + + headers = headers or {} + headers['Authorization'] = ', '.join(header) + return headers + + +def prepare_bearer_uri(token, uri): + """Add a `Bearer Token`_ to the request URI. + Not recommended, use only if client can't use authorization header or body. + + http://www.example.com/path?access_token=h480djs93hd8 + + .. _`Bearer Token`: https://tools.ietf.org/html/rfc6750 + + :param token: + :param uri: + """ + return add_params_to_uri(uri, [(('access_token', token))]) + + +def prepare_bearer_headers(token, headers=None): + """Add a `Bearer Token`_ to the request URI. + Recommended method of passing bearer tokens. + + Authorization: Bearer h480djs93hd8 + + .. _`Bearer Token`: https://tools.ietf.org/html/rfc6750 + + :param token: + :param headers: + """ + headers = headers or {} + headers['Authorization'] = 'Bearer %s' % token + return headers + + +def prepare_bearer_body(token, body=''): + """Add a `Bearer Token`_ to the request body. + + access_token=h480djs93hd8 + + .. _`Bearer Token`: https://tools.ietf.org/html/rfc6750 + + :param token: + :param body: + """ + return add_params_to_qs(body, [(('access_token', token))]) + + +def random_token_generator(request, refresh_token=False): + """ + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :param refresh_token: + """ + return common.generate_token() + + +def signed_token_generator(private_pem, **kwargs): + """ + :param private_pem: + """ + def signed_token_generator(request): + request.claims = kwargs + return common.generate_signed_token(private_pem, request) + + return signed_token_generator + + +def get_token_from_header(request): + """ + Helper function to extract a token from the request header. + + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :return: Return the token or None if the Authorization header is malformed. + """ + token = None + + if 'Authorization' in request.headers: + split_header = request.headers.get('Authorization').split() + if len(split_header) == 2 and split_header[0].lower() == 'bearer': + token = split_header[1] + else: + token = request.access_token + + return token + + +class TokenBase: + __slots__ = () + + def __call__(self, request, refresh_token=False): + raise NotImplementedError('Subclasses must implement this method.') + + def validate_request(self, request): + """ + :param request: OAuthlib request. + :type request: oauthlib.common.Request + """ + raise NotImplementedError('Subclasses must implement this method.') + + def estimate_type(self, request): + """ + :param request: OAuthlib request. + :type request: oauthlib.common.Request + """ + raise NotImplementedError('Subclasses must implement this method.') + + +class BearerToken(TokenBase): + __slots__ = ( + 'request_validator', 'token_generator', + 'refresh_token_generator', 'expires_in' + ) + + def __init__(self, request_validator=None, token_generator=None, + expires_in=None, refresh_token_generator=None): + self.request_validator = request_validator + self.token_generator = token_generator or random_token_generator + self.refresh_token_generator = ( + refresh_token_generator or self.token_generator + ) + self.expires_in = expires_in or 3600 + + def create_token(self, request, refresh_token=False, **kwargs): + """ + Create a BearerToken, by default without refresh token. + + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :param refresh_token: + """ + if "save_token" in kwargs: + warnings.warn("`save_token` has been deprecated, it was not called internally." + "If you do, call `request_validator.save_token()` instead.", + DeprecationWarning) + + expires_in = self.expires_in(request) if callable(self.expires_in) else self.expires_in + + request.expires_in = expires_in + + token = { + 'access_token': self.token_generator(request), + 'expires_in': expires_in, + 'token_type': 'Bearer', + } + + # If provided, include - this is optional in some cases https://tools.ietf.org/html/rfc6749#section-3.3 but + # there is currently no mechanism to coordinate issuing a token for only a subset of the requested scopes so + # all tokens issued are for the entire set of requested scopes. + if request.scopes is not None: + token['scope'] = ' '.join(request.scopes) + + if refresh_token: + if (request.refresh_token and + not self.request_validator.rotate_refresh_token(request)): + token['refresh_token'] = request.refresh_token + else: + token['refresh_token'] = self.refresh_token_generator(request) + + token.update(request.extra_credentials or {}) + return OAuth2Token(token) + + def validate_request(self, request): + """ + :param request: OAuthlib request. + :type request: oauthlib.common.Request + """ + token = get_token_from_header(request) + return self.request_validator.validate_bearer_token( + token, request.scopes, request) + + def estimate_type(self, request): + """ + :param request: OAuthlib request. + :type request: oauthlib.common.Request + """ + if request.headers.get('Authorization', '').split(' ')[0].lower() == 'bearer': + return 9 + elif request.access_token is not None: + return 5 + else: + return 0 diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..7dc27b3dff480bbb89e113f478cec27945af8e2d --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc6749/utils.py @@ -0,0 +1,83 @@ +""" +oauthlib.utils +~~~~~~~~~~~~~~ + +This module contains utility methods used by various parts of the OAuth 2 spec. +""" +import datetime +import os +from urllib.parse import quote, urlparse + +from oauthlib.common import urldecode + + +def list_to_scope(scope): + """Convert a list of scopes to a space separated string.""" + if isinstance(scope, str) or scope is None: + return scope + elif isinstance(scope, (set, tuple, list)): + return " ".join([str(s) for s in scope]) + else: + raise ValueError("Invalid scope (%s), must be string, tuple, set, or list." % scope) + + +def scope_to_list(scope): + """Convert a space separated string to a list of scopes.""" + if isinstance(scope, (tuple, list, set)): + return [str(s) for s in scope] + elif scope is None: + return None + else: + return scope.strip().split(" ") + + +def params_from_uri(uri): + params = dict(urldecode(urlparse(uri).query)) + if 'scope' in params: + params['scope'] = scope_to_list(params['scope']) + return params + + +def host_from_uri(uri): + """Extract hostname and port from URI. + + Will use default port for HTTP and HTTPS if none is present in the URI. + """ + default_ports = { + 'HTTP': '80', + 'HTTPS': '443', + } + + sch, netloc, path, par, query, fra = urlparse(uri) + if ':' in netloc: + netloc, port = netloc.split(':', 1) + else: + port = default_ports.get(sch.upper()) + + return netloc, port + + +def escape(u): + """Escape a string in an OAuth-compatible fashion. + + TODO: verify whether this can in fact be used for OAuth 2 + + """ + if not isinstance(u, str): + raise ValueError('Only unicode objects are escapable.') + return quote(u.encode('utf-8'), safe=b'~') + + +def generate_age(issue_time): + """Generate a age parameter for MAC authentication draft 00.""" + td = datetime.datetime.now() - issue_time + age = (td.microseconds + (td.seconds + td.days * 24 * 3600) + * 10 ** 6) / 10 ** 6 + return str(age) + + +def is_secure_transport(uri): + """Check if the uri is over ssl.""" + if os.environ.get('OAUTHLIB_INSECURE_TRANSPORT'): + return True + return uri.lower().startswith('https://') diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc8628/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc8628/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..65891445e3e687f136fc12896a7d745b70b1594d --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc8628/__init__.py @@ -0,0 +1,16 @@ +""" +oauthlib.oauth2.rfc8628 +~~~~~~~~~~~~~~~~~~~~~~~ + +This module is an implementation of various logic needed +for consuming and providing OAuth 2.0 Device Authorization RFC8628. +""" + +from oauthlib.oauth2.rfc8628.errors import ( + SlowDownError, + AuthorizationPendingError, + ExpiredTokenError, +) +import logging + +log = logging.getLogger(__name__) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc8628/clients/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc8628/clients/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..130b52e3814bc40c0f483b1ab60b034d1374049c --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc8628/clients/__init__.py @@ -0,0 +1,8 @@ +""" +oauthlib.oauth2.rfc8628 +~~~~~~~~~~~~~~~~~~~~~~~ + +This module is an implementation of various logic needed +for consuming OAuth 2.0 Device Authorization RFC8628. +""" +from .device import DeviceClient diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc8628/clients/device.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc8628/clients/device.py new file mode 100644 index 0000000000000000000000000000000000000000..ee0ccf8d61897ac5dae0ecd9028f13a929006129 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc8628/clients/device.py @@ -0,0 +1,95 @@ +""" +oauthlib.oauth2.rfc8628 +~~~~~~~~~~~~~~~~~~~~~~~ + +This module is an implementation of various logic needed +for consuming and providing OAuth 2.0 Device Authorization RFC8628. +""" +from oauthlib.common import add_params_to_uri +from oauthlib.oauth2 import BackendApplicationClient, Client +from oauthlib.oauth2.rfc6749.errors import InsecureTransportError +from oauthlib.oauth2.rfc6749.parameters import prepare_token_request +from oauthlib.oauth2.rfc6749.utils import is_secure_transport, list_to_scope + + +class DeviceClient(Client): + + """A public client utilizing the device authorization workflow. + + The client can request an access token using a device code and + a public client id associated with the device code as defined + in RFC8628. + + The device authorization grant type can be used to obtain both + access tokens and refresh tokens and is intended to be used in + a scenario where the device being authorized does not have a + user interface that is suitable for performing authentication. + """ + + grant_type = 'urn:ietf:params:oauth:grant-type:device_code' + + def __init__(self, client_id, **kwargs): + super().__init__(client_id, **kwargs) + self.client_secret = kwargs.get('client_secret') + + def prepare_request_uri(self, uri, scope=None, **kwargs): + if not is_secure_transport(uri): + raise InsecureTransportError() + + scope = self.scope if scope is None else scope + params = [(('client_id', self.client_id)), (('grant_type', self.grant_type))] + + if self.client_secret is not None: + params.append(('client_secret', self.client_secret)) + + if scope: + params.append(('scope', list_to_scope(scope))) + + for k,v in kwargs.items(): + if v: + params.append((str(k), v)) + + return add_params_to_uri(uri, params) + + def prepare_request_body(self, device_code, body='', scope=None, + include_client_id=False, **kwargs): + """Add device_code to request body + + The client makes a request to the token endpoint by adding the + device_code as a parameter using the + "application/x-www-form-urlencoded" format to the HTTP request + body. + + :param body: Existing request body (URL encoded string) to embed parameters + into. This may contain extra parameters. Default ''. + :param scope: The scope of the access request as described by + `Section 3.3`_. + + :param include_client_id: `True` to send the `client_id` in the + body of the upstream request. This is required + if the client is not authenticating with the + authorization server as described in + `Section 3.2.1`_. False otherwise (default). + :type include_client_id: Boolean + + :param kwargs: Extra credentials to include in the token request. + + The prepared body will include all provided device_code as well as + the ``grant_type`` parameter set to + ``urn:ietf:params:oauth:grant-type:device_code``:: + + >>> from oauthlib.oauth2 import DeviceClient + >>> client = DeviceClient('your_id', 'your_code') + >>> client.prepare_request_body(scope=['hello', 'world']) + 'grant_type=urn:ietf:params:oauth:grant-type:device_code&scope=hello+world' + + .. _`Section 3.2.1`: https://datatracker.ietf.org/doc/html/rfc6749#section-3.2.1 + .. _`Section 3.3`: https://datatracker.ietf.org/doc/html/rfc6749#section-3.3 + .. _`Section 3.4`: https://datatracker.ietf.org/doc/html/rfc8628#section-3.4 + """ + + kwargs['client_id'] = self.client_id + kwargs['include_client_id'] = include_client_id + scope = self.scope if scope is None else scope + return prepare_token_request(self.grant_type, body=body, device_code=device_code, + scope=scope, **kwargs) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc8628/endpoints/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc8628/endpoints/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..dc834797cd7e0f1f3f5f0c997a2f00cd5d7c0d38 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc8628/endpoints/__init__.py @@ -0,0 +1,10 @@ +""" +oauthlib.oauth2.rfc8628 +~~~~~~~~~~~~~~~~~~~~~~~ + +This module is an implementation of various logic needed +for consuming and providing OAuth 2.0 Device Authorization RFC8628. +""" + +from .device_authorization import DeviceAuthorizationEndpoint +from .pre_configured import DeviceApplicationServer diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc8628/endpoints/device_authorization.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc8628/endpoints/device_authorization.py new file mode 100644 index 0000000000000000000000000000000000000000..3f38a540590a9d38d42e3d84a79f6acfe440cac7 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc8628/endpoints/device_authorization.py @@ -0,0 +1,232 @@ +""" +oauthlib.oauth2.rfc8628 +~~~~~~~~~~~~~~~~~~~~~~~ + +This module is an implementation of various logic needed +for consuming and providing OAuth 2.0 RFC8628. +""" + +import logging +from typing import Callable + +from oauthlib.common import Request, generate_token +from oauthlib.oauth2.rfc6749 import errors +from oauthlib.oauth2.rfc6749.endpoints.base import ( + BaseEndpoint, + catch_errors_and_unavailability, +) + +log = logging.getLogger(__name__) + + +class DeviceAuthorizationEndpoint(BaseEndpoint): + """DeviceAuthorization endpoint - used by the client to initiate + the authorization flow by requesting a set of verification codes + from the authorization server by making an HTTP "POST" request to + the device authorization endpoint. + + The client authentication requirements of Section 3.2.1 of [RFC6749] + apply to requests on this endpoint, which means that confidential + clients (those that have established client credentials) authenticate + in the same manner as when making requests to the token endpoint, and + public clients provide the "client_id" parameter to identify + themselves. + """ + + def __init__( + self, + request_validator, + verification_uri, + expires_in=1800, + interval=None, + verification_uri_complete=None, + user_code_generator: Callable[[None], str] = None, + ): + """ + :param request_validator: An instance of RequestValidator. + :type request_validator: oauthlib.oauth2.rfc6749.RequestValidator. + :param verification_uri: a string containing the URL that can be polled by the client application + :param expires_in: a number that represents the lifetime of the `user_code` and `device_code` + :param interval: an option number that represents the number of seconds between each poll requests + :param verification_uri_complete: a string of a function that can be called with `user_data` as parameter + :param user_code_generator: a callable that returns a configurable user code + """ + self.request_validator = request_validator + self._expires_in = expires_in + self._interval = interval + self._verification_uri = verification_uri + self._verification_uri_complete = verification_uri_complete + self.user_code_generator = user_code_generator + + BaseEndpoint.__init__(self) + + @property + def interval(self): + """The minimum amount of time in seconds that the client + SHOULD wait between polling requests to the token endpoint. If no + value is provided, clients MUST use 5 as the default. + """ + return self._interval + + @property + def expires_in(self): + """The lifetime in seconds of the "device_code" and "user_code".""" + return self._expires_in + + @property + def verification_uri(self): + """The end-user verification URI on the authorization + server. The URI should be short and easy to remember as end users + will be asked to manually type it into their user agent. + """ + return self._verification_uri + + def verification_uri_complete(self, user_code): + if not self._verification_uri_complete: + return None + if isinstance(self._verification_uri_complete, str): + return self._verification_uri_complete.format(user_code=user_code) + if callable(self._verification_uri_complete): + return self._verification_uri_complete(user_code) + return None + + @catch_errors_and_unavailability + def validate_device_authorization_request(self, request): + """Validate the device authorization request. + + The client_id is required if the client is not authenticating with the + authorization server as described in `Section 3.2.1. of [RFC6749]`_. + The client identifier as described in `Section 2.2 of [RFC6749]`_. + + .. _`Section 3.2.1. of [RFC6749]`: https://www.rfc-editor.org/rfc/rfc6749#section-3.2.1 + .. _`Section 2.2 of [RFC6749]`: https://www.rfc-editor.org/rfc/rfc6749#section-2.2 + """ + + # First check duplicate parameters + for param in ("client_id", "scope"): + try: + duplicate_params = request.duplicate_params + except ValueError: + raise errors.InvalidRequestFatalError( + description="Unable to parse query string", request=request + ) + if param in duplicate_params: + raise errors.InvalidRequestFatalError( + description="Duplicate %s parameter." % param, request=request + ) + + # the "application/x-www-form-urlencoded" format, per Appendix B of [RFC6749] + # https://www.rfc-editor.org/rfc/rfc6749#appendix-B + if request.headers["Content-Type"] != "application/x-www-form-urlencoded": + raise errors.InvalidRequestError( + "Content-Type must be application/x-www-form-urlencoded", + request=request, + ) + + # REQUIRED. The client identifier as described in Section 2.2. + # https://tools.ietf.org/html/rfc6749#section-2.2 + # TODO: extract client_id an helper validation function. + if not request.client_id: + raise errors.MissingClientIdError(request=request) + + if not self.request_validator.validate_client_id(request.client_id, request): + raise errors.InvalidClientIdError(request=request) + + # The client authentication requirements of Section 3.2.1 of [RFC6749] + # apply to requests on this endpoint, which means that confidential + # clients (those that have established client credentials) authenticate + # in the same manner as when making requests to the token endpoint, and + # public clients provide the "client_id" parameter to identify + # themselves. + self._raise_on_invalid_client(request) + + @catch_errors_and_unavailability + def create_device_authorization_response( + self, uri, http_method="POST", body=None, headers=None + ): + """ + Generate a unique device verification code and an end-user code that are valid for a limited time. + Include them in the HTTP response body using the "application/json" format [RFC8259] with a + 200 (OK) status code, as described in `Section-3.2`_. + + :param uri: The full URI of the token request. + :type uri: str + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :param user_code_generator: + A callable that returns a string for the user code. + This allows the caller to decide how the `user_code` should be formatted. + :type user_code_generator: Callable[[], str] + :return: A tuple of three elements: + 1. A dict of headers to set on the response. + 2. The response body as a string. + 3. The response status code as an integer. + :rtype: tuple + + The response contains the following parameters: + + device_code + **REQUIRED.** The device verification code. + + user_code + **REQUIRED.** The end-user verification code. + + verification_uri + **REQUIRED.** The end-user verification URI on the authorization server. + The URI should be short and easy to remember as end users will be asked + to manually type it into their user agent. + + verification_uri_complete + **OPTIONAL.** A verification URI that includes the `user_code` (or + other information with the same function as the `user_code`), which is + designed for non-textual transmission. + + expires_in + **REQUIRED.** The lifetime in seconds of the `device_code` and `user_code`. + + interval + **OPTIONAL.** The minimum amount of time in seconds that the client + SHOULD wait between polling requests to the token endpoint. If no + value is provided, clients MUST use 5 as the default. + + **For example:** + + .. code-block:: http + + HTTP/1.1 200 OK + Content-Type: application/json + Cache-Control: no-store + + { + "device_code": "GmRhmhcxhwAzkoEqiMEg_DnyEysNkuNhszIySk9eS", + "user_code": "WDJB-MJHT", + "verification_uri": "https://example.com/device", + "verification_uri_complete": + "https://example.com/device?user_code=WDJB-MJHT", + "expires_in": 1800, + "interval": 5 + } + + .. _`Section-3.2`: https://www.rfc-editor.org/rfc/rfc8628#section-3.2 + """ + request = Request(uri, http_method, body, headers) + self.validate_device_authorization_request(request) + log.debug("Pre resource owner authorization validation ok for %r.", request) + + headers = {} + user_code = self.user_code_generator() if self.user_code_generator else generate_token() + data = { + "verification_uri": self.verification_uri, + "expires_in": self.expires_in, + "user_code": user_code, + "device_code": generate_token(), + } + if self.interval is not None: + data["interval"] = self.interval + + + verification_uri_complete = self.verification_uri_complete(user_code) + if verification_uri_complete: + data["verification_uri_complete"] = verification_uri_complete + + return headers, data, 200 diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc8628/endpoints/pre_configured.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc8628/endpoints/pre_configured.py new file mode 100644 index 0000000000000000000000000000000000000000..6cce683304ba7d86c78533fec44862f4568f7bfc --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc8628/endpoints/pre_configured.py @@ -0,0 +1,36 @@ +from oauthlib.oauth2.rfc8628.endpoints.device_authorization import ( + DeviceAuthorizationEndpoint, +) + +from typing import Callable, Optional +from oauthlib.openid.connect.core.request_validator import RequestValidator + + +class DeviceApplicationServer(DeviceAuthorizationEndpoint): + """An all-in-one endpoint featuring Authorization code grant and Bearer tokens.""" + + def __init__( + self, + request_validator: RequestValidator, + verification_uri: str, + interval: int = 5, + verification_uri_complete: Optional[str] = None, # noqa: FA100 + user_code_generator: Callable[[None], str] = None, + **kwargs, + ): + """Construct a new web application server. + + :param request_validator: An implementation of + oauthlib.oauth2.rfc8626.RequestValidator. + :param interval: How long the device needs to wait before polling the server + :param verification_uri: the verification_uri to be send back. + :param user_code_generator: a callable that allows the user code to be configured. + """ + DeviceAuthorizationEndpoint.__init__( + self, + request_validator, + interval=interval, + verification_uri=verification_uri, + user_code_generator=user_code_generator, + verification_uri_complete=verification_uri_complete, + ) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc8628/errors.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc8628/errors.py new file mode 100644 index 0000000000000000000000000000000000000000..a43593898ebb927d70d45f1482f1e5da465d3949 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc8628/errors.py @@ -0,0 +1,55 @@ +from oauthlib.oauth2.rfc6749.errors import OAuth2Error + +""" +oauthlib.oauth2.rfc8628.errors +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Error used both by OAuth2 clients and providers to represent the spec +defined error responses specific to the the device grant +""" + + +class AuthorizationPendingError(OAuth2Error): + """ + For the device authorization grant; + The authorization request is still pending as the end user hasn't + yet completed the user-interaction steps (Section 3.3). The + client SHOULD repeat the access token request to the token + endpoint (a process known as polling). Before each new request, + the client MUST wait at least the number of seconds specified by + the "interval" parameter of the device authorization response, + or 5 seconds if none was provided, and respect any + increase in the polling interval required by the "slow_down" + error. + """ + + error = "authorization_pending" + + +class SlowDownError(OAuth2Error): + """ + A variant of "authorization_pending", the authorization request is + still pending and polling should continue, but the interval MUST + be increased by 5 seconds for this and all subsequent requests. + """ + + error = "slow_down" + + +class ExpiredTokenError(OAuth2Error): + """ + The "device_code" has expired, and the device authorization + session has concluded. The client MAY commence a new device + authorization request but SHOULD wait for user interaction before + restarting to avoid unnecessary polling. + """ + + error = "expired_token" + + +class AccessDenied(OAuth2Error): + """ + The authorization request was denied. + """ + + error = "access_denied" diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc8628/grant_types/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc8628/grant_types/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..418dba775d13c97c76086b1882753165d7e154c6 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc8628/grant_types/__init__.py @@ -0,0 +1 @@ +from oauthlib.oauth2.rfc8628.grant_types.device_code import DeviceCodeGrant diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc8628/grant_types/device_code.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc8628/grant_types/device_code.py new file mode 100644 index 0000000000000000000000000000000000000000..082daf0fccf04fdc33656ddb3f833419648949d2 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc8628/grant_types/device_code.py @@ -0,0 +1,111 @@ +from __future__ import annotations +import json + +from typing import Callable + +from oauthlib import common # noqa: TC001 + +from oauthlib.oauth2.rfc6749 import errors as rfc6749_errors +from oauthlib.oauth2.rfc6749.grant_types.base import GrantTypeBase + + +class DeviceCodeGrant(GrantTypeBase): + def create_authorization_response( + self, request: common.Request, token_handler: Callable + ) -> tuple[dict, str, int]: + """ + Validate the device flow request -> create the access token + -> persist the token -> return the token. + """ + headers = self._get_default_headers() + try: + self.validate_token_request(request) + except rfc6749_errors.OAuth2Error as e: + headers.update(e.headers) + return headers, e.json, e.status_code + + token = token_handler.create_token(request, refresh_token=False) + + for modifier in self._token_modifiers: + token = modifier(token) + + self.request_validator.save_token(token, request) + + return self.create_token_response(request, token_handler) + + def validate_token_request(self, request: common.Request) -> None: + """ + Performs the necessary check against the request to ensure + it's allowed to retrieve a token. + """ + for validator in self.custom_validators.pre_token: + validator(request) + + if not getattr(request, "grant_type", None): + raise rfc6749_errors.InvalidRequestError( + "Request is missing grant type.", request=request + ) + + if request.grant_type != "urn:ietf:params:oauth:grant-type:device_code": + raise rfc6749_errors.UnsupportedGrantTypeError(request=request) + + for param in ("grant_type", "scope"): + if param in request.duplicate_params: + raise rfc6749_errors.InvalidRequestError( + description=f"Duplicate {param} parameter.", request=request + ) + + if not self.request_validator.authenticate_client(request): + raise rfc6749_errors.InvalidClientError(request=request) + elif not hasattr(request.client, "client_id"): + raise NotImplementedError( + "Authenticate client must set the " + "request.client.client_id attribute " + "in authenticate_client." + ) + + # Ensure client is authorized use of this grant type + self.validate_grant_type(request) + + request.client_id = request.client_id or request.client.client_id + self.validate_scopes(request) + + for validator in self.custom_validators.post_token: + validator(request) + + def create_token_response( + self, request: common.Request, token_handler: Callable + ) -> tuple[dict, str, int]: + """Return token or error in json format. + + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :param token_handler: A token handler instance, for example of type + oauthlib.oauth2.BearerToken. + + If the access token request is valid and authorized, the + authorization server issues an access token and optional refresh + token as described in `Section 5.1`_. If the request failed client + authentication or is invalid, the authorization server returns an + error response as described in `Section 5.2`_. + .. _`Section 5.1`: https://tools.ietf.org/html/rfc6749#section-5.1 + .. _`Section 5.2`: https://tools.ietf.org/html/rfc6749#section-5.2 + """ + headers = self._get_default_headers() + try: + if self.request_validator.client_authentication_required( + request + ) and not self.request_validator.authenticate_client(request): + raise rfc6749_errors.InvalidClientError(request=request) + + self.validate_token_request(request) + + except rfc6749_errors.OAuth2Error as e: + headers.update(e.headers) + return headers, e.json, e.status_code + + token = token_handler.create_token(request, self.refresh_token) + + self.request_validator.save_token(token, request) + + return headers, json.dumps(token), 200 diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc8628/request_validator.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc8628/request_validator.py new file mode 100644 index 0000000000000000000000000000000000000000..70ee7824e92dae12b5b44d073f11c708ebd07577 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/oauth2/rfc8628/request_validator.py @@ -0,0 +1,25 @@ +from oauthlib.oauth2 import RequestValidator as OAuth2RequestValidator + + +class RequestValidator(OAuth2RequestValidator): + def client_authentication_required(self, request, *args, **kwargs): + """Determine if client authentication is required for current request. + + According to the rfc8628, client authentication is required in the following cases: + - Device Authorization Request follows the, the client authentication requirements + of Section 3.2.1 of [RFC6749] apply to requests on this endpoint, which means that + confidential clients (those that have established client credentials) authenticate + in the same manner as when making requests to the token endpoint, and + public clients provide the "client_id" parameter to identify themselves, + see `Section 3.1`_. + + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :rtype: True or False + + Method is used by: + - Device Authorization Request + + .. _`Section 3.1`: https://www.rfc-editor.org/rfc/rfc8628#section-3.1 + """ + return True diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/openid/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/openid/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e3174374798f21cc8d5f23e67b2077f14234a1a6 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/openid/__init__.py @@ -0,0 +1,7 @@ +""" +oauthlib.openid +~~~~~~~~~~~~~~ + +""" +from .connect.core.endpoints import Server, UserInfoEndpoint +from .connect.core.request_validator import RequestValidator diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/openid/connect/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/openid/connect/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/openid/connect/core/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/openid/connect/core/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/openid/connect/core/endpoints/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/openid/connect/core/endpoints/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..7017ff4f3211d2007ee5d297cfff47c0a5b27fd4 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/openid/connect/core/endpoints/__init__.py @@ -0,0 +1,9 @@ +""" +oauthlib.oopenid.core +~~~~~~~~~~~~~~~~~~~~~~~ + +This module is an implementation of various logic needed +for consuming and providing OpenID Connect +""" +from .pre_configured import Server +from .userinfo import UserInfoEndpoint diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/openid/connect/core/endpoints/pre_configured.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/openid/connect/core/endpoints/pre_configured.py new file mode 100644 index 0000000000000000000000000000000000000000..17df516d17e9409dedc8902fa3a9ae38195c2067 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/openid/connect/core/endpoints/pre_configured.py @@ -0,0 +1,139 @@ +""" +oauthlib.openid.connect.core.endpoints.pre_configured +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +This module is an implementation of various endpoints needed +for providing OpenID Connect servers. +""" + +from oauthlib.oauth2.rfc6749.endpoints import ( + AuthorizationEndpoint, + IntrospectEndpoint, + ResourceEndpoint, + RevocationEndpoint, + TokenEndpoint, +) +from oauthlib.oauth2.rfc6749.grant_types import ( + AuthorizationCodeGrant as OAuth2AuthorizationCodeGrant, + ClientCredentialsGrant, + ImplicitGrant as OAuth2ImplicitGrant, + ResourceOwnerPasswordCredentialsGrant, +) +from oauthlib.oauth2.rfc8628.grant_types import DeviceCodeGrant +from oauthlib.oauth2.rfc6749.tokens import BearerToken + +from ..grant_types import ( + AuthorizationCodeGrant, + HybridGrant, + ImplicitGrant, + RefreshTokenGrant, +) +from ..grant_types.dispatchers import ( + AuthorizationCodeGrantDispatcher, + AuthorizationTokenGrantDispatcher, + ImplicitTokenGrantDispatcher, +) +from ..tokens import JWTToken +from .userinfo import UserInfoEndpoint + + +class Server( + AuthorizationEndpoint, + IntrospectEndpoint, + TokenEndpoint, + ResourceEndpoint, + RevocationEndpoint, + UserInfoEndpoint, +): + """ + An all-in-one endpoint featuring all four major grant types + and extension grants. + """ + + def __init__( + self, + request_validator, + token_expires_in=None, + token_generator=None, + refresh_token_generator=None, + *args, + **kwargs, + ): + """Construct a new all-grants-in-one server. + + :param request_validator: An implementation of + oauthlib.oauth2.RequestValidator. + :param token_expires_in: An int or a function to generate a token + expiration offset (in seconds) given a + oauthlib.common.Request object. + :param token_generator: A function to generate a token from a request. + :param refresh_token_generator: A function to generate a token from a + request for the refresh token. + :param kwargs: Extra parameters to pass to authorization-, + token-, resource-, and revocation-endpoint constructors. + """ + self.auth_grant = OAuth2AuthorizationCodeGrant(request_validator) + self.implicit_grant = OAuth2ImplicitGrant(request_validator) + self.password_grant = ResourceOwnerPasswordCredentialsGrant(request_validator) + self.credentials_grant = ClientCredentialsGrant(request_validator) + self.refresh_grant = RefreshTokenGrant(request_validator) + self.openid_connect_auth = AuthorizationCodeGrant(request_validator) + self.openid_connect_implicit = ImplicitGrant(request_validator) + self.openid_connect_hybrid = HybridGrant(request_validator) + self.device_code_grant = DeviceCodeGrant(request_validator, **kwargs) + + self.bearer = BearerToken( + request_validator, token_generator, token_expires_in, refresh_token_generator + ) + + self.jwt = JWTToken( + request_validator, token_generator, token_expires_in, refresh_token_generator + ) + + self.auth_grant_choice = AuthorizationCodeGrantDispatcher( + default_grant=self.auth_grant, oidc_grant=self.openid_connect_auth + ) + self.implicit_grant_choice = ImplicitTokenGrantDispatcher( + default_grant=self.implicit_grant, oidc_grant=self.openid_connect_implicit + ) + + # See http://openid.net/specs/oauth-v2-multiple-response-types-1_0.html#Combinations for valid combinations + # internally our AuthorizationEndpoint will ensure they can appear in any order for any valid combination + AuthorizationEndpoint.__init__( + self, + default_response_type="code", + response_types={ + "code": self.auth_grant_choice, + "token": self.implicit_grant_choice, + "id_token": self.openid_connect_implicit, + "id_token token": self.openid_connect_implicit, + "code token": self.openid_connect_hybrid, + "code id_token": self.openid_connect_hybrid, + "code id_token token": self.openid_connect_hybrid, + "none": self.auth_grant, + }, + default_token_type=self.bearer, + ) + + self.token_grant_choice = AuthorizationTokenGrantDispatcher( + request_validator, default_grant=self.auth_grant, oidc_grant=self.openid_connect_auth + ) + + TokenEndpoint.__init__( + self, + default_grant_type="authorization_code", + grant_types={ + "authorization_code": self.token_grant_choice, + "password": self.password_grant, + "client_credentials": self.credentials_grant, + "refresh_token": self.refresh_grant, + "urn:ietf:params:oauth:grant-type:device_code": self.device_code_grant, + }, + default_token_type=self.bearer, + ) + ResourceEndpoint.__init__( + self, default_token="Bearer", token_types={"Bearer": self.bearer, "JWT": self.jwt} + ) + RevocationEndpoint.__init__(self, request_validator) + IntrospectEndpoint.__init__(self, request_validator) + UserInfoEndpoint.__init__(self, request_validator) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/openid/connect/core/endpoints/userinfo.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/openid/connect/core/endpoints/userinfo.py new file mode 100644 index 0000000000000000000000000000000000000000..7aa2bbe97d34fa5afce4168256aafe1c8ce5fcae --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/openid/connect/core/endpoints/userinfo.py @@ -0,0 +1,106 @@ +""" +oauthlib.openid.connect.core.endpoints.userinfo +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +This module is an implementation of userinfo endpoint. +""" +import json +import logging + +from oauthlib.common import Request +from oauthlib.oauth2.rfc6749 import errors +from oauthlib.oauth2.rfc6749.endpoints.base import ( + BaseEndpoint, catch_errors_and_unavailability, +) +from oauthlib.oauth2.rfc6749.tokens import BearerToken + +log = logging.getLogger(__name__) + + +class UserInfoEndpoint(BaseEndpoint): + """Authorizes access to userinfo resource. + """ + def __init__(self, request_validator): + self.bearer = BearerToken(request_validator, None, None, None) + self.request_validator = request_validator + BaseEndpoint.__init__(self) + + @catch_errors_and_unavailability + def create_userinfo_response(self, uri, http_method='GET', body=None, headers=None): + """Validate BearerToken and return userinfo from RequestValidator + + The UserInfo Endpoint MUST return a + content-type header to indicate which format is being returned. The + content-type of the HTTP response MUST be application/json if the + response body is a text JSON object; the response body SHOULD be encoded + using UTF-8. + """ + request = Request(uri, http_method, body, headers) + request.scopes = ["openid"] + self.validate_userinfo_request(request) + + claims = self.request_validator.get_userinfo_claims(request) + if claims is None: + log.error('Userinfo MUST have claims for %r.', request) + raise errors.ServerError(status_code=500) + + if isinstance(claims, dict): + resp_headers = { + 'Content-Type': 'application/json' + } + if "sub" not in claims: + log.error('Userinfo MUST have "sub" for %r.', request) + raise errors.ServerError(status_code=500) + body = json.dumps(claims) + elif isinstance(claims, str): + resp_headers = { + 'Content-Type': 'application/jwt' + } + body = claims + else: + log.error('Userinfo return unknown response for %r.', request) + raise errors.ServerError(status_code=500) + log.debug('Userinfo access valid for %r.', request) + return resp_headers, body, 200 + + def validate_userinfo_request(self, request): + """Ensure the request is valid. + + 5.3.1. UserInfo Request + The Client sends the UserInfo Request using either HTTP GET or HTTP + POST. The Access Token obtained from an OpenID Connect Authentication + Request MUST be sent as a Bearer Token, per `Section 2`_ of OAuth 2.0 + Bearer Token Usage [RFC6750]. + + It is RECOMMENDED that the request use the HTTP GET method and the + Access Token be sent using the Authorization header field. + + The following is a non-normative example of a UserInfo Request: + + .. code-block:: http + + GET /userinfo HTTP/1.1 + Host: server.example.com + Authorization: Bearer SlAV32hkKG + + 5.3.3. UserInfo Error Response + When an error condition occurs, the UserInfo Endpoint returns an Error + Response as defined in `Section 3`_ of OAuth 2.0 Bearer Token Usage + [RFC6750]. (HTTP errors unrelated to RFC 6750 are returned to the User + Agent using the appropriate HTTP status code.) + + The following is a non-normative example of a UserInfo Error Response: + + .. code-block:: http + + HTTP/1.1 401 Unauthorized + WWW-Authenticate: Bearer error="invalid_token", + error_description="The Access Token expired" + + .. _`Section 2`: https://datatracker.ietf.org/doc/html/rfc6750#section-2 + .. _`Section 3`: https://datatracker.ietf.org/doc/html/rfc6750#section-3 + """ + if not self.bearer.validate_request(request): + raise errors.InvalidTokenError() + if "openid" not in request.scopes: + raise errors.InsufficientScopeError() diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/openid/connect/core/exceptions.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/openid/connect/core/exceptions.py new file mode 100644 index 0000000000000000000000000000000000000000..291cf13776871147e9660a35254bb70a595fc791 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/openid/connect/core/exceptions.py @@ -0,0 +1,150 @@ +""" +oauthlib.oauth2.rfc6749.errors +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Error used both by OAuth 2 clients and providers to represent the spec +defined error responses for all four core grant types. +""" +import inspect +import sys + +from oauthlib.oauth2.rfc6749.errors import FatalClientError, OAuth2Error + + +class FatalOpenIDClientError(FatalClientError): + pass + + +class OpenIDClientError(OAuth2Error): + pass + + +class InteractionRequired(OpenIDClientError): + """ + The Authorization Server requires End-User interaction to proceed. + + This error MAY be returned when the prompt parameter value in the + Authentication Request is none, but the Authentication Request cannot be + completed without displaying a user interface for End-User interaction. + """ + error = 'interaction_required' + status_code = 401 + + +class LoginRequired(OpenIDClientError): + """ + The Authorization Server requires End-User authentication. + + This error MAY be returned when the prompt parameter value in the + Authentication Request is none, but the Authentication Request cannot be + completed without displaying a user interface for End-User authentication. + """ + error = 'login_required' + status_code = 401 + + +class AccountSelectionRequired(OpenIDClientError): + """ + The End-User is REQUIRED to select a session at the Authorization Server. + + The End-User MAY be authenticated at the Authorization Server with + different associated accounts, but the End-User did not select a session. + This error MAY be returned when the prompt parameter value in the + Authentication Request is none, but the Authentication Request cannot be + completed without displaying a user interface to prompt for a session to + use. + """ + error = 'account_selection_required' + + +class ConsentRequired(OpenIDClientError): + """ + The Authorization Server requires End-User consent. + + This error MAY be returned when the prompt parameter value in the + Authentication Request is none, but the Authentication Request cannot be + completed without displaying a user interface for End-User consent. + """ + error = 'consent_required' + status_code = 401 + + +class InvalidRequestURI(OpenIDClientError): + """ + The request_uri in the Authorization Request returns an error or + contains invalid data. + """ + error = 'invalid_request_uri' + description = ('The request_uri in the Authorization Request returns an ' + 'error or contains invalid data.') + + +class InvalidRequestObject(OpenIDClientError): + """ + The request parameter contains an invalid Request Object. + """ + error = 'invalid_request_object' + description = 'The request parameter contains an invalid Request Object.' + + +class RequestNotSupported(OpenIDClientError): + """ + The OP does not support use of the request parameter. + """ + error = 'request_not_supported' + description = 'The request parameter is not supported.' + + +class RequestURINotSupported(OpenIDClientError): + """ + The OP does not support use of the request_uri parameter. + """ + error = 'request_uri_not_supported' + description = 'The request_uri parameter is not supported.' + + +class RegistrationNotSupported(OpenIDClientError): + """ + The OP does not support use of the registration parameter. + """ + error = 'registration_not_supported' + description = 'The registration parameter is not supported.' + + +class InvalidTokenError(OAuth2Error): + """ + The access token provided is expired, revoked, malformed, or + invalid for other reasons. The resource SHOULD respond with + the HTTP 401 (Unauthorized) status code. The client MAY + request a new access token and retry the protected resource + request. + """ + error = 'invalid_token' + status_code = 401 + description = ("The access token provided is expired, revoked, malformed, " + "or invalid for other reasons.") + + +class InsufficientScopeError(OAuth2Error): + """ + The request requires higher privileges than provided by the + access token. The resource server SHOULD respond with the HTTP + 403 (Forbidden) status code and MAY include the "scope" + attribute with the scope necessary to access the protected + resource. + """ + error = 'insufficient_scope' + status_code = 403 + description = ("The request requires higher privileges than provided by " + "the access token.") + + +def raise_from_error(error, params=None): + kwargs = { + 'description': params.get('error_description'), + 'uri': params.get('error_uri'), + 'state': params.get('state') + } + for _, cls in inspect.getmembers(sys.modules[__name__], inspect.isclass): + if cls.error == error: + raise cls(**kwargs) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/openid/connect/core/grant_types/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/openid/connect/core/grant_types/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..8dad5f607b9856704b3f074d31f3b701ddfafd94 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/openid/connect/core/grant_types/__init__.py @@ -0,0 +1,13 @@ +""" +oauthlib.openid.connect.core.grant_types +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +""" +from .authorization_code import AuthorizationCodeGrant +from .base import GrantTypeBase +from .dispatchers import ( + AuthorizationCodeGrantDispatcher, AuthorizationTokenGrantDispatcher, + ImplicitTokenGrantDispatcher, +) +from .hybrid import HybridGrant +from .implicit import ImplicitGrant +from .refresh_token import RefreshTokenGrant diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/openid/connect/core/grant_types/authorization_code.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/openid/connect/core/grant_types/authorization_code.py new file mode 100644 index 0000000000000000000000000000000000000000..6b2dcc3bdd12d27d280890494286efc5f0c9befb --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/openid/connect/core/grant_types/authorization_code.py @@ -0,0 +1,43 @@ +""" +oauthlib.openid.connect.core.grant_types +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +""" +import logging + +from oauthlib.oauth2.rfc6749.grant_types.authorization_code import ( + AuthorizationCodeGrant as OAuth2AuthorizationCodeGrant, +) + +from .base import GrantTypeBase + +log = logging.getLogger(__name__) + + +class AuthorizationCodeGrant(GrantTypeBase): + + def __init__(self, request_validator=None, **kwargs): + self.proxy_target = OAuth2AuthorizationCodeGrant( + request_validator=request_validator, **kwargs) + self.custom_validators.post_auth.append( + self.openid_authorization_validator) + self.register_token_modifier(self.add_id_token) + + def add_id_token(self, token, token_handler, request): + """ + Construct an initial version of id_token, and let the + request_validator sign or encrypt it. + + The authorization_code version of this method is used to + retrieve the nonce accordingly to the code storage. + """ + # Treat it as normal OAuth 2 auth code request if openid is not present + if not request.scopes or 'openid' not in request.scopes: + return token + + nonce = self.request_validator.get_authorization_code_nonce( + request.client_id, + request.code, + request.redirect_uri, + request + ) + return super().add_id_token(token, token_handler, request, nonce=nonce) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/openid/connect/core/grant_types/base.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/openid/connect/core/grant_types/base.py new file mode 100644 index 0000000000000000000000000000000000000000..29d583eb1c0677a5167ddccee274d596e3b1aca7 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/openid/connect/core/grant_types/base.py @@ -0,0 +1,330 @@ +import base64 +import hashlib +import logging +import time +from json import loads + +from oauthlib.oauth2.rfc6749.errors import ( + ConsentRequired, InvalidRequestError, LoginRequired, +) + +log = logging.getLogger(__name__) + + +class GrantTypeBase: + + # Just proxy the majority of method calls through to the + # proxy_target grant type handler, which will usually be either + # the standard OAuth2 AuthCode or Implicit grant types. + def __getattr__(self, attr): + return getattr(self.proxy_target, attr) + + def __setattr__(self, attr, value): + proxied_attrs = {'refresh_token', 'response_types'} + if attr in proxied_attrs: + setattr(self.proxy_target, attr, value) + else: + super(OpenIDConnectBase, self).__setattr__(attr, value) + + def validate_authorization_request(self, request): + """Validates the OpenID Connect authorization request parameters. + + :returns: (list of scopes, dict of request info) + """ + return self.proxy_target.validate_authorization_request(request) + + def _inflate_claims(self, request): + # this may be called multiple times in a single request so make sure we only de-serialize the claims once + if request.claims and not isinstance(request.claims, dict): + # specific claims are requested during the Authorization Request and may be requested for inclusion + # in either the id_token or the UserInfo endpoint response + # see http://openid.net/specs/openid-connect-core-1_0.html#ClaimsParameter + try: + request.claims = loads(request.claims) + except Exception as ex: + raise InvalidRequestError(description="Malformed claims parameter", + uri="http://openid.net/specs/openid-connect-core-1_0.html#ClaimsParameter") + + def id_token_hash(self, value, hashfunc=hashlib.sha256): + """ + Its value is the base64url encoding of the left-most half of the + hash of the octets of the ASCII representation of the access_token + value, where the hash algorithm used is the hash algorithm used in + the alg Header Parameter of the ID Token's JOSE Header. + + For instance, if the alg is RS256, hash the access_token value + with SHA-256, then take the left-most 128 bits and + base64url-encode them. + For instance, if the alg is HS512, hash the code value with + SHA-512, then take the left-most 256 bits and base64url-encode + them. The c_hash value is a case-sensitive string. + + Example of hash from OIDC specification (bound to a JWS using RS256): + + code: + Qcb0Orv1zh30vL1MPRsbm-diHiMwcLyZvn1arpZv-Jxf_11jnpEX3Tgfvk + + c_hash: + LDktKdoQak3Pk0cnXxCltA + """ + digest = hashfunc(value.encode()).digest() + left_most = len(digest) // 2 + return base64.urlsafe_b64encode(digest[:left_most]).decode().rstrip("=") + + def add_id_token(self, token, token_handler, request, nonce=None): + """ + Construct an initial version of id_token, and let the + request_validator sign or encrypt it. + + The initial version can contain the fields below, accordingly + to the spec: + - aud + - iat + - nonce + - at_hash + - c_hash + """ + # Treat it as normal OAuth 2 auth code request if openid is not present + if not request.scopes or 'openid' not in request.scopes: + return token + + # Only add an id token on auth/token step if asked for. + if request.response_type and 'id_token' not in request.response_type: + return token + + # Implementation mint its own id_token without help. + id_token = self.request_validator.get_id_token(token, token_handler, request) + if id_token: + token['id_token'] = id_token + return token + + # Fallback for asking some help from oauthlib framework. + # Start with technicals fields bound to the specification. + id_token = {} + id_token['aud'] = request.client_id + id_token['iat'] = int(time.time()) + + # nonce is REQUIRED when response_type value is: + # - id_token token (Implicit) + # - id_token (Implicit) + # - code id_token (Hybrid) + # - code id_token token (Hybrid) + # + # nonce is OPTIONAL when response_type value is: + # - code (Authorization Code) + # - code token (Hybrid) + if nonce is not None: + id_token["nonce"] = nonce + + # at_hash is REQUIRED when response_type value is: + # - id_token token (Implicit) + # - code id_token token (Hybrid) + # + # at_hash is OPTIONAL when: + # - code (Authorization code) + # - code id_token (Hybrid) + # - code token (Hybrid) + # + # at_hash MAY NOT be used when: + # - id_token (Implicit) + if "access_token" in token: + id_token["at_hash"] = self.id_token_hash(token["access_token"]) + + # c_hash is REQUIRED when response_type value is: + # - code id_token (Hybrid) + # - code id_token token (Hybrid) + # + # c_hash is OPTIONAL for others. + if "code" in token: + id_token["c_hash"] = self.id_token_hash(token["code"]) + + # Call request_validator to complete/sign/encrypt id_token + token['id_token'] = self.request_validator.finalize_id_token(id_token, token, token_handler, request) + + return token + + def openid_authorization_validator(self, request): + """Perform OpenID Connect specific authorization request validation. + + nonce + OPTIONAL. String value used to associate a Client session with + an ID Token, and to mitigate replay attacks. The value is + passed through unmodified from the Authentication Request to + the ID Token. Sufficient entropy MUST be present in the nonce + values used to prevent attackers from guessing values + + display + OPTIONAL. ASCII string value that specifies how the + Authorization Server displays the authentication and consent + user interface pages to the End-User. The defined values are: + + page - The Authorization Server SHOULD display the + authentication and consent UI consistent with a full User + Agent page view. If the display parameter is not specified, + this is the default display mode. + + popup - The Authorization Server SHOULD display the + authentication and consent UI consistent with a popup User + Agent window. The popup User Agent window should be of an + appropriate size for a login-focused dialog and should not + obscure the entire window that it is popping up over. + + touch - The Authorization Server SHOULD display the + authentication and consent UI consistent with a device that + leverages a touch interface. + + wap - The Authorization Server SHOULD display the + authentication and consent UI consistent with a "feature + phone" type display. + + The Authorization Server MAY also attempt to detect the + capabilities of the User Agent and present an appropriate + display. + + prompt + OPTIONAL. Space delimited, case sensitive list of ASCII string + values that specifies whether the Authorization Server prompts + the End-User for reauthentication and consent. The defined + values are: + + none - The Authorization Server MUST NOT display any + authentication or consent user interface pages. An error is + returned if an End-User is not already authenticated or the + Client does not have pre-configured consent for the + requested Claims or does not fulfill other conditions for + processing the request. The error code will typically be + login_required, interaction_required, or another code + defined in Section 3.1.2.6. This can be used as a method to + check for existing authentication and/or consent. + + login - The Authorization Server SHOULD prompt the End-User + for reauthentication. If it cannot reauthenticate the + End-User, it MUST return an error, typically + login_required. + + consent - The Authorization Server SHOULD prompt the + End-User for consent before returning information to the + Client. If it cannot obtain consent, it MUST return an + error, typically consent_required. + + select_account - The Authorization Server SHOULD prompt the + End-User to select a user account. This enables an End-User + who has multiple accounts at the Authorization Server to + select amongst the multiple accounts that they might have + current sessions for. If it cannot obtain an account + selection choice made by the End-User, it MUST return an + error, typically account_selection_required. + + The prompt parameter can be used by the Client to make sure + that the End-User is still present for the current session or + to bring attention to the request. If this parameter contains + none with any other value, an error is returned. + + max_age + OPTIONAL. Maximum Authentication Age. Specifies the allowable + elapsed time in seconds since the last time the End-User was + actively authenticated by the OP. If the elapsed time is + greater than this value, the OP MUST attempt to actively + re-authenticate the End-User. (The max_age request parameter + corresponds to the OpenID 2.0 PAPE [OpenID.PAPE] max_auth_age + request parameter.) When max_age is used, the ID Token returned + MUST include an auth_time Claim Value. + + ui_locales + OPTIONAL. End-User's preferred languages and scripts for the + user interface, represented as a space-separated list of BCP47 + [RFC5646] language tag values, ordered by preference. For + instance, the value "fr-CA fr en" represents a preference for + French as spoken in Canada, then French (without a region + designation), followed by English (without a region + designation). An error SHOULD NOT result if some or all of the + requested locales are not supported by the OpenID Provider. + + id_token_hint + OPTIONAL. ID Token previously issued by the Authorization + Server being passed as a hint about the End-User's current or + past authenticated session with the Client. If the End-User + identified by the ID Token is logged in or is logged in by the + request, then the Authorization Server returns a positive + response; otherwise, it SHOULD return an error, such as + login_required. When possible, an id_token_hint SHOULD be + present when prompt=none is used and an invalid_request error + MAY be returned if it is not; however, the server SHOULD + respond successfully when possible, even if it is not present. + The Authorization Server need not be listed as an audience of + the ID Token when it is used as an id_token_hint value. If the + ID Token received by the RP from the OP is encrypted, to use it + as an id_token_hint, the Client MUST decrypt the signed ID + Token contained within the encrypted ID Token. The Client MAY + re-encrypt the signed ID token to the Authentication Server + using a key that enables the server to decrypt the ID Token, + and use the re-encrypted ID token as the id_token_hint value. + + login_hint + OPTIONAL. Hint to the Authorization Server about the login + identifier the End-User might use to log in (if necessary). + This hint can be used by an RP if it first asks the End-User + for their e-mail address (or other identifier) and then wants + to pass that value as a hint to the discovered authorization + service. It is RECOMMENDED that the hint value match the value + used for discovery. This value MAY also be a phone number in + the format specified for the phone_number Claim. The use of + this parameter is left to the OP's discretion. + + acr_values + OPTIONAL. Requested Authentication Context Class Reference + values. Space-separated string that specifies the acr values + that the Authorization Server is being requested to use for + processing this Authentication Request, with the values + appearing in order of preference. The Authentication Context + Class satisfied by the authentication performed is returned as + the acr Claim Value, as specified in Section 2. The acr Claim + is requested as a Voluntary Claim by this parameter. + """ + + # Treat it as normal OAuth 2 auth code request if openid is not present + if not request.scopes or 'openid' not in request.scopes: + return {} + + prompt = request.prompt if request.prompt else [] + if hasattr(prompt, 'split'): + prompt = prompt.strip().split() + prompt = set(prompt) + + if 'none' in prompt: + + if len(prompt) > 1: + msg = "Prompt none is mutually exclusive with other values." + raise InvalidRequestError(request=request, description=msg) + + if not self.request_validator.validate_silent_login(request): + raise LoginRequired(request=request) + + if not self.request_validator.validate_silent_authorization(request): + raise ConsentRequired(request=request) + + self._inflate_claims(request) + + if not self.request_validator.validate_user_match( + request.id_token_hint, request.scopes, request.claims, request): + msg = "Session user does not match client supplied user." + raise LoginRequired(request=request, description=msg) + + ui_locales = request.ui_locales if request.ui_locales else [] + if hasattr(ui_locales, 'split'): + ui_locales = ui_locales.strip().split() + + request_info = { + 'display': request.display, + 'nonce': request.nonce, + 'prompt': prompt, + 'ui_locales': ui_locales, + 'id_token_hint': request.id_token_hint, + 'login_hint': request.login_hint, + 'claims': request.claims + } + + return request_info + + +OpenIDConnectBase = GrantTypeBase diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/openid/connect/core/grant_types/dispatchers.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/openid/connect/core/grant_types/dispatchers.py new file mode 100644 index 0000000000000000000000000000000000000000..7e0739684f107801ff094f98190efcd17ae438c1 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/openid/connect/core/grant_types/dispatchers.py @@ -0,0 +1,101 @@ +import logging + +log = logging.getLogger(__name__) + + +class Dispatcher: + default_grant = None + oidc_grant = None + + +class AuthorizationCodeGrantDispatcher(Dispatcher): + """ + This is an adapter class that will route simple Authorization Code + requests, those that have `response_type=code` and a scope including + `openid` to either the `default_grant` or the `oidc_grant` based on + the scopes requested. + """ + def __init__(self, default_grant=None, oidc_grant=None): + self.default_grant = default_grant + self.oidc_grant = oidc_grant + + def _handler_for_request(self, request): + handler = self.default_grant + + if request.scopes and "openid" in request.scopes: + handler = self.oidc_grant + + log.debug('Selecting handler for request %r.', handler) + return handler + + def create_authorization_response(self, request, token_handler): + """Read scope and route to the designated handler.""" + return self._handler_for_request(request).create_authorization_response(request, token_handler) + + def validate_authorization_request(self, request): + """Read scope and route to the designated handler.""" + return self._handler_for_request(request).validate_authorization_request(request) + + +class ImplicitTokenGrantDispatcher(Dispatcher): + """ + This is an adapter class that will route simple Authorization + requests, those that have `id_token` in `response_type` and a scope + including `openid` to either the `default_grant` or the `oidc_grant` + based on the scopes requested. + """ + def __init__(self, default_grant=None, oidc_grant=None): + self.default_grant = default_grant + self.oidc_grant = oidc_grant + + def _handler_for_request(self, request): + handler = self.default_grant + + if request.scopes and "openid" in request.scopes and 'id_token' in request.response_type: + handler = self.oidc_grant + + log.debug('Selecting handler for request %r.', handler) + return handler + + def create_authorization_response(self, request, token_handler): + """Read scope and route to the designated handler.""" + return self._handler_for_request(request).create_authorization_response(request, token_handler) + + def validate_authorization_request(self, request): + """Read scope and route to the designated handler.""" + return self._handler_for_request(request).validate_authorization_request(request) + + +class AuthorizationTokenGrantDispatcher(Dispatcher): + """ + This is an adapter class that will route simple Token requests, those that authorization_code have a scope + including 'openid' to either the default_grant or the oidc_grant based on the scopes requested. + """ + def __init__(self, request_validator, default_grant=None, oidc_grant=None): + self.default_grant = default_grant + self.oidc_grant = oidc_grant + self.request_validator = request_validator + + def _handler_for_request(self, request): + handler = self.default_grant + scopes = () + parameters = dict(request.decoded_body) + client_id = parameters.get('client_id') + code = parameters.get('code') + redirect_uri = parameters.get('redirect_uri') + + # If code is not present fallback to `default_grant` which will + # raise an error for the missing `code` in `create_token_response` step. + if code: + scopes = self.request_validator.get_authorization_code_scopes(client_id, code, redirect_uri, request) + + if 'openid' in scopes: + handler = self.oidc_grant + + log.debug('Selecting handler for request %r.', handler) + return handler + + def create_token_response(self, request, token_handler): + """Read scope and route to the designated handler.""" + handler = self._handler_for_request(request) + return handler.create_token_response(request, token_handler) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/openid/connect/core/grant_types/hybrid.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/openid/connect/core/grant_types/hybrid.py new file mode 100644 index 0000000000000000000000000000000000000000..9c1fc702f9496c592b06437e774c5d492c1b7de3 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/openid/connect/core/grant_types/hybrid.py @@ -0,0 +1,62 @@ +""" +oauthlib.openid.connect.core.grant_types +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +""" +import logging + +from oauthlib.oauth2.rfc6749.errors import InvalidRequestError +from oauthlib.oauth2.rfc6749.grant_types.authorization_code import ( + AuthorizationCodeGrant as OAuth2AuthorizationCodeGrant, +) + +from ..request_validator import RequestValidator +from .base import GrantTypeBase + +log = logging.getLogger(__name__) + + +class HybridGrant(GrantTypeBase): + + def __init__(self, request_validator=None, **kwargs): + self.request_validator = request_validator or RequestValidator() + + self.proxy_target = OAuth2AuthorizationCodeGrant( + request_validator=request_validator, **kwargs) + # All hybrid response types should be fragment-encoded. + self.proxy_target.default_response_mode = "fragment" + self.register_response_type('code id_token') + self.register_response_type('code token') + self.register_response_type('code id_token token') + self.custom_validators.post_auth.append( + self.openid_authorization_validator) + # Hybrid flows can return the id_token from the authorization + # endpoint as part of the 'code' response + self.register_code_modifier(self.add_token) + self.register_code_modifier(self.add_id_token) + self.register_token_modifier(self.add_id_token) + + def add_id_token(self, token, token_handler, request): + return super().add_id_token(token, token_handler, request, nonce=request.nonce) + + def openid_authorization_validator(self, request): + """Additional validation when following the Authorization Code flow. + """ + request_info = super().openid_authorization_validator(request) + if not request_info: # returns immediately if OAuth2.0 + return request_info + + # REQUIRED if the Response Type of the request is `code + # id_token` or `code id_token token` and OPTIONAL when the + # Response Type of the request is `code token`. It is a string + # value used to associate a Client session with an ID Token, + # and to mitigate replay attacks. The value is passed through + # unmodified from the Authentication Request to the ID + # Token. Sufficient entropy MUST be present in the `nonce` + # values used to prevent attackers from guessing values. For + # implementation notes, see Section 15.5.2. + if request.response_type in ["code id_token", "code id_token token"] and not request.nonce: + raise InvalidRequestError( + request=request, + description='Request is missing mandatory nonce parameter.' + ) + return request_info diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/openid/connect/core/grant_types/implicit.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/openid/connect/core/grant_types/implicit.py new file mode 100644 index 0000000000000000000000000000000000000000..a4fe6049bc29a8d9b202b9e0244a1b6c559d99d6 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/openid/connect/core/grant_types/implicit.py @@ -0,0 +1,51 @@ +""" +oauthlib.openid.connect.core.grant_types +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +""" +import logging + +from oauthlib.oauth2.rfc6749.errors import InvalidRequestError +from oauthlib.oauth2.rfc6749.grant_types.implicit import ( + ImplicitGrant as OAuth2ImplicitGrant, +) + +from .base import GrantTypeBase + +log = logging.getLogger(__name__) + + +class ImplicitGrant(GrantTypeBase): + + def __init__(self, request_validator=None, **kwargs): + self.proxy_target = OAuth2ImplicitGrant( + request_validator=request_validator, **kwargs) + self.register_response_type('id_token') + self.register_response_type('id_token token') + self.custom_validators.post_auth.append( + self.openid_authorization_validator) + self.register_token_modifier(self.add_id_token) + + def add_id_token(self, token, token_handler, request): + if 'state' not in token and request.state: + token['state'] = request.state + return super().add_id_token(token, token_handler, request, nonce=request.nonce) + + def openid_authorization_validator(self, request): + """Additional validation when following the implicit flow. + """ + request_info = super().openid_authorization_validator(request) + if not request_info: # returns immediately if OAuth2.0 + return request_info + + # REQUIRED. String value used to associate a Client session with an ID + # Token, and to mitigate replay attacks. The value is passed through + # unmodified from the Authentication Request to the ID Token. + # Sufficient entropy MUST be present in the nonce values used to + # prevent attackers from guessing values. For implementation notes, see + # Section 15.5.2. + if not request.nonce: + raise InvalidRequestError( + request=request, + description='Request is missing mandatory nonce parameter.' + ) + return request_info diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/openid/connect/core/grant_types/refresh_token.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/openid/connect/core/grant_types/refresh_token.py new file mode 100644 index 0000000000000000000000000000000000000000..43e4499c53c77a7f1486981f84e7fe49cf71f545 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/openid/connect/core/grant_types/refresh_token.py @@ -0,0 +1,34 @@ +""" +oauthlib.openid.connect.core.grant_types +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +""" +import logging + +from oauthlib.oauth2.rfc6749.grant_types.refresh_token import ( + RefreshTokenGrant as OAuth2RefreshTokenGrant, +) + +from .base import GrantTypeBase + +log = logging.getLogger(__name__) + + +class RefreshTokenGrant(GrantTypeBase): + + def __init__(self, request_validator=None, **kwargs): + self.proxy_target = OAuth2RefreshTokenGrant( + request_validator=request_validator, **kwargs) + self.register_token_modifier(self.add_id_token) + + def add_id_token(self, token, token_handler, request): + """ + Construct an initial version of id_token, and let the + request_validator sign or encrypt it. + + The authorization_code version of this method is used to + retrieve the nonce accordingly to the code storage. + """ + if not self.request_validator.refresh_id_token(request): + return token + + return super().add_id_token(token, token_handler, request) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/openid/connect/core/request_validator.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/openid/connect/core/request_validator.py new file mode 100644 index 0000000000000000000000000000000000000000..e3cea79b774d58cae6cc99be28334f76e1d67b56 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/openid/connect/core/request_validator.py @@ -0,0 +1,320 @@ +""" +oauthlib.openid.connect.core.request_validator +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +""" +import logging + +from oauthlib.oauth2.rfc6749.request_validator import ( + RequestValidator as OAuth2RequestValidator, +) + +log = logging.getLogger(__name__) + + +class RequestValidator(OAuth2RequestValidator): + + def get_authorization_code_scopes(self, client_id, code, redirect_uri, request): + """ Extracts scopes from saved authorization code. + + The scopes returned by this method is used to route token requests + based on scopes passed to Authorization Code requests. + + With that the token endpoint knows when to include OpenIDConnect + id_token in token response only based on authorization code scopes. + + Only code param should be sufficient to retrieve grant code from + any storage you are using, `client_id` and `redirect_uri` can have a + blank value `""` don't forget to check it before using those values + in a select query if a database is used. + + :param client_id: Unicode client identifier + :param code: Unicode authorization code grant + :param redirect_uri: Unicode absolute URI + :return: A list of scope + + Method is used by: + - Authorization Token Grant Dispatcher + """ + raise NotImplementedError('Subclasses must implement this method.') + + def get_authorization_code_nonce(self, client_id, code, redirect_uri, request): + """ Extracts nonce from saved authorization code. + + If present in the Authentication Request, Authorization + Servers MUST include a nonce Claim in the ID Token with the + Claim Value being the nonce value sent in the Authentication + Request. Authorization Servers SHOULD perform no other + processing on nonce values used. The nonce value is a + case-sensitive string. + + Only code param should be sufficient to retrieve grant code from + any storage you are using. However, `client_id` and `redirect_uri` + have been validated and can be used also. + + :param client_id: Unicode client identifier + :param code: Unicode authorization code grant + :param redirect_uri: Unicode absolute URI + :return: Unicode nonce + + Method is used by: + - Authorization Token Grant Dispatcher + """ + raise NotImplementedError('Subclasses must implement this method.') + + def get_jwt_bearer_token(self, token, token_handler, request): + """Get JWT Bearer token or OpenID Connect ID token + + If using OpenID Connect this SHOULD call `oauthlib.oauth2.RequestValidator.get_id_token` + + :param token: A Bearer token dict + :param token_handler: the token handler (BearerToken class) + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :return: The JWT Bearer token or OpenID Connect ID token (a JWS signed JWT) + + Method is used by JWT Bearer and OpenID Connect tokens: + - JWTToken.create_token + """ + raise NotImplementedError('Subclasses must implement this method.') + + def get_id_token(self, token, token_handler, request): + """Get OpenID Connect ID token + + This method is OPTIONAL and is NOT RECOMMENDED. + `finalize_id_token` SHOULD be implemented instead. However, if you + want a full control over the minting of the `id_token`, you + MAY want to override `get_id_token` instead of using + `finalize_id_token`. + + In the OpenID Connect workflows when an ID Token is requested this method is called. + Subclasses should implement the construction, signing and optional encryption of the + ID Token as described in the OpenID Connect spec. + + In addition to the standard OAuth2 request properties, the request may also contain + these OIDC specific properties which are useful to this method: + + - nonce, if workflow is implicit or hybrid and it was provided + - claims, if provided to the original Authorization Code request + + The token parameter is a dict which may contain an ``access_token`` entry, in which + case the resulting ID Token *should* include a calculated ``at_hash`` claim. + + Similarly, when the request parameter has a ``code`` property defined, the ID Token + *should* include a calculated ``c_hash`` claim. + + http://openid.net/specs/openid-connect-core-1_0.html (sections `3.1.3.6`_, `3.2.2.10`_, `3.3.2.11`_) + + .. _`3.1.3.6`: http://openid.net/specs/openid-connect-core-1_0.html#CodeIDToken + .. _`3.2.2.10`: http://openid.net/specs/openid-connect-core-1_0.html#ImplicitIDToken + .. _`3.3.2.11`: http://openid.net/specs/openid-connect-core-1_0.html#HybridIDToken + + :param token: A Bearer token dict + :param token_handler: the token handler (BearerToken class) + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :return: The ID Token (a JWS signed JWT) + """ + return None + + def finalize_id_token(self, id_token, token, token_handler, request): + """Finalize OpenID Connect ID token & Sign or Encrypt. + + In the OpenID Connect workflows when an ID Token is requested + this method is called. Subclasses should implement the + construction, signing and optional encryption of the ID Token + as described in the OpenID Connect spec. + + The `id_token` parameter is a dict containing a couple of OIDC + technical fields related to the specification. Prepopulated + attributes are: + + - `aud`, equals to `request.client_id`. + - `iat`, equals to current time. + - `nonce`, if present, is equals to the `nonce` from the + authorization request. + - `at_hash`, hash of `access_token`, if relevant. + - `c_hash`, hash of `code`, if relevant. + + This method MUST provide required fields as below: + + - `iss`, REQUIRED. Issuer Identifier for the Issuer of the response. + - `sub`, REQUIRED. Subject Identifier + - `exp`, REQUIRED. Expiration time on or after which the ID + Token MUST NOT be accepted by the RP when performing + authentication with the OP. + + Additional claims must be added, note that `request.scope` + should be used to determine the list of claims. + + More information can be found at `OpenID Connect Core#Claims`_ + + .. _`OpenID Connect Core#Claims`: https://openid.net/specs/openid-connect-core-1_0.html#Claims + + :param id_token: A dict containing technical fields of id_token + :param token: A Bearer token dict + :param token_handler: the token handler (BearerToken class) + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :return: The ID Token (a JWS signed JWT or JWE encrypted JWT) + """ + raise NotImplementedError('Subclasses must implement this method.') + + def validate_jwt_bearer_token(self, token, scopes, request): + """Ensure the JWT Bearer token or OpenID Connect ID token are valids and authorized access to scopes. + + If using OpenID Connect this SHOULD call `oauthlib.oauth2.RequestValidator.get_id_token` + + If not using OpenID Connect this can `return None` to avoid 5xx rather 401/3 response. + + OpenID connect core 1.0 describe how to validate an id_token: + - http://openid.net/specs/openid-connect-core-1_0.html#IDTokenValidation + - http://openid.net/specs/openid-connect-core-1_0.html#ImplicitIDTValidation + - http://openid.net/specs/openid-connect-core-1_0.html#HybridIDTValidation + - http://openid.net/specs/openid-connect-core-1_0.html#HybridIDTValidation2 + + :param token: Unicode Bearer token + :param scopes: List of scopes (defined by you) + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :rtype: True or False + + Method is indirectly used by all core OpenID connect JWT token issuing grant types: + - Authorization Code Grant + - Implicit Grant + - Hybrid Grant + """ + raise NotImplementedError('Subclasses must implement this method.') + + def validate_id_token(self, token, scopes, request): + """Ensure the id token is valid and authorized access to scopes. + + OpenID connect core 1.0 describe how to validate an id_token: + - http://openid.net/specs/openid-connect-core-1_0.html#IDTokenValidation + - http://openid.net/specs/openid-connect-core-1_0.html#ImplicitIDTValidation + - http://openid.net/specs/openid-connect-core-1_0.html#HybridIDTValidation + - http://openid.net/specs/openid-connect-core-1_0.html#HybridIDTValidation2 + + :param token: Unicode Bearer token + :param scopes: List of scopes (defined by you) + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :rtype: True or False + + Method is indirectly used by all core OpenID connect JWT token issuing grant types: + - Authorization Code Grant + - Implicit Grant + - Hybrid Grant + """ + raise NotImplementedError('Subclasses must implement this method.') + + def validate_silent_authorization(self, request): + """Ensure the logged in user has authorized silent OpenID authorization. + + Silent OpenID authorization allows access tokens and id tokens to be + granted to clients without any user prompt or interaction. + + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :rtype: True or False + + Method is used by: + - OpenIDConnectAuthCode + - OpenIDConnectImplicit + - OpenIDConnectHybrid + """ + raise NotImplementedError('Subclasses must implement this method.') + + def validate_silent_login(self, request): + """Ensure session user has authorized silent OpenID login. + + If no user is logged in or has not authorized silent login, this + method should return False. + + If the user is logged in but associated with multiple accounts and + not selected which one to link to the token then this method should + raise an oauthlib.oauth2.AccountSelectionRequired error. + + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :rtype: True or False + + Method is used by: + - OpenIDConnectAuthCode + - OpenIDConnectImplicit + - OpenIDConnectHybrid + """ + raise NotImplementedError('Subclasses must implement this method.') + + def validate_user_match(self, id_token_hint, scopes, claims, request): + """Ensure client supplied user id hint matches session user. + + If the sub claim or id_token_hint is supplied then the session + user must match the given ID. + + :param id_token_hint: User identifier string. + :param scopes: List of OAuth 2 scopes and OpenID claims (strings). + :param claims: OpenID Connect claims dict. + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :rtype: True or False + + Method is used by: + - OpenIDConnectAuthCode + - OpenIDConnectImplicit + - OpenIDConnectHybrid + """ + raise NotImplementedError('Subclasses must implement this method.') + + def get_userinfo_claims(self, request): + """Return the UserInfo claims in JSON or Signed or Encrypted. + + The UserInfo Claims MUST be returned as the members of a JSON object + unless a signed or encrypted response was requested during Client + Registration. The Claims defined in Section 5.1 can be returned, as can + additional Claims not specified there. + + For privacy reasons, OpenID Providers MAY elect to not return values for + some requested Claims. + + If a Claim is not returned, that Claim Name SHOULD be omitted from the + JSON object representing the Claims; it SHOULD NOT be present with a + null or empty string value. + + The sub (subject) Claim MUST always be returned in the UserInfo + Response. + + Upon receipt of the UserInfo Request, the UserInfo Endpoint MUST return + the JSON Serialization of the UserInfo Response as in Section 13.3 in + the HTTP response body unless a different format was specified during + Registration [OpenID.Registration]. + + If the UserInfo Response is signed and/or encrypted, then the Claims are + returned in a JWT and the content-type MUST be application/jwt. The + response MAY be encrypted without also being signed. If both signing and + encryption are requested, the response MUST be signed then encrypted, + with the result being a Nested JWT, as defined in [JWT]. + + If signed, the UserInfo Response SHOULD contain the Claims iss (issuer) + and aud (audience) as members. The iss value SHOULD be the OP's Issuer + Identifier URL. The aud value SHOULD be or include the RP's Client ID + value. + + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :rtype: Claims as a dict OR JWT/JWS/JWE as a string + + Method is used by: + UserInfoEndpoint + """ + + def refresh_id_token(self, request): + """Whether the id token should be refreshed. Default, True + + :param request: OAuthlib request. + :type request: oauthlib.common.Request + :rtype: True or False + + Method is used by: + RefreshTokenGrant + """ + return True diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/openid/connect/core/tokens.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/openid/connect/core/tokens.py new file mode 100644 index 0000000000000000000000000000000000000000..3ab35492434d31f1f45741233b9ceb31eb54b96a --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/openid/connect/core/tokens.py @@ -0,0 +1,45 @@ +""" +authlib.openid.connect.core.tokens +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +This module contains methods for adding JWT tokens to requests. +""" +from oauthlib.oauth2.rfc6749.tokens import ( + TokenBase, get_token_from_header, random_token_generator, +) + + +class JWTToken(TokenBase): + __slots__ = ( + 'request_validator', 'token_generator', + 'refresh_token_generator', 'expires_in' + ) + + def __init__(self, request_validator=None, token_generator=None, + expires_in=None, refresh_token_generator=None): + self.request_validator = request_validator + self.token_generator = token_generator or random_token_generator + self.refresh_token_generator = ( + refresh_token_generator or self.token_generator + ) + self.expires_in = expires_in or 3600 + + def create_token(self, request, refresh_token=False): + """Create a JWT Token, using requestvalidator method.""" + + expires_in = self.expires_in(request) if callable(self.expires_in) else self.expires_in + + request.expires_in = expires_in + + return self.request_validator.get_jwt_bearer_token(None, None, request) + + def validate_request(self, request): + token = get_token_from_header(request) + return self.request_validator.validate_jwt_bearer_token( + token, request.scopes, request) + + def estimate_type(self, request): + token = get_token_from_header(request) + if token and token.startswith('ey') and token.count('.') in (2, 4): + return 10 + return 0 diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/signals.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/signals.py new file mode 100644 index 0000000000000000000000000000000000000000..9538d098ce5384cb174cc0fb263ae9735011ad2d --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/signals.py @@ -0,0 +1,41 @@ +""" + Implements signals based on blinker if available, otherwise + falls silently back to a noop. Shamelessly stolen from flask.signals: + https://github.com/mitsuhiko/flask/blob/master/flask/signals.py +""" +signals_available = False +try: + from blinker import Namespace + signals_available = True +except ImportError: + class Namespace: + def signal(self, name, doc=None): + return _FakeSignal(name, doc) + + class _FakeSignal: + """If blinker is unavailable, create a fake class with the same + interface that allows sending of signals but will fail with an + error on anything else. Instead of doing anything on send, it + will just ignore the arguments and do nothing instead. + """ + + def __init__(self, name, doc=None): + self.name = name + self.__doc__ = doc + def _fail(self, *args, **kwargs): + raise RuntimeError('signalling support is unavailable ' + 'because the blinker library is ' + 'not installed.') + def send(*a, **kw): + return None + connect = disconnect = has_receivers_for = receivers_for = \ + temporarily_connected_to = connected_to = _fail + del _fail + +# The namespace for code signals. If you are not oauthlib code, do +# not put signals in here. Create your own namespace instead. +_signals = Namespace() + + +# Core signals. +scope_changed = _signals.signal('scope-changed') diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/uri_validate.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/uri_validate.py new file mode 100644 index 0000000000000000000000000000000000000000..69d2c9506a33035076d022bc1ba34229dae48fba --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/oauthlib/uri_validate.py @@ -0,0 +1,189 @@ +""" +Regex for URIs + +These regex are directly derived from the collected ABNF in RFC3986 +(except for DIGIT, ALPHA and HEXDIG, defined by RFC2234). + +They should be processed with re.VERBOSE. + +Thanks Mark Nottingham for this code - https://gist.github.com/138549 +""" +import re + +# basics + +DIGIT = r"[\x30-\x39]" + +ALPHA = r"[\x41-\x5A\x61-\x7A]" + +HEXDIG = r"[\x30-\x39A-Fa-f]" + +# pct-encoded = "%" HEXDIG HEXDIG +pct_encoded = r" %% %(HEXDIG)s %(HEXDIG)s" % locals() + +# unreserved = ALPHA / DIGIT / "-" / "." / "_" / "~" +unreserved = r"(?: %(ALPHA)s | %(DIGIT)s | \- | \. | _ | ~ )" % locals() + +# gen-delims = ":" / "/" / "?" / "#" / "[" / "]" / "@" +gen_delims = r"(?: : | / | \? | \# | \[ | \] | @ )" + +# sub-delims = "!" / "$" / "&" / "'" / "(" / ")" +# / "*" / "+" / "," / ";" / "=" +sub_delims = r"""(?: ! | \$ | & | ' | \( | \) | + \* | \+ | , | ; | = )""" + +# pchar = unreserved / pct-encoded / sub-delims / ":" / "@" +pchar = r"(?: %(unreserved)s | %(pct_encoded)s | %(sub_delims)s | : | @ )" % locals( +) + +# reserved = gen-delims / sub-delims +reserved = r"(?: %(gen_delims)s | %(sub_delims)s )" % locals() + + +# scheme + +# scheme = ALPHA *( ALPHA / DIGIT / "+" / "-" / "." ) +scheme = r"%(ALPHA)s (?: %(ALPHA)s | %(DIGIT)s | \+ | \- | \. )*" % locals() + + +# authority + +# dec-octet = DIGIT ; 0-9 +# / %x31-39 DIGIT ; 10-99 +# / "1" 2DIGIT ; 100-199 +# / "2" %x30-34 DIGIT ; 200-249 +# / "25" %x30-35 ; 250-255 +dec_octet = r"""(?: %(DIGIT)s | + [\x31-\x39] %(DIGIT)s | + 1 %(DIGIT)s{2} | + 2 [\x30-\x34] %(DIGIT)s | + 25 [\x30-\x35] + ) +""" % locals() + +# IPv4address = dec-octet "." dec-octet "." dec-octet "." dec-octet +IPv4address = r"%(dec_octet)s \. %(dec_octet)s \. %(dec_octet)s \. %(dec_octet)s" % locals( +) + +# IPv6address +IPv6address = r"([A-Fa-f0-9:]+[:$])[A-Fa-f0-9]{1,4}" + +# IPvFuture = "v" 1*HEXDIG "." 1*( unreserved / sub-delims / ":" ) +IPvFuture = r"v %(HEXDIG)s+ \. (?: %(unreserved)s | %(sub_delims)s | : )+" % locals() + +# IP-literal = "[" ( IPv6address / IPvFuture ) "]" +IP_literal = r"\[ (?: %(IPv6address)s | %(IPvFuture)s ) \]" % locals() + +# reg-name = *( unreserved / pct-encoded / sub-delims ) +reg_name = r"(?: %(unreserved)s | %(pct_encoded)s | %(sub_delims)s )*" % locals() + +# userinfo = *( unreserved / pct-encoded / sub-delims / ":" ) +userinfo = r"(?: %(unreserved)s | %(pct_encoded)s | %(sub_delims)s | : )" % locals( +) + +# host = IP-literal / IPv4address / reg-name +host = r"(?: %(IP_literal)s | %(IPv4address)s | %(reg_name)s )" % locals() + +# port = *DIGIT +port = r"(?: %(DIGIT)s )*" % locals() + +# authority = [ userinfo "@" ] host [ ":" port ] +authority = r"(?: %(userinfo)s @)? %(host)s (?: : %(port)s)?" % locals() + +# Path + +# segment = *pchar +segment = r"%(pchar)s*" % locals() + +# segment-nz = 1*pchar +segment_nz = r"%(pchar)s+" % locals() + +# segment-nz-nc = 1*( unreserved / pct-encoded / sub-delims / "@" ) +# ; non-zero-length segment without any colon ":" +segment_nz_nc = r"(?: %(unreserved)s | %(pct_encoded)s | %(sub_delims)s | @ )+" % locals() + +# path-abempty = *( "/" segment ) +path_abempty = r"(?: / %(segment)s )*" % locals() + +# path-absolute = "/" [ segment-nz *( "/" segment ) ] +path_absolute = r"/ (?: %(segment_nz)s (?: / %(segment)s )* )?" % locals() + +# path-noscheme = segment-nz-nc *( "/" segment ) +path_noscheme = r"%(segment_nz_nc)s (?: / %(segment)s )*" % locals() + +# path-rootless = segment-nz *( "/" segment ) +path_rootless = r"%(segment_nz)s (?: / %(segment)s )*" % locals() + +# path-empty = 0 +path_empty = r"" # FIXME + +# path = path-abempty ; begins with "/" or is empty +# / path-absolute ; begins with "/" but not "//" +# / path-noscheme ; begins with a non-colon segment +# / path-rootless ; begins with a segment +# / path-empty ; zero characters +path = r"""(?: %(path_abempty)s | + %(path_absolute)s | + %(path_noscheme)s | + %(path_rootless)s | + %(path_empty)s + ) +""" % locals() + +### Query and Fragment + +# query = *( pchar / "/" / "?" ) +query = r"(?: %(pchar)s | / | \? )*" % locals() + +# fragment = *( pchar / "/" / "?" ) +fragment = r"(?: %(pchar)s | / | \? )*" % locals() + +# URIs + +# hier-part = "//" authority path-abempty +# / path-absolute +# / path-rootless +# / path-empty +hier_part = r"""(?: (?: // %(authority)s %(path_abempty)s ) | + %(path_absolute)s | + %(path_rootless)s | + %(path_empty)s + ) +""" % locals() + +# relative-part = "//" authority path-abempty +# / path-absolute +# / path-noscheme +# / path-empty +relative_part = r"""(?: (?: // %(authority)s %(path_abempty)s ) | + %(path_absolute)s | + %(path_noscheme)s | + %(path_empty)s + ) +""" % locals() + +# relative-ref = relative-part [ "?" query ] [ "#" fragment ] +relative_ref = r"%(relative_part)s (?: \? %(query)s)? (?: \# %(fragment)s)?" % locals( +) + +# URI = scheme ":" hier-part [ "?" query ] [ "#" fragment ] +URI = r"^(?: %(scheme)s : %(hier_part)s (?: \? %(query)s )? (?: \# %(fragment)s )? )$" % locals( +) + +# URI-reference = URI / relative-ref +URI_reference = r"^(?: %(URI)s | %(relative_ref)s )$" % locals() + +# absolute-URI = scheme ":" hier-part [ "?" query ] +absolute_URI = r"^(?: %(scheme)s : %(hier_part)s (?: \? %(query)s )? )$" % locals() # noqa: N816 + + +def is_uri(uri): + return re.match(URI, uri, re.VERBOSE) + + +def is_uri_reference(uri): + return re.match(URI_reference, uri, re.VERBOSE) + + +def is_absolute_uri(uri): + return re.match(absolute_URI, uri, re.VERBOSE) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf-2.3.0.dist-info/INSTALLER b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf-2.3.0.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf-2.3.0.dist-info/INSTALLER @@ -0,0 +1 @@ +pip diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf-2.3.0.dist-info/LICENSE b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf-2.3.0.dist-info/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..eb208dada03bbd9975633d6f42eb036c09e33125 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf-2.3.0.dist-info/LICENSE @@ -0,0 +1,29 @@ +BSD 3-Clause License + +Copyright (c) 2018, Omry Yadan +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +1. Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +2. Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + +3. Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from + this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf-2.3.0.dist-info/METADATA b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf-2.3.0.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..6d3ab67e16a7dfc446c5e7f878569d4fa7e79d6e --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf-2.3.0.dist-info/METADATA @@ -0,0 +1,64 @@ +Metadata-Version: 2.1 +Name: omegaconf +Version: 2.3.0 +Summary: A flexible configuration library +Home-page: https://github.com/omry/omegaconf +Author: Omry Yadan +Author-email: omry@yadan.net +Keywords: yaml configuration config +Classifier: Programming Language :: Python :: 3.6 +Classifier: Programming Language :: Python :: 3.7 +Classifier: Programming Language :: Python :: 3.8 +Classifier: Programming Language :: Python :: 3.9 +Classifier: Programming Language :: Python :: 3.10 +Classifier: Programming Language :: Python :: 3.11 +Classifier: License :: OSI Approved :: BSD License +Classifier: Operating System :: OS Independent +Requires-Python: >=3.6 +Description-Content-Type: text/markdown +License-File: LICENSE +Requires-Dist: antlr4-python3-runtime (==4.9.*) +Requires-Dist: PyYAML (>=5.1.0) +Requires-Dist: dataclasses ; python_version == "3.6" + +# OmegaConf +| | Description | +| --- | --- | +| Project | [![PyPI version](https://badge.fury.io/py/omegaconf.svg)](https://badge.fury.io/py/omegaconf)[![Downloads](https://pepy.tech/badge/omegaconf/month)](https://pepy.tech/project/omegaconf?versions=1.4.*&versions=2.0.*&versions=2.1.*)![PyPI - Python Version](https://img.shields.io/pypi/pyversions/omegaconf.svg) | +| Code quality| [![CircleCI](https://img.shields.io/circleci/build/github/omry/omegaconf?logo=s&token=5de2f8dc2a0dd78438520575431aa533150806e3)](https://circleci.com/gh/omry/omegaconf)[![Coverage Status](https://coveralls.io/repos/github/omry/omegaconf/badge.svg)](https://coveralls.io/github/omry/omegaconf)[![Total alerts](https://img.shields.io/lgtm/alerts/g/omry/omegaconf.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/omry/omegaconf/alerts/)[![Language grade: Python](https://img.shields.io/lgtm/grade/python/g/omry/omegaconf.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/omry/omegaconf/context:python)| +| Docs and support |[![Documentation Status](https://readthedocs.org/projects/omegaconf/badge/?version=2.0_branch)](https://omegaconf.readthedocs.io/en/2.1_branch/)[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/omry/omegaconf/master?filepath=docs%2Fnotebook%2FTutorial.ipynb)| + + +OmegaConf is a hierarchical configuration system, with support for merging configurations from multiple sources (YAML config files, dataclasses/objects and CLI arguments) +providing a consistent API regardless of how the configuration was created. + +## Releases + +### Stable (2.2) +OmegaConf 2.2 is the current stable version. +* [What's new](https://github.com/omry/omegaconf/releases/tag/v2.2.1) +* [Documentation](https://omegaconf.readthedocs.io/en/2.2_branch/) +* [Source code](https://github.com/omry/omegaconf/tree/2.2_branch) + +Install with `pip install --upgrade omegaconf` + +### Previous release (2.1) +OmegaConf 2.1 is the current stable version. +* [What's new](https://github.com/omry/omegaconf/releases/tag/v2.1.1) +* [Documentation](https://omegaconf.readthedocs.io/en/2.1_branch/) +* [Slides](https://docs.google.com/presentation/d/e/2PACX-1vT_UIV7hCnquIbLUm4NnkUpXvPEh33IKiUEvPRF850WKA8opOlZOszjKdZ3tPmf8u7hGNP6HpqS-NT5/pub?start=false&loop=false&delayms=3000) +* [Source code](https://github.com/omry/omegaconf/tree/2.1_branch) + +Install with `pip install omegaconf==2.1` + +### Previous release (2.0) + +* [What's new](https://github.com/omry/omegaconf/releases/tag/v2.0.0) +* [Documentation](https://omegaconf.readthedocs.io/en/2.0_branch/) +* [Slides](https://docs.google.com/presentation/d/e/2PACX-1vT_UIV7hCnquIbLUm4NnkUpXvPEh33IKiUEvPRF850WKA8opOlZOszjKdZ3tPmf8u7hGNP6HpqS-NT5/pub?start=false&loop=false&delayms=3000) +* [Source code](https://github.com/omry/omegaconf/tree/2.0_branch) + +Install with `pip install omegaconf==2.0.6` + +## Live tutorial +Run the live tutorial: 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a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf-2.3.0.dist-info/top_level.txt b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf-2.3.0.dist-info/top_level.txt new file mode 100644 index 0000000000000000000000000000000000000000..fcd2b36b800878631de7b82d0218569415c6a323 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf-2.3.0.dist-info/top_level.txt @@ -0,0 +1,2 @@ +omegaconf +pydevd_plugins diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..1670cf08fa977560c2ad1ef2d98910824c1bdc9b --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/__init__.py @@ -0,0 +1,65 @@ +from .base import Container, DictKeyType, Node, SCMode, UnionNode +from .dictconfig import DictConfig +from .errors import ( + KeyValidationError, + MissingMandatoryValue, + ReadonlyConfigError, + UnsupportedValueType, + ValidationError, +) +from .listconfig import ListConfig +from .nodes import ( + AnyNode, + BooleanNode, + BytesNode, + EnumNode, + FloatNode, + IntegerNode, + PathNode, + StringNode, + ValueNode, +) +from .omegaconf import ( + II, + MISSING, + SI, + OmegaConf, + Resolver, + flag_override, + open_dict, + read_write, +) +from .version import __version__ + +__all__ = [ + "__version__", + "MissingMandatoryValue", + "ValidationError", + "ReadonlyConfigError", + "UnsupportedValueType", + "KeyValidationError", + "Container", + "UnionNode", + "ListConfig", + "DictConfig", + "DictKeyType", + "OmegaConf", + "Resolver", + "SCMode", + "flag_override", + "read_write", + "open_dict", + "Node", + "ValueNode", + "AnyNode", + "IntegerNode", + "StringNode", + "BytesNode", + "PathNode", + "BooleanNode", + "EnumNode", + "FloatNode", + "MISSING", + "SI", + "II", +] diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/_impl.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/_impl.py new file mode 100644 index 0000000000000000000000000000000000000000..0c30ef6a239e65302c484b361773a00f85c70909 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/_impl.py @@ -0,0 +1,101 @@ +from typing import Any + +from omegaconf import MISSING, Container, DictConfig, ListConfig, Node, ValueNode +from omegaconf.errors import ConfigTypeError, InterpolationToMissingValueError + +from ._utils import _DEFAULT_MARKER_, _get_value + + +def _resolve_container_value(cfg: Container, key: Any) -> None: + node = cfg._get_child(key) + assert isinstance(node, Node) + if node._is_interpolation(): + try: + resolved = node._dereference_node() + except InterpolationToMissingValueError: + node._set_value(MISSING) + else: + if isinstance(resolved, Container): + _resolve(resolved) + if isinstance(resolved, Container) and isinstance(node, ValueNode): + cfg[key] = resolved + else: + node._set_value(_get_value(resolved)) + else: + _resolve(node) + + +def _resolve(cfg: Node) -> Node: + assert isinstance(cfg, Node) + if cfg._is_interpolation(): + try: + resolved = cfg._dereference_node() + except InterpolationToMissingValueError: + cfg._set_value(MISSING) + else: + cfg._set_value(resolved._value()) + + if isinstance(cfg, DictConfig): + for k in cfg.keys(): + _resolve_container_value(cfg, k) + + elif isinstance(cfg, ListConfig): + for i in range(len(cfg)): + _resolve_container_value(cfg, i) + + return cfg + + +def select_value( + cfg: Container, + key: str, + *, + default: Any = _DEFAULT_MARKER_, + throw_on_resolution_failure: bool = True, + throw_on_missing: bool = False, + absolute_key: bool = False, +) -> Any: + node = select_node( + cfg=cfg, + key=key, + throw_on_resolution_failure=throw_on_resolution_failure, + throw_on_missing=throw_on_missing, + absolute_key=absolute_key, + ) + + node_not_found = node is None + if node_not_found or node._is_missing(): + if default is not _DEFAULT_MARKER_: + return default + else: + return None + + return _get_value(node) + + +def select_node( + cfg: Container, + key: str, + *, + throw_on_resolution_failure: bool = True, + throw_on_missing: bool = False, + absolute_key: bool = False, +) -> Any: + try: + # for non relative keys, the interpretation can be: + # 1. relative to cfg + # 2. relative to the config root + # This is controlled by the absolute_key flag. By default, such keys are relative to cfg. + if not absolute_key and not key.startswith("."): + key = f".{key}" + + cfg, key = cfg._resolve_key_and_root(key) + _root, _last_key, node = cfg._select_impl( + key, + throw_on_missing=throw_on_missing, + throw_on_resolution_failure=throw_on_resolution_failure, + ) + except ConfigTypeError: + return None + + return node diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/_utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..ffec246a38d278831bce26c6f7c233b72f34c1fe --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/_utils.py @@ -0,0 +1,1039 @@ +import copy +import os +import pathlib +import re +import string +import sys +import types +import warnings +from contextlib import contextmanager +from enum import Enum +from textwrap import dedent +from typing import ( + Any, + Dict, + Iterator, + List, + Optional, + Tuple, + Type, + Union, + get_type_hints, +) + +import yaml + +from .errors import ( + ConfigIndexError, + ConfigTypeError, + ConfigValueError, + GrammarParseError, + OmegaConfBaseException, + ValidationError, +) +from .grammar_parser import SIMPLE_INTERPOLATION_PATTERN, parse + +try: + import dataclasses + +except ImportError: # pragma: no cover + dataclasses = None # type: ignore # pragma: no cover + +try: + import attr + +except ImportError: # pragma: no cover + attr = None # type: ignore # pragma: no cover + +NoneType: Type[None] = type(None) + +BUILTIN_VALUE_TYPES: Tuple[Type[Any], ...] = ( + int, + float, + bool, + str, + bytes, + NoneType, +) + +# Regexprs to match key paths like: a.b, a[b], ..a[c].d, etc. +# We begin by matching the head (in these examples: a, a, ..a). +# This can be read as "dots followed by any character but `.` or `[`" +# Note that a key starting with brackets, like [a], is purposedly *not* +# matched here and will instead be handled in the next regex below (this +# is to keep this regex simple). +KEY_PATH_HEAD = re.compile(r"(\.)*[^.[]*") +# Then we match other keys. The following expression matches one key and can +# be read as a choice between two syntaxes: +# - `.` followed by anything except `.` or `[` (ex: .b, .d) +# - `[` followed by anything then `]` (ex: [b], [c]) +KEY_PATH_OTHER = re.compile(r"\.([^.[]*)|\[(.*?)\]") + + +# source: https://yaml.org/type/bool.html +YAML_BOOL_TYPES = [ + "y", + "Y", + "yes", + "Yes", + "YES", + "n", + "N", + "no", + "No", + "NO", + "true", + "True", + "TRUE", + "false", + "False", + "FALSE", + "on", + "On", + "ON", + "off", + "Off", + "OFF", +] + + +class Marker: + def __init__(self, desc: str): + self.desc = desc + + def __repr__(self) -> str: + return self.desc + + +# To be used as default value when `None` is not an option. +_DEFAULT_MARKER_: Any = Marker("_DEFAULT_MARKER_") + + +class OmegaConfDumper(yaml.Dumper): # type: ignore + str_representer_added = False + + @staticmethod + def str_representer(dumper: yaml.Dumper, data: str) -> yaml.ScalarNode: + with_quotes = yaml_is_bool(data) or is_int(data) or is_float(data) + return dumper.represent_scalar( + yaml.resolver.BaseResolver.DEFAULT_SCALAR_TAG, + data, + style=("'" if with_quotes else None), + ) + + +def get_omega_conf_dumper() -> Type[OmegaConfDumper]: + if not OmegaConfDumper.str_representer_added: + OmegaConfDumper.add_representer(str, OmegaConfDumper.str_representer) + OmegaConfDumper.str_representer_added = True + return OmegaConfDumper + + +def yaml_is_bool(b: str) -> bool: + return b in YAML_BOOL_TYPES + + +def get_yaml_loader() -> Any: + class OmegaConfLoader(yaml.SafeLoader): # type: ignore + def construct_mapping(self, node: yaml.Node, deep: bool = False) -> Any: + keys = set() + for key_node, value_node in node.value: + if key_node.tag != yaml.resolver.BaseResolver.DEFAULT_SCALAR_TAG: + continue + if key_node.value in keys: + raise yaml.constructor.ConstructorError( + "while constructing a mapping", + node.start_mark, + f"found duplicate key {key_node.value}", + key_node.start_mark, + ) + keys.add(key_node.value) + return super().construct_mapping(node, deep=deep) + + loader = OmegaConfLoader + loader.add_implicit_resolver( + "tag:yaml.org,2002:float", + re.compile( + """^(?: + [-+]?[0-9]+(?:_[0-9]+)*\\.[0-9_]*(?:[eE][-+]?[0-9]+)? + |[-+]?[0-9]+(?:_[0-9]+)*(?:[eE][-+]?[0-9]+) + |\\.[0-9]+(?:_[0-9]+)*(?:[eE][-+][0-9]+)? + |[-+]?[0-9]+(?:_[0-9]+)*(?::[0-5]?[0-9])+\\.[0-9_]* + |[-+]?\\.(?:inf|Inf|INF) + |\\.(?:nan|NaN|NAN))$""", + re.X, + ), + list("-+0123456789."), + ) + loader.yaml_implicit_resolvers = { + key: [ + (tag, regexp) + for tag, regexp in resolvers + if tag != "tag:yaml.org,2002:timestamp" + ] + for key, resolvers in loader.yaml_implicit_resolvers.items() + } + + loader.add_constructor( + "tag:yaml.org,2002:python/object/apply:pathlib.Path", + lambda loader, node: pathlib.Path(*loader.construct_sequence(node)), + ) + loader.add_constructor( + "tag:yaml.org,2002:python/object/apply:pathlib.PosixPath", + lambda loader, node: pathlib.PosixPath(*loader.construct_sequence(node)), + ) + loader.add_constructor( + "tag:yaml.org,2002:python/object/apply:pathlib.WindowsPath", + lambda loader, node: pathlib.WindowsPath(*loader.construct_sequence(node)), + ) + + return loader + + +def _get_class(path: str) -> type: + from importlib import import_module + + module_path, _, class_name = path.rpartition(".") + mod = import_module(module_path) + try: + klass: type = getattr(mod, class_name) + except AttributeError: + raise ImportError(f"Class {class_name} is not in module {module_path}") + return klass + + +def is_union_annotation(type_: Any) -> bool: + if sys.version_info >= (3, 10): # pragma: no cover + if isinstance(type_, types.UnionType): + return True + return getattr(type_, "__origin__", None) is Union + + +def _resolve_optional(type_: Any) -> Tuple[bool, Any]: + """Check whether `type_` is equivalent to `typing.Optional[T]` for some T.""" + if is_union_annotation(type_): + args = type_.__args__ + if NoneType in args: + optional = True + args = tuple(a for a in args if a is not NoneType) + else: + optional = False + if len(args) == 1: + return optional, args[0] + elif len(args) >= 2: + return optional, Union[args] + else: + assert False + + if type_ is Any: + return True, Any + + if type_ in (None, NoneType): + return True, NoneType + + return False, type_ + + +def _is_optional(obj: Any, key: Optional[Union[int, str]] = None) -> bool: + """Check `obj` metadata to see if the given node is optional.""" + from .base import Container, Node + + if key is not None: + assert isinstance(obj, Container) + obj = obj._get_node(key) + assert isinstance(obj, Node) + return obj._is_optional() + + +def _resolve_forward(type_: Type[Any], module: str) -> Type[Any]: + import typing # lgtm [py/import-and-import-from] + + forward = typing.ForwardRef if hasattr(typing, "ForwardRef") else typing._ForwardRef # type: ignore + if type(type_) is forward: + return _get_class(f"{module}.{type_.__forward_arg__}") + else: + if is_dict_annotation(type_): + kt, vt = get_dict_key_value_types(type_) + if kt is not None: + kt = _resolve_forward(kt, module=module) + if vt is not None: + vt = _resolve_forward(vt, module=module) + return Dict[kt, vt] # type: ignore + if is_list_annotation(type_): + et = get_list_element_type(type_) + if et is not None: + et = _resolve_forward(et, module=module) + return List[et] # type: ignore + if is_tuple_annotation(type_): + its = get_tuple_item_types(type_) + its = tuple(_resolve_forward(it, module=module) for it in its) + return Tuple[its] # type: ignore + + return type_ + + +def extract_dict_subclass_data(obj: Any, parent: Any) -> Optional[Dict[str, Any]]: + """Check if obj is an instance of a subclass of Dict. If so, extract the Dict keys/values.""" + from omegaconf.omegaconf import _maybe_wrap + + is_type = isinstance(obj, type) + obj_type = obj if is_type else type(obj) + subclasses_dict = is_dict_subclass(obj_type) + + if subclasses_dict: + warnings.warn( + f"Class `{obj_type.__name__}` subclasses `Dict`." + + " Subclassing `Dict` in Structured Config classes is deprecated," + + " see github.com/omry/omegaconf/issues/663", + UserWarning, + stacklevel=9, + ) + + if is_type: + return None + elif subclasses_dict: + dict_subclass_data = {} + key_type, element_type = get_dict_key_value_types(obj_type) + for name, value in obj.items(): + is_optional, type_ = _resolve_optional(element_type) + type_ = _resolve_forward(type_, obj.__module__) + try: + dict_subclass_data[name] = _maybe_wrap( + ref_type=type_, + is_optional=is_optional, + key=name, + value=value, + parent=parent, + ) + except ValidationError as ex: + format_and_raise( + node=None, key=name, value=value, cause=ex, msg=str(ex) + ) + return dict_subclass_data + else: + return None + + +def get_attr_class_fields(obj: Any) -> List["attr.Attribute[Any]"]: + is_type = isinstance(obj, type) + obj_type = obj if is_type else type(obj) + fields = attr.fields_dict(obj_type).values() + return [f for f in fields if f.metadata.get("omegaconf_ignore") is not True] + + +def get_attr_data(obj: Any, allow_objects: Optional[bool] = None) -> Dict[str, Any]: + from omegaconf.omegaconf import OmegaConf, _maybe_wrap + + flags = {"allow_objects": allow_objects} if allow_objects is not None else {} + + from omegaconf import MISSING + + d = {} + is_type = isinstance(obj, type) + obj_type = obj if is_type else type(obj) + dummy_parent = OmegaConf.create({}, flags=flags) + dummy_parent._metadata.object_type = obj_type + resolved_hints = get_type_hints(obj_type) + + for attrib in get_attr_class_fields(obj): + name = attrib.name + is_optional, type_ = _resolve_optional(resolved_hints[name]) + type_ = _resolve_forward(type_, obj.__module__) + if not is_type: + value = getattr(obj, name) + else: + value = attrib.default + if value == attr.NOTHING: + value = MISSING + if is_union_annotation(type_) and not is_supported_union_annotation(type_): + e = ConfigValueError( + f"Unions of containers are not supported:\n{name}: {type_str(type_)}" + ) + format_and_raise(node=None, key=None, value=value, cause=e, msg=str(e)) + + try: + d[name] = _maybe_wrap( + ref_type=type_, + is_optional=is_optional, + key=name, + value=value, + parent=dummy_parent, + ) + except (ValidationError, GrammarParseError) as ex: + format_and_raise( + node=dummy_parent, key=name, value=value, cause=ex, msg=str(ex) + ) + d[name]._set_parent(None) + dict_subclass_data = extract_dict_subclass_data(obj=obj, parent=dummy_parent) + if dict_subclass_data is not None: + d.update(dict_subclass_data) + return d + + +def get_dataclass_fields(obj: Any) -> List["dataclasses.Field[Any]"]: + fields = dataclasses.fields(obj) + return [f for f in fields if f.metadata.get("omegaconf_ignore") is not True] + + +def get_dataclass_data( + obj: Any, allow_objects: Optional[bool] = None +) -> Dict[str, Any]: + from omegaconf.omegaconf import MISSING, OmegaConf, _maybe_wrap + + flags = {"allow_objects": allow_objects} if allow_objects is not None else {} + d = {} + is_type = isinstance(obj, type) + obj_type = get_type_of(obj) + dummy_parent = OmegaConf.create({}, flags=flags) + dummy_parent._metadata.object_type = obj_type + resolved_hints = get_type_hints(obj_type) + for field in get_dataclass_fields(obj): + name = field.name + is_optional, type_ = _resolve_optional(resolved_hints[field.name]) + type_ = _resolve_forward(type_, obj.__module__) + has_default = field.default != dataclasses.MISSING + has_default_factory = field.default_factory != dataclasses.MISSING + + if not is_type: + value = getattr(obj, name) + else: + if has_default: + value = field.default + elif has_default_factory: + value = field.default_factory() # type: ignore + else: + value = MISSING + + if is_union_annotation(type_) and not is_supported_union_annotation(type_): + e = ConfigValueError( + f"Unions of containers are not supported:\n{name}: {type_str(type_)}" + ) + format_and_raise(node=None, key=None, value=value, cause=e, msg=str(e)) + try: + d[name] = _maybe_wrap( + ref_type=type_, + is_optional=is_optional, + key=name, + value=value, + parent=dummy_parent, + ) + except (ValidationError, GrammarParseError) as ex: + format_and_raise( + node=dummy_parent, key=name, value=value, cause=ex, msg=str(ex) + ) + d[name]._set_parent(None) + dict_subclass_data = extract_dict_subclass_data(obj=obj, parent=dummy_parent) + if dict_subclass_data is not None: + d.update(dict_subclass_data) + return d + + +def is_dataclass(obj: Any) -> bool: + from omegaconf.base import Node + + if dataclasses is None or isinstance(obj, Node): + return False + return dataclasses.is_dataclass(obj) + + +def is_attr_class(obj: Any) -> bool: + from omegaconf.base import Node + + if attr is None or isinstance(obj, Node): + return False + return attr.has(obj) + + +def is_structured_config(obj: Any) -> bool: + return is_attr_class(obj) or is_dataclass(obj) + + +def is_dataclass_frozen(type_: Any) -> bool: + return type_.__dataclass_params__.frozen # type: ignore + + +def is_attr_frozen(type_: type) -> bool: + # This is very hacky and probably fragile as well. + # Unfortunately currently there isn't an official API in attr that can detect that. + # noinspection PyProtectedMember + return type_.__setattr__ == attr._make._frozen_setattrs # type: ignore + + +def get_type_of(class_or_object: Any) -> Type[Any]: + type_ = class_or_object + if not isinstance(type_, type): + type_ = type(class_or_object) + assert isinstance(type_, type) + return type_ + + +def is_structured_config_frozen(obj: Any) -> bool: + type_ = get_type_of(obj) + + if is_dataclass(type_): + return is_dataclass_frozen(type_) + if is_attr_class(type_): + return is_attr_frozen(type_) + return False + + +def get_structured_config_init_field_names(obj: Any) -> List[str]: + fields: Union[List["dataclasses.Field[Any]"], List["attr.Attribute[Any]"]] + if is_dataclass(obj): + fields = get_dataclass_fields(obj) + elif is_attr_class(obj): + fields = get_attr_class_fields(obj) + else: + raise ValueError(f"Unsupported type: {type(obj).__name__}") + return [f.name for f in fields if f.init] + + +def get_structured_config_data( + obj: Any, allow_objects: Optional[bool] = None +) -> Dict[str, Any]: + if is_dataclass(obj): + return get_dataclass_data(obj, allow_objects=allow_objects) + elif is_attr_class(obj): + return get_attr_data(obj, allow_objects=allow_objects) + else: + raise ValueError(f"Unsupported type: {type(obj).__name__}") + + +class ValueKind(Enum): + VALUE = 0 + MANDATORY_MISSING = 1 + INTERPOLATION = 2 + + +def _is_missing_value(value: Any) -> bool: + from omegaconf import Node + + if isinstance(value, Node): + value = value._value() + return _is_missing_literal(value) + + +def _is_missing_literal(value: Any) -> bool: + # Uses literal '???' instead of the MISSING const for performance reasons. + return isinstance(value, str) and value == "???" + + +def _is_none( + value: Any, resolve: bool = False, throw_on_resolution_failure: bool = True +) -> bool: + from omegaconf import Node + + if not isinstance(value, Node): + return value is None + + if resolve: + value = value._maybe_dereference_node( + throw_on_resolution_failure=throw_on_resolution_failure + ) + if not throw_on_resolution_failure and value is None: + # Resolution failure: consider that it is *not* None. + return False + assert isinstance(value, Node) + + return value._is_none() + + +def get_value_kind( + value: Any, strict_interpolation_validation: bool = False +) -> ValueKind: + """ + Determine the kind of a value + Examples: + VALUE: "10", "20", True + MANDATORY_MISSING: "???" + INTERPOLATION: "${foo.bar}", "${foo.${bar}}", "${foo:bar}", "[${foo}, ${bar}]", + "ftp://${host}/path", "${foo:${bar}, [true], {'baz': ${baz}}}" + + :param value: Input to classify. + :param strict_interpolation_validation: If `True`, then when `value` is a string + containing "${", it is parsed to validate the interpolation syntax. If `False`, + this parsing step is skipped: this is more efficient, but will not detect errors. + """ + + if _is_missing_value(value): + return ValueKind.MANDATORY_MISSING + + if _is_interpolation(value, strict_interpolation_validation): + return ValueKind.INTERPOLATION + + return ValueKind.VALUE + + +def _is_interpolation(v: Any, strict_interpolation_validation: bool = False) -> bool: + from omegaconf import Node + + if isinstance(v, Node): + v = v._value() + + if isinstance(v, str) and _is_interpolation_string( + v, strict_interpolation_validation + ): + return True + return False + + +def _is_interpolation_string(value: str, strict_interpolation_validation: bool) -> bool: + # We identify potential interpolations by the presence of "${" in the string. + # Note that escaped interpolations (ex: "esc: \${bar}") are identified as + # interpolations: this is intended, since they must be processed as interpolations + # for the string to be properly un-escaped. + # Keep in mind that invalid interpolations will only be detected when + # `strict_interpolation_validation` is True. + if "${" in value: + if strict_interpolation_validation: + # First try the cheap regex matching that detects common interpolations. + if SIMPLE_INTERPOLATION_PATTERN.match(value) is None: + # If no match, do the more expensive grammar parsing to detect errors. + parse(value) + return True + return False + + +def _is_special(value: Any) -> bool: + """Special values are None, MISSING, and interpolation.""" + return _is_none(value) or get_value_kind(value) in ( + ValueKind.MANDATORY_MISSING, + ValueKind.INTERPOLATION, + ) + + +def is_float(st: str) -> bool: + try: + float(st) + return True + except ValueError: + return False + + +def is_int(st: str) -> bool: + try: + int(st) + return True + except ValueError: + return False + + +def is_primitive_list(obj: Any) -> bool: + return isinstance(obj, (list, tuple)) + + +def is_primitive_dict(obj: Any) -> bool: + t = get_type_of(obj) + return t is dict + + +def is_dict_annotation(type_: Any) -> bool: + if type_ in (dict, Dict): + return True + origin = getattr(type_, "__origin__", None) + # type_dict is a bit hard to detect. + # this support is tentative, if it eventually causes issues in other areas it may be dropped. + if sys.version_info < (3, 7, 0): # pragma: no cover + typed_dict = hasattr(type_, "__base__") and type_.__base__ == Dict + return origin is Dict or type_ is Dict or typed_dict + else: # pragma: no cover + typed_dict = hasattr(type_, "__base__") and type_.__base__ == dict + return origin is dict or typed_dict + + +def is_list_annotation(type_: Any) -> bool: + if type_ in (list, List): + return True + origin = getattr(type_, "__origin__", None) + if sys.version_info < (3, 7, 0): + return origin is List or type_ is List # pragma: no cover + else: + return origin is list # pragma: no cover + + +def is_tuple_annotation(type_: Any) -> bool: + if type_ in (tuple, Tuple): + return True + origin = getattr(type_, "__origin__", None) + if sys.version_info < (3, 7, 0): + return origin is Tuple or type_ is Tuple # pragma: no cover + else: + return origin is tuple # pragma: no cover + + +def is_supported_union_annotation(obj: Any) -> bool: + """Currently only primitive types are supported in Unions, e.g. Union[int, str]""" + if not is_union_annotation(obj): + return False + args = obj.__args__ + return all(is_primitive_type_annotation(arg) for arg in args) + + +def is_dict_subclass(type_: Any) -> bool: + return type_ is not None and isinstance(type_, type) and issubclass(type_, Dict) + + +def is_dict(obj: Any) -> bool: + return is_primitive_dict(obj) or is_dict_annotation(obj) or is_dict_subclass(obj) + + +def is_primitive_container(obj: Any) -> bool: + return is_primitive_list(obj) or is_primitive_dict(obj) + + +def get_list_element_type(ref_type: Optional[Type[Any]]) -> Any: + args = getattr(ref_type, "__args__", None) + if ref_type is not List and args is not None and args[0]: + element_type = args[0] + else: + element_type = Any + return element_type + + +def get_tuple_item_types(ref_type: Type[Any]) -> Tuple[Any, ...]: + args = getattr(ref_type, "__args__", None) + if args in (None, ()): + args = (Any, ...) + assert isinstance(args, tuple) + return args + + +def get_dict_key_value_types(ref_type: Any) -> Tuple[Any, Any]: + args = getattr(ref_type, "__args__", None) + if args is None: + bases = getattr(ref_type, "__orig_bases__", None) + if bases is not None and len(bases) > 0: + args = getattr(bases[0], "__args__", None) + + key_type: Any + element_type: Any + if ref_type is None or ref_type == Dict: + key_type = Any + element_type = Any + else: + if args is not None: + key_type = args[0] + element_type = args[1] + else: + key_type = Any + element_type = Any + + return key_type, element_type + + +def is_valid_value_annotation(type_: Any) -> bool: + _, type_ = _resolve_optional(type_) + return ( + type_ is Any + or is_primitive_type_annotation(type_) + or is_structured_config(type_) + or is_container_annotation(type_) + or is_supported_union_annotation(type_) + ) + + +def _valid_dict_key_annotation_type(type_: Any) -> bool: + from omegaconf import DictKeyType + + return type_ is None or type_ is Any or issubclass(type_, DictKeyType.__args__) # type: ignore + + +def is_primitive_type_annotation(type_: Any) -> bool: + type_ = get_type_of(type_) + return issubclass(type_, (Enum, pathlib.Path)) or type_ in BUILTIN_VALUE_TYPES + + +def _get_value(value: Any) -> Any: + from .base import Container, UnionNode + from .nodes import ValueNode + + if isinstance(value, ValueNode): + return value._value() + elif isinstance(value, Container): + boxed = value._value() + if boxed is None or _is_missing_literal(boxed) or _is_interpolation(boxed): + return boxed + elif isinstance(value, UnionNode): + boxed = value._value() + if boxed is None or _is_missing_literal(boxed) or _is_interpolation(boxed): + return boxed + else: + return _get_value(boxed) # pass through value of boxed node + + # return primitives and regular OmegaConf Containers as is + return value + + +def get_type_hint(obj: Any, key: Any = None) -> Optional[Type[Any]]: + from omegaconf import Container, Node + + if isinstance(obj, Container): + if key is not None: + obj = obj._get_node(key) + else: + if key is not None: + raise ValueError("Key must only be provided when obj is a container") + + if isinstance(obj, Node): + ref_type = obj._metadata.ref_type + if obj._is_optional() and ref_type is not Any: + return Optional[ref_type] # type: ignore + else: + return ref_type + else: + return Any # type: ignore + + +def _raise(ex: Exception, cause: Exception) -> None: + # Set the environment variable OC_CAUSE=1 to get a stacktrace that includes the + # causing exception. + env_var = os.environ["OC_CAUSE"] if "OC_CAUSE" in os.environ else None + debugging = sys.gettrace() is not None + full_backtrace = (debugging and not env_var == "0") or (env_var == "1") + if full_backtrace: + ex.__cause__ = cause + else: + ex.__cause__ = None + raise ex.with_traceback(sys.exc_info()[2]) # set env var OC_CAUSE=1 for full trace + + +def format_and_raise( + node: Any, + key: Any, + value: Any, + msg: str, + cause: Exception, + type_override: Any = None, +) -> None: + from omegaconf import OmegaConf + from omegaconf.base import Node + + if isinstance(cause, AssertionError): + raise + + if isinstance(cause, OmegaConfBaseException) and cause._initialized: + ex = cause + if type_override is not None: + ex = type_override(str(cause)) + ex.__dict__ = copy.deepcopy(cause.__dict__) + _raise(ex, cause) + + object_type: Optional[Type[Any]] + object_type_str: Optional[str] = None + ref_type: Optional[Type[Any]] + ref_type_str: Optional[str] + + child_node: Optional[Node] = None + if node is None: + full_key = key if key is not None else "" + object_type = None + ref_type = None + ref_type_str = None + else: + if key is not None and not node._is_none(): + child_node = node._get_node(key, validate_access=False) + + try: + full_key = node._get_full_key(key=key) + except Exception as exc: + # Since we are handling an exception, raising a different one here would + # be misleading. Instead, we display it in the key. + full_key = f"" + + object_type = OmegaConf.get_type(node) + object_type_str = type_str(object_type) + + ref_type = get_type_hint(node) + ref_type_str = type_str(ref_type) + + msg = string.Template(msg).safe_substitute( + REF_TYPE=ref_type_str, + OBJECT_TYPE=object_type_str, + KEY=key, + FULL_KEY=full_key, + VALUE=value, + VALUE_TYPE=type_str(type(value), include_module_name=True), + KEY_TYPE=f"{type(key).__name__}", + ) + + if ref_type not in (None, Any): + template = dedent( + """\ + $MSG + full_key: $FULL_KEY + reference_type=$REF_TYPE + object_type=$OBJECT_TYPE""" + ) + else: + template = dedent( + """\ + $MSG + full_key: $FULL_KEY + object_type=$OBJECT_TYPE""" + ) + s = string.Template(template=template) + + message = s.substitute( + REF_TYPE=ref_type_str, OBJECT_TYPE=object_type_str, MSG=msg, FULL_KEY=full_key + ) + exception_type = type(cause) if type_override is None else type_override + if exception_type == TypeError: + exception_type = ConfigTypeError + elif exception_type == IndexError: + exception_type = ConfigIndexError + + ex = exception_type(f"{message}") + if issubclass(exception_type, OmegaConfBaseException): + ex._initialized = True + ex.msg = message + ex.parent_node = node + ex.child_node = child_node + ex.key = key + ex.full_key = full_key + ex.value = value + ex.object_type = object_type + ex.object_type_str = object_type_str + ex.ref_type = ref_type + ex.ref_type_str = ref_type_str + + _raise(ex, cause) + + +def type_str(t: Any, include_module_name: bool = False) -> str: + is_optional, t = _resolve_optional(t) + if t is NoneType: + return str(t.__name__) + if t is Any: + return "Any" + if t is ...: + return "..." + + if hasattr(t, "__name__"): + name = str(t.__name__) + elif getattr(t, "_name", None) is not None: # pragma: no cover + name = str(t._name) + elif getattr(t, "__origin__", None) is not None: # pragma: no cover + name = type_str(t.__origin__) + else: + name = str(t) + if name.startswith("typing."): # pragma: no cover + name = name[len("typing.") :] + + args = getattr(t, "__args__", None) + if args is not None: + args = ", ".join( + [type_str(t, include_module_name=include_module_name) for t in t.__args__] + ) + ret = f"{name}[{args}]" + else: + ret = name + if include_module_name: + if ( + hasattr(t, "__module__") + and t.__module__ != "builtins" + and t.__module__ != "typing" + and not t.__module__.startswith("omegaconf.") + ): + module_prefix = str(t.__module__) + "." + else: + module_prefix = "" + ret = module_prefix + ret + if is_optional: + return f"Optional[{ret}]" + else: + return ret + + +def _ensure_container(target: Any, flags: Optional[Dict[str, bool]] = None) -> Any: + from omegaconf import OmegaConf + + if is_primitive_container(target): + assert isinstance(target, (list, dict)) + target = OmegaConf.create(target, flags=flags) + elif is_structured_config(target): + target = OmegaConf.structured(target, flags=flags) + elif not OmegaConf.is_config(target): + raise ValueError( + "Invalid input. Supports one of " + + "[dict,list,DictConfig,ListConfig,dataclass,dataclass instance,attr class,attr class instance]" + ) + + return target + + +def is_generic_list(type_: Any) -> bool: + """ + Checks if a type is a generic list, for example: + list returns False + typing.List returns False + typing.List[T] returns True + + :param type_: variable type + :return: bool + """ + return is_list_annotation(type_) and get_list_element_type(type_) is not None + + +def is_generic_dict(type_: Any) -> bool: + """ + Checks if a type is a generic dict, for example: + list returns False + typing.List returns False + typing.List[T] returns True + + :param type_: variable type + :return: bool + """ + return is_dict_annotation(type_) and len(get_dict_key_value_types(type_)) > 0 + + +def is_container_annotation(type_: Any) -> bool: + return is_list_annotation(type_) or is_dict_annotation(type_) + + +def split_key(key: str) -> List[str]: + """ + Split a full key path into its individual components. + + This is similar to `key.split(".")` but also works with the getitem syntax: + "a.b" -> ["a", "b"] + "a[b]" -> ["a", "b"] + ".a.b[c].d" -> ["", "a", "b", "c", "d"] + "[a].b" -> ["a", "b"] + """ + # Obtain the first part of the key (in docstring examples: a, a, .a, '') + first = KEY_PATH_HEAD.match(key) + assert first is not None + first_stop = first.span()[1] + + # `tokens` will contain all elements composing the key. + tokens = key[0:first_stop].split(".") + + # Optimization in case `key` has no other component: we are done. + if first_stop == len(key): + return tokens + + if key[first_stop] == "[" and not tokens[-1]: + # This is a special case where the first key starts with brackets, e.g. + # [a] or ..[a]. In that case there is an extra "" in `tokens` that we + # need to get rid of: + # [a] -> tokens = [""] but we would like [] + # ..[a] -> tokens = ["", "", ""] but we would like ["", ""] + tokens.pop() + + # Identify other key elements (in docstring examples: b, b, b/c/d, b) + others = KEY_PATH_OTHER.findall(key[first_stop:]) + + # There are two groups in the `KEY_PATH_OTHER` regex: one for keys starting + # with a dot (.b, .d) and one for keys starting with a bracket ([b], [c]). + # Only one group can be non-empty. + tokens += [dot_key if dot_key else bracket_key for dot_key, bracket_key in others] + + return tokens + + +# Similar to Python 3.7+'s `contextlib.nullcontext` (which should be used instead, +# once support for Python 3.6 is dropped). +@contextmanager +def nullcontext(enter_result: Any = None) -> Iterator[Any]: + yield enter_result diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/base.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/base.py new file mode 100644 index 0000000000000000000000000000000000000000..8b013d1d9fac789cc28e8ea0a23e586610a32f43 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/base.py @@ -0,0 +1,962 @@ +import copy +import sys +from abc import ABC, abstractmethod +from collections import defaultdict +from dataclasses import dataclass, field +from enum import Enum +from typing import Any, Dict, Iterator, List, Optional, Set, Tuple, Type, Union + +from antlr4 import ParserRuleContext + +from ._utils import ( + _DEFAULT_MARKER_, + NoneType, + ValueKind, + _get_value, + _is_interpolation, + _is_missing_value, + _is_special, + format_and_raise, + get_value_kind, + is_union_annotation, + is_valid_value_annotation, + split_key, + type_str, +) +from .errors import ( + ConfigKeyError, + ConfigTypeError, + InterpolationKeyError, + InterpolationResolutionError, + InterpolationToMissingValueError, + InterpolationValidationError, + MissingMandatoryValue, + UnsupportedInterpolationType, + ValidationError, +) +from .grammar.gen.OmegaConfGrammarParser import OmegaConfGrammarParser +from .grammar_parser import parse +from .grammar_visitor import GrammarVisitor + +DictKeyType = Union[str, bytes, int, Enum, float, bool] + + +@dataclass +class Metadata: + + ref_type: Union[Type[Any], Any] + + object_type: Union[Type[Any], Any] + + optional: bool + + key: Any + + # Flags have 3 modes: + # unset : inherit from parent (None if no parent specifies) + # set to true: flag is true + # set to false: flag is false + flags: Optional[Dict[str, bool]] = None + + # If True, when checking the value of a flag, if the flag is not set None is returned + # otherwise, the parent node is queried. + flags_root: bool = False + + resolver_cache: Dict[str, Any] = field(default_factory=lambda: defaultdict(dict)) + + def __post_init__(self) -> None: + if self.flags is None: + self.flags = {} + + @property + def type_hint(self) -> Union[Type[Any], Any]: + """Compute `type_hint` from `self.optional` and `self.ref_type`""" + # For compatibility with pickled OmegaConf objects created using older + # versions of OmegaConf, we store `ref_type` and `object_type` + # separately (rather than storing `type_hint` directly). + if self.optional: + return Optional[self.ref_type] + else: + return self.ref_type + + +@dataclass +class ContainerMetadata(Metadata): + key_type: Any = None + element_type: Any = None + + def __post_init__(self) -> None: + if self.ref_type is None: + self.ref_type = Any + assert self.key_type is Any or isinstance(self.key_type, type) + if self.element_type is not None: + if not is_valid_value_annotation(self.element_type): + raise ValidationError( + f"Unsupported value type: '{type_str(self.element_type, include_module_name=True)}'" + ) + + if self.flags is None: + self.flags = {} + + +class Node(ABC): + _metadata: Metadata + + _parent: Optional["Box"] + _flags_cache: Optional[Dict[str, Optional[bool]]] + + def __init__(self, parent: Optional["Box"], metadata: Metadata): + self.__dict__["_metadata"] = metadata + self.__dict__["_parent"] = parent + self.__dict__["_flags_cache"] = None + + def __getstate__(self) -> Dict[str, Any]: + # Overridden to ensure that the flags cache is cleared on serialization. + state_dict = copy.copy(self.__dict__) + del state_dict["_flags_cache"] + return state_dict + + def __setstate__(self, state_dict: Dict[str, Any]) -> None: + self.__dict__.update(state_dict) + self.__dict__["_flags_cache"] = None + + def _set_parent(self, parent: Optional["Box"]) -> None: + assert parent is None or isinstance(parent, Box) + self.__dict__["_parent"] = parent + self._invalidate_flags_cache() + + def _invalidate_flags_cache(self) -> None: + self.__dict__["_flags_cache"] = None + + def _get_parent(self) -> Optional["Box"]: + parent = self.__dict__["_parent"] + assert parent is None or isinstance(parent, Box) + return parent + + def _get_parent_container(self) -> Optional["Container"]: + """ + Like _get_parent, but returns the grandparent + in the case where `self` is wrapped by a UnionNode. + """ + parent = self.__dict__["_parent"] + assert parent is None or isinstance(parent, Box) + + if isinstance(parent, UnionNode): + grandparent = parent.__dict__["_parent"] + assert grandparent is None or isinstance(grandparent, Container) + return grandparent + else: + assert parent is None or isinstance(parent, Container) + return parent + + def _set_flag( + self, + flags: Union[List[str], str], + values: Union[List[Optional[bool]], Optional[bool]], + ) -> "Node": + if isinstance(flags, str): + flags = [flags] + + if values is None or isinstance(values, bool): + values = [values] + + if len(values) == 1: + values = len(flags) * values + + if len(flags) != len(values): + raise ValueError("Inconsistent lengths of input flag names and values") + + for idx, flag in enumerate(flags): + value = values[idx] + if value is None: + assert self._metadata.flags is not None + if flag in self._metadata.flags: + del self._metadata.flags[flag] + else: + assert self._metadata.flags is not None + self._metadata.flags[flag] = value + self._invalidate_flags_cache() + return self + + def _get_node_flag(self, flag: str) -> Optional[bool]: + """ + :param flag: flag to inspect + :return: the state of the flag on this node. + """ + assert self._metadata.flags is not None + return self._metadata.flags.get(flag) + + def _get_flag(self, flag: str) -> Optional[bool]: + cache = self.__dict__["_flags_cache"] + if cache is None: + cache = self.__dict__["_flags_cache"] = {} + + ret = cache.get(flag, _DEFAULT_MARKER_) + if ret is _DEFAULT_MARKER_: + ret = self._get_flag_no_cache(flag) + cache[flag] = ret + assert ret is None or isinstance(ret, bool) + return ret + + def _get_flag_no_cache(self, flag: str) -> Optional[bool]: + """ + Returns True if this config node flag is set + A flag is set if node.set_flag(True) was called + or one if it's parents is flag is set + :return: + """ + flags = self._metadata.flags + assert flags is not None + if flag in flags and flags[flag] is not None: + return flags[flag] + + if self._is_flags_root(): + return None + + parent = self._get_parent() + if parent is None: + return None + else: + # noinspection PyProtectedMember + return parent._get_flag(flag) + + def _format_and_raise( + self, + key: Any, + value: Any, + cause: Exception, + msg: Optional[str] = None, + type_override: Any = None, + ) -> None: + format_and_raise( + node=self, + key=key, + value=value, + msg=str(cause) if msg is None else msg, + cause=cause, + type_override=type_override, + ) + assert False + + @abstractmethod + def _get_full_key(self, key: Optional[Union[DictKeyType, int]]) -> str: + ... + + def _dereference_node(self) -> "Node": + node = self._dereference_node_impl(throw_on_resolution_failure=True) + assert node is not None + return node + + def _maybe_dereference_node( + self, + throw_on_resolution_failure: bool = False, + memo: Optional[Set[int]] = None, + ) -> Optional["Node"]: + return self._dereference_node_impl( + throw_on_resolution_failure=throw_on_resolution_failure, + memo=memo, + ) + + def _dereference_node_impl( + self, + throw_on_resolution_failure: bool, + memo: Optional[Set[int]] = None, + ) -> Optional["Node"]: + if not self._is_interpolation(): + return self + + parent = self._get_parent_container() + if parent is None: + if throw_on_resolution_failure: + raise InterpolationResolutionError( + "Cannot resolve interpolation for a node without a parent" + ) + return None + assert parent is not None + key = self._key() + return parent._resolve_interpolation_from_parse_tree( + parent=parent, + key=key, + value=self, + parse_tree=parse(_get_value(self)), + throw_on_resolution_failure=throw_on_resolution_failure, + memo=memo, + ) + + def _get_root(self) -> "Container": + root: Optional[Box] = self._get_parent() + if root is None: + assert isinstance(self, Container) + return self + assert root is not None and isinstance(root, Box) + while root._get_parent() is not None: + root = root._get_parent() + assert root is not None and isinstance(root, Box) + assert root is not None and isinstance(root, Container) + return root + + def _is_missing(self) -> bool: + """ + Check if the node's value is `???` (does *not* resolve interpolations). + """ + return _is_missing_value(self) + + def _is_none(self) -> bool: + """ + Check if the node's value is `None` (does *not* resolve interpolations). + """ + return self._value() is None + + @abstractmethod + def __eq__(self, other: Any) -> bool: + ... + + @abstractmethod + def __ne__(self, other: Any) -> bool: + ... + + @abstractmethod + def __hash__(self) -> int: + ... + + @abstractmethod + def _value(self) -> Any: + ... + + @abstractmethod + def _set_value(self, value: Any, flags: Optional[Dict[str, bool]] = None) -> None: + ... + + @abstractmethod + def _is_optional(self) -> bool: + ... + + @abstractmethod + def _is_interpolation(self) -> bool: + ... + + def _key(self) -> Any: + return self._metadata.key + + def _set_key(self, key: Any) -> None: + self._metadata.key = key + + def _is_flags_root(self) -> bool: + return self._metadata.flags_root + + def _set_flags_root(self, flags_root: bool) -> None: + if self._metadata.flags_root != flags_root: + self._metadata.flags_root = flags_root + self._invalidate_flags_cache() + + def _has_ref_type(self) -> bool: + return self._metadata.ref_type is not Any + + +class Box(Node): + """ + Base class for nodes that can contain other nodes. + Concrete subclasses include DictConfig, ListConfig, and UnionNode. + """ + + _content: Any + + def __init__(self, parent: Optional["Box"], metadata: Metadata): + super().__init__(parent=parent, metadata=metadata) + self.__dict__["_content"] = None + + def __copy__(self) -> Any: + # real shallow copy is impossible because of the reference to the parent. + return copy.deepcopy(self) + + def _re_parent(self) -> None: + from .dictconfig import DictConfig + from .listconfig import ListConfig + + # update parents of first level Config nodes to self + + if isinstance(self, DictConfig): + content = self.__dict__["_content"] + if isinstance(content, dict): + for _key, value in self.__dict__["_content"].items(): + if value is not None: + value._set_parent(self) + if isinstance(value, Box): + value._re_parent() + elif isinstance(self, ListConfig): + content = self.__dict__["_content"] + if isinstance(content, list): + for item in self.__dict__["_content"]: + if item is not None: + item._set_parent(self) + if isinstance(item, Box): + item._re_parent() + elif isinstance(self, UnionNode): + content = self.__dict__["_content"] + if isinstance(content, Node): + content._set_parent(self) + if isinstance(content, Box): # pragma: no cover + # No coverage here as support for containers inside + # UnionNode is not yet implemented + content._re_parent() + + +class Container(Box): + """ + Container tagging interface + """ + + _metadata: ContainerMetadata + + @abstractmethod + def _get_child( + self, + key: Any, + validate_access: bool = True, + validate_key: bool = True, + throw_on_missing_value: bool = False, + throw_on_missing_key: bool = False, + ) -> Union[Optional[Node], List[Optional[Node]]]: + ... + + @abstractmethod + def _get_node( + self, + key: Any, + validate_access: bool = True, + validate_key: bool = True, + throw_on_missing_value: bool = False, + throw_on_missing_key: bool = False, + ) -> Union[Optional[Node], List[Optional[Node]]]: + ... + + @abstractmethod + def __delitem__(self, key: Any) -> None: + ... + + @abstractmethod + def __setitem__(self, key: Any, value: Any) -> None: + ... + + @abstractmethod + def __iter__(self) -> Iterator[Any]: + ... + + @abstractmethod + def __getitem__(self, key_or_index: Any) -> Any: + ... + + def _resolve_key_and_root(self, key: str) -> Tuple["Container", str]: + orig = key + if not key.startswith("."): + return self._get_root(), key + else: + root: Optional[Container] = self + assert key.startswith(".") + while True: + assert root is not None + key = key[1:] + if not key.startswith("."): + break + root = root._get_parent_container() + if root is None: + raise ConfigKeyError(f"Error resolving key '{orig}'") + + return root, key + + def _select_impl( + self, + key: str, + throw_on_missing: bool, + throw_on_resolution_failure: bool, + memo: Optional[Set[int]] = None, + ) -> Tuple[Optional["Container"], Optional[str], Optional[Node]]: + """ + Select a value using dot separated key sequence + """ + from .omegaconf import _select_one + + if key == "": + return self, "", self + + split = split_key(key) + root: Optional[Container] = self + for i in range(len(split) - 1): + if root is None: + break + + k = split[i] + ret, _ = _select_one( + c=root, + key=k, + throw_on_missing=throw_on_missing, + throw_on_type_error=throw_on_resolution_failure, + ) + if isinstance(ret, Node): + ret = ret._maybe_dereference_node( + throw_on_resolution_failure=throw_on_resolution_failure, + memo=memo, + ) + + if ret is not None and not isinstance(ret, Container): + parent_key = ".".join(split[0 : i + 1]) + child_key = split[i + 1] + raise ConfigTypeError( + f"Error trying to access {key}: node `{parent_key}` " + f"is not a container and thus cannot contain `{child_key}`" + ) + root = ret + + if root is None: + return None, None, None + + last_key = split[-1] + value, _ = _select_one( + c=root, + key=last_key, + throw_on_missing=throw_on_missing, + throw_on_type_error=throw_on_resolution_failure, + ) + if value is None: + return root, last_key, None + + if memo is not None: + vid = id(value) + if vid in memo: + raise InterpolationResolutionError("Recursive interpolation detected") + # push to memo "stack" + memo.add(vid) + + try: + value = root._maybe_resolve_interpolation( + parent=root, + key=last_key, + value=value, + throw_on_resolution_failure=throw_on_resolution_failure, + memo=memo, + ) + finally: + if memo is not None: + # pop from memo "stack" + memo.remove(vid) + + return root, last_key, value + + def _resolve_interpolation_from_parse_tree( + self, + parent: Optional["Container"], + value: "Node", + key: Any, + parse_tree: OmegaConfGrammarParser.ConfigValueContext, + throw_on_resolution_failure: bool, + memo: Optional[Set[int]], + ) -> Optional["Node"]: + """ + Resolve an interpolation. + + This happens in two steps: + 1. The parse tree is visited, which outputs either a `Node` (e.g., + for node interpolations "${foo}"), a string (e.g., for string + interpolations "hello ${name}", or any other arbitrary value + (e.g., or custom interpolations "${foo:bar}"). + 2. This output is potentially validated and converted when the node + being resolved (`value`) is typed. + + If an error occurs in one of the above steps, an `InterpolationResolutionError` + (or a subclass of it) is raised, *unless* `throw_on_resolution_failure` is set + to `False` (in which case the return value is `None`). + + :param parent: Parent of the node being resolved. + :param value: Node being resolved. + :param key: The associated key in the parent. + :param parse_tree: The parse tree as obtained from `grammar_parser.parse()`. + :param throw_on_resolution_failure: If `False`, then exceptions raised during + the resolution of the interpolation are silenced, and instead `None` is + returned. + + :return: A `Node` that contains the interpolation result. This may be an existing + node in the config (in the case of a node interpolation "${foo}"), or a new + node that is created to wrap the interpolated value. It is `None` if and only if + `throw_on_resolution_failure` is `False` and an error occurs during resolution. + """ + + try: + resolved = self.resolve_parse_tree( + parse_tree=parse_tree, node=value, key=key, memo=memo + ) + except InterpolationResolutionError: + if throw_on_resolution_failure: + raise + return None + + return self._validate_and_convert_interpolation_result( + parent=parent, + value=value, + key=key, + resolved=resolved, + throw_on_resolution_failure=throw_on_resolution_failure, + ) + + def _validate_and_convert_interpolation_result( + self, + parent: Optional["Container"], + value: "Node", + key: Any, + resolved: Any, + throw_on_resolution_failure: bool, + ) -> Optional["Node"]: + from .nodes import AnyNode, InterpolationResultNode, ValueNode + + # If the output is not a Node already (e.g., because it is the output of a + # custom resolver), then we will need to wrap it within a Node. + must_wrap = not isinstance(resolved, Node) + + # If the node is typed, validate (and possibly convert) the result. + if isinstance(value, ValueNode) and not isinstance(value, AnyNode): + res_value = _get_value(resolved) + try: + conv_value = value.validate_and_convert(res_value) + except ValidationError as e: + if throw_on_resolution_failure: + self._format_and_raise( + key=key, + value=res_value, + cause=e, + msg=f"While dereferencing interpolation '{value}': {e}", + type_override=InterpolationValidationError, + ) + return None + + # If the converted value is of the same type, it means that no conversion + # was actually needed. As a result, we can keep the original `resolved` + # (and otherwise, the converted value must be wrapped into a new node). + if type(conv_value) != type(res_value): + must_wrap = True + resolved = conv_value + + if must_wrap: + return InterpolationResultNode(value=resolved, key=key, parent=parent) + else: + assert isinstance(resolved, Node) + return resolved + + def _validate_not_dereferencing_to_parent(self, node: Node, target: Node) -> None: + parent: Optional[Node] = node + while parent is not None: + if parent is target: + raise InterpolationResolutionError( + "Interpolation to parent node detected" + ) + parent = parent._get_parent() + + def _resolve_node_interpolation( + self, inter_key: str, memo: Optional[Set[int]] + ) -> "Node": + """A node interpolation is of the form `${foo.bar}`""" + try: + root_node, inter_key = self._resolve_key_and_root(inter_key) + except ConfigKeyError as exc: + raise InterpolationKeyError( + f"ConfigKeyError while resolving interpolation: {exc}" + ).with_traceback(sys.exc_info()[2]) + + try: + parent, last_key, value = root_node._select_impl( + inter_key, + throw_on_missing=True, + throw_on_resolution_failure=True, + memo=memo, + ) + except MissingMandatoryValue as exc: + raise InterpolationToMissingValueError( + f"MissingMandatoryValue while resolving interpolation: {exc}" + ).with_traceback(sys.exc_info()[2]) + + if parent is None or value is None: + raise InterpolationKeyError(f"Interpolation key '{inter_key}' not found") + else: + self._validate_not_dereferencing_to_parent(node=self, target=value) + return value + + def _evaluate_custom_resolver( + self, + key: Any, + node: Node, + inter_type: str, + inter_args: Tuple[Any, ...], + inter_args_str: Tuple[str, ...], + ) -> Any: + from omegaconf import OmegaConf + + resolver = OmegaConf._get_resolver(inter_type) + if resolver is not None: + root_node = self._get_root() + return resolver( + root_node, + self, + node, + inter_args, + inter_args_str, + ) + else: + raise UnsupportedInterpolationType( + f"Unsupported interpolation type {inter_type}" + ) + + def _maybe_resolve_interpolation( + self, + parent: Optional["Container"], + key: Any, + value: Node, + throw_on_resolution_failure: bool, + memo: Optional[Set[int]] = None, + ) -> Optional[Node]: + value_kind = get_value_kind(value) + if value_kind != ValueKind.INTERPOLATION: + return value + + parse_tree = parse(_get_value(value)) + return self._resolve_interpolation_from_parse_tree( + parent=parent, + value=value, + key=key, + parse_tree=parse_tree, + throw_on_resolution_failure=throw_on_resolution_failure, + memo=memo if memo is not None else set(), + ) + + def resolve_parse_tree( + self, + parse_tree: ParserRuleContext, + node: Node, + memo: Optional[Set[int]] = None, + key: Optional[Any] = None, + ) -> Any: + """ + Resolve a given parse tree into its value. + + We make no assumption here on the type of the tree's root, so that the + return value may be of any type. + """ + + def node_interpolation_callback( + inter_key: str, memo: Optional[Set[int]] + ) -> Optional["Node"]: + return self._resolve_node_interpolation(inter_key=inter_key, memo=memo) + + def resolver_interpolation_callback( + name: str, args: Tuple[Any, ...], args_str: Tuple[str, ...] + ) -> Any: + return self._evaluate_custom_resolver( + key=key, + node=node, + inter_type=name, + inter_args=args, + inter_args_str=args_str, + ) + + visitor = GrammarVisitor( + node_interpolation_callback=node_interpolation_callback, + resolver_interpolation_callback=resolver_interpolation_callback, + memo=memo, + ) + try: + return visitor.visit(parse_tree) + except InterpolationResolutionError: + raise + except Exception as exc: + # Other kinds of exceptions are wrapped in an `InterpolationResolutionError`. + raise InterpolationResolutionError( + f"{type(exc).__name__} raised while resolving interpolation: {exc}" + ).with_traceback(sys.exc_info()[2]) + + def _invalidate_flags_cache(self) -> None: + from .dictconfig import DictConfig + from .listconfig import ListConfig + + # invalidate subtree cache only if the cache is initialized in this node. + + if self.__dict__["_flags_cache"] is not None: + self.__dict__["_flags_cache"] = None + if isinstance(self, DictConfig): + content = self.__dict__["_content"] + if isinstance(content, dict): + for value in self.__dict__["_content"].values(): + value._invalidate_flags_cache() + elif isinstance(self, ListConfig): + content = self.__dict__["_content"] + if isinstance(content, list): + for item in self.__dict__["_content"]: + item._invalidate_flags_cache() + + +class SCMode(Enum): + DICT = 1 # Convert to plain dict + DICT_CONFIG = 2 # Keep as OmegaConf DictConfig + INSTANTIATE = 3 # Create a dataclass or attrs class instance + + +class UnionNode(Box): + """ + This class handles Union type hints. The `_content` attribute is either a + child node that is compatible with the given Union ref_type, or it is a + special value (None or MISSING or interpolation). + + Much of the logic for e.g. value assignment and type validation is + delegated to the child node. As such, UnionNode functions as a + "pass-through" node. User apps and downstream libraries should not need to + know about UnionNode (assuming they only use OmegaConf's public API). + """ + + _parent: Optional[Container] + _content: Union[Node, None, str] + + def __init__( + self, + content: Any, + ref_type: Any, + is_optional: bool = True, + key: Any = None, + parent: Optional[Box] = None, + ) -> None: + try: + if not is_union_annotation(ref_type): # pragma: no cover + msg = ( + f"UnionNode got unexpected ref_type {ref_type}. Please file a bug" + + " report at https://github.com/omry/omegaconf/issues" + ) + raise AssertionError(msg) + if not isinstance(parent, (Container, NoneType)): + raise ConfigTypeError("Parent type is not omegaconf.Container") + super().__init__( + parent=parent, + metadata=Metadata( + ref_type=ref_type, + object_type=None, + optional=is_optional, + key=key, + flags={"convert": False}, + ), + ) + self._set_value(content) + except Exception as ex: + format_and_raise(node=None, key=key, value=content, msg=str(ex), cause=ex) + + def _get_full_key(self, key: Optional[Union[DictKeyType, int]]) -> str: + parent = self._get_parent() + if parent is None: + if self._metadata.key is None: + return "" + else: + return str(self._metadata.key) + else: + return parent._get_full_key(self._metadata.key) + + def __eq__(self, other: Any) -> bool: + content = self.__dict__["_content"] + if isinstance(content, Node): + ret = content.__eq__(other) + elif isinstance(other, Node): + ret = other.__eq__(content) + else: + ret = content.__eq__(other) + assert isinstance(ret, (bool, type(NotImplemented))) + return ret + + def __ne__(self, other: Any) -> bool: + x = self.__eq__(other) + if x is NotImplemented: + return NotImplemented + return not x + + def __hash__(self) -> int: + return hash(self.__dict__["_content"]) + + def _value(self) -> Union[Node, None, str]: + content = self.__dict__["_content"] + assert isinstance(content, (Node, NoneType, str)) + return content + + def _set_value(self, value: Any, flags: Optional[Dict[str, bool]] = None) -> None: + previous_content = self.__dict__["_content"] + previous_metadata = self.__dict__["_metadata"] + try: + self._set_value_impl(value, flags) + except Exception as e: + self.__dict__["_content"] = previous_content + self.__dict__["_metadata"] = previous_metadata + raise e + + def _set_value_impl( + self, value: Any, flags: Optional[Dict[str, bool]] = None + ) -> None: + from omegaconf.omegaconf import _node_wrap + + ref_type = self._metadata.ref_type + type_hint = self._metadata.type_hint + + value = _get_value(value) + if _is_special(value): + assert isinstance(value, (str, NoneType)) + if value is None: + if not self._is_optional(): + raise ValidationError( + f"Value '$VALUE' is incompatible with type hint '{type_str(type_hint)}'" + ) + self.__dict__["_content"] = value + elif isinstance(value, Container): + raise ValidationError( + f"Cannot assign container '$VALUE' of type '$VALUE_TYPE' to {type_str(type_hint)}" + ) + else: + for candidate_ref_type in ref_type.__args__: + try: + self.__dict__["_content"] = _node_wrap( + value=value, + ref_type=candidate_ref_type, + is_optional=False, + key=None, + parent=self, + ) + break + except ValidationError: + continue + else: + raise ValidationError( + f"Value '$VALUE' of type '$VALUE_TYPE' is incompatible with type hint '{type_str(type_hint)}'" + ) + + def _is_optional(self) -> bool: + return self.__dict__["_metadata"].optional is True + + def _is_interpolation(self) -> bool: + return _is_interpolation(self.__dict__["_content"]) + + def __str__(self) -> str: + return str(self.__dict__["_content"]) + + def __repr__(self) -> str: + return repr(self.__dict__["_content"]) + + def __deepcopy__(self, memo: Dict[int, Any]) -> "UnionNode": + res = object.__new__(type(self)) + for key, value in self.__dict__.items(): + if key not in ("_content", "_parent"): + res.__dict__[key] = copy.deepcopy(value, memo=memo) + + src_content = self.__dict__["_content"] + if isinstance(src_content, Node): + old_parent = src_content.__dict__["_parent"] + try: + src_content.__dict__["_parent"] = None + content_copy = copy.deepcopy(src_content, memo=memo) + content_copy.__dict__["_parent"] = res + finally: + src_content.__dict__["_parent"] = old_parent + else: + # None and strings can be assigned as is + content_copy = src_content + + res.__dict__["_content"] = content_copy + res.__dict__["_parent"] = self.__dict__["_parent"] + return res diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/basecontainer.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/basecontainer.py new file mode 100644 index 0000000000000000000000000000000000000000..575df3c72105f9563d29bca8077d7e3fb79742b2 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/basecontainer.py @@ -0,0 +1,916 @@ +import copy +import sys +from abc import ABC, abstractmethod +from enum import Enum +from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Tuple, Union + +import yaml + +from ._utils import ( + _DEFAULT_MARKER_, + ValueKind, + _ensure_container, + _get_value, + _is_interpolation, + _is_missing_value, + _is_none, + _is_special, + _resolve_optional, + get_structured_config_data, + get_type_hint, + get_value_kind, + get_yaml_loader, + is_container_annotation, + is_dict_annotation, + is_list_annotation, + is_primitive_dict, + is_primitive_type_annotation, + is_structured_config, + is_tuple_annotation, + is_union_annotation, +) +from .base import ( + Box, + Container, + ContainerMetadata, + DictKeyType, + Node, + SCMode, + UnionNode, +) +from .errors import ( + ConfigCycleDetectedException, + ConfigTypeError, + InterpolationResolutionError, + KeyValidationError, + MissingMandatoryValue, + OmegaConfBaseException, + ReadonlyConfigError, + ValidationError, +) + +if TYPE_CHECKING: + from .dictconfig import DictConfig # pragma: no cover + + +class BaseContainer(Container, ABC): + _resolvers: ClassVar[Dict[str, Any]] = {} + + def __init__(self, parent: Optional[Box], metadata: ContainerMetadata): + if not (parent is None or isinstance(parent, Box)): + raise ConfigTypeError("Parent type is not omegaconf.Box") + super().__init__(parent=parent, metadata=metadata) + + def _get_child( + self, + key: Any, + validate_access: bool = True, + validate_key: bool = True, + throw_on_missing_value: bool = False, + throw_on_missing_key: bool = False, + ) -> Union[Optional[Node], List[Optional[Node]]]: + """Like _get_node, passing through to the nearest concrete Node.""" + child = self._get_node( + key=key, + validate_access=validate_access, + validate_key=validate_key, + throw_on_missing_value=throw_on_missing_value, + throw_on_missing_key=throw_on_missing_key, + ) + if isinstance(child, UnionNode) and not _is_special(child): + value = child._value() + assert isinstance(value, Node) and not isinstance(value, UnionNode) + child = value + return child + + def _resolve_with_default( + self, + key: Union[DictKeyType, int], + value: Node, + default_value: Any = _DEFAULT_MARKER_, + ) -> Any: + """returns the value with the specified key, like obj.key and obj['key']""" + if _is_missing_value(value): + if default_value is not _DEFAULT_MARKER_: + return default_value + raise MissingMandatoryValue("Missing mandatory value: $FULL_KEY") + + resolved_node = self._maybe_resolve_interpolation( + parent=self, + key=key, + value=value, + throw_on_resolution_failure=True, + ) + + return _get_value(resolved_node) + + def __str__(self) -> str: + return self.__repr__() + + def __repr__(self) -> str: + if self.__dict__["_content"] is None: + return "None" + elif self._is_interpolation() or self._is_missing(): + v = self.__dict__["_content"] + return f"'{v}'" + else: + return self.__dict__["_content"].__repr__() # type: ignore + + # Support pickle + def __getstate__(self) -> Dict[str, Any]: + dict_copy = copy.copy(self.__dict__) + + # no need to serialize the flags cache, it can be re-constructed later + dict_copy.pop("_flags_cache", None) + + dict_copy["_metadata"] = copy.copy(dict_copy["_metadata"]) + ref_type = self._metadata.ref_type + if is_container_annotation(ref_type): + if is_dict_annotation(ref_type): + dict_copy["_metadata"].ref_type = Dict + elif is_list_annotation(ref_type): + dict_copy["_metadata"].ref_type = List + else: + assert False + if sys.version_info < (3, 7): # pragma: no cover + element_type = self._metadata.element_type + if is_union_annotation(element_type): + raise OmegaConfBaseException( + "Serializing structured configs with `Union` element type requires python >= 3.7" + ) + return dict_copy + + # Support pickle + def __setstate__(self, d: Dict[str, Any]) -> None: + from omegaconf import DictConfig + from omegaconf._utils import is_generic_dict, is_generic_list + + if isinstance(self, DictConfig): + key_type = d["_metadata"].key_type + + # backward compatibility to load OmegaConf 2.0 configs + if key_type is None: + key_type = Any + d["_metadata"].key_type = key_type + + element_type = d["_metadata"].element_type + + # backward compatibility to load OmegaConf 2.0 configs + if element_type is None: + element_type = Any + d["_metadata"].element_type = element_type + + ref_type = d["_metadata"].ref_type + if is_container_annotation(ref_type): + if is_generic_dict(ref_type): + d["_metadata"].ref_type = Dict[key_type, element_type] # type: ignore + elif is_generic_list(ref_type): + d["_metadata"].ref_type = List[element_type] # type: ignore + else: + assert False + + d["_flags_cache"] = None + self.__dict__.update(d) + + @abstractmethod + def __delitem__(self, key: Any) -> None: + ... + + def __len__(self) -> int: + if self._is_none() or self._is_missing() or self._is_interpolation(): + return 0 + content = self.__dict__["_content"] + return len(content) + + def merge_with_cli(self) -> None: + args_list = sys.argv[1:] + self.merge_with_dotlist(args_list) + + def merge_with_dotlist(self, dotlist: List[str]) -> None: + from omegaconf import OmegaConf + + def fail() -> None: + raise ValueError("Input list must be a list or a tuple of strings") + + if not isinstance(dotlist, (list, tuple)): + fail() + + for arg in dotlist: + if not isinstance(arg, str): + fail() + + idx = arg.find("=") + if idx == -1: + key = arg + value = None + else: + key = arg[0:idx] + value = arg[idx + 1 :] + value = yaml.load(value, Loader=get_yaml_loader()) + + OmegaConf.update(self, key, value) + + def is_empty(self) -> bool: + """return true if config is empty""" + return len(self.__dict__["_content"]) == 0 + + @staticmethod + def _to_content( + conf: Container, + resolve: bool, + throw_on_missing: bool, + enum_to_str: bool = False, + structured_config_mode: SCMode = SCMode.DICT, + ) -> Union[None, Any, str, Dict[DictKeyType, Any], List[Any]]: + from omegaconf import MISSING, DictConfig, ListConfig + + def convert(val: Node) -> Any: + value = val._value() + if enum_to_str and isinstance(value, Enum): + value = f"{value.name}" + + return value + + def get_node_value(key: Union[DictKeyType, int]) -> Any: + try: + node = conf._get_child(key, throw_on_missing_value=throw_on_missing) + except MissingMandatoryValue as e: + conf._format_and_raise(key=key, value=None, cause=e) + assert isinstance(node, Node) + if resolve: + try: + node = node._dereference_node() + except InterpolationResolutionError as e: + conf._format_and_raise(key=key, value=None, cause=e) + + if isinstance(node, Container): + value = BaseContainer._to_content( + node, + resolve=resolve, + throw_on_missing=throw_on_missing, + enum_to_str=enum_to_str, + structured_config_mode=structured_config_mode, + ) + else: + value = convert(node) + return value + + if conf._is_none(): + return None + elif conf._is_missing(): + if throw_on_missing: + conf._format_and_raise( + key=None, + value=None, + cause=MissingMandatoryValue("Missing mandatory value"), + ) + else: + return MISSING + elif not resolve and conf._is_interpolation(): + inter = conf._value() + assert isinstance(inter, str) + return inter + + if resolve: + _conf = conf._dereference_node() + assert isinstance(_conf, Container) + conf = _conf + + if isinstance(conf, DictConfig): + if ( + conf._metadata.object_type not in (dict, None) + and structured_config_mode == SCMode.DICT_CONFIG + ): + return conf + if structured_config_mode == SCMode.INSTANTIATE and is_structured_config( + conf._metadata.object_type + ): + return conf._to_object() + + retdict: Dict[DictKeyType, Any] = {} + for key in conf.keys(): + value = get_node_value(key) + if enum_to_str and isinstance(key, Enum): + key = f"{key.name}" + retdict[key] = value + return retdict + elif isinstance(conf, ListConfig): + retlist: List[Any] = [] + for index in range(len(conf)): + item = get_node_value(index) + retlist.append(item) + + return retlist + assert False + + @staticmethod + def _map_merge(dest: "BaseContainer", src: "BaseContainer") -> None: + """merge src into dest and return a new copy, does not modified input""" + from omegaconf import AnyNode, DictConfig, ValueNode + + assert isinstance(dest, DictConfig) + assert isinstance(src, DictConfig) + src_type = src._metadata.object_type + src_ref_type = get_type_hint(src) + assert src_ref_type is not None + + # If source DictConfig is: + # - None => set the destination DictConfig to None + # - an interpolation => set the destination DictConfig to be the same interpolation + if src._is_none() or src._is_interpolation(): + dest._set_value(src._value()) + _update_types(node=dest, ref_type=src_ref_type, object_type=src_type) + return + + dest._validate_merge(value=src) + + def expand(node: Container) -> None: + rt = node._metadata.ref_type + val: Any + if rt is not Any: + if is_dict_annotation(rt): + val = {} + elif is_list_annotation(rt) or is_tuple_annotation(rt): + val = [] + else: + val = rt + elif isinstance(node, DictConfig): + val = {} + else: + assert False + + node._set_value(val) + + if ( + src._is_missing() + and not dest._is_missing() + and is_structured_config(src_ref_type) + ): + # Replace `src` with a prototype of its corresponding structured config + # whose fields are all missing (to avoid overwriting fields in `dest`). + assert src_type is None # src missing, so src's object_type should be None + src_type = src_ref_type + src = _create_structured_with_missing_fields( + ref_type=src_ref_type, object_type=src_type + ) + + if (dest._is_interpolation() or dest._is_missing()) and not src._is_missing(): + expand(dest) + + src_items = list(src) if not src._is_missing() else [] + for key in src_items: + src_node = src._get_node(key, validate_access=False) + dest_node = dest._get_node(key, validate_access=False) + assert isinstance(src_node, Node) + assert dest_node is None or isinstance(dest_node, Node) + src_value = _get_value(src_node) + + src_vk = get_value_kind(src_node) + src_node_missing = src_vk is ValueKind.MANDATORY_MISSING + + if isinstance(dest_node, DictConfig): + dest_node._validate_merge(value=src_node) + + if ( + isinstance(dest_node, Container) + and dest_node._is_none() + and not src_node_missing + and not _is_none(src_node, resolve=True) + ): + expand(dest_node) + + if dest_node is not None and dest_node._is_interpolation(): + target_node = dest_node._maybe_dereference_node() + if isinstance(target_node, Container): + dest[key] = target_node + dest_node = dest._get_node(key) + + is_optional, et = _resolve_optional(dest._metadata.element_type) + if dest_node is None and is_structured_config(et) and not src_node_missing: + # merging into a new node. Use element_type as a base + dest[key] = DictConfig( + et, parent=dest, ref_type=et, is_optional=is_optional + ) + dest_node = dest._get_node(key) + + if dest_node is not None: + if isinstance(dest_node, BaseContainer): + if isinstance(src_node, BaseContainer): + dest_node._merge_with(src_node) + elif not src_node_missing: + dest.__setitem__(key, src_node) + else: + if isinstance(src_node, BaseContainer): + dest.__setitem__(key, src_node) + else: + assert isinstance(dest_node, (ValueNode, UnionNode)) + assert isinstance(src_node, (ValueNode, UnionNode)) + try: + if isinstance(dest_node, AnyNode): + if src_node_missing: + node = copy.copy(src_node) + # if src node is missing, use the value from the dest_node, + # but validate it against the type of the src node before assigment + node._set_value(dest_node._value()) + else: + node = src_node + dest.__setitem__(key, node) + else: + if not src_node_missing: + dest_node._set_value(src_value) + + except (ValidationError, ReadonlyConfigError) as e: + dest._format_and_raise(key=key, value=src_value, cause=e) + else: + from omegaconf import open_dict + + if is_structured_config(src_type): + # verified to be compatible above in _validate_merge + with open_dict(dest): + dest[key] = src._get_node(key) + else: + dest[key] = src._get_node(key) + + _update_types(node=dest, ref_type=src_ref_type, object_type=src_type) + + # explicit flags on the source config are replacing the flag values in the destination + flags = src._metadata.flags + assert flags is not None + for flag, value in flags.items(): + if value is not None: + dest._set_flag(flag, value) + + @staticmethod + def _list_merge(dest: Any, src: Any) -> None: + from omegaconf import DictConfig, ListConfig, OmegaConf + + assert isinstance(dest, ListConfig) + assert isinstance(src, ListConfig) + + if src._is_none(): + dest._set_value(None) + elif src._is_missing(): + # do not change dest if src is MISSING. + if dest._metadata.element_type is Any: + dest._metadata.element_type = src._metadata.element_type + elif src._is_interpolation(): + dest._set_value(src._value()) + else: + temp_target = ListConfig(content=[], parent=dest._get_parent()) + temp_target.__dict__["_metadata"] = copy.deepcopy( + dest.__dict__["_metadata"] + ) + is_optional, et = _resolve_optional(dest._metadata.element_type) + if is_structured_config(et): + prototype = DictConfig(et, ref_type=et, is_optional=is_optional) + for item in src._iter_ex(resolve=False): + if isinstance(item, DictConfig): + item = OmegaConf.merge(prototype, item) + temp_target.append(item) + else: + for item in src._iter_ex(resolve=False): + temp_target.append(item) + + dest.__dict__["_content"] = temp_target.__dict__["_content"] + + # explicit flags on the source config are replacing the flag values in the destination + flags = src._metadata.flags + assert flags is not None + for flag, value in flags.items(): + if value is not None: + dest._set_flag(flag, value) + + def merge_with( + self, + *others: Union[ + "BaseContainer", Dict[str, Any], List[Any], Tuple[Any, ...], Any + ], + ) -> None: + try: + self._merge_with(*others) + except Exception as e: + self._format_and_raise(key=None, value=None, cause=e) + + def _merge_with( + self, + *others: Union[ + "BaseContainer", Dict[str, Any], List[Any], Tuple[Any, ...], Any + ], + ) -> None: + from .dictconfig import DictConfig + from .listconfig import ListConfig + + """merge a list of other Config objects into this one, overriding as needed""" + for other in others: + if other is None: + raise ValueError("Cannot merge with a None config") + + my_flags = {} + if self._get_flag("allow_objects") is True: + my_flags = {"allow_objects": True} + other = _ensure_container(other, flags=my_flags) + + if isinstance(self, DictConfig) and isinstance(other, DictConfig): + BaseContainer._map_merge(self, other) + elif isinstance(self, ListConfig) and isinstance(other, ListConfig): + BaseContainer._list_merge(self, other) + else: + raise TypeError("Cannot merge DictConfig with ListConfig") + + # recursively correct the parent hierarchy after the merge + self._re_parent() + + # noinspection PyProtectedMember + def _set_item_impl(self, key: Any, value: Any) -> None: + """ + Changes the value of the node key with the desired value. If the node key doesn't + exist it creates a new one. + """ + from .nodes import AnyNode, ValueNode + + if isinstance(value, Node): + do_deepcopy = not self._get_flag("no_deepcopy_set_nodes") + if not do_deepcopy and isinstance(value, Box): + # if value is from the same config, perform a deepcopy no matter what. + if self._get_root() is value._get_root(): + do_deepcopy = True + + if do_deepcopy: + value = copy.deepcopy(value) + value._set_parent(None) + + try: + old = value._key() + value._set_key(key) + self._validate_set(key, value) + finally: + value._set_key(old) + else: + self._validate_set(key, value) + + if self._get_flag("readonly"): + raise ReadonlyConfigError("Cannot change read-only config container") + + input_is_node = isinstance(value, Node) + target_node_ref = self._get_node(key) + assert target_node_ref is None or isinstance(target_node_ref, Node) + + input_is_typed_vnode = isinstance(value, ValueNode) and not isinstance( + value, AnyNode + ) + + def get_target_type_hint(val: Any) -> Any: + if not is_structured_config(val): + type_hint = self._metadata.element_type + else: + target = self._get_node(key) + if target is None: + type_hint = self._metadata.element_type + else: + assert isinstance(target, Node) + type_hint = target._metadata.type_hint + return type_hint + + target_type_hint = get_target_type_hint(value) + _, target_ref_type = _resolve_optional(target_type_hint) + + def assign(value_key: Any, val: Node) -> None: + assert val._get_parent() is None + v = val + v._set_parent(self) + v._set_key(value_key) + _deep_update_type_hint(node=v, type_hint=self._metadata.element_type) + self.__dict__["_content"][value_key] = v + + if input_is_typed_vnode and not is_union_annotation(target_ref_type): + assign(key, value) + else: + # input is not a ValueNode, can be primitive or box + + special_value = _is_special(value) + # We use the `Node._set_value` method if the target node exists and: + # 1. the target has an explicit ref_type, or + # 2. the target is an AnyNode and the input is a primitive type. + should_set_value = target_node_ref is not None and ( + target_node_ref._has_ref_type() + or ( + isinstance(target_node_ref, AnyNode) + and is_primitive_type_annotation(value) + ) + ) + if should_set_value: + if special_value and isinstance(value, Node): + value = value._value() + self.__dict__["_content"][key]._set_value(value) + elif input_is_node: + if ( + special_value + and ( + is_container_annotation(target_ref_type) + or is_structured_config(target_ref_type) + ) + or is_primitive_type_annotation(target_ref_type) + or is_union_annotation(target_ref_type) + ): + value = _get_value(value) + self._wrap_value_and_set(key, value, target_type_hint) + else: + assign(key, value) + else: + self._wrap_value_and_set(key, value, target_type_hint) + + def _wrap_value_and_set(self, key: Any, val: Any, type_hint: Any) -> None: + from omegaconf.omegaconf import _maybe_wrap + + is_optional, ref_type = _resolve_optional(type_hint) + + try: + wrapped = _maybe_wrap( + ref_type=ref_type, + key=key, + value=val, + is_optional=is_optional, + parent=self, + ) + except ValidationError as e: + self._format_and_raise(key=key, value=val, cause=e) + self.__dict__["_content"][key] = wrapped + + @staticmethod + def _item_eq( + c1: Container, + k1: Union[DictKeyType, int], + c2: Container, + k2: Union[DictKeyType, int], + ) -> bool: + v1 = c1._get_child(k1) + v2 = c2._get_child(k2) + assert v1 is not None and v2 is not None + + assert isinstance(v1, Node) + assert isinstance(v2, Node) + + if v1._is_none() and v2._is_none(): + return True + + if v1._is_missing() and v2._is_missing(): + return True + + v1_inter = v1._is_interpolation() + v2_inter = v2._is_interpolation() + dv1: Optional[Node] = v1 + dv2: Optional[Node] = v2 + + if v1_inter: + dv1 = v1._maybe_dereference_node() + if v2_inter: + dv2 = v2._maybe_dereference_node() + + if v1_inter and v2_inter: + if dv1 is None or dv2 is None: + return v1 == v2 + else: + # both are not none, if both are containers compare as container + if isinstance(dv1, Container) and isinstance(dv2, Container): + if dv1 != dv2: + return False + dv1 = _get_value(dv1) + dv2 = _get_value(dv2) + return dv1 == dv2 + elif not v1_inter and not v2_inter: + v1 = _get_value(v1) + v2 = _get_value(v2) + ret = v1 == v2 + assert isinstance(ret, bool) + return ret + else: + dv1 = _get_value(dv1) + dv2 = _get_value(dv2) + ret = dv1 == dv2 + assert isinstance(ret, bool) + return ret + + def _is_optional(self) -> bool: + return self.__dict__["_metadata"].optional is True + + def _is_interpolation(self) -> bool: + return _is_interpolation(self.__dict__["_content"]) + + @abstractmethod + def _validate_get(self, key: Any, value: Any = None) -> None: + ... + + @abstractmethod + def _validate_set(self, key: Any, value: Any) -> None: + ... + + def _value(self) -> Any: + return self.__dict__["_content"] + + def _get_full_key(self, key: Union[DictKeyType, int, slice, None]) -> str: + from .listconfig import ListConfig + from .omegaconf import _select_one + + if not isinstance(key, (int, str, Enum, float, bool, slice, bytes, type(None))): + return "" + + def _slice_to_str(x: slice) -> str: + if x.step is not None: + return f"{x.start}:{x.stop}:{x.step}" + else: + return f"{x.start}:{x.stop}" + + def prepand( + full_key: str, + parent_type: Any, + cur_type: Any, + key: Optional[Union[DictKeyType, int, slice]], + ) -> str: + if key is None: + return full_key + + if isinstance(key, slice): + key = _slice_to_str(key) + elif isinstance(key, Enum): + key = key.name + else: + key = str(key) + + assert isinstance(key, str) + + if issubclass(parent_type, ListConfig): + if full_key != "": + if issubclass(cur_type, ListConfig): + full_key = f"[{key}]{full_key}" + else: + full_key = f"[{key}].{full_key}" + else: + full_key = f"[{key}]" + else: + if full_key == "": + full_key = key + else: + if issubclass(cur_type, ListConfig): + full_key = f"{key}{full_key}" + else: + full_key = f"{key}.{full_key}" + return full_key + + if key is not None and key != "": + assert isinstance(self, Container) + cur, _ = _select_one( + c=self, key=str(key), throw_on_missing=False, throw_on_type_error=False + ) + if cur is None: + cur = self + full_key = prepand("", type(cur), None, key) + if cur._key() is not None: + full_key = prepand( + full_key, type(cur._get_parent()), type(cur), cur._key() + ) + else: + full_key = prepand("", type(cur._get_parent()), type(cur), cur._key()) + else: + cur = self + if cur._key() is None: + return "" + full_key = self._key() + + assert cur is not None + memo = {id(cur)} # remember already visited nodes so as to detect cycles + while cur._get_parent() is not None: + cur = cur._get_parent() + if id(cur) in memo: + raise ConfigCycleDetectedException( + f"Cycle when iterating over parents of key `{key!s}`" + ) + memo.add(id(cur)) + assert cur is not None + if cur._key() is not None: + full_key = prepand( + full_key, type(cur._get_parent()), type(cur), cur._key() + ) + + return full_key + + +def _create_structured_with_missing_fields( + ref_type: type, object_type: Optional[type] = None +) -> "DictConfig": + from . import MISSING, DictConfig + + cfg_data = get_structured_config_data(ref_type) + for v in cfg_data.values(): + v._set_value(MISSING) + + cfg = DictConfig(cfg_data) + cfg._metadata.optional, cfg._metadata.ref_type = _resolve_optional(ref_type) + cfg._metadata.object_type = object_type + + return cfg + + +def _update_types(node: Node, ref_type: Any, object_type: Optional[type]) -> None: + if object_type is not None and not is_primitive_dict(object_type): + node._metadata.object_type = object_type + + if node._metadata.ref_type is Any: + _deep_update_type_hint(node, ref_type) + + +def _deep_update_type_hint(node: Node, type_hint: Any) -> None: + """Ensure node is compatible with type_hint, mutating if necessary.""" + from omegaconf import DictConfig, ListConfig + + from ._utils import get_dict_key_value_types, get_list_element_type + + if type_hint is Any: + return + + _shallow_validate_type_hint(node, type_hint) + + new_is_optional, new_ref_type = _resolve_optional(type_hint) + node._metadata.ref_type = new_ref_type + node._metadata.optional = new_is_optional + + if is_list_annotation(new_ref_type) and isinstance(node, ListConfig): + new_element_type = get_list_element_type(new_ref_type) + node._metadata.element_type = new_element_type + if not _is_special(node): + for i in range(len(node)): + _deep_update_subnode(node, i, new_element_type) + + if is_dict_annotation(new_ref_type) and isinstance(node, DictConfig): + new_key_type, new_element_type = get_dict_key_value_types(new_ref_type) + node._metadata.key_type = new_key_type + node._metadata.element_type = new_element_type + if not _is_special(node): + for key in node: + if new_key_type is not Any and not isinstance(key, new_key_type): + raise KeyValidationError( + f"Key {key!r} ({type(key).__name__}) is incompatible" + + f" with key type hint '{new_key_type.__name__}'" + ) + _deep_update_subnode(node, key, new_element_type) + + +def _deep_update_subnode(node: BaseContainer, key: Any, value_type_hint: Any) -> None: + """Get node[key] and ensure it is compatible with value_type_hint, mutating if necessary.""" + subnode = node._get_node(key) + assert isinstance(subnode, Node) + if _is_special(subnode): + # Ensure special values are wrapped in a Node subclass that + # is compatible with the type hint. + node._wrap_value_and_set(key, subnode._value(), value_type_hint) + subnode = node._get_node(key) + assert isinstance(subnode, Node) + _deep_update_type_hint(subnode, value_type_hint) + + +def _shallow_validate_type_hint(node: Node, type_hint: Any) -> None: + """Error if node's type, content and metadata are not compatible with type_hint.""" + from omegaconf import DictConfig, ListConfig, ValueNode + + is_optional, ref_type = _resolve_optional(type_hint) + + vk = get_value_kind(node) + + if node._is_none(): + if not is_optional: + value = _get_value(node) + raise ValidationError( + f"Value {value!r} ({type(value).__name__})" + + f" is incompatible with type hint '{ref_type.__name__}'" + ) + return + elif vk in (ValueKind.MANDATORY_MISSING, ValueKind.INTERPOLATION): + return + elif vk == ValueKind.VALUE: + if is_primitive_type_annotation(ref_type) and isinstance(node, ValueNode): + value = node._value() + if not isinstance(value, ref_type): + raise ValidationError( + f"Value {value!r} ({type(value).__name__})" + + f" is incompatible with type hint '{ref_type.__name__}'" + ) + elif is_structured_config(ref_type) and isinstance(node, DictConfig): + return + elif is_dict_annotation(ref_type) and isinstance(node, DictConfig): + return + elif is_list_annotation(ref_type) and isinstance(node, ListConfig): + return + else: + if isinstance(node, ValueNode): + value = node._value() + raise ValidationError( + f"Value {value!r} ({type(value).__name__})" + + f" is incompatible with type hint '{ref_type}'" + ) + else: + raise ValidationError( + f"'{type(node).__name__}' is incompatible" + + f" with type hint '{ref_type}'" + ) + + else: + assert False diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/dictconfig.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/dictconfig.py new file mode 100644 index 0000000000000000000000000000000000000000..da7c8957f1c2e30b4c1889997c85c2a33cf72f01 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/dictconfig.py @@ -0,0 +1,776 @@ +import copy +from enum import Enum +from typing import ( + Any, + Dict, + ItemsView, + Iterable, + Iterator, + KeysView, + List, + MutableMapping, + Optional, + Sequence, + Tuple, + Type, + Union, +) + +from ._utils import ( + _DEFAULT_MARKER_, + ValueKind, + _get_value, + _is_interpolation, + _is_missing_literal, + _is_missing_value, + _is_none, + _resolve_optional, + _valid_dict_key_annotation_type, + format_and_raise, + get_structured_config_data, + get_structured_config_init_field_names, + get_type_of, + get_value_kind, + is_container_annotation, + is_dict, + is_primitive_dict, + is_structured_config, + is_structured_config_frozen, + type_str, +) +from .base import Box, Container, ContainerMetadata, DictKeyType, Node +from .basecontainer import BaseContainer +from .errors import ( + ConfigAttributeError, + ConfigKeyError, + ConfigTypeError, + InterpolationResolutionError, + KeyValidationError, + MissingMandatoryValue, + OmegaConfBaseException, + ReadonlyConfigError, + ValidationError, +) +from .nodes import EnumNode, ValueNode + + +class DictConfig(BaseContainer, MutableMapping[Any, Any]): + + _metadata: ContainerMetadata + _content: Union[Dict[DictKeyType, Node], None, str] + + def __init__( + self, + content: Union[Dict[DictKeyType, Any], "DictConfig", Any], + key: Any = None, + parent: Optional[Box] = None, + ref_type: Union[Any, Type[Any]] = Any, + key_type: Union[Any, Type[Any]] = Any, + element_type: Union[Any, Type[Any]] = Any, + is_optional: bool = True, + flags: Optional[Dict[str, bool]] = None, + ) -> None: + try: + if isinstance(content, DictConfig): + if flags is None: + flags = content._metadata.flags + super().__init__( + parent=parent, + metadata=ContainerMetadata( + key=key, + optional=is_optional, + ref_type=ref_type, + object_type=dict, + key_type=key_type, + element_type=element_type, + flags=flags, + ), + ) + + if not _valid_dict_key_annotation_type(key_type): + raise KeyValidationError(f"Unsupported key type {key_type}") + + if is_structured_config(content) or is_structured_config(ref_type): + self._set_value(content, flags=flags) + if is_structured_config_frozen(content) or is_structured_config_frozen( + ref_type + ): + self._set_flag("readonly", True) + + else: + if isinstance(content, DictConfig): + metadata = copy.deepcopy(content._metadata) + metadata.key = key + metadata.ref_type = ref_type + metadata.optional = is_optional + metadata.element_type = element_type + metadata.key_type = key_type + self.__dict__["_metadata"] = metadata + self._set_value(content, flags=flags) + except Exception as ex: + format_and_raise(node=None, key=key, value=None, cause=ex, msg=str(ex)) + + def __deepcopy__(self, memo: Dict[int, Any]) -> "DictConfig": + res = DictConfig(None) + res.__dict__["_metadata"] = copy.deepcopy(self.__dict__["_metadata"], memo=memo) + res.__dict__["_flags_cache"] = copy.deepcopy( + self.__dict__["_flags_cache"], memo=memo + ) + + src_content = self.__dict__["_content"] + if isinstance(src_content, dict): + content_copy = {} + for k, v in src_content.items(): + old_parent = v.__dict__["_parent"] + try: + v.__dict__["_parent"] = None + vc = copy.deepcopy(v, memo=memo) + vc.__dict__["_parent"] = res + content_copy[k] = vc + finally: + v.__dict__["_parent"] = old_parent + else: + # None and strings can be assigned as is + content_copy = src_content + + res.__dict__["_content"] = content_copy + # parent is retained, but not copied + res.__dict__["_parent"] = self.__dict__["_parent"] + return res + + def copy(self) -> "DictConfig": + return copy.copy(self) + + def _is_typed(self) -> bool: + return self._metadata.object_type not in (Any, None) and not is_dict( + self._metadata.object_type + ) + + def _validate_get(self, key: Any, value: Any = None) -> None: + is_typed = self._is_typed() + + is_struct = self._get_flag("struct") is True + if key not in self.__dict__["_content"]: + if is_typed: + # do not raise an exception if struct is explicitly set to False + if self._get_node_flag("struct") is False: + return + if is_typed or is_struct: + if is_typed: + assert self._metadata.object_type not in (dict, None) + msg = f"Key '{key}' not in '{self._metadata.object_type.__name__}'" + else: + msg = f"Key '{key}' is not in struct" + self._format_and_raise( + key=key, value=value, cause=ConfigAttributeError(msg) + ) + + def _validate_set(self, key: Any, value: Any) -> None: + from omegaconf import OmegaConf + + vk = get_value_kind(value) + if vk == ValueKind.INTERPOLATION: + return + if _is_none(value): + self._validate_non_optional(key, value) + return + if vk == ValueKind.MANDATORY_MISSING or value is None: + return + + target = self._get_node(key) if key is not None else self + + target_has_ref_type = isinstance( + target, DictConfig + ) and target._metadata.ref_type not in (Any, dict) + is_valid_target = target is None or not target_has_ref_type + + if is_valid_target: + return + + assert isinstance(target, Node) + + target_type = target._metadata.ref_type + value_type = OmegaConf.get_type(value) + + if is_dict(value_type) and is_dict(target_type): + return + if is_container_annotation(target_type) and not is_container_annotation( + value_type + ): + raise ValidationError( + f"Cannot assign {type_str(value_type)} to {type_str(target_type)}" + ) + + if target_type is not None and value_type is not None: + origin = getattr(target_type, "__origin__", target_type) + if not issubclass(value_type, origin): + self._raise_invalid_value(value, value_type, target_type) + + def _validate_merge(self, value: Any) -> None: + from omegaconf import OmegaConf + + dest = self + src = value + + self._validate_non_optional(None, src) + + dest_obj_type = OmegaConf.get_type(dest) + src_obj_type = OmegaConf.get_type(src) + + if dest._is_missing() and src._metadata.object_type not in (dict, None): + self._validate_set(key=None, value=_get_value(src)) + + if src._is_missing(): + return + + validation_error = ( + dest_obj_type is not None + and src_obj_type is not None + and is_structured_config(dest_obj_type) + and not src._is_none() + and not is_dict(src_obj_type) + and not issubclass(src_obj_type, dest_obj_type) + ) + if validation_error: + msg = ( + f"Merge error: {type_str(src_obj_type)} is not a " + f"subclass of {type_str(dest_obj_type)}. value: {src}" + ) + raise ValidationError(msg) + + def _validate_non_optional(self, key: Optional[DictKeyType], value: Any) -> None: + if _is_none(value, resolve=True, throw_on_resolution_failure=False): + + if key is not None: + child = self._get_node(key) + if child is not None: + assert isinstance(child, Node) + field_is_optional = child._is_optional() + else: + field_is_optional, _ = _resolve_optional( + self._metadata.element_type + ) + else: + field_is_optional = self._is_optional() + + if not field_is_optional: + self._format_and_raise( + key=key, + value=value, + cause=ValidationError("field '$FULL_KEY' is not Optional"), + ) + + def _raise_invalid_value( + self, value: Any, value_type: Any, target_type: Any + ) -> None: + assert value_type is not None + assert target_type is not None + msg = ( + f"Invalid type assigned: {type_str(value_type)} is not a " + f"subclass of {type_str(target_type)}. value: {value}" + ) + raise ValidationError(msg) + + def _validate_and_normalize_key(self, key: Any) -> DictKeyType: + return self._s_validate_and_normalize_key(self._metadata.key_type, key) + + def _s_validate_and_normalize_key(self, key_type: Any, key: Any) -> DictKeyType: + if key_type is Any: + for t in DictKeyType.__args__: # type: ignore + if isinstance(key, t): + return key # type: ignore + raise KeyValidationError("Incompatible key type '$KEY_TYPE'") + elif key_type is bool and key in [0, 1]: + # Python treats True as 1 and False as 0 when used as dict keys + # assert hash(0) == hash(False) + # assert hash(1) == hash(True) + return bool(key) + elif key_type in (str, bytes, int, float, bool): # primitive type + if not isinstance(key, key_type): + raise KeyValidationError( + f"Key $KEY ($KEY_TYPE) is incompatible with ({key_type.__name__})" + ) + + return key # type: ignore + elif issubclass(key_type, Enum): + try: + return EnumNode.validate_and_convert_to_enum(key_type, key) + except ValidationError: + valid = ", ".join([x for x in key_type.__members__.keys()]) + raise KeyValidationError( + f"Key '$KEY' is incompatible with the enum type '{key_type.__name__}', valid: [{valid}]" + ) + else: + assert False, f"Unsupported key type {key_type}" + + def __setitem__(self, key: DictKeyType, value: Any) -> None: + try: + self.__set_impl(key=key, value=value) + except AttributeError as e: + self._format_and_raise( + key=key, value=value, type_override=ConfigKeyError, cause=e + ) + except Exception as e: + self._format_and_raise(key=key, value=value, cause=e) + + def __set_impl(self, key: DictKeyType, value: Any) -> None: + key = self._validate_and_normalize_key(key) + self._set_item_impl(key, value) + + # hide content while inspecting in debugger + def __dir__(self) -> Iterable[str]: + if self._is_missing() or self._is_none(): + return [] + return self.__dict__["_content"].keys() # type: ignore + + def __setattr__(self, key: str, value: Any) -> None: + """ + Allow assigning attributes to DictConfig + :param key: + :param value: + :return: + """ + try: + self.__set_impl(key, value) + except Exception as e: + if isinstance(e, OmegaConfBaseException) and e._initialized: + raise e + self._format_and_raise(key=key, value=value, cause=e) + assert False + + def __getattr__(self, key: str) -> Any: + """ + Allow accessing dictionary values as attributes + :param key: + :return: + """ + if key == "__name__": + raise AttributeError() + + try: + return self._get_impl( + key=key, default_value=_DEFAULT_MARKER_, validate_key=False + ) + except ConfigKeyError as e: + self._format_and_raise( + key=key, value=None, cause=e, type_override=ConfigAttributeError + ) + except Exception as e: + self._format_and_raise(key=key, value=None, cause=e) + + def __getitem__(self, key: DictKeyType) -> Any: + """ + Allow map style access + :param key: + :return: + """ + + try: + return self._get_impl(key=key, default_value=_DEFAULT_MARKER_) + except AttributeError as e: + self._format_and_raise( + key=key, value=None, cause=e, type_override=ConfigKeyError + ) + except Exception as e: + self._format_and_raise(key=key, value=None, cause=e) + + def __delattr__(self, key: str) -> None: + """ + Allow deleting dictionary values as attributes + :param key: + :return: + """ + if self._get_flag("readonly"): + self._format_and_raise( + key=key, + value=None, + cause=ReadonlyConfigError( + "DictConfig in read-only mode does not support deletion" + ), + ) + try: + del self.__dict__["_content"][key] + except KeyError: + msg = "Attribute not found: '$KEY'" + self._format_and_raise(key=key, value=None, cause=ConfigAttributeError(msg)) + + def __delitem__(self, key: DictKeyType) -> None: + key = self._validate_and_normalize_key(key) + if self._get_flag("readonly"): + self._format_and_raise( + key=key, + value=None, + cause=ReadonlyConfigError( + "DictConfig in read-only mode does not support deletion" + ), + ) + if self._get_flag("struct"): + self._format_and_raise( + key=key, + value=None, + cause=ConfigTypeError( + "DictConfig in struct mode does not support deletion" + ), + ) + if self._is_typed() and self._get_node_flag("struct") is not False: + self._format_and_raise( + key=key, + value=None, + cause=ConfigTypeError( + f"{type_str(self._metadata.object_type)} (DictConfig) does not support deletion" + ), + ) + + try: + del self.__dict__["_content"][key] + except KeyError: + msg = "Key not found: '$KEY'" + self._format_and_raise(key=key, value=None, cause=ConfigKeyError(msg)) + + def get(self, key: DictKeyType, default_value: Any = None) -> Any: + """Return the value for `key` if `key` is in the dictionary, else + `default_value` (defaulting to `None`).""" + try: + return self._get_impl(key=key, default_value=default_value) + except KeyValidationError as e: + self._format_and_raise(key=key, value=None, cause=e) + + def _get_impl( + self, key: DictKeyType, default_value: Any, validate_key: bool = True + ) -> Any: + try: + node = self._get_child( + key=key, throw_on_missing_key=True, validate_key=validate_key + ) + except (ConfigAttributeError, ConfigKeyError): + if default_value is not _DEFAULT_MARKER_: + return default_value + else: + raise + assert isinstance(node, Node) + return self._resolve_with_default( + key=key, value=node, default_value=default_value + ) + + def _get_node( + self, + key: DictKeyType, + validate_access: bool = True, + validate_key: bool = True, + throw_on_missing_value: bool = False, + throw_on_missing_key: bool = False, + ) -> Optional[Node]: + try: + key = self._validate_and_normalize_key(key) + except KeyValidationError: + if validate_access and validate_key: + raise + else: + if throw_on_missing_key: + raise ConfigAttributeError + else: + return None + + if validate_access: + self._validate_get(key) + + value: Optional[Node] = self.__dict__["_content"].get(key) + if value is None: + if throw_on_missing_key: + raise ConfigKeyError(f"Missing key {key!s}") + elif throw_on_missing_value and value._is_missing(): + raise MissingMandatoryValue("Missing mandatory value: $KEY") + return value + + def pop(self, key: DictKeyType, default: Any = _DEFAULT_MARKER_) -> Any: + try: + if self._get_flag("readonly"): + raise ReadonlyConfigError("Cannot pop from read-only node") + if self._get_flag("struct"): + raise ConfigTypeError("DictConfig in struct mode does not support pop") + if self._is_typed() and self._get_node_flag("struct") is not False: + raise ConfigTypeError( + f"{type_str(self._metadata.object_type)} (DictConfig) does not support pop" + ) + key = self._validate_and_normalize_key(key) + node = self._get_child(key=key, validate_access=False) + if node is not None: + assert isinstance(node, Node) + value = self._resolve_with_default( + key=key, value=node, default_value=default + ) + + del self[key] + return value + else: + if default is not _DEFAULT_MARKER_: + return default + else: + full = self._get_full_key(key=key) + if full != key: + raise ConfigKeyError( + f"Key not found: '{key!s}' (path: '{full}')" + ) + else: + raise ConfigKeyError(f"Key not found: '{key!s}'") + except Exception as e: + self._format_and_raise(key=key, value=None, cause=e) + + def keys(self) -> KeysView[DictKeyType]: + if self._is_missing() or self._is_interpolation() or self._is_none(): + return {}.keys() + ret = self.__dict__["_content"].keys() + assert isinstance(ret, KeysView) + return ret + + def __contains__(self, key: object) -> bool: + """ + A key is contained in a DictConfig if there is an associated value and + it is not a mandatory missing value ('???'). + :param key: + :return: + """ + + try: + key = self._validate_and_normalize_key(key) + except KeyValidationError: + return False + + try: + node = self._get_child(key) + assert node is None or isinstance(node, Node) + except (KeyError, AttributeError): + node = None + + if node is None: + return False + else: + try: + self._resolve_with_default(key=key, value=node) + return True + except InterpolationResolutionError: + # Interpolations that fail count as existing. + return True + except MissingMandatoryValue: + # Missing values count as *not* existing. + return False + + def __iter__(self) -> Iterator[DictKeyType]: + return iter(self.keys()) + + def items(self) -> ItemsView[DictKeyType, Any]: + return dict(self.items_ex(resolve=True, keys=None)).items() + + def setdefault(self, key: DictKeyType, default: Any = None) -> Any: + if key in self: + ret = self.__getitem__(key) + else: + ret = default + self.__setitem__(key, default) + return ret + + def items_ex( + self, resolve: bool = True, keys: Optional[Sequence[DictKeyType]] = None + ) -> List[Tuple[DictKeyType, Any]]: + items: List[Tuple[DictKeyType, Any]] = [] + + if self._is_none(): + self._format_and_raise( + key=None, + value=None, + cause=TypeError("Cannot iterate a DictConfig object representing None"), + ) + if self._is_missing(): + raise MissingMandatoryValue("Cannot iterate a missing DictConfig") + + for key in self.keys(): + if resolve: + value = self[key] + else: + value = self.__dict__["_content"][key] + if isinstance(value, ValueNode): + value = value._value() + if keys is None or key in keys: + items.append((key, value)) + + return items + + def __eq__(self, other: Any) -> bool: + if other is None: + return self.__dict__["_content"] is None + if is_primitive_dict(other) or is_structured_config(other): + other = DictConfig(other, flags={"allow_objects": True}) + return DictConfig._dict_conf_eq(self, other) + if isinstance(other, DictConfig): + return DictConfig._dict_conf_eq(self, other) + if self._is_missing(): + return _is_missing_literal(other) + return NotImplemented + + def __ne__(self, other: Any) -> bool: + x = self.__eq__(other) + if x is not NotImplemented: + return not x + return NotImplemented + + def __hash__(self) -> int: + return hash(str(self)) + + def _promote(self, type_or_prototype: Optional[Type[Any]]) -> None: + """ + Retypes a node. + This should only be used in rare circumstances, where you want to dynamically change + the runtime structured-type of a DictConfig. + It will change the type and add the additional fields based on the input class or object + """ + if type_or_prototype is None: + return + if not is_structured_config(type_or_prototype): + raise ValueError(f"Expected structured config class: {type_or_prototype}") + + from omegaconf import OmegaConf + + proto: DictConfig = OmegaConf.structured(type_or_prototype) + object_type = proto._metadata.object_type + # remove the type to prevent assignment validation from rejecting the promotion. + proto._metadata.object_type = None + self.merge_with(proto) + # restore the type. + self._metadata.object_type = object_type + + def _set_value(self, value: Any, flags: Optional[Dict[str, bool]] = None) -> None: + try: + previous_content = self.__dict__["_content"] + self._set_value_impl(value, flags) + except Exception as e: + self.__dict__["_content"] = previous_content + raise e + + def _set_value_impl( + self, value: Any, flags: Optional[Dict[str, bool]] = None + ) -> None: + from omegaconf import MISSING, flag_override + + if flags is None: + flags = {} + + assert not isinstance(value, ValueNode) + self._validate_set(key=None, value=value) + + if _is_none(value, resolve=True): + self.__dict__["_content"] = None + self._metadata.object_type = None + elif _is_interpolation(value, strict_interpolation_validation=True): + self.__dict__["_content"] = value + self._metadata.object_type = None + elif _is_missing_value(value): + self.__dict__["_content"] = MISSING + self._metadata.object_type = None + else: + self.__dict__["_content"] = {} + if is_structured_config(value): + self._metadata.object_type = None + ao = self._get_flag("allow_objects") + data = get_structured_config_data(value, allow_objects=ao) + with flag_override(self, ["struct", "readonly"], False): + for k, v in data.items(): + self.__setitem__(k, v) + self._metadata.object_type = get_type_of(value) + + elif isinstance(value, DictConfig): + self._metadata.flags = copy.deepcopy(flags) + with flag_override(self, ["struct", "readonly"], False): + for k, v in value.__dict__["_content"].items(): + self.__setitem__(k, v) + self._metadata.object_type = value._metadata.object_type + + elif isinstance(value, dict): + with flag_override(self, ["struct", "readonly"], False): + for k, v in value.items(): + self.__setitem__(k, v) + self._metadata.object_type = dict + + else: # pragma: no cover + msg = f"Unsupported value type: {value}" + raise ValidationError(msg) + + @staticmethod + def _dict_conf_eq(d1: "DictConfig", d2: "DictConfig") -> bool: + + d1_none = d1.__dict__["_content"] is None + d2_none = d2.__dict__["_content"] is None + if d1_none and d2_none: + return True + if d1_none != d2_none: + return False + + assert isinstance(d1, DictConfig) + assert isinstance(d2, DictConfig) + if len(d1) != len(d2): + return False + if d1._is_missing() or d2._is_missing(): + return d1._is_missing() is d2._is_missing() + + for k, v in d1.items_ex(resolve=False): + if k not in d2.__dict__["_content"]: + return False + if not BaseContainer._item_eq(d1, k, d2, k): + return False + + return True + + def _to_object(self) -> Any: + """ + Instantiate an instance of `self._metadata.object_type`. + This requires `self` to be a structured config. + Nested subconfigs are converted by calling `OmegaConf.to_object`. + """ + from omegaconf import OmegaConf + + object_type = self._metadata.object_type + assert is_structured_config(object_type) + init_field_names = set(get_structured_config_init_field_names(object_type)) + + init_field_items: Dict[str, Any] = {} + non_init_field_items: Dict[str, Any] = {} + for k in self.keys(): + assert isinstance(k, str) + node = self._get_child(k) + assert isinstance(node, Node) + try: + node = node._dereference_node() + except InterpolationResolutionError as e: + self._format_and_raise(key=k, value=None, cause=e) + if node._is_missing(): + if k not in init_field_names: + continue # MISSING is ignored for init=False fields + self._format_and_raise( + key=k, + value=None, + cause=MissingMandatoryValue( + "Structured config of type `$OBJECT_TYPE` has missing mandatory value: $KEY" + ), + ) + if isinstance(node, Container): + v = OmegaConf.to_object(node) + else: + v = node._value() + + if k in init_field_names: + init_field_items[k] = v + else: + non_init_field_items[k] = v + + try: + result = object_type(**init_field_items) + except TypeError as exc: + self._format_and_raise( + key=None, + value=None, + cause=exc, + msg="Could not create instance of `$OBJECT_TYPE`: " + str(exc), + ) + + for k, v in non_init_field_items.items(): + setattr(result, k, v) + return result diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/errors.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/errors.py new file mode 100644 index 0000000000000000000000000000000000000000..60525ef3c62fb89093c0b5aa1147ae6f80b61062 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/errors.py @@ -0,0 +1,141 @@ +from typing import Any, Optional, Type + + +class OmegaConfBaseException(Exception): + # would ideally be typed Optional[Node] + parent_node: Any + child_node: Any + key: Any + full_key: Optional[str] + value: Any + msg: Optional[str] + cause: Optional[Exception] + object_type: Optional[Type[Any]] + object_type_str: Optional[str] + ref_type: Optional[Type[Any]] + ref_type_str: Optional[str] + + _initialized: bool = False + + def __init__(self, *_args: Any, **_kwargs: Any) -> None: + self.parent_node = None + self.child_node = None + self.key = None + self.full_key = None + self.value = None + self.msg = None + self.object_type = None + self.ref_type = None + + +class MissingMandatoryValue(OmegaConfBaseException): + """Thrown when a variable flagged with '???' value is accessed to + indicate that the value was not set""" + + +class KeyValidationError(OmegaConfBaseException, ValueError): + """ + Thrown when an a key of invalid type is used + """ + + +class ValidationError(OmegaConfBaseException, ValueError): + """ + Thrown when a value fails validation + """ + + +class UnsupportedValueType(ValidationError, ValueError): + """ + Thrown when an input value is not of supported type + """ + + +class ReadonlyConfigError(OmegaConfBaseException): + """ + Thrown when someone tries to modify a frozen config + """ + + +class InterpolationResolutionError(OmegaConfBaseException, ValueError): + """ + Base class for exceptions raised when resolving an interpolation. + """ + + +class UnsupportedInterpolationType(InterpolationResolutionError): + """ + Thrown when an attempt to use an unregistered interpolation is made + """ + + +class InterpolationKeyError(InterpolationResolutionError): + """ + Thrown when a node does not exist when resolving an interpolation. + """ + + +class InterpolationToMissingValueError(InterpolationResolutionError): + """ + Thrown when a node interpolation points to a node that is set to ???. + """ + + +class InterpolationValidationError(InterpolationResolutionError, ValidationError): + """ + Thrown when the result of an interpolation fails the validation step. + """ + + +class ConfigKeyError(OmegaConfBaseException, KeyError): + """ + Thrown from DictConfig when a regular dict access would have caused a KeyError. + """ + + msg: str + + def __init__(self, msg: str) -> None: + super().__init__(msg) + self.msg = msg + + def __str__(self) -> str: + """ + Workaround to nasty KeyError quirk: https://bugs.python.org/issue2651 + """ + return self.msg + + +class ConfigAttributeError(OmegaConfBaseException, AttributeError): + """ + Thrown from a config object when a regular access would have caused an AttributeError. + """ + + +class ConfigTypeError(OmegaConfBaseException, TypeError): + """ + Thrown from a config object when a regular access would have caused a TypeError. + """ + + +class ConfigIndexError(OmegaConfBaseException, IndexError): + """ + Thrown from a config object when a regular access would have caused an IndexError. + """ + + +class ConfigValueError(OmegaConfBaseException, ValueError): + """ + Thrown from a config object when a regular access would have caused a ValueError. + """ + + +class ConfigCycleDetectedException(OmegaConfBaseException): + """ + Thrown when a cycle is detected in the graph made by config nodes. + """ + + +class GrammarParseError(OmegaConfBaseException): + """ + Thrown when failing to parse an expression according to the ANTLR grammar. + """ diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/grammar/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/grammar/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/grammar/gen/OmegaConfGrammarLexer.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/grammar/gen/OmegaConfGrammarLexer.py new file mode 100644 index 0000000000000000000000000000000000000000..1bf9416fab12aaf7eb45c73b620037d4800e3496 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/grammar/gen/OmegaConfGrammarLexer.py @@ -0,0 +1,337 @@ +# Generated from /tmp/build-via-sdist-fm63w174/omegaconf-2.3.0/omegaconf/grammar/OmegaConfGrammarLexer.g4 by ANTLR 4.9.3 +from antlr4 import * +from io import StringIO +import sys +if sys.version_info[1] > 5: + from typing import TextIO +else: + from typing.io import TextIO + + + +def serializedATN(): + with StringIO() as buf: + buf.write("\3\u608b\ua72a\u8133\ub9ed\u417c\u3be7\u7786\u5964\2\36") + buf.write("\u01e7\b\1\b\1\b\1\b\1\b\1\4\2\t\2\4\3\t\3\4\4\t\4\4\5") + buf.write("\t\5\4\6\t\6\4\7\t\7\4\b\t\b\4\t\t\t\4\n\t\n\4\13\t\13") + buf.write("\4\f\t\f\4\r\t\r\4\16\t\16\4\17\t\17\4\20\t\20\4\21\t") + buf.write("\21\4\22\t\22\4\23\t\23\4\24\t\24\4\25\t\25\4\26\t\26") + buf.write("\4\27\t\27\4\30\t\30\4\31\t\31\4\32\t\32\4\33\t\33\4\34") + 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buf.write("\3\2\2\2\u01df\u01dd\3\2\2\2\u01df\u01e0\3\2\2\2\u01e0") + buf.write("\u01e1\3\2\2\2\u01e1\u01e2\b\65\4\2\u01e2n\3\2\2\2\u01e3") + buf.write("\u01e4\7&\2\2\u01e4\u01e5\3\2\2\2\u01e5\u01e6\b\66\4\2") + buf.write("\u01e6p\3\2\2\2\66\2\3\4\5\6x}\u0080\u008d\u0095\u009f") + buf.write("\u00a4\u00b0\u00b6\u00bb\u00ca\u00ce\u00d2\u00d5\u00da") + buf.write("\u00de\u00e4\u00e9\u00ee\u00f1\u00f5\u00f9\u00fd\u0102") + buf.write("\u0106\u0110\u0113\u0120\u012b\u0130\u0132\u014c\u014e") + buf.write("\u0153\u0157\u015d\u0161\u0167\u017e\u018c\u019a\u01a5") + buf.write("\u01aa\u01bf\u01cd\u01d8\u01df\22\t\6\2\7\4\2\t\3\2\7") + buf.write("\3\2\6\2\2\7\5\2\7\6\2\t\16\2\4\3\2\t\f\2\t\r\2\t\24\2") + buf.write("\t\4\2\t\25\2\t\32\2\t\33\2") + return buf.getvalue() + + +class OmegaConfGrammarLexer(Lexer): + + atn = ATNDeserializer().deserialize(serializedATN()) + + decisionsToDFA = [ DFA(ds, i) for i, ds in enumerate(atn.decisionToState) ] + + VALUE_MODE = 1 + INTERPOLATION_MODE = 2 + QUOTED_SINGLE_MODE = 3 + QUOTED_DOUBLE_MODE = 4 + + ANY_STR = 1 + ESC_INTER = 2 + TOP_ESC = 3 + INTER_OPEN = 4 + BRACE_OPEN = 5 + BRACE_CLOSE = 6 + QUOTE_OPEN_SINGLE = 7 + QUOTE_OPEN_DOUBLE = 8 + COMMA = 9 + BRACKET_OPEN = 10 + BRACKET_CLOSE = 11 + COLON = 12 + FLOAT = 13 + INT = 14 + BOOL = 15 + NULL = 16 + UNQUOTED_CHAR = 17 + ID = 18 + ESC = 19 + WS = 20 + INTER_CLOSE = 21 + DOT = 22 + INTER_KEY = 23 + MATCHING_QUOTE_CLOSE = 24 + QUOTED_ESC = 25 + DOLLAR = 26 + INTER_BRACKET_OPEN = 27 + INTER_BRACKET_CLOSE = 28 + + channelNames = [ u"DEFAULT_TOKEN_CHANNEL", u"HIDDEN" ] + + modeNames = [ "DEFAULT_MODE", "VALUE_MODE", "INTERPOLATION_MODE", "QUOTED_SINGLE_MODE", + "QUOTED_DOUBLE_MODE" ] + + literalNames = [ "", + "'.'", "'['", "']'" ] + + symbolicNames = [ "", + "ANY_STR", "ESC_INTER", "TOP_ESC", "INTER_OPEN", "BRACE_OPEN", + "BRACE_CLOSE", "QUOTE_OPEN_SINGLE", "QUOTE_OPEN_DOUBLE", "COMMA", + "BRACKET_OPEN", "BRACKET_CLOSE", "COLON", "FLOAT", "INT", "BOOL", + "NULL", "UNQUOTED_CHAR", "ID", "ESC", "WS", "INTER_CLOSE", "DOT", + "INTER_KEY", "MATCHING_QUOTE_CLOSE", "QUOTED_ESC", "DOLLAR", + "INTER_BRACKET_OPEN", "INTER_BRACKET_CLOSE" ] + + ruleNames = [ "CHAR", "DIGIT", "INT_UNSIGNED", "ESC_BACKSLASH", "TOP_INTER_OPEN", + "ANY_STR", "ESC_INTER", "TOP_ESC", "BACKSLASHES", "DOLLAR", + "INTER_OPEN", "BRACE_OPEN", "BRACE_CLOSE", "QUOTE_OPEN_SINGLE", + "QUOTE_OPEN_DOUBLE", "COMMA", "BRACKET_OPEN", "BRACKET_CLOSE", + "COLON", "POINT_FLOAT", "EXPONENT_FLOAT", "FLOAT", "INT", + "BOOL", "NULL", "UNQUOTED_CHAR", "ID", "ESC", "WS", "NESTED_INTER_OPEN", + "INTER_COLON", "INTER_CLOSE", "DOT", "INTER_BRACKET_OPEN", + "INTER_BRACKET_CLOSE", "INTER_ID", "INTER_KEY", "QSINGLE_INTER_OPEN", + "MATCHING_QUOTE_CLOSE", "QSINGLE_STR", "QSINGLE_ESC_INTER", + "QSINGLE_ESC_QUOTE", "QUOTED_ESC", "QSINGLE_BACKSLASHES", + "QSINGLE_DOLLAR", "QDOUBLE_INTER_OPEN", "QDOUBLE_CLOSE", + "QDOUBLE_STR", "QDOUBLE_ESC_INTER", "QDOUBLE_ESC_QUOTE", + "QDOUBLE_ESC", "QDOUBLE_BACKSLASHES", "QDOUBLE_DOLLAR" ] + + grammarFileName = "OmegaConfGrammarLexer.g4" + + def __init__(self, input=None, output:TextIO = sys.stdout): + super().__init__(input, output) + self.checkVersion("4.9.3") + self._interp = LexerATNSimulator(self, self.atn, self.decisionsToDFA, PredictionContextCache()) + self._actions = None + self._predicates = None + + diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/grammar/gen/OmegaConfGrammarParser.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/grammar/gen/OmegaConfGrammarParser.py new file mode 100644 index 0000000000000000000000000000000000000000..024cbab87fddd6ed3b4abb63105589f673b0e864 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/grammar/gen/OmegaConfGrammarParser.py @@ -0,0 +1,1595 @@ +# Generated from /tmp/build-via-sdist-fm63w174/omegaconf-2.3.0/omegaconf/grammar/OmegaConfGrammarParser.g4 by ANTLR 4.9.3 +# encoding: utf-8 +from antlr4 import * +from io import StringIO +import sys +if sys.version_info[1] > 5: + from typing import TextIO +else: + from typing.io import TextIO + + +def serializedATN(): + with StringIO() as buf: + buf.write("\3\u608b\ua72a\u8133\ub9ed\u417c\u3be7\u7786\u5964\3\36") + buf.write("\u00b7\4\2\t\2\4\3\t\3\4\4\t\4\4\5\t\5\4\6\t\6\4\7\t\7") + buf.write("\4\b\t\b\4\t\t\t\4\n\t\n\4\13\t\13\4\f\t\f\4\r\t\r\4\16") + buf.write("\t\16\4\17\t\17\4\20\t\20\4\21\t\21\3\2\3\2\3\2\3\3\3") + buf.write("\3\3\3\3\4\3\4\3\4\3\4\3\4\3\4\6\4/\n\4\r\4\16\4\60\3") + buf.write("\5\3\5\3\5\3\5\5\5\67\n\5\3\6\3\6\5\6;\n\6\3\6\3\6\3\7") + buf.write("\3\7\3\7\3\7\7\7C\n\7\f\7\16\7F\13\7\5\7H\n\7\3\7\3\7") + buf.write("\3\b\3\b\3\b\3\b\3\t\3\t\3\t\5\tS\n\t\7\tU\n\t\f\t\16") + buf.write("\tX\13\t\3\t\3\t\5\t\\\n\t\6\t^\n\t\r\t\16\t_\5\tb\n\t") + buf.write("\3\n\3\n\5\nf\n\n\3\13\3\13\7\13j\n\13\f\13\16\13m\13") + buf.write("\13\3\13\3\13\3\13\3\13\3\13\5\13t\n\13\3\13\3\13\3\13") + 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buf.write("\u008e\31\3\2\2\2\u008f\u0092\5\22\n\2\u0090\u0092\7\24") + buf.write("\2\2\u0091\u008f\3\2\2\2\u0091\u0090\3\2\2\2\u0092\u009a") + buf.write("\3\2\2\2\u0093\u0096\7\30\2\2\u0094\u0097\5\22\n\2\u0095") + buf.write("\u0097\7\24\2\2\u0096\u0094\3\2\2\2\u0096\u0095\3\2\2") + buf.write("\2\u0097\u0099\3\2\2\2\u0098\u0093\3\2\2\2\u0099\u009c") + buf.write("\3\2\2\2\u009a\u0098\3\2\2\2\u009a\u009b\3\2\2\2\u009b") + buf.write("\33\3\2\2\2\u009c\u009a\3\2\2\2\u009d\u009f\t\2\2\2\u009e") + buf.write("\u00a0\5\6\4\2\u009f\u009e\3\2\2\2\u009f\u00a0\3\2\2\2") + buf.write("\u00a0\u00a1\3\2\2\2\u00a1\u00a2\7\32\2\2\u00a2\35\3\2") + buf.write("\2\2\u00a3\u00ae\7\24\2\2\u00a4\u00ae\7\22\2\2\u00a5\u00ae") + buf.write("\7\20\2\2\u00a6\u00ae\7\17\2\2\u00a7\u00ae\7\21\2\2\u00a8") + buf.write("\u00ae\7\23\2\2\u00a9\u00ae\7\16\2\2\u00aa\u00ae\7\25") + buf.write("\2\2\u00ab\u00ae\7\26\2\2\u00ac\u00ae\5\22\n\2\u00ad\u00a3") + buf.write("\3\2\2\2\u00ad\u00a4\3\2\2\2\u00ad\u00a5\3\2\2\2\u00ad") + buf.write("\u00a6\3\2\2\2\u00ad\u00a7\3\2\2\2\u00ad\u00a8\3\2\2\2") + buf.write("\u00ad\u00a9\3\2\2\2\u00ad\u00aa\3\2\2\2\u00ad\u00ab\3") + buf.write("\2\2\2\u00ad\u00ac\3\2\2\2\u00ae\u00af\3\2\2\2\u00af\u00ad") + buf.write("\3\2\2\2\u00af\u00b0\3\2\2\2\u00b0\37\3\2\2\2\u00b1\u00b3") + buf.write("\t\3\2\2\u00b2\u00b1\3\2\2\2\u00b3\u00b4\3\2\2\2\u00b4") + buf.write("\u00b2\3\2\2\2\u00b4\u00b5\3\2\2\2\u00b5!\3\2\2\2\33.") + buf.write("\60\66:DGRV[_aeks{}\u0086\u008d\u0091\u0096\u009a\u009f") + buf.write("\u00ad\u00af\u00b4") + return buf.getvalue() + + +class OmegaConfGrammarParser ( Parser ): + + grammarFileName = "OmegaConfGrammarParser.g4" + + atn = ATNDeserializer().deserialize(serializedATN()) + + decisionsToDFA = [ DFA(ds, i) for i, ds in enumerate(atn.decisionToState) ] + + sharedContextCache = PredictionContextCache() + + literalNames = [ "", "", "", "", + "", "", "", "", + "", "", "", "", + "", "", "", "", + "", "", "", "", + "", "", "'.'", "", "", + "", "", "'['", "']'" ] + + symbolicNames = [ "", "ANY_STR", "ESC_INTER", "TOP_ESC", "INTER_OPEN", + "BRACE_OPEN", "BRACE_CLOSE", "QUOTE_OPEN_SINGLE", + "QUOTE_OPEN_DOUBLE", "COMMA", "BRACKET_OPEN", "BRACKET_CLOSE", + "COLON", "FLOAT", "INT", "BOOL", "NULL", "UNQUOTED_CHAR", + "ID", "ESC", "WS", "INTER_CLOSE", "DOT", "INTER_KEY", + "MATCHING_QUOTE_CLOSE", "QUOTED_ESC", "DOLLAR", "INTER_BRACKET_OPEN", + "INTER_BRACKET_CLOSE" ] + + RULE_configValue = 0 + RULE_singleElement = 1 + RULE_text = 2 + RULE_element = 3 + RULE_listContainer = 4 + RULE_dictContainer = 5 + RULE_dictKeyValuePair = 6 + RULE_sequence = 7 + RULE_interpolation = 8 + RULE_interpolationNode = 9 + RULE_interpolationResolver = 10 + RULE_configKey = 11 + RULE_resolverName = 12 + RULE_quotedValue = 13 + RULE_primitive = 14 + RULE_dictKey = 15 + + ruleNames = [ "configValue", "singleElement", "text", "element", "listContainer", + "dictContainer", "dictKeyValuePair", "sequence", "interpolation", + "interpolationNode", "interpolationResolver", "configKey", + "resolverName", "quotedValue", "primitive", "dictKey" ] + + EOF = Token.EOF + ANY_STR=1 + ESC_INTER=2 + TOP_ESC=3 + INTER_OPEN=4 + BRACE_OPEN=5 + BRACE_CLOSE=6 + QUOTE_OPEN_SINGLE=7 + QUOTE_OPEN_DOUBLE=8 + COMMA=9 + BRACKET_OPEN=10 + BRACKET_CLOSE=11 + COLON=12 + FLOAT=13 + INT=14 + BOOL=15 + NULL=16 + UNQUOTED_CHAR=17 + ID=18 + ESC=19 + WS=20 + INTER_CLOSE=21 + DOT=22 + INTER_KEY=23 + MATCHING_QUOTE_CLOSE=24 + QUOTED_ESC=25 + DOLLAR=26 + INTER_BRACKET_OPEN=27 + INTER_BRACKET_CLOSE=28 + + def __init__(self, input:TokenStream, output:TextIO = sys.stdout): + super().__init__(input, output) + self.checkVersion("4.9.3") + self._interp = ParserATNSimulator(self, self.atn, self.decisionsToDFA, self.sharedContextCache) + self._predicates = None + + + + + class ConfigValueContext(ParserRuleContext): + __slots__ = 'parser' + + def __init__(self, parser, parent:ParserRuleContext=None, invokingState:int=-1): + super().__init__(parent, invokingState) + self.parser = parser + + def text(self): + return self.getTypedRuleContext(OmegaConfGrammarParser.TextContext,0) + + + def EOF(self): + return self.getToken(OmegaConfGrammarParser.EOF, 0) + + def getRuleIndex(self): + return OmegaConfGrammarParser.RULE_configValue + + def enterRule(self, listener:ParseTreeListener): + if hasattr( listener, "enterConfigValue" ): + listener.enterConfigValue(self) + + def exitRule(self, listener:ParseTreeListener): + if hasattr( listener, "exitConfigValue" ): + listener.exitConfigValue(self) + + def accept(self, visitor:ParseTreeVisitor): + if hasattr( visitor, "visitConfigValue" ): + return visitor.visitConfigValue(self) + else: + return visitor.visitChildren(self) + + + + + def configValue(self): + + localctx = OmegaConfGrammarParser.ConfigValueContext(self, self._ctx, self.state) + self.enterRule(localctx, 0, self.RULE_configValue) + try: + self.enterOuterAlt(localctx, 1) + self.state = 32 + self.text() + self.state = 33 + self.match(OmegaConfGrammarParser.EOF) + except RecognitionException as re: + localctx.exception = re + self._errHandler.reportError(self, re) + self._errHandler.recover(self, re) + finally: + self.exitRule() + return localctx + + + class SingleElementContext(ParserRuleContext): + __slots__ = 'parser' + + def __init__(self, parser, parent:ParserRuleContext=None, invokingState:int=-1): + super().__init__(parent, invokingState) + self.parser = parser + + def element(self): + return self.getTypedRuleContext(OmegaConfGrammarParser.ElementContext,0) + + + def EOF(self): + return self.getToken(OmegaConfGrammarParser.EOF, 0) + + def getRuleIndex(self): + return OmegaConfGrammarParser.RULE_singleElement + + def enterRule(self, listener:ParseTreeListener): + if hasattr( listener, "enterSingleElement" ): + listener.enterSingleElement(self) + + def exitRule(self, listener:ParseTreeListener): + if hasattr( listener, "exitSingleElement" ): + listener.exitSingleElement(self) + + def accept(self, visitor:ParseTreeVisitor): + if hasattr( visitor, "visitSingleElement" ): + return visitor.visitSingleElement(self) + else: + return visitor.visitChildren(self) + + + + + def singleElement(self): + + localctx = OmegaConfGrammarParser.SingleElementContext(self, self._ctx, self.state) + self.enterRule(localctx, 2, self.RULE_singleElement) + try: + self.enterOuterAlt(localctx, 1) + self.state = 35 + self.element() + self.state = 36 + self.match(OmegaConfGrammarParser.EOF) + except RecognitionException as re: + localctx.exception = re + self._errHandler.reportError(self, re) + self._errHandler.recover(self, re) + finally: + self.exitRule() + return localctx + + + class TextContext(ParserRuleContext): + __slots__ = 'parser' + + def __init__(self, parser, parent:ParserRuleContext=None, invokingState:int=-1): + super().__init__(parent, invokingState) + self.parser = parser + + def interpolation(self, i:int=None): + if i is None: + return self.getTypedRuleContexts(OmegaConfGrammarParser.InterpolationContext) + else: + return self.getTypedRuleContext(OmegaConfGrammarParser.InterpolationContext,i) + + + def ANY_STR(self, i:int=None): + if i is None: + return self.getTokens(OmegaConfGrammarParser.ANY_STR) + else: + return self.getToken(OmegaConfGrammarParser.ANY_STR, i) + + def ESC(self, i:int=None): + if i is None: + return self.getTokens(OmegaConfGrammarParser.ESC) + else: + return self.getToken(OmegaConfGrammarParser.ESC, i) + + def ESC_INTER(self, i:int=None): + if i is None: + return self.getTokens(OmegaConfGrammarParser.ESC_INTER) + else: + return self.getToken(OmegaConfGrammarParser.ESC_INTER, i) + + def TOP_ESC(self, i:int=None): + if i is None: + return self.getTokens(OmegaConfGrammarParser.TOP_ESC) + else: + return self.getToken(OmegaConfGrammarParser.TOP_ESC, i) + + def QUOTED_ESC(self, i:int=None): + if i is None: + return self.getTokens(OmegaConfGrammarParser.QUOTED_ESC) + else: + return self.getToken(OmegaConfGrammarParser.QUOTED_ESC, i) + + def getRuleIndex(self): + return OmegaConfGrammarParser.RULE_text + + def enterRule(self, listener:ParseTreeListener): + if hasattr( listener, "enterText" ): + listener.enterText(self) + + def exitRule(self, listener:ParseTreeListener): + if hasattr( listener, "exitText" ): + listener.exitText(self) + + def accept(self, visitor:ParseTreeVisitor): + if hasattr( visitor, "visitText" ): + return visitor.visitText(self) + else: + return visitor.visitChildren(self) + + + + + def text(self): + + localctx = OmegaConfGrammarParser.TextContext(self, self._ctx, self.state) + self.enterRule(localctx, 4, self.RULE_text) + self._la = 0 # Token type + try: + self.enterOuterAlt(localctx, 1) + self.state = 44 + self._errHandler.sync(self) + _la = self._input.LA(1) + while True: + self.state = 44 + self._errHandler.sync(self) + token = self._input.LA(1) + if token in [OmegaConfGrammarParser.INTER_OPEN]: + self.state = 38 + self.interpolation() + pass + elif token in [OmegaConfGrammarParser.ANY_STR]: + self.state = 39 + self.match(OmegaConfGrammarParser.ANY_STR) + pass + elif token in [OmegaConfGrammarParser.ESC]: + self.state = 40 + self.match(OmegaConfGrammarParser.ESC) + pass + elif token in [OmegaConfGrammarParser.ESC_INTER]: + self.state = 41 + self.match(OmegaConfGrammarParser.ESC_INTER) + pass + elif token in [OmegaConfGrammarParser.TOP_ESC]: + self.state = 42 + self.match(OmegaConfGrammarParser.TOP_ESC) + pass + elif token in [OmegaConfGrammarParser.QUOTED_ESC]: + self.state = 43 + self.match(OmegaConfGrammarParser.QUOTED_ESC) + pass + else: + raise NoViableAltException(self) + + self.state = 46 + self._errHandler.sync(self) + _la = self._input.LA(1) + if not ((((_la) & ~0x3f) == 0 and ((1 << _la) & ((1 << OmegaConfGrammarParser.ANY_STR) | (1 << OmegaConfGrammarParser.ESC_INTER) | (1 << OmegaConfGrammarParser.TOP_ESC) | (1 << OmegaConfGrammarParser.INTER_OPEN) | (1 << OmegaConfGrammarParser.ESC) | (1 << OmegaConfGrammarParser.QUOTED_ESC))) != 0)): + break + + except RecognitionException as re: + localctx.exception = re + self._errHandler.reportError(self, re) + self._errHandler.recover(self, re) + finally: + self.exitRule() + return localctx + + + class ElementContext(ParserRuleContext): + __slots__ = 'parser' + + def __init__(self, parser, parent:ParserRuleContext=None, invokingState:int=-1): + super().__init__(parent, invokingState) + self.parser = parser + + def primitive(self): + return self.getTypedRuleContext(OmegaConfGrammarParser.PrimitiveContext,0) + + + def quotedValue(self): + return self.getTypedRuleContext(OmegaConfGrammarParser.QuotedValueContext,0) + + + def listContainer(self): + return self.getTypedRuleContext(OmegaConfGrammarParser.ListContainerContext,0) + + + def dictContainer(self): + return self.getTypedRuleContext(OmegaConfGrammarParser.DictContainerContext,0) + + + def getRuleIndex(self): + return OmegaConfGrammarParser.RULE_element + + def enterRule(self, listener:ParseTreeListener): + if hasattr( listener, "enterElement" ): + listener.enterElement(self) + + def exitRule(self, listener:ParseTreeListener): + if hasattr( listener, "exitElement" ): + listener.exitElement(self) + + def accept(self, visitor:ParseTreeVisitor): + if hasattr( visitor, "visitElement" ): + return visitor.visitElement(self) + else: + return visitor.visitChildren(self) + + + + + def element(self): + + localctx = OmegaConfGrammarParser.ElementContext(self, self._ctx, self.state) + self.enterRule(localctx, 6, self.RULE_element) + try: + self.state = 52 + self._errHandler.sync(self) + token = self._input.LA(1) + if token in [OmegaConfGrammarParser.INTER_OPEN, OmegaConfGrammarParser.COLON, OmegaConfGrammarParser.FLOAT, OmegaConfGrammarParser.INT, OmegaConfGrammarParser.BOOL, OmegaConfGrammarParser.NULL, OmegaConfGrammarParser.UNQUOTED_CHAR, OmegaConfGrammarParser.ID, OmegaConfGrammarParser.ESC, OmegaConfGrammarParser.WS]: + self.enterOuterAlt(localctx, 1) + self.state = 48 + self.primitive() + pass + elif token in [OmegaConfGrammarParser.QUOTE_OPEN_SINGLE, OmegaConfGrammarParser.QUOTE_OPEN_DOUBLE]: + self.enterOuterAlt(localctx, 2) + self.state = 49 + self.quotedValue() + pass + elif token in [OmegaConfGrammarParser.BRACKET_OPEN]: + self.enterOuterAlt(localctx, 3) + self.state = 50 + self.listContainer() + pass + elif token in [OmegaConfGrammarParser.BRACE_OPEN]: + self.enterOuterAlt(localctx, 4) + self.state = 51 + self.dictContainer() + pass + else: + raise NoViableAltException(self) + + except RecognitionException as re: + localctx.exception = re + self._errHandler.reportError(self, re) + self._errHandler.recover(self, re) + finally: + self.exitRule() + return localctx + + + class ListContainerContext(ParserRuleContext): + __slots__ = 'parser' + + def __init__(self, parser, parent:ParserRuleContext=None, invokingState:int=-1): + super().__init__(parent, invokingState) + self.parser = parser + + def BRACKET_OPEN(self): + return self.getToken(OmegaConfGrammarParser.BRACKET_OPEN, 0) + + def BRACKET_CLOSE(self): + return self.getToken(OmegaConfGrammarParser.BRACKET_CLOSE, 0) + + def sequence(self): + return self.getTypedRuleContext(OmegaConfGrammarParser.SequenceContext,0) + + + def getRuleIndex(self): + return OmegaConfGrammarParser.RULE_listContainer + + def enterRule(self, listener:ParseTreeListener): + if hasattr( listener, "enterListContainer" ): + listener.enterListContainer(self) + + def exitRule(self, listener:ParseTreeListener): + if hasattr( listener, "exitListContainer" ): + listener.exitListContainer(self) + + def accept(self, visitor:ParseTreeVisitor): + if hasattr( visitor, "visitListContainer" ): + return visitor.visitListContainer(self) + else: + return visitor.visitChildren(self) + + + + + def listContainer(self): + + localctx = OmegaConfGrammarParser.ListContainerContext(self, self._ctx, self.state) + self.enterRule(localctx, 8, self.RULE_listContainer) + self._la = 0 # Token type + try: + self.enterOuterAlt(localctx, 1) + self.state = 54 + self.match(OmegaConfGrammarParser.BRACKET_OPEN) + self.state = 56 + self._errHandler.sync(self) + _la = self._input.LA(1) + if (((_la) & ~0x3f) == 0 and ((1 << _la) & ((1 << OmegaConfGrammarParser.INTER_OPEN) | (1 << OmegaConfGrammarParser.BRACE_OPEN) | (1 << OmegaConfGrammarParser.QUOTE_OPEN_SINGLE) | (1 << OmegaConfGrammarParser.QUOTE_OPEN_DOUBLE) | (1 << OmegaConfGrammarParser.COMMA) | (1 << OmegaConfGrammarParser.BRACKET_OPEN) | (1 << OmegaConfGrammarParser.COLON) | (1 << OmegaConfGrammarParser.FLOAT) | (1 << OmegaConfGrammarParser.INT) | (1 << OmegaConfGrammarParser.BOOL) | (1 << OmegaConfGrammarParser.NULL) | (1 << OmegaConfGrammarParser.UNQUOTED_CHAR) | (1 << OmegaConfGrammarParser.ID) | (1 << OmegaConfGrammarParser.ESC) | (1 << OmegaConfGrammarParser.WS))) != 0): + self.state = 55 + self.sequence() + + + self.state = 58 + self.match(OmegaConfGrammarParser.BRACKET_CLOSE) + except RecognitionException as re: + localctx.exception = re + self._errHandler.reportError(self, re) + self._errHandler.recover(self, re) + finally: + self.exitRule() + return localctx + + + class DictContainerContext(ParserRuleContext): + __slots__ = 'parser' + + def __init__(self, parser, parent:ParserRuleContext=None, invokingState:int=-1): + super().__init__(parent, invokingState) + self.parser = parser + + def BRACE_OPEN(self): + return self.getToken(OmegaConfGrammarParser.BRACE_OPEN, 0) + + def BRACE_CLOSE(self): + return self.getToken(OmegaConfGrammarParser.BRACE_CLOSE, 0) + + def dictKeyValuePair(self, i:int=None): + if i is None: + return self.getTypedRuleContexts(OmegaConfGrammarParser.DictKeyValuePairContext) + else: + return self.getTypedRuleContext(OmegaConfGrammarParser.DictKeyValuePairContext,i) + + + def COMMA(self, i:int=None): + if i is None: + return self.getTokens(OmegaConfGrammarParser.COMMA) + else: + return self.getToken(OmegaConfGrammarParser.COMMA, i) + + def getRuleIndex(self): + return OmegaConfGrammarParser.RULE_dictContainer + + def enterRule(self, listener:ParseTreeListener): + if hasattr( listener, "enterDictContainer" ): + listener.enterDictContainer(self) + + def exitRule(self, listener:ParseTreeListener): + if hasattr( listener, "exitDictContainer" ): + listener.exitDictContainer(self) + + def accept(self, visitor:ParseTreeVisitor): + if hasattr( visitor, "visitDictContainer" ): + return visitor.visitDictContainer(self) + else: + return visitor.visitChildren(self) + + + + + def dictContainer(self): + + localctx = OmegaConfGrammarParser.DictContainerContext(self, self._ctx, self.state) + self.enterRule(localctx, 10, self.RULE_dictContainer) + self._la = 0 # Token type + try: + self.enterOuterAlt(localctx, 1) + self.state = 60 + self.match(OmegaConfGrammarParser.BRACE_OPEN) + self.state = 69 + self._errHandler.sync(self) + _la = self._input.LA(1) + if (((_la) & ~0x3f) == 0 and ((1 << _la) & ((1 << OmegaConfGrammarParser.FLOAT) | (1 << OmegaConfGrammarParser.INT) | (1 << OmegaConfGrammarParser.BOOL) | (1 << OmegaConfGrammarParser.NULL) | (1 << OmegaConfGrammarParser.UNQUOTED_CHAR) | (1 << OmegaConfGrammarParser.ID) | (1 << OmegaConfGrammarParser.ESC) | (1 << OmegaConfGrammarParser.WS))) != 0): + self.state = 61 + self.dictKeyValuePair() + self.state = 66 + self._errHandler.sync(self) + _la = self._input.LA(1) + while _la==OmegaConfGrammarParser.COMMA: + self.state = 62 + self.match(OmegaConfGrammarParser.COMMA) + self.state = 63 + self.dictKeyValuePair() + self.state = 68 + self._errHandler.sync(self) + _la = self._input.LA(1) + + + + self.state = 71 + self.match(OmegaConfGrammarParser.BRACE_CLOSE) + except RecognitionException as re: + localctx.exception = re + self._errHandler.reportError(self, re) + self._errHandler.recover(self, re) + finally: + self.exitRule() + return localctx + + + class DictKeyValuePairContext(ParserRuleContext): + __slots__ = 'parser' + + def __init__(self, parser, parent:ParserRuleContext=None, invokingState:int=-1): + super().__init__(parent, invokingState) + self.parser = parser + + def dictKey(self): + return self.getTypedRuleContext(OmegaConfGrammarParser.DictKeyContext,0) + + + def COLON(self): + return self.getToken(OmegaConfGrammarParser.COLON, 0) + + def element(self): + return self.getTypedRuleContext(OmegaConfGrammarParser.ElementContext,0) + + + def getRuleIndex(self): + return OmegaConfGrammarParser.RULE_dictKeyValuePair + + def enterRule(self, listener:ParseTreeListener): + if hasattr( listener, "enterDictKeyValuePair" ): + listener.enterDictKeyValuePair(self) + + def exitRule(self, listener:ParseTreeListener): + if hasattr( listener, "exitDictKeyValuePair" ): + listener.exitDictKeyValuePair(self) + + def accept(self, visitor:ParseTreeVisitor): + if hasattr( visitor, "visitDictKeyValuePair" ): + return visitor.visitDictKeyValuePair(self) + else: + return visitor.visitChildren(self) + + + + + def dictKeyValuePair(self): + + localctx = OmegaConfGrammarParser.DictKeyValuePairContext(self, self._ctx, self.state) + self.enterRule(localctx, 12, self.RULE_dictKeyValuePair) + try: + self.enterOuterAlt(localctx, 1) + self.state = 73 + self.dictKey() + self.state = 74 + self.match(OmegaConfGrammarParser.COLON) + self.state = 75 + self.element() + except RecognitionException as re: + localctx.exception = re + self._errHandler.reportError(self, re) + self._errHandler.recover(self, re) + finally: + self.exitRule() + return localctx + + + class SequenceContext(ParserRuleContext): + __slots__ = 'parser' + + def __init__(self, parser, parent:ParserRuleContext=None, invokingState:int=-1): + super().__init__(parent, invokingState) + self.parser = parser + + def element(self, i:int=None): + if i is None: + return self.getTypedRuleContexts(OmegaConfGrammarParser.ElementContext) + else: + return self.getTypedRuleContext(OmegaConfGrammarParser.ElementContext,i) + + + def COMMA(self, i:int=None): + if i is None: + return self.getTokens(OmegaConfGrammarParser.COMMA) + else: + return self.getToken(OmegaConfGrammarParser.COMMA, i) + + def getRuleIndex(self): + return OmegaConfGrammarParser.RULE_sequence + + def enterRule(self, listener:ParseTreeListener): + if hasattr( listener, "enterSequence" ): + listener.enterSequence(self) + + def exitRule(self, listener:ParseTreeListener): + if hasattr( listener, "exitSequence" ): + listener.exitSequence(self) + + def accept(self, visitor:ParseTreeVisitor): + if hasattr( visitor, "visitSequence" ): + return visitor.visitSequence(self) + else: + return visitor.visitChildren(self) + + + + + def sequence(self): + + localctx = OmegaConfGrammarParser.SequenceContext(self, self._ctx, self.state) + self.enterRule(localctx, 14, self.RULE_sequence) + self._la = 0 # Token type + try: + self.state = 95 + self._errHandler.sync(self) + token = self._input.LA(1) + if token in [OmegaConfGrammarParser.INTER_OPEN, OmegaConfGrammarParser.BRACE_OPEN, OmegaConfGrammarParser.QUOTE_OPEN_SINGLE, OmegaConfGrammarParser.QUOTE_OPEN_DOUBLE, OmegaConfGrammarParser.BRACKET_OPEN, OmegaConfGrammarParser.COLON, OmegaConfGrammarParser.FLOAT, OmegaConfGrammarParser.INT, OmegaConfGrammarParser.BOOL, OmegaConfGrammarParser.NULL, OmegaConfGrammarParser.UNQUOTED_CHAR, OmegaConfGrammarParser.ID, OmegaConfGrammarParser.ESC, OmegaConfGrammarParser.WS]: + self.enterOuterAlt(localctx, 1) + self.state = 77 + self.element() + self.state = 84 + self._errHandler.sync(self) + _la = self._input.LA(1) + while _la==OmegaConfGrammarParser.COMMA: + self.state = 78 + self.match(OmegaConfGrammarParser.COMMA) + self.state = 80 + self._errHandler.sync(self) + _la = self._input.LA(1) + if (((_la) & ~0x3f) == 0 and ((1 << _la) & ((1 << OmegaConfGrammarParser.INTER_OPEN) | (1 << OmegaConfGrammarParser.BRACE_OPEN) | (1 << OmegaConfGrammarParser.QUOTE_OPEN_SINGLE) | (1 << OmegaConfGrammarParser.QUOTE_OPEN_DOUBLE) | (1 << OmegaConfGrammarParser.BRACKET_OPEN) | (1 << OmegaConfGrammarParser.COLON) | (1 << OmegaConfGrammarParser.FLOAT) | (1 << OmegaConfGrammarParser.INT) | (1 << OmegaConfGrammarParser.BOOL) | (1 << OmegaConfGrammarParser.NULL) | (1 << OmegaConfGrammarParser.UNQUOTED_CHAR) | (1 << OmegaConfGrammarParser.ID) | (1 << OmegaConfGrammarParser.ESC) | (1 << OmegaConfGrammarParser.WS))) != 0): + self.state = 79 + self.element() + + + self.state = 86 + self._errHandler.sync(self) + _la = self._input.LA(1) + + pass + elif token in [OmegaConfGrammarParser.COMMA]: + self.enterOuterAlt(localctx, 2) + self.state = 91 + self._errHandler.sync(self) + _la = self._input.LA(1) + while True: + self.state = 87 + self.match(OmegaConfGrammarParser.COMMA) + self.state = 89 + self._errHandler.sync(self) + _la = self._input.LA(1) + if (((_la) & ~0x3f) == 0 and ((1 << _la) & ((1 << OmegaConfGrammarParser.INTER_OPEN) | (1 << OmegaConfGrammarParser.BRACE_OPEN) | (1 << OmegaConfGrammarParser.QUOTE_OPEN_SINGLE) | (1 << OmegaConfGrammarParser.QUOTE_OPEN_DOUBLE) | (1 << OmegaConfGrammarParser.BRACKET_OPEN) | (1 << OmegaConfGrammarParser.COLON) | (1 << OmegaConfGrammarParser.FLOAT) | (1 << OmegaConfGrammarParser.INT) | (1 << OmegaConfGrammarParser.BOOL) | (1 << OmegaConfGrammarParser.NULL) | (1 << OmegaConfGrammarParser.UNQUOTED_CHAR) | (1 << OmegaConfGrammarParser.ID) | (1 << OmegaConfGrammarParser.ESC) | (1 << OmegaConfGrammarParser.WS))) != 0): + self.state = 88 + self.element() + + + self.state = 93 + self._errHandler.sync(self) + _la = self._input.LA(1) + if not (_la==OmegaConfGrammarParser.COMMA): + break + + pass + else: + raise NoViableAltException(self) + + except RecognitionException as re: + localctx.exception = re + self._errHandler.reportError(self, re) + self._errHandler.recover(self, re) + finally: + self.exitRule() + return localctx + + + class InterpolationContext(ParserRuleContext): + __slots__ = 'parser' + + def __init__(self, parser, parent:ParserRuleContext=None, invokingState:int=-1): + super().__init__(parent, invokingState) + self.parser = parser + + def interpolationNode(self): + return self.getTypedRuleContext(OmegaConfGrammarParser.InterpolationNodeContext,0) + + + def interpolationResolver(self): + return self.getTypedRuleContext(OmegaConfGrammarParser.InterpolationResolverContext,0) + + + def getRuleIndex(self): + return OmegaConfGrammarParser.RULE_interpolation + + def enterRule(self, listener:ParseTreeListener): + if hasattr( listener, "enterInterpolation" ): + listener.enterInterpolation(self) + + def exitRule(self, listener:ParseTreeListener): + if hasattr( listener, "exitInterpolation" ): + listener.exitInterpolation(self) + + def accept(self, visitor:ParseTreeVisitor): + if hasattr( visitor, "visitInterpolation" ): + return visitor.visitInterpolation(self) + else: + return visitor.visitChildren(self) + + + + + def interpolation(self): + + localctx = OmegaConfGrammarParser.InterpolationContext(self, self._ctx, self.state) + self.enterRule(localctx, 16, self.RULE_interpolation) + try: + self.state = 99 + self._errHandler.sync(self) + la_ = self._interp.adaptivePredict(self._input,11,self._ctx) + if la_ == 1: + self.enterOuterAlt(localctx, 1) + self.state = 97 + self.interpolationNode() + pass + + elif la_ == 2: + self.enterOuterAlt(localctx, 2) + self.state = 98 + self.interpolationResolver() + pass + + + except RecognitionException as re: + localctx.exception = re + self._errHandler.reportError(self, re) + self._errHandler.recover(self, re) + finally: + self.exitRule() + return localctx + + + class InterpolationNodeContext(ParserRuleContext): + __slots__ = 'parser' + + def __init__(self, parser, parent:ParserRuleContext=None, invokingState:int=-1): + super().__init__(parent, invokingState) + self.parser = parser + + def INTER_OPEN(self): + return self.getToken(OmegaConfGrammarParser.INTER_OPEN, 0) + + def INTER_CLOSE(self): + return self.getToken(OmegaConfGrammarParser.INTER_CLOSE, 0) + + def configKey(self, i:int=None): + if i is None: + return self.getTypedRuleContexts(OmegaConfGrammarParser.ConfigKeyContext) + else: + return self.getTypedRuleContext(OmegaConfGrammarParser.ConfigKeyContext,i) + + + def BRACKET_OPEN(self, i:int=None): + if i is None: + return self.getTokens(OmegaConfGrammarParser.BRACKET_OPEN) + else: + return self.getToken(OmegaConfGrammarParser.BRACKET_OPEN, i) + + def BRACKET_CLOSE(self, i:int=None): + if i is None: + return self.getTokens(OmegaConfGrammarParser.BRACKET_CLOSE) + else: + return self.getToken(OmegaConfGrammarParser.BRACKET_CLOSE, i) + + def DOT(self, i:int=None): + if i is None: + return self.getTokens(OmegaConfGrammarParser.DOT) + else: + return self.getToken(OmegaConfGrammarParser.DOT, i) + + def getRuleIndex(self): + return OmegaConfGrammarParser.RULE_interpolationNode + + def enterRule(self, listener:ParseTreeListener): + if hasattr( listener, "enterInterpolationNode" ): + listener.enterInterpolationNode(self) + + def exitRule(self, listener:ParseTreeListener): + if hasattr( listener, "exitInterpolationNode" ): + listener.exitInterpolationNode(self) + + def accept(self, visitor:ParseTreeVisitor): + if hasattr( visitor, "visitInterpolationNode" ): + return visitor.visitInterpolationNode(self) + else: + return visitor.visitChildren(self) + + + + + def interpolationNode(self): + + localctx = OmegaConfGrammarParser.InterpolationNodeContext(self, self._ctx, self.state) + self.enterRule(localctx, 18, self.RULE_interpolationNode) + self._la = 0 # Token type + try: + self.enterOuterAlt(localctx, 1) + self.state = 101 + self.match(OmegaConfGrammarParser.INTER_OPEN) + self.state = 105 + self._errHandler.sync(self) + _la = self._input.LA(1) + while _la==OmegaConfGrammarParser.DOT: + self.state = 102 + self.match(OmegaConfGrammarParser.DOT) + self.state = 107 + self._errHandler.sync(self) + _la = self._input.LA(1) + + self.state = 113 + self._errHandler.sync(self) + token = self._input.LA(1) + if token in [OmegaConfGrammarParser.INTER_OPEN, OmegaConfGrammarParser.ID, OmegaConfGrammarParser.INTER_KEY]: + self.state = 108 + self.configKey() + pass + elif token in [OmegaConfGrammarParser.BRACKET_OPEN]: + self.state = 109 + self.match(OmegaConfGrammarParser.BRACKET_OPEN) + self.state = 110 + self.configKey() + self.state = 111 + self.match(OmegaConfGrammarParser.BRACKET_CLOSE) + pass + else: + raise NoViableAltException(self) + + self.state = 123 + self._errHandler.sync(self) + _la = self._input.LA(1) + while _la==OmegaConfGrammarParser.BRACKET_OPEN or _la==OmegaConfGrammarParser.DOT: + self.state = 121 + self._errHandler.sync(self) + token = self._input.LA(1) + if token in [OmegaConfGrammarParser.DOT]: + self.state = 115 + self.match(OmegaConfGrammarParser.DOT) + self.state = 116 + self.configKey() + pass + elif token in [OmegaConfGrammarParser.BRACKET_OPEN]: + self.state = 117 + self.match(OmegaConfGrammarParser.BRACKET_OPEN) + self.state = 118 + self.configKey() + self.state = 119 + self.match(OmegaConfGrammarParser.BRACKET_CLOSE) + pass + else: + raise NoViableAltException(self) + + self.state = 125 + self._errHandler.sync(self) + _la = self._input.LA(1) + + self.state = 126 + self.match(OmegaConfGrammarParser.INTER_CLOSE) + except RecognitionException as re: + localctx.exception = re + self._errHandler.reportError(self, re) + self._errHandler.recover(self, re) + finally: + self.exitRule() + return localctx + + + class InterpolationResolverContext(ParserRuleContext): + __slots__ = 'parser' + + def __init__(self, parser, parent:ParserRuleContext=None, invokingState:int=-1): + super().__init__(parent, invokingState) + self.parser = parser + + def INTER_OPEN(self): + return self.getToken(OmegaConfGrammarParser.INTER_OPEN, 0) + + def resolverName(self): + return self.getTypedRuleContext(OmegaConfGrammarParser.ResolverNameContext,0) + + + def COLON(self): + return self.getToken(OmegaConfGrammarParser.COLON, 0) + + def BRACE_CLOSE(self): + return self.getToken(OmegaConfGrammarParser.BRACE_CLOSE, 0) + + def sequence(self): + return self.getTypedRuleContext(OmegaConfGrammarParser.SequenceContext,0) + + + def getRuleIndex(self): + return OmegaConfGrammarParser.RULE_interpolationResolver + + def enterRule(self, listener:ParseTreeListener): + if hasattr( listener, "enterInterpolationResolver" ): + listener.enterInterpolationResolver(self) + + def exitRule(self, listener:ParseTreeListener): + if hasattr( listener, "exitInterpolationResolver" ): + listener.exitInterpolationResolver(self) + + def accept(self, visitor:ParseTreeVisitor): + if hasattr( visitor, "visitInterpolationResolver" ): + return visitor.visitInterpolationResolver(self) + else: + return visitor.visitChildren(self) + + + + + def interpolationResolver(self): + + localctx = OmegaConfGrammarParser.InterpolationResolverContext(self, self._ctx, self.state) + self.enterRule(localctx, 20, self.RULE_interpolationResolver) + self._la = 0 # Token type + try: + self.enterOuterAlt(localctx, 1) + self.state = 128 + self.match(OmegaConfGrammarParser.INTER_OPEN) + self.state = 129 + self.resolverName() + self.state = 130 + self.match(OmegaConfGrammarParser.COLON) + self.state = 132 + self._errHandler.sync(self) + _la = self._input.LA(1) + if (((_la) & ~0x3f) == 0 and ((1 << _la) & ((1 << OmegaConfGrammarParser.INTER_OPEN) | (1 << OmegaConfGrammarParser.BRACE_OPEN) | (1 << OmegaConfGrammarParser.QUOTE_OPEN_SINGLE) | (1 << OmegaConfGrammarParser.QUOTE_OPEN_DOUBLE) | (1 << OmegaConfGrammarParser.COMMA) | (1 << OmegaConfGrammarParser.BRACKET_OPEN) | (1 << OmegaConfGrammarParser.COLON) | (1 << OmegaConfGrammarParser.FLOAT) | (1 << OmegaConfGrammarParser.INT) | (1 << OmegaConfGrammarParser.BOOL) | (1 << OmegaConfGrammarParser.NULL) | (1 << OmegaConfGrammarParser.UNQUOTED_CHAR) | (1 << OmegaConfGrammarParser.ID) | (1 << OmegaConfGrammarParser.ESC) | (1 << OmegaConfGrammarParser.WS))) != 0): + self.state = 131 + self.sequence() + + + self.state = 134 + self.match(OmegaConfGrammarParser.BRACE_CLOSE) + except RecognitionException as re: + localctx.exception = re + self._errHandler.reportError(self, re) + self._errHandler.recover(self, re) + finally: + self.exitRule() + return localctx + + + class ConfigKeyContext(ParserRuleContext): + __slots__ = 'parser' + + def __init__(self, parser, parent:ParserRuleContext=None, invokingState:int=-1): + super().__init__(parent, invokingState) + self.parser = parser + + def interpolation(self): + return self.getTypedRuleContext(OmegaConfGrammarParser.InterpolationContext,0) + + + def ID(self): + return self.getToken(OmegaConfGrammarParser.ID, 0) + + def INTER_KEY(self): + return self.getToken(OmegaConfGrammarParser.INTER_KEY, 0) + + def getRuleIndex(self): + return OmegaConfGrammarParser.RULE_configKey + + def enterRule(self, listener:ParseTreeListener): + if hasattr( listener, "enterConfigKey" ): + listener.enterConfigKey(self) + + def exitRule(self, listener:ParseTreeListener): + if hasattr( listener, "exitConfigKey" ): + listener.exitConfigKey(self) + + def accept(self, visitor:ParseTreeVisitor): + if hasattr( visitor, "visitConfigKey" ): + return visitor.visitConfigKey(self) + else: + return visitor.visitChildren(self) + + + + + def configKey(self): + + localctx = OmegaConfGrammarParser.ConfigKeyContext(self, self._ctx, self.state) + self.enterRule(localctx, 22, self.RULE_configKey) + try: + self.state = 139 + self._errHandler.sync(self) + token = self._input.LA(1) + if token in [OmegaConfGrammarParser.INTER_OPEN]: + self.enterOuterAlt(localctx, 1) + self.state = 136 + self.interpolation() + pass + elif token in [OmegaConfGrammarParser.ID]: + self.enterOuterAlt(localctx, 2) + self.state = 137 + self.match(OmegaConfGrammarParser.ID) + pass + elif token in [OmegaConfGrammarParser.INTER_KEY]: + self.enterOuterAlt(localctx, 3) + self.state = 138 + self.match(OmegaConfGrammarParser.INTER_KEY) + pass + else: + raise NoViableAltException(self) + + except RecognitionException as re: + localctx.exception = re + self._errHandler.reportError(self, re) + self._errHandler.recover(self, re) + finally: + self.exitRule() + return localctx + + + class ResolverNameContext(ParserRuleContext): + __slots__ = 'parser' + + def __init__(self, parser, parent:ParserRuleContext=None, invokingState:int=-1): + super().__init__(parent, invokingState) + self.parser = parser + + def interpolation(self, i:int=None): + if i is None: + return self.getTypedRuleContexts(OmegaConfGrammarParser.InterpolationContext) + else: + return self.getTypedRuleContext(OmegaConfGrammarParser.InterpolationContext,i) + + + def ID(self, i:int=None): + if i is None: + return self.getTokens(OmegaConfGrammarParser.ID) + else: + return self.getToken(OmegaConfGrammarParser.ID, i) + + def DOT(self, i:int=None): + if i is None: + return self.getTokens(OmegaConfGrammarParser.DOT) + else: + return self.getToken(OmegaConfGrammarParser.DOT, i) + + def getRuleIndex(self): + return OmegaConfGrammarParser.RULE_resolverName + + def enterRule(self, listener:ParseTreeListener): + if hasattr( listener, "enterResolverName" ): + listener.enterResolverName(self) + + def exitRule(self, listener:ParseTreeListener): + if hasattr( listener, "exitResolverName" ): + listener.exitResolverName(self) + + def accept(self, visitor:ParseTreeVisitor): + if hasattr( visitor, "visitResolverName" ): + return visitor.visitResolverName(self) + else: + return visitor.visitChildren(self) + + + + + def resolverName(self): + + localctx = OmegaConfGrammarParser.ResolverNameContext(self, self._ctx, self.state) + self.enterRule(localctx, 24, self.RULE_resolverName) + self._la = 0 # Token type + try: + self.enterOuterAlt(localctx, 1) + self.state = 143 + self._errHandler.sync(self) + token = self._input.LA(1) + if token in [OmegaConfGrammarParser.INTER_OPEN]: + self.state = 141 + self.interpolation() + pass + elif token in [OmegaConfGrammarParser.ID]: + self.state = 142 + self.match(OmegaConfGrammarParser.ID) + pass + else: + raise NoViableAltException(self) + + self.state = 152 + self._errHandler.sync(self) + _la = self._input.LA(1) + while _la==OmegaConfGrammarParser.DOT: + self.state = 145 + self.match(OmegaConfGrammarParser.DOT) + self.state = 148 + self._errHandler.sync(self) + token = self._input.LA(1) + if token in [OmegaConfGrammarParser.INTER_OPEN]: + self.state = 146 + self.interpolation() + pass + elif token in [OmegaConfGrammarParser.ID]: + self.state = 147 + self.match(OmegaConfGrammarParser.ID) + pass + else: + raise NoViableAltException(self) + + self.state = 154 + self._errHandler.sync(self) + _la = self._input.LA(1) + + except RecognitionException as re: + localctx.exception = re + self._errHandler.reportError(self, re) + self._errHandler.recover(self, re) + finally: + self.exitRule() + return localctx + + + class QuotedValueContext(ParserRuleContext): + __slots__ = 'parser' + + def __init__(self, parser, parent:ParserRuleContext=None, invokingState:int=-1): + super().__init__(parent, invokingState) + self.parser = parser + + def MATCHING_QUOTE_CLOSE(self): + return self.getToken(OmegaConfGrammarParser.MATCHING_QUOTE_CLOSE, 0) + + def QUOTE_OPEN_SINGLE(self): + return self.getToken(OmegaConfGrammarParser.QUOTE_OPEN_SINGLE, 0) + + def QUOTE_OPEN_DOUBLE(self): + return self.getToken(OmegaConfGrammarParser.QUOTE_OPEN_DOUBLE, 0) + + def text(self): + return self.getTypedRuleContext(OmegaConfGrammarParser.TextContext,0) + + + def getRuleIndex(self): + return OmegaConfGrammarParser.RULE_quotedValue + + def enterRule(self, listener:ParseTreeListener): + if hasattr( listener, "enterQuotedValue" ): + listener.enterQuotedValue(self) + + def exitRule(self, listener:ParseTreeListener): + if hasattr( listener, "exitQuotedValue" ): + listener.exitQuotedValue(self) + + def accept(self, visitor:ParseTreeVisitor): + if hasattr( visitor, "visitQuotedValue" ): + return visitor.visitQuotedValue(self) + else: + return visitor.visitChildren(self) + + + + + def quotedValue(self): + + localctx = OmegaConfGrammarParser.QuotedValueContext(self, self._ctx, self.state) + self.enterRule(localctx, 26, self.RULE_quotedValue) + self._la = 0 # Token type + try: + self.enterOuterAlt(localctx, 1) + self.state = 155 + _la = self._input.LA(1) + if not(_la==OmegaConfGrammarParser.QUOTE_OPEN_SINGLE or _la==OmegaConfGrammarParser.QUOTE_OPEN_DOUBLE): + self._errHandler.recoverInline(self) + else: + self._errHandler.reportMatch(self) + self.consume() + self.state = 157 + self._errHandler.sync(self) + _la = self._input.LA(1) + if (((_la) & ~0x3f) == 0 and ((1 << _la) & ((1 << OmegaConfGrammarParser.ANY_STR) | (1 << OmegaConfGrammarParser.ESC_INTER) | (1 << OmegaConfGrammarParser.TOP_ESC) | (1 << OmegaConfGrammarParser.INTER_OPEN) | (1 << OmegaConfGrammarParser.ESC) | (1 << OmegaConfGrammarParser.QUOTED_ESC))) != 0): + self.state = 156 + self.text() + + + self.state = 159 + self.match(OmegaConfGrammarParser.MATCHING_QUOTE_CLOSE) + except RecognitionException as re: + localctx.exception = re + self._errHandler.reportError(self, re) + self._errHandler.recover(self, re) + finally: + self.exitRule() + return localctx + + + class PrimitiveContext(ParserRuleContext): + __slots__ = 'parser' + + def __init__(self, parser, parent:ParserRuleContext=None, invokingState:int=-1): + super().__init__(parent, invokingState) + self.parser = parser + + def ID(self, i:int=None): + if i is None: + return self.getTokens(OmegaConfGrammarParser.ID) + else: + return self.getToken(OmegaConfGrammarParser.ID, i) + + def NULL(self, i:int=None): + if i is None: + return self.getTokens(OmegaConfGrammarParser.NULL) + else: + return self.getToken(OmegaConfGrammarParser.NULL, i) + + def INT(self, i:int=None): + if i is None: + return self.getTokens(OmegaConfGrammarParser.INT) + else: + return self.getToken(OmegaConfGrammarParser.INT, i) + + def FLOAT(self, i:int=None): + if i is None: + return self.getTokens(OmegaConfGrammarParser.FLOAT) + else: + return self.getToken(OmegaConfGrammarParser.FLOAT, i) + + def BOOL(self, i:int=None): + if i is None: + return self.getTokens(OmegaConfGrammarParser.BOOL) + else: + return self.getToken(OmegaConfGrammarParser.BOOL, i) + + def UNQUOTED_CHAR(self, i:int=None): + if i is None: + return self.getTokens(OmegaConfGrammarParser.UNQUOTED_CHAR) + else: + return self.getToken(OmegaConfGrammarParser.UNQUOTED_CHAR, i) + + def COLON(self, i:int=None): + if i is None: + return self.getTokens(OmegaConfGrammarParser.COLON) + else: + return self.getToken(OmegaConfGrammarParser.COLON, i) + + def ESC(self, i:int=None): + if i is None: + return self.getTokens(OmegaConfGrammarParser.ESC) + else: + return self.getToken(OmegaConfGrammarParser.ESC, i) + + def WS(self, i:int=None): + if i is None: + return self.getTokens(OmegaConfGrammarParser.WS) + else: + return self.getToken(OmegaConfGrammarParser.WS, i) + + def interpolation(self, i:int=None): + if i is None: + return self.getTypedRuleContexts(OmegaConfGrammarParser.InterpolationContext) + else: + return self.getTypedRuleContext(OmegaConfGrammarParser.InterpolationContext,i) + + + def getRuleIndex(self): + return OmegaConfGrammarParser.RULE_primitive + + def enterRule(self, listener:ParseTreeListener): + if hasattr( listener, "enterPrimitive" ): + listener.enterPrimitive(self) + + def exitRule(self, listener:ParseTreeListener): + if hasattr( listener, "exitPrimitive" ): + listener.exitPrimitive(self) + + def accept(self, visitor:ParseTreeVisitor): + if hasattr( visitor, "visitPrimitive" ): + return visitor.visitPrimitive(self) + else: + return visitor.visitChildren(self) + + + + + def primitive(self): + + localctx = OmegaConfGrammarParser.PrimitiveContext(self, self._ctx, self.state) + self.enterRule(localctx, 28, self.RULE_primitive) + self._la = 0 # Token type + try: + self.enterOuterAlt(localctx, 1) + self.state = 171 + self._errHandler.sync(self) + _la = self._input.LA(1) + while True: + self.state = 171 + self._errHandler.sync(self) + token = self._input.LA(1) + if token in [OmegaConfGrammarParser.ID]: + self.state = 161 + self.match(OmegaConfGrammarParser.ID) + pass + elif token in [OmegaConfGrammarParser.NULL]: + self.state = 162 + self.match(OmegaConfGrammarParser.NULL) + pass + elif token in [OmegaConfGrammarParser.INT]: + self.state = 163 + self.match(OmegaConfGrammarParser.INT) + pass + elif token in [OmegaConfGrammarParser.FLOAT]: + self.state = 164 + self.match(OmegaConfGrammarParser.FLOAT) + pass + elif token in [OmegaConfGrammarParser.BOOL]: + self.state = 165 + self.match(OmegaConfGrammarParser.BOOL) + pass + elif token in [OmegaConfGrammarParser.UNQUOTED_CHAR]: + self.state = 166 + self.match(OmegaConfGrammarParser.UNQUOTED_CHAR) + pass + elif token in [OmegaConfGrammarParser.COLON]: + self.state = 167 + self.match(OmegaConfGrammarParser.COLON) + pass + elif token in [OmegaConfGrammarParser.ESC]: + self.state = 168 + self.match(OmegaConfGrammarParser.ESC) + pass + elif token in [OmegaConfGrammarParser.WS]: + self.state = 169 + self.match(OmegaConfGrammarParser.WS) + pass + elif token in [OmegaConfGrammarParser.INTER_OPEN]: + self.state = 170 + self.interpolation() + pass + else: + raise NoViableAltException(self) + + self.state = 173 + self._errHandler.sync(self) + _la = self._input.LA(1) + if not ((((_la) & ~0x3f) == 0 and ((1 << _la) & ((1 << OmegaConfGrammarParser.INTER_OPEN) | (1 << OmegaConfGrammarParser.COLON) | (1 << OmegaConfGrammarParser.FLOAT) | (1 << OmegaConfGrammarParser.INT) | (1 << OmegaConfGrammarParser.BOOL) | (1 << OmegaConfGrammarParser.NULL) | (1 << OmegaConfGrammarParser.UNQUOTED_CHAR) | (1 << OmegaConfGrammarParser.ID) | (1 << OmegaConfGrammarParser.ESC) | (1 << OmegaConfGrammarParser.WS))) != 0)): + break + + except RecognitionException as re: + localctx.exception = re + self._errHandler.reportError(self, re) + self._errHandler.recover(self, re) + finally: + self.exitRule() + return localctx + + + class DictKeyContext(ParserRuleContext): + __slots__ = 'parser' + + def __init__(self, parser, parent:ParserRuleContext=None, invokingState:int=-1): + super().__init__(parent, invokingState) + self.parser = parser + + def ID(self, i:int=None): + if i is None: + return self.getTokens(OmegaConfGrammarParser.ID) + else: + return self.getToken(OmegaConfGrammarParser.ID, i) + + def NULL(self, i:int=None): + if i is None: + return self.getTokens(OmegaConfGrammarParser.NULL) + else: + return self.getToken(OmegaConfGrammarParser.NULL, i) + + def INT(self, i:int=None): + if i is None: + return self.getTokens(OmegaConfGrammarParser.INT) + else: + return self.getToken(OmegaConfGrammarParser.INT, i) + + def FLOAT(self, i:int=None): + if i is None: + return self.getTokens(OmegaConfGrammarParser.FLOAT) + else: + return self.getToken(OmegaConfGrammarParser.FLOAT, i) + + def BOOL(self, i:int=None): + if i is None: + return self.getTokens(OmegaConfGrammarParser.BOOL) + else: + return self.getToken(OmegaConfGrammarParser.BOOL, i) + + def UNQUOTED_CHAR(self, i:int=None): + if i is None: + return self.getTokens(OmegaConfGrammarParser.UNQUOTED_CHAR) + else: + return self.getToken(OmegaConfGrammarParser.UNQUOTED_CHAR, i) + + def ESC(self, i:int=None): + if i is None: + return self.getTokens(OmegaConfGrammarParser.ESC) + else: + return self.getToken(OmegaConfGrammarParser.ESC, i) + + def WS(self, i:int=None): + if i is None: + return self.getTokens(OmegaConfGrammarParser.WS) + else: + return self.getToken(OmegaConfGrammarParser.WS, i) + + def getRuleIndex(self): + return OmegaConfGrammarParser.RULE_dictKey + + def enterRule(self, listener:ParseTreeListener): + if hasattr( listener, "enterDictKey" ): + listener.enterDictKey(self) + + def exitRule(self, listener:ParseTreeListener): + if hasattr( listener, "exitDictKey" ): + listener.exitDictKey(self) + + def accept(self, visitor:ParseTreeVisitor): + if hasattr( visitor, "visitDictKey" ): + return visitor.visitDictKey(self) + else: + return visitor.visitChildren(self) + + + + + def dictKey(self): + + localctx = OmegaConfGrammarParser.DictKeyContext(self, self._ctx, self.state) + self.enterRule(localctx, 30, self.RULE_dictKey) + self._la = 0 # Token type + try: + self.enterOuterAlt(localctx, 1) + self.state = 176 + self._errHandler.sync(self) + _la = self._input.LA(1) + while True: + self.state = 175 + _la = self._input.LA(1) + if not((((_la) & ~0x3f) == 0 and ((1 << _la) & ((1 << OmegaConfGrammarParser.FLOAT) | (1 << OmegaConfGrammarParser.INT) | (1 << OmegaConfGrammarParser.BOOL) | (1 << OmegaConfGrammarParser.NULL) | (1 << OmegaConfGrammarParser.UNQUOTED_CHAR) | (1 << OmegaConfGrammarParser.ID) | (1 << OmegaConfGrammarParser.ESC) | (1 << OmegaConfGrammarParser.WS))) != 0)): + self._errHandler.recoverInline(self) + else: + self._errHandler.reportMatch(self) + self.consume() + self.state = 178 + self._errHandler.sync(self) + _la = self._input.LA(1) + if not ((((_la) & ~0x3f) == 0 and ((1 << _la) & ((1 << OmegaConfGrammarParser.FLOAT) | (1 << OmegaConfGrammarParser.INT) | (1 << OmegaConfGrammarParser.BOOL) | (1 << OmegaConfGrammarParser.NULL) | (1 << OmegaConfGrammarParser.UNQUOTED_CHAR) | (1 << OmegaConfGrammarParser.ID) | (1 << OmegaConfGrammarParser.ESC) | (1 << OmegaConfGrammarParser.WS))) != 0)): + break + + except RecognitionException as re: + localctx.exception = re + self._errHandler.reportError(self, re) + self._errHandler.recover(self, re) + finally: + self.exitRule() + return localctx + + + + + diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/grammar/gen/OmegaConfGrammarParserListener.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/grammar/gen/OmegaConfGrammarParserListener.py new file mode 100644 index 0000000000000000000000000000000000000000..d149348181e910757b6a3d6388073b951fac4678 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/grammar/gen/OmegaConfGrammarParserListener.py @@ -0,0 +1,156 @@ +# Generated from /tmp/build-via-sdist-fm63w174/omegaconf-2.3.0/omegaconf/grammar/OmegaConfGrammarParser.g4 by ANTLR 4.9.3 +from antlr4 import * +if __name__ is not None and "." in __name__: + from .OmegaConfGrammarParser import OmegaConfGrammarParser +else: + from OmegaConfGrammarParser import OmegaConfGrammarParser + +# This class defines a complete listener for a parse tree produced by OmegaConfGrammarParser. +class OmegaConfGrammarParserListener(ParseTreeListener): + + # Enter a parse tree produced by OmegaConfGrammarParser#configValue. + def enterConfigValue(self, ctx:OmegaConfGrammarParser.ConfigValueContext): + pass + + # Exit a parse tree produced by OmegaConfGrammarParser#configValue. + def exitConfigValue(self, ctx:OmegaConfGrammarParser.ConfigValueContext): + pass + + + # Enter a parse tree produced by OmegaConfGrammarParser#singleElement. + def enterSingleElement(self, ctx:OmegaConfGrammarParser.SingleElementContext): + pass + + # Exit a parse tree produced by OmegaConfGrammarParser#singleElement. + def exitSingleElement(self, ctx:OmegaConfGrammarParser.SingleElementContext): + pass + + + # Enter a parse tree produced by OmegaConfGrammarParser#text. + def enterText(self, ctx:OmegaConfGrammarParser.TextContext): + pass + + # Exit a parse tree produced by OmegaConfGrammarParser#text. + def exitText(self, ctx:OmegaConfGrammarParser.TextContext): + pass + + + # Enter a parse tree produced by OmegaConfGrammarParser#element. + def enterElement(self, ctx:OmegaConfGrammarParser.ElementContext): + pass + + # Exit a parse tree produced by OmegaConfGrammarParser#element. + def exitElement(self, ctx:OmegaConfGrammarParser.ElementContext): + pass + + + # Enter a parse tree produced by OmegaConfGrammarParser#listContainer. + def enterListContainer(self, ctx:OmegaConfGrammarParser.ListContainerContext): + pass + + # Exit a parse tree produced by OmegaConfGrammarParser#listContainer. + def exitListContainer(self, ctx:OmegaConfGrammarParser.ListContainerContext): + pass + + + # Enter a parse tree produced by OmegaConfGrammarParser#dictContainer. + def enterDictContainer(self, ctx:OmegaConfGrammarParser.DictContainerContext): + pass + + # Exit a parse tree produced by OmegaConfGrammarParser#dictContainer. + def exitDictContainer(self, ctx:OmegaConfGrammarParser.DictContainerContext): + pass + + + # Enter a parse tree produced by OmegaConfGrammarParser#dictKeyValuePair. + def enterDictKeyValuePair(self, ctx:OmegaConfGrammarParser.DictKeyValuePairContext): + pass + + # Exit a parse tree produced by OmegaConfGrammarParser#dictKeyValuePair. + def exitDictKeyValuePair(self, ctx:OmegaConfGrammarParser.DictKeyValuePairContext): + pass + + + # Enter a parse tree produced by OmegaConfGrammarParser#sequence. + def enterSequence(self, ctx:OmegaConfGrammarParser.SequenceContext): + pass + + # Exit a parse tree produced by OmegaConfGrammarParser#sequence. + def exitSequence(self, ctx:OmegaConfGrammarParser.SequenceContext): + pass + + + # Enter a parse tree produced by OmegaConfGrammarParser#interpolation. + def enterInterpolation(self, ctx:OmegaConfGrammarParser.InterpolationContext): + pass + + # Exit a parse tree produced by OmegaConfGrammarParser#interpolation. + def exitInterpolation(self, ctx:OmegaConfGrammarParser.InterpolationContext): + pass + + + # Enter a parse tree produced by OmegaConfGrammarParser#interpolationNode. + def enterInterpolationNode(self, ctx:OmegaConfGrammarParser.InterpolationNodeContext): + pass + + # Exit a parse tree produced by OmegaConfGrammarParser#interpolationNode. + def exitInterpolationNode(self, ctx:OmegaConfGrammarParser.InterpolationNodeContext): + pass + + + # Enter a parse tree produced by OmegaConfGrammarParser#interpolationResolver. + def enterInterpolationResolver(self, ctx:OmegaConfGrammarParser.InterpolationResolverContext): + pass + + # Exit a parse tree produced by OmegaConfGrammarParser#interpolationResolver. + def exitInterpolationResolver(self, ctx:OmegaConfGrammarParser.InterpolationResolverContext): + pass + + + # Enter a parse tree produced by OmegaConfGrammarParser#configKey. + def enterConfigKey(self, ctx:OmegaConfGrammarParser.ConfigKeyContext): + pass + + # Exit a parse tree produced by OmegaConfGrammarParser#configKey. + def exitConfigKey(self, ctx:OmegaConfGrammarParser.ConfigKeyContext): + pass + + + # Enter a parse tree produced by OmegaConfGrammarParser#resolverName. + def enterResolverName(self, ctx:OmegaConfGrammarParser.ResolverNameContext): + pass + + # Exit a parse tree produced by OmegaConfGrammarParser#resolverName. + def exitResolverName(self, ctx:OmegaConfGrammarParser.ResolverNameContext): + pass + + + # Enter a parse tree produced by OmegaConfGrammarParser#quotedValue. + def enterQuotedValue(self, ctx:OmegaConfGrammarParser.QuotedValueContext): + pass + + # Exit a parse tree produced by OmegaConfGrammarParser#quotedValue. + def exitQuotedValue(self, ctx:OmegaConfGrammarParser.QuotedValueContext): + pass + + + # Enter a parse tree produced by OmegaConfGrammarParser#primitive. + def enterPrimitive(self, ctx:OmegaConfGrammarParser.PrimitiveContext): + pass + + # Exit a parse tree produced by OmegaConfGrammarParser#primitive. + def exitPrimitive(self, ctx:OmegaConfGrammarParser.PrimitiveContext): + pass + + + # Enter a parse tree produced by OmegaConfGrammarParser#dictKey. + def enterDictKey(self, ctx:OmegaConfGrammarParser.DictKeyContext): + pass + + # Exit a parse tree produced by OmegaConfGrammarParser#dictKey. + def exitDictKey(self, ctx:OmegaConfGrammarParser.DictKeyContext): + pass + + + +del OmegaConfGrammarParser \ No newline at end of file diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/grammar/gen/OmegaConfGrammarParserVisitor.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/grammar/gen/OmegaConfGrammarParserVisitor.py new file mode 100644 index 0000000000000000000000000000000000000000..ed247dd92f6654d2c862e848204a4679cc23e3e3 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/grammar/gen/OmegaConfGrammarParserVisitor.py @@ -0,0 +1,93 @@ +# Generated from /tmp/build-via-sdist-fm63w174/omegaconf-2.3.0/omegaconf/grammar/OmegaConfGrammarParser.g4 by ANTLR 4.9.3 +from antlr4 import * +if __name__ is not None and "." in __name__: + from .OmegaConfGrammarParser import OmegaConfGrammarParser +else: + from OmegaConfGrammarParser import OmegaConfGrammarParser + +# This class defines a complete generic visitor for a parse tree produced by OmegaConfGrammarParser. + +class OmegaConfGrammarParserVisitor(ParseTreeVisitor): + + # Visit a parse tree produced by OmegaConfGrammarParser#configValue. + def visitConfigValue(self, ctx:OmegaConfGrammarParser.ConfigValueContext): + return self.visitChildren(ctx) + + + # Visit a parse tree produced by OmegaConfGrammarParser#singleElement. + def visitSingleElement(self, ctx:OmegaConfGrammarParser.SingleElementContext): + return self.visitChildren(ctx) + + + # Visit a parse tree produced by OmegaConfGrammarParser#text. + def visitText(self, ctx:OmegaConfGrammarParser.TextContext): + return self.visitChildren(ctx) + + + # Visit a parse tree produced by OmegaConfGrammarParser#element. + def visitElement(self, ctx:OmegaConfGrammarParser.ElementContext): + return self.visitChildren(ctx) + + + # Visit a parse tree produced by OmegaConfGrammarParser#listContainer. + def visitListContainer(self, ctx:OmegaConfGrammarParser.ListContainerContext): + return self.visitChildren(ctx) + + + # Visit a parse tree produced by OmegaConfGrammarParser#dictContainer. + def visitDictContainer(self, ctx:OmegaConfGrammarParser.DictContainerContext): + return self.visitChildren(ctx) + + + # Visit a parse tree produced by OmegaConfGrammarParser#dictKeyValuePair. + def visitDictKeyValuePair(self, ctx:OmegaConfGrammarParser.DictKeyValuePairContext): + return self.visitChildren(ctx) + + + # Visit a parse tree produced by OmegaConfGrammarParser#sequence. + def visitSequence(self, ctx:OmegaConfGrammarParser.SequenceContext): + return self.visitChildren(ctx) + + + # Visit a parse tree produced by OmegaConfGrammarParser#interpolation. + def visitInterpolation(self, ctx:OmegaConfGrammarParser.InterpolationContext): + return self.visitChildren(ctx) + + + # Visit a parse tree produced by OmegaConfGrammarParser#interpolationNode. + def visitInterpolationNode(self, ctx:OmegaConfGrammarParser.InterpolationNodeContext): + return self.visitChildren(ctx) + + + # Visit a parse tree produced by OmegaConfGrammarParser#interpolationResolver. + def visitInterpolationResolver(self, ctx:OmegaConfGrammarParser.InterpolationResolverContext): + return self.visitChildren(ctx) + + + # Visit a parse tree produced by OmegaConfGrammarParser#configKey. + def visitConfigKey(self, ctx:OmegaConfGrammarParser.ConfigKeyContext): + return self.visitChildren(ctx) + + + # Visit a parse tree produced by OmegaConfGrammarParser#resolverName. + def visitResolverName(self, ctx:OmegaConfGrammarParser.ResolverNameContext): + return self.visitChildren(ctx) + + + # Visit a parse tree produced by OmegaConfGrammarParser#quotedValue. + def visitQuotedValue(self, ctx:OmegaConfGrammarParser.QuotedValueContext): + return self.visitChildren(ctx) + + + # Visit a parse tree produced by OmegaConfGrammarParser#primitive. + def visitPrimitive(self, ctx:OmegaConfGrammarParser.PrimitiveContext): + return self.visitChildren(ctx) + + + # Visit a parse tree produced by OmegaConfGrammarParser#dictKey. + def visitDictKey(self, ctx:OmegaConfGrammarParser.DictKeyContext): + return self.visitChildren(ctx) + + + +del OmegaConfGrammarParser \ No newline at end of file diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/grammar/gen/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/grammar/gen/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/grammar_parser.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/grammar_parser.py new file mode 100644 index 0000000000000000000000000000000000000000..3c883c2cfd49b442a90bda0c7777cccca706e82d --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/grammar_parser.py @@ -0,0 +1,144 @@ +import re +import threading +from typing import Any + +from antlr4 import CommonTokenStream, InputStream, ParserRuleContext +from antlr4.error.ErrorListener import ErrorListener + +from .errors import GrammarParseError + +# Import from visitor in order to check the presence of generated grammar files +# files in a single place. +from .grammar_visitor import ( # type: ignore + OmegaConfGrammarLexer, + OmegaConfGrammarParser, +) + +# Used to cache grammar objects to avoid re-creating them on each call to `parse()`. +# We use a per-thread cache to make it thread-safe. +_grammar_cache = threading.local() + +# Build regex pattern to efficiently identify typical interpolations. +# See test `test_match_simple_interpolation_pattern` for examples. +_config_key = r"[$\w]+" # foo, $0, $bar, $foo_$bar123$ +_key_maybe_brackets = f"{_config_key}|\\[{_config_key}\\]" # foo, [foo], [$bar] +_node_access = f"\\.{_key_maybe_brackets}" # .foo, [foo], [$bar] +_node_path = f"(\\.)*({_key_maybe_brackets})({_node_access})*" # [foo].bar, .foo[bar] +_node_inter = f"\\${{\\s*{_node_path}\\s*}}" # node interpolation ${foo.bar} +_id = "[a-zA-Z_][\\w\\-]*" # foo, foo_bar, foo-bar, abc123 +_resolver_name = f"({_id}(\\.{_id})*)?" # foo, ns.bar3, ns_1.ns_2.b0z +_arg = r"[a-zA-Z_0-9/\-\+.$%*@?|]+" # string representing a resolver argument +_args = f"{_arg}(\\s*,\\s*{_arg})*" # list of resolver arguments +_resolver_inter = f"\\${{\\s*{_resolver_name}\\s*:\\s*{_args}?\\s*}}" # ${foo:bar} +_inter = f"({_node_inter}|{_resolver_inter})" # any kind of interpolation +_outer = "([^$]|\\$(?!{))+" # any character except $ (unless not followed by {) +SIMPLE_INTERPOLATION_PATTERN = re.compile( + f"({_outer})?({_inter}({_outer})?)+$", flags=re.ASCII +) +# NOTE: SIMPLE_INTERPOLATION_PATTERN must not generate false positive matches: +# it must not accept anything that isn't a valid interpolation (per the +# interpolation grammar defined in `omegaconf/grammar/*.g4`). + + +class OmegaConfErrorListener(ErrorListener): # type: ignore + def syntaxError( + self, + recognizer: Any, + offending_symbol: Any, + line: Any, + column: Any, + msg: Any, + e: Any, + ) -> None: + raise GrammarParseError(str(e) if msg is None else msg) from e + + def reportAmbiguity( + self, + recognizer: Any, + dfa: Any, + startIndex: Any, + stopIndex: Any, + exact: Any, + ambigAlts: Any, + configs: Any, + ) -> None: + raise GrammarParseError("ANTLR error: Ambiguity") # pragma: no cover + + def reportAttemptingFullContext( + self, + recognizer: Any, + dfa: Any, + startIndex: Any, + stopIndex: Any, + conflictingAlts: Any, + configs: Any, + ) -> None: + # Note: for now we raise an error to be safe. However this is mostly a + # performance warning, so in the future this may be relaxed if we need + # to change the grammar in such a way that this warning cannot be + # avoided (another option would be to switch to SLL parsing mode). + raise GrammarParseError( + "ANTLR error: Attempting Full Context" + ) # pragma: no cover + + def reportContextSensitivity( + self, + recognizer: Any, + dfa: Any, + startIndex: Any, + stopIndex: Any, + prediction: Any, + configs: Any, + ) -> None: + raise GrammarParseError("ANTLR error: ContextSensitivity") # pragma: no cover + + +def parse( + value: str, parser_rule: str = "configValue", lexer_mode: str = "DEFAULT_MODE" +) -> ParserRuleContext: + """ + Parse interpolated string `value` (and return the parse tree). + """ + l_mode = getattr(OmegaConfGrammarLexer, lexer_mode) + istream = InputStream(value) + + cached = getattr(_grammar_cache, "data", None) + if cached is None: + error_listener = OmegaConfErrorListener() + lexer = OmegaConfGrammarLexer(istream) + lexer.removeErrorListeners() + lexer.addErrorListener(error_listener) + lexer.mode(l_mode) + token_stream = CommonTokenStream(lexer) + parser = OmegaConfGrammarParser(token_stream) + parser.removeErrorListeners() + parser.addErrorListener(error_listener) + + # The two lines below could be enabled in the future if we decide to switch + # to SLL prediction mode. Warning though, it has not been fully tested yet! + # from antlr4 import PredictionMode + # parser._interp.predictionMode = PredictionMode.SLL + + # Note that although the input stream `istream` is implicitly cached within + # the lexer, it will be replaced by a new input next time the lexer is re-used. + _grammar_cache.data = lexer, token_stream, parser + + else: + lexer, token_stream, parser = cached + # Replace the old input stream with the new one. + lexer.inputStream = istream + # Initialize the lexer / token stream / parser to process the new input. + lexer.mode(l_mode) + token_stream.setTokenSource(lexer) + parser.reset() + + try: + return getattr(parser, parser_rule)() + except Exception as exc: + if type(exc) is Exception and str(exc) == "Empty Stack": + # This exception is raised by antlr when trying to pop a mode while + # no mode has been pushed. We convert it into an `GrammarParseError` + # to facilitate exception handling from the caller. + raise GrammarParseError("Empty Stack") + else: + raise diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/grammar_visitor.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/grammar_visitor.py new file mode 100644 index 0000000000000000000000000000000000000000..1771516dcf78bbc692512d58d4dea55850b957b7 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/grammar_visitor.py @@ -0,0 +1,392 @@ +import sys +import warnings +from itertools import zip_longest +from typing import ( + TYPE_CHECKING, + Any, + Callable, + Dict, + Generator, + List, + Optional, + Set, + Tuple, + Union, +) + +from antlr4 import TerminalNode + +from .errors import InterpolationResolutionError + +if TYPE_CHECKING: + from .base import Node # noqa F401 + +try: + from omegaconf.grammar.gen.OmegaConfGrammarLexer import OmegaConfGrammarLexer + from omegaconf.grammar.gen.OmegaConfGrammarParser import OmegaConfGrammarParser + from omegaconf.grammar.gen.OmegaConfGrammarParserVisitor import ( + OmegaConfGrammarParserVisitor, + ) + +except ModuleNotFoundError: # pragma: no cover + print( + "Error importing OmegaConf's generated parsers, run `python setup.py antlr` to regenerate.", + file=sys.stderr, + ) + sys.exit(1) + + +class GrammarVisitor(OmegaConfGrammarParserVisitor): + def __init__( + self, + node_interpolation_callback: Callable[ + [str, Optional[Set[int]]], + Optional["Node"], + ], + resolver_interpolation_callback: Callable[..., Any], + memo: Optional[Set[int]], + **kw: Dict[Any, Any], + ): + """ + Constructor. + + :param node_interpolation_callback: Callback function that is called when + needing to resolve a node interpolation. This function should take a single + string input which is the key's dot path (ex: `"foo.bar"`). + + :param resolver_interpolation_callback: Callback function that is called when + needing to resolve a resolver interpolation. This function should accept + three keyword arguments: `name` (str, the name of the resolver), + `args` (tuple, the inputs to the resolver), and `args_str` (tuple, + the string representation of the inputs to the resolver). + + :param kw: Additional keyword arguments to be forwarded to parent class. + """ + super().__init__(**kw) + self.node_interpolation_callback = node_interpolation_callback + self.resolver_interpolation_callback = resolver_interpolation_callback + self.memo = memo + + def aggregateResult(self, aggregate: List[Any], nextResult: Any) -> List[Any]: + raise NotImplementedError + + def defaultResult(self) -> List[Any]: + # Raising an exception because not currently used (like `aggregateResult()`). + raise NotImplementedError + + def visitConfigKey(self, ctx: OmegaConfGrammarParser.ConfigKeyContext) -> str: + from ._utils import _get_value + + # interpolation | ID | INTER_KEY + assert ctx.getChildCount() == 1 + child = ctx.getChild(0) + if isinstance(child, OmegaConfGrammarParser.InterpolationContext): + res = _get_value(self.visitInterpolation(child)) + if not isinstance(res, str): + raise InterpolationResolutionError( + f"The following interpolation is used to denote a config key and " + f"thus should return a string, but instead returned `{res}` of " + f"type `{type(res)}`: {ctx.getChild(0).getText()}" + ) + return res + else: + assert isinstance(child, TerminalNode) and isinstance( + child.symbol.text, str + ) + return child.symbol.text + + def visitConfigValue(self, ctx: OmegaConfGrammarParser.ConfigValueContext) -> Any: + # text EOF + assert ctx.getChildCount() == 2 + return self.visit(ctx.getChild(0)) + + def visitDictKey(self, ctx: OmegaConfGrammarParser.DictKeyContext) -> Any: + return self._createPrimitive(ctx) + + def visitDictContainer( + self, ctx: OmegaConfGrammarParser.DictContainerContext + ) -> Dict[Any, Any]: + # BRACE_OPEN (dictKeyValuePair (COMMA dictKeyValuePair)*)? BRACE_CLOSE + assert ctx.getChildCount() >= 2 + return dict( + self.visitDictKeyValuePair(ctx.getChild(i)) + for i in range(1, ctx.getChildCount() - 1, 2) + ) + + def visitElement(self, ctx: OmegaConfGrammarParser.ElementContext) -> Any: + # primitive | quotedValue | listContainer | dictContainer + assert ctx.getChildCount() == 1 + return self.visit(ctx.getChild(0)) + + def visitInterpolation( + self, ctx: OmegaConfGrammarParser.InterpolationContext + ) -> Any: + assert ctx.getChildCount() == 1 # interpolationNode | interpolationResolver + return self.visit(ctx.getChild(0)) + + def visitInterpolationNode( + self, ctx: OmegaConfGrammarParser.InterpolationNodeContext + ) -> Optional["Node"]: + # INTER_OPEN + # DOT* // relative interpolation? + # (configKey | BRACKET_OPEN configKey BRACKET_CLOSE) // foo, [foo] + # (DOT configKey | BRACKET_OPEN configKey BRACKET_CLOSE)* // .foo, [foo], .foo[bar], [foo].bar[baz] + # INTER_CLOSE; + + assert ctx.getChildCount() >= 3 + + inter_key_tokens = [] # parsed elements of the dot path + for child in ctx.getChildren(): + if isinstance(child, TerminalNode): + s = child.symbol + if s.type in [ + OmegaConfGrammarLexer.DOT, + OmegaConfGrammarLexer.BRACKET_OPEN, + OmegaConfGrammarLexer.BRACKET_CLOSE, + ]: + inter_key_tokens.append(s.text) + else: + assert s.type in ( + OmegaConfGrammarLexer.INTER_OPEN, + OmegaConfGrammarLexer.INTER_CLOSE, + ) + else: + assert isinstance(child, OmegaConfGrammarParser.ConfigKeyContext) + inter_key_tokens.append(self.visitConfigKey(child)) + + inter_key = "".join(inter_key_tokens) + return self.node_interpolation_callback(inter_key, self.memo) + + def visitInterpolationResolver( + self, ctx: OmegaConfGrammarParser.InterpolationResolverContext + ) -> Any: + + # INTER_OPEN resolverName COLON sequence? BRACE_CLOSE + assert 4 <= ctx.getChildCount() <= 5 + + resolver_name = self.visit(ctx.getChild(1)) + maybe_seq = ctx.getChild(3) + args = [] + args_str = [] + if isinstance(maybe_seq, TerminalNode): # means there are no args + assert maybe_seq.symbol.type == OmegaConfGrammarLexer.BRACE_CLOSE + else: + assert isinstance(maybe_seq, OmegaConfGrammarParser.SequenceContext) + for val, txt in self.visitSequence(maybe_seq): + args.append(val) + args_str.append(txt) + + return self.resolver_interpolation_callback( + name=resolver_name, + args=tuple(args), + args_str=tuple(args_str), + ) + + def visitDictKeyValuePair( + self, ctx: OmegaConfGrammarParser.DictKeyValuePairContext + ) -> Tuple[Any, Any]: + from ._utils import _get_value + + assert ctx.getChildCount() == 3 # dictKey COLON element + key = self.visit(ctx.getChild(0)) + colon = ctx.getChild(1) + assert ( + isinstance(colon, TerminalNode) + and colon.symbol.type == OmegaConfGrammarLexer.COLON + ) + value = _get_value(self.visitElement(ctx.getChild(2))) + return key, value + + def visitListContainer( + self, ctx: OmegaConfGrammarParser.ListContainerContext + ) -> List[Any]: + # BRACKET_OPEN sequence? BRACKET_CLOSE; + assert ctx.getChildCount() in (2, 3) + if ctx.getChildCount() == 2: + return [] + sequence = ctx.getChild(1) + assert isinstance(sequence, OmegaConfGrammarParser.SequenceContext) + return list(val for val, _ in self.visitSequence(sequence)) # ignore raw text + + def visitPrimitive(self, ctx: OmegaConfGrammarParser.PrimitiveContext) -> Any: + return self._createPrimitive(ctx) + + def visitQuotedValue(self, ctx: OmegaConfGrammarParser.QuotedValueContext) -> str: + # (QUOTE_OPEN_SINGLE | QUOTE_OPEN_DOUBLE) text? MATCHING_QUOTE_CLOSE + n = ctx.getChildCount() + assert n in [2, 3] + return str(self.visit(ctx.getChild(1))) if n == 3 else "" + + def visitResolverName(self, ctx: OmegaConfGrammarParser.ResolverNameContext) -> str: + from ._utils import _get_value + + # (interpolation | ID) (DOT (interpolation | ID))* + assert ctx.getChildCount() >= 1 + items = [] + for child in list(ctx.getChildren())[::2]: + if isinstance(child, TerminalNode): + assert child.symbol.type == OmegaConfGrammarLexer.ID + items.append(child.symbol.text) + else: + assert isinstance(child, OmegaConfGrammarParser.InterpolationContext) + item = _get_value(self.visitInterpolation(child)) + if not isinstance(item, str): + raise InterpolationResolutionError( + f"The name of a resolver must be a string, but the interpolation " + f"{child.getText()} resolved to `{item}` which is of type " + f"{type(item)}" + ) + items.append(item) + return ".".join(items) + + def visitSequence( + self, ctx: OmegaConfGrammarParser.SequenceContext + ) -> Generator[Any, None, None]: + from ._utils import _get_value + + # (element (COMMA element?)*) | (COMMA element?)+ + assert ctx.getChildCount() >= 1 + + # DEPRECATED: remove in 2.2 (revert #571) + def empty_str_warning() -> None: + txt = ctx.getText() + warnings.warn( + f"In the sequence `{txt}` some elements are missing: please replace " + f"them with empty quoted strings. " + f"See https://github.com/omry/omegaconf/issues/572 for details.", + category=UserWarning, + ) + + is_previous_comma = True # whether previous child was a comma (init to True) + for child in ctx.getChildren(): + if isinstance(child, OmegaConfGrammarParser.ElementContext): + # Also preserve the original text representation of `child` so + # as to allow backward compatibility with old resolvers (registered + # with `legacy_register_resolver()`). Note that we cannot just cast + # the value to string later as for instance `null` would become "None". + yield _get_value(self.visitElement(child)), child.getText() + is_previous_comma = False + else: + assert ( + isinstance(child, TerminalNode) + and child.symbol.type == OmegaConfGrammarLexer.COMMA + ) + if is_previous_comma: + empty_str_warning() + yield "", "" + else: + is_previous_comma = True + if is_previous_comma: + # Trailing comma. + empty_str_warning() + yield "", "" + + def visitSingleElement( + self, ctx: OmegaConfGrammarParser.SingleElementContext + ) -> Any: + # element EOF + assert ctx.getChildCount() == 2 + return self.visit(ctx.getChild(0)) + + def visitText(self, ctx: OmegaConfGrammarParser.TextContext) -> Any: + # (interpolation | ANY_STR | ESC | ESC_INTER | TOP_ESC | QUOTED_ESC)+ + + # Single interpolation? If yes, return its resolved value "as is". + if ctx.getChildCount() == 1: + c = ctx.getChild(0) + if isinstance(c, OmegaConfGrammarParser.InterpolationContext): + return self.visitInterpolation(c) + + # Otherwise, concatenate string representations together. + return self._unescape(list(ctx.getChildren())) + + def _createPrimitive( + self, + ctx: Union[ + OmegaConfGrammarParser.PrimitiveContext, + OmegaConfGrammarParser.DictKeyContext, + ], + ) -> Any: + # (ID | NULL | INT | FLOAT | BOOL | UNQUOTED_CHAR | COLON | ESC | WS | interpolation)+ + if ctx.getChildCount() == 1: + child = ctx.getChild(0) + if isinstance(child, OmegaConfGrammarParser.InterpolationContext): + return self.visitInterpolation(child) + assert isinstance(child, TerminalNode) + symbol = child.symbol + # Parse primitive types. + if symbol.type in ( + OmegaConfGrammarLexer.ID, + OmegaConfGrammarLexer.UNQUOTED_CHAR, + OmegaConfGrammarLexer.COLON, + ): + return symbol.text + elif symbol.type == OmegaConfGrammarLexer.NULL: + return None + elif symbol.type == OmegaConfGrammarLexer.INT: + return int(symbol.text) + elif symbol.type == OmegaConfGrammarLexer.FLOAT: + return float(symbol.text) + elif symbol.type == OmegaConfGrammarLexer.BOOL: + return symbol.text.lower() == "true" + elif symbol.type == OmegaConfGrammarLexer.ESC: + return self._unescape([child]) + elif symbol.type == OmegaConfGrammarLexer.WS: # pragma: no cover + # A single WS should have been "consumed" by another token. + raise AssertionError("WS should never be reached") + assert False, symbol.type + # Concatenation of multiple items ==> un-escape the concatenation. + return self._unescape(list(ctx.getChildren())) + + def _unescape( + self, + seq: List[Union[TerminalNode, OmegaConfGrammarParser.InterpolationContext]], + ) -> str: + """ + Concatenate all symbols / interpolations in `seq`, unescaping symbols as needed. + + Interpolations are resolved and cast to string *WITHOUT* escaping their result + (it is assumed that whatever escaping is required was already handled during the + resolving of the interpolation). + """ + chrs = [] + for node, next_node in zip_longest(seq, seq[1:]): + if isinstance(node, TerminalNode): + s = node.symbol + if s.type == OmegaConfGrammarLexer.ESC_INTER: + # `ESC_INTER` is of the form `\\...\${`: the formula below computes + # the number of characters to keep at the end of the string to remove + # the correct number of backslashes. + text = s.text[-(len(s.text) // 2 + 1) :] + elif ( + # Character sequence identified as requiring un-escaping. + s.type == OmegaConfGrammarLexer.ESC + or ( + # At top level, we need to un-escape backslashes that precede + # an interpolation. + s.type == OmegaConfGrammarLexer.TOP_ESC + and isinstance( + next_node, OmegaConfGrammarParser.InterpolationContext + ) + ) + or ( + # In a quoted sring, we need to un-escape backslashes that + # either end the string, or are followed by an interpolation. + s.type == OmegaConfGrammarLexer.QUOTED_ESC + and ( + next_node is None + or isinstance( + next_node, OmegaConfGrammarParser.InterpolationContext + ) + ) + ) + ): + text = s.text[1::2] # un-escape the sequence + else: + text = s.text # keep the original text + else: + assert isinstance(node, OmegaConfGrammarParser.InterpolationContext) + text = str(self.visitInterpolation(node)) + chrs.append(text) + + return "".join(chrs) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/listconfig.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/listconfig.py new file mode 100644 index 0000000000000000000000000000000000000000..c3b0f18a4e1cc69f033fec788fe674a2c81dc700 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/listconfig.py @@ -0,0 +1,679 @@ +import copy +import itertools +from typing import ( + Any, + Callable, + Dict, + Iterable, + Iterator, + List, + MutableSequence, + Optional, + Tuple, + Type, + Union, +) + +from ._utils import ( + ValueKind, + _is_missing_literal, + _is_none, + _resolve_optional, + format_and_raise, + get_value_kind, + is_int, + is_primitive_list, + is_structured_config, + type_str, +) +from .base import Box, ContainerMetadata, Node +from .basecontainer import BaseContainer +from .errors import ( + ConfigAttributeError, + ConfigTypeError, + ConfigValueError, + KeyValidationError, + MissingMandatoryValue, + ReadonlyConfigError, + ValidationError, +) + + +class ListConfig(BaseContainer, MutableSequence[Any]): + + _content: Union[List[Node], None, str] + + def __init__( + self, + content: Union[List[Any], Tuple[Any, ...], "ListConfig", str, None], + key: Any = None, + parent: Optional[Box] = None, + element_type: Union[Type[Any], Any] = Any, + is_optional: bool = True, + ref_type: Union[Type[Any], Any] = Any, + flags: Optional[Dict[str, bool]] = None, + ) -> None: + try: + if isinstance(content, ListConfig): + if flags is None: + flags = content._metadata.flags + super().__init__( + parent=parent, + metadata=ContainerMetadata( + ref_type=ref_type, + object_type=list, + key=key, + optional=is_optional, + element_type=element_type, + key_type=int, + flags=flags, + ), + ) + + if isinstance(content, ListConfig): + metadata = copy.deepcopy(content._metadata) + metadata.key = key + metadata.ref_type = ref_type + metadata.optional = is_optional + metadata.element_type = element_type + self.__dict__["_metadata"] = metadata + self._set_value(value=content, flags=flags) + except Exception as ex: + format_and_raise(node=None, key=key, value=None, cause=ex, msg=str(ex)) + + def _validate_get(self, key: Any, value: Any = None) -> None: + if not isinstance(key, (int, slice)): + raise KeyValidationError( + "ListConfig indices must be integers or slices, not $KEY_TYPE" + ) + + def _validate_set(self, key: Any, value: Any) -> None: + from omegaconf import OmegaConf + + self._validate_get(key, value) + + if self._get_flag("readonly"): + raise ReadonlyConfigError("ListConfig is read-only") + + if 0 <= key < self.__len__(): + target = self._get_node(key) + if target is not None: + assert isinstance(target, Node) + if value is None and not target._is_optional(): + raise ValidationError( + "$FULL_KEY is not optional and cannot be assigned None" + ) + + vk = get_value_kind(value) + if vk == ValueKind.MANDATORY_MISSING: + return + else: + is_optional, target_type = _resolve_optional(self._metadata.element_type) + value_type = OmegaConf.get_type(value) + + if (value_type is None and not is_optional) or ( + is_structured_config(target_type) + and value_type is not None + and not issubclass(value_type, target_type) + ): + msg = ( + f"Invalid type assigned: {type_str(value_type)} is not a " + f"subclass of {type_str(target_type)}. value: {value}" + ) + raise ValidationError(msg) + + def __deepcopy__(self, memo: Dict[int, Any]) -> "ListConfig": + res = ListConfig(None) + res.__dict__["_metadata"] = copy.deepcopy(self.__dict__["_metadata"], memo=memo) + res.__dict__["_flags_cache"] = copy.deepcopy( + self.__dict__["_flags_cache"], memo=memo + ) + + src_content = self.__dict__["_content"] + if isinstance(src_content, list): + content_copy: List[Optional[Node]] = [] + for v in src_content: + old_parent = v.__dict__["_parent"] + try: + v.__dict__["_parent"] = None + vc = copy.deepcopy(v, memo=memo) + vc.__dict__["_parent"] = res + content_copy.append(vc) + finally: + v.__dict__["_parent"] = old_parent + else: + # None and strings can be assigned as is + content_copy = src_content + + res.__dict__["_content"] = content_copy + res.__dict__["_parent"] = self.__dict__["_parent"] + + return res + + def copy(self) -> "ListConfig": + return copy.copy(self) + + # hide content while inspecting in debugger + def __dir__(self) -> Iterable[str]: + if self._is_missing() or self._is_none(): + return [] + return [str(x) for x in range(0, len(self))] + + def __setattr__(self, key: str, value: Any) -> None: + self._format_and_raise( + key=key, + value=value, + cause=ConfigAttributeError("ListConfig does not support attribute access"), + ) + assert False + + def __getattr__(self, key: str) -> Any: + # PyCharm is sometimes inspecting __members__, be sure to tell it we don't have that. + if key == "__members__": + raise AttributeError() + + if key == "__name__": + raise AttributeError() + + if is_int(key): + return self.__getitem__(int(key)) + else: + self._format_and_raise( + key=key, + value=None, + cause=ConfigAttributeError( + "ListConfig does not support attribute access" + ), + ) + + def __getitem__(self, index: Union[int, slice]) -> Any: + try: + if self._is_missing(): + raise MissingMandatoryValue("ListConfig is missing") + self._validate_get(index, None) + if self._is_none(): + raise TypeError( + "ListConfig object representing None is not subscriptable" + ) + + assert isinstance(self.__dict__["_content"], list) + if isinstance(index, slice): + result = [] + start, stop, step = self._correct_index_params(index) + for slice_idx in itertools.islice( + range(0, len(self)), start, stop, step + ): + val = self._resolve_with_default( + key=slice_idx, value=self.__dict__["_content"][slice_idx] + ) + result.append(val) + if index.step and index.step < 0: + result.reverse() + return result + else: + return self._resolve_with_default( + key=index, value=self.__dict__["_content"][index] + ) + except Exception as e: + self._format_and_raise(key=index, value=None, cause=e) + + def _correct_index_params(self, index: slice) -> Tuple[int, int, int]: + start = index.start + stop = index.stop + step = index.step + if index.start and index.start < 0: + start = self.__len__() + index.start + if index.stop and index.stop < 0: + stop = self.__len__() + index.stop + if index.step and index.step < 0: + step = abs(step) + if start and stop: + if start > stop: + start, stop = stop + 1, start + 1 + else: + start = stop = 0 + elif not start and stop: + start = list(range(self.__len__() - 1, stop, -step))[0] + stop = None + elif start and not stop: + stop = start + 1 + start = (stop - 1) % step + else: + start = (self.__len__() - 1) % step + return start, stop, step + + def _set_at_index(self, index: Union[int, slice], value: Any) -> None: + self._set_item_impl(index, value) + + def __setitem__(self, index: Union[int, slice], value: Any) -> None: + try: + if isinstance(index, slice): + _ = iter(value) # check iterable + self_indices = index.indices(len(self)) + indexes = range(*self_indices) + + # Ensure lengths match for extended slice assignment + if index.step not in (None, 1): + if len(indexes) != len(value): + raise ValueError( + f"attempt to assign sequence of size {len(value)}" + f" to extended slice of size {len(indexes)}" + ) + + # Initialize insertion offsets for empty slices + if len(indexes) == 0: + curr_index = self_indices[0] - 1 + val_i = -1 + + work_copy = self.copy() # For atomicity manipulate a copy + + # Delete and optionally replace non empty slices + only_removed = 0 + for val_i, i in enumerate(indexes): + curr_index = i - only_removed + del work_copy[curr_index] + if val_i < len(value): + work_copy.insert(curr_index, value[val_i]) + else: + only_removed += 1 + + # Insert any remaining input items + for val_i in range(val_i + 1, len(value)): + curr_index += 1 + work_copy.insert(curr_index, value[val_i]) + + # Reinitialize self with work_copy + self.clear() + self.extend(work_copy) + else: + self._set_at_index(index, value) + except Exception as e: + self._format_and_raise(key=index, value=value, cause=e) + + def append(self, item: Any) -> None: + content = self.__dict__["_content"] + index = len(content) + content.append(None) + try: + self._set_item_impl(index, item) + except Exception as e: + del content[index] + self._format_and_raise(key=index, value=item, cause=e) + assert False + + def _update_keys(self) -> None: + for i in range(len(self)): + node = self._get_node(i) + if node is not None: + assert isinstance(node, Node) + node._metadata.key = i + + def insert(self, index: int, item: Any) -> None: + from omegaconf.omegaconf import _maybe_wrap + + try: + if self._get_flag("readonly"): + raise ReadonlyConfigError("Cannot insert into a read-only ListConfig") + if self._is_none(): + raise TypeError( + "Cannot insert into ListConfig object representing None" + ) + if self._is_missing(): + raise MissingMandatoryValue("Cannot insert into missing ListConfig") + + try: + assert isinstance(self.__dict__["_content"], list) + # insert place holder + self.__dict__["_content"].insert(index, None) + is_optional, ref_type = _resolve_optional(self._metadata.element_type) + node = _maybe_wrap( + ref_type=ref_type, + key=index, + value=item, + is_optional=is_optional, + parent=self, + ) + self._validate_set(key=index, value=node) + self._set_at_index(index, node) + self._update_keys() + except Exception: + del self.__dict__["_content"][index] + self._update_keys() + raise + except Exception as e: + self._format_and_raise(key=index, value=item, cause=e) + assert False + + def extend(self, lst: Iterable[Any]) -> None: + assert isinstance(lst, (tuple, list, ListConfig)) + for x in lst: + self.append(x) + + def remove(self, x: Any) -> None: + del self[self.index(x)] + + def __delitem__(self, key: Union[int, slice]) -> None: + if self._get_flag("readonly"): + self._format_and_raise( + key=key, + value=None, + cause=ReadonlyConfigError( + "Cannot delete item from read-only ListConfig" + ), + ) + del self.__dict__["_content"][key] + self._update_keys() + + def clear(self) -> None: + del self[:] + + def index( + self, x: Any, start: Optional[int] = None, end: Optional[int] = None + ) -> int: + if start is None: + start = 0 + if end is None: + end = len(self) + assert start >= 0 + assert end <= len(self) + found_idx = -1 + for idx in range(start, end): + item = self[idx] + if x == item: + found_idx = idx + break + if found_idx != -1: + return found_idx + else: + self._format_and_raise( + key=None, + value=None, + cause=ConfigValueError("Item not found in ListConfig"), + ) + assert False + + def count(self, x: Any) -> int: + c = 0 + for item in self: + if item == x: + c = c + 1 + return c + + def _get_node( + self, + key: Union[int, slice], + validate_access: bool = True, + validate_key: bool = True, + throw_on_missing_value: bool = False, + throw_on_missing_key: bool = False, + ) -> Union[Optional[Node], List[Optional[Node]]]: + try: + if self._is_none(): + raise TypeError( + "Cannot get_node from a ListConfig object representing None" + ) + if self._is_missing(): + raise MissingMandatoryValue("Cannot get_node from a missing ListConfig") + assert isinstance(self.__dict__["_content"], list) + if validate_access: + self._validate_get(key) + + value = self.__dict__["_content"][key] + if value is not None: + if isinstance(key, slice): + assert isinstance(value, list) + for v in value: + if throw_on_missing_value and v._is_missing(): + raise MissingMandatoryValue("Missing mandatory value") + else: + assert isinstance(value, Node) + if throw_on_missing_value and value._is_missing(): + raise MissingMandatoryValue("Missing mandatory value: $KEY") + return value + except (IndexError, TypeError, MissingMandatoryValue, KeyValidationError) as e: + if isinstance(e, MissingMandatoryValue) and throw_on_missing_value: + raise + if validate_access: + self._format_and_raise(key=key, value=None, cause=e) + assert False + else: + return None + + def get(self, index: int, default_value: Any = None) -> Any: + try: + if self._is_none(): + raise TypeError("Cannot get from a ListConfig object representing None") + if self._is_missing(): + raise MissingMandatoryValue("Cannot get from a missing ListConfig") + self._validate_get(index, None) + assert isinstance(self.__dict__["_content"], list) + return self._resolve_with_default( + key=index, + value=self.__dict__["_content"][index], + default_value=default_value, + ) + except Exception as e: + self._format_and_raise(key=index, value=None, cause=e) + assert False + + def pop(self, index: int = -1) -> Any: + try: + if self._get_flag("readonly"): + raise ReadonlyConfigError("Cannot pop from read-only ListConfig") + if self._is_none(): + raise TypeError("Cannot pop from a ListConfig object representing None") + if self._is_missing(): + raise MissingMandatoryValue("Cannot pop from a missing ListConfig") + + assert isinstance(self.__dict__["_content"], list) + node = self._get_child(index) + assert isinstance(node, Node) + ret = self._resolve_with_default(key=index, value=node, default_value=None) + del self.__dict__["_content"][index] + self._update_keys() + return ret + except KeyValidationError as e: + self._format_and_raise( + key=index, value=None, cause=e, type_override=ConfigTypeError + ) + assert False + except Exception as e: + self._format_and_raise(key=index, value=None, cause=e) + assert False + + def sort( + self, key: Optional[Callable[[Any], Any]] = None, reverse: bool = False + ) -> None: + try: + if self._get_flag("readonly"): + raise ReadonlyConfigError("Cannot sort a read-only ListConfig") + if self._is_none(): + raise TypeError("Cannot sort a ListConfig object representing None") + if self._is_missing(): + raise MissingMandatoryValue("Cannot sort a missing ListConfig") + + if key is None: + + def key1(x: Any) -> Any: + return x._value() + + else: + + def key1(x: Any) -> Any: + return key(x._value()) # type: ignore + + assert isinstance(self.__dict__["_content"], list) + self.__dict__["_content"].sort(key=key1, reverse=reverse) + + except Exception as e: + self._format_and_raise(key=None, value=None, cause=e) + assert False + + def __eq__(self, other: Any) -> bool: + if isinstance(other, (list, tuple)) or other is None: + other = ListConfig(other, flags={"allow_objects": True}) + return ListConfig._list_eq(self, other) + if other is None or isinstance(other, ListConfig): + return ListConfig._list_eq(self, other) + if self._is_missing(): + return _is_missing_literal(other) + return NotImplemented + + def __ne__(self, other: Any) -> bool: + x = self.__eq__(other) + if x is not NotImplemented: + return not x + return NotImplemented + + def __hash__(self) -> int: + return hash(str(self)) + + def __iter__(self) -> Iterator[Any]: + return self._iter_ex(resolve=True) + + class ListIterator(Iterator[Any]): + def __init__(self, lst: Any, resolve: bool) -> None: + self.resolve = resolve + self.iterator = iter(lst.__dict__["_content"]) + self.index = 0 + from .nodes import ValueNode + + self.ValueNode = ValueNode + + def __next__(self) -> Any: + + x = next(self.iterator) + if self.resolve: + x = x._dereference_node() + if x._is_missing(): + raise MissingMandatoryValue(f"Missing value at index {self.index}") + + self.index = self.index + 1 + if isinstance(x, self.ValueNode): + return x._value() + else: + # Must be omegaconf.Container. not checking for perf reasons. + if x._is_none(): + return None + return x + + def __repr__(self) -> str: # pragma: no cover + return f"ListConfig.ListIterator(resolve={self.resolve})" + + def _iter_ex(self, resolve: bool) -> Iterator[Any]: + try: + if self._is_none(): + raise TypeError("Cannot iterate a ListConfig object representing None") + if self._is_missing(): + raise MissingMandatoryValue("Cannot iterate a missing ListConfig") + + return ListConfig.ListIterator(self, resolve) + except (TypeError, MissingMandatoryValue) as e: + self._format_and_raise(key=None, value=None, cause=e) + assert False + + def __add__(self, other: Union[List[Any], "ListConfig"]) -> "ListConfig": + # res is sharing this list's parent to allow interpolation to work as expected + res = ListConfig(parent=self._get_parent(), content=[]) + res.extend(self) + res.extend(other) + return res + + def __radd__(self, other: Union[List[Any], "ListConfig"]) -> "ListConfig": + # res is sharing this list's parent to allow interpolation to work as expected + res = ListConfig(parent=self._get_parent(), content=[]) + res.extend(other) + res.extend(self) + return res + + def __iadd__(self, other: Iterable[Any]) -> "ListConfig": + self.extend(other) + return self + + def __contains__(self, item: Any) -> bool: + if self._is_none(): + raise TypeError( + "Cannot check if an item is in a ListConfig object representing None" + ) + if self._is_missing(): + raise MissingMandatoryValue( + "Cannot check if an item is in missing ListConfig" + ) + + lst = self.__dict__["_content"] + for x in lst: + x = x._dereference_node() + if x == item: + return True + return False + + def _set_value(self, value: Any, flags: Optional[Dict[str, bool]] = None) -> None: + try: + previous_content = self.__dict__["_content"] + previous_metadata = self.__dict__["_metadata"] + self._set_value_impl(value, flags) + except Exception as e: + self.__dict__["_content"] = previous_content + self.__dict__["_metadata"] = previous_metadata + raise e + + def _set_value_impl( + self, value: Any, flags: Optional[Dict[str, bool]] = None + ) -> None: + from omegaconf import MISSING, flag_override + + if flags is None: + flags = {} + + vk = get_value_kind(value, strict_interpolation_validation=True) + if _is_none(value): + if not self._is_optional(): + raise ValidationError( + "Non optional ListConfig cannot be constructed from None" + ) + self.__dict__["_content"] = None + self._metadata.object_type = None + elif vk is ValueKind.MANDATORY_MISSING: + self.__dict__["_content"] = MISSING + self._metadata.object_type = None + elif vk == ValueKind.INTERPOLATION: + self.__dict__["_content"] = value + self._metadata.object_type = None + else: + if not (is_primitive_list(value) or isinstance(value, ListConfig)): + type_ = type(value) + msg = f"Invalid value assigned: {type_.__name__} is not a ListConfig, list or tuple." + raise ValidationError(msg) + + self.__dict__["_content"] = [] + if isinstance(value, ListConfig): + self._metadata.flags = copy.deepcopy(flags) + # disable struct and readonly for the construction phase + # retaining other flags like allow_objects. The real flags are restored at the end of this function + with flag_override(self, ["struct", "readonly"], False): + for item in value._iter_ex(resolve=False): + self.append(item) + elif is_primitive_list(value): + with flag_override(self, ["struct", "readonly"], False): + for item in value: + self.append(item) + self._metadata.object_type = list + + @staticmethod + def _list_eq(l1: Optional["ListConfig"], l2: Optional["ListConfig"]) -> bool: + l1_none = l1.__dict__["_content"] is None + l2_none = l2.__dict__["_content"] is None + if l1_none and l2_none: + return True + if l1_none != l2_none: + return False + + assert isinstance(l1, ListConfig) + assert isinstance(l2, ListConfig) + if len(l1) != len(l2): + return False + for i in range(len(l1)): + if not BaseContainer._item_eq(l1, i, l2, i): + return False + + return True diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/nodes.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/nodes.py new file mode 100644 index 0000000000000000000000000000000000000000..1f02cde65801fc9b7443595fc80bd8017dd8273b --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/nodes.py @@ -0,0 +1,545 @@ +import copy +import math +import sys +from abc import abstractmethod +from enum import Enum +from pathlib import Path +from typing import Any, Dict, Optional, Type, Union + +from omegaconf._utils import ( + ValueKind, + _is_interpolation, + get_type_of, + get_value_kind, + is_primitive_container, + type_str, +) +from omegaconf.base import Box, DictKeyType, Metadata, Node +from omegaconf.errors import ReadonlyConfigError, UnsupportedValueType, ValidationError + + +class ValueNode(Node): + _val: Any + + def __init__(self, parent: Optional[Box], value: Any, metadata: Metadata): + from omegaconf import read_write + + super().__init__(parent=parent, metadata=metadata) + with read_write(self): + self._set_value(value) # lgtm [py/init-calls-subclass] + + def _value(self) -> Any: + return self._val + + def _set_value(self, value: Any, flags: Optional[Dict[str, bool]] = None) -> None: + if self._get_flag("readonly"): + raise ReadonlyConfigError("Cannot set value of read-only config node") + + if isinstance(value, str) and get_value_kind( + value, strict_interpolation_validation=True + ) in ( + ValueKind.INTERPOLATION, + ValueKind.MANDATORY_MISSING, + ): + self._val = value + else: + self._val = self.validate_and_convert(value) + + def _strict_validate_type(self, value: Any) -> None: + ref_type = self._metadata.ref_type + if isinstance(ref_type, type) and type(value) is not ref_type: + type_hint = type_str(self._metadata.type_hint) + raise ValidationError( + f"Value '$VALUE' of type '$VALUE_TYPE' is incompatible with type hint '{type_hint}'" + ) + + def validate_and_convert(self, value: Any) -> Any: + """ + Validates input and converts to canonical form + :param value: input value + :return: converted value ("100" may be converted to 100 for example) + """ + if value is None: + if self._is_optional(): + return None + ref_type_str = type_str(self._metadata.ref_type) + raise ValidationError( + f"Incompatible value '{value}' for field of type '{ref_type_str}'" + ) + + # Subclasses can assume that `value` is not None in + # `_validate_and_convert_impl()` and in `_strict_validate_type()`. + if self._get_flag("convert") is False: + self._strict_validate_type(value) + return value + else: + return self._validate_and_convert_impl(value) + + @abstractmethod + def _validate_and_convert_impl(self, value: Any) -> Any: + ... + + def __str__(self) -> str: + return str(self._val) + + def __repr__(self) -> str: + return repr(self._val) if hasattr(self, "_val") else "__INVALID__" + + def __eq__(self, other: Any) -> bool: + if isinstance(other, AnyNode): + return self._val == other._val # type: ignore + else: + return self._val == other # type: ignore + + def __ne__(self, other: Any) -> bool: + x = self.__eq__(other) + assert x is not NotImplemented + return not x + + def __hash__(self) -> int: + return hash(self._val) + + def _deepcopy_impl(self, res: Any, memo: Dict[int, Any]) -> None: + res.__dict__["_metadata"] = copy.deepcopy(self._metadata, memo=memo) + # shallow copy for value to support non-copyable value + res.__dict__["_val"] = self._val + + # parent is retained, but not copied + res.__dict__["_parent"] = self._parent + + def _is_optional(self) -> bool: + return self._metadata.optional + + def _is_interpolation(self) -> bool: + return _is_interpolation(self._value()) + + def _get_full_key(self, key: Optional[Union[DictKeyType, int]]) -> str: + parent = self._get_parent() + if parent is None: + if self._metadata.key is None: + return "" + else: + return str(self._metadata.key) + else: + return parent._get_full_key(self._metadata.key) + + +class AnyNode(ValueNode): + def __init__( + self, + value: Any = None, + key: Any = None, + parent: Optional[Box] = None, + flags: Optional[Dict[str, bool]] = None, + ): + super().__init__( + parent=parent, + value=value, + metadata=Metadata( + ref_type=Any, object_type=None, key=key, optional=True, flags=flags + ), + ) + + def _validate_and_convert_impl(self, value: Any) -> Any: + from ._utils import is_primitive_type_annotation + + # allow_objects is internal and not an official API. use at your own risk. + # Please be aware that this support is subject to change without notice. + # If this is deemed useful and supportable it may become an official API. + + if self._get_flag( + "allow_objects" + ) is not True and not is_primitive_type_annotation(value): + t = get_type_of(value) + raise UnsupportedValueType( + f"Value '{t.__name__}' is not a supported primitive type" + ) + return value + + def __deepcopy__(self, memo: Dict[int, Any]) -> "AnyNode": + res = AnyNode() + self._deepcopy_impl(res, memo) + return res + + +class StringNode(ValueNode): + def __init__( + self, + value: Any = None, + key: Any = None, + parent: Optional[Box] = None, + is_optional: bool = True, + flags: Optional[Dict[str, bool]] = None, + ): + super().__init__( + parent=parent, + value=value, + metadata=Metadata( + key=key, + optional=is_optional, + ref_type=str, + object_type=str, + flags=flags, + ), + ) + + def _validate_and_convert_impl(self, value: Any) -> str: + from omegaconf import OmegaConf + + if ( + OmegaConf.is_config(value) + or is_primitive_container(value) + or isinstance(value, bytes) + ): + raise ValidationError("Cannot convert '$VALUE_TYPE' to string: '$VALUE'") + return str(value) + + def __deepcopy__(self, memo: Dict[int, Any]) -> "StringNode": + res = StringNode() + self._deepcopy_impl(res, memo) + return res + + +class PathNode(ValueNode): + def __init__( + self, + value: Any = None, + key: Any = None, + parent: Optional[Box] = None, + is_optional: bool = True, + flags: Optional[Dict[str, bool]] = None, + ): + super().__init__( + parent=parent, + value=value, + metadata=Metadata( + key=key, + optional=is_optional, + ref_type=Path, + object_type=Path, + flags=flags, + ), + ) + + def _strict_validate_type(self, value: Any) -> None: + if not isinstance(value, Path): + raise ValidationError( + "Value '$VALUE' of type '$VALUE_TYPE' is not an instance of 'pathlib.Path'" + ) + + def _validate_and_convert_impl(self, value: Any) -> Path: + if not isinstance(value, (str, Path)): + raise ValidationError( + "Value '$VALUE' of type '$VALUE_TYPE' could not be converted to Path" + ) + + return Path(value) + + def __deepcopy__(self, memo: Dict[int, Any]) -> "PathNode": + res = PathNode() + self._deepcopy_impl(res, memo) + return res + + +class IntegerNode(ValueNode): + def __init__( + self, + value: Any = None, + key: Any = None, + parent: Optional[Box] = None, + is_optional: bool = True, + flags: Optional[Dict[str, bool]] = None, + ): + super().__init__( + parent=parent, + value=value, + metadata=Metadata( + key=key, + optional=is_optional, + ref_type=int, + object_type=int, + flags=flags, + ), + ) + + def _validate_and_convert_impl(self, value: Any) -> int: + try: + if type(value) in (str, int): + val = int(value) + else: + raise ValueError() + except ValueError: + raise ValidationError( + "Value '$VALUE' of type '$VALUE_TYPE' could not be converted to Integer" + ) + return val + + def __deepcopy__(self, memo: Dict[int, Any]) -> "IntegerNode": + res = IntegerNode() + self._deepcopy_impl(res, memo) + return res + + +class BytesNode(ValueNode): + def __init__( + self, + value: Any = None, + key: Any = None, + parent: Optional[Box] = None, + is_optional: bool = True, + flags: Optional[Dict[str, bool]] = None, + ): + super().__init__( + parent=parent, + value=value, + metadata=Metadata( + key=key, + optional=is_optional, + ref_type=bytes, + object_type=bytes, + flags=flags, + ), + ) + + def _validate_and_convert_impl(self, value: Any) -> bytes: + if not isinstance(value, bytes): + raise ValidationError( + "Value '$VALUE' of type '$VALUE_TYPE' is not of type 'bytes'" + ) + return value + + def __deepcopy__(self, memo: Dict[int, Any]) -> "BytesNode": + res = BytesNode() + self._deepcopy_impl(res, memo) + return res + + +class FloatNode(ValueNode): + def __init__( + self, + value: Any = None, + key: Any = None, + parent: Optional[Box] = None, + is_optional: bool = True, + flags: Optional[Dict[str, bool]] = None, + ): + super().__init__( + parent=parent, + value=value, + metadata=Metadata( + key=key, + optional=is_optional, + ref_type=float, + object_type=float, + flags=flags, + ), + ) + + def _validate_and_convert_impl(self, value: Any) -> float: + try: + if type(value) in (float, str, int): + return float(value) + else: + raise ValueError() + except ValueError: + raise ValidationError( + "Value '$VALUE' of type '$VALUE_TYPE' could not be converted to Float" + ) + + def __eq__(self, other: Any) -> bool: + if isinstance(other, ValueNode): + other_val = other._val + else: + other_val = other + if self._val is None and other is None: + return True + if self._val is None and other is not None: + return False + if self._val is not None and other is None: + return False + nan1 = math.isnan(self._val) if isinstance(self._val, float) else False + nan2 = math.isnan(other_val) if isinstance(other_val, float) else False + return self._val == other_val or (nan1 and nan2) + + def __hash__(self) -> int: + return hash(self._val) + + def __deepcopy__(self, memo: Dict[int, Any]) -> "FloatNode": + res = FloatNode() + self._deepcopy_impl(res, memo) + return res + + +class BooleanNode(ValueNode): + def __init__( + self, + value: Any = None, + key: Any = None, + parent: Optional[Box] = None, + is_optional: bool = True, + flags: Optional[Dict[str, bool]] = None, + ): + super().__init__( + parent=parent, + value=value, + metadata=Metadata( + key=key, + optional=is_optional, + ref_type=bool, + object_type=bool, + flags=flags, + ), + ) + + def _validate_and_convert_impl(self, value: Any) -> bool: + if isinstance(value, bool): + return value + if isinstance(value, int): + return value != 0 + elif isinstance(value, str): + try: + return self._validate_and_convert_impl(int(value)) + except ValueError as e: + if value.lower() in ("yes", "y", "on", "true"): + return True + elif value.lower() in ("no", "n", "off", "false"): + return False + else: + raise ValidationError( + "Value '$VALUE' is not a valid bool (type $VALUE_TYPE)" + ).with_traceback(sys.exc_info()[2]) from e + else: + raise ValidationError( + "Value '$VALUE' is not a valid bool (type $VALUE_TYPE)" + ) + + def __deepcopy__(self, memo: Dict[int, Any]) -> "BooleanNode": + res = BooleanNode() + self._deepcopy_impl(res, memo) + return res + + +class EnumNode(ValueNode): # lgtm [py/missing-equals] : Intentional. + """ + NOTE: EnumNode is serialized to yaml as a string ("Color.BLUE"), not as a fully qualified yaml type. + this means serialization to YAML of a typed config (with EnumNode) will not retain the type of the Enum + when loaded. + This is intentional, Please open an issue against OmegaConf if you wish to discuss this decision. + """ + + def __init__( + self, + enum_type: Type[Enum], + value: Optional[Union[Enum, str]] = None, + key: Any = None, + parent: Optional[Box] = None, + is_optional: bool = True, + flags: Optional[Dict[str, bool]] = None, + ): + if not isinstance(enum_type, type) or not issubclass(enum_type, Enum): + raise ValidationError( + f"EnumNode can only operate on Enum subclasses ({enum_type})" + ) + self.fields: Dict[str, str] = {} + self.enum_type: Type[Enum] = enum_type + for name, constant in enum_type.__members__.items(): + self.fields[name] = constant.value + super().__init__( + parent=parent, + value=value, + metadata=Metadata( + key=key, + optional=is_optional, + ref_type=enum_type, + object_type=enum_type, + flags=flags, + ), + ) + + def _strict_validate_type(self, value: Any) -> None: + ref_type = self._metadata.ref_type + if not isinstance(value, ref_type): + type_hint = type_str(self._metadata.type_hint) + raise ValidationError( + f"Value '$VALUE' of type '$VALUE_TYPE' is incompatible with type hint '{type_hint}'" + ) + + def _validate_and_convert_impl(self, value: Any) -> Enum: + return self.validate_and_convert_to_enum(enum_type=self.enum_type, value=value) + + @staticmethod + def validate_and_convert_to_enum(enum_type: Type[Enum], value: Any) -> Enum: + if not isinstance(value, (str, int)) and not isinstance(value, enum_type): + raise ValidationError( + f"Value $VALUE ($VALUE_TYPE) is not a valid input for {enum_type}" + ) + + if isinstance(value, enum_type): + return value + + try: + if isinstance(value, (float, bool)): + raise ValueError + + if isinstance(value, int): + return enum_type(value) + + if isinstance(value, str): + prefix = f"{enum_type.__name__}." + if value.startswith(prefix): + value = value[len(prefix) :] + return enum_type[value] + + assert False + + except (ValueError, KeyError) as e: + valid = ", ".join([x for x in enum_type.__members__.keys()]) + raise ValidationError( + f"Invalid value '$VALUE', expected one of [{valid}]" + ).with_traceback(sys.exc_info()[2]) from e + + def __deepcopy__(self, memo: Dict[int, Any]) -> "EnumNode": + res = EnumNode(enum_type=self.enum_type) + self._deepcopy_impl(res, memo) + return res + + +class InterpolationResultNode(ValueNode): + """ + Special node type, used to wrap interpolation results. + """ + + def __init__( + self, + value: Any, + key: Any = None, + parent: Optional[Box] = None, + flags: Optional[Dict[str, bool]] = None, + ): + super().__init__( + parent=parent, + value=value, + metadata=Metadata( + ref_type=Any, object_type=None, key=key, optional=True, flags=flags + ), + ) + # In general we should not try to write into interpolation results. + if flags is None or "readonly" not in flags: + self._set_flag("readonly", True) + + def _set_value(self, value: Any, flags: Optional[Dict[str, bool]] = None) -> None: + if self._get_flag("readonly"): + raise ReadonlyConfigError("Cannot set value of read-only config node") + self._val = self.validate_and_convert(value) + + def _validate_and_convert_impl(self, value: Any) -> Any: + # Interpolation results may be anything. + return value + + def __deepcopy__(self, memo: Dict[int, Any]) -> "InterpolationResultNode": + # Currently there should be no need to deep-copy such nodes. + raise NotImplementedError + + def _is_interpolation(self) -> bool: + # The result of an interpolation cannot be itself an interpolation. + return False diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/omegaconf.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/omegaconf.py new file mode 100644 index 0000000000000000000000000000000000000000..efde14a4e266ede954c479c8809cdb8ab80cb2d5 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/omegaconf.py @@ -0,0 +1,1157 @@ +"""OmegaConf module""" +import copy +import inspect +import io +import os +import pathlib +import sys +import warnings +from collections import defaultdict +from contextlib import contextmanager +from enum import Enum +from textwrap import dedent +from typing import ( + IO, + Any, + Callable, + Dict, + Generator, + Iterable, + List, + Optional, + Set, + Tuple, + Type, + Union, + overload, +) + +import yaml + +from . import DictConfig, DictKeyType, ListConfig +from ._utils import ( + _DEFAULT_MARKER_, + _ensure_container, + _get_value, + format_and_raise, + get_dict_key_value_types, + get_list_element_type, + get_omega_conf_dumper, + get_type_of, + is_attr_class, + is_dataclass, + is_dict_annotation, + is_int, + is_list_annotation, + is_primitive_container, + is_primitive_dict, + is_primitive_list, + is_structured_config, + is_tuple_annotation, + is_union_annotation, + nullcontext, + split_key, + type_str, +) +from .base import Box, Container, Node, SCMode, UnionNode +from .basecontainer import BaseContainer +from .errors import ( + MissingMandatoryValue, + OmegaConfBaseException, + UnsupportedInterpolationType, + ValidationError, +) +from .nodes import ( + AnyNode, + BooleanNode, + BytesNode, + EnumNode, + FloatNode, + IntegerNode, + PathNode, + StringNode, + ValueNode, +) + +MISSING: Any = "???" + +Resolver = Callable[..., Any] + + +def II(interpolation: str) -> Any: + """ + Equivalent to ``${interpolation}`` + + :param interpolation: + :return: input ``${node}`` with type Any + """ + return "${" + interpolation + "}" + + +def SI(interpolation: str) -> Any: + """ + Use this for String interpolation, for example ``"http://${host}:${port}"`` + + :param interpolation: interpolation string + :return: input interpolation with type ``Any`` + """ + return interpolation + + +def register_default_resolvers() -> None: + from omegaconf.resolvers import oc + + OmegaConf.register_new_resolver("oc.create", oc.create) + OmegaConf.register_new_resolver("oc.decode", oc.decode) + OmegaConf.register_new_resolver("oc.deprecated", oc.deprecated) + OmegaConf.register_new_resolver("oc.env", oc.env) + OmegaConf.register_new_resolver("oc.select", oc.select) + OmegaConf.register_new_resolver("oc.dict.keys", oc.dict.keys) + OmegaConf.register_new_resolver("oc.dict.values", oc.dict.values) + + +class OmegaConf: + """OmegaConf primary class""" + + def __init__(self) -> None: + raise NotImplementedError("Use one of the static construction functions") + + @staticmethod + def structured( + obj: Any, + parent: Optional[BaseContainer] = None, + flags: Optional[Dict[str, bool]] = None, + ) -> Any: + return OmegaConf.create(obj, parent, flags) + + @staticmethod + @overload + def create( + obj: str, + parent: Optional[BaseContainer] = None, + flags: Optional[Dict[str, bool]] = None, + ) -> Union[DictConfig, ListConfig]: + ... + + @staticmethod + @overload + def create( + obj: Union[List[Any], Tuple[Any, ...]], + parent: Optional[BaseContainer] = None, + flags: Optional[Dict[str, bool]] = None, + ) -> ListConfig: + ... + + @staticmethod + @overload + def create( + obj: DictConfig, + parent: Optional[BaseContainer] = None, + flags: Optional[Dict[str, bool]] = None, + ) -> DictConfig: + ... + + @staticmethod + @overload + def create( + obj: ListConfig, + parent: Optional[BaseContainer] = None, + flags: Optional[Dict[str, bool]] = None, + ) -> ListConfig: + ... + + @staticmethod + @overload + def create( + obj: Optional[Dict[Any, Any]] = None, + parent: Optional[BaseContainer] = None, + flags: Optional[Dict[str, bool]] = None, + ) -> DictConfig: + ... + + @staticmethod + def create( # noqa F811 + obj: Any = _DEFAULT_MARKER_, + parent: Optional[BaseContainer] = None, + flags: Optional[Dict[str, bool]] = None, + ) -> Union[DictConfig, ListConfig]: + return OmegaConf._create_impl( + obj=obj, + parent=parent, + flags=flags, + ) + + @staticmethod + def load(file_: Union[str, pathlib.Path, IO[Any]]) -> Union[DictConfig, ListConfig]: + from ._utils import get_yaml_loader + + if isinstance(file_, (str, pathlib.Path)): + with io.open(os.path.abspath(file_), "r", encoding="utf-8") as f: + obj = yaml.load(f, Loader=get_yaml_loader()) + elif getattr(file_, "read", None): + obj = yaml.load(file_, Loader=get_yaml_loader()) + else: + raise TypeError("Unexpected file type") + + if obj is not None and not isinstance(obj, (list, dict, str)): + raise IOError( # pragma: no cover + f"Invalid loaded object type: {type(obj).__name__}" + ) + + ret: Union[DictConfig, ListConfig] + if obj is None: + ret = OmegaConf.create() + else: + ret = OmegaConf.create(obj) + return ret + + @staticmethod + def save( + config: Any, f: Union[str, pathlib.Path, IO[Any]], resolve: bool = False + ) -> None: + """ + Save as configuration object to a file + + :param config: omegaconf.Config object (DictConfig or ListConfig). + :param f: filename or file object + :param resolve: True to save a resolved config (defaults to False) + """ + if is_dataclass(config) or is_attr_class(config): + config = OmegaConf.create(config) + data = OmegaConf.to_yaml(config, resolve=resolve) + if isinstance(f, (str, pathlib.Path)): + with io.open(os.path.abspath(f), "w", encoding="utf-8") as file: + file.write(data) + elif hasattr(f, "write"): + f.write(data) + f.flush() + else: + raise TypeError("Unexpected file type") + + @staticmethod + def from_cli(args_list: Optional[List[str]] = None) -> DictConfig: + if args_list is None: + # Skip program name + args_list = sys.argv[1:] + return OmegaConf.from_dotlist(args_list) + + @staticmethod + def from_dotlist(dotlist: List[str]) -> DictConfig: + """ + Creates config from the content sys.argv or from the specified args list of not None + + :param dotlist: A list of dotlist-style strings, e.g. ``["foo.bar=1", "baz=qux"]``. + :return: A ``DictConfig`` object created from the dotlist. + """ + conf = OmegaConf.create() + conf.merge_with_dotlist(dotlist) + return conf + + @staticmethod + def merge( + *configs: Union[ + DictConfig, + ListConfig, + Dict[DictKeyType, Any], + List[Any], + Tuple[Any, ...], + Any, + ], + ) -> Union[ListConfig, DictConfig]: + """ + Merge a list of previously created configs into a single one + + :param configs: Input configs + :return: the merged config object. + """ + assert len(configs) > 0 + target = copy.deepcopy(configs[0]) + target = _ensure_container(target) + assert isinstance(target, (DictConfig, ListConfig)) + + with flag_override(target, "readonly", False): + target.merge_with(*configs[1:]) + turned_readonly = target._get_flag("readonly") is True + + if turned_readonly: + OmegaConf.set_readonly(target, True) + + return target + + @staticmethod + def unsafe_merge( + *configs: Union[ + DictConfig, + ListConfig, + Dict[DictKeyType, Any], + List[Any], + Tuple[Any, ...], + Any, + ], + ) -> Union[ListConfig, DictConfig]: + """ + Merge a list of previously created configs into a single one + This is much faster than OmegaConf.merge() as the input configs are not copied. + However, the input configs must not be used after this operation as will become inconsistent. + + :param configs: Input configs + :return: the merged config object. + """ + assert len(configs) > 0 + target = configs[0] + target = _ensure_container(target) + assert isinstance(target, (DictConfig, ListConfig)) + + with flag_override( + target, ["readonly", "no_deepcopy_set_nodes"], [False, True] + ): + target.merge_with(*configs[1:]) + turned_readonly = target._get_flag("readonly") is True + + if turned_readonly: + OmegaConf.set_readonly(target, True) + + return target + + @staticmethod + def register_resolver(name: str, resolver: Resolver) -> None: + warnings.warn( + dedent( + """\ + register_resolver() is deprecated. + See https://github.com/omry/omegaconf/issues/426 for migration instructions. + """ + ), + stacklevel=2, + ) + return OmegaConf.legacy_register_resolver(name, resolver) + + # This function will eventually be deprecated and removed. + @staticmethod + def legacy_register_resolver(name: str, resolver: Resolver) -> None: + assert callable(resolver), "resolver must be callable" + # noinspection PyProtectedMember + assert ( + name not in BaseContainer._resolvers + ), f"resolver '{name}' is already registered" + + def resolver_wrapper( + config: BaseContainer, + parent: BaseContainer, + node: Node, + args: Tuple[Any, ...], + args_str: Tuple[str, ...], + ) -> Any: + cache = OmegaConf.get_cache(config)[name] + # "Un-escape " spaces and commas. + args_unesc = [x.replace(r"\ ", " ").replace(r"\,", ",") for x in args_str] + + # Nested interpolations behave in a potentially surprising way with + # legacy resolvers (they remain as strings, e.g., "${foo}"). If any + # input looks like an interpolation we thus raise an exception. + try: + bad_arg = next(i for i in args_unesc if "${" in i) + except StopIteration: + pass + else: + raise ValueError( + f"Resolver '{name}' was called with argument '{bad_arg}' that appears " + f"to be an interpolation. Nested interpolations are not supported for " + f"resolvers registered with `[legacy_]register_resolver()`, please use " + f"`register_new_resolver()` instead (see " + f"https://github.com/omry/omegaconf/issues/426 for migration instructions)." + ) + key = args_str + val = cache[key] if key in cache else resolver(*args_unesc) + cache[key] = val + return val + + # noinspection PyProtectedMember + BaseContainer._resolvers[name] = resolver_wrapper + + @staticmethod + def register_new_resolver( + name: str, + resolver: Resolver, + *, + replace: bool = False, + use_cache: bool = False, + ) -> None: + """ + Register a resolver. + + :param name: Name of the resolver. + :param resolver: Callable whose arguments are provided in the interpolation, + e.g., with ${foo:x,0,${y.z}} these arguments are respectively "x" (str), + 0 (int) and the value of ``y.z``. + :param replace: If set to ``False`` (default), then a ``ValueError`` is raised if + an existing resolver has already been registered with the same name. + If set to ``True``, then the new resolver replaces the previous one. + NOTE: The cache on existing config objects is not affected, use + ``OmegaConf.clear_cache(cfg)`` to clear it. + :param use_cache: Whether the resolver's outputs should be cached. The cache is + based only on the string literals representing the resolver arguments, e.g., + ${foo:${bar}} will always return the same value regardless of the value of + ``bar`` if the cache is enabled for ``foo``. + """ + if not callable(resolver): + raise TypeError("resolver must be callable") + if not name: + raise ValueError("cannot use an empty resolver name") + + if not replace and OmegaConf.has_resolver(name): + raise ValueError(f"resolver '{name}' is already registered") + + try: + sig: Optional[inspect.Signature] = inspect.signature(resolver) + except ValueError: + sig = None + + def _should_pass(special: str) -> bool: + ret = sig is not None and special in sig.parameters + if ret and use_cache: + raise ValueError( + f"use_cache=True is incompatible with functions that receive the {special}" + ) + return ret + + pass_parent = _should_pass("_parent_") + pass_node = _should_pass("_node_") + pass_root = _should_pass("_root_") + + def resolver_wrapper( + config: BaseContainer, + parent: Container, + node: Node, + args: Tuple[Any, ...], + args_str: Tuple[str, ...], + ) -> Any: + if use_cache: + cache = OmegaConf.get_cache(config)[name] + try: + return cache[args_str] + except KeyError: + pass + + # Call resolver. + kwargs: Dict[str, Node] = {} + if pass_parent: + kwargs["_parent_"] = parent + if pass_node: + kwargs["_node_"] = node + if pass_root: + kwargs["_root_"] = config + + ret = resolver(*args, **kwargs) + + if use_cache: + cache[args_str] = ret + return ret + + # noinspection PyProtectedMember + BaseContainer._resolvers[name] = resolver_wrapper + + @classmethod + def has_resolver(cls, name: str) -> bool: + return cls._get_resolver(name) is not None + + # noinspection PyProtectedMember + @staticmethod + def clear_resolvers() -> None: + """ + Clear(remove) all OmegaConf resolvers, then re-register OmegaConf's default resolvers. + """ + BaseContainer._resolvers = {} + register_default_resolvers() + + @classmethod + def clear_resolver(cls, name: str) -> bool: + """ + Clear(remove) any resolver only if it exists. + + Returns a bool: True if resolver is removed and False if not removed. + + .. warning: + This method can remove deafult resolvers as well. + + :param name: Name of the resolver. + :return: A bool (``True`` if resolver is removed, ``False`` if not found before removing). + """ + if cls.has_resolver(name): + BaseContainer._resolvers.pop(name) + return True + else: + # return False if resolver does not exist + return False + + @staticmethod + def get_cache(conf: BaseContainer) -> Dict[str, Any]: + return conf._metadata.resolver_cache + + @staticmethod + def set_cache(conf: BaseContainer, cache: Dict[str, Any]) -> None: + conf._metadata.resolver_cache = copy.deepcopy(cache) + + @staticmethod + def clear_cache(conf: BaseContainer) -> None: + OmegaConf.set_cache(conf, defaultdict(dict, {})) + + @staticmethod + def copy_cache(from_config: BaseContainer, to_config: BaseContainer) -> None: + OmegaConf.set_cache(to_config, OmegaConf.get_cache(from_config)) + + @staticmethod + def set_readonly(conf: Node, value: Optional[bool]) -> None: + # noinspection PyProtectedMember + conf._set_flag("readonly", value) + + @staticmethod + def is_readonly(conf: Node) -> Optional[bool]: + # noinspection PyProtectedMember + return conf._get_flag("readonly") + + @staticmethod + def set_struct(conf: Container, value: Optional[bool]) -> None: + # noinspection PyProtectedMember + conf._set_flag("struct", value) + + @staticmethod + def is_struct(conf: Container) -> Optional[bool]: + # noinspection PyProtectedMember + return conf._get_flag("struct") + + @staticmethod + def masked_copy(conf: DictConfig, keys: Union[str, List[str]]) -> DictConfig: + """ + Create a masked copy of of this config that contains a subset of the keys + + :param conf: DictConfig object + :param keys: keys to preserve in the copy + :return: The masked ``DictConfig`` object. + """ + from .dictconfig import DictConfig + + if not isinstance(conf, DictConfig): + raise ValueError("masked_copy is only supported for DictConfig") + + if isinstance(keys, str): + keys = [keys] + content = {key: value for key, value in conf.items_ex(resolve=False, keys=keys)} + return DictConfig(content=content) + + @staticmethod + def to_container( + cfg: Any, + *, + resolve: bool = False, + throw_on_missing: bool = False, + enum_to_str: bool = False, + structured_config_mode: SCMode = SCMode.DICT, + ) -> Union[Dict[DictKeyType, Any], List[Any], None, str, Any]: + """ + Resursively converts an OmegaConf config to a primitive container (dict or list). + + :param cfg: the config to convert + :param resolve: True to resolve all values + :param throw_on_missing: When True, raise MissingMandatoryValue if any missing values are present. + When False (the default), replace missing values with the string "???" in the output container. + :param enum_to_str: True to convert Enum keys and values to strings + :param structured_config_mode: Specify how Structured Configs (DictConfigs backed by a dataclass) are handled. + - By default (``structured_config_mode=SCMode.DICT``) structured configs are converted to plain dicts. + - If ``structured_config_mode=SCMode.DICT_CONFIG``, structured config nodes will remain as DictConfig. + - If ``structured_config_mode=SCMode.INSTANTIATE``, this function will instantiate structured configs + (DictConfigs backed by a dataclass), by creating an instance of the underlying dataclass. + + See also OmegaConf.to_object. + :return: A dict or a list representing this config as a primitive container. + """ + if not OmegaConf.is_config(cfg): + raise ValueError( + f"Input cfg is not an OmegaConf config object ({type_str(type(cfg))})" + ) + + return BaseContainer._to_content( + cfg, + resolve=resolve, + throw_on_missing=throw_on_missing, + enum_to_str=enum_to_str, + structured_config_mode=structured_config_mode, + ) + + @staticmethod + def to_object(cfg: Any) -> Union[Dict[DictKeyType, Any], List[Any], None, str, Any]: + """ + Resursively converts an OmegaConf config to a primitive container (dict or list). + Any DictConfig objects backed by dataclasses or attrs classes are instantiated + as instances of those backing classes. + + This is an alias for OmegaConf.to_container(..., resolve=True, throw_on_missing=True, + structured_config_mode=SCMode.INSTANTIATE) + + :param cfg: the config to convert + :return: A dict or a list or dataclass representing this config. + """ + return OmegaConf.to_container( + cfg=cfg, + resolve=True, + throw_on_missing=True, + enum_to_str=False, + structured_config_mode=SCMode.INSTANTIATE, + ) + + @staticmethod + def is_missing(cfg: Any, key: DictKeyType) -> bool: + assert isinstance(cfg, Container) + try: + node = cfg._get_child(key) + if node is None: + return False + assert isinstance(node, Node) + return node._is_missing() + except (UnsupportedInterpolationType, KeyError, AttributeError): + return False + + @staticmethod + def is_interpolation(node: Any, key: Optional[Union[int, str]] = None) -> bool: + if key is not None: + assert isinstance(node, Container) + target = node._get_child(key) + else: + target = node + if target is not None: + assert isinstance(target, Node) + return target._is_interpolation() + return False + + @staticmethod + def is_list(obj: Any) -> bool: + from . import ListConfig + + return isinstance(obj, ListConfig) + + @staticmethod + def is_dict(obj: Any) -> bool: + from . import DictConfig + + return isinstance(obj, DictConfig) + + @staticmethod + def is_config(obj: Any) -> bool: + from . import Container + + return isinstance(obj, Container) + + @staticmethod + def get_type(obj: Any, key: Optional[str] = None) -> Optional[Type[Any]]: + if key is not None: + c = obj._get_child(key) + else: + c = obj + return OmegaConf._get_obj_type(c) + + @staticmethod + def select( + cfg: Container, + key: str, + *, + default: Any = _DEFAULT_MARKER_, + throw_on_resolution_failure: bool = True, + throw_on_missing: bool = False, + ) -> Any: + """ + :param cfg: Config node to select from + :param key: Key to select + :param default: Default value to return if key is not found + :param throw_on_resolution_failure: Raise an exception if an interpolation + resolution error occurs, otherwise return None + :param throw_on_missing: Raise an exception if an attempt to select a missing key (with the value '???') + is made, otherwise return None + :return: selected value or None if not found. + """ + from ._impl import select_value + + try: + return select_value( + cfg=cfg, + key=key, + default=default, + throw_on_resolution_failure=throw_on_resolution_failure, + throw_on_missing=throw_on_missing, + ) + except Exception as e: + format_and_raise(node=cfg, key=key, value=None, cause=e, msg=str(e)) + + @staticmethod + def update( + cfg: Container, + key: str, + value: Any = None, + *, + merge: bool = True, + force_add: bool = False, + ) -> None: + """ + Updates a dot separated key sequence to a value + + :param cfg: input config to update + :param key: key to update (can be a dot separated path) + :param value: value to set, if value if a list or a dict it will be merged or set + depending on merge_config_values + :param merge: If value is a dict or a list, True (default) to merge + into the destination, False to replace the destination. + :param force_add: insert the entire path regardless of Struct flag or Structured Config nodes. + """ + + split = split_key(key) + root = cfg + for i in range(len(split) - 1): + k = split[i] + # if next_root is a primitive (string, int etc) replace it with an empty map + next_root, key_ = _select_one(root, k, throw_on_missing=False) + if not isinstance(next_root, Container): + if force_add: + with flag_override(root, "struct", False): + root[key_] = {} + else: + root[key_] = {} + root = root[key_] + + last = split[-1] + + assert isinstance( + root, Container + ), f"Unexpected type for root: {type(root).__name__}" + + last_key: Union[str, int] = last + if isinstance(root, ListConfig): + last_key = int(last) + + ctx = flag_override(root, "struct", False) if force_add else nullcontext() + with ctx: + if merge and (OmegaConf.is_config(value) or is_primitive_container(value)): + assert isinstance(root, BaseContainer) + node = root._get_child(last_key) + if OmegaConf.is_config(node): + assert isinstance(node, BaseContainer) + node.merge_with(value) + return + + if OmegaConf.is_dict(root): + assert isinstance(last_key, str) + root.__setattr__(last_key, value) + elif OmegaConf.is_list(root): + assert isinstance(last_key, int) + root.__setitem__(last_key, value) + else: + assert False + + @staticmethod + def to_yaml(cfg: Any, *, resolve: bool = False, sort_keys: bool = False) -> str: + """ + returns a yaml dump of this config object. + + :param cfg: Config object, Structured Config type or instance + :param resolve: if True, will return a string with the interpolations resolved, otherwise + interpolations are preserved + :param sort_keys: If True, will print dict keys in sorted order. default False. + :return: A string containing the yaml representation. + """ + cfg = _ensure_container(cfg) + container = OmegaConf.to_container(cfg, resolve=resolve, enum_to_str=True) + return yaml.dump( # type: ignore + container, + default_flow_style=False, + allow_unicode=True, + sort_keys=sort_keys, + Dumper=get_omega_conf_dumper(), + ) + + @staticmethod + def resolve(cfg: Container) -> None: + """ + Resolves all interpolations in the given config object in-place. + + :param cfg: An OmegaConf container (DictConfig, ListConfig) + Raises a ValueError if the input object is not an OmegaConf container. + """ + import omegaconf._impl + + if not OmegaConf.is_config(cfg): + # Since this function is mutating the input object in-place, it doesn't make sense to + # auto-convert the input object to an OmegaConf container + raise ValueError( + f"Invalid config type ({type(cfg).__name__}), expected an OmegaConf Container" + ) + omegaconf._impl._resolve(cfg) + + @staticmethod + def missing_keys(cfg: Any) -> Set[str]: + """ + Returns a set of missing keys in a dotlist style. + + :param cfg: An ``OmegaConf.Container``, + or a convertible object via ``OmegaConf.create`` (dict, list, ...). + :return: set of strings of the missing keys. + :raises ValueError: On input not representing a config. + """ + cfg = _ensure_container(cfg) + missings: Set[str] = set() + + def gather(_cfg: Container) -> None: + itr: Iterable[Any] + if isinstance(_cfg, ListConfig): + itr = range(len(_cfg)) + else: + itr = _cfg + + for key in itr: + if OmegaConf.is_missing(_cfg, key): + missings.add(_cfg._get_full_key(key)) + elif OmegaConf.is_config(_cfg[key]): + gather(_cfg[key]) + + gather(cfg) + return missings + + # === private === # + + @staticmethod + def _create_impl( # noqa F811 + obj: Any = _DEFAULT_MARKER_, + parent: Optional[BaseContainer] = None, + flags: Optional[Dict[str, bool]] = None, + ) -> Union[DictConfig, ListConfig]: + try: + from ._utils import get_yaml_loader + from .dictconfig import DictConfig + from .listconfig import ListConfig + + if obj is _DEFAULT_MARKER_: + obj = {} + if isinstance(obj, str): + obj = yaml.load(obj, Loader=get_yaml_loader()) + if obj is None: + return OmegaConf.create({}, parent=parent, flags=flags) + elif isinstance(obj, str): + return OmegaConf.create({obj: None}, parent=parent, flags=flags) + else: + assert isinstance(obj, (list, dict)) + return OmegaConf.create(obj, parent=parent, flags=flags) + + else: + if ( + is_primitive_dict(obj) + or OmegaConf.is_dict(obj) + or is_structured_config(obj) + or obj is None + ): + if isinstance(obj, DictConfig): + return DictConfig( + content=obj, + parent=parent, + ref_type=obj._metadata.ref_type, + is_optional=obj._metadata.optional, + key_type=obj._metadata.key_type, + element_type=obj._metadata.element_type, + flags=flags, + ) + else: + obj_type = OmegaConf.get_type(obj) + key_type, element_type = get_dict_key_value_types(obj_type) + return DictConfig( + content=obj, + parent=parent, + key_type=key_type, + element_type=element_type, + flags=flags, + ) + elif is_primitive_list(obj) or OmegaConf.is_list(obj): + if isinstance(obj, ListConfig): + return ListConfig( + content=obj, + parent=parent, + element_type=obj._metadata.element_type, + ref_type=obj._metadata.ref_type, + is_optional=obj._metadata.optional, + flags=flags, + ) + else: + obj_type = OmegaConf.get_type(obj) + element_type = get_list_element_type(obj_type) + return ListConfig( + content=obj, + parent=parent, + element_type=element_type, + ref_type=Any, + is_optional=True, + flags=flags, + ) + else: + if isinstance(obj, type): + raise ValidationError( + f"Input class '{obj.__name__}' is not a structured config. " + "did you forget to decorate it as a dataclass?" + ) + else: + raise ValidationError( + f"Object of unsupported type: '{type(obj).__name__}'" + ) + except OmegaConfBaseException as e: + format_and_raise(node=None, key=None, value=None, msg=str(e), cause=e) + assert False + + @staticmethod + def _get_obj_type(c: Any) -> Optional[Type[Any]]: + if is_structured_config(c): + return get_type_of(c) + elif c is None: + return None + elif isinstance(c, DictConfig): + if c._is_none(): + return None + elif c._is_missing(): + return None + else: + if is_structured_config(c._metadata.object_type): + return c._metadata.object_type + else: + return dict + elif isinstance(c, ListConfig): + return list + elif isinstance(c, ValueNode): + return type(c._value()) + elif isinstance(c, UnionNode): + return type(_get_value(c)) + elif isinstance(c, dict): + return dict + elif isinstance(c, (list, tuple)): + return list + else: + return get_type_of(c) + + @staticmethod + def _get_resolver( + name: str, + ) -> Optional[ + Callable[ + [Container, Container, Node, Tuple[Any, ...], Tuple[str, ...]], + Any, + ] + ]: + # noinspection PyProtectedMember + return ( + BaseContainer._resolvers[name] if name in BaseContainer._resolvers else None + ) + + +# register all default resolvers +register_default_resolvers() + + +@contextmanager +def flag_override( + config: Node, + names: Union[List[str], str], + values: Union[List[Optional[bool]], Optional[bool]], +) -> Generator[Node, None, None]: + if isinstance(names, str): + names = [names] + if values is None or isinstance(values, bool): + values = [values] + + prev_states = [config._get_node_flag(name) for name in names] + + try: + config._set_flag(names, values) + yield config + finally: + config._set_flag(names, prev_states) + + +@contextmanager +def read_write(config: Node) -> Generator[Node, None, None]: + prev_state = config._get_node_flag("readonly") + try: + OmegaConf.set_readonly(config, False) + yield config + finally: + OmegaConf.set_readonly(config, prev_state) + + +@contextmanager +def open_dict(config: Container) -> Generator[Container, None, None]: + prev_state = config._get_node_flag("struct") + try: + OmegaConf.set_struct(config, False) + yield config + finally: + OmegaConf.set_struct(config, prev_state) + + +# === private === # + + +def _node_wrap( + parent: Optional[Box], + is_optional: bool, + value: Any, + key: Any, + ref_type: Any = Any, +) -> Node: + node: Node + if is_dict_annotation(ref_type) or (is_primitive_dict(value) and ref_type is Any): + key_type, element_type = get_dict_key_value_types(ref_type) + node = DictConfig( + content=value, + key=key, + parent=parent, + ref_type=ref_type, + is_optional=is_optional, + key_type=key_type, + element_type=element_type, + ) + elif (is_list_annotation(ref_type) or is_tuple_annotation(ref_type)) or ( + type(value) in (list, tuple) and ref_type is Any + ): + element_type = get_list_element_type(ref_type) + node = ListConfig( + content=value, + key=key, + parent=parent, + is_optional=is_optional, + element_type=element_type, + ref_type=ref_type, + ) + elif is_structured_config(ref_type) or is_structured_config(value): + key_type, element_type = get_dict_key_value_types(value) + node = DictConfig( + ref_type=ref_type, + is_optional=is_optional, + content=value, + key=key, + parent=parent, + key_type=key_type, + element_type=element_type, + ) + elif is_union_annotation(ref_type): + node = UnionNode( + content=value, + ref_type=ref_type, + is_optional=is_optional, + key=key, + parent=parent, + ) + elif ref_type == Any or ref_type is None: + node = AnyNode(value=value, key=key, parent=parent) + elif isinstance(ref_type, type) and issubclass(ref_type, Enum): + node = EnumNode( + enum_type=ref_type, + value=value, + key=key, + parent=parent, + is_optional=is_optional, + ) + elif ref_type == int: + node = IntegerNode(value=value, key=key, parent=parent, is_optional=is_optional) + elif ref_type == float: + node = FloatNode(value=value, key=key, parent=parent, is_optional=is_optional) + elif ref_type == bool: + node = BooleanNode(value=value, key=key, parent=parent, is_optional=is_optional) + elif ref_type == str: + node = StringNode(value=value, key=key, parent=parent, is_optional=is_optional) + elif ref_type == bytes: + node = BytesNode(value=value, key=key, parent=parent, is_optional=is_optional) + elif ref_type == pathlib.Path: + node = PathNode(value=value, key=key, parent=parent, is_optional=is_optional) + else: + if parent is not None and parent._get_flag("allow_objects") is True: + if type(value) in (list, tuple): + node = ListConfig( + content=value, + key=key, + parent=parent, + ref_type=ref_type, + is_optional=is_optional, + ) + elif is_primitive_dict(value): + node = DictConfig( + content=value, + key=key, + parent=parent, + ref_type=ref_type, + is_optional=is_optional, + ) + else: + node = AnyNode(value=value, key=key, parent=parent) + else: + raise ValidationError(f"Unexpected type annotation: {type_str(ref_type)}") + return node + + +def _maybe_wrap( + ref_type: Any, + key: Any, + value: Any, + is_optional: bool, + parent: Optional[BaseContainer], +) -> Node: + # if already a node, update key and parent and return as is. + # NOTE: that this mutate the input node! + if isinstance(value, Node): + value._set_key(key) + value._set_parent(parent) + return value + else: + return _node_wrap( + ref_type=ref_type, + parent=parent, + is_optional=is_optional, + value=value, + key=key, + ) + + +def _select_one( + c: Container, key: str, throw_on_missing: bool, throw_on_type_error: bool = True +) -> Tuple[Optional[Node], Union[str, int]]: + from .dictconfig import DictConfig + from .listconfig import ListConfig + + ret_key: Union[str, int] = key + assert isinstance(c, Container), f"Unexpected type: {c}" + if c._is_none(): + return None, ret_key + + if isinstance(c, DictConfig): + assert isinstance(ret_key, str) + val = c._get_child(ret_key, validate_access=False) + elif isinstance(c, ListConfig): + assert isinstance(ret_key, str) + if not is_int(ret_key): + if throw_on_type_error: + raise TypeError( + f"Index '{ret_key}' ({type(ret_key).__name__}) is not an int" + ) + else: + val = None + else: + ret_key = int(ret_key) + if ret_key < 0 or ret_key + 1 > len(c): + val = None + else: + val = c._get_child(ret_key) + else: + assert False + + if val is not None: + assert isinstance(val, Node) + if val._is_missing(): + if throw_on_missing: + raise MissingMandatoryValue( + f"Missing mandatory value: {c._get_full_key(ret_key)}" + ) + else: + return val, ret_key + + assert val is None or isinstance(val, Node) + return val, ret_key diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/py.typed b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/py.typed new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/resolvers/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/resolvers/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..1b0bb71168722281af5ddecb46f79e36f63c71e4 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/resolvers/__init__.py @@ -0,0 +1,5 @@ +from omegaconf.resolvers import oc + +__all__ = [ + "oc", +] diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/resolvers/oc/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/resolvers/oc/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..c177ab4f1b35dfa5502a9b6359439bfca180ba1d --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/resolvers/oc/__init__.py @@ -0,0 +1,113 @@ +import os +import string +import warnings +from typing import Any, Optional + +from omegaconf import Container, Node +from omegaconf._utils import _DEFAULT_MARKER_, _get_value +from omegaconf.basecontainer import BaseContainer +from omegaconf.errors import ConfigKeyError +from omegaconf.grammar_parser import parse +from omegaconf.resolvers.oc import dict + + +def create(obj: Any, _parent_: Container) -> Any: + """Create a config object from `obj`, similar to `OmegaConf.create`""" + from omegaconf import OmegaConf + + assert isinstance(_parent_, BaseContainer) + return OmegaConf.create(obj, parent=_parent_) + + +def env(key: str, default: Any = _DEFAULT_MARKER_) -> Optional[str]: + """ + :param key: Environment variable key + :param default: Optional default value to use in case the key environment variable is not set. + If default is not a string, it is converted with str(default). + None default is returned as is. + :return: The environment variable 'key'. If the environment variable is not set and a default is + provided, the default is used. If used, the default is converted to a string with str(default). + If the default is None, None is returned (without a string conversion). + """ + try: + return os.environ[key] + except KeyError: + if default is not _DEFAULT_MARKER_: + return str(default) if default is not None else None + else: + raise KeyError(f"Environment variable '{key}' not found") + + +def decode(expr: Optional[str], _parent_: Container, _node_: Node) -> Any: + """ + Parse and evaluate `expr` according to the `singleElement` rule of the grammar. + + If `expr` is `None`, then return `None`. + """ + if expr is None: + return None + + if not isinstance(expr, str): + raise TypeError( + f"`oc.decode` can only take strings or None as input, " + f"but `{expr}` is of type {type(expr).__name__}" + ) + + parse_tree = parse(expr, parser_rule="singleElement", lexer_mode="VALUE_MODE") + val = _parent_.resolve_parse_tree(parse_tree, node=_node_) + return _get_value(val) + + +def deprecated( + key: str, + message: str = "'$OLD_KEY' is deprecated. Change your code and config to use '$NEW_KEY'", + *, + _parent_: Container, + _node_: Node, +) -> Any: + from omegaconf._impl import select_node + + if not isinstance(key, str): + raise TypeError( + f"oc.deprecated: interpolation key type is not a string ({type(key).__name__})" + ) + + if not isinstance(message, str): + raise TypeError( + f"oc.deprecated: interpolation message type is not a string ({type(message).__name__})" + ) + + full_key = _node_._get_full_key(key=None) + target_node = select_node(_parent_, key, absolute_key=True) + if target_node is None: + raise ConfigKeyError( + f"In oc.deprecated resolver at '{full_key}': Key not found: '{key}'" + ) + new_key = target_node._get_full_key(key=None) + msg = string.Template(message).safe_substitute( + OLD_KEY=full_key, + NEW_KEY=new_key, + ) + warnings.warn(category=UserWarning, message=msg) + return target_node + + +def select( + key: str, + default: Any = _DEFAULT_MARKER_, + *, + _parent_: Container, +) -> Any: + from omegaconf._impl import select_value + + return select_value(cfg=_parent_, key=key, absolute_key=True, default=default) + + +__all__ = [ + "create", + "decode", + "deprecated", + "dict", + "env", + "select", +] diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/resolvers/oc/dict.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/resolvers/oc/dict.py new file mode 100644 index 0000000000000000000000000000000000000000..adbfd16ac47f2dced46f509c8dfdc8516ba97f4a --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/resolvers/oc/dict.py @@ -0,0 +1,83 @@ +from typing import Any, List + +from omegaconf import AnyNode, Container, DictConfig, ListConfig +from omegaconf._utils import Marker +from omegaconf.basecontainer import BaseContainer +from omegaconf.errors import ConfigKeyError + +_DEFAULT_SELECT_MARKER_: Any = Marker("_DEFAULT_SELECT_MARKER_") + + +def keys( + key: str, + _parent_: Container, +) -> ListConfig: + from omegaconf import OmegaConf + + assert isinstance(_parent_, BaseContainer) + + in_dict = _get_and_validate_dict_input( + key, parent=_parent_, resolver_name="oc.dict.keys" + ) + + ret = OmegaConf.create(list(in_dict.keys()), parent=_parent_) + assert isinstance(ret, ListConfig) + return ret + + +def values(key: str, _root_: BaseContainer, _parent_: Container) -> ListConfig: + assert isinstance(_parent_, BaseContainer) + in_dict = _get_and_validate_dict_input( + key, parent=_parent_, resolver_name="oc.dict.values" + ) + + content = in_dict._content + assert isinstance(content, dict) + + ret = ListConfig([]) + if key.startswith("."): + key = f".{key}" # extra dot to compensate for extra level of nesting within ret ListConfig + for k in content: + ref_node = AnyNode(f"${{{key}.{k!s}}}") + ret.append(ref_node) + + # Finalize result by setting proper type and parent. + element_type: Any = in_dict._metadata.element_type + ret._metadata.element_type = element_type + ret._metadata.ref_type = List[element_type] + ret._set_parent(_parent_) + + return ret + + +def _get_and_validate_dict_input( + key: str, + parent: BaseContainer, + resolver_name: str, +) -> DictConfig: + from omegaconf._impl import select_value + + if not isinstance(key, str): + raise TypeError( + f"`{resolver_name}` requires a string as input, but obtained `{key}` " + f"of type: {type(key).__name__}" + ) + + in_dict = select_value( + parent, + key, + throw_on_missing=True, + absolute_key=True, + default=_DEFAULT_SELECT_MARKER_, + ) + + if in_dict is _DEFAULT_SELECT_MARKER_: + raise ConfigKeyError(f"Key not found: '{key}'") + + if not isinstance(in_dict, DictConfig): + raise TypeError( + f"`{resolver_name}` cannot be applied to objects of type: " + f"{type(in_dict).__name__}" + ) + + return in_dict diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/version.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/version.py new file mode 100644 index 0000000000000000000000000000000000000000..b4989b2ef4670f866a92767403c3f63204a06f81 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/omegaconf/version.py @@ -0,0 +1,13 @@ +import sys # pragma: no cover + +__version__ = "2.3.0" + +msg = """OmegaConf 2.0 and above is compatible with Python 3.6 and newer. +You have the following options: +1. Upgrade to Python 3.6 or newer. + This is highly recommended. new features will not be added to OmegaConf 1.4. +2. Continue using OmegaConf 1.4: + You can pip install 'OmegaConf<1.5' to do that. +""" +if sys.version_info < (3, 6): + raise ImportError(msg) # pragma: no cover diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..8fb62d5a8e2137a0dce4cdcbb196f141f3da8afb --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/__init__.py @@ -0,0 +1,49 @@ +#-*- coding: utf-8 -*- +""" +This package is an implementation of the OpenID specification in +Python. It contains code for both server and consumer +implementations. For information on implementing an OpenID consumer, +see the C{L{openid.consumer.consumer}} module. For information on +implementing an OpenID server, see the C{L{openid.server.server}} +module. + +@contact: U{http://github.com/necaris/python3-openid/} + +@copyright: (C) 2005-2008 JanRain, Inc., 2012-2017 Rami Chowdhury + +@license: Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + U{http://www.apache.org/licenses/LICENSE-2.0} + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions + and limitations under the License. +""" + +version_info = (3, 2, 0) + + + + +__version__ = ".".join(str(x) for x in version_info) + +__all__ = [ + 'association', + 'consumer', + 'cryptutil', + 'dh', + 'extension', + 'extensions', + 'fetchers', + 'kvform', + 'message', + 'oidutil', + 'server', + 'sreg', + 'store', + 'urinorm', + 'yadis', +] diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/association.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/association.py new file mode 100644 index 0000000000000000000000000000000000000000..94d68d71ac0a18dcbe8fb12f9f99ff1127163eb0 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/association.py @@ -0,0 +1,562 @@ +#-*-test-case-name: openid.test.test_association-*- +#-*- coding: utf-8 -*- +""" +This module contains code for dealing with associations between +consumers and servers. Associations contain a shared secret that is +used to sign C{openid.mode=id_res} messages. + +Users of the library should not usually need to interact directly with +associations. The L{store}, L{server} +and L{consumer} objects will create and manage +the associations. The consumer and server code will make use of a +C{L{SessionNegotiator}} when managing associations, which enables +users to express a preference for what kind of associations should be +allowed, and what kind of exchange should be done to establish the +association. + +@var default_negotiator: A C{L{SessionNegotiator}} that allows all + association types that are specified by the OpenID + specification. It prefers to use HMAC-SHA1/DH-SHA1, if it's + available. If HMAC-SHA256 is not supported by your Python runtime, + HMAC-SHA256 and DH-SHA256 will not be available. + +@var encrypted_negotiator: A C{L{SessionNegotiator}} that + does not support C{'no-encryption'} associations. It prefers + HMAC-SHA1/DH-SHA1 association types if available. +""" +import time +import functools + +from openid import cryptutil +from openid import kvform +from openid import oidutil +from openid.message import OPENID_NS + +__all__ = [ + 'default_negotiator', + 'encrypted_negotiator', + 'SessionNegotiator', + 'Association', +] + +all_association_types = [ + 'HMAC-SHA1', + 'HMAC-SHA256', +] + +if hasattr(cryptutil, 'hmacSha256'): + supported_association_types = list(all_association_types) + + default_association_order = [ + ('HMAC-SHA1', 'DH-SHA1'), + ('HMAC-SHA1', 'no-encryption'), + ('HMAC-SHA256', 'DH-SHA256'), + ('HMAC-SHA256', 'no-encryption'), + ] + + only_encrypted_association_order = [ + ('HMAC-SHA1', 'DH-SHA1'), + ('HMAC-SHA256', 'DH-SHA256'), + ] +else: + supported_association_types = ['HMAC-SHA1'] + + default_association_order = [ + ('HMAC-SHA1', 'DH-SHA1'), + ('HMAC-SHA1', 'no-encryption'), + ] + + only_encrypted_association_order = [ + ('HMAC-SHA1', 'DH-SHA1'), + ] + + +def getSessionTypes(assoc_type): + """Return the allowed session types for a given association type""" + assoc_to_session = { + 'HMAC-SHA1': ['DH-SHA1', 'no-encryption'], + 'HMAC-SHA256': ['DH-SHA256', 'no-encryption'], + } + return assoc_to_session.get(assoc_type, []) + + +def checkSessionType(assoc_type, session_type): + """Check to make sure that this pair of assoc type and session + type are allowed""" + if session_type not in getSessionTypes(assoc_type): + raise ValueError('Session type %r not valid for assocation type %r' % + (session_type, assoc_type)) + + +class SessionNegotiator(object): + """A session negotiator controls the allowed and preferred + association types and association session types. Both the + C{L{Consumer}} and + C{L{Server}} use negotiators when + creating associations. + + You can create and use negotiators if you: + + - Do not want to do Diffie-Hellman key exchange because you use + transport-layer encryption (e.g. SSL) + + - Want to use only SHA-256 associations + + - Do not want to support plain-text associations over a non-secure + channel + + It is up to you to set a policy for what kinds of associations to + accept. By default, the library will make any kind of association + that is allowed in the OpenID 2.0 specification. + + Use of negotiators in the library + ================================= + + When a consumer makes an association request, it calls + C{L{getAllowedType}} to get the preferred association type and + association session type. + + The server gets a request for a particular association/session + type and calls C{L{isAllowed}} to determine if it should + create an association. If it is supported, negotiation is + complete. If it is not, the server calls C{L{getAllowedType}} to + get an allowed association type to return to the consumer. + + If the consumer gets an error response indicating that the + requested association/session type is not supported by the server + that contains an assocation/session type to try, it calls + C{L{isAllowed}} to determine if it should try again with the + given combination of association/session type. + + @ivar allowed_types: A list of association/session types that are + allowed by the server. The order of the pairs in this list + determines preference. If an association/session type comes + earlier in the list, the library is more likely to use that + type. + @type allowed_types: [(str, str)] + """ + + def __init__(self, allowed_types): + self.setAllowedTypes(allowed_types) + + def copy(self): + return self.__class__(list(self.allowed_types)) + + def setAllowedTypes(self, allowed_types): + """Set the allowed association types, checking to make sure + each combination is valid.""" + for (assoc_type, session_type) in allowed_types: + checkSessionType(assoc_type, session_type) + + self.allowed_types = allowed_types + + def addAllowedType(self, assoc_type, session_type=None): + """Add an association type and session type to the allowed + types list. The assocation/session pairs are tried in the + order that they are added.""" + if self.allowed_types is None: + self.allowed_types = [] + + if session_type is None: + available = getSessionTypes(assoc_type) + + if not available: + raise ValueError('No session available for association type %r' + % (assoc_type, )) + + for session_type in getSessionTypes(assoc_type): + self.addAllowedType(assoc_type, session_type) + else: + checkSessionType(assoc_type, session_type) + self.allowed_types.append((assoc_type, session_type)) + + def isAllowed(self, assoc_type, session_type): + """Is this combination of association type and session type allowed?""" + assoc_good = (assoc_type, session_type) in self.allowed_types + matches = session_type in getSessionTypes(assoc_type) + return assoc_good and matches + + def getAllowedType(self): + """Get a pair of assocation type and session type that are + supported""" + try: + return self.allowed_types[0] + except IndexError: + return (None, None) + + +default_negotiator = SessionNegotiator(default_association_order) +encrypted_negotiator = SessionNegotiator(only_encrypted_association_order) + + +def getSecretSize(assoc_type): + if assoc_type == 'HMAC-SHA1': + return 20 + elif assoc_type == 'HMAC-SHA256': + return 32 + else: + raise ValueError('Unsupported association type: %r' % (assoc_type, )) + + +@functools.total_ordering +class Association(object): + """ + This class represents an association between a server and a + consumer. In general, users of this library will never see + instances of this object. The only exception is if you implement + a custom C{L{OpenIDStore}}. + + If you do implement such a store, it will need to store the values + of the C{L{handle}}, C{L{secret}}, C{L{issued}}, C{L{lifetime}}, and + C{L{assoc_type}} instance variables. + + @ivar handle: This is the handle the server gave this association. + + @type handle: C{str} + + + @ivar secret: This is the shared secret the server generated for + this association. + + @type secret: C{str} + + + @ivar issued: This is the time this association was issued, in + seconds since 00:00 GMT, January 1, 1970. (ie, a unix + timestamp) + + @type issued: C{int} + + + @ivar lifetime: This is the amount of time this association is + good for, measured in seconds since the association was + issued. + + @type lifetime: C{int} + + + @ivar assoc_type: This is the type of association this instance + represents. The only valid value of this field at this time + is C{'HMAC-SHA1'}, but new types may be defined in the future. + + @type assoc_type: C{str} + + + @sort: __init__, fromExpiresIn, expiresIn, __eq__, __ne__, + handle, secret, issued, lifetime, assoc_type + """ + + # The ordering and name of keys as stored by serialize + assoc_keys = [ + 'version', + 'handle', + 'secret', + 'issued', + 'lifetime', + 'assoc_type', + ] + + _macs = { + 'HMAC-SHA1': cryptutil.hmacSha1, + 'HMAC-SHA256': cryptutil.hmacSha256, + } + + @classmethod + def fromExpiresIn(cls, expires_in, handle, secret, assoc_type): + """ + This is an alternate constructor used by the OpenID consumer + library to create associations. C{L{OpenIDStore + }} implementations + shouldn't use this constructor. + + + @param expires_in: This is the amount of time this association + is good for, measured in seconds since the association was + issued. + + @type expires_in: C{int} + + + @param handle: This is the handle the server gave this + association. + + @type handle: C{str} + + + @param secret: This is the shared secret the server generated + for this association. + + @type secret: C{str} + + + @param assoc_type: This is the type of association this + instance represents. The only valid value of this field + at this time is C{'HMAC-SHA1'}, but new types may be + defined in the future. + + @type assoc_type: C{str} + """ + issued = int(time.time()) + lifetime = expires_in + return cls(handle, secret, issued, lifetime, assoc_type) + + def __init__(self, handle, secret, issued, lifetime, assoc_type): + """ + This is the standard constructor for creating an association. + + + @param handle: This is the handle the server gave this + association. + + @type handle: C{str} + + + @param secret: This is the shared secret the server generated + for this association. + + @type secret: C{str} + + + @param issued: This is the time this association was issued, + in seconds since 00:00 GMT, January 1, 1970. (ie, a unix + timestamp) + + @type issued: C{int} + + + @param lifetime: This is the amount of time this association + is good for, measured in seconds since the association was + issued. + + @type lifetime: C{int} + + + @param assoc_type: This is the type of association this + instance represents. The only valid value of this field + at this time is C{'HMAC-SHA1'}, but new types may be + defined in the future. + + @type assoc_type: C{str} + """ + if assoc_type not in all_association_types: + fmt = '%r is not a supported association type' + raise ValueError(fmt % (assoc_type, )) + + # secret_size = getSecretSize(assoc_type) + # if len(secret) != secret_size: + # fmt = 'Wrong size secret (%s bytes) for association type %s' + # raise ValueError(fmt % (len(secret), assoc_type)) + + self.handle = handle + + if isinstance(secret, str): + secret = secret.encode("utf-8") # should be bytes + self.secret = secret + + self.issued = issued + self.lifetime = lifetime + self.assoc_type = assoc_type + + @property + def expiresIn(self, now=None): + """ + This returns the number of seconds this association is still + valid for, or C{0} if the association is no longer valid. + + + @return: The number of seconds this association is still valid + for, or C{0} if the association is no longer valid. + + @rtype: C{int} + """ + if now is None: + now = int(time.time()) + + return max(0, self.issued + self.lifetime - now) + + def __lt__(self, other): + """ + Compare two C{L{Association}} instances to determine relative + ordering. + + Currently compares object lifetimes -- C{L{Association}} A < B + if A.lifetime < B.lifetime. + """ + return self.lifetime < other.lifetime + + def __eq__(self, other): + """ + This checks to see if two C{L{Association}} instances + represent the same association. + + + @return: C{True} if the two instances represent the same + association, C{False} otherwise. + + @rtype: C{bool} + """ + return type(self) is type(other) and self.__dict__ == other.__dict__ + + def __ne__(self, other): + """ + This checks to see if two C{L{Association}} instances + represent different associations. + + + @return: C{True} if the two instances represent different + associations, C{False} otherwise. + + @rtype: C{bool} + """ + return not (self == other) + + def serialize(self): + """ + Convert an association to KV form. + + @return: String in KV form suitable for deserialization by + deserialize. + + @rtype: str + """ + data = { + 'version': '2', + 'handle': self.handle, + 'secret': oidutil.toBase64(self.secret), + 'issued': str(int(self.issued)), + 'lifetime': str(int(self.lifetime)), + 'assoc_type': self.assoc_type + } + + assert len(data) == len(self.assoc_keys) + + pairs = [] + for field_name in self.assoc_keys: + pairs.append((field_name, data[field_name])) + + return kvform.seqToKV(pairs, strict=True) + + @classmethod + def deserialize(cls, assoc_s): + """ + Parse an association as stored by serialize(). + + inverse of serialize + + @param assoc_s: Association as serialized by serialize() + @type assoc_s: bytes + + @return: instance of this class + """ + pairs = kvform.kvToSeq(assoc_s, strict=True) + keys = [] + values = [] + for k, v in pairs: + keys.append(k) + values.append(v) + + if keys != cls.assoc_keys: + raise ValueError('Unexpected key values: %r', keys) + + version, handle, secret, issued, lifetime, assoc_type = values + if version != '2': + raise ValueError('Unknown version: %r' % version) + issued = int(issued) + lifetime = int(lifetime) + secret = oidutil.fromBase64(secret) + return cls(handle, secret, issued, lifetime, assoc_type) + + def sign(self, pairs): + """ + Generate a signature for a sequence of (key, value) pairs + + + @param pairs: The pairs to sign, in order + @type pairs: sequence of (str, str) + + + @return: The binary signature of this sequence of pairs + @rtype: bytes + """ + kv = kvform.seqToKV(pairs) + + try: + mac = self._macs[self.assoc_type] + except KeyError: + raise ValueError('Unknown association type: %r' % + (self.assoc_type, )) + + return mac(self.secret, kv) + + def getMessageSignature(self, message): + """Return the signature of a message. + + If I am not a sign-all association, the message must have a + signed list. + + @return: the signature, base64 encoded + + @rtype: bytes + + @raises ValueError: If there is no signed list and I am not a sign-all + type of association. + """ + pairs = self._makePairs(message) + return oidutil.toBase64(self.sign(pairs)) + + def signMessage(self, message): + """Add a signature (and a signed list) to a message. + + @return: a new Message object with a signature + @rtype: L{openid.message.Message} + """ + if (message.hasKey(OPENID_NS, 'sig') or + message.hasKey(OPENID_NS, 'signed')): + raise ValueError('Message already has signed list or signature') + + extant_handle = message.getArg(OPENID_NS, 'assoc_handle') + if extant_handle and extant_handle != self.handle: + raise ValueError("Message has a different association handle") + + signed_message = message.copy() + signed_message.setArg(OPENID_NS, 'assoc_handle', self.handle) + message_keys = list(signed_message.toPostArgs().keys()) + signed_list = [k[7:] for k in message_keys if k.startswith('openid.')] + signed_list.append('signed') + signed_list.sort() + signed_message.setArg(OPENID_NS, 'signed', ','.join(signed_list)) + sig = self.getMessageSignature(signed_message) + signed_message.setArg(OPENID_NS, 'sig', sig) + return signed_message + + def checkMessageSignature(self, message): + """Given a message with a signature, calculate a new signature + and return whether it matches the signature in the message. + + @raises ValueError: if the message has no signature or no signature + can be calculated for it. + """ + message_sig = message.getArg(OPENID_NS, 'sig') + if not message_sig: + raise ValueError("%s has no sig." % (message, )) + calculated_sig = self.getMessageSignature(message) + # remember, getMessageSignature returns bytes + calculated_sig = calculated_sig.decode('utf-8') + return cryptutil.const_eq(calculated_sig, message_sig) + + def _makePairs(self, message): + signed = message.getArg(OPENID_NS, 'signed') + if not signed: + raise ValueError('Message has no signed list: %s' % (message, )) + + signed_list = signed.split(',') + pairs = [] + data = message.toPostArgs() + for field in signed_list: + pairs.append((field, data.get('openid.' + field, ''))) + return pairs + + def __repr__(self): + return "<%s.%s %s %s>" % (self.__class__.__module__, + self.__class__.__name__, self.assoc_type, + self.handle) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/codecutil.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/codecutil.py new file mode 100644 index 0000000000000000000000000000000000000000..d8b9fe962a893a67c5643946f755e73b062b9cd9 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/codecutil.py @@ -0,0 +1,91 @@ +import codecs + +try: + chr(0x10000) +except ValueError: + # narrow python build + UCSCHAR = [ + (0xA0, 0xD7FF), + (0xF900, 0xFDCF), + (0xFDF0, 0xFFEF), + ] + + IPRIVATE = [ + (0xE000, 0xF8FF), + ] +else: + UCSCHAR = [ + (0xA0, 0xD7FF), + (0xF900, 0xFDCF), + (0xFDF0, 0xFFEF), + (0x10000, 0x1FFFD), + (0x20000, 0x2FFFD), + (0x30000, 0x3FFFD), + (0x40000, 0x4FFFD), + (0x50000, 0x5FFFD), + (0x60000, 0x6FFFD), + (0x70000, 0x7FFFD), + (0x80000, 0x8FFFD), + (0x90000, 0x9FFFD), + (0xA0000, 0xAFFFD), + (0xB0000, 0xBFFFD), + (0xC0000, 0xCFFFD), + (0xD0000, 0xDFFFD), + (0xE1000, 0xEFFFD), + ] + + IPRIVATE = [ + (0xE000, 0xF8FF), + (0xF0000, 0xFFFFD), + (0x100000, 0x10FFFD), + ] + +_ESCAPE_RANGES = UCSCHAR + IPRIVATE + + +def _in_escape_range(octet): + for start, end in _ESCAPE_RANGES: + if start <= octet <= end: + return True + return False + + +def _starts_surrogate_pair(character): + char_value = ord(character) + return 0xD800 <= char_value <= 0xDBFF + + +def _ends_surrogate_pair(character): + char_value = ord(character) + return 0xDC00 <= char_value <= 0xDFFF + + +def _pct_encoded_replacements(chunk): + replacements = [] + chunk_iter = iter(chunk) + for character in chunk_iter: + codepoint = ord(character) + if _in_escape_range(codepoint): + for char in chr(codepoint).encode("utf-8"): + replacements.append("%%%X" % char) + elif _starts_surrogate_pair(character): + next_character = next(chunk_iter) + for char in (character + next_character).encode("utf-8"): + replacements.append("%%%X" % char) + else: + replacements.append(chr(codepoint)) + return replacements + + +def _pct_escape_handler(err): + ''' + Encoding error handler that does percent-escaping of Unicode, to be used + with codecs.register_error + TODO: replace use of this with urllib.parse.quote as appropriate + ''' + chunk = err.object[err.start:err.end] + replacements = _pct_encoded_replacements(chunk) + return ("".join(replacements), err.end) + + +codecs.register_error("oid_percent_escape", _pct_escape_handler) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/consumer/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/consumer/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..aab51a293c3106c0d637c737226f6820c54269fc --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/consumer/__init__.py @@ -0,0 +1,6 @@ +""" +This package contains the portions of the library used only when +implementing an OpenID consumer. +""" + +__all__ = ['consumer', 'discover'] diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/consumer/consumer.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/consumer/consumer.py new file mode 100644 index 0000000000000000000000000000000000000000..ea390ecfb6157b62d5c751564ff5ecf9075115e1 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/consumer/consumer.py @@ -0,0 +1,1925 @@ +# -*- test-case-name: openid.test.test_consumer -*- +"""OpenID support for Relying Parties (aka Consumers). + +This module documents the main interface with the OpenID consumer +library. The only part of the library which has to be used and isn't +documented in full here is the store required to create an +C{L{Consumer}} instance. More on the abstract store type and +concrete implementations of it that are provided in the documentation +for the C{L{__init__}} method of the +C{L{Consumer}} class. + + +OVERVIEW +======== + + The OpenID identity verification process most commonly uses the + following steps, as visible to the user of this library: + + 1. The user enters their OpenID into a field on the consumer's + site, and hits a login button. + + 2. The consumer site discovers the user's OpenID provider using + the Yadis protocol. + + 3. The consumer site sends the browser a redirect to the + OpenID provider. This is the authentication request as + described in the OpenID specification. + + 4. The OpenID provider's site sends the browser a redirect + back to the consumer site. This redirect contains the + provider's response to the authentication request. + + The most important part of the flow to note is the consumer's site + must handle two separate HTTP requests in order to perform the + full identity check. + + +LIBRARY DESIGN +============== + + This consumer library is designed with that flow in mind. The + goal is to make it as easy as possible to perform the above steps + securely. + + At a high level, there are two important parts in the consumer + library. The first important part is this module, which contains + the interface to actually use this library. The second is the + C{L{openid.store.interface}} module, which describes the + interface to use if you need to create a custom method for storing + the state this library needs to maintain between requests. + + In general, the second part is less important for users of the + library to know about, as several implementations are provided + which cover a wide variety of situations in which consumers may + use the library. + + This module contains a class, C{L{Consumer}}, with methods + corresponding to the actions necessary in each of steps 2, 3, and + 4 described in the overview. Use of this library should be as easy + as creating an C{L{Consumer}} instance and calling the methods + appropriate for the action the site wants to take. + + +SESSIONS, STORES, AND STATELESS MODE +==================================== + + The C{L{Consumer}} object keeps track of two types of state: + + 1. State of the user's current authentication attempt. Things like + the identity URL, the list of endpoints discovered for that + URL, and in case where some endpoints are unreachable, the list + of endpoints already tried. This state needs to be held from + Consumer.begin() to Consumer.complete(), but it is only applicable + to a single session with a single user agent, and at the end of + the authentication process (i.e. when an OP replies with either + C{id_res} or C{cancel}) it may be discarded. + + 2. State of relationships with servers, i.e. shared secrets + (associations) with servers and nonces seen on signed messages. + This information should persist from one session to the next and + should not be bound to a particular user-agent. + + + These two types of storage are reflected in the first two arguments of + Consumer's constructor, C{session} and C{store}. C{session} is a + dict-like object and we hope your web framework provides you with one + of these bound to the user agent. C{store} is an instance of + L{openid.store.interface.OpenIDStore}. + + Since the store does hold secrets shared between your application and the + OpenID provider, you should be careful about how you use it in a shared + hosting environment. If the filesystem or database permissions of your + web host allow strangers to read from them, do not store your data there! + If you have no safe place to store your data, construct your consumer + with C{None} for the store, and it will operate only in stateless mode. + Stateless mode may be slower, put more load on the OpenID provider, and + trusts the provider to keep you safe from replay attacks. + + + Several store implementation are provided, and the interface is + fully documented so that custom stores can be used as well. See + the documentation for the C{L{Consumer}} class for more + information on the interface for stores. The implementations that + are provided allow the consumer site to store the necessary data + in several different ways, including several SQL databases and + normal files on disk. + + +IMMEDIATE MODE +============== + + In the flow described above, the user may need to confirm to the + OpenID provider that it's ok to disclose his or her identity. + The provider may draw pages asking for information from the user + before it redirects the browser back to the consumer's site. This + is generally transparent to the consumer site, so it is typically + ignored as an implementation detail. + + There can be times, however, where the consumer site wants to get + a response immediately. When this is the case, the consumer can + put the library in immediate mode. In immediate mode, there is an + extra response possible from the server, which is essentially the + server reporting that it doesn't have enough information to answer + the question yet. + + +USING THIS LIBRARY +================== + + Integrating this library into an application is usually a + relatively straightforward process. The process should basically + follow this plan: + + Add an OpenID login field somewhere on your site. When an OpenID + is entered in that field and the form is submitted, it should make + a request to your site which includes that OpenID URL. + + First, the application should L{instantiate a Consumer} + with a session for per-user state and store for shared state. + using the store of choice. + + Next, the application should call the 'C{L{begin}}' method on the + C{L{Consumer}} instance. This method takes the OpenID URL. The + C{L{begin}} method returns an C{L{AuthRequest}} + object. + + Next, the application should call the + C{L{redirectURL}} method on the + C{L{AuthRequest}} object. The parameter C{return_to} is the URL + that the OpenID server will send the user back to after attempting + to verify his or her identity. The C{realm} parameter is the + URL (or URL pattern) that identifies your web site to the user + when he or she is authorizing it. Send a redirect to the + resulting URL to the user's browser. + + That's the first half of the authentication process. The second + half of the process is done after the user's OpenID Provider sends the + user's browser a redirect back to your site to complete their + login. + + When that happens, the user will contact your site at the URL + given as the C{return_to} URL to the + C{L{redirectURL}} call made + above. The request will have several query parameters added to + the URL by the OpenID provider as the information necessary to + finish the request. + + Get a C{L{Consumer}} instance with the same session and store as + before and call its C{L{complete}} method, + passing in all the received query arguments. + + There are multiple possible return types possible from that + method. These indicate whether or not the login was successful, + and include any additional information appropriate for their type. + +@var SUCCESS: constant used as the status for + L{SuccessResponse} objects. + +@var FAILURE: constant used as the status for + L{FailureResponse} objects. + +@var CANCEL: constant used as the status for + L{CancelResponse} objects. + +@var SETUP_NEEDED: constant used as the status for + L{SetupNeededResponse} + objects. +""" + +import copy +import logging +from urllib.parse import urlparse, urldefrag, parse_qsl + +from openid import fetchers + +from openid.consumer.discover import discover, OpenIDServiceEndpoint, \ + DiscoveryFailure, OPENID_1_0_TYPE, OPENID_1_1_TYPE, OPENID_2_0_TYPE +from openid.message import Message, OPENID_NS, OPENID2_NS, OPENID1_NS, \ + IDENTIFIER_SELECT, no_default, BARE_NS +from openid import cryptutil +from openid import oidutil +from openid.association import Association, default_negotiator, \ + SessionNegotiator +from openid.dh import DiffieHellman +from openid.store.nonce import mkNonce, split as splitNonce +from openid.yadis.manager import Discovery +from openid import urinorm + +__all__ = [ + 'AuthRequest', + 'Consumer', + 'SuccessResponse', + 'SetupNeededResponse', + 'CancelResponse', + 'FailureResponse', + 'SUCCESS', + 'FAILURE', + 'CANCEL', + 'SETUP_NEEDED', +] + +logger = logging.getLogger(__name__) + +def makeKVPost(request_message, server_url): + """Make a Direct Request to an OpenID Provider and return the + result as a Message object. + + @raises openid.fetchers.HTTPFetchingError: if an error is + encountered in making the HTTP post. + + @rtype: L{openid.message.Message} + """ + # XXX: TESTME + resp = fetchers.fetch(server_url, body=request_message.toURLEncoded()) + + # Process response in separate function that can be shared by async code. + return _httpResponseToMessage(resp, server_url) + + +def _httpResponseToMessage(response, server_url): + """Adapt a POST response to a Message. + + @type response: L{openid.fetchers.HTTPResponse} + @param response: Result of a POST to an OpenID endpoint. + + @rtype: L{openid.message.Message} + + @raises openid.fetchers.HTTPFetchingError: if the server returned a + status of other than 200 or 400. + + @raises ServerError: if the server returned an OpenID error. + """ + # Should this function be named Message.fromHTTPResponse instead? + response_message = Message.fromKVForm(response.body) + if response.status == 400: + raise ServerError.fromMessage(response_message) + + elif response.status not in (200, 206): + fmt = 'bad status code from server %s: %s' + error_message = fmt % (server_url, response.status) + raise fetchers.HTTPFetchingError(error_message) + + return response_message + + +class Consumer(object): + """An OpenID consumer implementation that performs discovery and + does session management. + + @ivar consumer: an instance of an object implementing the OpenID + protocol, but doing no discovery or session management. + + @type consumer: GenericConsumer + + @ivar session: A dictionary-like object representing the user's + session data. This is used for keeping state of the OpenID + transaction when the user is redirected to the server. + + @cvar session_key_prefix: A string that is prepended to session + keys to ensure that they are unique. This variable may be + changed to suit your application. + """ + session_key_prefix = "_openid_consumer_" + + _token = 'last_token' + + _discover = staticmethod(discover) + + def __init__(self, session, store, consumer_class=None): + """Initialize a Consumer instance. + + You should create a new instance of the Consumer object with + every HTTP request that handles OpenID transactions. + + @param session: See L{the session instance variable} + + @param store: an object that implements the interface in + C{L{openid.store.interface.OpenIDStore}}. Several + implementations are provided, to cover common database + environments. + + @type store: C{L{openid.store.interface.OpenIDStore}} + + @see: L{openid.store.interface} + @see: L{openid.store} + """ + self.session = session + if consumer_class is None: + consumer_class = GenericConsumer + self.consumer = consumer_class(store) + self._token_key = self.session_key_prefix + self._token + + def begin(self, user_url, anonymous=False): + """Start the OpenID authentication process. See steps 1-2 in + the overview at the top of this file. + + @param user_url: Identity URL given by the user. This method + performs a textual transformation of the URL to try and + make sure it is normalized. For example, a user_url of + example.com will be normalized to http://example.com/ + normalizing and resolving any redirects the server might + issue. + + @type user_url: unicode + + @param anonymous: Whether to make an anonymous request of the OpenID + provider. Such a request does not ask for an authorization + assertion for an OpenID identifier, but may be used with + extensions to pass other data. e.g. "I don't care who you are, + but I'd like to know your time zone." + + @type anonymous: bool + + @returns: An object containing the discovered information will + be returned, with a method for building a redirect URL to + the server, as described in step 3 of the overview. This + object may also be used to add extension arguments to the + request, using its + L{addExtensionArg} + method. + + @returntype: L{AuthRequest} + + @raises openid.consumer.discover.DiscoveryFailure: when I fail to + find an OpenID server for this URL. If the C{yadis} package + is available, L{openid.consumer.discover.DiscoveryFailure} is + an alias for C{yadis.discover.DiscoveryFailure}. + """ + disco = Discovery(self.session, user_url, self.session_key_prefix) + try: + service = disco.getNextService(self._discover) + except fetchers.HTTPFetchingError as why: + raise DiscoveryFailure('Error fetching XRDS document: %s' % + (why.why, ), None) + + if service is None: + raise DiscoveryFailure('No usable OpenID services found for %s' % + (user_url, ), None) + else: + return self.beginWithoutDiscovery(service, anonymous) + + def beginWithoutDiscovery(self, service, anonymous=False): + """Start OpenID verification without doing OpenID server + discovery. This method is used internally by Consumer.begin + after discovery is performed, and exists to provide an + interface for library users needing to perform their own + discovery. + + @param service: an OpenID service endpoint descriptor. This + object and factories for it are found in the + L{openid.consumer.discover} module. + + @type service: + L{OpenIDServiceEndpoint} + + @returns: an OpenID authentication request object. + + @rtype: L{AuthRequest} + + @See: Openid.consumer.consumer.Consumer.begin + @see: openid.consumer.discover + """ + auth_req = self.consumer.begin(service) + self.session[self._token_key] = auth_req.endpoint + + try: + auth_req.setAnonymous(anonymous) + except ValueError as why: + raise ProtocolError(str(why)) + + return auth_req + + def complete(self, query, current_url): + """Called to interpret the server's response to an OpenID + request. It is called in step 4 of the flow described in the + consumer overview. + + @param query: A dictionary of the query parameters for this + HTTP request. + + @param current_url: The URL used to invoke the application. + Extract the URL from your application's web + request framework and specify it here to have it checked + against the openid.return_to value in the response. If + the return_to URL check fails, the status of the + completion will be FAILURE. + + @returns: a subclass of Response. The type of response is + indicated by the status attribute, which will be one of + SUCCESS, CANCEL, FAILURE, or SETUP_NEEDED. + + @see: L{SuccessResponse} + @see: L{CancelResponse} + @see: L{SetupNeededResponse} + @see: L{FailureResponse} + """ + + endpoint = self.session.get(self._token_key) + + message = Message.fromPostArgs(query) + response = self.consumer.complete(message, endpoint, current_url) + + try: + del self.session[self._token_key] + except KeyError: + pass + + if (response.status in ['success', 'cancel'] and + response.identity_url is not None): + + disco = Discovery(self.session, response.identity_url, + self.session_key_prefix) + # This is OK to do even if we did not do discovery in + # the first place. + disco.cleanup(force=True) + + return response + + def setAssociationPreference(self, association_preferences): + """Set the order in which association types/sessions should be + attempted. For instance, to only allow HMAC-SHA256 + associations created with a DH-SHA256 association session: + + >>> consumer.setAssociationPreference([('HMAC-SHA256', 'DH-SHA256')]) + + Any association type/association type pair that is not in this + list will not be attempted at all. + + @param association_preferences: The list of allowed + (association type, association session type) pairs that + should be allowed for this consumer to use, in order from + most preferred to least preferred. + @type association_preferences: [(str, str)] + + @returns: None + + @see: C{L{openid.association.SessionNegotiator}} + """ + self.consumer.negotiator = SessionNegotiator(association_preferences) + + +class DiffieHellmanSHA1ConsumerSession(object): + session_type = 'DH-SHA1' + hash_func = staticmethod(cryptutil.sha1) + secret_size = 20 + allowed_assoc_types = ['HMAC-SHA1'] + + def __init__(self, dh=None): + if dh is None: + dh = DiffieHellman.fromDefaults() + + self.dh = dh + + def getRequest(self): + cpub = cryptutil.longToBase64(self.dh.public) + + args = {'dh_consumer_public': cpub} + + if not self.dh.usingDefaultValues(): + args.update({ + 'dh_modulus': cryptutil.longToBase64(self.dh.modulus), + 'dh_gen': cryptutil.longToBase64(self.dh.generator), + }) + + return args + + def extractSecret(self, response): + dh_server_public64 = response.getArg(OPENID_NS, 'dh_server_public', + no_default) + enc_mac_key64 = response.getArg(OPENID_NS, 'enc_mac_key', no_default) + dh_server_public = cryptutil.base64ToLong(dh_server_public64) + enc_mac_key = oidutil.fromBase64(enc_mac_key64) + return self.dh.xorSecret(dh_server_public, enc_mac_key, self.hash_func) + + +class DiffieHellmanSHA256ConsumerSession(DiffieHellmanSHA1ConsumerSession): + session_type = 'DH-SHA256' + hash_func = staticmethod(cryptutil.sha256) + secret_size = 32 + allowed_assoc_types = ['HMAC-SHA256'] + + +class PlainTextConsumerSession(object): + session_type = 'no-encryption' + allowed_assoc_types = ['HMAC-SHA1', 'HMAC-SHA256'] + + def getRequest(self): + return {} + + def extractSecret(self, response): + mac_key64 = response.getArg(OPENID_NS, 'mac_key', no_default) + return oidutil.fromBase64(mac_key64) + + +class SetupNeededError(Exception): + """Internally-used exception that indicates that an immediate-mode + request cancelled.""" + + def __init__(self, user_setup_url=None): + Exception.__init__(self, user_setup_url) + self.user_setup_url = user_setup_url + + +class ProtocolError(ValueError): + """Exception that indicates that a message violated the + protocol. It is raised and caught internally to this file.""" + + +class TypeURIMismatch(ProtocolError): + """A protocol error arising from type URIs mismatching + """ + + def __init__(self, expected, endpoint): + ProtocolError.__init__(self, expected, endpoint) + self.expected = expected + self.endpoint = endpoint + + def __str__(self): + s = '<%s.%s: Required type %s not found in %s for endpoint %s>' % ( + self.__class__.__module__, self.__class__.__name__, self.expected, + self.endpoint.type_uris, self.endpoint) + return s + + +class ServerError(Exception): + """Exception that is raised when the server returns a 400 response + code to a direct request.""" + + def __init__(self, error_text, error_code, message): + Exception.__init__(self, error_text) + self.error_text = error_text + self.error_code = error_code + self.message = message + + def fromMessage(cls, message): + """Generate a ServerError instance, extracting the error text + and the error code from the message.""" + error_text = message.getArg(OPENID_NS, 'error', + '') + error_code = message.getArg(OPENID_NS, 'error_code') + return cls(error_text, error_code, message) + + fromMessage = classmethod(fromMessage) + + +class GenericConsumer(object): + """This is the implementation of the common logic for OpenID + consumers. It is unaware of the application in which it is + running. + + @ivar negotiator: An object that controls the kind of associations + that the consumer makes. It defaults to + C{L{openid.association.default_negotiator}}. Assign a + different negotiator to it if you have specific requirements + for how associations are made. + @type negotiator: C{L{openid.association.SessionNegotiator}} + """ + + # The name of the query parameter that gets added to the return_to + # URL when using OpenID1. You can change this value if you want or + # need a different name, but don't make it start with openid, + # because it's not a standard protocol thing for OpenID1. For + # OpenID2, the library will take care of the nonce using standard + # OpenID query parameter names. + openid1_nonce_query_arg_name = 'janrain_nonce' + + # Another query parameter that gets added to the return_to for + # OpenID 1; if the user's session state is lost, use this claimed + # identifier to do discovery when verifying the response. + openid1_return_to_identifier_name = 'openid1_claimed_id' + + session_types = { + 'DH-SHA1': DiffieHellmanSHA1ConsumerSession, + 'DH-SHA256': DiffieHellmanSHA256ConsumerSession, + 'no-encryption': PlainTextConsumerSession, + } + + _discover = staticmethod(discover) + + def __init__(self, store): + self.store = store + self.negotiator = default_negotiator.copy() + + def begin(self, service_endpoint): + """Create an AuthRequest object for the specified + service_endpoint. This method will create an association if + necessary.""" + if self.store is None: + assoc = None + else: + assoc = self._getAssociation(service_endpoint) + + request = AuthRequest(service_endpoint, assoc) + request.return_to_args[self.openid1_nonce_query_arg_name] = mkNonce() + + if request.message.isOpenID1(): + request.return_to_args[self.openid1_return_to_identifier_name] = \ + request.endpoint.claimed_id + + return request + + def complete(self, message, endpoint, return_to): + """Process the OpenID message, using the specified endpoint + and return_to URL as context. This method will handle any + OpenID message that is sent to the return_to URL. + """ + mode = message.getArg(OPENID_NS, 'mode', '') + + modeMethod = getattr(self, '_complete_' + mode, self._completeInvalid) + + return modeMethod(message, endpoint, return_to) + + def _complete_cancel(self, message, endpoint, _): + return CancelResponse(endpoint) + + def _complete_error(self, message, endpoint, _): + error = message.getArg(OPENID_NS, 'error') + contact = message.getArg(OPENID_NS, 'contact') + reference = message.getArg(OPENID_NS, 'reference') + + return FailureResponse( + endpoint, error, contact=contact, reference=reference) + + def _complete_setup_needed(self, message, endpoint, _): + if not message.isOpenID2(): + return self._completeInvalid(message, endpoint, _) + + user_setup_url = message.getArg(OPENID2_NS, 'user_setup_url') + return SetupNeededResponse(endpoint, user_setup_url) + + def _complete_id_res(self, message, endpoint, return_to): + try: + self._checkSetupNeeded(message) + except SetupNeededError as why: + return SetupNeededResponse(endpoint, why.user_setup_url) + else: + try: + return self._doIdRes(message, endpoint, return_to) + except (ProtocolError, DiscoveryFailure) as why: + return FailureResponse(endpoint, why) + + def _completeInvalid(self, message, endpoint, _): + mode = message.getArg(OPENID_NS, 'mode', '') + return FailureResponse(endpoint, 'Invalid openid.mode: %r' % (mode, )) + + def _checkReturnTo(self, message, return_to): + """Check an OpenID message and its openid.return_to value + against a return_to URL from an application. Return True on + success, False on failure. + """ + # Check the openid.return_to args against args in the original + # message. + try: + self._verifyReturnToArgs(message.toPostArgs()) + except ProtocolError as why: + logger.exception("Verifying return_to arguments: %s" % (why, )) + return False + + # Check the return_to base URL against the one in the message. + msg_return_to = message.getArg(OPENID_NS, 'return_to') + + # The URL scheme, authority, and path MUST be the same between + # the two URLs. + app_parts = urlparse(urinorm.urinorm(return_to)) + msg_parts = urlparse(urinorm.urinorm(msg_return_to)) + + # (addressing scheme, network location, path) must be equal in + # both URLs. + for part in range(0, 3): + if app_parts[part] != msg_parts[part]: + return False + + return True + + _makeKVPost = staticmethod(makeKVPost) + + def _checkSetupNeeded(self, message): + """Check an id_res message to see if it is a + checkid_immediate cancel response. + + @raises SetupNeededError: if it is a checkid_immediate cancellation + """ + # In OpenID 1, we check to see if this is a cancel from + # immediate mode by the presence of the user_setup_url + # parameter. + if message.isOpenID1(): + user_setup_url = message.getArg(OPENID1_NS, 'user_setup_url') + if user_setup_url is not None: + raise SetupNeededError(user_setup_url) + + def _doIdRes(self, message, endpoint, return_to): + """Handle id_res responses that are not cancellations of + immediate mode requests. + + @param message: the response paramaters. + @param endpoint: the discovered endpoint object. May be None. + + @raises ProtocolError: If the message contents are not + well-formed according to the OpenID specification. This + includes missing fields or not signing fields that should + be signed. + + @raises DiscoveryFailure: If the subject of the id_res message + does not match the supplied endpoint, and discovery on the + identifier in the message fails (this should only happen + when using OpenID 2) + + @returntype: L{Response} + """ + # Checks for presence of appropriate fields (and checks + # signed list fields) + self._idResCheckForFields(message) + + if not self._checkReturnTo(message, return_to): + raise ProtocolError( + "return_to does not match return URL. Expected %r, got %r" % + (return_to, message.getArg(OPENID_NS, 'return_to'))) + + # Verify discovery information: + endpoint = self._verifyDiscoveryResults(message, endpoint) + logger.info("Received id_res response from %s using association %s" % + (endpoint.server_url, + message.getArg(OPENID_NS, 'assoc_handle'))) + + self._idResCheckSignature(message, endpoint.server_url) + + # Will raise a ProtocolError if the nonce is bad + self._idResCheckNonce(message, endpoint) + + signed_list_str = message.getArg(OPENID_NS, 'signed', no_default) + signed_list = signed_list_str.split(',') + signed_fields = ["openid." + s for s in signed_list] + return SuccessResponse(endpoint, message, signed_fields) + + def _idResGetNonceOpenID1(self, message, endpoint): + """Extract the nonce from an OpenID 1 response. Return the + nonce from the BARE_NS since we independently check the + return_to arguments are the same as those in the response + message. + + See the openid1_nonce_query_arg_name class variable + + @returns: The nonce as a string or None + """ + return message.getArg(BARE_NS, self.openid1_nonce_query_arg_name) + + def _idResCheckNonce(self, message, endpoint): + if message.isOpenID1(): + # This indicates that the nonce was generated by the consumer + nonce = self._idResGetNonceOpenID1(message, endpoint) + server_url = '' + else: + nonce = message.getArg(OPENID2_NS, 'response_nonce') + server_url = endpoint.server_url + + if nonce is None: + raise ProtocolError('Nonce missing from response') + + try: + timestamp, salt = splitNonce(nonce) + except ValueError as why: + raise ProtocolError('Malformed nonce: %s' % (why, )) + + if (self.store is not None and + not self.store.useNonce(server_url, timestamp, salt)): + raise ProtocolError('Nonce already used or out of range') + + def _idResCheckSignature(self, message, server_url): + assoc_handle = message.getArg(OPENID_NS, 'assoc_handle') + if self.store is None: + assoc = None + else: + assoc = self.store.getAssociation(server_url, assoc_handle) + + if assoc: + if assoc.expiresIn <= 0: + # XXX: It might be a good idea sometimes to re-start the + # authentication with a new association. Doing it + # automatically opens the possibility for + # denial-of-service by a server that just returns expired + # associations (or really short-lived associations) + raise ProtocolError('Association with %s expired' % + (server_url, )) + + if not assoc.checkMessageSignature(message): + raise ProtocolError('Bad signature') + + else: + # It's not an association we know about. Stateless mode is our + # only possible path for recovery. + # XXX - async framework will not want to block on this call to + # _checkAuth. + if not self._checkAuth(message, server_url): + raise ProtocolError('Server denied check_authentication') + + def _idResCheckForFields(self, message): + # XXX: this should be handled by the code that processes the + # response (that is, if a field is missing, we should not have + # to explicitly check that it's present, just make sure that + # the fields are actually being used by the rest of the code + # in tests). Although, which fields are signed does need to be + # checked somewhere. + basic_fields = ['return_to', 'assoc_handle', 'sig', 'signed'] + basic_sig_fields = ['return_to', 'identity'] + + require_fields = { + OPENID2_NS: basic_fields + ['op_endpoint'], + OPENID1_NS: basic_fields + ['identity'], + } + + require_sigs = { + OPENID2_NS: + basic_sig_fields + + ['response_nonce', 'claimed_id', 'assoc_handle', 'op_endpoint'], + OPENID1_NS: + basic_sig_fields, + } + + for field in require_fields[message.getOpenIDNamespace()]: + if not message.hasKey(OPENID_NS, field): + raise ProtocolError('Missing required field %r' % (field, )) + + signed_list_str = message.getArg(OPENID_NS, 'signed', no_default) + signed_list = signed_list_str.split(',') + + for field in require_sigs[message.getOpenIDNamespace()]: + # Field is present and not in signed list + if message.hasKey(OPENID_NS, field) and field not in signed_list: + raise ProtocolError('"%s" not signed' % (field, )) + + def _verifyReturnToArgs(query): + """Verify that the arguments in the return_to URL are present in this + response. + """ + # NOTE -- query came from Message.toPostArgs, which returns a dict of + # {str: str} + message = Message.fromPostArgs(query) + return_to = message.getArg(OPENID_NS, 'return_to') + + if return_to is None: + raise ProtocolError('Response has no return_to') + + parsed_url = urlparse(return_to) + rt_query = parsed_url[4] + parsed_args = parse_qsl(rt_query) + + # NOTE -- parsed_args will be a dict of {bytes: bytes}, however it + # will be checked against return values from Message methods which are + # {str: str}. We need to compare apples to apples. + for rt_key, rt_value in parsed_args: + try: + value = query[rt_key] + if rt_value != value: + format = ("parameter %s value %r does not match " + "return_to's value %r") + raise ProtocolError(format % (rt_key, value, rt_value)) + except KeyError: + format = "return_to parameter %s absent from query %r" + raise ProtocolError(format % (rt_key, query)) + + # Make sure all non-OpenID arguments in the response are also + # in the signed return_to. + bare_args = message.getArgs(BARE_NS) + for pair in bare_args.items(): + if pair not in parsed_args: + raise ProtocolError("Parameter %s not in return_to URL" % + (pair[0], )) + + _verifyReturnToArgs = staticmethod(_verifyReturnToArgs) + + def _verifyDiscoveryResults(self, resp_msg, endpoint=None): + """ + Extract the information from an OpenID assertion message and + verify it against the original + + @param endpoint: The endpoint that resulted from doing discovery + @param resp_msg: The id_res message object + + @returns: the verified endpoint + """ + if resp_msg.getOpenIDNamespace() == OPENID2_NS: + return self._verifyDiscoveryResultsOpenID2(resp_msg, endpoint) + else: + return self._verifyDiscoveryResultsOpenID1(resp_msg, endpoint) + + def _verifyDiscoveryResultsOpenID2(self, resp_msg, endpoint): + to_match = OpenIDServiceEndpoint() + to_match.type_uris = [OPENID_2_0_TYPE] + to_match.claimed_id = resp_msg.getArg(OPENID2_NS, 'claimed_id') + to_match.local_id = resp_msg.getArg(OPENID2_NS, 'identity') + + # Raises a KeyError when the op_endpoint is not present + to_match.server_url = resp_msg.getArg(OPENID2_NS, 'op_endpoint', + no_default) + + # claimed_id and identifier must both be present or both + # be absent + if (to_match.claimed_id is None and to_match.local_id is not None): + raise ProtocolError( + 'openid.identity is present without openid.claimed_id') + + elif (to_match.claimed_id is not None and to_match.local_id is None): + raise ProtocolError( + 'openid.claimed_id is present without openid.identity') + + # This is a response without identifiers, so there's really no + # checking that we can do, so return an endpoint that's for + # the specified `openid.op_endpoint' + elif to_match.claimed_id is None: + return OpenIDServiceEndpoint.fromOPEndpointURL(to_match.server_url) + + # The claimed ID doesn't match, so we have to do discovery + # again. This covers not using sessions, OP identifier + # endpoints and responses that didn't match the original + # request. + if not endpoint: + logger.info('No pre-discovered information supplied.') + endpoint = self._discoverAndVerify(to_match.claimed_id, [to_match]) + elif endpoint.isOPIdentifier(): + logger.info( + 'Pre-discovered information based on OP-ID; need to rediscover.' + ) + endpoint = self._discoverAndVerify(to_match.claimed_id, [to_match]) + else: + # The claimed ID matches, so we use the endpoint that we + # discovered in initiation. This should be the most common + # case. + try: + self._verifyDiscoverySingle(endpoint, to_match) + except ProtocolError as e: + logger.exception( + "Error attempting to use stored discovery information: " + + str(e)) + logger.info("Attempting discovery to verify endpoint") + endpoint = self._discoverAndVerify(to_match.claimed_id, + [to_match]) + + # The endpoint we return should have the claimed ID from the + # message we just verified, fragment and all. + if endpoint.claimed_id != to_match.claimed_id: + endpoint = copy.copy(endpoint) + endpoint.claimed_id = to_match.claimed_id + return endpoint + + def _verifyDiscoveryResultsOpenID1(self, resp_msg, endpoint): + claimed_id = resp_msg.getArg(BARE_NS, + self.openid1_return_to_identifier_name) + + if endpoint is None and claimed_id is None: + raise RuntimeError( + 'When using OpenID 1, the claimed ID must be supplied, ' + 'either by passing it through as a return_to parameter ' + 'or by using a session, and supplied to the GenericConsumer ' + 'as the argument to complete()') + elif endpoint is not None and claimed_id is None: + claimed_id = endpoint.claimed_id + + to_match = OpenIDServiceEndpoint() + to_match.type_uris = [OPENID_1_1_TYPE] + to_match.local_id = resp_msg.getArg(OPENID1_NS, 'identity') + # Restore delegate information from the initiation phase + to_match.claimed_id = claimed_id + + if to_match.local_id is None: + raise ProtocolError('Missing required field openid.identity') + + to_match_1_0 = copy.copy(to_match) + to_match_1_0.type_uris = [OPENID_1_0_TYPE] + + if endpoint is not None: + try: + try: + self._verifyDiscoverySingle(endpoint, to_match) + except TypeURIMismatch: + self._verifyDiscoverySingle(endpoint, to_match_1_0) + except ProtocolError as e: + logger.exception( + "Error attempting to use stored discovery information: " + + str(e)) + logger.info("Attempting discovery to verify endpoint") + else: + return endpoint + + # Endpoint is either bad (failed verification) or None + return self._discoverAndVerify(claimed_id, [to_match, to_match_1_0]) + + def _verifyDiscoverySingle(self, endpoint, to_match): + """Verify that the given endpoint matches the information + extracted from the OpenID assertion, and raise an exception if + there is a mismatch. + + @type endpoint: openid.consumer.discover.OpenIDServiceEndpoint + @type to_match: openid.consumer.discover.OpenIDServiceEndpoint + + @rtype: NoneType + + @raises ProtocolError: when the endpoint does not match the + discovered information. + """ + # Every type URI that's in the to_match endpoint has to be + # present in the discovered endpoint. + for type_uri in to_match.type_uris: + if not endpoint.usesExtension(type_uri): + raise TypeURIMismatch(type_uri, endpoint) + + # Fragments do not influence discovery, so we can't compare a + # claimed identifier with a fragment to discovered information. + defragged_claimed_id, _ = urldefrag(to_match.claimed_id) + if defragged_claimed_id != endpoint.claimed_id: + raise ProtocolError( + 'Claimed ID does not match (different subjects!), ' + 'Expected %s, got %s' % + (defragged_claimed_id, endpoint.claimed_id)) + + if to_match.getLocalID() != endpoint.getLocalID(): + raise ProtocolError('local_id mismatch. Expected %s, got %s' % + (to_match.getLocalID(), endpoint.getLocalID())) + + # If the server URL is None, this must be an OpenID 1 + # response, because op_endpoint is a required parameter in + # OpenID 2. In that case, we don't actually care what the + # discovered server_url is, because signature checking or + # check_auth should take care of that check for us. + if to_match.server_url is None: + assert to_match.preferredNamespace() == OPENID1_NS, ( + """The code calling this must ensure that OpenID 2 + responses have a non-none `openid.op_endpoint' and + that it is set as the `server_url' attribute of the + `to_match' endpoint.""") + + elif to_match.server_url != endpoint.server_url: + raise ProtocolError('OP Endpoint mismatch. Expected %s, got %s' % + (to_match.server_url, endpoint.server_url)) + + def _discoverAndVerify(self, claimed_id, to_match_endpoints): + """Given an endpoint object created from the information in an + OpenID response, perform discovery and verify the discovery + results, returning the matching endpoint that is the result of + doing that discovery. + + @type to_match: openid.consumer.discover.OpenIDServiceEndpoint + @param to_match: The endpoint whose information we're confirming + + @rtype: openid.consumer.discover.OpenIDServiceEndpoint + @returns: The result of performing discovery on the claimed + identifier in `to_match' + + @raises DiscoveryFailure: when discovery fails. + """ + logger.info('Performing discovery on %s' % (claimed_id, )) + _, services = self._discover(claimed_id) + if not services: + raise DiscoveryFailure('No OpenID information found at %s' % + (claimed_id, ), None) + return self._verifyDiscoveredServices(claimed_id, services, + to_match_endpoints) + + def _verifyDiscoveredServices(self, claimed_id, services, + to_match_endpoints): + """See @L{_discoverAndVerify}""" + + # Search the services resulting from discovery to find one + # that matches the information from the assertion + failure_messages = [] + for endpoint in services: + for to_match_endpoint in to_match_endpoints: + try: + self._verifyDiscoverySingle(endpoint, to_match_endpoint) + except ProtocolError as why: + failure_messages.append(str(why)) + else: + # It matches, so discover verification has + # succeeded. Return this endpoint. + return endpoint + else: + logger.error('Discovery verification failure for %s' % + (claimed_id, )) + for failure_message in failure_messages: + logger.error(' * Endpoint mismatch: ' + failure_message) + + raise DiscoveryFailure( + 'No matching endpoint found after discovering %s' % + (claimed_id, ), None) + + def _checkAuth(self, message, server_url): + """Make a check_authentication request to verify this message. + + @returns: True if the request is valid. + @rtype: bool + """ + logger.info('Using OpenID check_authentication') + request = self._createCheckAuthRequest(message) + if request is None: + return False + try: + response = self._makeKVPost(request, server_url) + except (fetchers.HTTPFetchingError, ServerError) as e: + e0 = e.args[0] + logger.exception('check_authentication failed: %s' % e0) + return False + else: + return self._processCheckAuthResponse(response, server_url) + + def _createCheckAuthRequest(self, message): + """Generate a check_authentication request message given an + id_res message. + """ + signed = message.getArg(OPENID_NS, 'signed') + if signed: + if isinstance(signed, bytes): + signed = str(signed, encoding="utf-8") + for k in signed.split(','): + logger.info(k) + val = message.getAliasedArg(k) + + # Signed value is missing + if val is None: + logger.info('Missing signed field %r' % (k, )) + return None + + check_auth_message = message.copy() + check_auth_message.setArg(OPENID_NS, 'mode', 'check_authentication') + return check_auth_message + + def _processCheckAuthResponse(self, response, server_url): + """Process the response message from a check_authentication + request, invalidating associations if requested. + """ + is_valid = response.getArg(OPENID_NS, 'is_valid', 'false') + + invalidate_handle = response.getArg(OPENID_NS, 'invalidate_handle') + if invalidate_handle is not None: + logger.info('Received "invalidate_handle" from server %s' % + (server_url, )) + if self.store is None: + logger.error('Unexpectedly got invalidate_handle without ' + 'a store!') + else: + self.store.removeAssociation(server_url, invalidate_handle) + + if is_valid == 'true': + return True + else: + logger.error('Server responds that checkAuth call is not valid') + return False + + def _getAssociation(self, endpoint): + """Get an association for the endpoint's server_url. + + First try seeing if we have a good association in the + store. If we do not, then attempt to negotiate an association + with the server. + + If we negotiate a good association, it will get stored. + + @returns: A valid association for the endpoint's server_url or None + @rtype: openid.association.Association or NoneType + """ + assoc = self.store.getAssociation(endpoint.server_url) + + if assoc is None or assoc.expiresIn <= 0: + assoc = self._negotiateAssociation(endpoint) + if assoc is not None: + self.store.storeAssociation(endpoint.server_url, assoc) + + return assoc + + def _negotiateAssociation(self, endpoint): + """Make association requests to the server, attempting to + create a new association. + + @returns: a new association object + + @rtype: L{openid.association.Association} + """ + # Get our preferred session/association type from the negotiatior. + assoc_type, session_type = self.negotiator.getAllowedType() + + try: + assoc = self._requestAssociation(endpoint, assoc_type, + session_type) + except ServerError as why: + supportedTypes = self._extractSupportedAssociationType( + why, endpoint, assoc_type) + if supportedTypes is not None: + assoc_type, session_type = supportedTypes + # Attempt to create an association from the assoc_type + # and session_type that the server told us it + # supported. + try: + assoc = self._requestAssociation(endpoint, assoc_type, + session_type) + except ServerError as why: + # Do not keep trying, since it rejected the + # association type that it told us to use. + logger.error( + 'Server %s refused its suggested association ' + 'type: session_type=%s, assoc_type=%s' % ( + endpoint.server_url, session_type, assoc_type)) + return None + else: + return assoc + else: + return assoc + + def _extractSupportedAssociationType(self, server_error, endpoint, + assoc_type): + """Handle ServerErrors resulting from association requests. + + @returns: If server replied with an C{unsupported-type} error, + return a tuple of supported C{association_type}, C{session_type}. + Otherwise logs the error and returns None. + @rtype: tuple or None + """ + # Any error message whose code is not 'unsupported-type' + # should be considered a total failure. + if server_error.error_code != 'unsupported-type' or \ + server_error.message.isOpenID1(): + logger.error( + 'Server error when requesting an association from %r: %s' % + (endpoint.server_url, server_error.error_text)) + return None + + # The server didn't like the association/session type + # that we sent, and it sent us back a message that + # might tell us how to handle it. + logger.error('Unsupported association type %s: %s' % + (assoc_type, server_error.error_text, )) + + # Extract the session_type and assoc_type from the + # error message + assoc_type = server_error.message.getArg(OPENID_NS, 'assoc_type') + session_type = server_error.message.getArg(OPENID_NS, 'session_type') + + if assoc_type is None or session_type is None: + logger.error('Server responded with unsupported association ' + 'session but did not supply a fallback.') + return None + elif not self.negotiator.isAllowed(assoc_type, session_type): + fmt = ('Server sent unsupported session/association type: ' + 'session_type=%s, assoc_type=%s') + logger.error(fmt % (session_type, assoc_type)) + return None + else: + return assoc_type, session_type + + def _requestAssociation(self, endpoint, assoc_type, session_type): + """Make and process one association request to this endpoint's + OP endpoint URL. + + @returns: An association object or None if the association + processing failed. + + @raises ServerError: when the remote OpenID server returns an error. + """ + assoc_session, args = self._createAssociateRequest( + endpoint, assoc_type, session_type) + + try: + response = self._makeKVPost(args, endpoint.server_url) + except fetchers.HTTPFetchingError as why: + logger.exception('openid.associate request failed: %s' % (why, )) + return None + + try: + assoc = self._extractAssociation(response, assoc_session) + except KeyError as why: + logger.exception( + 'Missing required parameter in response from %s: %s' % + (endpoint.server_url, why)) + return None + except ProtocolError as why: + logger.exception('Protocol error parsing response from %s: %s' % + (endpoint.server_url, why)) + return None + else: + return assoc + + def _createAssociateRequest(self, endpoint, assoc_type, session_type): + """Create an association request for the given assoc_type and + session_type. + + @param endpoint: The endpoint whose server_url will be + queried. The important bit about the endpoint is whether + it's in compatiblity mode (OpenID 1.1) + + @param assoc_type: The association type that the request + should ask for. + @type assoc_type: str + + @param session_type: The session type that should be used in + the association request. The session_type is used to + create an association session object, and that session + object is asked for any additional fields that it needs to + add to the request. + @type session_type: str + + @returns: a pair of the association session object and the + request message that will be sent to the server. + @rtype: (association session type (depends on session_type), + openid.message.Message) + """ + session_type_class = self.session_types[session_type] + assoc_session = session_type_class() + + args = { + 'mode': 'associate', + 'assoc_type': assoc_type, + } + + if not endpoint.compatibilityMode(): + args['ns'] = OPENID2_NS + + # Leave out the session type if we're in compatibility mode + # *and* it's no-encryption. + if (not endpoint.compatibilityMode() or + assoc_session.session_type != 'no-encryption'): + args['session_type'] = assoc_session.session_type + + args.update(assoc_session.getRequest()) + message = Message.fromOpenIDArgs(args) + return assoc_session, message + + def _getOpenID1SessionType(self, assoc_response): + """Given an association response message, extract the OpenID + 1.X session type. + + This function mostly takes care of the 'no-encryption' default + behavior in OpenID 1. + + If the association type is plain-text, this function will + return 'no-encryption' + + @returns: The association type for this message + @rtype: str + + @raises KeyError: when the session_type field is absent. + """ + # If it's an OpenID 1 message, allow session_type to default + # to None (which signifies "no-encryption") + session_type = assoc_response.getArg(OPENID1_NS, 'session_type') + + # Handle the differences between no-encryption association + # respones in OpenID 1 and 2: + + # no-encryption is not really a valid session type for + # OpenID 1, but we'll accept it anyway, while issuing a + # warning. + if session_type == 'no-encryption': + logger.warning('OpenID server sent "no-encryption"' + 'for OpenID 1.X') + + # Missing or empty session type is the way to flag a + # 'no-encryption' response. Change the session type to + # 'no-encryption' so that it can be handled in the same + # way as OpenID 2 'no-encryption' respones. + elif session_type == '' or session_type is None: + session_type = 'no-encryption' + + return session_type + + def _extractAssociation(self, assoc_response, assoc_session): + """Attempt to extract an association from the response, given + the association response message and the established + association session. + + @param assoc_response: The association response message from + the server + @type assoc_response: openid.message.Message + + @param assoc_session: The association session object that was + used when making the request + @type assoc_session: depends on the session type of the request + + @raises ProtocolError: when data is malformed + @raises KeyError: when a field is missing + + @rtype: openid.association.Association + """ + # Extract the common fields from the response, raising an + # exception if they are not found + assoc_type = assoc_response.getArg(OPENID_NS, 'assoc_type', no_default) + assoc_handle = assoc_response.getArg(OPENID_NS, 'assoc_handle', + no_default) + + # expires_in is a base-10 string. The Python parsing will + # accept literals that have whitespace around them and will + # accept negative values. Neither of these are really in-spec, + # but we think it's OK to accept them. + expires_in_str = assoc_response.getArg(OPENID_NS, 'expires_in', + no_default) + try: + expires_in = int(expires_in_str) + except ValueError as why: + raise ProtocolError('Invalid expires_in field: %s' % (why, )) + + # OpenID 1 has funny association session behaviour. + if assoc_response.isOpenID1(): + session_type = self._getOpenID1SessionType(assoc_response) + else: + session_type = assoc_response.getArg(OPENID2_NS, 'session_type', + no_default) + + # Session type mismatch + if assoc_session.session_type != session_type: + if (assoc_response.isOpenID1() and + session_type == 'no-encryption'): + # In OpenID 1, any association request can result in a + # 'no-encryption' association response. Setting + # assoc_session to a new no-encryption session should + # make the rest of this function work properly for + # that case. + assoc_session = PlainTextConsumerSession() + else: + # Any other mismatch, regardless of protocol version + # results in the failure of the association session + # altogether. + fmt = 'Session type mismatch. Expected %r, got %r' + message = fmt % (assoc_session.session_type, session_type) + raise ProtocolError(message) + + # Make sure assoc_type is valid for session_type + if assoc_type not in assoc_session.allowed_assoc_types: + fmt = 'Unsupported assoc_type for session %s returned: %s' + raise ProtocolError(fmt % (assoc_session.session_type, assoc_type)) + + # Delegate to the association session to extract the secret + # from the response, however is appropriate for that session + # type. + try: + secret = assoc_session.extractSecret(assoc_response) + except ValueError as why: + fmt = 'Malformed response for %s session: %s' + raise ProtocolError(fmt % (assoc_session.session_type, why)) + + return Association.fromExpiresIn(expires_in, assoc_handle, secret, + assoc_type) + + +class AuthRequest(object): + """An object that holds the state necessary for generating an + OpenID authentication request. This object holds the association + with the server and the discovered information with which the + request will be made. + + It is separate from the consumer because you may wish to add + things to the request before sending it on its way to the + server. It also has serialization options that let you encode the + authentication request as a URL or as a form POST. + """ + + def __init__(self, endpoint, assoc): + """ + Creates a new AuthRequest object. This just stores each + argument in an appropriately named field. + + Users of this library should not create instances of this + class. Instances of this class are created by the library + when needed. + """ + self.assoc = assoc + self.endpoint = endpoint + self.return_to_args = {} + self.message = Message(endpoint.preferredNamespace()) + self._anonymous = False + + def setAnonymous(self, is_anonymous): + """Set whether this request should be made anonymously. If a + request is anonymous, the identifier will not be sent in the + request. This is only useful if you are making another kind of + request with an extension in this request. + + Anonymous requests are not allowed when the request is made + with OpenID 1. + + @raises ValueError: when attempting to set an OpenID1 request + as anonymous + """ + if is_anonymous and self.message.isOpenID1(): + raise ValueError('OpenID 1 requests MUST include the ' + 'identifier in the request') + else: + self._anonymous = is_anonymous + + def addExtension(self, extension_request): + """Add an extension to this checkid request. + + @param extension_request: An object that implements the + extension interface for adding arguments to an OpenID + message. + """ + extension_request.toMessage(self.message) + + def addExtensionArg(self, namespace, key, value): + """Add an extension argument to this OpenID authentication + request. + + Use caution when adding arguments, because they will be + URL-escaped and appended to the redirect URL, which can easily + get quite long. + + @param namespace: The namespace for the extension. For + example, the simple registration extension uses the + namespace C{sreg}. + + @type namespace: str + + @param key: The key within the extension namespace. For + example, the nickname field in the simple registration + extension's key is C{nickname}. + + @type key: str + + @param value: The value to provide to the server for this + argument. + + @type value: str + """ + self.message.setArg(namespace, key, value) + + def getMessage(self, realm, return_to=None, immediate=False): + """Produce a L{openid.message.Message} representing this request. + + @param realm: The URL (or URL pattern) that identifies your + web site to the user when she is authorizing it. + + @type realm: str + + @param return_to: The URL that the OpenID provider will send the + user back to after attempting to verify her identity. + + Not specifying a return_to URL means that the user will not + be returned to the site issuing the request upon its + completion. + + @type return_to: str + + @param immediate: If True, the OpenID provider is to send back + a response immediately, useful for behind-the-scenes + authentication attempts. Otherwise the OpenID provider + may engage the user before providing a response. This is + the default case, as the user may need to provide + credentials or approve the request before a positive + response can be sent. + + @type immediate: bool + + @returntype: L{openid.message.Message} + """ + if return_to: + return_to = oidutil.appendArgs(return_to, self.return_to_args) + elif immediate: + raise ValueError( + '"return_to" is mandatory when using "checkid_immediate"') + elif self.message.isOpenID1(): + raise ValueError('"return_to" is mandatory for OpenID 1 requests') + elif self.return_to_args: + raise ValueError('extra "return_to" arguments were specified, ' + 'but no return_to was specified') + + if immediate: + mode = 'checkid_immediate' + else: + mode = 'checkid_setup' + + message = self.message.copy() + if message.isOpenID1(): + realm_key = 'trust_root' + else: + realm_key = 'realm' + + message.updateArgs(OPENID_NS, { + realm_key: realm, + 'mode': mode, + 'return_to': return_to, + }) + + if not self._anonymous: + if self.endpoint.isOPIdentifier(): + # This will never happen when we're in compatibility + # mode, as long as isOPIdentifier() returns False + # whenever preferredNamespace() returns OPENID1_NS. + claimed_id = request_identity = IDENTIFIER_SELECT + else: + request_identity = self.endpoint.getLocalID() + claimed_id = self.endpoint.claimed_id + + # This is true for both OpenID 1 and 2 + message.setArg(OPENID_NS, 'identity', request_identity) + + if message.isOpenID2(): + message.setArg(OPENID2_NS, 'claimed_id', claimed_id) + + if self.assoc: + message.setArg(OPENID_NS, 'assoc_handle', self.assoc.handle) + assoc_log_msg = 'with association %s' % (self.assoc.handle, ) + else: + assoc_log_msg = 'using stateless mode.' + + logger.info("Generated %s request to %s %s" % + (mode, self.endpoint.server_url, assoc_log_msg)) + + return message + + def redirectURL(self, realm, return_to=None, immediate=False): + """Returns a URL with an encoded OpenID request. + + The resulting URL is the OpenID provider's endpoint URL with + parameters appended as query arguments. You should redirect + the user agent to this URL. + + OpenID 2.0 endpoints also accept POST requests, see + C{L{shouldSendRedirect}} and C{L{formMarkup}}. + + @param realm: The URL (or URL pattern) that identifies your + web site to the user when she is authorizing it. + + @type realm: str + + @param return_to: The URL that the OpenID provider will send the + user back to after attempting to verify her identity. + + Not specifying a return_to URL means that the user will not + be returned to the site issuing the request upon its + completion. + + @type return_to: str + + @param immediate: If True, the OpenID provider is to send back + a response immediately, useful for behind-the-scenes + authentication attempts. Otherwise the OpenID provider + may engage the user before providing a response. This is + the default case, as the user may need to provide + credentials or approve the request before a positive + response can be sent. + + @type immediate: bool + + @returns: The URL to redirect the user agent to. + + @returntype: str + """ + message = self.getMessage(realm, return_to, immediate) + return message.toURL(self.endpoint.server_url) + + def formMarkup(self, + realm, + return_to=None, + immediate=False, + form_tag_attrs=None): + """Get html for a form to submit this request to the IDP. + + @param form_tag_attrs: Dictionary of attributes to be added to + the form tag. 'accept-charset' and 'enctype' have defaults + that can be overridden. If a value is supplied for + 'action' or 'method', it will be replaced. + @type form_tag_attrs: {unicode: unicode} + """ + message = self.getMessage(realm, return_to, immediate) + return message.toFormMarkup(self.endpoint.server_url, form_tag_attrs) + + def htmlMarkup(self, + realm, + return_to=None, + immediate=False, + form_tag_attrs=None): + """Get an autosubmitting HTML page that submits this request to the + IDP. This is just a wrapper for formMarkup. + + @see: formMarkup + + @returns: str + """ + return oidutil.autoSubmitHTML( + self.formMarkup(realm, return_to, immediate, form_tag_attrs)) + + def shouldSendRedirect(self): + """Should this OpenID authentication request be sent as a HTTP + redirect or as a POST (form submission)? + + @rtype: bool + """ + return self.endpoint.compatibilityMode() + + +FAILURE = 'failure' +SUCCESS = 'success' +CANCEL = 'cancel' +SETUP_NEEDED = 'setup_needed' + + +class Response(object): + status = None + + def setEndpoint(self, endpoint): + self.endpoint = endpoint + if endpoint is None: + self.identity_url = None + else: + self.identity_url = endpoint.claimed_id + + def getDisplayIdentifier(self): + """Return the display identifier for this response. + + The display identifier is related to the Claimed Identifier, but the + two are not always identical. The display identifier is something the + user should recognize as what they entered, whereas the response's + claimed identifier (in the L{identity_url} attribute) may have extra + information for better persistence. + + URLs will be stripped of their fragments for display. XRIs will + display the human-readable identifier (i-name) instead of the + persistent identifier (i-number). + + Use the display identifier in your user interface. Use + L{identity_url} for querying your database or authorization server. + """ + if self.endpoint is not None: + return self.endpoint.getDisplayIdentifier() + return None + + +class SuccessResponse(Response): + """A response with a status of SUCCESS. Indicates that this request is a + successful acknowledgement from the OpenID server that the + supplied URL is, indeed controlled by the requesting agent. + + @ivar identity_url: The identity URL that has been authenticated; + the Claimed Identifier. + See also L{getDisplayIdentifier}. + + @ivar endpoint: The endpoint that authenticated the identifier. You + may access other discovered information related to this endpoint, + such as the CanonicalID of an XRI, through this object. + @type endpoint: + L{OpenIDServiceEndpoint} + + @ivar signed_fields: The arguments in the server's response that + were signed and verified. + + @cvar status: SUCCESS + """ + + status = SUCCESS + + def __init__(self, endpoint, message, signed_fields=None): + # Don't use setEndpoint, because endpoint should never be None + # for a successfull transaction. + self.endpoint = endpoint + self.identity_url = endpoint.claimed_id + + self.message = message + + if signed_fields is None: + signed_fields = [] + self.signed_fields = signed_fields + + def isOpenID1(self): + """Was this authentication response an OpenID 1 authentication + response? + """ + return self.message.isOpenID1() + + def isSigned(self, ns_uri, ns_key): + """Return whether a particular key is signed, regardless of + its namespace alias + """ + return self.message.getKey(ns_uri, ns_key) in self.signed_fields + + def getSigned(self, ns_uri, ns_key, default=None): + """Return the specified signed field if available, + otherwise return default + """ + if self.isSigned(ns_uri, ns_key): + return self.message.getArg(ns_uri, ns_key, default) + else: + return default + + def getSignedNS(self, ns_uri): + """Get signed arguments from the response message. Return a + dict of all arguments in the specified namespace. If any of + the arguments are not signed, return None. + """ + msg_args = self.message.getArgs(ns_uri) + + for key in msg_args.keys(): + if not self.isSigned(ns_uri, key): + logger.info( + "SuccessResponse.getSignedNS: (%s, %s) not signed." % + (ns_uri, key)) + return None + + return msg_args + + def extensionResponse(self, namespace_uri, require_signed): + """Return response arguments in the specified namespace. + + @param namespace_uri: The namespace URI of the arguments to be + returned. + + @param require_signed: True if the arguments should be among + those signed in the response, False if you don't care. + + If require_signed is True and the arguments are not signed, + return None. + """ + if require_signed: + return self.getSignedNS(namespace_uri) + else: + return self.message.getArgs(namespace_uri) + + def getReturnTo(self): + """Get the openid.return_to argument from this response. + + This is useful for verifying that this request was initiated + by this consumer. + + @returns: The return_to URL supplied to the server on the + initial request, or C{None} if the response did not contain + an C{openid.return_to} argument. + + @returntype: str + """ + return self.getSigned(OPENID_NS, 'return_to') + + def __eq__(self, other): + return ((self.endpoint == other.endpoint) and + (self.identity_url == other.identity_url) and + (self.message == other.message) and + (self.signed_fields == other.signed_fields) and + (self.status == other.status)) + + def __ne__(self, other): + return not (self == other) + + def __repr__(self): + return '<%s.%s id=%r signed=%r>' % ( + self.__class__.__module__, self.__class__.__name__, + self.identity_url, self.signed_fields) + + +class FailureResponse(Response): + """A response with a status of FAILURE. Indicates that the OpenID + protocol has failed. This could be locally or remotely triggered. + + @ivar identity_url: The identity URL for which authenitcation was + attempted, if it can be determined. Otherwise, None. + + @ivar message: A message indicating why the request failed, if one + is supplied. otherwise, None. + + @cvar status: FAILURE + """ + + status = FAILURE + + def __init__(self, endpoint, message=None, contact=None, reference=None): + self.setEndpoint(endpoint) + self.message = message + self.contact = contact + self.reference = reference + + def __repr__(self): + return "<%s.%s id=%r message=%r>" % (self.__class__.__module__, + self.__class__.__name__, + self.identity_url, self.message) + + +class CancelResponse(Response): + """A response with a status of CANCEL. Indicates that the user + cancelled the OpenID authentication request. + + @ivar identity_url: The identity URL for which authenitcation was + attempted, if it can be determined. Otherwise, None. + + @cvar status: CANCEL + """ + + status = CANCEL + + def __init__(self, endpoint): + self.setEndpoint(endpoint) + + +class SetupNeededResponse(Response): + """A response with a status of SETUP_NEEDED. Indicates that the + request was in immediate mode, and the server is unable to + authenticate the user without further interaction. + + @ivar identity_url: The identity URL for which authenitcation was + attempted. + + @ivar setup_url: A URL that can be used to send the user to the + server to set up for authentication. The user should be + redirected in to the setup_url, either in the current window + or in a new browser window. C{None} in OpenID 2.0. + + @cvar status: SETUP_NEEDED + """ + + status = SETUP_NEEDED + + def __init__(self, endpoint, setup_url=None): + self.setEndpoint(endpoint) + self.setup_url = setup_url diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/consumer/discover.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/consumer/discover.py new file mode 100644 index 0000000000000000000000000000000000000000..bfe987dd7f4ade478b8a7b44bb3767b9e74e102c --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/consumer/discover.py @@ -0,0 +1,467 @@ +# -*- test-case-name: openid.test.test_discover -*- +"""Functions to discover OpenID endpoints from identifiers. +""" + +__all__ = [ + 'DiscoveryFailure', + 'OPENID_1_0_NS', + 'OPENID_1_0_TYPE', + 'OPENID_1_1_TYPE', + 'OPENID_2_0_TYPE', + 'OPENID_IDP_2_0_TYPE', + 'OpenIDServiceEndpoint', + 'discover', +] + +import urllib.parse +import logging + +from openid import fetchers, urinorm + +from openid import yadis +from openid.yadis.etxrd import nsTag, XRDSError, XRD_NS_2_0 +from openid.yadis.services import applyFilter as extractServices +from openid.yadis.discover import discover as yadisDiscover +from openid.yadis.discover import DiscoveryFailure +from openid.yadis import xrires, filters +from openid.yadis import xri + +from openid.consumer import html_parse + +OPENID_1_0_NS = 'http://openid.net/xmlns/1.0' +OPENID_IDP_2_0_TYPE = 'http://specs.openid.net/auth/2.0/server' +OPENID_2_0_TYPE = 'http://specs.openid.net/auth/2.0/signon' +OPENID_1_1_TYPE = 'http://openid.net/signon/1.1' +OPENID_1_0_TYPE = 'http://openid.net/signon/1.0' + +from openid.message import OPENID1_NS as OPENID_1_0_MESSAGE_NS +from openid.message import OPENID2_NS as OPENID_2_0_MESSAGE_NS + +logger = logging.getLogger(__name__) + + +class OpenIDServiceEndpoint(object): + """Object representing an OpenID service endpoint. + + @ivar identity_url: the verified identifier. + @ivar canonicalID: For XRI, the persistent identifier. + """ + + # OpenID service type URIs, listed in order of preference. The + # ordering of this list affects yadis and XRI service discovery. + openid_type_uris = [ + OPENID_IDP_2_0_TYPE, + OPENID_2_0_TYPE, + OPENID_1_1_TYPE, + OPENID_1_0_TYPE, + ] + + def __init__(self): + self.claimed_id = None + self.server_url = None + self.type_uris = [] + self.local_id = None + self.canonicalID = None + self.used_yadis = False # whether this came from an XRDS + self.display_identifier = None + + def usesExtension(self, extension_uri): + return extension_uri in self.type_uris + + def preferredNamespace(self): + if (OPENID_IDP_2_0_TYPE in self.type_uris or + OPENID_2_0_TYPE in self.type_uris): + return OPENID_2_0_MESSAGE_NS + else: + return OPENID_1_0_MESSAGE_NS + + def supportsType(self, type_uri): + """Does this endpoint support this type? + + I consider C{/server} endpoints to implicitly support C{/signon}. + """ + return ((type_uri in self.type_uris) or + (type_uri == OPENID_2_0_TYPE and self.isOPIdentifier())) + + def getDisplayIdentifier(self): + """Return the display_identifier if set, else return the claimed_id. + """ + if self.display_identifier is not None: + return self.display_identifier + if self.claimed_id is None: + return None + else: + return urllib.parse.urldefrag(self.claimed_id)[0] + + def compatibilityMode(self): + return self.preferredNamespace() != OPENID_2_0_MESSAGE_NS + + def isOPIdentifier(self): + return OPENID_IDP_2_0_TYPE in self.type_uris + + def parseService(self, yadis_url, uri, type_uris, service_element): + """Set the state of this object based on the contents of the + service element.""" + self.type_uris = type_uris + self.server_url = uri + self.used_yadis = True + + if not self.isOPIdentifier(): + # XXX: This has crappy implications for Service elements + # that contain both 'server' and 'signon' Types. But + # that's a pathological configuration anyway, so I don't + # think I care. + self.local_id = findOPLocalIdentifier(service_element, + self.type_uris) + self.claimed_id = yadis_url + + def getLocalID(self): + """Return the identifier that should be sent as the + openid.identity parameter to the server.""" + # I looked at this conditional and thought "ah-hah! there's the bug!" + # but Python actually makes that one big expression somehow, i.e. + # "x is x is x" is not the same thing as "(x is x) is x". + # That's pretty weird, dude. -- kmt, 1/07 + if (self.local_id is self.canonicalID is None): + return self.claimed_id + else: + return self.local_id or self.canonicalID + + def fromBasicServiceEndpoint(cls, endpoint): + """Create a new instance of this class from the endpoint + object passed in. + + @return: None or OpenIDServiceEndpoint for this endpoint object""" + type_uris = endpoint.matchTypes(cls.openid_type_uris) + + # If any Type URIs match and there is an endpoint URI + # specified, then this is an OpenID endpoint + if type_uris and endpoint.uri is not None: + openid_endpoint = cls() + openid_endpoint.parseService(endpoint.yadis_url, endpoint.uri, + endpoint.type_uris, + endpoint.service_element) + else: + openid_endpoint = None + + return openid_endpoint + + fromBasicServiceEndpoint = classmethod(fromBasicServiceEndpoint) + + def fromHTML(cls, uri, html): + """Parse the given document as HTML looking for an OpenID + + @rtype: [OpenIDServiceEndpoint] + """ + discovery_types = [ + (OPENID_2_0_TYPE, 'openid2.provider', 'openid2.local_id'), + (OPENID_1_1_TYPE, 'openid.server', 'openid.delegate'), + ] + + link_attrs = html_parse.parseLinkAttrs(html) + services = [] + for type_uri, op_endpoint_rel, local_id_rel in discovery_types: + op_endpoint_url = html_parse.findFirstHref(link_attrs, + op_endpoint_rel) + if op_endpoint_url is None: + continue + + service = cls() + service.claimed_id = uri + service.local_id = html_parse.findFirstHref(link_attrs, + local_id_rel) + service.server_url = op_endpoint_url + service.type_uris = [type_uri] + + services.append(service) + + return services + + fromHTML = classmethod(fromHTML) + + def fromXRDS(cls, uri, xrds): + """Parse the given document as XRDS looking for OpenID services. + + @rtype: [OpenIDServiceEndpoint] + + @raises XRDSError: When the XRDS does not parse. + + @since: 2.1.0 + """ + return extractServices(uri, xrds, cls) + + fromXRDS = classmethod(fromXRDS) + + def fromDiscoveryResult(cls, discoveryResult): + """Create endpoints from a DiscoveryResult. + + @type discoveryResult: L{DiscoveryResult} + + @rtype: list of L{OpenIDServiceEndpoint} + + @raises XRDSError: When the XRDS does not parse. + + @since: 2.1.0 + """ + if discoveryResult.isXRDS(): + method = cls.fromXRDS + else: + method = cls.fromHTML + return method(discoveryResult.normalized_uri, + discoveryResult.response_text) + + fromDiscoveryResult = classmethod(fromDiscoveryResult) + + def fromOPEndpointURL(cls, op_endpoint_url): + """Construct an OP-Identifier OpenIDServiceEndpoint object for + a given OP Endpoint URL + + @param op_endpoint_url: The URL of the endpoint + @rtype: OpenIDServiceEndpoint + """ + service = cls() + service.server_url = op_endpoint_url + service.type_uris = [OPENID_IDP_2_0_TYPE] + return service + + fromOPEndpointURL = classmethod(fromOPEndpointURL) + + def __str__(self): + return ("<%s.%s " + "server_url=%r " + "claimed_id=%r " + "local_id=%r " + "canonicalID=%r " + "used_yadis=%s " + ">" % (self.__class__.__module__, self.__class__.__name__, + self.server_url, self.claimed_id, self.local_id, + self.canonicalID, self.used_yadis)) + + +def findOPLocalIdentifier(service_element, type_uris): + """Find the OP-Local Identifier for this xrd:Service element. + + This considers openid:Delegate to be a synonym for xrd:LocalID if + both OpenID 1.X and OpenID 2.0 types are present. If only OpenID + 1.X is present, it returns the value of openid:Delegate. If only + OpenID 2.0 is present, it returns the value of xrd:LocalID. If + there is more than one LocalID tag and the values are different, + it raises a DiscoveryFailure. This is also triggered when the + xrd:LocalID and openid:Delegate tags are different. + + @param service_element: The xrd:Service element + @type service_element: ElementTree.Node + + @param type_uris: The xrd:Type values present in this service + element. This function could extract them, but higher level + code needs to do that anyway. + @type type_uris: [str] + + @raises DiscoveryFailure: when discovery fails. + + @returns: The OP-Local Identifier for this service element, if one + is present, or None otherwise. + @rtype: str or unicode or NoneType + """ + # XXX: Test this function on its own! + + # Build the list of tags that could contain the OP-Local Identifier + local_id_tags = [] + if (OPENID_1_1_TYPE in type_uris or OPENID_1_0_TYPE in type_uris): + local_id_tags.append(nsTag(OPENID_1_0_NS, 'Delegate')) + + if OPENID_2_0_TYPE in type_uris: + local_id_tags.append(nsTag(XRD_NS_2_0, 'LocalID')) + + # Walk through all the matching tags and make sure that they all + # have the same value + local_id = None + for local_id_tag in local_id_tags: + for local_id_element in service_element.findall(local_id_tag): + if local_id is None: + local_id = local_id_element.text + elif local_id != local_id_element.text: + format = 'More than one %r tag found in one service element' + message = format % (local_id_tag, ) + raise DiscoveryFailure(message, None) + + return local_id + + +def normalizeURL(url): + """Normalize a URL, converting normalization failures to + DiscoveryFailure""" + try: + normalized = urinorm.urinorm(url) + except ValueError as why: + raise DiscoveryFailure('Normalizing identifier: %s' % (why, ), None) + else: + return urllib.parse.urldefrag(normalized)[0] + + +def normalizeXRI(xri): + """Normalize an XRI, stripping its scheme if present""" + if xri.startswith("xri://"): + xri = xri[6:] + return xri + + +def arrangeByType(service_list, preferred_types): + """Rearrange service_list in a new list so services are ordered by + types listed in preferred_types. Return the new list.""" + + def enumerate(elts): + """Return an iterable that pairs the index of an element with + that element. + + For Python 2.2 compatibility""" + return list(zip(list(range(len(elts))), elts)) + + def bestMatchingService(service): + """Return the index of the first matching type, or something + higher if no type matches. + + This provides an ordering in which service elements that + contain a type that comes earlier in the preferred types list + come before service elements that come later. If a service + element has more than one type, the most preferred one wins. + """ + for i, t in enumerate(preferred_types): + if preferred_types[i] in service.type_uris: + return i + + return len(preferred_types) + + # Build a list with the service elements in tuples whose + # comparison will prefer the one with the best matching service + prio_services = [(bestMatchingService(s), orig_index, s) + for (orig_index, s) in enumerate(service_list)] + prio_services.sort() + + # Now that the services are sorted by priority, remove the sort + # keys from the list. + for i in range(len(prio_services)): + prio_services[i] = prio_services[i][2] + + return prio_services + + +def getOPOrUserServices(openid_services): + """Extract OP Identifier services. If none found, return the + rest, sorted with most preferred first according to + OpenIDServiceEndpoint.openid_type_uris. + + openid_services is a list of OpenIDServiceEndpoint objects. + + Returns a list of OpenIDServiceEndpoint objects.""" + + op_services = arrangeByType(openid_services, [OPENID_IDP_2_0_TYPE]) + + openid_services = arrangeByType(openid_services, + OpenIDServiceEndpoint.openid_type_uris) + + return op_services or openid_services + + +def discoverYadis(uri): + """Discover OpenID services for a URI. Tries Yadis and falls back + on old-style discovery if Yadis fails. + + @param uri: normalized identity URL + @type uri: str + + @return: (claimed_id, services) + @rtype: (str, list(OpenIDServiceEndpoint)) + + @raises DiscoveryFailure: when discovery fails. + """ + # Might raise a yadis.discover.DiscoveryFailure if no document + # came back for that URI at all. I don't think falling back + # to OpenID 1.0 discovery on the same URL will help, so don't + # bother to catch it. + response = yadisDiscover(uri) + + yadis_url = response.normalized_uri + body = response.response_text + try: + openid_services = OpenIDServiceEndpoint.fromXRDS(yadis_url, body) + except XRDSError: + # Does not parse as a Yadis XRDS file + openid_services = [] + + if not openid_services: + # Either not an XRDS or there are no OpenID services. + + if response.isXRDS(): + # if we got the Yadis content-type or followed the Yadis + # header, re-fetch the document without following the Yadis + # header, with no Accept header. + return discoverNoYadis(uri) + + # Try to parse the response as HTML. + # + openid_services = OpenIDServiceEndpoint.fromHTML(yadis_url, body) + + return (yadis_url, getOPOrUserServices(openid_services)) + + +def discoverXRI(iname): + endpoints = [] + iname = normalizeXRI(iname) + try: + canonicalID, services = xrires.ProxyResolver().query( + iname, OpenIDServiceEndpoint.openid_type_uris) + + if canonicalID is None: + raise XRDSError('No CanonicalID found for XRI %r' % (iname, )) + + flt = filters.mkFilter(OpenIDServiceEndpoint) + for service_element in services: + endpoints.extend(flt.getServiceEndpoints(iname, service_element)) + except XRDSError: + logger.exception('xrds error on ' + iname) + + for endpoint in endpoints: + # Is there a way to pass this through the filter to the endpoint + # constructor instead of tacking it on after? + endpoint.canonicalID = canonicalID + endpoint.claimed_id = canonicalID + endpoint.display_identifier = iname + + # FIXME: returned xri should probably be in some normal form + return iname, getOPOrUserServices(endpoints) + + +def discoverNoYadis(uri): + http_resp = fetchers.fetch(uri) + if http_resp.status not in (200, 206): + raise DiscoveryFailure( + 'HTTP Response status from identity URL host is not 200. ' + 'Got status %r' % (http_resp.status, ), http_resp) + + claimed_id = http_resp.final_url + openid_services = OpenIDServiceEndpoint.fromHTML(claimed_id, + http_resp.body) + return claimed_id, openid_services + + +def discoverURI(uri): + parsed = urllib.parse.urlparse(uri) + if parsed[0] and parsed[1]: + if parsed[0] not in ['http', 'https']: + raise DiscoveryFailure('URI scheme is not HTTP or HTTPS', None) + else: + uri = 'http://' + uri + + uri = normalizeURL(uri) + claimed_id, openid_services = discoverYadis(uri) + claimed_id = normalizeURL(claimed_id) + return claimed_id, openid_services + + +def discover(identifier): + if xri.identifierScheme(identifier) == "XRI": + return discoverXRI(identifier) + else: + return discoverURI(identifier) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/consumer/html_parse.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/consumer/html_parse.py new file mode 100644 index 0000000000000000000000000000000000000000..cc154eb5b682aa08fe76463d73db3bd490a25576 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/consumer/html_parse.py @@ -0,0 +1,278 @@ +""" +This module implements a VERY limited parser that finds tags in +the head of HTML or XHTML documents and parses out their attributes +according to the OpenID spec. It is a liberal parser, but it requires +these things from the data in order to work: + + - There must be an open tag + + - There must be an open tag inside of the tag + + - Only s that are found inside of the tag are parsed + (this is by design) + + - The parser follows the OpenID specification in resolving the + attributes of the link tags. This means that the attributes DO NOT + get resolved as they would by an XML or HTML parser. In particular, + only certain entities get replaced, and href attributes do not get + resolved relative to a base URL. + +From http://openid.net/specs.bml#linkrel: + + - The openid.server URL MUST be an absolute URL. OpenID consumers + MUST NOT attempt to resolve relative URLs. + + - The openid.server URL MUST NOT include entities other than &, + <, >, and ". + +The parser ignores SGML comments and . Both kinds of +quoting are allowed for attributes. + +The parser deals with invalid markup in these ways: + + - Tag names are not case-sensitive + + - The tag is accepted even when it is not at the top level + + - The tag is accepted even when it is not a direct child of + the tag, but a tag must be an ancestor of the + tag + + - tags are accepted even when they are not direct children of + the tag, but a tag must be an ancestor of the + tag + + - If there is no closing tag for an open or tag, the + remainder of the document is viewed as being inside of the tag. If + there is no closing tag for a tag, the link tag is treated + as a short tag. Exceptions to this rule are that closes + and or closes + + - Attributes of the tag are not required to be quoted. + + - In the case of duplicated attribute names, the attribute coming + last in the tag will be the value returned. + + - Any text that does not parse as an attribute within a link tag will + be ignored. (e.g. will ignore + pumpkin) + + - If there are more than one or tag, the parser only + looks inside of the first one. + + - The contents of + +''', flags) + +tag_expr = r''' +# Starts with the tag name at a word boundary, where the tag name is +# not a namespace +<%(tag_name)s\b(?!:) + +# All of the stuff up to a ">", hopefully attributes. +(?P[^>]*?) + +(?: # Match a short tag + /> + +| # Match a full tag + > + + (?P.*?) + + # Closed by + (?: # One of the specified close tags + + + # End of the string + | \Z + + ) + +) +''' + + +def tagMatcher(tag_name, *close_tags): + if close_tags: + options = '|'.join((tag_name, ) + close_tags) + closers = '(?:%s)' % (options, ) + else: + closers = tag_name + + expr = tag_expr % locals() + return re.compile(expr, flags) + + +# Must contain at least an open html and an open head tag +html_find = tagMatcher('html') +head_find = tagMatcher('head', 'body') +link_find = re.compile(r'\w+)= + +# Then either a quoted or unquoted attribute +(?: + + # Match everything that\'s between matching quote marks + (?P["\'])(?P.*?)(?P=qopen) +| + + # If the value is not quoted, match up to whitespace + (?P(?:[^\s<>/]|/(?!>))+) +) + +| + +(?P[<>]) +''', flags) + +# Entity replacement: +replacements = { + 'amp': '&', + 'lt': '<', + 'gt': '>', + 'quot': '"', +} + +ent_replace = re.compile(r'&(%s);' % '|'.join(list(replacements.keys()))) + + +def replaceEnt(mo): + "Replace the entities that are specified by OpenID" + return replacements.get(mo.group(1), mo.group()) + + +def parseLinkAttrs(html, ignore_errors=False): + """Find all link tags in a string representing a HTML document and + return a list of their attributes. + + @param html: the text to parse + @type html: str or unicode + + @param ignore_errors: whether to return despite e.g. parsing errors + @type ignore_errors: bool + + @return: A list of dictionaries of attributes, one for each link tag + @rtype: [[(type(html), type(html))]] + """ + if isinstance(html, bytes): + # Attempt to decode as UTF-8, since that's the most modern -- also + # try Latin-1, since that's suggested by HTTP/1.1. If neither of + # those works, fall over. + try: + html = html.decode("utf-8") + except UnicodeDecodeError: + try: + html = html.decode("latin1") + except UnicodeDecodeError: + if ignore_errors: + # Optionally ignore the errors and act as if no link attrs + # were found here + return [] + else: + raise AssertionError("Unreadable HTML!") + + stripped = removed_re.sub('', html) + html_mo = html_find.search(stripped) + if html_mo is None or html_mo.start('contents') == -1: + return [] + + start, end = html_mo.span('contents') + head_mo = head_find.search(stripped, start, end) + if head_mo is None or head_mo.start('contents') == -1: + return [] + + start, end = head_mo.span('contents') + link_mos = link_find.finditer(stripped, head_mo.start(), head_mo.end()) + + matches = [] + for link_mo in link_mos: + start = link_mo.start() + 5 + link_attrs = {} + for attr_mo in attr_find.finditer(stripped, start): + if attr_mo.lastgroup == 'end_link': + break + + # Either q_val or unq_val must be present, but not both + # unq_val is a True (non-empty) value if it is present + attr_name, q_val, unq_val = attr_mo.group('attr_name', 'q_val', + 'unq_val') + attr_val = ent_replace.sub(replaceEnt, unq_val or q_val) + + link_attrs[attr_name] = attr_val + + matches.append(link_attrs) + + return matches + + +def relMatches(rel_attr, target_rel): + """Does this target_rel appear in the rel_str?""" + # XXX: TESTME + rels = rel_attr.strip().split() + for rel in rels: + rel = rel.lower() + if rel == target_rel: + return 1 + + return 0 + + +def linkHasRel(link_attrs, target_rel): + """Does this link have target_rel as a relationship?""" + # XXX: TESTME + rel_attr = link_attrs.get('rel') + return rel_attr and relMatches(rel_attr, target_rel) + + +def findLinksRel(link_attrs_list, target_rel): + """Filter the list of link attributes on whether it has target_rel + as a relationship.""" + # XXX: TESTME + matchesTarget = lambda attrs: linkHasRel(attrs, target_rel) + return list(filter(matchesTarget, link_attrs_list)) + + +def findFirstHref(link_attrs_list, target_rel): + """Return the value of the href attribute for the first link tag + in the list that has target_rel as a relationship.""" + # XXX: TESTME + matches = findLinksRel(link_attrs_list, target_rel) + if not matches: + return None + first = matches[0] + return first.get('href') diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/cryptutil.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/cryptutil.py new file mode 100644 index 0000000000000000000000000000000000000000..7fdd628519b6bb676c2d6a709ed28a3d33f61acc --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/cryptutil.py @@ -0,0 +1,154 @@ +"""Module containing a cryptographic-quality source of randomness and +other cryptographically useful functionality + +Python 2.4 needs no external support for this module, nor does Python +2.3 on a system with /dev/urandom. + +Other configurations will need a quality source of random bytes and +access to a function that will convert binary strings to long +integers. This module will work with the Python Cryptography Toolkit +(pycrypto) if it is present. pycrypto can be found with a search +engine, but is currently found at: + +http://www.amk.ca/python/code/crypto +""" + +__all__ = [ + 'base64ToLong', + 'binaryToLong', + 'hmacSha1', + 'hmacSha256', + 'longToBase64', + 'longToBinary', + 'randomString', + 'randrange', + 'sha1', + 'sha256', +] + +import hmac +import os +import random + +from openid.oidutil import toBase64, fromBase64 + +import hashlib + + +class HashContainer(object): + def __init__(self, hash_constructor): + self.new = hash_constructor + self.digest_size = hash_constructor().digest_size + + +sha1_module = HashContainer(hashlib.sha1) +sha256_module = HashContainer(hashlib.sha256) + + +def hmacSha1(key, text): + if isinstance(key, str): + key = bytes(key, encoding="utf-8") + if isinstance(text, str): + text = bytes(text, encoding="utf-8") + return hmac.new(key, text, sha1_module).digest() + + +def sha1(s): + if isinstance(s, str): + s = bytes(s, encoding="utf-8") + return sha1_module.new(s).digest() + + +def hmacSha256(key, text): + if isinstance(key, str): + key = bytes(key, encoding="utf-8") + if isinstance(text, str): + text = bytes(text, encoding="utf-8") + return hmac.new(key, text, sha256_module).digest() + + +def sha256(s): + if isinstance(s, str): + s = bytes(s, encoding="utf-8") + return sha256_module.new(s).digest() + + +SHA256_AVAILABLE = True + +try: + from Crypto.Util.number import long_to_bytes, bytes_to_long +except ImportError: + # In the case where we don't have pycrypto installed, define substitute + # functionality. + + import pickle + + def longToBinary(l): + if l == 0: + return b'\x00' + b = bytearray(pickle.encode_long(l)) + b.reverse() + return bytes(b) + + def binaryToLong(s): + if isinstance(s, str): + s = s.encode("utf-8") + b = bytearray(s) + b.reverse() + return pickle.decode_long(bytes(b)) +else: + # We have pycrypto, so wrap its functions instead. + + def longToBinary(l): + if l < 0: + raise ValueError('This function only supports positive integers') + + bytestring = long_to_bytes(l) + if bytestring[0] > 127: + return b'\x00' + bytestring + else: + return bytestring + + def binaryToLong(bytestring): + if not bytestring: + raise ValueError('Empty string passed to strToLong') + + if bytestring[0] > 127: + raise ValueError('This function only supports positive integers') + + return bytes_to_long(bytestring) + + +# A cryptographically safe source of random bytes +getBytes = os.urandom + +# A randrange function that works for longs +randrange = random.randrange + + +def longToBase64(l): + return toBase64(longToBinary(l)) + + +def base64ToLong(s): + return binaryToLong(fromBase64(s)) + + +def randomString(length, chrs=None): + """Produce a string of length random bytes, chosen from chrs.""" + if chrs is None: + return getBytes(length) + else: + n = len(chrs) + return ''.join([chrs[randrange(n)] for _ in range(length)]) + + +def const_eq(s1, s2): + if len(s1) != len(s2): + return False + + result = True + for i in range(len(s1)): + result = result and (s1[i] == s2[i]) + + return result diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/dh.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/dh.py new file mode 100644 index 0000000000000000000000000000000000000000..5de7e4ecbbceb67cc766d1bc4015752c1b3a5fec --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/dh.py @@ -0,0 +1,47 @@ +from openid import cryptutil + + +def strxor(x, y): + if len(x) != len(y): + raise ValueError('Inputs to strxor must have the same length') + + if isinstance(x, str): + x = x.encode("utf-8") + if isinstance(y, str): + y = y.encode("utf-8") + + return bytes([a ^ b for a, b in zip(x, y)]) + + +class DiffieHellman(object): + DEFAULT_MOD = 155172898181473697471232257763715539915724801966915404479707795314057629378541917580651227423698188993727816152646631438561595825688188889951272158842675419950341258706556549803580104870537681476726513255747040765857479291291572334510643245094715007229621094194349783925984760375594985848253359305585439638443 + + DEFAULT_GEN = 2 + + def fromDefaults(cls): + return cls(cls.DEFAULT_MOD, cls.DEFAULT_GEN) + + fromDefaults = classmethod(fromDefaults) + + def __init__(self, modulus, generator): + self.modulus = int(modulus) + self.generator = int(generator) + + self._setPrivate(cryptutil.randrange(1, modulus - 1)) + + def _setPrivate(self, private): + """This is here to make testing easier""" + self.private = private + self.public = pow(self.generator, self.private, self.modulus) + + def usingDefaultValues(self): + return (self.modulus == self.DEFAULT_MOD and + self.generator == self.DEFAULT_GEN) + + def getSharedSecret(self, composite): + return pow(composite, self.private, self.modulus) + + def xorSecret(self, composite, secret, hash_func): + dh_shared = self.getSharedSecret(composite) + hashed_dh_shared = hash_func(cryptutil.longToBinary(dh_shared)) + return strxor(secret, hashed_dh_shared) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/extension.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/extension.py new file mode 100644 index 0000000000000000000000000000000000000000..0052f0033b88a9d6480cf9de767caa5ce67cb64d --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/extension.py @@ -0,0 +1,51 @@ +import warnings + +from openid import message as message_module + + +class Extension(object): + """An interface for OpenID extensions. + + @ivar ns_uri: The namespace to which to add the arguments for this + extension + """ + ns_uri = None + ns_alias = None + + def getExtensionArgs(self): + """Get the string arguments that should be added to an OpenID + message for this extension. + + @returns: A dictionary of completely non-namespaced arguments + to be added. For example, if the extension's alias is + 'uncle', and this method returns {'meat':'Hot Rats'}, the + final message will contain {'openid.uncle.meat':'Hot Rats'} + """ + raise NotImplementedError() + + def toMessage(self, message=None): + """Add the arguments from this extension to the provided + message, or create a new message containing only those + arguments. + + @returns: The message with the extension arguments added + """ + if message is None: + warnings.warn( + 'Passing None to Extension.toMessage is deprecated. ' + 'Creating a message assuming you want OpenID 2.', + DeprecationWarning, + stacklevel=2) + message = message_module.Message(message_module.OPENID2_NS) + + implicit = message.isOpenID1() + + try: + message.namespaces.addAlias( + self.ns_uri, self.ns_alias, implicit=implicit) + except KeyError: + if message.namespaces.getAlias(self.ns_uri) != self.ns_alias: + raise + + message.updateArgs(self.ns_uri, self.getExtensionArgs()) + return message diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/extensions/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/extensions/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..710b200268d9b29772d21bada5b25eb3429a4b74 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/extensions/__init__.py @@ -0,0 +1,5 @@ +"""OpenID Extension modules.""" + +__all__ = ['ax', 'pape', 'sreg'] + +from openid.extensions.draft import pape5 as pape diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/extensions/ax.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/extensions/ax.py new file mode 100644 index 0000000000000000000000000000000000000000..d36fd00872cf20be9a61470b09b5026a8f886dfa --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/extensions/ax.py @@ -0,0 +1,781 @@ +# -*- test-case-name: openid.test.test_ax -*- +"""Implements the OpenID Attribute Exchange specification, version 1.0. + +@since: 2.1.0 +""" + +__all__ = [ + 'AttributeRequest', + 'FetchRequest', + 'FetchResponse', + 'StoreRequest', + 'StoreResponse', +] + +from openid import extension +from openid.server.trustroot import TrustRoot +from openid.message import NamespaceMap, OPENID_NS + +# Use this as the 'count' value for an attribute in a FetchRequest to +# ask for as many values as the OP can provide. +UNLIMITED_VALUES = "unlimited" + +# Minimum supported alias length in characters. Here for +# completeness. +MINIMUM_SUPPORTED_ALIAS_LENGTH = 32 + + +def checkAlias(alias): + """ + Check an alias for invalid characters; raise AXError if any are + found. Return None if the alias is valid. + """ + if ',' in alias: + raise AXError("Alias %r must not contain comma" % (alias, )) + if '.' in alias: + raise AXError("Alias %r must not contain period" % (alias, )) + + +class AXError(ValueError): + """Results from data that does not meet the attribute exchange 1.0 + specification""" + + +class NotAXMessage(AXError): + """Raised when there is no Attribute Exchange mode in the message.""" + + def __repr__(self): + return self.__class__.__name__ + + def __str__(self): + return self.__class__.__name__ + + +class AXMessage(extension.Extension): + """Abstract class containing common code for attribute exchange messages + + @cvar ns_alias: The preferred namespace alias for attribute + exchange messages + + @cvar mode: The type of this attribute exchange message. This must + be overridden in subclasses. + """ + + # This class is abstract, so it's OK that it doesn't override the + # abstract method in Extension: + # + #pylint:disable-msg=W0223 + + ns_alias = 'ax' + ns_uri = 'http://openid.net/srv/ax/1.0' + mode = None # NOTE mode is only ever set to a str value, see below + + def _checkMode(self, ax_args): + """Raise an exception if the mode in the attribute exchange + arguments does not match what is expected for this class. + + @raises NotAXMessage: When there is no mode value in ax_args at all. + + @raises AXError: When mode does not match. + """ + mode = ax_args.get('mode') + if isinstance(mode, bytes): + mode = str(mode, encoding="utf-8") + if mode != self.mode: + if not mode: + raise NotAXMessage() + else: + raise AXError('Expected mode %r; got %r' % (self.mode, mode)) + + def _newArgs(self): + """Return a set of attribute exchange arguments containing the + basic information that must be in every attribute exchange + message. + """ + return {'mode': self.mode} + + +class AttrInfo(object): + """Represents a single attribute in an attribute exchange + request. This should be added to an AXRequest object in order to + request the attribute. + + @ivar required: Whether the attribute will be marked as required + when presented to the subject of the attribute exchange + request. + @type required: bool + + @ivar count: How many values of this type to request from the + subject. Defaults to one. + @type count: int + + @ivar type_uri: The identifier that determines what the attribute + represents and how it is serialized. For example, one type URI + representing dates could represent a Unix timestamp in base 10 + and another could represent a human-readable string. + @type type_uri: str + + @ivar alias: The name that should be given to this alias in the + request. If it is not supplied, a generic name will be + assigned. For example, if you want to call a Unix timestamp + value 'tstamp', set its alias to that value. If two attributes + in the same message request to use the same alias, the request + will fail to be generated. + @type alias: str or NoneType + """ + + # It's OK that this class doesn't have public methods (it's just a + # holder for a bunch of attributes): + # + #pylint:disable-msg=R0903 + + def __init__(self, type_uri, count=1, required=False, alias=None): + self.required = required + self.count = count + self.type_uri = type_uri + self.alias = alias + + if self.alias is not None: + checkAlias(self.alias) + + def wantsUnlimitedValues(self): + """ + When processing a request for this attribute, the OP should + call this method to determine whether all available attribute + values were requested. If self.count == UNLIMITED_VALUES, + this returns True. Otherwise this returns False, in which + case self.count is an integer. + """ + return self.count == UNLIMITED_VALUES + + +def toTypeURIs(namespace_map, alias_list_s): + """Given a namespace mapping and a string containing a + comma-separated list of namespace aliases, return a list of type + URIs that correspond to those aliases. + + @param namespace_map: The mapping from namespace URI to alias + @type namespace_map: openid.message.NamespaceMap + + @param alias_list_s: The string containing the comma-separated + list of aliases. May also be None for convenience. + @type alias_list_s: str or NoneType + + @returns: The list of namespace URIs that corresponds to the + supplied list of aliases. If the string was zero-length or + None, an empty list will be returned. + + @raise KeyError: If an alias is present in the list of aliases but + is not present in the namespace map. + """ + uris = [] + + if alias_list_s: + for alias in alias_list_s.split(','): + type_uri = namespace_map.getNamespaceURI(alias) + if type_uri is None: + raise KeyError('No type is defined for attribute name %r' % + (alias, )) + else: + uris.append(type_uri) + + return uris + + +class FetchRequest(AXMessage): + """An attribute exchange 'fetch_request' message. This message is + sent by a relying party when it wishes to obtain attributes about + the subject of an OpenID authentication request. + + @ivar requested_attributes: The attributes that have been + requested thus far, indexed by the type URI. + @type requested_attributes: {str:AttrInfo} + + @ivar update_url: A URL that will accept responses for this + attribute exchange request, even in the absence of the user + who made this request. + """ + mode = 'fetch_request' + + def __init__(self, update_url=None): + AXMessage.__init__(self) + self.requested_attributes = {} + self.update_url = update_url + + def add(self, attribute): + """Add an attribute to this attribute exchange request. + + @param attribute: The attribute that is being requested + @type attribute: C{L{AttrInfo}} + + @returns: None + + @raise KeyError: when the requested attribute is already + present in this fetch request. + """ + if attribute.type_uri in self.requested_attributes: + raise KeyError('The attribute %r has already been requested' % + (attribute.type_uri, )) + + self.requested_attributes[attribute.type_uri] = attribute + + def getExtensionArgs(self): + """Get the serialized form of this attribute fetch request. + + @returns: The fetch request message parameters + @rtype: {unicode:unicode} + """ + aliases = NamespaceMap() + + required = [] + if_available = [] + + ax_args = self._newArgs() + + for type_uri, attribute in self.requested_attributes.items(): + if attribute.alias is None: + alias = aliases.add(type_uri) + else: + # This will raise an exception when the second + # attribute with the same alias is added. I think it + # would be better to complain at the time that the + # attribute is added to this object so that the code + # that is adding it is identified in the stack trace, + # but it's more work to do so, and it won't be 100% + # accurate anyway, since the attributes are + # mutable. So for now, just live with the fact that + # we'll learn about the error later. + # + # The other possible approach is to hide the error and + # generate a new alias on the fly. I think that would + # probably be bad. + alias = aliases.addAlias(type_uri, attribute.alias) + + if attribute.required: + required.append(alias) + else: + if_available.append(alias) + + if attribute.count != 1: + ax_args['count.' + alias] = str(attribute.count) + + ax_args['type.' + alias] = type_uri + + if required: + ax_args['required'] = ','.join(required) + + if if_available: + ax_args['if_available'] = ','.join(if_available) + + return ax_args + + def getRequiredAttrs(self): + """Get the type URIs for all attributes that have been marked + as required. + + @returns: A list of the type URIs for attributes that have + been marked as required. + @rtype: [str] + """ + required = [] + for type_uri, attribute in self.requested_attributes.items(): + if attribute.required: + required.append(type_uri) + + return required + + def fromOpenIDRequest(cls, openid_request): + """Extract a FetchRequest from an OpenID message + + @param openid_request: The OpenID authentication request + containing the attribute fetch request + @type openid_request: C{L{openid.server.server.CheckIDRequest}} + + @rtype: C{L{FetchRequest}} or C{None} + @returns: The FetchRequest extracted from the message or None, if + the message contained no AX extension. + + @raises KeyError: if the AuthRequest is not consistent in its use + of namespace aliases. + + @raises AXError: When parseExtensionArgs would raise same. + + @see: L{parseExtensionArgs} + """ + message = openid_request.message + ax_args = message.getArgs(cls.ns_uri) + self = cls() + try: + self.parseExtensionArgs(ax_args) + except NotAXMessage as err: + return None + + if self.update_url: + # Update URL must match the openid.realm of the underlying + # OpenID 2 message. + realm = message.getArg(OPENID_NS, 'realm', + message.getArg(OPENID_NS, 'return_to')) + + if not realm: + raise AXError( + ("Cannot validate update_url %r " + "against absent realm") + % (self.update_url, )) + + tr = TrustRoot.parse(realm) + if not tr.validateURL(self.update_url): + raise AXError( + "Update URL %r failed validation against realm %r" % + (self.update_url, realm, )) + + return self + + fromOpenIDRequest = classmethod(fromOpenIDRequest) + + def parseExtensionArgs(self, ax_args): + """Given attribute exchange arguments, populate this FetchRequest. + + @param ax_args: Attribute Exchange arguments from the request. + As returned from L{Message.getArgs}. + @type ax_args: dict + + @raises KeyError: if the message is not consistent in its use + of namespace aliases. + + @raises NotAXMessage: If ax_args does not include an Attribute Exchange + mode. + + @raises AXError: If the data to be parsed does not follow the + attribute exchange specification. At least when + 'if_available' or 'required' is not specified for a + particular attribute type. + """ + # Raises an exception if the mode is not the expected value + self._checkMode(ax_args) + + aliases = NamespaceMap() + + for key, value in ax_args.items(): + if key.startswith('type.'): + alias = key[5:] + type_uri = value + aliases.addAlias(type_uri, alias) + + count_key = 'count.' + alias + count_s = ax_args.get(count_key) + if count_s: + try: + count = int(count_s) + if count <= 0: + raise AXError( + "Count %r must be greater than zero, got %r" % + (count_key, count_s, )) + except ValueError: + if count_s != UNLIMITED_VALUES: + raise AXError("Invalid count value for %r: %r" % + (count_key, count_s, )) + count = count_s + else: + count = 1 + + self.add(AttrInfo(type_uri, alias=alias, count=count)) + + required = toTypeURIs(aliases, ax_args.get('required')) + + for type_uri in required: + self.requested_attributes[type_uri].required = True + + if_available = toTypeURIs(aliases, ax_args.get('if_available')) + + all_type_uris = required + if_available + + for type_uri in aliases.iterNamespaceURIs(): + if type_uri not in all_type_uris: + raise AXError('Type URI %r was in the request but not ' + 'present in "required" or "if_available"' % + (type_uri, )) + + self.update_url = ax_args.get('update_url') + + def iterAttrs(self): + """Iterate over the AttrInfo objects that are + contained in this fetch_request. + """ + return iter(self.requested_attributes.values()) + + def __iter__(self): + """Iterate over the attribute type URIs in this fetch_request + """ + return iter(self.requested_attributes) + + def has_key(self, type_uri): + """Is the given type URI present in this fetch_request? + """ + return type_uri in self.requested_attributes + + __contains__ = has_key + + +class AXKeyValueMessage(AXMessage): + """An abstract class that implements a message that has attribute + keys and values. It contains the common code between + fetch_response and store_request. + """ + + # This class is abstract, so it's OK that it doesn't override the + # abstract method in Extension: + # + #pylint:disable-msg=W0223 + + def __init__(self): + AXMessage.__init__(self) + self.data = {} + + def addValue(self, type_uri, value): + """Add a single value for the given attribute type to the + message. If there are already values specified for this type, + this value will be sent in addition to the values already + specified. + + @param type_uri: The URI for the attribute + + @param value: The value to add to the response to the relying + party for this attribute + @type value: unicode + + @returns: None + """ + try: + values = self.data[type_uri] + except KeyError: + values = self.data[type_uri] = [] + + values.append(value) + + def setValues(self, type_uri, values): + """Set the values for the given attribute type. This replaces + any values that have already been set for this attribute. + + @param type_uri: The URI for the attribute + + @param values: A list of values to send for this attribute. + @type values: [unicode] + """ + + self.data[type_uri] = values + + def _getExtensionKVArgs(self, aliases=None): + """Get the extension arguments for the key/value pairs + contained in this message. + + @param aliases: An alias mapping. Set to None if you don't + care about the aliases for this request. + """ + if aliases is None: + aliases = NamespaceMap() + + ax_args = {} + + for type_uri, values in self.data.items(): + alias = aliases.add(type_uri) + + ax_args['type.' + alias] = type_uri + ax_args['count.' + alias] = str(len(values)) + + for i, value in enumerate(values): + key = 'value.%s.%d' % (alias, i + 1) + ax_args[key] = value + + return ax_args + + def parseExtensionArgs(self, ax_args): + """Parse attribute exchange key/value arguments into this + object. + + @param ax_args: The attribute exchange fetch_response + arguments, with namespacing removed. + @type ax_args: {unicode:unicode} + + @returns: None + + @raises ValueError: If the message has bad values for + particular fields + + @raises KeyError: If the namespace mapping is bad or required + arguments are missing + """ + self._checkMode(ax_args) + + aliases = NamespaceMap() + + for key, value in ax_args.items(): + if key.startswith('type.'): + type_uri = value + alias = key[5:] + checkAlias(alias) + aliases.addAlias(type_uri, alias) + + for type_uri, alias in aliases.items(): + try: + count_s = ax_args['count.' + alias] + except KeyError: + value = ax_args['value.' + alias] + + if value == '': + values = [] + else: + values = [value] + else: + count = int(count_s) + values = [] + for i in range(1, count + 1): + value_key = 'value.%s.%d' % (alias, i) + value = ax_args[value_key] + values.append(value) + + self.data[type_uri] = values + + def getSingle(self, type_uri, default=None): + """Get a single value for an attribute. If no value was sent + for this attribute, use the supplied default. If there is more + than one value for this attribute, this method will fail. + + @type type_uri: str + @param type_uri: The URI for the attribute + + @param default: The value to return if the attribute was not + sent in the fetch_response. + + @returns: The value of the attribute in the fetch_response + message, or the default supplied + @rtype: unicode or NoneType + + @raises ValueError: If there is more than one value for this + parameter in the fetch_response message. + @raises KeyError: If the attribute was not sent in this response + """ + values = self.data.get(type_uri) + if not values: + return default + elif len(values) == 1: + return values[0] + else: + raise AXError('More than one value present for %r' % (type_uri, )) + + def get(self, type_uri): + """Get the list of values for this attribute in the + fetch_response. + + XXX: what to do if the values are not present? default + parameter? this is funny because it's always supposed to + return a list, so the default may break that, though it's + provided by the user's code, so it might be okay. If no + default is supplied, should the return be None or []? + + @param type_uri: The URI of the attribute + + @returns: The list of values for this attribute in the + response. May be an empty list. + @rtype: [unicode] + + @raises KeyError: If the attribute was not sent in the response + """ + return self.data[type_uri] + + def count(self, type_uri): + """Get the number of responses for a particular attribute in + this fetch_response message. + + @param type_uri: The URI of the attribute + + @returns: The number of values sent for this attribute + + @raises KeyError: If the attribute was not sent in the + response. KeyError will not be raised if the number of + values was zero. + """ + return len(self.get(type_uri)) + + +class FetchResponse(AXKeyValueMessage): + """A fetch_response attribute exchange message + """ + mode = 'fetch_response' + + def __init__(self, request=None, update_url=None): + """ + @param request: When supplied, I will use namespace aliases + that match those in this request. I will also check to + make sure I do not respond with attributes that were not + requested. + + @type request: L{FetchRequest} + + @param update_url: By default, C{update_url} is taken from the + request. But if you do not supply the request, you may set + the C{update_url} here. + + @type update_url: str + """ + AXKeyValueMessage.__init__(self) + self.update_url = update_url + self.request = request + + def getExtensionArgs(self): + """Serialize this object into arguments in the attribute + exchange namespace + + @returns: The dictionary of unqualified attribute exchange + arguments that represent this fetch_response. + @rtype: {unicode;unicode} + """ + + aliases = NamespaceMap() + + zero_value_types = [] + + if self.request is not None: + # Validate the data in the context of the request (the + # same attributes should be present in each, and the + # counts in the response must be no more than the counts + # in the request) + + for type_uri in self.data: + if type_uri not in self.request: + raise KeyError( + 'Response attribute not present in request: %r' % + (type_uri, )) + + for attr_info in self.request.iterAttrs(): + # Copy the aliases from the request so that reading + # the response in light of the request is easier + if attr_info.alias is None: + aliases.add(attr_info.type_uri) + else: + aliases.addAlias(attr_info.type_uri, attr_info.alias) + + try: + values = self.data[attr_info.type_uri] + except KeyError: + values = [] + zero_value_types.append(attr_info) + + if (attr_info.count != UNLIMITED_VALUES) and \ + (attr_info.count < len(values)): + raise AXError( + 'More than the number of requested values were ' + 'specified for %r' % (attr_info.type_uri, )) + + kv_args = self._getExtensionKVArgs(aliases) + + # Add the KV args into the response with the args that are + # unique to the fetch_response + ax_args = self._newArgs() + + # For each requested attribute, put its type/alias and count + # into the response even if no data were returned. + for attr_info in zero_value_types: + alias = aliases.getAlias(attr_info.type_uri) + kv_args['type.' + alias] = attr_info.type_uri + kv_args['count.' + alias] = '0' + + update_url = ((self.request and self.request.update_url) or + self.update_url) + + if update_url: + ax_args['update_url'] = update_url + + ax_args.update(kv_args) + + return ax_args + + def parseExtensionArgs(self, ax_args): + """@see: {Extension.parseExtensionArgs}""" + super(FetchResponse, self).parseExtensionArgs(ax_args) + self.update_url = ax_args.get('update_url') + + def fromSuccessResponse(cls, success_response, signed=True): + """Construct a FetchResponse object from an OpenID library + SuccessResponse object. + + @param success_response: A successful id_res response object + @type success_response: openid.consumer.consumer.SuccessResponse + + @param signed: Whether non-signed args should be + processsed. If True (the default), only signed arguments + will be processsed. + @type signed: bool + + @returns: A FetchResponse containing the data from the OpenID + message, or None if the SuccessResponse did not contain AX + extension data. + + @raises AXError: when the AX data cannot be parsed. + """ + self = cls() + ax_args = success_response.extensionResponse(self.ns_uri, signed) + + try: + self.parseExtensionArgs(ax_args) + except NotAXMessage as err: + return None + else: + return self + + fromSuccessResponse = classmethod(fromSuccessResponse) + + +class StoreRequest(AXKeyValueMessage): + """A store request attribute exchange message representation + """ + mode = 'store_request' + + def __init__(self, aliases=None): + """ + @param aliases: The namespace aliases to use when making this + store request. Leave as None to use defaults. + """ + super(StoreRequest, self).__init__() + self.aliases = aliases + + def getExtensionArgs(self): + """ + @see: L{Extension.getExtensionArgs} + """ + ax_args = self._newArgs() + kv_args = self._getExtensionKVArgs(self.aliases) + ax_args.update(kv_args) + return ax_args + + +class StoreResponse(AXMessage): + """An indication that the store request was processed along with + this OpenID transaction. + """ + + SUCCESS_MODE = 'store_response_success' + FAILURE_MODE = 'store_response_failure' + + def __init__(self, succeeded=True, error_message=None): + AXMessage.__init__(self) + + if succeeded and error_message is not None: + raise AXError('An error message may only be included in a ' + 'failing fetch response') + if succeeded: + self.mode = self.SUCCESS_MODE + else: + self.mode = self.FAILURE_MODE + + self.error_message = error_message + + def succeeded(self): + """Was this response a success response?""" + return self.mode == self.SUCCESS_MODE + + def getExtensionArgs(self): + """@see: {Extension.getExtensionArgs}""" + ax_args = self._newArgs() + if not self.succeeded() and self.error_message: + ax_args['error'] = self.error_message + + return ax_args diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/extensions/draft/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/extensions/draft/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/extensions/draft/pape2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/extensions/draft/pape2.py new file mode 100644 index 0000000000000000000000000000000000000000..d0587e316080de8846e7a2f80075b995395a878a --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/extensions/draft/pape2.py @@ -0,0 +1,285 @@ +"""An implementation of the OpenID Provider Authentication Policy +Extension 1.0 + +@see: http://openid.net/developers/specs/ + +@since: 2.1.0 +""" + +__all__ = [ + 'Request', + 'Response', + 'ns_uri', + 'AUTH_PHISHING_RESISTANT', + 'AUTH_MULTI_FACTOR', + 'AUTH_MULTI_FACTOR_PHYSICAL', +] + +from openid.extension import Extension +import re + +ns_uri = "http://specs.openid.net/extensions/pape/1.0" + +AUTH_MULTI_FACTOR_PHYSICAL = \ + 'http://schemas.openid.net/pape/policies/2007/06/multi-factor-physical' +AUTH_MULTI_FACTOR = \ + 'http://schemas.openid.net/pape/policies/2007/06/multi-factor' +AUTH_PHISHING_RESISTANT = \ + 'http://schemas.openid.net/pape/policies/2007/06/phishing-resistant' + +TIME_VALIDATOR = re.compile('^\d\d\d\d-\d\d-\d\dT\d\d:\d\d:\d\dZ$') + + +class Request(Extension): + """A Provider Authentication Policy request, sent from a relying + party to a provider + + @ivar preferred_auth_policies: The authentication policies that + the relying party prefers + @type preferred_auth_policies: [str] + + @ivar max_auth_age: The maximum time, in seconds, that the relying + party wants to allow to have elapsed before the user must + re-authenticate + @type max_auth_age: int or NoneType + """ + + ns_alias = 'pape' + + def __init__(self, preferred_auth_policies=None, max_auth_age=None): + super(Request, self).__init__() + if not preferred_auth_policies: + preferred_auth_policies = [] + + self.preferred_auth_policies = preferred_auth_policies + self.max_auth_age = max_auth_age + + def __bool__(self): + return bool(self.preferred_auth_policies or + self.max_auth_age is not None) + + def addPolicyURI(self, policy_uri): + """Add an acceptable authentication policy URI to this request + + This method is intended to be used by the relying party to add + acceptable authentication types to the request. + + @param policy_uri: The identifier for the preferred type of + authentication. + @see: http://openid.net/specs/openid-provider-authentication-policy-extension-1_0-01.html#auth_policies + """ + if policy_uri not in self.preferred_auth_policies: + self.preferred_auth_policies.append(policy_uri) + + def getExtensionArgs(self): + """@see: C{L{Extension.getExtensionArgs}} + """ + ns_args = { + 'preferred_auth_policies': ' '.join(self.preferred_auth_policies) + } + + if self.max_auth_age is not None: + ns_args['max_auth_age'] = str(self.max_auth_age) + + return ns_args + + def fromOpenIDRequest(cls, request): + """Instantiate a Request object from the arguments in a + C{checkid_*} OpenID message + """ + self = cls() + args = request.message.getArgs(self.ns_uri) + + if args == {}: + return None + + self.parseExtensionArgs(args) + return self + + fromOpenIDRequest = classmethod(fromOpenIDRequest) + + def parseExtensionArgs(self, args): + """Set the state of this request to be that expressed in these + PAPE arguments + + @param args: The PAPE arguments without a namespace + + @rtype: None + + @raises ValueError: When the max_auth_age is not parseable as + an integer + """ + + # preferred_auth_policies is a space-separated list of policy URIs + self.preferred_auth_policies = [] + + policies_str = args.get('preferred_auth_policies') + if policies_str: + if isinstance(policies_str, bytes): + policies_str = str(policies_str, encoding="utf-8") + for uri in policies_str.split(' '): + if uri not in self.preferred_auth_policies: + self.preferred_auth_policies.append(uri) + + # max_auth_age is base-10 integer number of seconds + max_auth_age_str = args.get('max_auth_age') + self.max_auth_age = None + + if max_auth_age_str: + try: + self.max_auth_age = int(max_auth_age_str) + except ValueError: + pass + + def preferredTypes(self, supported_types): + """Given a list of authentication policy URIs that a provider + supports, this method returns the subsequence of those types + that are preferred by the relying party. + + @param supported_types: A sequence of authentication policy + type URIs that are supported by a provider + + @returns: The sub-sequence of the supported types that are + preferred by the relying party. This list will be ordered + in the order that the types appear in the supported_types + sequence, and may be empty if the provider does not prefer + any of the supported authentication types. + + @returntype: [str] + """ + return list( + filter(self.preferred_auth_policies.__contains__, supported_types)) + + +Request.ns_uri = ns_uri + + +class Response(Extension): + """A Provider Authentication Policy response, sent from a provider + to a relying party + """ + + ns_alias = 'pape' + + def __init__(self, + auth_policies=None, + auth_time=None, + nist_auth_level=None): + super(Response, self).__init__() + if auth_policies: + self.auth_policies = auth_policies + else: + self.auth_policies = [] + + self.auth_time = auth_time + self.nist_auth_level = nist_auth_level + + def addPolicyURI(self, policy_uri): + """Add a authentication policy to this response + + This method is intended to be used by the provider to add a + policy that the provider conformed to when authenticating the user. + + @param policy_uri: The identifier for the preferred type of + authentication. + @see: http://openid.net/specs/openid-provider-authentication-policy-extension-1_0-01.html#auth_policies + """ + if policy_uri not in self.auth_policies: + self.auth_policies.append(policy_uri) + + def fromSuccessResponse(cls, success_response): + """Create a C{L{Response}} object from a successful OpenID + library response + (C{L{openid.consumer.consumer.SuccessResponse}}) response + message + + @param success_response: A SuccessResponse from consumer.complete() + @type success_response: C{L{openid.consumer.consumer.SuccessResponse}} + + @rtype: Response or None + @returns: A provider authentication policy response from the + data that was supplied with the C{id_res} response or None + if the provider sent no signed PAPE response arguments. + """ + self = cls() + + # PAPE requires that the args be signed. + args = success_response.getSignedNS(self.ns_uri) + + # Only try to construct a PAPE response if the arguments were + # signed in the OpenID response. If not, return None. + if args is not None: + self.parseExtensionArgs(args) + return self + else: + return None + + def parseExtensionArgs(self, args, strict=False): + """Parse the provider authentication policy arguments into the + internal state of this object + + @param args: unqualified provider authentication policy + arguments + + @param strict: Whether to raise an exception when bad data is + encountered + + @returns: None. The data is parsed into the internal fields of + this object. + """ + policies_str = args.get('auth_policies') + if policies_str and policies_str != 'none': + self.auth_policies = policies_str.split(' ') + + nist_level_str = args.get('nist_auth_level') + if nist_level_str: + try: + nist_level = int(nist_level_str) + except ValueError: + if strict: + raise ValueError( + 'nist_auth_level must be an integer between ' + 'zero and four, inclusive') + else: + self.nist_auth_level = None + else: + if 0 <= nist_level < 5: + self.nist_auth_level = nist_level + + auth_time = args.get('auth_time') + if auth_time: + if TIME_VALIDATOR.match(auth_time): + self.auth_time = auth_time + elif strict: + raise ValueError("auth_time must be in RFC3339 format") + + fromSuccessResponse = classmethod(fromSuccessResponse) + + def getExtensionArgs(self): + """@see: C{L{Extension.getExtensionArgs}} + """ + if len(self.auth_policies) == 0: + ns_args = { + 'auth_policies': 'none', + } + else: + ns_args = { + 'auth_policies': ' '.join(self.auth_policies), + } + + if self.nist_auth_level is not None: + if self.nist_auth_level not in list(range(0, 5)): + raise ValueError('nist_auth_level must be an integer between ' + 'zero and four, inclusive') + ns_args['nist_auth_level'] = str(self.nist_auth_level) + + if self.auth_time is not None: + if not TIME_VALIDATOR.match(self.auth_time): + raise ValueError('auth_time must be in RFC3339 format') + + ns_args['auth_time'] = self.auth_time + + return ns_args + + +Response.ns_uri = ns_uri diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/extensions/draft/pape5.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/extensions/draft/pape5.py new file mode 100644 index 0000000000000000000000000000000000000000..e413250241d781b1ece5aec66ab5027bd0c3ec78 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/extensions/draft/pape5.py @@ -0,0 +1,481 @@ +"""An implementation of the OpenID Provider Authentication Policy +Extension 1.0, Draft 5 + +@see: http://openid.net/developers/specs/ + +@since: 2.1.0 +""" + +__all__ = [ + 'Request', + 'Response', + 'ns_uri', + 'AUTH_PHISHING_RESISTANT', + 'AUTH_MULTI_FACTOR', + 'AUTH_MULTI_FACTOR_PHYSICAL', + 'LEVELS_NIST', + 'LEVELS_JISA', +] + +from openid.extension import Extension +import warnings +import re + +ns_uri = "http://specs.openid.net/extensions/pape/1.0" + +AUTH_MULTI_FACTOR_PHYSICAL = \ + 'http://schemas.openid.net/pape/policies/2007/06/multi-factor-physical' +AUTH_MULTI_FACTOR = \ + 'http://schemas.openid.net/pape/policies/2007/06/multi-factor' +AUTH_PHISHING_RESISTANT = \ + 'http://schemas.openid.net/pape/policies/2007/06/phishing-resistant' +AUTH_NONE = \ + 'http://schemas.openid.net/pape/policies/2007/06/none' + +TIME_VALIDATOR = re.compile(r'^\d\d\d\d-\d\d-\d\dT\d\d:\d\d:\d\dZ$') + +LEVELS_NIST = 'http://csrc.nist.gov/publications/nistpubs/800-63/SP800-63V1_0_2.pdf' +LEVELS_JISA = 'http://www.jisa.or.jp/spec/auth_level.html' + + +class PAPEExtension(Extension): + _default_auth_level_aliases = { + 'nist': LEVELS_NIST, + 'jisa': LEVELS_JISA, + } + + def __init__(self): + self.auth_level_aliases = self._default_auth_level_aliases.copy() + + def _addAuthLevelAlias(self, auth_level_uri, alias=None): + """Add an auth level URI alias to this request. + + @param auth_level_uri: The auth level URI to send in the + request. + + @param alias: The namespace alias to use for this auth level + in this message. May be None if the alias is not + important. + """ + if alias is None: + try: + alias = self._getAlias(auth_level_uri) + except KeyError: + alias = self._generateAlias() + else: + existing_uri = self.auth_level_aliases.get(alias) + if existing_uri is not None and existing_uri != auth_level_uri: + raise KeyError('Attempting to redefine alias %r from %r to %r', + alias, existing_uri, auth_level_uri) + + self.auth_level_aliases[alias] = auth_level_uri + + def _generateAlias(self): + """Return an unused auth level alias""" + for i in range(1000): + alias = 'cust%d' % (i, ) + if alias not in self.auth_level_aliases: + return alias + + raise RuntimeError('Could not find an unused alias (tried 1000!)') + + def _getAlias(self, auth_level_uri): + """Return the alias for the specified auth level URI. + + @raises KeyError: if no alias is defined + """ + for (alias, existing_uri) in self.auth_level_aliases.items(): + if auth_level_uri == existing_uri: + return alias + + raise KeyError(auth_level_uri) + + +class Request(PAPEExtension): + """A Provider Authentication Policy request, sent from a relying + party to a provider + + @ivar preferred_auth_policies: The authentication policies that + the relying party prefers + @type preferred_auth_policies: [str] + + @ivar max_auth_age: The maximum time, in seconds, that the relying + party wants to allow to have elapsed before the user must + re-authenticate + @type max_auth_age: int or NoneType + + @ivar preferred_auth_level_types: Ordered list of authentication + level namespace URIs + + @type preferred_auth_level_types: [str] + """ + + ns_alias = 'pape' + + def __init__(self, + preferred_auth_policies=None, + max_auth_age=None, + preferred_auth_level_types=None): + super(Request, self).__init__() + if preferred_auth_policies is None: + preferred_auth_policies = [] + + self.preferred_auth_policies = preferred_auth_policies + self.max_auth_age = max_auth_age + self.preferred_auth_level_types = [] + + if preferred_auth_level_types is not None: + for auth_level in preferred_auth_level_types: + self.addAuthLevel(auth_level) + + def __bool__(self): + return bool(self.preferred_auth_policies or + self.max_auth_age is not None or + self.preferred_auth_level_types) + + def addPolicyURI(self, policy_uri): + """Add an acceptable authentication policy URI to this request + + This method is intended to be used by the relying party to add + acceptable authentication types to the request. + + @param policy_uri: The identifier for the preferred type of + authentication. + @see: http://openid.net/specs/openid-provider-authentication-policy-extension-1_0-05.html#auth_policies + """ + if policy_uri not in self.preferred_auth_policies: + self.preferred_auth_policies.append(policy_uri) + + def addAuthLevel(self, auth_level_uri, alias=None): + self._addAuthLevelAlias(auth_level_uri, alias) + if auth_level_uri not in self.preferred_auth_level_types: + self.preferred_auth_level_types.append(auth_level_uri) + + def getExtensionArgs(self): + """@see: C{L{Extension.getExtensionArgs}} + """ + ns_args = { + 'preferred_auth_policies': ' '.join(self.preferred_auth_policies), + } + + if self.max_auth_age is not None: + ns_args['max_auth_age'] = str(self.max_auth_age) + + if self.preferred_auth_level_types: + preferred_types = [] + + for auth_level_uri in self.preferred_auth_level_types: + alias = self._getAlias(auth_level_uri) + ns_args['auth_level.ns.%s' % (alias, )] = auth_level_uri + preferred_types.append(alias) + + ns_args['preferred_auth_level_types'] = ' '.join(preferred_types) + + return ns_args + + def fromOpenIDRequest(cls, request): + """Instantiate a Request object from the arguments in a + C{checkid_*} OpenID message + """ + self = cls() + args = request.message.getArgs(self.ns_uri) + is_openid1 = request.message.isOpenID1() + + if args == {}: + return None + + self.parseExtensionArgs(args, is_openid1) + return self + + fromOpenIDRequest = classmethod(fromOpenIDRequest) + + def parseExtensionArgs(self, args, is_openid1, strict=False): + """Set the state of this request to be that expressed in these + PAPE arguments + + @param args: The PAPE arguments without a namespace + + @param strict: Whether to raise an exception if the input is + out of spec or otherwise malformed. If strict is false, + malformed input will be ignored. + + @param is_openid1: Whether the input should be treated as part + of an OpenID1 request + + @rtype: None + + @raises ValueError: When the max_auth_age is not parseable as + an integer + """ + + # preferred_auth_policies is a space-separated list of policy URIs + self.preferred_auth_policies = [] + + policies_str = args.get('preferred_auth_policies') + if policies_str: + if isinstance(policies_str, bytes): + policies_str = str(policies_str, encoding="utf-8") + for uri in policies_str.split(' '): + if uri not in self.preferred_auth_policies: + self.preferred_auth_policies.append(uri) + + # max_auth_age is base-10 integer number of seconds + max_auth_age_str = args.get('max_auth_age') + self.max_auth_age = None + + if max_auth_age_str: + try: + self.max_auth_age = int(max_auth_age_str) + except ValueError: + if strict: + raise + + # Parse auth level information + preferred_auth_level_types = args.get('preferred_auth_level_types') + if preferred_auth_level_types: + aliases = preferred_auth_level_types.strip().split() + + for alias in aliases: + key = 'auth_level.ns.%s' % (alias, ) + try: + uri = args[key] + except KeyError: + if is_openid1: + uri = self._default_auth_level_aliases.get(alias) + else: + uri = None + + if uri is None: + if strict: + raise ValueError('preferred auth level %r is not ' + 'defined in this message' % (alias, )) + else: + self.addAuthLevel(uri, alias) + + def preferredTypes(self, supported_types): + """Given a list of authentication policy URIs that a provider + supports, this method returns the subsequence of those types + that are preferred by the relying party. + + @param supported_types: A sequence of authentication policy + type URIs that are supported by a provider + + @returns: The sub-sequence of the supported types that are + preferred by the relying party. This list will be ordered + in the order that the types appear in the supported_types + sequence, and may be empty if the provider does not prefer + any of the supported authentication types. + + @returntype: [str] + """ + return list( + filter(self.preferred_auth_policies.__contains__, supported_types)) + + +Request.ns_uri = ns_uri + + +class Response(PAPEExtension): + """A Provider Authentication Policy response, sent from a provider + to a relying party + + @ivar auth_policies: List of authentication policies conformed to + by this OpenID assertion, represented as policy URIs + """ + + ns_alias = 'pape' + + def __init__(self, auth_policies=None, auth_time=None, auth_levels=None): + super(Response, self).__init__() + if auth_policies: + self.auth_policies = auth_policies + else: + self.auth_policies = [] + + self.auth_time = auth_time + self.auth_levels = {} + + if auth_levels is None: + auth_levels = {} + + for uri, level in auth_levels.items(): + self.setAuthLevel(uri, level) + + def setAuthLevel(self, level_uri, level, alias=None): + """Set the value for the given auth level type. + + @param level: string representation of an authentication level + valid for level_uri + + @param alias: An optional namespace alias for the given auth + level URI. May be omitted if the alias is not + significant. The library will use a reasonable default for + widely-used auth level types. + """ + self._addAuthLevelAlias(level_uri, alias) + self.auth_levels[level_uri] = level + + def getAuthLevel(self, level_uri): + """Return the auth level for the specified auth level + identifier + + @returns: A string that should map to the auth levels defined + for the auth level type + + @raises KeyError: If the auth level type is not present in + this message + """ + return self.auth_levels[level_uri] + + def _getNISTAuthLevel(self): + try: + return int(self.getAuthLevel(LEVELS_NIST)) + except KeyError: + return None + + nist_auth_level = property( + _getNISTAuthLevel, + doc="Backward-compatibility accessor for the NIST auth level") + + def addPolicyURI(self, policy_uri): + """Add a authentication policy to this response + + This method is intended to be used by the provider to add a + policy that the provider conformed to when authenticating the user. + + @param policy_uri: The identifier for the preferred type of + authentication. + @see: http://openid.net/specs/openid-provider-authentication-policy-extension-1_0-01.html#auth_policies + """ + if policy_uri == AUTH_NONE: + raise RuntimeError( + 'To send no policies, do not set any on the response.') + + if policy_uri not in self.auth_policies: + self.auth_policies.append(policy_uri) + + def fromSuccessResponse(cls, success_response): + """Create a C{L{Response}} object from a successful OpenID + library response + (C{L{openid.consumer.consumer.SuccessResponse}}) response + message + + @param success_response: A SuccessResponse from consumer.complete() + @type success_response: C{L{openid.consumer.consumer.SuccessResponse}} + + @rtype: Response or None + @returns: A provider authentication policy response from the + data that was supplied with the C{id_res} response or None + if the provider sent no signed PAPE response arguments. + """ + self = cls() + + # PAPE requires that the args be signed. + args = success_response.getSignedNS(self.ns_uri) + is_openid1 = success_response.isOpenID1() + + # Only try to construct a PAPE response if the arguments were + # signed in the OpenID response. If not, return None. + if args is not None: + self.parseExtensionArgs(args, is_openid1) + return self + else: + return None + + def parseExtensionArgs(self, args, is_openid1, strict=False): + """Parse the provider authentication policy arguments into the + internal state of this object + + @param args: unqualified provider authentication policy + arguments + + @param strict: Whether to raise an exception when bad data is + encountered + + @returns: None. The data is parsed into the internal fields of + this object. + """ + policies_str = args.get('auth_policies') + if policies_str: + auth_policies = policies_str.split(' ') + elif strict: + raise ValueError('Missing auth_policies') + else: + auth_policies = [] + + if (len(auth_policies) > 1 and strict and AUTH_NONE in auth_policies): + raise ValueError('Got some auth policies, as well as the special ' + '"none" URI: %r' % (auth_policies, )) + + if 'none' in auth_policies: + msg = '"none" used as a policy URI (see PAPE draft < 5)' + if strict: + raise ValueError(msg) + else: + warnings.warn(msg, stacklevel=2) + + auth_policies = [ + u for u in auth_policies if u not in ['none', AUTH_NONE] + ] + + self.auth_policies = auth_policies + + for (key, val) in args.items(): + if key.startswith('auth_level.'): + alias = key[11:] + + # skip the already-processed namespace declarations + if alias.startswith('ns.'): + continue + + try: + uri = args['auth_level.ns.%s' % (alias, )] + except KeyError: + if is_openid1: + uri = self._default_auth_level_aliases.get(alias) + else: + uri = None + + if uri is None: + if strict: + raise ValueError('Undefined auth level alias: %r' % + (alias, )) + else: + self.setAuthLevel(uri, val, alias) + + auth_time = args.get('auth_time') + if auth_time: + if TIME_VALIDATOR.match(auth_time): + self.auth_time = auth_time + elif strict: + raise ValueError("auth_time must be in RFC3339 format") + + fromSuccessResponse = classmethod(fromSuccessResponse) + + def getExtensionArgs(self): + """@see: C{L{Extension.getExtensionArgs}} + """ + if len(self.auth_policies) == 0: + ns_args = { + 'auth_policies': AUTH_NONE, + } + else: + ns_args = { + 'auth_policies': ' '.join(self.auth_policies), + } + + for level_type, level in self.auth_levels.items(): + alias = self._getAlias(level_type) + ns_args['auth_level.ns.%s' % (alias, )] = level_type + ns_args['auth_level.%s' % (alias, )] = str(level) + + if self.auth_time is not None: + if not TIME_VALIDATOR.match(self.auth_time): + raise ValueError('auth_time must be in RFC3339 format') + + ns_args['auth_time'] = self.auth_time + + return ns_args + + +Response.ns_uri = ns_uri diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/extensions/sreg.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/extensions/sreg.py new file mode 100644 index 0000000000000000000000000000000000000000..b626d378e51be7d1e847d49459c97d4895c07d0d --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/extensions/sreg.py @@ -0,0 +1,529 @@ +"""Simple registration request and response parsing and object representation + +This module contains objects representing simple registration requests +and responses that can be used with both OpenID relying parties and +OpenID providers. + + 1. The relying party creates a request object and adds it to the + C{L{AuthRequest}} object + before making the C{checkid_} request to the OpenID provider:: + + auth_request.addExtension(SRegRequest(required=['email'])) + + 2. The OpenID provider extracts the simple registration request from + the OpenID request using C{L{SRegRequest.fromOpenIDRequest}}, + gets the user's approval and data, creates a C{L{SRegResponse}} + object and adds it to the C{id_res} response:: + + sreg_req = SRegRequest.fromOpenIDRequest(checkid_request) + # [ get the user's approval and data, informing the user that + # the fields in sreg_response were requested ] + sreg_resp = SRegResponse.extractResponse(sreg_req, user_data) + sreg_resp.toMessage(openid_response.fields) + + 3. The relying party uses C{L{SRegResponse.fromSuccessResponse}} to + extract the data from the OpenID response:: + + sreg_resp = SRegResponse.fromSuccessResponse(success_response) + +@since: 2.0 + +@var sreg_data_fields: The names of the data fields that are listed in + the sreg spec, and a description of them in English + +@var sreg_uri: The preferred URI to use for the simple registration + namespace and XRD Type value +""" + +from openid.message import registerNamespaceAlias, \ + NamespaceAliasRegistrationError +from openid.extension import Extension +import logging + +logger = logging.getLogger(__name__) + +try: + str #pylint:disable-msg=W0104 +except NameError: + # For Python 2.2 + str = (str, str) #pylint:disable-msg=W0622 + +__all__ = [ + 'SRegRequest', + 'SRegResponse', + 'data_fields', + 'ns_uri', + 'ns_uri_1_0', + 'ns_uri_1_1', + 'supportsSReg', +] + +# The data fields that are listed in the sreg spec +data_fields = { + 'fullname': 'Full Name', + 'nickname': 'Nickname', + 'dob': 'Date of Birth', + 'email': 'E-mail Address', + 'gender': 'Gender', + 'postcode': 'Postal Code', + 'country': 'Country', + 'language': 'Language', + 'timezone': 'Time Zone', +} + + +def checkFieldName(field_name): + """Check to see that the given value is a valid simple + registration data field name. + + @raise ValueError: if the field name is not a valid simple + registration data field name + """ + if field_name not in data_fields: + raise ValueError('%r is not a defined simple registration field' % + (field_name, )) + + +# URI used in the wild for Yadis documents advertising simple +# registration support +ns_uri_1_0 = 'http://openid.net/sreg/1.0' + +# URI in the draft specification for simple registration 1.1 +# +ns_uri_1_1 = 'http://openid.net/extensions/sreg/1.1' + +# This attribute will always hold the preferred URI to use when adding +# sreg support to an XRDS file or in an OpenID namespace declaration. +ns_uri = ns_uri_1_1 + +try: + registerNamespaceAlias(ns_uri_1_1, 'sreg') +except NamespaceAliasRegistrationError as e: + logger.exception('registerNamespaceAlias(%r, %r) failed: %s' % + (ns_uri_1_1, 'sreg', str(e), )) + + +def supportsSReg(endpoint): + """Does the given endpoint advertise support for simple + registration? + + @param endpoint: The endpoint object as returned by OpenID discovery + @type endpoint: openid.consumer.discover.OpenIDEndpoint + + @returns: Whether an sreg type was advertised by the endpoint + @rtype: bool + """ + return (endpoint.usesExtension(ns_uri_1_1) or + endpoint.usesExtension(ns_uri_1_0)) + + +class SRegNamespaceError(ValueError): + """The simple registration namespace was not found and could not + be created using the expected name (there's another extension + using the name 'sreg') + + This is not I{illegal}, for OpenID 2, although it probably + indicates a problem, since it's not expected that other extensions + will re-use the alias that is in use for OpenID 1. + + If this is an OpenID 1 request, then there is no recourse. This + should not happen unless some code has modified the namespaces for + the message that is being processed. + """ + + +def getSRegNS(message): + """Extract the simple registration namespace URI from the given + OpenID message. Handles OpenID 1 and 2, as well as both sreg + namespace URIs found in the wild, as well as missing namespace + definitions (for OpenID 1) + + @param message: The OpenID message from which to parse simple + registration fields. This may be a request or response message. + @type message: C{L{openid.message.Message}} + + @returns: the sreg namespace URI for the supplied message. The + message may be modified to define a simple registration + namespace. + @rtype: C{str} + + @raise ValueError: when using OpenID 1 if the message defines + the 'sreg' alias to be something other than a simple + registration type. + """ + # See if there exists an alias for one of the two defined simple + # registration types. + for sreg_ns_uri in [ns_uri_1_1, ns_uri_1_0]: + alias = message.namespaces.getAlias(sreg_ns_uri) + if alias is not None: + break + else: + # There is no alias for either of the types, so try to add + # one. We default to using the modern value (1.1) + sreg_ns_uri = ns_uri_1_1 + try: + message.namespaces.addAlias(ns_uri_1_1, 'sreg') + except KeyError as why: + # An alias for the string 'sreg' already exists, but it's + # defined for something other than simple registration + raise SRegNamespaceError(why) + + # we know that sreg_ns_uri defined, because it's defined in the + # else clause of the loop as well, so disable the warning + return sreg_ns_uri #pylint:disable-msg=W0631 + + +class SRegRequest(Extension): + """An object to hold the state of a simple registration request. + + @ivar required: A list of the required fields in this simple + registration request + @type required: [str] + + @ivar optional: A list of the optional fields in this simple + registration request + @type optional: [str] + + @ivar policy_url: The policy URL that was provided with the request + @type policy_url: str or NoneType + + @group Consumer: requestField, requestFields, getExtensionArgs, addToOpenIDRequest + @group Server: fromOpenIDRequest, parseExtensionArgs + """ + + ns_alias = 'sreg' + + def __init__(self, + required=None, + optional=None, + policy_url=None, + sreg_ns_uri=ns_uri): + """Initialize an empty simple registration request""" + Extension.__init__(self) + self.required = [] + self.optional = [] + self.policy_url = policy_url + self.ns_uri = sreg_ns_uri + + if required: + self.requestFields(required, required=True, strict=True) + + if optional: + self.requestFields(optional, required=False, strict=True) + + # Assign getSRegNS to a static method so that it can be + # overridden for testing. + _getSRegNS = staticmethod(getSRegNS) + + def fromOpenIDRequest(cls, request): + """Create a simple registration request that contains the + fields that were requested in the OpenID request with the + given arguments + + @param request: The OpenID request + @type request: openid.server.CheckIDRequest + + @returns: The newly created simple registration request + @rtype: C{L{SRegRequest}} + """ + self = cls() + + # Since we're going to mess with namespace URI mapping, don't + # mutate the object that was passed in. + message = request.message.copy() + + self.ns_uri = self._getSRegNS(message) + args = message.getArgs(self.ns_uri) + self.parseExtensionArgs(args) + + return self + + fromOpenIDRequest = classmethod(fromOpenIDRequest) + + def parseExtensionArgs(self, args, strict=False): + """Parse the unqualified simple registration request + parameters and add them to this object. + + This method is essentially the inverse of + C{L{getExtensionArgs}}. This method restores the serialized simple + registration request fields. + + If you are extracting arguments from a standard OpenID + checkid_* request, you probably want to use C{L{fromOpenIDRequest}}, + which will extract the sreg namespace and arguments from the + OpenID request. This method is intended for cases where the + OpenID server needs more control over how the arguments are + parsed than that method provides. + + >>> args = message.getArgs(ns_uri) + >>> request.parseExtensionArgs(args) + + @param args: The unqualified simple registration arguments + @type args: {str:str} + + @param strict: Whether requests with fields that are not + defined in the simple registration specification should be + tolerated (and ignored) + @type strict: bool + + @returns: None; updates this object + """ + for list_name in ['required', 'optional']: + required = (list_name == 'required') + items = args.get(list_name) + if items: + for field_name in items.split(','): + try: + self.requestField(field_name, required, strict) + except ValueError: + if strict: + raise + + self.policy_url = args.get('policy_url') + + def allRequestedFields(self): + """A list of all of the simple registration fields that were + requested, whether they were required or optional. + + @rtype: [str] + """ + return self.required + self.optional + + def wereFieldsRequested(self): + """Have any simple registration fields been requested? + + @rtype: bool + """ + return bool(self.allRequestedFields()) + + def __contains__(self, field_name): + """Was this field in the request?""" + return (field_name in self.required or field_name in self.optional) + + def requestField(self, field_name, required=False, strict=False): + """Request the specified field from the OpenID user + + @param field_name: the unqualified simple registration field name + @type field_name: str + + @param required: whether the given field should be presented + to the user as being a required to successfully complete + the request + + @param strict: whether to raise an exception when a field is + added to a request more than once + + @raise ValueError: when the field requested is not a simple + registration field or strict is set and the field was + requested more than once + """ + checkFieldName(field_name) + + if strict: + if field_name in self.required or field_name in self.optional: + raise ValueError('That field has already been requested') + else: + if field_name in self.required: + return + + if field_name in self.optional: + if required: + self.optional.remove(field_name) + else: + return + + if required: + self.required.append(field_name) + else: + self.optional.append(field_name) + + def requestFields(self, field_names, required=False, strict=False): + """Add the given list of fields to the request + + @param field_names: The simple registration data fields to request + @type field_names: [str] + + @param required: Whether these values should be presented to + the user as required + + @param strict: whether to raise an exception when a field is + added to a request more than once + + @raise ValueError: when a field requested is not a simple + registration field or strict is set and a field was + requested more than once + """ + if isinstance(field_names, str): + raise TypeError('Fields should be passed as a list of ' + 'strings (not %r)' % (type(field_names), )) + + for field_name in field_names: + self.requestField(field_name, required, strict=strict) + + def getExtensionArgs(self): + """Get a dictionary of unqualified simple registration + arguments representing this request. + + This method is essentially the inverse of + C{L{parseExtensionArgs}}. This method serializes the simple + registration request fields. + + @rtype: {str:str} + """ + args = {} + + if self.required: + args['required'] = ','.join(self.required) + + if self.optional: + args['optional'] = ','.join(self.optional) + + if self.policy_url: + args['policy_url'] = self.policy_url + + return args + + +class SRegResponse(Extension): + """Represents the data returned in a simple registration response + inside of an OpenID C{id_res} response. This object will be + created by the OpenID server, added to the C{id_res} response + object, and then extracted from the C{id_res} message by the + Consumer. + + @ivar data: The simple registration data, keyed by the unqualified + simple registration name of the field (i.e. nickname is keyed + by C{'nickname'}) + + @ivar ns_uri: The URI under which the simple registration data was + stored in the response message. + + @group Server: extractResponse + @group Consumer: fromSuccessResponse + @group Read-only dictionary interface: keys, iterkeys, items, iteritems, + __iter__, get, __getitem__, keys, has_key + """ + + ns_alias = 'sreg' + + def __init__(self, data=None, sreg_ns_uri=ns_uri): + Extension.__init__(self) + if data is None: + self.data = {} + else: + self.data = data + + self.ns_uri = sreg_ns_uri + + def extractResponse(cls, request, data): + """Take a C{L{SRegRequest}} and a dictionary of simple + registration values and create a C{L{SRegResponse}} + object containing that data. + + @param request: The simple registration request object + @type request: SRegRequest + + @param data: The simple registration data for this + response, as a dictionary from unqualified simple + registration field name to string (unicode) value. For + instance, the nickname should be stored under the key + 'nickname'. + @type data: {str:str} + + @returns: a simple registration response object + @rtype: SRegResponse + """ + self = cls() + self.ns_uri = request.ns_uri + for field in request.allRequestedFields(): + value = data.get(field) + if value is not None: + self.data[field] = value + return self + + extractResponse = classmethod(extractResponse) + + # Assign getSRegArgs to a static method so that it can be + # overridden for testing + _getSRegNS = staticmethod(getSRegNS) + + def fromSuccessResponse(cls, success_response, signed_only=True): + """Create a C{L{SRegResponse}} object from a successful OpenID + library response + (C{L{openid.consumer.consumer.SuccessResponse}}) response + message + + @param success_response: A SuccessResponse from consumer.complete() + @type success_response: C{L{openid.consumer.consumer.SuccessResponse}} + + @param signed_only: Whether to process only data that was + signed in the id_res message from the server. + @type signed_only: bool + + @rtype: SRegResponse + @returns: A simple registration response containing the data + that was supplied with the C{id_res} response. + """ + self = cls() + self.ns_uri = self._getSRegNS(success_response.message) + if signed_only: + args = success_response.getSignedNS(self.ns_uri) + else: + args = success_response.message.getArgs(self.ns_uri) + + if not args: + return None + + for field_name in data_fields: + if field_name in args: + self.data[field_name] = args[field_name] + + return self + + fromSuccessResponse = classmethod(fromSuccessResponse) + + def getExtensionArgs(self): + """Get the fields to put in the simple registration namespace + when adding them to an id_res message. + + @see: openid.extension + """ + return self.data + + # Read-only dictionary interface + def get(self, field_name, default=None): + """Like dict.get, except that it checks that the field name is + defined by the simple registration specification""" + checkFieldName(field_name) + return self.data.get(field_name, default) + + def items(self): + """All of the data values in this simple registration response + """ + return list(self.data.items()) + + def iteritems(self): + return iter(self.data.items()) + + def keys(self): + return list(self.data.keys()) + + def iterkeys(self): + return iter(self.data.keys()) + + def has_key(self, key): + return key in self + + def __contains__(self, field_name): + checkFieldName(field_name) + return field_name in self.data + + def __iter__(self): + return iter(self.data) + + def __getitem__(self, field_name): + checkFieldName(field_name) + return self.data[field_name] + + def __bool__(self): + return bool(self.data) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/fetchers.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/fetchers.py new file mode 100644 index 0000000000000000000000000000000000000000..c109cd2fd026f8414ae5d53012232891f107e2a6 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/fetchers.py @@ -0,0 +1,493 @@ +# -*- test-case-name: openid.test.test_fetchers -*- +""" +This module contains the HTTP fetcher interface and several implementations. +""" + +__all__ = [ + 'fetch', 'getDefaultFetcher', 'setDefaultFetcher', 'HTTPResponse', + 'HTTPFetcher', 'createHTTPFetcher', 'HTTPFetchingError', 'HTTPError' +] + +import urllib.request +import urllib.error +import urllib.parse +import http.client + +import time +import io +import sys +import contextlib + +import openid +import openid.urinorm + +# Try to import httplib2 for caching support +# http://bitworking.org/projects/httplib2/ +try: + import httplib2 +except ImportError: + # httplib2 not available + httplib2 = None + +# try to import pycurl, which will let us use CurlHTTPFetcher +try: + import pycurl +except ImportError: + pycurl = None + +USER_AGENT = "python-openid/%s (%s)" % (openid.__version__, sys.platform) +MAX_RESPONSE_KB = 1024 + + +def fetch(url, body=None, headers=None): + """Invoke the fetch method on the default fetcher. Most users + should need only this method. + + @raises Exception: any exceptions that may be raised by the default fetcher + """ + fetcher = getDefaultFetcher() + return fetcher.fetch(url, body, headers) + + +def createHTTPFetcher(): + """Create a default HTTP fetcher instance + + prefers Curl to urllib2.""" + if pycurl is None: + fetcher = Urllib2Fetcher() + else: + fetcher = CurlHTTPFetcher() + + return fetcher + + +# Contains the currently set HTTP fetcher. If it is set to None, the +# library will call createHTTPFetcher() to set it. Do not access this +# variable outside of this module. +_default_fetcher = None + + +def getDefaultFetcher(): + """Return the default fetcher instance + if no fetcher has been set, it will create a default fetcher. + + @return: the default fetcher + @rtype: HTTPFetcher + """ + global _default_fetcher + + if _default_fetcher is None: + setDefaultFetcher(createHTTPFetcher()) + + return _default_fetcher + + +def setDefaultFetcher(fetcher, wrap_exceptions=True): + """Set the default fetcher + + @param fetcher: The fetcher to use as the default HTTP fetcher + @type fetcher: HTTPFetcher + + @param wrap_exceptions: Whether to wrap exceptions thrown by the + fetcher wil HTTPFetchingError so that they may be caught + easier. By default, exceptions will be wrapped. In general, + unwrapped fetchers are useful for debugging of fetching errors + or if your fetcher raises well-known exceptions that you would + like to catch. + @type wrap_exceptions: bool + """ + global _default_fetcher + if fetcher is None or not wrap_exceptions: + _default_fetcher = fetcher + else: + _default_fetcher = ExceptionWrappingFetcher(fetcher) + + +def usingCurl(): + """Whether the currently set HTTP fetcher is a Curl HTTP fetcher.""" + fetcher = getDefaultFetcher() + if isinstance(fetcher, ExceptionWrappingFetcher): + fetcher = fetcher.fetcher + return isinstance(fetcher, CurlHTTPFetcher) + + +class HTTPResponse(object): + """XXX document attributes""" + headers = None + status = None + body = None + final_url = None + + def __init__(self, final_url=None, status=None, headers=None, body=None): + self.final_url = final_url + self.status = status + self.headers = headers + self.body = body + + def __repr__(self): + return "<%s status %s for %s>" % (self.__class__.__name__, self.status, + self.final_url) + + +class HTTPFetcher(object): + """ + This class is the interface for openid HTTP fetchers. This + interface is only important if you need to write a new fetcher for + some reason. + """ + + def fetch(self, url, body=None, headers=None): + """ + This performs an HTTP POST or GET, following redirects along + the way. If a body is specified, then the request will be a + POST. Otherwise, it will be a GET. + + + @param headers: HTTP headers to include with the request + @type headers: {str:str} + + @return: An object representing the server's HTTP response. If + there are network or protocol errors, an exception will be + raised. HTTP error responses, like 404 or 500, do not + cause exceptions. + + @rtype: L{HTTPResponse} + + @raise Exception: Different implementations will raise + different errors based on the underlying HTTP library. + """ + raise NotImplementedError + + +def _allowedURL(url): + parsed = urllib.parse.urlparse(url) + # scheme is the first item in the tuple + return parsed[0] in ('http', 'https') + + +class HTTPFetchingError(Exception): + """Exception that is wrapped around all exceptions that are raised + by the underlying fetcher when using the ExceptionWrappingFetcher + + @ivar why: The exception that caused this exception + """ + + def __init__(self, why=None): + Exception.__init__(self, why) + self.why = why + + +class ExceptionWrappingFetcher(HTTPFetcher): + """Fetcher that wraps another fetcher, causing all exceptions + + @cvar uncaught_exceptions: Exceptions that should be exposed to the + user if they are raised by the fetch call + """ + + uncaught_exceptions = (SystemExit, KeyboardInterrupt, MemoryError) + + def __init__(self, fetcher): + self.fetcher = fetcher + + def fetch(self, *args, **kwargs): + try: + return self.fetcher.fetch(*args, **kwargs) + except self.uncaught_exceptions: + raise + except: + exc_cls, exc_inst = sys.exc_info()[:2] + if exc_inst is None: + # string exceptions + exc_inst = exc_cls + + raise HTTPFetchingError(why=exc_inst) + + +class Urllib2Fetcher(HTTPFetcher): + """An C{L{HTTPFetcher}} that uses urllib2. + """ + + # Parameterized for the benefit of testing frameworks, see + # http://trac.openidenabled.com/trac/ticket/85 + urlopen = staticmethod(urllib.request.urlopen) + + def fetch(self, url, body=None, headers=None): + if not _allowedURL(url): + raise ValueError('Bad URL scheme: %r' % (url, )) + + if headers is None: + headers = {} + + headers.setdefault('User-Agent', "%s Python-urllib/%s" % + (USER_AGENT, urllib.request.__version__)) + + if isinstance(body, str): + body = bytes(body, encoding="utf-8") + + req = urllib.request.Request(url, data=body, headers=headers) + + url_resource = None + try: + url_resource = self.urlopen(req) + with contextlib.closing(url_resource): + return self._makeResponse(url_resource) + except urllib.error.HTTPError as why: + with contextlib.closing(why): + resp = self._makeResponse(why) + return resp + except (urllib.error.URLError, http.client.BadStatusLine) as why: + raise + except Exception as why: + raise AssertionError(why) + + def _makeResponse(self, urllib2_response): + ''' + Construct an HTTPResponse from the the urllib response. Attempt to + decode the response body from bytes to str if the necessary information + is available. + ''' + resp = HTTPResponse() + resp.body = urllib2_response.read(MAX_RESPONSE_KB * 1024) + resp.final_url = urllib2_response.geturl() + resp.headers = self._lowerCaseKeys( + dict(list(urllib2_response.info().items()))) + + if hasattr(urllib2_response, 'code'): + resp.status = urllib2_response.code + else: + resp.status = 200 + + _, extra_dict = self._parseHeaderValue( + resp.headers.get("content-type", "")) + # Try to decode the response body to a string, if there's a + # charset known; fall back to ISO-8859-1 otherwise, since that's + # what's suggested in HTTP/1.1 + charset = extra_dict.get('charset', 'latin1') + try: + resp.body = resp.body.decode(charset) + except Exception: + pass + + return resp + + def _lowerCaseKeys(self, headers_dict): + new_dict = {} + for k, v in headers_dict.items(): + new_dict[k.lower()] = v + return new_dict + + def _parseHeaderValue(self, header_value): + """ + Parse out a complex header value (such as Content-Type, with a value + like "text/html; charset=utf-8") into a main value and a dictionary of + extra information (in this case, 'text/html' and {'charset': 'utf8'}). + """ + values = header_value.split(';', 1) + if len(values) == 1: + # There's no extra info -- return the main value and an empty dict + return values[0], {} + main_value, extra_values = values[0], values[1].split(';') + extra_dict = {} + for value_string in extra_values: + try: + key, value = value_string.split('=', 1) + extra_dict[key.strip()] = value.strip() + except ValueError: + # Can't unpack it -- must be malformed. Ignore + pass + return main_value, extra_dict + + +class HTTPError(HTTPFetchingError): + """ + This exception is raised by the C{L{CurlHTTPFetcher}} when it + encounters an exceptional situation fetching a URL. + """ + pass + + +# XXX: define what we mean by paranoid, and make sure it is. +class CurlHTTPFetcher(HTTPFetcher): + """ + An C{L{HTTPFetcher}} that uses pycurl for fetching. + See U{http://pycurl.sourceforge.net/}. + """ + ALLOWED_TIME = 20 # seconds + + def __init__(self): + HTTPFetcher.__init__(self) + if pycurl is None: + raise RuntimeError('Cannot find pycurl library') + + def _parseHeaders(self, header_file): + header_file.seek(0) + + # Remove all non "name: value" header lines from the input + lines = [line.decode().strip() for line in header_file if b':' in line] + + headers = {} + for line in lines: + try: + name, value = line.split(':', 1) + except ValueError: + raise HTTPError("Malformed HTTP header line in response: %r" % + (line, )) + + value = value.strip() + + # HTTP headers are case-insensitive + name = name.lower() + headers[name] = value + + return headers + + def _checkURL(self, url): + # XXX: document that this can be overridden to match desired policy + # XXX: make sure url is well-formed and routeable + return _allowedURL(url) + + def fetch(self, url, body=None, headers=None): + stop = int(time.time()) + self.ALLOWED_TIME + off = self.ALLOWED_TIME + + if headers is None: + headers = {} + + headers.setdefault('User-Agent', + "%s %s" % (USER_AGENT, pycurl.version, )) + + header_list = [] + if headers is not None: + for header_name, header_value in headers.items(): + header = '%s: %s' % (header_name, header_value) + header_list.append(header.encode()) + + c = pycurl.Curl() + try: + c.setopt(pycurl.NOSIGNAL, 1) + + if header_list: + c.setopt(pycurl.HTTPHEADER, header_list) + + # Presence of a body indicates that we should do a POST + if body is not None: + c.setopt(pycurl.POST, 1) + c.setopt(pycurl.POSTFIELDS, body) + + while off > 0: + if not self._checkURL(url): + raise HTTPError("Fetching URL not allowed: %r" % (url, )) + + data = io.BytesIO() + + def write_data(chunk): + if data.tell() > (1024 * MAX_RESPONSE_KB): + return 0 + else: + return data.write(chunk) + + response_header_data = io.BytesIO() + c.setopt(pycurl.WRITEFUNCTION, write_data) + c.setopt(pycurl.HEADERFUNCTION, response_header_data.write) + c.setopt(pycurl.TIMEOUT, off) + c.setopt(pycurl.URL, openid.urinorm.urinorm(url)) + + c.perform() + + response_headers = self._parseHeaders(response_header_data) + code = c.getinfo(pycurl.RESPONSE_CODE) + if code in [301, 302, 303, 307]: + url = response_headers.get('location') + if url is None: + raise HTTPError( + 'Redirect (%s) returned without a location' % code) + + # Redirects are always GETs + c.setopt(pycurl.POST, 0) + + # There is no way to reset POSTFIELDS to empty and + # reuse the connection, but we only use it once. + else: + resp = HTTPResponse() + resp.headers = response_headers + resp.status = code + resp.final_url = url + resp.body = data.getvalue().decode() + return resp + + off = stop - int(time.time()) + + raise HTTPError("Timed out fetching: %r" % (url, )) + finally: + c.close() + + +class HTTPLib2Fetcher(HTTPFetcher): + """A fetcher that uses C{httplib2} for performing HTTP + requests. This implementation supports HTTP caching. + + @see: http://bitworking.org/projects/httplib2/ + """ + + def __init__(self, cache=None): + """@param cache: An object suitable for use as an C{httplib2} + cache. If a string is passed, it is assumed to be a + directory name. + """ + if httplib2 is None: + raise RuntimeError('Cannot find httplib2 library. ' + 'See http://bitworking.org/projects/httplib2/') + + super(HTTPLib2Fetcher, self).__init__() + + # An instance of the httplib2 object that performs HTTP requests + self.httplib2 = httplib2.Http(cache) + + # We want httplib2 to raise exceptions for errors, just like + # the other fetchers. + self.httplib2.force_exception_to_status_code = False + + def fetch(self, url, body=None, headers=None): + """Perform an HTTP request + + @raises Exception: Any exception that can be raised by httplib2 + + @see: C{L{HTTPFetcher.fetch}} + """ + if body: + method = 'POST' + else: + method = 'GET' + + if headers is None: + headers = {} + + # httplib2 doesn't check to make sure that the URL's scheme is + # 'http' so we do it here. + if not (url.startswith('http://') or url.startswith('https://')): + raise ValueError('URL is not a HTTP URL: %r' % (url, )) + + httplib2_response, content = self.httplib2.request( + url, method, body=body, headers=headers) + + # Translate the httplib2 response to our HTTP response abstraction + + # When a 400 is returned, there is no "content-location" + # header set. This seems like a bug to me. I can't think of a + # case where we really care about the final URL when it is an + # error response, but being careful about it can't hurt. + try: + final_url = httplib2_response['content-location'] + except KeyError: + # We're assuming that no redirects occurred + assert not httplib2_response.previous + + # And this should never happen for a successful response + assert httplib2_response.status != 200 + final_url = url + + return HTTPResponse( + body=content.decode(), # TODO Don't assume ASCII + final_url=final_url, + headers=dict(list(httplib2_response.items())), + status=httplib2_response.status, ) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/kvform.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/kvform.py new file mode 100644 index 0000000000000000000000000000000000000000..f8985d9aec59d02fc22f8ec343ce89ff97472f71 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/kvform.py @@ -0,0 +1,134 @@ +import logging + +logger = logging.getLogger(__name__) + +__all__ = ['seqToKV', 'kvToSeq', 'dictToKV', 'kvToDict'] + + +class KVFormError(ValueError): + pass + + +def seqToKV(seq, strict=False): + """Represent a sequence of pairs of strings as newline-terminated + key:value pairs. The pairs are generated in the order given. + + @param seq: The pairs + @type seq: [(str, (unicode|str))] + + @return: A string representation of the sequence + @rtype: bytes + """ + + def err(msg): + formatted = 'seqToKV warning: %s: %r' % (msg, seq) + if strict: + raise KVFormError(formatted) + else: + logger.warning(formatted) + + lines = [] + for k, v in seq: + if isinstance(k, bytes): + k = k.decode('utf-8') + elif not isinstance(k, str): + err('Converting key to string: %r' % k) + k = str(k) + + if '\n' in k: + raise KVFormError( + 'Invalid input for seqToKV: key contains newline: %r' % (k, )) + + if ':' in k: + raise KVFormError( + 'Invalid input for seqToKV: key contains colon: %r' % (k, )) + + if k.strip() != k: + err('Key has whitespace at beginning or end: %r' % (k, )) + + if isinstance(v, bytes): + v = v.decode('utf-8') + elif not isinstance(v, str): + err('Converting value to string: %r' % (v, )) + v = str(v) + + if '\n' in v: + raise KVFormError( + 'Invalid input for seqToKV: value contains newline: %r' % + (v, )) + + if v.strip() != v: + err('Value has whitespace at beginning or end: %r' % (v, )) + + lines.append(k + ':' + v + '\n') + + return ''.join(lines).encode('utf-8') + + +def kvToSeq(data, strict=False): + """ + + After one parse, seqToKV and kvToSeq are inverses, with no warnings:: + + seq = kvToSeq(s) + seqToKV(kvToSeq(seq)) == seq + + @return str + """ + + def err(msg): + formatted = 'kvToSeq warning: %s: %r' % (msg, data) + if strict: + raise KVFormError(formatted) + else: + logger.warning(formatted) + + if isinstance(data, bytes): + data = data.decode("utf-8") + + lines = data.split('\n') + if lines[-1]: + err('Does not end in a newline') + else: + del lines[-1] + + pairs = [] + line_num = 0 + for line in lines: + line_num += 1 + + # Ignore blank lines + if not line.strip(): + continue + + pair = line.split(':', 1) + if len(pair) == 2: + k, v = pair + k_s = k.strip() + if k_s != k: + fmt = ('In line %d, ignoring leading or trailing ' + 'whitespace in key %r') + err(fmt % (line_num, k)) + + if not k_s: + err('In line %d, got empty key' % (line_num, )) + + v_s = v.strip() + if v_s != v: + fmt = ('In line %d, ignoring leading or trailing ' + 'whitespace in value %r') + err(fmt % (line_num, v)) + + pairs.append((k_s, v_s)) + else: + err('Line %d does not contain a colon' % line_num) + + return pairs + + +def dictToKV(d): + return seqToKV(sorted(d.items())) + + +def kvToDict(s): + return dict(kvToSeq(s)) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/message.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/message.py new file mode 100644 index 0000000000000000000000000000000000000000..325c14d414e3244df57909376d8f5196d65ac2c1 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/message.py @@ -0,0 +1,678 @@ +"""Extension argument processing code +""" +__all__ = [ + 'Message', 'NamespaceMap', 'no_default', 'registerNamespaceAlias', + 'OPENID_NS', 'BARE_NS', 'OPENID1_NS', 'OPENID2_NS', 'SREG_URI', + 'IDENTIFIER_SELECT' +] + +import copy +import warnings +import urllib.request +import urllib.error + +from openid import oidutil +from openid import kvform +try: + ElementTree = oidutil.importElementTree() +except ImportError: + # No elementtree found, so give up, but don't fail to import, + # since we have fallbacks. + ElementTree = None + +# This doesn't REALLY belong here, but where is better? +IDENTIFIER_SELECT = 'http://specs.openid.net/auth/2.0/identifier_select' + +# URI for Simple Registration extension, the only commonly deployed +# OpenID 1.x extension, and so a special case +SREG_URI = 'http://openid.net/sreg/1.0' + +# The OpenID 1.X namespace URI +OPENID1_NS = 'http://openid.net/signon/1.0' +THE_OTHER_OPENID1_NS = 'http://openid.net/signon/1.1' + +OPENID1_NAMESPACES = OPENID1_NS, THE_OTHER_OPENID1_NS + +# The OpenID 2.0 namespace URI +OPENID2_NS = 'http://specs.openid.net/auth/2.0' + +# The namespace consisting of pairs with keys that are prefixed with +# "openid." but not in another namespace. +NULL_NAMESPACE = oidutil.Symbol('Null namespace') + +# The null namespace, when it is an allowed OpenID namespace +OPENID_NS = oidutil.Symbol('OpenID namespace') + +# The top-level namespace, excluding all pairs with keys that start +# with "openid." +BARE_NS = oidutil.Symbol('Bare namespace') + +# Limit, in bytes, of identity provider and return_to URLs, including +# response payload. See OpenID 1.1 specification, Appendix D. +OPENID1_URL_LIMIT = 2047 + +# All OpenID protocol fields. Used to check namespace aliases. +OPENID_PROTOCOL_FIELDS = [ + 'ns', + 'mode', + 'error', + 'return_to', + 'contact', + 'reference', + 'signed', + 'assoc_type', + 'session_type', + 'dh_modulus', + 'dh_gen', + 'dh_consumer_public', + 'claimed_id', + 'identity', + 'realm', + 'invalidate_handle', + 'op_endpoint', + 'response_nonce', + 'sig', + 'assoc_handle', + 'trust_root', + 'openid', +] + + +class UndefinedOpenIDNamespace(ValueError): + """Raised if the generic OpenID namespace is accessed when there + is no OpenID namespace set for this message.""" + + +class InvalidOpenIDNamespace(ValueError): + """Raised if openid.ns is not a recognized value. + + For recognized values, see L{Message.allowed_openid_namespaces} + """ + + def __str__(self): + s = "Invalid OpenID Namespace" + if self.args: + s += " %r" % (self.args[0], ) + return s + + +# Sentinel used for Message implementation to indicate that getArg +# should raise an exception instead of returning a default. +no_default = object() + +# Global namespace / alias registration map. See +# registerNamespaceAlias. +registered_aliases = {} + + +class NamespaceAliasRegistrationError(Exception): + """ + Raised when an alias or namespace URI has already been registered. + """ + pass + + +def registerNamespaceAlias(namespace_uri, alias): + """ + Registers a (namespace URI, alias) mapping in a global namespace + alias map. Raises NamespaceAliasRegistrationError if either the + namespace URI or alias has already been registered with a + different value. This function is required if you want to use a + namespace with an OpenID 1 message. + """ + global registered_aliases + + if registered_aliases.get(alias) == namespace_uri: + return + + if namespace_uri in list(registered_aliases.values()): + raise NamespaceAliasRegistrationError( + 'Namespace uri %r already registered' % (namespace_uri, )) + + if alias in registered_aliases: + raise NamespaceAliasRegistrationError('Alias %r already registered' % + (alias, )) + + registered_aliases[alias] = namespace_uri + + +class Message(object): + """ + In the implementation of this object, None represents the global + namespace as well as a namespace with no key. + + @cvar namespaces: A dictionary specifying specific + namespace-URI to alias mappings that should be used when + generating namespace aliases. + + @ivar ns_args: two-level dictionary of the values in this message, + grouped by namespace URI. The first level is the namespace + URI. + """ + + allowed_openid_namespaces = [OPENID1_NS, THE_OTHER_OPENID1_NS, OPENID2_NS] + + def __init__(self, openid_namespace=None): + """Create an empty Message. + + @raises InvalidOpenIDNamespace: if openid_namespace is not in + L{Message.allowed_openid_namespaces} + """ + self.args = {} + self.namespaces = NamespaceMap() + if openid_namespace is None: + self._openid_ns_uri = None + else: + implicit = openid_namespace in OPENID1_NAMESPACES + self.setOpenIDNamespace(openid_namespace, implicit) + + @classmethod + def fromPostArgs(cls, args): + """Construct a Message containing a set of POST arguments. + + """ + self = cls() + + # Partition into "openid." args and bare args + openid_args = {} + for key, value in args.items(): + if isinstance(value, list): + raise TypeError("query dict must have one value for each key, " + "not lists of values. Query is %r" % (args, )) + + try: + prefix, rest = key.split('.', 1) + except ValueError: + prefix = None + + if prefix != 'openid': + self.args[(BARE_NS, key)] = value + else: + openid_args[rest] = value + + self._fromOpenIDArgs(openid_args) + + return self + + @classmethod + def fromOpenIDArgs(cls, openid_args): + """Construct a Message from a parsed KVForm message. + + @raises InvalidOpenIDNamespace: if openid.ns is not in + L{Message.allowed_openid_namespaces} + """ + self = cls() + self._fromOpenIDArgs(openid_args) + return self + + def _fromOpenIDArgs(self, openid_args): + ns_args = [] + + # Resolve namespaces + for rest, value in openid_args.items(): + try: + ns_alias, ns_key = rest.split('.', 1) + except ValueError: + ns_alias = NULL_NAMESPACE + ns_key = rest + + if ns_alias == 'ns': + self.namespaces.addAlias(value, ns_key) + elif ns_alias == NULL_NAMESPACE and ns_key == 'ns': + # null namespace + self.setOpenIDNamespace(value, False) + else: + ns_args.append((ns_alias, ns_key, value)) + + # Implicitly set an OpenID namespace definition (OpenID 1) + if not self.getOpenIDNamespace(): + self.setOpenIDNamespace(OPENID1_NS, True) + + # Actually put the pairs into the appropriate namespaces + for (ns_alias, ns_key, value) in ns_args: + ns_uri = self.namespaces.getNamespaceURI(ns_alias) + if ns_uri is None: + # we found a namespaced arg without a namespace URI defined + ns_uri = self._getDefaultNamespace(ns_alias) + if ns_uri is None: + ns_uri = self.getOpenIDNamespace() + ns_key = '%s.%s' % (ns_alias, ns_key) + else: + self.namespaces.addAlias(ns_uri, ns_alias, implicit=True) + + self.setArg(ns_uri, ns_key, value) + + def _getDefaultNamespace(self, mystery_alias): + """OpenID 1 compatibility: look for a default namespace URI to + use for this alias.""" + global registered_aliases + # Only try to map an alias to a default if it's an + # OpenID 1.x message. + if self.isOpenID1(): + return registered_aliases.get(mystery_alias) + else: + return None + + def setOpenIDNamespace(self, openid_ns_uri, implicit): + """Set the OpenID namespace URI used in this message. + + @raises InvalidOpenIDNamespace: if the namespace is not in + L{Message.allowed_openid_namespaces} + """ + if isinstance(openid_ns_uri, bytes): + openid_ns_uri = str(openid_ns_uri, encoding="utf-8") + if openid_ns_uri not in self.allowed_openid_namespaces: + raise InvalidOpenIDNamespace(openid_ns_uri) + + self.namespaces.addAlias(openid_ns_uri, NULL_NAMESPACE, implicit) + self._openid_ns_uri = openid_ns_uri + + def getOpenIDNamespace(self): + return self._openid_ns_uri + + def isOpenID1(self): + return self.getOpenIDNamespace() in OPENID1_NAMESPACES + + def isOpenID2(self): + return self.getOpenIDNamespace() == OPENID2_NS + + def fromKVForm(cls, kvform_string): + """Create a Message from a KVForm string""" + return cls.fromOpenIDArgs(kvform.kvToDict(kvform_string)) + + fromKVForm = classmethod(fromKVForm) + + def copy(self): + return copy.deepcopy(self) + + def toPostArgs(self): + """ + Return all arguments with openid. in front of namespaced arguments. + @return bytes + """ + args = {} + + # Add namespace definitions to the output + for ns_uri, alias in self.namespaces.items(): + if self.namespaces.isImplicit(ns_uri): + continue + if alias == NULL_NAMESPACE: + ns_key = 'openid.ns' + else: + ns_key = 'openid.ns.' + alias + args[ns_key] = oidutil.toUnicode(ns_uri) + + for (ns_uri, ns_key), value in self.args.items(): + key = self.getKey(ns_uri, ns_key) + # Ensure the resulting value is an UTF-8 encoded *bytestring*. + args[key] = oidutil.toUnicode(value) + + return args + + def toArgs(self): + """Return all namespaced arguments, failing if any + non-namespaced arguments exist.""" + # FIXME - undocumented exception + post_args = self.toPostArgs() + kvargs = {} + for k, v in post_args.items(): + if not k.startswith('openid.'): + raise ValueError( + 'This message can only be encoded as a POST, because it ' + 'contains arguments that are not prefixed with "openid."') + else: + kvargs[k[7:]] = v + + return kvargs + + def toFormMarkup(self, + action_url, + form_tag_attrs=None, + submit_text="Continue"): + """Generate HTML form markup that contains the values in this + message, to be HTTP POSTed as x-www-form-urlencoded UTF-8. + + @param action_url: The URL to which the form will be POSTed + @type action_url: str + + @param form_tag_attrs: Dictionary of attributes to be added to + the form tag. 'accept-charset' and 'enctype' have defaults + that can be overridden. If a value is supplied for + 'action' or 'method', it will be replaced. + @type form_tag_attrs: {unicode: unicode} + + @param submit_text: The text that will appear on the submit + button for this form. + @type submit_text: unicode + + @returns: A string containing (X)HTML markup for a form that + encodes the values in this Message object. + @rtype: str + """ + if ElementTree is None: + raise RuntimeError('This function requires ElementTree.') + + assert action_url is not None + + form = ElementTree.Element('form') + + if form_tag_attrs: + for name, attr in form_tag_attrs.items(): + form.attrib[name] = attr + + form.attrib['action'] = oidutil.toUnicode(action_url) + form.attrib['method'] = 'post' + form.attrib['accept-charset'] = 'UTF-8' + form.attrib['enctype'] = 'application/x-www-form-urlencoded' + + for name, value in self.toPostArgs().items(): + attrs = { + 'type': 'hidden', + 'name': oidutil.toUnicode(name), + 'value': oidutil.toUnicode(value) + } + form.append(ElementTree.Element('input', attrs)) + + submit = ElementTree.Element( + 'input', + {'type': 'submit', + 'value': oidutil.toUnicode(submit_text)}) + form.append(submit) + + return str(ElementTree.tostring(form, encoding='utf-8'), + encoding="utf-8") + + def toURL(self, base_url): + """Generate a GET URL with the parameters in this message + attached as query parameters.""" + return oidutil.appendArgs(base_url, self.toPostArgs()) + + def toKVForm(self): + """Generate a KVForm string that contains the parameters in + this message. This will fail if the message contains arguments + outside of the 'openid.' prefix. + """ + return kvform.dictToKV(self.toArgs()) + + def toURLEncoded(self): + """Generate an x-www-urlencoded string""" + args = sorted(self.toPostArgs().items()) + return urllib.parse.urlencode(args) + + def _fixNS(self, namespace): + """Convert an input value into the internally used values of + this object + + @param namespace: The string or constant to convert + @type namespace: str or unicode or BARE_NS or OPENID_NS + """ + if isinstance(namespace, bytes): + namespace = str(namespace, encoding="utf-8") + + if namespace == OPENID_NS: + if self._openid_ns_uri is None: + raise UndefinedOpenIDNamespace('OpenID namespace not set') + else: + namespace = self._openid_ns_uri + + if namespace != BARE_NS and not isinstance(namespace, str): + raise TypeError( + "Namespace must be BARE_NS, OPENID_NS or a string. got %r" % + (namespace, )) + + if namespace != BARE_NS and ':' not in namespace: + fmt = 'OpenID 2.0 namespace identifiers SHOULD be URIs. Got %r' + warnings.warn(fmt % (namespace, ), DeprecationWarning) + + if namespace == 'sreg': + fmt = 'Using %r instead of "sreg" as namespace' + warnings.warn( + fmt % (SREG_URI, ), + DeprecationWarning, ) + return SREG_URI + + return namespace + + def hasKey(self, namespace, ns_key): + namespace = self._fixNS(namespace) + return (namespace, ns_key) in self.args + + def getKey(self, namespace, ns_key): + """Get the key for a particular namespaced argument""" + namespace = self._fixNS(namespace) + if namespace == BARE_NS: + return ns_key + + ns_alias = self.namespaces.getAlias(namespace) + + # No alias is defined, so no key can exist + if ns_alias is None: + return None + + if ns_alias == NULL_NAMESPACE: + tail = ns_key + else: + tail = '%s.%s' % (ns_alias, ns_key) + + return 'openid.' + tail + + def getArg(self, namespace, key, default=None): + """Get a value for a namespaced key. + + @param namespace: The namespace in the message for this key + @type namespace: str + + @param key: The key to get within this namespace + @type key: str + + @param default: The value to use if this key is absent from + this message. Using the special value + openid.message.no_default will result in this method + raising a KeyError instead of returning the default. + + @rtype: str or the type of default + @raises KeyError: if default is no_default + @raises UndefinedOpenIDNamespace: if the message has not yet + had an OpenID namespace set + """ + namespace = self._fixNS(namespace) + args_key = (namespace, key) + try: + return self.args[args_key] + except KeyError: + if default is no_default: + raise KeyError((namespace, key)) + else: + return default + + def getArgs(self, namespace): + """Get the arguments that are defined for this namespace URI + + @returns: mapping from namespaced keys to values + @returntype: dict of {str:bytes} + """ + namespace = self._fixNS(namespace) + args = [] + for ((pair_ns, ns_key), value) in self.args.items(): + if pair_ns == namespace: + if isinstance(ns_key, bytes): + k = str(ns_key, encoding="utf-8") + else: + k = ns_key + if isinstance(value, bytes): + v = str(value, encoding="utf-8") + else: + v = value + args.append((k, v)) + return dict(args) + + def updateArgs(self, namespace, updates): + """Set multiple key/value pairs in one call + + @param updates: The values to set + @type updates: {unicode:unicode} + """ + namespace = self._fixNS(namespace) + for k, v in updates.items(): + self.setArg(namespace, k, v) + + def setArg(self, namespace, key, value): + """Set a single argument in this namespace""" + assert key is not None + assert value is not None + namespace = self._fixNS(namespace) + # try to ensure that internally it's consistent, at least: str -> str + if isinstance(value, bytes): + value = str(value, encoding="utf-8") + self.args[(namespace, key)] = value + if not (namespace is BARE_NS): + self.namespaces.add(namespace) + + def delArg(self, namespace, key): + namespace = self._fixNS(namespace) + del self.args[(namespace, key)] + + def __repr__(self): + return "<%s.%s %r>" % (self.__class__.__module__, + self.__class__.__name__, self.args) + + def __eq__(self, other): + return self.args == other.args + + def __ne__(self, other): + return not (self == other) + + def getAliasedArg(self, aliased_key, default=None): + if aliased_key == 'ns': + return self.getOpenIDNamespace() + + if aliased_key.startswith('ns.'): + uri = self.namespaces.getNamespaceURI(aliased_key[3:]) + if uri is None: + if default == no_default: + raise KeyError + else: + return default + else: + return uri + + try: + alias, key = aliased_key.split('.', 1) + except ValueError: + # need more than x values to unpack + ns = None + else: + ns = self.namespaces.getNamespaceURI(alias) + + if ns is None: + key = aliased_key + ns = self.getOpenIDNamespace() + + return self.getArg(ns, key, default) + + +class NamespaceMap(object): + """Maintains a bijective map between namespace uris and aliases. + """ + + def __init__(self): + self.alias_to_namespace = {} + self.namespace_to_alias = {} + self.implicit_namespaces = [] + + def getAlias(self, namespace_uri): + return self.namespace_to_alias.get(namespace_uri) + + def getNamespaceURI(self, alias): + return self.alias_to_namespace.get(alias) + + def iterNamespaceURIs(self): + """Return an iterator over the namespace URIs""" + return iter(self.namespace_to_alias) + + def iterAliases(self): + """Return an iterator over the aliases""" + return iter(self.alias_to_namespace) + + def items(self): + """Iterate over the mapping + + @returns: iterator of (namespace_uri, alias) + """ + return self.namespace_to_alias.items() + + def addAlias(self, namespace_uri, desired_alias, implicit=False): + """Add an alias from this namespace URI to the desired alias + """ + if isinstance(namespace_uri, bytes): + namespace_uri = str(namespace_uri, encoding="utf-8") + # Check that desired_alias is not an openid protocol field as + # per the spec. + assert desired_alias not in OPENID_PROTOCOL_FIELDS, \ + "%r is not an allowed namespace alias" % (desired_alias,) + + # Check that desired_alias does not contain a period as per + # the spec. + if isinstance(desired_alias, str): + assert '.' not in desired_alias, \ + "%r must not contain a dot" % (desired_alias,) + + # Check that there is not a namespace already defined for + # the desired alias + current_namespace_uri = self.alias_to_namespace.get(desired_alias) + if (current_namespace_uri is not None and + current_namespace_uri != namespace_uri): + + fmt = ('Cannot map %r to alias %r. ' + '%r is already mapped to alias %r') + + msg = fmt % (namespace_uri, desired_alias, current_namespace_uri, + desired_alias) + raise KeyError(msg) + + # Check that there is not already a (different) alias for + # this namespace URI + alias = self.namespace_to_alias.get(namespace_uri) + if alias is not None and alias != desired_alias: + fmt = ('Cannot map %r to alias %r. ' + 'It is already mapped to alias %r') + raise KeyError(fmt % (namespace_uri, desired_alias, alias)) + + assert (desired_alias == NULL_NAMESPACE or + type(desired_alias) in [str, str]), repr(desired_alias) + assert namespace_uri not in self.implicit_namespaces + self.alias_to_namespace[desired_alias] = namespace_uri + self.namespace_to_alias[namespace_uri] = desired_alias + if implicit: + self.implicit_namespaces.append(namespace_uri) + return desired_alias + + def add(self, namespace_uri): + """Add this namespace URI to the mapping, without caring what + alias it ends up with""" + # See if this namespace is already mapped to an alias + alias = self.namespace_to_alias.get(namespace_uri) + if alias is not None: + return alias + + # Fall back to generating a numerical alias + i = 0 + while True: + alias = 'ext' + str(i) + try: + self.addAlias(namespace_uri, alias) + except KeyError: + i += 1 + else: + return alias + + assert False, "Not reached" + + def isDefined(self, namespace_uri): + return namespace_uri in self.namespace_to_alias + + def __contains__(self, namespace_uri): + return self.isDefined(namespace_uri) + + def isImplicit(self, namespace_uri): + return namespace_uri in self.implicit_namespaces diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/oidutil.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/oidutil.py new file mode 100644 index 0000000000000000000000000000000000000000..26f8960353bbddbd0403cc00807ac2360e23c65e --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/oidutil.py @@ -0,0 +1,235 @@ +"""This module contains general utility code that is used throughout +the library. +""" + +__all__ = [ + 'log', 'appendArgs', 'toBase64', 'fromBase64', 'autoSubmitHTML', + 'toUnicode' +] + +import binascii +import logging + +# import urllib.parse as urlparse +from urllib.parse import urlencode + +logger = logging.getLogger(__name__) + +xxe_safe_elementtree_modules = [ + 'defusedxml.cElementTree', + 'defusedxml.ElementTree', +] + +elementtree_modules = [ + 'xml.etree.cElementTree', + 'xml.etree.ElementTree', + 'cElementTree', + 'elementtree.ElementTree', +] + + +def toUnicode(value): + """Returns the given argument as a unicode object. + + @param value: A UTF-8 encoded string or a unicode (coercable) object + @type message: str or unicode + + @returns: Unicode object representing the input value. + """ + if isinstance(value, bytes): + return value.decode('utf-8') + return str(value) + + +def autoSubmitHTML(form, title='OpenID transaction in progress'): + if isinstance(form, bytes): + form = str(form, encoding="utf-8") + if isinstance(title, bytes): + title = str(title, encoding="utf-8") + html = """ + + + %s + + +%s + + + +""" % (title, form) + return html + + +def importSafeElementTree(module_names=None): + """Find a working ElementTree implementation that is not vulnerable + to XXE, using `defusedxml`. + + >>> XXESafeElementTree = importSafeElementTree() + + @param module_names: The names of modules to try to use as + a safe ElementTree. Defaults to C{L{xxe_safe_elementtree_modules}} + + @returns: An ElementTree module that is not vulnerable to XXE. + """ + if module_names is None: + module_names = xxe_safe_elementtree_modules + try: + return importElementTree(module_names) + except ImportError: + raise ImportError('Unable to find a ElementTree module ' + 'that is not vulnerable to XXE. ' + 'Tried importing %r' % (module_names, )) + + +def importElementTree(module_names=None): + """Find a working ElementTree implementation, trying the standard + places that such a thing might show up. + + >>> ElementTree = importElementTree() + + @param module_names: The names of modules to try to use as + ElementTree. Defaults to C{L{elementtree_modules}} + + @returns: An ElementTree module + """ + if module_names is None: + module_names = elementtree_modules + + for mod_name in module_names: + try: + ElementTree = __import__(mod_name, None, None, ['unused']) + except ImportError: + pass + else: + # Make sure it can actually parse XML + try: + ElementTree.XML('') + except (SystemExit, MemoryError, AssertionError): + raise + except: + logger.exception( + 'Not using ElementTree library %r because it failed to ' + 'parse a trivial document: %s' % mod_name) + else: + return ElementTree + else: + raise ImportError('No ElementTree library found. ' + 'You may need to install one. ' + 'Tried importing %r' % (module_names, )) + + +def log(message, level=0): + """Handle a log message from the OpenID library. + + This is a legacy function which redirects to logger.error. + The logging module should be used instead of this + + @param message: A string containing a debugging message from the + OpenID library + @type message: str + + @param level: The severity of the log message. This parameter is + currently unused, but in the future, the library may indicate + more important information with a higher level value. + @type level: int or None + + @returns: Nothing. + """ + + logger.error("This is a legacy log message, please use the " + "logging module. Message: %s", message) + + +def appendArgs(url, args): + """Append query arguments to a HTTP(s) URL. If the URL already has + query arguemtns, these arguments will be added, and the existing + arguments will be preserved. Duplicate arguments will not be + detected or collapsed (both will appear in the output). + + @param url: The url to which the arguments will be appended + @type url: str + + @param args: The query arguments to add to the URL. If a + dictionary is passed, the items will be sorted before + appending them to the URL. If a sequence of pairs is passed, + the order of the sequence will be preserved. + @type args: A dictionary from string to string, or a sequence of + pairs of strings. + + @returns: The URL with the parameters added + @rtype: str + """ + if hasattr(args, 'items'): + args = sorted(args.items()) + else: + args = list(args) + + if not isinstance(url, str): + url = str(url, encoding="utf-8") + + if not args: + return url + + if '?' in url: + sep = '&' + else: + sep = '?' + + # Map unicode to UTF-8 if present. Do not make any assumptions + # about the encodings of plain bytes (str). + i = 0 + for k, v in args: + if not isinstance(k, bytes): + k = k.encode('utf-8') + + if not isinstance(v, bytes): + v = v.encode('utf-8') + + args[i] = (k, v) + i += 1 + + return '%s%s%s' % (url, sep, urlencode(args)) + + +def toBase64(s): + """Represent string / bytes s as base64, omitting newlines""" + if isinstance(s, str): + s = s.encode("utf-8") + return binascii.b2a_base64(s)[:-1] + + +def fromBase64(s): + if isinstance(s, str): + s = s.encode("utf-8") + try: + return binascii.a2b_base64(s) + except binascii.Error as why: + # Convert to a common exception type + raise ValueError(str(why)) + + +class Symbol(object): + """This class implements an object that compares equal to others + of the same type that have the same name. These are distict from + str or unicode objects. + """ + + def __init__(self, name): + self.name = name + + def __eq__(self, other): + return type(self) is type(other) and self.name == other.name + + def __ne__(self, other): + return not (self == other) + + def __hash__(self): + return hash((self.__class__, self.name)) + + def __repr__(self): + return '' % (self.name, ) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/server/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/server/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..c8fde25736a66e63bb5d32aedbd69ac86d3ba90e --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/server/__init__.py @@ -0,0 +1,6 @@ +""" +This package contains the portions of the library used only when +implementing an OpenID server. See L{openid.server.server}. +""" + +__all__ = ['server', 'trustroot'] diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/server/server.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/server/server.py new file mode 100644 index 0000000000000000000000000000000000000000..fa4fae22f2f0b9701e0f19b3ce93e95d10dcac9c --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/server/server.py @@ -0,0 +1,1824 @@ +# -*- test-case-name: openid.test.test_server -*- +"""OpenID server protocol and logic. + +Overview +======== + + An OpenID server must perform three tasks: + + 1. Examine the incoming request to determine its nature and validity. + + 2. Make a decision about how to respond to this request. + + 3. Format the response according to the protocol. + + The first and last of these tasks may performed by + the L{decodeRequest} and + L{encodeResponse} methods of the + L{Server} object. Who gets to do the intermediate task -- deciding + how to respond to the request -- will depend on what type of request it + is. + + If it's a request to authenticate a user (a X{C{checkid_setup}} or + X{C{checkid_immediate}} request), you need to decide if you will assert + that this user may claim the identity in question. Exactly how you do + that is a matter of application policy, but it generally involves making + sure the user has an account with your system and is logged in, checking + to see if that identity is hers to claim, and verifying with the user that + she does consent to releasing that information to the party making the + request. + + Examine the properties of the L{CheckIDRequest} object, optionally + check L{CheckIDRequest.returnToVerified}, and and when you've come + to a decision, form a response by calling L{CheckIDRequest.answer}. + + Other types of requests relate to establishing associations between client + and server and verifying the authenticity of previous communications. + L{Server} contains all the logic and data necessary to respond to + such requests; just pass the request to L{Server.handleRequest}. + + +OpenID Extensions +================= + + Do you want to provide other information for your users + in addition to authentication? Version 2.0 of the OpenID + protocol allows consumers to add extensions to their requests. + For example, with sites using the U{Simple Registration + Extension}, + a user can agree to have their nickname and e-mail address sent to a + site when they sign up. + + Since extensions do not change the way OpenID authentication works, + code to handle extension requests may be completely separate from the + L{OpenIDRequest} class here. But you'll likely want data sent back by + your extension to be signed. L{OpenIDResponse} provides methods with + which you can add data to it which can be signed with the other data in + the OpenID signature. + + For example:: + + # when request is a checkid_* request + response = request.answer(True) + # this will a signed 'openid.sreg.timezone' parameter to the response + # as well as a namespace declaration for the openid.sreg namespace + response.fields.setArg('http://openid.net/sreg/1.0', 'timezone', 'America/Los_Angeles') + + There are helper modules for a number of extensions, including + L{Attribute Exchange}, + L{PAPE}, and + L{Simple Registration} in the L{openid.extensions} + package. + +Stores +====== + + The OpenID server needs to maintain state between requests in order + to function. Its mechanism for doing this is called a store. The + store interface is defined in C{L{openid.store.interface.OpenIDStore}}. + Additionally, several concrete store implementations are provided, so that + most sites won't need to implement a custom store. For a store backed + by flat files on disk, see C{L{openid.store.filestore.FileOpenIDStore}}. + For stores based on MySQL or SQLite, see the C{L{openid.store.sqlstore}} + module. + + +Upgrading +========= + +From 1.0 to 1.1 +--------------- + + The keys by which a server looks up associations in its store have changed + in version 1.2 of this library. If your store has entries created from + version 1.0 code, you should empty it. + +From 1.1 to 2.0 +--------------- + + One of the additions to the OpenID protocol was a specified nonce + format for one-way nonces. As a result, the nonce table in the store + has changed. You'll need to run contrib/upgrade-store-1.1-to-2.0 to + upgrade your store, or you'll encounter errors about the wrong number + of columns in the oid_nonces table. + + If you've written your own custom store or code that interacts + directly with it, you'll need to review the change notes in + L{openid.store.interface}. + +@group Requests: OpenIDRequest, AssociateRequest, CheckIDRequest, + CheckAuthRequest + +@group Responses: OpenIDResponse + +@group HTTP Codes: HTTP_OK, HTTP_REDIRECT, HTTP_ERROR + +@group Response Encodings: ENCODE_KVFORM, ENCODE_HTML_FORM, ENCODE_URL +""" + +import time +import warnings +import logging +from copy import deepcopy + +from openid import cryptutil +from openid import oidutil +from openid import kvform +from openid.dh import DiffieHellman +from openid.store.nonce import mkNonce +from openid.server.trustroot import TrustRoot, verifyReturnTo +from openid.association import Association, default_negotiator, getSecretSize +from openid.message import Message, InvalidOpenIDNamespace, \ + OPENID_NS, OPENID2_NS, IDENTIFIER_SELECT, OPENID1_URL_LIMIT +from openid.urinorm import urinorm + +logger = logging.getLogger(__name__) + +HTTP_OK = 200 +HTTP_REDIRECT = 302 +HTTP_ERROR = 400 + +BROWSER_REQUEST_MODES = ['checkid_setup', 'checkid_immediate'] + +ENCODE_KVFORM = ('kvform', ) +ENCODE_URL = ('URL/redirect', ) +ENCODE_HTML_FORM = ('HTML form', ) + +UNUSED = None + + +class OpenIDRequest(object): + """I represent an incoming OpenID request. + + @cvar mode: the C{X{openid.mode}} of this request. + @type mode: str + """ + mode = None + + +class CheckAuthRequest(OpenIDRequest): + """A request to verify the validity of a previous response. + + @cvar mode: "X{C{check_authentication}}" + @type mode: str + + @ivar assoc_handle: The X{association handle} the response was signed with. + @type assoc_handle: str + @ivar signed: The message with the signature which wants checking. + @type signed: L{Message} + + @ivar invalidate_handle: An X{association handle} the client is asking + about the validity of. Optional, may be C{None}. + @type invalidate_handle: str + + @see: U{OpenID Specs, Mode: check_authentication + } + """ + mode = "check_authentication" + + required_fields = ["identity", "return_to", "response_nonce"] + + def __init__(self, assoc_handle, signed, invalidate_handle=None): + """Construct me. + + These parameters are assigned directly as class attributes, see + my L{class documentation} for their descriptions. + + @type assoc_handle: str + @type signed: L{Message} + @type invalidate_handle: str + """ + self.assoc_handle = assoc_handle + self.signed = signed + self.invalidate_handle = invalidate_handle + self.namespace = OPENID2_NS + + @classmethod + def fromMessage(klass, message, op_endpoint=UNUSED): + """Construct me from an OpenID Message. + + @param message: An OpenID check_authentication Message + @type message: L{openid.message.Message} + + @returntype: L{CheckAuthRequest} + """ + self = klass.__new__(klass) + self.message = message + self.namespace = message.getOpenIDNamespace() + self.assoc_handle = message.getArg(OPENID_NS, 'assoc_handle') + self.sig = message.getArg(OPENID_NS, 'sig') + + if (self.assoc_handle is None or self.sig is None): + fmt = "%s request missing required parameter from message %s" + raise ProtocolError(message, text=fmt % (self.mode, message)) + + self.invalidate_handle = message.getArg(OPENID_NS, 'invalidate_handle') + + self.signed = message.copy() + # openid.mode is currently check_authentication because + # that's the mode of this request. But the signature + # was made on something with a different openid.mode. + # http://article.gmane.org/gmane.comp.web.openid.general/537 + if self.signed.hasKey(OPENID_NS, "mode"): + self.signed.setArg(OPENID_NS, "mode", "id_res") + + return self + + def answer(self, signatory): + """Respond to this request. + + Given a L{Signatory}, I can check the validity of the signature and + the X{C{invalidate_handle}}. + + @param signatory: The L{Signatory} to use to check the signature. + @type signatory: L{Signatory} + + @returns: A response with an X{C{is_valid}} (and, if + appropriate X{C{invalidate_handle}}) field. + @returntype: L{OpenIDResponse} + """ + is_valid = signatory.verify(self.assoc_handle, self.signed) + # Now invalidate that assoc_handle so it this checkAuth message cannot + # be replayed. + signatory.invalidate(self.assoc_handle, dumb=True) + response = OpenIDResponse(self) + valid_str = (is_valid and "true") or "false" + response.fields.setArg(OPENID_NS, 'is_valid', valid_str) + + if self.invalidate_handle: + assoc = signatory.getAssociation( + self.invalidate_handle, dumb=False) + if not assoc: + response.fields.setArg(OPENID_NS, 'invalidate_handle', + self.invalidate_handle) + return response + + def __str__(self): + if self.invalidate_handle: + ih = " invalidate? %r" % (self.invalidate_handle, ) + else: + ih = "" + s = "<%s handle: %r sig: %r: signed: %r%s>" % ( + self.__class__.__name__, self.assoc_handle, self.sig, self.signed, + ih) + return s + + +class PlainTextServerSession(object): + """An object that knows how to handle association requests with no + session type. + + @cvar session_type: The session_type for this association + session. There is no type defined for plain-text in the OpenID + specification, so we use 'no-encryption'. + @type session_type: str + + @see: U{OpenID Specs, Mode: associate + } + @see: AssociateRequest + """ + session_type = 'no-encryption' + allowed_assoc_types = ['HMAC-SHA1', 'HMAC-SHA256'] + + def fromMessage(cls, unused_request): + return cls() + + fromMessage = classmethod(fromMessage) + + def answer(self, secret): + return {'mac_key': oidutil.toBase64(secret)} + + +class DiffieHellmanSHA1ServerSession(object): + """An object that knows how to handle association requests with the + Diffie-Hellman session type. + + @cvar session_type: The session_type for this association + session. + @type session_type: str + + @ivar dh: The Diffie-Hellman algorithm values for this request + @type dh: DiffieHellman + + @ivar consumer_pubkey: The public key sent by the consumer in the + associate request + @type consumer_pubkey: long + + @see: U{OpenID Specs, Mode: associate + } + @see: AssociateRequest + """ + session_type = 'DH-SHA1' + hash_func = staticmethod(cryptutil.sha1) + allowed_assoc_types = ['HMAC-SHA1'] + + def __init__(self, dh, consumer_pubkey): + self.dh = dh + self.consumer_pubkey = consumer_pubkey + + def fromMessage(cls, message): + """ + @param message: The associate request message + @type message: openid.message.Message + + @returntype: L{DiffieHellmanSHA1ServerSession} + + @raises ProtocolError: When parameters required to establish the + session are missing. + """ + dh_modulus = message.getArg(OPENID_NS, 'dh_modulus') + dh_gen = message.getArg(OPENID_NS, 'dh_gen') + if (dh_modulus is None and dh_gen is not None or dh_gen is None and + dh_modulus is not None): + + if dh_modulus is None: + missing = 'modulus' + else: + missing = 'generator' + + raise ProtocolError( + message, 'If non-default modulus or generator is ' + 'supplied, both must be supplied. Missing %s' % (missing, )) + + if dh_modulus or dh_gen: + dh_modulus = cryptutil.base64ToLong(dh_modulus) + dh_gen = cryptutil.base64ToLong(dh_gen) + dh = DiffieHellman(dh_modulus, dh_gen) + else: + dh = DiffieHellman.fromDefaults() + + consumer_pubkey = message.getArg(OPENID_NS, 'dh_consumer_public') + if consumer_pubkey is None: + raise ProtocolError(message, "Public key for DH-SHA1 session " + "not found in message %s" % (message, )) + + consumer_pubkey = cryptutil.base64ToLong(consumer_pubkey) + + return cls(dh, consumer_pubkey) + + fromMessage = classmethod(fromMessage) + + def answer(self, secret): + mac_key = self.dh.xorSecret(self.consumer_pubkey, secret, + self.hash_func) + return { + 'dh_server_public': cryptutil.longToBase64(self.dh.public), + 'enc_mac_key': oidutil.toBase64(mac_key), + } + + +class DiffieHellmanSHA256ServerSession(DiffieHellmanSHA1ServerSession): + session_type = 'DH-SHA256' + hash_func = staticmethod(cryptutil.sha256) + allowed_assoc_types = ['HMAC-SHA256'] + + +class AssociateRequest(OpenIDRequest): + """A request to establish an X{association}. + + @cvar mode: "X{C{check_authentication}}" + @type mode: str + + @ivar assoc_type: The type of association. The protocol currently only + defines one value for this, "X{C{HMAC-SHA1}}". + @type assoc_type: str + + @ivar session: An object that knows how to handle association + requests of a certain type. + + @see: U{OpenID Specs, Mode: associate + } + """ + + mode = "associate" + + session_classes = { + 'no-encryption': PlainTextServerSession, + 'DH-SHA1': DiffieHellmanSHA1ServerSession, + 'DH-SHA256': DiffieHellmanSHA256ServerSession, + } + + def __init__(self, session, assoc_type): + """Construct me. + + The session is assigned directly as a class attribute. See my + L{class documentation} for its description. + """ + super(AssociateRequest, self).__init__() + self.session = session + self.assoc_type = assoc_type + self.namespace = OPENID2_NS + + def fromMessage(klass, message, op_endpoint=UNUSED): + """Construct me from an OpenID Message. + + @param message: The OpenID associate request + @type message: openid.message.Message + + @returntype: L{AssociateRequest} + """ + if message.isOpenID1(): + session_type = message.getArg(OPENID_NS, 'session_type') + if session_type == 'no-encryption': + logger.warning( + 'Received OpenID 1 request with a no-encryption ' + 'assocaition session type. Continuing anyway.') + elif not session_type: + session_type = 'no-encryption' + else: + session_type = message.getArg(OPENID2_NS, 'session_type') + if session_type is None: + raise ProtocolError( + message, text="session_type missing from request") + + try: + session_class = klass.session_classes[session_type] + except KeyError: + raise ProtocolError(message, + "Unknown session type %r" % (session_type, )) + + try: + session = session_class.fromMessage(message) + except ValueError as why: + raise ProtocolError(message, 'Error parsing %s session: %s' % + (session_class.session_type, why)) + + assoc_type = message.getArg(OPENID_NS, 'assoc_type', 'HMAC-SHA1') + if assoc_type not in session.allowed_assoc_types: + fmt = 'Session type %s does not support association type %s' + raise ProtocolError(message, fmt % (session_type, assoc_type)) + + self = klass(session, assoc_type) + self.message = message + self.namespace = message.getOpenIDNamespace() + return self + + fromMessage = classmethod(fromMessage) + + def answer(self, assoc): + """Respond to this request with an X{association}. + + @param assoc: The association to send back. + @type assoc: L{openid.association.Association} + + @returns: A response with the association information, encrypted + to the consumer's X{public key} if appropriate. + @returntype: L{OpenIDResponse} + """ + response = OpenIDResponse(self) + response.fields.updateArgs(OPENID_NS, { + 'expires_in': str(assoc.expiresIn), + 'assoc_type': self.assoc_type, + 'assoc_handle': assoc.handle, + }) + response.fields.updateArgs(OPENID_NS, + self.session.answer(assoc.secret)) + + if not (self.session.session_type == 'no-encryption' and + self.message.isOpenID1()): + # The session type "no-encryption" did not have a name + # in OpenID v1, it was just omitted. + response.fields.setArg(OPENID_NS, 'session_type', + self.session.session_type) + + return response + + def answerUnsupported(self, + message, + preferred_association_type=None, + preferred_session_type=None): + """Respond to this request indicating that the association + type or association session type is not supported.""" + if self.message.isOpenID1(): + raise ProtocolError(self.message) + + response = OpenIDResponse(self) + response.fields.setArg(OPENID_NS, 'error_code', 'unsupported-type') + response.fields.setArg(OPENID_NS, 'error', message) + + if preferred_association_type: + response.fields.setArg(OPENID_NS, 'assoc_type', + preferred_association_type) + + if preferred_session_type: + response.fields.setArg(OPENID_NS, 'session_type', + preferred_session_type) + + return response + + +class CheckIDRequest(OpenIDRequest): + """A request to confirm the identity of a user. + + This class handles requests for openid modes X{C{checkid_immediate}} + and X{C{checkid_setup}}. + + @cvar mode: "X{C{checkid_immediate}}" or "X{C{checkid_setup}}" + @type mode: str + + @ivar immediate: Is this an immediate-mode request? + @type immediate: bool + + @ivar identity: The OP-local identifier being checked. + @type identity: str + + @ivar claimed_id: The claimed identifier. Not present in OpenID 1.x + messages. + @type claimed_id: str + + @ivar trust_root: "Are you Frank?" asks the checkid request. "Who wants + to know?" C{trust_root}, that's who. This URL identifies the party + making the request, and the user will use that to make her decision + about what answer she trusts them to have. Referred to as "realm" in + OpenID 2.0. + @type trust_root: str + + @ivar return_to: The URL to send the user agent back to to reply to this + request. + @type return_to: str + + @ivar assoc_handle: Provided in smart mode requests, a handle for a + previously established association. C{None} for dumb mode requests. + @type assoc_handle: str + """ + + def __init__(self, + identity, + return_to, + trust_root=None, + immediate=False, + assoc_handle=None, + op_endpoint=None, + claimed_id=None): + """Construct me. + + These parameters are assigned directly as class attributes, see + my L{class documentation} for their descriptions. + + @raises MalformedReturnURL: When the C{return_to} URL is not a URL. + """ + self.assoc_handle = assoc_handle + self.identity = identity + self.claimed_id = claimed_id or identity + self.return_to = return_to + self.trust_root = trust_root or return_to + self.op_endpoint = op_endpoint + assert self.op_endpoint is not None + if immediate: + self.immediate = True + self.mode = "checkid_immediate" + else: + self.immediate = False + self.mode = "checkid_setup" + + if self.return_to is not None and \ + not TrustRoot.parse(self.return_to): + raise MalformedReturnURL(None, self.return_to) + if not self.trustRootValid(): + raise UntrustedReturnURL(None, self.return_to, self.trust_root) + self.message = None + + def _getNamespace(self): + warnings.warn( + 'The "namespace" attribute of CheckIDRequest objects ' + 'is deprecated. Use "message.getOpenIDNamespace()" ' + 'instead', + DeprecationWarning, + stacklevel=2) + return self.message.getOpenIDNamespace() + + namespace = property(_getNamespace) + + def fromMessage(klass, message, op_endpoint): + """Construct me from an OpenID message. + + @raises ProtocolError: When not all required parameters are present + in the message. + + @raises MalformedReturnURL: When the C{return_to} URL is not a URL. + + @raises UntrustedReturnURL: When the C{return_to} URL is outside + the C{trust_root}. + + @param message: An OpenID checkid_* request Message + @type message: openid.message.Message + + @param op_endpoint: The endpoint URL of the server that this + message was sent to. + @type op_endpoint: str + + @returntype: L{CheckIDRequest} + """ + self = klass.__new__(klass) + self.message = message + self.op_endpoint = op_endpoint + mode = message.getArg(OPENID_NS, 'mode') + if mode == "checkid_immediate": + self.immediate = True + self.mode = "checkid_immediate" + else: + self.immediate = False + self.mode = "checkid_setup" + + self.return_to = message.getArg(OPENID_NS, 'return_to') + if message.isOpenID1() and not self.return_to: + fmt = "Missing required field 'return_to' from %r" + raise ProtocolError(message, text=fmt % (message, )) + + self.identity = message.getArg(OPENID_NS, 'identity') + self.claimed_id = message.getArg(OPENID_NS, 'claimed_id') + if message.isOpenID1(): + if self.identity is None: + s = "OpenID 1 message did not contain openid.identity" + raise ProtocolError(message, text=s) + else: + if self.identity and not self.claimed_id: + s = ("OpenID 2.0 message contained openid.identity but not " + "claimed_id") + raise ProtocolError(message, text=s) + elif self.claimed_id and not self.identity: + s = ("OpenID 2.0 message contained openid.claimed_id but not " + "identity") + raise ProtocolError(message, text=s) + + # There's a case for making self.trust_root be a TrustRoot + # here. But if TrustRoot isn't currently part of the "public" API, + # I'm not sure it's worth doing. + + if message.isOpenID1(): + trust_root_param = 'trust_root' + else: + trust_root_param = 'realm' + + # Using 'or' here is slightly different than sending a default + # argument to getArg, as it will treat no value and an empty + # string as equivalent. + self.trust_root = (message.getArg(OPENID_NS, trust_root_param) or + self.return_to) + + if not message.isOpenID1(): + if self.return_to is self.trust_root is None: + raise ProtocolError( + message, + "openid.realm required when " + "openid.return_to absent") + + self.assoc_handle = message.getArg(OPENID_NS, 'assoc_handle') + + # Using TrustRoot.parse here is a bit misleading, as we're not + # parsing return_to as a trust root at all. However, valid URLs + # are valid trust roots, so we can use this to get an idea if it + # is a valid URL. Not all trust roots are valid return_to URLs, + # however (particularly ones with wildcards), so this is still a + # little sketchy. + if self.return_to is not None and \ + not TrustRoot.parse(self.return_to): + raise MalformedReturnURL(message, self.return_to) + + # I first thought that checking to see if the return_to is within + # the trust_root is premature here, a logic-not-decoding thing. But + # it was argued that this is really part of data validation. A + # request with an invalid trust_root/return_to is broken regardless of + # application, right? + if not self.trustRootValid(): + raise UntrustedReturnURL(message, self.return_to, self.trust_root) + + return self + + fromMessage = classmethod(fromMessage) + + def idSelect(self): + """Is the identifier to be selected by the IDP? + + @returntype: bool + """ + # So IDPs don't have to import the constant + return self.identity == IDENTIFIER_SELECT + + def trustRootValid(self): + """Is my return_to under my trust_root? + + @returntype: bool + """ + if not self.trust_root: + return True + tr = TrustRoot.parse(self.trust_root) + if tr is None: + raise MalformedTrustRoot(self.message, self.trust_root) + + if self.return_to is not None: + return tr.validateURL(self.return_to) + else: + return True + + def returnToVerified(self): + """Does the relying party publish the return_to URL for this + response under the realm? It is up to the provider to set a + policy for what kinds of realms should be allowed. This + return_to URL verification reduces vulnerability to data-theft + attacks based on open proxies, cross-site-scripting, or open + redirectors. + + This check should only be performed after making sure that the + return_to URL matches the realm. + + @see: L{trustRootValid} + + @raises openid.yadis.discover.DiscoveryFailure: if the realm + URL does not support Yadis discovery (and so does not + support the verification process). + + @raises openid.fetchers.HTTPFetchingError: if the realm URL + is not reachable. When this is the case, the RP may be hosted + on the user's intranet. + + @returntype: bool + + @returns: True if the realm publishes a document with the + return_to URL listed + + @since: 2.1.0 + """ + return verifyReturnTo(self.trust_root, self.return_to) + + def answer(self, allow, server_url=None, identity=None, claimed_id=None): + """Respond to this request. + + @param allow: Allow this user to claim this identity, and allow the + consumer to have this information? + @type allow: bool + + @param server_url: DEPRECATED. Passing C{op_endpoint} to the + L{Server} constructor makes this optional. + + When an OpenID 1.x immediate mode request does not succeed, + it gets back a URL where the request may be carried out + in a not-so-immediate fashion. Pass my URL in here (the + fully qualified address of this server's endpoint, i.e. + C{http://example.com/server}), and I will use it as a base for the + URL for a new request. + + Optional for requests where C{CheckIDRequest.immediate} is C{False} + or C{allow} is C{True}. + + @type server_url: str + + @param identity: The OP-local identifier to answer with. Only for use + when the relying party requested identifier selection. + @type identity: str or None + + @param claimed_id: The claimed identifier to answer with, for use + with identifier selection in the case where the claimed identifier + and the OP-local identifier differ, i.e. when the claimed_id uses + delegation. + + If C{identity} is provided but this is not, C{claimed_id} will + default to the value of C{identity}. When answering requests + that did not ask for identifier selection, the response + C{claimed_id} will default to that of the request. + + This parameter is new in OpenID 2.0. + @type claimed_id: str or None + + @returntype: L{OpenIDResponse} + + @change: Version 2.0 deprecates C{server_url} and adds C{claimed_id}. + + @raises NoReturnError: when I do not have a return_to. + """ + assert self.message is not None + + if not self.return_to: + raise NoReturnToError + + if not server_url: + if not self.message.isOpenID1() and not self.op_endpoint: + # In other words, that warning I raised in Server.__init__? + # You should pay attention to it now. + raise RuntimeError("%s should be constructed with op_endpoint " + "to respond to OpenID 2.0 messages." % + (self, )) + server_url = self.op_endpoint + + if allow: + mode = 'id_res' + elif self.message.isOpenID1(): + if self.immediate: + mode = 'id_res' + else: + mode = 'cancel' + else: + if self.immediate: + mode = 'setup_needed' + else: + mode = 'cancel' + + response = OpenIDResponse(self) + + if claimed_id and self.message.isOpenID1(): + namespace = self.message.getOpenIDNamespace() + raise VersionError("claimed_id is new in OpenID 2.0 and not " + "available for %s" % (namespace, )) + + if allow: + if self.identity == IDENTIFIER_SELECT: + if not identity: + raise ValueError( + "This request uses IdP-driven identifier selection." + "You must supply an identifier in the response.") + response_identity = identity + response_claimed_id = claimed_id or identity + + elif self.identity: + if identity and (self.identity != identity): + normalized_request_identity = urinorm(self.identity) + normalized_answer_identity = urinorm(identity) + + if (normalized_request_identity != + normalized_answer_identity): + raise ValueError( + "Request was for identity %r, cannot reply " + "with identity %r" % (self.identity, identity)) + + # The "identity" value in the response shall always be + # the same as that in the request, otherwise the RP is + # likely to not validate the response. + response_identity = self.identity + response_claimed_id = self.claimed_id + else: + if identity: + raise ValueError( + "This request specified no identity and you " + "supplied %r" % (identity, )) + response_identity = None + + if self.message.isOpenID1() and response_identity is None: + raise ValueError( + "Request was an OpenID 1 request, so response must " + "include an identifier.") + + response.fields.updateArgs(OPENID_NS, { + 'mode': mode, + 'return_to': self.return_to, + 'response_nonce': mkNonce(), + }) + + if server_url: + response.fields.setArg(OPENID_NS, 'op_endpoint', server_url) + + if response_identity is not None: + response.fields.setArg(OPENID_NS, 'identity', + response_identity) + if self.message.isOpenID2(): + response.fields.setArg(OPENID_NS, 'claimed_id', + response_claimed_id) + else: + response.fields.setArg(OPENID_NS, 'mode', mode) + if self.immediate: + if self.message.isOpenID1() and not server_url: + raise ValueError("setup_url is required for allow=False " + "in OpenID 1.x immediate mode.") + # Make a new request just like me, but with immediate=False. + setup_request = self.__class__( + self.identity, + self.return_to, + self.trust_root, + immediate=False, + assoc_handle=self.assoc_handle, + op_endpoint=self.op_endpoint, + claimed_id=self.claimed_id) + + # XXX: This API is weird. + setup_request.message = self.message + + setup_url = setup_request.encodeToURL(server_url) + response.fields.setArg(OPENID_NS, 'user_setup_url', setup_url) + + return response + + def encodeToURL(self, server_url): + """Encode this request as a URL to GET. + + @param server_url: URL of the OpenID server to make this request of. + @type server_url: str + + @returntype: str + + @raises NoReturnError: when I do not have a return_to. + """ + if not self.return_to: + raise NoReturnToError + + # Imported from the alternate reality where these classes are used + # in both the client and server code, so Requests are Encodable too. + # That's right, code imported from alternate realities all for the + # love of you, id_res/user_setup_url. + q = { + 'mode': self.mode, + 'identity': self.identity, + 'claimed_id': self.claimed_id, + 'return_to': self.return_to + } + if self.trust_root: + if self.message.isOpenID1(): + q['trust_root'] = self.trust_root + else: + q['realm'] = self.trust_root + if self.assoc_handle: + q['assoc_handle'] = self.assoc_handle + + response = Message(self.message.getOpenIDNamespace()) + response.updateArgs(OPENID_NS, q) + return response.toURL(server_url) + + def getCancelURL(self): + """Get the URL to cancel this request. + + Useful for creating a "Cancel" button on a web form so that operation + can be carried out directly without another trip through the server. + + (Except you probably want to make another trip through the server so + that it knows that the user did make a decision. Or you could simulate + this method by doing C{.answer(False).encodeToURL()}) + + @returntype: str + @returns: The return_to URL with openid.mode = cancel. + + @raises NoReturnError: when I do not have a return_to. + """ + if not self.return_to: + raise NoReturnToError + + if self.immediate: + raise ValueError("Cancel is not an appropriate response to " + "immediate mode requests.") + + response = Message(self.message.getOpenIDNamespace()) + response.setArg(OPENID_NS, 'mode', 'cancel') + return response.toURL(self.return_to) + + def __repr__(self): + return '<%s id:%r im:%s tr:%r ah:%r>' % ( + self.__class__.__name__, self.identity, self.immediate, + self.trust_root, self.assoc_handle) + + +class OpenIDResponse(object): + """I am a response to an OpenID request. + + @ivar request: The request I respond to. + @type request: L{OpenIDRequest} + + @ivar fields: My parameters as a dictionary with each key mapping to + one value. Keys are parameter names with no leading "C{openid.}". + e.g. "C{identity}" and "C{mac_key}", never "C{openid.identity}". + @type fields: L{openid.message.Message} + + @ivar signed: The names of the fields which should be signed. + @type signed: list of str + """ + + # Implementer's note: In a more symmetric client/server + # implementation, there would be more types of OpenIDResponse + # object and they would have validated attributes according to the + # type of response. But as it is, Response objects in a server are + # basically write-only, their only job is to go out over the wire, + # so this is just a loose wrapper around OpenIDResponse.fields. + + def __init__(self, request): + """Make a response to an L{OpenIDRequest}. + + @type request: L{OpenIDRequest} + """ + self.request = request + self.fields = Message(request.namespace) + + def __str__(self): + return "%s for %s: %s" % (self.__class__.__name__, + self.request.__class__.__name__, self.fields) + + def toFormMarkup(self, form_tag_attrs=None): + """Returns the form markup for this response. + + @param form_tag_attrs: Dictionary of attributes to be added to + the form tag. 'accept-charset' and 'enctype' have defaults + that can be overridden. If a value is supplied for + 'action' or 'method', it will be replaced. + + @returntype: str + + @since: 2.1.0 + """ + return self.fields.toFormMarkup( + self.request.return_to, form_tag_attrs=form_tag_attrs) + + def toHTML(self, form_tag_attrs=None): + """Returns an HTML document that auto-submits the form markup + for this response. + + @returntype: str + + @see: toFormMarkup + + @since: 2.1.? + """ + return oidutil.autoSubmitHTML(self.toFormMarkup(form_tag_attrs)) + + def renderAsForm(self): + """Returns True if this response's encoding is + ENCODE_HTML_FORM. Convenience method for server authors. + + @returntype: bool + + @since: 2.1.0 + """ + return self.whichEncoding() == ENCODE_HTML_FORM + + def needsSigning(self): + """Does this response require signing? + + @returntype: bool + """ + return self.fields.getArg(OPENID_NS, 'mode') == 'id_res' + + # implements IEncodable + def whichEncoding(self): + """How should I be encoded? + + @returns: one of ENCODE_URL, ENCODE_HTML_FORM, or ENCODE_KVFORM. + + @change: 2.1.0 added the ENCODE_HTML_FORM response. + """ + if self.request.mode in BROWSER_REQUEST_MODES: + if self.fields.getOpenIDNamespace() == OPENID2_NS and \ + len(self.encodeToURL()) > OPENID1_URL_LIMIT: + return ENCODE_HTML_FORM + else: + return ENCODE_URL + else: + return ENCODE_KVFORM + + def encodeToURL(self): + """Encode a response as a URL for the user agent to GET. + + You will generally use this URL with a HTTP redirect. + + @returns: A URL to direct the user agent back to. + @returntype: str + """ + return self.fields.toURL(self.request.return_to) + + def addExtension(self, extension_response): + """ + Add an extension response to this response message. + + @param extension_response: An object that implements the + extension interface for adding arguments to an OpenID + message. + @type extension_response: L{openid.extension} + + @returntype: None + """ + extension_response.toMessage(self.fields) + + def encodeToKVForm(self): + """Encode a response in key-value colon/newline format. + + This is a machine-readable format used to respond to messages which + came directly from the consumer and not through the user agent. + + @see: OpenID Specs, + U{Key-Value Colon/Newline format} + + @returntype: str + """ + return self.fields.toKVForm() + + +class WebResponse(object): + """I am a response to an OpenID request in terms a web server understands. + + I generally come from an L{Encoder}, either directly or from + L{Server.encodeResponse}. + + @ivar code: The HTTP code of this response. + @type code: int + + @ivar headers: Headers to include in this response. + @type headers: dict + + @ivar body: The body of this response. + @type body: str + """ + + def __init__(self, code=HTTP_OK, headers=None, body=b""): + """Construct me. + + These parameters are assigned directly as class attributes, see + my L{class documentation} for their descriptions. + """ + self.code = code + if headers is not None: + self.headers = headers + else: + self.headers = {} + if isinstance(body, bytes): + body = str(body, encoding="utf-8") + self.body = body + + +class Signatory(object): + """I sign things. + + I also check signatures. + + All my state is encapsulated in an + L{OpenIDStore}, which means + I'm not generally pickleable but I am easy to reconstruct. + + @cvar SECRET_LIFETIME: The number of seconds a secret remains valid. + @type SECRET_LIFETIME: int + """ + + SECRET_LIFETIME = 14 * 24 * 60 * 60 # 14 days, in seconds + + # keys have a bogus server URL in them because the filestore + # really does expect that key to be a URL. This seems a little + # silly for the server store, since I expect there to be only one + # server URL. + _normal_key = 'http://localhost/|normal' + _dumb_key = 'http://localhost/|dumb' + + def __init__(self, store): + """Create a new Signatory. + + @param store: The back-end where my associations are stored. + @type store: L{openid.store.interface.OpenIDStore} + """ + assert store is not None + self.store = store + + def verify(self, assoc_handle, message): + """Verify that the signature for some data is valid. + + @param assoc_handle: The handle of the association used to sign the + data. + @type assoc_handle: str + + @param message: The signed message to verify + @type message: openid.message.Message + + @returns: C{True} if the signature is valid, C{False} if not. + @returntype: bool + """ + assoc = self.getAssociation(assoc_handle, dumb=True) + if not assoc: + logger.error("failed to get assoc with handle %r to verify " + "message %r" % (assoc_handle, message)) + return False + + try: + valid = assoc.checkMessageSignature(message) + except ValueError as ex: + logger.exception("Error in verifying %s with %s: %s" % + (message, assoc, ex)) + return False + return valid + + def sign(self, response): + """Sign a response. + + I take a L{OpenIDResponse}, create a signature for everything + in its L{signed} list, and return a new + copy of the response object with that signature included. + + @param response: A response to sign. + @type response: L{OpenIDResponse} + + @returns: A signed copy of the response. + @returntype: L{OpenIDResponse} + """ + signed_response = deepcopy(response) + assoc_handle = response.request.assoc_handle + if assoc_handle: + # normal mode + # disabling expiration check because even if the association + # is expired, we still need to know some properties of the + # association so that we may preserve those properties when + # creating the fallback association. + assoc = self.getAssociation( + assoc_handle, dumb=False, checkExpiration=False) + + if not assoc or assoc.expiresIn <= 0: + # fall back to dumb mode + signed_response.fields.setArg(OPENID_NS, 'invalidate_handle', + assoc_handle) + assoc_type = assoc and assoc.assoc_type or 'HMAC-SHA1' + if assoc and assoc.expiresIn <= 0: + # now do the clean-up that the disabled checkExpiration + # code didn't get to do. + self.invalidate(assoc_handle, dumb=False) + assoc = self.createAssociation( + dumb=True, assoc_type=assoc_type) + else: + # dumb mode. + assoc = self.createAssociation(dumb=True) + + try: + signed_response.fields = assoc.signMessage(signed_response.fields) + except kvform.KVFormError as err: + raise EncodingError(response, explanation=str(err)) + return signed_response + + def createAssociation(self, dumb=True, assoc_type='HMAC-SHA1'): + """Make a new association. + + @param dumb: Is this association for a dumb-mode transaction? + @type dumb: bool + + @param assoc_type: The type of association to create. Currently + there is only one type defined, C{HMAC-SHA1}. + @type assoc_type: str + + @returns: the new association. + @returntype: L{openid.association.Association} + """ + secret = cryptutil.getBytes(getSecretSize(assoc_type)) + uniq = oidutil.toBase64(cryptutil.getBytes(4)) + handle = '{%s}{%x}{%s}' % (assoc_type, int(time.time()), uniq) + + assoc = Association.fromExpiresIn(self.SECRET_LIFETIME, handle, secret, + assoc_type) + + if dumb: + key = self._dumb_key + else: + key = self._normal_key + self.store.storeAssociation(key, assoc) + return assoc + + def getAssociation(self, assoc_handle, dumb, checkExpiration=True): + """Get the association with the specified handle. + + @type assoc_handle: str + + @param dumb: Is this association used with dumb mode? + @type dumb: bool + + @returns: the association, or None if no valid association with that + handle was found. + @returntype: L{openid.association.Association} + """ + # Hmm. We've created an interface that deals almost entirely with + # assoc_handles. The only place outside the Signatory that uses this + # (and thus the only place that ever sees Association objects) is + # when creating a response to an association request, as it must have + # the association's secret. + + if assoc_handle is None: + raise ValueError("assoc_handle must not be None") + + if dumb: + key = self._dumb_key + else: + key = self._normal_key + assoc = self.store.getAssociation(key, assoc_handle) + if assoc is not None and assoc.expiresIn <= 0: + logger.info("requested %sdumb key %r is expired (by %s seconds)" % + ((not dumb) and 'not-' or '', assoc_handle, + assoc.expiresIn)) + if checkExpiration: + self.store.removeAssociation(key, assoc_handle) + assoc = None + return assoc + + def invalidate(self, assoc_handle, dumb): + """Invalidates the association with the given handle. + + @type assoc_handle: str + + @param dumb: Is this association used with dumb mode? + @type dumb: bool + """ + if dumb: + key = self._dumb_key + else: + key = self._normal_key + self.store.removeAssociation(key, assoc_handle) + + +class Encoder(object): + """I encode responses in to L{WebResponses}. + + If you don't like L{WebResponses}, you can do + your own handling of L{OpenIDResponses} with + L{OpenIDResponse.whichEncoding}, L{OpenIDResponse.encodeToURL}, and + L{OpenIDResponse.encodeToKVForm}. + """ + + responseFactory = WebResponse + + def encode(self, response): + """Encode a response to a L{WebResponse}. + + @raises EncodingError: When I can't figure out how to encode this + message. + """ + encode_as = response.whichEncoding() + if encode_as == ENCODE_KVFORM: + wr = self.responseFactory(body=response.encodeToKVForm()) + if isinstance(response, Exception): + wr.code = HTTP_ERROR + elif encode_as == ENCODE_URL: + location = response.encodeToURL() + wr = self.responseFactory( + code=HTTP_REDIRECT, headers={'location': location}) + elif encode_as == ENCODE_HTML_FORM: + wr = self.responseFactory(code=HTTP_OK, body=response.toHTML()) + else: + # Can't encode this to a protocol message. You should probably + # render it to HTML and show it to the user. + raise EncodingError(response) + return wr + + +class SigningEncoder(Encoder): + """I encode responses in to L{WebResponses}, signing them when required. + """ + + def __init__(self, signatory): + """Create a L{SigningEncoder}. + + @param signatory: The L{Signatory} I will make signatures with. + @type signatory: L{Signatory} + """ + self.signatory = signatory + + def encode(self, response): + """Encode a response to a L{WebResponse}, signing it first if appropriate. + + @raises EncodingError: When I can't figure out how to encode this + message. + + @raises AlreadySigned: When this response is already signed. + + @returntype: L{WebResponse} + """ + # the isinstance is a bit of a kludge... it means there isn't really + # an adapter to make the interfaces quite match. + if (not isinstance(response, Exception)) and response.needsSigning(): + if not self.signatory: + raise ValueError("Must have a store to sign this request: %s" % + (response, ), response) + if response.fields.hasKey(OPENID_NS, 'sig'): + raise AlreadySigned(response) + response = self.signatory.sign(response) + return super(SigningEncoder, self).encode(response) + + +class Decoder(object): + """I decode an incoming web request in to a L{OpenIDRequest}. + """ + + _handlers = { + 'checkid_setup': CheckIDRequest.fromMessage, + 'checkid_immediate': CheckIDRequest.fromMessage, + 'check_authentication': CheckAuthRequest.fromMessage, + 'associate': AssociateRequest.fromMessage, + } + + def __init__(self, server): + """Construct a Decoder. + + @param server: The server which I am decoding requests for. + (Necessary because some replies reference their server.) + @type server: L{Server} + """ + self.server = server + + def decode(self, query): + """I transform query parameters into an L{OpenIDRequest}. + + If the query does not seem to be an OpenID request at all, I return + C{None}. + + @param query: The query parameters as a dictionary with each + key mapping to one value. + @type query: dict + + @raises ProtocolError: When the query does not seem to be a valid + OpenID request. + + @returntype: L{OpenIDRequest} + """ + if not query: + return None + + try: + message = Message.fromPostArgs(query) + except InvalidOpenIDNamespace as err: + # It's useful to have a Message attached to a ProtocolError, so we + # override the bad ns value to build a Message out of it. Kinda + # kludgy, since it's made of lies, but the parts that aren't lies + # are more useful than a 'None'. + query = query.copy() + query['openid.ns'] = OPENID2_NS + message = Message.fromPostArgs(query) + raise ProtocolError(message, str(err)) + + mode = message.getArg(OPENID_NS, 'mode') + if not mode: + fmt = "No mode value in message %s" + raise ProtocolError(message, text=fmt % (message, )) + + handler = self._handlers.get(mode, self.defaultDecoder) + return handler(message, self.server.op_endpoint) + + def defaultDecoder(self, message, server): + """Called to decode queries when no handler for that mode is found. + + @raises ProtocolError: This implementation always raises + L{ProtocolError}. + """ + mode = message.getArg(OPENID_NS, 'mode') + fmt = "Unrecognized OpenID mode %r" + raise ProtocolError(message, text=fmt % (mode, )) + + +class Server(object): + """I handle requests for an OpenID server. + + Some types of requests (those which are not C{checkid} requests) may be + handed to my L{handleRequest} method, and I will take care of it and + return a response. + + For your convenience, I also provide an interface to L{Decoder.decode} + and L{SigningEncoder.encode} through my methods L{decodeRequest} and + L{encodeResponse}. + + All my state is encapsulated in an + L{OpenIDStore}, which means + I'm not generally pickleable but I am easy to reconstruct. + + Example:: + + oserver = Server(FileOpenIDStore(data_path), "http://example.com/op") + request = oserver.decodeRequest(query) + if request.mode in ['checkid_immediate', 'checkid_setup']: + if self.isAuthorized(request.identity, request.trust_root): + response = request.answer(True) + elif request.immediate: + response = request.answer(False) + else: + self.showDecidePage(request) + return + else: + response = oserver.handleRequest(request) + + webresponse = oserver.encode(response) + + @ivar signatory: I'm using this for associate requests and to sign things. + @type signatory: L{Signatory} + + @ivar decoder: I'm using this to decode things. + @type decoder: L{Decoder} + + @ivar encoder: I'm using this to encode things. + @type encoder: L{Encoder} + + @ivar op_endpoint: My URL. + @type op_endpoint: str + + @ivar negotiator: I use this to determine which kinds of + associations I can make and how. + @type negotiator: L{openid.association.SessionNegotiator} + """ + + def __init__(self, + store, + op_endpoint=None, + signatoryClass=Signatory, + encoderClass=SigningEncoder, + decoderClass=Decoder): + """A new L{Server}. + + @param store: The back-end where my associations are stored. + @type store: L{openid.store.interface.OpenIDStore} + + @param op_endpoint: My URL, the fully qualified address of this + server's endpoint, i.e. C{http://example.com/server} + @type op_endpoint: str + + @change: C{op_endpoint} is new in library version 2.0. It + currently defaults to C{None} for compatibility with + earlier versions of the library, but you must provide it + if you want to respond to any version 2 OpenID requests. + """ + self.store = store + self.signatory = signatoryClass(self.store) + self.encoder = encoderClass(self.signatory) + self.decoder = decoderClass(self) + self.negotiator = default_negotiator.copy() + + if not op_endpoint: + warnings.warn( + "%s.%s constructor requires op_endpoint parameter " + "for OpenID 2.0 servers" % + (self.__class__.__module__, self.__class__.__name__), + stacklevel=2) + self.op_endpoint = op_endpoint + + def handleRequest(self, request): + """Handle a request. + + Give me a request, I will give you a response. Unless it's a type + of request I cannot handle myself, in which case I will raise + C{NotImplementedError}. In that case, you can handle it yourself, + or add a method to me for handling that request type. + + @raises NotImplementedError: When I do not have a handler defined + for that type of request. + + @returntype: L{OpenIDResponse} + """ + handler = getattr(self, 'openid_' + request.mode, None) + if handler is not None: + return handler(request) + else: + raise NotImplementedError( + "%s has no handler for a request of mode %r." % + (self, request.mode)) + + def openid_check_authentication(self, request): + """Handle and respond to C{check_authentication} requests. + + @returntype: L{OpenIDResponse} + """ + return request.answer(self.signatory) + + def openid_associate(self, request): + """Handle and respond to C{associate} requests. + + @returntype: L{OpenIDResponse} + """ + # XXX: TESTME + assoc_type = request.assoc_type + session_type = request.session.session_type + if self.negotiator.isAllowed(assoc_type, session_type): + assoc = self.signatory.createAssociation( + dumb=False, assoc_type=assoc_type) + return request.answer(assoc) + else: + message = ('Association type %r is not supported with ' + 'session type %r' % (assoc_type, session_type)) + (preferred_assoc_type, preferred_session_type) = \ + self.negotiator.getAllowedType() + return request.answerUnsupported(message, preferred_assoc_type, + preferred_session_type) + + def decodeRequest(self, query): + """Transform query parameters into an L{OpenIDRequest}. + + If the query does not seem to be an OpenID request at all, I return + C{None}. + + @param query: The query parameters as a dictionary with each + key mapping to one value. + @type query: dict + + @raises ProtocolError: When the query does not seem to be a valid + OpenID request. + + @returntype: L{OpenIDRequest} + + @see: L{Decoder.decode} + """ + return self.decoder.decode(query) + + def encodeResponse(self, response): + """Encode a response to a L{WebResponse}, signing it first if appropriate. + + @raises EncodingError: When I can't figure out how to encode this + message. + + @raises AlreadySigned: When this response is already signed. + + @returntype: L{WebResponse} + + @see: L{SigningEncoder.encode} + """ + return self.encoder.encode(response) + + +class ProtocolError(Exception): + """A message did not conform to the OpenID protocol. + + @ivar message: The query that is failing to be a valid OpenID request. + @type message: openid.message.Message + """ + + def __init__(self, message, text=None, reference=None, contact=None): + """When an error occurs. + + @param message: The message that is failing to be a valid + OpenID request. + @type message: openid.message.Message + + @param text: A message about the encountered error. Set as C{args[0]}. + @type text: str + """ + self.openid_message = message + self.reference = reference + self.contact = contact + assert type(message) not in [str, str] + Exception.__init__(self, text) + + def getReturnTo(self): + """Get the return_to argument from the request, if any. + + @returntype: str + """ + if self.openid_message is None: + return None + else: + return self.openid_message.getArg(OPENID_NS, 'return_to') + + def hasReturnTo(self): + """Did this request have a return_to parameter? + + @returntype: bool + """ + return self.getReturnTo() is not None + + def toMessage(self): + """Generate a Message object for sending to the relying party, + after encoding. + """ + namespace = self.openid_message.getOpenIDNamespace() + reply = Message(namespace) + reply.setArg(OPENID_NS, 'mode', 'error') + reply.setArg(OPENID_NS, 'error', str(self)) + + if self.contact is not None: + reply.setArg(OPENID_NS, 'contact', str(self.contact)) + + if self.reference is not None: + reply.setArg(OPENID_NS, 'reference', str(self.reference)) + + return reply + + # implements IEncodable + + def encodeToURL(self): + return self.toMessage().toURL(self.getReturnTo()) + + def encodeToKVForm(self): + return self.toMessage().toKVForm() + + def toFormMarkup(self): + """Encode to HTML form markup for POST. + + @since: 2.1.0 + """ + return self.toMessage().toFormMarkup(self.getReturnTo()) + + def toHTML(self): + """Encode to a full HTML page, wrapping the form markup in a page + that will autosubmit the form. + + @since: 2.1.? + """ + return oidutil.autoSubmitHTML(self.toFormMarkup()) + + def whichEncoding(self): + """How should I be encoded? + + @returns: one of ENCODE_URL, ENCODE_KVFORM, or None. If None, + I cannot be encoded as a protocol message and should be + displayed to the user. + """ + if self.hasReturnTo(): + if self.openid_message.getOpenIDNamespace() == OPENID2_NS and \ + len(self.encodeToURL()) > OPENID1_URL_LIMIT: + return ENCODE_HTML_FORM + else: + return ENCODE_URL + + if self.openid_message is None: + return None + + mode = self.openid_message.getArg(OPENID_NS, 'mode') + if mode: + if mode not in BROWSER_REQUEST_MODES: + return ENCODE_KVFORM + + # According to the OpenID spec as of this writing, we are probably + # supposed to switch on request type here (GET versus POST) to figure + # out if we're supposed to print machine-readable or human-readable + # content at this point. GET/POST seems like a pretty lousy way of + # making the distinction though, as it's just as possible that the + # user agent could have mistakenly been directed to post to the + # server URL. + + # Basically, if your request was so broken that you didn't manage to + # include an openid.mode, I'm not going to worry too much about + # returning you something you can't parse. + return None + + +class VersionError(Exception): + """Raised when an operation was attempted that is not compatible with + the protocol version being used.""" + + +class NoReturnToError(Exception): + """Raised when a response to a request cannot be generated because + the request contains no return_to URL. + """ + pass + + +class EncodingError(Exception): + """Could not encode this as a protocol message. + + You should probably render it and show it to the user. + + @ivar response: The response that failed to encode. + @type response: L{OpenIDResponse} + """ + + def __init__(self, response, explanation=None): + Exception.__init__(self, response) + self.response = response + self.explanation = explanation + + def __str__(self): + if self.explanation: + s = '%s: %s' % (self.__class__.__name__, self.explanation) + else: + s = '%s for Response %s' % (self.__class__.__name__, self.response) + return s + + +class AlreadySigned(EncodingError): + """This response is already signed.""" + + +class UntrustedReturnURL(ProtocolError): + """A return_to is outside the trust_root.""" + + def __init__(self, message, return_to, trust_root): + ProtocolError.__init__(self, message) + self.return_to = return_to + self.trust_root = trust_root + + def __str__(self): + return "return_to %r not under trust_root %r" % (self.return_to, + self.trust_root) + + +class MalformedReturnURL(ProtocolError): + """The return_to URL doesn't look like a valid URL.""" + + def __init__(self, openid_message, return_to): + self.return_to = return_to + ProtocolError.__init__(self, openid_message) + + +class MalformedTrustRoot(ProtocolError): + """The trust root is not well-formed. + + @see: OpenID Specs, U{openid.trust_root} + """ + pass + + +#class IEncodable: # Interface +# def encodeToURL(return_to): +# """Encode a response as a URL for redirection. +# +# @returns: A URL to direct the user agent back to. +# @returntype: str +# """ +# pass +# +# def encodeToKvform(): +# """Encode a response in key-value colon/newline format. +# +# This is a machine-readable format used to respond to messages which +# came directly from the consumer and not through the user agent. +# +# @see: OpenID Specs, +# U{Key-Value Colon/Newline format} +# +# @returntype: str +# """ +# pass +# +# def whichEncoding(): +# """How should I be encoded? +# +# @returns: one of ENCODE_URL, ENCODE_KVFORM, or None. If None, +# I cannot be encoded as a protocol message and should be +# displayed to the user. +# """ +# pass diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/server/trustroot.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/server/trustroot.py new file mode 100644 index 0000000000000000000000000000000000000000..9d0cb032f4eb8017b3d8f7096e486078e540f491 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/server/trustroot.py @@ -0,0 +1,456 @@ +# -*- test-case-name: openid.test.test_rpverify -*- +""" +This module contains the C{L{TrustRoot}} class, which helps handle +trust root checking. This module is used by the +C{L{openid.server.server}} module, but it is also available to server +implementers who wish to use it for additional trust root checking. + +It also implements relying party return_to URL verification, based on +the realm. +""" + +__all__ = [ + 'TrustRoot', + 'RP_RETURN_TO_URL_TYPE', + 'extractReturnToURLs', + 'returnToMatches', + 'verifyReturnTo', +] + +from openid import urinorm +from openid.yadis import services + +from urllib.parse import urlparse, urlunparse +import re +import logging + +logger = logging.getLogger(__name__) + +############################################ +_protocols = ['http', 'https'] +_top_level_domains = [ + 'ac', 'ad', 'ae', 'aero', 'af', 'ag', 'ai', 'al', 'am', 'an', 'ao', 'aq', + 'ar', 'arpa', 'as', 'asia', 'at', 'au', 'aw', 'ax', 'az', 'ba', 'bb', 'bd', + 'be', 'bf', 'bg', 'bh', 'bi', 'biz', 'bj', 'bm', 'bn', 'bo', 'br', 'bs', + 'bt', 'bv', 'bw', 'by', 'bz', 'ca', 'cat', 'cc', 'cd', 'cf', 'cg', 'ch', + 'ci', 'ck', 'cl', 'cm', 'cn', 'co', 'com', 'coop', 'cr', 'cu', 'cv', 'cx', + 'cy', 'cz', 'de', 'dj', 'dk', 'dm', 'do', 'dz', 'ec', 'edu', 'ee', 'eg', + 'er', 'es', 'et', 'eu', 'fi', 'fj', 'fk', 'fm', 'fo', 'fr', 'ga', 'gb', + 'gd', 'ge', 'gf', 'gg', 'gh', 'gi', 'gl', 'gm', 'gn', 'gov', 'gp', 'gq', + 'gr', 'gs', 'gt', 'gu', 'gw', 'gy', 'hk', 'hm', 'hn', 'hr', 'ht', 'hu', + 'id', 'ie', 'il', 'im', 'in', 'info', 'int', 'io', 'iq', 'ir', 'is', 'it', + 'je', 'jm', 'jo', 'jobs', 'jp', 'ke', 'kg', 'kh', 'ki', 'km', 'kn', 'kp', + 'kr', 'kw', 'ky', 'kz', 'la', 'lb', 'lc', 'li', 'lk', 'lr', 'ls', 'lt', + 'lu', 'lv', 'ly', 'ma', 'mc', 'md', 'me', 'mg', 'mh', 'mil', 'mk', 'ml', + 'mm', 'mn', 'mo', 'mobi', 'mp', 'mq', 'mr', 'ms', 'mt', 'mu', 'museum', + 'mv', 'mw', 'mx', 'my', 'mz', 'na', 'name', 'nc', 'ne', 'net', 'nf', 'ng', + 'ni', 'nl', 'no', 'np', 'nr', 'nu', 'nz', 'om', 'org', 'pa', 'pe', 'pf', + 'pg', 'ph', 'pk', 'pl', 'pm', 'pn', 'pr', 'pro', 'ps', 'pt', 'pw', 'py', + 'qa', 're', 'ro', 'rs', 'ru', 'rw', 'sa', 'sb', 'sc', 'sd', 'se', 'sg', + 'sh', 'si', 'sj', 'sk', 'sl', 'sm', 'sn', 'so', 'sr', 'st', 'su', 'sv', + 'sy', 'sz', 'tc', 'td', 'tel', 'tf', 'tg', 'th', 'tj', 'tk', 'tl', 'tm', + 'tn', 'to', 'tp', 'tr', 'travel', 'tt', 'tv', 'tw', 'tz', 'ua', 'ug', 'uk', + 'us', 'uy', 'uz', 'va', 'vc', 've', 'vg', 'vi', 'vn', 'vu', 'wf', 'ws', + 'xn--0zwm56d', 'xn--11b5bs3a9aj6g', 'xn--80akhbyknj4f', 'xn--9t4b11yi5a', + 'xn--deba0ad', 'xn--g6w251d', 'xn--hgbk6aj7f53bba', 'xn--hlcj6aya9esc7a', + 'xn--jxalpdlp', 'xn--kgbechtv', 'xn--zckzah', 'ye', 'yt', 'yu', 'za', 'zm', + 'zw' +] + +# Build from RFC3986, section 3.2.2. Used to reject hosts with invalid +# characters. +host_segment_re = re.compile( + r"(?:[-a-zA-Z0-9!$&'\(\)\*+,;=._~]|%[a-zA-Z0-9]{2})+$") + + +class RealmVerificationRedirected(Exception): + """Attempting to verify this realm resulted in a redirect. + + @since: 2.1.0 + """ + + def __init__(self, relying_party_url, rp_url_after_redirects): + self.relying_party_url = relying_party_url + self.rp_url_after_redirects = rp_url_after_redirects + + def __str__(self): + return ("Attempting to verify %r resulted in " + "redirect to %r" % (self.relying_party_url, + self.rp_url_after_redirects)) + + +def _parseURL(url): + try: + url = urinorm.urinorm(url) + except ValueError: + return None + proto, netloc, path, params, query, frag = urlparse(url) + if not path: + # Python <2.4 does not parse URLs with no path properly + if not query and '?' in netloc: + netloc, query = netloc.split('?', 1) + + path = '/' + + path = urlunparse(('', '', path, params, query, frag)) + + if ':' in netloc: + try: + host, port = netloc.split(':') + except ValueError: + return None + + if not re.match(r'\d+$', port): + return None + else: + host = netloc + port = '' + + host = host.lower() + if not host_segment_re.match(host): + return None + + return proto, host, port, path + + +class TrustRoot(object): + """ + This class represents an OpenID trust root. The C{L{parse}} + classmethod accepts a trust root string, producing a + C{L{TrustRoot}} object. The method OpenID server implementers + would be most likely to use is the C{L{isSane}} method, which + checks the trust root for given patterns that indicate that the + trust root is too broad or points to a local network resource. + + @sort: parse, isSane + """ + + def __init__(self, unparsed, proto, wildcard, host, port, path): + self.unparsed = unparsed + self.proto = proto + self.wildcard = wildcard + self.host = host + self.port = port + self.path = path + + def isSane(self): + """ + This method checks the to see if a trust root represents a + reasonable (sane) set of URLs. 'http://*.com/', for example + is not a reasonable pattern, as it cannot meaningfully specify + the site claiming it. This function attempts to find many + related examples, but it can only work via heuristics. + Negative responses from this method should be treated as + advisory, used only to alert the user to examine the trust + root carefully. + + + @return: Whether the trust root is sane + + @rtype: C{bool} + """ + + if self.host == 'localhost': + return True + + host_parts = self.host.split('.') + if self.wildcard: + assert host_parts[0] == '', host_parts + del host_parts[0] + + # If it's an absolute domain name, remove the empty string + # from the end. + if host_parts and not host_parts[-1]: + del host_parts[-1] + + if not host_parts: + return False + + # Do not allow adjacent dots + if '' in host_parts: + return False + + tld = host_parts[-1] + if tld not in _top_level_domains: + return False + + if len(host_parts) == 1: + return False + + if self.wildcard: + if len(tld) == 2 and len(host_parts[-2]) <= 3: + # It's a 2-letter tld with a short second to last segment + # so there needs to be more than two segments specified + # (e.g. *.co.uk is insane) + return len(host_parts) > 2 + + # Passed all tests for insanity. + return True + + def validateURL(self, url): + """ + Validates a URL against this trust root. + + + @param url: The URL to check + + @type url: C{str} + + + @return: Whether the given URL is within this trust root. + + @rtype: C{bool} + """ + + url_parts = _parseURL(url) + if url_parts is None: + return False + + proto, host, port, path = url_parts + + if proto != self.proto: + return False + + if port != self.port: + return False + + if '*' in host: + return False + + if not self.wildcard: + if host != self.host: + return False + elif ((not host.endswith(self.host)) and ('.' + host) != self.host): + return False + + if path != self.path: + path_len = len(self.path) + trust_prefix = self.path[:path_len] + url_prefix = path[:path_len] + + # must be equal up to the length of the path, at least + if trust_prefix != url_prefix: + return False + + # These characters must be on the boundary between the end + # of the trust root's path and the start of the URL's + # path. + if '?' in self.path: + allowed = '&' + else: + allowed = '?/' + + return (self.path[-1] in allowed or path[path_len] in allowed) + + return True + + def parse(cls, trust_root): + """ + This method creates a C{L{TrustRoot}} instance from the given + input, if possible. + + + @param trust_root: This is the trust root to parse into a + C{L{TrustRoot}} object. + + @type trust_root: C{str} + + + @return: A C{L{TrustRoot}} instance if trust_root parses as a + trust root, C{None} otherwise. + + @rtype: C{NoneType} or C{L{TrustRoot}} + """ + url_parts = _parseURL(trust_root) + if url_parts is None: + return None + + proto, host, port, path = url_parts + + # check for valid prototype + if proto not in _protocols: + return None + + # check for URI fragment + if path.find('#') != -1: + return None + + # extract wildcard if it is there + if host.find('*', 1) != -1: + # wildcard must be at start of domain: *.foo.com, not foo.*.com + return None + + if host.startswith('*'): + # Starts with star, so must have a dot after it (if a + # domain is specified) + if len(host) > 1 and host[1] != '.': + return None + + host = host[1:] + wilcard = True + else: + wilcard = False + + # we have a valid trust root + tr = cls(trust_root, proto, wilcard, host, port, path) + + return tr + + parse = classmethod(parse) + + def checkSanity(cls, trust_root_string): + """str -> bool + + is this a sane trust root? + """ + trust_root = cls.parse(trust_root_string) + if trust_root is None: + return False + else: + return trust_root.isSane() + + checkSanity = classmethod(checkSanity) + + def checkURL(cls, trust_root, url): + """quick func for validating a url against a trust root. See the + TrustRoot class if you need more control.""" + tr = cls.parse(trust_root) + return tr is not None and tr.validateURL(url) + + checkURL = classmethod(checkURL) + + def buildDiscoveryURL(self): + """Return a discovery URL for this realm. + + This function does not check to make sure that the realm is + valid. Its behaviour on invalid inputs is undefined. + + @rtype: str + + @returns: The URL upon which relying party discovery should be run + in order to verify the return_to URL + + @since: 2.1.0 + """ + if self.wildcard: + # Use "www." in place of the star + assert self.host.startswith('.'), self.host + www_domain = 'www' + self.host + return '%s://%s%s' % (self.proto, www_domain, self.path) + else: + return self.unparsed + + def __repr__(self): + return "TrustRoot(%r, %r, %r, %r, %r, %r)" % ( + self.unparsed, self.proto, self.wildcard, self.host, self.port, + self.path) + + def __str__(self): + return repr(self) + + +# The URI for relying party discovery, used in realm verification. +# +# XXX: This should probably live somewhere else (like in +# openid.consumer or openid.yadis somewhere) +RP_RETURN_TO_URL_TYPE = 'http://specs.openid.net/auth/2.0/return_to' + + +def _extractReturnURL(endpoint): + """If the endpoint is a relying party OpenID return_to endpoint, + return the endpoint URL. Otherwise, return None. + + This function is intended to be used as a filter for the Yadis + filtering interface. + + @see: C{L{openid.yadis.services}} + @see: C{L{openid.yadis.filters}} + + @param endpoint: An XRDS BasicServiceEndpoint, as returned by + performing Yadis dicovery. + + @returns: The endpoint URL or None if the endpoint is not a + relying party endpoint. + @rtype: str or NoneType + """ + if endpoint.matchTypes([RP_RETURN_TO_URL_TYPE]): + return endpoint.uri + else: + return None + + +def returnToMatches(allowed_return_to_urls, return_to): + """Is the return_to URL under one of the supplied allowed + return_to URLs? + + @since: 2.1.0 + """ + + for allowed_return_to in allowed_return_to_urls: + # A return_to pattern works the same as a realm, except that + # it's not allowed to use a wildcard. We'll model this by + # parsing it as a realm, and not trying to match it if it has + # a wildcard. + + return_realm = TrustRoot.parse(allowed_return_to) + if ( # Parses as a trust root + return_realm is not None and + + # Does not have a wildcard + not return_realm.wildcard and + + # Matches the return_to that we passed in with it + return_realm.validateURL(return_to)): + return True + + # No URL in the list matched + return False + + +def getAllowedReturnURLs(relying_party_url): + """Given a relying party discovery URL return a list of return_to URLs. + + @since: 2.1.0 + """ + (rp_url_after_redirects, return_to_urls) = services.getServiceEndpoints( + relying_party_url, _extractReturnURL) + + if rp_url_after_redirects != relying_party_url: + # Verification caused a redirect + raise RealmVerificationRedirected(relying_party_url, + rp_url_after_redirects) + + return return_to_urls + + +# _vrfy parameter is there to make testing easier +def verifyReturnTo(realm_str, return_to, _vrfy=getAllowedReturnURLs): + """Verify that a return_to URL is valid for the given realm. + + This function builds a discovery URL, performs Yadis discovery on + it, makes sure that the URL does not redirect, parses out the + return_to URLs, and finally checks to see if the current return_to + URL matches the return_to. + + @raises DiscoveryFailure: When Yadis discovery fails + @returns: True if the return_to URL is valid for the realm + + @since: 2.1.0 + """ + realm = TrustRoot.parse(realm_str) + if realm is None: + # The realm does not parse as a URL pattern + return False + + try: + allowable_urls = _vrfy(realm.buildDiscoveryURL()) + except RealmVerificationRedirected as err: + logger.exception(str(err)) + return False + + if returnToMatches(allowable_urls, return_to): + return True + else: + logger.error("Failed to validate return_to %r for realm %r, was not " + "in %s" % (return_to, realm_str, allowable_urls)) + return False diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/sreg.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/sreg.py new file mode 100644 index 0000000000000000000000000000000000000000..d665a5d041c811fe35952161ff3282e27c32af88 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/sreg.py @@ -0,0 +1,7 @@ +"""moved to L{openid.extensions.sreg}""" + +import warnings +warnings.warn("openid.sreg has moved to openid.extensions.sreg", + DeprecationWarning) + +from openid.extensions.sreg import * diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/store/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/store/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..76509b51988bca323b0ee107ea5779a90d4a8b21 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/store/__init__.py @@ -0,0 +1,8 @@ +""" +This package contains the modules related to this library's use of +persistent storage. + +@sort: interface, filestore, sqlstore, memstore +""" + +__all__ = ['interface', 'filestore', 'sqlstore', 'memstore', 'nonce'] diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/store/filestore.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/store/filestore.py new file mode 100644 index 0000000000000000000000000000000000000000..f5f2954035d5efdab769b22edfb54187d81e8f3a --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/store/filestore.py @@ -0,0 +1,399 @@ +""" +This module contains an C{L{OpenIDStore}} implementation backed by +flat files. +""" + +import string +import os +import os.path +import time +import logging + +from errno import EEXIST, ENOENT + +from tempfile import mkstemp + +from openid.association import Association +from openid.store.interface import OpenIDStore +from openid.store import nonce +from openid import cryptutil, oidutil + +logger = logging.getLogger(__name__) + +_filename_allowed = string.ascii_letters + string.digits + '.' +_isFilenameSafe = set(_filename_allowed).__contains__ + + +def _safe64(s): + h64 = oidutil.toBase64(cryptutil.sha1(s)) + # to be able to manipulate it, make it a bytearray + h64 = bytearray(h64) + h64 = h64.replace(b'+', b'_') + h64 = h64.replace(b'/', b'.') + h64 = h64.replace(b'=', b'') + return bytes(h64) + + +def _filenameEscape(s): + filename_chunks = [] + for c in s: + if _isFilenameSafe(c): + filename_chunks.append(c) + else: + filename_chunks.append('_%02X' % ord(c)) + return ''.join(filename_chunks) + + +def _removeIfPresent(filename): + """Attempt to remove a file, returning whether the file existed at + the time of the call. + + str -> bool + """ + try: + os.unlink(filename) + except OSError as why: + if why.errno == ENOENT: + # Someone beat us to it, but it's gone, so that's OK + return 0 + else: + raise + else: + # File was present + return 1 + + +def _ensureDir(dir_name): + """Create dir_name as a directory if it does not exist. If it + exists, make sure that it is, in fact, a directory. + + Can raise OSError + + str -> NoneType + """ + try: + os.makedirs(dir_name) + except OSError as why: + if why.errno != EEXIST or not os.path.isdir(dir_name): + raise + + +class FileOpenIDStore(OpenIDStore): + """ + This is a filesystem-based store for OpenID associations and + nonces. This store should be safe for use in concurrent systems + on both windows and unix (excluding NFS filesystems). There are a + couple race conditions in the system, but those failure cases have + been set up in such a way that the worst-case behavior is someone + having to try to log in a second time. + + Most of the methods of this class are implementation details. + People wishing to just use this store need only pay attention to + the C{L{__init__}} method. + + Methods of this object can raise OSError if unexpected filesystem + conditions, such as bad permissions or missing directories, occur. + """ + + def __init__(self, directory): + """ + Initializes a new FileOpenIDStore. This initializes the + nonce and association directories, which are subdirectories of + the directory passed in. + + @param directory: This is the directory to put the store + directories in. + + @type directory: C{str} + """ + # Make absolute + directory = os.path.normpath(os.path.abspath(directory)) + + self.nonce_dir = os.path.join(directory, 'nonces') + + self.association_dir = os.path.join(directory, 'associations') + + # Temp dir must be on the same filesystem as the assciations + # directory + self.temp_dir = os.path.join(directory, 'temp') + + self.max_nonce_age = 6 * 60 * 60 # Six hours, in seconds + + self._setup() + + def _setup(self): + """Make sure that the directories in which we store our data + exist. + + () -> NoneType + """ + _ensureDir(self.nonce_dir) + _ensureDir(self.association_dir) + _ensureDir(self.temp_dir) + + def _mktemp(self): + """Create a temporary file on the same filesystem as + self.association_dir. + + The temporary directory should not be cleaned if there are any + processes using the store. If there is no active process using + the store, it is safe to remove all of the files in the + temporary directory. + + () -> (file, str) + """ + fd, name = mkstemp(dir=self.temp_dir) + try: + file_obj = os.fdopen(fd, 'wb') + return file_obj, name + except: + _removeIfPresent(name) + raise + + def getAssociationFilename(self, server_url, handle): + """Create a unique filename for a given server url and + handle. This implementation does not assume anything about the + format of the handle. The filename that is returned will + contain the domain name from the server URL for ease of human + inspection of the data directory. + + (str, str) -> str + """ + if server_url.find('://') == -1: + raise ValueError('Bad server URL: %r' % server_url) + + proto, rest = server_url.split('://', 1) + domain = _filenameEscape(rest.split('/', 1)[0]) + url_hash = _safe64(server_url) + if handle: + handle_hash = _safe64(handle) + else: + handle_hash = '' + + filename = '%s-%s-%s-%s' % (proto, domain, url_hash, handle_hash) + + return os.path.join(self.association_dir, filename) + + def storeAssociation(self, server_url, association): + """Store an association in the association directory. + + (str, Association) -> NoneType + """ + association_s = association.serialize() # NOTE: UTF-8 encoded bytes + filename = self.getAssociationFilename(server_url, association.handle) + tmp_file, tmp = self._mktemp() + + try: + try: + tmp_file.write(association_s) + os.fsync(tmp_file.fileno()) + finally: + tmp_file.close() + + try: + os.rename(tmp, filename) + except OSError as why: + if why.errno != EEXIST: + raise + + # We only expect EEXIST to happen only on Windows. It's + # possible that we will succeed in unlinking the existing + # file, but not in putting the temporary file in place. + try: + os.unlink(filename) + except OSError as why: + if why.errno == ENOENT: + pass + else: + raise + + # Now the target should not exist. Try renaming again, + # giving up if it fails. + os.rename(tmp, filename) + except: + # If there was an error, don't leave the temporary file + # around. + _removeIfPresent(tmp) + raise + + def getAssociation(self, server_url, handle=None): + """Retrieve an association. If no handle is specified, return + the association with the latest expiration. + + (str, str or NoneType) -> Association or NoneType + """ + if handle is None: + handle = '' + + # The filename with the empty handle is a prefix of all other + # associations for the given server URL. + filename = self.getAssociationFilename(server_url, handle) + + if handle: + return self._getAssociation(filename) + else: + association_files = os.listdir(self.association_dir) + matching_files = [] + # strip off the path to do the comparison + name = os.path.basename(filename) + for association_file in association_files: + if association_file.startswith(name): + matching_files.append(association_file) + + matching_associations = [] + # read the matching files and sort by time issued + for name in matching_files: + full_name = os.path.join(self.association_dir, name) + association = self._getAssociation(full_name) + if association is not None: + matching_associations.append( + (association.issued, association)) + + matching_associations.sort() + + # return the most recently issued one. + if matching_associations: + (_, assoc) = matching_associations[-1] + return assoc + else: + return None + + def _getAssociation(self, filename): + try: + assoc_file = open(filename, 'rb') + except IOError as why: + if why.errno == ENOENT: + # No association exists for that URL and handle + return None + else: + raise + + try: + assoc_s = assoc_file.read() + finally: + assoc_file.close() + + try: + association = Association.deserialize(assoc_s) + except ValueError: + _removeIfPresent(filename) + return None + + # Clean up expired associations + if association.expiresIn == 0: + _removeIfPresent(filename) + return None + else: + return association + + def removeAssociation(self, server_url, handle): + """Remove an association if it exists. Do nothing if it does not. + + (str, str) -> bool + """ + assoc = self.getAssociation(server_url, handle) + if assoc is None: + return 0 + else: + filename = self.getAssociationFilename(server_url, handle) + return _removeIfPresent(filename) + + def useNonce(self, server_url, timestamp, salt): + """Return whether this nonce is valid. + + str -> bool + """ + if abs(timestamp - time.time()) > nonce.SKEW: + return False + + if server_url: + proto, rest = server_url.split('://', 1) + else: + # Create empty proto / rest values for empty server_url, + # which is part of a consumer-generated nonce. + proto, rest = '', '' + + domain = _filenameEscape(rest.split('/', 1)[0]) + url_hash = _safe64(server_url) + salt_hash = _safe64(salt) + + filename = '%08x-%s-%s-%s-%s' % (timestamp, proto, domain, url_hash, + salt_hash) + + filename = os.path.join(self.nonce_dir, filename) + try: + fd = os.open(filename, os.O_CREAT | os.O_EXCL | os.O_WRONLY, 0o200) + except OSError as why: + if why.errno == EEXIST: + return False + else: + raise + else: + os.close(fd) + return True + + def _allAssocs(self): + all_associations = [] + + association_filenames = [ + os.path.join(self.association_dir, filename) + for filename in os.listdir(self.association_dir) + ] + for association_filename in association_filenames: + try: + association_file = open(association_filename, 'rb') + except IOError as why: + if why.errno == ENOENT: + logger.exception("%s disappeared during %s._allAssocs" % ( + association_filename, self.__class__.__name__)) + else: + raise + else: + try: + assoc_s = association_file.read() + finally: + association_file.close() + + # Remove expired or corrupted associations + try: + association = Association.deserialize(assoc_s) + except ValueError: + _removeIfPresent(association_filename) + else: + all_associations.append( + (association_filename, association)) + + return all_associations + + def cleanup(self): + """Remove expired entries from the database. This is + potentially expensive, so only run when it is acceptable to + take time. + + () -> NoneType + """ + self.cleanupAssociations() + self.cleanupNonces() + + def cleanupAssociations(self): + removed = 0 + for assoc_filename, assoc in self._allAssocs(): + if assoc.expiresIn == 0: + _removeIfPresent(assoc_filename) + removed += 1 + return removed + + def cleanupNonces(self): + nonces = os.listdir(self.nonce_dir) + now = time.time() + + removed = 0 + # Check all nonces for expiry + for nonce_fname in nonces: + timestamp = nonce_fname.split('-', 1)[0] + timestamp = int(timestamp, 16) + if abs(timestamp - now) > nonce.SKEW: + filename = os.path.join(self.nonce_dir, nonce_fname) + _removeIfPresent(filename) + removed += 1 + return removed diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/store/interface.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/store/interface.py new file mode 100644 index 0000000000000000000000000000000000000000..63776572d00834f96150b83c28d99c51347f0035 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/store/interface.py @@ -0,0 +1,198 @@ +""" +This module contains the definition of the C{L{OpenIDStore}} +interface. +""" + + +class OpenIDStore(object): + """ + This is the interface for the store objects the OpenID library + uses. It is a single class that provides all of the persistence + mechanisms that the OpenID library needs, for both servers and + consumers. + + @change: Version 2.0 removed the C{storeNonce}, C{getAuthKey}, and C{isDumb} + methods, and changed the behavior of the C{L{useNonce}} method + to support one-way nonces. It added C{L{cleanupNonces}}, + C{L{cleanupAssociations}}, and C{L{cleanup}}. + + @sort: storeAssociation, getAssociation, removeAssociation, + useNonce + """ + + def storeAssociation(self, server_url, association): + """ + This method puts a C{L{Association + }} object into storage, + retrievable by server URL and handle. + + + @param server_url: The URL of the identity server that this + association is with. Because of the way the server + portion of the library uses this interface, don't assume + there are any limitations on the character set of the + input string. In particular, expect to see unescaped + non-url-safe characters in the server_url field. + + @type server_url: C{str} + + + @param association: The C{L{Association + }} to store. + + @type association: C{L{Association + }} + + + @return: C{None} + + @rtype: C{NoneType} + """ + raise NotImplementedError + + def getAssociation(self, server_url, handle=None): + """ + This method returns an C{L{Association + }} object from storage that + matches the server URL and, if specified, handle. It returns + C{None} if no such association is found or if the matching + association is expired. + + If no handle is specified, the store may return any + association which matches the server URL. If multiple + associations are valid, the recommended return value for this + method is the one most recently issued. + + This method is allowed (and encouraged) to garbage collect + expired associations when found. This method must not return + expired associations. + + + @param server_url: The URL of the identity server to get the + association for. Because of the way the server portion of + the library uses this interface, don't assume there are + any limitations on the character set of the input string. + In particular, expect to see unescaped non-url-safe + characters in the server_url field. + + @type server_url: C{str} + + + @param handle: This optional parameter is the handle of the + specific association to get. If no specific handle is + provided, any valid association matching the server URL is + returned. + + @type handle: C{str} or C{NoneType} + + + @return: The C{L{Association + }} for the given identity + server. + + @rtype: C{L{Association }} or + C{NoneType} + """ + raise NotImplementedError + + def removeAssociation(self, server_url, handle): + """ + This method removes the matching association if it's found, + and returns whether the association was removed or not. + + + @param server_url: The URL of the identity server the + association to remove belongs to. Because of the way the + server portion of the library uses this interface, don't + assume there are any limitations on the character set of + the input string. In particular, expect to see unescaped + non-url-safe characters in the server_url field. + + @type server_url: C{str} + + + @param handle: This is the handle of the association to + remove. If there isn't an association found that matches + both the given URL and handle, then there was no matching + handle found. + + @type handle: C{str} + + + @return: Returns whether or not the given association existed. + + @rtype: C{bool} or C{int} + """ + raise NotImplementedError + + def useNonce(self, server_url, timestamp, salt): + """Called when using a nonce. + + This method should return C{True} if the nonce has not been + used before, and store it for a while to make sure nobody + tries to use the same value again. If the nonce has already + been used or the timestamp is not current, return C{False}. + + You may use L{openid.store.nonce.SKEW} for your timestamp window. + + @change: In earlier versions, round-trip nonces were used and + a nonce was only valid if it had been previously stored + with C{storeNonce}. Version 2.0 uses one-way nonces, + requiring a different implementation here that does not + depend on a C{storeNonce} call. (C{storeNonce} is no + longer part of the interface.) + + @param server_url: The URL of the server from which the nonce + originated. + + @type server_url: C{str} + + @param timestamp: The time that the nonce was created (to the + nearest second), in seconds since January 1 1970 UTC. + @type timestamp: C{int} + + @param salt: A random string that makes two nonces from the + same server issued during the same second unique. + @type salt: str + + @return: Whether or not the nonce was valid. + + @rtype: C{bool} + """ + raise NotImplementedError + + def cleanupNonces(self): + """Remove expired nonces from the store. + + Discards any nonce from storage that is old enough that its + timestamp would not pass L{useNonce}. + + This method is not called in the normal operation of the + library. It provides a way for store admins to keep + their storage from filling up with expired data. + + @return: the number of nonces expired. + @returntype: int + """ + raise NotImplementedError + + def cleanupAssociations(self): + """Remove expired associations from the store. + + This method is not called in the normal operation of the + library. It provides a way for store admins to keep + their storage from filling up with expired data. + + @return: the number of associations expired. + @returntype: int + """ + raise NotImplementedError + + def cleanup(self): + """Shortcut for C{L{cleanupNonces}()}, C{L{cleanupAssociations}()}. + + This method is not called in the normal operation of the + library. It provides a way for store admins to keep + their storage from filling up with expired data. + """ + return self.cleanupNonces(), self.cleanupAssociations() diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/store/memstore.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/store/memstore.py new file mode 100644 index 0000000000000000000000000000000000000000..21e3e69c09170e7263fdd795280dd7b24a033452 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/store/memstore.py @@ -0,0 +1,126 @@ +"""A simple store using only in-process memory.""" + +from openid.store import nonce + +import copy +import time + + +class ServerAssocs(object): + def __init__(self): + self.assocs = {} + + def set(self, assoc): + self.assocs[assoc.handle] = assoc + + def get(self, handle): + return self.assocs.get(handle) + + def remove(self, handle): + try: + del self.assocs[handle] + except KeyError: + return False + else: + return True + + def best(self): + """Returns association with the oldest issued date. + + or None if there are no associations. + """ + best = None + for assoc in list(self.assocs.values()): + if best is None or best.issued < assoc.issued: + best = assoc + return best + + def cleanup(self): + """Remove expired associations. + + @return: tuple of (removed associations, remaining associations) + """ + remove = [] + for handle, assoc in self.assocs.items(): + if assoc.expiresIn == 0: + remove.append(handle) + for handle in remove: + del self.assocs[handle] + return len(remove), len(self.assocs) + + +class MemoryStore(object): + """In-process memory store. + + Use for single long-running processes. No persistence supplied. + """ + + def __init__(self): + self.server_assocs = {} + self.nonces = {} + + def _getServerAssocs(self, server_url): + try: + return self.server_assocs[server_url] + except KeyError: + assocs = self.server_assocs[server_url] = ServerAssocs() + return assocs + + def storeAssociation(self, server_url, assoc): + assocs = self._getServerAssocs(server_url) + assocs.set(copy.deepcopy(assoc)) + + def getAssociation(self, server_url, handle=None): + assocs = self._getServerAssocs(server_url) + if handle is None: + return assocs.best() + else: + return assocs.get(handle) + + def removeAssociation(self, server_url, handle): + assocs = self._getServerAssocs(server_url) + return assocs.remove(handle) + + def useNonce(self, server_url, timestamp, salt): + if abs(timestamp - time.time()) > nonce.SKEW: + return False + + anonce = (str(server_url), int(timestamp), str(salt)) + if anonce in self.nonces: + return False + else: + self.nonces[anonce] = None + return True + + def cleanupNonces(self): + now = time.time() + expired = [] + for anonce in self.nonces.keys(): + if abs(anonce[1] - now) > nonce.SKEW: + # removing items while iterating over the set could be bad. + expired.append(anonce) + + for anonce in expired: + del self.nonces[anonce] + return len(expired) + + def cleanupAssociations(self): + remove_urls = [] + removed_assocs = 0 + for server_url, assocs in self.server_assocs.items(): + removed, remaining = assocs.cleanup() + removed_assocs += removed + if not remaining: + remove_urls.append(server_url) + + # Remove entries from server_assocs that had none remaining. + for server_url in remove_urls: + del self.server_assocs[server_url] + return removed_assocs + + def __eq__(self, other): + return ((self.server_assocs == other.server_assocs) and + (self.nonces == other.nonces)) + + def __ne__(self, other): + return not (self == other) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/store/nonce.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/store/nonce.py new file mode 100644 index 0000000000000000000000000000000000000000..06c12149a4b12c4aa1ae32073838a0abe0e9c357 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/store/nonce.py @@ -0,0 +1,101 @@ +__all__ = [ + 'split', + 'mkNonce', + 'checkTimestamp', +] + +from openid import cryptutil +from time import strptime, strftime, gmtime, time +from calendar import timegm +import string + +NONCE_CHARS = string.ascii_letters + string.digits + +# Keep nonces for five hours (allow five hours for the combination of +# request time and clock skew). This is probably way more than is +# necessary, but there is not much overhead in storing nonces. +SKEW = 60 * 60 * 5 + +time_fmt = '%Y-%m-%dT%H:%M:%SZ' +time_str_len = len('0000-00-00T00:00:00Z') + + +def split(nonce_string): + """Extract a timestamp from the given nonce string + + @param nonce_string: the nonce from which to extract the timestamp + @type nonce_string: str + + @returns: A pair of a Unix timestamp and the salt characters + @returntype: (int, str) + + @raises ValueError: if the nonce does not start with a correctly + formatted time string + """ + timestamp_str = nonce_string[:time_str_len] + try: + timestamp = timegm(strptime(timestamp_str, time_fmt)) + except AssertionError: # Python 2.2 + timestamp = -1 + if timestamp < 0: + raise ValueError('time out of range') + return timestamp, nonce_string[time_str_len:] + + +def checkTimestamp(nonce_string, allowed_skew=SKEW, now=None): + """Is the timestamp that is part of the specified nonce string + within the allowed clock-skew of the current time? + + @param nonce_string: The nonce that is being checked + @type nonce_string: str + + @param allowed_skew: How many seconds should be allowed for + completing the request, allowing for clock skew. + @type allowed_skew: int + + @param now: The current time, as a Unix timestamp + @type now: int + + @returntype: bool + @returns: Whether the timestamp is correctly formatted and within + the allowed skew of the current time. + """ + try: + stamp, _ = split(nonce_string) + except ValueError: + return False + else: + if now is None: + now = time() + + # Time after which we should not use the nonce + past = now - allowed_skew + + # Time that is too far in the future for us to allow + future = now + allowed_skew + + # the stamp is not too far in the future and is not too far in + # the past + return past <= stamp <= future + + +def mkNonce(when=None): + """Generate a nonce with the current timestamp + + @param when: Unix timestamp representing the issue time of the + nonce. Defaults to the current time. + @type when: int + + @returntype: str + @returns: A string that should be usable as a one-way nonce + + @see: time + """ + salt = cryptutil.randomString(6, NONCE_CHARS) + if when is None: + t = gmtime() + else: + t = gmtime(when) + + time_str = strftime(time_fmt, t) + return time_str + salt diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/store/sqlstore.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/store/sqlstore.py new file mode 100644 index 0000000000000000000000000000000000000000..1fb6a34563427221de56ae0c4188d4ba91d15b9f --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/store/sqlstore.py @@ -0,0 +1,510 @@ +""" +This module contains C{L{OpenIDStore}} implementations that use +various SQL databases to back them. + +Example of how to initialize a store database:: + +python -c 'from openid.store import sqlstore; import pysqlite2.dbapi2;' + 'sqlstore.SQLiteStore(pysqlite2.dbapi2.connect("cstore.db")).createTables()' +""" +import re +import time + +from openid.association import Association +from openid.store.interface import OpenIDStore +from openid.store import nonce + + +def _inTxn(func): + def wrapped(self, *args, **kwargs): + return self._callInTransaction(func, self, *args, **kwargs) + + if hasattr(func, '__name__'): + try: + wrapped.__name__ = func.__name__[4:] + except TypeError: + pass + + if hasattr(func, '__doc__'): + wrapped.__doc__ = func.__doc__ + + return wrapped + + +class SQLStore(OpenIDStore): + """ + This is the parent class for the SQL stores, which contains the + logic common to all of the SQL stores. + + The table names used are determined by the class variables + C{L{associations_table}} and + C{L{nonces_table}}. To change the name of the tables used, pass + new table names into the constructor. + + To create the tables with the proper schema, see the + C{L{createTables}} method. + + This class shouldn't be used directly. Use one of its subclasses + instead, as those contain the code necessary to use a specific + database. + + All methods other than C{L{__init__}} and C{L{createTables}} + should be considered implementation details. + + + @cvar associations_table: This is the default name of the table to + keep associations in + + @cvar nonces_table: This is the default name of the table to keep + nonces in. + + + @sort: __init__, createTables + """ + + associations_table = 'oid_associations' + nonces_table = 'oid_nonces' + + def __init__(self, conn, associations_table=None, nonces_table=None): + """ + This creates a new SQLStore instance. It requires an + established database connection be given to it, and it allows + overriding the default table names. + + + @param conn: This must be an established connection to a + database of the correct type for the SQLStore subclass + you're using. + + @type conn: A python database API compatible connection + object. + + + @param associations_table: This is an optional parameter to + specify the name of the table used for storing + associations. The default value is specified in + C{L{SQLStore.associations_table}}. + + @type associations_table: C{str} + + + @param nonces_table: This is an optional parameter to specify + the name of the table used for storing nonces. The + default value is specified in C{L{SQLStore.nonces_table}}. + + @type nonces_table: C{str} + """ + self.conn = conn + self.cur = None + self._statement_cache = {} + self._table_names = { + 'associations': associations_table or self.associations_table, + 'nonces': nonces_table or self.nonces_table, + } + self.max_nonce_age = 6 * 60 * 60 # Six hours, in seconds + + # DB API extension: search for "Connection Attributes .Error, + # .ProgrammingError, etc." in + # http://www.python.org/dev/peps/pep-0249/ + if (hasattr(self.conn, 'IntegrityError') and + hasattr(self.conn, 'OperationalError')): + self.exceptions = self.conn + + if not (hasattr(self.exceptions, 'IntegrityError') and + hasattr(self.exceptions, 'OperationalError')): + raise RuntimeError("Error using database connection module " + "(Maybe it can't be imported?)") + + def blobDecode(self, blob): + """Convert a blob as returned by the SQL engine into a str object. + + str -> str""" + return blob + + def blobEncode(self, s): + """Convert a str object into the necessary object for storing + in the database as a blob.""" + return s + + def _getSQL(self, sql_name): + try: + return self._statement_cache[sql_name] + except KeyError: + sql = getattr(self, sql_name) + sql %= self._table_names + self._statement_cache[sql_name] = sql + return sql + + def _execSQL(self, sql_name, *args): + sql = self._getSQL(sql_name) + + # Kludge because we have reports of postgresql not quoting + # arguments if they are passed in as unicode instead of str. + # Currently the strings in our tables just have ascii in them, + # so this ought to be safe. + def unicode_to_str(arg): + if isinstance(arg, str): + return str(arg) + else: + return arg + + str_args = list(map(unicode_to_str, args)) + self.cur.execute(sql, str_args) + + def __getattr__(self, attr): + # if the attribute starts with db_, use a default + # implementation that looks up the appropriate SQL statement + # as an attribute of this object and executes it. + if attr[:3] == 'db_': + sql_name = attr[3:] + '_sql' + + def func(*args): + return self._execSQL(sql_name, *args) + + setattr(self, attr, func) + return func + else: + raise AttributeError('Attribute %r not found' % (attr, )) + + def _callInTransaction(self, func, *args, **kwargs): + """Execute the given function inside of a transaction, with an + open cursor. If no exception is raised, the transaction is + comitted, otherwise it is rolled back.""" + # No nesting of transactions + self.conn.rollback() + + try: + self.cur = self.conn.cursor() + try: + ret = func(*args, **kwargs) + finally: + self.cur.close() + self.cur = None + except: + self.conn.rollback() + raise + else: + self.conn.commit() + + return ret + + def txn_createTables(self): + """ + This method creates the database tables necessary for this + store to work. It should not be called if the tables already + exist. + """ + self.db_create_nonce() + self.db_create_assoc() + + createTables = _inTxn(txn_createTables) + + def txn_storeAssociation(self, server_url, association): + """Set the association for the server URL. + + Association -> NoneType + """ + a = association + self.db_set_assoc(server_url, a.handle, + self.blobEncode(a.secret), a.issued, a.lifetime, + a.assoc_type) + + storeAssociation = _inTxn(txn_storeAssociation) + + def txn_getAssociation(self, server_url, handle=None): + """Get the most recent association that has been set for this + server URL and handle. + + str -> NoneType or Association + """ + if handle is not None: + self.db_get_assoc(server_url, handle) + else: + self.db_get_assocs(server_url) + + rows = self.cur.fetchall() + if len(rows) == 0: + return None + else: + associations = [] + for values in rows: + values = list(values) + values[1] = self.blobDecode(values[1]) + assoc = Association(*values) + if assoc.expiresIn == 0: + self.txn_removeAssociation(server_url, assoc.handle) + else: + associations.append((assoc.issued, assoc)) + + if associations: + associations.sort() + return associations[-1][1] + else: + return None + + getAssociation = _inTxn(txn_getAssociation) + + def txn_removeAssociation(self, server_url, handle): + """Remove the association for the given server URL and handle, + returning whether the association existed at all. + + (str, str) -> bool + """ + self.db_remove_assoc(server_url, handle) + return self.cur.rowcount > 0 # -1 is undefined + + removeAssociation = _inTxn(txn_removeAssociation) + + def txn_useNonce(self, server_url, timestamp, salt): + """Return whether this nonce is present, and if it is, then + remove it from the set. + + str -> bool""" + if abs(timestamp - time.time()) > nonce.SKEW: + return False + + try: + self.db_add_nonce(server_url, timestamp, salt) + except self.exceptions.IntegrityError: + # The key uniqueness check failed + return False + else: + # The nonce was successfully added + return True + + useNonce = _inTxn(txn_useNonce) + + def txn_cleanupNonces(self): + self.db_clean_nonce(int(time.time()) - nonce.SKEW) + return self.cur.rowcount + + cleanupNonces = _inTxn(txn_cleanupNonces) + + def txn_cleanupAssociations(self): + self.db_clean_assoc(int(time.time())) + return self.cur.rowcount + + cleanupAssociations = _inTxn(txn_cleanupAssociations) + + +class SQLiteStore(SQLStore): + """ + This is an SQLite-based specialization of C{L{SQLStore}}. + + To create an instance, see C{L{SQLStore.__init__}}. To create the + tables it will use, see C{L{SQLStore.createTables}}. + + All other methods are implementation details. + """ + + create_nonce_sql = """ + CREATE TABLE %(nonces)s ( + server_url VARCHAR, + timestamp INTEGER, + salt CHAR(40), + UNIQUE(server_url, timestamp, salt) + ); + """ + + create_assoc_sql = """ + CREATE TABLE %(associations)s + ( + server_url VARCHAR(2047), + handle VARCHAR(255), + secret BLOB(128), + issued INTEGER, + lifetime INTEGER, + assoc_type VARCHAR(64), + PRIMARY KEY (server_url, handle) + ); + """ + + set_assoc_sql = ('INSERT OR REPLACE INTO %(associations)s ' + '(server_url, handle, secret, issued, ' + 'lifetime, assoc_type) ' + 'VALUES (?, ?, ?, ?, ?, ?);') + get_assocs_sql = ('SELECT handle, secret, issued, lifetime, assoc_type ' + 'FROM %(associations)s WHERE server_url = ?;') + get_assoc_sql = ( + 'SELECT handle, secret, issued, lifetime, assoc_type ' + 'FROM %(associations)s WHERE server_url = ? AND handle = ?;') + + get_expired_sql = ('SELECT server_url ' + 'FROM %(associations)s WHERE issued + lifetime < ?;') + + remove_assoc_sql = ('DELETE FROM %(associations)s ' + 'WHERE server_url = ? AND handle = ?;') + + clean_assoc_sql = 'DELETE FROM %(associations)s WHERE issued + lifetime < ?;' + + add_nonce_sql = 'INSERT INTO %(nonces)s VALUES (?, ?, ?);' + + clean_nonce_sql = 'DELETE FROM %(nonces)s WHERE timestamp < ?;' + + def blobEncode(self, s): + return memoryview(s) + + def useNonce(self, *args, **kwargs): + # Older versions of the sqlite wrapper do not raise + # IntegrityError as they should, so we have to detect the + # message from the OperationalError. + try: + return super(SQLiteStore, self).useNonce(*args, **kwargs) + except self.exceptions.OperationalError as why: + if re.match('^columns .* are not unique$', str(why)): + return False + else: + raise + + +class MySQLStore(SQLStore): + """ + This is a MySQL-based specialization of C{L{SQLStore}}. + + Uses InnoDB tables for transaction support. + + To create an instance, see C{L{SQLStore.__init__}}. To create the + tables it will use, see C{L{SQLStore.createTables}}. + + All other methods are implementation details. + """ + + try: + import MySQLdb as exceptions + except ImportError: + exceptions = None + + create_nonce_sql = """ + CREATE TABLE %(nonces)s ( + server_url BLOB NOT NULL, + timestamp INTEGER NOT NULL, + salt CHAR(40) NOT NULL, + PRIMARY KEY (server_url(255), timestamp, salt) + ) + ENGINE=InnoDB; + """ + + create_assoc_sql = """ + CREATE TABLE %(associations)s + ( + server_url BLOB NOT NULL, + handle VARCHAR(255) NOT NULL, + secret BLOB NOT NULL, + issued INTEGER NOT NULL, + lifetime INTEGER NOT NULL, + assoc_type VARCHAR(64) NOT NULL, + PRIMARY KEY (server_url(255), handle) + ) + ENGINE=InnoDB; + """ + + set_assoc_sql = ('REPLACE INTO %(associations)s ' + 'VALUES (%%s, %%s, %%s, %%s, %%s, %%s);') + get_assocs_sql = ('SELECT handle, secret, issued, lifetime, assoc_type' + ' FROM %(associations)s WHERE server_url = %%s;') + get_expired_sql = ('SELECT server_url ' + 'FROM %(associations)s WHERE issued + lifetime < %%s;') + + get_assoc_sql = ( + 'SELECT handle, secret, issued, lifetime, assoc_type' + ' FROM %(associations)s WHERE server_url = %%s AND handle = %%s;') + remove_assoc_sql = ('DELETE FROM %(associations)s ' + 'WHERE server_url = %%s AND handle = %%s;') + + clean_assoc_sql = 'DELETE FROM %(associations)s WHERE issued + lifetime < %%s;' + + add_nonce_sql = 'INSERT INTO %(nonces)s VALUES (%%s, %%s, %%s);' + + clean_nonce_sql = 'DELETE FROM %(nonces)s WHERE timestamp < %%s;' + + +class PostgreSQLStore(SQLStore): + """ + This is a PostgreSQL-based specialization of C{L{SQLStore}}. + + To create an instance, see C{L{SQLStore.__init__}}. To create the + tables it will use, see C{L{SQLStore.createTables}}. + + All other methods are implementation details. + """ + + try: + import psycopg2 + except ImportError: + from psycopg2cffi import compat + compat.register() + + exceptions = None + + create_nonce_sql = """ + CREATE TABLE %(nonces)s ( + server_url VARCHAR(2047) NOT NULL, + timestamp INTEGER NOT NULL, + salt CHAR(40) NOT NULL, + PRIMARY KEY (server_url, timestamp, salt) + ); + """ + + create_assoc_sql = """ + CREATE TABLE %(associations)s + ( + server_url VARCHAR(2047) NOT NULL, + handle VARCHAR(255) NOT NULL, + secret BYTEA NOT NULL, + issued INTEGER NOT NULL, + lifetime INTEGER NOT NULL, + assoc_type VARCHAR(64) NOT NULL, + PRIMARY KEY (server_url, handle), + CONSTRAINT secret_length_constraint CHECK (LENGTH(secret) <= 128) + ); + """ + + def db_set_assoc(self, server_url, handle, secret, issued, lifetime, + assoc_type): + """ + Set an association. This is implemented as a method because + REPLACE INTO is not supported by PostgreSQL (and is not + standard SQL). + """ + result = self.db_get_assoc(server_url, handle) + rows = self.cur.fetchall() + if len(rows): + # Update the table since this associations already exists. + return self.db_update_assoc(secret, issued, lifetime, assoc_type, + server_url, handle) + else: + # Insert a new record because this association wasn't + # found. + return self.db_new_assoc(server_url, handle, secret, issued, + lifetime, assoc_type) + + new_assoc_sql = ('INSERT INTO %(associations)s ' + 'VALUES (%%s, %%s, %%s, %%s, %%s, %%s);') + update_assoc_sql = ('UPDATE %(associations)s SET ' + 'secret = %%s, issued = %%s, ' + 'lifetime = %%s, assoc_type = %%s ' + 'WHERE server_url = %%s AND handle = %%s;') + get_assocs_sql = ('SELECT handle, secret, issued, lifetime, assoc_type' + ' FROM %(associations)s WHERE server_url = %%s;') + get_expired_sql = ('SELECT server_url ' + 'FROM %(associations)s WHERE issued + lifetime < %%s;') + + get_assoc_sql = ( + 'SELECT handle, secret, issued, lifetime, assoc_type' + ' FROM %(associations)s WHERE server_url = %%s AND handle = %%s;') + remove_assoc_sql = ('DELETE FROM %(associations)s ' + 'WHERE server_url = %%s AND handle = %%s;') + + clean_assoc_sql = 'DELETE FROM %(associations)s WHERE issued + lifetime < %%s;' + + add_nonce_sql = 'INSERT INTO %(nonces)s VALUES (%%s, %%s, %%s);' + + clean_nonce_sql = 'DELETE FROM %(nonces)s WHERE timestamp < %%s;' + + def blobEncode(self, blob): + from psycopg2 import Binary + + return Binary(blob) + + def blobDecode(self, blob): + return blob.tobytes() diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/urinorm.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/urinorm.py new file mode 100644 index 0000000000000000000000000000000000000000..5fb49b4d4646167ba74bfe6140e5762285a258bf --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/urinorm.py @@ -0,0 +1,161 @@ +import re + +from openid import codecutil # registers 'oid_percent_escape' encoding handler + +# from appendix B of rfc 3986 (http://www.ietf.org/rfc/rfc3986.txt) +uri_pattern = r'^(([^:/?#]+):)?(//([^/?#]*))?([^?#]*)(\?([^#]*))?(#(.*))?' +uri_re = re.compile(uri_pattern) + +# gen-delims = ":" / "/" / "?" / "#" / "[" / "]" / "@" +# +# sub-delims = "!" / "$" / "&" / "'" / "(" / ")" +# / "*" / "+" / "," / ";" / "=" +# +# unreserved = ALPHA / DIGIT / "-" / "." / "_" / "~" + +uri_illegal_char_re = re.compile(r"[^-A-Za-z0-9:/?#[\]@!$&'()*+,;=._~%]", + re.UNICODE) + +authority_pattern = r'^([^@]*@)?([^:]*)(:.*)?' +authority_re = re.compile(authority_pattern) + +pct_encoded_pattern = r'%([0-9A-Fa-f]{2})' +pct_encoded_re = re.compile(pct_encoded_pattern) + +_unreserved = [False] * 256 +for _ in range(ord('A'), ord('Z') + 1): + _unreserved[_] = True +for _ in range(ord('0'), ord('9') + 1): + _unreserved[_] = True +for _ in range(ord('a'), ord('z') + 1): + _unreserved[_] = True +_unreserved[ord('-')] = True +_unreserved[ord('.')] = True +_unreserved[ord('_')] = True +_unreserved[ord('~')] = True + + +def _pct_encoded_replace_unreserved(mo): + try: + i = int(mo.group(1), 16) + if _unreserved[i]: + return chr(i) + else: + return mo.group().upper() + + except ValueError: + return mo.group() + + +def _pct_encoded_replace(mo): + try: + return chr(int(mo.group(1), 16)) + except ValueError: + return mo.group() + + +def remove_dot_segments(path): + result_segments = [] + + while path: + if path.startswith('../'): + path = path[3:] + elif path.startswith('./'): + path = path[2:] + elif path.startswith('/./'): + path = path[2:] + elif path == '/.': + path = '/' + elif path.startswith('/../'): + path = path[3:] + if result_segments: + result_segments.pop() + elif path == '/..': + path = '/' + if result_segments: + result_segments.pop() + elif path == '..' or path == '.': + path = '' + else: + i = 0 + if path[0] == '/': + i = 1 + i = path.find('/', i) + if i == -1: + i = len(path) + result_segments.append(path[:i]) + path = path[i:] + + return ''.join(result_segments) + + +def urinorm(uri): + ''' + Normalize a URI + ''' + # TODO: use urllib.parse instead of these complex regular expressions + if isinstance(uri, bytes): + uri = str(uri, encoding='utf-8') + + uri = uri.encode('ascii', errors='oid_percent_escape').decode('utf-8') + # _escapeme_re.sub(_pct_escape_unicode, uri).encode('ascii').decode() + + illegal_mo = uri_illegal_char_re.search(uri) + if illegal_mo: + raise ValueError('Illegal characters in URI: %r at position %s' % + (illegal_mo.group(), illegal_mo.start())) + + uri_mo = uri_re.match(uri) + + scheme = uri_mo.group(2) + if scheme is None: + raise ValueError('No scheme specified') + + scheme = scheme.lower() + if scheme not in ('http', 'https'): + raise ValueError('Not an absolute HTTP or HTTPS URI: %r' % (uri, )) + + authority = uri_mo.group(4) + if authority is None: + raise ValueError('Not an absolute URI: %r' % (uri, )) + + authority_mo = authority_re.match(authority) + if authority_mo is None: + raise ValueError('URI does not have a valid authority: %r' % (uri, )) + + userinfo, host, port = authority_mo.groups() + + if userinfo is None: + userinfo = '' + + if '%' in host: + host = host.lower() + host = pct_encoded_re.sub(_pct_encoded_replace, host) + host = host.encode('idna').decode() + else: + host = host.lower() + + if port: + if (port == ':' or (scheme == 'http' and port == ':80') or + (scheme == 'https' and port == ':443')): + port = '' + else: + port = '' + + authority = userinfo + host + port + + path = uri_mo.group(5) + path = pct_encoded_re.sub(_pct_encoded_replace_unreserved, path) + path = remove_dot_segments(path) + if not path: + path = '/' + + query = uri_mo.group(6) + if query is None: + query = '' + + fragment = uri_mo.group(8) + if fragment is None: + fragment = '' + + return scheme + '://' + authority + path + query + fragment diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/yadis/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/yadis/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ef806e87508dac5c9701356b287e311b281af402 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/yadis/__init__.py @@ -0,0 +1,17 @@ +#-*- coding: utf-8 -*- + +__all__ = [ + 'constants', + 'discover', + 'etxrd', + 'filters', + 'manager', + 'parsehtml', + 'services', + 'xri', + 'xrires', +] + +version_info = (2, 0, 0) + +__version__ = ".".join(str(x) for x in version_info) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/yadis/accept.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/yadis/accept.py new file mode 100644 index 0000000000000000000000000000000000000000..2f18aa662a7ab2751b4666a82b23d2b6b121379e --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/yadis/accept.py @@ -0,0 +1,137 @@ +"""Functions for generating and parsing HTTP Accept: headers for +supporting server-directed content negotiation. +""" + + +def generateAcceptHeader(*elements): + """Generate an accept header value + + [str or (str, float)] -> str + """ + parts = [] + for element in elements: + if type(element) is str: + qs = "1.0" + mtype = element + else: + mtype, q = element + q = float(q) + if q > 1 or q <= 0: + raise ValueError('Invalid preference factor: %r' % q) + + qs = '%0.1f' % (q, ) + + parts.append((qs, mtype)) + + parts.sort() + chunks = [] + for q, mtype in parts: + if q == '1.0': + chunks.append(mtype) + else: + chunks.append('%s; q=%s' % (mtype, q)) + + return ', '.join(chunks) + + +def parseAcceptHeader(value): + """Parse an accept header, ignoring any accept-extensions + + returns a list of tuples containing main MIME type, MIME subtype, + and quality markdown. + + str -> [(str, str, float)] + """ + chunks = [chunk.strip() for chunk in value.split(',')] + accept = [] + for chunk in chunks: + parts = [s.strip() for s in chunk.split(';')] + + mtype = parts.pop(0) + if '/' not in mtype: + # This is not a MIME type, so ignore the bad data + continue + + main, sub = mtype.split('/', 1) + + for ext in parts: + if '=' in ext: + k, v = ext.split('=', 1) + if k == 'q': + try: + q = float(v) + break + except ValueError: + # Ignore poorly formed q-values + pass + else: + q = 1.0 + + accept.append((q, main, sub)) + + accept.sort() + accept.reverse() + return [(main, sub, q) for (q, main, sub) in accept] + + +def matchTypes(accept_types, have_types): + """Given the result of parsing an Accept: header, and the + available MIME types, return the acceptable types with their + quality markdowns. + + For example: + + >>> acceptable = parseAcceptHeader('text/html, text/plain; q=0.5') + >>> matchTypes(acceptable, ['text/plain', 'text/html', 'image/jpeg']) + [('text/html', 1.0), ('text/plain', 0.5)] + + + Type signature: ([(str, str, float)], [str]) -> [(str, float)] + """ + if not accept_types: + # Accept all of them + default = 1 + else: + default = 0 + + match_main = {} + match_sub = {} + for (main, sub, q) in accept_types: + if main == '*': + default = max(default, q) + continue + elif sub == '*': + match_main[main] = max(match_main.get(main, 0), q) + else: + match_sub[(main, sub)] = max(match_sub.get((main, sub), 0), q) + + accepted_list = [] + order_maintainer = 0 + for mtype in have_types: + main, sub = mtype.split('/') + if (main, sub) in match_sub: + q = match_sub[(main, sub)] + else: + q = match_main.get(main, default) + + if q: + accepted_list.append((1 - q, order_maintainer, q, mtype)) + order_maintainer += 1 + + accepted_list.sort() + return [(mtype, q) for (_, _, q, mtype) in accepted_list] + + +def getAcceptable(accept_header, have_types): + """Parse the accept header and return a list of available types in + preferred order. If a type is unacceptable, it will not be in the + resulting list. + + This is a convenience wrapper around matchTypes and + parseAcceptHeader. + + (str, [str]) -> [str] + """ + accepted = parseAcceptHeader(accept_header) + preferred = matchTypes(accepted, have_types) + return [mtype for (mtype, _) in preferred] diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/yadis/constants.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/yadis/constants.py new file mode 100644 index 0000000000000000000000000000000000000000..5d3072387ebda2f5c8f1b9d8bc63f27a80208af9 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/yadis/constants.py @@ -0,0 +1,12 @@ +__all__ = ['YADIS_HEADER_NAME', 'YADIS_CONTENT_TYPE', 'YADIS_ACCEPT_HEADER'] +from openid.yadis.accept import generateAcceptHeader + +YADIS_HEADER_NAME = 'X-XRDS-Location' +YADIS_CONTENT_TYPE = 'application/xrds+xml' + +# A value suitable for using as an accept header when performing YADIS +# discovery, unless the application has special requirements +YADIS_ACCEPT_HEADER = generateAcceptHeader( + ('text/html', 0.3), + ('application/xhtml+xml', 0.5), + (YADIS_CONTENT_TYPE, 1.0), ) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/yadis/discover.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/yadis/discover.py new file mode 100644 index 0000000000000000000000000000000000000000..af11b1036b17aa4ec2ec5fe3b4116151550658f0 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/yadis/discover.py @@ -0,0 +1,169 @@ +# -*- test-case-name: openid.test.test_yadis_discover -*- +__all__ = ['discover', 'DiscoveryResult', 'DiscoveryFailure'] + +from io import StringIO + +from openid import fetchers + +from openid.yadis.constants import \ + YADIS_HEADER_NAME, YADIS_CONTENT_TYPE, YADIS_ACCEPT_HEADER +from openid.yadis.parsehtml import MetaNotFound, findHTMLMeta + + +class DiscoveryFailure(Exception): + """Raised when a YADIS protocol error occurs in the discovery process""" + identity_url = None + + def __init__(self, message, http_response): + Exception.__init__(self, message) + self.http_response = http_response + + +class DiscoveryResult(object): + """Contains the result of performing Yadis discovery on a URI""" + + # The URI that was passed to the fetcher + request_uri = None + + # The result of following redirects from the request_uri + normalized_uri = None + + # The URI from which the response text was returned (set to + # None if there was no XRDS document found) + xrds_uri = None + + # The content-type returned with the response_text + content_type = None + + # The document returned from the xrds_uri + response_text = None + + def __init__(self, request_uri): + """Initialize the state of the object + + sets all attributes to None except the request_uri + """ + self.request_uri = request_uri + + def usedYadisLocation(self): + """Was the Yadis protocol's indirection used?""" + if self.xrds_uri is None: + return False + return self.normalized_uri != self.xrds_uri + + def isXRDS(self): + """Is the response text supposed to be an XRDS document?""" + return (self.usedYadisLocation() or + self.content_type == YADIS_CONTENT_TYPE) + + +def discover(uri): + """Discover services for a given URI. + + @param uri: The identity URI as a well-formed http or https + URI. The well-formedness and the protocol are not checked, but + the results of this function are undefined if those properties + do not hold. + + @return: DiscoveryResult object + + @raises Exception: Any exception that can be raised by fetching a URL with + the given fetcher. + @raises DiscoveryFailure: When the HTTP response does not have a 200 code. + """ + result = DiscoveryResult(uri) + resp = fetchers.fetch(uri, headers={'Accept': YADIS_ACCEPT_HEADER}) + if resp.status not in (200, 206): + raise DiscoveryFailure( + 'HTTP Response status from identity URL host is not 200. ' + 'Got status %r' % (resp.status, ), resp) + + # Note the URL after following redirects + result.normalized_uri = resp.final_url + + # Attempt to find out where to go to discover the document + # or if we already have it + result.content_type = resp.headers.get('content-type') + + result.xrds_uri = whereIsYadis(resp) + + if result.xrds_uri and result.usedYadisLocation(): + resp = fetchers.fetch(result.xrds_uri) + if resp.status not in (200, 206): + exc = DiscoveryFailure( + 'HTTP Response status from Yadis host is not 200. ' + 'Got status %r' % (resp.status, ), resp) + exc.identity_url = result.normalized_uri + raise exc + result.content_type = resp.headers.get('content-type') + + result.response_text = resp.body + return result + + +def whereIsYadis(resp): + """Given a HTTPResponse, return the location of the Yadis document. + + May be the URL just retrieved, another URL, or None if no suitable URL can + be found. + + [non-blocking] + + @returns: str or None + """ + # Attempt to find out where to go to discover the document + # or if we already have it + content_type = resp.headers.get('content-type') + + # According to the spec, the content-type header must be an exact + # match, or else we have to look for an indirection. + if (content_type and + content_type.split(';', 1)[0].lower() == YADIS_CONTENT_TYPE): + return resp.final_url + else: + # Try the header + yadis_loc = resp.headers.get(YADIS_HEADER_NAME.lower()) + + if not yadis_loc: + # Parse as HTML if the header is missing. + # + # XXX: do we want to do something with content-type, like + # have a whitelist or a blacklist (for detecting that it's + # HTML)? + + # Decode body by encoding of file + content_type = content_type or '' + encoding = content_type.rsplit(';', 1) + if (len(encoding) == 2 and + encoding[1].strip().startswith('charset=')): + encoding = encoding[1].split('=', 1)[1].strip() + else: + encoding = 'utf-8' + + if isinstance(resp.body, bytes): + try: + content = resp.body.decode(encoding) + except UnicodeError: + # All right, the detected encoding has failed. Try with + # UTF-8 (even if there was no detected encoding and we've + # defaulted to UTF-8, it's not that expensive an operation) + try: + content = resp.body.decode('utf-8') + except UnicodeError: + # At this point the content cannot be decoded to a str + # using the detected encoding or falling back to utf-8, + # so we have to resort to replacing undecodable chars. + # This *will* result in broken content but there isn't + # anything else that can be done. + content = resp.body.decode(encoding, 'replace') + else: + content = resp.body + + try: + yadis_loc = findHTMLMeta(StringIO(content)) + except (MetaNotFound, UnicodeError): + # UnicodeError: Response body could not be encoded and xrds + # location could not be found before troubles occur. + pass + + return yadis_loc diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/yadis/etxrd.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/yadis/etxrd.py new file mode 100644 index 0000000000000000000000000000000000000000..85e739afa9476d91a0db776d7565516008353614 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/yadis/etxrd.py @@ -0,0 +1,318 @@ +# -*- test-case-name: yadis.test.test_etxrd -*- +""" +ElementTree interface to an XRD document. +""" + +__all__ = [ + 'nsTag', + 'mkXRDTag', + 'isXRDS', + 'parseXRDS', + 'getCanonicalID', + 'getYadisXRD', + 'getPriorityStrict', + 'getPriority', + 'prioSort', + 'iterServices', + 'expandService', + 'expandServices', +] + +import sys +import random +import functools + +from datetime import datetime +from time import strptime + +from openid.oidutil import importElementTree, importSafeElementTree + +ElementTree = importElementTree() +SafeElementTree = importSafeElementTree() + +from openid.yadis import xri + + +class XRDSError(Exception): + """An error with the XRDS document.""" + + # The exception that triggered this exception + reason = None + + +class XRDSFraud(XRDSError): + """Raised when there's an assertion in the XRDS that it does not have + the authority to make. + """ + + +def parseXRDS(text): + """Parse the given text as an XRDS document. + + @return: ElementTree containing an XRDS document + + @raises XRDSError: When there is a parse error or the document does + not contain an XRDS. + """ + try: + # lxml prefers to parse bytestrings, and occasionally chokes on a + # combination of text strings and declared XML encodings -- see + # https://github.com/necaris/python3-openid/issues/19 + # To avoid this, we ensure that the 'text' we're parsing is actually + # a bytestring + bytestring = text.encode('utf8') if isinstance(text, str) else text + element = SafeElementTree.XML(bytestring) + except (SystemExit, MemoryError, AssertionError, ImportError): + raise + except Exception as why: + exc = XRDSError('Error parsing document as XML') + exc.reason = why + raise exc + else: + tree = ElementTree.ElementTree(element) + if not isXRDS(tree): + raise XRDSError('Not an XRDS document') + + return tree + + +XRD_NS_2_0 = 'xri://$xrd*($v*2.0)' +XRDS_NS = 'xri://$xrds' + + +def nsTag(ns, t): + return '{%s}%s' % (ns, t) + + +def mkXRDTag(t): + """basestring -> basestring + + Create a tag name in the XRD 2.0 XML namespace suitable for using + with ElementTree + """ + return nsTag(XRD_NS_2_0, t) + + +def mkXRDSTag(t): + """basestring -> basestring + + Create a tag name in the XRDS XML namespace suitable for using + with ElementTree + """ + return nsTag(XRDS_NS, t) + + +# Tags that are used in Yadis documents +root_tag = mkXRDSTag('XRDS') +service_tag = mkXRDTag('Service') +xrd_tag = mkXRDTag('XRD') +type_tag = mkXRDTag('Type') +uri_tag = mkXRDTag('URI') +expires_tag = mkXRDTag('Expires') + +# Other XRD tags +canonicalID_tag = mkXRDTag('CanonicalID') + + +def isXRDS(xrd_tree): + """Is this document an XRDS document?""" + root = xrd_tree.getroot() + return root.tag == root_tag + + +def getYadisXRD(xrd_tree): + """Return the XRD element that should contain the Yadis services""" + xrd = None + + # for the side-effect of assigning the last one in the list to the + # xrd variable + for xrd in xrd_tree.findall(xrd_tag): + pass + + # There were no elements found, or else xrd would be set to the + # last one + if xrd is None: + raise XRDSError('No XRD present in tree') + + return xrd + + +def getXRDExpiration(xrd_element, default=None): + """Return the expiration date of this XRD element, or None if no + expiration was specified. + + @type xrd_element: ElementTree node + + @param default: The value to use as the expiration if no + expiration was specified in the XRD. + + @rtype: datetime.datetime + + @raises ValueError: If the xrd:Expires element is present, but its + contents are not formatted according to the specification. + """ + expires_element = xrd_element.find(expires_tag) + if expires_element is None: + return default + else: + expires_string = expires_element.text + + # Will raise ValueError if the string is not the expected format + expires_time = strptime(expires_string, "%Y-%m-%dT%H:%M:%SZ") + return datetime(*expires_time[0:6]) + + +def getCanonicalID(iname, xrd_tree): + """Return the CanonicalID from this XRDS document. + + @param iname: the XRI being resolved. + @type iname: unicode + + @param xrd_tree: The XRDS output from the resolver. + @type xrd_tree: ElementTree + + @returns: The XRI CanonicalID or None. + @returntype: unicode or None + """ + xrd_list = xrd_tree.findall(xrd_tag) + xrd_list.reverse() + + try: + canonicalID = xri.XRI(xrd_list[0].findall(canonicalID_tag)[0].text) + except IndexError: + return None + + childID = canonicalID.lower() + + for xrd in xrd_list[1:]: + parent_sought = childID.rsplit("!", 1)[0] + parent = xri.XRI(xrd.findtext(canonicalID_tag)) + if parent_sought != parent.lower(): + raise XRDSFraud("%r can not come from %s" % (childID, parent)) + + childID = parent_sought + + root = xri.rootAuthority(iname) + if not xri.providerIsAuthoritative(root, childID): + raise XRDSFraud("%r can not come from root %r" % (childID, root)) + + return canonicalID + + +@functools.total_ordering +class _Max(object): + """ + Value that compares greater than any other value. + + Should only be used as a singleton. Implemented for use as a + priority value for when a priority is not specified. + """ + + def __lt__(self, other): + return isinstance(other, self.__class__) + + def __eq__(self, other): + return isinstance(other, self.__class__) + + +Max = _Max() + + +def getPriorityStrict(element): + """Get the priority of this element. + + Raises ValueError if the value of the priority is invalid. If no + priority is specified, it returns a value that compares greater + than any other value. + """ + prio_str = element.get('priority') + if prio_str is not None: + prio_val = int(prio_str) + if prio_val >= 0: + return prio_val + else: + raise ValueError('Priority values must be non-negative integers') + + # Any errors in parsing the priority fall through to here + return Max + + +def getPriority(element): + """Get the priority of this element + + Returns Max if no priority is specified or the priority value is invalid. + """ + try: + return getPriorityStrict(element) + except ValueError: + return Max + + +def prioSort(elements): + """Sort a list of elements that have priority attributes""" + # Randomize the services before sorting so that equal priority + # elements are load-balanced. + random.shuffle(elements) + + sorted_elems = sorted(elements, key=getPriority) + return sorted_elems + + +def iterServices(xrd_tree): + """Return an iterable over the Service elements in the Yadis XRD + + sorted by priority""" + xrd = getYadisXRD(xrd_tree) + return prioSort(xrd.findall(service_tag)) + + +def sortedURIs(service_element): + """Given a Service element, return a list of the contents of all + URI tags in priority order.""" + return [ + uri_element.text + for uri_element in prioSort(service_element.findall(uri_tag)) + ] + + +def getTypeURIs(service_element): + """Given a Service element, return a list of the contents of all + Type tags""" + return [ + type_element.text for type_element in service_element.findall(type_tag) + ] + + +def expandService(service_element): + """Take a service element and expand it into an iterator of: + ([type_uri], uri, service_element) + """ + uris = sortedURIs(service_element) + if not uris: + uris = [None] + + expanded = [] + for uri in uris: + type_uris = getTypeURIs(service_element) + expanded.append((type_uris, uri, service_element)) + + return expanded + + +def expandServices(service_elements): + """Take a sorted iterator of service elements and expand it into a + sorted iterator of: + ([type_uri], uri, service_element) + + There may be more than one item in the resulting list for each + service element if there is more than one URI or type for a + service, but each triple will be unique. + + If there is no URI or Type for a Service element, it will not + appear in the result. + """ + expanded = [] + for service_element in service_elements: + expanded.extend(expandService(service_element)) + + return expanded diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/yadis/filters.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/yadis/filters.py new file mode 100644 index 0000000000000000000000000000000000000000..81bafee634420472de68b41306975616ab0fc50b --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/yadis/filters.py @@ -0,0 +1,213 @@ +"""This module contains functions and classes used for extracting +endpoint information out of a Yadis XRD file using the ElementTree XML +parser. +""" + +__all__ = [ + 'BasicServiceEndpoint', + 'mkFilter', + 'IFilter', + 'TransformFilterMaker', + 'CompoundFilter', +] + +from openid.yadis.etxrd import expandService +try: + from collections.abc import Callable +except ImportError: + from collections import Callable + + +class BasicServiceEndpoint(object): + """Generic endpoint object that contains parsed service + information, as well as a reference to the service element from + which it was generated. If there is more than one xrd:Type or + xrd:URI in the xrd:Service, this object represents just one of + those pairs. + + This object can be used as a filter, because it implements + fromBasicServiceEndpoint. + + The simplest kind of filter you can write implements + fromBasicServiceEndpoint, which takes one of these objects. + """ + + def __init__(self, yadis_url, type_uris, uri, service_element): + self.type_uris = type_uris + self.yadis_url = yadis_url + self.uri = uri + self.service_element = service_element + + def matchTypes(self, type_uris): + """Query this endpoint to see if it has any of the given type + URIs. This is useful for implementing other endpoint classes + that e.g. need to check for the presence of multiple versions + of a single protocol. + + @param type_uris: The URIs that you wish to check + @type type_uris: iterable of str + + @return: all types that are in both in type_uris and + self.type_uris + """ + return [uri for uri in type_uris if uri in self.type_uris] + + def fromBasicServiceEndpoint(endpoint): + """Trivial transform from a basic endpoint to itself. This + method exists to allow BasicServiceEndpoint to be used as a + filter. + + If you are subclassing this object, re-implement this function. + + @param endpoint: An instance of BasicServiceEndpoint + @return: The object that was passed in, with no processing. + """ + return endpoint + + fromBasicServiceEndpoint = staticmethod(fromBasicServiceEndpoint) + + +class IFilter(object): + """Interface for Yadis filter objects. Other filter-like things + are convertable to this class.""" + + def getServiceEndpoints(self, yadis_url, service_element): + """Returns an iterator of endpoint objects""" + raise NotImplementedError + + +class TransformFilterMaker(object): + """Take a list of basic filters and makes a filter that transforms + the basic filter into a top-level filter. This is mostly useful + for the implementation of mkFilter, which should only be needed + for special cases or internal use by this library. + + This object is useful for creating simple filters for services + that use one URI and are specified by one Type (we expect most + Types will fit this paradigm). + + Creates a BasicServiceEndpoint object and apply the filter + functions to it until one of them returns a value. + """ + + def __init__(self, filter_functions): + """Initialize the filter maker's state + + @param filter_functions: The endpoint transformer functions to + apply to the basic endpoint. These are called in turn + until one of them does not return None, and the result of + that transformer is returned. + """ + self.filter_functions = filter_functions + + def getServiceEndpoints(self, yadis_url, service_element): + """Returns an iterator of endpoint objects produced by the + filter functions.""" + endpoints = [] + + # Do an expansion of the service element by xrd:Type and xrd:URI + for type_uris, uri, _ in expandService(service_element): + + # Create a basic endpoint object to represent this + # yadis_url, Service, Type, URI combination + endpoint = BasicServiceEndpoint(yadis_url, type_uris, uri, + service_element) + + e = self.applyFilters(endpoint) + if e is not None: + endpoints.append(e) + + return endpoints + + def applyFilters(self, endpoint): + """Apply filter functions to an endpoint until one of them + returns non-None.""" + for filter_function in self.filter_functions: + e = filter_function(endpoint) + if e is not None: + # Once one of the filters has returned an + # endpoint, do not apply any more. + return e + + return None + + +class CompoundFilter(object): + """Create a new filter that applies a set of filters to an endpoint + and collects their results. + """ + + def __init__(self, subfilters): + self.subfilters = subfilters + + def getServiceEndpoints(self, yadis_url, service_element): + """Generate all endpoint objects for all of the subfilters of + this filter and return their concatenation.""" + endpoints = [] + for subfilter in self.subfilters: + endpoints.extend( + subfilter.getServiceEndpoints(yadis_url, service_element)) + return endpoints + + +# Exception raised when something is not able to be turned into a filter +filter_type_error = TypeError( + 'Expected a filter, an endpoint, a callable or a list of any of these.') + + +def mkFilter(parts): + """Convert a filter-convertable thing into a filter + + @param parts: a filter, an endpoint, a callable, or a list of any of these. + """ + # Convert the parts into a list, and pass to mkCompoundFilter + if parts is None: + parts = [BasicServiceEndpoint] + + try: + parts = list(parts) + except TypeError: + return mkCompoundFilter([parts]) + else: + return mkCompoundFilter(parts) + + +def mkCompoundFilter(parts): + """Create a filter out of a list of filter-like things + + Used by mkFilter + + @param parts: list of filter, endpoint, callable or list of any of these + """ + # Separate into a list of callables and a list of filter objects + transformers = [] + filters = [] + for subfilter in parts: + try: + subfilter = list(subfilter) + except TypeError: + # If it's not an iterable + if hasattr(subfilter, 'getServiceEndpoints'): + # It's a full filter + filters.append(subfilter) + elif hasattr(subfilter, 'fromBasicServiceEndpoint'): + # It's an endpoint object, so put its endpoint + # conversion attribute into the list of endpoint + # transformers + transformers.append(subfilter.fromBasicServiceEndpoint) + elif isinstance(subfilter, Callable): + # It's a simple callable, so add it to the list of + # endpoint transformers + transformers.append(subfilter) + else: + raise filter_type_error + else: + filters.append(mkCompoundFilter(subfilter)) + + if transformers: + filters.append(TransformFilterMaker(transformers)) + + if len(filters) == 1: + return filters[0] + else: + return CompoundFilter(filters) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/yadis/manager.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/yadis/manager.py new file mode 100644 index 0000000000000000000000000000000000000000..9c9a042f6dd15c66249ffdc78c2a0406dda011c8 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/yadis/manager.py @@ -0,0 +1,195 @@ +class YadisServiceManager(object): + """Holds the state of a list of selected Yadis services, managing + storing it in a session and iterating over the services in order.""" + + def __init__(self, starting_url, yadis_url, services, session_key): + # The URL that was used to initiate the Yadis protocol + self.starting_url = starting_url + + # The URL after following redirects (the identifier) + self.yadis_url = yadis_url + + # List of service elements + self.services = list(services) + + self.session_key = session_key + + # Reference to the current service object + self._current = None + + def __len__(self): + """How many untried services remain?""" + return len(self.services) + + def __iter__(self): + return self + + def __next__(self): + """Return the next service + + self.current() will continue to return that service until the + next call to this method.""" + try: + self._current = self.services.pop(0) + except IndexError: + raise StopIteration + else: + return self._current + + def current(self): + """Return the current service. + + Returns None if there are no services left. + """ + return self._current + + def forURL(self, url): + return url in [self.starting_url, self.yadis_url] + + def started(self): + """Has the first service been returned?""" + return self._current is not None + + def store(self, session): + """Store this object in the session, by its session key.""" + session[self.session_key] = self + + +class Discovery(object): + """State management for discovery. + + High-level usage pattern is to call .getNextService(discover) in + order to find the next available service for this user for this + session. Once a request completes, call .finish() to clean up the + session state. + + @ivar session: a dict-like object that stores state unique to the + requesting user-agent. This object must be able to store + serializable objects. + + @ivar url: the URL that is used to make the discovery request + + @ivar session_key_suffix: The suffix that will be used to identify + this object in the session object. + """ + + DEFAULT_SUFFIX = 'auth' + PREFIX = '_yadis_services_' + + def __init__(self, session, url, session_key_suffix=None): + """Initialize a discovery object""" + self.session = session + self.url = url + if session_key_suffix is None: + session_key_suffix = self.DEFAULT_SUFFIX + + self.session_key_suffix = session_key_suffix + + def getNextService(self, discover): + """Return the next authentication service for the pair of + user_input and session. This function handles fallback. + + + @param discover: a callable that takes a URL and returns a + list of services + + @type discover: str -> [service] + + + @return: the next available service + """ + manager = self.getManager() + if manager is not None and not manager: + self.destroyManager() + + if not manager: + yadis_url, services = discover(self.url) + manager = self.createManager(services, yadis_url) + + if manager: + service = next(manager) + manager.store(self.session) + else: + service = None + + return service + + def cleanup(self, force=False): + """Clean up Yadis-related services in the session and return + the most-recently-attempted service from the manager, if one + exists. + + @param force: True if the manager should be deleted regardless + of whether it's a manager for self.url. + + @return: current service endpoint object or None if there is + no current service + """ + manager = self.getManager(force=force) + if manager is not None: + service = manager.current() + self.destroyManager(force=force) + else: + service = None + + return service + + ### Lower-level methods + + def getSessionKey(self): + """Get the session key for this starting URL and suffix + + @return: The session key + @rtype: str + """ + return self.PREFIX + self.session_key_suffix + + def getManager(self, force=False): + """Extract the YadisServiceManager for this object's URL and + suffix from the session. + + @param force: True if the manager should be returned + regardless of whether it's a manager for self.url. + + @return: The current YadisServiceManager, if it's for this + URL, or else None + """ + manager = self.session.get(self.getSessionKey()) + if (manager is not None and (manager.forURL(self.url) or force)): + return manager + else: + return None + + def createManager(self, services, yadis_url=None): + """Create a new YadisService Manager for this starting URL and + suffix, and store it in the session. + + @raises KeyError: When I already have a manager. + + @return: A new YadisServiceManager or None + """ + key = self.getSessionKey() + if self.getManager(): + raise KeyError('There is already a %r manager for %r' % + (key, self.url)) + + if not services: + return None + + manager = YadisServiceManager(self.url, yadis_url, services, key) + manager.store(self.session) + return manager + + def destroyManager(self, force=False): + """Delete any YadisServiceManager with this starting URL and + suffix from the session. + + If there is no service manager or the service manager is for a + different URL, it silently does nothing. + + @param force: True if the manager should be deleted regardless + of whether it's a manager for self.url. + """ + if self.getManager(force=force) is not None: + key = self.getSessionKey() + del self.session[key] diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/yadis/parsehtml.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/yadis/parsehtml.py new file mode 100644 index 0000000000000000000000000000000000000000..c78711196c8ad2d56af07d1f12b1b9d65b078d07 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/yadis/parsehtml.py @@ -0,0 +1,207 @@ +__all__ = ['findHTMLMeta', 'MetaNotFound'] + +from html.parser import HTMLParser +import html.entities +import re +import sys + +from openid.yadis.constants import YADIS_HEADER_NAME + +# Size of the chunks to search at a time (also the amount that gets +# read at a time) +CHUNK_SIZE = 1024 * 16 # 16 KB + + +class ParseDone(Exception): + """Exception to hold the URI that was located when the parse is + finished. If the parse finishes without finding the URI, set it to + None.""" + + +class MetaNotFound(Exception): + """Exception to hold the content of the page if we did not find + the appropriate tag""" + + +re_flags = re.IGNORECASE | re.UNICODE | re.VERBOSE +ent_pat = r''' +& + +(?: \#x (?P [a-f0-9]+ ) +| \# (?P \d+ ) +| (?P \w+ ) +) + +;''' + +ent_re = re.compile(ent_pat, re_flags) + + +def substituteMO(mo): + if mo.lastgroup == 'hex': + codepoint = int(mo.group('hex'), 16) + elif mo.lastgroup == 'dec': + codepoint = int(mo.group('dec')) + else: + assert mo.lastgroup == 'word' + codepoint = html.entities.name2codepoint.get(mo.group('word')) + + if codepoint is None: + return mo.group() + else: + return chr(codepoint) + + +def substituteEntities(s): + return ent_re.sub(substituteMO, s) + + +class YadisHTMLParser(HTMLParser): + """Parser that finds a meta http-equiv tag in the head of a html + document. + + When feeding in data, if the tag is matched or it will never be + found, the parser will raise ParseDone with the uri as the first + attribute. + + Parsing state diagram + ===================== + + Any unlisted input does not affect the state:: + + 1, 2, 5 8 + +--------------------------+ +-+ + | | | | + 4 | 3 1, 2, 5, 7 v | v + TOP -> HTML -> HEAD ----------> TERMINATED + | | ^ | ^ ^ + | | 3 | | | | + | +------------+ +-> FOUND ------+ | + | 6 8 | + | 1, 2 | + +------------------------------------+ + + 1. any of , , -> TERMINATE + 2. -> TERMINATE + 3. -> HEAD + 4. -> HTML + 5. -> TERMINATE + 6. -> FOUND + 7. -> TERMINATE + 8. Any input -> TERMINATE + """ + TOP = 0 + HTML = 1 + HEAD = 2 + FOUND = 3 + TERMINATED = 4 + + def __init__(self): + if (sys.version_info.minor <= 2): + # Python 3.2 and below actually require the `strict` argument + # to `html.parser.HTMLParser` -- otherwise it's deprecated and + # we don't want to pass it + super(YadisHTMLParser, self).__init__(strict=False) + else: + super(YadisHTMLParser, self).__init__() + self.phase = self.TOP + + def _terminate(self): + self.phase = self.TERMINATED + raise ParseDone(None) + + def handle_endtag(self, tag): + # If we ever see an end of head, body, or html, bail out right away. + # [1] + if tag in ['head', 'body', 'html']: + self._terminate() + + def handle_starttag(self, tag, attrs): + # if we ever see a start body tag, bail out right away, since + # we want to prevent the meta tag from appearing in the body + # [2] + if tag == 'body': + self._terminate() + + if self.phase == self.TOP: + # At the top level, allow a html tag or a head tag to move + # to the head or html phase + if tag == 'head': + # [3] + self.phase = self.HEAD + elif tag == 'html': + # [4] + self.phase = self.HTML + + elif self.phase == self.HTML: + # if we are in the html tag, allow a head tag to move to + # the HEAD phase. If we get another html tag, then bail + # out + if tag == 'head': + # [3] + self.phase = self.HEAD + elif tag == 'html': + # [5] + self._terminate() + + elif self.phase == self.HEAD: + # If we are in the head phase, look for the appropriate + # meta tag. If we get a head or body tag, bail out. + if tag == 'meta': + attrs_d = dict(attrs) + http_equiv = attrs_d.get('http-equiv', '').lower() + if http_equiv == YADIS_HEADER_NAME.lower(): + raw_attr = attrs_d.get('content') + yadis_loc = substituteEntities(raw_attr) + # [6] + self.phase = self.FOUND + raise ParseDone(yadis_loc) + + elif tag in ('head', 'html'): + # [5], [7] + self._terminate() + + def feed(self, chars): + # [8] + if self.phase in (self.TERMINATED, self.FOUND): + self._terminate() + + return super(YadisHTMLParser, self).feed(chars) + + +def findHTMLMeta(stream): + """Look for a meta http-equiv tag with the YADIS header name. + + @param stream: Source of the html text + @type stream: Object that implements a read() method that works + like file.read + + @return: The URI from which to fetch the XRDS document + @rtype: str + + @raises MetaNotFound: raised with the content that was + searched as the first parameter. + """ + parser = YadisHTMLParser() + chunks = [] + + while 1: + chunk = stream.read(CHUNK_SIZE) + if not chunk: + # End of file + break + + chunks.append(chunk) + try: + parser.feed(chunk) + except ParseDone as why: + uri = why.args[0] + if uri is None: + # Parse finished, but we may need the rest of the file + chunks.append(stream.read()) + break + else: + return uri + + content = ''.join(chunks) + raise MetaNotFound(content) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/yadis/services.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/yadis/services.py new file mode 100644 index 0000000000000000000000000000000000000000..2092a9cae77db1d4d4a21e0fa9b878ba209e605e --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/yadis/services.py @@ -0,0 +1,56 @@ +# -*- test-case-name: openid.test.test_services -*- + +from openid.yadis.filters import mkFilter +from openid.yadis.discover import discover, DiscoveryFailure +from openid.yadis.etxrd import parseXRDS, iterServices, XRDSError + + +def getServiceEndpoints(input_url, flt=None): + """Perform the Yadis protocol on the input URL and return an + iterable of resulting endpoint objects. + + @param flt: A filter object or something that is convertable to + a filter object (using mkFilter) that will be used to generate + endpoint objects. This defaults to generating BasicEndpoint + objects. + + @param input_url: The URL on which to perform the Yadis protocol + + @return: The normalized identity URL and an iterable of endpoint + objects generated by the filter function. + + @rtype: (str, [endpoint]) + + @raises DiscoveryFailure: when Yadis fails to obtain an XRDS document. + """ + result = discover(input_url) + try: + endpoints = applyFilter(result.normalized_uri, result.response_text, + flt) + except XRDSError as err: + raise DiscoveryFailure(str(err), None) + return (result.normalized_uri, endpoints) + + +def applyFilter(normalized_uri, xrd_data, flt=None): + """Generate an iterable of endpoint objects given this input data, + presumably from the result of performing the Yadis protocol. + + @param normalized_uri: The input URL, after following redirects, + as in the Yadis protocol. + + + @param xrd_data: The XML text the XRDS file fetched from the + normalized URI. + @type xrd_data: str + + """ + flt = mkFilter(flt) + et = parseXRDS(xrd_data) + + endpoints = [] + for service_element in iterServices(et): + endpoints.extend( + flt.getServiceEndpoints(normalized_uri, service_element)) + + return endpoints diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/yadis/xri.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/yadis/xri.py new file mode 100644 index 0000000000000000000000000000000000000000..621d30465c22118957c925ca23078ee44e93d599 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/yadis/xri.py @@ -0,0 +1,122 @@ +# -*- test-case-name: openid.test.test_xri -*- +"""Utility functions for handling XRIs. + +@see: XRI Syntax v2.0 at the U{OASIS XRI Technical Committee} +""" + +import re +from functools import reduce + +from openid import codecutil # registers 'oid_percent_escape' encoding handler + +XRI_AUTHORITIES = ['!', '=', '@', '+', '$', '('] + + +def identifierScheme(identifier): + """Determine if this identifier is an XRI or URI. + + @returns: C{"XRI"} or C{"URI"} + """ + if identifier.startswith('xri://') or (identifier and + identifier[0] in XRI_AUTHORITIES): + return "XRI" + else: + return "URI" + + +def toIRINormal(xri): + """Transform an XRI to IRI-normal form.""" + if not xri.startswith('xri://'): + xri = 'xri://' + xri + return escapeForIRI(xri) + + +_xref_re = re.compile(r'\((.*?)\)') + + +def _escape_xref(xref_match): + """Escape things that need to be escaped if they're in a cross-reference. + """ + xref = xref_match.group() + xref = xref.replace('/', '%2F') + xref = xref.replace('?', '%3F') + xref = xref.replace('#', '%23') + return xref + + +def escapeForIRI(xri): + """Escape things that need to be escaped when transforming to an IRI.""" + xri = xri.replace('%', '%25') + xri = _xref_re.sub(_escape_xref, xri) + return xri + + +def toURINormal(xri): + """Transform an XRI to URI normal form.""" + return iriToURI(toIRINormal(xri)) + + +def iriToURI(iri): + """Transform an IRI to a URI by escaping unicode.""" + # According to RFC 3987, section 3.1, "Mapping of IRIs to URIs" + if isinstance(iri, bytes): + iri = str(iri, encoding="utf-8") + return iri.encode('ascii', errors='oid_percent_escape').decode() + + +def providerIsAuthoritative(providerID, canonicalID): + """Is this provider ID authoritative for this XRI? + + @returntype: bool + """ + # XXX: can't use rsplit until we require python >= 2.4. + lastbang = canonicalID.rindex('!') + parent = canonicalID[:lastbang] + return parent == providerID + + +def rootAuthority(xri): + """Return the root authority for an XRI. + + Example:: + + rootAuthority("xri://@example") == "xri://@" + + @type xri: unicode + @returntype: unicode + """ + if xri.startswith('xri://'): + xri = xri[6:] + authority = xri.split('/', 1)[0] + if authority[0] == '(': + # Cross-reference. + # XXX: This is incorrect if someone nests cross-references so there + # is another close-paren in there. Hopefully nobody does that + # before we have a real xriparse function. Hopefully nobody does + # that *ever*. + root = authority[:authority.index(')') + 1] + elif authority[0] in XRI_AUTHORITIES: + # Other XRI reference. + root = authority[0] + else: + # IRI reference. XXX: Can IRI authorities have segments? + segments = authority.split('!') + segments = reduce(list.__add__, [s.split('*') for s in segments]) + root = segments[0] + + return XRI(root) + + +def XRI(xri): + """An XRI object allowing comparison of XRI. + + Ideally, this would do full normalization and provide comparsion + operators as per XRI Syntax. Right now, it just does a bit of + canonicalization by ensuring the xri scheme is present. + + @param xri: an xri string + @type xri: unicode + """ + if not xri.startswith('xri://'): + xri = 'xri://' + xri + return xri diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/yadis/xrires.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/yadis/xrires.py new file mode 100644 index 0000000000000000000000000000000000000000..a7140294f09b0bbd5d88690c2a8503fa78397447 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/openid/yadis/xrires.py @@ -0,0 +1,123 @@ +# -*- test-case-name: openid.test.test_xrires -*- +"""XRI resolution. +""" + +from urllib.parse import urlencode +from openid import fetchers +from openid.yadis import etxrd +from openid.yadis.xri import toURINormal +from openid.yadis.services import iterServices + +DEFAULT_PROXY = 'http://proxy.xri.net/' + + +class ProxyResolver(object): + """Python interface to a remote XRI proxy resolver. + """ + + def __init__(self, proxy_url=DEFAULT_PROXY): + self.proxy_url = proxy_url + + def queryURL(self, xri, service_type=None): + """Build a URL to query the proxy resolver. + + @param xri: An XRI to resolve. + @type xri: unicode + + @param service_type: The service type to resolve, if you desire + service endpoint selection. A service type is a URI. + @type service_type: str + + @returns: a URL + @returntype: str + """ + # Trim off the xri:// prefix. The proxy resolver didn't accept it + # when this code was written, but that may (or may not) change for + # XRI Resolution 2.0 Working Draft 11. + qxri = toURINormal(xri)[6:] + hxri = self.proxy_url + qxri + args = { + # XXX: If the proxy resolver will ensure that it doesn't return + # bogus CanonicalIDs (as per Steve's message of 15 Aug 2006 + # 11:13:42), then we could ask for application/xrd+xml instead, + # which would give us a bit less to process. + '_xrd_r': 'application/xrds+xml', + } + if service_type: + args['_xrd_t'] = service_type + else: + # Don't perform service endpoint selection. + args['_xrd_r'] += ';sep=false' + query = _appendArgs(hxri, args) + return query + + def query(self, xri, service_types): + """Resolve some services for an XRI. + + Note: I don't implement any service endpoint selection beyond what + the resolver I'm querying does, so the Services I return may well + include Services that were not of the types you asked for. + + May raise fetchers.HTTPFetchingError or L{etxrd.XRDSError} if + the fetching or parsing don't go so well. + + @param xri: An XRI to resolve. + @type xri: unicode + + @param service_types: A list of services types to query for. Service + types are URIs. + @type service_types: list of str + + @returns: tuple of (CanonicalID, Service elements) + @returntype: (unicode, list of C{ElementTree.Element}s) + """ + # FIXME: No test coverage! + services = [] + # Make a seperate request to the proxy resolver for each service + # type, as, if it is following Refs, it could return a different + # XRDS for each. + + canonicalID = None + + for service_type in service_types: + url = self.queryURL(xri, service_type) + response = fetchers.fetch(url) + if response.status not in (200, 206): + # XXX: sucks to fail silently. + # print "response not OK:", response + continue + et = etxrd.parseXRDS(response.body) + canonicalID = etxrd.getCanonicalID(xri, et) + some_services = list(iterServices(et)) + services.extend(some_services) + # TODO: + # * If we do get hits for multiple service_types, we're almost + # certainly going to have duplicated service entries and + # broken priority ordering. + return canonicalID, services + + +def _appendArgs(url, args): + """Append some arguments to an HTTP query. + """ + # to be merged with oidutil.appendArgs when we combine the projects. + if hasattr(args, 'items'): + args = list(args.items()) + args.sort() + + if len(args) == 0: + return url + + # According to XRI Resolution section "QXRI query parameters": + # + # """If the original QXRI had a null query component (only a leading + # question mark), or a query component consisting of only question + # marks, one additional leading question mark MUST be added when + # adding any XRI resolution parameters.""" + + if '?' in url.rstrip('?'): + sep = '&' + else: + sep = '?' + + return '%s%s%s' % (url, sep, urlencode(args)) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum-3.4.0.dist-info/INSTALLER b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum-3.4.0.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum-3.4.0.dist-info/INSTALLER @@ -0,0 +1 @@ +pip diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum-3.4.0.dist-info/METADATA b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum-3.4.0.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..9abd08e29444230e00a1bfa74011e230d66bf1b9 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum-3.4.0.dist-info/METADATA @@ -0,0 +1,117 @@ +Metadata-Version: 2.3 +Name: opt_einsum +Version: 3.4.0 +Summary: Path optimization of einsum functions. +Author-email: Daniel Smith +License-Expression: MIT +License-File: LICENSE +Classifier: Development Status :: 5 - Production/Stable +Classifier: Intended Audience :: Developers +Classifier: Intended Audience :: Science/Research +Classifier: License :: OSI Approved :: MIT License +Classifier: Programming Language :: Python +Classifier: Programming Language :: Python :: 3 +Classifier: Programming Language :: Python :: 3 :: Only +Classifier: Programming Language :: Python :: 3.9 +Classifier: Programming Language :: Python :: 3.10 +Classifier: Programming Language :: Python :: 3.11 +Classifier: Programming Language :: Python :: 3.12 +Classifier: Programming Language :: Python :: 3.13 +Classifier: Programming Language :: Python :: Implementation :: CPython +Classifier: Programming Language :: Python :: Implementation :: PyPy +Classifier: Topic :: Software Development :: Libraries :: Python Modules +Requires-Python: >=3.8 +Description-Content-Type: text/markdown + +# Optimized Einsum + +[![Tests](https://github.com/dgasmith/opt_einsum/actions/workflows/Tests.yml/badge.svg)](https://github.com/dgasmith/opt_einsum/actions/workflows/Tests.yml) +[![codecov](https://codecov.io/gh/dgasmith/opt_einsum/branch/master/graph/badge.svg)](https://codecov.io/gh/dgasmith/opt_einsum) +[![Anaconda-Server Badge](https://anaconda.org/conda-forge/opt_einsum/badges/version.svg)](https://anaconda.org/conda-forge/opt_einsum) +[![PyPI](https://img.shields.io/pypi/v/opt_einsum.svg)](https://pypi.org/project/opt-einsum/#description) +[![PyPIStats](https://img.shields.io/pypi/dm/opt_einsum)](https://pypistats.org/packages/opt-einsum) +[![Documentation Status](https://github.com/dgasmith/opt_einsum/actions/workflows/Docs.yaml/badge.svg)](https://dgasmith.github.io/opt_einsum/) +[![DOI](https://joss.theoj.org/papers/10.21105/joss.00753/status.svg)](https://doi.org/10.21105/joss.00753) + +## Optimized Einsum: A tensor contraction order optimizer + +Optimized einsum can significantly reduce the overall execution time of einsum-like expressions (e.g., +[`np.einsum`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.einsum.html), +[`dask.array.einsum`](https://docs.dask.org/en/latest/array-api.html#dask.array.einsum), +[`pytorch.einsum`](https://pytorch.org/docs/stable/torch.html#torch.einsum), +[`tensorflow.einsum`](https://www.tensorflow.org/api_docs/python/tf/einsum), +) +by optimizing the expression's contraction order and dispatching many +operations to canonical BLAS, cuBLAS, or other specialized routines. + +Optimized +einsum is agnostic to the backend and can handle NumPy, Dask, PyTorch, +Tensorflow, CuPy, Sparse, Theano, JAX, and Autograd arrays as well as potentially +any library which conforms to a standard API. See the +[**documentation**](https://dgasmith.github.io/opt_einsum/) for more +information. + +## Example usage + +The [`opt_einsum.contract`](https://dgasmith.github.io/opt_einsum/api_reference#opt_einsumcontract) +function can often act as a drop-in replacement for `einsum` +functions without further changes to the code while providing superior performance. +Here, a tensor contraction is performed with and without optimization: + +```python +import numpy as np +from opt_einsum import contract + +N = 10 +C = np.random.rand(N, N) +I = np.random.rand(N, N, N, N) + +%timeit np.einsum('pi,qj,ijkl,rk,sl->pqrs', C, C, I, C, C) +1 loops, best of 3: 934 ms per loop + +%timeit contract('pi,qj,ijkl,rk,sl->pqrs', C, C, I, C, C) +1000 loops, best of 3: 324 us per loop +``` + +In this particular example, we see a ~3000x performance improvement which is +not uncommon when compared against unoptimized contractions. See the [backend +examples](https://dgasmith.github.io/opt_einsum/getting_started/backends) +for more information on using other backends. + +## Features + +The algorithms found in this repository often power the `einsum` optimizations +in many of the above projects. For example, the optimization of `np.einsum` +has been passed upstream and most of the same features that can be found in +this repository can be enabled with `np.einsum(..., optimize=True)`. However, +this repository often has more up to date algorithms for complex contractions. + +The following capabilities are enabled by `opt_einsum`: + +* Inspect [detailed information](https://dgasmith.github.io/opt_einsum/paths/introduction) about the path chosen. +* Perform contractions with [numerous backends](https://dgasmith.github.io/opt_einsum/getting_started/backends), including on the GPU and with libraries such as [TensorFlow](https://www.tensorflow.org) and [PyTorch](https://pytorch.org). +* Generate [reusable expressions](https://dgasmith.github.io/opt_einsum/getting_started/reusing_paths), potentially with [constant tensors](https://dgasmith.github.io/opt_einsum/getting_started/reusing_paths#specifying-constants), that can be compiled for greater performance. +* Use an arbitrary number of indices to find contractions for [hundreds or even thousands of tensors](https://dgasmith.github.io/opt_einsum/examples/large_expr_with_greedy). +* Share [intermediate computations](https://dgasmith.github.io/opt_einsum/getting_started/sharing_intermediates) among multiple contractions. +* Compute gradients of tensor contractions using [autograd](https://github.com/HIPS/autograd) or [jax](https://github.com/google/jax) + +Please see the [documentation](https://dgasmith.github.io/opt_einsum/index) for more features! + +## Installation + +`opt_einsum` can either be installed via `pip install opt_einsum` or from conda `conda install opt_einsum -c conda-forge`. +See the installation [documentation](https://dgasmith.github.io/opt_einsum/getting_started/install) for further methods. + +## Citation + +If this code has benefited your research, please support us by citing: + +Daniel G. A. Smith and Johnnie Gray, opt_einsum - A Python package for optimizing contraction order for einsum-like expressions. *Journal of Open Source Software*, **2018**, 3(26), 753 + +DOI: + +## Contributing + +All contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome. + +A detailed overview on how to contribute can be found in the [contributing guide](https://github.com/dgasmith/opt_einsum/blob/master/.github/CONTRIBUTING.md). diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum-3.4.0.dist-info/RECORD b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum-3.4.0.dist-info/RECORD new file mode 100644 index 0000000000000000000000000000000000000000..e061f27bcff5d4897fcb5f052a6aa37f443c9432 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum-3.4.0.dist-info/RECORD @@ -0,0 +1,61 @@ 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a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum-3.4.0.dist-info/licenses/LICENSE b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum-3.4.0.dist-info/licenses/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..e3d77df3e14b5a4316cfa8acbfef044812c41768 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum-3.4.0.dist-info/licenses/LICENSE @@ -0,0 +1,22 @@ +The MIT License (MIT) + +Copyright (c) 2014 Daniel Smith + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. + diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..820bfb1d6d341540515f93551a028287482bddd0 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/__init__.py @@ -0,0 +1,29 @@ +"""Main init function for opt_einsum.""" + +from opt_einsum import blas, helpers, path_random, paths +from opt_einsum._version import __version__ +from opt_einsum.contract import contract, contract_expression, contract_path +from opt_einsum.parser import get_symbol +from opt_einsum.path_random import RandomGreedy +from opt_einsum.paths import BranchBound, DynamicProgramming +from opt_einsum.sharing import shared_intermediates + +__all__ = [ + "__version__", + "blas", + "helpers", + "path_random", + "paths", + "contract", + "contract_expression", + "contract_path", + "get_symbol", + "RandomGreedy", + "BranchBound", + "DynamicProgramming", + "shared_intermediates", +] + + +paths.register_path_fn("random-greedy", path_random.random_greedy) +paths.register_path_fn("random-greedy-128", path_random.random_greedy_128) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/_version.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/_version.py new file mode 100644 index 0000000000000000000000000000000000000000..95d91a25346e8b0e970514912bf0876d217cf7e5 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/_version.py @@ -0,0 +1,16 @@ +# file generated by setuptools_scm +# don't change, don't track in version control +TYPE_CHECKING = False +if TYPE_CHECKING: + from typing import Tuple, Union + VERSION_TUPLE = Tuple[Union[int, str], ...] +else: + VERSION_TUPLE = object + +version: str +__version__: str +__version_tuple__: VERSION_TUPLE +version_tuple: VERSION_TUPLE + +__version__ = version = '3.4.0' +__version_tuple__ = version_tuple = (3, 4, 0) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/backends/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/backends/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..487f685de3edaea97bd16361fa1242f390cc8578 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/backends/__init__.py @@ -0,0 +1,28 @@ +"""Compute backends for opt_einsum.""" + +# Backends +from opt_einsum.backends.cupy import to_cupy +from opt_einsum.backends.dispatch import ( + build_expression, + evaluate_constants, + get_func, + has_backend, + has_einsum, + has_tensordot, +) +from opt_einsum.backends.tensorflow import to_tensorflow +from opt_einsum.backends.theano import to_theano +from opt_einsum.backends.torch import to_torch + +__all__ = [ + "get_func", + "has_einsum", + "has_tensordot", + "build_expression", + "evaluate_constants", + "has_backend", + "to_tensorflow", + "to_theano", + "to_cupy", + "to_torch", +] diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/backends/cupy.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/backends/cupy.py new file mode 100644 index 0000000000000000000000000000000000000000..34e47639d265636a24d67c2a0ac1b0f442bcc029 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/backends/cupy.py @@ -0,0 +1,32 @@ +"""Required functions for optimized contractions of numpy arrays using cupy.""" + +from opt_einsum.helpers import has_array_interface +from opt_einsum.sharing import to_backend_cache_wrap + +__all__ = ["to_cupy", "build_expression", "evaluate_constants"] + + +@to_backend_cache_wrap +def to_cupy(array): # pragma: no cover + import cupy + + if has_array_interface(array): + return cupy.asarray(array) + + return array + + +def build_expression(_, expr): # pragma: no cover + """Build a cupy function based on ``arrays`` and ``expr``.""" + + def cupy_contract(*arrays): + return expr._contract([to_cupy(x) for x in arrays], backend="cupy").get() + + return cupy_contract + + +def evaluate_constants(const_arrays, expr): # pragma: no cover + """Convert constant arguments to cupy arrays, and perform any possible + constant contractions. + """ + return expr(*[to_cupy(x) for x in const_arrays], backend="cupy", evaluate_constants=True) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/backends/dispatch.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/backends/dispatch.py new file mode 100644 index 0000000000000000000000000000000000000000..dd716424f6768038a46513fa364e8ba9e58241b6 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/backends/dispatch.py @@ -0,0 +1,156 @@ +"""Handles dispatching array operations to the correct backend library, as well +as converting arrays to backend formats and then potentially storing them as +constants. +""" + +import importlib +from typing import Any, Dict, Tuple + +from opt_einsum.backends import cupy as _cupy +from opt_einsum.backends import jax as _jax +from opt_einsum.backends import object_arrays +from opt_einsum.backends import tensorflow as _tensorflow +from opt_einsum.backends import theano as _theano +from opt_einsum.backends import torch as _torch + +__all__ = [ + "get_func", + "has_einsum", + "has_tensordot", + "build_expression", + "evaluate_constants", + "has_backend", +] + +# known non top-level imports +_aliases = { + "dask": "dask.array", + "theano": "theano.tensor", + "torch": "opt_einsum.backends.torch", + "jax": "jax.numpy", + "jaxlib": "jax.numpy", + "autograd": "autograd.numpy", + "mars": "mars.tensor", +} + + +def _import_func(func: str, backend: str, default: Any = None) -> Any: + """Try and import ``{backend}.{func}``. + If library is installed and func is found, return the func; + otherwise if default is provided, return default; + otherwise raise an error. + """ + try: + lib = importlib.import_module(_aliases.get(backend, backend)) + return getattr(lib, func) if default is None else getattr(lib, func, default) + except AttributeError: + error_msg = ( + "{} doesn't seem to provide the function {} - see " + "https://optimized-einsum.readthedocs.io/en/latest/backends.html " + "for details on which functions are required for which contractions." + ) + raise AttributeError(error_msg.format(backend, func)) + + +# manually cache functions as python2 doesn't support functools.lru_cache +# other libs will be added to this if needed, but pre-populate with numpy +_cached_funcs: Dict[Tuple[str, str], Any] = { + ("einsum", "object"): object_arrays.object_einsum, +} + +try: + import numpy as np # type: ignore + + _cached_funcs[("tensordot", "numpy")] = np.tensordot + _cached_funcs[("transpose", "numpy")] = np.transpose + _cached_funcs[("einsum", "numpy")] = np.einsum + # also pre-populate with the arbitrary object backend + _cached_funcs[("tensordot", "object")] = np.tensordot + _cached_funcs[("transpose", "object")] = np.transpose +except ModuleNotFoundError: + pass + + +def get_func(func: str, backend: str = "numpy", default: Any = None) -> Any: + """Return ``{backend}.{func}``, e.g. ``numpy.einsum``, + or a default func if provided. Cache result. + """ + try: + return _cached_funcs[func, backend] + except KeyError: + fn = _import_func(func, backend, default) + _cached_funcs[func, backend] = fn + return fn + + +# mark libs with einsum, else try to use tensordot/transpose as much as possible +_has_einsum: Dict[str, bool] = {} + + +def has_einsum(backend: str) -> bool: + """Check if ``{backend}.einsum`` exists, cache result for performance.""" + try: + return _has_einsum[backend] + except KeyError: + try: + get_func("einsum", backend) + _has_einsum[backend] = True + except AttributeError: + _has_einsum[backend] = False + + return _has_einsum[backend] + + +_has_tensordot: Dict[str, bool] = {} + + +def has_tensordot(backend: str) -> bool: + """Check if ``{backend}.tensordot`` exists, cache result for performance.""" + try: + return _has_tensordot[backend] + except KeyError: + try: + get_func("tensordot", backend) + _has_tensordot[backend] = True + except AttributeError: + _has_tensordot[backend] = False + + return _has_tensordot[backend] + + +# Dispatch to correct expression backend +# these are the backends which support explicit to-and-from numpy conversion +CONVERT_BACKENDS = { + "tensorflow": _tensorflow.build_expression, + "theano": _theano.build_expression, + "cupy": _cupy.build_expression, + "torch": _torch.build_expression, + "jax": _jax.build_expression, +} + +EVAL_CONSTS_BACKENDS = { + "tensorflow": _tensorflow.evaluate_constants, + "theano": _theano.evaluate_constants, + "cupy": _cupy.evaluate_constants, + "torch": _torch.evaluate_constants, + "jax": _jax.evaluate_constants, +} + + +def build_expression(backend, arrays, expr): + """Build an expression, based on ``expr`` and initial arrays ``arrays``, + that evaluates using backend ``backend``. + """ + return CONVERT_BACKENDS[backend](arrays, expr) + + +def evaluate_constants(backend, arrays, expr): + """Convert constant arrays to the correct backend, and perform as much of + the contraction of ``expr`` with these as possible. + """ + return EVAL_CONSTS_BACKENDS[backend](arrays, expr) + + +def has_backend(backend: str) -> bool: + """Checks if the backend is known.""" + return backend.lower() in CONVERT_BACKENDS diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/backends/jax.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/backends/jax.py new file mode 100644 index 0000000000000000000000000000000000000000..45aa4b28d4cf31328eb21afc8f78cf478ca79769 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/backends/jax.py @@ -0,0 +1,45 @@ +"""Required functions for optimized contractions of numpy arrays using jax.""" + +from opt_einsum.sharing import to_backend_cache_wrap + +__all__ = ["build_expression", "evaluate_constants"] + +_JAX = None + + +def _get_jax_and_to_jax(): + global _JAX + if _JAX is None: + import jax # type: ignore + + @to_backend_cache_wrap + @jax.jit + def to_jax(x): + return x + + _JAX = jax, to_jax + + return _JAX + + +def build_expression(_, expr): # pragma: no cover + """Build a jax function based on ``arrays`` and ``expr``.""" + jax, _ = _get_jax_and_to_jax() + + jax_expr = jax.jit(expr._contract) + + def jax_contract(*arrays): + import numpy as np # type: ignore + + return np.asarray(jax_expr(arrays)) + + return jax_contract + + +def evaluate_constants(const_arrays, expr): # pragma: no cover + """Convert constant arguments to jax arrays, and perform any possible + constant contractions. + """ + jax, to_jax = _get_jax_and_to_jax() + + return expr(*[to_jax(x) for x in const_arrays], backend="jax", evaluate_constants=True) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/backends/object_arrays.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/backends/object_arrays.py new file mode 100644 index 0000000000000000000000000000000000000000..8954a1800e78cdb809b546677613a5c4c98ffb7c --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/backends/object_arrays.py @@ -0,0 +1,59 @@ +"""Functions for performing contractions with array elements which are objects.""" + +import functools +import operator + +from opt_einsum.typing import ArrayType + + +def object_einsum(eq: str, *arrays: ArrayType) -> ArrayType: + """A ``einsum`` implementation for ``numpy`` arrays with object dtype. + The loop is performed in python, meaning the objects themselves need + only to implement ``__mul__`` and ``__add__`` for the contraction to be + computed. This may be useful when, for example, computing expressions of + tensors with symbolic elements, but note it will be very slow when compared + to ``numpy.einsum`` and numeric data types! + + Parameters + ---------- + eq : str + The contraction string, should specify output. + arrays : sequence of arrays + These can be any indexable arrays as long as addition and + multiplication is defined on the elements. + + Returns: + ------- + out : numpy.ndarray + The output tensor, with ``dtype=object``. + """ + import numpy as np # type: ignore + + # when called by ``opt_einsum`` we will always be given a full eq + lhs, output = eq.split("->") + inputs = lhs.split(",") + + sizes = {} + for term, array in zip(inputs, arrays): + for k, d in zip(term, array.shape): + sizes[k] = d + + out_size = tuple(sizes[k] for k in output) + out = np.empty(out_size, dtype=object) + + inner = tuple(k for k in sizes if k not in output) + inner_size = tuple(sizes[k] for k in inner) + + for coo_o in np.ndindex(*out_size): + coord = dict(zip(output, coo_o)) + + def gen_inner_sum(): + for coo_i in np.ndindex(*inner_size): + coord.update(dict(zip(inner, coo_i))) + locs = (tuple(coord[k] for k in term) for term in inputs) + elements = (array[loc] for array, loc in zip(arrays, locs)) + yield functools.reduce(operator.mul, elements) + + out[coo_o] = functools.reduce(operator.add, gen_inner_sum()) + + return out diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/backends/tensorflow.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/backends/tensorflow.py new file mode 100644 index 0000000000000000000000000000000000000000..a6544f8d16b77e5ac5bcebaa132bd44909338df9 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/backends/tensorflow.py @@ -0,0 +1,123 @@ +"""Required functions for optimized contractions of numpy arrays using tensorflow.""" + +from opt_einsum.helpers import has_array_interface +from opt_einsum.sharing import to_backend_cache_wrap + +__all__ = ["to_tensorflow", "build_expression", "evaluate_constants"] + +_CACHED_TF_DEVICE = None + + +def _get_tensorflow_and_device(): + global _CACHED_TF_DEVICE + + if _CACHED_TF_DEVICE is None: + import tensorflow as tf # type: ignore + + try: + eager = tf.executing_eagerly() + except AttributeError: + try: + eager = tf.contrib.eager.in_eager_mode() + except AttributeError: + eager = False + + device = tf.test.gpu_device_name() + if not device: + device = "cpu" + + _CACHED_TF_DEVICE = tf, device, eager + + return _CACHED_TF_DEVICE + + +@to_backend_cache_wrap(constants=True) +def to_tensorflow(array, constant=False): + """Convert a numpy array to a ``tensorflow.placeholder`` instance.""" + tf, device, eager = _get_tensorflow_and_device() + + if eager: + if has_array_interface(array): + with tf.device(device): + return tf.convert_to_tensor(array) + + return array + + if has_array_interface(array): + if constant: + return tf.convert_to_tensor(array) + + return tf.placeholder(array.dtype, array.shape) + + return array + + +# Standard graph mode + + +def build_expression_graph(arrays, expr): + """Build a tensorflow function based on ``arrays`` and ``expr``.""" + tf, _, _ = _get_tensorflow_and_device() + + placeholders = [to_tensorflow(array) for array in arrays] + graph = expr._contract(placeholders, backend="tensorflow") + + def tensorflow_contract(*arrays): + session = tf.get_default_session() + # only want to feed placeholders - constant tensors already have values + feed_dict = {p: a for p, a in zip(placeholders, arrays) if p.op.type == "Placeholder"} + return session.run(graph, feed_dict=feed_dict) + + return tensorflow_contract + + +def evaluate_constants_graph(const_arrays, expr): + """Convert constant arguments to tensorflow constants, and perform any + possible constant contractions. Requires evaluating a tensorflow graph. + """ + tf, _, _ = _get_tensorflow_and_device() + + # compute the partial graph of new inputs + const_arrays = [to_tensorflow(x, constant=True) for x in const_arrays] + new_ops, new_contraction_list = expr(*const_arrays, backend="tensorflow", evaluate_constants=True) + + # evaluate the new inputs and convert back to tensorflow, maintaining None as non-consts + session = tf.get_default_session() + new_consts = iter(session.run([x for x in new_ops if x is not None])) + new_ops = [None if x is None else to_tensorflow(next(new_consts), constant=True) for x in new_ops] + + return new_ops, new_contraction_list + + +# Eager execution mode + + +def build_expression_eager(_, expr): + """Build a eager tensorflow function based on ``arrays`` and ``expr``.""" + + def tensorflow_eager_contract(*arrays): + return expr._contract([to_tensorflow(x) for x in arrays], backend="tensorflow").numpy() + + return tensorflow_eager_contract + + +def evaluate_constants_eager(const_arrays, expr): + """Convert constant arguments to tensorflow_eager arrays, and perform any + possible constant contractions. + """ + return expr(*[to_tensorflow(x) for x in const_arrays], backend="tensorflow", evaluate_constants=True) + + +# Dispatch to eager or graph mode + + +def build_expression(arrays, expr): + _, _, eager = _get_tensorflow_and_device() + fn = build_expression_eager if eager else build_expression_graph + return fn(arrays, expr) + + +def evaluate_constants(const_arrays, expr): + _, _, eager = _get_tensorflow_and_device() + fn = evaluate_constants_eager if eager else evaluate_constants_graph + return fn(const_arrays, expr) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/backends/theano.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/backends/theano.py new file mode 100644 index 0000000000000000000000000000000000000000..5b54aab0740785fe868f13ee355f20b78a5179ea --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/backends/theano.py @@ -0,0 +1,48 @@ +"""Required functions for optimized contractions of numpy arrays using theano.""" + +from opt_einsum.helpers import has_array_interface +from opt_einsum.sharing import to_backend_cache_wrap + +__all__ = ["to_theano", "build_expression", "evaluate_constants"] + + +@to_backend_cache_wrap(constants=True) +def to_theano(array, constant=False): + """Convert a numpy array to ``theano.tensor.TensorType`` instance.""" + import theano # type: ignore + + if has_array_interface(array): + if constant: + return theano.tensor.constant(array) + + return theano.tensor.TensorType(dtype=array.dtype, broadcastable=[False] * len(array.shape))() + + return array + + +def build_expression(arrays, expr): + """Build a theano function based on ``arrays`` and ``expr``.""" + import theano + + in_vars = [to_theano(array) for array in arrays] + out_var = expr._contract(in_vars, backend="theano") + + # don't supply constants to graph + graph_ins = [x for x in in_vars if not isinstance(x, theano.tensor.TensorConstant)] + graph = theano.function(graph_ins, out_var) + + def theano_contract(*arrays): + return graph(*[x for x in arrays if not isinstance(x, theano.tensor.TensorConstant)]) + + return theano_contract + + +def evaluate_constants(const_arrays, expr): + # compute the partial graph of new inputs + const_arrays = [to_theano(x, constant=True) for x in const_arrays] + new_ops, new_contraction_list = expr(*const_arrays, backend="theano", evaluate_constants=True) + + # evaluate the new inputs and convert to theano shared tensors + new_ops = [None if x is None else to_theano(x.eval(), constant=True) for x in new_ops] + + return new_ops, new_contraction_list diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/backends/torch.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/backends/torch.py new file mode 100644 index 0000000000000000000000000000000000000000..f7f01f265ad6b3961ff0707e831ddceeaf6af4bb --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/backends/torch.py @@ -0,0 +1,130 @@ +"""Required functions for optimized contractions of numpy arrays using pytorch.""" + +from opt_einsum.helpers import has_array_interface +from opt_einsum.parser import convert_to_valid_einsum_chars +from opt_einsum.sharing import to_backend_cache_wrap + +__all__ = [ + "transpose", + "einsum", + "tensordot", + "to_torch", + "build_expression", + "evaluate_constants", +] + +_TORCH_DEVICE = None +_TORCH_HAS_TENSORDOT = None + +_torch_symbols_base = "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ" + + +def _get_torch_and_device(): + global _TORCH_DEVICE + global _TORCH_HAS_TENSORDOT + + if _TORCH_DEVICE is None: + import torch # type: ignore + + device = "cuda" if torch.cuda.is_available() else "cpu" + _TORCH_DEVICE = torch, device + _TORCH_HAS_TENSORDOT = hasattr(torch, "tensordot") + + return _TORCH_DEVICE + + +def transpose(a, axes): + """Normal torch transpose is only valid for 2D matrices.""" + return a.permute(*axes) + + +def einsum(equation, *operands, **kwargs): + """Variadic version of torch.einsum to match numpy api.""" + # rename symbols to support PyTorch 0.4.1 and earlier, + # which allow only symbols a-z. + equation = convert_to_valid_einsum_chars(equation) + + torch, _ = _get_torch_and_device() + return torch.einsum(equation, operands) + + +def tensordot(x, y, axes=2): + """Simple translation of tensordot syntax to einsum.""" + torch, _ = _get_torch_and_device() + + if _TORCH_HAS_TENSORDOT: + return torch.tensordot(x, y, dims=axes) + + xnd = x.ndimension() + ynd = y.ndimension() + + # convert int argument to (list[int], list[int]) + if isinstance(axes, int): + axes = range(xnd - axes, xnd), range(axes) + + # convert (int, int) to (list[int], list[int]) + if isinstance(axes[0], int): + axes = (axes[0],), axes[1] + if isinstance(axes[1], int): + axes = axes[0], (axes[1],) + + # initialize empty indices + x_ix = [None] * xnd + y_ix = [None] * ynd + out_ix = [] + + # fill in repeated indices + available_ix = iter(_torch_symbols_base) + for ax1, ax2 in zip(*axes): + repeat = next(available_ix) + x_ix[ax1] = repeat + y_ix[ax2] = repeat + + # fill in the rest, and maintain output order + for i in range(xnd): + if x_ix[i] is None: + leave = next(available_ix) + x_ix[i] = leave + out_ix.append(leave) + for i in range(ynd): + if y_ix[i] is None: + leave = next(available_ix) + y_ix[i] = leave + out_ix.append(leave) + + # form full string and contract! + einsum_str = "{},{}->{}".format(*map("".join, (x_ix, y_ix, out_ix))) + return einsum(einsum_str, x, y) + + +@to_backend_cache_wrap +def to_torch(array): + torch, device = _get_torch_and_device() + + if has_array_interface(array): + return torch.from_numpy(array).to(device) + + return array + + +def build_expression(_, expr): # pragma: no cover + """Build a torch function based on ``arrays`` and ``expr``.""" + + def torch_contract(*arrays): + torch_arrays = [to_torch(x) for x in arrays] + torch_out = expr._contract(torch_arrays, backend="torch") + + if torch_out.device.type == "cpu": + return torch_out.numpy() + + return torch_out.cpu().numpy() + + return torch_contract + + +def evaluate_constants(const_arrays, expr): + """Convert constant arguments to torch, and perform any possible constant + contractions. + """ + const_arrays = [to_torch(x) for x in const_arrays] + return expr(*const_arrays, backend="torch", evaluate_constants=True) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/blas.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/blas.py new file mode 100644 index 0000000000000000000000000000000000000000..7d119b5197da90dc42ff7fd609c54132db8c5e93 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/blas.py @@ -0,0 +1,122 @@ +"""Determines if a contraction can use BLAS or not.""" + +from typing import List, Sequence, Tuple, Union + +from opt_einsum.typing import ArrayIndexType + +__all__ = ["can_blas"] + + +def can_blas( + inputs: List[str], + result: str, + idx_removed: ArrayIndexType, + shapes: Union[Sequence[Tuple[int]], None] = None, +) -> Union[str, bool]: + """Checks if we can use a BLAS call. + + Parameters + ---------- + inputs : list of str + Specifies the subscripts for summation. + result : str + Resulting summation. + idx_removed : set + Indices that are removed in the summation + shapes : sequence of tuple[int], optional + If given, check also that none of the indices are broadcast dimensions. + + Returns: + ------- + type : str or bool + The type of BLAS call to be used or False if none. + + Notes: + ----- + We assume several operations are not efficient such as a transposed + DDOT, therefore 'ijk,jki->' should prefer einsum. These return the blas + type appended with "/EINSUM" to differentiate when they can still be done + with tensordot if required, e.g. when a backend has no einsum. + + Examples: + -------- + >>> can_blas(['ij', 'jk'], 'ik', set('j')) + 'GEMM' + + >>> can_blas(['ijj', 'jk'], 'ik', set('j')) + False + + >>> can_blas(['ab', 'cd'], 'abcd', set()) + 'OUTER/EINSUM' + + >>> # looks like GEMM but actually 'j' is broadcast: + >>> can_blas(['ij', 'jk'], 'ik', set('j'), shapes=[(4, 1), (5, 6)]) + False + """ + # Can only do two + if len(inputs) != 2: + return False + + input_left, input_right = inputs + + for c in set(input_left + input_right): + # can't deal with repeated indices on same input or more than 2 total + nl, nr = input_left.count(c), input_right.count(c) + if (nl > 1) or (nr > 1) or (nl + nr > 2): + return False + + # can't do implicit summation or dimension collapse e.g. + # "ab,bc->c" (implicitly sum over 'a') + # "ab,ca->ca" (take diagonal of 'a') + if nl + nr - 1 == int(c in result): + return False + + # check for broadcast indices e.g: + # "ij,jk->ik" (but one of the 'j' dimensions is broadcast up) + if shapes is not None: + for c in idx_removed: + if shapes[0][input_left.find(c)] != shapes[1][input_right.find(c)]: + return False + + # Prefer einsum if not removing indices + # (N.B. tensordot outer faster for large arrays?) + if len(idx_removed) == 0: + return "OUTER/EINSUM" + + # Build a few temporaries + sets = [set(x) for x in inputs] + keep_left = sets[0] - idx_removed + keep_right = sets[1] - idx_removed + rs = len(idx_removed) + + # DDOT + if inputs[0] == inputs[1]: + return "DOT" + + # DDOT does not make sense if you have to transpose - prefer einsum + elif sets[0] == sets[1]: + return "DOT/EINSUM" + + # GEMM no transpose + if input_left[-rs:] == input_right[:rs]: + return "GEMM" + + # GEMM transpose both + elif input_left[:rs] == input_right[-rs:]: + return "GEMM" + + # GEMM transpose right + elif input_left[-rs:] == input_right[-rs:]: + return "GEMM" + + # GEMM transpose left + elif input_left[:rs] == input_right[:rs]: + return "GEMM" + + # Einsum is faster than vectordot if we have to copy + elif (len(keep_left) == 0) or (len(keep_right) == 0): + return "GEMV/EINSUM" + + # Conventional tensordot + else: + return "TDOT" diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/contract.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/contract.py new file mode 100644 index 0000000000000000000000000000000000000000..153d060d25f24fa748741205803e3d3f29d4bfc2 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/contract.py @@ -0,0 +1,1043 @@ +"""Contains the primary optimization and contraction routines.""" + +from decimal import Decimal +from functools import lru_cache +from typing import Any, Collection, Dict, Iterable, List, Literal, Optional, Sequence, Tuple, Union, overload + +from opt_einsum import backends, blas, helpers, parser, paths, sharing +from opt_einsum.typing import ( + ArrayIndexType, + ArrayShaped, + ArrayType, + BackendType, + ContractionListType, + OptimizeKind, + PathType, + TensorShapeType, +) + +__all__ = [ + "contract_path", + "contract", + "format_const_einsum_str", + "ContractExpression", + "shape_only", +] + +## Common types + +_MemoryLimit = Union[None, int, Decimal, Literal["max_input"]] + + +class PathInfo: + """A printable object to contain information about a contraction path.""" + + def __init__( + self, + contraction_list: ContractionListType, + input_subscripts: str, + output_subscript: str, + indices: ArrayIndexType, + path: PathType, + scale_list: Sequence[int], + naive_cost: int, + opt_cost: int, + size_list: Sequence[int], + size_dict: Dict[str, int], + ): + self.contraction_list = contraction_list + self.input_subscripts = input_subscripts + self.output_subscript = output_subscript + self.path = path + self.indices = indices + self.scale_list = scale_list + self.naive_cost = Decimal(naive_cost) + self.opt_cost = Decimal(opt_cost) + self.speedup = self.naive_cost / max(self.opt_cost, Decimal(1)) + self.size_list = size_list + self.size_dict = size_dict + + self.shapes = [tuple(size_dict[k] for k in ks) for ks in input_subscripts.split(",")] + self.eq = f"{input_subscripts}->{output_subscript}" + self.largest_intermediate = Decimal(max(size_list, default=1)) + + def __repr__(self) -> str: + # Return the path along with a nice string representation + header = ("scaling", "BLAS", "current", "remaining") + + path_print = [ + f" Complete contraction: {self.eq}\n", + f" Naive scaling: {len(self.indices)}\n", + f" Optimized scaling: {max(self.scale_list, default=0)}\n", + f" Naive FLOP count: {self.naive_cost:.3e}\n", + f" Optimized FLOP count: {self.opt_cost:.3e}\n", + f" Theoretical speedup: {self.speedup:.3e}\n", + f" Largest intermediate: {self.largest_intermediate:.3e} elements\n", + "-" * 80 + "\n", + "{:>6} {:>11} {:>22} {:>37}\n".format(*header), + "-" * 80, + ] + + for n, contraction in enumerate(self.contraction_list): + _, _, einsum_str, remaining, do_blas = contraction + + if remaining is not None: + remaining_str = ",".join(remaining) + "->" + self.output_subscript + else: + remaining_str = "..." + size_remaining = max(0, 56 - max(22, len(einsum_str))) + + path_run = ( + self.scale_list[n], + do_blas, + einsum_str, + remaining_str, + size_remaining, + ) + path_print.append("\n{:>4} {:>14} {:>22} {:>{}}".format(*path_run)) + + return "".join(path_print) + + +def _choose_memory_arg(memory_limit: _MemoryLimit, size_list: List[int]) -> Optional[int]: + if memory_limit == "max_input": + return max(size_list) + + if isinstance(memory_limit, str): + raise ValueError("memory_limit must be None, int, or the string Literal['max_input'].") + + if memory_limit is None: + return None + + if memory_limit < 1: + if memory_limit == -1: + return None + else: + raise ValueError("Memory limit must be larger than 0, or -1") + + return int(memory_limit) + + +_EinsumDefaultKeys = Literal["order", "casting", "dtype", "out"] + + +def _filter_einsum_defaults(kwargs: Dict[_EinsumDefaultKeys, Any]) -> Dict[_EinsumDefaultKeys, Any]: + """Filters out default contract kwargs to pass to various backends.""" + kwargs = kwargs.copy() + ret: Dict[_EinsumDefaultKeys, Any] = {} + if (order := kwargs.pop("order", "K")) != "K": + ret["order"] = order + + if (casting := kwargs.pop("casting", "safe")) != "safe": + ret["casting"] = casting + + if (dtype := kwargs.pop("dtype", None)) is not None: + ret["dtype"] = dtype + + if (out := kwargs.pop("out", None)) is not None: + ret["out"] = out + + ret.update(kwargs) + return ret + + +# Overlaod for contract(einsum_string, *operands) +@overload +def contract_path( + subscripts: str, + *operands: ArrayType, + use_blas: bool = True, + optimize: OptimizeKind = True, + memory_limit: _MemoryLimit = None, + shapes: bool = False, + **kwargs: Any, +) -> Tuple[PathType, PathInfo]: ... + + +# Overlaod for contract(operand, indices, operand, indices, ....) +@overload +def contract_path( + subscripts: ArrayType, + *operands: Union[ArrayType, Collection[int]], + use_blas: bool = True, + optimize: OptimizeKind = True, + memory_limit: _MemoryLimit = None, + shapes: bool = False, + **kwargs: Any, +) -> Tuple[PathType, PathInfo]: ... + + +def contract_path( + subscripts: Any, + *operands: Any, + use_blas: bool = True, + optimize: OptimizeKind = True, + memory_limit: _MemoryLimit = None, + shapes: bool = False, + **kwargs: Any, +) -> Tuple[PathType, PathInfo]: + """Find a contraction order `path`, without performing the contraction. + + Parameters: + subscripts: Specifies the subscripts for summation. + *operands: These are the arrays for the operation. + use_blas: Do you use BLAS for valid operations, may use extra memory for more intermediates. + optimize: Choose the type of path the contraction will be optimized with. + - if a list is given uses this as the path. + - `'optimal'` An algorithm that explores all possible ways of + contracting the listed tensors. Scales factorially with the number of + terms in the contraction. + - `'dp'` A faster (but essentially optimal) algorithm that uses + dynamic programming to exhaustively search all contraction paths + without outer-products. + - `'greedy'` An cheap algorithm that heuristically chooses the best + pairwise contraction at each step. Scales linearly in the number of + terms in the contraction. + - `'random-greedy'` Run a randomized version of the greedy algorithm + 32 times and pick the best path. + - `'random-greedy-128'` Run a randomized version of the greedy + algorithm 128 times and pick the best path. + - `'branch-all'` An algorithm like optimal but that restricts itself + to searching 'likely' paths. Still scales factorially. + - `'branch-2'` An even more restricted version of 'branch-all' that + only searches the best two options at each step. Scales exponentially + with the number of terms in the contraction. + - `'auto'` Choose the best of the above algorithms whilst aiming to + keep the path finding time below 1ms. + - `'auto-hq'` Aim for a high quality contraction, choosing the best + of the above algorithms whilst aiming to keep the path finding time + below 1sec. + + memory_limit: Give the upper bound of the largest intermediate tensor contract will build. + - None or -1 means there is no limit + - `max_input` means the limit is set as largest input tensor + - a positive integer is taken as an explicit limit on the number of elements + + The default is None. Note that imposing a limit can make contractions + exponentially slower to perform. + + shapes: Whether ``contract_path`` should assume arrays (the default) or array shapes have been supplied. + + Returns: + path: The optimized einsum contraciton path + PathInfo: A printable object containing various information about the path found. + + Notes: + The resulting path indicates which terms of the input contraction should be + contracted first, the result of this contraction is then appended to the end of + the contraction list. + + Examples: + We can begin with a chain dot example. In this case, it is optimal to + contract the b and c tensors represented by the first element of the path (1, + 2). The resulting tensor is added to the end of the contraction and the + remaining contraction, `(0, 1)`, is then executed. + + ```python + a = np.random.rand(2, 2) + b = np.random.rand(2, 5) + c = np.random.rand(5, 2) + path_info = opt_einsum.contract_path('ij,jk,kl->il', a, b, c) + print(path_info[0]) + #> [(1, 2), (0, 1)] + print(path_info[1]) + #> Complete contraction: ij,jk,kl->il + #> Naive scaling: 4 + #> Optimized scaling: 3 + #> Naive FLOP count: 1.600e+02 + #> Optimized FLOP count: 5.600e+01 + #> Theoretical speedup: 2.857 + #> Largest intermediate: 4.000e+00 elements + #> ------------------------------------------------------------------------- + #> scaling current remaining + #> ------------------------------------------------------------------------- + #> 3 kl,jk->jl ij,jl->il + #> 3 jl,ij->il il->il + ``` + + A more complex index transformation example. + + ```python + I = np.random.rand(10, 10, 10, 10) + C = np.random.rand(10, 10) + path_info = oe.contract_path('ea,fb,abcd,gc,hd->efgh', C, C, I, C, C) + + print(path_info[0]) + #> [(0, 2), (0, 3), (0, 2), (0, 1)] + print(path_info[1]) + #> Complete contraction: ea,fb,abcd,gc,hd->efgh + #> Naive scaling: 8 + #> Optimized scaling: 5 + #> Naive FLOP count: 8.000e+08 + #> Optimized FLOP count: 8.000e+05 + #> Theoretical speedup: 1000.000 + #> Largest intermediate: 1.000e+04 elements + #> -------------------------------------------------------------------------- + #> scaling current remaining + #> -------------------------------------------------------------------------- + #> 5 abcd,ea->bcde fb,gc,hd,bcde->efgh + #> 5 bcde,fb->cdef gc,hd,cdef->efgh + #> 5 cdef,gc->defg hd,defg->efgh + #> 5 defg,hd->efgh efgh->efgh + ``` + """ + if (optimize is True) or (optimize is None): + optimize = "auto" + + # Hidden option, only einsum should call this + einsum_call_arg = kwargs.pop("einsum_call", False) + if len(kwargs): + raise TypeError(f"Did not understand the following kwargs: {kwargs.keys()}") + + # Python side parsing + operands_ = [subscripts] + list(operands) + input_subscripts, output_subscript, operands_prepped = parser.parse_einsum_input(operands_, shapes=shapes) + + # Build a few useful list and sets + input_list = input_subscripts.split(",") + input_sets = [frozenset(x) for x in input_list] + if shapes: + input_shapes = operands_prepped + else: + input_shapes = [parser.get_shape(x) for x in operands_prepped] + output_set = frozenset(output_subscript) + indices = frozenset(input_subscripts.replace(",", "")) + + # Get length of each unique dimension and ensure all dimensions are correct + size_dict: Dict[str, int] = {} + for tnum, term in enumerate(input_list): + sh = input_shapes[tnum] + + if len(sh) != len(term): + raise ValueError( + f"Einstein sum subscript '{input_list[tnum]}' does not contain the " + f"correct number of indices for operand {tnum}." + ) + for cnum, char in enumerate(term): + dim = int(sh[cnum]) + + if char in size_dict: + # For broadcasting cases we always want the largest dim size + if size_dict[char] == 1: + size_dict[char] = dim + elif dim not in (1, size_dict[char]): + raise ValueError( + f"Size of label '{char}' for operand {tnum} ({size_dict[char]}) does not match previous " + f"terms ({dim})." + ) + else: + size_dict[char] = dim + + # Compute size of each input array plus the output array + size_list = [helpers.compute_size_by_dict(term, size_dict) for term in input_list + [output_subscript]] + memory_arg = _choose_memory_arg(memory_limit, size_list) + + num_ops = len(input_list) + + # Compute naive cost + # This is not quite right, need to look into exactly how einsum does this + # indices_in_input = input_subscripts.replace(',', '') + inner_product = (sum(len(x) for x in input_sets) - len(indices)) > 0 + naive_cost = helpers.flop_count(indices, inner_product, num_ops, size_dict) + + # Compute the path + if optimize is False: + path_tuple: PathType = [tuple(range(num_ops))] + elif not isinstance(optimize, (str, paths.PathOptimizer)): + # Custom path supplied + path_tuple = optimize # type: ignore + elif num_ops <= 2: + # Nothing to be optimized + path_tuple = [tuple(range(num_ops))] + elif isinstance(optimize, paths.PathOptimizer): + # Custom path optimizer supplied + path_tuple = optimize(input_sets, output_set, size_dict, memory_arg) + else: + path_optimizer = paths.get_path_fn(optimize) + path_tuple = path_optimizer(input_sets, output_set, size_dict, memory_arg) + + cost_list = [] + scale_list = [] + size_list = [] + contraction_list = [] + + # Build contraction tuple (positions, gemm, einsum_str, remaining) + for cnum, contract_inds in enumerate(path_tuple): + # Make sure we remove inds from right to left + contract_inds = tuple(sorted(contract_inds, reverse=True)) + + contract_tuple = helpers.find_contraction(contract_inds, input_sets, output_set) + out_inds, input_sets, idx_removed, idx_contract = contract_tuple + + # Compute cost, scale, and size + cost = helpers.flop_count(idx_contract, bool(idx_removed), len(contract_inds), size_dict) + cost_list.append(cost) + scale_list.append(len(idx_contract)) + size_list.append(helpers.compute_size_by_dict(out_inds, size_dict)) + + tmp_inputs = [input_list.pop(x) for x in contract_inds] + tmp_shapes = [input_shapes.pop(x) for x in contract_inds] + + if use_blas: + do_blas = blas.can_blas(tmp_inputs, "".join(out_inds), idx_removed, tmp_shapes) + else: + do_blas = False + + # Last contraction + if (cnum - len(path_tuple)) == -1: + idx_result = output_subscript + else: + # use tensordot order to minimize transpositions + all_input_inds = "".join(tmp_inputs) + idx_result = "".join(sorted(out_inds, key=all_input_inds.find)) + + shp_result = parser.find_output_shape(tmp_inputs, tmp_shapes, idx_result) + + input_list.append(idx_result) + input_shapes.append(shp_result) + + einsum_str = ",".join(tmp_inputs) + "->" + idx_result + + # for large expressions saving the remaining terms at each step can + # incur a large memory footprint - and also be messy to print + if len(input_list) <= 20: + remaining: Optional[Tuple[str, ...]] = tuple(input_list) + else: + remaining = None + + contraction = (contract_inds, idx_removed, einsum_str, remaining, do_blas) + contraction_list.append(contraction) + + opt_cost = sum(cost_list) + + if einsum_call_arg: + return operands_prepped, contraction_list # type: ignore + + path_print = PathInfo( + contraction_list, + input_subscripts, + output_subscript, + indices, + path_tuple, + scale_list, + naive_cost, + opt_cost, + size_list, + size_dict, + ) + + return path_tuple, path_print + + +@sharing.einsum_cache_wrap +def _einsum(*operands: Any, **kwargs: Any) -> ArrayType: + """Base einsum, but with pre-parse for valid characters if a string is given.""" + fn = backends.get_func("einsum", kwargs.pop("backend", "numpy")) + + if not isinstance(operands[0], str): + return fn(*operands, **kwargs) + + einsum_str, operands = operands[0], operands[1:] + + # Do we need to temporarily map indices into [a-z,A-Z] range? + if not parser.has_valid_einsum_chars_only(einsum_str): + # Explicitly find output str first so as to maintain order + if "->" not in einsum_str: + einsum_str += "->" + parser.find_output_str(einsum_str) + + einsum_str = parser.convert_to_valid_einsum_chars(einsum_str) + + kwargs = _filter_einsum_defaults(kwargs) # type: ignore + return fn(einsum_str, *operands, **kwargs) + + +def _default_transpose(x: ArrayType, axes: Tuple[int, ...]) -> ArrayType: + # most libraries implement a method version + return x.transpose(axes) + + +@sharing.transpose_cache_wrap +def _transpose(x: ArrayType, axes: Tuple[int, ...], backend: str = "numpy") -> ArrayType: + """Base transpose.""" + fn = backends.get_func("transpose", backend, _default_transpose) + return fn(x, axes) + + +@sharing.tensordot_cache_wrap +def _tensordot(x: ArrayType, y: ArrayType, axes: Tuple[int, ...], backend: str = "numpy") -> ArrayType: + """Base tensordot.""" + fn = backends.get_func("tensordot", backend) + return fn(x, y, axes=axes) + + +# Rewrite einsum to handle different cases + + +@overload +def contract( + subscripts: str, + *operands: ArrayType, + out: ArrayType = ..., + use_blas: bool = ..., + optimize: OptimizeKind = ..., + memory_limit: _MemoryLimit = ..., + backend: BackendType = ..., + **kwargs: Any, +) -> ArrayType: ... + + +@overload +def contract( + subscripts: ArrayType, + *operands: Union[ArrayType, Collection[int]], + out: ArrayType = ..., + use_blas: bool = ..., + optimize: OptimizeKind = ..., + memory_limit: _MemoryLimit = ..., + backend: BackendType = ..., + **kwargs: Any, +) -> ArrayType: ... + + +def contract( + subscripts: Union[str, ArrayType], + *operands: Union[ArrayType, Collection[int]], + out: Optional[ArrayType] = None, + use_blas: bool = True, + optimize: OptimizeKind = True, + memory_limit: _MemoryLimit = None, + backend: BackendType = "auto", + **kwargs: Any, +) -> ArrayType: + """Evaluates the Einstein summation convention on the operands. A drop in + replacement for NumPy's einsum function that optimizes the order of contraction + to reduce overall scaling at the cost of several intermediate arrays. + + Parameters: + subscripts: Specifies the subscripts for summation. + *operands: These are the arrays for the operation. + out: A output array in which set the resulting output. + use_blas: Do you use BLAS for valid operations, may use extra memory for more intermediates. + optimize:- Choose the type of path the contraction will be optimized with + - if a list is given uses this as the path. + - `'optimal'` An algorithm that explores all possible ways of + contracting the listed tensors. Scales factorially with the number of + terms in the contraction. + - `'dp'` A faster (but essentially optimal) algorithm that uses + dynamic programming to exhaustively search all contraction paths + without outer-products. + - `'greedy'` An cheap algorithm that heuristically chooses the best + pairwise contraction at each step. Scales linearly in the number of + terms in the contraction. + - `'random-greedy'` Run a randomized version of the greedy algorithm + 32 times and pick the best path. + - `'random-greedy-128'` Run a randomized version of the greedy + algorithm 128 times and pick the best path. + - `'branch-all'` An algorithm like optimal but that restricts itself + to searching 'likely' paths. Still scales factorially. + - `'branch-2'` An even more restricted version of 'branch-all' that + only searches the best two options at each step. Scales exponentially + with the number of terms in the contraction. + - `'auto', None, True` Choose the best of the above algorithms whilst aiming to + keep the path finding time below 1ms. + - `'auto-hq'` Aim for a high quality contraction, choosing the best + of the above algorithms whilst aiming to keep the path finding time + below 1sec. + - `False` will not optimize the contraction. + + memory_limit:- Give the upper bound of the largest intermediate tensor contract will build. + - None or -1 means there is no limit. + - `max_input` means the limit is set as largest input tensor. + - A positive integer is taken as an explicit limit on the number of elements. + + The default is None. Note that imposing a limit can make contractions + exponentially slower to perform. + + backend: Which library to use to perform the required ``tensordot``, ``transpose`` + and ``einsum`` calls. Should match the types of arrays supplied, See + `contract_expression` for generating expressions which convert + numpy arrays to and from the backend library automatically. + + Returns: + The result of the einsum expression. + + Notes: + This function should produce a result identical to that of NumPy's einsum + function. The primary difference is ``contract`` will attempt to form + intermediates which reduce the overall scaling of the given einsum contraction. + By default the worst intermediate formed will be equal to that of the largest + input array. For large einsum expressions with many input arrays this can + provide arbitrarily large (1000 fold+) speed improvements. + + For contractions with just two tensors this function will attempt to use + NumPy's built-in BLAS functionality to ensure that the given operation is + performed optimally. When NumPy is linked to a threaded BLAS, potential + speedups are on the order of 20-100 for a six core machine. + """ + if (optimize is True) or (optimize is None): + optimize = "auto" + + operands_list = [subscripts] + list(operands) + + # If no optimization, run pure einsum + if optimize is False: + return _einsum(*operands_list, out=out, **kwargs) + + # Grab non-einsum kwargs + gen_expression = kwargs.pop("_gen_expression", False) + constants_dict = kwargs.pop("_constants_dict", {}) + + if gen_expression: + full_str = operands_list[0] + + # Build the contraction list and operand + contraction_list: ContractionListType + operands, contraction_list = contract_path( # type: ignore + *operands_list, optimize=optimize, memory_limit=memory_limit, einsum_call=True, use_blas=use_blas + ) + + # check if performing contraction or just building expression + if gen_expression: + return ContractExpression(full_str, contraction_list, constants_dict, **kwargs) + + return _core_contract(operands, contraction_list, backend=backend, out=out, **kwargs) + + +@lru_cache(None) +def _infer_backend_class_cached(cls: type) -> str: + return cls.__module__.split(".")[0] + + +def infer_backend(x: Any) -> str: + return _infer_backend_class_cached(x.__class__) + + +def parse_backend(arrays: Sequence[ArrayType], backend: Optional[str]) -> str: + """Find out what backend we should use, dipatching based on the first + array if ``backend='auto'`` is specified. + """ + if (backend != "auto") and (backend is not None): + return backend + backend = infer_backend(arrays[0]) + + # some arrays will be defined in modules that don't implement tensordot + # etc. so instead default to numpy + if not backends.has_tensordot(backend): + return "numpy" + + return backend + + +def _core_contract( + operands_: Sequence[ArrayType], + contraction_list: ContractionListType, + backend: Optional[str] = "auto", + evaluate_constants: bool = False, + out: Optional[ArrayType] = None, + **kwargs: Any, +) -> ArrayType: + """Inner loop used to perform an actual contraction given the output + from a ``contract_path(..., einsum_call=True)`` call. + """ + # Special handling if out is specified + specified_out = out is not None + + operands = list(operands_) + backend = parse_backend(operands, backend) + + # try and do as much as possible without einsum if not available + no_einsum = not backends.has_einsum(backend) + + # Start contraction loop + for num, contraction in enumerate(contraction_list): + inds, idx_rm, einsum_str, _, blas_flag = contraction + + # check if we are performing the pre-pass of an expression with constants, + # if so, break out upon finding first non-constant (None) operand + if evaluate_constants and any(operands[x] is None for x in inds): + return operands, contraction_list[num:] + + tmp_operands = [operands.pop(x) for x in inds] + + # Do we need to deal with the output? + handle_out = specified_out and ((num + 1) == len(contraction_list)) + + # Call tensordot (check if should prefer einsum, but only if available) + if blas_flag and ("EINSUM" not in blas_flag or no_einsum): # type: ignore + # Checks have already been handled + input_str, results_index = einsum_str.split("->") + input_left, input_right = input_str.split(",") + + tensor_result = "".join(s for s in input_left + input_right if s not in idx_rm) + + if idx_rm: + # Find indices to contract over + left_pos, right_pos = [], [] + for s in idx_rm: + left_pos.append(input_left.find(s)) + right_pos.append(input_right.find(s)) + + # Construct the axes tuples in a canonical order + axes = tuple(zip(*sorted(zip(left_pos, right_pos)))) + else: + # Ensure axes is always pair of tuples + axes = ((), ()) + + # Contract! + new_view = _tensordot(*tmp_operands, axes=axes, backend=backend, **kwargs) + + # Build a new view if needed + if (tensor_result != results_index) or handle_out: + transpose = tuple(map(tensor_result.index, results_index)) + new_view = _transpose(new_view, axes=transpose, backend=backend) + + if handle_out: + out[:] = new_view # type: ignore + + else: + # Call einsum + out_kwarg: Union[None, ArrayType] = None + if handle_out: + out_kwarg = out + new_view = _einsum(einsum_str, *tmp_operands, backend=backend, out=out_kwarg, **kwargs) + + # Append new items and dereference what we can + operands.append(new_view) + del tmp_operands, new_view + + if specified_out: + return out + else: + return operands[0] + + +def format_const_einsum_str(einsum_str: str, constants: Iterable[int]) -> str: + """Add brackets to the constant terms in ``einsum_str``. For example: + + >>> format_const_einsum_str('ab,bc,cd->ad', [0, 2]) + 'bc,[ab,cd]->ad' + + No-op if there are no constants. + """ + if not constants: + return einsum_str + + if "->" in einsum_str: + lhs, rhs = einsum_str.split("->") + arrow = "->" + else: + lhs, rhs, arrow = einsum_str, "", "" + + wrapped_terms = [f"[{t}]" if i in constants else t for i, t in enumerate(lhs.split(","))] + + formatted_einsum_str = "{}{}{}".format(",".join(wrapped_terms), arrow, rhs) + + # merge adjacent constants + formatted_einsum_str = formatted_einsum_str.replace("],[", ",") + return formatted_einsum_str + + +class ContractExpression: + """Helper class for storing an explicit ``contraction_list`` which can + then be repeatedly called solely with the array arguments. + """ + + def __init__( + self, + contraction: str, + contraction_list: ContractionListType, + constants_dict: Dict[int, ArrayType], + **kwargs: Any, + ): + self.contraction = format_const_einsum_str(contraction, constants_dict.keys()) + self.contraction_list = contraction_list + self.kwargs = kwargs + + # need to know _full_num_args to parse constants with, and num_args to call with + self._full_num_args = contraction.count(",") + 1 + self.num_args = self._full_num_args - len(constants_dict) + + # likewise need to know full contraction list + self._full_contraction_list = contraction_list + + self._constants_dict = constants_dict + self._evaluated_constants: Dict[str, Any] = {} + self._backend_expressions: Dict[str, Any] = {} + + def evaluate_constants(self, backend: Optional[str] = "auto") -> None: + """Convert any constant operands to the correct backend form, and + perform as many contractions as possible to create a new list of + operands, stored in ``self._evaluated_constants[backend]``. This also + makes sure ``self.contraction_list`` only contains the remaining, + non-const operations. + """ + # prepare a list of operands, with `None` for non-consts + tmp_const_ops = [self._constants_dict.get(i, None) for i in range(self._full_num_args)] + backend = parse_backend(tmp_const_ops, backend) + + # get the new list of operands with constant operations performed, and remaining contractions + try: + new_ops, new_contraction_list = backends.evaluate_constants(backend, tmp_const_ops, self) + except KeyError: + new_ops, new_contraction_list = self(*tmp_const_ops, backend=backend, evaluate_constants=True) + + self._evaluated_constants[backend] = new_ops + self.contraction_list = new_contraction_list + + def _get_evaluated_constants(self, backend: str) -> List[Optional[ArrayType]]: + """Retrieve or generate the cached list of constant operators (mixed + in with None representing non-consts) and the remaining contraction + list. + """ + try: + return self._evaluated_constants[backend] + except KeyError: + self.evaluate_constants(backend) + return self._evaluated_constants[backend] + + def _get_backend_expression(self, arrays: Sequence[ArrayType], backend: str) -> Any: + try: + return self._backend_expressions[backend] + except KeyError: + fn = backends.build_expression(backend, arrays, self) + self._backend_expressions[backend] = fn + return fn + + def _contract( + self, + arrays: Sequence[ArrayType], + out: Optional[ArrayType] = None, + backend: Optional[str] = "auto", + evaluate_constants: bool = False, + ) -> ArrayType: + """The normal, core contraction.""" + contraction_list = self._full_contraction_list if evaluate_constants else self.contraction_list + + return _core_contract( + list(arrays), + contraction_list, + out=out, + backend=backend, + evaluate_constants=evaluate_constants, + **self.kwargs, + ) + + def _contract_with_conversion( + self, + arrays: Sequence[ArrayType], + out: Optional[ArrayType], + backend: str, + evaluate_constants: bool = False, + ) -> ArrayType: + """Special contraction, i.e., contraction with a different backend + but converting to and from that backend. Retrieves or generates a + cached expression using ``arrays`` as templates, then calls it + with ``arrays``. + + If ``evaluate_constants=True``, perform a partial contraction that + prepares the constant tensors and operations with the right backend. + """ + # convert consts to correct type & find reduced contraction list + if evaluate_constants: + return backends.evaluate_constants(backend, arrays, self) + + result = self._get_backend_expression(arrays, backend)(*arrays) + + if out is not None: + out[()] = result + return out + + return result + + def __call__( + self, + *arrays: ArrayType, + out: Union[None, ArrayType] = None, + backend: str = "auto", + evaluate_constants: bool = False, + ) -> ArrayType: + """Evaluate this expression with a set of arrays. + + Parameters: + arrays: The arrays to supply as input to the expression. + out: If specified, output the result into this array. + backend: Perform the contraction with this backend library. If numpy arrays + are supplied then try to convert them to and from the correct + backend array type. + evaluate_constants: Pre-evaluates constants with the appropriate backend. + + Returns: + The contracted result. + """ + backend = parse_backend(arrays, backend) + + correct_num_args = self._full_num_args if evaluate_constants else self.num_args + + if len(arrays) != correct_num_args: + raise ValueError( + f"This `ContractExpression` takes exactly {self.num_args} array arguments " + f"but received {len(arrays)}." + ) + + if self._constants_dict and not evaluate_constants: + # fill in the missing non-constant terms with newly supplied arrays + ops_var, ops_const = iter(arrays), self._get_evaluated_constants(backend) + ops: Sequence[ArrayType] = [next(ops_var) if op is None else op for op in ops_const] + else: + ops = arrays + + try: + # Check if the backend requires special preparation / calling + # but also ignore non-numpy arrays -> assume user wants same type back + if backends.has_backend(backend) and all(infer_backend(x) == "numpy" for x in arrays): + return self._contract_with_conversion(ops, out, backend, evaluate_constants=evaluate_constants) + + return self._contract(ops, out=out, backend=backend, evaluate_constants=evaluate_constants) + + except ValueError as err: + original_msg = str(err.args) if err.args else "" + msg = ( + "Internal error while evaluating `ContractExpression`. Note that few checks are performed" + " - the number and rank of the array arguments must match the original expression. " + f"The internal error was: '{original_msg}'", + ) + err.args = msg + raise + + def __repr__(self) -> str: + if self._constants_dict: + constants_repr = f", constants={sorted(self._constants_dict)}" + else: + constants_repr = "" + return f"" + + def __str__(self) -> str: + s = [self.__repr__()] + for i, c in enumerate(self.contraction_list): + s.append(f"\n {i + 1}. ") + s.append(f"'{c[2]}'" + (f" [{c[-1]}]" if c[-1] else "")) + s.append(f"\neinsum_kwargs={self.kwargs}") + return "".join(s) + + +def shape_only(shape: TensorShapeType) -> ArrayShaped: + """Dummy ``numpy.ndarray`` which has a shape only - for generating + contract expressions. + """ + return ArrayShaped(shape) + + +# Overlaod for contract(einsum_string, *operands) +@overload +def contract_expression( + subscripts: str, + *operands: Union[ArrayType, TensorShapeType], + constants: Union[Collection[int], None] = ..., + use_blas: bool = ..., + optimize: OptimizeKind = ..., + memory_limit: _MemoryLimit = ..., + **kwargs: Any, +) -> ContractExpression: ... + + +# Overlaod for contract(operand, indices, operand, indices, ....) +@overload +def contract_expression( + subscripts: Union[ArrayType, TensorShapeType], + *operands: Union[ArrayType, TensorShapeType, Collection[int]], + constants: Union[Collection[int], None] = ..., + use_blas: bool = ..., + optimize: OptimizeKind = ..., + memory_limit: _MemoryLimit = ..., + **kwargs: Any, +) -> ContractExpression: ... + + +def contract_expression( + subscripts: Union[str, ArrayType, TensorShapeType], + *shapes: Union[ArrayType, TensorShapeType, Collection[int]], + constants: Union[Collection[int], None] = None, + use_blas: bool = True, + optimize: OptimizeKind = True, + memory_limit: _MemoryLimit = None, + **kwargs: Any, +) -> ContractExpression: + """Generate a reusable expression for a given contraction with + specific shapes, which can, for example, be cached. + + Parameters: + + subscripts: Specifies the subscripts for summation. + shapes: Shapes of the arrays to optimize the contraction for. + constants: The indices of any constant arguments in `shapes`, in which case the + actual array should be supplied at that position rather than just a + shape. If these are specified, then constant parts of the contraction + between calls will be reused. Additionally, if a GPU-enabled backend is + used for example, then the constant tensors will be kept on the GPU, + minimizing transfers. + kwargs: Passed on to `contract_path` or `einsum`. See `contract`. + + Returns: + Callable with signature `expr(*arrays, out=None, backend='numpy')` where the array's shapes should match `shapes`. + + Notes: + The `out` keyword argument should be supplied to the generated expression + rather than this function. + The `backend` keyword argument should also be supplied to the generated + expression. If numpy arrays are supplied, if possible they will be + converted to and back from the correct backend array type. + The generated expression will work with any arrays which have + the same rank (number of dimensions) as the original shapes, however, if + the actual sizes are different, the expression may no longer be optimal. + Constant operations will be computed upon the first call with a particular + backend, then subsequently reused. + + Examples: + Basic usage: + + ```python + expr = contract_expression("ab,bc->ac", (3, 4), (4, 5)) + a, b = np.random.rand(3, 4), np.random.rand(4, 5) + c = expr(a, b) + np.allclose(c, a @ b) + #> True + ``` + + Supply `a` as a constant: + + ```python + expr = contract_expression("ab,bc->ac", a, (4, 5), constants=[0]) + expr + #> ac', constants=[0])> + + c = expr(b) + np.allclose(c, a @ b) + #> True + ``` + + """ + if not optimize: + raise ValueError("Can only generate expressions for optimized contractions.") + + for arg in ("out", "backend"): + if kwargs.get(arg, None) is not None: + raise ValueError( + f"'{arg}' should only be specified when calling a " "`ContractExpression`, not when building it." + ) + + if not isinstance(subscripts, str): + subscripts, shapes = parser.convert_interleaved_input((subscripts,) + shapes) + + kwargs["_gen_expression"] = True + + # build dict of constant indices mapped to arrays + constants = constants or () + constants_dict = {i: shapes[i] for i in constants} + kwargs["_constants_dict"] = constants_dict + + # apart from constant arguments, make dummy arrays + dummy_arrays = [s if i in constants else shape_only(s) for i, s in enumerate(shapes)] # type: ignore + + return contract( + subscripts, *dummy_arrays, use_blas=use_blas, optimize=optimize, memory_limit=memory_limit, **kwargs + ) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/helpers.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/helpers.py new file mode 100644 index 0000000000000000000000000000000000000000..3d8b41e95443a2db7592f458e70d0f8b605cea45 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/helpers.py @@ -0,0 +1,151 @@ +"""Contains helper functions for opt_einsum testing scripts.""" + +from typing import Any, Collection, Dict, FrozenSet, Iterable, List, Tuple, overload + +from opt_einsum.typing import ArrayIndexType, ArrayType + +__all__ = ["compute_size_by_dict", "find_contraction", "flop_count"] + +_valid_chars = "abcdefghijklmopqABC" +_sizes = [2, 3, 4, 5, 4, 3, 2, 6, 5, 4, 3, 2, 5, 7, 4, 3, 2, 3, 4] +_default_dim_dict = dict(zip(_valid_chars, _sizes)) + + +@overload +def compute_size_by_dict(indices: Iterable[int], idx_dict: List[int]) -> int: ... + + +@overload +def compute_size_by_dict(indices: Collection[str], idx_dict: Dict[str, int]) -> int: ... + + +def compute_size_by_dict(indices: Any, idx_dict: Any) -> int: + """Computes the product of the elements in indices based on the dictionary + idx_dict. + + Parameters + ---------- + indices : iterable + Indices to base the product on. + idx_dict : dictionary + Dictionary of index _sizes + + Returns: + ------- + ret : int + The resulting product. + + Examples: + -------- + >>> compute_size_by_dict('abbc', {'a': 2, 'b':3, 'c':5}) + 90 + + """ + ret = 1 + for i in indices: # lgtm [py/iteration-string-and-sequence] + ret *= idx_dict[i] + return ret + + +def find_contraction( + positions: Collection[int], + input_sets: List[ArrayIndexType], + output_set: ArrayIndexType, +) -> Tuple[FrozenSet[str], List[ArrayIndexType], ArrayIndexType, ArrayIndexType]: + """Finds the contraction for a given set of input and output sets. + + Parameters + ---------- + positions : iterable + Integer positions of terms used in the contraction. + input_sets : list + List of sets that represent the lhs side of the einsum subscript + output_set : set + Set that represents the rhs side of the overall einsum subscript + + Returns: + ------- + new_result : set + The indices of the resulting contraction + remaining : list + List of sets that have not been contracted, the new set is appended to + the end of this list + idx_removed : set + Indices removed from the entire contraction + idx_contraction : set + The indices used in the current contraction + + Examples: + -------- + # A simple dot product test case + >>> pos = (0, 1) + >>> isets = [set('ab'), set('bc')] + >>> oset = set('ac') + >>> find_contraction(pos, isets, oset) + ({'a', 'c'}, [{'a', 'c'}], {'b'}, {'a', 'b', 'c'}) + + # A more complex case with additional terms in the contraction + >>> pos = (0, 2) + >>> isets = [set('abd'), set('ac'), set('bdc')] + >>> oset = set('ac') + >>> find_contraction(pos, isets, oset) + ({'a', 'c'}, [{'a', 'c'}, {'a', 'c'}], {'b', 'd'}, {'a', 'b', 'c', 'd'}) + """ + remaining = list(input_sets) + inputs = (remaining.pop(i) for i in sorted(positions, reverse=True)) + idx_contract = frozenset.union(*inputs) + idx_remain = output_set.union(*remaining) + + new_result = idx_remain & idx_contract + idx_removed = idx_contract - new_result + remaining.append(new_result) + + return new_result, remaining, idx_removed, idx_contract + + +def flop_count( + idx_contraction: Collection[str], + inner: bool, + num_terms: int, + size_dictionary: Dict[str, int], +) -> int: + """Computes the number of FLOPS in the contraction. + + Parameters + ---------- + idx_contraction : iterable + The indices involved in the contraction + inner : bool + Does this contraction require an inner product? + num_terms : int + The number of terms in a contraction + size_dictionary : dict + The size of each of the indices in idx_contraction + + Returns: + ------- + flop_count : int + The total number of FLOPS required for the contraction. + + Examples: + -------- + >>> flop_count('abc', False, 1, {'a': 2, 'b':3, 'c':5}) + 30 + + >>> flop_count('abc', True, 2, {'a': 2, 'b':3, 'c':5}) + 60 + + """ + overall_size = compute_size_by_dict(idx_contraction, size_dictionary) + op_factor = max(1, num_terms - 1) + if inner: + op_factor += 1 + + return overall_size * op_factor + + +def has_array_interface(array: ArrayType) -> ArrayType: + if hasattr(array, "__array_interface__"): + return True + else: + return False diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/parser.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/parser.py new file mode 100644 index 0000000000000000000000000000000000000000..316a421cf5b509a283152e9537b022f670fe67a9 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/parser.py @@ -0,0 +1,415 @@ +"""A functionally equivalent parser of the numpy.einsum input parser.""" + +import itertools +from typing import Any, Dict, Iterator, List, Sequence, Tuple + +from opt_einsum.typing import ArrayType, TensorShapeType + +__all__ = [ + "is_valid_einsum_char", + "has_valid_einsum_chars_only", + "get_symbol", + "get_shape", + "gen_unused_symbols", + "convert_to_valid_einsum_chars", + "alpha_canonicalize", + "find_output_str", + "find_output_shape", + "possibly_convert_to_numpy", + "parse_einsum_input", +] + +_einsum_symbols_base = "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ" + + +def is_valid_einsum_char(x: str) -> bool: + """Check if the character ``x`` is valid for numpy einsum. + + **Examples:** + + ```python + is_valid_einsum_char("a") + #> True + + is_valid_einsum_char("Ǵ") + #> False + ``` + """ + return (x in _einsum_symbols_base) or (x in ",->.") + + +def has_valid_einsum_chars_only(einsum_str: str) -> bool: + """Check if ``einsum_str`` contains only valid characters for numpy einsum. + + **Examples:** + + ```python + has_valid_einsum_chars_only("abAZ") + #> True + + has_valid_einsum_chars_only("Över") + #> False + ``` + """ + return all(map(is_valid_einsum_char, einsum_str)) + + +def get_symbol(i: int) -> str: + """Get the symbol corresponding to int ``i`` - runs through the usual 52 + letters before resorting to unicode characters, starting at ``chr(192)`` and skipping surrogates. + + **Examples:** + + ```python + get_symbol(2) + #> 'c' + + get_symbol(200) + #> 'Ŕ' + + get_symbol(20000) + #> '京' + ``` + """ + if i < 52: + return _einsum_symbols_base[i] + elif i >= 55296: + # Skip chr(57343) - chr(55296) as surrogates + return chr(i + 2048) + else: + return chr(i + 140) + + +def gen_unused_symbols(used: str, n: int) -> Iterator[str]: + """Generate ``n`` symbols that are not already in ``used``. + + **Examples:** + ```python + list(oe.parser.gen_unused_symbols("abd", 2)) + #> ['c', 'e'] + ``` + """ + i = cnt = 0 + while cnt < n: + s = get_symbol(i) + i += 1 + if s in used: + continue + yield s + cnt += 1 + + +def convert_to_valid_einsum_chars(einsum_str: str) -> str: + """Convert the str ``einsum_str`` to contain only the alphabetic characters + valid for numpy einsum. If there are too many symbols, let the backend + throw an error. + + Examples: + -------- + >>> oe.parser.convert_to_valid_einsum_chars("Ĥěļļö") + 'cbdda' + """ + symbols = sorted(set(einsum_str) - set(",->")) + replacer = {x: get_symbol(i) for i, x in enumerate(symbols)} + return "".join(replacer.get(x, x) for x in einsum_str) + + +def alpha_canonicalize(equation: str) -> str: + """Alpha convert an equation in an order-independent canonical way. + + Examples: + -------- + >>> oe.parser.alpha_canonicalize("dcba") + 'abcd' + + >>> oe.parser.alpha_canonicalize("Ĥěļļö") + 'abccd' + """ + rename: Dict[str, str] = {} + for name in equation: + if name in ".,->": + continue + if name not in rename: + rename[name] = get_symbol(len(rename)) + return "".join(rename.get(x, x) for x in equation) + + +def find_output_str(subscripts: str) -> str: + """Find the output string for the inputs ``subscripts`` under canonical einstein summation rules. + That is, repeated indices are summed over by default. + + Examples: + -------- + >>> oe.parser.find_output_str("ab,bc") + 'ac' + + >>> oe.parser.find_output_str("a,b") + 'ab' + + >>> oe.parser.find_output_str("a,a,b,b") + '' + """ + tmp_subscripts = subscripts.replace(",", "") + return "".join(s for s in sorted(set(tmp_subscripts)) if tmp_subscripts.count(s) == 1) + + +def find_output_shape(inputs: List[str], shapes: List[TensorShapeType], output: str) -> TensorShapeType: + """Find the output shape for given inputs, shapes and output string, taking + into account broadcasting. + + Examples: + -------- + >>> oe.parser.find_output_shape(["ab", "bc"], [(2, 3), (3, 4)], "ac") + (2, 4) + + # Broadcasting is accounted for + >>> oe.parser.find_output_shape(["a", "a"], [(4, ), (1, )], "a") + (4,) + """ + return tuple(max(shape[loc] for shape, loc in zip(shapes, [x.find(c) for x in inputs]) if loc >= 0) for c in output) + + +_BaseTypes = (bool, int, float, complex, str, bytes) + + +def get_shape(x: Any) -> TensorShapeType: + """Get the shape of the array-like object `x`. If `x` is not array-like, raise an error. + + Array-like objects are those that have a `shape` attribute, are sequences of BaseTypes, or are BaseTypes. + BaseTypes are defined as `bool`, `int`, `float`, `complex`, `str`, and `bytes`. + """ + if hasattr(x, "shape"): + return x.shape + elif isinstance(x, _BaseTypes): + return () + elif isinstance(x, Sequence): + shape = [] + while isinstance(x, Sequence) and not isinstance(x, _BaseTypes): + shape.append(len(x)) + x = x[0] + return tuple(shape) + else: + raise ValueError(f"Cannot determine the shape of {x}, can only determine the shape of array-like objects.") + + +def possibly_convert_to_numpy(x: Any) -> Any: + """Convert things without a 'shape' to ndarrays, but leave everything else. + + Examples: + -------- + >>> oe.parser.possibly_convert_to_numpy(5) + array(5) + + >>> oe.parser.possibly_convert_to_numpy([5, 3]) + array([5, 3]) + + >>> oe.parser.possibly_convert_to_numpy(np.array([5, 3])) + array([5, 3]) + + # Any class with a shape is passed through + >>> class Shape: + ... def __init__(self, shape): + ... self.shape = shape + ... + + >>> myshape = Shape((5, 5)) + >>> oe.parser.possibly_convert_to_numpy(myshape) + <__main__.Shape object at 0x10f850710> + """ + if not hasattr(x, "shape"): + try: + import numpy as np # type: ignore + except ModuleNotFoundError: + raise ModuleNotFoundError( + "numpy is required to convert non-array objects to arrays. This function will be deprecated in the future." + ) + + return np.asanyarray(x) + else: + return x + + +def convert_subscripts(old_sub: List[Any], symbol_map: Dict[Any, Any]) -> str: + """Convert user custom subscripts list to subscript string according to `symbol_map`. + + Examples: + -------- + >>> oe.parser.convert_subscripts(['abc', 'def'], {'abc':'a', 'def':'b'}) + 'ab' + >>> oe.parser.convert_subscripts([Ellipsis, object], {object:'a'}) + '...a' + """ + new_sub = "" + for s in old_sub: + if s is Ellipsis: + new_sub += "..." + else: + # no need to try/except here because symbol_map has already been checked + new_sub += symbol_map[s] + return new_sub + + +def convert_interleaved_input(operands: Sequence[Any]) -> Tuple[str, Tuple[Any, ...]]: + """Convert 'interleaved' input to standard einsum input.""" + tmp_operands = list(operands) + operand_list = [] + subscript_list = [] + for _ in range(len(operands) // 2): + operand_list.append(tmp_operands.pop(0)) + subscript_list.append(tmp_operands.pop(0)) + + output_list = tmp_operands[-1] if len(tmp_operands) else None + + # build a map from user symbols to single-character symbols based on `get_symbol` + # The map retains the intrinsic order of user symbols + try: + # collect all user symbols + symbol_set = set(itertools.chain.from_iterable(subscript_list)) + + # remove Ellipsis because it can not be compared with other objects + symbol_set.discard(Ellipsis) + + # build the map based on sorted user symbols, retaining the order we lost in the `set` + symbol_map = {symbol: get_symbol(idx) for idx, symbol in enumerate(sorted(symbol_set))} + + except TypeError: # unhashable or uncomparable object + raise TypeError( + "For this input type lists must contain either Ellipsis " + "or hashable and comparable object (e.g. int, str)." + ) + + subscripts = ",".join(convert_subscripts(sub, symbol_map) for sub in subscript_list) + if output_list is not None: + subscripts += "->" + subscripts += convert_subscripts(output_list, symbol_map) + + return subscripts, tuple(operand_list) + + +def parse_einsum_input(operands: Any, shapes: bool = False) -> Tuple[str, str, List[ArrayType]]: + """A reproduction of einsum c side einsum parsing in python. + + Parameters: + operands: Intakes the same inputs as `contract_path`, but NOT the keyword args. The only + supported keyword argument is: + shapes: Whether ``parse_einsum_input`` should assume arrays (the default) or + array shapes have been supplied. + + Returns: + input_strings: Parsed input strings + output_string: Parsed output string + operands: The operands to use in the numpy contraction + + Examples: + The operand list is simplified to reduce printing: + + ```python + >>> a = np.random.rand(4, 4) + >>> b = np.random.rand(4, 4, 4) + >>> parse_einsum_input(('...a,...a->...', a, b)) + ('za,xza', 'xz', [a, b]) + + >>> parse_einsum_input((a, [Ellipsis, 0], b, [Ellipsis, 0])) + ('za,xza', 'xz', [a, b]) + ``` + """ + if len(operands) == 0: + raise ValueError("No input operands") + + if isinstance(operands[0], str): + subscripts = operands[0].replace(" ", "") + if shapes: + if any(hasattr(o, "shape") for o in operands[1:]): + raise ValueError( + "shapes is set to True but given at least one operand looks like an array" + " (at least one operand has a shape attribute). " + ) + operands = operands[1:] + else: + subscripts, operands = convert_interleaved_input(operands) + + if shapes: + operand_shapes = operands + else: + operand_shapes = [get_shape(o) for o in operands] + + # Check for proper "->" + if ("-" in subscripts) or (">" in subscripts): + invalid = (subscripts.count("-") > 1) or (subscripts.count(">") > 1) + if invalid or (subscripts.count("->") != 1): + raise ValueError("Subscripts can only contain one '->'.") + + # Parse ellipses + if "." in subscripts: + used = subscripts.replace(".", "").replace(",", "").replace("->", "") + ellipse_inds = "".join(gen_unused_symbols(used, max(len(x) for x in operand_shapes))) + longest = 0 + + # Do we have an output to account for? + if "->" in subscripts: + input_tmp, output_sub = subscripts.split("->") + split_subscripts = input_tmp.split(",") + out_sub = True + else: + split_subscripts = subscripts.split(",") + out_sub = False + + for num, sub in enumerate(split_subscripts): + if "." in sub: + if (sub.count(".") != 3) or (sub.count("...") != 1): + raise ValueError("Invalid Ellipses.") + + # Take into account numerical values + if operand_shapes[num] == (): + ellipse_count = 0 + else: + ellipse_count = max(len(operand_shapes[num]), 1) - (len(sub) - 3) + + if ellipse_count > longest: + longest = ellipse_count + + if ellipse_count < 0: + raise ValueError("Ellipses lengths do not match.") + elif ellipse_count == 0: + split_subscripts[num] = sub.replace("...", "") + else: + split_subscripts[num] = sub.replace("...", ellipse_inds[-ellipse_count:]) + + subscripts = ",".join(split_subscripts) + + # Figure out output ellipses + if longest == 0: + out_ellipse = "" + else: + out_ellipse = ellipse_inds[-longest:] + + if out_sub: + subscripts += "->" + output_sub.replace("...", out_ellipse) + else: + # Special care for outputless ellipses + output_subscript = find_output_str(subscripts) + normal_inds = "".join(sorted(set(output_subscript) - set(out_ellipse))) + + subscripts += "->" + out_ellipse + normal_inds + + # Build output string if does not exist + if "->" in subscripts: + input_subscripts, output_subscript = subscripts.split("->") + else: + input_subscripts, output_subscript = subscripts, find_output_str(subscripts) + + # Make sure output subscripts are unique and in the input + for char in output_subscript: + if output_subscript.count(char) != 1: + raise ValueError(f"Output character '{char}' appeared more than once in the output.") + if char not in input_subscripts: + raise ValueError(f"Output character '{char}' did not appear in the input") + + # Make sure number operands is equivalent to the number of terms + if len(input_subscripts.split(",")) != len(operands): + raise ValueError( + f"Number of einsum subscripts, {len(input_subscripts.split(','))}, must be equal to the " + f"number of operands, {len(operands)}." + ) + + return input_subscripts, output_subscript, operands diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/path_random.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/path_random.py new file mode 100644 index 0000000000000000000000000000000000000000..137682296a23642d67292d8140431c7ded608470 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/path_random.py @@ -0,0 +1,398 @@ +"""Support for random optimizers, including the random-greedy path.""" + +import functools +import heapq +import math +import time +from collections import deque +from decimal import Decimal +from random import choices as random_choices +from random import seed as random_seed +from typing import Any, Dict, Generator, Iterable, List, Optional, Tuple, Union + +from opt_einsum import helpers, paths +from opt_einsum.typing import ArrayIndexType, ArrayType, PathType + +__all__ = ["RandomGreedy", "random_greedy", "random_greedy_128"] + + +class RandomOptimizer(paths.PathOptimizer): + """Base class for running any random path finder that benefits + from repeated calling, possibly in a parallel fashion. Custom random + optimizers should subclass this, and the `setup` method should be + implemented with the following signature: + + ```python + def setup(self, inputs, output, size_dict): + # custom preparation here ... + return trial_fn, trial_args + ``` + + Where `trial_fn` itself should have the signature:: + + ```python + def trial_fn(r, *trial_args): + # custom computation of path here + return ssa_path, cost, size + ``` + + Where `r` is the run number and could for example be used to seed a + random number generator. See `RandomGreedy` for an example. + + + Parameters: + max_repeats: The maximum number of repeat trials to have. + max_time: The maximum amount of time to run the algorithm for. + minimize: Whether to favour paths that minimize the total estimated flop-count or + the size of the largest intermediate created. + parallel: Whether to parallelize the random trials, by default `False`. If + `True`, use a `concurrent.futures.ProcessPoolExecutor` with the same + number of processes as cores. If an integer is specified, use that many + processes instead. Finally, you can supply a custom executor-pool which + should have an API matching that of the python 3 standard library + module `concurrent.futures`. Namely, a `submit` method that returns + `Future` objects, themselves with `result` and `cancel` methods. + pre_dispatch: If running in parallel, how many jobs to pre-dispatch so as to avoid + submitting all jobs at once. Should also be more than twice the number + of workers to avoid under-subscription. Default: 128. + + Attributes: + path: The best path found so far. + costs: The list of each trial's costs found so far. + sizes: The list of each trial's largest intermediate size so far. + """ + + def __init__( + self, + max_repeats: int = 32, + max_time: Optional[float] = None, + minimize: str = "flops", + parallel: Union[bool, Decimal, int] = False, + pre_dispatch: int = 128, + ): + if minimize not in ("flops", "size"): + raise ValueError("`minimize` should be one of {'flops', 'size'}.") + + self.max_repeats = max_repeats + self.max_time = max_time + self.minimize = minimize + self.better = paths.get_better_fn(minimize) + self._parallel: Union[bool, Decimal, int] = False + self.parallel = parallel + self.pre_dispatch = pre_dispatch + + self.costs: List[int] = [] + self.sizes: List[int] = [] + self.best: Dict[str, Any] = {"flops": float("inf"), "size": float("inf")} + + self._repeats_start = 0 + self._executor: Any + self._futures: Any + + @property + def path(self) -> PathType: + """The best path found so far.""" + return paths.ssa_to_linear(self.best["ssa_path"]) + + @property + def parallel(self) -> Union[bool, Decimal, int]: + return self._parallel + + @parallel.setter + def parallel(self, parallel: Union[bool, Decimal, int]) -> None: + # shutdown any previous executor if we are managing it + if getattr(self, "_managing_executor", False): + self._executor.shutdown() + + self._parallel = parallel + self._managing_executor = False + + if parallel is False: + self._executor = None + return + + if parallel is True: + from concurrent.futures import ProcessPoolExecutor + + self._executor = ProcessPoolExecutor() + self._managing_executor = True + return + + if isinstance(parallel, (int, Decimal)): + from concurrent.futures import ProcessPoolExecutor + + self._executor = ProcessPoolExecutor(int(parallel)) + self._managing_executor = True + return + + # assume a pool-executor has been supplied + self._executor = parallel + + def _gen_results_parallel(self, repeats: Iterable[int], trial_fn: Any, args: Any) -> Generator[Any, None, None]: + """Lazily generate results from an executor without submitting all jobs at once.""" + self._futures = deque() + + # the idea here is to submit at least ``pre_dispatch`` jobs *before* we + # yield any results, then do both in tandem, before draining the queue + for r in repeats: + if len(self._futures) < self.pre_dispatch: + self._futures.append(self._executor.submit(trial_fn, r, *args)) + continue + yield self._futures.popleft().result() + + while self._futures: + yield self._futures.popleft().result() + + def _cancel_futures(self) -> None: + if self._executor is not None: + for f in self._futures: + f.cancel() + + def setup( + self, + inputs: List[ArrayIndexType], + output: ArrayIndexType, + size_dict: Dict[str, int], + ) -> Tuple[Any, Any]: + raise NotImplementedError + + def __call__( + self, + inputs: List[ArrayIndexType], + output: ArrayIndexType, + size_dict: Dict[str, int], + memory_limit: Optional[int] = None, + ) -> PathType: + self._check_args_against_first_call(inputs, output, size_dict) + + # start a timer? + if self.max_time is not None: + t0 = time.time() + + trial_fn, trial_args = self.setup(inputs, output, size_dict) + + r_start = self._repeats_start + len(self.costs) + r_stop = r_start + self.max_repeats + repeats = range(r_start, r_stop) + + # create the trials lazily + if self._executor is not None: + trials = self._gen_results_parallel(repeats, trial_fn, trial_args) + else: + trials = (trial_fn(r, *trial_args) for r in repeats) + + # assess the trials + for ssa_path, cost, size in trials: + # keep track of all costs and sizes + self.costs.append(cost) + self.sizes.append(size) + + # check if we have found a new best + found_new_best = self.better(cost, size, self.best["flops"], self.best["size"]) + + if found_new_best: + self.best["flops"] = cost + self.best["size"] = size + self.best["ssa_path"] = ssa_path + + # check if we have run out of time + if (self.max_time is not None) and (time.time() > t0 + self.max_time): + break + + self._cancel_futures() + return self.path + + def __del__(self): + # if we created the parallel pool-executor, shut it down + if getattr(self, "_managing_executor", False): + self._executor.shutdown() + + +def thermal_chooser(queue, remaining, nbranch=8, temperature=1, rel_temperature=True): + """A contraction 'chooser' that weights possible contractions using a + Boltzmann distribution. Explicitly, given costs `c_i` (with `c_0` the + smallest), the relative weights, `w_i`, are computed as: + + $$w_i = exp( -(c_i - c_0) / temperature)$$ + + Additionally, if `rel_temperature` is set, scale `temperature` by + `abs(c_0)` to account for likely fluctuating cost magnitudes during the + course of a contraction. + + Parameters: + queue: The heapified list of candidate contractions. + remaining: Mapping of remaining inputs' indices to the ssa id. + temperature: When choosing a possible contraction, its relative probability will be + proportional to `exp(-cost / temperature)`. Thus the larger + `temperature` is, the further random paths will stray from the normal + 'greedy' path. Conversely, if set to zero, only paths with exactly the + same cost as the best at each step will be explored. + rel_temperature: Whether to normalize the `temperature` at each step to the scale of + the best cost. This is generally beneficial as the magnitude of costs + can vary significantly throughout a contraction. + nbranch: How many potential paths to calculate probability for and choose from at each step. + + Returns: + cost + k1 + k2 + k3 + """ + n = 0 + choices = [] + while queue and n < nbranch: + cost, k1, k2, k12 = heapq.heappop(queue) + if k1 not in remaining or k2 not in remaining: + continue # candidate is obsolete + choices.append((cost, k1, k2, k12)) + n += 1 + + if n == 0: + return None + if n == 1: + return choices[0] + + costs = [choice[0][0] for choice in choices] + cmin = costs[0] + + # adjust by the overall scale to account for fluctuating absolute costs + if rel_temperature: + temperature *= max(1, abs(cmin)) + + # compute relative probability for each potential contraction + if temperature == 0.0: + energies = [1 if c == cmin else 0 for c in costs] + else: + # shift by cmin for numerical reasons + energies = [math.exp(-(c - cmin) / temperature) for c in costs] + + # randomly choose a contraction based on energies + (chosen,) = random_choices(range(n), weights=energies) + cost, k1, k2, k12 = choices.pop(chosen) + + # put the other choice back in the heap + for other in choices: + heapq.heappush(queue, other) + + return cost, k1, k2, k12 + + +def ssa_path_compute_cost( + ssa_path: PathType, + inputs: List[ArrayIndexType], + output: ArrayIndexType, + size_dict: Dict[str, int], +) -> Tuple[int, int]: + """Compute the flops and max size of an ssa path.""" + inputs = list(map(frozenset, inputs)) + output = frozenset(output) + remaining = set(range(len(inputs))) + total_cost = 0 + max_size = 0 + + for i, j in ssa_path: + k12, flops12 = paths.calc_k12_flops(inputs, output, remaining, i, j, size_dict) # type: ignore + remaining.discard(i) + remaining.discard(j) + remaining.add(len(inputs)) + inputs.append(k12) + total_cost += flops12 + max_size = max(max_size, helpers.compute_size_by_dict(k12, size_dict)) + + return total_cost, max_size + + +def _trial_greedy_ssa_path_and_cost( + r: int, + inputs: List[ArrayIndexType], + output: ArrayIndexType, + size_dict: Dict[str, int], + choose_fn: Any, + cost_fn: Any, +) -> Tuple[PathType, int, int]: + """A single, repeatable, greedy trial run. **Returns:** ``ssa_path`` and cost.""" + if r == 0: + # always start with the standard greedy approach + choose_fn = None + + random_seed(r) + + ssa_path = paths.ssa_greedy_optimize(inputs, output, size_dict, choose_fn, cost_fn) + cost, size = ssa_path_compute_cost(ssa_path, inputs, output, size_dict) + + return ssa_path, cost, size + + +class RandomGreedy(RandomOptimizer): + def __init__( + self, + cost_fn: str = "memory-removed-jitter", + temperature: float = 1.0, + rel_temperature: bool = True, + nbranch: int = 8, + **kwargs: Any, + ): + """Parameters: + cost_fn: A function that returns a heuristic 'cost' of a potential contraction + with which to sort candidates. Should have signature + `cost_fn(size12, size1, size2, k12, k1, k2)`. + temperature: When choosing a possible contraction, its relative probability will be + proportional to `exp(-cost / temperature)`. Thus the larger + `temperature` is, the further random paths will stray from the normal + 'greedy' path. Conversely, if set to zero, only paths with exactly the + same cost as the best at each step will be explored. + rel_temperature: Whether to normalize the ``temperature`` at each step to the scale of + the best cost. This is generally beneficial as the magnitude of costs + can vary significantly throughout a contraction. If False, the + algorithm will end up branching when the absolute cost is low, but + stick to the 'greedy' path when the cost is high - this can also be + beneficial. + nbranch: How many potential paths to calculate probability for and choose from at each step. + kwargs: Supplied to RandomOptimizer. + """ + self.cost_fn = cost_fn + self.temperature = temperature + self.rel_temperature = rel_temperature + self.nbranch = nbranch + super().__init__(**kwargs) + + @property + def choose_fn(self) -> Any: + """The function that chooses which contraction to take - make this a + property so that ``temperature`` and ``nbranch`` etc. can be updated + between runs. + """ + if self.nbranch == 1: + return None + + return functools.partial( + thermal_chooser, + temperature=self.temperature, + nbranch=self.nbranch, + rel_temperature=self.rel_temperature, + ) + + def setup( + self, + inputs: List[ArrayIndexType], + output: ArrayIndexType, + size_dict: Dict[str, int], + ) -> Tuple[Any, Any]: + fn = _trial_greedy_ssa_path_and_cost + args = (inputs, output, size_dict, self.choose_fn, self.cost_fn) + return fn, args + + +def random_greedy( + inputs: List[ArrayIndexType], + output: ArrayIndexType, + idx_dict: Dict[str, int], + memory_limit: Optional[int] = None, + **optimizer_kwargs: Any, +) -> ArrayType: + """A simple wrapper around the `RandomGreedy` optimizer.""" + optimizer = RandomGreedy(**optimizer_kwargs) + return optimizer(inputs, output, idx_dict, memory_limit) + + +random_greedy_128 = functools.partial(random_greedy, max_repeats=128) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/paths.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/paths.py new file mode 100644 index 0000000000000000000000000000000000000000..5c38c45d734ade28a4b52bc75693d2dd023855ff --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/paths.py @@ -0,0 +1,1394 @@ +"""Contains the path technology behind opt_einsum in addition to several path helpers.""" + +import bisect +import functools +import heapq +import itertools +import operator +import random +import re +from collections import Counter, defaultdict +from typing import Any, Callable, Dict, FrozenSet, Generator, List, Optional, Sequence, Set, Tuple, Union +from typing import Counter as CounterType + +from opt_einsum.helpers import compute_size_by_dict, flop_count +from opt_einsum.typing import ArrayIndexType, PathSearchFunctionType, PathType, TensorShapeType + +__all__ = [ + "optimal", + "BranchBound", + "branch", + "greedy", + "auto", + "auto_hq", + "get_path_fn", + "DynamicProgramming", + "dynamic_programming", +] + +_UNLIMITED_MEM = {-1, None, float("inf")} + + +class PathOptimizer: + r"""Base class for different path optimizers to inherit from. + + Subclassed optimizers should define a call method with signature: + + ```python + def __call__(self, inputs: List[ArrayIndexType], output: ArrayIndexType, size_dict: dict[str, int], memory_limit: int | None = None) -> list[tuple[int, ...]]: + \"\"\" + Parameters: + inputs: The indices of each input array. + outputs: The output indices + size_dict: The size of each index + memory_limit: If given, the maximum allowed memory. + \"\"\" + # ... compute path here ... + return path + ``` + + where `path` is a list of int-tuples specifying a contraction order. + """ + + def _check_args_against_first_call( + self, + inputs: List[ArrayIndexType], + output: ArrayIndexType, + size_dict: Dict[str, int], + ) -> None: + """Utility that stateful optimizers can use to ensure they are not + called with different contractions across separate runs. + """ + args = (inputs, output, size_dict) + if not hasattr(self, "_first_call_args"): + # simply set the attribute as currently there is no global PathOptimizer init + self._first_call_args = args + elif args != self._first_call_args: + raise ValueError( + "The arguments specifying the contraction that this path optimizer " + "instance was called with have changed - try creating a new instance." + ) + + def __call__( + self, + inputs: List[ArrayIndexType], + output: ArrayIndexType, + size_dict: Dict[str, int], + memory_limit: Optional[int] = None, + ) -> PathType: + raise NotImplementedError + + +def ssa_to_linear(ssa_path: PathType) -> PathType: + """Convert a path with static single assignment ids to a path with recycled + linear ids. + + Example: + ```python + ssa_to_linear([(0, 3), (2, 4), (1, 5)]) + #> [(0, 3), (1, 2), (0, 1)] + ``` + """ + # ids = np.arange(1 + max(map(max, ssa_path)), dtype=np.int32) # type: ignore + # path = [] + # for ssa_ids in ssa_path: + # path.append(tuple(int(ids[ssa_id]) for ssa_id in ssa_ids)) + # for ssa_id in ssa_ids: + # ids[ssa_id:] -= 1 + # return path + + n = sum(map(len, ssa_path)) - len(ssa_path) + 1 + ids = list(range(n)) + path = [] + ssa = n + for scon in ssa_path: + con = sorted([bisect.bisect_left(ids, s) for s in scon]) + for j in reversed(con): + ids.pop(j) + ids.append(ssa) + path.append(con) + ssa += 1 + return [tuple(x) for x in path] + + # N = sum(map(len, ssa_path)) - len(ssa_path) + 1 + # ids = list(range(N)) + # ids = np.arange(1 + max(map(max, ssa_path)), dtype=np.int32) + # path = [] + # ssa = N + # for scon in ssa_path: + # con = sorted(map(ids.index, scon)) + # for j in reversed(con): + # ids.pop(j) + # ids.append(ssa) + # path.append(con) + # ssa += 1 + # return path + + +def linear_to_ssa(path: PathType) -> PathType: + """Convert a path with recycled linear ids to a path with static single + assignment ids. + + Exmaple: + ```python + linear_to_ssa([(0, 3), (1, 2), (0, 1)]) + #> [(0, 3), (2, 4), (1, 5)] + ``` + """ + num_inputs = sum(map(len, path)) - len(path) + 1 + linear_to_ssa = list(range(num_inputs)) + new_ids = itertools.count(num_inputs) + ssa_path = [] + for ids in path: + ssa_path.append(tuple(linear_to_ssa[id_] for id_ in ids)) + for id_ in sorted(ids, reverse=True): + del linear_to_ssa[id_] + linear_to_ssa.append(next(new_ids)) + return ssa_path + + +def calc_k12_flops( + inputs: Tuple[FrozenSet[str]], + output: FrozenSet[str], + remaining: FrozenSet[int], + i: int, + j: int, + size_dict: Dict[str, int], +) -> Tuple[FrozenSet[str], int]: + """Calculate the resulting indices and flops for a potential pairwise + contraction - used in the recursive (optimal/branch) algorithms. + + Parameters: + inputs: The indices of each tensor in this contraction, note this includes + tensors unavailable to contract as static single assignment is used:> + contracted tensors are not removed from the list. + output: The set of output indices for the whole contraction. + remaining: *The set of indices (corresponding to ``inputs``) of tensors still available to contract. + i: Index of potential tensor to contract. + j: Index of potential tensor to contract. + size_dict: Size mapping of all the indices. + + Returns: + k12: The resulting indices of the potential tensor. + cost: Estimated flop count of operation. + """ + k1, k2 = inputs[i], inputs[j] + either = k1 | k2 + shared = k1 & k2 + keep = frozenset.union(output, *map(inputs.__getitem__, remaining - {i, j})) + + k12 = either & keep + cost = flop_count(either, bool(shared - keep), 2, size_dict) + + return k12, cost + + +def _compute_oversize_flops( + inputs: Tuple[FrozenSet[str]], + remaining: List[ArrayIndexType], + output: ArrayIndexType, + size_dict: Dict[str, int], +) -> int: + """Compute the flop count for a contraction of all remaining arguments. This + is used when a memory limit means that no pairwise contractions can be made. + """ + idx_contraction = frozenset.union(*map(inputs.__getitem__, remaining)) # type: ignore + inner = idx_contraction - output + num_terms = len(remaining) + return flop_count(idx_contraction, bool(inner), num_terms, size_dict) + + +def optimal( + inputs: List[ArrayIndexType], + output: ArrayIndexType, + size_dict: Dict[str, int], + memory_limit: Optional[int] = None, +) -> PathType: + """Computes all possible pair contractions in a depth-first recursive manner, + sieving results based on `memory_limit` and the best path found so far. + + Parameters: + inputs: List of sets that represent the lhs side of the einsum subscript. + output: Set that represents the rhs side of the overall einsum subscript. + size_dict: Dictionary of index sizes. + memory_limit: The maximum number of elements in a temporary array. + + Returns: + path: The optimal contraction order within the memory limit constraint. + + Examples: + ```python + isets = [set('abd'), set('ac'), set('bdc')] + oset = set('') + idx_sizes = {'a': 1, 'b':2, 'c':3, 'd':4} + optimal(isets, oset, idx_sizes, 5000) + #> [(0, 2), (0, 1)] + ``` + """ + inputs_set = tuple(map(frozenset, inputs)) + output_set = frozenset(output) + + best_flops = {"flops": float("inf")} + best_ssa_path = {"ssa_path": (tuple(range(len(inputs))),)} + size_cache: Dict[FrozenSet[str], int] = {} + result_cache: Dict[Tuple[ArrayIndexType, ArrayIndexType], Tuple[FrozenSet[str], int]] = {} + + def _optimal_iterate(path, remaining, inputs, flops): + # reached end of path (only ever get here if flops is best found so far) + if len(remaining) == 1: + best_flops["flops"] = flops + best_ssa_path["ssa_path"] = path + return + + # check all possible remaining paths + for i, j in itertools.combinations(remaining, 2): + if i > j: + i, j = j, i + key = (inputs[i], inputs[j]) + try: + k12, flops12 = result_cache[key] + except KeyError: + k12, flops12 = result_cache[key] = calc_k12_flops(inputs, output_set, remaining, i, j, size_dict) + + # sieve based on current best flops + new_flops = flops + flops12 + if new_flops >= best_flops["flops"]: + continue + + # sieve based on memory limit + if memory_limit not in _UNLIMITED_MEM: + try: + size12 = size_cache[k12] + except KeyError: + size12 = size_cache[k12] = compute_size_by_dict(k12, size_dict) + + # possibly terminate this path with an all-terms einsum + if size12 > memory_limit: + new_flops = flops + _compute_oversize_flops(inputs, remaining, output_set, size_dict) + if new_flops < best_flops["flops"]: + best_flops["flops"] = new_flops + best_ssa_path["ssa_path"] = path + (tuple(remaining),) + continue + + # add contraction and recurse into all remaining + _optimal_iterate( + path=path + ((i, j),), + inputs=inputs + (k12,), + remaining=remaining - {i, j} | {len(inputs)}, + flops=new_flops, + ) + + _optimal_iterate(path=(), inputs=inputs_set, remaining=set(range(len(inputs))), flops=0) + + return ssa_to_linear(best_ssa_path["ssa_path"]) + + +# functions for comparing which of two paths is 'better' + + +def better_flops_first(flops: int, size: int, best_flops: int, best_size: int) -> bool: + return (flops, size) < (best_flops, best_size) + + +def better_size_first(flops: int, size: int, best_flops: int, best_size: int) -> bool: + return (size, flops) < (best_size, best_flops) + + +_BETTER_FNS = { + "flops": better_flops_first, + "size": better_size_first, +} + + +def get_better_fn(key: str) -> Callable[[int, int, int, int], bool]: + return _BETTER_FNS[key] + + +# functions for assigning a heuristic 'cost' to a potential contraction + + +def cost_memory_removed(size12: int, size1: int, size2: int, k12: int, k1: int, k2: int) -> float: + """The default heuristic cost, corresponding to the total reduction in + memory of performing a contraction. + """ + return size12 - size1 - size2 + + +def cost_memory_removed_jitter(size12: int, size1: int, size2: int, k12: int, k1: int, k2: int) -> float: + """Like memory-removed, but with a slight amount of noise that breaks ties + and thus jumbles the contractions a bit. + """ + return random.gauss(1.0, 0.01) * (size12 - size1 - size2) + + +_COST_FNS = { + "memory-removed": cost_memory_removed, + "memory-removed-jitter": cost_memory_removed_jitter, +} + + +class BranchBound(PathOptimizer): + def __init__( + self, + nbranch: Optional[int] = None, + cutoff_flops_factor: int = 4, + minimize: str = "flops", + cost_fn: str = "memory-removed", + ): + """Explores possible pair contractions in a depth-first recursive manner like + the `optimal` approach, but with extra heuristic early pruning of branches + as well sieving by `memory_limit` and the best path found so far. + + + Parameters: + nbranch: How many branches to explore at each contraction step. If None, explore + all possible branches. If an integer, branch into this many paths at + each step. Defaults to None. + cutoff_flops_factor: If at any point, a path is doing this much worse than the best path + found so far was, terminate it. The larger this is made, the more paths + will be fully explored and the slower the algorithm. Defaults to 4. + minimize: Whether to optimize the path with regard primarily to the total + estimated flop-count, or the size of the largest intermediate. The + option not chosen will still be used as a secondary criterion. + cost_fn: A function that returns a heuristic 'cost' of a potential contraction + with which to sort candidates. Should have signature + `cost_fn(size12, size1, size2, k12, k1, k2)`. + """ + if (nbranch is not None) and nbranch < 1: + raise ValueError(f"The number of branches must be at least one, `nbranch={nbranch}`.") + + self.nbranch = nbranch + self.cutoff_flops_factor = cutoff_flops_factor + self.minimize = minimize + self.cost_fn: Any = _COST_FNS.get(cost_fn, cost_fn) + + self.better = get_better_fn(minimize) + self.best: Dict[str, Any] = {"flops": float("inf"), "size": float("inf")} + self.best_progress: Dict[int, float] = defaultdict(lambda: float("inf")) + + @property + def path(self) -> PathType: + return ssa_to_linear(self.best["ssa_path"]) + + def __call__( + self, + inputs_: List[ArrayIndexType], + output_: ArrayIndexType, + size_dict: Dict[str, int], + memory_limit: Optional[int] = None, + ) -> PathType: + """Parameters: + inputs_: List of sets that represent the lhs side of the einsum subscript + output_: Set that represents the rhs side of the overall einsum subscript + size_dict: Dictionary of index sizes + memory_limit: The maximum number of elements in a temporary array. + + Returns: + path: The contraction order within the memory limit constraint. + + Examples: + ```python + isets = [set('abd'), set('ac'), set('bdc')] + oset = set('') + idx_sizes = {'a': 1, 'b':2, 'c':3, 'd':4} + optimal(isets, oset, idx_sizes, 5000) + #> [(0, 2), (0, 1)] + """ + self._check_args_against_first_call(inputs_, output_, size_dict) + + inputs: Tuple[FrozenSet[str]] = tuple(map(frozenset, inputs_)) # type: ignore + output: FrozenSet[str] = frozenset(output_) + + size_cache = {k: compute_size_by_dict(k, size_dict) for k in inputs} + result_cache: Dict[Tuple[FrozenSet[str], FrozenSet[str]], Tuple[FrozenSet[str], int]] = {} + + def _branch_iterate(path, inputs, remaining, flops, size): + # reached end of path (only ever get here if flops is best found so far) + if len(remaining) == 1: + self.best["size"] = size + self.best["flops"] = flops + self.best["ssa_path"] = path + return + + def _assess_candidate(k1: FrozenSet[str], k2: FrozenSet[str], i: int, j: int) -> Any: + # find resulting indices and flops + try: + k12, flops12 = result_cache[k1, k2] + except KeyError: + k12, flops12 = result_cache[k1, k2] = calc_k12_flops(inputs, output, remaining, i, j, size_dict) + + try: + size12 = size_cache[k12] + except KeyError: + size12 = size_cache[k12] = compute_size_by_dict(k12, size_dict) + + new_flops = flops + flops12 + new_size = max(size, size12) + + # sieve based on current best i.e. check flops and size still better + if not self.better(new_flops, new_size, self.best["flops"], self.best["size"]): + return None + + # compare to how the best method was doing as this point + if new_flops < self.best_progress[len(inputs)]: + self.best_progress[len(inputs)] = new_flops + # sieve based on current progress relative to best + elif new_flops > self.cutoff_flops_factor * self.best_progress[len(inputs)]: + return None + + # sieve based on memory limit + if (memory_limit not in _UNLIMITED_MEM) and (size12 > memory_limit): # type: ignore + # terminate path here, but check all-terms contract first + new_flops = flops + _compute_oversize_flops(inputs, remaining, output_, size_dict) + if new_flops < self.best["flops"]: + self.best["flops"] = new_flops + self.best["ssa_path"] = path + (tuple(remaining),) + return None + + # set cost heuristic in order to locally sort possible contractions + size1, size2 = size_cache[inputs[i]], size_cache[inputs[j]] + cost = self.cost_fn(size12, size1, size2, k12, k1, k2) + + return cost, flops12, new_flops, new_size, (i, j), k12 + + # check all possible remaining paths + candidates = [] + for i, j in itertools.combinations(remaining, 2): + if i > j: + i, j = j, i + k1, k2 = inputs[i], inputs[j] + + # initially ignore outer products + if k1.isdisjoint(k2): + continue + + candidate = _assess_candidate(k1, k2, i, j) + if candidate: + heapq.heappush(candidates, candidate) + + # assess outer products if nothing left + if not candidates: + for i, j in itertools.combinations(remaining, 2): + if i > j: + i, j = j, i + k1, k2 = inputs[i], inputs[j] + candidate = _assess_candidate(k1, k2, i, j) + if candidate: + heapq.heappush(candidates, candidate) + + # recurse into all or some of the best candidate contractions + bi = 0 + while (self.nbranch is None or bi < self.nbranch) and candidates: + _, _, new_flops, new_size, (i, j), k12 = heapq.heappop(candidates) + _branch_iterate( + path=path + ((i, j),), + inputs=inputs + (k12,), + remaining=(remaining - {i, j}) | {len(inputs)}, + flops=new_flops, + size=new_size, + ) + bi += 1 + + _branch_iterate(path=(), inputs=inputs, remaining=set(range(len(inputs))), flops=0, size=0) + + return self.path + + +def branch( + inputs: List[ArrayIndexType], + output: ArrayIndexType, + size_dict: Dict[str, int], + memory_limit: Optional[int] = None, + nbranch: Optional[int] = None, + cutoff_flops_factor: int = 4, + minimize: str = "flops", + cost_fn: str = "memory-removed", +) -> PathType: + optimizer = BranchBound( + nbranch=nbranch, cutoff_flops_factor=cutoff_flops_factor, minimize=minimize, cost_fn=cost_fn + ) + return optimizer(inputs, output, size_dict, memory_limit) + + +branch_all = functools.partial(branch, nbranch=None) +branch_2 = functools.partial(branch, nbranch=2) +branch_1 = functools.partial(branch, nbranch=1) + +GreedyCostType = Tuple[int, int, int] +GreedyContractionType = Tuple[GreedyCostType, ArrayIndexType, ArrayIndexType, ArrayIndexType] # Cost, t1,t2->t3 + + +def _get_candidate( + output: ArrayIndexType, + sizes: Dict[str, int], + remaining: Dict[ArrayIndexType, int], + footprints: Dict[ArrayIndexType, int], + dim_ref_counts: Dict[int, Set[str]], + k1: ArrayIndexType, + k2: ArrayIndexType, + cost_fn: Any, +) -> GreedyContractionType: + either = k1 | k2 + two = k1 & k2 + one = either - two + k12 = (either & output) | (two & dim_ref_counts[3]) | (one & dim_ref_counts[2]) + cost = cost_fn( + compute_size_by_dict(k12, sizes), + footprints[k1], + footprints[k2], + k12, + k1, + k2, + ) + id1 = remaining[k1] + id2 = remaining[k2] + if id1 > id2: + k1, id1, k2, id2 = k2, id2, k1, id1 + cost = cost, id2, id1 # break ties to ensure determinism + return cost, k1, k2, k12 + + +def _push_candidate( + output: ArrayIndexType, + sizes: Dict[str, Any], + remaining: Dict[ArrayIndexType, int], + footprints: Dict[ArrayIndexType, int], + dim_ref_counts: Dict[int, Set[str]], + k1: ArrayIndexType, + k2s: List[ArrayIndexType], + queue: List[GreedyContractionType], + push_all: bool, + cost_fn: Any, +) -> None: + candidates = (_get_candidate(output, sizes, remaining, footprints, dim_ref_counts, k1, k2, cost_fn) for k2 in k2s) + if push_all: + # want to do this if we e.g. are using a custom 'choose_fn' + for candidate in candidates: + heapq.heappush(queue, candidate) + else: + heapq.heappush(queue, min(candidates)) + + +def _update_ref_counts( + dim_to_keys: Dict[str, Set[ArrayIndexType]], + dim_ref_counts: Dict[int, Set[str]], + dims: ArrayIndexType, +) -> None: + for dim in dims: + count = len(dim_to_keys[dim]) + if count <= 1: + dim_ref_counts[2].discard(dim) + dim_ref_counts[3].discard(dim) + elif count == 2: + dim_ref_counts[2].add(dim) + dim_ref_counts[3].discard(dim) + else: + dim_ref_counts[2].add(dim) + dim_ref_counts[3].add(dim) + + +def _simple_chooser(queue, remaining): + """Default contraction chooser that simply takes the minimum cost option.""" + cost, k1, k2, k12 = heapq.heappop(queue) + if k1 not in remaining or k2 not in remaining: + return None # candidate is obsolete + return cost, k1, k2, k12 + + +def ssa_greedy_optimize( + inputs: List[ArrayIndexType], + output: ArrayIndexType, + sizes: Dict[str, int], + choose_fn: Any = None, + cost_fn: Any = "memory-removed", +) -> PathType: + """This is the core function for :func:`greedy` but produces a path with + static single assignment ids rather than recycled linear ids. + SSA ids are cheaper to work with and easier to reason about. + """ + if len(inputs) == 1: + # Perform a single contraction to match output shape. + return [(0,)] + + # set the function that assigns a heuristic cost to a possible contraction + cost_fn = _COST_FNS.get(cost_fn, cost_fn) + + # set the function that chooses which contraction to take + if choose_fn is None: + choose_fn = _simple_chooser + push_all = False + else: + # assume chooser wants access to all possible contractions + push_all = True + + # A dim that is common to all tensors might as well be an output dim, since it + # cannot be contracted until the final step. This avoids an expensive all-pairs + # comparison to search for possible contractions at each step, leading to speedup + # in many practical problems where all tensors share a common batch dimension. + fs_inputs = [frozenset(x) for x in inputs] + output = frozenset(output) | frozenset.intersection(*fs_inputs) + + # Deduplicate shapes by eagerly computing Hadamard products. + remaining: Dict[ArrayIndexType, int] = {} # key -> ssa_id + ssa_ids = itertools.count(len(fs_inputs)) + ssa_path: List[TensorShapeType] = [] + for ssa_id, key in enumerate(fs_inputs): + if key in remaining: + ssa_path.append((remaining[key], ssa_id)) + remaining[key] = next(ssa_ids) + else: + remaining[key] = ssa_id + + # Keep track of possible contraction dims. + dim_to_keys = defaultdict(set) + for key in remaining: + for dim in key - output: + dim_to_keys[dim].add(key) + + # Keep track of the number of tensors using each dim; when the dim is no longer + # used it can be contracted. Since we specialize to binary ops, we only care about + # ref counts of >=2 or >=3. + dim_ref_counts = { + count: {dim for dim, keys in dim_to_keys.items() if len(keys) >= count} - output for count in [2, 3] + } + + # Compute separable part of the objective function for contractions. + footprints = {key: compute_size_by_dict(key, sizes) for key in remaining} + + # Find initial candidate contractions. + queue: List[GreedyContractionType] = [] + for dim, dim_keys in dim_to_keys.items(): + dim_keys_list = sorted(dim_keys, key=remaining.__getitem__) + for i, k1 in enumerate(dim_keys_list[:-1]): + k2s_guess = dim_keys_list[1 + i :] + _push_candidate( + output, + sizes, + remaining, + footprints, + dim_ref_counts, + k1, + k2s_guess, + queue, + push_all, + cost_fn, + ) + + # Greedily contract pairs of tensors. + while queue: + con = choose_fn(queue, remaining) + if con is None: + continue # allow choose_fn to flag all candidates obsolete + cost, k1, k2, k12 = con + + ssa_id1 = remaining.pop(k1) + ssa_id2 = remaining.pop(k2) + for dim in k1 - output: + dim_to_keys[dim].remove(k1) + for dim in k2 - output: + dim_to_keys[dim].remove(k2) + ssa_path.append((ssa_id1, ssa_id2)) + if k12 in remaining: + ssa_path.append((remaining[k12], next(ssa_ids))) + else: + for dim in k12 - output: + dim_to_keys[dim].add(k12) + remaining[k12] = next(ssa_ids) + _update_ref_counts(dim_to_keys, dim_ref_counts, k1 | k2 - output) + footprints[k12] = compute_size_by_dict(k12, sizes) + + # Find new candidate contractions. + k1 = k12 + k2s = {k2 for dim in k1 for k2 in dim_to_keys[dim]} + k2s.discard(k1) + if k2s: + _push_candidate( + output, + sizes, + remaining, + footprints, + dim_ref_counts, + k1, + list(k2s), + queue, + push_all, + cost_fn, + ) + + # Greedily compute pairwise outer products. + final_queue = [(compute_size_by_dict(key & output, sizes), ssa_id, key) for key, ssa_id in remaining.items()] + heapq.heapify(final_queue) + _, ssa_id1, k1 = heapq.heappop(final_queue) + while final_queue: + _, ssa_id2, k2 = heapq.heappop(final_queue) + ssa_path.append((min(ssa_id1, ssa_id2), max(ssa_id1, ssa_id2))) + k12 = (k1 | k2) & output + cost = compute_size_by_dict(k12, sizes) + ssa_id12 = next(ssa_ids) + _, ssa_id1, k1 = heapq.heappushpop(final_queue, (cost, ssa_id12, k12)) + + return ssa_path + + +def greedy( + inputs: List[ArrayIndexType], + output: ArrayIndexType, + size_dict: Dict[str, int], + memory_limit: Optional[int] = None, + choose_fn: Any = None, + cost_fn: str = "memory-removed", +) -> PathType: + """Finds the path by a three stage algorithm: + + 1. Eagerly compute Hadamard products. + 2. Greedily compute contractions to maximize `removed_size` + 3. Greedily compute outer products. + + This algorithm scales quadratically with respect to the + maximum number of elements sharing a common dim. + + Parameters: + inputs: List of sets that represent the lhs side of the einsum subscript + output: Set that represents the rhs side of the overall einsum subscript + size_dict: Dictionary of index sizes + memory_limit: The maximum number of elements in a temporary array + choose_fn: A function that chooses which contraction to perform from the queue + cost_fn: A function that assigns a potential contraction a cost. + + Returns: + path: The contraction order (a list of tuples of ints). + + Examples: + ```python + isets = [set('abd'), set('ac'), set('bdc')] + oset = set('') + idx_sizes = {'a': 1, 'b':2, 'c':3, 'd':4} + greedy(isets, oset, idx_sizes) + #> [(0, 2), (0, 1)] + ``` + """ + if memory_limit not in _UNLIMITED_MEM: + return branch(inputs, output, size_dict, memory_limit, nbranch=1, cost_fn=cost_fn) # type: ignore + + ssa_path = ssa_greedy_optimize(inputs, output, size_dict, cost_fn=cost_fn, choose_fn=choose_fn) + return ssa_to_linear(ssa_path) + + +def _tree_to_sequence(tree: Tuple[Any, ...]) -> PathType: + """Converts a contraction tree to a contraction path as it has to be + returned by path optimizers. A contraction tree can either be an int + (=no contraction) or a tuple containing the terms to be contracted. An + arbitrary number (>= 1) of terms can be contracted at once. Note that + contractions are commutative, e.g. (j, k, l) = (k, l, j). Note that in + general, solutions are not unique. + + Parameters: + c: Contraction tree + + Returns: + path: Contraction path + + Examples: + ```python + _tree_to_sequence(((1,2),(0,(4,5,3)))) + #> [(1, 2), (1, 2, 3), (0, 2), (0, 1)] + ``` + """ + # ((1,2),(0,(4,5,3))) --> [(1, 2), (1, 2, 3), (0, 2), (0, 1)] + # + # 0 0 0 (1,2) --> ((1,2),(0,(3,4,5))) + # 1 3 (1,2) --> (0,(3,4,5)) + # 2 --> 4 --> (3,4,5) + # 3 5 + # 4 (1,2) + # 5 + # + # this function iterates through the table shown above from right to left; + + if type(tree) == int: # noqa: E721 + return [] + + c: List[Tuple[Any, ...]] = [tree] # list of remaining contractions (lower part of columns shown above) + t: List[int] = [] # list of elementary tensors (upper part of columns) + s: List[Tuple[int, ...]] = [] # resulting contraction sequence + + while len(c) > 0: + j = c.pop(-1) + s.insert(0, ()) + + for i in sorted([i for i in j if type(i) == int]): # noqa: E721 + s[0] += (sum(1 for q in t if q < i),) + t.insert(s[0][-1], i) + + for i_tup in [i_tup for i_tup in j if type(i_tup) != int]: # noqa: E721 + s[0] += (len(t) + len(c),) + c.append(i_tup) + + return s + + +def _find_disconnected_subgraphs(inputs: List[FrozenSet[int]], output: FrozenSet[int]) -> List[FrozenSet[int]]: + """Finds disconnected subgraphs in the given list of inputs. Inputs are + connected if they share summation indices. Note: Disconnected subgraphs + can be contracted independently before forming outer products. + + Parameters: + inputs: List of sets that represent the lhs side of the einsum subscript + output: Set that represents the rhs side of the overall einsum subscript + + Returns: + subgraphs: List containing sets of indices for each subgraph + + Examples: + ```python + _find_disconnected_subgraphs([set("ab"), set("c"), set("ad")], set("bd")) + #> [{0, 2}, {1}] + + _find_disconnected_subgraphs([set("ab"), set("c"), set("ad")], set("abd")) + #> [{0}, {1}, {2}] + ``` + """ + subgraphs = [] + unused_inputs = set(range(len(inputs))) + + i_sum = frozenset.union(*inputs) - output # all summation indices + + while len(unused_inputs) > 0: + g = set() + q = [unused_inputs.pop()] + while len(q) > 0: + j = q.pop() + g.add(j) + i_tmp = i_sum & inputs[j] + n = {k for k in unused_inputs if len(i_tmp & inputs[k]) > 0} + q.extend(n) + unused_inputs.difference_update(n) + + subgraphs.append(g) + + return [frozenset(x) for x in subgraphs] + + +def _bitmap_select(s: int, seq: List[FrozenSet[int]]) -> Generator[FrozenSet[int], None, None]: + """Select elements of ``seq`` which are marked by the bitmap set ``s``. + + E.g.: + + >>> list(_bitmap_select(0b11010, ['A', 'B', 'C', 'D', 'E'])) + ['B', 'D', 'E'] + """ + return (x for x, b in zip(seq, bin(s)[:1:-1]) if b == "1") + + +def _dp_calc_legs(g, all_tensors, s, inputs, i1_cut_i2_wo_output, i1_union_i2): + """Calculates the effective outer indices of the intermediate tensor + corresponding to the subgraph ``s``. + """ + # set of remaining tensors (=g-s) + r = g & (all_tensors ^ s) + # indices of remaining indices: + if r: + i_r = frozenset.union(*_bitmap_select(r, inputs)) + else: + i_r = frozenset() + # contraction indices: + i_contract = i1_cut_i2_wo_output - i_r + return i1_union_i2 - i_contract + + +def _dp_compare_flops( + cost1: int, + cost2: int, + i1_union_i2: Set[int], + size_dict: List[int], + cost_cap: int, + s1: int, + s2: int, + xn: Dict[int, Any], + g: int, + all_tensors: int, + inputs: List[FrozenSet[int]], + i1_cut_i2_wo_output: Set[int], + memory_limit: Optional[int], + contract1: Union[int, Tuple[int]], + contract2: Union[int, Tuple[int]], +) -> None: + """Performs the inner comparison of whether the two subgraphs (the bitmaps + `s1` and `s2`) should be merged and added to the dynamic programming + search. Will skip for a number of reasons: + + 1. If the number of operations to form `s = s1 | s2` including previous + contractions is above the cost-cap. + 2. If we've already found a better way of making `s`. + 3. If the intermediate tensor corresponding to `s` is going to break the + memory limit. + """ + # TODO: Odd usage with an Iterable[int] to map a dict of type List[int] + cost = cost1 + cost2 + compute_size_by_dict(i1_union_i2, size_dict) + if cost <= cost_cap: + s = s1 | s2 + if s not in xn or cost < xn[s][1]: + i = _dp_calc_legs(g, all_tensors, s, inputs, i1_cut_i2_wo_output, i1_union_i2) + mem = compute_size_by_dict(i, size_dict) + if memory_limit is None or mem <= memory_limit: + xn[s] = (i, cost, (contract1, contract2)) + + +def _dp_compare_size( + cost1: int, + cost2: int, + i1_union_i2: Set[int], + size_dict: List[int], + cost_cap: int, + s1: int, + s2: int, + xn: Dict[int, Any], + g: int, + all_tensors: int, + inputs: List[FrozenSet[int]], + i1_cut_i2_wo_output: Set[int], + memory_limit: Optional[int], + contract1: Union[int, Tuple[int]], + contract2: Union[int, Tuple[int]], +) -> None: + """Like `_dp_compare_flops` but sieves the potential contraction based + on the size of the intermediate tensor created, rather than the number of + operations, and so calculates that first. + """ + s = s1 | s2 + i = _dp_calc_legs(g, all_tensors, s, inputs, i1_cut_i2_wo_output, i1_union_i2) + mem = compute_size_by_dict(i, size_dict) + cost = max(cost1, cost2, mem) + if cost <= cost_cap: + if s not in xn or cost < xn[s][1]: + if memory_limit is None or mem <= memory_limit: + xn[s] = (i, cost, (contract1, contract2)) + + +def _dp_compare_write( + cost1: int, + cost2: int, + i1_union_i2: Set[int], + size_dict: List[int], + cost_cap: int, + s1: int, + s2: int, + xn: Dict[int, Any], + g: int, + all_tensors: int, + inputs: List[FrozenSet[int]], + i1_cut_i2_wo_output: Set[int], + memory_limit: Optional[int], + contract1: Union[int, Tuple[int]], + contract2: Union[int, Tuple[int]], +) -> None: + """Like ``_dp_compare_flops`` but sieves the potential contraction based + on the total size of memory created, rather than the number of + operations, and so calculates that first. + """ + s = s1 | s2 + i = _dp_calc_legs(g, all_tensors, s, inputs, i1_cut_i2_wo_output, i1_union_i2) + mem = compute_size_by_dict(i, size_dict) + cost = cost1 + cost2 + mem + if cost <= cost_cap: + if s not in xn or cost < xn[s][1]: + if memory_limit is None or mem <= memory_limit: + xn[s] = (i, cost, (contract1, contract2)) + + +DEFAULT_COMBO_FACTOR = 64 + + +def _dp_compare_combo( + cost1: int, + cost2: int, + i1_union_i2: Set[int], + size_dict: List[int], + cost_cap: int, + s1: int, + s2: int, + xn: Dict[int, Any], + g: int, + all_tensors: int, + inputs: List[FrozenSet[int]], + i1_cut_i2_wo_output: Set[int], + memory_limit: Optional[int], + contract1: Union[int, Tuple[int]], + contract2: Union[int, Tuple[int]], + factor: Union[int, float] = DEFAULT_COMBO_FACTOR, + combine: Callable = sum, +) -> None: + """Like ``_dp_compare_flops`` but sieves the potential contraction based + on some combination of both the flops and size,. + """ + s = s1 | s2 + i = _dp_calc_legs(g, all_tensors, s, inputs, i1_cut_i2_wo_output, i1_union_i2) + mem = compute_size_by_dict(i, size_dict) + f = compute_size_by_dict(i1_union_i2, size_dict) + cost = cost1 + cost2 + combine((f, factor * mem)) + if cost <= cost_cap: + if s not in xn or cost < xn[s][1]: + if memory_limit is None or mem <= memory_limit: + xn[s] = (i, cost, (contract1, contract2)) + + +minimize_finder = re.compile(r"(flops|size|write|combo|limit)-*(\d*)") + + +@functools.lru_cache(128) +def _parse_minimize(minimize: Union[str, Callable]) -> Tuple[Callable, Union[int, float]]: + """This works out what local scoring function to use for the dp algorithm + as well as a `naive_scale` to account for the memory_limit checks. + """ + if minimize == "flops": + return _dp_compare_flops, 1 + elif minimize == "size": + return _dp_compare_size, 1 + elif minimize == "write": + return _dp_compare_write, 1 + elif callable(minimize): + # default to naive_scale=inf for this and remaining options + # as otherwise memory_limit check can cause problems + return minimize, float("inf") + + # parse out a customized value for the combination factor + match = minimize_finder.fullmatch(minimize) + if match is None: + raise ValueError(f"Couldn't parse `minimize` value: {minimize}.") + + minimize, custom_factor = match.groups() + factor = float(custom_factor) if custom_factor else DEFAULT_COMBO_FACTOR + if minimize == "combo": + return functools.partial(_dp_compare_combo, factor=factor, combine=sum), float("inf") + elif minimize == "limit": + return functools.partial(_dp_compare_combo, factor=factor, combine=max), float("inf") + else: + raise ValueError(f"Couldn't parse `minimize` value: {minimize}.") + + +def simple_tree_tuple(seq: Sequence[Tuple[int, ...]]) -> Tuple[Any, ...]: + """Make a simple left to right binary tree out of iterable `seq`. + + ```python + tuple_nest([1, 2, 3, 4]) + #> (((1, 2), 3), 4) + ``` + + """ + return functools.reduce(lambda x, y: (x, y), seq) + + +def _dp_parse_out_single_term_ops( + inputs: List[FrozenSet[int]], all_inds: Tuple[str, ...], ind_counts: CounterType[str] +) -> Tuple[List[FrozenSet[int]], List[Tuple[int]], List[Union[int, Tuple[int]]]]: + """Take `inputs` and parse for single term index operations, i.e. where + an index appears on one tensor and nowhere else. + + If a term is completely reduced to a scalar in this way it can be removed + to `inputs_done`. If only some indices can be summed then add a 'single + term contraction' that will perform this summation. + """ + i_single = frozenset(i for i, c in enumerate(all_inds) if ind_counts[c] == 1) + inputs_parsed: List[FrozenSet[int]] = [] + inputs_done: List[Tuple[int]] = [] + inputs_contractions: List[Union[int, Tuple[int]]] = [] + for j, i in enumerate(inputs): + i_reduced = i - i_single + if (not i_reduced) and (len(i) > 0): + # input reduced to scalar already - remove + inputs_done.append((j,)) + else: + # if the input has any index reductions, add single contraction + inputs_parsed.append(i_reduced) + inputs_contractions.append((j,) if i_reduced != i else j) + + return inputs_parsed, inputs_done, inputs_contractions + + +class DynamicProgramming(PathOptimizer): + """Finds the optimal path of pairwise contractions without intermediate outer + products based a dynamic programming approach presented in + Phys. Rev. E 90, 033315 (2014) (the corresponding preprint is publicly + available at https://arxiv.org/abs/1304.6112). This method is especially + well-suited in the area of tensor network states, where it usually + outperforms all the other optimization strategies. + + This algorithm shows exponential scaling with the number of inputs + in the worst case scenario (see example below). If the graph to be + contracted consists of disconnected subgraphs, the algorithm scales + linearly in the number of disconnected subgraphs and only exponentially + with the number of inputs per subgraph. + + Parameters: + minimize: What to minimize: + - 'flops' - minimize the number of flops + - 'size' - minimize the size of the largest intermediate + - 'write' - minimize the size of all intermediate tensors + - 'combo' - minimize `flops + alpha * write` summed over intermediates, a default ratio of alpha=64 + is used, or it can be customized with `f'combo-{alpha}'` + - 'limit' - minimize `max(flops, alpha * write)` summed over intermediates, a default ratio of alpha=64 + is used, or it can be customized with `f'limit-{alpha}'` + - callable - a custom local cost function + + cost_cap: How to implement cost-capping: + - True - iteratively increase the cost-cap + - False - implement no cost-cap at all + - int - use explicit cost cap + + search_outer: In rare circumstances the optimal contraction may involve an outer + product, this option allows searching such contractions but may well + slow down the path finding considerably on all but very small graphs. + """ + + def __init__(self, minimize: str = "flops", cost_cap: Union[bool, int] = True, search_outer: bool = False) -> None: + self.minimize = minimize + self.search_outer = search_outer + self.cost_cap = cost_cap + + def __call__( + self, + inputs_: List[ArrayIndexType], + output_: ArrayIndexType, + size_dict_: Dict[str, int], + memory_limit_: Optional[int] = None, + ) -> PathType: + """Parameters: + inputs_: List of sets that represent the lhs side of the einsum subscript + output_: Set that represents the rhs side of the overall einsum subscript + size_dict_: Dictionary of index sizes + memory_limit_: The maximum number of elements in a temporary array. + + Returns: + path: The contraction order (a list of tuples of ints). + + Examples: + ```python + n_in = 3 # exponential scaling + n_out = 2 # linear scaling + s = dict() + i_all = [] + for _ in range(n_out): + i = [set() for _ in range(n_in)] + for j in range(n_in): + for k in range(j+1, n_in): + c = oe.get_symbol(len(s)) + i[j].add(c) + i[k].add(c) + s[c] = 2 + i_all.extend(i) + o = DynamicProgramming() + o(i_all, set(), s) + #> [(1, 2), (0, 4), (1, 2), (0, 2), (0, 1)] + ``` + """ + _check_contraction, naive_scale = _parse_minimize(self.minimize) + _check_outer = (lambda x: True) if self.search_outer else (lambda x: x) + + ind_counts = Counter(itertools.chain(*inputs_, output_)) + all_inds = tuple(ind_counts) + + # convert all indices to integers (makes set operations ~10 % faster) + symbol2int = {c: j for j, c in enumerate(all_inds)} + inputs = [frozenset(symbol2int[c] for c in i) for i in inputs_] + output = frozenset(symbol2int[c] for c in output_) + size_dict_canonical = {symbol2int[c]: v for c, v in size_dict_.items() if c in symbol2int} + size_dict = [size_dict_canonical[j] for j in range(len(size_dict_canonical))] + naive_cost = naive_scale * len(inputs) * functools.reduce(operator.mul, size_dict, 1) + + inputs, inputs_done, inputs_contractions = _dp_parse_out_single_term_ops(inputs, all_inds, ind_counts) + + if not inputs: + # nothing left to do after single axis reductions! + return _tree_to_sequence(simple_tree_tuple(inputs_done)) + + # a list of all necessary contraction expressions for each of the + # disconnected subgraphs and their size + subgraph_contractions = inputs_done + subgraph_contractions_size = [1] * len(inputs_done) + + if self.search_outer: + # optimize everything together if we are considering outer products + subgraphs = [frozenset(range(len(inputs)))] + else: + subgraphs = _find_disconnected_subgraphs(inputs, output) + + # the bitmap set of all tensors is computed as it is needed to + # compute set differences: s1 - s2 transforms into + # s1 & (all_tensors ^ s2) + all_tensors = (1 << len(inputs)) - 1 + + for g in subgraphs: + # dynamic programming approach to compute x[n] for subgraph g; + # x[n][set of n tensors] = (indices, cost, contraction) + # the set of n tensors is represented by a bitmap: if bit j is 1, + # tensor j is in the set, e.g. 0b100101 = {0,2,5}; set unions + # (intersections) can then be computed by bitwise or (and); + x: List[Any] = [None] * 2 + [{} for j in range(len(g) - 1)] + x[1] = {1 << j: (inputs[j], 0, inputs_contractions[j]) for j in g} + + # convert set of tensors g to a bitmap set: + bitmap_g = functools.reduce(lambda x, y: x | y, (1 << j for j in g)) + + # try to find contraction with cost <= cost_cap and increase + # cost_cap successively if no such contraction is found; + # this is a major performance improvement; start with product of + # output index dimensions as initial cost_cap + subgraph_inds = frozenset.union(*_bitmap_select(bitmap_g, inputs)) + if self.cost_cap is True: + cost_cap = compute_size_by_dict(subgraph_inds & output, size_dict) + elif self.cost_cap is False: + cost_cap = float("inf") # type: ignore + else: + cost_cap = self.cost_cap + # set the factor to increase the cost by each iteration (ensure > 1) + if len(subgraph_inds) == 0: + cost_increment = 2 + else: + cost_increment = max(min(map(size_dict.__getitem__, subgraph_inds)), 2) + + while len(x[-1]) == 0: + for n in range(2, len(x[1]) + 1): + xn = x[n] + + # try to combine solutions from x[m] and x[n-m] + for m in range(1, n // 2 + 1): + for s1, (i1, cost1, contract1) in x[m].items(): + for s2, (i2, cost2, contract2) in x[n - m].items(): + # can only merge if s1 and s2 are disjoint + # and avoid e.g. s1={0}, s2={1} and s1={1}, s2={0} + if (not s1 & s2) and (m != n - m or s1 < s2): + i1_cut_i2_wo_output = (i1 & i2) - output + + # maybe ignore outer products: + if _check_outer(i1_cut_i2_wo_output): + i1_union_i2 = i1 | i2 + _check_contraction( + cost1, + cost2, + i1_union_i2, + size_dict, + cost_cap, + s1, + s2, + xn, + bitmap_g, + all_tensors, + inputs, + i1_cut_i2_wo_output, + memory_limit_, + contract1, + contract2, + ) + + if (cost_cap > naive_cost) and (len(x[-1]) == 0): + raise RuntimeError("No contraction found for given `memory_limit`.") + + # increase cost cap for next iteration: + cost_cap = cost_increment * cost_cap + + i, cost, contraction = list(x[-1].values())[0] + subgraph_contractions.append(contraction) + subgraph_contractions_size.append(compute_size_by_dict(i, size_dict)) + + # sort the subgraph contractions by the size of the subgraphs in + # ascending order (will give the cheapest contractions); note that + # outer products should be performed pairwise (to use BLAS functions) + subgraph_contractions = [ + subgraph_contractions[j] + for j in sorted( + range(len(subgraph_contractions_size)), + key=subgraph_contractions_size.__getitem__, + ) + ] + + # build the final contraction tree + tree = simple_tree_tuple(subgraph_contractions) + return _tree_to_sequence(tree) + + +def dynamic_programming( + inputs: List[ArrayIndexType], + output: ArrayIndexType, + size_dict: Dict[str, int], + memory_limit: Optional[int] = None, + **kwargs: Any, +) -> PathType: + optimizer = DynamicProgramming(**kwargs) + return optimizer(inputs, output, size_dict, memory_limit) + + +_AUTO_CHOICES = {} +for i in range(1, 5): + _AUTO_CHOICES[i] = optimal +for i in range(5, 7): + _AUTO_CHOICES[i] = branch_all +for i in range(7, 9): + _AUTO_CHOICES[i] = branch_2 +for i in range(9, 15): + _AUTO_CHOICES[i] = branch_1 + + +def auto( + inputs: List[ArrayIndexType], + output: ArrayIndexType, + size_dict: Dict[str, int], + memory_limit: Optional[int] = None, +) -> PathType: + """Finds the contraction path by automatically choosing the method based on + how many input arguments there are. + """ + return _AUTO_CHOICES.get(len(inputs), greedy)(inputs, output, size_dict, memory_limit) + + +_AUTO_HQ_CHOICES = {} +for i in range(1, 6): + _AUTO_HQ_CHOICES[i] = optimal +for i in range(6, 17): + _AUTO_HQ_CHOICES[i] = dynamic_programming + + +def auto_hq( + inputs: List[ArrayIndexType], + output: ArrayIndexType, + size_dict: Dict[str, int], + memory_limit: Optional[int] = None, +) -> PathType: + """Finds the contraction path by automatically choosing the method based on + how many input arguments there are, but targeting a more generous + amount of search time than ``'auto'``. + """ + from opt_einsum.path_random import random_greedy_128 + + return _AUTO_HQ_CHOICES.get(len(inputs), random_greedy_128)(inputs, output, size_dict, memory_limit) + + +_PATH_OPTIONS: Dict[str, PathSearchFunctionType] = { + "auto": auto, + "auto-hq": auto_hq, + "optimal": optimal, + "branch-all": branch_all, + "branch-2": branch_2, + "branch-1": branch_1, + "greedy": greedy, + "eager": greedy, + "opportunistic": greedy, + "dp": dynamic_programming, + "dynamic-programming": dynamic_programming, +} + + +def register_path_fn(name: str, fn: PathSearchFunctionType) -> None: + """Add path finding function ``fn`` as an option with ``name``.""" + if name in _PATH_OPTIONS: + raise KeyError(f"Path optimizer '{name}' already exists.") + + _PATH_OPTIONS[name.lower()] = fn + + +def get_path_fn(path_type: str) -> PathSearchFunctionType: + """Get the correct path finding function from str ``path_type``.""" + path_type = path_type.lower() + if path_type not in _PATH_OPTIONS: + raise KeyError(f"Path optimizer '{path_type}' not found, valid options are {set(_PATH_OPTIONS.keys())}.") + + return _PATH_OPTIONS[path_type] diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/sharing.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/sharing.py new file mode 100644 index 0000000000000000000000000000000000000000..c9ee583262e43e76d2f66559ad3cce29532d04fe --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/sharing.py @@ -0,0 +1,216 @@ +"""A module for sharing intermediates between contractions. + +Copyright (c) 2018 Uber Technologies +""" + +import contextlib +import functools +import numbers +import threading +from collections import Counter, defaultdict +from typing import Any, Dict, Generator, List, Optional, Tuple, Union +from typing import Counter as CounterType + +from opt_einsum.parser import alpha_canonicalize, parse_einsum_input +from opt_einsum.typing import ArrayType + +CacheKeyType = Union[Tuple[str, str, int, Tuple[int, ...]], Tuple[str, int]] +CacheType = Dict[CacheKeyType, ArrayType] + +__all__ = [ + "currently_sharing", + "get_sharing_cache", + "shared_intermediates", + "count_cached_ops", + "transpose_cache_wrap", + "einsum_cache_wrap", + "to_backend_cache_wrap", +] + +_SHARING_STACK: Dict[int, List[CacheType]] = defaultdict(list) + + +def currently_sharing() -> bool: + """Check if we are currently sharing a cache -- thread specific.""" + return threading.get_ident() in _SHARING_STACK + + +def get_sharing_cache() -> CacheType: + """Return the most recent sharing cache -- thread specific.""" + return _SHARING_STACK[threading.get_ident()][-1] + + +def _add_sharing_cache(cache: CacheType) -> Any: + _SHARING_STACK[threading.get_ident()].append(cache) + + +def _remove_sharing_cache() -> None: + tid = threading.get_ident() + _SHARING_STACK[tid].pop() + if not _SHARING_STACK[tid]: + del _SHARING_STACK[tid] + + +@contextlib.contextmanager +def shared_intermediates( + cache: Optional[CacheType] = None, +) -> Generator[CacheType, None, None]: + """Context in which contract intermediate results are shared. + + Note that intermediate computations will not be garbage collected until + 1. this context exits, and + 2. the yielded cache is garbage collected (if it was captured). + + **Parameters:** + + - **cache** - *(dict)* If specified, a user-stored dict in which intermediate results will be stored. This can be used to interleave sharing contexts. + + **Returns:** + + - **cache** - *(dict)* A dictionary in which sharing results are stored. If ignored, + sharing results will be garbage collected when this context is + exited. This dict can be passed to another context to resume + sharing. + """ + if cache is None: + cache = {} + _add_sharing_cache(cache) + try: + yield cache + finally: + _remove_sharing_cache() + + +def count_cached_ops(cache: CacheType) -> CounterType[str]: + """Returns a counter of the types of each op in the cache. + This is useful for profiling to increase sharing. + """ + return Counter(key[0] for key in cache.keys()) + + +def _save_tensors(*tensors: ArrayType) -> None: + """Save tensors in the cache to prevent their ids from being recycled. + This is needed to prevent false cache lookups. + """ + cache = get_sharing_cache() + for tensor in tensors: + cache["tensor", id(tensor)] = tensor + + +def _memoize(key: CacheKeyType, fn: Any, *args: Any, **kwargs: Any) -> ArrayType: + """Memoize ``fn(*args, **kwargs)`` using the given ``key``. + Results will be stored in the innermost ``cache`` yielded by + :func:`shared_intermediates`. + """ + cache = get_sharing_cache() + if key in cache: + return cache[key] + result = fn(*args, **kwargs) + cache[key] = result + return result + + +def transpose_cache_wrap(transpose: Any) -> Any: + """Decorates a ``transpose()`` implementation to be memoized inside a + :func:`shared_intermediates` context. + """ + + @functools.wraps(transpose) + def cached_transpose(a, axes, backend="numpy"): + if not currently_sharing(): + return transpose(a, axes, backend=backend) + + # hash by axes + _save_tensors(a) + axes = tuple(axes) + key = "transpose", backend, id(a), axes + return _memoize(key, transpose, a, axes, backend=backend) + + return cached_transpose + + +def tensordot_cache_wrap(tensordot: Any) -> Any: + """Decorates a ``tensordot()`` implementation to be memoized inside a + :func:`shared_intermediates` context. + """ + + @functools.wraps(tensordot) + def cached_tensordot(x, y, axes=2, backend="numpy"): + if not currently_sharing(): + return tensordot(x, y, axes, backend=backend) + + # hash based on the (axes_x,axes_y) form of axes + _save_tensors(x, y) + if isinstance(axes, numbers.Number): + axes = ( + list(range(len(x.shape)))[len(x.shape) - axes :], + list(range(len(y.shape)))[:axes], + ) + axes = tuple(axes[0]), tuple(axes[1]) + key = "tensordot", backend, id(x), id(y), axes + return _memoize(key, tensordot, x, y, axes, backend=backend) + + return cached_tensordot + + +def einsum_cache_wrap(einsum: Any) -> Any: + """Decorates an ``einsum()`` implementation to be memoized inside a + :func:`shared_intermediates` context. + """ + + @functools.wraps(einsum) + def cached_einsum(*args, **kwargs): + if not currently_sharing(): + return einsum(*args, **kwargs) + + # hash modulo commutativity by computing a canonical ordering and names + backend = kwargs.pop("backend", "numpy") + equation = args[0] + inputs, output, operands = parse_einsum_input(args) + inputs = inputs.split(",") + + _save_tensors(*operands) + + # Build canonical key + canonical = sorted(zip(inputs, map(id, operands)), key=lambda x: x[1]) + canonical_ids = tuple(id_ for _, id_ in canonical) + canonical_inputs = ",".join(input_ for input_, _ in canonical) + canonical_equation = alpha_canonicalize(canonical_inputs + "->" + output) + + key = "einsum", backend, canonical_equation, canonical_ids + return _memoize(key, einsum, equation, *operands, backend=backend) + + return cached_einsum + + +def to_backend_cache_wrap(to_backend: Any = None, constants: Any = False) -> Any: + """Decorates an ``to_backend()`` implementation to be memoized inside a + :func:`shared_intermediates` context (e.g. ``to_cupy``, ``to_torch``). + """ + # manage the case that decorator is called with args + if to_backend is None: + return functools.partial(to_backend_cache_wrap, constants=constants) + + if constants: + + @functools.wraps(to_backend) + def cached_to_backend(array, constant=False): + if not currently_sharing(): + return to_backend(array, constant=constant) + + # hash by id + key = to_backend.__name__, id(array), constant + return _memoize(key, to_backend, array, constant=constant) + + else: + + @functools.wraps(to_backend) + def cached_to_backend(array): + if not currently_sharing(): + return to_backend(array) + + # hash by id + key = to_backend.__name__, id(array) + return _memoize(key, to_backend, array) + + return cached_to_backend diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/testing.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/testing.py new file mode 100644 index 0000000000000000000000000000000000000000..2f18b3a867d771c43b9ff7d7b2edf45fe93237f2 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/testing.py @@ -0,0 +1,224 @@ +"""Testing routines for opt_einsum.""" + +import random +from typing import Any, Dict, List, Literal, Optional, Tuple, Union, overload + +import pytest + +from opt_einsum.parser import get_symbol +from opt_einsum.typing import ArrayType, PathType, TensorShapeType + +_valid_chars = "abcdefghijklmopqABC" +_sizes = [2, 3, 4, 5, 4, 3, 2, 6, 5, 4, 3, 2, 5, 7, 4, 3, 2, 3, 4] +_default_dim_dict = dict(zip(_valid_chars, _sizes)) + + +def build_shapes(string: str, dimension_dict: Optional[Dict[str, int]] = None) -> Tuple[TensorShapeType, ...]: + """Builds random tensor shapes for testing. + + Parameters: + string: List of tensor strings to build + dimension_dict: Dictionary of index sizes, defaults to indices size of 2-7 + + Returns: + The resulting shapes. + + Examples: + ```python + >>> shapes = build_shapes('abbc', {'a': 2, 'b':3, 'c':5}) + >>> shapes + [(2, 3), (3, 3, 5), (5,)] + ``` + + """ + if dimension_dict is None: + dimension_dict = _default_dim_dict + + shapes = [] + terms = string.split("->")[0].split(",") + for term in terms: + dims = [dimension_dict[x] for x in term] + shapes.append(tuple(dims)) + return tuple(shapes) + + +def build_views( + string: str, dimension_dict: Optional[Dict[str, int]] = None, array_function: Optional[Any] = None +) -> Tuple[ArrayType]: + """Builds random numpy arrays for testing. + + Parameters: + string: List of tensor strings to build + dimension_dict: Dictionary of index _sizes + array_function: Function to build the arrays, defaults to np.random.rand + + Returns: + The resulting views. + + Examples: + ```python + >>> view = build_views('abbc', {'a': 2, 'b':3, 'c':5}) + >>> view[0].shape + (2, 3, 3, 5) + ``` + + """ + if array_function is None: + np = pytest.importorskip("numpy") + array_function = np.random.rand + + views = [] + for shape in build_shapes(string, dimension_dict=dimension_dict): + if shape: + views.append(array_function(*shape)) + else: + views.append(random.random()) + return tuple(views) + + +@overload +def rand_equation( + n: int, + regularity: int, + n_out: int = ..., + d_min: int = ..., + d_max: int = ..., + seed: Optional[int] = ..., + global_dim: bool = ..., + *, + return_size_dict: Literal[True], +) -> Tuple[str, PathType, Dict[str, int]]: ... + + +@overload +def rand_equation( + n: int, + regularity: int, + n_out: int = ..., + d_min: int = ..., + d_max: int = ..., + seed: Optional[int] = ..., + global_dim: bool = ..., + return_size_dict: Literal[False] = ..., +) -> Tuple[str, PathType]: ... + + +def rand_equation( + n: int, + regularity: int, + n_out: int = 0, + d_min: int = 2, + d_max: int = 9, + seed: Optional[int] = None, + global_dim: bool = False, + return_size_dict: bool = False, +) -> Union[Tuple[str, PathType, Dict[str, int]], Tuple[str, PathType]]: + """Generate a random contraction and shapes. + + Parameters: + n: Number of array arguments. + regularity: 'Regularity' of the contraction graph. This essentially determines how + many indices each tensor shares with others on average. + n_out: Number of output indices (i.e. the number of non-contracted indices). + Defaults to 0, i.e., a contraction resulting in a scalar. + d_min: Minimum dimension size. + d_max: Maximum dimension size. + seed: If not None, seed numpy's random generator with this. + global_dim: Add a global, 'broadcast', dimension to every operand. + return_size_dict: Return the mapping of indices to sizes. + + Returns: + eq: The equation string. + shapes: The array shapes. + size_dict: The dict of index sizes, only returned if ``return_size_dict=True``. + + Examples: + ```python + >>> eq, shapes = rand_equation(n=10, regularity=4, n_out=5, seed=42) + >>> eq + 'oyeqn,tmaq,skpo,vg,hxui,n,fwxmr,hitplcj,kudlgfv,rywjsb->cebda' + + >>> shapes + [(9, 5, 4, 5, 4), + (4, 4, 8, 5), + (9, 4, 6, 9), + (6, 6), + (6, 9, 7, 8), + (4,), + (9, 3, 9, 4, 9), + (6, 8, 4, 6, 8, 6, 3), + (4, 7, 8, 8, 6, 9, 6), + (9, 5, 3, 3, 9, 5)] + ``` + """ + np = pytest.importorskip("numpy") + if seed is not None: + np.random.seed(seed) + + # total number of indices + num_inds = n * regularity // 2 + n_out + inputs = ["" for _ in range(n)] + output = [] + + size_dict = {get_symbol(i): np.random.randint(d_min, d_max + 1) for i in range(num_inds)} + + # generate a list of indices to place either once or twice + def gen(): + for i, ix in enumerate(size_dict): + # generate an outer index + if i < n_out: + output.append(ix) + yield ix + # generate a bond + else: + yield ix + yield ix + + # add the indices randomly to the inputs + for i, ix in enumerate(np.random.permutation(list(gen()))): + # make sure all inputs have at least one index + if i < n: + inputs[i] += ix + else: + # don't add any traces on same op + where = np.random.randint(0, n) + while ix in inputs[where]: + where = np.random.randint(0, n) + + inputs[where] += ix + + # possibly add the same global dim to every arg + if global_dim: + gdim = get_symbol(num_inds) + size_dict[gdim] = np.random.randint(d_min, d_max + 1) + for i in range(n): + inputs[i] += gdim + output += gdim + + # randomly transpose the output indices and form equation + output = "".join(np.random.permutation(output)) # type: ignore + eq = "{}->{}".format(",".join(inputs), output) + + # make the shapes + shapes = [tuple(size_dict[ix] for ix in op) for op in inputs] + + ret = (eq, shapes) + + if return_size_dict: + return ret + (size_dict,) + else: + return ret + + +def build_arrays_from_tuples(path: PathType) -> List[Any]: + """Build random numpy arrays from a path. + + Parameters: + path: The path to build arrays from. + + Returns: + The resulting arrays. + """ + np = pytest.importorskip("numpy") + + return [np.random.rand(*x) for x in path] diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/tests/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/tests/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/tests/test_backends.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/tests/test_backends.py new file mode 100644 index 0000000000000000000000000000000000000000..8145fb16c1a612d4cc4856cc0370cfeb89512c83 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/tests/test_backends.py @@ -0,0 +1,462 @@ +from typing import Set + +import pytest + +from opt_einsum import backends, contract, contract_expression, sharing +from opt_einsum.contract import ArrayShaped, infer_backend, parse_backend +from opt_einsum.testing import build_views + +try: + # needed so tensorflow doesn't allocate all gpu mem + try: + from tensorflow import ConfigProto # type: ignore + from tensorflow import Session as TFSession + except ImportError: + from tensorflow.compat.v1 import ConfigProto # type: ignore + from tensorflow.compat.v1 import Session as TFSession + _TF_CONFIG = ConfigProto() + _TF_CONFIG.gpu_options.allow_growth = True +except ImportError: + pass + + +tests = [ + "ab,bc->ca", + "abc,bcd,dea", + "abc,def->fedcba", + "abc,bcd,df->fa", + # test 'prefer einsum' ops + "ijk,ikj", + "i,j->ij", + "ijk,k->ij", + "AB,BC->CA", +] + + +@pytest.mark.parametrize("string", tests) +def test_tensorflow(string: str) -> None: + np = pytest.importorskip("numpy") + pytest.importorskip("tensorflow") + + views = build_views(string) + ein = contract(string, *views, optimize=False, use_blas=False) + opt = np.empty_like(ein) + + shps = [v.shape for v in views] + expr = contract_expression(string, *shps, optimize=True) + + sess = TFSession(config=_TF_CONFIG) + with sess.as_default(): + expr(*views, backend="tensorflow", out=opt) + sess.close() + + assert np.allclose(ein, opt) + + # test non-conversion mode + tensorflow_views = [backends.to_tensorflow(view) for view in views] + expr(*tensorflow_views) + + +@pytest.mark.parametrize("constants", [{0, 1}, {0, 2}, {1, 2}]) +def test_tensorflow_with_constants(constants: Set[int]) -> None: + np = pytest.importorskip("numpy") + tf = pytest.importorskip("tensorflow") + + eq = "ij,jk,kl->li" + shapes = (2, 3), (3, 4), (4, 5) + (non_const,) = {0, 1, 2} - constants + ops = [np.random.rand(*shp) if i in constants else shp for i, shp in enumerate(shapes)] + var = np.random.rand(*shapes[non_const]) + res_exp = contract(eq, *(ops[i] if i in constants else var for i in range(3))) + + expr = contract_expression(eq, *ops, constants=constants) + + # check tensorflow + with TFSession(config=_TF_CONFIG).as_default(): + res_got = expr(var, backend="tensorflow") + assert all( + array is None or infer_backend(array) == "tensorflow" for array in expr._evaluated_constants["tensorflow"] + ) + assert np.allclose(res_exp, res_got) + + # check can call with numpy still + res_got2 = expr(var, backend="numpy") + assert np.allclose(res_exp, res_got2) + + # check tensorflow call returns tensorflow still + res_got3 = expr(backends.to_tensorflow(var)) + assert isinstance(res_got3, tf.Tensor) + + +@pytest.mark.parametrize("string", tests) +def test_tensorflow_with_sharing(string: str) -> None: + np = pytest.importorskip("numpy") + tf = pytest.importorskip("tensorflow") + + views = build_views(string) + ein = contract(string, *views, optimize=False, use_blas=False) + + shps = [v.shape for v in views] + expr = contract_expression(string, *shps, optimize=True) + + sess = TFSession(config=_TF_CONFIG) + + with sess.as_default(), sharing.shared_intermediates() as cache: + tfl1 = expr(*views, backend="tensorflow") + assert sharing.get_sharing_cache() is cache + cache_sz = len(cache) + assert cache_sz > 0 + tfl2 = expr(*views, backend="tensorflow") + assert len(cache) == cache_sz + + assert all(isinstance(t, tf.Tensor) for t in cache.values()) + + assert np.allclose(ein, tfl1) + assert np.allclose(ein, tfl2) + + +@pytest.mark.parametrize("string", tests) +def test_theano(string: str) -> None: + np = pytest.importorskip("numpy") + theano = pytest.importorskip("theano") + + views = build_views(string) + ein = contract(string, *views, optimize=False, use_blas=False) + shps = [v.shape for v in views] + + expr = contract_expression(string, *shps, optimize=True) + + opt = expr(*views, backend="theano") + assert np.allclose(ein, opt) + + # test non-conversion mode + theano_views = [backends.to_theano(view) for view in views] + theano_opt = expr(*theano_views) + assert isinstance(theano_opt, theano.tensor.TensorVariable) + + +@pytest.mark.parametrize("constants", [{0, 1}, {0, 2}, {1, 2}]) +def test_theano_with_constants(constants: Set[int]) -> None: + np = pytest.importorskip("numpy") + theano = pytest.importorskip("theano") + + eq = "ij,jk,kl->li" + shapes = (2, 3), (3, 4), (4, 5) + (non_const,) = {0, 1, 2} - constants + ops = [np.random.rand(*shp) if i in constants else shp for i, shp in enumerate(shapes)] + var = np.random.rand(*shapes[non_const]) + res_exp = contract(eq, *(ops[i] if i in constants else var for i in range(3))) + + expr = contract_expression(eq, *ops, constants=constants) + + # check theano + res_got = expr(var, backend="theano") + assert all(array is None or infer_backend(array) == "theano" for array in expr._evaluated_constants["theano"]) + assert np.allclose(res_exp, res_got) + + # check can call with numpy still + res_got2 = expr(var, backend="numpy") + assert np.allclose(res_exp, res_got2) + + # check theano call returns theano still + res_got3 = expr(backends.to_theano(var)) + assert isinstance(res_got3, theano.tensor.TensorVariable) + + +@pytest.mark.parametrize("string", tests) +def test_theano_with_sharing(string: str) -> None: + np = pytest.importorskip("numpy") + theano = pytest.importorskip("theano") + + views = build_views(string) + ein = contract(string, *views, optimize=False, use_blas=False) + + shps = [v.shape for v in views] + expr = contract_expression(string, *shps, optimize=True) + + with sharing.shared_intermediates() as cache: + thn1 = expr(*views, backend="theano") + assert sharing.get_sharing_cache() is cache + cache_sz = len(cache) + assert cache_sz > 0 + thn2 = expr(*views, backend="theano") + assert len(cache) == cache_sz + + assert all(isinstance(t, theano.tensor.TensorVariable) for t in cache.values()) + + assert np.allclose(ein, thn1) + assert np.allclose(ein, thn2) + + +@pytest.mark.parametrize("string", tests) +def test_cupy(string: str) -> None: + np = pytest.importorskip("numpy") # pragma: no cover + cupy = pytest.importorskip("cupy") + + views = build_views(string) + ein = contract(string, *views, optimize=False, use_blas=False) + shps = [v.shape for v in views] + + expr = contract_expression(string, *shps, optimize=True) + + opt = expr(*views, backend="cupy") + assert np.allclose(ein, opt) + + # test non-conversion mode + cupy_views = [backends.to_cupy(view) for view in views] + cupy_opt = expr(*cupy_views) + assert isinstance(cupy_opt, cupy.ndarray) + assert np.allclose(ein, cupy.asnumpy(cupy_opt)) + + +@pytest.mark.parametrize("constants", [{0, 1}, {0, 2}, {1, 2}]) +def test_cupy_with_constants(constants: Set[int]) -> None: + np = pytest.importorskip("numpy") # pragma: no cover + cupy = pytest.importorskip("cupy") + + eq = "ij,jk,kl->li" + shapes = (2, 3), (3, 4), (4, 5) + (non_const,) = {0, 1, 2} - constants + ops = [np.random.rand(*shp) if i in constants else shp for i, shp in enumerate(shapes)] + var = np.random.rand(*shapes[non_const]) + res_exp = contract(eq, *(ops[i] if i in constants else var for i in range(3))) + + expr = contract_expression(eq, *ops, constants=constants) + + # check cupy + res_got = expr(var, backend="cupy") + # check cupy versions of constants exist + assert all(array is None or infer_backend(array) == "cupy" for array in expr._evaluated_constants["cupy"]) + assert np.allclose(res_exp, res_got) + + # check can call with numpy still + res_got2 = expr(var, backend="numpy") + assert np.allclose(res_exp, res_got2) + + # check cupy call returns cupy still + res_got3 = expr(cupy.asarray(var)) + assert isinstance(res_got3, cupy.ndarray) + assert np.allclose(res_exp, res_got3.get()) + + +@pytest.mark.parametrize("string", tests) +def test_jax(string: str) -> None: + np = pytest.importorskip("numpy") # pragma: no cover + pytest.importorskip("jax") + + views = build_views(string) + ein = contract(string, *views, optimize=False, use_blas=False) + shps = [v.shape for v in views] + + expr = contract_expression(string, *shps, optimize=True) + + opt = expr(*views, backend="jax") + assert np.allclose(ein, opt) + assert isinstance(opt, np.ndarray) + + +@pytest.mark.parametrize("constants", [{0, 1}, {0, 2}, {1, 2}]) +def test_jax_with_constants(constants: Set[int]) -> None: + jax = pytest.importorskip("jax") + key = jax.random.PRNGKey(42) + + eq = "ij,jk,kl->li" + shapes = (2, 3), (3, 4), (4, 5) + (non_const,) = {0, 1, 2} - constants + ops = [jax.random.uniform(key, shp) if i in constants else shp for i, shp in enumerate(shapes)] + var = jax.random.uniform(key, shapes[non_const]) + res_exp = contract(eq, *(ops[i] if i in constants else var for i in range(3))) + + expr = contract_expression(eq, *ops, constants=constants) + + # check jax + res_got = expr(var, backend="jax") + # check jax versions of constants exist + assert all(array is None or infer_backend(array).startswith("jax") for array in expr._evaluated_constants["jax"]) + assert jax.numpy.sum(jax.numpy.abs(res_exp - res_got)) < 1e-8 + + +def test_jax_jit_gradient() -> None: + jax = pytest.importorskip("jax") + key = jax.random.PRNGKey(42) + + eq = "ij,jk,kl->" + shapes = (2, 3), (3, 4), (4, 2) + views = [jax.random.uniform(key, s) for s in shapes] + expr = contract_expression(eq, *shapes) + x0 = expr(*views) + + jit_expr = jax.jit(expr) + x1 = jit_expr(*views).item() + assert x1 == pytest.approx(x0, rel=1e-5) + + # jax only takes gradient w.r.t first argument + grad_expr = jax.jit(jax.grad(lambda views: expr(*views))) + view_grads = grad_expr(views) + assert all(v1.shape == v2.shape for v1, v2 in zip(views, view_grads)) + + # taking a step along the gradient should reduce our 'loss' + new_views = [v - 0.001 * dv for v, dv in zip(views, view_grads)] + x2 = jit_expr(*new_views).item() + assert x2 < x1 + + +def test_autograd_gradient() -> None: + np = pytest.importorskip("numpy") + autograd = pytest.importorskip("autograd") + + eq = "ij,jk,kl->" + shapes = (2, 3), (3, 4), (4, 2) + views = [np.random.randn(*s) for s in shapes] + expr = contract_expression(eq, *shapes) + x0 = expr(*views) + + # autograd only takes gradient w.r.t first argument + grad_expr = autograd.grad(lambda views: expr(*views)) + view_grads = grad_expr(views) + assert all(v1.shape == v2.shape for v1, v2 in zip(views, view_grads)) + + # taking a step along the gradient should reduce our 'loss' + new_views = [v - 0.001 * dv for v, dv in zip(views, view_grads)] + x1 = expr(*new_views) + assert x1 < x0 + + +@pytest.mark.parametrize("string", tests) +def test_dask(string: str) -> None: + np = pytest.importorskip("numpy") + da = pytest.importorskip("dask.array") + + views = build_views(string) + ein = contract(string, *views, optimize=False, use_blas=False) + shps = [v.shape for v in views] + expr = contract_expression(string, *shps, optimize=True) + + # test non-conversion mode + da_views = [da.from_array(x, chunks=(2)) for x in views] + da_opt = expr(*da_views) + + # check type is maintained when not using numpy arrays + assert isinstance(da_opt, da.Array) + + assert np.allclose(ein, np.array(da_opt)) + + # try raw contract + da_opt = contract(string, *da_views) + assert isinstance(da_opt, da.Array) + assert np.allclose(ein, np.array(da_opt)) + + +@pytest.mark.parametrize("string", tests) +def test_sparse(string: str) -> None: + np = pytest.importorskip("numpy") + sparse = pytest.importorskip("sparse") + + views = build_views(string) + + # sparsify views so they don't become dense during contraction + for view in views: + np.random.seed(42) + mask = np.random.choice([False, True], view.shape, True, [0.05, 0.95]) + view[mask] = 0 + + ein = contract(string, *views, optimize=False, use_blas=False) + shps = [v.shape for v in views] + expr = contract_expression(string, *shps, optimize=True) + + # test non-conversion mode + sparse_views = [sparse.COO.from_numpy(x) for x in views] + sparse_opt = expr(*sparse_views) + + # If the expression returns a float, stop here + if not ein.shape: + assert pytest.approx(ein) == 0.0 + return + + # check type is maintained when not using numpy arrays + assert isinstance(sparse_opt, sparse.COO) + assert np.allclose(ein, sparse_opt.todense()) + + # try raw contract + sparse_opt = contract(string, *sparse_views) + assert isinstance(sparse_opt, sparse.COO) + assert np.allclose(ein, sparse_opt.todense()) + + +@pytest.mark.parametrize("string", tests) +def test_torch(string: str) -> None: + torch = pytest.importorskip("torch") + + views = build_views(string, array_function=torch.rand) + ein = torch.einsum(string, *views) + + shps = [v.shape for v in views] + expr = contract_expression(string, *shps, optimize=True) + + opt = expr(*views, backend="torch") + torch.testing.assert_close(ein, opt) + + # test non-conversion mode + torch_views = [backends.to_torch(view) for view in views] + torch_opt = expr(*torch_views) + assert isinstance(torch_opt, torch.Tensor) + torch.testing.assert_close(ein, torch_opt) + + +@pytest.mark.parametrize("constants", [{0, 1}, {0, 2}, {1, 2}]) +def test_torch_with_constants(constants: Set[int]) -> None: + torch = pytest.importorskip("torch") + + eq = "ij,jk,kl->li" + shapes = (2, 3), (3, 4), (4, 5) + (non_const,) = {0, 1, 2} - constants + ops = [torch.rand(*shp) if i in constants else shp for i, shp in enumerate(shapes)] + var = torch.rand(*shapes[non_const]) + res_exp = contract(eq, *(ops[i] if i in constants else var for i in range(3)), backend="torch") + + expr = contract_expression(eq, *ops, constants=constants) + + # check torch + res_got = expr(var, backend="torch") + assert all(array is None or infer_backend(array) == "torch" for array in expr._evaluated_constants["torch"]) + torch.testing.assert_close(res_exp, res_got) + + # check can call with numpy still + res_got2 = expr(var, backend="torch") + torch.testing.assert_close(res_exp, res_got2) + + # check torch call returns torch still + res_got3 = expr(backends.to_torch(var)) + assert isinstance(res_got3, torch.Tensor) + torch.testing.assert_close(res_exp, res_got3) + + +def test_auto_backend_custom_array_no_tensordot() -> None: + x = ArrayShaped((1, 2, 3)) + # Shaped is an array-like object defined by opt_einsum - which has no TDOT + assert infer_backend(x) == "opt_einsum" + assert parse_backend([x], "auto") == "numpy" + assert parse_backend([x], None) == "numpy" + + +@pytest.mark.parametrize("string", tests) +def test_object_arrays_backend(string: str) -> None: + np = pytest.importorskip("numpy") + views = build_views(string) + ein = contract(string, *views, optimize=False, use_blas=False) + assert ein.dtype != object + + shps = [v.shape for v in views] + expr = contract_expression(string, *shps, optimize=True) + + obj_views = [view.astype(object) for view in views] + + # try raw contract + obj_opt = contract(string, *obj_views, backend="object") + assert obj_opt.dtype == object + assert np.allclose(ein, obj_opt.astype(float)) + + # test expression + obj_opt = expr(*obj_views, backend="object") + assert obj_opt.dtype == object + assert np.allclose(ein, obj_opt.astype(float)) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/tests/test_blas.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/tests/test_blas.py new file mode 100644 index 0000000000000000000000000000000000000000..28b723a5b435a9a6a24105495e99093721dcbc14 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/tests/test_blas.py @@ -0,0 +1,81 @@ +""" +Tests the BLAS capability for the opt_einsum module. +""" + +from typing import Any + +import pytest + +from opt_einsum import blas, contract + +blas_tests = [ + # DOT + ((["k", "k"], "", set("k")), "DOT"), # DDOT + ((["ijk", "ijk"], "", set("ijk")), "DOT"), # DDOT + # GEMV? + # GEMM + ((["ij", "jk"], "ik", set("j")), "GEMM"), # GEMM N N + ((["ijl", "jlk"], "ik", set("jl")), "GEMM"), # GEMM N N Tensor + ((["ij", "kj"], "ik", set("j")), "GEMM"), # GEMM N T + ((["ijl", "kjl"], "ik", set("jl")), "GEMM"), # GEMM N T Tensor + ((["ji", "jk"], "ik", set("j")), "GEMM"), # GEMM T N + ((["jli", "jlk"], "ik", set("jl")), "GEMM"), # GEMM T N Tensor + ((["ji", "kj"], "ik", set("j")), "GEMM"), # GEMM T T + ((["jli", "kjl"], "ik", set("jl")), "GEMM"), # GEMM T T Tensor + # GEMM with final transpose + ((["ij", "jk"], "ki", set("j")), "GEMM"), # GEMM N N + ((["ijl", "jlk"], "ki", set("jl")), "GEMM"), # GEMM N N Tensor + ((["ij", "kj"], "ki", set("j")), "GEMM"), # GEMM N T + ((["ijl", "kjl"], "ki", set("jl")), "GEMM"), # GEMM N T Tensor + ((["ji", "jk"], "ki", set("j")), "GEMM"), # GEMM T N + ((["jli", "jlk"], "ki", set("jl")), "GEMM"), # GEMM T N Tensor + ((["ji", "kj"], "ki", set("j")), "GEMM"), # GEMM T T + ((["jli", "kjl"], "ki", set("jl")), "GEMM"), # GEMM T T Tensor + # Tensor Dot (requires copy), lets not deal with this for now + ((["ilj", "jlk"], "ik", set("jl")), "TDOT"), # FT GEMM N N Tensor + ((["ijl", "ljk"], "ik", set("jl")), "TDOT"), # ST GEMM N N Tensor + ((["ilj", "kjl"], "ik", set("jl")), "TDOT"), # FT GEMM N T Tensor + ((["ijl", "klj"], "ik", set("jl")), "TDOT"), # ST GEMM N T Tensor + ((["lji", "jlk"], "ik", set("jl")), "TDOT"), # FT GEMM T N Tensor + ((["jli", "ljk"], "ik", set("jl")), "TDOT"), # ST GEMM T N Tensor + ((["lji", "jlk"], "ik", set("jl")), "TDOT"), # FT GEMM T N Tensor + ((["jli", "ljk"], "ik", set("jl")), "TDOT"), # ST GEMM T N Tensor + # Tensor Dot (requires copy), lets not deal with this for now with transpose + ((["ilj", "jlk"], "ik", set("lj")), "TDOT"), # FT GEMM N N Tensor + ((["ijl", "ljk"], "ik", set("lj")), "TDOT"), # ST GEMM N N Tensor + ((["ilj", "kjl"], "ik", set("lj")), "TDOT"), # FT GEMM N T Tensor + ((["ijl", "klj"], "ik", set("lj")), "TDOT"), # ST GEMM N T Tensor + ((["lji", "jlk"], "ik", set("lj")), "TDOT"), # FT GEMM T N Tensor + ((["jli", "ljk"], "ik", set("lj")), "TDOT"), # ST GEMM T N Tensor + ((["lji", "jlk"], "ik", set("lj")), "TDOT"), # FT GEMM T N Tensor + ((["jli", "ljk"], "ik", set("lj")), "TDOT"), # ST GEMM T N Tensor + # Other + ((["ijk", "ikj"], "", set("ijk")), "DOT/EINSUM"), # Transpose DOT + ((["i", "j"], "ij", set()), "OUTER/EINSUM"), # Outer + ((["ijk", "ik"], "j", set("ik")), "GEMV/EINSUM"), # Matrix-vector + ((["ijj", "jk"], "ik", set("j")), False), # Double index + ((["ijk", "j"], "ij", set()), False), # Index sum 1 + ((["ij", "ij"], "ij", set()), False), # Index sum 2 +] + + +@pytest.mark.parametrize("inp,benchmark", blas_tests) +def test_can_blas(inp: Any, benchmark: bool) -> None: + result = blas.can_blas(*inp) + assert result == benchmark + + +def test_blas_out() -> None: + np = pytest.importorskip("numpy") + + a = np.random.rand(4, 4) + b = np.random.rand(4, 4) + c = np.random.rand(4, 4) + d = np.empty((4, 4)) + + contract("ij,jk->ik", a, b, out=d) + np.testing.assert_allclose(d, np.dot(a, b)) + assert np.allclose(d, np.dot(a, b)) + + contract("ij,jk,kl->il", a, b, c, out=d) + np.testing.assert_allclose(d, np.dot(a, b).dot(c)) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/tests/test_contract.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/tests/test_contract.py new file mode 100644 index 0000000000000000000000000000000000000000..72d90f7e2fd78d3ffccbecd94a647a945bbb9a13 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/tests/test_contract.py @@ -0,0 +1,279 @@ +""" +Tets a series of opt_einsum contraction paths to ensure the results are the same for different paths +""" + +from typing import Any, List + +import pytest + +from opt_einsum import contract, contract_expression, contract_path +from opt_einsum.paths import _PATH_OPTIONS, linear_to_ssa, ssa_to_linear +from opt_einsum.testing import build_views, rand_equation +from opt_einsum.typing import OptimizeKind + +# NumPy is required for the majority of this file +np = pytest.importorskip("numpy") + + +tests = [ + # Test scalar-like operations + "a,->a", + "ab,->ab", + ",ab,->ab", + ",,->", + # Test hadamard-like products + "a,ab,abc->abc", + "a,b,ab->ab", + # Test index-transformations + "ea,fb,gc,hd,abcd->efgh", + "ea,fb,abcd,gc,hd->efgh", + "abcd,ea,fb,gc,hd->efgh", + # Test complex contractions + "acdf,jbje,gihb,hfac,gfac,gifabc,hfac", + "acdf,jbje,gihb,hfac,gfac,gifabc,hfac", + "cd,bdhe,aidb,hgca,gc,hgibcd,hgac", + "abhe,hidj,jgba,hiab,gab", + "bde,cdh,agdb,hica,ibd,hgicd,hiac", + "chd,bde,agbc,hiad,hgc,hgi,hiad", + "chd,bde,agbc,hiad,bdi,cgh,agdb", + "bdhe,acad,hiab,agac,hibd", + # Test collapse + "ab,ab,c->", + "ab,ab,c->c", + "ab,ab,cd,cd->", + "ab,ab,cd,cd->ac", + "ab,ab,cd,cd->cd", + "ab,ab,cd,cd,ef,ef->", + # Test outer prodcuts + "ab,cd,ef->abcdef", + "ab,cd,ef->acdf", + "ab,cd,de->abcde", + "ab,cd,de->be", + "ab,bcd,cd->abcd", + "ab,bcd,cd->abd", + # Random test cases that have previously failed + "eb,cb,fb->cef", + "dd,fb,be,cdb->cef", + "bca,cdb,dbf,afc->", + "dcc,fce,ea,dbf->ab", + "fdf,cdd,ccd,afe->ae", + "abcd,ad", + "ed,fcd,ff,bcf->be", + "baa,dcf,af,cde->be", + "bd,db,eac->ace", + "fff,fae,bef,def->abd", + "efc,dbc,acf,fd->abe", + # Inner products + "ab,ab", + "ab,ba", + "abc,abc", + "abc,bac", + "abc,cba", + # GEMM test cases + "ab,bc", + "ab,cb", + "ba,bc", + "ba,cb", + "abcd,cd", + "abcd,ab", + "abcd,cdef", + "abcd,cdef->feba", + "abcd,efdc", + # Inner than dot + "aab,bc->ac", + "ab,bcc->ac", + "aab,bcc->ac", + "baa,bcc->ac", + "aab,ccb->ac", + # Randomly build test caes + "aab,fa,df,ecc->bde", + "ecb,fef,bad,ed->ac", + "bcf,bbb,fbf,fc->", + "bb,ff,be->e", + "bcb,bb,fc,fff->", + "fbb,dfd,fc,fc->", + "afd,ba,cc,dc->bf", + "adb,bc,fa,cfc->d", + "bbd,bda,fc,db->acf", + "dba,ead,cad->bce", + "aef,fbc,dca->bde", +] + + +@pytest.mark.parametrize("optimize", (True, False, None)) +def test_contract_plain_types(optimize: OptimizeKind) -> None: + expr = "ij,jk,kl->il" + ops = [np.random.rand(2, 2), np.random.rand(2, 2), np.random.rand(2, 2)] + + path = contract_path(expr, *ops, optimize=optimize) + assert len(path) == 2 + + result = contract(expr, *ops, optimize=optimize) + assert result.shape == (2, 2) + + +@pytest.mark.parametrize("string", tests) +@pytest.mark.parametrize("optimize", _PATH_OPTIONS) +def test_compare(optimize: OptimizeKind, string: str) -> None: + views = build_views(string) + + ein = contract(string, *views, optimize=False, use_blas=False) + opt = contract(string, *views, optimize=optimize, use_blas=False) + assert np.allclose(ein, opt) + + +@pytest.mark.parametrize("string", tests) +def test_drop_in_replacement(string: str) -> None: + views = build_views(string) + opt = contract(string, *views) + assert np.allclose(opt, np.einsum(string, *views)) + + +@pytest.mark.parametrize("string", tests) +@pytest.mark.parametrize("optimize", _PATH_OPTIONS) +def test_compare_greek(optimize: OptimizeKind, string: str) -> None: + views = build_views(string) + + ein = contract(string, *views, optimize=False, use_blas=False) + + # convert to greek + string = "".join(chr(ord(c) + 848) if c not in ",->." else c for c in string) + + opt = contract(string, *views, optimize=optimize, use_blas=False) + assert np.allclose(ein, opt) + + +@pytest.mark.parametrize("string", tests) +@pytest.mark.parametrize("optimize", _PATH_OPTIONS) +def test_compare_blas(optimize: OptimizeKind, string: str) -> None: + views = build_views(string) + + ein = contract(string, *views, optimize=False) + opt = contract(string, *views, optimize=optimize) + assert np.allclose(ein, opt) + + +@pytest.mark.parametrize("string", tests) +@pytest.mark.parametrize("optimize", _PATH_OPTIONS) +def test_compare_blas_greek(optimize: OptimizeKind, string: str) -> None: + views = build_views(string) + + ein = contract(string, *views, optimize=False) + + # convert to greek + string = "".join(chr(ord(c) + 848) if c not in ",->." else c for c in string) + + opt = contract(string, *views, optimize=optimize) + assert np.allclose(ein, opt) + + +def test_some_non_alphabet_maintains_order() -> None: + # 'c beta a' should automatically go to -> 'a c beta' + string = "c" + chr(ord("b") + 848) + "a" + # but beta will be temporarily replaced with 'b' for which 'cba->abc' + # so check manual output kicks in: + x = np.random.rand(2, 3, 4) + assert np.allclose(contract(string, x), contract("cxa", x)) + + +def test_printing(): + string = "bbd,bda,fc,db->acf" + views = build_views(string) + + ein = contract_path(string, *views) + assert len(str(ein[1])) == 728 + + +@pytest.mark.parametrize("string", tests) +@pytest.mark.parametrize("optimize", _PATH_OPTIONS) +@pytest.mark.parametrize("use_blas", [False, True]) +@pytest.mark.parametrize("out_spec", [False, True]) +def test_contract_expressions(string: str, optimize: OptimizeKind, use_blas: bool, out_spec: bool) -> None: + views = build_views(string) + shapes = [view.shape if hasattr(view, "shape") else () for view in views] + expected = contract(string, *views, optimize=False, use_blas=False) + + expr = contract_expression(string, *shapes, optimize=optimize, use_blas=use_blas) + + if out_spec and ("->" in string) and (string[-2:] != "->"): + (out,) = build_views(string.split("->")[1]) + expr(*views, out=out) + else: + out = expr(*views) + + assert np.allclose(out, expected) + + # check representations + assert string in expr.__repr__() + assert string in expr.__str__() + + +def test_contract_expression_interleaved_input() -> None: + x, y, z = (np.random.randn(2, 2) for _ in "xyz") + expected = np.einsum(x, [0, 1], y, [1, 2], z, [2, 3], [3, 0]) + xshp, yshp, zshp = ((2, 2) for _ in "xyz") + expr = contract_expression(xshp, [0, 1], yshp, [1, 2], zshp, [2, 3], [3, 0]) + out = expr(x, y, z) + assert np.allclose(out, expected) + + +@pytest.mark.parametrize( + "string,constants", + [ + ("hbc,bdef,cdkj,ji,ikeh,lfo", [1, 2, 3, 4]), + ("bdef,cdkj,ji,ikeh,hbc,lfo", [0, 1, 2, 3]), + ("hbc,bdef,cdkj,ji,ikeh,lfo", [1, 2, 3, 4]), + ("hbc,bdef,cdkj,ji,ikeh,lfo", [1, 2, 3, 4]), + ("ijab,acd,bce,df,ef->ji", [1, 2, 3, 4]), + ("ab,cd,ad,cb", [1, 3]), + ("ab,bc,cd", [0, 1]), + ], +) +def test_contract_expression_with_constants(string: str, constants: List[int]) -> None: + views = build_views(string) + expected = contract(string, *views, optimize=False, use_blas=False) + + shapes = [view.shape if hasattr(view, "shape") else () for view in views] + + expr_args: List[Any] = [] + ctrc_args = [] + for i, (shape, view) in enumerate(zip(shapes, views)): + if i in constants: + expr_args.append(view) + else: + expr_args.append(shape) + ctrc_args.append(view) + + expr = contract_expression(string, *expr_args, constants=constants) + out = expr(*ctrc_args) + assert np.allclose(expected, out) + + +@pytest.mark.parametrize("optimize", ["greedy", "optimal"]) +@pytest.mark.parametrize("n", [4, 5]) +@pytest.mark.parametrize("reg", [2, 3]) +@pytest.mark.parametrize("n_out", [0, 2, 4]) +@pytest.mark.parametrize("global_dim", [False, True]) +def test_rand_equation(optimize: OptimizeKind, n: int, reg: int, n_out: int, global_dim: bool) -> None: + eq, _, size_dict = rand_equation(n, reg, n_out, d_min=2, d_max=5, seed=42, return_size_dict=True) + views = build_views(eq, size_dict) + + expected = contract(eq, *views, optimize=False) + actual = contract(eq, *views, optimize=optimize) + + assert np.allclose(expected, actual) + + +@pytest.mark.parametrize("equation", tests) +def test_linear_vs_ssa(equation: str) -> None: + views = build_views(equation) + linear_path, _ = contract_path(equation, *views) + ssa_path = linear_to_ssa(linear_path) + linear_path2 = ssa_to_linear(ssa_path) + assert linear_path2 == linear_path + + +def test_contract_path_supply_shapes() -> None: + eq = "ab,bc,cd" + shps = [(2, 3), (3, 4), (4, 5)] + contract_path(eq, *shps, shapes=True) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/tests/test_edge_cases.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/tests/test_edge_cases.py new file mode 100644 index 0000000000000000000000000000000000000000..483553173e21f9e57a839bdb906d8be2b309f883 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/tests/test_edge_cases.py @@ -0,0 +1,152 @@ +""" +Tets a series of opt_einsum contraction paths to ensure the results are the same for different paths +""" + +from typing import Any, Tuple + +import pytest + +from opt_einsum import contract, contract_expression, contract_path +from opt_einsum.typing import PathType + +# NumPy is required for the majority of this file +np = pytest.importorskip("numpy") + + +def test_contract_expression_checks() -> None: + # check optimize needed + with pytest.raises(ValueError): + contract_expression("ab,bc->ac", (2, 3), (3, 4), optimize=False) + + # check sizes are still checked + with pytest.raises(ValueError): + contract_expression("ab,bc->ac", (2, 3), (3, 4), (42, 42)) + + # check if out given + out = np.empty((2, 4)) + with pytest.raises(ValueError): + contract_expression("ab,bc->ac", (2, 3), (3, 4), out=out) + + # check still get errors when wrong ranks supplied to expression + expr = contract_expression("ab,bc->ac", (2, 3), (3, 4)) + + # too few arguments + with pytest.raises(ValueError) as err: + expr(np.random.rand(2, 3)) + assert "`ContractExpression` takes exactly 2" in str(err.value) + + # too many arguments + with pytest.raises(ValueError) as err: + expr(np.random.rand(2, 3), np.random.rand(2, 3), np.random.rand(2, 3)) + assert "`ContractExpression` takes exactly 2" in str(err.value) + + # wrong shapes + with pytest.raises(ValueError) as err: + expr(np.random.rand(2, 3, 4), np.random.rand(3, 4)) + assert "Internal error while evaluating `ContractExpression`" in str(err.value) + with pytest.raises(ValueError) as err: + expr(np.random.rand(2, 4), np.random.rand(3, 4, 5)) + assert "Internal error while evaluating `ContractExpression`" in str(err.value) + with pytest.raises(ValueError) as err: + expr(np.random.rand(2, 3), np.random.rand(3, 4), out=np.random.rand(2, 4, 6)) + assert "Internal error while evaluating `ContractExpression`" in str(err.value) + + # should only be able to specify out + with pytest.raises(TypeError) as err_type: + expr(np.random.rand(2, 3), np.random.rand(3, 4), order="F") # type: ignore + assert "got an unexpected keyword" in str(err_type.value) + + +def test_broadcasting_contraction() -> None: + a = np.random.rand(1, 5, 4) + b = np.random.rand(4, 6) + c = np.random.rand(5, 6) + d = np.random.rand(10) + + ein_scalar = contract("ijk,kl,jl", a, b, c, optimize=False) + opt_scalar = contract("ijk,kl,jl", a, b, c, optimize=True) + assert np.allclose(ein_scalar, opt_scalar) + + result = ein_scalar * d + + ein = contract("ijk,kl,jl,i->i", a, b, c, d, optimize=False) + opt = contract("ijk,kl,jl,i->i", a, b, c, d, optimize=True) + + assert np.allclose(ein, result) + assert np.allclose(opt, result) + + +def test_broadcasting_contraction2() -> None: + a = np.random.rand(1, 1, 5, 4) + b = np.random.rand(4, 6) + c = np.random.rand(5, 6) + d = np.random.rand(7, 7) + + ein_scalar = contract("abjk,kl,jl", a, b, c, optimize=False) + opt_scalar = contract("abjk,kl,jl", a, b, c, optimize=True) + assert np.allclose(ein_scalar, opt_scalar) + + result = ein_scalar * d + + ein = contract("abjk,kl,jl,ab->ab", a, b, c, d, optimize=False) + opt = contract("abjk,kl,jl,ab->ab", a, b, c, d, optimize=True) + + assert np.allclose(ein, result) + assert np.allclose(opt, result) + + +def test_broadcasting_contraction3() -> None: + a = np.random.rand(1, 5, 4) + b = np.random.rand(4, 1, 6) + c = np.random.rand(5, 6) + d = np.random.rand(7, 7) + + ein = contract("ajk,kbl,jl,ab->ab", a, b, c, d, optimize=False) + opt = contract("ajk,kbl,jl,ab->ab", a, b, c, d, optimize=True) + + assert np.allclose(ein, opt) + + +def test_broadcasting_contraction4() -> None: + a = np.arange(64).reshape(2, 4, 8) + ein = contract("obk,ijk->ioj", a, a, optimize=False) + opt = contract("obk,ijk->ioj", a, a, optimize=True) + + assert np.allclose(ein, opt) + + +def test_can_blas_on_healed_broadcast_dimensions() -> None: + expr = contract_expression("ab,bc,bd->acd", (5, 4), (1, 5), (4, 20)) + # first contraction involves broadcasting + assert expr.contraction_list[0][2] == "bc,ab->bca" + assert expr.contraction_list[0][-1] is False + # but then is healed GEMM is usable + assert expr.contraction_list[1][2] == "bca,bd->acd" + assert expr.contraction_list[1][-1] == "GEMM" + + +def test_pathinfo_for_empty_contraction() -> None: + eq = "->" + arrays = (1.0,) + path: PathType = [] + _, info = contract_path(eq, *arrays, optimize=path) + # some info is built lazily, so check repr + assert repr(info) + assert info.largest_intermediate == 1 + + +@pytest.mark.parametrize( + "expression, operands", + [ + [",,->", (5, 5.0, 2.0j)], + ["ab,->", ([[5, 5], [2.0, 1]], 2.0j)], + ["ab,bc->ac", ([[5, 5], [2.0, 1]], [[2.0, 1], [3.0, 4]])], + ["ab,->", ([[5, 5], [2.0, 1]], True)], + ], +) +def test_contract_with_assumed_shapes(expression: str, operands: Tuple[Any]) -> None: + """Test that we can contract with assumed shapes, and that the output is correct. This is required as we need to infer intermediate shape sizes.""" + + benchmark = np.einsum(expression, *operands) + result = contract(expression, *operands, optimize=True) + assert np.allclose(benchmark, result) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/tests/test_input.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/tests/test_input.py new file mode 100644 index 0000000000000000000000000000000000000000..24b1ead3fb342204c7161759b5fdefb1a261a840 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/tests/test_input.py @@ -0,0 +1,279 @@ +""" +Tests the input parsing for opt_einsum. Duplicates the np.einsum input tests. +""" + +from typing import Any, List + +import pytest + +from opt_einsum import contract, contract_path +from opt_einsum.typing import ArrayType + +np = pytest.importorskip("numpy") + + +def build_views(string: str) -> List[ArrayType]: + """Builds random numpy arrays for testing by using a fixed size dictionary and an input string.""" + + chars = "abcdefghij" + sizes_array = np.array([2, 3, 4, 5, 4, 3, 2, 6, 5, 4]) + sizes = dict(zip(chars, sizes_array)) + + views = [] + + string = string.replace("...", "ij") + + terms = string.split("->")[0].split(",") + for term in terms: + dims = [sizes[x] for x in term] + views.append(np.random.rand(*dims)) + return views + + +def test_type_errors() -> None: + # subscripts must be a string + with pytest.raises(TypeError): + contract(0, 0) + + # out parameter must be an array + with pytest.raises(TypeError): + contract("", 0, out="test") + + # order parameter must be a valid order + # changed in Numpy 1.19, see https://github.com/numpy/numpy/commit/35b0a051c19265f5643f6011ee11e31d30c8bc4c + with pytest.raises((TypeError, ValueError)): + contract("", 0, order="W") # type: ignore + + # casting parameter must be a valid casting + with pytest.raises(ValueError): + contract("", 0, casting="blah") # type: ignore + + # dtype parameter must be a valid dtype + with pytest.raises(TypeError): + contract("", 0, dtype="bad_data_type") + + # other keyword arguments are rejected + with pytest.raises(TypeError): + contract("", 0, bad_arg=0) + + # issue 4528 revealed a segfault with this call + with pytest.raises(TypeError): + contract(*(None,) * 63) + + # Cannot have two -> + with pytest.raises(ValueError): + contract("->,->", 0, 5) + + # Undefined symbol lhs + with pytest.raises(ValueError): + contract("&,a->", 0, 5) + + # Undefined symbol rhs + with pytest.raises(ValueError): + contract("a,a->&", 0, 5) + + with pytest.raises(ValueError): + contract("a,a->&", 0, 5) + + # Catch ellipsis errors + string = "...a->...a" + views = build_views(string) + + # Subscript list must contain Ellipsis or (hashable && comparable) object + with pytest.raises(TypeError): + contract(views[0], [Ellipsis, 0], [Ellipsis, ["a"]]) + + with pytest.raises(TypeError): + contract(views[0], [Ellipsis, {}], [Ellipsis, "a"]) + + +@pytest.mark.parametrize("contract_fn", [contract, contract_path]) +def test_value_errors(contract_fn: Any) -> None: + with pytest.raises(ValueError): + contract_fn("") + + # subscripts must be a string + with pytest.raises(TypeError): + contract_fn(0, 0) + + # invalid subscript character + with pytest.raises(ValueError): + contract_fn("i%...", [0, 0]) + with pytest.raises(ValueError): + contract_fn("...j$", [0, 0]) + with pytest.raises(ValueError): + contract_fn("i->&", [0, 0]) + + with pytest.raises(ValueError): + contract_fn("") + # number of operands must match count in subscripts string + with pytest.raises(ValueError): + contract_fn("", 0, 0) + with pytest.raises(ValueError): + contract_fn(",", 0, [0], [0]) + with pytest.raises(ValueError): + contract_fn(",", [0]) + + # can't have more subscripts than dimensions in the operand + with pytest.raises(ValueError): + contract_fn("i", 0) + with pytest.raises(ValueError): + contract_fn("ij", [0, 0]) + with pytest.raises(ValueError): + contract_fn("...i", 0) + with pytest.raises(ValueError): + contract_fn("i...j", [0, 0]) + with pytest.raises(ValueError): + contract_fn("i...", 0) + with pytest.raises(ValueError): + contract_fn("ij...", [0, 0]) + + # invalid ellipsis + with pytest.raises(ValueError): + contract_fn("i..", [0, 0]) + with pytest.raises(ValueError): + contract_fn(".i...", [0, 0]) + with pytest.raises(ValueError): + contract_fn("j->..j", [0, 0]) + with pytest.raises(ValueError): + contract_fn("j->.j...", [0, 0]) + + # invalid subscript character + with pytest.raises(ValueError): + contract_fn("i%...", [0, 0]) + with pytest.raises(ValueError): + contract_fn("...j$", [0, 0]) + with pytest.raises(ValueError): + contract_fn("i->&", [0, 0]) + + # output subscripts must appear in input + with pytest.raises(ValueError): + contract_fn("i->ij", [0, 0]) + + # output subscripts may only be specified once + with pytest.raises(ValueError): + contract_fn("ij->jij", [[0, 0], [0, 0]]) + + # dimensions much match when being collapsed + with pytest.raises(ValueError): + contract_fn("ii", np.arange(6).reshape(2, 3)) + with pytest.raises(ValueError): + contract_fn("ii->i", np.arange(6).reshape(2, 3)) + + # broadcasting to new dimensions must be enabled explicitly + with pytest.raises(ValueError): + contract_fn("i", np.arange(6).reshape(2, 3)) + + with pytest.raises(TypeError): + contract_fn("ij->ij", [[0, 1], [0, 1]], bad_kwarg=True) + + +@pytest.mark.parametrize( + "string", + [ + # Ellipse + "...a->...", + "a...->...", + "a...a->...a", + "...,...", + "a,b", + "...a,...b", + ], +) +def test_compare(string: str) -> None: + views = build_views(string) + + ein = contract(string, *views, optimize=False) + opt = contract(string, *views) + assert np.allclose(ein, opt) + + opt = contract(string, *views, optimize="optimal") + assert np.allclose(ein, opt) + + +def test_ellipse_input1() -> None: + string = "...a->..." + views = build_views(string) + + ein = contract(string, *views, optimize=False) + opt = contract(views[0], [Ellipsis, 0], [Ellipsis]) + assert np.allclose(ein, opt) + + +def test_ellipse_input2() -> None: + string = "...a" + views = build_views(string) + + ein = contract(string, *views, optimize=False) + opt = contract(views[0], [Ellipsis, 0]) + assert np.allclose(ein, opt) + + +def test_ellipse_input3() -> None: + string = "...a->...a" + views = build_views(string) + + ein = contract(string, *views, optimize=False) + opt = contract(views[0], [Ellipsis, 0], [Ellipsis, 0]) + assert np.allclose(ein, opt) + + +def test_ellipse_input4() -> None: + string = "...b,...a->..." + views = build_views(string) + + ein = contract(string, *views, optimize=False) + opt = contract(views[0], [Ellipsis, 1], views[1], [Ellipsis, 0], [Ellipsis]) + assert np.allclose(ein, opt) + + +def test_singleton_dimension_broadcast() -> None: + # singleton dimensions broadcast (gh-10343) + p = np.ones((10, 2)) + q = np.ones((1, 2)) + + ein = contract("ij,ij->j", p, q, optimize=False) + opt = contract("ij,ij->j", p, q, optimize=True) + assert np.allclose(ein, opt) + assert np.allclose(opt, [10.0, 10.0]) + + p = np.ones((1, 5)) + q = np.ones((5, 5)) + + for optimize in (True, False): + res1 = (contract("...ij,...jk->...ik", p, p, optimize=optimize),) + res2 = contract("...ij,...jk->...ik", p, q, optimize=optimize) + assert np.allclose(res1, res2) + assert np.allclose(res2, np.full((1, 5), 5)) + + +def test_large_int_input_format() -> None: + string = "ab,bc,cd" + x, y, z = build_views(string) + string_output = contract(string, x, y, z) + int_output = contract(x, (1000, 1001), y, (1001, 1002), z, (1002, 1003)) + assert np.allclose(string_output, int_output) + for i in range(10): + transpose_output = contract(x, (i + 1, i)) + assert np.allclose(transpose_output, x.T) + + +def test_hashable_object_input_format() -> None: + string = "ab,bc,cd" + x, y, z = build_views(string) + string_output = contract(string, x, y, z) + hash_output1 = contract(x, ("left", "bond1"), y, ("bond1", "bond2"), z, ("bond2", "right")) + hash_output2 = contract( + x, + ("left", "bond1"), + y, + ("bond1", "bond2"), + z, + ("bond2", "right"), + ("left", "right"), + ) + assert np.allclose(string_output, hash_output1) + assert np.allclose(hash_output1, hash_output2) + for i in range(1, 10): + transpose_output = contract(x, ("b" * i, "a" * i)) + assert np.allclose(transpose_output, x.T) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/tests/test_parser.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/tests/test_parser.py new file mode 100644 index 0000000000000000000000000000000000000000..b39bf8b4bdbb2b790641e51d96cd44bb5ef88762 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/tests/test_parser.py @@ -0,0 +1,74 @@ +""" +Directly tests various parser utility functions. +""" + +from typing import Any, Tuple + +import pytest + +from opt_einsum.parser import get_shape, get_symbol, parse_einsum_input +from opt_einsum.testing import build_arrays_from_tuples + + +def test_get_symbol() -> None: + assert get_symbol(2) == "c" + assert get_symbol(200000) == "\U00031540" + # Ensure we skip surrogates '[\uD800-\uDFFF]' + assert get_symbol(55295) == "\ud88b" + assert get_symbol(55296) == "\ue000" + assert get_symbol(57343) == "\ue7ff" + + +def test_parse_einsum_input() -> None: + eq = "ab,bc,cd" + ops = build_arrays_from_tuples([(2, 3), (3, 4), (4, 5)]) + input_subscripts, output_subscript, operands = parse_einsum_input([eq, *ops]) + assert input_subscripts == eq + assert output_subscript == "ad" + assert operands == ops + + +def test_parse_einsum_input_shapes_error() -> None: + eq = "ab,bc,cd" + ops = build_arrays_from_tuples([(2, 3), (3, 4), (4, 5)]) + + with pytest.raises(ValueError): + _ = parse_einsum_input([eq, *ops], shapes=True) + + +def test_parse_einsum_input_shapes() -> None: + eq = "ab,bc,cd" + shapes = [(2, 3), (3, 4), (4, 5)] + input_subscripts, output_subscript, operands = parse_einsum_input([eq, *shapes], shapes=True) + assert input_subscripts == eq + assert output_subscript == "ad" + assert shapes == operands + + +def test_parse_with_ellisis() -> None: + eq = "...a,ab" + shapes = [(2, 3), (3, 4)] + input_subscripts, output_subscript, operands = parse_einsum_input([eq, *shapes], shapes=True) + assert input_subscripts == "da,ab" + assert output_subscript == "db" + assert shapes == operands + + +@pytest.mark.parametrize( + "array, shape", + [ + [[5], (1,)], + [[5, 5], (2,)], + [(5, 5), (2,)], + [[[[[[5, 2]]]]], (1, 1, 1, 1, 2)], + [[[[[["abcdef", "b"]]]]], (1, 1, 1, 1, 2)], + ["A", ()], + [b"A", ()], + [True, ()], + [5, ()], + [5.0, ()], + [5.0 + 0j, ()], + ], +) +def test_get_shapes(array: Any, shape: Tuple[int]) -> None: + assert get_shape(array) == shape diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/tests/test_paths.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/tests/test_paths.py new file mode 100644 index 0000000000000000000000000000000000000000..22ed1429e6bc98a9c9c3bdcff0871fae57ff647a --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/tests/test_paths.py @@ -0,0 +1,534 @@ +""" +Tests the accuracy of the opt_einsum paths in addition to unit tests for +the various path helper functions. +""" + +import itertools +from concurrent.futures import ProcessPoolExecutor +from typing import Any, Dict, List, Optional + +import pytest + +import opt_einsum as oe +from opt_einsum.testing import build_shapes, rand_equation +from opt_einsum.typing import ArrayIndexType, OptimizeKind, PathType, TensorShapeType + +explicit_path_tests = { + "GEMM1": ( + [set("abd"), set("ac"), set("bdc")], + set(""), + {"a": 1, "b": 2, "c": 3, "d": 4}, + ), + "Inner1": ( + [set("abcd"), set("abc"), set("bc")], + set(""), + {"a": 5, "b": 2, "c": 3, "d": 4}, + ), +} + +# note that these tests have no unique solution due to the chosen dimensions +path_edge_tests = [ + ["greedy", "eb,cb,fb->cef", ((0, 2), (0, 1))], + ["branch-all", "eb,cb,fb->cef", ((0, 2), (0, 1))], + ["branch-2", "eb,cb,fb->cef", ((0, 2), (0, 1))], + ["optimal", "eb,cb,fb->cef", ((0, 2), (0, 1))], + ["dp", "eb,cb,fb->cef", ((1, 2), (0, 1))], + ["greedy", "dd,fb,be,cdb->cef", ((0, 3), (0, 1), (0, 1))], + ["branch-all", "dd,fb,be,cdb->cef", ((0, 3), (0, 1), (0, 1))], + ["branch-2", "dd,fb,be,cdb->cef", ((0, 3), (0, 1), (0, 1))], + ["optimal", "dd,fb,be,cdb->cef", ((0, 3), (0, 1), (0, 1))], + ["optimal", "dd,fb,be,cdb->cef", ((0, 3), (0, 1), (0, 1))], + ["dp", "dd,fb,be,cdb->cef", ((0, 3), (0, 2), (0, 1))], + ["greedy", "bca,cdb,dbf,afc->", ((1, 2), (0, 2), (0, 1))], + ["branch-all", "bca,cdb,dbf,afc->", ((1, 2), (0, 2), (0, 1))], + ["branch-2", "bca,cdb,dbf,afc->", ((1, 2), (0, 2), (0, 1))], + ["optimal", "bca,cdb,dbf,afc->", ((1, 2), (0, 2), (0, 1))], + ["dp", "bca,cdb,dbf,afc->", ((1, 2), (1, 2), (0, 1))], + ["greedy", "dcc,fce,ea,dbf->ab", ((1, 2), (0, 1), (0, 1))], + ["branch-all", "dcc,fce,ea,dbf->ab", ((1, 2), (0, 2), (0, 1))], + ["branch-2", "dcc,fce,ea,dbf->ab", ((1, 2), (0, 2), (0, 1))], + ["optimal", "dcc,fce,ea,dbf->ab", ((1, 2), (0, 2), (0, 1))], + ["dp", "dcc,fce,ea,dbf->ab", ((1, 2), (0, 2), (0, 1))], +] + +# note that these tests have no unique solution due to the chosen dimensions +path_scalar_tests = [ + [ + "a,->a", + 1, + ], + ["ab,->ab", 1], + [",a,->a", 2], + [",,a,->a", 3], + [",,->", 2], +] + + +def check_path(test_output: PathType, benchmark: PathType, bypass: bool = False) -> bool: + if not isinstance(test_output, list): + return False + + if len(test_output) != len(benchmark): + return False + + ret = True + for pos in range(len(test_output)): + ret &= isinstance(test_output[pos], tuple) + ret &= test_output[pos] == list(benchmark)[pos] + return ret + + +def assert_contract_order(func: Any, test_data: Any, max_size: int, benchmark: PathType) -> None: + test_output = func(test_data[0], test_data[1], test_data[2], max_size) + assert check_path(test_output, benchmark) + + +def test_size_by_dict() -> None: + sizes_dict = {} + for ind, val in zip("abcdez", [2, 5, 9, 11, 13, 0]): + sizes_dict[ind] = val + + path_func = oe.helpers.compute_size_by_dict + + assert 1 == path_func("", sizes_dict) + assert 2 == path_func("a", sizes_dict) + assert 5 == path_func("b", sizes_dict) + + assert 0 == path_func("z", sizes_dict) + assert 0 == path_func("az", sizes_dict) + assert 0 == path_func("zbc", sizes_dict) + + assert 104 == path_func("aaae", sizes_dict) + assert 12870 == path_func("abcde", sizes_dict) + + +def test_flop_cost() -> None: + size_dict = {v: 10 for v in "abcdef"} + + # Loop over an array + assert 10 == oe.helpers.flop_count("a", False, 1, size_dict) + + # Hadamard product (*) + assert 10 == oe.helpers.flop_count("a", False, 2, size_dict) + assert 100 == oe.helpers.flop_count("ab", False, 2, size_dict) + + # Inner product (+, *) + assert 20 == oe.helpers.flop_count("a", True, 2, size_dict) + assert 200 == oe.helpers.flop_count("ab", True, 2, size_dict) + + # Inner product x3 (+, *, *) + assert 30 == oe.helpers.flop_count("a", True, 3, size_dict) + + # GEMM + assert 2000 == oe.helpers.flop_count("abc", True, 2, size_dict) + + +def test_bad_path_option() -> None: + with pytest.raises(KeyError): + oe.contract("a,b,c", [1], [2], [3], optimize="optimall", shapes=True) # type: ignore + + +def test_explicit_path() -> None: + pytest.importorskip("numpy") + x = oe.contract("a,b,c", [1], [2], [3], optimize=[(1, 2), (0, 1)]) + assert x.item() == 6 + + +def test_path_optimal() -> None: + test_func = oe.paths.optimal + + test_data = explicit_path_tests["GEMM1"] + assert_contract_order(test_func, test_data, 5000, [(0, 2), (0, 1)]) + assert_contract_order(test_func, test_data, 0, [(0, 1, 2)]) + + +def test_path_greedy() -> None: + test_func = oe.paths.greedy + + test_data = explicit_path_tests["GEMM1"] + assert_contract_order(test_func, test_data, 5000, [(0, 2), (0, 1)]) + assert_contract_order(test_func, test_data, 0, [(0, 1, 2)]) + + +def test_memory_paths() -> None: + expression = "abc,bdef,fghj,cem,mhk,ljk->adgl" + + views = build_shapes(expression) + + # Test tiny memory limit + path_ret = oe.contract_path(expression, *views, optimize="optimal", memory_limit=5, shapes=True) + assert check_path(path_ret[0], [(0, 1, 2, 3, 4, 5)]) + + path_ret = oe.contract_path(expression, *views, optimize="greedy", memory_limit=5, shapes=True) + assert check_path(path_ret[0], [(0, 1, 2, 3, 4, 5)]) + + # Check the possibilities, greedy is capped + path_ret = oe.contract_path(expression, *views, optimize="optimal", memory_limit=-1, shapes=True) + assert check_path(path_ret[0], [(0, 3), (0, 4), (0, 2), (0, 2), (0, 1)]) + + path_ret = oe.contract_path(expression, *views, optimize="greedy", memory_limit=-1, shapes=True) + assert check_path(path_ret[0], [(0, 3), (0, 4), (0, 2), (0, 2), (0, 1)]) + + +@pytest.mark.parametrize("alg,expression,order", path_edge_tests) +def test_path_edge_cases(alg: OptimizeKind, expression: str, order: PathType) -> None: + views = build_shapes(expression) + + # Test tiny memory limit + path_ret = oe.contract_path(expression, *views, optimize=alg, shapes=True) + assert check_path(path_ret[0], order) + + +@pytest.mark.parametrize("expression,order", path_scalar_tests) +@pytest.mark.parametrize("alg", oe.paths._PATH_OPTIONS) +def test_path_scalar_cases(alg: OptimizeKind, expression: str, order: PathType) -> None: + views = build_shapes(expression) + + # Test tiny memory limit + path_ret = oe.contract_path(expression, *views, optimize=alg, shapes=True) + # print(path_ret[0]) + assert len(path_ret[0]) == order + + +def test_optimal_edge_cases() -> None: + # Edge test5 + expression = "a,ac,ab,ad,cd,bd,bc->" + edge_test4 = build_shapes(expression, dimension_dict={"a": 20, "b": 20, "c": 20, "d": 20}) + path, _ = oe.contract_path(expression, *edge_test4, optimize="greedy", memory_limit="max_input", shapes=True) + assert check_path(path, [(0, 1), (0, 1, 2, 3, 4, 5)]) + + path, _ = oe.contract_path(expression, *edge_test4, optimize="optimal", memory_limit="max_input", shapes=True) + assert check_path(path, [(0, 1), (0, 1, 2, 3, 4, 5)]) + + +def test_greedy_edge_cases() -> None: + expression = "abc,cfd,dbe,efa" + dim_dict = {k: 20 for k in expression.replace(",", "")} + tensors = build_shapes(expression, dimension_dict=dim_dict) + + path, _ = oe.contract_path(expression, *tensors, optimize="greedy", memory_limit="max_input", shapes=True) + assert check_path(path, [(0, 1, 2, 3)]) + + path, _ = oe.contract_path(expression, *tensors, optimize="greedy", memory_limit=-1, shapes=True) + assert check_path(path, [(0, 1), (0, 2), (0, 1)]) + + +def test_dp_edge_cases_dimension_1() -> None: + eq = "nlp,nlq,pl->n" + shapes = [(1, 1, 1), (1, 1, 1), (1, 1)] + info = oe.contract_path(eq, *shapes, shapes=True, optimize="dp")[1] + assert max(info.scale_list) == 3 + + +def test_dp_edge_cases_all_singlet_indices() -> None: + eq = "a,bcd,efg->" + shapes = [(2,), (2, 2, 2), (2, 2, 2)] + info = oe.contract_path(eq, *shapes, shapes=True, optimize="dp")[1] + assert max(info.scale_list) == 3 + + +def test_custom_dp_can_optimize_for_outer_products() -> None: + eq = "a,b,abc->c" + + da, db, dc = 2, 2, 3 + shapes = [(da,), (db,), (da, db, dc)] + + opt1 = oe.DynamicProgramming(search_outer=False) + opt2 = oe.DynamicProgramming(search_outer=True) + + info1 = oe.contract_path(eq, *shapes, shapes=True, optimize=opt1)[1] + info2 = oe.contract_path(eq, *shapes, shapes=True, optimize=opt2)[1] + + assert info2.opt_cost < info1.opt_cost + + +def test_custom_dp_can_optimize_for_size() -> None: + eq, shapes = rand_equation(10, 4, seed=43) + + opt1 = oe.DynamicProgramming(minimize="flops") + opt2 = oe.DynamicProgramming(minimize="size") + + info1 = oe.contract_path(eq, *shapes, shapes=True, optimize=opt1)[1] + info2 = oe.contract_path(eq, *shapes, shapes=True, optimize=opt2)[1] + + assert info1.opt_cost < info2.opt_cost + assert info1.largest_intermediate > info2.largest_intermediate + + +def test_custom_dp_can_set_cost_cap() -> None: + eq, shapes = rand_equation(5, 3, seed=42) + opt1 = oe.DynamicProgramming(cost_cap=True) + opt2 = oe.DynamicProgramming(cost_cap=False) + opt3 = oe.DynamicProgramming(cost_cap=100) + info1 = oe.contract_path(eq, *shapes, shapes=True, optimize=opt1)[1] + info2 = oe.contract_path(eq, *shapes, shapes=True, optimize=opt2)[1] + info3 = oe.contract_path(eq, *shapes, shapes=True, optimize=opt3)[1] + assert info1.opt_cost == info2.opt_cost == info3.opt_cost + + +@pytest.mark.parametrize( + "minimize,cost,width,path", + [ + ("flops", 663054, 18900, [(4, 5), (2, 5), (2, 7), (5, 6), (1, 5), (1, 4), (0, 3), (0, 2), (0, 1)]), + ("size", 1114440, 2016, [(2, 7), (3, 8), (3, 7), (2, 6), (1, 5), (1, 4), (1, 3), (1, 2), (0, 1)]), + ("write", 983790, 2016, [(0, 8), (3, 4), (1, 4), (5, 6), (1, 5), (0, 4), (0, 3), (1, 2), (0, 1)]), + ("combo", 973518, 2016, [(4, 5), (2, 5), (6, 7), (2, 6), (1, 5), (1, 4), (0, 3), (0, 2), (0, 1)]), + ("limit", 983832, 2016, [(2, 7), (3, 4), (0, 4), (3, 6), (2, 5), (0, 4), (0, 3), (1, 2), (0, 1)]), + ("combo-256", 983790, 2016, [(0, 8), (3, 4), (1, 4), (5, 6), (1, 5), (0, 4), (0, 3), (1, 2), (0, 1)]), + ("limit-256", 983832, 2016, [(2, 7), (3, 4), (0, 4), (3, 6), (2, 5), (0, 4), (0, 3), (1, 2), (0, 1)]), + ], +) +def test_custom_dp_can_set_minimize(minimize: str, cost: int, width: int, path: PathType) -> None: + eq, shapes = rand_equation(10, 4, seed=43) + opt = oe.DynamicProgramming(minimize=minimize) + info = oe.contract_path(eq, *shapes, shapes=True, optimize=opt)[1] + assert info.path == path + assert info.opt_cost == cost + assert info.largest_intermediate == width + + +def test_dp_errors_when_no_contractions_found() -> None: + eq, shapes = rand_equation(10, 3, seed=42) + + # first get the actual minimum cost + opt = oe.DynamicProgramming(minimize="size") + _, info = oe.contract_path(eq, *shapes, shapes=True, optimize=opt) + mincost = info.largest_intermediate + + # check we can still find it without minimizing size explicitly + oe.contract_path(eq, *shapes, shapes=True, memory_limit=mincost, optimize="dp") + + # but check just below this threshold raises + with pytest.raises(RuntimeError): + oe.contract_path(eq, *shapes, shapes=True, memory_limit=mincost - 1, optimize="dp") + + +@pytest.mark.parametrize("optimize", ["greedy", "branch-2", "branch-all", "optimal", "dp"]) +def test_can_optimize_outer_products(optimize: OptimizeKind) -> None: + a, b, c = ((10, 10) for _ in range(3)) + d = (10, 2) + + assert oe.contract_path("ab,cd,ef,fg", a, b, c, d, optimize=optimize, shapes=True)[0] == [ + (2, 3), + (0, 2), + (0, 1), + ] + + +@pytest.mark.parametrize("num_symbols", [2, 3, 26, 26 + 26, 256 - 140, 300]) +def test_large_path(num_symbols: int) -> None: + symbols = "".join(oe.get_symbol(i) for i in range(num_symbols)) + dimension_dict = dict(zip(symbols, itertools.cycle([2, 3, 4]))) + expression = ",".join(symbols[t : t + 2] for t in range(num_symbols - 1)) + tensors = build_shapes(expression, dimension_dict=dimension_dict) + + # Check that path construction does not crash + oe.contract_path(expression, *tensors, optimize="greedy", shapes=True) + + +def test_custom_random_greedy() -> None: + np = pytest.importorskip("numpy") + + eq, shapes = rand_equation(10, 4, seed=42) + views = list(map(np.ones, shapes)) + + with pytest.raises(ValueError): + oe.RandomGreedy(minimize="something") + + optimizer = oe.RandomGreedy(max_repeats=10, minimize="flops") + path, path_info = oe.contract_path(eq, *views, optimize=optimizer) + + assert len(optimizer.costs) == 10 + assert len(optimizer.sizes) == 10 + + assert path == optimizer.path + assert optimizer.best["flops"] == min(optimizer.costs) + assert path_info.largest_intermediate == optimizer.best["size"] + assert path_info.opt_cost == optimizer.best["flops"] + + # check can change settings and run again + optimizer.temperature = 0.0 + optimizer.max_repeats = 6 + path, path_info = oe.contract_path(eq, *views, optimize=optimizer) + + assert len(optimizer.costs) == 16 + assert len(optimizer.sizes) == 16 + + assert path == optimizer.path + assert optimizer.best["size"] == min(optimizer.sizes) + assert path_info.largest_intermediate == optimizer.best["size"] + assert path_info.opt_cost == optimizer.best["flops"] + + # check error if we try and reuse the optimizer on a different expression + eq, shapes = rand_equation(10, 4, seed=41) + views = list(map(np.ones, shapes)) + with pytest.raises(ValueError): + path, path_info = oe.contract_path(eq, *views, optimize=optimizer) + + +def test_custom_branchbound() -> None: + np = pytest.importorskip("numpy") + + eq, shapes = rand_equation(8, 4, seed=42) + views = list(map(np.ones, shapes)) + optimizer = oe.BranchBound(nbranch=2, cutoff_flops_factor=10, minimize="size") + + path, path_info = oe.contract_path(eq, *views, optimize=optimizer) + + assert path == optimizer.path + assert path_info.largest_intermediate == optimizer.best["size"] + assert path_info.opt_cost == optimizer.best["flops"] + + # tweak settings and run again + optimizer.nbranch = 3 + optimizer.cutoff_flops_factor = 4 + path, path_info = oe.contract_path(eq, *views, optimize=optimizer) + + assert path == optimizer.path + assert path_info.largest_intermediate == optimizer.best["size"] + assert path_info.opt_cost == optimizer.best["flops"] + + # check error if we try and reuse the optimizer on a different expression + eq, shapes = rand_equation(8, 4, seed=41) + views = list(map(np.ones, shapes)) + with pytest.raises(ValueError): + path, path_info = oe.contract_path(eq, *views, optimize=optimizer) + + +def test_branchbound_validation() -> None: + with pytest.raises(ValueError): + oe.BranchBound(nbranch=0) + + +def test_parallel_random_greedy() -> None: + np = pytest.importorskip("numpy") + + pool = ProcessPoolExecutor(2) + + eq, shapes = rand_equation(10, 4, seed=42) + views = list(map(np.ones, shapes)) + + optimizer = oe.RandomGreedy(max_repeats=10, parallel=pool) + path, path_info = oe.contract_path(eq, *views, optimize=optimizer) + + assert len(optimizer.costs) == 10 + assert len(optimizer.sizes) == 10 + + assert path == optimizer.path + assert optimizer.parallel is pool + assert optimizer._executor is pool + assert optimizer.best["flops"] == min(optimizer.costs) + assert path_info.largest_intermediate == optimizer.best["size"] + assert path_info.opt_cost == optimizer.best["flops"] + + # now switch to max time algorithm + optimizer.max_repeats = int(1e6) + optimizer.max_time = 0.2 + optimizer.parallel = 2 + + path, path_info = oe.contract_path(eq, *views, optimize=optimizer) + + assert len(optimizer.costs) > 10 + assert len(optimizer.sizes) > 10 + + assert path == optimizer.path + assert optimizer.best["flops"] == min(optimizer.costs) + assert path_info.largest_intermediate == optimizer.best["size"] + assert path_info.opt_cost == optimizer.best["flops"] + + optimizer.parallel = True + assert optimizer._executor is not None + assert optimizer._executor is not pool + + are_done = [f.running() or f.done() for f in optimizer._futures] + assert all(are_done) + + +def test_custom_path_optimizer() -> None: + np = pytest.importorskip("numpy") + + class NaiveOptimizer(oe.paths.PathOptimizer): + def __call__( + self, + inputs: List[ArrayIndexType], + output: ArrayIndexType, + size_dict: Dict[str, int], + memory_limit: Optional[int] = None, + ) -> PathType: + self.was_used = True + return [(0, 1)] * (len(inputs) - 1) + + eq, shapes = rand_equation(5, 3, seed=42, d_max=3) + views = list(map(np.ones, shapes)) + + exp = oe.contract(eq, *views, optimize=False) + + optimizer = NaiveOptimizer() + out = oe.contract(eq, *views, optimize=optimizer) + assert exp == out + assert optimizer.was_used + + +def test_custom_random_optimizer() -> None: + np = pytest.importorskip("numpy") + + class NaiveRandomOptimizer(oe.path_random.RandomOptimizer): + @staticmethod + def random_path( + r: int, n: int, inputs: List[ArrayIndexType], output: ArrayIndexType, size_dict: Dict[str, int] + ) -> Any: + """Picks a completely random contraction order.""" + np.random.seed(r) + ssa_path: List[TensorShapeType] = [] + remaining = set(range(n)) + while len(remaining) > 1: + i, j = np.random.choice(list(remaining), size=2, replace=False) + remaining.add(n + len(ssa_path)) + remaining.remove(i) + remaining.remove(j) + ssa_path.append((i, j)) + cost, size = oe.path_random.ssa_path_compute_cost(ssa_path, inputs, output, size_dict) + return ssa_path, cost, size + + def setup(self, inputs: Any, output: Any, size_dict: Any) -> Any: + self.was_used = True + n = len(inputs) + trial_fn = self.random_path + trial_args = (n, inputs, output, size_dict) + return trial_fn, trial_args + + eq, shapes = rand_equation(5, 3, seed=42, d_max=3) + views = list(map(np.ones, shapes)) + + exp = oe.contract(eq, *views, optimize=False) + + optimizer = NaiveRandomOptimizer(max_repeats=16) + out = oe.contract(eq, *views, optimize=optimizer) + assert exp == out + assert optimizer.was_used + + assert len(optimizer.costs) == 16 + + +def test_optimizer_registration() -> None: + def custom_optimizer( + inputs: List[ArrayIndexType], output: ArrayIndexType, size_dict: Dict[str, int], memory_limit: Optional[int] + ) -> PathType: + return [(0, 1)] * (len(inputs) - 1) + + with pytest.raises(KeyError): + oe.paths.register_path_fn("optimal", custom_optimizer) + + oe.paths.register_path_fn("custom", custom_optimizer) + assert "custom" in oe.paths._PATH_OPTIONS + + eq = "ab,bc,cd" + shapes = [(2, 3), (3, 4), (4, 5)] + path, _ = oe.contract_path(eq, *shapes, shapes=True, optimize="custom") # type: ignore + assert path == [(0, 1), (0, 1)] + del oe.paths._PATH_OPTIONS["custom"] + + +def test_path_with_assumed_shapes() -> None: + path, _ = oe.contract_path("ab,bc,cd", [[5, 3]], [[2], [4]], [[3, 2]]) + assert path == [(0, 1), (0, 1)] diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/tests/test_sharing.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/tests/test_sharing.py new file mode 100644 index 0000000000000000000000000000000000000000..8b0f1c08e521b14c2e0c74015b1b0b6b46cf982a --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/tests/test_sharing.py @@ -0,0 +1,390 @@ +import itertools +import weakref +from collections import Counter +from typing import Any + +import pytest + +from opt_einsum import contract, contract_expression, contract_path, get_symbol, shared_intermediates +from opt_einsum.backends import to_cupy, to_torch +from opt_einsum.contract import _einsum +from opt_einsum.parser import parse_einsum_input +from opt_einsum.sharing import count_cached_ops, currently_sharing, get_sharing_cache +from opt_einsum.testing import build_views +from opt_einsum.typing import BackendType + +pytest.importorskip("numpy") + +try: + import numpy as np # type: ignore + + numpy_if_found = "numpy" +except ImportError: + numpy_if_found = pytest.param("numpy", marks=[pytest.mark.skip(reason="NumPy not installed.")]) # type: ignore + +try: + import cupy # noqa + + cupy_if_found = "cupy" +except ImportError: + cupy_if_found = pytest.param("cupy", marks=[pytest.mark.skip(reason="CuPy not installed.")]) # type: ignore + +try: + import torch # type: ignore # noqa + + torch_if_found = "torch" +except ImportError: + torch_if_found = pytest.param("torch", marks=[pytest.mark.skip(reason="PyTorch not installed.")]) # type: ignore + +backends = [numpy_if_found, torch_if_found, cupy_if_found] +equations = [ + "ab,bc->ca", + "abc,bcd,dea", + "abc,def->fedcba", + "abc,bcd,df->fa", + # test 'prefer einsum' ops + "ijk,ikj", + "i,j->ij", + "ijk,k->ij", + "AB,BC->CA", +] +to_backend = { + "numpy": lambda x: x, + "torch": to_torch, + "cupy": to_cupy, +} + + +@pytest.mark.parametrize("eq", equations) +@pytest.mark.parametrize("backend", backends) +def test_sharing_value(eq: str, backend: BackendType) -> None: + views = build_views(eq) + shapes = [v.shape for v in views] + expr = contract_expression(eq, *shapes) + + expected = expr(*views, backend=backend) + with shared_intermediates(): + actual = expr(*views, backend=backend) + + assert (actual == expected).all() + + +@pytest.mark.parametrize("backend", backends) +def test_complete_sharing(backend: BackendType) -> None: + eq = "ab,bc,cd->" + views = build_views(eq) + expr = contract_expression(eq, *(v.shape for v in views)) + + print("-" * 40) + print("Without sharing:") + with shared_intermediates() as cache: + expr(*views, backend=backend) + expected = count_cached_ops(cache) + + print("-" * 40) + print("With sharing:") + with shared_intermediates() as cache: + expr(*views, backend=backend) + expr(*views, backend=backend) + actual = count_cached_ops(cache) + + print("-" * 40) + print(f"Without sharing: {expected} expressions") + print(f"With sharing: {actual} expressions") + assert actual == expected + + +@pytest.mark.parametrize("backend", backends) +def test_sharing_reused_cache(backend: BackendType) -> None: + eq = "ab,bc,cd->" + views = build_views(eq) + expr = contract_expression(eq, *(v.shape for v in views)) + + print("-" * 40) + print("Without sharing:") + with shared_intermediates() as cache: + expr(*views, backend=backend) + expected = count_cached_ops(cache) + + print("-" * 40) + print("With sharing:") + with shared_intermediates() as cache: + expr(*views, backend=backend) + with shared_intermediates(cache): + expr(*views, backend=backend) + actual = count_cached_ops(cache) + + print("-" * 40) + print(f"Without sharing: {expected} expressions") + print(f"With sharing: {actual} expressions") + assert actual == expected + + +@pytest.mark.parametrize("backend", backends) +def test_no_sharing_separate_cache(backend: BackendType) -> None: + eq = "ab,bc,cd->" + views = build_views(eq) + expr = contract_expression(eq, *(v.shape for v in views)) + + print("-" * 40) + print("Without sharing:") + with shared_intermediates() as cache: + expr(*views, backend=backend) + expected = count_cached_ops(cache) + expected.update(count_cached_ops(cache)) # we expect double + + print("-" * 40) + print("With sharing:") + with shared_intermediates() as cache1: + expr(*views, backend=backend) + actual = count_cached_ops(cache1) + with shared_intermediates() as cache2: + expr(*views, backend=backend) + actual.update(count_cached_ops(cache2)) + + print("-" * 40) + print(f"Without sharing: {expected} expressions") + print(f"With sharing: {actual} expressions") + assert actual == expected + + +@pytest.mark.parametrize("backend", backends) +def test_sharing_nesting(backend: BackendType) -> None: + eqs = ["ab,bc,cd->a", "ab,bc,cd->b", "ab,bc,cd->c", "ab,bc,cd->c"] + views = build_views(eqs[0]) + shapes = [v.shape for v in views] + refs: Any = weakref.WeakValueDictionary() + + def method1(views): + with shared_intermediates(): + w = contract_expression(eqs[0], *shapes)(*views, backend=backend) + x = contract_expression(eqs[2], *shapes)(*views, backend=backend) + result = contract_expression("a,b->", w.shape, x.shape)(w, x, backend=backend) + refs["w"] = w + refs["x"] = x + del w, x + assert "w" in refs + assert "x" in refs + assert "w" not in refs, "cache leakage" + assert "x" not in refs, "cache leakage" + return result + + def method2(views): + with shared_intermediates(): + y = contract_expression(eqs[2], *shapes)(*views, backend=backend) + z = contract_expression(eqs[3], *shapes)(*views, backend=backend) + refs["y"] = y + refs["z"] = z + result = contract_expression("c,d->", y.shape, z.shape)(y, z, backend=backend) + result = result + method1(views) # nest method1 in method2 + del y, z + assert "y" in refs + assert "z" in refs + assert "y" not in refs + assert "z" not in refs + + method1(views) + method2(views) + + +@pytest.mark.parametrize("eq", equations) +@pytest.mark.parametrize("backend", backends) +def test_sharing_modulo_commutativity(eq: str, backend: BackendType) -> None: + ops = tuple(to_backend[backend](x) for x in build_views(eq)) + inputs, output, _ = parse_einsum_input([eq] + list(ops)) + inputs_list = inputs.split(",") + + print("-" * 40) + print("Without sharing:") + with shared_intermediates() as cache: + _einsum(eq, *ops, backend=backend) + expected = count_cached_ops(cache) + + print("-" * 40) + print("With sharing:") + with shared_intermediates() as cache: + for permuted in itertools.permutations(zip(inputs_list, ops)): + permuted_inputs = [p[0] for p in permuted] + permuted_ops = [p[1] for p in permuted] + permuted_eq = "{}->{}".format(",".join(permuted_inputs), output) + _einsum(permuted_eq, *permuted_ops, backend=backend) + actual = count_cached_ops(cache) + + print("-" * 40) + print(f"Without sharing: {expected} expressions") + print(f"With sharing: {actual} expressions") + assert actual == expected + + +@pytest.mark.parametrize("backend", backends) +def test_partial_sharing(backend: BackendType) -> None: + eq = "ab,bc,de->" + x, y, z1 = build_views(eq) # type: ignore + z2 = 2.0 * z1 - 1.0 + expr = contract_expression(eq, x.shape, y.shape, z1.shape) + + print("-" * 40) + print("Without sharing:") + num_exprs_nosharing: Any = Counter() + with shared_intermediates() as cache: + expr(x, y, z1, backend=backend) + num_exprs_nosharing.update(count_cached_ops(cache)) + with shared_intermediates() as cache: + expr(x, y, z2, backend=backend) + num_exprs_nosharing.update(count_cached_ops(cache)) + + print("-" * 40) + print("With sharing:") + with shared_intermediates() as cache: + expr(x, y, z1, backend=backend) + expr(x, y, z2, backend=backend) + num_exprs_sharing = count_cached_ops(cache) + + print("-" * 40) + print(f"Without sharing: {num_exprs_nosharing} expressions") + print(f"With sharing: {num_exprs_sharing} expressions") + assert num_exprs_nosharing["einsum"] > num_exprs_sharing["einsum"] + + +@pytest.mark.parametrize("backend", backends) +def test_sharing_with_constants(backend: BackendType) -> None: + inputs = "ij,jk,kl" + outputs = "ijkl" + equations = [f"{inputs}->{output}" for output in outputs] + shapes = (2, 3), (3, 4), (4, 5) + constants = {0, 2} + ops = [np.random.rand(*shp) if i in constants else shp for i, shp in enumerate(shapes)] + var = np.random.rand(*shapes[1]) + + expected = [contract_expression(eq, *shapes)(ops[0], var, ops[2]) for eq in equations] + + with shared_intermediates(): + actual = [contract_expression(eq, *ops, constants=constants)(var) for eq in equations] + + for dim, expected_dim, actual_dim in zip(outputs, expected, actual): + assert np.allclose(expected_dim, actual_dim), f"error at {dim}" + + +@pytest.mark.parametrize("size", [3, 4, 5]) +@pytest.mark.parametrize("backend", backends) +def test_chain(size: int, backend: BackendType) -> None: + xs = [np.random.rand(2, 2) for _ in range(size)] + shapes = [x.shape for x in xs] + alphabet = "".join(get_symbol(i) for i in range(size + 1)) + names = [alphabet[i : i + 2] for i in range(size)] + inputs = ",".join(names) + + with shared_intermediates(): + print(inputs) + for i in range(size + 1): + target = alphabet[i] + eq = f"{inputs}->{target}" + path_info = contract_path(eq, *xs) + print(path_info[1]) + expr = contract_expression(eq, *shapes) + expr(*xs, backend=backend) + print("-" * 40) + + +@pytest.mark.parametrize("size", [3, 4, 5, 10]) +@pytest.mark.parametrize("backend", backends) +def test_chain_2(size: int, backend: BackendType) -> None: + xs = [np.random.rand(2, 2) for _ in range(size)] + shapes = [x.shape for x in xs] + alphabet = "".join(get_symbol(i) for i in range(size + 1)) + names = [alphabet[i : i + 2] for i in range(size)] + inputs = ",".join(names) + + with shared_intermediates(): + print(inputs) + for i in range(size): + target = alphabet[i : i + 2] + eq = f"{inputs}->{target}" + path_info = contract_path(eq, *xs) + print(path_info[1]) + expr = contract_expression(eq, *shapes) + expr(*xs, backend=backend) + print("-" * 40) + + +def _compute_cost(cache): + counts = count_cached_ops(cache) + return counts["einsum"] + counts["tensordot"] + + +@pytest.mark.parametrize("backend", backends) +def test_chain_2_growth(backend: BackendType) -> None: + sizes = list(range(1, 21)) + costs = [] + for size in sizes: + xs = [np.random.rand(2, 2) for _ in range(size)] + alphabet = "".join(get_symbol(i) for i in range(size + 1)) + names = [alphabet[i : i + 2] for i in range(size)] + inputs = ",".join(names) + + with shared_intermediates() as cache: + for i in range(size): + target = alphabet[i : i + 2] + eq = f"{inputs}->{target}" + expr = contract_expression(eq, *(x.shape for x in xs)) + expr(*xs, backend=backend) + costs.append(_compute_cost(cache)) + + print(f"sizes = {repr(sizes)}") + print(f"costs = {repr(costs)}") + for size, cost in zip(sizes, costs): + print(f"{size}\t{cost}") + + +@pytest.mark.parametrize("size", [3, 4, 5]) +@pytest.mark.parametrize("backend", backends) +def test_chain_sharing(size: int, backend: BackendType) -> None: + xs = [np.random.rand(2, 2) for _ in range(size)] + alphabet = "".join(get_symbol(i) for i in range(size + 1)) + names = [alphabet[i : i + 2] for i in range(size)] + inputs = ",".join(names) + + num_exprs_nosharing = 0 + for i in range(size + 1): + with shared_intermediates() as cache: + target = alphabet[i] + eq = f"{inputs}->{target}" + expr = contract_expression(eq, *tuple(x.shape for x in xs)) + expr(*xs, backend=backend) + num_exprs_nosharing += _compute_cost(cache) + + with shared_intermediates() as cache: + print(inputs) + for i in range(size + 1): + target = alphabet[i] + eq = f"{inputs}->{target}" + path_info = contract_path(eq, *xs) + print(path_info[1]) + expr = contract_expression(eq, *[x.shape for x in xs]) + expr(*xs, backend=backend) + num_exprs_sharing = _compute_cost(cache) + + print("-" * 40) + print(f"Without sharing: {num_exprs_nosharing} expressions") + print(f"With sharing: {num_exprs_sharing} expressions") + assert num_exprs_nosharing > num_exprs_sharing + + +def test_multithreaded_sharing() -> None: + from multiprocessing.pool import ThreadPool + + def fn(): + x, y, z = build_views("ab,bc,cd") + + with shared_intermediates(): + contract("ab,bc,cd->a", x, y, z) + contract("ab,bc,cd->b", x, y, z) + + return len(get_sharing_cache()) + + expected = fn() + pool = ThreadPool(8) + fs = [pool.apply_async(fn) for _ in range(16)] + assert not currently_sharing() + assert [f.get() for f in fs] == [expected] * 16 + pool.close() diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/typing.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/typing.py new file mode 100644 index 0000000000000000000000000000000000000000..a807621f7263520a26d6b7634e94b254e51803b1 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/opt_einsum/typing.py @@ -0,0 +1,27 @@ +"""Types used in the opt_einsum package.""" + +from collections import namedtuple +from typing import Any, Callable, Collection, Dict, FrozenSet, List, Literal, Optional, Tuple, Union + +TensorShapeType = Tuple[int, ...] +PathType = Collection[TensorShapeType] + +ArrayType = Any + +ArrayIndexType = FrozenSet[str] +ArrayShaped = namedtuple("ArrayShaped", ["shape"]) + +ContractionListType = List[Tuple[Any, ArrayIndexType, str, Optional[Tuple[str, ...]], Union[str, bool]]] +PathSearchFunctionType = Callable[[List[ArrayIndexType], ArrayIndexType, Dict[str, int], Optional[int]], PathType] + +# Contract kwargs +OptimizeKind = Union[ + None, + bool, + Literal[ + "optimal", "dp", "greedy", "random-greedy", "random-greedy-128", "branch-all", "branch-2", "auto", "auto-hq" + ], + PathType, + PathSearchFunctionType, +] +BackendType = Literal["auto", "object", "autograd", "cupy", "dask", "jax", "theano", "tensorflow", "torch", "libjax"] diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/optree-0.19.0.dist-info/INSTALLER b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/optree-0.19.0.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/optree-0.19.0.dist-info/INSTALLER @@ -0,0 +1 @@ +pip diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/optree-0.19.0.dist-info/METADATA b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/optree-0.19.0.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..7e97283da42a13977ee563f359f8d9e00a12b8e3 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/optree-0.19.0.dist-info/METADATA @@ -0,0 +1,776 @@ +Metadata-Version: 2.4 +Name: optree +Version: 0.19.0 +Summary: Optimized PyTree Utilities. +Author: OpTree Contributors +License-Expression: Apache-2.0 +Project-URL: Homepage, https://github.com/metaopt/optree +Project-URL: Repository, https://github.com/metaopt/optree +Project-URL: Documentation, https://optree.readthedocs.io +Project-URL: Bug Report, https://github.com/metaopt/optree/issues +Keywords: PyTree,Tree Manipulation,Tree Traversal,Functional Programming +Classifier: Development Status :: 4 - Beta +Classifier: Programming Language :: C++ +Classifier: Programming Language :: Python :: 3 +Classifier: Programming Language :: Python :: 3.9 +Classifier: Programming Language :: Python :: 3.10 +Classifier: Programming Language :: Python :: 3.11 +Classifier: Programming Language :: Python :: 3.12 +Classifier: Programming Language :: Python :: 3.13 +Classifier: Programming Language :: Python :: 3.14 +Classifier: Programming Language :: Python :: 3.15 +Classifier: Programming Language :: Python :: Free Threading :: 4 - Resilient +Classifier: Programming Language :: Python :: Implementation :: CPython +Classifier: Programming Language :: Python :: Implementation :: PyPy +Classifier: Operating System :: POSIX :: Linux +Classifier: Operating System :: MacOS +Classifier: Operating System :: Microsoft :: Windows +Classifier: Operating System :: iOS +Classifier: Operating System :: Android +Classifier: Intended Audience :: Developers +Classifier: Intended Audience :: Education +Classifier: Intended Audience :: Science/Research +Classifier: Topic :: Utilities +Requires-Python: >=3.9 +Description-Content-Type: text/markdown +License-File: LICENSE +Requires-Dist: typing-extensions>=4.6.0 +Requires-Dist: typing-extensions>=4.12.0; python_version >= "3.13" +Provides-Extra: jax +Requires-Dist: jax; extra == "jax" +Provides-Extra: numpy +Requires-Dist: numpy; extra == "numpy" +Provides-Extra: torch +Requires-Dist: torch; extra == "torch" +Provides-Extra: lint +Requires-Dist: cpplint; extra == "lint" +Requires-Dist: doc8; extra == "lint" +Requires-Dist: mypy; extra == "lint" +Requires-Dist: pre-commit; extra == "lint" +Requires-Dist: pyenchant; extra == "lint" +Requires-Dist: pylint[spelling]; extra == "lint" +Requires-Dist: ruff; extra == "lint" +Requires-Dist: xdoctest; extra == "lint" +Provides-Extra: test +Requires-Dist: pytest; extra == "test" +Requires-Dist: pytest-cov; extra == "test" +Requires-Dist: covdefaults; extra == "test" +Requires-Dist: rich; extra == "test" +Requires-Dist: typing-extensions==4.6.0; (python_version < "3.13" and platform_system == "Linux") and extra == "test" +Requires-Dist: typing-extensions==4.6.0; (python_version < "3.13" and platform_system == "Darwin") and extra == "test" +Requires-Dist: typing-extensions==4.6.0; (python_version < "3.13" and platform_system == "Windows") and extra == "test" +Requires-Dist: typing-extensions==4.12.0; (python_version >= "3.13" and platform_system == "Linux") and extra == "test" +Requires-Dist: typing-extensions==4.12.0; (python_version >= "3.13" and platform_system == "Darwin") and extra == "test" +Requires-Dist: typing-extensions==4.12.0; (python_version >= "3.13" and platform_system == "Windows") and extra == "test" +Provides-Extra: docs +Requires-Dist: sphinx~=8.0; extra == "docs" +Requires-Dist: sphinx-autoapi; extra == "docs" +Requires-Dist: sphinx-autobuild; extra == "docs" +Requires-Dist: sphinx-autodoc-typehints; extra == "docs" +Requires-Dist: sphinx-copybutton; extra == "docs" +Requires-Dist: sphinx-rtd-theme; extra == "docs" +Requires-Dist: sphinxcontrib-bibtex; extra == "docs" +Requires-Dist: docutils; extra == "docs" +Requires-Dist: jax[cpu]; extra == "docs" +Requires-Dist: numpy; extra == "docs" +Requires-Dist: torch; extra == "docs" +Dynamic: license-file + + + +# OpTree + +![Python 3.9+](https://img.shields.io/badge/Python-3.9%2B-brightgreen) +[![PyPI](https://img.shields.io/pypi/v/optree?logo=pypi)](https://pypi.org/project/optree) +![GitHub Workflow Status](https://img.shields.io/github/actions/workflow/status/metaopt/optree/build.yml?label=build&logo=github) +![GitHub Workflow Status](https://img.shields.io/github/actions/workflow/status/metaopt/optree/tests.yml?label=tests&logo=github) +[![Codecov](https://img.shields.io/codecov/c/github/metaopt/optree/main?logo=codecov)](https://codecov.io/gh/metaopt/optree) +[![Documentation Status](https://img.shields.io/readthedocs/optree?logo=readthedocs)](https://optree.readthedocs.io) +[![Downloads](https://static.pepy.tech/personalized-badge/optree?period=total&left_color=gray&right_color=blue&left_text=downloads)](https://pepy.tech/project/optree) +[![GitHub Repo Stars](https://img.shields.io/github/stars/metaopt/optree?color=brightgreen&logo=github)](https://github.com/metaopt/optree/stargazers) + +Optimized PyTree Utilities. + +-------------------------------------------------------------------------------- + +### Table of Contents + +- [Installation](#installation) +- [PyTrees](#pytrees) + - [Tree Nodes and Leaves](#tree-nodes-and-leaves) + - [Built-in PyTree Node Types](#built-in-pytree-node-types) + - [Registering a Container-like Custom Type as Non-leaf Nodes](#registering-a-container-like-custom-type-as-non-leaf-nodes) + - [Notes about the PyTree Type Registry](#notes-about-the-pytree-type-registry) + - [`None` is Non-leaf Node vs. `None` is Leaf](#none-is-non-leaf-node-vs-none-is-leaf) + - [Key Ordering for Dictionaries](#key-ordering-for-dictionaries) +- [Changelog](#changelog) +- [License](#license) + +-------------------------------------------------------------------------------- + +## Installation + +Install from PyPI ([![PyPI](https://img.shields.io/pypi/v/optree?logo=pypi)](https://pypi.org/project/optree) / ![Status](https://img.shields.io/pypi/status/optree)): + +```bash +pip3 install --upgrade optree +``` + +Install from conda-forge ([![conda-forge](https://img.shields.io/conda/v/conda-forge/optree?logo=condaforge)](https://anaconda.org/conda-forge/optree)): + +```bash +conda install conda-forge::optree +``` + +Install the latest version from GitHub: + +```bash +pip3 install git+https://github.com/metaopt/optree.git#egg=optree +``` + +Or, clone this repo and install manually: + +```bash +git clone --depth=1 https://github.com/metaopt/optree.git && cd optree + +pip3 install . +``` + +The following options are available while building the Python C extension from the source: + +```bash +export CMAKE_COMMAND="/path/to/custom/cmake" +export CMAKE_BUILD_TYPE="Debug" +export CMAKE_CXX_STANDARD="20" # C++17 is tested on Linux/macOS (C++20 is required on Windows) +export OPTREE_CXX_WERROR="OFF" +export _GLIBCXX_USE_CXX11_ABI="1" +export pybind11_DIR="/path/to/custom/pybind11" + +pip3 install . +``` + +Compiling from the source requires Python 3.9+, a compiler (`gcc` / `clang` / `icc` / `cl.exe`) that supports C++20 and a `cmake` installation. + +-------------------------------------------------------------------------------- + +## PyTrees + +A PyTree is a recursive structure that can be an arbitrarily nested Python container (e.g., `tuple`, `list`, `dict`, `OrderedDict`, `NamedTuple`, etc.) or an opaque Python object. +The key concepts of tree operations are tree flattening and its inverse (tree unflattening). +Additional tree operations can be performed based on these two basic functions (e.g., `tree_map = tree_unflatten ∘ map ∘ tree_flatten`). + +Tree flattening is traversing the entire tree in a left-to-right depth-first manner and returning the leaves of the tree in a deterministic order. + +```python +>>> tree = {'b': (2, [3, 4]), 'a': 1, 'c': 5, 'd': 6} +>>> optree.tree_flatten(tree) +([1, 2, 3, 4, 5, 6], PyTreeSpec({'a': *, 'b': (*, [*, *]), 'c': *, 'd': *})) +>>> optree.tree_flatten(1) +([1], PyTreeSpec(*)) +>>> optree.tree_flatten(None) +([], PyTreeSpec(None)) +>>> optree.tree_map(lambda x: x**2, tree) +{'b': (4, [9, 16]), 'a': 1, 'c': 25, 'd': 36} +``` + +This usually implies that the equal pytrees return equal lists of leaves and the same tree structure. +See also section [Key Ordering for Dictionaries](#key-ordering-for-dictionaries). + +```python +>>> {'a': [1, 2], 'b': [3]} == {'b': [3], 'a': [1, 2]} +True +>>> optree.tree_leaves({'a': [1, 2], 'b': [3]}) == optree.tree_leaves({'b': [3], 'a': [1, 2]}) +True +>>> optree.tree_structure({'a': [1, 2], 'b': [3]}) == optree.tree_structure({'b': [3], 'a': [1, 2]}) +True +>>> optree.tree_map(lambda x: x**2, {'a': [1, 2], 'b': [3]}) +{'a': [1, 4], 'b': [9]} +>>> optree.tree_map(lambda x: x**2, {'b': [3], 'a': [1, 2]}) +{'b': [9], 'a': [1, 4]} +``` + +To flatten [`dict`](https://docs.python.org/3/library/stdtypes.html#dict) and [`collections.defaultdict`](https://docs.python.org/3/library/collections.html#collections.defaultdict) objects with the insertion order preserved, use the `dict_insertion_ordered` context manager: + +```python +>>> tree = {'b': (2, [3, 4]), 'a': 1, 'c': None, 'd': 5} +>>> optree.tree_flatten(tree) +( + [1, 2, 3, 4, 5], + PyTreeSpec({'a': *, 'b': (*, [*, *]), 'c': None, 'd': *}) +) +>>> with optree.dict_insertion_ordered(True, namespace='some-namespace'): +... optree.tree_flatten(tree, namespace='some-namespace') +( + [2, 3, 4, 1, 5], + PyTreeSpec({'b': (*, [*, *]), 'a': *, 'c': None, 'd': *}, namespace='some-namespace') +) +``` + +> [!TIP] +> +> Since OpTree v0.14.1, a new namespace `optree.pytree` is introduced as aliases for `optree.tree_*` functions. The following examples are equivalent to the above: +> +> ```python +> >>> import optree.pytree as pt +> >>> tree = {'b': (2, [3, 4]), 'a': 1, 'c': 5, 'd': 6} +> >>> pt.flatten(tree) +> ([1, 2, 3, 4, 5, 6], PyTreeSpec({'a': *, 'b': (*, [*, *]), 'c': *, 'd': *})) +> >>> pt.flatten(1) +> ([1], PyTreeSpec(*)) +> >>> pt.flatten(None) +> ([], PyTreeSpec(None)) +> >>> pt.map(lambda x: x**2, tree) +> {'b': (4, [9, 16]), 'a': 1, 'c': 25, 'd': 36} +> >>> pt.map(lambda x: x**2, {'a': [1, 2], 'b': [3]}) +> {'a': [1, 4], 'b': [9]} +> >>> pt.map(lambda x: x**2, {'b': [3], 'a': [1, 2]}) +> {'b': [9], 'a': [1, 4]} +> ``` +> +> Since OpTree v0.16.0, a re-export API `optree.pytree.reexport(...)` is available to create a new module that exports all the `optree.pytree` APIs with a given namespace. +> This is useful for downstream libraries to create their own pytree utilities without passing the `namespace` argument explicitly. +> +> ```python +> # foo/__init__.py +> import optree +> pytree = optree.pytree.reexport(namespace='foo') +> del optree +> +> # foo/bar.py +> from foo import pytree +> +> @pytree.dataclasses.dataclass +> class Bar: +> a: int +> b: float +> +> # User code +> In [1]: import foo +> +> In [2]: foo.pytree.flatten({'a': 1, 'b': 2, 'c': foo.bar.Bar(3, 4.0)}) +> Out[2]: +> ( +> [1, 2, 3, 4.0], +> PyTreeSpec({'a': *, 'b': *, 'c': CustomTreeNode(Bar[()], [*, *])}, namespace='foo') +> ) +> +> In [3]: foo.pytree.functools.reduce(lambda x, y: x * y, {'a': 1, 'b': 2, 'c': foo.bar.Bar(3, 4.0)}) +> Out[3]: 24.0 +> ``` + +### Tree Nodes and Leaves + +A tree is a collection of non-leaf nodes and leaf nodes, where the leaf nodes are opaque objects having no children to flatten. +`optree.tree_flatten(...)` will flatten the tree and return a list of leaf nodes while the non-leaf nodes will be stored in the tree structure specification. + +#### Built-in PyTree Node Types + +OpTree out-of-box supports the following Python container types in the global registry: + +- [`tuple`](https://docs.python.org/3/library/stdtypes.html#tuple) +- [`list`](https://docs.python.org/3/library/stdtypes.html#list) +- [`dict`](https://docs.python.org/3/library/stdtypes.html#dict) +- [`collections.namedtuple`](https://docs.python.org/3/library/collections.html#collections.namedtuple) and its subclasses +- [`collections.OrderedDict`](https://docs.python.org/3/library/collections.html#collections.OrderedDict) +- [`collections.defaultdict`](https://docs.python.org/3/library/collections.html#collections.defaultdict) +- [`collections.deque`](https://docs.python.org/3/library/collections.html#collections.deque) +- [`PyStructSequence`](https://docs.python.org/3/c-api/tuple.html#struct-sequence-objects) types created by C API [`PyStructSequence_NewType`](https://docs.python.org/3/c-api/tuple.html#c.PyStructSequence_NewType) + +which are considered non-leaf nodes in the tree. + +Python objects whose type is not registered will be treated as leaf nodes. +The registry lookup uses the `is` operator to determine whether the type is matched. +So subclasses will need to be explicitly registered in the registry, otherwise, objects of that type will be considered leaves. +The [`NoneType`](https://docs.python.org/3/library/constants.html#None) is a special case discussed in section [`None` is non-leaf Node vs. `None` is Leaf](#none-is-non-leaf-node-vs-none-is-leaf). + +#### Registering a Container-like Custom Type as Non-leaf Nodes + +A container-like Python type can be registered in the type registry with a pair of functions that specify: + +- `flatten_func(container) -> (children, metadata, entries)`: convert an instance of the container type to a `(children, metadata, entries)` triple, where `children` is an iterable of subtrees and `entries` is an iterable of path entries of the container (e.g., indices or keys). +- `unflatten_func(metadata, children) -> container`: convert such a pair back to an instance of the container type. + +The `metadata` is some necessary data apart from the children to reconstruct the container, e.g., the keys of the dictionary (the children are values). + +The `entries` can be omitted (only returns a pair) or is optional to implement (returns `None`). If so, use `range(len(children))` (i.e., flat indices) as path entries of the current node. The signature for the flatten function can be one of the following: + +- `flatten_func(container) -> (children, metadata, entries)` +- `flatten_func(container) -> (children, metadata, None)` +- `flatten_func(container) -> (children, metadata)` + +The following examples show how to register custom types and utilize them for `tree_flatten` and `tree_map`. Please refer to section [Notes about the PyTree Type Registry](#notes-about-the-pytree-type-registry) for more information. + +```python +# Register a Python type with lambda functions +optree.register_pytree_node( + set, + # (set) -> (children, metadata, None) + lambda s: (sorted(s), None, None), + # (metadata, children) -> (set) + lambda _, children: set(children), + namespace='set', +) + +# Register a Python type into a namespace +import torch + +class Torch2NumpyEntry(optree.PyTreeEntry): + def __call__(self, obj): + assert self.entry == 0 + return obj.cpu().detach().numpy() + + def codify(self, node=''): + assert self.entry == 0 + return f'{node}.cpu().detach().numpy()' + +optree.register_pytree_node( + torch.Tensor, + # (tensor) -> (children, metadata) + flatten_func=lambda tensor: ( + (tensor.cpu().detach().numpy(),), + {'dtype': tensor.dtype, 'device': tensor.device, 'requires_grad': tensor.requires_grad}, + ), + # (metadata, children) -> tensor + unflatten_func=lambda metadata, children: torch.tensor(children[0], **metadata), + path_entry_type=Torch2NumpyEntry, + namespace='torch2numpy', +) +``` + +```python +>>> tree = {'weight': torch.ones(size=(1, 2)).cuda(), 'bias': torch.zeros(size=(2,))} +>>> tree +{'weight': tensor([[1., 1.]], device='cuda:0'), 'bias': tensor([0., 0.])} + +# Flatten without specifying the namespace +>>> optree.tree_flatten(tree) # `torch.Tensor`s are leaf nodes +([tensor([0., 0.]), tensor([[1., 1.]], device='cuda:0')], PyTreeSpec({'bias': *, 'weight': *})) + +# Flatten with the namespace +>>> leaves, treespec = optree.tree_flatten(tree, namespace='torch2numpy') +>>> leaves, treespec +( + [array([0., 0.], dtype=float32), array([[1., 1.]], dtype=float32)], + PyTreeSpec( + { + 'bias': CustomTreeNode(Tensor[{'dtype': torch.float32, 'device': device(type='cpu'), 'requires_grad': False}], [*]), + 'weight': CustomTreeNode(Tensor[{'dtype': torch.float32, 'device': device(type='cuda', index=0), 'requires_grad': False}], [*]) + }, + namespace='torch2numpy' + ) +) + +# `entries` are not defined and use `range(len(children))` +>>> optree.tree_paths(tree, namespace='torch2numpy') +[('bias', 0), ('weight', 0)] + +# Custom path entry type defines the pytree access behavior +>>> optree.tree_accessors(tree, namespace='torch2numpy') +[ + PyTreeAccessor(*['bias'].cpu().detach().numpy(), (MappingEntry(key='bias', type=), Torch2NumpyEntry(entry=0, type=))), + PyTreeAccessor(*['weight'].cpu().detach().numpy(), (MappingEntry(key='weight', type=), Torch2NumpyEntry(entry=0, type=))) +] + +# Unflatten back to a copy of the original object +>>> optree.tree_unflatten(treespec, leaves) +{'weight': tensor([[1., 1.]], device='cuda:0'), 'bias': tensor([0., 0.])} +``` + +Users can also extend the pytree registry by decorating the custom class and defining an instance method `tree_flatten` and a class method `tree_unflatten`. + +```python +from collections import UserDict + +@optree.register_pytree_node_class(namespace='mydict') +class MyDict(UserDict): + TREE_PATH_ENTRY_TYPE = optree.MappingEntry # used by accessor APIs + + def __tree_flatten__(self): # -> (children, metadata, entries) + reversed_keys = sorted(self.keys(), reverse=True) + return ( + [self[key] for key in reversed_keys], # children + reversed_keys, # metadata + reversed_keys, # entries + ) + + @classmethod + def __tree_unflatten__(cls, metadata, children): + return cls(zip(metadata, children)) +``` + +```python +>>> tree = MyDict(b=4, a=(2, 3), c=MyDict({'d': 5, 'f': 6})) + +# Flatten without specifying the namespace +>>> optree.tree_flatten_with_path(tree) # `MyDict`s are leaf nodes +( + [()], + [MyDict(b=4, a=(2, 3), c=MyDict({'d': 5, 'f': 6}))], + PyTreeSpec(*) +) + +# Flatten with the namespace +>>> optree.tree_flatten_with_path(tree, namespace='mydict') +( + [('c', 'f'), ('c', 'd'), ('b',), ('a', 0), ('a', 1)], + [6, 5, 4, 2, 3], + PyTreeSpec( + CustomTreeNode(MyDict[['c', 'b', 'a']], [CustomTreeNode(MyDict[['f', 'd']], [*, *]), *, (*, *)]), + namespace='mydict' + ) +) +>>> optree.tree_flatten_with_accessor(tree, namespace='mydict') +( + [ + PyTreeAccessor(*['c']['f'], (MappingEntry(key='c', type=), MappingEntry(key='f', type=))), + PyTreeAccessor(*['c']['d'], (MappingEntry(key='c', type=), MappingEntry(key='d', type=))), + PyTreeAccessor(*['b'], (MappingEntry(key='b', type=),)), + PyTreeAccessor(*['a'][0], (MappingEntry(key='a', type=), SequenceEntry(index=0, type=))), + PyTreeAccessor(*['a'][1], (MappingEntry(key='a', type=), SequenceEntry(index=1, type=))) + ], + [6, 5, 4, 2, 3], + PyTreeSpec( + CustomTreeNode(MyDict[['c', 'b', 'a']], [CustomTreeNode(MyDict[['f', 'd']], [*, *]), *, (*, *)]), + namespace='mydict' + ) +) +``` + +#### Notes about the PyTree Type Registry + +There are several key attributes of the pytree type registry: + +1. **The type registry is interpreter-dependent.** This means registering a custom type in the registry affects all modules that use OpTree in the same Python interpreter. + +> [!WARNING] +> For safety reasons, a `namespace` must be specified while registering a custom type. It is +> used to isolate the behavior of flattening and unflattening a pytree node type. This is to +> prevent accidental collisions between different libraries that may register the same type. + +2. **The elements in the type registry are immutable.** Users can neither register the same type twice in the same namespace (i.e., update the type registry), nor remove a type from the type registry. To update the behavior of an already registered type, simply register it again with another `namespace`. + +3. **Users cannot modify the behavior of already registered built-in types** listed in [Built-in PyTree Node Types](#built-in-pytree-node-types), such as key order sorting for `dict` and `collections.defaultdict`. + +4. **Inherited subclasses are not implicitly registered.** The registry lookup uses `type(obj) is registered_type` rather than `isinstance(obj, registered_type)`. Users need to register the subclasses explicitly. To register all subclasses, it is easy to implement with [`metaclass`](https://docs.python.org/3/reference/datamodel.html#metaclasses) or [`__init_subclass__`](https://docs.python.org/3/reference/datamodel.html#customizing-class-creation), for example: + + ```python + from collections import UserDict + + @optree.register_pytree_node_class(namespace='mydict') + class MyDict(UserDict): + TREE_PATH_ENTRY_TYPE = optree.MappingEntry # used by accessor APIs + + def __init_subclass__(cls): # define this in the base class + super().__init_subclass__() + # Register a subclass to namespace 'mydict' + optree.register_pytree_node_class(cls, namespace='mydict') + + def __tree_flatten__(self): # -> (children, metadata, entries) + reversed_keys = sorted(self.keys(), reverse=True) + return ( + [self[key] for key in reversed_keys], # children + reversed_keys, # metadata + reversed_keys, # entries + ) + + @classmethod + def __tree_unflatten__(cls, metadata, children): + return cls(zip(metadata, children)) + + # Subclasses will be automatically registered in namespace 'mydict' + class MyAnotherDict(MyDict): + pass + ``` + + ```python + >>> tree = MyDict(b=4, a=(2, 3), c=MyAnotherDict({'d': 5, 'f': 6})) + >>> optree.tree_flatten_with_path(tree, namespace='mydict') + ( + [('c', 'f'), ('c', 'd'), ('b',), ('a', 0), ('a', 1)], + [6, 5, 4, 2, 3], + PyTreeSpec( + CustomTreeNode(MyDict[['c', 'b', 'a']], [CustomTreeNode(MyAnotherDict[['f', 'd']], [*, *]), *, (*, *)]), + namespace='mydict' + ) + ) + >>> optree.tree_accessors(tree, namespace='mydict') + [ + PyTreeAccessor(*['c']['f'], (MappingEntry(key='c', type=), MappingEntry(key='f', type=))), + PyTreeAccessor(*['c']['d'], (MappingEntry(key='c', type=), MappingEntry(key='d', type=))), + PyTreeAccessor(*['b'], (MappingEntry(key='b', type=),)), + PyTreeAccessor(*['a'][0], (MappingEntry(key='a', type=), SequenceEntry(index=0, type=))), + PyTreeAccessor(*['a'][1], (MappingEntry(key='a', type=), SequenceEntry(index=1, type=))) + ] + ``` + +5. **Be careful about the potential infinite recursion of the custom flatten function.** The returned `children` from the custom flatten function are considered subtrees. They will be further flattened recursively. The `children` can have the same type as the current node. Users must design their termination condition carefully. + + ```python + import numpy as np + import torch + + optree.register_pytree_node( + np.ndarray, + # Children are nested lists of Python objects + lambda array: (np.atleast_1d(array).tolist(), array.ndim == 0), + lambda scalar, rows: np.asarray(rows) if not scalar else np.asarray(rows[0]), + namespace='numpy1', + ) + + optree.register_pytree_node( + np.ndarray, + # Children are Python objects + lambda array: ( + list(array.ravel()), # list(1DArray[T]) -> List[T] + dict(shape=array.shape, dtype=array.dtype) + ), + lambda metadata, children: np.asarray(children, dtype=metadata['dtype']).reshape(metadata['shape']), + namespace='numpy2', + ) + + optree.register_pytree_node( + np.ndarray, + # Returns a list of `np.ndarray`s without termination condition + lambda array: ([array.ravel()], array.dtype), + lambda shape, children: children[0].reshape(shape), + namespace='numpy3', + ) + + optree.register_pytree_node( + torch.Tensor, + # Children are nested lists of Python objects + lambda tensor: (torch.atleast_1d(tensor).tolist(), tensor.ndim == 0), + lambda scalar, rows: torch.tensor(rows) if not scalar else torch.tensor(rows[0]), + namespace='torch1', + ) + + optree.register_pytree_node( + torch.Tensor, + # Returns a list of `torch.Tensor`s without termination condition + lambda tensor: ( + list(tensor.view(-1)), # list(1DTensor[T]) -> List[0DTensor[T]] (STILL TENSORS!) + tensor.shape + ), + lambda shape, children: torch.stack(children).reshape(shape), + namespace='torch2', + ) + ``` + + ```python + >>> optree.tree_flatten(np.arange(9).reshape(3, 3), namespace='numpy1') + ( + [0, 1, 2, 3, 4, 5, 6, 7, 8], + PyTreeSpec( + CustomTreeNode(ndarray[False], [[*, *, *], [*, *, *], [*, *, *]]), + namespace='numpy1' + ) + ) + # Implicitly casts `float`s to `np.float64` + >>> optree.tree_map(lambda x: x + 1.5, np.arange(9).reshape(3, 3), namespace='numpy1') + array([[1.5, 2.5, 3.5], + [4.5, 5.5, 6.5], + [7.5, 8.5, 9.5]]) + + >>> optree.tree_flatten(np.arange(9).reshape(3, 3), namespace='numpy2') + ( + [0, 1, 2, 3, 4, 5, 6, 7, 8], + PyTreeSpec( + CustomTreeNode(ndarray[{'shape': (3, 3), 'dtype': dtype('int64')}], [*, *, *, *, *, *, *, *, *]), + namespace='numpy2' + ) + ) + # Explicitly casts `float`s to `np.int64` + >>> optree.tree_map(lambda x: x + 1.5, np.arange(9).reshape(3, 3), namespace='numpy2') + array([[1, 2, 3], + [4, 5, 6], + [7, 8, 9]]) + + # Children are also `np.ndarray`s, recurse without termination condition. + >>> optree.tree_flatten(np.arange(9).reshape(3, 3), namespace='numpy3') + Traceback (most recent call last): + ... + RecursionError: Maximum recursion depth exceeded during flattening the tree. + + >>> optree.tree_flatten(torch.arange(9).reshape(3, 3), namespace='torch1') + ( + [0, 1, 2, 3, 4, 5, 6, 7, 8], + PyTreeSpec( + CustomTreeNode(Tensor[False], [[*, *, *], [*, *, *], [*, *, *]]), + namespace='torch1' + ) + ) + # Implicitly casts `float`s to `torch.float32` + >>> optree.tree_map(lambda x: x + 1.5, torch.arange(9).reshape(3, 3), namespace='torch1') + tensor([[1.5000, 2.5000, 3.5000], + [4.5000, 5.5000, 6.5000], + [7.5000, 8.5000, 9.5000]]) + + # Children are also `torch.Tensor`s, recurse without termination condition. + >>> optree.tree_flatten(torch.arange(9).reshape(3, 3), namespace='torch2') + Traceback (most recent call last): + ... + RecursionError: Maximum recursion depth exceeded during flattening the tree. + ``` + +### `None` is Non-leaf Node vs. `None` is Leaf + +The [`None`](https://docs.python.org/3/library/constants.html#None) object is a special object in the Python language. +It serves some of the same purposes as `null` (a pointer does not point to anything) in other programming languages, which denotes a variable is empty or marks default parameters. +However, the `None` object is a singleton object rather than a pointer. +It may also serve as a sentinel value. +In addition, if a function has returned without any return value or the return statement is omitted, the function will also implicitly return the `None` object. + +By default, the `None` object is considered a non-leaf node in the tree with arity 0, i.e., _**a non-leaf node that has no children**_. +This is like the behavior of an empty tuple. +While flattening a tree, it will remain in the tree structure definitions rather than in the leaves list. + +```python +>>> tree = {'b': (2, [3, 4]), 'a': 1, 'c': None, 'd': 5} +>>> optree.tree_flatten(tree) +([1, 2, 3, 4, 5], PyTreeSpec({'a': *, 'b': (*, [*, *]), 'c': None, 'd': *})) +>>> optree.tree_flatten(tree, none_is_leaf=True) +([1, 2, 3, 4, None, 5], PyTreeSpec({'a': *, 'b': (*, [*, *]), 'c': *, 'd': *}, NoneIsLeaf)) +>>> optree.tree_flatten(1) +([1], PyTreeSpec(*)) +>>> optree.tree_flatten(None) +([], PyTreeSpec(None)) +>>> optree.tree_flatten(None, none_is_leaf=True) +([None], PyTreeSpec(*, NoneIsLeaf)) +``` + +OpTree provides a keyword argument `none_is_leaf` to determine whether to consider the `None` object as a leaf, like other opaque objects. +If `none_is_leaf=True`, the `None` object will be placed in the leaves list. +Otherwise, the `None` object will remain in the tree structure specification. + +```python +>>> import torch + +>>> linear = torch.nn.Linear(in_features=3, out_features=2, bias=False) +>>> linear._parameters # a container has None +OrderedDict({ + 'weight': Parameter containing: + tensor([[-0.6677, 0.5209, 0.3295], + [-0.4876, -0.3142, 0.1785]], requires_grad=True), + 'bias': None +}) + +>>> optree.tree_map(torch.zeros_like, linear._parameters) +OrderedDict({ + 'weight': tensor([[0., 0., 0.], + [0., 0., 0.]]), + 'bias': None +}) + +>>> optree.tree_map(torch.zeros_like, linear._parameters, none_is_leaf=True) +Traceback (most recent call last): + ... +TypeError: zeros_like(): argument 'input' (position 1) must be Tensor, not NoneType + +>>> optree.tree_map(lambda t: torch.zeros_like(t) if t is not None else 0, linear._parameters, none_is_leaf=True) +OrderedDict({ + 'weight': tensor([[0., 0., 0.], + [0., 0., 0.]]), + 'bias': 0 +}) +``` + +### Key Ordering for Dictionaries + +The built-in Python dictionary (i.e., [`builtins.dict`](https://docs.python.org/3/library/stdtypes.html#dict)) is an unordered mapping that holds the keys and values. +The leaves of a dictionary are the values. Although since Python 3.6, the built-in dictionary is insertion ordered ([PEP 468](https://peps.python.org/pep-0468)). +The dictionary equality operator (`==`) does not check for key ordering. +To ensure [referential transparency](https://en.wikipedia.org/wiki/Referential_transparency) that "equal `dict`" implies "equal ordering of leaves", the order of values of the dictionary is sorted by the keys. +This behavior is also applied to [`collections.defaultdict`](https://docs.python.org/3/library/collections.html#collections.defaultdict). + +```python +>>> optree.tree_flatten({'a': [1, 2], 'b': [3]}) +([1, 2, 3], PyTreeSpec({'a': [*, *], 'b': [*]})) +>>> optree.tree_flatten({'b': [3], 'a': [1, 2]}) +([1, 2, 3], PyTreeSpec({'a': [*, *], 'b': [*]})) +``` + +If users want to keep the values in the insertion order in pytree traversal, they should use [`collections.OrderedDict`](https://docs.python.org/3/library/collections.html#collections.OrderedDict), which will take the order of keys under consideration: + +```python +>>> OrderedDict([('a', [1, 2]), ('b', [3])]) == OrderedDict([('b', [3]), ('a', [1, 2])]) +False +>>> optree.tree_flatten(OrderedDict([('a', [1, 2]), ('b', [3])])) +([1, 2, 3], PyTreeSpec(OrderedDict({'a': [*, *], 'b': [*]}))) +>>> optree.tree_flatten(OrderedDict([('b', [3]), ('a', [1, 2])])) +([3, 1, 2], PyTreeSpec(OrderedDict({'b': [*], 'a': [*, *]}))) +``` + +To flatten [`builtins.dict`](https://docs.python.org/3/library/stdtypes.html#dict) and [`collections.defaultdict`](https://docs.python.org/3/library/collections.html#collections.defaultdict) objects with the insertion order preserved, use the `dict_insertion_ordered` context manager: + +```python +>>> tree = {'b': (2, [3, 4]), 'a': 1, 'c': None, 'd': 5} +>>> optree.tree_flatten(tree) +( + [1, 2, 3, 4, 5], + PyTreeSpec({'a': *, 'b': (*, [*, *]), 'c': None, 'd': *}) +) +>>> with optree.dict_insertion_ordered(True, namespace='some-namespace'): +... optree.tree_flatten(tree, namespace='some-namespace') +( + [2, 3, 4, 1, 5], + PyTreeSpec({'b': (*, [*, *]), 'a': *, 'c': None, 'd': *}, namespace='some-namespace') +) +``` + +**Since OpTree v0.9.0, the key order of the reconstructed output dictionaries from `tree_unflatten` is guaranteed to be consistent with the key order of the input dictionaries in `tree_flatten`.** + +```python +>>> leaves, treespec = optree.tree_flatten({'b': [3], 'a': [1, 2]}) +>>> leaves, treespec +([1, 2, 3], PyTreeSpec({'a': [*, *], 'b': [*]})) +>>> optree.tree_unflatten(treespec, leaves) +{'b': [3], 'a': [1, 2]} +>>> optree.tree_map(lambda x: x, {'b': [3], 'a': [1, 2]}) +{'b': [3], 'a': [1, 2]} +>>> optree.tree_map(lambda x: x + 1, {'b': [3], 'a': [1, 2]}) +{'b': [4], 'a': [2, 3]} +``` + +This property is also preserved during serialization/deserialization. + +```python +>>> leaves, treespec = optree.tree_flatten({'b': [3], 'a': [1, 2]}) +>>> leaves, treespec +([1, 2, 3], PyTreeSpec({'a': [*, *], 'b': [*]})) +>>> restored_treespec = pickle.loads(pickle.dumps(treespec)) +>>> optree.tree_unflatten(treespec, leaves) +{'b': [3], 'a': [1, 2]} +>>> optree.tree_unflatten(restored_treespec, leaves) +{'b': [3], 'a': [1, 2]} +``` + +> [!NOTE] +> Note that there are no restrictions on the `dict` to require the keys to be comparable (sortable). +> There can be multiple types of keys in the dictionary. +> The keys are sorted in ascending order by `key=lambda k: k` first if capable otherwise fallback to `key=lambda k: (f'{k.__class__.__module__}.{k.__class__.__qualname__}', k)`. This handles most cases. +> +> ```python +> >>> sorted({1: 2, 1.5: 1}.keys()) +> [1, 1.5] +> >>> sorted({'a': 3, 1: 2, 1.5: 1}.keys()) +> Traceback (most recent call last): +> ... +> TypeError: '<' not supported between instances of 'int' and 'str' +> >>> sorted({'a': 3, 1: 2, 1.5: 1}.keys(), key=lambda k: (f'{k.__class__.__module__}.{k.__class__.__qualname__}', k)) +> [1.5, 1, 'a'] +> ``` + +-------------------------------------------------------------------------------- + +## Changelog + +See [CHANGELOG.md](https://github.com/metaopt/optree/blob/HEAD/CHANGELOG.md). + +-------------------------------------------------------------------------------- + +## License + +OpTree is released under the Apache License 2.0. + +OpTree is heavily based on JAX's implementation of the PyTree utility, with deep refactoring and several improvements. +The original licenses can be found at [JAX's Apache License 2.0](https://github.com/google/jax/blob/HEAD/LICENSE) and [Tensorflow's Apache License 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All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/optree-0.19.0.dist-info/top_level.txt b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/optree-0.19.0.dist-info/top_level.txt new file mode 100644 index 0000000000000000000000000000000000000000..baa1ff25cd6d7f321c1e43579b55bc42e0c4b53d --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/optree-0.19.0.dist-info/top_level.txt @@ -0,0 +1 @@ +optree diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/optree/_C.pyi b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/optree/_C.pyi new file mode 100644 index 0000000000000000000000000000000000000000..81de1d634f1e5fb20a05481f1368438ab5795cbd --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/optree/_C.pyi @@ -0,0 +1,226 @@ +# Copyright 2022-2025 MetaOPT Team. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +# pylint: disable=all + +import builtins +import enum +import sys +from collections.abc import Callable, Collection, Iterable, Iterator +from types import MappingProxyType +from typing import Any, ClassVar, Final, final +from typing_extensions import Self # Python 3.11+ + +from optree.typing import ( + FlattenFunc, + MetaData, + PyTree, + PyTreeAccessor, + PyTreeEntry, + T, + U, + UnflattenFunc, +) + +# Set if the type allows subclassing (see CPython's Include/object.h) +Py_TPFLAGS_BASETYPE: Final[int] # (1UL << 10) + +# Meta-information during build-time +BUILDTIME_METADATA: Final[MappingProxyType[str, Any]] +PY_VERSION: Final[str] +PY_VERSION_HEX: Final[int] +if sys.implementation.name == 'pypy': # noqa: PYI002 + PYPY_VERSION: Final[str] + PYPY_VERSION_NUM: Final[int] + PYPY_VERSION_HEX: Final[int] +Py_DEBUG: Final[bool] +Py_GIL_DISABLED: Final[bool] +PYBIND11_VERSION_HEX: Final[int] +PYBIND11_INTERNALS_VERSION: Final[int] +PYBIND11_INTERNALS_ID: Final[str] +PYBIND11_MODULE_LOCAL_ID: Final[str] +PYBIND11_HAS_NATIVE_ENUM: Final[bool] +PYBIND11_HAS_INTERNALS_WITH_SMART_HOLDER_SUPPORT: Final[bool] +PYBIND11_HAS_SUBINTERPRETER_SUPPORT: Final[bool] +GLIBCXX_USE_CXX11_ABI: Final[bool] +OPTREE_HAS_SUBINTERPRETER_SUPPORT: Final[bool] +OPTREE_HAS_READ_WRITE_LOCK: Final[bool] + +@final +class InternalError(SystemError): ... + +@final +class PyTreeKind(enum.IntEnum): + CUSTOM = 0 # a custom type + LEAF = enum.auto() # an opaque leaf node + NONE = enum.auto() # None + TUPLE = enum.auto() # a tuple + LIST = enum.auto() # a list + DICT = enum.auto() # a dict + NAMEDTUPLE = enum.auto() # a collections.namedtuple + ORDEREDDICT = enum.auto() # a collections.OrderedDict + DEFAULTDICT = enum.auto() # a collections.defaultdict + DEQUE = enum.auto() # a collections.deque + STRUCTSEQUENCE = enum.auto() # a PyStructSequence + + NUM_KINDS: ClassVar[int] + +MAX_RECURSION_DEPTH: Final[int] + +@final +class PyTreeSpec: + num_nodes: int + num_leaves: int + num_children: int + none_is_leaf: bool + namespace: str + type: builtins.type | None + kind: PyTreeKind + def unflatten(self, leaves: Iterable[T], /) -> PyTree[T]: ... + def flatten_up_to(self, tree: PyTree[T], /) -> list[PyTree[T]]: ... + def broadcast_to_common_suffix(self, other: Self, /) -> Self: ... + def transform( + self, + /, + f_node: Callable[[Self], Self] | None = None, + f_leaf: Callable[[Self], Self] | None = None, + ) -> Self: ... + def compose(self, inner: Self, /) -> Self: ... + def traverse( + self, + leaves: Iterable[T], + /, + f_node: Callable[[Collection[U]], U] | None = None, + f_leaf: Callable[[T], U] | None = None, + ) -> U: ... + def walk( + self, + leaves: Iterable[T], + /, + f_node: Callable[[builtins.type, MetaData, tuple[U, ...]], U] | None = None, + f_leaf: Callable[[T], U] | None = None, + ) -> U: ... + def paths(self, /) -> list[tuple[Any, ...]]: ... + def accessors(self, /) -> list[PyTreeAccessor]: ... + def entries(self, /) -> list[Any]: ... + def entry(self, index: int, /) -> Any: ... + def children(self, /) -> list[Self]: ... + def child(self, index: int, /) -> Self: ... + def one_level(self, /) -> Self | None: ... + def is_leaf(self, /, *, strict: bool = True) -> bool: ... + def is_one_level(self, /) -> bool: ... + def is_prefix(self, other: Self, /, *, strict: bool = False) -> bool: ... + def is_suffix(self, other: Self, /, *, strict: bool = False) -> bool: ... + def __eq__(self, other: object, /) -> bool: ... + def __ne__(self, other: object, /) -> bool: ... + def __lt__(self, other: object, /) -> bool: ... + def __le__(self, other: object, /) -> bool: ... + def __gt__(self, other: object, /) -> bool: ... + def __ge__(self, other: object, /) -> bool: ... + def __hash__(self, /) -> int: ... + def __len__(self, /) -> int: ... + +@final +class PyTreeIter(Iterator[T]): + def __init__( + self, + tree: PyTree[T], + /, + leaf_predicate: Callable[[T], bool] | None = None, + none_is_leaf: bool = False, + namespace: str = '', + ) -> None: ... + def __iter__(self, /) -> Self: ... + def __next__(self, /) -> T: ... + +# Functions +def flatten( + tree: PyTree[T], + /, + leaf_predicate: Callable[[T], bool] | None = None, + none_is_leaf: bool = False, + namespace: str = '', +) -> tuple[list[T], PyTreeSpec]: ... +def flatten_with_path( + tree: PyTree[T], + /, + leaf_predicate: Callable[[T], bool] | None = None, + none_is_leaf: bool = False, + namespace: str = '', +) -> tuple[list[tuple[Any, ...]], list[T], PyTreeSpec]: ... + +# Constructors +def make_leaf( + none_is_leaf: bool = False, + namespace: str = '', # unused +) -> PyTreeSpec: ... +def make_none( + none_is_leaf: bool = False, + namespace: str = '', # unused +) -> PyTreeSpec: ... +def make_from_collection( + collection: Collection[PyTreeSpec], + /, + none_is_leaf: bool = False, + namespace: str = '', +) -> PyTreeSpec: ... + +# Utility functions +def is_leaf( + obj: T, + /, + leaf_predicate: Callable[[T], bool] | None = None, + none_is_leaf: bool = False, + namespace: str = '', +) -> bool: ... +def is_namedtuple(obj: object | type, /) -> bool: ... +def is_namedtuple_instance(obj: object, /) -> bool: ... +def is_namedtuple_class(cls: type, /) -> bool: ... +def namedtuple_fields(obj: tuple | type[tuple], /) -> tuple[str, ...]: ... +def is_structseq(obj: object | type, /) -> bool: ... +def is_structseq_instance(obj: object, /) -> bool: ... +def is_structseq_class(cls: type, /) -> bool: ... +def structseq_fields(obj: tuple | type[tuple], /) -> tuple[str, ...]: ... + +# Registration functions +def register_node( + cls: type[Collection[T]], + /, + flatten_func: FlattenFunc[T], + unflatten_func: UnflattenFunc[T], + path_entry_type: type[PyTreeEntry], + namespace: str = '', +) -> None: ... +def unregister_node( + cls: type, + /, + namespace: str = '', +) -> None: ... +def is_dict_insertion_ordered( + namespace: str = '', + inherit_global_namespace: bool = True, +) -> bool: ... +def set_dict_insertion_ordered( + mode: bool, + /, + namespace: str = '', +) -> None: ... +def get_registry_size(namespace: str | None = None) -> int: ... +def get_num_interpreters_seen() -> int: ... +def get_num_interpreters_alive() -> int: ... +def get_alive_interpreter_ids() -> set[int]: ... +def is_current_interpreter_main() -> bool: ... +def get_current_interpreter_id() -> int: ... +def get_main_interpreter_id() -> int: ... diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/optree/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/optree/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..6b076607e9a6375ea040d2bc79401bba07747ad3 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/optree/__init__.py @@ -0,0 +1,237 @@ +# Copyright 2022-2025 MetaOPT Team. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""OpTree: Optimized PyTree Utilities.""" + +from optree import accessors, dataclasses, functools, integrations, pytree, treespec, typing +from optree.accessors import ( + AutoEntry, + DataclassEntry, + FlattenedEntry, + GetAttrEntry, + GetItemEntry, + MappingEntry, + NamedTupleEntry, + PyTreeAccessor, + PyTreeEntry, + SequenceEntry, + StructSequenceEntry, +) +from optree.ops import ( + MAX_RECURSION_DEPTH, + NONE_IS_LEAF, + NONE_IS_NODE, + all_leaves, + broadcast_common, + broadcast_prefix, + prefix_errors, + tree_accessors, + tree_all, + tree_any, + tree_broadcast_common, + tree_broadcast_map, + tree_broadcast_map_with_accessor, + tree_broadcast_map_with_path, + tree_broadcast_prefix, + tree_flatten, + tree_flatten_one_level, + tree_flatten_with_accessor, + tree_flatten_with_path, + tree_is_leaf, + tree_iter, + tree_leaves, + tree_map, + tree_map_, + tree_map_with_accessor, + tree_map_with_accessor_, + tree_map_with_path, + tree_map_with_path_, + tree_max, + tree_min, + tree_partition, + tree_paths, + tree_reduce, + tree_replace_nones, + tree_structure, + tree_sum, + tree_transpose, + tree_transpose_map, + tree_transpose_map_with_accessor, + tree_transpose_map_with_path, + tree_unflatten, + treespec_accessors, + treespec_child, + treespec_children, + treespec_defaultdict, + treespec_deque, + treespec_dict, + treespec_entries, + treespec_entry, + treespec_from_collection, + treespec_is_leaf, + treespec_is_one_level, + treespec_is_prefix, + treespec_is_strict_leaf, + treespec_is_suffix, + treespec_leaf, + treespec_list, + treespec_namedtuple, + treespec_none, + treespec_one_level, + treespec_ordereddict, + treespec_paths, + treespec_structseq, + treespec_transform, + treespec_tuple, +) +from optree.registry import ( + dict_insertion_ordered, + register_pytree_node, + register_pytree_node_class, + unregister_pytree_node, +) +from optree.typing import ( + CustomTreeNode, + FlattenFunc, + PyTree, + PyTreeDef, + PyTreeKind, + PyTreeSpec, + PyTreeTypeVar, + UnflattenFunc, + is_namedtuple, + is_namedtuple_class, + is_namedtuple_instance, + is_structseq, + is_structseq_class, + is_structseq_instance, + namedtuple_fields, + structseq_fields, +) +from optree.version import __version__ as __version__ # pylint: disable=useless-import-alias + + +__all__ = [ + # Tree operations + 'MAX_RECURSION_DEPTH', + 'NONE_IS_NODE', + 'NONE_IS_LEAF', + 'tree_flatten', + 'tree_flatten_with_path', + 'tree_flatten_with_accessor', + 'tree_unflatten', + 'tree_iter', + 'tree_leaves', + 'tree_structure', + 'tree_paths', + 'tree_accessors', + 'tree_is_leaf', + 'all_leaves', + 'tree_map', + 'tree_map_', + 'tree_map_with_path', + 'tree_map_with_path_', + 'tree_map_with_accessor', + 'tree_map_with_accessor_', + 'tree_replace_nones', + 'tree_partition', + 'tree_transpose', + 'tree_transpose_map', + 'tree_transpose_map_with_path', + 'tree_transpose_map_with_accessor', + 'tree_broadcast_prefix', + 'broadcast_prefix', + 'tree_broadcast_common', + 'broadcast_common', + 'tree_broadcast_map', + 'tree_broadcast_map_with_path', + 'tree_broadcast_map_with_accessor', + 'tree_reduce', + 'tree_sum', + 'tree_max', + 'tree_min', + 'tree_all', + 'tree_any', + 'tree_flatten_one_level', + 'prefix_errors', + 'treespec_paths', + 'treespec_accessors', + 'treespec_entries', + 'treespec_entry', + 'treespec_children', + 'treespec_child', + 'treespec_one_level', + 'treespec_transform', + 'treespec_is_leaf', + 'treespec_is_strict_leaf', + 'treespec_is_one_level', + 'treespec_is_prefix', + 'treespec_is_suffix', + 'treespec_leaf', + 'treespec_none', + 'treespec_tuple', + 'treespec_list', + 'treespec_dict', + 'treespec_namedtuple', + 'treespec_ordereddict', + 'treespec_defaultdict', + 'treespec_deque', + 'treespec_structseq', + 'treespec_from_collection', + # Accessor + 'PyTreeEntry', + 'GetAttrEntry', + 'GetItemEntry', + 'FlattenedEntry', + 'AutoEntry', + 'SequenceEntry', + 'MappingEntry', + 'NamedTupleEntry', + 'StructSequenceEntry', + 'DataclassEntry', + 'PyTreeAccessor', + # Registry + 'register_pytree_node', + 'register_pytree_node_class', + 'unregister_pytree_node', + 'dict_insertion_ordered', + # Typing + 'PyTreeSpec', + 'PyTreeDef', + 'PyTreeKind', + 'PyTree', + 'PyTreeTypeVar', + 'CustomTreeNode', + 'FlattenFunc', + 'UnflattenFunc', + 'is_namedtuple', + 'is_namedtuple_class', + 'is_namedtuple_instance', + 'namedtuple_fields', + 'is_structseq', + 'is_structseq_class', + 'is_structseq_instance', + 'structseq_fields', +] + +MAX_RECURSION_DEPTH: int = MAX_RECURSION_DEPTH +"""Maximum recursion depth for pytree traversal. + +This limit prevents infinite recursion from causing an overflow of the C stack +and crashing Python. +""" +NONE_IS_NODE: bool = NONE_IS_NODE # literal constant +"""Literal constant that treats :data:`None` as a pytree non-leaf node.""" +NONE_IS_LEAF: bool = NONE_IS_LEAF # literal constant +"""Literal constant that treats :data:`None` as a pytree leaf node.""" diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/optree/accessors.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/optree/accessors.py new file mode 100644 index 0000000000000000000000000000000000000000..0285e240e7e7e750b234b232bb5c1b3a9aafc69b --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/optree/accessors.py @@ -0,0 +1,443 @@ +# Copyright 2022-2025 MetaOPT Team. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Access support for pytrees.""" + +from __future__ import annotations + +import dataclasses +import sys +from collections.abc import Iterable, Mapping, Sequence +from typing import TYPE_CHECKING, Any, ClassVar, Generic, Literal, TypeVar, overload +from typing_extensions import Self # Python 3.11+ + +import optree._C as _C +from optree._C import PyTreeKind + + +if TYPE_CHECKING: + import builtins + + from optree.typing import NamedTuple, StructSequence + + +__all__ = [ + 'PyTreeEntry', + 'GetItemEntry', + 'GetAttrEntry', + 'FlattenedEntry', + 'AutoEntry', + 'SequenceEntry', + 'MappingEntry', + 'NamedTupleEntry', + 'StructSequenceEntry', + 'DataclassEntry', + 'PyTreeAccessor', +] + + +SLOTS = {'slots': True} if sys.version_info >= (3, 10) else {} # Python 3.10+ + + +@dataclasses.dataclass(init=True, repr=False, eq=False, frozen=True, **SLOTS) +class PyTreeEntry: + """Base class for path entries.""" + + entry: Any + type: builtins.type + kind: PyTreeKind + + def __post_init__(self, /) -> None: + """Post-initialize the path entry.""" + if self.kind == PyTreeKind.LEAF: + raise ValueError('Cannot create a leaf path entry.') + if self.kind == PyTreeKind.NONE: + raise ValueError('Cannot create a path entry for None.') + + def __call__(self, obj: Any, /) -> Any: + """Get the child object.""" + try: + return obj[self.entry] # should be overridden + except TypeError as ex: + raise TypeError( + f'{self.__class__!r} cannot access through {obj!r} via entry {self.entry!r}', + ) from ex + + def __add__(self, other: object, /) -> PyTreeAccessor: + """Join the path entry with another path entry or accessor.""" + if isinstance(other, PyTreeEntry): + return PyTreeAccessor((self, other)) + if isinstance(other, PyTreeAccessor): + return PyTreeAccessor((self, *other)) + return NotImplemented + + def __eq__(self, other: object, /) -> bool: + """Check if the path entries are equal.""" + return isinstance(other, PyTreeEntry) and ( + ( + self.entry, + self.type, + self.kind, + self.__class__.__call__.__code__.co_code, + self.__class__.codify.__code__.co_code, + ) + == ( + other.entry, + other.type, + other.kind, + other.__class__.__call__.__code__.co_code, + other.__class__.codify.__code__.co_code, + ) + ) + + def __hash__(self, /) -> int: + """Get the hash of the path entry.""" + return hash( + ( + self.entry, + self.type, + self.kind, + self.__class__.__call__.__code__.co_code, + self.__class__.codify.__code__.co_code, + ), + ) + + def __repr__(self, /) -> str: + """Get the representation of the path entry.""" + return f'{self.__class__.__name__}(entry={self.entry!r}, type={self.type!r})' + + def codify(self, /, node: str = '') -> str: + """Generate code for accessing the path entry.""" + return f'{node}[]' # should be overridden + + +del SLOTS + + +_T = TypeVar('_T') +_T_co = TypeVar('_T_co', covariant=True) +_KT_co = TypeVar('_KT_co', covariant=True) +_VT_co = TypeVar('_VT_co', covariant=True) + + +class AutoEntry(PyTreeEntry): + """A generic path entry class that determines the entry type on creation automatically.""" + + __slots__: ClassVar[tuple[()]] = () + + def __new__( # type: ignore[misc] + cls, + /, + entry: Any, + type: builtins.type, # pylint: disable=redefined-builtin + kind: PyTreeKind, + ) -> PyTreeEntry: + """Create a new path entry.""" + # pylint: disable-next=import-outside-toplevel + from optree.typing import is_namedtuple_class, is_structseq_class + + if cls is not AutoEntry: + # Use the subclass type if the type is explicitly specified + return super().__new__(cls) + + if kind != PyTreeKind.CUSTOM: + raise ValueError(f'Cannot create an automatic path entry for PyTreeKind {kind!r}.') + + # Dispatch the path entry type based on the node type + path_entry_type: builtins.type[PyTreeEntry] + if is_structseq_class(type): + path_entry_type = StructSequenceEntry + elif is_namedtuple_class(type): + path_entry_type = NamedTupleEntry + elif dataclasses.is_dataclass(type): + path_entry_type = DataclassEntry + elif issubclass(type, Mapping): + path_entry_type = MappingEntry + elif issubclass(type, Sequence): + path_entry_type = SequenceEntry + else: + path_entry_type = FlattenedEntry + + if not issubclass(path_entry_type, AutoEntry): + # The __init__() method will not be called if the returned instance is not a subtype of + # AutoEntry. We should return an initialized instance. Return a fully-initialized + # instance of the dispatched type. + return path_entry_type(entry, type, kind) + + # The __init__() method will be called if the returned instance is a subtype of AutoEntry. + # We should return an uninitialized instance. The __init__() method will initialize it. + # But we will never reach here because the dispatched type is never a subtype of AutoEntry. + raise NotImplementedError('Unreachable code.') + + +class GetItemEntry(PyTreeEntry): + """A generic path entry class for nodes that access their children by :meth:`__getitem__`.""" + + __slots__: ClassVar[tuple[()]] = () + + def __call__(self, obj: Any, /) -> Any: + """Get the child object.""" + return obj[self.entry] + + def codify(self, /, node: str = '') -> str: + """Generate code for accessing the path entry.""" + return f'{node}[{self.entry!r}]' + + +class GetAttrEntry(PyTreeEntry): + """A generic path entry class for nodes that access their children by :meth:`__getattr__`.""" + + __slots__: ClassVar[tuple[()]] = () + + entry: str + + @property + def name(self, /) -> str: + """Get the attribute name.""" + return self.entry + + def __call__(self, obj: Any, /) -> Any: + """Get the child object.""" + return getattr(obj, self.name) + + def codify(self, /, node: str = '') -> str: + """Generate code for accessing the path entry.""" + return f'{node}.{self.name}' + + +class FlattenedEntry(PyTreeEntry): # pylint: disable=too-few-public-methods + """A fallback path entry class for flattened objects.""" + + __slots__: ClassVar[tuple[()]] = () + + +class SequenceEntry(GetItemEntry, Generic[_T_co]): + """A path entry class for sequences.""" + + __slots__: ClassVar[tuple[()]] = () + + entry: int + type: builtins.type[Sequence[_T_co]] + + @property + def index(self, /) -> int: + """Get the index.""" + return self.entry + + def __call__(self, obj: Sequence[_T_co], /) -> _T_co: + """Get the child object.""" + return obj[self.index] + + def __repr__(self, /) -> str: + """Get the representation of the path entry.""" + return f'{self.__class__.__name__}(index={self.index!r}, type={self.type!r})' + + +class MappingEntry(GetItemEntry, Generic[_KT_co, _VT_co]): + """A path entry class for mappings.""" + + __slots__: ClassVar[tuple[()]] = () + + entry: _KT_co + type: builtins.type[Mapping[_KT_co, _VT_co]] + + @property + def key(self, /) -> _KT_co: + """Get the key.""" + return self.entry + + def __call__(self, obj: Mapping[_KT_co, _VT_co], /) -> _VT_co: + """Get the child object.""" + return obj[self.key] + + def __repr__(self, /) -> str: + """Get the representation of the path entry.""" + return f'{self.__class__.__name__}(key={self.key!r}, type={self.type!r})' + + +class NamedTupleEntry(SequenceEntry[_T]): + """A path entry class for namedtuple objects.""" + + __slots__: ClassVar[tuple[()]] = () + + entry: int + type: builtins.type[NamedTuple[_T]] # type: ignore[type-arg] + kind: Literal[PyTreeKind.NAMEDTUPLE] + + @property + def fields(self, /) -> tuple[str, ...]: + """Get the field names.""" + from optree.typing import namedtuple_fields # pylint: disable=import-outside-toplevel + + return namedtuple_fields(self.type) + + @property + def field(self, /) -> str: + """Get the field name.""" + return self.fields[self.entry] + + def __repr__(self, /) -> str: + """Get the representation of the path entry.""" + return f'{self.__class__.__name__}(field={self.field!r}, type={self.type!r})' + + def codify(self, /, node: str = '') -> str: + """Generate code for accessing the path entry.""" + return f'{node}.{self.field}' + + +class StructSequenceEntry(SequenceEntry[_T]): + """A path entry class for PyStructSequence objects.""" + + __slots__: ClassVar[tuple[()]] = () + + entry: int + type: builtins.type[StructSequence[_T]] + kind: Literal[PyTreeKind.STRUCTSEQUENCE] + + @property + def fields(self, /) -> tuple[str, ...]: + """Get the field names.""" + from optree.typing import structseq_fields # pylint: disable=import-outside-toplevel + + return structseq_fields(self.type) + + @property + def field(self, /) -> str: + """Get the field name.""" + return self.fields[self.entry] + + def __repr__(self, /) -> str: + """Get the representation of the path entry.""" + return f'{self.__class__.__name__}(field={self.field!r}, type={self.type!r})' + + def codify(self, /, node: str = '') -> str: + """Generate code for accessing the path entry.""" + return f'{node}.{self.field}' + + +class DataclassEntry(GetAttrEntry): + """A path entry class for dataclasses.""" + + __slots__: ClassVar[tuple[()]] = () + + entry: str | int # type: ignore[assignment] + + @property + def fields(self, /) -> tuple[str, ...]: # pragma: no cover + """Get all field names.""" + return tuple(f.name for f in dataclasses.fields(self.type)) + + @property + def init_fields(self, /) -> tuple[str, ...]: + """Get the init field names.""" + return tuple(f.name for f in dataclasses.fields(self.type) if f.init) + + @property + def field(self, /) -> str: + """Get the field name.""" + if isinstance(self.entry, int): + return self.init_fields[self.entry] + return self.entry + + @property + def name(self, /) -> str: + """Get the attribute name.""" + return self.field + + def __repr__(self, /) -> str: + """Get the representation of the path entry.""" + return f'{self.__class__.__name__}(field={self.field!r}, type={self.type!r})' + + +class PyTreeAccessor(tuple[PyTreeEntry, ...]): + """A path class for PyTrees.""" + + __slots__: ClassVar[tuple[()]] = () + + @property + def path(self, /) -> tuple[Any, ...]: + """Get the path of the accessor.""" + return tuple(e.entry for e in self) + + def __new__(cls, /, path: Iterable[PyTreeEntry] = ()) -> Self: + """Create a new accessor instance.""" + if not isinstance(path, (list, tuple)): + path = tuple(path) + if not all(isinstance(p, PyTreeEntry) for p in path): + raise TypeError(f'Expected a path of PyTreeEntry, got {path!r}.') + return super().__new__(cls, path) + + def __call__(self, obj: Any, /) -> Any: + """Get the child object.""" + for entry in self: + obj = entry(obj) + return obj + + @overload # type: ignore[override] + def __getitem__(self, index: int, /) -> PyTreeEntry: ... + + @overload + def __getitem__(self, index: slice, /) -> Self: ... + + def __getitem__(self, index: int | slice, /) -> PyTreeEntry | Self: + """Get the child path entry or an accessor for a subpath.""" + if isinstance(index, slice): + return self.__class__(super().__getitem__(index)) + return super().__getitem__(index) + + def __add__(self, other: object, /) -> Self: + """Join the accessor with another path entry or accessor.""" + if isinstance(other, PyTreeEntry): + return self.__class__((*self, other)) + if isinstance(other, PyTreeAccessor): + return self.__class__((*self, *other)) + return NotImplemented + + def __mul__(self, value: int, /) -> Self: # type: ignore[override] + """Repeat the accessor.""" + return self.__class__(super().__mul__(value)) + + def __rmul__(self, value: int, /) -> Self: # type: ignore[override] + """Repeat the accessor.""" + return self.__class__(super().__rmul__(value)) + + def __eq__(self, other: object, /) -> bool: + """Check if the accessors are equal.""" + return isinstance(other, PyTreeAccessor) and super().__eq__(other) + + def __hash__(self, /) -> int: + """Get the hash of the accessor.""" + return super().__hash__() + + def __repr__(self, /) -> str: + """Get the representation of the accessor.""" + return f'{self.__class__.__name__}({self.codify()}, {super().__repr__()})' + + def codify(self, /, root: str = '*') -> str: + """Generate code for accessing the path.""" + string = root + for entry in self: + string = entry.codify(string) + return string + + +# These classes are used internally in the C++ side for accessor APIs +_name, _cls = '', object +for _name in __all__: + _cls = globals()[_name] + if not isinstance(_cls, type): # pragma: no cover + raise TypeError(f'Expected a class, got {_cls!r}.') + _cls.__module__ = 'optree' + setattr(_C, _name, _cls) +del _name, _cls diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/optree/dataclasses.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/optree/dataclasses.py new file mode 100644 index 0000000000000000000000000000000000000000..f531f2e88c7b9f176c249060751cbcdf20856f01 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/optree/dataclasses.py @@ -0,0 +1,509 @@ +# Copyright 2022-2025 MetaOPT Team. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""PyTree integration with :mod:`dataclasses`. + +This module implements PyTree integration with :mod:`dataclasses` by redefining the :func:`field`, +:func:`dataclass`, and :func:`make_dataclass` functions. Other APIs are re-exported from the +original :mod:`dataclasses` module. + +The PyTree integration allows dataclasses to be flattened and unflattened recursively. The fields +are stored in a special attribute named ``__optree_dataclass_fields__`` in the dataclass. + +>>> import math +... import optree +... +>>> @optree.dataclasses.dataclass(namespace='my_module') +... class Point: +... x: float +... y: float +... z: float = 0.0 +... norm: float = optree.dataclasses.field(init=False, pytree_node=False) +... +... def __post_init__(self) -> None: +... self.norm = math.hypot(self.x, self.y, self.z) +... +>>> point = Point(2.0, 6.0, 3.0) +>>> point +Point(x=2.0, y=6.0, z=3.0, norm=7.0) +>>> # Flatten without specifying the namespace +>>> optree.tree_flatten(point) # `Point`s are leaf nodes +([Point(x=2.0, y=6.0, z=3.0, norm=7.0)], PyTreeSpec(*)) +>>> # Flatten with the namespace +>>> accessors, leaves, treespec = optree.tree_flatten_with_accessor(point, namespace='my_module') +>>> accessors, leaves, treespec # doctest: +IGNORE_WHITESPACE,ELLIPSIS +( + [ + PyTreeAccessor(*.x, (DataclassEntry(field='x', type=),)), + PyTreeAccessor(*.y, (DataclassEntry(field='y', type=),)), + PyTreeAccessor(*.z, (DataclassEntry(field='z', type=),)) + ], + [2.0, 6.0, 3.0], + PyTreeSpec(CustomTreeNode(Point[()], [*, *, *]), namespace='my_module') +) +>>> point == optree.tree_unflatten(treespec, leaves) +True +""" + +# pylint: disable=too-many-arguments + +from __future__ import annotations + +import contextlib +import dataclasses +import functools +import inspect +import sys +import types +from dataclasses import * # noqa: F401,F403,RUF100 # pylint: disable=wildcard-import,unused-wildcard-import +from typing import TYPE_CHECKING, Any, Callable, Literal, Protocol, TypeVar, overload +from typing_extensions import dataclass_transform # Python 3.11+ + + +if TYPE_CHECKING: + from collections.abc import Iterable + + +# Redefine `field`, `dataclasses`, and `make_dataclasses`. +# The remaining APIs are re-exported from the original package. +__all__ = [*dataclasses.__all__] + + +_FIELDS = '__optree_dataclass_fields__' +_PYTREE_NODE_DEFAULT: bool = True + + +_T = TypeVar('_T') +_U = TypeVar('_U') +_TypeT = TypeVar('_TypeT', bound=type) + + +@overload # type: ignore[no-redef] +def field( + *, + default: _T, + init: bool = True, + repr: bool = True, # pylint: disable=redefined-builtin + hash: bool | None = None, # pylint: disable=redefined-builtin + compare: bool = True, + metadata: dict[Any, Any] | None = None, + kw_only: bool | Literal[dataclasses.MISSING] = dataclasses.MISSING, # type: ignore[valid-type] # Python 3.10+ + doc: str | None = None, # Python 3.14+ + pytree_node: bool | None = None, +) -> _T: ... + + +@overload +def field( + *, + default_factory: Callable[[], _T], + init: bool = True, + repr: bool = True, # pylint: disable=redefined-builtin + hash: bool | None = None, # pylint: disable=redefined-builtin + compare: bool = True, + metadata: dict[Any, Any] | None = None, + kw_only: bool | Literal[dataclasses.MISSING] = dataclasses.MISSING, # type: ignore[valid-type] # Python 3.10+ + doc: str | None = None, # Python 3.14+ + pytree_node: bool | None = None, +) -> _T: ... + + +@overload +def field( + *, + init: bool = True, + repr: bool = True, # pylint: disable=redefined-builtin + hash: bool | None = None, # pylint: disable=redefined-builtin + compare: bool = True, + metadata: dict[Any, Any] | None = None, + kw_only: bool | Literal[dataclasses.MISSING] = dataclasses.MISSING, # type: ignore[valid-type] # Python 3.10+ + doc: str | None = None, # Python 3.14+ + pytree_node: bool | None = None, +) -> Any: ... + + +def field( # noqa: D417 # pylint: disable=function-redefined + *, + default: Any = dataclasses.MISSING, + default_factory: Any = dataclasses.MISSING, + init: bool = True, + repr: bool = True, # pylint: disable=redefined-builtin + hash: bool | None = None, # pylint: disable=redefined-builtin + compare: bool = True, + metadata: dict[Any, Any] | None = None, + kw_only: bool | Literal[dataclasses.MISSING] = dataclasses.MISSING, # type: ignore[valid-type] # Python 3.10+ + doc: str | None = None, # Python 3.14+ + pytree_node: bool | None = None, +) -> Any: + """Field factory for :func:`dataclass`. + + This factory function is used to define the fields in a dataclass. It is similar to the field + factory :func:`dataclasses.field`, but with an additional ``pytree_node`` parameter. If + ``pytree_node`` is :data:`True` (default), the field will be considered a child node in the + PyTree structure which can be recursively flattened and unflattened. Otherwise, the field will + be considered as PyTree metadata. + + Setting ``pytree_node`` in the field factory is equivalent to setting a key ``'pytree_node'`` in + ``metadata`` in the original field factory. The ``pytree_node`` value can be accessed using + ``field.metadata['pytree_node']``. If ``pytree_node`` is :data:`None`, the value + ``metadata.get('pytree_node', True)`` will be used. + + .. note:: + If a field is considered a child node, it must be included in the argument list of the + :meth:`__init__` method, i.e., passes ``init=True`` in the field factory. + + Args: + pytree_node (bool or None, optional): Whether the field is a PyTree node. + **kwargs (optional): Optional keyword arguments passed to :func:`dataclasses.field`. + + Returns: + dataclasses.Field: The field defined using the provided arguments with + ``field.metadata['pytree_node']`` set. + """ + metadata = (metadata or {}).copy() + if pytree_node is None: + pytree_node = metadata.get('pytree_node', _PYTREE_NODE_DEFAULT) + metadata['pytree_node'] = pytree_node + + kwargs = { + 'default': default, + 'default_factory': default_factory, + 'init': init, + 'repr': repr, + 'hash': hash, + 'compare': compare, + 'metadata': metadata, + } + + if sys.version_info >= (3, 10): # pragma: >=3.10 cover + kwargs['kw_only'] = kw_only + elif kw_only is not dataclasses.MISSING: # pragma: <3.10 cover + raise TypeError("field() got an unexpected keyword argument 'kw_only'") + + if sys.version_info >= (3, 14): # pragma: >=3.14 cover + kwargs['doc'] = doc + elif doc is not None: # pragma: <3.14 cover + raise TypeError("field() got an unexpected keyword argument 'doc'") + + if not init and pytree_node: + raise TypeError( + '`pytree_node=True` is not allowed for non-init fields. ' + f'Please explicitly set `{__name__}.field(init=False, pytree_node=False)`.', + ) + + return dataclasses.field(**kwargs) # pylint: disable=invalid-field-call + + +@overload # type: ignore[no-redef] +def dataclass( + *, + init: bool = True, + repr: bool = True, # pylint: disable=redefined-builtin + eq: bool = True, + order: bool = False, + unsafe_hash: bool = False, + frozen: bool = False, + match_args: bool = True, # Python 3.10+ + kw_only: bool = False, # Python 3.10+ + slots: bool = False, # Python 3.10+ + weakref_slot: bool = False, # Python 3.11+ + namespace: str, +) -> Callable[[_TypeT], _TypeT]: ... + + +@overload +def dataclass( + cls: _TypeT, + /, + *, + init: bool = True, + repr: bool = True, # pylint: disable=redefined-builtin + eq: bool = True, + order: bool = False, + unsafe_hash: bool = False, + frozen: bool = False, + match_args: bool = True, # Python 3.10+ + kw_only: bool = False, # Python 3.10+ + slots: bool = False, # Python 3.10+ + weakref_slot: bool = False, # Python 3.11+ + namespace: str, +) -> _TypeT: ... + + +@dataclass_transform(field_specifiers=(field,)) +def dataclass( # noqa: C901,D417 # pylint: disable=function-redefined,too-many-locals,too-many-branches + cls: _TypeT | None = None, + /, + *, + init: bool = True, + repr: bool = True, # pylint: disable=redefined-builtin + eq: bool = True, + order: bool = False, + unsafe_hash: bool = False, + frozen: bool = False, + match_args: bool = True, # Python 3.10+ + kw_only: bool = False, # Python 3.10+ + slots: bool = False, # Python 3.10+ + weakref_slot: bool = False, # Python 3.11+ + namespace: str, +) -> _TypeT | Callable[[_TypeT], _TypeT]: + """Dataclass decorator with PyTree integration. + + Args: + cls (type or None, optional): The class to decorate. If :data:`None`, return a decorator. + namespace (str): The registry namespace used for the PyTree registration. + **kwargs (optional): Optional keyword arguments passed to :func:`dataclasses.dataclass`. + + Returns: + type or callable: The decorated class with PyTree integration or decorator function. + """ + # pylint: disable-next=import-outside-toplevel + from optree.registry import __GLOBAL_NAMESPACE as GLOBAL_NAMESPACE + + kwargs = { + 'init': init, + 'repr': repr, + 'eq': eq, + 'order': order, + 'unsafe_hash': unsafe_hash, + 'frozen': frozen, + } + + if sys.version_info >= (3, 10): # pragma: >=3.10 cover + kwargs['match_args'] = match_args + kwargs['kw_only'] = kw_only + kwargs['slots'] = slots + elif match_args is not True: # pragma: <3.10 cover + raise TypeError("dataclass() got an unexpected keyword argument 'match_args'") + elif kw_only is not False: # pragma: <3.10 cover + raise TypeError("dataclass() got an unexpected keyword argument 'kw_only'") + elif slots is not False: # pragma: <3.10 cover + raise TypeError("dataclass() got an unexpected keyword argument 'slots'") + + if sys.version_info >= (3, 11): # pragma: >=3.11 cover + kwargs['weakref_slot'] = weakref_slot + elif weakref_slot is not False: # pragma: <3.11 cover + raise TypeError("dataclass() got an unexpected keyword argument 'weakref_slot'") + + if cls is None: + + def decorator(cls: _TypeT) -> _TypeT: + return dataclass(cls, namespace=namespace, **kwargs) # type: ignore[call-overload] + + return decorator + + if not inspect.isclass(cls): + raise TypeError(f'@{__name__}.dataclass() can only be used with classes, not {cls!r}.') + if _FIELDS in cls.__dict__: + raise TypeError( + f'@{__name__}.dataclass() cannot be applied to {cls.__name__} more than once.', + ) + if namespace is not GLOBAL_NAMESPACE and not isinstance(namespace, str): + raise TypeError(f'The namespace must be a string, got {namespace!r}.') + if namespace == '': + namespace = GLOBAL_NAMESPACE + + cls = dataclasses.dataclass(cls, **kwargs) # type: ignore[assignment] + + children_fields = {} + metadata_fields = {} + for f in dataclasses.fields(cls): + if f.metadata.get('pytree_node', _PYTREE_NODE_DEFAULT): + if not f.init: + raise TypeError( + f'PyTree node field {f.name!r} must be included in `__init__()`. ' + f'Or you can explicitly set `{__name__}.field(init=False, pytree_node=False)`.', + ) + children_fields[f.name] = f + elif f.init: + metadata_fields[f.name] = f + + children_field_names = tuple(children_fields) + children_fields = types.MappingProxyType(children_fields) + metadata_fields = types.MappingProxyType(metadata_fields) + setattr(cls, _FIELDS, (children_fields, metadata_fields)) + + def flatten_func( + obj: _T, + /, + ) -> tuple[ + tuple[_U, ...], + tuple[tuple[str, Any], ...], + tuple[str, ...], + ]: + children = tuple(getattr(obj, name) for name in children_field_names) + metadata = tuple((name, getattr(obj, name)) for name in metadata_fields) + return children, metadata, children_field_names + + # pylint: disable-next=line-too-long + def unflatten_func(metadata: tuple[tuple[str, Any], ...], children: tuple[_U, ...], /) -> _T: # type: ignore[type-var] + kwargs = dict(zip(children_field_names, children)) + kwargs.update(metadata) + return cls(**kwargs) + + from optree.accessors import DataclassEntry # pylint: disable=import-outside-toplevel + from optree.registry import register_pytree_node # pylint: disable=import-outside-toplevel + + return register_pytree_node( # type: ignore[return-value] + cls, + flatten_func, + unflatten_func, # type: ignore[arg-type] + path_entry_type=DataclassEntry, + namespace=namespace, + ) + + +class _DataclassDecorator(Protocol[_TypeT]): # pylint: disable=too-few-public-methods + def __call__( # pylint: disable=arguments-differ + self, + cls: _TypeT, + /, + *, + init: bool = True, + repr: bool = True, # pylint: disable=redefined-builtin + eq: bool = True, + order: bool = False, + unsafe_hash: bool = False, + frozen: bool = False, + match_args: bool = True, + kw_only: bool = False, + slots: bool = False, + weakref_slot: bool = False, + ) -> _TypeT: + raise NotImplementedError + + +# pylint: disable-next=function-redefined,too-many-locals,too-many-branches +def make_dataclass( # type: ignore[no-redef] # noqa: C901,D417 + cls_name: str, + # pylint: disable-next=redefined-outer-name + fields: Iterable[str | tuple[str, Any] | tuple[str, Any, Any]], + *, + bases: tuple[type, ...] = (), + ns: dict[str, Any] | None = None, # redirect to `namespace` to `dataclasses.make_dataclass()` + init: bool = True, + repr: bool = True, # pylint: disable=redefined-builtin + eq: bool = True, + order: bool = False, + unsafe_hash: bool = False, + frozen: bool = False, + match_args: bool = True, # Python 3.10+ + kw_only: bool = False, # Python 3.10+ + slots: bool = False, # Python 3.10+ + weakref_slot: bool = False, # Python 3.11+ + module: str | None = None, # Python 3.12+ + decorator: _DataclassDecorator[_TypeT] = dataclasses.dataclass, # type: ignore[assignment] # Python 3.14+ + namespace: str, # the PyTree registration namespace +) -> _TypeT: + """Make a new dynamically created dataclass with PyTree integration. + + The dataclass name will be ``cls_name``. ``fields`` is an iterable of either (name), (name, type), + or (name, type, Field) objects. If type is omitted, use the string :data:`typing.Any`. Field + objects are created by the equivalent of calling :func:`field` (name, type [, Field-info]). + + The ``namespace`` parameter is the PyTree registration namespace which should be a string. The + ``namespace`` in the original :func:`dataclasses.make_dataclass` function is renamed to ``ns`` + to avoid conflicts. + + The remaining parameters are passed to :func:`dataclasses.make_dataclass`. + See :func:`dataclasses.make_dataclass` for more information. + + Args: + cls_name: The name of the dataclass. + fields (Iterable[str | tuple[str, Any] | tuple[str, Any, Any]]): An iterable of either + (name), (name, type), or (name, type, Field) objects. + namespace (str): The registry namespace used for the PyTree registration. + ns (dict or None, optional): The namespace used in dynamic type creation. + See :func:`dataclasses.make_dataclass` and the builtin :func:`type` function for more + information. + **kwargs (optional): Optional keyword arguments passed to :func:`dataclasses.make_dataclass`. + + Returns: + type: The dynamically created dataclass with PyTree integration. + """ + # pylint: disable-next=import-outside-toplevel + from optree.registry import __GLOBAL_NAMESPACE as GLOBAL_NAMESPACE + + if isinstance(namespace, dict) or namespace is None: # type: ignore[unreachable] + if ns is GLOBAL_NAMESPACE or isinstance(ns, str): # type: ignore[unreachable] + ns, namespace = namespace, ns + elif ns is None: + raise TypeError("make_dataclass() missing 1 required keyword-only argument: 'ns'") + if namespace is not GLOBAL_NAMESPACE and not isinstance(namespace, str): + raise TypeError(f'The namespace must be a string, got {namespace!r}.') + if namespace == '': + namespace = GLOBAL_NAMESPACE + + dataclass_kwargs = { + 'init': init, + 'repr': repr, + 'eq': eq, + 'order': order, + 'unsafe_hash': unsafe_hash, + 'frozen': frozen, + } + make_dataclass_kwargs = { + 'bases': bases, + 'namespace': ns, + } + + if sys.version_info >= (3, 10): # pragma: >=3.10 cover + dataclass_kwargs['match_args'] = match_args + dataclass_kwargs['kw_only'] = kw_only + dataclass_kwargs['slots'] = slots + elif match_args is not True: # pragma: <3.10 cover + raise TypeError("make_dataclass() got an unexpected keyword argument 'match_args'") + elif kw_only is not False: # pragma: <3.10 cover + raise TypeError("make_dataclass() got an unexpected keyword argument 'kw_only'") + elif slots is not False: # pragma: <3.10 cover + raise TypeError("make_dataclass() got an unexpected keyword argument 'slots'") + + if sys.version_info >= (3, 11): # pragma: >=3.11 cover + dataclass_kwargs['weakref_slot'] = weakref_slot + elif weakref_slot is not False: # pragma: <3.11 cover + raise TypeError("make_dataclass() got an unexpected keyword argument 'weakref_slot'") + + if sys.version_info >= (3, 12): # pragma: >=3.12 cover + if module is None: + try: + # pylint: disable-next=protected-access + module = sys._getframemodulename(1) or '__main__' # type: ignore[attr-defined] + except AttributeError: # pragma: no cover + with contextlib.suppress(AttributeError, ValueError): + # pylint: disable-next=protected-access + module = sys._getframe(1).f_globals.get('__name__', '__main__') + make_dataclass_kwargs['module'] = module + elif module is not None: # pragma: <3.12 cover + raise TypeError("make_dataclass() got an unexpected keyword argument 'module'") + + registered_by_decorator = False + if sys.version_info >= (3, 14): # pragma: >=3.14 cover + if decorator in (dataclasses.dataclass, dataclass): + decorator = functools.partial(dataclass, namespace=namespace) + registered_by_decorator = True + make_dataclass_kwargs['decorator'] = decorator + elif decorator is not dataclasses.dataclass: # pragma: <3.14 cover + raise TypeError("make_dataclass() got an unexpected keyword argument 'decorator'") + + cls: _TypeT = dataclasses.make_dataclass( # type: ignore[assignment] + cls_name, + fields=fields, + **dataclass_kwargs, # type: ignore[arg-type] + **make_dataclass_kwargs, # type: ignore[arg-type] + ) + if not registered_by_decorator: # pragma: <3.14 cover + dataclass_kwargs.pop('slots', None) # already defined in `make_dataclass()` + dataclass_kwargs.pop('weakref_slot', None) # already used in `make_dataclass()` + cls = dataclass(cls, **dataclass_kwargs, namespace=namespace) # type: ignore[call-overload] + return cls diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/optree/functools.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/optree/functools.py new file mode 100644 index 0000000000000000000000000000000000000000..06ca397e72e9b82ee0cfa7e8417bd4244c873904 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/optree/functools.py @@ -0,0 +1,169 @@ +# Copyright 2022-2025 MetaOPT Team. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""PyTree integration with :mod:`functools`.""" + +from __future__ import annotations + +import functools +from typing import TYPE_CHECKING, Any, Callable, ClassVar +from typing_extensions import Self # Python 3.11+ + +from optree import registry +from optree.accessors import GetAttrEntry +from optree.ops import tree_reduce as reduce +from optree.typing import CustomTreeNode, T + + +if TYPE_CHECKING: + from optree.accessors import PyTreeEntry + + +__all__ = [ + 'partial', + 'reduce', +] + + +class _HashablePartialShim: + """A shim object that delegates :meth:`__call__`, :meth:`__eq__`, and :meth:`__hash__` to a :func:`functools.partial` object.""" # pylint: disable=line-too-long + + __slots__: ClassVar[tuple[str, ...]] = ('args', 'func', 'keywords', 'partial_func') + + func: Callable[..., Any] + args: tuple[Any, ...] + keywords: dict[str, Any] + + def __init__(self, partial_func: functools.partial, /) -> None: + self.partial_func: functools.partial = partial_func + + def __call__(self, /, *args: Any, **kwargs: Any) -> Any: + return self.partial_func(*args, **kwargs) + + def __eq__(self, other: object, /) -> bool: + if isinstance(other, _HashablePartialShim): + return self.partial_func == other.partial_func + return self.partial_func == other + + def __hash__(self, /) -> int: + return hash(self.partial_func) + + def __repr__(self, /) -> str: + return repr(self.partial_func) + + +# pylint: disable-next=protected-access +@registry.register_pytree_node_class(namespace=registry.__GLOBAL_NAMESPACE) +class partial( # noqa: N801 # pylint: disable=invalid-name,too-few-public-methods + functools.partial, + CustomTreeNode[T], +): + """A version of :func:`functools.partial` that works in pytrees. + + Use it for partial function evaluation in a way that is compatible with transformations, + e.g., ``partial(func, *args, **kwargs)``. + + (You need to explicitly opt-in to this behavior because we did not want to give + :func:`functools.partial` different semantics than normal function closures.) + + For example, here is a basic usage of :class:`partial` in a manner similar to + :func:`functools.partial`: + + >>> import operator + >>> import torch + >>> add_one = partial(operator.add, torch.ones(())) + >>> add_one(torch.tensor([[1, 2], [3, 4]])) + tensor([[2., 3.], + [4., 5.]]) + + Pytree compatibility means that the resulting partial function can be passed as an argument + within tree-map functions, which is not possible with a standard :func:`functools.partial` + function: + + >>> def call_func_on_cuda(f, *args, **kwargs): + ... f, args, kwargs = tree_map(lambda t: t.cuda(), (f, args, kwargs)) + ... return f(*args, **kwargs) + ... + >>> # doctest: +SKIP + >>> tree_map(lambda t: t.cuda(), add_one) + optree.functools.partial(, tensor(1., device='cuda:0')) + >>> call_func_on_cuda(add_one, torch.tensor([[1, 2], [3, 4]])) + tensor([[2., 3.], + [4., 5.]], device='cuda:0') + + Passing zero arguments to :class:`partial` effectively wraps the original function, making it a + valid argument in tree-map functions: + + >>> # doctest: +SKIP + >>> call_func_on_cuda(partial(torch.add), torch.tensor(1), torch.tensor(2)) + tensor(3, device='cuda:0') + + Had we passed :func:`operator.add` to ``call_func_on_cuda`` directly, it would have resulted in + a :class:`TypeError` or :class:`AttributeError`. + """ + + __slots__: ClassVar[tuple[()]] = () + + func: Callable[..., Any] + args: tuple[T, ...] + keywords: dict[str, T] + + TREE_PATH_ENTRY_TYPE: ClassVar[type[PyTreeEntry]] = GetAttrEntry + + def __new__(cls, func: Callable[..., Any], /, *args: T, **keywords: T) -> Self: + """Create a new :class:`partial` instance.""" + # In Python 3.10+, if func is itself a functools.partial instance, functools.partial.__new__ + # would merge the arguments of this partial instance with the arguments of the func. We box + # func in a class that does not (yet) have a `func` attribute to defeat this optimization, + # since we care exactly which arguments are considered part of the pytree. + if isinstance(func, functools.partial): + original_func = func + func = _HashablePartialShim(original_func) + assert not hasattr(func, 'func'), 'shimmed function should not have a `func` attribute' + out = super().__new__(cls, func, *args, **keywords) + func.func = original_func.func + func.args = original_func.args + func.keywords = original_func.keywords + return out + + return super().__new__(cls, func, *args, **keywords) + + def __repr__(self, /) -> str: + """Return a string representation of the :class:`partial` instance.""" + args = [repr(self.func)] + args.extend(repr(x) for x in self.args) + args.extend(f'{k}={v!r}' for (k, v) in self.keywords.items()) + return f'{self.__class__.__module__}.{self.__class__.__qualname__}({", ".join(args)})' + + def __tree_flatten__( # type: ignore[override] + self, + /, + ) -> tuple[ + tuple[tuple[T, ...], dict[str, T]], + Callable[..., Any], + tuple[str, str], + ]: + """Flatten the :class:`partial` instance into children and metadata.""" + return (self.args, self.keywords), self.func, ('args', 'keywords') + + @classmethod + def __tree_unflatten__( # type: ignore[override] + cls, + metadata: Callable[..., Any], + children: tuple[tuple[T, ...], dict[str, T]], + /, + ) -> Self: + """Unflatten the children and metadata into a :class:`partial` instance.""" + args, keywords = children + return cls(metadata, *args, **keywords) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/optree/integrations/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/optree/integrations/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..986ad144f7ae89f4b51630e85c47ea0c8792ed69 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/optree/integrations/__init__.py @@ -0,0 +1,49 @@ +# Copyright 2022-2025 MetaOPT Team. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Integrations with third-party libraries.""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + + +if TYPE_CHECKING: + from types import ModuleType + + from optree.integrations import jax, numpy, torch + + +SUBMODULES: frozenset[str] = frozenset({'jax', 'numpy', 'torch'}) + + +def __dir__() -> list[str]: + return [*sorted(SUBMODULES), 'SUBMODULES'] + + +def __getattr__(name: str, /) -> ModuleType: + if name in SUBMODULES: + import importlib # pylint: disable=import-outside-toplevel + import sys # pylint: disable=import-outside-toplevel + + module = sys.modules[__name__] + + submodule = importlib.import_module(f'{__name__}.{name}') # pragma: no cover + setattr(module, name, submodule) # pragma: no cover + return submodule # pragma: no cover + + raise AttributeError(f'module {__name__!r} has no attribute {name!r}') + + +del TYPE_CHECKING diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/optree/integrations/jax.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/optree/integrations/jax.py new file mode 100644 index 0000000000000000000000000000000000000000..95c35a92963628805088e884c5f179afa7837942 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/optree/integrations/jax.py @@ -0,0 +1,293 @@ +# Copyright 2022-2025 MetaOPT Team. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +# This file is modified from: +# https://github.com/google/jax/blob/jax-v0.4.20/jax/_src/flatten_util.py +# ============================================================================== +# Copyright 2018 The JAX Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Integration with JAX.""" + +# pragma: jax cover file +# pylint: disable=import-error + +from __future__ import annotations + +import contextlib +import itertools +import warnings +from operator import itemgetter +from types import FunctionType +from typing import Any, Callable +from typing_extensions import TypeAlias # Python 3.10+ + +import jax.numpy as jnp +from jax import Array, lax +from jax._src import dtypes +from jax.typing import ArrayLike + +from optree.ops import tree_flatten, tree_unflatten +from optree.typing import PyTreeSpec, PyTreeTypeVar +from optree.utils import safe_zip, total_order_sorted + + +__all__ = ['ArrayLikeTree', 'ArrayTree', 'tree_ravel'] + + +# pylint: disable-next=invalid-name +ArrayLikeTree: TypeAlias = PyTreeTypeVar('ArrayLikeTree', ArrayLike) # type: ignore[valid-type] +# pylint: disable-next=invalid-name +ArrayTree: TypeAlias = PyTreeTypeVar('ArrayTree', Array) # type: ignore[valid-type] + + +# Vendor from https://github.com/google/jax/blob/jax-v0.4.20/jax/_src/util.py +class HashablePartial: # pragma: no cover + """A hashable version of :class:`functools.partial`.""" + + func: FunctionType + args: tuple[Any, ...] + kwargs: dict[str, Any] + + def __init__(self, func: FunctionType | HashablePartial, /, *args: Any, **kwargs: Any) -> None: + """Construct a :class:`HashablePartial` instance.""" + if not callable(func): + raise TypeError(f'Expected a callable, got {func!r}.') + + if isinstance(func, HashablePartial): + self.func = func.func + self.args = func.args + args + self.kwargs = {**func.kwargs, **kwargs} + elif isinstance(func, FunctionType): + self.func = func # type: ignore[assignment] + self.args = args + self.kwargs = kwargs + else: + raise TypeError(f'Expected a function, got {func!r}.') + + def __eq__(self, other: object, /) -> bool: + return ( + type(other) is HashablePartial # pylint: disable=unidiomatic-typecheck + and self.func.__code__ == other.func.__code__ + and (self.args, self.kwargs) == (other.args, other.kwargs) + ) + + def __hash__(self, /) -> int: + return hash( + ( + self.func.__code__, + self.args, + tuple(total_order_sorted(self.kwargs.items(), key=itemgetter(0))), + ), + ) + + def __call__(self, /, *args: Any, **kwargs: Any) -> Any: + kwargs = {**self.kwargs, **kwargs} + return self.func(*self.args, *args, **kwargs) + + +with contextlib.suppress(ImportError): # pragma: no cover + # pylint: disable-next=ungrouped-imports + from jax._src.util import HashablePartial # type: ignore[no-redef] # noqa: F811,RUF100 + + +def tree_ravel( + tree: ArrayLikeTree, + /, + is_leaf: Callable[[Any], bool] | None = None, + *, + none_is_leaf: bool = False, + namespace: str = '', +) -> tuple[Array, Callable[[Array], ArrayTree]]: + r"""Ravel (flatten) a pytree of arrays down to a 1D array. + + >>> tree = { + ... 'layer1': { + ... 'weight': jnp.arange(0, 6, dtype=jnp.float32).reshape((2, 3)), + ... 'bias': jnp.arange(6, 8, dtype=jnp.float32).reshape((2,)), + ... }, + ... 'layer2': { + ... 'weight': jnp.arange(8, 10, dtype=jnp.float32).reshape((1, 2)), + ... 'bias': jnp.arange(10, 11, dtype=jnp.float32).reshape((1,)), + ... }, + ... } + >>> tree # doctest: +IGNORE_WHITESPACE + { + 'layer1': { + 'weight': Array([[0., 1., 2.], + [3., 4., 5.]], dtype=float32), + 'bias': Array([6., 7.], dtype=float32) + }, + 'layer2': { + 'weight': Array([[8., 9.]], dtype=float32), + 'bias': Array([10.], dtype=float32) + } + } + >>> flat, unravel_func = tree_ravel(tree) + >>> flat + Array([ 6., 7., 0., 1., 2., 3., 4., 5., 10., 8., 9.], dtype=float32) + >>> unravel_func(flat) # doctest: +IGNORE_WHITESPACE + { + 'layer1': { + 'weight': Array([[0., 1., 2.], + [3., 4., 5.]], dtype=float32), + 'bias': Array([6., 7.], dtype=float32) + }, + 'layer2': { + 'weight': Array([[8., 9.]], dtype=float32), + 'bias': Array([10.], dtype=float32) + } + } + + Args: + tree (pytree): a pytree of arrays and scalars to ravel. + is_leaf (callable, optional): An optionally specified function that will be called at each + flattening step. It should return a boolean, with :data:`True` stopping the traversal + and the whole subtree being treated as a leaf, and :data:`False` indicating the + flattening should traverse the current object. + none_is_leaf (bool, optional): Whether to treat :data:`None` as a leaf. If :data:`False`, + :data:`None` is a non-leaf node with arity 0. Thus :data:`None` is contained in the + treespec rather than in the leaves list and :data:`None` will remain in the result + pytree. (default: :data:`False`) + namespace (str, optional): The registry namespace used for custom pytree node types. + (default: :const:`''`, i.e., the global namespace) + + Returns: + A pair ``(array, unravel_func)`` where the first element is a 1D array representing the + flattened and concatenated leaf values, with ``dtype`` determined by promoting the + ``dtype``\s of leaf values, and the second element is a callable for unflattening a 1D array + of the same length back to a pytree of the same structure as the input ``tree``. If the + input pytree is empty (i.e. has no leaves) then as a convention a 1D empty array of the + default dtype is returned in the first component of the output. + """ + leaves, treespec = tree_flatten( + tree, + is_leaf=is_leaf, + none_is_leaf=none_is_leaf, + namespace=namespace, + ) + flat, unravel_flat = _ravel_leaves(leaves) + return flat, HashablePartial(_tree_unravel, treespec, unravel_flat) # type: ignore[arg-type] + + +ravel_pytree = tree_ravel + + +def _tree_unravel( + treespec: PyTreeSpec, + unravel_flat: Callable[[Array], list[ArrayLike]], + flat: Array, + /, +) -> ArrayTree: + return tree_unflatten(treespec, unravel_flat(flat)) + + +def _ravel_leaves( + leaves: list[ArrayLike], + /, +) -> tuple[ + Array, + Callable[[Array], list[ArrayLike]], +]: + if not leaves: + return (jnp.zeros(0), _unravel_empty) + + from_dtypes = tuple(dtypes.dtype(leaf) for leaf in leaves) + to_dtype = dtypes.result_type(*from_dtypes) + sizes = tuple(jnp.size(leaf) for leaf in leaves) + shapes = tuple(jnp.shape(leaf) for leaf in leaves) + indices = tuple(itertools.accumulate(sizes)) + + if all(dt == to_dtype for dt in from_dtypes): + # Skip any dtype conversion, resulting in a dtype-polymorphic `unravel`. + # See https://github.com/google/jax/issues/7809. + raveled = jnp.concatenate([jnp.ravel(leaf) for leaf in leaves]) + return ( + raveled, + HashablePartial(_unravel_leaves_single_dtype, indices, shapes), # type: ignore[arg-type] + ) + + # When there is more than one distinct input dtype, we perform type conversions and produce a + # dtype-specific unravel function. + raveled = jnp.concatenate( + [jnp.ravel(lax.convert_element_type(leaf, to_dtype)) for leaf in leaves], + ) + return ( + raveled, + HashablePartial(_unravel_leaves, indices, shapes, from_dtypes, to_dtype), # type: ignore[arg-type] + ) + + +def _unravel_empty(flat: Array, /) -> list[ArrayLike]: + if jnp.shape(flat) != (0,): + raise ValueError( + f'The unravel function expected an array of shape {(0,)}, got shape {jnp.shape(flat)}.', + ) + + return [] + + +def _unravel_leaves_single_dtype( + indices: tuple[int, ...], + shapes: tuple[tuple[int, ...], ...], + flat: Array, + /, +) -> list[Array]: + if jnp.shape(flat) != (indices[-1],): + raise ValueError( + f'The unravel function expected an array of shape {(indices[-1],)}, ' + f'got shape {jnp.shape(flat)}.', + ) + + chunks = jnp.split(flat, indices[:-1]) + return [chunk.reshape(shape) for chunk, shape in safe_zip(chunks, shapes)] + + +def _unravel_leaves( + indices: tuple[int, ...], + shapes: tuple[tuple[int, ...], ...], + from_dtypes: tuple[jnp.dtype, ...], + to_dtype: jnp.dtype, + flat: Array, + /, +) -> list[Array]: + if jnp.shape(flat) != (indices[-1],): + raise ValueError( + f'The unravel function expected an array of shape {(indices[-1],)}, ' + f'got shape {jnp.shape(flat)}.', + ) + array_dtype = dtypes.dtype(flat) + if array_dtype != to_dtype: + raise ValueError( + f'The unravel function expected an array of dtype {to_dtype}, got dtype {array_dtype}.', + ) + + chunks = jnp.split(flat, indices[:-1]) + with warnings.catch_warnings(): + warnings.simplefilter('ignore') # ignore complex-to-real cast warning + return [ + lax.convert_element_type(chunk.reshape(shape), dtype) + for chunk, shape, dtype in safe_zip(chunks, shapes, from_dtypes) + ] diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/optree/integrations/numpy.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/optree/integrations/numpy.py new file mode 100644 index 0000000000000000000000000000000000000000..aab70bc52a7c0c5f3c9599d39aa5e54b93b0e9be --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/optree/integrations/numpy.py @@ -0,0 +1,218 @@ +# Copyright 2022-2025 MetaOPT Team. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Integration with NumPy.""" + +# pragma: numpy cover file +# pylint: disable=import-error + +from __future__ import annotations + +import functools +import itertools +import warnings +from typing import Any, Callable +from typing_extensions import TypeAlias # Python 3.10+ + +import numpy as np +from numpy.typing import ArrayLike + +from optree.ops import tree_flatten, tree_unflatten +from optree.typing import PyTreeSpec, PyTreeTypeVar +from optree.utils import safe_zip + + +__all__ = ['ArrayLikeTree', 'ArrayTree', 'tree_ravel'] + + +# pylint: disable-next=invalid-name +ArrayLikeTree: TypeAlias = PyTreeTypeVar('ArrayLikeTree', ArrayLike) # type: ignore[valid-type] +# pylint: disable-next=invalid-name +ArrayTree: TypeAlias = PyTreeTypeVar('ArrayTree', np.ndarray) # type: ignore[valid-type] + + +def tree_ravel( + tree: ArrayLikeTree, + /, + is_leaf: Callable[[Any], bool] | None = None, + *, + none_is_leaf: bool = False, + namespace: str = '', +) -> tuple[np.ndarray, Callable[[np.ndarray], ArrayTree]]: + r"""Ravel (flatten) a pytree of arrays down to a 1D array. + + >>> tree = { + ... 'layer1': { + ... 'weight': np.arange(0, 6, dtype=np.float32).reshape((2, 3)), + ... 'bias': np.arange(6, 8, dtype=np.float32).reshape((2,)), + ... }, + ... 'layer2': { + ... 'weight': np.arange(8, 10, dtype=np.float32).reshape((1, 2)), + ... 'bias': np.arange(10, 11, dtype=np.float32).reshape((1,)), + ... }, + ... } + >>> tree # doctest: +IGNORE_WHITESPACE + { + 'layer1': { + 'weight': array([[0., 1., 2.], + [3., 4., 5.]], dtype=float32), + 'bias': array([6., 7.], dtype=float32) + }, + 'layer2': { + 'weight': array([[8., 9.]], dtype=float32), + 'bias': array([10.], dtype=float32) + } + } + >>> flat, unravel_func = tree_ravel(tree) + >>> flat + array([ 6., 7., 0., 1., 2., 3., 4., 5., 10., 8., 9.], dtype=float32) + >>> unravel_func(flat) # doctest: +IGNORE_WHITESPACE + { + 'layer1': { + 'weight': array([[0., 1., 2.], + [3., 4., 5.]], dtype=float32), + 'bias': array([6., 7.], dtype=float32) + }, + 'layer2': { + 'weight': array([[8., 9.]], dtype=float32), + 'bias': array([10.], dtype=float32) + } + } + + Args: + tree (pytree): a pytree of arrays and scalars to ravel. + is_leaf (callable, optional): An optionally specified function that will be called at each + flattening step. It should return a boolean, with :data:`True` stopping the traversal + and the whole subtree being treated as a leaf, and :data:`False` indicating the + flattening should traverse the current object. + none_is_leaf (bool, optional): Whether to treat :data:`None` as a leaf. If :data:`False`, + :data:`None` is a non-leaf node with arity 0. Thus :data:`None` is contained in the + treespec rather than in the leaves list and :data:`None` will remain in the result + pytree. (default: :data:`False`) + namespace (str, optional): The registry namespace used for custom pytree node types. + (default: :const:`''`, i.e., the global namespace) + + Returns: + A pair ``(array, unravel_func)`` where the first element is a 1D array representing the + flattened and concatenated leaf values, with ``dtype`` determined by promoting the + ``dtype``\s of leaf values, and the second element is a callable for unflattening a 1D array + of the same length back to a pytree of the same structure as the input ``tree``. If the + input pytree is empty (i.e. has no leaves) then as a convention a 1D empty array of the + default dtype is returned in the first component of the output. + """ + leaves, treespec = tree_flatten( + tree, + is_leaf=is_leaf, + none_is_leaf=none_is_leaf, + namespace=namespace, + ) + flat, unravel_flat = _ravel_leaves(leaves) + return flat, functools.partial(_tree_unravel, treespec, unravel_flat) + + +ravel_pytree = tree_ravel + + +def _tree_unravel( + treespec: PyTreeSpec, + unravel_flat: Callable[[np.ndarray], list[np.ndarray]], + flat: np.ndarray, + /, +) -> ArrayTree: + return tree_unflatten(treespec, unravel_flat(flat)) + + +def _ravel_leaves( + leaves: list[np.ndarray], + /, +) -> tuple[ + np.ndarray, + Callable[[np.ndarray], list[np.ndarray]], +]: + if not leaves: + return (np.zeros(0), _unravel_empty) + + from_dtypes = tuple(np.result_type(leaf) for leaf in leaves) + to_dtype = np.result_type(*leaves) + sizes = tuple(np.size(leaf) for leaf in leaves) + shapes = tuple(np.shape(leaf) for leaf in leaves) + indices = tuple(itertools.accumulate(sizes)) + + if all(dt == to_dtype for dt in from_dtypes): + # Skip any dtype conversion, resulting in a dtype-polymorphic `unravel`. + raveled = np.concatenate([np.ravel(leaf) for leaf in leaves]) + return ( + raveled, + functools.partial(_unravel_leaves_single_dtype, indices, shapes), + ) + + # When there is more than one distinct input dtype, we perform type conversions and produce a + # dtype-specific unravel function. + raveled = np.concatenate([np.ravel(leaf).astype(to_dtype) for leaf in leaves]) + return ( + raveled, + functools.partial(_unravel_leaves, indices, shapes, from_dtypes, to_dtype), + ) + + +def _unravel_empty(flat: np.ndarray, /) -> list[np.ndarray]: + if np.shape(flat) != (0,): + raise ValueError( + f'The unravel function expected an array of shape {(0,)}, got shape {np.shape(flat)}.', + ) + return [] + + +def _unravel_leaves_single_dtype( + indices: tuple[int, ...], + shapes: tuple[tuple[int, ...], ...], + flat: np.ndarray, + /, +) -> list[np.ndarray]: + if np.shape(flat) != (indices[-1],): + raise ValueError( + f'The unravel function expected an array of shape {(indices[-1],)}, ' + f'got shape {np.shape(flat)}.', + ) + + chunks = np.split(flat, indices[:-1]) + return [chunk.reshape(shape) for chunk, shape in safe_zip(chunks, shapes)] + + +def _unravel_leaves( + indices: tuple[int, ...], + shapes: tuple[tuple[int, ...], ...], + from_dtypes: tuple[np.dtype, ...], + to_dtype: np.dtype, + flat: np.ndarray, + /, +) -> list[np.ndarray]: + if np.shape(flat) != (indices[-1],): + raise ValueError( + f'The unravel function expected an array of shape {(indices[-1],)}, ' + f'got shape {np.shape(flat)}.', + ) + array_dtype = np.result_type(flat) + if array_dtype != to_dtype: + raise ValueError( + f'The unravel function expected an array of dtype {to_dtype}, got dtype {array_dtype}.', + ) + + chunks = np.split(flat, indices[:-1]) + with warnings.catch_warnings(): + warnings.simplefilter('ignore') # ignore complex-to-real cast warning + return [ + chunk.reshape(shape).astype(dtype) + for chunk, shape, dtype in safe_zip(chunks, shapes, from_dtypes) + ] diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/optree/integrations/torch.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/optree/integrations/torch.py new file mode 100644 index 0000000000000000000000000000000000000000..e156e34354cbe5f84f2e0691a9c12cb2a66835e8 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/optree/integrations/torch.py @@ -0,0 +1,222 @@ +# Copyright 2022-2025 MetaOPT Team. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Integration with PyTorch.""" + +# pragma: torch cover file +# pylint: disable=import-error + +from __future__ import annotations + +import functools +import warnings +from typing import Any, Callable +from typing_extensions import TypeAlias # Python 3.10+ + +import torch + +from optree.ops import tree_flatten, tree_unflatten +from optree.typing import PyTreeSpec, PyTreeTypeVar +from optree.utils import safe_zip + + +__all__ = ['TensorTree', 'tree_ravel'] + + +# pylint: disable-next=invalid-name +TensorTree: TypeAlias = PyTreeTypeVar('TensorTree', torch.Tensor) # type: ignore[valid-type] + + +def tree_ravel( + tree: TensorTree, + /, + is_leaf: Callable[[Any], bool] | None = None, + *, + none_is_leaf: bool = False, + namespace: str = '', +) -> tuple[torch.Tensor, Callable[[torch.Tensor], TensorTree]]: + r"""Ravel (flatten) a pytree of tensors down to a 1D tensor. + + >>> tree = { + ... 'layer1': { + ... 'weight': torch.arange(0, 6, dtype=torch.float64).reshape((2, 3)), + ... 'bias': torch.arange(6, 8, dtype=torch.float64).reshape((2,)), + ... }, + ... 'layer2': { + ... 'weight': torch.arange(8, 10, dtype=torch.float64).reshape((1, 2)), + ... 'bias': torch.arange(10, 11, dtype=torch.float64).reshape((1,)), + ... }, + ... } + >>> tree # doctest: +IGNORE_WHITESPACE + { + 'layer1': { + 'weight': tensor([[0., 1., 2.], + [3., 4., 5.]], dtype=torch.float64), + 'bias': tensor([6., 7.], dtype=torch.float64) + }, + 'layer2': { + 'weight': tensor([[8., 9.]], dtype=torch.float64), + 'bias': tensor([10.], dtype=torch.float64) + } + } + >>> flat, unravel_func = tree_ravel(tree) + >>> flat + tensor([ 6., 7., 0., 1., 2., 3., 4., 5., 10., 8., 9.], dtype=torch.float64) + >>> unravel_func(flat) # doctest: +IGNORE_WHITESPACE + { + 'layer1': { + 'weight': tensor([[0., 1., 2.], + [3., 4., 5.]], dtype=torch.float64), + 'bias': tensor([6., 7.], dtype=torch.float64) + }, + 'layer2': { + 'weight': tensor([[8., 9.]], dtype=torch.float64), + 'bias': tensor([10.], dtype=torch.float64) + } + } + + Args: + tree (pytree): a pytree of tensors to ravel. + is_leaf (callable, optional): An optionally specified function that will be called at each + flattening step. It should return a boolean, with :data:`True` stopping the traversal + and the whole subtree being treated as a leaf, and :data:`False` indicating the + flattening should traverse the current object. + none_is_leaf (bool, optional): Whether to treat :data:`None` as a leaf. If :data:`False`, + :data:`None` is a non-leaf node with arity 0. Thus :data:`None` is contained in the + treespec rather than in the leaves list and :data:`None` will remain in the result + pytree. (default: :data:`False`) + namespace (str, optional): The registry namespace used for custom pytree node types. + (default: :const:`''`, i.e., the global namespace) + + Returns: + A pair ``(tensor, unravel_func)`` where the first element is a 1D tensor representing the + flattened and concatenated leaf values, with ``dtype`` determined by promoting the + ``dtype``\s of leaf values, and the second element is a callable for unflattening a 1D tensor + of the same length back to a pytree of the same structure as the input ``tree``. If the + input pytree is empty (i.e. has no leaves) then as a convention a 1D empty tensor of the + default dtype is returned in the first component of the output. + """ + leaves, treespec = tree_flatten( + tree, + is_leaf=is_leaf, + none_is_leaf=none_is_leaf, + namespace=namespace, + ) + flat, unravel_flat = _ravel_leaves(leaves) + return flat, functools.partial(_tree_unravel, treespec, unravel_flat) + + +ravel_pytree = tree_ravel + + +def _tree_unravel( + treespec: PyTreeSpec, + unravel_flat: Callable[[torch.Tensor], list[torch.Tensor]], + flat: torch.Tensor, + /, +) -> TensorTree: + return tree_unflatten(treespec, unravel_flat(flat)) + + +def _ravel_leaves( + leaves: list[torch.Tensor], + /, +) -> tuple[ + torch.Tensor, + Callable[[torch.Tensor], list[torch.Tensor]], +]: + if not leaves: + return (torch.zeros(0), _unravel_empty) + if not all(torch.is_tensor(leaf) for leaf in leaves): + raise ValueError('All leaves must be tensors.') + + from_dtypes = tuple(leaf.dtype for leaf in leaves) + to_dtype = from_dtypes[0] + for from_dtype in from_dtypes[1:]: + to_dtype = torch.promote_types(to_dtype, from_dtype) + sizes = tuple(leaf.numel() for leaf in leaves) + shapes = tuple(leaf.shape for leaf in leaves) + + if all(dt == to_dtype for dt in from_dtypes): + # Skip any dtype conversion, resulting in a dtype-polymorphic `unravel`. + raveled = torch.cat([torch.ravel(leaf) for leaf in leaves]) + return ( + raveled, + functools.partial(_unravel_leaves_single_dtype, sizes, shapes), + ) + + # When there is more than one distinct input dtype, we perform type conversions and produce a + # dtype-specific unravel function. + raveled = torch.cat([torch.ravel(leaf).to(to_dtype) for leaf in leaves]) + return ( + raveled, + functools.partial(_unravel_leaves, sizes, shapes, from_dtypes, to_dtype), + ) + + +def _unravel_empty(flat: torch.Tensor, /) -> list[torch.Tensor]: + if not torch.is_tensor(flat): + raise ValueError(f'Expected a tensor to unravel, got {type(flat)!r}.') + if flat.shape != (0,): + raise ValueError( + f'The unravel function expected a tensor of shape {(0,)}, got shape {flat.shape}.', + ) + return [] + + +def _unravel_leaves_single_dtype( + sizes: tuple[int, ...], + shapes: tuple[tuple[int, ...], ...], + flat: torch.Tensor, + /, +) -> list[torch.Tensor]: + if not torch.is_tensor(flat): + raise ValueError(f'Expected a tensor to unravel, got {type(flat)!r}.') + if flat.shape != (sum(sizes),): + raise ValueError( + f'The unravel function expected a tensor of shape {(sum(sizes),)}, ' + f'got shape {flat.shape}.', + ) + + chunks = torch.split(flat, list(sizes)) + return [chunk.reshape(shape) for chunk, shape in safe_zip(chunks, shapes)] + + +def _unravel_leaves( + sizes: tuple[int, ...], + shapes: tuple[tuple[int, ...], ...], + from_dtypes: tuple[torch.dtype, ...], + to_dtype: torch.dtype, + flat: torch.Tensor, + /, +) -> list[torch.Tensor]: + if not torch.is_tensor(flat): + raise ValueError(f'Expected a tensor to unravel, got {type(flat)!r}.') + if flat.shape != (sum(sizes),): + raise ValueError( + f'The unravel function expected a tensor of shape {(sum(sizes),)}, ' + f'got shape {flat.shape}.', + ) + if flat.dtype != to_dtype: + raise ValueError( + f'The unravel function expected a tensor of dtype {to_dtype}, got dtype {flat.dtype}.', + ) + + chunks = torch.split(flat, list(sizes)) + with warnings.catch_warnings(): + warnings.simplefilter('ignore') # ignore complex-to-real cast warning + return [ + chunk.reshape(shape).to(dtype) + for chunk, shape, dtype in safe_zip(chunks, shapes, from_dtypes) + ] diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/optree/ops.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/optree/ops.py new file mode 100644 index 0000000000000000000000000000000000000000..d5d653759debe0178513c1bf879f80b865a55ac4 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/optree/ops.py @@ -0,0 +1,3731 @@ +# Copyright 2022-2025 MetaOPT Team. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""OpTree: Optimized PyTree Utilities.""" + +# pylint: disable=too-many-lines + +from __future__ import annotations + +import difflib +import functools +import itertools +import textwrap +from collections import OrderedDict, defaultdict, deque +from typing import TYPE_CHECKING, Any, Callable, ClassVar, Generic, overload + +import optree._C as _C +from optree.accessors import PyTreeAccessor +from optree.typing import NamedTuple, T, is_namedtuple_instance, is_structseq_instance + + +if TYPE_CHECKING: + import builtins + from collections.abc import Collection, Iterable, Mapping + + from optree.accessors import PyTreeEntry + from optree.typing import ( + MetaData, + PyTree, + PyTreeKind, + PyTreeSpec, + S, + StructSequence, + U, + UnflattenFunc, + ) + + +__all__ = [ + 'MAX_RECURSION_DEPTH', + 'NONE_IS_NODE', + 'NONE_IS_LEAF', + 'tree_flatten', + 'tree_flatten_with_path', + 'tree_flatten_with_accessor', + 'tree_unflatten', + 'tree_iter', + 'tree_leaves', + 'tree_structure', + 'tree_paths', + 'tree_accessors', + 'tree_is_leaf', + 'all_leaves', + 'tree_map', + 'tree_map_', + 'tree_map_with_path', + 'tree_map_with_path_', + 'tree_map_with_accessor', + 'tree_map_with_accessor_', + 'tree_replace_nones', + 'tree_partition', + 'tree_transpose', + 'tree_transpose_map', + 'tree_transpose_map_with_path', + 'tree_transpose_map_with_accessor', + 'tree_broadcast_prefix', + 'broadcast_prefix', + 'tree_broadcast_common', + 'broadcast_common', + 'tree_broadcast_map', + 'tree_broadcast_map_with_path', + 'tree_broadcast_map_with_accessor', + 'tree_reduce', + 'tree_sum', + 'tree_max', + 'tree_min', + 'tree_all', + 'tree_any', + 'tree_flatten_one_level', + 'treespec_paths', + 'treespec_accessors', + 'treespec_entries', + 'treespec_entry', + 'treespec_children', + 'treespec_child', + 'treespec_one_level', + 'treespec_transform', + 'treespec_is_leaf', + 'treespec_is_strict_leaf', + 'treespec_is_one_level', + 'treespec_is_prefix', + 'treespec_is_suffix', + 'treespec_leaf', + 'treespec_none', + 'treespec_tuple', + 'treespec_list', + 'treespec_dict', + 'treespec_namedtuple', + 'treespec_ordereddict', + 'treespec_defaultdict', + 'treespec_deque', + 'treespec_structseq', + 'treespec_from_collection', + 'prefix_errors', +] + +MAX_RECURSION_DEPTH: int = _C.MAX_RECURSION_DEPTH +"""Maximum recursion depth for pytree traversal. + +This limit prevents infinite recursion from causing an overflow of the C stack +and crashing Python. +""" +NONE_IS_NODE: bool = False # literal constant +"""Literal constant that treats :data:`None` as a pytree non-leaf node.""" +NONE_IS_LEAF: bool = True # literal constant +"""Literal constant that treats :data:`None` as a pytree leaf node.""" + + +def tree_flatten( + tree: PyTree[T], + /, + is_leaf: Callable[[T], bool] | None = None, + *, + none_is_leaf: bool = False, + namespace: str = '', +) -> tuple[list[T], PyTreeSpec]: + """Flatten a pytree. + + See also :func:`tree_flatten_with_path` and :func:`tree_unflatten`. + + The flattening order (i.e., the order of elements in the output list) is deterministic, + corresponding to a left-to-right depth-first tree traversal. + + >>> tree = {'b': (2, [3, 4]), 'a': 1, 'c': None, 'd': 5} + >>> tree_flatten(tree) # doctest: +IGNORE_WHITESPACE + ( + [1, 2, 3, 4, 5], + PyTreeSpec({'a': *, 'b': (*, [*, *]), 'c': None, 'd': *}) + ) + >>> tree_flatten(tree, none_is_leaf=True) # doctest: +IGNORE_WHITESPACE + ( + [1, 2, 3, 4, None, 5], + PyTreeSpec({'a': *, 'b': (*, [*, *]), 'c': *, 'd': *}, NoneIsLeaf) + ) + >>> tree_flatten(1) + ([1], PyTreeSpec(*)) + >>> tree_flatten(None) + ([], PyTreeSpec(None)) + >>> tree_flatten(None, none_is_leaf=True) + ([None], PyTreeSpec(*, NoneIsLeaf)) + + For unordered dictionaries, :class:`dict` and :class:`collections.defaultdict`, the order is + dependent on the **sorted** keys in the dictionary. Please use :class:`collections.OrderedDict` + if you want to keep the keys in the insertion order. + + >>> from collections import OrderedDict + >>> tree = OrderedDict([('b', (2, [3, 4])), ('a', 1), ('c', None), ('d', 5)]) + >>> tree_flatten(tree) # doctest: +IGNORE_WHITESPACE + ( + [2, 3, 4, 1, 5], + PyTreeSpec(OrderedDict({'b': (*, [*, *]), 'a': *, 'c': None, 'd': *})) + ) + >>> tree_flatten(tree, none_is_leaf=True) # doctest: +IGNORE_WHITESPACE + ( + [2, 3, 4, 1, None, 5], + PyTreeSpec(OrderedDict({'b': (*, [*, *]), 'a': *, 'c': *, 'd': *}), NoneIsLeaf) + ) + + Args: + tree (pytree): A pytree to flatten. + is_leaf (callable, optional): An optionally specified function that will be called at each + flattening step. It should return a boolean, with :data:`True` stopping the traversal + and the whole subtree being treated as a leaf, and :data:`False` indicating the + flattening should traverse the current object. + none_is_leaf (bool, optional): Whether to treat :data:`None` as a leaf. If :data:`False`, + :data:`None` is a non-leaf node with arity 0. Thus :data:`None` is contained in the + treespec rather than in the leaves list and :data:`None` will remain in the result + pytree. (default: :data:`False`) + namespace (str, optional): The registry namespace used for custom pytree node types. + (default: :const:`''`, i.e., the global namespace) + + Returns: + A pair ``(leaves, treespec)`` where the first element is a list of leaf values and the + second element is a treespec representing the structure of the pytree. + """ + return _C.flatten(tree, is_leaf, none_is_leaf, namespace) + + +def tree_flatten_with_path( + tree: PyTree[T], + /, + is_leaf: Callable[[T], bool] | None = None, + *, + none_is_leaf: bool = False, + namespace: str = '', +) -> tuple[list[tuple[Any, ...]], list[T], PyTreeSpec]: + """Flatten a pytree and additionally record the paths. + + See also :func:`tree_flatten`, :func:`tree_paths`, and :func:`treespec_paths`. + + The flattening order (i.e., the order of elements in the output list) is deterministic, + corresponding to a left-to-right depth-first tree traversal. + + >>> tree = {'b': (2, [3, 4]), 'a': 1, 'c': None, 'd': 5} + >>> tree_flatten_with_path(tree) # doctest: +IGNORE_WHITESPACE + ( + [('a',), ('b', 0), ('b', 1, 0), ('b', 1, 1), ('d',)], + [1, 2, 3, 4, 5], + PyTreeSpec({'a': *, 'b': (*, [*, *]), 'c': None, 'd': *}) + ) + >>> tree_flatten_with_path(tree, none_is_leaf=True) # doctest: +IGNORE_WHITESPACE + ( + [('a',), ('b', 0), ('b', 1, 0), ('b', 1, 1), ('c',), ('d',)], + [1, 2, 3, 4, None, 5], + PyTreeSpec({'a': *, 'b': (*, [*, *]), 'c': *, 'd': *}, NoneIsLeaf) + ) + >>> tree_flatten_with_path(1) + ([()], [1], PyTreeSpec(*)) + >>> tree_flatten_with_path(None) + ([], [], PyTreeSpec(None)) + >>> tree_flatten_with_path(None, none_is_leaf=True) + ([()], [None], PyTreeSpec(*, NoneIsLeaf)) + + For unordered dictionaries, :class:`dict` and :class:`collections.defaultdict`, the order is + dependent on the **sorted** keys in the dictionary. Please use :class:`collections.OrderedDict` + if you want to keep the keys in the insertion order. + + >>> from collections import OrderedDict + >>> tree = OrderedDict([('b', (2, [3, 4])), ('a', 1), ('c', None), ('d', 5)]) + >>> tree_flatten_with_path(tree) # doctest: +IGNORE_WHITESPACE + ( + [('b', 0), ('b', 1, 0), ('b', 1, 1), ('a',), ('d',)], + [2, 3, 4, 1, 5], + PyTreeSpec(OrderedDict({'b': (*, [*, *]), 'a': *, 'c': None, 'd': *})) + ) + >>> tree_flatten_with_path(tree, none_is_leaf=True) # doctest: +IGNORE_WHITESPACE + ( + [('b', 0), ('b', 1, 0), ('b', 1, 1), ('a',), ('c',), ('d',)], + [2, 3, 4, 1, None, 5], + PyTreeSpec(OrderedDict({'b': (*, [*, *]), 'a': *, 'c': *, 'd': *}), NoneIsLeaf) + ) + + Args: + tree (pytree): A pytree to flatten. + is_leaf (callable, optional): An optionally specified function that will be called at each + flattening step. It should return a boolean, with :data:`True` stopping the traversal + and the whole subtree being treated as a leaf, and :data:`False` indicating the + flattening should traverse the current object. + none_is_leaf (bool, optional): Whether to treat :data:`None` as a leaf. If :data:`False`, + :data:`None` is a non-leaf node with arity 0. Thus :data:`None` is contained in the + treespec rather than in the leaves list and :data:`None` will remain in the result + pytree. (default: :data:`False`) + namespace (str, optional): The registry namespace used for custom pytree node types. + (default: :const:`''`, i.e., the global namespace) + + Returns: + A triple ``(paths, leaves, treespec)``. The first element is a list of paths to the leaf + values, where each path is a tuple of the index or keys. The second element is a list of + leaf values and the last element is a treespec representing the structure of the pytree. + """ + return _C.flatten_with_path(tree, is_leaf, none_is_leaf, namespace) + + +def tree_flatten_with_accessor( + tree: PyTree[T], + /, + is_leaf: Callable[[T], bool] | None = None, + *, + none_is_leaf: bool = False, + namespace: str = '', +) -> tuple[list[PyTreeAccessor], list[T], PyTreeSpec]: + """Flatten a pytree and additionally record the accessors. + + See also :func:`tree_flatten`, :func:`tree_accessors`, and :func:`treespec_accessors`. + + The flattening order (i.e., the order of elements in the output list) is deterministic, + corresponding to a left-to-right depth-first tree traversal. + + >>> tree = {'b': (2, [3, 4]), 'a': 1, 'c': None, 'd': 5} + >>> tree_flatten_with_accessor(tree) # doctest: +IGNORE_WHITESPACE + ( + [ + PyTreeAccessor(*['a'], (MappingEntry(key='a', type=),)), + PyTreeAccessor(*['b'][0], (MappingEntry(key='b', type=), SequenceEntry(index=0, type=))), + PyTreeAccessor(*['b'][1][0], (MappingEntry(key='b', type=), SequenceEntry(index=1, type=), SequenceEntry(index=0, type=))), + PyTreeAccessor(*['b'][1][1], (MappingEntry(key='b', type=), SequenceEntry(index=1, type=), SequenceEntry(index=1, type=))), + PyTreeAccessor(*['d'], (MappingEntry(key='d', type=),)) + ], + [1, 2, 3, 4, 5], + PyTreeSpec({'a': *, 'b': (*, [*, *]), 'c': None, 'd': *}) + ) + >>> tree_flatten_with_accessor(tree, none_is_leaf=True) # doctest: +IGNORE_WHITESPACE + ( + [ + PyTreeAccessor(*['a'], (MappingEntry(key='a', type=),)), + PyTreeAccessor(*['b'][0], (MappingEntry(key='b', type=), SequenceEntry(index=0, type=))), + PyTreeAccessor(*['b'][1][0], (MappingEntry(key='b', type=), SequenceEntry(index=1, type=), SequenceEntry(index=0, type=))), + PyTreeAccessor(*['b'][1][1], (MappingEntry(key='b', type=), SequenceEntry(index=1, type=), SequenceEntry(index=1, type=))), + PyTreeAccessor(*['c'], (MappingEntry(key='c', type=),)), + PyTreeAccessor(*['d'], (MappingEntry(key='d', type=),)) + ], + [1, 2, 3, 4, None, 5], + PyTreeSpec({'a': *, 'b': (*, [*, *]), 'c': *, 'd': *}, NoneIsLeaf) + ) + >>> tree_flatten_with_accessor(1) + ([PyTreeAccessor(*, ())], [1], PyTreeSpec(*)) + >>> tree_flatten_with_accessor(None) + ([], [], PyTreeSpec(None)) + >>> tree_flatten_with_accessor(None, none_is_leaf=True) + ([PyTreeAccessor(*, ())], [None], PyTreeSpec(*, NoneIsLeaf)) + + For unordered dictionaries, :class:`dict` and :class:`collections.defaultdict`, the order is + dependent on the **sorted** keys in the dictionary. Please use :class:`collections.OrderedDict` + if you want to keep the keys in the insertion order. + + >>> from collections import OrderedDict + >>> tree = OrderedDict([('b', (2, [3, 4])), ('a', 1), ('c', None), ('d', 5)]) + >>> tree_flatten_with_accessor(tree) # doctest: +IGNORE_WHITESPACE + ( + [ + PyTreeAccessor(*['b'][0], (MappingEntry(key='b', type=), SequenceEntry(index=0, type=))), + PyTreeAccessor(*['b'][1][0], (MappingEntry(key='b', type=), SequenceEntry(index=1, type=), SequenceEntry(index=0, type=))), + PyTreeAccessor(*['b'][1][1], (MappingEntry(key='b', type=), SequenceEntry(index=1, type=), SequenceEntry(index=1, type=))), + PyTreeAccessor(*['a'], (MappingEntry(key='a', type=),)), + PyTreeAccessor(*['d'], (MappingEntry(key='d', type=),)) + ], + [2, 3, 4, 1, 5], + PyTreeSpec(OrderedDict({'b': (*, [*, *]), 'a': *, 'c': None, 'd': *})) + ) + >>> tree_flatten_with_accessor(tree, none_is_leaf=True) # doctest: +IGNORE_WHITESPACE + ( + [ + PyTreeAccessor(*['b'][0], (MappingEntry(key='b', type=), SequenceEntry(index=0, type=))), + PyTreeAccessor(*['b'][1][0], (MappingEntry(key='b', type=), SequenceEntry(index=1, type=), SequenceEntry(index=0, type=))), + PyTreeAccessor(*['b'][1][1], (MappingEntry(key='b', type=), SequenceEntry(index=1, type=), SequenceEntry(index=1, type=))), + PyTreeAccessor(*['a'], (MappingEntry(key='a', type=),)), + PyTreeAccessor(*['c'], (MappingEntry(key='c', type=),)), + PyTreeAccessor(*['d'], (MappingEntry(key='d', type=),)) + ], + [2, 3, 4, 1, None, 5], + PyTreeSpec(OrderedDict({'b': (*, [*, *]), 'a': *, 'c': *, 'd': *}), NoneIsLeaf) + ) + + Args: + tree (pytree): A pytree to flatten. + is_leaf (callable, optional): An optionally specified function that will be called at each + flattening step. It should return a boolean, with :data:`True` stopping the traversal + and the whole subtree being treated as a leaf, and :data:`False` indicating the + flattening should traverse the current object. + none_is_leaf (bool, optional): Whether to treat :data:`None` as a leaf. If :data:`False`, + :data:`None` is a non-leaf node with arity 0. Thus :data:`None` is contained in the + treespec rather than in the leaves list and :data:`None` will remain in the result + pytree. (default: :data:`False`) + namespace (str, optional): The registry namespace used for custom pytree node types. + (default: :const:`''`, i.e., the global namespace) + + Returns: + A triple ``(accessors, leaves, treespec)``. The first element is a list of accessors to the + leaf values. The second element is a list of leaf values and the last element is a treespec + representing the structure of the pytree. + """ # pylint: disable=line-too-long + leaves, treespec = _C.flatten(tree, is_leaf, none_is_leaf, namespace) + return treespec.accessors(), leaves, treespec + + +def tree_unflatten(treespec: PyTreeSpec, leaves: Iterable[T]) -> PyTree[T]: + """Reconstruct a pytree from the treespec and the leaves. + + The inverse of :func:`tree_flatten`. + + >>> tree = {'b': (2, [3, 4]), 'a': 1, 'c': None, 'd': 5} + >>> leaves, treespec = tree_flatten(tree) + >>> tree == tree_unflatten(treespec, leaves) + True + + Args: + treespec (PyTreeSpec): The treespec to reconstruct. + leaves (iterable): The list of leaves to use for reconstruction. The list must match the + number of leaves of the treespec. + + Returns: + The reconstructed pytree, containing the ``leaves`` placed in the structure described by + ``treespec``. + """ + return treespec.unflatten(leaves) + + +def tree_iter( + tree: PyTree[T], + /, + is_leaf: Callable[[T], bool] | None = None, + *, + none_is_leaf: bool = False, + namespace: str = '', +) -> Iterable[T]: + """Get an iterator over the leaves of a pytree. + + See also :func:`tree_flatten` and :func:`tree_leaves`. + + >>> tree = {'b': (2, [3, 4]), 'a': 1, 'c': None, 'd': 5} + >>> list(tree_iter(tree)) + [1, 2, 3, 4, 5] + >>> list(tree_iter(tree, none_is_leaf=True)) + [1, 2, 3, 4, None, 5] + >>> list(tree_iter(1)) + [1] + >>> list(tree_iter(None)) + [] + >>> list(tree_iter(None, none_is_leaf=True)) + [None] + + Args: + tree (pytree): A pytree to iterate over. + is_leaf (callable, optional): An optionally specified function that will be called at each + flattening step. It should return a boolean, with :data:`True` stopping the traversal + and the whole subtree being treated as a leaf, and :data:`False` indicating the + flattening should traverse the current object. + none_is_leaf (bool, optional): Whether to treat :data:`None` as a leaf. If :data:`False`, + :data:`None` is a non-leaf node with arity 0. Thus :data:`None` is contained in the + treespec rather than in the leaves list and :data:`None` will remain in the result + pytree. (default: :data:`False`) + namespace (str, optional): The registry namespace used for custom pytree node types. + (default: :const:`''`, i.e., the global namespace) + + Returns: + An iterator over the leaf values. + """ + return _C.PyTreeIter(tree, is_leaf, none_is_leaf, namespace) + + +def tree_leaves( + tree: PyTree[T], + /, + is_leaf: Callable[[T], bool] | None = None, + *, + none_is_leaf: bool = False, + namespace: str = '', +) -> list[T]: + """Get the leaves of a pytree. + + See also :func:`tree_flatten` and :func:`tree_iter`. + + >>> tree = {'b': (2, [3, 4]), 'a': 1, 'c': None, 'd': 5} + >>> tree_leaves(tree) + [1, 2, 3, 4, 5] + >>> tree_leaves(tree, none_is_leaf=True) + [1, 2, 3, 4, None, 5] + >>> tree_leaves(1) + [1] + >>> tree_leaves(None) + [] + >>> tree_leaves(None, none_is_leaf=True) + [None] + + Args: + tree (pytree): A pytree to flatten. + is_leaf (callable, optional): An optionally specified function that will be called at each + flattening step. It should return a boolean, with :data:`True` stopping the traversal + and the whole subtree being treated as a leaf, and :data:`False` indicating the + flattening should traverse the current object. + none_is_leaf (bool, optional): Whether to treat :data:`None` as a leaf. If :data:`False`, + :data:`None` is a non-leaf node with arity 0. Thus :data:`None` is contained in the + treespec rather than in the leaves list and :data:`None` will remain in the result + pytree. (default: :data:`False`) + namespace (str, optional): The registry namespace used for custom pytree node types. + (default: :const:`''`, i.e., the global namespace) + + Returns: + A list of leaf values. + """ + return _C.flatten(tree, is_leaf, none_is_leaf, namespace)[0] + + +def tree_structure( + tree: PyTree[T], + /, + is_leaf: Callable[[T], bool] | None = None, + *, + none_is_leaf: bool = False, + namespace: str = '', +) -> PyTreeSpec: + """Get the treespec for a pytree. + + See also :func:`tree_flatten`. + + >>> tree = {'b': (2, [3, 4]), 'a': 1, 'c': None, 'd': 5} + >>> tree_structure(tree) + PyTreeSpec({'a': *, 'b': (*, [*, *]), 'c': None, 'd': *}) + >>> tree_structure(tree, none_is_leaf=True) + PyTreeSpec({'a': *, 'b': (*, [*, *]), 'c': *, 'd': *}, NoneIsLeaf) + >>> tree_structure(1) + PyTreeSpec(*) + >>> tree_structure(None) + PyTreeSpec(None) + >>> tree_structure(None, none_is_leaf=True) + PyTreeSpec(*, NoneIsLeaf) + + Args: + tree (pytree): A pytree to flatten. + is_leaf (callable, optional): An optionally specified function that will be called at each + flattening step. It should return a boolean, with :data:`True` stopping the traversal + and the whole subtree being treated as a leaf, and :data:`False` indicating the + flattening should traverse the current object. + none_is_leaf (bool, optional): Whether to treat :data:`None` as a leaf. If :data:`False`, + :data:`None` is a non-leaf node with arity 0. Thus :data:`None` is contained in the + treespec rather than in the leaves list and :data:`None` will remain in the result + pytree. (default: :data:`False`) + namespace (str, optional): The registry namespace used for custom pytree node types. + (default: :const:`''`, i.e., the global namespace) + + Returns: + A treespec object representing the structure of the pytree. + """ + return _C.flatten(tree, is_leaf, none_is_leaf, namespace)[1] + + +def tree_paths( + tree: PyTree[T], + /, + is_leaf: Callable[[T], bool] | None = None, + *, + none_is_leaf: bool = False, + namespace: str = '', +) -> list[tuple[Any, ...]]: + """Get the path entries to the leaves of a pytree. + + See also :func:`tree_flatten`, :func:`tree_flatten_with_path`, and :func:`treespec_paths`. + + >>> tree = {'b': (2, [3, 4]), 'a': 1, 'c': None, 'd': 5} + >>> tree_paths(tree) + [('a',), ('b', 0), ('b', 1, 0), ('b', 1, 1), ('d',)] + >>> tree_paths(tree, none_is_leaf=True) + [('a',), ('b', 0), ('b', 1, 0), ('b', 1, 1), ('c',), ('d',)] + >>> tree_paths(1) + [()] + >>> tree_paths(None) + [] + >>> tree_paths(None, none_is_leaf=True) + [()] + + Args: + tree (pytree): A pytree to flatten. + is_leaf (callable, optional): An optionally specified function that will be called at each + flattening step. It should return a boolean, with :data:`True` stopping the traversal + and the whole subtree being treated as a leaf, and :data:`False` indicating the + flattening should traverse the current object. + none_is_leaf (bool, optional): Whether to treat :data:`None` as a leaf. If :data:`False`, + :data:`None` is a non-leaf node with arity 0. Thus :data:`None` is contained in the + treespec rather than in the leaves list and :data:`None` will remain in the result + pytree. (default: :data:`False`) + namespace (str, optional): The registry namespace used for custom pytree node types. + (default: :const:`''`, i.e., the global namespace) + + Returns: + A list of paths to the leaf values, where each path is a tuple of the index or keys. + """ + return _C.flatten_with_path(tree, is_leaf, none_is_leaf, namespace)[0] + + +def tree_accessors( + tree: PyTree[T], + /, + is_leaf: Callable[[T], bool] | None = None, + *, + none_is_leaf: bool = False, + namespace: str = '', +) -> list[PyTreeAccessor]: + """Get the accessors to the leaves of a pytree. + + See also :func:`tree_flatten`, :func:`tree_flatten_with_accessor`, and + :func:`treespec_accessors`. + + >>> tree = {'b': (2, [3, 4]), 'a': 1, 'c': None, 'd': 5} + >>> tree_accessors(tree) # doctest: +IGNORE_WHITESPACE + [ + PyTreeAccessor(*['a'], (MappingEntry(key='a', type=),)), + PyTreeAccessor(*['b'][0], (MappingEntry(key='b', type=), SequenceEntry(index=0, type=))), + PyTreeAccessor(*['b'][1][0], (MappingEntry(key='b', type=), SequenceEntry(index=1, type=), SequenceEntry(index=0, type=))), + PyTreeAccessor(*['b'][1][1], (MappingEntry(key='b', type=), SequenceEntry(index=1, type=), SequenceEntry(index=1, type=))), + PyTreeAccessor(*['d'], (MappingEntry(key='d', type=),)) + ] + >>> tree_accessors(tree, none_is_leaf=True) # doctest: +IGNORE_WHITESPACE + [ + PyTreeAccessor(*['a'], (MappingEntry(key='a', type=),)), + PyTreeAccessor(*['b'][0], (MappingEntry(key='b', type=), SequenceEntry(index=0, type=))), + PyTreeAccessor(*['b'][1][0], (MappingEntry(key='b', type=), SequenceEntry(index=1, type=), SequenceEntry(index=0, type=))), + PyTreeAccessor(*['b'][1][1], (MappingEntry(key='b', type=), SequenceEntry(index=1, type=), SequenceEntry(index=1, type=))), + PyTreeAccessor(*['c'], (MappingEntry(key='c', type=),)), + PyTreeAccessor(*['d'], (MappingEntry(key='d', type=),)) + ] + >>> tree_accessors(1) + [PyTreeAccessor(*, ())] + >>> tree_accessors(None) + [] + >>> tree_accessors(None, none_is_leaf=True) + [PyTreeAccessor(*, ())] + + Args: + tree (pytree): A pytree to flatten. + is_leaf (callable, optional): An optionally specified function that will be called at each + flattening step. It should return a boolean, with :data:`True` stopping the traversal + and the whole subtree being treated as a leaf, and :data:`False` indicating the + flattening should traverse the current object. + none_is_leaf (bool, optional): Whether to treat :data:`None` as a leaf. If :data:`False`, + :data:`None` is a non-leaf node with arity 0. Thus :data:`None` is contained in the + treespec rather than in the leaves list and :data:`None` will remain in the result + pytree. (default: :data:`False`) + namespace (str, optional): The registry namespace used for custom pytree node types. + (default: :const:`''`, i.e., the global namespace) + + Returns: + A list of accessors to the leaf values. + """ # pylint: disable=line-too-long + return _C.flatten(tree, is_leaf, none_is_leaf, namespace)[1].accessors() + + +def tree_is_leaf( + tree: PyTree[T], + /, + is_leaf: Callable[[T], bool] | None = None, + *, + none_is_leaf: bool = False, + namespace: str = '', +) -> bool: + """Test whether the given object is a leaf node. + + See also :func:`tree_flatten`, :func:`tree_leaves`, and :func:`all_leaves`. + + >>> tree_is_leaf(1) + True + >>> tree_is_leaf(None) + False + >>> tree_is_leaf(None, none_is_leaf=True) + True + >>> tree_is_leaf({'a': 1, 'b': (2, 3)}) + False + + Args: + tree (pytree): A pytree to check if it is a leaf node. + is_leaf (callable, optional): An optionally specified function that will be called at each + flattening step. It should return a boolean, with :data:`True` stopping the traversal + and the whole subtree being treated as a leaf, and :data:`False` indicating the + flattening should traverse the current object. + none_is_leaf (bool, optional): Whether to treat :data:`None` as a leaf. If :data:`False`, + :data:`None` is a non-leaf node with arity 0. Thus :data:`None` is contained in the + treespec rather than a leaf. (default: :data:`False`) + namespace (str, optional): The registry namespace used for custom pytree node types. + (default: :const:`''`, i.e., the global namespace) + + Returns: + A boolean indicating if the given object is a leaf node. + """ + return _C.is_leaf(tree, is_leaf, none_is_leaf, namespace) # type: ignore[arg-type] + + +def all_leaves( + iterable: Iterable[PyTree[T]], + /, + is_leaf: Callable[[T], bool] | None = None, + *, + none_is_leaf: bool = False, + namespace: str = '', +) -> bool: + """Test whether all elements in the given iterable are leaves. + + See also :func:`tree_flatten`, :func:`tree_leaves`, and :func:`tree_is_leaf`. + + >>> tree = {'a': [1, 2, 3]} + >>> all_leaves(tree_leaves(tree)) + True + >>> all_leaves([tree]) + False + >>> all_leaves([1, 2, None, 3]) + False + >>> all_leaves([1, 2, None, 3], none_is_leaf=True) + True + + Note that this function iterates and checks the elements in the input iterable object, which + uses the :func:`iter` function. For dictionaries, ``iter(d)`` for a dictionary ``d`` iterates + the keys of the dictionary, not the values. + + >>> list({'a': 1, 'b': (2, 3)}) + ['a', 'b'] + >>> all_leaves({'a': 1, 'b': (2, 3)}) + True + + This function is useful in advanced cases. For example, if a library allows arbitrary map + operations on a flat list of leaves it may want to check if the result is still a flat list + of leaves. + + Args: + iterable (iterable): An iterable of objects. + is_leaf (callable, optional): An optionally specified function that will be called at each + flattening step. It should return a boolean, with :data:`True` stopping the traversal + and the whole subtree being treated as a leaf, and :data:`False` indicating the + flattening should traverse the current object. + none_is_leaf (bool, optional): Whether to treat :data:`None` as a leaf. If :data:`False`, + :data:`None` is a non-leaf node with arity 0. Thus :data:`None` is contained in the + treespec rather than a leaf. (default: :data:`False`) + namespace (str, optional): The registry namespace used for custom pytree node types. + (default: :const:`''`, i.e., the global namespace) + + Returns: + A boolean indicating if all elements in the input iterable are leaves. + """ + return all( + tree_is_leaf( + item, + is_leaf=is_leaf, + none_is_leaf=none_is_leaf, + namespace=namespace, + ) + for item in iterable + ) + + +def tree_map( + func: Callable[..., U], + tree: PyTree[T], + /, + *rests: PyTree[S], + is_leaf: Callable[[T], bool] | None = None, + none_is_leaf: bool = False, + namespace: str = '', +) -> PyTree[U]: + """Map a multi-input function over pytree args to produce a new pytree. + + See also :func:`tree_map_`, :func:`tree_map_with_path`, :func:`tree_map_with_path_`, + and :func:`tree_broadcast_map`. + + >>> tree_map(lambda x: x + 1, {'x': 7, 'y': (42, 64)}) + {'x': 8, 'y': (43, 65)} + >>> tree_map(lambda x: x + 1, {'x': 7, 'y': (42, 64), 'z': None}) + {'x': 8, 'y': (43, 65), 'z': None} + >>> tree_map(lambda x: x is None, {'x': 7, 'y': (42, 64), 'z': None}) + {'x': False, 'y': (False, False), 'z': None} + >>> tree_map(lambda x: x is None, {'x': 7, 'y': (42, 64), 'z': None}, none_is_leaf=True) + {'x': False, 'y': (False, False), 'z': True} + + If multiple inputs are given, the structure of the tree is taken from the first input; + subsequent inputs need only have ``tree`` as a prefix: + + >>> tree_map(lambda x, y: [x] + y, [5, 6], [[7, 9], [1, 2]]) + [[5, 7, 9], [6, 1, 2]] + + Args: + func (callable): A function that takes ``1 + len(rests)`` arguments, to be applied at the + corresponding leaves of the pytrees. + tree (pytree): A pytree to be mapped over, with each leaf providing the first positional + argument to function ``func``. + rests (tuple of pytree): A tuple of pytrees, each of which has the same structure as + ``tree`` or has ``tree`` as a prefix. + is_leaf (callable, optional): An optionally specified function that will be called at each + flattening step. It should return a boolean, with :data:`True` stopping the traversal + and the whole subtree being treated as a leaf, and :data:`False` indicating the + flattening should traverse the current object. + none_is_leaf (bool, optional): Whether to treat :data:`None` as a leaf. If :data:`False`, + :data:`None` is a non-leaf node with arity 0. Thus :data:`None` is contained in the + treespec rather than in the leaves list and :data:`None` will remain in the result + pytree. (default: :data:`False`) + namespace (str, optional): The registry namespace used for custom pytree node types. + (default: :const:`''`, i.e., the global namespace) + + Returns: + A new pytree with the same structure as ``tree`` but with the value at each leaf given by + ``func(x, *xs)`` where ``x`` is the value at the corresponding leaf in ``tree`` and ``xs`` + is the tuple of values at corresponding nodes in ``rests``. + """ + leaves, treespec = _C.flatten(tree, is_leaf, none_is_leaf, namespace) + flat_args = [leaves] + [treespec.flatten_up_to(r) for r in rests] + return treespec.unflatten(map(func, *flat_args)) + + +def tree_map_( + func: Callable[..., Any], + tree: PyTree[T], + /, + *rests: PyTree[S], + is_leaf: Callable[[T], bool] | None = None, + none_is_leaf: bool = False, + namespace: str = '', +) -> PyTree[T]: + """Like :func:`tree_map`, but do an inplace call on each leaf and return the original tree. + + See also :func:`tree_map`, :func:`tree_map_with_path`, and :func:`tree_map_with_path_`. + + Args: + func (callable): A function that takes ``1 + len(rests)`` arguments, to be applied at the + corresponding leaves of the pytrees. + tree (pytree): A pytree to be mapped over, with each leaf providing the first positional + argument to function ``func``. + rests (tuple of pytree): A tuple of pytrees, each of which has the same structure as + ``tree`` or has ``tree`` as a prefix. + is_leaf (callable, optional): An optionally specified function that will be called at each + flattening step. It should return a boolean, with :data:`True` stopping the traversal + and the whole subtree being treated as a leaf, and :data:`False` indicating the + flattening should traverse the current object. + none_is_leaf (bool, optional): Whether to treat :data:`None` as a leaf. If :data:`False`, + :data:`None` is a non-leaf node with arity 0. Thus :data:`None` is contained in the + treespec rather than in the leaves list and :data:`None` will remain in the result + pytree. (default: :data:`False`) + namespace (str, optional): The registry namespace used for custom pytree node types. + (default: :const:`''`, i.e., the global namespace) + + Returns: + The original ``tree`` with the values at each leaf modified by the side effect of function + ``func(x, *xs)`` (not the return value) where ``x`` is the value at the corresponding leaf + in ``tree`` and ``xs`` is the tuple of values at corresponding nodes in ``rests``. + """ + leaves, treespec = _C.flatten(tree, is_leaf, none_is_leaf, namespace) + flat_args = [leaves] + [treespec.flatten_up_to(r) for r in rests] + deque(map(func, *flat_args), maxlen=0) # consume and exhaust the iterable + return tree + + +def tree_map_with_path( + func: Callable[..., U], + tree: PyTree[T], + /, + *rests: PyTree[S], + is_leaf: Callable[[T], bool] | None = None, + none_is_leaf: bool = False, + namespace: str = '', +) -> PyTree[U]: + """Map a multi-input function over pytree args as well as the tree paths to produce a new pytree. + + See also :func:`tree_map`, :func:`tree_map_`, and :func:`tree_map_with_path_`. + + >>> tree_map_with_path(lambda p, x: (len(p), x), {'x': 7, 'y': (42, 64)}) + {'x': (1, 7), 'y': ((2, 42), (2, 64))} + >>> tree_map_with_path(lambda p, x: x + len(p), {'x': 7, 'y': (42, 64), 'z': None}) + {'x': 8, 'y': (44, 66), 'z': None} + >>> tree_map_with_path(lambda p, x: p, {'x': 7, 'y': (42, 64), 'z': {1.5: None}}) + {'x': ('x',), 'y': (('y', 0), ('y', 1)), 'z': {1.5: None}} + >>> tree_map_with_path(lambda p, x: p, {'x': 7, 'y': (42, 64), 'z': {1.5: None}}, none_is_leaf=True) + {'x': ('x',), 'y': (('y', 0), ('y', 1)), 'z': {1.5: ('z', 1.5)}} + + Args: + func (callable): A function that takes ``2 + len(rests)`` arguments, to be applied at the + corresponding leaves of the pytrees with extra paths. + tree (pytree): A pytree to be mapped over, with each leaf providing the second positional + argument and the corresponding path providing the first positional argument to function + ``func``. + rests (tuple of pytree): A tuple of pytrees, each of which has the same structure as + ``tree`` or has ``tree`` as a prefix. + is_leaf (callable, optional): An optionally specified function that will be called at each + flattening step. It should return a boolean, with :data:`True` stopping the traversal + and the whole subtree being treated as a leaf, and :data:`False` indicating the + flattening should traverse the current object. + none_is_leaf (bool, optional): Whether to treat :data:`None` as a leaf. If :data:`False`, + :data:`None` is a non-leaf node with arity 0. Thus :data:`None` is contained in the + treespec rather than in the leaves list and :data:`None` will remain in the result + pytree. (default: :data:`False`) + namespace (str, optional): The registry namespace used for custom pytree node types. + (default: :const:`''`, i.e., the global namespace) + + Returns: + A new pytree with the same structure as ``tree`` but with the value at each leaf given by + ``func(p, x, *xs)`` where ``(p, x)`` are the path and value at the corresponding leaf in + ``tree`` and ``xs`` is the tuple of values at corresponding nodes in ``rests``. + """ + paths, leaves, treespec = _C.flatten_with_path(tree, is_leaf, none_is_leaf, namespace) + flat_args = [leaves] + [treespec.flatten_up_to(r) for r in rests] + return treespec.unflatten(map(func, paths, *flat_args)) + + +def tree_map_with_path_( + func: Callable[..., Any], + tree: PyTree[T], + /, + *rests: PyTree[S], + is_leaf: Callable[[T], bool] | None = None, + none_is_leaf: bool = False, + namespace: str = '', +) -> PyTree[T]: + """Like :func:`tree_map_with_path`, but do an inplace call on each leaf and return the original tree. + + See also :func:`tree_map`, :func:`tree_map_`, and :func:`tree_map_with_path`. + + Args: + func (callable): A function that takes ``2 + len(rests)`` arguments, to be applied at the + corresponding leaves of the pytrees with extra paths. + tree (pytree): A pytree to be mapped over, with each leaf providing the second positional + argument and the corresponding path providing the first positional argument to function + ``func``. + rests (tuple of pytree): A tuple of pytrees, each of which has the same structure as + ``tree`` or has ``tree`` as a prefix. + is_leaf (callable, optional): An optionally specified function that will be called at each + flattening step. It should return a boolean, with :data:`True` stopping the traversal + and the whole subtree being treated as a leaf, and :data:`False` indicating the + flattening should traverse the current object. + none_is_leaf (bool, optional): Whether to treat :data:`None` as a leaf. If :data:`False`, + :data:`None` is a non-leaf node with arity 0. Thus :data:`None` is contained in the + treespec rather than in the leaves list and :data:`None` will remain in the result + pytree. (default: :data:`False`) + namespace (str, optional): The registry namespace used for custom pytree node types. + (default: :const:`''`, i.e., the global namespace) + + Returns: + The original ``tree`` with the values at each leaf modified by the side effect of function + ``func(p, x, *xs)`` (not the return value) where ``(p, x)`` are the path and value at the + corresponding leaf in ``tree`` and ``xs`` is the tuple of values at corresponding nodes in + ``rests``. + """ + paths, leaves, treespec = _C.flatten_with_path(tree, is_leaf, none_is_leaf, namespace) + flat_args = [leaves] + [treespec.flatten_up_to(r) for r in rests] + deque(map(func, paths, *flat_args), maxlen=0) # consume and exhaust the iterable + return tree + + +def tree_map_with_accessor( + func: Callable[..., U], + tree: PyTree[T], + /, + *rests: PyTree[S], + is_leaf: Callable[[T], bool] | None = None, + none_is_leaf: bool = False, + namespace: str = '', +) -> PyTree[U]: + """Map a multi-input function over pytree args as well as the tree accessors to produce a new pytree. + + See also :func:`tree_map`, :func:`tree_map_`, and :func:`tree_map_with_accessor_`. + + >>> tree_map_with_accessor(lambda a, x: f'{a.codify("tree")} = {x!r}', {'x': 7, 'y': (42, 64)}) + {'x': "tree['x'] = 7", 'y': ("tree['y'][0] = 42", "tree['y'][1] = 64")} + >>> tree_map_with_accessor(lambda a, x: x + len(a), {'x': 7, 'y': (42, 64), 'z': None}) + {'x': 8, 'y': (44, 66), 'z': None} + >>> tree_map_with_accessor( # doctest: +IGNORE_WHITESPACE,ELLIPSIS + ... lambda a, x: a, + ... {'x': 7, 'y': (42, 64), 'z': {1.5: None}}, + ... ) + { + 'x': PyTreeAccessor(*['x'], ...), + 'y': ( + PyTreeAccessor(*['y'][0], ...), + PyTreeAccessor(*['y'][1], ...) + ), + 'z': {1.5: None} + } + >>> tree_map_with_accessor( # doctest: +IGNORE_WHITESPACE,ELLIPSIS + ... lambda a, x: a, + ... {'x': 7, 'y': (42, 64), 'z': {1.5: None}}, + ... none_is_leaf=True, + ... ) + { + 'x': PyTreeAccessor(*['x'], ...), + 'y': ( + PyTreeAccessor(*['y'][0], ...), + PyTreeAccessor(*['y'][1], ...) + ), + 'z': { + 1.5: PyTreeAccessor(*['z'][1.5], ...) + } + } + + Args: + func (callable): A function that takes ``2 + len(rests)`` arguments, to be applied at the + corresponding leaves of the pytrees with extra accessors. + tree (pytree): A pytree to be mapped over, with each leaf providing the second positional + argument and the corresponding accessor providing the first positional argument to + function ``func``. + rests (tuple of pytree): A tuple of pytrees, each of which has the same structure as + ``tree`` or has ``tree`` as a prefix. + is_leaf (callable, optional): An optionally specified function that will be called at each + flattening step. It should return a boolean, with :data:`True` stopping the traversal + and the whole subtree being treated as a leaf, and :data:`False` indicating the + flattening should traverse the current object. + none_is_leaf (bool, optional): Whether to treat :data:`None` as a leaf. If :data:`False`, + :data:`None` is a non-leaf node with arity 0. Thus :data:`None` is contained in the + treespec rather than in the leaves list and :data:`None` will remain in the result + pytree. (default: :data:`False`) + namespace (str, optional): The registry namespace used for custom pytree node types. + (default: :const:`''`, i.e., the global namespace) + + Returns: + A new pytree with the same structure as ``tree`` but with the value at each leaf given by + ``func(a, x, *xs)`` where ``(a, x)`` are the accessor and value at the corresponding leaf in + ``tree`` and ``xs`` is the tuple of values at corresponding nodes in ``rests``. + """ + leaves, treespec = _C.flatten(tree, is_leaf, none_is_leaf, namespace) + flat_args = [leaves] + [treespec.flatten_up_to(r) for r in rests] + return treespec.unflatten(map(func, treespec.accessors(), *flat_args)) + + +def tree_map_with_accessor_( + func: Callable[..., Any], + tree: PyTree[T], + /, + *rests: PyTree[S], + is_leaf: Callable[[T], bool] | None = None, + none_is_leaf: bool = False, + namespace: str = '', +) -> PyTree[T]: + """Like :func:`tree_map_with_accessor`, but do an inplace call on each leaf and return the original tree. + + See also :func:`tree_map`, :func:`tree_map_`, and :func:`tree_map_with_accessor`. + + Args: + func (callable): A function that takes ``2 + len(rests)`` arguments, to be applied at the + corresponding leaves of the pytrees with extra accessors. + tree (pytree): A pytree to be mapped over, with each leaf providing the second positional + argument and the corresponding accessor providing the first positional argument to + function ``func``. + rests (tuple of pytree): A tuple of pytrees, each of which has the same structure as + ``tree`` or has ``tree`` as a prefix. + is_leaf (callable, optional): An optionally specified function that will be called at each + flattening step. It should return a boolean, with :data:`True` stopping the traversal + and the whole subtree being treated as a leaf, and :data:`False` indicating the + flattening should traverse the current object. + none_is_leaf (bool, optional): Whether to treat :data:`None` as a leaf. If :data:`False`, + :data:`None` is a non-leaf node with arity 0. Thus :data:`None` is contained in the + treespec rather than in the leaves list and :data:`None` will remain in the result + pytree. (default: :data:`False`) + namespace (str, optional): The registry namespace used for custom pytree node types. + (default: :const:`''`, i.e., the global namespace) + + Returns: + The original ``tree`` with the values at each leaf modified by the side effect of function + ``func(a, x, *xs)`` (not the return value) where ``(a, x)`` are the accessor and value at + the corresponding leaf in ``tree`` and ``xs`` is the tuple of values at corresponding nodes + in ``rests``. + """ + leaves, treespec = _C.flatten(tree, is_leaf, none_is_leaf, namespace) + flat_args = [leaves] + [treespec.flatten_up_to(r) for r in rests] + deque(map(func, treespec.accessors(), *flat_args), maxlen=0) # consume and exhaust the iterable + return tree + + +def tree_replace_nones( + sentinel: S, + tree: PyTree[T | None], + /, + namespace: str = '', +) -> PyTree[T | S]: + """Replace :data:`None` in ``tree`` with ``sentinel``. + + See also :func:`tree_flatten` and :func:`tree_map`. + + >>> tree_replace_nones(0, {'a': 1, 'b': None, 'c': (2, None)}) + {'a': 1, 'b': 0, 'c': (2, 0)} + >>> tree_replace_nones(0, None) + 0 + + Args: + sentinel (object): The value to replace :data:`None` with. + tree (pytree): A pytree to be transformed. + namespace (str, optional): The registry namespace used for custom pytree node types. + (default: :const:`''`, i.e., the global namespace) + + Returns: + A new pytree with the same structure as ``tree`` but with :data:`None` replaced. + """ + if tree is None: + return sentinel # type: ignore[unreachable] + return tree_map( + lambda x: x if x is not None else sentinel, + tree, + none_is_leaf=True, + namespace=namespace, + ) + + +@overload +def tree_partition( + predicate: Callable[[T], bool], + tree: PyTree[T], + /, + is_leaf: Callable[[T], bool] | None = None, + *, + fillvalue: None = None, + none_is_leaf: bool = False, + namespace: str = '', +) -> tuple[PyTree[T | None], PyTree[T | None]]: ... + + +@overload +def tree_partition( + predicate: Callable[[T], bool], + tree: PyTree[T], + /, + is_leaf: Callable[[T], bool] | None = None, + *, + fillvalue: S, + none_is_leaf: bool = False, + namespace: str = '', +) -> tuple[PyTree[T | S], PyTree[T | S]]: ... + + +def tree_partition( + predicate: Callable[[T], bool], + tree: PyTree[T], + /, + is_leaf: Callable[[T], bool] | None = None, + *, + fillvalue: S = None, # type: ignore[assignment] + none_is_leaf: bool = False, + namespace: str = '', +) -> tuple[PyTree[T | S], PyTree[T | S]]: + """Partition a tree into the left and right parts by the given predicate function. + + See also :func:`tree_transpose_map`. + + >>> left, right = tree_partition(lambda x: x > 10, {'x': 7, 'y': (42, 64)}) + >>> left + {'x': None, 'y': (42, 64)} + >>> right + {'x': 7, 'y': (None, None)} + + Instead of :data:`None`, one can also use a different sentinel value: + + >>> sentinel = object() + >>> left, right = tree_partition(lambda x: x > 10, {'x': 7, 'y': (42, 64)}, fillvalue=sentinel) + >>> left # doctest: +ELLIPSIS + {'x': , 'y': (42, 64)} + >>> right # doctest: +ELLIPSIS + {'x': 7, 'y': (, )} + + Args: + predicate (callable): A function that takes a leaf value as argument and splits/partitions + it into the left or right tree based on the ``predicate``'s return value. + tree (pytree): A pytree to be split, with each leaf providing the first positional + argument to function ``predicate``. + is_leaf (callable, optional): An optionally specified function that will be called at each + flattening step. It should return a boolean, with :data:`True` stopping the traversal + and the whole subtree being treated as a leaf, and :data:`False` indicating the + flattening should traverse the current object. + fillvalue (object, optional): A sentinel value to retain the tree structure. + (default: :data:`None`) + none_is_leaf (bool, optional): Whether to treat :data:`None` as a leaf. If :data:`False`, + :data:`None` is a non-leaf node with arity 0. Thus :data:`None` is contained in the + treespec rather than in the leaves list and :data:`None` will remain in the result + pytree. (default: :data:`False`) + namespace (str, optional): The registry namespace used for custom pytree node types. + (default: :const:`''`, i.e., the global namespace) + + Returns: + Two pytrees with the same structure as ``tree`` but with orthogonal leaves based on the + ``predicate`` function. The first pytree contains all leaves where ``predicate`` evaluates + to ``True``, the second for ``False``. The removed nodes in both trees are filled with + ``fillvalue`` to keep the original tree structure. + """ + return tree_transpose_map( + lambda x: (x, fillvalue) if predicate(x) else (fillvalue, x), # type: ignore[arg-type,return-value] + tree, + inner_treespec=tree_structure((0, 0), none_is_leaf=none_is_leaf), # type: ignore[arg-type] + is_leaf=is_leaf, + none_is_leaf=none_is_leaf, + namespace=namespace, + ) + + +def tree_transpose( + outer_treespec: PyTreeSpec, + inner_treespec: PyTreeSpec, + tree: PyTree[T], + /, + is_leaf: Callable[[T], bool] | None = None, +) -> PyTree[T]: # PyTree[PyTree[T]] + """Transform a tree having tree structure (outer, inner) into one having structure (inner, outer). + + See also :func:`tree_flatten`, :func:`tree_structure`, and :func:`tree_transpose_map`. + + >>> outer_treespec = tree_structure({'a': 1, 'b': 2, 'c': (3, 4)}) + >>> outer_treespec + PyTreeSpec({'a': *, 'b': *, 'c': (*, *)}) + >>> inner_treespec = tree_structure((1, 2)) + >>> inner_treespec + PyTreeSpec((*, *)) + >>> tree = {'a': (1, 2), 'b': (3, 4), 'c': ((5, 6), (7, 8))} + >>> tree_transpose(outer_treespec, inner_treespec, tree) + ({'a': 1, 'b': 3, 'c': (5, 7)}, {'a': 2, 'b': 4, 'c': (6, 8)}) + + For performance reasons, this function is only checks for the number of leaves in the input + pytree, not the structure. The result is only enumerated up to the original order of leaves in + ``tree``, then transpose depends on the number of leaves in structure (inner, outer). The caller + is responsible for ensuring that the input pytree has a prefix structure of ``outer_treespec`` + followed by a prefix structure of ``inner_treespec``. Otherwise, the result may be incorrect. + + >>> tree_transpose(outer_treespec, inner_treespec, list(range(1, 9))) + ({'a': 1, 'b': 3, 'c': (5, 7)}, {'a': 2, 'b': 4, 'c': (6, 8)}) + + Args: + outer_treespec (PyTreeSpec): A treespec object representing the outer structure of the pytree. + inner_treespec (PyTreeSpec): A treespec object representing the inner structure of the pytree. + tree (pytree): A pytree to be transposed. + is_leaf (callable, optional): An optionally specified function that will be called at each + flattening step. It should return a boolean, with :data:`True` stopping the traversal + and the whole subtree being treated as a leaf, and :data:`False` indicating the + flattening should traverse the current object. + + Returns: + A new pytree with the same structure as ``inner_treespec`` but with the value at each leaf + having the same structure as ``outer_treespec``. + """ + if outer_treespec.none_is_leaf != inner_treespec.none_is_leaf: + raise ValueError('Tree structures must have the same none_is_leaf value.') + outer_size = outer_treespec.num_leaves + inner_size = inner_treespec.num_leaves + if outer_size == 0 or inner_size == 0: + raise ValueError('Tree structures must have at least one leaf.') + if ( + outer_treespec.namespace + and inner_treespec.namespace + and outer_treespec.namespace != inner_treespec.namespace + ): + raise ValueError( + f'Tree structures must have the same namespace, ' + f'got {outer_treespec.namespace!r} vs. {inner_treespec.namespace!r}.', + ) + + leaves, treespec = tree_flatten( + tree, + is_leaf=is_leaf, + none_is_leaf=outer_treespec.none_is_leaf, + namespace=outer_treespec.namespace or inner_treespec.namespace, + ) + if treespec.num_leaves != outer_size * inner_size: + expected_treespec = outer_treespec.compose(inner_treespec) + raise TypeError(f'Tree structure mismatch; expected: {expected_treespec}, got: {treespec}.') + + grouped = [ + leaves[offset : offset + inner_size] + for offset in range(0, outer_size * inner_size, inner_size) + ] + transposed = zip(*grouped) + subtrees = map(outer_treespec.unflatten, transposed) + return inner_treespec.unflatten(subtrees) # type: ignore[arg-type] + + +def tree_transpose_map( + func: Callable[..., PyTree[U]], + tree: PyTree[T], + /, + *rests: PyTree[S], + inner_treespec: PyTreeSpec | None = None, + is_leaf: Callable[[T], bool] | None = None, + none_is_leaf: bool = False, + namespace: str = '', +) -> PyTree[U]: # PyTree[PyTree[U]] + """Map a multi-input function over pytree args to produce a new pytree with transposed structure. + + See also :func:`tree_map`, :func:`tree_map_with_path`, and :func:`tree_transpose`. + + >>> comp = {'a': 1, 'b': (6j, -3 + 4j), 'c': [-5.0, 2j]} + >>> real, imag, mod = tree_transpose_map(lambda z: (z.real, z.imag, abs(z)), comp) + >>> real + {'a': 1, 'b': (0.0, -3.0), 'c': [-5.0, 0.0]} + >>> imag + {'a': 0, 'b': (6.0, 4.0), 'c': [0.0, 2.0]} + >>> mod + {'a': 1, 'b': (6.0, 5.0), 'c': [5.0, 2.0]} + + >>> tree = {'b': (2, [3, 4]), 'a': 1, 'c': (5, 6)} + >>> tree_transpose_map( # doctest: +IGNORE_WHITESPACE + ... lambda x: {'identity': x, 'double': 2 * x}, + ... tree, + ... ) + { + 'identity': {'b': (2, [3, 4]), 'a': 1, 'c': (5, 6)}, + 'double': {'b': (4, [6, 8]), 'a': 2, 'c': (10, 12)} + } + >>> tree_transpose_map( # doctest: +IGNORE_WHITESPACE + ... lambda x: {'identity': x, 'double': (x, x)}, + ... tree, + ... ) + { + 'identity': {'b': (2, [3, 4]), 'a': 1, 'c': (5, 6)}, + 'double': ( + {'b': (2, [3, 4]), 'a': 1, 'c': (5, 6)}, + {'b': (2, [3, 4]), 'a': 1, 'c': (5, 6)} + ) + } + >>> tree_transpose_map( # doctest: +IGNORE_WHITESPACE + ... lambda x: {'identity': x, 'double': (x, x)}, + ... tree, + ... inner_treespec=tree_structure({'identity': 0, 'double': 0}), + ... ) + { + 'identity': {'b': (2, [3, 4]), 'a': 1, 'c': (5, 6)}, + 'double': {'b': ((2, 2), [(3, 3), (4, 4)]), 'a': (1, 1), 'c': ((5, 5), (6, 6))} + } + + Args: + func (callable): A function that takes ``1 + len(rests)`` arguments, to be applied at the + corresponding leaves of the pytrees. + tree (pytree): A pytree to be mapped over, with each leaf providing the first positional + argument to function ``func``. + rests (tuple of pytree): A tuple of pytrees, each of which has the same structure as + ``tree`` or has ``tree`` as a prefix. + inner_treespec (PyTreeSpec, optional): The treespec object representing the inner structure + of the result pytree. If not specified, the inner structure is inferred from the result + of the function ``func`` on the first leaf. (default: :data:`None`) + is_leaf (callable, optional): An optionally specified function that will be called at each + flattening step. It should return a boolean, with :data:`True` stopping the traversal + and the whole subtree being treated as a leaf, and :data:`False` indicating the + flattening should traverse the current object. + none_is_leaf (bool, optional): Whether to treat :data:`None` as a leaf. If :data:`False`, + :data:`None` is a non-leaf node with arity 0. Thus :data:`None` is contained in the + treespec rather than in the leaves list and :data:`None` will remain in the result + pytree. (default: :data:`False`) + namespace (str, optional): The registry namespace used for custom pytree node types. + (default: :const:`''`, i.e., the global namespace) + + Returns: + A new nested pytree with the same structure as ``inner_treespec`` but with the value at each + leaf having the same structure as ``tree``. The subtree at each leaf is given by the result + of function ``func(x, *xs)`` where ``x`` is the value at the corresponding leaf in ``tree`` + and ``xs`` is the tuple of values at corresponding nodes in ``rests``. + """ + leaves, outer_treespec = _C.flatten(tree, is_leaf, none_is_leaf, namespace) + if outer_treespec.num_leaves == 0: + raise ValueError(f'The outer structure must have at least one leaf. Got: {outer_treespec}.') + flat_args = [leaves] + [outer_treespec.flatten_up_to(r) for r in rests] + outputs = list(map(func, *flat_args)) + + if inner_treespec is None: + inner_treespec = tree_structure( + outputs[0], + is_leaf=is_leaf, # type: ignore[arg-type] + none_is_leaf=none_is_leaf, + namespace=namespace, + ) + if inner_treespec.num_leaves == 0: + raise ValueError(f'The inner structure must have at least one leaf. Got: {inner_treespec}.') + + grouped = [inner_treespec.flatten_up_to(o) for o in outputs] + transposed = zip(*grouped) + subtrees = map(outer_treespec.unflatten, transposed) + return inner_treespec.unflatten(subtrees) # type: ignore[arg-type] + + +def tree_transpose_map_with_path( + func: Callable[..., PyTree[U]], + tree: PyTree[T], + /, + *rests: PyTree[S], + inner_treespec: PyTreeSpec | None = None, + is_leaf: Callable[[T], bool] | None = None, + none_is_leaf: bool = False, + namespace: str = '', +) -> PyTree[U]: # PyTree[PyTree[U]] + """Map a multi-input function over pytree args as well as the tree paths to produce a new pytree with transposed structure. + + See also :func:`tree_map_with_path`, :func:`tree_transpose_map`, and :func:`tree_transpose`. + + >>> tree = {'b': (2, [3, 4]), 'a': 1, 'c': (5, 6)} + >>> tree_transpose_map_with_path( # doctest: +IGNORE_WHITESPACE + ... lambda p, x: {'depth': len(p), 'value': x}, + ... tree, + ... ) + { + 'depth': {'b': (2, [3, 3]), 'a': 1, 'c': (2, 2)}, + 'value': {'b': (2, [3, 4]), 'a': 1, 'c': (5, 6)} + } + >>> tree_transpose_map_with_path( # doctest: +IGNORE_WHITESPACE + ... lambda p, x: {'path': p, 'value': x}, + ... tree, + ... inner_treespec=tree_structure({'path': 0, 'value': 0}), + ... ) + { + 'path': { + 'b': (('b', 0), [('b', 1, 0), ('b', 1, 1)]), + 'a': ('a',), + 'c': (('c', 0), ('c', 1)) + }, + 'value': {'b': (2, [3, 4]), 'a': 1, 'c': (5, 6)} + } + + Args: + func (callable): A function that takes ``2 + len(rests)`` arguments, to be applied at the + corresponding leaves of the pytrees with extra paths. + tree (pytree): A pytree to be mapped over, with each leaf providing the second positional + argument and the corresponding path providing the first positional argument to function + ``func``. + rests (tuple of pytree): A tuple of pytrees, each of which has the same structure as + ``tree`` or has ``tree`` as a prefix. + inner_treespec (PyTreeSpec, optional): The treespec object representing the inner structure + of the result pytree. If not specified, the inner structure is inferred from the result + of the function ``func`` on the first leaf. (default: :data:`None`) + is_leaf (callable, optional): An optionally specified function that will be called at each + flattening step. It should return a boolean, with :data:`True` stopping the traversal + and the whole subtree being treated as a leaf, and :data:`False` indicating the + flattening should traverse the current object. + none_is_leaf (bool, optional): Whether to treat :data:`None` as a leaf. If :data:`False`, + :data:`None` is a non-leaf node with arity 0. Thus :data:`None` is contained in the + treespec rather than in the leaves list and :data:`None` will remain in the result + pytree. (default: :data:`False`) + namespace (str, optional): The registry namespace used for custom pytree node types. + (default: :const:`''`, i.e., the global namespace) + + Returns: + A new nested pytree with the same structure as ``inner_treespec`` but with the value at each + leaf having the same structure as ``tree``. The subtree at each leaf is given by the result + of function ``func(p, x, *xs)`` where ``(p, x)`` are the path and value at the corresponding + leaf in ``tree`` and ``xs`` is the tuple of values at corresponding nodes in ``rests``. + """ # pylint: disable=line-too-long + paths, leaves, outer_treespec = _C.flatten_with_path(tree, is_leaf, none_is_leaf, namespace) + if outer_treespec.num_leaves == 0: + raise ValueError(f'The outer structure must have at least one leaf. Got: {outer_treespec}.') + flat_args = [leaves] + [outer_treespec.flatten_up_to(r) for r in rests] + outputs = list(map(func, paths, *flat_args)) + + if inner_treespec is None: + inner_treespec = tree_structure( + outputs[0], + is_leaf=is_leaf, # type: ignore[arg-type] + none_is_leaf=none_is_leaf, + namespace=namespace, + ) + if inner_treespec.num_leaves == 0: + raise ValueError(f'The inner structure must have at least one leaf. Got: {inner_treespec}.') + + grouped = [inner_treespec.flatten_up_to(o) for o in outputs] + transposed = zip(*grouped) + subtrees = map(outer_treespec.unflatten, transposed) + return inner_treespec.unflatten(subtrees) # type: ignore[arg-type] + + +def tree_transpose_map_with_accessor( + func: Callable[..., PyTree[U]], + tree: PyTree[T], + /, + *rests: PyTree[S], + inner_treespec: PyTreeSpec | None = None, + is_leaf: Callable[[T], bool] | None = None, + none_is_leaf: bool = False, + namespace: str = '', +) -> PyTree[U]: # PyTree[PyTree[U]] + """Map a multi-input function over pytree args as well as the tree accessors to produce a new pytree with transposed structure. + + See also :func:`tree_map_with_accessor`, :func:`tree_transpose_map`, and :func:`tree_transpose`. + + >>> tree = {'b': (2, [3, 4]), 'a': 1, 'c': (5, 6)} + >>> tree_transpose_map_with_accessor( # doctest: +IGNORE_WHITESPACE + ... lambda a, x: {'depth': len(a), 'code': a.codify('tree'), 'value': x}, + ... tree, + ... ) + { + 'depth': { + 'b': (2, [3, 3]), + 'a': 1, + 'c': (2, 2) + }, + 'code': { + 'b': ("tree['b'][0]", ["tree['b'][1][0]", "tree['b'][1][1]"]), + 'a': "tree['a']", + 'c': ("tree['c'][0]", "tree['c'][1]") + }, + 'value': { + 'b': (2, [3, 4]), + 'a': 1, + 'c': (5, 6) + } + } + >>> tree_transpose_map_with_accessor( # doctest: +IGNORE_WHITESPACE,ELLIPSIS + ... lambda a, x: {'path': a.path, 'accessor': a, 'value': x}, + ... tree, + ... inner_treespec=tree_structure({'path': 0, 'accessor': 0, 'value': 0}), + ... ) + { + 'path': { + 'b': (('b', 0), [('b', 1, 0), ('b', 1, 1)]), + 'a': ('a',), + 'c': (('c', 0), ('c', 1)) + }, + 'accessor': { + 'b': ( + PyTreeAccessor(*['b'][0], ...), + [ + PyTreeAccessor(*['b'][1][0], ...), + PyTreeAccessor(*['b'][1][1], ...) + ] + ), + 'a': PyTreeAccessor(*['a'], ...), + 'c': ( + PyTreeAccessor(*['c'][0], ...), + PyTreeAccessor(*['c'][1], ...) + ) + }, + 'value': {'b': (2, [3, 4]), 'a': 1, 'c': (5, 6)} + } + + Args: + func (callable): A function that takes ``2 + len(rests)`` arguments, to be applied at the + corresponding leaves of the pytrees with extra accessors. + tree (pytree): A pytree to be mapped over, with each leaf providing the second positional + argument and the corresponding accessor providing the first positional argument to + function ``func``. + rests (tuple of pytree): A tuple of pytrees, each of which has the same structure as + ``tree`` or has ``tree`` as a prefix. + inner_treespec (PyTreeSpec, optional): The treespec object representing the inner structure + of the result pytree. If not specified, the inner structure is inferred from the result + of the function ``func`` on the first leaf. (default: :data:`None`) + is_leaf (callable, optional): An optionally specified function that will be called at each + flattening step. It should return a boolean, with :data:`True` stopping the traversal + and the whole subtree being treated as a leaf, and :data:`False` indicating the + flattening should traverse the current object. + none_is_leaf (bool, optional): Whether to treat :data:`None` as a leaf. If :data:`False`, + :data:`None` is a non-leaf node with arity 0. Thus :data:`None` is contained in the + treespec rather than in the leaves list and :data:`None` will remain in the result + pytree. (default: :data:`False`) + namespace (str, optional): The registry namespace used for custom pytree node types. + (default: :const:`''`, i.e., the global namespace) + + Returns: + A new nested pytree with the same structure as ``inner_treespec`` but with the value at each + leaf having the same structure as ``tree``. The subtree at each leaf is given by the result of + function ``func(a, x, *xs)`` where ``(a, x)`` are the accessor and value at the corresponding + leaf in ``tree`` and ``xs`` is the tuple of values at corresponding nodes in ``rests``. + """ # pylint: disable=line-too-long + leaves, outer_treespec = _C.flatten(tree, is_leaf, none_is_leaf, namespace) + if outer_treespec.num_leaves == 0: + raise ValueError(f'The outer structure must have at least one leaf. Got: {outer_treespec}.') + flat_args = [leaves] + [outer_treespec.flatten_up_to(r) for r in rests] + outputs = list(map(func, outer_treespec.accessors(), *flat_args)) + + if inner_treespec is None: + inner_treespec = tree_structure( + outputs[0], + is_leaf=is_leaf, # type: ignore[arg-type] + none_is_leaf=none_is_leaf, + namespace=namespace, + ) + if inner_treespec.num_leaves == 0: + raise ValueError(f'The inner structure must have at least one leaf. Got: {inner_treespec}.') + + grouped = [inner_treespec.flatten_up_to(o) for o in outputs] + transposed = zip(*grouped) + subtrees = map(outer_treespec.unflatten, transposed) + return inner_treespec.unflatten(subtrees) # type: ignore[arg-type] + + +def tree_broadcast_prefix( + prefix_tree: PyTree[T], + full_tree: PyTree[S], + /, + is_leaf: Callable[[T], bool] | None = None, + *, + none_is_leaf: bool = False, + namespace: str = '', +) -> PyTree[T]: # PyTree[PyTree[T]] + """Return a pytree of same structure of ``full_tree`` with broadcasted subtrees in ``prefix_tree``. + + See also :func:`broadcast_prefix`, :func:`tree_broadcast_common`, and :func:`treespec_is_prefix`. + + If a ``prefix_tree`` is a prefix of a ``full_tree``, this means the ``full_tree`` can be + constructed by replacing the leaves of ``prefix_tree`` with appropriate **subtrees**. + + This function returns a pytree with the same size as ``full_tree``. The leaves are replicated + from ``prefix_tree``. The number of replicas is determined by the corresponding subtree in + ``full_tree``. + + >>> tree_broadcast_prefix(1, [2, 3, 4]) + [1, 1, 1] + >>> tree_broadcast_prefix([1, 2, 3], [4, 5, 6]) + [1, 2, 3] + >>> tree_broadcast_prefix([1, 2, 3], [4, 5, 6, 7]) + Traceback (most recent call last): + ... + ValueError: list arity mismatch; expected: 3, got: 4; list: [4, 5, 6, 7]. + >>> tree_broadcast_prefix([1, 2, 3], [4, 5, (6, 7)]) + [1, 2, (3, 3)] + >>> tree_broadcast_prefix([1, 2, 3], [4, 5, {'a': 6, 'b': 7, 'c': (None, 8)}]) + [1, 2, {'a': 3, 'b': 3, 'c': (None, 3)}] + >>> tree_broadcast_prefix([1, 2, 3], [4, 5, {'a': 6, 'b': 7, 'c': (None, 8)}], none_is_leaf=True) + [1, 2, {'a': 3, 'b': 3, 'c': (3, 3)}] + + Args: + prefix_tree (pytree): A pytree with the prefix structure of ``full_tree``. + full_tree (pytree): A pytree with the suffix structure of ``prefix_tree``. + is_leaf (callable, optional): An optionally specified function that will be called at each + flattening step. It should return a boolean, with :data:`True` stopping the traversal + and the whole subtree being treated as a leaf, and :data:`False` indicating the + flattening should traverse the current object. + none_is_leaf (bool, optional): Whether to treat :data:`None` as a leaf. If :data:`False`, + :data:`None` is a non-leaf node with arity 0. Thus :data:`None` is contained in the + treespec rather than in the leaves list and :data:`None` will remain in the result + pytree. (default: :data:`False`) + namespace (str, optional): The registry namespace used for custom pytree node types. + (default: :const:`''`, i.e., the global namespace) + + Returns: + A pytree of same structure of ``full_tree`` with broadcasted subtrees in ``prefix_tree``. + """ + + def broadcast_leaves(x: T, subtree: PyTree[S]) -> PyTree[T]: + subtreespec = tree_structure( + subtree, + is_leaf=is_leaf, # type: ignore[arg-type] + none_is_leaf=none_is_leaf, + namespace=namespace, + ) + return subtreespec.unflatten(itertools.repeat(x, subtreespec.num_leaves)) + + # If prefix_tree is not a tree prefix of full_tree, this code can raise a ValueError; + # use prefix_errors to find disagreements and raise more precise error messages. + # errors = prefix_errors( + # prefix_tree, + # full_tree, + # is_leaf=is_leaf, + # none_is_leaf=none_is_leaf, + # namespace=namespace, + # ) + return tree_map( + broadcast_leaves, # type: ignore[arg-type] + prefix_tree, + full_tree, + is_leaf=is_leaf, + none_is_leaf=none_is_leaf, + namespace=namespace, + ) + + +def broadcast_prefix( + prefix_tree: PyTree[T], + full_tree: PyTree[S], + /, + is_leaf: Callable[[T], bool] | None = None, + *, + none_is_leaf: bool = False, + namespace: str = '', +) -> list[T]: + """Return a list of broadcasted leaves in ``prefix_tree`` to match the number of leaves in ``full_tree``. + + See also :func:`tree_broadcast_prefix`, :func:`broadcast_common`, and :func:`treespec_is_prefix`. + + If a ``prefix_tree`` is a prefix of a ``full_tree``, this means the ``full_tree`` can be + constructed by replacing the leaves of ``prefix_tree`` with appropriate **subtrees**. + + This function returns a list of leaves with the same size as ``full_tree``. The leaves are + replicated from ``prefix_tree``. The number of replicas is determined by the corresponding + subtree in ``full_tree``. + + >>> broadcast_prefix(1, [2, 3, 4]) + [1, 1, 1] + >>> broadcast_prefix([1, 2, 3], [4, 5, 6]) + [1, 2, 3] + >>> broadcast_prefix([1, 2, 3], [4, 5, 6, 7]) + Traceback (most recent call last): + ... + ValueError: list arity mismatch; expected: 3, got: 4; list: [4, 5, 6, 7]. + >>> broadcast_prefix([1, 2, 3], [4, 5, (6, 7)]) + [1, 2, 3, 3] + >>> broadcast_prefix([1, 2, 3], [4, 5, {'a': 6, 'b': 7, 'c': (None, 8)}]) + [1, 2, 3, 3, 3] + >>> broadcast_prefix([1, 2, 3], [4, 5, {'a': 6, 'b': 7, 'c': (None, 8)}], none_is_leaf=True) + [1, 2, 3, 3, 3, 3] + + Args: + prefix_tree (pytree): A pytree with the prefix structure of ``full_tree``. + full_tree (pytree): A pytree with the suffix structure of ``prefix_tree``. + is_leaf (callable, optional): An optionally specified function that will be called at each + flattening step. It should return a boolean, with :data:`True` stopping the traversal + and the whole subtree being treated as a leaf, and :data:`False` indicating the + flattening should traverse the current object. + none_is_leaf (bool, optional): Whether to treat :data:`None` as a leaf. If :data:`False`, + :data:`None` is a non-leaf node with arity 0. Thus :data:`None` is contained in the + treespec rather than in the leaves list and :data:`None` will remain in the result + pytree. (default: :data:`False`) + namespace (str, optional): The registry namespace used for custom pytree node types. + (default: :const:`''`, i.e., the global namespace) + + Returns: + A list of leaves in ``prefix_tree`` broadcasted to match the number of leaves in ``full_tree``. + """ + result: list[T] = [] + + def add_leaves(x: T, subtree: PyTree[S]) -> None: + subtreespec = tree_structure( + subtree, + is_leaf=is_leaf, # type: ignore[arg-type] + none_is_leaf=none_is_leaf, + namespace=namespace, + ) + result.extend(itertools.repeat(x, subtreespec.num_leaves)) + + # If prefix_tree is not a tree prefix of full_tree, this code can raise a ValueError; + # use prefix_errors to find disagreements and raise more precise error messages. + # errors = prefix_errors( + # prefix_tree, + # full_tree, + # is_leaf=is_leaf, + # none_is_leaf=none_is_leaf, + # namespace=namespace, + # ) + tree_map_( + add_leaves, + prefix_tree, + full_tree, + is_leaf=is_leaf, + none_is_leaf=none_is_leaf, + namespace=namespace, + ) + return result + + +def tree_broadcast_common( + tree: PyTree[T], + other_tree: PyTree[T], + /, + is_leaf: Callable[[T], bool] | None = None, + *, + none_is_leaf: bool = False, + namespace: str = '', +) -> tuple[PyTree[T], PyTree[T]]: + """Return two pytrees of common suffix structure of ``tree`` and ``other_tree`` with broadcasted subtrees. + + See also :func:`broadcast_common`, :func:`tree_broadcast_prefix`, and :func:`treespec_is_prefix`. + + If a ``suffix_tree`` is a suffix of a ``tree``, this means the ``suffix_tree`` can be + constructed by replacing the leaves of ``tree`` with appropriate **subtrees**. + + This function returns two pytrees with the same structure. The tree structure is the common + suffix structure of ``tree`` and ``other_tree``. The leaves are replicated from ``tree`` and + ``other_tree``. The number of replicas is determined by the corresponding subtree in the suffix + structure. + + >>> tree_broadcast_common(1, [2, 3, 4]) + ([1, 1, 1], [2, 3, 4]) + >>> tree_broadcast_common([1, 2, 3], [4, 5, 6]) + ([1, 2, 3], [4, 5, 6]) + >>> tree_broadcast_common([1, 2, 3], [4, 5, 6, 7]) + Traceback (most recent call last): + ... + ValueError: list arity mismatch; expected: 3, got: 4. + >>> tree_broadcast_common([1, (2, 3), 4], [5, 6, (7, 8)]) + ([1, (2, 3), (4, 4)], [5, (6, 6), (7, 8)]) + >>> tree_broadcast_common([1, {'a': (2, 3)}, 4], [5, 6, {'a': 7, 'b': 8, 'c': (None, 9)}]) + ([1, {'a': (2, 3)}, {'a': 4, 'b': 4, 'c': (None, 4)}], + [5, {'a': (6, 6)}, {'a': 7, 'b': 8, 'c': (None, 9)}]) + >>> tree_broadcast_common([1, {'a': (2, 3)}, 4], [5, 6, {'a': 7, 'b': 8, 'c': (None, 9)}], none_is_leaf=True) + ([1, {'a': (2, 3)}, {'a': 4, 'b': 4, 'c': (4, 4)}], + [5, {'a': (6, 6)}, {'a': 7, 'b': 8, 'c': (None, 9)}]) + >>> tree_broadcast_common([1, None], [None, 2]) + ([None, None], [None, None]) + >>> tree_broadcast_common([1, None], [None, 2], none_is_leaf=True) + ([1, None], [None, 2]) + + Args: + tree (pytree): A pytree that has a common suffix structure with ``other_tree``. + other_tree (pytree): A pytree that has a common suffix structure with ``tree``. + is_leaf (callable, optional): An optionally specified function that will be called at each + flattening step. It should return a boolean, with :data:`True` stopping the traversal + and the whole subtree being treated as a leaf, and :data:`False` indicating the + flattening should traverse the current object. + none_is_leaf (bool, optional): Whether to treat :data:`None` as a leaf. If :data:`False`, + :data:`None` is a non-leaf node with arity 0. Thus :data:`None` is contained in the + treespec rather than in the leaves list and :data:`None` will remain in the result + pytree. (default: :data:`False`) + namespace (str, optional): The registry namespace used for custom pytree node types. + (default: :const:`''`, i.e., the global namespace) + + Returns: + Two pytrees of common suffix structure of ``tree`` and ``other_tree`` with broadcasted subtrees. + """ + leaves, treespec = _C.flatten(tree, is_leaf, none_is_leaf, namespace) + other_leaves, other_treespec = _C.flatten(other_tree, is_leaf, none_is_leaf, namespace) + common_suffix_treespec = treespec.broadcast_to_common_suffix(other_treespec) + + sentinel: T = object() # type: ignore[assignment] + common_suffix_tree: PyTree[T] = common_suffix_treespec.unflatten( + itertools.repeat(sentinel, common_suffix_treespec.num_leaves), + ) + + def broadcast_leaves(x: T, subtree: PyTree[T]) -> PyTree[T]: + subtreespec = tree_structure( + subtree, + is_leaf=is_leaf, + none_is_leaf=none_is_leaf, + namespace=namespace, + ) + return subtreespec.unflatten(itertools.repeat(x, subtreespec.num_leaves)) + + broadcasted_tree: PyTree[T] = treespec.unflatten( + map( + broadcast_leaves, # type: ignore[arg-type] + leaves, + treespec.flatten_up_to(common_suffix_tree), + ), + ) + other_broadcasted_tree: PyTree[T] = other_treespec.unflatten( + map( + broadcast_leaves, # type: ignore[arg-type] + other_leaves, + other_treespec.flatten_up_to(common_suffix_tree), + ), + ) + return broadcasted_tree, other_broadcasted_tree + + +def broadcast_common( + tree: PyTree[T], + other_tree: PyTree[T], + /, + is_leaf: Callable[[T], bool] | None = None, + *, + none_is_leaf: bool = False, + namespace: str = '', +) -> tuple[list[T], list[T]]: + """Return two lists of broadcasted leaves in ``tree`` and ``other_tree`` to match the number of leaves in the common suffix structure. + + See also :func:`tree_broadcast_common`, :func:`broadcast_prefix`, and :func:`treespec_is_prefix`. + + If a ``suffix_tree`` is a suffix of a ``tree``, this means the ``suffix_tree`` can be + constructed by replacing the leaves of ``tree`` with appropriate **subtrees**. + + This function returns two pytrees with the same structure. The tree structure is the common + suffix structure of ``tree`` and ``other_tree``. The leaves are replicated from ``tree`` and + ``other_tree``. The number of replicas is determined by the corresponding subtree in the suffix + structure. + + >>> broadcast_common(1, [2, 3, 4]) + ([1, 1, 1], [2, 3, 4]) + >>> broadcast_common([1, 2, 3], [4, 5, 6]) + ([1, 2, 3], [4, 5, 6]) + >>> broadcast_common([1, 2, 3], [4, 5, 6, 7]) + Traceback (most recent call last): + ... + ValueError: list arity mismatch; expected: 3, got: 4. + >>> broadcast_common([1, (2, 3), 4], [5, 6, (7, 8)]) + ([1, 2, 3, 4, 4], [5, 6, 6, 7, 8]) + >>> broadcast_common([1, {'a': (2, 3)}, 4], [5, 6, {'a': 7, 'b': 8, 'c': (None, 9)}]) + ([1, 2, 3, 4, 4, 4], [5, 6, 6, 7, 8, 9]) + >>> broadcast_common([1, {'a': (2, 3)}, 4], [5, 6, {'a': 7, 'b': 8, 'c': (None, 9)}], none_is_leaf=True) + ([1, 2, 3, 4, 4, 4, 4], [5, 6, 6, 7, 8, None, 9]) + >>> broadcast_common([1, None], [None, 2]) + ([], []) + >>> broadcast_common([1, None], [None, 2], none_is_leaf=True) + ([1, None], [None, 2]) + + Args: + tree (pytree): A pytree that has a common suffix structure with ``other_tree``. + other_tree (pytree): A pytree that has a common suffix structure with ``tree``. + is_leaf (callable, optional): An optionally specified function that will be called at each + flattening step. It should return a boolean, with :data:`True` stopping the traversal + and the whole subtree being treated as a leaf, and :data:`False` indicating the + flattening should traverse the current object. + none_is_leaf (bool, optional): Whether to treat :data:`None` as a leaf. If :data:`False`, + :data:`None` is a non-leaf node with arity 0. Thus :data:`None` is contained in the + treespec rather than in the leaves list and :data:`None` will remain in the result + pytree. (default: :data:`False`) + namespace (str, optional): The registry namespace used for custom pytree node types. + (default: :const:`''`, i.e., the global namespace) + + Returns: + Two lists of leaves in ``tree`` and ``other_tree`` broadcasted to match the number of leaves + in the common suffix structure. + """ # pylint: disable=line-too-long + broadcasted_tree, other_broadcasted_tree = tree_broadcast_common( + tree, + other_tree, + is_leaf=is_leaf, + none_is_leaf=none_is_leaf, + namespace=namespace, + ) + + broadcasted_leaves: list[T] = [] + other_broadcasted_leaves: list[T] = [] + + def add_leaves(x: T, y: T) -> None: + broadcasted_leaves.append(x) + other_broadcasted_leaves.append(y) + + tree_map_( + add_leaves, + broadcasted_tree, + other_broadcasted_tree, + is_leaf=is_leaf, + none_is_leaf=none_is_leaf, + namespace=namespace, + ) + return broadcasted_leaves, other_broadcasted_leaves + + +def _tree_broadcast_common( + tree: PyTree[T], + /, + *rests: PyTree[T], + is_leaf: Callable[[T], bool] | None = None, + none_is_leaf: bool = False, + namespace: str = '', +) -> tuple[PyTree[T], ...]: + if not rests: + return (tree,) + if len(rests) == 1: + return tree_broadcast_common( + tree, + rests[0], + is_leaf=is_leaf, + none_is_leaf=none_is_leaf, + namespace=namespace, + ) + + broadcasted_tree = tree + broadcasted_rests = list(rests) + for _ in range(2): + for i, rest in enumerate(rests): + broadcasted_tree, broadcasted_rests[i] = tree_broadcast_common( + broadcasted_tree, + rest, + is_leaf=is_leaf, + none_is_leaf=none_is_leaf, + namespace=namespace, + ) + + return (broadcasted_tree, *broadcasted_rests) + + +# pylint: disable-next=too-many-locals +def tree_broadcast_map( + func: Callable[..., U], + tree: PyTree[T], + /, + *rests: PyTree[T], + is_leaf: Callable[[T], bool] | None = None, + none_is_leaf: bool = False, + namespace: str = '', +) -> PyTree[U]: + """Map a multi-input function over pytree args to produce a new pytree. + + See also :func:`tree_broadcast_map_with_path`, :func:`tree_map`, :func:`tree_map_`, + and :func:`tree_map_with_path`. + + If only one input is provided, this function is the same as :func:`tree_map`: + + >>> tree_broadcast_map(lambda x: x + 1, {'x': 7, 'y': (42, 64)}) + {'x': 8, 'y': (43, 65)} + >>> tree_broadcast_map(lambda x: x + 1, {'x': 7, 'y': (42, 64), 'z': None}) + {'x': 8, 'y': (43, 65), 'z': None} + >>> tree_broadcast_map(lambda x: x is None, {'x': 7, 'y': (42, 64), 'z': None}) + {'x': False, 'y': (False, False), 'z': None} + >>> tree_broadcast_map(lambda x: x is None, {'x': 7, 'y': (42, 64), 'z': None}, none_is_leaf=True) + {'x': False, 'y': (False, False), 'z': True} + + If multiple inputs are given, all input trees will be broadcasted to the common suffix structure + of all inputs: + + >>> tree_broadcast_map(lambda x, y: x * y, [5, 6, (3, 4)], [{'a': 7, 'b': 9}, [1, 2], 8]) + [{'a': 35, 'b': 45}, [6, 12], (24, 32)] + + Args: + func (callable): A function that takes ``1 + len(rests)`` arguments, to be applied at the + corresponding leaves of the pytrees. + tree (pytree): A pytree to be mapped over, with each leaf providing the first positional + argument to function ``func``. + rests (tuple of pytree): A tuple of pytrees, they should have a common suffix structure with + each other and with ``tree``. + is_leaf (callable, optional): An optionally specified function that will be called at each + flattening step. It should return a boolean, with :data:`True` stopping the traversal + and the whole subtree being treated as a leaf, and :data:`False` indicating the + flattening should traverse the current object. + none_is_leaf (bool, optional): Whether to treat :data:`None` as a leaf. If :data:`False`, + :data:`None` is a non-leaf node with arity 0. Thus :data:`None` is contained in the + treespec rather than in the leaves list and :data:`None` will remain in the result + pytree. (default: :data:`False`) + namespace (str, optional): The registry namespace used for custom pytree node types. + (default: :const:`''`, i.e., the global namespace) + + Returns: + A new pytree with the structure as the common suffix structure of ``tree`` and ``rests`` but + with the value at each leaf given by ``func(x, *xs)`` where ``x`` is the value at the + corresponding leaf (may be broadcasted) in ``tree`` and ``xs`` is the tuple of values at + corresponding leaves (may be broadcasted) in ``rests``. + """ + return tree_map( + func, + *_tree_broadcast_common( + tree, + *rests, + is_leaf=is_leaf, + none_is_leaf=none_is_leaf, + namespace=namespace, + ), + is_leaf=is_leaf, + none_is_leaf=none_is_leaf, + namespace=namespace, + ) + + +# pylint: disable-next=too-many-locals +def tree_broadcast_map_with_path( + func: Callable[..., U], + tree: PyTree[T], + /, + *rests: PyTree[T], + is_leaf: Callable[[T], bool] | None = None, + none_is_leaf: bool = False, + namespace: str = '', +) -> PyTree[U]: + """Map a multi-input function over pytree args as well as the tree paths to produce a new pytree. + + See also :func:`tree_broadcast_map`, :func:`tree_map`, :func:`tree_map_`, + and :func:`tree_map_with_path`. + + If only one input is provided, this function is the same as :func:`tree_map`: + + >>> tree_broadcast_map_with_path(lambda p, x: (len(p), x), {'x': 7, 'y': (42, 64)}) + {'x': (1, 7), 'y': ((2, 42), (2, 64))} + >>> tree_broadcast_map_with_path(lambda p, x: x + len(p), {'x': 7, 'y': (42, 64), 'z': None}) + {'x': 8, 'y': (44, 66), 'z': None} + >>> tree_broadcast_map_with_path(lambda p, x: p, {'x': 7, 'y': (42, 64), 'z': {1.5: None}}) + {'x': ('x',), 'y': (('y', 0), ('y', 1)), 'z': {1.5: None}} + >>> tree_broadcast_map_with_path(lambda p, x: p, {'x': 7, 'y': (42, 64), 'z': {1.5: None}}, none_is_leaf=True) + {'x': ('x',), 'y': (('y', 0), ('y', 1)), 'z': {1.5: ('z', 1.5)}} + + If multiple inputs are given, all input trees will be broadcasted to the common suffix structure + of all inputs: + + >>> tree_broadcast_map_with_path( # doctest: +IGNORE_WHITESPACE + ... lambda p, x, y: (p, x * y), + ... [5, 6, (3, 4)], + ... [{'a': 7, 'b': 9}, [1, 2], 8], + ... ) + [ + {'a': ((0, 'a'), 35), 'b': ((0, 'b'), 45)}, + [((1, 0), 6), ((1, 1), 12)], + (((2, 0), 24), ((2, 1), 32)) + ] + + Args: + func (callable): A function that takes ``2 + len(rests)`` arguments, to be applied at the + corresponding leaves of the pytrees with extra paths. + tree (pytree): A pytree to be mapped over, with each leaf providing the first positional + argument to function ``func``. + rests (tuple of pytree): A tuple of pytrees, they should have a common suffix structure with + each other and with ``tree``. + is_leaf (callable, optional): An optionally specified function that will be called at each + flattening step. It should return a boolean, with :data:`True` stopping the traversal + and the whole subtree being treated as a leaf, and :data:`False` indicating the + flattening should traverse the current object. + none_is_leaf (bool, optional): Whether to treat :data:`None` as a leaf. If :data:`False`, + :data:`None` is a non-leaf node with arity 0. Thus :data:`None` is contained in the + treespec rather than in the leaves list and :data:`None` will remain in the result + pytree. (default: :data:`False`) + namespace (str, optional): The registry namespace used for custom pytree node types. + (default: :const:`''`, i.e., the global namespace) + + Returns: + A new pytree with the structure as the common suffix structure of ``tree`` and ``rests`` but + with the value at each leaf given by ``func(p, x, *xs)`` where ``(p, x)`` are the path and + value at the corresponding leaf (may be broadcasted) in ``tree`` and ``xs`` is the tuple of + values at corresponding leaves (may be broadcasted) in ``rests``. + """ + return tree_map_with_path( + func, + *_tree_broadcast_common( + tree, + *rests, + is_leaf=is_leaf, + none_is_leaf=none_is_leaf, + namespace=namespace, + ), + is_leaf=is_leaf, + none_is_leaf=none_is_leaf, + namespace=namespace, + ) + + +def tree_broadcast_map_with_accessor( + func: Callable[..., U], + tree: PyTree[T], + /, + *rests: PyTree[T], + is_leaf: Callable[[T], bool] | None = None, + none_is_leaf: bool = False, + namespace: str = '', +) -> PyTree[U]: + """Map a multi-input function over pytree args as well as the tree accessors to produce a new pytree. + + See also :func:`tree_broadcast_map`, :func:`tree_map`, :func:`tree_map_`, + and :func:`tree_map_with_accessor`. + + If only one input is provided, this function is the same as :func:`tree_map`: + + >>> tree_broadcast_map_with_accessor(lambda a, x: (len(a), x), {'x': 7, 'y': (42, 64)}) + {'x': (1, 7), 'y': ((2, 42), (2, 64))} + >>> tree_broadcast_map_with_accessor(lambda a, x: x + len(a), {'x': 7, 'y': (42, 64), 'z': None}) + {'x': 8, 'y': (44, 66), 'z': None} + >>> tree_broadcast_map_with_accessor( # doctest: +IGNORE_WHITESPACE + ... lambda a, x: a.codify('tree'), + ... {'x': 7, 'y': (42, 64), 'z': {1.5: None}}, + ... ) + { + 'x': "tree['x']", + 'y': ("tree['y'][0]", "tree['y'][1]"), + 'z': {1.5: None} + } + >>> tree_broadcast_map_with_accessor( # doctest: +IGNORE_WHITESPACE + ... lambda a, x: a.codify('tree'), + ... {'x': 7, 'y': (42, 64), 'z': {1.5: None}}, + ... none_is_leaf=True, + ... ) + { + 'x': "tree['x']", + 'y': ("tree['y'][0]", "tree['y'][1]"), + 'z': {1.5: "tree['z'][1.5]"} + } + + If multiple inputs are given, all input trees will be broadcasted to the common suffix structure + of all inputs: + + >>> tree_broadcast_map_with_accessor( # doctest: +IGNORE_WHITESPACE + ... lambda a, x, y: f'{a.codify("tree")} = {x * y}', + ... [5, 6, (3, 4)], + ... [{'a': 7, 'b': 9}, [1, 2], 8], + ... ) + [ + {'a': "tree[0]['a'] = 35", 'b': "tree[0]['b'] = 45"}, + ['tree[1][0] = 6', 'tree[1][1] = 12'], + ('tree[2][0] = 24', 'tree[2][1] = 32') + ] + + Args: + func (callable): A function that takes ``2 + len(rests)`` arguments, to be applied at the + corresponding leaves of the pytrees with extra accessors. + tree (pytree): A pytree to be mapped over, with each leaf providing the first positional + argument to function ``func``. + rests (tuple of pytree): A tuple of pytrees, they should have a common suffix structure with + each other and with ``tree``. + is_leaf (callable, optional): An optionally specified function that will be called at each + flattening step. It should return a boolean, with :data:`True` stopping the traversal + and the whole subtree being treated as a leaf, and :data:`False` indicating the + flattening should traverse the current object. + none_is_leaf (bool, optional): Whether to treat :data:`None` as a leaf. If :data:`False`, + :data:`None` is a non-leaf node with arity 0. Thus :data:`None` is contained in the + treespec rather than in the leaves list and :data:`None` will remain in the result + pytree. (default: :data:`False`) + namespace (str, optional): The registry namespace used for custom pytree node types. + (default: :const:`''`, i.e., the global namespace) + + Returns: + A new pytree with the structure as the common suffix structure of ``tree`` and ``rests`` but + with the value at each leaf given by ``func(a, x, *xs)`` where ``(a, x)`` are the accessor + and value at the corresponding leaf (may be broadcasted) in ``tree`` and ``xs`` is the tuple + of values at corresponding leaves (may be broadcasted) in ``rests``. + """ + return tree_map_with_accessor( + func, + *_tree_broadcast_common( + tree, + *rests, + is_leaf=is_leaf, + none_is_leaf=none_is_leaf, + namespace=namespace, + ), + is_leaf=is_leaf, + none_is_leaf=none_is_leaf, + namespace=namespace, + ) + + +# pylint: disable-next=missing-class-docstring,too-few-public-methods +class MissingSentinel: # pragma: no cover + __slots__: ClassVar[tuple[()]] = () + + def __repr__(self) -> str: + return '' + + +__MISSING: T = MissingSentinel() # type: ignore[valid-type] +del MissingSentinel + + +@overload +def tree_reduce( + func: Callable[[T, T], T], + tree: PyTree[T], + /, + *, + is_leaf: Callable[[T], bool] | None = None, + none_is_leaf: bool = False, + namespace: str = '', +) -> T: ... + + +@overload +def tree_reduce( + func: Callable[[T, S], T], + tree: PyTree[S], + /, + initial: T = __MISSING, + *, + is_leaf: Callable[[S], bool] | None = None, + none_is_leaf: bool = False, + namespace: str = '', +) -> T: ... + + +def tree_reduce( + func: Callable[[T, S], T], + tree: PyTree[S], + /, + initial: T = __MISSING, + *, + is_leaf: Callable[[S], bool] | None = None, + none_is_leaf: bool = False, + namespace: str = '', +) -> T: + """Traversal through a pytree and reduce the leaves in left-to-right depth-first order. + + See also :func:`tree_leaves` and :func:`tree_sum`. + + >>> tree_reduce(lambda x, y: x + y, {'x': 1, 'y': (2, 3)}) + 6 + >>> tree_reduce(lambda x, y: x + y, {'x': 1, 'y': (2, None), 'z': 3}) # `None` is a non-leaf node with arity 0 by default + 6 + >>> tree_reduce(lambda x, y: x and y, {'x': 1, 'y': (2, None), 'z': 3}) + 3 + >>> tree_reduce(lambda x, y: x and y, {'x': 1, 'y': (2, None), 'z': 3}, none_is_leaf=True) + None + + Args: + func (callable): A function that takes two arguments and returns a value of the same type. + tree (pytree): A pytree to be traversed. + initial (object, optional): An initial value to be used for the reduction. If not provided, + the first leaf value is used as the initial value. + is_leaf (callable, optional): An optionally specified function that will be called at each + flattening step. It should return a boolean, with :data:`True` stopping the traversal + and the whole subtree being treated as a leaf, and :data:`False` indicating the + flattening should traverse the current object. + none_is_leaf (bool, optional): Whether to treat :data:`None` as a leaf. If :data:`False`, + :data:`None` is a non-leaf node with arity 0. Thus :data:`None` is contained in the + treespec rather than in the leaves list and :data:`None` will remain in the result + pytree. (default: :data:`False`) + namespace (str, optional): The registry namespace used for custom pytree node types. + (default: :const:`''`, i.e., the global namespace) + + Returns: + The result of reducing the leaves of the pytree using ``func``. + """ # pylint: disable=line-too-long + leaves = tree_leaves(tree, is_leaf=is_leaf, none_is_leaf=none_is_leaf, namespace=namespace) + if initial is __MISSING: + return functools.reduce(func, leaves) # type: ignore[arg-type,return-value] + return functools.reduce(func, leaves, initial) + + +def tree_sum( + tree: PyTree[T], + /, + start: T = 0, # type: ignore[assignment] + *, + is_leaf: Callable[[T], bool] | None = None, + none_is_leaf: bool = False, + namespace: str = '', +) -> T: + """Sum ``start`` and leaf values in ``tree`` in left-to-right depth-first order and return the total. + + See also :func:`tree_leaves` and :func:`tree_reduce`. + + >>> tree_sum({'x': 1, 'y': (2, 3)}) + 6 + >>> tree_sum({'x': 1, 'y': (2, None), 'z': 3}) # `None` is a non-leaf node with arity 0 by default + 6 + >>> tree_sum({'x': 1, 'y': (2, None), 'z': 3}, none_is_leaf=True) + Traceback (most recent call last): + ... + TypeError: unsupported operand type(s) for +: 'int' and 'NoneType' + >>> tree_sum({'x': 'a', 'y': ('b', None), 'z': 'c'}, start='') + 'abc' + >>> tree_sum({'x': [1], 'y': ([2], [None]), 'z': [3]}, start=[], is_leaf=lambda x: isinstance(x, list)) + [1, 2, None, 3] + + Args: + tree (pytree): A pytree to be traversed. + start (object, optional): An initial value to be used for the sum. (default: :data:`0`) + is_leaf (callable, optional): An optionally specified function that will be called at each + flattening step. It should return a boolean, with :data:`True` stopping the traversal + and the whole subtree being treated as a leaf, and :data:`False` indicating the + flattening should traverse the current object. + none_is_leaf (bool, optional): Whether to treat :data:`None` as a leaf. If :data:`False`, + :data:`None` is a non-leaf node with arity 0. Thus :data:`None` is contained in the + treespec rather than in the leaves list and :data:`None` will remain in the result + pytree. (default: :data:`False`) + namespace (str, optional): The registry namespace used for custom pytree node types. + (default: :const:`''`, i.e., the global namespace) + + Returns: + The total sum of ``start`` and leaf values in ``tree``. + """ + leaves = tree_leaves(tree, is_leaf=is_leaf, none_is_leaf=none_is_leaf, namespace=namespace) + # sum() rejects string values for `start` parameter + if isinstance(start, str): + return ''.join([start, *leaves]) # type: ignore[list-item,return-value] + if isinstance(start, (bytes, bytearray)): + return b''.join([start, *leaves]) # type: ignore[list-item,return-value] + return sum(leaves, start) # type: ignore[call-overload] + + +@overload +def tree_max( + tree: PyTree[T], + /, + *, + is_leaf: Callable[[T], bool] | None = None, + key: Callable[[T], Any] | None = None, + none_is_leaf: bool = False, + namespace: str = '', +) -> T: ... + + +@overload +def tree_max( + tree: PyTree[T], + /, + *, + default: T = __MISSING, + key: Callable[[T], Any] | None = None, + is_leaf: Callable[[T], bool] | None = None, + none_is_leaf: bool = False, + namespace: str = '', +) -> T: ... + + +def tree_max( + tree: PyTree[T], + /, + *, + default: T = __MISSING, + key: Callable[[T], Any] | None = None, + is_leaf: Callable[[T], bool] | None = None, + none_is_leaf: bool = False, + namespace: str = '', +) -> T: + """Return the maximum leaf value in ``tree``. + + See also :func:`tree_leaves` and :func:`tree_min`. + + >>> tree_max({}) + Traceback (most recent call last): + ... + ValueError: max() iterable argument is empty + >>> tree_max({}, default=0) + 0 + >>> tree_max({'x': 0, 'y': (2, 1)}) + 2 + >>> tree_max({'x': 0, 'y': (2, 1)}, key=lambda x: -x) + 0 + >>> tree_max({'a': None}) # `None` is a non-leaf node with arity 0 by default + Traceback (most recent call last): + ... + ValueError: max() iterable argument is empty + >>> tree_max({'a': None}, default=0) # `None` is a non-leaf node with arity 0 by default + 0 + >>> tree_max({'a': None}, none_is_leaf=True) + None + >>> tree_max(None) # `None` is a non-leaf node with arity 0 by default + Traceback (most recent call last): + ... + ValueError: max() iterable argument is empty + >>> tree_max(None, default=0) + 0 + >>> tree_max(None, none_is_leaf=True) + None + + Args: + tree (pytree): A pytree to be traversed. + default (object, optional): The default value to return if ``tree`` is empty. If the ``tree`` + is empty and ``default`` is not specified, raise a :exc:`ValueError`. + key (callable or None, optional): A one-argument ordering function like that used for + :meth:`list.sort`. + is_leaf (callable, optional): An optionally specified function that will be called at each + flattening step. It should return a boolean, with :data:`True` stopping the traversal + and the whole subtree being treated as a leaf, and :data:`False` indicating the + flattening should traverse the current object. + none_is_leaf (bool, optional): Whether to treat :data:`None` as a leaf. If :data:`False`, + :data:`None` is a non-leaf node with arity 0. Thus :data:`None` is contained in the + treespec rather than in the leaves list and :data:`None` will remain in the result + pytree. (default: :data:`False`) + namespace (str, optional): The registry namespace used for custom pytree node types. + (default: :const:`''`, i.e., the global namespace) + + Returns: + The maximum leaf value in ``tree``. + """ + leaves = tree_leaves(tree, is_leaf=is_leaf, none_is_leaf=none_is_leaf, namespace=namespace) + if default is __MISSING: + return max(leaves, key=key) # type: ignore[type-var,arg-type] + return max(leaves, default=default, key=key) # type: ignore[type-var,arg-type] + + +@overload +def tree_min( + tree: PyTree[T], + /, + *, + key: Callable[[T], Any] | None = None, + is_leaf: Callable[[T], bool] | None = None, + none_is_leaf: bool = False, + namespace: str = '', +) -> T: ... + + +@overload +def tree_min( + tree: PyTree[T], + /, + *, + default: T = __MISSING, + key: Callable[[T], Any] | None = None, + is_leaf: Callable[[T], bool] | None = None, + none_is_leaf: bool = False, + namespace: str = '', +) -> T: ... + + +def tree_min( + tree: PyTree[T], + /, + *, + default: T = __MISSING, + key: Callable[[T], Any] | None = None, + is_leaf: Callable[[T], bool] | None = None, + none_is_leaf: bool = False, + namespace: str = '', +) -> T: + """Return the minimum leaf value in ``tree``. + + See also :func:`tree_leaves` and :func:`tree_max`. + + >>> tree_min({}) + Traceback (most recent call last): + ... + ValueError: min() iterable argument is empty + >>> tree_min({}, default=0) + 0 + >>> tree_min({'x': 0, 'y': (2, 1)}) + 0 + >>> tree_min({'x': 0, 'y': (2, 1)}, key=lambda x: -x) + 2 + >>> tree_min({'a': None}) # `None` is a non-leaf node with arity 0 by default + Traceback (most recent call last): + ... + ValueError: min() iterable argument is empty + >>> tree_min({'a': None}, default=0) # `None` is a non-leaf node with arity 0 by default + 0 + >>> tree_min({'a': None}, none_is_leaf=True) + None + >>> tree_min(None) # `None` is a non-leaf node with arity 0 by default + Traceback (most recent call last): + ... + ValueError: min() iterable argument is empty + >>> tree_min(None, default=0) + 0 + >>> tree_min(None, none_is_leaf=True) + None + + Args: + tree (pytree): A pytree to be traversed. + default (object, optional): The default value to return if ``tree`` is empty. If the ``tree`` + is empty and ``default`` is not specified, raise a :exc:`ValueError`. + key (callable or None, optional): A one-argument ordering function like that used for + :meth:`list.sort`. + is_leaf (callable, optional): An optionally specified function that will be called at each + flattening step. It should return a boolean, with :data:`True` stopping the traversal + and the whole subtree being treated as a leaf, and :data:`False` indicating the + flattening should traverse the current object. + none_is_leaf (bool, optional): Whether to treat :data:`None` as a leaf. If :data:`False`, + :data:`None` is a non-leaf node with arity 0. Thus :data:`None` is contained in the + treespec rather than in the leaves list and :data:`None` will remain in the result + pytree. (default: :data:`False`) + namespace (str, optional): The registry namespace used for custom pytree node types. + (default: :const:`''`, i.e., the global namespace) + + Returns: + The minimum leaf value in ``tree``. + """ + leaves = tree_leaves(tree, is_leaf=is_leaf, none_is_leaf=none_is_leaf, namespace=namespace) + if default is __MISSING: + return min(leaves, key=key) # type: ignore[type-var,arg-type] + return min(leaves, default=default, key=key) # type: ignore[type-var,arg-type] + + +def tree_all( + tree: PyTree[T], + /, + *, + is_leaf: Callable[[T], bool] | None = None, + none_is_leaf: bool = False, + namespace: str = '', +) -> bool: + """Test whether all leaves in ``tree`` are true (or if ``tree`` is empty). + + See also :func:`tree_leaves` and :func:`tree_any`. + + >>> tree_all({}) + True + >>> tree_all({'x': 1, 'y': (2, 3)}) + True + >>> tree_all({'x': 1, 'y': (2, None), 'z': 3}) # `None` is a non-leaf node with arity 0 by default + True + >>> tree_all({'x': 1, 'y': (2, None), 'z': 3}, none_is_leaf=True) + False + >>> tree_all(None) # `None` is a non-leaf node with arity 0 by default + True + >>> tree_all(None, none_is_leaf=True) + False + + Args: + tree (pytree): A pytree to be traversed. + is_leaf (callable, optional): An optionally specified function that will be called at each + flattening step. It should return a boolean, with :data:`True` stopping the traversal + and the whole subtree being treated as a leaf, and :data:`False` indicating the + flattening should traverse the current object. + none_is_leaf (bool, optional): Whether to treat :data:`None` as a leaf. If :data:`False`, + :data:`None` is a non-leaf node with arity 0. Thus :data:`None` is contained in the + treespec rather than in the leaves list and :data:`None` will remain in the result + pytree. (default: :data:`False`) + namespace (str, optional): The registry namespace used for custom pytree node types. + (default: :const:`''`, i.e., the global namespace) + + Returns: + :data:`True` if all leaves in ``tree`` are true, or if ``tree`` is empty. + Otherwise, :data:`False`. + """ + return all( + tree_iter( + tree, # type: ignore[arg-type] + is_leaf=is_leaf, # type: ignore[arg-type] + none_is_leaf=none_is_leaf, + namespace=namespace, + ), + ) + + +def tree_any( + tree: PyTree[T], + /, + *, + is_leaf: Callable[[T], bool] | None = None, + none_is_leaf: bool = False, + namespace: str = '', +) -> bool: + """Test whether any leaves in ``tree`` are true (or :data:`False` if ``tree`` is empty). + + See also :func:`tree_leaves` and :func:`tree_all`. + + >>> tree_any({}) + False + >>> tree_any({'x': 0, 'y': (2, 0)}) + True + >>> tree_any({'a': None}) # `None` is a non-leaf node with arity 0 by default + False + >>> tree_any({'a': None}, none_is_leaf=True) # `None` is evaluated as false + False + >>> tree_any(None) # `None` is a non-leaf node with arity 0 by default + False + >>> tree_any(None, none_is_leaf=True) # `None` is evaluated as false + False + + Args: + tree (pytree): A pytree to be traversed. + is_leaf (callable, optional): An optionally specified function that will be called at each + flattening step. It should return a boolean, with :data:`True` stopping the traversal + and the whole subtree being treated as a leaf, and :data:`False` indicating the + flattening should traverse the current object. + none_is_leaf (bool, optional): Whether to treat :data:`None` as a leaf. If :data:`False`, + :data:`None` is a non-leaf node with arity 0. Thus :data:`None` is contained in the + treespec rather than in the leaves list and :data:`None` will remain in the result + pytree. (default: :data:`False`) + namespace (str, optional): The registry namespace used for custom pytree node types. + (default: :const:`''`, i.e., the global namespace) + + Returns: + :data:`True` if any leaves in ``tree`` are true. Otherwise, :data:`False`. + If ``tree`` is empty, return :data:`False`. + """ + return any( + tree_iter( + tree, # type: ignore[arg-type] + is_leaf=is_leaf, # type: ignore[arg-type] + none_is_leaf=none_is_leaf, + namespace=namespace, + ), + ) + + +class FlattenOneLevelOutput(NamedTuple, Generic[T]): + """The output of :func:`tree_flatten_one_level`.""" + + children: list[PyTree[T]] + """A list of one-level children of the pytree node.""" + + metadata: MetaData + """The metadata used to reconstruct the pytree node.""" + + entries: tuple[Any, ...] + """A tuple of path entries to the children.""" + + unflatten_func: UnflattenFunc[PyTree[T]] + """A function that can be used to unflatten the metadata and children back to the pytree node.""" + + +# Subclass the namedtuple class to allow assigning new attributes. +class FlattenOneLevelOutputEx(FlattenOneLevelOutput[T]): + """The output of :func:`tree_flatten_one_level`.""" + + type: builtins.type[Collection[T]] + """The type of the pytree node.""" + + path_entry_type: builtins.type[PyTreeEntry] + """The type of the path entry for the pytree node.""" + + kind: PyTreeKind + """The kind of the pytree node.""" + + +def tree_flatten_one_level( + tree: PyTree[T], + /, + is_leaf: Callable[[T], bool] | None = None, + *, + none_is_leaf: bool = False, + namespace: str = '', +) -> FlattenOneLevelOutputEx[T]: + """Flatten the pytree one level, returning a 4-tuple of children, metadata, path entries, and an unflatten function. + + See also :func:`tree_flatten` and :func:`tree_flatten_with_path`. + + >>> children, metadata, entries, unflatten_func = tree_flatten_one_level({'b': (2, [3, 4]), 'a': 1, 'c': None, 'd': 5}) + >>> children, metadata, entries + ([1, (2, [3, 4]), None, 5], ['a', 'b', 'c', 'd'], ('a', 'b', 'c', 'd')) + >>> unflatten_func(metadata, children) + {'a': 1, 'b': (2, [3, 4]), 'c': None, 'd': 5} + >>> children, metadata, entries, unflatten_func = tree_flatten_one_level([{'a': 1, 'b': (2, 3)}, (4, 5)]) + >>> children, metadata, entries + ([{'a': 1, 'b': (2, 3)}, (4, 5)], None, (0, 1)) + >>> unflatten_func(metadata, children) + [{'a': 1, 'b': (2, 3)}, (4, 5)] + + Args: + tree (pytree): A pytree to be traversed. + is_leaf (callable, optional): An optionally specified function that will be called at each + flattening step. It should return a boolean, with :data:`True` stopping the traversal + and the whole subtree being treated as a leaf, and :data:`False` indicating the + flattening should traverse the current object. + none_is_leaf (bool, optional): Whether to treat :data:`None` as a leaf. If :data:`False`, + :data:`None` is a non-leaf node with arity 0. Thus :data:`None` is contained in the + treespec rather than in the leaves list and :data:`None` will remain in the result + pytree. (default: :data:`False`) + namespace (str, optional): The registry namespace used for custom pytree node types. + (default: :const:`''`, i.e., the global namespace) + + Returns: + A 4-tuple ``(children, metadata, entries, unflatten_func)``. The first element is a list of + one-level children of the pytree node. The second element is the metadata used to + reconstruct the pytree node. The third element is a tuple of path entries to the children. + The fourth element is a function that can be used to unflatten the metadata and + children back to the pytree node. + """ # pylint: disable=line-too-long + node_type = type(tree) + if (tree is None and none_is_leaf) or (is_leaf is not None and is_leaf(tree)): # type: ignore[unreachable,arg-type] + raise ValueError(f'Cannot flatten leaf-type: {node_type} (node: {tree!r}).') + + from optree.registry import register_pytree_node # pylint: disable=import-outside-toplevel + + handler = register_pytree_node.get(node_type, namespace=namespace) + if handler is None: + raise ValueError(f'Cannot flatten leaf-type: {node_type} (node: {tree!r}).') + + flattened = tuple(handler.flatten_func(tree)) + if len(flattened) == 2: + flattened = (*flattened, None) + elif len(flattened) != 3: + raise RuntimeError( + f'PyTree custom flatten function for type {node_type} should return a 2- or 3-tuple, ' + f'got {len(flattened)}.', + ) + flattened: tuple[Iterable[PyTree[T]], MetaData, Iterable[Any] | None] + children, metadata, entries = flattened + children = list(children) + entries = tuple(range(len(children)) if entries is None else entries) + if len(children) != len(entries): + raise RuntimeError( + f'PyTree custom flatten function for type {node_type} returned inconsistent ' + f'number of children ({len(children)}) and number of entries ({len(entries)}).', + ) + + output = FlattenOneLevelOutputEx( + children=children, + metadata=metadata, + entries=entries, + unflatten_func=handler.unflatten_func, + ) + output.type = node_type + output.path_entry_type = handler.path_entry_type + output.kind = handler.kind + return output + + +def treespec_paths(treespec: PyTreeSpec, /) -> list[tuple[Any, ...]]: + """Return a list of paths to the leaves of a treespec. + + See also :func:`tree_flatten_with_path`, :func:`tree_paths`, and :meth:`PyTreeSpec.paths`. + + >>> treespec = tree_structure({'b': 3, 'a': (0, [1, 2]), 'c': (4, None)}) + >>> treespec + PyTreeSpec({'a': (*, [*, *]), 'b': *, 'c': (*, None)}) + >>> treespec_paths(treespec) + [('a', 0), ('a', 1, 0), ('a', 1, 1), ('b',), ('c', 0)] + """ + return treespec.paths() + + +def treespec_accessors(treespec: PyTreeSpec, /) -> list[PyTreeAccessor]: + """Return a list of accessors to the leaves of a treespec. + + See also :func:`tree_flatten_with_accessor`, :func:`tree_accessors`, + and :meth:`PyTreeSpec.accessors`. + + >>> treespec = tree_structure({'b': 3, 'a': (0, [1, 2]), 'c': (4, None)}) + >>> treespec + PyTreeSpec({'a': (*, [*, *]), 'b': *, 'c': (*, None)}) + >>> treespec_accessors(treespec) # doctest: +IGNORE_WHITESPACE,ELLIPSIS + [ + PyTreeAccessor(*['a'][0], ...), + PyTreeAccessor(*['a'][1][0], ...), + PyTreeAccessor(*['a'][1][1], ...), + PyTreeAccessor(*['b'], ...), + PyTreeAccessor(*['c'][0], ...) + ] + >>> treespec_accessors(treespec_leaf()) + [PyTreeAccessor(*, ())] + >>> treespec_accessors(treespec_none()) + [] + """ + return treespec.accessors() + + +def treespec_entries(treespec: PyTreeSpec, /) -> list[Any]: + """Return a list of one-level entries of a treespec to its children. + + See also :func:`treespec_entry`, :func:`treespec_paths`, :func:`treespec_children`, + and :meth:`PyTreeSpec.entries`. + + >>> treespec = tree_structure({'b': 3, 'a': (0, [1, 2]), 'c': (4, None)}) + >>> treespec + PyTreeSpec({'a': (*, [*, *]), 'b': *, 'c': (*, None)}) + >>> treespec_entries(treespec) + ['a', 'b', 'c'] + """ + return treespec.entries() + + +def treespec_entry(treespec: PyTreeSpec, index: int, /) -> Any: + """Return the entry of a treespec at the given index. + + See also :func:`treespec_entries`, :func:`treespec_children`, and :meth:`PyTreeSpec.entry`. + """ + return treespec.entry(index) + + +def treespec_children(treespec: PyTreeSpec, /) -> list[PyTreeSpec]: + """Return a list of treespecs for the children of a treespec. + + See also :func:`treespec_child`, :func:`treespec_paths`, :func:`treespec_entries`, + :func:`treespec_one_level`, and :meth:`PyTreeSpec.children`. + + >>> treespec = tree_structure({'b': 3, 'a': (0, [1, 2]), 'c': (4, None)}) + >>> treespec + PyTreeSpec({'a': (*, [*, *]), 'b': *, 'c': (*, None)}) + >>> treespec_children(treespec) + [PyTreeSpec((*, [*, *])), PyTreeSpec(*), PyTreeSpec((*, None))] + """ + return treespec.children() + + +def treespec_child(treespec: PyTreeSpec, index: int, /) -> PyTreeSpec: + """Return the treespec of the child of a treespec at the given index. + + See also :func:`treespec_children`, :func:`treespec_entries`, and :meth:`PyTreeSpec.child`. + """ + return treespec.child(index) + + +def treespec_one_level(treespec: PyTreeSpec, /) -> PyTreeSpec | None: + """Return the one-level tree structure of the treespec or :data:`None` if the treespec is a leaf. + + See also :func:`treespec_children`, :func:`treespec_is_one_level`, and :meth:`PyTreeSpec.one_level`. + + >>> treespec = tree_structure({'b': 3, 'a': (0, [1, 2]), 'c': (4, None)}) + >>> treespec + PyTreeSpec({'a': (*, [*, *]), 'b': *, 'c': (*, None)}) + >>> treespec_one_level(treespec) + PyTreeSpec({'a': *, 'b': *, 'c': *}) + """ + return treespec.one_level() + + +def treespec_transform( + treespec: PyTreeSpec, + /, + f_node: Callable[[PyTreeSpec], PyTreeSpec] | None = None, + f_leaf: Callable[[PyTreeSpec], PyTreeSpec] | None = None, +) -> PyTreeSpec: + """Transform a treespec by applying functions to its nodes and leaves. + + See also :func:`treespec_children`, :func:`treespec_is_leaf`, and :meth:`PyTreeSpec.transform`. + + >>> treespec = tree_structure({'b': 3, 'a': (0, [1, 2]), 'c': (4, None)}) + >>> treespec + PyTreeSpec({'a': (*, [*, *]), 'b': *, 'c': (*, None)}) + >>> treespec_transform(treespec, lambda spec: treespec_dict(zip(spec.entries(), spec.children()))) + PyTreeSpec({'a': {0: *, 1: {0: *, 1: *}}, 'b': *, 'c': {0: *, 1: {}}}) + >>> treespec_transform( + ... treespec, + ... lambda spec: ( + ... treespec_ordereddict(zip(spec.entries(), spec.children())) + ... if spec.type is dict + ... else spec + ... ), + ... ) + PyTreeSpec(OrderedDict({'a': (*, [*, *]), 'b': *, 'c': (*, None)})) + >>> treespec_transform( + ... treespec, + ... lambda spec: ( + ... treespec_ordereddict(tree_unflatten(spec, spec.children())) + ... if spec.type is dict + ... else spec + ... ), + ... ) + PyTreeSpec(OrderedDict({'b': (*, [*, *]), 'a': *, 'c': (*, None)})) + >>> treespec_transform(treespec, lambda spec: treespec_tuple(spec.children())) + PyTreeSpec(((*, (*, *)), *, (*, ()))) + >>> treespec_transform( + ... treespec, + ... lambda spec: ( + ... treespec_list(spec.children()) + ... if spec.type is tuple + ... else spec + ... ), + ... ) + PyTreeSpec({'a': [*, [*, *]], 'b': *, 'c': [*, None]}) + >>> treespec_transform(treespec, None, lambda spec: tree_structure((1, [2]))) + PyTreeSpec({'a': ((*, [*]), [(*, [*]), (*, [*])]), 'b': (*, [*]), 'c': ((*, [*]), None)}) + + Args: + treespec (PyTreeSpec): A treespec to transform. + f_node (callable, optional): A function to apply to each non-leaf node. It takes a treespec + and returns a new treespec. If :data:`None`, the node is left unchanged. + (default: :data:`None`) + f_leaf (callable, optional): A function to apply to each leaf node. It takes a treespec and + returns a new treespec. If :data:`None`, the leaf is left unchanged. + (default: :data:`None`) + + Returns: + A new treespec with the transformations applied. + """ + return treespec.transform(f_node, f_leaf) + + +def treespec_is_leaf(treespec: PyTreeSpec, /, *, strict: bool = True) -> bool: + """Return whether the treespec is a leaf that has no children. + + See also :func:`treespec_is_strict_leaf` and :meth:`PyTreeSpec.is_leaf`. + + This function is equivalent to ``treespec.is_leaf(strict=strict)``. If ``strict=True``, it will + return :data:`True` if and only if the treespec represents a strict leaf. If ``strict=False``, + it will return :data:`True` if the treespec represents a strict leaf or :data:`None` or an empty + container (e.g., an empty tuple). + + >>> treespec_is_leaf(tree_structure(1)) + True + >>> treespec_is_leaf(tree_structure((1, 2))) + False + >>> treespec_is_leaf(tree_structure(None)) + False + >>> treespec_is_leaf(tree_structure(None), strict=False) + True + >>> treespec_is_leaf(tree_structure(None, none_is_leaf=False)) + False + >>> treespec_is_leaf(tree_structure(None, none_is_leaf=True)) + True + >>> treespec_is_leaf(tree_structure(())) + False + >>> treespec_is_leaf(tree_structure(()), strict=False) + True + >>> treespec_is_leaf(tree_structure([])) + False + >>> treespec_is_leaf(tree_structure([]), strict=False) + True + + Args: + treespec (PyTreeSpec): A treespec. + strict (bool, optional): Whether not to treat :data:`None` or an empty + container (e.g., an empty tuple) as a leaf. (default: :data:`True`) + + Returns: + :data:`True` if the treespec represents a leaf that has no children, otherwise, :data:`False`. + """ + if strict: + return treespec.num_nodes == 1 and treespec.num_leaves == 1 + return treespec.num_nodes == 1 + + +def treespec_is_strict_leaf(treespec: PyTreeSpec, /) -> bool: + """Return whether the treespec is a strict leaf. + + See also :func:`treespec_is_leaf` and :meth:`PyTreeSpec.is_leaf`. + + This function respects the ``none_is_leaf`` setting in the treespec. It is equivalent to + ``treespec.is_leaf(strict=True)``. It will return :data:`True` if and only if the treespec + represents a strict leaf. + + >>> treespec_is_strict_leaf(tree_structure(1)) + True + >>> treespec_is_strict_leaf(tree_structure((1, 2))) + False + >>> treespec_is_strict_leaf(tree_structure(None)) + False + >>> treespec_is_strict_leaf(tree_structure(None, none_is_leaf=False)) + False + >>> treespec_is_strict_leaf(tree_structure(None, none_is_leaf=True)) + True + >>> treespec_is_strict_leaf(tree_structure(())) + False + >>> treespec_is_strict_leaf(tree_structure([])) + False + + Args: + treespec (PyTreeSpec): A treespec. + + Returns: + :data:`True` if the treespec represents a strict leaf, otherwise, :data:`False`. + """ + return treespec.num_nodes == 1 and treespec.num_leaves == 1 + + +def treespec_is_one_level(treespec: PyTreeSpec, /) -> bool: + """Return whether the treespec is a one-level tree structure. + + See also :func:`treespec_is_leaf`, :func:`treespec_one_level`, and :meth:`PyTreeSpec.is_one_level`. + + >>> treespec_is_one_level(tree_structure(1)) + False + >>> treespec_is_one_level(tree_structure((1, 2))) + True + >>> treespec_is_one_level(tree_structure({'a': 1, 'b': 2, 'c': 3})) + True + >>> treespec_is_one_level(tree_structure({'a': 1, 'b': (2, 3), 'c': 4})) + False + >>> treespec_is_one_level(tree_structure(None)) + True + """ + return ( + treespec.num_nodes == treespec.num_children + 1 + and treespec.num_leaves == treespec.num_children + ) + + +def treespec_is_prefix( + treespec: PyTreeSpec, + other_treespec: PyTreeSpec, + /, + *, + strict: bool = False, +) -> bool: + """Return whether ``treespec`` is a prefix of ``other_treespec``. + + See also :func:`treespec_is_suffix` and :meth:`PyTreeSpec.is_prefix`. + + Args: + treespec (PyTreeSpec): A treespec. + other_treespec (PyTreeSpec): Another treespec to compare against. + strict (bool, optional): If :data:`True`, the treespec must be a strict prefix (not equal). + (default: :data:`False`) + + Returns: + :data:`True` if ``treespec`` is a prefix of ``other_treespec``, otherwise, :data:`False`. + """ + return treespec.is_prefix(other_treespec, strict=strict) + + +def treespec_is_suffix( + treespec: PyTreeSpec, + other_treespec: PyTreeSpec, + /, + *, + strict: bool = False, +) -> bool: + """Return whether ``treespec`` is a suffix of ``other_treespec``. + + See also :func:`treespec_is_prefix` and :meth:`PyTreeSpec.is_suffix`. + + Args: + treespec (PyTreeSpec): A treespec. + other_treespec (PyTreeSpec): Another treespec to compare against. + strict (bool, optional): If :data:`True`, the treespec must be a strict suffix (not equal). + (default: :data:`False`) + + Returns: + :data:`True` if ``treespec`` is a suffix of ``other_treespec``, otherwise, :data:`False`. + """ + return treespec.is_suffix(other_treespec, strict=strict) + + +def treespec_leaf( + *, + none_is_leaf: bool = False, + namespace: str = '', # unused +) -> PyTreeSpec: + """Make a treespec representing a leaf node. + + See also :func:`tree_structure`, :func:`treespec_none`, and :func:`treespec_tuple`. + + >>> treespec_leaf() + PyTreeSpec(*) + >>> treespec_leaf(none_is_leaf=True) + PyTreeSpec(*, NoneIsLeaf) + >>> treespec_leaf(none_is_leaf=False) == treespec_leaf(none_is_leaf=True) + False + >>> treespec_leaf() == tree_structure(1) + True + >>> treespec_leaf(none_is_leaf=True) == tree_structure(1, none_is_leaf=True) + True + >>> treespec_leaf(none_is_leaf=True) == tree_structure(None, none_is_leaf=True) + True + >>> treespec_leaf(none_is_leaf=True) == tree_structure(None, none_is_leaf=False) + False + >>> treespec_leaf(none_is_leaf=True) == treespec_none(none_is_leaf=True) + True + >>> treespec_leaf(none_is_leaf=True) == treespec_none(none_is_leaf=False) + False + >>> treespec_leaf(none_is_leaf=False) == treespec_none(none_is_leaf=True) + False + >>> treespec_leaf(none_is_leaf=False) == treespec_none(none_is_leaf=False) + False + + Args: + none_is_leaf (bool, optional): Whether to treat :data:`None` as a leaf. If :data:`False`, + :data:`None` is a non-leaf node with arity 0. Thus :data:`None` is contained in the + treespec rather than in the leaves list and :data:`None` will remain in the result + pytree. (default: :data:`False`) + namespace (str, optional): The registry namespace used for custom pytree node types. + (default: :const:`''`, i.e., the global namespace) + + Returns: + A treespec representing a leaf node. + """ + return _C.make_leaf( + none_is_leaf, + namespace, # unused + ) + + +def treespec_none( + *, + none_is_leaf: bool = False, + namespace: str = '', # unused +) -> PyTreeSpec: + """Make a treespec representing a :data:`None` node. + + See also :func:`tree_structure`, :func:`treespec_leaf`, and :func:`treespec_tuple`. + + >>> treespec_none() + PyTreeSpec(None) + >>> treespec_none(none_is_leaf=True) + PyTreeSpec(*, NoneIsLeaf) + >>> treespec_none(none_is_leaf=False) == treespec_none(none_is_leaf=True) + False + >>> treespec_none() == tree_structure(None) + True + >>> treespec_none() == tree_structure(1) + False + >>> treespec_none(none_is_leaf=True) == tree_structure(1, none_is_leaf=True) + True + >>> treespec_none(none_is_leaf=True) == tree_structure(None, none_is_leaf=True) + True + >>> treespec_none(none_is_leaf=True) == tree_structure(None, none_is_leaf=False) + False + >>> treespec_none(none_is_leaf=True) == treespec_leaf(none_is_leaf=True) + True + >>> treespec_none(none_is_leaf=False) == treespec_leaf(none_is_leaf=True) + False + >>> treespec_none(none_is_leaf=True) == treespec_leaf(none_is_leaf=False) + False + >>> treespec_none(none_is_leaf=False) == treespec_leaf(none_is_leaf=False) + False + + Args: + none_is_leaf (bool, optional): Whether to treat :data:`None` as a leaf. If :data:`False`, + :data:`None` is a non-leaf node with arity 0. Thus :data:`None` is contained in the + treespec rather than in the leaves list and :data:`None` will remain in the result + pytree. (default: :data:`False`) + namespace (str, optional): The registry namespace used for custom pytree node types. + (default: :const:`''`, i.e., the global namespace) + + Returns: + A treespec representing a :data:`None` node. + """ + return _C.make_none( + none_is_leaf, + namespace, # unused + ) + + +def treespec_tuple( + iterable: Iterable[PyTreeSpec] = (), + /, + *, + none_is_leaf: bool = False, + namespace: str = '', +) -> PyTreeSpec: + """Make a tuple treespec from an iterable of child treespecs. + + See also :func:`tree_structure`, :func:`treespec_leaf`, and :func:`treespec_none`. + + >>> treespec_tuple([treespec_leaf(), treespec_leaf()]) + PyTreeSpec((*, *)) + >>> treespec_tuple([treespec_leaf(), treespec_leaf(), treespec_none()]) + PyTreeSpec((*, *, None)) + >>> treespec_tuple() + PyTreeSpec(()) + >>> treespec_tuple([treespec_leaf(), treespec_tuple([treespec_leaf(), treespec_leaf()])]) + PyTreeSpec((*, (*, *))) + >>> treespec_tuple([treespec_leaf(), tree_structure({'a': 1, 'b': 2})]) + PyTreeSpec((*, {'a': *, 'b': *})) + >>> treespec_tuple([treespec_leaf(), tree_structure({'a': 1, 'b': 2}, none_is_leaf=True)]) + Traceback (most recent call last): + ... + ValueError: Expected treespec(s) with `none_is_leaf=False`. + + Args: + iterable (iterable of PyTreeSpec, optional): An iterable of child treespecs. They must have + the same ``none_is_leaf`` and ``namespace`` values. + none_is_leaf (bool, optional): Whether to treat :data:`None` as a leaf. If :data:`False`, + :data:`None` is a non-leaf node with arity 0. Thus :data:`None` is contained in the + treespec rather than in the leaves list and :data:`None` will remain in the result + pytree. (default: :data:`False`) + namespace (str, optional): The registry namespace used for custom pytree node types. + (default: :const:`''`, i.e., the global namespace) + + Returns: + A treespec representing a tuple node with the given children. + """ + return _C.make_from_collection( + tuple(iterable), + none_is_leaf, + namespace, + ) + + +def treespec_list( + iterable: Iterable[PyTreeSpec] = (), + /, + *, + none_is_leaf: bool = False, + namespace: str = '', +) -> PyTreeSpec: + """Make a list treespec from an iterable of child treespecs. + + See also :func:`tree_structure`, :func:`treespec_leaf`, and :func:`treespec_none`. + + >>> treespec_list([treespec_leaf(), treespec_leaf()]) + PyTreeSpec([*, *]) + >>> treespec_list([treespec_leaf(), treespec_leaf(), treespec_none()]) + PyTreeSpec([*, *, None]) + >>> treespec_list() + PyTreeSpec([]) + >>> treespec_list([treespec_leaf(), treespec_tuple([treespec_leaf(), treespec_leaf()])]) + PyTreeSpec([*, (*, *)]) + >>> treespec_list([treespec_leaf(), tree_structure({'a': 1, 'b': 2})]) + PyTreeSpec([*, {'a': *, 'b': *}]) + >>> treespec_list([treespec_leaf(), tree_structure({'a': 1, 'b': 2}, none_is_leaf=True)]) + Traceback (most recent call last): + ... + ValueError: Expected treespec(s) with `none_is_leaf=False`. + + Args: + iterable (iterable of PyTreeSpec, optional): An iterable of child treespecs. They must have + the same ``none_is_leaf`` and ``namespace`` values. + none_is_leaf (bool, optional): Whether to treat :data:`None` as a leaf. If :data:`False`, + :data:`None` is a non-leaf node with arity 0. Thus :data:`None` is contained in the + treespec rather than in the leaves list and :data:`None` will remain in the result + pytree. (default: :data:`False`) + namespace (str, optional): The registry namespace used for custom pytree node types. + (default: :const:`''`, i.e., the global namespace) + + Returns: + A treespec representing a list node with the given children. + """ + return _C.make_from_collection( + list(iterable), + none_is_leaf, + namespace, + ) + + +def treespec_dict( + mapping: Mapping[Any, PyTreeSpec] | Iterable[tuple[Any, PyTreeSpec]] = (), + /, + *, + none_is_leaf: bool = False, + namespace: str = '', + **kwargs: PyTreeSpec, +) -> PyTreeSpec: + """Make a dict treespec from a dict of child treespecs. + + See also :func:`tree_structure`, :func:`treespec_leaf`, and :func:`treespec_none`. + + >>> treespec_dict({'a': treespec_leaf(), 'b': treespec_leaf()}) + PyTreeSpec({'a': *, 'b': *}) + >>> treespec_dict([('b', treespec_leaf()), ('c', treespec_leaf()), ('a', treespec_none())]) + PyTreeSpec({'a': None, 'b': *, 'c': *}) + >>> treespec_dict() + PyTreeSpec({}) + >>> treespec_dict(a=treespec_leaf(), b=treespec_tuple([treespec_leaf(), treespec_leaf()])) + PyTreeSpec({'a': *, 'b': (*, *)}) + >>> treespec_dict({'a': treespec_leaf(), 'b': tree_structure([1, 2])}) + PyTreeSpec({'a': *, 'b': [*, *]}) + >>> treespec_dict({'a': treespec_leaf(), 'b': tree_structure([1, 2], none_is_leaf=True)}) + Traceback (most recent call last): + ... + ValueError: Expected treespec(s) with `none_is_leaf=False`. + + Args: + mapping (mapping of PyTreeSpec, optional): A mapping of child treespecs. They must have the + same ``none_is_leaf`` and ``namespace`` values. + none_is_leaf (bool, optional): Whether to treat :data:`None` as a leaf. If :data:`False`, + :data:`None` is a non-leaf node with arity 0. Thus :data:`None` is contained in the + treespec rather than in the leaves list and :data:`None` will remain in the result + pytree. (default: :data:`False`) + namespace (str, optional): The registry namespace used for custom pytree node types. + (default: :const:`''`, i.e., the global namespace) + **kwargs (PyTreeSpec, optional): Additional child treespecs to add to the mapping. + + Returns: + A treespec representing a dict node with the given children. + """ + return _C.make_from_collection( + dict(mapping, **kwargs), + none_is_leaf, + namespace, + ) + + +def treespec_namedtuple( + namedtuple: NamedTuple[PyTreeSpec], # type: ignore[type-arg] + /, + *, + none_is_leaf: bool = False, + namespace: str = '', +) -> PyTreeSpec: + """Make a namedtuple treespec from a namedtuple of child treespecs. + + See also :func:`tree_structure`, :func:`treespec_leaf`, and :func:`treespec_none`. + + >>> from collections import namedtuple + >>> Point = namedtuple('Point', ['x', 'y']) + >>> treespec_namedtuple(Point(x=treespec_leaf(), y=treespec_leaf())) + PyTreeSpec(Point(x=*, y=*)) + >>> treespec_namedtuple(Point(x=treespec_leaf(), y=treespec_tuple([treespec_leaf(), treespec_leaf()]))) + PyTreeSpec(Point(x=*, y=(*, *))) + >>> treespec_namedtuple(Point(x=treespec_leaf(), y=tree_structure([1, 2]))) + PyTreeSpec(Point(x=*, y=[*, *])) + >>> treespec_namedtuple(Point(x=treespec_leaf(), y=tree_structure([1, 2], none_is_leaf=True))) + Traceback (most recent call last): + ... + ValueError: Expected treespec(s) with `none_is_leaf=False`. + + Args: + namedtuple (namedtuple of PyTreeSpec): A namedtuple of child treespecs. They must have the + same ``none_is_leaf`` and ``namespace`` values. + none_is_leaf (bool, optional): Whether to treat :data:`None` as a leaf. If :data:`False`, + :data:`None` is a non-leaf node with arity 0. Thus :data:`None` is contained in the + treespec rather than in the leaves list and :data:`None` will remain in the result + pytree. (default: :data:`False`) + namespace (str, optional): The registry namespace used for custom pytree node types. + (default: :const:`''`, i.e., the global namespace) + + Returns: + A treespec representing a namedtuple node with the given children. + """ + if not is_namedtuple_instance(namedtuple): + raise ValueError(f'Expected a namedtuple of PyTreeSpec(s), got {namedtuple!r}.') + return _C.make_from_collection( + namedtuple, + none_is_leaf, + namespace, + ) + + +def treespec_ordereddict( + mapping: Mapping[Any, PyTreeSpec] | Iterable[tuple[Any, PyTreeSpec]] = (), + /, + *, + none_is_leaf: bool = False, + namespace: str = '', + **kwargs: PyTreeSpec, +) -> PyTreeSpec: + """Make an OrderedDict treespec from an OrderedDict of child treespecs. + + See also :func:`tree_structure`, :func:`treespec_leaf`, and :func:`treespec_none`. + + >>> treespec_ordereddict({'a': treespec_leaf(), 'b': treespec_leaf()}) + PyTreeSpec(OrderedDict({'a': *, 'b': *})) + >>> treespec_ordereddict([('b', treespec_leaf()), ('c', treespec_leaf()), ('a', treespec_none())]) + PyTreeSpec(OrderedDict({'b': *, 'c': *, 'a': None})) + >>> treespec_ordereddict() + PyTreeSpec(OrderedDict()) + >>> treespec_ordereddict(a=treespec_leaf(), b=treespec_tuple([treespec_leaf(), treespec_leaf()])) + PyTreeSpec(OrderedDict({'a': *, 'b': (*, *)})) + >>> treespec_ordereddict({'a': treespec_leaf(), 'b': tree_structure([1, 2])}) + PyTreeSpec(OrderedDict({'a': *, 'b': [*, *]})) + >>> treespec_ordereddict({'a': treespec_leaf(), 'b': tree_structure([1, 2], none_is_leaf=True)}) + Traceback (most recent call last): + ... + ValueError: Expected treespec(s) with `none_is_leaf=False`. + + Args: + mapping (mapping of PyTreeSpec, optional): A mapping of child treespecs. They must have the + same ``none_is_leaf`` and ``namespace`` values. + none_is_leaf (bool, optional): Whether to treat :data:`None` as a leaf. If :data:`False`, + :data:`None` is a non-leaf node with arity 0. Thus :data:`None` is contained in the + treespec rather than in the leaves list and :data:`None` will remain in the result + pytree. (default: :data:`False`) + namespace (str, optional): The registry namespace used for custom pytree node types. + (default: :const:`''`, i.e., the global namespace) + **kwargs (PyTreeSpec, optional): Additional child treespecs to add to the mapping. + + Returns: + A treespec representing an OrderedDict node with the given children. + """ + return _C.make_from_collection( + OrderedDict(mapping, **kwargs), + none_is_leaf, + namespace, + ) + + +def treespec_defaultdict( + default_factory: Callable[[], Any] | None = None, + mapping: Mapping[Any, PyTreeSpec] | Iterable[tuple[Any, PyTreeSpec]] = (), + /, + *, + none_is_leaf: bool = False, + namespace: str = '', + **kwargs: PyTreeSpec, +) -> PyTreeSpec: + """Make a defaultdict treespec from a defaultdict of child treespecs. + + See also :func:`tree_structure`, :func:`treespec_leaf`, and :func:`treespec_none`. + + >>> treespec_defaultdict(int, {'a': treespec_leaf(), 'b': treespec_leaf()}) + PyTreeSpec(defaultdict(, {'a': *, 'b': *})) + >>> treespec_defaultdict(int, [('b', treespec_leaf()), ('c', treespec_leaf()), ('a', treespec_none())]) + PyTreeSpec(defaultdict(, {'a': None, 'b': *, 'c': *})) + >>> treespec_defaultdict() + PyTreeSpec(defaultdict(None, {})) + >>> treespec_defaultdict(int) + PyTreeSpec(defaultdict(, {})) + >>> treespec_defaultdict(int, a=treespec_leaf(), b=treespec_tuple([treespec_leaf(), treespec_leaf()])) + PyTreeSpec(defaultdict(, {'a': *, 'b': (*, *)})) + >>> treespec_defaultdict(int, {'a': treespec_leaf(), 'b': tree_structure([1, 2])}) + PyTreeSpec(defaultdict(, {'a': *, 'b': [*, *]})) + >>> treespec_defaultdict(int, {'a': treespec_leaf(), 'b': tree_structure([1, 2], none_is_leaf=True)}) + Traceback (most recent call last): + ... + ValueError: Expected treespec(s) with `none_is_leaf=False`. + + Args: + default_factory (callable or None, optional): A factory function that will be used to create + a missing value. (default: :data:`None`) + mapping (mapping of PyTreeSpec, optional): A mapping of child treespecs. They must have the + same ``none_is_leaf`` and ``namespace`` values. + none_is_leaf (bool, optional): Whether to treat :data:`None` as a leaf. If :data:`False`, + :data:`None` is a non-leaf node with arity 0. Thus :data:`None` is contained in the + treespec rather than in the leaves list and :data:`None` will remain in the result + pytree. (default: :data:`False`) + namespace (str, optional): The registry namespace used for custom pytree node types. + (default: :const:`''`, i.e., the global namespace) + **kwargs (PyTreeSpec, optional): Additional child treespecs to add to the mapping. + + Returns: + A treespec representing a defaultdict node with the given children. + """ + return _C.make_from_collection( + defaultdict(default_factory, mapping, **kwargs), + none_is_leaf, + namespace, + ) + + +def treespec_deque( + iterable: Iterable[PyTreeSpec] = (), + /, + maxlen: int | None = None, + *, + none_is_leaf: bool = False, + namespace: str = '', +) -> PyTreeSpec: + """Make a deque treespec from a deque of child treespecs. + + See also :func:`tree_structure`, :func:`treespec_leaf`, and :func:`treespec_none`. + + >>> treespec_deque([treespec_leaf(), treespec_leaf()]) + PyTreeSpec(deque([*, *])) + >>> treespec_deque([treespec_leaf(), treespec_leaf(), treespec_none()], maxlen=5) + PyTreeSpec(deque([*, *, None], maxlen=5)) + >>> treespec_deque() + PyTreeSpec(deque([])) + >>> treespec_deque([treespec_leaf(), treespec_tuple([treespec_leaf(), treespec_leaf()])]) + PyTreeSpec(deque([*, (*, *)])) + >>> treespec_deque([treespec_leaf(), tree_structure({'a': 1, 'b': 2})], maxlen=5) + PyTreeSpec(deque([*, {'a': *, 'b': *}], maxlen=5)) + >>> treespec_deque([treespec_leaf(), tree_structure({'a': 1, 'b': 2}, none_is_leaf=True)], maxlen=5) + Traceback (most recent call last): + ... + ValueError: Expected treespec(s) with `none_is_leaf=False`. + + Args: + iterable (iterable of PyTreeSpec, optional): An iterable of child treespecs. They must have + the same ``none_is_leaf`` and ``namespace`` values. + maxlen (int or None, optional): The maximum size of a deque or :data:`None` if unbounded. + (default: :data:`None`) + none_is_leaf (bool, optional): Whether to treat :data:`None` as a leaf. If :data:`False`, + :data:`None` is a non-leaf node with arity 0. Thus :data:`None` is contained in the + treespec rather than in the leaves list and :data:`None` will remain in the result + pytree. (default: :data:`False`) + namespace (str, optional): The registry namespace used for custom pytree node types. + (default: :const:`''`, i.e., the global namespace) + + Returns: + A treespec representing a deque node with the given children. + """ + return _C.make_from_collection( + deque(iterable, maxlen=maxlen), + none_is_leaf, + namespace, + ) + + +def treespec_structseq( + structseq: StructSequence[PyTreeSpec], + /, + *, + none_is_leaf: bool = False, + namespace: str = '', +) -> PyTreeSpec: + """Make a PyStructSequence treespec from a PyStructSequence of child treespecs. + + See also :func:`tree_structure`, :func:`treespec_leaf`, and :func:`treespec_none`. + + Args: + structseq (PyStructSequence of PyTreeSpec): A PyStructSequence of child treespecs. They must + have the same ``none_is_leaf`` and ``namespace`` values. + none_is_leaf (bool, optional): Whether to treat :data:`None` as a leaf. If :data:`False`, + :data:`None` is a non-leaf node with arity 0. Thus :data:`None` is contained in the + treespec rather than in the leaves list and :data:`None` will remain in the result + pytree. (default: :data:`False`) + namespace (str, optional): The registry namespace used for custom pytree node types. + (default: :const:`''`, i.e., the global namespace) + + Returns: + A treespec representing a PyStructSequence node with the given children. + """ + if not is_structseq_instance(structseq): + raise ValueError(f'Expected a PyStructSequence of PyTreeSpec(s), got {structseq!r}.') + return _C.make_from_collection( + structseq, + none_is_leaf, + namespace, + ) + + +def treespec_from_collection( + collection: Collection[PyTreeSpec], + /, + *, + none_is_leaf: bool = False, + namespace: str = '', +) -> PyTreeSpec: + """Make a treespec from a collection of child treespecs. + + See also :func:`tree_structure`, :func:`treespec_leaf`, and :func:`treespec_none`. + + >>> treespec_from_collection(None) + PyTreeSpec(None) + >>> treespec_from_collection(None, none_is_leaf=True) + PyTreeSpec(*, NoneIsLeaf) + >>> treespec_from_collection(object()) + PyTreeSpec(*) + >>> treespec_from_collection([treespec_leaf(), treespec_none()]) + PyTreeSpec([*, None]) + >>> treespec_from_collection({'a': treespec_leaf(), 'b': treespec_none()}) + PyTreeSpec({'a': *, 'b': None}) + >>> treespec_from_collection(deque([treespec_leaf(), tree_structure({'a': 1, 'b': 2})], maxlen=5)) + PyTreeSpec(deque([*, {'a': *, 'b': *}], maxlen=5)) + >>> treespec_from_collection({'a': treespec_leaf(), 'b': (treespec_leaf(), treespec_none())}) + Traceback (most recent call last): + ... + ValueError: Expected a(n) dict of PyTreeSpec(s), got {'a': PyTreeSpec(*), 'b': (PyTreeSpec(*), PyTreeSpec(None))}. + >>> treespec_from_collection([treespec_leaf(), tree_structure({'a': 1, 'b': 2}, none_is_leaf=True)]) + Traceback (most recent call last): + ... + ValueError: Expected treespec(s) with `none_is_leaf=False`. + + + Args: + collection (collection of PyTreeSpec): A collection of child treespecs. They must have the + same ``none_is_leaf`` and ``namespace`` values. + none_is_leaf (bool, optional): Whether to treat :data:`None` as a leaf. If :data:`False`, + :data:`None` is a non-leaf node with arity 0. Thus :data:`None` is contained in the + treespec rather than in the leaves list and :data:`None` will remain in the result + pytree. (default: :data:`False`) + namespace (str, optional): The registry namespace used for custom pytree node types. + (default: :const:`''`, i.e., the global namespace) + + Returns: + A treespec representing the same structure of the collection with the given children. + """ + return _C.make_from_collection(collection, none_is_leaf, namespace) + + +STANDARD_DICT_TYPES: frozenset[type] = frozenset({dict, OrderedDict, defaultdict}) + + +def prefix_errors( # noqa: C901 + prefix_tree: PyTree[T], + full_tree: PyTree[S], + /, + is_leaf: Callable[[T], bool] | None = None, + *, + none_is_leaf: bool = False, + namespace: str = '', +) -> list[Callable[[str], ValueError]]: + """Return a list of errors that would be raised by :func:`broadcast_prefix`. + + See also :func:`broadcast_prefix` and :func:`tree_broadcast_prefix`. + + Args: + prefix_tree (pytree): A pytree with the prefix structure of ``full_tree``. + full_tree (pytree): A pytree with the suffix structure of ``prefix_tree``. + is_leaf (callable, optional): An optionally specified function that will be called at each + flattening step. It should return a boolean, with :data:`True` stopping the traversal + and the whole subtree being treated as a leaf, and :data:`False` indicating the + flattening should traverse the current object. + none_is_leaf (bool, optional): Whether to treat :data:`None` as a leaf. If :data:`False`, + :data:`None` is a non-leaf node with arity 0. Thus :data:`None` is contained in the + treespec rather than in the leaves list and :data:`None` will remain in the result + pytree. (default: :data:`False`) + namespace (str, optional): The registry namespace used for custom pytree node types. + (default: :const:`''`, i.e., the global namespace) + + Returns: + A list of callables that take a name string and return a :exc:`ValueError` describing the + structure mismatch. An empty list indicates that ``prefix_tree`` is a valid prefix of + ``full_tree``. + """ + + def helper( # pylint: disable=too-many-locals + accessor: PyTreeAccessor, + prefix_subtree: PyTree[T], + full_subtree: PyTree[S], + ) -> Iterable[Callable[[str], ValueError]]: + # A leaf is a valid prefix of any tree + if tree_is_leaf( + prefix_subtree, + is_leaf=is_leaf, + none_is_leaf=none_is_leaf, + namespace=namespace, + ): + return + + # The subtrees may disagree because their roots are of different types: + prefix_tree_type = type(prefix_subtree) + full_tree_type = type(full_subtree) + both_standard_dict = ( + prefix_tree_type in STANDARD_DICT_TYPES and full_tree_type in STANDARD_DICT_TYPES + ) + both_deque = prefix_tree_type is deque and full_tree_type is deque # type: ignore[comparison-overlap] + if ( + prefix_tree_type is not full_tree_type + and not both_standard_dict # special handling for dictionary types + ): + yield lambda name: ValueError( + f'pytree structure error: different types at key path\n' + f' {accessor.codify(name) if accessor else name + " tree root"}\n' + f'At that key path, the prefix pytree {name} has a subtree of type\n' + f' {type(prefix_subtree)}\n' + f'but at the same key path the full pytree has a subtree of different type\n' + f' {type(full_subtree)}.', + ) + return # don't look for more errors in this subtree + + # Or they may disagree if their roots have different numbers of children (note that because both + # prefix_tree and full_tree have the same type at this point, and because prefix_tree is not a + # leaf, each can be flattened once): + prefix_tree_one_level_output = ( + prefix_tree_children, + prefix_tree_metadata, + prefix_tree_entries, + _, + ) = tree_flatten_one_level( + prefix_subtree, + none_is_leaf=none_is_leaf, + namespace=namespace, + ) + full_tree_one_level_output = ( + full_tree_children, + full_tree_metadata, + full_tree_entries, + _, + ) = tree_flatten_one_level( + full_subtree, + none_is_leaf=none_is_leaf, + namespace=namespace, + ) + # Special handling for dictionary types + if both_standard_dict: + prefix_tree_keys: list[Any] = ( + prefix_tree_metadata # type: ignore[assignment] + if prefix_tree_type is not defaultdict # type: ignore[comparison-overlap] + else prefix_tree_metadata[1] # type: ignore[index] + ) + full_tree_keys: list[Any] = ( + full_tree_metadata # type: ignore[assignment] + if full_tree_type is not defaultdict # type: ignore[comparison-overlap] + else full_tree_metadata[1] # type: ignore[index] + ) + prefix_tree_keys_set = set(prefix_tree_keys) + full_tree_keys_set = set(full_tree_keys) + if prefix_tree_keys_set != full_tree_keys_set: + missing_keys = sorted(prefix_tree_keys_set.difference(full_tree_keys_set)) + extra_keys = sorted(full_tree_keys_set.difference(prefix_tree_keys_set)) + key_difference = '' + if missing_keys: + key_difference += f'\nmissing key(s):\n {missing_keys}' + if extra_keys: + key_difference += f'\nextra key(s):\n {extra_keys}' + yield lambda name: ValueError( + f'pytree structure error: different pytree keys at key path\n' + f' {accessor.codify(name) if accessor else name + " tree root"}\n' + f'At that key path, the prefix pytree {name} has a subtree of type\n' + f' {prefix_tree_type}\n' + f'with {len(prefix_tree_keys)} key(s)\n' + f' {prefix_tree_keys}\n' + f'but at the same key path the full pytree has a subtree of type\n' + f' {full_tree_type}\n' + f'but with {len(full_tree_keys)} key(s)\n' + f' {full_tree_keys}{key_difference}', + ) + return # don't look for more errors in this subtree + + # If the keys agree, we should ensure that the children are in the same order: + full_tree_children = [full_subtree[k] for k in prefix_tree_keys] # type: ignore[misc] + + if len(prefix_tree_children) != len(full_tree_children): + yield lambda name: ValueError( + f'pytree structure error: different numbers of pytree children at key path\n' + f' {accessor.codify(name) if accessor else name + " tree root"}\n' + f'At that key path, the prefix pytree {name} has a subtree of type\n' + f' {prefix_tree_type}\n' + f'with {len(prefix_tree_children)} children, ' + f'but at the same key path the full pytree has a subtree of the same ' + f'type but with {len(full_tree_children)} children.', + ) + return # don't look for more errors in this subtree + + # Or they may disagree if their roots have different pytree metadata: + if ( + prefix_tree_metadata != full_tree_metadata + and (not both_deque) # ignore maxlen mismatch for deque + and ( + # Special handling for dictionary types already done in the keys check above + not both_standard_dict + ) + ): + prefix_tree_metadata_repr = repr(prefix_tree_metadata) + full_tree_metadata_repr = repr(full_tree_metadata) + metadata_diff = textwrap.indent( + '\n'.join( + difflib.ndiff( + prefix_tree_metadata_repr.splitlines(), + full_tree_metadata_repr.splitlines(), + ), + ), + prefix=' ', + ) + yield lambda name: ValueError( + f'pytree structure error: different pytree metadata at key path\n' + f' {accessor.codify(name) if accessor else name + " tree root"}\n' + f'At that key path, the prefix pytree {name} has a subtree of type\n' + f' {prefix_tree_type}\n' + f'with metadata\n' + f' {prefix_tree_metadata_repr}\n' + f'but at the same key path the full pytree has a subtree of the same ' + f'type but with metadata\n' + f' {full_tree_metadata_repr}\n' + f'so the diff in the metadata at these pytree nodes is\n' + f'{metadata_diff}', + ) + return # don't look for more errors in this subtree + + # If the root types and numbers of children agree, there must be an error in a subtree, + # so recurse: + entries = [ + prefix_tree_one_level_output.path_entry_type( + e, + prefix_tree_type, + prefix_tree_one_level_output.kind, + ) + for e in prefix_tree_entries + ] + entries_ = [ + full_tree_one_level_output.path_entry_type( + e, + full_tree_type, + full_tree_one_level_output.kind, + ) + for e in full_tree_entries + ] + assert ( + both_standard_dict # special handling for dictionary types already done in the keys check above + or entries == entries_ + ), f'equal pytree nodes gave different keys: {entries} and {entries_}' + # pylint: disable-next=invalid-name + for e, t1, t2 in zip(entries, prefix_tree_children, full_tree_children): + yield from helper(accessor + e, t1, t2) + + return list(helper(PyTreeAccessor(), prefix_tree, full_tree)) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/optree/py.typed b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/optree/py.typed new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/optree/pytree.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/optree/pytree.py new file mode 100644 index 0000000000000000000000000000000000000000..33a0a8e8bd068c030720276957bd5504247ba02b --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/optree/pytree.py @@ -0,0 +1,392 @@ +# Copyright 2022-2025 MetaOPT Team. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +r"""Utilities for working with ``PyTree``\s. + +The :mod:`optree.pytree` namespace contains aliases of ``optree.tree_*`` utilities. + +>>> import optree.pytree as pytree +>>> tree = {'b': (2, [3, 4]), 'a': 1, 'c': None, 'd': 5} +>>> leaves, treespec = pytree.flatten(tree) +>>> leaves, treespec # doctest: +IGNORE_WHITESPACE +( + [1, 2, 3, 4, 5], + PyTreeSpec({'a': *, 'b': (*, [*, *]), 'c': None, 'd': *}) +) +>>> tree == pytree.unflatten(treespec, leaves) +True + +.. versionadded:: 0.14.1 +""" + +from __future__ import annotations + +import functools as _functools +import inspect as _inspect +import sys as _sys +from builtins import all as _all +from types import ModuleType as _ModuleType +from typing import TYPE_CHECKING as _TYPE_CHECKING + +import optree.dataclasses as dataclasses +import optree.functools as functools +from optree.accessors import PyTreeEntry +from optree.ops import tree_accessors as accessors +from optree.ops import tree_all as all # pylint: disable=redefined-builtin +from optree.ops import tree_any as any # pylint: disable=redefined-builtin +from optree.ops import tree_broadcast_common as broadcast_common +from optree.ops import tree_broadcast_map as broadcast_map +from optree.ops import tree_broadcast_map_with_accessor as broadcast_map_with_accessor +from optree.ops import tree_broadcast_map_with_path as broadcast_map_with_path +from optree.ops import tree_broadcast_prefix as broadcast_prefix +from optree.ops import tree_flatten as flatten +from optree.ops import tree_flatten_one_level as flatten_one_level +from optree.ops import tree_flatten_with_accessor as flatten_with_accessor +from optree.ops import tree_flatten_with_path as flatten_with_path +from optree.ops import tree_is_leaf as is_leaf +from optree.ops import tree_iter as iter # pylint: disable=redefined-builtin +from optree.ops import tree_leaves as leaves +from optree.ops import tree_map as map # pylint: disable=redefined-builtin +from optree.ops import tree_map_ as map_ +from optree.ops import tree_map_with_accessor as map_with_accessor +from optree.ops import tree_map_with_accessor_ as map_with_accessor_ +from optree.ops import tree_map_with_path as map_with_path +from optree.ops import tree_map_with_path_ as map_with_path_ +from optree.ops import tree_max as max # pylint: disable=redefined-builtin +from optree.ops import tree_min as min # pylint: disable=redefined-builtin +from optree.ops import tree_partition as partition +from optree.ops import tree_paths as paths +from optree.ops import tree_reduce as reduce +from optree.ops import tree_replace_nones as replace_nones +from optree.ops import tree_structure as structure +from optree.ops import tree_sum as sum # pylint: disable=redefined-builtin +from optree.ops import tree_transpose as transpose +from optree.ops import tree_transpose_map as transpose_map +from optree.ops import tree_transpose_map_with_accessor as transpose_map_with_accessor +from optree.ops import tree_transpose_map_with_path as transpose_map_with_path +from optree.ops import tree_unflatten as unflatten +from optree.registry import dict_insertion_ordered +from optree.registry import register_pytree_node as register_node +from optree.registry import register_pytree_node_class as register_node_class +from optree.registry import unregister_pytree_node as unregister_node +from optree.typing import PyTreeKind, PyTreeSpec +from optree.version import __version__ as __version__ # pylint: disable=useless-import-alias + + +__all__ = [ + 'reexport', + 'PyTreeSpec', + 'PyTreeKind', + 'PyTreeEntry', + 'flatten', + 'flatten_with_path', + 'flatten_with_accessor', + 'unflatten', + 'iter', + 'leaves', + 'structure', + 'paths', + 'accessors', + 'is_leaf', + 'map', + 'map_', + 'map_with_path', + 'map_with_path_', + 'map_with_accessor', + 'map_with_accessor_', + 'replace_nones', + 'partition', + 'transpose', + 'transpose_map', + 'transpose_map_with_path', + 'transpose_map_with_accessor', + 'broadcast_prefix', + 'broadcast_common', + 'broadcast_map', + 'broadcast_map_with_path', + 'broadcast_map_with_accessor', + 'reduce', + 'sum', + 'max', + 'min', + 'all', + 'any', + 'flatten_one_level', + 'register_node', + 'register_node_class', + 'unregister_node', + 'dict_insertion_ordered', +] + + +if _TYPE_CHECKING: + from collections.abc import Callable, Iterable + from typing import Any, TypeVar # pylint: disable=ungrouped-imports + from typing_extensions import ParamSpec # Python 3.10+ + + _P = ParamSpec('_P') + _T = TypeVar('_T') + + +class ReexportedModule(_ModuleType): + """A module that re-exports APIs from another module.""" + + __doc__: str + + def __init__( + self, + /, + name: str, + *, + namespace: str, + original: _ModuleType, + doc: str | None = None, + __all__: Iterable[str] | None = None, + __dir__: Iterable[str] | None = None, + extra_members: dict[str, Any] | None = None, + ) -> None: + doc = doc or ( + f'Re-exports :mod:`{original.__name__}` as :mod:`{name}` ' + f'with namespace :const:`{namespace!r}`.' + ) + super().__init__(name, doc) + + if __all__ is None: # pragma: no branch + __all__ = {n for n in original.__all__ if n != 'reexport'} + __all__ = set(__all__) + if __dir__ is None: # pragma: no branch + __dir__ = {n for n in original.__dir__() if not n.startswith('_') and n != 'reexport'} + __dir__ = set(__dir__).intersection(__all__) + + if extra_members: + for key, value in extra_members.items(): + setattr(self, key, value) + __dir__.update(extra_members) + + self.__namespace = namespace + self.__original = original + self.__all_set = __all__ + self.__all = sorted(__all__) + self.__dir = sorted(__dir__) + + @property + def __all__(self, /) -> list[str]: + """Return the list of attributes available in this module.""" + return self.__all + + def __dir__(self, /) -> list[str]: + """Return the list of attributes available in this module.""" + return self.__dir.copy() + + def __getattr__(self, name: str, /) -> Any: + """Get an attribute from the re-exported module.""" + if name in self.__all_set: + attr = getattr(self.__original, name) + if _inspect.isfunction(attr): + attr = self.__reexport__(attr) + setattr(self, name, attr) + return attr + raise AttributeError(f'module {self.__name__!r} has no attribute {name!r}') + + def __reexport__(self, func: Callable[_P, _T], /) -> Callable[_P, _T]: + """Re-export a function with the default namespace.""" + sig = _inspect.signature(func) + if 'namespace' not in sig.parameters: + + @_functools.wraps(func) + def wrapped(*args: _P.args, **kwargs: _P.kwargs) -> _T: + return func(*args, **kwargs) + else: + + @_functools.wraps(func) + def wrapped( # type: ignore[valid-type] + *args: _P.args, + namespace: str = self.__namespace, + **kwargs: _P.kwargs, + ) -> _T: + return func(*args, namespace=namespace, **kwargs) # type: ignore[arg-type] + + if func.__doc__: # pragma: no branch + wrapped.__doc__ = func.__doc__.replace( + "(default: :const:`''`, i.e., the global namespace)", + f'(default: :const:`{self.__namespace!r}`)', + ) + wrapped.__signature__ = sig.replace( # type: ignore[attr-defined] + parameters=[ + p if p.name != 'namespace' else p.replace(default=self.__namespace) + for p in sig.parameters.values() + ], + ) + + if callable(getattr(func, 'get', None)): + wrapped.get = self.__reexport__(func.get) # type: ignore[attr-defined] + + return wrapped + + +if _TYPE_CHECKING: + # pylint: disable-next=missing-class-docstring,too-few-public-methods + class ReexportedPyTreeModule(ReexportedModule): + __version__: str + functools: _ModuleType + dataclasses: _ModuleType + + PyTreeSpec: type[PyTreeSpec] = PyTreeSpec + PyTreeKind: type[PyTreeKind] = PyTreeKind + PyTreeEntry: type[PyTreeEntry] = PyTreeEntry + flatten = staticmethod(flatten) + flatten_with_path = staticmethod(flatten_with_path) + flatten_with_accessor = staticmethod(flatten_with_accessor) + unflatten = staticmethod(unflatten) + iter = staticmethod(iter) + leaves = staticmethod(leaves) + structure = staticmethod(structure) + paths = staticmethod(paths) + accessors = staticmethod(accessors) + is_leaf = staticmethod(is_leaf) + map = staticmethod(map) + map_ = staticmethod(map_) + map_with_path = staticmethod(map_with_path) + map_with_path_ = staticmethod(map_with_path_) + map_with_accessor = staticmethod(map_with_accessor) + map_with_accessor_ = staticmethod(map_with_accessor_) + replace_nones = staticmethod(replace_nones) + partition = staticmethod(partition) + transpose = staticmethod(transpose) + transpose_map = staticmethod(transpose_map) + transpose_map_with_path = staticmethod(transpose_map_with_path) + transpose_map_with_accessor = staticmethod(transpose_map_with_accessor) + broadcast_prefix = staticmethod(broadcast_prefix) + broadcast_common = staticmethod(broadcast_common) + broadcast_map = staticmethod(broadcast_map) + broadcast_map_with_path = staticmethod(broadcast_map_with_path) + broadcast_map_with_accessor = staticmethod(broadcast_map_with_accessor) + reduce = staticmethod(reduce) + sum = staticmethod(sum) + max = staticmethod(max) + min = staticmethod(min) + all = staticmethod(all) + any = staticmethod(any) + flatten_one_level = staticmethod(flatten_one_level) + register_node = staticmethod(register_node) + register_node_class = staticmethod(register_node_class) + unregister_node = staticmethod(unregister_node) + dict_insertion_ordered = staticmethod(dict_insertion_ordered) + + def reexport(*, namespace: str, module: str | None = None) -> ReexportedPyTreeModule: + """Re-export a pytree utility module with the given namespace as default.""" + raise NotImplementedError('reexport() is not available in type checking mode') + +else: + + def reexport(*, namespace: str, module: str | None = None) -> _ModuleType: # type: ignore[misc] + """Re-export a pytree utility module with the given namespace as default. + + >>> import optree + >>> pytree = optree.pytree.reexport(namespace='my-pkg', module='my_pkg.pytree') + >>> pytree.flatten({'a': 1, 'b': 2}) + ([1, 2], PyTreeSpec({'a': *, 'b': *})) + + This function is useful for downstream libraries that want to re-export the pytree utilities + with their own namespace: + + .. code-block:: python + + # foo/__init__.py + import optree + pytree = optree.pytree.reexport(namespace='foo') + del optree + + # foo/bar.py + from foo import pytree + + @pytree.dataclasses.dataclass + class Bar: + a: int + b: float + + # User code + In [1]: import foo + + In [2]: foo.pytree.flatten({'a': 1, 'b': 2, 'c': foo.bar.Bar(3, 4.0)})) + Out[2]: + ( + [1, 2, 3, 4.0], + PyTreeSpec({'a': *, 'b': *, 'c': CustomTreeNode(Bar[()], [*, *])}, namespace='foo') + ) + + In [3]: foo.pytree.functools.reduce(lambda x, y: x * y, {'a': 1, 'b': 2, 'c': foo.bar.Bar(3, 4.0)})) + Out[3]: 24.0 + + .. versionadded:: 0.16.0 + + Args: + namespace (str): The namespace to use in the re-exported module. + module (str, optional): The name of the re-exported module. + If not provided, defaults to ``.pytree``. The caller module is determined + by inspecting the stack frame. + + Returns: + The re-exported module. + """ + # pylint: disable-next=import-outside-toplevel + from optree.registry import __GLOBAL_NAMESPACE as GLOBAL_NAMESPACE + + if namespace is GLOBAL_NAMESPACE: + namespace = '' + elif not isinstance(namespace, str): + raise TypeError(f'The namespace must be a string, got {namespace!r}.') + + if module is None: + try: + # pylint: disable-next=protected-access + caller_module = _sys._getframemodulename(1) or '__main__' # type: ignore[attr-defined] + except AttributeError: # pragma: no cover + try: + # pylint: disable-next=protected-access + caller_module = _sys._getframe(1).f_globals.get('__name__', '__main__') + except (AttributeError, ValueError): + caller_module = '__main__' + module = f'{caller_module}.pytree' + if not module or not _all(part.isidentifier() for part in module.split('.')): + raise ValueError(f'invalid module name: {module!r}') + + for module_name in (module, f'{module}.dataclasses', f'{module}.functools'): + if module_name in _sys.modules: + raise ValueError(f'module {module_name!r} already exists') + + reexported_dataclasses = ReexportedModule( + f'{module}.dataclasses', + namespace=namespace, + original=dataclasses, + ) + reexported_functools = ReexportedModule( + f'{module}.functools', + namespace=namespace, + original=functools, + ) + mod: ReexportedPyTreeModule = ReexportedModule( # type: ignore[assignment] + module, + namespace=namespace, + original=_sys.modules[__name__], + extra_members={ + '__version__': __version__, + 'dataclasses': reexported_dataclasses, + 'functools': reexported_functools, + }, + ) + _sys.modules[module] = mod + _sys.modules[f'{module}.dataclasses'] = reexported_dataclasses + _sys.modules[f'{module}.functools'] = reexported_functools + return mod diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/optree/registry.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/optree/registry.py new file mode 100644 index 0000000000000000000000000000000000000000..c01e14bb8f078319a345c74742bbc82b578e72d9 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/optree/registry.py @@ -0,0 +1,953 @@ +# Copyright 2022-2025 MetaOPT Team. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Registry for custom pytree node types.""" + +# pylint: disable=too-many-lines + +from __future__ import annotations + +import contextlib +import dataclasses +import functools +import inspect +import sys +from collections import OrderedDict, defaultdict, deque, namedtuple +from operator import itemgetter, methodcaller +from threading import Lock +from typing import TYPE_CHECKING, Any, Callable, ClassVar, Generic, NamedTuple, TypeVar, overload + +import optree._C as _C +from optree.accessors import ( + AutoEntry, + MappingEntry, + NamedTupleEntry, + PyTreeEntry, + SequenceEntry, + StructSequenceEntry, +) +from optree.typing import ( + Children, + MetaData, + PyTreeKind, + StructSequence, + T, + is_namedtuple_class, + is_structseq_class, +) +from optree.utils import safe_zip, total_order_sorted, unzip2 + + +if TYPE_CHECKING: + import builtins + from collections.abc import Collection, Generator, Iterable + + from optree.typing import KT, VT, CustomTreeNode, FlattenFunc, UnflattenFunc + + # pylint: disable-next=invalid-name + CustomTreeNodeType = TypeVar('CustomTreeNodeType', bound=type[CustomTreeNode]) + + +__all__ = [ + 'register_pytree_node', + 'register_pytree_node_class', + 'unregister_pytree_node', + 'dict_insertion_ordered', +] + + +SLOTS = {'slots': True} if sys.version_info >= (3, 10) else {} # Python 3.10+ + + +@dataclasses.dataclass(init=True, repr=True, eq=True, frozen=True, **SLOTS) +class PyTreeNodeRegistryEntry(Generic[T]): + """A dataclass that stores the information of a pytree node type.""" + + type: builtins.type[Collection[T]] + flatten_func: FlattenFunc[T] + unflatten_func: UnflattenFunc[T] + + if sys.version_info >= (3, 10): # pragma: >=3.10 cover + _: dataclasses.KW_ONLY # Python 3.10+ + + path_entry_type: builtins.type[PyTreeEntry] = AutoEntry + kind: PyTreeKind = PyTreeKind.CUSTOM + namespace: str = '' + + +del SLOTS + + +# pylint: disable-next=missing-class-docstring,too-few-public-methods +class GlobalNamespace: # pragma: no cover + __slots__: ClassVar[tuple[()]] = () + + def __repr__(self, /) -> str: + return '' + + +__GLOBAL_NAMESPACE: str = GlobalNamespace() # type: ignore[assignment] +__REGISTRY_LOCK: Lock = Lock() +del GlobalNamespace + + +if TYPE_CHECKING: + from typing_extensions import ParamSpec # Python 3.10+ + + _P = ParamSpec('_P') + _T = TypeVar('_T') + _GetP = ParamSpec('_GetP') + _GetT = TypeVar('_GetT') + + class _CallableWithGet(Generic[_P, _T, _GetP, _GetT]): + def __call__(self, /, *args: _P.args, **kwargs: _P.kwargs) -> _T: + raise NotImplementedError + + # pylint: disable-next=missing-function-docstring + def get(self, /, *args: _GetP.args, **kwargs: _GetP.kwargs) -> _GetT: + raise NotImplementedError + + +def _add_get( + get: Callable[_GetP, _GetT], + /, +) -> Callable[ + [Callable[_P, _T]], + _CallableWithGet[_P, _T, _GetP, _GetT], +]: + def decorator(func: Callable[_P, _T], /) -> _CallableWithGet[_P, _T, _GetP, _GetT]: + func.get = get # type: ignore[attr-defined] + return func # type: ignore[return-value] + + return decorator + + +@overload +def pytree_node_registry_get( + cls: type, + /, + *, + namespace: str = '', +) -> PyTreeNodeRegistryEntry | None: ... + + +@overload +def pytree_node_registry_get( + cls: None = None, + /, + *, + namespace: str = '', +) -> dict[type, PyTreeNodeRegistryEntry]: ... + + +# pylint: disable-next=too-many-return-statements,too-many-branches +def pytree_node_registry_get( # noqa: C901 + cls: type | None = None, + /, + *, + namespace: str = '', +) -> dict[type, PyTreeNodeRegistryEntry] | PyTreeNodeRegistryEntry | None: + """Look up the pytree node registry. + + >>> register_pytree_node.get() # doctest: +IGNORE_WHITESPACE,ELLIPSIS + { + : PyTreeNodeRegistryEntry( + type=, + flatten_func=, + unflatten_func=, + path_entry_type=, + kind=, + namespace='' + ), + : PyTreeNodeRegistryEntry( + type=, + flatten_func=, + unflatten_func=, + path_entry_type=, + kind=, + namespace='' + ), + : PyTreeNodeRegistryEntry( + type=, + flatten_func=, + unflatten_func=, + path_entry_type=, + kind=, + namespace='' + ), + ... + } + >>> register_pytree_node.get(defaultdict) # doctest: +IGNORE_WHITESPACE,ELLIPSIS + PyTreeNodeRegistryEntry( + type=, + flatten_func=, + unflatten_func=, + path_entry_type=, + kind=, + namespace='' + ) + >>> register_pytree_node.get(frozenset) # frozenset is considered as a leaf node + None + + Args: + cls (type or None, optional): The class of the pytree node to retrieve. If not provided, all + the registered pytree nodes in the namespace are returned. + namespace (str, optional): The namespace of the registry to retrieve. If not provided, the + global namespace is used. + + Returns: + If the ``cls`` is not provided, a dictionary of all the registered pytree nodes in the + namespace is returned. If the ``cls`` is provided, the corresponding registry entry is + returned if the ``cls`` is registered as a pytree node. Otherwise, :data:`None` is returned, + i.e., the ``cls`` is represented as a leaf node. + """ + if namespace is __GLOBAL_NAMESPACE: + namespace = '' + if ( + cls is not None + and cls is not namedtuple # noqa: PYI024 + and not inspect.isclass(cls) + ): + raise TypeError(f'Expected a class or None, got {cls!r}.') # pragma: !=3.9 cover + if not isinstance(namespace, str): + raise TypeError( # pragma: !=3.9 cover + f'The namespace must be a string, got {namespace!r}.', + ) + + if cls is None: + namespaces = frozenset({namespace, ''}) + with __REGISTRY_LOCK: + registry = { + handler.type: handler + for handler in _NODETYPE_REGISTRY.values() + if handler.namespace in namespaces + } + if _C.is_dict_insertion_ordered(namespace): + registry[dict] = _DICT_INSERTION_ORDERED_REGISTRY_ENTRY + registry[defaultdict] = _DEFAULTDICT_INSERTION_ORDERED_REGISTRY_ENTRY + return registry + + if namespace != '': + handler = _NODETYPE_REGISTRY.get((namespace, cls)) + if handler is not None: + return handler + + if _C.is_dict_insertion_ordered(namespace): + if cls is dict: + return _DICT_INSERTION_ORDERED_REGISTRY_ENTRY + if cls is defaultdict: + return _DEFAULTDICT_INSERTION_ORDERED_REGISTRY_ENTRY + + handler = _NODETYPE_REGISTRY.get(cls) + if handler is not None: + return handler + if is_structseq_class(cls): + return _NODETYPE_REGISTRY.get(StructSequence) + if is_namedtuple_class(cls): + return _NODETYPE_REGISTRY.get(namedtuple) # type: ignore[call-overload] # noqa: PYI024 + return None + + +@_add_get(pytree_node_registry_get) +def register_pytree_node( + cls: type[Collection[T]], + /, + flatten_func: FlattenFunc[T], + unflatten_func: UnflattenFunc[T], + *, + path_entry_type: type[PyTreeEntry] = AutoEntry, + namespace: str, +) -> type[Collection[T]]: + """Extend the set of types that are considered internal nodes in pytrees. + + See also :func:`register_pytree_node_class` and :func:`unregister_pytree_node`. + + The ``namespace`` argument is used to avoid collisions that occur when different libraries + register the same Python type with different behaviors. It is recommended to add a unique prefix + to the namespace to avoid conflicts with other libraries. Namespaces can also be used to specify + the same class in different namespaces for different use cases. + + .. warning:: + For safety reasons, a ``namespace`` must be specified while registering a custom type. It is + used to isolate the behavior of flattening and unflattening a pytree node type. This is to + prevent accidental collisions between different libraries that may register the same type. + + Args: + cls (type): A Python type to treat as an internal pytree node. + flatten_func (callable): A function to be used during flattening, taking an instance of ``cls`` + and returning a triple or optionally a pair, with (1) an iterable for the children to be + flattened recursively, and (2) some hashable metadata to be stored in the treespec and + to be passed to the ``unflatten_func``, and (3) (optional) an iterable for the tree path + entries to the corresponding children. If the entries are not provided or given by + :data:`None`, then `range(len(children))` will be used. + unflatten_func (callable): A function taking two arguments: the metadata that was returned + by ``flatten_func`` and stored in the treespec, and the unflattened children. The + function should return an instance of ``cls``. + path_entry_type (type, optional): The type of the path entry to be used in the treespec. + (default: :class:`AutoEntry`) + namespace (str): A non-empty string that uniquely identifies the namespace of the type registry. + This is used to isolate the registry from other modules that might register a different + custom behavior for the same type. + + Returns: + The same type as the input ``cls``. + + Raises: + TypeError: If the input type is not a class. + TypeError: If the path entry class is not a subclass of :class:`PyTreeEntry`. + TypeError: If the namespace is not a string. + ValueError: If the namespace is an empty string. + ValueError: If the type is already registered in the registry. + + .. versionadded:: 0.12.0 + The ``path_entry_type`` argument to specify the path entry type used in + :meth:`PyTreeSpec.accessors` and :func:`tree_flatten_with_accessor`. + If not provided, :class:`AutoEntry` will be used. + + Examples: + >>> # Register a Python type with lambda functions + >>> register_pytree_node( + ... set, + ... lambda s: (sorted(s), None, None), + ... lambda _, children: set(children), + ... namespace='set', + ... ) + + + >>> # Register a Python type into a namespace + >>> import torch + >>> register_pytree_node( + ... torch.Tensor, + ... flatten_func=lambda tensor: ( + ... (tensor.cpu().detach().numpy(),), + ... {'dtype': tensor.dtype, 'device': tensor.device, 'requires_grad': tensor.requires_grad}, + ... ), + ... unflatten_func=lambda metadata, children: torch.tensor(children[0], **metadata), + ... namespace='torch2numpy', + ... ) + + + >>> # doctest: +SKIP + >>> tree = {'weight': torch.ones(size=(1, 2)).cuda(), 'bias': torch.zeros(size=(2,))} + >>> tree + {'weight': tensor([[1., 1.]], device='cuda:0'), 'bias': tensor([0., 0.])} + + >>> # Flatten without specifying the namespace + >>> tree_flatten(tree) # `torch.Tensor`s are leaf nodes + ([tensor([0., 0.]), tensor([[1., 1.]], device='cuda:0')], PyTreeSpec({'bias': *, 'weight': *})) + + >>> # Flatten with the namespace + >>> tree_flatten(tree, namespace='torch2numpy') + ( + [array([0., 0.], dtype=float32), array([[1., 1.]], dtype=float32)], + PyTreeSpec( + { + 'bias': CustomTreeNode(Tensor[{'dtype': torch.float32, 'device': device(type='cpu'), 'requires_grad': False}], [*]), + 'weight': CustomTreeNode(Tensor[{'dtype': torch.float32, 'device': device(type='cuda', index=0), 'requires_grad': False}], [*]) + }, + namespace='torch2numpy' + ) + ) + + >>> # Register the same type with a different namespace for different behaviors + >>> def tensor2flatparam(tensor): + ... return [torch.nn.Parameter(tensor.reshape(-1))], tensor.shape, None + ... + ... def flatparam2tensor(metadata, children): + ... return children[0].reshape(metadata) + ... + ... register_pytree_node( + ... torch.Tensor, + ... flatten_func=tensor2flatparam, + ... unflatten_func=flatparam2tensor, + ... namespace='tensor2flatparam', + ... ) + + + >>> # Flatten with the new namespace + >>> tree_flatten(tree, namespace='tensor2flatparam') + ( + [ + Parameter containing: tensor([0., 0.], requires_grad=True), + Parameter containing: tensor([1., 1.], device='cuda:0', requires_grad=True) + ], + PyTreeSpec( + { + 'bias': CustomTreeNode(Tensor[torch.Size([2])], [*]), + 'weight': CustomTreeNode(Tensor[torch.Size([1, 2])], [*]) + }, + namespace='tensor2flatparam' + ) + ) + """ # pylint: disable=line-too-long + if not inspect.isclass(cls): + raise TypeError(f'Expected a class, got {cls!r}.') + if not (inspect.isclass(path_entry_type) and issubclass(path_entry_type, PyTreeEntry)): + raise TypeError(f'Expected a subclass of PyTreeEntry, got {path_entry_type!r}.') + if namespace is not __GLOBAL_NAMESPACE and not isinstance(namespace, str): + raise TypeError(f'The namespace must be a string, got {namespace!r}.') + if namespace == '': + raise ValueError('The namespace cannot be an empty string.') + + registration_key: type | tuple[str, type] + if namespace is __GLOBAL_NAMESPACE: + registration_key = cls + namespace = '' + else: + registration_key = (namespace, cls) + + with __REGISTRY_LOCK: + _C.register_node( + cls, + flatten_func, + unflatten_func, + path_entry_type, + namespace, + ) + _NODETYPE_REGISTRY[registration_key] = PyTreeNodeRegistryEntry( + cls, + flatten_func, + unflatten_func, + path_entry_type=path_entry_type, + namespace=namespace, + ) + return cls + + +del pytree_node_registry_get, _add_get + + +@overload +def register_pytree_node_class( + cls: str | None = None, + /, + *, + path_entry_type: type[PyTreeEntry] | None = None, + namespace: str | None = None, +) -> Callable[[CustomTreeNodeType], CustomTreeNodeType]: ... + + +@overload +def register_pytree_node_class( + cls: CustomTreeNodeType, + /, + *, + path_entry_type: type[PyTreeEntry] | None, + namespace: str, +) -> CustomTreeNodeType: ... + + +# pylint: disable-next=too-many-branches +def register_pytree_node_class( # noqa: C901 + cls: CustomTreeNodeType | str | None = None, + /, + *, + path_entry_type: type[PyTreeEntry] | None = None, + namespace: str | None = None, +) -> CustomTreeNodeType | Callable[[CustomTreeNodeType], CustomTreeNodeType]: + """Extend the set of types that are considered internal nodes in pytrees. + + See also :func:`register_pytree_node` and :func:`unregister_pytree_node`. + + The ``namespace`` argument is used to avoid collisions that occur when different libraries + register the same Python type with different behaviors. It is recommended to add a unique prefix + to the namespace to avoid conflicts with other libraries. Namespaces can also be used to specify + the same class in different namespaces for different use cases. + + .. warning:: + For safety reasons, a ``namespace`` must be specified while registering a custom type. It is + used to isolate the behavior of flattening and unflattening a pytree node type. This is to + prevent accidental collisions between different libraries that may register the same type. + + Args: + cls (type, optional): A Python type to treat as an internal pytree node. + path_entry_type (type, optional): The type of the path entry to be used in the treespec. + (default: :class:`AutoEntry`) + namespace (str, optional): A non-empty string that uniquely identifies the namespace of the + type registry. This is used to isolate the registry from other modules that might + register a different custom behavior for the same type. + + Returns: + The same type as the input ``cls`` if the argument presents. Otherwise, return a decorator + function that registers the class as a pytree node. + + Raises: + TypeError: If the path entry class is not a subclass of :class:`PyTreeEntry`. + TypeError: If the namespace is not a string. + TypeError: If the class does not define the required method pairs. + ValueError: If the namespace is an empty string. + ValueError: If the type is already registered in the registry. + + .. versionadded:: 0.12.0 + The ``TREE_PATH_ENTRY_TYPE`` class variable to specify the path entry type used in + :meth:`PyTreeSpec.accessors` and :func:`tree_flatten_with_accessor`. + If not provided, :class:`AutoEntry` will be used. + + .. versionadded:: 0.18.0 + Previously, this function looked for methods named ``tree_flatten`` and ``tree_unflatten`` + for the given class. Since version 0.18.0, it prefers methods named ``__tree_flatten__`` + and ``__tree_unflatten__`` instead. The old method names are still supported for + backward compatibility, but it is recommended to use the new method names. + The method resolution follows this priority: + 1. If both ``__tree_flatten__`` and ``__tree_unflatten__`` are defined, use them directly. + 2. If both ``tree_flatten`` and ``tree_unflatten`` are defined, wrap them as dunder methods. + 3. If neither complete pair is available, raise a :exc:`TypeError` suggesting the new method names. + + This function is a thin wrapper around :func:`register_pytree_node`, and provides a + class-oriented interface: + + .. code-block:: python + + @register_pytree_node_class(namespace='foo') + class Special: + TREE_PATH_ENTRY_TYPE = GetAttrEntry + + def __init__(self, x, y): + self.x = x + self.y = y + + def __tree_flatten__(self): + return ((self.x, self.y), None, ('x', 'y')) + + @classmethod + def __tree_unflatten__(cls, metadata, children): + return cls(*children) + + @register_pytree_node_class('mylist') + class MyList(UserList): + TREE_PATH_ENTRY_TYPE = SequenceEntry + + def __tree_flatten__(self): + return self.data, None, None + + @classmethod + def __tree_unflatten__(cls, metadata, children): + return cls(*children) + + # Legacy style (still supported but not recommended) + @register_pytree_node_class(namespace='legacy') + class LegacyStyleMyList(UserList): + def tree_flatten(self): + # Implementation automatically wrapped as __tree_flatten__ + return self.data, None, None + + @classmethod + def tree_unflatten(cls, metadata, children): + # Implementation automatically wrapped as __tree_unflatten__ + return cls(*children) + """ + if cls is __GLOBAL_NAMESPACE or isinstance(cls, str): + if namespace is not None: + raise ValueError('Cannot specify `namespace` when the first argument is a string.') + if cls == '': + raise ValueError('The namespace cannot be an empty string.') + cls, namespace = None, cls + + if namespace is None: + raise ValueError('Must specify `namespace` when the first argument is a class.') + if namespace is not __GLOBAL_NAMESPACE and not isinstance(namespace, str): + raise TypeError(f'The namespace must be a string, got {namespace!r}') + if namespace == '': + raise ValueError('The namespace cannot be an empty string.') + + if cls is None: + + def decorator(cls: CustomTreeNodeType, /) -> CustomTreeNodeType: + return register_pytree_node_class( + cls, + path_entry_type=path_entry_type, + namespace=namespace, + ) + + return decorator + + if not inspect.isclass(cls): + raise TypeError(f'Expected a class, got {cls!r}.') + if path_entry_type is None: + path_entry_type = getattr(cls, 'TREE_PATH_ENTRY_TYPE', AutoEntry) + if not (inspect.isclass(path_entry_type) and issubclass(path_entry_type, PyTreeEntry)): + raise TypeError(f'Expected a subclass of PyTreeEntry, got {path_entry_type!r}.') + + # Check for dunder-styled methods first (preferred since 0.18.0) + if not all( + callable(getattr(cls, method, None)) + for method in ('__tree_flatten__', '__tree_unflatten__') + ): + # Check for old-styled methods (backward compatibility) + if not all( + callable(getattr(cls, method, None)) for method in ('tree_flatten', 'tree_unflatten') + ): + raise TypeError( + f'{cls!r} must define both `__tree_flatten__` and `__tree_unflatten__` methods ' + 'for registration as a pytree node.', + ) + + # Add dunder-styled wrapper methods to the class + # pylint: disable=no-member + + @functools.wraps(cls.tree_flatten) + def __tree_flatten__( # noqa: N807 + self: CustomTreeNode[T], + /, + ) -> tuple[Children[T], MetaData] | tuple[Children[T], MetaData, Iterable[Any] | None]: + return self.tree_flatten() # type: ignore[attr-defined] + + @classmethod # type: ignore[misc] + @functools.wraps(getattr(cls.tree_unflatten, '__func__', cls.tree_unflatten)) + def __tree_unflatten__( # noqa: N807 + cls: type[CustomTreeNode[T]], + metadata: MetaData, + children: Children[T], + /, + ) -> CustomTreeNode[T]: + return cls.tree_unflatten(metadata, children) # type: ignore[attr-defined] + + # pylint: enable=no-member + + cls.__tree_flatten__ = __tree_flatten__ + cls.__tree_unflatten__ = __tree_unflatten__ + + register_pytree_node( + cls, + methodcaller('__tree_flatten__'), + cls.__tree_unflatten__, + path_entry_type=path_entry_type, + namespace=namespace, + ) + return cls + + +def unregister_pytree_node(cls: type, /, *, namespace: str) -> PyTreeNodeRegistryEntry: + """Remove a type from the pytree node registry. + + See also :func:`register_pytree_node` and :func:`register_pytree_node_class`. + + This function is the inverse operation of function :func:`register_pytree_node`. + + Args: + cls (type): A Python type to remove from the pytree node registry. + namespace (str): The namespace of the pytree node registry to remove the type from. + + Returns: + The removed registry entry. + + Raises: + TypeError: If the input type is not a class. + TypeError: If the namespace is not a string. + ValueError: If the namespace is an empty string. + ValueError: If the type is a built-in type that cannot be unregistered. + ValueError: If the type is not found in the registry. + + Examples: + >>> # Register a Python type with lambda functions + >>> register_pytree_node( + ... set, + ... lambda s: (sorted(s), None, None), + ... lambda _, children: set(children), + ... namespace='temp', + ... ) + + + >>> # Unregister the Python type + >>> unregister_pytree_node(set, namespace='temp') + """ + if not inspect.isclass(cls): + raise TypeError(f'Expected a class, got {cls!r}.') + if namespace is not __GLOBAL_NAMESPACE and not isinstance(namespace, str): + raise TypeError(f'The namespace must be a string, got {namespace!r}.') + if namespace == '': + raise ValueError('The namespace cannot be an empty string.') + + registration_key: type | tuple[str, type] + if namespace is __GLOBAL_NAMESPACE: + registration_key = cls + namespace = '' + else: + registration_key = (namespace, cls) + + with __REGISTRY_LOCK: + _C.unregister_node(cls, namespace) + return _NODETYPE_REGISTRY.pop(registration_key) + + +@contextlib.contextmanager +def dict_insertion_ordered(mode: bool, /, *, namespace: str) -> Generator[None]: + """Context manager to temporarily set the dictionary sorting mode. + + This context manager is used to temporarily set the dictionary sorting mode for a specific + namespace. The dictionary sorting mode is used to determine whether the keys of a dictionary + should be sorted or keep the insertion order when flattening a pytree. + + >>> tree = {'b': (2, [3, 4]), 'a': 1, 'c': None, 'd': 5} + >>> tree_flatten(tree) # doctest: +IGNORE_WHITESPACE + ( + [1, 2, 3, 4, 5], + PyTreeSpec({'a': *, 'b': (*, [*, *]), 'c': None, 'd': *}) + ) + >>> with dict_insertion_ordered(True, namespace='some-namespace'): # doctest: +IGNORE_WHITESPACE + ... tree_flatten(tree, namespace='some-namespace') + ( + [2, 3, 4, 1, 5], + PyTreeSpec({'b': (*, [*, *]), 'a': *, 'c': None, 'd': *}, namespace='some-namespace') + ) + + .. warning:: + The dictionary sorting mode is a global setting and is **not thread-safe**. It is + recommended to use this context manager in a single-threaded environment. + + Args: + mode (bool): The dictionary sorting mode to set. + namespace (str): The namespace to set the dictionary sorting mode for. + """ + if namespace is not __GLOBAL_NAMESPACE and not isinstance(namespace, str): + raise TypeError(f'The namespace must be a string, got {namespace!r}.') + if namespace == '': + raise ValueError('The namespace cannot be an empty string.') + if namespace is __GLOBAL_NAMESPACE: + namespace = '' + + with __REGISTRY_LOCK: + prev = _C.is_dict_insertion_ordered(namespace, inherit_global_namespace=False) + _C.set_dict_insertion_ordered(bool(mode), namespace) + + try: + yield + finally: + with __REGISTRY_LOCK: + _C.set_dict_insertion_ordered(prev, namespace) + + +def _sorted_items(items: Iterable[tuple[KT, VT]], /) -> list[tuple[KT, VT]]: + return total_order_sorted(items, key=itemgetter(0)) + + +def _none_flatten(_: None, /) -> tuple[tuple[()], None]: + return (), None + + +def _none_unflatten(_: None, children: Iterable[Any], /) -> None: + sentinel = object() + if next(iter(children), sentinel) is not sentinel: + raise ValueError('Expected no children.') + + +def _tuple_flatten(tup: tuple[T, ...], /) -> tuple[tuple[T, ...], None]: + return tup, None + + +def _tuple_unflatten(_: None, children: Iterable[T], /) -> tuple[T, ...]: + return tuple(children) + + +def _list_flatten(lst: list[T], /) -> tuple[list[T], None]: + return lst, None + + +def _list_unflatten(_: None, children: Iterable[T], /) -> list[T]: + return list(children) + + +def _dict_flatten(dct: dict[KT, VT], /) -> tuple[tuple[VT, ...], list[KT], tuple[KT, ...]]: + keys, values = unzip2(_sorted_items(dct.items())) + return values, list(keys), keys + + +def _dict_unflatten(keys: list[KT], values: Iterable[VT], /) -> dict[KT, VT]: + return dict(safe_zip(keys, values)) + + +def _dict_insertion_ordered_flatten( + dct: dict[KT, VT], + /, +) -> tuple[ + tuple[VT, ...], + list[KT], + tuple[KT, ...], +]: + keys, values = unzip2(dct.items()) + return values, list(keys), keys + + +def _dict_insertion_ordered_unflatten(keys: list[KT], values: Iterable[VT], /) -> dict[KT, VT]: + return dict(safe_zip(keys, values)) + + +def _ordereddict_flatten( + dct: OrderedDict[KT, VT], + /, +) -> tuple[ + tuple[VT, ...], + list[KT], + tuple[KT, ...], +]: + keys, values = unzip2(dct.items()) + return values, list(keys), keys + + +def _ordereddict_unflatten(keys: list[KT], values: Iterable[VT], /) -> OrderedDict[KT, VT]: + return OrderedDict(safe_zip(keys, values)) + + +def _defaultdict_flatten( + dct: defaultdict[KT, VT], + /, +) -> tuple[ + tuple[VT, ...], + tuple[Callable[[], VT] | None, list[KT]], + tuple[KT, ...], +]: + values, dict_metadata, entries = _dict_flatten(dct) + return values, (dct.default_factory, dict_metadata), entries + + +def _defaultdict_unflatten( + metadata: tuple[Callable[[], VT], list[KT]], + values: Iterable[VT], + /, +) -> defaultdict[KT, VT]: + default_factory, dict_metadata = metadata + return defaultdict(default_factory, _dict_unflatten(dict_metadata, values)) + + +def _defaultdict_insertion_ordered_flatten( + dct: defaultdict[KT, VT], + /, +) -> tuple[ + tuple[VT, ...], + tuple[Callable[[], VT] | None, list[KT]], + tuple[KT, ...], +]: + values, dict_metadata, entries = _dict_insertion_ordered_flatten(dct) + return values, (dct.default_factory, dict_metadata), entries + + +def _defaultdict_insertion_ordered_unflatten( + metadata: tuple[Callable[[], VT], list[KT]], + values: Iterable[VT], + /, +) -> defaultdict[KT, VT]: + default_factory, dict_metadata = metadata + return defaultdict(default_factory, _dict_insertion_ordered_unflatten(dict_metadata, values)) + + +def _deque_flatten(deq: deque[T], /) -> tuple[deque[T], int | None]: + return deq, deq.maxlen + + +def _deque_unflatten(maxlen: int | None, children: Iterable[T], /) -> deque[T]: + return deque(children, maxlen=maxlen) + + +def _namedtuple_flatten(tup: NamedTuple[T], /) -> tuple[tuple[T, ...], type[NamedTuple[T]]]: # type: ignore[type-arg] + return tup, type(tup) + + +# pylint: disable-next=line-too-long +def _namedtuple_unflatten(cls: type[NamedTuple[T]], children: Iterable[T], /) -> NamedTuple[T]: # type: ignore[type-arg] + return cls(*children) # type: ignore[call-overload] + + +def _structseq_flatten(seq: StructSequence[T], /) -> tuple[tuple[T, ...], type[StructSequence[T]]]: + return seq, type(seq) + + +def _structseq_unflatten( + cls: type[StructSequence[T]], + children: Iterable[T], + /, +) -> StructSequence[T]: + return cls(children) + + +_NODETYPE_REGISTRY: dict[type | tuple[str, type], PyTreeNodeRegistryEntry] = { + type(None): PyTreeNodeRegistryEntry( + type(None), # type: ignore[arg-type] + _none_flatten, + _none_unflatten, + path_entry_type=PyTreeEntry, + kind=PyTreeKind.NONE, + ), + tuple: PyTreeNodeRegistryEntry( + tuple, + _tuple_flatten, + _tuple_unflatten, + path_entry_type=SequenceEntry, + kind=PyTreeKind.TUPLE, + ), + list: PyTreeNodeRegistryEntry( + list, + _list_flatten, + _list_unflatten, + path_entry_type=SequenceEntry, + kind=PyTreeKind.LIST, + ), + dict: PyTreeNodeRegistryEntry( + dict, + _dict_flatten, + _dict_unflatten, + path_entry_type=MappingEntry, + kind=PyTreeKind.DICT, + ), + namedtuple: PyTreeNodeRegistryEntry( # type: ignore[dict-item] # noqa: PYI024 + namedtuple, # type: ignore[arg-type] # noqa: PYI024 + _namedtuple_flatten, + _namedtuple_unflatten, + path_entry_type=NamedTupleEntry, + kind=PyTreeKind.NAMEDTUPLE, + ), + OrderedDict: PyTreeNodeRegistryEntry( + OrderedDict, + _ordereddict_flatten, + _ordereddict_unflatten, + path_entry_type=MappingEntry, + kind=PyTreeKind.ORDEREDDICT, + ), + defaultdict: PyTreeNodeRegistryEntry( + defaultdict, + _defaultdict_flatten, + _defaultdict_unflatten, + path_entry_type=MappingEntry, + kind=PyTreeKind.DEFAULTDICT, + ), + deque: PyTreeNodeRegistryEntry( + deque, + _deque_flatten, + _deque_unflatten, + path_entry_type=SequenceEntry, + kind=PyTreeKind.DEQUE, + ), + StructSequence: PyTreeNodeRegistryEntry( + StructSequence, + _structseq_flatten, + _structseq_unflatten, + path_entry_type=StructSequenceEntry, + kind=PyTreeKind.STRUCTSEQUENCE, + ), +} + + +_DICT_INSERTION_ORDERED_REGISTRY_ENTRY = PyTreeNodeRegistryEntry( + dict, + _dict_insertion_ordered_flatten, + _dict_insertion_ordered_unflatten, + path_entry_type=MappingEntry, + kind=PyTreeKind.DICT, +) +_DEFAULTDICT_INSERTION_ORDERED_REGISTRY_ENTRY = PyTreeNodeRegistryEntry( + defaultdict, + _defaultdict_insertion_ordered_flatten, + _defaultdict_insertion_ordered_unflatten, + path_entry_type=MappingEntry, + kind=PyTreeKind.DEFAULTDICT, +) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/optree/treespec.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/optree/treespec.py new file mode 100644 index 0000000000000000000000000000000000000000..07ec08ef80c0348792fb22920b7911e0c56d6273 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/optree/treespec.py @@ -0,0 +1,55 @@ +# Copyright 2022-2025 MetaOPT Team. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""The :mod:`optree.treespec` namespace contains constructors for class :class:`optree.PyTreeSpec`. + +>>> import optree.treespec as treespec +>>> treespec.leaf() +PyTreeSpec(*) +>>> treespec.none() +PyTreeSpec(None) +>>> treespec.dict({'a': treespec.leaf(), 'b': treespec.leaf()}) +PyTreeSpec({'a': *, 'b': *}) + +.. versionadded:: 0.14.1 +""" + +from __future__ import annotations + +from optree.ops import treespec_defaultdict as defaultdict +from optree.ops import treespec_deque as deque +from optree.ops import treespec_dict as dict # pylint: disable=redefined-builtin +from optree.ops import treespec_from_collection as from_collection +from optree.ops import treespec_leaf as leaf +from optree.ops import treespec_list as list # pylint: disable=redefined-builtin +from optree.ops import treespec_namedtuple as namedtuple +from optree.ops import treespec_none as none +from optree.ops import treespec_ordereddict as ordereddict +from optree.ops import treespec_structseq as structseq +from optree.ops import treespec_tuple as tuple # pylint: disable=redefined-builtin + + +__all__ = [ + 'leaf', + 'none', + 'tuple', + 'list', + 'dict', + 'namedtuple', + 'ordereddict', + 'defaultdict', + 'deque', + 'structseq', + 'from_collection', +] diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/optree/typing.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/optree/typing.py new file mode 100644 index 0000000000000000000000000000000000000000..0e41e8b00b0c187ef3cda4b582387f66fc785976 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/optree/typing.py @@ -0,0 +1,582 @@ +# Copyright 2022-2025 MetaOPT Team. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Typing utilities for OpTree.""" + +from __future__ import annotations + +import abc +import functools +import platform +import sys +import threading +import types +from builtins import dict as Dict # noqa: N812 +from builtins import list as List # noqa: N812 +from builtins import tuple as Tuple # noqa: N812 +from collections import OrderedDict +from collections import defaultdict as DefaultDict # noqa: N812 +from collections import deque as Deque # noqa: N812 +from collections.abc import ( + Collection, + Hashable, + ItemsView, + Iterable, + Iterator, + KeysView, + Sequence, + ValuesView, +) +from typing import ( + Any, + Callable, + ClassVar, + Final, + ForwardRef, + Generic, + Optional, + Protocol, + TypeVar, + Union, + final, + get_origin, + runtime_checkable, +) +from typing_extensions import ( + NamedTuple, # Generic NamedTuple: Python 3.11+ + Never, # Python 3.11+ + ParamSpec, # Python 3.10+ + Self, # Python 3.11+ + TypeAlias, # Python 3.10+ + TypeAliasType, # Python 3.12+ +) +from weakref import WeakKeyDictionary + +import optree._C as _C +from optree._C import PyTreeKind, PyTreeSpec +from optree.accessors import ( + AutoEntry, + DataclassEntry, + FlattenedEntry, + GetAttrEntry, + GetItemEntry, + MappingEntry, + NamedTupleEntry, + PyTreeAccessor, + PyTreeEntry, + SequenceEntry, + StructSequenceEntry, +) + + +__all__ = [ + 'PyTreeSpec', + 'PyTreeDef', + 'PyTreeKind', + 'PyTree', + 'PyTreeTypeVar', + 'CustomTreeNode', + 'Children', + 'MetaData', + 'FlattenFunc', + 'UnflattenFunc', + 'PyTreeEntry', + 'GetItemEntry', + 'GetAttrEntry', + 'FlattenedEntry', + 'AutoEntry', + 'SequenceEntry', + 'MappingEntry', + 'NamedTupleEntry', + 'StructSequenceEntry', + 'DataclassEntry', + 'PyTreeAccessor', + 'is_namedtuple', + 'is_namedtuple_class', + 'is_namedtuple_instance', + 'namedtuple_fields', + 'is_structseq', + 'is_structseq_class', + 'is_structseq_instance', + 'structseq_fields', + 'T', + 'S', + 'U', + 'KT', + 'VT', + 'P', + 'F', + 'Iterable', + 'Sequence', + 'Tuple', + 'List', + 'Dict', + 'NamedTuple', + 'OrderedDict', + 'DefaultDict', + 'Deque', + 'StructSequence', +] + + +PyTreeDef: TypeAlias = PyTreeSpec # alias + +T = TypeVar('T') +S = TypeVar('S') +U = TypeVar('U') +KT = TypeVar('KT') +VT = TypeVar('VT') +P = ParamSpec('P') +F = TypeVar('F', bound=Callable[..., Any]) + + +Children: TypeAlias = Iterable[T] +MetaData: TypeAlias = Optional[Hashable] + + +@runtime_checkable +class CustomTreeNode(Protocol[T]): # pylint: disable=too-few-public-methods + """The abstract base class for custom pytree nodes.""" + + def __tree_flatten__( + self, + /, + ) -> ( + # Use `range(num_children)` as path entries + tuple[Children[T], MetaData] + | + # With optionally implemented path entries + tuple[Children[T], MetaData, Iterable[Any] | None] + ): + """Flatten the custom pytree node into children and metadata.""" + + @classmethod + def __tree_unflatten__(cls, metadata: MetaData, children: Children[T], /) -> Self: + """Unflatten the children and metadata into the custom pytree node.""" + + +_UnionType = type(Union[int, str]) + + +try: # pragma: no cover + from typing import _tp_cache # type: ignore[attr-defined] # pylint: disable=ungrouped-imports +except ImportError: # pragma: no cover + + def _tp_cache(func: Callable[P, T], /) -> Callable[P, T]: + cached = functools.lru_cache(func) + + @functools.wraps(func) + def inner(*args: P.args, **kwargs: P.kwargs) -> T: + try: + return cached(*args, **kwargs) # type: ignore[arg-type] + except TypeError: + # All real errors (not unhashable args) are raised below. + return func(*args, **kwargs) + + return inner + + +class PyTree(Generic[T]): # pragma: no cover + """Generic PyTree type. + + >>> import torch + >>> TensorTree = PyTree[torch.Tensor] + >>> TensorTree # doctest: +IGNORE_WHITESPACE + typing.Union[torch.Tensor, + tuple[ForwardRef('PyTree[torch.Tensor]'), ...], + list[ForwardRef('PyTree[torch.Tensor]')], + dict[typing.Any, ForwardRef('PyTree[torch.Tensor]')], + collections.deque[ForwardRef('PyTree[torch.Tensor]')], + optree.typing.CustomTreeNode[ForwardRef('PyTree[torch.Tensor]')]] + """ + + __slots__: ClassVar[tuple[()]] = () + __instances__: ClassVar[ + WeakKeyDictionary[ + TypeAliasType, + tuple[type | TypeAliasType, str | None], + ] + ] = WeakKeyDictionary() + __instance_lock__: ClassVar[threading.Lock] = threading.Lock() + + @_tp_cache + def __class_getitem__( # noqa: C901 # pylint: disable=too-many-branches + cls, + item: ( + type[T] + | TypeAliasType + | tuple[type[T] | TypeAliasType] + | tuple[type[T] | TypeAliasType, str | None] + ), + ) -> TypeAliasType: + """Instantiate a PyTree type with the given type.""" + if not isinstance(item, tuple): + item = (item, None) + if len(item) == 1: + item = (item[0], None) + elif len(item) != 2: + raise TypeError( + f'{cls.__name__}[...] only supports a tuple of 2 items, ' + f'a parameter and a string of type name, got {item!r}.', + ) + param, name = item + if name is not None and not isinstance(name, str): + raise TypeError( + f'{cls.__name__}[...] only supports a tuple of 2 items, ' + f'a parameter and a string of type name, got {item!r}.', + ) + + if isinstance(param, _UnionType) and get_origin(param) is Union: # type: ignore[unreachable] + with cls.__instance_lock__: # type: ignore[unreachable] + try: + if param in cls.__instances__: + return param # PyTree[PyTree[T]] -> PyTree[T] + except TypeError: + pass # non-hashable type + + if name is not None: + recurse_ref = ForwardRef(name) + elif isinstance(param, TypeVar): + recurse_ref = ForwardRef(f'{cls.__name__}[{param.__name__}]') # type: ignore[unreachable] + elif isinstance(param, type): + if param.__module__ == 'builtins': + typename = param.__qualname__ + else: + try: + typename = f'{param.__module__}.{param.__qualname__}' + except AttributeError: + typename = f'{param.__module__}.{param.__name__}' + recurse_ref = ForwardRef(f'{cls.__name__}[{typename}]') + else: + recurse_ref = ForwardRef(f'{cls.__name__}[{param!r}]') + + pytree_alias = Union[ + param, # type: ignore[valid-type] + Tuple[recurse_ref, ...], # type: ignore[valid-type] # Tuple, NamedTuple, PyStructSequence + List[recurse_ref], # type: ignore[valid-type] + Dict[Any, recurse_ref], # type: ignore[valid-type] # Dict, OrderedDict, DefaultDict + Deque[recurse_ref], # type: ignore[valid-type] + CustomTreeNode[recurse_ref], # type: ignore[valid-type] + ] + + with cls.__instance_lock__: + cls.__instances__[pytree_alias] = (param, name) # type: ignore[index] + return pytree_alias # type: ignore[return-value] + + def __new__(cls, /) -> Never: # pylint: disable=arguments-differ + """Prohibit instantiation.""" + raise TypeError('Cannot instantiate special typing classes.') + + def __init_subclass__(cls, /, *args: Any, **kwargs: Any) -> Never: + """Prohibit subclassing.""" + raise TypeError('Cannot subclass special typing classes.') + + def __getitem__(self, key: Any, /) -> PyTree[T] | T: + """Emulate collection-like behavior.""" + raise NotImplementedError + + def __getattr__(self, name: str, /) -> PyTree[T] | T: + """Emulate dataclass-like behavior.""" + raise NotImplementedError + + def __contains__(self, key: Any, /) -> bool: + """Emulate collection-like behavior.""" + raise NotImplementedError + + def __len__(self, /) -> int: + """Emulate collection-like behavior.""" + raise NotImplementedError + + def __iter__(self, /) -> Iterator[PyTree[T] | T | Any]: + """Emulate collection-like behavior.""" + raise NotImplementedError + + def index(self, key: Any, /) -> int: + """Emulate sequence-like behavior.""" + raise NotImplementedError + + def count(self, key: Any, /) -> int: + """Emulate sequence-like behavior.""" + raise NotImplementedError + + def get(self, key: Any, /, default: S | None = None) -> PyTree[T] | T | S | None: + """Emulate mapping-like behavior.""" + raise NotImplementedError + + def keys(self, /) -> KeysView[Any]: + """Emulate mapping-like behavior.""" + raise NotImplementedError + + def values(self, /) -> ValuesView[PyTree[T] | T]: + """Emulate mapping-like behavior.""" + raise NotImplementedError + + def items(self, /) -> ItemsView[Any, PyTree[T] | T]: + """Emulate mapping-like behavior.""" + raise NotImplementedError + + +# pylint: disable-next=too-few-public-methods +class PyTreeTypeVar: # pragma: no cover + """Type variable for PyTree. + + >>> import torch + >>> TensorTree = PyTreeTypeVar('TensorTree', torch.Tensor) + >>> TensorTree # doctest: +IGNORE_WHITESPACE + typing.Union[torch.Tensor, + tuple[ForwardRef('TensorTree'), ...], + list[ForwardRef('TensorTree')], + dict[typing.Any, ForwardRef('TensorTree')], + collections.deque[ForwardRef('TensorTree')], + optree.typing.CustomTreeNode[ForwardRef('TensorTree')]] + """ + + @_tp_cache + def __new__(cls, /, name: str, param: type | TypeAliasType) -> TypeAliasType: # type: ignore[misc] + """Instantiate a PyTree type variable with the given name and parameter.""" + if not isinstance(name, str): + raise TypeError(f'{cls.__name__} only supports a string of type name, got {name!r}.') + return PyTree[param, name] # type: ignore[misc,valid-type] + + def __init_subclass__(cls, /, *args: Any, **kwargs: Any) -> Never: + """Prohibit subclassing.""" + raise TypeError('Cannot subclass special typing classes.') + + +class FlattenFunc(Protocol[T]): # pylint: disable=too-few-public-methods + """The type stub class for flatten functions.""" + + @abc.abstractmethod + def __call__( + self, + container: Collection[T], + /, + ) -> tuple[Children[T], MetaData] | tuple[Children[T], MetaData, Iterable[Any] | None]: + """Flatten the container into children and metadata.""" + + +class UnflattenFunc(Protocol[T]): # pylint: disable=too-few-public-methods + """The type stub class for unflatten functions.""" + + @abc.abstractmethod + def __call__(self, metadata: MetaData, children: Children[T], /) -> Collection[T]: + """Unflatten the children and metadata back into the container.""" + + +def _override_with_( + cxx_implementation: Callable[P, T], + /, +) -> Callable[[Callable[P, T]], Callable[P, T]]: + """Decorator to override the Python implementation with the C++ implementation. + + >>> @_override_with_(any) + ... def my_any(iterable): + ... for elem in iterable: + ... if elem: + ... return True + ... return False + ... + >>> my_any([False, False, True, False, False, True]) # run at C speed + True + """ + + def wrapper(python_implementation: Callable[P, T], /) -> Callable[P, T]: + @functools.wraps(python_implementation) + def wrapped(*args: P.args, **kwargs: P.kwargs) -> T: + return cxx_implementation(*args, **kwargs) + + wrapped.__cxx_implementation__ = cxx_implementation # type: ignore[attr-defined] + wrapped.__python_implementation__ = python_implementation # type: ignore[attr-defined] + + return wrapped + + return wrapper + + +@_override_with_(_C.is_namedtuple) +def is_namedtuple(obj: object | type, /) -> bool: + """Return whether the object is an instance of namedtuple or a subclass of namedtuple.""" + cls = obj if isinstance(obj, type) else type(obj) + return is_namedtuple_class(cls) + + +@_override_with_(_C.is_namedtuple_instance) +def is_namedtuple_instance(obj: object, /) -> bool: + """Return whether the object is an instance of namedtuple.""" + return is_namedtuple_class(type(obj)) + + +@_override_with_(_C.is_namedtuple_class) +def is_namedtuple_class(cls: type, /) -> bool: + """Return whether the class is a subclass of namedtuple.""" + return ( + isinstance(cls, type) + and issubclass(cls, tuple) + and isinstance(getattr(cls, '_fields', None), tuple) + # pylint: disable-next=unidiomatic-typecheck + and all(type(field) is str for field in cls._fields) # type: ignore[attr-defined] + and callable(getattr(cls, '_make', None)) + and callable(getattr(cls, '_asdict', None)) + ) + + +@_override_with_(_C.namedtuple_fields) +def namedtuple_fields(obj: tuple | type[tuple], /) -> tuple[str, ...]: + """Return the field names of a namedtuple.""" + if isinstance(obj, type): + cls = obj + if not is_namedtuple_class(cls): + raise TypeError(f'Expected a collections.namedtuple type, got {cls!r}.') + else: + cls = type(obj) + if not is_namedtuple_class(cls): + raise TypeError(f'Expected an instance of collections.namedtuple type, got {obj!r}.') + return cls._fields # type: ignore[attr-defined] + + +_T_co = TypeVar('_T_co', covariant=True) + + +class StructSequenceMeta(type): + """The metaclass for PyStructSequence stub type.""" + + def __subclasscheck__(cls, subclass: type, /) -> bool: + """Return whether the class is a PyStructSequence type. + + >>> import time + >>> issubclass(time.struct_time, StructSequence) + True + >>> class MyTuple(tuple): + ... n_fields = 2 + ... n_sequence_fields = 2 + ... n_unnamed_fields = 0 + >>> issubclass(MyTuple, StructSequence) + False + """ + return is_structseq_class(subclass) + + def __instancecheck__(cls, instance: Any, /) -> bool: + """Return whether the object is a PyStructSequence instance. + + >>> import sys + >>> isinstance(sys.float_info, StructSequence) + True + >>> isinstance((1, 2), StructSequence) + False + """ + return is_structseq_instance(instance) + + +# Reference: https://github.com/python/typeshed/blob/main/stdlib/_typeshed/__init__.pyi +# This is an internal CPython type that is like, but subtly different from a NamedTuple. +# `StructSequence` classes are unsubclassable, so are all decorated with `@final`. +# pylint: disable-next=invalid-name,missing-class-docstring +@final +class StructSequence(tuple[_T_co, ...], metaclass=StructSequenceMeta): + """A generic type stub for CPython's ``PyStructSequence`` type.""" + + __slots__: ClassVar[tuple[()]] = () + + n_fields: Final[ClassVar[int]] # type: ignore[misc] # pylint: disable=invalid-name + n_sequence_fields: Final[ClassVar[int]] # type: ignore[misc] # pylint: disable=invalid-name + n_unnamed_fields: Final[ClassVar[int]] # type: ignore[misc] # pylint: disable=invalid-name + + def __init_subclass__(cls, /) -> Never: + """Prohibit subclassing.""" + raise TypeError("type 'StructSequence' is not an acceptable base type") + + # pylint: disable-next=unused-argument,redefined-builtin + def __new__(cls, /, sequence: Iterable[_T_co], dict: dict[str, Any] = ...) -> Self: + """Create a new :class:`StructSequence` instance.""" + raise NotImplementedError + + +structseq: TypeAlias = StructSequence # noqa: PYI042 # pylint: disable=invalid-name + +del StructSequenceMeta + + +@_override_with_(_C.is_structseq) +def is_structseq(obj: object | type, /) -> bool: + """Return whether the object is an instance of PyStructSequence or a class of PyStructSequence.""" + cls = obj if isinstance(obj, type) else type(obj) + return is_structseq_class(cls) + + +@_override_with_(_C.is_structseq_instance) +def is_structseq_instance(obj: object, /) -> bool: + """Return whether the object is an instance of PyStructSequence.""" + return is_structseq_class(type(obj)) + + +# Set if the type allows subclassing (see CPython's Include/object.h) +Py_TPFLAGS_BASETYPE: int = _C.Py_TPFLAGS_BASETYPE # (1UL << 10) # pylint: disable=invalid-name + + +@_override_with_(_C.is_structseq_class) +def is_structseq_class(cls: type, /) -> bool: + """Return whether the class is a class of PyStructSequence.""" + if ( + isinstance(cls, type) + # Check direct inheritance from `tuple` rather than `issubclass(cls, tuple)` + and cls.__bases__ == (tuple,) + # Check PyStructSequence members + and isinstance(getattr(cls, 'n_fields', None), int) + and isinstance(getattr(cls, 'n_sequence_fields', None), int) + and isinstance(getattr(cls, 'n_unnamed_fields', None), int) + ): + # Check the type does not allow subclassing + if platform.python_implementation() == 'PyPy': # pragma: pypy cover + try: + types.new_class('subclass', bases=(cls,)) + except (AssertionError, TypeError): + return True + return False + return not bool(cls.__flags__ & Py_TPFLAGS_BASETYPE) # pragma: pypy no cover + return False + + +# pylint: disable-next=line-too-long +StructSequenceFieldType: type[types.MemberDescriptorType] = type(type(sys.version_info).major) # type: ignore[assignment] + + +@_override_with_(_C.structseq_fields) +def structseq_fields(obj: tuple | type[tuple], /) -> tuple[str, ...]: + """Return the field names of a PyStructSequence.""" + if isinstance(obj, type): + cls = obj + if not is_structseq_class(cls): + raise TypeError(f'Expected a PyStructSequence type, got {cls!r}.') + else: + cls = type(obj) + if not is_structseq_class(cls): + raise TypeError(f'Expected an instance of PyStructSequence type, got {obj!r}.') + + if platform.python_implementation() == 'PyPy': # pragma: pypy cover + indices_by_name = { + name: member.index # type: ignore[attr-defined] + for name, member in vars(cls).items() + if isinstance(member, StructSequenceFieldType) + } + fields = sorted(indices_by_name, key=indices_by_name.get) # type: ignore[arg-type] + else: # pragma: pypy no cover + fields = [ + name + for name, member in vars(cls).items() + if isinstance(member, StructSequenceFieldType) + ] + return tuple(fields[: cls.n_sequence_fields]) # type: ignore[attr-defined] + + +del _tp_cache diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/optree/utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/optree/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..5a725ee00a0e544347ed76d5651cb7fd69f9aa29 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/optree/utils.py @@ -0,0 +1,117 @@ +# Copyright 2022-2025 MetaOPT Team. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Utility functions for OpTree.""" + +from __future__ import annotations + +from collections.abc import Iterable, Sequence +from typing import TYPE_CHECKING, Any, Callable, overload + + +if TYPE_CHECKING: + from optree.typing import S, T, U + + +def total_order_sorted( + iterable: Iterable[T], + /, + *, + key: Callable[[T], Any] | None = None, + reverse: bool = False, +) -> list[T]: + """Sort an iterable in a total order. + + This is useful for sorting objects that are not comparable, e.g., dictionaries with different + types of keys. + """ + sequence = list(iterable) + + try: + # Sort directly if possible + return sorted(sequence, key=key, reverse=reverse) # type: ignore[type-var,arg-type] + except TypeError: + if key is None: + + def key_fn(x: T) -> tuple[str, Any]: + return (f'{x.__class__.__module__}.{x.__class__.__qualname__}', x) + + else: + + def key_fn(x: T) -> tuple[str, Any]: + y = key(x) + return (f'{y.__class__.__module__}.{y.__class__.__qualname__}', y) + + try: + # Add `{obj.__class__.__module__}.{obj.__class__.__qualname__}` to the key order to make + # it sortable between different types (e.g., `int` vs. `str`) + return sorted(sequence, key=key_fn, reverse=reverse) + except TypeError: # cannot sort the keys (e.g., user-defined types) + return sequence # fallback to original order + + +@overload +def safe_zip( + iter1: Iterable[T], + /, +) -> zip[tuple[T]]: ... + + +@overload +def safe_zip( + iter1: Iterable[T], + iter2: Iterable[S], + /, +) -> zip[tuple[T, S]]: ... + + +@overload +def safe_zip( + iter1: Iterable[T], + iter2: Iterable[S], + iter3: Iterable[U], + /, +) -> zip[tuple[T, S, U]]: ... + + +@overload +def safe_zip( + iter1: Iterable[Any], + iter2: Iterable[Any], + iter3: Iterable[Any], + iter4: Iterable[Any], + /, + *iters: Iterable[Any], +) -> zip[tuple[Any, ...]]: ... + + +def safe_zip(*args: Iterable[Any]) -> zip[tuple[Any, ...]]: + """Strict zip that requires all arguments to be the same length.""" + seqs = [arg if isinstance(arg, Sequence) else list(arg) for arg in args] + if len(set(map(len, seqs))) > 1: + raise ValueError(f'length mismatch: {list(map(len, seqs))}') + return zip(*seqs) + + +def unzip2(xys: Iterable[tuple[T, S]], /) -> tuple[tuple[T, ...], tuple[S, ...]]: + """Unzip sequence of length-2 tuples into two tuples.""" + # Note: we deliberately don't use zip(*xys) because it is lazily evaluated, + # is too permissive about inputs, and does not guarantee a length-2 output. + # For example, for empty dict: tuple(zip(*{}.items())) -> () + xs = [] + ys = [] + for x, y in xys: + xs.append(x) + ys.append(y) + return tuple(xs), tuple(ys) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/optree/version.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/optree/version.py new file mode 100644 index 0000000000000000000000000000000000000000..65efee704e36c50408d09eb502418178b860869b --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/optree/version.py @@ -0,0 +1,61 @@ +# Copyright 2022-2025 MetaOPT Team. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""OpTree: Optimized PyTree Utilities.""" + +# pylint: disable=invalid-name + +__version__ = '0.19.0' +__license__ = 'Apache-2.0' +__author__ = 'OpTree Contributors' +__release__ = True + +if not __release__: + import subprocess + from pathlib import Path + + root_dir = Path(__file__).absolute().parent.parent + try: + prefix, sep, suffix = ( + subprocess.check_output( # noqa: S603 + [ # noqa: S607 + 'git', + f'--git-dir={root_dir / ".git"}', + 'describe', + '--abbrev=7', + ], + cwd=root_dir, + stderr=subprocess.DEVNULL, + text=True, + encoding='utf-8', + timeout=120.0, + ) + .strip() + .lstrip('v') + .replace('-', '.dev', 1) + .replace('-', '+', 1) + .partition('.dev') + ) + if sep: + version_prefix, dot, version_tail = prefix.rpartition('.') + prefix = f'{version_prefix}{dot}{int(version_tail) + 1}' + __version__ = f'{prefix}{sep}{suffix}' + del version_prefix, dot, version_tail + else: + __version__ = prefix + del prefix, sep, suffix + except (OSError, RuntimeError, subprocess.SubprocessError): + pass + + del Path, subprocess, root_dir diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging-25.0.dist-info/INSTALLER b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging-25.0.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..f79e4cb9aaf0b2d9e8ba78861e2071317b2384b3 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging-25.0.dist-info/INSTALLER @@ -0,0 +1 @@ +conda \ No newline at end of file diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging-25.0.dist-info/METADATA b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging-25.0.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..10b290a6cd1770506ae281a5fa469f6c110c364c --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging-25.0.dist-info/METADATA @@ -0,0 +1,105 @@ +Metadata-Version: 2.4 +Name: packaging +Version: 25.0 +Summary: Core utilities for Python packages +Author-email: Donald Stufft +Requires-Python: >=3.8 +Description-Content-Type: text/x-rst +Classifier: Development Status :: 5 - Production/Stable +Classifier: Intended Audience :: Developers +Classifier: License :: OSI Approved :: Apache Software License +Classifier: License :: OSI Approved :: BSD License +Classifier: Programming Language :: Python +Classifier: Programming Language :: Python :: 3 +Classifier: Programming Language :: Python :: 3 :: Only +Classifier: Programming Language :: Python :: 3.8 +Classifier: Programming Language :: Python :: 3.9 +Classifier: Programming Language :: Python :: 3.10 +Classifier: Programming Language :: Python :: 3.11 +Classifier: Programming Language :: Python :: 3.12 +Classifier: Programming Language :: Python :: 3.13 +Classifier: Programming Language :: Python :: Implementation :: CPython +Classifier: Programming Language :: Python :: Implementation :: PyPy +Classifier: Typing :: Typed +License-File: LICENSE +License-File: LICENSE.APACHE +License-File: LICENSE.BSD +Project-URL: Documentation, https://packaging.pypa.io/ +Project-URL: Source, https://github.com/pypa/packaging + +packaging +========= + +.. start-intro + +Reusable core utilities for various Python Packaging +`interoperability specifications `_. + +This library provides utilities that implement the interoperability +specifications which have clearly one correct behaviour (eg: :pep:`440`) +or benefit greatly from having a single shared implementation (eg: :pep:`425`). + +.. end-intro + +The ``packaging`` project includes the following: version handling, specifiers, +markers, requirements, tags, utilities. + +Documentation +------------- + +The `documentation`_ provides information and the API for the following: + +- Version Handling +- Specifiers +- Markers +- Requirements +- Tags +- Utilities + +Installation +------------ + +Use ``pip`` to install these utilities:: + + pip install packaging + +The ``packaging`` library uses calendar-based versioning (``YY.N``). + +Discussion +---------- + +If you run into bugs, you can file them in our `issue tracker`_. + +You can also join ``#pypa`` on Freenode to ask questions or get involved. + + +.. _`documentation`: https://packaging.pypa.io/ +.. _`issue tracker`: https://github.com/pypa/packaging/issues + + +Code of Conduct +--------------- + +Everyone interacting in the packaging project's codebases, issue trackers, chat +rooms, and mailing lists is expected to follow the `PSF Code of Conduct`_. + +.. _PSF Code of Conduct: https://github.com/pypa/.github/blob/main/CODE_OF_CONDUCT.md + +Contributing +------------ + +The ``CONTRIBUTING.rst`` file outlines how to contribute to this project as +well as how to report a potential security issue. The documentation for this +project also covers information about `project development`_ and `security`_. + +.. _`project development`: https://packaging.pypa.io/en/latest/development/ +.. _`security`: https://packaging.pypa.io/en/latest/security/ + +Project History +--------------- + +Please review the ``CHANGELOG.rst`` file or the `Changelog documentation`_ for +recent changes and project history. + +.. _`Changelog documentation`: https://packaging.pypa.io/en/latest/changelog/ + diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging-25.0.dist-info/RECORD b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging-25.0.dist-info/RECORD new file mode 100644 index 0000000000000000000000000000000000000000..6a1f82a905a05b778a928630200667a9e0002cfa --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging-25.0.dist-info/RECORD @@ -0,0 +1,42 @@ 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a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging-25.0.dist-info/direct_url.json b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging-25.0.dist-info/direct_url.json new file mode 100644 index 0000000000000000000000000000000000000000..536c485ead1d1ee8386bd0f96bdc7b2e2fc85bb9 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging-25.0.dist-info/direct_url.json @@ -0,0 +1 @@ +{"dir_info": {}, "url": "file:///home/task_176104877067765/conda-bld/packaging_1761049113113/work"} \ No newline at end of file diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging-25.0.dist-info/licenses/LICENSE b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging-25.0.dist-info/licenses/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..6f62d44e4ef733c0e713afcd2371fed7f2b3de67 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging-25.0.dist-info/licenses/LICENSE @@ -0,0 +1,3 @@ +This software is made available under the terms of *either* of the licenses +found in LICENSE.APACHE or LICENSE.BSD. Contributions to this software is made +under the terms of *both* these licenses. diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging-25.0.dist-info/licenses/LICENSE.APACHE b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging-25.0.dist-info/licenses/LICENSE.APACHE new file mode 100644 index 0000000000000000000000000000000000000000..f433b1a53f5b830a205fd2df78e2b34974656c7b --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging-25.0.dist-info/licenses/LICENSE.APACHE @@ -0,0 +1,177 @@ + + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + + 2. Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..d45c22cfd88e2b88976b46d4171a502b602658ec --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging/__init__.py @@ -0,0 +1,15 @@ +# This file is dual licensed under the terms of the Apache License, Version +# 2.0, and the BSD License. See the LICENSE file in the root of this repository +# for complete details. + +__title__ = "packaging" +__summary__ = "Core utilities for Python packages" +__uri__ = "https://github.com/pypa/packaging" + +__version__ = "25.0" + +__author__ = "Donald Stufft and individual contributors" +__email__ = "donald@stufft.io" + +__license__ = "BSD-2-Clause or Apache-2.0" +__copyright__ = f"2014 {__author__}" diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging/_elffile.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging/_elffile.py new file mode 100644 index 0000000000000000000000000000000000000000..7a5afc33b0a2401cf509bb6a8d1143d5230324d9 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging/_elffile.py @@ -0,0 +1,109 @@ +""" +ELF file parser. + +This provides a class ``ELFFile`` that parses an ELF executable in a similar +interface to ``ZipFile``. Only the read interface is implemented. + +Based on: https://gist.github.com/lyssdod/f51579ae8d93c8657a5564aefc2ffbca +ELF header: https://refspecs.linuxfoundation.org/elf/gabi4+/ch4.eheader.html +""" + +from __future__ import annotations + +import enum +import os +import struct +from typing import IO + + +class ELFInvalid(ValueError): + pass + + +class EIClass(enum.IntEnum): + C32 = 1 + C64 = 2 + + +class EIData(enum.IntEnum): + Lsb = 1 + Msb = 2 + + +class EMachine(enum.IntEnum): + I386 = 3 + S390 = 22 + Arm = 40 + X8664 = 62 + AArc64 = 183 + + +class ELFFile: + """ + Representation of an ELF executable. + """ + + def __init__(self, f: IO[bytes]) -> None: + self._f = f + + try: + ident = self._read("16B") + except struct.error as e: + raise ELFInvalid("unable to parse identification") from e + magic = bytes(ident[:4]) + if magic != b"\x7fELF": + raise ELFInvalid(f"invalid magic: {magic!r}") + + self.capacity = ident[4] # Format for program header (bitness). + self.encoding = ident[5] # Data structure encoding (endianness). + + try: + # e_fmt: Format for program header. + # p_fmt: Format for section header. + # p_idx: Indexes to find p_type, p_offset, and p_filesz. + e_fmt, self._p_fmt, self._p_idx = { + (1, 1): ("HHIIIIIHHH", ">IIIIIIII", (0, 1, 4)), # 32-bit MSB. + (2, 1): ("HHIQQQIHHH", ">IIQQQQQQ", (0, 2, 5)), # 64-bit MSB. + }[(self.capacity, self.encoding)] + except KeyError as e: + raise ELFInvalid( + f"unrecognized capacity ({self.capacity}) or encoding ({self.encoding})" + ) from e + + try: + ( + _, + self.machine, # Architecture type. + _, + _, + self._e_phoff, # Offset of program header. + _, + self.flags, # Processor-specific flags. + _, + self._e_phentsize, # Size of section. + self._e_phnum, # Number of sections. + ) = self._read(e_fmt) + except struct.error as e: + raise ELFInvalid("unable to parse machine and section information") from e + + def _read(self, fmt: str) -> tuple[int, ...]: + return struct.unpack(fmt, self._f.read(struct.calcsize(fmt))) + + @property + def interpreter(self) -> str | None: + """ + The path recorded in the ``PT_INTERP`` section header. + """ + for index in range(self._e_phnum): + self._f.seek(self._e_phoff + self._e_phentsize * index) + try: + data = self._read(self._p_fmt) + except struct.error: + continue + if data[self._p_idx[0]] != 3: # Not PT_INTERP. + continue + self._f.seek(data[self._p_idx[1]]) + return os.fsdecode(self._f.read(data[self._p_idx[2]])).strip("\0") + return None diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging/_manylinux.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging/_manylinux.py new file mode 100644 index 0000000000000000000000000000000000000000..95f55762e86f08af76aaa95f5cd147d4517c6d66 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging/_manylinux.py @@ -0,0 +1,262 @@ +from __future__ import annotations + +import collections +import contextlib +import functools +import os +import re +import sys +import warnings +from typing import Generator, Iterator, NamedTuple, Sequence + +from ._elffile import EIClass, EIData, ELFFile, EMachine + +EF_ARM_ABIMASK = 0xFF000000 +EF_ARM_ABI_VER5 = 0x05000000 +EF_ARM_ABI_FLOAT_HARD = 0x00000400 + + +# `os.PathLike` not a generic type until Python 3.9, so sticking with `str` +# as the type for `path` until then. +@contextlib.contextmanager +def _parse_elf(path: str) -> Generator[ELFFile | None, None, None]: + try: + with open(path, "rb") as f: + yield ELFFile(f) + except (OSError, TypeError, ValueError): + yield None + + +def _is_linux_armhf(executable: str) -> bool: + # hard-float ABI can be detected from the ELF header of the running + # process + # https://static.docs.arm.com/ihi0044/g/aaelf32.pdf + with _parse_elf(executable) as f: + return ( + f is not None + and f.capacity == EIClass.C32 + and f.encoding == EIData.Lsb + and f.machine == EMachine.Arm + and f.flags & EF_ARM_ABIMASK == EF_ARM_ABI_VER5 + and f.flags & EF_ARM_ABI_FLOAT_HARD == EF_ARM_ABI_FLOAT_HARD + ) + + +def _is_linux_i686(executable: str) -> bool: + with _parse_elf(executable) as f: + return ( + f is not None + and f.capacity == EIClass.C32 + and f.encoding == EIData.Lsb + and f.machine == EMachine.I386 + ) + + +def _have_compatible_abi(executable: str, archs: Sequence[str]) -> bool: + if "armv7l" in archs: + return _is_linux_armhf(executable) + if "i686" in archs: + return _is_linux_i686(executable) + allowed_archs = { + "x86_64", + "aarch64", + "ppc64", + "ppc64le", + "s390x", + "loongarch64", + "riscv64", + } + return any(arch in allowed_archs for arch in archs) + + +# If glibc ever changes its major version, we need to know what the last +# minor version was, so we can build the complete list of all versions. +# For now, guess what the highest minor version might be, assume it will +# be 50 for testing. Once this actually happens, update the dictionary +# with the actual value. +_LAST_GLIBC_MINOR: dict[int, int] = collections.defaultdict(lambda: 50) + + +class _GLibCVersion(NamedTuple): + major: int + minor: int + + +def _glibc_version_string_confstr() -> str | None: + """ + Primary implementation of glibc_version_string using os.confstr. + """ + # os.confstr is quite a bit faster than ctypes.DLL. It's also less likely + # to be broken or missing. This strategy is used in the standard library + # platform module. + # https://github.com/python/cpython/blob/fcf1d003bf4f0100c/Lib/platform.py#L175-L183 + try: + # Should be a string like "glibc 2.17". + version_string: str | None = os.confstr("CS_GNU_LIBC_VERSION") + assert version_string is not None + _, version = version_string.rsplit() + except (AssertionError, AttributeError, OSError, ValueError): + # os.confstr() or CS_GNU_LIBC_VERSION not available (or a bad value)... + return None + return version + + +def _glibc_version_string_ctypes() -> str | None: + """ + Fallback implementation of glibc_version_string using ctypes. + """ + try: + import ctypes + except ImportError: + return None + + # ctypes.CDLL(None) internally calls dlopen(NULL), and as the dlopen + # manpage says, "If filename is NULL, then the returned handle is for the + # main program". This way we can let the linker do the work to figure out + # which libc our process is actually using. + # + # We must also handle the special case where the executable is not a + # dynamically linked executable. This can occur when using musl libc, + # for example. In this situation, dlopen() will error, leading to an + # OSError. Interestingly, at least in the case of musl, there is no + # errno set on the OSError. The single string argument used to construct + # OSError comes from libc itself and is therefore not portable to + # hard code here. In any case, failure to call dlopen() means we + # can proceed, so we bail on our attempt. + try: + process_namespace = ctypes.CDLL(None) + except OSError: + return None + + try: + gnu_get_libc_version = process_namespace.gnu_get_libc_version + except AttributeError: + # Symbol doesn't exist -> therefore, we are not linked to + # glibc. + return None + + # Call gnu_get_libc_version, which returns a string like "2.5" + gnu_get_libc_version.restype = ctypes.c_char_p + version_str: str = gnu_get_libc_version() + # py2 / py3 compatibility: + if not isinstance(version_str, str): + version_str = version_str.decode("ascii") + + return version_str + + +def _glibc_version_string() -> str | None: + """Returns glibc version string, or None if not using glibc.""" + return _glibc_version_string_confstr() or _glibc_version_string_ctypes() + + +def _parse_glibc_version(version_str: str) -> tuple[int, int]: + """Parse glibc version. + + We use a regexp instead of str.split because we want to discard any + random junk that might come after the minor version -- this might happen + in patched/forked versions of glibc (e.g. Linaro's version of glibc + uses version strings like "2.20-2014.11"). See gh-3588. + """ + m = re.match(r"(?P[0-9]+)\.(?P[0-9]+)", version_str) + if not m: + warnings.warn( + f"Expected glibc version with 2 components major.minor, got: {version_str}", + RuntimeWarning, + stacklevel=2, + ) + return -1, -1 + return int(m.group("major")), int(m.group("minor")) + + +@functools.lru_cache +def _get_glibc_version() -> tuple[int, int]: + version_str = _glibc_version_string() + if version_str is None: + return (-1, -1) + return _parse_glibc_version(version_str) + + +# From PEP 513, PEP 600 +def _is_compatible(arch: str, version: _GLibCVersion) -> bool: + sys_glibc = _get_glibc_version() + if sys_glibc < version: + return False + # Check for presence of _manylinux module. + try: + import _manylinux + except ImportError: + return True + if hasattr(_manylinux, "manylinux_compatible"): + result = _manylinux.manylinux_compatible(version[0], version[1], arch) + if result is not None: + return bool(result) + return True + if version == _GLibCVersion(2, 5): + if hasattr(_manylinux, "manylinux1_compatible"): + return bool(_manylinux.manylinux1_compatible) + if version == _GLibCVersion(2, 12): + if hasattr(_manylinux, "manylinux2010_compatible"): + return bool(_manylinux.manylinux2010_compatible) + if version == _GLibCVersion(2, 17): + if hasattr(_manylinux, "manylinux2014_compatible"): + return bool(_manylinux.manylinux2014_compatible) + return True + + +_LEGACY_MANYLINUX_MAP = { + # CentOS 7 w/ glibc 2.17 (PEP 599) + (2, 17): "manylinux2014", + # CentOS 6 w/ glibc 2.12 (PEP 571) + (2, 12): "manylinux2010", + # CentOS 5 w/ glibc 2.5 (PEP 513) + (2, 5): "manylinux1", +} + + +def platform_tags(archs: Sequence[str]) -> Iterator[str]: + """Generate manylinux tags compatible to the current platform. + + :param archs: Sequence of compatible architectures. + The first one shall be the closest to the actual architecture and be the part of + platform tag after the ``linux_`` prefix, e.g. ``x86_64``. + The ``linux_`` prefix is assumed as a prerequisite for the current platform to + be manylinux-compatible. + + :returns: An iterator of compatible manylinux tags. + """ + if not _have_compatible_abi(sys.executable, archs): + return + # Oldest glibc to be supported regardless of architecture is (2, 17). + too_old_glibc2 = _GLibCVersion(2, 16) + if set(archs) & {"x86_64", "i686"}: + # On x86/i686 also oldest glibc to be supported is (2, 5). + too_old_glibc2 = _GLibCVersion(2, 4) + current_glibc = _GLibCVersion(*_get_glibc_version()) + glibc_max_list = [current_glibc] + # We can assume compatibility across glibc major versions. + # https://sourceware.org/bugzilla/show_bug.cgi?id=24636 + # + # Build a list of maximum glibc versions so that we can + # output the canonical list of all glibc from current_glibc + # down to too_old_glibc2, including all intermediary versions. + for glibc_major in range(current_glibc.major - 1, 1, -1): + glibc_minor = _LAST_GLIBC_MINOR[glibc_major] + glibc_max_list.append(_GLibCVersion(glibc_major, glibc_minor)) + for arch in archs: + for glibc_max in glibc_max_list: + if glibc_max.major == too_old_glibc2.major: + min_minor = too_old_glibc2.minor + else: + # For other glibc major versions oldest supported is (x, 0). + min_minor = -1 + for glibc_minor in range(glibc_max.minor, min_minor, -1): + glibc_version = _GLibCVersion(glibc_max.major, glibc_minor) + tag = "manylinux_{}_{}".format(*glibc_version) + if _is_compatible(arch, glibc_version): + yield f"{tag}_{arch}" + # Handle the legacy manylinux1, manylinux2010, manylinux2014 tags. + if glibc_version in _LEGACY_MANYLINUX_MAP: + legacy_tag = _LEGACY_MANYLINUX_MAP[glibc_version] + if _is_compatible(arch, glibc_version): + yield f"{legacy_tag}_{arch}" diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging/_musllinux.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging/_musllinux.py new file mode 100644 index 0000000000000000000000000000000000000000..d2bf30b56319ba862c5c9a1a39a87c6d1cb68718 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging/_musllinux.py @@ -0,0 +1,85 @@ +"""PEP 656 support. + +This module implements logic to detect if the currently running Python is +linked against musl, and what musl version is used. +""" + +from __future__ import annotations + +import functools +import re +import subprocess +import sys +from typing import Iterator, NamedTuple, Sequence + +from ._elffile import ELFFile + + +class _MuslVersion(NamedTuple): + major: int + minor: int + + +def _parse_musl_version(output: str) -> _MuslVersion | None: + lines = [n for n in (n.strip() for n in output.splitlines()) if n] + if len(lines) < 2 or lines[0][:4] != "musl": + return None + m = re.match(r"Version (\d+)\.(\d+)", lines[1]) + if not m: + return None + return _MuslVersion(major=int(m.group(1)), minor=int(m.group(2))) + + +@functools.lru_cache +def _get_musl_version(executable: str) -> _MuslVersion | None: + """Detect currently-running musl runtime version. + + This is done by checking the specified executable's dynamic linking + information, and invoking the loader to parse its output for a version + string. If the loader is musl, the output would be something like:: + + musl libc (x86_64) + Version 1.2.2 + Dynamic Program Loader + """ + try: + with open(executable, "rb") as f: + ld = ELFFile(f).interpreter + except (OSError, TypeError, ValueError): + return None + if ld is None or "musl" not in ld: + return None + proc = subprocess.run([ld], stderr=subprocess.PIPE, text=True) + return _parse_musl_version(proc.stderr) + + +def platform_tags(archs: Sequence[str]) -> Iterator[str]: + """Generate musllinux tags compatible to the current platform. + + :param archs: Sequence of compatible architectures. + The first one shall be the closest to the actual architecture and be the part of + platform tag after the ``linux_`` prefix, e.g. ``x86_64``. + The ``linux_`` prefix is assumed as a prerequisite for the current platform to + be musllinux-compatible. + + :returns: An iterator of compatible musllinux tags. + """ + sys_musl = _get_musl_version(sys.executable) + if sys_musl is None: # Python not dynamically linked against musl. + return + for arch in archs: + for minor in range(sys_musl.minor, -1, -1): + yield f"musllinux_{sys_musl.major}_{minor}_{arch}" + + +if __name__ == "__main__": # pragma: no cover + import sysconfig + + plat = sysconfig.get_platform() + assert plat.startswith("linux-"), "not linux" + + print("plat:", plat) + print("musl:", _get_musl_version(sys.executable)) + print("tags:", end=" ") + for t in platform_tags(re.sub(r"[.-]", "_", plat.split("-", 1)[-1])): + print(t, end="\n ") diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging/_parser.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging/_parser.py new file mode 100644 index 0000000000000000000000000000000000000000..0007c0aa64aeae24c7e0ca90790a25ba51dbf7d6 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging/_parser.py @@ -0,0 +1,353 @@ +"""Handwritten parser of dependency specifiers. + +The docstring for each __parse_* function contains EBNF-inspired grammar representing +the implementation. +""" + +from __future__ import annotations + +import ast +from typing import NamedTuple, Sequence, Tuple, Union + +from ._tokenizer import DEFAULT_RULES, Tokenizer + + +class Node: + def __init__(self, value: str) -> None: + self.value = value + + def __str__(self) -> str: + return self.value + + def __repr__(self) -> str: + return f"<{self.__class__.__name__}('{self}')>" + + def serialize(self) -> str: + raise NotImplementedError + + +class Variable(Node): + def serialize(self) -> str: + return str(self) + + +class Value(Node): + def serialize(self) -> str: + return f'"{self}"' + + +class Op(Node): + def serialize(self) -> str: + return str(self) + + +MarkerVar = Union[Variable, Value] +MarkerItem = Tuple[MarkerVar, Op, MarkerVar] +MarkerAtom = Union[MarkerItem, Sequence["MarkerAtom"]] +MarkerList = Sequence[Union["MarkerList", MarkerAtom, str]] + + +class ParsedRequirement(NamedTuple): + name: str + url: str + extras: list[str] + specifier: str + marker: MarkerList | None + + +# -------------------------------------------------------------------------------------- +# Recursive descent parser for dependency specifier +# -------------------------------------------------------------------------------------- +def parse_requirement(source: str) -> ParsedRequirement: + return _parse_requirement(Tokenizer(source, rules=DEFAULT_RULES)) + + +def _parse_requirement(tokenizer: Tokenizer) -> ParsedRequirement: + """ + requirement = WS? IDENTIFIER WS? extras WS? requirement_details + """ + tokenizer.consume("WS") + + name_token = tokenizer.expect( + "IDENTIFIER", expected="package name at the start of dependency specifier" + ) + name = name_token.text + tokenizer.consume("WS") + + extras = _parse_extras(tokenizer) + tokenizer.consume("WS") + + url, specifier, marker = _parse_requirement_details(tokenizer) + tokenizer.expect("END", expected="end of dependency specifier") + + return ParsedRequirement(name, url, extras, specifier, marker) + + +def _parse_requirement_details( + tokenizer: Tokenizer, +) -> tuple[str, str, MarkerList | None]: + """ + requirement_details = AT URL (WS requirement_marker?)? + | specifier WS? (requirement_marker)? + """ + + specifier = "" + url = "" + marker = None + + if tokenizer.check("AT"): + tokenizer.read() + tokenizer.consume("WS") + + url_start = tokenizer.position + url = tokenizer.expect("URL", expected="URL after @").text + if tokenizer.check("END", peek=True): + return (url, specifier, marker) + + tokenizer.expect("WS", expected="whitespace after URL") + + # The input might end after whitespace. + if tokenizer.check("END", peek=True): + return (url, specifier, marker) + + marker = _parse_requirement_marker( + tokenizer, span_start=url_start, after="URL and whitespace" + ) + else: + specifier_start = tokenizer.position + specifier = _parse_specifier(tokenizer) + tokenizer.consume("WS") + + if tokenizer.check("END", peek=True): + return (url, specifier, marker) + + marker = _parse_requirement_marker( + tokenizer, + span_start=specifier_start, + after=( + "version specifier" + if specifier + else "name and no valid version specifier" + ), + ) + + return (url, specifier, marker) + + +def _parse_requirement_marker( + tokenizer: Tokenizer, *, span_start: int, after: str +) -> MarkerList: + """ + requirement_marker = SEMICOLON marker WS? + """ + + if not tokenizer.check("SEMICOLON"): + tokenizer.raise_syntax_error( + f"Expected end or semicolon (after {after})", + span_start=span_start, + ) + tokenizer.read() + + marker = _parse_marker(tokenizer) + tokenizer.consume("WS") + + return marker + + +def _parse_extras(tokenizer: Tokenizer) -> list[str]: + """ + extras = (LEFT_BRACKET wsp* extras_list? wsp* RIGHT_BRACKET)? + """ + if not tokenizer.check("LEFT_BRACKET", peek=True): + return [] + + with tokenizer.enclosing_tokens( + "LEFT_BRACKET", + "RIGHT_BRACKET", + around="extras", + ): + tokenizer.consume("WS") + extras = _parse_extras_list(tokenizer) + tokenizer.consume("WS") + + return extras + + +def _parse_extras_list(tokenizer: Tokenizer) -> list[str]: + """ + extras_list = identifier (wsp* ',' wsp* identifier)* + """ + extras: list[str] = [] + + if not tokenizer.check("IDENTIFIER"): + return extras + + extras.append(tokenizer.read().text) + + while True: + tokenizer.consume("WS") + if tokenizer.check("IDENTIFIER", peek=True): + tokenizer.raise_syntax_error("Expected comma between extra names") + elif not tokenizer.check("COMMA"): + break + + tokenizer.read() + tokenizer.consume("WS") + + extra_token = tokenizer.expect("IDENTIFIER", expected="extra name after comma") + extras.append(extra_token.text) + + return extras + + +def _parse_specifier(tokenizer: Tokenizer) -> str: + """ + specifier = LEFT_PARENTHESIS WS? version_many WS? RIGHT_PARENTHESIS + | WS? version_many WS? + """ + with tokenizer.enclosing_tokens( + "LEFT_PARENTHESIS", + "RIGHT_PARENTHESIS", + around="version specifier", + ): + tokenizer.consume("WS") + parsed_specifiers = _parse_version_many(tokenizer) + tokenizer.consume("WS") + + return parsed_specifiers + + +def _parse_version_many(tokenizer: Tokenizer) -> str: + """ + version_many = (SPECIFIER (WS? COMMA WS? SPECIFIER)*)? + """ + parsed_specifiers = "" + while tokenizer.check("SPECIFIER"): + span_start = tokenizer.position + parsed_specifiers += tokenizer.read().text + if tokenizer.check("VERSION_PREFIX_TRAIL", peek=True): + tokenizer.raise_syntax_error( + ".* suffix can only be used with `==` or `!=` operators", + span_start=span_start, + span_end=tokenizer.position + 1, + ) + if tokenizer.check("VERSION_LOCAL_LABEL_TRAIL", peek=True): + tokenizer.raise_syntax_error( + "Local version label can only be used with `==` or `!=` operators", + span_start=span_start, + span_end=tokenizer.position, + ) + tokenizer.consume("WS") + if not tokenizer.check("COMMA"): + break + parsed_specifiers += tokenizer.read().text + tokenizer.consume("WS") + + return parsed_specifiers + + +# -------------------------------------------------------------------------------------- +# Recursive descent parser for marker expression +# -------------------------------------------------------------------------------------- +def parse_marker(source: str) -> MarkerList: + return _parse_full_marker(Tokenizer(source, rules=DEFAULT_RULES)) + + +def _parse_full_marker(tokenizer: Tokenizer) -> MarkerList: + retval = _parse_marker(tokenizer) + tokenizer.expect("END", expected="end of marker expression") + return retval + + +def _parse_marker(tokenizer: Tokenizer) -> MarkerList: + """ + marker = marker_atom (BOOLOP marker_atom)+ + """ + expression = [_parse_marker_atom(tokenizer)] + while tokenizer.check("BOOLOP"): + token = tokenizer.read() + expr_right = _parse_marker_atom(tokenizer) + expression.extend((token.text, expr_right)) + return expression + + +def _parse_marker_atom(tokenizer: Tokenizer) -> MarkerAtom: + """ + marker_atom = WS? LEFT_PARENTHESIS WS? marker WS? RIGHT_PARENTHESIS WS? + | WS? marker_item WS? + """ + + tokenizer.consume("WS") + if tokenizer.check("LEFT_PARENTHESIS", peek=True): + with tokenizer.enclosing_tokens( + "LEFT_PARENTHESIS", + "RIGHT_PARENTHESIS", + around="marker expression", + ): + tokenizer.consume("WS") + marker: MarkerAtom = _parse_marker(tokenizer) + tokenizer.consume("WS") + else: + marker = _parse_marker_item(tokenizer) + tokenizer.consume("WS") + return marker + + +def _parse_marker_item(tokenizer: Tokenizer) -> MarkerItem: + """ + marker_item = WS? marker_var WS? marker_op WS? marker_var WS? + """ + tokenizer.consume("WS") + marker_var_left = _parse_marker_var(tokenizer) + tokenizer.consume("WS") + marker_op = _parse_marker_op(tokenizer) + tokenizer.consume("WS") + marker_var_right = _parse_marker_var(tokenizer) + tokenizer.consume("WS") + return (marker_var_left, marker_op, marker_var_right) + + +def _parse_marker_var(tokenizer: Tokenizer) -> MarkerVar: + """ + marker_var = VARIABLE | QUOTED_STRING + """ + if tokenizer.check("VARIABLE"): + return process_env_var(tokenizer.read().text.replace(".", "_")) + elif tokenizer.check("QUOTED_STRING"): + return process_python_str(tokenizer.read().text) + else: + tokenizer.raise_syntax_error( + message="Expected a marker variable or quoted string" + ) + + +def process_env_var(env_var: str) -> Variable: + if env_var in ("platform_python_implementation", "python_implementation"): + return Variable("platform_python_implementation") + else: + return Variable(env_var) + + +def process_python_str(python_str: str) -> Value: + value = ast.literal_eval(python_str) + return Value(str(value)) + + +def _parse_marker_op(tokenizer: Tokenizer) -> Op: + """ + marker_op = IN | NOT IN | OP + """ + if tokenizer.check("IN"): + tokenizer.read() + return Op("in") + elif tokenizer.check("NOT"): + tokenizer.read() + tokenizer.expect("WS", expected="whitespace after 'not'") + tokenizer.expect("IN", expected="'in' after 'not'") + return Op("not in") + elif tokenizer.check("OP"): + return Op(tokenizer.read().text) + else: + return tokenizer.raise_syntax_error( + "Expected marker operator, one of <=, <, !=, ==, >=, >, ~=, ===, in, not in" + ) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging/_structures.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging/_structures.py new file mode 100644 index 0000000000000000000000000000000000000000..90a6465f9682c886363eea5327dac64bf623a6ff --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging/_structures.py @@ -0,0 +1,61 @@ +# This file is dual licensed under the terms of the Apache License, Version +# 2.0, and the BSD License. See the LICENSE file in the root of this repository +# for complete details. + + +class InfinityType: + def __repr__(self) -> str: + return "Infinity" + + def __hash__(self) -> int: + return hash(repr(self)) + + def __lt__(self, other: object) -> bool: + return False + + def __le__(self, other: object) -> bool: + return False + + def __eq__(self, other: object) -> bool: + return isinstance(other, self.__class__) + + def __gt__(self, other: object) -> bool: + return True + + def __ge__(self, other: object) -> bool: + return True + + def __neg__(self: object) -> "NegativeInfinityType": + return NegativeInfinity + + +Infinity = InfinityType() + + +class NegativeInfinityType: + def __repr__(self) -> str: + return "-Infinity" + + def __hash__(self) -> int: + return hash(repr(self)) + + def __lt__(self, other: object) -> bool: + return True + + def __le__(self, other: object) -> bool: + return True + + def __eq__(self, other: object) -> bool: + return isinstance(other, self.__class__) + + def __gt__(self, other: object) -> bool: + return False + + def __ge__(self, other: object) -> bool: + return False + + def __neg__(self: object) -> InfinityType: + return Infinity + + +NegativeInfinity = NegativeInfinityType() diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging/_tokenizer.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging/_tokenizer.py new file mode 100644 index 0000000000000000000000000000000000000000..d28a9b6cf5daca9fa3ce5a8c8ae583a57ee84f12 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging/_tokenizer.py @@ -0,0 +1,195 @@ +from __future__ import annotations + +import contextlib +import re +from dataclasses import dataclass +from typing import Iterator, NoReturn + +from .specifiers import Specifier + + +@dataclass +class Token: + name: str + text: str + position: int + + +class ParserSyntaxError(Exception): + """The provided source text could not be parsed correctly.""" + + def __init__( + self, + message: str, + *, + source: str, + span: tuple[int, int], + ) -> None: + self.span = span + self.message = message + self.source = source + + super().__init__() + + def __str__(self) -> str: + marker = " " * self.span[0] + "~" * (self.span[1] - self.span[0]) + "^" + return "\n ".join([self.message, self.source, marker]) + + +DEFAULT_RULES: dict[str, str | re.Pattern[str]] = { + "LEFT_PARENTHESIS": r"\(", + "RIGHT_PARENTHESIS": r"\)", + "LEFT_BRACKET": r"\[", + "RIGHT_BRACKET": r"\]", + "SEMICOLON": r";", + "COMMA": r",", + "QUOTED_STRING": re.compile( + r""" + ( + ('[^']*') + | + ("[^"]*") + ) + """, + re.VERBOSE, + ), + "OP": r"(===|==|~=|!=|<=|>=|<|>)", + "BOOLOP": r"\b(or|and)\b", + "IN": r"\bin\b", + "NOT": r"\bnot\b", + "VARIABLE": re.compile( + r""" + \b( + python_version + |python_full_version + |os[._]name + |sys[._]platform + |platform_(release|system) + |platform[._](version|machine|python_implementation) + |python_implementation + |implementation_(name|version) + |extras? + |dependency_groups + )\b + """, + re.VERBOSE, + ), + "SPECIFIER": re.compile( + Specifier._operator_regex_str + Specifier._version_regex_str, + re.VERBOSE | re.IGNORECASE, + ), + "AT": r"\@", + "URL": r"[^ \t]+", + "IDENTIFIER": r"\b[a-zA-Z0-9][a-zA-Z0-9._-]*\b", + "VERSION_PREFIX_TRAIL": r"\.\*", + "VERSION_LOCAL_LABEL_TRAIL": r"\+[a-z0-9]+(?:[-_\.][a-z0-9]+)*", + "WS": r"[ \t]+", + "END": r"$", +} + + +class Tokenizer: + """Context-sensitive token parsing. + + Provides methods to examine the input stream to check whether the next token + matches. + """ + + def __init__( + self, + source: str, + *, + rules: dict[str, str | re.Pattern[str]], + ) -> None: + self.source = source + self.rules: dict[str, re.Pattern[str]] = { + name: re.compile(pattern) for name, pattern in rules.items() + } + self.next_token: Token | None = None + self.position = 0 + + def consume(self, name: str) -> None: + """Move beyond provided token name, if at current position.""" + if self.check(name): + self.read() + + def check(self, name: str, *, peek: bool = False) -> bool: + """Check whether the next token has the provided name. + + By default, if the check succeeds, the token *must* be read before + another check. If `peek` is set to `True`, the token is not loaded and + would need to be checked again. + """ + assert self.next_token is None, ( + f"Cannot check for {name!r}, already have {self.next_token!r}" + ) + assert name in self.rules, f"Unknown token name: {name!r}" + + expression = self.rules[name] + + match = expression.match(self.source, self.position) + if match is None: + return False + if not peek: + self.next_token = Token(name, match[0], self.position) + return True + + def expect(self, name: str, *, expected: str) -> Token: + """Expect a certain token name next, failing with a syntax error otherwise. + + The token is *not* read. + """ + if not self.check(name): + raise self.raise_syntax_error(f"Expected {expected}") + return self.read() + + def read(self) -> Token: + """Consume the next token and return it.""" + token = self.next_token + assert token is not None + + self.position += len(token.text) + self.next_token = None + + return token + + def raise_syntax_error( + self, + message: str, + *, + span_start: int | None = None, + span_end: int | None = None, + ) -> NoReturn: + """Raise ParserSyntaxError at the given position.""" + span = ( + self.position if span_start is None else span_start, + self.position if span_end is None else span_end, + ) + raise ParserSyntaxError( + message, + source=self.source, + span=span, + ) + + @contextlib.contextmanager + def enclosing_tokens( + self, open_token: str, close_token: str, *, around: str + ) -> Iterator[None]: + if self.check(open_token): + open_position = self.position + self.read() + else: + open_position = None + + yield + + if open_position is None: + return + + if not self.check(close_token): + self.raise_syntax_error( + f"Expected matching {close_token} for {open_token}, after {around}", + span_start=open_position, + ) + + self.read() diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging/licenses/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging/licenses/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..6f7f9e6289dabe5bbc45cc8f01ac2449da890d02 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging/licenses/__init__.py @@ -0,0 +1,145 @@ +####################################################################################### +# +# Adapted from: +# https://github.com/pypa/hatch/blob/5352e44/backend/src/hatchling/licenses/parse.py +# +# MIT License +# +# Copyright (c) 2017-present Ofek Lev +# +# Permission is hereby granted, free of charge, to any person obtaining a copy of this +# software and associated documentation files (the "Software"), to deal in the Software +# without restriction, including without limitation the rights to use, copy, modify, +# merge, publish, distribute, sublicense, and/or sell copies of the Software, and to +# permit persons to whom the Software is furnished to do so, subject to the following +# conditions: +# +# The above copyright notice and this permission notice shall be included in all copies +# or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, +# INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A +# PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT +# HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF +# CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE +# OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +# +# +# With additional allowance of arbitrary `LicenseRef-` identifiers, not just +# `LicenseRef-Public-Domain` and `LicenseRef-Proprietary`. +# +####################################################################################### +from __future__ import annotations + +import re +from typing import NewType, cast + +from packaging.licenses._spdx import EXCEPTIONS, LICENSES + +__all__ = [ + "InvalidLicenseExpression", + "NormalizedLicenseExpression", + "canonicalize_license_expression", +] + +license_ref_allowed = re.compile("^[A-Za-z0-9.-]*$") + +NormalizedLicenseExpression = NewType("NormalizedLicenseExpression", str) + + +class InvalidLicenseExpression(ValueError): + """Raised when a license-expression string is invalid + + >>> canonicalize_license_expression("invalid") + Traceback (most recent call last): + ... + packaging.licenses.InvalidLicenseExpression: Invalid license expression: 'invalid' + """ + + +def canonicalize_license_expression( + raw_license_expression: str, +) -> NormalizedLicenseExpression: + if not raw_license_expression: + message = f"Invalid license expression: {raw_license_expression!r}" + raise InvalidLicenseExpression(message) + + # Pad any parentheses so tokenization can be achieved by merely splitting on + # whitespace. + license_expression = raw_license_expression.replace("(", " ( ").replace(")", " ) ") + licenseref_prefix = "LicenseRef-" + license_refs = { + ref.lower(): "LicenseRef-" + ref[len(licenseref_prefix) :] + for ref in license_expression.split() + if ref.lower().startswith(licenseref_prefix.lower()) + } + + # Normalize to lower case so we can look up licenses/exceptions + # and so boolean operators are Python-compatible. + license_expression = license_expression.lower() + + tokens = license_expression.split() + + # Rather than implementing boolean logic, we create an expression that Python can + # parse. Everything that is not involved with the grammar itself is treated as + # `False` and the expression should evaluate as such. + python_tokens = [] + for token in tokens: + if token not in {"or", "and", "with", "(", ")"}: + python_tokens.append("False") + elif token == "with": + python_tokens.append("or") + elif token == "(" and python_tokens and python_tokens[-1] not in {"or", "and"}: + message = f"Invalid license expression: {raw_license_expression!r}" + raise InvalidLicenseExpression(message) + else: + python_tokens.append(token) + + python_expression = " ".join(python_tokens) + try: + invalid = eval(python_expression, globals(), locals()) + except Exception: + invalid = True + + if invalid is not False: + message = f"Invalid license expression: {raw_license_expression!r}" + raise InvalidLicenseExpression(message) from None + + # Take a final pass to check for unknown licenses/exceptions. + normalized_tokens = [] + for token in tokens: + if token in {"or", "and", "with", "(", ")"}: + normalized_tokens.append(token.upper()) + continue + + if normalized_tokens and normalized_tokens[-1] == "WITH": + if token not in EXCEPTIONS: + message = f"Unknown license exception: {token!r}" + raise InvalidLicenseExpression(message) + + normalized_tokens.append(EXCEPTIONS[token]["id"]) + else: + if token.endswith("+"): + final_token = token[:-1] + suffix = "+" + else: + final_token = token + suffix = "" + + if final_token.startswith("licenseref-"): + if not license_ref_allowed.match(final_token): + message = f"Invalid licenseref: {final_token!r}" + raise InvalidLicenseExpression(message) + normalized_tokens.append(license_refs[final_token] + suffix) + else: + if final_token not in LICENSES: + message = f"Unknown license: {final_token!r}" + raise InvalidLicenseExpression(message) + normalized_tokens.append(LICENSES[final_token]["id"] + suffix) + + normalized_expression = " ".join(normalized_tokens) + + return cast( + NormalizedLicenseExpression, + normalized_expression.replace("( ", "(").replace(" )", ")"), + ) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging/licenses/_spdx.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging/licenses/_spdx.py new file mode 100644 index 0000000000000000000000000000000000000000..eac22276a34ccd73fc9d70c67ca318a49eb11e77 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging/licenses/_spdx.py @@ -0,0 +1,759 @@ + +from __future__ import annotations + +from typing import TypedDict + +class SPDXLicense(TypedDict): + id: str + deprecated: bool + +class SPDXException(TypedDict): + id: str + deprecated: bool + + +VERSION = '3.25.0' + +LICENSES: dict[str, SPDXLicense] = { + '0bsd': {'id': '0BSD', 'deprecated': False}, + '3d-slicer-1.0': {'id': '3D-Slicer-1.0', 'deprecated': False}, + 'aal': {'id': 'AAL', 'deprecated': False}, + 'abstyles': {'id': 'Abstyles', 'deprecated': False}, + 'adacore-doc': {'id': 'AdaCore-doc', 'deprecated': False}, + 'adobe-2006': {'id': 'Adobe-2006', 'deprecated': False}, + 'adobe-display-postscript': {'id': 'Adobe-Display-PostScript', 'deprecated': False}, + 'adobe-glyph': {'id': 'Adobe-Glyph', 'deprecated': False}, + 'adobe-utopia': {'id': 'Adobe-Utopia', 'deprecated': False}, + 'adsl': {'id': 'ADSL', 'deprecated': False}, + 'afl-1.1': {'id': 'AFL-1.1', 'deprecated': False}, + 'afl-1.2': {'id': 'AFL-1.2', 'deprecated': False}, + 'afl-2.0': {'id': 'AFL-2.0', 'deprecated': False}, + 'afl-2.1': {'id': 'AFL-2.1', 'deprecated': False}, + 'afl-3.0': {'id': 'AFL-3.0', 'deprecated': False}, + 'afmparse': {'id': 'Afmparse', 'deprecated': False}, + 'agpl-1.0': {'id': 'AGPL-1.0', 'deprecated': True}, + 'agpl-1.0-only': {'id': 'AGPL-1.0-only', 'deprecated': False}, + 'agpl-1.0-or-later': {'id': 'AGPL-1.0-or-later', 'deprecated': False}, + 'agpl-3.0': {'id': 'AGPL-3.0', 'deprecated': True}, + 'agpl-3.0-only': {'id': 'AGPL-3.0-only', 'deprecated': False}, + 'agpl-3.0-or-later': {'id': 'AGPL-3.0-or-later', 'deprecated': False}, + 'aladdin': {'id': 'Aladdin', 'deprecated': False}, + 'amd-newlib': {'id': 'AMD-newlib', 'deprecated': False}, + 'amdplpa': {'id': 'AMDPLPA', 'deprecated': False}, + 'aml': {'id': 'AML', 'deprecated': False}, + 'aml-glslang': {'id': 'AML-glslang', 'deprecated': False}, + 'ampas': {'id': 'AMPAS', 'deprecated': False}, + 'antlr-pd': {'id': 'ANTLR-PD', 'deprecated': False}, + 'antlr-pd-fallback': {'id': 'ANTLR-PD-fallback', 'deprecated': False}, + 'any-osi': {'id': 'any-OSI', 'deprecated': False}, + 'apache-1.0': {'id': 'Apache-1.0', 'deprecated': False}, + 'apache-1.1': {'id': 'Apache-1.1', 'deprecated': False}, + 'apache-2.0': {'id': 'Apache-2.0', 'deprecated': False}, + 'apafml': {'id': 'APAFML', 'deprecated': False}, + 'apl-1.0': {'id': 'APL-1.0', 'deprecated': False}, + 'app-s2p': {'id': 'App-s2p', 'deprecated': False}, + 'apsl-1.0': {'id': 'APSL-1.0', 'deprecated': False}, + 'apsl-1.1': {'id': 'APSL-1.1', 'deprecated': False}, + 'apsl-1.2': {'id': 'APSL-1.2', 'deprecated': False}, + 'apsl-2.0': {'id': 'APSL-2.0', 'deprecated': False}, + 'arphic-1999': {'id': 'Arphic-1999', 'deprecated': False}, + 'artistic-1.0': {'id': 'Artistic-1.0', 'deprecated': False}, + 'artistic-1.0-cl8': {'id': 'Artistic-1.0-cl8', 'deprecated': False}, + 'artistic-1.0-perl': {'id': 'Artistic-1.0-Perl', 'deprecated': False}, + 'artistic-2.0': {'id': 'Artistic-2.0', 'deprecated': False}, + 'aswf-digital-assets-1.0': {'id': 'ASWF-Digital-Assets-1.0', 'deprecated': False}, + 'aswf-digital-assets-1.1': {'id': 'ASWF-Digital-Assets-1.1', 'deprecated': False}, + 'baekmuk': {'id': 'Baekmuk', 'deprecated': False}, + 'bahyph': {'id': 'Bahyph', 'deprecated': False}, + 'barr': {'id': 'Barr', 'deprecated': False}, + 'bcrypt-solar-designer': {'id': 'bcrypt-Solar-Designer', 'deprecated': False}, + 'beerware': {'id': 'Beerware', 'deprecated': False}, + 'bitstream-charter': {'id': 'Bitstream-Charter', 'deprecated': False}, + 'bitstream-vera': {'id': 'Bitstream-Vera', 'deprecated': False}, + 'bittorrent-1.0': {'id': 'BitTorrent-1.0', 'deprecated': False}, + 'bittorrent-1.1': {'id': 'BitTorrent-1.1', 'deprecated': False}, + 'blessing': {'id': 'blessing', 'deprecated': False}, + 'blueoak-1.0.0': {'id': 'BlueOak-1.0.0', 'deprecated': False}, + 'boehm-gc': {'id': 'Boehm-GC', 'deprecated': False}, + 'borceux': {'id': 'Borceux', 'deprecated': False}, + 'brian-gladman-2-clause': {'id': 'Brian-Gladman-2-Clause', 'deprecated': False}, + 'brian-gladman-3-clause': {'id': 'Brian-Gladman-3-Clause', 'deprecated': False}, + 'bsd-1-clause': {'id': 'BSD-1-Clause', 'deprecated': False}, + 'bsd-2-clause': {'id': 'BSD-2-Clause', 'deprecated': False}, + 'bsd-2-clause-darwin': {'id': 'BSD-2-Clause-Darwin', 'deprecated': False}, + 'bsd-2-clause-first-lines': {'id': 'BSD-2-Clause-first-lines', 'deprecated': False}, + 'bsd-2-clause-freebsd': {'id': 'BSD-2-Clause-FreeBSD', 'deprecated': True}, + 'bsd-2-clause-netbsd': {'id': 'BSD-2-Clause-NetBSD', 'deprecated': True}, + 'bsd-2-clause-patent': {'id': 'BSD-2-Clause-Patent', 'deprecated': False}, + 'bsd-2-clause-views': {'id': 'BSD-2-Clause-Views', 'deprecated': False}, + 'bsd-3-clause': {'id': 'BSD-3-Clause', 'deprecated': False}, + 'bsd-3-clause-acpica': {'id': 'BSD-3-Clause-acpica', 'deprecated': False}, + 'bsd-3-clause-attribution': {'id': 'BSD-3-Clause-Attribution', 'deprecated': False}, + 'bsd-3-clause-clear': {'id': 'BSD-3-Clause-Clear', 'deprecated': False}, + 'bsd-3-clause-flex': {'id': 'BSD-3-Clause-flex', 'deprecated': False}, + 'bsd-3-clause-hp': {'id': 'BSD-3-Clause-HP', 'deprecated': False}, + 'bsd-3-clause-lbnl': {'id': 'BSD-3-Clause-LBNL', 'deprecated': False}, + 'bsd-3-clause-modification': {'id': 'BSD-3-Clause-Modification', 'deprecated': False}, + 'bsd-3-clause-no-military-license': {'id': 'BSD-3-Clause-No-Military-License', 'deprecated': False}, + 'bsd-3-clause-no-nuclear-license': {'id': 'BSD-3-Clause-No-Nuclear-License', 'deprecated': False}, + 'bsd-3-clause-no-nuclear-license-2014': {'id': 'BSD-3-Clause-No-Nuclear-License-2014', 'deprecated': False}, + 'bsd-3-clause-no-nuclear-warranty': {'id': 'BSD-3-Clause-No-Nuclear-Warranty', 'deprecated': False}, + 'bsd-3-clause-open-mpi': {'id': 'BSD-3-Clause-Open-MPI', 'deprecated': False}, + 'bsd-3-clause-sun': {'id': 'BSD-3-Clause-Sun', 'deprecated': False}, + 'bsd-4-clause': {'id': 'BSD-4-Clause', 'deprecated': False}, + 'bsd-4-clause-shortened': {'id': 'BSD-4-Clause-Shortened', 'deprecated': False}, + 'bsd-4-clause-uc': {'id': 'BSD-4-Clause-UC', 'deprecated': False}, + 'bsd-4.3reno': {'id': 'BSD-4.3RENO', 'deprecated': False}, + 'bsd-4.3tahoe': {'id': 'BSD-4.3TAHOE', 'deprecated': False}, + 'bsd-advertising-acknowledgement': {'id': 'BSD-Advertising-Acknowledgement', 'deprecated': False}, + 'bsd-attribution-hpnd-disclaimer': {'id': 'BSD-Attribution-HPND-disclaimer', 'deprecated': False}, + 'bsd-inferno-nettverk': {'id': 'BSD-Inferno-Nettverk', 'deprecated': False}, + 'bsd-protection': {'id': 'BSD-Protection', 'deprecated': False}, + 'bsd-source-beginning-file': {'id': 'BSD-Source-beginning-file', 'deprecated': False}, + 'bsd-source-code': {'id': 'BSD-Source-Code', 'deprecated': False}, + 'bsd-systemics': {'id': 'BSD-Systemics', 'deprecated': False}, + 'bsd-systemics-w3works': {'id': 'BSD-Systemics-W3Works', 'deprecated': False}, + 'bsl-1.0': {'id': 'BSL-1.0', 'deprecated': False}, + 'busl-1.1': {'id': 'BUSL-1.1', 'deprecated': False}, + 'bzip2-1.0.5': {'id': 'bzip2-1.0.5', 'deprecated': True}, + 'bzip2-1.0.6': {'id': 'bzip2-1.0.6', 'deprecated': False}, + 'c-uda-1.0': {'id': 'C-UDA-1.0', 'deprecated': False}, + 'cal-1.0': {'id': 'CAL-1.0', 'deprecated': False}, + 'cal-1.0-combined-work-exception': {'id': 'CAL-1.0-Combined-Work-Exception', 'deprecated': False}, + 'caldera': {'id': 'Caldera', 'deprecated': False}, + 'caldera-no-preamble': {'id': 'Caldera-no-preamble', 'deprecated': False}, + 'catharon': {'id': 'Catharon', 'deprecated': False}, + 'catosl-1.1': {'id': 'CATOSL-1.1', 'deprecated': False}, + 'cc-by-1.0': {'id': 'CC-BY-1.0', 'deprecated': False}, + 'cc-by-2.0': {'id': 'CC-BY-2.0', 'deprecated': False}, + 'cc-by-2.5': {'id': 'CC-BY-2.5', 'deprecated': False}, + 'cc-by-2.5-au': {'id': 'CC-BY-2.5-AU', 'deprecated': False}, + 'cc-by-3.0': {'id': 'CC-BY-3.0', 'deprecated': False}, + 'cc-by-3.0-at': {'id': 'CC-BY-3.0-AT', 'deprecated': False}, + 'cc-by-3.0-au': {'id': 'CC-BY-3.0-AU', 'deprecated': False}, + 'cc-by-3.0-de': {'id': 'CC-BY-3.0-DE', 'deprecated': False}, + 'cc-by-3.0-igo': {'id': 'CC-BY-3.0-IGO', 'deprecated': False}, + 'cc-by-3.0-nl': {'id': 'CC-BY-3.0-NL', 'deprecated': False}, + 'cc-by-3.0-us': {'id': 'CC-BY-3.0-US', 'deprecated': False}, + 'cc-by-4.0': {'id': 'CC-BY-4.0', 'deprecated': False}, + 'cc-by-nc-1.0': {'id': 'CC-BY-NC-1.0', 'deprecated': False}, + 'cc-by-nc-2.0': {'id': 'CC-BY-NC-2.0', 'deprecated': False}, + 'cc-by-nc-2.5': {'id': 'CC-BY-NC-2.5', 'deprecated': False}, + 'cc-by-nc-3.0': {'id': 'CC-BY-NC-3.0', 'deprecated': False}, + 'cc-by-nc-3.0-de': {'id': 'CC-BY-NC-3.0-DE', 'deprecated': False}, + 'cc-by-nc-4.0': {'id': 'CC-BY-NC-4.0', 'deprecated': False}, + 'cc-by-nc-nd-1.0': {'id': 'CC-BY-NC-ND-1.0', 'deprecated': False}, + 'cc-by-nc-nd-2.0': {'id': 'CC-BY-NC-ND-2.0', 'deprecated': False}, + 'cc-by-nc-nd-2.5': {'id': 'CC-BY-NC-ND-2.5', 'deprecated': False}, + 'cc-by-nc-nd-3.0': {'id': 'CC-BY-NC-ND-3.0', 'deprecated': False}, + 'cc-by-nc-nd-3.0-de': {'id': 'CC-BY-NC-ND-3.0-DE', 'deprecated': False}, + 'cc-by-nc-nd-3.0-igo': {'id': 'CC-BY-NC-ND-3.0-IGO', 'deprecated': False}, + 'cc-by-nc-nd-4.0': {'id': 'CC-BY-NC-ND-4.0', 'deprecated': False}, + 'cc-by-nc-sa-1.0': {'id': 'CC-BY-NC-SA-1.0', 'deprecated': False}, + 'cc-by-nc-sa-2.0': {'id': 'CC-BY-NC-SA-2.0', 'deprecated': False}, + 'cc-by-nc-sa-2.0-de': {'id': 'CC-BY-NC-SA-2.0-DE', 'deprecated': False}, + 'cc-by-nc-sa-2.0-fr': {'id': 'CC-BY-NC-SA-2.0-FR', 'deprecated': False}, + 'cc-by-nc-sa-2.0-uk': {'id': 'CC-BY-NC-SA-2.0-UK', 'deprecated': False}, + 'cc-by-nc-sa-2.5': {'id': 'CC-BY-NC-SA-2.5', 'deprecated': False}, + 'cc-by-nc-sa-3.0': {'id': 'CC-BY-NC-SA-3.0', 'deprecated': False}, + 'cc-by-nc-sa-3.0-de': {'id': 'CC-BY-NC-SA-3.0-DE', 'deprecated': False}, + 'cc-by-nc-sa-3.0-igo': {'id': 'CC-BY-NC-SA-3.0-IGO', 'deprecated': False}, + 'cc-by-nc-sa-4.0': {'id': 'CC-BY-NC-SA-4.0', 'deprecated': False}, + 'cc-by-nd-1.0': {'id': 'CC-BY-ND-1.0', 'deprecated': False}, + 'cc-by-nd-2.0': {'id': 'CC-BY-ND-2.0', 'deprecated': False}, + 'cc-by-nd-2.5': {'id': 'CC-BY-ND-2.5', 'deprecated': False}, + 'cc-by-nd-3.0': {'id': 'CC-BY-ND-3.0', 'deprecated': False}, + 'cc-by-nd-3.0-de': {'id': 'CC-BY-ND-3.0-DE', 'deprecated': False}, + 'cc-by-nd-4.0': {'id': 'CC-BY-ND-4.0', 'deprecated': False}, + 'cc-by-sa-1.0': {'id': 'CC-BY-SA-1.0', 'deprecated': False}, + 'cc-by-sa-2.0': {'id': 'CC-BY-SA-2.0', 'deprecated': False}, + 'cc-by-sa-2.0-uk': {'id': 'CC-BY-SA-2.0-UK', 'deprecated': False}, + 'cc-by-sa-2.1-jp': {'id': 'CC-BY-SA-2.1-JP', 'deprecated': False}, + 'cc-by-sa-2.5': {'id': 'CC-BY-SA-2.5', 'deprecated': False}, + 'cc-by-sa-3.0': {'id': 'CC-BY-SA-3.0', 'deprecated': False}, + 'cc-by-sa-3.0-at': {'id': 'CC-BY-SA-3.0-AT', 'deprecated': False}, + 'cc-by-sa-3.0-de': {'id': 'CC-BY-SA-3.0-DE', 'deprecated': False}, + 'cc-by-sa-3.0-igo': {'id': 'CC-BY-SA-3.0-IGO', 'deprecated': False}, + 'cc-by-sa-4.0': {'id': 'CC-BY-SA-4.0', 'deprecated': False}, + 'cc-pddc': {'id': 'CC-PDDC', 'deprecated': False}, + 'cc0-1.0': {'id': 'CC0-1.0', 'deprecated': False}, + 'cddl-1.0': {'id': 'CDDL-1.0', 'deprecated': False}, + 'cddl-1.1': {'id': 'CDDL-1.1', 'deprecated': False}, + 'cdl-1.0': {'id': 'CDL-1.0', 'deprecated': False}, + 'cdla-permissive-1.0': {'id': 'CDLA-Permissive-1.0', 'deprecated': False}, + 'cdla-permissive-2.0': {'id': 'CDLA-Permissive-2.0', 'deprecated': False}, + 'cdla-sharing-1.0': {'id': 'CDLA-Sharing-1.0', 'deprecated': False}, + 'cecill-1.0': {'id': 'CECILL-1.0', 'deprecated': False}, + 'cecill-1.1': {'id': 'CECILL-1.1', 'deprecated': False}, + 'cecill-2.0': {'id': 'CECILL-2.0', 'deprecated': False}, + 'cecill-2.1': {'id': 'CECILL-2.1', 'deprecated': False}, + 'cecill-b': {'id': 'CECILL-B', 'deprecated': False}, + 'cecill-c': {'id': 'CECILL-C', 'deprecated': False}, + 'cern-ohl-1.1': {'id': 'CERN-OHL-1.1', 'deprecated': False}, + 'cern-ohl-1.2': {'id': 'CERN-OHL-1.2', 'deprecated': False}, + 'cern-ohl-p-2.0': {'id': 'CERN-OHL-P-2.0', 'deprecated': False}, + 'cern-ohl-s-2.0': {'id': 'CERN-OHL-S-2.0', 'deprecated': False}, + 'cern-ohl-w-2.0': {'id': 'CERN-OHL-W-2.0', 'deprecated': False}, + 'cfitsio': {'id': 'CFITSIO', 'deprecated': False}, + 'check-cvs': {'id': 'check-cvs', 'deprecated': False}, + 'checkmk': {'id': 'checkmk', 'deprecated': False}, + 'clartistic': {'id': 'ClArtistic', 'deprecated': False}, + 'clips': {'id': 'Clips', 'deprecated': False}, + 'cmu-mach': {'id': 'CMU-Mach', 'deprecated': False}, + 'cmu-mach-nodoc': {'id': 'CMU-Mach-nodoc', 'deprecated': False}, + 'cnri-jython': {'id': 'CNRI-Jython', 'deprecated': False}, + 'cnri-python': {'id': 'CNRI-Python', 'deprecated': False}, + 'cnri-python-gpl-compatible': {'id': 'CNRI-Python-GPL-Compatible', 'deprecated': False}, + 'coil-1.0': {'id': 'COIL-1.0', 'deprecated': False}, + 'community-spec-1.0': {'id': 'Community-Spec-1.0', 'deprecated': False}, + 'condor-1.1': {'id': 'Condor-1.1', 'deprecated': False}, + 'copyleft-next-0.3.0': {'id': 'copyleft-next-0.3.0', 'deprecated': False}, + 'copyleft-next-0.3.1': {'id': 'copyleft-next-0.3.1', 'deprecated': False}, + 'cornell-lossless-jpeg': {'id': 'Cornell-Lossless-JPEG', 'deprecated': False}, + 'cpal-1.0': {'id': 'CPAL-1.0', 'deprecated': False}, + 'cpl-1.0': {'id': 'CPL-1.0', 'deprecated': False}, + 'cpol-1.02': {'id': 'CPOL-1.02', 'deprecated': False}, + 'cronyx': {'id': 'Cronyx', 'deprecated': False}, + 'crossword': {'id': 'Crossword', 'deprecated': False}, + 'crystalstacker': {'id': 'CrystalStacker', 'deprecated': False}, + 'cua-opl-1.0': {'id': 'CUA-OPL-1.0', 'deprecated': False}, + 'cube': {'id': 'Cube', 'deprecated': False}, + 'curl': {'id': 'curl', 'deprecated': False}, + 'cve-tou': {'id': 'cve-tou', 'deprecated': False}, + 'd-fsl-1.0': {'id': 'D-FSL-1.0', 'deprecated': False}, + 'dec-3-clause': {'id': 'DEC-3-Clause', 'deprecated': False}, + 'diffmark': {'id': 'diffmark', 'deprecated': False}, + 'dl-de-by-2.0': {'id': 'DL-DE-BY-2.0', 'deprecated': False}, + 'dl-de-zero-2.0': {'id': 'DL-DE-ZERO-2.0', 'deprecated': False}, + 'doc': {'id': 'DOC', 'deprecated': False}, + 'docbook-schema': {'id': 'DocBook-Schema', 'deprecated': False}, + 'docbook-xml': {'id': 'DocBook-XML', 'deprecated': False}, + 'dotseqn': {'id': 'Dotseqn', 'deprecated': False}, + 'drl-1.0': {'id': 'DRL-1.0', 'deprecated': False}, + 'drl-1.1': {'id': 'DRL-1.1', 'deprecated': False}, + 'dsdp': {'id': 'DSDP', 'deprecated': False}, + 'dtoa': {'id': 'dtoa', 'deprecated': False}, + 'dvipdfm': {'id': 'dvipdfm', 'deprecated': False}, + 'ecl-1.0': {'id': 'ECL-1.0', 'deprecated': False}, + 'ecl-2.0': {'id': 'ECL-2.0', 'deprecated': False}, + 'ecos-2.0': {'id': 'eCos-2.0', 'deprecated': True}, + 'efl-1.0': {'id': 'EFL-1.0', 'deprecated': False}, + 'efl-2.0': {'id': 'EFL-2.0', 'deprecated': False}, + 'egenix': {'id': 'eGenix', 'deprecated': False}, + 'elastic-2.0': {'id': 'Elastic-2.0', 'deprecated': False}, + 'entessa': {'id': 'Entessa', 'deprecated': False}, + 'epics': {'id': 'EPICS', 'deprecated': False}, + 'epl-1.0': {'id': 'EPL-1.0', 'deprecated': False}, + 'epl-2.0': {'id': 'EPL-2.0', 'deprecated': False}, + 'erlpl-1.1': {'id': 'ErlPL-1.1', 'deprecated': False}, + 'etalab-2.0': {'id': 'etalab-2.0', 'deprecated': False}, + 'eudatagrid': {'id': 'EUDatagrid', 'deprecated': False}, + 'eupl-1.0': {'id': 'EUPL-1.0', 'deprecated': False}, + 'eupl-1.1': {'id': 'EUPL-1.1', 'deprecated': False}, + 'eupl-1.2': {'id': 'EUPL-1.2', 'deprecated': False}, + 'eurosym': {'id': 'Eurosym', 'deprecated': False}, + 'fair': {'id': 'Fair', 'deprecated': False}, + 'fbm': {'id': 'FBM', 'deprecated': False}, + 'fdk-aac': {'id': 'FDK-AAC', 'deprecated': False}, + 'ferguson-twofish': {'id': 'Ferguson-Twofish', 'deprecated': False}, + 'frameworx-1.0': {'id': 'Frameworx-1.0', 'deprecated': False}, + 'freebsd-doc': {'id': 'FreeBSD-DOC', 'deprecated': False}, + 'freeimage': {'id': 'FreeImage', 'deprecated': False}, + 'fsfap': {'id': 'FSFAP', 'deprecated': False}, + 'fsfap-no-warranty-disclaimer': {'id': 'FSFAP-no-warranty-disclaimer', 'deprecated': False}, + 'fsful': {'id': 'FSFUL', 'deprecated': False}, + 'fsfullr': {'id': 'FSFULLR', 'deprecated': False}, + 'fsfullrwd': {'id': 'FSFULLRWD', 'deprecated': False}, + 'ftl': {'id': 'FTL', 'deprecated': False}, + 'furuseth': {'id': 'Furuseth', 'deprecated': False}, + 'fwlw': {'id': 'fwlw', 'deprecated': False}, + 'gcr-docs': {'id': 'GCR-docs', 'deprecated': False}, + 'gd': {'id': 'GD', 'deprecated': False}, + 'gfdl-1.1': {'id': 'GFDL-1.1', 'deprecated': True}, + 'gfdl-1.1-invariants-only': {'id': 'GFDL-1.1-invariants-only', 'deprecated': False}, + 'gfdl-1.1-invariants-or-later': {'id': 'GFDL-1.1-invariants-or-later', 'deprecated': False}, + 'gfdl-1.1-no-invariants-only': {'id': 'GFDL-1.1-no-invariants-only', 'deprecated': False}, + 'gfdl-1.1-no-invariants-or-later': {'id': 'GFDL-1.1-no-invariants-or-later', 'deprecated': False}, + 'gfdl-1.1-only': {'id': 'GFDL-1.1-only', 'deprecated': False}, + 'gfdl-1.1-or-later': {'id': 'GFDL-1.1-or-later', 'deprecated': False}, + 'gfdl-1.2': {'id': 'GFDL-1.2', 'deprecated': True}, + 'gfdl-1.2-invariants-only': {'id': 'GFDL-1.2-invariants-only', 'deprecated': False}, + 'gfdl-1.2-invariants-or-later': {'id': 'GFDL-1.2-invariants-or-later', 'deprecated': False}, + 'gfdl-1.2-no-invariants-only': {'id': 'GFDL-1.2-no-invariants-only', 'deprecated': False}, + 'gfdl-1.2-no-invariants-or-later': {'id': 'GFDL-1.2-no-invariants-or-later', 'deprecated': False}, + 'gfdl-1.2-only': {'id': 'GFDL-1.2-only', 'deprecated': False}, + 'gfdl-1.2-or-later': {'id': 'GFDL-1.2-or-later', 'deprecated': False}, + 'gfdl-1.3': {'id': 'GFDL-1.3', 'deprecated': True}, + 'gfdl-1.3-invariants-only': {'id': 'GFDL-1.3-invariants-only', 'deprecated': False}, + 'gfdl-1.3-invariants-or-later': {'id': 'GFDL-1.3-invariants-or-later', 'deprecated': False}, + 'gfdl-1.3-no-invariants-only': {'id': 'GFDL-1.3-no-invariants-only', 'deprecated': False}, + 'gfdl-1.3-no-invariants-or-later': {'id': 'GFDL-1.3-no-invariants-or-later', 'deprecated': False}, + 'gfdl-1.3-only': {'id': 'GFDL-1.3-only', 'deprecated': False}, + 'gfdl-1.3-or-later': {'id': 'GFDL-1.3-or-later', 'deprecated': False}, + 'giftware': {'id': 'Giftware', 'deprecated': False}, + 'gl2ps': {'id': 'GL2PS', 'deprecated': False}, + 'glide': {'id': 'Glide', 'deprecated': False}, + 'glulxe': {'id': 'Glulxe', 'deprecated': False}, + 'glwtpl': {'id': 'GLWTPL', 'deprecated': False}, + 'gnuplot': {'id': 'gnuplot', 'deprecated': False}, + 'gpl-1.0': {'id': 'GPL-1.0', 'deprecated': True}, + 'gpl-1.0+': {'id': 'GPL-1.0+', 'deprecated': True}, + 'gpl-1.0-only': {'id': 'GPL-1.0-only', 'deprecated': False}, + 'gpl-1.0-or-later': {'id': 'GPL-1.0-or-later', 'deprecated': False}, + 'gpl-2.0': {'id': 'GPL-2.0', 'deprecated': True}, + 'gpl-2.0+': {'id': 'GPL-2.0+', 'deprecated': True}, + 'gpl-2.0-only': {'id': 'GPL-2.0-only', 'deprecated': False}, + 'gpl-2.0-or-later': {'id': 'GPL-2.0-or-later', 'deprecated': False}, + 'gpl-2.0-with-autoconf-exception': {'id': 'GPL-2.0-with-autoconf-exception', 'deprecated': True}, + 'gpl-2.0-with-bison-exception': {'id': 'GPL-2.0-with-bison-exception', 'deprecated': True}, + 'gpl-2.0-with-classpath-exception': {'id': 'GPL-2.0-with-classpath-exception', 'deprecated': True}, + 'gpl-2.0-with-font-exception': {'id': 'GPL-2.0-with-font-exception', 'deprecated': True}, + 'gpl-2.0-with-gcc-exception': {'id': 'GPL-2.0-with-GCC-exception', 'deprecated': True}, + 'gpl-3.0': {'id': 'GPL-3.0', 'deprecated': True}, + 'gpl-3.0+': {'id': 'GPL-3.0+', 'deprecated': True}, + 'gpl-3.0-only': {'id': 'GPL-3.0-only', 'deprecated': False}, + 'gpl-3.0-or-later': {'id': 'GPL-3.0-or-later', 'deprecated': False}, + 'gpl-3.0-with-autoconf-exception': {'id': 'GPL-3.0-with-autoconf-exception', 'deprecated': True}, + 'gpl-3.0-with-gcc-exception': {'id': 'GPL-3.0-with-GCC-exception', 'deprecated': True}, + 'graphics-gems': {'id': 'Graphics-Gems', 'deprecated': False}, + 'gsoap-1.3b': {'id': 'gSOAP-1.3b', 'deprecated': False}, + 'gtkbook': {'id': 'gtkbook', 'deprecated': False}, + 'gutmann': {'id': 'Gutmann', 'deprecated': False}, + 'haskellreport': {'id': 'HaskellReport', 'deprecated': False}, + 'hdparm': {'id': 'hdparm', 'deprecated': False}, + 'hidapi': {'id': 'HIDAPI', 'deprecated': False}, + 'hippocratic-2.1': {'id': 'Hippocratic-2.1', 'deprecated': False}, + 'hp-1986': {'id': 'HP-1986', 'deprecated': False}, + 'hp-1989': {'id': 'HP-1989', 'deprecated': False}, + 'hpnd': {'id': 'HPND', 'deprecated': False}, + 'hpnd-dec': {'id': 'HPND-DEC', 'deprecated': False}, + 'hpnd-doc': {'id': 'HPND-doc', 'deprecated': False}, + 'hpnd-doc-sell': {'id': 'HPND-doc-sell', 'deprecated': False}, + 'hpnd-export-us': {'id': 'HPND-export-US', 'deprecated': False}, + 'hpnd-export-us-acknowledgement': {'id': 'HPND-export-US-acknowledgement', 'deprecated': False}, + 'hpnd-export-us-modify': {'id': 'HPND-export-US-modify', 'deprecated': False}, + 'hpnd-export2-us': {'id': 'HPND-export2-US', 'deprecated': False}, + 'hpnd-fenneberg-livingston': {'id': 'HPND-Fenneberg-Livingston', 'deprecated': False}, + 'hpnd-inria-imag': {'id': 'HPND-INRIA-IMAG', 'deprecated': False}, + 'hpnd-intel': {'id': 'HPND-Intel', 'deprecated': False}, + 'hpnd-kevlin-henney': {'id': 'HPND-Kevlin-Henney', 'deprecated': False}, + 'hpnd-markus-kuhn': {'id': 'HPND-Markus-Kuhn', 'deprecated': False}, + 'hpnd-merchantability-variant': {'id': 'HPND-merchantability-variant', 'deprecated': False}, + 'hpnd-mit-disclaimer': {'id': 'HPND-MIT-disclaimer', 'deprecated': False}, + 'hpnd-netrek': {'id': 'HPND-Netrek', 'deprecated': False}, + 'hpnd-pbmplus': {'id': 'HPND-Pbmplus', 'deprecated': False}, + 'hpnd-sell-mit-disclaimer-xserver': {'id': 'HPND-sell-MIT-disclaimer-xserver', 'deprecated': False}, + 'hpnd-sell-regexpr': {'id': 'HPND-sell-regexpr', 'deprecated': False}, + 'hpnd-sell-variant': {'id': 'HPND-sell-variant', 'deprecated': False}, + 'hpnd-sell-variant-mit-disclaimer': {'id': 'HPND-sell-variant-MIT-disclaimer', 'deprecated': False}, + 'hpnd-sell-variant-mit-disclaimer-rev': {'id': 'HPND-sell-variant-MIT-disclaimer-rev', 'deprecated': False}, + 'hpnd-uc': {'id': 'HPND-UC', 'deprecated': False}, + 'hpnd-uc-export-us': {'id': 'HPND-UC-export-US', 'deprecated': False}, + 'htmltidy': {'id': 'HTMLTIDY', 'deprecated': False}, + 'ibm-pibs': {'id': 'IBM-pibs', 'deprecated': False}, + 'icu': {'id': 'ICU', 'deprecated': False}, + 'iec-code-components-eula': {'id': 'IEC-Code-Components-EULA', 'deprecated': False}, + 'ijg': {'id': 'IJG', 'deprecated': False}, + 'ijg-short': {'id': 'IJG-short', 'deprecated': False}, + 'imagemagick': {'id': 'ImageMagick', 'deprecated': False}, + 'imatix': {'id': 'iMatix', 'deprecated': False}, + 'imlib2': {'id': 'Imlib2', 'deprecated': False}, + 'info-zip': {'id': 'Info-ZIP', 'deprecated': False}, + 'inner-net-2.0': {'id': 'Inner-Net-2.0', 'deprecated': False}, + 'intel': {'id': 'Intel', 'deprecated': False}, + 'intel-acpi': {'id': 'Intel-ACPI', 'deprecated': False}, + 'interbase-1.0': {'id': 'Interbase-1.0', 'deprecated': False}, + 'ipa': {'id': 'IPA', 'deprecated': False}, + 'ipl-1.0': {'id': 'IPL-1.0', 'deprecated': False}, + 'isc': {'id': 'ISC', 'deprecated': False}, + 'isc-veillard': {'id': 'ISC-Veillard', 'deprecated': False}, + 'jam': {'id': 'Jam', 'deprecated': False}, + 'jasper-2.0': {'id': 'JasPer-2.0', 'deprecated': False}, + 'jpl-image': {'id': 'JPL-image', 'deprecated': False}, + 'jpnic': {'id': 'JPNIC', 'deprecated': False}, + 'json': {'id': 'JSON', 'deprecated': False}, + 'kastrup': {'id': 'Kastrup', 'deprecated': False}, + 'kazlib': {'id': 'Kazlib', 'deprecated': False}, + 'knuth-ctan': {'id': 'Knuth-CTAN', 'deprecated': False}, + 'lal-1.2': {'id': 'LAL-1.2', 'deprecated': False}, + 'lal-1.3': {'id': 'LAL-1.3', 'deprecated': False}, + 'latex2e': {'id': 'Latex2e', 'deprecated': False}, + 'latex2e-translated-notice': {'id': 'Latex2e-translated-notice', 'deprecated': False}, + 'leptonica': {'id': 'Leptonica', 'deprecated': False}, + 'lgpl-2.0': {'id': 'LGPL-2.0', 'deprecated': True}, + 'lgpl-2.0+': {'id': 'LGPL-2.0+', 'deprecated': True}, + 'lgpl-2.0-only': {'id': 'LGPL-2.0-only', 'deprecated': False}, + 'lgpl-2.0-or-later': {'id': 'LGPL-2.0-or-later', 'deprecated': False}, + 'lgpl-2.1': {'id': 'LGPL-2.1', 'deprecated': True}, + 'lgpl-2.1+': {'id': 'LGPL-2.1+', 'deprecated': True}, + 'lgpl-2.1-only': {'id': 'LGPL-2.1-only', 'deprecated': False}, + 'lgpl-2.1-or-later': {'id': 'LGPL-2.1-or-later', 'deprecated': False}, + 'lgpl-3.0': {'id': 'LGPL-3.0', 'deprecated': True}, + 'lgpl-3.0+': {'id': 'LGPL-3.0+', 'deprecated': True}, + 'lgpl-3.0-only': {'id': 'LGPL-3.0-only', 'deprecated': False}, + 'lgpl-3.0-or-later': {'id': 'LGPL-3.0-or-later', 'deprecated': False}, + 'lgpllr': {'id': 'LGPLLR', 'deprecated': False}, + 'libpng': {'id': 'Libpng', 'deprecated': False}, + 'libpng-2.0': {'id': 'libpng-2.0', 'deprecated': False}, + 'libselinux-1.0': {'id': 'libselinux-1.0', 'deprecated': False}, + 'libtiff': {'id': 'libtiff', 'deprecated': False}, + 'libutil-david-nugent': {'id': 'libutil-David-Nugent', 'deprecated': False}, + 'liliq-p-1.1': {'id': 'LiLiQ-P-1.1', 'deprecated': False}, + 'liliq-r-1.1': {'id': 'LiLiQ-R-1.1', 'deprecated': False}, + 'liliq-rplus-1.1': {'id': 'LiLiQ-Rplus-1.1', 'deprecated': False}, + 'linux-man-pages-1-para': {'id': 'Linux-man-pages-1-para', 'deprecated': False}, + 'linux-man-pages-copyleft': {'id': 'Linux-man-pages-copyleft', 'deprecated': False}, + 'linux-man-pages-copyleft-2-para': {'id': 'Linux-man-pages-copyleft-2-para', 'deprecated': False}, + 'linux-man-pages-copyleft-var': {'id': 'Linux-man-pages-copyleft-var', 'deprecated': False}, + 'linux-openib': {'id': 'Linux-OpenIB', 'deprecated': False}, + 'loop': {'id': 'LOOP', 'deprecated': False}, + 'lpd-document': {'id': 'LPD-document', 'deprecated': False}, + 'lpl-1.0': {'id': 'LPL-1.0', 'deprecated': False}, + 'lpl-1.02': {'id': 'LPL-1.02', 'deprecated': False}, + 'lppl-1.0': {'id': 'LPPL-1.0', 'deprecated': False}, + 'lppl-1.1': {'id': 'LPPL-1.1', 'deprecated': False}, + 'lppl-1.2': {'id': 'LPPL-1.2', 'deprecated': False}, + 'lppl-1.3a': {'id': 'LPPL-1.3a', 'deprecated': False}, + 'lppl-1.3c': {'id': 'LPPL-1.3c', 'deprecated': False}, + 'lsof': {'id': 'lsof', 'deprecated': False}, + 'lucida-bitmap-fonts': {'id': 'Lucida-Bitmap-Fonts', 'deprecated': False}, + 'lzma-sdk-9.11-to-9.20': {'id': 'LZMA-SDK-9.11-to-9.20', 'deprecated': False}, + 'lzma-sdk-9.22': {'id': 'LZMA-SDK-9.22', 'deprecated': False}, + 'mackerras-3-clause': {'id': 'Mackerras-3-Clause', 'deprecated': False}, + 'mackerras-3-clause-acknowledgment': {'id': 'Mackerras-3-Clause-acknowledgment', 'deprecated': False}, + 'magaz': {'id': 'magaz', 'deprecated': False}, + 'mailprio': {'id': 'mailprio', 'deprecated': False}, + 'makeindex': {'id': 'MakeIndex', 'deprecated': False}, + 'martin-birgmeier': {'id': 'Martin-Birgmeier', 'deprecated': False}, + 'mcphee-slideshow': {'id': 'McPhee-slideshow', 'deprecated': False}, + 'metamail': {'id': 'metamail', 'deprecated': False}, + 'minpack': {'id': 'Minpack', 'deprecated': False}, + 'miros': {'id': 'MirOS', 'deprecated': False}, + 'mit': {'id': 'MIT', 'deprecated': False}, + 'mit-0': {'id': 'MIT-0', 'deprecated': False}, + 'mit-advertising': {'id': 'MIT-advertising', 'deprecated': False}, + 'mit-cmu': {'id': 'MIT-CMU', 'deprecated': False}, + 'mit-enna': {'id': 'MIT-enna', 'deprecated': False}, + 'mit-feh': {'id': 'MIT-feh', 'deprecated': False}, + 'mit-festival': {'id': 'MIT-Festival', 'deprecated': False}, + 'mit-khronos-old': {'id': 'MIT-Khronos-old', 'deprecated': False}, + 'mit-modern-variant': {'id': 'MIT-Modern-Variant', 'deprecated': False}, + 'mit-open-group': {'id': 'MIT-open-group', 'deprecated': False}, + 'mit-testregex': {'id': 'MIT-testregex', 'deprecated': False}, + 'mit-wu': {'id': 'MIT-Wu', 'deprecated': False}, + 'mitnfa': {'id': 'MITNFA', 'deprecated': False}, + 'mmixware': {'id': 'MMIXware', 'deprecated': False}, + 'motosoto': {'id': 'Motosoto', 'deprecated': False}, + 'mpeg-ssg': {'id': 'MPEG-SSG', 'deprecated': False}, + 'mpi-permissive': {'id': 'mpi-permissive', 'deprecated': False}, + 'mpich2': {'id': 'mpich2', 'deprecated': False}, + 'mpl-1.0': {'id': 'MPL-1.0', 'deprecated': False}, + 'mpl-1.1': {'id': 'MPL-1.1', 'deprecated': False}, + 'mpl-2.0': {'id': 'MPL-2.0', 'deprecated': False}, + 'mpl-2.0-no-copyleft-exception': {'id': 'MPL-2.0-no-copyleft-exception', 'deprecated': False}, + 'mplus': {'id': 'mplus', 'deprecated': False}, + 'ms-lpl': {'id': 'MS-LPL', 'deprecated': False}, + 'ms-pl': {'id': 'MS-PL', 'deprecated': False}, + 'ms-rl': {'id': 'MS-RL', 'deprecated': False}, + 'mtll': {'id': 'MTLL', 'deprecated': False}, + 'mulanpsl-1.0': {'id': 'MulanPSL-1.0', 'deprecated': False}, + 'mulanpsl-2.0': {'id': 'MulanPSL-2.0', 'deprecated': False}, + 'multics': {'id': 'Multics', 'deprecated': False}, + 'mup': {'id': 'Mup', 'deprecated': False}, + 'naist-2003': {'id': 'NAIST-2003', 'deprecated': False}, + 'nasa-1.3': {'id': 'NASA-1.3', 'deprecated': False}, + 'naumen': {'id': 'Naumen', 'deprecated': False}, + 'nbpl-1.0': {'id': 'NBPL-1.0', 'deprecated': False}, + 'ncbi-pd': {'id': 'NCBI-PD', 'deprecated': False}, + 'ncgl-uk-2.0': {'id': 'NCGL-UK-2.0', 'deprecated': False}, + 'ncl': {'id': 'NCL', 'deprecated': False}, + 'ncsa': {'id': 'NCSA', 'deprecated': False}, + 'net-snmp': {'id': 'Net-SNMP', 'deprecated': True}, + 'netcdf': {'id': 'NetCDF', 'deprecated': False}, + 'newsletr': {'id': 'Newsletr', 'deprecated': False}, + 'ngpl': {'id': 'NGPL', 'deprecated': False}, + 'nicta-1.0': {'id': 'NICTA-1.0', 'deprecated': False}, + 'nist-pd': {'id': 'NIST-PD', 'deprecated': False}, + 'nist-pd-fallback': {'id': 'NIST-PD-fallback', 'deprecated': False}, + 'nist-software': {'id': 'NIST-Software', 'deprecated': False}, + 'nlod-1.0': {'id': 'NLOD-1.0', 'deprecated': False}, + 'nlod-2.0': {'id': 'NLOD-2.0', 'deprecated': False}, + 'nlpl': {'id': 'NLPL', 'deprecated': False}, + 'nokia': {'id': 'Nokia', 'deprecated': False}, + 'nosl': {'id': 'NOSL', 'deprecated': False}, + 'noweb': {'id': 'Noweb', 'deprecated': False}, + 'npl-1.0': {'id': 'NPL-1.0', 'deprecated': False}, + 'npl-1.1': {'id': 'NPL-1.1', 'deprecated': False}, + 'nposl-3.0': {'id': 'NPOSL-3.0', 'deprecated': False}, + 'nrl': {'id': 'NRL', 'deprecated': False}, + 'ntp': {'id': 'NTP', 'deprecated': False}, + 'ntp-0': {'id': 'NTP-0', 'deprecated': False}, + 'nunit': {'id': 'Nunit', 'deprecated': True}, + 'o-uda-1.0': {'id': 'O-UDA-1.0', 'deprecated': False}, + 'oar': {'id': 'OAR', 'deprecated': False}, + 'occt-pl': {'id': 'OCCT-PL', 'deprecated': False}, + 'oclc-2.0': {'id': 'OCLC-2.0', 'deprecated': False}, + 'odbl-1.0': {'id': 'ODbL-1.0', 'deprecated': False}, + 'odc-by-1.0': {'id': 'ODC-By-1.0', 'deprecated': False}, + 'offis': {'id': 'OFFIS', 'deprecated': False}, + 'ofl-1.0': {'id': 'OFL-1.0', 'deprecated': False}, + 'ofl-1.0-no-rfn': {'id': 'OFL-1.0-no-RFN', 'deprecated': False}, + 'ofl-1.0-rfn': {'id': 'OFL-1.0-RFN', 'deprecated': False}, + 'ofl-1.1': {'id': 'OFL-1.1', 'deprecated': False}, + 'ofl-1.1-no-rfn': {'id': 'OFL-1.1-no-RFN', 'deprecated': False}, + 'ofl-1.1-rfn': {'id': 'OFL-1.1-RFN', 'deprecated': False}, + 'ogc-1.0': {'id': 'OGC-1.0', 'deprecated': False}, + 'ogdl-taiwan-1.0': {'id': 'OGDL-Taiwan-1.0', 'deprecated': False}, + 'ogl-canada-2.0': {'id': 'OGL-Canada-2.0', 'deprecated': False}, + 'ogl-uk-1.0': {'id': 'OGL-UK-1.0', 'deprecated': False}, + 'ogl-uk-2.0': {'id': 'OGL-UK-2.0', 'deprecated': False}, + 'ogl-uk-3.0': {'id': 'OGL-UK-3.0', 'deprecated': False}, + 'ogtsl': {'id': 'OGTSL', 'deprecated': False}, + 'oldap-1.1': {'id': 'OLDAP-1.1', 'deprecated': False}, + 'oldap-1.2': {'id': 'OLDAP-1.2', 'deprecated': False}, + 'oldap-1.3': {'id': 'OLDAP-1.3', 'deprecated': False}, + 'oldap-1.4': {'id': 'OLDAP-1.4', 'deprecated': False}, + 'oldap-2.0': {'id': 'OLDAP-2.0', 'deprecated': False}, + 'oldap-2.0.1': {'id': 'OLDAP-2.0.1', 'deprecated': False}, + 'oldap-2.1': {'id': 'OLDAP-2.1', 'deprecated': False}, + 'oldap-2.2': {'id': 'OLDAP-2.2', 'deprecated': False}, + 'oldap-2.2.1': {'id': 'OLDAP-2.2.1', 'deprecated': False}, + 'oldap-2.2.2': {'id': 'OLDAP-2.2.2', 'deprecated': False}, + 'oldap-2.3': {'id': 'OLDAP-2.3', 'deprecated': False}, + 'oldap-2.4': {'id': 'OLDAP-2.4', 'deprecated': False}, + 'oldap-2.5': {'id': 'OLDAP-2.5', 'deprecated': False}, + 'oldap-2.6': {'id': 'OLDAP-2.6', 'deprecated': False}, + 'oldap-2.7': {'id': 'OLDAP-2.7', 'deprecated': False}, + 'oldap-2.8': {'id': 'OLDAP-2.8', 'deprecated': False}, + 'olfl-1.3': {'id': 'OLFL-1.3', 'deprecated': False}, + 'oml': {'id': 'OML', 'deprecated': False}, + 'openpbs-2.3': {'id': 'OpenPBS-2.3', 'deprecated': False}, + 'openssl': {'id': 'OpenSSL', 'deprecated': False}, + 'openssl-standalone': {'id': 'OpenSSL-standalone', 'deprecated': False}, + 'openvision': {'id': 'OpenVision', 'deprecated': False}, + 'opl-1.0': {'id': 'OPL-1.0', 'deprecated': False}, + 'opl-uk-3.0': {'id': 'OPL-UK-3.0', 'deprecated': False}, + 'opubl-1.0': {'id': 'OPUBL-1.0', 'deprecated': False}, + 'oset-pl-2.1': {'id': 'OSET-PL-2.1', 'deprecated': False}, + 'osl-1.0': {'id': 'OSL-1.0', 'deprecated': False}, + 'osl-1.1': {'id': 'OSL-1.1', 'deprecated': False}, + 'osl-2.0': {'id': 'OSL-2.0', 'deprecated': False}, + 'osl-2.1': {'id': 'OSL-2.1', 'deprecated': False}, + 'osl-3.0': {'id': 'OSL-3.0', 'deprecated': False}, + 'padl': {'id': 'PADL', 'deprecated': False}, + 'parity-6.0.0': {'id': 'Parity-6.0.0', 'deprecated': False}, + 'parity-7.0.0': {'id': 'Parity-7.0.0', 'deprecated': False}, + 'pddl-1.0': {'id': 'PDDL-1.0', 'deprecated': False}, + 'php-3.0': {'id': 'PHP-3.0', 'deprecated': False}, + 'php-3.01': {'id': 'PHP-3.01', 'deprecated': False}, + 'pixar': {'id': 'Pixar', 'deprecated': False}, + 'pkgconf': {'id': 'pkgconf', 'deprecated': False}, + 'plexus': {'id': 'Plexus', 'deprecated': False}, + 'pnmstitch': {'id': 'pnmstitch', 'deprecated': False}, + 'polyform-noncommercial-1.0.0': {'id': 'PolyForm-Noncommercial-1.0.0', 'deprecated': False}, + 'polyform-small-business-1.0.0': {'id': 'PolyForm-Small-Business-1.0.0', 'deprecated': False}, + 'postgresql': {'id': 'PostgreSQL', 'deprecated': False}, + 'ppl': {'id': 'PPL', 'deprecated': False}, + 'psf-2.0': {'id': 'PSF-2.0', 'deprecated': False}, + 'psfrag': {'id': 'psfrag', 'deprecated': False}, + 'psutils': {'id': 'psutils', 'deprecated': False}, + 'python-2.0': {'id': 'Python-2.0', 'deprecated': False}, + 'python-2.0.1': {'id': 'Python-2.0.1', 'deprecated': False}, + 'python-ldap': {'id': 'python-ldap', 'deprecated': False}, + 'qhull': {'id': 'Qhull', 'deprecated': False}, + 'qpl-1.0': {'id': 'QPL-1.0', 'deprecated': False}, + 'qpl-1.0-inria-2004': {'id': 'QPL-1.0-INRIA-2004', 'deprecated': False}, + 'radvd': {'id': 'radvd', 'deprecated': False}, + 'rdisc': {'id': 'Rdisc', 'deprecated': False}, + 'rhecos-1.1': {'id': 'RHeCos-1.1', 'deprecated': False}, + 'rpl-1.1': {'id': 'RPL-1.1', 'deprecated': False}, + 'rpl-1.5': {'id': 'RPL-1.5', 'deprecated': False}, + 'rpsl-1.0': {'id': 'RPSL-1.0', 'deprecated': False}, + 'rsa-md': {'id': 'RSA-MD', 'deprecated': False}, + 'rscpl': {'id': 'RSCPL', 'deprecated': False}, + 'ruby': {'id': 'Ruby', 'deprecated': False}, + 'ruby-pty': {'id': 'Ruby-pty', 'deprecated': False}, + 'sax-pd': {'id': 'SAX-PD', 'deprecated': False}, + 'sax-pd-2.0': {'id': 'SAX-PD-2.0', 'deprecated': False}, + 'saxpath': {'id': 'Saxpath', 'deprecated': False}, + 'scea': {'id': 'SCEA', 'deprecated': False}, + 'schemereport': {'id': 'SchemeReport', 'deprecated': False}, + 'sendmail': {'id': 'Sendmail', 'deprecated': False}, + 'sendmail-8.23': {'id': 'Sendmail-8.23', 'deprecated': False}, + 'sgi-b-1.0': {'id': 'SGI-B-1.0', 'deprecated': False}, + 'sgi-b-1.1': {'id': 'SGI-B-1.1', 'deprecated': False}, + 'sgi-b-2.0': {'id': 'SGI-B-2.0', 'deprecated': False}, + 'sgi-opengl': {'id': 'SGI-OpenGL', 'deprecated': False}, + 'sgp4': {'id': 'SGP4', 'deprecated': False}, + 'shl-0.5': {'id': 'SHL-0.5', 'deprecated': False}, + 'shl-0.51': {'id': 'SHL-0.51', 'deprecated': False}, + 'simpl-2.0': {'id': 'SimPL-2.0', 'deprecated': False}, + 'sissl': {'id': 'SISSL', 'deprecated': False}, + 'sissl-1.2': {'id': 'SISSL-1.2', 'deprecated': False}, + 'sl': {'id': 'SL', 'deprecated': False}, + 'sleepycat': {'id': 'Sleepycat', 'deprecated': False}, + 'smlnj': {'id': 'SMLNJ', 'deprecated': False}, + 'smppl': {'id': 'SMPPL', 'deprecated': False}, + 'snia': {'id': 'SNIA', 'deprecated': False}, + 'snprintf': {'id': 'snprintf', 'deprecated': False}, + 'softsurfer': {'id': 'softSurfer', 'deprecated': False}, + 'soundex': {'id': 'Soundex', 'deprecated': False}, + 'spencer-86': {'id': 'Spencer-86', 'deprecated': False}, + 'spencer-94': {'id': 'Spencer-94', 'deprecated': False}, + 'spencer-99': {'id': 'Spencer-99', 'deprecated': False}, + 'spl-1.0': {'id': 'SPL-1.0', 'deprecated': False}, + 'ssh-keyscan': {'id': 'ssh-keyscan', 'deprecated': False}, + 'ssh-openssh': {'id': 'SSH-OpenSSH', 'deprecated': False}, + 'ssh-short': {'id': 'SSH-short', 'deprecated': False}, + 'ssleay-standalone': {'id': 'SSLeay-standalone', 'deprecated': False}, + 'sspl-1.0': {'id': 'SSPL-1.0', 'deprecated': False}, + 'standardml-nj': {'id': 'StandardML-NJ', 'deprecated': True}, + 'sugarcrm-1.1.3': {'id': 'SugarCRM-1.1.3', 'deprecated': False}, + 'sun-ppp': {'id': 'Sun-PPP', 'deprecated': False}, + 'sun-ppp-2000': {'id': 'Sun-PPP-2000', 'deprecated': False}, + 'sunpro': {'id': 'SunPro', 'deprecated': False}, + 'swl': {'id': 'SWL', 'deprecated': False}, + 'swrule': {'id': 'swrule', 'deprecated': False}, + 'symlinks': {'id': 'Symlinks', 'deprecated': False}, + 'tapr-ohl-1.0': {'id': 'TAPR-OHL-1.0', 'deprecated': False}, + 'tcl': {'id': 'TCL', 'deprecated': False}, + 'tcp-wrappers': {'id': 'TCP-wrappers', 'deprecated': False}, + 'termreadkey': {'id': 'TermReadKey', 'deprecated': False}, + 'tgppl-1.0': {'id': 'TGPPL-1.0', 'deprecated': False}, + 'threeparttable': {'id': 'threeparttable', 'deprecated': False}, + 'tmate': {'id': 'TMate', 'deprecated': False}, + 'torque-1.1': {'id': 'TORQUE-1.1', 'deprecated': False}, + 'tosl': {'id': 'TOSL', 'deprecated': False}, + 'tpdl': {'id': 'TPDL', 'deprecated': False}, + 'tpl-1.0': {'id': 'TPL-1.0', 'deprecated': False}, + 'ttwl': {'id': 'TTWL', 'deprecated': False}, + 'ttyp0': {'id': 'TTYP0', 'deprecated': False}, + 'tu-berlin-1.0': {'id': 'TU-Berlin-1.0', 'deprecated': False}, + 'tu-berlin-2.0': {'id': 'TU-Berlin-2.0', 'deprecated': False}, + 'ubuntu-font-1.0': {'id': 'Ubuntu-font-1.0', 'deprecated': False}, + 'ucar': {'id': 'UCAR', 'deprecated': False}, + 'ucl-1.0': {'id': 'UCL-1.0', 'deprecated': False}, + 'ulem': {'id': 'ulem', 'deprecated': False}, + 'umich-merit': {'id': 'UMich-Merit', 'deprecated': False}, + 'unicode-3.0': {'id': 'Unicode-3.0', 'deprecated': False}, + 'unicode-dfs-2015': {'id': 'Unicode-DFS-2015', 'deprecated': False}, + 'unicode-dfs-2016': {'id': 'Unicode-DFS-2016', 'deprecated': False}, + 'unicode-tou': {'id': 'Unicode-TOU', 'deprecated': False}, + 'unixcrypt': {'id': 'UnixCrypt', 'deprecated': False}, + 'unlicense': {'id': 'Unlicense', 'deprecated': False}, + 'upl-1.0': {'id': 'UPL-1.0', 'deprecated': False}, + 'urt-rle': {'id': 'URT-RLE', 'deprecated': False}, + 'vim': {'id': 'Vim', 'deprecated': False}, + 'vostrom': {'id': 'VOSTROM', 'deprecated': False}, + 'vsl-1.0': {'id': 'VSL-1.0', 'deprecated': False}, + 'w3c': {'id': 'W3C', 'deprecated': False}, + 'w3c-19980720': {'id': 'W3C-19980720', 'deprecated': False}, + 'w3c-20150513': {'id': 'W3C-20150513', 'deprecated': False}, + 'w3m': {'id': 'w3m', 'deprecated': False}, + 'watcom-1.0': {'id': 'Watcom-1.0', 'deprecated': False}, + 'widget-workshop': {'id': 'Widget-Workshop', 'deprecated': False}, + 'wsuipa': {'id': 'Wsuipa', 'deprecated': False}, + 'wtfpl': {'id': 'WTFPL', 'deprecated': False}, + 'wxwindows': {'id': 'wxWindows', 'deprecated': True}, + 'x11': {'id': 'X11', 'deprecated': False}, + 'x11-distribute-modifications-variant': {'id': 'X11-distribute-modifications-variant', 'deprecated': False}, + 'x11-swapped': {'id': 'X11-swapped', 'deprecated': False}, + 'xdebug-1.03': {'id': 'Xdebug-1.03', 'deprecated': False}, + 'xerox': {'id': 'Xerox', 'deprecated': False}, + 'xfig': {'id': 'Xfig', 'deprecated': False}, + 'xfree86-1.1': {'id': 'XFree86-1.1', 'deprecated': False}, + 'xinetd': {'id': 'xinetd', 'deprecated': False}, + 'xkeyboard-config-zinoviev': {'id': 'xkeyboard-config-Zinoviev', 'deprecated': False}, + 'xlock': {'id': 'xlock', 'deprecated': False}, + 'xnet': {'id': 'Xnet', 'deprecated': False}, + 'xpp': {'id': 'xpp', 'deprecated': False}, + 'xskat': {'id': 'XSkat', 'deprecated': False}, + 'xzoom': {'id': 'xzoom', 'deprecated': False}, + 'ypl-1.0': {'id': 'YPL-1.0', 'deprecated': False}, + 'ypl-1.1': {'id': 'YPL-1.1', 'deprecated': False}, + 'zed': {'id': 'Zed', 'deprecated': False}, + 'zeeff': {'id': 'Zeeff', 'deprecated': False}, + 'zend-2.0': {'id': 'Zend-2.0', 'deprecated': False}, + 'zimbra-1.3': {'id': 'Zimbra-1.3', 'deprecated': False}, + 'zimbra-1.4': {'id': 'Zimbra-1.4', 'deprecated': False}, + 'zlib': {'id': 'Zlib', 'deprecated': False}, + 'zlib-acknowledgement': {'id': 'zlib-acknowledgement', 'deprecated': False}, + 'zpl-1.1': {'id': 'ZPL-1.1', 'deprecated': False}, + 'zpl-2.0': {'id': 'ZPL-2.0', 'deprecated': False}, + 'zpl-2.1': {'id': 'ZPL-2.1', 'deprecated': False}, +} + +EXCEPTIONS: dict[str, SPDXException] = { + '389-exception': {'id': '389-exception', 'deprecated': False}, + 'asterisk-exception': {'id': 'Asterisk-exception', 'deprecated': False}, + 'asterisk-linking-protocols-exception': {'id': 'Asterisk-linking-protocols-exception', 'deprecated': False}, + 'autoconf-exception-2.0': {'id': 'Autoconf-exception-2.0', 'deprecated': False}, + 'autoconf-exception-3.0': {'id': 'Autoconf-exception-3.0', 'deprecated': False}, + 'autoconf-exception-generic': {'id': 'Autoconf-exception-generic', 'deprecated': False}, + 'autoconf-exception-generic-3.0': {'id': 'Autoconf-exception-generic-3.0', 'deprecated': False}, + 'autoconf-exception-macro': {'id': 'Autoconf-exception-macro', 'deprecated': False}, + 'bison-exception-1.24': {'id': 'Bison-exception-1.24', 'deprecated': False}, + 'bison-exception-2.2': {'id': 'Bison-exception-2.2', 'deprecated': False}, + 'bootloader-exception': {'id': 'Bootloader-exception', 'deprecated': False}, + 'classpath-exception-2.0': {'id': 'Classpath-exception-2.0', 'deprecated': False}, + 'clisp-exception-2.0': {'id': 'CLISP-exception-2.0', 'deprecated': False}, + 'cryptsetup-openssl-exception': {'id': 'cryptsetup-OpenSSL-exception', 'deprecated': False}, + 'digirule-foss-exception': {'id': 'DigiRule-FOSS-exception', 'deprecated': False}, + 'ecos-exception-2.0': {'id': 'eCos-exception-2.0', 'deprecated': False}, + 'erlang-otp-linking-exception': {'id': 'erlang-otp-linking-exception', 'deprecated': False}, + 'fawkes-runtime-exception': {'id': 'Fawkes-Runtime-exception', 'deprecated': False}, + 'fltk-exception': {'id': 'FLTK-exception', 'deprecated': False}, + 'fmt-exception': {'id': 'fmt-exception', 'deprecated': False}, + 'font-exception-2.0': {'id': 'Font-exception-2.0', 'deprecated': False}, + 'freertos-exception-2.0': {'id': 'freertos-exception-2.0', 'deprecated': False}, + 'gcc-exception-2.0': {'id': 'GCC-exception-2.0', 'deprecated': False}, + 'gcc-exception-2.0-note': {'id': 'GCC-exception-2.0-note', 'deprecated': False}, + 'gcc-exception-3.1': {'id': 'GCC-exception-3.1', 'deprecated': False}, + 'gmsh-exception': {'id': 'Gmsh-exception', 'deprecated': False}, + 'gnat-exception': {'id': 'GNAT-exception', 'deprecated': False}, + 'gnome-examples-exception': {'id': 'GNOME-examples-exception', 'deprecated': False}, + 'gnu-compiler-exception': {'id': 'GNU-compiler-exception', 'deprecated': False}, + 'gnu-javamail-exception': {'id': 'gnu-javamail-exception', 'deprecated': False}, + 'gpl-3.0-interface-exception': {'id': 'GPL-3.0-interface-exception', 'deprecated': False}, + 'gpl-3.0-linking-exception': {'id': 'GPL-3.0-linking-exception', 'deprecated': False}, + 'gpl-3.0-linking-source-exception': {'id': 'GPL-3.0-linking-source-exception', 'deprecated': False}, + 'gpl-cc-1.0': {'id': 'GPL-CC-1.0', 'deprecated': False}, + 'gstreamer-exception-2005': {'id': 'GStreamer-exception-2005', 'deprecated': False}, + 'gstreamer-exception-2008': {'id': 'GStreamer-exception-2008', 'deprecated': False}, + 'i2p-gpl-java-exception': {'id': 'i2p-gpl-java-exception', 'deprecated': False}, + 'kicad-libraries-exception': {'id': 'KiCad-libraries-exception', 'deprecated': False}, + 'lgpl-3.0-linking-exception': {'id': 'LGPL-3.0-linking-exception', 'deprecated': False}, + 'libpri-openh323-exception': {'id': 'libpri-OpenH323-exception', 'deprecated': False}, + 'libtool-exception': {'id': 'Libtool-exception', 'deprecated': False}, + 'linux-syscall-note': {'id': 'Linux-syscall-note', 'deprecated': False}, + 'llgpl': {'id': 'LLGPL', 'deprecated': False}, + 'llvm-exception': {'id': 'LLVM-exception', 'deprecated': False}, + 'lzma-exception': {'id': 'LZMA-exception', 'deprecated': False}, + 'mif-exception': {'id': 'mif-exception', 'deprecated': False}, + 'nokia-qt-exception-1.1': {'id': 'Nokia-Qt-exception-1.1', 'deprecated': True}, + 'ocaml-lgpl-linking-exception': {'id': 'OCaml-LGPL-linking-exception', 'deprecated': False}, + 'occt-exception-1.0': {'id': 'OCCT-exception-1.0', 'deprecated': False}, + 'openjdk-assembly-exception-1.0': {'id': 'OpenJDK-assembly-exception-1.0', 'deprecated': False}, + 'openvpn-openssl-exception': {'id': 'openvpn-openssl-exception', 'deprecated': False}, + 'pcre2-exception': {'id': 'PCRE2-exception', 'deprecated': False}, + 'ps-or-pdf-font-exception-20170817': {'id': 'PS-or-PDF-font-exception-20170817', 'deprecated': False}, + 'qpl-1.0-inria-2004-exception': {'id': 'QPL-1.0-INRIA-2004-exception', 'deprecated': False}, + 'qt-gpl-exception-1.0': {'id': 'Qt-GPL-exception-1.0', 'deprecated': False}, + 'qt-lgpl-exception-1.1': {'id': 'Qt-LGPL-exception-1.1', 'deprecated': False}, + 'qwt-exception-1.0': {'id': 'Qwt-exception-1.0', 'deprecated': False}, + 'romic-exception': {'id': 'romic-exception', 'deprecated': False}, + 'rrdtool-floss-exception-2.0': {'id': 'RRDtool-FLOSS-exception-2.0', 'deprecated': False}, + 'sane-exception': {'id': 'SANE-exception', 'deprecated': False}, + 'shl-2.0': {'id': 'SHL-2.0', 'deprecated': False}, + 'shl-2.1': {'id': 'SHL-2.1', 'deprecated': False}, + 'stunnel-exception': {'id': 'stunnel-exception', 'deprecated': False}, + 'swi-exception': {'id': 'SWI-exception', 'deprecated': False}, + 'swift-exception': {'id': 'Swift-exception', 'deprecated': False}, + 'texinfo-exception': {'id': 'Texinfo-exception', 'deprecated': False}, + 'u-boot-exception-2.0': {'id': 'u-boot-exception-2.0', 'deprecated': False}, + 'ubdl-exception': {'id': 'UBDL-exception', 'deprecated': False}, + 'universal-foss-exception-1.0': {'id': 'Universal-FOSS-exception-1.0', 'deprecated': False}, + 'vsftpd-openssl-exception': {'id': 'vsftpd-openssl-exception', 'deprecated': False}, + 'wxwindows-exception-3.1': {'id': 'WxWindows-exception-3.1', 'deprecated': False}, + 'x11vnc-openssl-exception': {'id': 'x11vnc-openssl-exception', 'deprecated': False}, +} diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging/markers.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging/markers.py new file mode 100644 index 0000000000000000000000000000000000000000..e7cea57297a23809548fcd0d344740796bae945b --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging/markers.py @@ -0,0 +1,362 @@ +# This file is dual licensed under the terms of the Apache License, Version +# 2.0, and the BSD License. See the LICENSE file in the root of this repository +# for complete details. + +from __future__ import annotations + +import operator +import os +import platform +import sys +from typing import AbstractSet, Any, Callable, Literal, TypedDict, Union, cast + +from ._parser import MarkerAtom, MarkerList, Op, Value, Variable +from ._parser import parse_marker as _parse_marker +from ._tokenizer import ParserSyntaxError +from .specifiers import InvalidSpecifier, Specifier +from .utils import canonicalize_name + +__all__ = [ + "EvaluateContext", + "InvalidMarker", + "Marker", + "UndefinedComparison", + "UndefinedEnvironmentName", + "default_environment", +] + +Operator = Callable[[str, Union[str, AbstractSet[str]]], bool] +EvaluateContext = Literal["metadata", "lock_file", "requirement"] +MARKERS_ALLOWING_SET = {"extras", "dependency_groups"} + + +class InvalidMarker(ValueError): + """ + An invalid marker was found, users should refer to PEP 508. + """ + + +class UndefinedComparison(ValueError): + """ + An invalid operation was attempted on a value that doesn't support it. + """ + + +class UndefinedEnvironmentName(ValueError): + """ + A name was attempted to be used that does not exist inside of the + environment. + """ + + +class Environment(TypedDict): + implementation_name: str + """The implementation's identifier, e.g. ``'cpython'``.""" + + implementation_version: str + """ + The implementation's version, e.g. ``'3.13.0a2'`` for CPython 3.13.0a2, or + ``'7.3.13'`` for PyPy3.10 v7.3.13. + """ + + os_name: str + """ + The value of :py:data:`os.name`. The name of the operating system dependent module + imported, e.g. ``'posix'``. + """ + + platform_machine: str + """ + Returns the machine type, e.g. ``'i386'``. + + An empty string if the value cannot be determined. + """ + + platform_release: str + """ + The system's release, e.g. ``'2.2.0'`` or ``'NT'``. + + An empty string if the value cannot be determined. + """ + + platform_system: str + """ + The system/OS name, e.g. ``'Linux'``, ``'Windows'`` or ``'Java'``. + + An empty string if the value cannot be determined. + """ + + platform_version: str + """ + The system's release version, e.g. ``'#3 on degas'``. + + An empty string if the value cannot be determined. + """ + + python_full_version: str + """ + The Python version as string ``'major.minor.patchlevel'``. + + Note that unlike the Python :py:data:`sys.version`, this value will always include + the patchlevel (it defaults to 0). + """ + + platform_python_implementation: str + """ + A string identifying the Python implementation, e.g. ``'CPython'``. + """ + + python_version: str + """The Python version as string ``'major.minor'``.""" + + sys_platform: str + """ + This string contains a platform identifier that can be used to append + platform-specific components to :py:data:`sys.path`, for instance. + + For Unix systems, except on Linux and AIX, this is the lowercased OS name as + returned by ``uname -s`` with the first part of the version as returned by + ``uname -r`` appended, e.g. ``'sunos5'`` or ``'freebsd8'``, at the time when Python + was built. + """ + + +def _normalize_extra_values(results: Any) -> Any: + """ + Normalize extra values. + """ + if isinstance(results[0], tuple): + lhs, op, rhs = results[0] + if isinstance(lhs, Variable) and lhs.value == "extra": + normalized_extra = canonicalize_name(rhs.value) + rhs = Value(normalized_extra) + elif isinstance(rhs, Variable) and rhs.value == "extra": + normalized_extra = canonicalize_name(lhs.value) + lhs = Value(normalized_extra) + results[0] = lhs, op, rhs + return results + + +def _format_marker( + marker: list[str] | MarkerAtom | str, first: bool | None = True +) -> str: + assert isinstance(marker, (list, tuple, str)) + + # Sometimes we have a structure like [[...]] which is a single item list + # where the single item is itself it's own list. In that case we want skip + # the rest of this function so that we don't get extraneous () on the + # outside. + if ( + isinstance(marker, list) + and len(marker) == 1 + and isinstance(marker[0], (list, tuple)) + ): + return _format_marker(marker[0]) + + if isinstance(marker, list): + inner = (_format_marker(m, first=False) for m in marker) + if first: + return " ".join(inner) + else: + return "(" + " ".join(inner) + ")" + elif isinstance(marker, tuple): + return " ".join([m.serialize() for m in marker]) + else: + return marker + + +_operators: dict[str, Operator] = { + "in": lambda lhs, rhs: lhs in rhs, + "not in": lambda lhs, rhs: lhs not in rhs, + "<": operator.lt, + "<=": operator.le, + "==": operator.eq, + "!=": operator.ne, + ">=": operator.ge, + ">": operator.gt, +} + + +def _eval_op(lhs: str, op: Op, rhs: str | AbstractSet[str]) -> bool: + if isinstance(rhs, str): + try: + spec = Specifier("".join([op.serialize(), rhs])) + except InvalidSpecifier: + pass + else: + return spec.contains(lhs, prereleases=True) + + oper: Operator | None = _operators.get(op.serialize()) + if oper is None: + raise UndefinedComparison(f"Undefined {op!r} on {lhs!r} and {rhs!r}.") + + return oper(lhs, rhs) + + +def _normalize( + lhs: str, rhs: str | AbstractSet[str], key: str +) -> tuple[str, str | AbstractSet[str]]: + # PEP 685 – Comparison of extra names for optional distribution dependencies + # https://peps.python.org/pep-0685/ + # > When comparing extra names, tools MUST normalize the names being + # > compared using the semantics outlined in PEP 503 for names + if key == "extra": + assert isinstance(rhs, str), "extra value must be a string" + return (canonicalize_name(lhs), canonicalize_name(rhs)) + if key in MARKERS_ALLOWING_SET: + if isinstance(rhs, str): # pragma: no cover + return (canonicalize_name(lhs), canonicalize_name(rhs)) + else: + return (canonicalize_name(lhs), {canonicalize_name(v) for v in rhs}) + + # other environment markers don't have such standards + return lhs, rhs + + +def _evaluate_markers( + markers: MarkerList, environment: dict[str, str | AbstractSet[str]] +) -> bool: + groups: list[list[bool]] = [[]] + + for marker in markers: + assert isinstance(marker, (list, tuple, str)) + + if isinstance(marker, list): + groups[-1].append(_evaluate_markers(marker, environment)) + elif isinstance(marker, tuple): + lhs, op, rhs = marker + + if isinstance(lhs, Variable): + environment_key = lhs.value + lhs_value = environment[environment_key] + rhs_value = rhs.value + else: + lhs_value = lhs.value + environment_key = rhs.value + rhs_value = environment[environment_key] + assert isinstance(lhs_value, str), "lhs must be a string" + lhs_value, rhs_value = _normalize(lhs_value, rhs_value, key=environment_key) + groups[-1].append(_eval_op(lhs_value, op, rhs_value)) + else: + assert marker in ["and", "or"] + if marker == "or": + groups.append([]) + + return any(all(item) for item in groups) + + +def format_full_version(info: sys._version_info) -> str: + version = f"{info.major}.{info.minor}.{info.micro}" + kind = info.releaselevel + if kind != "final": + version += kind[0] + str(info.serial) + return version + + +def default_environment() -> Environment: + iver = format_full_version(sys.implementation.version) + implementation_name = sys.implementation.name + return { + "implementation_name": implementation_name, + "implementation_version": iver, + "os_name": os.name, + "platform_machine": platform.machine(), + "platform_release": platform.release(), + "platform_system": platform.system(), + "platform_version": platform.version(), + "python_full_version": platform.python_version(), + "platform_python_implementation": platform.python_implementation(), + "python_version": ".".join(platform.python_version_tuple()[:2]), + "sys_platform": sys.platform, + } + + +class Marker: + def __init__(self, marker: str) -> None: + # Note: We create a Marker object without calling this constructor in + # packaging.requirements.Requirement. If any additional logic is + # added here, make sure to mirror/adapt Requirement. + try: + self._markers = _normalize_extra_values(_parse_marker(marker)) + # The attribute `_markers` can be described in terms of a recursive type: + # MarkerList = List[Union[Tuple[Node, ...], str, MarkerList]] + # + # For example, the following expression: + # python_version > "3.6" or (python_version == "3.6" and os_name == "unix") + # + # is parsed into: + # [ + # (, ')>, ), + # 'and', + # [ + # (, , ), + # 'or', + # (, , ) + # ] + # ] + except ParserSyntaxError as e: + raise InvalidMarker(str(e)) from e + + def __str__(self) -> str: + return _format_marker(self._markers) + + def __repr__(self) -> str: + return f"" + + def __hash__(self) -> int: + return hash((self.__class__.__name__, str(self))) + + def __eq__(self, other: Any) -> bool: + if not isinstance(other, Marker): + return NotImplemented + + return str(self) == str(other) + + def evaluate( + self, + environment: dict[str, str] | None = None, + context: EvaluateContext = "metadata", + ) -> bool: + """Evaluate a marker. + + Return the boolean from evaluating the given marker against the + environment. environment is an optional argument to override all or + part of the determined environment. The *context* parameter specifies what + context the markers are being evaluated for, which influences what markers + are considered valid. Acceptable values are "metadata" (for core metadata; + default), "lock_file", and "requirement" (i.e. all other situations). + + The environment is determined from the current Python process. + """ + current_environment = cast( + "dict[str, str | AbstractSet[str]]", default_environment() + ) + if context == "lock_file": + current_environment.update( + extras=frozenset(), dependency_groups=frozenset() + ) + elif context == "metadata": + current_environment["extra"] = "" + if environment is not None: + current_environment.update(environment) + # The API used to allow setting extra to None. We need to handle this + # case for backwards compatibility. + if "extra" in current_environment and current_environment["extra"] is None: + current_environment["extra"] = "" + + return _evaluate_markers( + self._markers, _repair_python_full_version(current_environment) + ) + + +def _repair_python_full_version( + env: dict[str, str | AbstractSet[str]], +) -> dict[str, str | AbstractSet[str]]: + """ + Work around platform.python_version() returning something that is not PEP 440 + compliant for non-tagged Python builds. + """ + python_full_version = cast(str, env["python_full_version"]) + if python_full_version.endswith("+"): + env["python_full_version"] = f"{python_full_version}local" + return env diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging/metadata.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging/metadata.py new file mode 100644 index 0000000000000000000000000000000000000000..3bd8602d36c53374f1f10a0716d28fdc64e2d3ac --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging/metadata.py @@ -0,0 +1,862 @@ +from __future__ import annotations + +import email.feedparser +import email.header +import email.message +import email.parser +import email.policy +import pathlib +import sys +import typing +from typing import ( + Any, + Callable, + Generic, + Literal, + TypedDict, + cast, +) + +from . import licenses, requirements, specifiers, utils +from . import version as version_module +from .licenses import NormalizedLicenseExpression + +T = typing.TypeVar("T") + + +if sys.version_info >= (3, 11): # pragma: no cover + ExceptionGroup = ExceptionGroup +else: # pragma: no cover + + class ExceptionGroup(Exception): + """A minimal implementation of :external:exc:`ExceptionGroup` from Python 3.11. + + If :external:exc:`ExceptionGroup` is already defined by Python itself, + that version is used instead. + """ + + message: str + exceptions: list[Exception] + + def __init__(self, message: str, exceptions: list[Exception]) -> None: + self.message = message + self.exceptions = exceptions + + def __repr__(self) -> str: + return f"{self.__class__.__name__}({self.message!r}, {self.exceptions!r})" + + +class InvalidMetadata(ValueError): + """A metadata field contains invalid data.""" + + field: str + """The name of the field that contains invalid data.""" + + def __init__(self, field: str, message: str) -> None: + self.field = field + super().__init__(message) + + +# The RawMetadata class attempts to make as few assumptions about the underlying +# serialization formats as possible. The idea is that as long as a serialization +# formats offer some very basic primitives in *some* way then we can support +# serializing to and from that format. +class RawMetadata(TypedDict, total=False): + """A dictionary of raw core metadata. + + Each field in core metadata maps to a key of this dictionary (when data is + provided). The key is lower-case and underscores are used instead of dashes + compared to the equivalent core metadata field. Any core metadata field that + can be specified multiple times or can hold multiple values in a single + field have a key with a plural name. See :class:`Metadata` whose attributes + match the keys of this dictionary. + + Core metadata fields that can be specified multiple times are stored as a + list or dict depending on which is appropriate for the field. Any fields + which hold multiple values in a single field are stored as a list. + + """ + + # Metadata 1.0 - PEP 241 + metadata_version: str + name: str + version: str + platforms: list[str] + summary: str + description: str + keywords: list[str] + home_page: str + author: str + author_email: str + license: str + + # Metadata 1.1 - PEP 314 + supported_platforms: list[str] + download_url: str + classifiers: list[str] + requires: list[str] + provides: list[str] + obsoletes: list[str] + + # Metadata 1.2 - PEP 345 + maintainer: str + maintainer_email: str + requires_dist: list[str] + provides_dist: list[str] + obsoletes_dist: list[str] + requires_python: str + requires_external: list[str] + project_urls: dict[str, str] + + # Metadata 2.0 + # PEP 426 attempted to completely revamp the metadata format + # but got stuck without ever being able to build consensus on + # it and ultimately ended up withdrawn. + # + # However, a number of tools had started emitting METADATA with + # `2.0` Metadata-Version, so for historical reasons, this version + # was skipped. + + # Metadata 2.1 - PEP 566 + description_content_type: str + provides_extra: list[str] + + # Metadata 2.2 - PEP 643 + dynamic: list[str] + + # Metadata 2.3 - PEP 685 + # No new fields were added in PEP 685, just some edge case were + # tightened up to provide better interoptability. + + # Metadata 2.4 - PEP 639 + license_expression: str + license_files: list[str] + + +_STRING_FIELDS = { + "author", + "author_email", + "description", + "description_content_type", + "download_url", + "home_page", + "license", + "license_expression", + "maintainer", + "maintainer_email", + "metadata_version", + "name", + "requires_python", + "summary", + "version", +} + +_LIST_FIELDS = { + "classifiers", + "dynamic", + "license_files", + "obsoletes", + "obsoletes_dist", + "platforms", + "provides", + "provides_dist", + "provides_extra", + "requires", + "requires_dist", + "requires_external", + "supported_platforms", +} + +_DICT_FIELDS = { + "project_urls", +} + + +def _parse_keywords(data: str) -> list[str]: + """Split a string of comma-separated keywords into a list of keywords.""" + return [k.strip() for k in data.split(",")] + + +def _parse_project_urls(data: list[str]) -> dict[str, str]: + """Parse a list of label/URL string pairings separated by a comma.""" + urls = {} + for pair in data: + # Our logic is slightly tricky here as we want to try and do + # *something* reasonable with malformed data. + # + # The main thing that we have to worry about, is data that does + # not have a ',' at all to split the label from the Value. There + # isn't a singular right answer here, and we will fail validation + # later on (if the caller is validating) so it doesn't *really* + # matter, but since the missing value has to be an empty str + # and our return value is dict[str, str], if we let the key + # be the missing value, then they'd have multiple '' values that + # overwrite each other in a accumulating dict. + # + # The other potentional issue is that it's possible to have the + # same label multiple times in the metadata, with no solid "right" + # answer with what to do in that case. As such, we'll do the only + # thing we can, which is treat the field as unparseable and add it + # to our list of unparsed fields. + parts = [p.strip() for p in pair.split(",", 1)] + parts.extend([""] * (max(0, 2 - len(parts)))) # Ensure 2 items + + # TODO: The spec doesn't say anything about if the keys should be + # considered case sensitive or not... logically they should + # be case-preserving and case-insensitive, but doing that + # would open up more cases where we might have duplicate + # entries. + label, url = parts + if label in urls: + # The label already exists in our set of urls, so this field + # is unparseable, and we can just add the whole thing to our + # unparseable data and stop processing it. + raise KeyError("duplicate labels in project urls") + urls[label] = url + + return urls + + +def _get_payload(msg: email.message.Message, source: bytes | str) -> str: + """Get the body of the message.""" + # If our source is a str, then our caller has managed encodings for us, + # and we don't need to deal with it. + if isinstance(source, str): + payload = msg.get_payload() + assert isinstance(payload, str) + return payload + # If our source is a bytes, then we're managing the encoding and we need + # to deal with it. + else: + bpayload = msg.get_payload(decode=True) + assert isinstance(bpayload, bytes) + try: + return bpayload.decode("utf8", "strict") + except UnicodeDecodeError as exc: + raise ValueError("payload in an invalid encoding") from exc + + +# The various parse_FORMAT functions here are intended to be as lenient as +# possible in their parsing, while still returning a correctly typed +# RawMetadata. +# +# To aid in this, we also generally want to do as little touching of the +# data as possible, except where there are possibly some historic holdovers +# that make valid data awkward to work with. +# +# While this is a lower level, intermediate format than our ``Metadata`` +# class, some light touch ups can make a massive difference in usability. + +# Map METADATA fields to RawMetadata. +_EMAIL_TO_RAW_MAPPING = { + "author": "author", + "author-email": "author_email", + "classifier": "classifiers", + "description": "description", + "description-content-type": "description_content_type", + "download-url": "download_url", + "dynamic": "dynamic", + "home-page": "home_page", + "keywords": "keywords", + "license": "license", + "license-expression": "license_expression", + "license-file": "license_files", + "maintainer": "maintainer", + "maintainer-email": "maintainer_email", + "metadata-version": "metadata_version", + "name": "name", + "obsoletes": "obsoletes", + "obsoletes-dist": "obsoletes_dist", + "platform": "platforms", + "project-url": "project_urls", + "provides": "provides", + "provides-dist": "provides_dist", + "provides-extra": "provides_extra", + "requires": "requires", + "requires-dist": "requires_dist", + "requires-external": "requires_external", + "requires-python": "requires_python", + "summary": "summary", + "supported-platform": "supported_platforms", + "version": "version", +} +_RAW_TO_EMAIL_MAPPING = {raw: email for email, raw in _EMAIL_TO_RAW_MAPPING.items()} + + +def parse_email(data: bytes | str) -> tuple[RawMetadata, dict[str, list[str]]]: + """Parse a distribution's metadata stored as email headers (e.g. from ``METADATA``). + + This function returns a two-item tuple of dicts. The first dict is of + recognized fields from the core metadata specification. Fields that can be + parsed and translated into Python's built-in types are converted + appropriately. All other fields are left as-is. Fields that are allowed to + appear multiple times are stored as lists. + + The second dict contains all other fields from the metadata. This includes + any unrecognized fields. It also includes any fields which are expected to + be parsed into a built-in type but were not formatted appropriately. Finally, + any fields that are expected to appear only once but are repeated are + included in this dict. + + """ + raw: dict[str, str | list[str] | dict[str, str]] = {} + unparsed: dict[str, list[str]] = {} + + if isinstance(data, str): + parsed = email.parser.Parser(policy=email.policy.compat32).parsestr(data) + else: + parsed = email.parser.BytesParser(policy=email.policy.compat32).parsebytes(data) + + # We have to wrap parsed.keys() in a set, because in the case of multiple + # values for a key (a list), the key will appear multiple times in the + # list of keys, but we're avoiding that by using get_all(). + for name in frozenset(parsed.keys()): + # Header names in RFC are case insensitive, so we'll normalize to all + # lower case to make comparisons easier. + name = name.lower() + + # We use get_all() here, even for fields that aren't multiple use, + # because otherwise someone could have e.g. two Name fields, and we + # would just silently ignore it rather than doing something about it. + headers = parsed.get_all(name) or [] + + # The way the email module works when parsing bytes is that it + # unconditionally decodes the bytes as ascii using the surrogateescape + # handler. When you pull that data back out (such as with get_all() ), + # it looks to see if the str has any surrogate escapes, and if it does + # it wraps it in a Header object instead of returning the string. + # + # As such, we'll look for those Header objects, and fix up the encoding. + value = [] + # Flag if we have run into any issues processing the headers, thus + # signalling that the data belongs in 'unparsed'. + valid_encoding = True + for h in headers: + # It's unclear if this can return more types than just a Header or + # a str, so we'll just assert here to make sure. + assert isinstance(h, (email.header.Header, str)) + + # If it's a header object, we need to do our little dance to get + # the real data out of it. In cases where there is invalid data + # we're going to end up with mojibake, but there's no obvious, good + # way around that without reimplementing parts of the Header object + # ourselves. + # + # That should be fine since, if mojibacked happens, this key is + # going into the unparsed dict anyways. + if isinstance(h, email.header.Header): + # The Header object stores it's data as chunks, and each chunk + # can be independently encoded, so we'll need to check each + # of them. + chunks: list[tuple[bytes, str | None]] = [] + for bin, encoding in email.header.decode_header(h): + try: + bin.decode("utf8", "strict") + except UnicodeDecodeError: + # Enable mojibake. + encoding = "latin1" + valid_encoding = False + else: + encoding = "utf8" + chunks.append((bin, encoding)) + + # Turn our chunks back into a Header object, then let that + # Header object do the right thing to turn them into a + # string for us. + value.append(str(email.header.make_header(chunks))) + # This is already a string, so just add it. + else: + value.append(h) + + # We've processed all of our values to get them into a list of str, + # but we may have mojibake data, in which case this is an unparsed + # field. + if not valid_encoding: + unparsed[name] = value + continue + + raw_name = _EMAIL_TO_RAW_MAPPING.get(name) + if raw_name is None: + # This is a bit of a weird situation, we've encountered a key that + # we don't know what it means, so we don't know whether it's meant + # to be a list or not. + # + # Since we can't really tell one way or another, we'll just leave it + # as a list, even though it may be a single item list, because that's + # what makes the most sense for email headers. + unparsed[name] = value + continue + + # If this is one of our string fields, then we'll check to see if our + # value is a list of a single item. If it is then we'll assume that + # it was emitted as a single string, and unwrap the str from inside + # the list. + # + # If it's any other kind of data, then we haven't the faintest clue + # what we should parse it as, and we have to just add it to our list + # of unparsed stuff. + if raw_name in _STRING_FIELDS and len(value) == 1: + raw[raw_name] = value[0] + # If this is one of our list of string fields, then we can just assign + # the value, since email *only* has strings, and our get_all() call + # above ensures that this is a list. + elif raw_name in _LIST_FIELDS: + raw[raw_name] = value + # Special Case: Keywords + # The keywords field is implemented in the metadata spec as a str, + # but it conceptually is a list of strings, and is serialized using + # ", ".join(keywords), so we'll do some light data massaging to turn + # this into what it logically is. + elif raw_name == "keywords" and len(value) == 1: + raw[raw_name] = _parse_keywords(value[0]) + # Special Case: Project-URL + # The project urls is implemented in the metadata spec as a list of + # specially-formatted strings that represent a key and a value, which + # is fundamentally a mapping, however the email format doesn't support + # mappings in a sane way, so it was crammed into a list of strings + # instead. + # + # We will do a little light data massaging to turn this into a map as + # it logically should be. + elif raw_name == "project_urls": + try: + raw[raw_name] = _parse_project_urls(value) + except KeyError: + unparsed[name] = value + # Nothing that we've done has managed to parse this, so it'll just + # throw it in our unparseable data and move on. + else: + unparsed[name] = value + + # We need to support getting the Description from the message payload in + # addition to getting it from the the headers. This does mean, though, there + # is the possibility of it being set both ways, in which case we put both + # in 'unparsed' since we don't know which is right. + try: + payload = _get_payload(parsed, data) + except ValueError: + unparsed.setdefault("description", []).append( + parsed.get_payload(decode=isinstance(data, bytes)) # type: ignore[call-overload] + ) + else: + if payload: + # Check to see if we've already got a description, if so then both + # it, and this body move to unparseable. + if "description" in raw: + description_header = cast(str, raw.pop("description")) + unparsed.setdefault("description", []).extend( + [description_header, payload] + ) + elif "description" in unparsed: + unparsed["description"].append(payload) + else: + raw["description"] = payload + + # We need to cast our `raw` to a metadata, because a TypedDict only support + # literal key names, but we're computing our key names on purpose, but the + # way this function is implemented, our `TypedDict` can only have valid key + # names. + return cast(RawMetadata, raw), unparsed + + +_NOT_FOUND = object() + + +# Keep the two values in sync. +_VALID_METADATA_VERSIONS = ["1.0", "1.1", "1.2", "2.1", "2.2", "2.3", "2.4"] +_MetadataVersion = Literal["1.0", "1.1", "1.2", "2.1", "2.2", "2.3", "2.4"] + +_REQUIRED_ATTRS = frozenset(["metadata_version", "name", "version"]) + + +class _Validator(Generic[T]): + """Validate a metadata field. + + All _process_*() methods correspond to a core metadata field. The method is + called with the field's raw value. If the raw value is valid it is returned + in its "enriched" form (e.g. ``version.Version`` for the ``Version`` field). + If the raw value is invalid, :exc:`InvalidMetadata` is raised (with a cause + as appropriate). + """ + + name: str + raw_name: str + added: _MetadataVersion + + def __init__( + self, + *, + added: _MetadataVersion = "1.0", + ) -> None: + self.added = added + + def __set_name__(self, _owner: Metadata, name: str) -> None: + self.name = name + self.raw_name = _RAW_TO_EMAIL_MAPPING[name] + + def __get__(self, instance: Metadata, _owner: type[Metadata]) -> T: + # With Python 3.8, the caching can be replaced with functools.cached_property(). + # No need to check the cache as attribute lookup will resolve into the + # instance's __dict__ before __get__ is called. + cache = instance.__dict__ + value = instance._raw.get(self.name) + + # To make the _process_* methods easier, we'll check if the value is None + # and if this field is NOT a required attribute, and if both of those + # things are true, we'll skip the the converter. This will mean that the + # converters never have to deal with the None union. + if self.name in _REQUIRED_ATTRS or value is not None: + try: + converter: Callable[[Any], T] = getattr(self, f"_process_{self.name}") + except AttributeError: + pass + else: + value = converter(value) + + cache[self.name] = value + try: + del instance._raw[self.name] # type: ignore[misc] + except KeyError: + pass + + return cast(T, value) + + def _invalid_metadata( + self, msg: str, cause: Exception | None = None + ) -> InvalidMetadata: + exc = InvalidMetadata( + self.raw_name, msg.format_map({"field": repr(self.raw_name)}) + ) + exc.__cause__ = cause + return exc + + def _process_metadata_version(self, value: str) -> _MetadataVersion: + # Implicitly makes Metadata-Version required. + if value not in _VALID_METADATA_VERSIONS: + raise self._invalid_metadata(f"{value!r} is not a valid metadata version") + return cast(_MetadataVersion, value) + + def _process_name(self, value: str) -> str: + if not value: + raise self._invalid_metadata("{field} is a required field") + # Validate the name as a side-effect. + try: + utils.canonicalize_name(value, validate=True) + except utils.InvalidName as exc: + raise self._invalid_metadata( + f"{value!r} is invalid for {{field}}", cause=exc + ) from exc + else: + return value + + def _process_version(self, value: str) -> version_module.Version: + if not value: + raise self._invalid_metadata("{field} is a required field") + try: + return version_module.parse(value) + except version_module.InvalidVersion as exc: + raise self._invalid_metadata( + f"{value!r} is invalid for {{field}}", cause=exc + ) from exc + + def _process_summary(self, value: str) -> str: + """Check the field contains no newlines.""" + if "\n" in value: + raise self._invalid_metadata("{field} must be a single line") + return value + + def _process_description_content_type(self, value: str) -> str: + content_types = {"text/plain", "text/x-rst", "text/markdown"} + message = email.message.EmailMessage() + message["content-type"] = value + + content_type, parameters = ( + # Defaults to `text/plain` if parsing failed. + message.get_content_type().lower(), + message["content-type"].params, + ) + # Check if content-type is valid or defaulted to `text/plain` and thus was + # not parseable. + if content_type not in content_types or content_type not in value.lower(): + raise self._invalid_metadata( + f"{{field}} must be one of {list(content_types)}, not {value!r}" + ) + + charset = parameters.get("charset", "UTF-8") + if charset != "UTF-8": + raise self._invalid_metadata( + f"{{field}} can only specify the UTF-8 charset, not {list(charset)}" + ) + + markdown_variants = {"GFM", "CommonMark"} + variant = parameters.get("variant", "GFM") # Use an acceptable default. + if content_type == "text/markdown" and variant not in markdown_variants: + raise self._invalid_metadata( + f"valid Markdown variants for {{field}} are {list(markdown_variants)}, " + f"not {variant!r}", + ) + return value + + def _process_dynamic(self, value: list[str]) -> list[str]: + for dynamic_field in map(str.lower, value): + if dynamic_field in {"name", "version", "metadata-version"}: + raise self._invalid_metadata( + f"{dynamic_field!r} is not allowed as a dynamic field" + ) + elif dynamic_field not in _EMAIL_TO_RAW_MAPPING: + raise self._invalid_metadata( + f"{dynamic_field!r} is not a valid dynamic field" + ) + return list(map(str.lower, value)) + + def _process_provides_extra( + self, + value: list[str], + ) -> list[utils.NormalizedName]: + normalized_names = [] + try: + for name in value: + normalized_names.append(utils.canonicalize_name(name, validate=True)) + except utils.InvalidName as exc: + raise self._invalid_metadata( + f"{name!r} is invalid for {{field}}", cause=exc + ) from exc + else: + return normalized_names + + def _process_requires_python(self, value: str) -> specifiers.SpecifierSet: + try: + return specifiers.SpecifierSet(value) + except specifiers.InvalidSpecifier as exc: + raise self._invalid_metadata( + f"{value!r} is invalid for {{field}}", cause=exc + ) from exc + + def _process_requires_dist( + self, + value: list[str], + ) -> list[requirements.Requirement]: + reqs = [] + try: + for req in value: + reqs.append(requirements.Requirement(req)) + except requirements.InvalidRequirement as exc: + raise self._invalid_metadata( + f"{req!r} is invalid for {{field}}", cause=exc + ) from exc + else: + return reqs + + def _process_license_expression( + self, value: str + ) -> NormalizedLicenseExpression | None: + try: + return licenses.canonicalize_license_expression(value) + except ValueError as exc: + raise self._invalid_metadata( + f"{value!r} is invalid for {{field}}", cause=exc + ) from exc + + def _process_license_files(self, value: list[str]) -> list[str]: + paths = [] + for path in value: + if ".." in path: + raise self._invalid_metadata( + f"{path!r} is invalid for {{field}}, " + "parent directory indicators are not allowed" + ) + if "*" in path: + raise self._invalid_metadata( + f"{path!r} is invalid for {{field}}, paths must be resolved" + ) + if ( + pathlib.PurePosixPath(path).is_absolute() + or pathlib.PureWindowsPath(path).is_absolute() + ): + raise self._invalid_metadata( + f"{path!r} is invalid for {{field}}, paths must be relative" + ) + if pathlib.PureWindowsPath(path).as_posix() != path: + raise self._invalid_metadata( + f"{path!r} is invalid for {{field}}, paths must use '/' delimiter" + ) + paths.append(path) + return paths + + +class Metadata: + """Representation of distribution metadata. + + Compared to :class:`RawMetadata`, this class provides objects representing + metadata fields instead of only using built-in types. Any invalid metadata + will cause :exc:`InvalidMetadata` to be raised (with a + :py:attr:`~BaseException.__cause__` attribute as appropriate). + """ + + _raw: RawMetadata + + @classmethod + def from_raw(cls, data: RawMetadata, *, validate: bool = True) -> Metadata: + """Create an instance from :class:`RawMetadata`. + + If *validate* is true, all metadata will be validated. All exceptions + related to validation will be gathered and raised as an :class:`ExceptionGroup`. + """ + ins = cls() + ins._raw = data.copy() # Mutations occur due to caching enriched values. + + if validate: + exceptions: list[Exception] = [] + try: + metadata_version = ins.metadata_version + metadata_age = _VALID_METADATA_VERSIONS.index(metadata_version) + except InvalidMetadata as metadata_version_exc: + exceptions.append(metadata_version_exc) + metadata_version = None + + # Make sure to check for the fields that are present, the required + # fields (so their absence can be reported). + fields_to_check = frozenset(ins._raw) | _REQUIRED_ATTRS + # Remove fields that have already been checked. + fields_to_check -= {"metadata_version"} + + for key in fields_to_check: + try: + if metadata_version: + # Can't use getattr() as that triggers descriptor protocol which + # will fail due to no value for the instance argument. + try: + field_metadata_version = cls.__dict__[key].added + except KeyError: + exc = InvalidMetadata(key, f"unrecognized field: {key!r}") + exceptions.append(exc) + continue + field_age = _VALID_METADATA_VERSIONS.index( + field_metadata_version + ) + if field_age > metadata_age: + field = _RAW_TO_EMAIL_MAPPING[key] + exc = InvalidMetadata( + field, + f"{field} introduced in metadata version " + f"{field_metadata_version}, not {metadata_version}", + ) + exceptions.append(exc) + continue + getattr(ins, key) + except InvalidMetadata as exc: + exceptions.append(exc) + + if exceptions: + raise ExceptionGroup("invalid metadata", exceptions) + + return ins + + @classmethod + def from_email(cls, data: bytes | str, *, validate: bool = True) -> Metadata: + """Parse metadata from email headers. + + If *validate* is true, the metadata will be validated. All exceptions + related to validation will be gathered and raised as an :class:`ExceptionGroup`. + """ + raw, unparsed = parse_email(data) + + if validate: + exceptions: list[Exception] = [] + for unparsed_key in unparsed: + if unparsed_key in _EMAIL_TO_RAW_MAPPING: + message = f"{unparsed_key!r} has invalid data" + else: + message = f"unrecognized field: {unparsed_key!r}" + exceptions.append(InvalidMetadata(unparsed_key, message)) + + if exceptions: + raise ExceptionGroup("unparsed", exceptions) + + try: + return cls.from_raw(raw, validate=validate) + except ExceptionGroup as exc_group: + raise ExceptionGroup( + "invalid or unparsed metadata", exc_group.exceptions + ) from None + + metadata_version: _Validator[_MetadataVersion] = _Validator() + """:external:ref:`core-metadata-metadata-version` + (required; validated to be a valid metadata version)""" + # `name` is not normalized/typed to NormalizedName so as to provide access to + # the original/raw name. + name: _Validator[str] = _Validator() + """:external:ref:`core-metadata-name` + (required; validated using :func:`~packaging.utils.canonicalize_name` and its + *validate* parameter)""" + version: _Validator[version_module.Version] = _Validator() + """:external:ref:`core-metadata-version` (required)""" + dynamic: _Validator[list[str] | None] = _Validator( + added="2.2", + ) + """:external:ref:`core-metadata-dynamic` + (validated against core metadata field names and lowercased)""" + platforms: _Validator[list[str] | None] = _Validator() + """:external:ref:`core-metadata-platform`""" + supported_platforms: _Validator[list[str] | None] = _Validator(added="1.1") + """:external:ref:`core-metadata-supported-platform`""" + summary: _Validator[str | None] = _Validator() + """:external:ref:`core-metadata-summary` (validated to contain no newlines)""" + description: _Validator[str | None] = _Validator() # TODO 2.1: can be in body + """:external:ref:`core-metadata-description`""" + description_content_type: _Validator[str | None] = _Validator(added="2.1") + """:external:ref:`core-metadata-description-content-type` (validated)""" + keywords: _Validator[list[str] | None] = _Validator() + """:external:ref:`core-metadata-keywords`""" + home_page: _Validator[str | None] = _Validator() + """:external:ref:`core-metadata-home-page`""" + download_url: _Validator[str | None] = _Validator(added="1.1") + """:external:ref:`core-metadata-download-url`""" + author: _Validator[str | None] = _Validator() + """:external:ref:`core-metadata-author`""" + author_email: _Validator[str | None] = _Validator() + """:external:ref:`core-metadata-author-email`""" + maintainer: _Validator[str | None] = _Validator(added="1.2") + """:external:ref:`core-metadata-maintainer`""" + maintainer_email: _Validator[str | None] = _Validator(added="1.2") + """:external:ref:`core-metadata-maintainer-email`""" + license: _Validator[str | None] = _Validator() + """:external:ref:`core-metadata-license`""" + license_expression: _Validator[NormalizedLicenseExpression | None] = _Validator( + added="2.4" + ) + """:external:ref:`core-metadata-license-expression`""" + license_files: _Validator[list[str] | None] = _Validator(added="2.4") + """:external:ref:`core-metadata-license-file`""" + classifiers: _Validator[list[str] | None] = _Validator(added="1.1") + """:external:ref:`core-metadata-classifier`""" + requires_dist: _Validator[list[requirements.Requirement] | None] = _Validator( + added="1.2" + ) + """:external:ref:`core-metadata-requires-dist`""" + requires_python: _Validator[specifiers.SpecifierSet | None] = _Validator( + added="1.2" + ) + """:external:ref:`core-metadata-requires-python`""" + # Because `Requires-External` allows for non-PEP 440 version specifiers, we + # don't do any processing on the values. + requires_external: _Validator[list[str] | None] = _Validator(added="1.2") + """:external:ref:`core-metadata-requires-external`""" + project_urls: _Validator[dict[str, str] | None] = _Validator(added="1.2") + """:external:ref:`core-metadata-project-url`""" + # PEP 685 lets us raise an error if an extra doesn't pass `Name` validation + # regardless of metadata version. + provides_extra: _Validator[list[utils.NormalizedName] | None] = _Validator( + added="2.1", + ) + """:external:ref:`core-metadata-provides-extra`""" + provides_dist: _Validator[list[str] | None] = _Validator(added="1.2") + """:external:ref:`core-metadata-provides-dist`""" + obsoletes_dist: _Validator[list[str] | None] = _Validator(added="1.2") + """:external:ref:`core-metadata-obsoletes-dist`""" + requires: _Validator[list[str] | None] = _Validator(added="1.1") + """``Requires`` (deprecated)""" + provides: _Validator[list[str] | None] = _Validator(added="1.1") + """``Provides`` (deprecated)""" + obsoletes: _Validator[list[str] | None] = _Validator(added="1.1") + """``Obsoletes`` (deprecated)""" diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging/py.typed b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging/py.typed new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging/requirements.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging/requirements.py new file mode 100644 index 0000000000000000000000000000000000000000..4e068c9567def3564f238a76fe7ab46b569f33e5 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging/requirements.py @@ -0,0 +1,91 @@ +# This file is dual licensed under the terms of the Apache License, Version +# 2.0, and the BSD License. See the LICENSE file in the root of this repository +# for complete details. +from __future__ import annotations + +from typing import Any, Iterator + +from ._parser import parse_requirement as _parse_requirement +from ._tokenizer import ParserSyntaxError +from .markers import Marker, _normalize_extra_values +from .specifiers import SpecifierSet +from .utils import canonicalize_name + + +class InvalidRequirement(ValueError): + """ + An invalid requirement was found, users should refer to PEP 508. + """ + + +class Requirement: + """Parse a requirement. + + Parse a given requirement string into its parts, such as name, specifier, + URL, and extras. Raises InvalidRequirement on a badly-formed requirement + string. + """ + + # TODO: Can we test whether something is contained within a requirement? + # If so how do we do that? Do we need to test against the _name_ of + # the thing as well as the version? What about the markers? + # TODO: Can we normalize the name and extra name? + + def __init__(self, requirement_string: str) -> None: + try: + parsed = _parse_requirement(requirement_string) + except ParserSyntaxError as e: + raise InvalidRequirement(str(e)) from e + + self.name: str = parsed.name + self.url: str | None = parsed.url or None + self.extras: set[str] = set(parsed.extras or []) + self.specifier: SpecifierSet = SpecifierSet(parsed.specifier) + self.marker: Marker | None = None + if parsed.marker is not None: + self.marker = Marker.__new__(Marker) + self.marker._markers = _normalize_extra_values(parsed.marker) + + def _iter_parts(self, name: str) -> Iterator[str]: + yield name + + if self.extras: + formatted_extras = ",".join(sorted(self.extras)) + yield f"[{formatted_extras}]" + + if self.specifier: + yield str(self.specifier) + + if self.url: + yield f"@ {self.url}" + if self.marker: + yield " " + + if self.marker: + yield f"; {self.marker}" + + def __str__(self) -> str: + return "".join(self._iter_parts(self.name)) + + def __repr__(self) -> str: + return f"" + + def __hash__(self) -> int: + return hash( + ( + self.__class__.__name__, + *self._iter_parts(canonicalize_name(self.name)), + ) + ) + + def __eq__(self, other: Any) -> bool: + if not isinstance(other, Requirement): + return NotImplemented + + return ( + canonicalize_name(self.name) == canonicalize_name(other.name) + and self.extras == other.extras + and self.specifier == other.specifier + and self.url == other.url + and self.marker == other.marker + ) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging/specifiers.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging/specifiers.py new file mode 100644 index 0000000000000000000000000000000000000000..c8448043006f1c60ee92df69da47986d10229b1d --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging/specifiers.py @@ -0,0 +1,1019 @@ +# This file is dual licensed under the terms of the Apache License, Version +# 2.0, and the BSD License. See the LICENSE file in the root of this repository +# for complete details. +""" +.. testsetup:: + + from packaging.specifiers import Specifier, SpecifierSet, InvalidSpecifier + from packaging.version import Version +""" + +from __future__ import annotations + +import abc +import itertools +import re +from typing import Callable, Iterable, Iterator, TypeVar, Union + +from .utils import canonicalize_version +from .version import Version + +UnparsedVersion = Union[Version, str] +UnparsedVersionVar = TypeVar("UnparsedVersionVar", bound=UnparsedVersion) +CallableOperator = Callable[[Version, str], bool] + + +def _coerce_version(version: UnparsedVersion) -> Version: + if not isinstance(version, Version): + version = Version(version) + return version + + +class InvalidSpecifier(ValueError): + """ + Raised when attempting to create a :class:`Specifier` with a specifier + string that is invalid. + + >>> Specifier("lolwat") + Traceback (most recent call last): + ... + packaging.specifiers.InvalidSpecifier: Invalid specifier: 'lolwat' + """ + + +class BaseSpecifier(metaclass=abc.ABCMeta): + @abc.abstractmethod + def __str__(self) -> str: + """ + Returns the str representation of this Specifier-like object. This + should be representative of the Specifier itself. + """ + + @abc.abstractmethod + def __hash__(self) -> int: + """ + Returns a hash value for this Specifier-like object. + """ + + @abc.abstractmethod + def __eq__(self, other: object) -> bool: + """ + Returns a boolean representing whether or not the two Specifier-like + objects are equal. + + :param other: The other object to check against. + """ + + @property + @abc.abstractmethod + def prereleases(self) -> bool | None: + """Whether or not pre-releases as a whole are allowed. + + This can be set to either ``True`` or ``False`` to explicitly enable or disable + prereleases or it can be set to ``None`` (the default) to use default semantics. + """ + + @prereleases.setter + def prereleases(self, value: bool) -> None: + """Setter for :attr:`prereleases`. + + :param value: The value to set. + """ + + @abc.abstractmethod + def contains(self, item: str, prereleases: bool | None = None) -> bool: + """ + Determines if the given item is contained within this specifier. + """ + + @abc.abstractmethod + def filter( + self, iterable: Iterable[UnparsedVersionVar], prereleases: bool | None = None + ) -> Iterator[UnparsedVersionVar]: + """ + Takes an iterable of items and filters them so that only items which + are contained within this specifier are allowed in it. + """ + + +class Specifier(BaseSpecifier): + """This class abstracts handling of version specifiers. + + .. tip:: + + It is generally not required to instantiate this manually. You should instead + prefer to work with :class:`SpecifierSet` instead, which can parse + comma-separated version specifiers (which is what package metadata contains). + """ + + _operator_regex_str = r""" + (?P(~=|==|!=|<=|>=|<|>|===)) + """ + _version_regex_str = r""" + (?P + (?: + # The identity operators allow for an escape hatch that will + # do an exact string match of the version you wish to install. + # This will not be parsed by PEP 440 and we cannot determine + # any semantic meaning from it. This operator is discouraged + # but included entirely as an escape hatch. + (?<====) # Only match for the identity operator + \s* + [^\s;)]* # The arbitrary version can be just about anything, + # we match everything except for whitespace, a + # semi-colon for marker support, and a closing paren + # since versions can be enclosed in them. + ) + | + (?: + # The (non)equality operators allow for wild card and local + # versions to be specified so we have to define these two + # operators separately to enable that. + (?<===|!=) # Only match for equals and not equals + + \s* + v? + (?:[0-9]+!)? # epoch + [0-9]+(?:\.[0-9]+)* # release + + # You cannot use a wild card and a pre-release, post-release, a dev or + # local version together so group them with a | and make them optional. + (?: + \.\* # Wild card syntax of .* + | + (?: # pre release + [-_\.]? + (alpha|beta|preview|pre|a|b|c|rc) + [-_\.]? + [0-9]* + )? + (?: # post release + (?:-[0-9]+)|(?:[-_\.]?(post|rev|r)[-_\.]?[0-9]*) + )? + (?:[-_\.]?dev[-_\.]?[0-9]*)? # dev release + (?:\+[a-z0-9]+(?:[-_\.][a-z0-9]+)*)? # local + )? + ) + | + (?: + # The compatible operator requires at least two digits in the + # release segment. + (?<=~=) # Only match for the compatible operator + + \s* + v? + (?:[0-9]+!)? # epoch + [0-9]+(?:\.[0-9]+)+ # release (We have a + instead of a *) + (?: # pre release + [-_\.]? + (alpha|beta|preview|pre|a|b|c|rc) + [-_\.]? + [0-9]* + )? + (?: # post release + (?:-[0-9]+)|(?:[-_\.]?(post|rev|r)[-_\.]?[0-9]*) + )? + (?:[-_\.]?dev[-_\.]?[0-9]*)? # dev release + ) + | + (?: + # All other operators only allow a sub set of what the + # (non)equality operators do. Specifically they do not allow + # local versions to be specified nor do they allow the prefix + # matching wild cards. + (?=": "greater_than_equal", + "<": "less_than", + ">": "greater_than", + "===": "arbitrary", + } + + def __init__(self, spec: str = "", prereleases: bool | None = None) -> None: + """Initialize a Specifier instance. + + :param spec: + The string representation of a specifier which will be parsed and + normalized before use. + :param prereleases: + This tells the specifier if it should accept prerelease versions if + applicable or not. The default of ``None`` will autodetect it from the + given specifiers. + :raises InvalidSpecifier: + If the given specifier is invalid (i.e. bad syntax). + """ + match = self._regex.search(spec) + if not match: + raise InvalidSpecifier(f"Invalid specifier: {spec!r}") + + self._spec: tuple[str, str] = ( + match.group("operator").strip(), + match.group("version").strip(), + ) + + # Store whether or not this Specifier should accept prereleases + self._prereleases = prereleases + + # https://github.com/python/mypy/pull/13475#pullrequestreview-1079784515 + @property # type: ignore[override] + def prereleases(self) -> bool: + # If there is an explicit prereleases set for this, then we'll just + # blindly use that. + if self._prereleases is not None: + return self._prereleases + + # Look at all of our specifiers and determine if they are inclusive + # operators, and if they are if they are including an explicit + # prerelease. + operator, version = self._spec + if operator in ["==", ">=", "<=", "~=", "===", ">", "<"]: + # The == specifier can include a trailing .*, if it does we + # want to remove before parsing. + if operator == "==" and version.endswith(".*"): + version = version[:-2] + + # Parse the version, and if it is a pre-release than this + # specifier allows pre-releases. + if Version(version).is_prerelease: + return True + + return False + + @prereleases.setter + def prereleases(self, value: bool) -> None: + self._prereleases = value + + @property + def operator(self) -> str: + """The operator of this specifier. + + >>> Specifier("==1.2.3").operator + '==' + """ + return self._spec[0] + + @property + def version(self) -> str: + """The version of this specifier. + + >>> Specifier("==1.2.3").version + '1.2.3' + """ + return self._spec[1] + + def __repr__(self) -> str: + """A representation of the Specifier that shows all internal state. + + >>> Specifier('>=1.0.0') + =1.0.0')> + >>> Specifier('>=1.0.0', prereleases=False) + =1.0.0', prereleases=False)> + >>> Specifier('>=1.0.0', prereleases=True) + =1.0.0', prereleases=True)> + """ + pre = ( + f", prereleases={self.prereleases!r}" + if self._prereleases is not None + else "" + ) + + return f"<{self.__class__.__name__}({str(self)!r}{pre})>" + + def __str__(self) -> str: + """A string representation of the Specifier that can be round-tripped. + + >>> str(Specifier('>=1.0.0')) + '>=1.0.0' + >>> str(Specifier('>=1.0.0', prereleases=False)) + '>=1.0.0' + """ + return "{}{}".format(*self._spec) + + @property + def _canonical_spec(self) -> tuple[str, str]: + canonical_version = canonicalize_version( + self._spec[1], + strip_trailing_zero=(self._spec[0] != "~="), + ) + return self._spec[0], canonical_version + + def __hash__(self) -> int: + return hash(self._canonical_spec) + + def __eq__(self, other: object) -> bool: + """Whether or not the two Specifier-like objects are equal. + + :param other: The other object to check against. + + The value of :attr:`prereleases` is ignored. + + >>> Specifier("==1.2.3") == Specifier("== 1.2.3.0") + True + >>> (Specifier("==1.2.3", prereleases=False) == + ... Specifier("==1.2.3", prereleases=True)) + True + >>> Specifier("==1.2.3") == "==1.2.3" + True + >>> Specifier("==1.2.3") == Specifier("==1.2.4") + False + >>> Specifier("==1.2.3") == Specifier("~=1.2.3") + False + """ + if isinstance(other, str): + try: + other = self.__class__(str(other)) + except InvalidSpecifier: + return NotImplemented + elif not isinstance(other, self.__class__): + return NotImplemented + + return self._canonical_spec == other._canonical_spec + + def _get_operator(self, op: str) -> CallableOperator: + operator_callable: CallableOperator = getattr( + self, f"_compare_{self._operators[op]}" + ) + return operator_callable + + def _compare_compatible(self, prospective: Version, spec: str) -> bool: + # Compatible releases have an equivalent combination of >= and ==. That + # is that ~=2.2 is equivalent to >=2.2,==2.*. This allows us to + # implement this in terms of the other specifiers instead of + # implementing it ourselves. The only thing we need to do is construct + # the other specifiers. + + # We want everything but the last item in the version, but we want to + # ignore suffix segments. + prefix = _version_join( + list(itertools.takewhile(_is_not_suffix, _version_split(spec)))[:-1] + ) + + # Add the prefix notation to the end of our string + prefix += ".*" + + return self._get_operator(">=")(prospective, spec) and self._get_operator("==")( + prospective, prefix + ) + + def _compare_equal(self, prospective: Version, spec: str) -> bool: + # We need special logic to handle prefix matching + if spec.endswith(".*"): + # In the case of prefix matching we want to ignore local segment. + normalized_prospective = canonicalize_version( + prospective.public, strip_trailing_zero=False + ) + # Get the normalized version string ignoring the trailing .* + normalized_spec = canonicalize_version(spec[:-2], strip_trailing_zero=False) + # Split the spec out by bangs and dots, and pretend that there is + # an implicit dot in between a release segment and a pre-release segment. + split_spec = _version_split(normalized_spec) + + # Split the prospective version out by bangs and dots, and pretend + # that there is an implicit dot in between a release segment and + # a pre-release segment. + split_prospective = _version_split(normalized_prospective) + + # 0-pad the prospective version before shortening it to get the correct + # shortened version. + padded_prospective, _ = _pad_version(split_prospective, split_spec) + + # Shorten the prospective version to be the same length as the spec + # so that we can determine if the specifier is a prefix of the + # prospective version or not. + shortened_prospective = padded_prospective[: len(split_spec)] + + return shortened_prospective == split_spec + else: + # Convert our spec string into a Version + spec_version = Version(spec) + + # If the specifier does not have a local segment, then we want to + # act as if the prospective version also does not have a local + # segment. + if not spec_version.local: + prospective = Version(prospective.public) + + return prospective == spec_version + + def _compare_not_equal(self, prospective: Version, spec: str) -> bool: + return not self._compare_equal(prospective, spec) + + def _compare_less_than_equal(self, prospective: Version, spec: str) -> bool: + # NB: Local version identifiers are NOT permitted in the version + # specifier, so local version labels can be universally removed from + # the prospective version. + return Version(prospective.public) <= Version(spec) + + def _compare_greater_than_equal(self, prospective: Version, spec: str) -> bool: + # NB: Local version identifiers are NOT permitted in the version + # specifier, so local version labels can be universally removed from + # the prospective version. + return Version(prospective.public) >= Version(spec) + + def _compare_less_than(self, prospective: Version, spec_str: str) -> bool: + # Convert our spec to a Version instance, since we'll want to work with + # it as a version. + spec = Version(spec_str) + + # Check to see if the prospective version is less than the spec + # version. If it's not we can short circuit and just return False now + # instead of doing extra unneeded work. + if not prospective < spec: + return False + + # This special case is here so that, unless the specifier itself + # includes is a pre-release version, that we do not accept pre-release + # versions for the version mentioned in the specifier (e.g. <3.1 should + # not match 3.1.dev0, but should match 3.0.dev0). + if not spec.is_prerelease and prospective.is_prerelease: + if Version(prospective.base_version) == Version(spec.base_version): + return False + + # If we've gotten to here, it means that prospective version is both + # less than the spec version *and* it's not a pre-release of the same + # version in the spec. + return True + + def _compare_greater_than(self, prospective: Version, spec_str: str) -> bool: + # Convert our spec to a Version instance, since we'll want to work with + # it as a version. + spec = Version(spec_str) + + # Check to see if the prospective version is greater than the spec + # version. If it's not we can short circuit and just return False now + # instead of doing extra unneeded work. + if not prospective > spec: + return False + + # This special case is here so that, unless the specifier itself + # includes is a post-release version, that we do not accept + # post-release versions for the version mentioned in the specifier + # (e.g. >3.1 should not match 3.0.post0, but should match 3.2.post0). + if not spec.is_postrelease and prospective.is_postrelease: + if Version(prospective.base_version) == Version(spec.base_version): + return False + + # Ensure that we do not allow a local version of the version mentioned + # in the specifier, which is technically greater than, to match. + if prospective.local is not None: + if Version(prospective.base_version) == Version(spec.base_version): + return False + + # If we've gotten to here, it means that prospective version is both + # greater than the spec version *and* it's not a pre-release of the + # same version in the spec. + return True + + def _compare_arbitrary(self, prospective: Version, spec: str) -> bool: + return str(prospective).lower() == str(spec).lower() + + def __contains__(self, item: str | Version) -> bool: + """Return whether or not the item is contained in this specifier. + + :param item: The item to check for. + + This is used for the ``in`` operator and behaves the same as + :meth:`contains` with no ``prereleases`` argument passed. + + >>> "1.2.3" in Specifier(">=1.2.3") + True + >>> Version("1.2.3") in Specifier(">=1.2.3") + True + >>> "1.0.0" in Specifier(">=1.2.3") + False + >>> "1.3.0a1" in Specifier(">=1.2.3") + False + >>> "1.3.0a1" in Specifier(">=1.2.3", prereleases=True) + True + """ + return self.contains(item) + + def contains(self, item: UnparsedVersion, prereleases: bool | None = None) -> bool: + """Return whether or not the item is contained in this specifier. + + :param item: + The item to check for, which can be a version string or a + :class:`Version` instance. + :param prereleases: + Whether or not to match prereleases with this Specifier. If set to + ``None`` (the default), it uses :attr:`prereleases` to determine + whether or not prereleases are allowed. + + >>> Specifier(">=1.2.3").contains("1.2.3") + True + >>> Specifier(">=1.2.3").contains(Version("1.2.3")) + True + >>> Specifier(">=1.2.3").contains("1.0.0") + False + >>> Specifier(">=1.2.3").contains("1.3.0a1") + False + >>> Specifier(">=1.2.3", prereleases=True).contains("1.3.0a1") + True + >>> Specifier(">=1.2.3").contains("1.3.0a1", prereleases=True) + True + """ + + # Determine if prereleases are to be allowed or not. + if prereleases is None: + prereleases = self.prereleases + + # Normalize item to a Version, this allows us to have a shortcut for + # "2.0" in Specifier(">=2") + normalized_item = _coerce_version(item) + + # Determine if we should be supporting prereleases in this specifier + # or not, if we do not support prereleases than we can short circuit + # logic if this version is a prereleases. + if normalized_item.is_prerelease and not prereleases: + return False + + # Actually do the comparison to determine if this item is contained + # within this Specifier or not. + operator_callable: CallableOperator = self._get_operator(self.operator) + return operator_callable(normalized_item, self.version) + + def filter( + self, iterable: Iterable[UnparsedVersionVar], prereleases: bool | None = None + ) -> Iterator[UnparsedVersionVar]: + """Filter items in the given iterable, that match the specifier. + + :param iterable: + An iterable that can contain version strings and :class:`Version` instances. + The items in the iterable will be filtered according to the specifier. + :param prereleases: + Whether or not to allow prereleases in the returned iterator. If set to + ``None`` (the default), it will be intelligently decide whether to allow + prereleases or not (based on the :attr:`prereleases` attribute, and + whether the only versions matching are prereleases). + + This method is smarter than just ``filter(Specifier().contains, [...])`` + because it implements the rule from :pep:`440` that a prerelease item + SHOULD be accepted if no other versions match the given specifier. + + >>> list(Specifier(">=1.2.3").filter(["1.2", "1.3", "1.5a1"])) + ['1.3'] + >>> list(Specifier(">=1.2.3").filter(["1.2", "1.2.3", "1.3", Version("1.4")])) + ['1.2.3', '1.3', ] + >>> list(Specifier(">=1.2.3").filter(["1.2", "1.5a1"])) + ['1.5a1'] + >>> list(Specifier(">=1.2.3").filter(["1.3", "1.5a1"], prereleases=True)) + ['1.3', '1.5a1'] + >>> list(Specifier(">=1.2.3", prereleases=True).filter(["1.3", "1.5a1"])) + ['1.3', '1.5a1'] + """ + + yielded = False + found_prereleases = [] + + kw = {"prereleases": prereleases if prereleases is not None else True} + + # Attempt to iterate over all the values in the iterable and if any of + # them match, yield them. + for version in iterable: + parsed_version = _coerce_version(version) + + if self.contains(parsed_version, **kw): + # If our version is a prerelease, and we were not set to allow + # prereleases, then we'll store it for later in case nothing + # else matches this specifier. + if parsed_version.is_prerelease and not ( + prereleases or self.prereleases + ): + found_prereleases.append(version) + # Either this is not a prerelease, or we should have been + # accepting prereleases from the beginning. + else: + yielded = True + yield version + + # Now that we've iterated over everything, determine if we've yielded + # any values, and if we have not and we have any prereleases stored up + # then we will go ahead and yield the prereleases. + if not yielded and found_prereleases: + for version in found_prereleases: + yield version + + +_prefix_regex = re.compile(r"^([0-9]+)((?:a|b|c|rc)[0-9]+)$") + + +def _version_split(version: str) -> list[str]: + """Split version into components. + + The split components are intended for version comparison. The logic does + not attempt to retain the original version string, so joining the + components back with :func:`_version_join` may not produce the original + version string. + """ + result: list[str] = [] + + epoch, _, rest = version.rpartition("!") + result.append(epoch or "0") + + for item in rest.split("."): + match = _prefix_regex.search(item) + if match: + result.extend(match.groups()) + else: + result.append(item) + return result + + +def _version_join(components: list[str]) -> str: + """Join split version components into a version string. + + This function assumes the input came from :func:`_version_split`, where the + first component must be the epoch (either empty or numeric), and all other + components numeric. + """ + epoch, *rest = components + return f"{epoch}!{'.'.join(rest)}" + + +def _is_not_suffix(segment: str) -> bool: + return not any( + segment.startswith(prefix) for prefix in ("dev", "a", "b", "rc", "post") + ) + + +def _pad_version(left: list[str], right: list[str]) -> tuple[list[str], list[str]]: + left_split, right_split = [], [] + + # Get the release segment of our versions + left_split.append(list(itertools.takewhile(lambda x: x.isdigit(), left))) + right_split.append(list(itertools.takewhile(lambda x: x.isdigit(), right))) + + # Get the rest of our versions + left_split.append(left[len(left_split[0]) :]) + right_split.append(right[len(right_split[0]) :]) + + # Insert our padding + left_split.insert(1, ["0"] * max(0, len(right_split[0]) - len(left_split[0]))) + right_split.insert(1, ["0"] * max(0, len(left_split[0]) - len(right_split[0]))) + + return ( + list(itertools.chain.from_iterable(left_split)), + list(itertools.chain.from_iterable(right_split)), + ) + + +class SpecifierSet(BaseSpecifier): + """This class abstracts handling of a set of version specifiers. + + It can be passed a single specifier (``>=3.0``), a comma-separated list of + specifiers (``>=3.0,!=3.1``), or no specifier at all. + """ + + def __init__( + self, + specifiers: str | Iterable[Specifier] = "", + prereleases: bool | None = None, + ) -> None: + """Initialize a SpecifierSet instance. + + :param specifiers: + The string representation of a specifier or a comma-separated list of + specifiers which will be parsed and normalized before use. + May also be an iterable of ``Specifier`` instances, which will be used + as is. + :param prereleases: + This tells the SpecifierSet if it should accept prerelease versions if + applicable or not. The default of ``None`` will autodetect it from the + given specifiers. + + :raises InvalidSpecifier: + If the given ``specifiers`` are not parseable than this exception will be + raised. + """ + + if isinstance(specifiers, str): + # Split on `,` to break each individual specifier into its own item, and + # strip each item to remove leading/trailing whitespace. + split_specifiers = [s.strip() for s in specifiers.split(",") if s.strip()] + + # Make each individual specifier a Specifier and save in a frozen set + # for later. + self._specs = frozenset(map(Specifier, split_specifiers)) + else: + # Save the supplied specifiers in a frozen set. + self._specs = frozenset(specifiers) + + # Store our prereleases value so we can use it later to determine if + # we accept prereleases or not. + self._prereleases = prereleases + + @property + def prereleases(self) -> bool | None: + # If we have been given an explicit prerelease modifier, then we'll + # pass that through here. + if self._prereleases is not None: + return self._prereleases + + # If we don't have any specifiers, and we don't have a forced value, + # then we'll just return None since we don't know if this should have + # pre-releases or not. + if not self._specs: + return None + + # Otherwise we'll see if any of the given specifiers accept + # prereleases, if any of them do we'll return True, otherwise False. + return any(s.prereleases for s in self._specs) + + @prereleases.setter + def prereleases(self, value: bool) -> None: + self._prereleases = value + + def __repr__(self) -> str: + """A representation of the specifier set that shows all internal state. + + Note that the ordering of the individual specifiers within the set may not + match the input string. + + >>> SpecifierSet('>=1.0.0,!=2.0.0') + =1.0.0')> + >>> SpecifierSet('>=1.0.0,!=2.0.0', prereleases=False) + =1.0.0', prereleases=False)> + >>> SpecifierSet('>=1.0.0,!=2.0.0', prereleases=True) + =1.0.0', prereleases=True)> + """ + pre = ( + f", prereleases={self.prereleases!r}" + if self._prereleases is not None + else "" + ) + + return f"" + + def __str__(self) -> str: + """A string representation of the specifier set that can be round-tripped. + + Note that the ordering of the individual specifiers within the set may not + match the input string. + + >>> str(SpecifierSet(">=1.0.0,!=1.0.1")) + '!=1.0.1,>=1.0.0' + >>> str(SpecifierSet(">=1.0.0,!=1.0.1", prereleases=False)) + '!=1.0.1,>=1.0.0' + """ + return ",".join(sorted(str(s) for s in self._specs)) + + def __hash__(self) -> int: + return hash(self._specs) + + def __and__(self, other: SpecifierSet | str) -> SpecifierSet: + """Return a SpecifierSet which is a combination of the two sets. + + :param other: The other object to combine with. + + >>> SpecifierSet(">=1.0.0,!=1.0.1") & '<=2.0.0,!=2.0.1' + =1.0.0')> + >>> SpecifierSet(">=1.0.0,!=1.0.1") & SpecifierSet('<=2.0.0,!=2.0.1') + =1.0.0')> + """ + if isinstance(other, str): + other = SpecifierSet(other) + elif not isinstance(other, SpecifierSet): + return NotImplemented + + specifier = SpecifierSet() + specifier._specs = frozenset(self._specs | other._specs) + + if self._prereleases is None and other._prereleases is not None: + specifier._prereleases = other._prereleases + elif self._prereleases is not None and other._prereleases is None: + specifier._prereleases = self._prereleases + elif self._prereleases == other._prereleases: + specifier._prereleases = self._prereleases + else: + raise ValueError( + "Cannot combine SpecifierSets with True and False prerelease overrides." + ) + + return specifier + + def __eq__(self, other: object) -> bool: + """Whether or not the two SpecifierSet-like objects are equal. + + :param other: The other object to check against. + + The value of :attr:`prereleases` is ignored. + + >>> SpecifierSet(">=1.0.0,!=1.0.1") == SpecifierSet(">=1.0.0,!=1.0.1") + True + >>> (SpecifierSet(">=1.0.0,!=1.0.1", prereleases=False) == + ... SpecifierSet(">=1.0.0,!=1.0.1", prereleases=True)) + True + >>> SpecifierSet(">=1.0.0,!=1.0.1") == ">=1.0.0,!=1.0.1" + True + >>> SpecifierSet(">=1.0.0,!=1.0.1") == SpecifierSet(">=1.0.0") + False + >>> SpecifierSet(">=1.0.0,!=1.0.1") == SpecifierSet(">=1.0.0,!=1.0.2") + False + """ + if isinstance(other, (str, Specifier)): + other = SpecifierSet(str(other)) + elif not isinstance(other, SpecifierSet): + return NotImplemented + + return self._specs == other._specs + + def __len__(self) -> int: + """Returns the number of specifiers in this specifier set.""" + return len(self._specs) + + def __iter__(self) -> Iterator[Specifier]: + """ + Returns an iterator over all the underlying :class:`Specifier` instances + in this specifier set. + + >>> sorted(SpecifierSet(">=1.0.0,!=1.0.1"), key=str) + [, =1.0.0')>] + """ + return iter(self._specs) + + def __contains__(self, item: UnparsedVersion) -> bool: + """Return whether or not the item is contained in this specifier. + + :param item: The item to check for. + + This is used for the ``in`` operator and behaves the same as + :meth:`contains` with no ``prereleases`` argument passed. + + >>> "1.2.3" in SpecifierSet(">=1.0.0,!=1.0.1") + True + >>> Version("1.2.3") in SpecifierSet(">=1.0.0,!=1.0.1") + True + >>> "1.0.1" in SpecifierSet(">=1.0.0,!=1.0.1") + False + >>> "1.3.0a1" in SpecifierSet(">=1.0.0,!=1.0.1") + False + >>> "1.3.0a1" in SpecifierSet(">=1.0.0,!=1.0.1", prereleases=True) + True + """ + return self.contains(item) + + def contains( + self, + item: UnparsedVersion, + prereleases: bool | None = None, + installed: bool | None = None, + ) -> bool: + """Return whether or not the item is contained in this SpecifierSet. + + :param item: + The item to check for, which can be a version string or a + :class:`Version` instance. + :param prereleases: + Whether or not to match prereleases with this SpecifierSet. If set to + ``None`` (the default), it uses :attr:`prereleases` to determine + whether or not prereleases are allowed. + + >>> SpecifierSet(">=1.0.0,!=1.0.1").contains("1.2.3") + True + >>> SpecifierSet(">=1.0.0,!=1.0.1").contains(Version("1.2.3")) + True + >>> SpecifierSet(">=1.0.0,!=1.0.1").contains("1.0.1") + False + >>> SpecifierSet(">=1.0.0,!=1.0.1").contains("1.3.0a1") + False + >>> SpecifierSet(">=1.0.0,!=1.0.1", prereleases=True).contains("1.3.0a1") + True + >>> SpecifierSet(">=1.0.0,!=1.0.1").contains("1.3.0a1", prereleases=True) + True + """ + # Ensure that our item is a Version instance. + if not isinstance(item, Version): + item = Version(item) + + # Determine if we're forcing a prerelease or not, if we're not forcing + # one for this particular filter call, then we'll use whatever the + # SpecifierSet thinks for whether or not we should support prereleases. + if prereleases is None: + prereleases = self.prereleases + + # We can determine if we're going to allow pre-releases by looking to + # see if any of the underlying items supports them. If none of them do + # and this item is a pre-release then we do not allow it and we can + # short circuit that here. + # Note: This means that 1.0.dev1 would not be contained in something + # like >=1.0.devabc however it would be in >=1.0.debabc,>0.0.dev0 + if not prereleases and item.is_prerelease: + return False + + if installed and item.is_prerelease: + item = Version(item.base_version) + + # We simply dispatch to the underlying specs here to make sure that the + # given version is contained within all of them. + # Note: This use of all() here means that an empty set of specifiers + # will always return True, this is an explicit design decision. + return all(s.contains(item, prereleases=prereleases) for s in self._specs) + + def filter( + self, iterable: Iterable[UnparsedVersionVar], prereleases: bool | None = None + ) -> Iterator[UnparsedVersionVar]: + """Filter items in the given iterable, that match the specifiers in this set. + + :param iterable: + An iterable that can contain version strings and :class:`Version` instances. + The items in the iterable will be filtered according to the specifier. + :param prereleases: + Whether or not to allow prereleases in the returned iterator. If set to + ``None`` (the default), it will be intelligently decide whether to allow + prereleases or not (based on the :attr:`prereleases` attribute, and + whether the only versions matching are prereleases). + + This method is smarter than just ``filter(SpecifierSet(...).contains, [...])`` + because it implements the rule from :pep:`440` that a prerelease item + SHOULD be accepted if no other versions match the given specifier. + + >>> list(SpecifierSet(">=1.2.3").filter(["1.2", "1.3", "1.5a1"])) + ['1.3'] + >>> list(SpecifierSet(">=1.2.3").filter(["1.2", "1.3", Version("1.4")])) + ['1.3', ] + >>> list(SpecifierSet(">=1.2.3").filter(["1.2", "1.5a1"])) + [] + >>> list(SpecifierSet(">=1.2.3").filter(["1.3", "1.5a1"], prereleases=True)) + ['1.3', '1.5a1'] + >>> list(SpecifierSet(">=1.2.3", prereleases=True).filter(["1.3", "1.5a1"])) + ['1.3', '1.5a1'] + + An "empty" SpecifierSet will filter items based on the presence of prerelease + versions in the set. + + >>> list(SpecifierSet("").filter(["1.3", "1.5a1"])) + ['1.3'] + >>> list(SpecifierSet("").filter(["1.5a1"])) + ['1.5a1'] + >>> list(SpecifierSet("", prereleases=True).filter(["1.3", "1.5a1"])) + ['1.3', '1.5a1'] + >>> list(SpecifierSet("").filter(["1.3", "1.5a1"], prereleases=True)) + ['1.3', '1.5a1'] + """ + # Determine if we're forcing a prerelease or not, if we're not forcing + # one for this particular filter call, then we'll use whatever the + # SpecifierSet thinks for whether or not we should support prereleases. + if prereleases is None: + prereleases = self.prereleases + + # If we have any specifiers, then we want to wrap our iterable in the + # filter method for each one, this will act as a logical AND amongst + # each specifier. + if self._specs: + for spec in self._specs: + iterable = spec.filter(iterable, prereleases=bool(prereleases)) + return iter(iterable) + # If we do not have any specifiers, then we need to have a rough filter + # which will filter out any pre-releases, unless there are no final + # releases. + else: + filtered: list[UnparsedVersionVar] = [] + found_prereleases: list[UnparsedVersionVar] = [] + + for item in iterable: + parsed_version = _coerce_version(item) + + # Store any item which is a pre-release for later unless we've + # already found a final version or we are accepting prereleases + if parsed_version.is_prerelease and not prereleases: + if not filtered: + found_prereleases.append(item) + else: + filtered.append(item) + + # If we've found no items except for pre-releases, then we'll go + # ahead and use the pre-releases + if not filtered and found_prereleases and prereleases is None: + return iter(found_prereleases) + + return iter(filtered) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging/tags.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging/tags.py new file mode 100644 index 0000000000000000000000000000000000000000..8522f59c4f2d0039c1a02ea9aef66fe017dbdb20 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging/tags.py @@ -0,0 +1,656 @@ +# This file is dual licensed under the terms of the Apache License, Version +# 2.0, and the BSD License. See the LICENSE file in the root of this repository +# for complete details. + +from __future__ import annotations + +import logging +import platform +import re +import struct +import subprocess +import sys +import sysconfig +from importlib.machinery import EXTENSION_SUFFIXES +from typing import ( + Iterable, + Iterator, + Sequence, + Tuple, + cast, +) + +from . import _manylinux, _musllinux + +logger = logging.getLogger(__name__) + +PythonVersion = Sequence[int] +AppleVersion = Tuple[int, int] + +INTERPRETER_SHORT_NAMES: dict[str, str] = { + "python": "py", # Generic. + "cpython": "cp", + "pypy": "pp", + "ironpython": "ip", + "jython": "jy", +} + + +_32_BIT_INTERPRETER = struct.calcsize("P") == 4 + + +class Tag: + """ + A representation of the tag triple for a wheel. + + Instances are considered immutable and thus are hashable. Equality checking + is also supported. + """ + + __slots__ = ["_abi", "_hash", "_interpreter", "_platform"] + + def __init__(self, interpreter: str, abi: str, platform: str) -> None: + self._interpreter = interpreter.lower() + self._abi = abi.lower() + self._platform = platform.lower() + # The __hash__ of every single element in a Set[Tag] will be evaluated each time + # that a set calls its `.disjoint()` method, which may be called hundreds of + # times when scanning a page of links for packages with tags matching that + # Set[Tag]. Pre-computing the value here produces significant speedups for + # downstream consumers. + self._hash = hash((self._interpreter, self._abi, self._platform)) + + @property + def interpreter(self) -> str: + return self._interpreter + + @property + def abi(self) -> str: + return self._abi + + @property + def platform(self) -> str: + return self._platform + + def __eq__(self, other: object) -> bool: + if not isinstance(other, Tag): + return NotImplemented + + return ( + (self._hash == other._hash) # Short-circuit ASAP for perf reasons. + and (self._platform == other._platform) + and (self._abi == other._abi) + and (self._interpreter == other._interpreter) + ) + + def __hash__(self) -> int: + return self._hash + + def __str__(self) -> str: + return f"{self._interpreter}-{self._abi}-{self._platform}" + + def __repr__(self) -> str: + return f"<{self} @ {id(self)}>" + + +def parse_tag(tag: str) -> frozenset[Tag]: + """ + Parses the provided tag (e.g. `py3-none-any`) into a frozenset of Tag instances. + + Returning a set is required due to the possibility that the tag is a + compressed tag set. + """ + tags = set() + interpreters, abis, platforms = tag.split("-") + for interpreter in interpreters.split("."): + for abi in abis.split("."): + for platform_ in platforms.split("."): + tags.add(Tag(interpreter, abi, platform_)) + return frozenset(tags) + + +def _get_config_var(name: str, warn: bool = False) -> int | str | None: + value: int | str | None = sysconfig.get_config_var(name) + if value is None and warn: + logger.debug( + "Config variable '%s' is unset, Python ABI tag may be incorrect", name + ) + return value + + +def _normalize_string(string: str) -> str: + return string.replace(".", "_").replace("-", "_").replace(" ", "_") + + +def _is_threaded_cpython(abis: list[str]) -> bool: + """ + Determine if the ABI corresponds to a threaded (`--disable-gil`) build. + + The threaded builds are indicated by a "t" in the abiflags. + """ + if len(abis) == 0: + return False + # expect e.g., cp313 + m = re.match(r"cp\d+(.*)", abis[0]) + if not m: + return False + abiflags = m.group(1) + return "t" in abiflags + + +def _abi3_applies(python_version: PythonVersion, threading: bool) -> bool: + """ + Determine if the Python version supports abi3. + + PEP 384 was first implemented in Python 3.2. The threaded (`--disable-gil`) + builds do not support abi3. + """ + return len(python_version) > 1 and tuple(python_version) >= (3, 2) and not threading + + +def _cpython_abis(py_version: PythonVersion, warn: bool = False) -> list[str]: + py_version = tuple(py_version) # To allow for version comparison. + abis = [] + version = _version_nodot(py_version[:2]) + threading = debug = pymalloc = ucs4 = "" + with_debug = _get_config_var("Py_DEBUG", warn) + has_refcount = hasattr(sys, "gettotalrefcount") + # Windows doesn't set Py_DEBUG, so checking for support of debug-compiled + # extension modules is the best option. + # https://github.com/pypa/pip/issues/3383#issuecomment-173267692 + has_ext = "_d.pyd" in EXTENSION_SUFFIXES + if with_debug or (with_debug is None and (has_refcount or has_ext)): + debug = "d" + if py_version >= (3, 13) and _get_config_var("Py_GIL_DISABLED", warn): + threading = "t" + if py_version < (3, 8): + with_pymalloc = _get_config_var("WITH_PYMALLOC", warn) + if with_pymalloc or with_pymalloc is None: + pymalloc = "m" + if py_version < (3, 3): + unicode_size = _get_config_var("Py_UNICODE_SIZE", warn) + if unicode_size == 4 or ( + unicode_size is None and sys.maxunicode == 0x10FFFF + ): + ucs4 = "u" + elif debug: + # Debug builds can also load "normal" extension modules. + # We can also assume no UCS-4 or pymalloc requirement. + abis.append(f"cp{version}{threading}") + abis.insert(0, f"cp{version}{threading}{debug}{pymalloc}{ucs4}") + return abis + + +def cpython_tags( + python_version: PythonVersion | None = None, + abis: Iterable[str] | None = None, + platforms: Iterable[str] | None = None, + *, + warn: bool = False, +) -> Iterator[Tag]: + """ + Yields the tags for a CPython interpreter. + + The tags consist of: + - cp-- + - cp-abi3- + - cp-none- + - cp-abi3- # Older Python versions down to 3.2. + + If python_version only specifies a major version then user-provided ABIs and + the 'none' ABItag will be used. + + If 'abi3' or 'none' are specified in 'abis' then they will be yielded at + their normal position and not at the beginning. + """ + if not python_version: + python_version = sys.version_info[:2] + + interpreter = f"cp{_version_nodot(python_version[:2])}" + + if abis is None: + if len(python_version) > 1: + abis = _cpython_abis(python_version, warn) + else: + abis = [] + abis = list(abis) + # 'abi3' and 'none' are explicitly handled later. + for explicit_abi in ("abi3", "none"): + try: + abis.remove(explicit_abi) + except ValueError: + pass + + platforms = list(platforms or platform_tags()) + for abi in abis: + for platform_ in platforms: + yield Tag(interpreter, abi, platform_) + + threading = _is_threaded_cpython(abis) + use_abi3 = _abi3_applies(python_version, threading) + if use_abi3: + yield from (Tag(interpreter, "abi3", platform_) for platform_ in platforms) + yield from (Tag(interpreter, "none", platform_) for platform_ in platforms) + + if use_abi3: + for minor_version in range(python_version[1] - 1, 1, -1): + for platform_ in platforms: + version = _version_nodot((python_version[0], minor_version)) + interpreter = f"cp{version}" + yield Tag(interpreter, "abi3", platform_) + + +def _generic_abi() -> list[str]: + """ + Return the ABI tag based on EXT_SUFFIX. + """ + # The following are examples of `EXT_SUFFIX`. + # We want to keep the parts which are related to the ABI and remove the + # parts which are related to the platform: + # - linux: '.cpython-310-x86_64-linux-gnu.so' => cp310 + # - mac: '.cpython-310-darwin.so' => cp310 + # - win: '.cp310-win_amd64.pyd' => cp310 + # - win: '.pyd' => cp37 (uses _cpython_abis()) + # - pypy: '.pypy38-pp73-x86_64-linux-gnu.so' => pypy38_pp73 + # - graalpy: '.graalpy-38-native-x86_64-darwin.dylib' + # => graalpy_38_native + + ext_suffix = _get_config_var("EXT_SUFFIX", warn=True) + if not isinstance(ext_suffix, str) or ext_suffix[0] != ".": + raise SystemError("invalid sysconfig.get_config_var('EXT_SUFFIX')") + parts = ext_suffix.split(".") + if len(parts) < 3: + # CPython3.7 and earlier uses ".pyd" on Windows. + return _cpython_abis(sys.version_info[:2]) + soabi = parts[1] + if soabi.startswith("cpython"): + # non-windows + abi = "cp" + soabi.split("-")[1] + elif soabi.startswith("cp"): + # windows + abi = soabi.split("-")[0] + elif soabi.startswith("pypy"): + abi = "-".join(soabi.split("-")[:2]) + elif soabi.startswith("graalpy"): + abi = "-".join(soabi.split("-")[:3]) + elif soabi: + # pyston, ironpython, others? + abi = soabi + else: + return [] + return [_normalize_string(abi)] + + +def generic_tags( + interpreter: str | None = None, + abis: Iterable[str] | None = None, + platforms: Iterable[str] | None = None, + *, + warn: bool = False, +) -> Iterator[Tag]: + """ + Yields the tags for a generic interpreter. + + The tags consist of: + - -- + + The "none" ABI will be added if it was not explicitly provided. + """ + if not interpreter: + interp_name = interpreter_name() + interp_version = interpreter_version(warn=warn) + interpreter = "".join([interp_name, interp_version]) + if abis is None: + abis = _generic_abi() + else: + abis = list(abis) + platforms = list(platforms or platform_tags()) + if "none" not in abis: + abis.append("none") + for abi in abis: + for platform_ in platforms: + yield Tag(interpreter, abi, platform_) + + +def _py_interpreter_range(py_version: PythonVersion) -> Iterator[str]: + """ + Yields Python versions in descending order. + + After the latest version, the major-only version will be yielded, and then + all previous versions of that major version. + """ + if len(py_version) > 1: + yield f"py{_version_nodot(py_version[:2])}" + yield f"py{py_version[0]}" + if len(py_version) > 1: + for minor in range(py_version[1] - 1, -1, -1): + yield f"py{_version_nodot((py_version[0], minor))}" + + +def compatible_tags( + python_version: PythonVersion | None = None, + interpreter: str | None = None, + platforms: Iterable[str] | None = None, +) -> Iterator[Tag]: + """ + Yields the sequence of tags that are compatible with a specific version of Python. + + The tags consist of: + - py*-none- + - -none-any # ... if `interpreter` is provided. + - py*-none-any + """ + if not python_version: + python_version = sys.version_info[:2] + platforms = list(platforms or platform_tags()) + for version in _py_interpreter_range(python_version): + for platform_ in platforms: + yield Tag(version, "none", platform_) + if interpreter: + yield Tag(interpreter, "none", "any") + for version in _py_interpreter_range(python_version): + yield Tag(version, "none", "any") + + +def _mac_arch(arch: str, is_32bit: bool = _32_BIT_INTERPRETER) -> str: + if not is_32bit: + return arch + + if arch.startswith("ppc"): + return "ppc" + + return "i386" + + +def _mac_binary_formats(version: AppleVersion, cpu_arch: str) -> list[str]: + formats = [cpu_arch] + if cpu_arch == "x86_64": + if version < (10, 4): + return [] + formats.extend(["intel", "fat64", "fat32"]) + + elif cpu_arch == "i386": + if version < (10, 4): + return [] + formats.extend(["intel", "fat32", "fat"]) + + elif cpu_arch == "ppc64": + # TODO: Need to care about 32-bit PPC for ppc64 through 10.2? + if version > (10, 5) or version < (10, 4): + return [] + formats.append("fat64") + + elif cpu_arch == "ppc": + if version > (10, 6): + return [] + formats.extend(["fat32", "fat"]) + + if cpu_arch in {"arm64", "x86_64"}: + formats.append("universal2") + + if cpu_arch in {"x86_64", "i386", "ppc64", "ppc", "intel"}: + formats.append("universal") + + return formats + + +def mac_platforms( + version: AppleVersion | None = None, arch: str | None = None +) -> Iterator[str]: + """ + Yields the platform tags for a macOS system. + + The `version` parameter is a two-item tuple specifying the macOS version to + generate platform tags for. The `arch` parameter is the CPU architecture to + generate platform tags for. Both parameters default to the appropriate value + for the current system. + """ + version_str, _, cpu_arch = platform.mac_ver() + if version is None: + version = cast("AppleVersion", tuple(map(int, version_str.split(".")[:2]))) + if version == (10, 16): + # When built against an older macOS SDK, Python will report macOS 10.16 + # instead of the real version. + version_str = subprocess.run( + [ + sys.executable, + "-sS", + "-c", + "import platform; print(platform.mac_ver()[0])", + ], + check=True, + env={"SYSTEM_VERSION_COMPAT": "0"}, + stdout=subprocess.PIPE, + text=True, + ).stdout + version = cast("AppleVersion", tuple(map(int, version_str.split(".")[:2]))) + else: + version = version + if arch is None: + arch = _mac_arch(cpu_arch) + else: + arch = arch + + if (10, 0) <= version and version < (11, 0): + # Prior to Mac OS 11, each yearly release of Mac OS bumped the + # "minor" version number. The major version was always 10. + major_version = 10 + for minor_version in range(version[1], -1, -1): + compat_version = major_version, minor_version + binary_formats = _mac_binary_formats(compat_version, arch) + for binary_format in binary_formats: + yield f"macosx_{major_version}_{minor_version}_{binary_format}" + + if version >= (11, 0): + # Starting with Mac OS 11, each yearly release bumps the major version + # number. The minor versions are now the midyear updates. + minor_version = 0 + for major_version in range(version[0], 10, -1): + compat_version = major_version, minor_version + binary_formats = _mac_binary_formats(compat_version, arch) + for binary_format in binary_formats: + yield f"macosx_{major_version}_{minor_version}_{binary_format}" + + if version >= (11, 0): + # Mac OS 11 on x86_64 is compatible with binaries from previous releases. + # Arm64 support was introduced in 11.0, so no Arm binaries from previous + # releases exist. + # + # However, the "universal2" binary format can have a + # macOS version earlier than 11.0 when the x86_64 part of the binary supports + # that version of macOS. + major_version = 10 + if arch == "x86_64": + for minor_version in range(16, 3, -1): + compat_version = major_version, minor_version + binary_formats = _mac_binary_formats(compat_version, arch) + for binary_format in binary_formats: + yield f"macosx_{major_version}_{minor_version}_{binary_format}" + else: + for minor_version in range(16, 3, -1): + compat_version = major_version, minor_version + binary_format = "universal2" + yield f"macosx_{major_version}_{minor_version}_{binary_format}" + + +def ios_platforms( + version: AppleVersion | None = None, multiarch: str | None = None +) -> Iterator[str]: + """ + Yields the platform tags for an iOS system. + + :param version: A two-item tuple specifying the iOS version to generate + platform tags for. Defaults to the current iOS version. + :param multiarch: The CPU architecture+ABI to generate platform tags for - + (the value used by `sys.implementation._multiarch` e.g., + `arm64_iphoneos` or `x84_64_iphonesimulator`). Defaults to the current + multiarch value. + """ + if version is None: + # if iOS is the current platform, ios_ver *must* be defined. However, + # it won't exist for CPython versions before 3.13, which causes a mypy + # error. + _, release, _, _ = platform.ios_ver() # type: ignore[attr-defined, unused-ignore] + version = cast("AppleVersion", tuple(map(int, release.split(".")[:2]))) + + if multiarch is None: + multiarch = sys.implementation._multiarch + multiarch = multiarch.replace("-", "_") + + ios_platform_template = "ios_{major}_{minor}_{multiarch}" + + # Consider any iOS major.minor version from the version requested, down to + # 12.0. 12.0 is the first iOS version that is known to have enough features + # to support CPython. Consider every possible minor release up to X.9. There + # highest the minor has ever gone is 8 (14.8 and 15.8) but having some extra + # candidates that won't ever match doesn't really hurt, and it saves us from + # having to keep an explicit list of known iOS versions in the code. Return + # the results descending order of version number. + + # If the requested major version is less than 12, there won't be any matches. + if version[0] < 12: + return + + # Consider the actual X.Y version that was requested. + yield ios_platform_template.format( + major=version[0], minor=version[1], multiarch=multiarch + ) + + # Consider every minor version from X.0 to the minor version prior to the + # version requested by the platform. + for minor in range(version[1] - 1, -1, -1): + yield ios_platform_template.format( + major=version[0], minor=minor, multiarch=multiarch + ) + + for major in range(version[0] - 1, 11, -1): + for minor in range(9, -1, -1): + yield ios_platform_template.format( + major=major, minor=minor, multiarch=multiarch + ) + + +def android_platforms( + api_level: int | None = None, abi: str | None = None +) -> Iterator[str]: + """ + Yields the :attr:`~Tag.platform` tags for Android. If this function is invoked on + non-Android platforms, the ``api_level`` and ``abi`` arguments are required. + + :param int api_level: The maximum `API level + `__ to return. Defaults + to the current system's version, as returned by ``platform.android_ver``. + :param str abi: The `Android ABI `__, + e.g. ``arm64_v8a``. Defaults to the current system's ABI , as returned by + ``sysconfig.get_platform``. Hyphens and periods will be replaced with + underscores. + """ + if platform.system() != "Android" and (api_level is None or abi is None): + raise TypeError( + "on non-Android platforms, the api_level and abi arguments are required" + ) + + if api_level is None: + # Python 3.13 was the first version to return platform.system() == "Android", + # and also the first version to define platform.android_ver(). + api_level = platform.android_ver().api_level # type: ignore[attr-defined] + + if abi is None: + abi = sysconfig.get_platform().split("-")[-1] + abi = _normalize_string(abi) + + # 16 is the minimum API level known to have enough features to support CPython + # without major patching. Yield every API level from the maximum down to the + # minimum, inclusive. + min_api_level = 16 + for ver in range(api_level, min_api_level - 1, -1): + yield f"android_{ver}_{abi}" + + +def _linux_platforms(is_32bit: bool = _32_BIT_INTERPRETER) -> Iterator[str]: + linux = _normalize_string(sysconfig.get_platform()) + if not linux.startswith("linux_"): + # we should never be here, just yield the sysconfig one and return + yield linux + return + if is_32bit: + if linux == "linux_x86_64": + linux = "linux_i686" + elif linux == "linux_aarch64": + linux = "linux_armv8l" + _, arch = linux.split("_", 1) + archs = {"armv8l": ["armv8l", "armv7l"]}.get(arch, [arch]) + yield from _manylinux.platform_tags(archs) + yield from _musllinux.platform_tags(archs) + for arch in archs: + yield f"linux_{arch}" + + +def _generic_platforms() -> Iterator[str]: + yield _normalize_string(sysconfig.get_platform()) + + +def platform_tags() -> Iterator[str]: + """ + Provides the platform tags for this installation. + """ + if platform.system() == "Darwin": + return mac_platforms() + elif platform.system() == "iOS": + return ios_platforms() + elif platform.system() == "Android": + return android_platforms() + elif platform.system() == "Linux": + return _linux_platforms() + else: + return _generic_platforms() + + +def interpreter_name() -> str: + """ + Returns the name of the running interpreter. + + Some implementations have a reserved, two-letter abbreviation which will + be returned when appropriate. + """ + name = sys.implementation.name + return INTERPRETER_SHORT_NAMES.get(name) or name + + +def interpreter_version(*, warn: bool = False) -> str: + """ + Returns the version of the running interpreter. + """ + version = _get_config_var("py_version_nodot", warn=warn) + if version: + version = str(version) + else: + version = _version_nodot(sys.version_info[:2]) + return version + + +def _version_nodot(version: PythonVersion) -> str: + return "".join(map(str, version)) + + +def sys_tags(*, warn: bool = False) -> Iterator[Tag]: + """ + Returns the sequence of tag triples for the running interpreter. + + The order of the sequence corresponds to priority order for the + interpreter, from most to least important. + """ + + interp_name = interpreter_name() + if interp_name == "cp": + yield from cpython_tags(warn=warn) + else: + yield from generic_tags() + + if interp_name == "pp": + interp = "pp3" + elif interp_name == "cp": + interp = "cp" + interpreter_version(warn=warn) + else: + interp = None + yield from compatible_tags(interpreter=interp) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging/utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..23450953df74eccd9c13cd2a955ce09d1f968565 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging/utils.py @@ -0,0 +1,163 @@ +# This file is dual licensed under the terms of the Apache License, Version +# 2.0, and the BSD License. See the LICENSE file in the root of this repository +# for complete details. + +from __future__ import annotations + +import functools +import re +from typing import NewType, Tuple, Union, cast + +from .tags import Tag, parse_tag +from .version import InvalidVersion, Version, _TrimmedRelease + +BuildTag = Union[Tuple[()], Tuple[int, str]] +NormalizedName = NewType("NormalizedName", str) + + +class InvalidName(ValueError): + """ + An invalid distribution name; users should refer to the packaging user guide. + """ + + +class InvalidWheelFilename(ValueError): + """ + An invalid wheel filename was found, users should refer to PEP 427. + """ + + +class InvalidSdistFilename(ValueError): + """ + An invalid sdist filename was found, users should refer to the packaging user guide. + """ + + +# Core metadata spec for `Name` +_validate_regex = re.compile( + r"^([A-Z0-9]|[A-Z0-9][A-Z0-9._-]*[A-Z0-9])$", re.IGNORECASE +) +_canonicalize_regex = re.compile(r"[-_.]+") +_normalized_regex = re.compile(r"^([a-z0-9]|[a-z0-9]([a-z0-9-](?!--))*[a-z0-9])$") +# PEP 427: The build number must start with a digit. +_build_tag_regex = re.compile(r"(\d+)(.*)") + + +def canonicalize_name(name: str, *, validate: bool = False) -> NormalizedName: + if validate and not _validate_regex.match(name): + raise InvalidName(f"name is invalid: {name!r}") + # This is taken from PEP 503. + value = _canonicalize_regex.sub("-", name).lower() + return cast(NormalizedName, value) + + +def is_normalized_name(name: str) -> bool: + return _normalized_regex.match(name) is not None + + +@functools.singledispatch +def canonicalize_version( + version: Version | str, *, strip_trailing_zero: bool = True +) -> str: + """ + Return a canonical form of a version as a string. + + >>> canonicalize_version('1.0.1') + '1.0.1' + + Per PEP 625, versions may have multiple canonical forms, differing + only by trailing zeros. + + >>> canonicalize_version('1.0.0') + '1' + >>> canonicalize_version('1.0.0', strip_trailing_zero=False) + '1.0.0' + + Invalid versions are returned unaltered. + + >>> canonicalize_version('foo bar baz') + 'foo bar baz' + """ + return str(_TrimmedRelease(str(version)) if strip_trailing_zero else version) + + +@canonicalize_version.register +def _(version: str, *, strip_trailing_zero: bool = True) -> str: + try: + parsed = Version(version) + except InvalidVersion: + # Legacy versions cannot be normalized + return version + return canonicalize_version(parsed, strip_trailing_zero=strip_trailing_zero) + + +def parse_wheel_filename( + filename: str, +) -> tuple[NormalizedName, Version, BuildTag, frozenset[Tag]]: + if not filename.endswith(".whl"): + raise InvalidWheelFilename( + f"Invalid wheel filename (extension must be '.whl'): {filename!r}" + ) + + filename = filename[:-4] + dashes = filename.count("-") + if dashes not in (4, 5): + raise InvalidWheelFilename( + f"Invalid wheel filename (wrong number of parts): {filename!r}" + ) + + parts = filename.split("-", dashes - 2) + name_part = parts[0] + # See PEP 427 for the rules on escaping the project name. + if "__" in name_part or re.match(r"^[\w\d._]*$", name_part, re.UNICODE) is None: + raise InvalidWheelFilename(f"Invalid project name: {filename!r}") + name = canonicalize_name(name_part) + + try: + version = Version(parts[1]) + except InvalidVersion as e: + raise InvalidWheelFilename( + f"Invalid wheel filename (invalid version): {filename!r}" + ) from e + + if dashes == 5: + build_part = parts[2] + build_match = _build_tag_regex.match(build_part) + if build_match is None: + raise InvalidWheelFilename( + f"Invalid build number: {build_part} in {filename!r}" + ) + build = cast(BuildTag, (int(build_match.group(1)), build_match.group(2))) + else: + build = () + tags = parse_tag(parts[-1]) + return (name, version, build, tags) + + +def parse_sdist_filename(filename: str) -> tuple[NormalizedName, Version]: + if filename.endswith(".tar.gz"): + file_stem = filename[: -len(".tar.gz")] + elif filename.endswith(".zip"): + file_stem = filename[: -len(".zip")] + else: + raise InvalidSdistFilename( + f"Invalid sdist filename (extension must be '.tar.gz' or '.zip'):" + f" {filename!r}" + ) + + # We are requiring a PEP 440 version, which cannot contain dashes, + # so we split on the last dash. + name_part, sep, version_part = file_stem.rpartition("-") + if not sep: + raise InvalidSdistFilename(f"Invalid sdist filename: {filename!r}") + + name = canonicalize_name(name_part) + + try: + version = Version(version_part) + except InvalidVersion as e: + raise InvalidSdistFilename( + f"Invalid sdist filename (invalid version): {filename!r}" + ) from e + + return (name, version) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging/version.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging/version.py new file mode 100644 index 0000000000000000000000000000000000000000..c9bbda20e463b8d9389ecd65f74af33810a02bdd --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/packaging/version.py @@ -0,0 +1,582 @@ +# This file is dual licensed under the terms of the Apache License, Version +# 2.0, and the BSD License. See the LICENSE file in the root of this repository +# for complete details. +""" +.. testsetup:: + + from packaging.version import parse, Version +""" + +from __future__ import annotations + +import itertools +import re +from typing import Any, Callable, NamedTuple, SupportsInt, Tuple, Union + +from ._structures import Infinity, InfinityType, NegativeInfinity, NegativeInfinityType + +__all__ = ["VERSION_PATTERN", "InvalidVersion", "Version", "parse"] + +LocalType = Tuple[Union[int, str], ...] + +CmpPrePostDevType = Union[InfinityType, NegativeInfinityType, Tuple[str, int]] +CmpLocalType = Union[ + NegativeInfinityType, + Tuple[Union[Tuple[int, str], Tuple[NegativeInfinityType, Union[int, str]]], ...], +] +CmpKey = Tuple[ + int, + Tuple[int, ...], + CmpPrePostDevType, + CmpPrePostDevType, + CmpPrePostDevType, + CmpLocalType, +] +VersionComparisonMethod = Callable[[CmpKey, CmpKey], bool] + + +class _Version(NamedTuple): + epoch: int + release: tuple[int, ...] + dev: tuple[str, int] | None + pre: tuple[str, int] | None + post: tuple[str, int] | None + local: LocalType | None + + +def parse(version: str) -> Version: + """Parse the given version string. + + >>> parse('1.0.dev1') + + + :param version: The version string to parse. + :raises InvalidVersion: When the version string is not a valid version. + """ + return Version(version) + + +class InvalidVersion(ValueError): + """Raised when a version string is not a valid version. + + >>> Version("invalid") + Traceback (most recent call last): + ... + packaging.version.InvalidVersion: Invalid version: 'invalid' + """ + + +class _BaseVersion: + _key: tuple[Any, ...] + + def __hash__(self) -> int: + return hash(self._key) + + # Please keep the duplicated `isinstance` check + # in the six comparisons hereunder + # unless you find a way to avoid adding overhead function calls. + def __lt__(self, other: _BaseVersion) -> bool: + if not isinstance(other, _BaseVersion): + return NotImplemented + + return self._key < other._key + + def __le__(self, other: _BaseVersion) -> bool: + if not isinstance(other, _BaseVersion): + return NotImplemented + + return self._key <= other._key + + def __eq__(self, other: object) -> bool: + if not isinstance(other, _BaseVersion): + return NotImplemented + + return self._key == other._key + + def __ge__(self, other: _BaseVersion) -> bool: + if not isinstance(other, _BaseVersion): + return NotImplemented + + return self._key >= other._key + + def __gt__(self, other: _BaseVersion) -> bool: + if not isinstance(other, _BaseVersion): + return NotImplemented + + return self._key > other._key + + def __ne__(self, other: object) -> bool: + if not isinstance(other, _BaseVersion): + return NotImplemented + + return self._key != other._key + + +# Deliberately not anchored to the start and end of the string, to make it +# easier for 3rd party code to reuse +_VERSION_PATTERN = r""" + v? + (?: + (?:(?P[0-9]+)!)? # epoch + (?P[0-9]+(?:\.[0-9]+)*) # release segment + (?P
                                          # pre-release
+            [-_\.]?
+            (?Palpha|a|beta|b|preview|pre|c|rc)
+            [-_\.]?
+            (?P[0-9]+)?
+        )?
+        (?P                                         # post release
+            (?:-(?P[0-9]+))
+            |
+            (?:
+                [-_\.]?
+                (?Ppost|rev|r)
+                [-_\.]?
+                (?P[0-9]+)?
+            )
+        )?
+        (?P                                          # dev release
+            [-_\.]?
+            (?Pdev)
+            [-_\.]?
+            (?P[0-9]+)?
+        )?
+    )
+    (?:\+(?P[a-z0-9]+(?:[-_\.][a-z0-9]+)*))?       # local version
+"""
+
+VERSION_PATTERN = _VERSION_PATTERN
+"""
+A string containing the regular expression used to match a valid version.
+
+The pattern is not anchored at either end, and is intended for embedding in larger
+expressions (for example, matching a version number as part of a file name). The
+regular expression should be compiled with the ``re.VERBOSE`` and ``re.IGNORECASE``
+flags set.
+
+:meta hide-value:
+"""
+
+
+class Version(_BaseVersion):
+    """This class abstracts handling of a project's versions.
+
+    A :class:`Version` instance is comparison aware and can be compared and
+    sorted using the standard Python interfaces.
+
+    >>> v1 = Version("1.0a5")
+    >>> v2 = Version("1.0")
+    >>> v1
+    
+    >>> v2
+    
+    >>> v1 < v2
+    True
+    >>> v1 == v2
+    False
+    >>> v1 > v2
+    False
+    >>> v1 >= v2
+    False
+    >>> v1 <= v2
+    True
+    """
+
+    _regex = re.compile(r"^\s*" + VERSION_PATTERN + r"\s*$", re.VERBOSE | re.IGNORECASE)
+    _key: CmpKey
+
+    def __init__(self, version: str) -> None:
+        """Initialize a Version object.
+
+        :param version:
+            The string representation of a version which will be parsed and normalized
+            before use.
+        :raises InvalidVersion:
+            If the ``version`` does not conform to PEP 440 in any way then this
+            exception will be raised.
+        """
+
+        # Validate the version and parse it into pieces
+        match = self._regex.search(version)
+        if not match:
+            raise InvalidVersion(f"Invalid version: {version!r}")
+
+        # Store the parsed out pieces of the version
+        self._version = _Version(
+            epoch=int(match.group("epoch")) if match.group("epoch") else 0,
+            release=tuple(int(i) for i in match.group("release").split(".")),
+            pre=_parse_letter_version(match.group("pre_l"), match.group("pre_n")),
+            post=_parse_letter_version(
+                match.group("post_l"), match.group("post_n1") or match.group("post_n2")
+            ),
+            dev=_parse_letter_version(match.group("dev_l"), match.group("dev_n")),
+            local=_parse_local_version(match.group("local")),
+        )
+
+        # Generate a key which will be used for sorting
+        self._key = _cmpkey(
+            self._version.epoch,
+            self._version.release,
+            self._version.pre,
+            self._version.post,
+            self._version.dev,
+            self._version.local,
+        )
+
+    def __repr__(self) -> str:
+        """A representation of the Version that shows all internal state.
+
+        >>> Version('1.0.0')
+        
+        """
+        return f""
+
+    def __str__(self) -> str:
+        """A string representation of the version that can be round-tripped.
+
+        >>> str(Version("1.0a5"))
+        '1.0a5'
+        """
+        parts = []
+
+        # Epoch
+        if self.epoch != 0:
+            parts.append(f"{self.epoch}!")
+
+        # Release segment
+        parts.append(".".join(str(x) for x in self.release))
+
+        # Pre-release
+        if self.pre is not None:
+            parts.append("".join(str(x) for x in self.pre))
+
+        # Post-release
+        if self.post is not None:
+            parts.append(f".post{self.post}")
+
+        # Development release
+        if self.dev is not None:
+            parts.append(f".dev{self.dev}")
+
+        # Local version segment
+        if self.local is not None:
+            parts.append(f"+{self.local}")
+
+        return "".join(parts)
+
+    @property
+    def epoch(self) -> int:
+        """The epoch of the version.
+
+        >>> Version("2.0.0").epoch
+        0
+        >>> Version("1!2.0.0").epoch
+        1
+        """
+        return self._version.epoch
+
+    @property
+    def release(self) -> tuple[int, ...]:
+        """The components of the "release" segment of the version.
+
+        >>> Version("1.2.3").release
+        (1, 2, 3)
+        >>> Version("2.0.0").release
+        (2, 0, 0)
+        >>> Version("1!2.0.0.post0").release
+        (2, 0, 0)
+
+        Includes trailing zeroes but not the epoch or any pre-release / development /
+        post-release suffixes.
+        """
+        return self._version.release
+
+    @property
+    def pre(self) -> tuple[str, int] | None:
+        """The pre-release segment of the version.
+
+        >>> print(Version("1.2.3").pre)
+        None
+        >>> Version("1.2.3a1").pre
+        ('a', 1)
+        >>> Version("1.2.3b1").pre
+        ('b', 1)
+        >>> Version("1.2.3rc1").pre
+        ('rc', 1)
+        """
+        return self._version.pre
+
+    @property
+    def post(self) -> int | None:
+        """The post-release number of the version.
+
+        >>> print(Version("1.2.3").post)
+        None
+        >>> Version("1.2.3.post1").post
+        1
+        """
+        return self._version.post[1] if self._version.post else None
+
+    @property
+    def dev(self) -> int | None:
+        """The development number of the version.
+
+        >>> print(Version("1.2.3").dev)
+        None
+        >>> Version("1.2.3.dev1").dev
+        1
+        """
+        return self._version.dev[1] if self._version.dev else None
+
+    @property
+    def local(self) -> str | None:
+        """The local version segment of the version.
+
+        >>> print(Version("1.2.3").local)
+        None
+        >>> Version("1.2.3+abc").local
+        'abc'
+        """
+        if self._version.local:
+            return ".".join(str(x) for x in self._version.local)
+        else:
+            return None
+
+    @property
+    def public(self) -> str:
+        """The public portion of the version.
+
+        >>> Version("1.2.3").public
+        '1.2.3'
+        >>> Version("1.2.3+abc").public
+        '1.2.3'
+        >>> Version("1!1.2.3dev1+abc").public
+        '1!1.2.3.dev1'
+        """
+        return str(self).split("+", 1)[0]
+
+    @property
+    def base_version(self) -> str:
+        """The "base version" of the version.
+
+        >>> Version("1.2.3").base_version
+        '1.2.3'
+        >>> Version("1.2.3+abc").base_version
+        '1.2.3'
+        >>> Version("1!1.2.3dev1+abc").base_version
+        '1!1.2.3'
+
+        The "base version" is the public version of the project without any pre or post
+        release markers.
+        """
+        parts = []
+
+        # Epoch
+        if self.epoch != 0:
+            parts.append(f"{self.epoch}!")
+
+        # Release segment
+        parts.append(".".join(str(x) for x in self.release))
+
+        return "".join(parts)
+
+    @property
+    def is_prerelease(self) -> bool:
+        """Whether this version is a pre-release.
+
+        >>> Version("1.2.3").is_prerelease
+        False
+        >>> Version("1.2.3a1").is_prerelease
+        True
+        >>> Version("1.2.3b1").is_prerelease
+        True
+        >>> Version("1.2.3rc1").is_prerelease
+        True
+        >>> Version("1.2.3dev1").is_prerelease
+        True
+        """
+        return self.dev is not None or self.pre is not None
+
+    @property
+    def is_postrelease(self) -> bool:
+        """Whether this version is a post-release.
+
+        >>> Version("1.2.3").is_postrelease
+        False
+        >>> Version("1.2.3.post1").is_postrelease
+        True
+        """
+        return self.post is not None
+
+    @property
+    def is_devrelease(self) -> bool:
+        """Whether this version is a development release.
+
+        >>> Version("1.2.3").is_devrelease
+        False
+        >>> Version("1.2.3.dev1").is_devrelease
+        True
+        """
+        return self.dev is not None
+
+    @property
+    def major(self) -> int:
+        """The first item of :attr:`release` or ``0`` if unavailable.
+
+        >>> Version("1.2.3").major
+        1
+        """
+        return self.release[0] if len(self.release) >= 1 else 0
+
+    @property
+    def minor(self) -> int:
+        """The second item of :attr:`release` or ``0`` if unavailable.
+
+        >>> Version("1.2.3").minor
+        2
+        >>> Version("1").minor
+        0
+        """
+        return self.release[1] if len(self.release) >= 2 else 0
+
+    @property
+    def micro(self) -> int:
+        """The third item of :attr:`release` or ``0`` if unavailable.
+
+        >>> Version("1.2.3").micro
+        3
+        >>> Version("1").micro
+        0
+        """
+        return self.release[2] if len(self.release) >= 3 else 0
+
+
+class _TrimmedRelease(Version):
+    @property
+    def release(self) -> tuple[int, ...]:
+        """
+        Release segment without any trailing zeros.
+
+        >>> _TrimmedRelease('1.0.0').release
+        (1,)
+        >>> _TrimmedRelease('0.0').release
+        (0,)
+        """
+        rel = super().release
+        nonzeros = (index for index, val in enumerate(rel) if val)
+        last_nonzero = max(nonzeros, default=0)
+        return rel[: last_nonzero + 1]
+
+
+def _parse_letter_version(
+    letter: str | None, number: str | bytes | SupportsInt | None
+) -> tuple[str, int] | None:
+    if letter:
+        # We consider there to be an implicit 0 in a pre-release if there is
+        # not a numeral associated with it.
+        if number is None:
+            number = 0
+
+        # We normalize any letters to their lower case form
+        letter = letter.lower()
+
+        # We consider some words to be alternate spellings of other words and
+        # in those cases we want to normalize the spellings to our preferred
+        # spelling.
+        if letter == "alpha":
+            letter = "a"
+        elif letter == "beta":
+            letter = "b"
+        elif letter in ["c", "pre", "preview"]:
+            letter = "rc"
+        elif letter in ["rev", "r"]:
+            letter = "post"
+
+        return letter, int(number)
+
+    assert not letter
+    if number:
+        # We assume if we are given a number, but we are not given a letter
+        # then this is using the implicit post release syntax (e.g. 1.0-1)
+        letter = "post"
+
+        return letter, int(number)
+
+    return None
+
+
+_local_version_separators = re.compile(r"[\._-]")
+
+
+def _parse_local_version(local: str | None) -> LocalType | None:
+    """
+    Takes a string like abc.1.twelve and turns it into ("abc", 1, "twelve").
+    """
+    if local is not None:
+        return tuple(
+            part.lower() if not part.isdigit() else int(part)
+            for part in _local_version_separators.split(local)
+        )
+    return None
+
+
+def _cmpkey(
+    epoch: int,
+    release: tuple[int, ...],
+    pre: tuple[str, int] | None,
+    post: tuple[str, int] | None,
+    dev: tuple[str, int] | None,
+    local: LocalType | None,
+) -> CmpKey:
+    # When we compare a release version, we want to compare it with all of the
+    # trailing zeros removed. So we'll use a reverse the list, drop all the now
+    # leading zeros until we come to something non zero, then take the rest
+    # re-reverse it back into the correct order and make it a tuple and use
+    # that for our sorting key.
+    _release = tuple(
+        reversed(list(itertools.dropwhile(lambda x: x == 0, reversed(release))))
+    )
+
+    # We need to "trick" the sorting algorithm to put 1.0.dev0 before 1.0a0.
+    # We'll do this by abusing the pre segment, but we _only_ want to do this
+    # if there is not a pre or a post segment. If we have one of those then
+    # the normal sorting rules will handle this case correctly.
+    if pre is None and post is None and dev is not None:
+        _pre: CmpPrePostDevType = NegativeInfinity
+    # Versions without a pre-release (except as noted above) should sort after
+    # those with one.
+    elif pre is None:
+        _pre = Infinity
+    else:
+        _pre = pre
+
+    # Versions without a post segment should sort before those with one.
+    if post is None:
+        _post: CmpPrePostDevType = NegativeInfinity
+
+    else:
+        _post = post
+
+    # Versions without a development segment should sort after those with one.
+    if dev is None:
+        _dev: CmpPrePostDevType = Infinity
+
+    else:
+        _dev = dev
+
+    if local is None:
+        # Versions without a local segment should sort before those with one.
+        _local: CmpLocalType = NegativeInfinity
+    else:
+        # Versions with a local segment need that segment parsed to implement
+        # the sorting rules in PEP440.
+        # - Alpha numeric segments sort before numeric segments
+        # - Alpha numeric segments sort lexicographically
+        # - Numeric segments sort numerically
+        # - Shorter versions sort before longer versions when the prefixes
+        #   match exactly
+        _local = tuple(
+            (i, "") if isinstance(i, int) else (NegativeInfinity, i) for i in local
+        )
+
+    return epoch, _release, _pre, _post, _dev, _local
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ca2eba20432924304517be99d5113bc9f57614d2
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/__init__.py
@@ -0,0 +1,367 @@
+from __future__ import annotations
+
+import os
+import warnings
+
+__docformat__ = "restructuredtext"
+
+# Let users know if they're missing any of our hard dependencies
+_hard_dependencies = ("numpy", "pytz", "dateutil")
+_missing_dependencies = []
+
+for _dependency in _hard_dependencies:
+    try:
+        __import__(_dependency)
+    except ImportError as _e:  # pragma: no cover
+        _missing_dependencies.append(f"{_dependency}: {_e}")
+
+if _missing_dependencies:  # pragma: no cover
+    raise ImportError(
+        "Unable to import required dependencies:\n" + "\n".join(_missing_dependencies)
+    )
+del _hard_dependencies, _dependency, _missing_dependencies
+
+try:
+    # numpy compat
+    from pandas.compat import (
+        is_numpy_dev as _is_numpy_dev,  # pyright: ignore[reportUnusedImport] # noqa: F401
+    )
+except ImportError as _err:  # pragma: no cover
+    _module = _err.name
+    raise ImportError(
+        f"C extension: {_module} not built. If you want to import "
+        "pandas from the source directory, you may need to run "
+        "'python setup.py build_ext' to build the C extensions first."
+    ) from _err
+
+from pandas._config import (
+    get_option,
+    set_option,
+    reset_option,
+    describe_option,
+    option_context,
+    options,
+)
+
+# let init-time option registration happen
+import pandas.core.config_init  # pyright: ignore[reportUnusedImport] # noqa: F401
+
+from pandas.core.api import (
+    # dtype
+    ArrowDtype,
+    Int8Dtype,
+    Int16Dtype,
+    Int32Dtype,
+    Int64Dtype,
+    UInt8Dtype,
+    UInt16Dtype,
+    UInt32Dtype,
+    UInt64Dtype,
+    Float32Dtype,
+    Float64Dtype,
+    CategoricalDtype,
+    PeriodDtype,
+    IntervalDtype,
+    DatetimeTZDtype,
+    StringDtype,
+    BooleanDtype,
+    # missing
+    NA,
+    isna,
+    isnull,
+    notna,
+    notnull,
+    # indexes
+    Index,
+    CategoricalIndex,
+    RangeIndex,
+    MultiIndex,
+    IntervalIndex,
+    TimedeltaIndex,
+    DatetimeIndex,
+    PeriodIndex,
+    IndexSlice,
+    # tseries
+    NaT,
+    Period,
+    period_range,
+    Timedelta,
+    timedelta_range,
+    Timestamp,
+    date_range,
+    bdate_range,
+    Interval,
+    interval_range,
+    DateOffset,
+    # conversion
+    to_numeric,
+    to_datetime,
+    to_timedelta,
+    # misc
+    Flags,
+    Grouper,
+    factorize,
+    unique,
+    value_counts,
+    NamedAgg,
+    array,
+    Categorical,
+    set_eng_float_format,
+    Series,
+    DataFrame,
+)
+
+from pandas.core.dtypes.dtypes import SparseDtype
+
+from pandas.tseries.api import infer_freq
+from pandas.tseries import offsets
+
+from pandas.core.computation.api import eval
+
+from pandas.core.reshape.api import (
+    concat,
+    lreshape,
+    melt,
+    wide_to_long,
+    merge,
+    merge_asof,
+    merge_ordered,
+    crosstab,
+    pivot,
+    pivot_table,
+    get_dummies,
+    from_dummies,
+    cut,
+    qcut,
+)
+
+from pandas import api, arrays, errors, io, plotting, tseries
+from pandas import testing
+from pandas.util._print_versions import show_versions
+
+from pandas.io.api import (
+    # excel
+    ExcelFile,
+    ExcelWriter,
+    read_excel,
+    # parsers
+    read_csv,
+    read_fwf,
+    read_table,
+    # pickle
+    read_pickle,
+    to_pickle,
+    # pytables
+    HDFStore,
+    read_hdf,
+    # sql
+    read_sql,
+    read_sql_query,
+    read_sql_table,
+    # misc
+    read_clipboard,
+    read_parquet,
+    read_orc,
+    read_feather,
+    read_gbq,
+    read_html,
+    read_xml,
+    read_json,
+    read_stata,
+    read_sas,
+    read_spss,
+)
+
+from pandas.io.json._normalize import json_normalize
+
+from pandas.util._tester import test
+
+# use the closest tagged version if possible
+_built_with_meson = False
+try:
+    from pandas._version_meson import (  # pyright: ignore [reportMissingImports]
+        __version__,
+        __git_version__,
+    )
+
+    _built_with_meson = True
+except ImportError:
+    from pandas._version import get_versions
+
+    v = get_versions()
+    __version__ = v.get("closest-tag", v["version"])
+    __git_version__ = v.get("full-revisionid")
+    del get_versions, v
+
+# GH#55043 - deprecation of the data_manager option
+if "PANDAS_DATA_MANAGER" in os.environ:
+    warnings.warn(
+        "The env variable PANDAS_DATA_MANAGER is set. The data_manager option is "
+        "deprecated and will be removed in a future version. Only the BlockManager "
+        "will be available. Unset this environment variable to silence this warning.",
+        FutureWarning,
+        stacklevel=2,
+    )
+
+del warnings, os
+
+# module level doc-string
+__doc__ = """
+pandas - a powerful data analysis and manipulation library for Python
+=====================================================================
+
+**pandas** is a Python package providing fast, flexible, and expressive data
+structures designed to make working with "relational" or "labeled" data both
+easy and intuitive. It aims to be the fundamental high-level building block for
+doing practical, **real world** data analysis in Python. Additionally, it has
+the broader goal of becoming **the most powerful and flexible open source data
+analysis / manipulation tool available in any language**. It is already well on
+its way toward this goal.
+
+Main Features
+-------------
+Here are just a few of the things that pandas does well:
+
+  - Easy handling of missing data in floating point as well as non-floating
+    point data.
+  - Size mutability: columns can be inserted and deleted from DataFrame and
+    higher dimensional objects
+  - Automatic and explicit data alignment: objects can be explicitly aligned
+    to a set of labels, or the user can simply ignore the labels and let
+    `Series`, `DataFrame`, etc. automatically align the data for you in
+    computations.
+  - Powerful, flexible group by functionality to perform split-apply-combine
+    operations on data sets, for both aggregating and transforming data.
+  - Make it easy to convert ragged, differently-indexed data in other Python
+    and NumPy data structures into DataFrame objects.
+  - Intelligent label-based slicing, fancy indexing, and subsetting of large
+    data sets.
+  - Intuitive merging and joining data sets.
+  - Flexible reshaping and pivoting of data sets.
+  - Hierarchical labeling of axes (possible to have multiple labels per tick).
+  - Robust IO tools for loading data from flat files (CSV and delimited),
+    Excel files, databases, and saving/loading data from the ultrafast HDF5
+    format.
+  - Time series-specific functionality: date range generation and frequency
+    conversion, moving window statistics, date shifting and lagging.
+"""
+
+# Use __all__ to let type checkers know what is part of the public API.
+# Pandas is not (yet) a py.typed library: the public API is determined
+# based on the documentation.
+__all__ = [
+    "ArrowDtype",
+    "BooleanDtype",
+    "Categorical",
+    "CategoricalDtype",
+    "CategoricalIndex",
+    "DataFrame",
+    "DateOffset",
+    "DatetimeIndex",
+    "DatetimeTZDtype",
+    "ExcelFile",
+    "ExcelWriter",
+    "Flags",
+    "Float32Dtype",
+    "Float64Dtype",
+    "Grouper",
+    "HDFStore",
+    "Index",
+    "IndexSlice",
+    "Int16Dtype",
+    "Int32Dtype",
+    "Int64Dtype",
+    "Int8Dtype",
+    "Interval",
+    "IntervalDtype",
+    "IntervalIndex",
+    "MultiIndex",
+    "NA",
+    "NaT",
+    "NamedAgg",
+    "Period",
+    "PeriodDtype",
+    "PeriodIndex",
+    "RangeIndex",
+    "Series",
+    "SparseDtype",
+    "StringDtype",
+    "Timedelta",
+    "TimedeltaIndex",
+    "Timestamp",
+    "UInt16Dtype",
+    "UInt32Dtype",
+    "UInt64Dtype",
+    "UInt8Dtype",
+    "api",
+    "array",
+    "arrays",
+    "bdate_range",
+    "concat",
+    "crosstab",
+    "cut",
+    "date_range",
+    "describe_option",
+    "errors",
+    "eval",
+    "factorize",
+    "get_dummies",
+    "from_dummies",
+    "get_option",
+    "infer_freq",
+    "interval_range",
+    "io",
+    "isna",
+    "isnull",
+    "json_normalize",
+    "lreshape",
+    "melt",
+    "merge",
+    "merge_asof",
+    "merge_ordered",
+    "notna",
+    "notnull",
+    "offsets",
+    "option_context",
+    "options",
+    "period_range",
+    "pivot",
+    "pivot_table",
+    "plotting",
+    "qcut",
+    "read_clipboard",
+    "read_csv",
+    "read_excel",
+    "read_feather",
+    "read_fwf",
+    "read_gbq",
+    "read_hdf",
+    "read_html",
+    "read_json",
+    "read_orc",
+    "read_parquet",
+    "read_pickle",
+    "read_sas",
+    "read_spss",
+    "read_sql",
+    "read_sql_query",
+    "read_sql_table",
+    "read_stata",
+    "read_table",
+    "read_xml",
+    "reset_option",
+    "set_eng_float_format",
+    "set_option",
+    "show_versions",
+    "test",
+    "testing",
+    "timedelta_range",
+    "to_datetime",
+    "to_numeric",
+    "to_pickle",
+    "to_timedelta",
+    "tseries",
+    "unique",
+    "value_counts",
+    "wide_to_long",
+]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_config/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_config/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..838b6affd283633c2269cb91db4bf9eaebda318e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_config/__init__.py
@@ -0,0 +1,57 @@
+"""
+pandas._config is considered explicitly upstream of everything else in pandas,
+should have no intra-pandas dependencies.
+
+importing `dates` and `display` ensures that keys needed by _libs
+are initialized.
+"""
+__all__ = [
+    "config",
+    "detect_console_encoding",
+    "get_option",
+    "set_option",
+    "reset_option",
+    "describe_option",
+    "option_context",
+    "options",
+    "using_copy_on_write",
+    "warn_copy_on_write",
+]
+from pandas._config import config
+from pandas._config import dates  # pyright: ignore[reportUnusedImport]  # noqa: F401
+from pandas._config.config import (
+    _global_config,
+    describe_option,
+    get_option,
+    option_context,
+    options,
+    reset_option,
+    set_option,
+)
+from pandas._config.display import detect_console_encoding
+
+
+def using_copy_on_write() -> bool:
+    _mode_options = _global_config["mode"]
+    return (
+        _mode_options["copy_on_write"] is True
+        and _mode_options["data_manager"] == "block"
+    )
+
+
+def warn_copy_on_write() -> bool:
+    _mode_options = _global_config["mode"]
+    return (
+        _mode_options["copy_on_write"] == "warn"
+        and _mode_options["data_manager"] == "block"
+    )
+
+
+def using_nullable_dtypes() -> bool:
+    _mode_options = _global_config["mode"]
+    return _mode_options["nullable_dtypes"]
+
+
+def using_string_dtype() -> bool:
+    _mode_options = _global_config["future"]
+    return _mode_options["infer_string"]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_config/config.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_config/config.py
new file mode 100644
index 0000000000000000000000000000000000000000..c391939d22491099652e13ad81e83b201f140b60
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_config/config.py
@@ -0,0 +1,948 @@
+"""
+The config module holds package-wide configurables and provides
+a uniform API for working with them.
+
+Overview
+========
+
+This module supports the following requirements:
+- options are referenced using keys in dot.notation, e.g. "x.y.option - z".
+- keys are case-insensitive.
+- functions should accept partial/regex keys, when unambiguous.
+- options can be registered by modules at import time.
+- options can be registered at init-time (via core.config_init)
+- options have a default value, and (optionally) a description and
+  validation function associated with them.
+- options can be deprecated, in which case referencing them
+  should produce a warning.
+- deprecated options can optionally be rerouted to a replacement
+  so that accessing a deprecated option reroutes to a differently
+  named option.
+- options can be reset to their default value.
+- all option can be reset to their default value at once.
+- all options in a certain sub - namespace can be reset at once.
+- the user can set / get / reset or ask for the description of an option.
+- a developer can register and mark an option as deprecated.
+- you can register a callback to be invoked when the option value
+  is set or reset. Changing the stored value is considered misuse, but
+  is not verboten.
+
+Implementation
+==============
+
+- Data is stored using nested dictionaries, and should be accessed
+  through the provided API.
+
+- "Registered options" and "Deprecated options" have metadata associated
+  with them, which are stored in auxiliary dictionaries keyed on the
+  fully-qualified key, e.g. "x.y.z.option".
+
+- the config_init module is imported by the package's __init__.py file.
+  placing any register_option() calls there will ensure those options
+  are available as soon as pandas is loaded. If you use register_option
+  in a module, it will only be available after that module is imported,
+  which you should be aware of.
+
+- `config_prefix` is a context_manager (for use with the `with` keyword)
+  which can save developers some typing, see the docstring.
+
+"""
+
+from __future__ import annotations
+
+from contextlib import (
+    ContextDecorator,
+    contextmanager,
+)
+import re
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Generic,
+    NamedTuple,
+    cast,
+)
+import warnings
+
+from pandas._typing import (
+    F,
+    T,
+)
+from pandas.util._exceptions import find_stack_level
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Generator,
+        Iterable,
+    )
+
+
+class DeprecatedOption(NamedTuple):
+    key: str
+    msg: str | None
+    rkey: str | None
+    removal_ver: str | None
+
+
+class RegisteredOption(NamedTuple):
+    key: str
+    defval: object
+    doc: str
+    validator: Callable[[object], Any] | None
+    cb: Callable[[str], Any] | None
+
+
+# holds deprecated option metadata
+_deprecated_options: dict[str, DeprecatedOption] = {}
+
+# holds registered option metadata
+_registered_options: dict[str, RegisteredOption] = {}
+
+# holds the current values for registered options
+_global_config: dict[str, Any] = {}
+
+# keys which have a special meaning
+_reserved_keys: list[str] = ["all"]
+
+
+class OptionError(AttributeError, KeyError):
+    """
+    Exception raised for pandas.options.
+
+    Backwards compatible with KeyError checks.
+
+    Examples
+    --------
+    >>> pd.options.context
+    Traceback (most recent call last):
+    OptionError: No such option
+    """
+
+
+#
+# User API
+
+
+def _get_single_key(pat: str, silent: bool) -> str:
+    keys = _select_options(pat)
+    if len(keys) == 0:
+        if not silent:
+            _warn_if_deprecated(pat)
+        raise OptionError(f"No such keys(s): {repr(pat)}")
+    if len(keys) > 1:
+        raise OptionError("Pattern matched multiple keys")
+    key = keys[0]
+
+    if not silent:
+        _warn_if_deprecated(key)
+
+    key = _translate_key(key)
+
+    return key
+
+
+def _get_option(pat: str, silent: bool = False) -> Any:
+    key = _get_single_key(pat, silent)
+
+    # walk the nested dict
+    root, k = _get_root(key)
+    return root[k]
+
+
+def _set_option(*args, **kwargs) -> None:
+    # must at least 1 arg deal with constraints later
+    nargs = len(args)
+    if not nargs or nargs % 2 != 0:
+        raise ValueError("Must provide an even number of non-keyword arguments")
+
+    # default to false
+    silent = kwargs.pop("silent", False)
+
+    if kwargs:
+        kwarg = next(iter(kwargs.keys()))
+        raise TypeError(f'_set_option() got an unexpected keyword argument "{kwarg}"')
+
+    for k, v in zip(args[::2], args[1::2]):
+        key = _get_single_key(k, silent)
+
+        o = _get_registered_option(key)
+        if o and o.validator:
+            o.validator(v)
+
+        # walk the nested dict
+        root, k_root = _get_root(key)
+        root[k_root] = v
+
+        if o.cb:
+            if silent:
+                with warnings.catch_warnings(record=True):
+                    o.cb(key)
+            else:
+                o.cb(key)
+
+
+def _describe_option(pat: str = "", _print_desc: bool = True) -> str | None:
+    keys = _select_options(pat)
+    if len(keys) == 0:
+        raise OptionError("No such keys(s)")
+
+    s = "\n".join([_build_option_description(k) for k in keys])
+
+    if _print_desc:
+        print(s)
+        return None
+    return s
+
+
+def _reset_option(pat: str, silent: bool = False) -> None:
+    keys = _select_options(pat)
+
+    if len(keys) == 0:
+        raise OptionError("No such keys(s)")
+
+    if len(keys) > 1 and len(pat) < 4 and pat != "all":
+        raise ValueError(
+            "You must specify at least 4 characters when "
+            "resetting multiple keys, use the special keyword "
+            '"all" to reset all the options to their default value'
+        )
+
+    for k in keys:
+        _set_option(k, _registered_options[k].defval, silent=silent)
+
+
+def get_default_val(pat: str):
+    key = _get_single_key(pat, silent=True)
+    return _get_registered_option(key).defval
+
+
+class DictWrapper:
+    """provide attribute-style access to a nested dict"""
+
+    d: dict[str, Any]
+
+    def __init__(self, d: dict[str, Any], prefix: str = "") -> None:
+        object.__setattr__(self, "d", d)
+        object.__setattr__(self, "prefix", prefix)
+
+    def __setattr__(self, key: str, val: Any) -> None:
+        prefix = object.__getattribute__(self, "prefix")
+        if prefix:
+            prefix += "."
+        prefix += key
+        # you can't set new keys
+        # can you can't overwrite subtrees
+        if key in self.d and not isinstance(self.d[key], dict):
+            _set_option(prefix, val)
+        else:
+            raise OptionError("You can only set the value of existing options")
+
+    def __getattr__(self, key: str):
+        prefix = object.__getattribute__(self, "prefix")
+        if prefix:
+            prefix += "."
+        prefix += key
+        try:
+            v = object.__getattribute__(self, "d")[key]
+        except KeyError as err:
+            raise OptionError("No such option") from err
+        if isinstance(v, dict):
+            return DictWrapper(v, prefix)
+        else:
+            return _get_option(prefix)
+
+    def __dir__(self) -> list[str]:
+        return list(self.d.keys())
+
+
+# For user convenience,  we'd like to have the available options described
+# in the docstring. For dev convenience we'd like to generate the docstrings
+# dynamically instead of maintaining them by hand. To this, we use the
+# class below which wraps functions inside a callable, and converts
+# __doc__ into a property function. The doctsrings below are templates
+# using the py2.6+ advanced formatting syntax to plug in a concise list
+# of options, and option descriptions.
+
+
+class CallableDynamicDoc(Generic[T]):
+    def __init__(self, func: Callable[..., T], doc_tmpl: str) -> None:
+        self.__doc_tmpl__ = doc_tmpl
+        self.__func__ = func
+
+    def __call__(self, *args, **kwds) -> T:
+        return self.__func__(*args, **kwds)
+
+    # error: Signature of "__doc__" incompatible with supertype "object"
+    @property
+    def __doc__(self) -> str:  # type: ignore[override]
+        opts_desc = _describe_option("all", _print_desc=False)
+        opts_list = pp_options_list(list(_registered_options.keys()))
+        return self.__doc_tmpl__.format(opts_desc=opts_desc, opts_list=opts_list)
+
+
+_get_option_tmpl = """
+get_option(pat)
+
+Retrieves the value of the specified option.
+
+Available options:
+
+{opts_list}
+
+Parameters
+----------
+pat : str
+    Regexp which should match a single option.
+    Note: partial matches are supported for convenience, but unless you use the
+    full option name (e.g. x.y.z.option_name), your code may break in future
+    versions if new options with similar names are introduced.
+
+Returns
+-------
+result : the value of the option
+
+Raises
+------
+OptionError : if no such option exists
+
+Notes
+-----
+Please reference the :ref:`User Guide ` for more information.
+
+The available options with its descriptions:
+
+{opts_desc}
+
+Examples
+--------
+>>> pd.get_option('display.max_columns')  # doctest: +SKIP
+4
+"""
+
+_set_option_tmpl = """
+set_option(pat, value)
+
+Sets the value of the specified option.
+
+Available options:
+
+{opts_list}
+
+Parameters
+----------
+pat : str
+    Regexp which should match a single option.
+    Note: partial matches are supported for convenience, but unless you use the
+    full option name (e.g. x.y.z.option_name), your code may break in future
+    versions if new options with similar names are introduced.
+value : object
+    New value of option.
+
+Returns
+-------
+None
+
+Raises
+------
+OptionError if no such option exists
+
+Notes
+-----
+Please reference the :ref:`User Guide ` for more information.
+
+The available options with its descriptions:
+
+{opts_desc}
+
+Examples
+--------
+>>> pd.set_option('display.max_columns', 4)
+>>> df = pd.DataFrame([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]])
+>>> df
+   0  1  ...  3   4
+0  1  2  ...  4   5
+1  6  7  ...  9  10
+[2 rows x 5 columns]
+>>> pd.reset_option('display.max_columns')
+"""
+
+_describe_option_tmpl = """
+describe_option(pat, _print_desc=False)
+
+Prints the description for one or more registered options.
+
+Call with no arguments to get a listing for all registered options.
+
+Available options:
+
+{opts_list}
+
+Parameters
+----------
+pat : str
+    Regexp pattern. All matching keys will have their description displayed.
+_print_desc : bool, default True
+    If True (default) the description(s) will be printed to stdout.
+    Otherwise, the description(s) will be returned as a unicode string
+    (for testing).
+
+Returns
+-------
+None by default, the description(s) as a unicode string if _print_desc
+is False
+
+Notes
+-----
+Please reference the :ref:`User Guide ` for more information.
+
+The available options with its descriptions:
+
+{opts_desc}
+
+Examples
+--------
+>>> pd.describe_option('display.max_columns')  # doctest: +SKIP
+display.max_columns : int
+    If max_cols is exceeded, switch to truncate view...
+"""
+
+_reset_option_tmpl = """
+reset_option(pat)
+
+Reset one or more options to their default value.
+
+Pass "all" as argument to reset all options.
+
+Available options:
+
+{opts_list}
+
+Parameters
+----------
+pat : str/regex
+    If specified only options matching `prefix*` will be reset.
+    Note: partial matches are supported for convenience, but unless you
+    use the full option name (e.g. x.y.z.option_name), your code may break
+    in future versions if new options with similar names are introduced.
+
+Returns
+-------
+None
+
+Notes
+-----
+Please reference the :ref:`User Guide ` for more information.
+
+The available options with its descriptions:
+
+{opts_desc}
+
+Examples
+--------
+>>> pd.reset_option('display.max_columns')  # doctest: +SKIP
+"""
+
+# bind the functions with their docstrings into a Callable
+# and use that as the functions exposed in pd.api
+get_option = CallableDynamicDoc(_get_option, _get_option_tmpl)
+set_option = CallableDynamicDoc(_set_option, _set_option_tmpl)
+reset_option = CallableDynamicDoc(_reset_option, _reset_option_tmpl)
+describe_option = CallableDynamicDoc(_describe_option, _describe_option_tmpl)
+options = DictWrapper(_global_config)
+
+#
+# Functions for use by pandas developers, in addition to User - api
+
+
+class option_context(ContextDecorator):
+    """
+    Context manager to temporarily set options in the `with` statement context.
+
+    You need to invoke as ``option_context(pat, val, [(pat, val), ...])``.
+
+    Examples
+    --------
+    >>> from pandas import option_context
+    >>> with option_context('display.max_rows', 10, 'display.max_columns', 5):
+    ...     pass
+    """
+
+    def __init__(self, *args) -> None:
+        if len(args) % 2 != 0 or len(args) < 2:
+            raise ValueError(
+                "Need to invoke as option_context(pat, val, [(pat, val), ...])."
+            )
+
+        self.ops = list(zip(args[::2], args[1::2]))
+
+    def __enter__(self) -> None:
+        self.undo = [(pat, _get_option(pat)) for pat, val in self.ops]
+
+        for pat, val in self.ops:
+            _set_option(pat, val, silent=True)
+
+    def __exit__(self, *args) -> None:
+        if self.undo:
+            for pat, val in self.undo:
+                _set_option(pat, val, silent=True)
+
+
+def register_option(
+    key: str,
+    defval: object,
+    doc: str = "",
+    validator: Callable[[object], Any] | None = None,
+    cb: Callable[[str], Any] | None = None,
+) -> None:
+    """
+    Register an option in the package-wide pandas config object
+
+    Parameters
+    ----------
+    key : str
+        Fully-qualified key, e.g. "x.y.option - z".
+    defval : object
+        Default value of the option.
+    doc : str
+        Description of the option.
+    validator : Callable, optional
+        Function of a single argument, should raise `ValueError` if
+        called with a value which is not a legal value for the option.
+    cb
+        a function of a single argument "key", which is called
+        immediately after an option value is set/reset. key is
+        the full name of the option.
+
+    Raises
+    ------
+    ValueError if `validator` is specified and `defval` is not a valid value.
+
+    """
+    import keyword
+    import tokenize
+
+    key = key.lower()
+
+    if key in _registered_options:
+        raise OptionError(f"Option '{key}' has already been registered")
+    if key in _reserved_keys:
+        raise OptionError(f"Option '{key}' is a reserved key")
+
+    # the default value should be legal
+    if validator:
+        validator(defval)
+
+    # walk the nested dict, creating dicts as needed along the path
+    path = key.split(".")
+
+    for k in path:
+        if not re.match("^" + tokenize.Name + "$", k):
+            raise ValueError(f"{k} is not a valid identifier")
+        if keyword.iskeyword(k):
+            raise ValueError(f"{k} is a python keyword")
+
+    cursor = _global_config
+    msg = "Path prefix to option '{option}' is already an option"
+
+    for i, p in enumerate(path[:-1]):
+        if not isinstance(cursor, dict):
+            raise OptionError(msg.format(option=".".join(path[:i])))
+        if p not in cursor:
+            cursor[p] = {}
+        cursor = cursor[p]
+
+    if not isinstance(cursor, dict):
+        raise OptionError(msg.format(option=".".join(path[:-1])))
+
+    cursor[path[-1]] = defval  # initialize
+
+    # save the option metadata
+    _registered_options[key] = RegisteredOption(
+        key=key, defval=defval, doc=doc, validator=validator, cb=cb
+    )
+
+
+def deprecate_option(
+    key: str,
+    msg: str | None = None,
+    rkey: str | None = None,
+    removal_ver: str | None = None,
+) -> None:
+    """
+    Mark option `key` as deprecated, if code attempts to access this option,
+    a warning will be produced, using `msg` if given, or a default message
+    if not.
+    if `rkey` is given, any access to the key will be re-routed to `rkey`.
+
+    Neither the existence of `key` nor that if `rkey` is checked. If they
+    do not exist, any subsequence access will fail as usual, after the
+    deprecation warning is given.
+
+    Parameters
+    ----------
+    key : str
+        Name of the option to be deprecated.
+        must be a fully-qualified option name (e.g "x.y.z.rkey").
+    msg : str, optional
+        Warning message to output when the key is referenced.
+        if no message is given a default message will be emitted.
+    rkey : str, optional
+        Name of an option to reroute access to.
+        If specified, any referenced `key` will be
+        re-routed to `rkey` including set/get/reset.
+        rkey must be a fully-qualified option name (e.g "x.y.z.rkey").
+        used by the default message if no `msg` is specified.
+    removal_ver : str, optional
+        Specifies the version in which this option will
+        be removed. used by the default message if no `msg` is specified.
+
+    Raises
+    ------
+    OptionError
+        If the specified key has already been deprecated.
+    """
+    key = key.lower()
+
+    if key in _deprecated_options:
+        raise OptionError(f"Option '{key}' has already been defined as deprecated.")
+
+    _deprecated_options[key] = DeprecatedOption(key, msg, rkey, removal_ver)
+
+
+#
+# functions internal to the module
+
+
+def _select_options(pat: str) -> list[str]:
+    """
+    returns a list of keys matching `pat`
+
+    if pat=="all", returns all registered options
+    """
+    # short-circuit for exact key
+    if pat in _registered_options:
+        return [pat]
+
+    # else look through all of them
+    keys = sorted(_registered_options.keys())
+    if pat == "all":  # reserved key
+        return keys
+
+    return [k for k in keys if re.search(pat, k, re.I)]
+
+
+def _get_root(key: str) -> tuple[dict[str, Any], str]:
+    path = key.split(".")
+    cursor = _global_config
+    for p in path[:-1]:
+        cursor = cursor[p]
+    return cursor, path[-1]
+
+
+def _is_deprecated(key: str) -> bool:
+    """Returns True if the given option has been deprecated"""
+    key = key.lower()
+    return key in _deprecated_options
+
+
+def _get_deprecated_option(key: str):
+    """
+    Retrieves the metadata for a deprecated option, if `key` is deprecated.
+
+    Returns
+    -------
+    DeprecatedOption (namedtuple) if key is deprecated, None otherwise
+    """
+    try:
+        d = _deprecated_options[key]
+    except KeyError:
+        return None
+    else:
+        return d
+
+
+def _get_registered_option(key: str):
+    """
+    Retrieves the option metadata if `key` is a registered option.
+
+    Returns
+    -------
+    RegisteredOption (namedtuple) if key is deprecated, None otherwise
+    """
+    return _registered_options.get(key)
+
+
+def _translate_key(key: str) -> str:
+    """
+    if key id deprecated and a replacement key defined, will return the
+    replacement key, otherwise returns `key` as - is
+    """
+    d = _get_deprecated_option(key)
+    if d:
+        return d.rkey or key
+    else:
+        return key
+
+
+def _warn_if_deprecated(key: str) -> bool:
+    """
+    Checks if `key` is a deprecated option and if so, prints a warning.
+
+    Returns
+    -------
+    bool - True if `key` is deprecated, False otherwise.
+    """
+    d = _get_deprecated_option(key)
+    if d:
+        if d.msg:
+            warnings.warn(
+                d.msg,
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+        else:
+            msg = f"'{key}' is deprecated"
+            if d.removal_ver:
+                msg += f" and will be removed in {d.removal_ver}"
+            if d.rkey:
+                msg += f", please use '{d.rkey}' instead."
+            else:
+                msg += ", please refrain from using it."
+
+            warnings.warn(msg, FutureWarning, stacklevel=find_stack_level())
+        return True
+    return False
+
+
+def _build_option_description(k: str) -> str:
+    """Builds a formatted description of a registered option and prints it"""
+    o = _get_registered_option(k)
+    d = _get_deprecated_option(k)
+
+    s = f"{k} "
+
+    if o.doc:
+        s += "\n".join(o.doc.strip().split("\n"))
+    else:
+        s += "No description available."
+
+    if o:
+        s += f"\n    [default: {o.defval}] [currently: {_get_option(k, True)}]"
+
+    if d:
+        rkey = d.rkey or ""
+        s += "\n    (Deprecated"
+        s += f", use `{rkey}` instead."
+        s += ")"
+
+    return s
+
+
+def pp_options_list(keys: Iterable[str], width: int = 80, _print: bool = False):
+    """Builds a concise listing of available options, grouped by prefix"""
+    from itertools import groupby
+    from textwrap import wrap
+
+    def pp(name: str, ks: Iterable[str]) -> list[str]:
+        pfx = "- " + name + ".[" if name else ""
+        ls = wrap(
+            ", ".join(ks),
+            width,
+            initial_indent=pfx,
+            subsequent_indent="  ",
+            break_long_words=False,
+        )
+        if ls and ls[-1] and name:
+            ls[-1] = ls[-1] + "]"
+        return ls
+
+    ls: list[str] = []
+    singles = [x for x in sorted(keys) if x.find(".") < 0]
+    if singles:
+        ls += pp("", singles)
+    keys = [x for x in keys if x.find(".") >= 0]
+
+    for k, g in groupby(sorted(keys), lambda x: x[: x.rfind(".")]):
+        ks = [x[len(k) + 1 :] for x in list(g)]
+        ls += pp(k, ks)
+    s = "\n".join(ls)
+    if _print:
+        print(s)
+    else:
+        return s
+
+
+#
+# helpers
+
+
+@contextmanager
+def config_prefix(prefix: str) -> Generator[None, None, None]:
+    """
+    contextmanager for multiple invocations of API with a common prefix
+
+    supported API functions: (register / get / set )__option
+
+    Warning: This is not thread - safe, and won't work properly if you import
+    the API functions into your module using the "from x import y" construct.
+
+    Example
+    -------
+    import pandas._config.config as cf
+    with cf.config_prefix("display.font"):
+        cf.register_option("color", "red")
+        cf.register_option("size", " 5 pt")
+        cf.set_option(size, " 6 pt")
+        cf.get_option(size)
+        ...
+
+        etc'
+
+    will register options "display.font.color", "display.font.size", set the
+    value of "display.font.size"... and so on.
+    """
+    # Note: reset_option relies on set_option, and on key directly
+    # it does not fit in to this monkey-patching scheme
+
+    global register_option, get_option, set_option
+
+    def wrap(func: F) -> F:
+        def inner(key: str, *args, **kwds):
+            pkey = f"{prefix}.{key}"
+            return func(pkey, *args, **kwds)
+
+        return cast(F, inner)
+
+    _register_option = register_option
+    _get_option = get_option
+    _set_option = set_option
+    set_option = wrap(set_option)
+    get_option = wrap(get_option)
+    register_option = wrap(register_option)
+    try:
+        yield
+    finally:
+        set_option = _set_option
+        get_option = _get_option
+        register_option = _register_option
+
+
+# These factories and methods are handy for use as the validator
+# arg in register_option
+
+
+def is_type_factory(_type: type[Any]) -> Callable[[Any], None]:
+    """
+
+    Parameters
+    ----------
+    `_type` - a type to be compared against (e.g. type(x) == `_type`)
+
+    Returns
+    -------
+    validator - a function of a single argument x , which raises
+                ValueError if type(x) is not equal to `_type`
+
+    """
+
+    def inner(x) -> None:
+        if type(x) != _type:
+            raise ValueError(f"Value must have type '{_type}'")
+
+    return inner
+
+
+def is_instance_factory(_type) -> Callable[[Any], None]:
+    """
+
+    Parameters
+    ----------
+    `_type` - the type to be checked against
+
+    Returns
+    -------
+    validator - a function of a single argument x , which raises
+                ValueError if x is not an instance of `_type`
+
+    """
+    if isinstance(_type, (tuple, list)):
+        _type = tuple(_type)
+        type_repr = "|".join(map(str, _type))
+    else:
+        type_repr = f"'{_type}'"
+
+    def inner(x) -> None:
+        if not isinstance(x, _type):
+            raise ValueError(f"Value must be an instance of {type_repr}")
+
+    return inner
+
+
+def is_one_of_factory(legal_values) -> Callable[[Any], None]:
+    callables = [c for c in legal_values if callable(c)]
+    legal_values = [c for c in legal_values if not callable(c)]
+
+    def inner(x) -> None:
+        if x not in legal_values:
+            if not any(c(x) for c in callables):
+                uvals = [str(lval) for lval in legal_values]
+                pp_values = "|".join(uvals)
+                msg = f"Value must be one of {pp_values}"
+                if len(callables):
+                    msg += " or a callable"
+                raise ValueError(msg)
+
+    return inner
+
+
+def is_nonnegative_int(value: object) -> None:
+    """
+    Verify that value is None or a positive int.
+
+    Parameters
+    ----------
+    value : None or int
+            The `value` to be checked.
+
+    Raises
+    ------
+    ValueError
+        When the value is not None or is a negative integer
+    """
+    if value is None:
+        return
+
+    elif isinstance(value, int):
+        if value >= 0:
+            return
+
+    msg = "Value must be a nonnegative integer or None"
+    raise ValueError(msg)
+
+
+# common type validators, for convenience
+# usage: register_option(... , validator = is_int)
+is_int = is_type_factory(int)
+is_bool = is_type_factory(bool)
+is_float = is_type_factory(float)
+is_str = is_type_factory(str)
+is_text = is_instance_factory((str, bytes))
+
+
+def is_callable(obj) -> bool:
+    """
+
+    Parameters
+    ----------
+    `obj` - the object to be checked
+
+    Returns
+    -------
+    validator - returns True if object is callable
+        raises ValueError otherwise.
+
+    """
+    if not callable(obj):
+        raise ValueError("Value must be a callable")
+    return True
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_config/dates.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_config/dates.py
new file mode 100644
index 0000000000000000000000000000000000000000..b37831f96eb73bf2f128929a1769db6c141eebad
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_config/dates.py
@@ -0,0 +1,25 @@
+"""
+config for datetime formatting
+"""
+from __future__ import annotations
+
+from pandas._config import config as cf
+
+pc_date_dayfirst_doc = """
+: boolean
+    When True, prints and parses dates with the day first, eg 20/01/2005
+"""
+
+pc_date_yearfirst_doc = """
+: boolean
+    When True, prints and parses dates with the year first, eg 2005/01/20
+"""
+
+with cf.config_prefix("display"):
+    # Needed upstream of `_libs` because these are used in tslibs.parsing
+    cf.register_option(
+        "date_dayfirst", False, pc_date_dayfirst_doc, validator=cf.is_bool
+    )
+    cf.register_option(
+        "date_yearfirst", False, pc_date_yearfirst_doc, validator=cf.is_bool
+    )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_config/display.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_config/display.py
new file mode 100644
index 0000000000000000000000000000000000000000..df2c3ad36c855d77c33d80c78c3d83ab3c09d5f9
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_config/display.py
@@ -0,0 +1,62 @@
+"""
+Unopinionated display configuration.
+"""
+
+from __future__ import annotations
+
+import locale
+import sys
+
+from pandas._config import config as cf
+
+# -----------------------------------------------------------------------------
+# Global formatting options
+_initial_defencoding: str | None = None
+
+
+def detect_console_encoding() -> str:
+    """
+    Try to find the most capable encoding supported by the console.
+    slightly modified from the way IPython handles the same issue.
+    """
+    global _initial_defencoding
+
+    encoding = None
+    try:
+        encoding = sys.stdout.encoding or sys.stdin.encoding
+    except (AttributeError, OSError):
+        pass
+
+    # try again for something better
+    if not encoding or "ascii" in encoding.lower():
+        try:
+            encoding = locale.getpreferredencoding()
+        except locale.Error:
+            # can be raised by locale.setlocale(), which is
+            #  called by getpreferredencoding
+            #  (on some systems, see stdlib locale docs)
+            pass
+
+    # when all else fails. this will usually be "ascii"
+    if not encoding or "ascii" in encoding.lower():
+        encoding = sys.getdefaultencoding()
+
+    # GH#3360, save the reported defencoding at import time
+    # MPL backends may change it. Make available for debugging.
+    if not _initial_defencoding:
+        _initial_defencoding = sys.getdefaultencoding()
+
+    return encoding
+
+
+pc_encoding_doc = """
+: str/unicode
+    Defaults to the detected encoding of the console.
+    Specifies the encoding to be used for strings returned by to_string,
+    these are generally strings meant to be displayed on the console.
+"""
+
+with cf.config_prefix("display"):
+    cf.register_option(
+        "encoding", detect_console_encoding(), pc_encoding_doc, validator=cf.is_text
+    )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_config/localization.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_config/localization.py
new file mode 100644
index 0000000000000000000000000000000000000000..5c1a0ff1395334a55baa6c5d77a71635872fe824
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_config/localization.py
@@ -0,0 +1,172 @@
+"""
+Helpers for configuring locale settings.
+
+Name `localization` is chosen to avoid overlap with builtin `locale` module.
+"""
+from __future__ import annotations
+
+from contextlib import contextmanager
+import locale
+import platform
+import re
+import subprocess
+from typing import TYPE_CHECKING
+
+from pandas._config.config import options
+
+if TYPE_CHECKING:
+    from collections.abc import Generator
+
+
+@contextmanager
+def set_locale(
+    new_locale: str | tuple[str, str], lc_var: int = locale.LC_ALL
+) -> Generator[str | tuple[str, str], None, None]:
+    """
+    Context manager for temporarily setting a locale.
+
+    Parameters
+    ----------
+    new_locale : str or tuple
+        A string of the form .. For example to set
+        the current locale to US English with a UTF8 encoding, you would pass
+        "en_US.UTF-8".
+    lc_var : int, default `locale.LC_ALL`
+        The category of the locale being set.
+
+    Notes
+    -----
+    This is useful when you want to run a particular block of code under a
+    particular locale, without globally setting the locale. This probably isn't
+    thread-safe.
+    """
+    # getlocale is not always compliant with setlocale, use setlocale. GH#46595
+    current_locale = locale.setlocale(lc_var)
+
+    try:
+        locale.setlocale(lc_var, new_locale)
+        normalized_code, normalized_encoding = locale.getlocale()
+        if normalized_code is not None and normalized_encoding is not None:
+            yield f"{normalized_code}.{normalized_encoding}"
+        else:
+            yield new_locale
+    finally:
+        locale.setlocale(lc_var, current_locale)
+
+
+def can_set_locale(lc: str, lc_var: int = locale.LC_ALL) -> bool:
+    """
+    Check to see if we can set a locale, and subsequently get the locale,
+    without raising an Exception.
+
+    Parameters
+    ----------
+    lc : str
+        The locale to attempt to set.
+    lc_var : int, default `locale.LC_ALL`
+        The category of the locale being set.
+
+    Returns
+    -------
+    bool
+        Whether the passed locale can be set
+    """
+    try:
+        with set_locale(lc, lc_var=lc_var):
+            pass
+    except (ValueError, locale.Error):
+        # horrible name for a Exception subclass
+        return False
+    else:
+        return True
+
+
+def _valid_locales(locales: list[str] | str, normalize: bool) -> list[str]:
+    """
+    Return a list of normalized locales that do not throw an ``Exception``
+    when set.
+
+    Parameters
+    ----------
+    locales : str
+        A string where each locale is separated by a newline.
+    normalize : bool
+        Whether to call ``locale.normalize`` on each locale.
+
+    Returns
+    -------
+    valid_locales : list
+        A list of valid locales.
+    """
+    return [
+        loc
+        for loc in (
+            locale.normalize(loc.strip()) if normalize else loc.strip()
+            for loc in locales
+        )
+        if can_set_locale(loc)
+    ]
+
+
+def get_locales(
+    prefix: str | None = None,
+    normalize: bool = True,
+) -> list[str]:
+    """
+    Get all the locales that are available on the system.
+
+    Parameters
+    ----------
+    prefix : str
+        If not ``None`` then return only those locales with the prefix
+        provided. For example to get all English language locales (those that
+        start with ``"en"``), pass ``prefix="en"``.
+    normalize : bool
+        Call ``locale.normalize`` on the resulting list of available locales.
+        If ``True``, only locales that can be set without throwing an
+        ``Exception`` are returned.
+
+    Returns
+    -------
+    locales : list of strings
+        A list of locale strings that can be set with ``locale.setlocale()``.
+        For example::
+
+            locale.setlocale(locale.LC_ALL, locale_string)
+
+    On error will return an empty list (no locale available, e.g. Windows)
+
+    """
+    if platform.system() in ("Linux", "Darwin"):
+        raw_locales = subprocess.check_output(["locale", "-a"])
+    else:
+        # Other platforms e.g. windows platforms don't define "locale -a"
+        #  Note: is_platform_windows causes circular import here
+        return []
+
+    try:
+        # raw_locales is "\n" separated list of locales
+        # it may contain non-decodable parts, so split
+        # extract what we can and then rejoin.
+        split_raw_locales = raw_locales.split(b"\n")
+        out_locales = []
+        for x in split_raw_locales:
+            try:
+                out_locales.append(str(x, encoding=options.display.encoding))
+            except UnicodeError:
+                # 'locale -a' is used to populated 'raw_locales' and on
+                # Redhat 7 Linux (and maybe others) prints locale names
+                # using windows-1252 encoding.  Bug only triggered by
+                # a few special characters and when there is an
+                # extensive list of installed locales.
+                out_locales.append(str(x, encoding="windows-1252"))
+
+    except TypeError:
+        pass
+
+    if prefix is None:
+        return _valid_locales(out_locales, normalize)
+
+    pattern = re.compile(f"{prefix}.*")
+    found = pattern.findall("\n".join(out_locales))
+    return _valid_locales(found, normalize)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..26a872a90e4934c929d168228125f2e43936c774
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/__init__.py
@@ -0,0 +1,27 @@
+__all__ = [
+    "NaT",
+    "NaTType",
+    "OutOfBoundsDatetime",
+    "Period",
+    "Timedelta",
+    "Timestamp",
+    "iNaT",
+    "Interval",
+]
+
+
+# Below imports needs to happen first to ensure pandas top level
+# module gets monkeypatched with the pandas_datetime_CAPI
+# see pandas_datetime_exec in pd_datetime.c
+import pandas._libs.pandas_parser  # isort: skip # type: ignore[reportUnusedImport]
+import pandas._libs.pandas_datetime  # noqa: F401 # isort: skip # type: ignore[reportUnusedImport]
+from pandas._libs.interval import Interval
+from pandas._libs.tslibs import (
+    NaT,
+    NaTType,
+    OutOfBoundsDatetime,
+    Period,
+    Timedelta,
+    Timestamp,
+    iNaT,
+)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/algos.pyi b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/algos.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..caf5425dfc7b44cbb2fea103e56d4584709eca1e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/algos.pyi
@@ -0,0 +1,416 @@
+from typing import Any
+
+import numpy as np
+
+from pandas._typing import npt
+
+class Infinity:
+    def __eq__(self, other) -> bool: ...
+    def __ne__(self, other) -> bool: ...
+    def __lt__(self, other) -> bool: ...
+    def __le__(self, other) -> bool: ...
+    def __gt__(self, other) -> bool: ...
+    def __ge__(self, other) -> bool: ...
+
+class NegInfinity:
+    def __eq__(self, other) -> bool: ...
+    def __ne__(self, other) -> bool: ...
+    def __lt__(self, other) -> bool: ...
+    def __le__(self, other) -> bool: ...
+    def __gt__(self, other) -> bool: ...
+    def __ge__(self, other) -> bool: ...
+
+def unique_deltas(
+    arr: np.ndarray,  # const int64_t[:]
+) -> np.ndarray: ...  # np.ndarray[np.int64, ndim=1]
+def is_lexsorted(list_of_arrays: list[npt.NDArray[np.int64]]) -> bool: ...
+def groupsort_indexer(
+    index: np.ndarray,  # const int64_t[:]
+    ngroups: int,
+) -> tuple[
+    np.ndarray,  # ndarray[int64_t, ndim=1]
+    np.ndarray,  # ndarray[int64_t, ndim=1]
+]: ...
+def kth_smallest(
+    arr: np.ndarray,  # numeric[:]
+    k: int,
+) -> Any: ...  # numeric
+
+# ----------------------------------------------------------------------
+# Pairwise correlation/covariance
+
+def nancorr(
+    mat: npt.NDArray[np.float64],  # const float64_t[:, :]
+    cov: bool = ...,
+    minp: int | None = ...,
+) -> npt.NDArray[np.float64]: ...  # ndarray[float64_t, ndim=2]
+def nancorr_spearman(
+    mat: npt.NDArray[np.float64],  # ndarray[float64_t, ndim=2]
+    minp: int = ...,
+) -> npt.NDArray[np.float64]: ...  # ndarray[float64_t, ndim=2]
+
+# ----------------------------------------------------------------------
+
+def validate_limit(nobs: int | None, limit=...) -> int: ...
+def get_fill_indexer(
+    mask: npt.NDArray[np.bool_],
+    limit: int | None = None,
+) -> npt.NDArray[np.intp]: ...
+def pad(
+    old: np.ndarray,  # ndarray[numeric_object_t]
+    new: np.ndarray,  # ndarray[numeric_object_t]
+    limit=...,
+) -> npt.NDArray[np.intp]: ...  # np.ndarray[np.intp, ndim=1]
+def pad_inplace(
+    values: np.ndarray,  # numeric_object_t[:]
+    mask: np.ndarray,  # uint8_t[:]
+    limit=...,
+) -> None: ...
+def pad_2d_inplace(
+    values: np.ndarray,  # numeric_object_t[:, :]
+    mask: np.ndarray,  # const uint8_t[:, :]
+    limit=...,
+) -> None: ...
+def backfill(
+    old: np.ndarray,  # ndarray[numeric_object_t]
+    new: np.ndarray,  # ndarray[numeric_object_t]
+    limit=...,
+) -> npt.NDArray[np.intp]: ...  # np.ndarray[np.intp, ndim=1]
+def backfill_inplace(
+    values: np.ndarray,  # numeric_object_t[:]
+    mask: np.ndarray,  # uint8_t[:]
+    limit=...,
+) -> None: ...
+def backfill_2d_inplace(
+    values: np.ndarray,  # numeric_object_t[:, :]
+    mask: np.ndarray,  # const uint8_t[:, :]
+    limit=...,
+) -> None: ...
+def is_monotonic(
+    arr: np.ndarray,  # ndarray[numeric_object_t, ndim=1]
+    timelike: bool,
+) -> tuple[bool, bool, bool]: ...
+
+# ----------------------------------------------------------------------
+# rank_1d, rank_2d
+# ----------------------------------------------------------------------
+
+def rank_1d(
+    values: np.ndarray,  # ndarray[numeric_object_t, ndim=1]
+    labels: np.ndarray | None = ...,  # const int64_t[:]=None
+    is_datetimelike: bool = ...,
+    ties_method=...,
+    ascending: bool = ...,
+    pct: bool = ...,
+    na_option=...,
+    mask: npt.NDArray[np.bool_] | None = ...,
+) -> np.ndarray: ...  # np.ndarray[float64_t, ndim=1]
+def rank_2d(
+    in_arr: np.ndarray,  # ndarray[numeric_object_t, ndim=2]
+    axis: int = ...,
+    is_datetimelike: bool = ...,
+    ties_method=...,
+    ascending: bool = ...,
+    na_option=...,
+    pct: bool = ...,
+) -> np.ndarray: ...  # np.ndarray[float64_t, ndim=1]
+def diff_2d(
+    arr: np.ndarray,  # ndarray[diff_t, ndim=2]
+    out: np.ndarray,  # ndarray[out_t, ndim=2]
+    periods: int,
+    axis: int,
+    datetimelike: bool = ...,
+) -> None: ...
+def ensure_platform_int(arr: object) -> npt.NDArray[np.intp]: ...
+def ensure_object(arr: object) -> npt.NDArray[np.object_]: ...
+def ensure_float64(arr: object) -> npt.NDArray[np.float64]: ...
+def ensure_int8(arr: object) -> npt.NDArray[np.int8]: ...
+def ensure_int16(arr: object) -> npt.NDArray[np.int16]: ...
+def ensure_int32(arr: object) -> npt.NDArray[np.int32]: ...
+def ensure_int64(arr: object) -> npt.NDArray[np.int64]: ...
+def ensure_uint64(arr: object) -> npt.NDArray[np.uint64]: ...
+def take_1d_int8_int8(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_1d_int8_int32(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_1d_int8_int64(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_1d_int8_float64(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_1d_int16_int16(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_1d_int16_int32(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_1d_int16_int64(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_1d_int16_float64(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_1d_int32_int32(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_1d_int32_int64(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_1d_int32_float64(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_1d_int64_int64(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_1d_int64_float64(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_1d_float32_float32(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_1d_float32_float64(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_1d_float64_float64(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_1d_object_object(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_1d_bool_bool(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_1d_bool_object(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis0_int8_int8(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis0_int8_int32(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis0_int8_int64(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis0_int8_float64(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis0_int16_int16(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis0_int16_int32(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis0_int16_int64(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis0_int16_float64(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis0_int32_int32(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis0_int32_int64(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis0_int32_float64(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis0_int64_int64(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis0_int64_float64(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis0_float32_float32(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis0_float32_float64(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis0_float64_float64(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis0_object_object(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis0_bool_bool(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis0_bool_object(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis1_int8_int8(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis1_int8_int32(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis1_int8_int64(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis1_int8_float64(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis1_int16_int16(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis1_int16_int32(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis1_int16_int64(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis1_int16_float64(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis1_int32_int32(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis1_int32_int64(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis1_int32_float64(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis1_int64_int64(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis1_int64_float64(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis1_float32_float32(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis1_float32_float64(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis1_float64_float64(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis1_object_object(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis1_bool_bool(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis1_bool_object(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_multi_int8_int8(
+    values: np.ndarray,
+    indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]],
+    out: np.ndarray,
+    fill_value=...,
+) -> None: ...
+def take_2d_multi_int8_int32(
+    values: np.ndarray,
+    indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]],
+    out: np.ndarray,
+    fill_value=...,
+) -> None: ...
+def take_2d_multi_int8_int64(
+    values: np.ndarray,
+    indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]],
+    out: np.ndarray,
+    fill_value=...,
+) -> None: ...
+def take_2d_multi_int8_float64(
+    values: np.ndarray,
+    indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]],
+    out: np.ndarray,
+    fill_value=...,
+) -> None: ...
+def take_2d_multi_int16_int16(
+    values: np.ndarray,
+    indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]],
+    out: np.ndarray,
+    fill_value=...,
+) -> None: ...
+def take_2d_multi_int16_int32(
+    values: np.ndarray,
+    indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]],
+    out: np.ndarray,
+    fill_value=...,
+) -> None: ...
+def take_2d_multi_int16_int64(
+    values: np.ndarray,
+    indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]],
+    out: np.ndarray,
+    fill_value=...,
+) -> None: ...
+def take_2d_multi_int16_float64(
+    values: np.ndarray,
+    indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]],
+    out: np.ndarray,
+    fill_value=...,
+) -> None: ...
+def take_2d_multi_int32_int32(
+    values: np.ndarray,
+    indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]],
+    out: np.ndarray,
+    fill_value=...,
+) -> None: ...
+def take_2d_multi_int32_int64(
+    values: np.ndarray,
+    indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]],
+    out: np.ndarray,
+    fill_value=...,
+) -> None: ...
+def take_2d_multi_int32_float64(
+    values: np.ndarray,
+    indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]],
+    out: np.ndarray,
+    fill_value=...,
+) -> None: ...
+def take_2d_multi_int64_float64(
+    values: np.ndarray,
+    indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]],
+    out: np.ndarray,
+    fill_value=...,
+) -> None: ...
+def take_2d_multi_float32_float32(
+    values: np.ndarray,
+    indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]],
+    out: np.ndarray,
+    fill_value=...,
+) -> None: ...
+def take_2d_multi_float32_float64(
+    values: np.ndarray,
+    indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]],
+    out: np.ndarray,
+    fill_value=...,
+) -> None: ...
+def take_2d_multi_float64_float64(
+    values: np.ndarray,
+    indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]],
+    out: np.ndarray,
+    fill_value=...,
+) -> None: ...
+def take_2d_multi_object_object(
+    values: np.ndarray,
+    indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]],
+    out: np.ndarray,
+    fill_value=...,
+) -> None: ...
+def take_2d_multi_bool_bool(
+    values: np.ndarray,
+    indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]],
+    out: np.ndarray,
+    fill_value=...,
+) -> None: ...
+def take_2d_multi_bool_object(
+    values: np.ndarray,
+    indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]],
+    out: np.ndarray,
+    fill_value=...,
+) -> None: ...
+def take_2d_multi_int64_int64(
+    values: np.ndarray,
+    indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]],
+    out: np.ndarray,
+    fill_value=...,
+) -> None: ...
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/arrays.pyi b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/arrays.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..86f69c3cdfc75f3101a8e0afe59f9b102d90ad79
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/arrays.pyi
@@ -0,0 +1,40 @@
+from typing import Sequence
+
+import numpy as np
+
+from pandas._typing import (
+    AxisInt,
+    DtypeObj,
+    Self,
+    Shape,
+)
+
+class NDArrayBacked:
+    _dtype: DtypeObj
+    _ndarray: np.ndarray
+    def __init__(self, values: np.ndarray, dtype: DtypeObj) -> None: ...
+    @classmethod
+    def _simple_new(cls, values: np.ndarray, dtype: DtypeObj): ...
+    def _from_backing_data(self, values: np.ndarray): ...
+    def __setstate__(self, state): ...
+    def __len__(self) -> int: ...
+    @property
+    def shape(self) -> Shape: ...
+    @property
+    def ndim(self) -> int: ...
+    @property
+    def size(self) -> int: ...
+    @property
+    def nbytes(self) -> int: ...
+    def copy(self, order=...): ...
+    def delete(self, loc, axis=...): ...
+    def swapaxes(self, axis1, axis2): ...
+    def repeat(self, repeats: int | Sequence[int], axis: int | None = ...): ...
+    def reshape(self, *args, **kwargs): ...
+    def ravel(self, order=...): ...
+    @property
+    def T(self): ...
+    @classmethod
+    def _concat_same_type(
+        cls, to_concat: Sequence[Self], axis: AxisInt = ...
+    ) -> Self: ...
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/byteswap.cpython-310-x86_64-linux-gnu.so b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/byteswap.cpython-310-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..1e2d684397cfc5b34dd293d51bc2412ee74890e6
Binary files /dev/null and b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/byteswap.cpython-310-x86_64-linux-gnu.so differ
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/byteswap.pyi b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/byteswap.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..bb0dbfc6a50b1bb7cd509dc5b3dfeed55ad70b09
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/byteswap.pyi
@@ -0,0 +1,5 @@
+def read_float_with_byteswap(data: bytes, offset: int, byteswap: bool) -> float: ...
+def read_double_with_byteswap(data: bytes, offset: int, byteswap: bool) -> float: ...
+def read_uint16_with_byteswap(data: bytes, offset: int, byteswap: bool) -> int: ...
+def read_uint32_with_byteswap(data: bytes, offset: int, byteswap: bool) -> int: ...
+def read_uint64_with_byteswap(data: bytes, offset: int, byteswap: bool) -> int: ...
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/groupby.pyi b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/groupby.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..eebd9331ab393d832c8c0099084a95a6aed0c460
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/groupby.pyi
@@ -0,0 +1,217 @@
+from typing import Literal
+
+import numpy as np
+
+from pandas._typing import npt
+
+def group_median_float64(
+    out: np.ndarray,  # ndarray[float64_t, ndim=2]
+    counts: npt.NDArray[np.int64],
+    values: np.ndarray,  # ndarray[float64_t, ndim=2]
+    labels: npt.NDArray[np.int64],
+    min_count: int = ...,  # Py_ssize_t
+    mask: np.ndarray | None = ...,
+    result_mask: np.ndarray | None = ...,
+) -> None: ...
+def group_cumprod(
+    out: np.ndarray,  # float64_t[:, ::1]
+    values: np.ndarray,  # const float64_t[:, :]
+    labels: np.ndarray,  # const int64_t[:]
+    ngroups: int,
+    is_datetimelike: bool,
+    skipna: bool = ...,
+    mask: np.ndarray | None = ...,
+    result_mask: np.ndarray | None = ...,
+) -> None: ...
+def group_cumsum(
+    out: np.ndarray,  # int64float_t[:, ::1]
+    values: np.ndarray,  # ndarray[int64float_t, ndim=2]
+    labels: np.ndarray,  # const int64_t[:]
+    ngroups: int,
+    is_datetimelike: bool,
+    skipna: bool = ...,
+    mask: np.ndarray | None = ...,
+    result_mask: np.ndarray | None = ...,
+) -> None: ...
+def group_shift_indexer(
+    out: np.ndarray,  # int64_t[::1]
+    labels: np.ndarray,  # const int64_t[:]
+    ngroups: int,
+    periods: int,
+) -> None: ...
+def group_fillna_indexer(
+    out: np.ndarray,  # ndarray[intp_t]
+    labels: np.ndarray,  # ndarray[int64_t]
+    sorted_labels: npt.NDArray[np.intp],
+    mask: npt.NDArray[np.uint8],
+    limit: int,  # int64_t
+    dropna: bool,
+) -> None: ...
+def group_any_all(
+    out: np.ndarray,  # uint8_t[::1]
+    values: np.ndarray,  # const uint8_t[::1]
+    labels: np.ndarray,  # const int64_t[:]
+    mask: np.ndarray,  # const uint8_t[::1]
+    val_test: Literal["any", "all"],
+    skipna: bool,
+    result_mask: np.ndarray | None,
+) -> None: ...
+def group_sum(
+    out: np.ndarray,  # complexfloatingintuint_t[:, ::1]
+    counts: np.ndarray,  # int64_t[::1]
+    values: np.ndarray,  # ndarray[complexfloatingintuint_t, ndim=2]
+    labels: np.ndarray,  # const intp_t[:]
+    mask: np.ndarray | None,
+    result_mask: np.ndarray | None = ...,
+    min_count: int = ...,
+    is_datetimelike: bool = ...,
+    initial: object = ...,
+) -> None: ...
+def group_prod(
+    out: np.ndarray,  # int64float_t[:, ::1]
+    counts: np.ndarray,  # int64_t[::1]
+    values: np.ndarray,  # ndarray[int64float_t, ndim=2]
+    labels: np.ndarray,  # const intp_t[:]
+    mask: np.ndarray | None,
+    result_mask: np.ndarray | None = ...,
+    min_count: int = ...,
+) -> None: ...
+def group_var(
+    out: np.ndarray,  # floating[:, ::1]
+    counts: np.ndarray,  # int64_t[::1]
+    values: np.ndarray,  # ndarray[floating, ndim=2]
+    labels: np.ndarray,  # const intp_t[:]
+    min_count: int = ...,  # Py_ssize_t
+    ddof: int = ...,  # int64_t
+    mask: np.ndarray | None = ...,
+    result_mask: np.ndarray | None = ...,
+    is_datetimelike: bool = ...,
+    name: str = ...,
+) -> None: ...
+def group_skew(
+    out: np.ndarray,  # float64_t[:, ::1]
+    counts: np.ndarray,  # int64_t[::1]
+    values: np.ndarray,  # ndarray[float64_T, ndim=2]
+    labels: np.ndarray,  # const intp_t[::1]
+    mask: np.ndarray | None = ...,
+    result_mask: np.ndarray | None = ...,
+    skipna: bool = ...,
+) -> None: ...
+def group_mean(
+    out: np.ndarray,  # floating[:, ::1]
+    counts: np.ndarray,  # int64_t[::1]
+    values: np.ndarray,  # ndarray[floating, ndim=2]
+    labels: np.ndarray,  # const intp_t[:]
+    min_count: int = ...,  # Py_ssize_t
+    is_datetimelike: bool = ...,  # bint
+    mask: np.ndarray | None = ...,
+    result_mask: np.ndarray | None = ...,
+) -> None: ...
+def group_ohlc(
+    out: np.ndarray,  # floatingintuint_t[:, ::1]
+    counts: np.ndarray,  # int64_t[::1]
+    values: np.ndarray,  # ndarray[floatingintuint_t, ndim=2]
+    labels: np.ndarray,  # const intp_t[:]
+    min_count: int = ...,
+    mask: np.ndarray | None = ...,
+    result_mask: np.ndarray | None = ...,
+) -> None: ...
+def group_quantile(
+    out: npt.NDArray[np.float64],
+    values: np.ndarray,  # ndarray[numeric, ndim=1]
+    labels: npt.NDArray[np.intp],
+    mask: npt.NDArray[np.uint8],
+    qs: npt.NDArray[np.float64],  # const
+    starts: npt.NDArray[np.int64],
+    ends: npt.NDArray[np.int64],
+    interpolation: Literal["linear", "lower", "higher", "nearest", "midpoint"],
+    result_mask: np.ndarray | None,
+    is_datetimelike: bool,
+) -> None: ...
+def group_last(
+    out: np.ndarray,  # rank_t[:, ::1]
+    counts: np.ndarray,  # int64_t[::1]
+    values: np.ndarray,  # ndarray[rank_t, ndim=2]
+    labels: np.ndarray,  # const int64_t[:]
+    mask: npt.NDArray[np.bool_] | None,
+    result_mask: npt.NDArray[np.bool_] | None = ...,
+    min_count: int = ...,  # Py_ssize_t
+    is_datetimelike: bool = ...,
+    skipna: bool = ...,
+) -> None: ...
+def group_nth(
+    out: np.ndarray,  # rank_t[:, ::1]
+    counts: np.ndarray,  # int64_t[::1]
+    values: np.ndarray,  # ndarray[rank_t, ndim=2]
+    labels: np.ndarray,  # const int64_t[:]
+    mask: npt.NDArray[np.bool_] | None,
+    result_mask: npt.NDArray[np.bool_] | None = ...,
+    min_count: int = ...,  # int64_t
+    rank: int = ...,  # int64_t
+    is_datetimelike: bool = ...,
+    skipna: bool = ...,
+) -> None: ...
+def group_rank(
+    out: np.ndarray,  # float64_t[:, ::1]
+    values: np.ndarray,  # ndarray[rank_t, ndim=2]
+    labels: np.ndarray,  # const int64_t[:]
+    ngroups: int,
+    is_datetimelike: bool,
+    ties_method: Literal["average", "min", "max", "first", "dense"] = ...,
+    ascending: bool = ...,
+    pct: bool = ...,
+    na_option: Literal["keep", "top", "bottom"] = ...,
+    mask: npt.NDArray[np.bool_] | None = ...,
+) -> None: ...
+def group_max(
+    out: np.ndarray,  # groupby_t[:, ::1]
+    counts: np.ndarray,  # int64_t[::1]
+    values: np.ndarray,  # ndarray[groupby_t, ndim=2]
+    labels: np.ndarray,  # const int64_t[:]
+    min_count: int = ...,
+    is_datetimelike: bool = ...,
+    mask: np.ndarray | None = ...,
+    result_mask: np.ndarray | None = ...,
+) -> None: ...
+def group_min(
+    out: np.ndarray,  # groupby_t[:, ::1]
+    counts: np.ndarray,  # int64_t[::1]
+    values: np.ndarray,  # ndarray[groupby_t, ndim=2]
+    labels: np.ndarray,  # const int64_t[:]
+    min_count: int = ...,
+    is_datetimelike: bool = ...,
+    mask: np.ndarray | None = ...,
+    result_mask: np.ndarray | None = ...,
+) -> None: ...
+def group_idxmin_idxmax(
+    out: npt.NDArray[np.intp],
+    counts: npt.NDArray[np.int64],
+    values: np.ndarray,  # ndarray[groupby_t, ndim=2]
+    labels: npt.NDArray[np.intp],
+    min_count: int = ...,
+    is_datetimelike: bool = ...,
+    mask: np.ndarray | None = ...,
+    name: str = ...,
+    skipna: bool = ...,
+    result_mask: np.ndarray | None = ...,
+) -> None: ...
+def group_cummin(
+    out: np.ndarray,  # groupby_t[:, ::1]
+    values: np.ndarray,  # ndarray[groupby_t, ndim=2]
+    labels: np.ndarray,  # const int64_t[:]
+    ngroups: int,
+    is_datetimelike: bool,
+    mask: np.ndarray | None = ...,
+    result_mask: np.ndarray | None = ...,
+    skipna: bool = ...,
+) -> None: ...
+def group_cummax(
+    out: np.ndarray,  # groupby_t[:, ::1]
+    values: np.ndarray,  # ndarray[groupby_t, ndim=2]
+    labels: np.ndarray,  # const int64_t[:]
+    ngroups: int,
+    is_datetimelike: bool,
+    mask: np.ndarray | None = ...,
+    result_mask: np.ndarray | None = ...,
+    skipna: bool = ...,
+) -> None: ...
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/hashing.pyi b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/hashing.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..8361026e4a87d462e04c53f7f1f8aee8a7f6ffe0
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/hashing.pyi
@@ -0,0 +1,9 @@
+import numpy as np
+
+from pandas._typing import npt
+
+def hash_object_array(
+    arr: npt.NDArray[np.object_],
+    key: str,
+    encoding: str = ...,
+) -> npt.NDArray[np.uint64]: ...
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/hashtable.pyi b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/hashtable.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..555ec73acd9b2e9ccbb5ea145db77862e2c54dbf
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/hashtable.pyi
@@ -0,0 +1,252 @@
+from typing import (
+    Any,
+    Hashable,
+    Literal,
+)
+
+import numpy as np
+
+from pandas._typing import npt
+
+def unique_label_indices(
+    labels: np.ndarray,  # const int64_t[:]
+) -> np.ndarray: ...
+
+class Factorizer:
+    count: int
+    uniques: Any
+    def __init__(self, size_hint: int) -> None: ...
+    def get_count(self) -> int: ...
+    def factorize(
+        self,
+        values: np.ndarray,
+        na_sentinel=...,
+        na_value=...,
+        mask=...,
+    ) -> npt.NDArray[np.intp]: ...
+
+class ObjectFactorizer(Factorizer):
+    table: PyObjectHashTable
+    uniques: ObjectVector
+
+class Int64Factorizer(Factorizer):
+    table: Int64HashTable
+    uniques: Int64Vector
+
+class UInt64Factorizer(Factorizer):
+    table: UInt64HashTable
+    uniques: UInt64Vector
+
+class Int32Factorizer(Factorizer):
+    table: Int32HashTable
+    uniques: Int32Vector
+
+class UInt32Factorizer(Factorizer):
+    table: UInt32HashTable
+    uniques: UInt32Vector
+
+class Int16Factorizer(Factorizer):
+    table: Int16HashTable
+    uniques: Int16Vector
+
+class UInt16Factorizer(Factorizer):
+    table: UInt16HashTable
+    uniques: UInt16Vector
+
+class Int8Factorizer(Factorizer):
+    table: Int8HashTable
+    uniques: Int8Vector
+
+class UInt8Factorizer(Factorizer):
+    table: UInt8HashTable
+    uniques: UInt8Vector
+
+class Float64Factorizer(Factorizer):
+    table: Float64HashTable
+    uniques: Float64Vector
+
+class Float32Factorizer(Factorizer):
+    table: Float32HashTable
+    uniques: Float32Vector
+
+class Complex64Factorizer(Factorizer):
+    table: Complex64HashTable
+    uniques: Complex64Vector
+
+class Complex128Factorizer(Factorizer):
+    table: Complex128HashTable
+    uniques: Complex128Vector
+
+class Int64Vector:
+    def __init__(self, *args) -> None: ...
+    def __len__(self) -> int: ...
+    def to_array(self) -> npt.NDArray[np.int64]: ...
+
+class Int32Vector:
+    def __init__(self, *args) -> None: ...
+    def __len__(self) -> int: ...
+    def to_array(self) -> npt.NDArray[np.int32]: ...
+
+class Int16Vector:
+    def __init__(self, *args) -> None: ...
+    def __len__(self) -> int: ...
+    def to_array(self) -> npt.NDArray[np.int16]: ...
+
+class Int8Vector:
+    def __init__(self, *args) -> None: ...
+    def __len__(self) -> int: ...
+    def to_array(self) -> npt.NDArray[np.int8]: ...
+
+class UInt64Vector:
+    def __init__(self, *args) -> None: ...
+    def __len__(self) -> int: ...
+    def to_array(self) -> npt.NDArray[np.uint64]: ...
+
+class UInt32Vector:
+    def __init__(self, *args) -> None: ...
+    def __len__(self) -> int: ...
+    def to_array(self) -> npt.NDArray[np.uint32]: ...
+
+class UInt16Vector:
+    def __init__(self, *args) -> None: ...
+    def __len__(self) -> int: ...
+    def to_array(self) -> npt.NDArray[np.uint16]: ...
+
+class UInt8Vector:
+    def __init__(self, *args) -> None: ...
+    def __len__(self) -> int: ...
+    def to_array(self) -> npt.NDArray[np.uint8]: ...
+
+class Float64Vector:
+    def __init__(self, *args) -> None: ...
+    def __len__(self) -> int: ...
+    def to_array(self) -> npt.NDArray[np.float64]: ...
+
+class Float32Vector:
+    def __init__(self, *args) -> None: ...
+    def __len__(self) -> int: ...
+    def to_array(self) -> npt.NDArray[np.float32]: ...
+
+class Complex128Vector:
+    def __init__(self, *args) -> None: ...
+    def __len__(self) -> int: ...
+    def to_array(self) -> npt.NDArray[np.complex128]: ...
+
+class Complex64Vector:
+    def __init__(self, *args) -> None: ...
+    def __len__(self) -> int: ...
+    def to_array(self) -> npt.NDArray[np.complex64]: ...
+
+class StringVector:
+    def __init__(self, *args) -> None: ...
+    def __len__(self) -> int: ...
+    def to_array(self) -> npt.NDArray[np.object_]: ...
+
+class ObjectVector:
+    def __init__(self, *args) -> None: ...
+    def __len__(self) -> int: ...
+    def to_array(self) -> npt.NDArray[np.object_]: ...
+
+class HashTable:
+    # NB: The base HashTable class does _not_ actually have these methods;
+    #  we are putting them here for the sake of mypy to avoid
+    #  reproducing them in each subclass below.
+    def __init__(self, size_hint: int = ..., uses_mask: bool = ...) -> None: ...
+    def __len__(self) -> int: ...
+    def __contains__(self, key: Hashable) -> bool: ...
+    def sizeof(self, deep: bool = ...) -> int: ...
+    def get_state(self) -> dict[str, int]: ...
+    # TODO: `val/key` type is subclass-specific
+    def get_item(self, val): ...  # TODO: return type?
+    def set_item(self, key, val) -> None: ...
+    def get_na(self): ...  # TODO: return type?
+    def set_na(self, val) -> None: ...
+    def map_locations(
+        self,
+        values: np.ndarray,  # np.ndarray[subclass-specific]
+        mask: npt.NDArray[np.bool_] | None = ...,
+    ) -> None: ...
+    def lookup(
+        self,
+        values: np.ndarray,  # np.ndarray[subclass-specific]
+        mask: npt.NDArray[np.bool_] | None = ...,
+    ) -> npt.NDArray[np.intp]: ...
+    def get_labels(
+        self,
+        values: np.ndarray,  # np.ndarray[subclass-specific]
+        uniques,  # SubclassTypeVector
+        count_prior: int = ...,
+        na_sentinel: int = ...,
+        na_value: object = ...,
+        mask=...,
+    ) -> npt.NDArray[np.intp]: ...
+    def unique(
+        self,
+        values: np.ndarray,  # np.ndarray[subclass-specific]
+        return_inverse: bool = ...,
+        mask=...,
+    ) -> (
+        tuple[
+            np.ndarray,  # np.ndarray[subclass-specific]
+            npt.NDArray[np.intp],
+        ]
+        | np.ndarray
+    ): ...  # np.ndarray[subclass-specific]
+    def factorize(
+        self,
+        values: np.ndarray,  # np.ndarray[subclass-specific]
+        na_sentinel: int = ...,
+        na_value: object = ...,
+        mask=...,
+        ignore_na: bool = True,
+    ) -> tuple[np.ndarray, npt.NDArray[np.intp]]: ...  # np.ndarray[subclass-specific]
+
+class Complex128HashTable(HashTable): ...
+class Complex64HashTable(HashTable): ...
+class Float64HashTable(HashTable): ...
+class Float32HashTable(HashTable): ...
+
+class Int64HashTable(HashTable):
+    # Only Int64HashTable has get_labels_groupby, map_keys_to_values
+    def get_labels_groupby(
+        self,
+        values: npt.NDArray[np.int64],  # const int64_t[:]
+    ) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.int64]]: ...
+    def map_keys_to_values(
+        self,
+        keys: npt.NDArray[np.int64],
+        values: npt.NDArray[np.int64],  # const int64_t[:]
+    ) -> None: ...
+
+class Int32HashTable(HashTable): ...
+class Int16HashTable(HashTable): ...
+class Int8HashTable(HashTable): ...
+class UInt64HashTable(HashTable): ...
+class UInt32HashTable(HashTable): ...
+class UInt16HashTable(HashTable): ...
+class UInt8HashTable(HashTable): ...
+class StringHashTable(HashTable): ...
+class PyObjectHashTable(HashTable): ...
+class IntpHashTable(HashTable): ...
+
+def duplicated(
+    values: np.ndarray,
+    keep: Literal["last", "first", False] = ...,
+    mask: npt.NDArray[np.bool_] | None = ...,
+) -> npt.NDArray[np.bool_]: ...
+def mode(
+    values: np.ndarray, dropna: bool, mask: npt.NDArray[np.bool_] | None = ...
+) -> np.ndarray: ...
+def value_count(
+    values: np.ndarray,
+    dropna: bool,
+    mask: npt.NDArray[np.bool_] | None = ...,
+) -> tuple[np.ndarray, npt.NDArray[np.int64], int]: ...  # np.ndarray[same-as-values]
+
+# arr and values should have same dtype
+def ismember(
+    arr: np.ndarray,
+    values: np.ndarray,
+) -> npt.NDArray[np.bool_]: ...
+def object_hash(obj) -> int: ...
+def objects_are_equal(a, b) -> bool: ...
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/index.pyi b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/index.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..9c3791a642768ccfb13f681a31ac993c606c2304
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/index.pyi
@@ -0,0 +1,103 @@
+import numpy as np
+
+from pandas._typing import npt
+
+from pandas import MultiIndex
+from pandas.core.arrays import ExtensionArray
+
+multiindex_nulls_shift: int
+
+class IndexEngine:
+    over_size_threshold: bool
+    def __init__(self, values: np.ndarray) -> None: ...
+    def __contains__(self, val: object) -> bool: ...
+
+    # -> int | slice | np.ndarray[bool]
+    def get_loc(self, val: object) -> int | slice | np.ndarray: ...
+    def sizeof(self, deep: bool = ...) -> int: ...
+    def __sizeof__(self) -> int: ...
+    @property
+    def is_unique(self) -> bool: ...
+    @property
+    def is_monotonic_increasing(self) -> bool: ...
+    @property
+    def is_monotonic_decreasing(self) -> bool: ...
+    @property
+    def is_mapping_populated(self) -> bool: ...
+    def clear_mapping(self): ...
+    def get_indexer(self, values: np.ndarray) -> npt.NDArray[np.intp]: ...
+    def get_indexer_non_unique(
+        self,
+        targets: np.ndarray,
+    ) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]: ...
+
+class MaskedIndexEngine(IndexEngine):
+    def __init__(self, values: object) -> None: ...
+    def get_indexer_non_unique(
+        self, targets: object
+    ) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]: ...
+
+class Float64Engine(IndexEngine): ...
+class Float32Engine(IndexEngine): ...
+class Complex128Engine(IndexEngine): ...
+class Complex64Engine(IndexEngine): ...
+class Int64Engine(IndexEngine): ...
+class Int32Engine(IndexEngine): ...
+class Int16Engine(IndexEngine): ...
+class Int8Engine(IndexEngine): ...
+class UInt64Engine(IndexEngine): ...
+class UInt32Engine(IndexEngine): ...
+class UInt16Engine(IndexEngine): ...
+class UInt8Engine(IndexEngine): ...
+class ObjectEngine(IndexEngine): ...
+class DatetimeEngine(Int64Engine): ...
+class TimedeltaEngine(DatetimeEngine): ...
+class PeriodEngine(Int64Engine): ...
+class BoolEngine(UInt8Engine): ...
+class MaskedFloat64Engine(MaskedIndexEngine): ...
+class MaskedFloat32Engine(MaskedIndexEngine): ...
+class MaskedComplex128Engine(MaskedIndexEngine): ...
+class MaskedComplex64Engine(MaskedIndexEngine): ...
+class MaskedInt64Engine(MaskedIndexEngine): ...
+class MaskedInt32Engine(MaskedIndexEngine): ...
+class MaskedInt16Engine(MaskedIndexEngine): ...
+class MaskedInt8Engine(MaskedIndexEngine): ...
+class MaskedUInt64Engine(MaskedIndexEngine): ...
+class MaskedUInt32Engine(MaskedIndexEngine): ...
+class MaskedUInt16Engine(MaskedIndexEngine): ...
+class MaskedUInt8Engine(MaskedIndexEngine): ...
+class MaskedBoolEngine(MaskedUInt8Engine): ...
+
+class StringObjectEngine(ObjectEngine):
+    def __init__(self, values: object, na_value) -> None: ...
+
+class BaseMultiIndexCodesEngine:
+    levels: list[np.ndarray]
+    offsets: np.ndarray  # ndarray[uint64_t, ndim=1]
+
+    def __init__(
+        self,
+        levels: list[np.ndarray],  # all entries hashable
+        labels: list[np.ndarray],  # all entries integer-dtyped
+        offsets: np.ndarray,  # np.ndarray[np.uint64, ndim=1]
+    ) -> None: ...
+    def get_indexer(self, target: npt.NDArray[np.object_]) -> npt.NDArray[np.intp]: ...
+    def _extract_level_codes(self, target: MultiIndex) -> np.ndarray: ...
+
+class ExtensionEngine:
+    def __init__(self, values: ExtensionArray) -> None: ...
+    def __contains__(self, val: object) -> bool: ...
+    def get_loc(self, val: object) -> int | slice | np.ndarray: ...
+    def get_indexer(self, values: np.ndarray) -> npt.NDArray[np.intp]: ...
+    def get_indexer_non_unique(
+        self,
+        targets: np.ndarray,
+    ) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]: ...
+    @property
+    def is_unique(self) -> bool: ...
+    @property
+    def is_monotonic_increasing(self) -> bool: ...
+    @property
+    def is_monotonic_decreasing(self) -> bool: ...
+    def sizeof(self, deep: bool = ...) -> int: ...
+    def clear_mapping(self): ...
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/indexing.cpython-310-x86_64-linux-gnu.so b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/indexing.cpython-310-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..341562b045543017dc4668fc61988e7ab60bb00a
Binary files /dev/null and b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/indexing.cpython-310-x86_64-linux-gnu.so differ
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/indexing.pyi b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/indexing.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..3ae5c5044a2f75452fa57ba578af2c7b4c78ec96
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/indexing.pyi
@@ -0,0 +1,17 @@
+from typing import (
+    Generic,
+    TypeVar,
+)
+
+from pandas.core.indexing import IndexingMixin
+
+_IndexingMixinT = TypeVar("_IndexingMixinT", bound=IndexingMixin)
+
+class NDFrameIndexerBase(Generic[_IndexingMixinT]):
+    name: str
+    # in practice obj is either a DataFrame or a Series
+    obj: _IndexingMixinT
+
+    def __init__(self, name: str, obj: _IndexingMixinT) -> None: ...
+    @property
+    def ndim(self) -> int: ...
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/internals.pyi b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/internals.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..ffe6c7730bcdcfb8af1407bf55b59170c4093699
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/internals.pyi
@@ -0,0 +1,94 @@
+from typing import (
+    Iterator,
+    Sequence,
+    final,
+    overload,
+)
+import weakref
+
+import numpy as np
+
+from pandas._typing import (
+    ArrayLike,
+    Self,
+    npt,
+)
+
+from pandas import Index
+from pandas.core.internals.blocks import Block as B
+
+def slice_len(slc: slice, objlen: int = ...) -> int: ...
+def get_concat_blkno_indexers(
+    blknos_list: list[npt.NDArray[np.intp]],
+) -> list[tuple[npt.NDArray[np.intp], BlockPlacement]]: ...
+def get_blkno_indexers(
+    blknos: np.ndarray,  # int64_t[:]
+    group: bool = ...,
+) -> list[tuple[int, slice | np.ndarray]]: ...
+def get_blkno_placements(
+    blknos: np.ndarray,
+    group: bool = ...,
+) -> Iterator[tuple[int, BlockPlacement]]: ...
+def update_blklocs_and_blknos(
+    blklocs: npt.NDArray[np.intp],
+    blknos: npt.NDArray[np.intp],
+    loc: int,
+    nblocks: int,
+) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]: ...
+@final
+class BlockPlacement:
+    def __init__(self, val: int | slice | np.ndarray) -> None: ...
+    @property
+    def indexer(self) -> np.ndarray | slice: ...
+    @property
+    def as_array(self) -> np.ndarray: ...
+    @property
+    def as_slice(self) -> slice: ...
+    @property
+    def is_slice_like(self) -> bool: ...
+    @overload
+    def __getitem__(
+        self, loc: slice | Sequence[int] | npt.NDArray[np.intp]
+    ) -> BlockPlacement: ...
+    @overload
+    def __getitem__(self, loc: int) -> int: ...
+    def __iter__(self) -> Iterator[int]: ...
+    def __len__(self) -> int: ...
+    def delete(self, loc) -> BlockPlacement: ...
+    def add(self, other) -> BlockPlacement: ...
+    def append(self, others: list[BlockPlacement]) -> BlockPlacement: ...
+    def tile_for_unstack(self, factor: int) -> npt.NDArray[np.intp]: ...
+
+class Block:
+    _mgr_locs: BlockPlacement
+    ndim: int
+    values: ArrayLike
+    refs: BlockValuesRefs
+    def __init__(
+        self,
+        values: ArrayLike,
+        placement: BlockPlacement,
+        ndim: int,
+        refs: BlockValuesRefs | None = ...,
+    ) -> None: ...
+    def slice_block_rows(self, slicer: slice) -> Self: ...
+
+class BlockManager:
+    blocks: tuple[B, ...]
+    axes: list[Index]
+    _known_consolidated: bool
+    _is_consolidated: bool
+    _blknos: np.ndarray
+    _blklocs: np.ndarray
+    def __init__(
+        self, blocks: tuple[B, ...], axes: list[Index], verify_integrity=...
+    ) -> None: ...
+    def get_slice(self, slobj: slice, axis: int = ...) -> Self: ...
+    def _rebuild_blknos_and_blklocs(self) -> None: ...
+
+class BlockValuesRefs:
+    referenced_blocks: list[weakref.ref]
+    def __init__(self, blk: Block | None = ...) -> None: ...
+    def add_reference(self, blk: Block) -> None: ...
+    def add_index_reference(self, index: Index) -> None: ...
+    def has_reference(self) -> bool: ...
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/interval.pyi b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/interval.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..587fdf84f2f85520713352bbcab29804c95621e5
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/interval.pyi
@@ -0,0 +1,174 @@
+from typing import (
+    Any,
+    Generic,
+    TypeVar,
+    overload,
+)
+
+import numpy as np
+import numpy.typing as npt
+
+from pandas._typing import (
+    IntervalClosedType,
+    Timedelta,
+    Timestamp,
+)
+
+VALID_CLOSED: frozenset[str]
+
+_OrderableScalarT = TypeVar("_OrderableScalarT", int, float)
+_OrderableTimesT = TypeVar("_OrderableTimesT", Timestamp, Timedelta)
+_OrderableT = TypeVar("_OrderableT", int, float, Timestamp, Timedelta)
+
+class _LengthDescriptor:
+    @overload
+    def __get__(
+        self, instance: Interval[_OrderableScalarT], owner: Any
+    ) -> _OrderableScalarT: ...
+    @overload
+    def __get__(
+        self, instance: Interval[_OrderableTimesT], owner: Any
+    ) -> Timedelta: ...
+
+class _MidDescriptor:
+    @overload
+    def __get__(self, instance: Interval[_OrderableScalarT], owner: Any) -> float: ...
+    @overload
+    def __get__(
+        self, instance: Interval[_OrderableTimesT], owner: Any
+    ) -> _OrderableTimesT: ...
+
+class IntervalMixin:
+    @property
+    def closed_left(self) -> bool: ...
+    @property
+    def closed_right(self) -> bool: ...
+    @property
+    def open_left(self) -> bool: ...
+    @property
+    def open_right(self) -> bool: ...
+    @property
+    def is_empty(self) -> bool: ...
+    def _check_closed_matches(self, other: IntervalMixin, name: str = ...) -> None: ...
+
+class Interval(IntervalMixin, Generic[_OrderableT]):
+    @property
+    def left(self: Interval[_OrderableT]) -> _OrderableT: ...
+    @property
+    def right(self: Interval[_OrderableT]) -> _OrderableT: ...
+    @property
+    def closed(self) -> IntervalClosedType: ...
+    mid: _MidDescriptor
+    length: _LengthDescriptor
+    def __init__(
+        self,
+        left: _OrderableT,
+        right: _OrderableT,
+        closed: IntervalClosedType = ...,
+    ) -> None: ...
+    def __hash__(self) -> int: ...
+    @overload
+    def __contains__(
+        self: Interval[Timedelta], key: Timedelta | Interval[Timedelta]
+    ) -> bool: ...
+    @overload
+    def __contains__(
+        self: Interval[Timestamp], key: Timestamp | Interval[Timestamp]
+    ) -> bool: ...
+    @overload
+    def __contains__(
+        self: Interval[_OrderableScalarT],
+        key: _OrderableScalarT | Interval[_OrderableScalarT],
+    ) -> bool: ...
+    @overload
+    def __add__(
+        self: Interval[_OrderableTimesT], y: Timedelta
+    ) -> Interval[_OrderableTimesT]: ...
+    @overload
+    def __add__(
+        self: Interval[int], y: _OrderableScalarT
+    ) -> Interval[_OrderableScalarT]: ...
+    @overload
+    def __add__(self: Interval[float], y: float) -> Interval[float]: ...
+    @overload
+    def __radd__(
+        self: Interval[_OrderableTimesT], y: Timedelta
+    ) -> Interval[_OrderableTimesT]: ...
+    @overload
+    def __radd__(
+        self: Interval[int], y: _OrderableScalarT
+    ) -> Interval[_OrderableScalarT]: ...
+    @overload
+    def __radd__(self: Interval[float], y: float) -> Interval[float]: ...
+    @overload
+    def __sub__(
+        self: Interval[_OrderableTimesT], y: Timedelta
+    ) -> Interval[_OrderableTimesT]: ...
+    @overload
+    def __sub__(
+        self: Interval[int], y: _OrderableScalarT
+    ) -> Interval[_OrderableScalarT]: ...
+    @overload
+    def __sub__(self: Interval[float], y: float) -> Interval[float]: ...
+    @overload
+    def __rsub__(
+        self: Interval[_OrderableTimesT], y: Timedelta
+    ) -> Interval[_OrderableTimesT]: ...
+    @overload
+    def __rsub__(
+        self: Interval[int], y: _OrderableScalarT
+    ) -> Interval[_OrderableScalarT]: ...
+    @overload
+    def __rsub__(self: Interval[float], y: float) -> Interval[float]: ...
+    @overload
+    def __mul__(
+        self: Interval[int], y: _OrderableScalarT
+    ) -> Interval[_OrderableScalarT]: ...
+    @overload
+    def __mul__(self: Interval[float], y: float) -> Interval[float]: ...
+    @overload
+    def __rmul__(
+        self: Interval[int], y: _OrderableScalarT
+    ) -> Interval[_OrderableScalarT]: ...
+    @overload
+    def __rmul__(self: Interval[float], y: float) -> Interval[float]: ...
+    @overload
+    def __truediv__(
+        self: Interval[int], y: _OrderableScalarT
+    ) -> Interval[_OrderableScalarT]: ...
+    @overload
+    def __truediv__(self: Interval[float], y: float) -> Interval[float]: ...
+    @overload
+    def __floordiv__(
+        self: Interval[int], y: _OrderableScalarT
+    ) -> Interval[_OrderableScalarT]: ...
+    @overload
+    def __floordiv__(self: Interval[float], y: float) -> Interval[float]: ...
+    def overlaps(self: Interval[_OrderableT], other: Interval[_OrderableT]) -> bool: ...
+
+def intervals_to_interval_bounds(
+    intervals: np.ndarray, validate_closed: bool = ...
+) -> tuple[np.ndarray, np.ndarray, IntervalClosedType]: ...
+
+class IntervalTree(IntervalMixin):
+    def __init__(
+        self,
+        left: np.ndarray,
+        right: np.ndarray,
+        closed: IntervalClosedType = ...,
+        leaf_size: int = ...,
+    ) -> None: ...
+    @property
+    def mid(self) -> np.ndarray: ...
+    @property
+    def length(self) -> np.ndarray: ...
+    def get_indexer(self, target) -> npt.NDArray[np.intp]: ...
+    def get_indexer_non_unique(
+        self, target
+    ) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]: ...
+    _na_count: int
+    @property
+    def is_overlapping(self) -> bool: ...
+    @property
+    def is_monotonic_increasing(self) -> bool: ...
+    def clear_mapping(self) -> None: ...
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/join.pyi b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/join.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..1d4e8c90bc5593eae650319e9ca1b58cbd7eed73
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/join.pyi
@@ -0,0 +1,79 @@
+import numpy as np
+
+from pandas._typing import npt
+
+def inner_join(
+    left: np.ndarray,  # const intp_t[:]
+    right: np.ndarray,  # const intp_t[:]
+    max_groups: int,
+    sort: bool = ...,
+) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]: ...
+def left_outer_join(
+    left: np.ndarray,  # const intp_t[:]
+    right: np.ndarray,  # const intp_t[:]
+    max_groups: int,
+    sort: bool = ...,
+) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]: ...
+def full_outer_join(
+    left: np.ndarray,  # const intp_t[:]
+    right: np.ndarray,  # const intp_t[:]
+    max_groups: int,
+) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]: ...
+def ffill_indexer(
+    indexer: np.ndarray,  # const intp_t[:]
+) -> npt.NDArray[np.intp]: ...
+def left_join_indexer_unique(
+    left: np.ndarray,  # ndarray[join_t]
+    right: np.ndarray,  # ndarray[join_t]
+) -> npt.NDArray[np.intp]: ...
+def left_join_indexer(
+    left: np.ndarray,  # ndarray[join_t]
+    right: np.ndarray,  # ndarray[join_t]
+) -> tuple[
+    np.ndarray,  # np.ndarray[join_t]
+    npt.NDArray[np.intp],
+    npt.NDArray[np.intp],
+]: ...
+def inner_join_indexer(
+    left: np.ndarray,  # ndarray[join_t]
+    right: np.ndarray,  # ndarray[join_t]
+) -> tuple[
+    np.ndarray,  # np.ndarray[join_t]
+    npt.NDArray[np.intp],
+    npt.NDArray[np.intp],
+]: ...
+def outer_join_indexer(
+    left: np.ndarray,  # ndarray[join_t]
+    right: np.ndarray,  # ndarray[join_t]
+) -> tuple[
+    np.ndarray,  # np.ndarray[join_t]
+    npt.NDArray[np.intp],
+    npt.NDArray[np.intp],
+]: ...
+def asof_join_backward_on_X_by_Y(
+    left_values: np.ndarray,  # ndarray[numeric_t]
+    right_values: np.ndarray,  # ndarray[numeric_t]
+    left_by_values: np.ndarray,  # const int64_t[:]
+    right_by_values: np.ndarray,  # const int64_t[:]
+    allow_exact_matches: bool = ...,
+    tolerance: np.number | float | None = ...,
+    use_hashtable: bool = ...,
+) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]: ...
+def asof_join_forward_on_X_by_Y(
+    left_values: np.ndarray,  # ndarray[numeric_t]
+    right_values: np.ndarray,  # ndarray[numeric_t]
+    left_by_values: np.ndarray,  # const int64_t[:]
+    right_by_values: np.ndarray,  # const int64_t[:]
+    allow_exact_matches: bool = ...,
+    tolerance: np.number | float | None = ...,
+    use_hashtable: bool = ...,
+) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]: ...
+def asof_join_nearest_on_X_by_Y(
+    left_values: np.ndarray,  # ndarray[numeric_t]
+    right_values: np.ndarray,  # ndarray[numeric_t]
+    left_by_values: np.ndarray,  # const int64_t[:]
+    right_by_values: np.ndarray,  # const int64_t[:]
+    allow_exact_matches: bool = ...,
+    tolerance: np.number | float | None = ...,
+    use_hashtable: bool = ...,
+) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]: ...
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/json.cpython-310-x86_64-linux-gnu.so b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/json.cpython-310-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..8e23e2241f455840d8b4d4015d7cbdddd410122d
Binary files /dev/null and b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/json.cpython-310-x86_64-linux-gnu.so differ
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/json.pyi b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/json.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..bc4fe68573b94290cfba2e66950c1b6d45ccf0dc
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/json.pyi
@@ -0,0 +1,23 @@
+from typing import (
+    Any,
+    Callable,
+)
+
+def ujson_dumps(
+    obj: Any,
+    ensure_ascii: bool = ...,
+    double_precision: int = ...,
+    indent: int = ...,
+    orient: str = ...,
+    date_unit: str = ...,
+    iso_dates: bool = ...,
+    default_handler: None
+    | Callable[[Any], str | float | bool | list | dict | None] = ...,
+) -> str: ...
+def ujson_loads(
+    s: str,
+    precise_float: bool = ...,
+    numpy: bool = ...,
+    dtype: None = ...,
+    labelled: bool = ...,
+) -> Any: ...
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/lib.pyi b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/lib.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..71a4d3ae2575ffd646568080485d9afada47d599
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/lib.pyi
@@ -0,0 +1,216 @@
+# TODO(npdtypes): Many types specified here can be made more specific/accurate;
+#  the more specific versions are specified in comments
+from decimal import Decimal
+from typing import (
+    Any,
+    Callable,
+    Final,
+    Generator,
+    Hashable,
+    Literal,
+    TypeAlias,
+    overload,
+)
+
+import numpy as np
+
+from pandas._libs.interval import Interval
+from pandas._libs.tslibs import Period
+from pandas._typing import (
+    ArrayLike,
+    DtypeObj,
+    TypeGuard,
+    npt,
+)
+
+# placeholder until we can specify np.ndarray[object, ndim=2]
+ndarray_obj_2d = np.ndarray
+
+from enum import Enum
+
+class _NoDefault(Enum):
+    no_default = ...
+
+no_default: Final = _NoDefault.no_default
+NoDefault: TypeAlias = Literal[_NoDefault.no_default]
+
+i8max: int
+u8max: int
+
+def is_np_dtype(dtype: object, kinds: str | None = ...) -> TypeGuard[np.dtype]: ...
+def item_from_zerodim(val: object) -> object: ...
+def infer_dtype(value: object, skipna: bool = ...) -> str: ...
+def is_iterator(obj: object) -> bool: ...
+def is_scalar(val: object) -> bool: ...
+def is_list_like(obj: object, allow_sets: bool = ...) -> bool: ...
+def is_pyarrow_array(obj: object) -> bool: ...
+def is_period(val: object) -> TypeGuard[Period]: ...
+def is_interval(obj: object) -> TypeGuard[Interval]: ...
+def is_decimal(obj: object) -> TypeGuard[Decimal]: ...
+def is_complex(obj: object) -> TypeGuard[complex]: ...
+def is_bool(obj: object) -> TypeGuard[bool | np.bool_]: ...
+def is_integer(obj: object) -> TypeGuard[int | np.integer]: ...
+def is_int_or_none(obj) -> bool: ...
+def is_float(obj: object) -> TypeGuard[float]: ...
+def is_interval_array(values: np.ndarray) -> bool: ...
+def is_datetime64_array(values: np.ndarray, skipna: bool = True) -> bool: ...
+def is_timedelta_or_timedelta64_array(
+    values: np.ndarray, skipna: bool = True
+) -> bool: ...
+def is_datetime_with_singletz_array(values: np.ndarray) -> bool: ...
+def is_time_array(values: np.ndarray, skipna: bool = ...): ...
+def is_date_array(values: np.ndarray, skipna: bool = ...): ...
+def is_datetime_array(values: np.ndarray, skipna: bool = ...): ...
+def is_string_array(values: np.ndarray, skipna: bool = ...): ...
+def is_float_array(values: np.ndarray): ...
+def is_integer_array(values: np.ndarray, skipna: bool = ...): ...
+def is_bool_array(values: np.ndarray, skipna: bool = ...): ...
+def fast_multiget(
+    mapping: dict,
+    keys: np.ndarray,  # object[:]
+    default=...,
+) -> np.ndarray: ...
+def fast_unique_multiple_list_gen(gen: Generator, sort: bool = ...) -> list: ...
+def fast_unique_multiple_list(lists: list, sort: bool | None = ...) -> list: ...
+def map_infer(
+    arr: np.ndarray,
+    f: Callable[[Any], Any],
+    convert: bool = ...,
+    ignore_na: bool = ...,
+) -> np.ndarray: ...
+@overload
+def maybe_convert_objects(
+    objects: npt.NDArray[np.object_],
+    *,
+    try_float: bool = ...,
+    safe: bool = ...,
+    convert_numeric: bool = ...,
+    convert_non_numeric: Literal[False] = ...,
+    convert_string: Literal[False] = ...,
+    convert_to_nullable_dtype: Literal[False] = ...,
+    dtype_if_all_nat: DtypeObj | None = ...,
+) -> npt.NDArray[np.object_ | np.number]: ...
+@overload
+def maybe_convert_objects(
+    objects: npt.NDArray[np.object_],
+    *,
+    try_float: bool = ...,
+    safe: bool = ...,
+    convert_numeric: bool = ...,
+    convert_non_numeric: bool = ...,
+    convert_string: bool = ...,
+    convert_to_nullable_dtype: Literal[True] = ...,
+    dtype_if_all_nat: DtypeObj | None = ...,
+) -> ArrayLike: ...
+@overload
+def maybe_convert_objects(
+    objects: npt.NDArray[np.object_],
+    *,
+    try_float: bool = ...,
+    safe: bool = ...,
+    convert_numeric: bool = ...,
+    convert_non_numeric: bool = ...,
+    convert_string: bool = ...,
+    convert_to_nullable_dtype: bool = ...,
+    dtype_if_all_nat: DtypeObj | None = ...,
+) -> ArrayLike: ...
+@overload
+def maybe_convert_numeric(
+    values: npt.NDArray[np.object_],
+    na_values: set,
+    convert_empty: bool = ...,
+    coerce_numeric: bool = ...,
+    convert_to_masked_nullable: Literal[False] = ...,
+) -> tuple[np.ndarray, None]: ...
+@overload
+def maybe_convert_numeric(
+    values: npt.NDArray[np.object_],
+    na_values: set,
+    convert_empty: bool = ...,
+    coerce_numeric: bool = ...,
+    *,
+    convert_to_masked_nullable: Literal[True],
+) -> tuple[np.ndarray, np.ndarray]: ...
+
+# TODO: restrict `arr`?
+def ensure_string_array(
+    arr,
+    na_value: object = ...,
+    convert_na_value: bool = ...,
+    copy: bool = ...,
+    skipna: bool = ...,
+) -> npt.NDArray[np.object_]: ...
+def convert_nans_to_NA(
+    arr: npt.NDArray[np.object_],
+) -> npt.NDArray[np.object_]: ...
+def fast_zip(ndarrays: list) -> npt.NDArray[np.object_]: ...
+
+# TODO: can we be more specific about rows?
+def to_object_array_tuples(rows: object) -> ndarray_obj_2d: ...
+def tuples_to_object_array(
+    tuples: npt.NDArray[np.object_],
+) -> ndarray_obj_2d: ...
+
+# TODO: can we be more specific about rows?
+def to_object_array(rows: object, min_width: int = ...) -> ndarray_obj_2d: ...
+def dicts_to_array(dicts: list, columns: list) -> ndarray_obj_2d: ...
+def maybe_booleans_to_slice(
+    mask: npt.NDArray[np.uint8],
+) -> slice | npt.NDArray[np.uint8]: ...
+def maybe_indices_to_slice(
+    indices: npt.NDArray[np.intp],
+    max_len: int,
+) -> slice | npt.NDArray[np.intp]: ...
+def is_all_arraylike(obj: list) -> bool: ...
+
+# -----------------------------------------------------------------
+# Functions which in reality take memoryviews
+
+def memory_usage_of_objects(arr: np.ndarray) -> int: ...  # object[:]  # np.int64
+def map_infer_mask(
+    arr: np.ndarray,
+    f: Callable[[Any], Any],
+    mask: np.ndarray,  # const uint8_t[:]
+    convert: bool = ...,
+    na_value: Any = ...,
+    dtype: np.dtype = ...,
+) -> np.ndarray: ...
+def indices_fast(
+    index: npt.NDArray[np.intp],
+    labels: np.ndarray,  # const int64_t[:]
+    keys: list,
+    sorted_labels: list[npt.NDArray[np.int64]],
+) -> dict[Hashable, npt.NDArray[np.intp]]: ...
+def generate_slices(
+    labels: np.ndarray, ngroups: int  # const intp_t[:]
+) -> tuple[npt.NDArray[np.int64], npt.NDArray[np.int64]]: ...
+def count_level_2d(
+    mask: np.ndarray,  # ndarray[uint8_t, ndim=2, cast=True],
+    labels: np.ndarray,  # const intp_t[:]
+    max_bin: int,
+) -> np.ndarray: ...  # np.ndarray[np.int64, ndim=2]
+def get_level_sorter(
+    codes: np.ndarray,  # const int64_t[:]
+    starts: np.ndarray,  # const intp_t[:]
+) -> np.ndarray: ...  # np.ndarray[np.intp, ndim=1]
+def generate_bins_dt64(
+    values: npt.NDArray[np.int64],
+    binner: np.ndarray,  # const int64_t[:]
+    closed: object = ...,
+    hasnans: bool = ...,
+) -> np.ndarray: ...  # np.ndarray[np.int64, ndim=1]
+def array_equivalent_object(
+    left: npt.NDArray[np.object_],
+    right: npt.NDArray[np.object_],
+) -> bool: ...
+def has_infs(arr: np.ndarray) -> bool: ...  # const floating[:]
+def has_only_ints_or_nan(arr: np.ndarray) -> bool: ...  # const floating[:]
+def get_reverse_indexer(
+    indexer: np.ndarray,  # const intp_t[:]
+    length: int,
+) -> npt.NDArray[np.intp]: ...
+def is_bool_list(obj: list) -> bool: ...
+def dtypes_all_equal(types: list[DtypeObj]) -> bool: ...
+def is_range_indexer(
+    left: np.ndarray, n: int  # np.ndarray[np.int64, ndim=1]
+) -> bool: ...
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/missing.pyi b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/missing.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..282dcee3ed6cfdd5cd83e5c78a69c422831b4cac
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/missing.pyi
@@ -0,0 +1,16 @@
+import numpy as np
+from numpy import typing as npt
+
+class NAType:
+    def __new__(cls, *args, **kwargs): ...
+
+NA: NAType
+
+def is_matching_na(
+    left: object, right: object, nan_matches_none: bool = ...
+) -> bool: ...
+def isposinf_scalar(val: object) -> bool: ...
+def isneginf_scalar(val: object) -> bool: ...
+def checknull(val: object, inf_as_na: bool = ...) -> bool: ...
+def isnaobj(arr: np.ndarray, inf_as_na: bool = ...) -> npt.NDArray[np.bool_]: ...
+def is_numeric_na(values: np.ndarray) -> npt.NDArray[np.bool_]: ...
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/ops.pyi b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/ops.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..6738a1dff4a9eb272f3e6c88a31b0bd7386aebd2
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/ops.pyi
@@ -0,0 +1,51 @@
+from typing import (
+    Any,
+    Callable,
+    Iterable,
+    Literal,
+    TypeAlias,
+    overload,
+)
+
+import numpy as np
+
+from pandas._typing import npt
+
+_BinOp: TypeAlias = Callable[[Any, Any], Any]
+_BoolOp: TypeAlias = Callable[[Any, Any], bool]
+
+def scalar_compare(
+    values: np.ndarray,  # object[:]
+    val: object,
+    op: _BoolOp,  # {operator.eq, operator.ne, ...}
+) -> npt.NDArray[np.bool_]: ...
+def vec_compare(
+    left: npt.NDArray[np.object_],
+    right: npt.NDArray[np.object_],
+    op: _BoolOp,  # {operator.eq, operator.ne, ...}
+) -> npt.NDArray[np.bool_]: ...
+def scalar_binop(
+    values: np.ndarray,  # object[:]
+    val: object,
+    op: _BinOp,  # binary operator
+) -> np.ndarray: ...
+def vec_binop(
+    left: np.ndarray,  # object[:]
+    right: np.ndarray,  # object[:]
+    op: _BinOp,  # binary operator
+) -> np.ndarray: ...
+@overload
+def maybe_convert_bool(
+    arr: npt.NDArray[np.object_],
+    true_values: Iterable | None = None,
+    false_values: Iterable | None = None,
+    convert_to_masked_nullable: Literal[False] = ...,
+) -> tuple[np.ndarray, None]: ...
+@overload
+def maybe_convert_bool(
+    arr: npt.NDArray[np.object_],
+    true_values: Iterable = ...,
+    false_values: Iterable = ...,
+    *,
+    convert_to_masked_nullable: Literal[True],
+) -> tuple[np.ndarray, np.ndarray]: ...
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/ops_dispatch.cpython-310-x86_64-linux-gnu.so b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/ops_dispatch.cpython-310-x86_64-linux-gnu.so
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index 0000000000000000000000000000000000000000..0c0fba5ce2335a0cb6e3b8ffc5667f6f395f7449
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diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/ops_dispatch.pyi b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/ops_dispatch.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..91b5a4dbaaebc177191d3189f12e4e20d56ca0fa
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/ops_dispatch.pyi
@@ -0,0 +1,5 @@
+import numpy as np
+
+def maybe_dispatch_ufunc_to_dunder_op(
+    self, ufunc: np.ufunc, method: str, *inputs, **kwargs
+): ...
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/pandas_datetime.cpython-310-x86_64-linux-gnu.so b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/pandas_datetime.cpython-310-x86_64-linux-gnu.so
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index 0000000000000000000000000000000000000000..f19af041c19ed3db47fbe71aa32dc06936407b76
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diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/pandas_parser.cpython-310-x86_64-linux-gnu.so b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/pandas_parser.cpython-310-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..981d010ab4c67d56b309bd168521351596213fdf
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diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/parsers.pyi b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/parsers.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..253bb7303cefb81f61692c8d7bd9812a191d9ac5
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/parsers.pyi
@@ -0,0 +1,77 @@
+from typing import (
+    Hashable,
+    Literal,
+)
+
+import numpy as np
+
+from pandas._typing import (
+    ArrayLike,
+    Dtype,
+    npt,
+)
+
+STR_NA_VALUES: set[str]
+DEFAULT_BUFFER_HEURISTIC: int
+
+def sanitize_objects(
+    values: npt.NDArray[np.object_],
+    na_values: set,
+) -> int: ...
+
+class TextReader:
+    unnamed_cols: set[str]
+    table_width: int  # int64_t
+    leading_cols: int  # int64_t
+    header: list[list[int]]  # non-negative integers
+    def __init__(
+        self,
+        source,
+        delimiter: bytes | str = ...,  # single-character only
+        header=...,
+        header_start: int = ...,  # int64_t
+        header_end: int = ...,  # uint64_t
+        index_col=...,
+        names=...,
+        tokenize_chunksize: int = ...,  # int64_t
+        delim_whitespace: bool = ...,
+        converters=...,
+        skipinitialspace: bool = ...,
+        escapechar: bytes | str | None = ...,  # single-character only
+        doublequote: bool = ...,
+        quotechar: str | bytes | None = ...,  # at most 1 character
+        quoting: int = ...,
+        lineterminator: bytes | str | None = ...,  # at most 1 character
+        comment=...,
+        decimal: bytes | str = ...,  # single-character only
+        thousands: bytes | str | None = ...,  # single-character only
+        dtype: Dtype | dict[Hashable, Dtype] = ...,
+        usecols=...,
+        error_bad_lines: bool = ...,
+        warn_bad_lines: bool = ...,
+        na_filter: bool = ...,
+        na_values=...,
+        na_fvalues=...,
+        keep_default_na: bool = ...,
+        true_values=...,
+        false_values=...,
+        allow_leading_cols: bool = ...,
+        skiprows=...,
+        skipfooter: int = ...,  # int64_t
+        verbose: bool = ...,
+        float_precision: Literal["round_trip", "legacy", "high"] | None = ...,
+        skip_blank_lines: bool = ...,
+        encoding_errors: bytes | str = ...,
+    ) -> None: ...
+    def set_noconvert(self, i: int) -> None: ...
+    def remove_noconvert(self, i: int) -> None: ...
+    def close(self) -> None: ...
+    def read(self, rows: int | None = ...) -> dict[int, ArrayLike]: ...
+    def read_low_memory(self, rows: int | None) -> list[dict[int, ArrayLike]]: ...
+
+# _maybe_upcast, na_values are only exposed for testing
+na_values: dict
+
+def _maybe_upcast(
+    arr, use_dtype_backend: bool = ..., dtype_backend: str = ...
+) -> np.ndarray: ...
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/properties.cpython-310-x86_64-linux-gnu.so b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/properties.cpython-310-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..904c8f08799738e066b048afbd1782e41cb9437e
Binary files /dev/null and b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/properties.cpython-310-x86_64-linux-gnu.so differ
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/properties.pyi b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/properties.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..aaa44a0cf47bf8635727ea9f354227de72bbff29
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/properties.pyi
@@ -0,0 +1,27 @@
+from typing import (
+    Sequence,
+    overload,
+)
+
+from pandas._typing import (
+    AnyArrayLike,
+    DataFrame,
+    Index,
+    Series,
+)
+
+# note: this is a lie to make type checkers happy (they special
+# case property). cache_readonly uses attribute names similar to
+# property (fget) but it does not provide fset and fdel.
+cache_readonly = property
+
+class AxisProperty:
+    axis: int
+    def __init__(self, axis: int = ..., doc: str = ...) -> None: ...
+    @overload
+    def __get__(self, obj: DataFrame | Series, type) -> Index: ...
+    @overload
+    def __get__(self, obj: None, type) -> AxisProperty: ...
+    def __set__(
+        self, obj: DataFrame | Series, value: AnyArrayLike | Sequence
+    ) -> None: ...
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/reshape.pyi b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/reshape.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..110687fcd0c313c45e8b025083fa5790fb9913b1
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/reshape.pyi
@@ -0,0 +1,16 @@
+import numpy as np
+
+from pandas._typing import npt
+
+def unstack(
+    values: np.ndarray,  # reshape_t[:, :]
+    mask: np.ndarray,  # const uint8_t[:]
+    stride: int,
+    length: int,
+    width: int,
+    new_values: np.ndarray,  # reshape_t[:, :]
+    new_mask: np.ndarray,  # uint8_t[:, :]
+) -> None: ...
+def explode(
+    values: npt.NDArray[np.object_],
+) -> tuple[npt.NDArray[np.object_], npt.NDArray[np.int64]]: ...
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/sas.pyi b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/sas.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..5d65e2b56b5916ed1e76e1409e4f75c652ee8fc9
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/sas.pyi
@@ -0,0 +1,7 @@
+from pandas.io.sas.sas7bdat import SAS7BDATReader
+
+class Parser:
+    def __init__(self, parser: SAS7BDATReader) -> None: ...
+    def read(self, nrows: int) -> None: ...
+
+def get_subheader_index(signature: bytes) -> int: ...
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/sparse.pyi b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/sparse.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..536265b25425e9e7ecabd37f095115442a7cb209
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/sparse.pyi
@@ -0,0 +1,51 @@
+from typing import Sequence
+
+import numpy as np
+
+from pandas._typing import (
+    Self,
+    npt,
+)
+
+class SparseIndex:
+    length: int
+    npoints: int
+    def __init__(self) -> None: ...
+    @property
+    def ngaps(self) -> int: ...
+    @property
+    def nbytes(self) -> int: ...
+    @property
+    def indices(self) -> npt.NDArray[np.int32]: ...
+    def equals(self, other) -> bool: ...
+    def lookup(self, index: int) -> np.int32: ...
+    def lookup_array(self, indexer: npt.NDArray[np.int32]) -> npt.NDArray[np.int32]: ...
+    def to_int_index(self) -> IntIndex: ...
+    def to_block_index(self) -> BlockIndex: ...
+    def intersect(self, y_: SparseIndex) -> Self: ...
+    def make_union(self, y_: SparseIndex) -> Self: ...
+
+class IntIndex(SparseIndex):
+    indices: npt.NDArray[np.int32]
+    def __init__(
+        self, length: int, indices: Sequence[int], check_integrity: bool = ...
+    ) -> None: ...
+
+class BlockIndex(SparseIndex):
+    nblocks: int
+    blocs: np.ndarray
+    blengths: np.ndarray
+    def __init__(
+        self, length: int, blocs: np.ndarray, blengths: np.ndarray
+    ) -> None: ...
+
+    # Override to have correct parameters
+    def intersect(self, other: SparseIndex) -> Self: ...
+    def make_union(self, y: SparseIndex) -> Self: ...
+
+def make_mask_object_ndarray(
+    arr: npt.NDArray[np.object_], fill_value
+) -> npt.NDArray[np.bool_]: ...
+def get_blocks(
+    indices: npt.NDArray[np.int32],
+) -> tuple[npt.NDArray[np.int32], npt.NDArray[np.int32]]: ...
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/testing.pyi b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/testing.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..01da496975f512b204defb06fcd19a36e235f97a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/testing.pyi
@@ -0,0 +1,12 @@
+def assert_dict_equal(a, b, compare_keys: bool = ...): ...
+def assert_almost_equal(
+    a,
+    b,
+    rtol: float = ...,
+    atol: float = ...,
+    check_dtype: bool = ...,
+    obj=...,
+    lobj=...,
+    robj=...,
+    index_values=...,
+): ...
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/tslib.pyi b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/tslib.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..5a340c1d88bc439663c2e482c9eb7270f8ebc5c6
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/tslib.pyi
@@ -0,0 +1,37 @@
+from datetime import tzinfo
+
+import numpy as np
+
+from pandas._typing import npt
+
+def format_array_from_datetime(
+    values: npt.NDArray[np.int64],
+    tz: tzinfo | None = ...,
+    format: str | None = ...,
+    na_rep: str | float = ...,
+    reso: int = ...,  # NPY_DATETIMEUNIT
+) -> npt.NDArray[np.object_]: ...
+def array_with_unit_to_datetime(
+    values: npt.NDArray[np.object_],
+    unit: str,
+    errors: str = ...,
+) -> tuple[np.ndarray, tzinfo | None]: ...
+def first_non_null(values: np.ndarray) -> int: ...
+def array_to_datetime(
+    values: npt.NDArray[np.object_],
+    errors: str = ...,
+    dayfirst: bool = ...,
+    yearfirst: bool = ...,
+    utc: bool = ...,
+    creso: int = ...,
+) -> tuple[np.ndarray, tzinfo | None]: ...
+
+# returned ndarray may be object dtype or datetime64[ns]
+
+def array_to_datetime_with_tz(
+    values: npt.NDArray[np.object_],
+    tz: tzinfo,
+    dayfirst: bool,
+    yearfirst: bool,
+    creso: int,
+) -> npt.NDArray[np.int64]: ...
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/tslibs/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/tslibs/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..88a9a259ac8ec87255b25b3e15375b1c92099999
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/tslibs/__init__.py
@@ -0,0 +1,87 @@
+__all__ = [
+    "dtypes",
+    "localize_pydatetime",
+    "NaT",
+    "NaTType",
+    "iNaT",
+    "nat_strings",
+    "OutOfBoundsDatetime",
+    "OutOfBoundsTimedelta",
+    "IncompatibleFrequency",
+    "Period",
+    "Resolution",
+    "Timedelta",
+    "normalize_i8_timestamps",
+    "is_date_array_normalized",
+    "dt64arr_to_periodarr",
+    "delta_to_nanoseconds",
+    "ints_to_pydatetime",
+    "ints_to_pytimedelta",
+    "get_resolution",
+    "Timestamp",
+    "tz_convert_from_utc_single",
+    "tz_convert_from_utc",
+    "to_offset",
+    "Tick",
+    "BaseOffset",
+    "tz_compare",
+    "is_unitless",
+    "astype_overflowsafe",
+    "get_unit_from_dtype",
+    "periods_per_day",
+    "periods_per_second",
+    "guess_datetime_format",
+    "add_overflowsafe",
+    "get_supported_dtype",
+    "is_supported_dtype",
+]
+
+from pandas._libs.tslibs import dtypes  # pylint: disable=import-self
+from pandas._libs.tslibs.conversion import localize_pydatetime
+from pandas._libs.tslibs.dtypes import (
+    Resolution,
+    periods_per_day,
+    periods_per_second,
+)
+from pandas._libs.tslibs.nattype import (
+    NaT,
+    NaTType,
+    iNaT,
+    nat_strings,
+)
+from pandas._libs.tslibs.np_datetime import (
+    OutOfBoundsDatetime,
+    OutOfBoundsTimedelta,
+    add_overflowsafe,
+    astype_overflowsafe,
+    get_supported_dtype,
+    is_supported_dtype,
+    is_unitless,
+    py_get_unit_from_dtype as get_unit_from_dtype,
+)
+from pandas._libs.tslibs.offsets import (
+    BaseOffset,
+    Tick,
+    to_offset,
+)
+from pandas._libs.tslibs.parsing import guess_datetime_format
+from pandas._libs.tslibs.period import (
+    IncompatibleFrequency,
+    Period,
+)
+from pandas._libs.tslibs.timedeltas import (
+    Timedelta,
+    delta_to_nanoseconds,
+    ints_to_pytimedelta,
+)
+from pandas._libs.tslibs.timestamps import Timestamp
+from pandas._libs.tslibs.timezones import tz_compare
+from pandas._libs.tslibs.tzconversion import tz_convert_from_utc_single
+from pandas._libs.tslibs.vectorized import (
+    dt64arr_to_periodarr,
+    get_resolution,
+    ints_to_pydatetime,
+    is_date_array_normalized,
+    normalize_i8_timestamps,
+    tz_convert_from_utc,
+)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/tslibs/base.cpython-310-x86_64-linux-gnu.so b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/tslibs/base.cpython-310-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..71df370b808efdc3206ff0e741393e8834703812
Binary files /dev/null and b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/tslibs/base.cpython-310-x86_64-linux-gnu.so differ
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/tslibs/ccalendar.cpython-310-x86_64-linux-gnu.so b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/tslibs/ccalendar.cpython-310-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..bb9d88adfc4cfa22b69954314c06c9bfd1452d1a
Binary files /dev/null and b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/tslibs/ccalendar.cpython-310-x86_64-linux-gnu.so differ
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/tslibs/ccalendar.pyi b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/tslibs/ccalendar.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..993f18a61d74aaa643e9790df70ede618b917223
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/tslibs/ccalendar.pyi
@@ -0,0 +1,12 @@
+DAYS: list[str]
+MONTH_ALIASES: dict[int, str]
+MONTH_NUMBERS: dict[str, int]
+MONTHS: list[str]
+int_to_weekday: dict[int, str]
+
+def get_firstbday(year: int, month: int) -> int: ...
+def get_lastbday(year: int, month: int) -> int: ...
+def get_day_of_year(year: int, month: int, day: int) -> int: ...
+def get_iso_calendar(year: int, month: int, day: int) -> tuple[int, int, int]: ...
+def get_week_of_year(year: int, month: int, day: int) -> int: ...
+def get_days_in_month(year: int, month: int) -> int: ...
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/tslibs/conversion.pyi b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/tslibs/conversion.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..26affae577f4d3f4ecda2ac9c1bc0cb748a35d4d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/tslibs/conversion.pyi
@@ -0,0 +1,14 @@
+from datetime import (
+    datetime,
+    tzinfo,
+)
+
+import numpy as np
+
+DT64NS_DTYPE: np.dtype
+TD64NS_DTYPE: np.dtype
+
+def localize_pydatetime(dt: datetime, tz: tzinfo | None) -> datetime: ...
+def cast_from_unit_vectorized(
+    values: np.ndarray, unit: str, out_unit: str = ...
+) -> np.ndarray: ...
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/tslibs/dtypes.pyi b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/tslibs/dtypes.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..7fdeb88d498ac88b6d023e25409e8f756b12c07a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/tslibs/dtypes.pyi
@@ -0,0 +1,83 @@
+from enum import Enum
+
+OFFSET_TO_PERIOD_FREQSTR: dict[str, str]
+
+def periods_per_day(reso: int = ...) -> int: ...
+def periods_per_second(reso: int) -> int: ...
+def abbrev_to_npy_unit(abbrev: str | None) -> int: ...
+def freq_to_period_freqstr(freq_n: int, freq_name: str) -> str: ...
+
+class PeriodDtypeBase:
+    _dtype_code: int  # PeriodDtypeCode
+    _n: int
+
+    # actually __cinit__
+    def __new__(cls, code: int, n: int): ...
+    @property
+    def _freq_group_code(self) -> int: ...
+    @property
+    def _resolution_obj(self) -> Resolution: ...
+    def _get_to_timestamp_base(self) -> int: ...
+    @property
+    def _freqstr(self) -> str: ...
+    def __hash__(self) -> int: ...
+    def _is_tick_like(self) -> bool: ...
+    @property
+    def _creso(self) -> int: ...
+    @property
+    def _td64_unit(self) -> str: ...
+
+class FreqGroup(Enum):
+    FR_ANN: int
+    FR_QTR: int
+    FR_MTH: int
+    FR_WK: int
+    FR_BUS: int
+    FR_DAY: int
+    FR_HR: int
+    FR_MIN: int
+    FR_SEC: int
+    FR_MS: int
+    FR_US: int
+    FR_NS: int
+    FR_UND: int
+    @staticmethod
+    def from_period_dtype_code(code: int) -> FreqGroup: ...
+
+class Resolution(Enum):
+    RESO_NS: int
+    RESO_US: int
+    RESO_MS: int
+    RESO_SEC: int
+    RESO_MIN: int
+    RESO_HR: int
+    RESO_DAY: int
+    RESO_MTH: int
+    RESO_QTR: int
+    RESO_YR: int
+    def __lt__(self, other: Resolution) -> bool: ...
+    def __ge__(self, other: Resolution) -> bool: ...
+    @property
+    def attrname(self) -> str: ...
+    @classmethod
+    def from_attrname(cls, attrname: str) -> Resolution: ...
+    @classmethod
+    def get_reso_from_freqstr(cls, freq: str) -> Resolution: ...
+    @property
+    def attr_abbrev(self) -> str: ...
+
+class NpyDatetimeUnit(Enum):
+    NPY_FR_Y: int
+    NPY_FR_M: int
+    NPY_FR_W: int
+    NPY_FR_D: int
+    NPY_FR_h: int
+    NPY_FR_m: int
+    NPY_FR_s: int
+    NPY_FR_ms: int
+    NPY_FR_us: int
+    NPY_FR_ns: int
+    NPY_FR_ps: int
+    NPY_FR_fs: int
+    NPY_FR_as: int
+    NPY_FR_GENERIC: int
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/tslibs/fields.pyi b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/tslibs/fields.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..c6cfd44e9f6ab76ee4bc3be7c765569991514d18
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/tslibs/fields.pyi
@@ -0,0 +1,62 @@
+import numpy as np
+
+from pandas._typing import npt
+
+def build_field_sarray(
+    dtindex: npt.NDArray[np.int64],  # const int64_t[:]
+    reso: int,  # NPY_DATETIMEUNIT
+) -> np.ndarray: ...
+def month_position_check(fields, weekdays) -> str | None: ...
+def get_date_name_field(
+    dtindex: npt.NDArray[np.int64],  # const int64_t[:]
+    field: str,
+    locale: str | None = ...,
+    reso: int = ...,  # NPY_DATETIMEUNIT
+) -> npt.NDArray[np.object_]: ...
+def get_start_end_field(
+    dtindex: npt.NDArray[np.int64],
+    field: str,
+    freqstr: str | None = ...,
+    month_kw: int = ...,
+    reso: int = ...,  # NPY_DATETIMEUNIT
+) -> npt.NDArray[np.bool_]: ...
+def get_date_field(
+    dtindex: npt.NDArray[np.int64],  # const int64_t[:]
+    field: str,
+    reso: int = ...,  # NPY_DATETIMEUNIT
+) -> npt.NDArray[np.int32]: ...
+def get_timedelta_field(
+    tdindex: npt.NDArray[np.int64],  # const int64_t[:]
+    field: str,
+    reso: int = ...,  # NPY_DATETIMEUNIT
+) -> npt.NDArray[np.int32]: ...
+def get_timedelta_days(
+    tdindex: npt.NDArray[np.int64],  # const int64_t[:]
+    reso: int = ...,  # NPY_DATETIMEUNIT
+) -> npt.NDArray[np.int64]: ...
+def isleapyear_arr(
+    years: np.ndarray,
+) -> npt.NDArray[np.bool_]: ...
+def build_isocalendar_sarray(
+    dtindex: npt.NDArray[np.int64],  # const int64_t[:]
+    reso: int,  # NPY_DATETIMEUNIT
+) -> np.ndarray: ...
+def _get_locale_names(name_type: str, locale: str | None = ...): ...
+
+class RoundTo:
+    @property
+    def MINUS_INFTY(self) -> int: ...
+    @property
+    def PLUS_INFTY(self) -> int: ...
+    @property
+    def NEAREST_HALF_EVEN(self) -> int: ...
+    @property
+    def NEAREST_HALF_PLUS_INFTY(self) -> int: ...
+    @property
+    def NEAREST_HALF_MINUS_INFTY(self) -> int: ...
+
+def round_nsint64(
+    values: npt.NDArray[np.int64],
+    mode: RoundTo,
+    nanos: int,
+) -> npt.NDArray[np.int64]: ...
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/tslibs/nattype.pyi b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/tslibs/nattype.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..bd86a6fdc2174a38bdd0845019e0afd6f2aecbf1
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/tslibs/nattype.pyi
@@ -0,0 +1,141 @@
+from datetime import (
+    datetime,
+    timedelta,
+    tzinfo as _tzinfo,
+)
+import typing
+
+import numpy as np
+
+from pandas._libs.tslibs.period import Period
+from pandas._typing import Self
+
+NaT: NaTType
+iNaT: int
+nat_strings: set[str]
+
+_NaTComparisonTypes: typing.TypeAlias = (
+    datetime | timedelta | Period | np.datetime64 | np.timedelta64
+)
+
+class _NatComparison:
+    def __call__(self, other: _NaTComparisonTypes) -> bool: ...
+
+class NaTType:
+    _value: np.int64
+    @property
+    def value(self) -> int: ...
+    @property
+    def asm8(self) -> np.datetime64: ...
+    def to_datetime64(self) -> np.datetime64: ...
+    def to_numpy(
+        self, dtype: np.dtype | str | None = ..., copy: bool = ...
+    ) -> np.datetime64 | np.timedelta64: ...
+    @property
+    def is_leap_year(self) -> bool: ...
+    @property
+    def is_month_start(self) -> bool: ...
+    @property
+    def is_quarter_start(self) -> bool: ...
+    @property
+    def is_year_start(self) -> bool: ...
+    @property
+    def is_month_end(self) -> bool: ...
+    @property
+    def is_quarter_end(self) -> bool: ...
+    @property
+    def is_year_end(self) -> bool: ...
+    @property
+    def day_of_year(self) -> float: ...
+    @property
+    def dayofyear(self) -> float: ...
+    @property
+    def days_in_month(self) -> float: ...
+    @property
+    def daysinmonth(self) -> float: ...
+    @property
+    def day_of_week(self) -> float: ...
+    @property
+    def dayofweek(self) -> float: ...
+    @property
+    def week(self) -> float: ...
+    @property
+    def weekofyear(self) -> float: ...
+    def day_name(self) -> float: ...
+    def month_name(self) -> float: ...
+    def weekday(self) -> float: ...
+    def isoweekday(self) -> float: ...
+    def total_seconds(self) -> float: ...
+    def today(self, *args, **kwargs) -> NaTType: ...
+    def now(self, *args, **kwargs) -> NaTType: ...
+    def to_pydatetime(self) -> NaTType: ...
+    def date(self) -> NaTType: ...
+    def round(self) -> NaTType: ...
+    def floor(self) -> NaTType: ...
+    def ceil(self) -> NaTType: ...
+    @property
+    def tzinfo(self) -> None: ...
+    @property
+    def tz(self) -> None: ...
+    def tz_convert(self, tz: _tzinfo | str | None) -> NaTType: ...
+    def tz_localize(
+        self,
+        tz: _tzinfo | str | None,
+        ambiguous: str = ...,
+        nonexistent: str = ...,
+    ) -> NaTType: ...
+    def replace(
+        self,
+        year: int | None = ...,
+        month: int | None = ...,
+        day: int | None = ...,
+        hour: int | None = ...,
+        minute: int | None = ...,
+        second: int | None = ...,
+        microsecond: int | None = ...,
+        nanosecond: int | None = ...,
+        tzinfo: _tzinfo | None = ...,
+        fold: int | None = ...,
+    ) -> NaTType: ...
+    @property
+    def year(self) -> float: ...
+    @property
+    def quarter(self) -> float: ...
+    @property
+    def month(self) -> float: ...
+    @property
+    def day(self) -> float: ...
+    @property
+    def hour(self) -> float: ...
+    @property
+    def minute(self) -> float: ...
+    @property
+    def second(self) -> float: ...
+    @property
+    def millisecond(self) -> float: ...
+    @property
+    def microsecond(self) -> float: ...
+    @property
+    def nanosecond(self) -> float: ...
+    # inject Timedelta properties
+    @property
+    def days(self) -> float: ...
+    @property
+    def microseconds(self) -> float: ...
+    @property
+    def nanoseconds(self) -> float: ...
+    # inject Period properties
+    @property
+    def qyear(self) -> float: ...
+    def __eq__(self, other: object) -> bool: ...
+    def __ne__(self, other: object) -> bool: ...
+    __lt__: _NatComparison
+    __le__: _NatComparison
+    __gt__: _NatComparison
+    __ge__: _NatComparison
+    def __sub__(self, other: Self | timedelta | datetime) -> Self: ...
+    def __rsub__(self, other: Self | timedelta | datetime) -> Self: ...
+    def __add__(self, other: Self | timedelta | datetime) -> Self: ...
+    def __radd__(self, other: Self | timedelta | datetime) -> Self: ...
+    def __hash__(self) -> int: ...
+    def as_unit(self, unit: str, round_ok: bool = ...) -> NaTType: ...
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/tslibs/np_datetime.pyi b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/tslibs/np_datetime.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..00ef35c50e53251d5ca6f6c6d5ad28a67a695a21
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/tslibs/np_datetime.pyi
@@ -0,0 +1,27 @@
+import numpy as np
+
+from pandas._typing import npt
+
+class OutOfBoundsDatetime(ValueError): ...
+class OutOfBoundsTimedelta(ValueError): ...
+
+# only exposed for testing
+def py_get_unit_from_dtype(dtype: np.dtype): ...
+def py_td64_to_tdstruct(td64: int, unit: int) -> dict: ...
+def astype_overflowsafe(
+    values: np.ndarray,
+    dtype: np.dtype,
+    copy: bool = ...,
+    round_ok: bool = ...,
+    is_coerce: bool = ...,
+) -> np.ndarray: ...
+def is_unitless(dtype: np.dtype) -> bool: ...
+def compare_mismatched_resolutions(
+    left: np.ndarray, right: np.ndarray, op
+) -> npt.NDArray[np.bool_]: ...
+def add_overflowsafe(
+    left: npt.NDArray[np.int64],
+    right: npt.NDArray[np.int64],
+) -> npt.NDArray[np.int64]: ...
+def get_supported_dtype(dtype: np.dtype) -> np.dtype: ...
+def is_supported_dtype(dtype: np.dtype) -> bool: ...
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/tslibs/offsets.pyi b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/tslibs/offsets.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..7eb8dc0813868de9ca96c086f5506619719937f4
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/tslibs/offsets.pyi
@@ -0,0 +1,287 @@
+from datetime import (
+    datetime,
+    time,
+    timedelta,
+)
+from typing import (
+    Any,
+    Collection,
+    Literal,
+    TypeVar,
+    overload,
+)
+
+import numpy as np
+
+from pandas._libs.tslibs.nattype import NaTType
+from pandas._typing import (
+    OffsetCalendar,
+    Self,
+    npt,
+)
+
+from .timedeltas import Timedelta
+
+_BaseOffsetT = TypeVar("_BaseOffsetT", bound=BaseOffset)
+_DatetimeT = TypeVar("_DatetimeT", bound=datetime)
+_TimedeltaT = TypeVar("_TimedeltaT", bound=timedelta)
+
+_relativedelta_kwds: set[str]
+prefix_mapping: dict[str, type]
+
+class ApplyTypeError(TypeError): ...
+
+class BaseOffset:
+    n: int
+    normalize: bool
+    def __init__(self, n: int = ..., normalize: bool = ...) -> None: ...
+    def __eq__(self, other) -> bool: ...
+    def __ne__(self, other) -> bool: ...
+    def __hash__(self) -> int: ...
+    @property
+    def kwds(self) -> dict: ...
+    @property
+    def base(self) -> BaseOffset: ...
+    @overload
+    def __add__(self, other: npt.NDArray[np.object_]) -> npt.NDArray[np.object_]: ...
+    @overload
+    def __add__(self, other: BaseOffset) -> Self: ...
+    @overload
+    def __add__(self, other: _DatetimeT) -> _DatetimeT: ...
+    @overload
+    def __add__(self, other: _TimedeltaT) -> _TimedeltaT: ...
+    @overload
+    def __radd__(self, other: npt.NDArray[np.object_]) -> npt.NDArray[np.object_]: ...
+    @overload
+    def __radd__(self, other: BaseOffset) -> Self: ...
+    @overload
+    def __radd__(self, other: _DatetimeT) -> _DatetimeT: ...
+    @overload
+    def __radd__(self, other: _TimedeltaT) -> _TimedeltaT: ...
+    @overload
+    def __radd__(self, other: NaTType) -> NaTType: ...
+    def __sub__(self, other: BaseOffset) -> Self: ...
+    @overload
+    def __rsub__(self, other: npt.NDArray[np.object_]) -> npt.NDArray[np.object_]: ...
+    @overload
+    def __rsub__(self, other: BaseOffset): ...
+    @overload
+    def __rsub__(self, other: _DatetimeT) -> _DatetimeT: ...
+    @overload
+    def __rsub__(self, other: _TimedeltaT) -> _TimedeltaT: ...
+    @overload
+    def __mul__(self, other: np.ndarray) -> np.ndarray: ...
+    @overload
+    def __mul__(self, other: int): ...
+    @overload
+    def __rmul__(self, other: np.ndarray) -> np.ndarray: ...
+    @overload
+    def __rmul__(self, other: int) -> Self: ...
+    def __neg__(self) -> Self: ...
+    def copy(self) -> Self: ...
+    @property
+    def name(self) -> str: ...
+    @property
+    def rule_code(self) -> str: ...
+    @property
+    def freqstr(self) -> str: ...
+    def _apply(self, other): ...
+    def _apply_array(self, dtarr: np.ndarray) -> np.ndarray: ...
+    def rollback(self, dt: datetime) -> datetime: ...
+    def rollforward(self, dt: datetime) -> datetime: ...
+    def is_on_offset(self, dt: datetime) -> bool: ...
+    def __setstate__(self, state) -> None: ...
+    def __getstate__(self): ...
+    @property
+    def nanos(self) -> int: ...
+    def is_anchored(self) -> bool: ...
+
+def _get_offset(name: str) -> BaseOffset: ...
+
+class SingleConstructorOffset(BaseOffset):
+    @classmethod
+    def _from_name(cls, suffix: None = ...): ...
+    def __reduce__(self): ...
+
+@overload
+def to_offset(freq: None, is_period: bool = ...) -> None: ...
+@overload
+def to_offset(freq: _BaseOffsetT, is_period: bool = ...) -> _BaseOffsetT: ...
+@overload
+def to_offset(freq: timedelta | str, is_period: bool = ...) -> BaseOffset: ...
+
+class Tick(SingleConstructorOffset):
+    _creso: int
+    _prefix: str
+    def __init__(self, n: int = ..., normalize: bool = ...) -> None: ...
+    @property
+    def delta(self) -> Timedelta: ...
+    @property
+    def nanos(self) -> int: ...
+
+def delta_to_tick(delta: timedelta) -> Tick: ...
+
+class Day(Tick): ...
+class Hour(Tick): ...
+class Minute(Tick): ...
+class Second(Tick): ...
+class Milli(Tick): ...
+class Micro(Tick): ...
+class Nano(Tick): ...
+
+class RelativeDeltaOffset(BaseOffset):
+    def __init__(self, n: int = ..., normalize: bool = ..., **kwds: Any) -> None: ...
+
+class BusinessMixin(SingleConstructorOffset):
+    def __init__(
+        self, n: int = ..., normalize: bool = ..., offset: timedelta = ...
+    ) -> None: ...
+
+class BusinessDay(BusinessMixin): ...
+
+class BusinessHour(BusinessMixin):
+    def __init__(
+        self,
+        n: int = ...,
+        normalize: bool = ...,
+        start: str | time | Collection[str | time] = ...,
+        end: str | time | Collection[str | time] = ...,
+        offset: timedelta = ...,
+    ) -> None: ...
+
+class WeekOfMonthMixin(SingleConstructorOffset):
+    def __init__(
+        self, n: int = ..., normalize: bool = ..., weekday: int = ...
+    ) -> None: ...
+
+class YearOffset(SingleConstructorOffset):
+    def __init__(
+        self, n: int = ..., normalize: bool = ..., month: int | None = ...
+    ) -> None: ...
+
+class BYearEnd(YearOffset): ...
+class BYearBegin(YearOffset): ...
+class YearEnd(YearOffset): ...
+class YearBegin(YearOffset): ...
+
+class QuarterOffset(SingleConstructorOffset):
+    def __init__(
+        self, n: int = ..., normalize: bool = ..., startingMonth: int | None = ...
+    ) -> None: ...
+
+class BQuarterEnd(QuarterOffset): ...
+class BQuarterBegin(QuarterOffset): ...
+class QuarterEnd(QuarterOffset): ...
+class QuarterBegin(QuarterOffset): ...
+class MonthOffset(SingleConstructorOffset): ...
+class MonthEnd(MonthOffset): ...
+class MonthBegin(MonthOffset): ...
+class BusinessMonthEnd(MonthOffset): ...
+class BusinessMonthBegin(MonthOffset): ...
+
+class SemiMonthOffset(SingleConstructorOffset):
+    def __init__(
+        self, n: int = ..., normalize: bool = ..., day_of_month: int | None = ...
+    ) -> None: ...
+
+class SemiMonthEnd(SemiMonthOffset): ...
+class SemiMonthBegin(SemiMonthOffset): ...
+
+class Week(SingleConstructorOffset):
+    def __init__(
+        self, n: int = ..., normalize: bool = ..., weekday: int | None = ...
+    ) -> None: ...
+
+class WeekOfMonth(WeekOfMonthMixin):
+    def __init__(
+        self, n: int = ..., normalize: bool = ..., week: int = ..., weekday: int = ...
+    ) -> None: ...
+
+class LastWeekOfMonth(WeekOfMonthMixin): ...
+
+class FY5253Mixin(SingleConstructorOffset):
+    def __init__(
+        self,
+        n: int = ...,
+        normalize: bool = ...,
+        weekday: int = ...,
+        startingMonth: int = ...,
+        variation: Literal["nearest", "last"] = ...,
+    ) -> None: ...
+
+class FY5253(FY5253Mixin): ...
+
+class FY5253Quarter(FY5253Mixin):
+    def __init__(
+        self,
+        n: int = ...,
+        normalize: bool = ...,
+        weekday: int = ...,
+        startingMonth: int = ...,
+        qtr_with_extra_week: int = ...,
+        variation: Literal["nearest", "last"] = ...,
+    ) -> None: ...
+
+class Easter(SingleConstructorOffset): ...
+
+class _CustomBusinessMonth(BusinessMixin):
+    def __init__(
+        self,
+        n: int = ...,
+        normalize: bool = ...,
+        weekmask: str = ...,
+        holidays: list | None = ...,
+        calendar: OffsetCalendar | None = ...,
+        offset: timedelta = ...,
+    ) -> None: ...
+
+class CustomBusinessDay(BusinessDay):
+    def __init__(
+        self,
+        n: int = ...,
+        normalize: bool = ...,
+        weekmask: str = ...,
+        holidays: list | None = ...,
+        calendar: OffsetCalendar | None = ...,
+        offset: timedelta = ...,
+    ) -> None: ...
+
+class CustomBusinessHour(BusinessHour):
+    def __init__(
+        self,
+        n: int = ...,
+        normalize: bool = ...,
+        weekmask: str = ...,
+        holidays: list | None = ...,
+        calendar: OffsetCalendar | None = ...,
+        start: str | time | Collection[str | time] = ...,
+        end: str | time | Collection[str | time] = ...,
+        offset: timedelta = ...,
+    ) -> None: ...
+
+class CustomBusinessMonthEnd(_CustomBusinessMonth): ...
+class CustomBusinessMonthBegin(_CustomBusinessMonth): ...
+class OffsetMeta(type): ...
+class DateOffset(RelativeDeltaOffset, metaclass=OffsetMeta): ...
+
+BDay = BusinessDay
+BMonthEnd = BusinessMonthEnd
+BMonthBegin = BusinessMonthBegin
+CBMonthEnd = CustomBusinessMonthEnd
+CBMonthBegin = CustomBusinessMonthBegin
+CDay = CustomBusinessDay
+
+def roll_qtrday(
+    other: datetime, n: int, month: int, day_opt: str, modby: int
+) -> int: ...
+
+INVALID_FREQ_ERR_MSG: Literal["Invalid frequency: {0}"]
+
+def shift_months(
+    dtindex: npt.NDArray[np.int64],
+    months: int,
+    day_opt: str | None = ...,
+    reso: int = ...,
+) -> npt.NDArray[np.int64]: ...
+
+_offset_map: dict[str, BaseOffset]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/tslibs/parsing.pyi b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/tslibs/parsing.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..40394f915d4b0f12d3631d997c46381757152bfe
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/tslibs/parsing.pyi
@@ -0,0 +1,33 @@
+from datetime import datetime
+
+import numpy as np
+
+from pandas._typing import npt
+
+class DateParseError(ValueError): ...
+
+def py_parse_datetime_string(
+    date_string: str,
+    dayfirst: bool = ...,
+    yearfirst: bool = ...,
+) -> datetime: ...
+def parse_datetime_string_with_reso(
+    date_string: str,
+    freq: str | None = ...,
+    dayfirst: bool | None = ...,
+    yearfirst: bool | None = ...,
+) -> tuple[datetime, str]: ...
+def _does_string_look_like_datetime(py_string: str) -> bool: ...
+def quarter_to_myear(year: int, quarter: int, freq: str) -> tuple[int, int]: ...
+def try_parse_dates(
+    values: npt.NDArray[np.object_],  # object[:]
+    parser,
+) -> npt.NDArray[np.object_]: ...
+def guess_datetime_format(
+    dt_str: str,
+    dayfirst: bool | None = ...,
+) -> str | None: ...
+def concat_date_cols(
+    date_cols: tuple,
+) -> npt.NDArray[np.object_]: ...
+def get_rule_month(source: str) -> str: ...
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/tslibs/period.pyi b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/tslibs/period.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..22f3bdbe668decaac0c53cf080ae5c3f098d7a48
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/tslibs/period.pyi
@@ -0,0 +1,135 @@
+from datetime import timedelta
+from typing import Literal
+
+import numpy as np
+
+from pandas._libs.tslibs.dtypes import PeriodDtypeBase
+from pandas._libs.tslibs.nattype import NaTType
+from pandas._libs.tslibs.offsets import BaseOffset
+from pandas._libs.tslibs.timestamps import Timestamp
+from pandas._typing import (
+    Frequency,
+    npt,
+)
+
+INVALID_FREQ_ERR_MSG: str
+DIFFERENT_FREQ: str
+
+class IncompatibleFrequency(ValueError): ...
+
+def periodarr_to_dt64arr(
+    periodarr: npt.NDArray[np.int64],  # const int64_t[:]
+    freq: int,
+) -> npt.NDArray[np.int64]: ...
+def period_asfreq_arr(
+    arr: npt.NDArray[np.int64],
+    freq1: int,
+    freq2: int,
+    end: bool,
+) -> npt.NDArray[np.int64]: ...
+def get_period_field_arr(
+    field: str,
+    arr: npt.NDArray[np.int64],  # const int64_t[:]
+    freq: int,
+) -> npt.NDArray[np.int64]: ...
+def from_ordinals(
+    values: npt.NDArray[np.int64],  # const int64_t[:]
+    freq: timedelta | BaseOffset | str,
+) -> npt.NDArray[np.int64]: ...
+def extract_ordinals(
+    values: npt.NDArray[np.object_],
+    freq: Frequency | int,
+) -> npt.NDArray[np.int64]: ...
+def extract_freq(
+    values: npt.NDArray[np.object_],
+) -> BaseOffset: ...
+def period_array_strftime(
+    values: npt.NDArray[np.int64],
+    dtype_code: int,
+    na_rep,
+    date_format: str | None,
+) -> npt.NDArray[np.object_]: ...
+
+# exposed for tests
+def period_asfreq(ordinal: int, freq1: int, freq2: int, end: bool) -> int: ...
+def period_ordinal(
+    y: int, m: int, d: int, h: int, min: int, s: int, us: int, ps: int, freq: int
+) -> int: ...
+def freq_to_dtype_code(freq: BaseOffset) -> int: ...
+def validate_end_alias(how: str) -> Literal["E", "S"]: ...
+
+class PeriodMixin:
+    @property
+    def end_time(self) -> Timestamp: ...
+    @property
+    def start_time(self) -> Timestamp: ...
+    def _require_matching_freq(self, other: BaseOffset, base: bool = ...) -> None: ...
+
+class Period(PeriodMixin):
+    ordinal: int  # int64_t
+    freq: BaseOffset
+    _dtype: PeriodDtypeBase
+
+    # error: "__new__" must return a class instance (got "Union[Period, NaTType]")
+    def __new__(  # type: ignore[misc]
+        cls,
+        value=...,
+        freq: int | str | BaseOffset | None = ...,
+        ordinal: int | None = ...,
+        year: int | None = ...,
+        month: int | None = ...,
+        quarter: int | None = ...,
+        day: int | None = ...,
+        hour: int | None = ...,
+        minute: int | None = ...,
+        second: int | None = ...,
+    ) -> Period | NaTType: ...
+    @classmethod
+    def _maybe_convert_freq(cls, freq) -> BaseOffset: ...
+    @classmethod
+    def _from_ordinal(cls, ordinal: int, freq: BaseOffset) -> Period: ...
+    @classmethod
+    def now(cls, freq: Frequency) -> Period: ...
+    def strftime(self, fmt: str | None) -> str: ...
+    def to_timestamp(
+        self,
+        freq: str | BaseOffset | None = ...,
+        how: str = ...,
+    ) -> Timestamp: ...
+    def asfreq(self, freq: str | BaseOffset, how: str = ...) -> Period: ...
+    @property
+    def freqstr(self) -> str: ...
+    @property
+    def is_leap_year(self) -> bool: ...
+    @property
+    def daysinmonth(self) -> int: ...
+    @property
+    def days_in_month(self) -> int: ...
+    @property
+    def qyear(self) -> int: ...
+    @property
+    def quarter(self) -> int: ...
+    @property
+    def day_of_year(self) -> int: ...
+    @property
+    def weekday(self) -> int: ...
+    @property
+    def day_of_week(self) -> int: ...
+    @property
+    def week(self) -> int: ...
+    @property
+    def weekofyear(self) -> int: ...
+    @property
+    def second(self) -> int: ...
+    @property
+    def minute(self) -> int: ...
+    @property
+    def hour(self) -> int: ...
+    @property
+    def day(self) -> int: ...
+    @property
+    def month(self) -> int: ...
+    @property
+    def year(self) -> int: ...
+    def __sub__(self, other) -> Period | BaseOffset: ...
+    def __add__(self, other) -> Period: ...
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/tslibs/strptime.pyi b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/tslibs/strptime.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..0ec1a1e25a2b3cfe974baebfe32d686435f73e11
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/tslibs/strptime.pyi
@@ -0,0 +1,14 @@
+import numpy as np
+
+from pandas._typing import npt
+
+def array_strptime(
+    values: npt.NDArray[np.object_],
+    fmt: str | None,
+    exact: bool = ...,
+    errors: str = ...,
+    utc: bool = ...,
+    creso: int = ...,  # NPY_DATETIMEUNIT
+) -> tuple[np.ndarray, np.ndarray]: ...
+
+# first ndarray is M8[ns], second is object ndarray of tzinfo | None
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/tslibs/timedeltas.pyi b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/tslibs/timedeltas.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..24ec6c8891a89a7af4042df066fec6bc9d7b0e04
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/tslibs/timedeltas.pyi
@@ -0,0 +1,174 @@
+from datetime import timedelta
+from typing import (
+    ClassVar,
+    Literal,
+    TypeAlias,
+    TypeVar,
+    overload,
+)
+
+import numpy as np
+
+from pandas._libs.tslibs import (
+    NaTType,
+    Tick,
+)
+from pandas._typing import (
+    Frequency,
+    Self,
+    npt,
+)
+
+# This should be kept consistent with the keys in the dict timedelta_abbrevs
+# in pandas/_libs/tslibs/timedeltas.pyx
+UnitChoices: TypeAlias = Literal[
+    "Y",
+    "y",
+    "M",
+    "W",
+    "w",
+    "D",
+    "d",
+    "days",
+    "day",
+    "hours",
+    "hour",
+    "hr",
+    "h",
+    "m",
+    "minute",
+    "min",
+    "minutes",
+    "T",
+    "t",
+    "s",
+    "seconds",
+    "sec",
+    "second",
+    "ms",
+    "milliseconds",
+    "millisecond",
+    "milli",
+    "millis",
+    "L",
+    "l",
+    "us",
+    "microseconds",
+    "microsecond",
+    "µs",
+    "micro",
+    "micros",
+    "u",
+    "ns",
+    "nanoseconds",
+    "nano",
+    "nanos",
+    "nanosecond",
+    "n",
+]
+_S = TypeVar("_S", bound=timedelta)
+
+def get_unit_for_round(freq, creso: int) -> int: ...
+def disallow_ambiguous_unit(unit: str | None) -> None: ...
+def ints_to_pytimedelta(
+    m8values: npt.NDArray[np.timedelta64],
+    box: bool = ...,
+) -> npt.NDArray[np.object_]: ...
+def array_to_timedelta64(
+    values: npt.NDArray[np.object_],
+    unit: str | None = ...,
+    errors: str = ...,
+) -> np.ndarray: ...  # np.ndarray[m8ns]
+def parse_timedelta_unit(unit: str | None) -> UnitChoices: ...
+def delta_to_nanoseconds(
+    delta: np.timedelta64 | timedelta | Tick,
+    reso: int = ...,  # NPY_DATETIMEUNIT
+    round_ok: bool = ...,
+) -> int: ...
+def floordiv_object_array(
+    left: np.ndarray, right: npt.NDArray[np.object_]
+) -> np.ndarray: ...
+def truediv_object_array(
+    left: np.ndarray, right: npt.NDArray[np.object_]
+) -> np.ndarray: ...
+
+class Timedelta(timedelta):
+    _creso: int
+    min: ClassVar[Timedelta]
+    max: ClassVar[Timedelta]
+    resolution: ClassVar[Timedelta]
+    value: int  # np.int64
+    _value: int  # np.int64
+    # error: "__new__" must return a class instance (got "Union[Timestamp, NaTType]")
+    def __new__(  # type: ignore[misc]
+        cls: type[_S],
+        value=...,
+        unit: str | None = ...,
+        **kwargs: float | np.integer | np.floating,
+    ) -> _S | NaTType: ...
+    @classmethod
+    def _from_value_and_reso(cls, value: np.int64, reso: int) -> Timedelta: ...
+    @property
+    def days(self) -> int: ...
+    @property
+    def seconds(self) -> int: ...
+    @property
+    def microseconds(self) -> int: ...
+    def total_seconds(self) -> float: ...
+    def to_pytimedelta(self) -> timedelta: ...
+    def to_timedelta64(self) -> np.timedelta64: ...
+    @property
+    def asm8(self) -> np.timedelta64: ...
+    # TODO: round/floor/ceil could return NaT?
+    def round(self, freq: Frequency) -> Self: ...
+    def floor(self, freq: Frequency) -> Self: ...
+    def ceil(self, freq: Frequency) -> Self: ...
+    @property
+    def resolution_string(self) -> str: ...
+    def __add__(self, other: timedelta) -> Timedelta: ...
+    def __radd__(self, other: timedelta) -> Timedelta: ...
+    def __sub__(self, other: timedelta) -> Timedelta: ...
+    def __rsub__(self, other: timedelta) -> Timedelta: ...
+    def __neg__(self) -> Timedelta: ...
+    def __pos__(self) -> Timedelta: ...
+    def __abs__(self) -> Timedelta: ...
+    def __mul__(self, other: float) -> Timedelta: ...
+    def __rmul__(self, other: float) -> Timedelta: ...
+    # error: Signature of "__floordiv__" incompatible with supertype "timedelta"
+    @overload  # type: ignore[override]
+    def __floordiv__(self, other: timedelta) -> int: ...
+    @overload
+    def __floordiv__(self, other: float) -> Timedelta: ...
+    @overload
+    def __floordiv__(
+        self, other: npt.NDArray[np.timedelta64]
+    ) -> npt.NDArray[np.intp]: ...
+    @overload
+    def __floordiv__(
+        self, other: npt.NDArray[np.number]
+    ) -> npt.NDArray[np.timedelta64] | Timedelta: ...
+    @overload
+    def __rfloordiv__(self, other: timedelta | str) -> int: ...
+    @overload
+    def __rfloordiv__(self, other: None | NaTType) -> NaTType: ...
+    @overload
+    def __rfloordiv__(self, other: np.ndarray) -> npt.NDArray[np.timedelta64]: ...
+    @overload
+    def __truediv__(self, other: timedelta) -> float: ...
+    @overload
+    def __truediv__(self, other: float) -> Timedelta: ...
+    def __mod__(self, other: timedelta) -> Timedelta: ...
+    def __divmod__(self, other: timedelta) -> tuple[int, Timedelta]: ...
+    def __le__(self, other: timedelta) -> bool: ...
+    def __lt__(self, other: timedelta) -> bool: ...
+    def __ge__(self, other: timedelta) -> bool: ...
+    def __gt__(self, other: timedelta) -> bool: ...
+    def __hash__(self) -> int: ...
+    def isoformat(self) -> str: ...
+    def to_numpy(
+        self, dtype: npt.DTypeLike = ..., copy: bool = False
+    ) -> np.timedelta64: ...
+    def view(self, dtype: npt.DTypeLike) -> object: ...
+    @property
+    def unit(self) -> str: ...
+    def as_unit(self, unit: str, round_ok: bool = ...) -> Timedelta: ...
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/tslibs/timestamps.pyi b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/tslibs/timestamps.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..c769b09d1b7a1adc4532dd367bcbc4cf522419cd
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/tslibs/timestamps.pyi
@@ -0,0 +1,241 @@
+from datetime import (
+    date as _date,
+    datetime,
+    time as _time,
+    timedelta,
+    tzinfo as _tzinfo,
+)
+from time import struct_time
+from typing import (
+    ClassVar,
+    Literal,
+    TypeAlias,
+    overload,
+)
+
+import numpy as np
+
+from pandas._libs.tslibs import (
+    BaseOffset,
+    NaTType,
+    Period,
+    Tick,
+    Timedelta,
+)
+from pandas._typing import (
+    Self,
+    TimestampNonexistent,
+)
+
+_TimeZones: TypeAlias = str | _tzinfo | None | int
+
+def integer_op_not_supported(obj: object) -> TypeError: ...
+
+class Timestamp(datetime):
+    _creso: int
+    min: ClassVar[Timestamp]
+    max: ClassVar[Timestamp]
+
+    resolution: ClassVar[Timedelta]
+    _value: int  # np.int64
+    # error: "__new__" must return a class instance (got "Union[Timestamp, NaTType]")
+    def __new__(  # type: ignore[misc]
+        cls: type[Self],
+        ts_input: np.integer | float | str | _date | datetime | np.datetime64 = ...,
+        year: int | None = ...,
+        month: int | None = ...,
+        day: int | None = ...,
+        hour: int | None = ...,
+        minute: int | None = ...,
+        second: int | None = ...,
+        microsecond: int | None = ...,
+        tzinfo: _tzinfo | None = ...,
+        *,
+        nanosecond: int | None = ...,
+        tz: _TimeZones = ...,
+        unit: str | int | None = ...,
+        fold: int | None = ...,
+    ) -> Self | NaTType: ...
+    @classmethod
+    def _from_value_and_reso(
+        cls, value: int, reso: int, tz: _TimeZones
+    ) -> Timestamp: ...
+    @property
+    def value(self) -> int: ...  # np.int64
+    @property
+    def year(self) -> int: ...
+    @property
+    def month(self) -> int: ...
+    @property
+    def day(self) -> int: ...
+    @property
+    def hour(self) -> int: ...
+    @property
+    def minute(self) -> int: ...
+    @property
+    def second(self) -> int: ...
+    @property
+    def microsecond(self) -> int: ...
+    @property
+    def nanosecond(self) -> int: ...
+    @property
+    def tzinfo(self) -> _tzinfo | None: ...
+    @property
+    def tz(self) -> _tzinfo | None: ...
+    @property
+    def fold(self) -> int: ...
+    @classmethod
+    def fromtimestamp(cls, ts: float, tz: _TimeZones = ...) -> Self: ...
+    @classmethod
+    def utcfromtimestamp(cls, ts: float) -> Self: ...
+    @classmethod
+    def today(cls, tz: _TimeZones = ...) -> Self: ...
+    @classmethod
+    def fromordinal(
+        cls,
+        ordinal: int,
+        tz: _TimeZones = ...,
+    ) -> Self: ...
+    @classmethod
+    def now(cls, tz: _TimeZones = ...) -> Self: ...
+    @classmethod
+    def utcnow(cls) -> Self: ...
+    # error: Signature of "combine" incompatible with supertype "datetime"
+    @classmethod
+    def combine(  # type: ignore[override]
+        cls, date: _date, time: _time
+    ) -> datetime: ...
+    @classmethod
+    def fromisoformat(cls, date_string: str) -> Self: ...
+    def strftime(self, format: str) -> str: ...
+    def __format__(self, fmt: str) -> str: ...
+    def toordinal(self) -> int: ...
+    def timetuple(self) -> struct_time: ...
+    def timestamp(self) -> float: ...
+    def utctimetuple(self) -> struct_time: ...
+    def date(self) -> _date: ...
+    def time(self) -> _time: ...
+    def timetz(self) -> _time: ...
+    # LSP violation: nanosecond is not present in datetime.datetime.replace
+    # and has positional args following it
+    def replace(  # type: ignore[override]
+        self,
+        year: int | None = ...,
+        month: int | None = ...,
+        day: int | None = ...,
+        hour: int | None = ...,
+        minute: int | None = ...,
+        second: int | None = ...,
+        microsecond: int | None = ...,
+        nanosecond: int | None = ...,
+        tzinfo: _tzinfo | type[object] | None = ...,
+        fold: int | None = ...,
+    ) -> Self: ...
+    # LSP violation: datetime.datetime.astimezone has a default value for tz
+    def astimezone(self, tz: _TimeZones) -> Self: ...  # type: ignore[override]
+    def ctime(self) -> str: ...
+    def isoformat(self, sep: str = ..., timespec: str = ...) -> str: ...
+    @classmethod
+    def strptime(
+        # Note: strptime is actually disabled and raises NotImplementedError
+        cls,
+        date_string: str,
+        format: str,
+    ) -> Self: ...
+    def utcoffset(self) -> timedelta | None: ...
+    def tzname(self) -> str | None: ...
+    def dst(self) -> timedelta | None: ...
+    def __le__(self, other: datetime) -> bool: ...  # type: ignore[override]
+    def __lt__(self, other: datetime) -> bool: ...  # type: ignore[override]
+    def __ge__(self, other: datetime) -> bool: ...  # type: ignore[override]
+    def __gt__(self, other: datetime) -> bool: ...  # type: ignore[override]
+    # error: Signature of "__add__" incompatible with supertype "date"/"datetime"
+    @overload  # type: ignore[override]
+    def __add__(self, other: np.ndarray) -> np.ndarray: ...
+    @overload
+    def __add__(self, other: timedelta | np.timedelta64 | Tick) -> Self: ...
+    def __radd__(self, other: timedelta) -> Self: ...
+    @overload  # type: ignore[override]
+    def __sub__(self, other: datetime) -> Timedelta: ...
+    @overload
+    def __sub__(self, other: timedelta | np.timedelta64 | Tick) -> Self: ...
+    def __hash__(self) -> int: ...
+    def weekday(self) -> int: ...
+    def isoweekday(self) -> int: ...
+    # Return type "Tuple[int, int, int]" of "isocalendar" incompatible with return
+    # type "_IsoCalendarDate" in supertype "date"
+    def isocalendar(self) -> tuple[int, int, int]: ...  # type: ignore[override]
+    @property
+    def is_leap_year(self) -> bool: ...
+    @property
+    def is_month_start(self) -> bool: ...
+    @property
+    def is_quarter_start(self) -> bool: ...
+    @property
+    def is_year_start(self) -> bool: ...
+    @property
+    def is_month_end(self) -> bool: ...
+    @property
+    def is_quarter_end(self) -> bool: ...
+    @property
+    def is_year_end(self) -> bool: ...
+    def to_pydatetime(self, warn: bool = ...) -> datetime: ...
+    def to_datetime64(self) -> np.datetime64: ...
+    def to_period(self, freq: BaseOffset | str | None = None) -> Period: ...
+    def to_julian_date(self) -> np.float64: ...
+    @property
+    def asm8(self) -> np.datetime64: ...
+    def tz_convert(self, tz: _TimeZones) -> Self: ...
+    # TODO: could return NaT?
+    def tz_localize(
+        self,
+        tz: _TimeZones,
+        ambiguous: bool | Literal["raise", "NaT"] = ...,
+        nonexistent: TimestampNonexistent = ...,
+    ) -> Self: ...
+    def normalize(self) -> Self: ...
+    # TODO: round/floor/ceil could return NaT?
+    def round(
+        self,
+        freq: str,
+        ambiguous: bool | Literal["raise", "NaT"] = ...,
+        nonexistent: TimestampNonexistent = ...,
+    ) -> Self: ...
+    def floor(
+        self,
+        freq: str,
+        ambiguous: bool | Literal["raise", "NaT"] = ...,
+        nonexistent: TimestampNonexistent = ...,
+    ) -> Self: ...
+    def ceil(
+        self,
+        freq: str,
+        ambiguous: bool | Literal["raise", "NaT"] = ...,
+        nonexistent: TimestampNonexistent = ...,
+    ) -> Self: ...
+    def day_name(self, locale: str | None = ...) -> str: ...
+    def month_name(self, locale: str | None = ...) -> str: ...
+    @property
+    def day_of_week(self) -> int: ...
+    @property
+    def dayofweek(self) -> int: ...
+    @property
+    def day_of_year(self) -> int: ...
+    @property
+    def dayofyear(self) -> int: ...
+    @property
+    def quarter(self) -> int: ...
+    @property
+    def week(self) -> int: ...
+    def to_numpy(
+        self, dtype: np.dtype | None = ..., copy: bool = ...
+    ) -> np.datetime64: ...
+    @property
+    def _date_repr(self) -> str: ...
+    @property
+    def days_in_month(self) -> int: ...
+    @property
+    def daysinmonth(self) -> int: ...
+    @property
+    def unit(self) -> str: ...
+    def as_unit(self, unit: str, round_ok: bool = ...) -> Timestamp: ...
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/tslibs/timezones.pyi b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/tslibs/timezones.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..4e9f0c6ae6c33447ebc86d3daf5bf5cedbe5b0cb
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/tslibs/timezones.pyi
@@ -0,0 +1,21 @@
+from datetime import (
+    datetime,
+    tzinfo,
+)
+from typing import Callable
+
+import numpy as np
+
+# imported from dateutil.tz
+dateutil_gettz: Callable[[str], tzinfo]
+
+def tz_standardize(tz: tzinfo) -> tzinfo: ...
+def tz_compare(start: tzinfo | None, end: tzinfo | None) -> bool: ...
+def infer_tzinfo(
+    start: datetime | None,
+    end: datetime | None,
+) -> tzinfo | None: ...
+def maybe_get_tz(tz: str | int | np.int64 | tzinfo | None) -> tzinfo | None: ...
+def get_timezone(tz: tzinfo) -> tzinfo | str: ...
+def is_utc(tz: tzinfo | None) -> bool: ...
+def is_fixed_offset(tz: tzinfo) -> bool: ...
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/tslibs/tzconversion.pyi b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/tslibs/tzconversion.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..2108fa0f35547191c3db683f8cc1015e5bda4abb
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/tslibs/tzconversion.pyi
@@ -0,0 +1,21 @@
+from datetime import (
+    timedelta,
+    tzinfo,
+)
+from typing import Iterable
+
+import numpy as np
+
+from pandas._typing import npt
+
+# tz_convert_from_utc_single exposed for testing
+def tz_convert_from_utc_single(
+    utc_val: np.int64, tz: tzinfo, creso: int = ...
+) -> np.int64: ...
+def tz_localize_to_utc(
+    vals: npt.NDArray[np.int64],
+    tz: tzinfo | None,
+    ambiguous: str | bool | Iterable[bool] | None = ...,
+    nonexistent: str | timedelta | np.timedelta64 | None = ...,
+    creso: int = ...,  # NPY_DATETIMEUNIT
+) -> npt.NDArray[np.int64]: ...
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/tslibs/vectorized.pyi b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/tslibs/vectorized.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..de19f592da62bbeb0b3ab267039abd902d4bb854
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/tslibs/vectorized.pyi
@@ -0,0 +1,43 @@
+"""
+For cython types that cannot be represented precisely, closest-available
+python equivalents are used, and the precise types kept as adjacent comments.
+"""
+from datetime import tzinfo
+
+import numpy as np
+
+from pandas._libs.tslibs.dtypes import Resolution
+from pandas._typing import npt
+
+def dt64arr_to_periodarr(
+    stamps: npt.NDArray[np.int64],
+    freq: int,
+    tz: tzinfo | None,
+    reso: int = ...,  # NPY_DATETIMEUNIT
+) -> npt.NDArray[np.int64]: ...
+def is_date_array_normalized(
+    stamps: npt.NDArray[np.int64],
+    tz: tzinfo | None,
+    reso: int,  # NPY_DATETIMEUNIT
+) -> bool: ...
+def normalize_i8_timestamps(
+    stamps: npt.NDArray[np.int64],
+    tz: tzinfo | None,
+    reso: int,  # NPY_DATETIMEUNIT
+) -> npt.NDArray[np.int64]: ...
+def get_resolution(
+    stamps: npt.NDArray[np.int64],
+    tz: tzinfo | None = ...,
+    reso: int = ...,  # NPY_DATETIMEUNIT
+) -> Resolution: ...
+def ints_to_pydatetime(
+    stamps: npt.NDArray[np.int64],
+    tz: tzinfo | None = ...,
+    box: str = ...,
+    reso: int = ...,  # NPY_DATETIMEUNIT
+) -> npt.NDArray[np.object_]: ...
+def tz_convert_from_utc(
+    stamps: npt.NDArray[np.int64],
+    tz: tzinfo | None,
+    reso: int = ...,  # NPY_DATETIMEUNIT
+) -> npt.NDArray[np.int64]: ...
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/window/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/window/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/window/aggregations.pyi b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/window/aggregations.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..a6cfbec9b15b9aa13a15dab3ff35b0d8761f1036
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/window/aggregations.pyi
@@ -0,0 +1,127 @@
+from typing import (
+    Any,
+    Callable,
+    Literal,
+)
+
+import numpy as np
+
+from pandas._typing import (
+    WindowingRankType,
+    npt,
+)
+
+def roll_sum(
+    values: np.ndarray,  # const float64_t[:]
+    start: np.ndarray,  # np.ndarray[np.int64]
+    end: np.ndarray,  # np.ndarray[np.int64]
+    minp: int,  # int64_t
+) -> np.ndarray: ...  # np.ndarray[float]
+def roll_mean(
+    values: np.ndarray,  # const float64_t[:]
+    start: np.ndarray,  # np.ndarray[np.int64]
+    end: np.ndarray,  # np.ndarray[np.int64]
+    minp: int,  # int64_t
+) -> np.ndarray: ...  # np.ndarray[float]
+def roll_var(
+    values: np.ndarray,  # const float64_t[:]
+    start: np.ndarray,  # np.ndarray[np.int64]
+    end: np.ndarray,  # np.ndarray[np.int64]
+    minp: int,  # int64_t
+    ddof: int = ...,
+) -> np.ndarray: ...  # np.ndarray[float]
+def roll_skew(
+    values: np.ndarray,  # np.ndarray[np.float64]
+    start: np.ndarray,  # np.ndarray[np.int64]
+    end: np.ndarray,  # np.ndarray[np.int64]
+    minp: int,  # int64_t
+) -> np.ndarray: ...  # np.ndarray[float]
+def roll_kurt(
+    values: np.ndarray,  # np.ndarray[np.float64]
+    start: np.ndarray,  # np.ndarray[np.int64]
+    end: np.ndarray,  # np.ndarray[np.int64]
+    minp: int,  # int64_t
+) -> np.ndarray: ...  # np.ndarray[float]
+def roll_median_c(
+    values: np.ndarray,  # np.ndarray[np.float64]
+    start: np.ndarray,  # np.ndarray[np.int64]
+    end: np.ndarray,  # np.ndarray[np.int64]
+    minp: int,  # int64_t
+) -> np.ndarray: ...  # np.ndarray[float]
+def roll_max(
+    values: np.ndarray,  # np.ndarray[np.float64]
+    start: np.ndarray,  # np.ndarray[np.int64]
+    end: np.ndarray,  # np.ndarray[np.int64]
+    minp: int,  # int64_t
+) -> np.ndarray: ...  # np.ndarray[float]
+def roll_min(
+    values: np.ndarray,  # np.ndarray[np.float64]
+    start: np.ndarray,  # np.ndarray[np.int64]
+    end: np.ndarray,  # np.ndarray[np.int64]
+    minp: int,  # int64_t
+) -> np.ndarray: ...  # np.ndarray[float]
+def roll_quantile(
+    values: np.ndarray,  # const float64_t[:]
+    start: np.ndarray,  # np.ndarray[np.int64]
+    end: np.ndarray,  # np.ndarray[np.int64]
+    minp: int,  # int64_t
+    quantile: float,  # float64_t
+    interpolation: Literal["linear", "lower", "higher", "nearest", "midpoint"],
+) -> np.ndarray: ...  # np.ndarray[float]
+def roll_rank(
+    values: np.ndarray,
+    start: np.ndarray,
+    end: np.ndarray,
+    minp: int,
+    percentile: bool,
+    method: WindowingRankType,
+    ascending: bool,
+) -> np.ndarray: ...  # np.ndarray[float]
+def roll_apply(
+    obj: object,
+    start: np.ndarray,  # np.ndarray[np.int64]
+    end: np.ndarray,  # np.ndarray[np.int64]
+    minp: int,  # int64_t
+    function: Callable[..., Any],
+    raw: bool,
+    args: tuple[Any, ...],
+    kwargs: dict[str, Any],
+) -> npt.NDArray[np.float64]: ...
+def roll_weighted_sum(
+    values: np.ndarray,  # const float64_t[:]
+    weights: np.ndarray,  # const float64_t[:]
+    minp: int,
+) -> np.ndarray: ...  # np.ndarray[np.float64]
+def roll_weighted_mean(
+    values: np.ndarray,  # const float64_t[:]
+    weights: np.ndarray,  # const float64_t[:]
+    minp: int,
+) -> np.ndarray: ...  # np.ndarray[np.float64]
+def roll_weighted_var(
+    values: np.ndarray,  # const float64_t[:]
+    weights: np.ndarray,  # const float64_t[:]
+    minp: int,  # int64_t
+    ddof: int,  # unsigned int
+) -> np.ndarray: ...  # np.ndarray[np.float64]
+def ewm(
+    vals: np.ndarray,  # const float64_t[:]
+    start: np.ndarray,  # const int64_t[:]
+    end: np.ndarray,  # const int64_t[:]
+    minp: int,
+    com: float,  # float64_t
+    adjust: bool,
+    ignore_na: bool,
+    deltas: np.ndarray | None = None,  # const float64_t[:]
+    normalize: bool = True,
+) -> np.ndarray: ...  # np.ndarray[np.float64]
+def ewmcov(
+    input_x: np.ndarray,  # const float64_t[:]
+    start: np.ndarray,  # const int64_t[:]
+    end: np.ndarray,  # const int64_t[:]
+    minp: int,
+    input_y: np.ndarray,  # const float64_t[:]
+    com: float,  # float64_t
+    adjust: bool,
+    ignore_na: bool,
+    bias: bool,
+) -> np.ndarray: ...  # np.ndarray[np.float64]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/window/indexers.pyi b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/window/indexers.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..c9bc64be34ac9a41d14fef33b0fc76bdf66527e9
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/window/indexers.pyi
@@ -0,0 +1,12 @@
+import numpy as np
+
+from pandas._typing import npt
+
+def calculate_variable_window_bounds(
+    num_values: int,  # int64_t
+    window_size: int,  # int64_t
+    min_periods,
+    center: bool,
+    closed: str | None,
+    index: np.ndarray,  # const int64_t[:]
+) -> tuple[npt.NDArray[np.int64], npt.NDArray[np.int64]]: ...
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/writers.pyi b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/writers.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..7b41856525dadf79a2bf4b29c7ddebfedaa880db
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_libs/writers.pyi
@@ -0,0 +1,20 @@
+import numpy as np
+
+from pandas._typing import ArrayLike
+
+def write_csv_rows(
+    data: list[ArrayLike],
+    data_index: np.ndarray,
+    nlevels: int,
+    cols: np.ndarray,
+    writer: object,  # _csv.writer
+) -> None: ...
+def convert_json_to_lines(arr: str) -> str: ...
+def max_len_string_array(
+    arr: np.ndarray,  # pandas_string[:]
+) -> int: ...
+def word_len(val: object) -> int: ...
+def string_array_replace_from_nan_rep(
+    arr: np.ndarray,  # np.ndarray[object, ndim=1]
+    nan_rep: object,
+) -> None: ...
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_testing/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_testing/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d7197f23ce1e4981fd7dcea5bdc4f8db8810f277
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_testing/__init__.py
@@ -0,0 +1,635 @@
+from __future__ import annotations
+
+from decimal import Decimal
+import operator
+import os
+from sys import byteorder
+from typing import (
+    TYPE_CHECKING,
+    Callable,
+    ContextManager,
+)
+import warnings
+
+import numpy as np
+
+from pandas._config import using_string_dtype
+from pandas._config.localization import (
+    can_set_locale,
+    get_locales,
+    set_locale,
+)
+
+from pandas.compat import pa_version_under10p1
+
+import pandas as pd
+from pandas import (
+    ArrowDtype,
+    DataFrame,
+    Index,
+    MultiIndex,
+    RangeIndex,
+    Series,
+)
+from pandas._testing._io import (
+    round_trip_localpath,
+    round_trip_pathlib,
+    round_trip_pickle,
+    write_to_compressed,
+)
+from pandas._testing._warnings import (
+    assert_produces_warning,
+    maybe_produces_warning,
+)
+from pandas._testing.asserters import (
+    assert_almost_equal,
+    assert_attr_equal,
+    assert_categorical_equal,
+    assert_class_equal,
+    assert_contains_all,
+    assert_copy,
+    assert_datetime_array_equal,
+    assert_dict_equal,
+    assert_equal,
+    assert_extension_array_equal,
+    assert_frame_equal,
+    assert_index_equal,
+    assert_indexing_slices_equivalent,
+    assert_interval_array_equal,
+    assert_is_sorted,
+    assert_is_valid_plot_return_object,
+    assert_metadata_equivalent,
+    assert_numpy_array_equal,
+    assert_period_array_equal,
+    assert_series_equal,
+    assert_sp_array_equal,
+    assert_timedelta_array_equal,
+    raise_assert_detail,
+)
+from pandas._testing.compat import (
+    get_dtype,
+    get_obj,
+)
+from pandas._testing.contexts import (
+    assert_cow_warning,
+    decompress_file,
+    ensure_clean,
+    raises_chained_assignment_error,
+    set_timezone,
+    use_numexpr,
+    with_csv_dialect,
+)
+from pandas.core.arrays import (
+    ArrowExtensionArray,
+    BaseMaskedArray,
+    NumpyExtensionArray,
+)
+from pandas.core.arrays._mixins import NDArrayBackedExtensionArray
+from pandas.core.construction import extract_array
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        Dtype,
+        NpDtype,
+    )
+
+
+UNSIGNED_INT_NUMPY_DTYPES: list[NpDtype] = ["uint8", "uint16", "uint32", "uint64"]
+UNSIGNED_INT_EA_DTYPES: list[Dtype] = ["UInt8", "UInt16", "UInt32", "UInt64"]
+SIGNED_INT_NUMPY_DTYPES: list[NpDtype] = [int, "int8", "int16", "int32", "int64"]
+SIGNED_INT_EA_DTYPES: list[Dtype] = ["Int8", "Int16", "Int32", "Int64"]
+ALL_INT_NUMPY_DTYPES = UNSIGNED_INT_NUMPY_DTYPES + SIGNED_INT_NUMPY_DTYPES
+ALL_INT_EA_DTYPES = UNSIGNED_INT_EA_DTYPES + SIGNED_INT_EA_DTYPES
+ALL_INT_DTYPES: list[Dtype] = [*ALL_INT_NUMPY_DTYPES, *ALL_INT_EA_DTYPES]
+
+FLOAT_NUMPY_DTYPES: list[NpDtype] = [float, "float32", "float64"]
+FLOAT_EA_DTYPES: list[Dtype] = ["Float32", "Float64"]
+ALL_FLOAT_DTYPES: list[Dtype] = [*FLOAT_NUMPY_DTYPES, *FLOAT_EA_DTYPES]
+
+COMPLEX_DTYPES: list[Dtype] = [complex, "complex64", "complex128"]
+if using_string_dtype():
+    STRING_DTYPES: list[Dtype] = ["U"]
+else:
+    STRING_DTYPES: list[Dtype] = [str, "str", "U"]  # type: ignore[no-redef]
+COMPLEX_FLOAT_DTYPES: list[Dtype] = [*COMPLEX_DTYPES, *FLOAT_NUMPY_DTYPES]
+
+DATETIME64_DTYPES: list[Dtype] = ["datetime64[ns]", "M8[ns]"]
+TIMEDELTA64_DTYPES: list[Dtype] = ["timedelta64[ns]", "m8[ns]"]
+
+BOOL_DTYPES: list[Dtype] = [bool, "bool"]
+BYTES_DTYPES: list[Dtype] = [bytes, "bytes"]
+OBJECT_DTYPES: list[Dtype] = [object, "object"]
+
+ALL_REAL_NUMPY_DTYPES = FLOAT_NUMPY_DTYPES + ALL_INT_NUMPY_DTYPES
+ALL_REAL_EXTENSION_DTYPES = FLOAT_EA_DTYPES + ALL_INT_EA_DTYPES
+ALL_REAL_DTYPES: list[Dtype] = [*ALL_REAL_NUMPY_DTYPES, *ALL_REAL_EXTENSION_DTYPES]
+ALL_NUMERIC_DTYPES: list[Dtype] = [*ALL_REAL_DTYPES, *COMPLEX_DTYPES]
+
+ALL_NUMPY_DTYPES = (
+    ALL_REAL_NUMPY_DTYPES
+    + COMPLEX_DTYPES
+    + STRING_DTYPES
+    + DATETIME64_DTYPES
+    + TIMEDELTA64_DTYPES
+    + BOOL_DTYPES
+    + OBJECT_DTYPES
+    + BYTES_DTYPES
+)
+
+NARROW_NP_DTYPES = [
+    np.float16,
+    np.float32,
+    np.int8,
+    np.int16,
+    np.int32,
+    np.uint8,
+    np.uint16,
+    np.uint32,
+]
+
+PYTHON_DATA_TYPES = [
+    str,
+    int,
+    float,
+    complex,
+    list,
+    tuple,
+    range,
+    dict,
+    set,
+    frozenset,
+    bool,
+    bytes,
+    bytearray,
+    memoryview,
+]
+
+ENDIAN = {"little": "<", "big": ">"}[byteorder]
+
+NULL_OBJECTS = [None, np.nan, pd.NaT, float("nan"), pd.NA, Decimal("NaN")]
+NP_NAT_OBJECTS = [
+    cls("NaT", unit)
+    for cls in [np.datetime64, np.timedelta64]
+    for unit in [
+        "Y",
+        "M",
+        "W",
+        "D",
+        "h",
+        "m",
+        "s",
+        "ms",
+        "us",
+        "ns",
+        "ps",
+        "fs",
+        "as",
+    ]
+]
+
+if not pa_version_under10p1:
+    import pyarrow as pa
+
+    UNSIGNED_INT_PYARROW_DTYPES = [pa.uint8(), pa.uint16(), pa.uint32(), pa.uint64()]
+    SIGNED_INT_PYARROW_DTYPES = [pa.int8(), pa.int16(), pa.int32(), pa.int64()]
+    ALL_INT_PYARROW_DTYPES = UNSIGNED_INT_PYARROW_DTYPES + SIGNED_INT_PYARROW_DTYPES
+    ALL_INT_PYARROW_DTYPES_STR_REPR = [
+        str(ArrowDtype(typ)) for typ in ALL_INT_PYARROW_DTYPES
+    ]
+
+    # pa.float16 doesn't seem supported
+    # https://github.com/apache/arrow/blob/master/python/pyarrow/src/arrow/python/helpers.cc#L86
+    FLOAT_PYARROW_DTYPES = [pa.float32(), pa.float64()]
+    FLOAT_PYARROW_DTYPES_STR_REPR = [
+        str(ArrowDtype(typ)) for typ in FLOAT_PYARROW_DTYPES
+    ]
+    DECIMAL_PYARROW_DTYPES = [pa.decimal128(7, 3)]
+    STRING_PYARROW_DTYPES = [pa.string()]
+    BINARY_PYARROW_DTYPES = [pa.binary()]
+
+    TIME_PYARROW_DTYPES = [
+        pa.time32("s"),
+        pa.time32("ms"),
+        pa.time64("us"),
+        pa.time64("ns"),
+    ]
+    DATE_PYARROW_DTYPES = [pa.date32(), pa.date64()]
+    DATETIME_PYARROW_DTYPES = [
+        pa.timestamp(unit=unit, tz=tz)
+        for unit in ["s", "ms", "us", "ns"]
+        for tz in [None, "UTC", "US/Pacific", "US/Eastern"]
+    ]
+    TIMEDELTA_PYARROW_DTYPES = [pa.duration(unit) for unit in ["s", "ms", "us", "ns"]]
+
+    BOOL_PYARROW_DTYPES = [pa.bool_()]
+
+    # TODO: Add container like pyarrow types:
+    #  https://arrow.apache.org/docs/python/api/datatypes.html#factory-functions
+    ALL_PYARROW_DTYPES = (
+        ALL_INT_PYARROW_DTYPES
+        + FLOAT_PYARROW_DTYPES
+        + DECIMAL_PYARROW_DTYPES
+        + STRING_PYARROW_DTYPES
+        + BINARY_PYARROW_DTYPES
+        + TIME_PYARROW_DTYPES
+        + DATE_PYARROW_DTYPES
+        + DATETIME_PYARROW_DTYPES
+        + TIMEDELTA_PYARROW_DTYPES
+        + BOOL_PYARROW_DTYPES
+    )
+    ALL_REAL_PYARROW_DTYPES_STR_REPR = (
+        ALL_INT_PYARROW_DTYPES_STR_REPR + FLOAT_PYARROW_DTYPES_STR_REPR
+    )
+else:
+    FLOAT_PYARROW_DTYPES_STR_REPR = []
+    ALL_INT_PYARROW_DTYPES_STR_REPR = []
+    ALL_PYARROW_DTYPES = []
+    ALL_REAL_PYARROW_DTYPES_STR_REPR = []
+
+ALL_REAL_NULLABLE_DTYPES = (
+    FLOAT_NUMPY_DTYPES + ALL_REAL_EXTENSION_DTYPES + ALL_REAL_PYARROW_DTYPES_STR_REPR
+)
+
+arithmetic_dunder_methods = [
+    "__add__",
+    "__radd__",
+    "__sub__",
+    "__rsub__",
+    "__mul__",
+    "__rmul__",
+    "__floordiv__",
+    "__rfloordiv__",
+    "__truediv__",
+    "__rtruediv__",
+    "__pow__",
+    "__rpow__",
+    "__mod__",
+    "__rmod__",
+]
+
+comparison_dunder_methods = ["__eq__", "__ne__", "__le__", "__lt__", "__ge__", "__gt__"]
+
+
+# -----------------------------------------------------------------------------
+# Comparators
+
+
+def box_expected(expected, box_cls, transpose: bool = True):
+    """
+    Helper function to wrap the expected output of a test in a given box_class.
+
+    Parameters
+    ----------
+    expected : np.ndarray, Index, Series
+    box_cls : {Index, Series, DataFrame}
+
+    Returns
+    -------
+    subclass of box_cls
+    """
+    if box_cls is pd.array:
+        if isinstance(expected, RangeIndex):
+            # pd.array would return an IntegerArray
+            expected = NumpyExtensionArray(np.asarray(expected._values))
+        else:
+            expected = pd.array(expected, copy=False)
+    elif box_cls is Index:
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", "Dtype inference", category=FutureWarning)
+            expected = Index(expected)
+    elif box_cls is Series:
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", "Dtype inference", category=FutureWarning)
+            expected = Series(expected)
+    elif box_cls is DataFrame:
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", "Dtype inference", category=FutureWarning)
+            expected = Series(expected).to_frame()
+        if transpose:
+            # for vector operations, we need a DataFrame to be a single-row,
+            #  not a single-column, in order to operate against non-DataFrame
+            #  vectors of the same length. But convert to two rows to avoid
+            #  single-row special cases in datetime arithmetic
+            expected = expected.T
+            expected = pd.concat([expected] * 2, ignore_index=True)
+    elif box_cls is np.ndarray or box_cls is np.array:
+        expected = np.array(expected)
+    elif box_cls is to_array:
+        expected = to_array(expected)
+    else:
+        raise NotImplementedError(box_cls)
+    return expected
+
+
+def to_array(obj):
+    """
+    Similar to pd.array, but does not cast numpy dtypes to nullable dtypes.
+    """
+    # temporary implementation until we get pd.array in place
+    dtype = getattr(obj, "dtype", None)
+
+    if dtype is None:
+        return np.asarray(obj)
+
+    return extract_array(obj, extract_numpy=True)
+
+
+class SubclassedSeries(Series):
+    _metadata = ["testattr", "name"]
+
+    @property
+    def _constructor(self):
+        # For testing, those properties return a generic callable, and not
+        # the actual class. In this case that is equivalent, but it is to
+        # ensure we don't rely on the property returning a class
+        # See https://github.com/pandas-dev/pandas/pull/46018 and
+        # https://github.com/pandas-dev/pandas/issues/32638 and linked issues
+        return lambda *args, **kwargs: SubclassedSeries(*args, **kwargs)
+
+    @property
+    def _constructor_expanddim(self):
+        return lambda *args, **kwargs: SubclassedDataFrame(*args, **kwargs)
+
+
+class SubclassedDataFrame(DataFrame):
+    _metadata = ["testattr"]
+
+    @property
+    def _constructor(self):
+        return lambda *args, **kwargs: SubclassedDataFrame(*args, **kwargs)
+
+    @property
+    def _constructor_sliced(self):
+        return lambda *args, **kwargs: SubclassedSeries(*args, **kwargs)
+
+
+def convert_rows_list_to_csv_str(rows_list: list[str]) -> str:
+    """
+    Convert list of CSV rows to single CSV-formatted string for current OS.
+
+    This method is used for creating expected value of to_csv() method.
+
+    Parameters
+    ----------
+    rows_list : List[str]
+        Each element represents the row of csv.
+
+    Returns
+    -------
+    str
+        Expected output of to_csv() in current OS.
+    """
+    sep = os.linesep
+    return sep.join(rows_list) + sep
+
+
+def external_error_raised(expected_exception: type[Exception]) -> ContextManager:
+    """
+    Helper function to mark pytest.raises that have an external error message.
+
+    Parameters
+    ----------
+    expected_exception : Exception
+        Expected error to raise.
+
+    Returns
+    -------
+    Callable
+        Regular `pytest.raises` function with `match` equal to `None`.
+    """
+    import pytest
+
+    return pytest.raises(expected_exception, match=None)
+
+
+cython_table = pd.core.common._cython_table.items()
+
+
+def get_cython_table_params(ndframe, func_names_and_expected):
+    """
+    Combine frame, functions from com._cython_table
+    keys and expected result.
+
+    Parameters
+    ----------
+    ndframe : DataFrame or Series
+    func_names_and_expected : Sequence of two items
+        The first item is a name of a NDFrame method ('sum', 'prod') etc.
+        The second item is the expected return value.
+
+    Returns
+    -------
+    list
+        List of three items (DataFrame, function, expected result)
+    """
+    results = []
+    for func_name, expected in func_names_and_expected:
+        results.append((ndframe, func_name, expected))
+        results += [
+            (ndframe, func, expected)
+            for func, name in cython_table
+            if name == func_name
+        ]
+    return results
+
+
+def get_op_from_name(op_name: str) -> Callable:
+    """
+    The operator function for a given op name.
+
+    Parameters
+    ----------
+    op_name : str
+        The op name, in form of "add" or "__add__".
+
+    Returns
+    -------
+    function
+        A function performing the operation.
+    """
+    short_opname = op_name.strip("_")
+    try:
+        op = getattr(operator, short_opname)
+    except AttributeError:
+        # Assume it is the reverse operator
+        rop = getattr(operator, short_opname[1:])
+        op = lambda x, y: rop(y, x)
+
+    return op
+
+
+# -----------------------------------------------------------------------------
+# Indexing test helpers
+
+
+def getitem(x):
+    return x
+
+
+def setitem(x):
+    return x
+
+
+def loc(x):
+    return x.loc
+
+
+def iloc(x):
+    return x.iloc
+
+
+def at(x):
+    return x.at
+
+
+def iat(x):
+    return x.iat
+
+
+# -----------------------------------------------------------------------------
+
+_UNITS = ["s", "ms", "us", "ns"]
+
+
+def get_finest_unit(left: str, right: str):
+    """
+    Find the higher of two datetime64 units.
+    """
+    if _UNITS.index(left) >= _UNITS.index(right):
+        return left
+    return right
+
+
+def shares_memory(left, right) -> bool:
+    """
+    Pandas-compat for np.shares_memory.
+    """
+    if isinstance(left, np.ndarray) and isinstance(right, np.ndarray):
+        return np.shares_memory(left, right)
+    elif isinstance(left, np.ndarray):
+        # Call with reversed args to get to unpacking logic below.
+        return shares_memory(right, left)
+
+    if isinstance(left, RangeIndex):
+        return False
+    if isinstance(left, MultiIndex):
+        return shares_memory(left._codes, right)
+    if isinstance(left, (Index, Series)):
+        if isinstance(right, (Index, Series)):
+            return shares_memory(left._values, right._values)
+        return shares_memory(left._values, right)
+
+    if isinstance(left, NDArrayBackedExtensionArray):
+        return shares_memory(left._ndarray, right)
+    if isinstance(left, pd.core.arrays.SparseArray):
+        return shares_memory(left.sp_values, right)
+    if isinstance(left, pd.core.arrays.IntervalArray):
+        return shares_memory(left._left, right) or shares_memory(left._right, right)
+
+    if isinstance(left, ArrowExtensionArray):
+        if isinstance(right, ArrowExtensionArray):
+            # https://github.com/pandas-dev/pandas/pull/43930#discussion_r736862669
+            left_pa_data = left._pa_array
+            right_pa_data = right._pa_array
+            left_buf1 = left_pa_data.chunk(0).buffers()[1]
+            right_buf1 = right_pa_data.chunk(0).buffers()[1]
+            return left_buf1.address == right_buf1.address
+        else:
+            # if we have one one ArrowExtensionArray and one other array, assume
+            # they can only share memory if they share the same numpy buffer
+            return np.shares_memory(left, right)
+
+    if isinstance(left, BaseMaskedArray) and isinstance(right, BaseMaskedArray):
+        # By convention, we'll say these share memory if they share *either*
+        #  the _data or the _mask
+        return np.shares_memory(left._data, right._data) or np.shares_memory(
+            left._mask, right._mask
+        )
+
+    if isinstance(left, DataFrame) and len(left._mgr.arrays) == 1:
+        arr = left._mgr.arrays[0]
+        return shares_memory(arr, right)
+
+    raise NotImplementedError(type(left), type(right))
+
+
+__all__ = [
+    "ALL_INT_EA_DTYPES",
+    "ALL_INT_NUMPY_DTYPES",
+    "ALL_NUMPY_DTYPES",
+    "ALL_REAL_NUMPY_DTYPES",
+    "assert_almost_equal",
+    "assert_attr_equal",
+    "assert_categorical_equal",
+    "assert_class_equal",
+    "assert_contains_all",
+    "assert_copy",
+    "assert_datetime_array_equal",
+    "assert_dict_equal",
+    "assert_equal",
+    "assert_extension_array_equal",
+    "assert_frame_equal",
+    "assert_index_equal",
+    "assert_indexing_slices_equivalent",
+    "assert_interval_array_equal",
+    "assert_is_sorted",
+    "assert_is_valid_plot_return_object",
+    "assert_metadata_equivalent",
+    "assert_numpy_array_equal",
+    "assert_period_array_equal",
+    "assert_produces_warning",
+    "assert_series_equal",
+    "assert_sp_array_equal",
+    "assert_timedelta_array_equal",
+    "assert_cow_warning",
+    "at",
+    "BOOL_DTYPES",
+    "box_expected",
+    "BYTES_DTYPES",
+    "can_set_locale",
+    "COMPLEX_DTYPES",
+    "convert_rows_list_to_csv_str",
+    "DATETIME64_DTYPES",
+    "decompress_file",
+    "ENDIAN",
+    "ensure_clean",
+    "external_error_raised",
+    "FLOAT_EA_DTYPES",
+    "FLOAT_NUMPY_DTYPES",
+    "get_cython_table_params",
+    "get_dtype",
+    "getitem",
+    "get_locales",
+    "get_finest_unit",
+    "get_obj",
+    "get_op_from_name",
+    "iat",
+    "iloc",
+    "loc",
+    "maybe_produces_warning",
+    "NARROW_NP_DTYPES",
+    "NP_NAT_OBJECTS",
+    "NULL_OBJECTS",
+    "OBJECT_DTYPES",
+    "raise_assert_detail",
+    "raises_chained_assignment_error",
+    "round_trip_localpath",
+    "round_trip_pathlib",
+    "round_trip_pickle",
+    "setitem",
+    "set_locale",
+    "set_timezone",
+    "shares_memory",
+    "SIGNED_INT_EA_DTYPES",
+    "SIGNED_INT_NUMPY_DTYPES",
+    "STRING_DTYPES",
+    "SubclassedDataFrame",
+    "SubclassedSeries",
+    "TIMEDELTA64_DTYPES",
+    "to_array",
+    "UNSIGNED_INT_EA_DTYPES",
+    "UNSIGNED_INT_NUMPY_DTYPES",
+    "use_numexpr",
+    "with_csv_dialect",
+    "write_to_compressed",
+]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_testing/_hypothesis.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_testing/_hypothesis.py
new file mode 100644
index 0000000000000000000000000000000000000000..084ca9c306d192a2543108249dbc345d1259be01
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_testing/_hypothesis.py
@@ -0,0 +1,93 @@
+"""
+Hypothesis data generator helpers.
+"""
+from datetime import datetime
+
+from hypothesis import strategies as st
+from hypothesis.extra.dateutil import timezones as dateutil_timezones
+from hypothesis.extra.pytz import timezones as pytz_timezones
+
+from pandas.compat import is_platform_windows
+
+import pandas as pd
+
+from pandas.tseries.offsets import (
+    BMonthBegin,
+    BMonthEnd,
+    BQuarterBegin,
+    BQuarterEnd,
+    BYearBegin,
+    BYearEnd,
+    MonthBegin,
+    MonthEnd,
+    QuarterBegin,
+    QuarterEnd,
+    YearBegin,
+    YearEnd,
+)
+
+OPTIONAL_INTS = st.lists(st.one_of(st.integers(), st.none()), max_size=10, min_size=3)
+
+OPTIONAL_FLOATS = st.lists(st.one_of(st.floats(), st.none()), max_size=10, min_size=3)
+
+OPTIONAL_TEXT = st.lists(st.one_of(st.none(), st.text()), max_size=10, min_size=3)
+
+OPTIONAL_DICTS = st.lists(
+    st.one_of(st.none(), st.dictionaries(st.text(), st.integers())),
+    max_size=10,
+    min_size=3,
+)
+
+OPTIONAL_LISTS = st.lists(
+    st.one_of(st.none(), st.lists(st.text(), max_size=10, min_size=3)),
+    max_size=10,
+    min_size=3,
+)
+
+OPTIONAL_ONE_OF_ALL = st.one_of(
+    OPTIONAL_DICTS, OPTIONAL_FLOATS, OPTIONAL_INTS, OPTIONAL_LISTS, OPTIONAL_TEXT
+)
+
+if is_platform_windows():
+    DATETIME_NO_TZ = st.datetimes(min_value=datetime(1900, 1, 1))
+else:
+    DATETIME_NO_TZ = st.datetimes()
+
+DATETIME_JAN_1_1900_OPTIONAL_TZ = st.datetimes(
+    min_value=pd.Timestamp(
+        1900, 1, 1
+    ).to_pydatetime(),  # pyright: ignore[reportGeneralTypeIssues]
+    max_value=pd.Timestamp(
+        1900, 1, 1
+    ).to_pydatetime(),  # pyright: ignore[reportGeneralTypeIssues]
+    timezones=st.one_of(st.none(), dateutil_timezones(), pytz_timezones()),
+)
+
+DATETIME_IN_PD_TIMESTAMP_RANGE_NO_TZ = st.datetimes(
+    min_value=pd.Timestamp.min.to_pydatetime(warn=False),
+    max_value=pd.Timestamp.max.to_pydatetime(warn=False),
+)
+
+INT_NEG_999_TO_POS_999 = st.integers(-999, 999)
+
+# The strategy for each type is registered in conftest.py, as they don't carry
+# enough runtime information (e.g. type hints) to infer how to build them.
+YQM_OFFSET = st.one_of(
+    *map(
+        st.from_type,
+        [
+            MonthBegin,
+            MonthEnd,
+            BMonthBegin,
+            BMonthEnd,
+            QuarterBegin,
+            QuarterEnd,
+            BQuarterBegin,
+            BQuarterEnd,
+            YearBegin,
+            YearEnd,
+            BYearBegin,
+            BYearEnd,
+        ],
+    )
+)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_testing/_io.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_testing/_io.py
new file mode 100644
index 0000000000000000000000000000000000000000..95977edb600ade42a8f8a1fada2b5085cee1da56
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_testing/_io.py
@@ -0,0 +1,170 @@
+from __future__ import annotations
+
+import gzip
+import io
+import pathlib
+import tarfile
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+)
+import uuid
+import zipfile
+
+from pandas.compat import (
+    get_bz2_file,
+    get_lzma_file,
+)
+from pandas.compat._optional import import_optional_dependency
+
+import pandas as pd
+from pandas._testing.contexts import ensure_clean
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        FilePath,
+        ReadPickleBuffer,
+    )
+
+    from pandas import (
+        DataFrame,
+        Series,
+    )
+
+# ------------------------------------------------------------------
+# File-IO
+
+
+def round_trip_pickle(
+    obj: Any, path: FilePath | ReadPickleBuffer | None = None
+) -> DataFrame | Series:
+    """
+    Pickle an object and then read it again.
+
+    Parameters
+    ----------
+    obj : any object
+        The object to pickle and then re-read.
+    path : str, path object or file-like object, default None
+        The path where the pickled object is written and then read.
+
+    Returns
+    -------
+    pandas object
+        The original object that was pickled and then re-read.
+    """
+    _path = path
+    if _path is None:
+        _path = f"__{uuid.uuid4()}__.pickle"
+    with ensure_clean(_path) as temp_path:
+        pd.to_pickle(obj, temp_path)
+        return pd.read_pickle(temp_path)
+
+
+def round_trip_pathlib(writer, reader, path: str | None = None):
+    """
+    Write an object to file specified by a pathlib.Path and read it back
+
+    Parameters
+    ----------
+    writer : callable bound to pandas object
+        IO writing function (e.g. DataFrame.to_csv )
+    reader : callable
+        IO reading function (e.g. pd.read_csv )
+    path : str, default None
+        The path where the object is written and then read.
+
+    Returns
+    -------
+    pandas object
+        The original object that was serialized and then re-read.
+    """
+    Path = pathlib.Path
+    if path is None:
+        path = "___pathlib___"
+    with ensure_clean(path) as path:
+        writer(Path(path))  # type: ignore[arg-type]
+        obj = reader(Path(path))  # type: ignore[arg-type]
+    return obj
+
+
+def round_trip_localpath(writer, reader, path: str | None = None):
+    """
+    Write an object to file specified by a py.path LocalPath and read it back.
+
+    Parameters
+    ----------
+    writer : callable bound to pandas object
+        IO writing function (e.g. DataFrame.to_csv )
+    reader : callable
+        IO reading function (e.g. pd.read_csv )
+    path : str, default None
+        The path where the object is written and then read.
+
+    Returns
+    -------
+    pandas object
+        The original object that was serialized and then re-read.
+    """
+    import pytest
+
+    LocalPath = pytest.importorskip("py.path").local
+    if path is None:
+        path = "___localpath___"
+    with ensure_clean(path) as path:
+        writer(LocalPath(path))
+        obj = reader(LocalPath(path))
+    return obj
+
+
+def write_to_compressed(compression, path, data, dest: str = "test") -> None:
+    """
+    Write data to a compressed file.
+
+    Parameters
+    ----------
+    compression : {'gzip', 'bz2', 'zip', 'xz', 'zstd'}
+        The compression type to use.
+    path : str
+        The file path to write the data.
+    data : str
+        The data to write.
+    dest : str, default "test"
+        The destination file (for ZIP only)
+
+    Raises
+    ------
+    ValueError : An invalid compression value was passed in.
+    """
+    args: tuple[Any, ...] = (data,)
+    mode = "wb"
+    method = "write"
+    compress_method: Callable
+
+    if compression == "zip":
+        compress_method = zipfile.ZipFile
+        mode = "w"
+        args = (dest, data)
+        method = "writestr"
+    elif compression == "tar":
+        compress_method = tarfile.TarFile
+        mode = "w"
+        file = tarfile.TarInfo(name=dest)
+        bytes = io.BytesIO(data)
+        file.size = len(data)
+        args = (file, bytes)
+        method = "addfile"
+    elif compression == "gzip":
+        compress_method = gzip.GzipFile
+    elif compression == "bz2":
+        compress_method = get_bz2_file()
+    elif compression == "zstd":
+        compress_method = import_optional_dependency("zstandard").open
+    elif compression == "xz":
+        compress_method = get_lzma_file()
+    else:
+        raise ValueError(f"Unrecognized compression type: {compression}")
+
+    with compress_method(path, mode=mode) as f:
+        getattr(f, method)(*args)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_testing/_warnings.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_testing/_warnings.py
new file mode 100644
index 0000000000000000000000000000000000000000..c9a287942f2dac5ddbaf49168db280ec2ba3f2c4
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_testing/_warnings.py
@@ -0,0 +1,232 @@
+from __future__ import annotations
+
+from contextlib import (
+    contextmanager,
+    nullcontext,
+)
+import inspect
+import re
+import sys
+from typing import (
+    TYPE_CHECKING,
+    Literal,
+    cast,
+)
+import warnings
+
+from pandas.compat import PY311
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Generator,
+        Sequence,
+    )
+
+
+@contextmanager
+def assert_produces_warning(
+    expected_warning: type[Warning] | bool | tuple[type[Warning], ...] | None = Warning,
+    filter_level: Literal[
+        "error", "ignore", "always", "default", "module", "once"
+    ] = "always",
+    check_stacklevel: bool = True,
+    raise_on_extra_warnings: bool = True,
+    match: str | None = None,
+) -> Generator[list[warnings.WarningMessage], None, None]:
+    """
+    Context manager for running code expected to either raise a specific warning,
+    multiple specific warnings, or not raise any warnings. Verifies that the code
+    raises the expected warning(s), and that it does not raise any other unexpected
+    warnings. It is basically a wrapper around ``warnings.catch_warnings``.
+
+    Parameters
+    ----------
+    expected_warning : {Warning, False, tuple[Warning, ...], None}, default Warning
+        The type of Exception raised. ``exception.Warning`` is the base
+        class for all warnings. To raise multiple types of exceptions,
+        pass them as a tuple. To check that no warning is returned,
+        specify ``False`` or ``None``.
+    filter_level : str or None, default "always"
+        Specifies whether warnings are ignored, displayed, or turned
+        into errors.
+        Valid values are:
+
+        * "error" - turns matching warnings into exceptions
+        * "ignore" - discard the warning
+        * "always" - always emit a warning
+        * "default" - print the warning the first time it is generated
+          from each location
+        * "module" - print the warning the first time it is generated
+          from each module
+        * "once" - print the warning the first time it is generated
+
+    check_stacklevel : bool, default True
+        If True, displays the line that called the function containing
+        the warning to show were the function is called. Otherwise, the
+        line that implements the function is displayed.
+    raise_on_extra_warnings : bool, default True
+        Whether extra warnings not of the type `expected_warning` should
+        cause the test to fail.
+    match : str, optional
+        Match warning message.
+
+    Examples
+    --------
+    >>> import warnings
+    >>> with assert_produces_warning():
+    ...     warnings.warn(UserWarning())
+    ...
+    >>> with assert_produces_warning(False):
+    ...     warnings.warn(RuntimeWarning())
+    ...
+    Traceback (most recent call last):
+        ...
+    AssertionError: Caused unexpected warning(s): ['RuntimeWarning'].
+    >>> with assert_produces_warning(UserWarning):
+    ...     warnings.warn(RuntimeWarning())
+    Traceback (most recent call last):
+        ...
+    AssertionError: Did not see expected warning of class 'UserWarning'.
+
+    ..warn:: This is *not* thread-safe.
+    """
+    __tracebackhide__ = True
+
+    with warnings.catch_warnings(record=True) as w:
+        warnings.simplefilter(filter_level)
+        try:
+            yield w
+        finally:
+            if expected_warning:
+                expected_warning = cast(type[Warning], expected_warning)
+                _assert_caught_expected_warning(
+                    caught_warnings=w,
+                    expected_warning=expected_warning,
+                    match=match,
+                    check_stacklevel=check_stacklevel,
+                )
+            if raise_on_extra_warnings:
+                _assert_caught_no_extra_warnings(
+                    caught_warnings=w,
+                    expected_warning=expected_warning,
+                )
+
+
+def maybe_produces_warning(warning: type[Warning], condition: bool, **kwargs):
+    """
+    Return a context manager that possibly checks a warning based on the condition
+    """
+    if condition:
+        return assert_produces_warning(warning, **kwargs)
+    else:
+        return nullcontext()
+
+
+def _assert_caught_expected_warning(
+    *,
+    caught_warnings: Sequence[warnings.WarningMessage],
+    expected_warning: type[Warning],
+    match: str | None,
+    check_stacklevel: bool,
+) -> None:
+    """Assert that there was the expected warning among the caught warnings."""
+    saw_warning = False
+    matched_message = False
+    unmatched_messages = []
+
+    for actual_warning in caught_warnings:
+        if issubclass(actual_warning.category, expected_warning):
+            saw_warning = True
+
+            if check_stacklevel:
+                _assert_raised_with_correct_stacklevel(actual_warning)
+
+            if match is not None:
+                if re.search(match, str(actual_warning.message)):
+                    matched_message = True
+                else:
+                    unmatched_messages.append(actual_warning.message)
+
+    if not saw_warning:
+        raise AssertionError(
+            f"Did not see expected warning of class "
+            f"{repr(expected_warning.__name__)}"
+        )
+
+    if match and not matched_message:
+        raise AssertionError(
+            f"Did not see warning {repr(expected_warning.__name__)} "
+            f"matching '{match}'. The emitted warning messages are "
+            f"{unmatched_messages}"
+        )
+
+
+def _assert_caught_no_extra_warnings(
+    *,
+    caught_warnings: Sequence[warnings.WarningMessage],
+    expected_warning: type[Warning] | bool | tuple[type[Warning], ...] | None,
+) -> None:
+    """Assert that no extra warnings apart from the expected ones are caught."""
+    extra_warnings = []
+
+    for actual_warning in caught_warnings:
+        if _is_unexpected_warning(actual_warning, expected_warning):
+            # GH#38630 pytest.filterwarnings does not suppress these.
+            if actual_warning.category == ResourceWarning:
+                # GH 44732: Don't make the CI flaky by filtering SSL-related
+                # ResourceWarning from dependencies
+                if "unclosed  bool:
+    """Check if the actual warning issued is unexpected."""
+    if actual_warning and not expected_warning:
+        return True
+    expected_warning = cast(type[Warning], expected_warning)
+    return bool(not issubclass(actual_warning.category, expected_warning))
+
+
+def _assert_raised_with_correct_stacklevel(
+    actual_warning: warnings.WarningMessage,
+) -> None:
+    # https://stackoverflow.com/questions/17407119/python-inspect-stack-is-slow
+    frame = inspect.currentframe()
+    for _ in range(4):
+        frame = frame.f_back  # type: ignore[union-attr]
+    try:
+        caller_filename = inspect.getfile(frame)  # type: ignore[arg-type]
+    finally:
+        # See note in
+        # https://docs.python.org/3/library/inspect.html#inspect.Traceback
+        del frame
+    msg = (
+        "Warning not set with correct stacklevel. "
+        f"File where warning is raised: {actual_warning.filename} != "
+        f"{caller_filename}. Warning message: {actual_warning.message}"
+    )
+    assert actual_warning.filename == caller_filename, msg
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_testing/asserters.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_testing/asserters.py
new file mode 100644
index 0000000000000000000000000000000000000000..a1f9844669c8c99848796e102878848d565bbd5c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_testing/asserters.py
@@ -0,0 +1,1459 @@
+from __future__ import annotations
+
+import operator
+from typing import (
+    TYPE_CHECKING,
+    Literal,
+    NoReturn,
+    cast,
+)
+
+import numpy as np
+
+from pandas._libs import lib
+from pandas._libs.missing import is_matching_na
+from pandas._libs.sparse import SparseIndex
+import pandas._libs.testing as _testing
+from pandas._libs.tslibs.np_datetime import compare_mismatched_resolutions
+
+from pandas.core.dtypes.common import (
+    is_bool,
+    is_float_dtype,
+    is_integer_dtype,
+    is_number,
+    is_numeric_dtype,
+    needs_i8_conversion,
+)
+from pandas.core.dtypes.dtypes import (
+    CategoricalDtype,
+    DatetimeTZDtype,
+    ExtensionDtype,
+    NumpyEADtype,
+)
+from pandas.core.dtypes.missing import array_equivalent
+
+import pandas as pd
+from pandas import (
+    Categorical,
+    DataFrame,
+    DatetimeIndex,
+    Index,
+    IntervalDtype,
+    IntervalIndex,
+    MultiIndex,
+    PeriodIndex,
+    RangeIndex,
+    Series,
+    TimedeltaIndex,
+)
+from pandas.core.arrays import (
+    DatetimeArray,
+    ExtensionArray,
+    IntervalArray,
+    PeriodArray,
+    TimedeltaArray,
+)
+from pandas.core.arrays.datetimelike import DatetimeLikeArrayMixin
+from pandas.core.arrays.string_ import StringDtype
+from pandas.core.indexes.api import safe_sort_index
+
+from pandas.io.formats.printing import pprint_thing
+
+if TYPE_CHECKING:
+    from pandas._typing import DtypeObj
+
+
+def assert_almost_equal(
+    left,
+    right,
+    check_dtype: bool | Literal["equiv"] = "equiv",
+    rtol: float = 1.0e-5,
+    atol: float = 1.0e-8,
+    **kwargs,
+) -> None:
+    """
+    Check that the left and right objects are approximately equal.
+
+    By approximately equal, we refer to objects that are numbers or that
+    contain numbers which may be equivalent to specific levels of precision.
+
+    Parameters
+    ----------
+    left : object
+    right : object
+    check_dtype : bool or {'equiv'}, default 'equiv'
+        Check dtype if both a and b are the same type. If 'equiv' is passed in,
+        then `RangeIndex` and `Index` with int64 dtype are also considered
+        equivalent when doing type checking.
+    rtol : float, default 1e-5
+        Relative tolerance.
+    atol : float, default 1e-8
+        Absolute tolerance.
+    """
+    if isinstance(left, Index):
+        assert_index_equal(
+            left,
+            right,
+            check_exact=False,
+            exact=check_dtype,
+            rtol=rtol,
+            atol=atol,
+            **kwargs,
+        )
+
+    elif isinstance(left, Series):
+        assert_series_equal(
+            left,
+            right,
+            check_exact=False,
+            check_dtype=check_dtype,
+            rtol=rtol,
+            atol=atol,
+            **kwargs,
+        )
+
+    elif isinstance(left, DataFrame):
+        assert_frame_equal(
+            left,
+            right,
+            check_exact=False,
+            check_dtype=check_dtype,
+            rtol=rtol,
+            atol=atol,
+            **kwargs,
+        )
+
+    else:
+        # Other sequences.
+        if check_dtype:
+            if is_number(left) and is_number(right):
+                # Do not compare numeric classes, like np.float64 and float.
+                pass
+            elif is_bool(left) and is_bool(right):
+                # Do not compare bool classes, like np.bool_ and bool.
+                pass
+            else:
+                if isinstance(left, np.ndarray) or isinstance(right, np.ndarray):
+                    obj = "numpy array"
+                else:
+                    obj = "Input"
+                assert_class_equal(left, right, obj=obj)
+
+        # if we have "equiv", this becomes True
+        _testing.assert_almost_equal(
+            left, right, check_dtype=bool(check_dtype), rtol=rtol, atol=atol, **kwargs
+        )
+
+
+def _check_isinstance(left, right, cls) -> None:
+    """
+    Helper method for our assert_* methods that ensures that
+    the two objects being compared have the right type before
+    proceeding with the comparison.
+
+    Parameters
+    ----------
+    left : The first object being compared.
+    right : The second object being compared.
+    cls : The class type to check against.
+
+    Raises
+    ------
+    AssertionError : Either `left` or `right` is not an instance of `cls`.
+    """
+    cls_name = cls.__name__
+
+    if not isinstance(left, cls):
+        raise AssertionError(
+            f"{cls_name} Expected type {cls}, found {type(left)} instead"
+        )
+    if not isinstance(right, cls):
+        raise AssertionError(
+            f"{cls_name} Expected type {cls}, found {type(right)} instead"
+        )
+
+
+def assert_dict_equal(left, right, compare_keys: bool = True) -> None:
+    _check_isinstance(left, right, dict)
+    _testing.assert_dict_equal(left, right, compare_keys=compare_keys)
+
+
+def assert_index_equal(
+    left: Index,
+    right: Index,
+    exact: bool | str = "equiv",
+    check_names: bool = True,
+    check_exact: bool = True,
+    check_categorical: bool = True,
+    check_order: bool = True,
+    rtol: float = 1.0e-5,
+    atol: float = 1.0e-8,
+    obj: str = "Index",
+) -> None:
+    """
+    Check that left and right Index are equal.
+
+    Parameters
+    ----------
+    left : Index
+    right : Index
+    exact : bool or {'equiv'}, default 'equiv'
+        Whether to check the Index class, dtype and inferred_type
+        are identical. If 'equiv', then RangeIndex can be substituted for
+        Index with an int64 dtype as well.
+    check_names : bool, default True
+        Whether to check the names attribute.
+    check_exact : bool, default True
+        Whether to compare number exactly.
+    check_categorical : bool, default True
+        Whether to compare internal Categorical exactly.
+    check_order : bool, default True
+        Whether to compare the order of index entries as well as their values.
+        If True, both indexes must contain the same elements, in the same order.
+        If False, both indexes must contain the same elements, but in any order.
+    rtol : float, default 1e-5
+        Relative tolerance. Only used when check_exact is False.
+    atol : float, default 1e-8
+        Absolute tolerance. Only used when check_exact is False.
+    obj : str, default 'Index'
+        Specify object name being compared, internally used to show appropriate
+        assertion message.
+
+    Examples
+    --------
+    >>> from pandas import testing as tm
+    >>> a = pd.Index([1, 2, 3])
+    >>> b = pd.Index([1, 2, 3])
+    >>> tm.assert_index_equal(a, b)
+    """
+    __tracebackhide__ = True
+
+    def _check_types(left, right, obj: str = "Index") -> None:
+        if not exact:
+            return
+
+        assert_class_equal(left, right, exact=exact, obj=obj)
+        assert_attr_equal("inferred_type", left, right, obj=obj)
+
+        # Skip exact dtype checking when `check_categorical` is False
+        if isinstance(left.dtype, CategoricalDtype) and isinstance(
+            right.dtype, CategoricalDtype
+        ):
+            if check_categorical:
+                assert_attr_equal("dtype", left, right, obj=obj)
+                assert_index_equal(left.categories, right.categories, exact=exact)
+            return
+
+        assert_attr_equal("dtype", left, right, obj=obj)
+
+    # instance validation
+    _check_isinstance(left, right, Index)
+
+    # class / dtype comparison
+    _check_types(left, right, obj=obj)
+
+    # level comparison
+    if left.nlevels != right.nlevels:
+        msg1 = f"{obj} levels are different"
+        msg2 = f"{left.nlevels}, {left}"
+        msg3 = f"{right.nlevels}, {right}"
+        raise_assert_detail(obj, msg1, msg2, msg3)
+
+    # length comparison
+    if len(left) != len(right):
+        msg1 = f"{obj} length are different"
+        msg2 = f"{len(left)}, {left}"
+        msg3 = f"{len(right)}, {right}"
+        raise_assert_detail(obj, msg1, msg2, msg3)
+
+    # If order doesn't matter then sort the index entries
+    if not check_order:
+        left = safe_sort_index(left)
+        right = safe_sort_index(right)
+
+    # MultiIndex special comparison for little-friendly error messages
+    if isinstance(left, MultiIndex):
+        right = cast(MultiIndex, right)
+
+        for level in range(left.nlevels):
+            lobj = f"MultiIndex level [{level}]"
+            try:
+                # try comparison on levels/codes to avoid densifying MultiIndex
+                assert_index_equal(
+                    left.levels[level],
+                    right.levels[level],
+                    exact=exact,
+                    check_names=check_names,
+                    check_exact=check_exact,
+                    check_categorical=check_categorical,
+                    rtol=rtol,
+                    atol=atol,
+                    obj=lobj,
+                )
+                assert_numpy_array_equal(left.codes[level], right.codes[level])
+            except AssertionError:
+                llevel = left.get_level_values(level)
+                rlevel = right.get_level_values(level)
+
+                assert_index_equal(
+                    llevel,
+                    rlevel,
+                    exact=exact,
+                    check_names=check_names,
+                    check_exact=check_exact,
+                    check_categorical=check_categorical,
+                    rtol=rtol,
+                    atol=atol,
+                    obj=lobj,
+                )
+            # get_level_values may change dtype
+            _check_types(left.levels[level], right.levels[level], obj=obj)
+
+    # skip exact index checking when `check_categorical` is False
+    elif check_exact and check_categorical:
+        if not left.equals(right):
+            mismatch = left._values != right._values
+
+            if not isinstance(mismatch, np.ndarray):
+                mismatch = cast("ExtensionArray", mismatch).fillna(True)
+
+            diff = np.sum(mismatch.astype(int)) * 100.0 / len(left)
+            msg = f"{obj} values are different ({np.round(diff, 5)} %)"
+            raise_assert_detail(obj, msg, left, right)
+    else:
+        # if we have "equiv", this becomes True
+        exact_bool = bool(exact)
+        _testing.assert_almost_equal(
+            left.values,
+            right.values,
+            rtol=rtol,
+            atol=atol,
+            check_dtype=exact_bool,
+            obj=obj,
+            lobj=left,
+            robj=right,
+        )
+
+    # metadata comparison
+    if check_names:
+        assert_attr_equal("names", left, right, obj=obj)
+    if isinstance(left, PeriodIndex) or isinstance(right, PeriodIndex):
+        assert_attr_equal("dtype", left, right, obj=obj)
+    if isinstance(left, IntervalIndex) or isinstance(right, IntervalIndex):
+        assert_interval_array_equal(left._values, right._values)
+
+    if check_categorical:
+        if isinstance(left.dtype, CategoricalDtype) or isinstance(
+            right.dtype, CategoricalDtype
+        ):
+            assert_categorical_equal(left._values, right._values, obj=f"{obj} category")
+
+
+def assert_class_equal(
+    left, right, exact: bool | str = True, obj: str = "Input"
+) -> None:
+    """
+    Checks classes are equal.
+    """
+    __tracebackhide__ = True
+
+    def repr_class(x):
+        if isinstance(x, Index):
+            # return Index as it is to include values in the error message
+            return x
+
+        return type(x).__name__
+
+    def is_class_equiv(idx: Index) -> bool:
+        """Classes that are a RangeIndex (sub-)instance or exactly an `Index` .
+
+        This only checks class equivalence. There is a separate check that the
+        dtype is int64.
+        """
+        return type(idx) is Index or isinstance(idx, RangeIndex)
+
+    if type(left) == type(right):
+        return
+
+    if exact == "equiv":
+        if is_class_equiv(left) and is_class_equiv(right):
+            return
+
+    msg = f"{obj} classes are different"
+    raise_assert_detail(obj, msg, repr_class(left), repr_class(right))
+
+
+def assert_attr_equal(attr: str, left, right, obj: str = "Attributes") -> None:
+    """
+    Check attributes are equal. Both objects must have attribute.
+
+    Parameters
+    ----------
+    attr : str
+        Attribute name being compared.
+    left : object
+    right : object
+    obj : str, default 'Attributes'
+        Specify object name being compared, internally used to show appropriate
+        assertion message
+    """
+    __tracebackhide__ = True
+
+    left_attr = getattr(left, attr)
+    right_attr = getattr(right, attr)
+
+    if left_attr is right_attr or is_matching_na(left_attr, right_attr):
+        # e.g. both np.nan, both NaT, both pd.NA, ...
+        return None
+
+    try:
+        result = left_attr == right_attr
+    except TypeError:
+        # datetimetz on rhs may raise TypeError
+        result = False
+    if (left_attr is pd.NA) ^ (right_attr is pd.NA):
+        result = False
+    elif not isinstance(result, bool):
+        result = result.all()
+
+    if not result:
+        msg = f'Attribute "{attr}" are different'
+        raise_assert_detail(obj, msg, left_attr, right_attr)
+    return None
+
+
+def assert_is_valid_plot_return_object(objs) -> None:
+    from matplotlib.artist import Artist
+    from matplotlib.axes import Axes
+
+    if isinstance(objs, (Series, np.ndarray)):
+        if isinstance(objs, Series):
+            objs = objs._values
+        for el in objs.ravel():
+            msg = (
+                "one of 'objs' is not a matplotlib Axes instance, "
+                f"type encountered {repr(type(el).__name__)}"
+            )
+            assert isinstance(el, (Axes, dict)), msg
+    else:
+        msg = (
+            "objs is neither an ndarray of Artist instances nor a single "
+            "ArtistArtist instance, tuple, or dict, 'objs' is a "
+            f"{repr(type(objs).__name__)}"
+        )
+        assert isinstance(objs, (Artist, tuple, dict)), msg
+
+
+def assert_is_sorted(seq) -> None:
+    """Assert that the sequence is sorted."""
+    if isinstance(seq, (Index, Series)):
+        seq = seq.values
+    # sorting does not change precisions
+    if isinstance(seq, np.ndarray):
+        assert_numpy_array_equal(seq, np.sort(np.array(seq)))
+    else:
+        assert_extension_array_equal(seq, seq[seq.argsort()])
+
+
+def assert_categorical_equal(
+    left,
+    right,
+    check_dtype: bool = True,
+    check_category_order: bool = True,
+    obj: str = "Categorical",
+) -> None:
+    """
+    Test that Categoricals are equivalent.
+
+    Parameters
+    ----------
+    left : Categorical
+    right : Categorical
+    check_dtype : bool, default True
+        Check that integer dtype of the codes are the same.
+    check_category_order : bool, default True
+        Whether the order of the categories should be compared, which
+        implies identical integer codes.  If False, only the resulting
+        values are compared.  The ordered attribute is
+        checked regardless.
+    obj : str, default 'Categorical'
+        Specify object name being compared, internally used to show appropriate
+        assertion message.
+    """
+    _check_isinstance(left, right, Categorical)
+
+    exact: bool | str
+    if isinstance(left.categories, RangeIndex) or isinstance(
+        right.categories, RangeIndex
+    ):
+        exact = "equiv"
+    else:
+        # We still want to require exact matches for Index
+        exact = True
+
+    if check_category_order:
+        assert_index_equal(
+            left.categories, right.categories, obj=f"{obj}.categories", exact=exact
+        )
+        assert_numpy_array_equal(
+            left.codes, right.codes, check_dtype=check_dtype, obj=f"{obj}.codes"
+        )
+    else:
+        try:
+            lc = left.categories.sort_values()
+            rc = right.categories.sort_values()
+        except TypeError:
+            # e.g. '<' not supported between instances of 'int' and 'str'
+            lc, rc = left.categories, right.categories
+        assert_index_equal(lc, rc, obj=f"{obj}.categories", exact=exact)
+        assert_index_equal(
+            left.categories.take(left.codes),
+            right.categories.take(right.codes),
+            obj=f"{obj}.values",
+            exact=exact,
+        )
+
+    assert_attr_equal("ordered", left, right, obj=obj)
+
+
+def assert_interval_array_equal(
+    left, right, exact: bool | Literal["equiv"] = "equiv", obj: str = "IntervalArray"
+) -> None:
+    """
+    Test that two IntervalArrays are equivalent.
+
+    Parameters
+    ----------
+    left, right : IntervalArray
+        The IntervalArrays to compare.
+    exact : bool or {'equiv'}, default 'equiv'
+        Whether to check the Index class, dtype and inferred_type
+        are identical. If 'equiv', then RangeIndex can be substituted for
+        Index with an int64 dtype as well.
+    obj : str, default 'IntervalArray'
+        Specify object name being compared, internally used to show appropriate
+        assertion message
+    """
+    _check_isinstance(left, right, IntervalArray)
+
+    kwargs = {}
+    if left._left.dtype.kind in "mM":
+        # We have a DatetimeArray or TimedeltaArray
+        kwargs["check_freq"] = False
+
+    assert_equal(left._left, right._left, obj=f"{obj}.left", **kwargs)
+    assert_equal(left._right, right._right, obj=f"{obj}.left", **kwargs)
+
+    assert_attr_equal("closed", left, right, obj=obj)
+
+
+def assert_period_array_equal(left, right, obj: str = "PeriodArray") -> None:
+    _check_isinstance(left, right, PeriodArray)
+
+    assert_numpy_array_equal(left._ndarray, right._ndarray, obj=f"{obj}._ndarray")
+    assert_attr_equal("dtype", left, right, obj=obj)
+
+
+def assert_datetime_array_equal(
+    left, right, obj: str = "DatetimeArray", check_freq: bool = True
+) -> None:
+    __tracebackhide__ = True
+    _check_isinstance(left, right, DatetimeArray)
+
+    assert_numpy_array_equal(left._ndarray, right._ndarray, obj=f"{obj}._ndarray")
+    if check_freq:
+        assert_attr_equal("freq", left, right, obj=obj)
+    assert_attr_equal("tz", left, right, obj=obj)
+
+
+def assert_timedelta_array_equal(
+    left, right, obj: str = "TimedeltaArray", check_freq: bool = True
+) -> None:
+    __tracebackhide__ = True
+    _check_isinstance(left, right, TimedeltaArray)
+    assert_numpy_array_equal(left._ndarray, right._ndarray, obj=f"{obj}._ndarray")
+    if check_freq:
+        assert_attr_equal("freq", left, right, obj=obj)
+
+
+def raise_assert_detail(
+    obj, message, left, right, diff=None, first_diff=None, index_values=None
+) -> NoReturn:
+    __tracebackhide__ = True
+
+    msg = f"""{obj} are different
+
+{message}"""
+
+    if isinstance(index_values, Index):
+        index_values = np.asarray(index_values)
+
+    if isinstance(index_values, np.ndarray):
+        msg += f"\n[index]: {pprint_thing(index_values)}"
+
+    if isinstance(left, np.ndarray):
+        left = pprint_thing(left)
+    elif isinstance(left, (CategoricalDtype, NumpyEADtype)):
+        left = repr(left)
+    elif isinstance(left, StringDtype):
+        # TODO(infer_string) this special case could be avoided if we have
+        # a more informative repr https://github.com/pandas-dev/pandas/issues/59342
+        left = f"StringDtype(storage={left.storage}, na_value={left.na_value})"
+
+    if isinstance(right, np.ndarray):
+        right = pprint_thing(right)
+    elif isinstance(right, (CategoricalDtype, NumpyEADtype)):
+        right = repr(right)
+    elif isinstance(right, StringDtype):
+        right = f"StringDtype(storage={right.storage}, na_value={right.na_value})"
+
+    msg += f"""
+[left]:  {left}
+[right]: {right}"""
+
+    if diff is not None:
+        msg += f"\n[diff]: {diff}"
+
+    if first_diff is not None:
+        msg += f"\n{first_diff}"
+
+    raise AssertionError(msg)
+
+
+def assert_numpy_array_equal(
+    left,
+    right,
+    strict_nan: bool = False,
+    check_dtype: bool | Literal["equiv"] = True,
+    err_msg=None,
+    check_same=None,
+    obj: str = "numpy array",
+    index_values=None,
+) -> None:
+    """
+    Check that 'np.ndarray' is equivalent.
+
+    Parameters
+    ----------
+    left, right : numpy.ndarray or iterable
+        The two arrays to be compared.
+    strict_nan : bool, default False
+        If True, consider NaN and None to be different.
+    check_dtype : bool, default True
+        Check dtype if both a and b are np.ndarray.
+    err_msg : str, default None
+        If provided, used as assertion message.
+    check_same : None|'copy'|'same', default None
+        Ensure left and right refer/do not refer to the same memory area.
+    obj : str, default 'numpy array'
+        Specify object name being compared, internally used to show appropriate
+        assertion message.
+    index_values : Index | numpy.ndarray, default None
+        optional index (shared by both left and right), used in output.
+    """
+    __tracebackhide__ = True
+
+    # instance validation
+    # Show a detailed error message when classes are different
+    assert_class_equal(left, right, obj=obj)
+    # both classes must be an np.ndarray
+    _check_isinstance(left, right, np.ndarray)
+
+    def _get_base(obj):
+        return obj.base if getattr(obj, "base", None) is not None else obj
+
+    left_base = _get_base(left)
+    right_base = _get_base(right)
+
+    if check_same == "same":
+        if left_base is not right_base:
+            raise AssertionError(f"{repr(left_base)} is not {repr(right_base)}")
+    elif check_same == "copy":
+        if left_base is right_base:
+            raise AssertionError(f"{repr(left_base)} is {repr(right_base)}")
+
+    def _raise(left, right, err_msg) -> NoReturn:
+        if err_msg is None:
+            if left.shape != right.shape:
+                raise_assert_detail(
+                    obj, f"{obj} shapes are different", left.shape, right.shape
+                )
+
+            diff = 0
+            for left_arr, right_arr in zip(left, right):
+                # count up differences
+                if not array_equivalent(left_arr, right_arr, strict_nan=strict_nan):
+                    diff += 1
+
+            diff = diff * 100.0 / left.size
+            msg = f"{obj} values are different ({np.round(diff, 5)} %)"
+            raise_assert_detail(obj, msg, left, right, index_values=index_values)
+
+        raise AssertionError(err_msg)
+
+    # compare shape and values
+    if not array_equivalent(left, right, strict_nan=strict_nan):
+        _raise(left, right, err_msg)
+
+    if check_dtype:
+        if isinstance(left, np.ndarray) and isinstance(right, np.ndarray):
+            assert_attr_equal("dtype", left, right, obj=obj)
+
+
+def assert_extension_array_equal(
+    left,
+    right,
+    check_dtype: bool | Literal["equiv"] = True,
+    index_values=None,
+    check_exact: bool | lib.NoDefault = lib.no_default,
+    rtol: float | lib.NoDefault = lib.no_default,
+    atol: float | lib.NoDefault = lib.no_default,
+    obj: str = "ExtensionArray",
+) -> None:
+    """
+    Check that left and right ExtensionArrays are equal.
+
+    Parameters
+    ----------
+    left, right : ExtensionArray
+        The two arrays to compare.
+    check_dtype : bool, default True
+        Whether to check if the ExtensionArray dtypes are identical.
+    index_values : Index | numpy.ndarray, default None
+        Optional index (shared by both left and right), used in output.
+    check_exact : bool, default False
+        Whether to compare number exactly.
+
+        .. versionchanged:: 2.2.0
+
+            Defaults to True for integer dtypes if none of
+            ``check_exact``, ``rtol`` and ``atol`` are specified.
+    rtol : float, default 1e-5
+        Relative tolerance. Only used when check_exact is False.
+    atol : float, default 1e-8
+        Absolute tolerance. Only used when check_exact is False.
+    obj : str, default 'ExtensionArray'
+        Specify object name being compared, internally used to show appropriate
+        assertion message.
+
+        .. versionadded:: 2.0.0
+
+    Notes
+    -----
+    Missing values are checked separately from valid values.
+    A mask of missing values is computed for each and checked to match.
+    The remaining all-valid values are cast to object dtype and checked.
+
+    Examples
+    --------
+    >>> from pandas import testing as tm
+    >>> a = pd.Series([1, 2, 3, 4])
+    >>> b, c = a.array, a.array
+    >>> tm.assert_extension_array_equal(b, c)
+    """
+    if (
+        check_exact is lib.no_default
+        and rtol is lib.no_default
+        and atol is lib.no_default
+    ):
+        check_exact = (
+            is_numeric_dtype(left.dtype)
+            and not is_float_dtype(left.dtype)
+            or is_numeric_dtype(right.dtype)
+            and not is_float_dtype(right.dtype)
+        )
+    elif check_exact is lib.no_default:
+        check_exact = False
+
+    rtol = rtol if rtol is not lib.no_default else 1.0e-5
+    atol = atol if atol is not lib.no_default else 1.0e-8
+
+    assert isinstance(left, ExtensionArray), "left is not an ExtensionArray"
+    assert isinstance(right, ExtensionArray), "right is not an ExtensionArray"
+    if check_dtype:
+        assert_attr_equal("dtype", left, right, obj=f"Attributes of {obj}")
+
+    if (
+        isinstance(left, DatetimeLikeArrayMixin)
+        and isinstance(right, DatetimeLikeArrayMixin)
+        and type(right) == type(left)
+    ):
+        # GH 52449
+        if not check_dtype and left.dtype.kind in "mM":
+            if not isinstance(left.dtype, np.dtype):
+                l_unit = cast(DatetimeTZDtype, left.dtype).unit
+            else:
+                l_unit = np.datetime_data(left.dtype)[0]
+            if not isinstance(right.dtype, np.dtype):
+                r_unit = cast(DatetimeTZDtype, right.dtype).unit
+            else:
+                r_unit = np.datetime_data(right.dtype)[0]
+            if (
+                l_unit != r_unit
+                and compare_mismatched_resolutions(
+                    left._ndarray, right._ndarray, operator.eq
+                ).all()
+            ):
+                return
+        # Avoid slow object-dtype comparisons
+        # np.asarray for case where we have a np.MaskedArray
+        assert_numpy_array_equal(
+            np.asarray(left.asi8),
+            np.asarray(right.asi8),
+            index_values=index_values,
+            obj=obj,
+        )
+        return
+
+    left_na = np.asarray(left.isna())
+    right_na = np.asarray(right.isna())
+    assert_numpy_array_equal(
+        left_na, right_na, obj=f"{obj} NA mask", index_values=index_values
+    )
+
+    # Specifically for StringArrayNumpySemantics, validate here we have a valid array
+    if (
+        isinstance(left.dtype, StringDtype)
+        and left.dtype.storage == "python"
+        and left.dtype.na_value is np.nan
+    ):
+        assert np.all(
+            [np.isnan(val) for val in left._ndarray[left_na]]  # type: ignore[attr-defined]
+        ), "wrong missing value sentinels"
+    if (
+        isinstance(right.dtype, StringDtype)
+        and right.dtype.storage == "python"
+        and right.dtype.na_value is np.nan
+    ):
+        assert np.all(
+            [np.isnan(val) for val in right._ndarray[right_na]]  # type: ignore[attr-defined]
+        ), "wrong missing value sentinels"
+
+    left_valid = left[~left_na].to_numpy(dtype=object)
+    right_valid = right[~right_na].to_numpy(dtype=object)
+    if check_exact:
+        assert_numpy_array_equal(
+            left_valid, right_valid, obj=obj, index_values=index_values
+        )
+    else:
+        _testing.assert_almost_equal(
+            left_valid,
+            right_valid,
+            check_dtype=bool(check_dtype),
+            rtol=rtol,
+            atol=atol,
+            obj=obj,
+            index_values=index_values,
+        )
+
+
+# This could be refactored to use the NDFrame.equals method
+def assert_series_equal(
+    left,
+    right,
+    check_dtype: bool | Literal["equiv"] = True,
+    check_index_type: bool | Literal["equiv"] = "equiv",
+    check_series_type: bool = True,
+    check_names: bool = True,
+    check_exact: bool | lib.NoDefault = lib.no_default,
+    check_datetimelike_compat: bool = False,
+    check_categorical: bool = True,
+    check_category_order: bool = True,
+    check_freq: bool = True,
+    check_flags: bool = True,
+    rtol: float | lib.NoDefault = lib.no_default,
+    atol: float | lib.NoDefault = lib.no_default,
+    obj: str = "Series",
+    *,
+    check_index: bool = True,
+    check_like: bool = False,
+) -> None:
+    """
+    Check that left and right Series are equal.
+
+    Parameters
+    ----------
+    left : Series
+    right : Series
+    check_dtype : bool, default True
+        Whether to check the Series dtype is identical.
+    check_index_type : bool or {'equiv'}, default 'equiv'
+        Whether to check the Index class, dtype and inferred_type
+        are identical.
+    check_series_type : bool, default True
+         Whether to check the Series class is identical.
+    check_names : bool, default True
+        Whether to check the Series and Index names attribute.
+    check_exact : bool, default False
+        Whether to compare number exactly.
+
+        .. versionchanged:: 2.2.0
+
+            Defaults to True for integer dtypes if none of
+            ``check_exact``, ``rtol`` and ``atol`` are specified.
+    check_datetimelike_compat : bool, default False
+        Compare datetime-like which is comparable ignoring dtype.
+    check_categorical : bool, default True
+        Whether to compare internal Categorical exactly.
+    check_category_order : bool, default True
+        Whether to compare category order of internal Categoricals.
+    check_freq : bool, default True
+        Whether to check the `freq` attribute on a DatetimeIndex or TimedeltaIndex.
+    check_flags : bool, default True
+        Whether to check the `flags` attribute.
+    rtol : float, default 1e-5
+        Relative tolerance. Only used when check_exact is False.
+    atol : float, default 1e-8
+        Absolute tolerance. Only used when check_exact is False.
+    obj : str, default 'Series'
+        Specify object name being compared, internally used to show appropriate
+        assertion message.
+    check_index : bool, default True
+        Whether to check index equivalence. If False, then compare only values.
+
+        .. versionadded:: 1.3.0
+    check_like : bool, default False
+        If True, ignore the order of the index. Must be False if check_index is False.
+        Note: same labels must be with the same data.
+
+        .. versionadded:: 1.5.0
+
+    Examples
+    --------
+    >>> from pandas import testing as tm
+    >>> a = pd.Series([1, 2, 3, 4])
+    >>> b = pd.Series([1, 2, 3, 4])
+    >>> tm.assert_series_equal(a, b)
+    """
+    __tracebackhide__ = True
+    check_exact_index = False if check_exact is lib.no_default else check_exact
+    if (
+        check_exact is lib.no_default
+        and rtol is lib.no_default
+        and atol is lib.no_default
+    ):
+        check_exact = (
+            is_numeric_dtype(left.dtype)
+            and not is_float_dtype(left.dtype)
+            or is_numeric_dtype(right.dtype)
+            and not is_float_dtype(right.dtype)
+        )
+    elif check_exact is lib.no_default:
+        check_exact = False
+
+    rtol = rtol if rtol is not lib.no_default else 1.0e-5
+    atol = atol if atol is not lib.no_default else 1.0e-8
+
+    if not check_index and check_like:
+        raise ValueError("check_like must be False if check_index is False")
+
+    # instance validation
+    _check_isinstance(left, right, Series)
+
+    if check_series_type:
+        assert_class_equal(left, right, obj=obj)
+
+    # length comparison
+    if len(left) != len(right):
+        msg1 = f"{len(left)}, {left.index}"
+        msg2 = f"{len(right)}, {right.index}"
+        raise_assert_detail(obj, "Series length are different", msg1, msg2)
+
+    if check_flags:
+        assert left.flags == right.flags, f"{repr(left.flags)} != {repr(right.flags)}"
+
+    if check_index:
+        # GH #38183
+        assert_index_equal(
+            left.index,
+            right.index,
+            exact=check_index_type,
+            check_names=check_names,
+            check_exact=check_exact_index,
+            check_categorical=check_categorical,
+            check_order=not check_like,
+            rtol=rtol,
+            atol=atol,
+            obj=f"{obj}.index",
+        )
+
+    if check_like:
+        left = left.reindex_like(right)
+
+    if check_freq and isinstance(left.index, (DatetimeIndex, TimedeltaIndex)):
+        lidx = left.index
+        ridx = right.index
+        assert lidx.freq == ridx.freq, (lidx.freq, ridx.freq)
+
+    if check_dtype:
+        # We want to skip exact dtype checking when `check_categorical`
+        # is False. We'll still raise if only one is a `Categorical`,
+        # regardless of `check_categorical`
+        if (
+            isinstance(left.dtype, CategoricalDtype)
+            and isinstance(right.dtype, CategoricalDtype)
+            and not check_categorical
+        ):
+            pass
+        else:
+            assert_attr_equal("dtype", left, right, obj=f"Attributes of {obj}")
+    if check_exact:
+        left_values = left._values
+        right_values = right._values
+        # Only check exact if dtype is numeric
+        if isinstance(left_values, ExtensionArray) and isinstance(
+            right_values, ExtensionArray
+        ):
+            assert_extension_array_equal(
+                left_values,
+                right_values,
+                check_dtype=check_dtype,
+                index_values=left.index,
+                obj=str(obj),
+            )
+        else:
+            # convert both to NumPy if not, check_dtype would raise earlier
+            lv, rv = left_values, right_values
+            if isinstance(left_values, ExtensionArray):
+                lv = left_values.to_numpy()
+            if isinstance(right_values, ExtensionArray):
+                rv = right_values.to_numpy()
+            assert_numpy_array_equal(
+                lv,
+                rv,
+                check_dtype=check_dtype,
+                obj=str(obj),
+                index_values=left.index,
+            )
+    elif check_datetimelike_compat and (
+        needs_i8_conversion(left.dtype) or needs_i8_conversion(right.dtype)
+    ):
+        # we want to check only if we have compat dtypes
+        # e.g. integer and M|m are NOT compat, but we can simply check
+        # the values in that case
+
+        # datetimelike may have different objects (e.g. datetime.datetime
+        # vs Timestamp) but will compare equal
+        if not Index(left._values).equals(Index(right._values)):
+            msg = (
+                f"[datetimelike_compat=True] {left._values} "
+                f"is not equal to {right._values}."
+            )
+            raise AssertionError(msg)
+    elif isinstance(left.dtype, IntervalDtype) and isinstance(
+        right.dtype, IntervalDtype
+    ):
+        assert_interval_array_equal(left.array, right.array)
+    elif isinstance(left.dtype, CategoricalDtype) or isinstance(
+        right.dtype, CategoricalDtype
+    ):
+        _testing.assert_almost_equal(
+            left._values,
+            right._values,
+            rtol=rtol,
+            atol=atol,
+            check_dtype=bool(check_dtype),
+            obj=str(obj),
+            index_values=left.index,
+        )
+    elif isinstance(left.dtype, ExtensionDtype) and isinstance(
+        right.dtype, ExtensionDtype
+    ):
+        assert_extension_array_equal(
+            left._values,
+            right._values,
+            rtol=rtol,
+            atol=atol,
+            check_dtype=check_dtype,
+            index_values=left.index,
+            obj=str(obj),
+        )
+    elif is_extension_array_dtype_and_needs_i8_conversion(
+        left.dtype, right.dtype
+    ) or is_extension_array_dtype_and_needs_i8_conversion(right.dtype, left.dtype):
+        assert_extension_array_equal(
+            left._values,
+            right._values,
+            check_dtype=check_dtype,
+            index_values=left.index,
+            obj=str(obj),
+        )
+    elif needs_i8_conversion(left.dtype) and needs_i8_conversion(right.dtype):
+        # DatetimeArray or TimedeltaArray
+        assert_extension_array_equal(
+            left._values,
+            right._values,
+            check_dtype=check_dtype,
+            index_values=left.index,
+            obj=str(obj),
+        )
+    else:
+        _testing.assert_almost_equal(
+            left._values,
+            right._values,
+            rtol=rtol,
+            atol=atol,
+            check_dtype=bool(check_dtype),
+            obj=str(obj),
+            index_values=left.index,
+        )
+
+    # metadata comparison
+    if check_names:
+        assert_attr_equal("name", left, right, obj=obj)
+
+    if check_categorical:
+        if isinstance(left.dtype, CategoricalDtype) or isinstance(
+            right.dtype, CategoricalDtype
+        ):
+            assert_categorical_equal(
+                left._values,
+                right._values,
+                obj=f"{obj} category",
+                check_category_order=check_category_order,
+            )
+
+
+# This could be refactored to use the NDFrame.equals method
+def assert_frame_equal(
+    left,
+    right,
+    check_dtype: bool | Literal["equiv"] = True,
+    check_index_type: bool | Literal["equiv"] = "equiv",
+    check_column_type: bool | Literal["equiv"] = "equiv",
+    check_frame_type: bool = True,
+    check_names: bool = True,
+    by_blocks: bool = False,
+    check_exact: bool | lib.NoDefault = lib.no_default,
+    check_datetimelike_compat: bool = False,
+    check_categorical: bool = True,
+    check_like: bool = False,
+    check_freq: bool = True,
+    check_flags: bool = True,
+    rtol: float | lib.NoDefault = lib.no_default,
+    atol: float | lib.NoDefault = lib.no_default,
+    obj: str = "DataFrame",
+) -> None:
+    """
+    Check that left and right DataFrame are equal.
+
+    This function is intended to compare two DataFrames and output any
+    differences. It is mostly intended for use in unit tests.
+    Additional parameters allow varying the strictness of the
+    equality checks performed.
+
+    Parameters
+    ----------
+    left : DataFrame
+        First DataFrame to compare.
+    right : DataFrame
+        Second DataFrame to compare.
+    check_dtype : bool, default True
+        Whether to check the DataFrame dtype is identical.
+    check_index_type : bool or {'equiv'}, default 'equiv'
+        Whether to check the Index class, dtype and inferred_type
+        are identical.
+    check_column_type : bool or {'equiv'}, default 'equiv'
+        Whether to check the columns class, dtype and inferred_type
+        are identical. Is passed as the ``exact`` argument of
+        :func:`assert_index_equal`.
+    check_frame_type : bool, default True
+        Whether to check the DataFrame class is identical.
+    check_names : bool, default True
+        Whether to check that the `names` attribute for both the `index`
+        and `column` attributes of the DataFrame is identical.
+    by_blocks : bool, default False
+        Specify how to compare internal data. If False, compare by columns.
+        If True, compare by blocks.
+    check_exact : bool, default False
+        Whether to compare number exactly.
+
+        .. versionchanged:: 2.2.0
+
+            Defaults to True for integer dtypes if none of
+            ``check_exact``, ``rtol`` and ``atol`` are specified.
+    check_datetimelike_compat : bool, default False
+        Compare datetime-like which is comparable ignoring dtype.
+    check_categorical : bool, default True
+        Whether to compare internal Categorical exactly.
+    check_like : bool, default False
+        If True, ignore the order of index & columns.
+        Note: index labels must match their respective rows
+        (same as in columns) - same labels must be with the same data.
+    check_freq : bool, default True
+        Whether to check the `freq` attribute on a DatetimeIndex or TimedeltaIndex.
+    check_flags : bool, default True
+        Whether to check the `flags` attribute.
+    rtol : float, default 1e-5
+        Relative tolerance. Only used when check_exact is False.
+    atol : float, default 1e-8
+        Absolute tolerance. Only used when check_exact is False.
+    obj : str, default 'DataFrame'
+        Specify object name being compared, internally used to show appropriate
+        assertion message.
+
+    See Also
+    --------
+    assert_series_equal : Equivalent method for asserting Series equality.
+    DataFrame.equals : Check DataFrame equality.
+
+    Examples
+    --------
+    This example shows comparing two DataFrames that are equal
+    but with columns of differing dtypes.
+
+    >>> from pandas.testing import assert_frame_equal
+    >>> df1 = pd.DataFrame({'a': [1, 2], 'b': [3, 4]})
+    >>> df2 = pd.DataFrame({'a': [1, 2], 'b': [3.0, 4.0]})
+
+    df1 equals itself.
+
+    >>> assert_frame_equal(df1, df1)
+
+    df1 differs from df2 as column 'b' is of a different type.
+
+    >>> assert_frame_equal(df1, df2)
+    Traceback (most recent call last):
+    ...
+    AssertionError: Attributes of DataFrame.iloc[:, 1] (column name="b") are different
+
+    Attribute "dtype" are different
+    [left]:  int64
+    [right]: float64
+
+    Ignore differing dtypes in columns with check_dtype.
+
+    >>> assert_frame_equal(df1, df2, check_dtype=False)
+    """
+    __tracebackhide__ = True
+    _rtol = rtol if rtol is not lib.no_default else 1.0e-5
+    _atol = atol if atol is not lib.no_default else 1.0e-8
+    _check_exact = check_exact if check_exact is not lib.no_default else False
+
+    # instance validation
+    _check_isinstance(left, right, DataFrame)
+
+    if check_frame_type:
+        assert isinstance(left, type(right))
+        # assert_class_equal(left, right, obj=obj)
+
+    # shape comparison
+    if left.shape != right.shape:
+        raise_assert_detail(
+            obj, f"{obj} shape mismatch", f"{repr(left.shape)}", f"{repr(right.shape)}"
+        )
+
+    if check_flags:
+        assert left.flags == right.flags, f"{repr(left.flags)} != {repr(right.flags)}"
+
+    # index comparison
+    assert_index_equal(
+        left.index,
+        right.index,
+        exact=check_index_type,
+        check_names=check_names,
+        check_exact=_check_exact,
+        check_categorical=check_categorical,
+        check_order=not check_like,
+        rtol=_rtol,
+        atol=_atol,
+        obj=f"{obj}.index",
+    )
+
+    # column comparison
+    assert_index_equal(
+        left.columns,
+        right.columns,
+        exact=check_column_type,
+        check_names=check_names,
+        check_exact=_check_exact,
+        check_categorical=check_categorical,
+        check_order=not check_like,
+        rtol=_rtol,
+        atol=_atol,
+        obj=f"{obj}.columns",
+    )
+
+    if check_like:
+        left = left.reindex_like(right)
+
+    # compare by blocks
+    if by_blocks:
+        rblocks = right._to_dict_of_blocks()
+        lblocks = left._to_dict_of_blocks()
+        for dtype in list(set(list(lblocks.keys()) + list(rblocks.keys()))):
+            assert dtype in lblocks
+            assert dtype in rblocks
+            assert_frame_equal(
+                lblocks[dtype], rblocks[dtype], check_dtype=check_dtype, obj=obj
+            )
+
+    # compare by columns
+    else:
+        for i, col in enumerate(left.columns):
+            # We have already checked that columns match, so we can do
+            #  fast location-based lookups
+            lcol = left._ixs(i, axis=1)
+            rcol = right._ixs(i, axis=1)
+
+            # GH #38183
+            # use check_index=False, because we do not want to run
+            # assert_index_equal for each column,
+            # as we already checked it for the whole dataframe before.
+            assert_series_equal(
+                lcol,
+                rcol,
+                check_dtype=check_dtype,
+                check_index_type=check_index_type,
+                check_exact=check_exact,
+                check_names=check_names,
+                check_datetimelike_compat=check_datetimelike_compat,
+                check_categorical=check_categorical,
+                check_freq=check_freq,
+                obj=f'{obj}.iloc[:, {i}] (column name="{col}")',
+                rtol=rtol,
+                atol=atol,
+                check_index=False,
+                check_flags=False,
+            )
+
+
+def assert_equal(left, right, **kwargs) -> None:
+    """
+    Wrapper for tm.assert_*_equal to dispatch to the appropriate test function.
+
+    Parameters
+    ----------
+    left, right : Index, Series, DataFrame, ExtensionArray, or np.ndarray
+        The two items to be compared.
+    **kwargs
+        All keyword arguments are passed through to the underlying assert method.
+    """
+    __tracebackhide__ = True
+
+    if isinstance(left, Index):
+        assert_index_equal(left, right, **kwargs)
+        if isinstance(left, (DatetimeIndex, TimedeltaIndex)):
+            assert left.freq == right.freq, (left.freq, right.freq)
+    elif isinstance(left, Series):
+        assert_series_equal(left, right, **kwargs)
+    elif isinstance(left, DataFrame):
+        assert_frame_equal(left, right, **kwargs)
+    elif isinstance(left, IntervalArray):
+        assert_interval_array_equal(left, right, **kwargs)
+    elif isinstance(left, PeriodArray):
+        assert_period_array_equal(left, right, **kwargs)
+    elif isinstance(left, DatetimeArray):
+        assert_datetime_array_equal(left, right, **kwargs)
+    elif isinstance(left, TimedeltaArray):
+        assert_timedelta_array_equal(left, right, **kwargs)
+    elif isinstance(left, ExtensionArray):
+        assert_extension_array_equal(left, right, **kwargs)
+    elif isinstance(left, np.ndarray):
+        assert_numpy_array_equal(left, right, **kwargs)
+    elif isinstance(left, str):
+        assert kwargs == {}
+        assert left == right
+    else:
+        assert kwargs == {}
+        assert_almost_equal(left, right)
+
+
+def assert_sp_array_equal(left, right) -> None:
+    """
+    Check that the left and right SparseArray are equal.
+
+    Parameters
+    ----------
+    left : SparseArray
+    right : SparseArray
+    """
+    _check_isinstance(left, right, pd.arrays.SparseArray)
+
+    assert_numpy_array_equal(left.sp_values, right.sp_values)
+
+    # SparseIndex comparison
+    assert isinstance(left.sp_index, SparseIndex)
+    assert isinstance(right.sp_index, SparseIndex)
+
+    left_index = left.sp_index
+    right_index = right.sp_index
+
+    if not left_index.equals(right_index):
+        raise_assert_detail(
+            "SparseArray.index", "index are not equal", left_index, right_index
+        )
+    else:
+        # Just ensure a
+        pass
+
+    assert_attr_equal("fill_value", left, right)
+    assert_attr_equal("dtype", left, right)
+    assert_numpy_array_equal(left.to_dense(), right.to_dense())
+
+
+def assert_contains_all(iterable, dic) -> None:
+    for k in iterable:
+        assert k in dic, f"Did not contain item: {repr(k)}"
+
+
+def assert_copy(iter1, iter2, **eql_kwargs) -> None:
+    """
+    iter1, iter2: iterables that produce elements
+    comparable with assert_almost_equal
+
+    Checks that the elements are equal, but not
+    the same object. (Does not check that items
+    in sequences are also not the same object)
+    """
+    for elem1, elem2 in zip(iter1, iter2):
+        assert_almost_equal(elem1, elem2, **eql_kwargs)
+        msg = (
+            f"Expected object {repr(type(elem1))} and object {repr(type(elem2))} to be "
+            "different objects, but they were the same object."
+        )
+        assert elem1 is not elem2, msg
+
+
+def is_extension_array_dtype_and_needs_i8_conversion(
+    left_dtype: DtypeObj, right_dtype: DtypeObj
+) -> bool:
+    """
+    Checks that we have the combination of an ExtensionArraydtype and
+    a dtype that should be converted to int64
+
+    Returns
+    -------
+    bool
+
+    Related to issue #37609
+    """
+    return isinstance(left_dtype, ExtensionDtype) and needs_i8_conversion(right_dtype)
+
+
+def assert_indexing_slices_equivalent(ser: Series, l_slc: slice, i_slc: slice) -> None:
+    """
+    Check that ser.iloc[i_slc] matches ser.loc[l_slc] and, if applicable,
+    ser[l_slc].
+    """
+    expected = ser.iloc[i_slc]
+
+    assert_series_equal(ser.loc[l_slc], expected)
+
+    if not is_integer_dtype(ser.index):
+        # For integer indices, .loc and plain getitem are position-based.
+        assert_series_equal(ser[l_slc], expected)
+
+
+def assert_metadata_equivalent(
+    left: DataFrame | Series, right: DataFrame | Series | None = None
+) -> None:
+    """
+    Check that ._metadata attributes are equivalent.
+    """
+    for attr in left._metadata:
+        val = getattr(left, attr, None)
+        if right is None:
+            assert val is None
+        else:
+            assert val == getattr(right, attr, None)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_testing/compat.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_testing/compat.py
new file mode 100644
index 0000000000000000000000000000000000000000..cc352ba7b8f2f5a5548d4d5749d3b48ac838aced
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_testing/compat.py
@@ -0,0 +1,29 @@
+"""
+Helpers for sharing tests between DataFrame/Series
+"""
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+from pandas import DataFrame
+
+if TYPE_CHECKING:
+    from pandas._typing import DtypeObj
+
+
+def get_dtype(obj) -> DtypeObj:
+    if isinstance(obj, DataFrame):
+        # Note: we are assuming only one column
+        return obj.dtypes.iat[0]
+    else:
+        return obj.dtype
+
+
+def get_obj(df: DataFrame, klass):
+    """
+    For sharing tests using frame_or_series, either return the DataFrame
+    unchanged or return it's first column as a Series.
+    """
+    if klass is DataFrame:
+        return df
+    return df._ixs(0, axis=1)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_testing/contexts.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_testing/contexts.py
new file mode 100644
index 0000000000000000000000000000000000000000..aa409a90a22f86b03dbcabcce28d4f4663876a4b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_testing/contexts.py
@@ -0,0 +1,261 @@
+from __future__ import annotations
+
+from contextlib import contextmanager
+import os
+from pathlib import Path
+import tempfile
+from typing import (
+    IO,
+    TYPE_CHECKING,
+    Any,
+)
+import uuid
+
+from pandas._config import using_copy_on_write
+
+from pandas.compat import (
+    PYPY,
+    WARNING_CHECK_DISABLED,
+)
+from pandas.errors import ChainedAssignmentError
+
+from pandas import set_option
+
+from pandas.io.common import get_handle
+
+if TYPE_CHECKING:
+    from collections.abc import Generator
+
+    from pandas._typing import (
+        BaseBuffer,
+        CompressionOptions,
+        FilePath,
+    )
+
+
+@contextmanager
+def decompress_file(
+    path: FilePath | BaseBuffer, compression: CompressionOptions
+) -> Generator[IO[bytes], None, None]:
+    """
+    Open a compressed file and return a file object.
+
+    Parameters
+    ----------
+    path : str
+        The path where the file is read from.
+
+    compression : {'gzip', 'bz2', 'zip', 'xz', 'zstd', None}
+        Name of the decompression to use
+
+    Returns
+    -------
+    file object
+    """
+    with get_handle(path, "rb", compression=compression, is_text=False) as handle:
+        yield handle.handle
+
+
+@contextmanager
+def set_timezone(tz: str) -> Generator[None, None, None]:
+    """
+    Context manager for temporarily setting a timezone.
+
+    Parameters
+    ----------
+    tz : str
+        A string representing a valid timezone.
+
+    Examples
+    --------
+    >>> from datetime import datetime
+    >>> from dateutil.tz import tzlocal
+    >>> tzlocal().tzname(datetime(2021, 1, 1))  # doctest: +SKIP
+    'IST'
+
+    >>> with set_timezone('US/Eastern'):
+    ...     tzlocal().tzname(datetime(2021, 1, 1))
+    ...
+    'EST'
+    """
+    import time
+
+    def setTZ(tz) -> None:
+        if hasattr(time, "tzset"):
+            if tz is None:
+                try:
+                    del os.environ["TZ"]
+                except KeyError:
+                    pass
+            else:
+                os.environ["TZ"] = tz
+                time.tzset()
+
+    orig_tz = os.environ.get("TZ")
+    setTZ(tz)
+    try:
+        yield
+    finally:
+        setTZ(orig_tz)
+
+
+@contextmanager
+def ensure_clean(
+    filename=None, return_filelike: bool = False, **kwargs: Any
+) -> Generator[Any, None, None]:
+    """
+    Gets a temporary path and agrees to remove on close.
+
+    This implementation does not use tempfile.mkstemp to avoid having a file handle.
+    If the code using the returned path wants to delete the file itself, windows
+    requires that no program has a file handle to it.
+
+    Parameters
+    ----------
+    filename : str (optional)
+        suffix of the created file.
+    return_filelike : bool (default False)
+        if True, returns a file-like which is *always* cleaned. Necessary for
+        savefig and other functions which want to append extensions.
+    **kwargs
+        Additional keywords are passed to open().
+
+    """
+    folder = Path(tempfile.gettempdir())
+
+    if filename is None:
+        filename = ""
+    filename = str(uuid.uuid4()) + filename
+    path = folder / filename
+
+    path.touch()
+
+    handle_or_str: str | IO = str(path)
+    encoding = kwargs.pop("encoding", None)
+    if return_filelike:
+        kwargs.setdefault("mode", "w+b")
+        if encoding is None and "b" not in kwargs["mode"]:
+            encoding = "utf-8"
+        handle_or_str = open(path, encoding=encoding, **kwargs)
+
+    try:
+        yield handle_or_str
+    finally:
+        if not isinstance(handle_or_str, str):
+            handle_or_str.close()
+        if path.is_file():
+            path.unlink()
+
+
+@contextmanager
+def with_csv_dialect(name: str, **kwargs) -> Generator[None, None, None]:
+    """
+    Context manager to temporarily register a CSV dialect for parsing CSV.
+
+    Parameters
+    ----------
+    name : str
+        The name of the dialect.
+    kwargs : mapping
+        The parameters for the dialect.
+
+    Raises
+    ------
+    ValueError : the name of the dialect conflicts with a builtin one.
+
+    See Also
+    --------
+    csv : Python's CSV library.
+    """
+    import csv
+
+    _BUILTIN_DIALECTS = {"excel", "excel-tab", "unix"}
+
+    if name in _BUILTIN_DIALECTS:
+        raise ValueError("Cannot override builtin dialect.")
+
+    csv.register_dialect(name, **kwargs)
+    try:
+        yield
+    finally:
+        csv.unregister_dialect(name)
+
+
+@contextmanager
+def use_numexpr(use, min_elements=None) -> Generator[None, None, None]:
+    from pandas.core.computation import expressions as expr
+
+    if min_elements is None:
+        min_elements = expr._MIN_ELEMENTS
+
+    olduse = expr.USE_NUMEXPR
+    oldmin = expr._MIN_ELEMENTS
+    set_option("compute.use_numexpr", use)
+    expr._MIN_ELEMENTS = min_elements
+    try:
+        yield
+    finally:
+        expr._MIN_ELEMENTS = oldmin
+        set_option("compute.use_numexpr", olduse)
+
+
+def raises_chained_assignment_error(warn=True, extra_warnings=(), extra_match=()):
+    from pandas._testing import assert_produces_warning
+
+    if not warn:
+        from contextlib import nullcontext
+
+        return nullcontext()
+
+    if (PYPY or WARNING_CHECK_DISABLED) and not extra_warnings:
+        from contextlib import nullcontext
+
+        return nullcontext()
+    elif (PYPY or WARNING_CHECK_DISABLED) and extra_warnings:
+        return assert_produces_warning(
+            extra_warnings,
+            match="|".join(extra_match),
+        )
+    else:
+        if using_copy_on_write():
+            warning = ChainedAssignmentError
+            match = (
+                "A value is trying to be set on a copy of a DataFrame or Series "
+                "through chained assignment"
+            )
+        else:
+            warning = FutureWarning  # type: ignore[assignment]
+            # TODO update match
+            match = "ChainedAssignmentError"
+        if extra_warnings:
+            warning = (warning, *extra_warnings)  # type: ignore[assignment]
+        return assert_produces_warning(
+            warning,
+            match="|".join((match, *extra_match)),
+        )
+
+
+def assert_cow_warning(warn=True, match=None, **kwargs):
+    """
+    Assert that a warning is raised in the CoW warning mode.
+
+    Parameters
+    ----------
+    warn : bool, default True
+        By default, check that a warning is raised. Can be turned off by passing False.
+    match : str
+        The warning message to match against, if different from the default.
+    kwargs
+        Passed through to assert_produces_warning
+    """
+    from pandas._testing import assert_produces_warning
+
+    if not warn or WARNING_CHECK_DISABLED:
+        from contextlib import nullcontext
+
+        return nullcontext()
+
+    if not match:
+        match = "Setting a value on a view"
+
+    return assert_produces_warning(FutureWarning, match=match, **kwargs)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_typing.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_typing.py
new file mode 100644
index 0000000000000000000000000000000000000000..3df9a47a35fca32547947560a8df1cea1d1863c2
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_typing.py
@@ -0,0 +1,525 @@
+from __future__ import annotations
+
+from collections.abc import (
+    Hashable,
+    Iterator,
+    Mapping,
+    MutableMapping,
+    Sequence,
+)
+from datetime import (
+    date,
+    datetime,
+    timedelta,
+    tzinfo,
+)
+from os import PathLike
+import sys
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Literal,
+    Optional,
+    Protocol,
+    Type as type_t,
+    TypeVar,
+    Union,
+    overload,
+)
+
+import numpy as np
+
+# To prevent import cycles place any internal imports in the branch below
+# and use a string literal forward reference to it in subsequent types
+# https://mypy.readthedocs.io/en/latest/common_issues.html#import-cycles
+if TYPE_CHECKING:
+    import numpy.typing as npt
+
+    from pandas._libs import (
+        NaTType,
+        Period,
+        Timedelta,
+        Timestamp,
+    )
+    from pandas._libs.tslibs import BaseOffset
+
+    from pandas.core.dtypes.dtypes import ExtensionDtype
+
+    from pandas import Interval
+    from pandas.arrays import (
+        DatetimeArray,
+        TimedeltaArray,
+    )
+    from pandas.core.arrays.base import ExtensionArray
+    from pandas.core.frame import DataFrame
+    from pandas.core.generic import NDFrame
+    from pandas.core.groupby.generic import (
+        DataFrameGroupBy,
+        GroupBy,
+        SeriesGroupBy,
+    )
+    from pandas.core.indexes.base import Index
+    from pandas.core.internals import (
+        ArrayManager,
+        BlockManager,
+        SingleArrayManager,
+        SingleBlockManager,
+    )
+    from pandas.core.resample import Resampler
+    from pandas.core.series import Series
+    from pandas.core.window.rolling import BaseWindow
+
+    from pandas.io.formats.format import EngFormatter
+    from pandas.tseries.holiday import AbstractHolidayCalendar
+
+    ScalarLike_co = Union[
+        int,
+        float,
+        complex,
+        str,
+        bytes,
+        np.generic,
+    ]
+
+    # numpy compatible types
+    NumpyValueArrayLike = Union[ScalarLike_co, npt.ArrayLike]
+    # Name "npt._ArrayLikeInt_co" is not defined  [name-defined]
+    NumpySorter = Optional[npt._ArrayLikeInt_co]  # type: ignore[name-defined]
+
+    from typing import SupportsIndex
+
+    if sys.version_info >= (3, 10):
+        from typing import TypeGuard  # pyright: ignore[reportUnusedImport]
+    else:
+        from typing_extensions import TypeGuard  # pyright: ignore[reportUnusedImport]
+
+    if sys.version_info >= (3, 11):
+        from typing import Self  # pyright: ignore[reportUnusedImport]
+    else:
+        from typing_extensions import Self  # pyright: ignore[reportUnusedImport]
+else:
+    npt: Any = None
+    Self: Any = None
+    TypeGuard: Any = None
+
+HashableT = TypeVar("HashableT", bound=Hashable)
+MutableMappingT = TypeVar("MutableMappingT", bound=MutableMapping)
+
+# array-like
+
+ArrayLike = Union["ExtensionArray", np.ndarray]
+AnyArrayLike = Union[ArrayLike, "Index", "Series"]
+TimeArrayLike = Union["DatetimeArray", "TimedeltaArray"]
+
+# list-like
+
+# from https://github.com/hauntsaninja/useful_types
+# includes Sequence-like objects but excludes str and bytes
+_T_co = TypeVar("_T_co", covariant=True)
+
+
+class SequenceNotStr(Protocol[_T_co]):
+    @overload
+    def __getitem__(self, index: SupportsIndex, /) -> _T_co:
+        ...
+
+    @overload
+    def __getitem__(self, index: slice, /) -> Sequence[_T_co]:
+        ...
+
+    def __contains__(self, value: object, /) -> bool:
+        ...
+
+    def __len__(self) -> int:
+        ...
+
+    def __iter__(self) -> Iterator[_T_co]:
+        ...
+
+    def index(self, value: Any, /, start: int = 0, stop: int = ...) -> int:
+        ...
+
+    def count(self, value: Any, /) -> int:
+        ...
+
+    def __reversed__(self) -> Iterator[_T_co]:
+        ...
+
+
+ListLike = Union[AnyArrayLike, SequenceNotStr, range]
+
+# scalars
+
+PythonScalar = Union[str, float, bool]
+DatetimeLikeScalar = Union["Period", "Timestamp", "Timedelta"]
+PandasScalar = Union["Period", "Timestamp", "Timedelta", "Interval"]
+Scalar = Union[PythonScalar, PandasScalar, np.datetime64, np.timedelta64, date]
+IntStrT = TypeVar("IntStrT", bound=Union[int, str])
+
+
+# timestamp and timedelta convertible types
+
+TimestampConvertibleTypes = Union[
+    "Timestamp", date, np.datetime64, np.int64, float, str
+]
+TimestampNonexistent = Union[
+    Literal["shift_forward", "shift_backward", "NaT", "raise"], timedelta
+]
+TimedeltaConvertibleTypes = Union[
+    "Timedelta", timedelta, np.timedelta64, np.int64, float, str
+]
+Timezone = Union[str, tzinfo]
+
+ToTimestampHow = Literal["s", "e", "start", "end"]
+
+# NDFrameT is stricter and ensures that the same subclass of NDFrame always is
+# used. E.g. `def func(a: NDFrameT) -> NDFrameT: ...` means that if a
+# Series is passed into a function, a Series is always returned and if a DataFrame is
+# passed in, a DataFrame is always returned.
+NDFrameT = TypeVar("NDFrameT", bound="NDFrame")
+
+NumpyIndexT = TypeVar("NumpyIndexT", np.ndarray, "Index")
+
+AxisInt = int
+Axis = Union[AxisInt, Literal["index", "columns", "rows"]]
+IndexLabel = Union[Hashable, Sequence[Hashable]]
+Level = Hashable
+Shape = tuple[int, ...]
+Suffixes = tuple[Optional[str], Optional[str]]
+Ordered = Optional[bool]
+JSONSerializable = Optional[Union[PythonScalar, list, dict]]
+Frequency = Union[str, "BaseOffset"]
+Axes = ListLike
+
+RandomState = Union[
+    int,
+    np.ndarray,
+    np.random.Generator,
+    np.random.BitGenerator,
+    np.random.RandomState,
+]
+
+# dtypes
+NpDtype = Union[str, np.dtype, type_t[Union[str, complex, bool, object]]]
+Dtype = Union["ExtensionDtype", NpDtype]
+AstypeArg = Union["ExtensionDtype", "npt.DTypeLike"]
+# DtypeArg specifies all allowable dtypes in a functions its dtype argument
+DtypeArg = Union[Dtype, dict[Hashable, Dtype]]
+DtypeObj = Union[np.dtype, "ExtensionDtype"]
+
+# converters
+ConvertersArg = dict[Hashable, Callable[[Dtype], Dtype]]
+
+# parse_dates
+ParseDatesArg = Union[
+    bool, list[Hashable], list[list[Hashable]], dict[Hashable, list[Hashable]]
+]
+
+# For functions like rename that convert one label to another
+Renamer = Union[Mapping[Any, Hashable], Callable[[Any], Hashable]]
+
+# to maintain type information across generic functions and parametrization
+T = TypeVar("T")
+
+# used in decorators to preserve the signature of the function it decorates
+# see https://mypy.readthedocs.io/en/stable/generics.html#declaring-decorators
+FuncType = Callable[..., Any]
+F = TypeVar("F", bound=FuncType)
+
+# types of vectorized key functions for DataFrame::sort_values and
+# DataFrame::sort_index, among others
+ValueKeyFunc = Optional[Callable[["Series"], Union["Series", AnyArrayLike]]]
+IndexKeyFunc = Optional[Callable[["Index"], Union["Index", AnyArrayLike]]]
+
+# types of `func` kwarg for DataFrame.aggregate and Series.aggregate
+AggFuncTypeBase = Union[Callable, str]
+AggFuncTypeDict = MutableMapping[
+    Hashable, Union[AggFuncTypeBase, list[AggFuncTypeBase]]
+]
+AggFuncType = Union[
+    AggFuncTypeBase,
+    list[AggFuncTypeBase],
+    AggFuncTypeDict,
+]
+AggObjType = Union[
+    "Series",
+    "DataFrame",
+    "GroupBy",
+    "SeriesGroupBy",
+    "DataFrameGroupBy",
+    "BaseWindow",
+    "Resampler",
+]
+
+PythonFuncType = Callable[[Any], Any]
+
+# filenames and file-like-objects
+AnyStr_co = TypeVar("AnyStr_co", str, bytes, covariant=True)
+AnyStr_contra = TypeVar("AnyStr_contra", str, bytes, contravariant=True)
+
+
+class BaseBuffer(Protocol):
+    @property
+    def mode(self) -> str:
+        # for _get_filepath_or_buffer
+        ...
+
+    def seek(self, __offset: int, __whence: int = ...) -> int:
+        # with one argument: gzip.GzipFile, bz2.BZ2File
+        # with two arguments: zip.ZipFile, read_sas
+        ...
+
+    def seekable(self) -> bool:
+        # for bz2.BZ2File
+        ...
+
+    def tell(self) -> int:
+        # for zip.ZipFile, read_stata, to_stata
+        ...
+
+
+class ReadBuffer(BaseBuffer, Protocol[AnyStr_co]):
+    def read(self, __n: int = ...) -> AnyStr_co:
+        # for BytesIOWrapper, gzip.GzipFile, bz2.BZ2File
+        ...
+
+
+class WriteBuffer(BaseBuffer, Protocol[AnyStr_contra]):
+    def write(self, __b: AnyStr_contra) -> Any:
+        # for gzip.GzipFile, bz2.BZ2File
+        ...
+
+    def flush(self) -> Any:
+        # for gzip.GzipFile, bz2.BZ2File
+        ...
+
+
+class ReadPickleBuffer(ReadBuffer[bytes], Protocol):
+    def readline(self) -> bytes:
+        ...
+
+
+class WriteExcelBuffer(WriteBuffer[bytes], Protocol):
+    def truncate(self, size: int | None = ...) -> int:
+        ...
+
+
+class ReadCsvBuffer(ReadBuffer[AnyStr_co], Protocol):
+    def __iter__(self) -> Iterator[AnyStr_co]:
+        # for engine=python
+        ...
+
+    def fileno(self) -> int:
+        # for _MMapWrapper
+        ...
+
+    def readline(self) -> AnyStr_co:
+        # for engine=python
+        ...
+
+    @property
+    def closed(self) -> bool:
+        # for enine=pyarrow
+        ...
+
+
+FilePath = Union[str, "PathLike[str]"]
+
+# for arbitrary kwargs passed during reading/writing files
+StorageOptions = Optional[dict[str, Any]]
+
+
+# compression keywords and compression
+CompressionDict = dict[str, Any]
+CompressionOptions = Optional[
+    Union[Literal["infer", "gzip", "bz2", "zip", "xz", "zstd", "tar"], CompressionDict]
+]
+
+# types in DataFrameFormatter
+FormattersType = Union[
+    list[Callable], tuple[Callable, ...], Mapping[Union[str, int], Callable]
+]
+ColspaceType = Mapping[Hashable, Union[str, int]]
+FloatFormatType = Union[str, Callable, "EngFormatter"]
+ColspaceArgType = Union[
+    str, int, Sequence[Union[str, int]], Mapping[Hashable, Union[str, int]]
+]
+
+# Arguments for fillna()
+FillnaOptions = Literal["backfill", "bfill", "ffill", "pad"]
+InterpolateOptions = Literal[
+    "linear",
+    "time",
+    "index",
+    "values",
+    "nearest",
+    "zero",
+    "slinear",
+    "quadratic",
+    "cubic",
+    "barycentric",
+    "polynomial",
+    "krogh",
+    "piecewise_polynomial",
+    "spline",
+    "pchip",
+    "akima",
+    "cubicspline",
+    "from_derivatives",
+]
+
+# internals
+Manager = Union[
+    "ArrayManager", "SingleArrayManager", "BlockManager", "SingleBlockManager"
+]
+SingleManager = Union["SingleArrayManager", "SingleBlockManager"]
+Manager2D = Union["ArrayManager", "BlockManager"]
+
+# indexing
+# PositionalIndexer -> valid 1D positional indexer, e.g. can pass
+# to ndarray.__getitem__
+# ScalarIndexer is for a single value as the index
+# SequenceIndexer is for list like or slices (but not tuples)
+# PositionalIndexerTuple is extends the PositionalIndexer for 2D arrays
+# These are used in various __getitem__ overloads
+# TODO(typing#684): add Ellipsis, see
+# https://github.com/python/typing/issues/684#issuecomment-548203158
+# https://bugs.python.org/issue41810
+# Using List[int] here rather than Sequence[int] to disallow tuples.
+ScalarIndexer = Union[int, np.integer]
+SequenceIndexer = Union[slice, list[int], np.ndarray]
+PositionalIndexer = Union[ScalarIndexer, SequenceIndexer]
+PositionalIndexerTuple = tuple[PositionalIndexer, PositionalIndexer]
+PositionalIndexer2D = Union[PositionalIndexer, PositionalIndexerTuple]
+if TYPE_CHECKING:
+    TakeIndexer = Union[Sequence[int], Sequence[np.integer], npt.NDArray[np.integer]]
+else:
+    TakeIndexer = Any
+
+# Shared by functions such as drop and astype
+IgnoreRaise = Literal["ignore", "raise"]
+
+# Windowing rank methods
+WindowingRankType = Literal["average", "min", "max"]
+
+# read_csv engines
+CSVEngine = Literal["c", "python", "pyarrow", "python-fwf"]
+
+# read_json engines
+JSONEngine = Literal["ujson", "pyarrow"]
+
+# read_xml parsers
+XMLParsers = Literal["lxml", "etree"]
+
+# read_html flavors
+HTMLFlavors = Literal["lxml", "html5lib", "bs4"]
+
+# Interval closed type
+IntervalLeftRight = Literal["left", "right"]
+IntervalClosedType = Union[IntervalLeftRight, Literal["both", "neither"]]
+
+# datetime and NaTType
+DatetimeNaTType = Union[datetime, "NaTType"]
+DateTimeErrorChoices = Union[IgnoreRaise, Literal["coerce"]]
+
+# sort_index
+SortKind = Literal["quicksort", "mergesort", "heapsort", "stable"]
+NaPosition = Literal["first", "last"]
+
+# Arguments for nsmalles and n_largest
+NsmallestNlargestKeep = Literal["first", "last", "all"]
+
+# quantile interpolation
+QuantileInterpolation = Literal["linear", "lower", "higher", "midpoint", "nearest"]
+
+# plotting
+PlottingOrientation = Literal["horizontal", "vertical"]
+
+# dropna
+AnyAll = Literal["any", "all"]
+
+# merge
+MergeHow = Literal["left", "right", "inner", "outer", "cross"]
+MergeValidate = Literal[
+    "one_to_one",
+    "1:1",
+    "one_to_many",
+    "1:m",
+    "many_to_one",
+    "m:1",
+    "many_to_many",
+    "m:m",
+]
+
+# join
+JoinHow = Literal["left", "right", "inner", "outer"]
+JoinValidate = Literal[
+    "one_to_one",
+    "1:1",
+    "one_to_many",
+    "1:m",
+    "many_to_one",
+    "m:1",
+    "many_to_many",
+    "m:m",
+]
+
+# reindex
+ReindexMethod = Union[FillnaOptions, Literal["nearest"]]
+
+MatplotlibColor = Union[str, Sequence[float]]
+TimeGrouperOrigin = Union[
+    "Timestamp", Literal["epoch", "start", "start_day", "end", "end_day"]
+]
+TimeAmbiguous = Union[Literal["infer", "NaT", "raise"], "npt.NDArray[np.bool_]"]
+TimeNonexistent = Union[
+    Literal["shift_forward", "shift_backward", "NaT", "raise"], timedelta
+]
+DropKeep = Literal["first", "last", False]
+CorrelationMethod = Union[
+    Literal["pearson", "kendall", "spearman"], Callable[[np.ndarray, np.ndarray], float]
+]
+AlignJoin = Literal["outer", "inner", "left", "right"]
+DtypeBackend = Literal["pyarrow", "numpy_nullable"]
+
+TimeUnit = Literal["s", "ms", "us", "ns"]
+OpenFileErrors = Literal[
+    "strict",
+    "ignore",
+    "replace",
+    "surrogateescape",
+    "xmlcharrefreplace",
+    "backslashreplace",
+    "namereplace",
+]
+
+# update
+UpdateJoin = Literal["left"]
+
+# applymap
+NaAction = Literal["ignore"]
+
+# from_dict
+FromDictOrient = Literal["columns", "index", "tight"]
+
+# to_gbc
+ToGbqIfexist = Literal["fail", "replace", "append"]
+
+# to_stata
+ToStataByteorder = Literal[">", "<", "little", "big"]
+
+# ExcelWriter
+ExcelWriterIfSheetExists = Literal["error", "new", "replace", "overlay"]
+
+# Offsets
+OffsetCalendar = Union[np.busdaycalendar, "AbstractHolidayCalendar"]
+
+# read_csv: usecols
+UsecolsArgType = Union[
+    SequenceNotStr[Hashable],
+    range,
+    AnyArrayLike,
+    Callable[[HashableT], bool],
+    None,
+]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_version.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_version.py
new file mode 100644
index 0000000000000000000000000000000000000000..a4d9a4555018d906c11433c95ef0277a7550c862
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_version.py
@@ -0,0 +1,692 @@
+# This file helps to compute a version number in source trees obtained from
+# git-archive tarball (such as those provided by githubs download-from-tag
+# feature). Distribution tarballs (built by setup.py sdist) and build
+# directories (produced by setup.py build) will contain a much shorter file
+# that just contains the computed version number.
+
+# This file is released into the public domain.
+# Generated by versioneer-0.28
+# https://github.com/python-versioneer/python-versioneer
+
+"""Git implementation of _version.py."""
+
+import errno
+import functools
+import os
+import re
+import subprocess
+import sys
+from typing import Callable
+
+
+def get_keywords():
+    """Get the keywords needed to look up the version information."""
+    # these strings will be replaced by git during git-archive.
+    # setup.py/versioneer.py will grep for the variable names, so they must
+    # each be defined on a line of their own. _version.py will just call
+    # get_keywords().
+    git_refnames = " (HEAD, tag: v2.3.3, origin/2.3.x)"
+    git_full = "9c8bc3e55188c8aff37207a74f1dd144980b8874"
+    git_date = "2025-09-30 00:12:09 +0200"
+    keywords = {"refnames": git_refnames, "full": git_full, "date": git_date}
+    return keywords
+
+
+class VersioneerConfig:
+    """Container for Versioneer configuration parameters."""
+
+
+def get_config():
+    """Create, populate and return the VersioneerConfig() object."""
+    # these strings are filled in when 'setup.py versioneer' creates
+    # _version.py
+    cfg = VersioneerConfig()
+    cfg.VCS = "git"
+    cfg.style = "pep440"
+    cfg.tag_prefix = "v"
+    cfg.parentdir_prefix = "pandas-"
+    cfg.versionfile_source = "pandas/_version.py"
+    cfg.verbose = False
+    return cfg
+
+
+class NotThisMethod(Exception):
+    """Exception raised if a method is not valid for the current scenario."""
+
+
+LONG_VERSION_PY: dict[str, str] = {}
+HANDLERS: dict[str, dict[str, Callable]] = {}
+
+
+def register_vcs_handler(vcs, method):  # decorator
+    """Create decorator to mark a method as the handler of a VCS."""
+
+    def decorate(f):
+        """Store f in HANDLERS[vcs][method]."""
+        if vcs not in HANDLERS:
+            HANDLERS[vcs] = {}
+        HANDLERS[vcs][method] = f
+        return f
+
+    return decorate
+
+
+def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False, env=None):
+    """Call the given command(s)."""
+    assert isinstance(commands, list)
+    process = None
+
+    popen_kwargs = {}
+    if sys.platform == "win32":
+        # This hides the console window if pythonw.exe is used
+        startupinfo = subprocess.STARTUPINFO()
+        startupinfo.dwFlags |= subprocess.STARTF_USESHOWWINDOW
+        popen_kwargs["startupinfo"] = startupinfo
+
+    for command in commands:
+        dispcmd = str([command] + args)
+        try:
+            # remember shell=False, so use git.cmd on windows, not just git
+            process = subprocess.Popen(
+                [command] + args,
+                cwd=cwd,
+                env=env,
+                stdout=subprocess.PIPE,
+                stderr=(subprocess.PIPE if hide_stderr else None),
+                **popen_kwargs,
+            )
+            break
+        except OSError:
+            e = sys.exc_info()[1]
+            if e.errno == errno.ENOENT:
+                continue
+            if verbose:
+                print(f"unable to run {dispcmd}")
+                print(e)
+            return None, None
+    else:
+        if verbose:
+            print(f"unable to find command, tried {commands}")
+        return None, None
+    stdout = process.communicate()[0].strip().decode()
+    if process.returncode != 0:
+        if verbose:
+            print(f"unable to run {dispcmd} (error)")
+            print(f"stdout was {stdout}")
+        return None, process.returncode
+    return stdout, process.returncode
+
+
+def versions_from_parentdir(parentdir_prefix, root, verbose):
+    """Try to determine the version from the parent directory name.
+
+    Source tarballs conventionally unpack into a directory that includes both
+    the project name and a version string. We will also support searching up
+    two directory levels for an appropriately named parent directory
+    """
+    rootdirs = []
+
+    for _ in range(3):
+        dirname = os.path.basename(root)
+        if dirname.startswith(parentdir_prefix):
+            return {
+                "version": dirname[len(parentdir_prefix) :],
+                "full-revisionid": None,
+                "dirty": False,
+                "error": None,
+                "date": None,
+            }
+        rootdirs.append(root)
+        root = os.path.dirname(root)  # up a level
+
+    if verbose:
+        print(
+            f"Tried directories {str(rootdirs)} \
+            but none started with prefix {parentdir_prefix}"
+        )
+    raise NotThisMethod("rootdir doesn't start with parentdir_prefix")
+
+
+@register_vcs_handler("git", "get_keywords")
+def git_get_keywords(versionfile_abs):
+    """Extract version information from the given file."""
+    # the code embedded in _version.py can just fetch the value of these
+    # keywords. When used from setup.py, we don't want to import _version.py,
+    # so we do it with a regexp instead. This function is not used from
+    # _version.py.
+    keywords = {}
+    try:
+        with open(versionfile_abs, encoding="utf-8") as fobj:
+            for line in fobj:
+                if line.strip().startswith("git_refnames ="):
+                    mo = re.search(r'=\s*"(.*)"', line)
+                    if mo:
+                        keywords["refnames"] = mo.group(1)
+                if line.strip().startswith("git_full ="):
+                    mo = re.search(r'=\s*"(.*)"', line)
+                    if mo:
+                        keywords["full"] = mo.group(1)
+                if line.strip().startswith("git_date ="):
+                    mo = re.search(r'=\s*"(.*)"', line)
+                    if mo:
+                        keywords["date"] = mo.group(1)
+    except OSError:
+        pass
+    return keywords
+
+
+@register_vcs_handler("git", "keywords")
+def git_versions_from_keywords(keywords, tag_prefix, verbose):
+    """Get version information from git keywords."""
+    if "refnames" not in keywords:
+        raise NotThisMethod("Short version file found")
+    date = keywords.get("date")
+    if date is not None:
+        # Use only the last line.  Previous lines may contain GPG signature
+        # information.
+        date = date.splitlines()[-1]
+
+        # git-2.2.0 added "%cI", which expands to an ISO-8601 -compliant
+        # datestamp. However we prefer "%ci" (which expands to an "ISO-8601
+        # -like" string, which we must then edit to make compliant), because
+        # it's been around since git-1.5.3, and it's too difficult to
+        # discover which version we're using, or to work around using an
+        # older one.
+        date = date.strip().replace(" ", "T", 1).replace(" ", "", 1)
+    refnames = keywords["refnames"].strip()
+    if refnames.startswith("$Format"):
+        if verbose:
+            print("keywords are unexpanded, not using")
+        raise NotThisMethod("unexpanded keywords, not a git-archive tarball")
+    refs = {r.strip() for r in refnames.strip("()").split(",")}
+    # starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of
+    # just "foo-1.0". If we see a "tag: " prefix, prefer those.
+    TAG = "tag: "
+    tags = {r[len(TAG) :] for r in refs if r.startswith(TAG)}
+    if not tags:
+        # Either we're using git < 1.8.3, or there really are no tags. We use
+        # a heuristic: assume all version tags have a digit. The old git %d
+        # expansion behaves like git log --decorate=short and strips out the
+        # refs/heads/ and refs/tags/ prefixes that would let us distinguish
+        # between branches and tags. By ignoring refnames without digits, we
+        # filter out many common branch names like "release" and
+        # "stabilization", as well as "HEAD" and "master".
+        tags = {r for r in refs if re.search(r"\d", r)}
+        if verbose:
+            print(f"discarding '{','.join(refs - tags)}', no digits")
+    if verbose:
+        print(f"likely tags: {','.join(sorted(tags))}")
+    for ref in sorted(tags):
+        # sorting will prefer e.g. "2.0" over "2.0rc1"
+        if ref.startswith(tag_prefix):
+            r = ref[len(tag_prefix) :]
+            # Filter out refs that exactly match prefix or that don't start
+            # with a number once the prefix is stripped (mostly a concern
+            # when prefix is '')
+            if not re.match(r"\d", r):
+                continue
+            if verbose:
+                print(f"picking {r}")
+            return {
+                "version": r,
+                "full-revisionid": keywords["full"].strip(),
+                "dirty": False,
+                "error": None,
+                "date": date,
+            }
+    # no suitable tags, so version is "0+unknown", but full hex is still there
+    if verbose:
+        print("no suitable tags, using unknown + full revision id")
+    return {
+        "version": "0+unknown",
+        "full-revisionid": keywords["full"].strip(),
+        "dirty": False,
+        "error": "no suitable tags",
+        "date": None,
+    }
+
+
+@register_vcs_handler("git", "pieces_from_vcs")
+def git_pieces_from_vcs(tag_prefix, root, verbose, runner=run_command):
+    """Get version from 'git describe' in the root of the source tree.
+
+    This only gets called if the git-archive 'subst' keywords were *not*
+    expanded, and _version.py hasn't already been rewritten with a short
+    version string, meaning we're inside a checked out source tree.
+    """
+    GITS = ["git"]
+    if sys.platform == "win32":
+        GITS = ["git.cmd", "git.exe"]
+
+    # GIT_DIR can interfere with correct operation of Versioneer.
+    # It may be intended to be passed to the Versioneer-versioned project,
+    # but that should not change where we get our version from.
+    env = os.environ.copy()
+    env.pop("GIT_DIR", None)
+    runner = functools.partial(runner, env=env)
+
+    _, rc = runner(GITS, ["rev-parse", "--git-dir"], cwd=root, hide_stderr=not verbose)
+    if rc != 0:
+        if verbose:
+            print(f"Directory {root} not under git control")
+        raise NotThisMethod("'git rev-parse --git-dir' returned error")
+
+    # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty]
+    # if there isn't one, this yields HEX[-dirty] (no NUM)
+    describe_out, rc = runner(
+        GITS,
+        [
+            "describe",
+            "--tags",
+            "--dirty",
+            "--always",
+            "--long",
+            "--match",
+            f"{tag_prefix}[[:digit:]]*",
+        ],
+        cwd=root,
+    )
+    # --long was added in git-1.5.5
+    if describe_out is None:
+        raise NotThisMethod("'git describe' failed")
+    describe_out = describe_out.strip()
+    full_out, rc = runner(GITS, ["rev-parse", "HEAD"], cwd=root)
+    if full_out is None:
+        raise NotThisMethod("'git rev-parse' failed")
+    full_out = full_out.strip()
+
+    pieces = {}
+    pieces["long"] = full_out
+    pieces["short"] = full_out[:7]  # maybe improved later
+    pieces["error"] = None
+
+    branch_name, rc = runner(GITS, ["rev-parse", "--abbrev-ref", "HEAD"], cwd=root)
+    # --abbrev-ref was added in git-1.6.3
+    if rc != 0 or branch_name is None:
+        raise NotThisMethod("'git rev-parse --abbrev-ref' returned error")
+    branch_name = branch_name.strip()
+
+    if branch_name == "HEAD":
+        # If we aren't exactly on a branch, pick a branch which represents
+        # the current commit. If all else fails, we are on a branchless
+        # commit.
+        branches, rc = runner(GITS, ["branch", "--contains"], cwd=root)
+        # --contains was added in git-1.5.4
+        if rc != 0 or branches is None:
+            raise NotThisMethod("'git branch --contains' returned error")
+        branches = branches.split("\n")
+
+        # Remove the first line if we're running detached
+        if "(" in branches[0]:
+            branches.pop(0)
+
+        # Strip off the leading "* " from the list of branches.
+        branches = [branch[2:] for branch in branches]
+        if "master" in branches:
+            branch_name = "master"
+        elif not branches:
+            branch_name = None
+        else:
+            # Pick the first branch that is returned. Good or bad.
+            branch_name = branches[0]
+
+    pieces["branch"] = branch_name
+
+    # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty]
+    # TAG might have hyphens.
+    git_describe = describe_out
+
+    # look for -dirty suffix
+    dirty = git_describe.endswith("-dirty")
+    pieces["dirty"] = dirty
+    if dirty:
+        git_describe = git_describe[: git_describe.rindex("-dirty")]
+
+    # now we have TAG-NUM-gHEX or HEX
+
+    if "-" in git_describe:
+        # TAG-NUM-gHEX
+        mo = re.search(r"^(.+)-(\d+)-g([0-9a-f]+)$", git_describe)
+        if not mo:
+            # unparsable. Maybe git-describe is misbehaving?
+            pieces["error"] = f"unable to parse git-describe output: '{describe_out}'"
+            return pieces
+
+        # tag
+        full_tag = mo.group(1)
+        if not full_tag.startswith(tag_prefix):
+            if verbose:
+                fmt = "tag '%s' doesn't start with prefix '%s'"
+                print(fmt % (full_tag, tag_prefix))
+            pieces[
+                "error"
+            ] = f"tag '{full_tag}' doesn't start with prefix '{tag_prefix}'"
+            return pieces
+        pieces["closest-tag"] = full_tag[len(tag_prefix) :]
+
+        # distance: number of commits since tag
+        pieces["distance"] = int(mo.group(2))
+
+        # commit: short hex revision ID
+        pieces["short"] = mo.group(3)
+
+    else:
+        # HEX: no tags
+        pieces["closest-tag"] = None
+        out, rc = runner(GITS, ["rev-list", "HEAD", "--left-right"], cwd=root)
+        pieces["distance"] = len(out.split())  # total number of commits
+
+    # commit date: see ISO-8601 comment in git_versions_from_keywords()
+    date = runner(GITS, ["show", "-s", "--format=%ci", "HEAD"], cwd=root)[0].strip()
+    # Use only the last line.  Previous lines may contain GPG signature
+    # information.
+    date = date.splitlines()[-1]
+    pieces["date"] = date.strip().replace(" ", "T", 1).replace(" ", "", 1)
+
+    return pieces
+
+
+def plus_or_dot(pieces) -> str:
+    """Return a + if we don't already have one, else return a ."""
+    if "+" in pieces.get("closest-tag", ""):
+        return "."
+    return "+"
+
+
+def render_pep440(pieces):
+    """Build up version string, with post-release "local version identifier".
+
+    Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you
+    get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty
+
+    Exceptions:
+    1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty]
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"] or pieces["dirty"]:
+            rendered += plus_or_dot(pieces)
+            rendered += f"{pieces['distance']}.g{pieces['short']}"
+            if pieces["dirty"]:
+                rendered += ".dirty"
+    else:
+        # exception #1
+        rendered = f"0+untagged.{pieces['distance']}.g{pieces['short']}"
+        if pieces["dirty"]:
+            rendered += ".dirty"
+    return rendered
+
+
+def render_pep440_branch(pieces):
+    """TAG[[.dev0]+DISTANCE.gHEX[.dirty]] .
+
+    The ".dev0" means not master branch. Note that .dev0 sorts backwards
+    (a feature branch will appear "older" than the master branch).
+
+    Exceptions:
+    1: no tags. 0[.dev0]+untagged.DISTANCE.gHEX[.dirty]
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"] or pieces["dirty"]:
+            if pieces["branch"] != "master":
+                rendered += ".dev0"
+            rendered += plus_or_dot(pieces)
+            rendered += f"{pieces['distance']}.g{pieces['short']}"
+            if pieces["dirty"]:
+                rendered += ".dirty"
+    else:
+        # exception #1
+        rendered = "0"
+        if pieces["branch"] != "master":
+            rendered += ".dev0"
+        rendered += f"+untagged.{pieces['distance']}.g{pieces['short']}"
+        if pieces["dirty"]:
+            rendered += ".dirty"
+    return rendered
+
+
+def pep440_split_post(ver):
+    """Split pep440 version string at the post-release segment.
+
+    Returns the release segments before the post-release and the
+    post-release version number (or -1 if no post-release segment is present).
+    """
+    vc = str.split(ver, ".post")
+    return vc[0], int(vc[1] or 0) if len(vc) == 2 else None
+
+
+def render_pep440_pre(pieces):
+    """TAG[.postN.devDISTANCE] -- No -dirty.
+
+    Exceptions:
+    1: no tags. 0.post0.devDISTANCE
+    """
+    if pieces["closest-tag"]:
+        if pieces["distance"]:
+            # update the post release segment
+            tag_version, post_version = pep440_split_post(pieces["closest-tag"])
+            rendered = tag_version
+            if post_version is not None:
+                rendered += f".post{post_version + 1}.dev{pieces['distance']}"
+            else:
+                rendered += f".post0.dev{pieces['distance']}"
+        else:
+            # no commits, use the tag as the version
+            rendered = pieces["closest-tag"]
+    else:
+        # exception #1
+        rendered = f"0.post0.dev{pieces['distance']}"
+    return rendered
+
+
+def render_pep440_post(pieces):
+    """TAG[.postDISTANCE[.dev0]+gHEX] .
+
+    The ".dev0" means dirty. Note that .dev0 sorts backwards
+    (a dirty tree will appear "older" than the corresponding clean one),
+    but you shouldn't be releasing software with -dirty anyways.
+
+    Exceptions:
+    1: no tags. 0.postDISTANCE[.dev0]
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"] or pieces["dirty"]:
+            rendered += f".post{pieces['distance']}"
+            if pieces["dirty"]:
+                rendered += ".dev0"
+            rendered += plus_or_dot(pieces)
+            rendered += f"g{pieces['short']}"
+    else:
+        # exception #1
+        rendered = f"0.post{pieces['distance']}"
+        if pieces["dirty"]:
+            rendered += ".dev0"
+        rendered += f"+g{pieces['short']}"
+    return rendered
+
+
+def render_pep440_post_branch(pieces):
+    """TAG[.postDISTANCE[.dev0]+gHEX[.dirty]] .
+
+    The ".dev0" means not master branch.
+
+    Exceptions:
+    1: no tags. 0.postDISTANCE[.dev0]+gHEX[.dirty]
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"] or pieces["dirty"]:
+            rendered += f".post{pieces['distance']}"
+            if pieces["branch"] != "master":
+                rendered += ".dev0"
+            rendered += plus_or_dot(pieces)
+            rendered += f"g{pieces['short']}"
+            if pieces["dirty"]:
+                rendered += ".dirty"
+    else:
+        # exception #1
+        rendered = f"0.post{pieces['distance']}"
+        if pieces["branch"] != "master":
+            rendered += ".dev0"
+        rendered += f"+g{pieces['short']}"
+        if pieces["dirty"]:
+            rendered += ".dirty"
+    return rendered
+
+
+def render_pep440_old(pieces):
+    """TAG[.postDISTANCE[.dev0]] .
+
+    The ".dev0" means dirty.
+
+    Exceptions:
+    1: no tags. 0.postDISTANCE[.dev0]
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"] or pieces["dirty"]:
+            rendered += f"0.post{pieces['distance']}"
+            if pieces["dirty"]:
+                rendered += ".dev0"
+    else:
+        # exception #1
+        rendered = f"0.post{pieces['distance']}"
+        if pieces["dirty"]:
+            rendered += ".dev0"
+    return rendered
+
+
+def render_git_describe(pieces):
+    """TAG[-DISTANCE-gHEX][-dirty].
+
+    Like 'git describe --tags --dirty --always'.
+
+    Exceptions:
+    1: no tags. HEX[-dirty]  (note: no 'g' prefix)
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"]:
+            rendered += f"-{pieces['distance']}-g{pieces['short']}"
+    else:
+        # exception #1
+        rendered = pieces["short"]
+    if pieces["dirty"]:
+        rendered += "-dirty"
+    return rendered
+
+
+def render_git_describe_long(pieces):
+    """TAG-DISTANCE-gHEX[-dirty].
+
+    Like 'git describe --tags --dirty --always -long'.
+    The distance/hash is unconditional.
+
+    Exceptions:
+    1: no tags. HEX[-dirty]  (note: no 'g' prefix)
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        rendered += f"-{pieces['distance']}-g{pieces['short']}"
+    else:
+        # exception #1
+        rendered = pieces["short"]
+    if pieces["dirty"]:
+        rendered += "-dirty"
+    return rendered
+
+
+def render(pieces, style):
+    """Render the given version pieces into the requested style."""
+    if pieces["error"]:
+        return {
+            "version": "unknown",
+            "full-revisionid": pieces.get("long"),
+            "dirty": None,
+            "error": pieces["error"],
+            "date": None,
+        }
+
+    if not style or style == "default":
+        style = "pep440"  # the default
+
+    if style == "pep440":
+        rendered = render_pep440(pieces)
+    elif style == "pep440-branch":
+        rendered = render_pep440_branch(pieces)
+    elif style == "pep440-pre":
+        rendered = render_pep440_pre(pieces)
+    elif style == "pep440-post":
+        rendered = render_pep440_post(pieces)
+    elif style == "pep440-post-branch":
+        rendered = render_pep440_post_branch(pieces)
+    elif style == "pep440-old":
+        rendered = render_pep440_old(pieces)
+    elif style == "git-describe":
+        rendered = render_git_describe(pieces)
+    elif style == "git-describe-long":
+        rendered = render_git_describe_long(pieces)
+    else:
+        raise ValueError(f"unknown style '{style}'")
+
+    return {
+        "version": rendered,
+        "full-revisionid": pieces["long"],
+        "dirty": pieces["dirty"],
+        "error": None,
+        "date": pieces.get("date"),
+    }
+
+
+def get_versions():
+    """Get version information or return default if unable to do so."""
+    # I am in _version.py, which lives at ROOT/VERSIONFILE_SOURCE. If we have
+    # __file__, we can work backwards from there to the root. Some
+    # py2exe/bbfreeze/non-CPython implementations don't do __file__, in which
+    # case we can only use expanded keywords.
+
+    cfg = get_config()
+    verbose = cfg.verbose
+
+    try:
+        return git_versions_from_keywords(get_keywords(), cfg.tag_prefix, verbose)
+    except NotThisMethod:
+        pass
+
+    try:
+        root = os.path.realpath(__file__)
+        # versionfile_source is the relative path from the top of the source
+        # tree (where the .git directory might live) to this file. Invert
+        # this to find the root from __file__.
+        for _ in cfg.versionfile_source.split("/"):
+            root = os.path.dirname(root)
+    except NameError:
+        return {
+            "version": "0+unknown",
+            "full-revisionid": None,
+            "dirty": None,
+            "error": "unable to find root of source tree",
+            "date": None,
+        }
+
+    try:
+        pieces = git_pieces_from_vcs(cfg.tag_prefix, root, verbose)
+        return render(pieces, cfg.style)
+    except NotThisMethod:
+        pass
+
+    try:
+        if cfg.parentdir_prefix:
+            return versions_from_parentdir(cfg.parentdir_prefix, root, verbose)
+    except NotThisMethod:
+        pass
+
+    return {
+        "version": "0+unknown",
+        "full-revisionid": None,
+        "dirty": None,
+        "error": "unable to compute version",
+        "date": None,
+    }
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_version_meson.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_version_meson.py
new file mode 100644
index 0000000000000000000000000000000000000000..f6c87a5bfbdbaf252f1b13ec01220ac1c58d9984
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/_version_meson.py
@@ -0,0 +1,2 @@
+__version__="2.3.3"
+__git_version__="9c8bc3e55188c8aff37207a74f1dd144980b8874"
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/api/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/api/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a0d42b6541fdf8817b996ef9804db8a87a2bcd2c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/api/__init__.py
@@ -0,0 +1,16 @@
+""" public toolkit API """
+from pandas.api import (
+    extensions,
+    indexers,
+    interchange,
+    types,
+    typing,
+)
+
+__all__ = [
+    "interchange",
+    "extensions",
+    "indexers",
+    "types",
+    "typing",
+]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/api/extensions/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/api/extensions/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ea5f1ba926899f9d11e34e70181ed77cae7ead1d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/api/extensions/__init__.py
@@ -0,0 +1,33 @@
+"""
+Public API for extending pandas objects.
+"""
+
+from pandas._libs.lib import no_default
+
+from pandas.core.dtypes.base import (
+    ExtensionDtype,
+    register_extension_dtype,
+)
+
+from pandas.core.accessor import (
+    register_dataframe_accessor,
+    register_index_accessor,
+    register_series_accessor,
+)
+from pandas.core.algorithms import take
+from pandas.core.arrays import (
+    ExtensionArray,
+    ExtensionScalarOpsMixin,
+)
+
+__all__ = [
+    "no_default",
+    "ExtensionDtype",
+    "register_extension_dtype",
+    "register_dataframe_accessor",
+    "register_index_accessor",
+    "register_series_accessor",
+    "take",
+    "ExtensionArray",
+    "ExtensionScalarOpsMixin",
+]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/api/indexers/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/api/indexers/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..78357f11dc3b79f13490b91c69ef5457fbfa9768
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/api/indexers/__init__.py
@@ -0,0 +1,17 @@
+"""
+Public API for Rolling Window Indexers.
+"""
+
+from pandas.core.indexers import check_array_indexer
+from pandas.core.indexers.objects import (
+    BaseIndexer,
+    FixedForwardWindowIndexer,
+    VariableOffsetWindowIndexer,
+)
+
+__all__ = [
+    "check_array_indexer",
+    "BaseIndexer",
+    "FixedForwardWindowIndexer",
+    "VariableOffsetWindowIndexer",
+]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/api/interchange/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/api/interchange/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..2f3a73bc46b3109c3c13e1a3468a69aed2ffb2e8
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/api/interchange/__init__.py
@@ -0,0 +1,8 @@
+"""
+Public API for DataFrame interchange protocol.
+"""
+
+from pandas.core.interchange.dataframe_protocol import DataFrame
+from pandas.core.interchange.from_dataframe import from_dataframe
+
+__all__ = ["from_dataframe", "DataFrame"]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/api/types/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/api/types/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..c601086bb9f86a633d6a3f2245779fea9428bf95
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/api/types/__init__.py
@@ -0,0 +1,23 @@
+"""
+Public toolkit API.
+"""
+
+from pandas._libs.lib import infer_dtype
+
+from pandas.core.dtypes.api import *  # noqa: F403
+from pandas.core.dtypes.concat import union_categoricals
+from pandas.core.dtypes.dtypes import (
+    CategoricalDtype,
+    DatetimeTZDtype,
+    IntervalDtype,
+    PeriodDtype,
+)
+
+__all__ = [
+    "infer_dtype",
+    "union_categoricals",
+    "CategoricalDtype",
+    "DatetimeTZDtype",
+    "IntervalDtype",
+    "PeriodDtype",
+]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/api/typing/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/api/typing/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..9b5d2cb06b523508b15025be804a66daaaaf7a45
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/api/typing/__init__.py
@@ -0,0 +1,55 @@
+"""
+Public API classes that store intermediate results useful for type-hinting.
+"""
+
+from pandas._libs import NaTType
+from pandas._libs.missing import NAType
+
+from pandas.core.groupby import (
+    DataFrameGroupBy,
+    SeriesGroupBy,
+)
+from pandas.core.resample import (
+    DatetimeIndexResamplerGroupby,
+    PeriodIndexResamplerGroupby,
+    Resampler,
+    TimedeltaIndexResamplerGroupby,
+    TimeGrouper,
+)
+from pandas.core.window import (
+    Expanding,
+    ExpandingGroupby,
+    ExponentialMovingWindow,
+    ExponentialMovingWindowGroupby,
+    Rolling,
+    RollingGroupby,
+    Window,
+)
+
+# TODO: Can't import Styler without importing jinja2
+# from pandas.io.formats.style import Styler
+from pandas.io.json._json import JsonReader
+from pandas.io.stata import StataReader
+
+__all__ = [
+    "DataFrameGroupBy",
+    "DatetimeIndexResamplerGroupby",
+    "Expanding",
+    "ExpandingGroupby",
+    "ExponentialMovingWindow",
+    "ExponentialMovingWindowGroupby",
+    "JsonReader",
+    "NaTType",
+    "NAType",
+    "PeriodIndexResamplerGroupby",
+    "Resampler",
+    "Rolling",
+    "RollingGroupby",
+    "SeriesGroupBy",
+    "StataReader",
+    # See TODO above
+    # "Styler",
+    "TimedeltaIndexResamplerGroupby",
+    "TimeGrouper",
+    "Window",
+]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/arrays/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/arrays/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a11755275d00e070bea6ab73a881b98d0b976551
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/arrays/__init__.py
@@ -0,0 +1,53 @@
+"""
+All of pandas' ExtensionArrays.
+
+See :ref:`extending.extension-types` for more.
+"""
+from pandas.core.arrays import (
+    ArrowExtensionArray,
+    ArrowStringArray,
+    BooleanArray,
+    Categorical,
+    DatetimeArray,
+    FloatingArray,
+    IntegerArray,
+    IntervalArray,
+    NumpyExtensionArray,
+    PeriodArray,
+    SparseArray,
+    StringArray,
+    TimedeltaArray,
+)
+
+__all__ = [
+    "ArrowExtensionArray",
+    "ArrowStringArray",
+    "BooleanArray",
+    "Categorical",
+    "DatetimeArray",
+    "FloatingArray",
+    "IntegerArray",
+    "IntervalArray",
+    "NumpyExtensionArray",
+    "PeriodArray",
+    "SparseArray",
+    "StringArray",
+    "TimedeltaArray",
+]
+
+
+def __getattr__(name: str) -> type[NumpyExtensionArray]:
+    if name == "PandasArray":
+        # GH#53694
+        import warnings
+
+        from pandas.util._exceptions import find_stack_level
+
+        warnings.warn(
+            "PandasArray has been renamed NumpyExtensionArray. Use that "
+            "instead. This alias will be removed in a future version.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        return NumpyExtensionArray
+    raise AttributeError(f"module 'pandas.arrays' has no attribute '{name}'")
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/compat/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/compat/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..c8554be0a18b1bb6a7ad6a0b43fe5cfc29f27864
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/compat/__init__.py
@@ -0,0 +1,213 @@
+"""
+compat
+======
+
+Cross-compatible functions for different versions of Python.
+
+Other items:
+* platform checker
+"""
+from __future__ import annotations
+
+import os
+import platform
+import sys
+from typing import TYPE_CHECKING
+
+from pandas.compat._constants import (
+    IS64,
+    ISMUSL,
+    PY310,
+    PY311,
+    PY312,
+    PY314,
+    PYPY,
+    WARNING_CHECK_DISABLED,
+)
+import pandas.compat.compressors
+from pandas.compat.numpy import is_numpy_dev
+from pandas.compat.pyarrow import (
+    HAS_PYARROW,
+    pa_version_under10p1,
+    pa_version_under11p0,
+    pa_version_under13p0,
+    pa_version_under14p0,
+    pa_version_under14p1,
+    pa_version_under16p0,
+    pa_version_under17p0,
+    pa_version_under18p0,
+    pa_version_under19p0,
+    pa_version_under20p0,
+    pa_version_under21p0,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import F
+
+
+def set_function_name(f: F, name: str, cls: type) -> F:
+    """
+    Bind the name/qualname attributes of the function.
+    """
+    f.__name__ = name
+    f.__qualname__ = f"{cls.__name__}.{name}"
+    f.__module__ = cls.__module__
+    return f
+
+
+def is_platform_little_endian() -> bool:
+    """
+    Checking if the running platform is little endian.
+
+    Returns
+    -------
+    bool
+        True if the running platform is little endian.
+    """
+    return sys.byteorder == "little"
+
+
+def is_platform_windows() -> bool:
+    """
+    Checking if the running platform is windows.
+
+    Returns
+    -------
+    bool
+        True if the running platform is windows.
+    """
+    return sys.platform in ["win32", "cygwin"]
+
+
+def is_platform_linux() -> bool:
+    """
+    Checking if the running platform is linux.
+
+    Returns
+    -------
+    bool
+        True if the running platform is linux.
+    """
+    return sys.platform == "linux"
+
+
+def is_platform_mac() -> bool:
+    """
+    Checking if the running platform is mac.
+
+    Returns
+    -------
+    bool
+        True if the running platform is mac.
+    """
+    return sys.platform == "darwin"
+
+
+def is_platform_arm() -> bool:
+    """
+    Checking if the running platform use ARM architecture.
+
+    Returns
+    -------
+    bool
+        True if the running platform uses ARM architecture.
+    """
+    return platform.machine() in ("arm64", "aarch64") or platform.machine().startswith(
+        "armv"
+    )
+
+
+def is_platform_power() -> bool:
+    """
+    Checking if the running platform use Power architecture.
+
+    Returns
+    -------
+    bool
+        True if the running platform uses ARM architecture.
+    """
+    return platform.machine() in ("ppc64", "ppc64le")
+
+
+def is_ci_environment() -> bool:
+    """
+    Checking if running in a continuous integration environment by checking
+    the PANDAS_CI environment variable.
+
+    Returns
+    -------
+    bool
+        True if the running in a continuous integration environment.
+    """
+    return os.environ.get("PANDAS_CI", "0") == "1"
+
+
+def get_lzma_file() -> type[pandas.compat.compressors.LZMAFile]:
+    """
+    Importing the `LZMAFile` class from the `lzma` module.
+
+    Returns
+    -------
+    class
+        The `LZMAFile` class from the `lzma` module.
+
+    Raises
+    ------
+    RuntimeError
+        If the `lzma` module was not imported correctly, or didn't exist.
+    """
+    if not pandas.compat.compressors.has_lzma:
+        raise RuntimeError(
+            "lzma module not available. "
+            "A Python re-install with the proper dependencies, "
+            "might be required to solve this issue."
+        )
+    return pandas.compat.compressors.LZMAFile
+
+
+def get_bz2_file() -> type[pandas.compat.compressors.BZ2File]:
+    """
+    Importing the `BZ2File` class from the `bz2` module.
+
+    Returns
+    -------
+    class
+        The `BZ2File` class from the `bz2` module.
+
+    Raises
+    ------
+    RuntimeError
+        If the `bz2` module was not imported correctly, or didn't exist.
+    """
+    if not pandas.compat.compressors.has_bz2:
+        raise RuntimeError(
+            "bz2 module not available. "
+            "A Python re-install with the proper dependencies, "
+            "might be required to solve this issue."
+        )
+    return pandas.compat.compressors.BZ2File
+
+
+__all__ = [
+    "is_numpy_dev",
+    "pa_version_under10p1",
+    "pa_version_under11p0",
+    "pa_version_under13p0",
+    "pa_version_under14p0",
+    "pa_version_under14p1",
+    "pa_version_under16p0",
+    "pa_version_under17p0",
+    "pa_version_under18p0",
+    "pa_version_under19p0",
+    "pa_version_under20p0",
+    "pa_version_under21p0",
+    "HAS_PYARROW",
+    "IS64",
+    "ISMUSL",
+    "PY310",
+    "PY311",
+    "PY312",
+    "PY314",
+    "PYPY",
+    "WARNING_CHECK_DISABLED",
+]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/compat/_constants.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/compat/_constants.py
new file mode 100644
index 0000000000000000000000000000000000000000..fb011603712c61ae1dcc745148a6dfb2c05b803f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/compat/_constants.py
@@ -0,0 +1,34 @@
+"""
+_constants
+======
+
+Constants relevant for the Python implementation.
+"""
+
+from __future__ import annotations
+
+import platform
+import sys
+import sysconfig
+
+IS64 = sys.maxsize > 2**32
+
+PY310 = sys.version_info >= (3, 10)
+PY311 = sys.version_info >= (3, 11)
+PY312 = sys.version_info >= (3, 12)
+PY314 = sys.version_info >= (3, 14)
+PYPY = platform.python_implementation() == "PyPy"
+ISMUSL = "musl" in (sysconfig.get_config_var("HOST_GNU_TYPE") or "")
+REF_COUNT = 2 if PY311 else 3
+WARNING_CHECK_DISABLED = PY314
+
+
+__all__ = [
+    "IS64",
+    "ISMUSL",
+    "PY310",
+    "PY311",
+    "PY312",
+    "PY314",
+    "PYPY",
+]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/compat/_optional.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/compat/_optional.py
new file mode 100644
index 0000000000000000000000000000000000000000..2bc6cd46f09a7e4b103658f9c2ec9a69d93d00b9
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/compat/_optional.py
@@ -0,0 +1,168 @@
+from __future__ import annotations
+
+import importlib
+import sys
+from typing import TYPE_CHECKING
+import warnings
+
+from pandas.util._exceptions import find_stack_level
+
+from pandas.util.version import Version
+
+if TYPE_CHECKING:
+    import types
+
+# Update install.rst & setup.cfg when updating versions!
+
+VERSIONS = {
+    "adbc-driver-postgresql": "0.8.0",
+    "adbc-driver-sqlite": "0.8.0",
+    "bs4": "4.11.2",
+    "blosc": "1.21.3",
+    "bottleneck": "1.3.6",
+    "dataframe-api-compat": "0.1.7",
+    "fastparquet": "2022.12.0",
+    "fsspec": "2022.11.0",
+    "html5lib": "1.1",
+    "hypothesis": "6.46.1",
+    "gcsfs": "2022.11.0",
+    "jinja2": "3.1.2",
+    "lxml.etree": "4.9.2",
+    "matplotlib": "3.6.3",
+    "numba": "0.56.4",
+    "numexpr": "2.8.4",
+    "odfpy": "1.4.1",
+    "openpyxl": "3.1.0",
+    "pandas_gbq": "0.19.0",
+    "psycopg2": "2.9.6",  # (dt dec pq3 ext lo64)
+    "pymysql": "1.0.2",
+    "pyarrow": "10.0.1",
+    "pyreadstat": "1.2.0",
+    "pytest": "7.3.2",
+    "python-calamine": "0.1.7",
+    "pyxlsb": "1.0.10",
+    "s3fs": "2022.11.0",
+    "scipy": "1.10.0",
+    "sqlalchemy": "2.0.0",
+    "tables": "3.8.0",
+    "tabulate": "0.9.0",
+    "xarray": "2022.12.0",
+    "xlrd": "2.0.1",
+    "xlsxwriter": "3.0.5",
+    "zstandard": "0.19.0",
+    "tzdata": "2022.7",
+    "qtpy": "2.3.0",
+    "pyqt5": "5.15.9",
+}
+
+# A mapping from import name to package name (on PyPI) for packages where
+# these two names are different.
+
+INSTALL_MAPPING = {
+    "bs4": "beautifulsoup4",
+    "bottleneck": "Bottleneck",
+    "jinja2": "Jinja2",
+    "lxml.etree": "lxml",
+    "odf": "odfpy",
+    "pandas_gbq": "pandas-gbq",
+    "python_calamine": "python-calamine",
+    "sqlalchemy": "SQLAlchemy",
+    "tables": "pytables",
+}
+
+
+def get_version(module: types.ModuleType) -> str:
+    version = getattr(module, "__version__", None)
+
+    if version is None:
+        raise ImportError(f"Can't determine version for {module.__name__}")
+    if module.__name__ == "psycopg2":
+        # psycopg2 appends " (dt dec pq3 ext lo64)" to it's version
+        version = version.split()[0]
+    return version
+
+
+def import_optional_dependency(
+    name: str,
+    extra: str = "",
+    errors: str = "raise",
+    min_version: str | None = None,
+):
+    """
+    Import an optional dependency.
+
+    By default, if a dependency is missing an ImportError with a nice
+    message will be raised. If a dependency is present, but too old,
+    we raise.
+
+    Parameters
+    ----------
+    name : str
+        The module name.
+    extra : str
+        Additional text to include in the ImportError message.
+    errors : str {'raise', 'warn', 'ignore'}
+        What to do when a dependency is not found or its version is too old.
+
+        * raise : Raise an ImportError
+        * warn : Only applicable when a module's version is to old.
+          Warns that the version is too old and returns None
+        * ignore: If the module is not installed, return None, otherwise,
+          return the module, even if the version is too old.
+          It's expected that users validate the version locally when
+          using ``errors="ignore"`` (see. ``io/html.py``)
+    min_version : str, default None
+        Specify a minimum version that is different from the global pandas
+        minimum version required.
+    Returns
+    -------
+    maybe_module : Optional[ModuleType]
+        The imported module, when found and the version is correct.
+        None is returned when the package is not found and `errors`
+        is False, or when the package's version is too old and `errors`
+        is ``'warn'`` or ``'ignore'``.
+    """
+    assert errors in {"warn", "raise", "ignore"}
+
+    package_name = INSTALL_MAPPING.get(name)
+    install_name = package_name if package_name is not None else name
+
+    msg = (
+        f"Missing optional dependency '{install_name}'. {extra} "
+        f"Use pip or conda to install {install_name}."
+    )
+    try:
+        module = importlib.import_module(name)
+    except ImportError:
+        if errors == "raise":
+            raise ImportError(msg)
+        return None
+
+    # Handle submodules: if we have submodule, grab parent module from sys.modules
+    parent = name.split(".")[0]
+    if parent != name:
+        install_name = parent
+        module_to_get = sys.modules[install_name]
+    else:
+        module_to_get = module
+    minimum_version = min_version if min_version is not None else VERSIONS.get(parent)
+    if minimum_version:
+        version = get_version(module_to_get)
+        if version and Version(version) < Version(minimum_version):
+            msg = (
+                f"Pandas requires version '{minimum_version}' or newer of '{parent}' "
+                f"(version '{version}' currently installed)."
+            )
+            if errors == "warn":
+                warnings.warn(
+                    msg,
+                    UserWarning,
+                    stacklevel=find_stack_level(),
+                )
+                return None
+            elif errors == "raise":
+                raise ImportError(msg)
+            else:
+                return None
+
+    return module
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/compat/compressors.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/compat/compressors.py
new file mode 100644
index 0000000000000000000000000000000000000000..1f31e34c092c9672559ca2f5194cb1da7083d03b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/compat/compressors.py
@@ -0,0 +1,77 @@
+"""
+Patched ``BZ2File`` and ``LZMAFile`` to handle pickle protocol 5.
+"""
+
+from __future__ import annotations
+
+from pickle import PickleBuffer
+
+from pandas.compat._constants import PY310
+
+try:
+    import bz2
+
+    has_bz2 = True
+except ImportError:
+    has_bz2 = False
+
+try:
+    import lzma
+
+    has_lzma = True
+except ImportError:
+    has_lzma = False
+
+
+def flatten_buffer(
+    b: bytes | bytearray | memoryview | PickleBuffer,
+) -> bytes | bytearray | memoryview:
+    """
+    Return some 1-D `uint8` typed buffer.
+
+    Coerces anything that does not match that description to one that does
+    without copying if possible (otherwise will copy).
+    """
+
+    if isinstance(b, (bytes, bytearray)):
+        return b
+
+    if not isinstance(b, PickleBuffer):
+        b = PickleBuffer(b)
+
+    try:
+        # coerce to 1-D `uint8` C-contiguous `memoryview` zero-copy
+        return b.raw()
+    except BufferError:
+        # perform in-memory copy if buffer is not contiguous
+        return memoryview(b).tobytes("A")
+
+
+if has_bz2:
+
+    class BZ2File(bz2.BZ2File):
+        if not PY310:
+
+            def write(self, b) -> int:
+                # Workaround issue where `bz2.BZ2File` expects `len`
+                # to return the number of bytes in `b` by converting
+                # `b` into something that meets that constraint with
+                # minimal copying.
+                #
+                # Note: This is fixed in Python 3.10.
+                return super().write(flatten_buffer(b))
+
+
+if has_lzma:
+
+    class LZMAFile(lzma.LZMAFile):
+        if not PY310:
+
+            def write(self, b) -> int:
+                # Workaround issue where `lzma.LZMAFile` expects `len`
+                # to return the number of bytes in `b` by converting
+                # `b` into something that meets that constraint with
+                # minimal copying.
+                #
+                # Note: This is fixed in Python 3.10.
+                return super().write(flatten_buffer(b))
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/compat/numpy/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/compat/numpy/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a06761d03887bc2a2dd186fe2d3ac781f7222aeb
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/compat/numpy/__init__.py
@@ -0,0 +1,53 @@
+""" support numpy compatibility across versions """
+import warnings
+
+import numpy as np
+
+from pandas.util.version import Version
+
+# numpy versioning
+_np_version = np.__version__
+_nlv = Version(_np_version)
+np_version_lt1p23 = _nlv < Version("1.23")
+np_version_gte1p24 = _nlv >= Version("1.24")
+np_version_gte1p24p3 = _nlv >= Version("1.24.3")
+np_version_gte1p25 = _nlv >= Version("1.25")
+np_version_gt2 = _nlv >= Version("2.0.0")
+is_numpy_dev = _nlv.dev is not None
+_min_numpy_ver = "1.22.4"
+
+
+if _nlv < Version(_min_numpy_ver):
+    raise ImportError(
+        f"this version of pandas is incompatible with numpy < {_min_numpy_ver}\n"
+        f"your numpy version is {_np_version}.\n"
+        f"Please upgrade numpy to >= {_min_numpy_ver} to use this pandas version"
+    )
+
+
+np_long: type
+np_ulong: type
+
+if np_version_gt2:
+    try:
+        with warnings.catch_warnings():
+            warnings.filterwarnings(
+                "ignore",
+                r".*In the future `np\.long` will be defined as.*",
+                FutureWarning,
+            )
+            np_long = np.long  # type: ignore[attr-defined]
+            np_ulong = np.ulong  # type: ignore[attr-defined]
+    except AttributeError:
+        np_long = np.int_
+        np_ulong = np.uint
+else:
+    np_long = np.int_
+    np_ulong = np.uint
+
+
+__all__ = [
+    "np",
+    "_np_version",
+    "is_numpy_dev",
+]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/compat/numpy/function.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/compat/numpy/function.py
new file mode 100644
index 0000000000000000000000000000000000000000..4df30f7f4a8a79984ca6de521ac058bd30fd8faf
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/compat/numpy/function.py
@@ -0,0 +1,418 @@
+"""
+For compatibility with numpy libraries, pandas functions or methods have to
+accept '*args' and '**kwargs' parameters to accommodate numpy arguments that
+are not actually used or respected in the pandas implementation.
+
+To ensure that users do not abuse these parameters, validation is performed in
+'validators.py' to make sure that any extra parameters passed correspond ONLY
+to those in the numpy signature. Part of that validation includes whether or
+not the user attempted to pass in non-default values for these extraneous
+parameters. As we want to discourage users from relying on these parameters
+when calling the pandas implementation, we want them only to pass in the
+default values for these parameters.
+
+This module provides a set of commonly used default arguments for functions and
+methods that are spread throughout the codebase. This module will make it
+easier to adjust to future upstream changes in the analogous numpy signatures.
+"""
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    TypeVar,
+    cast,
+    overload,
+)
+
+import numpy as np
+from numpy import ndarray
+
+from pandas._libs.lib import (
+    is_bool,
+    is_integer,
+)
+from pandas.errors import UnsupportedFunctionCall
+from pandas.util._validators import (
+    validate_args,
+    validate_args_and_kwargs,
+    validate_kwargs,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        Axis,
+        AxisInt,
+    )
+
+    AxisNoneT = TypeVar("AxisNoneT", Axis, None)
+
+
+class CompatValidator:
+    def __init__(
+        self,
+        defaults,
+        fname=None,
+        method: str | None = None,
+        max_fname_arg_count=None,
+    ) -> None:
+        self.fname = fname
+        self.method = method
+        self.defaults = defaults
+        self.max_fname_arg_count = max_fname_arg_count
+
+    def __call__(
+        self,
+        args,
+        kwargs,
+        fname=None,
+        max_fname_arg_count=None,
+        method: str | None = None,
+    ) -> None:
+        if not args and not kwargs:
+            return None
+
+        fname = self.fname if fname is None else fname
+        max_fname_arg_count = (
+            self.max_fname_arg_count
+            if max_fname_arg_count is None
+            else max_fname_arg_count
+        )
+        method = self.method if method is None else method
+
+        if method == "args":
+            validate_args(fname, args, max_fname_arg_count, self.defaults)
+        elif method == "kwargs":
+            validate_kwargs(fname, kwargs, self.defaults)
+        elif method == "both":
+            validate_args_and_kwargs(
+                fname, args, kwargs, max_fname_arg_count, self.defaults
+            )
+        else:
+            raise ValueError(f"invalid validation method '{method}'")
+
+
+ARGMINMAX_DEFAULTS = {"out": None}
+validate_argmin = CompatValidator(
+    ARGMINMAX_DEFAULTS, fname="argmin", method="both", max_fname_arg_count=1
+)
+validate_argmax = CompatValidator(
+    ARGMINMAX_DEFAULTS, fname="argmax", method="both", max_fname_arg_count=1
+)
+
+
+def process_skipna(skipna: bool | ndarray | None, args) -> tuple[bool, Any]:
+    if isinstance(skipna, ndarray) or skipna is None:
+        args = (skipna,) + args
+        skipna = True
+
+    return skipna, args
+
+
+def validate_argmin_with_skipna(skipna: bool | ndarray | None, args, kwargs) -> bool:
+    """
+    If 'Series.argmin' is called via the 'numpy' library, the third parameter
+    in its signature is 'out', which takes either an ndarray or 'None', so
+    check if the 'skipna' parameter is either an instance of ndarray or is
+    None, since 'skipna' itself should be a boolean
+    """
+    skipna, args = process_skipna(skipna, args)
+    validate_argmin(args, kwargs)
+    return skipna
+
+
+def validate_argmax_with_skipna(skipna: bool | ndarray | None, args, kwargs) -> bool:
+    """
+    If 'Series.argmax' is called via the 'numpy' library, the third parameter
+    in its signature is 'out', which takes either an ndarray or 'None', so
+    check if the 'skipna' parameter is either an instance of ndarray or is
+    None, since 'skipna' itself should be a boolean
+    """
+    skipna, args = process_skipna(skipna, args)
+    validate_argmax(args, kwargs)
+    return skipna
+
+
+ARGSORT_DEFAULTS: dict[str, int | str | None] = {}
+ARGSORT_DEFAULTS["axis"] = -1
+ARGSORT_DEFAULTS["kind"] = "quicksort"
+ARGSORT_DEFAULTS["order"] = None
+ARGSORT_DEFAULTS["kind"] = None
+ARGSORT_DEFAULTS["stable"] = None
+
+
+validate_argsort = CompatValidator(
+    ARGSORT_DEFAULTS, fname="argsort", max_fname_arg_count=0, method="both"
+)
+
+# two different signatures of argsort, this second validation for when the
+# `kind` param is supported
+ARGSORT_DEFAULTS_KIND: dict[str, int | None] = {}
+ARGSORT_DEFAULTS_KIND["axis"] = -1
+ARGSORT_DEFAULTS_KIND["order"] = None
+ARGSORT_DEFAULTS_KIND["stable"] = None
+validate_argsort_kind = CompatValidator(
+    ARGSORT_DEFAULTS_KIND, fname="argsort", max_fname_arg_count=0, method="both"
+)
+
+
+def validate_argsort_with_ascending(ascending: bool | int | None, args, kwargs) -> bool:
+    """
+    If 'Categorical.argsort' is called via the 'numpy' library, the first
+    parameter in its signature is 'axis', which takes either an integer or
+    'None', so check if the 'ascending' parameter has either integer type or is
+    None, since 'ascending' itself should be a boolean
+    """
+    if is_integer(ascending) or ascending is None:
+        args = (ascending,) + args
+        ascending = True
+
+    validate_argsort_kind(args, kwargs, max_fname_arg_count=3)
+    ascending = cast(bool, ascending)
+    return ascending
+
+
+CLIP_DEFAULTS: dict[str, Any] = {"out": None}
+validate_clip = CompatValidator(
+    CLIP_DEFAULTS, fname="clip", method="both", max_fname_arg_count=3
+)
+
+
+@overload
+def validate_clip_with_axis(axis: ndarray, args, kwargs) -> None:
+    ...
+
+
+@overload
+def validate_clip_with_axis(axis: AxisNoneT, args, kwargs) -> AxisNoneT:
+    ...
+
+
+def validate_clip_with_axis(
+    axis: ndarray | AxisNoneT, args, kwargs
+) -> AxisNoneT | None:
+    """
+    If 'NDFrame.clip' is called via the numpy library, the third parameter in
+    its signature is 'out', which can takes an ndarray, so check if the 'axis'
+    parameter is an instance of ndarray, since 'axis' itself should either be
+    an integer or None
+    """
+    if isinstance(axis, ndarray):
+        args = (axis,) + args
+        # error: Incompatible types in assignment (expression has type "None",
+        # variable has type "Union[ndarray[Any, Any], str, int]")
+        axis = None  # type: ignore[assignment]
+
+    validate_clip(args, kwargs)
+    # error: Incompatible return value type (got "Union[ndarray[Any, Any],
+    # str, int]", expected "Union[str, int, None]")
+    return axis  # type: ignore[return-value]
+
+
+CUM_FUNC_DEFAULTS: dict[str, Any] = {}
+CUM_FUNC_DEFAULTS["dtype"] = None
+CUM_FUNC_DEFAULTS["out"] = None
+validate_cum_func = CompatValidator(
+    CUM_FUNC_DEFAULTS, method="both", max_fname_arg_count=1
+)
+validate_cumsum = CompatValidator(
+    CUM_FUNC_DEFAULTS, fname="cumsum", method="both", max_fname_arg_count=1
+)
+
+
+def validate_cum_func_with_skipna(skipna: bool, args, kwargs, name) -> bool:
+    """
+    If this function is called via the 'numpy' library, the third parameter in
+    its signature is 'dtype', which takes either a 'numpy' dtype or 'None', so
+    check if the 'skipna' parameter is a boolean or not
+    """
+    if not is_bool(skipna):
+        args = (skipna,) + args
+        skipna = True
+    elif isinstance(skipna, np.bool_):
+        skipna = bool(skipna)
+
+    validate_cum_func(args, kwargs, fname=name)
+    return skipna
+
+
+ALLANY_DEFAULTS: dict[str, bool | None] = {}
+ALLANY_DEFAULTS["dtype"] = None
+ALLANY_DEFAULTS["out"] = None
+ALLANY_DEFAULTS["keepdims"] = False
+ALLANY_DEFAULTS["axis"] = None
+validate_all = CompatValidator(
+    ALLANY_DEFAULTS, fname="all", method="both", max_fname_arg_count=1
+)
+validate_any = CompatValidator(
+    ALLANY_DEFAULTS, fname="any", method="both", max_fname_arg_count=1
+)
+
+LOGICAL_FUNC_DEFAULTS = {"out": None, "keepdims": False}
+validate_logical_func = CompatValidator(LOGICAL_FUNC_DEFAULTS, method="kwargs")
+
+MINMAX_DEFAULTS = {"axis": None, "dtype": None, "out": None, "keepdims": False}
+validate_min = CompatValidator(
+    MINMAX_DEFAULTS, fname="min", method="both", max_fname_arg_count=1
+)
+validate_max = CompatValidator(
+    MINMAX_DEFAULTS, fname="max", method="both", max_fname_arg_count=1
+)
+
+RESHAPE_DEFAULTS: dict[str, str] = {"order": "C"}
+validate_reshape = CompatValidator(
+    RESHAPE_DEFAULTS, fname="reshape", method="both", max_fname_arg_count=1
+)
+
+REPEAT_DEFAULTS: dict[str, Any] = {"axis": None}
+validate_repeat = CompatValidator(
+    REPEAT_DEFAULTS, fname="repeat", method="both", max_fname_arg_count=1
+)
+
+ROUND_DEFAULTS: dict[str, Any] = {"out": None}
+validate_round = CompatValidator(
+    ROUND_DEFAULTS, fname="round", method="both", max_fname_arg_count=1
+)
+
+SORT_DEFAULTS: dict[str, int | str | None] = {}
+SORT_DEFAULTS["axis"] = -1
+SORT_DEFAULTS["kind"] = "quicksort"
+SORT_DEFAULTS["order"] = None
+validate_sort = CompatValidator(SORT_DEFAULTS, fname="sort", method="kwargs")
+
+STAT_FUNC_DEFAULTS: dict[str, Any | None] = {}
+STAT_FUNC_DEFAULTS["dtype"] = None
+STAT_FUNC_DEFAULTS["out"] = None
+
+SUM_DEFAULTS = STAT_FUNC_DEFAULTS.copy()
+SUM_DEFAULTS["axis"] = None
+SUM_DEFAULTS["keepdims"] = False
+SUM_DEFAULTS["initial"] = None
+
+PROD_DEFAULTS = SUM_DEFAULTS.copy()
+
+MEAN_DEFAULTS = SUM_DEFAULTS.copy()
+
+MEDIAN_DEFAULTS = STAT_FUNC_DEFAULTS.copy()
+MEDIAN_DEFAULTS["overwrite_input"] = False
+MEDIAN_DEFAULTS["keepdims"] = False
+
+STAT_FUNC_DEFAULTS["keepdims"] = False
+
+validate_stat_func = CompatValidator(STAT_FUNC_DEFAULTS, method="kwargs")
+validate_sum = CompatValidator(
+    SUM_DEFAULTS, fname="sum", method="both", max_fname_arg_count=1
+)
+validate_prod = CompatValidator(
+    PROD_DEFAULTS, fname="prod", method="both", max_fname_arg_count=1
+)
+validate_mean = CompatValidator(
+    MEAN_DEFAULTS, fname="mean", method="both", max_fname_arg_count=1
+)
+validate_median = CompatValidator(
+    MEDIAN_DEFAULTS, fname="median", method="both", max_fname_arg_count=1
+)
+
+STAT_DDOF_FUNC_DEFAULTS: dict[str, bool | None] = {}
+STAT_DDOF_FUNC_DEFAULTS["dtype"] = None
+STAT_DDOF_FUNC_DEFAULTS["out"] = None
+STAT_DDOF_FUNC_DEFAULTS["keepdims"] = False
+validate_stat_ddof_func = CompatValidator(STAT_DDOF_FUNC_DEFAULTS, method="kwargs")
+
+TAKE_DEFAULTS: dict[str, str | None] = {}
+TAKE_DEFAULTS["out"] = None
+TAKE_DEFAULTS["mode"] = "raise"
+validate_take = CompatValidator(TAKE_DEFAULTS, fname="take", method="kwargs")
+
+
+def validate_take_with_convert(convert: ndarray | bool | None, args, kwargs) -> bool:
+    """
+    If this function is called via the 'numpy' library, the third parameter in
+    its signature is 'axis', which takes either an ndarray or 'None', so check
+    if the 'convert' parameter is either an instance of ndarray or is None
+    """
+    if isinstance(convert, ndarray) or convert is None:
+        args = (convert,) + args
+        convert = True
+
+    validate_take(args, kwargs, max_fname_arg_count=3, method="both")
+    return convert
+
+
+TRANSPOSE_DEFAULTS = {"axes": None}
+validate_transpose = CompatValidator(
+    TRANSPOSE_DEFAULTS, fname="transpose", method="both", max_fname_arg_count=0
+)
+
+
+def validate_groupby_func(name: str, args, kwargs, allowed=None) -> None:
+    """
+    'args' and 'kwargs' should be empty, except for allowed kwargs because all
+    of their necessary parameters are explicitly listed in the function
+    signature
+    """
+    if allowed is None:
+        allowed = []
+
+    kwargs = set(kwargs) - set(allowed)
+
+    if len(args) + len(kwargs) > 0:
+        raise UnsupportedFunctionCall(
+            "numpy operations are not valid with groupby. "
+            f"Use .groupby(...).{name}() instead"
+        )
+
+
+RESAMPLER_NUMPY_OPS = ("min", "max", "sum", "prod", "mean", "std", "var")
+
+
+def validate_resampler_func(method: str, args, kwargs) -> None:
+    """
+    'args' and 'kwargs' should be empty because all of their necessary
+    parameters are explicitly listed in the function signature
+    """
+    if len(args) + len(kwargs) > 0:
+        if method in RESAMPLER_NUMPY_OPS:
+            raise UnsupportedFunctionCall(
+                "numpy operations are not valid with resample. "
+                f"Use .resample(...).{method}() instead"
+            )
+        raise TypeError("too many arguments passed in")
+
+
+def validate_minmax_axis(axis: AxisInt | None, ndim: int = 1) -> None:
+    """
+    Ensure that the axis argument passed to min, max, argmin, or argmax is zero
+    or None, as otherwise it will be incorrectly ignored.
+
+    Parameters
+    ----------
+    axis : int or None
+    ndim : int, default 1
+
+    Raises
+    ------
+    ValueError
+    """
+    if axis is None:
+        return
+    if axis >= ndim or (axis < 0 and ndim + axis < 0):
+        raise ValueError(f"`axis` must be fewer than the number of dimensions ({ndim})")
+
+
+_validation_funcs = {
+    "median": validate_median,
+    "mean": validate_mean,
+    "min": validate_min,
+    "max": validate_max,
+    "sum": validate_sum,
+    "prod": validate_prod,
+}
+
+
+def validate_func(fname, args, kwargs) -> None:
+    if fname not in _validation_funcs:
+        return validate_stat_func(args, kwargs, fname=fname)
+
+    validation_func = _validation_funcs[fname]
+    return validation_func(args, kwargs)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/compat/pickle_compat.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/compat/pickle_compat.py
new file mode 100644
index 0000000000000000000000000000000000000000..cd98087c06c18634304c29d88837017a6952a4fc
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/compat/pickle_compat.py
@@ -0,0 +1,262 @@
+"""
+Support pre-0.12 series pickle compatibility.
+"""
+from __future__ import annotations
+
+import contextlib
+import copy
+import io
+import pickle as pkl
+from typing import TYPE_CHECKING
+
+import numpy as np
+
+from pandas._libs.arrays import NDArrayBacked
+from pandas._libs.tslibs import BaseOffset
+
+from pandas import Index
+from pandas.core.arrays import (
+    DatetimeArray,
+    PeriodArray,
+    TimedeltaArray,
+)
+from pandas.core.internals import BlockManager
+
+if TYPE_CHECKING:
+    from collections.abc import Generator
+
+
+def load_reduce(self) -> None:
+    stack = self.stack
+    args = stack.pop()
+    func = stack[-1]
+
+    try:
+        stack[-1] = func(*args)
+        return
+    except TypeError as err:
+        # If we have a deprecated function,
+        # try to replace and try again.
+
+        msg = "_reconstruct: First argument must be a sub-type of ndarray"
+
+        if msg in str(err):
+            try:
+                cls = args[0]
+                stack[-1] = object.__new__(cls)
+                return
+            except TypeError:
+                pass
+        elif args and isinstance(args[0], type) and issubclass(args[0], BaseOffset):
+            # TypeError: object.__new__(Day) is not safe, use Day.__new__()
+            cls = args[0]
+            stack[-1] = cls.__new__(*args)
+            return
+        elif args and issubclass(args[0], PeriodArray):
+            cls = args[0]
+            stack[-1] = NDArrayBacked.__new__(*args)
+            return
+
+        raise
+
+
+# If classes are moved, provide compat here.
+_class_locations_map = {
+    ("pandas.core.sparse.array", "SparseArray"): ("pandas.core.arrays", "SparseArray"),
+    # 15477
+    ("pandas.core.base", "FrozenNDArray"): ("numpy", "ndarray"),
+    # Re-routing unpickle block logic to go through _unpickle_block instead
+    # for pandas <= 1.3.5
+    ("pandas.core.internals.blocks", "new_block"): (
+        "pandas._libs.internals",
+        "_unpickle_block",
+    ),
+    ("pandas.core.indexes.frozen", "FrozenNDArray"): ("numpy", "ndarray"),
+    ("pandas.core.base", "FrozenList"): ("pandas.core.indexes.frozen", "FrozenList"),
+    # 10890
+    ("pandas.core.series", "TimeSeries"): ("pandas.core.series", "Series"),
+    ("pandas.sparse.series", "SparseTimeSeries"): (
+        "pandas.core.sparse.series",
+        "SparseSeries",
+    ),
+    # 12588, extensions moving
+    ("pandas._sparse", "BlockIndex"): ("pandas._libs.sparse", "BlockIndex"),
+    ("pandas.tslib", "Timestamp"): ("pandas._libs.tslib", "Timestamp"),
+    # 18543 moving period
+    ("pandas._period", "Period"): ("pandas._libs.tslibs.period", "Period"),
+    ("pandas._libs.period", "Period"): ("pandas._libs.tslibs.period", "Period"),
+    # 18014 moved __nat_unpickle from _libs.tslib-->_libs.tslibs.nattype
+    ("pandas.tslib", "__nat_unpickle"): (
+        "pandas._libs.tslibs.nattype",
+        "__nat_unpickle",
+    ),
+    ("pandas._libs.tslib", "__nat_unpickle"): (
+        "pandas._libs.tslibs.nattype",
+        "__nat_unpickle",
+    ),
+    # 15998 top-level dirs moving
+    ("pandas.sparse.array", "SparseArray"): (
+        "pandas.core.arrays.sparse",
+        "SparseArray",
+    ),
+    ("pandas.indexes.base", "_new_Index"): ("pandas.core.indexes.base", "_new_Index"),
+    ("pandas.indexes.base", "Index"): ("pandas.core.indexes.base", "Index"),
+    ("pandas.indexes.numeric", "Int64Index"): (
+        "pandas.core.indexes.base",
+        "Index",  # updated in 50775
+    ),
+    ("pandas.indexes.range", "RangeIndex"): ("pandas.core.indexes.range", "RangeIndex"),
+    ("pandas.indexes.multi", "MultiIndex"): ("pandas.core.indexes.multi", "MultiIndex"),
+    ("pandas.tseries.index", "_new_DatetimeIndex"): (
+        "pandas.core.indexes.datetimes",
+        "_new_DatetimeIndex",
+    ),
+    ("pandas.tseries.index", "DatetimeIndex"): (
+        "pandas.core.indexes.datetimes",
+        "DatetimeIndex",
+    ),
+    ("pandas.tseries.period", "PeriodIndex"): (
+        "pandas.core.indexes.period",
+        "PeriodIndex",
+    ),
+    # 19269, arrays moving
+    ("pandas.core.categorical", "Categorical"): ("pandas.core.arrays", "Categorical"),
+    # 19939, add timedeltaindex, float64index compat from 15998 move
+    ("pandas.tseries.tdi", "TimedeltaIndex"): (
+        "pandas.core.indexes.timedeltas",
+        "TimedeltaIndex",
+    ),
+    ("pandas.indexes.numeric", "Float64Index"): (
+        "pandas.core.indexes.base",
+        "Index",  # updated in 50775
+    ),
+    # 50775, remove Int64Index, UInt64Index & Float64Index from codabase
+    ("pandas.core.indexes.numeric", "Int64Index"): (
+        "pandas.core.indexes.base",
+        "Index",
+    ),
+    ("pandas.core.indexes.numeric", "UInt64Index"): (
+        "pandas.core.indexes.base",
+        "Index",
+    ),
+    ("pandas.core.indexes.numeric", "Float64Index"): (
+        "pandas.core.indexes.base",
+        "Index",
+    ),
+    ("pandas.core.arrays.sparse.dtype", "SparseDtype"): (
+        "pandas.core.dtypes.dtypes",
+        "SparseDtype",
+    ),
+}
+
+
+# our Unpickler sub-class to override methods and some dispatcher
+# functions for compat and uses a non-public class of the pickle module.
+
+
+class Unpickler(pkl._Unpickler):
+    def find_class(self, module, name):
+        # override superclass
+        key = (module, name)
+        module, name = _class_locations_map.get(key, key)
+        return super().find_class(module, name)
+
+
+Unpickler.dispatch = copy.copy(Unpickler.dispatch)
+Unpickler.dispatch[pkl.REDUCE[0]] = load_reduce
+
+
+def load_newobj(self) -> None:
+    args = self.stack.pop()
+    cls = self.stack[-1]
+
+    # compat
+    if issubclass(cls, Index):
+        obj = object.__new__(cls)
+    elif issubclass(cls, DatetimeArray) and not args:
+        arr = np.array([], dtype="M8[ns]")
+        obj = cls.__new__(cls, arr, arr.dtype)
+    elif issubclass(cls, TimedeltaArray) and not args:
+        arr = np.array([], dtype="m8[ns]")
+        obj = cls.__new__(cls, arr, arr.dtype)
+    elif cls is BlockManager and not args:
+        obj = cls.__new__(cls, (), [], False)
+    else:
+        obj = cls.__new__(cls, *args)
+
+    self.stack[-1] = obj
+
+
+Unpickler.dispatch[pkl.NEWOBJ[0]] = load_newobj
+
+
+def load_newobj_ex(self) -> None:
+    kwargs = self.stack.pop()
+    args = self.stack.pop()
+    cls = self.stack.pop()
+
+    # compat
+    if issubclass(cls, Index):
+        obj = object.__new__(cls)
+    else:
+        obj = cls.__new__(cls, *args, **kwargs)
+    self.append(obj)
+
+
+try:
+    Unpickler.dispatch[pkl.NEWOBJ_EX[0]] = load_newobj_ex
+except (AttributeError, KeyError):
+    pass
+
+
+def load(fh, encoding: str | None = None, is_verbose: bool = False):
+    """
+    Load a pickle, with a provided encoding,
+
+    Parameters
+    ----------
+    fh : a filelike object
+    encoding : an optional encoding
+    is_verbose : show exception output
+    """
+    try:
+        fh.seek(0)
+        if encoding is not None:
+            up = Unpickler(fh, encoding=encoding)
+        else:
+            up = Unpickler(fh)
+        # "Unpickler" has no attribute "is_verbose"  [attr-defined]
+        up.is_verbose = is_verbose  # type: ignore[attr-defined]
+
+        return up.load()
+    except (ValueError, TypeError):
+        raise
+
+
+def loads(
+    bytes_object: bytes,
+    *,
+    fix_imports: bool = True,
+    encoding: str = "ASCII",
+    errors: str = "strict",
+):
+    """
+    Analogous to pickle._loads.
+    """
+    fd = io.BytesIO(bytes_object)
+    return Unpickler(
+        fd, fix_imports=fix_imports, encoding=encoding, errors=errors
+    ).load()
+
+
+@contextlib.contextmanager
+def patch_pickle() -> Generator[None, None, None]:
+    """
+    Temporarily patch pickle to use our unpickler.
+    """
+    orig_loads = pkl.loads
+    try:
+        setattr(pkl, "loads", loads)
+        yield
+    finally:
+        setattr(pkl, "loads", orig_loads)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/compat/pyarrow.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/compat/pyarrow.py
new file mode 100644
index 0000000000000000000000000000000000000000..aacf2cd85c8ca3a9c1604d5314a7c247998e00c8
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/compat/pyarrow.py
@@ -0,0 +1,39 @@
+""" support pyarrow compatibility across versions """
+
+from __future__ import annotations
+
+from pandas.util.version import Version
+
+try:
+    import pyarrow as pa
+
+    _palv = Version(Version(pa.__version__).base_version)
+    pa_version_under10p1 = _palv < Version("10.0.1")
+    pa_version_under11p0 = _palv < Version("11.0.0")
+    pa_version_under12p0 = _palv < Version("12.0.0")
+    pa_version_under13p0 = _palv < Version("13.0.0")
+    pa_version_under14p0 = _palv < Version("14.0.0")
+    pa_version_under14p1 = _palv < Version("14.0.1")
+    pa_version_under15p0 = _palv < Version("15.0.0")
+    pa_version_under16p0 = _palv < Version("16.0.0")
+    pa_version_under17p0 = _palv < Version("17.0.0")
+    pa_version_under18p0 = _palv < Version("18.0.0")
+    pa_version_under19p0 = _palv < Version("19.0.0")
+    pa_version_under20p0 = _palv < Version("20.0.0")
+    pa_version_under21p0 = _palv < Version("21.0.0")
+    HAS_PYARROW = True
+except ImportError:
+    pa_version_under10p1 = True
+    pa_version_under11p0 = True
+    pa_version_under12p0 = True
+    pa_version_under13p0 = True
+    pa_version_under14p0 = True
+    pa_version_under14p1 = True
+    pa_version_under15p0 = True
+    pa_version_under16p0 = True
+    pa_version_under17p0 = True
+    pa_version_under18p0 = True
+    pa_version_under19p0 = True
+    pa_version_under20p0 = True
+    pa_version_under21p0 = True
+    HAS_PYARROW = False
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/conftest.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/conftest.py
new file mode 100644
index 0000000000000000000000000000000000000000..35fe5cb475cde0e6ea6c181c6326b06cadc763ea
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/conftest.py
@@ -0,0 +1,2065 @@
+"""
+This file is very long and growing, but it was decided to not split it yet, as
+it's still manageable (2020-03-17, ~1.1k LoC). See gh-31989
+
+Instead of splitting it was decided to define sections here:
+- Configuration / Settings
+- Autouse fixtures
+- Common arguments
+- Missing values & co.
+- Classes
+- Indices
+- Series'
+- DataFrames
+- Operators & Operations
+- Data sets/files
+- Time zones
+- Dtypes
+- Misc
+"""
+from __future__ import annotations
+
+from collections import abc
+from datetime import (
+    date,
+    datetime,
+    time,
+    timedelta,
+    timezone,
+)
+from decimal import Decimal
+import operator
+import os
+from typing import (
+    TYPE_CHECKING,
+    Callable,
+)
+
+from dateutil.tz import (
+    tzlocal,
+    tzutc,
+)
+import hypothesis
+from hypothesis import strategies as st
+import numpy as np
+import pytest
+from pytz import (
+    FixedOffset,
+    utc,
+)
+
+from pandas._config.config import _get_option
+
+import pandas.util._test_decorators as td
+
+from pandas.core.dtypes.dtypes import (
+    DatetimeTZDtype,
+    IntervalDtype,
+)
+
+import pandas as pd
+from pandas import (
+    CategoricalIndex,
+    DataFrame,
+    Interval,
+    IntervalIndex,
+    Period,
+    RangeIndex,
+    Series,
+    Timedelta,
+    Timestamp,
+    date_range,
+    period_range,
+    timedelta_range,
+)
+import pandas._testing as tm
+from pandas.core import ops
+from pandas.core.indexes.api import (
+    Index,
+    MultiIndex,
+)
+from pandas.util.version import Version
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Hashable,
+        Iterator,
+    )
+
+try:
+    import pyarrow as pa
+except ImportError:
+    has_pyarrow = False
+else:
+    del pa
+    has_pyarrow = True
+
+import zoneinfo
+
+try:
+    zoneinfo.ZoneInfo("UTC")
+except zoneinfo.ZoneInfoNotFoundError:
+    zoneinfo = None  # type: ignore[assignment]
+
+
+# ----------------------------------------------------------------
+# Configuration / Settings
+# ----------------------------------------------------------------
+# pytest
+
+
+def pytest_addoption(parser) -> None:
+    parser.addoption(
+        "--no-strict-data-files",
+        action="store_false",
+        help="Don't fail if a test is skipped for missing data file.",
+    )
+
+
+def ignore_doctest_warning(item: pytest.Item, path: str, message: str) -> None:
+    """Ignore doctest warning.
+
+    Parameters
+    ----------
+    item : pytest.Item
+        pytest test item.
+    path : str
+        Module path to Python object, e.g. "pandas.core.frame.DataFrame.append". A
+        warning will be filtered when item.name ends with in given path. So it is
+        sufficient to specify e.g. "DataFrame.append".
+    message : str
+        Message to be filtered.
+    """
+    if item.name.endswith(path):
+        item.add_marker(pytest.mark.filterwarnings(f"ignore:{message}"))
+
+
+def pytest_collection_modifyitems(items, config) -> None:
+    is_doctest = config.getoption("--doctest-modules") or config.getoption(
+        "--doctest-cython", default=False
+    )
+
+    # Warnings from doctests that can be ignored; place reason in comment above.
+    # Each entry specifies (path, message) - see the ignore_doctest_warning function
+    ignored_doctest_warnings = [
+        ("is_int64_dtype", "is_int64_dtype is deprecated"),
+        ("is_interval_dtype", "is_interval_dtype is deprecated"),
+        ("is_period_dtype", "is_period_dtype is deprecated"),
+        ("is_datetime64tz_dtype", "is_datetime64tz_dtype is deprecated"),
+        ("is_categorical_dtype", "is_categorical_dtype is deprecated"),
+        ("is_sparse", "is_sparse is deprecated"),
+        ("DataFrameGroupBy.fillna", "DataFrameGroupBy.fillna is deprecated"),
+        ("NDFrame.replace", "The 'method' keyword"),
+        ("NDFrame.replace", "Series.replace without 'value'"),
+        ("NDFrame.clip", "Downcasting behavior in Series and DataFrame methods"),
+        ("Series.idxmin", "The behavior of Series.idxmin"),
+        ("Series.idxmax", "The behavior of Series.idxmax"),
+        ("SeriesGroupBy.fillna", "SeriesGroupBy.fillna is deprecated"),
+        ("SeriesGroupBy.idxmin", "The behavior of Series.idxmin"),
+        ("SeriesGroupBy.idxmax", "The behavior of Series.idxmax"),
+        # Docstring divides by zero to show behavior difference
+        ("missing.mask_zero_div_zero", "divide by zero encountered"),
+        (
+            "to_pydatetime",
+            "The behavior of DatetimeProperties.to_pydatetime is deprecated",
+        ),
+        (
+            "pandas.core.generic.NDFrame.bool",
+            "(Series|DataFrame).bool is now deprecated and will be removed "
+            "in future version of pandas",
+        ),
+        (
+            "pandas.core.generic.NDFrame.first",
+            "first is deprecated and will be removed in a future version. "
+            "Please create a mask and filter using `.loc` instead",
+        ),
+        (
+            "Resampler.fillna",
+            "DatetimeIndexResampler.fillna is deprecated",
+        ),
+        (
+            "DataFrameGroupBy.fillna",
+            "DataFrameGroupBy.fillna with 'method' is deprecated",
+        ),
+        (
+            "DataFrameGroupBy.fillna",
+            "DataFrame.fillna with 'method' is deprecated",
+        ),
+        ("read_parquet", "Passing a BlockManager to DataFrame is deprecated"),
+    ]
+
+    if is_doctest:
+        for item in items:
+            for path, message in ignored_doctest_warnings:
+                ignore_doctest_warning(item, path, message)
+
+
+hypothesis_health_checks = [hypothesis.HealthCheck.too_slow]
+if Version(hypothesis.__version__) >= Version("6.83.2"):
+    hypothesis_health_checks.append(hypothesis.HealthCheck.differing_executors)
+
+# Hypothesis
+hypothesis.settings.register_profile(
+    "ci",
+    # Hypothesis timing checks are tuned for scalars by default, so we bump
+    # them from 200ms to 500ms per test case as the global default.  If this
+    # is too short for a specific test, (a) try to make it faster, and (b)
+    # if it really is slow add `@settings(deadline=...)` with a working value,
+    # or `deadline=None` to entirely disable timeouts for that test.
+    # 2022-02-09: Changed deadline from 500 -> None. Deadline leads to
+    # non-actionable, flaky CI failures (# GH 24641, 44969, 45118, 44969)
+    deadline=None,
+    suppress_health_check=tuple(hypothesis_health_checks),
+)
+hypothesis.settings.load_profile("ci")
+
+# Registering these strategies makes them globally available via st.from_type,
+# which is use for offsets in tests/tseries/offsets/test_offsets_properties.py
+for name in "MonthBegin MonthEnd BMonthBegin BMonthEnd".split():
+    cls = getattr(pd.tseries.offsets, name)
+    st.register_type_strategy(
+        cls, st.builds(cls, n=st.integers(-99, 99), normalize=st.booleans())
+    )
+
+for name in "YearBegin YearEnd BYearBegin BYearEnd".split():
+    cls = getattr(pd.tseries.offsets, name)
+    st.register_type_strategy(
+        cls,
+        st.builds(
+            cls,
+            n=st.integers(-5, 5),
+            normalize=st.booleans(),
+            month=st.integers(min_value=1, max_value=12),
+        ),
+    )
+
+for name in "QuarterBegin QuarterEnd BQuarterBegin BQuarterEnd".split():
+    cls = getattr(pd.tseries.offsets, name)
+    st.register_type_strategy(
+        cls,
+        st.builds(
+            cls,
+            n=st.integers(-24, 24),
+            normalize=st.booleans(),
+            startingMonth=st.integers(min_value=1, max_value=12),
+        ),
+    )
+
+
+# ----------------------------------------------------------------
+# Autouse fixtures
+# ----------------------------------------------------------------
+
+
+# https://github.com/pytest-dev/pytest/issues/11873
+# Would like to avoid autouse=True, but cannot as of pytest 8.0.0
+@pytest.fixture(autouse=True)
+def add_doctest_imports(doctest_namespace) -> None:
+    """
+    Make `np` and `pd` names available for doctests.
+    """
+    doctest_namespace["np"] = np
+    doctest_namespace["pd"] = pd
+
+
+@pytest.fixture(autouse=True)
+def configure_tests() -> None:
+    """
+    Configure settings for all tests and test modules.
+    """
+    pd.set_option("chained_assignment", "raise")
+
+
+# ----------------------------------------------------------------
+# Common arguments
+# ----------------------------------------------------------------
+@pytest.fixture(params=[0, 1, "index", "columns"], ids=lambda x: f"axis={repr(x)}")
+def axis(request):
+    """
+    Fixture for returning the axis numbers of a DataFrame.
+    """
+    return request.param
+
+
+axis_frame = axis
+
+
+@pytest.fixture(params=[1, "columns"], ids=lambda x: f"axis={repr(x)}")
+def axis_1(request):
+    """
+    Fixture for returning aliases of axis 1 of a DataFrame.
+    """
+    return request.param
+
+
+@pytest.fixture(params=[True, False, None])
+def observed(request):
+    """
+    Pass in the observed keyword to groupby for [True, False]
+    This indicates whether categoricals should return values for
+    values which are not in the grouper [False / None], or only values which
+    appear in the grouper [True]. [None] is supported for future compatibility
+    if we decide to change the default (and would need to warn if this
+    parameter is not passed).
+    """
+    return request.param
+
+
+@pytest.fixture(params=[True, False, None])
+def ordered(request):
+    """
+    Boolean 'ordered' parameter for Categorical.
+    """
+    return request.param
+
+
+@pytest.fixture(params=[True, False])
+def skipna(request):
+    """
+    Boolean 'skipna' parameter.
+    """
+    return request.param
+
+
+@pytest.fixture(params=["first", "last", False])
+def keep(request):
+    """
+    Valid values for the 'keep' parameter used in
+    .duplicated or .drop_duplicates
+    """
+    return request.param
+
+
+@pytest.fixture(params=["both", "neither", "left", "right"])
+def inclusive_endpoints_fixture(request):
+    """
+    Fixture for trying all interval 'inclusive' parameters.
+    """
+    return request.param
+
+
+@pytest.fixture(params=["left", "right", "both", "neither"])
+def closed(request):
+    """
+    Fixture for trying all interval closed parameters.
+    """
+    return request.param
+
+
+@pytest.fixture(params=["left", "right", "both", "neither"])
+def other_closed(request):
+    """
+    Secondary closed fixture to allow parametrizing over all pairs of closed.
+    """
+    return request.param
+
+
+@pytest.fixture(
+    params=[
+        None,
+        "gzip",
+        "bz2",
+        "zip",
+        "xz",
+        "tar",
+        pytest.param("zstd", marks=td.skip_if_no("zstandard")),
+    ]
+)
+def compression(request):
+    """
+    Fixture for trying common compression types in compression tests.
+    """
+    return request.param
+
+
+@pytest.fixture(
+    params=[
+        "gzip",
+        "bz2",
+        "zip",
+        "xz",
+        "tar",
+        pytest.param("zstd", marks=td.skip_if_no("zstandard")),
+    ]
+)
+def compression_only(request):
+    """
+    Fixture for trying common compression types in compression tests excluding
+    uncompressed case.
+    """
+    return request.param
+
+
+@pytest.fixture(params=[True, False])
+def writable(request):
+    """
+    Fixture that an array is writable.
+    """
+    return request.param
+
+
+@pytest.fixture(params=["inner", "outer", "left", "right"])
+def join_type(request):
+    """
+    Fixture for trying all types of join operations.
+    """
+    return request.param
+
+
+@pytest.fixture(params=["nlargest", "nsmallest"])
+def nselect_method(request):
+    """
+    Fixture for trying all nselect methods.
+    """
+    return request.param
+
+
+# ----------------------------------------------------------------
+# Missing values & co.
+# ----------------------------------------------------------------
+@pytest.fixture(params=tm.NULL_OBJECTS, ids=lambda x: type(x).__name__)
+def nulls_fixture(request):
+    """
+    Fixture for each null type in pandas.
+    """
+    return request.param
+
+
+nulls_fixture2 = nulls_fixture  # Generate cartesian product of nulls_fixture
+
+
+@pytest.fixture(params=[None, np.nan, pd.NaT])
+def unique_nulls_fixture(request):
+    """
+    Fixture for each null type in pandas, each null type exactly once.
+    """
+    return request.param
+
+
+# Generate cartesian product of unique_nulls_fixture:
+unique_nulls_fixture2 = unique_nulls_fixture
+
+
+@pytest.fixture(params=tm.NP_NAT_OBJECTS, ids=lambda x: type(x).__name__)
+def np_nat_fixture(request):
+    """
+    Fixture for each NaT type in numpy.
+    """
+    return request.param
+
+
+# Generate cartesian product of np_nat_fixture:
+np_nat_fixture2 = np_nat_fixture
+
+
+# ----------------------------------------------------------------
+# Classes
+# ----------------------------------------------------------------
+
+
+@pytest.fixture(params=[DataFrame, Series])
+def frame_or_series(request):
+    """
+    Fixture to parametrize over DataFrame and Series.
+    """
+    return request.param
+
+
+@pytest.fixture(params=[Index, Series], ids=["index", "series"])
+def index_or_series(request):
+    """
+    Fixture to parametrize over Index and Series, made necessary by a mypy
+    bug, giving an error:
+
+    List item 0 has incompatible type "Type[Series]"; expected "Type[PandasObject]"
+
+    See GH#29725
+    """
+    return request.param
+
+
+# Generate cartesian product of index_or_series fixture:
+index_or_series2 = index_or_series
+
+
+@pytest.fixture(params=[Index, Series, pd.array], ids=["index", "series", "array"])
+def index_or_series_or_array(request):
+    """
+    Fixture to parametrize over Index, Series, and ExtensionArray
+    """
+    return request.param
+
+
+@pytest.fixture(params=[Index, Series, DataFrame, pd.array], ids=lambda x: x.__name__)
+def box_with_array(request):
+    """
+    Fixture to test behavior for Index, Series, DataFrame, and pandas Array
+    classes
+    """
+    return request.param
+
+
+box_with_array2 = box_with_array
+
+
+@pytest.fixture
+def dict_subclass() -> type[dict]:
+    """
+    Fixture for a dictionary subclass.
+    """
+
+    class TestSubDict(dict):
+        def __init__(self, *args, **kwargs) -> None:
+            dict.__init__(self, *args, **kwargs)
+
+    return TestSubDict
+
+
+@pytest.fixture
+def non_dict_mapping_subclass() -> type[abc.Mapping]:
+    """
+    Fixture for a non-mapping dictionary subclass.
+    """
+
+    class TestNonDictMapping(abc.Mapping):
+        def __init__(self, underlying_dict) -> None:
+            self._data = underlying_dict
+
+        def __getitem__(self, key):
+            return self._data.__getitem__(key)
+
+        def __iter__(self) -> Iterator:
+            return self._data.__iter__()
+
+        def __len__(self) -> int:
+            return self._data.__len__()
+
+    return TestNonDictMapping
+
+
+# ----------------------------------------------------------------
+# Indices
+# ----------------------------------------------------------------
+@pytest.fixture
+def multiindex_year_month_day_dataframe_random_data():
+    """
+    DataFrame with 3 level MultiIndex (year, month, day) covering
+    first 100 business days from 2000-01-01 with random data
+    """
+    tdf = DataFrame(
+        np.random.default_rng(2).standard_normal((100, 4)),
+        columns=Index(list("ABCD")),
+        index=date_range("2000-01-01", periods=100, freq="B"),
+    )
+    ymd = tdf.groupby([lambda x: x.year, lambda x: x.month, lambda x: x.day]).sum()
+    # use int64 Index, to make sure things work
+    ymd.index = ymd.index.set_levels([lev.astype("i8") for lev in ymd.index.levels])
+    ymd.index.set_names(["year", "month", "day"], inplace=True)
+    return ymd
+
+
+@pytest.fixture
+def lexsorted_two_level_string_multiindex() -> MultiIndex:
+    """
+    2-level MultiIndex, lexsorted, with string names.
+    """
+    return MultiIndex(
+        levels=[["foo", "bar", "baz", "qux"], ["one", "two", "three"]],
+        codes=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3], [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
+        names=["first", "second"],
+    )
+
+
+@pytest.fixture
+def multiindex_dataframe_random_data(
+    lexsorted_two_level_string_multiindex,
+) -> DataFrame:
+    """DataFrame with 2 level MultiIndex with random data"""
+    index = lexsorted_two_level_string_multiindex
+    return DataFrame(
+        np.random.default_rng(2).standard_normal((10, 3)),
+        index=index,
+        columns=Index(["A", "B", "C"], name="exp"),
+    )
+
+
+def _create_multiindex():
+    """
+    MultiIndex used to test the general functionality of this object
+    """
+
+    # See Also: tests.multi.conftest.idx
+    major_axis = Index(["foo", "bar", "baz", "qux"])
+    minor_axis = Index(["one", "two"])
+
+    major_codes = np.array([0, 0, 1, 2, 3, 3])
+    minor_codes = np.array([0, 1, 0, 1, 0, 1])
+    index_names = ["first", "second"]
+    return MultiIndex(
+        levels=[major_axis, minor_axis],
+        codes=[major_codes, minor_codes],
+        names=index_names,
+        verify_integrity=False,
+    )
+
+
+def _create_mi_with_dt64tz_level():
+    """
+    MultiIndex with a level that is a tzaware DatetimeIndex.
+    """
+    # GH#8367 round trip with pickle
+    return MultiIndex.from_product(
+        [[1, 2], ["a", "b"], date_range("20130101", periods=3, tz="US/Eastern")],
+        names=["one", "two", "three"],
+    )
+
+
+indices_dict = {
+    "object": Index([f"pandas_{i}" for i in range(100)], dtype=object),
+    "string": Index([f"pandas_{i}" for i in range(100)], dtype="str"),
+    "datetime": date_range("2020-01-01", periods=100),
+    "datetime-tz": date_range("2020-01-01", periods=100, tz="US/Pacific"),
+    "period": period_range("2020-01-01", periods=100, freq="D"),
+    "timedelta": timedelta_range(start="1 day", periods=100, freq="D"),
+    "range": RangeIndex(100),
+    "int8": Index(np.arange(100), dtype="int8"),
+    "int16": Index(np.arange(100), dtype="int16"),
+    "int32": Index(np.arange(100), dtype="int32"),
+    "int64": Index(np.arange(100), dtype="int64"),
+    "uint8": Index(np.arange(100), dtype="uint8"),
+    "uint16": Index(np.arange(100), dtype="uint16"),
+    "uint32": Index(np.arange(100), dtype="uint32"),
+    "uint64": Index(np.arange(100), dtype="uint64"),
+    "float32": Index(np.arange(100), dtype="float32"),
+    "float64": Index(np.arange(100), dtype="float64"),
+    "bool-object": Index([True, False] * 5, dtype=object),
+    "bool-dtype": Index([True, False] * 5, dtype=bool),
+    "complex64": Index(
+        np.arange(100, dtype="complex64") + 1.0j * np.arange(100, dtype="complex64")
+    ),
+    "complex128": Index(
+        np.arange(100, dtype="complex128") + 1.0j * np.arange(100, dtype="complex128")
+    ),
+    "categorical": CategoricalIndex(list("abcd") * 25),
+    "interval": IntervalIndex.from_breaks(np.linspace(0, 100, num=101)),
+    "empty": Index([]),
+    "tuples": MultiIndex.from_tuples(zip(["foo", "bar", "baz"], [1, 2, 3])),
+    "mi-with-dt64tz-level": _create_mi_with_dt64tz_level(),
+    "multi": _create_multiindex(),
+    "repeats": Index([0, 0, 1, 1, 2, 2]),
+    "nullable_int": Index(np.arange(100), dtype="Int64"),
+    "nullable_uint": Index(np.arange(100), dtype="UInt16"),
+    "nullable_float": Index(np.arange(100), dtype="Float32"),
+    "nullable_bool": Index(np.arange(100).astype(bool), dtype="boolean"),
+    "string-python": Index(
+        pd.array([f"pandas_{i}" for i in range(100)], dtype="string[python]")
+    ),
+}
+if has_pyarrow:
+    idx = Index(pd.array([f"pandas_{i}" for i in range(100)], dtype="string[pyarrow]"))
+    indices_dict["string-pyarrow"] = idx
+
+
+@pytest.fixture(params=indices_dict.keys())
+def index(request):
+    """
+    Fixture for many "simple" kinds of indices.
+
+    These indices are unlikely to cover corner cases, e.g.
+        - no names
+        - no NaTs/NaNs
+        - no values near implementation bounds
+        - ...
+    """
+    # copy to avoid mutation, e.g. setting .name
+    return indices_dict[request.param].copy()
+
+
+# Needed to generate cartesian product of indices
+index_fixture2 = index
+
+
+@pytest.fixture(
+    params=[
+        key for key, value in indices_dict.items() if not isinstance(value, MultiIndex)
+    ]
+)
+def index_flat(request):
+    """
+    index fixture, but excluding MultiIndex cases.
+    """
+    key = request.param
+    return indices_dict[key].copy()
+
+
+# Alias so we can test with cartesian product of index_flat
+index_flat2 = index_flat
+
+
+@pytest.fixture(
+    params=[
+        key
+        for key, value in indices_dict.items()
+        if not (
+            key.startswith(("int", "uint", "float"))
+            or key in ["range", "empty", "repeats", "bool-dtype"]
+        )
+        and not isinstance(value, MultiIndex)
+    ]
+)
+def index_with_missing(request):
+    """
+    Fixture for indices with missing values.
+
+    Integer-dtype and empty cases are excluded because they cannot hold missing
+    values.
+
+    MultiIndex is excluded because isna() is not defined for MultiIndex.
+    """
+
+    # GH 35538. Use deep copy to avoid illusive bug on np-dev
+    # GHA pipeline that writes into indices_dict despite copy
+    ind = indices_dict[request.param].copy(deep=True)
+    vals = ind.values.copy()
+    if request.param in ["tuples", "mi-with-dt64tz-level", "multi"]:
+        # For setting missing values in the top level of MultiIndex
+        vals = ind.tolist()
+        vals[0] = (None,) + vals[0][1:]
+        vals[-1] = (None,) + vals[-1][1:]
+        return MultiIndex.from_tuples(vals)
+    else:
+        vals[0] = None
+        vals[-1] = None
+        return type(ind)(vals)
+
+
+# ----------------------------------------------------------------
+# Series'
+# ----------------------------------------------------------------
+@pytest.fixture
+def string_series() -> Series:
+    """
+    Fixture for Series of floats with Index of unique strings
+    """
+    return Series(
+        np.arange(30, dtype=np.float64) * 1.1,
+        index=Index([f"i_{i}" for i in range(30)]),
+        name="series",
+    )
+
+
+@pytest.fixture
+def object_series() -> Series:
+    """
+    Fixture for Series of dtype object with Index of unique strings
+    """
+    data = [f"foo_{i}" for i in range(30)]
+    index = Index([f"bar_{i}" for i in range(30)])
+    return Series(data, index=index, name="objects", dtype=object)
+
+
+@pytest.fixture
+def datetime_series() -> Series:
+    """
+    Fixture for Series of floats with DatetimeIndex
+    """
+    return Series(
+        np.random.default_rng(2).standard_normal(30),
+        index=date_range("2000-01-01", periods=30, freq="B"),
+        name="ts",
+    )
+
+
+def _create_series(index):
+    """Helper for the _series dict"""
+    size = len(index)
+    data = np.random.default_rng(2).standard_normal(size)
+    return Series(data, index=index, name="a", copy=False)
+
+
+_series = {
+    f"series-with-{index_id}-index": _create_series(index)
+    for index_id, index in indices_dict.items()
+}
+
+
+@pytest.fixture
+def series_with_simple_index(index) -> Series:
+    """
+    Fixture for tests on series with changing types of indices.
+    """
+    return _create_series(index)
+
+
+_narrow_series = {
+    f"{dtype.__name__}-series": Series(
+        range(30), index=[f"i-{i}" for i in range(30)], name="a", dtype=dtype
+    )
+    for dtype in tm.NARROW_NP_DTYPES
+}
+
+
+_index_or_series_objs = {**indices_dict, **_series, **_narrow_series}
+
+
+@pytest.fixture(params=_index_or_series_objs.keys())
+def index_or_series_obj(request):
+    """
+    Fixture for tests on indexes, series and series with a narrow dtype
+    copy to avoid mutation, e.g. setting .name
+    """
+    return _index_or_series_objs[request.param].copy(deep=True)
+
+
+_typ_objects_series = {
+    f"{dtype.__name__}-series": Series(dtype) for dtype in tm.PYTHON_DATA_TYPES
+}
+
+
+_index_or_series_memory_objs = {
+    **indices_dict,
+    **_series,
+    **_narrow_series,
+    **_typ_objects_series,
+}
+
+
+@pytest.fixture(params=_index_or_series_memory_objs.keys())
+def index_or_series_memory_obj(request):
+    """
+    Fixture for tests on indexes, series, series with a narrow dtype and
+    series with empty objects type
+    copy to avoid mutation, e.g. setting .name
+    """
+    return _index_or_series_memory_objs[request.param].copy(deep=True)
+
+
+# ----------------------------------------------------------------
+# DataFrames
+# ----------------------------------------------------------------
+@pytest.fixture
+def int_frame() -> DataFrame:
+    """
+    Fixture for DataFrame of ints with index of unique strings
+
+    Columns are ['A', 'B', 'C', 'D']
+    """
+    return DataFrame(
+        np.ones((30, 4), dtype=np.int64),
+        index=Index([f"foo_{i}" for i in range(30)]),
+        columns=Index(list("ABCD")),
+    )
+
+
+@pytest.fixture
+def float_frame() -> DataFrame:
+    """
+    Fixture for DataFrame of floats with index of unique strings
+
+    Columns are ['A', 'B', 'C', 'D'].
+    """
+    return DataFrame(
+        np.random.default_rng(2).standard_normal((30, 4)),
+        index=Index([f"foo_{i}" for i in range(30)]),
+        columns=Index(list("ABCD")),
+    )
+
+
+@pytest.fixture
+def rand_series_with_duplicate_datetimeindex() -> Series:
+    """
+    Fixture for Series with a DatetimeIndex that has duplicates.
+    """
+    dates = [
+        datetime(2000, 1, 2),
+        datetime(2000, 1, 2),
+        datetime(2000, 1, 2),
+        datetime(2000, 1, 3),
+        datetime(2000, 1, 3),
+        datetime(2000, 1, 3),
+        datetime(2000, 1, 4),
+        datetime(2000, 1, 4),
+        datetime(2000, 1, 4),
+        datetime(2000, 1, 5),
+    ]
+
+    return Series(np.random.default_rng(2).standard_normal(len(dates)), index=dates)
+
+
+# ----------------------------------------------------------------
+# Scalars
+# ----------------------------------------------------------------
+@pytest.fixture(
+    params=[
+        (Interval(left=0, right=5), IntervalDtype("int64", "right")),
+        (Interval(left=0.1, right=0.5), IntervalDtype("float64", "right")),
+        (Period("2012-01", freq="M"), "period[M]"),
+        (Period("2012-02-01", freq="D"), "period[D]"),
+        (
+            Timestamp("2011-01-01", tz="US/Eastern"),
+            DatetimeTZDtype(unit="s", tz="US/Eastern"),
+        ),
+        (Timedelta(seconds=500), "timedelta64[ns]"),
+    ]
+)
+def ea_scalar_and_dtype(request):
+    return request.param
+
+
+# ----------------------------------------------------------------
+# Operators & Operations
+# ----------------------------------------------------------------
+
+
+@pytest.fixture(params=tm.arithmetic_dunder_methods)
+def all_arithmetic_operators(request):
+    """
+    Fixture for dunder names for common arithmetic operations.
+    """
+    return request.param
+
+
+@pytest.fixture(
+    params=[
+        operator.add,
+        ops.radd,
+        operator.sub,
+        ops.rsub,
+        operator.mul,
+        ops.rmul,
+        operator.truediv,
+        ops.rtruediv,
+        operator.floordiv,
+        ops.rfloordiv,
+        operator.mod,
+        ops.rmod,
+        operator.pow,
+        ops.rpow,
+        operator.eq,
+        operator.ne,
+        operator.lt,
+        operator.le,
+        operator.gt,
+        operator.ge,
+        operator.and_,
+        ops.rand_,
+        operator.xor,
+        ops.rxor,
+        operator.or_,
+        ops.ror_,
+    ]
+)
+def all_binary_operators(request):
+    """
+    Fixture for operator and roperator arithmetic, comparison, and logical ops.
+    """
+    return request.param
+
+
+@pytest.fixture(
+    params=[
+        operator.add,
+        ops.radd,
+        operator.sub,
+        ops.rsub,
+        operator.mul,
+        ops.rmul,
+        operator.truediv,
+        ops.rtruediv,
+        operator.floordiv,
+        ops.rfloordiv,
+        operator.mod,
+        ops.rmod,
+        operator.pow,
+        ops.rpow,
+    ]
+)
+def all_arithmetic_functions(request):
+    """
+    Fixture for operator and roperator arithmetic functions.
+
+    Notes
+    -----
+    This includes divmod and rdivmod, whereas all_arithmetic_operators
+    does not.
+    """
+    return request.param
+
+
+_all_numeric_reductions = [
+    "count",
+    "sum",
+    "max",
+    "min",
+    "mean",
+    "prod",
+    "std",
+    "var",
+    "median",
+    "kurt",
+    "skew",
+    "sem",
+]
+
+
+@pytest.fixture(params=_all_numeric_reductions)
+def all_numeric_reductions(request):
+    """
+    Fixture for numeric reduction names.
+    """
+    return request.param
+
+
+_all_boolean_reductions = ["all", "any"]
+
+
+@pytest.fixture(params=_all_boolean_reductions)
+def all_boolean_reductions(request):
+    """
+    Fixture for boolean reduction names.
+    """
+    return request.param
+
+
+_all_reductions = _all_numeric_reductions + _all_boolean_reductions
+
+
+@pytest.fixture(params=_all_reductions)
+def all_reductions(request):
+    """
+    Fixture for all (boolean + numeric) reduction names.
+    """
+    return request.param
+
+
+@pytest.fixture(
+    params=[
+        operator.eq,
+        operator.ne,
+        operator.gt,
+        operator.ge,
+        operator.lt,
+        operator.le,
+    ]
+)
+def comparison_op(request):
+    """
+    Fixture for operator module comparison functions.
+    """
+    return request.param
+
+
+@pytest.fixture(params=["__le__", "__lt__", "__ge__", "__gt__"])
+def compare_operators_no_eq_ne(request):
+    """
+    Fixture for dunder names for compare operations except == and !=
+
+    * >=
+    * >
+    * <
+    * <=
+    """
+    return request.param
+
+
+@pytest.fixture(
+    params=["__and__", "__rand__", "__or__", "__ror__", "__xor__", "__rxor__"]
+)
+def all_logical_operators(request):
+    """
+    Fixture for dunder names for common logical operations
+
+    * |
+    * &
+    * ^
+    """
+    return request.param
+
+
+_all_numeric_accumulations = ["cumsum", "cumprod", "cummin", "cummax"]
+
+
+@pytest.fixture(params=_all_numeric_accumulations)
+def all_numeric_accumulations(request):
+    """
+    Fixture for numeric accumulation names
+    """
+    return request.param
+
+
+# ----------------------------------------------------------------
+# Data sets/files
+# ----------------------------------------------------------------
+@pytest.fixture
+def strict_data_files(pytestconfig):
+    """
+    Returns the configuration for the test setting `--no-strict-data-files`.
+    """
+    return pytestconfig.getoption("--no-strict-data-files")
+
+
+@pytest.fixture
+def datapath(strict_data_files: str) -> Callable[..., str]:
+    """
+    Get the path to a data file.
+
+    Parameters
+    ----------
+    path : str
+        Path to the file, relative to ``pandas/tests/``
+
+    Returns
+    -------
+    path including ``pandas/tests``.
+
+    Raises
+    ------
+    ValueError
+        If the path doesn't exist and the --no-strict-data-files option is not set.
+    """
+    BASE_PATH = os.path.join(os.path.dirname(__file__), "tests")
+
+    def deco(*args):
+        path = os.path.join(BASE_PATH, *args)
+        if not os.path.exists(path):
+            if strict_data_files:
+                raise ValueError(
+                    f"Could not find file {path} and --no-strict-data-files is not set."
+                )
+            pytest.skip(f"Could not find {path}.")
+        return path
+
+    return deco
+
+
+# ----------------------------------------------------------------
+# Time zones
+# ----------------------------------------------------------------
+TIMEZONES = [
+    None,
+    "UTC",
+    "US/Eastern",
+    "Asia/Tokyo",
+    "dateutil/US/Pacific",
+    "dateutil/Asia/Singapore",
+    "+01:15",
+    "-02:15",
+    "UTC+01:15",
+    "UTC-02:15",
+    tzutc(),
+    tzlocal(),
+    FixedOffset(300),
+    FixedOffset(0),
+    FixedOffset(-300),
+    timezone.utc,
+    timezone(timedelta(hours=1)),
+    timezone(timedelta(hours=-1), name="foo"),
+]
+if zoneinfo is not None:
+    TIMEZONES.extend(
+        [
+            zoneinfo.ZoneInfo("US/Pacific"),  # type: ignore[list-item]
+            zoneinfo.ZoneInfo("UTC"),  # type: ignore[list-item]
+        ]
+    )
+TIMEZONE_IDS = [repr(i) for i in TIMEZONES]
+
+
+@td.parametrize_fixture_doc(str(TIMEZONE_IDS))
+@pytest.fixture(params=TIMEZONES, ids=TIMEZONE_IDS)
+def tz_naive_fixture(request):
+    """
+    Fixture for trying timezones including default (None): {0}
+    """
+    return request.param
+
+
+@td.parametrize_fixture_doc(str(TIMEZONE_IDS[1:]))
+@pytest.fixture(params=TIMEZONES[1:], ids=TIMEZONE_IDS[1:])
+def tz_aware_fixture(request):
+    """
+    Fixture for trying explicit timezones: {0}
+    """
+    return request.param
+
+
+# Generate cartesian product of tz_aware_fixture:
+tz_aware_fixture2 = tz_aware_fixture
+
+
+_UTCS = ["utc", "dateutil/UTC", utc, tzutc(), timezone.utc]
+if zoneinfo is not None:
+    _UTCS.append(zoneinfo.ZoneInfo("UTC"))
+
+
+@pytest.fixture(params=_UTCS)
+def utc_fixture(request):
+    """
+    Fixture to provide variants of UTC timezone strings and tzinfo objects.
+    """
+    return request.param
+
+
+utc_fixture2 = utc_fixture
+
+
+@pytest.fixture(params=["s", "ms", "us", "ns"])
+def unit(request):
+    """
+    datetime64 units we support.
+    """
+    return request.param
+
+
+unit2 = unit
+
+
+# ----------------------------------------------------------------
+# Dtypes
+# ----------------------------------------------------------------
+@pytest.fixture(params=tm.STRING_DTYPES)
+def string_dtype(request):
+    """
+    Parametrized fixture for string dtypes.
+
+    * str
+    * 'str'
+    * 'U'
+    """
+    return request.param
+
+
+@pytest.fixture(
+    params=[
+        ("python", pd.NA),
+        pytest.param(("pyarrow", pd.NA), marks=td.skip_if_no("pyarrow")),
+        pytest.param(("pyarrow", np.nan), marks=td.skip_if_no("pyarrow")),
+        ("python", np.nan),
+    ],
+    ids=[
+        "string=string[python]",
+        "string=string[pyarrow]",
+        "string=str[pyarrow]",
+        "string=str[python]",
+    ],
+)
+def string_dtype_no_object(request):
+    """
+    Parametrized fixture for string dtypes.
+    * 'string[python]' (NA variant)
+    * 'string[pyarrow]' (NA variant)
+    * 'str' (NaN variant, with pyarrow)
+    * 'str' (NaN variant, without pyarrow)
+    """
+    # need to instantiate the StringDtype here instead of in the params
+    # to avoid importing pyarrow during test collection
+    storage, na_value = request.param
+    return pd.StringDtype(storage, na_value)
+
+
+@pytest.fixture(
+    params=[
+        "string[python]",
+        pytest.param("string[pyarrow]", marks=td.skip_if_no("pyarrow")),
+    ]
+)
+def nullable_string_dtype(request):
+    """
+    Parametrized fixture for string dtypes.
+
+    * 'string[python]'
+    * 'string[pyarrow]'
+    """
+    return request.param
+
+
+@pytest.fixture(
+    params=[
+        pytest.param(("pyarrow", np.nan), marks=td.skip_if_no("pyarrow")),
+        pytest.param(("pyarrow", pd.NA), marks=td.skip_if_no("pyarrow")),
+    ]
+)
+def pyarrow_string_dtype(request):
+    """
+    Parametrized fixture for string dtypes backed by Pyarrow.
+
+    * 'str[pyarrow]'
+    * 'string[pyarrow]'
+    """
+    return pd.StringDtype(*request.param)
+
+
+@pytest.fixture(
+    params=[
+        "python",
+        pytest.param("pyarrow", marks=td.skip_if_no("pyarrow")),
+    ]
+)
+def string_storage(request):
+    """
+    Parametrized fixture for pd.options.mode.string_storage.
+
+    * 'python'
+    * 'pyarrow'
+    """
+    return request.param
+
+
+@pytest.fixture(
+    params=[
+        ("python", pd.NA),
+        pytest.param(("pyarrow", pd.NA), marks=td.skip_if_no("pyarrow")),
+        pytest.param(("pyarrow", np.nan), marks=td.skip_if_no("pyarrow")),
+        ("python", np.nan),
+    ],
+    ids=[
+        "string=string[python]",
+        "string=string[pyarrow]",
+        "string=str[pyarrow]",
+        "string=str[python]",
+    ],
+)
+def string_dtype_arguments(request):
+    """
+    Parametrized fixture for StringDtype storage and na_value.
+
+    * 'python' + pd.NA
+    * 'pyarrow' + pd.NA
+    * 'pyarrow' + np.nan
+    """
+    return request.param
+
+
+@pytest.fixture(
+    params=[
+        "numpy_nullable",
+        pytest.param("pyarrow", marks=td.skip_if_no("pyarrow")),
+    ]
+)
+def dtype_backend(request):
+    """
+    Parametrized fixture for pd.options.mode.string_storage.
+
+    * 'python'
+    * 'pyarrow'
+    """
+    return request.param
+
+
+# Alias so we can test with cartesian product of string_storage
+string_storage2 = string_storage
+string_dtype_arguments2 = string_dtype_arguments
+
+
+@pytest.fixture(params=tm.BYTES_DTYPES)
+def bytes_dtype(request):
+    """
+    Parametrized fixture for bytes dtypes.
+
+    * bytes
+    * 'bytes'
+    """
+    return request.param
+
+
+@pytest.fixture(params=tm.OBJECT_DTYPES)
+def object_dtype(request):
+    """
+    Parametrized fixture for object dtypes.
+
+    * object
+    * 'object'
+    """
+    return request.param
+
+
+@pytest.fixture(
+    params=[
+        np.dtype("object"),
+        ("python", pd.NA),
+        pytest.param(("pyarrow", pd.NA), marks=td.skip_if_no("pyarrow")),
+        pytest.param(("pyarrow", np.nan), marks=td.skip_if_no("pyarrow")),
+        ("python", np.nan),
+    ],
+    ids=[
+        "string=object",
+        "string=string[python]",
+        "string=string[pyarrow]",
+        "string=str[pyarrow]",
+        "string=str[python]",
+    ],
+)
+def any_string_dtype(request):
+    """
+    Parametrized fixture for string dtypes.
+    * 'object'
+    * 'string[python]' (NA variant)
+    * 'string[pyarrow]' (NA variant)
+    * 'str' (NaN variant, with pyarrow)
+    * 'str' (NaN variant, without pyarrow)
+    """
+    if isinstance(request.param, np.dtype):
+        return request.param
+    else:
+        # need to instantiate the StringDtype here instead of in the params
+        # to avoid importing pyarrow during test collection
+        storage, na_value = request.param
+        return pd.StringDtype(storage, na_value)
+
+
+@pytest.fixture(params=tm.DATETIME64_DTYPES)
+def datetime64_dtype(request):
+    """
+    Parametrized fixture for datetime64 dtypes.
+
+    * 'datetime64[ns]'
+    * 'M8[ns]'
+    """
+    return request.param
+
+
+@pytest.fixture(params=tm.TIMEDELTA64_DTYPES)
+def timedelta64_dtype(request):
+    """
+    Parametrized fixture for timedelta64 dtypes.
+
+    * 'timedelta64[ns]'
+    * 'm8[ns]'
+    """
+    return request.param
+
+
+@pytest.fixture
+def fixed_now_ts() -> Timestamp:
+    """
+    Fixture emits fixed Timestamp.now()
+    """
+    return Timestamp(  # pyright: ignore[reportGeneralTypeIssues]
+        year=2021, month=1, day=1, hour=12, minute=4, second=13, microsecond=22
+    )
+
+
+@pytest.fixture(params=tm.FLOAT_NUMPY_DTYPES)
+def float_numpy_dtype(request):
+    """
+    Parameterized fixture for float dtypes.
+
+    * float
+    * 'float32'
+    * 'float64'
+    """
+    return request.param
+
+
+@pytest.fixture(params=tm.FLOAT_EA_DTYPES)
+def float_ea_dtype(request):
+    """
+    Parameterized fixture for float dtypes.
+
+    * 'Float32'
+    * 'Float64'
+    """
+    return request.param
+
+
+@pytest.fixture(params=tm.ALL_FLOAT_DTYPES)
+def any_float_dtype(request):
+    """
+    Parameterized fixture for float dtypes.
+
+    * float
+    * 'float32'
+    * 'float64'
+    * 'Float32'
+    * 'Float64'
+    """
+    return request.param
+
+
+@pytest.fixture(params=tm.COMPLEX_DTYPES)
+def complex_dtype(request):
+    """
+    Parameterized fixture for complex dtypes.
+
+    * complex
+    * 'complex64'
+    * 'complex128'
+    """
+    return request.param
+
+
+@pytest.fixture(params=tm.COMPLEX_FLOAT_DTYPES)
+def complex_or_float_dtype(request):
+    """
+    Parameterized fixture for complex and numpy float dtypes.
+
+    * complex
+    * 'complex64'
+    * 'complex128'
+    * float
+    * 'float32'
+    * 'float64'
+    """
+    return request.param
+
+
+@pytest.fixture(params=tm.SIGNED_INT_NUMPY_DTYPES)
+def any_signed_int_numpy_dtype(request):
+    """
+    Parameterized fixture for signed integer dtypes.
+
+    * int
+    * 'int8'
+    * 'int16'
+    * 'int32'
+    * 'int64'
+    """
+    return request.param
+
+
+@pytest.fixture(params=tm.UNSIGNED_INT_NUMPY_DTYPES)
+def any_unsigned_int_numpy_dtype(request):
+    """
+    Parameterized fixture for unsigned integer dtypes.
+
+    * 'uint8'
+    * 'uint16'
+    * 'uint32'
+    * 'uint64'
+    """
+    return request.param
+
+
+@pytest.fixture(params=tm.ALL_INT_NUMPY_DTYPES)
+def any_int_numpy_dtype(request):
+    """
+    Parameterized fixture for any integer dtype.
+
+    * int
+    * 'int8'
+    * 'uint8'
+    * 'int16'
+    * 'uint16'
+    * 'int32'
+    * 'uint32'
+    * 'int64'
+    * 'uint64'
+    """
+    return request.param
+
+
+@pytest.fixture(params=tm.ALL_INT_EA_DTYPES)
+def any_int_ea_dtype(request):
+    """
+    Parameterized fixture for any nullable integer dtype.
+
+    * 'UInt8'
+    * 'Int8'
+    * 'UInt16'
+    * 'Int16'
+    * 'UInt32'
+    * 'Int32'
+    * 'UInt64'
+    * 'Int64'
+    """
+    return request.param
+
+
+@pytest.fixture(params=tm.ALL_INT_DTYPES)
+def any_int_dtype(request):
+    """
+    Parameterized fixture for any nullable integer dtype.
+
+    * int
+    * 'int8'
+    * 'uint8'
+    * 'int16'
+    * 'uint16'
+    * 'int32'
+    * 'uint32'
+    * 'int64'
+    * 'uint64'
+    * 'UInt8'
+    * 'Int8'
+    * 'UInt16'
+    * 'Int16'
+    * 'UInt32'
+    * 'Int32'
+    * 'UInt64'
+    * 'Int64'
+    """
+    return request.param
+
+
+@pytest.fixture(params=tm.ALL_INT_EA_DTYPES + tm.FLOAT_EA_DTYPES)
+def any_numeric_ea_dtype(request):
+    """
+    Parameterized fixture for any nullable integer dtype and
+    any float ea dtypes.
+
+    * 'UInt8'
+    * 'Int8'
+    * 'UInt16'
+    * 'Int16'
+    * 'UInt32'
+    * 'Int32'
+    * 'UInt64'
+    * 'Int64'
+    * 'Float32'
+    * 'Float64'
+    """
+    return request.param
+
+
+#  Unsupported operand types for + ("List[Union[str, ExtensionDtype, dtype[Any],
+#  Type[object]]]" and "List[str]")
+@pytest.fixture(
+    params=tm.ALL_INT_EA_DTYPES
+    + tm.FLOAT_EA_DTYPES
+    + tm.ALL_INT_PYARROW_DTYPES_STR_REPR
+    + tm.FLOAT_PYARROW_DTYPES_STR_REPR  # type: ignore[operator]
+)
+def any_numeric_ea_and_arrow_dtype(request):
+    """
+    Parameterized fixture for any nullable integer dtype and
+    any float ea dtypes.
+
+    * 'UInt8'
+    * 'Int8'
+    * 'UInt16'
+    * 'Int16'
+    * 'UInt32'
+    * 'Int32'
+    * 'UInt64'
+    * 'Int64'
+    * 'Float32'
+    * 'Float64'
+    * 'uint8[pyarrow]'
+    * 'int8[pyarrow]'
+    * 'uint16[pyarrow]'
+    * 'int16[pyarrow]'
+    * 'uint32[pyarrow]'
+    * 'int32[pyarrow]'
+    * 'uint64[pyarrow]'
+    * 'int64[pyarrow]'
+    * 'float32[pyarrow]'
+    * 'float64[pyarrow]'
+    """
+    return request.param
+
+
+@pytest.fixture(params=tm.SIGNED_INT_EA_DTYPES)
+def any_signed_int_ea_dtype(request):
+    """
+    Parameterized fixture for any signed nullable integer dtype.
+
+    * 'Int8'
+    * 'Int16'
+    * 'Int32'
+    * 'Int64'
+    """
+    return request.param
+
+
+@pytest.fixture(params=tm.ALL_REAL_NUMPY_DTYPES)
+def any_real_numpy_dtype(request):
+    """
+    Parameterized fixture for any (purely) real numeric dtype.
+
+    * int
+    * 'int8'
+    * 'uint8'
+    * 'int16'
+    * 'uint16'
+    * 'int32'
+    * 'uint32'
+    * 'int64'
+    * 'uint64'
+    * float
+    * 'float32'
+    * 'float64'
+    """
+    return request.param
+
+
+@pytest.fixture(params=tm.ALL_REAL_DTYPES)
+def any_real_numeric_dtype(request):
+    """
+    Parameterized fixture for any (purely) real numeric dtype.
+
+    * int
+    * 'int8'
+    * 'uint8'
+    * 'int16'
+    * 'uint16'
+    * 'int32'
+    * 'uint32'
+    * 'int64'
+    * 'uint64'
+    * float
+    * 'float32'
+    * 'float64'
+
+    and associated ea dtypes.
+    """
+    return request.param
+
+
+@pytest.fixture(params=tm.ALL_NUMPY_DTYPES)
+def any_numpy_dtype(request):
+    """
+    Parameterized fixture for all numpy dtypes.
+
+    * bool
+    * 'bool'
+    * int
+    * 'int8'
+    * 'uint8'
+    * 'int16'
+    * 'uint16'
+    * 'int32'
+    * 'uint32'
+    * 'int64'
+    * 'uint64'
+    * float
+    * 'float32'
+    * 'float64'
+    * complex
+    * 'complex64'
+    * 'complex128'
+    * str
+    * 'str'
+    * 'U'
+    * bytes
+    * 'bytes'
+    * 'datetime64[ns]'
+    * 'M8[ns]'
+    * 'timedelta64[ns]'
+    * 'm8[ns]'
+    * object
+    * 'object'
+    """
+    return request.param
+
+
+@pytest.fixture(params=tm.ALL_REAL_NULLABLE_DTYPES)
+def any_real_nullable_dtype(request):
+    """
+    Parameterized fixture for all real dtypes that can hold NA.
+
+    * float
+    * 'float32'
+    * 'float64'
+    * 'Float32'
+    * 'Float64'
+    * 'UInt8'
+    * 'UInt16'
+    * 'UInt32'
+    * 'UInt64'
+    * 'Int8'
+    * 'Int16'
+    * 'Int32'
+    * 'Int64'
+    * 'uint8[pyarrow]'
+    * 'uint16[pyarrow]'
+    * 'uint32[pyarrow]'
+    * 'uint64[pyarrow]'
+    * 'int8[pyarrow]'
+    * 'int16[pyarrow]'
+    * 'int32[pyarrow]'
+    * 'int64[pyarrow]'
+    * 'float[pyarrow]'
+    * 'double[pyarrow]'
+    """
+    return request.param
+
+
+@pytest.fixture(params=tm.ALL_NUMERIC_DTYPES)
+def any_numeric_dtype(request):
+    """
+    Parameterized fixture for all numeric dtypes.
+
+    * int
+    * 'int8'
+    * 'uint8'
+    * 'int16'
+    * 'uint16'
+    * 'int32'
+    * 'uint32'
+    * 'int64'
+    * 'uint64'
+    * float
+    * 'float32'
+    * 'float64'
+    * complex
+    * 'complex64'
+    * 'complex128'
+    * 'UInt8'
+    * 'Int8'
+    * 'UInt16'
+    * 'Int16'
+    * 'UInt32'
+    * 'Int32'
+    * 'UInt64'
+    * 'Int64'
+    * 'Float32'
+    * 'Float64'
+    """
+    return request.param
+
+
+# categoricals are handled separately
+_any_skipna_inferred_dtype = [
+    ("string", ["a", np.nan, "c"]),
+    ("string", ["a", pd.NA, "c"]),
+    ("mixed", ["a", pd.NaT, "c"]),  # pd.NaT not considered valid by is_string_array
+    ("bytes", [b"a", np.nan, b"c"]),
+    ("empty", [np.nan, np.nan, np.nan]),
+    ("empty", []),
+    ("mixed-integer", ["a", np.nan, 2]),
+    ("mixed", ["a", np.nan, 2.0]),
+    ("floating", [1.0, np.nan, 2.0]),
+    ("integer", [1, np.nan, 2]),
+    ("mixed-integer-float", [1, np.nan, 2.0]),
+    ("decimal", [Decimal(1), np.nan, Decimal(2)]),
+    ("boolean", [True, np.nan, False]),
+    ("boolean", [True, pd.NA, False]),
+    ("datetime64", [np.datetime64("2013-01-01"), np.nan, np.datetime64("2018-01-01")]),
+    ("datetime", [Timestamp("20130101"), np.nan, Timestamp("20180101")]),
+    ("date", [date(2013, 1, 1), np.nan, date(2018, 1, 1)]),
+    ("complex", [1 + 1j, np.nan, 2 + 2j]),
+    # The following dtype is commented out due to GH 23554
+    # ('timedelta64', [np.timedelta64(1, 'D'),
+    #                  np.nan, np.timedelta64(2, 'D')]),
+    ("timedelta", [timedelta(1), np.nan, timedelta(2)]),
+    ("time", [time(1), np.nan, time(2)]),
+    ("period", [Period(2013), pd.NaT, Period(2018)]),
+    ("interval", [Interval(0, 1), np.nan, Interval(0, 2)]),
+]
+ids, _ = zip(*_any_skipna_inferred_dtype)  # use inferred type as fixture-id
+
+
+@pytest.fixture(params=_any_skipna_inferred_dtype, ids=ids)
+def any_skipna_inferred_dtype(request):
+    """
+    Fixture for all inferred dtypes from _libs.lib.infer_dtype
+
+    The covered (inferred) types are:
+    * 'string'
+    * 'empty'
+    * 'bytes'
+    * 'mixed'
+    * 'mixed-integer'
+    * 'mixed-integer-float'
+    * 'floating'
+    * 'integer'
+    * 'decimal'
+    * 'boolean'
+    * 'datetime64'
+    * 'datetime'
+    * 'date'
+    * 'timedelta'
+    * 'time'
+    * 'period'
+    * 'interval'
+
+    Returns
+    -------
+    inferred_dtype : str
+        The string for the inferred dtype from _libs.lib.infer_dtype
+    values : np.ndarray
+        An array of object dtype that will be inferred to have
+        `inferred_dtype`
+
+    Examples
+    --------
+    >>> from pandas._libs import lib
+    >>>
+    >>> def test_something(any_skipna_inferred_dtype):
+    ...     inferred_dtype, values = any_skipna_inferred_dtype
+    ...     # will pass
+    ...     assert lib.infer_dtype(values, skipna=True) == inferred_dtype
+    """
+    inferred_dtype, values = request.param
+    values = np.array(values, dtype=object)  # object dtype to avoid casting
+
+    # correctness of inference tested in tests/dtypes/test_inference.py
+    return inferred_dtype, values
+
+
+# ----------------------------------------------------------------
+# Misc
+# ----------------------------------------------------------------
+@pytest.fixture
+def ip():
+    """
+    Get an instance of IPython.InteractiveShell.
+
+    Will raise a skip if IPython is not installed.
+    """
+    pytest.importorskip("IPython", minversion="6.0.0")
+    from IPython.core.interactiveshell import InteractiveShell
+
+    # GH#35711 make sure sqlite history file handle is not leaked
+    from traitlets.config import Config  # isort:skip
+
+    c = Config()
+    c.HistoryManager.hist_file = ":memory:"
+
+    return InteractiveShell(config=c)
+
+
+@pytest.fixture(params=["bsr", "coo", "csc", "csr", "dia", "dok", "lil"])
+def spmatrix(request):
+    """
+    Yields scipy sparse matrix classes.
+    """
+    sparse = pytest.importorskip("scipy.sparse")
+
+    return getattr(sparse, request.param + "_matrix")
+
+
+@pytest.fixture(
+    params=[
+        getattr(pd.offsets, o)
+        for o in pd.offsets.__all__
+        if issubclass(getattr(pd.offsets, o), pd.offsets.Tick) and o != "Tick"
+    ]
+)
+def tick_classes(request):
+    """
+    Fixture for Tick based datetime offsets available for a time series.
+    """
+    return request.param
+
+
+@pytest.fixture(params=[None, lambda x: x])
+def sort_by_key(request):
+    """
+    Simple fixture for testing keys in sorting methods.
+    Tests None (no key) and the identity key.
+    """
+    return request.param
+
+
+@pytest.fixture(
+    params=[
+        ("foo", None, None),
+        ("Egon", "Venkman", None),
+        ("NCC1701D", "NCC1701D", "NCC1701D"),
+        # possibly-matching NAs
+        (np.nan, np.nan, np.nan),
+        (np.nan, pd.NaT, None),
+        (np.nan, pd.NA, None),
+        (pd.NA, pd.NA, pd.NA),
+    ]
+)
+def names(request) -> tuple[Hashable, Hashable, Hashable]:
+    """
+    A 3-tuple of names, the first two for operands, the last for a result.
+    """
+    return request.param
+
+
+@pytest.fixture(params=[tm.setitem, tm.loc, tm.iloc])
+def indexer_sli(request):
+    """
+    Parametrize over __setitem__, loc.__setitem__, iloc.__setitem__
+    """
+    return request.param
+
+
+@pytest.fixture(params=[tm.loc, tm.iloc])
+def indexer_li(request):
+    """
+    Parametrize over loc.__getitem__, iloc.__getitem__
+    """
+    return request.param
+
+
+@pytest.fixture(params=[tm.setitem, tm.iloc])
+def indexer_si(request):
+    """
+    Parametrize over __setitem__, iloc.__setitem__
+    """
+    return request.param
+
+
+@pytest.fixture(params=[tm.setitem, tm.loc])
+def indexer_sl(request):
+    """
+    Parametrize over __setitem__, loc.__setitem__
+    """
+    return request.param
+
+
+@pytest.fixture(params=[tm.at, tm.loc])
+def indexer_al(request):
+    """
+    Parametrize over at.__setitem__, loc.__setitem__
+    """
+    return request.param
+
+
+@pytest.fixture(params=[tm.iat, tm.iloc])
+def indexer_ial(request):
+    """
+    Parametrize over iat.__setitem__, iloc.__setitem__
+    """
+    return request.param
+
+
+@pytest.fixture
+def using_array_manager() -> bool:
+    """
+    Fixture to check if the array manager is being used.
+    """
+    return _get_option("mode.data_manager", silent=True) == "array"
+
+
+@pytest.fixture
+def using_copy_on_write() -> bool:
+    """
+    Fixture to check if Copy-on-Write is enabled.
+    """
+    return (
+        pd.options.mode.copy_on_write is True
+        and _get_option("mode.data_manager", silent=True) == "block"
+    )
+
+
+@pytest.fixture
+def warn_copy_on_write() -> bool:
+    """
+    Fixture to check if Copy-on-Write is in warning mode.
+    """
+    return (
+        pd.options.mode.copy_on_write == "warn"
+        and _get_option("mode.data_manager", silent=True) == "block"
+    )
+
+
+@pytest.fixture
+def using_infer_string() -> bool:
+    """
+    Fixture to check if infer string option is enabled.
+    """
+    return pd.options.future.infer_string is True
+
+
+warsaws = ["Europe/Warsaw", "dateutil/Europe/Warsaw"]
+if zoneinfo is not None:
+    warsaws.append(zoneinfo.ZoneInfo("Europe/Warsaw"))  # type: ignore[arg-type]
+
+
+@pytest.fixture(params=warsaws)
+def warsaw(request) -> str:
+    """
+    tzinfo for Europe/Warsaw using pytz, dateutil, or zoneinfo.
+    """
+    return request.param
+
+
+@pytest.fixture()
+def arrow_string_storage():
+    return ("pyarrow", "pyarrow_numpy")
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/_numba/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/_numba/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/_numba/executor.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/_numba/executor.py
new file mode 100644
index 0000000000000000000000000000000000000000..0a26acb7df60a20a253ca3b864568c984bb86e3d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/_numba/executor.py
@@ -0,0 +1,239 @@
+from __future__ import annotations
+
+import functools
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import Scalar
+
+import numpy as np
+
+from pandas.compat._optional import import_optional_dependency
+
+
+@functools.cache
+def generate_apply_looper(func, nopython=True, nogil=True, parallel=False):
+    if TYPE_CHECKING:
+        import numba
+    else:
+        numba = import_optional_dependency("numba")
+    nb_compat_func = numba.extending.register_jitable(func)
+
+    @numba.jit(nopython=nopython, nogil=nogil, parallel=parallel)
+    def nb_looper(values, axis):
+        # Operate on the first row/col in order to get
+        # the output shape
+        if axis == 0:
+            first_elem = values[:, 0]
+            dim0 = values.shape[1]
+        else:
+            first_elem = values[0]
+            dim0 = values.shape[0]
+        res0 = nb_compat_func(first_elem)
+        # Use np.asarray to get shape for
+        # https://github.com/numba/numba/issues/4202#issuecomment-1185981507
+        buf_shape = (dim0,) + np.atleast_1d(np.asarray(res0)).shape
+        if axis == 0:
+            buf_shape = buf_shape[::-1]
+        buff = np.empty(buf_shape)
+
+        if axis == 1:
+            buff[0] = res0
+            for i in numba.prange(1, values.shape[0]):
+                buff[i] = nb_compat_func(values[i])
+        else:
+            buff[:, 0] = res0
+            for j in numba.prange(1, values.shape[1]):
+                buff[:, j] = nb_compat_func(values[:, j])
+        return buff
+
+    return nb_looper
+
+
+@functools.cache
+def make_looper(func, result_dtype, is_grouped_kernel, nopython, nogil, parallel):
+    if TYPE_CHECKING:
+        import numba
+    else:
+        numba = import_optional_dependency("numba")
+
+    if is_grouped_kernel:
+
+        @numba.jit(nopython=nopython, nogil=nogil, parallel=parallel)
+        def column_looper(
+            values: np.ndarray,
+            labels: np.ndarray,
+            ngroups: int,
+            min_periods: int,
+            *args,
+        ):
+            result = np.empty((values.shape[0], ngroups), dtype=result_dtype)
+            na_positions = {}
+            for i in numba.prange(values.shape[0]):
+                output, na_pos = func(
+                    values[i], result_dtype, labels, ngroups, min_periods, *args
+                )
+                result[i] = output
+                if len(na_pos) > 0:
+                    na_positions[i] = np.array(na_pos)
+            return result, na_positions
+
+    else:
+
+        @numba.jit(nopython=nopython, nogil=nogil, parallel=parallel)
+        def column_looper(
+            values: np.ndarray,
+            start: np.ndarray,
+            end: np.ndarray,
+            min_periods: int,
+            *args,
+        ):
+            result = np.empty((values.shape[0], len(start)), dtype=result_dtype)
+            na_positions = {}
+            for i in numba.prange(values.shape[0]):
+                output, na_pos = func(
+                    values[i], result_dtype, start, end, min_periods, *args
+                )
+                result[i] = output
+                if len(na_pos) > 0:
+                    na_positions[i] = np.array(na_pos)
+            return result, na_positions
+
+    return column_looper
+
+
+default_dtype_mapping: dict[np.dtype, Any] = {
+    np.dtype("int8"): np.int64,
+    np.dtype("int16"): np.int64,
+    np.dtype("int32"): np.int64,
+    np.dtype("int64"): np.int64,
+    np.dtype("uint8"): np.uint64,
+    np.dtype("uint16"): np.uint64,
+    np.dtype("uint32"): np.uint64,
+    np.dtype("uint64"): np.uint64,
+    np.dtype("float32"): np.float64,
+    np.dtype("float64"): np.float64,
+    np.dtype("complex64"): np.complex128,
+    np.dtype("complex128"): np.complex128,
+}
+
+
+# TODO: Preserve complex dtypes
+
+float_dtype_mapping: dict[np.dtype, Any] = {
+    np.dtype("int8"): np.float64,
+    np.dtype("int16"): np.float64,
+    np.dtype("int32"): np.float64,
+    np.dtype("int64"): np.float64,
+    np.dtype("uint8"): np.float64,
+    np.dtype("uint16"): np.float64,
+    np.dtype("uint32"): np.float64,
+    np.dtype("uint64"): np.float64,
+    np.dtype("float32"): np.float64,
+    np.dtype("float64"): np.float64,
+    np.dtype("complex64"): np.float64,
+    np.dtype("complex128"): np.float64,
+}
+
+identity_dtype_mapping: dict[np.dtype, Any] = {
+    np.dtype("int8"): np.int8,
+    np.dtype("int16"): np.int16,
+    np.dtype("int32"): np.int32,
+    np.dtype("int64"): np.int64,
+    np.dtype("uint8"): np.uint8,
+    np.dtype("uint16"): np.uint16,
+    np.dtype("uint32"): np.uint32,
+    np.dtype("uint64"): np.uint64,
+    np.dtype("float32"): np.float32,
+    np.dtype("float64"): np.float64,
+    np.dtype("complex64"): np.complex64,
+    np.dtype("complex128"): np.complex128,
+}
+
+
+def generate_shared_aggregator(
+    func: Callable[..., Scalar],
+    dtype_mapping: dict[np.dtype, np.dtype],
+    is_grouped_kernel: bool,
+    nopython: bool,
+    nogil: bool,
+    parallel: bool,
+):
+    """
+    Generate a Numba function that loops over the columns 2D object and applies
+    a 1D numba kernel over each column.
+
+    Parameters
+    ----------
+    func : function
+        aggregation function to be applied to each column
+    dtype_mapping: dict or None
+        If not None, maps a dtype to a result dtype.
+        Otherwise, will fall back to default mapping.
+    is_grouped_kernel: bool, default False
+        Whether func operates using the group labels (True)
+        or using starts/ends arrays
+
+        If true, you also need to pass the number of groups to this function
+    nopython : bool
+        nopython to be passed into numba.jit
+    nogil : bool
+        nogil to be passed into numba.jit
+    parallel : bool
+        parallel to be passed into numba.jit
+
+    Returns
+    -------
+    Numba function
+    """
+
+    # A wrapper around the looper function,
+    # to dispatch based on dtype since numba is unable to do that in nopython mode
+
+    # It also post-processes the values by inserting nans where number of observations
+    # is less than min_periods
+    # Cannot do this in numba nopython mode
+    # (you'll run into type-unification error when you cast int -> float)
+    def looper_wrapper(
+        values,
+        start=None,
+        end=None,
+        labels=None,
+        ngroups=None,
+        min_periods: int = 0,
+        **kwargs,
+    ):
+        result_dtype = dtype_mapping[values.dtype]
+        column_looper = make_looper(
+            func, result_dtype, is_grouped_kernel, nopython, nogil, parallel
+        )
+        # Need to unpack kwargs since numba only supports *args
+        if is_grouped_kernel:
+            result, na_positions = column_looper(
+                values, labels, ngroups, min_periods, *kwargs.values()
+            )
+        else:
+            result, na_positions = column_looper(
+                values, start, end, min_periods, *kwargs.values()
+            )
+        if result.dtype.kind == "i":
+            # Look if na_positions is not empty
+            # If so, convert the whole block
+            # This is OK since int dtype cannot hold nan,
+            # so if min_periods not satisfied for 1 col, it is not satisfied for
+            # all columns at that index
+            for na_pos in na_positions.values():
+                if len(na_pos) > 0:
+                    result = result.astype("float64")
+                    break
+        # TODO: Optimize this
+        for i, na_pos in na_positions.items():
+            if len(na_pos) > 0:
+                result[i, na_pos] = np.nan
+        return result
+
+    return looper_wrapper
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/_numba/extensions.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/_numba/extensions.py
new file mode 100644
index 0000000000000000000000000000000000000000..b05f12295a729176aadae19bb1d17f3191f98806
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/_numba/extensions.py
@@ -0,0 +1,585 @@
+# Disable type checking for this module since numba's internals
+# are not typed, and we use numba's internals via its extension API
+# mypy: ignore-errors
+"""
+Utility classes/functions to let numba recognize
+pandas Index/Series/DataFrame
+
+Mostly vendored from https://github.com/numba/numba/blob/main/numba/tests/pdlike_usecase.py
+"""
+
+from __future__ import annotations
+
+from contextlib import contextmanager
+import operator
+
+import numba
+from numba import types
+from numba.core import cgutils
+from numba.core.datamodel import models
+from numba.core.extending import (
+    NativeValue,
+    box,
+    lower_builtin,
+    make_attribute_wrapper,
+    overload,
+    overload_attribute,
+    overload_method,
+    register_model,
+    type_callable,
+    typeof_impl,
+    unbox,
+)
+from numba.core.imputils import impl_ret_borrowed
+import numpy as np
+
+from pandas._libs import lib
+
+from pandas.core.indexes.base import Index
+from pandas.core.indexing import _iLocIndexer
+from pandas.core.internals import SingleBlockManager
+from pandas.core.series import Series
+
+
+# Helper function to hack around fact that Index casts numpy string dtype to object
+#
+# Idea is to set an attribute on a Index called _numba_data
+# that is the original data, or the object data casted to numpy string dtype,
+# with a context manager that is unset afterwards
+@contextmanager
+def set_numba_data(index: Index):
+    numba_data = index._data
+    if numba_data.dtype in (object, "string"):
+        numba_data = np.asarray(numba_data)
+        if not lib.is_string_array(numba_data):
+            raise ValueError(
+                "The numba engine only supports using string or numeric column names"
+            )
+        numba_data = numba_data.astype("U")
+    try:
+        index._numba_data = numba_data
+        yield index
+    finally:
+        del index._numba_data
+
+
+# TODO: Range index support
+# (this currently lowers OK, but does not round-trip)
+class IndexType(types.Type):
+    """
+    The type class for Index objects.
+    """
+
+    def __init__(self, dtype, layout, pyclass: any) -> None:
+        self.pyclass = pyclass
+        name = f"index({dtype}, {layout})"
+        self.dtype = dtype
+        self.layout = layout
+        super().__init__(name)
+
+    @property
+    def key(self):
+        return self.pyclass, self.dtype, self.layout
+
+    @property
+    def as_array(self):
+        return types.Array(self.dtype, 1, self.layout)
+
+    def copy(self, dtype=None, ndim: int = 1, layout=None):
+        assert ndim == 1
+        if dtype is None:
+            dtype = self.dtype
+        layout = layout or self.layout
+        return type(self)(dtype, layout, self.pyclass)
+
+
+class SeriesType(types.Type):
+    """
+    The type class for Series objects.
+    """
+
+    def __init__(self, dtype, index, namety) -> None:
+        assert isinstance(index, IndexType)
+        self.dtype = dtype
+        self.index = index
+        self.values = types.Array(self.dtype, 1, "C")
+        self.namety = namety
+        name = f"series({dtype}, {index}, {namety})"
+        super().__init__(name)
+
+    @property
+    def key(self):
+        return self.dtype, self.index, self.namety
+
+    @property
+    def as_array(self):
+        return self.values
+
+    def copy(self, dtype=None, ndim: int = 1, layout: str = "C"):
+        assert ndim == 1
+        assert layout == "C"
+        if dtype is None:
+            dtype = self.dtype
+        return type(self)(dtype, self.index, self.namety)
+
+
+@typeof_impl.register(Index)
+def typeof_index(val, c):
+    """
+    This will assume that only strings are in object dtype
+    index.
+    (you should check this before this gets lowered down to numba)
+    """
+    # arrty = typeof_impl(val._data, c)
+    arrty = typeof_impl(val._numba_data, c)
+    assert arrty.ndim == 1
+    return IndexType(arrty.dtype, arrty.layout, type(val))
+
+
+@typeof_impl.register(Series)
+def typeof_series(val, c):
+    index = typeof_impl(val.index, c)
+    arrty = typeof_impl(val.values, c)
+    namety = typeof_impl(val.name, c)
+    assert arrty.ndim == 1
+    assert arrty.layout == "C"
+    return SeriesType(arrty.dtype, index, namety)
+
+
+@type_callable(Series)
+def type_series_constructor(context):
+    def typer(data, index, name=None):
+        if isinstance(index, IndexType) and isinstance(data, types.Array):
+            assert data.ndim == 1
+            if name is None:
+                name = types.intp
+            return SeriesType(data.dtype, index, name)
+
+    return typer
+
+
+@type_callable(Index)
+def type_index_constructor(context):
+    def typer(data, hashmap=None):
+        if isinstance(data, types.Array):
+            assert data.layout == "C"
+            assert data.ndim == 1
+            assert hashmap is None or isinstance(hashmap, types.DictType)
+            return IndexType(data.dtype, layout=data.layout, pyclass=Index)
+
+    return typer
+
+
+# Backend extensions for Index and Series and Frame
+@register_model(IndexType)
+class IndexModel(models.StructModel):
+    def __init__(self, dmm, fe_type) -> None:
+        # We don't want the numpy string scalar type in our hashmap
+        members = [
+            ("data", fe_type.as_array),
+            # This is an attempt to emulate our hashtable code with a numba
+            # typed dict
+            # It maps from values in the index to their integer positions in the array
+            ("hashmap", types.DictType(fe_type.dtype, types.intp)),
+            # Pointer to the Index object this was created from, or that it
+            # boxes to
+            # https://numba.discourse.group/t/qst-how-to-cache-the-boxing-of-an-object/2128/2?u=lithomas1
+            ("parent", types.pyobject),
+        ]
+        models.StructModel.__init__(self, dmm, fe_type, members)
+
+
+@register_model(SeriesType)
+class SeriesModel(models.StructModel):
+    def __init__(self, dmm, fe_type) -> None:
+        members = [
+            ("index", fe_type.index),
+            ("values", fe_type.as_array),
+            ("name", fe_type.namety),
+        ]
+        models.StructModel.__init__(self, dmm, fe_type, members)
+
+
+make_attribute_wrapper(IndexType, "data", "_data")
+make_attribute_wrapper(IndexType, "hashmap", "hashmap")
+
+make_attribute_wrapper(SeriesType, "index", "index")
+make_attribute_wrapper(SeriesType, "values", "values")
+make_attribute_wrapper(SeriesType, "name", "name")
+
+
+@lower_builtin(Series, types.Array, IndexType)
+def pdseries_constructor(context, builder, sig, args):
+    data, index = args
+    series = cgutils.create_struct_proxy(sig.return_type)(context, builder)
+    series.index = index
+    series.values = data
+    series.name = context.get_constant(types.intp, 0)
+    return impl_ret_borrowed(context, builder, sig.return_type, series._getvalue())
+
+
+@lower_builtin(Series, types.Array, IndexType, types.intp)
+@lower_builtin(Series, types.Array, IndexType, types.float64)
+@lower_builtin(Series, types.Array, IndexType, types.unicode_type)
+def pdseries_constructor_with_name(context, builder, sig, args):
+    data, index, name = args
+    series = cgutils.create_struct_proxy(sig.return_type)(context, builder)
+    series.index = index
+    series.values = data
+    series.name = name
+    return impl_ret_borrowed(context, builder, sig.return_type, series._getvalue())
+
+
+@lower_builtin(Index, types.Array, types.DictType, types.pyobject)
+def index_constructor_2arg(context, builder, sig, args):
+    (data, hashmap, parent) = args
+    index = cgutils.create_struct_proxy(sig.return_type)(context, builder)
+
+    index.data = data
+    index.hashmap = hashmap
+    index.parent = parent
+    return impl_ret_borrowed(context, builder, sig.return_type, index._getvalue())
+
+
+@lower_builtin(Index, types.Array, types.DictType)
+def index_constructor_2arg_parent(context, builder, sig, args):
+    # Basically same as index_constructor_1arg, but also lets you specify the
+    # parent object
+    (data, hashmap) = args
+    index = cgutils.create_struct_proxy(sig.return_type)(context, builder)
+
+    index.data = data
+    index.hashmap = hashmap
+    return impl_ret_borrowed(context, builder, sig.return_type, index._getvalue())
+
+
+@lower_builtin(Index, types.Array)
+def index_constructor_1arg(context, builder, sig, args):
+    from numba.typed import Dict
+
+    key_type = sig.return_type.dtype
+    value_type = types.intp
+
+    def index_impl(data):
+        return Index(data, Dict.empty(key_type, value_type))
+
+    return context.compile_internal(builder, index_impl, sig, args)
+
+
+# Helper to convert the unicodecharseq (numpy string scalar) into a unicode_type
+# (regular string)
+def maybe_cast_str(x):
+    # Dummy function that numba can overload
+    pass
+
+
+@overload(maybe_cast_str)
+def maybe_cast_str_impl(x):
+    """Converts numba UnicodeCharSeq (numpy string scalar) -> unicode type (string).
+    Is a no-op for other types."""
+    if isinstance(x, types.UnicodeCharSeq):
+        return lambda x: str(x)
+    else:
+        return lambda x: x
+
+
+@unbox(IndexType)
+def unbox_index(typ, obj, c):
+    """
+    Convert a Index object to a native structure.
+
+    Note: Object dtype is not allowed here
+    """
+    data_obj = c.pyapi.object_getattr_string(obj, "_numba_data")
+    index = cgutils.create_struct_proxy(typ)(c.context, c.builder)
+    # If we see an object array, assume its been validated as only containing strings
+    # We still need to do the conversion though
+    index.data = c.unbox(typ.as_array, data_obj).value
+    typed_dict_obj = c.pyapi.unserialize(c.pyapi.serialize_object(numba.typed.Dict))
+    # Create an empty typed dict in numba for the hashmap for indexing
+    # equiv of numba.typed.Dict.empty(typ.dtype, types.intp)
+    arr_type_obj = c.pyapi.unserialize(c.pyapi.serialize_object(typ.dtype))
+    intp_type_obj = c.pyapi.unserialize(c.pyapi.serialize_object(types.intp))
+    hashmap_obj = c.pyapi.call_method(
+        typed_dict_obj, "empty", (arr_type_obj, intp_type_obj)
+    )
+    index.hashmap = c.unbox(types.DictType(typ.dtype, types.intp), hashmap_obj).value
+    # Set the parent for speedy boxing.
+    index.parent = obj
+
+    # Decrefs
+    c.pyapi.decref(data_obj)
+    c.pyapi.decref(arr_type_obj)
+    c.pyapi.decref(intp_type_obj)
+    c.pyapi.decref(typed_dict_obj)
+
+    return NativeValue(index._getvalue())
+
+
+@unbox(SeriesType)
+def unbox_series(typ, obj, c):
+    """
+    Convert a Series object to a native structure.
+    """
+    index_obj = c.pyapi.object_getattr_string(obj, "index")
+    values_obj = c.pyapi.object_getattr_string(obj, "values")
+    name_obj = c.pyapi.object_getattr_string(obj, "name")
+
+    series = cgutils.create_struct_proxy(typ)(c.context, c.builder)
+    series.index = c.unbox(typ.index, index_obj).value
+    series.values = c.unbox(typ.values, values_obj).value
+    series.name = c.unbox(typ.namety, name_obj).value
+
+    # Decrefs
+    c.pyapi.decref(index_obj)
+    c.pyapi.decref(values_obj)
+    c.pyapi.decref(name_obj)
+
+    return NativeValue(series._getvalue())
+
+
+@box(IndexType)
+def box_index(typ, val, c):
+    """
+    Convert a native index structure to a Index object.
+
+    If our native index is of a numpy string dtype, we'll cast it to
+    object.
+    """
+    # First build a Numpy array object, then wrap it in a Index
+    index = cgutils.create_struct_proxy(typ)(c.context, c.builder, value=val)
+
+    res = cgutils.alloca_once_value(c.builder, index.parent)
+
+    # Does parent exist?
+    # (it means already boxed once, or Index same as original df.index or df.columns)
+    # xref https://github.com/numba/numba/blob/596e8a55334cc46854e3192766e643767bd7c934/numba/core/boxing.py#L593C17-L593C17
+    with c.builder.if_else(cgutils.is_not_null(c.builder, index.parent)) as (
+        has_parent,
+        otherwise,
+    ):
+        with has_parent:
+            c.pyapi.incref(index.parent)
+        with otherwise:
+            # TODO: preserve the original class for the index
+            # Also need preserve the name of the Index
+            # class_obj = c.pyapi.unserialize(c.pyapi.serialize_object(typ.pyclass))
+            class_obj = c.pyapi.unserialize(c.pyapi.serialize_object(Index))
+            array_obj = c.box(typ.as_array, index.data)
+            if isinstance(typ.dtype, types.UnicodeCharSeq):
+                # We converted to numpy string dtype, convert back
+                # to object since _simple_new won't do that for uss
+                object_str_obj = c.pyapi.unserialize(c.pyapi.serialize_object("object"))
+                array_obj = c.pyapi.call_method(array_obj, "astype", (object_str_obj,))
+                c.pyapi.decref(object_str_obj)
+            # this is basically Index._simple_new(array_obj, name_obj) in python
+            index_obj = c.pyapi.call_method(class_obj, "_simple_new", (array_obj,))
+            index.parent = index_obj
+            c.builder.store(index_obj, res)
+
+            # Decrefs
+            c.pyapi.decref(class_obj)
+            c.pyapi.decref(array_obj)
+    return c.builder.load(res)
+
+
+@box(SeriesType)
+def box_series(typ, val, c):
+    """
+    Convert a native series structure to a Series object.
+    """
+    series = cgutils.create_struct_proxy(typ)(c.context, c.builder, value=val)
+    series_const_obj = c.pyapi.unserialize(c.pyapi.serialize_object(Series._from_mgr))
+    mgr_const_obj = c.pyapi.unserialize(
+        c.pyapi.serialize_object(SingleBlockManager.from_array)
+    )
+    index_obj = c.box(typ.index, series.index)
+    array_obj = c.box(typ.as_array, series.values)
+    name_obj = c.box(typ.namety, series.name)
+    # This is basically equivalent of
+    # pd.Series(data=array_obj, index=index_obj)
+    # To improve perf, we will construct the Series from a manager
+    # object to avoid checks.
+    # We'll also set the name attribute manually to avoid validation
+    mgr_obj = c.pyapi.call_function_objargs(
+        mgr_const_obj,
+        (
+            array_obj,
+            index_obj,
+        ),
+    )
+    mgr_axes_obj = c.pyapi.object_getattr_string(mgr_obj, "axes")
+    # Series._constructor_from_mgr(mgr, axes)
+    series_obj = c.pyapi.call_function_objargs(
+        series_const_obj, (mgr_obj, mgr_axes_obj)
+    )
+    c.pyapi.object_setattr_string(series_obj, "_name", name_obj)
+
+    # Decrefs
+    c.pyapi.decref(series_const_obj)
+    c.pyapi.decref(mgr_axes_obj)
+    c.pyapi.decref(mgr_obj)
+    c.pyapi.decref(mgr_const_obj)
+    c.pyapi.decref(index_obj)
+    c.pyapi.decref(array_obj)
+    c.pyapi.decref(name_obj)
+
+    return series_obj
+
+
+# Add common series reductions (e.g. mean, sum),
+# and also add common binops (e.g. add, sub, mul, div)
+def generate_series_reduction(ser_reduction, ser_method):
+    @overload_method(SeriesType, ser_reduction)
+    def series_reduction(series):
+        def series_reduction_impl(series):
+            return ser_method(series.values)
+
+        return series_reduction_impl
+
+    return series_reduction
+
+
+def generate_series_binop(binop):
+    @overload(binop)
+    def series_binop(series1, value):
+        if isinstance(series1, SeriesType):
+            if isinstance(value, SeriesType):
+
+                def series_binop_impl(series1, series2):
+                    # TODO: Check index matching?
+                    return Series(
+                        binop(series1.values, series2.values),
+                        series1.index,
+                        series1.name,
+                    )
+
+                return series_binop_impl
+            else:
+
+                def series_binop_impl(series1, value):
+                    return Series(
+                        binop(series1.values, value), series1.index, series1.name
+                    )
+
+                return series_binop_impl
+
+    return series_binop
+
+
+series_reductions = [
+    ("sum", np.sum),
+    ("mean", np.mean),
+    # Disabled due to discrepancies between numba std. dev
+    # and pandas std. dev (no way to specify dof)
+    # ("std", np.std),
+    # ("var", np.var),
+    ("min", np.min),
+    ("max", np.max),
+]
+for reduction, reduction_method in series_reductions:
+    generate_series_reduction(reduction, reduction_method)
+
+series_binops = [operator.add, operator.sub, operator.mul, operator.truediv]
+
+for ser_binop in series_binops:
+    generate_series_binop(ser_binop)
+
+
+# get_loc on Index
+@overload_method(IndexType, "get_loc")
+def index_get_loc(index, item):
+    def index_get_loc_impl(index, item):
+        # Initialize the hash table if not initialized
+        if len(index.hashmap) == 0:
+            for i, val in enumerate(index._data):
+                index.hashmap[val] = i
+        return index.hashmap[item]
+
+    return index_get_loc_impl
+
+
+# Indexing for Series/Index
+@overload(operator.getitem)
+def series_indexing(series, item):
+    if isinstance(series, SeriesType):
+
+        def series_getitem(series, item):
+            loc = series.index.get_loc(item)
+            return series.iloc[loc]
+
+        return series_getitem
+
+
+@overload(operator.getitem)
+def index_indexing(index, idx):
+    if isinstance(index, IndexType):
+
+        def index_getitem(index, idx):
+            return index._data[idx]
+
+        return index_getitem
+
+
+class IlocType(types.Type):
+    def __init__(self, obj_type) -> None:
+        self.obj_type = obj_type
+        name = f"iLocIndexer({obj_type})"
+        super().__init__(name=name)
+
+    @property
+    def key(self):
+        return self.obj_type
+
+
+@typeof_impl.register(_iLocIndexer)
+def typeof_iloc(val, c):
+    objtype = typeof_impl(val.obj, c)
+    return IlocType(objtype)
+
+
+@type_callable(_iLocIndexer)
+def type_iloc_constructor(context):
+    def typer(obj):
+        if isinstance(obj, SeriesType):
+            return IlocType(obj)
+
+    return typer
+
+
+@lower_builtin(_iLocIndexer, SeriesType)
+def iloc_constructor(context, builder, sig, args):
+    (obj,) = args
+    iloc_indexer = cgutils.create_struct_proxy(sig.return_type)(context, builder)
+    iloc_indexer.obj = obj
+    return impl_ret_borrowed(
+        context, builder, sig.return_type, iloc_indexer._getvalue()
+    )
+
+
+@register_model(IlocType)
+class ILocModel(models.StructModel):
+    def __init__(self, dmm, fe_type) -> None:
+        members = [("obj", fe_type.obj_type)]
+        models.StructModel.__init__(self, dmm, fe_type, members)
+
+
+make_attribute_wrapper(IlocType, "obj", "obj")
+
+
+@overload_attribute(SeriesType, "iloc")
+def series_iloc(series):
+    def get(series):
+        return _iLocIndexer(series)
+
+    return get
+
+
+@overload(operator.getitem)
+def iloc_getitem(iloc_indexer, i):
+    if isinstance(iloc_indexer, IlocType):
+
+        def getitem_impl(iloc_indexer, i):
+            return iloc_indexer.obj.values[i]
+
+        return getitem_impl
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/_numba/kernels/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/_numba/kernels/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..1116c61c4ca8e48d94a6c9c6222aaf545d989e86
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/_numba/kernels/__init__.py
@@ -0,0 +1,27 @@
+from pandas.core._numba.kernels.mean_ import (
+    grouped_mean,
+    sliding_mean,
+)
+from pandas.core._numba.kernels.min_max_ import (
+    grouped_min_max,
+    sliding_min_max,
+)
+from pandas.core._numba.kernels.sum_ import (
+    grouped_sum,
+    sliding_sum,
+)
+from pandas.core._numba.kernels.var_ import (
+    grouped_var,
+    sliding_var,
+)
+
+__all__ = [
+    "sliding_mean",
+    "grouped_mean",
+    "sliding_sum",
+    "grouped_sum",
+    "sliding_var",
+    "grouped_var",
+    "sliding_min_max",
+    "grouped_min_max",
+]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/_numba/kernels/mean_.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/_numba/kernels/mean_.py
new file mode 100644
index 0000000000000000000000000000000000000000..f415804781753372a5715b6ffee6a7ab8cc70b64
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/_numba/kernels/mean_.py
@@ -0,0 +1,196 @@
+"""
+Numba 1D mean kernels that can be shared by
+* Dataframe / Series
+* groupby
+* rolling / expanding
+
+Mirrors pandas/_libs/window/aggregation.pyx
+"""
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import numba
+import numpy as np
+
+from pandas.core._numba.kernels.shared import is_monotonic_increasing
+from pandas.core._numba.kernels.sum_ import grouped_kahan_sum
+
+if TYPE_CHECKING:
+    from pandas._typing import npt
+
+
+@numba.jit(nopython=True, nogil=True, parallel=False)
+def add_mean(
+    val: float,
+    nobs: int,
+    sum_x: float,
+    neg_ct: int,
+    compensation: float,
+    num_consecutive_same_value: int,
+    prev_value: float,
+) -> tuple[int, float, int, float, int, float]:
+    if not np.isnan(val):
+        nobs += 1
+        y = val - compensation
+        t = sum_x + y
+        compensation = t - sum_x - y
+        sum_x = t
+        if val < 0:
+            neg_ct += 1
+
+        if val == prev_value:
+            num_consecutive_same_value += 1
+        else:
+            num_consecutive_same_value = 1
+        prev_value = val
+
+    return nobs, sum_x, neg_ct, compensation, num_consecutive_same_value, prev_value
+
+
+@numba.jit(nopython=True, nogil=True, parallel=False)
+def remove_mean(
+    val: float, nobs: int, sum_x: float, neg_ct: int, compensation: float
+) -> tuple[int, float, int, float]:
+    if not np.isnan(val):
+        nobs -= 1
+        y = -val - compensation
+        t = sum_x + y
+        compensation = t - sum_x - y
+        sum_x = t
+        if val < 0:
+            neg_ct -= 1
+    return nobs, sum_x, neg_ct, compensation
+
+
+@numba.jit(nopython=True, nogil=True, parallel=False)
+def sliding_mean(
+    values: np.ndarray,
+    result_dtype: np.dtype,
+    start: np.ndarray,
+    end: np.ndarray,
+    min_periods: int,
+) -> tuple[np.ndarray, list[int]]:
+    N = len(start)
+    nobs = 0
+    sum_x = 0.0
+    neg_ct = 0
+    compensation_add = 0.0
+    compensation_remove = 0.0
+
+    is_monotonic_increasing_bounds = is_monotonic_increasing(
+        start
+    ) and is_monotonic_increasing(end)
+
+    output = np.empty(N, dtype=result_dtype)
+
+    for i in range(N):
+        s = start[i]
+        e = end[i]
+        if i == 0 or not is_monotonic_increasing_bounds:
+            prev_value = values[s]
+            num_consecutive_same_value = 0
+
+            for j in range(s, e):
+                val = values[j]
+                (
+                    nobs,
+                    sum_x,
+                    neg_ct,
+                    compensation_add,
+                    num_consecutive_same_value,
+                    prev_value,
+                ) = add_mean(
+                    val,
+                    nobs,
+                    sum_x,
+                    neg_ct,
+                    compensation_add,
+                    num_consecutive_same_value,
+                    prev_value,  # pyright: ignore[reportGeneralTypeIssues]
+                )
+        else:
+            for j in range(start[i - 1], s):
+                val = values[j]
+                nobs, sum_x, neg_ct, compensation_remove = remove_mean(
+                    val, nobs, sum_x, neg_ct, compensation_remove
+                )
+
+            for j in range(end[i - 1], e):
+                val = values[j]
+                (
+                    nobs,
+                    sum_x,
+                    neg_ct,
+                    compensation_add,
+                    num_consecutive_same_value,
+                    prev_value,
+                ) = add_mean(
+                    val,
+                    nobs,
+                    sum_x,
+                    neg_ct,
+                    compensation_add,
+                    num_consecutive_same_value,
+                    prev_value,  # pyright: ignore[reportGeneralTypeIssues]
+                )
+
+        if nobs >= min_periods and nobs > 0:
+            result = sum_x / nobs
+            if num_consecutive_same_value >= nobs:
+                result = prev_value
+            elif neg_ct == 0 and result < 0:
+                result = 0
+            elif neg_ct == nobs and result > 0:
+                result = 0
+        else:
+            result = np.nan
+
+        output[i] = result
+
+        if not is_monotonic_increasing_bounds:
+            nobs = 0
+            sum_x = 0.0
+            neg_ct = 0
+            compensation_remove = 0.0
+
+    # na_position is empty list since float64 can already hold nans
+    # Do list comprehension, since numba cannot figure out that na_pos is
+    # empty list of ints on its own
+    na_pos = [0 for i in range(0)]
+    return output, na_pos
+
+
+@numba.jit(nopython=True, nogil=True, parallel=False)
+def grouped_mean(
+    values: np.ndarray,
+    result_dtype: np.dtype,
+    labels: npt.NDArray[np.intp],
+    ngroups: int,
+    min_periods: int,
+) -> tuple[np.ndarray, list[int]]:
+    output, nobs_arr, comp_arr, consecutive_counts, prev_vals = grouped_kahan_sum(
+        values, result_dtype, labels, ngroups
+    )
+
+    # Post-processing, replace sums that don't satisfy min_periods
+    for lab in range(ngroups):
+        nobs = nobs_arr[lab]
+        num_consecutive_same_value = consecutive_counts[lab]
+        prev_value = prev_vals[lab]
+        sum_x = output[lab]
+        if nobs >= min_periods:
+            if num_consecutive_same_value >= nobs:
+                result = prev_value * nobs
+            else:
+                result = sum_x
+        else:
+            result = np.nan
+        result /= nobs
+        output[lab] = result
+
+    # na_position is empty list since float64 can already hold nans
+    # Do list comprehension, since numba cannot figure out that na_pos is
+    # empty list of ints on its own
+    na_pos = [0 for i in range(0)]
+    return output, na_pos
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/_numba/kernels/min_max_.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/_numba/kernels/min_max_.py
new file mode 100644
index 0000000000000000000000000000000000000000..c9803980e64a6bf2ec5b274acd582850e3a07420
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/_numba/kernels/min_max_.py
@@ -0,0 +1,125 @@
+"""
+Numba 1D min/max kernels that can be shared by
+* Dataframe / Series
+* groupby
+* rolling / expanding
+
+Mirrors pandas/_libs/window/aggregation.pyx
+"""
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import numba
+import numpy as np
+
+if TYPE_CHECKING:
+    from pandas._typing import npt
+
+
+@numba.jit(nopython=True, nogil=True, parallel=False)
+def sliding_min_max(
+    values: np.ndarray,
+    result_dtype: np.dtype,
+    start: np.ndarray,
+    end: np.ndarray,
+    min_periods: int,
+    is_max: bool,
+) -> tuple[np.ndarray, list[int]]:
+    N = len(start)
+    nobs = 0
+    output = np.empty(N, dtype=result_dtype)
+    na_pos = []
+    # Use deque once numba supports it
+    # https://github.com/numba/numba/issues/7417
+    Q: list = []
+    W: list = []
+    for i in range(N):
+        curr_win_size = end[i] - start[i]
+        if i == 0:
+            st = start[i]
+        else:
+            st = end[i - 1]
+
+        for k in range(st, end[i]):
+            ai = values[k]
+            if not np.isnan(ai):
+                nobs += 1
+            elif is_max:
+                ai = -np.inf
+            else:
+                ai = np.inf
+            # Discard previous entries if we find new min or max
+            if is_max:
+                while Q and ((ai >= values[Q[-1]]) or values[Q[-1]] != values[Q[-1]]):
+                    Q.pop()
+            else:
+                while Q and ((ai <= values[Q[-1]]) or values[Q[-1]] != values[Q[-1]]):
+                    Q.pop()
+            Q.append(k)
+            W.append(k)
+
+        # Discard entries outside and left of current window
+        while Q and Q[0] <= start[i] - 1:
+            Q.pop(0)
+        while W and W[0] <= start[i] - 1:
+            if not np.isnan(values[W[0]]):
+                nobs -= 1
+            W.pop(0)
+
+        # Save output based on index in input value array
+        if Q and curr_win_size > 0 and nobs >= min_periods:
+            output[i] = values[Q[0]]
+        else:
+            if values.dtype.kind != "i":
+                output[i] = np.nan
+            else:
+                na_pos.append(i)
+
+    return output, na_pos
+
+
+@numba.jit(nopython=True, nogil=True, parallel=False)
+def grouped_min_max(
+    values: np.ndarray,
+    result_dtype: np.dtype,
+    labels: npt.NDArray[np.intp],
+    ngroups: int,
+    min_periods: int,
+    is_max: bool,
+) -> tuple[np.ndarray, list[int]]:
+    N = len(labels)
+    nobs = np.zeros(ngroups, dtype=np.int64)
+    na_pos = []
+    output = np.empty(ngroups, dtype=result_dtype)
+
+    for i in range(N):
+        lab = labels[i]
+        val = values[i]
+        if lab < 0:
+            continue
+
+        if values.dtype.kind == "i" or not np.isnan(val):
+            nobs[lab] += 1
+        else:
+            # NaN value cannot be a min/max value
+            continue
+
+        if nobs[lab] == 1:
+            # First element in group, set output equal to this
+            output[lab] = val
+            continue
+
+        if is_max:
+            if val > output[lab]:
+                output[lab] = val
+        else:
+            if val < output[lab]:
+                output[lab] = val
+
+    # Set labels that don't satisfy min_periods as np.nan
+    for lab, count in enumerate(nobs):
+        if count < min_periods:
+            na_pos.append(lab)
+
+    return output, na_pos
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/_numba/kernels/shared.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/_numba/kernels/shared.py
new file mode 100644
index 0000000000000000000000000000000000000000..c52372fe6b08f3ec9ec6e836341e08ac804d50f3
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/_numba/kernels/shared.py
@@ -0,0 +1,29 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import numba
+
+if TYPE_CHECKING:
+    import numpy as np
+
+
+@numba.jit(
+    # error: Any? not callable
+    numba.boolean(numba.int64[:]),  # type: ignore[misc]
+    nopython=True,
+    nogil=True,
+    parallel=False,
+)
+def is_monotonic_increasing(bounds: np.ndarray) -> bool:
+    """Check if int64 values are monotonically increasing."""
+    n = len(bounds)
+    if n < 2:
+        return True
+    prev = bounds[0]
+    for i in range(1, n):
+        cur = bounds[i]
+        if cur < prev:
+            return False
+        prev = cur
+    return True
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/_numba/kernels/sum_.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/_numba/kernels/sum_.py
new file mode 100644
index 0000000000000000000000000000000000000000..94db84267ceecb83234fc2e0b231566a2fdffd66
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/_numba/kernels/sum_.py
@@ -0,0 +1,244 @@
+"""
+Numba 1D sum kernels that can be shared by
+* Dataframe / Series
+* groupby
+* rolling / expanding
+
+Mirrors pandas/_libs/window/aggregation.pyx
+"""
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    Any,
+)
+
+import numba
+from numba.extending import register_jitable
+import numpy as np
+
+if TYPE_CHECKING:
+    from pandas._typing import npt
+
+from pandas.core._numba.kernels.shared import is_monotonic_increasing
+
+
+@numba.jit(nopython=True, nogil=True, parallel=False)
+def add_sum(
+    val: Any,
+    nobs: int,
+    sum_x: Any,
+    compensation: Any,
+    num_consecutive_same_value: int,
+    prev_value: Any,
+) -> tuple[int, Any, Any, int, Any]:
+    if not np.isnan(val):
+        nobs += 1
+        y = val - compensation
+        t = sum_x + y
+        compensation = t - sum_x - y
+        sum_x = t
+
+        if val == prev_value:
+            num_consecutive_same_value += 1
+        else:
+            num_consecutive_same_value = 1
+        prev_value = val
+
+    return nobs, sum_x, compensation, num_consecutive_same_value, prev_value
+
+
+@numba.jit(nopython=True, nogil=True, parallel=False)
+def remove_sum(
+    val: Any, nobs: int, sum_x: Any, compensation: Any
+) -> tuple[int, Any, Any]:
+    if not np.isnan(val):
+        nobs -= 1
+        y = -val - compensation
+        t = sum_x + y
+        compensation = t - sum_x - y
+        sum_x = t
+    return nobs, sum_x, compensation
+
+
+@numba.jit(nopython=True, nogil=True, parallel=False)
+def sliding_sum(
+    values: np.ndarray,
+    result_dtype: np.dtype,
+    start: np.ndarray,
+    end: np.ndarray,
+    min_periods: int,
+) -> tuple[np.ndarray, list[int]]:
+    dtype = values.dtype
+
+    na_val: object = np.nan
+    if dtype.kind == "i":
+        na_val = 0
+
+    N = len(start)
+    nobs = 0
+    sum_x = 0
+    compensation_add = 0
+    compensation_remove = 0
+    na_pos = []
+
+    is_monotonic_increasing_bounds = is_monotonic_increasing(
+        start
+    ) and is_monotonic_increasing(end)
+
+    output = np.empty(N, dtype=result_dtype)
+
+    for i in range(N):
+        s = start[i]
+        e = end[i]
+        if i == 0 or not is_monotonic_increasing_bounds:
+            prev_value = values[s]
+            num_consecutive_same_value = 0
+
+            for j in range(s, e):
+                val = values[j]
+                (
+                    nobs,
+                    sum_x,
+                    compensation_add,
+                    num_consecutive_same_value,
+                    prev_value,
+                ) = add_sum(
+                    val,
+                    nobs,
+                    sum_x,
+                    compensation_add,
+                    num_consecutive_same_value,
+                    prev_value,
+                )
+        else:
+            for j in range(start[i - 1], s):
+                val = values[j]
+                nobs, sum_x, compensation_remove = remove_sum(
+                    val, nobs, sum_x, compensation_remove
+                )
+
+            for j in range(end[i - 1], e):
+                val = values[j]
+                (
+                    nobs,
+                    sum_x,
+                    compensation_add,
+                    num_consecutive_same_value,
+                    prev_value,
+                ) = add_sum(
+                    val,
+                    nobs,
+                    sum_x,
+                    compensation_add,
+                    num_consecutive_same_value,
+                    prev_value,
+                )
+
+        if nobs == 0 == min_periods:
+            result: object = 0
+        elif nobs >= min_periods:
+            if num_consecutive_same_value >= nobs:
+                result = prev_value * nobs
+            else:
+                result = sum_x
+        else:
+            result = na_val
+            if dtype.kind == "i":
+                na_pos.append(i)
+
+        output[i] = result
+
+        if not is_monotonic_increasing_bounds:
+            nobs = 0
+            sum_x = 0
+            compensation_remove = 0
+
+    return output, na_pos
+
+
+# Mypy/pyright don't like the fact that the decorator is untyped
+@register_jitable  # type: ignore[misc]
+def grouped_kahan_sum(
+    values: np.ndarray,
+    result_dtype: np.dtype,
+    labels: npt.NDArray[np.intp],
+    ngroups: int,
+) -> tuple[
+    np.ndarray, npt.NDArray[np.int64], np.ndarray, npt.NDArray[np.int64], np.ndarray
+]:
+    N = len(labels)
+
+    nobs_arr = np.zeros(ngroups, dtype=np.int64)
+    comp_arr = np.zeros(ngroups, dtype=values.dtype)
+    consecutive_counts = np.zeros(ngroups, dtype=np.int64)
+    prev_vals = np.zeros(ngroups, dtype=values.dtype)
+    output = np.zeros(ngroups, dtype=result_dtype)
+
+    for i in range(N):
+        lab = labels[i]
+        val = values[i]
+
+        if lab < 0:
+            continue
+
+        sum_x = output[lab]
+        nobs = nobs_arr[lab]
+        compensation_add = comp_arr[lab]
+        num_consecutive_same_value = consecutive_counts[lab]
+        prev_value = prev_vals[lab]
+
+        (
+            nobs,
+            sum_x,
+            compensation_add,
+            num_consecutive_same_value,
+            prev_value,
+        ) = add_sum(
+            val,
+            nobs,
+            sum_x,
+            compensation_add,
+            num_consecutive_same_value,
+            prev_value,
+        )
+
+        output[lab] = sum_x
+        consecutive_counts[lab] = num_consecutive_same_value
+        prev_vals[lab] = prev_value
+        comp_arr[lab] = compensation_add
+        nobs_arr[lab] = nobs
+    return output, nobs_arr, comp_arr, consecutive_counts, prev_vals
+
+
+@numba.jit(nopython=True, nogil=True, parallel=False)
+def grouped_sum(
+    values: np.ndarray,
+    result_dtype: np.dtype,
+    labels: npt.NDArray[np.intp],
+    ngroups: int,
+    min_periods: int,
+) -> tuple[np.ndarray, list[int]]:
+    na_pos = []
+
+    output, nobs_arr, comp_arr, consecutive_counts, prev_vals = grouped_kahan_sum(
+        values, result_dtype, labels, ngroups
+    )
+
+    # Post-processing, replace sums that don't satisfy min_periods
+    for lab in range(ngroups):
+        nobs = nobs_arr[lab]
+        num_consecutive_same_value = consecutive_counts[lab]
+        prev_value = prev_vals[lab]
+        sum_x = output[lab]
+        if nobs >= min_periods:
+            if num_consecutive_same_value >= nobs:
+                result = prev_value * nobs
+            else:
+                result = sum_x
+        else:
+            result = sum_x  # Don't change val, will be replaced by nan later
+            na_pos.append(lab)
+        output[lab] = result
+
+    return output, na_pos
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/_numba/kernels/var_.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/_numba/kernels/var_.py
new file mode 100644
index 0000000000000000000000000000000000000000..c63d0b90b0fc3f4a4a532b9ce85e57d8bd823fb1
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/_numba/kernels/var_.py
@@ -0,0 +1,245 @@
+"""
+Numba 1D var kernels that can be shared by
+* Dataframe / Series
+* groupby
+* rolling / expanding
+
+Mirrors pandas/_libs/window/aggregation.pyx
+"""
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import numba
+import numpy as np
+
+if TYPE_CHECKING:
+    from pandas._typing import npt
+
+from pandas.core._numba.kernels.shared import is_monotonic_increasing
+
+
+@numba.jit(nopython=True, nogil=True, parallel=False)
+def add_var(
+    val: float,
+    nobs: int,
+    mean_x: float,
+    ssqdm_x: float,
+    compensation: float,
+    num_consecutive_same_value: int,
+    prev_value: float,
+) -> tuple[int, float, float, float, int, float]:
+    if not np.isnan(val):
+        if val == prev_value:
+            num_consecutive_same_value += 1
+        else:
+            num_consecutive_same_value = 1
+        prev_value = val
+
+        nobs += 1
+        prev_mean = mean_x - compensation
+        y = val - compensation
+        t = y - mean_x
+        compensation = t + mean_x - y
+        delta = t
+        if nobs:
+            mean_x += delta / nobs
+        else:
+            mean_x = 0
+        ssqdm_x += (val - prev_mean) * (val - mean_x)
+    return nobs, mean_x, ssqdm_x, compensation, num_consecutive_same_value, prev_value
+
+
+@numba.jit(nopython=True, nogil=True, parallel=False)
+def remove_var(
+    val: float, nobs: int, mean_x: float, ssqdm_x: float, compensation: float
+) -> tuple[int, float, float, float]:
+    if not np.isnan(val):
+        nobs -= 1
+        if nobs:
+            prev_mean = mean_x - compensation
+            y = val - compensation
+            t = y - mean_x
+            compensation = t + mean_x - y
+            delta = t
+            mean_x -= delta / nobs
+            ssqdm_x -= (val - prev_mean) * (val - mean_x)
+        else:
+            mean_x = 0
+            ssqdm_x = 0
+    return nobs, mean_x, ssqdm_x, compensation
+
+
+@numba.jit(nopython=True, nogil=True, parallel=False)
+def sliding_var(
+    values: np.ndarray,
+    result_dtype: np.dtype,
+    start: np.ndarray,
+    end: np.ndarray,
+    min_periods: int,
+    ddof: int = 1,
+) -> tuple[np.ndarray, list[int]]:
+    N = len(start)
+    nobs = 0
+    mean_x = 0.0
+    ssqdm_x = 0.0
+    compensation_add = 0.0
+    compensation_remove = 0.0
+
+    min_periods = max(min_periods, 1)
+    is_monotonic_increasing_bounds = is_monotonic_increasing(
+        start
+    ) and is_monotonic_increasing(end)
+
+    output = np.empty(N, dtype=result_dtype)
+
+    for i in range(N):
+        s = start[i]
+        e = end[i]
+        if i == 0 or not is_monotonic_increasing_bounds:
+            prev_value = values[s]
+            num_consecutive_same_value = 0
+
+            for j in range(s, e):
+                val = values[j]
+                (
+                    nobs,
+                    mean_x,
+                    ssqdm_x,
+                    compensation_add,
+                    num_consecutive_same_value,
+                    prev_value,
+                ) = add_var(
+                    val,
+                    nobs,
+                    mean_x,
+                    ssqdm_x,
+                    compensation_add,
+                    num_consecutive_same_value,
+                    prev_value,
+                )
+        else:
+            for j in range(start[i - 1], s):
+                val = values[j]
+                nobs, mean_x, ssqdm_x, compensation_remove = remove_var(
+                    val, nobs, mean_x, ssqdm_x, compensation_remove
+                )
+
+            for j in range(end[i - 1], e):
+                val = values[j]
+                (
+                    nobs,
+                    mean_x,
+                    ssqdm_x,
+                    compensation_add,
+                    num_consecutive_same_value,
+                    prev_value,
+                ) = add_var(
+                    val,
+                    nobs,
+                    mean_x,
+                    ssqdm_x,
+                    compensation_add,
+                    num_consecutive_same_value,
+                    prev_value,
+                )
+
+        if nobs >= min_periods and nobs > ddof:
+            if nobs == 1 or num_consecutive_same_value >= nobs:
+                result = 0.0
+            else:
+                result = ssqdm_x / (nobs - ddof)
+        else:
+            result = np.nan
+
+        output[i] = result
+
+        if not is_monotonic_increasing_bounds:
+            nobs = 0
+            mean_x = 0.0
+            ssqdm_x = 0.0
+            compensation_remove = 0.0
+
+    # na_position is empty list since float64 can already hold nans
+    # Do list comprehension, since numba cannot figure out that na_pos is
+    # empty list of ints on its own
+    na_pos = [0 for i in range(0)]
+    return output, na_pos
+
+
+@numba.jit(nopython=True, nogil=True, parallel=False)
+def grouped_var(
+    values: np.ndarray,
+    result_dtype: np.dtype,
+    labels: npt.NDArray[np.intp],
+    ngroups: int,
+    min_periods: int,
+    ddof: int = 1,
+) -> tuple[np.ndarray, list[int]]:
+    N = len(labels)
+
+    nobs_arr = np.zeros(ngroups, dtype=np.int64)
+    comp_arr = np.zeros(ngroups, dtype=values.dtype)
+    consecutive_counts = np.zeros(ngroups, dtype=np.int64)
+    prev_vals = np.zeros(ngroups, dtype=values.dtype)
+    output = np.zeros(ngroups, dtype=result_dtype)
+    means = np.zeros(ngroups, dtype=result_dtype)
+
+    for i in range(N):
+        lab = labels[i]
+        val = values[i]
+
+        if lab < 0:
+            continue
+
+        mean_x = means[lab]
+        ssqdm_x = output[lab]
+        nobs = nobs_arr[lab]
+        compensation_add = comp_arr[lab]
+        num_consecutive_same_value = consecutive_counts[lab]
+        prev_value = prev_vals[lab]
+
+        (
+            nobs,
+            mean_x,
+            ssqdm_x,
+            compensation_add,
+            num_consecutive_same_value,
+            prev_value,
+        ) = add_var(
+            val,
+            nobs,
+            mean_x,
+            ssqdm_x,
+            compensation_add,
+            num_consecutive_same_value,
+            prev_value,
+        )
+
+        output[lab] = ssqdm_x
+        means[lab] = mean_x
+        consecutive_counts[lab] = num_consecutive_same_value
+        prev_vals[lab] = prev_value
+        comp_arr[lab] = compensation_add
+        nobs_arr[lab] = nobs
+
+    # Post-processing, replace vars that don't satisfy min_periods
+    for lab in range(ngroups):
+        nobs = nobs_arr[lab]
+        num_consecutive_same_value = consecutive_counts[lab]
+        ssqdm_x = output[lab]
+        if nobs >= min_periods and nobs > ddof:
+            if nobs == 1 or num_consecutive_same_value >= nobs:
+                result = 0.0
+            else:
+                result = ssqdm_x / (nobs - ddof)
+        else:
+            result = np.nan
+        output[lab] = result
+
+    # Second pass to get the std.dev
+    # na_position is empty list since float64 can already hold nans
+    # Do list comprehension, since numba cannot figure out that na_pos is
+    # empty list of ints on its own
+    na_pos = [0 for i in range(0)]
+    return output, na_pos
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/accessor.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/accessor.py
new file mode 100644
index 0000000000000000000000000000000000000000..698abb2ec4989b2f95f6b2c012f03873bba328cb
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/accessor.py
@@ -0,0 +1,340 @@
+"""
+
+accessor.py contains base classes for implementing accessor properties
+that can be mixed into or pinned onto other pandas classes.
+
+"""
+from __future__ import annotations
+
+from typing import (
+    Callable,
+    final,
+)
+import warnings
+
+from pandas.util._decorators import doc
+from pandas.util._exceptions import find_stack_level
+
+
+class DirNamesMixin:
+    _accessors: set[str] = set()
+    _hidden_attrs: frozenset[str] = frozenset()
+
+    @final
+    def _dir_deletions(self) -> set[str]:
+        """
+        Delete unwanted __dir__ for this object.
+        """
+        return self._accessors | self._hidden_attrs
+
+    def _dir_additions(self) -> set[str]:
+        """
+        Add additional __dir__ for this object.
+        """
+        return {accessor for accessor in self._accessors if hasattr(self, accessor)}
+
+    def __dir__(self) -> list[str]:
+        """
+        Provide method name lookup and completion.
+
+        Notes
+        -----
+        Only provide 'public' methods.
+        """
+        rv = set(super().__dir__())
+        rv = (rv - self._dir_deletions()) | self._dir_additions()
+        return sorted(rv)
+
+
+class PandasDelegate:
+    """
+    Abstract base class for delegating methods/properties.
+    """
+
+    def _delegate_property_get(self, name: str, *args, **kwargs):
+        raise TypeError(f"You cannot access the property {name}")
+
+    def _delegate_property_set(self, name: str, value, *args, **kwargs):
+        raise TypeError(f"The property {name} cannot be set")
+
+    def _delegate_method(self, name: str, *args, **kwargs):
+        raise TypeError(f"You cannot call method {name}")
+
+    @classmethod
+    def _add_delegate_accessors(
+        cls,
+        delegate,
+        accessors: list[str],
+        typ: str,
+        overwrite: bool = False,
+        accessor_mapping: Callable[[str], str] = lambda x: x,
+        raise_on_missing: bool = True,
+    ) -> None:
+        """
+        Add accessors to cls from the delegate class.
+
+        Parameters
+        ----------
+        cls
+            Class to add the methods/properties to.
+        delegate
+            Class to get methods/properties and doc-strings.
+        accessors : list of str
+            List of accessors to add.
+        typ : {'property', 'method'}
+        overwrite : bool, default False
+            Overwrite the method/property in the target class if it exists.
+        accessor_mapping: Callable, default lambda x: x
+            Callable to map the delegate's function to the cls' function.
+        raise_on_missing: bool, default True
+            Raise if an accessor does not exist on delegate.
+            False skips the missing accessor.
+        """
+
+        def _create_delegator_property(name: str):
+            def _getter(self):
+                return self._delegate_property_get(name)
+
+            def _setter(self, new_values):
+                return self._delegate_property_set(name, new_values)
+
+            _getter.__name__ = name
+            _setter.__name__ = name
+
+            return property(
+                fget=_getter,
+                fset=_setter,
+                doc=getattr(delegate, accessor_mapping(name)).__doc__,
+            )
+
+        def _create_delegator_method(name: str):
+            def f(self, *args, **kwargs):
+                return self._delegate_method(name, *args, **kwargs)
+
+            f.__name__ = name
+            f.__doc__ = getattr(delegate, accessor_mapping(name)).__doc__
+
+            return f
+
+        for name in accessors:
+            if (
+                not raise_on_missing
+                and getattr(delegate, accessor_mapping(name), None) is None
+            ):
+                continue
+
+            if typ == "property":
+                f = _create_delegator_property(name)
+            else:
+                f = _create_delegator_method(name)
+
+            # don't overwrite existing methods/properties
+            if overwrite or not hasattr(cls, name):
+                setattr(cls, name, f)
+
+
+def delegate_names(
+    delegate,
+    accessors: list[str],
+    typ: str,
+    overwrite: bool = False,
+    accessor_mapping: Callable[[str], str] = lambda x: x,
+    raise_on_missing: bool = True,
+):
+    """
+    Add delegated names to a class using a class decorator.  This provides
+    an alternative usage to directly calling `_add_delegate_accessors`
+    below a class definition.
+
+    Parameters
+    ----------
+    delegate : object
+        The class to get methods/properties & doc-strings.
+    accessors : Sequence[str]
+        List of accessor to add.
+    typ : {'property', 'method'}
+    overwrite : bool, default False
+       Overwrite the method/property in the target class if it exists.
+    accessor_mapping: Callable, default lambda x: x
+        Callable to map the delegate's function to the cls' function.
+    raise_on_missing: bool, default True
+        Raise if an accessor does not exist on delegate.
+        False skips the missing accessor.
+
+    Returns
+    -------
+    callable
+        A class decorator.
+
+    Examples
+    --------
+    @delegate_names(Categorical, ["categories", "ordered"], "property")
+    class CategoricalAccessor(PandasDelegate):
+        [...]
+    """
+
+    def add_delegate_accessors(cls):
+        cls._add_delegate_accessors(
+            delegate,
+            accessors,
+            typ,
+            overwrite=overwrite,
+            accessor_mapping=accessor_mapping,
+            raise_on_missing=raise_on_missing,
+        )
+        return cls
+
+    return add_delegate_accessors
+
+
+# Ported with modifications from xarray; licence at LICENSES/XARRAY_LICENSE
+# https://github.com/pydata/xarray/blob/master/xarray/core/extensions.py
+# 1. We don't need to catch and re-raise AttributeErrors as RuntimeErrors
+# 2. We use a UserWarning instead of a custom Warning
+
+
+class CachedAccessor:
+    """
+    Custom property-like object.
+
+    A descriptor for caching accessors.
+
+    Parameters
+    ----------
+    name : str
+        Namespace that will be accessed under, e.g. ``df.foo``.
+    accessor : cls
+        Class with the extension methods.
+
+    Notes
+    -----
+    For accessor, The class's __init__ method assumes that one of
+    ``Series``, ``DataFrame`` or ``Index`` as the
+    single argument ``data``.
+    """
+
+    def __init__(self, name: str, accessor) -> None:
+        self._name = name
+        self._accessor = accessor
+
+    def __get__(self, obj, cls):
+        if obj is None:
+            # we're accessing the attribute of the class, i.e., Dataset.geo
+            return self._accessor
+        accessor_obj = self._accessor(obj)
+        # Replace the property with the accessor object. Inspired by:
+        # https://www.pydanny.com/cached-property.html
+        # We need to use object.__setattr__ because we overwrite __setattr__ on
+        # NDFrame
+        object.__setattr__(obj, self._name, accessor_obj)
+        return accessor_obj
+
+
+@doc(klass="", others="")
+def _register_accessor(name: str, cls):
+    """
+    Register a custom accessor on {klass} objects.
+
+    Parameters
+    ----------
+    name : str
+        Name under which the accessor should be registered. A warning is issued
+        if this name conflicts with a preexisting attribute.
+
+    Returns
+    -------
+    callable
+        A class decorator.
+
+    See Also
+    --------
+    register_dataframe_accessor : Register a custom accessor on DataFrame objects.
+    register_series_accessor : Register a custom accessor on Series objects.
+    register_index_accessor : Register a custom accessor on Index objects.
+
+    Notes
+    -----
+    When accessed, your accessor will be initialized with the pandas object
+    the user is interacting with. So the signature must be
+
+    .. code-block:: python
+
+        def __init__(self, pandas_object):  # noqa: E999
+            ...
+
+    For consistency with pandas methods, you should raise an ``AttributeError``
+    if the data passed to your accessor has an incorrect dtype.
+
+    >>> pd.Series(['a', 'b']).dt
+    Traceback (most recent call last):
+    ...
+    AttributeError: Can only use .dt accessor with datetimelike values
+
+    Examples
+    --------
+    In your library code::
+
+        import pandas as pd
+
+        @pd.api.extensions.register_dataframe_accessor("geo")
+        class GeoAccessor:
+            def __init__(self, pandas_obj):
+                self._obj = pandas_obj
+
+            @property
+            def center(self):
+                # return the geographic center point of this DataFrame
+                lat = self._obj.latitude
+                lon = self._obj.longitude
+                return (float(lon.mean()), float(lat.mean()))
+
+            def plot(self):
+                # plot this array's data on a map, e.g., using Cartopy
+                pass
+
+    Back in an interactive IPython session:
+
+        .. code-block:: ipython
+
+            In [1]: ds = pd.DataFrame({{"longitude": np.linspace(0, 10),
+               ...:                    "latitude": np.linspace(0, 20)}})
+            In [2]: ds.geo.center
+            Out[2]: (5.0, 10.0)
+            In [3]: ds.geo.plot()  # plots data on a map
+    """
+
+    def decorator(accessor):
+        if hasattr(cls, name):
+            warnings.warn(
+                f"registration of accessor {repr(accessor)} under name "
+                f"{repr(name)} for type {repr(cls)} is overriding a preexisting "
+                f"attribute with the same name.",
+                UserWarning,
+                stacklevel=find_stack_level(),
+            )
+        setattr(cls, name, CachedAccessor(name, accessor))
+        cls._accessors.add(name)
+        return accessor
+
+    return decorator
+
+
+@doc(_register_accessor, klass="DataFrame")
+def register_dataframe_accessor(name: str):
+    from pandas import DataFrame
+
+    return _register_accessor(name, DataFrame)
+
+
+@doc(_register_accessor, klass="Series")
+def register_series_accessor(name: str):
+    from pandas import Series
+
+    return _register_accessor(name, Series)
+
+
+@doc(_register_accessor, klass="Index")
+def register_index_accessor(name: str):
+    from pandas import Index
+
+    return _register_accessor(name, Index)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/algorithms.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/algorithms.py
new file mode 100644
index 0000000000000000000000000000000000000000..c6084880bea5d0b8771bf31be1f1e6f5fecdc094
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/algorithms.py
@@ -0,0 +1,1747 @@
+"""
+Generic data algorithms. This module is experimental at the moment and not
+intended for public consumption
+"""
+from __future__ import annotations
+
+import decimal
+import operator
+from textwrap import dedent
+from typing import (
+    TYPE_CHECKING,
+    Literal,
+    cast,
+)
+import warnings
+
+import numpy as np
+
+from pandas._libs import (
+    algos,
+    hashtable as htable,
+    iNaT,
+    lib,
+)
+from pandas._typing import (
+    AnyArrayLike,
+    ArrayLike,
+    AxisInt,
+    DtypeObj,
+    TakeIndexer,
+    npt,
+)
+from pandas.util._decorators import doc
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.cast import (
+    construct_1d_object_array_from_listlike,
+    np_find_common_type,
+)
+from pandas.core.dtypes.common import (
+    ensure_float64,
+    ensure_object,
+    ensure_platform_int,
+    is_array_like,
+    is_bool_dtype,
+    is_complex_dtype,
+    is_dict_like,
+    is_extension_array_dtype,
+    is_float_dtype,
+    is_integer,
+    is_integer_dtype,
+    is_list_like,
+    is_object_dtype,
+    is_signed_integer_dtype,
+    needs_i8_conversion,
+)
+from pandas.core.dtypes.concat import concat_compat
+from pandas.core.dtypes.dtypes import (
+    BaseMaskedDtype,
+    CategoricalDtype,
+    ExtensionDtype,
+    NumpyEADtype,
+)
+from pandas.core.dtypes.generic import (
+    ABCDatetimeArray,
+    ABCExtensionArray,
+    ABCIndex,
+    ABCMultiIndex,
+    ABCSeries,
+    ABCTimedeltaArray,
+)
+from pandas.core.dtypes.missing import (
+    isna,
+    na_value_for_dtype,
+)
+
+from pandas.core.array_algos.take import take_nd
+from pandas.core.construction import (
+    array as pd_array,
+    ensure_wrapped_if_datetimelike,
+    extract_array,
+)
+from pandas.core.indexers import validate_indices
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        ListLike,
+        NumpySorter,
+        NumpyValueArrayLike,
+    )
+
+    from pandas import (
+        Categorical,
+        Index,
+        Series,
+    )
+    from pandas.core.arrays import (
+        BaseMaskedArray,
+        ExtensionArray,
+    )
+
+
+# --------------- #
+# dtype access    #
+# --------------- #
+def _ensure_data(values: ArrayLike) -> np.ndarray:
+    """
+    routine to ensure that our data is of the correct
+    input dtype for lower-level routines
+
+    This will coerce:
+    - ints -> int64
+    - uint -> uint64
+    - bool -> uint8
+    - datetimelike -> i8
+    - datetime64tz -> i8 (in local tz)
+    - categorical -> codes
+
+    Parameters
+    ----------
+    values : np.ndarray or ExtensionArray
+
+    Returns
+    -------
+    np.ndarray
+    """
+
+    if not isinstance(values, ABCMultiIndex):
+        # extract_array would raise
+        values = extract_array(values, extract_numpy=True)
+
+    if is_object_dtype(values.dtype):
+        return ensure_object(np.asarray(values))
+
+    elif isinstance(values.dtype, BaseMaskedDtype):
+        # i.e. BooleanArray, FloatingArray, IntegerArray
+        values = cast("BaseMaskedArray", values)
+        if not values._hasna:
+            # No pd.NAs -> We can avoid an object-dtype cast (and copy) GH#41816
+            #  recurse to avoid re-implementing logic for eg bool->uint8
+            return _ensure_data(values._data)
+        return np.asarray(values)
+
+    elif isinstance(values.dtype, CategoricalDtype):
+        # NB: cases that go through here should NOT be using _reconstruct_data
+        #  on the back-end.
+        values = cast("Categorical", values)
+        return values.codes
+
+    elif is_bool_dtype(values.dtype):
+        if isinstance(values, np.ndarray):
+            # i.e. actually dtype == np.dtype("bool")
+            return np.asarray(values).view("uint8")
+        else:
+            # e.g. Sparse[bool, False]  # TODO: no test cases get here
+            return np.asarray(values).astype("uint8", copy=False)
+
+    elif is_integer_dtype(values.dtype):
+        return np.asarray(values)
+
+    elif is_float_dtype(values.dtype):
+        # Note: checking `values.dtype == "float128"` raises on Windows and 32bit
+        # error: Item "ExtensionDtype" of "Union[Any, ExtensionDtype, dtype[Any]]"
+        # has no attribute "itemsize"
+        if values.dtype.itemsize in [2, 12, 16]:  # type: ignore[union-attr]
+            # we dont (yet) have float128 hashtable support
+            return ensure_float64(values)
+        return np.asarray(values)
+
+    elif is_complex_dtype(values.dtype):
+        return cast(np.ndarray, values)
+
+    # datetimelike
+    elif needs_i8_conversion(values.dtype):
+        npvalues = values.view("i8")
+        npvalues = cast(np.ndarray, npvalues)
+        return npvalues
+
+    # we have failed, return object
+    values = np.asarray(values, dtype=object)
+    return ensure_object(values)
+
+
+def _reconstruct_data(
+    values: ArrayLike, dtype: DtypeObj, original: AnyArrayLike
+) -> ArrayLike:
+    """
+    reverse of _ensure_data
+
+    Parameters
+    ----------
+    values : np.ndarray or ExtensionArray
+    dtype : np.dtype or ExtensionDtype
+    original : AnyArrayLike
+
+    Returns
+    -------
+    ExtensionArray or np.ndarray
+    """
+    if isinstance(values, ABCExtensionArray) and values.dtype == dtype:
+        # Catch DatetimeArray/TimedeltaArray
+        return values
+
+    if not isinstance(dtype, np.dtype):
+        # i.e. ExtensionDtype; note we have ruled out above the possibility
+        #  that values.dtype == dtype
+        cls = dtype.construct_array_type()
+
+        values = cls._from_sequence(values, dtype=dtype)
+
+    else:
+        values = values.astype(dtype, copy=False)
+
+    return values
+
+
+def _ensure_arraylike(values, func_name: str) -> ArrayLike:
+    """
+    ensure that we are arraylike if not already
+    """
+    if not isinstance(values, (ABCIndex, ABCSeries, ABCExtensionArray, np.ndarray)):
+        # GH#52986
+        if func_name != "isin-targets":
+            # Make an exception for the comps argument in isin.
+            warnings.warn(
+                f"{func_name} with argument that is not not a Series, Index, "
+                "ExtensionArray, or np.ndarray is deprecated and will raise in a "
+                "future version.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+
+        inferred = lib.infer_dtype(values, skipna=False)
+        if inferred in ["mixed", "string", "mixed-integer"]:
+            # "mixed-integer" to ensure we do not cast ["ss", 42] to str GH#22160
+            if isinstance(values, tuple):
+                values = list(values)
+            values = construct_1d_object_array_from_listlike(values)
+        else:
+            values = np.asarray(values)
+    return values
+
+
+_hashtables = {
+    "complex128": htable.Complex128HashTable,
+    "complex64": htable.Complex64HashTable,
+    "float64": htable.Float64HashTable,
+    "float32": htable.Float32HashTable,
+    "uint64": htable.UInt64HashTable,
+    "uint32": htable.UInt32HashTable,
+    "uint16": htable.UInt16HashTable,
+    "uint8": htable.UInt8HashTable,
+    "int64": htable.Int64HashTable,
+    "int32": htable.Int32HashTable,
+    "int16": htable.Int16HashTable,
+    "int8": htable.Int8HashTable,
+    "string": htable.StringHashTable,
+    "object": htable.PyObjectHashTable,
+}
+
+
+def _get_hashtable_algo(values: np.ndarray):
+    """
+    Parameters
+    ----------
+    values : np.ndarray
+
+    Returns
+    -------
+    htable : HashTable subclass
+    values : ndarray
+    """
+    values = _ensure_data(values)
+
+    ndtype = _check_object_for_strings(values)
+    hashtable = _hashtables[ndtype]
+    return hashtable, values
+
+
+def _check_object_for_strings(values: np.ndarray) -> str:
+    """
+    Check if we can use string hashtable instead of object hashtable.
+
+    Parameters
+    ----------
+    values : ndarray
+
+    Returns
+    -------
+    str
+    """
+    ndtype = values.dtype.name
+    if ndtype == "object":
+        # it's cheaper to use a String Hash Table than Object; we infer
+        # including nulls because that is the only difference between
+        # StringHashTable and ObjectHashtable
+        if lib.is_string_array(values, skipna=False):
+            ndtype = "string"
+    return ndtype
+
+
+# --------------- #
+# top-level algos #
+# --------------- #
+
+
+def unique(values):
+    """
+    Return unique values based on a hash table.
+
+    Uniques are returned in order of appearance. This does NOT sort.
+
+    Significantly faster than numpy.unique for long enough sequences.
+    Includes NA values.
+
+    Parameters
+    ----------
+    values : 1d array-like
+
+    Returns
+    -------
+    numpy.ndarray or ExtensionArray
+
+        The return can be:
+
+        * Index : when the input is an Index
+        * Categorical : when the input is a Categorical dtype
+        * ndarray : when the input is a Series/ndarray
+
+        Return numpy.ndarray or ExtensionArray.
+
+    See Also
+    --------
+    Index.unique : Return unique values from an Index.
+    Series.unique : Return unique values of Series object.
+
+    Examples
+    --------
+    >>> pd.unique(pd.Series([2, 1, 3, 3]))
+    array([2, 1, 3])
+
+    >>> pd.unique(pd.Series([2] + [1] * 5))
+    array([2, 1])
+
+    >>> pd.unique(pd.Series([pd.Timestamp("20160101"), pd.Timestamp("20160101")]))
+    array(['2016-01-01T00:00:00.000000000'], dtype='datetime64[ns]')
+
+    >>> pd.unique(
+    ...     pd.Series(
+    ...         [
+    ...             pd.Timestamp("20160101", tz="US/Eastern"),
+    ...             pd.Timestamp("20160101", tz="US/Eastern"),
+    ...         ]
+    ...     )
+    ... )
+    
+    ['2016-01-01 00:00:00-05:00']
+    Length: 1, dtype: datetime64[ns, US/Eastern]
+
+    >>> pd.unique(
+    ...     pd.Index(
+    ...         [
+    ...             pd.Timestamp("20160101", tz="US/Eastern"),
+    ...             pd.Timestamp("20160101", tz="US/Eastern"),
+    ...         ]
+    ...     )
+    ... )
+    DatetimeIndex(['2016-01-01 00:00:00-05:00'],
+            dtype='datetime64[ns, US/Eastern]',
+            freq=None)
+
+    >>> pd.unique(np.array(list("baabc"), dtype="O"))
+    array(['b', 'a', 'c'], dtype=object)
+
+    An unordered Categorical will return categories in the
+    order of appearance.
+
+    >>> pd.unique(pd.Series(pd.Categorical(list("baabc"))))
+    ['b', 'a', 'c']
+    Categories (3, object): ['a', 'b', 'c']
+
+    >>> pd.unique(pd.Series(pd.Categorical(list("baabc"), categories=list("abc"))))
+    ['b', 'a', 'c']
+    Categories (3, object): ['a', 'b', 'c']
+
+    An ordered Categorical preserves the category ordering.
+
+    >>> pd.unique(
+    ...     pd.Series(
+    ...         pd.Categorical(list("baabc"), categories=list("abc"), ordered=True)
+    ...     )
+    ... )
+    ['b', 'a', 'c']
+    Categories (3, object): ['a' < 'b' < 'c']
+
+    An array of tuples
+
+    >>> pd.unique(pd.Series([("a", "b"), ("b", "a"), ("a", "c"), ("b", "a")]).values)
+    array([('a', 'b'), ('b', 'a'), ('a', 'c')], dtype=object)
+    """
+    return unique_with_mask(values)
+
+
+def nunique_ints(values: ArrayLike) -> int:
+    """
+    Return the number of unique values for integer array-likes.
+
+    Significantly faster than pandas.unique for long enough sequences.
+    No checks are done to ensure input is integral.
+
+    Parameters
+    ----------
+    values : 1d array-like
+
+    Returns
+    -------
+    int : The number of unique values in ``values``
+    """
+    if len(values) == 0:
+        return 0
+    values = _ensure_data(values)
+    # bincount requires intp
+    result = (np.bincount(values.ravel().astype("intp")) != 0).sum()
+    return result
+
+
+def unique_with_mask(values, mask: npt.NDArray[np.bool_] | None = None):
+    """See algorithms.unique for docs. Takes a mask for masked arrays."""
+    values = _ensure_arraylike(values, func_name="unique")
+
+    if isinstance(values.dtype, ExtensionDtype):
+        # Dispatch to extension dtype's unique.
+        return values.unique()
+
+    original = values
+    hashtable, values = _get_hashtable_algo(values)
+
+    table = hashtable(len(values))
+    if mask is None:
+        uniques = table.unique(values)
+        uniques = _reconstruct_data(uniques, original.dtype, original)
+        return uniques
+
+    else:
+        uniques, mask = table.unique(values, mask=mask)
+        uniques = _reconstruct_data(uniques, original.dtype, original)
+        assert mask is not None  # for mypy
+        return uniques, mask.astype("bool")
+
+
+unique1d = unique
+
+
+_MINIMUM_COMP_ARR_LEN = 1_000_000
+
+
+def isin(comps: ListLike, values: ListLike) -> npt.NDArray[np.bool_]:
+    """
+    Compute the isin boolean array.
+
+    Parameters
+    ----------
+    comps : list-like
+    values : list-like
+
+    Returns
+    -------
+    ndarray[bool]
+        Same length as `comps`.
+    """
+    if not is_list_like(comps):
+        raise TypeError(
+            "only list-like objects are allowed to be passed "
+            f"to isin(), you passed a `{type(comps).__name__}`"
+        )
+    if not is_list_like(values):
+        raise TypeError(
+            "only list-like objects are allowed to be passed "
+            f"to isin(), you passed a `{type(values).__name__}`"
+        )
+
+    if not isinstance(values, (ABCIndex, ABCSeries, ABCExtensionArray, np.ndarray)):
+        orig_values = list(values)
+        values = _ensure_arraylike(orig_values, func_name="isin-targets")
+
+        if (
+            len(values) > 0
+            and values.dtype.kind in "iufcb"
+            and not is_signed_integer_dtype(comps)
+        ):
+            # GH#46485 Use object to avoid upcast to float64 later
+            # TODO: Share with _find_common_type_compat
+            values = construct_1d_object_array_from_listlike(orig_values)
+
+    elif isinstance(values, ABCMultiIndex):
+        # Avoid raising in extract_array
+        values = np.array(values)
+    else:
+        values = extract_array(values, extract_numpy=True, extract_range=True)
+
+    comps_array = _ensure_arraylike(comps, func_name="isin")
+    comps_array = extract_array(comps_array, extract_numpy=True)
+    if not isinstance(comps_array, np.ndarray):
+        # i.e. Extension Array
+        return comps_array.isin(values)
+
+    elif needs_i8_conversion(comps_array.dtype):
+        # Dispatch to DatetimeLikeArrayMixin.isin
+        return pd_array(comps_array).isin(values)
+    elif needs_i8_conversion(values.dtype) and not is_object_dtype(comps_array.dtype):
+        # e.g. comps_array are integers and values are datetime64s
+        return np.zeros(comps_array.shape, dtype=bool)
+        # TODO: not quite right ... Sparse/Categorical
+    elif needs_i8_conversion(values.dtype):
+        return isin(comps_array, values.astype(object))
+
+    elif isinstance(values.dtype, ExtensionDtype):
+        return isin(np.asarray(comps_array), np.asarray(values))
+
+    # GH16012
+    # Ensure np.isin doesn't get object types or it *may* throw an exception
+    # Albeit hashmap has O(1) look-up (vs. O(logn) in sorted array),
+    # isin is faster for small sizes
+    if (
+        len(comps_array) > _MINIMUM_COMP_ARR_LEN
+        and len(values) <= 26
+        and comps_array.dtype != object
+    ):
+        # If the values include nan we need to check for nan explicitly
+        # since np.nan it not equal to np.nan
+        if isna(values).any():
+
+            def f(c, v):
+                return np.logical_or(np.isin(c, v).ravel(), np.isnan(c))
+
+        else:
+            f = lambda a, b: np.isin(a, b).ravel()
+
+    else:
+        common = np_find_common_type(values.dtype, comps_array.dtype)
+        values = values.astype(common, copy=False)
+        comps_array = comps_array.astype(common, copy=False)
+        f = htable.ismember
+
+    return f(comps_array, values)
+
+
+def factorize_array(
+    values: np.ndarray,
+    use_na_sentinel: bool = True,
+    size_hint: int | None = None,
+    na_value: object = None,
+    mask: npt.NDArray[np.bool_] | None = None,
+) -> tuple[npt.NDArray[np.intp], np.ndarray]:
+    """
+    Factorize a numpy array to codes and uniques.
+
+    This doesn't do any coercion of types or unboxing before factorization.
+
+    Parameters
+    ----------
+    values : ndarray
+    use_na_sentinel : bool, default True
+        If True, the sentinel -1 will be used for NaN values. If False,
+        NaN values will be encoded as non-negative integers and will not drop the
+        NaN from the uniques of the values.
+    size_hint : int, optional
+        Passed through to the hashtable's 'get_labels' method
+    na_value : object, optional
+        A value in `values` to consider missing. Note: only use this
+        parameter when you know that you don't have any values pandas would
+        consider missing in the array (NaN for float data, iNaT for
+        datetimes, etc.).
+    mask : ndarray[bool], optional
+        If not None, the mask is used as indicator for missing values
+        (True = missing, False = valid) instead of `na_value` or
+        condition "val != val".
+
+    Returns
+    -------
+    codes : ndarray[np.intp]
+    uniques : ndarray
+    """
+    original = values
+    if values.dtype.kind in "mM":
+        # _get_hashtable_algo will cast dt64/td64 to i8 via _ensure_data, so we
+        #  need to do the same to na_value. We are assuming here that the passed
+        #  na_value is an appropriately-typed NaT.
+        # e.g. test_where_datetimelike_categorical
+        na_value = iNaT
+
+    hash_klass, values = _get_hashtable_algo(values)
+
+    table = hash_klass(size_hint or len(values))
+    uniques, codes = table.factorize(
+        values,
+        na_sentinel=-1,
+        na_value=na_value,
+        mask=mask,
+        ignore_na=use_na_sentinel,
+    )
+
+    # re-cast e.g. i8->dt64/td64, uint8->bool
+    uniques = _reconstruct_data(uniques, original.dtype, original)
+
+    codes = ensure_platform_int(codes)
+    return codes, uniques
+
+
+@doc(
+    values=dedent(
+        """\
+    values : sequence
+        A 1-D sequence. Sequences that aren't pandas objects are
+        coerced to ndarrays before factorization.
+    """
+    ),
+    sort=dedent(
+        """\
+    sort : bool, default False
+        Sort `uniques` and shuffle `codes` to maintain the
+        relationship.
+    """
+    ),
+    size_hint=dedent(
+        """\
+    size_hint : int, optional
+        Hint to the hashtable sizer.
+    """
+    ),
+)
+def factorize(
+    values,
+    sort: bool = False,
+    use_na_sentinel: bool = True,
+    size_hint: int | None = None,
+) -> tuple[np.ndarray, np.ndarray | Index]:
+    """
+    Encode the object as an enumerated type or categorical variable.
+
+    This method is useful for obtaining a numeric representation of an
+    array when all that matters is identifying distinct values. `factorize`
+    is available as both a top-level function :func:`pandas.factorize`,
+    and as a method :meth:`Series.factorize` and :meth:`Index.factorize`.
+
+    Parameters
+    ----------
+    {values}{sort}
+    use_na_sentinel : bool, default True
+        If True, the sentinel -1 will be used for NaN values. If False,
+        NaN values will be encoded as non-negative integers and will not drop the
+        NaN from the uniques of the values.
+
+        .. versionadded:: 1.5.0
+    {size_hint}\
+
+    Returns
+    -------
+    codes : ndarray
+        An integer ndarray that's an indexer into `uniques`.
+        ``uniques.take(codes)`` will have the same values as `values`.
+    uniques : ndarray, Index, or Categorical
+        The unique valid values. When `values` is Categorical, `uniques`
+        is a Categorical. When `values` is some other pandas object, an
+        `Index` is returned. Otherwise, a 1-D ndarray is returned.
+
+        .. note::
+
+           Even if there's a missing value in `values`, `uniques` will
+           *not* contain an entry for it.
+
+    See Also
+    --------
+    cut : Discretize continuous-valued array.
+    unique : Find the unique value in an array.
+
+    Notes
+    -----
+    Reference :ref:`the user guide ` for more examples.
+
+    Examples
+    --------
+    These examples all show factorize as a top-level method like
+    ``pd.factorize(values)``. The results are identical for methods like
+    :meth:`Series.factorize`.
+
+    >>> codes, uniques = pd.factorize(np.array(['b', 'b', 'a', 'c', 'b'], dtype="O"))
+    >>> codes
+    array([0, 0, 1, 2, 0])
+    >>> uniques
+    array(['b', 'a', 'c'], dtype=object)
+
+    With ``sort=True``, the `uniques` will be sorted, and `codes` will be
+    shuffled so that the relationship is the maintained.
+
+    >>> codes, uniques = pd.factorize(np.array(['b', 'b', 'a', 'c', 'b'], dtype="O"),
+    ...                               sort=True)
+    >>> codes
+    array([1, 1, 0, 2, 1])
+    >>> uniques
+    array(['a', 'b', 'c'], dtype=object)
+
+    When ``use_na_sentinel=True`` (the default), missing values are indicated in
+    the `codes` with the sentinel value ``-1`` and missing values are not
+    included in `uniques`.
+
+    >>> codes, uniques = pd.factorize(np.array(['b', None, 'a', 'c', 'b'], dtype="O"))
+    >>> codes
+    array([ 0, -1,  1,  2,  0])
+    >>> uniques
+    array(['b', 'a', 'c'], dtype=object)
+
+    Thus far, we've only factorized lists (which are internally coerced to
+    NumPy arrays). When factorizing pandas objects, the type of `uniques`
+    will differ. For Categoricals, a `Categorical` is returned.
+
+    >>> cat = pd.Categorical(['a', 'a', 'c'], categories=['a', 'b', 'c'])
+    >>> codes, uniques = pd.factorize(cat)
+    >>> codes
+    array([0, 0, 1])
+    >>> uniques
+    ['a', 'c']
+    Categories (3, object): ['a', 'b', 'c']
+
+    Notice that ``'b'`` is in ``uniques.categories``, despite not being
+    present in ``cat.values``.
+
+    For all other pandas objects, an Index of the appropriate type is
+    returned.
+
+    >>> cat = pd.Series(['a', 'a', 'c'])
+    >>> codes, uniques = pd.factorize(cat)
+    >>> codes
+    array([0, 0, 1])
+    >>> uniques
+    Index(['a', 'c'], dtype='object')
+
+    If NaN is in the values, and we want to include NaN in the uniques of the
+    values, it can be achieved by setting ``use_na_sentinel=False``.
+
+    >>> values = np.array([1, 2, 1, np.nan])
+    >>> codes, uniques = pd.factorize(values)  # default: use_na_sentinel=True
+    >>> codes
+    array([ 0,  1,  0, -1])
+    >>> uniques
+    array([1., 2.])
+
+    >>> codes, uniques = pd.factorize(values, use_na_sentinel=False)
+    >>> codes
+    array([0, 1, 0, 2])
+    >>> uniques
+    array([ 1.,  2., nan])
+    """
+    # Implementation notes: This method is responsible for 3 things
+    # 1.) coercing data to array-like (ndarray, Index, extension array)
+    # 2.) factorizing codes and uniques
+    # 3.) Maybe boxing the uniques in an Index
+    #
+    # Step 2 is dispatched to extension types (like Categorical). They are
+    # responsible only for factorization. All data coercion, sorting and boxing
+    # should happen here.
+    if isinstance(values, (ABCIndex, ABCSeries)):
+        return values.factorize(sort=sort, use_na_sentinel=use_na_sentinel)
+
+    values = _ensure_arraylike(values, func_name="factorize")
+    original = values
+
+    if (
+        isinstance(values, (ABCDatetimeArray, ABCTimedeltaArray))
+        and values.freq is not None
+    ):
+        # The presence of 'freq' means we can fast-path sorting and know there
+        #  aren't NAs
+        codes, uniques = values.factorize(sort=sort)
+        return codes, uniques
+
+    elif not isinstance(values, np.ndarray):
+        # i.e. ExtensionArray
+        codes, uniques = values.factorize(use_na_sentinel=use_na_sentinel)
+
+    else:
+        values = np.asarray(values)  # convert DTA/TDA/MultiIndex
+
+        if not use_na_sentinel and values.dtype == object:
+            # factorize can now handle differentiating various types of null values.
+            # These can only occur when the array has object dtype.
+            # However, for backwards compatibility we only use the null for the
+            # provided dtype. This may be revisited in the future, see GH#48476.
+            null_mask = isna(values)
+            if null_mask.any():
+                na_value = na_value_for_dtype(values.dtype, compat=False)
+                # Don't modify (potentially user-provided) array
+                values = np.where(null_mask, na_value, values)
+
+        codes, uniques = factorize_array(
+            values,
+            use_na_sentinel=use_na_sentinel,
+            size_hint=size_hint,
+        )
+
+    if sort and len(uniques) > 0:
+        uniques, codes = safe_sort(
+            uniques,
+            codes,
+            use_na_sentinel=use_na_sentinel,
+            assume_unique=True,
+            verify=False,
+        )
+
+    uniques = _reconstruct_data(uniques, original.dtype, original)
+
+    return codes, uniques
+
+
+def value_counts(
+    values,
+    sort: bool = True,
+    ascending: bool = False,
+    normalize: bool = False,
+    bins=None,
+    dropna: bool = True,
+) -> Series:
+    """
+    Compute a histogram of the counts of non-null values.
+
+    Parameters
+    ----------
+    values : ndarray (1-d)
+    sort : bool, default True
+        Sort by values
+    ascending : bool, default False
+        Sort in ascending order
+    normalize: bool, default False
+        If True then compute a relative histogram
+    bins : integer, optional
+        Rather than count values, group them into half-open bins,
+        convenience for pd.cut, only works with numeric data
+    dropna : bool, default True
+        Don't include counts of NaN
+
+    Returns
+    -------
+    Series
+    """
+    warnings.warn(
+        # GH#53493
+        "pandas.value_counts is deprecated and will be removed in a "
+        "future version. Use pd.Series(obj).value_counts() instead.",
+        FutureWarning,
+        stacklevel=find_stack_level(),
+    )
+    return value_counts_internal(
+        values,
+        sort=sort,
+        ascending=ascending,
+        normalize=normalize,
+        bins=bins,
+        dropna=dropna,
+    )
+
+
+def value_counts_internal(
+    values,
+    sort: bool = True,
+    ascending: bool = False,
+    normalize: bool = False,
+    bins=None,
+    dropna: bool = True,
+) -> Series:
+    from pandas import (
+        Index,
+        Series,
+    )
+
+    index_name = getattr(values, "name", None)
+    name = "proportion" if normalize else "count"
+
+    if bins is not None:
+        from pandas.core.reshape.tile import cut
+
+        if isinstance(values, Series):
+            values = values._values
+
+        try:
+            ii = cut(values, bins, include_lowest=True)
+        except TypeError as err:
+            raise TypeError("bins argument only works with numeric data.") from err
+
+        # count, remove nulls (from the index), and but the bins
+        result = ii.value_counts(dropna=dropna)
+        result.name = name
+        result = result[result.index.notna()]
+        result.index = result.index.astype("interval")
+        result = result.sort_index()
+
+        # if we are dropna and we have NO values
+        if dropna and (result._values == 0).all():
+            result = result.iloc[0:0]
+
+        # normalizing is by len of all (regardless of dropna)
+        counts = np.array([len(ii)])
+
+    else:
+        if is_extension_array_dtype(values):
+            # handle Categorical and sparse,
+            result = Series(values, copy=False)._values.value_counts(dropna=dropna)
+            result.name = name
+            result.index.name = index_name
+            counts = result._values
+            if not isinstance(counts, np.ndarray):
+                # e.g. ArrowExtensionArray
+                counts = np.asarray(counts)
+
+        elif isinstance(values, ABCMultiIndex):
+            # GH49558
+            levels = list(range(values.nlevels))
+            result = (
+                Series(index=values, name=name)
+                .groupby(level=levels, dropna=dropna)
+                .size()
+            )
+            result.index.names = values.names
+            counts = result._values
+
+        else:
+            values = _ensure_arraylike(values, func_name="value_counts")
+            keys, counts, _ = value_counts_arraylike(values, dropna)
+            if keys.dtype == np.float16:
+                keys = keys.astype(np.float32)
+
+            # For backwards compatibility, we let Index do its normal type
+            #  inference, _except_ for if if infers from object to bool.
+            idx = Index(keys)
+            if idx.dtype in [bool, "string"] and keys.dtype == object:
+                idx = idx.astype(object)
+            elif (
+                idx.dtype != keys.dtype  # noqa: PLR1714  # # pylint: disable=R1714
+                and idx.dtype != "string"
+            ):
+                warnings.warn(
+                    # GH#56161
+                    "The behavior of value_counts with object-dtype is deprecated. "
+                    "In a future version, this will *not* perform dtype inference "
+                    "on the resulting index. To retain the old behavior, use "
+                    "`result.index = result.index.infer_objects()`",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+            idx.name = index_name
+
+            result = Series(counts, index=idx, name=name, copy=False)
+
+    if sort:
+        result = result.sort_values(ascending=ascending)
+
+    if normalize:
+        result = result / counts.sum()
+
+    return result
+
+
+# Called once from SparseArray, otherwise could be private
+def value_counts_arraylike(
+    values: np.ndarray, dropna: bool, mask: npt.NDArray[np.bool_] | None = None
+) -> tuple[ArrayLike, npt.NDArray[np.int64], int]:
+    """
+    Parameters
+    ----------
+    values : np.ndarray
+    dropna : bool
+    mask : np.ndarray[bool] or None, default None
+
+    Returns
+    -------
+    uniques : np.ndarray
+    counts : np.ndarray[np.int64]
+    """
+    original = values
+    values = _ensure_data(values)
+
+    keys, counts, na_counter = htable.value_count(values, dropna, mask=mask)
+
+    if needs_i8_conversion(original.dtype):
+        # datetime, timedelta, or period
+
+        if dropna:
+            mask = keys != iNaT
+            keys, counts = keys[mask], counts[mask]
+
+    res_keys = _reconstruct_data(keys, original.dtype, original)
+    return res_keys, counts, na_counter
+
+
+def duplicated(
+    values: ArrayLike,
+    keep: Literal["first", "last", False] = "first",
+    mask: npt.NDArray[np.bool_] | None = None,
+) -> npt.NDArray[np.bool_]:
+    """
+    Return boolean ndarray denoting duplicate values.
+
+    Parameters
+    ----------
+    values : np.ndarray or ExtensionArray
+        Array over which to check for duplicate values.
+    keep : {'first', 'last', False}, default 'first'
+        - ``first`` : Mark duplicates as ``True`` except for the first
+          occurrence.
+        - ``last`` : Mark duplicates as ``True`` except for the last
+          occurrence.
+        - False : Mark all duplicates as ``True``.
+    mask : ndarray[bool], optional
+        array indicating which elements to exclude from checking
+
+    Returns
+    -------
+    duplicated : ndarray[bool]
+    """
+    values = _ensure_data(values)
+    return htable.duplicated(values, keep=keep, mask=mask)
+
+
+def mode(
+    values: ArrayLike, dropna: bool = True, mask: npt.NDArray[np.bool_] | None = None
+) -> ArrayLike:
+    """
+    Returns the mode(s) of an array.
+
+    Parameters
+    ----------
+    values : array-like
+        Array over which to check for duplicate values.
+    dropna : bool, default True
+        Don't consider counts of NaN/NaT.
+
+    Returns
+    -------
+    np.ndarray or ExtensionArray
+    """
+    values = _ensure_arraylike(values, func_name="mode")
+    original = values
+
+    if needs_i8_conversion(values.dtype):
+        # Got here with ndarray; dispatch to DatetimeArray/TimedeltaArray.
+        values = ensure_wrapped_if_datetimelike(values)
+        values = cast("ExtensionArray", values)
+        return values._mode(dropna=dropna)
+
+    values = _ensure_data(values)
+
+    npresult, res_mask = htable.mode(values, dropna=dropna, mask=mask)
+    if res_mask is not None:
+        return npresult, res_mask  # type: ignore[return-value]
+
+    try:
+        npresult = safe_sort(npresult)
+    except TypeError as err:
+        warnings.warn(
+            f"Unable to sort modes: {err}",
+            stacklevel=find_stack_level(),
+        )
+
+    result = _reconstruct_data(npresult, original.dtype, original)
+    return result
+
+
+def rank(
+    values: ArrayLike,
+    axis: AxisInt = 0,
+    method: str = "average",
+    na_option: str = "keep",
+    ascending: bool = True,
+    pct: bool = False,
+) -> npt.NDArray[np.float64]:
+    """
+    Rank the values along a given axis.
+
+    Parameters
+    ----------
+    values : np.ndarray or ExtensionArray
+        Array whose values will be ranked. The number of dimensions in this
+        array must not exceed 2.
+    axis : int, default 0
+        Axis over which to perform rankings.
+    method : {'average', 'min', 'max', 'first', 'dense'}, default 'average'
+        The method by which tiebreaks are broken during the ranking.
+    na_option : {'keep', 'top'}, default 'keep'
+        The method by which NaNs are placed in the ranking.
+        - ``keep``: rank each NaN value with a NaN ranking
+        - ``top``: replace each NaN with either +/- inf so that they
+                   there are ranked at the top
+    ascending : bool, default True
+        Whether or not the elements should be ranked in ascending order.
+    pct : bool, default False
+        Whether or not to the display the returned rankings in integer form
+        (e.g. 1, 2, 3) or in percentile form (e.g. 0.333..., 0.666..., 1).
+    """
+    is_datetimelike = needs_i8_conversion(values.dtype)
+    values = _ensure_data(values)
+
+    if values.ndim == 1:
+        ranks = algos.rank_1d(
+            values,
+            is_datetimelike=is_datetimelike,
+            ties_method=method,
+            ascending=ascending,
+            na_option=na_option,
+            pct=pct,
+        )
+    elif values.ndim == 2:
+        ranks = algos.rank_2d(
+            values,
+            axis=axis,
+            is_datetimelike=is_datetimelike,
+            ties_method=method,
+            ascending=ascending,
+            na_option=na_option,
+            pct=pct,
+        )
+    else:
+        raise TypeError("Array with ndim > 2 are not supported.")
+
+    return ranks
+
+
+# ---- #
+# take #
+# ---- #
+
+
+def take(
+    arr,
+    indices: TakeIndexer,
+    axis: AxisInt = 0,
+    allow_fill: bool = False,
+    fill_value=None,
+):
+    """
+    Take elements from an array.
+
+    Parameters
+    ----------
+    arr : array-like or scalar value
+        Non array-likes (sequences/scalars without a dtype) are coerced
+        to an ndarray.
+
+        .. deprecated:: 2.1.0
+            Passing an argument other than a numpy.ndarray, ExtensionArray,
+            Index, or Series is deprecated.
+
+    indices : sequence of int or one-dimensional np.ndarray of int
+        Indices to be taken.
+    axis : int, default 0
+        The axis over which to select values.
+    allow_fill : bool, default False
+        How to handle negative values in `indices`.
+
+        * False: negative values in `indices` indicate positional indices
+          from the right (the default). This is similar to :func:`numpy.take`.
+
+        * True: negative values in `indices` indicate
+          missing values. These values are set to `fill_value`. Any other
+          negative values raise a ``ValueError``.
+
+    fill_value : any, optional
+        Fill value to use for NA-indices when `allow_fill` is True.
+        This may be ``None``, in which case the default NA value for
+        the type (``self.dtype.na_value``) is used.
+
+        For multi-dimensional `arr`, each *element* is filled with
+        `fill_value`.
+
+    Returns
+    -------
+    ndarray or ExtensionArray
+        Same type as the input.
+
+    Raises
+    ------
+    IndexError
+        When `indices` is out of bounds for the array.
+    ValueError
+        When the indexer contains negative values other than ``-1``
+        and `allow_fill` is True.
+
+    Notes
+    -----
+    When `allow_fill` is False, `indices` may be whatever dimensionality
+    is accepted by NumPy for `arr`.
+
+    When `allow_fill` is True, `indices` should be 1-D.
+
+    See Also
+    --------
+    numpy.take : Take elements from an array along an axis.
+
+    Examples
+    --------
+    >>> import pandas as pd
+
+    With the default ``allow_fill=False``, negative numbers indicate
+    positional indices from the right.
+
+    >>> pd.api.extensions.take(np.array([10, 20, 30]), [0, 0, -1])
+    array([10, 10, 30])
+
+    Setting ``allow_fill=True`` will place `fill_value` in those positions.
+
+    >>> pd.api.extensions.take(np.array([10, 20, 30]), [0, 0, -1], allow_fill=True)
+    array([10., 10., nan])
+
+    >>> pd.api.extensions.take(np.array([10, 20, 30]), [0, 0, -1], allow_fill=True,
+    ...      fill_value=-10)
+    array([ 10,  10, -10])
+    """
+    if not isinstance(arr, (np.ndarray, ABCExtensionArray, ABCIndex, ABCSeries)):
+        # GH#52981
+        warnings.warn(
+            "pd.api.extensions.take accepting non-standard inputs is deprecated "
+            "and will raise in a future version. Pass either a numpy.ndarray, "
+            "ExtensionArray, Index, or Series instead.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+
+    if not is_array_like(arr):
+        arr = np.asarray(arr)
+
+    indices = ensure_platform_int(indices)
+
+    if allow_fill:
+        # Pandas style, -1 means NA
+        validate_indices(indices, arr.shape[axis])
+        result = take_nd(
+            arr, indices, axis=axis, allow_fill=True, fill_value=fill_value
+        )
+    else:
+        # NumPy style
+        result = arr.take(indices, axis=axis)
+    return result
+
+
+# ------------ #
+# searchsorted #
+# ------------ #
+
+
+def searchsorted(
+    arr: ArrayLike,
+    value: NumpyValueArrayLike | ExtensionArray,
+    side: Literal["left", "right"] = "left",
+    sorter: NumpySorter | None = None,
+) -> npt.NDArray[np.intp] | np.intp:
+    """
+    Find indices where elements should be inserted to maintain order.
+
+    Find the indices into a sorted array `arr` (a) such that, if the
+    corresponding elements in `value` were inserted before the indices,
+    the order of `arr` would be preserved.
+
+    Assuming that `arr` is sorted:
+
+    ======  ================================
+    `side`  returned index `i` satisfies
+    ======  ================================
+    left    ``arr[i-1] < value <= self[i]``
+    right   ``arr[i-1] <= value < self[i]``
+    ======  ================================
+
+    Parameters
+    ----------
+    arr: np.ndarray, ExtensionArray, Series
+        Input array. If `sorter` is None, then it must be sorted in
+        ascending order, otherwise `sorter` must be an array of indices
+        that sort it.
+    value : array-like or scalar
+        Values to insert into `arr`.
+    side : {'left', 'right'}, optional
+        If 'left', the index of the first suitable location found is given.
+        If 'right', return the last such index.  If there is no suitable
+        index, return either 0 or N (where N is the length of `self`).
+    sorter : 1-D array-like, optional
+        Optional array of integer indices that sort array a into ascending
+        order. They are typically the result of argsort.
+
+    Returns
+    -------
+    array of ints or int
+        If value is array-like, array of insertion points.
+        If value is scalar, a single integer.
+
+    See Also
+    --------
+    numpy.searchsorted : Similar method from NumPy.
+    """
+    if sorter is not None:
+        sorter = ensure_platform_int(sorter)
+
+    if (
+        isinstance(arr, np.ndarray)
+        and arr.dtype.kind in "iu"
+        and (is_integer(value) or is_integer_dtype(value))
+    ):
+        # if `arr` and `value` have different dtypes, `arr` would be
+        # recast by numpy, causing a slow search.
+        # Before searching below, we therefore try to give `value` the
+        # same dtype as `arr`, while guarding against integer overflows.
+        iinfo = np.iinfo(arr.dtype.type)
+        value_arr = np.array([value]) if is_integer(value) else np.array(value)
+        if (value_arr >= iinfo.min).all() and (value_arr <= iinfo.max).all():
+            # value within bounds, so no overflow, so can convert value dtype
+            # to dtype of arr
+            dtype = arr.dtype
+        else:
+            dtype = value_arr.dtype
+
+        if is_integer(value):
+            # We know that value is int
+            value = cast(int, dtype.type(value))
+        else:
+            value = pd_array(cast(ArrayLike, value), dtype=dtype)
+    else:
+        # E.g. if `arr` is an array with dtype='datetime64[ns]'
+        # and `value` is a pd.Timestamp, we may need to convert value
+        arr = ensure_wrapped_if_datetimelike(arr)
+
+    # Argument 1 to "searchsorted" of "ndarray" has incompatible type
+    # "Union[NumpyValueArrayLike, ExtensionArray]"; expected "NumpyValueArrayLike"
+    return arr.searchsorted(value, side=side, sorter=sorter)  # type: ignore[arg-type]
+
+
+# ---- #
+# diff #
+# ---- #
+
+_diff_special = {"float64", "float32", "int64", "int32", "int16", "int8"}
+
+
+def diff(arr, n: int, axis: AxisInt = 0):
+    """
+    difference of n between self,
+    analogous to s-s.shift(n)
+
+    Parameters
+    ----------
+    arr : ndarray or ExtensionArray
+    n : int
+        number of periods
+    axis : {0, 1}
+        axis to shift on
+    stacklevel : int, default 3
+        The stacklevel for the lost dtype warning.
+
+    Returns
+    -------
+    shifted
+    """
+
+    n = int(n)
+    na = np.nan
+    dtype = arr.dtype
+
+    is_bool = is_bool_dtype(dtype)
+    if is_bool:
+        op = operator.xor
+    else:
+        op = operator.sub
+
+    if isinstance(dtype, NumpyEADtype):
+        # NumpyExtensionArray cannot necessarily hold shifted versions of itself.
+        arr = arr.to_numpy()
+        dtype = arr.dtype
+
+    if not isinstance(arr, np.ndarray):
+        # i.e ExtensionArray
+        if hasattr(arr, f"__{op.__name__}__"):
+            if axis != 0:
+                raise ValueError(f"cannot diff {type(arr).__name__} on axis={axis}")
+            return op(arr, arr.shift(n))
+        else:
+            raise TypeError(
+                f"{type(arr).__name__} has no 'diff' method. "
+                "Convert to a suitable dtype prior to calling 'diff'."
+            )
+
+    is_timedelta = False
+    if arr.dtype.kind in "mM":
+        dtype = np.int64
+        arr = arr.view("i8")
+        na = iNaT
+        is_timedelta = True
+
+    elif is_bool:
+        # We have to cast in order to be able to hold np.nan
+        dtype = np.object_
+
+    elif dtype.kind in "iu":
+        # We have to cast in order to be able to hold np.nan
+
+        # int8, int16 are incompatible with float64,
+        # see https://github.com/cython/cython/issues/2646
+        if arr.dtype.name in ["int8", "int16"]:
+            dtype = np.float32
+        else:
+            dtype = np.float64
+
+    orig_ndim = arr.ndim
+    if orig_ndim == 1:
+        # reshape so we can always use algos.diff_2d
+        arr = arr.reshape(-1, 1)
+        # TODO: require axis == 0
+
+    dtype = np.dtype(dtype)
+    out_arr = np.empty(arr.shape, dtype=dtype)
+
+    na_indexer = [slice(None)] * 2
+    na_indexer[axis] = slice(None, n) if n >= 0 else slice(n, None)
+    out_arr[tuple(na_indexer)] = na
+
+    if arr.dtype.name in _diff_special:
+        # TODO: can diff_2d dtype specialization troubles be fixed by defining
+        #  out_arr inside diff_2d?
+        algos.diff_2d(arr, out_arr, n, axis, datetimelike=is_timedelta)
+    else:
+        # To keep mypy happy, _res_indexer is a list while res_indexer is
+        #  a tuple, ditto for lag_indexer.
+        _res_indexer = [slice(None)] * 2
+        _res_indexer[axis] = slice(n, None) if n >= 0 else slice(None, n)
+        res_indexer = tuple(_res_indexer)
+
+        _lag_indexer = [slice(None)] * 2
+        _lag_indexer[axis] = slice(None, -n) if n > 0 else slice(-n, None)
+        lag_indexer = tuple(_lag_indexer)
+
+        out_arr[res_indexer] = op(arr[res_indexer], arr[lag_indexer])
+
+    if is_timedelta:
+        out_arr = out_arr.view("timedelta64[ns]")
+
+    if orig_ndim == 1:
+        out_arr = out_arr[:, 0]
+    return out_arr
+
+
+# --------------------------------------------------------------------
+# Helper functions
+
+
+# Note: safe_sort is in algorithms.py instead of sorting.py because it is
+#  low-dependency, is used in this module, and used private methods from
+#  this module.
+def safe_sort(
+    values: Index | ArrayLike,
+    codes: npt.NDArray[np.intp] | None = None,
+    use_na_sentinel: bool = True,
+    assume_unique: bool = False,
+    verify: bool = True,
+) -> AnyArrayLike | tuple[AnyArrayLike, np.ndarray]:
+    """
+    Sort ``values`` and reorder corresponding ``codes``.
+
+    ``values`` should be unique if ``codes`` is not None.
+    Safe for use with mixed types (int, str), orders ints before strs.
+
+    Parameters
+    ----------
+    values : list-like
+        Sequence; must be unique if ``codes`` is not None.
+    codes : np.ndarray[intp] or None, default None
+        Indices to ``values``. All out of bound indices are treated as
+        "not found" and will be masked with ``-1``.
+    use_na_sentinel : bool, default True
+        If True, the sentinel -1 will be used for NaN values. If False,
+        NaN values will be encoded as non-negative integers and will not drop the
+        NaN from the uniques of the values.
+    assume_unique : bool, default False
+        When True, ``values`` are assumed to be unique, which can speed up
+        the calculation. Ignored when ``codes`` is None.
+    verify : bool, default True
+        Check if codes are out of bound for the values and put out of bound
+        codes equal to ``-1``. If ``verify=False``, it is assumed there
+        are no out of bound codes. Ignored when ``codes`` is None.
+
+    Returns
+    -------
+    ordered : AnyArrayLike
+        Sorted ``values``
+    new_codes : ndarray
+        Reordered ``codes``; returned when ``codes`` is not None.
+
+    Raises
+    ------
+    TypeError
+        * If ``values`` is not list-like or if ``codes`` is neither None
+        nor list-like
+        * If ``values`` cannot be sorted
+    ValueError
+        * If ``codes`` is not None and ``values`` contain duplicates.
+    """
+    if not isinstance(values, (np.ndarray, ABCExtensionArray, ABCIndex)):
+        raise TypeError(
+            "Only np.ndarray, ExtensionArray, and Index objects are allowed to "
+            "be passed to safe_sort as values"
+        )
+
+    sorter = None
+    ordered: AnyArrayLike
+
+    if (
+        not isinstance(values.dtype, ExtensionDtype)
+        and lib.infer_dtype(values, skipna=False) == "mixed-integer"
+    ):
+        ordered = _sort_mixed(values)
+    else:
+        try:
+            sorter = values.argsort()
+            ordered = values.take(sorter)
+        except (TypeError, decimal.InvalidOperation):
+            # Previous sorters failed or were not applicable, try `_sort_mixed`
+            # which would work, but which fails for special case of 1d arrays
+            # with tuples.
+            if values.size and isinstance(values[0], tuple):
+                # error: Argument 1 to "_sort_tuples" has incompatible type
+                # "Union[Index, ExtensionArray, ndarray[Any, Any]]"; expected
+                # "ndarray[Any, Any]"
+                ordered = _sort_tuples(values)  # type: ignore[arg-type]
+            else:
+                ordered = _sort_mixed(values)
+
+    # codes:
+
+    if codes is None:
+        return ordered
+
+    if not is_list_like(codes):
+        raise TypeError(
+            "Only list-like objects or None are allowed to "
+            "be passed to safe_sort as codes"
+        )
+    codes = ensure_platform_int(np.asarray(codes))
+
+    if not assume_unique and not len(unique(values)) == len(values):
+        raise ValueError("values should be unique if codes is not None")
+
+    if sorter is None:
+        # mixed types
+        # error: Argument 1 to "_get_hashtable_algo" has incompatible type
+        # "Union[Index, ExtensionArray, ndarray[Any, Any]]"; expected
+        # "ndarray[Any, Any]"
+        hash_klass, values = _get_hashtable_algo(values)  # type: ignore[arg-type]
+        t = hash_klass(len(values))
+        t.map_locations(values)
+        sorter = ensure_platform_int(t.lookup(ordered))
+
+    if use_na_sentinel:
+        # take_nd is faster, but only works for na_sentinels of -1
+        order2 = sorter.argsort()
+        if verify:
+            mask = (codes < -len(values)) | (codes >= len(values))
+            codes[mask] = 0
+        else:
+            mask = None
+        new_codes = take_nd(order2, codes, fill_value=-1)
+    else:
+        reverse_indexer = np.empty(len(sorter), dtype=int)
+        reverse_indexer.put(sorter, np.arange(len(sorter)))
+        # Out of bound indices will be masked with `-1` next, so we
+        # may deal with them here without performance loss using `mode='wrap'`
+        new_codes = reverse_indexer.take(codes, mode="wrap")
+
+        if use_na_sentinel:
+            mask = codes == -1
+            if verify:
+                mask = mask | (codes < -len(values)) | (codes >= len(values))
+
+    if use_na_sentinel and mask is not None:
+        np.putmask(new_codes, mask, -1)
+
+    return ordered, ensure_platform_int(new_codes)
+
+
+def _sort_mixed(values) -> AnyArrayLike:
+    """order ints before strings before nulls in 1d arrays"""
+    str_pos = np.array([isinstance(x, str) for x in values], dtype=bool)
+    null_pos = np.array([isna(x) for x in values], dtype=bool)
+    num_pos = ~str_pos & ~null_pos
+    str_argsort = np.argsort(values[str_pos])
+    num_argsort = np.argsort(values[num_pos])
+    # convert boolean arrays to positional indices, then order by underlying values
+    str_locs = str_pos.nonzero()[0].take(str_argsort)
+    num_locs = num_pos.nonzero()[0].take(num_argsort)
+    null_locs = null_pos.nonzero()[0]
+    locs = np.concatenate([num_locs, str_locs, null_locs])
+    return values.take(locs)
+
+
+def _sort_tuples(values: np.ndarray) -> np.ndarray:
+    """
+    Convert array of tuples (1d) to array of arrays (2d).
+    We need to keep the columns separately as they contain different types and
+    nans (can't use `np.sort` as it may fail when str and nan are mixed in a
+    column as types cannot be compared).
+    """
+    from pandas.core.internals.construction import to_arrays
+    from pandas.core.sorting import lexsort_indexer
+
+    arrays, _ = to_arrays(values, None)
+    indexer = lexsort_indexer(arrays, orders=True)
+    return values[indexer]
+
+
+def union_with_duplicates(
+    lvals: ArrayLike | Index, rvals: ArrayLike | Index
+) -> ArrayLike | Index:
+    """
+    Extracts the union from lvals and rvals with respect to duplicates and nans in
+    both arrays.
+
+    Parameters
+    ----------
+    lvals: np.ndarray or ExtensionArray
+        left values which is ordered in front.
+    rvals: np.ndarray or ExtensionArray
+        right values ordered after lvals.
+
+    Returns
+    -------
+    np.ndarray or ExtensionArray
+        Containing the unsorted union of both arrays.
+
+    Notes
+    -----
+    Caller is responsible for ensuring lvals.dtype == rvals.dtype.
+    """
+    from pandas import Series
+
+    with warnings.catch_warnings():
+        # filter warning from object dtype inference; we will end up discarding
+        # the index here, so the deprecation does not affect the end result here.
+        warnings.filterwarnings(
+            "ignore",
+            "The behavior of value_counts with object-dtype is deprecated",
+            category=FutureWarning,
+        )
+        l_count = value_counts_internal(lvals, dropna=False)
+        r_count = value_counts_internal(rvals, dropna=False)
+    l_count, r_count = l_count.align(r_count, fill_value=0)
+    final_count = np.maximum(l_count.values, r_count.values)
+    final_count = Series(final_count, index=l_count.index, dtype="int", copy=False)
+    if isinstance(lvals, ABCMultiIndex) and isinstance(rvals, ABCMultiIndex):
+        unique_vals = lvals.append(rvals).unique()
+    else:
+        if isinstance(lvals, ABCIndex):
+            lvals = lvals._values
+        if isinstance(rvals, ABCIndex):
+            rvals = rvals._values
+        # error: List item 0 has incompatible type "Union[ExtensionArray,
+        # ndarray[Any, Any], Index]"; expected "Union[ExtensionArray,
+        # ndarray[Any, Any]]"
+        combined = concat_compat([lvals, rvals])  # type: ignore[list-item]
+        unique_vals = unique(combined)
+        unique_vals = ensure_wrapped_if_datetimelike(unique_vals)
+    repeats = final_count.reindex(unique_vals).values
+    return np.repeat(unique_vals, repeats)
+
+
+def map_array(
+    arr: ArrayLike,
+    mapper,
+    na_action: Literal["ignore"] | None = None,
+    convert: bool = True,
+) -> np.ndarray | ExtensionArray | Index:
+    """
+    Map values using an input mapping or function.
+
+    Parameters
+    ----------
+    mapper : function, dict, or Series
+        Mapping correspondence.
+    na_action : {None, 'ignore'}, default None
+        If 'ignore', propagate NA values, without passing them to the
+        mapping correspondence.
+    convert : bool, default True
+        Try to find better dtype for elementwise function results. If
+        False, leave as dtype=object.
+
+    Returns
+    -------
+    Union[ndarray, Index, ExtensionArray]
+        The output of the mapping function applied to the array.
+        If the function returns a tuple with more than one element
+        a MultiIndex will be returned.
+    """
+    if na_action not in (None, "ignore"):
+        msg = f"na_action must either be 'ignore' or None, {na_action} was passed"
+        raise ValueError(msg)
+
+    # we can fastpath dict/Series to an efficient map
+    # as we know that we are not going to have to yield
+    # python types
+    if is_dict_like(mapper):
+        if isinstance(mapper, dict) and hasattr(mapper, "__missing__"):
+            # If a dictionary subclass defines a default value method,
+            # convert mapper to a lookup function (GH #15999).
+            dict_with_default = mapper
+            mapper = lambda x: dict_with_default[
+                np.nan if isinstance(x, float) and np.isnan(x) else x
+            ]
+        else:
+            # Dictionary does not have a default. Thus it's safe to
+            # convert to an Series for efficiency.
+            # we specify the keys here to handle the
+            # possibility that they are tuples
+
+            # The return value of mapping with an empty mapper is
+            # expected to be pd.Series(np.nan, ...). As np.nan is
+            # of dtype float64 the return value of this method should
+            # be float64 as well
+            from pandas import Series
+
+            if len(mapper) == 0:
+                mapper = Series(mapper, dtype=np.float64)
+            else:
+                mapper = Series(mapper)
+
+    if isinstance(mapper, ABCSeries):
+        if na_action == "ignore":
+            mapper = mapper[mapper.index.notna()]
+
+        # Since values were input this means we came from either
+        # a dict or a series and mapper should be an index
+        indexer = mapper.index.get_indexer(arr)
+        new_values = take_nd(mapper._values, indexer)
+
+        return new_values
+
+    if not len(arr):
+        return arr.copy()
+
+    # we must convert to python types
+    values = arr.astype(object, copy=False)
+    if na_action is None:
+        return lib.map_infer(values, mapper, convert=convert)
+    else:
+        return lib.map_infer_mask(
+            values, mapper, mask=isna(values).view(np.uint8), convert=convert
+        )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/api.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/api.py
new file mode 100644
index 0000000000000000000000000000000000000000..2cfe5ffc0170d61bca248bca43dda7c2f082496f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/api.py
@@ -0,0 +1,140 @@
+from pandas._libs import (
+    NaT,
+    Period,
+    Timedelta,
+    Timestamp,
+)
+from pandas._libs.missing import NA
+
+from pandas.core.dtypes.dtypes import (
+    ArrowDtype,
+    CategoricalDtype,
+    DatetimeTZDtype,
+    IntervalDtype,
+    PeriodDtype,
+)
+from pandas.core.dtypes.missing import (
+    isna,
+    isnull,
+    notna,
+    notnull,
+)
+
+from pandas.core.algorithms import (
+    factorize,
+    unique,
+    value_counts,
+)
+from pandas.core.arrays import Categorical
+from pandas.core.arrays.boolean import BooleanDtype
+from pandas.core.arrays.floating import (
+    Float32Dtype,
+    Float64Dtype,
+)
+from pandas.core.arrays.integer import (
+    Int8Dtype,
+    Int16Dtype,
+    Int32Dtype,
+    Int64Dtype,
+    UInt8Dtype,
+    UInt16Dtype,
+    UInt32Dtype,
+    UInt64Dtype,
+)
+from pandas.core.arrays.string_ import StringDtype
+from pandas.core.construction import array
+from pandas.core.flags import Flags
+from pandas.core.groupby import (
+    Grouper,
+    NamedAgg,
+)
+from pandas.core.indexes.api import (
+    CategoricalIndex,
+    DatetimeIndex,
+    Index,
+    IntervalIndex,
+    MultiIndex,
+    PeriodIndex,
+    RangeIndex,
+    TimedeltaIndex,
+)
+from pandas.core.indexes.datetimes import (
+    bdate_range,
+    date_range,
+)
+from pandas.core.indexes.interval import (
+    Interval,
+    interval_range,
+)
+from pandas.core.indexes.period import period_range
+from pandas.core.indexes.timedeltas import timedelta_range
+from pandas.core.indexing import IndexSlice
+from pandas.core.series import Series
+from pandas.core.tools.datetimes import to_datetime
+from pandas.core.tools.numeric import to_numeric
+from pandas.core.tools.timedeltas import to_timedelta
+
+from pandas.io.formats.format import set_eng_float_format
+from pandas.tseries.offsets import DateOffset
+
+# DataFrame needs to be imported after NamedAgg to avoid a circular import
+from pandas.core.frame import DataFrame  # isort:skip
+
+__all__ = [
+    "array",
+    "ArrowDtype",
+    "bdate_range",
+    "BooleanDtype",
+    "Categorical",
+    "CategoricalDtype",
+    "CategoricalIndex",
+    "DataFrame",
+    "DateOffset",
+    "date_range",
+    "DatetimeIndex",
+    "DatetimeTZDtype",
+    "factorize",
+    "Flags",
+    "Float32Dtype",
+    "Float64Dtype",
+    "Grouper",
+    "Index",
+    "IndexSlice",
+    "Int16Dtype",
+    "Int32Dtype",
+    "Int64Dtype",
+    "Int8Dtype",
+    "Interval",
+    "IntervalDtype",
+    "IntervalIndex",
+    "interval_range",
+    "isna",
+    "isnull",
+    "MultiIndex",
+    "NA",
+    "NamedAgg",
+    "NaT",
+    "notna",
+    "notnull",
+    "Period",
+    "PeriodDtype",
+    "PeriodIndex",
+    "period_range",
+    "RangeIndex",
+    "Series",
+    "set_eng_float_format",
+    "StringDtype",
+    "Timedelta",
+    "TimedeltaIndex",
+    "timedelta_range",
+    "Timestamp",
+    "to_datetime",
+    "to_numeric",
+    "to_timedelta",
+    "UInt16Dtype",
+    "UInt32Dtype",
+    "UInt64Dtype",
+    "UInt8Dtype",
+    "unique",
+    "value_counts",
+]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/apply.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/apply.py
new file mode 100644
index 0000000000000000000000000000000000000000..fafc9ee1b69283f75c9aa4b72768d1fd92f5a70a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/apply.py
@@ -0,0 +1,2057 @@
+from __future__ import annotations
+
+import abc
+from collections import defaultdict
+import functools
+from functools import partial
+import inspect
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Literal,
+    cast,
+)
+import warnings
+
+import numpy as np
+
+from pandas._config import option_context
+
+from pandas._libs import lib
+from pandas._libs.internals import BlockValuesRefs
+from pandas._typing import (
+    AggFuncType,
+    AggFuncTypeBase,
+    AggFuncTypeDict,
+    AggObjType,
+    Axis,
+    AxisInt,
+    NDFrameT,
+    npt,
+)
+from pandas.compat._optional import import_optional_dependency
+from pandas.errors import SpecificationError
+from pandas.util._decorators import cache_readonly
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.cast import is_nested_object
+from pandas.core.dtypes.common import (
+    is_dict_like,
+    is_extension_array_dtype,
+    is_list_like,
+    is_numeric_dtype,
+    is_sequence,
+)
+from pandas.core.dtypes.dtypes import (
+    CategoricalDtype,
+    ExtensionDtype,
+)
+from pandas.core.dtypes.generic import (
+    ABCDataFrame,
+    ABCNDFrame,
+    ABCSeries,
+)
+
+from pandas.core._numba.executor import generate_apply_looper
+import pandas.core.common as com
+from pandas.core.construction import ensure_wrapped_if_datetimelike
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Generator,
+        Hashable,
+        Iterable,
+        MutableMapping,
+        Sequence,
+    )
+
+    from pandas import (
+        DataFrame,
+        Index,
+        Series,
+    )
+    from pandas.core.groupby import GroupBy
+    from pandas.core.resample import Resampler
+    from pandas.core.window.rolling import BaseWindow
+
+
+ResType = dict[int, Any]
+
+
+def frame_apply(
+    obj: DataFrame,
+    func: AggFuncType,
+    axis: Axis = 0,
+    raw: bool = False,
+    result_type: str | None = None,
+    by_row: Literal[False, "compat"] = "compat",
+    engine: str = "python",
+    engine_kwargs: dict[str, bool] | None = None,
+    args=None,
+    kwargs=None,
+) -> FrameApply:
+    """construct and return a row or column based frame apply object"""
+    axis = obj._get_axis_number(axis)
+    klass: type[FrameApply]
+    if axis == 0:
+        klass = FrameRowApply
+    elif axis == 1:
+        klass = FrameColumnApply
+
+    _, func, _, _ = reconstruct_func(func, **kwargs)
+    assert func is not None
+
+    return klass(
+        obj,
+        func,
+        raw=raw,
+        result_type=result_type,
+        by_row=by_row,
+        engine=engine,
+        engine_kwargs=engine_kwargs,
+        args=args,
+        kwargs=kwargs,
+    )
+
+
+class Apply(metaclass=abc.ABCMeta):
+    axis: AxisInt
+
+    def __init__(
+        self,
+        obj: AggObjType,
+        func: AggFuncType,
+        raw: bool,
+        result_type: str | None,
+        *,
+        by_row: Literal[False, "compat", "_compat"] = "compat",
+        engine: str = "python",
+        engine_kwargs: dict[str, bool] | None = None,
+        args,
+        kwargs,
+    ) -> None:
+        self.obj = obj
+        self.raw = raw
+
+        assert by_row is False or by_row in ["compat", "_compat"]
+        self.by_row = by_row
+
+        self.args = args or ()
+        self.kwargs = kwargs or {}
+
+        self.engine = engine
+        self.engine_kwargs = {} if engine_kwargs is None else engine_kwargs
+
+        if result_type not in [None, "reduce", "broadcast", "expand"]:
+            raise ValueError(
+                "invalid value for result_type, must be one "
+                "of {None, 'reduce', 'broadcast', 'expand'}"
+            )
+
+        self.result_type = result_type
+
+        self.func = func
+
+    @abc.abstractmethod
+    def apply(self) -> DataFrame | Series:
+        pass
+
+    @abc.abstractmethod
+    def agg_or_apply_list_like(
+        self, op_name: Literal["agg", "apply"]
+    ) -> DataFrame | Series:
+        pass
+
+    @abc.abstractmethod
+    def agg_or_apply_dict_like(
+        self, op_name: Literal["agg", "apply"]
+    ) -> DataFrame | Series:
+        pass
+
+    def agg(self) -> DataFrame | Series | None:
+        """
+        Provide an implementation for the aggregators.
+
+        Returns
+        -------
+        Result of aggregation, or None if agg cannot be performed by
+        this method.
+        """
+        obj = self.obj
+        func = self.func
+        args = self.args
+        kwargs = self.kwargs
+
+        if isinstance(func, str):
+            return self.apply_str()
+
+        if is_dict_like(func):
+            return self.agg_dict_like()
+        elif is_list_like(func):
+            # we require a list, but not a 'str'
+            return self.agg_list_like()
+
+        if callable(func):
+            f = com.get_cython_func(func)
+            if f and not args and not kwargs:
+                warn_alias_replacement(obj, func, f)
+                return getattr(obj, f)()
+
+        # caller can react
+        return None
+
+    def transform(self) -> DataFrame | Series:
+        """
+        Transform a DataFrame or Series.
+
+        Returns
+        -------
+        DataFrame or Series
+            Result of applying ``func`` along the given axis of the
+            Series or DataFrame.
+
+        Raises
+        ------
+        ValueError
+            If the transform function fails or does not transform.
+        """
+        obj = self.obj
+        func = self.func
+        axis = self.axis
+        args = self.args
+        kwargs = self.kwargs
+
+        is_series = obj.ndim == 1
+
+        if obj._get_axis_number(axis) == 1:
+            assert not is_series
+            return obj.T.transform(func, 0, *args, **kwargs).T
+
+        if is_list_like(func) and not is_dict_like(func):
+            func = cast(list[AggFuncTypeBase], func)
+            # Convert func equivalent dict
+            if is_series:
+                func = {com.get_callable_name(v) or v: v for v in func}
+            else:
+                func = {col: func for col in obj}
+
+        if is_dict_like(func):
+            func = cast(AggFuncTypeDict, func)
+            return self.transform_dict_like(func)
+
+        # func is either str or callable
+        func = cast(AggFuncTypeBase, func)
+        try:
+            result = self.transform_str_or_callable(func)
+        except TypeError:
+            raise
+        except Exception as err:
+            raise ValueError("Transform function failed") from err
+
+        # Functions that transform may return empty Series/DataFrame
+        # when the dtype is not appropriate
+        if (
+            isinstance(result, (ABCSeries, ABCDataFrame))
+            and result.empty
+            and not obj.empty
+        ):
+            raise ValueError("Transform function failed")
+        # error: Argument 1 to "__get__" of "AxisProperty" has incompatible type
+        # "Union[Series, DataFrame, GroupBy[Any], SeriesGroupBy,
+        # DataFrameGroupBy, BaseWindow, Resampler]"; expected "Union[DataFrame,
+        # Series]"
+        if not isinstance(result, (ABCSeries, ABCDataFrame)) or not result.index.equals(
+            obj.index  # type: ignore[arg-type]
+        ):
+            raise ValueError("Function did not transform")
+
+        return result
+
+    def transform_dict_like(self, func) -> DataFrame:
+        """
+        Compute transform in the case of a dict-like func
+        """
+        from pandas.core.reshape.concat import concat
+
+        obj = self.obj
+        args = self.args
+        kwargs = self.kwargs
+
+        # transform is currently only for Series/DataFrame
+        assert isinstance(obj, ABCNDFrame)
+
+        if len(func) == 0:
+            raise ValueError("No transform functions were provided")
+
+        func = self.normalize_dictlike_arg("transform", obj, func)
+
+        results: dict[Hashable, DataFrame | Series] = {}
+        for name, how in func.items():
+            colg = obj._gotitem(name, ndim=1)
+            results[name] = colg.transform(how, 0, *args, **kwargs)
+        return concat(results, axis=1)
+
+    def transform_str_or_callable(self, func) -> DataFrame | Series:
+        """
+        Compute transform in the case of a string or callable func
+        """
+        obj = self.obj
+        args = self.args
+        kwargs = self.kwargs
+
+        if isinstance(func, str):
+            return self._apply_str(obj, func, *args, **kwargs)
+
+        if not args and not kwargs:
+            f = com.get_cython_func(func)
+            if f:
+                warn_alias_replacement(obj, func, f)
+                return getattr(obj, f)()
+
+        # Two possible ways to use a UDF - apply or call directly
+        try:
+            return obj.apply(func, args=args, **kwargs)
+        except Exception:
+            return func(obj, *args, **kwargs)
+
+    def agg_list_like(self) -> DataFrame | Series:
+        """
+        Compute aggregation in the case of a list-like argument.
+
+        Returns
+        -------
+        Result of aggregation.
+        """
+        return self.agg_or_apply_list_like(op_name="agg")
+
+    def compute_list_like(
+        self,
+        op_name: Literal["agg", "apply"],
+        selected_obj: Series | DataFrame,
+        kwargs: dict[str, Any],
+    ) -> tuple[list[Hashable] | Index, list[Any]]:
+        """
+        Compute agg/apply results for like-like input.
+
+        Parameters
+        ----------
+        op_name : {"agg", "apply"}
+            Operation being performed.
+        selected_obj : Series or DataFrame
+            Data to perform operation on.
+        kwargs : dict
+            Keyword arguments to pass to the functions.
+
+        Returns
+        -------
+        keys : list[Hashable] or Index
+            Index labels for result.
+        results : list
+            Data for result. When aggregating with a Series, this can contain any
+            Python objects.
+        """
+        func = cast(list[AggFuncTypeBase], self.func)
+        obj = self.obj
+
+        results = []
+        keys = []
+
+        # degenerate case
+        if selected_obj.ndim == 1:
+            for a in func:
+                colg = obj._gotitem(selected_obj.name, ndim=1, subset=selected_obj)
+                args = (
+                    [self.axis, *self.args]
+                    if include_axis(op_name, colg)
+                    else self.args
+                )
+                new_res = getattr(colg, op_name)(a, *args, **kwargs)
+                results.append(new_res)
+
+                # make sure we find a good name
+                name = com.get_callable_name(a) or a
+                keys.append(name)
+
+        else:
+            indices = []
+            for index, col in enumerate(selected_obj):
+                colg = obj._gotitem(col, ndim=1, subset=selected_obj.iloc[:, index])
+                args = (
+                    [self.axis, *self.args]
+                    if include_axis(op_name, colg)
+                    else self.args
+                )
+                new_res = getattr(colg, op_name)(func, *args, **kwargs)
+                results.append(new_res)
+                indices.append(index)
+            # error: Incompatible types in assignment (expression has type "Any |
+            # Index", variable has type "list[Any | Callable[..., Any] | str]")
+            keys = selected_obj.columns.take(indices)  # type: ignore[assignment]
+
+        return keys, results
+
+    def wrap_results_list_like(
+        self, keys: Iterable[Hashable], results: list[Series | DataFrame]
+    ):
+        from pandas.core.reshape.concat import concat
+
+        obj = self.obj
+
+        try:
+            return concat(results, keys=keys, axis=1, sort=False)
+        except TypeError as err:
+            # we are concatting non-NDFrame objects,
+            # e.g. a list of scalars
+            from pandas import Series
+
+            result = Series(results, index=keys, name=obj.name)
+            if is_nested_object(result):
+                raise ValueError(
+                    "cannot combine transform and aggregation operations"
+                ) from err
+            return result
+
+    def agg_dict_like(self) -> DataFrame | Series:
+        """
+        Compute aggregation in the case of a dict-like argument.
+
+        Returns
+        -------
+        Result of aggregation.
+        """
+        return self.agg_or_apply_dict_like(op_name="agg")
+
+    def compute_dict_like(
+        self,
+        op_name: Literal["agg", "apply"],
+        selected_obj: Series | DataFrame,
+        selection: Hashable | Sequence[Hashable],
+        kwargs: dict[str, Any],
+    ) -> tuple[list[Hashable], list[Any]]:
+        """
+        Compute agg/apply results for dict-like input.
+
+        Parameters
+        ----------
+        op_name : {"agg", "apply"}
+            Operation being performed.
+        selected_obj : Series or DataFrame
+            Data to perform operation on.
+        selection : hashable or sequence of hashables
+            Used by GroupBy, Window, and Resample if selection is applied to the object.
+        kwargs : dict
+            Keyword arguments to pass to the functions.
+
+        Returns
+        -------
+        keys : list[hashable]
+            Index labels for result.
+        results : list
+            Data for result. When aggregating with a Series, this can contain any
+            Python object.
+        """
+        from pandas.core.groupby.generic import (
+            DataFrameGroupBy,
+            SeriesGroupBy,
+        )
+
+        obj = self.obj
+        is_groupby = isinstance(obj, (DataFrameGroupBy, SeriesGroupBy))
+        func = cast(AggFuncTypeDict, self.func)
+        func = self.normalize_dictlike_arg(op_name, selected_obj, func)
+
+        is_non_unique_col = (
+            selected_obj.ndim == 2
+            and selected_obj.columns.nunique() < len(selected_obj.columns)
+        )
+
+        if selected_obj.ndim == 1:
+            # key only used for output
+            colg = obj._gotitem(selection, ndim=1)
+            results = [getattr(colg, op_name)(how, **kwargs) for _, how in func.items()]
+            keys = list(func.keys())
+        elif not is_groupby and is_non_unique_col:
+            # key used for column selection and output
+            # GH#51099
+            results = []
+            keys = []
+            for key, how in func.items():
+                indices = selected_obj.columns.get_indexer_for([key])
+                labels = selected_obj.columns.take(indices)
+                label_to_indices = defaultdict(list)
+                for index, label in zip(indices, labels):
+                    label_to_indices[label].append(index)
+
+                key_data = [
+                    getattr(selected_obj._ixs(indice, axis=1), op_name)(how, **kwargs)
+                    for label, indices in label_to_indices.items()
+                    for indice in indices
+                ]
+
+                keys += [key] * len(key_data)
+                results += key_data
+        else:
+            # key used for column selection and output
+            results = [
+                getattr(obj._gotitem(key, ndim=1), op_name)(how, **kwargs)
+                for key, how in func.items()
+            ]
+            keys = list(func.keys())
+
+        return keys, results
+
+    def wrap_results_dict_like(
+        self,
+        selected_obj: Series | DataFrame,
+        result_index: list[Hashable],
+        result_data: list,
+    ):
+        from pandas import Index
+        from pandas.core.reshape.concat import concat
+
+        obj = self.obj
+
+        # Avoid making two isinstance calls in all and any below
+        is_ndframe = [isinstance(r, ABCNDFrame) for r in result_data]
+
+        if all(is_ndframe):
+            results = dict(zip(result_index, result_data))
+            keys_to_use: Iterable[Hashable]
+            keys_to_use = [k for k in result_index if not results[k].empty]
+            # Have to check, if at least one DataFrame is not empty.
+            keys_to_use = keys_to_use if keys_to_use != [] else result_index
+            if selected_obj.ndim == 2:
+                # keys are columns, so we can preserve names
+                ktu = Index(keys_to_use)
+                ktu._set_names(selected_obj.columns.names)
+                keys_to_use = ktu
+
+            axis: AxisInt = 0 if isinstance(obj, ABCSeries) else 1
+            result = concat(
+                {k: results[k] for k in keys_to_use},
+                axis=axis,
+                keys=keys_to_use,
+            )
+        elif any(is_ndframe):
+            # There is a mix of NDFrames and scalars
+            raise ValueError(
+                "cannot perform both aggregation "
+                "and transformation operations "
+                "simultaneously"
+            )
+        else:
+            from pandas import Series
+
+            # we have a list of scalars
+            # GH 36212 use name only if obj is a series
+            if obj.ndim == 1:
+                obj = cast("Series", obj)
+                name = obj.name
+            else:
+                name = None
+
+            result = Series(result_data, index=result_index, name=name)
+
+        return result
+
+    def apply_str(self) -> DataFrame | Series:
+        """
+        Compute apply in case of a string.
+
+        Returns
+        -------
+        result: Series or DataFrame
+        """
+        # Caller is responsible for checking isinstance(self.f, str)
+        func = cast(str, self.func)
+
+        obj = self.obj
+
+        from pandas.core.groupby.generic import (
+            DataFrameGroupBy,
+            SeriesGroupBy,
+        )
+
+        # Support for `frame.transform('method')`
+        # Some methods (shift, etc.) require the axis argument, others
+        # don't, so inspect and insert if necessary.
+        method = getattr(obj, func, None)
+        if callable(method):
+            sig = inspect.getfullargspec(method)
+            arg_names = (*sig.args, *sig.kwonlyargs)
+            if self.axis != 0 and (
+                "axis" not in arg_names or func in ("corrwith", "skew")
+            ):
+                raise ValueError(f"Operation {func} does not support axis=1")
+            if "axis" in arg_names:
+                if isinstance(obj, (SeriesGroupBy, DataFrameGroupBy)):
+                    # Try to avoid FutureWarning for deprecated axis keyword;
+                    # If self.axis matches the axis we would get by not passing
+                    #  axis, we safely exclude the keyword.
+
+                    default_axis = 0
+                    if func in ["idxmax", "idxmin"]:
+                        # DataFrameGroupBy.idxmax, idxmin axis defaults to self.axis,
+                        # whereas other axis keywords default to 0
+                        default_axis = self.obj.axis
+
+                    if default_axis != self.axis:
+                        self.kwargs["axis"] = self.axis
+                else:
+                    self.kwargs["axis"] = self.axis
+        return self._apply_str(obj, func, *self.args, **self.kwargs)
+
+    def apply_list_or_dict_like(self) -> DataFrame | Series:
+        """
+        Compute apply in case of a list-like or dict-like.
+
+        Returns
+        -------
+        result: Series, DataFrame, or None
+            Result when self.func is a list-like or dict-like, None otherwise.
+        """
+
+        if self.engine == "numba":
+            raise NotImplementedError(
+                "The 'numba' engine doesn't support list-like/"
+                "dict likes of callables yet."
+            )
+
+        if self.axis == 1 and isinstance(self.obj, ABCDataFrame):
+            return self.obj.T.apply(self.func, 0, args=self.args, **self.kwargs).T
+
+        func = self.func
+        kwargs = self.kwargs
+
+        if is_dict_like(func):
+            result = self.agg_or_apply_dict_like(op_name="apply")
+        else:
+            result = self.agg_or_apply_list_like(op_name="apply")
+
+        result = reconstruct_and_relabel_result(result, func, **kwargs)
+
+        return result
+
+    def normalize_dictlike_arg(
+        self, how: str, obj: DataFrame | Series, func: AggFuncTypeDict
+    ) -> AggFuncTypeDict:
+        """
+        Handler for dict-like argument.
+
+        Ensures that necessary columns exist if obj is a DataFrame, and
+        that a nested renamer is not passed. Also normalizes to all lists
+        when values consists of a mix of list and non-lists.
+        """
+        assert how in ("apply", "agg", "transform")
+
+        # Can't use func.values(); wouldn't work for a Series
+        if (
+            how == "agg"
+            and isinstance(obj, ABCSeries)
+            and any(is_list_like(v) for _, v in func.items())
+        ) or (any(is_dict_like(v) for _, v in func.items())):
+            # GH 15931 - deprecation of renaming keys
+            raise SpecificationError("nested renamer is not supported")
+
+        if obj.ndim != 1:
+            # Check for missing columns on a frame
+            from pandas import Index
+
+            cols = Index(list(func.keys())).difference(obj.columns, sort=True)
+            if len(cols) > 0:
+                raise KeyError(f"Column(s) {list(cols)} do not exist")
+
+        aggregator_types = (list, tuple, dict)
+
+        # if we have a dict of any non-scalars
+        # eg. {'A' : ['mean']}, normalize all to
+        # be list-likes
+        # Cannot use func.values() because arg may be a Series
+        if any(isinstance(x, aggregator_types) for _, x in func.items()):
+            new_func: AggFuncTypeDict = {}
+            for k, v in func.items():
+                if not isinstance(v, aggregator_types):
+                    new_func[k] = [v]
+                else:
+                    new_func[k] = v
+            func = new_func
+        return func
+
+    def _apply_str(self, obj, func: str, *args, **kwargs):
+        """
+        if arg is a string, then try to operate on it:
+        - try to find a function (or attribute) on obj
+        - try to find a numpy function
+        - raise
+        """
+        assert isinstance(func, str)
+
+        if hasattr(obj, func):
+            f = getattr(obj, func)
+            if callable(f):
+                return f(*args, **kwargs)
+
+            # people may aggregate on a non-callable attribute
+            # but don't let them think they can pass args to it
+            assert len(args) == 0
+            assert len([kwarg for kwarg in kwargs if kwarg not in ["axis"]]) == 0
+            return f
+        elif hasattr(np, func) and hasattr(obj, "__array__"):
+            # in particular exclude Window
+            f = getattr(np, func)
+            return f(obj, *args, **kwargs)
+        else:
+            msg = f"'{func}' is not a valid function for '{type(obj).__name__}' object"
+            raise AttributeError(msg)
+
+
+class NDFrameApply(Apply):
+    """
+    Methods shared by FrameApply and SeriesApply but
+    not GroupByApply or ResamplerWindowApply
+    """
+
+    obj: DataFrame | Series
+
+    @property
+    def index(self) -> Index:
+        return self.obj.index
+
+    @property
+    def agg_axis(self) -> Index:
+        return self.obj._get_agg_axis(self.axis)
+
+    def agg_or_apply_list_like(
+        self, op_name: Literal["agg", "apply"]
+    ) -> DataFrame | Series:
+        obj = self.obj
+        kwargs = self.kwargs
+
+        if op_name == "apply":
+            if isinstance(self, FrameApply):
+                by_row = self.by_row
+
+            elif isinstance(self, SeriesApply):
+                by_row = "_compat" if self.by_row else False
+            else:
+                by_row = False
+            kwargs = {**kwargs, "by_row": by_row}
+
+        if getattr(obj, "axis", 0) == 1:
+            raise NotImplementedError("axis other than 0 is not supported")
+
+        keys, results = self.compute_list_like(op_name, obj, kwargs)
+        result = self.wrap_results_list_like(keys, results)
+        return result
+
+    def agg_or_apply_dict_like(
+        self, op_name: Literal["agg", "apply"]
+    ) -> DataFrame | Series:
+        assert op_name in ["agg", "apply"]
+        obj = self.obj
+
+        kwargs = {}
+        if op_name == "apply":
+            by_row = "_compat" if self.by_row else False
+            kwargs.update({"by_row": by_row})
+
+        if getattr(obj, "axis", 0) == 1:
+            raise NotImplementedError("axis other than 0 is not supported")
+
+        selection = None
+        result_index, result_data = self.compute_dict_like(
+            op_name, obj, selection, kwargs
+        )
+        result = self.wrap_results_dict_like(obj, result_index, result_data)
+        return result
+
+
+class FrameApply(NDFrameApply):
+    obj: DataFrame
+
+    def __init__(
+        self,
+        obj: AggObjType,
+        func: AggFuncType,
+        raw: bool,
+        result_type: str | None,
+        *,
+        by_row: Literal[False, "compat"] = False,
+        engine: str = "python",
+        engine_kwargs: dict[str, bool] | None = None,
+        args,
+        kwargs,
+    ) -> None:
+        if by_row is not False and by_row != "compat":
+            raise ValueError(f"by_row={by_row} not allowed")
+        super().__init__(
+            obj,
+            func,
+            raw,
+            result_type,
+            by_row=by_row,
+            engine=engine,
+            engine_kwargs=engine_kwargs,
+            args=args,
+            kwargs=kwargs,
+        )
+
+    # ---------------------------------------------------------------
+    # Abstract Methods
+
+    @property
+    @abc.abstractmethod
+    def result_index(self) -> Index:
+        pass
+
+    @property
+    @abc.abstractmethod
+    def result_columns(self) -> Index:
+        pass
+
+    @property
+    @abc.abstractmethod
+    def series_generator(self) -> Generator[Series, None, None]:
+        pass
+
+    @staticmethod
+    @functools.cache
+    @abc.abstractmethod
+    def generate_numba_apply_func(
+        func, nogil=True, nopython=True, parallel=False
+    ) -> Callable[[npt.NDArray, Index, Index], dict[int, Any]]:
+        pass
+
+    @abc.abstractmethod
+    def apply_with_numba(self):
+        pass
+
+    def validate_values_for_numba(self):
+        # Validate column dtyps all OK
+        for colname, dtype in self.obj.dtypes.items():
+            if not is_numeric_dtype(dtype):
+                raise ValueError(
+                    f"Column {colname} must have a numeric dtype. "
+                    f"Found '{dtype}' instead"
+                )
+            if is_extension_array_dtype(dtype):
+                raise ValueError(
+                    f"Column {colname} is backed by an extension array, "
+                    f"which is not supported by the numba engine."
+                )
+
+    @abc.abstractmethod
+    def wrap_results_for_axis(
+        self, results: ResType, res_index: Index
+    ) -> DataFrame | Series:
+        pass
+
+    # ---------------------------------------------------------------
+
+    @property
+    def res_columns(self) -> Index:
+        return self.result_columns
+
+    @property
+    def columns(self) -> Index:
+        return self.obj.columns
+
+    @cache_readonly
+    def values(self):
+        return self.obj.values
+
+    def apply(self) -> DataFrame | Series:
+        """compute the results"""
+
+        # dispatch to handle list-like or dict-like
+        if is_list_like(self.func):
+            if self.engine == "numba":
+                raise NotImplementedError(
+                    "the 'numba' engine doesn't support lists of callables yet"
+                )
+            return self.apply_list_or_dict_like()
+
+        # all empty
+        if len(self.columns) == 0 and len(self.index) == 0:
+            return self.apply_empty_result()
+
+        # string dispatch
+        if isinstance(self.func, str):
+            if self.engine == "numba":
+                raise NotImplementedError(
+                    "the 'numba' engine doesn't support using "
+                    "a string as the callable function"
+                )
+            return self.apply_str()
+
+        # ufunc
+        elif isinstance(self.func, np.ufunc):
+            if self.engine == "numba":
+                raise NotImplementedError(
+                    "the 'numba' engine doesn't support "
+                    "using a numpy ufunc as the callable function"
+                )
+            with np.errstate(all="ignore"):
+                results = self.obj._mgr.apply("apply", func=self.func)
+            # _constructor will retain self.index and self.columns
+            return self.obj._constructor_from_mgr(results, axes=results.axes)
+
+        # broadcasting
+        if self.result_type == "broadcast":
+            if self.engine == "numba":
+                raise NotImplementedError(
+                    "the 'numba' engine doesn't support result_type='broadcast'"
+                )
+            return self.apply_broadcast(self.obj)
+
+        # one axis empty
+        elif not all(self.obj.shape):
+            return self.apply_empty_result()
+
+        # raw
+        elif self.raw:
+            return self.apply_raw(engine=self.engine, engine_kwargs=self.engine_kwargs)
+
+        return self.apply_standard()
+
+    def agg(self):
+        obj = self.obj
+        axis = self.axis
+
+        # TODO: Avoid having to change state
+        self.obj = self.obj if self.axis == 0 else self.obj.T
+        self.axis = 0
+
+        result = None
+        try:
+            result = super().agg()
+        finally:
+            self.obj = obj
+            self.axis = axis
+
+        if axis == 1:
+            result = result.T if result is not None else result
+
+        if result is None:
+            result = self.obj.apply(self.func, axis, args=self.args, **self.kwargs)
+
+        return result
+
+    def apply_empty_result(self):
+        """
+        we have an empty result; at least 1 axis is 0
+
+        we will try to apply the function to an empty
+        series in order to see if this is a reduction function
+        """
+        assert callable(self.func)
+
+        # we are not asked to reduce or infer reduction
+        # so just return a copy of the existing object
+        if self.result_type not in ["reduce", None]:
+            return self.obj.copy()
+
+        # we may need to infer
+        should_reduce = self.result_type == "reduce"
+
+        from pandas import Series
+
+        if not should_reduce:
+            try:
+                if self.axis == 0:
+                    r = self.func(
+                        Series([], dtype=np.float64), *self.args, **self.kwargs
+                    )
+                else:
+                    r = self.func(
+                        Series(index=self.columns, dtype=np.float64),
+                        *self.args,
+                        **self.kwargs,
+                    )
+            except Exception:
+                pass
+            else:
+                should_reduce = not isinstance(r, Series)
+
+        if should_reduce:
+            if len(self.agg_axis):
+                r = self.func(Series([], dtype=np.float64), *self.args, **self.kwargs)
+            else:
+                r = np.nan
+
+            return self.obj._constructor_sliced(r, index=self.agg_axis)
+        else:
+            return self.obj.copy()
+
+    def apply_raw(self, engine="python", engine_kwargs=None):
+        """apply to the values as a numpy array"""
+
+        def wrap_function(func):
+            """
+            Wrap user supplied function to work around numpy issue.
+
+            see https://github.com/numpy/numpy/issues/8352
+            """
+
+            def wrapper(*args, **kwargs):
+                result = func(*args, **kwargs)
+                if isinstance(result, str):
+                    result = np.array(result, dtype=object)
+                return result
+
+            return wrapper
+
+        if engine == "numba":
+            engine_kwargs = {} if engine_kwargs is None else engine_kwargs
+
+            # error: Argument 1 to "__call__" of "_lru_cache_wrapper" has
+            # incompatible type "Callable[..., Any] | str | list[Callable
+            # [..., Any] | str] | dict[Hashable,Callable[..., Any] | str |
+            # list[Callable[..., Any] | str]]"; expected "Hashable"
+            nb_looper = generate_apply_looper(
+                self.func, **engine_kwargs  # type: ignore[arg-type]
+            )
+            result = nb_looper(self.values, self.axis)
+            # If we made the result 2-D, squeeze it back to 1-D
+            result = np.squeeze(result)
+        else:
+            result = np.apply_along_axis(
+                wrap_function(self.func),
+                self.axis,
+                self.values,
+                *self.args,
+                **self.kwargs,
+            )
+
+        # TODO: mixed type case
+        if result.ndim == 2:
+            return self.obj._constructor(result, index=self.index, columns=self.columns)
+        else:
+            return self.obj._constructor_sliced(result, index=self.agg_axis)
+
+    def apply_broadcast(self, target: DataFrame) -> DataFrame:
+        assert callable(self.func)
+
+        result_values = np.empty_like(target.values)
+
+        # axis which we want to compare compliance
+        result_compare = target.shape[0]
+
+        for i, col in enumerate(target.columns):
+            res = self.func(target[col], *self.args, **self.kwargs)
+            ares = np.asarray(res).ndim
+
+            # must be a scalar or 1d
+            if ares > 1:
+                raise ValueError("too many dims to broadcast")
+            if ares == 1:
+                # must match return dim
+                if result_compare != len(res):
+                    raise ValueError("cannot broadcast result")
+
+            result_values[:, i] = res
+
+        # we *always* preserve the original index / columns
+        result = self.obj._constructor(
+            result_values, index=target.index, columns=target.columns
+        )
+        return result
+
+    def apply_standard(self):
+        if self.engine == "python":
+            results, res_index = self.apply_series_generator()
+        else:
+            results, res_index = self.apply_series_numba()
+
+        # wrap results
+        return self.wrap_results(results, res_index)
+
+    def apply_series_generator(self) -> tuple[ResType, Index]:
+        assert callable(self.func)
+
+        series_gen = self.series_generator
+        res_index = self.result_index
+
+        results = {}
+
+        with option_context("mode.chained_assignment", None):
+            for i, v in enumerate(series_gen):
+                # ignore SettingWithCopy here in case the user mutates
+                results[i] = self.func(v, *self.args, **self.kwargs)
+                if isinstance(results[i], ABCSeries):
+                    # If we have a view on v, we need to make a copy because
+                    #  series_generator will swap out the underlying data
+                    results[i] = results[i].copy(deep=False)
+
+        return results, res_index
+
+    def apply_series_numba(self):
+        if self.engine_kwargs.get("parallel", False):
+            raise NotImplementedError(
+                "Parallel apply is not supported when raw=False and engine='numba'"
+            )
+        if not self.obj.index.is_unique or not self.columns.is_unique:
+            raise NotImplementedError(
+                "The index/columns must be unique when raw=False and engine='numba'"
+            )
+        self.validate_values_for_numba()
+        results = self.apply_with_numba()
+        return results, self.result_index
+
+    def wrap_results(self, results: ResType, res_index: Index) -> DataFrame | Series:
+        from pandas import Series
+
+        # see if we can infer the results
+        if len(results) > 0 and 0 in results and is_sequence(results[0]):
+            return self.wrap_results_for_axis(results, res_index)
+
+        # dict of scalars
+
+        # the default dtype of an empty Series is `object`, but this
+        # code can be hit by df.mean() where the result should have dtype
+        # float64 even if it's an empty Series.
+        constructor_sliced = self.obj._constructor_sliced
+        if len(results) == 0 and constructor_sliced is Series:
+            result = constructor_sliced(results, dtype=np.float64)
+        else:
+            result = constructor_sliced(results)
+        result.index = res_index
+
+        return result
+
+    def apply_str(self) -> DataFrame | Series:
+        # Caller is responsible for checking isinstance(self.func, str)
+        # TODO: GH#39993 - Avoid special-casing by replacing with lambda
+        if self.func == "size":
+            # Special-cased because DataFrame.size returns a single scalar
+            obj = self.obj
+            value = obj.shape[self.axis]
+            return obj._constructor_sliced(value, index=self.agg_axis)
+        return super().apply_str()
+
+
+class FrameRowApply(FrameApply):
+    axis: AxisInt = 0
+
+    @property
+    def series_generator(self) -> Generator[Series, None, None]:
+        return (self.obj._ixs(i, axis=1) for i in range(len(self.columns)))
+
+    @staticmethod
+    @functools.cache
+    def generate_numba_apply_func(
+        func, nogil=True, nopython=True, parallel=False
+    ) -> Callable[[npt.NDArray, Index, Index], dict[int, Any]]:
+        numba = import_optional_dependency("numba")
+        from pandas import Series
+
+        # Import helper from extensions to cast string object -> np strings
+        # Note: This also has the side effect of loading our numba extensions
+        from pandas.core._numba.extensions import maybe_cast_str
+
+        jitted_udf = numba.extending.register_jitable(func)
+
+        # Currently the parallel argument doesn't get passed through here
+        # (it's disabled) since the dicts in numba aren't thread-safe.
+        @numba.jit(nogil=nogil, nopython=nopython, parallel=parallel)
+        def numba_func(values, col_names, df_index):
+            results = {}
+            for j in range(values.shape[1]):
+                # Create the series
+                ser = Series(
+                    values[:, j], index=df_index, name=maybe_cast_str(col_names[j])
+                )
+                results[j] = jitted_udf(ser)
+            return results
+
+        return numba_func
+
+    def apply_with_numba(self) -> dict[int, Any]:
+        nb_func = self.generate_numba_apply_func(
+            cast(Callable, self.func), **self.engine_kwargs
+        )
+        from pandas.core._numba.extensions import set_numba_data
+
+        index = self.obj.index
+        columns = self.obj.columns
+
+        # Convert from numba dict to regular dict
+        # Our isinstance checks in the df constructor don't pass for numbas typed dict
+        with set_numba_data(index) as index, set_numba_data(columns) as columns:
+            res = dict(nb_func(self.values, columns, index))
+        return res
+
+    @property
+    def result_index(self) -> Index:
+        return self.columns
+
+    @property
+    def result_columns(self) -> Index:
+        return self.index
+
+    def wrap_results_for_axis(
+        self, results: ResType, res_index: Index
+    ) -> DataFrame | Series:
+        """return the results for the rows"""
+
+        if self.result_type == "reduce":
+            # e.g. test_apply_dict GH#8735
+            res = self.obj._constructor_sliced(results)
+            res.index = res_index
+            return res
+
+        elif self.result_type is None and all(
+            isinstance(x, dict) for x in results.values()
+        ):
+            # Our operation was a to_dict op e.g.
+            #  test_apply_dict GH#8735, test_apply_reduce_to_dict GH#25196 #37544
+            res = self.obj._constructor_sliced(results)
+            res.index = res_index
+            return res
+
+        try:
+            result = self.obj._constructor(data=results)
+        except ValueError as err:
+            if "All arrays must be of the same length" in str(err):
+                # e.g. result = [[2, 3], [1.5], ['foo', 'bar']]
+                #  see test_agg_listlike_result GH#29587
+                res = self.obj._constructor_sliced(results)
+                res.index = res_index
+                return res
+            else:
+                raise
+
+        if not isinstance(results[0], ABCSeries):
+            if len(result.index) == len(self.res_columns):
+                result.index = self.res_columns
+
+        if len(result.columns) == len(res_index):
+            result.columns = res_index
+
+        return result
+
+
+class FrameColumnApply(FrameApply):
+    axis: AxisInt = 1
+
+    def apply_broadcast(self, target: DataFrame) -> DataFrame:
+        result = super().apply_broadcast(target.T)
+        return result.T
+
+    @property
+    def series_generator(self) -> Generator[Series, None, None]:
+        values = self.values
+        values = ensure_wrapped_if_datetimelike(values)
+        assert len(values) > 0
+
+        # We create one Series object, and will swap out the data inside
+        #  of it.  Kids: don't do this at home.
+        ser = self.obj._ixs(0, axis=0)
+        mgr = ser._mgr
+
+        is_view = mgr.blocks[0].refs.has_reference()  # type: ignore[union-attr]
+
+        if isinstance(ser.dtype, ExtensionDtype):
+            # values will be incorrect for this block
+            # TODO(EA2D): special case would be unnecessary with 2D EAs
+            obj = self.obj
+            for i in range(len(obj)):
+                yield obj._ixs(i, axis=0)
+
+        else:
+            for arr, name in zip(values, self.index):
+                # GH#35462 re-pin mgr in case setitem changed it
+                ser._mgr = mgr
+                mgr.set_values(arr)
+                object.__setattr__(ser, "_name", name)
+                if not is_view:
+                    # In apply_series_generator we store the a shallow copy of the
+                    # result, which potentially increases the ref count of this reused
+                    # `ser` object (depending on the result of the applied function)
+                    # -> if that happened and `ser` is already a copy, then we reset
+                    # the refs here to avoid triggering a unnecessary CoW inside the
+                    # applied function (https://github.com/pandas-dev/pandas/pull/56212)
+                    mgr.blocks[0].refs = BlockValuesRefs(mgr.blocks[0])  # type: ignore[union-attr]
+                yield ser
+
+    @staticmethod
+    @functools.cache
+    def generate_numba_apply_func(
+        func, nogil=True, nopython=True, parallel=False
+    ) -> Callable[[npt.NDArray, Index, Index], dict[int, Any]]:
+        numba = import_optional_dependency("numba")
+        from pandas import Series
+        from pandas.core._numba.extensions import maybe_cast_str
+
+        jitted_udf = numba.extending.register_jitable(func)
+
+        @numba.jit(nogil=nogil, nopython=nopython, parallel=parallel)
+        def numba_func(values, col_names_index, index):
+            results = {}
+            # Currently the parallel argument doesn't get passed through here
+            # (it's disabled) since the dicts in numba aren't thread-safe.
+            for i in range(values.shape[0]):
+                # Create the series
+                # TODO: values corrupted without the copy
+                ser = Series(
+                    values[i].copy(),
+                    index=col_names_index,
+                    name=maybe_cast_str(index[i]),
+                )
+                results[i] = jitted_udf(ser)
+
+            return results
+
+        return numba_func
+
+    def apply_with_numba(self) -> dict[int, Any]:
+        nb_func = self.generate_numba_apply_func(
+            cast(Callable, self.func), **self.engine_kwargs
+        )
+
+        from pandas.core._numba.extensions import set_numba_data
+
+        # Convert from numba dict to regular dict
+        # Our isinstance checks in the df constructor don't pass for numbas typed dict
+        with set_numba_data(self.obj.index) as index, set_numba_data(
+            self.columns
+        ) as columns:
+            res = dict(nb_func(self.values, columns, index))
+
+        return res
+
+    @property
+    def result_index(self) -> Index:
+        return self.index
+
+    @property
+    def result_columns(self) -> Index:
+        return self.columns
+
+    def wrap_results_for_axis(
+        self, results: ResType, res_index: Index
+    ) -> DataFrame | Series:
+        """return the results for the columns"""
+        result: DataFrame | Series
+
+        # we have requested to expand
+        if self.result_type == "expand":
+            result = self.infer_to_same_shape(results, res_index)
+
+        # we have a non-series and don't want inference
+        elif not isinstance(results[0], ABCSeries):
+            result = self.obj._constructor_sliced(results)
+            result.index = res_index
+
+        # we may want to infer results
+        else:
+            result = self.infer_to_same_shape(results, res_index)
+
+        return result
+
+    def infer_to_same_shape(self, results: ResType, res_index: Index) -> DataFrame:
+        """infer the results to the same shape as the input object"""
+        result = self.obj._constructor(data=results)
+        result = result.T
+
+        # set the index
+        result.index = res_index
+
+        # infer dtypes
+        result = result.infer_objects(copy=False)
+
+        return result
+
+
+class SeriesApply(NDFrameApply):
+    obj: Series
+    axis: AxisInt = 0
+    by_row: Literal[False, "compat", "_compat"]  # only relevant for apply()
+
+    def __init__(
+        self,
+        obj: Series,
+        func: AggFuncType,
+        *,
+        convert_dtype: bool | lib.NoDefault = lib.no_default,
+        by_row: Literal[False, "compat", "_compat"] = "compat",
+        args,
+        kwargs,
+    ) -> None:
+        if convert_dtype is lib.no_default:
+            convert_dtype = True
+        else:
+            warnings.warn(
+                "the convert_dtype parameter is deprecated and will be removed in a "
+                "future version.  Do ``ser.astype(object).apply()`` "
+                "instead if you want ``convert_dtype=False``.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+        self.convert_dtype = convert_dtype
+
+        super().__init__(
+            obj,
+            func,
+            raw=False,
+            result_type=None,
+            by_row=by_row,
+            args=args,
+            kwargs=kwargs,
+        )
+
+    def apply(self) -> DataFrame | Series:
+        obj = self.obj
+
+        if len(obj) == 0:
+            return self.apply_empty_result()
+
+        # dispatch to handle list-like or dict-like
+        if is_list_like(self.func):
+            return self.apply_list_or_dict_like()
+
+        if isinstance(self.func, str):
+            # if we are a string, try to dispatch
+            return self.apply_str()
+
+        if self.by_row == "_compat":
+            return self.apply_compat()
+
+        # self.func is Callable
+        return self.apply_standard()
+
+    def agg(self):
+        result = super().agg()
+        if result is None:
+            obj = self.obj
+            func = self.func
+            # string, list-like, and dict-like are entirely handled in super
+            assert callable(func)
+
+            # GH53325: The setup below is just to keep current behavior while emitting a
+            # deprecation message. In the future this will all be replaced with a simple
+            # `result = f(self.obj, *self.args, **self.kwargs)`.
+            try:
+                result = obj.apply(func, args=self.args, **self.kwargs)
+            except (ValueError, AttributeError, TypeError):
+                result = func(obj, *self.args, **self.kwargs)
+            else:
+                msg = (
+                    f"using {func} in {type(obj).__name__}.agg cannot aggregate and "
+                    f"has been deprecated. Use {type(obj).__name__}.transform to "
+                    f"keep behavior unchanged."
+                )
+                warnings.warn(msg, FutureWarning, stacklevel=find_stack_level())
+
+        return result
+
+    def apply_empty_result(self) -> Series:
+        obj = self.obj
+        return obj._constructor(dtype=obj.dtype, index=obj.index).__finalize__(
+            obj, method="apply"
+        )
+
+    def apply_compat(self):
+        """compat apply method for funcs in listlikes and dictlikes.
+
+         Used for each callable when giving listlikes and dictlikes of callables to
+         apply. Needed for compatibility with Pandas < v2.1.
+
+        .. versionadded:: 2.1.0
+        """
+        obj = self.obj
+        func = self.func
+
+        if callable(func):
+            f = com.get_cython_func(func)
+            if f and not self.args and not self.kwargs:
+                return obj.apply(func, by_row=False)
+
+        try:
+            result = obj.apply(func, by_row="compat")
+        except (ValueError, AttributeError, TypeError):
+            result = obj.apply(func, by_row=False)
+        return result
+
+    def apply_standard(self) -> DataFrame | Series:
+        # caller is responsible for ensuring that f is Callable
+        func = cast(Callable, self.func)
+        obj = self.obj
+
+        if isinstance(func, np.ufunc):
+            with np.errstate(all="ignore"):
+                return func(obj, *self.args, **self.kwargs)
+        elif not self.by_row:
+            return func(obj, *self.args, **self.kwargs)
+
+        if self.args or self.kwargs:
+            # _map_values does not support args/kwargs
+            def curried(x):
+                return func(x, *self.args, **self.kwargs)
+
+        else:
+            curried = func
+
+        # row-wise access
+        # apply doesn't have a `na_action` keyword and for backward compat reasons
+        # we need to give `na_action="ignore"` for categorical data.
+        # TODO: remove the `na_action="ignore"` when that default has been changed in
+        #  Categorical (GH51645).
+        action = "ignore" if isinstance(obj.dtype, CategoricalDtype) else None
+        mapped = obj._map_values(
+            mapper=curried, na_action=action, convert=self.convert_dtype
+        )
+
+        if len(mapped) and isinstance(mapped[0], ABCSeries):
+            # GH#43986 Need to do list(mapped) in order to get treated as nested
+            #  See also GH#25959 regarding EA support
+            return obj._constructor_expanddim(list(mapped), index=obj.index)
+        else:
+            return obj._constructor(mapped, index=obj.index).__finalize__(
+                obj, method="apply"
+            )
+
+
+class GroupByApply(Apply):
+    obj: GroupBy | Resampler | BaseWindow
+
+    def __init__(
+        self,
+        obj: GroupBy[NDFrameT],
+        func: AggFuncType,
+        *,
+        args,
+        kwargs,
+    ) -> None:
+        kwargs = kwargs.copy()
+        self.axis = obj.obj._get_axis_number(kwargs.get("axis", 0))
+        super().__init__(
+            obj,
+            func,
+            raw=False,
+            result_type=None,
+            args=args,
+            kwargs=kwargs,
+        )
+
+    def apply(self):
+        raise NotImplementedError
+
+    def transform(self):
+        raise NotImplementedError
+
+    def agg_or_apply_list_like(
+        self, op_name: Literal["agg", "apply"]
+    ) -> DataFrame | Series:
+        obj = self.obj
+        kwargs = self.kwargs
+        if op_name == "apply":
+            kwargs = {**kwargs, "by_row": False}
+
+        if getattr(obj, "axis", 0) == 1:
+            raise NotImplementedError("axis other than 0 is not supported")
+
+        if obj._selected_obj.ndim == 1:
+            # For SeriesGroupBy this matches _obj_with_exclusions
+            selected_obj = obj._selected_obj
+        else:
+            selected_obj = obj._obj_with_exclusions
+
+        # Only set as_index=True on groupby objects, not Window or Resample
+        # that inherit from this class.
+        with com.temp_setattr(
+            obj, "as_index", True, condition=hasattr(obj, "as_index")
+        ):
+            keys, results = self.compute_list_like(op_name, selected_obj, kwargs)
+        result = self.wrap_results_list_like(keys, results)
+        return result
+
+    def agg_or_apply_dict_like(
+        self, op_name: Literal["agg", "apply"]
+    ) -> DataFrame | Series:
+        from pandas.core.groupby.generic import (
+            DataFrameGroupBy,
+            SeriesGroupBy,
+        )
+
+        assert op_name in ["agg", "apply"]
+
+        obj = self.obj
+        kwargs = {}
+        if op_name == "apply":
+            by_row = "_compat" if self.by_row else False
+            kwargs.update({"by_row": by_row})
+
+        if getattr(obj, "axis", 0) == 1:
+            raise NotImplementedError("axis other than 0 is not supported")
+
+        selected_obj = obj._selected_obj
+        selection = obj._selection
+
+        is_groupby = isinstance(obj, (DataFrameGroupBy, SeriesGroupBy))
+
+        # Numba Groupby engine/engine-kwargs passthrough
+        if is_groupby:
+            engine = self.kwargs.get("engine", None)
+            engine_kwargs = self.kwargs.get("engine_kwargs", None)
+            kwargs.update({"engine": engine, "engine_kwargs": engine_kwargs})
+
+        with com.temp_setattr(
+            obj, "as_index", True, condition=hasattr(obj, "as_index")
+        ):
+            result_index, result_data = self.compute_dict_like(
+                op_name, selected_obj, selection, kwargs
+            )
+        result = self.wrap_results_dict_like(selected_obj, result_index, result_data)
+        return result
+
+
+class ResamplerWindowApply(GroupByApply):
+    axis: AxisInt = 0
+    obj: Resampler | BaseWindow
+
+    def __init__(
+        self,
+        obj: Resampler | BaseWindow,
+        func: AggFuncType,
+        *,
+        args,
+        kwargs,
+    ) -> None:
+        super(GroupByApply, self).__init__(
+            obj,
+            func,
+            raw=False,
+            result_type=None,
+            args=args,
+            kwargs=kwargs,
+        )
+
+    def apply(self):
+        raise NotImplementedError
+
+    def transform(self):
+        raise NotImplementedError
+
+
+def reconstruct_func(
+    func: AggFuncType | None, **kwargs
+) -> tuple[bool, AggFuncType, tuple[str, ...] | None, npt.NDArray[np.intp] | None]:
+    """
+    This is the internal function to reconstruct func given if there is relabeling
+    or not and also normalize the keyword to get new order of columns.
+
+    If named aggregation is applied, `func` will be None, and kwargs contains the
+    column and aggregation function information to be parsed;
+    If named aggregation is not applied, `func` is either string (e.g. 'min') or
+    Callable, or list of them (e.g. ['min', np.max]), or the dictionary of column name
+    and str/Callable/list of them (e.g. {'A': 'min'}, or {'A': [np.min, lambda x: x]})
+
+    If relabeling is True, will return relabeling, reconstructed func, column
+    names, and the reconstructed order of columns.
+    If relabeling is False, the columns and order will be None.
+
+    Parameters
+    ----------
+    func: agg function (e.g. 'min' or Callable) or list of agg functions
+        (e.g. ['min', np.max]) or dictionary (e.g. {'A': ['min', np.max]}).
+    **kwargs: dict, kwargs used in is_multi_agg_with_relabel and
+        normalize_keyword_aggregation function for relabelling
+
+    Returns
+    -------
+    relabelling: bool, if there is relabelling or not
+    func: normalized and mangled func
+    columns: tuple of column names
+    order: array of columns indices
+
+    Examples
+    --------
+    >>> reconstruct_func(None, **{"foo": ("col", "min")})
+    (True, defaultdict(, {'col': ['min']}), ('foo',), array([0]))
+
+    >>> reconstruct_func("min")
+    (False, 'min', None, None)
+    """
+    relabeling = func is None and is_multi_agg_with_relabel(**kwargs)
+    columns: tuple[str, ...] | None = None
+    order: npt.NDArray[np.intp] | None = None
+
+    if not relabeling:
+        if isinstance(func, list) and len(func) > len(set(func)):
+            # GH 28426 will raise error if duplicated function names are used and
+            # there is no reassigned name
+            raise SpecificationError(
+                "Function names must be unique if there is no new column names "
+                "assigned"
+            )
+        if func is None:
+            # nicer error message
+            raise TypeError("Must provide 'func' or tuples of '(column, aggfunc).")
+
+    if relabeling:
+        # error: Incompatible types in assignment (expression has type
+        # "MutableMapping[Hashable, list[Callable[..., Any] | str]]", variable has type
+        # "Callable[..., Any] | str | list[Callable[..., Any] | str] |
+        # MutableMapping[Hashable, Callable[..., Any] | str | list[Callable[..., Any] |
+        # str]] | None")
+        func, columns, order = normalize_keyword_aggregation(  # type: ignore[assignment]
+            kwargs
+        )
+    assert func is not None
+
+    return relabeling, func, columns, order
+
+
+def is_multi_agg_with_relabel(**kwargs) -> bool:
+    """
+    Check whether kwargs passed to .agg look like multi-agg with relabeling.
+
+    Parameters
+    ----------
+    **kwargs : dict
+
+    Returns
+    -------
+    bool
+
+    Examples
+    --------
+    >>> is_multi_agg_with_relabel(a="max")
+    False
+    >>> is_multi_agg_with_relabel(a_max=("a", "max"), a_min=("a", "min"))
+    True
+    >>> is_multi_agg_with_relabel()
+    False
+    """
+    return all(isinstance(v, tuple) and len(v) == 2 for v in kwargs.values()) and (
+        len(kwargs) > 0
+    )
+
+
+def normalize_keyword_aggregation(
+    kwargs: dict,
+) -> tuple[
+    MutableMapping[Hashable, list[AggFuncTypeBase]],
+    tuple[str, ...],
+    npt.NDArray[np.intp],
+]:
+    """
+    Normalize user-provided "named aggregation" kwargs.
+    Transforms from the new ``Mapping[str, NamedAgg]`` style kwargs
+    to the old Dict[str, List[scalar]]].
+
+    Parameters
+    ----------
+    kwargs : dict
+
+    Returns
+    -------
+    aggspec : dict
+        The transformed kwargs.
+    columns : tuple[str, ...]
+        The user-provided keys.
+    col_idx_order : List[int]
+        List of columns indices.
+
+    Examples
+    --------
+    >>> normalize_keyword_aggregation({"output": ("input", "sum")})
+    (defaultdict(, {'input': ['sum']}), ('output',), array([0]))
+    """
+    from pandas.core.indexes.base import Index
+
+    # Normalize the aggregation functions as Mapping[column, List[func]],
+    # process normally, then fixup the names.
+    # TODO: aggspec type: typing.Dict[str, List[AggScalar]]
+    aggspec = defaultdict(list)
+    order = []
+    columns, pairs = list(zip(*kwargs.items()))
+
+    for column, aggfunc in pairs:
+        aggspec[column].append(aggfunc)
+        order.append((column, com.get_callable_name(aggfunc) or aggfunc))
+
+    # uniquify aggfunc name if duplicated in order list
+    uniquified_order = _make_unique_kwarg_list(order)
+
+    # GH 25719, due to aggspec will change the order of assigned columns in aggregation
+    # uniquified_aggspec will store uniquified order list and will compare it with order
+    # based on index
+    aggspec_order = [
+        (column, com.get_callable_name(aggfunc) or aggfunc)
+        for column, aggfuncs in aggspec.items()
+        for aggfunc in aggfuncs
+    ]
+    uniquified_aggspec = _make_unique_kwarg_list(aggspec_order)
+
+    # get the new index of columns by comparison
+    col_idx_order = Index(uniquified_aggspec).get_indexer(uniquified_order)
+    return aggspec, columns, col_idx_order
+
+
+def _make_unique_kwarg_list(
+    seq: Sequence[tuple[Any, Any]]
+) -> Sequence[tuple[Any, Any]]:
+    """
+    Uniquify aggfunc name of the pairs in the order list
+
+    Examples:
+    --------
+    >>> kwarg_list = [('a', ''), ('a', ''), ('b', '')]
+    >>> _make_unique_kwarg_list(kwarg_list)
+    [('a', '_0'), ('a', '_1'), ('b', '')]
+    """
+    return [
+        (pair[0], f"{pair[1]}_{seq[:i].count(pair)}") if seq.count(pair) > 1 else pair
+        for i, pair in enumerate(seq)
+    ]
+
+
+def relabel_result(
+    result: DataFrame | Series,
+    func: dict[str, list[Callable | str]],
+    columns: Iterable[Hashable],
+    order: Iterable[int],
+) -> dict[Hashable, Series]:
+    """
+    Internal function to reorder result if relabelling is True for
+    dataframe.agg, and return the reordered result in dict.
+
+    Parameters:
+    ----------
+    result: Result from aggregation
+    func: Dict of (column name, funcs)
+    columns: New columns name for relabelling
+    order: New order for relabelling
+
+    Examples
+    --------
+    >>> from pandas.core.apply import relabel_result
+    >>> result = pd.DataFrame(
+    ...     {"A": [np.nan, 2, np.nan], "C": [6, np.nan, np.nan], "B": [np.nan, 4, 2.5]},
+    ...     index=["max", "mean", "min"]
+    ... )
+    >>> funcs = {"A": ["max"], "C": ["max"], "B": ["mean", "min"]}
+    >>> columns = ("foo", "aab", "bar", "dat")
+    >>> order = [0, 1, 2, 3]
+    >>> result_in_dict = relabel_result(result, funcs, columns, order)
+    >>> pd.DataFrame(result_in_dict, index=columns)
+           A    C    B
+    foo  2.0  NaN  NaN
+    aab  NaN  6.0  NaN
+    bar  NaN  NaN  4.0
+    dat  NaN  NaN  2.5
+    """
+    from pandas.core.indexes.base import Index
+
+    reordered_indexes = [
+        pair[0] for pair in sorted(zip(columns, order), key=lambda t: t[1])
+    ]
+    reordered_result_in_dict: dict[Hashable, Series] = {}
+    idx = 0
+
+    reorder_mask = not isinstance(result, ABCSeries) and len(result.columns) > 1
+    for col, fun in func.items():
+        s = result[col].dropna()
+
+        # In the `_aggregate`, the callable names are obtained and used in `result`, and
+        # these names are ordered alphabetically. e.g.
+        #           C2   C1
+        #    1  NaN
+        # amax     NaN  4.0
+        # max      NaN  4.0
+        # sum     18.0  6.0
+        # Therefore, the order of functions for each column could be shuffled
+        # accordingly so need to get the callable name if it is not parsed names, and
+        # reorder the aggregated result for each column.
+        # e.g. if df.agg(c1=("C2", sum), c2=("C2", lambda x: min(x))), correct order is
+        # [sum, ], but in `result`, it will be [, sum], and we need to
+        # reorder so that aggregated values map to their functions regarding the order.
+
+        # However there is only one column being used for aggregation, not need to
+        # reorder since the index is not sorted, and keep as is in `funcs`, e.g.
+        #         A
+        # min   1.0
+        # mean  1.5
+        # mean  1.5
+        if reorder_mask:
+            fun = [
+                com.get_callable_name(f) if not isinstance(f, str) else f for f in fun
+            ]
+            col_idx_order = Index(s.index).get_indexer(fun)
+            s = s.iloc[col_idx_order]
+
+        # assign the new user-provided "named aggregation" as index names, and reindex
+        # it based on the whole user-provided names.
+        s.index = reordered_indexes[idx : idx + len(fun)]
+        reordered_result_in_dict[col] = s.reindex(columns, copy=False)
+        idx = idx + len(fun)
+    return reordered_result_in_dict
+
+
+def reconstruct_and_relabel_result(result, func, **kwargs) -> DataFrame | Series:
+    from pandas import DataFrame
+
+    relabeling, func, columns, order = reconstruct_func(func, **kwargs)
+
+    if relabeling:
+        # This is to keep the order to columns occurrence unchanged, and also
+        # keep the order of new columns occurrence unchanged
+
+        # For the return values of reconstruct_func, if relabeling is
+        # False, columns and order will be None.
+        assert columns is not None
+        assert order is not None
+
+        result_in_dict = relabel_result(result, func, columns, order)
+        result = DataFrame(result_in_dict, index=columns)
+
+    return result
+
+
+# TODO: Can't use, because mypy doesn't like us setting __name__
+#   error: "partial[Any]" has no attribute "__name__"
+# the type is:
+#   typing.Sequence[Callable[..., ScalarResult]]
+#     -> typing.Sequence[Callable[..., ScalarResult]]:
+
+
+def _managle_lambda_list(aggfuncs: Sequence[Any]) -> Sequence[Any]:
+    """
+    Possibly mangle a list of aggfuncs.
+
+    Parameters
+    ----------
+    aggfuncs : Sequence
+
+    Returns
+    -------
+    mangled: list-like
+        A new AggSpec sequence, where lambdas have been converted
+        to have unique names.
+
+    Notes
+    -----
+    If just one aggfunc is passed, the name will not be mangled.
+    """
+    if len(aggfuncs) <= 1:
+        # don't mangle for .agg([lambda x: .])
+        return aggfuncs
+    i = 0
+    mangled_aggfuncs = []
+    for aggfunc in aggfuncs:
+        if com.get_callable_name(aggfunc) == "":
+            aggfunc = partial(aggfunc)
+            aggfunc.__name__ = f""
+            i += 1
+        mangled_aggfuncs.append(aggfunc)
+
+    return mangled_aggfuncs
+
+
+def maybe_mangle_lambdas(agg_spec: Any) -> Any:
+    """
+    Make new lambdas with unique names.
+
+    Parameters
+    ----------
+    agg_spec : Any
+        An argument to GroupBy.agg.
+        Non-dict-like `agg_spec` are pass through as is.
+        For dict-like `agg_spec` a new spec is returned
+        with name-mangled lambdas.
+
+    Returns
+    -------
+    mangled : Any
+        Same type as the input.
+
+    Examples
+    --------
+    >>> maybe_mangle_lambdas('sum')
+    'sum'
+    >>> maybe_mangle_lambdas([lambda: 1, lambda: 2])  # doctest: +SKIP
+    [,
+     .f(*args, **kwargs)>]
+    """
+    is_dict = is_dict_like(agg_spec)
+    if not (is_dict or is_list_like(agg_spec)):
+        return agg_spec
+    mangled_aggspec = type(agg_spec)()  # dict or OrderedDict
+
+    if is_dict:
+        for key, aggfuncs in agg_spec.items():
+            if is_list_like(aggfuncs) and not is_dict_like(aggfuncs):
+                mangled_aggfuncs = _managle_lambda_list(aggfuncs)
+            else:
+                mangled_aggfuncs = aggfuncs
+
+            mangled_aggspec[key] = mangled_aggfuncs
+    else:
+        mangled_aggspec = _managle_lambda_list(agg_spec)
+
+    return mangled_aggspec
+
+
+def validate_func_kwargs(
+    kwargs: dict,
+) -> tuple[list[str], list[str | Callable[..., Any]]]:
+    """
+    Validates types of user-provided "named aggregation" kwargs.
+    `TypeError` is raised if aggfunc is not `str` or callable.
+
+    Parameters
+    ----------
+    kwargs : dict
+
+    Returns
+    -------
+    columns : List[str]
+        List of user-provided keys.
+    func : List[Union[str, callable[...,Any]]]
+        List of user-provided aggfuncs
+
+    Examples
+    --------
+    >>> validate_func_kwargs({'one': 'min', 'two': 'max'})
+    (['one', 'two'], ['min', 'max'])
+    """
+    tuple_given_message = "func is expected but received {} in **kwargs."
+    columns = list(kwargs)
+    func = []
+    for col_func in kwargs.values():
+        if not (isinstance(col_func, str) or callable(col_func)):
+            raise TypeError(tuple_given_message.format(type(col_func).__name__))
+        func.append(col_func)
+    if not columns:
+        no_arg_message = "Must provide 'func' or named aggregation **kwargs."
+        raise TypeError(no_arg_message)
+    return columns, func
+
+
+def include_axis(op_name: Literal["agg", "apply"], colg: Series | DataFrame) -> bool:
+    return isinstance(colg, ABCDataFrame) or (
+        isinstance(colg, ABCSeries) and op_name == "agg"
+    )
+
+
+def warn_alias_replacement(
+    obj: AggObjType,
+    func: Callable,
+    alias: str,
+) -> None:
+    if alias.startswith("np."):
+        full_alias = alias
+    else:
+        full_alias = f"{type(obj).__name__}.{alias}"
+        alias = f'"{alias}"'
+    warnings.warn(
+        f"The provided callable {func} is currently using "
+        f"{full_alias}. In a future version of pandas, "
+        f"the provided callable will be used directly. To keep current "
+        f"behavior pass the string {alias} instead.",
+        category=FutureWarning,
+        stacklevel=find_stack_level(),
+    )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/array_algos/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/array_algos/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a7655a013c6cf3fca754086fdeb29b806220d5e4
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/array_algos/__init__.py
@@ -0,0 +1,9 @@
+"""
+core.array_algos is for algorithms that operate on ndarray and ExtensionArray.
+These should:
+
+- Assume that any Index, Series, or DataFrame objects have already been unwrapped.
+- Assume that any list arguments have already been cast to ndarray/EA.
+- Not depend on Index, Series, or DataFrame, nor import any of these.
+- May dispatch to ExtensionArray methods, but should not import from core.arrays.
+"""
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/array_algos/datetimelike_accumulations.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/array_algos/datetimelike_accumulations.py
new file mode 100644
index 0000000000000000000000000000000000000000..825fe60ee6cf88a2186a5f501c8696cecaf2657d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/array_algos/datetimelike_accumulations.py
@@ -0,0 +1,67 @@
+"""
+datetimelke_accumulations.py is for accumulations of datetimelike extension arrays
+"""
+
+from __future__ import annotations
+
+from typing import Callable
+
+import numpy as np
+
+from pandas._libs import iNaT
+
+from pandas.core.dtypes.missing import isna
+
+
+def _cum_func(
+    func: Callable,
+    values: np.ndarray,
+    *,
+    skipna: bool = True,
+):
+    """
+    Accumulations for 1D datetimelike arrays.
+
+    Parameters
+    ----------
+    func : np.cumsum, np.maximum.accumulate, np.minimum.accumulate
+    values : np.ndarray
+        Numpy array with the values (can be of any dtype that support the
+        operation). Values is changed is modified inplace.
+    skipna : bool, default True
+        Whether to skip NA.
+    """
+    try:
+        fill_value = {
+            np.maximum.accumulate: np.iinfo(np.int64).min,
+            np.cumsum: 0,
+            np.minimum.accumulate: np.iinfo(np.int64).max,
+        }[func]
+    except KeyError:
+        raise ValueError(f"No accumulation for {func} implemented on BaseMaskedArray")
+
+    mask = isna(values)
+    y = values.view("i8")
+    y[mask] = fill_value
+
+    if not skipna:
+        mask = np.maximum.accumulate(mask)
+
+    result = func(y)
+    result[mask] = iNaT
+
+    if values.dtype.kind in "mM":
+        return result.view(values.dtype.base)
+    return result
+
+
+def cumsum(values: np.ndarray, *, skipna: bool = True) -> np.ndarray:
+    return _cum_func(np.cumsum, values, skipna=skipna)
+
+
+def cummin(values: np.ndarray, *, skipna: bool = True):
+    return _cum_func(np.minimum.accumulate, values, skipna=skipna)
+
+
+def cummax(values: np.ndarray, *, skipna: bool = True):
+    return _cum_func(np.maximum.accumulate, values, skipna=skipna)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/array_algos/masked_accumulations.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/array_algos/masked_accumulations.py
new file mode 100644
index 0000000000000000000000000000000000000000..ad9e96d398a242dc64de2018b749fd2dbca7ed78
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/array_algos/masked_accumulations.py
@@ -0,0 +1,90 @@
+"""
+masked_accumulations.py is for accumulation algorithms using a mask-based approach
+for missing values.
+"""
+
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    Callable,
+)
+
+import numpy as np
+
+if TYPE_CHECKING:
+    from pandas._typing import npt
+
+
+def _cum_func(
+    func: Callable,
+    values: np.ndarray,
+    mask: npt.NDArray[np.bool_],
+    *,
+    skipna: bool = True,
+):
+    """
+    Accumulations for 1D masked array.
+
+    We will modify values in place to replace NAs with the appropriate fill value.
+
+    Parameters
+    ----------
+    func : np.cumsum, np.cumprod, np.maximum.accumulate, np.minimum.accumulate
+    values : np.ndarray
+        Numpy array with the values (can be of any dtype that support the
+        operation).
+    mask : np.ndarray
+        Boolean numpy array (True values indicate missing values).
+    skipna : bool, default True
+        Whether to skip NA.
+    """
+    dtype_info: np.iinfo | np.finfo
+    if values.dtype.kind == "f":
+        dtype_info = np.finfo(values.dtype.type)
+    elif values.dtype.kind in "iu":
+        dtype_info = np.iinfo(values.dtype.type)
+    elif values.dtype.kind == "b":
+        # Max value of bool is 1, but since we are setting into a boolean
+        # array, 255 is fine as well. Min value has to be 0 when setting
+        # into the boolean array.
+        dtype_info = np.iinfo(np.uint8)
+    else:
+        raise NotImplementedError(
+            f"No masked accumulation defined for dtype {values.dtype.type}"
+        )
+    try:
+        fill_value = {
+            np.cumprod: 1,
+            np.maximum.accumulate: dtype_info.min,
+            np.cumsum: 0,
+            np.minimum.accumulate: dtype_info.max,
+        }[func]
+    except KeyError:
+        raise NotImplementedError(
+            f"No accumulation for {func} implemented on BaseMaskedArray"
+        )
+
+    values[mask] = fill_value
+
+    if not skipna:
+        mask = np.maximum.accumulate(mask)
+
+    values = func(values)
+    return values, mask
+
+
+def cumsum(values: np.ndarray, mask: npt.NDArray[np.bool_], *, skipna: bool = True):
+    return _cum_func(np.cumsum, values, mask, skipna=skipna)
+
+
+def cumprod(values: np.ndarray, mask: npt.NDArray[np.bool_], *, skipna: bool = True):
+    return _cum_func(np.cumprod, values, mask, skipna=skipna)
+
+
+def cummin(values: np.ndarray, mask: npt.NDArray[np.bool_], *, skipna: bool = True):
+    return _cum_func(np.minimum.accumulate, values, mask, skipna=skipna)
+
+
+def cummax(values: np.ndarray, mask: npt.NDArray[np.bool_], *, skipna: bool = True):
+    return _cum_func(np.maximum.accumulate, values, mask, skipna=skipna)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/array_algos/masked_reductions.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/array_algos/masked_reductions.py
new file mode 100644
index 0000000000000000000000000000000000000000..6bf97729a79b1436fdf466eade06e0049984c1d0
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/array_algos/masked_reductions.py
@@ -0,0 +1,201 @@
+"""
+masked_reductions.py is for reduction algorithms using a mask-based approach
+for missing values.
+"""
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    Callable,
+)
+import warnings
+
+import numpy as np
+
+from pandas._libs import missing as libmissing
+
+from pandas.core.nanops import check_below_min_count
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        AxisInt,
+        npt,
+    )
+
+
+def _reductions(
+    func: Callable,
+    values: np.ndarray,
+    mask: npt.NDArray[np.bool_],
+    *,
+    skipna: bool = True,
+    min_count: int = 0,
+    axis: AxisInt | None = None,
+    **kwargs,
+):
+    """
+    Sum, mean or product for 1D masked array.
+
+    Parameters
+    ----------
+    func : np.sum or np.prod
+    values : np.ndarray
+        Numpy array with the values (can be of any dtype that support the
+        operation).
+    mask : np.ndarray[bool]
+        Boolean numpy array (True values indicate missing values).
+    skipna : bool, default True
+        Whether to skip NA.
+    min_count : int, default 0
+        The required number of valid values to perform the operation. If fewer than
+        ``min_count`` non-NA values are present the result will be NA.
+    axis : int, optional, default None
+    """
+    if not skipna:
+        if mask.any() or check_below_min_count(values.shape, None, min_count):
+            return libmissing.NA
+        else:
+            return func(values, axis=axis, **kwargs)
+    else:
+        if check_below_min_count(values.shape, mask, min_count) and (
+            axis is None or values.ndim == 1
+        ):
+            return libmissing.NA
+
+        if values.dtype == np.dtype(object):
+            # object dtype does not support `where` without passing an initial
+            values = values[~mask]
+            return func(values, axis=axis, **kwargs)
+        return func(values, where=~mask, axis=axis, **kwargs)
+
+
+def sum(
+    values: np.ndarray,
+    mask: npt.NDArray[np.bool_],
+    *,
+    skipna: bool = True,
+    min_count: int = 0,
+    axis: AxisInt | None = None,
+):
+    return _reductions(
+        np.sum, values=values, mask=mask, skipna=skipna, min_count=min_count, axis=axis
+    )
+
+
+def prod(
+    values: np.ndarray,
+    mask: npt.NDArray[np.bool_],
+    *,
+    skipna: bool = True,
+    min_count: int = 0,
+    axis: AxisInt | None = None,
+):
+    return _reductions(
+        np.prod, values=values, mask=mask, skipna=skipna, min_count=min_count, axis=axis
+    )
+
+
+def _minmax(
+    func: Callable,
+    values: np.ndarray,
+    mask: npt.NDArray[np.bool_],
+    *,
+    skipna: bool = True,
+    axis: AxisInt | None = None,
+):
+    """
+    Reduction for 1D masked array.
+
+    Parameters
+    ----------
+    func : np.min or np.max
+    values : np.ndarray
+        Numpy array with the values (can be of any dtype that support the
+        operation).
+    mask : np.ndarray[bool]
+        Boolean numpy array (True values indicate missing values).
+    skipna : bool, default True
+        Whether to skip NA.
+    axis : int, optional, default None
+    """
+    if not skipna:
+        if mask.any() or not values.size:
+            # min/max with empty array raise in numpy, pandas returns NA
+            return libmissing.NA
+        else:
+            return func(values, axis=axis)
+    else:
+        subset = values[~mask]
+        if subset.size:
+            return func(subset, axis=axis)
+        else:
+            # min/max with empty array raise in numpy, pandas returns NA
+            return libmissing.NA
+
+
+def min(
+    values: np.ndarray,
+    mask: npt.NDArray[np.bool_],
+    *,
+    skipna: bool = True,
+    axis: AxisInt | None = None,
+):
+    return _minmax(np.min, values=values, mask=mask, skipna=skipna, axis=axis)
+
+
+def max(
+    values: np.ndarray,
+    mask: npt.NDArray[np.bool_],
+    *,
+    skipna: bool = True,
+    axis: AxisInt | None = None,
+):
+    return _minmax(np.max, values=values, mask=mask, skipna=skipna, axis=axis)
+
+
+def mean(
+    values: np.ndarray,
+    mask: npt.NDArray[np.bool_],
+    *,
+    skipna: bool = True,
+    axis: AxisInt | None = None,
+):
+    if not values.size or mask.all():
+        return libmissing.NA
+    return _reductions(np.mean, values=values, mask=mask, skipna=skipna, axis=axis)
+
+
+def var(
+    values: np.ndarray,
+    mask: npt.NDArray[np.bool_],
+    *,
+    skipna: bool = True,
+    axis: AxisInt | None = None,
+    ddof: int = 1,
+):
+    if not values.size or mask.all():
+        return libmissing.NA
+
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore", RuntimeWarning)
+        return _reductions(
+            np.var, values=values, mask=mask, skipna=skipna, axis=axis, ddof=ddof
+        )
+
+
+def std(
+    values: np.ndarray,
+    mask: npt.NDArray[np.bool_],
+    *,
+    skipna: bool = True,
+    axis: AxisInt | None = None,
+    ddof: int = 1,
+):
+    if not values.size or mask.all():
+        return libmissing.NA
+
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore", RuntimeWarning)
+        return _reductions(
+            np.std, values=values, mask=mask, skipna=skipna, axis=axis, ddof=ddof
+        )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/array_algos/putmask.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/array_algos/putmask.py
new file mode 100644
index 0000000000000000000000000000000000000000..f65d2d20e028e36b35a397d8ac973f184ce1412c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/array_algos/putmask.py
@@ -0,0 +1,149 @@
+"""
+EA-compatible analogue to np.putmask
+"""
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    Any,
+)
+
+import numpy as np
+
+from pandas._libs import lib
+
+from pandas.core.dtypes.cast import infer_dtype_from
+from pandas.core.dtypes.common import is_list_like
+
+from pandas.core.arrays import ExtensionArray
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        ArrayLike,
+        npt,
+    )
+
+    from pandas import MultiIndex
+
+
+def putmask_inplace(values: ArrayLike, mask: npt.NDArray[np.bool_], value: Any) -> None:
+    """
+    ExtensionArray-compatible implementation of np.putmask.  The main
+    difference is we do not handle repeating or truncating like numpy.
+
+    Parameters
+    ----------
+    values: np.ndarray or ExtensionArray
+    mask : np.ndarray[bool]
+        We assume extract_bool_array has already been called.
+    value : Any
+    """
+
+    if (
+        not isinstance(values, np.ndarray)
+        or (values.dtype == object and not lib.is_scalar(value))
+        # GH#43424: np.putmask raises TypeError if we cannot cast between types with
+        # rule = "safe", a stricter guarantee we may not have here
+        or (
+            isinstance(value, np.ndarray) and not np.can_cast(value.dtype, values.dtype)
+        )
+    ):
+        # GH#19266 using np.putmask gives unexpected results with listlike value
+        #  along with object dtype
+        if is_list_like(value) and len(value) == len(values):
+            values[mask] = value[mask]
+        else:
+            values[mask] = value
+    else:
+        # GH#37833 np.putmask is more performant than __setitem__
+        np.putmask(values, mask, value)
+
+
+def putmask_without_repeat(
+    values: np.ndarray, mask: npt.NDArray[np.bool_], new: Any
+) -> None:
+    """
+    np.putmask will truncate or repeat if `new` is a listlike with
+    len(new) != len(values).  We require an exact match.
+
+    Parameters
+    ----------
+    values : np.ndarray
+    mask : np.ndarray[bool]
+    new : Any
+    """
+    if getattr(new, "ndim", 0) >= 1:
+        new = new.astype(values.dtype, copy=False)
+
+    # TODO: this prob needs some better checking for 2D cases
+    nlocs = mask.sum()
+    if nlocs > 0 and is_list_like(new) and getattr(new, "ndim", 1) == 1:
+        shape = np.shape(new)
+        # np.shape compat for if setitem_datetimelike_compat
+        #  changed arraylike to list e.g. test_where_dt64_2d
+        if nlocs == shape[-1]:
+            # GH#30567
+            # If length of ``new`` is less than the length of ``values``,
+            # `np.putmask` would first repeat the ``new`` array and then
+            # assign the masked values hence produces incorrect result.
+            # `np.place` on the other hand uses the ``new`` values at it is
+            # to place in the masked locations of ``values``
+            np.place(values, mask, new)
+            # i.e. values[mask] = new
+        elif mask.shape[-1] == shape[-1] or shape[-1] == 1:
+            np.putmask(values, mask, new)
+        else:
+            raise ValueError("cannot assign mismatch length to masked array")
+    else:
+        np.putmask(values, mask, new)
+
+
+def validate_putmask(
+    values: ArrayLike | MultiIndex, mask: np.ndarray
+) -> tuple[npt.NDArray[np.bool_], bool]:
+    """
+    Validate mask and check if this putmask operation is a no-op.
+    """
+    mask = extract_bool_array(mask)
+    if mask.shape != values.shape:
+        raise ValueError("putmask: mask and data must be the same size")
+
+    noop = not mask.any()
+    return mask, noop
+
+
+def extract_bool_array(mask: ArrayLike) -> npt.NDArray[np.bool_]:
+    """
+    If we have a SparseArray or BooleanArray, convert it to ndarray[bool].
+    """
+    if isinstance(mask, ExtensionArray):
+        # We could have BooleanArray, Sparse[bool], ...
+        #  Except for BooleanArray, this is equivalent to just
+        #  np.asarray(mask, dtype=bool)
+        mask = mask.to_numpy(dtype=bool, na_value=False)
+
+    mask = np.asarray(mask, dtype=bool)
+    return mask
+
+
+def setitem_datetimelike_compat(values: np.ndarray, num_set: int, other):
+    """
+    Parameters
+    ----------
+    values : np.ndarray
+    num_set : int
+        For putmask, this is mask.sum()
+    other : Any
+    """
+    if values.dtype == object:
+        dtype, _ = infer_dtype_from(other)
+
+        if lib.is_np_dtype(dtype, "mM"):
+            # https://github.com/numpy/numpy/issues/12550
+            #  timedelta64 will incorrectly cast to int
+            if not is_list_like(other):
+                other = [other] * num_set
+            else:
+                other = list(other)
+
+    return other
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/array_algos/quantile.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/array_algos/quantile.py
new file mode 100644
index 0000000000000000000000000000000000000000..5c933294fb944f04dd3e9a64e4731ea4349254f9
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/array_algos/quantile.py
@@ -0,0 +1,226 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import numpy as np
+
+from pandas.core.dtypes.missing import (
+    isna,
+    na_value_for_dtype,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        ArrayLike,
+        Scalar,
+        npt,
+    )
+
+
+def quantile_compat(
+    values: ArrayLike, qs: npt.NDArray[np.float64], interpolation: str
+) -> ArrayLike:
+    """
+    Compute the quantiles of the given values for each quantile in `qs`.
+
+    Parameters
+    ----------
+    values : np.ndarray or ExtensionArray
+    qs : np.ndarray[float64]
+    interpolation : str
+
+    Returns
+    -------
+    np.ndarray or ExtensionArray
+    """
+    if isinstance(values, np.ndarray):
+        fill_value = na_value_for_dtype(values.dtype, compat=False)
+        mask = isna(values)
+        return quantile_with_mask(values, mask, fill_value, qs, interpolation)
+    else:
+        return values._quantile(qs, interpolation)
+
+
+def quantile_with_mask(
+    values: np.ndarray,
+    mask: npt.NDArray[np.bool_],
+    fill_value,
+    qs: npt.NDArray[np.float64],
+    interpolation: str,
+) -> np.ndarray:
+    """
+    Compute the quantiles of the given values for each quantile in `qs`.
+
+    Parameters
+    ----------
+    values : np.ndarray
+        For ExtensionArray, this is _values_for_factorize()[0]
+    mask : np.ndarray[bool]
+        mask = isna(values)
+        For ExtensionArray, this is computed before calling _value_for_factorize
+    fill_value : Scalar
+        The value to interpret fill NA entries with
+        For ExtensionArray, this is _values_for_factorize()[1]
+    qs : np.ndarray[float64]
+    interpolation : str
+        Type of interpolation
+
+    Returns
+    -------
+    np.ndarray
+
+    Notes
+    -----
+    Assumes values is already 2D.  For ExtensionArray this means np.atleast_2d
+    has been called on _values_for_factorize()[0]
+
+    Quantile is computed along axis=1.
+    """
+    assert values.shape == mask.shape
+    if values.ndim == 1:
+        # unsqueeze, operate, re-squeeze
+        values = np.atleast_2d(values)
+        mask = np.atleast_2d(mask)
+        res_values = quantile_with_mask(values, mask, fill_value, qs, interpolation)
+        return res_values[0]
+
+    assert values.ndim == 2
+
+    is_empty = values.shape[1] == 0
+
+    if is_empty:
+        # create the array of na_values
+        # 2d len(values) * len(qs)
+        flat = np.array([fill_value] * len(qs))
+        result = np.repeat(flat, len(values)).reshape(len(values), len(qs))
+    else:
+        result = _nanpercentile(
+            values,
+            qs * 100.0,
+            na_value=fill_value,
+            mask=mask,
+            interpolation=interpolation,
+        )
+
+        result = np.asarray(result)
+        result = result.T
+
+    return result
+
+
+def _nanpercentile_1d(
+    values: np.ndarray,
+    mask: npt.NDArray[np.bool_],
+    qs: npt.NDArray[np.float64],
+    na_value: Scalar,
+    interpolation: str,
+) -> Scalar | np.ndarray:
+    """
+    Wrapper for np.percentile that skips missing values, specialized to
+    1-dimensional case.
+
+    Parameters
+    ----------
+    values : array over which to find quantiles
+    mask : ndarray[bool]
+        locations in values that should be considered missing
+    qs : np.ndarray[float64] of quantile indices to find
+    na_value : scalar
+        value to return for empty or all-null values
+    interpolation : str
+
+    Returns
+    -------
+    quantiles : scalar or array
+    """
+    # mask is Union[ExtensionArray, ndarray]
+    values = values[~mask]
+
+    if len(values) == 0:
+        # Can't pass dtype=values.dtype here bc we might have na_value=np.nan
+        #  with values.dtype=int64 see test_quantile_empty
+        # equiv: 'np.array([na_value] * len(qs))' but much faster
+        return np.full(len(qs), na_value)
+
+    return np.percentile(
+        values,
+        qs,
+        # error: No overload variant of "percentile" matches argument
+        # types "ndarray[Any, Any]", "ndarray[Any, dtype[floating[_64Bit]]]"
+        # , "Dict[str, str]"  [call-overload]
+        method=interpolation,  # type: ignore[call-overload]
+    )
+
+
+def _nanpercentile(
+    values: np.ndarray,
+    qs: npt.NDArray[np.float64],
+    *,
+    na_value,
+    mask: npt.NDArray[np.bool_],
+    interpolation: str,
+):
+    """
+    Wrapper for np.percentile that skips missing values.
+
+    Parameters
+    ----------
+    values : np.ndarray[ndim=2]  over which to find quantiles
+    qs : np.ndarray[float64] of quantile indices to find
+    na_value : scalar
+        value to return for empty or all-null values
+    mask : np.ndarray[bool]
+        locations in values that should be considered missing
+    interpolation : str
+
+    Returns
+    -------
+    quantiles : scalar or array
+    """
+
+    if values.dtype.kind in "mM":
+        # need to cast to integer to avoid rounding errors in numpy
+        result = _nanpercentile(
+            values.view("i8"),
+            qs=qs,
+            na_value=na_value.view("i8"),
+            mask=mask,
+            interpolation=interpolation,
+        )
+
+        # Note: we have to do `astype` and not view because in general we
+        #  have float result at this point, not i8
+        return result.astype(values.dtype)
+
+    if mask.any():
+        # Caller is responsible for ensuring mask shape match
+        assert mask.shape == values.shape
+        result = [
+            _nanpercentile_1d(val, m, qs, na_value, interpolation=interpolation)
+            for (val, m) in zip(list(values), list(mask))
+        ]
+        if values.dtype.kind == "f":
+            # preserve itemsize
+            result = np.asarray(result, dtype=values.dtype).T
+        else:
+            result = np.asarray(result).T
+            if (
+                result.dtype != values.dtype
+                and not mask.all()
+                and (result == result.astype(values.dtype, copy=False)).all()
+            ):
+                # mask.all() will never get cast back to int
+                # e.g. values id integer dtype and result is floating dtype,
+                #  only cast back to integer dtype if result values are all-integer.
+                result = result.astype(values.dtype, copy=False)
+        return result
+    else:
+        return np.percentile(
+            values,
+            qs,
+            axis=1,
+            # error: No overload variant of "percentile" matches argument types
+            # "ndarray[Any, Any]", "ndarray[Any, dtype[floating[_64Bit]]]",
+            # "int", "Dict[str, str]"  [call-overload]
+            method=interpolation,  # type: ignore[call-overload]
+        )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/array_algos/replace.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/array_algos/replace.py
new file mode 100644
index 0000000000000000000000000000000000000000..16239ed8c46970f38159ae12bc9a45da31ffd617
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/array_algos/replace.py
@@ -0,0 +1,155 @@
+"""
+Methods used by Block.replace and related methods.
+"""
+from __future__ import annotations
+
+import operator
+import re
+from re import Pattern
+from typing import (
+    TYPE_CHECKING,
+    Any,
+)
+
+import numpy as np
+
+from pandas.core.dtypes.common import (
+    is_bool,
+    is_re,
+    is_re_compilable,
+)
+from pandas.core.dtypes.missing import isna
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        ArrayLike,
+        Scalar,
+        npt,
+    )
+
+
+def should_use_regex(regex: bool, to_replace: Any) -> bool:
+    """
+    Decide whether to treat `to_replace` as a regular expression.
+    """
+    if is_re(to_replace):
+        regex = True
+
+    regex = regex and is_re_compilable(to_replace)
+
+    # Don't use regex if the pattern is empty.
+    regex = regex and re.compile(to_replace).pattern != ""
+    return regex
+
+
+def compare_or_regex_search(
+    a: ArrayLike, b: Scalar | Pattern, regex: bool, mask: npt.NDArray[np.bool_]
+) -> ArrayLike:
+    """
+    Compare two array-like inputs of the same shape or two scalar values
+
+    Calls operator.eq or re.search, depending on regex argument. If regex is
+    True, perform an element-wise regex matching.
+
+    Parameters
+    ----------
+    a : array-like
+    b : scalar or regex pattern
+    regex : bool
+    mask : np.ndarray[bool]
+
+    Returns
+    -------
+    mask : array-like of bool
+    """
+    if isna(b):
+        return ~mask
+
+    def _check_comparison_types(
+        result: ArrayLike | bool, a: ArrayLike, b: Scalar | Pattern
+    ):
+        """
+        Raises an error if the two arrays (a,b) cannot be compared.
+        Otherwise, returns the comparison result as expected.
+        """
+        if is_bool(result) and isinstance(a, np.ndarray):
+            type_names = [type(a).__name__, type(b).__name__]
+
+            type_names[0] = f"ndarray(dtype={a.dtype})"
+
+            raise TypeError(
+                f"Cannot compare types {repr(type_names[0])} and {repr(type_names[1])}"
+            )
+
+    if not regex or not should_use_regex(regex, b):
+        # TODO: should use missing.mask_missing?
+        op = lambda x: operator.eq(x, b)
+    else:
+        op = np.vectorize(
+            lambda x: bool(re.search(b, x))
+            if isinstance(x, str) and isinstance(b, (str, Pattern))
+            else False
+        )
+
+    # GH#32621 use mask to avoid comparing to NAs
+    if isinstance(a, np.ndarray) and mask is not None:
+        a = a[mask]
+        result = op(a)
+
+        if isinstance(result, np.ndarray):
+            # The shape of the mask can differ to that of the result
+            # since we may compare only a subset of a's or b's elements
+            tmp = np.zeros(mask.shape, dtype=np.bool_)
+            np.place(tmp, mask, result)
+            result = tmp
+    else:
+        result = op(a)
+
+    _check_comparison_types(result, a, b)
+    return result
+
+
+def replace_regex(
+    values: ArrayLike, rx: re.Pattern, value, mask: npt.NDArray[np.bool_] | None
+) -> None:
+    """
+    Parameters
+    ----------
+    values : ArrayLike
+        Object dtype.
+    rx : re.Pattern
+    value : Any
+    mask : np.ndarray[bool], optional
+
+    Notes
+    -----
+    Alters values in-place.
+    """
+
+    # deal with replacing values with objects (strings) that match but
+    # whose replacement is not a string (numeric, nan, object)
+    if isna(value) or not isinstance(value, str):
+
+        def re_replacer(s):
+            if is_re(rx) and isinstance(s, str):
+                return value if rx.search(s) is not None else s
+            else:
+                return s
+
+    else:
+        # value is guaranteed to be a string here, s can be either a string
+        # or null if it's null it gets returned
+        def re_replacer(s):
+            if is_re(rx) and isinstance(s, str):
+                return rx.sub(value, s)
+            else:
+                return s
+
+    f = np.vectorize(re_replacer, otypes=[np.object_])
+
+    if mask is None:
+        values[:] = f(values)
+    else:
+        if values.ndim != mask.ndim:
+            mask = np.broadcast_to(mask, values.shape)
+        values[mask] = f(values[mask])
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/array_algos/take.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/array_algos/take.py
new file mode 100644
index 0000000000000000000000000000000000000000..ac674e31586e72040a8e1313f232a00299b961ee
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/array_algos/take.py
@@ -0,0 +1,594 @@
+from __future__ import annotations
+
+import functools
+from typing import (
+    TYPE_CHECKING,
+    cast,
+    overload,
+)
+
+import numpy as np
+
+from pandas._libs import (
+    algos as libalgos,
+    lib,
+)
+
+from pandas.core.dtypes.cast import maybe_promote
+from pandas.core.dtypes.common import (
+    ensure_platform_int,
+    is_1d_only_ea_dtype,
+)
+from pandas.core.dtypes.missing import na_value_for_dtype
+
+from pandas.core.construction import ensure_wrapped_if_datetimelike
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        ArrayLike,
+        AxisInt,
+        npt,
+    )
+
+    from pandas.core.arrays._mixins import NDArrayBackedExtensionArray
+    from pandas.core.arrays.base import ExtensionArray
+
+
+@overload
+def take_nd(
+    arr: np.ndarray,
+    indexer,
+    axis: AxisInt = ...,
+    fill_value=...,
+    allow_fill: bool = ...,
+) -> np.ndarray:
+    ...
+
+
+@overload
+def take_nd(
+    arr: ExtensionArray,
+    indexer,
+    axis: AxisInt = ...,
+    fill_value=...,
+    allow_fill: bool = ...,
+) -> ArrayLike:
+    ...
+
+
+def take_nd(
+    arr: ArrayLike,
+    indexer,
+    axis: AxisInt = 0,
+    fill_value=lib.no_default,
+    allow_fill: bool = True,
+) -> ArrayLike:
+    """
+    Specialized Cython take which sets NaN values in one pass
+
+    This dispatches to ``take`` defined on ExtensionArrays.
+
+    Note: this function assumes that the indexer is a valid(ated) indexer with
+    no out of bound indices.
+
+    Parameters
+    ----------
+    arr : np.ndarray or ExtensionArray
+        Input array.
+    indexer : ndarray
+        1-D array of indices to take, subarrays corresponding to -1 value
+        indices are filed with fill_value
+    axis : int, default 0
+        Axis to take from
+    fill_value : any, default np.nan
+        Fill value to replace -1 values with
+    allow_fill : bool, default True
+        If False, indexer is assumed to contain no -1 values so no filling
+        will be done.  This short-circuits computation of a mask.  Result is
+        undefined if allow_fill == False and -1 is present in indexer.
+
+    Returns
+    -------
+    subarray : np.ndarray or ExtensionArray
+        May be the same type as the input, or cast to an ndarray.
+    """
+    if fill_value is lib.no_default:
+        fill_value = na_value_for_dtype(arr.dtype, compat=False)
+    elif lib.is_np_dtype(arr.dtype, "mM"):
+        dtype, fill_value = maybe_promote(arr.dtype, fill_value)
+        if arr.dtype != dtype:
+            # EA.take is strict about returning a new object of the same type
+            # so for that case cast upfront
+            arr = arr.astype(dtype)
+
+    if not isinstance(arr, np.ndarray):
+        # i.e. ExtensionArray,
+        # includes for EA to catch DatetimeArray, TimedeltaArray
+        if not is_1d_only_ea_dtype(arr.dtype):
+            # i.e. DatetimeArray, TimedeltaArray
+            arr = cast("NDArrayBackedExtensionArray", arr)
+            return arr.take(
+                indexer, fill_value=fill_value, allow_fill=allow_fill, axis=axis
+            )
+
+        return arr.take(indexer, fill_value=fill_value, allow_fill=allow_fill)
+
+    arr = np.asarray(arr)
+    return _take_nd_ndarray(arr, indexer, axis, fill_value, allow_fill)
+
+
+def _take_nd_ndarray(
+    arr: np.ndarray,
+    indexer: npt.NDArray[np.intp] | None,
+    axis: AxisInt,
+    fill_value,
+    allow_fill: bool,
+) -> np.ndarray:
+    if indexer is None:
+        indexer = np.arange(arr.shape[axis], dtype=np.intp)
+        dtype, fill_value = arr.dtype, arr.dtype.type()
+    else:
+        indexer = ensure_platform_int(indexer)
+
+    dtype, fill_value, mask_info = _take_preprocess_indexer_and_fill_value(
+        arr, indexer, fill_value, allow_fill
+    )
+
+    flip_order = False
+    if arr.ndim == 2 and arr.flags.f_contiguous:
+        flip_order = True
+
+    if flip_order:
+        arr = arr.T
+        axis = arr.ndim - axis - 1
+
+    # at this point, it's guaranteed that dtype can hold both the arr values
+    # and the fill_value
+    out_shape_ = list(arr.shape)
+    out_shape_[axis] = len(indexer)
+    out_shape = tuple(out_shape_)
+    if arr.flags.f_contiguous and axis == arr.ndim - 1:
+        # minor tweak that can make an order-of-magnitude difference
+        # for dataframes initialized directly from 2-d ndarrays
+        # (s.t. df.values is c-contiguous and df._mgr.blocks[0] is its
+        # f-contiguous transpose)
+        out = np.empty(out_shape, dtype=dtype, order="F")
+    else:
+        out = np.empty(out_shape, dtype=dtype)
+
+    func = _get_take_nd_function(
+        arr.ndim, arr.dtype, out.dtype, axis=axis, mask_info=mask_info
+    )
+    func(arr, indexer, out, fill_value)
+
+    if flip_order:
+        out = out.T
+    return out
+
+
+def take_1d(
+    arr: ArrayLike,
+    indexer: npt.NDArray[np.intp],
+    fill_value=None,
+    allow_fill: bool = True,
+    mask: npt.NDArray[np.bool_] | None = None,
+) -> ArrayLike:
+    """
+    Specialized version for 1D arrays. Differences compared to `take_nd`:
+
+    - Assumes input array has already been converted to numpy array / EA
+    - Assumes indexer is already guaranteed to be intp dtype ndarray
+    - Only works for 1D arrays
+
+    To ensure the lowest possible overhead.
+
+    Note: similarly to `take_nd`, this function assumes that the indexer is
+    a valid(ated) indexer with no out of bound indices.
+
+    Parameters
+    ----------
+    arr : np.ndarray or ExtensionArray
+        Input array.
+    indexer : ndarray
+        1-D array of indices to take (validated indices, intp dtype).
+    fill_value : any, default np.nan
+        Fill value to replace -1 values with
+    allow_fill : bool, default True
+        If False, indexer is assumed to contain no -1 values so no filling
+        will be done.  This short-circuits computation of a mask. Result is
+        undefined if allow_fill == False and -1 is present in indexer.
+    mask : np.ndarray, optional, default None
+        If `allow_fill` is True, and the mask (where indexer == -1) is already
+        known, it can be passed to avoid recomputation.
+    """
+    if not isinstance(arr, np.ndarray):
+        # ExtensionArray -> dispatch to their method
+        return arr.take(indexer, fill_value=fill_value, allow_fill=allow_fill)
+
+    if not allow_fill:
+        return arr.take(indexer)
+
+    dtype, fill_value, mask_info = _take_preprocess_indexer_and_fill_value(
+        arr, indexer, fill_value, True, mask
+    )
+
+    # at this point, it's guaranteed that dtype can hold both the arr values
+    # and the fill_value
+    out = np.empty(indexer.shape, dtype=dtype)
+
+    func = _get_take_nd_function(
+        arr.ndim, arr.dtype, out.dtype, axis=0, mask_info=mask_info
+    )
+    func(arr, indexer, out, fill_value)
+
+    return out
+
+
+def take_2d_multi(
+    arr: np.ndarray,
+    indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]],
+    fill_value=np.nan,
+) -> np.ndarray:
+    """
+    Specialized Cython take which sets NaN values in one pass.
+    """
+    # This is only called from one place in DataFrame._reindex_multi,
+    #  so we know indexer is well-behaved.
+    assert indexer is not None
+    assert indexer[0] is not None
+    assert indexer[1] is not None
+
+    row_idx, col_idx = indexer
+
+    row_idx = ensure_platform_int(row_idx)
+    col_idx = ensure_platform_int(col_idx)
+    indexer = row_idx, col_idx
+    mask_info = None
+
+    # check for promotion based on types only (do this first because
+    # it's faster than computing a mask)
+    dtype, fill_value = maybe_promote(arr.dtype, fill_value)
+    if dtype != arr.dtype:
+        # check if promotion is actually required based on indexer
+        row_mask = row_idx == -1
+        col_mask = col_idx == -1
+        row_needs = row_mask.any()
+        col_needs = col_mask.any()
+        mask_info = (row_mask, col_mask), (row_needs, col_needs)
+
+        if not (row_needs or col_needs):
+            # if not, then depromote, set fill_value to dummy
+            # (it won't be used but we don't want the cython code
+            # to crash when trying to cast it to dtype)
+            dtype, fill_value = arr.dtype, arr.dtype.type()
+
+    # at this point, it's guaranteed that dtype can hold both the arr values
+    # and the fill_value
+    out_shape = len(row_idx), len(col_idx)
+    out = np.empty(out_shape, dtype=dtype)
+
+    func = _take_2d_multi_dict.get((arr.dtype.name, out.dtype.name), None)
+    if func is None and arr.dtype != out.dtype:
+        func = _take_2d_multi_dict.get((out.dtype.name, out.dtype.name), None)
+        if func is not None:
+            func = _convert_wrapper(func, out.dtype)
+
+    if func is not None:
+        func(arr, indexer, out=out, fill_value=fill_value)
+    else:
+        # test_reindex_multi
+        _take_2d_multi_object(
+            arr, indexer, out, fill_value=fill_value, mask_info=mask_info
+        )
+
+    return out
+
+
+@functools.lru_cache
+def _get_take_nd_function_cached(
+    ndim: int, arr_dtype: np.dtype, out_dtype: np.dtype, axis: AxisInt
+):
+    """
+    Part of _get_take_nd_function below that doesn't need `mask_info` and thus
+    can be cached (mask_info potentially contains a numpy ndarray which is not
+    hashable and thus cannot be used as argument for cached function).
+    """
+    tup = (arr_dtype.name, out_dtype.name)
+    if ndim == 1:
+        func = _take_1d_dict.get(tup, None)
+    elif ndim == 2:
+        if axis == 0:
+            func = _take_2d_axis0_dict.get(tup, None)
+        else:
+            func = _take_2d_axis1_dict.get(tup, None)
+    if func is not None:
+        return func
+
+    # We get here with string, uint, float16, and complex dtypes that could
+    #  potentially be handled in algos_take_helper.
+    #  Also a couple with (M8[ns], object) and (m8[ns], object)
+    tup = (out_dtype.name, out_dtype.name)
+    if ndim == 1:
+        func = _take_1d_dict.get(tup, None)
+    elif ndim == 2:
+        if axis == 0:
+            func = _take_2d_axis0_dict.get(tup, None)
+        else:
+            func = _take_2d_axis1_dict.get(tup, None)
+    if func is not None:
+        func = _convert_wrapper(func, out_dtype)
+        return func
+
+    return None
+
+
+def _get_take_nd_function(
+    ndim: int,
+    arr_dtype: np.dtype,
+    out_dtype: np.dtype,
+    axis: AxisInt = 0,
+    mask_info=None,
+):
+    """
+    Get the appropriate "take" implementation for the given dimension, axis
+    and dtypes.
+    """
+    func = None
+    if ndim <= 2:
+        # for this part we don't need `mask_info` -> use the cached algo lookup
+        func = _get_take_nd_function_cached(ndim, arr_dtype, out_dtype, axis)
+
+    if func is None:
+
+        def func(arr, indexer, out, fill_value=np.nan) -> None:
+            indexer = ensure_platform_int(indexer)
+            _take_nd_object(
+                arr, indexer, out, axis=axis, fill_value=fill_value, mask_info=mask_info
+            )
+
+    return func
+
+
+def _view_wrapper(f, arr_dtype=None, out_dtype=None, fill_wrap=None):
+    def wrapper(
+        arr: np.ndarray, indexer: np.ndarray, out: np.ndarray, fill_value=np.nan
+    ) -> None:
+        if arr_dtype is not None:
+            arr = arr.view(arr_dtype)
+        if out_dtype is not None:
+            out = out.view(out_dtype)
+        if fill_wrap is not None:
+            # FIXME: if we get here with dt64/td64 we need to be sure we have
+            #  matching resos
+            if fill_value.dtype.kind == "m":
+                fill_value = fill_value.astype("m8[ns]")
+            else:
+                fill_value = fill_value.astype("M8[ns]")
+            fill_value = fill_wrap(fill_value)
+
+        f(arr, indexer, out, fill_value=fill_value)
+
+    return wrapper
+
+
+def _convert_wrapper(f, conv_dtype):
+    def wrapper(
+        arr: np.ndarray, indexer: np.ndarray, out: np.ndarray, fill_value=np.nan
+    ) -> None:
+        if conv_dtype == object:
+            # GH#39755 avoid casting dt64/td64 to integers
+            arr = ensure_wrapped_if_datetimelike(arr)
+        arr = arr.astype(conv_dtype)
+        f(arr, indexer, out, fill_value=fill_value)
+
+    return wrapper
+
+
+_take_1d_dict = {
+    ("int8", "int8"): libalgos.take_1d_int8_int8,
+    ("int8", "int32"): libalgos.take_1d_int8_int32,
+    ("int8", "int64"): libalgos.take_1d_int8_int64,
+    ("int8", "float64"): libalgos.take_1d_int8_float64,
+    ("int16", "int16"): libalgos.take_1d_int16_int16,
+    ("int16", "int32"): libalgos.take_1d_int16_int32,
+    ("int16", "int64"): libalgos.take_1d_int16_int64,
+    ("int16", "float64"): libalgos.take_1d_int16_float64,
+    ("int32", "int32"): libalgos.take_1d_int32_int32,
+    ("int32", "int64"): libalgos.take_1d_int32_int64,
+    ("int32", "float64"): libalgos.take_1d_int32_float64,
+    ("int64", "int64"): libalgos.take_1d_int64_int64,
+    ("int64", "float64"): libalgos.take_1d_int64_float64,
+    ("float32", "float32"): libalgos.take_1d_float32_float32,
+    ("float32", "float64"): libalgos.take_1d_float32_float64,
+    ("float64", "float64"): libalgos.take_1d_float64_float64,
+    ("object", "object"): libalgos.take_1d_object_object,
+    ("bool", "bool"): _view_wrapper(libalgos.take_1d_bool_bool, np.uint8, np.uint8),
+    ("bool", "object"): _view_wrapper(libalgos.take_1d_bool_object, np.uint8, None),
+    ("datetime64[ns]", "datetime64[ns]"): _view_wrapper(
+        libalgos.take_1d_int64_int64, np.int64, np.int64, np.int64
+    ),
+    ("timedelta64[ns]", "timedelta64[ns]"): _view_wrapper(
+        libalgos.take_1d_int64_int64, np.int64, np.int64, np.int64
+    ),
+}
+
+_take_2d_axis0_dict = {
+    ("int8", "int8"): libalgos.take_2d_axis0_int8_int8,
+    ("int8", "int32"): libalgos.take_2d_axis0_int8_int32,
+    ("int8", "int64"): libalgos.take_2d_axis0_int8_int64,
+    ("int8", "float64"): libalgos.take_2d_axis0_int8_float64,
+    ("int16", "int16"): libalgos.take_2d_axis0_int16_int16,
+    ("int16", "int32"): libalgos.take_2d_axis0_int16_int32,
+    ("int16", "int64"): libalgos.take_2d_axis0_int16_int64,
+    ("int16", "float64"): libalgos.take_2d_axis0_int16_float64,
+    ("int32", "int32"): libalgos.take_2d_axis0_int32_int32,
+    ("int32", "int64"): libalgos.take_2d_axis0_int32_int64,
+    ("int32", "float64"): libalgos.take_2d_axis0_int32_float64,
+    ("int64", "int64"): libalgos.take_2d_axis0_int64_int64,
+    ("int64", "float64"): libalgos.take_2d_axis0_int64_float64,
+    ("float32", "float32"): libalgos.take_2d_axis0_float32_float32,
+    ("float32", "float64"): libalgos.take_2d_axis0_float32_float64,
+    ("float64", "float64"): libalgos.take_2d_axis0_float64_float64,
+    ("object", "object"): libalgos.take_2d_axis0_object_object,
+    ("bool", "bool"): _view_wrapper(
+        libalgos.take_2d_axis0_bool_bool, np.uint8, np.uint8
+    ),
+    ("bool", "object"): _view_wrapper(
+        libalgos.take_2d_axis0_bool_object, np.uint8, None
+    ),
+    ("datetime64[ns]", "datetime64[ns]"): _view_wrapper(
+        libalgos.take_2d_axis0_int64_int64, np.int64, np.int64, fill_wrap=np.int64
+    ),
+    ("timedelta64[ns]", "timedelta64[ns]"): _view_wrapper(
+        libalgos.take_2d_axis0_int64_int64, np.int64, np.int64, fill_wrap=np.int64
+    ),
+}
+
+_take_2d_axis1_dict = {
+    ("int8", "int8"): libalgos.take_2d_axis1_int8_int8,
+    ("int8", "int32"): libalgos.take_2d_axis1_int8_int32,
+    ("int8", "int64"): libalgos.take_2d_axis1_int8_int64,
+    ("int8", "float64"): libalgos.take_2d_axis1_int8_float64,
+    ("int16", "int16"): libalgos.take_2d_axis1_int16_int16,
+    ("int16", "int32"): libalgos.take_2d_axis1_int16_int32,
+    ("int16", "int64"): libalgos.take_2d_axis1_int16_int64,
+    ("int16", "float64"): libalgos.take_2d_axis1_int16_float64,
+    ("int32", "int32"): libalgos.take_2d_axis1_int32_int32,
+    ("int32", "int64"): libalgos.take_2d_axis1_int32_int64,
+    ("int32", "float64"): libalgos.take_2d_axis1_int32_float64,
+    ("int64", "int64"): libalgos.take_2d_axis1_int64_int64,
+    ("int64", "float64"): libalgos.take_2d_axis1_int64_float64,
+    ("float32", "float32"): libalgos.take_2d_axis1_float32_float32,
+    ("float32", "float64"): libalgos.take_2d_axis1_float32_float64,
+    ("float64", "float64"): libalgos.take_2d_axis1_float64_float64,
+    ("object", "object"): libalgos.take_2d_axis1_object_object,
+    ("bool", "bool"): _view_wrapper(
+        libalgos.take_2d_axis1_bool_bool, np.uint8, np.uint8
+    ),
+    ("bool", "object"): _view_wrapper(
+        libalgos.take_2d_axis1_bool_object, np.uint8, None
+    ),
+    ("datetime64[ns]", "datetime64[ns]"): _view_wrapper(
+        libalgos.take_2d_axis1_int64_int64, np.int64, np.int64, fill_wrap=np.int64
+    ),
+    ("timedelta64[ns]", "timedelta64[ns]"): _view_wrapper(
+        libalgos.take_2d_axis1_int64_int64, np.int64, np.int64, fill_wrap=np.int64
+    ),
+}
+
+_take_2d_multi_dict = {
+    ("int8", "int8"): libalgos.take_2d_multi_int8_int8,
+    ("int8", "int32"): libalgos.take_2d_multi_int8_int32,
+    ("int8", "int64"): libalgos.take_2d_multi_int8_int64,
+    ("int8", "float64"): libalgos.take_2d_multi_int8_float64,
+    ("int16", "int16"): libalgos.take_2d_multi_int16_int16,
+    ("int16", "int32"): libalgos.take_2d_multi_int16_int32,
+    ("int16", "int64"): libalgos.take_2d_multi_int16_int64,
+    ("int16", "float64"): libalgos.take_2d_multi_int16_float64,
+    ("int32", "int32"): libalgos.take_2d_multi_int32_int32,
+    ("int32", "int64"): libalgos.take_2d_multi_int32_int64,
+    ("int32", "float64"): libalgos.take_2d_multi_int32_float64,
+    ("int64", "int64"): libalgos.take_2d_multi_int64_int64,
+    ("int64", "float64"): libalgos.take_2d_multi_int64_float64,
+    ("float32", "float32"): libalgos.take_2d_multi_float32_float32,
+    ("float32", "float64"): libalgos.take_2d_multi_float32_float64,
+    ("float64", "float64"): libalgos.take_2d_multi_float64_float64,
+    ("object", "object"): libalgos.take_2d_multi_object_object,
+    ("bool", "bool"): _view_wrapper(
+        libalgos.take_2d_multi_bool_bool, np.uint8, np.uint8
+    ),
+    ("bool", "object"): _view_wrapper(
+        libalgos.take_2d_multi_bool_object, np.uint8, None
+    ),
+    ("datetime64[ns]", "datetime64[ns]"): _view_wrapper(
+        libalgos.take_2d_multi_int64_int64, np.int64, np.int64, fill_wrap=np.int64
+    ),
+    ("timedelta64[ns]", "timedelta64[ns]"): _view_wrapper(
+        libalgos.take_2d_multi_int64_int64, np.int64, np.int64, fill_wrap=np.int64
+    ),
+}
+
+
+def _take_nd_object(
+    arr: np.ndarray,
+    indexer: npt.NDArray[np.intp],
+    out: np.ndarray,
+    axis: AxisInt,
+    fill_value,
+    mask_info,
+) -> None:
+    if mask_info is not None:
+        mask, needs_masking = mask_info
+    else:
+        mask = indexer == -1
+        needs_masking = mask.any()
+    if arr.dtype != out.dtype:
+        arr = arr.astype(out.dtype)
+    if arr.shape[axis] > 0:
+        arr.take(indexer, axis=axis, out=out)
+    if needs_masking:
+        outindexer = [slice(None)] * arr.ndim
+        outindexer[axis] = mask
+        out[tuple(outindexer)] = fill_value
+
+
+def _take_2d_multi_object(
+    arr: np.ndarray,
+    indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]],
+    out: np.ndarray,
+    fill_value,
+    mask_info,
+) -> None:
+    # this is not ideal, performance-wise, but it's better than raising
+    # an exception (best to optimize in Cython to avoid getting here)
+    row_idx, col_idx = indexer  # both np.intp
+    if mask_info is not None:
+        (row_mask, col_mask), (row_needs, col_needs) = mask_info
+    else:
+        row_mask = row_idx == -1
+        col_mask = col_idx == -1
+        row_needs = row_mask.any()
+        col_needs = col_mask.any()
+    if fill_value is not None:
+        if row_needs:
+            out[row_mask, :] = fill_value
+        if col_needs:
+            out[:, col_mask] = fill_value
+    for i, u_ in enumerate(row_idx):
+        if u_ != -1:
+            for j, v in enumerate(col_idx):
+                if v != -1:
+                    out[i, j] = arr[u_, v]
+
+
+def _take_preprocess_indexer_and_fill_value(
+    arr: np.ndarray,
+    indexer: npt.NDArray[np.intp],
+    fill_value,
+    allow_fill: bool,
+    mask: npt.NDArray[np.bool_] | None = None,
+):
+    mask_info: tuple[np.ndarray | None, bool] | None = None
+
+    if not allow_fill:
+        dtype, fill_value = arr.dtype, arr.dtype.type()
+        mask_info = None, False
+    else:
+        # check for promotion based on types only (do this first because
+        # it's faster than computing a mask)
+        dtype, fill_value = maybe_promote(arr.dtype, fill_value)
+        if dtype != arr.dtype:
+            # check if promotion is actually required based on indexer
+            if mask is not None:
+                needs_masking = True
+            else:
+                mask = indexer == -1
+                needs_masking = bool(mask.any())
+            mask_info = mask, needs_masking
+            if not needs_masking:
+                # if not, then depromote, set fill_value to dummy
+                # (it won't be used but we don't want the cython code
+                # to crash when trying to cast it to dtype)
+                dtype, fill_value = arr.dtype, arr.dtype.type()
+
+    return dtype, fill_value, mask_info
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/array_algos/transforms.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/array_algos/transforms.py
new file mode 100644
index 0000000000000000000000000000000000000000..ec67244949e3db92cc811b19cdfcd5d1dd2b4de8
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/array_algos/transforms.py
@@ -0,0 +1,50 @@
+"""
+transforms.py is for shape-preserving functions.
+"""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import numpy as np
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        AxisInt,
+        Scalar,
+    )
+
+
+def shift(
+    values: np.ndarray, periods: int, axis: AxisInt, fill_value: Scalar
+) -> np.ndarray:
+    new_values = values
+
+    if periods == 0 or values.size == 0:
+        return new_values.copy()
+
+    # make sure array sent to np.roll is c_contiguous
+    f_ordered = values.flags.f_contiguous
+    if f_ordered:
+        new_values = new_values.T
+        axis = new_values.ndim - axis - 1
+
+    if new_values.size:
+        new_values = np.roll(
+            new_values,
+            np.intp(periods),
+            axis=axis,
+        )
+
+    axis_indexer = [slice(None)] * values.ndim
+    if periods > 0:
+        axis_indexer[axis] = slice(None, periods)
+    else:
+        axis_indexer[axis] = slice(periods, None)
+    new_values[tuple(axis_indexer)] = fill_value
+
+    # restore original order
+    if f_ordered:
+        new_values = new_values.T
+
+    return new_values
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arraylike.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arraylike.py
new file mode 100644
index 0000000000000000000000000000000000000000..fd585b3e1946880ca79f97b0b6d4a2990bdd32d1
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arraylike.py
@@ -0,0 +1,530 @@
+"""
+Methods that can be shared by many array-like classes or subclasses:
+    Series
+    Index
+    ExtensionArray
+"""
+from __future__ import annotations
+
+import operator
+from typing import Any
+
+import numpy as np
+
+from pandas._libs import lib
+from pandas._libs.ops_dispatch import maybe_dispatch_ufunc_to_dunder_op
+
+from pandas.core.dtypes.generic import ABCNDFrame
+
+from pandas.core import roperator
+from pandas.core.construction import extract_array
+from pandas.core.ops.common import unpack_zerodim_and_defer
+
+REDUCTION_ALIASES = {
+    "maximum": "max",
+    "minimum": "min",
+    "add": "sum",
+    "multiply": "prod",
+}
+
+
+class OpsMixin:
+    # -------------------------------------------------------------
+    # Comparisons
+
+    def _cmp_method(self, other, op):
+        return NotImplemented
+
+    @unpack_zerodim_and_defer("__eq__")
+    def __eq__(self, other):
+        return self._cmp_method(other, operator.eq)
+
+    @unpack_zerodim_and_defer("__ne__")
+    def __ne__(self, other):
+        return self._cmp_method(other, operator.ne)
+
+    @unpack_zerodim_and_defer("__lt__")
+    def __lt__(self, other):
+        return self._cmp_method(other, operator.lt)
+
+    @unpack_zerodim_and_defer("__le__")
+    def __le__(self, other):
+        return self._cmp_method(other, operator.le)
+
+    @unpack_zerodim_and_defer("__gt__")
+    def __gt__(self, other):
+        return self._cmp_method(other, operator.gt)
+
+    @unpack_zerodim_and_defer("__ge__")
+    def __ge__(self, other):
+        return self._cmp_method(other, operator.ge)
+
+    # -------------------------------------------------------------
+    # Logical Methods
+
+    def _logical_method(self, other, op):
+        return NotImplemented
+
+    @unpack_zerodim_and_defer("__and__")
+    def __and__(self, other):
+        return self._logical_method(other, operator.and_)
+
+    @unpack_zerodim_and_defer("__rand__")
+    def __rand__(self, other):
+        return self._logical_method(other, roperator.rand_)
+
+    @unpack_zerodim_and_defer("__or__")
+    def __or__(self, other):
+        return self._logical_method(other, operator.or_)
+
+    @unpack_zerodim_and_defer("__ror__")
+    def __ror__(self, other):
+        return self._logical_method(other, roperator.ror_)
+
+    @unpack_zerodim_and_defer("__xor__")
+    def __xor__(self, other):
+        return self._logical_method(other, operator.xor)
+
+    @unpack_zerodim_and_defer("__rxor__")
+    def __rxor__(self, other):
+        return self._logical_method(other, roperator.rxor)
+
+    # -------------------------------------------------------------
+    # Arithmetic Methods
+
+    def _arith_method(self, other, op):
+        return NotImplemented
+
+    @unpack_zerodim_and_defer("__add__")
+    def __add__(self, other):
+        """
+        Get Addition of DataFrame and other, column-wise.
+
+        Equivalent to ``DataFrame.add(other)``.
+
+        Parameters
+        ----------
+        other : scalar, sequence, Series, dict or DataFrame
+            Object to be added to the DataFrame.
+
+        Returns
+        -------
+        DataFrame
+            The result of adding ``other`` to DataFrame.
+
+        See Also
+        --------
+        DataFrame.add : Add a DataFrame and another object, with option for index-
+            or column-oriented addition.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'height': [1.5, 2.6], 'weight': [500, 800]},
+        ...                   index=['elk', 'moose'])
+        >>> df
+               height  weight
+        elk       1.5     500
+        moose     2.6     800
+
+        Adding a scalar affects all rows and columns.
+
+        >>> df[['height', 'weight']] + 1.5
+               height  weight
+        elk       3.0   501.5
+        moose     4.1   801.5
+
+        Each element of a list is added to a column of the DataFrame, in order.
+
+        >>> df[['height', 'weight']] + [0.5, 1.5]
+               height  weight
+        elk       2.0   501.5
+        moose     3.1   801.5
+
+        Keys of a dictionary are aligned to the DataFrame, based on column names;
+        each value in the dictionary is added to the corresponding column.
+
+        >>> df[['height', 'weight']] + {'height': 0.5, 'weight': 1.5}
+               height  weight
+        elk       2.0   501.5
+        moose     3.1   801.5
+
+        When `other` is a :class:`Series`, the index of `other` is aligned with the
+        columns of the DataFrame.
+
+        >>> s1 = pd.Series([0.5, 1.5], index=['weight', 'height'])
+        >>> df[['height', 'weight']] + s1
+               height  weight
+        elk       3.0   500.5
+        moose     4.1   800.5
+
+        Even when the index of `other` is the same as the index of the DataFrame,
+        the :class:`Series` will not be reoriented. If index-wise alignment is desired,
+        :meth:`DataFrame.add` should be used with `axis='index'`.
+
+        >>> s2 = pd.Series([0.5, 1.5], index=['elk', 'moose'])
+        >>> df[['height', 'weight']] + s2
+               elk  height  moose  weight
+        elk    NaN     NaN    NaN     NaN
+        moose  NaN     NaN    NaN     NaN
+
+        >>> df[['height', 'weight']].add(s2, axis='index')
+               height  weight
+        elk       2.0   500.5
+        moose     4.1   801.5
+
+        When `other` is a :class:`DataFrame`, both columns names and the
+        index are aligned.
+
+        >>> other = pd.DataFrame({'height': [0.2, 0.4, 0.6]},
+        ...                      index=['elk', 'moose', 'deer'])
+        >>> df[['height', 'weight']] + other
+               height  weight
+        deer      NaN     NaN
+        elk       1.7     NaN
+        moose     3.0     NaN
+        """
+        return self._arith_method(other, operator.add)
+
+    @unpack_zerodim_and_defer("__radd__")
+    def __radd__(self, other):
+        return self._arith_method(other, roperator.radd)
+
+    @unpack_zerodim_and_defer("__sub__")
+    def __sub__(self, other):
+        return self._arith_method(other, operator.sub)
+
+    @unpack_zerodim_and_defer("__rsub__")
+    def __rsub__(self, other):
+        return self._arith_method(other, roperator.rsub)
+
+    @unpack_zerodim_and_defer("__mul__")
+    def __mul__(self, other):
+        return self._arith_method(other, operator.mul)
+
+    @unpack_zerodim_and_defer("__rmul__")
+    def __rmul__(self, other):
+        return self._arith_method(other, roperator.rmul)
+
+    @unpack_zerodim_and_defer("__truediv__")
+    def __truediv__(self, other):
+        return self._arith_method(other, operator.truediv)
+
+    @unpack_zerodim_and_defer("__rtruediv__")
+    def __rtruediv__(self, other):
+        return self._arith_method(other, roperator.rtruediv)
+
+    @unpack_zerodim_and_defer("__floordiv__")
+    def __floordiv__(self, other):
+        return self._arith_method(other, operator.floordiv)
+
+    @unpack_zerodim_and_defer("__rfloordiv")
+    def __rfloordiv__(self, other):
+        return self._arith_method(other, roperator.rfloordiv)
+
+    @unpack_zerodim_and_defer("__mod__")
+    def __mod__(self, other):
+        return self._arith_method(other, operator.mod)
+
+    @unpack_zerodim_and_defer("__rmod__")
+    def __rmod__(self, other):
+        return self._arith_method(other, roperator.rmod)
+
+    @unpack_zerodim_and_defer("__divmod__")
+    def __divmod__(self, other):
+        return self._arith_method(other, divmod)
+
+    @unpack_zerodim_and_defer("__rdivmod__")
+    def __rdivmod__(self, other):
+        return self._arith_method(other, roperator.rdivmod)
+
+    @unpack_zerodim_and_defer("__pow__")
+    def __pow__(self, other):
+        return self._arith_method(other, operator.pow)
+
+    @unpack_zerodim_and_defer("__rpow__")
+    def __rpow__(self, other):
+        return self._arith_method(other, roperator.rpow)
+
+
+# -----------------------------------------------------------------------------
+# Helpers to implement __array_ufunc__
+
+
+def array_ufunc(self, ufunc: np.ufunc, method: str, *inputs: Any, **kwargs: Any):
+    """
+    Compatibility with numpy ufuncs.
+
+    See also
+    --------
+    numpy.org/doc/stable/reference/arrays.classes.html#numpy.class.__array_ufunc__
+    """
+    from pandas.core.frame import (
+        DataFrame,
+        Series,
+    )
+    from pandas.core.generic import NDFrame
+    from pandas.core.internals import (
+        ArrayManager,
+        BlockManager,
+    )
+
+    cls = type(self)
+
+    kwargs = _standardize_out_kwarg(**kwargs)
+
+    # for binary ops, use our custom dunder methods
+    result = maybe_dispatch_ufunc_to_dunder_op(self, ufunc, method, *inputs, **kwargs)
+    if result is not NotImplemented:
+        return result
+
+    # Determine if we should defer.
+    no_defer = (
+        np.ndarray.__array_ufunc__,
+        cls.__array_ufunc__,
+    )
+
+    for item in inputs:
+        higher_priority = (
+            hasattr(item, "__array_priority__")
+            and item.__array_priority__ > self.__array_priority__
+        )
+        has_array_ufunc = (
+            hasattr(item, "__array_ufunc__")
+            and type(item).__array_ufunc__ not in no_defer
+            and not isinstance(item, self._HANDLED_TYPES)
+        )
+        if higher_priority or has_array_ufunc:
+            return NotImplemented
+
+    # align all the inputs.
+    types = tuple(type(x) for x in inputs)
+    alignable = [x for x, t in zip(inputs, types) if issubclass(t, NDFrame)]
+
+    if len(alignable) > 1:
+        # This triggers alignment.
+        # At the moment, there aren't any ufuncs with more than two inputs
+        # so this ends up just being x1.index | x2.index, but we write
+        # it to handle *args.
+        set_types = set(types)
+        if len(set_types) > 1 and {DataFrame, Series}.issubset(set_types):
+            # We currently don't handle ufunc(DataFrame, Series)
+            # well. Previously this raised an internal ValueError. We might
+            # support it someday, so raise a NotImplementedError.
+            raise NotImplementedError(
+                f"Cannot apply ufunc {ufunc} to mixed DataFrame and Series inputs."
+            )
+        axes = self.axes
+        for obj in alignable[1:]:
+            # this relies on the fact that we aren't handling mixed
+            # series / frame ufuncs.
+            for i, (ax1, ax2) in enumerate(zip(axes, obj.axes)):
+                axes[i] = ax1.union(ax2)
+
+        reconstruct_axes = dict(zip(self._AXIS_ORDERS, axes))
+        inputs = tuple(
+            x.reindex(**reconstruct_axes) if issubclass(t, NDFrame) else x
+            for x, t in zip(inputs, types)
+        )
+    else:
+        reconstruct_axes = dict(zip(self._AXIS_ORDERS, self.axes))
+
+    if self.ndim == 1:
+        names = [getattr(x, "name") for x in inputs if hasattr(x, "name")]
+        name = names[0] if len(set(names)) == 1 else None
+        reconstruct_kwargs = {"name": name}
+    else:
+        reconstruct_kwargs = {}
+
+    def reconstruct(result):
+        if ufunc.nout > 1:
+            # np.modf, np.frexp, np.divmod
+            return tuple(_reconstruct(x) for x in result)
+
+        return _reconstruct(result)
+
+    def _reconstruct(result):
+        if lib.is_scalar(result):
+            return result
+
+        if result.ndim != self.ndim:
+            if method == "outer":
+                raise NotImplementedError
+            return result
+        if isinstance(result, (BlockManager, ArrayManager)):
+            # we went through BlockManager.apply e.g. np.sqrt
+            result = self._constructor_from_mgr(result, axes=result.axes)
+        else:
+            # we converted an array, lost our axes
+            result = self._constructor(
+                result, **reconstruct_axes, **reconstruct_kwargs, copy=False
+            )
+        # TODO: When we support multiple values in __finalize__, this
+        # should pass alignable to `__finalize__` instead of self.
+        # Then `np.add(a, b)` would consider attrs from both a and b
+        # when a and b are NDFrames.
+        if len(alignable) == 1:
+            result = result.__finalize__(self)
+        return result
+
+    if "out" in kwargs:
+        # e.g. test_multiindex_get_loc
+        result = dispatch_ufunc_with_out(self, ufunc, method, *inputs, **kwargs)
+        return reconstruct(result)
+
+    if method == "reduce":
+        # e.g. test.series.test_ufunc.test_reduce
+        result = dispatch_reduction_ufunc(self, ufunc, method, *inputs, **kwargs)
+        if result is not NotImplemented:
+            return result
+
+    # We still get here with kwargs `axis` for e.g. np.maximum.accumulate
+    #  and `dtype` and `keepdims` for np.ptp
+
+    if self.ndim > 1 and (len(inputs) > 1 or ufunc.nout > 1):
+        # Just give up on preserving types in the complex case.
+        # In theory we could preserve them for them.
+        # * nout>1 is doable if BlockManager.apply took nout and
+        #   returned a Tuple[BlockManager].
+        # * len(inputs) > 1 is doable when we know that we have
+        #   aligned blocks / dtypes.
+
+        # e.g. my_ufunc, modf, logaddexp, heaviside, subtract, add
+        inputs = tuple(np.asarray(x) for x in inputs)
+        # Note: we can't use default_array_ufunc here bc reindexing means
+        #  that `self` may not be among `inputs`
+        result = getattr(ufunc, method)(*inputs, **kwargs)
+    elif self.ndim == 1:
+        # ufunc(series, ...)
+        inputs = tuple(extract_array(x, extract_numpy=True) for x in inputs)
+        result = getattr(ufunc, method)(*inputs, **kwargs)
+    else:
+        # ufunc(dataframe)
+        if method == "__call__" and not kwargs:
+            # for np.(..) calls
+            # kwargs cannot necessarily be handled block-by-block, so only
+            # take this path if there are no kwargs
+            mgr = inputs[0]._mgr
+            result = mgr.apply(getattr(ufunc, method))
+        else:
+            # otherwise specific ufunc methods (eg np..accumulate(..))
+            # Those can have an axis keyword and thus can't be called block-by-block
+            result = default_array_ufunc(inputs[0], ufunc, method, *inputs, **kwargs)
+            # e.g. np.negative (only one reached), with "where" and "out" in kwargs
+
+    result = reconstruct(result)
+    return result
+
+
+def _standardize_out_kwarg(**kwargs) -> dict:
+    """
+    If kwargs contain "out1" and "out2", replace that with a tuple "out"
+
+    np.divmod, np.modf, np.frexp can have either `out=(out1, out2)` or
+    `out1=out1, out2=out2)`
+    """
+    if "out" not in kwargs and "out1" in kwargs and "out2" in kwargs:
+        out1 = kwargs.pop("out1")
+        out2 = kwargs.pop("out2")
+        out = (out1, out2)
+        kwargs["out"] = out
+    return kwargs
+
+
+def dispatch_ufunc_with_out(self, ufunc: np.ufunc, method: str, *inputs, **kwargs):
+    """
+    If we have an `out` keyword, then call the ufunc without `out` and then
+    set the result into the given `out`.
+    """
+
+    # Note: we assume _standardize_out_kwarg has already been called.
+    out = kwargs.pop("out")
+    where = kwargs.pop("where", None)
+
+    result = getattr(ufunc, method)(*inputs, **kwargs)
+
+    if result is NotImplemented:
+        return NotImplemented
+
+    if isinstance(result, tuple):
+        # i.e. np.divmod, np.modf, np.frexp
+        if not isinstance(out, tuple) or len(out) != len(result):
+            raise NotImplementedError
+
+        for arr, res in zip(out, result):
+            _assign_where(arr, res, where)
+
+        return out
+
+    if isinstance(out, tuple):
+        if len(out) == 1:
+            out = out[0]
+        else:
+            raise NotImplementedError
+
+    _assign_where(out, result, where)
+    return out
+
+
+def _assign_where(out, result, where) -> None:
+    """
+    Set a ufunc result into 'out', masking with a 'where' argument if necessary.
+    """
+    if where is None:
+        # no 'where' arg passed to ufunc
+        out[:] = result
+    else:
+        np.putmask(out, where, result)
+
+
+def default_array_ufunc(self, ufunc: np.ufunc, method: str, *inputs, **kwargs):
+    """
+    Fallback to the behavior we would get if we did not define __array_ufunc__.
+
+    Notes
+    -----
+    We are assuming that `self` is among `inputs`.
+    """
+    if not any(x is self for x in inputs):
+        raise NotImplementedError
+
+    new_inputs = [x if x is not self else np.asarray(x) for x in inputs]
+
+    return getattr(ufunc, method)(*new_inputs, **kwargs)
+
+
+def dispatch_reduction_ufunc(self, ufunc: np.ufunc, method: str, *inputs, **kwargs):
+    """
+    Dispatch ufunc reductions to self's reduction methods.
+    """
+    assert method == "reduce"
+
+    if len(inputs) != 1 or inputs[0] is not self:
+        return NotImplemented
+
+    if ufunc.__name__ not in REDUCTION_ALIASES:
+        return NotImplemented
+
+    method_name = REDUCTION_ALIASES[ufunc.__name__]
+
+    # NB: we are assuming that min/max represent minimum/maximum methods,
+    #  which would not be accurate for e.g. Timestamp.min
+    if not hasattr(self, method_name):
+        return NotImplemented
+
+    if self.ndim > 1:
+        if isinstance(self, ABCNDFrame):
+            # TODO: test cases where this doesn't hold, i.e. 2D DTA/TDA
+            kwargs["numeric_only"] = False
+
+        if "axis" not in kwargs:
+            # For DataFrame reductions we don't want the default axis=0
+            # Note: np.min is not a ufunc, but uses array_function_dispatch,
+            #  so calls DataFrame.min (without ever getting here) with the np.min
+            #  default of axis=None, which DataFrame.min catches and changes to axis=0.
+            # np.minimum.reduce(df) gets here bc axis is not in kwargs,
+            #  so we set axis=0 to match the behaviorof np.minimum.reduce(df.values)
+            kwargs["axis"] = 0
+
+    # By default, numpy's reductions do not skip NaNs, so we have to
+    #  pass skipna=False
+    return getattr(self, method_name)(skipna=False, **kwargs)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..245a171fea74bc9409a315b64d157a37b3da6eaa
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/__init__.py
@@ -0,0 +1,43 @@
+from pandas.core.arrays.arrow import ArrowExtensionArray
+from pandas.core.arrays.base import (
+    ExtensionArray,
+    ExtensionOpsMixin,
+    ExtensionScalarOpsMixin,
+)
+from pandas.core.arrays.boolean import BooleanArray
+from pandas.core.arrays.categorical import Categorical
+from pandas.core.arrays.datetimes import DatetimeArray
+from pandas.core.arrays.floating import FloatingArray
+from pandas.core.arrays.integer import IntegerArray
+from pandas.core.arrays.interval import IntervalArray
+from pandas.core.arrays.masked import BaseMaskedArray
+from pandas.core.arrays.numpy_ import NumpyExtensionArray
+from pandas.core.arrays.period import (
+    PeriodArray,
+    period_array,
+)
+from pandas.core.arrays.sparse import SparseArray
+from pandas.core.arrays.string_ import StringArray
+from pandas.core.arrays.string_arrow import ArrowStringArray
+from pandas.core.arrays.timedeltas import TimedeltaArray
+
+__all__ = [
+    "ArrowExtensionArray",
+    "ExtensionArray",
+    "ExtensionOpsMixin",
+    "ExtensionScalarOpsMixin",
+    "ArrowStringArray",
+    "BaseMaskedArray",
+    "BooleanArray",
+    "Categorical",
+    "DatetimeArray",
+    "FloatingArray",
+    "IntegerArray",
+    "IntervalArray",
+    "NumpyExtensionArray",
+    "PeriodArray",
+    "period_array",
+    "SparseArray",
+    "StringArray",
+    "TimedeltaArray",
+]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/_arrow_string_mixins.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/_arrow_string_mixins.py
new file mode 100644
index 0000000000000000000000000000000000000000..c99ee7d02a2260fb48b40d2bf5048fc960d47b2e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/_arrow_string_mixins.py
@@ -0,0 +1,377 @@
+from __future__ import annotations
+
+from functools import partial
+import re
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Literal,
+)
+
+import numpy as np
+
+from pandas._libs import lib
+from pandas.compat import (
+    pa_version_under10p1,
+    pa_version_under11p0,
+    pa_version_under13p0,
+    pa_version_under17p0,
+    pa_version_under21p0,
+)
+
+if not pa_version_under10p1:
+    import pyarrow as pa
+    import pyarrow.compute as pc
+
+if TYPE_CHECKING:
+    from collections.abc import Callable
+
+    from pandas._typing import (
+        Scalar,
+        Self,
+    )
+
+
+class ArrowStringArrayMixin:
+    _pa_array: pa.ChunkedArray
+
+    def __init__(self, *args, **kwargs) -> None:
+        raise NotImplementedError
+
+    def _convert_bool_result(self, result, na=lib.no_default, method_name=None):
+        # Convert a bool-dtype result to the appropriate result type
+        raise NotImplementedError
+
+    def _convert_int_result(self, result):
+        # Convert an integer-dtype result to the appropriate result type
+        raise NotImplementedError
+
+    def _apply_elementwise(self, func: Callable) -> list[list[Any]]:
+        raise NotImplementedError
+
+    def _str_len(self):
+        result = pc.utf8_length(self._pa_array)
+        return self._convert_int_result(result)
+
+    def _str_lower(self) -> Self:
+        return type(self)(pc.utf8_lower(self._pa_array))
+
+    def _str_upper(self) -> Self:
+        return type(self)(pc.utf8_upper(self._pa_array))
+
+    def _str_strip(self, to_strip=None) -> Self:
+        if to_strip is None:
+            result = pc.utf8_trim_whitespace(self._pa_array)
+        else:
+            result = pc.utf8_trim(self._pa_array, characters=to_strip)
+        return type(self)(result)
+
+    def _str_lstrip(self, to_strip=None) -> Self:
+        if to_strip is None:
+            result = pc.utf8_ltrim_whitespace(self._pa_array)
+        else:
+            result = pc.utf8_ltrim(self._pa_array, characters=to_strip)
+        return type(self)(result)
+
+    def _str_rstrip(self, to_strip=None) -> Self:
+        if to_strip is None:
+            result = pc.utf8_rtrim_whitespace(self._pa_array)
+        else:
+            result = pc.utf8_rtrim(self._pa_array, characters=to_strip)
+        return type(self)(result)
+
+    def _str_pad(
+        self,
+        width: int,
+        side: Literal["left", "right", "both"] = "left",
+        fillchar: str = " ",
+    ):
+        if side == "left":
+            pa_pad = pc.utf8_lpad
+        elif side == "right":
+            pa_pad = pc.utf8_rpad
+        elif side == "both":
+            if pa_version_under17p0:
+                # GH#59624 fall back to object dtype
+                from pandas import array as pd_array
+
+                obj_arr = self.astype(object, copy=False)  # type: ignore[attr-defined]
+                obj = pd_array(obj_arr, dtype=object)
+                result = obj._str_pad(width, side, fillchar)  # type: ignore[attr-defined]
+                return type(self)._from_sequence(result, dtype=self.dtype)  # type: ignore[attr-defined]
+            else:
+                # GH#54792
+                # https://github.com/apache/arrow/issues/15053#issuecomment-2317032347
+                lean_left = (width % 2) == 0
+                pa_pad = partial(pc.utf8_center, lean_left_on_odd_padding=lean_left)
+        else:
+            raise ValueError(
+                f"Invalid side: {side}. Side must be one of 'left', 'right', 'both'"
+            )
+        return type(self)(pa_pad(self._pa_array, width=width, padding=fillchar))
+
+    def _str_get(self, i: int):
+        lengths = pc.utf8_length(self._pa_array)
+        if i >= 0:
+            out_of_bounds = pc.greater_equal(i, lengths)
+            start = i
+            stop = i + 1
+            step = 1
+        else:
+            out_of_bounds = pc.greater(-i, lengths)
+            start = i
+            stop = i - 1
+            step = -1
+        not_out_of_bounds = pc.invert(out_of_bounds.fill_null(True))
+        selected = pc.utf8_slice_codeunits(
+            self._pa_array, start=start, stop=stop, step=step
+        )
+        null_value = pa.scalar(None, type=self._pa_array.type)
+        result = pc.if_else(not_out_of_bounds, selected, null_value)
+        return type(self)(result)
+
+    def _str_slice(
+        self, start: int | None = None, stop: int | None = None, step: int | None = None
+    ):
+        if pa_version_under11p0:
+            # GH#59724
+            result = self._apply_elementwise(lambda val: val[start:stop:step])
+            return type(self)(pa.chunked_array(result, type=self._pa_array.type))
+        if start is None:
+            if step is not None and step < 0:
+                # GH#59710
+                start = -1
+            else:
+                start = 0
+        if step is None:
+            step = 1
+        return type(self)(
+            pc.utf8_slice_codeunits(self._pa_array, start=start, stop=stop, step=step)
+        )
+
+    def _str_slice_replace(
+        self, start: int | None = None, stop: int | None = None, repl: str | None = None
+    ):
+        if repl is None:
+            repl = ""
+        if start is None:
+            start = 0
+        if stop is None:
+            stop = np.iinfo(np.int64).max
+        return type(self)(pc.utf8_replace_slice(self._pa_array, start, stop, repl))
+
+    def _str_replace(
+        self,
+        pat: str | re.Pattern,
+        repl: str | Callable,
+        n: int = -1,
+        case: bool = True,
+        flags: int = 0,
+        regex: bool = True,
+    ) -> Self:
+        if (
+            isinstance(pat, re.Pattern)
+            or callable(repl)
+            or not case
+            or flags
+            or (
+                isinstance(repl, str)
+                and (r"\g<" in repl or re.search(r"\\\d", repl) is not None)
+            )
+        ):
+            raise NotImplementedError(
+                "replace is not supported with a re.Pattern, callable repl, "
+                "case=False, flags!=0, or when the replacement string contains "
+                "named group references (\\g<...>, \\d+)"
+            )
+
+        func = pc.replace_substring_regex if regex else pc.replace_substring
+        # https://github.com/apache/arrow/issues/39149
+        # GH 56404, unexpected behavior with negative max_replacements with pyarrow.
+        pa_max_replacements = None if n < 0 else n
+        result = func(
+            self._pa_array,
+            pattern=pat,
+            replacement=repl,
+            max_replacements=pa_max_replacements,
+        )
+        return type(self)(result)
+
+    def _str_capitalize(self) -> Self:
+        return type(self)(pc.utf8_capitalize(self._pa_array))
+
+    def _str_title(self):
+        return type(self)(pc.utf8_title(self._pa_array))
+
+    def _str_swapcase(self):
+        return type(self)(pc.utf8_swapcase(self._pa_array))
+
+    def _str_removeprefix(self, prefix: str):
+        if not pa_version_under13p0:
+            starts_with = pc.starts_with(self._pa_array, pattern=prefix)
+            removed = pc.utf8_slice_codeunits(self._pa_array, len(prefix))
+            result = pc.if_else(starts_with, removed, self._pa_array)
+            return type(self)(result)
+        predicate = lambda val: val.removeprefix(prefix)
+        result = self._apply_elementwise(predicate)
+        return type(self)(pa.chunked_array(result))
+
+    def _str_removesuffix(self, suffix: str):
+        ends_with = pc.ends_with(self._pa_array, pattern=suffix)
+        removed = pc.utf8_slice_codeunits(self._pa_array, 0, stop=-len(suffix))
+        result = pc.if_else(ends_with, removed, self._pa_array)
+        return type(self)(result)
+
+    def _str_startswith(
+        self, pat: str | tuple[str, ...], na: Scalar | lib.NoDefault = lib.no_default
+    ):
+        if isinstance(pat, str):
+            result = pc.starts_with(self._pa_array, pattern=pat)
+        else:
+            if len(pat) == 0:
+                # For empty tuple we return null for missing values and False
+                #  for valid values.
+                result = pc.if_else(pc.is_null(self._pa_array), None, False)
+            else:
+                result = pc.starts_with(self._pa_array, pattern=pat[0])
+
+                for p in pat[1:]:
+                    result = pc.or_(result, pc.starts_with(self._pa_array, pattern=p))
+        return self._convert_bool_result(result, na=na, method_name="startswith")
+
+    def _str_endswith(
+        self, pat: str | tuple[str, ...], na: Scalar | lib.NoDefault = lib.no_default
+    ):
+        if isinstance(pat, str):
+            result = pc.ends_with(self._pa_array, pattern=pat)
+        else:
+            if len(pat) == 0:
+                # For empty tuple we return null for missing values and False
+                #  for valid values.
+                result = pc.if_else(pc.is_null(self._pa_array), None, False)
+            else:
+                result = pc.ends_with(self._pa_array, pattern=pat[0])
+
+                for p in pat[1:]:
+                    result = pc.or_(result, pc.ends_with(self._pa_array, pattern=p))
+        return self._convert_bool_result(result, na=na, method_name="endswith")
+
+    def _str_isalnum(self):
+        result = pc.utf8_is_alnum(self._pa_array)
+        return self._convert_bool_result(result)
+
+    def _str_isalpha(self):
+        result = pc.utf8_is_alpha(self._pa_array)
+        return self._convert_bool_result(result)
+
+    def _str_isdecimal(self):
+        result = pc.utf8_is_decimal(self._pa_array)
+        return self._convert_bool_result(result)
+
+    def _str_isdigit(self):
+        if pa_version_under21p0:
+            # https://github.com/pandas-dev/pandas/issues/61466
+            res_list = self._apply_elementwise(str.isdigit)
+            return self._convert_bool_result(
+                pa.chunked_array(res_list, type=pa.bool_())
+            )
+        result = pc.utf8_is_digit(self._pa_array)
+        return self._convert_bool_result(result)
+
+    def _str_islower(self):
+        result = pc.utf8_is_lower(self._pa_array)
+        return self._convert_bool_result(result)
+
+    def _str_isnumeric(self):
+        result = pc.utf8_is_numeric(self._pa_array)
+        return self._convert_bool_result(result)
+
+    def _str_isspace(self):
+        result = pc.utf8_is_space(self._pa_array)
+        return self._convert_bool_result(result)
+
+    def _str_istitle(self):
+        result = pc.utf8_is_title(self._pa_array)
+        return self._convert_bool_result(result)
+
+    def _str_isupper(self):
+        result = pc.utf8_is_upper(self._pa_array)
+        return self._convert_bool_result(result)
+
+    def _str_contains(
+        self,
+        pat,
+        case: bool = True,
+        flags: int = 0,
+        na: Scalar | lib.NoDefault = lib.no_default,
+        regex: bool = True,
+    ):
+        if flags:
+            raise NotImplementedError(f"contains not implemented with {flags=}")
+
+        if regex:
+            pa_contains = pc.match_substring_regex
+        else:
+            pa_contains = pc.match_substring
+        result = pa_contains(self._pa_array, pat, ignore_case=not case)
+        return self._convert_bool_result(result, na=na, method_name="contains")
+
+    def _str_match(
+        self,
+        pat: str,
+        case: bool = True,
+        flags: int = 0,
+        na: Scalar | lib.NoDefault = lib.no_default,
+    ):
+        if not pat.startswith("^"):
+            pat = f"^({pat})"
+        return self._str_contains(pat, case, flags, na, regex=True)
+
+    def _str_fullmatch(
+        self,
+        pat: str,
+        case: bool = True,
+        flags: int = 0,
+        na: Scalar | lib.NoDefault = lib.no_default,
+    ):
+        if (not pat.endswith("$") or pat.endswith("\\$")) and not pat.startswith("^"):
+            pat = f"^({pat})$"
+        elif not pat.endswith("$") or pat.endswith("\\$"):
+            pat = f"^({pat[1:]})$"
+        elif not pat.startswith("^"):
+            pat = f"^({pat[0:-1]})$"
+        return self._str_match(pat, case, flags, na)
+
+    def _str_find(self, sub: str, start: int = 0, end: int | None = None):
+        if (
+            pa_version_under13p0
+            and not (start != 0 and end is not None)
+            and not (start == 0 and end is None)
+        ):
+            # GH#59562
+            res_list = self._apply_elementwise(lambda val: val.find(sub, start, end))
+            return self._convert_int_result(pa.chunked_array(res_list))
+
+        if (start == 0 or start is None) and end is None:
+            result = pc.find_substring(self._pa_array, sub)
+        else:
+            if sub == "":
+                # GH#56792
+                res_list = self._apply_elementwise(
+                    lambda val: val.find(sub, start, end)
+                )
+                return self._convert_int_result(pa.chunked_array(res_list))
+            if start is None:
+                start_offset = 0
+                start = 0
+            elif start < 0:
+                start_offset = pc.add(start, pc.utf8_length(self._pa_array))
+                start_offset = pc.if_else(pc.less(start_offset, 0), 0, start_offset)
+            else:
+                start_offset = start
+            slices = pc.utf8_slice_codeunits(self._pa_array, start, stop=end)
+            result = pc.find_substring(slices, sub)
+            found = pc.not_equal(result, pa.scalar(-1, type=result.type))
+            offset_result = pc.add(result, start_offset)
+            result = pc.if_else(found, offset_result, -1)
+        return self._convert_int_result(result)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/_mixins.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/_mixins.py
new file mode 100644
index 0000000000000000000000000000000000000000..cb6861a8dd00ff29edb398f0a8cc6ca73205c78d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/_mixins.py
@@ -0,0 +1,544 @@
+from __future__ import annotations
+
+from functools import wraps
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Literal,
+    cast,
+    overload,
+)
+
+import numpy as np
+
+from pandas._libs import lib
+from pandas._libs.arrays import NDArrayBacked
+from pandas._libs.tslibs import is_supported_dtype
+from pandas._typing import (
+    ArrayLike,
+    AxisInt,
+    Dtype,
+    F,
+    FillnaOptions,
+    PositionalIndexer2D,
+    PositionalIndexerTuple,
+    ScalarIndexer,
+    Self,
+    SequenceIndexer,
+    Shape,
+    TakeIndexer,
+    npt,
+)
+from pandas.errors import AbstractMethodError
+from pandas.util._decorators import doc
+from pandas.util._validators import (
+    validate_bool_kwarg,
+    validate_fillna_kwargs,
+    validate_insert_loc,
+)
+
+from pandas.core.dtypes.common import pandas_dtype
+from pandas.core.dtypes.dtypes import (
+    DatetimeTZDtype,
+    ExtensionDtype,
+    PeriodDtype,
+)
+from pandas.core.dtypes.missing import array_equivalent
+
+from pandas.core import missing
+from pandas.core.algorithms import (
+    take,
+    unique,
+    value_counts_internal as value_counts,
+)
+from pandas.core.array_algos.quantile import quantile_with_mask
+from pandas.core.array_algos.transforms import shift
+from pandas.core.arrays.base import ExtensionArray
+from pandas.core.construction import extract_array
+from pandas.core.indexers import check_array_indexer
+from pandas.core.sorting import nargminmax
+
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+
+    from pandas._typing import (
+        NumpySorter,
+        NumpyValueArrayLike,
+    )
+
+    from pandas import Series
+
+
+def ravel_compat(meth: F) -> F:
+    """
+    Decorator to ravel a 2D array before passing it to a cython operation,
+    then reshape the result to our own shape.
+    """
+
+    @wraps(meth)
+    def method(self, *args, **kwargs):
+        if self.ndim == 1:
+            return meth(self, *args, **kwargs)
+
+        flags = self._ndarray.flags
+        flat = self.ravel("K")
+        result = meth(flat, *args, **kwargs)
+        order = "F" if flags.f_contiguous else "C"
+        return result.reshape(self.shape, order=order)
+
+    return cast(F, method)
+
+
+class NDArrayBackedExtensionArray(NDArrayBacked, ExtensionArray):
+    """
+    ExtensionArray that is backed by a single NumPy ndarray.
+    """
+
+    _ndarray: np.ndarray
+
+    # scalar used to denote NA value inside our self._ndarray, e.g. -1
+    #  for Categorical, iNaT for Period. Outside of object dtype,
+    #  self.isna() should be exactly locations in self._ndarray with
+    #  _internal_fill_value.
+    _internal_fill_value: Any
+
+    def _box_func(self, x):
+        """
+        Wrap numpy type in our dtype.type if necessary.
+        """
+        return x
+
+    def _validate_scalar(self, value):
+        # used by NDArrayBackedExtensionIndex.insert
+        raise AbstractMethodError(self)
+
+    # ------------------------------------------------------------------------
+
+    def view(self, dtype: Dtype | None = None) -> ArrayLike:
+        # We handle datetime64, datetime64tz, timedelta64, and period
+        #  dtypes here. Everything else we pass through to the underlying
+        #  ndarray.
+        if dtype is None or dtype is self.dtype:
+            return self._from_backing_data(self._ndarray)
+
+        if isinstance(dtype, type):
+            # we sometimes pass non-dtype objects, e.g np.ndarray;
+            #  pass those through to the underlying ndarray
+            return self._ndarray.view(dtype)
+
+        dtype = pandas_dtype(dtype)
+        arr = self._ndarray
+
+        if isinstance(dtype, PeriodDtype):
+            cls = dtype.construct_array_type()
+            return cls(arr.view("i8"), dtype=dtype)
+        elif isinstance(dtype, DatetimeTZDtype):
+            dt_cls = dtype.construct_array_type()
+            dt64_values = arr.view(f"M8[{dtype.unit}]")
+            return dt_cls._simple_new(dt64_values, dtype=dtype)
+        elif lib.is_np_dtype(dtype, "M") and is_supported_dtype(dtype):
+            from pandas.core.arrays import DatetimeArray
+
+            dt64_values = arr.view(dtype)
+            return DatetimeArray._simple_new(dt64_values, dtype=dtype)
+
+        elif lib.is_np_dtype(dtype, "m") and is_supported_dtype(dtype):
+            from pandas.core.arrays import TimedeltaArray
+
+            td64_values = arr.view(dtype)
+            return TimedeltaArray._simple_new(td64_values, dtype=dtype)
+
+        # error: Argument "dtype" to "view" of "_ArrayOrScalarCommon" has incompatible
+        # type "Union[ExtensionDtype, dtype[Any]]"; expected "Union[dtype[Any], None,
+        # type, _SupportsDType, str, Union[Tuple[Any, int], Tuple[Any, Union[int,
+        # Sequence[int]]], List[Any], _DTypeDict, Tuple[Any, Any]]]"
+        return arr.view(dtype=dtype)  # type: ignore[arg-type]
+
+    def take(
+        self,
+        indices: TakeIndexer,
+        *,
+        allow_fill: bool = False,
+        fill_value: Any = None,
+        axis: AxisInt = 0,
+    ) -> Self:
+        if allow_fill:
+            fill_value = self._validate_scalar(fill_value)
+
+        new_data = take(
+            self._ndarray,
+            indices,
+            allow_fill=allow_fill,
+            fill_value=fill_value,
+            axis=axis,
+        )
+        return self._from_backing_data(new_data)
+
+    # ------------------------------------------------------------------------
+
+    def equals(self, other) -> bool:
+        if type(self) is not type(other):
+            return False
+        if self.dtype != other.dtype:
+            return False
+        return bool(array_equivalent(self._ndarray, other._ndarray, dtype_equal=True))
+
+    @classmethod
+    def _from_factorized(cls, values, original):
+        assert values.dtype == original._ndarray.dtype
+        return original._from_backing_data(values)
+
+    def _values_for_argsort(self) -> np.ndarray:
+        return self._ndarray
+
+    def _values_for_factorize(self):
+        return self._ndarray, self._internal_fill_value
+
+    def _hash_pandas_object(
+        self, *, encoding: str, hash_key: str, categorize: bool
+    ) -> npt.NDArray[np.uint64]:
+        from pandas.core.util.hashing import hash_array
+
+        values = self._ndarray
+        return hash_array(
+            values, encoding=encoding, hash_key=hash_key, categorize=categorize
+        )
+
+    # Signature of "argmin" incompatible with supertype "ExtensionArray"
+    def argmin(self, axis: AxisInt = 0, skipna: bool = True):  # type: ignore[override]
+        # override base class by adding axis keyword
+        validate_bool_kwarg(skipna, "skipna")
+        if not skipna and self._hasna:
+            raise NotImplementedError
+        return nargminmax(self, "argmin", axis=axis)
+
+    # Signature of "argmax" incompatible with supertype "ExtensionArray"
+    def argmax(self, axis: AxisInt = 0, skipna: bool = True):  # type: ignore[override]
+        # override base class by adding axis keyword
+        validate_bool_kwarg(skipna, "skipna")
+        if not skipna and self._hasna:
+            raise NotImplementedError
+        return nargminmax(self, "argmax", axis=axis)
+
+    def unique(self) -> Self:
+        new_data = unique(self._ndarray)
+        return self._from_backing_data(new_data)
+
+    @classmethod
+    @doc(ExtensionArray._concat_same_type)
+    def _concat_same_type(
+        cls,
+        to_concat: Sequence[Self],
+        axis: AxisInt = 0,
+    ) -> Self:
+        if not lib.dtypes_all_equal([x.dtype for x in to_concat]):
+            dtypes = {str(x.dtype) for x in to_concat}
+            raise ValueError("to_concat must have the same dtype", dtypes)
+
+        return super()._concat_same_type(to_concat, axis=axis)
+
+    @doc(ExtensionArray.searchsorted)
+    def searchsorted(
+        self,
+        value: NumpyValueArrayLike | ExtensionArray,
+        side: Literal["left", "right"] = "left",
+        sorter: NumpySorter | None = None,
+    ) -> npt.NDArray[np.intp] | np.intp:
+        npvalue = self._validate_setitem_value(value)
+        return self._ndarray.searchsorted(npvalue, side=side, sorter=sorter)
+
+    @doc(ExtensionArray.shift)
+    def shift(self, periods: int = 1, fill_value=None):
+        # NB: shift is always along axis=0
+        axis = 0
+        fill_value = self._validate_scalar(fill_value)
+        new_values = shift(self._ndarray, periods, axis, fill_value)
+
+        return self._from_backing_data(new_values)
+
+    def __setitem__(self, key, value) -> None:
+        key = check_array_indexer(self, key)
+        value = self._validate_setitem_value(value)
+        self._ndarray[key] = value
+
+    def _validate_setitem_value(self, value):
+        return value
+
+    @overload
+    def __getitem__(self, key: ScalarIndexer) -> Any:
+        ...
+
+    @overload
+    def __getitem__(
+        self,
+        key: SequenceIndexer | PositionalIndexerTuple,
+    ) -> Self:
+        ...
+
+    def __getitem__(
+        self,
+        key: PositionalIndexer2D,
+    ) -> Self | Any:
+        if lib.is_integer(key):
+            # fast-path
+            result = self._ndarray[key]
+            if self.ndim == 1:
+                return self._box_func(result)
+            return self._from_backing_data(result)
+
+        # error: Incompatible types in assignment (expression has type "ExtensionArray",
+        # variable has type "Union[int, slice, ndarray]")
+        key = extract_array(key, extract_numpy=True)  # type: ignore[assignment]
+        key = check_array_indexer(self, key)
+        result = self._ndarray[key]
+        if lib.is_scalar(result):
+            return self._box_func(result)
+
+        result = self._from_backing_data(result)
+        return result
+
+    def _fill_mask_inplace(
+        self, method: str, limit: int | None, mask: npt.NDArray[np.bool_]
+    ) -> None:
+        # (for now) when self.ndim == 2, we assume axis=0
+        func = missing.get_fill_func(method, ndim=self.ndim)
+        func(self._ndarray.T, limit=limit, mask=mask.T)
+
+    def _pad_or_backfill(
+        self,
+        *,
+        method: FillnaOptions,
+        limit: int | None = None,
+        limit_area: Literal["inside", "outside"] | None = None,
+        copy: bool = True,
+    ) -> Self:
+        mask = self.isna()
+        if mask.any():
+            # (for now) when self.ndim == 2, we assume axis=0
+            func = missing.get_fill_func(method, ndim=self.ndim)
+
+            npvalues = self._ndarray.T
+            if copy:
+                npvalues = npvalues.copy()
+            func(npvalues, limit=limit, limit_area=limit_area, mask=mask.T)
+            npvalues = npvalues.T
+
+            if copy:
+                new_values = self._from_backing_data(npvalues)
+            else:
+                new_values = self
+
+        else:
+            if copy:
+                new_values = self.copy()
+            else:
+                new_values = self
+        return new_values
+
+    @doc(ExtensionArray.fillna)
+    def fillna(
+        self, value=None, method=None, limit: int | None = None, copy: bool = True
+    ) -> Self:
+        value, method = validate_fillna_kwargs(
+            value, method, validate_scalar_dict_value=False
+        )
+
+        mask = self.isna()
+        # error: Argument 2 to "check_value_size" has incompatible type
+        # "ExtensionArray"; expected "ndarray"
+        value = missing.check_value_size(
+            value, mask, len(self)  # type: ignore[arg-type]
+        )
+
+        if mask.any():
+            if method is not None:
+                # (for now) when self.ndim == 2, we assume axis=0
+                func = missing.get_fill_func(method, ndim=self.ndim)
+                npvalues = self._ndarray.T
+                if copy:
+                    npvalues = npvalues.copy()
+                func(npvalues, limit=limit, mask=mask.T)
+                npvalues = npvalues.T
+
+                # TODO: NumpyExtensionArray didn't used to copy, need tests
+                #  for this
+                new_values = self._from_backing_data(npvalues)
+            else:
+                # fill with value
+                if copy:
+                    new_values = self.copy()
+                else:
+                    new_values = self[:]
+                new_values[mask] = value
+        else:
+            # We validate the fill_value even if there is nothing to fill
+            if value is not None:
+                self._validate_setitem_value(value)
+
+            if not copy:
+                new_values = self[:]
+            else:
+                new_values = self.copy()
+        return new_values
+
+    # ------------------------------------------------------------------------
+    # Reductions
+
+    def _wrap_reduction_result(self, axis: AxisInt | None, result):
+        if axis is None or self.ndim == 1:
+            return self._box_func(result)
+        return self._from_backing_data(result)
+
+    # ------------------------------------------------------------------------
+    # __array_function__ methods
+
+    def _putmask(self, mask: npt.NDArray[np.bool_], value) -> None:
+        """
+        Analogue to np.putmask(self, mask, value)
+
+        Parameters
+        ----------
+        mask : np.ndarray[bool]
+        value : scalar or listlike
+
+        Raises
+        ------
+        TypeError
+            If value cannot be cast to self.dtype.
+        """
+        value = self._validate_setitem_value(value)
+
+        np.putmask(self._ndarray, mask, value)
+
+    def _where(self: Self, mask: npt.NDArray[np.bool_], value) -> Self:
+        """
+        Analogue to np.where(mask, self, value)
+
+        Parameters
+        ----------
+        mask : np.ndarray[bool]
+        value : scalar or listlike
+
+        Raises
+        ------
+        TypeError
+            If value cannot be cast to self.dtype.
+        """
+        value = self._validate_setitem_value(value)
+
+        res_values = np.where(mask, self._ndarray, value)
+        if res_values.dtype != self._ndarray.dtype:
+            raise AssertionError(
+                # GH#56410
+                "Something has gone wrong, please report a bug at "
+                "github.com/pandas-dev/pandas/"
+            )
+        return self._from_backing_data(res_values)
+
+    # ------------------------------------------------------------------------
+    # Index compat methods
+
+    def insert(self, loc: int, item) -> Self:
+        """
+        Make new ExtensionArray inserting new item at location. Follows
+        Python list.append semantics for negative values.
+
+        Parameters
+        ----------
+        loc : int
+        item : object
+
+        Returns
+        -------
+        type(self)
+        """
+        loc = validate_insert_loc(loc, len(self))
+
+        code = self._validate_scalar(item)
+
+        new_vals = np.concatenate(
+            (
+                self._ndarray[:loc],
+                np.asarray([code], dtype=self._ndarray.dtype),
+                self._ndarray[loc:],
+            )
+        )
+        return self._from_backing_data(new_vals)
+
+    # ------------------------------------------------------------------------
+    # Additional array methods
+    #  These are not part of the EA API, but we implement them because
+    #  pandas assumes they're there.
+
+    def value_counts(self, dropna: bool = True) -> Series:
+        """
+        Return a Series containing counts of unique values.
+
+        Parameters
+        ----------
+        dropna : bool, default True
+            Don't include counts of NA values.
+
+        Returns
+        -------
+        Series
+        """
+        if self.ndim != 1:
+            raise NotImplementedError
+
+        from pandas import (
+            Index,
+            Series,
+        )
+
+        if dropna:
+            # error: Unsupported operand type for ~ ("ExtensionArray")
+            values = self[~self.isna()]._ndarray  # type: ignore[operator]
+        else:
+            values = self._ndarray
+
+        result = value_counts(values, sort=False, dropna=dropna)
+
+        index_arr = self._from_backing_data(np.asarray(result.index._data))
+        index = Index(index_arr, name=result.index.name)
+        return Series(result._values, index=index, name=result.name, copy=False)
+
+    def _quantile(
+        self,
+        qs: npt.NDArray[np.float64],
+        interpolation: str,
+    ) -> Self:
+        # TODO: disable for Categorical if not ordered?
+
+        mask = np.asarray(self.isna())
+        arr = self._ndarray
+        fill_value = self._internal_fill_value
+
+        res_values = quantile_with_mask(arr, mask, fill_value, qs, interpolation)
+        if res_values.dtype == self._ndarray.dtype:
+            return self._from_backing_data(res_values)
+        else:
+            # e.g. test_quantile_empty we are empty integer dtype and res_values
+            #  has floating dtype
+            # TODO: technically __init__ isn't defined here.
+            #  Should we raise NotImplementedError and handle this on NumpyEA?
+            return type(self)(res_values)  # type: ignore[call-arg]
+
+    # ------------------------------------------------------------------------
+    # numpy-like methods
+
+    @classmethod
+    def _empty(cls, shape: Shape, dtype: ExtensionDtype) -> Self:
+        """
+        Analogous to np.empty(shape, dtype=dtype)
+
+        Parameters
+        ----------
+        shape : tuple[int]
+        dtype : ExtensionDtype
+        """
+        # The base implementation uses a naive approach to find the dtype
+        #  for the backing ndarray
+        arr = cls._from_sequence([], dtype=dtype)
+        backing = np.empty(shape, dtype=arr._ndarray.dtype)
+        return arr._from_backing_data(backing)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/_ranges.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/_ranges.py
new file mode 100644
index 0000000000000000000000000000000000000000..3e89391324ad4a90235da230250758662822678f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/_ranges.py
@@ -0,0 +1,207 @@
+"""
+Helper functions to generate range-like data for DatetimeArray
+(and possibly TimedeltaArray/PeriodArray)
+"""
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import numpy as np
+
+from pandas._libs.lib import i8max
+from pandas._libs.tslibs import (
+    BaseOffset,
+    OutOfBoundsDatetime,
+    Timedelta,
+    Timestamp,
+    iNaT,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import npt
+
+
+def generate_regular_range(
+    start: Timestamp | Timedelta | None,
+    end: Timestamp | Timedelta | None,
+    periods: int | None,
+    freq: BaseOffset,
+    unit: str = "ns",
+) -> npt.NDArray[np.intp]:
+    """
+    Generate a range of dates or timestamps with the spans between dates
+    described by the given `freq` DateOffset.
+
+    Parameters
+    ----------
+    start : Timedelta, Timestamp or None
+        First point of produced date range.
+    end : Timedelta, Timestamp or None
+        Last point of produced date range.
+    periods : int or None
+        Number of periods in produced date range.
+    freq : Tick
+        Describes space between dates in produced date range.
+    unit : str, default "ns"
+        The resolution the output is meant to represent.
+
+    Returns
+    -------
+    ndarray[np.int64]
+        Representing the given resolution.
+    """
+    istart = start._value if start is not None else None
+    iend = end._value if end is not None else None
+    freq.nanos  # raises if non-fixed frequency
+    td = Timedelta(freq)
+    b: int
+    e: int
+    try:
+        td = td.as_unit(unit, round_ok=False)
+    except ValueError as err:
+        raise ValueError(
+            f"freq={freq} is incompatible with unit={unit}. "
+            "Use a lower freq or a higher unit instead."
+        ) from err
+    stride = int(td._value)
+
+    if periods is None and istart is not None and iend is not None:
+        b = istart
+        # cannot just use e = Timestamp(end) + 1 because arange breaks when
+        # stride is too large, see GH10887
+        e = b + (iend - b) // stride * stride + stride // 2 + 1
+    elif istart is not None and periods is not None:
+        b = istart
+        e = _generate_range_overflow_safe(b, periods, stride, side="start")
+    elif iend is not None and periods is not None:
+        e = iend + stride
+        b = _generate_range_overflow_safe(e, periods, stride, side="end")
+    else:
+        raise ValueError(
+            "at least 'start' or 'end' should be specified if a 'period' is given."
+        )
+
+    with np.errstate(over="raise"):
+        # If the range is sufficiently large, np.arange may overflow
+        #  and incorrectly return an empty array if not caught.
+        try:
+            values = np.arange(b, e, stride, dtype=np.int64)
+        except FloatingPointError:
+            xdr = [b]
+            while xdr[-1] != e:
+                xdr.append(xdr[-1] + stride)
+            values = np.array(xdr[:-1], dtype=np.int64)
+    return values
+
+
+def _generate_range_overflow_safe(
+    endpoint: int, periods: int, stride: int, side: str = "start"
+) -> int:
+    """
+    Calculate the second endpoint for passing to np.arange, checking
+    to avoid an integer overflow.  Catch OverflowError and re-raise
+    as OutOfBoundsDatetime.
+
+    Parameters
+    ----------
+    endpoint : int
+        nanosecond timestamp of the known endpoint of the desired range
+    periods : int
+        number of periods in the desired range
+    stride : int
+        nanoseconds between periods in the desired range
+    side : {'start', 'end'}
+        which end of the range `endpoint` refers to
+
+    Returns
+    -------
+    other_end : int
+
+    Raises
+    ------
+    OutOfBoundsDatetime
+    """
+    # GH#14187 raise instead of incorrectly wrapping around
+    assert side in ["start", "end"]
+
+    i64max = np.uint64(i8max)
+    msg = f"Cannot generate range with {side}={endpoint} and periods={periods}"
+
+    with np.errstate(over="raise"):
+        # if periods * strides cannot be multiplied within the *uint64* bounds,
+        #  we cannot salvage the operation by recursing, so raise
+        try:
+            addend = np.uint64(periods) * np.uint64(np.abs(stride))
+        except FloatingPointError as err:
+            raise OutOfBoundsDatetime(msg) from err
+
+    if np.abs(addend) <= i64max:
+        # relatively easy case without casting concerns
+        return _generate_range_overflow_safe_signed(endpoint, periods, stride, side)
+
+    elif (endpoint > 0 and side == "start" and stride > 0) or (
+        endpoint < 0 < stride and side == "end"
+    ):
+        # no chance of not-overflowing
+        raise OutOfBoundsDatetime(msg)
+
+    elif side == "end" and endpoint - stride <= i64max < endpoint:
+        # in _generate_regular_range we added `stride` thereby overflowing
+        #  the bounds.  Adjust to fix this.
+        return _generate_range_overflow_safe(
+            endpoint - stride, periods - 1, stride, side
+        )
+
+    # split into smaller pieces
+    mid_periods = periods // 2
+    remaining = periods - mid_periods
+    assert 0 < remaining < periods, (remaining, periods, endpoint, stride)
+
+    midpoint = int(_generate_range_overflow_safe(endpoint, mid_periods, stride, side))
+    return _generate_range_overflow_safe(midpoint, remaining, stride, side)
+
+
+def _generate_range_overflow_safe_signed(
+    endpoint: int, periods: int, stride: int, side: str
+) -> int:
+    """
+    A special case for _generate_range_overflow_safe where `periods * stride`
+    can be calculated without overflowing int64 bounds.
+    """
+    assert side in ["start", "end"]
+    if side == "end":
+        stride *= -1
+
+    with np.errstate(over="raise"):
+        addend = np.int64(periods) * np.int64(stride)
+        try:
+            # easy case with no overflows
+            result = np.int64(endpoint) + addend
+            if result == iNaT:
+                # Putting this into a DatetimeArray/TimedeltaArray
+                #  would incorrectly be interpreted as NaT
+                raise OverflowError
+            return int(result)
+        except (FloatingPointError, OverflowError):
+            # with endpoint negative and addend positive we risk
+            #  FloatingPointError; with reversed signed we risk OverflowError
+            pass
+
+        # if stride and endpoint had opposite signs, then endpoint + addend
+        #  should never overflow.  so they must have the same signs
+        assert (stride > 0 and endpoint >= 0) or (stride < 0 and endpoint <= 0)
+
+        if stride > 0:
+            # watch out for very special case in which we just slightly
+            #  exceed implementation bounds, but when passing the result to
+            #  np.arange will get a result slightly within the bounds
+
+            uresult = np.uint64(endpoint) + np.uint64(addend)
+            i64max = np.uint64(i8max)
+            assert uresult > i64max
+            if uresult <= i64max + np.uint64(stride):
+                return int(uresult)
+
+    raise OutOfBoundsDatetime(
+        f"Cannot generate range with {side}={endpoint} and periods={periods}"
+    )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/_utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..6b46396d5efdfa4301a5362c8a5a71678345479b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/_utils.py
@@ -0,0 +1,63 @@
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    Any,
+)
+
+import numpy as np
+
+from pandas._libs import lib
+from pandas.errors import LossySetitemError
+
+from pandas.core.dtypes.cast import np_can_hold_element
+from pandas.core.dtypes.common import is_numeric_dtype
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        ArrayLike,
+        npt,
+    )
+
+
+def to_numpy_dtype_inference(
+    arr: ArrayLike, dtype: npt.DTypeLike | None, na_value, hasna: bool
+) -> tuple[npt.DTypeLike, Any]:
+    if dtype is None and is_numeric_dtype(arr.dtype):
+        dtype_given = False
+        if hasna:
+            if arr.dtype.kind == "b":
+                dtype = np.dtype(np.object_)
+            else:
+                if arr.dtype.kind in "iu":
+                    dtype = np.dtype(np.float64)
+                else:
+                    dtype = arr.dtype.numpy_dtype  # type: ignore[union-attr]
+                if na_value is lib.no_default:
+                    na_value = np.nan
+        else:
+            dtype = arr.dtype.numpy_dtype  # type: ignore[union-attr]
+    elif dtype is not None:
+        dtype = np.dtype(dtype)
+        dtype_given = True
+    else:
+        dtype_given = True
+
+    if na_value is lib.no_default:
+        if dtype is None or not hasna:
+            na_value = arr.dtype.na_value
+        elif dtype.kind == "f":  # type: ignore[union-attr]
+            na_value = np.nan
+        elif dtype.kind == "M":  # type: ignore[union-attr]
+            na_value = np.datetime64("nat")
+        elif dtype.kind == "m":  # type: ignore[union-attr]
+            na_value = np.timedelta64("nat")
+        else:
+            na_value = arr.dtype.na_value
+
+    if not dtype_given and hasna:
+        try:
+            np_can_hold_element(dtype, na_value)  # type: ignore[arg-type]
+        except LossySetitemError:
+            dtype = np.dtype(np.object_)
+    return dtype, na_value
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/arrow/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/arrow/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5fc50f786fc6a6c51f78ef9ebd4ee6ed26a2bab3
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/arrow/__init__.py
@@ -0,0 +1,7 @@
+from pandas.core.arrays.arrow.accessors import (
+    ListAccessor,
+    StructAccessor,
+)
+from pandas.core.arrays.arrow.array import ArrowExtensionArray
+
+__all__ = ["ArrowExtensionArray", "StructAccessor", "ListAccessor"]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/arrow/_arrow_utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/arrow/_arrow_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..285c3fd465ffcb9f507ebd1b3a0e3e6f55b76987
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/arrow/_arrow_utils.py
@@ -0,0 +1,50 @@
+from __future__ import annotations
+
+import numpy as np
+import pyarrow
+
+
+def pyarrow_array_to_numpy_and_mask(
+    arr, dtype: np.dtype
+) -> tuple[np.ndarray, np.ndarray]:
+    """
+    Convert a primitive pyarrow.Array to a numpy array and boolean mask based
+    on the buffers of the Array.
+
+    At the moment pyarrow.BooleanArray is not supported.
+
+    Parameters
+    ----------
+    arr : pyarrow.Array
+    dtype : numpy.dtype
+
+    Returns
+    -------
+    (data, mask)
+        Tuple of two numpy arrays with the raw data (with specified dtype) and
+        a boolean mask (validity mask, so False means missing)
+    """
+    dtype = np.dtype(dtype)
+
+    if pyarrow.types.is_null(arr.type):
+        # No initialization of data is needed since everything is null
+        data = np.empty(len(arr), dtype=dtype)
+        mask = np.zeros(len(arr), dtype=bool)
+        return data, mask
+    buflist = arr.buffers()
+    # Since Arrow buffers might contain padding and the data might be offset,
+    # the buffer gets sliced here before handing it to numpy.
+    # See also https://github.com/pandas-dev/pandas/issues/40896
+    offset = arr.offset * dtype.itemsize
+    length = len(arr) * dtype.itemsize
+    data_buf = buflist[1][offset : offset + length]
+    data = np.frombuffer(data_buf, dtype=dtype)
+    bitmask = buflist[0]
+    if bitmask is not None:
+        mask = pyarrow.BooleanArray.from_buffers(
+            pyarrow.bool_(), len(arr), [None, bitmask], offset=arr.offset
+        )
+        mask = np.asarray(mask)
+    else:
+        mask = np.ones(len(arr), dtype=bool)
+    return data, mask
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/arrow/accessors.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/arrow/accessors.py
new file mode 100644
index 0000000000000000000000000000000000000000..65f0784eaa3fd45e278cef083c3a606023827da0
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/arrow/accessors.py
@@ -0,0 +1,473 @@
+"""Accessors for arrow-backed data."""
+
+from __future__ import annotations
+
+from abc import (
+    ABCMeta,
+    abstractmethod,
+)
+from typing import (
+    TYPE_CHECKING,
+    cast,
+)
+
+from pandas.compat import (
+    pa_version_under10p1,
+    pa_version_under11p0,
+)
+
+from pandas.core.dtypes.common import is_list_like
+
+if not pa_version_under10p1:
+    import pyarrow as pa
+    import pyarrow.compute as pc
+
+    from pandas.core.dtypes.dtypes import ArrowDtype
+
+if TYPE_CHECKING:
+    from collections.abc import Iterator
+
+    from pandas import (
+        DataFrame,
+        Series,
+    )
+
+
+class ArrowAccessor(metaclass=ABCMeta):
+    @abstractmethod
+    def __init__(self, data, validation_msg: str) -> None:
+        self._data = data
+        self._validation_msg = validation_msg
+        self._validate(data)
+
+    @abstractmethod
+    def _is_valid_pyarrow_dtype(self, pyarrow_dtype) -> bool:
+        pass
+
+    def _validate(self, data):
+        dtype = data.dtype
+        if pa_version_under10p1 or not isinstance(dtype, ArrowDtype):
+            # Raise AttributeError so that inspect can handle non-struct Series.
+            raise AttributeError(self._validation_msg.format(dtype=dtype))
+
+        if not self._is_valid_pyarrow_dtype(dtype.pyarrow_dtype):
+            # Raise AttributeError so that inspect can handle invalid Series.
+            raise AttributeError(self._validation_msg.format(dtype=dtype))
+
+    @property
+    def _pa_array(self):
+        return self._data.array._pa_array
+
+
+class ListAccessor(ArrowAccessor):
+    """
+    Accessor object for list data properties of the Series values.
+
+    Parameters
+    ----------
+    data : Series
+        Series containing Arrow list data.
+    """
+
+    def __init__(self, data=None) -> None:
+        super().__init__(
+            data,
+            validation_msg="Can only use the '.list' accessor with "
+            "'list[pyarrow]' dtype, not {dtype}.",
+        )
+
+    def _is_valid_pyarrow_dtype(self, pyarrow_dtype) -> bool:
+        return (
+            pa.types.is_list(pyarrow_dtype)
+            or pa.types.is_fixed_size_list(pyarrow_dtype)
+            or pa.types.is_large_list(pyarrow_dtype)
+        )
+
+    def len(self) -> Series:
+        """
+        Return the length of each list in the Series.
+
+        Returns
+        -------
+        pandas.Series
+            The length of each list.
+
+        Examples
+        --------
+        >>> import pyarrow as pa
+        >>> s = pd.Series(
+        ...     [
+        ...         [1, 2, 3],
+        ...         [3],
+        ...     ],
+        ...     dtype=pd.ArrowDtype(pa.list_(
+        ...         pa.int64()
+        ...     ))
+        ... )
+        >>> s.list.len()
+        0    3
+        1    1
+        dtype: int32[pyarrow]
+        """
+        from pandas import Series
+
+        value_lengths = pc.list_value_length(self._pa_array)
+        return Series(value_lengths, dtype=ArrowDtype(value_lengths.type))
+
+    def __getitem__(self, key: int | slice) -> Series:
+        """
+        Index or slice lists in the Series.
+
+        Parameters
+        ----------
+        key : int | slice
+            Index or slice of indices to access from each list.
+
+        Returns
+        -------
+        pandas.Series
+            The list at requested index.
+
+        Examples
+        --------
+        >>> import pyarrow as pa
+        >>> s = pd.Series(
+        ...     [
+        ...         [1, 2, 3],
+        ...         [3],
+        ...     ],
+        ...     dtype=pd.ArrowDtype(pa.list_(
+        ...         pa.int64()
+        ...     ))
+        ... )
+        >>> s.list[0]
+        0    1
+        1    3
+        dtype: int64[pyarrow]
+        """
+        from pandas import Series
+
+        if isinstance(key, int):
+            # TODO: Support negative key but pyarrow does not allow
+            # element index to be an array.
+            # if key < 0:
+            #     key = pc.add(key, pc.list_value_length(self._pa_array))
+            element = pc.list_element(self._pa_array, key)
+            return Series(element, dtype=ArrowDtype(element.type))
+        elif isinstance(key, slice):
+            if pa_version_under11p0:
+                raise NotImplementedError(
+                    f"List slice not supported by pyarrow {pa.__version__}."
+                )
+
+            # TODO: Support negative start/stop/step, ideally this would be added
+            # upstream in pyarrow.
+            start, stop, step = key.start, key.stop, key.step
+            if start is None:
+                # TODO: When adding negative step support
+                #  this should be setto last element of array
+                # when step is negative.
+                start = 0
+            if step is None:
+                step = 1
+            sliced = pc.list_slice(self._pa_array, start, stop, step)
+            return Series(sliced, dtype=ArrowDtype(sliced.type))
+        else:
+            raise ValueError(f"key must be an int or slice, got {type(key).__name__}")
+
+    def __iter__(self) -> Iterator:
+        raise TypeError(f"'{type(self).__name__}' object is not iterable")
+
+    def flatten(self) -> Series:
+        """
+        Flatten list values.
+
+        Returns
+        -------
+        pandas.Series
+            The data from all lists in the series flattened.
+
+        Examples
+        --------
+        >>> import pyarrow as pa
+        >>> s = pd.Series(
+        ...     [
+        ...         [1, 2, 3],
+        ...         [3],
+        ...     ],
+        ...     dtype=pd.ArrowDtype(pa.list_(
+        ...         pa.int64()
+        ...     ))
+        ... )
+        >>> s.list.flatten()
+        0    1
+        1    2
+        2    3
+        3    3
+        dtype: int64[pyarrow]
+        """
+        from pandas import Series
+
+        flattened = pc.list_flatten(self._pa_array)
+        return Series(flattened, dtype=ArrowDtype(flattened.type))
+
+
+class StructAccessor(ArrowAccessor):
+    """
+    Accessor object for structured data properties of the Series values.
+
+    Parameters
+    ----------
+    data : Series
+        Series containing Arrow struct data.
+    """
+
+    def __init__(self, data=None) -> None:
+        super().__init__(
+            data,
+            validation_msg=(
+                "Can only use the '.struct' accessor with 'struct[pyarrow]' "
+                "dtype, not {dtype}."
+            ),
+        )
+
+    def _is_valid_pyarrow_dtype(self, pyarrow_dtype) -> bool:
+        return pa.types.is_struct(pyarrow_dtype)
+
+    @property
+    def dtypes(self) -> Series:
+        """
+        Return the dtype object of each child field of the struct.
+
+        Returns
+        -------
+        pandas.Series
+            The data type of each child field.
+
+        Examples
+        --------
+        >>> import pyarrow as pa
+        >>> s = pd.Series(
+        ...     [
+        ...         {"version": 1, "project": "pandas"},
+        ...         {"version": 2, "project": "pandas"},
+        ...         {"version": 1, "project": "numpy"},
+        ...     ],
+        ...     dtype=pd.ArrowDtype(pa.struct(
+        ...         [("version", pa.int64()), ("project", pa.string())]
+        ...     ))
+        ... )
+        >>> s.struct.dtypes
+        version     int64[pyarrow]
+        project    string[pyarrow]
+        dtype: object
+        """
+        from pandas import (
+            Index,
+            Series,
+        )
+
+        pa_type = self._data.dtype.pyarrow_dtype
+        types = [ArrowDtype(struct.type) for struct in pa_type]
+        names = [struct.name for struct in pa_type]
+        return Series(types, index=Index(names))
+
+    def field(
+        self,
+        name_or_index: list[str]
+        | list[bytes]
+        | list[int]
+        | pc.Expression
+        | bytes
+        | str
+        | int,
+    ) -> Series:
+        """
+        Extract a child field of a struct as a Series.
+
+        Parameters
+        ----------
+        name_or_index : str | bytes | int | expression | list
+            Name or index of the child field to extract.
+
+            For list-like inputs, this will index into a nested
+            struct.
+
+        Returns
+        -------
+        pandas.Series
+            The data corresponding to the selected child field.
+
+        See Also
+        --------
+        Series.struct.explode : Return all child fields as a DataFrame.
+
+        Notes
+        -----
+        The name of the resulting Series will be set using the following
+        rules:
+
+        - For string, bytes, or integer `name_or_index` (or a list of these, for
+          a nested selection), the Series name is set to the selected
+          field's name.
+        - For a :class:`pyarrow.compute.Expression`, this is set to
+          the string form of the expression.
+        - For list-like `name_or_index`, the name will be set to the
+          name of the final field selected.
+
+        Examples
+        --------
+        >>> import pyarrow as pa
+        >>> s = pd.Series(
+        ...     [
+        ...         {"version": 1, "project": "pandas"},
+        ...         {"version": 2, "project": "pandas"},
+        ...         {"version": 1, "project": "numpy"},
+        ...     ],
+        ...     dtype=pd.ArrowDtype(pa.struct(
+        ...         [("version", pa.int64()), ("project", pa.string())]
+        ...     ))
+        ... )
+
+        Extract by field name.
+
+        >>> s.struct.field("project")
+        0    pandas
+        1    pandas
+        2     numpy
+        Name: project, dtype: string[pyarrow]
+
+        Extract by field index.
+
+        >>> s.struct.field(0)
+        0    1
+        1    2
+        2    1
+        Name: version, dtype: int64[pyarrow]
+
+        Or an expression
+
+        >>> import pyarrow.compute as pc
+        >>> s.struct.field(pc.field("project"))
+        0    pandas
+        1    pandas
+        2     numpy
+        Name: project, dtype: string[pyarrow]
+
+        For nested struct types, you can pass a list of values to index
+        multiple levels:
+
+        >>> version_type = pa.struct([
+        ...     ("major", pa.int64()),
+        ...     ("minor", pa.int64()),
+        ... ])
+        >>> s = pd.Series(
+        ...     [
+        ...         {"version": {"major": 1, "minor": 5}, "project": "pandas"},
+        ...         {"version": {"major": 2, "minor": 1}, "project": "pandas"},
+        ...         {"version": {"major": 1, "minor": 26}, "project": "numpy"},
+        ...     ],
+        ...     dtype=pd.ArrowDtype(pa.struct(
+        ...         [("version", version_type), ("project", pa.string())]
+        ...     ))
+        ... )
+        >>> s.struct.field(["version", "minor"])
+        0     5
+        1     1
+        2    26
+        Name: minor, dtype: int64[pyarrow]
+        >>> s.struct.field([0, 0])
+        0    1
+        1    2
+        2    1
+        Name: major, dtype: int64[pyarrow]
+        """
+        from pandas import Series
+
+        def get_name(
+            level_name_or_index: list[str]
+            | list[bytes]
+            | list[int]
+            | pc.Expression
+            | bytes
+            | str
+            | int,
+            data: pa.ChunkedArray,
+        ):
+            if isinstance(level_name_or_index, int):
+                name = data.type.field(level_name_or_index).name
+            elif isinstance(level_name_or_index, (str, bytes)):
+                name = level_name_or_index
+            elif isinstance(level_name_or_index, pc.Expression):
+                name = str(level_name_or_index)
+            elif is_list_like(level_name_or_index):
+                # For nested input like [2, 1, 2]
+                # iteratively get the struct and field name. The last
+                # one is used for the name of the index.
+                level_name_or_index = list(reversed(level_name_or_index))
+                selected = data
+                while level_name_or_index:
+                    # we need the cast, otherwise mypy complains about
+                    # getting ints, bytes, or str here, which isn't possible.
+                    level_name_or_index = cast(list, level_name_or_index)
+                    name_or_index = level_name_or_index.pop()
+                    name = get_name(name_or_index, selected)
+                    selected = selected.type.field(selected.type.get_field_index(name))
+                    name = selected.name
+            else:
+                raise ValueError(
+                    "name_or_index must be an int, str, bytes, "
+                    "pyarrow.compute.Expression, or list of those"
+                )
+            return name
+
+        pa_arr = self._data.array._pa_array
+        name = get_name(name_or_index, pa_arr)
+        field_arr = pc.struct_field(pa_arr, name_or_index)
+
+        return Series(
+            field_arr,
+            dtype=ArrowDtype(field_arr.type),
+            index=self._data.index,
+            name=name,
+        )
+
+    def explode(self) -> DataFrame:
+        """
+        Extract all child fields of a struct as a DataFrame.
+
+        Returns
+        -------
+        pandas.DataFrame
+            The data corresponding to all child fields.
+
+        See Also
+        --------
+        Series.struct.field : Return a single child field as a Series.
+
+        Examples
+        --------
+        >>> import pyarrow as pa
+        >>> s = pd.Series(
+        ...     [
+        ...         {"version": 1, "project": "pandas"},
+        ...         {"version": 2, "project": "pandas"},
+        ...         {"version": 1, "project": "numpy"},
+        ...     ],
+        ...     dtype=pd.ArrowDtype(pa.struct(
+        ...         [("version", pa.int64()), ("project", pa.string())]
+        ...     ))
+        ... )
+
+        >>> s.struct.explode()
+           version project
+        0        1  pandas
+        1        2  pandas
+        2        1   numpy
+        """
+        from pandas import concat
+
+        pa_type = self._pa_array.type
+        return concat(
+            [self.field(i) for i in range(pa_type.num_fields)], axis="columns"
+        )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/arrow/array.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/arrow/array.py
new file mode 100644
index 0000000000000000000000000000000000000000..e16a126ac10ee3ee3a00d8d967ad2a038e37ff5c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/arrow/array.py
@@ -0,0 +1,2969 @@
+from __future__ import annotations
+
+import functools
+import operator
+from pathlib import Path
+import re
+import textwrap
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Literal,
+    cast,
+)
+import unicodedata
+import warnings
+
+import numpy as np
+
+from pandas._libs import lib
+from pandas._libs.tslibs import (
+    NaT,
+    Timedelta,
+    Timestamp,
+    timezones,
+)
+from pandas.compat import (
+    pa_version_under10p1,
+    pa_version_under11p0,
+    pa_version_under13p0,
+)
+from pandas.util._decorators import doc
+from pandas.util._exceptions import find_stack_level
+from pandas.util._validators import validate_fillna_kwargs
+
+from pandas.core.dtypes.cast import (
+    can_hold_element,
+    infer_dtype_from_scalar,
+)
+from pandas.core.dtypes.common import (
+    is_array_like,
+    is_bool_dtype,
+    is_float_dtype,
+    is_integer,
+    is_list_like,
+    is_numeric_dtype,
+    is_scalar,
+    is_string_dtype,
+)
+from pandas.core.dtypes.dtypes import DatetimeTZDtype
+from pandas.core.dtypes.missing import isna
+
+from pandas.core import (
+    algorithms as algos,
+    missing,
+    ops,
+    roperator,
+)
+from pandas.core.algorithms import map_array
+from pandas.core.arraylike import OpsMixin
+from pandas.core.arrays._arrow_string_mixins import ArrowStringArrayMixin
+from pandas.core.arrays._utils import to_numpy_dtype_inference
+from pandas.core.arrays.base import (
+    ExtensionArray,
+    ExtensionArraySupportsAnyAll,
+)
+from pandas.core.arrays.masked import BaseMaskedArray
+from pandas.core.arrays.string_ import StringDtype
+import pandas.core.common as com
+from pandas.core.indexers import (
+    check_array_indexer,
+    unpack_tuple_and_ellipses,
+    validate_indices,
+)
+from pandas.core.nanops import check_below_min_count
+from pandas.core.strings.base import BaseStringArrayMethods
+
+from pandas.io._util import _arrow_dtype_mapping
+from pandas.tseries.frequencies import to_offset
+
+if not pa_version_under10p1:
+    import pyarrow as pa
+    import pyarrow.compute as pc
+
+    from pandas.core.dtypes.dtypes import ArrowDtype
+
+    ARROW_CMP_FUNCS = {
+        "eq": pc.equal,
+        "ne": pc.not_equal,
+        "lt": pc.less,
+        "gt": pc.greater,
+        "le": pc.less_equal,
+        "ge": pc.greater_equal,
+    }
+
+    ARROW_LOGICAL_FUNCS = {
+        "and_": pc.and_kleene,
+        "rand_": lambda x, y: pc.and_kleene(y, x),
+        "or_": pc.or_kleene,
+        "ror_": lambda x, y: pc.or_kleene(y, x),
+        "xor": pc.xor,
+        "rxor": lambda x, y: pc.xor(y, x),
+    }
+
+    ARROW_BIT_WISE_FUNCS = {
+        "and_": pc.bit_wise_and,
+        "rand_": lambda x, y: pc.bit_wise_and(y, x),
+        "or_": pc.bit_wise_or,
+        "ror_": lambda x, y: pc.bit_wise_or(y, x),
+        "xor": pc.bit_wise_xor,
+        "rxor": lambda x, y: pc.bit_wise_xor(y, x),
+    }
+
+    def cast_for_truediv(
+        arrow_array: pa.ChunkedArray, pa_object: pa.Array | pa.Scalar
+    ) -> tuple[pa.ChunkedArray, pa.Array | pa.Scalar]:
+        # Ensure int / int -> float mirroring Python/Numpy behavior
+        # as pc.divide_checked(int, int) -> int
+        if pa.types.is_integer(arrow_array.type) and pa.types.is_integer(
+            pa_object.type
+        ):
+            # GH: 56645.
+            # https://github.com/apache/arrow/issues/35563
+            return pc.cast(arrow_array, pa.float64(), safe=False), pc.cast(
+                pa_object, pa.float64(), safe=False
+            )
+
+        return arrow_array, pa_object
+
+    def floordiv_compat(
+        left: pa.ChunkedArray | pa.Array | pa.Scalar,
+        right: pa.ChunkedArray | pa.Array | pa.Scalar,
+    ) -> pa.ChunkedArray:
+        # TODO: Replace with pyarrow floordiv kernel.
+        # https://github.com/apache/arrow/issues/39386
+        if pa.types.is_integer(left.type) and pa.types.is_integer(right.type):
+            divided = pc.divide_checked(left, right)
+            if pa.types.is_signed_integer(divided.type):
+                # GH 56676
+                has_remainder = pc.not_equal(pc.multiply(divided, right), left)
+                has_one_negative_operand = pc.less(
+                    pc.bit_wise_xor(left, right),
+                    pa.scalar(0, type=divided.type),
+                )
+                result = pc.if_else(
+                    pc.and_(
+                        has_remainder,
+                        has_one_negative_operand,
+                    ),
+                    # GH: 55561
+                    pc.subtract(divided, pa.scalar(1, type=divided.type)),
+                    divided,
+                )
+            else:
+                result = divided
+            result = result.cast(left.type)
+        else:
+            divided = pc.divide(left, right)
+            result = pc.floor(divided)
+        return result
+
+    ARROW_ARITHMETIC_FUNCS = {
+        "add": pc.add_checked,
+        "radd": lambda x, y: pc.add_checked(y, x),
+        "sub": pc.subtract_checked,
+        "rsub": lambda x, y: pc.subtract_checked(y, x),
+        "mul": pc.multiply_checked,
+        "rmul": lambda x, y: pc.multiply_checked(y, x),
+        "truediv": lambda x, y: pc.divide(*cast_for_truediv(x, y)),
+        "rtruediv": lambda x, y: pc.divide(*cast_for_truediv(y, x)),
+        "floordiv": lambda x, y: floordiv_compat(x, y),
+        "rfloordiv": lambda x, y: floordiv_compat(y, x),
+        "mod": NotImplemented,
+        "rmod": NotImplemented,
+        "divmod": NotImplemented,
+        "rdivmod": NotImplemented,
+        "pow": pc.power_checked,
+        "rpow": lambda x, y: pc.power_checked(y, x),
+    }
+
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+
+    from pandas._typing import (
+        ArrayLike,
+        AxisInt,
+        Dtype,
+        FillnaOptions,
+        InterpolateOptions,
+        Iterator,
+        NpDtype,
+        NumpySorter,
+        NumpyValueArrayLike,
+        PositionalIndexer,
+        Scalar,
+        Self,
+        SortKind,
+        TakeIndexer,
+        TimeAmbiguous,
+        TimeNonexistent,
+        npt,
+    )
+
+    from pandas import Series
+    from pandas.core.arrays.datetimes import DatetimeArray
+    from pandas.core.arrays.timedeltas import TimedeltaArray
+
+
+def get_unit_from_pa_dtype(pa_dtype):
+    # https://github.com/pandas-dev/pandas/pull/50998#discussion_r1100344804
+    if pa_version_under11p0:
+        unit = str(pa_dtype).split("[", 1)[-1][:-1]
+        if unit not in ["s", "ms", "us", "ns"]:
+            raise ValueError(pa_dtype)
+        return unit
+    return pa_dtype.unit
+
+
+def to_pyarrow_type(
+    dtype: ArrowDtype | pa.DataType | Dtype | None,
+) -> pa.DataType | None:
+    """
+    Convert dtype to a pyarrow type instance.
+    """
+    if isinstance(dtype, ArrowDtype):
+        return dtype.pyarrow_dtype
+    elif isinstance(dtype, pa.DataType):
+        return dtype
+    elif isinstance(dtype, DatetimeTZDtype):
+        return pa.timestamp(dtype.unit, dtype.tz)
+    elif dtype:
+        try:
+            # Accepts python types too
+            # Doesn't handle all numpy types
+            return pa.from_numpy_dtype(dtype)
+        except pa.ArrowNotImplementedError:
+            pass
+    return None
+
+
+class ArrowExtensionArray(  # type: ignore[misc]
+    OpsMixin,
+    ExtensionArraySupportsAnyAll,
+    ArrowStringArrayMixin,
+    BaseStringArrayMethods,
+):
+    """
+    Pandas ExtensionArray backed by a PyArrow ChunkedArray.
+
+    .. warning::
+
+       ArrowExtensionArray is considered experimental. The implementation and
+       parts of the API may change without warning.
+
+    Parameters
+    ----------
+    values : pyarrow.Array or pyarrow.ChunkedArray
+
+    Attributes
+    ----------
+    None
+
+    Methods
+    -------
+    None
+
+    Returns
+    -------
+    ArrowExtensionArray
+
+    Notes
+    -----
+    Most methods are implemented using `pyarrow compute functions. `__
+    Some methods may either raise an exception or raise a ``PerformanceWarning`` if an
+    associated compute function is not available based on the installed version of PyArrow.
+
+    Please install the latest version of PyArrow to enable the best functionality and avoid
+    potential bugs in prior versions of PyArrow.
+
+    Examples
+    --------
+    Create an ArrowExtensionArray with :func:`pandas.array`:
+
+    >>> pd.array([1, 1, None], dtype="int64[pyarrow]")
+    
+    [1, 1, ]
+    Length: 3, dtype: int64[pyarrow]
+    """  # noqa: E501 (http link too long)
+
+    _pa_array: pa.ChunkedArray
+    _dtype: ArrowDtype
+
+    def __init__(self, values: pa.Array | pa.ChunkedArray) -> None:
+        if pa_version_under10p1:
+            msg = "pyarrow>=10.0.1 is required for PyArrow backed ArrowExtensionArray."
+            raise ImportError(msg)
+        if isinstance(values, pa.Array):
+            self._pa_array = pa.chunked_array([values])
+        elif isinstance(values, pa.ChunkedArray):
+            self._pa_array = values
+        else:
+            raise ValueError(
+                f"Unsupported type '{type(values)}' for ArrowExtensionArray"
+            )
+        self._dtype = ArrowDtype(self._pa_array.type)
+
+    @classmethod
+    def _from_sequence(cls, scalars, *, dtype: Dtype | None = None, copy: bool = False):
+        """
+        Construct a new ExtensionArray from a sequence of scalars.
+        """
+        pa_type = to_pyarrow_type(dtype)
+        pa_array = cls._box_pa_array(scalars, pa_type=pa_type, copy=copy)
+        arr = cls(pa_array)
+        return arr
+
+    @classmethod
+    def _from_sequence_of_strings(
+        cls, strings, *, dtype: Dtype | None = None, copy: bool = False
+    ):
+        """
+        Construct a new ExtensionArray from a sequence of strings.
+        """
+        pa_type = to_pyarrow_type(dtype)
+        if (
+            pa_type is None
+            or pa.types.is_binary(pa_type)
+            or pa.types.is_string(pa_type)
+            or pa.types.is_large_string(pa_type)
+        ):
+            # pa_type is None: Let pa.array infer
+            # pa_type is string/binary: scalars already correct type
+            scalars = strings
+        elif pa.types.is_timestamp(pa_type):
+            from pandas.core.tools.datetimes import to_datetime
+
+            scalars = to_datetime(strings, errors="raise")
+        elif pa.types.is_date(pa_type):
+            from pandas.core.tools.datetimes import to_datetime
+
+            scalars = to_datetime(strings, errors="raise").date
+        elif pa.types.is_duration(pa_type):
+            from pandas.core.tools.timedeltas import to_timedelta
+
+            scalars = to_timedelta(strings, errors="raise")
+            if pa_type.unit != "ns":
+                # GH51175: test_from_sequence_of_strings_pa_array
+                # attempt to parse as int64 reflecting pyarrow's
+                # duration to string casting behavior
+                mask = isna(scalars)
+                if not isinstance(strings, (pa.Array, pa.ChunkedArray)):
+                    strings = pa.array(strings, type=pa.string(), from_pandas=True)
+                strings = pc.if_else(mask, None, strings)
+                try:
+                    scalars = strings.cast(pa.int64())
+                except pa.ArrowInvalid:
+                    pass
+        elif pa.types.is_time(pa_type):
+            from pandas.core.tools.times import to_time
+
+            # "coerce" to allow "null times" (None) to not raise
+            scalars = to_time(strings, errors="coerce")
+        elif pa.types.is_boolean(pa_type):
+            # pyarrow string->bool casting is case-insensitive:
+            #   "true" or "1" -> True
+            #   "false" or "0" -> False
+            # Note: BooleanArray was previously used to parse these strings
+            #   and allows "1.0" and "0.0". Pyarrow casting does not support
+            #   this, but we allow it here.
+            if isinstance(strings, (pa.Array, pa.ChunkedArray)):
+                scalars = strings
+            else:
+                scalars = pa.array(strings, type=pa.string(), from_pandas=True)
+            scalars = pc.if_else(pc.equal(scalars, "1.0"), "1", scalars)
+            scalars = pc.if_else(pc.equal(scalars, "0.0"), "0", scalars)
+            scalars = scalars.cast(pa.bool_())
+        elif (
+            pa.types.is_integer(pa_type)
+            or pa.types.is_floating(pa_type)
+            or pa.types.is_decimal(pa_type)
+        ):
+            from pandas.core.tools.numeric import to_numeric
+
+            scalars = to_numeric(strings, errors="raise")
+        else:
+            raise NotImplementedError(
+                f"Converting strings to {pa_type} is not implemented."
+            )
+        return cls._from_sequence(scalars, dtype=pa_type, copy=copy)
+
+    @classmethod
+    def _box_pa(
+        cls, value, pa_type: pa.DataType | None = None
+    ) -> pa.Array | pa.ChunkedArray | pa.Scalar:
+        """
+        Box value into a pyarrow Array, ChunkedArray or Scalar.
+
+        Parameters
+        ----------
+        value : any
+        pa_type : pa.DataType | None
+
+        Returns
+        -------
+        pa.Array or pa.ChunkedArray or pa.Scalar
+        """
+        if isinstance(value, pa.Scalar) or not is_list_like(value):
+            return cls._box_pa_scalar(value, pa_type)
+        return cls._box_pa_array(value, pa_type)
+
+    @classmethod
+    def _box_pa_scalar(cls, value, pa_type: pa.DataType | None = None) -> pa.Scalar:
+        """
+        Box value into a pyarrow Scalar.
+
+        Parameters
+        ----------
+        value : any
+        pa_type : pa.DataType | None
+
+        Returns
+        -------
+        pa.Scalar
+        """
+        if isinstance(value, pa.Scalar):
+            pa_scalar = value
+        elif isna(value):
+            pa_scalar = pa.scalar(None, type=pa_type)
+        else:
+            # Workaround https://github.com/apache/arrow/issues/37291
+            if isinstance(value, Timedelta):
+                if pa_type is None:
+                    pa_type = pa.duration(value.unit)
+                elif value.unit != pa_type.unit:
+                    value = value.as_unit(pa_type.unit)
+                value = value._value
+            elif isinstance(value, Timestamp):
+                if pa_type is None:
+                    pa_type = pa.timestamp(value.unit, tz=value.tz)
+                elif value.unit != pa_type.unit:
+                    value = value.as_unit(pa_type.unit)
+                value = value._value
+
+            pa_scalar = pa.scalar(value, type=pa_type, from_pandas=True)
+
+        if pa_type is not None and pa_scalar.type != pa_type:
+            pa_scalar = pa_scalar.cast(pa_type)
+
+        return pa_scalar
+
+    @classmethod
+    def _box_pa_array(
+        cls, value, pa_type: pa.DataType | None = None, copy: bool = False
+    ) -> pa.Array | pa.ChunkedArray:
+        """
+        Box value into a pyarrow Array or ChunkedArray.
+
+        Parameters
+        ----------
+        value : Sequence
+        pa_type : pa.DataType | None
+
+        Returns
+        -------
+        pa.Array or pa.ChunkedArray
+        """
+        if isinstance(value, cls):
+            pa_array = value._pa_array
+        elif isinstance(value, (pa.Array, pa.ChunkedArray)):
+            pa_array = value
+        elif isinstance(value, BaseMaskedArray):
+            # GH 52625
+            if copy:
+                value = value.copy()
+            pa_array = value.__arrow_array__()
+        else:
+            if (
+                isinstance(value, np.ndarray)
+                and pa_type is not None
+                and (
+                    pa.types.is_large_binary(pa_type)
+                    or pa.types.is_large_string(pa_type)
+                )
+            ):
+                # See https://github.com/apache/arrow/issues/35289
+                value = value.tolist()
+            elif copy and is_array_like(value):
+                # pa array should not get updated when numpy array is updated
+                value = value.copy()
+
+            if (
+                pa_type is not None
+                and pa.types.is_duration(pa_type)
+                and (not isinstance(value, np.ndarray) or value.dtype.kind not in "mi")
+            ):
+                # Workaround https://github.com/apache/arrow/issues/37291
+                from pandas.core.tools.timedeltas import to_timedelta
+
+                value = to_timedelta(value, unit=pa_type.unit).as_unit(pa_type.unit)
+                value = value.to_numpy()
+
+            try:
+                pa_array = pa.array(value, type=pa_type, from_pandas=True)
+            except (pa.ArrowInvalid, pa.ArrowTypeError):
+                # GH50430: let pyarrow infer type, then cast
+                pa_array = pa.array(value, from_pandas=True)
+
+            if pa_type is None and pa.types.is_duration(pa_array.type):
+                # Workaround https://github.com/apache/arrow/issues/37291
+                from pandas.core.tools.timedeltas import to_timedelta
+
+                value = to_timedelta(value)
+                value = value.to_numpy()
+                pa_array = pa.array(value, type=pa_type, from_pandas=True)
+
+            if pa.types.is_duration(pa_array.type) and pa_array.null_count > 0:
+                # GH52843: upstream bug for duration types when originally
+                # constructed with data containing numpy NaT.
+                # https://github.com/apache/arrow/issues/35088
+                arr = cls(pa_array)
+                arr = arr.fillna(arr.dtype.na_value)
+                pa_array = arr._pa_array
+
+        if pa_type is not None and pa_array.type != pa_type:
+            if pa.types.is_dictionary(pa_type):
+                pa_array = pa_array.dictionary_encode()
+            else:
+                try:
+                    pa_array = pa_array.cast(pa_type)
+                except (
+                    pa.ArrowInvalid,
+                    pa.ArrowTypeError,
+                    pa.ArrowNotImplementedError,
+                ):
+                    if pa.types.is_string(pa_array.type) or pa.types.is_large_string(
+                        pa_array.type
+                    ):
+                        # TODO: Move logic in _from_sequence_of_strings into
+                        # _box_pa_array
+                        return cls._from_sequence_of_strings(
+                            value, dtype=pa_type
+                        )._pa_array
+                    else:
+                        raise
+
+        return pa_array
+
+    def __getitem__(self, item: PositionalIndexer):
+        """Select a subset of self.
+
+        Parameters
+        ----------
+        item : int, slice, or ndarray
+            * int: The position in 'self' to get.
+            * slice: A slice object, where 'start', 'stop', and 'step' are
+              integers or None
+            * ndarray: A 1-d boolean NumPy ndarray the same length as 'self'
+
+        Returns
+        -------
+        item : scalar or ExtensionArray
+
+        Notes
+        -----
+        For scalar ``item``, return a scalar value suitable for the array's
+        type. This should be an instance of ``self.dtype.type``.
+        For slice ``key``, return an instance of ``ExtensionArray``, even
+        if the slice is length 0 or 1.
+        For a boolean mask, return an instance of ``ExtensionArray``, filtered
+        to the values where ``item`` is True.
+        """
+        item = check_array_indexer(self, item)
+
+        if isinstance(item, np.ndarray):
+            if not len(item):
+                # Removable once we migrate StringDtype[pyarrow] to ArrowDtype[string]
+                if (
+                    isinstance(self._dtype, StringDtype)
+                    and self._dtype.storage == "pyarrow"
+                ):
+                    # TODO(infer_string) should this be large_string?
+                    pa_dtype = pa.string()
+                else:
+                    pa_dtype = self._dtype.pyarrow_dtype
+                return type(self)(pa.chunked_array([], type=pa_dtype))
+            elif item.dtype.kind in "iu":
+                return self.take(item)
+            elif item.dtype.kind == "b":
+                return type(self)(self._pa_array.filter(item))
+            else:
+                raise IndexError(
+                    "Only integers, slices and integer or "
+                    "boolean arrays are valid indices."
+                )
+        elif isinstance(item, tuple):
+            item = unpack_tuple_and_ellipses(item)
+
+        if item is Ellipsis:
+            # TODO: should be handled by pyarrow?
+            item = slice(None)
+
+        if is_scalar(item) and not is_integer(item):
+            # e.g. "foo" or 2.5
+            # exception message copied from numpy
+            raise IndexError(
+                r"only integers, slices (`:`), ellipsis (`...`), numpy.newaxis "
+                r"(`None`) and integer or boolean arrays are valid indices"
+            )
+        # We are not an array indexer, so maybe e.g. a slice or integer
+        # indexer. We dispatch to pyarrow.
+        if isinstance(item, slice):
+            # Arrow bug https://github.com/apache/arrow/issues/38768
+            if item.start == item.stop:
+                pass
+            elif (
+                item.stop is not None
+                and item.stop < -len(self)
+                and item.step is not None
+                and item.step < 0
+            ):
+                item = slice(item.start, None, item.step)
+
+        value = self._pa_array[item]
+        if isinstance(value, pa.ChunkedArray):
+            return type(self)(value)
+        else:
+            pa_type = self._pa_array.type
+            scalar = value.as_py()
+            if scalar is None:
+                return self._dtype.na_value
+            elif pa.types.is_timestamp(pa_type) and pa_type.unit != "ns":
+                # GH 53326
+                return Timestamp(scalar).as_unit(pa_type.unit)
+            elif pa.types.is_duration(pa_type) and pa_type.unit != "ns":
+                # GH 53326
+                return Timedelta(scalar).as_unit(pa_type.unit)
+            else:
+                return scalar
+
+    def __iter__(self) -> Iterator[Any]:
+        """
+        Iterate over elements of the array.
+        """
+        na_value = self._dtype.na_value
+        # GH 53326
+        pa_type = self._pa_array.type
+        box_timestamp = pa.types.is_timestamp(pa_type) and pa_type.unit != "ns"
+        box_timedelta = pa.types.is_duration(pa_type) and pa_type.unit != "ns"
+        for value in self._pa_array:
+            val = value.as_py()
+            if val is None:
+                yield na_value
+            elif box_timestamp:
+                yield Timestamp(val).as_unit(pa_type.unit)
+            elif box_timedelta:
+                yield Timedelta(val).as_unit(pa_type.unit)
+            else:
+                yield val
+
+    def __arrow_array__(self, type=None):
+        """Convert myself to a pyarrow ChunkedArray."""
+        return self._pa_array
+
+    def __array__(
+        self, dtype: NpDtype | None = None, copy: bool | None = None
+    ) -> np.ndarray:
+        """Correctly construct numpy arrays when passed to `np.asarray()`."""
+        if copy is False:
+            warnings.warn(
+                "Starting with NumPy 2.0, the behavior of the 'copy' keyword has "
+                "changed and passing 'copy=False' raises an error when returning "
+                "a zero-copy NumPy array is not possible. pandas will follow "
+                "this behavior starting with pandas 3.0.\nThis conversion to "
+                "NumPy requires a copy, but 'copy=False' was passed. Consider "
+                "using 'np.asarray(..)' instead.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+        elif copy is None:
+            # `to_numpy(copy=False)` has the meaning of NumPy `copy=None`.
+            copy = False
+
+        return self.to_numpy(dtype=dtype, copy=copy)
+
+    def __invert__(self) -> Self:
+        # This is a bit wise op for integer types
+        if pa.types.is_integer(self._pa_array.type):
+            return type(self)(pc.bit_wise_not(self._pa_array))
+        elif pa.types.is_string(self._pa_array.type) or pa.types.is_large_string(
+            self._pa_array.type
+        ):
+            # Raise TypeError instead of pa.ArrowNotImplementedError
+            raise TypeError("__invert__ is not supported for string dtypes")
+        else:
+            return type(self)(pc.invert(self._pa_array))
+
+    def __neg__(self) -> Self:
+        try:
+            return type(self)(pc.negate_checked(self._pa_array))
+        except pa.ArrowNotImplementedError as err:
+            raise TypeError(
+                f"unary '-' not supported for dtype '{self.dtype}'"
+            ) from err
+
+    def __pos__(self) -> Self:
+        return type(self)(self._pa_array)
+
+    def __abs__(self) -> Self:
+        return type(self)(pc.abs_checked(self._pa_array))
+
+    # GH 42600: __getstate__/__setstate__ not necessary once
+    # https://issues.apache.org/jira/browse/ARROW-10739 is addressed
+    def __getstate__(self):
+        state = self.__dict__.copy()
+        state["_pa_array"] = self._pa_array.combine_chunks()
+        return state
+
+    def __setstate__(self, state) -> None:
+        if "_data" in state:
+            data = state.pop("_data")
+        else:
+            data = state["_pa_array"]
+        state["_pa_array"] = pa.chunked_array(data)
+        self.__dict__.update(state)
+
+    def _cmp_method(self, other, op):
+        pc_func = ARROW_CMP_FUNCS[op.__name__]
+        if isinstance(other, (ExtensionArray, np.ndarray, list)):
+            try:
+                boxed = self._box_pa(other)
+            except pa.lib.ArrowInvalid:
+                # e.g. GH#60228 [1, "b"] we have to operate pointwise
+                res_values = [op(x, y) for x, y in zip(self, other)]
+                result = pa.array(res_values, type=pa.bool_(), from_pandas=True)
+            else:
+                try:
+                    result = pc_func(self._pa_array, boxed)
+                except pa.ArrowNotImplementedError:
+                    result = ops.invalid_comparison(self, other, op)
+                    result = pa.array(result, type=pa.bool_())
+
+        elif is_scalar(other):
+            try:
+                result = pc_func(self._pa_array, self._box_pa(other))
+            except (pa.lib.ArrowNotImplementedError, pa.lib.ArrowInvalid):
+                mask = isna(self) | isna(other)
+                valid = ~mask
+                result = np.zeros(len(self), dtype="bool")
+                np_array = np.array(self)
+                try:
+                    result[valid] = op(np_array[valid], other)
+                except TypeError:
+                    result = ops.invalid_comparison(self, other, op)
+                result = pa.array(result, type=pa.bool_())
+                result = pc.if_else(valid, result, None)
+        else:
+            raise NotImplementedError(
+                f"{op.__name__} not implemented for {type(other)}"
+            )
+        return ArrowExtensionArray(result)
+
+    def _op_method_error_message(self, other, op) -> str:
+        if hasattr(other, "dtype"):
+            other_type = f"dtype '{other.dtype}'"
+        else:
+            other_type = f"object of type {type(other)}"
+        return (
+            f"operation '{op.__name__}' not supported for "
+            f"dtype '{self.dtype}' with {other_type}"
+        )
+
+    def _evaluate_op_method(self, other, op, arrow_funcs) -> Self:
+        pa_type = self._pa_array.type
+        other_original = other
+        other = self._box_pa(other)
+
+        if (
+            pa.types.is_string(pa_type)
+            or pa.types.is_large_string(pa_type)
+            or pa.types.is_binary(pa_type)
+        ):
+            if op in [operator.add, roperator.radd]:
+                sep = pa.scalar("", type=pa_type)
+                try:
+                    if op is operator.add:
+                        result = pc.binary_join_element_wise(self._pa_array, other, sep)
+                    elif op is roperator.radd:
+                        result = pc.binary_join_element_wise(other, self._pa_array, sep)
+                except pa.ArrowNotImplementedError as err:
+                    raise TypeError(
+                        self._op_method_error_message(other_original, op)
+                    ) from err
+                return type(self)(result)
+            elif op in [operator.mul, roperator.rmul]:
+                binary = self._pa_array
+                integral = other
+                if not pa.types.is_integer(integral.type):
+                    raise TypeError("Can only string multiply by an integer.")
+                pa_integral = pc.if_else(pc.less(integral, 0), 0, integral)
+                result = pc.binary_repeat(binary, pa_integral)
+                return type(self)(result)
+        elif (
+            pa.types.is_string(other.type)
+            or pa.types.is_binary(other.type)
+            or pa.types.is_large_string(other.type)
+        ) and op in [operator.mul, roperator.rmul]:
+            binary = other
+            integral = self._pa_array
+            if not pa.types.is_integer(integral.type):
+                raise TypeError("Can only string multiply by an integer.")
+            pa_integral = pc.if_else(pc.less(integral, 0), 0, integral)
+            result = pc.binary_repeat(binary, pa_integral)
+            return type(self)(result)
+        if (
+            isinstance(other, pa.Scalar)
+            and pc.is_null(other).as_py()
+            and op.__name__ in ARROW_LOGICAL_FUNCS
+        ):
+            # pyarrow kleene ops require null to be typed
+            other = other.cast(pa_type)
+
+        pc_func = arrow_funcs[op.__name__]
+        if pc_func is NotImplemented:
+            if pa.types.is_string(pa_type) or pa.types.is_large_string(pa_type):
+                raise TypeError(self._op_method_error_message(other_original, op))
+            raise NotImplementedError(f"{op.__name__} not implemented.")
+
+        try:
+            result = pc_func(self._pa_array, other)
+        except pa.ArrowNotImplementedError as err:
+            raise TypeError(self._op_method_error_message(other_original, op)) from err
+        return type(self)(result)
+
+    def _logical_method(self, other, op):
+        # For integer types `^`, `|`, `&` are bitwise operators and return
+        # integer types. Otherwise these are boolean ops.
+        if pa.types.is_integer(self._pa_array.type):
+            return self._evaluate_op_method(other, op, ARROW_BIT_WISE_FUNCS)
+        elif (
+            (
+                pa.types.is_string(self._pa_array.type)
+                or pa.types.is_large_string(self._pa_array.type)
+            )
+            and op in (roperator.ror_, roperator.rand_, roperator.rxor)
+            and isinstance(other, np.ndarray)
+            and other.dtype == bool
+        ):
+            # GH#60234 backward compatibility for the move to StringDtype in 3.0
+            op_name = op.__name__[1:].strip("_")
+            warnings.warn(
+                f"'{op_name}' operations between boolean dtype and {self.dtype} are "
+                "deprecated and will raise in a future version. Explicitly "
+                "cast the strings to a boolean dtype before operating instead.",
+                DeprecationWarning,
+                stacklevel=find_stack_level(),
+            )
+            return op(other, self.astype(bool))
+        else:
+            return self._evaluate_op_method(other, op, ARROW_LOGICAL_FUNCS)
+
+    def _arith_method(self, other, op) -> Self | npt.NDArray[np.object_]:
+        if (
+            op in [operator.truediv, roperator.rtruediv]
+            and isinstance(other, Path)
+            and (
+                pa.types.is_string(self._pa_array.type)
+                or pa.types.is_large_string(self._pa_array.type)
+            )
+        ):
+            # GH#61940
+            return np.array(
+                [
+                    op(x, other) if isinstance(x, str) else self.dtype.na_value
+                    for x in self
+                ],
+                dtype=object,
+            )
+        return self._evaluate_op_method(other, op, ARROW_ARITHMETIC_FUNCS)
+
+    def equals(self, other) -> bool:
+        if not isinstance(other, ArrowExtensionArray):
+            return False
+        # I'm told that pyarrow makes __eq__ behave like pandas' equals;
+        #  TODO: is this documented somewhere?
+        return self._pa_array == other._pa_array
+
+    @property
+    def dtype(self) -> ArrowDtype:
+        """
+        An instance of 'ExtensionDtype'.
+        """
+        return self._dtype
+
+    @property
+    def nbytes(self) -> int:
+        """
+        The number of bytes needed to store this object in memory.
+        """
+        return self._pa_array.nbytes
+
+    def __len__(self) -> int:
+        """
+        Length of this array.
+
+        Returns
+        -------
+        length : int
+        """
+        return len(self._pa_array)
+
+    def __contains__(self, key) -> bool:
+        # https://github.com/pandas-dev/pandas/pull/51307#issuecomment-1426372604
+        if isna(key) and key is not self.dtype.na_value:
+            if self.dtype.kind == "f" and lib.is_float(key):
+                return pc.any(pc.is_nan(self._pa_array)).as_py()
+
+            # e.g. date or timestamp types we do not allow None here to match pd.NA
+            return False
+            # TODO: maybe complex? object?
+
+        return bool(super().__contains__(key))
+
+    @property
+    def _hasna(self) -> bool:
+        return self._pa_array.null_count > 0
+
+    def isna(self) -> npt.NDArray[np.bool_]:
+        """
+        Boolean NumPy array indicating if each value is missing.
+
+        This should return a 1-D array the same length as 'self'.
+        """
+        # GH51630: fast paths
+        null_count = self._pa_array.null_count
+        if null_count == 0:
+            return np.zeros(len(self), dtype=np.bool_)
+        elif null_count == len(self):
+            return np.ones(len(self), dtype=np.bool_)
+
+        return self._pa_array.is_null().to_numpy()
+
+    def any(self, *, skipna: bool = True, **kwargs):
+        """
+        Return whether any element is truthy.
+
+        Returns False unless there is at least one element that is truthy.
+        By default, NAs are skipped. If ``skipna=False`` is specified and
+        missing values are present, similar :ref:`Kleene logic `
+        is used as for logical operations.
+
+        Parameters
+        ----------
+        skipna : bool, default True
+            Exclude NA values. If the entire array is NA and `skipna` is
+            True, then the result will be False, as for an empty array.
+            If `skipna` is False, the result will still be True if there is
+            at least one element that is truthy, otherwise NA will be returned
+            if there are NA's present.
+
+        Returns
+        -------
+        bool or :attr:`pandas.NA`
+
+        See Also
+        --------
+        ArrowExtensionArray.all : Return whether all elements are truthy.
+
+        Examples
+        --------
+        The result indicates whether any element is truthy (and by default
+        skips NAs):
+
+        >>> pd.array([True, False, True], dtype="boolean[pyarrow]").any()
+        True
+        >>> pd.array([True, False, pd.NA], dtype="boolean[pyarrow]").any()
+        True
+        >>> pd.array([False, False, pd.NA], dtype="boolean[pyarrow]").any()
+        False
+        >>> pd.array([], dtype="boolean[pyarrow]").any()
+        False
+        >>> pd.array([pd.NA], dtype="boolean[pyarrow]").any()
+        False
+        >>> pd.array([pd.NA], dtype="float64[pyarrow]").any()
+        False
+
+        With ``skipna=False``, the result can be NA if this is logically
+        required (whether ``pd.NA`` is True or False influences the result):
+
+        >>> pd.array([True, False, pd.NA], dtype="boolean[pyarrow]").any(skipna=False)
+        True
+        >>> pd.array([1, 0, pd.NA], dtype="boolean[pyarrow]").any(skipna=False)
+        True
+        >>> pd.array([False, False, pd.NA], dtype="boolean[pyarrow]").any(skipna=False)
+        
+        >>> pd.array([0, 0, pd.NA], dtype="boolean[pyarrow]").any(skipna=False)
+        
+        """
+        return self._reduce("any", skipna=skipna, **kwargs)
+
+    def all(self, *, skipna: bool = True, **kwargs):
+        """
+        Return whether all elements are truthy.
+
+        Returns True unless there is at least one element that is falsey.
+        By default, NAs are skipped. If ``skipna=False`` is specified and
+        missing values are present, similar :ref:`Kleene logic `
+        is used as for logical operations.
+
+        Parameters
+        ----------
+        skipna : bool, default True
+            Exclude NA values. If the entire array is NA and `skipna` is
+            True, then the result will be True, as for an empty array.
+            If `skipna` is False, the result will still be False if there is
+            at least one element that is falsey, otherwise NA will be returned
+            if there are NA's present.
+
+        Returns
+        -------
+        bool or :attr:`pandas.NA`
+
+        See Also
+        --------
+        ArrowExtensionArray.any : Return whether any element is truthy.
+
+        Examples
+        --------
+        The result indicates whether all elements are truthy (and by default
+        skips NAs):
+
+        >>> pd.array([True, True, pd.NA], dtype="boolean[pyarrow]").all()
+        True
+        >>> pd.array([1, 1, pd.NA], dtype="boolean[pyarrow]").all()
+        True
+        >>> pd.array([True, False, pd.NA], dtype="boolean[pyarrow]").all()
+        False
+        >>> pd.array([], dtype="boolean[pyarrow]").all()
+        True
+        >>> pd.array([pd.NA], dtype="boolean[pyarrow]").all()
+        True
+        >>> pd.array([pd.NA], dtype="float64[pyarrow]").all()
+        True
+
+        With ``skipna=False``, the result can be NA if this is logically
+        required (whether ``pd.NA`` is True or False influences the result):
+
+        >>> pd.array([True, True, pd.NA], dtype="boolean[pyarrow]").all(skipna=False)
+        
+        >>> pd.array([1, 1, pd.NA], dtype="boolean[pyarrow]").all(skipna=False)
+        
+        >>> pd.array([True, False, pd.NA], dtype="boolean[pyarrow]").all(skipna=False)
+        False
+        >>> pd.array([1, 0, pd.NA], dtype="boolean[pyarrow]").all(skipna=False)
+        False
+        """
+        return self._reduce("all", skipna=skipna, **kwargs)
+
+    def argsort(
+        self,
+        *,
+        ascending: bool = True,
+        kind: SortKind = "quicksort",
+        na_position: str = "last",
+        **kwargs,
+    ) -> np.ndarray:
+        order = "ascending" if ascending else "descending"
+        null_placement = {"last": "at_end", "first": "at_start"}.get(na_position, None)
+        if null_placement is None:
+            raise ValueError(f"invalid na_position: {na_position}")
+
+        result = pc.array_sort_indices(
+            self._pa_array, order=order, null_placement=null_placement
+        )
+        np_result = result.to_numpy()
+        return np_result.astype(np.intp, copy=False)
+
+    def _argmin_max(self, skipna: bool, method: str) -> int:
+        if self._pa_array.length() in (0, self._pa_array.null_count) or (
+            self._hasna and not skipna
+        ):
+            # For empty or all null, pyarrow returns -1 but pandas expects TypeError
+            # For skipna=False and data w/ null, pandas expects NotImplementedError
+            # let ExtensionArray.arg{max|min} raise
+            return getattr(super(), f"arg{method}")(skipna=skipna)
+
+        data = self._pa_array
+        if pa.types.is_duration(data.type):
+            data = data.cast(pa.int64())
+
+        value = getattr(pc, method)(data, skip_nulls=skipna)
+        return pc.index(data, value).as_py()
+
+    def argmin(self, skipna: bool = True) -> int:
+        return self._argmin_max(skipna, "min")
+
+    def argmax(self, skipna: bool = True) -> int:
+        return self._argmin_max(skipna, "max")
+
+    def copy(self) -> Self:
+        """
+        Return a shallow copy of the array.
+
+        Underlying ChunkedArray is immutable, so a deep copy is unnecessary.
+
+        Returns
+        -------
+        type(self)
+        """
+        return type(self)(self._pa_array)
+
+    def dropna(self) -> Self:
+        """
+        Return ArrowExtensionArray without NA values.
+
+        Returns
+        -------
+        ArrowExtensionArray
+        """
+        return type(self)(pc.drop_null(self._pa_array))
+
+    def _pad_or_backfill(
+        self,
+        *,
+        method: FillnaOptions,
+        limit: int | None = None,
+        limit_area: Literal["inside", "outside"] | None = None,
+        copy: bool = True,
+    ) -> Self:
+        if not self._hasna:
+            # TODO(CoW): Not necessary anymore when CoW is the default
+            return self.copy()
+
+        if limit is None and limit_area is None:
+            method = missing.clean_fill_method(method)
+            try:
+                if method == "pad":
+                    return type(self)(pc.fill_null_forward(self._pa_array))
+                elif method == "backfill":
+                    return type(self)(pc.fill_null_backward(self._pa_array))
+            except pa.ArrowNotImplementedError:
+                # ArrowNotImplementedError: Function 'coalesce' has no kernel
+                #   matching input types (duration[ns], duration[ns])
+                # TODO: remove try/except wrapper if/when pyarrow implements
+                #   a kernel for duration types.
+                pass
+
+        # TODO(3.0): after EA.fillna 'method' deprecation is enforced, we can remove
+        #  this method entirely.
+        return super()._pad_or_backfill(
+            method=method, limit=limit, limit_area=limit_area, copy=copy
+        )
+
+    @doc(ExtensionArray.fillna)
+    def fillna(
+        self,
+        value: object | ArrayLike | None = None,
+        method: FillnaOptions | None = None,
+        limit: int | None = None,
+        copy: bool = True,
+    ) -> Self:
+        value, method = validate_fillna_kwargs(value, method)
+
+        if not self._hasna:
+            # TODO(CoW): Not necessary anymore when CoW is the default
+            return self.copy()
+
+        if limit is not None:
+            return super().fillna(value=value, method=method, limit=limit, copy=copy)
+
+        if method is not None:
+            return super().fillna(method=method, limit=limit, copy=copy)
+
+        if isinstance(value, (np.ndarray, ExtensionArray)):
+            # Similar to check_value_size, but we do not mask here since we may
+            #  end up passing it to the super() method.
+            if len(value) != len(self):
+                raise ValueError(
+                    f"Length of 'value' does not match. Got ({len(value)}) "
+                    f" expected {len(self)}"
+                )
+
+        try:
+            fill_value = self._box_pa(value, pa_type=self._pa_array.type)
+        except pa.ArrowTypeError as err:
+            msg = f"Invalid value '{value!s}' for dtype '{self.dtype}'"
+            raise TypeError(msg) from err
+
+        try:
+            return type(self)(pc.fill_null(self._pa_array, fill_value=fill_value))
+        except pa.ArrowNotImplementedError:
+            # ArrowNotImplementedError: Function 'coalesce' has no kernel
+            #   matching input types (duration[ns], duration[ns])
+            # TODO: remove try/except wrapper if/when pyarrow implements
+            #   a kernel for duration types.
+            pass
+
+        return super().fillna(value=value, method=method, limit=limit, copy=copy)
+
+    def isin(self, values: ArrayLike) -> npt.NDArray[np.bool_]:
+        # short-circuit to return all False array.
+        if not len(values):
+            return np.zeros(len(self), dtype=bool)
+
+        result = pc.is_in(self._pa_array, value_set=pa.array(values, from_pandas=True))
+        # pyarrow 2.0.0 returned nulls, so we explicitly specify dtype to convert nulls
+        # to False
+        return np.array(result, dtype=np.bool_)
+
+    def _values_for_factorize(self) -> tuple[np.ndarray, Any]:
+        """
+        Return an array and missing value suitable for factorization.
+
+        Returns
+        -------
+        values : ndarray
+        na_value : pd.NA
+
+        Notes
+        -----
+        The values returned by this method are also used in
+        :func:`pandas.util.hash_pandas_object`.
+        """
+        values = self._pa_array.to_numpy()
+        return values, self.dtype.na_value
+
+    @doc(ExtensionArray.factorize)
+    def factorize(
+        self,
+        use_na_sentinel: bool = True,
+    ) -> tuple[np.ndarray, ExtensionArray]:
+        null_encoding = "mask" if use_na_sentinel else "encode"
+
+        data = self._pa_array
+        pa_type = data.type
+        if pa_version_under11p0 and pa.types.is_duration(pa_type):
+            # https://github.com/apache/arrow/issues/15226#issuecomment-1376578323
+            data = data.cast(pa.int64())
+
+        if pa.types.is_dictionary(data.type):
+            encoded = data
+        else:
+            encoded = data.dictionary_encode(null_encoding=null_encoding)
+        if encoded.length() == 0:
+            indices = np.array([], dtype=np.intp)
+            uniques = type(self)(pa.chunked_array([], type=encoded.type.value_type))
+        else:
+            # GH 54844
+            combined = encoded.combine_chunks()
+            pa_indices = combined.indices
+            if pa_indices.null_count > 0:
+                pa_indices = pc.fill_null(pa_indices, -1)
+            indices = pa_indices.to_numpy(zero_copy_only=False, writable=True).astype(
+                np.intp, copy=False
+            )
+            uniques = type(self)(combined.dictionary)
+
+        if pa_version_under11p0 and pa.types.is_duration(pa_type):
+            uniques = cast(ArrowExtensionArray, uniques.astype(self.dtype))
+        return indices, uniques
+
+    def reshape(self, *args, **kwargs):
+        raise NotImplementedError(
+            f"{type(self)} does not support reshape "
+            f"as backed by a 1D pyarrow.ChunkedArray."
+        )
+
+    def round(self, decimals: int = 0, *args, **kwargs) -> Self:
+        """
+        Round each value in the array a to the given number of decimals.
+
+        Parameters
+        ----------
+        decimals : int, default 0
+            Number of decimal places to round to. If decimals is negative,
+            it specifies the number of positions to the left of the decimal point.
+        *args, **kwargs
+            Additional arguments and keywords have no effect.
+
+        Returns
+        -------
+        ArrowExtensionArray
+            Rounded values of the ArrowExtensionArray.
+
+        See Also
+        --------
+        DataFrame.round : Round values of a DataFrame.
+        Series.round : Round values of a Series.
+        """
+        return type(self)(pc.round(self._pa_array, ndigits=decimals))
+
+    @doc(ExtensionArray.searchsorted)
+    def searchsorted(
+        self,
+        value: NumpyValueArrayLike | ExtensionArray,
+        side: Literal["left", "right"] = "left",
+        sorter: NumpySorter | None = None,
+    ) -> npt.NDArray[np.intp] | np.intp:
+        if self._hasna:
+            raise ValueError(
+                "searchsorted requires array to be sorted, which is impossible "
+                "with NAs present."
+            )
+        if isinstance(value, ExtensionArray):
+            value = value.astype(object)
+        # Base class searchsorted would cast to object, which is *much* slower.
+        dtype = None
+        if isinstance(self.dtype, ArrowDtype):
+            pa_dtype = self.dtype.pyarrow_dtype
+            if (
+                pa.types.is_timestamp(pa_dtype) or pa.types.is_duration(pa_dtype)
+            ) and pa_dtype.unit == "ns":
+                # np.array[datetime/timedelta].searchsorted(datetime/timedelta)
+                # erroneously fails when numpy type resolution is nanoseconds
+                dtype = object
+        return self.to_numpy(dtype=dtype).searchsorted(value, side=side, sorter=sorter)
+
+    def take(
+        self,
+        indices: TakeIndexer,
+        allow_fill: bool = False,
+        fill_value: Any = None,
+    ) -> ArrowExtensionArray:
+        """
+        Take elements from an array.
+
+        Parameters
+        ----------
+        indices : sequence of int or one-dimensional np.ndarray of int
+            Indices to be taken.
+        allow_fill : bool, default False
+            How to handle negative values in `indices`.
+
+            * False: negative values in `indices` indicate positional indices
+              from the right (the default). This is similar to
+              :func:`numpy.take`.
+
+            * True: negative values in `indices` indicate
+              missing values. These values are set to `fill_value`. Any other
+              other negative values raise a ``ValueError``.
+
+        fill_value : any, optional
+            Fill value to use for NA-indices when `allow_fill` is True.
+            This may be ``None``, in which case the default NA value for
+            the type, ``self.dtype.na_value``, is used.
+
+            For many ExtensionArrays, there will be two representations of
+            `fill_value`: a user-facing "boxed" scalar, and a low-level
+            physical NA value. `fill_value` should be the user-facing version,
+            and the implementation should handle translating that to the
+            physical version for processing the take if necessary.
+
+        Returns
+        -------
+        ExtensionArray
+
+        Raises
+        ------
+        IndexError
+            When the indices are out of bounds for the array.
+        ValueError
+            When `indices` contains negative values other than ``-1``
+            and `allow_fill` is True.
+
+        See Also
+        --------
+        numpy.take
+        api.extensions.take
+
+        Notes
+        -----
+        ExtensionArray.take is called by ``Series.__getitem__``, ``.loc``,
+        ``iloc``, when `indices` is a sequence of values. Additionally,
+        it's called by :meth:`Series.reindex`, or any other method
+        that causes realignment, with a `fill_value`.
+        """
+        indices_array = np.asanyarray(indices)
+
+        if len(self._pa_array) == 0 and (indices_array >= 0).any():
+            raise IndexError("cannot do a non-empty take")
+        if indices_array.size > 0 and indices_array.max() >= len(self._pa_array):
+            raise IndexError("out of bounds value in 'indices'.")
+
+        if allow_fill:
+            fill_mask = indices_array < 0
+            if fill_mask.any():
+                validate_indices(indices_array, len(self._pa_array))
+                # TODO(ARROW-9433): Treat negative indices as NULL
+                indices_array = pa.array(indices_array, mask=fill_mask)
+                result = self._pa_array.take(indices_array)
+                if isna(fill_value):
+                    return type(self)(result)
+                # TODO: ArrowNotImplementedError: Function fill_null has no
+                # kernel matching input types (array[string], scalar[string])
+                result = type(self)(result)
+                result[fill_mask] = fill_value
+                return result
+                # return type(self)(pc.fill_null(result, pa.scalar(fill_value)))
+            else:
+                # Nothing to fill
+                return type(self)(self._pa_array.take(indices))
+        else:  # allow_fill=False
+            # TODO(ARROW-9432): Treat negative indices as indices from the right.
+            if (indices_array < 0).any():
+                # Don't modify in-place
+                indices_array = np.copy(indices_array)
+                indices_array[indices_array < 0] += len(self._pa_array)
+            return type(self)(self._pa_array.take(indices_array))
+
+    def _maybe_convert_datelike_array(self):
+        """Maybe convert to a datelike array."""
+        pa_type = self._pa_array.type
+        if pa.types.is_timestamp(pa_type):
+            return self._to_datetimearray()
+        elif pa.types.is_duration(pa_type):
+            return self._to_timedeltaarray()
+        return self
+
+    def _to_datetimearray(self) -> DatetimeArray:
+        """Convert a pyarrow timestamp typed array to a DatetimeArray."""
+        from pandas.core.arrays.datetimes import (
+            DatetimeArray,
+            tz_to_dtype,
+        )
+
+        pa_type = self._pa_array.type
+        assert pa.types.is_timestamp(pa_type)
+        np_dtype = np.dtype(f"M8[{pa_type.unit}]")
+        dtype = tz_to_dtype(pa_type.tz, pa_type.unit)
+        np_array = self._pa_array.to_numpy()
+        np_array = np_array.astype(np_dtype)
+        return DatetimeArray._simple_new(np_array, dtype=dtype)
+
+    def _to_timedeltaarray(self) -> TimedeltaArray:
+        """Convert a pyarrow duration typed array to a TimedeltaArray."""
+        from pandas.core.arrays.timedeltas import TimedeltaArray
+
+        pa_type = self._pa_array.type
+        assert pa.types.is_duration(pa_type)
+        np_dtype = np.dtype(f"m8[{pa_type.unit}]")
+        np_array = self._pa_array.to_numpy()
+        np_array = np_array.astype(np_dtype)
+        return TimedeltaArray._simple_new(np_array, dtype=np_dtype)
+
+    def _values_for_json(self) -> np.ndarray:
+        if is_numeric_dtype(self.dtype):
+            return np.asarray(self, dtype=object)
+        return super()._values_for_json()
+
+    @doc(ExtensionArray.to_numpy)
+    def to_numpy(
+        self,
+        dtype: npt.DTypeLike | None = None,
+        copy: bool = False,
+        na_value: object = lib.no_default,
+    ) -> np.ndarray:
+        original_na_value = na_value
+        dtype, na_value = to_numpy_dtype_inference(self, dtype, na_value, self._hasna)
+        pa_type = self._pa_array.type
+        if not self._hasna or isna(na_value) or pa.types.is_null(pa_type):
+            data = self
+        else:
+            data = self.fillna(na_value)
+            copy = False
+
+        if pa.types.is_timestamp(pa_type) or pa.types.is_duration(pa_type):
+            # GH 55997
+            if dtype != object and na_value is self.dtype.na_value:
+                na_value = lib.no_default
+            result = data._maybe_convert_datelike_array().to_numpy(
+                dtype=dtype, na_value=na_value
+            )
+        elif pa.types.is_time(pa_type) or pa.types.is_date(pa_type):
+            # convert to list of python datetime.time objects before
+            # wrapping in ndarray
+            result = np.array(list(data), dtype=dtype)
+            if data._hasna:
+                result[data.isna()] = na_value
+        elif pa.types.is_null(pa_type):
+            if dtype is not None and isna(na_value):
+                na_value = None
+            result = np.full(len(data), fill_value=na_value, dtype=dtype)
+        elif not data._hasna or (
+            pa.types.is_floating(pa_type)
+            and (
+                na_value is np.nan
+                or original_na_value is lib.no_default
+                and is_float_dtype(dtype)
+            )
+        ):
+            result = data._pa_array.to_numpy()
+            if dtype is not None:
+                result = result.astype(dtype, copy=False)
+            if copy:
+                result = result.copy()
+        else:
+            if dtype is None:
+                empty = pa.array([], type=pa_type).to_numpy(zero_copy_only=False)
+                if can_hold_element(empty, na_value):
+                    dtype = empty.dtype
+                else:
+                    dtype = np.object_
+            result = np.empty(len(data), dtype=dtype)
+            mask = data.isna()
+            result[mask] = na_value
+            result[~mask] = data[~mask]._pa_array.to_numpy()
+        return result
+
+    def map(self, mapper, na_action=None):
+        if is_numeric_dtype(self.dtype):
+            return map_array(self.to_numpy(), mapper, na_action=na_action)
+        else:
+            return super().map(mapper, na_action)
+
+    @doc(ExtensionArray.duplicated)
+    def duplicated(
+        self, keep: Literal["first", "last", False] = "first"
+    ) -> npt.NDArray[np.bool_]:
+        pa_type = self._pa_array.type
+        if pa.types.is_floating(pa_type) or pa.types.is_integer(pa_type):
+            values = self.to_numpy(na_value=0)
+        elif pa.types.is_boolean(pa_type):
+            values = self.to_numpy(na_value=False)
+        elif pa.types.is_temporal(pa_type):
+            if pa_type.bit_width == 32:
+                pa_type = pa.int32()
+            else:
+                pa_type = pa.int64()
+            arr = self.astype(ArrowDtype(pa_type))
+            values = arr.to_numpy(na_value=0)
+        else:
+            # factorize the values to avoid the performance penalty of
+            # converting to object dtype
+            values = self.factorize()[0]
+
+        mask = self.isna() if self._hasna else None
+        return algos.duplicated(values, keep=keep, mask=mask)
+
+    def unique(self) -> Self:
+        """
+        Compute the ArrowExtensionArray of unique values.
+
+        Returns
+        -------
+        ArrowExtensionArray
+        """
+        pa_type = self._pa_array.type
+
+        if pa_version_under11p0 and pa.types.is_duration(pa_type):
+            # https://github.com/apache/arrow/issues/15226#issuecomment-1376578323
+            data = self._pa_array.cast(pa.int64())
+        else:
+            data = self._pa_array
+
+        pa_result = pc.unique(data)
+
+        if pa_version_under11p0 and pa.types.is_duration(pa_type):
+            pa_result = pa_result.cast(pa_type)
+
+        return type(self)(pa_result)
+
+    def value_counts(self, dropna: bool = True) -> Series:
+        """
+        Return a Series containing counts of each unique value.
+
+        Parameters
+        ----------
+        dropna : bool, default True
+            Don't include counts of missing values.
+
+        Returns
+        -------
+        counts : Series
+
+        See Also
+        --------
+        Series.value_counts
+        """
+        pa_type = self._pa_array.type
+        if pa_version_under11p0 and pa.types.is_duration(pa_type):
+            # https://github.com/apache/arrow/issues/15226#issuecomment-1376578323
+            data = self._pa_array.cast(pa.int64())
+        else:
+            data = self._pa_array
+
+        from pandas import (
+            Index,
+            Series,
+        )
+
+        vc = data.value_counts()
+
+        values = vc.field(0)
+        counts = vc.field(1)
+        if dropna and data.null_count > 0:
+            mask = values.is_valid()
+            values = values.filter(mask)
+            counts = counts.filter(mask)
+
+        if pa_version_under11p0 and pa.types.is_duration(pa_type):
+            values = values.cast(pa_type)
+
+        counts = ArrowExtensionArray(counts)
+
+        index = Index(type(self)(values))
+
+        return Series(counts, index=index, name="count", copy=False)
+
+    @classmethod
+    def _concat_same_type(cls, to_concat) -> Self:
+        """
+        Concatenate multiple ArrowExtensionArrays.
+
+        Parameters
+        ----------
+        to_concat : sequence of ArrowExtensionArrays
+
+        Returns
+        -------
+        ArrowExtensionArray
+        """
+        chunks = [array for ea in to_concat for array in ea._pa_array.iterchunks()]
+        if to_concat[0].dtype == "string":
+            # StringDtype has no attribute pyarrow_dtype
+            pa_dtype = pa.large_string()
+        else:
+            pa_dtype = to_concat[0].dtype.pyarrow_dtype
+        arr = pa.chunked_array(chunks, type=pa_dtype)
+        return cls(arr)
+
+    def _accumulate(
+        self, name: str, *, skipna: bool = True, **kwargs
+    ) -> ArrowExtensionArray | ExtensionArray:
+        """
+        Return an ExtensionArray performing an accumulation operation.
+
+        The underlying data type might change.
+
+        Parameters
+        ----------
+        name : str
+            Name of the function, supported values are:
+            - cummin
+            - cummax
+            - cumsum
+            - cumprod
+        skipna : bool, default True
+            If True, skip NA values.
+        **kwargs
+            Additional keyword arguments passed to the accumulation function.
+            Currently, there is no supported kwarg.
+
+        Returns
+        -------
+        array
+
+        Raises
+        ------
+        NotImplementedError : subclass does not define accumulations
+        """
+        if is_string_dtype(self):
+            return self._str_accumulate(name=name, skipna=skipna, **kwargs)
+
+        pyarrow_name = {
+            "cummax": "cumulative_max",
+            "cummin": "cumulative_min",
+            "cumprod": "cumulative_prod_checked",
+            "cumsum": "cumulative_sum_checked",
+        }.get(name, name)
+        pyarrow_meth = getattr(pc, pyarrow_name, None)
+        if pyarrow_meth is None:
+            return super()._accumulate(name, skipna=skipna, **kwargs)
+
+        data_to_accum = self._pa_array
+
+        pa_dtype = data_to_accum.type
+
+        convert_to_int = (
+            pa.types.is_temporal(pa_dtype) and name in ["cummax", "cummin"]
+        ) or (pa.types.is_duration(pa_dtype) and name == "cumsum")
+
+        if convert_to_int:
+            if pa_dtype.bit_width == 32:
+                data_to_accum = data_to_accum.cast(pa.int32())
+            else:
+                data_to_accum = data_to_accum.cast(pa.int64())
+
+        try:
+            result = pyarrow_meth(data_to_accum, skip_nulls=skipna, **kwargs)
+        except pa.ArrowNotImplementedError as err:
+            msg = f"operation '{name}' not supported for dtype '{self.dtype}'"
+            raise TypeError(msg) from err
+
+        if convert_to_int:
+            result = result.cast(pa_dtype)
+
+        return type(self)(result)
+
+    def _str_accumulate(
+        self, name: str, *, skipna: bool = True, **kwargs
+    ) -> ArrowExtensionArray | ExtensionArray:
+        """
+        Accumulate implementation for strings, see `_accumulate` docstring for details.
+
+        pyarrow.compute does not implement these methods for strings.
+        """
+        if name == "cumprod":
+            msg = f"operation '{name}' not supported for dtype '{self.dtype}'"
+            raise TypeError(msg)
+
+        # We may need to strip out trailing NA values
+        tail: pa.array | None = None
+        na_mask: pa.array | None = None
+        pa_array = self._pa_array
+        np_func = {
+            "cumsum": np.cumsum,
+            "cummin": np.minimum.accumulate,
+            "cummax": np.maximum.accumulate,
+        }[name]
+
+        if self._hasna:
+            na_mask = pc.is_null(pa_array)
+            if pc.all(na_mask) == pa.scalar(True):
+                return type(self)(pa_array)
+            if skipna:
+                if name == "cumsum":
+                    pa_array = pc.fill_null(pa_array, "")
+                else:
+                    # We can retain the running min/max by forward/backward filling.
+                    pa_array = pc.fill_null_forward(pa_array)
+                    pa_array = pc.fill_null_backward(pa_array)
+            else:
+                # When not skipping NA values, the result should be null from
+                # the first NA value onward.
+                idx = pc.index(na_mask, True).as_py()
+                tail = pa.nulls(len(pa_array) - idx, type=pa_array.type)
+                pa_array = pa_array[:idx]
+
+        # error: Cannot call function of unknown type
+        pa_result = pa.array(np_func(pa_array), type=pa_array.type)  # type: ignore[operator]
+
+        if tail is not None:
+            pa_result = pa.concat_arrays([pa_result, tail])
+        elif na_mask is not None:
+            pa_result = pc.if_else(na_mask, None, pa_result)
+
+        result = type(self)(pa_result)
+        return result
+
+    def _reduce_pyarrow(self, name: str, *, skipna: bool = True, **kwargs) -> pa.Scalar:
+        """
+        Return a pyarrow scalar result of performing the reduction operation.
+
+        Parameters
+        ----------
+        name : str
+            Name of the function, supported values are:
+            { any, all, min, max, sum, mean, median, prod,
+            std, var, sem, kurt, skew }.
+        skipna : bool, default True
+            If True, skip NaN values.
+        **kwargs
+            Additional keyword arguments passed to the reduction function.
+            Currently, `ddof` is the only supported kwarg.
+
+        Returns
+        -------
+        pyarrow scalar
+
+        Raises
+        ------
+        TypeError : subclass does not define reductions
+        """
+        pa_type = self._pa_array.type
+
+        data_to_reduce = self._pa_array
+
+        cast_kwargs = {} if pa_version_under13p0 else {"safe": False}
+
+        if name in ["any", "all"] and (
+            pa.types.is_integer(pa_type)
+            or pa.types.is_floating(pa_type)
+            or pa.types.is_duration(pa_type)
+            or pa.types.is_decimal(pa_type)
+        ):
+            # pyarrow only supports any/all for boolean dtype, we allow
+            #  for other dtypes, matching our non-pyarrow behavior
+
+            if pa.types.is_duration(pa_type):
+                data_to_cmp = self._pa_array.cast(pa.int64())
+            else:
+                data_to_cmp = self._pa_array
+
+            not_eq = pc.not_equal(data_to_cmp, 0)
+            data_to_reduce = not_eq
+
+        elif name in ["min", "max", "sum"] and pa.types.is_duration(pa_type):
+            data_to_reduce = self._pa_array.cast(pa.int64())
+
+        elif name in ["median", "mean", "std", "sem"] and pa.types.is_temporal(pa_type):
+            nbits = pa_type.bit_width
+            if nbits == 32:
+                data_to_reduce = self._pa_array.cast(pa.int32())
+            else:
+                data_to_reduce = self._pa_array.cast(pa.int64())
+
+        if name == "sem":
+
+            def pyarrow_meth(data, skip_nulls, **kwargs):
+                numerator = pc.stddev(data, skip_nulls=skip_nulls, **kwargs)
+                denominator = pc.sqrt_checked(pc.count(self._pa_array))
+                return pc.divide_checked(numerator, denominator)
+
+        elif name == "sum" and (
+            pa.types.is_string(pa_type) or pa.types.is_large_string(pa_type)
+        ):
+
+            def pyarrow_meth(data, skip_nulls, min_count=0):  # type: ignore[misc]
+                mask = pc.is_null(data) if data.null_count > 0 else None
+                if skip_nulls:
+                    if min_count > 0 and check_below_min_count(
+                        (len(data),),
+                        None if mask is None else mask.to_numpy(),
+                        min_count,
+                    ):
+                        return pa.scalar(None, type=data.type)
+                    if data.null_count > 0:
+                        # binary_join returns null if there is any null ->
+                        # have to filter out any nulls
+                        data = data.filter(pc.invert(mask))
+                else:
+                    if mask is not None or check_below_min_count(
+                        (len(data),), None, min_count
+                    ):
+                        return pa.scalar(None, type=data.type)
+
+                if pa.types.is_large_string(data.type):
+                    # binary_join only supports string, not large_string
+                    data = data.cast(pa.string())
+                data_list = pa.ListArray.from_arrays(
+                    [0, len(data)], data.combine_chunks()
+                )[0]
+                return pc.binary_join(data_list, "")
+
+        else:
+            pyarrow_name = {
+                "median": "quantile",
+                "prod": "product",
+                "std": "stddev",
+                "var": "variance",
+            }.get(name, name)
+            # error: Incompatible types in assignment
+            # (expression has type "Optional[Any]", variable has type
+            # "Callable[[Any, Any, KwArg(Any)], Any]")
+            pyarrow_meth = getattr(pc, pyarrow_name, None)  # type: ignore[assignment]
+            if pyarrow_meth is None:
+                # Let ExtensionArray._reduce raise the TypeError
+                return super()._reduce(name, skipna=skipna, **kwargs)
+
+        # GH51624: pyarrow defaults to min_count=1, pandas behavior is min_count=0
+        if name in ["any", "all"] and "min_count" not in kwargs:
+            kwargs["min_count"] = 0
+        elif name == "median":
+            # GH 52679: Use quantile instead of approximate_median
+            kwargs["q"] = 0.5
+
+        try:
+            result = pyarrow_meth(data_to_reduce, skip_nulls=skipna, **kwargs)
+        except (AttributeError, NotImplementedError, TypeError) as err:
+            msg = (
+                f"'{type(self).__name__}' with dtype {self.dtype} "
+                f"does not support reduction '{name}' with pyarrow "
+                f"version {pa.__version__}. '{name}' may be supported by "
+                f"upgrading pyarrow."
+            )
+            raise TypeError(msg) from err
+        if name == "median":
+            # GH 52679: Use quantile instead of approximate_median; returns array
+            result = result[0]
+        if pc.is_null(result).as_py():
+            return result
+
+        if name in ["min", "max", "sum"] and pa.types.is_duration(pa_type):
+            result = result.cast(pa_type)
+        if name in ["median", "mean"] and pa.types.is_temporal(pa_type):
+            if not pa_version_under13p0:
+                nbits = pa_type.bit_width
+                if nbits == 32:
+                    result = result.cast(pa.int32(), **cast_kwargs)
+                else:
+                    result = result.cast(pa.int64(), **cast_kwargs)
+            result = result.cast(pa_type)
+        if name in ["std", "sem"] and pa.types.is_temporal(pa_type):
+            result = result.cast(pa.int64(), **cast_kwargs)
+            if pa.types.is_duration(pa_type):
+                result = result.cast(pa_type)
+            elif pa.types.is_time(pa_type):
+                unit = get_unit_from_pa_dtype(pa_type)
+                result = result.cast(pa.duration(unit))
+            elif pa.types.is_date(pa_type):
+                # go with closest available unit, i.e. "s"
+                result = result.cast(pa.duration("s"))
+            else:
+                # i.e. timestamp
+                result = result.cast(pa.duration(pa_type.unit))
+
+        return result
+
+    def _reduce(
+        self, name: str, *, skipna: bool = True, keepdims: bool = False, **kwargs
+    ):
+        """
+        Return a scalar result of performing the reduction operation.
+
+        Parameters
+        ----------
+        name : str
+            Name of the function, supported values are:
+            { any, all, min, max, sum, mean, median, prod,
+            std, var, sem, kurt, skew }.
+        skipna : bool, default True
+            If True, skip NaN values.
+        **kwargs
+            Additional keyword arguments passed to the reduction function.
+            Currently, `ddof` is the only supported kwarg.
+
+        Returns
+        -------
+        scalar
+
+        Raises
+        ------
+        TypeError : subclass does not define reductions
+        """
+        result = self._reduce_calc(name, skipna=skipna, keepdims=keepdims, **kwargs)
+        if isinstance(result, pa.Array):
+            return type(self)(result)
+        else:
+            return result
+
+    def _reduce_calc(
+        self, name: str, *, skipna: bool = True, keepdims: bool = False, **kwargs
+    ):
+        pa_result = self._reduce_pyarrow(name, skipna=skipna, **kwargs)
+
+        if keepdims:
+            if isinstance(pa_result, pa.Scalar):
+                result = pa.array([pa_result.as_py()], type=pa_result.type)
+            else:
+                result = pa.array(
+                    [pa_result],
+                    type=to_pyarrow_type(infer_dtype_from_scalar(pa_result)[0]),
+                )
+            return result
+
+        if pc.is_null(pa_result).as_py():
+            return self.dtype.na_value
+        elif isinstance(pa_result, pa.Scalar):
+            return pa_result.as_py()
+        else:
+            return pa_result
+
+    def _explode(self):
+        """
+        See Series.explode.__doc__.
+        """
+        # child class explode method supports only list types; return
+        # default implementation for non list types.
+        if not hasattr(self.dtype, "pyarrow_dtype") or (
+            not pa.types.is_list(self.dtype.pyarrow_dtype)
+        ):
+            return super()._explode()
+        values = self
+        counts = pa.compute.list_value_length(values._pa_array)
+        counts = counts.fill_null(1).to_numpy()
+        fill_value = pa.scalar([None], type=self._pa_array.type)
+        mask = counts == 0
+        if mask.any():
+            values = values.copy()
+            values[mask] = fill_value
+            counts = counts.copy()
+            counts[mask] = 1
+        values = values.fillna(fill_value)
+        values = type(self)(pa.compute.list_flatten(values._pa_array))
+        return values, counts
+
+    def __setitem__(self, key, value) -> None:
+        """Set one or more values inplace.
+
+        Parameters
+        ----------
+        key : int, ndarray, or slice
+            When called from, e.g. ``Series.__setitem__``, ``key`` will be
+            one of
+
+            * scalar int
+            * ndarray of integers.
+            * boolean ndarray
+            * slice object
+
+        value : ExtensionDtype.type, Sequence[ExtensionDtype.type], or object
+            value or values to be set of ``key``.
+
+        Returns
+        -------
+        None
+        """
+        # GH50085: unwrap 1D indexers
+        if isinstance(key, tuple) and len(key) == 1:
+            key = key[0]
+
+        key = check_array_indexer(self, key)
+        value = self._maybe_convert_setitem_value(value)
+
+        if com.is_null_slice(key):
+            # fast path (GH50248)
+            data = self._if_else(True, value, self._pa_array)
+
+        elif is_integer(key):
+            # fast path
+            key = cast(int, key)
+            n = len(self)
+            if key < 0:
+                key += n
+            if not 0 <= key < n:
+                raise IndexError(
+                    f"index {key} is out of bounds for axis 0 with size {n}"
+                )
+            if isinstance(value, pa.Scalar):
+                value = value.as_py()
+            elif is_list_like(value):
+                raise ValueError("Length of indexer and values mismatch")
+            chunks = [
+                *self._pa_array[:key].chunks,
+                pa.array([value], type=self._pa_array.type, from_pandas=True),
+                *self._pa_array[key + 1 :].chunks,
+            ]
+            data = pa.chunked_array(chunks).combine_chunks()
+
+        elif is_bool_dtype(key):
+            key = np.asarray(key, dtype=np.bool_)
+            data = self._replace_with_mask(self._pa_array, key, value)
+
+        elif is_scalar(value) or isinstance(value, pa.Scalar):
+            mask = np.zeros(len(self), dtype=np.bool_)
+            mask[key] = True
+            data = self._if_else(mask, value, self._pa_array)
+
+        else:
+            indices = np.arange(len(self))[key]
+            if len(indices) != len(value):
+                raise ValueError("Length of indexer and values mismatch")
+            if len(indices) == 0:
+                return
+            argsort = np.argsort(indices)
+            indices = indices[argsort]
+            value = value.take(argsort)
+            mask = np.zeros(len(self), dtype=np.bool_)
+            mask[indices] = True
+            data = self._replace_with_mask(self._pa_array, mask, value)
+
+        if isinstance(data, pa.Array):
+            data = pa.chunked_array([data])
+        self._pa_array = data
+
+    def _rank_calc(
+        self,
+        *,
+        axis: AxisInt = 0,
+        method: str = "average",
+        na_option: str = "keep",
+        ascending: bool = True,
+        pct: bool = False,
+    ):
+        if axis != 0:
+            ranked = super()._rank(
+                axis=axis,
+                method=method,
+                na_option=na_option,
+                ascending=ascending,
+                pct=pct,
+            )
+            # keep dtypes consistent with the implementation below
+            if method == "average" or pct:
+                pa_type = pa.float64()
+            else:
+                pa_type = pa.uint64()
+            result = pa.array(ranked, type=pa_type, from_pandas=True)
+            return result
+
+        data = self._pa_array.combine_chunks()
+        sort_keys = "ascending" if ascending else "descending"
+        null_placement = "at_start" if na_option == "top" else "at_end"
+        tiebreaker = "min" if method == "average" else method
+
+        result = pc.rank(
+            data,
+            sort_keys=sort_keys,
+            null_placement=null_placement,
+            tiebreaker=tiebreaker,
+        )
+
+        if na_option == "keep":
+            mask = pc.is_null(self._pa_array)
+            null = pa.scalar(None, type=result.type)
+            result = pc.if_else(mask, null, result)
+
+        if method == "average":
+            result_max = pc.rank(
+                data,
+                sort_keys=sort_keys,
+                null_placement=null_placement,
+                tiebreaker="max",
+            )
+            result_max = result_max.cast(pa.float64())
+            result_min = result.cast(pa.float64())
+            result = pc.divide(pc.add(result_min, result_max), 2)
+
+        if pct:
+            if not pa.types.is_floating(result.type):
+                result = result.cast(pa.float64())
+            if method == "dense":
+                divisor = pc.max(result)
+            else:
+                divisor = pc.count(result)
+            result = pc.divide(result, divisor)
+
+        return result
+
+    def _rank(
+        self,
+        *,
+        axis: AxisInt = 0,
+        method: str = "average",
+        na_option: str = "keep",
+        ascending: bool = True,
+        pct: bool = False,
+    ):
+        """
+        See Series.rank.__doc__.
+        """
+        return self._convert_rank_result(
+            self._rank_calc(
+                axis=axis,
+                method=method,
+                na_option=na_option,
+                ascending=ascending,
+                pct=pct,
+            )
+        )
+
+    def _quantile(self, qs: npt.NDArray[np.float64], interpolation: str) -> Self:
+        """
+        Compute the quantiles of self for each quantile in `qs`.
+
+        Parameters
+        ----------
+        qs : np.ndarray[float64]
+        interpolation: str
+
+        Returns
+        -------
+        same type as self
+        """
+        pa_dtype = self._pa_array.type
+
+        data = self._pa_array
+        if pa.types.is_temporal(pa_dtype):
+            # https://github.com/apache/arrow/issues/33769 in these cases
+            #  we can cast to ints and back
+            nbits = pa_dtype.bit_width
+            if nbits == 32:
+                data = data.cast(pa.int32())
+            else:
+                data = data.cast(pa.int64())
+
+        result = pc.quantile(data, q=qs, interpolation=interpolation)
+
+        if pa.types.is_temporal(pa_dtype):
+            if pa.types.is_floating(result.type):
+                result = pc.floor(result)
+            nbits = pa_dtype.bit_width
+            if nbits == 32:
+                result = result.cast(pa.int32())
+            else:
+                result = result.cast(pa.int64())
+            result = result.cast(pa_dtype)
+
+        return type(self)(result)
+
+    def _mode(self, dropna: bool = True) -> Self:
+        """
+        Returns the mode(s) of the ExtensionArray.
+
+        Always returns `ExtensionArray` even if only one value.
+
+        Parameters
+        ----------
+        dropna : bool, default True
+            Don't consider counts of NA values.
+
+        Returns
+        -------
+        same type as self
+            Sorted, if possible.
+        """
+        pa_type = self._pa_array.type
+        if pa.types.is_temporal(pa_type):
+            nbits = pa_type.bit_width
+            if nbits == 32:
+                data = self._pa_array.cast(pa.int32())
+            elif nbits == 64:
+                data = self._pa_array.cast(pa.int64())
+            else:
+                raise NotImplementedError(pa_type)
+        else:
+            data = self._pa_array
+
+        if dropna:
+            data = data.drop_null()
+
+        res = pc.value_counts(data)
+        most_common = res.field("values").filter(
+            pc.equal(res.field("counts"), pc.max(res.field("counts")))
+        )
+
+        if pa.types.is_temporal(pa_type):
+            most_common = most_common.cast(pa_type)
+
+        most_common = most_common.take(pc.array_sort_indices(most_common))
+        return type(self)(most_common)
+
+    def _maybe_convert_setitem_value(self, value):
+        """Maybe convert value to be pyarrow compatible."""
+        try:
+            value = self._box_pa(value, self._pa_array.type)
+        except pa.ArrowTypeError as err:
+            msg = f"Invalid value '{value!s}' for dtype '{self.dtype}'"
+            raise TypeError(msg) from err
+        return value
+
+    def interpolate(
+        self,
+        *,
+        method: InterpolateOptions,
+        axis: int,
+        index,
+        limit,
+        limit_direction,
+        limit_area,
+        copy: bool,
+        **kwargs,
+    ) -> Self:
+        """
+        See NDFrame.interpolate.__doc__.
+        """
+        # NB: we return type(self) even if copy=False
+        if not self.dtype._is_numeric:
+            raise TypeError(f"Cannot interpolate with {self.dtype} dtype")
+
+        mask = self.isna()
+        if self.dtype.kind == "f":
+            data = self._pa_array.to_numpy()
+        elif self.dtype.kind in "iu":
+            data = self.to_numpy(dtype="f8", na_value=0.0)
+        else:
+            raise NotImplementedError(
+                f"interpolate is not implemented for dtype={self.dtype}"
+            )
+
+        missing.interpolate_2d_inplace(
+            data,
+            method=method,
+            axis=0,
+            index=index,
+            limit=limit,
+            limit_direction=limit_direction,
+            limit_area=limit_area,
+            mask=mask,
+            **kwargs,
+        )
+        return type(self)(self._box_pa_array(pa.array(data, mask=mask)))
+
+    @classmethod
+    def _if_else(
+        cls,
+        cond: npt.NDArray[np.bool_] | bool,
+        left: ArrayLike | Scalar,
+        right: ArrayLike | Scalar,
+    ):
+        """
+        Choose values based on a condition.
+
+        Analogous to pyarrow.compute.if_else, with logic
+        to fallback to numpy for unsupported types.
+
+        Parameters
+        ----------
+        cond : npt.NDArray[np.bool_] or bool
+        left : ArrayLike | Scalar
+        right : ArrayLike | Scalar
+
+        Returns
+        -------
+        pa.Array
+        """
+        try:
+            return pc.if_else(cond, left, right)
+        except pa.ArrowNotImplementedError:
+            pass
+
+        def _to_numpy_and_type(value) -> tuple[np.ndarray, pa.DataType | None]:
+            if isinstance(value, (pa.Array, pa.ChunkedArray)):
+                pa_type = value.type
+            elif isinstance(value, pa.Scalar):
+                pa_type = value.type
+                value = value.as_py()
+            else:
+                pa_type = None
+            return np.array(value, dtype=object), pa_type
+
+        left, left_type = _to_numpy_and_type(left)
+        right, right_type = _to_numpy_and_type(right)
+        pa_type = left_type or right_type
+        result = np.where(cond, left, right)
+        return pa.array(result, type=pa_type, from_pandas=True)
+
+    @classmethod
+    def _replace_with_mask(
+        cls,
+        values: pa.Array | pa.ChunkedArray,
+        mask: npt.NDArray[np.bool_] | bool,
+        replacements: ArrayLike | Scalar,
+    ):
+        """
+        Replace items selected with a mask.
+
+        Analogous to pyarrow.compute.replace_with_mask, with logic
+        to fallback to numpy for unsupported types.
+
+        Parameters
+        ----------
+        values : pa.Array or pa.ChunkedArray
+        mask : npt.NDArray[np.bool_] or bool
+        replacements : ArrayLike or Scalar
+            Replacement value(s)
+
+        Returns
+        -------
+        pa.Array or pa.ChunkedArray
+        """
+        if isinstance(replacements, pa.ChunkedArray):
+            # replacements must be array or scalar, not ChunkedArray
+            replacements = replacements.combine_chunks()
+        if isinstance(values, pa.ChunkedArray) and pa.types.is_boolean(values.type):
+            # GH#52059 replace_with_mask segfaults for chunked array
+            # https://github.com/apache/arrow/issues/34634
+            values = values.combine_chunks()
+        try:
+            return pc.replace_with_mask(values, mask, replacements)
+        except pa.ArrowNotImplementedError:
+            pass
+        if isinstance(replacements, pa.Array):
+            replacements = np.array(replacements, dtype=object)
+        elif isinstance(replacements, pa.Scalar):
+            replacements = replacements.as_py()
+        result = np.array(values, dtype=object)
+        result[mask] = replacements
+        return pa.array(result, type=values.type, from_pandas=True)
+
+    # ------------------------------------------------------------------
+    # GroupBy Methods
+
+    def _to_masked(self):
+        pa_dtype = self._pa_array.type
+
+        if pa.types.is_floating(pa_dtype) or pa.types.is_integer(pa_dtype):
+            na_value = 1
+        elif pa.types.is_boolean(pa_dtype):
+            na_value = True
+        else:
+            raise NotImplementedError
+
+        dtype = _arrow_dtype_mapping()[pa_dtype]
+        mask = self.isna()
+        arr = self.to_numpy(dtype=dtype.numpy_dtype, na_value=na_value)
+        return dtype.construct_array_type()(arr, mask)
+
+    def _groupby_op(
+        self,
+        *,
+        how: str,
+        has_dropped_na: bool,
+        min_count: int,
+        ngroups: int,
+        ids: npt.NDArray[np.intp],
+        **kwargs,
+    ):
+        if isinstance(self.dtype, StringDtype):
+            if how in [
+                "prod",
+                "mean",
+                "median",
+                "cumsum",
+                "cumprod",
+                "std",
+                "sem",
+                "var",
+                "skew",
+            ]:
+                raise TypeError(
+                    f"dtype '{self.dtype}' does not support operation '{how}'"
+                )
+            return super()._groupby_op(
+                how=how,
+                has_dropped_na=has_dropped_na,
+                min_count=min_count,
+                ngroups=ngroups,
+                ids=ids,
+                **kwargs,
+            )
+
+        # maybe convert to a compatible dtype optimized for groupby
+        values: ExtensionArray
+        pa_type = self._pa_array.type
+        if pa.types.is_timestamp(pa_type):
+            values = self._to_datetimearray()
+        elif pa.types.is_duration(pa_type):
+            values = self._to_timedeltaarray()
+        else:
+            values = self._to_masked()
+
+        result = values._groupby_op(
+            how=how,
+            has_dropped_na=has_dropped_na,
+            min_count=min_count,
+            ngroups=ngroups,
+            ids=ids,
+            **kwargs,
+        )
+        if isinstance(result, np.ndarray):
+            return result
+        return type(self)._from_sequence(result, copy=False)
+
+    def _apply_elementwise(self, func: Callable) -> list[list[Any]]:
+        """Apply a callable to each element while maintaining the chunking structure."""
+        return [
+            [
+                None if val is None else func(val)
+                for val in chunk.to_numpy(zero_copy_only=False)
+            ]
+            for chunk in self._pa_array.iterchunks()
+        ]
+
+    def _convert_bool_result(self, result, na=lib.no_default, method_name=None):
+        if na is not lib.no_default and not isna(
+            na
+        ):  # pyright: ignore [reportGeneralTypeIssues]
+            result = result.fill_null(na)
+        return type(self)(result)
+
+    def _convert_int_result(self, result):
+        return type(self)(result)
+
+    def _convert_rank_result(self, result):
+        return type(self)(result)
+
+    def _str_count(self, pat: str, flags: int = 0):
+        if flags:
+            raise NotImplementedError(f"count not implemented with {flags=}")
+        return type(self)(pc.count_substring_regex(self._pa_array, pat))
+
+    def _str_repeat(self, repeats: int | Sequence[int]):
+        if not isinstance(repeats, int):
+            raise NotImplementedError(
+                f"repeat is not implemented when repeats is {type(repeats).__name__}"
+            )
+        else:
+            return type(self)(pc.binary_repeat(self._pa_array, repeats))
+
+    def _str_join(self, sep: str):
+        if pa.types.is_string(self._pa_array.type) or pa.types.is_large_string(
+            self._pa_array.type
+        ):
+            result = self._apply_elementwise(list)
+            result = pa.chunked_array(result, type=pa.list_(pa.string()))
+        else:
+            result = self._pa_array
+        return type(self)(pc.binary_join(result, sep))
+
+    def _str_partition(self, sep: str, expand: bool):
+        predicate = lambda val: val.partition(sep)
+        result = self._apply_elementwise(predicate)
+        return type(self)(pa.chunked_array(result))
+
+    def _str_rpartition(self, sep: str, expand: bool):
+        predicate = lambda val: val.rpartition(sep)
+        result = self._apply_elementwise(predicate)
+        return type(self)(pa.chunked_array(result))
+
+    def _str_casefold(self):
+        predicate = lambda val: val.casefold()
+        result = self._apply_elementwise(predicate)
+        return type(self)(pa.chunked_array(result))
+
+    def _str_encode(self, encoding: str, errors: str = "strict"):
+        predicate = lambda val: val.encode(encoding, errors)
+        result = self._apply_elementwise(predicate)
+        return type(self)(pa.chunked_array(result))
+
+    def _str_extract(self, pat: str, flags: int = 0, expand: bool = True):
+        if flags:
+            raise NotImplementedError("Only flags=0 is implemented.")
+        groups = re.compile(pat).groupindex.keys()
+        if len(groups) == 0:
+            raise ValueError(f"{pat=} must contain a symbolic group name.")
+        result = pc.extract_regex(self._pa_array, pat)
+        if expand:
+            return {
+                col: type(self)(pc.struct_field(result, [i]))
+                for col, i in zip(groups, range(result.type.num_fields))
+            }
+        else:
+            return type(self)(pc.struct_field(result, [0]))
+
+    def _str_findall(self, pat: str, flags: int = 0):
+        regex = re.compile(pat, flags=flags)
+        predicate = lambda val: regex.findall(val)
+        result = self._apply_elementwise(predicate)
+        return type(self)(pa.chunked_array(result))
+
+    def _str_get_dummies(self, sep: str = "|"):
+        split = pc.split_pattern(self._pa_array, sep)
+        flattened_values = pc.list_flatten(split)
+        uniques = flattened_values.unique()
+        uniques_sorted = uniques.take(pa.compute.array_sort_indices(uniques))
+        lengths = pc.list_value_length(split).fill_null(0).to_numpy()
+        n_rows = len(self)
+        n_cols = len(uniques)
+        indices = pc.index_in(flattened_values, uniques_sorted).to_numpy()
+        indices = indices + np.arange(n_rows).repeat(lengths) * n_cols
+        dummies = np.zeros(n_rows * n_cols, dtype=np.bool_)
+        dummies[indices] = True
+        dummies = dummies.reshape((n_rows, n_cols))
+        result = type(self)(pa.array(list(dummies)))
+        return result, uniques_sorted.to_pylist()
+
+    def _str_index(self, sub: str, start: int = 0, end: int | None = None):
+        predicate = lambda val: val.index(sub, start, end)
+        result = self._apply_elementwise(predicate)
+        return type(self)(pa.chunked_array(result))
+
+    def _str_rindex(self, sub: str, start: int = 0, end: int | None = None):
+        predicate = lambda val: val.rindex(sub, start, end)
+        result = self._apply_elementwise(predicate)
+        return type(self)(pa.chunked_array(result))
+
+    def _str_normalize(self, form: str):
+        predicate = lambda val: unicodedata.normalize(form, val)
+        result = self._apply_elementwise(predicate)
+        return type(self)(pa.chunked_array(result))
+
+    def _str_rfind(self, sub: str, start: int = 0, end=None):
+        predicate = lambda val: val.rfind(sub, start, end)
+        result = self._apply_elementwise(predicate)
+        return type(self)(pa.chunked_array(result))
+
+    def _str_split(
+        self,
+        pat: str | None = None,
+        n: int | None = -1,
+        expand: bool = False,
+        regex: bool | None = None,
+    ):
+        if n in {-1, 0}:
+            n = None
+        if pat is None:
+            split_func = pc.utf8_split_whitespace
+        elif regex:
+            split_func = functools.partial(pc.split_pattern_regex, pattern=pat)
+        else:
+            split_func = functools.partial(pc.split_pattern, pattern=pat)
+        return type(self)(split_func(self._pa_array, max_splits=n))
+
+    def _str_rsplit(self, pat: str | None = None, n: int | None = -1):
+        if n in {-1, 0}:
+            n = None
+        if pat is None:
+            return type(self)(
+                pc.utf8_split_whitespace(self._pa_array, max_splits=n, reverse=True)
+            )
+        else:
+            return type(self)(
+                pc.split_pattern(self._pa_array, pat, max_splits=n, reverse=True)
+            )
+
+    def _str_translate(self, table: dict[int, str]):
+        predicate = lambda val: val.translate(table)
+        result = self._apply_elementwise(predicate)
+        return type(self)(pa.chunked_array(result))
+
+    def _str_wrap(self, width: int, **kwargs):
+        kwargs["width"] = width
+        tw = textwrap.TextWrapper(**kwargs)
+        predicate = lambda val: "\n".join(tw.wrap(val))
+        result = self._apply_elementwise(predicate)
+        return type(self)(pa.chunked_array(result))
+
+    @property
+    def _dt_days(self):
+        return type(self)(
+            pa.array(self._to_timedeltaarray().days, from_pandas=True, type=pa.int32())
+        )
+
+    @property
+    def _dt_hours(self):
+        return type(self)(
+            pa.array(
+                [
+                    td.components.hours if td is not NaT else None
+                    for td in self._to_timedeltaarray()
+                ],
+                type=pa.int32(),
+            )
+        )
+
+    @property
+    def _dt_minutes(self):
+        return type(self)(
+            pa.array(
+                [
+                    td.components.minutes if td is not NaT else None
+                    for td in self._to_timedeltaarray()
+                ],
+                type=pa.int32(),
+            )
+        )
+
+    @property
+    def _dt_seconds(self):
+        return type(self)(
+            pa.array(
+                self._to_timedeltaarray().seconds, from_pandas=True, type=pa.int32()
+            )
+        )
+
+    @property
+    def _dt_milliseconds(self):
+        return type(self)(
+            pa.array(
+                [
+                    td.components.milliseconds if td is not NaT else None
+                    for td in self._to_timedeltaarray()
+                ],
+                type=pa.int32(),
+            )
+        )
+
+    @property
+    def _dt_microseconds(self):
+        return type(self)(
+            pa.array(
+                self._to_timedeltaarray().microseconds,
+                from_pandas=True,
+                type=pa.int32(),
+            )
+        )
+
+    @property
+    def _dt_nanoseconds(self):
+        return type(self)(
+            pa.array(
+                self._to_timedeltaarray().nanoseconds, from_pandas=True, type=pa.int32()
+            )
+        )
+
+    def _dt_to_pytimedelta(self):
+        data = self._pa_array.to_pylist()
+        if self._dtype.pyarrow_dtype.unit == "ns":
+            data = [None if ts is None else ts.to_pytimedelta() for ts in data]
+        return np.array(data, dtype=object)
+
+    def _dt_total_seconds(self):
+        return type(self)(
+            pa.array(self._to_timedeltaarray().total_seconds(), from_pandas=True)
+        )
+
+    def _dt_as_unit(self, unit: str):
+        if pa.types.is_date(self.dtype.pyarrow_dtype):
+            raise NotImplementedError("as_unit not implemented for date types")
+        pd_array = self._maybe_convert_datelike_array()
+        # Don't just cast _pa_array in order to follow pandas unit conversion rules
+        return type(self)(pa.array(pd_array.as_unit(unit), from_pandas=True))
+
+    @property
+    def _dt_year(self):
+        return type(self)(pc.year(self._pa_array))
+
+    @property
+    def _dt_day(self):
+        return type(self)(pc.day(self._pa_array))
+
+    @property
+    def _dt_day_of_week(self):
+        return type(self)(pc.day_of_week(self._pa_array))
+
+    _dt_dayofweek = _dt_day_of_week
+    _dt_weekday = _dt_day_of_week
+
+    @property
+    def _dt_day_of_year(self):
+        return type(self)(pc.day_of_year(self._pa_array))
+
+    _dt_dayofyear = _dt_day_of_year
+
+    @property
+    def _dt_hour(self):
+        return type(self)(pc.hour(self._pa_array))
+
+    def _dt_isocalendar(self):
+        return type(self)(pc.iso_calendar(self._pa_array))
+
+    @property
+    def _dt_is_leap_year(self):
+        return type(self)(pc.is_leap_year(self._pa_array))
+
+    @property
+    def _dt_is_month_start(self):
+        return type(self)(pc.equal(pc.day(self._pa_array), 1))
+
+    @property
+    def _dt_is_month_end(self):
+        result = pc.equal(
+            pc.days_between(
+                pc.floor_temporal(self._pa_array, unit="day"),
+                pc.ceil_temporal(self._pa_array, unit="month"),
+            ),
+            1,
+        )
+        return type(self)(result)
+
+    @property
+    def _dt_is_year_start(self):
+        return type(self)(
+            pc.and_(
+                pc.equal(pc.month(self._pa_array), 1),
+                pc.equal(pc.day(self._pa_array), 1),
+            )
+        )
+
+    @property
+    def _dt_is_year_end(self):
+        return type(self)(
+            pc.and_(
+                pc.equal(pc.month(self._pa_array), 12),
+                pc.equal(pc.day(self._pa_array), 31),
+            )
+        )
+
+    @property
+    def _dt_is_quarter_start(self):
+        result = pc.equal(
+            pc.floor_temporal(self._pa_array, unit="quarter"),
+            pc.floor_temporal(self._pa_array, unit="day"),
+        )
+        return type(self)(result)
+
+    @property
+    def _dt_is_quarter_end(self):
+        result = pc.equal(
+            pc.days_between(
+                pc.floor_temporal(self._pa_array, unit="day"),
+                pc.ceil_temporal(self._pa_array, unit="quarter"),
+            ),
+            1,
+        )
+        return type(self)(result)
+
+    @property
+    def _dt_days_in_month(self):
+        result = pc.days_between(
+            pc.floor_temporal(self._pa_array, unit="month"),
+            pc.ceil_temporal(self._pa_array, unit="month"),
+        )
+        return type(self)(result)
+
+    _dt_daysinmonth = _dt_days_in_month
+
+    @property
+    def _dt_microsecond(self):
+        return type(self)(pc.microsecond(self._pa_array))
+
+    @property
+    def _dt_minute(self):
+        return type(self)(pc.minute(self._pa_array))
+
+    @property
+    def _dt_month(self):
+        return type(self)(pc.month(self._pa_array))
+
+    @property
+    def _dt_nanosecond(self):
+        return type(self)(pc.nanosecond(self._pa_array))
+
+    @property
+    def _dt_quarter(self):
+        return type(self)(pc.quarter(self._pa_array))
+
+    @property
+    def _dt_second(self):
+        return type(self)(pc.second(self._pa_array))
+
+    @property
+    def _dt_date(self):
+        return type(self)(self._pa_array.cast(pa.date32()))
+
+    @property
+    def _dt_time(self):
+        unit = (
+            self.dtype.pyarrow_dtype.unit
+            if self.dtype.pyarrow_dtype.unit in {"us", "ns"}
+            else "ns"
+        )
+        return type(self)(self._pa_array.cast(pa.time64(unit)))
+
+    @property
+    def _dt_tz(self):
+        return timezones.maybe_get_tz(self.dtype.pyarrow_dtype.tz)
+
+    @property
+    def _dt_unit(self):
+        return self.dtype.pyarrow_dtype.unit
+
+    def _dt_normalize(self):
+        return type(self)(pc.floor_temporal(self._pa_array, 1, "day"))
+
+    def _dt_strftime(self, format: str):
+        return type(self)(pc.strftime(self._pa_array, format=format))
+
+    def _round_temporally(
+        self,
+        method: Literal["ceil", "floor", "round"],
+        freq,
+        ambiguous: TimeAmbiguous = "raise",
+        nonexistent: TimeNonexistent = "raise",
+    ):
+        if ambiguous != "raise":
+            raise NotImplementedError("ambiguous is not supported.")
+        if nonexistent != "raise":
+            raise NotImplementedError("nonexistent is not supported.")
+        offset = to_offset(freq)
+        if offset is None:
+            raise ValueError(f"Must specify a valid frequency: {freq}")
+        pa_supported_unit = {
+            "Y": "year",
+            "YS": "year",
+            "Q": "quarter",
+            "QS": "quarter",
+            "M": "month",
+            "MS": "month",
+            "W": "week",
+            "D": "day",
+            "h": "hour",
+            "min": "minute",
+            "s": "second",
+            "ms": "millisecond",
+            "us": "microsecond",
+            "ns": "nanosecond",
+        }
+        unit = pa_supported_unit.get(offset._prefix, None)
+        if unit is None:
+            raise ValueError(f"{freq=} is not supported")
+        multiple = offset.n
+        rounding_method = getattr(pc, f"{method}_temporal")
+        return type(self)(rounding_method(self._pa_array, multiple=multiple, unit=unit))
+
+    def _dt_ceil(
+        self,
+        freq,
+        ambiguous: TimeAmbiguous = "raise",
+        nonexistent: TimeNonexistent = "raise",
+    ):
+        return self._round_temporally("ceil", freq, ambiguous, nonexistent)
+
+    def _dt_floor(
+        self,
+        freq,
+        ambiguous: TimeAmbiguous = "raise",
+        nonexistent: TimeNonexistent = "raise",
+    ):
+        return self._round_temporally("floor", freq, ambiguous, nonexistent)
+
+    def _dt_round(
+        self,
+        freq,
+        ambiguous: TimeAmbiguous = "raise",
+        nonexistent: TimeNonexistent = "raise",
+    ):
+        return self._round_temporally("round", freq, ambiguous, nonexistent)
+
+    def _dt_day_name(self, locale: str | None = None):
+        if locale is None:
+            locale = "C"
+        return type(self)(pc.strftime(self._pa_array, format="%A", locale=locale))
+
+    def _dt_month_name(self, locale: str | None = None):
+        if locale is None:
+            locale = "C"
+        return type(self)(pc.strftime(self._pa_array, format="%B", locale=locale))
+
+    def _dt_to_pydatetime(self):
+        if pa.types.is_date(self.dtype.pyarrow_dtype):
+            raise ValueError(
+                f"to_pydatetime cannot be called with {self.dtype.pyarrow_dtype} type. "
+                "Convert to pyarrow timestamp type."
+            )
+        data = self._pa_array.to_pylist()
+        if self._dtype.pyarrow_dtype.unit == "ns":
+            data = [None if ts is None else ts.to_pydatetime(warn=False) for ts in data]
+        return np.array(data, dtype=object)
+
+    def _dt_tz_localize(
+        self,
+        tz,
+        ambiguous: TimeAmbiguous = "raise",
+        nonexistent: TimeNonexistent = "raise",
+    ):
+        if ambiguous != "raise":
+            raise NotImplementedError(f"{ambiguous=} is not supported")
+        nonexistent_pa = {
+            "raise": "raise",
+            "shift_backward": "earliest",
+            "shift_forward": "latest",
+        }.get(
+            nonexistent, None  # type: ignore[arg-type]
+        )
+        if nonexistent_pa is None:
+            raise NotImplementedError(f"{nonexistent=} is not supported")
+        if tz is None:
+            result = self._pa_array.cast(pa.timestamp(self.dtype.pyarrow_dtype.unit))
+        else:
+            result = pc.assume_timezone(
+                self._pa_array, str(tz), ambiguous=ambiguous, nonexistent=nonexistent_pa
+            )
+        return type(self)(result)
+
+    def _dt_tz_convert(self, tz):
+        if self.dtype.pyarrow_dtype.tz is None:
+            raise TypeError(
+                "Cannot convert tz-naive timestamps, use tz_localize to localize"
+            )
+        current_unit = self.dtype.pyarrow_dtype.unit
+        result = self._pa_array.cast(pa.timestamp(current_unit, tz))
+        return type(self)(result)
+
+
+def transpose_homogeneous_pyarrow(
+    arrays: Sequence[ArrowExtensionArray],
+) -> list[ArrowExtensionArray]:
+    """Transpose arrow extension arrays in a list, but faster.
+
+    Input should be a list of arrays of equal length and all have the same
+    dtype. The caller is responsible for ensuring validity of input data.
+    """
+    arrays = list(arrays)
+    nrows, ncols = len(arrays[0]), len(arrays)
+    indices = np.arange(nrows * ncols).reshape(ncols, nrows).T.flatten()
+    arr = pa.chunked_array([chunk for arr in arrays for chunk in arr._pa_array.chunks])
+    arr = arr.take(indices)
+    return [ArrowExtensionArray(arr.slice(i * ncols, ncols)) for i in range(nrows)]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/arrow/extension_types.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/arrow/extension_types.py
new file mode 100644
index 0000000000000000000000000000000000000000..72bfd6f2212f8fae6ea7786599de44beaeb3f902
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/arrow/extension_types.py
@@ -0,0 +1,174 @@
+from __future__ import annotations
+
+import json
+from typing import TYPE_CHECKING
+
+import pyarrow
+
+from pandas.compat import pa_version_under14p1
+
+from pandas.core.dtypes.dtypes import (
+    IntervalDtype,
+    PeriodDtype,
+)
+
+from pandas.core.arrays.interval import VALID_CLOSED
+
+if TYPE_CHECKING:
+    from pandas._typing import IntervalClosedType
+
+
+class ArrowPeriodType(pyarrow.ExtensionType):
+    def __init__(self, freq) -> None:
+        # attributes need to be set first before calling
+        # super init (as that calls serialize)
+        self._freq = freq
+        pyarrow.ExtensionType.__init__(self, pyarrow.int64(), "pandas.period")
+
+    @property
+    def freq(self):
+        return self._freq
+
+    def __arrow_ext_serialize__(self) -> bytes:
+        metadata = {"freq": self.freq}
+        return json.dumps(metadata).encode()
+
+    @classmethod
+    def __arrow_ext_deserialize__(cls, storage_type, serialized) -> ArrowPeriodType:
+        metadata = json.loads(serialized.decode())
+        return ArrowPeriodType(metadata["freq"])
+
+    def __eq__(self, other):
+        if isinstance(other, pyarrow.BaseExtensionType):
+            return type(self) == type(other) and self.freq == other.freq
+        else:
+            return NotImplemented
+
+    def __ne__(self, other) -> bool:
+        return not self == other
+
+    def __hash__(self) -> int:
+        return hash((str(self), self.freq))
+
+    def to_pandas_dtype(self) -> PeriodDtype:
+        return PeriodDtype(freq=self.freq)
+
+
+# register the type with a dummy instance
+_period_type = ArrowPeriodType("D")
+pyarrow.register_extension_type(_period_type)
+
+
+class ArrowIntervalType(pyarrow.ExtensionType):
+    def __init__(self, subtype, closed: IntervalClosedType) -> None:
+        # attributes need to be set first before calling
+        # super init (as that calls serialize)
+        assert closed in VALID_CLOSED
+        self._closed: IntervalClosedType = closed
+        if not isinstance(subtype, pyarrow.DataType):
+            subtype = pyarrow.type_for_alias(str(subtype))
+        self._subtype = subtype
+
+        storage_type = pyarrow.struct([("left", subtype), ("right", subtype)])
+        pyarrow.ExtensionType.__init__(self, storage_type, "pandas.interval")
+
+    @property
+    def subtype(self):
+        return self._subtype
+
+    @property
+    def closed(self) -> IntervalClosedType:
+        return self._closed
+
+    def __arrow_ext_serialize__(self) -> bytes:
+        metadata = {"subtype": str(self.subtype), "closed": self.closed}
+        return json.dumps(metadata).encode()
+
+    @classmethod
+    def __arrow_ext_deserialize__(cls, storage_type, serialized) -> ArrowIntervalType:
+        metadata = json.loads(serialized.decode())
+        subtype = pyarrow.type_for_alias(metadata["subtype"])
+        closed = metadata["closed"]
+        return ArrowIntervalType(subtype, closed)
+
+    def __eq__(self, other):
+        if isinstance(other, pyarrow.BaseExtensionType):
+            return (
+                type(self) == type(other)
+                and self.subtype == other.subtype
+                and self.closed == other.closed
+            )
+        else:
+            return NotImplemented
+
+    def __ne__(self, other) -> bool:
+        return not self == other
+
+    def __hash__(self) -> int:
+        return hash((str(self), str(self.subtype), self.closed))
+
+    def to_pandas_dtype(self) -> IntervalDtype:
+        return IntervalDtype(self.subtype.to_pandas_dtype(), self.closed)
+
+
+# register the type with a dummy instance
+_interval_type = ArrowIntervalType(pyarrow.int64(), "left")
+pyarrow.register_extension_type(_interval_type)
+
+
+_ERROR_MSG = """\
+Disallowed deserialization of 'arrow.py_extension_type':
+storage_type = {storage_type}
+serialized = {serialized}
+pickle disassembly:\n{pickle_disassembly}
+
+Reading of untrusted Parquet or Feather files with a PyExtensionType column
+allows arbitrary code execution.
+If you trust this file, you can enable reading the extension type by one of:
+
+- upgrading to pyarrow >= 14.0.1, and call `pa.PyExtensionType.set_auto_load(True)`
+- install pyarrow-hotfix (`pip install pyarrow-hotfix`) and disable it by running
+  `import pyarrow_hotfix; pyarrow_hotfix.uninstall()`
+
+We strongly recommend updating your Parquet/Feather files to use extension types
+derived from `pyarrow.ExtensionType` instead, and register this type explicitly.
+"""
+
+
+def patch_pyarrow():
+    # starting from pyarrow 14.0.1, it has its own mechanism
+    if not pa_version_under14p1:
+        return
+
+    # if https://github.com/pitrou/pyarrow-hotfix was installed and enabled
+    if getattr(pyarrow, "_hotfix_installed", False):
+        return
+
+    class ForbiddenExtensionType(pyarrow.ExtensionType):
+        def __arrow_ext_serialize__(self):
+            return b""
+
+        @classmethod
+        def __arrow_ext_deserialize__(cls, storage_type, serialized):
+            import io
+            import pickletools
+
+            out = io.StringIO()
+            pickletools.dis(serialized, out)
+            raise RuntimeError(
+                _ERROR_MSG.format(
+                    storage_type=storage_type,
+                    serialized=serialized,
+                    pickle_disassembly=out.getvalue(),
+                )
+            )
+
+    pyarrow.unregister_extension_type("arrow.py_extension_type")
+    pyarrow.register_extension_type(
+        ForbiddenExtensionType(pyarrow.null(), "arrow.py_extension_type")
+    )
+
+    pyarrow._hotfix_installed = True
+
+
+patch_pyarrow()
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/base.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/base.py
new file mode 100644
index 0000000000000000000000000000000000000000..62458b89f9c089fb663c2524b4dd585000336712
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/base.py
@@ -0,0 +1,2612 @@
+"""
+An interface for extending pandas with custom arrays.
+
+.. warning::
+
+   This is an experimental API and subject to breaking changes
+   without warning.
+"""
+from __future__ import annotations
+
+import operator
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    ClassVar,
+    Literal,
+    cast,
+    overload,
+)
+import warnings
+
+import numpy as np
+
+from pandas._libs import (
+    algos as libalgos,
+    lib,
+)
+from pandas.compat import set_function_name
+from pandas.compat.numpy import function as nv
+from pandas.errors import AbstractMethodError
+from pandas.util._decorators import (
+    Appender,
+    Substitution,
+    cache_readonly,
+)
+from pandas.util._exceptions import find_stack_level
+from pandas.util._validators import (
+    validate_bool_kwarg,
+    validate_fillna_kwargs,
+    validate_insert_loc,
+)
+
+from pandas.core.dtypes.cast import maybe_cast_pointwise_result
+from pandas.core.dtypes.common import (
+    is_list_like,
+    is_scalar,
+    pandas_dtype,
+)
+from pandas.core.dtypes.dtypes import ExtensionDtype
+from pandas.core.dtypes.generic import (
+    ABCDataFrame,
+    ABCIndex,
+    ABCSeries,
+)
+from pandas.core.dtypes.missing import isna
+
+from pandas.core import (
+    arraylike,
+    missing,
+    roperator,
+)
+from pandas.core.algorithms import (
+    duplicated,
+    factorize_array,
+    isin,
+    map_array,
+    mode,
+    rank,
+    unique,
+)
+from pandas.core.array_algos.quantile import quantile_with_mask
+from pandas.core.missing import _fill_limit_area_1d
+from pandas.core.sorting import (
+    nargminmax,
+    nargsort,
+)
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Iterator,
+        Sequence,
+    )
+
+    from pandas._typing import (
+        ArrayLike,
+        AstypeArg,
+        AxisInt,
+        Dtype,
+        DtypeObj,
+        FillnaOptions,
+        InterpolateOptions,
+        NumpySorter,
+        NumpyValueArrayLike,
+        PositionalIndexer,
+        ScalarIndexer,
+        Self,
+        SequenceIndexer,
+        Shape,
+        SortKind,
+        TakeIndexer,
+        npt,
+    )
+
+    from pandas import Index
+
+_extension_array_shared_docs: dict[str, str] = {}
+
+
+class ExtensionArray:
+    """
+    Abstract base class for custom 1-D array types.
+
+    pandas will recognize instances of this class as proper arrays
+    with a custom type and will not attempt to coerce them to objects. They
+    may be stored directly inside a :class:`DataFrame` or :class:`Series`.
+
+    Attributes
+    ----------
+    dtype
+    nbytes
+    ndim
+    shape
+
+    Methods
+    -------
+    argsort
+    astype
+    copy
+    dropna
+    duplicated
+    factorize
+    fillna
+    equals
+    insert
+    interpolate
+    isin
+    isna
+    ravel
+    repeat
+    searchsorted
+    shift
+    take
+    tolist
+    unique
+    view
+    _accumulate
+    _concat_same_type
+    _explode
+    _formatter
+    _from_factorized
+    _from_sequence
+    _from_sequence_of_strings
+    _hash_pandas_object
+    _pad_or_backfill
+    _reduce
+    _values_for_argsort
+    _values_for_factorize
+
+    Notes
+    -----
+    The interface includes the following abstract methods that must be
+    implemented by subclasses:
+
+    * _from_sequence
+    * _from_factorized
+    * __getitem__
+    * __len__
+    * __eq__
+    * dtype
+    * nbytes
+    * isna
+    * take
+    * copy
+    * _concat_same_type
+    * interpolate
+
+    A default repr displaying the type, (truncated) data, length,
+    and dtype is provided. It can be customized or replaced by
+    by overriding:
+
+    * __repr__ : A default repr for the ExtensionArray.
+    * _formatter : Print scalars inside a Series or DataFrame.
+
+    Some methods require casting the ExtensionArray to an ndarray of Python
+    objects with ``self.astype(object)``, which may be expensive. When
+    performance is a concern, we highly recommend overriding the following
+    methods:
+
+    * fillna
+    * _pad_or_backfill
+    * dropna
+    * unique
+    * factorize / _values_for_factorize
+    * argsort, argmax, argmin / _values_for_argsort
+    * searchsorted
+    * map
+
+    The remaining methods implemented on this class should be performant,
+    as they only compose abstract methods. Still, a more efficient
+    implementation may be available, and these methods can be overridden.
+
+    One can implement methods to handle array accumulations or reductions.
+
+    * _accumulate
+    * _reduce
+
+    One can implement methods to handle parsing from strings that will be used
+    in methods such as ``pandas.io.parsers.read_csv``.
+
+    * _from_sequence_of_strings
+
+    This class does not inherit from 'abc.ABCMeta' for performance reasons.
+    Methods and properties required by the interface raise
+    ``pandas.errors.AbstractMethodError`` and no ``register`` method is
+    provided for registering virtual subclasses.
+
+    ExtensionArrays are limited to 1 dimension.
+
+    They may be backed by none, one, or many NumPy arrays. For example,
+    ``pandas.Categorical`` is an extension array backed by two arrays,
+    one for codes and one for categories. An array of IPv6 address may
+    be backed by a NumPy structured array with two fields, one for the
+    lower 64 bits and one for the upper 64 bits. Or they may be backed
+    by some other storage type, like Python lists. Pandas makes no
+    assumptions on how the data are stored, just that it can be converted
+    to a NumPy array.
+    The ExtensionArray interface does not impose any rules on how this data
+    is stored. However, currently, the backing data cannot be stored in
+    attributes called ``.values`` or ``._values`` to ensure full compatibility
+    with pandas internals. But other names as ``.data``, ``._data``,
+    ``._items``, ... can be freely used.
+
+    If implementing NumPy's ``__array_ufunc__`` interface, pandas expects
+    that
+
+    1. You defer by returning ``NotImplemented`` when any Series are present
+       in `inputs`. Pandas will extract the arrays and call the ufunc again.
+    2. You define a ``_HANDLED_TYPES`` tuple as an attribute on the class.
+       Pandas inspect this to determine whether the ufunc is valid for the
+       types present.
+
+    See :ref:`extending.extension.ufunc` for more.
+
+    By default, ExtensionArrays are not hashable.  Immutable subclasses may
+    override this behavior.
+
+    Examples
+    --------
+    Please see the following:
+
+    https://github.com/pandas-dev/pandas/blob/main/pandas/tests/extension/list/array.py
+    """
+
+    # '_typ' is for pandas.core.dtypes.generic.ABCExtensionArray.
+    # Don't override this.
+    _typ = "extension"
+
+    # similar to __array_priority__, positions ExtensionArray after Index,
+    #  Series, and DataFrame.  EA subclasses may override to choose which EA
+    #  subclass takes priority. If overriding, the value should always be
+    #  strictly less than 2000 to be below Index.__pandas_priority__.
+    __pandas_priority__ = 1000
+
+    # ------------------------------------------------------------------------
+    # Constructors
+    # ------------------------------------------------------------------------
+
+    @classmethod
+    def _from_sequence(cls, scalars, *, dtype: Dtype | None = None, copy: bool = False):
+        """
+        Construct a new ExtensionArray from a sequence of scalars.
+
+        Parameters
+        ----------
+        scalars : Sequence
+            Each element will be an instance of the scalar type for this
+            array, ``cls.dtype.type`` or be converted into this type in this method.
+        dtype : dtype, optional
+            Construct for this particular dtype. This should be a Dtype
+            compatible with the ExtensionArray.
+        copy : bool, default False
+            If True, copy the underlying data.
+
+        Returns
+        -------
+        ExtensionArray
+
+        Examples
+        --------
+        >>> pd.arrays.IntegerArray._from_sequence([4, 5])
+        
+        [4, 5]
+        Length: 2, dtype: Int64
+        """
+        raise AbstractMethodError(cls)
+
+    @classmethod
+    def _from_scalars(cls, scalars, *, dtype: DtypeObj) -> Self:
+        """
+        Strict analogue to _from_sequence, allowing only sequences of scalars
+        that should be specifically inferred to the given dtype.
+
+        Parameters
+        ----------
+        scalars : sequence
+        dtype : ExtensionDtype
+
+        Raises
+        ------
+        TypeError or ValueError
+
+        Notes
+        -----
+        This is called in a try/except block when casting the result of a
+        pointwise operation.
+        """
+        try:
+            return cls._from_sequence(scalars, dtype=dtype, copy=False)
+        except (ValueError, TypeError):
+            raise
+        except Exception:
+            warnings.warn(
+                "_from_scalars should only raise ValueError or TypeError. "
+                "Consider overriding _from_scalars where appropriate.",
+                stacklevel=find_stack_level(),
+            )
+            raise
+
+    @classmethod
+    def _from_sequence_of_strings(
+        cls, strings, *, dtype: Dtype | None = None, copy: bool = False
+    ):
+        """
+        Construct a new ExtensionArray from a sequence of strings.
+
+        Parameters
+        ----------
+        strings : Sequence
+            Each element will be an instance of the scalar type for this
+            array, ``cls.dtype.type``.
+        dtype : dtype, optional
+            Construct for this particular dtype. This should be a Dtype
+            compatible with the ExtensionArray.
+        copy : bool, default False
+            If True, copy the underlying data.
+
+        Returns
+        -------
+        ExtensionArray
+
+        Examples
+        --------
+        >>> pd.arrays.IntegerArray._from_sequence_of_strings(["1", "2", "3"])
+        
+        [1, 2, 3]
+        Length: 3, dtype: Int64
+        """
+        raise AbstractMethodError(cls)
+
+    @classmethod
+    def _from_factorized(cls, values, original):
+        """
+        Reconstruct an ExtensionArray after factorization.
+
+        Parameters
+        ----------
+        values : ndarray
+            An integer ndarray with the factorized values.
+        original : ExtensionArray
+            The original ExtensionArray that factorize was called on.
+
+        See Also
+        --------
+        factorize : Top-level factorize method that dispatches here.
+        ExtensionArray.factorize : Encode the extension array as an enumerated type.
+
+        Examples
+        --------
+        >>> interv_arr = pd.arrays.IntervalArray([pd.Interval(0, 1),
+        ...                                      pd.Interval(1, 5), pd.Interval(1, 5)])
+        >>> codes, uniques = pd.factorize(interv_arr)
+        >>> pd.arrays.IntervalArray._from_factorized(uniques, interv_arr)
+        
+        [(0, 1], (1, 5]]
+        Length: 2, dtype: interval[int64, right]
+        """
+        raise AbstractMethodError(cls)
+
+    # ------------------------------------------------------------------------
+    # Must be a Sequence
+    # ------------------------------------------------------------------------
+    @overload
+    def __getitem__(self, item: ScalarIndexer) -> Any:
+        ...
+
+    @overload
+    def __getitem__(self, item: SequenceIndexer) -> Self:
+        ...
+
+    def __getitem__(self, item: PositionalIndexer) -> Self | Any:
+        """
+        Select a subset of self.
+
+        Parameters
+        ----------
+        item : int, slice, or ndarray
+            * int: The position in 'self' to get.
+
+            * slice: A slice object, where 'start', 'stop', and 'step' are
+              integers or None
+
+            * ndarray: A 1-d boolean NumPy ndarray the same length as 'self'
+
+            * list[int]:  A list of int
+
+        Returns
+        -------
+        item : scalar or ExtensionArray
+
+        Notes
+        -----
+        For scalar ``item``, return a scalar value suitable for the array's
+        type. This should be an instance of ``self.dtype.type``.
+
+        For slice ``key``, return an instance of ``ExtensionArray``, even
+        if the slice is length 0 or 1.
+
+        For a boolean mask, return an instance of ``ExtensionArray``, filtered
+        to the values where ``item`` is True.
+        """
+        raise AbstractMethodError(self)
+
+    def __setitem__(self, key, value) -> None:
+        """
+        Set one or more values inplace.
+
+        This method is not required to satisfy the pandas extension array
+        interface.
+
+        Parameters
+        ----------
+        key : int, ndarray, or slice
+            When called from, e.g. ``Series.__setitem__``, ``key`` will be
+            one of
+
+            * scalar int
+            * ndarray of integers.
+            * boolean ndarray
+            * slice object
+
+        value : ExtensionDtype.type, Sequence[ExtensionDtype.type], or object
+            value or values to be set of ``key``.
+
+        Returns
+        -------
+        None
+        """
+        # Some notes to the ExtensionArray implementer who may have ended up
+        # here. While this method is not required for the interface, if you
+        # *do* choose to implement __setitem__, then some semantics should be
+        # observed:
+        #
+        # * Setting multiple values : ExtensionArrays should support setting
+        #   multiple values at once, 'key' will be a sequence of integers and
+        #  'value' will be a same-length sequence.
+        #
+        # * Broadcasting : For a sequence 'key' and a scalar 'value',
+        #   each position in 'key' should be set to 'value'.
+        #
+        # * Coercion : Most users will expect basic coercion to work. For
+        #   example, a string like '2018-01-01' is coerced to a datetime
+        #   when setting on a datetime64ns array. In general, if the
+        #   __init__ method coerces that value, then so should __setitem__
+        # Note, also, that Series/DataFrame.where internally use __setitem__
+        # on a copy of the data.
+        raise NotImplementedError(f"{type(self)} does not implement __setitem__.")
+
+    def __len__(self) -> int:
+        """
+        Length of this array
+
+        Returns
+        -------
+        length : int
+        """
+        raise AbstractMethodError(self)
+
+    def __iter__(self) -> Iterator[Any]:
+        """
+        Iterate over elements of the array.
+        """
+        # This needs to be implemented so that pandas recognizes extension
+        # arrays as list-like. The default implementation makes successive
+        # calls to ``__getitem__``, which may be slower than necessary.
+        for i in range(len(self)):
+            yield self[i]
+
+    def __contains__(self, item: object) -> bool | np.bool_:
+        """
+        Return for `item in self`.
+        """
+        # GH37867
+        # comparisons of any item to pd.NA always return pd.NA, so e.g. "a" in [pd.NA]
+        # would raise a TypeError. The implementation below works around that.
+        if is_scalar(item) and isna(item):
+            if not self._can_hold_na:
+                return False
+            elif item is self.dtype.na_value or isinstance(item, self.dtype.type):
+                return self._hasna
+            else:
+                return False
+        else:
+            # error: Item "ExtensionArray" of "Union[ExtensionArray, ndarray]" has no
+            # attribute "any"
+            return (item == self).any()  # type: ignore[union-attr]
+
+    # error: Signature of "__eq__" incompatible with supertype "object"
+    def __eq__(self, other: object) -> ArrayLike:  # type: ignore[override]
+        """
+        Return for `self == other` (element-wise equality).
+        """
+        # Implementer note: this should return a boolean numpy ndarray or
+        # a boolean ExtensionArray.
+        # When `other` is one of Series, Index, or DataFrame, this method should
+        # return NotImplemented (to ensure that those objects are responsible for
+        # first unpacking the arrays, and then dispatch the operation to the
+        # underlying arrays)
+        raise AbstractMethodError(self)
+
+    # error: Signature of "__ne__" incompatible with supertype "object"
+    def __ne__(self, other: object) -> ArrayLike:  # type: ignore[override]
+        """
+        Return for `self != other` (element-wise in-equality).
+        """
+        # error: Unsupported operand type for ~ ("ExtensionArray")
+        return ~(self == other)  # type: ignore[operator]
+
+    def to_numpy(
+        self,
+        dtype: npt.DTypeLike | None = None,
+        copy: bool = False,
+        na_value: object = lib.no_default,
+    ) -> np.ndarray:
+        """
+        Convert to a NumPy ndarray.
+
+        This is similar to :meth:`numpy.asarray`, but may provide additional control
+        over how the conversion is done.
+
+        Parameters
+        ----------
+        dtype : str or numpy.dtype, optional
+            The dtype to pass to :meth:`numpy.asarray`.
+        copy : bool, default False
+            Whether to ensure that the returned value is a not a view on
+            another array. Note that ``copy=False`` does not *ensure* that
+            ``to_numpy()`` is no-copy. Rather, ``copy=True`` ensure that
+            a copy is made, even if not strictly necessary.
+        na_value : Any, optional
+            The value to use for missing values. The default value depends
+            on `dtype` and the type of the array.
+
+        Returns
+        -------
+        numpy.ndarray
+        """
+        result = np.asarray(self, dtype=dtype)
+        if copy or na_value is not lib.no_default:
+            result = result.copy()
+        if na_value is not lib.no_default:
+            result[self.isna()] = na_value
+        return result
+
+    # ------------------------------------------------------------------------
+    # Required attributes
+    # ------------------------------------------------------------------------
+
+    @property
+    def dtype(self) -> ExtensionDtype:
+        """
+        An instance of ExtensionDtype.
+
+        Examples
+        --------
+        >>> pd.array([1, 2, 3]).dtype
+        Int64Dtype()
+        """
+        raise AbstractMethodError(self)
+
+    @property
+    def shape(self) -> Shape:
+        """
+        Return a tuple of the array dimensions.
+
+        Examples
+        --------
+        >>> arr = pd.array([1, 2, 3])
+        >>> arr.shape
+        (3,)
+        """
+        return (len(self),)
+
+    @property
+    def size(self) -> int:
+        """
+        The number of elements in the array.
+        """
+        # error: Incompatible return value type (got "signedinteger[_64Bit]",
+        # expected "int")  [return-value]
+        return np.prod(self.shape)  # type: ignore[return-value]
+
+    @property
+    def ndim(self) -> int:
+        """
+        Extension Arrays are only allowed to be 1-dimensional.
+
+        Examples
+        --------
+        >>> arr = pd.array([1, 2, 3])
+        >>> arr.ndim
+        1
+        """
+        return 1
+
+    @property
+    def nbytes(self) -> int:
+        """
+        The number of bytes needed to store this object in memory.
+
+        Examples
+        --------
+        >>> pd.array([1, 2, 3]).nbytes
+        27
+        """
+        # If this is expensive to compute, return an approximate lower bound
+        # on the number of bytes needed.
+        raise AbstractMethodError(self)
+
+    # ------------------------------------------------------------------------
+    # Additional Methods
+    # ------------------------------------------------------------------------
+
+    @overload
+    def astype(self, dtype: npt.DTypeLike, copy: bool = ...) -> np.ndarray:
+        ...
+
+    @overload
+    def astype(self, dtype: ExtensionDtype, copy: bool = ...) -> ExtensionArray:
+        ...
+
+    @overload
+    def astype(self, dtype: AstypeArg, copy: bool = ...) -> ArrayLike:
+        ...
+
+    def astype(self, dtype: AstypeArg, copy: bool = True) -> ArrayLike:
+        """
+        Cast to a NumPy array or ExtensionArray with 'dtype'.
+
+        Parameters
+        ----------
+        dtype : str or dtype
+            Typecode or data-type to which the array is cast.
+        copy : bool, default True
+            Whether to copy the data, even if not necessary. If False,
+            a copy is made only if the old dtype does not match the
+            new dtype.
+
+        Returns
+        -------
+        np.ndarray or pandas.api.extensions.ExtensionArray
+            An ``ExtensionArray`` if ``dtype`` is ``ExtensionDtype``,
+            otherwise a Numpy ndarray with ``dtype`` for its dtype.
+
+        Examples
+        --------
+        >>> arr = pd.array([1, 2, 3])
+        >>> arr
+        
+        [1, 2, 3]
+        Length: 3, dtype: Int64
+
+        Casting to another ``ExtensionDtype`` returns an ``ExtensionArray``:
+
+        >>> arr1 = arr.astype('Float64')
+        >>> arr1
+        
+        [1.0, 2.0, 3.0]
+        Length: 3, dtype: Float64
+        >>> arr1.dtype
+        Float64Dtype()
+
+        Otherwise, we will get a Numpy ndarray:
+
+        >>> arr2 = arr.astype('float64')
+        >>> arr2
+        array([1., 2., 3.])
+        >>> arr2.dtype
+        dtype('float64')
+        """
+        dtype = pandas_dtype(dtype)
+        if dtype == self.dtype:
+            if not copy:
+                return self
+            else:
+                return self.copy()
+
+        if isinstance(dtype, ExtensionDtype):
+            cls = dtype.construct_array_type()
+            return cls._from_sequence(self, dtype=dtype, copy=copy)
+
+        elif lib.is_np_dtype(dtype, "M"):
+            from pandas.core.arrays import DatetimeArray
+
+            return DatetimeArray._from_sequence(self, dtype=dtype, copy=copy)
+
+        elif lib.is_np_dtype(dtype, "m"):
+            from pandas.core.arrays import TimedeltaArray
+
+            return TimedeltaArray._from_sequence(self, dtype=dtype, copy=copy)
+
+        if not copy:
+            return np.asarray(self, dtype=dtype)
+        else:
+            return np.array(self, dtype=dtype, copy=copy)
+
+    def isna(self) -> np.ndarray | ExtensionArraySupportsAnyAll:
+        """
+        A 1-D array indicating if each value is missing.
+
+        Returns
+        -------
+        numpy.ndarray or pandas.api.extensions.ExtensionArray
+            In most cases, this should return a NumPy ndarray. For
+            exceptional cases like ``SparseArray``, where returning
+            an ndarray would be expensive, an ExtensionArray may be
+            returned.
+
+        Notes
+        -----
+        If returning an ExtensionArray, then
+
+        * ``na_values._is_boolean`` should be True
+        * `na_values` should implement :func:`ExtensionArray._reduce`
+        * ``na_values.any`` and ``na_values.all`` should be implemented
+
+        Examples
+        --------
+        >>> arr = pd.array([1, 2, np.nan, np.nan])
+        >>> arr.isna()
+        array([False, False,  True,  True])
+        """
+        raise AbstractMethodError(self)
+
+    @property
+    def _hasna(self) -> bool:
+        # GH#22680
+        """
+        Equivalent to `self.isna().any()`.
+
+        Some ExtensionArray subclasses may be able to optimize this check.
+        """
+        return bool(self.isna().any())
+
+    def _values_for_argsort(self) -> np.ndarray:
+        """
+        Return values for sorting.
+
+        Returns
+        -------
+        ndarray
+            The transformed values should maintain the ordering between values
+            within the array.
+
+        See Also
+        --------
+        ExtensionArray.argsort : Return the indices that would sort this array.
+
+        Notes
+        -----
+        The caller is responsible for *not* modifying these values in-place, so
+        it is safe for implementers to give views on ``self``.
+
+        Functions that use this (e.g. ``ExtensionArray.argsort``) should ignore
+        entries with missing values in the original array (according to
+        ``self.isna()``). This means that the corresponding entries in the returned
+        array don't need to be modified to sort correctly.
+
+        Examples
+        --------
+        In most cases, this is the underlying Numpy array of the ``ExtensionArray``:
+
+        >>> arr = pd.array([1, 2, 3])
+        >>> arr._values_for_argsort()
+        array([1, 2, 3])
+        """
+        # Note: this is used in `ExtensionArray.argsort/argmin/argmax`.
+        return np.array(self)
+
+    def argsort(
+        self,
+        *,
+        ascending: bool = True,
+        kind: SortKind = "quicksort",
+        na_position: str = "last",
+        **kwargs,
+    ) -> np.ndarray:
+        """
+        Return the indices that would sort this array.
+
+        Parameters
+        ----------
+        ascending : bool, default True
+            Whether the indices should result in an ascending
+            or descending sort.
+        kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, optional
+            Sorting algorithm.
+        na_position : {'first', 'last'}, default 'last'
+            If ``'first'``, put ``NaN`` values at the beginning.
+            If ``'last'``, put ``NaN`` values at the end.
+        *args, **kwargs:
+            Passed through to :func:`numpy.argsort`.
+
+        Returns
+        -------
+        np.ndarray[np.intp]
+            Array of indices that sort ``self``. If NaN values are contained,
+            NaN values are placed at the end.
+
+        See Also
+        --------
+        numpy.argsort : Sorting implementation used internally.
+
+        Examples
+        --------
+        >>> arr = pd.array([3, 1, 2, 5, 4])
+        >>> arr.argsort()
+        array([1, 2, 0, 4, 3])
+        """
+        # Implementer note: You have two places to override the behavior of
+        # argsort.
+        # 1. _values_for_argsort : construct the values passed to np.argsort
+        # 2. argsort : total control over sorting. In case of overriding this,
+        #    it is recommended to also override argmax/argmin
+        ascending = nv.validate_argsort_with_ascending(ascending, (), kwargs)
+
+        values = self._values_for_argsort()
+        return nargsort(
+            values,
+            kind=kind,
+            ascending=ascending,
+            na_position=na_position,
+            mask=np.asarray(self.isna()),
+        )
+
+    def argmin(self, skipna: bool = True) -> int:
+        """
+        Return the index of minimum value.
+
+        In case of multiple occurrences of the minimum value, the index
+        corresponding to the first occurrence is returned.
+
+        Parameters
+        ----------
+        skipna : bool, default True
+
+        Returns
+        -------
+        int
+
+        See Also
+        --------
+        ExtensionArray.argmax : Return the index of the maximum value.
+
+        Examples
+        --------
+        >>> arr = pd.array([3, 1, 2, 5, 4])
+        >>> arr.argmin()
+        1
+        """
+        # Implementer note: You have two places to override the behavior of
+        # argmin.
+        # 1. _values_for_argsort : construct the values used in nargminmax
+        # 2. argmin itself : total control over sorting.
+        validate_bool_kwarg(skipna, "skipna")
+        if not skipna and self._hasna:
+            raise NotImplementedError
+        return nargminmax(self, "argmin")
+
+    def argmax(self, skipna: bool = True) -> int:
+        """
+        Return the index of maximum value.
+
+        In case of multiple occurrences of the maximum value, the index
+        corresponding to the first occurrence is returned.
+
+        Parameters
+        ----------
+        skipna : bool, default True
+
+        Returns
+        -------
+        int
+
+        See Also
+        --------
+        ExtensionArray.argmin : Return the index of the minimum value.
+
+        Examples
+        --------
+        >>> arr = pd.array([3, 1, 2, 5, 4])
+        >>> arr.argmax()
+        3
+        """
+        # Implementer note: You have two places to override the behavior of
+        # argmax.
+        # 1. _values_for_argsort : construct the values used in nargminmax
+        # 2. argmax itself : total control over sorting.
+        validate_bool_kwarg(skipna, "skipna")
+        if not skipna and self._hasna:
+            raise NotImplementedError
+        return nargminmax(self, "argmax")
+
+    def interpolate(
+        self,
+        *,
+        method: InterpolateOptions,
+        axis: int,
+        index: Index,
+        limit,
+        limit_direction,
+        limit_area,
+        copy: bool,
+        **kwargs,
+    ) -> Self:
+        """
+        See DataFrame.interpolate.__doc__.
+
+        Examples
+        --------
+        >>> arr = pd.arrays.NumpyExtensionArray(np.array([0, 1, np.nan, 3]))
+        >>> arr.interpolate(method="linear",
+        ...                 limit=3,
+        ...                 limit_direction="forward",
+        ...                 index=pd.Index([1, 2, 3, 4]),
+        ...                 fill_value=1,
+        ...                 copy=False,
+        ...                 axis=0,
+        ...                 limit_area="inside"
+        ...                 )
+        
+        [0.0, 1.0, 2.0, 3.0]
+        Length: 4, dtype: float64
+        """
+        # NB: we return type(self) even if copy=False
+        raise NotImplementedError(
+            f"{type(self).__name__} does not implement interpolate"
+        )
+
+    def _pad_or_backfill(
+        self,
+        *,
+        method: FillnaOptions,
+        limit: int | None = None,
+        limit_area: Literal["inside", "outside"] | None = None,
+        copy: bool = True,
+    ) -> Self:
+        """
+        Pad or backfill values, used by Series/DataFrame ffill and bfill.
+
+        Parameters
+        ----------
+        method : {'backfill', 'bfill', 'pad', 'ffill'}
+            Method to use for filling holes in reindexed Series:
+
+            * pad / ffill: propagate last valid observation forward to next valid.
+            * backfill / bfill: use NEXT valid observation to fill gap.
+
+        limit : int, default None
+            This is the maximum number of consecutive
+            NaN values to forward/backward fill. In other words, if there is
+            a gap with more than this number of consecutive NaNs, it will only
+            be partially filled. If method is not specified, this is the
+            maximum number of entries along the entire axis where NaNs will be
+            filled.
+
+        copy : bool, default True
+            Whether to make a copy of the data before filling. If False, then
+            the original should be modified and no new memory should be allocated.
+            For ExtensionArray subclasses that cannot do this, it is at the
+            author's discretion whether to ignore "copy=False" or to raise.
+            The base class implementation ignores the keyword if any NAs are
+            present.
+
+        Returns
+        -------
+        Same type as self
+
+        Examples
+        --------
+        >>> arr = pd.array([np.nan, np.nan, 2, 3, np.nan, np.nan])
+        >>> arr._pad_or_backfill(method="backfill", limit=1)
+        
+        [, 2, 2, 3, , ]
+        Length: 6, dtype: Int64
+        """
+
+        # If a 3rd-party EA has implemented this functionality in fillna,
+        #  we warn that they need to implement _pad_or_backfill instead.
+        if (
+            type(self).fillna is not ExtensionArray.fillna
+            and type(self)._pad_or_backfill is ExtensionArray._pad_or_backfill
+        ):
+            # Check for _pad_or_backfill here allows us to call
+            #  super()._pad_or_backfill without getting this warning
+            warnings.warn(
+                "ExtensionArray.fillna 'method' keyword is deprecated. "
+                "In a future version. arr._pad_or_backfill will be called "
+                "instead. 3rd-party ExtensionArray authors need to implement "
+                "_pad_or_backfill.",
+                DeprecationWarning,
+                stacklevel=find_stack_level(),
+            )
+            if limit_area is not None:
+                raise NotImplementedError(
+                    f"{type(self).__name__} does not implement limit_area "
+                    "(added in pandas 2.2). 3rd-party ExtnsionArray authors "
+                    "need to add this argument to _pad_or_backfill."
+                )
+            return self.fillna(method=method, limit=limit)
+
+        mask = self.isna()
+
+        if mask.any():
+            # NB: the base class does not respect the "copy" keyword
+            meth = missing.clean_fill_method(method)
+
+            npmask = np.asarray(mask)
+            if limit_area is not None and not npmask.all():
+                _fill_limit_area_1d(npmask, limit_area)
+            if meth == "pad":
+                indexer = libalgos.get_fill_indexer(npmask, limit=limit)
+                return self.take(indexer, allow_fill=True)
+            else:
+                # i.e. meth == "backfill"
+                indexer = libalgos.get_fill_indexer(npmask[::-1], limit=limit)[::-1]
+                return self[::-1].take(indexer, allow_fill=True)
+
+        else:
+            if not copy:
+                return self
+            new_values = self.copy()
+        return new_values
+
+    def fillna(
+        self,
+        value: object | ArrayLike | None = None,
+        method: FillnaOptions | None = None,
+        limit: int | None = None,
+        copy: bool = True,
+    ) -> Self:
+        """
+        Fill NA/NaN values using the specified method.
+
+        Parameters
+        ----------
+        value : scalar, array-like
+            If a scalar value is passed it is used to fill all missing values.
+            Alternatively, an array-like "value" can be given. It's expected
+            that the array-like have the same length as 'self'.
+        method : {'backfill', 'bfill', 'pad', 'ffill', None}, default None
+            Method to use for filling holes in reindexed Series:
+
+            * pad / ffill: propagate last valid observation forward to next valid.
+            * backfill / bfill: use NEXT valid observation to fill gap.
+
+            .. deprecated:: 2.1.0
+
+        limit : int, default None
+            If method is specified, this is the maximum number of consecutive
+            NaN values to forward/backward fill. In other words, if there is
+            a gap with more than this number of consecutive NaNs, it will only
+            be partially filled. If method is not specified, this is the
+            maximum number of entries along the entire axis where NaNs will be
+            filled.
+
+            .. deprecated:: 2.1.0
+
+        copy : bool, default True
+            Whether to make a copy of the data before filling. If False, then
+            the original should be modified and no new memory should be allocated.
+            For ExtensionArray subclasses that cannot do this, it is at the
+            author's discretion whether to ignore "copy=False" or to raise.
+            The base class implementation ignores the keyword in pad/backfill
+            cases.
+
+        Returns
+        -------
+        ExtensionArray
+            With NA/NaN filled.
+
+        Examples
+        --------
+        >>> arr = pd.array([np.nan, np.nan, 2, 3, np.nan, np.nan])
+        >>> arr.fillna(0)
+        
+        [0, 0, 2, 3, 0, 0]
+        Length: 6, dtype: Int64
+        """
+        if method is not None:
+            warnings.warn(
+                f"The 'method' keyword in {type(self).__name__}.fillna is "
+                "deprecated and will be removed in a future version.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+
+        value, method = validate_fillna_kwargs(value, method)
+
+        mask = self.isna()
+        # error: Argument 2 to "check_value_size" has incompatible type
+        # "ExtensionArray"; expected "ndarray"
+        value = missing.check_value_size(
+            value, mask, len(self)  # type: ignore[arg-type]
+        )
+
+        if mask.any():
+            if method is not None:
+                meth = missing.clean_fill_method(method)
+
+                npmask = np.asarray(mask)
+                if meth == "pad":
+                    indexer = libalgos.get_fill_indexer(npmask, limit=limit)
+                    return self.take(indexer, allow_fill=True)
+                else:
+                    # i.e. meth == "backfill"
+                    indexer = libalgos.get_fill_indexer(npmask[::-1], limit=limit)[::-1]
+                    return self[::-1].take(indexer, allow_fill=True)
+            else:
+                # fill with value
+                if not copy:
+                    new_values = self[:]
+                else:
+                    new_values = self.copy()
+                new_values[mask] = value
+        else:
+            if not copy:
+                new_values = self[:]
+            else:
+                new_values = self.copy()
+        return new_values
+
+    def dropna(self) -> Self:
+        """
+        Return ExtensionArray without NA values.
+
+        Returns
+        -------
+
+        Examples
+        --------
+        >>> pd.array([1, 2, np.nan]).dropna()
+        
+        [1, 2]
+        Length: 2, dtype: Int64
+        """
+        # error: Unsupported operand type for ~ ("ExtensionArray")
+        return self[~self.isna()]  # type: ignore[operator]
+
+    def duplicated(
+        self, keep: Literal["first", "last", False] = "first"
+    ) -> npt.NDArray[np.bool_]:
+        """
+        Return boolean ndarray denoting duplicate values.
+
+        Parameters
+        ----------
+        keep : {'first', 'last', False}, default 'first'
+            - ``first`` : Mark duplicates as ``True`` except for the first occurrence.
+            - ``last`` : Mark duplicates as ``True`` except for the last occurrence.
+            - False : Mark all duplicates as ``True``.
+
+        Returns
+        -------
+        ndarray[bool]
+
+        Examples
+        --------
+        >>> pd.array([1, 1, 2, 3, 3], dtype="Int64").duplicated()
+        array([False,  True, False, False,  True])
+        """
+        mask = self.isna().astype(np.bool_, copy=False)
+        return duplicated(values=self, keep=keep, mask=mask)
+
+    def shift(self, periods: int = 1, fill_value: object = None) -> ExtensionArray:
+        """
+        Shift values by desired number.
+
+        Newly introduced missing values are filled with
+        ``self.dtype.na_value``.
+
+        Parameters
+        ----------
+        periods : int, default 1
+            The number of periods to shift. Negative values are allowed
+            for shifting backwards.
+
+        fill_value : object, optional
+            The scalar value to use for newly introduced missing values.
+            The default is ``self.dtype.na_value``.
+
+        Returns
+        -------
+        ExtensionArray
+            Shifted.
+
+        Notes
+        -----
+        If ``self`` is empty or ``periods`` is 0, a copy of ``self`` is
+        returned.
+
+        If ``periods > len(self)``, then an array of size
+        len(self) is returned, with all values filled with
+        ``self.dtype.na_value``.
+
+        For 2-dimensional ExtensionArrays, we are always shifting along axis=0.
+
+        Examples
+        --------
+        >>> arr = pd.array([1, 2, 3])
+        >>> arr.shift(2)
+        
+        [, , 1]
+        Length: 3, dtype: Int64
+        """
+        # Note: this implementation assumes that `self.dtype.na_value` can be
+        # stored in an instance of your ExtensionArray with `self.dtype`.
+        if not len(self) or periods == 0:
+            return self.copy()
+
+        if isna(fill_value):
+            fill_value = self.dtype.na_value
+
+        empty = self._from_sequence(
+            [fill_value] * min(abs(periods), len(self)), dtype=self.dtype
+        )
+        if periods > 0:
+            a = empty
+            b = self[:-periods]
+        else:
+            a = self[abs(periods) :]
+            b = empty
+        return self._concat_same_type([a, b])
+
+    def unique(self) -> Self:
+        """
+        Compute the ExtensionArray of unique values.
+
+        Returns
+        -------
+        pandas.api.extensions.ExtensionArray
+
+        Examples
+        --------
+        >>> arr = pd.array([1, 2, 3, 1, 2, 3])
+        >>> arr.unique()
+        
+        [1, 2, 3]
+        Length: 3, dtype: Int64
+        """
+        uniques = unique(self.astype(object))
+        return self._from_sequence(uniques, dtype=self.dtype)
+
+    def searchsorted(
+        self,
+        value: NumpyValueArrayLike | ExtensionArray,
+        side: Literal["left", "right"] = "left",
+        sorter: NumpySorter | None = None,
+    ) -> npt.NDArray[np.intp] | np.intp:
+        """
+        Find indices where elements should be inserted to maintain order.
+
+        Find the indices into a sorted array `self` (a) such that, if the
+        corresponding elements in `value` were inserted before the indices,
+        the order of `self` would be preserved.
+
+        Assuming that `self` is sorted:
+
+        ======  ================================
+        `side`  returned index `i` satisfies
+        ======  ================================
+        left    ``self[i-1] < value <= self[i]``
+        right   ``self[i-1] <= value < self[i]``
+        ======  ================================
+
+        Parameters
+        ----------
+        value : array-like, list or scalar
+            Value(s) to insert into `self`.
+        side : {'left', 'right'}, optional
+            If 'left', the index of the first suitable location found is given.
+            If 'right', return the last such index.  If there is no suitable
+            index, return either 0 or N (where N is the length of `self`).
+        sorter : 1-D array-like, optional
+            Optional array of integer indices that sort array a into ascending
+            order. They are typically the result of argsort.
+
+        Returns
+        -------
+        array of ints or int
+            If value is array-like, array of insertion points.
+            If value is scalar, a single integer.
+
+        See Also
+        --------
+        numpy.searchsorted : Similar method from NumPy.
+
+        Examples
+        --------
+        >>> arr = pd.array([1, 2, 3, 5])
+        >>> arr.searchsorted([4])
+        array([3])
+        """
+        # Note: the base tests provided by pandas only test the basics.
+        # We do not test
+        # 1. Values outside the range of the `data_for_sorting` fixture
+        # 2. Values between the values in the `data_for_sorting` fixture
+        # 3. Missing values.
+        arr = self.astype(object)
+        if isinstance(value, ExtensionArray):
+            value = value.astype(object)
+        return arr.searchsorted(value, side=side, sorter=sorter)
+
+    def equals(self, other: object) -> bool:
+        """
+        Return if another array is equivalent to this array.
+
+        Equivalent means that both arrays have the same shape and dtype, and
+        all values compare equal. Missing values in the same location are
+        considered equal (in contrast with normal equality).
+
+        Parameters
+        ----------
+        other : ExtensionArray
+            Array to compare to this Array.
+
+        Returns
+        -------
+        boolean
+            Whether the arrays are equivalent.
+
+        Examples
+        --------
+        >>> arr1 = pd.array([1, 2, np.nan])
+        >>> arr2 = pd.array([1, 2, np.nan])
+        >>> arr1.equals(arr2)
+        True
+        """
+        if type(self) != type(other):
+            return False
+        other = cast(ExtensionArray, other)
+        if self.dtype != other.dtype:
+            return False
+        elif len(self) != len(other):
+            return False
+        else:
+            equal_values = self == other
+            if isinstance(equal_values, ExtensionArray):
+                # boolean array with NA -> fill with False
+                equal_values = equal_values.fillna(False)
+            # error: Unsupported left operand type for & ("ExtensionArray")
+            equal_na = self.isna() & other.isna()  # type: ignore[operator]
+            return bool((equal_values | equal_na).all())
+
+    def isin(self, values: ArrayLike) -> npt.NDArray[np.bool_]:
+        """
+        Pointwise comparison for set containment in the given values.
+
+        Roughly equivalent to `np.array([x in values for x in self])`
+
+        Parameters
+        ----------
+        values : np.ndarray or ExtensionArray
+
+        Returns
+        -------
+        np.ndarray[bool]
+
+        Examples
+        --------
+        >>> arr = pd.array([1, 2, 3])
+        >>> arr.isin([1])
+        
+        [True, False, False]
+        Length: 3, dtype: boolean
+        """
+        return isin(np.asarray(self), values)
+
+    def _values_for_factorize(self) -> tuple[np.ndarray, Any]:
+        """
+        Return an array and missing value suitable for factorization.
+
+        Returns
+        -------
+        values : ndarray
+            An array suitable for factorization. This should maintain order
+            and be a supported dtype (Float64, Int64, UInt64, String, Object).
+            By default, the extension array is cast to object dtype.
+        na_value : object
+            The value in `values` to consider missing. This will be treated
+            as NA in the factorization routines, so it will be coded as
+            `-1` and not included in `uniques`. By default,
+            ``np.nan`` is used.
+
+        Notes
+        -----
+        The values returned by this method are also used in
+        :func:`pandas.util.hash_pandas_object`. If needed, this can be
+        overridden in the ``self._hash_pandas_object()`` method.
+
+        Examples
+        --------
+        >>> pd.array([1, 2, 3])._values_for_factorize()
+        (array([1, 2, 3], dtype=object), nan)
+        """
+        return self.astype(object), np.nan
+
+    def factorize(
+        self,
+        use_na_sentinel: bool = True,
+    ) -> tuple[np.ndarray, ExtensionArray]:
+        """
+        Encode the extension array as an enumerated type.
+
+        Parameters
+        ----------
+        use_na_sentinel : bool, default True
+            If True, the sentinel -1 will be used for NaN values. If False,
+            NaN values will be encoded as non-negative integers and will not drop the
+            NaN from the uniques of the values.
+
+            .. versionadded:: 1.5.0
+
+        Returns
+        -------
+        codes : ndarray
+            An integer NumPy array that's an indexer into the original
+            ExtensionArray.
+        uniques : ExtensionArray
+            An ExtensionArray containing the unique values of `self`.
+
+            .. note::
+
+               uniques will *not* contain an entry for the NA value of
+               the ExtensionArray if there are any missing values present
+               in `self`.
+
+        See Also
+        --------
+        factorize : Top-level factorize method that dispatches here.
+
+        Notes
+        -----
+        :meth:`pandas.factorize` offers a `sort` keyword as well.
+
+        Examples
+        --------
+        >>> idx1 = pd.PeriodIndex(["2014-01", "2014-01", "2014-02", "2014-02",
+        ...                       "2014-03", "2014-03"], freq="M")
+        >>> arr, idx = idx1.factorize()
+        >>> arr
+        array([0, 0, 1, 1, 2, 2])
+        >>> idx
+        PeriodIndex(['2014-01', '2014-02', '2014-03'], dtype='period[M]')
+        """
+        # Implementer note: There are two ways to override the behavior of
+        # pandas.factorize
+        # 1. _values_for_factorize and _from_factorize.
+        #    Specify the values passed to pandas' internal factorization
+        #    routines, and how to convert from those values back to the
+        #    original ExtensionArray.
+        # 2. ExtensionArray.factorize.
+        #    Complete control over factorization.
+        arr, na_value = self._values_for_factorize()
+
+        codes, uniques = factorize_array(
+            arr, use_na_sentinel=use_na_sentinel, na_value=na_value
+        )
+
+        uniques_ea = self._from_factorized(uniques, self)
+        return codes, uniques_ea
+
+    _extension_array_shared_docs[
+        "repeat"
+    ] = """
+        Repeat elements of a %(klass)s.
+
+        Returns a new %(klass)s where each element of the current %(klass)s
+        is repeated consecutively a given number of times.
+
+        Parameters
+        ----------
+        repeats : int or array of ints
+            The number of repetitions for each element. This should be a
+            non-negative integer. Repeating 0 times will return an empty
+            %(klass)s.
+        axis : None
+            Must be ``None``. Has no effect but is accepted for compatibility
+            with numpy.
+
+        Returns
+        -------
+        %(klass)s
+            Newly created %(klass)s with repeated elements.
+
+        See Also
+        --------
+        Series.repeat : Equivalent function for Series.
+        Index.repeat : Equivalent function for Index.
+        numpy.repeat : Similar method for :class:`numpy.ndarray`.
+        ExtensionArray.take : Take arbitrary positions.
+
+        Examples
+        --------
+        >>> cat = pd.Categorical(['a', 'b', 'c'])
+        >>> cat
+        ['a', 'b', 'c']
+        Categories (3, object): ['a', 'b', 'c']
+        >>> cat.repeat(2)
+        ['a', 'a', 'b', 'b', 'c', 'c']
+        Categories (3, object): ['a', 'b', 'c']
+        >>> cat.repeat([1, 2, 3])
+        ['a', 'b', 'b', 'c', 'c', 'c']
+        Categories (3, object): ['a', 'b', 'c']
+        """
+
+    @Substitution(klass="ExtensionArray")
+    @Appender(_extension_array_shared_docs["repeat"])
+    def repeat(self, repeats: int | Sequence[int], axis: AxisInt | None = None) -> Self:
+        nv.validate_repeat((), {"axis": axis})
+        ind = np.arange(len(self)).repeat(repeats)
+        return self.take(ind)
+
+    # ------------------------------------------------------------------------
+    # Indexing methods
+    # ------------------------------------------------------------------------
+
+    def take(
+        self,
+        indices: TakeIndexer,
+        *,
+        allow_fill: bool = False,
+        fill_value: Any = None,
+    ) -> Self:
+        """
+        Take elements from an array.
+
+        Parameters
+        ----------
+        indices : sequence of int or one-dimensional np.ndarray of int
+            Indices to be taken.
+        allow_fill : bool, default False
+            How to handle negative values in `indices`.
+
+            * False: negative values in `indices` indicate positional indices
+              from the right (the default). This is similar to
+              :func:`numpy.take`.
+
+            * True: negative values in `indices` indicate
+              missing values. These values are set to `fill_value`. Any other
+              other negative values raise a ``ValueError``.
+
+        fill_value : any, optional
+            Fill value to use for NA-indices when `allow_fill` is True.
+            This may be ``None``, in which case the default NA value for
+            the type, ``self.dtype.na_value``, is used.
+
+            For many ExtensionArrays, there will be two representations of
+            `fill_value`: a user-facing "boxed" scalar, and a low-level
+            physical NA value. `fill_value` should be the user-facing version,
+            and the implementation should handle translating that to the
+            physical version for processing the take if necessary.
+
+        Returns
+        -------
+        ExtensionArray
+
+        Raises
+        ------
+        IndexError
+            When the indices are out of bounds for the array.
+        ValueError
+            When `indices` contains negative values other than ``-1``
+            and `allow_fill` is True.
+
+        See Also
+        --------
+        numpy.take : Take elements from an array along an axis.
+        api.extensions.take : Take elements from an array.
+
+        Notes
+        -----
+        ExtensionArray.take is called by ``Series.__getitem__``, ``.loc``,
+        ``iloc``, when `indices` is a sequence of values. Additionally,
+        it's called by :meth:`Series.reindex`, or any other method
+        that causes realignment, with a `fill_value`.
+
+        Examples
+        --------
+        Here's an example implementation, which relies on casting the
+        extension array to object dtype. This uses the helper method
+        :func:`pandas.api.extensions.take`.
+
+        .. code-block:: python
+
+           def take(self, indices, allow_fill=False, fill_value=None):
+               from pandas.core.algorithms import take
+
+               # If the ExtensionArray is backed by an ndarray, then
+               # just pass that here instead of coercing to object.
+               data = self.astype(object)
+
+               if allow_fill and fill_value is None:
+                   fill_value = self.dtype.na_value
+
+               # fill value should always be translated from the scalar
+               # type for the array, to the physical storage type for
+               # the data, before passing to take.
+
+               result = take(data, indices, fill_value=fill_value,
+                             allow_fill=allow_fill)
+               return self._from_sequence(result, dtype=self.dtype)
+        """
+        # Implementer note: The `fill_value` parameter should be a user-facing
+        # value, an instance of self.dtype.type. When passed `fill_value=None`,
+        # the default of `self.dtype.na_value` should be used.
+        # This may differ from the physical storage type your ExtensionArray
+        # uses. In this case, your implementation is responsible for casting
+        # the user-facing type to the storage type, before using
+        # pandas.api.extensions.take
+        raise AbstractMethodError(self)
+
+    def copy(self) -> Self:
+        """
+        Return a copy of the array.
+
+        Returns
+        -------
+        ExtensionArray
+
+        Examples
+        --------
+        >>> arr = pd.array([1, 2, 3])
+        >>> arr2 = arr.copy()
+        >>> arr[0] = 2
+        >>> arr2
+        
+        [1, 2, 3]
+        Length: 3, dtype: Int64
+        """
+        raise AbstractMethodError(self)
+
+    def view(self, dtype: Dtype | None = None) -> ArrayLike:
+        """
+        Return a view on the array.
+
+        Parameters
+        ----------
+        dtype : str, np.dtype, or ExtensionDtype, optional
+            Default None.
+
+        Returns
+        -------
+        ExtensionArray or np.ndarray
+            A view on the :class:`ExtensionArray`'s data.
+
+        Examples
+        --------
+        This gives view on the underlying data of an ``ExtensionArray`` and is not a
+        copy. Modifications on either the view or the original ``ExtensionArray``
+        will be reflectd on the underlying data:
+
+        >>> arr = pd.array([1, 2, 3])
+        >>> arr2 = arr.view()
+        >>> arr[0] = 2
+        >>> arr2
+        
+        [2, 2, 3]
+        Length: 3, dtype: Int64
+        """
+        # NB:
+        # - This must return a *new* object referencing the same data, not self.
+        # - The only case that *must* be implemented is with dtype=None,
+        #   giving a view with the same dtype as self.
+        if dtype is not None:
+            raise NotImplementedError(dtype)
+        return self[:]
+
+    # ------------------------------------------------------------------------
+    # Printing
+    # ------------------------------------------------------------------------
+
+    def __repr__(self) -> str:
+        if self.ndim > 1:
+            return self._repr_2d()
+
+        from pandas.io.formats.printing import format_object_summary
+
+        # the short repr has no trailing newline, while the truncated
+        # repr does. So we include a newline in our template, and strip
+        # any trailing newlines from format_object_summary
+        data = format_object_summary(
+            self, self._formatter(), indent_for_name=False
+        ).rstrip(", \n")
+        class_name = f"<{type(self).__name__}>\n"
+        footer = self._get_repr_footer()
+        return f"{class_name}{data}\n{footer}"
+
+    def _get_repr_footer(self) -> str:
+        # GH#24278
+        if self.ndim > 1:
+            return f"Shape: {self.shape}, dtype: {self.dtype}"
+        return f"Length: {len(self)}, dtype: {self.dtype}"
+
+    def _repr_2d(self) -> str:
+        from pandas.io.formats.printing import format_object_summary
+
+        # the short repr has no trailing newline, while the truncated
+        # repr does. So we include a newline in our template, and strip
+        # any trailing newlines from format_object_summary
+        lines = [
+            format_object_summary(x, self._formatter(), indent_for_name=False).rstrip(
+                ", \n"
+            )
+            for x in self
+        ]
+        data = ",\n".join(lines)
+        class_name = f"<{type(self).__name__}>"
+        footer = self._get_repr_footer()
+        return f"{class_name}\n[\n{data}\n]\n{footer}"
+
+    def _formatter(self, boxed: bool = False) -> Callable[[Any], str | None]:
+        """
+        Formatting function for scalar values.
+
+        This is used in the default '__repr__'. The returned formatting
+        function receives instances of your scalar type.
+
+        Parameters
+        ----------
+        boxed : bool, default False
+            An indicated for whether or not your array is being printed
+            within a Series, DataFrame, or Index (True), or just by
+            itself (False). This may be useful if you want scalar values
+            to appear differently within a Series versus on its own (e.g.
+            quoted or not).
+
+        Returns
+        -------
+        Callable[[Any], str]
+            A callable that gets instances of the scalar type and
+            returns a string. By default, :func:`repr` is used
+            when ``boxed=False`` and :func:`str` is used when
+            ``boxed=True``.
+
+        Examples
+        --------
+        >>> class MyExtensionArray(pd.arrays.NumpyExtensionArray):
+        ...     def _formatter(self, boxed=False):
+        ...         return lambda x: '*' + str(x) + '*' if boxed else repr(x) + '*'
+        >>> MyExtensionArray(np.array([1, 2, 3, 4]))
+        
+        [1*, 2*, 3*, 4*]
+        Length: 4, dtype: int64
+        """
+        if boxed:
+            return str
+        return repr
+
+    # ------------------------------------------------------------------------
+    # Reshaping
+    # ------------------------------------------------------------------------
+
+    def transpose(self, *axes: int) -> ExtensionArray:
+        """
+        Return a transposed view on this array.
+
+        Because ExtensionArrays are always 1D, this is a no-op.  It is included
+        for compatibility with np.ndarray.
+
+        Returns
+        -------
+        ExtensionArray
+
+        Examples
+        --------
+        >>> pd.array([1, 2, 3]).transpose()
+        
+        [1, 2, 3]
+        Length: 3, dtype: Int64
+        """
+        return self[:]
+
+    @property
+    def T(self) -> ExtensionArray:
+        return self.transpose()
+
+    def ravel(self, order: Literal["C", "F", "A", "K"] | None = "C") -> ExtensionArray:
+        """
+        Return a flattened view on this array.
+
+        Parameters
+        ----------
+        order : {None, 'C', 'F', 'A', 'K'}, default 'C'
+
+        Returns
+        -------
+        ExtensionArray
+
+        Notes
+        -----
+        - Because ExtensionArrays are 1D-only, this is a no-op.
+        - The "order" argument is ignored, is for compatibility with NumPy.
+
+        Examples
+        --------
+        >>> pd.array([1, 2, 3]).ravel()
+        
+        [1, 2, 3]
+        Length: 3, dtype: Int64
+        """
+        return self
+
+    @classmethod
+    def _concat_same_type(cls, to_concat: Sequence[Self]) -> Self:
+        """
+        Concatenate multiple array of this dtype.
+
+        Parameters
+        ----------
+        to_concat : sequence of this type
+
+        Returns
+        -------
+        ExtensionArray
+
+        Examples
+        --------
+        >>> arr1 = pd.array([1, 2, 3])
+        >>> arr2 = pd.array([4, 5, 6])
+        >>> pd.arrays.IntegerArray._concat_same_type([arr1, arr2])
+        
+        [1, 2, 3, 4, 5, 6]
+        Length: 6, dtype: Int64
+        """
+        # Implementer note: this method will only be called with a sequence of
+        # ExtensionArrays of this class and with the same dtype as self. This
+        # should allow "easy" concatenation (no upcasting needed), and result
+        # in a new ExtensionArray of the same dtype.
+        # Note: this strict behaviour is only guaranteed starting with pandas 1.1
+        raise AbstractMethodError(cls)
+
+    # The _can_hold_na attribute is set to True so that pandas internals
+    # will use the ExtensionDtype.na_value as the NA value in operations
+    # such as take(), reindex(), shift(), etc.  In addition, those results
+    # will then be of the ExtensionArray subclass rather than an array
+    # of objects
+    @cache_readonly
+    def _can_hold_na(self) -> bool:
+        return self.dtype._can_hold_na
+
+    def _accumulate(
+        self, name: str, *, skipna: bool = True, **kwargs
+    ) -> ExtensionArray:
+        """
+        Return an ExtensionArray performing an accumulation operation.
+
+        The underlying data type might change.
+
+        Parameters
+        ----------
+        name : str
+            Name of the function, supported values are:
+            - cummin
+            - cummax
+            - cumsum
+            - cumprod
+        skipna : bool, default True
+            If True, skip NA values.
+        **kwargs
+            Additional keyword arguments passed to the accumulation function.
+            Currently, there is no supported kwarg.
+
+        Returns
+        -------
+        array
+
+        Raises
+        ------
+        NotImplementedError : subclass does not define accumulations
+
+        Examples
+        --------
+        >>> arr = pd.array([1, 2, 3])
+        >>> arr._accumulate(name='cumsum')
+        
+        [1, 3, 6]
+        Length: 3, dtype: Int64
+        """
+        raise NotImplementedError(f"cannot perform {name} with type {self.dtype}")
+
+    def _reduce(
+        self, name: str, *, skipna: bool = True, keepdims: bool = False, **kwargs
+    ):
+        """
+        Return a scalar result of performing the reduction operation.
+
+        Parameters
+        ----------
+        name : str
+            Name of the function, supported values are:
+            { any, all, min, max, sum, mean, median, prod,
+            std, var, sem, kurt, skew }.
+        skipna : bool, default True
+            If True, skip NaN values.
+        keepdims : bool, default False
+            If False, a scalar is returned.
+            If True, the result has dimension with size one along the reduced axis.
+
+            .. versionadded:: 2.1
+
+               This parameter is not required in the _reduce signature to keep backward
+               compatibility, but will become required in the future. If the parameter
+               is not found in the method signature, a FutureWarning will be emitted.
+        **kwargs
+            Additional keyword arguments passed to the reduction function.
+            Currently, `ddof` is the only supported kwarg.
+
+        Returns
+        -------
+        scalar
+
+        Raises
+        ------
+        TypeError : subclass does not define reductions
+
+        Examples
+        --------
+        >>> pd.array([1, 2, 3])._reduce("min")
+        1
+        """
+        meth = getattr(self, name, None)
+        if meth is None:
+            raise TypeError(
+                f"'{type(self).__name__}' with dtype {self.dtype} "
+                f"does not support reduction '{name}'"
+            )
+        result = meth(skipna=skipna, **kwargs)
+        if keepdims:
+            result = np.array([result])
+
+        return result
+
+    # https://github.com/python/typeshed/issues/2148#issuecomment-520783318
+    # Incompatible types in assignment (expression has type "None", base class
+    # "object" defined the type as "Callable[[object], int]")
+    __hash__: ClassVar[None]  # type: ignore[assignment]
+
+    # ------------------------------------------------------------------------
+    # Non-Optimized Default Methods; in the case of the private methods here,
+    #  these are not guaranteed to be stable across pandas versions.
+
+    def _values_for_json(self) -> np.ndarray:
+        """
+        Specify how to render our entries in to_json.
+
+        Notes
+        -----
+        The dtype on the returned ndarray is not restricted, but for non-native
+        types that are not specifically handled in objToJSON.c, to_json is
+        liable to raise. In these cases, it may be safer to return an ndarray
+        of strings.
+        """
+        return np.asarray(self)
+
+    def _hash_pandas_object(
+        self, *, encoding: str, hash_key: str, categorize: bool
+    ) -> npt.NDArray[np.uint64]:
+        """
+        Hook for hash_pandas_object.
+
+        Default is to use the values returned by _values_for_factorize.
+
+        Parameters
+        ----------
+        encoding : str
+            Encoding for data & key when strings.
+        hash_key : str
+            Hash_key for string key to encode.
+        categorize : bool
+            Whether to first categorize object arrays before hashing. This is more
+            efficient when the array contains duplicate values.
+
+        Returns
+        -------
+        np.ndarray[uint64]
+
+        Examples
+        --------
+        >>> pd.array([1, 2])._hash_pandas_object(encoding='utf-8',
+        ...                                      hash_key="1000000000000000",
+        ...                                      categorize=False
+        ...                                      )
+        array([ 6238072747940578789, 15839785061582574730], dtype=uint64)
+        """
+        from pandas.core.util.hashing import hash_array
+
+        values, _ = self._values_for_factorize()
+        return hash_array(
+            values, encoding=encoding, hash_key=hash_key, categorize=categorize
+        )
+
+    def _explode(self) -> tuple[Self, npt.NDArray[np.uint64]]:
+        """
+        Transform each element of list-like to a row.
+
+        For arrays that do not contain list-like elements the default
+        implementation of this method just returns a copy and an array
+        of ones (unchanged index).
+
+        Returns
+        -------
+        ExtensionArray
+            Array with the exploded values.
+        np.ndarray[uint64]
+            The original lengths of each list-like for determining the
+            resulting index.
+
+        See Also
+        --------
+        Series.explode : The method on the ``Series`` object that this
+            extension array method is meant to support.
+
+        Examples
+        --------
+        >>> import pyarrow as pa
+        >>> a = pd.array([[1, 2, 3], [4], [5, 6]],
+        ...              dtype=pd.ArrowDtype(pa.list_(pa.int64())))
+        >>> a._explode()
+        (
+        [1, 2, 3, 4, 5, 6]
+        Length: 6, dtype: int64[pyarrow], array([3, 1, 2], dtype=int32))
+        """
+        values = self.copy()
+        counts = np.ones(shape=(len(self),), dtype=np.uint64)
+        return values, counts
+
+    def tolist(self) -> list:
+        """
+        Return a list of the values.
+
+        These are each a scalar type, which is a Python scalar
+        (for str, int, float) or a pandas scalar
+        (for Timestamp/Timedelta/Interval/Period)
+
+        Returns
+        -------
+        list
+
+        Examples
+        --------
+        >>> arr = pd.array([1, 2, 3])
+        >>> arr.tolist()
+        [1, 2, 3]
+        """
+        if self.ndim > 1:
+            return [x.tolist() for x in self]
+        return list(self)
+
+    def delete(self, loc: PositionalIndexer) -> Self:
+        indexer = np.delete(np.arange(len(self)), loc)
+        return self.take(indexer)
+
+    def insert(self, loc: int, item) -> Self:
+        """
+        Insert an item at the given position.
+
+        Parameters
+        ----------
+        loc : int
+        item : scalar-like
+
+        Returns
+        -------
+        same type as self
+
+        Notes
+        -----
+        This method should be both type and dtype-preserving.  If the item
+        cannot be held in an array of this type/dtype, either ValueError or
+        TypeError should be raised.
+
+        The default implementation relies on _from_sequence to raise on invalid
+        items.
+
+        Examples
+        --------
+        >>> arr = pd.array([1, 2, 3])
+        >>> arr.insert(2, -1)
+        
+        [1, 2, -1, 3]
+        Length: 4, dtype: Int64
+        """
+        loc = validate_insert_loc(loc, len(self))
+
+        item_arr = type(self)._from_sequence([item], dtype=self.dtype)
+
+        return type(self)._concat_same_type([self[:loc], item_arr, self[loc:]])
+
+    def _putmask(self, mask: npt.NDArray[np.bool_], value) -> None:
+        """
+        Analogue to np.putmask(self, mask, value)
+
+        Parameters
+        ----------
+        mask : np.ndarray[bool]
+        value : scalar or listlike
+            If listlike, must be arraylike with same length as self.
+
+        Returns
+        -------
+        None
+
+        Notes
+        -----
+        Unlike np.putmask, we do not repeat listlike values with mismatched length.
+        'value' should either be a scalar or an arraylike with the same length
+        as self.
+        """
+        if is_list_like(value):
+            val = value[mask]
+        else:
+            val = value
+
+        self[mask] = val
+
+    def _where(self, mask: npt.NDArray[np.bool_], value) -> Self:
+        """
+        Analogue to np.where(mask, self, value)
+
+        Parameters
+        ----------
+        mask : np.ndarray[bool]
+        value : scalar or listlike
+
+        Returns
+        -------
+        same type as self
+        """
+        result = self.copy()
+
+        if is_list_like(value):
+            val = value[~mask]
+        else:
+            val = value
+
+        result[~mask] = val
+        return result
+
+    # TODO(3.0): this can be removed once GH#33302 deprecation is enforced
+    def _fill_mask_inplace(
+        self, method: str, limit: int | None, mask: npt.NDArray[np.bool_]
+    ) -> None:
+        """
+        Replace values in locations specified by 'mask' using pad or backfill.
+
+        See also
+        --------
+        ExtensionArray.fillna
+        """
+        func = missing.get_fill_func(method)
+        npvalues = self.astype(object)
+        # NB: if we don't copy mask here, it may be altered inplace, which
+        #  would mess up the `self[mask] = ...` below.
+        func(npvalues, limit=limit, mask=mask.copy())
+        new_values = self._from_sequence(npvalues, dtype=self.dtype)
+        self[mask] = new_values[mask]
+
+    def _rank(
+        self,
+        *,
+        axis: AxisInt = 0,
+        method: str = "average",
+        na_option: str = "keep",
+        ascending: bool = True,
+        pct: bool = False,
+    ):
+        """
+        See Series.rank.__doc__.
+        """
+        if axis != 0:
+            raise NotImplementedError
+
+        return rank(
+            self._values_for_argsort(),
+            axis=axis,
+            method=method,
+            na_option=na_option,
+            ascending=ascending,
+            pct=pct,
+        )
+
+    @classmethod
+    def _empty(cls, shape: Shape, dtype: ExtensionDtype):
+        """
+        Create an ExtensionArray with the given shape and dtype.
+
+        See also
+        --------
+        ExtensionDtype.empty
+            ExtensionDtype.empty is the 'official' public version of this API.
+        """
+        # Implementer note: while ExtensionDtype.empty is the public way to
+        # call this method, it is still required to implement this `_empty`
+        # method as well (it is called internally in pandas)
+        obj = cls._from_sequence([], dtype=dtype)
+
+        taker = np.broadcast_to(np.intp(-1), shape)
+        result = obj.take(taker, allow_fill=True)
+        if not isinstance(result, cls) or dtype != result.dtype:
+            raise NotImplementedError(
+                f"Default 'empty' implementation is invalid for dtype='{dtype}'"
+            )
+        return result
+
+    def _quantile(self, qs: npt.NDArray[np.float64], interpolation: str) -> Self:
+        """
+        Compute the quantiles of self for each quantile in `qs`.
+
+        Parameters
+        ----------
+        qs : np.ndarray[float64]
+        interpolation: str
+
+        Returns
+        -------
+        same type as self
+        """
+        mask = np.asarray(self.isna())
+        arr = np.asarray(self)
+        fill_value = np.nan
+
+        res_values = quantile_with_mask(arr, mask, fill_value, qs, interpolation)
+        return type(self)._from_sequence(res_values)
+
+    def _mode(self, dropna: bool = True) -> Self:
+        """
+        Returns the mode(s) of the ExtensionArray.
+
+        Always returns `ExtensionArray` even if only one value.
+
+        Parameters
+        ----------
+        dropna : bool, default True
+            Don't consider counts of NA values.
+
+        Returns
+        -------
+        same type as self
+            Sorted, if possible.
+        """
+        # error: Incompatible return value type (got "Union[ExtensionArray,
+        # ndarray[Any, Any]]", expected "Self")
+        return mode(self, dropna=dropna)  # type: ignore[return-value]
+
+    def __array_ufunc__(self, ufunc: np.ufunc, method: str, *inputs, **kwargs):
+        if any(
+            isinstance(other, (ABCSeries, ABCIndex, ABCDataFrame)) for other in inputs
+        ):
+            return NotImplemented
+
+        result = arraylike.maybe_dispatch_ufunc_to_dunder_op(
+            self, ufunc, method, *inputs, **kwargs
+        )
+        if result is not NotImplemented:
+            return result
+
+        if "out" in kwargs:
+            return arraylike.dispatch_ufunc_with_out(
+                self, ufunc, method, *inputs, **kwargs
+            )
+
+        if method == "reduce":
+            result = arraylike.dispatch_reduction_ufunc(
+                self, ufunc, method, *inputs, **kwargs
+            )
+            if result is not NotImplemented:
+                return result
+
+        return arraylike.default_array_ufunc(self, ufunc, method, *inputs, **kwargs)
+
+    def map(self, mapper, na_action=None):
+        """
+        Map values using an input mapping or function.
+
+        Parameters
+        ----------
+        mapper : function, dict, or Series
+            Mapping correspondence.
+        na_action : {None, 'ignore'}, default None
+            If 'ignore', propagate NA values, without passing them to the
+            mapping correspondence. If 'ignore' is not supported, a
+            ``NotImplementedError`` should be raised.
+
+        Returns
+        -------
+        Union[ndarray, Index, ExtensionArray]
+            The output of the mapping function applied to the array.
+            If the function returns a tuple with more than one element
+            a MultiIndex will be returned.
+        """
+        return map_array(self, mapper, na_action=na_action)
+
+    # ------------------------------------------------------------------------
+    # GroupBy Methods
+
+    def _groupby_op(
+        self,
+        *,
+        how: str,
+        has_dropped_na: bool,
+        min_count: int,
+        ngroups: int,
+        ids: npt.NDArray[np.intp],
+        **kwargs,
+    ) -> ArrayLike:
+        """
+        Dispatch GroupBy reduction or transformation operation.
+
+        This is an *experimental* API to allow ExtensionArray authors to implement
+        reductions and transformations. The API is subject to change.
+
+        Parameters
+        ----------
+        how : {'any', 'all', 'sum', 'prod', 'min', 'max', 'mean', 'median',
+               'median', 'var', 'std', 'sem', 'nth', 'last', 'ohlc',
+               'cumprod', 'cumsum', 'cummin', 'cummax', 'rank'}
+        has_dropped_na : bool
+        min_count : int
+        ngroups : int
+        ids : np.ndarray[np.intp]
+            ids[i] gives the integer label for the group that self[i] belongs to.
+        **kwargs : operation-specific
+            'any', 'all' -> ['skipna']
+            'var', 'std', 'sem' -> ['ddof']
+            'cumprod', 'cumsum', 'cummin', 'cummax' -> ['skipna']
+            'rank' -> ['ties_method', 'ascending', 'na_option', 'pct']
+
+        Returns
+        -------
+        np.ndarray or ExtensionArray
+        """
+        from pandas.core.arrays.string_ import StringDtype
+        from pandas.core.groupby.ops import WrappedCythonOp
+
+        kind = WrappedCythonOp.get_kind_from_how(how)
+        op = WrappedCythonOp(how=how, kind=kind, has_dropped_na=has_dropped_na)
+
+        initial: Any = 0
+        # GH#43682
+        if isinstance(self.dtype, StringDtype):
+            # StringArray
+            if op.how in [
+                "prod",
+                "mean",
+                "median",
+                "cumsum",
+                "cumprod",
+                "std",
+                "sem",
+                "var",
+                "skew",
+            ]:
+                raise TypeError(
+                    f"dtype '{self.dtype}' does not support operation '{how}'"
+                )
+            if op.how not in ["any", "all"]:
+                # Fail early to avoid conversion to object
+                op._get_cython_function(op.kind, op.how, np.dtype(object), False)
+
+            arr = self
+            if op.how == "sum":
+                initial = ""
+                # https://github.com/pandas-dev/pandas/issues/60229
+                # All NA should result in the empty string.
+                if min_count == 0:
+                    arr = arr.fillna("")
+            npvalues = arr.to_numpy(object, na_value=np.nan)
+        else:
+            raise NotImplementedError(
+                f"function is not implemented for this dtype: {self.dtype}"
+            )
+
+        res_values = op._cython_op_ndim_compat(
+            npvalues,
+            min_count=min_count,
+            ngroups=ngroups,
+            comp_ids=ids,
+            mask=None,
+            initial=initial,
+            **kwargs,
+        )
+
+        if op.how in op.cast_blocklist:
+            # i.e. how in ["rank"], since other cast_blocklist methods don't go
+            #  through cython_operation
+            return res_values
+
+        if isinstance(self.dtype, StringDtype):
+            dtype = self.dtype
+            string_array_cls = dtype.construct_array_type()
+            return string_array_cls._from_sequence(res_values, dtype=dtype)
+
+        else:
+            raise NotImplementedError
+
+
+class ExtensionArraySupportsAnyAll(ExtensionArray):
+    def any(self, *, skipna: bool = True) -> bool:
+        raise AbstractMethodError(self)
+
+    def all(self, *, skipna: bool = True) -> bool:
+        raise AbstractMethodError(self)
+
+
+class ExtensionOpsMixin:
+    """
+    A base class for linking the operators to their dunder names.
+
+    .. note::
+
+       You may want to set ``__array_priority__`` if you want your
+       implementation to be called when involved in binary operations
+       with NumPy arrays.
+    """
+
+    @classmethod
+    def _create_arithmetic_method(cls, op):
+        raise AbstractMethodError(cls)
+
+    @classmethod
+    def _add_arithmetic_ops(cls) -> None:
+        setattr(cls, "__add__", cls._create_arithmetic_method(operator.add))
+        setattr(cls, "__radd__", cls._create_arithmetic_method(roperator.radd))
+        setattr(cls, "__sub__", cls._create_arithmetic_method(operator.sub))
+        setattr(cls, "__rsub__", cls._create_arithmetic_method(roperator.rsub))
+        setattr(cls, "__mul__", cls._create_arithmetic_method(operator.mul))
+        setattr(cls, "__rmul__", cls._create_arithmetic_method(roperator.rmul))
+        setattr(cls, "__pow__", cls._create_arithmetic_method(operator.pow))
+        setattr(cls, "__rpow__", cls._create_arithmetic_method(roperator.rpow))
+        setattr(cls, "__mod__", cls._create_arithmetic_method(operator.mod))
+        setattr(cls, "__rmod__", cls._create_arithmetic_method(roperator.rmod))
+        setattr(cls, "__floordiv__", cls._create_arithmetic_method(operator.floordiv))
+        setattr(
+            cls, "__rfloordiv__", cls._create_arithmetic_method(roperator.rfloordiv)
+        )
+        setattr(cls, "__truediv__", cls._create_arithmetic_method(operator.truediv))
+        setattr(cls, "__rtruediv__", cls._create_arithmetic_method(roperator.rtruediv))
+        setattr(cls, "__divmod__", cls._create_arithmetic_method(divmod))
+        setattr(cls, "__rdivmod__", cls._create_arithmetic_method(roperator.rdivmod))
+
+    @classmethod
+    def _create_comparison_method(cls, op):
+        raise AbstractMethodError(cls)
+
+    @classmethod
+    def _add_comparison_ops(cls) -> None:
+        setattr(cls, "__eq__", cls._create_comparison_method(operator.eq))
+        setattr(cls, "__ne__", cls._create_comparison_method(operator.ne))
+        setattr(cls, "__lt__", cls._create_comparison_method(operator.lt))
+        setattr(cls, "__gt__", cls._create_comparison_method(operator.gt))
+        setattr(cls, "__le__", cls._create_comparison_method(operator.le))
+        setattr(cls, "__ge__", cls._create_comparison_method(operator.ge))
+
+    @classmethod
+    def _create_logical_method(cls, op):
+        raise AbstractMethodError(cls)
+
+    @classmethod
+    def _add_logical_ops(cls) -> None:
+        setattr(cls, "__and__", cls._create_logical_method(operator.and_))
+        setattr(cls, "__rand__", cls._create_logical_method(roperator.rand_))
+        setattr(cls, "__or__", cls._create_logical_method(operator.or_))
+        setattr(cls, "__ror__", cls._create_logical_method(roperator.ror_))
+        setattr(cls, "__xor__", cls._create_logical_method(operator.xor))
+        setattr(cls, "__rxor__", cls._create_logical_method(roperator.rxor))
+
+
+class ExtensionScalarOpsMixin(ExtensionOpsMixin):
+    """
+    A mixin for defining ops on an ExtensionArray.
+
+    It is assumed that the underlying scalar objects have the operators
+    already defined.
+
+    Notes
+    -----
+    If you have defined a subclass MyExtensionArray(ExtensionArray), then
+    use MyExtensionArray(ExtensionArray, ExtensionScalarOpsMixin) to
+    get the arithmetic operators.  After the definition of MyExtensionArray,
+    insert the lines
+
+    MyExtensionArray._add_arithmetic_ops()
+    MyExtensionArray._add_comparison_ops()
+
+    to link the operators to your class.
+
+    .. note::
+
+       You may want to set ``__array_priority__`` if you want your
+       implementation to be called when involved in binary operations
+       with NumPy arrays.
+    """
+
+    @classmethod
+    def _create_method(cls, op, coerce_to_dtype: bool = True, result_dtype=None):
+        """
+        A class method that returns a method that will correspond to an
+        operator for an ExtensionArray subclass, by dispatching to the
+        relevant operator defined on the individual elements of the
+        ExtensionArray.
+
+        Parameters
+        ----------
+        op : function
+            An operator that takes arguments op(a, b)
+        coerce_to_dtype : bool, default True
+            boolean indicating whether to attempt to convert
+            the result to the underlying ExtensionArray dtype.
+            If it's not possible to create a new ExtensionArray with the
+            values, an ndarray is returned instead.
+
+        Returns
+        -------
+        Callable[[Any, Any], Union[ndarray, ExtensionArray]]
+            A method that can be bound to a class. When used, the method
+            receives the two arguments, one of which is the instance of
+            this class, and should return an ExtensionArray or an ndarray.
+
+            Returning an ndarray may be necessary when the result of the
+            `op` cannot be stored in the ExtensionArray. The dtype of the
+            ndarray uses NumPy's normal inference rules.
+
+        Examples
+        --------
+        Given an ExtensionArray subclass called MyExtensionArray, use
+
+            __add__ = cls._create_method(operator.add)
+
+        in the class definition of MyExtensionArray to create the operator
+        for addition, that will be based on the operator implementation
+        of the underlying elements of the ExtensionArray
+        """
+
+        def _binop(self, other):
+            def convert_values(param):
+                if isinstance(param, ExtensionArray) or is_list_like(param):
+                    ovalues = param
+                else:  # Assume its an object
+                    ovalues = [param] * len(self)
+                return ovalues
+
+            if isinstance(other, (ABCSeries, ABCIndex, ABCDataFrame)):
+                # rely on pandas to unbox and dispatch to us
+                return NotImplemented
+
+            lvalues = self
+            rvalues = convert_values(other)
+
+            # If the operator is not defined for the underlying objects,
+            # a TypeError should be raised
+            res = [op(a, b) for (a, b) in zip(lvalues, rvalues)]
+
+            def _maybe_convert(arr):
+                if coerce_to_dtype:
+                    # https://github.com/pandas-dev/pandas/issues/22850
+                    # We catch all regular exceptions here, and fall back
+                    # to an ndarray.
+                    res = maybe_cast_pointwise_result(arr, self.dtype, same_dtype=False)
+                    if not isinstance(res, type(self)):
+                        # exception raised in _from_sequence; ensure we have ndarray
+                        res = np.asarray(arr)
+                else:
+                    res = np.asarray(arr, dtype=result_dtype)
+                return res
+
+            if op.__name__ in {"divmod", "rdivmod"}:
+                a, b = zip(*res)
+                return _maybe_convert(a), _maybe_convert(b)
+
+            return _maybe_convert(res)
+
+        op_name = f"__{op.__name__}__"
+        return set_function_name(_binop, op_name, cls)
+
+    @classmethod
+    def _create_arithmetic_method(cls, op):
+        return cls._create_method(op)
+
+    @classmethod
+    def _create_comparison_method(cls, op):
+        return cls._create_method(op, coerce_to_dtype=False, result_dtype=bool)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/boolean.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/boolean.py
new file mode 100644
index 0000000000000000000000000000000000000000..04e6f0a0bcdde9a11550fcec8274e09fe8429430
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/boolean.py
@@ -0,0 +1,407 @@
+from __future__ import annotations
+
+import numbers
+from typing import (
+    TYPE_CHECKING,
+    ClassVar,
+    cast,
+)
+
+import numpy as np
+
+from pandas._libs import (
+    lib,
+    missing as libmissing,
+)
+
+from pandas.core.dtypes.common import is_list_like
+from pandas.core.dtypes.dtypes import register_extension_dtype
+from pandas.core.dtypes.missing import isna
+
+from pandas.core import ops
+from pandas.core.array_algos import masked_accumulations
+from pandas.core.arrays.masked import (
+    BaseMaskedArray,
+    BaseMaskedDtype,
+)
+
+if TYPE_CHECKING:
+    import pyarrow
+
+    from pandas._typing import (
+        Dtype,
+        DtypeObj,
+        Self,
+        npt,
+        type_t,
+    )
+
+
+@register_extension_dtype
+class BooleanDtype(BaseMaskedDtype):
+    """
+    Extension dtype for boolean data.
+
+    .. warning::
+
+       BooleanDtype is considered experimental. The implementation and
+       parts of the API may change without warning.
+
+    Attributes
+    ----------
+    None
+
+    Methods
+    -------
+    None
+
+    Examples
+    --------
+    >>> pd.BooleanDtype()
+    BooleanDtype
+    """
+
+    name: ClassVar[str] = "boolean"
+
+    # https://github.com/python/mypy/issues/4125
+    # error: Signature of "type" incompatible with supertype "BaseMaskedDtype"
+    @property
+    def type(self) -> type:  # type: ignore[override]
+        return np.bool_
+
+    @property
+    def kind(self) -> str:
+        return "b"
+
+    @property
+    def numpy_dtype(self) -> np.dtype:
+        return np.dtype("bool")
+
+    @classmethod
+    def construct_array_type(cls) -> type_t[BooleanArray]:
+        """
+        Return the array type associated with this dtype.
+
+        Returns
+        -------
+        type
+        """
+        return BooleanArray
+
+    def __repr__(self) -> str:
+        return "BooleanDtype"
+
+    @property
+    def _is_boolean(self) -> bool:
+        return True
+
+    @property
+    def _is_numeric(self) -> bool:
+        return True
+
+    def __from_arrow__(
+        self, array: pyarrow.Array | pyarrow.ChunkedArray
+    ) -> BooleanArray:
+        """
+        Construct BooleanArray from pyarrow Array/ChunkedArray.
+        """
+        import pyarrow
+
+        if array.type != pyarrow.bool_() and not pyarrow.types.is_null(array.type):
+            raise TypeError(f"Expected array of boolean type, got {array.type} instead")
+
+        if isinstance(array, pyarrow.Array):
+            chunks = [array]
+            length = len(array)
+        else:
+            # pyarrow.ChunkedArray
+            chunks = array.chunks
+            length = array.length()
+
+        if pyarrow.types.is_null(array.type):
+            mask = np.ones(length, dtype=bool)
+            # No need to init data, since all null
+            data = np.empty(length, dtype=bool)
+            return BooleanArray(data, mask)
+
+        results = []
+        for arr in chunks:
+            buflist = arr.buffers()
+            data = pyarrow.BooleanArray.from_buffers(
+                arr.type, len(arr), [None, buflist[1]], offset=arr.offset
+            ).to_numpy(zero_copy_only=False)
+            if arr.null_count != 0:
+                mask = pyarrow.BooleanArray.from_buffers(
+                    arr.type, len(arr), [None, buflist[0]], offset=arr.offset
+                ).to_numpy(zero_copy_only=False)
+                mask = ~mask
+            else:
+                mask = np.zeros(len(arr), dtype=bool)
+
+            bool_arr = BooleanArray(data, mask)
+            results.append(bool_arr)
+
+        if not results:
+            return BooleanArray(
+                np.array([], dtype=np.bool_), np.array([], dtype=np.bool_)
+            )
+        else:
+            return BooleanArray._concat_same_type(results)
+
+
+def coerce_to_array(
+    values, mask=None, copy: bool = False
+) -> tuple[np.ndarray, np.ndarray]:
+    """
+    Coerce the input values array to numpy arrays with a mask.
+
+    Parameters
+    ----------
+    values : 1D list-like
+    mask : bool 1D array, optional
+    copy : bool, default False
+        if True, copy the input
+
+    Returns
+    -------
+    tuple of (values, mask)
+    """
+    if isinstance(values, BooleanArray):
+        if mask is not None:
+            raise ValueError("cannot pass mask for BooleanArray input")
+        values, mask = values._data, values._mask
+        if copy:
+            values = values.copy()
+            mask = mask.copy()
+        return values, mask
+
+    mask_values = None
+    if isinstance(values, np.ndarray) and values.dtype == np.bool_:
+        if copy:
+            values = values.copy()
+    elif isinstance(values, np.ndarray) and values.dtype.kind in "iufcb":
+        mask_values = isna(values)
+
+        values_bool = np.zeros(len(values), dtype=bool)
+        values_bool[~mask_values] = values[~mask_values].astype(bool)
+
+        if not np.all(
+            values_bool[~mask_values].astype(values.dtype) == values[~mask_values]
+        ):
+            raise TypeError("Need to pass bool-like values")
+
+        values = values_bool
+    else:
+        values_object = np.asarray(values, dtype=object)
+
+        inferred_dtype = lib.infer_dtype(values_object, skipna=True)
+        integer_like = ("floating", "integer", "mixed-integer-float")
+        if inferred_dtype not in ("boolean", "empty") + integer_like:
+            raise TypeError("Need to pass bool-like values")
+
+        # mypy does not narrow the type of mask_values to npt.NDArray[np.bool_]
+        # within this branch, it assumes it can also be None
+        mask_values = cast("npt.NDArray[np.bool_]", isna(values_object))
+        values = np.zeros(len(values), dtype=bool)
+        values[~mask_values] = values_object[~mask_values].astype(bool)
+
+        # if the values were integer-like, validate it were actually 0/1's
+        if (inferred_dtype in integer_like) and not (
+            np.all(
+                values[~mask_values].astype(float)
+                == values_object[~mask_values].astype(float)
+            )
+        ):
+            raise TypeError("Need to pass bool-like values")
+
+    if mask is None and mask_values is None:
+        mask = np.zeros(values.shape, dtype=bool)
+    elif mask is None:
+        mask = mask_values
+    else:
+        if isinstance(mask, np.ndarray) and mask.dtype == np.bool_:
+            if mask_values is not None:
+                mask = mask | mask_values
+            else:
+                if copy:
+                    mask = mask.copy()
+        else:
+            mask = np.array(mask, dtype=bool)
+            if mask_values is not None:
+                mask = mask | mask_values
+
+    if values.shape != mask.shape:
+        raise ValueError("values.shape and mask.shape must match")
+
+    return values, mask
+
+
+class BooleanArray(BaseMaskedArray):
+    """
+    Array of boolean (True/False) data with missing values.
+
+    This is a pandas Extension array for boolean data, under the hood
+    represented by 2 numpy arrays: a boolean array with the data and
+    a boolean array with the mask (True indicating missing).
+
+    BooleanArray implements Kleene logic (sometimes called three-value
+    logic) for logical operations. See :ref:`boolean.kleene` for more.
+
+    To construct an BooleanArray from generic array-like input, use
+    :func:`pandas.array` specifying ``dtype="boolean"`` (see examples
+    below).
+
+    .. warning::
+
+       BooleanArray is considered experimental. The implementation and
+       parts of the API may change without warning.
+
+    Parameters
+    ----------
+    values : numpy.ndarray
+        A 1-d boolean-dtype array with the data.
+    mask : numpy.ndarray
+        A 1-d boolean-dtype array indicating missing values (True
+        indicates missing).
+    copy : bool, default False
+        Whether to copy the `values` and `mask` arrays.
+
+    Attributes
+    ----------
+    None
+
+    Methods
+    -------
+    None
+
+    Returns
+    -------
+    BooleanArray
+
+    Examples
+    --------
+    Create an BooleanArray with :func:`pandas.array`:
+
+    >>> pd.array([True, False, None], dtype="boolean")
+    
+    [True, False, ]
+    Length: 3, dtype: boolean
+    """
+
+    # The value used to fill '_data' to avoid upcasting
+    _internal_fill_value = False
+    # Fill values used for any/all
+    # Incompatible types in assignment (expression has type "bool", base class
+    # "BaseMaskedArray" defined the type as "")
+    _truthy_value = True  # type: ignore[assignment]
+    _falsey_value = False  # type: ignore[assignment]
+    _TRUE_VALUES = {"True", "TRUE", "true", "1", "1.0"}
+    _FALSE_VALUES = {"False", "FALSE", "false", "0", "0.0"}
+
+    @classmethod
+    def _simple_new(cls, values: np.ndarray, mask: npt.NDArray[np.bool_]) -> Self:
+        result = super()._simple_new(values, mask)
+        result._dtype = BooleanDtype()
+        return result
+
+    def __init__(
+        self, values: np.ndarray, mask: np.ndarray, copy: bool = False
+    ) -> None:
+        if not (isinstance(values, np.ndarray) and values.dtype == np.bool_):
+            raise TypeError(
+                "values should be boolean numpy array. Use "
+                "the 'pd.array' function instead"
+            )
+        self._dtype = BooleanDtype()
+        super().__init__(values, mask, copy=copy)
+
+    @property
+    def dtype(self) -> BooleanDtype:
+        return self._dtype
+
+    @classmethod
+    def _from_sequence_of_strings(
+        cls,
+        strings: list[str],
+        *,
+        dtype: Dtype | None = None,
+        copy: bool = False,
+        true_values: list[str] | None = None,
+        false_values: list[str] | None = None,
+    ) -> BooleanArray:
+        true_values_union = cls._TRUE_VALUES.union(true_values or [])
+        false_values_union = cls._FALSE_VALUES.union(false_values or [])
+
+        def map_string(s) -> bool:
+            if s in true_values_union:
+                return True
+            elif s in false_values_union:
+                return False
+            else:
+                raise ValueError(f"{s} cannot be cast to bool")
+
+        scalars = np.array(strings, dtype=object)
+        mask = isna(scalars)
+        scalars[~mask] = list(map(map_string, scalars[~mask]))
+        return cls._from_sequence(scalars, dtype=dtype, copy=copy)
+
+    _HANDLED_TYPES = (np.ndarray, numbers.Number, bool, np.bool_)
+
+    @classmethod
+    def _coerce_to_array(
+        cls, value, *, dtype: DtypeObj, copy: bool = False
+    ) -> tuple[np.ndarray, np.ndarray]:
+        if dtype:
+            assert dtype == "boolean"
+        return coerce_to_array(value, copy=copy)
+
+    def _logical_method(self, other, op):
+        assert op.__name__ in {"or_", "ror_", "and_", "rand_", "xor", "rxor"}
+        other_is_scalar = lib.is_scalar(other)
+        mask = None
+
+        if isinstance(other, BooleanArray):
+            other, mask = other._data, other._mask
+        elif is_list_like(other):
+            other = np.asarray(other, dtype="bool")
+            if other.ndim > 1:
+                raise NotImplementedError("can only perform ops with 1-d structures")
+            other, mask = coerce_to_array(other, copy=False)
+        elif isinstance(other, np.bool_):
+            other = other.item()
+
+        if other_is_scalar and other is not libmissing.NA and not lib.is_bool(other):
+            raise TypeError(
+                "'other' should be pandas.NA or a bool. "
+                f"Got {type(other).__name__} instead."
+            )
+
+        if not other_is_scalar and len(self) != len(other):
+            raise ValueError("Lengths must match")
+
+        if op.__name__ in {"or_", "ror_"}:
+            result, mask = ops.kleene_or(self._data, other, self._mask, mask)
+        elif op.__name__ in {"and_", "rand_"}:
+            result, mask = ops.kleene_and(self._data, other, self._mask, mask)
+        else:
+            # i.e. xor, rxor
+            result, mask = ops.kleene_xor(self._data, other, self._mask, mask)
+
+        # i.e. BooleanArray
+        return self._maybe_mask_result(result, mask)
+
+    def _accumulate(
+        self, name: str, *, skipna: bool = True, **kwargs
+    ) -> BaseMaskedArray:
+        data = self._data
+        mask = self._mask
+        if name in ("cummin", "cummax"):
+            op = getattr(masked_accumulations, name)
+            data, mask = op(data, mask, skipna=skipna, **kwargs)
+            return self._simple_new(data, mask)
+        else:
+            from pandas.core.arrays import IntegerArray
+
+            return IntegerArray(data.astype(int), mask)._accumulate(
+                name, skipna=skipna, **kwargs
+            )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/categorical.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/categorical.py
new file mode 100644
index 0000000000000000000000000000000000000000..8bee4740b39510bf049aadc348d9e911915fe239
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/categorical.py
@@ -0,0 +1,3111 @@
+from __future__ import annotations
+
+from csv import QUOTE_NONNUMERIC
+from functools import partial
+import operator
+from shutil import get_terminal_size
+from typing import (
+    TYPE_CHECKING,
+    Literal,
+    cast,
+    overload,
+)
+import warnings
+
+import numpy as np
+
+from pandas._config import get_option
+
+from pandas._libs import (
+    NaT,
+    algos as libalgos,
+    lib,
+)
+from pandas._libs.arrays import NDArrayBacked
+from pandas.compat.numpy import function as nv
+from pandas.util._exceptions import find_stack_level
+from pandas.util._validators import validate_bool_kwarg
+
+from pandas.core.dtypes.cast import (
+    coerce_indexer_dtype,
+    find_common_type,
+)
+from pandas.core.dtypes.common import (
+    ensure_int64,
+    ensure_platform_int,
+    is_any_real_numeric_dtype,
+    is_bool_dtype,
+    is_dict_like,
+    is_hashable,
+    is_integer_dtype,
+    is_list_like,
+    is_scalar,
+    needs_i8_conversion,
+    pandas_dtype,
+)
+from pandas.core.dtypes.dtypes import (
+    ArrowDtype,
+    CategoricalDtype,
+    CategoricalDtypeType,
+    ExtensionDtype,
+)
+from pandas.core.dtypes.generic import (
+    ABCIndex,
+    ABCSeries,
+)
+from pandas.core.dtypes.missing import (
+    is_valid_na_for_dtype,
+    isna,
+)
+
+from pandas.core import (
+    algorithms,
+    arraylike,
+    ops,
+)
+from pandas.core.accessor import (
+    PandasDelegate,
+    delegate_names,
+)
+from pandas.core.algorithms import (
+    factorize,
+    take_nd,
+)
+from pandas.core.arrays._mixins import (
+    NDArrayBackedExtensionArray,
+    ravel_compat,
+)
+from pandas.core.base import (
+    ExtensionArray,
+    NoNewAttributesMixin,
+    PandasObject,
+)
+import pandas.core.common as com
+from pandas.core.construction import (
+    extract_array,
+    sanitize_array,
+)
+from pandas.core.ops.common import unpack_zerodim_and_defer
+from pandas.core.sorting import nargsort
+from pandas.core.strings.object_array import ObjectStringArrayMixin
+
+from pandas.io.formats import console
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Hashable,
+        Iterator,
+        Sequence,
+    )
+
+    from pandas._typing import (
+        ArrayLike,
+        AstypeArg,
+        AxisInt,
+        Dtype,
+        DtypeObj,
+        NpDtype,
+        Ordered,
+        Self,
+        Shape,
+        SortKind,
+        npt,
+    )
+
+    from pandas import (
+        DataFrame,
+        Index,
+        Series,
+    )
+
+
+def _cat_compare_op(op):
+    opname = f"__{op.__name__}__"
+    fill_value = op is operator.ne
+
+    @unpack_zerodim_and_defer(opname)
+    def func(self, other):
+        hashable = is_hashable(other)
+        if is_list_like(other) and len(other) != len(self) and not hashable:
+            # in hashable case we may have a tuple that is itself a category
+            raise ValueError("Lengths must match.")
+
+        if not self.ordered:
+            if opname in ["__lt__", "__gt__", "__le__", "__ge__"]:
+                raise TypeError(
+                    "Unordered Categoricals can only compare equality or not"
+                )
+        if isinstance(other, Categorical):
+            # Two Categoricals can only be compared if the categories are
+            # the same (maybe up to ordering, depending on ordered)
+
+            msg = "Categoricals can only be compared if 'categories' are the same."
+            if not self._categories_match_up_to_permutation(other):
+                raise TypeError(msg)
+
+            if not self.ordered and not self.categories.equals(other.categories):
+                # both unordered and different order
+                other_codes = recode_for_categories(
+                    other.codes, other.categories, self.categories, copy=False
+                )
+            else:
+                other_codes = other._codes
+
+            ret = op(self._codes, other_codes)
+            mask = (self._codes == -1) | (other_codes == -1)
+            if mask.any():
+                ret[mask] = fill_value
+            return ret
+
+        if hashable:
+            if other in self.categories:
+                i = self._unbox_scalar(other)
+                ret = op(self._codes, i)
+
+                if opname not in {"__eq__", "__ge__", "__gt__"}:
+                    # GH#29820 performance trick; get_loc will always give i>=0,
+                    #  so in the cases (__ne__, __le__, __lt__) the setting
+                    #  here is a no-op, so can be skipped.
+                    mask = self._codes == -1
+                    ret[mask] = fill_value
+                return ret
+            else:
+                return ops.invalid_comparison(self, other, op)
+        else:
+            # allow categorical vs object dtype array comparisons for equality
+            # these are only positional comparisons
+            if opname not in ["__eq__", "__ne__"]:
+                raise TypeError(
+                    f"Cannot compare a Categorical for op {opname} with "
+                    f"type {type(other)}.\nIf you want to compare values, "
+                    "use 'np.asarray(cat)  other'."
+                )
+
+            if isinstance(other, ExtensionArray) and needs_i8_conversion(other.dtype):
+                # We would return NotImplemented here, but that messes up
+                #  ExtensionIndex's wrapped methods
+                return op(other, self)
+            return getattr(np.array(self), opname)(np.array(other))
+
+    func.__name__ = opname
+
+    return func
+
+
+def contains(cat, key, container) -> bool:
+    """
+    Helper for membership check for ``key`` in ``cat``.
+
+    This is a helper method for :method:`__contains__`
+    and :class:`CategoricalIndex.__contains__`.
+
+    Returns True if ``key`` is in ``cat.categories`` and the
+    location of ``key`` in ``categories`` is in ``container``.
+
+    Parameters
+    ----------
+    cat : :class:`Categorical`or :class:`categoricalIndex`
+    key : a hashable object
+        The key to check membership for.
+    container : Container (e.g. list-like or mapping)
+        The container to check for membership in.
+
+    Returns
+    -------
+    is_in : bool
+        True if ``key`` is in ``self.categories`` and location of
+        ``key`` in ``categories`` is in ``container``, else False.
+
+    Notes
+    -----
+    This method does not check for NaN values. Do that separately
+    before calling this method.
+    """
+    hash(key)
+
+    # get location of key in categories.
+    # If a KeyError, the key isn't in categories, so logically
+    #  can't be in container either.
+    try:
+        loc = cat.categories.get_loc(key)
+    except (KeyError, TypeError):
+        return False
+
+    # loc is the location of key in categories, but also the *value*
+    # for key in container. So, `key` may be in categories,
+    # but still not in `container`. Example ('b' in categories,
+    # but not in values):
+    # 'b' in Categorical(['a'], categories=['a', 'b'])  # False
+    if is_scalar(loc):
+        return loc in container
+    else:
+        # if categories is an IntervalIndex, loc is an array.
+        return any(loc_ in container for loc_ in loc)
+
+
+class Categorical(NDArrayBackedExtensionArray, PandasObject, ObjectStringArrayMixin):
+    """
+    Represent a categorical variable in classic R / S-plus fashion.
+
+    `Categoricals` can only take on a limited, and usually fixed, number
+    of possible values (`categories`). In contrast to statistical categorical
+    variables, a `Categorical` might have an order, but numerical operations
+    (additions, divisions, ...) are not possible.
+
+    All values of the `Categorical` are either in `categories` or `np.nan`.
+    Assigning values outside of `categories` will raise a `ValueError`. Order
+    is defined by the order of the `categories`, not lexical order of the
+    values.
+
+    Parameters
+    ----------
+    values : list-like
+        The values of the categorical. If categories are given, values not in
+        categories will be replaced with NaN.
+    categories : Index-like (unique), optional
+        The unique categories for this categorical. If not given, the
+        categories are assumed to be the unique values of `values` (sorted, if
+        possible, otherwise in the order in which they appear).
+    ordered : bool, default False
+        Whether or not this categorical is treated as a ordered categorical.
+        If True, the resulting categorical will be ordered.
+        An ordered categorical respects, when sorted, the order of its
+        `categories` attribute (which in turn is the `categories` argument, if
+        provided).
+    dtype : CategoricalDtype
+        An instance of ``CategoricalDtype`` to use for this categorical.
+
+    Attributes
+    ----------
+    categories : Index
+        The categories of this categorical.
+    codes : ndarray
+        The codes (integer positions, which point to the categories) of this
+        categorical, read only.
+    ordered : bool
+        Whether or not this Categorical is ordered.
+    dtype : CategoricalDtype
+        The instance of ``CategoricalDtype`` storing the ``categories``
+        and ``ordered``.
+
+    Methods
+    -------
+    from_codes
+    __array__
+
+    Raises
+    ------
+    ValueError
+        If the categories do not validate.
+    TypeError
+        If an explicit ``ordered=True`` is given but no `categories` and the
+        `values` are not sortable.
+
+    See Also
+    --------
+    CategoricalDtype : Type for categorical data.
+    CategoricalIndex : An Index with an underlying ``Categorical``.
+
+    Notes
+    -----
+    See the `user guide
+    `__
+    for more.
+
+    Examples
+    --------
+    >>> pd.Categorical([1, 2, 3, 1, 2, 3])
+    [1, 2, 3, 1, 2, 3]
+    Categories (3, int64): [1, 2, 3]
+
+    >>> pd.Categorical(['a', 'b', 'c', 'a', 'b', 'c'])
+    ['a', 'b', 'c', 'a', 'b', 'c']
+    Categories (3, object): ['a', 'b', 'c']
+
+    Missing values are not included as a category.
+
+    >>> c = pd.Categorical([1, 2, 3, 1, 2, 3, np.nan])
+    >>> c
+    [1, 2, 3, 1, 2, 3, NaN]
+    Categories (3, int64): [1, 2, 3]
+
+    However, their presence is indicated in the `codes` attribute
+    by code `-1`.
+
+    >>> c.codes
+    array([ 0,  1,  2,  0,  1,  2, -1], dtype=int8)
+
+    Ordered `Categoricals` can be sorted according to the custom order
+    of the categories and can have a min and max value.
+
+    >>> c = pd.Categorical(['a', 'b', 'c', 'a', 'b', 'c'], ordered=True,
+    ...                    categories=['c', 'b', 'a'])
+    >>> c
+    ['a', 'b', 'c', 'a', 'b', 'c']
+    Categories (3, object): ['c' < 'b' < 'a']
+    >>> c.min()
+    'c'
+    """
+
+    # For comparisons, so that numpy uses our implementation if the compare
+    # ops, which raise
+    __array_priority__ = 1000
+    # tolist is not actually deprecated, just suppressed in the __dir__
+    _hidden_attrs = PandasObject._hidden_attrs | frozenset(["tolist"])
+    _typ = "categorical"
+
+    _dtype: CategoricalDtype
+
+    @classmethod
+    # error: Argument 2 of "_simple_new" is incompatible with supertype
+    # "NDArrayBacked"; supertype defines the argument type as
+    # "Union[dtype[Any], ExtensionDtype]"
+    def _simple_new(  # type: ignore[override]
+        cls, codes: np.ndarray, dtype: CategoricalDtype
+    ) -> Self:
+        # NB: This is not _quite_ as simple as the "usual" _simple_new
+        codes = coerce_indexer_dtype(codes, dtype.categories)
+        dtype = CategoricalDtype(ordered=False).update_dtype(dtype)
+        return super()._simple_new(codes, dtype)
+
+    def __init__(
+        self,
+        values,
+        categories=None,
+        ordered=None,
+        dtype: Dtype | None = None,
+        fastpath: bool | lib.NoDefault = lib.no_default,
+        copy: bool = True,
+    ) -> None:
+        if fastpath is not lib.no_default:
+            # GH#20110
+            warnings.warn(
+                "The 'fastpath' keyword in Categorical is deprecated and will "
+                "be removed in a future version. Use Categorical.from_codes instead",
+                DeprecationWarning,
+                stacklevel=find_stack_level(),
+            )
+        else:
+            fastpath = False
+
+        dtype = CategoricalDtype._from_values_or_dtype(
+            values, categories, ordered, dtype
+        )
+        # At this point, dtype is always a CategoricalDtype, but
+        # we may have dtype.categories be None, and we need to
+        # infer categories in a factorization step further below
+
+        if fastpath:
+            codes = coerce_indexer_dtype(values, dtype.categories)
+            dtype = CategoricalDtype(ordered=False).update_dtype(dtype)
+            super().__init__(codes, dtype)
+            return
+
+        if not is_list_like(values):
+            # GH#38433
+            raise TypeError("Categorical input must be list-like")
+
+        # null_mask indicates missing values we want to exclude from inference.
+        # This means: only missing values in list-likes (not arrays/ndframes).
+        null_mask = np.array(False)
+
+        # sanitize input
+        vdtype = getattr(values, "dtype", None)
+        if isinstance(vdtype, CategoricalDtype):
+            if dtype.categories is None:
+                dtype = CategoricalDtype(values.categories, dtype.ordered)
+        elif not isinstance(values, (ABCIndex, ABCSeries, ExtensionArray)):
+            values = com.convert_to_list_like(values)
+            if isinstance(values, list) and len(values) == 0:
+                # By convention, empty lists result in object dtype:
+                values = np.array([], dtype=object)
+            elif isinstance(values, np.ndarray):
+                if values.ndim > 1:
+                    # preempt sanitize_array from raising ValueError
+                    raise NotImplementedError(
+                        "> 1 ndim Categorical are not supported at this time"
+                    )
+                values = sanitize_array(values, None)
+            else:
+                # i.e. must be a list
+                arr = sanitize_array(values, None)
+                null_mask = isna(arr)
+                if null_mask.any():
+                    # We remove null values here, then below will re-insert
+                    #  them, grep "full_codes"
+                    arr_list = [values[idx] for idx in np.where(~null_mask)[0]]
+
+                    # GH#44900 Do not cast to float if we have only missing values
+                    if arr_list or arr.dtype == "object":
+                        sanitize_dtype = None
+                    else:
+                        sanitize_dtype = arr.dtype
+
+                    arr = sanitize_array(arr_list, None, dtype=sanitize_dtype)
+                values = arr
+
+        if dtype.categories is None:
+            if isinstance(values.dtype, ArrowDtype) and issubclass(
+                values.dtype.type, CategoricalDtypeType
+            ):
+                arr = values._pa_array.combine_chunks()
+                categories = arr.dictionary.to_pandas(types_mapper=ArrowDtype)
+                codes = arr.indices.to_numpy()
+                dtype = CategoricalDtype(categories, values.dtype.pyarrow_dtype.ordered)
+            else:
+                if not isinstance(values, ABCIndex):
+                    # in particular RangeIndex xref test_index_equal_range_categories
+                    values = sanitize_array(values, None)
+                try:
+                    codes, categories = factorize(values, sort=True)
+                except TypeError as err:
+                    codes, categories = factorize(values, sort=False)
+                    if dtype.ordered:
+                        # raise, as we don't have a sortable data structure and so
+                        # the user should give us one by specifying categories
+                        raise TypeError(
+                            "'values' is not ordered, please "
+                            "explicitly specify the categories order "
+                            "by passing in a categories argument."
+                        ) from err
+
+                # we're inferring from values
+                dtype = CategoricalDtype(categories, dtype.ordered)
+
+        elif isinstance(values.dtype, CategoricalDtype):
+            old_codes = extract_array(values)._codes
+            codes = recode_for_categories(
+                old_codes, values.dtype.categories, dtype.categories, copy=copy
+            )
+
+        else:
+            codes = _get_codes_for_values(values, dtype.categories)
+
+        if null_mask.any():
+            # Reinsert -1 placeholders for previously removed missing values
+            full_codes = -np.ones(null_mask.shape, dtype=codes.dtype)
+            full_codes[~null_mask] = codes
+            codes = full_codes
+
+        dtype = CategoricalDtype(ordered=False).update_dtype(dtype)
+        arr = coerce_indexer_dtype(codes, dtype.categories)
+        super().__init__(arr, dtype)
+
+    @property
+    def dtype(self) -> CategoricalDtype:
+        """
+        The :class:`~pandas.api.types.CategoricalDtype` for this instance.
+
+        Examples
+        --------
+        >>> cat = pd.Categorical(['a', 'b'], ordered=True)
+        >>> cat
+        ['a', 'b']
+        Categories (2, object): ['a' < 'b']
+        >>> cat.dtype
+        CategoricalDtype(categories=['a', 'b'], ordered=True, categories_dtype=object)
+        """
+        return self._dtype
+
+    @property
+    def _internal_fill_value(self) -> int:
+        # using the specific numpy integer instead of python int to get
+        #  the correct dtype back from _quantile in the all-NA case
+        dtype = self._ndarray.dtype
+        return dtype.type(-1)
+
+    @classmethod
+    def _from_sequence(
+        cls, scalars, *, dtype: Dtype | None = None, copy: bool = False
+    ) -> Self:
+        return cls(scalars, dtype=dtype, copy=copy)
+
+    @classmethod
+    def _from_scalars(cls, scalars, *, dtype: DtypeObj) -> Self:
+        if dtype is None:
+            # The _from_scalars strictness doesn't make much sense in this case.
+            raise NotImplementedError
+
+        res = cls._from_sequence(scalars, dtype=dtype)
+
+        # if there are any non-category elements in scalars, these will be
+        #  converted to NAs in res.
+        mask = isna(scalars)
+        if not (mask == res.isna()).all():
+            # Some non-category element in scalars got converted to NA in res.
+            raise ValueError
+        return res
+
+    @overload
+    def astype(self, dtype: npt.DTypeLike, copy: bool = ...) -> np.ndarray:
+        ...
+
+    @overload
+    def astype(self, dtype: ExtensionDtype, copy: bool = ...) -> ExtensionArray:
+        ...
+
+    @overload
+    def astype(self, dtype: AstypeArg, copy: bool = ...) -> ArrayLike:
+        ...
+
+    def astype(self, dtype: AstypeArg, copy: bool = True) -> ArrayLike:
+        """
+        Coerce this type to another dtype
+
+        Parameters
+        ----------
+        dtype : numpy dtype or pandas type
+        copy : bool, default True
+            By default, astype always returns a newly allocated object.
+            If copy is set to False and dtype is categorical, the original
+            object is returned.
+        """
+        dtype = pandas_dtype(dtype)
+        if self.dtype is dtype:
+            result = self.copy() if copy else self
+
+        elif isinstance(dtype, CategoricalDtype):
+            # GH 10696/18593/18630
+            dtype = self.dtype.update_dtype(dtype)
+            self = self.copy() if copy else self
+            result = self._set_dtype(dtype)
+
+        elif isinstance(dtype, ExtensionDtype):
+            return super().astype(dtype, copy=copy)
+
+        elif dtype.kind in "iu" and self.isna().any():
+            raise ValueError("Cannot convert float NaN to integer")
+
+        elif len(self.codes) == 0 or len(self.categories) == 0:
+            # For NumPy 1.x compatibility we cannot use copy=None.  And
+            # `copy=False` has the meaning of `copy=None` here:
+            if not copy:
+                result = np.asarray(self, dtype=dtype)
+            else:
+                result = np.array(self, dtype=dtype)
+
+        else:
+            # GH8628 (PERF): astype category codes instead of astyping array
+            new_cats = self.categories._values
+
+            try:
+                new_cats = new_cats.astype(dtype=dtype, copy=copy)
+                fill_value = self.categories._na_value
+                if not is_valid_na_for_dtype(fill_value, dtype):
+                    fill_value = lib.item_from_zerodim(
+                        np.array(self.categories._na_value).astype(dtype)
+                    )
+            except (
+                TypeError,  # downstream error msg for CategoricalIndex is misleading
+                ValueError,
+            ):
+                msg = f"Cannot cast {self.categories.dtype} dtype to {dtype}"
+                raise ValueError(msg)
+
+            result = take_nd(
+                new_cats, ensure_platform_int(self._codes), fill_value=fill_value
+            )
+
+        return result
+
+    def to_list(self):
+        """
+        Alias for tolist.
+        """
+        # GH#51254
+        warnings.warn(
+            "Categorical.to_list is deprecated and will be removed in a future "
+            "version. Use obj.tolist() instead",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        return self.tolist()
+
+    @classmethod
+    def _from_inferred_categories(
+        cls, inferred_categories, inferred_codes, dtype, true_values=None
+    ) -> Self:
+        """
+        Construct a Categorical from inferred values.
+
+        For inferred categories (`dtype` is None) the categories are sorted.
+        For explicit `dtype`, the `inferred_categories` are cast to the
+        appropriate type.
+
+        Parameters
+        ----------
+        inferred_categories : Index
+        inferred_codes : Index
+        dtype : CategoricalDtype or 'category'
+        true_values : list, optional
+            If none are provided, the default ones are
+            "True", "TRUE", and "true."
+
+        Returns
+        -------
+        Categorical
+        """
+        from pandas import (
+            Index,
+            to_datetime,
+            to_numeric,
+            to_timedelta,
+        )
+
+        cats = Index(inferred_categories)
+        known_categories = (
+            isinstance(dtype, CategoricalDtype) and dtype.categories is not None
+        )
+
+        if known_categories:
+            # Convert to a specialized type with `dtype` if specified.
+            if is_any_real_numeric_dtype(dtype.categories.dtype):
+                cats = to_numeric(inferred_categories, errors="coerce")
+            elif lib.is_np_dtype(dtype.categories.dtype, "M"):
+                cats = to_datetime(inferred_categories, errors="coerce")
+            elif lib.is_np_dtype(dtype.categories.dtype, "m"):
+                cats = to_timedelta(inferred_categories, errors="coerce")
+            elif is_bool_dtype(dtype.categories.dtype):
+                if true_values is None:
+                    true_values = ["True", "TRUE", "true"]
+
+                # error: Incompatible types in assignment (expression has type
+                # "ndarray", variable has type "Index")
+                cats = cats.isin(true_values)  # type: ignore[assignment]
+
+        if known_categories:
+            # Recode from observation order to dtype.categories order.
+            categories = dtype.categories
+            codes = recode_for_categories(inferred_codes, cats, categories)
+        elif not cats.is_monotonic_increasing:
+            # Sort categories and recode for unknown categories.
+            unsorted = cats.copy()
+            categories = cats.sort_values()
+
+            codes = recode_for_categories(inferred_codes, unsorted, categories)
+            dtype = CategoricalDtype(categories, ordered=False)
+        else:
+            dtype = CategoricalDtype(cats, ordered=False)
+            codes = inferred_codes
+
+        return cls._simple_new(codes, dtype=dtype)
+
+    @classmethod
+    def from_codes(
+        cls,
+        codes,
+        categories=None,
+        ordered=None,
+        dtype: Dtype | None = None,
+        validate: bool = True,
+    ) -> Self:
+        """
+        Make a Categorical type from codes and categories or dtype.
+
+        This constructor is useful if you already have codes and
+        categories/dtype and so do not need the (computation intensive)
+        factorization step, which is usually done on the constructor.
+
+        If your data does not follow this convention, please use the normal
+        constructor.
+
+        Parameters
+        ----------
+        codes : array-like of int
+            An integer array, where each integer points to a category in
+            categories or dtype.categories, or else is -1 for NaN.
+        categories : index-like, optional
+            The categories for the categorical. Items need to be unique.
+            If the categories are not given here, then they must be provided
+            in `dtype`.
+        ordered : bool, optional
+            Whether or not this categorical is treated as an ordered
+            categorical. If not given here or in `dtype`, the resulting
+            categorical will be unordered.
+        dtype : CategoricalDtype or "category", optional
+            If :class:`CategoricalDtype`, cannot be used together with
+            `categories` or `ordered`.
+        validate : bool, default True
+            If True, validate that the codes are valid for the dtype.
+            If False, don't validate that the codes are valid. Be careful about skipping
+            validation, as invalid codes can lead to severe problems, such as segfaults.
+
+            .. versionadded:: 2.1.0
+
+        Returns
+        -------
+        Categorical
+
+        Examples
+        --------
+        >>> dtype = pd.CategoricalDtype(['a', 'b'], ordered=True)
+        >>> pd.Categorical.from_codes(codes=[0, 1, 0, 1], dtype=dtype)
+        ['a', 'b', 'a', 'b']
+        Categories (2, object): ['a' < 'b']
+        """
+        dtype = CategoricalDtype._from_values_or_dtype(
+            categories=categories, ordered=ordered, dtype=dtype
+        )
+        if dtype.categories is None:
+            msg = (
+                "The categories must be provided in 'categories' or "
+                "'dtype'. Both were None."
+            )
+            raise ValueError(msg)
+
+        if validate:
+            # beware: non-valid codes may segfault
+            codes = cls._validate_codes_for_dtype(codes, dtype=dtype)
+
+        return cls._simple_new(codes, dtype=dtype)
+
+    # ------------------------------------------------------------------
+    # Categories/Codes/Ordered
+
+    @property
+    def categories(self) -> Index:
+        """
+        The categories of this categorical.
+
+        Setting assigns new values to each category (effectively a rename of
+        each individual category).
+
+        The assigned value has to be a list-like object. All items must be
+        unique and the number of items in the new categories must be the same
+        as the number of items in the old categories.
+
+        Raises
+        ------
+        ValueError
+            If the new categories do not validate as categories or if the
+            number of new categories is unequal the number of old categories
+
+        See Also
+        --------
+        rename_categories : Rename categories.
+        reorder_categories : Reorder categories.
+        add_categories : Add new categories.
+        remove_categories : Remove the specified categories.
+        remove_unused_categories : Remove categories which are not used.
+        set_categories : Set the categories to the specified ones.
+
+        Examples
+        --------
+        For :class:`pandas.Series`:
+
+        >>> ser = pd.Series(['a', 'b', 'c', 'a'], dtype='category')
+        >>> ser.cat.categories
+        Index(['a', 'b', 'c'], dtype='object')
+
+        >>> raw_cat = pd.Categorical(['a', 'b', 'c', 'a'], categories=['b', 'c', 'd'])
+        >>> ser = pd.Series(raw_cat)
+        >>> ser.cat.categories
+        Index(['b', 'c', 'd'], dtype='object')
+
+        For :class:`pandas.Categorical`:
+
+        >>> cat = pd.Categorical(['a', 'b'], ordered=True)
+        >>> cat.categories
+        Index(['a', 'b'], dtype='object')
+
+        For :class:`pandas.CategoricalIndex`:
+
+        >>> ci = pd.CategoricalIndex(['a', 'c', 'b', 'a', 'c', 'b'])
+        >>> ci.categories
+        Index(['a', 'b', 'c'], dtype='object')
+
+        >>> ci = pd.CategoricalIndex(['a', 'c'], categories=['c', 'b', 'a'])
+        >>> ci.categories
+        Index(['c', 'b', 'a'], dtype='object')
+        """
+        return self.dtype.categories
+
+    @property
+    def ordered(self) -> Ordered:
+        """
+        Whether the categories have an ordered relationship.
+
+        Examples
+        --------
+        For :class:`pandas.Series`:
+
+        >>> ser = pd.Series(['a', 'b', 'c', 'a'], dtype='category')
+        >>> ser.cat.ordered
+        False
+
+        >>> raw_cat = pd.Categorical(['a', 'b', 'c', 'a'], ordered=True)
+        >>> ser = pd.Series(raw_cat)
+        >>> ser.cat.ordered
+        True
+
+        For :class:`pandas.Categorical`:
+
+        >>> cat = pd.Categorical(['a', 'b'], ordered=True)
+        >>> cat.ordered
+        True
+
+        >>> cat = pd.Categorical(['a', 'b'], ordered=False)
+        >>> cat.ordered
+        False
+
+        For :class:`pandas.CategoricalIndex`:
+
+        >>> ci = pd.CategoricalIndex(['a', 'b'], ordered=True)
+        >>> ci.ordered
+        True
+
+        >>> ci = pd.CategoricalIndex(['a', 'b'], ordered=False)
+        >>> ci.ordered
+        False
+        """
+        return self.dtype.ordered
+
+    @property
+    def codes(self) -> np.ndarray:
+        """
+        The category codes of this categorical index.
+
+        Codes are an array of integers which are the positions of the actual
+        values in the categories array.
+
+        There is no setter, use the other categorical methods and the normal item
+        setter to change values in the categorical.
+
+        Returns
+        -------
+        ndarray[int]
+            A non-writable view of the ``codes`` array.
+
+        Examples
+        --------
+        For :class:`pandas.Categorical`:
+
+        >>> cat = pd.Categorical(['a', 'b'], ordered=True)
+        >>> cat.codes
+        array([0, 1], dtype=int8)
+
+        For :class:`pandas.CategoricalIndex`:
+
+        >>> ci = pd.CategoricalIndex(['a', 'b', 'c', 'a', 'b', 'c'])
+        >>> ci.codes
+        array([0, 1, 2, 0, 1, 2], dtype=int8)
+
+        >>> ci = pd.CategoricalIndex(['a', 'c'], categories=['c', 'b', 'a'])
+        >>> ci.codes
+        array([2, 0], dtype=int8)
+        """
+        v = self._codes.view()
+        v.flags.writeable = False
+        return v
+
+    def _set_categories(self, categories, fastpath: bool = False) -> None:
+        """
+        Sets new categories inplace
+
+        Parameters
+        ----------
+        fastpath : bool, default False
+           Don't perform validation of the categories for uniqueness or nulls
+
+        Examples
+        --------
+        >>> c = pd.Categorical(['a', 'b'])
+        >>> c
+        ['a', 'b']
+        Categories (2, object): ['a', 'b']
+
+        >>> c._set_categories(pd.Index(['a', 'c']))
+        >>> c
+        ['a', 'c']
+        Categories (2, object): ['a', 'c']
+        """
+        if fastpath:
+            new_dtype = CategoricalDtype._from_fastpath(categories, self.ordered)
+        else:
+            new_dtype = CategoricalDtype(categories, ordered=self.ordered)
+        if (
+            not fastpath
+            and self.dtype.categories is not None
+            and len(new_dtype.categories) != len(self.dtype.categories)
+        ):
+            raise ValueError(
+                "new categories need to have the same number of "
+                "items as the old categories!"
+            )
+
+        super().__init__(self._ndarray, new_dtype)
+
+    def _set_dtype(self, dtype: CategoricalDtype) -> Self:
+        """
+        Internal method for directly updating the CategoricalDtype
+
+        Parameters
+        ----------
+        dtype : CategoricalDtype
+
+        Notes
+        -----
+        We don't do any validation here. It's assumed that the dtype is
+        a (valid) instance of `CategoricalDtype`.
+        """
+        codes = recode_for_categories(self.codes, self.categories, dtype.categories)
+        return type(self)._simple_new(codes, dtype=dtype)
+
+    def set_ordered(self, value: bool) -> Self:
+        """
+        Set the ordered attribute to the boolean value.
+
+        Parameters
+        ----------
+        value : bool
+           Set whether this categorical is ordered (True) or not (False).
+        """
+        new_dtype = CategoricalDtype(self.categories, ordered=value)
+        cat = self.copy()
+        NDArrayBacked.__init__(cat, cat._ndarray, new_dtype)
+        return cat
+
+    def as_ordered(self) -> Self:
+        """
+        Set the Categorical to be ordered.
+
+        Returns
+        -------
+        Categorical
+            Ordered Categorical.
+
+        Examples
+        --------
+        For :class:`pandas.Series`:
+
+        >>> ser = pd.Series(['a', 'b', 'c', 'a'], dtype='category')
+        >>> ser.cat.ordered
+        False
+        >>> ser = ser.cat.as_ordered()
+        >>> ser.cat.ordered
+        True
+
+        For :class:`pandas.CategoricalIndex`:
+
+        >>> ci = pd.CategoricalIndex(['a', 'b', 'c', 'a'])
+        >>> ci.ordered
+        False
+        >>> ci = ci.as_ordered()
+        >>> ci.ordered
+        True
+        """
+        return self.set_ordered(True)
+
+    def as_unordered(self) -> Self:
+        """
+        Set the Categorical to be unordered.
+
+        Returns
+        -------
+        Categorical
+            Unordered Categorical.
+
+        Examples
+        --------
+        For :class:`pandas.Series`:
+
+        >>> raw_cat = pd.Categorical(['a', 'b', 'c', 'a'], ordered=True)
+        >>> ser = pd.Series(raw_cat)
+        >>> ser.cat.ordered
+        True
+        >>> ser = ser.cat.as_unordered()
+        >>> ser.cat.ordered
+        False
+
+        For :class:`pandas.CategoricalIndex`:
+
+        >>> ci = pd.CategoricalIndex(['a', 'b', 'c', 'a'], ordered=True)
+        >>> ci.ordered
+        True
+        >>> ci = ci.as_unordered()
+        >>> ci.ordered
+        False
+        """
+        return self.set_ordered(False)
+
+    def set_categories(self, new_categories, ordered=None, rename: bool = False):
+        """
+        Set the categories to the specified new categories.
+
+        ``new_categories`` can include new categories (which will result in
+        unused categories) or remove old categories (which results in values
+        set to ``NaN``). If ``rename=True``, the categories will simply be renamed
+        (less or more items than in old categories will result in values set to
+        ``NaN`` or in unused categories respectively).
+
+        This method can be used to perform more than one action of adding,
+        removing, and reordering simultaneously and is therefore faster than
+        performing the individual steps via the more specialised methods.
+
+        On the other hand this methods does not do checks (e.g., whether the
+        old categories are included in the new categories on a reorder), which
+        can result in surprising changes, for example when using special string
+        dtypes, which does not considers a S1 string equal to a single char
+        python string.
+
+        Parameters
+        ----------
+        new_categories : Index-like
+           The categories in new order.
+        ordered : bool, default False
+           Whether or not the categorical is treated as a ordered categorical.
+           If not given, do not change the ordered information.
+        rename : bool, default False
+           Whether or not the new_categories should be considered as a rename
+           of the old categories or as reordered categories.
+
+        Returns
+        -------
+        Categorical with reordered categories.
+
+        Raises
+        ------
+        ValueError
+            If new_categories does not validate as categories
+
+        See Also
+        --------
+        rename_categories : Rename categories.
+        reorder_categories : Reorder categories.
+        add_categories : Add new categories.
+        remove_categories : Remove the specified categories.
+        remove_unused_categories : Remove categories which are not used.
+
+        Examples
+        --------
+        For :class:`pandas.Series`:
+
+        >>> raw_cat = pd.Categorical(['a', 'b', 'c', 'A'],
+        ...                           categories=['a', 'b', 'c'], ordered=True)
+        >>> ser = pd.Series(raw_cat)
+        >>> ser
+        0   a
+        1   b
+        2   c
+        3   NaN
+        dtype: category
+        Categories (3, object): ['a' < 'b' < 'c']
+
+        >>> ser.cat.set_categories(['A', 'B', 'C'], rename=True)
+        0   A
+        1   B
+        2   C
+        3   NaN
+        dtype: category
+        Categories (3, object): ['A' < 'B' < 'C']
+
+        For :class:`pandas.CategoricalIndex`:
+
+        >>> ci = pd.CategoricalIndex(['a', 'b', 'c', 'A'],
+        ...                          categories=['a', 'b', 'c'], ordered=True)
+        >>> ci
+        CategoricalIndex(['a', 'b', 'c', nan], categories=['a', 'b', 'c'],
+                         ordered=True, dtype='category')
+
+        >>> ci.set_categories(['A', 'b', 'c'])
+        CategoricalIndex([nan, 'b', 'c', nan], categories=['A', 'b', 'c'],
+                         ordered=True, dtype='category')
+        >>> ci.set_categories(['A', 'b', 'c'], rename=True)
+        CategoricalIndex(['A', 'b', 'c', nan], categories=['A', 'b', 'c'],
+                         ordered=True, dtype='category')
+        """
+
+        if ordered is None:
+            ordered = self.dtype.ordered
+        new_dtype = CategoricalDtype(new_categories, ordered=ordered)
+
+        cat = self.copy()
+        if rename:
+            if cat.dtype.categories is not None and len(new_dtype.categories) < len(
+                cat.dtype.categories
+            ):
+                # remove all _codes which are larger and set to -1/NaN
+                cat._codes[cat._codes >= len(new_dtype.categories)] = -1
+            codes = cat._codes
+        else:
+            codes = recode_for_categories(
+                cat.codes, cat.categories, new_dtype.categories
+            )
+        NDArrayBacked.__init__(cat, codes, new_dtype)
+        return cat
+
+    def rename_categories(self, new_categories) -> Self:
+        """
+        Rename categories.
+
+        Parameters
+        ----------
+        new_categories : list-like, dict-like or callable
+
+            New categories which will replace old categories.
+
+            * list-like: all items must be unique and the number of items in
+              the new categories must match the existing number of categories.
+
+            * dict-like: specifies a mapping from
+              old categories to new. Categories not contained in the mapping
+              are passed through and extra categories in the mapping are
+              ignored.
+
+            * callable : a callable that is called on all items in the old
+              categories and whose return values comprise the new categories.
+
+        Returns
+        -------
+        Categorical
+            Categorical with renamed categories.
+
+        Raises
+        ------
+        ValueError
+            If new categories are list-like and do not have the same number of
+            items than the current categories or do not validate as categories
+
+        See Also
+        --------
+        reorder_categories : Reorder categories.
+        add_categories : Add new categories.
+        remove_categories : Remove the specified categories.
+        remove_unused_categories : Remove categories which are not used.
+        set_categories : Set the categories to the specified ones.
+
+        Examples
+        --------
+        >>> c = pd.Categorical(['a', 'a', 'b'])
+        >>> c.rename_categories([0, 1])
+        [0, 0, 1]
+        Categories (2, int64): [0, 1]
+
+        For dict-like ``new_categories``, extra keys are ignored and
+        categories not in the dictionary are passed through
+
+        >>> c.rename_categories({'a': 'A', 'c': 'C'})
+        ['A', 'A', 'b']
+        Categories (2, object): ['A', 'b']
+
+        You may also provide a callable to create the new categories
+
+        >>> c.rename_categories(lambda x: x.upper())
+        ['A', 'A', 'B']
+        Categories (2, object): ['A', 'B']
+        """
+
+        if is_dict_like(new_categories):
+            new_categories = [
+                new_categories.get(item, item) for item in self.categories
+            ]
+        elif callable(new_categories):
+            new_categories = [new_categories(item) for item in self.categories]
+
+        cat = self.copy()
+        cat._set_categories(new_categories)
+        return cat
+
+    def reorder_categories(self, new_categories, ordered=None) -> Self:
+        """
+        Reorder categories as specified in new_categories.
+
+        ``new_categories`` need to include all old categories and no new category
+        items.
+
+        Parameters
+        ----------
+        new_categories : Index-like
+           The categories in new order.
+        ordered : bool, optional
+           Whether or not the categorical is treated as a ordered categorical.
+           If not given, do not change the ordered information.
+
+        Returns
+        -------
+        Categorical
+            Categorical with reordered categories.
+
+        Raises
+        ------
+        ValueError
+            If the new categories do not contain all old category items or any
+            new ones
+
+        See Also
+        --------
+        rename_categories : Rename categories.
+        add_categories : Add new categories.
+        remove_categories : Remove the specified categories.
+        remove_unused_categories : Remove categories which are not used.
+        set_categories : Set the categories to the specified ones.
+
+        Examples
+        --------
+        For :class:`pandas.Series`:
+
+        >>> ser = pd.Series(['a', 'b', 'c', 'a'], dtype='category')
+        >>> ser = ser.cat.reorder_categories(['c', 'b', 'a'], ordered=True)
+        >>> ser
+        0   a
+        1   b
+        2   c
+        3   a
+        dtype: category
+        Categories (3, object): ['c' < 'b' < 'a']
+
+        >>> ser.sort_values()
+        2   c
+        1   b
+        0   a
+        3   a
+        dtype: category
+        Categories (3, object): ['c' < 'b' < 'a']
+
+        For :class:`pandas.CategoricalIndex`:
+
+        >>> ci = pd.CategoricalIndex(['a', 'b', 'c', 'a'])
+        >>> ci
+        CategoricalIndex(['a', 'b', 'c', 'a'], categories=['a', 'b', 'c'],
+                         ordered=False, dtype='category')
+        >>> ci.reorder_categories(['c', 'b', 'a'], ordered=True)
+        CategoricalIndex(['a', 'b', 'c', 'a'], categories=['c', 'b', 'a'],
+                         ordered=True, dtype='category')
+        """
+        if (
+            len(self.categories) != len(new_categories)
+            or not self.categories.difference(new_categories).empty
+        ):
+            raise ValueError(
+                "items in new_categories are not the same as in old categories"
+            )
+        return self.set_categories(new_categories, ordered=ordered)
+
+    def add_categories(self, new_categories) -> Self:
+        """
+        Add new categories.
+
+        `new_categories` will be included at the last/highest place in the
+        categories and will be unused directly after this call.
+
+        Parameters
+        ----------
+        new_categories : category or list-like of category
+           The new categories to be included.
+
+        Returns
+        -------
+        Categorical
+            Categorical with new categories added.
+
+        Raises
+        ------
+        ValueError
+            If the new categories include old categories or do not validate as
+            categories
+
+        See Also
+        --------
+        rename_categories : Rename categories.
+        reorder_categories : Reorder categories.
+        remove_categories : Remove the specified categories.
+        remove_unused_categories : Remove categories which are not used.
+        set_categories : Set the categories to the specified ones.
+
+        Examples
+        --------
+        >>> c = pd.Categorical(['c', 'b', 'c'])
+        >>> c
+        ['c', 'b', 'c']
+        Categories (2, object): ['b', 'c']
+
+        >>> c.add_categories(['d', 'a'])
+        ['c', 'b', 'c']
+        Categories (4, object): ['b', 'c', 'd', 'a']
+        """
+
+        if not is_list_like(new_categories):
+            new_categories = [new_categories]
+        already_included = set(new_categories) & set(self.dtype.categories)
+        if len(already_included) != 0:
+            raise ValueError(
+                f"new categories must not include old categories: {already_included}"
+            )
+
+        if hasattr(new_categories, "dtype"):
+            from pandas import Series
+
+            dtype = find_common_type(
+                [self.dtype.categories.dtype, new_categories.dtype]
+            )
+            new_categories = Series(
+                list(self.dtype.categories) + list(new_categories), dtype=dtype
+            )
+        else:
+            new_categories = list(self.dtype.categories) + list(new_categories)
+
+        new_dtype = CategoricalDtype(new_categories, self.ordered)
+        cat = self.copy()
+        codes = coerce_indexer_dtype(cat._ndarray, new_dtype.categories)
+        NDArrayBacked.__init__(cat, codes, new_dtype)
+        return cat
+
+    def remove_categories(self, removals) -> Self:
+        """
+        Remove the specified categories.
+
+        `removals` must be included in the old categories. Values which were in
+        the removed categories will be set to NaN
+
+        Parameters
+        ----------
+        removals : category or list of categories
+           The categories which should be removed.
+
+        Returns
+        -------
+        Categorical
+            Categorical with removed categories.
+
+        Raises
+        ------
+        ValueError
+            If the removals are not contained in the categories
+
+        See Also
+        --------
+        rename_categories : Rename categories.
+        reorder_categories : Reorder categories.
+        add_categories : Add new categories.
+        remove_unused_categories : Remove categories which are not used.
+        set_categories : Set the categories to the specified ones.
+
+        Examples
+        --------
+        >>> c = pd.Categorical(['a', 'c', 'b', 'c', 'd'])
+        >>> c
+        ['a', 'c', 'b', 'c', 'd']
+        Categories (4, object): ['a', 'b', 'c', 'd']
+
+        >>> c.remove_categories(['d', 'a'])
+        [NaN, 'c', 'b', 'c', NaN]
+        Categories (2, object): ['b', 'c']
+        """
+        from pandas import Index
+
+        if not is_list_like(removals):
+            removals = [removals]
+
+        removals = Index(removals).unique().dropna()
+        new_categories = (
+            self.dtype.categories.difference(removals, sort=False)
+            if self.dtype.ordered is True
+            else self.dtype.categories.difference(removals)
+        )
+        not_included = removals.difference(self.dtype.categories)
+
+        if len(not_included) != 0:
+            not_included = set(not_included)
+            raise ValueError(f"removals must all be in old categories: {not_included}")
+
+        return self.set_categories(new_categories, ordered=self.ordered, rename=False)
+
+    def remove_unused_categories(self) -> Self:
+        """
+        Remove categories which are not used.
+
+        Returns
+        -------
+        Categorical
+            Categorical with unused categories dropped.
+
+        See Also
+        --------
+        rename_categories : Rename categories.
+        reorder_categories : Reorder categories.
+        add_categories : Add new categories.
+        remove_categories : Remove the specified categories.
+        set_categories : Set the categories to the specified ones.
+
+        Examples
+        --------
+        >>> c = pd.Categorical(['a', 'c', 'b', 'c', 'd'])
+        >>> c
+        ['a', 'c', 'b', 'c', 'd']
+        Categories (4, object): ['a', 'b', 'c', 'd']
+
+        >>> c[2] = 'a'
+        >>> c[4] = 'c'
+        >>> c
+        ['a', 'c', 'a', 'c', 'c']
+        Categories (4, object): ['a', 'b', 'c', 'd']
+
+        >>> c.remove_unused_categories()
+        ['a', 'c', 'a', 'c', 'c']
+        Categories (2, object): ['a', 'c']
+        """
+        idx, inv = np.unique(self._codes, return_inverse=True)
+
+        if idx.size != 0 and idx[0] == -1:  # na sentinel
+            idx, inv = idx[1:], inv - 1
+
+        new_categories = self.dtype.categories.take(idx)
+        new_dtype = CategoricalDtype._from_fastpath(
+            new_categories, ordered=self.ordered
+        )
+        new_codes = coerce_indexer_dtype(inv, new_dtype.categories)
+
+        cat = self.copy()
+        NDArrayBacked.__init__(cat, new_codes, new_dtype)
+        return cat
+
+    # ------------------------------------------------------------------
+
+    def map(
+        self,
+        mapper,
+        na_action: Literal["ignore"] | None | lib.NoDefault = lib.no_default,
+    ):
+        """
+        Map categories using an input mapping or function.
+
+        Maps the categories to new categories. If the mapping correspondence is
+        one-to-one the result is a :class:`~pandas.Categorical` which has the
+        same order property as the original, otherwise a :class:`~pandas.Index`
+        is returned. NaN values are unaffected.
+
+        If a `dict` or :class:`~pandas.Series` is used any unmapped category is
+        mapped to `NaN`. Note that if this happens an :class:`~pandas.Index`
+        will be returned.
+
+        Parameters
+        ----------
+        mapper : function, dict, or Series
+            Mapping correspondence.
+        na_action : {None, 'ignore'}, default 'ignore'
+            If 'ignore', propagate NaN values, without passing them to the
+            mapping correspondence.
+
+            .. deprecated:: 2.1.0
+
+               The default value of 'ignore' has been deprecated and will be changed to
+               None in the future.
+
+        Returns
+        -------
+        pandas.Categorical or pandas.Index
+            Mapped categorical.
+
+        See Also
+        --------
+        CategoricalIndex.map : Apply a mapping correspondence on a
+            :class:`~pandas.CategoricalIndex`.
+        Index.map : Apply a mapping correspondence on an
+            :class:`~pandas.Index`.
+        Series.map : Apply a mapping correspondence on a
+            :class:`~pandas.Series`.
+        Series.apply : Apply more complex functions on a
+            :class:`~pandas.Series`.
+
+        Examples
+        --------
+        >>> cat = pd.Categorical(['a', 'b', 'c'])
+        >>> cat
+        ['a', 'b', 'c']
+        Categories (3, object): ['a', 'b', 'c']
+        >>> cat.map(lambda x: x.upper(), na_action=None)
+        ['A', 'B', 'C']
+        Categories (3, object): ['A', 'B', 'C']
+        >>> cat.map({'a': 'first', 'b': 'second', 'c': 'third'}, na_action=None)
+        ['first', 'second', 'third']
+        Categories (3, object): ['first', 'second', 'third']
+
+        If the mapping is one-to-one the ordering of the categories is
+        preserved:
+
+        >>> cat = pd.Categorical(['a', 'b', 'c'], ordered=True)
+        >>> cat
+        ['a', 'b', 'c']
+        Categories (3, object): ['a' < 'b' < 'c']
+        >>> cat.map({'a': 3, 'b': 2, 'c': 1}, na_action=None)
+        [3, 2, 1]
+        Categories (3, int64): [3 < 2 < 1]
+
+        If the mapping is not one-to-one an :class:`~pandas.Index` is returned:
+
+        >>> cat.map({'a': 'first', 'b': 'second', 'c': 'first'}, na_action=None)
+        Index(['first', 'second', 'first'], dtype='object')
+
+        If a `dict` is used, all unmapped categories are mapped to `NaN` and
+        the result is an :class:`~pandas.Index`:
+
+        >>> cat.map({'a': 'first', 'b': 'second'}, na_action=None)
+        Index(['first', 'second', nan], dtype='object')
+        """
+        if na_action is lib.no_default:
+            warnings.warn(
+                "The default value of 'ignore' for the `na_action` parameter in "
+                "pandas.Categorical.map is deprecated and will be "
+                "changed to 'None' in a future version. Please set na_action to the "
+                "desired value to avoid seeing this warning",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+            na_action = "ignore"
+
+        assert callable(mapper) or is_dict_like(mapper)
+
+        new_categories = self.categories.map(mapper)
+
+        has_nans = np.any(self._codes == -1)
+
+        na_val = np.nan
+        if na_action is None and has_nans:
+            na_val = mapper(np.nan) if callable(mapper) else mapper.get(np.nan, np.nan)
+
+        if new_categories.is_unique and not new_categories.hasnans and na_val is np.nan:
+            new_dtype = CategoricalDtype(new_categories, ordered=self.ordered)
+            return self.from_codes(self._codes.copy(), dtype=new_dtype, validate=False)
+
+        if has_nans:
+            new_categories = new_categories.insert(len(new_categories), na_val)
+
+        return np.take(new_categories, self._codes)
+
+    __eq__ = _cat_compare_op(operator.eq)
+    __ne__ = _cat_compare_op(operator.ne)
+    __lt__ = _cat_compare_op(operator.lt)
+    __gt__ = _cat_compare_op(operator.gt)
+    __le__ = _cat_compare_op(operator.le)
+    __ge__ = _cat_compare_op(operator.ge)
+
+    # -------------------------------------------------------------
+    # Validators; ideally these can be de-duplicated
+
+    def _validate_setitem_value(self, value):
+        if not is_hashable(value):
+            # wrap scalars and hashable-listlikes in list
+            return self._validate_listlike(value)
+        else:
+            return self._validate_scalar(value)
+
+    def _validate_scalar(self, fill_value):
+        """
+        Convert a user-facing fill_value to a representation to use with our
+        underlying ndarray, raising TypeError if this is not possible.
+
+        Parameters
+        ----------
+        fill_value : object
+
+        Returns
+        -------
+        fill_value : int
+
+        Raises
+        ------
+        TypeError
+        """
+
+        if is_valid_na_for_dtype(fill_value, self.categories.dtype):
+            fill_value = -1
+        elif fill_value in self.categories:
+            fill_value = self._unbox_scalar(fill_value)
+        else:
+            raise TypeError(
+                "Cannot setitem on a Categorical with a new "
+                f"category ({fill_value}), set the categories first"
+            ) from None
+        return fill_value
+
+    @classmethod
+    def _validate_codes_for_dtype(cls, codes, *, dtype: CategoricalDtype) -> np.ndarray:
+        if isinstance(codes, ExtensionArray) and is_integer_dtype(codes.dtype):
+            # Avoid the implicit conversion of Int to object
+            if isna(codes).any():
+                raise ValueError("codes cannot contain NA values")
+            codes = codes.to_numpy(dtype=np.int64)
+        else:
+            codes = np.asarray(codes)
+        if len(codes) and codes.dtype.kind not in "iu":
+            raise ValueError("codes need to be array-like integers")
+
+        if len(codes) and (codes.max() >= len(dtype.categories) or codes.min() < -1):
+            raise ValueError("codes need to be between -1 and len(categories)-1")
+        return codes
+
+    # -------------------------------------------------------------
+
+    @ravel_compat
+    def __array__(
+        self, dtype: NpDtype | None = None, copy: bool | None = None
+    ) -> np.ndarray:
+        """
+        The numpy array interface.
+
+        Users should not call this directly. Rather, it is invoked by
+        :func:`numpy.array` and :func:`numpy.asarray`.
+
+        Parameters
+        ----------
+        dtype : np.dtype or None
+            Specifies the the dtype for the array.
+
+        copy : bool or None, optional
+            See :func:`numpy.asarray`.
+
+        Returns
+        -------
+        numpy.array
+            A numpy array of either the specified dtype or,
+            if dtype==None (default), the same dtype as
+            categorical.categories.dtype.
+
+        Examples
+        --------
+
+        >>> cat = pd.Categorical(['a', 'b'], ordered=True)
+
+        The following calls ``cat.__array__``
+
+        >>> np.asarray(cat)
+        array(['a', 'b'], dtype=object)
+        """
+        if copy is False:
+            warnings.warn(
+                "Starting with NumPy 2.0, the behavior of the 'copy' keyword has "
+                "changed and passing 'copy=False' raises an error when returning "
+                "a zero-copy NumPy array is not possible. pandas will follow "
+                "this behavior starting with pandas 3.0.\nThis conversion to "
+                "NumPy requires a copy, but 'copy=False' was passed. Consider "
+                "using 'np.asarray(..)' instead.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+
+        ret = take_nd(self.categories._values, self._codes)
+        # When we're a Categorical[ExtensionArray], like Interval,
+        # we need to ensure __array__ gets all the way to an
+        # ndarray.
+
+        # `take_nd` should already make a copy, so don't force again.
+        return np.asarray(ret, dtype=dtype)
+
+    def __array_ufunc__(self, ufunc: np.ufunc, method: str, *inputs, **kwargs):
+        # for binary ops, use our custom dunder methods
+        result = arraylike.maybe_dispatch_ufunc_to_dunder_op(
+            self, ufunc, method, *inputs, **kwargs
+        )
+        if result is not NotImplemented:
+            return result
+
+        if "out" in kwargs:
+            # e.g. test_numpy_ufuncs_out
+            return arraylike.dispatch_ufunc_with_out(
+                self, ufunc, method, *inputs, **kwargs
+            )
+
+        if method == "reduce":
+            # e.g. TestCategoricalAnalytics::test_min_max_ordered
+            result = arraylike.dispatch_reduction_ufunc(
+                self, ufunc, method, *inputs, **kwargs
+            )
+            if result is not NotImplemented:
+                return result
+
+        # for all other cases, raise for now (similarly as what happens in
+        # Series.__array_prepare__)
+        raise TypeError(
+            f"Object with dtype {self.dtype} cannot perform "
+            f"the numpy op {ufunc.__name__}"
+        )
+
+    def __setstate__(self, state) -> None:
+        """Necessary for making this object picklable"""
+        if not isinstance(state, dict):
+            return super().__setstate__(state)
+
+        if "_dtype" not in state:
+            state["_dtype"] = CategoricalDtype(state["_categories"], state["_ordered"])
+
+        if "_codes" in state and "_ndarray" not in state:
+            # backward compat, changed what is property vs attribute
+            state["_ndarray"] = state.pop("_codes")
+
+        super().__setstate__(state)
+
+    @property
+    def nbytes(self) -> int:
+        return self._codes.nbytes + self.dtype.categories.values.nbytes
+
+    def memory_usage(self, deep: bool = False) -> int:
+        """
+        Memory usage of my values
+
+        Parameters
+        ----------
+        deep : bool
+            Introspect the data deeply, interrogate
+            `object` dtypes for system-level memory consumption
+
+        Returns
+        -------
+        bytes used
+
+        Notes
+        -----
+        Memory usage does not include memory consumed by elements that
+        are not components of the array if deep=False
+
+        See Also
+        --------
+        numpy.ndarray.nbytes
+        """
+        return self._codes.nbytes + self.dtype.categories.memory_usage(deep=deep)
+
+    def isna(self) -> npt.NDArray[np.bool_]:
+        """
+        Detect missing values
+
+        Missing values (-1 in .codes) are detected.
+
+        Returns
+        -------
+        np.ndarray[bool] of whether my values are null
+
+        See Also
+        --------
+        isna : Top-level isna.
+        isnull : Alias of isna.
+        Categorical.notna : Boolean inverse of Categorical.isna.
+
+        """
+        return self._codes == -1
+
+    isnull = isna
+
+    def notna(self) -> npt.NDArray[np.bool_]:
+        """
+        Inverse of isna
+
+        Both missing values (-1 in .codes) and NA as a category are detected as
+        null.
+
+        Returns
+        -------
+        np.ndarray[bool] of whether my values are not null
+
+        See Also
+        --------
+        notna : Top-level notna.
+        notnull : Alias of notna.
+        Categorical.isna : Boolean inverse of Categorical.notna.
+
+        """
+        return ~self.isna()
+
+    notnull = notna
+
+    def value_counts(self, dropna: bool = True) -> Series:
+        """
+        Return a Series containing counts of each category.
+
+        Every category will have an entry, even those with a count of 0.
+
+        Parameters
+        ----------
+        dropna : bool, default True
+            Don't include counts of NaN.
+
+        Returns
+        -------
+        counts : Series
+
+        See Also
+        --------
+        Series.value_counts
+        """
+        from pandas import (
+            CategoricalIndex,
+            Series,
+        )
+
+        code, cat = self._codes, self.categories
+        ncat, mask = (len(cat), code >= 0)
+        ix, clean = np.arange(ncat), mask.all()
+
+        if dropna or clean:
+            obs = code if clean else code[mask]
+            count = np.bincount(obs, minlength=ncat or 0)
+        else:
+            count = np.bincount(np.where(mask, code, ncat))
+            ix = np.append(ix, -1)
+
+        ix = coerce_indexer_dtype(ix, self.dtype.categories)
+        ix = self._from_backing_data(ix)
+
+        return Series(
+            count, index=CategoricalIndex(ix), dtype="int64", name="count", copy=False
+        )
+
+    # error: Argument 2 of "_empty" is incompatible with supertype
+    # "NDArrayBackedExtensionArray"; supertype defines the argument type as
+    # "ExtensionDtype"
+    @classmethod
+    def _empty(  # type: ignore[override]
+        cls, shape: Shape, dtype: CategoricalDtype
+    ) -> Self:
+        """
+        Analogous to np.empty(shape, dtype=dtype)
+
+        Parameters
+        ----------
+        shape : tuple[int]
+        dtype : CategoricalDtype
+        """
+        arr = cls._from_sequence([], dtype=dtype)
+
+        # We have to use np.zeros instead of np.empty otherwise the resulting
+        #  ndarray may contain codes not supported by this dtype, in which
+        #  case repr(result) could segfault.
+        backing = np.zeros(shape, dtype=arr._ndarray.dtype)
+
+        return arr._from_backing_data(backing)
+
+    def _internal_get_values(self) -> ArrayLike:
+        """
+        Return the values.
+
+        For internal compatibility with pandas formatting.
+
+        Returns
+        -------
+        np.ndarray or ExtensionArray
+            A numpy array or ExtensionArray of the same dtype as
+            categorical.categories.dtype.
+        """
+        # if we are a datetime and period index, return Index to keep metadata
+        if needs_i8_conversion(self.categories.dtype):
+            return self.categories.take(self._codes, fill_value=NaT)._values
+        elif is_integer_dtype(self.categories.dtype) and -1 in self._codes:
+            return (
+                self.categories.astype("object")
+                .take(self._codes, fill_value=np.nan)
+                ._values
+            )
+        return np.array(self)
+
+    def check_for_ordered(self, op) -> None:
+        """assert that we are ordered"""
+        if not self.ordered:
+            raise TypeError(
+                f"Categorical is not ordered for operation {op}\n"
+                "you can use .as_ordered() to change the "
+                "Categorical to an ordered one\n"
+            )
+
+    def argsort(
+        self, *, ascending: bool = True, kind: SortKind = "quicksort", **kwargs
+    ):
+        """
+        Return the indices that would sort the Categorical.
+
+        Missing values are sorted at the end.
+
+        Parameters
+        ----------
+        ascending : bool, default True
+            Whether the indices should result in an ascending
+            or descending sort.
+        kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, optional
+            Sorting algorithm.
+        **kwargs:
+            passed through to :func:`numpy.argsort`.
+
+        Returns
+        -------
+        np.ndarray[np.intp]
+
+        See Also
+        --------
+        numpy.ndarray.argsort
+
+        Notes
+        -----
+        While an ordering is applied to the category values, arg-sorting
+        in this context refers more to organizing and grouping together
+        based on matching category values. Thus, this function can be
+        called on an unordered Categorical instance unlike the functions
+        'Categorical.min' and 'Categorical.max'.
+
+        Examples
+        --------
+        >>> pd.Categorical(['b', 'b', 'a', 'c']).argsort()
+        array([2, 0, 1, 3])
+
+        >>> cat = pd.Categorical(['b', 'b', 'a', 'c'],
+        ...                      categories=['c', 'b', 'a'],
+        ...                      ordered=True)
+        >>> cat.argsort()
+        array([3, 0, 1, 2])
+
+        Missing values are placed at the end
+
+        >>> cat = pd.Categorical([2, None, 1])
+        >>> cat.argsort()
+        array([2, 0, 1])
+        """
+        return super().argsort(ascending=ascending, kind=kind, **kwargs)
+
+    @overload
+    def sort_values(
+        self,
+        *,
+        inplace: Literal[False] = ...,
+        ascending: bool = ...,
+        na_position: str = ...,
+    ) -> Self:
+        ...
+
+    @overload
+    def sort_values(
+        self, *, inplace: Literal[True], ascending: bool = ..., na_position: str = ...
+    ) -> None:
+        ...
+
+    def sort_values(
+        self,
+        *,
+        inplace: bool = False,
+        ascending: bool = True,
+        na_position: str = "last",
+    ) -> Self | None:
+        """
+        Sort the Categorical by category value returning a new
+        Categorical by default.
+
+        While an ordering is applied to the category values, sorting in this
+        context refers more to organizing and grouping together based on
+        matching category values. Thus, this function can be called on an
+        unordered Categorical instance unlike the functions 'Categorical.min'
+        and 'Categorical.max'.
+
+        Parameters
+        ----------
+        inplace : bool, default False
+            Do operation in place.
+        ascending : bool, default True
+            Order ascending. Passing False orders descending. The
+            ordering parameter provides the method by which the
+            category values are organized.
+        na_position : {'first', 'last'} (optional, default='last')
+            'first' puts NaNs at the beginning
+            'last' puts NaNs at the end
+
+        Returns
+        -------
+        Categorical or None
+
+        See Also
+        --------
+        Categorical.sort
+        Series.sort_values
+
+        Examples
+        --------
+        >>> c = pd.Categorical([1, 2, 2, 1, 5])
+        >>> c
+        [1, 2, 2, 1, 5]
+        Categories (3, int64): [1, 2, 5]
+        >>> c.sort_values()
+        [1, 1, 2, 2, 5]
+        Categories (3, int64): [1, 2, 5]
+        >>> c.sort_values(ascending=False)
+        [5, 2, 2, 1, 1]
+        Categories (3, int64): [1, 2, 5]
+
+        >>> c = pd.Categorical([1, 2, 2, 1, 5])
+
+        'sort_values' behaviour with NaNs. Note that 'na_position'
+        is independent of the 'ascending' parameter:
+
+        >>> c = pd.Categorical([np.nan, 2, 2, np.nan, 5])
+        >>> c
+        [NaN, 2, 2, NaN, 5]
+        Categories (2, int64): [2, 5]
+        >>> c.sort_values()
+        [2, 2, 5, NaN, NaN]
+        Categories (2, int64): [2, 5]
+        >>> c.sort_values(ascending=False)
+        [5, 2, 2, NaN, NaN]
+        Categories (2, int64): [2, 5]
+        >>> c.sort_values(na_position='first')
+        [NaN, NaN, 2, 2, 5]
+        Categories (2, int64): [2, 5]
+        >>> c.sort_values(ascending=False, na_position='first')
+        [NaN, NaN, 5, 2, 2]
+        Categories (2, int64): [2, 5]
+        """
+        inplace = validate_bool_kwarg(inplace, "inplace")
+        if na_position not in ["last", "first"]:
+            raise ValueError(f"invalid na_position: {repr(na_position)}")
+
+        sorted_idx = nargsort(self, ascending=ascending, na_position=na_position)
+
+        if not inplace:
+            codes = self._codes[sorted_idx]
+            return self._from_backing_data(codes)
+        self._codes[:] = self._codes[sorted_idx]
+        return None
+
+    def _rank(
+        self,
+        *,
+        axis: AxisInt = 0,
+        method: str = "average",
+        na_option: str = "keep",
+        ascending: bool = True,
+        pct: bool = False,
+    ):
+        """
+        See Series.rank.__doc__.
+        """
+        if axis != 0:
+            raise NotImplementedError
+        vff = self._values_for_rank()
+        return algorithms.rank(
+            vff,
+            axis=axis,
+            method=method,
+            na_option=na_option,
+            ascending=ascending,
+            pct=pct,
+        )
+
+    def _values_for_rank(self) -> np.ndarray:
+        """
+        For correctly ranking ordered categorical data. See GH#15420
+
+        Ordered categorical data should be ranked on the basis of
+        codes with -1 translated to NaN.
+
+        Returns
+        -------
+        numpy.array
+
+        """
+        from pandas import Series
+
+        if self.ordered:
+            values = self.codes
+            mask = values == -1
+            if mask.any():
+                values = values.astype("float64")
+                values[mask] = np.nan
+        elif is_any_real_numeric_dtype(self.categories.dtype):
+            values = np.array(self)
+        else:
+            #  reorder the categories (so rank can use the float codes)
+            #  instead of passing an object array to rank
+            values = np.array(
+                self.rename_categories(
+                    Series(self.categories, copy=False).rank().values
+                )
+            )
+        return values
+
+    def _hash_pandas_object(
+        self, *, encoding: str, hash_key: str, categorize: bool
+    ) -> npt.NDArray[np.uint64]:
+        """
+        Hash a Categorical by hashing its categories, and then mapping the codes
+        to the hashes.
+
+        Parameters
+        ----------
+        encoding : str
+        hash_key : str
+        categorize : bool
+            Ignored for Categorical.
+
+        Returns
+        -------
+        np.ndarray[uint64]
+        """
+        # Note we ignore categorize, as we are already Categorical.
+        from pandas.core.util.hashing import hash_array
+
+        # Convert ExtensionArrays to ndarrays
+        values = np.asarray(self.categories._values)
+        hashed = hash_array(values, encoding, hash_key, categorize=False)
+
+        # we have uint64, as we don't directly support missing values
+        # we don't want to use take_nd which will coerce to float
+        # instead, directly construct the result with a
+        # max(np.uint64) as the missing value indicator
+        #
+        # TODO: GH#15362
+
+        mask = self.isna()
+        if len(hashed):
+            result = hashed.take(self._codes)
+        else:
+            result = np.zeros(len(mask), dtype="uint64")
+
+        if mask.any():
+            result[mask] = lib.u8max
+
+        return result
+
+    # ------------------------------------------------------------------
+    # NDArrayBackedExtensionArray compat
+
+    @property
+    def _codes(self) -> np.ndarray:
+        return self._ndarray
+
+    def _box_func(self, i: int):
+        if i == -1:
+            return np.nan
+        return self.categories[i]
+
+    def _unbox_scalar(self, key) -> int:
+        # searchsorted is very performance sensitive. By converting codes
+        # to same dtype as self.codes, we get much faster performance.
+        code = self.categories.get_loc(key)
+        code = self._ndarray.dtype.type(code)
+        return code
+
+    # ------------------------------------------------------------------
+
+    def __iter__(self) -> Iterator:
+        """
+        Returns an Iterator over the values of this Categorical.
+        """
+        if self.ndim == 1:
+            return iter(self._internal_get_values().tolist())
+        else:
+            return (self[n] for n in range(len(self)))
+
+    def __contains__(self, key) -> bool:
+        """
+        Returns True if `key` is in this Categorical.
+        """
+        # if key is a NaN, check if any NaN is in self.
+        if is_valid_na_for_dtype(key, self.categories.dtype):
+            return bool(self.isna().any())
+
+        return contains(self, key, container=self._codes)
+
+    # ------------------------------------------------------------------
+    # Rendering Methods
+
+    def _formatter(self, boxed: bool = False):
+        # Returning None here will cause format_array to do inference.
+        return None
+
+    def _repr_categories(self) -> list[str]:
+        """
+        return the base repr for the categories
+        """
+        max_categories = (
+            10
+            if get_option("display.max_categories") == 0
+            else get_option("display.max_categories")
+        )
+        from pandas.io.formats import format as fmt
+
+        formatter = None
+        if self.categories.dtype == "str":
+            # the extension array formatter defaults to boxed=True in format_array
+            # override here to boxed=False to be consistent with QUOTE_NONNUMERIC
+            formatter = cast(ExtensionArray, self.categories._values)._formatter(
+                boxed=False
+            )
+
+        format_array = partial(
+            fmt.format_array, formatter=formatter, quoting=QUOTE_NONNUMERIC
+        )
+        if len(self.categories) > max_categories:
+            num = max_categories // 2
+            head = format_array(self.categories[:num]._values)
+            tail = format_array(self.categories[-num:]._values)
+            category_strs = head + ["..."] + tail
+        else:
+            category_strs = format_array(self.categories._values)
+
+        # Strip all leading spaces, which format_array adds for columns...
+        category_strs = [x.strip() for x in category_strs]
+        return category_strs
+
+    def _get_repr_footer(self) -> str:
+        """
+        Returns a string representation of the footer.
+        """
+        category_strs = self._repr_categories()
+        dtype = str(self.categories.dtype)
+        levheader = f"Categories ({len(self.categories)}, {dtype}): "
+        width, _ = get_terminal_size()
+        max_width = get_option("display.width") or width
+        if console.in_ipython_frontend():
+            # 0 = no breaks
+            max_width = 0
+        levstring = ""
+        start = True
+        cur_col_len = len(levheader)  # header
+        sep_len, sep = (3, " < ") if self.ordered else (2, ", ")
+        linesep = f"{sep.rstrip()}\n"  # remove whitespace
+        for val in category_strs:
+            if max_width != 0 and cur_col_len + sep_len + len(val) > max_width:
+                levstring += linesep + (" " * (len(levheader) + 1))
+                cur_col_len = len(levheader) + 1  # header + a whitespace
+            elif not start:
+                levstring += sep
+                cur_col_len += len(val)
+            levstring += val
+            start = False
+        # replace to simple save space by
+        return f"{levheader}[{levstring.replace(' < ... < ', ' ... ')}]"
+
+    def _get_values_repr(self) -> str:
+        from pandas.io.formats import format as fmt
+
+        assert len(self) > 0
+
+        vals = self._internal_get_values()
+        fmt_values = fmt.format_array(
+            vals,
+            None,
+            float_format=None,
+            na_rep="NaN",
+            quoting=QUOTE_NONNUMERIC,
+        )
+
+        fmt_values = [i.strip() for i in fmt_values]
+        joined = ", ".join(fmt_values)
+        result = "[" + joined + "]"
+        return result
+
+    def __repr__(self) -> str:
+        """
+        String representation.
+        """
+        footer = self._get_repr_footer()
+        length = len(self)
+        max_len = 10
+        if length > max_len:
+            # In long cases we do not display all entries, so we add Length
+            #  information to the __repr__.
+            num = max_len // 2
+            head = self[:num]._get_values_repr()
+            tail = self[-(max_len - num) :]._get_values_repr()
+            body = f"{head[:-1]}, ..., {tail[1:]}"
+            length_info = f"Length: {len(self)}"
+            result = f"{body}\n{length_info}\n{footer}"
+        elif length > 0:
+            body = self._get_values_repr()
+            result = f"{body}\n{footer}"
+        else:
+            # In the empty case we use a comma instead of newline to get
+            #  a more compact __repr__
+            body = "[]"
+            result = f"{body}, {footer}"
+
+        return result
+
+    # ------------------------------------------------------------------
+
+    def _validate_listlike(self, value):
+        # NB: here we assume scalar-like tuples have already been excluded
+        value = extract_array(value, extract_numpy=True)
+
+        # require identical categories set
+        if isinstance(value, Categorical):
+            if self.dtype != value.dtype:
+                raise TypeError(
+                    "Cannot set a Categorical with another, "
+                    "without identical categories"
+                )
+            # dtype equality implies categories_match_up_to_permutation
+            value = self._encode_with_my_categories(value)
+            return value._codes
+
+        from pandas import Index
+
+        # tupleize_cols=False for e.g. test_fillna_iterable_category GH#41914
+        to_add = Index._with_infer(value, tupleize_cols=False).difference(
+            self.categories
+        )
+
+        # no assignments of values not in categories, but it's always ok to set
+        # something to np.nan
+        if len(to_add) and not isna(to_add).all():
+            raise TypeError(
+                "Cannot setitem on a Categorical with a new "
+                "category, set the categories first"
+            )
+
+        codes = self.categories.get_indexer(value)
+        return codes.astype(self._ndarray.dtype, copy=False)
+
+    def _reverse_indexer(self) -> dict[Hashable, npt.NDArray[np.intp]]:
+        """
+        Compute the inverse of a categorical, returning
+        a dict of categories -> indexers.
+
+        *This is an internal function*
+
+        Returns
+        -------
+        Dict[Hashable, np.ndarray[np.intp]]
+            dict of categories -> indexers
+
+        Examples
+        --------
+        >>> c = pd.Categorical(list('aabca'))
+        >>> c
+        ['a', 'a', 'b', 'c', 'a']
+        Categories (3, object): ['a', 'b', 'c']
+        >>> c.categories
+        Index(['a', 'b', 'c'], dtype='object')
+        >>> c.codes
+        array([0, 0, 1, 2, 0], dtype=int8)
+        >>> c._reverse_indexer()
+        {'a': array([0, 1, 4]), 'b': array([2]), 'c': array([3])}
+
+        """
+        categories = self.categories
+        r, counts = libalgos.groupsort_indexer(
+            ensure_platform_int(self.codes), categories.size
+        )
+        counts = ensure_int64(counts).cumsum()
+        _result = (r[start:end] for start, end in zip(counts, counts[1:]))
+        return dict(zip(categories, _result))
+
+    # ------------------------------------------------------------------
+    # Reductions
+
+    def _reduce(
+        self, name: str, *, skipna: bool = True, keepdims: bool = False, **kwargs
+    ):
+        result = super()._reduce(name, skipna=skipna, keepdims=keepdims, **kwargs)
+        if name in ["argmax", "argmin"]:
+            # don't wrap in Categorical!
+            return result
+        if keepdims:
+            return type(self)(result, dtype=self.dtype)
+        else:
+            return result
+
+    def min(self, *, skipna: bool = True, **kwargs):
+        """
+        The minimum value of the object.
+
+        Only ordered `Categoricals` have a minimum!
+
+        Raises
+        ------
+        TypeError
+            If the `Categorical` is not `ordered`.
+
+        Returns
+        -------
+        min : the minimum of this `Categorical`, NA value if empty
+        """
+        nv.validate_minmax_axis(kwargs.get("axis", 0))
+        nv.validate_min((), kwargs)
+        self.check_for_ordered("min")
+
+        if not len(self._codes):
+            return self.dtype.na_value
+
+        good = self._codes != -1
+        if not good.all():
+            if skipna and good.any():
+                pointer = self._codes[good].min()
+            else:
+                return np.nan
+        else:
+            pointer = self._codes.min()
+        return self._wrap_reduction_result(None, pointer)
+
+    def max(self, *, skipna: bool = True, **kwargs):
+        """
+        The maximum value of the object.
+
+        Only ordered `Categoricals` have a maximum!
+
+        Raises
+        ------
+        TypeError
+            If the `Categorical` is not `ordered`.
+
+        Returns
+        -------
+        max : the maximum of this `Categorical`, NA if array is empty
+        """
+        nv.validate_minmax_axis(kwargs.get("axis", 0))
+        nv.validate_max((), kwargs)
+        self.check_for_ordered("max")
+
+        if not len(self._codes):
+            return self.dtype.na_value
+
+        good = self._codes != -1
+        if not good.all():
+            if skipna and good.any():
+                pointer = self._codes[good].max()
+            else:
+                return np.nan
+        else:
+            pointer = self._codes.max()
+        return self._wrap_reduction_result(None, pointer)
+
+    def _mode(self, dropna: bool = True) -> Categorical:
+        codes = self._codes
+        mask = None
+        if dropna:
+            mask = self.isna()
+
+        res_codes = algorithms.mode(codes, mask=mask)
+        res_codes = cast(np.ndarray, res_codes)
+        assert res_codes.dtype == codes.dtype
+        res = self._from_backing_data(res_codes)
+        return res
+
+    # ------------------------------------------------------------------
+    # ExtensionArray Interface
+
+    def unique(self) -> Self:
+        """
+        Return the ``Categorical`` which ``categories`` and ``codes`` are
+        unique.
+
+        .. versionchanged:: 1.3.0
+
+            Previously, unused categories were dropped from the new categories.
+
+        Returns
+        -------
+        Categorical
+
+        See Also
+        --------
+        pandas.unique
+        CategoricalIndex.unique
+        Series.unique : Return unique values of Series object.
+
+        Examples
+        --------
+        >>> pd.Categorical(list("baabc")).unique()
+        ['b', 'a', 'c']
+        Categories (3, object): ['a', 'b', 'c']
+        >>> pd.Categorical(list("baab"), categories=list("abc"), ordered=True).unique()
+        ['b', 'a']
+        Categories (3, object): ['a' < 'b' < 'c']
+        """
+        # pylint: disable=useless-parent-delegation
+        return super().unique()
+
+    def equals(self, other: object) -> bool:
+        """
+        Returns True if categorical arrays are equal.
+
+        Parameters
+        ----------
+        other : `Categorical`
+
+        Returns
+        -------
+        bool
+        """
+        if not isinstance(other, Categorical):
+            return False
+        elif self._categories_match_up_to_permutation(other):
+            other = self._encode_with_my_categories(other)
+            return np.array_equal(self._codes, other._codes)
+        return False
+
+    @classmethod
+    def _concat_same_type(cls, to_concat: Sequence[Self], axis: AxisInt = 0) -> Self:
+        from pandas.core.dtypes.concat import union_categoricals
+
+        first = to_concat[0]
+        if axis >= first.ndim:
+            raise ValueError(
+                f"axis {axis} is out of bounds for array of dimension {first.ndim}"
+            )
+
+        if axis == 1:
+            # Flatten, concatenate then reshape
+            if not all(x.ndim == 2 for x in to_concat):
+                raise ValueError
+
+            # pass correctly-shaped to union_categoricals
+            tc_flat = []
+            for obj in to_concat:
+                tc_flat.extend([obj[:, i] for i in range(obj.shape[1])])
+
+            res_flat = cls._concat_same_type(tc_flat, axis=0)
+
+            result = res_flat.reshape(len(first), -1, order="F")
+            return result
+
+        result = union_categoricals(to_concat)
+        return result
+
+    # ------------------------------------------------------------------
+
+    def _encode_with_my_categories(self, other: Categorical) -> Categorical:
+        """
+        Re-encode another categorical using this Categorical's categories.
+
+        Notes
+        -----
+        This assumes we have already checked
+        self._categories_match_up_to_permutation(other).
+        """
+        # Indexing on codes is more efficient if categories are the same,
+        #  so we can apply some optimizations based on the degree of
+        #  dtype-matching.
+        codes = recode_for_categories(
+            other.codes, other.categories, self.categories, copy=False
+        )
+        return self._from_backing_data(codes)
+
+    def _categories_match_up_to_permutation(self, other: Categorical) -> bool:
+        """
+        Returns True if categoricals are the same dtype
+          same categories, and same ordered
+
+        Parameters
+        ----------
+        other : Categorical
+
+        Returns
+        -------
+        bool
+        """
+        return hash(self.dtype) == hash(other.dtype)
+
+    def describe(self) -> DataFrame:
+        """
+        Describes this Categorical
+
+        Returns
+        -------
+        description: `DataFrame`
+            A dataframe with frequency and counts by category.
+        """
+        counts = self.value_counts(dropna=False)
+        freqs = counts / counts.sum()
+
+        from pandas import Index
+        from pandas.core.reshape.concat import concat
+
+        result = concat([counts, freqs], axis=1)
+        result.columns = Index(["counts", "freqs"])
+        result.index.name = "categories"
+
+        return result
+
+    def isin(self, values: ArrayLike) -> npt.NDArray[np.bool_]:
+        """
+        Check whether `values` are contained in Categorical.
+
+        Return a boolean NumPy Array showing whether each element in
+        the Categorical matches an element in the passed sequence of
+        `values` exactly.
+
+        Parameters
+        ----------
+        values : np.ndarray or ExtensionArray
+            The sequence of values to test. Passing in a single string will
+            raise a ``TypeError``. Instead, turn a single string into a
+            list of one element.
+
+        Returns
+        -------
+        np.ndarray[bool]
+
+        Raises
+        ------
+        TypeError
+          * If `values` is not a set or list-like
+
+        See Also
+        --------
+        pandas.Series.isin : Equivalent method on Series.
+
+        Examples
+        --------
+        >>> s = pd.Categorical(['lama', 'cow', 'lama', 'beetle', 'lama',
+        ...                'hippo'])
+        >>> s.isin(['cow', 'lama'])
+        array([ True,  True,  True, False,  True, False])
+
+        Passing a single string as ``s.isin('lama')`` will raise an error. Use
+        a list of one element instead:
+
+        >>> s.isin(['lama'])
+        array([ True, False,  True, False,  True, False])
+        """
+        null_mask = np.asarray(isna(values))
+        code_values = self.categories.get_indexer_for(values)
+        code_values = code_values[null_mask | (code_values >= 0)]
+        return algorithms.isin(self.codes, code_values)
+
+    def _replace(self, *, to_replace, value, inplace: bool = False):
+        from pandas import Index
+
+        orig_dtype = self.dtype
+
+        inplace = validate_bool_kwarg(inplace, "inplace")
+        cat = self if inplace else self.copy()
+
+        mask = isna(np.asarray(value))
+        if mask.any():
+            removals = np.asarray(to_replace)[mask]
+            removals = cat.categories[cat.categories.isin(removals)]
+            new_cat = cat.remove_categories(removals)
+            NDArrayBacked.__init__(cat, new_cat.codes, new_cat.dtype)
+
+        ser = cat.categories.to_series()
+        ser = ser.replace(to_replace=to_replace, value=value)
+
+        all_values = Index(ser)
+
+        # GH51016: maintain order of existing categories
+        idxr = cat.categories.get_indexer_for(all_values)
+        locs = np.arange(len(ser))
+        locs = np.where(idxr == -1, locs, idxr)
+        locs = locs.argsort()
+
+        new_categories = ser.take(locs)
+        new_categories = new_categories.drop_duplicates(keep="first")
+        new_categories = Index(new_categories)
+        new_codes = recode_for_categories(
+            cat._codes, all_values, new_categories, copy=False
+        )
+        new_dtype = CategoricalDtype(new_categories, ordered=self.dtype.ordered)
+        NDArrayBacked.__init__(cat, new_codes, new_dtype)
+
+        if new_dtype != orig_dtype:
+            warnings.warn(
+                # GH#55147
+                "The behavior of Series.replace (and DataFrame.replace) with "
+                "CategoricalDtype is deprecated. In a future version, replace "
+                "will only be used for cases that preserve the categories. "
+                "To change the categories, use ser.cat.rename_categories "
+                "instead.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+        if not inplace:
+            return cat
+
+    # ------------------------------------------------------------------------
+    # String methods interface
+    def _str_map(
+        self, f, na_value=lib.no_default, dtype=np.dtype("object"), convert: bool = True
+    ):
+        # Optimization to apply the callable `f` to the categories once
+        # and rebuild the result by `take`ing from the result with the codes.
+        # Returns the same type as the object-dtype implementation though.
+        categories = self.categories
+        codes = self.codes
+        if categories.dtype == "string":
+            result = categories.array._str_map(f, na_value, dtype)  # type: ignore[attr-defined]
+            if (
+                categories.dtype.na_value is np.nan  # type: ignore[union-attr]
+                and is_bool_dtype(dtype)
+                and (na_value is lib.no_default or isna(na_value))
+            ):
+                # NaN propagates as False for functions with boolean return type
+                na_value = False
+        else:
+            from pandas.core.arrays import NumpyExtensionArray
+
+            result = NumpyExtensionArray(categories.to_numpy())._str_map(
+                f, na_value, dtype
+            )
+        return take_nd(result, codes, fill_value=na_value)
+
+    def _str_get_dummies(self, sep: str = "|"):
+        # sep may not be in categories. Just bail on this.
+        from pandas.core.arrays import NumpyExtensionArray
+
+        return NumpyExtensionArray(self.to_numpy(str, na_value="NaN"))._str_get_dummies(
+            sep
+        )
+
+    # ------------------------------------------------------------------------
+    # GroupBy Methods
+
+    def _groupby_op(
+        self,
+        *,
+        how: str,
+        has_dropped_na: bool,
+        min_count: int,
+        ngroups: int,
+        ids: npt.NDArray[np.intp],
+        **kwargs,
+    ):
+        from pandas.core.groupby.ops import WrappedCythonOp
+
+        kind = WrappedCythonOp.get_kind_from_how(how)
+        op = WrappedCythonOp(how=how, kind=kind, has_dropped_na=has_dropped_na)
+
+        dtype = self.dtype
+        if how in ["sum", "prod", "cumsum", "cumprod", "skew"]:
+            raise TypeError(f"{dtype} type does not support {how} operations")
+        if how in ["min", "max", "rank", "idxmin", "idxmax"] and not dtype.ordered:
+            # raise TypeError instead of NotImplementedError to ensure we
+            #  don't go down a group-by-group path, since in the empty-groups
+            #  case that would fail to raise
+            raise TypeError(f"Cannot perform {how} with non-ordered Categorical")
+        if how not in [
+            "rank",
+            "any",
+            "all",
+            "first",
+            "last",
+            "min",
+            "max",
+            "idxmin",
+            "idxmax",
+        ]:
+            if kind == "transform":
+                raise TypeError(f"{dtype} type does not support {how} operations")
+            raise TypeError(f"{dtype} dtype does not support aggregation '{how}'")
+
+        result_mask = None
+        mask = self.isna()
+        if how == "rank":
+            assert self.ordered  # checked earlier
+            npvalues = self._ndarray
+        elif how in ["first", "last", "min", "max", "idxmin", "idxmax"]:
+            npvalues = self._ndarray
+            result_mask = np.zeros(ngroups, dtype=bool)
+        else:
+            # any/all
+            npvalues = self.astype(bool)
+
+        res_values = op._cython_op_ndim_compat(
+            npvalues,
+            min_count=min_count,
+            ngroups=ngroups,
+            comp_ids=ids,
+            mask=mask,
+            result_mask=result_mask,
+            **kwargs,
+        )
+
+        if how in op.cast_blocklist:
+            return res_values
+        elif how in ["first", "last", "min", "max"]:
+            res_values[result_mask == 1] = -1
+        return self._from_backing_data(res_values)
+
+
+# The Series.cat accessor
+
+
+@delegate_names(
+    delegate=Categorical, accessors=["categories", "ordered"], typ="property"
+)
+@delegate_names(
+    delegate=Categorical,
+    accessors=[
+        "rename_categories",
+        "reorder_categories",
+        "add_categories",
+        "remove_categories",
+        "remove_unused_categories",
+        "set_categories",
+        "as_ordered",
+        "as_unordered",
+    ],
+    typ="method",
+)
+class CategoricalAccessor(PandasDelegate, PandasObject, NoNewAttributesMixin):
+    """
+    Accessor object for categorical properties of the Series values.
+
+    Parameters
+    ----------
+    data : Series or CategoricalIndex
+
+    Examples
+    --------
+    >>> s = pd.Series(list("abbccc")).astype("category")
+    >>> s
+    0    a
+    1    b
+    2    b
+    3    c
+    4    c
+    5    c
+    dtype: category
+    Categories (3, object): ['a', 'b', 'c']
+
+    >>> s.cat.categories
+    Index(['a', 'b', 'c'], dtype='object')
+
+    >>> s.cat.rename_categories(list("cba"))
+    0    c
+    1    b
+    2    b
+    3    a
+    4    a
+    5    a
+    dtype: category
+    Categories (3, object): ['c', 'b', 'a']
+
+    >>> s.cat.reorder_categories(list("cba"))
+    0    a
+    1    b
+    2    b
+    3    c
+    4    c
+    5    c
+    dtype: category
+    Categories (3, object): ['c', 'b', 'a']
+
+    >>> s.cat.add_categories(["d", "e"])
+    0    a
+    1    b
+    2    b
+    3    c
+    4    c
+    5    c
+    dtype: category
+    Categories (5, object): ['a', 'b', 'c', 'd', 'e']
+
+    >>> s.cat.remove_categories(["a", "c"])
+    0    NaN
+    1      b
+    2      b
+    3    NaN
+    4    NaN
+    5    NaN
+    dtype: category
+    Categories (1, object): ['b']
+
+    >>> s1 = s.cat.add_categories(["d", "e"])
+    >>> s1.cat.remove_unused_categories()
+    0    a
+    1    b
+    2    b
+    3    c
+    4    c
+    5    c
+    dtype: category
+    Categories (3, object): ['a', 'b', 'c']
+
+    >>> s.cat.set_categories(list("abcde"))
+    0    a
+    1    b
+    2    b
+    3    c
+    4    c
+    5    c
+    dtype: category
+    Categories (5, object): ['a', 'b', 'c', 'd', 'e']
+
+    >>> s.cat.as_ordered()
+    0    a
+    1    b
+    2    b
+    3    c
+    4    c
+    5    c
+    dtype: category
+    Categories (3, object): ['a' < 'b' < 'c']
+
+    >>> s.cat.as_unordered()
+    0    a
+    1    b
+    2    b
+    3    c
+    4    c
+    5    c
+    dtype: category
+    Categories (3, object): ['a', 'b', 'c']
+    """
+
+    def __init__(self, data) -> None:
+        self._validate(data)
+        self._parent = data.values
+        self._index = data.index
+        self._name = data.name
+        self._freeze()
+
+    @staticmethod
+    def _validate(data):
+        if not isinstance(data.dtype, CategoricalDtype):
+            raise AttributeError("Can only use .cat accessor with a 'category' dtype")
+
+    def _delegate_property_get(self, name: str):
+        return getattr(self._parent, name)
+
+    # error: Signature of "_delegate_property_set" incompatible with supertype
+    # "PandasDelegate"
+    def _delegate_property_set(self, name: str, new_values):  # type: ignore[override]
+        return setattr(self._parent, name, new_values)
+
+    @property
+    def codes(self) -> Series:
+        """
+        Return Series of codes as well as the index.
+
+        Examples
+        --------
+        >>> raw_cate = pd.Categorical(["a", "b", "c", "a"], categories=["a", "b"])
+        >>> ser = pd.Series(raw_cate)
+        >>> ser.cat.codes
+        0   0
+        1   1
+        2  -1
+        3   0
+        dtype: int8
+        """
+        from pandas import Series
+
+        return Series(self._parent.codes, index=self._index)
+
+    def _delegate_method(self, name: str, *args, **kwargs):
+        from pandas import Series
+
+        method = getattr(self._parent, name)
+        res = method(*args, **kwargs)
+        if res is not None:
+            return Series(res, index=self._index, name=self._name)
+
+
+# utility routines
+
+
+def _get_codes_for_values(
+    values: Index | Series | ExtensionArray | np.ndarray,
+    categories: Index,
+) -> np.ndarray:
+    """
+    utility routine to turn values into codes given the specified categories
+
+    If `values` is known to be a Categorical, use recode_for_categories instead.
+    """
+    codes = categories.get_indexer_for(values)
+    return coerce_indexer_dtype(codes, categories)
+
+
+def recode_for_categories(
+    codes: np.ndarray, old_categories, new_categories, copy: bool = True
+) -> np.ndarray:
+    """
+    Convert a set of codes for to a new set of categories
+
+    Parameters
+    ----------
+    codes : np.ndarray
+    old_categories, new_categories : Index
+    copy: bool, default True
+        Whether to copy if the codes are unchanged.
+
+    Returns
+    -------
+    new_codes : np.ndarray[np.int64]
+
+    Examples
+    --------
+    >>> old_cat = pd.Index(['b', 'a', 'c'])
+    >>> new_cat = pd.Index(['a', 'b'])
+    >>> codes = np.array([0, 1, 1, 2])
+    >>> recode_for_categories(codes, old_cat, new_cat)
+    array([ 1,  0,  0, -1], dtype=int8)
+    """
+    if len(old_categories) == 0:
+        # All null anyway, so just retain the nulls
+        if copy:
+            return codes.copy()
+        return codes
+    elif new_categories.equals(old_categories):
+        # Same categories, so no need to actually recode
+        if copy:
+            return codes.copy()
+        return codes
+
+    indexer = coerce_indexer_dtype(
+        new_categories.get_indexer_for(old_categories), new_categories
+    )
+    new_codes = take_nd(indexer, codes, fill_value=-1)
+    return new_codes
+
+
+def factorize_from_iterable(values) -> tuple[np.ndarray, Index]:
+    """
+    Factorize an input `values` into `categories` and `codes`. Preserves
+    categorical dtype in `categories`.
+
+    Parameters
+    ----------
+    values : list-like
+
+    Returns
+    -------
+    codes : ndarray
+    categories : Index
+        If `values` has a categorical dtype, then `categories` is
+        a CategoricalIndex keeping the categories and order of `values`.
+    """
+    from pandas import CategoricalIndex
+
+    if not is_list_like(values):
+        raise TypeError("Input must be list-like")
+
+    categories: Index
+
+    vdtype = getattr(values, "dtype", None)
+    if isinstance(vdtype, CategoricalDtype):
+        values = extract_array(values)
+        # The Categorical we want to build has the same categories
+        # as values but its codes are by def [0, ..., len(n_categories) - 1]
+        cat_codes = np.arange(len(values.categories), dtype=values.codes.dtype)
+        cat = Categorical.from_codes(cat_codes, dtype=values.dtype, validate=False)
+
+        categories = CategoricalIndex(cat)
+        codes = values.codes
+    else:
+        # The value of ordered is irrelevant since we don't use cat as such,
+        # but only the resulting categories, the order of which is independent
+        # from ordered. Set ordered to False as default. See GH #15457
+        cat = Categorical(values, ordered=False)
+        categories = cat.categories
+        codes = cat.codes
+    return codes, categories
+
+
+def factorize_from_iterables(iterables) -> tuple[list[np.ndarray], list[Index]]:
+    """
+    A higher-level wrapper over `factorize_from_iterable`.
+
+    Parameters
+    ----------
+    iterables : list-like of list-likes
+
+    Returns
+    -------
+    codes : list of ndarrays
+    categories : list of Indexes
+
+    Notes
+    -----
+    See `factorize_from_iterable` for more info.
+    """
+    if len(iterables) == 0:
+        # For consistency, it should return two empty lists.
+        return [], []
+
+    codes, categories = zip(*(factorize_from_iterable(it) for it in iterables))
+    return list(codes), list(categories)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/datetimelike.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/datetimelike.py
new file mode 100644
index 0000000000000000000000000000000000000000..cfe1f3acd914344d5ec161b40a8cd494f03353dc
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/datetimelike.py
@@ -0,0 +1,2583 @@
+from __future__ import annotations
+
+from datetime import (
+    datetime,
+    timedelta,
+)
+from functools import wraps
+import operator
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Literal,
+    Union,
+    cast,
+    final,
+    overload,
+)
+import warnings
+
+import numpy as np
+
+from pandas._config import using_string_dtype
+
+from pandas._libs import (
+    algos,
+    lib,
+)
+from pandas._libs.arrays import NDArrayBacked
+from pandas._libs.tslibs import (
+    BaseOffset,
+    IncompatibleFrequency,
+    NaT,
+    NaTType,
+    Period,
+    Resolution,
+    Tick,
+    Timedelta,
+    Timestamp,
+    add_overflowsafe,
+    astype_overflowsafe,
+    get_unit_from_dtype,
+    iNaT,
+    ints_to_pydatetime,
+    ints_to_pytimedelta,
+    periods_per_day,
+    to_offset,
+)
+from pandas._libs.tslibs.fields import (
+    RoundTo,
+    round_nsint64,
+)
+from pandas._libs.tslibs.np_datetime import compare_mismatched_resolutions
+from pandas._libs.tslibs.timedeltas import get_unit_for_round
+from pandas._libs.tslibs.timestamps import integer_op_not_supported
+from pandas._typing import (
+    ArrayLike,
+    AxisInt,
+    DatetimeLikeScalar,
+    Dtype,
+    DtypeObj,
+    F,
+    InterpolateOptions,
+    NpDtype,
+    PositionalIndexer2D,
+    PositionalIndexerTuple,
+    ScalarIndexer,
+    Self,
+    SequenceIndexer,
+    TimeAmbiguous,
+    TimeNonexistent,
+    npt,
+)
+from pandas.compat.numpy import function as nv
+from pandas.errors import (
+    AbstractMethodError,
+    InvalidComparison,
+    PerformanceWarning,
+)
+from pandas.util._decorators import (
+    Appender,
+    Substitution,
+    cache_readonly,
+)
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.cast import construct_1d_object_array_from_listlike
+from pandas.core.dtypes.common import (
+    is_all_strings,
+    is_integer_dtype,
+    is_list_like,
+    is_object_dtype,
+    is_string_dtype,
+    pandas_dtype,
+)
+from pandas.core.dtypes.dtypes import (
+    ArrowDtype,
+    CategoricalDtype,
+    DatetimeTZDtype,
+    ExtensionDtype,
+    PeriodDtype,
+)
+from pandas.core.dtypes.generic import (
+    ABCCategorical,
+    ABCMultiIndex,
+)
+from pandas.core.dtypes.missing import (
+    is_valid_na_for_dtype,
+    isna,
+)
+
+from pandas.core import (
+    algorithms,
+    missing,
+    nanops,
+    ops,
+)
+from pandas.core.algorithms import (
+    isin,
+    map_array,
+    unique1d,
+)
+from pandas.core.array_algos import datetimelike_accumulations
+from pandas.core.arraylike import OpsMixin
+from pandas.core.arrays._mixins import (
+    NDArrayBackedExtensionArray,
+    ravel_compat,
+)
+from pandas.core.arrays.arrow.array import ArrowExtensionArray
+from pandas.core.arrays.base import ExtensionArray
+from pandas.core.arrays.integer import IntegerArray
+import pandas.core.common as com
+from pandas.core.construction import (
+    array as pd_array,
+    ensure_wrapped_if_datetimelike,
+    extract_array,
+)
+from pandas.core.indexers import (
+    check_array_indexer,
+    check_setitem_lengths,
+)
+from pandas.core.ops.common import unpack_zerodim_and_defer
+from pandas.core.ops.invalid import (
+    invalid_comparison,
+    make_invalid_op,
+)
+
+from pandas.tseries import frequencies
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Iterator,
+        Sequence,
+    )
+
+    from pandas import Index
+    from pandas.core.arrays import (
+        DatetimeArray,
+        PeriodArray,
+        TimedeltaArray,
+    )
+
+DTScalarOrNaT = Union[DatetimeLikeScalar, NaTType]
+
+
+def _make_unpacked_invalid_op(op_name: str):
+    op = make_invalid_op(op_name)
+    return unpack_zerodim_and_defer(op_name)(op)
+
+
+def _period_dispatch(meth: F) -> F:
+    """
+    For PeriodArray methods, dispatch to DatetimeArray and re-wrap the results
+    in PeriodArray.  We cannot use ._ndarray directly for the affected
+    methods because the i8 data has different semantics on NaT values.
+    """
+
+    @wraps(meth)
+    def new_meth(self, *args, **kwargs):
+        if not isinstance(self.dtype, PeriodDtype):
+            return meth(self, *args, **kwargs)
+
+        arr = self.view("M8[ns]")
+        result = meth(arr, *args, **kwargs)
+        if result is NaT:
+            return NaT
+        elif isinstance(result, Timestamp):
+            return self._box_func(result._value)
+
+        res_i8 = result.view("i8")
+        return self._from_backing_data(res_i8)
+
+    return cast(F, new_meth)
+
+
+# error: Definition of "_concat_same_type" in base class "NDArrayBacked" is
+# incompatible with definition in base class "ExtensionArray"
+class DatetimeLikeArrayMixin(  # type: ignore[misc]
+    OpsMixin, NDArrayBackedExtensionArray
+):
+    """
+    Shared Base/Mixin class for DatetimeArray, TimedeltaArray, PeriodArray
+
+    Assumes that __new__/__init__ defines:
+        _ndarray
+
+    and that inheriting subclass implements:
+        freq
+    """
+
+    # _infer_matches -> which infer_dtype strings are close enough to our own
+    _infer_matches: tuple[str, ...]
+    _is_recognized_dtype: Callable[[DtypeObj], bool]
+    _recognized_scalars: tuple[type, ...]
+    _ndarray: np.ndarray
+    freq: BaseOffset | None
+
+    @cache_readonly
+    def _can_hold_na(self) -> bool:
+        return True
+
+    def __init__(
+        self, data, dtype: Dtype | None = None, freq=None, copy: bool = False
+    ) -> None:
+        raise AbstractMethodError(self)
+
+    @property
+    def _scalar_type(self) -> type[DatetimeLikeScalar]:
+        """
+        The scalar associated with this datelike
+
+        * PeriodArray : Period
+        * DatetimeArray : Timestamp
+        * TimedeltaArray : Timedelta
+        """
+        raise AbstractMethodError(self)
+
+    def _scalar_from_string(self, value: str) -> DTScalarOrNaT:
+        """
+        Construct a scalar type from a string.
+
+        Parameters
+        ----------
+        value : str
+
+        Returns
+        -------
+        Period, Timestamp, or Timedelta, or NaT
+            Whatever the type of ``self._scalar_type`` is.
+
+        Notes
+        -----
+        This should call ``self._check_compatible_with`` before
+        unboxing the result.
+        """
+        raise AbstractMethodError(self)
+
+    def _unbox_scalar(
+        self, value: DTScalarOrNaT
+    ) -> np.int64 | np.datetime64 | np.timedelta64:
+        """
+        Unbox the integer value of a scalar `value`.
+
+        Parameters
+        ----------
+        value : Period, Timestamp, Timedelta, or NaT
+            Depending on subclass.
+
+        Returns
+        -------
+        int
+
+        Examples
+        --------
+        >>> arr = pd.array(np.array(['1970-01-01'], 'datetime64[ns]'))
+        >>> arr._unbox_scalar(arr[0])
+        numpy.datetime64('1970-01-01T00:00:00.000000000')
+        """
+        raise AbstractMethodError(self)
+
+    def _check_compatible_with(self, other: DTScalarOrNaT) -> None:
+        """
+        Verify that `self` and `other` are compatible.
+
+        * DatetimeArray verifies that the timezones (if any) match
+        * PeriodArray verifies that the freq matches
+        * Timedelta has no verification
+
+        In each case, NaT is considered compatible.
+
+        Parameters
+        ----------
+        other
+
+        Raises
+        ------
+        Exception
+        """
+        raise AbstractMethodError(self)
+
+    # ------------------------------------------------------------------
+
+    def _box_func(self, x):
+        """
+        box function to get object from internal representation
+        """
+        raise AbstractMethodError(self)
+
+    def _box_values(self, values) -> np.ndarray:
+        """
+        apply box func to passed values
+        """
+        return lib.map_infer(values, self._box_func, convert=False)
+
+    def __iter__(self) -> Iterator:
+        if self.ndim > 1:
+            return (self[n] for n in range(len(self)))
+        else:
+            return (self._box_func(v) for v in self.asi8)
+
+    @property
+    def asi8(self) -> npt.NDArray[np.int64]:
+        """
+        Integer representation of the values.
+
+        Returns
+        -------
+        ndarray
+            An ndarray with int64 dtype.
+        """
+        # do not cache or you'll create a memory leak
+        return self._ndarray.view("i8")
+
+    # ----------------------------------------------------------------
+    # Rendering Methods
+
+    def _format_native_types(
+        self, *, na_rep: str | float = "NaT", date_format=None
+    ) -> npt.NDArray[np.object_]:
+        """
+        Helper method for astype when converting to strings.
+
+        Returns
+        -------
+        ndarray[str]
+        """
+        raise AbstractMethodError(self)
+
+    def _formatter(self, boxed: bool = False):
+        # TODO: Remove Datetime & DatetimeTZ formatters.
+        return "'{}'".format
+
+    # ----------------------------------------------------------------
+    # Array-Like / EA-Interface Methods
+
+    def __array__(
+        self, dtype: NpDtype | None = None, copy: bool | None = None
+    ) -> np.ndarray:
+        # used for Timedelta/DatetimeArray, overwritten by PeriodArray
+        if is_object_dtype(dtype):
+            if copy is False:
+                warnings.warn(
+                    "Starting with NumPy 2.0, the behavior of the 'copy' keyword has "
+                    "changed and passing 'copy=False' raises an error when returning "
+                    "a zero-copy NumPy array is not possible. pandas will follow this "
+                    "behavior starting with pandas 3.0.\nThis conversion to NumPy "
+                    "requires a copy, but 'copy=False' was passed. Consider using "
+                    "'np.asarray(..)' instead.",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+
+            return np.array(list(self), dtype=object)
+
+        if copy is True:
+            return np.array(self._ndarray, dtype=dtype)
+        return self._ndarray
+
+    @overload
+    def __getitem__(self, item: ScalarIndexer) -> DTScalarOrNaT:
+        ...
+
+    @overload
+    def __getitem__(
+        self,
+        item: SequenceIndexer | PositionalIndexerTuple,
+    ) -> Self:
+        ...
+
+    def __getitem__(self, key: PositionalIndexer2D) -> Self | DTScalarOrNaT:
+        """
+        This getitem defers to the underlying array, which by-definition can
+        only handle list-likes, slices, and integer scalars
+        """
+        # Use cast as we know we will get back a DatetimeLikeArray or DTScalar,
+        # but skip evaluating the Union at runtime for performance
+        # (see https://github.com/pandas-dev/pandas/pull/44624)
+        result = cast("Union[Self, DTScalarOrNaT]", super().__getitem__(key))
+        if lib.is_scalar(result):
+            return result
+        else:
+            # At this point we know the result is an array.
+            result = cast(Self, result)
+        result._freq = self._get_getitem_freq(key)
+        return result
+
+    def _get_getitem_freq(self, key) -> BaseOffset | None:
+        """
+        Find the `freq` attribute to assign to the result of a __getitem__ lookup.
+        """
+        is_period = isinstance(self.dtype, PeriodDtype)
+        if is_period:
+            freq = self.freq
+        elif self.ndim != 1:
+            freq = None
+        else:
+            key = check_array_indexer(self, key)  # maybe ndarray[bool] -> slice
+            freq = None
+            if isinstance(key, slice):
+                if self.freq is not None and key.step is not None:
+                    freq = key.step * self.freq
+                else:
+                    freq = self.freq
+            elif key is Ellipsis:
+                # GH#21282 indexing with Ellipsis is similar to a full slice,
+                #  should preserve `freq` attribute
+                freq = self.freq
+            elif com.is_bool_indexer(key):
+                new_key = lib.maybe_booleans_to_slice(key.view(np.uint8))
+                if isinstance(new_key, slice):
+                    return self._get_getitem_freq(new_key)
+        return freq
+
+    # error: Argument 1 of "__setitem__" is incompatible with supertype
+    # "ExtensionArray"; supertype defines the argument type as "Union[int,
+    # ndarray]"
+    def __setitem__(
+        self,
+        key: int | Sequence[int] | Sequence[bool] | slice,
+        value: NaTType | Any | Sequence[Any],
+    ) -> None:
+        # I'm fudging the types a bit here. "Any" above really depends
+        # on type(self). For PeriodArray, it's Period (or stuff coercible
+        # to a period in from_sequence). For DatetimeArray, it's Timestamp...
+        # I don't know if mypy can do that, possibly with Generics.
+        # https://mypy.readthedocs.io/en/latest/generics.html
+
+        no_op = check_setitem_lengths(key, value, self)
+
+        # Calling super() before the no_op short-circuit means that we raise
+        #  on invalid 'value' even if this is a no-op, e.g. wrong-dtype empty array.
+        super().__setitem__(key, value)
+
+        if no_op:
+            return
+
+        self._maybe_clear_freq()
+
+    def _maybe_clear_freq(self) -> None:
+        # inplace operations like __setitem__ may invalidate the freq of
+        # DatetimeArray and TimedeltaArray
+        pass
+
+    def astype(self, dtype, copy: bool = True):
+        # Some notes on cases we don't have to handle here in the base class:
+        #   1. PeriodArray.astype handles period -> period
+        #   2. DatetimeArray.astype handles conversion between tz.
+        #   3. DatetimeArray.astype handles datetime -> period
+        dtype = pandas_dtype(dtype)
+
+        if dtype == object:
+            if self.dtype.kind == "M":
+                self = cast("DatetimeArray", self)
+                # *much* faster than self._box_values
+                #  for e.g. test_get_loc_tuple_monotonic_above_size_cutoff
+                i8data = self.asi8
+                converted = ints_to_pydatetime(
+                    i8data,
+                    tz=self.tz,
+                    box="timestamp",
+                    reso=self._creso,
+                )
+                return converted
+
+            elif self.dtype.kind == "m":
+                return ints_to_pytimedelta(self._ndarray, box=True)
+
+            return self._box_values(self.asi8.ravel()).reshape(self.shape)
+
+        elif is_string_dtype(dtype):
+            if isinstance(dtype, ExtensionDtype):
+                arr_object = self._format_native_types(na_rep=dtype.na_value)  # type: ignore[arg-type]
+                cls = dtype.construct_array_type()
+                return cls._from_sequence(arr_object, dtype=dtype, copy=False)
+            else:
+                return self._format_native_types()
+
+        elif isinstance(dtype, ExtensionDtype):
+            return super().astype(dtype, copy=copy)
+        elif dtype.kind in "iu":
+            # we deliberately ignore int32 vs. int64 here.
+            # See https://github.com/pandas-dev/pandas/issues/24381 for more.
+            values = self.asi8
+            if dtype != np.int64:
+                raise TypeError(
+                    f"Converting from {self.dtype} to {dtype} is not supported. "
+                    "Do obj.astype('int64').astype(dtype) instead"
+                )
+
+            if copy:
+                values = values.copy()
+            return values
+        elif (dtype.kind in "mM" and self.dtype != dtype) or dtype.kind == "f":
+            # disallow conversion between datetime/timedelta,
+            # and conversions for any datetimelike to float
+            msg = f"Cannot cast {type(self).__name__} to dtype {dtype}"
+            raise TypeError(msg)
+        else:
+            return np.asarray(self, dtype=dtype)
+
+    @overload
+    def view(self) -> Self:
+        ...
+
+    @overload
+    def view(self, dtype: Literal["M8[ns]"]) -> DatetimeArray:
+        ...
+
+    @overload
+    def view(self, dtype: Literal["m8[ns]"]) -> TimedeltaArray:
+        ...
+
+    @overload
+    def view(self, dtype: Dtype | None = ...) -> ArrayLike:
+        ...
+
+    # pylint: disable-next=useless-parent-delegation
+    def view(self, dtype: Dtype | None = None) -> ArrayLike:
+        # we need to explicitly call super() method as long as the `@overload`s
+        #  are present in this file.
+        return super().view(dtype)
+
+    # ------------------------------------------------------------------
+    # Validation Methods
+    # TODO: try to de-duplicate these, ensure identical behavior
+
+    def _validate_comparison_value(self, other):
+        if isinstance(other, str):
+            try:
+                # GH#18435 strings get a pass from tzawareness compat
+                other = self._scalar_from_string(other)
+            except (ValueError, IncompatibleFrequency):
+                # failed to parse as Timestamp/Timedelta/Period
+                raise InvalidComparison(other)
+
+        if isinstance(other, self._recognized_scalars) or other is NaT:
+            other = self._scalar_type(other)
+            try:
+                self._check_compatible_with(other)
+            except (TypeError, IncompatibleFrequency) as err:
+                # e.g. tzawareness mismatch
+                raise InvalidComparison(other) from err
+
+        elif not is_list_like(other):
+            raise InvalidComparison(other)
+
+        elif len(other) != len(self):
+            raise ValueError("Lengths must match")
+
+        else:
+            try:
+                other = self._validate_listlike(other, allow_object=True)
+                self._check_compatible_with(other)
+            except (TypeError, IncompatibleFrequency) as err:
+                if is_object_dtype(getattr(other, "dtype", None)):
+                    # We will have to operate element-wise
+                    pass
+                else:
+                    raise InvalidComparison(other) from err
+
+        return other
+
+    def _validate_scalar(
+        self,
+        value,
+        *,
+        allow_listlike: bool = False,
+        unbox: bool = True,
+    ):
+        """
+        Validate that the input value can be cast to our scalar_type.
+
+        Parameters
+        ----------
+        value : object
+        allow_listlike: bool, default False
+            When raising an exception, whether the message should say
+            listlike inputs are allowed.
+        unbox : bool, default True
+            Whether to unbox the result before returning.  Note: unbox=False
+            skips the setitem compatibility check.
+
+        Returns
+        -------
+        self._scalar_type or NaT
+        """
+        if isinstance(value, self._scalar_type):
+            pass
+
+        elif isinstance(value, str):
+            # NB: Careful about tzawareness
+            try:
+                value = self._scalar_from_string(value)
+            except ValueError as err:
+                msg = self._validation_error_message(value, allow_listlike)
+                raise TypeError(msg) from err
+
+        elif is_valid_na_for_dtype(value, self.dtype):
+            # GH#18295
+            value = NaT
+
+        elif isna(value):
+            # if we are dt64tz and value is dt64("NaT"), dont cast to NaT,
+            #  or else we'll fail to raise in _unbox_scalar
+            msg = self._validation_error_message(value, allow_listlike)
+            raise TypeError(msg)
+
+        elif isinstance(value, self._recognized_scalars):
+            # error: Argument 1 to "Timestamp" has incompatible type "object"; expected
+            # "integer[Any] | float | str | date | datetime | datetime64"
+            value = self._scalar_type(value)  # type: ignore[arg-type]
+
+        else:
+            msg = self._validation_error_message(value, allow_listlike)
+            raise TypeError(msg)
+
+        if not unbox:
+            # NB: In general NDArrayBackedExtensionArray will unbox here;
+            #  this option exists to prevent a performance hit in
+            #  TimedeltaIndex.get_loc
+            return value
+        return self._unbox_scalar(value)
+
+    def _validation_error_message(self, value, allow_listlike: bool = False) -> str:
+        """
+        Construct an exception message on validation error.
+
+        Some methods allow only scalar inputs, while others allow either scalar
+        or listlike.
+
+        Parameters
+        ----------
+        allow_listlike: bool, default False
+
+        Returns
+        -------
+        str
+        """
+        if hasattr(value, "dtype") and getattr(value, "ndim", 0) > 0:
+            msg_got = f"{value.dtype} array"
+        else:
+            msg_got = f"'{type(value).__name__}'"
+        if allow_listlike:
+            msg = (
+                f"value should be a '{self._scalar_type.__name__}', 'NaT', "
+                f"or array of those. Got {msg_got} instead."
+            )
+        else:
+            msg = (
+                f"value should be a '{self._scalar_type.__name__}' or 'NaT'. "
+                f"Got {msg_got} instead."
+            )
+        return msg
+
+    def _validate_listlike(self, value, allow_object: bool = False):
+        if isinstance(value, type(self)):
+            if self.dtype.kind in "mM" and not allow_object:
+                # error: "DatetimeLikeArrayMixin" has no attribute "as_unit"
+                value = value.as_unit(self.unit, round_ok=False)  # type: ignore[attr-defined]
+            return value
+
+        if isinstance(value, list) and len(value) == 0:
+            # We treat empty list as our own dtype.
+            return type(self)._from_sequence([], dtype=self.dtype)
+
+        if hasattr(value, "dtype") and value.dtype == object:
+            # `array` below won't do inference if value is an Index or Series.
+            #  so do so here.  in the Index case, inferred_type may be cached.
+            if lib.infer_dtype(value) in self._infer_matches:
+                try:
+                    value = type(self)._from_sequence(value)
+                except (ValueError, TypeError):
+                    if allow_object:
+                        return value
+                    msg = self._validation_error_message(value, True)
+                    raise TypeError(msg)
+
+        # Do type inference if necessary up front (after unpacking
+        # NumpyExtensionArray)
+        # e.g. we passed PeriodIndex.values and got an ndarray of Periods
+        value = extract_array(value, extract_numpy=True)
+        value = pd_array(value)
+        value = extract_array(value, extract_numpy=True)
+
+        if is_all_strings(value):
+            # We got a StringArray
+            try:
+                # TODO: Could use from_sequence_of_strings if implemented
+                # Note: passing dtype is necessary for PeriodArray tests
+                value = type(self)._from_sequence(value, dtype=self.dtype)
+            except ValueError:
+                pass
+
+        if isinstance(value.dtype, CategoricalDtype):
+            # e.g. we have a Categorical holding self.dtype
+            if value.categories.dtype == self.dtype:
+                # TODO: do we need equal dtype or just comparable?
+                value = value._internal_get_values()
+                value = extract_array(value, extract_numpy=True)
+
+        if allow_object and is_object_dtype(value.dtype):
+            pass
+
+        elif not type(self)._is_recognized_dtype(value.dtype):
+            msg = self._validation_error_message(value, True)
+            raise TypeError(msg)
+
+        if self.dtype.kind in "mM" and not allow_object:
+            # error: "DatetimeLikeArrayMixin" has no attribute "as_unit"
+            value = value.as_unit(self.unit, round_ok=False)  # type: ignore[attr-defined]
+        return value
+
+    def _validate_setitem_value(self, value):
+        if is_list_like(value):
+            value = self._validate_listlike(value)
+        else:
+            return self._validate_scalar(value, allow_listlike=True)
+
+        return self._unbox(value)
+
+    @final
+    def _unbox(self, other) -> np.int64 | np.datetime64 | np.timedelta64 | np.ndarray:
+        """
+        Unbox either a scalar with _unbox_scalar or an instance of our own type.
+        """
+        if lib.is_scalar(other):
+            other = self._unbox_scalar(other)
+        else:
+            # same type as self
+            self._check_compatible_with(other)
+            other = other._ndarray
+        return other
+
+    # ------------------------------------------------------------------
+    # Additional array methods
+    #  These are not part of the EA API, but we implement them because
+    #  pandas assumes they're there.
+
+    @ravel_compat
+    def map(self, mapper, na_action=None):
+        from pandas import Index
+
+        result = map_array(self, mapper, na_action=na_action)
+        result = Index(result)
+
+        if isinstance(result, ABCMultiIndex):
+            return result.to_numpy()
+        else:
+            return result.array
+
+    def isin(self, values: ArrayLike) -> npt.NDArray[np.bool_]:
+        """
+        Compute boolean array of whether each value is found in the
+        passed set of values.
+
+        Parameters
+        ----------
+        values : np.ndarray or ExtensionArray
+
+        Returns
+        -------
+        ndarray[bool]
+        """
+        if values.dtype.kind in "fiuc":
+            # TODO: de-duplicate with equals, validate_comparison_value
+            return np.zeros(self.shape, dtype=bool)
+
+        values = ensure_wrapped_if_datetimelike(values)
+
+        if not isinstance(values, type(self)):
+            inferable = [
+                "timedelta",
+                "timedelta64",
+                "datetime",
+                "datetime64",
+                "date",
+                "period",
+            ]
+            if values.dtype == object:
+                values = lib.maybe_convert_objects(
+                    values,  # type: ignore[arg-type]
+                    convert_non_numeric=True,
+                    dtype_if_all_nat=self.dtype,
+                )
+                if values.dtype != object:
+                    return self.isin(values)
+
+                inferred = lib.infer_dtype(values, skipna=False)
+                if inferred not in inferable:
+                    if inferred == "string":
+                        pass
+
+                    elif "mixed" in inferred:
+                        return isin(self.astype(object), values)
+                    else:
+                        return np.zeros(self.shape, dtype=bool)
+
+            try:
+                values = type(self)._from_sequence(values)
+            except ValueError:
+                return isin(self.astype(object), values)
+            else:
+                warnings.warn(
+                    # GH#53111
+                    f"The behavior of 'isin' with dtype={self.dtype} and "
+                    "castable values (e.g. strings) is deprecated. In a "
+                    "future version, these will not be considered matching "
+                    "by isin. Explicitly cast to the appropriate dtype before "
+                    "calling isin instead.",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+
+        if self.dtype.kind in "mM":
+            self = cast("DatetimeArray | TimedeltaArray", self)
+            # error: Item "ExtensionArray" of "ExtensionArray | ndarray[Any, Any]"
+            # has no attribute "as_unit"
+            values = values.as_unit(self.unit)  # type: ignore[union-attr]
+
+        try:
+            # error: Argument 1 to "_check_compatible_with" of "DatetimeLikeArrayMixin"
+            # has incompatible type "ExtensionArray | ndarray[Any, Any]"; expected
+            # "Period | Timestamp | Timedelta | NaTType"
+            self._check_compatible_with(values)  # type: ignore[arg-type]
+        except (TypeError, ValueError):
+            # Includes tzawareness mismatch and IncompatibleFrequencyError
+            return np.zeros(self.shape, dtype=bool)
+
+        # error: Item "ExtensionArray" of "ExtensionArray | ndarray[Any, Any]"
+        # has no attribute "asi8"
+        return isin(self.asi8, values.asi8)  # type: ignore[union-attr]
+
+    # ------------------------------------------------------------------
+    # Null Handling
+
+    def isna(self) -> npt.NDArray[np.bool_]:
+        return self._isnan
+
+    @property  # NB: override with cache_readonly in immutable subclasses
+    def _isnan(self) -> npt.NDArray[np.bool_]:
+        """
+        return if each value is nan
+        """
+        return self.asi8 == iNaT
+
+    @property  # NB: override with cache_readonly in immutable subclasses
+    def _hasna(self) -> bool:
+        """
+        return if I have any nans; enables various perf speedups
+        """
+        return bool(self._isnan.any())
+
+    def _maybe_mask_results(
+        self, result: np.ndarray, fill_value=iNaT, convert=None
+    ) -> np.ndarray:
+        """
+        Parameters
+        ----------
+        result : np.ndarray
+        fill_value : object, default iNaT
+        convert : str, dtype or None
+
+        Returns
+        -------
+        result : ndarray with values replace by the fill_value
+
+        mask the result if needed, convert to the provided dtype if its not
+        None
+
+        This is an internal routine.
+        """
+        if self._hasna:
+            if convert:
+                result = result.astype(convert)
+            if fill_value is None:
+                fill_value = np.nan
+            np.putmask(result, self._isnan, fill_value)
+        return result
+
+    # ------------------------------------------------------------------
+    # Frequency Properties/Methods
+
+    @property
+    def freqstr(self) -> str | None:
+        """
+        Return the frequency object as a string if it's set, otherwise None.
+
+        Examples
+        --------
+        For DatetimeIndex:
+
+        >>> idx = pd.DatetimeIndex(["1/1/2020 10:00:00+00:00"], freq="D")
+        >>> idx.freqstr
+        'D'
+
+        The frequency can be inferred if there are more than 2 points:
+
+        >>> idx = pd.DatetimeIndex(["2018-01-01", "2018-01-03", "2018-01-05"],
+        ...                        freq="infer")
+        >>> idx.freqstr
+        '2D'
+
+        For PeriodIndex:
+
+        >>> idx = pd.PeriodIndex(["2023-1", "2023-2", "2023-3"], freq="M")
+        >>> idx.freqstr
+        'M'
+        """
+        if self.freq is None:
+            return None
+        return self.freq.freqstr
+
+    @property  # NB: override with cache_readonly in immutable subclasses
+    def inferred_freq(self) -> str | None:
+        """
+        Tries to return a string representing a frequency generated by infer_freq.
+
+        Returns None if it can't autodetect the frequency.
+
+        Examples
+        --------
+        For DatetimeIndex:
+
+        >>> idx = pd.DatetimeIndex(["2018-01-01", "2018-01-03", "2018-01-05"])
+        >>> idx.inferred_freq
+        '2D'
+
+        For TimedeltaIndex:
+
+        >>> tdelta_idx = pd.to_timedelta(["0 days", "10 days", "20 days"])
+        >>> tdelta_idx
+        TimedeltaIndex(['0 days', '10 days', '20 days'],
+                       dtype='timedelta64[ns]', freq=None)
+        >>> tdelta_idx.inferred_freq
+        '10D'
+        """
+        if self.ndim != 1:
+            return None
+        try:
+            return frequencies.infer_freq(self)
+        except ValueError:
+            return None
+
+    @property  # NB: override with cache_readonly in immutable subclasses
+    def _resolution_obj(self) -> Resolution | None:
+        freqstr = self.freqstr
+        if freqstr is None:
+            return None
+        try:
+            return Resolution.get_reso_from_freqstr(freqstr)
+        except KeyError:
+            return None
+
+    @property  # NB: override with cache_readonly in immutable subclasses
+    def resolution(self) -> str:
+        """
+        Returns day, hour, minute, second, millisecond or microsecond
+        """
+        # error: Item "None" of "Optional[Any]" has no attribute "attrname"
+        return self._resolution_obj.attrname  # type: ignore[union-attr]
+
+    # monotonicity/uniqueness properties are called via frequencies.infer_freq,
+    #  see GH#23789
+
+    @property
+    def _is_monotonic_increasing(self) -> bool:
+        return algos.is_monotonic(self.asi8, timelike=True)[0]
+
+    @property
+    def _is_monotonic_decreasing(self) -> bool:
+        return algos.is_monotonic(self.asi8, timelike=True)[1]
+
+    @property
+    def _is_unique(self) -> bool:
+        return len(unique1d(self.asi8.ravel("K"))) == self.size
+
+    # ------------------------------------------------------------------
+    # Arithmetic Methods
+
+    def _cmp_method(self, other, op):
+        if self.ndim > 1 and getattr(other, "shape", None) == self.shape:
+            # TODO: handle 2D-like listlikes
+            return op(self.ravel(), other.ravel()).reshape(self.shape)
+
+        try:
+            other = self._validate_comparison_value(other)
+        except InvalidComparison:
+            return invalid_comparison(self, other, op)
+
+        dtype = getattr(other, "dtype", None)
+        if is_object_dtype(dtype):
+            # We have to use comp_method_OBJECT_ARRAY instead of numpy
+            #  comparison otherwise it would raise when comparing to None
+            result = ops.comp_method_OBJECT_ARRAY(
+                op, np.asarray(self.astype(object)), other
+            )
+            return result
+        if other is NaT:
+            if op is operator.ne:
+                result = np.ones(self.shape, dtype=bool)
+            else:
+                result = np.zeros(self.shape, dtype=bool)
+            return result
+
+        if not isinstance(self.dtype, PeriodDtype):
+            self = cast(TimelikeOps, self)
+            if self._creso != other._creso:
+                if not isinstance(other, type(self)):
+                    # i.e. Timedelta/Timestamp, cast to ndarray and let
+                    #  compare_mismatched_resolutions handle broadcasting
+                    try:
+                        # GH#52080 see if we can losslessly cast to shared unit
+                        other = other.as_unit(self.unit, round_ok=False)
+                    except ValueError:
+                        other_arr = np.array(other.asm8)
+                        return compare_mismatched_resolutions(
+                            self._ndarray, other_arr, op
+                        )
+                else:
+                    other_arr = other._ndarray
+                    return compare_mismatched_resolutions(self._ndarray, other_arr, op)
+
+        other_vals = self._unbox(other)
+        # GH#37462 comparison on i8 values is almost 2x faster than M8/m8
+        result = op(self._ndarray.view("i8"), other_vals.view("i8"))
+
+        o_mask = isna(other)
+        mask = self._isnan | o_mask
+        if mask.any():
+            nat_result = op is operator.ne
+            np.putmask(result, mask, nat_result)
+
+        return result
+
+    # pow is invalid for all three subclasses; TimedeltaArray will override
+    #  the multiplication and division ops
+    __pow__ = _make_unpacked_invalid_op("__pow__")
+    __rpow__ = _make_unpacked_invalid_op("__rpow__")
+    __mul__ = _make_unpacked_invalid_op("__mul__")
+    __rmul__ = _make_unpacked_invalid_op("__rmul__")
+    __truediv__ = _make_unpacked_invalid_op("__truediv__")
+    __rtruediv__ = _make_unpacked_invalid_op("__rtruediv__")
+    __floordiv__ = _make_unpacked_invalid_op("__floordiv__")
+    __rfloordiv__ = _make_unpacked_invalid_op("__rfloordiv__")
+    __mod__ = _make_unpacked_invalid_op("__mod__")
+    __rmod__ = _make_unpacked_invalid_op("__rmod__")
+    __divmod__ = _make_unpacked_invalid_op("__divmod__")
+    __rdivmod__ = _make_unpacked_invalid_op("__rdivmod__")
+
+    @final
+    def _get_i8_values_and_mask(
+        self, other
+    ) -> tuple[int | npt.NDArray[np.int64], None | npt.NDArray[np.bool_]]:
+        """
+        Get the int64 values and b_mask to pass to add_overflowsafe.
+        """
+        if isinstance(other, Period):
+            i8values = other.ordinal
+            mask = None
+        elif isinstance(other, (Timestamp, Timedelta)):
+            i8values = other._value
+            mask = None
+        else:
+            # PeriodArray, DatetimeArray, TimedeltaArray
+            mask = other._isnan
+            i8values = other.asi8
+        return i8values, mask
+
+    @final
+    def _get_arithmetic_result_freq(self, other) -> BaseOffset | None:
+        """
+        Check if we can preserve self.freq in addition or subtraction.
+        """
+        # Adding or subtracting a Timedelta/Timestamp scalar is freq-preserving
+        #  whenever self.freq is a Tick
+        if isinstance(self.dtype, PeriodDtype):
+            return self.freq
+        elif not lib.is_scalar(other):
+            return None
+        elif isinstance(self.freq, Tick):
+            # In these cases
+            return self.freq
+        return None
+
+    @final
+    def _add_datetimelike_scalar(self, other) -> DatetimeArray:
+        if not lib.is_np_dtype(self.dtype, "m"):
+            raise TypeError(
+                f"cannot add {type(self).__name__} and {type(other).__name__}"
+            )
+
+        self = cast("TimedeltaArray", self)
+
+        from pandas.core.arrays import DatetimeArray
+        from pandas.core.arrays.datetimes import tz_to_dtype
+
+        assert other is not NaT
+        if isna(other):
+            # i.e. np.datetime64("NaT")
+            # In this case we specifically interpret NaT as a datetime, not
+            # the timedelta interpretation we would get by returning self + NaT
+            result = self._ndarray + NaT.to_datetime64().astype(f"M8[{self.unit}]")
+            # Preserve our resolution
+            return DatetimeArray._simple_new(result, dtype=result.dtype)
+
+        other = Timestamp(other)
+        self, other = self._ensure_matching_resos(other)
+        self = cast("TimedeltaArray", self)
+
+        other_i8, o_mask = self._get_i8_values_and_mask(other)
+        result = add_overflowsafe(self.asi8, np.asarray(other_i8, dtype="i8"))
+        res_values = result.view(f"M8[{self.unit}]")
+
+        dtype = tz_to_dtype(tz=other.tz, unit=self.unit)
+        res_values = result.view(f"M8[{self.unit}]")
+        new_freq = self._get_arithmetic_result_freq(other)
+        return DatetimeArray._simple_new(res_values, dtype=dtype, freq=new_freq)
+
+    @final
+    def _add_datetime_arraylike(self, other: DatetimeArray) -> DatetimeArray:
+        if not lib.is_np_dtype(self.dtype, "m"):
+            raise TypeError(
+                f"cannot add {type(self).__name__} and {type(other).__name__}"
+            )
+
+        # defer to DatetimeArray.__add__
+        return other + self
+
+    @final
+    def _sub_datetimelike_scalar(
+        self, other: datetime | np.datetime64
+    ) -> TimedeltaArray:
+        if self.dtype.kind != "M":
+            raise TypeError(f"cannot subtract a datelike from a {type(self).__name__}")
+
+        self = cast("DatetimeArray", self)
+        # subtract a datetime from myself, yielding a ndarray[timedelta64[ns]]
+
+        if isna(other):
+            # i.e. np.datetime64("NaT")
+            return self - NaT
+
+        ts = Timestamp(other)
+
+        self, ts = self._ensure_matching_resos(ts)
+        return self._sub_datetimelike(ts)
+
+    @final
+    def _sub_datetime_arraylike(self, other: DatetimeArray) -> TimedeltaArray:
+        if self.dtype.kind != "M":
+            raise TypeError(f"cannot subtract a datelike from a {type(self).__name__}")
+
+        if len(self) != len(other):
+            raise ValueError("cannot add indices of unequal length")
+
+        self = cast("DatetimeArray", self)
+
+        self, other = self._ensure_matching_resos(other)
+        return self._sub_datetimelike(other)
+
+    @final
+    def _sub_datetimelike(self, other: Timestamp | DatetimeArray) -> TimedeltaArray:
+        self = cast("DatetimeArray", self)
+
+        from pandas.core.arrays import TimedeltaArray
+
+        try:
+            self._assert_tzawareness_compat(other)
+        except TypeError as err:
+            new_message = str(err).replace("compare", "subtract")
+            raise type(err)(new_message) from err
+
+        other_i8, o_mask = self._get_i8_values_and_mask(other)
+        res_values = add_overflowsafe(self.asi8, np.asarray(-other_i8, dtype="i8"))
+        res_m8 = res_values.view(f"timedelta64[{self.unit}]")
+
+        new_freq = self._get_arithmetic_result_freq(other)
+        new_freq = cast("Tick | None", new_freq)
+        return TimedeltaArray._simple_new(res_m8, dtype=res_m8.dtype, freq=new_freq)
+
+    @final
+    def _add_period(self, other: Period) -> PeriodArray:
+        if not lib.is_np_dtype(self.dtype, "m"):
+            raise TypeError(f"cannot add Period to a {type(self).__name__}")
+
+        # We will wrap in a PeriodArray and defer to the reversed operation
+        from pandas.core.arrays.period import PeriodArray
+
+        i8vals = np.broadcast_to(other.ordinal, self.shape)
+        dtype = PeriodDtype(other.freq)
+        parr = PeriodArray(i8vals, dtype=dtype)
+        return parr + self
+
+    def _add_offset(self, offset):
+        raise AbstractMethodError(self)
+
+    def _add_timedeltalike_scalar(self, other):
+        """
+        Add a delta of a timedeltalike
+
+        Returns
+        -------
+        Same type as self
+        """
+        if isna(other):
+            # i.e np.timedelta64("NaT")
+            new_values = np.empty(self.shape, dtype="i8").view(self._ndarray.dtype)
+            new_values.fill(iNaT)
+            return type(self)._simple_new(new_values, dtype=self.dtype)
+
+        # PeriodArray overrides, so we only get here with DTA/TDA
+        self = cast("DatetimeArray | TimedeltaArray", self)
+        other = Timedelta(other)
+        self, other = self._ensure_matching_resos(other)
+        return self._add_timedeltalike(other)
+
+    def _add_timedelta_arraylike(self, other: TimedeltaArray):
+        """
+        Add a delta of a TimedeltaIndex
+
+        Returns
+        -------
+        Same type as self
+        """
+        # overridden by PeriodArray
+
+        if len(self) != len(other):
+            raise ValueError("cannot add indices of unequal length")
+
+        self = cast("DatetimeArray | TimedeltaArray", self)
+
+        self, other = self._ensure_matching_resos(other)
+        return self._add_timedeltalike(other)
+
+    @final
+    def _add_timedeltalike(self, other: Timedelta | TimedeltaArray):
+        self = cast("DatetimeArray | TimedeltaArray", self)
+
+        other_i8, o_mask = self._get_i8_values_and_mask(other)
+        new_values = add_overflowsafe(self.asi8, np.asarray(other_i8, dtype="i8"))
+        res_values = new_values.view(self._ndarray.dtype)
+
+        new_freq = self._get_arithmetic_result_freq(other)
+
+        # error: Argument "dtype" to "_simple_new" of "DatetimeArray" has
+        # incompatible type "Union[dtype[datetime64], DatetimeTZDtype,
+        # dtype[timedelta64]]"; expected "Union[dtype[datetime64], DatetimeTZDtype]"
+        return type(self)._simple_new(
+            res_values, dtype=self.dtype, freq=new_freq  # type: ignore[arg-type]
+        )
+
+    @final
+    def _add_nat(self):
+        """
+        Add pd.NaT to self
+        """
+        if isinstance(self.dtype, PeriodDtype):
+            raise TypeError(
+                f"Cannot add {type(self).__name__} and {type(NaT).__name__}"
+            )
+        self = cast("TimedeltaArray | DatetimeArray", self)
+
+        # GH#19124 pd.NaT is treated like a timedelta for both timedelta
+        # and datetime dtypes
+        result = np.empty(self.shape, dtype=np.int64)
+        result.fill(iNaT)
+        result = result.view(self._ndarray.dtype)  # preserve reso
+        # error: Argument "dtype" to "_simple_new" of "DatetimeArray" has
+        # incompatible type "Union[dtype[timedelta64], dtype[datetime64],
+        # DatetimeTZDtype]"; expected "Union[dtype[datetime64], DatetimeTZDtype]"
+        return type(self)._simple_new(
+            result, dtype=self.dtype, freq=None  # type: ignore[arg-type]
+        )
+
+    @final
+    def _sub_nat(self):
+        """
+        Subtract pd.NaT from self
+        """
+        # GH#19124 Timedelta - datetime is not in general well-defined.
+        # We make an exception for pd.NaT, which in this case quacks
+        # like a timedelta.
+        # For datetime64 dtypes by convention we treat NaT as a datetime, so
+        # this subtraction returns a timedelta64 dtype.
+        # For period dtype, timedelta64 is a close-enough return dtype.
+        result = np.empty(self.shape, dtype=np.int64)
+        result.fill(iNaT)
+        if self.dtype.kind in "mM":
+            # We can retain unit in dtype
+            self = cast("DatetimeArray| TimedeltaArray", self)
+            return result.view(f"timedelta64[{self.unit}]")
+        else:
+            return result.view("timedelta64[ns]")
+
+    @final
+    def _sub_periodlike(self, other: Period | PeriodArray) -> npt.NDArray[np.object_]:
+        # If the operation is well-defined, we return an object-dtype ndarray
+        # of DateOffsets.  Null entries are filled with pd.NaT
+        if not isinstance(self.dtype, PeriodDtype):
+            raise TypeError(
+                f"cannot subtract {type(other).__name__} from {type(self).__name__}"
+            )
+
+        self = cast("PeriodArray", self)
+        self._check_compatible_with(other)
+
+        other_i8, o_mask = self._get_i8_values_and_mask(other)
+        new_i8_data = add_overflowsafe(self.asi8, np.asarray(-other_i8, dtype="i8"))
+        new_data = np.array([self.freq.base * x for x in new_i8_data])
+
+        if o_mask is None:
+            # i.e. Period scalar
+            mask = self._isnan
+        else:
+            # i.e. PeriodArray
+            mask = self._isnan | o_mask
+        new_data[mask] = NaT
+        return new_data
+
+    @final
+    def _addsub_object_array(self, other: npt.NDArray[np.object_], op):
+        """
+        Add or subtract array-like of DateOffset objects
+
+        Parameters
+        ----------
+        other : np.ndarray[object]
+        op : {operator.add, operator.sub}
+
+        Returns
+        -------
+        np.ndarray[object]
+            Except in fastpath case with length 1 where we operate on the
+            contained scalar.
+        """
+        assert op in [operator.add, operator.sub]
+        if len(other) == 1 and self.ndim == 1:
+            # Note: without this special case, we could annotate return type
+            #  as ndarray[object]
+            # If both 1D then broadcasting is unambiguous
+            return op(self, other[0])
+
+        warnings.warn(
+            "Adding/subtracting object-dtype array to "
+            f"{type(self).__name__} not vectorized.",
+            PerformanceWarning,
+            stacklevel=find_stack_level(),
+        )
+
+        # Caller is responsible for broadcasting if necessary
+        assert self.shape == other.shape, (self.shape, other.shape)
+
+        res_values = op(self.astype("O"), np.asarray(other))
+        return res_values
+
+    def _accumulate(self, name: str, *, skipna: bool = True, **kwargs) -> Self:
+        if name not in {"cummin", "cummax"}:
+            raise TypeError(f"Accumulation {name} not supported for {type(self)}")
+
+        op = getattr(datetimelike_accumulations, name)
+        result = op(self.copy(), skipna=skipna, **kwargs)
+
+        return type(self)._simple_new(result, dtype=self.dtype)
+
+    @unpack_zerodim_and_defer("__add__")
+    def __add__(self, other):
+        other_dtype = getattr(other, "dtype", None)
+        other = ensure_wrapped_if_datetimelike(other)
+
+        # scalar others
+        if other is NaT:
+            result = self._add_nat()
+        elif isinstance(other, (Tick, timedelta, np.timedelta64)):
+            result = self._add_timedeltalike_scalar(other)
+        elif isinstance(other, BaseOffset):
+            # specifically _not_ a Tick
+            result = self._add_offset(other)
+        elif isinstance(other, (datetime, np.datetime64)):
+            result = self._add_datetimelike_scalar(other)
+        elif isinstance(other, Period) and lib.is_np_dtype(self.dtype, "m"):
+            result = self._add_period(other)
+        elif lib.is_integer(other):
+            # This check must come after the check for np.timedelta64
+            # as is_integer returns True for these
+            if not isinstance(self.dtype, PeriodDtype):
+                raise integer_op_not_supported(self)
+            obj = cast("PeriodArray", self)
+            result = obj._addsub_int_array_or_scalar(other * obj.dtype._n, operator.add)
+
+        # array-like others
+        elif lib.is_np_dtype(other_dtype, "m"):
+            # TimedeltaIndex, ndarray[timedelta64]
+            result = self._add_timedelta_arraylike(other)
+        elif is_object_dtype(other_dtype):
+            # e.g. Array/Index of DateOffset objects
+            result = self._addsub_object_array(other, operator.add)
+        elif lib.is_np_dtype(other_dtype, "M") or isinstance(
+            other_dtype, DatetimeTZDtype
+        ):
+            # DatetimeIndex, ndarray[datetime64]
+            return self._add_datetime_arraylike(other)
+        elif is_integer_dtype(other_dtype):
+            if not isinstance(self.dtype, PeriodDtype):
+                raise integer_op_not_supported(self)
+            obj = cast("PeriodArray", self)
+            result = obj._addsub_int_array_or_scalar(other * obj.dtype._n, operator.add)
+        else:
+            # Includes Categorical, other ExtensionArrays
+            # For PeriodDtype, if self is a TimedeltaArray and other is a
+            #  PeriodArray with  a timedelta-like (i.e. Tick) freq, this
+            #  operation is valid.  Defer to the PeriodArray implementation.
+            #  In remaining cases, this will end up raising TypeError.
+            return NotImplemented
+
+        if isinstance(result, np.ndarray) and lib.is_np_dtype(result.dtype, "m"):
+            from pandas.core.arrays import TimedeltaArray
+
+            return TimedeltaArray._from_sequence(result)
+        return result
+
+    def __radd__(self, other):
+        # alias for __add__
+        return self.__add__(other)
+
+    @unpack_zerodim_and_defer("__sub__")
+    def __sub__(self, other):
+        other_dtype = getattr(other, "dtype", None)
+        other = ensure_wrapped_if_datetimelike(other)
+
+        # scalar others
+        if other is NaT:
+            result = self._sub_nat()
+        elif isinstance(other, (Tick, timedelta, np.timedelta64)):
+            result = self._add_timedeltalike_scalar(-other)
+        elif isinstance(other, BaseOffset):
+            # specifically _not_ a Tick
+            result = self._add_offset(-other)
+        elif isinstance(other, (datetime, np.datetime64)):
+            result = self._sub_datetimelike_scalar(other)
+        elif lib.is_integer(other):
+            # This check must come after the check for np.timedelta64
+            # as is_integer returns True for these
+            if not isinstance(self.dtype, PeriodDtype):
+                raise integer_op_not_supported(self)
+            obj = cast("PeriodArray", self)
+            result = obj._addsub_int_array_or_scalar(other * obj.dtype._n, operator.sub)
+
+        elif isinstance(other, Period):
+            result = self._sub_periodlike(other)
+
+        # array-like others
+        elif lib.is_np_dtype(other_dtype, "m"):
+            # TimedeltaIndex, ndarray[timedelta64]
+            result = self._add_timedelta_arraylike(-other)
+        elif is_object_dtype(other_dtype):
+            # e.g. Array/Index of DateOffset objects
+            result = self._addsub_object_array(other, operator.sub)
+        elif lib.is_np_dtype(other_dtype, "M") or isinstance(
+            other_dtype, DatetimeTZDtype
+        ):
+            # DatetimeIndex, ndarray[datetime64]
+            result = self._sub_datetime_arraylike(other)
+        elif isinstance(other_dtype, PeriodDtype):
+            # PeriodIndex
+            result = self._sub_periodlike(other)
+        elif is_integer_dtype(other_dtype):
+            if not isinstance(self.dtype, PeriodDtype):
+                raise integer_op_not_supported(self)
+            obj = cast("PeriodArray", self)
+            result = obj._addsub_int_array_or_scalar(other * obj.dtype._n, operator.sub)
+        else:
+            # Includes ExtensionArrays, float_dtype
+            return NotImplemented
+
+        if isinstance(result, np.ndarray) and lib.is_np_dtype(result.dtype, "m"):
+            from pandas.core.arrays import TimedeltaArray
+
+            return TimedeltaArray._from_sequence(result)
+        return result
+
+    def __rsub__(self, other):
+        other_dtype = getattr(other, "dtype", None)
+        other_is_dt64 = lib.is_np_dtype(other_dtype, "M") or isinstance(
+            other_dtype, DatetimeTZDtype
+        )
+
+        if other_is_dt64 and lib.is_np_dtype(self.dtype, "m"):
+            # ndarray[datetime64] cannot be subtracted from self, so
+            # we need to wrap in DatetimeArray/Index and flip the operation
+            if lib.is_scalar(other):
+                # i.e. np.datetime64 object
+                return Timestamp(other) - self
+            if not isinstance(other, DatetimeLikeArrayMixin):
+                # Avoid down-casting DatetimeIndex
+                from pandas.core.arrays import DatetimeArray
+
+                other = DatetimeArray._from_sequence(other)
+            return other - self
+        elif self.dtype.kind == "M" and hasattr(other, "dtype") and not other_is_dt64:
+            # GH#19959 datetime - datetime is well-defined as timedelta,
+            # but any other type - datetime is not well-defined.
+            raise TypeError(
+                f"cannot subtract {type(self).__name__} from {type(other).__name__}"
+            )
+        elif isinstance(self.dtype, PeriodDtype) and lib.is_np_dtype(other_dtype, "m"):
+            # TODO: Can we simplify/generalize these cases at all?
+            raise TypeError(f"cannot subtract {type(self).__name__} from {other.dtype}")
+        elif lib.is_np_dtype(self.dtype, "m"):
+            self = cast("TimedeltaArray", self)
+            return (-self) + other
+
+        # We get here with e.g. datetime objects
+        return -(self - other)
+
+    def __iadd__(self, other) -> Self:
+        result = self + other
+        self[:] = result[:]
+
+        if not isinstance(self.dtype, PeriodDtype):
+            # restore freq, which is invalidated by setitem
+            self._freq = result.freq
+        return self
+
+    def __isub__(self, other) -> Self:
+        result = self - other
+        self[:] = result[:]
+
+        if not isinstance(self.dtype, PeriodDtype):
+            # restore freq, which is invalidated by setitem
+            self._freq = result.freq
+        return self
+
+    # --------------------------------------------------------------
+    # Reductions
+
+    @_period_dispatch
+    def _quantile(
+        self,
+        qs: npt.NDArray[np.float64],
+        interpolation: str,
+    ) -> Self:
+        return super()._quantile(qs=qs, interpolation=interpolation)
+
+    @_period_dispatch
+    def min(self, *, axis: AxisInt | None = None, skipna: bool = True, **kwargs):
+        """
+        Return the minimum value of the Array or minimum along
+        an axis.
+
+        See Also
+        --------
+        numpy.ndarray.min
+        Index.min : Return the minimum value in an Index.
+        Series.min : Return the minimum value in a Series.
+        """
+        nv.validate_min((), kwargs)
+        nv.validate_minmax_axis(axis, self.ndim)
+
+        result = nanops.nanmin(self._ndarray, axis=axis, skipna=skipna)
+        return self._wrap_reduction_result(axis, result)
+
+    @_period_dispatch
+    def max(self, *, axis: AxisInt | None = None, skipna: bool = True, **kwargs):
+        """
+        Return the maximum value of the Array or maximum along
+        an axis.
+
+        See Also
+        --------
+        numpy.ndarray.max
+        Index.max : Return the maximum value in an Index.
+        Series.max : Return the maximum value in a Series.
+        """
+        nv.validate_max((), kwargs)
+        nv.validate_minmax_axis(axis, self.ndim)
+
+        result = nanops.nanmax(self._ndarray, axis=axis, skipna=skipna)
+        return self._wrap_reduction_result(axis, result)
+
+    def mean(self, *, skipna: bool = True, axis: AxisInt | None = 0):
+        """
+        Return the mean value of the Array.
+
+        Parameters
+        ----------
+        skipna : bool, default True
+            Whether to ignore any NaT elements.
+        axis : int, optional, default 0
+
+        Returns
+        -------
+        scalar
+            Timestamp or Timedelta.
+
+        See Also
+        --------
+        numpy.ndarray.mean : Returns the average of array elements along a given axis.
+        Series.mean : Return the mean value in a Series.
+
+        Notes
+        -----
+        mean is only defined for Datetime and Timedelta dtypes, not for Period.
+
+        Examples
+        --------
+        For :class:`pandas.DatetimeIndex`:
+
+        >>> idx = pd.date_range('2001-01-01 00:00', periods=3)
+        >>> idx
+        DatetimeIndex(['2001-01-01', '2001-01-02', '2001-01-03'],
+                      dtype='datetime64[ns]', freq='D')
+        >>> idx.mean()
+        Timestamp('2001-01-02 00:00:00')
+
+        For :class:`pandas.TimedeltaIndex`:
+
+        >>> tdelta_idx = pd.to_timedelta([1, 2, 3], unit='D')
+        >>> tdelta_idx
+        TimedeltaIndex(['1 days', '2 days', '3 days'],
+                        dtype='timedelta64[ns]', freq=None)
+        >>> tdelta_idx.mean()
+        Timedelta('2 days 00:00:00')
+        """
+        if isinstance(self.dtype, PeriodDtype):
+            # See discussion in GH#24757
+            raise TypeError(
+                f"mean is not implemented for {type(self).__name__} since the "
+                "meaning is ambiguous.  An alternative is "
+                "obj.to_timestamp(how='start').mean()"
+            )
+
+        result = nanops.nanmean(
+            self._ndarray, axis=axis, skipna=skipna, mask=self.isna()
+        )
+        return self._wrap_reduction_result(axis, result)
+
+    @_period_dispatch
+    def median(self, *, axis: AxisInt | None = None, skipna: bool = True, **kwargs):
+        nv.validate_median((), kwargs)
+
+        if axis is not None and abs(axis) >= self.ndim:
+            raise ValueError("abs(axis) must be less than ndim")
+
+        result = nanops.nanmedian(self._ndarray, axis=axis, skipna=skipna)
+        return self._wrap_reduction_result(axis, result)
+
+    def _mode(self, dropna: bool = True):
+        mask = None
+        if dropna:
+            mask = self.isna()
+
+        i8modes = algorithms.mode(self.view("i8"), mask=mask)
+        npmodes = i8modes.view(self._ndarray.dtype)
+        npmodes = cast(np.ndarray, npmodes)
+        return self._from_backing_data(npmodes)
+
+    # ------------------------------------------------------------------
+    # GroupBy Methods
+
+    def _groupby_op(
+        self,
+        *,
+        how: str,
+        has_dropped_na: bool,
+        min_count: int,
+        ngroups: int,
+        ids: npt.NDArray[np.intp],
+        **kwargs,
+    ):
+        dtype = self.dtype
+        if dtype.kind == "M":
+            # Adding/multiplying datetimes is not valid
+            if how in ["sum", "prod", "cumsum", "cumprod", "var", "skew"]:
+                raise TypeError(f"datetime64 type does not support {how} operations")
+            if how in ["any", "all"]:
+                # GH#34479
+                warnings.warn(
+                    f"'{how}' with datetime64 dtypes is deprecated and will raise in a "
+                    f"future version. Use (obj != pd.Timestamp(0)).{how}() instead.",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+
+        elif isinstance(dtype, PeriodDtype):
+            # Adding/multiplying Periods is not valid
+            if how in ["sum", "prod", "cumsum", "cumprod", "var", "skew"]:
+                raise TypeError(f"Period type does not support {how} operations")
+            if how in ["any", "all"]:
+                # GH#34479
+                warnings.warn(
+                    f"'{how}' with PeriodDtype is deprecated and will raise in a "
+                    f"future version. Use (obj != pd.Period(0, freq)).{how}() instead.",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+        else:
+            # timedeltas we can add but not multiply
+            if how in ["prod", "cumprod", "skew", "var"]:
+                raise TypeError(f"timedelta64 type does not support {how} operations")
+
+        # All of the functions implemented here are ordinal, so we can
+        #  operate on the tz-naive equivalents
+        npvalues = self._ndarray.view("M8[ns]")
+
+        from pandas.core.groupby.ops import WrappedCythonOp
+
+        kind = WrappedCythonOp.get_kind_from_how(how)
+        op = WrappedCythonOp(how=how, kind=kind, has_dropped_na=has_dropped_na)
+
+        res_values = op._cython_op_ndim_compat(
+            npvalues,
+            min_count=min_count,
+            ngroups=ngroups,
+            comp_ids=ids,
+            mask=None,
+            **kwargs,
+        )
+
+        if op.how in op.cast_blocklist:
+            # i.e. how in ["rank"], since other cast_blocklist methods don't go
+            #  through cython_operation
+            return res_values
+
+        # We did a view to M8[ns] above, now we go the other direction
+        assert res_values.dtype == "M8[ns]"
+        if how in ["std", "sem"]:
+            from pandas.core.arrays import TimedeltaArray
+
+            if isinstance(self.dtype, PeriodDtype):
+                raise TypeError("'std' and 'sem' are not valid for PeriodDtype")
+            self = cast("DatetimeArray | TimedeltaArray", self)
+            new_dtype = f"m8[{self.unit}]"
+            res_values = res_values.view(new_dtype)
+            return TimedeltaArray._simple_new(res_values, dtype=res_values.dtype)
+
+        res_values = res_values.view(self._ndarray.dtype)
+        return self._from_backing_data(res_values)
+
+
+class DatelikeOps(DatetimeLikeArrayMixin):
+    """
+    Common ops for DatetimeIndex/PeriodIndex, but not TimedeltaIndex.
+    """
+
+    @Substitution(
+        URL="https://docs.python.org/3/library/datetime.html"
+        "#strftime-and-strptime-behavior"
+    )
+    def strftime(self, date_format: str) -> npt.NDArray[np.object_]:
+        """
+        Convert to Index using specified date_format.
+
+        Return an Index of formatted strings specified by date_format, which
+        supports the same string format as the python standard library. Details
+        of the string format can be found in `python string format
+        doc <%(URL)s>`__.
+
+        Formats supported by the C `strftime` API but not by the python string format
+        doc (such as `"%%R"`, `"%%r"`) are not officially supported and should be
+        preferably replaced with their supported equivalents (such as `"%%H:%%M"`,
+        `"%%I:%%M:%%S %%p"`).
+
+        Note that `PeriodIndex` support additional directives, detailed in
+        `Period.strftime`.
+
+        Parameters
+        ----------
+        date_format : str
+            Date format string (e.g. "%%Y-%%m-%%d").
+
+        Returns
+        -------
+        ndarray[object]
+            NumPy ndarray of formatted strings.
+
+        See Also
+        --------
+        to_datetime : Convert the given argument to datetime.
+        DatetimeIndex.normalize : Return DatetimeIndex with times to midnight.
+        DatetimeIndex.round : Round the DatetimeIndex to the specified freq.
+        DatetimeIndex.floor : Floor the DatetimeIndex to the specified freq.
+        Timestamp.strftime : Format a single Timestamp.
+        Period.strftime : Format a single Period.
+
+        Examples
+        --------
+        >>> rng = pd.date_range(pd.Timestamp("2018-03-10 09:00"),
+        ...                     periods=3, freq='s')
+        >>> rng.strftime('%%B %%d, %%Y, %%r')
+        Index(['March 10, 2018, 09:00:00 AM', 'March 10, 2018, 09:00:01 AM',
+               'March 10, 2018, 09:00:02 AM'],
+              dtype='object')
+        """
+        result = self._format_native_types(date_format=date_format, na_rep=np.nan)
+        if using_string_dtype():
+            from pandas import StringDtype
+
+            return pd_array(result, dtype=StringDtype(na_value=np.nan))  # type: ignore[return-value]
+        return result.astype(object, copy=False)
+
+
+_round_doc = """
+    Perform {op} operation on the data to the specified `freq`.
+
+    Parameters
+    ----------
+    freq : str or Offset
+        The frequency level to {op} the index to. Must be a fixed
+        frequency like 'S' (second) not 'ME' (month end). See
+        :ref:`frequency aliases ` for
+        a list of possible `freq` values.
+    ambiguous : 'infer', bool-ndarray, 'NaT', default 'raise'
+        Only relevant for DatetimeIndex:
+
+        - 'infer' will attempt to infer fall dst-transition hours based on
+          order
+        - bool-ndarray where True signifies a DST time, False designates
+          a non-DST time (note that this flag is only applicable for
+          ambiguous times)
+        - 'NaT' will return NaT where there are ambiguous times
+        - 'raise' will raise an AmbiguousTimeError if there are ambiguous
+          times.
+
+    nonexistent : 'shift_forward', 'shift_backward', 'NaT', timedelta, default 'raise'
+        A nonexistent time does not exist in a particular timezone
+        where clocks moved forward due to DST.
+
+        - 'shift_forward' will shift the nonexistent time forward to the
+          closest existing time
+        - 'shift_backward' will shift the nonexistent time backward to the
+          closest existing time
+        - 'NaT' will return NaT where there are nonexistent times
+        - timedelta objects will shift nonexistent times by the timedelta
+        - 'raise' will raise an NonExistentTimeError if there are
+          nonexistent times.
+
+    Returns
+    -------
+    DatetimeIndex, TimedeltaIndex, or Series
+        Index of the same type for a DatetimeIndex or TimedeltaIndex,
+        or a Series with the same index for a Series.
+
+    Raises
+    ------
+    ValueError if the `freq` cannot be converted.
+
+    Notes
+    -----
+    If the timestamps have a timezone, {op}ing will take place relative to the
+    local ("wall") time and re-localized to the same timezone. When {op}ing
+    near daylight savings time, use ``nonexistent`` and ``ambiguous`` to
+    control the re-localization behavior.
+
+    Examples
+    --------
+    **DatetimeIndex**
+
+    >>> rng = pd.date_range('1/1/2018 11:59:00', periods=3, freq='min')
+    >>> rng
+    DatetimeIndex(['2018-01-01 11:59:00', '2018-01-01 12:00:00',
+                   '2018-01-01 12:01:00'],
+                  dtype='datetime64[ns]', freq='min')
+    """
+
+_round_example = """>>> rng.round('h')
+    DatetimeIndex(['2018-01-01 12:00:00', '2018-01-01 12:00:00',
+                   '2018-01-01 12:00:00'],
+                  dtype='datetime64[ns]', freq=None)
+
+    **Series**
+
+    >>> pd.Series(rng).dt.round("h")
+    0   2018-01-01 12:00:00
+    1   2018-01-01 12:00:00
+    2   2018-01-01 12:00:00
+    dtype: datetime64[ns]
+
+    When rounding near a daylight savings time transition, use ``ambiguous`` or
+    ``nonexistent`` to control how the timestamp should be re-localized.
+
+    >>> rng_tz = pd.DatetimeIndex(["2021-10-31 03:30:00"], tz="Europe/Amsterdam")
+
+    >>> rng_tz.floor("2h", ambiguous=False)
+    DatetimeIndex(['2021-10-31 02:00:00+01:00'],
+                  dtype='datetime64[ns, Europe/Amsterdam]', freq=None)
+
+    >>> rng_tz.floor("2h", ambiguous=True)
+    DatetimeIndex(['2021-10-31 02:00:00+02:00'],
+                  dtype='datetime64[ns, Europe/Amsterdam]', freq=None)
+    """
+
+_floor_example = """>>> rng.floor('h')
+    DatetimeIndex(['2018-01-01 11:00:00', '2018-01-01 12:00:00',
+                   '2018-01-01 12:00:00'],
+                  dtype='datetime64[ns]', freq=None)
+
+    **Series**
+
+    >>> pd.Series(rng).dt.floor("h")
+    0   2018-01-01 11:00:00
+    1   2018-01-01 12:00:00
+    2   2018-01-01 12:00:00
+    dtype: datetime64[ns]
+
+    When rounding near a daylight savings time transition, use ``ambiguous`` or
+    ``nonexistent`` to control how the timestamp should be re-localized.
+
+    >>> rng_tz = pd.DatetimeIndex(["2021-10-31 03:30:00"], tz="Europe/Amsterdam")
+
+    >>> rng_tz.floor("2h", ambiguous=False)
+    DatetimeIndex(['2021-10-31 02:00:00+01:00'],
+                 dtype='datetime64[ns, Europe/Amsterdam]', freq=None)
+
+    >>> rng_tz.floor("2h", ambiguous=True)
+    DatetimeIndex(['2021-10-31 02:00:00+02:00'],
+                  dtype='datetime64[ns, Europe/Amsterdam]', freq=None)
+    """
+
+_ceil_example = """>>> rng.ceil('h')
+    DatetimeIndex(['2018-01-01 12:00:00', '2018-01-01 12:00:00',
+                   '2018-01-01 13:00:00'],
+                  dtype='datetime64[ns]', freq=None)
+
+    **Series**
+
+    >>> pd.Series(rng).dt.ceil("h")
+    0   2018-01-01 12:00:00
+    1   2018-01-01 12:00:00
+    2   2018-01-01 13:00:00
+    dtype: datetime64[ns]
+
+    When rounding near a daylight savings time transition, use ``ambiguous`` or
+    ``nonexistent`` to control how the timestamp should be re-localized.
+
+    >>> rng_tz = pd.DatetimeIndex(["2021-10-31 01:30:00"], tz="Europe/Amsterdam")
+
+    >>> rng_tz.ceil("h", ambiguous=False)
+    DatetimeIndex(['2021-10-31 02:00:00+01:00'],
+                  dtype='datetime64[ns, Europe/Amsterdam]', freq=None)
+
+    >>> rng_tz.ceil("h", ambiguous=True)
+    DatetimeIndex(['2021-10-31 02:00:00+02:00'],
+                  dtype='datetime64[ns, Europe/Amsterdam]', freq=None)
+    """
+
+
+class TimelikeOps(DatetimeLikeArrayMixin):
+    """
+    Common ops for TimedeltaIndex/DatetimeIndex, but not PeriodIndex.
+    """
+
+    _default_dtype: np.dtype
+
+    def __init__(
+        self, values, dtype=None, freq=lib.no_default, copy: bool = False
+    ) -> None:
+        warnings.warn(
+            # GH#55623
+            f"{type(self).__name__}.__init__ is deprecated and will be "
+            "removed in a future version. Use pd.array instead.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        if dtype is not None:
+            dtype = pandas_dtype(dtype)
+
+        values = extract_array(values, extract_numpy=True)
+        if isinstance(values, IntegerArray):
+            values = values.to_numpy("int64", na_value=iNaT)
+
+        inferred_freq = getattr(values, "_freq", None)
+        explicit_none = freq is None
+        freq = freq if freq is not lib.no_default else None
+
+        if isinstance(values, type(self)):
+            if explicit_none:
+                # don't inherit from values
+                pass
+            elif freq is None:
+                freq = values.freq
+            elif freq and values.freq:
+                freq = to_offset(freq)
+                freq = _validate_inferred_freq(freq, values.freq)
+
+            if dtype is not None and dtype != values.dtype:
+                # TODO: we only have tests for this for DTA, not TDA (2022-07-01)
+                raise TypeError(
+                    f"dtype={dtype} does not match data dtype {values.dtype}"
+                )
+
+            dtype = values.dtype
+            values = values._ndarray
+
+        elif dtype is None:
+            if isinstance(values, np.ndarray) and values.dtype.kind in "Mm":
+                dtype = values.dtype
+            else:
+                dtype = self._default_dtype
+                if isinstance(values, np.ndarray) and values.dtype == "i8":
+                    values = values.view(dtype)
+
+        if not isinstance(values, np.ndarray):
+            raise ValueError(
+                f"Unexpected type '{type(values).__name__}'. 'values' must be a "
+                f"{type(self).__name__}, ndarray, or Series or Index "
+                "containing one of those."
+            )
+        if values.ndim not in [1, 2]:
+            raise ValueError("Only 1-dimensional input arrays are supported.")
+
+        if values.dtype == "i8":
+            # for compat with datetime/timedelta/period shared methods,
+            #  we can sometimes get here with int64 values.  These represent
+            #  nanosecond UTC (or tz-naive) unix timestamps
+            if dtype is None:
+                dtype = self._default_dtype
+                values = values.view(self._default_dtype)
+            elif lib.is_np_dtype(dtype, "mM"):
+                values = values.view(dtype)
+            elif isinstance(dtype, DatetimeTZDtype):
+                kind = self._default_dtype.kind
+                new_dtype = f"{kind}8[{dtype.unit}]"
+                values = values.view(new_dtype)
+
+        dtype = self._validate_dtype(values, dtype)
+
+        if freq == "infer":
+            raise ValueError(
+                f"Frequency inference not allowed in {type(self).__name__}.__init__. "
+                "Use 'pd.array()' instead."
+            )
+
+        if copy:
+            values = values.copy()
+        if freq:
+            freq = to_offset(freq)
+            if values.dtype.kind == "m" and not isinstance(freq, Tick):
+                raise TypeError("TimedeltaArray/Index freq must be a Tick")
+
+        NDArrayBacked.__init__(self, values=values, dtype=dtype)
+        self._freq = freq
+
+        if inferred_freq is None and freq is not None:
+            type(self)._validate_frequency(self, freq)
+
+    @classmethod
+    def _validate_dtype(cls, values, dtype):
+        raise AbstractMethodError(cls)
+
+    @property
+    def freq(self):
+        """
+        Return the frequency object if it is set, otherwise None.
+        """
+        return self._freq
+
+    @freq.setter
+    def freq(self, value) -> None:
+        if value is not None:
+            value = to_offset(value)
+            self._validate_frequency(self, value)
+            if self.dtype.kind == "m" and not isinstance(value, Tick):
+                raise TypeError("TimedeltaArray/Index freq must be a Tick")
+
+            if self.ndim > 1:
+                raise ValueError("Cannot set freq with ndim > 1")
+
+        self._freq = value
+
+    @final
+    def _maybe_pin_freq(self, freq, validate_kwds: dict):
+        """
+        Constructor helper to pin the appropriate `freq` attribute.  Assumes
+        that self._freq is currently set to any freq inferred in
+        _from_sequence_not_strict.
+        """
+        if freq is None:
+            # user explicitly passed None -> override any inferred_freq
+            self._freq = None
+        elif freq == "infer":
+            # if self._freq is *not* None then we already inferred a freq
+            #  and there is nothing left to do
+            if self._freq is None:
+                # Set _freq directly to bypass duplicative _validate_frequency
+                # check.
+                self._freq = to_offset(self.inferred_freq)
+        elif freq is lib.no_default:
+            # user did not specify anything, keep inferred freq if the original
+            #  data had one, otherwise do nothing
+            pass
+        elif self._freq is None:
+            # We cannot inherit a freq from the data, so we need to validate
+            #  the user-passed freq
+            freq = to_offset(freq)
+            type(self)._validate_frequency(self, freq, **validate_kwds)
+            self._freq = freq
+        else:
+            # Otherwise we just need to check that the user-passed freq
+            #  doesn't conflict with the one we already have.
+            freq = to_offset(freq)
+            _validate_inferred_freq(freq, self._freq)
+
+    @final
+    @classmethod
+    def _validate_frequency(cls, index, freq: BaseOffset, **kwargs):
+        """
+        Validate that a frequency is compatible with the values of a given
+        Datetime Array/Index or Timedelta Array/Index
+
+        Parameters
+        ----------
+        index : DatetimeIndex or TimedeltaIndex
+            The index on which to determine if the given frequency is valid
+        freq : DateOffset
+            The frequency to validate
+        """
+        inferred = index.inferred_freq
+        if index.size == 0 or inferred == freq.freqstr:
+            return None
+
+        try:
+            on_freq = cls._generate_range(
+                start=index[0],
+                end=None,
+                periods=len(index),
+                freq=freq,
+                unit=index.unit,
+                **kwargs,
+            )
+            if not np.array_equal(index.asi8, on_freq.asi8):
+                raise ValueError
+        except ValueError as err:
+            if "non-fixed" in str(err):
+                # non-fixed frequencies are not meaningful for timedelta64;
+                #  we retain that error message
+                raise err
+            # GH#11587 the main way this is reached is if the `np.array_equal`
+            #  check above is False.  This can also be reached if index[0]
+            #  is `NaT`, in which case the call to `cls._generate_range` will
+            #  raise a ValueError, which we re-raise with a more targeted
+            #  message.
+            raise ValueError(
+                f"Inferred frequency {inferred} from passed values "
+                f"does not conform to passed frequency {freq.freqstr}"
+            ) from err
+
+    @classmethod
+    def _generate_range(
+        cls, start, end, periods: int | None, freq, *args, **kwargs
+    ) -> Self:
+        raise AbstractMethodError(cls)
+
+    # --------------------------------------------------------------
+
+    @cache_readonly
+    def _creso(self) -> int:
+        return get_unit_from_dtype(self._ndarray.dtype)
+
+    @cache_readonly
+    def unit(self) -> str:
+        # e.g. "ns", "us", "ms"
+        # error: Argument 1 to "dtype_to_unit" has incompatible type
+        # "ExtensionDtype"; expected "Union[DatetimeTZDtype, dtype[Any]]"
+        return dtype_to_unit(self.dtype)  # type: ignore[arg-type]
+
+    def as_unit(self, unit: str, round_ok: bool = True) -> Self:
+        if unit not in ["s", "ms", "us", "ns"]:
+            raise ValueError("Supported units are 's', 'ms', 'us', 'ns'")
+
+        dtype = np.dtype(f"{self.dtype.kind}8[{unit}]")
+        new_values = astype_overflowsafe(self._ndarray, dtype, round_ok=round_ok)
+
+        if isinstance(self.dtype, np.dtype):
+            new_dtype = new_values.dtype
+        else:
+            tz = cast("DatetimeArray", self).tz
+            new_dtype = DatetimeTZDtype(tz=tz, unit=unit)
+
+        # error: Unexpected keyword argument "freq" for "_simple_new" of
+        # "NDArrayBacked"  [call-arg]
+        return type(self)._simple_new(
+            new_values, dtype=new_dtype, freq=self.freq  # type: ignore[call-arg]
+        )
+
+    # TODO: annotate other as DatetimeArray | TimedeltaArray | Timestamp | Timedelta
+    #  with the return type matching input type.  TypeVar?
+    def _ensure_matching_resos(self, other):
+        if self._creso != other._creso:
+            # Just as with Timestamp/Timedelta, we cast to the higher resolution
+            if self._creso < other._creso:
+                self = self.as_unit(other.unit)
+            else:
+                other = other.as_unit(self.unit)
+        return self, other
+
+    # --------------------------------------------------------------
+
+    def __array_ufunc__(self, ufunc: np.ufunc, method: str, *inputs, **kwargs):
+        if (
+            ufunc in [np.isnan, np.isinf, np.isfinite]
+            and len(inputs) == 1
+            and inputs[0] is self
+        ):
+            # numpy 1.18 changed isinf and isnan to not raise on dt64/td64
+            return getattr(ufunc, method)(self._ndarray, **kwargs)
+
+        return super().__array_ufunc__(ufunc, method, *inputs, **kwargs)
+
+    def _round(self, freq, mode, ambiguous, nonexistent):
+        # round the local times
+        if isinstance(self.dtype, DatetimeTZDtype):
+            # operate on naive timestamps, then convert back to aware
+            self = cast("DatetimeArray", self)
+            naive = self.tz_localize(None)
+            result = naive._round(freq, mode, ambiguous, nonexistent)
+            return result.tz_localize(
+                self.tz, ambiguous=ambiguous, nonexistent=nonexistent
+            )
+
+        values = self.view("i8")
+        values = cast(np.ndarray, values)
+        nanos = get_unit_for_round(freq, self._creso)
+        if nanos == 0:
+            # GH 52761
+            return self.copy()
+        result_i8 = round_nsint64(values, mode, nanos)
+        result = self._maybe_mask_results(result_i8, fill_value=iNaT)
+        result = result.view(self._ndarray.dtype)
+        return self._simple_new(result, dtype=self.dtype)
+
+    @Appender((_round_doc + _round_example).format(op="round"))
+    def round(
+        self,
+        freq,
+        ambiguous: TimeAmbiguous = "raise",
+        nonexistent: TimeNonexistent = "raise",
+    ) -> Self:
+        return self._round(freq, RoundTo.NEAREST_HALF_EVEN, ambiguous, nonexistent)
+
+    @Appender((_round_doc + _floor_example).format(op="floor"))
+    def floor(
+        self,
+        freq,
+        ambiguous: TimeAmbiguous = "raise",
+        nonexistent: TimeNonexistent = "raise",
+    ) -> Self:
+        return self._round(freq, RoundTo.MINUS_INFTY, ambiguous, nonexistent)
+
+    @Appender((_round_doc + _ceil_example).format(op="ceil"))
+    def ceil(
+        self,
+        freq,
+        ambiguous: TimeAmbiguous = "raise",
+        nonexistent: TimeNonexistent = "raise",
+    ) -> Self:
+        return self._round(freq, RoundTo.PLUS_INFTY, ambiguous, nonexistent)
+
+    # --------------------------------------------------------------
+    # Reductions
+
+    def any(self, *, axis: AxisInt | None = None, skipna: bool = True) -> bool:
+        # GH#34479 the nanops call will issue a FutureWarning for non-td64 dtype
+        return nanops.nanany(self._ndarray, axis=axis, skipna=skipna, mask=self.isna())
+
+    def all(self, *, axis: AxisInt | None = None, skipna: bool = True) -> bool:
+        # GH#34479 the nanops call will issue a FutureWarning for non-td64 dtype
+
+        return nanops.nanall(self._ndarray, axis=axis, skipna=skipna, mask=self.isna())
+
+    # --------------------------------------------------------------
+    # Frequency Methods
+
+    def _maybe_clear_freq(self) -> None:
+        self._freq = None
+
+    def _with_freq(self, freq) -> Self:
+        """
+        Helper to get a view on the same data, with a new freq.
+
+        Parameters
+        ----------
+        freq : DateOffset, None, or "infer"
+
+        Returns
+        -------
+        Same type as self
+        """
+        # GH#29843
+        if freq is None:
+            # Always valid
+            pass
+        elif len(self) == 0 and isinstance(freq, BaseOffset):
+            # Always valid.  In the TimedeltaArray case, we require a Tick offset
+            if self.dtype.kind == "m" and not isinstance(freq, Tick):
+                raise TypeError("TimedeltaArray/Index freq must be a Tick")
+        else:
+            # As an internal method, we can ensure this assertion always holds
+            assert freq == "infer"
+            freq = to_offset(self.inferred_freq)
+
+        arr = self.view()
+        arr._freq = freq
+        return arr
+
+    # --------------------------------------------------------------
+    # ExtensionArray Interface
+
+    def _values_for_json(self) -> np.ndarray:
+        # Small performance bump vs the base class which calls np.asarray(self)
+        if isinstance(self.dtype, np.dtype):
+            return self._ndarray
+        return super()._values_for_json()
+
+    def factorize(
+        self,
+        use_na_sentinel: bool = True,
+        sort: bool = False,
+    ):
+        if self.freq is not None:
+            # We must be unique, so can short-circuit (and retain freq)
+            codes = np.arange(len(self), dtype=np.intp)
+            uniques = self.copy()  # TODO: copy or view?
+            if sort and self.freq.n < 0:
+                codes = codes[::-1]
+                uniques = uniques[::-1]
+            return codes, uniques
+
+        if sort:
+            # algorithms.factorize only passes sort=True here when freq is
+            #  not None, so this should not be reached.
+            raise NotImplementedError(
+                f"The 'sort' keyword in {type(self).__name__}.factorize is "
+                "ignored unless arr.freq is not None. To factorize with sort, "
+                "call pd.factorize(obj, sort=True) instead."
+            )
+        return super().factorize(use_na_sentinel=use_na_sentinel)
+
+    @classmethod
+    def _concat_same_type(
+        cls,
+        to_concat: Sequence[Self],
+        axis: AxisInt = 0,
+    ) -> Self:
+        new_obj = super()._concat_same_type(to_concat, axis)
+
+        obj = to_concat[0]
+
+        if axis == 0:
+            # GH 3232: If the concat result is evenly spaced, we can retain the
+            # original frequency
+            to_concat = [x for x in to_concat if len(x)]
+
+            if obj.freq is not None and all(x.freq == obj.freq for x in to_concat):
+                pairs = zip(to_concat[:-1], to_concat[1:])
+                if all(pair[0][-1] + obj.freq == pair[1][0] for pair in pairs):
+                    new_freq = obj.freq
+                    new_obj._freq = new_freq
+        return new_obj
+
+    def copy(self, order: str = "C") -> Self:
+        new_obj = super().copy(order=order)
+        new_obj._freq = self.freq
+        return new_obj
+
+    def interpolate(
+        self,
+        *,
+        method: InterpolateOptions,
+        axis: int,
+        index: Index,
+        limit,
+        limit_direction,
+        limit_area,
+        copy: bool,
+        **kwargs,
+    ) -> Self:
+        """
+        See NDFrame.interpolate.__doc__.
+        """
+        # NB: we return type(self) even if copy=False
+        if method != "linear":
+            raise NotImplementedError
+
+        if not copy:
+            out_data = self._ndarray
+        else:
+            out_data = self._ndarray.copy()
+
+        missing.interpolate_2d_inplace(
+            out_data,
+            method=method,
+            axis=axis,
+            index=index,
+            limit=limit,
+            limit_direction=limit_direction,
+            limit_area=limit_area,
+            **kwargs,
+        )
+        if not copy:
+            return self
+        return type(self)._simple_new(out_data, dtype=self.dtype)
+
+    # --------------------------------------------------------------
+    # Unsorted
+
+    @property
+    def _is_dates_only(self) -> bool:
+        """
+        Check if we are round times at midnight (and no timezone), which will
+        be given a more compact __repr__ than other cases. For TimedeltaArray
+        we are checking for multiples of 24H.
+        """
+        if not lib.is_np_dtype(self.dtype):
+            # i.e. we have a timezone
+            return False
+
+        values_int = self.asi8
+        consider_values = values_int != iNaT
+        reso = get_unit_from_dtype(self.dtype)
+        ppd = periods_per_day(reso)
+
+        # TODO: can we reuse is_date_array_normalized?  would need a skipna kwd
+        #  (first attempt at this was less performant than this implementation)
+        even_days = np.logical_and(consider_values, values_int % ppd != 0).sum() == 0
+        return even_days
+
+
+# -------------------------------------------------------------------
+# Shared Constructor Helpers
+
+
+def ensure_arraylike_for_datetimelike(
+    data, copy: bool, cls_name: str
+) -> tuple[ArrayLike, bool]:
+    if not hasattr(data, "dtype"):
+        # e.g. list, tuple
+        if not isinstance(data, (list, tuple)) and np.ndim(data) == 0:
+            # i.e. generator
+            data = list(data)
+
+        data = construct_1d_object_array_from_listlike(data)
+        copy = False
+    elif isinstance(data, ABCMultiIndex):
+        raise TypeError(f"Cannot create a {cls_name} from a MultiIndex.")
+    else:
+        data = extract_array(data, extract_numpy=True)
+
+    if isinstance(data, IntegerArray) or (
+        isinstance(data, ArrowExtensionArray) and data.dtype.kind in "iu"
+    ):
+        data = data.to_numpy("int64", na_value=iNaT)
+        copy = False
+    elif isinstance(data, ArrowExtensionArray):
+        data = data._maybe_convert_datelike_array()
+        data = data.to_numpy()
+        copy = False
+    elif not isinstance(data, (np.ndarray, ExtensionArray)):
+        # GH#24539 e.g. xarray, dask object
+        data = np.asarray(data)
+
+    elif isinstance(data, ABCCategorical):
+        # GH#18664 preserve tz in going DTI->Categorical->DTI
+        # TODO: cases where we need to do another pass through maybe_convert_dtype,
+        #  e.g. the categories are timedelta64s
+        data = data.categories.take(data.codes, fill_value=NaT)._values
+        copy = False
+
+    return data, copy
+
+
+@overload
+def validate_periods(periods: None) -> None:
+    ...
+
+
+@overload
+def validate_periods(periods: int | float) -> int:
+    ...
+
+
+def validate_periods(periods: int | float | None) -> int | None:
+    """
+    If a `periods` argument is passed to the Datetime/Timedelta Array/Index
+    constructor, cast it to an integer.
+
+    Parameters
+    ----------
+    periods : None, float, int
+
+    Returns
+    -------
+    periods : None or int
+
+    Raises
+    ------
+    TypeError
+        if periods is None, float, or int
+    """
+    if periods is not None:
+        if lib.is_float(periods):
+            warnings.warn(
+                # GH#56036
+                "Non-integer 'periods' in pd.date_range, pd.timedelta_range, "
+                "pd.period_range, and pd.interval_range are deprecated and "
+                "will raise in a future version.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+            periods = int(periods)
+        elif not lib.is_integer(periods):
+            raise TypeError(f"periods must be a number, got {periods}")
+    return periods
+
+
+def _validate_inferred_freq(
+    freq: BaseOffset | None, inferred_freq: BaseOffset | None
+) -> BaseOffset | None:
+    """
+    If the user passes a freq and another freq is inferred from passed data,
+    require that they match.
+
+    Parameters
+    ----------
+    freq : DateOffset or None
+    inferred_freq : DateOffset or None
+
+    Returns
+    -------
+    freq : DateOffset or None
+    """
+    if inferred_freq is not None:
+        if freq is not None and freq != inferred_freq:
+            raise ValueError(
+                f"Inferred frequency {inferred_freq} from passed "
+                "values does not conform to passed frequency "
+                f"{freq.freqstr}"
+            )
+        if freq is None:
+            freq = inferred_freq
+
+    return freq
+
+
+def dtype_to_unit(dtype: DatetimeTZDtype | np.dtype | ArrowDtype) -> str:
+    """
+    Return the unit str corresponding to the dtype's resolution.
+
+    Parameters
+    ----------
+    dtype : DatetimeTZDtype or np.dtype
+        If np.dtype, we assume it is a datetime64 dtype.
+
+    Returns
+    -------
+    str
+    """
+    if isinstance(dtype, DatetimeTZDtype):
+        return dtype.unit
+    elif isinstance(dtype, ArrowDtype):
+        if dtype.kind not in "mM":
+            raise ValueError(f"{dtype=} does not have a resolution.")
+        return dtype.pyarrow_dtype.unit
+    return np.datetime_data(dtype)[0]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/datetimes.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/datetimes.py
new file mode 100644
index 0000000000000000000000000000000000000000..0db25db02e75ad201e11ab0f6a6d10205060ea9a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/datetimes.py
@@ -0,0 +1,2837 @@
+from __future__ import annotations
+
+from datetime import (
+    datetime,
+    timedelta,
+    tzinfo,
+)
+from typing import (
+    TYPE_CHECKING,
+    cast,
+    overload,
+)
+import warnings
+
+import numpy as np
+
+from pandas._config import using_string_dtype
+
+from pandas._libs import (
+    lib,
+    tslib,
+)
+from pandas._libs.tslibs import (
+    BaseOffset,
+    NaT,
+    NaTType,
+    Resolution,
+    Timestamp,
+    astype_overflowsafe,
+    fields,
+    get_resolution,
+    get_supported_dtype,
+    get_unit_from_dtype,
+    ints_to_pydatetime,
+    is_date_array_normalized,
+    is_supported_dtype,
+    is_unitless,
+    normalize_i8_timestamps,
+    timezones,
+    to_offset,
+    tz_convert_from_utc,
+    tzconversion,
+)
+from pandas._libs.tslibs.dtypes import abbrev_to_npy_unit
+from pandas.errors import PerformanceWarning
+from pandas.util._exceptions import find_stack_level
+from pandas.util._validators import validate_inclusive
+
+from pandas.core.dtypes.common import (
+    DT64NS_DTYPE,
+    INT64_DTYPE,
+    is_bool_dtype,
+    is_float_dtype,
+    is_string_dtype,
+    pandas_dtype,
+)
+from pandas.core.dtypes.dtypes import (
+    DatetimeTZDtype,
+    ExtensionDtype,
+    PeriodDtype,
+)
+from pandas.core.dtypes.missing import isna
+
+from pandas.core.arrays import datetimelike as dtl
+from pandas.core.arrays._ranges import generate_regular_range
+import pandas.core.common as com
+
+from pandas.tseries.frequencies import get_period_alias
+from pandas.tseries.offsets import (
+    Day,
+    Tick,
+)
+
+if TYPE_CHECKING:
+    from collections.abc import Iterator
+
+    from pandas._typing import (
+        ArrayLike,
+        DateTimeErrorChoices,
+        DtypeObj,
+        IntervalClosedType,
+        Self,
+        TimeAmbiguous,
+        TimeNonexistent,
+        npt,
+    )
+
+    from pandas import DataFrame
+    from pandas.core.arrays import PeriodArray
+
+
+_ITER_CHUNKSIZE = 10_000
+
+
+@overload
+def tz_to_dtype(tz: tzinfo, unit: str = ...) -> DatetimeTZDtype:
+    ...
+
+
+@overload
+def tz_to_dtype(tz: None, unit: str = ...) -> np.dtype[np.datetime64]:
+    ...
+
+
+def tz_to_dtype(
+    tz: tzinfo | None, unit: str = "ns"
+) -> np.dtype[np.datetime64] | DatetimeTZDtype:
+    """
+    Return a datetime64[ns] dtype appropriate for the given timezone.
+
+    Parameters
+    ----------
+    tz : tzinfo or None
+    unit : str, default "ns"
+
+    Returns
+    -------
+    np.dtype or Datetime64TZDType
+    """
+    if tz is None:
+        return np.dtype(f"M8[{unit}]")
+    else:
+        return DatetimeTZDtype(tz=tz, unit=unit)
+
+
+def _field_accessor(name: str, field: str, docstring: str | None = None):
+    def f(self):
+        values = self._local_timestamps()
+
+        if field in self._bool_ops:
+            result: np.ndarray
+
+            if field.endswith(("start", "end")):
+                freq = self.freq
+                month_kw = 12
+                if freq:
+                    kwds = freq.kwds
+                    month_kw = kwds.get("startingMonth", kwds.get("month", 12))
+
+                result = fields.get_start_end_field(
+                    values, field, self.freqstr, month_kw, reso=self._creso
+                )
+            else:
+                result = fields.get_date_field(values, field, reso=self._creso)
+
+            # these return a boolean by-definition
+            return result
+
+        if field in self._object_ops:
+            result = fields.get_date_name_field(values, field, reso=self._creso)
+            result = self._maybe_mask_results(result, fill_value=None)
+
+        else:
+            result = fields.get_date_field(values, field, reso=self._creso)
+            result = self._maybe_mask_results(
+                result, fill_value=None, convert="float64"
+            )
+
+        return result
+
+    f.__name__ = name
+    f.__doc__ = docstring
+    return property(f)
+
+
+# error: Definition of "_concat_same_type" in base class "NDArrayBacked" is
+# incompatible with definition in base class "ExtensionArray"
+class DatetimeArray(dtl.TimelikeOps, dtl.DatelikeOps):  # type: ignore[misc]
+    """
+    Pandas ExtensionArray for tz-naive or tz-aware datetime data.
+
+    .. warning::
+
+       DatetimeArray is currently experimental, and its API may change
+       without warning. In particular, :attr:`DatetimeArray.dtype` is
+       expected to change to always be an instance of an ``ExtensionDtype``
+       subclass.
+
+    Parameters
+    ----------
+    values : Series, Index, DatetimeArray, ndarray
+        The datetime data.
+
+        For DatetimeArray `values` (or a Series or Index boxing one),
+        `dtype` and `freq` will be extracted from `values`.
+
+    dtype : numpy.dtype or DatetimeTZDtype
+        Note that the only NumPy dtype allowed is 'datetime64[ns]'.
+    freq : str or Offset, optional
+        The frequency.
+    copy : bool, default False
+        Whether to copy the underlying array of values.
+
+    Attributes
+    ----------
+    None
+
+    Methods
+    -------
+    None
+
+    Examples
+    --------
+    >>> pd.arrays.DatetimeArray._from_sequence(
+    ...    pd.DatetimeIndex(['2023-01-01', '2023-01-02'], freq='D'))
+    
+    ['2023-01-01 00:00:00', '2023-01-02 00:00:00']
+    Length: 2, dtype: datetime64[ns]
+    """
+
+    _typ = "datetimearray"
+    _internal_fill_value = np.datetime64("NaT", "ns")
+    _recognized_scalars = (datetime, np.datetime64)
+    _is_recognized_dtype = lambda x: lib.is_np_dtype(x, "M") or isinstance(
+        x, DatetimeTZDtype
+    )
+    _infer_matches = ("datetime", "datetime64", "date")
+
+    @property
+    def _scalar_type(self) -> type[Timestamp]:
+        return Timestamp
+
+    # define my properties & methods for delegation
+    _bool_ops: list[str] = [
+        "is_month_start",
+        "is_month_end",
+        "is_quarter_start",
+        "is_quarter_end",
+        "is_year_start",
+        "is_year_end",
+        "is_leap_year",
+    ]
+    _object_ops: list[str] = ["freq", "tz"]
+    _field_ops: list[str] = [
+        "year",
+        "month",
+        "day",
+        "hour",
+        "minute",
+        "second",
+        "weekday",
+        "dayofweek",
+        "day_of_week",
+        "dayofyear",
+        "day_of_year",
+        "quarter",
+        "days_in_month",
+        "daysinmonth",
+        "microsecond",
+        "nanosecond",
+    ]
+    _other_ops: list[str] = ["date", "time", "timetz"]
+    _datetimelike_ops: list[str] = (
+        _field_ops + _object_ops + _bool_ops + _other_ops + ["unit"]
+    )
+    _datetimelike_methods: list[str] = [
+        "to_period",
+        "tz_localize",
+        "tz_convert",
+        "normalize",
+        "strftime",
+        "round",
+        "floor",
+        "ceil",
+        "month_name",
+        "day_name",
+        "as_unit",
+    ]
+
+    # ndim is inherited from ExtensionArray, must exist to ensure
+    #  Timestamp.__richcmp__(DateTimeArray) operates pointwise
+
+    # ensure that operations with numpy arrays defer to our implementation
+    __array_priority__ = 1000
+
+    # -----------------------------------------------------------------
+    # Constructors
+
+    _dtype: np.dtype[np.datetime64] | DatetimeTZDtype
+    _freq: BaseOffset | None = None
+    _default_dtype = DT64NS_DTYPE  # used in TimeLikeOps.__init__
+
+    @classmethod
+    def _from_scalars(cls, scalars, *, dtype: DtypeObj) -> Self:
+        if lib.infer_dtype(scalars, skipna=True) not in ["datetime", "datetime64"]:
+            # TODO: require any NAs be valid-for-DTA
+            # TODO: if dtype is passed, check for tzawareness compat?
+            raise ValueError
+        return cls._from_sequence(scalars, dtype=dtype)
+
+    @classmethod
+    def _validate_dtype(cls, values, dtype):
+        # used in TimeLikeOps.__init__
+        dtype = _validate_dt64_dtype(dtype)
+        _validate_dt64_dtype(values.dtype)
+        if isinstance(dtype, np.dtype):
+            if values.dtype != dtype:
+                raise ValueError("Values resolution does not match dtype.")
+        else:
+            vunit = np.datetime_data(values.dtype)[0]
+            if vunit != dtype.unit:
+                raise ValueError("Values resolution does not match dtype.")
+        return dtype
+
+    # error: Signature of "_simple_new" incompatible with supertype "NDArrayBacked"
+    @classmethod
+    def _simple_new(  # type: ignore[override]
+        cls,
+        values: npt.NDArray[np.datetime64],
+        freq: BaseOffset | None = None,
+        dtype: np.dtype[np.datetime64] | DatetimeTZDtype = DT64NS_DTYPE,
+    ) -> Self:
+        assert isinstance(values, np.ndarray)
+        assert dtype.kind == "M"
+        if isinstance(dtype, np.dtype):
+            assert dtype == values.dtype
+            assert not is_unitless(dtype)
+        else:
+            # DatetimeTZDtype. If we have e.g. DatetimeTZDtype[us, UTC],
+            #  then values.dtype should be M8[us].
+            assert dtype._creso == get_unit_from_dtype(values.dtype)
+
+        result = super()._simple_new(values, dtype)
+        result._freq = freq
+        return result
+
+    @classmethod
+    def _from_sequence(cls, scalars, *, dtype=None, copy: bool = False):
+        return cls._from_sequence_not_strict(scalars, dtype=dtype, copy=copy)
+
+    @classmethod
+    def _from_sequence_not_strict(
+        cls,
+        data,
+        *,
+        dtype=None,
+        copy: bool = False,
+        tz=lib.no_default,
+        freq: str | BaseOffset | lib.NoDefault | None = lib.no_default,
+        dayfirst: bool = False,
+        yearfirst: bool = False,
+        ambiguous: TimeAmbiguous = "raise",
+    ) -> Self:
+        """
+        A non-strict version of _from_sequence, called from DatetimeIndex.__new__.
+        """
+
+        # if the user either explicitly passes tz=None or a tz-naive dtype, we
+        #  disallows inferring a tz.
+        explicit_tz_none = tz is None
+        if tz is lib.no_default:
+            tz = None
+        else:
+            tz = timezones.maybe_get_tz(tz)
+
+        dtype = _validate_dt64_dtype(dtype)
+        # if dtype has an embedded tz, capture it
+        tz = _validate_tz_from_dtype(dtype, tz, explicit_tz_none)
+
+        unit = None
+        if dtype is not None:
+            unit = dtl.dtype_to_unit(dtype)
+
+        data, copy = dtl.ensure_arraylike_for_datetimelike(
+            data, copy, cls_name="DatetimeArray"
+        )
+        inferred_freq = None
+        if isinstance(data, DatetimeArray):
+            inferred_freq = data.freq
+
+        subarr, tz = _sequence_to_dt64(
+            data,
+            copy=copy,
+            tz=tz,
+            dayfirst=dayfirst,
+            yearfirst=yearfirst,
+            ambiguous=ambiguous,
+            out_unit=unit,
+        )
+        # We have to call this again after possibly inferring a tz above
+        _validate_tz_from_dtype(dtype, tz, explicit_tz_none)
+        if tz is not None and explicit_tz_none:
+            raise ValueError(
+                "Passed data is timezone-aware, incompatible with 'tz=None'. "
+                "Use obj.tz_localize(None) instead."
+            )
+
+        data_unit = np.datetime_data(subarr.dtype)[0]
+        data_dtype = tz_to_dtype(tz, data_unit)
+        result = cls._simple_new(subarr, freq=inferred_freq, dtype=data_dtype)
+        if unit is not None and unit != result.unit:
+            # If unit was specified in user-passed dtype, cast to it here
+            result = result.as_unit(unit)
+
+        validate_kwds = {"ambiguous": ambiguous}
+        result._maybe_pin_freq(freq, validate_kwds)
+        return result
+
+    @classmethod
+    def _generate_range(
+        cls,
+        start,
+        end,
+        periods: int | None,
+        freq,
+        tz=None,
+        normalize: bool = False,
+        ambiguous: TimeAmbiguous = "raise",
+        nonexistent: TimeNonexistent = "raise",
+        inclusive: IntervalClosedType = "both",
+        *,
+        unit: str | None = None,
+    ) -> Self:
+        periods = dtl.validate_periods(periods)
+        if freq is None and any(x is None for x in [periods, start, end]):
+            raise ValueError("Must provide freq argument if no data is supplied")
+
+        if com.count_not_none(start, end, periods, freq) != 3:
+            raise ValueError(
+                "Of the four parameters: start, end, periods, "
+                "and freq, exactly three must be specified"
+            )
+        freq = to_offset(freq)
+
+        if start is not None:
+            start = Timestamp(start)
+
+        if end is not None:
+            end = Timestamp(end)
+
+        if start is NaT or end is NaT:
+            raise ValueError("Neither `start` nor `end` can be NaT")
+
+        if unit is not None:
+            if unit not in ["s", "ms", "us", "ns"]:
+                raise ValueError("'unit' must be one of 's', 'ms', 'us', 'ns'")
+        else:
+            unit = "ns"
+
+        if start is not None:
+            start = start.as_unit(unit, round_ok=False)
+        if end is not None:
+            end = end.as_unit(unit, round_ok=False)
+
+        left_inclusive, right_inclusive = validate_inclusive(inclusive)
+        start, end = _maybe_normalize_endpoints(start, end, normalize)
+        tz = _infer_tz_from_endpoints(start, end, tz)
+
+        if tz is not None:
+            # Localize the start and end arguments
+            start = _maybe_localize_point(start, freq, tz, ambiguous, nonexistent)
+            end = _maybe_localize_point(end, freq, tz, ambiguous, nonexistent)
+
+        if freq is not None:
+            # We break Day arithmetic (fixed 24 hour) here and opt for
+            # Day to mean calendar day (23/24/25 hour). Therefore, strip
+            # tz info from start and day to avoid DST arithmetic
+            if isinstance(freq, Day):
+                if start is not None:
+                    start = start.tz_localize(None)
+                if end is not None:
+                    end = end.tz_localize(None)
+
+            if isinstance(freq, Tick):
+                i8values = generate_regular_range(start, end, periods, freq, unit=unit)
+            else:
+                xdr = _generate_range(
+                    start=start, end=end, periods=periods, offset=freq, unit=unit
+                )
+                i8values = np.array([x._value for x in xdr], dtype=np.int64)
+
+            endpoint_tz = start.tz if start is not None else end.tz
+
+            if tz is not None and endpoint_tz is None:
+                if not timezones.is_utc(tz):
+                    # short-circuit tz_localize_to_utc which would make
+                    #  an unnecessary copy with UTC but be a no-op.
+                    creso = abbrev_to_npy_unit(unit)
+                    i8values = tzconversion.tz_localize_to_utc(
+                        i8values,
+                        tz,
+                        ambiguous=ambiguous,
+                        nonexistent=nonexistent,
+                        creso=creso,
+                    )
+
+                # i8values is localized datetime64 array -> have to convert
+                # start/end as well to compare
+                if start is not None:
+                    start = start.tz_localize(tz, ambiguous, nonexistent)
+                if end is not None:
+                    end = end.tz_localize(tz, ambiguous, nonexistent)
+        else:
+            # Create a linearly spaced date_range in local time
+            # Nanosecond-granularity timestamps aren't always correctly
+            # representable with doubles, so we limit the range that we
+            # pass to np.linspace as much as possible
+            periods = cast(int, periods)
+            i8values = (
+                np.linspace(0, end._value - start._value, periods, dtype="int64")
+                + start._value
+            )
+            if i8values.dtype != "i8":
+                # 2022-01-09 I (brock) am not sure if it is possible for this
+                #  to overflow and cast to e.g. f8, but if it does we need to cast
+                i8values = i8values.astype("i8")
+
+        if start == end:
+            if not left_inclusive and not right_inclusive:
+                i8values = i8values[1:-1]
+        else:
+            start_i8 = Timestamp(start)._value
+            end_i8 = Timestamp(end)._value
+            if not left_inclusive or not right_inclusive:
+                if not left_inclusive and len(i8values) and i8values[0] == start_i8:
+                    i8values = i8values[1:]
+                if not right_inclusive and len(i8values) and i8values[-1] == end_i8:
+                    i8values = i8values[:-1]
+
+        dt64_values = i8values.view(f"datetime64[{unit}]")
+        dtype = tz_to_dtype(tz, unit=unit)
+        return cls._simple_new(dt64_values, freq=freq, dtype=dtype)
+
+    # -----------------------------------------------------------------
+    # DatetimeLike Interface
+
+    def _unbox_scalar(self, value) -> np.datetime64:
+        if not isinstance(value, self._scalar_type) and value is not NaT:
+            raise ValueError("'value' should be a Timestamp.")
+        self._check_compatible_with(value)
+        if value is NaT:
+            return np.datetime64(value._value, self.unit)
+        else:
+            return value.as_unit(self.unit).asm8
+
+    def _scalar_from_string(self, value) -> Timestamp | NaTType:
+        return Timestamp(value, tz=self.tz)
+
+    def _check_compatible_with(self, other) -> None:
+        if other is NaT:
+            return
+        self._assert_tzawareness_compat(other)
+
+    # -----------------------------------------------------------------
+    # Descriptive Properties
+
+    def _box_func(self, x: np.datetime64) -> Timestamp | NaTType:
+        # GH#42228
+        value = x.view("i8")
+        ts = Timestamp._from_value_and_reso(value, reso=self._creso, tz=self.tz)
+        return ts
+
+    @property
+    # error: Return type "Union[dtype, DatetimeTZDtype]" of "dtype"
+    # incompatible with return type "ExtensionDtype" in supertype
+    # "ExtensionArray"
+    def dtype(self) -> np.dtype[np.datetime64] | DatetimeTZDtype:  # type: ignore[override]
+        """
+        The dtype for the DatetimeArray.
+
+        .. warning::
+
+           A future version of pandas will change dtype to never be a
+           ``numpy.dtype``. Instead, :attr:`DatetimeArray.dtype` will
+           always be an instance of an ``ExtensionDtype`` subclass.
+
+        Returns
+        -------
+        numpy.dtype or DatetimeTZDtype
+            If the values are tz-naive, then ``np.dtype('datetime64[ns]')``
+            is returned.
+
+            If the values are tz-aware, then the ``DatetimeTZDtype``
+            is returned.
+        """
+        return self._dtype
+
+    @property
+    def tz(self) -> tzinfo | None:
+        """
+        Return the timezone.
+
+        Returns
+        -------
+        datetime.tzinfo, pytz.tzinfo.BaseTZInfo, dateutil.tz.tz.tzfile, or None
+            Returns None when the array is tz-naive.
+
+        Examples
+        --------
+        For Series:
+
+        >>> s = pd.Series(["1/1/2020 10:00:00+00:00", "2/1/2020 11:00:00+00:00"])
+        >>> s = pd.to_datetime(s)
+        >>> s
+        0   2020-01-01 10:00:00+00:00
+        1   2020-02-01 11:00:00+00:00
+        dtype: datetime64[ns, UTC]
+        >>> s.dt.tz
+        datetime.timezone.utc
+
+        For DatetimeIndex:
+
+        >>> idx = pd.DatetimeIndex(["1/1/2020 10:00:00+00:00",
+        ...                         "2/1/2020 11:00:00+00:00"])
+        >>> idx.tz
+        datetime.timezone.utc
+        """
+        # GH 18595
+        return getattr(self.dtype, "tz", None)
+
+    @tz.setter
+    def tz(self, value):
+        # GH 3746: Prevent localizing or converting the index by setting tz
+        raise AttributeError(
+            "Cannot directly set timezone. Use tz_localize() "
+            "or tz_convert() as appropriate"
+        )
+
+    @property
+    def tzinfo(self) -> tzinfo | None:
+        """
+        Alias for tz attribute
+        """
+        return self.tz
+
+    @property  # NB: override with cache_readonly in immutable subclasses
+    def is_normalized(self) -> bool:
+        """
+        Returns True if all of the dates are at midnight ("no time")
+        """
+        return is_date_array_normalized(self.asi8, self.tz, reso=self._creso)
+
+    @property  # NB: override with cache_readonly in immutable subclasses
+    def _resolution_obj(self) -> Resolution:
+        return get_resolution(self.asi8, self.tz, reso=self._creso)
+
+    # ----------------------------------------------------------------
+    # Array-Like / EA-Interface Methods
+
+    def __array__(self, dtype=None, copy=None) -> np.ndarray:
+        if dtype is None and self.tz:
+            # The default for tz-aware is object, to preserve tz info
+            dtype = object
+
+        return super().__array__(dtype=dtype, copy=copy)
+
+    def __iter__(self) -> Iterator:
+        """
+        Return an iterator over the boxed values
+
+        Yields
+        ------
+        tstamp : Timestamp
+        """
+        if self.ndim > 1:
+            for i in range(len(self)):
+                yield self[i]
+        else:
+            # convert in chunks of 10k for efficiency
+            data = self.asi8
+            length = len(self)
+            chunksize = _ITER_CHUNKSIZE
+            chunks = (length // chunksize) + 1
+
+            for i in range(chunks):
+                start_i = i * chunksize
+                end_i = min((i + 1) * chunksize, length)
+                converted = ints_to_pydatetime(
+                    data[start_i:end_i],
+                    tz=self.tz,
+                    box="timestamp",
+                    reso=self._creso,
+                )
+                yield from converted
+
+    def astype(self, dtype, copy: bool = True):
+        # We handle
+        #   --> datetime
+        #   --> period
+        # DatetimeLikeArrayMixin Super handles the rest.
+        dtype = pandas_dtype(dtype)
+
+        if dtype == self.dtype:
+            if copy:
+                return self.copy()
+            return self
+
+        elif isinstance(dtype, ExtensionDtype):
+            if not isinstance(dtype, DatetimeTZDtype):
+                # e.g. Sparse[datetime64[ns]]
+                return super().astype(dtype, copy=copy)
+            elif self.tz is None:
+                # pre-2.0 this did self.tz_localize(dtype.tz), which did not match
+                #  the Series behavior which did
+                #  values.tz_localize("UTC").tz_convert(dtype.tz)
+                raise TypeError(
+                    "Cannot use .astype to convert from timezone-naive dtype to "
+                    "timezone-aware dtype. Use obj.tz_localize instead or "
+                    "series.dt.tz_localize instead"
+                )
+            else:
+                # tzaware unit conversion e.g. datetime64[s, UTC]
+                np_dtype = np.dtype(dtype.str)
+                res_values = astype_overflowsafe(self._ndarray, np_dtype, copy=copy)
+                return type(self)._simple_new(res_values, dtype=dtype, freq=self.freq)
+
+        elif (
+            self.tz is None
+            and lib.is_np_dtype(dtype, "M")
+            and not is_unitless(dtype)
+            and is_supported_dtype(dtype)
+        ):
+            # unit conversion e.g. datetime64[s]
+            res_values = astype_overflowsafe(self._ndarray, dtype, copy=True)
+            return type(self)._simple_new(res_values, dtype=res_values.dtype)
+            # TODO: preserve freq?
+
+        elif self.tz is not None and lib.is_np_dtype(dtype, "M"):
+            # pre-2.0 behavior for DTA/DTI was
+            #  values.tz_convert("UTC").tz_localize(None), which did not match
+            #  the Series behavior
+            raise TypeError(
+                "Cannot use .astype to convert from timezone-aware dtype to "
+                "timezone-naive dtype. Use obj.tz_localize(None) or "
+                "obj.tz_convert('UTC').tz_localize(None) instead."
+            )
+
+        elif (
+            self.tz is None
+            and lib.is_np_dtype(dtype, "M")
+            and dtype != self.dtype
+            and is_unitless(dtype)
+        ):
+            raise TypeError(
+                "Casting to unit-less dtype 'datetime64' is not supported. "
+                "Pass e.g. 'datetime64[ns]' instead."
+            )
+
+        elif isinstance(dtype, PeriodDtype):
+            return self.to_period(freq=dtype.freq)
+        return dtl.DatetimeLikeArrayMixin.astype(self, dtype, copy)
+
+    # -----------------------------------------------------------------
+    # Rendering Methods
+
+    def _format_native_types(
+        self, *, na_rep: str | float = "NaT", date_format=None, **kwargs
+    ) -> npt.NDArray[np.object_]:
+        if date_format is None and self._is_dates_only:
+            # Only dates and no timezone: provide a default format
+            date_format = "%Y-%m-%d"
+
+        return tslib.format_array_from_datetime(
+            self.asi8, tz=self.tz, format=date_format, na_rep=na_rep, reso=self._creso
+        )
+
+    # -----------------------------------------------------------------
+    # Comparison Methods
+
+    def _has_same_tz(self, other) -> bool:
+        # vzone shouldn't be None if value is non-datetime like
+        if isinstance(other, np.datetime64):
+            # convert to Timestamp as np.datetime64 doesn't have tz attr
+            other = Timestamp(other)
+
+        if not hasattr(other, "tzinfo"):
+            return False
+        other_tz = other.tzinfo
+        return timezones.tz_compare(self.tzinfo, other_tz)
+
+    def _assert_tzawareness_compat(self, other) -> None:
+        # adapted from _Timestamp._assert_tzawareness_compat
+        other_tz = getattr(other, "tzinfo", None)
+        other_dtype = getattr(other, "dtype", None)
+
+        if isinstance(other_dtype, DatetimeTZDtype):
+            # Get tzinfo from Series dtype
+            other_tz = other.dtype.tz
+        if other is NaT:
+            # pd.NaT quacks both aware and naive
+            pass
+        elif self.tz is None:
+            if other_tz is not None:
+                raise TypeError(
+                    "Cannot compare tz-naive and tz-aware datetime-like objects."
+                )
+        elif other_tz is None:
+            raise TypeError(
+                "Cannot compare tz-naive and tz-aware datetime-like objects"
+            )
+
+    # -----------------------------------------------------------------
+    # Arithmetic Methods
+
+    def _add_offset(self, offset: BaseOffset) -> Self:
+        assert not isinstance(offset, Tick)
+
+        if self.tz is not None:
+            values = self.tz_localize(None)
+        else:
+            values = self
+
+        try:
+            res_values = offset._apply_array(values._ndarray)
+            if res_values.dtype.kind == "i":
+                # error: Argument 1 to "view" of "ndarray" has incompatible type
+                # "dtype[datetime64] | DatetimeTZDtype"; expected
+                # "dtype[Any] | type[Any] | _SupportsDType[dtype[Any]]"
+                res_values = res_values.view(values.dtype)  # type: ignore[arg-type]
+        except NotImplementedError:
+            warnings.warn(
+                "Non-vectorized DateOffset being applied to Series or DatetimeIndex.",
+                PerformanceWarning,
+                stacklevel=find_stack_level(),
+            )
+            res_values = self.astype("O") + offset
+            # TODO(GH#55564): as_unit will be unnecessary
+            result = type(self)._from_sequence(res_values).as_unit(self.unit)
+            if not len(self):
+                # GH#30336 _from_sequence won't be able to infer self.tz
+                return result.tz_localize(self.tz)
+
+        else:
+            result = type(self)._simple_new(res_values, dtype=res_values.dtype)
+            if offset.normalize:
+                result = result.normalize()
+                result._freq = None
+
+            if self.tz is not None:
+                result = result.tz_localize(self.tz)
+
+        return result
+
+    # -----------------------------------------------------------------
+    # Timezone Conversion and Localization Methods
+
+    def _local_timestamps(self) -> npt.NDArray[np.int64]:
+        """
+        Convert to an i8 (unix-like nanosecond timestamp) representation
+        while keeping the local timezone and not using UTC.
+        This is used to calculate time-of-day information as if the timestamps
+        were timezone-naive.
+        """
+        if self.tz is None or timezones.is_utc(self.tz):
+            # Avoid the copy that would be made in tzconversion
+            return self.asi8
+        return tz_convert_from_utc(self.asi8, self.tz, reso=self._creso)
+
+    def tz_convert(self, tz) -> Self:
+        """
+        Convert tz-aware Datetime Array/Index from one time zone to another.
+
+        Parameters
+        ----------
+        tz : str, pytz.timezone, dateutil.tz.tzfile, datetime.tzinfo or None
+            Time zone for time. Corresponding timestamps would be converted
+            to this time zone of the Datetime Array/Index. A `tz` of None will
+            convert to UTC and remove the timezone information.
+
+        Returns
+        -------
+        Array or Index
+
+        Raises
+        ------
+        TypeError
+            If Datetime Array/Index is tz-naive.
+
+        See Also
+        --------
+        DatetimeIndex.tz : A timezone that has a variable offset from UTC.
+        DatetimeIndex.tz_localize : Localize tz-naive DatetimeIndex to a
+            given time zone, or remove timezone from a tz-aware DatetimeIndex.
+
+        Examples
+        --------
+        With the `tz` parameter, we can change the DatetimeIndex
+        to other time zones:
+
+        >>> dti = pd.date_range(start='2014-08-01 09:00',
+        ...                     freq='h', periods=3, tz='Europe/Berlin')
+
+        >>> dti
+        DatetimeIndex(['2014-08-01 09:00:00+02:00',
+                       '2014-08-01 10:00:00+02:00',
+                       '2014-08-01 11:00:00+02:00'],
+                      dtype='datetime64[ns, Europe/Berlin]', freq='h')
+
+        >>> dti.tz_convert('US/Central')
+        DatetimeIndex(['2014-08-01 02:00:00-05:00',
+                       '2014-08-01 03:00:00-05:00',
+                       '2014-08-01 04:00:00-05:00'],
+                      dtype='datetime64[ns, US/Central]', freq='h')
+
+        With the ``tz=None``, we can remove the timezone (after converting
+        to UTC if necessary):
+
+        >>> dti = pd.date_range(start='2014-08-01 09:00', freq='h',
+        ...                     periods=3, tz='Europe/Berlin')
+
+        >>> dti
+        DatetimeIndex(['2014-08-01 09:00:00+02:00',
+                       '2014-08-01 10:00:00+02:00',
+                       '2014-08-01 11:00:00+02:00'],
+                        dtype='datetime64[ns, Europe/Berlin]', freq='h')
+
+        >>> dti.tz_convert(None)
+        DatetimeIndex(['2014-08-01 07:00:00',
+                       '2014-08-01 08:00:00',
+                       '2014-08-01 09:00:00'],
+                        dtype='datetime64[ns]', freq='h')
+        """
+        tz = timezones.maybe_get_tz(tz)
+
+        if self.tz is None:
+            # tz naive, use tz_localize
+            raise TypeError(
+                "Cannot convert tz-naive timestamps, use tz_localize to localize"
+            )
+
+        # No conversion since timestamps are all UTC to begin with
+        dtype = tz_to_dtype(tz, unit=self.unit)
+        return self._simple_new(self._ndarray, dtype=dtype, freq=self.freq)
+
+    @dtl.ravel_compat
+    def tz_localize(
+        self,
+        tz,
+        ambiguous: TimeAmbiguous = "raise",
+        nonexistent: TimeNonexistent = "raise",
+    ) -> Self:
+        """
+        Localize tz-naive Datetime Array/Index to tz-aware Datetime Array/Index.
+
+        This method takes a time zone (tz) naive Datetime Array/Index object
+        and makes this time zone aware. It does not move the time to another
+        time zone.
+
+        This method can also be used to do the inverse -- to create a time
+        zone unaware object from an aware object. To that end, pass `tz=None`.
+
+        Parameters
+        ----------
+        tz : str, pytz.timezone, dateutil.tz.tzfile, datetime.tzinfo or None
+            Time zone to convert timestamps to. Passing ``None`` will
+            remove the time zone information preserving local time.
+        ambiguous : 'infer', 'NaT', bool array, default 'raise'
+            When clocks moved backward due to DST, ambiguous times may arise.
+            For example in Central European Time (UTC+01), when going from
+            03:00 DST to 02:00 non-DST, 02:30:00 local time occurs both at
+            00:30:00 UTC and at 01:30:00 UTC. In such a situation, the
+            `ambiguous` parameter dictates how ambiguous times should be
+            handled.
+
+            - 'infer' will attempt to infer fall dst-transition hours based on
+              order
+            - bool-ndarray where True signifies a DST time, False signifies a
+              non-DST time (note that this flag is only applicable for
+              ambiguous times)
+            - 'NaT' will return NaT where there are ambiguous times
+            - 'raise' will raise an AmbiguousTimeError if there are ambiguous
+              times.
+
+        nonexistent : 'shift_forward', 'shift_backward, 'NaT', timedelta, \
+default 'raise'
+            A nonexistent time does not exist in a particular timezone
+            where clocks moved forward due to DST.
+
+            - 'shift_forward' will shift the nonexistent time forward to the
+              closest existing time
+            - 'shift_backward' will shift the nonexistent time backward to the
+              closest existing time
+            - 'NaT' will return NaT where there are nonexistent times
+            - timedelta objects will shift nonexistent times by the timedelta
+            - 'raise' will raise an NonExistentTimeError if there are
+              nonexistent times.
+
+        Returns
+        -------
+        Same type as self
+            Array/Index converted to the specified time zone.
+
+        Raises
+        ------
+        TypeError
+            If the Datetime Array/Index is tz-aware and tz is not None.
+
+        See Also
+        --------
+        DatetimeIndex.tz_convert : Convert tz-aware DatetimeIndex from
+            one time zone to another.
+
+        Examples
+        --------
+        >>> tz_naive = pd.date_range('2018-03-01 09:00', periods=3)
+        >>> tz_naive
+        DatetimeIndex(['2018-03-01 09:00:00', '2018-03-02 09:00:00',
+                       '2018-03-03 09:00:00'],
+                      dtype='datetime64[ns]', freq='D')
+
+        Localize DatetimeIndex in US/Eastern time zone:
+
+        >>> tz_aware = tz_naive.tz_localize(tz='US/Eastern')
+        >>> tz_aware
+        DatetimeIndex(['2018-03-01 09:00:00-05:00',
+                       '2018-03-02 09:00:00-05:00',
+                       '2018-03-03 09:00:00-05:00'],
+                      dtype='datetime64[ns, US/Eastern]', freq=None)
+
+        With the ``tz=None``, we can remove the time zone information
+        while keeping the local time (not converted to UTC):
+
+        >>> tz_aware.tz_localize(None)
+        DatetimeIndex(['2018-03-01 09:00:00', '2018-03-02 09:00:00',
+                       '2018-03-03 09:00:00'],
+                      dtype='datetime64[ns]', freq=None)
+
+        Be careful with DST changes. When there is sequential data, pandas can
+        infer the DST time:
+
+        >>> s = pd.to_datetime(pd.Series(['2018-10-28 01:30:00',
+        ...                               '2018-10-28 02:00:00',
+        ...                               '2018-10-28 02:30:00',
+        ...                               '2018-10-28 02:00:00',
+        ...                               '2018-10-28 02:30:00',
+        ...                               '2018-10-28 03:00:00',
+        ...                               '2018-10-28 03:30:00']))
+        >>> s.dt.tz_localize('CET', ambiguous='infer')
+        0   2018-10-28 01:30:00+02:00
+        1   2018-10-28 02:00:00+02:00
+        2   2018-10-28 02:30:00+02:00
+        3   2018-10-28 02:00:00+01:00
+        4   2018-10-28 02:30:00+01:00
+        5   2018-10-28 03:00:00+01:00
+        6   2018-10-28 03:30:00+01:00
+        dtype: datetime64[ns, CET]
+
+        In some cases, inferring the DST is impossible. In such cases, you can
+        pass an ndarray to the ambiguous parameter to set the DST explicitly
+
+        >>> s = pd.to_datetime(pd.Series(['2018-10-28 01:20:00',
+        ...                               '2018-10-28 02:36:00',
+        ...                               '2018-10-28 03:46:00']))
+        >>> s.dt.tz_localize('CET', ambiguous=np.array([True, True, False]))
+        0   2018-10-28 01:20:00+02:00
+        1   2018-10-28 02:36:00+02:00
+        2   2018-10-28 03:46:00+01:00
+        dtype: datetime64[ns, CET]
+
+        If the DST transition causes nonexistent times, you can shift these
+        dates forward or backwards with a timedelta object or `'shift_forward'`
+        or `'shift_backwards'`.
+
+        >>> s = pd.to_datetime(pd.Series(['2015-03-29 02:30:00',
+        ...                               '2015-03-29 03:30:00']))
+        >>> s.dt.tz_localize('Europe/Warsaw', nonexistent='shift_forward')
+        0   2015-03-29 03:00:00+02:00
+        1   2015-03-29 03:30:00+02:00
+        dtype: datetime64[ns, Europe/Warsaw]
+
+        >>> s.dt.tz_localize('Europe/Warsaw', nonexistent='shift_backward')
+        0   2015-03-29 01:59:59.999999999+01:00
+        1   2015-03-29 03:30:00+02:00
+        dtype: datetime64[ns, Europe/Warsaw]
+
+        >>> s.dt.tz_localize('Europe/Warsaw', nonexistent=pd.Timedelta('1h'))
+        0   2015-03-29 03:30:00+02:00
+        1   2015-03-29 03:30:00+02:00
+        dtype: datetime64[ns, Europe/Warsaw]
+        """
+        nonexistent_options = ("raise", "NaT", "shift_forward", "shift_backward")
+        if nonexistent not in nonexistent_options and not isinstance(
+            nonexistent, timedelta
+        ):
+            raise ValueError(
+                "The nonexistent argument must be one of 'raise', "
+                "'NaT', 'shift_forward', 'shift_backward' or "
+                "a timedelta object"
+            )
+
+        if self.tz is not None:
+            if tz is None:
+                new_dates = tz_convert_from_utc(self.asi8, self.tz, reso=self._creso)
+            else:
+                raise TypeError("Already tz-aware, use tz_convert to convert.")
+        else:
+            tz = timezones.maybe_get_tz(tz)
+            # Convert to UTC
+
+            new_dates = tzconversion.tz_localize_to_utc(
+                self.asi8,
+                tz,
+                ambiguous=ambiguous,
+                nonexistent=nonexistent,
+                creso=self._creso,
+            )
+        new_dates_dt64 = new_dates.view(f"M8[{self.unit}]")
+        dtype = tz_to_dtype(tz, unit=self.unit)
+
+        freq = None
+        if timezones.is_utc(tz) or (len(self) == 1 and not isna(new_dates_dt64[0])):
+            # we can preserve freq
+            # TODO: Also for fixed-offsets
+            freq = self.freq
+        elif tz is None and self.tz is None:
+            # no-op
+            freq = self.freq
+        return self._simple_new(new_dates_dt64, dtype=dtype, freq=freq)
+
+    # ----------------------------------------------------------------
+    # Conversion Methods - Vectorized analogues of Timestamp methods
+
+    def to_pydatetime(self) -> npt.NDArray[np.object_]:
+        """
+        Return an ndarray of ``datetime.datetime`` objects.
+
+        Returns
+        -------
+        numpy.ndarray
+
+        Examples
+        --------
+        >>> idx = pd.date_range('2018-02-27', periods=3)
+        >>> idx.to_pydatetime()
+        array([datetime.datetime(2018, 2, 27, 0, 0),
+               datetime.datetime(2018, 2, 28, 0, 0),
+               datetime.datetime(2018, 3, 1, 0, 0)], dtype=object)
+        """
+        return ints_to_pydatetime(self.asi8, tz=self.tz, reso=self._creso)
+
+    def normalize(self) -> Self:
+        """
+        Convert times to midnight.
+
+        The time component of the date-time is converted to midnight i.e.
+        00:00:00. This is useful in cases, when the time does not matter.
+        Length is unaltered. The timezones are unaffected.
+
+        This method is available on Series with datetime values under
+        the ``.dt`` accessor, and directly on Datetime Array/Index.
+
+        Returns
+        -------
+        DatetimeArray, DatetimeIndex or Series
+            The same type as the original data. Series will have the same
+            name and index. DatetimeIndex will have the same name.
+
+        See Also
+        --------
+        floor : Floor the datetimes to the specified freq.
+        ceil : Ceil the datetimes to the specified freq.
+        round : Round the datetimes to the specified freq.
+
+        Examples
+        --------
+        >>> idx = pd.date_range(start='2014-08-01 10:00', freq='h',
+        ...                     periods=3, tz='Asia/Calcutta')
+        >>> idx
+        DatetimeIndex(['2014-08-01 10:00:00+05:30',
+                       '2014-08-01 11:00:00+05:30',
+                       '2014-08-01 12:00:00+05:30'],
+                        dtype='datetime64[ns, Asia/Calcutta]', freq='h')
+        >>> idx.normalize()
+        DatetimeIndex(['2014-08-01 00:00:00+05:30',
+                       '2014-08-01 00:00:00+05:30',
+                       '2014-08-01 00:00:00+05:30'],
+                       dtype='datetime64[ns, Asia/Calcutta]', freq=None)
+        """
+        new_values = normalize_i8_timestamps(self.asi8, self.tz, reso=self._creso)
+        dt64_values = new_values.view(self._ndarray.dtype)
+
+        dta = type(self)._simple_new(dt64_values, dtype=dt64_values.dtype)
+        dta = dta._with_freq("infer")
+        if self.tz is not None:
+            dta = dta.tz_localize(self.tz)
+        return dta
+
+    def to_period(self, freq=None) -> PeriodArray:
+        """
+        Cast to PeriodArray/PeriodIndex at a particular frequency.
+
+        Converts DatetimeArray/Index to PeriodArray/PeriodIndex.
+
+        Parameters
+        ----------
+        freq : str or Period, optional
+            One of pandas' :ref:`period aliases `
+            or an Period object. Will be inferred by default.
+
+        Returns
+        -------
+        PeriodArray/PeriodIndex
+
+        Raises
+        ------
+        ValueError
+            When converting a DatetimeArray/Index with non-regular values,
+            so that a frequency cannot be inferred.
+
+        See Also
+        --------
+        PeriodIndex: Immutable ndarray holding ordinal values.
+        DatetimeIndex.to_pydatetime: Return DatetimeIndex as object.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({"y": [1, 2, 3]},
+        ...                   index=pd.to_datetime(["2000-03-31 00:00:00",
+        ...                                         "2000-05-31 00:00:00",
+        ...                                         "2000-08-31 00:00:00"]))
+        >>> df.index.to_period("M")
+        PeriodIndex(['2000-03', '2000-05', '2000-08'],
+                    dtype='period[M]')
+
+        Infer the daily frequency
+
+        >>> idx = pd.date_range("2017-01-01", periods=2)
+        >>> idx.to_period()
+        PeriodIndex(['2017-01-01', '2017-01-02'],
+                    dtype='period[D]')
+        """
+        from pandas.core.arrays import PeriodArray
+
+        if self.tz is not None:
+            warnings.warn(
+                "Converting to PeriodArray/Index representation "
+                "will drop timezone information.",
+                UserWarning,
+                stacklevel=find_stack_level(),
+            )
+
+        if freq is None:
+            freq = self.freqstr or self.inferred_freq
+            if isinstance(self.freq, BaseOffset) and hasattr(
+                self.freq, "_period_dtype_code"
+            ):
+                freq = PeriodDtype(self.freq)._freqstr
+
+            if freq is None:
+                raise ValueError(
+                    "You must pass a freq argument as current index has none."
+                )
+
+            res = get_period_alias(freq)
+
+            #  https://github.com/pandas-dev/pandas/issues/33358
+            if res is None:
+                res = freq
+
+            freq = res
+        return PeriodArray._from_datetime64(self._ndarray, freq, tz=self.tz)
+
+    # -----------------------------------------------------------------
+    # Properties - Vectorized Timestamp Properties/Methods
+
+    def month_name(self, locale=None) -> npt.NDArray[np.object_]:
+        """
+        Return the month names with specified locale.
+
+        Parameters
+        ----------
+        locale : str, optional
+            Locale determining the language in which to return the month name.
+            Default is English locale (``'en_US.utf8'``). Use the command
+            ``locale -a`` on your terminal on Unix systems to find your locale
+            language code.
+
+        Returns
+        -------
+        Series or Index
+            Series or Index of month names.
+
+        Examples
+        --------
+        >>> s = pd.Series(pd.date_range(start='2018-01', freq='ME', periods=3))
+        >>> s
+        0   2018-01-31
+        1   2018-02-28
+        2   2018-03-31
+        dtype: datetime64[ns]
+        >>> s.dt.month_name()
+        0     January
+        1    February
+        2       March
+        dtype: object
+
+        >>> idx = pd.date_range(start='2018-01', freq='ME', periods=3)
+        >>> idx
+        DatetimeIndex(['2018-01-31', '2018-02-28', '2018-03-31'],
+                      dtype='datetime64[ns]', freq='ME')
+        >>> idx.month_name()
+        Index(['January', 'February', 'March'], dtype='object')
+
+        Using the ``locale`` parameter you can set a different locale language,
+        for example: ``idx.month_name(locale='pt_BR.utf8')`` will return month
+        names in Brazilian Portuguese language.
+
+        >>> idx = pd.date_range(start='2018-01', freq='ME', periods=3)
+        >>> idx
+        DatetimeIndex(['2018-01-31', '2018-02-28', '2018-03-31'],
+                      dtype='datetime64[ns]', freq='ME')
+        >>> idx.month_name(locale='pt_BR.utf8')  # doctest: +SKIP
+        Index(['Janeiro', 'Fevereiro', 'Março'], dtype='object')
+        """
+        values = self._local_timestamps()
+
+        result = fields.get_date_name_field(
+            values, "month_name", locale=locale, reso=self._creso
+        )
+        result = self._maybe_mask_results(result, fill_value=None)
+        if using_string_dtype():
+            from pandas import (
+                StringDtype,
+                array as pd_array,
+            )
+
+            return pd_array(result, dtype=StringDtype(na_value=np.nan))  # type: ignore[return-value]
+        return result
+
+    def day_name(self, locale=None) -> npt.NDArray[np.object_]:
+        """
+        Return the day names with specified locale.
+
+        Parameters
+        ----------
+        locale : str, optional
+            Locale determining the language in which to return the day name.
+            Default is English locale (``'en_US.utf8'``). Use the command
+            ``locale -a`` on your terminal on Unix systems to find your locale
+            language code.
+
+        Returns
+        -------
+        Series or Index
+            Series or Index of day names.
+
+        Examples
+        --------
+        >>> s = pd.Series(pd.date_range(start='2018-01-01', freq='D', periods=3))
+        >>> s
+        0   2018-01-01
+        1   2018-01-02
+        2   2018-01-03
+        dtype: datetime64[ns]
+        >>> s.dt.day_name()
+        0       Monday
+        1      Tuesday
+        2    Wednesday
+        dtype: object
+
+        >>> idx = pd.date_range(start='2018-01-01', freq='D', periods=3)
+        >>> idx
+        DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03'],
+                      dtype='datetime64[ns]', freq='D')
+        >>> idx.day_name()
+        Index(['Monday', 'Tuesday', 'Wednesday'], dtype='object')
+
+        Using the ``locale`` parameter you can set a different locale language,
+        for example: ``idx.day_name(locale='pt_BR.utf8')`` will return day
+        names in Brazilian Portuguese language.
+
+        >>> idx = pd.date_range(start='2018-01-01', freq='D', periods=3)
+        >>> idx
+        DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03'],
+                      dtype='datetime64[ns]', freq='D')
+        >>> idx.day_name(locale='pt_BR.utf8') # doctest: +SKIP
+        Index(['Segunda', 'Terça', 'Quarta'], dtype='object')
+        """
+        values = self._local_timestamps()
+
+        result = fields.get_date_name_field(
+            values, "day_name", locale=locale, reso=self._creso
+        )
+        result = self._maybe_mask_results(result, fill_value=None)
+        if using_string_dtype():
+            # TODO: no tests that check for dtype of result as of 2024-08-15
+            from pandas import (
+                StringDtype,
+                array as pd_array,
+            )
+
+            return pd_array(result, dtype=StringDtype(na_value=np.nan))  # type: ignore[return-value]
+        return result
+
+    @property
+    def time(self) -> npt.NDArray[np.object_]:
+        """
+        Returns numpy array of :class:`datetime.time` objects.
+
+        The time part of the Timestamps.
+
+        Examples
+        --------
+        For Series:
+
+        >>> s = pd.Series(["1/1/2020 10:00:00+00:00", "2/1/2020 11:00:00+00:00"])
+        >>> s = pd.to_datetime(s)
+        >>> s
+        0   2020-01-01 10:00:00+00:00
+        1   2020-02-01 11:00:00+00:00
+        dtype: datetime64[ns, UTC]
+        >>> s.dt.time
+        0    10:00:00
+        1    11:00:00
+        dtype: object
+
+        For DatetimeIndex:
+
+        >>> idx = pd.DatetimeIndex(["1/1/2020 10:00:00+00:00",
+        ...                         "2/1/2020 11:00:00+00:00"])
+        >>> idx.time
+        array([datetime.time(10, 0), datetime.time(11, 0)], dtype=object)
+        """
+        # If the Timestamps have a timezone that is not UTC,
+        # convert them into their i8 representation while
+        # keeping their timezone and not using UTC
+        timestamps = self._local_timestamps()
+
+        return ints_to_pydatetime(timestamps, box="time", reso=self._creso)
+
+    @property
+    def timetz(self) -> npt.NDArray[np.object_]:
+        """
+        Returns numpy array of :class:`datetime.time` objects with timezones.
+
+        The time part of the Timestamps.
+
+        Examples
+        --------
+        For Series:
+
+        >>> s = pd.Series(["1/1/2020 10:00:00+00:00", "2/1/2020 11:00:00+00:00"])
+        >>> s = pd.to_datetime(s)
+        >>> s
+        0   2020-01-01 10:00:00+00:00
+        1   2020-02-01 11:00:00+00:00
+        dtype: datetime64[ns, UTC]
+        >>> s.dt.timetz
+        0    10:00:00+00:00
+        1    11:00:00+00:00
+        dtype: object
+
+        For DatetimeIndex:
+
+        >>> idx = pd.DatetimeIndex(["1/1/2020 10:00:00+00:00",
+        ...                         "2/1/2020 11:00:00+00:00"])
+        >>> idx.timetz
+        array([datetime.time(10, 0, tzinfo=datetime.timezone.utc),
+        datetime.time(11, 0, tzinfo=datetime.timezone.utc)], dtype=object)
+        """
+        return ints_to_pydatetime(self.asi8, self.tz, box="time", reso=self._creso)
+
+    @property
+    def date(self) -> npt.NDArray[np.object_]:
+        """
+        Returns numpy array of python :class:`datetime.date` objects.
+
+        Namely, the date part of Timestamps without time and
+        timezone information.
+
+        Examples
+        --------
+        For Series:
+
+        >>> s = pd.Series(["1/1/2020 10:00:00+00:00", "2/1/2020 11:00:00+00:00"])
+        >>> s = pd.to_datetime(s)
+        >>> s
+        0   2020-01-01 10:00:00+00:00
+        1   2020-02-01 11:00:00+00:00
+        dtype: datetime64[ns, UTC]
+        >>> s.dt.date
+        0    2020-01-01
+        1    2020-02-01
+        dtype: object
+
+        For DatetimeIndex:
+
+        >>> idx = pd.DatetimeIndex(["1/1/2020 10:00:00+00:00",
+        ...                         "2/1/2020 11:00:00+00:00"])
+        >>> idx.date
+        array([datetime.date(2020, 1, 1), datetime.date(2020, 2, 1)], dtype=object)
+        """
+        # If the Timestamps have a timezone that is not UTC,
+        # convert them into their i8 representation while
+        # keeping their timezone and not using UTC
+        timestamps = self._local_timestamps()
+
+        return ints_to_pydatetime(timestamps, box="date", reso=self._creso)
+
+    def isocalendar(self) -> DataFrame:
+        """
+        Calculate year, week, and day according to the ISO 8601 standard.
+
+        Returns
+        -------
+        DataFrame
+            With columns year, week and day.
+
+        See Also
+        --------
+        Timestamp.isocalendar : Function return a 3-tuple containing ISO year,
+            week number, and weekday for the given Timestamp object.
+        datetime.date.isocalendar : Return a named tuple object with
+            three components: year, week and weekday.
+
+        Examples
+        --------
+        >>> idx = pd.date_range(start='2019-12-29', freq='D', periods=4)
+        >>> idx.isocalendar()
+                    year  week  day
+        2019-12-29  2019    52    7
+        2019-12-30  2020     1    1
+        2019-12-31  2020     1    2
+        2020-01-01  2020     1    3
+        >>> idx.isocalendar().week
+        2019-12-29    52
+        2019-12-30     1
+        2019-12-31     1
+        2020-01-01     1
+        Freq: D, Name: week, dtype: UInt32
+        """
+        from pandas import DataFrame
+
+        values = self._local_timestamps()
+        sarray = fields.build_isocalendar_sarray(values, reso=self._creso)
+        iso_calendar_df = DataFrame(
+            sarray, columns=["year", "week", "day"], dtype="UInt32"
+        )
+        if self._hasna:
+            iso_calendar_df.iloc[self._isnan] = None
+        return iso_calendar_df
+
+    year = _field_accessor(
+        "year",
+        "Y",
+        """
+        The year of the datetime.
+
+        Examples
+        --------
+        >>> datetime_series = pd.Series(
+        ...     pd.date_range("2000-01-01", periods=3, freq="YE")
+        ... )
+        >>> datetime_series
+        0   2000-12-31
+        1   2001-12-31
+        2   2002-12-31
+        dtype: datetime64[ns]
+        >>> datetime_series.dt.year
+        0    2000
+        1    2001
+        2    2002
+        dtype: int32
+        """,
+    )
+    month = _field_accessor(
+        "month",
+        "M",
+        """
+        The month as January=1, December=12.
+
+        Examples
+        --------
+        >>> datetime_series = pd.Series(
+        ...     pd.date_range("2000-01-01", periods=3, freq="ME")
+        ... )
+        >>> datetime_series
+        0   2000-01-31
+        1   2000-02-29
+        2   2000-03-31
+        dtype: datetime64[ns]
+        >>> datetime_series.dt.month
+        0    1
+        1    2
+        2    3
+        dtype: int32
+        """,
+    )
+    day = _field_accessor(
+        "day",
+        "D",
+        """
+        The day of the datetime.
+
+        Examples
+        --------
+        >>> datetime_series = pd.Series(
+        ...     pd.date_range("2000-01-01", periods=3, freq="D")
+        ... )
+        >>> datetime_series
+        0   2000-01-01
+        1   2000-01-02
+        2   2000-01-03
+        dtype: datetime64[ns]
+        >>> datetime_series.dt.day
+        0    1
+        1    2
+        2    3
+        dtype: int32
+        """,
+    )
+    hour = _field_accessor(
+        "hour",
+        "h",
+        """
+        The hours of the datetime.
+
+        Examples
+        --------
+        >>> datetime_series = pd.Series(
+        ...     pd.date_range("2000-01-01", periods=3, freq="h")
+        ... )
+        >>> datetime_series
+        0   2000-01-01 00:00:00
+        1   2000-01-01 01:00:00
+        2   2000-01-01 02:00:00
+        dtype: datetime64[ns]
+        >>> datetime_series.dt.hour
+        0    0
+        1    1
+        2    2
+        dtype: int32
+        """,
+    )
+    minute = _field_accessor(
+        "minute",
+        "m",
+        """
+        The minutes of the datetime.
+
+        Examples
+        --------
+        >>> datetime_series = pd.Series(
+        ...     pd.date_range("2000-01-01", periods=3, freq="min")
+        ... )
+        >>> datetime_series
+        0   2000-01-01 00:00:00
+        1   2000-01-01 00:01:00
+        2   2000-01-01 00:02:00
+        dtype: datetime64[ns]
+        >>> datetime_series.dt.minute
+        0    0
+        1    1
+        2    2
+        dtype: int32
+        """,
+    )
+    second = _field_accessor(
+        "second",
+        "s",
+        """
+        The seconds of the datetime.
+
+        Examples
+        --------
+        >>> datetime_series = pd.Series(
+        ...     pd.date_range("2000-01-01", periods=3, freq="s")
+        ... )
+        >>> datetime_series
+        0   2000-01-01 00:00:00
+        1   2000-01-01 00:00:01
+        2   2000-01-01 00:00:02
+        dtype: datetime64[ns]
+        >>> datetime_series.dt.second
+        0    0
+        1    1
+        2    2
+        dtype: int32
+        """,
+    )
+    microsecond = _field_accessor(
+        "microsecond",
+        "us",
+        """
+        The microseconds of the datetime.
+
+        Examples
+        --------
+        >>> datetime_series = pd.Series(
+        ...     pd.date_range("2000-01-01", periods=3, freq="us")
+        ... )
+        >>> datetime_series
+        0   2000-01-01 00:00:00.000000
+        1   2000-01-01 00:00:00.000001
+        2   2000-01-01 00:00:00.000002
+        dtype: datetime64[ns]
+        >>> datetime_series.dt.microsecond
+        0       0
+        1       1
+        2       2
+        dtype: int32
+        """,
+    )
+    nanosecond = _field_accessor(
+        "nanosecond",
+        "ns",
+        """
+        The nanoseconds of the datetime.
+
+        Examples
+        --------
+        >>> datetime_series = pd.Series(
+        ...     pd.date_range("2000-01-01", periods=3, freq="ns")
+        ... )
+        >>> datetime_series
+        0   2000-01-01 00:00:00.000000000
+        1   2000-01-01 00:00:00.000000001
+        2   2000-01-01 00:00:00.000000002
+        dtype: datetime64[ns]
+        >>> datetime_series.dt.nanosecond
+        0       0
+        1       1
+        2       2
+        dtype: int32
+        """,
+    )
+    _dayofweek_doc = """
+    The day of the week with Monday=0, Sunday=6.
+
+    Return the day of the week. It is assumed the week starts on
+    Monday, which is denoted by 0 and ends on Sunday which is denoted
+    by 6. This method is available on both Series with datetime
+    values (using the `dt` accessor) or DatetimeIndex.
+
+    Returns
+    -------
+    Series or Index
+        Containing integers indicating the day number.
+
+    See Also
+    --------
+    Series.dt.dayofweek : Alias.
+    Series.dt.weekday : Alias.
+    Series.dt.day_name : Returns the name of the day of the week.
+
+    Examples
+    --------
+    >>> s = pd.date_range('2016-12-31', '2017-01-08', freq='D').to_series()
+    >>> s.dt.dayofweek
+    2016-12-31    5
+    2017-01-01    6
+    2017-01-02    0
+    2017-01-03    1
+    2017-01-04    2
+    2017-01-05    3
+    2017-01-06    4
+    2017-01-07    5
+    2017-01-08    6
+    Freq: D, dtype: int32
+    """
+    day_of_week = _field_accessor("day_of_week", "dow", _dayofweek_doc)
+    dayofweek = day_of_week
+    weekday = day_of_week
+
+    day_of_year = _field_accessor(
+        "dayofyear",
+        "doy",
+        """
+        The ordinal day of the year.
+
+        Examples
+        --------
+        For Series:
+
+        >>> s = pd.Series(["1/1/2020 10:00:00+00:00", "2/1/2020 11:00:00+00:00"])
+        >>> s = pd.to_datetime(s)
+        >>> s
+        0   2020-01-01 10:00:00+00:00
+        1   2020-02-01 11:00:00+00:00
+        dtype: datetime64[ns, UTC]
+        >>> s.dt.dayofyear
+        0    1
+        1   32
+        dtype: int32
+
+        For DatetimeIndex:
+
+        >>> idx = pd.DatetimeIndex(["1/1/2020 10:00:00+00:00",
+        ...                         "2/1/2020 11:00:00+00:00"])
+        >>> idx.dayofyear
+        Index([1, 32], dtype='int32')
+        """,
+    )
+    dayofyear = day_of_year
+    quarter = _field_accessor(
+        "quarter",
+        "q",
+        """
+        The quarter of the date.
+
+        Examples
+        --------
+        For Series:
+
+        >>> s = pd.Series(["1/1/2020 10:00:00+00:00", "4/1/2020 11:00:00+00:00"])
+        >>> s = pd.to_datetime(s)
+        >>> s
+        0   2020-01-01 10:00:00+00:00
+        1   2020-04-01 11:00:00+00:00
+        dtype: datetime64[ns, UTC]
+        >>> s.dt.quarter
+        0    1
+        1    2
+        dtype: int32
+
+        For DatetimeIndex:
+
+        >>> idx = pd.DatetimeIndex(["1/1/2020 10:00:00+00:00",
+        ...                         "2/1/2020 11:00:00+00:00"])
+        >>> idx.quarter
+        Index([1, 1], dtype='int32')
+        """,
+    )
+    days_in_month = _field_accessor(
+        "days_in_month",
+        "dim",
+        """
+        The number of days in the month.
+
+        Examples
+        --------
+        >>> s = pd.Series(["1/1/2020 10:00:00+00:00", "2/1/2020 11:00:00+00:00"])
+        >>> s = pd.to_datetime(s)
+        >>> s
+        0   2020-01-01 10:00:00+00:00
+        1   2020-02-01 11:00:00+00:00
+        dtype: datetime64[ns, UTC]
+        >>> s.dt.daysinmonth
+        0    31
+        1    29
+        dtype: int32
+        """,
+    )
+    daysinmonth = days_in_month
+    _is_month_doc = """
+        Indicates whether the date is the {first_or_last} day of the month.
+
+        Returns
+        -------
+        Series or array
+            For Series, returns a Series with boolean values.
+            For DatetimeIndex, returns a boolean array.
+
+        See Also
+        --------
+        is_month_start : Return a boolean indicating whether the date
+            is the first day of the month.
+        is_month_end : Return a boolean indicating whether the date
+            is the last day of the month.
+
+        Examples
+        --------
+        This method is available on Series with datetime values under
+        the ``.dt`` accessor, and directly on DatetimeIndex.
+
+        >>> s = pd.Series(pd.date_range("2018-02-27", periods=3))
+        >>> s
+        0   2018-02-27
+        1   2018-02-28
+        2   2018-03-01
+        dtype: datetime64[ns]
+        >>> s.dt.is_month_start
+        0    False
+        1    False
+        2    True
+        dtype: bool
+        >>> s.dt.is_month_end
+        0    False
+        1    True
+        2    False
+        dtype: bool
+
+        >>> idx = pd.date_range("2018-02-27", periods=3)
+        >>> idx.is_month_start
+        array([False, False, True])
+        >>> idx.is_month_end
+        array([False, True, False])
+    """
+    is_month_start = _field_accessor(
+        "is_month_start", "is_month_start", _is_month_doc.format(first_or_last="first")
+    )
+
+    is_month_end = _field_accessor(
+        "is_month_end", "is_month_end", _is_month_doc.format(first_or_last="last")
+    )
+
+    is_quarter_start = _field_accessor(
+        "is_quarter_start",
+        "is_quarter_start",
+        """
+        Indicator for whether the date is the first day of a quarter.
+
+        Returns
+        -------
+        is_quarter_start : Series or DatetimeIndex
+            The same type as the original data with boolean values. Series will
+            have the same name and index. DatetimeIndex will have the same
+            name.
+
+        See Also
+        --------
+        quarter : Return the quarter of the date.
+        is_quarter_end : Similar property for indicating the quarter end.
+
+        Examples
+        --------
+        This method is available on Series with datetime values under
+        the ``.dt`` accessor, and directly on DatetimeIndex.
+
+        >>> df = pd.DataFrame({'dates': pd.date_range("2017-03-30",
+        ...                   periods=4)})
+        >>> df.assign(quarter=df.dates.dt.quarter,
+        ...           is_quarter_start=df.dates.dt.is_quarter_start)
+               dates  quarter  is_quarter_start
+        0 2017-03-30        1             False
+        1 2017-03-31        1             False
+        2 2017-04-01        2              True
+        3 2017-04-02        2             False
+
+        >>> idx = pd.date_range('2017-03-30', periods=4)
+        >>> idx
+        DatetimeIndex(['2017-03-30', '2017-03-31', '2017-04-01', '2017-04-02'],
+                      dtype='datetime64[ns]', freq='D')
+
+        >>> idx.is_quarter_start
+        array([False, False,  True, False])
+        """,
+    )
+    is_quarter_end = _field_accessor(
+        "is_quarter_end",
+        "is_quarter_end",
+        """
+        Indicator for whether the date is the last day of a quarter.
+
+        Returns
+        -------
+        is_quarter_end : Series or DatetimeIndex
+            The same type as the original data with boolean values. Series will
+            have the same name and index. DatetimeIndex will have the same
+            name.
+
+        See Also
+        --------
+        quarter : Return the quarter of the date.
+        is_quarter_start : Similar property indicating the quarter start.
+
+        Examples
+        --------
+        This method is available on Series with datetime values under
+        the ``.dt`` accessor, and directly on DatetimeIndex.
+
+        >>> df = pd.DataFrame({'dates': pd.date_range("2017-03-30",
+        ...                    periods=4)})
+        >>> df.assign(quarter=df.dates.dt.quarter,
+        ...           is_quarter_end=df.dates.dt.is_quarter_end)
+               dates  quarter    is_quarter_end
+        0 2017-03-30        1             False
+        1 2017-03-31        1              True
+        2 2017-04-01        2             False
+        3 2017-04-02        2             False
+
+        >>> idx = pd.date_range('2017-03-30', periods=4)
+        >>> idx
+        DatetimeIndex(['2017-03-30', '2017-03-31', '2017-04-01', '2017-04-02'],
+                      dtype='datetime64[ns]', freq='D')
+
+        >>> idx.is_quarter_end
+        array([False,  True, False, False])
+        """,
+    )
+    is_year_start = _field_accessor(
+        "is_year_start",
+        "is_year_start",
+        """
+        Indicate whether the date is the first day of a year.
+
+        Returns
+        -------
+        Series or DatetimeIndex
+            The same type as the original data with boolean values. Series will
+            have the same name and index. DatetimeIndex will have the same
+            name.
+
+        See Also
+        --------
+        is_year_end : Similar property indicating the last day of the year.
+
+        Examples
+        --------
+        This method is available on Series with datetime values under
+        the ``.dt`` accessor, and directly on DatetimeIndex.
+
+        >>> dates = pd.Series(pd.date_range("2017-12-30", periods=3))
+        >>> dates
+        0   2017-12-30
+        1   2017-12-31
+        2   2018-01-01
+        dtype: datetime64[ns]
+
+        >>> dates.dt.is_year_start
+        0    False
+        1    False
+        2    True
+        dtype: bool
+
+        >>> idx = pd.date_range("2017-12-30", periods=3)
+        >>> idx
+        DatetimeIndex(['2017-12-30', '2017-12-31', '2018-01-01'],
+                      dtype='datetime64[ns]', freq='D')
+
+        >>> idx.is_year_start
+        array([False, False,  True])
+        """,
+    )
+    is_year_end = _field_accessor(
+        "is_year_end",
+        "is_year_end",
+        """
+        Indicate whether the date is the last day of the year.
+
+        Returns
+        -------
+        Series or DatetimeIndex
+            The same type as the original data with boolean values. Series will
+            have the same name and index. DatetimeIndex will have the same
+            name.
+
+        See Also
+        --------
+        is_year_start : Similar property indicating the start of the year.
+
+        Examples
+        --------
+        This method is available on Series with datetime values under
+        the ``.dt`` accessor, and directly on DatetimeIndex.
+
+        >>> dates = pd.Series(pd.date_range("2017-12-30", periods=3))
+        >>> dates
+        0   2017-12-30
+        1   2017-12-31
+        2   2018-01-01
+        dtype: datetime64[ns]
+
+        >>> dates.dt.is_year_end
+        0    False
+        1     True
+        2    False
+        dtype: bool
+
+        >>> idx = pd.date_range("2017-12-30", periods=3)
+        >>> idx
+        DatetimeIndex(['2017-12-30', '2017-12-31', '2018-01-01'],
+                      dtype='datetime64[ns]', freq='D')
+
+        >>> idx.is_year_end
+        array([False,  True, False])
+        """,
+    )
+    is_leap_year = _field_accessor(
+        "is_leap_year",
+        "is_leap_year",
+        """
+        Boolean indicator if the date belongs to a leap year.
+
+        A leap year is a year, which has 366 days (instead of 365) including
+        29th of February as an intercalary day.
+        Leap years are years which are multiples of four with the exception
+        of years divisible by 100 but not by 400.
+
+        Returns
+        -------
+        Series or ndarray
+             Booleans indicating if dates belong to a leap year.
+
+        Examples
+        --------
+        This method is available on Series with datetime values under
+        the ``.dt`` accessor, and directly on DatetimeIndex.
+
+        >>> idx = pd.date_range("2012-01-01", "2015-01-01", freq="YE")
+        >>> idx
+        DatetimeIndex(['2012-12-31', '2013-12-31', '2014-12-31'],
+                      dtype='datetime64[ns]', freq='YE-DEC')
+        >>> idx.is_leap_year
+        array([ True, False, False])
+
+        >>> dates_series = pd.Series(idx)
+        >>> dates_series
+        0   2012-12-31
+        1   2013-12-31
+        2   2014-12-31
+        dtype: datetime64[ns]
+        >>> dates_series.dt.is_leap_year
+        0     True
+        1    False
+        2    False
+        dtype: bool
+        """,
+    )
+
+    def to_julian_date(self) -> npt.NDArray[np.float64]:
+        """
+        Convert Datetime Array to float64 ndarray of Julian Dates.
+        0 Julian date is noon January 1, 4713 BC.
+        https://en.wikipedia.org/wiki/Julian_day
+        """
+
+        # http://mysite.verizon.net/aesir_research/date/jdalg2.htm
+        year = np.asarray(self.year)
+        month = np.asarray(self.month)
+        day = np.asarray(self.day)
+        testarr = month < 3
+        year[testarr] -= 1
+        month[testarr] += 12
+        return (
+            day
+            + np.fix((153 * month - 457) / 5)
+            + 365 * year
+            + np.floor(year / 4)
+            - np.floor(year / 100)
+            + np.floor(year / 400)
+            + 1_721_118.5
+            + (
+                self.hour
+                + self.minute / 60
+                + self.second / 3600
+                + self.microsecond / 3600 / 10**6
+                + self.nanosecond / 3600 / 10**9
+            )
+            / 24
+        )
+
+    # -----------------------------------------------------------------
+    # Reductions
+
+    def std(
+        self,
+        axis=None,
+        dtype=None,
+        out=None,
+        ddof: int = 1,
+        keepdims: bool = False,
+        skipna: bool = True,
+    ):
+        """
+        Return sample standard deviation over requested axis.
+
+        Normalized by `N-1` by default. This can be changed using ``ddof``.
+
+        Parameters
+        ----------
+        axis : int, optional
+            Axis for the function to be applied on. For :class:`pandas.Series`
+            this parameter is unused and defaults to ``None``.
+        ddof : int, default 1
+            Degrees of Freedom. The divisor used in calculations is `N - ddof`,
+            where `N` represents the number of elements.
+        skipna : bool, default True
+            Exclude NA/null values. If an entire row/column is ``NA``, the result
+            will be ``NA``.
+
+        Returns
+        -------
+        Timedelta
+
+        See Also
+        --------
+        numpy.ndarray.std : Returns the standard deviation of the array elements
+            along given axis.
+        Series.std : Return sample standard deviation over requested axis.
+
+        Examples
+        --------
+        For :class:`pandas.DatetimeIndex`:
+
+        >>> idx = pd.date_range('2001-01-01 00:00', periods=3)
+        >>> idx
+        DatetimeIndex(['2001-01-01', '2001-01-02', '2001-01-03'],
+                      dtype='datetime64[ns]', freq='D')
+        >>> idx.std()
+        Timedelta('1 days 00:00:00')
+        """
+        # Because std is translation-invariant, we can get self.std
+        #  by calculating (self - Timestamp(0)).std, and we can do it
+        #  without creating a copy by using a view on self._ndarray
+        from pandas.core.arrays import TimedeltaArray
+
+        # Find the td64 dtype with the same resolution as our dt64 dtype
+        dtype_str = self._ndarray.dtype.name.replace("datetime64", "timedelta64")
+        dtype = np.dtype(dtype_str)
+
+        tda = TimedeltaArray._simple_new(self._ndarray.view(dtype), dtype=dtype)
+
+        return tda.std(axis=axis, out=out, ddof=ddof, keepdims=keepdims, skipna=skipna)
+
+
+# -------------------------------------------------------------------
+# Constructor Helpers
+
+
+def _sequence_to_dt64(
+    data: ArrayLike,
+    *,
+    copy: bool = False,
+    tz: tzinfo | None = None,
+    dayfirst: bool = False,
+    yearfirst: bool = False,
+    ambiguous: TimeAmbiguous = "raise",
+    out_unit: str | None = None,
+):
+    """
+    Parameters
+    ----------
+    data : np.ndarray or ExtensionArray
+        dtl.ensure_arraylike_for_datetimelike has already been called.
+    copy : bool, default False
+    tz : tzinfo or None, default None
+    dayfirst : bool, default False
+    yearfirst : bool, default False
+    ambiguous : str, bool, or arraylike, default 'raise'
+        See pandas._libs.tslibs.tzconversion.tz_localize_to_utc.
+    out_unit : str or None, default None
+        Desired output resolution.
+
+    Returns
+    -------
+    result : numpy.ndarray
+        The sequence converted to a numpy array with dtype ``datetime64[unit]``.
+        Where `unit` is "ns" unless specified otherwise by `out_unit`.
+    tz : tzinfo or None
+        Either the user-provided tzinfo or one inferred from the data.
+
+    Raises
+    ------
+    TypeError : PeriodDType data is passed
+    """
+
+    # By this point we are assured to have either a numpy array or Index
+    data, copy = maybe_convert_dtype(data, copy, tz=tz)
+    data_dtype = getattr(data, "dtype", None)
+
+    if out_unit is None:
+        out_unit = "ns"
+    out_dtype = np.dtype(f"M8[{out_unit}]")
+
+    if data_dtype == object or is_string_dtype(data_dtype):
+        # TODO: We do not have tests specific to string-dtypes,
+        #  also complex or categorical or other extension
+        data = cast(np.ndarray, data)
+        copy = False
+        if lib.infer_dtype(data, skipna=False) == "integer":
+            # Much more performant than going through array_to_datetime
+            data = data.astype(np.int64)
+        elif tz is not None and ambiguous == "raise":
+            obj_data = np.asarray(data, dtype=object)
+            result = tslib.array_to_datetime_with_tz(
+                obj_data,
+                tz=tz,
+                dayfirst=dayfirst,
+                yearfirst=yearfirst,
+                creso=abbrev_to_npy_unit(out_unit),
+            )
+            return result, tz
+        else:
+            converted, inferred_tz = objects_to_datetime64(
+                data,
+                dayfirst=dayfirst,
+                yearfirst=yearfirst,
+                allow_object=False,
+                out_unit=out_unit or "ns",
+            )
+            copy = False
+            if tz and inferred_tz:
+                #  two timezones: convert to intended from base UTC repr
+                # GH#42505 by convention, these are _already_ UTC
+                result = converted
+
+            elif inferred_tz:
+                tz = inferred_tz
+                result = converted
+
+            else:
+                result, _ = _construct_from_dt64_naive(
+                    converted, tz=tz, copy=copy, ambiguous=ambiguous
+                )
+            return result, tz
+
+        data_dtype = data.dtype
+
+    # `data` may have originally been a Categorical[datetime64[ns, tz]],
+    # so we need to handle these types.
+    if isinstance(data_dtype, DatetimeTZDtype):
+        # DatetimeArray -> ndarray
+        data = cast(DatetimeArray, data)
+        tz = _maybe_infer_tz(tz, data.tz)
+        result = data._ndarray
+
+    elif lib.is_np_dtype(data_dtype, "M"):
+        # tz-naive DatetimeArray or ndarray[datetime64]
+        if isinstance(data, DatetimeArray):
+            data = data._ndarray
+
+        data = cast(np.ndarray, data)
+        result, copy = _construct_from_dt64_naive(
+            data, tz=tz, copy=copy, ambiguous=ambiguous
+        )
+
+    else:
+        # must be integer dtype otherwise
+        # assume this data are epoch timestamps
+        if data.dtype != INT64_DTYPE:
+            data = data.astype(np.int64, copy=False)
+            copy = False
+        data = cast(np.ndarray, data)
+        result = data.view(out_dtype)
+
+    if copy:
+        result = result.copy()
+
+    assert isinstance(result, np.ndarray), type(result)
+    assert result.dtype.kind == "M"
+    assert result.dtype != "M8"
+    assert is_supported_dtype(result.dtype)
+    return result, tz
+
+
+def _construct_from_dt64_naive(
+    data: np.ndarray, *, tz: tzinfo | None, copy: bool, ambiguous: TimeAmbiguous
+) -> tuple[np.ndarray, bool]:
+    """
+    Convert datetime64 data to a supported dtype, localizing if necessary.
+    """
+    # Caller is responsible for ensuring
+    #  lib.is_np_dtype(data.dtype)
+
+    new_dtype = data.dtype
+    if not is_supported_dtype(new_dtype):
+        # Cast to the nearest supported unit, generally "s"
+        new_dtype = get_supported_dtype(new_dtype)
+        data = astype_overflowsafe(data, dtype=new_dtype, copy=False)
+        copy = False
+
+    if data.dtype.byteorder == ">":
+        # TODO: better way to handle this?  non-copying alternative?
+        #  without this, test_constructor_datetime64_bigendian fails
+        data = data.astype(data.dtype.newbyteorder("<"))
+        new_dtype = data.dtype
+        copy = False
+
+    if tz is not None:
+        # Convert tz-naive to UTC
+        # TODO: if tz is UTC, are there situations where we *don't* want a
+        #  copy?  tz_localize_to_utc always makes one.
+        shape = data.shape
+        if data.ndim > 1:
+            data = data.ravel()
+
+        data_unit = get_unit_from_dtype(new_dtype)
+        data = tzconversion.tz_localize_to_utc(
+            data.view("i8"), tz, ambiguous=ambiguous, creso=data_unit
+        )
+        data = data.view(new_dtype)
+        data = data.reshape(shape)
+
+    assert data.dtype == new_dtype, data.dtype
+    result = data
+
+    return result, copy
+
+
+def objects_to_datetime64(
+    data: np.ndarray,
+    dayfirst,
+    yearfirst,
+    utc: bool = False,
+    errors: DateTimeErrorChoices = "raise",
+    allow_object: bool = False,
+    out_unit: str = "ns",
+):
+    """
+    Convert data to array of timestamps.
+
+    Parameters
+    ----------
+    data : np.ndarray[object]
+    dayfirst : bool
+    yearfirst : bool
+    utc : bool, default False
+        Whether to convert/localize timestamps to UTC.
+    errors : {'raise', 'ignore', 'coerce'}
+    allow_object : bool
+        Whether to return an object-dtype ndarray instead of raising if the
+        data contains more than one timezone.
+    out_unit : str, default "ns"
+
+    Returns
+    -------
+    result : ndarray
+        np.datetime64[out_unit] if returned values represent wall times or UTC
+        timestamps.
+        object if mixed timezones
+    inferred_tz : tzinfo or None
+        If not None, then the datetime64 values in `result` denote UTC timestamps.
+
+    Raises
+    ------
+    ValueError : if data cannot be converted to datetimes
+    TypeError  : When a type cannot be converted to datetime
+    """
+    assert errors in ["raise", "ignore", "coerce"]
+
+    # if str-dtype, convert
+    data = np.asarray(data, dtype=np.object_)
+
+    result, tz_parsed = tslib.array_to_datetime(
+        data,
+        errors=errors,
+        utc=utc,
+        dayfirst=dayfirst,
+        yearfirst=yearfirst,
+        creso=abbrev_to_npy_unit(out_unit),
+    )
+
+    if tz_parsed is not None:
+        # We can take a shortcut since the datetime64 numpy array
+        #  is in UTC
+        return result, tz_parsed
+    elif result.dtype.kind == "M":
+        return result, tz_parsed
+    elif result.dtype == object:
+        # GH#23675 when called via `pd.to_datetime`, returning an object-dtype
+        #  array is allowed.  When called via `pd.DatetimeIndex`, we can
+        #  only accept datetime64 dtype, so raise TypeError if object-dtype
+        #  is returned, as that indicates the values can be recognized as
+        #  datetimes but they have conflicting timezones/awareness
+        if allow_object:
+            return result, tz_parsed
+        raise TypeError("DatetimeIndex has mixed timezones")
+    else:  # pragma: no cover
+        # GH#23675 this TypeError should never be hit, whereas the TypeError
+        #  in the object-dtype branch above is reachable.
+        raise TypeError(result)
+
+
+def maybe_convert_dtype(data, copy: bool, tz: tzinfo | None = None):
+    """
+    Convert data based on dtype conventions, issuing
+    errors where appropriate.
+
+    Parameters
+    ----------
+    data : np.ndarray or pd.Index
+    copy : bool
+    tz : tzinfo or None, default None
+
+    Returns
+    -------
+    data : np.ndarray or pd.Index
+    copy : bool
+
+    Raises
+    ------
+    TypeError : PeriodDType data is passed
+    """
+    if not hasattr(data, "dtype"):
+        # e.g. collections.deque
+        return data, copy
+
+    if is_float_dtype(data.dtype):
+        # pre-2.0 we treated these as wall-times, inconsistent with ints
+        # GH#23675, GH#45573 deprecated to treat symmetrically with integer dtypes.
+        # Note: data.astype(np.int64) fails ARM tests, see
+        # https://github.com/pandas-dev/pandas/issues/49468.
+        data = data.astype(DT64NS_DTYPE).view("i8")
+        copy = False
+
+    elif lib.is_np_dtype(data.dtype, "m") or is_bool_dtype(data.dtype):
+        # GH#29794 enforcing deprecation introduced in GH#23539
+        raise TypeError(f"dtype {data.dtype} cannot be converted to datetime64[ns]")
+    elif isinstance(data.dtype, PeriodDtype):
+        # Note: without explicitly raising here, PeriodIndex
+        #  test_setops.test_join_does_not_recur fails
+        raise TypeError(
+            "Passing PeriodDtype data is invalid. Use `data.to_timestamp()` instead"
+        )
+
+    elif isinstance(data.dtype, ExtensionDtype) and not isinstance(
+        data.dtype, DatetimeTZDtype
+    ):
+        # TODO: We have no tests for these
+        data = np.array(data, dtype=np.object_)
+        copy = False
+
+    return data, copy
+
+
+# -------------------------------------------------------------------
+# Validation and Inference
+
+
+def _maybe_infer_tz(tz: tzinfo | None, inferred_tz: tzinfo | None) -> tzinfo | None:
+    """
+    If a timezone is inferred from data, check that it is compatible with
+    the user-provided timezone, if any.
+
+    Parameters
+    ----------
+    tz : tzinfo or None
+    inferred_tz : tzinfo or None
+
+    Returns
+    -------
+    tz : tzinfo or None
+
+    Raises
+    ------
+    TypeError : if both timezones are present but do not match
+    """
+    if tz is None:
+        tz = inferred_tz
+    elif inferred_tz is None:
+        pass
+    elif not timezones.tz_compare(tz, inferred_tz):
+        raise TypeError(
+            f"data is already tz-aware {inferred_tz}, unable to "
+            f"set specified tz: {tz}"
+        )
+    return tz
+
+
+def _validate_dt64_dtype(dtype):
+    """
+    Check that a dtype, if passed, represents either a numpy datetime64[ns]
+    dtype or a pandas DatetimeTZDtype.
+
+    Parameters
+    ----------
+    dtype : object
+
+    Returns
+    -------
+    dtype : None, numpy.dtype, or DatetimeTZDtype
+
+    Raises
+    ------
+    ValueError : invalid dtype
+
+    Notes
+    -----
+    Unlike _validate_tz_from_dtype, this does _not_ allow non-existent
+    tz errors to go through
+    """
+    if dtype is not None:
+        dtype = pandas_dtype(dtype)
+        if dtype == np.dtype("M8"):
+            # no precision, disallowed GH#24806
+            msg = (
+                "Passing in 'datetime64' dtype with no precision is not allowed. "
+                "Please pass in 'datetime64[ns]' instead."
+            )
+            raise ValueError(msg)
+
+        if (
+            isinstance(dtype, np.dtype)
+            and (dtype.kind != "M" or not is_supported_dtype(dtype))
+        ) or not isinstance(dtype, (np.dtype, DatetimeTZDtype)):
+            raise ValueError(
+                f"Unexpected value for 'dtype': '{dtype}'. "
+                "Must be 'datetime64[s]', 'datetime64[ms]', 'datetime64[us]', "
+                "'datetime64[ns]' or DatetimeTZDtype'."
+            )
+
+        if getattr(dtype, "tz", None):
+            # https://github.com/pandas-dev/pandas/issues/18595
+            # Ensure that we have a standard timezone for pytz objects.
+            # Without this, things like adding an array of timedeltas and
+            # a  tz-aware Timestamp (with a tz specific to its datetime) will
+            # be incorrect(ish?) for the array as a whole
+            dtype = cast(DatetimeTZDtype, dtype)
+            dtype = DatetimeTZDtype(
+                unit=dtype.unit, tz=timezones.tz_standardize(dtype.tz)
+            )
+
+    return dtype
+
+
+def _validate_tz_from_dtype(
+    dtype, tz: tzinfo | None, explicit_tz_none: bool = False
+) -> tzinfo | None:
+    """
+    If the given dtype is a DatetimeTZDtype, extract the implied
+    tzinfo object from it and check that it does not conflict with the given
+    tz.
+
+    Parameters
+    ----------
+    dtype : dtype, str
+    tz : None, tzinfo
+    explicit_tz_none : bool, default False
+        Whether tz=None was passed explicitly, as opposed to lib.no_default.
+
+    Returns
+    -------
+    tz : consensus tzinfo
+
+    Raises
+    ------
+    ValueError : on tzinfo mismatch
+    """
+    if dtype is not None:
+        if isinstance(dtype, str):
+            try:
+                dtype = DatetimeTZDtype.construct_from_string(dtype)
+            except TypeError:
+                # Things like `datetime64[ns]`, which is OK for the
+                # constructors, but also nonsense, which should be validated
+                # but not by us. We *do* allow non-existent tz errors to
+                # go through
+                pass
+        dtz = getattr(dtype, "tz", None)
+        if dtz is not None:
+            if tz is not None and not timezones.tz_compare(tz, dtz):
+                raise ValueError("cannot supply both a tz and a dtype with a tz")
+            if explicit_tz_none:
+                raise ValueError("Cannot pass both a timezone-aware dtype and tz=None")
+            tz = dtz
+
+        if tz is not None and lib.is_np_dtype(dtype, "M"):
+            # We also need to check for the case where the user passed a
+            #  tz-naive dtype (i.e. datetime64[ns])
+            if tz is not None and not timezones.tz_compare(tz, dtz):
+                raise ValueError(
+                    "cannot supply both a tz and a "
+                    "timezone-naive dtype (i.e. datetime64[ns])"
+                )
+
+    return tz
+
+
+def _infer_tz_from_endpoints(
+    start: Timestamp, end: Timestamp, tz: tzinfo | None
+) -> tzinfo | None:
+    """
+    If a timezone is not explicitly given via `tz`, see if one can
+    be inferred from the `start` and `end` endpoints.  If more than one
+    of these inputs provides a timezone, require that they all agree.
+
+    Parameters
+    ----------
+    start : Timestamp
+    end : Timestamp
+    tz : tzinfo or None
+
+    Returns
+    -------
+    tz : tzinfo or None
+
+    Raises
+    ------
+    TypeError : if start and end timezones do not agree
+    """
+    try:
+        inferred_tz = timezones.infer_tzinfo(start, end)
+    except AssertionError as err:
+        # infer_tzinfo raises AssertionError if passed mismatched timezones
+        raise TypeError(
+            "Start and end cannot both be tz-aware with different timezones"
+        ) from err
+
+    inferred_tz = timezones.maybe_get_tz(inferred_tz)
+    tz = timezones.maybe_get_tz(tz)
+
+    if tz is not None and inferred_tz is not None:
+        if not timezones.tz_compare(inferred_tz, tz):
+            raise AssertionError("Inferred time zone not equal to passed time zone")
+
+    elif inferred_tz is not None:
+        tz = inferred_tz
+
+    return tz
+
+
+def _maybe_normalize_endpoints(
+    start: Timestamp | None, end: Timestamp | None, normalize: bool
+):
+    if normalize:
+        if start is not None:
+            start = start.normalize()
+
+        if end is not None:
+            end = end.normalize()
+
+    return start, end
+
+
+def _maybe_localize_point(
+    ts: Timestamp | None, freq, tz, ambiguous, nonexistent
+) -> Timestamp | None:
+    """
+    Localize a start or end Timestamp to the timezone of the corresponding
+    start or end Timestamp
+
+    Parameters
+    ----------
+    ts : start or end Timestamp to potentially localize
+    freq : Tick, DateOffset, or None
+    tz : str, timezone object or None
+    ambiguous: str, localization behavior for ambiguous times
+    nonexistent: str, localization behavior for nonexistent times
+
+    Returns
+    -------
+    ts : Timestamp
+    """
+    # Make sure start and end are timezone localized if:
+    # 1) freq = a Timedelta-like frequency (Tick)
+    # 2) freq = None i.e. generating a linspaced range
+    if ts is not None and ts.tzinfo is None:
+        # Note: We can't ambiguous='infer' a singular ambiguous time; however,
+        # we have historically defaulted ambiguous=False
+        ambiguous = ambiguous if ambiguous != "infer" else False
+        localize_args = {"ambiguous": ambiguous, "nonexistent": nonexistent, "tz": None}
+        if isinstance(freq, Tick) or freq is None:
+            localize_args["tz"] = tz
+        ts = ts.tz_localize(**localize_args)
+    return ts
+
+
+def _generate_range(
+    start: Timestamp | None,
+    end: Timestamp | None,
+    periods: int | None,
+    offset: BaseOffset,
+    *,
+    unit: str,
+):
+    """
+    Generates a sequence of dates corresponding to the specified time
+    offset. Similar to dateutil.rrule except uses pandas DateOffset
+    objects to represent time increments.
+
+    Parameters
+    ----------
+    start : Timestamp or None
+    end : Timestamp or None
+    periods : int or None
+    offset : DateOffset
+    unit : str
+
+    Notes
+    -----
+    * This method is faster for generating weekdays than dateutil.rrule
+    * At least two of (start, end, periods) must be specified.
+    * If both start and end are specified, the returned dates will
+    satisfy start <= date <= end.
+
+    Returns
+    -------
+    dates : generator object
+    """
+    offset = to_offset(offset)
+
+    # Argument 1 to "Timestamp" has incompatible type "Optional[Timestamp]";
+    # expected "Union[integer[Any], float, str, date, datetime64]"
+    start = Timestamp(start)  # type: ignore[arg-type]
+    if start is not NaT:
+        start = start.as_unit(unit)
+    else:
+        start = None
+
+    # Argument 1 to "Timestamp" has incompatible type "Optional[Timestamp]";
+    # expected "Union[integer[Any], float, str, date, datetime64]"
+    end = Timestamp(end)  # type: ignore[arg-type]
+    if end is not NaT:
+        end = end.as_unit(unit)
+    else:
+        end = None
+
+    if start and not offset.is_on_offset(start):
+        # Incompatible types in assignment (expression has type "datetime",
+        # variable has type "Optional[Timestamp]")
+        start = offset.rollforward(start)  # type: ignore[assignment]
+
+    elif end and not offset.is_on_offset(end):
+        # Incompatible types in assignment (expression has type "datetime",
+        # variable has type "Optional[Timestamp]")
+        end = offset.rollback(end)  # type: ignore[assignment]
+
+    # Unsupported operand types for < ("Timestamp" and "None")
+    if periods is None and end < start and offset.n >= 0:  # type: ignore[operator]
+        end = None
+        periods = 0
+
+    if end is None:
+        # error: No overload variant of "__radd__" of "BaseOffset" matches
+        # argument type "None"
+        end = start + (periods - 1) * offset  # type: ignore[operator]
+
+    if start is None:
+        # error: No overload variant of "__radd__" of "BaseOffset" matches
+        # argument type "None"
+        start = end - (periods - 1) * offset  # type: ignore[operator]
+
+    start = cast(Timestamp, start)
+    end = cast(Timestamp, end)
+
+    cur = start
+    if offset.n >= 0:
+        while cur <= end:
+            yield cur
+
+            if cur == end:
+                # GH#24252 avoid overflows by not performing the addition
+                # in offset.apply unless we have to
+                break
+
+            # faster than cur + offset
+            next_date = offset._apply(cur)
+            next_date = next_date.as_unit(unit)
+            if next_date <= cur:
+                raise ValueError(f"Offset {offset} did not increment date")
+            cur = next_date
+    else:
+        while cur >= end:
+            yield cur
+
+            if cur == end:
+                # GH#24252 avoid overflows by not performing the addition
+                # in offset.apply unless we have to
+                break
+
+            # faster than cur + offset
+            next_date = offset._apply(cur)
+            next_date = next_date.as_unit(unit)
+            if next_date >= cur:
+                raise ValueError(f"Offset {offset} did not decrement date")
+            cur = next_date
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/floating.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/floating.py
new file mode 100644
index 0000000000000000000000000000000000000000..74b8cfb65cbc7887b7d2a164121c90eda0833121
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/floating.py
@@ -0,0 +1,173 @@
+from __future__ import annotations
+
+from typing import ClassVar
+
+import numpy as np
+
+from pandas.core.dtypes.base import register_extension_dtype
+from pandas.core.dtypes.common import is_float_dtype
+
+from pandas.core.arrays.numeric import (
+    NumericArray,
+    NumericDtype,
+)
+
+
+class FloatingDtype(NumericDtype):
+    """
+    An ExtensionDtype to hold a single size of floating dtype.
+
+    These specific implementations are subclasses of the non-public
+    FloatingDtype. For example we have Float32Dtype to represent float32.
+
+    The attributes name & type are set when these subclasses are created.
+    """
+
+    _default_np_dtype = np.dtype(np.float64)
+    _checker = is_float_dtype
+
+    @classmethod
+    def construct_array_type(cls) -> type[FloatingArray]:
+        """
+        Return the array type associated with this dtype.
+
+        Returns
+        -------
+        type
+        """
+        return FloatingArray
+
+    @classmethod
+    def _get_dtype_mapping(cls) -> dict[np.dtype, FloatingDtype]:
+        return NUMPY_FLOAT_TO_DTYPE
+
+    @classmethod
+    def _safe_cast(cls, values: np.ndarray, dtype: np.dtype, copy: bool) -> np.ndarray:
+        """
+        Safely cast the values to the given dtype.
+
+        "safe" in this context means the casting is lossless.
+        """
+        # This is really only here for compatibility with IntegerDtype
+        # Here for compat with IntegerDtype
+        return values.astype(dtype, copy=copy)
+
+
+class FloatingArray(NumericArray):
+    """
+    Array of floating (optional missing) values.
+
+    .. warning::
+
+       FloatingArray is currently experimental, and its API or internal
+       implementation may change without warning. Especially the behaviour
+       regarding NaN (distinct from NA missing values) is subject to change.
+
+    We represent a FloatingArray with 2 numpy arrays:
+
+    - data: contains a numpy float array of the appropriate dtype
+    - mask: a boolean array holding a mask on the data, True is missing
+
+    To construct an FloatingArray from generic array-like input, use
+    :func:`pandas.array` with one of the float dtypes (see examples).
+
+    See :ref:`integer_na` for more.
+
+    Parameters
+    ----------
+    values : numpy.ndarray
+        A 1-d float-dtype array.
+    mask : numpy.ndarray
+        A 1-d boolean-dtype array indicating missing values.
+    copy : bool, default False
+        Whether to copy the `values` and `mask`.
+
+    Attributes
+    ----------
+    None
+
+    Methods
+    -------
+    None
+
+    Returns
+    -------
+    FloatingArray
+
+    Examples
+    --------
+    Create an FloatingArray with :func:`pandas.array`:
+
+    >>> pd.array([0.1, None, 0.3], dtype=pd.Float32Dtype())
+    
+    [0.1, , 0.3]
+    Length: 3, dtype: Float32
+
+    String aliases for the dtypes are also available. They are capitalized.
+
+    >>> pd.array([0.1, None, 0.3], dtype="Float32")
+    
+    [0.1, , 0.3]
+    Length: 3, dtype: Float32
+    """
+
+    _dtype_cls = FloatingDtype
+
+    # The value used to fill '_data' to avoid upcasting
+    _internal_fill_value = np.nan
+    # Fill values used for any/all
+    # Incompatible types in assignment (expression has type "float", base class
+    # "BaseMaskedArray" defined the type as "")
+    _truthy_value = 1.0  # type: ignore[assignment]
+    _falsey_value = 0.0  # type: ignore[assignment]
+
+
+_dtype_docstring = """
+An ExtensionDtype for {dtype} data.
+
+This dtype uses ``pd.NA`` as missing value indicator.
+
+Attributes
+----------
+None
+
+Methods
+-------
+None
+
+Examples
+--------
+For Float32Dtype:
+
+>>> ser = pd.Series([2.25, pd.NA], dtype=pd.Float32Dtype())
+>>> ser.dtype
+Float32Dtype()
+
+For Float64Dtype:
+
+>>> ser = pd.Series([2.25, pd.NA], dtype=pd.Float64Dtype())
+>>> ser.dtype
+Float64Dtype()
+"""
+
+# create the Dtype
+
+
+@register_extension_dtype
+class Float32Dtype(FloatingDtype):
+    type = np.float32
+    name: ClassVar[str] = "Float32"
+    __doc__ = _dtype_docstring.format(dtype="float32")
+
+
+@register_extension_dtype
+class Float64Dtype(FloatingDtype):
+    type = np.float64
+    name: ClassVar[str] = "Float64"
+    __doc__ = _dtype_docstring.format(dtype="float64")
+
+
+NUMPY_FLOAT_TO_DTYPE: dict[np.dtype, FloatingDtype] = {
+    np.dtype(np.float32): Float32Dtype(),
+    np.dtype(np.float64): Float64Dtype(),
+}
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/integer.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/integer.py
new file mode 100644
index 0000000000000000000000000000000000000000..f9384e25ba9d9f32caf826efc01b4eb58a454d65
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/integer.py
@@ -0,0 +1,272 @@
+from __future__ import annotations
+
+from typing import ClassVar
+
+import numpy as np
+
+from pandas.core.dtypes.base import register_extension_dtype
+from pandas.core.dtypes.common import is_integer_dtype
+
+from pandas.core.arrays.numeric import (
+    NumericArray,
+    NumericDtype,
+)
+
+
+class IntegerDtype(NumericDtype):
+    """
+    An ExtensionDtype to hold a single size & kind of integer dtype.
+
+    These specific implementations are subclasses of the non-public
+    IntegerDtype. For example, we have Int8Dtype to represent signed int 8s.
+
+    The attributes name & type are set when these subclasses are created.
+    """
+
+    _default_np_dtype = np.dtype(np.int64)
+    _checker = is_integer_dtype
+
+    @classmethod
+    def construct_array_type(cls) -> type[IntegerArray]:
+        """
+        Return the array type associated with this dtype.
+
+        Returns
+        -------
+        type
+        """
+        return IntegerArray
+
+    @classmethod
+    def _get_dtype_mapping(cls) -> dict[np.dtype, IntegerDtype]:
+        return NUMPY_INT_TO_DTYPE
+
+    @classmethod
+    def _safe_cast(cls, values: np.ndarray, dtype: np.dtype, copy: bool) -> np.ndarray:
+        """
+        Safely cast the values to the given dtype.
+
+        "safe" in this context means the casting is lossless. e.g. if 'values'
+        has a floating dtype, each value must be an integer.
+        """
+        try:
+            return values.astype(dtype, casting="safe", copy=copy)
+        except TypeError as err:
+            casted = values.astype(dtype, copy=copy)
+            if (casted == values).all():
+                return casted
+
+            raise TypeError(
+                f"cannot safely cast non-equivalent {values.dtype} to {np.dtype(dtype)}"
+            ) from err
+
+
+class IntegerArray(NumericArray):
+    """
+    Array of integer (optional missing) values.
+
+    Uses :attr:`pandas.NA` as the missing value.
+
+    .. warning::
+
+       IntegerArray is currently experimental, and its API or internal
+       implementation may change without warning.
+
+    We represent an IntegerArray with 2 numpy arrays:
+
+    - data: contains a numpy integer array of the appropriate dtype
+    - mask: a boolean array holding a mask on the data, True is missing
+
+    To construct an IntegerArray from generic array-like input, use
+    :func:`pandas.array` with one of the integer dtypes (see examples).
+
+    See :ref:`integer_na` for more.
+
+    Parameters
+    ----------
+    values : numpy.ndarray
+        A 1-d integer-dtype array.
+    mask : numpy.ndarray
+        A 1-d boolean-dtype array indicating missing values.
+    copy : bool, default False
+        Whether to copy the `values` and `mask`.
+
+    Attributes
+    ----------
+    None
+
+    Methods
+    -------
+    None
+
+    Returns
+    -------
+    IntegerArray
+
+    Examples
+    --------
+    Create an IntegerArray with :func:`pandas.array`.
+
+    >>> int_array = pd.array([1, None, 3], dtype=pd.Int32Dtype())
+    >>> int_array
+    
+    [1, , 3]
+    Length: 3, dtype: Int32
+
+    String aliases for the dtypes are also available. They are capitalized.
+
+    >>> pd.array([1, None, 3], dtype='Int32')
+    
+    [1, , 3]
+    Length: 3, dtype: Int32
+
+    >>> pd.array([1, None, 3], dtype='UInt16')
+    
+    [1, , 3]
+    Length: 3, dtype: UInt16
+    """
+
+    _dtype_cls = IntegerDtype
+
+    # The value used to fill '_data' to avoid upcasting
+    _internal_fill_value = 1
+    # Fill values used for any/all
+    # Incompatible types in assignment (expression has type "int", base class
+    # "BaseMaskedArray" defined the type as "")
+    _truthy_value = 1  # type: ignore[assignment]
+    _falsey_value = 0  # type: ignore[assignment]
+
+
+_dtype_docstring = """
+An ExtensionDtype for {dtype} integer data.
+
+Uses :attr:`pandas.NA` as its missing value, rather than :attr:`numpy.nan`.
+
+Attributes
+----------
+None
+
+Methods
+-------
+None
+
+Examples
+--------
+For Int8Dtype:
+
+>>> ser = pd.Series([2, pd.NA], dtype=pd.Int8Dtype())
+>>> ser.dtype
+Int8Dtype()
+
+For Int16Dtype:
+
+>>> ser = pd.Series([2, pd.NA], dtype=pd.Int16Dtype())
+>>> ser.dtype
+Int16Dtype()
+
+For Int32Dtype:
+
+>>> ser = pd.Series([2, pd.NA], dtype=pd.Int32Dtype())
+>>> ser.dtype
+Int32Dtype()
+
+For Int64Dtype:
+
+>>> ser = pd.Series([2, pd.NA], dtype=pd.Int64Dtype())
+>>> ser.dtype
+Int64Dtype()
+
+For UInt8Dtype:
+
+>>> ser = pd.Series([2, pd.NA], dtype=pd.UInt8Dtype())
+>>> ser.dtype
+UInt8Dtype()
+
+For UInt16Dtype:
+
+>>> ser = pd.Series([2, pd.NA], dtype=pd.UInt16Dtype())
+>>> ser.dtype
+UInt16Dtype()
+
+For UInt32Dtype:
+
+>>> ser = pd.Series([2, pd.NA], dtype=pd.UInt32Dtype())
+>>> ser.dtype
+UInt32Dtype()
+
+For UInt64Dtype:
+
+>>> ser = pd.Series([2, pd.NA], dtype=pd.UInt64Dtype())
+>>> ser.dtype
+UInt64Dtype()
+"""
+
+# create the Dtype
+
+
+@register_extension_dtype
+class Int8Dtype(IntegerDtype):
+    type = np.int8
+    name: ClassVar[str] = "Int8"
+    __doc__ = _dtype_docstring.format(dtype="int8")
+
+
+@register_extension_dtype
+class Int16Dtype(IntegerDtype):
+    type = np.int16
+    name: ClassVar[str] = "Int16"
+    __doc__ = _dtype_docstring.format(dtype="int16")
+
+
+@register_extension_dtype
+class Int32Dtype(IntegerDtype):
+    type = np.int32
+    name: ClassVar[str] = "Int32"
+    __doc__ = _dtype_docstring.format(dtype="int32")
+
+
+@register_extension_dtype
+class Int64Dtype(IntegerDtype):
+    type = np.int64
+    name: ClassVar[str] = "Int64"
+    __doc__ = _dtype_docstring.format(dtype="int64")
+
+
+@register_extension_dtype
+class UInt8Dtype(IntegerDtype):
+    type = np.uint8
+    name: ClassVar[str] = "UInt8"
+    __doc__ = _dtype_docstring.format(dtype="uint8")
+
+
+@register_extension_dtype
+class UInt16Dtype(IntegerDtype):
+    type = np.uint16
+    name: ClassVar[str] = "UInt16"
+    __doc__ = _dtype_docstring.format(dtype="uint16")
+
+
+@register_extension_dtype
+class UInt32Dtype(IntegerDtype):
+    type = np.uint32
+    name: ClassVar[str] = "UInt32"
+    __doc__ = _dtype_docstring.format(dtype="uint32")
+
+
+@register_extension_dtype
+class UInt64Dtype(IntegerDtype):
+    type = np.uint64
+    name: ClassVar[str] = "UInt64"
+    __doc__ = _dtype_docstring.format(dtype="uint64")
+
+
+NUMPY_INT_TO_DTYPE: dict[np.dtype, IntegerDtype] = {
+    np.dtype(np.int8): Int8Dtype(),
+    np.dtype(np.int16): Int16Dtype(),
+    np.dtype(np.int32): Int32Dtype(),
+    np.dtype(np.int64): Int64Dtype(),
+    np.dtype(np.uint8): UInt8Dtype(),
+    np.dtype(np.uint16): UInt16Dtype(),
+    np.dtype(np.uint32): UInt32Dtype(),
+    np.dtype(np.uint64): UInt64Dtype(),
+}
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/interval.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/interval.py
new file mode 100644
index 0000000000000000000000000000000000000000..da57e4ceed87e3a63e63e5ac7172c807a5e5b683
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/interval.py
@@ -0,0 +1,1930 @@
+from __future__ import annotations
+
+import operator
+from operator import (
+    le,
+    lt,
+)
+import textwrap
+from typing import (
+    TYPE_CHECKING,
+    Literal,
+    Union,
+    overload,
+)
+import warnings
+
+import numpy as np
+
+from pandas._libs import lib
+from pandas._libs.interval import (
+    VALID_CLOSED,
+    Interval,
+    IntervalMixin,
+    intervals_to_interval_bounds,
+)
+from pandas._libs.missing import NA
+from pandas._typing import (
+    ArrayLike,
+    AxisInt,
+    Dtype,
+    FillnaOptions,
+    IntervalClosedType,
+    NpDtype,
+    PositionalIndexer,
+    ScalarIndexer,
+    Self,
+    SequenceIndexer,
+    SortKind,
+    TimeArrayLike,
+    npt,
+)
+from pandas.compat.numpy import function as nv
+from pandas.errors import IntCastingNaNError
+from pandas.util._decorators import Appender
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.cast import (
+    LossySetitemError,
+    maybe_upcast_numeric_to_64bit,
+)
+from pandas.core.dtypes.common import (
+    is_float_dtype,
+    is_integer_dtype,
+    is_list_like,
+    is_object_dtype,
+    is_scalar,
+    is_string_dtype,
+    needs_i8_conversion,
+    pandas_dtype,
+)
+from pandas.core.dtypes.dtypes import (
+    CategoricalDtype,
+    IntervalDtype,
+)
+from pandas.core.dtypes.generic import (
+    ABCDataFrame,
+    ABCDatetimeIndex,
+    ABCIntervalIndex,
+    ABCPeriodIndex,
+)
+from pandas.core.dtypes.missing import (
+    is_valid_na_for_dtype,
+    isna,
+    notna,
+)
+
+from pandas.core.algorithms import (
+    isin,
+    take,
+    unique,
+    value_counts_internal as value_counts,
+)
+from pandas.core.arrays import ArrowExtensionArray
+from pandas.core.arrays.base import (
+    ExtensionArray,
+    _extension_array_shared_docs,
+)
+from pandas.core.arrays.datetimes import DatetimeArray
+from pandas.core.arrays.timedeltas import TimedeltaArray
+import pandas.core.common as com
+from pandas.core.construction import (
+    array as pd_array,
+    ensure_wrapped_if_datetimelike,
+    extract_array,
+)
+from pandas.core.indexers import check_array_indexer
+from pandas.core.ops import (
+    invalid_comparison,
+    unpack_zerodim_and_defer,
+)
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Iterator,
+        Sequence,
+    )
+
+    from pandas import (
+        Index,
+        Series,
+    )
+
+
+IntervalSide = Union[TimeArrayLike, np.ndarray]
+IntervalOrNA = Union[Interval, float]
+
+_interval_shared_docs: dict[str, str] = {}
+
+_shared_docs_kwargs = {
+    "klass": "IntervalArray",
+    "qualname": "arrays.IntervalArray",
+    "name": "",
+}
+
+
+_interval_shared_docs[
+    "class"
+] = """
+%(summary)s
+
+Parameters
+----------
+data : array-like (1-dimensional)
+    Array-like (ndarray, :class:`DateTimeArray`, :class:`TimeDeltaArray`) containing
+    Interval objects from which to build the %(klass)s.
+closed : {'left', 'right', 'both', 'neither'}, default 'right'
+    Whether the intervals are closed on the left-side, right-side, both or
+    neither.
+dtype : dtype or None, default None
+    If None, dtype will be inferred.
+copy : bool, default False
+    Copy the input data.
+%(name)s\
+verify_integrity : bool, default True
+    Verify that the %(klass)s is valid.
+
+Attributes
+----------
+left
+right
+closed
+mid
+length
+is_empty
+is_non_overlapping_monotonic
+%(extra_attributes)s\
+
+Methods
+-------
+from_arrays
+from_tuples
+from_breaks
+contains
+overlaps
+set_closed
+to_tuples
+%(extra_methods)s\
+
+See Also
+--------
+Index : The base pandas Index type.
+Interval : A bounded slice-like interval; the elements of an %(klass)s.
+interval_range : Function to create a fixed frequency IntervalIndex.
+cut : Bin values into discrete Intervals.
+qcut : Bin values into equal-sized Intervals based on rank or sample quantiles.
+
+Notes
+-----
+See the `user guide
+`__
+for more.
+
+%(examples)s\
+"""
+
+
+@Appender(
+    _interval_shared_docs["class"]
+    % {
+        "klass": "IntervalArray",
+        "summary": "Pandas array for interval data that are closed on the same side.",
+        "name": "",
+        "extra_attributes": "",
+        "extra_methods": "",
+        "examples": textwrap.dedent(
+            """\
+    Examples
+    --------
+    A new ``IntervalArray`` can be constructed directly from an array-like of
+    ``Interval`` objects:
+
+    >>> pd.arrays.IntervalArray([pd.Interval(0, 1), pd.Interval(1, 5)])
+    
+    [(0, 1], (1, 5]]
+    Length: 2, dtype: interval[int64, right]
+
+    It may also be constructed using one of the constructor
+    methods: :meth:`IntervalArray.from_arrays`,
+    :meth:`IntervalArray.from_breaks`, and :meth:`IntervalArray.from_tuples`.
+    """
+        ),
+    }
+)
+class IntervalArray(IntervalMixin, ExtensionArray):
+    can_hold_na = True
+    _na_value = _fill_value = np.nan
+
+    @property
+    def ndim(self) -> Literal[1]:
+        return 1
+
+    # To make mypy recognize the fields
+    _left: IntervalSide
+    _right: IntervalSide
+    _dtype: IntervalDtype
+
+    # ---------------------------------------------------------------------
+    # Constructors
+
+    def __new__(
+        cls,
+        data,
+        closed: IntervalClosedType | None = None,
+        dtype: Dtype | None = None,
+        copy: bool = False,
+        verify_integrity: bool = True,
+    ):
+        data = extract_array(data, extract_numpy=True)
+
+        if isinstance(data, cls):
+            left: IntervalSide = data._left
+            right: IntervalSide = data._right
+            closed = closed or data.closed
+            dtype = IntervalDtype(left.dtype, closed=closed)
+        else:
+            # don't allow scalars
+            if is_scalar(data):
+                msg = (
+                    f"{cls.__name__}(...) must be called with a collection "
+                    f"of some kind, {data} was passed"
+                )
+                raise TypeError(msg)
+
+            # might need to convert empty or purely na data
+            data = _maybe_convert_platform_interval(data)
+            left, right, infer_closed = intervals_to_interval_bounds(
+                data, validate_closed=closed is None
+            )
+            if left.dtype == object:
+                left = lib.maybe_convert_objects(left)
+                right = lib.maybe_convert_objects(right)
+            closed = closed or infer_closed
+
+            left, right, dtype = cls._ensure_simple_new_inputs(
+                left,
+                right,
+                closed=closed,
+                copy=copy,
+                dtype=dtype,
+            )
+
+        if verify_integrity:
+            cls._validate(left, right, dtype=dtype)
+
+        return cls._simple_new(
+            left,
+            right,
+            dtype=dtype,
+        )
+
+    @classmethod
+    def _simple_new(
+        cls,
+        left: IntervalSide,
+        right: IntervalSide,
+        dtype: IntervalDtype,
+    ) -> Self:
+        result = IntervalMixin.__new__(cls)
+        result._left = left
+        result._right = right
+        result._dtype = dtype
+
+        return result
+
+    @classmethod
+    def _ensure_simple_new_inputs(
+        cls,
+        left,
+        right,
+        closed: IntervalClosedType | None = None,
+        copy: bool = False,
+        dtype: Dtype | None = None,
+    ) -> tuple[IntervalSide, IntervalSide, IntervalDtype]:
+        """Ensure correctness of input parameters for cls._simple_new."""
+        from pandas.core.indexes.base import ensure_index
+
+        left = ensure_index(left, copy=copy)
+        left = maybe_upcast_numeric_to_64bit(left)
+
+        right = ensure_index(right, copy=copy)
+        right = maybe_upcast_numeric_to_64bit(right)
+
+        if closed is None and isinstance(dtype, IntervalDtype):
+            closed = dtype.closed
+
+        closed = closed or "right"
+
+        if dtype is not None:
+            # GH 19262: dtype must be an IntervalDtype to override inferred
+            dtype = pandas_dtype(dtype)
+            if isinstance(dtype, IntervalDtype):
+                if dtype.subtype is not None:
+                    left = left.astype(dtype.subtype)
+                    right = right.astype(dtype.subtype)
+            else:
+                msg = f"dtype must be an IntervalDtype, got {dtype}"
+                raise TypeError(msg)
+
+            if dtype.closed is None:
+                # possibly loading an old pickle
+                dtype = IntervalDtype(dtype.subtype, closed)
+            elif closed != dtype.closed:
+                raise ValueError("closed keyword does not match dtype.closed")
+
+        # coerce dtypes to match if needed
+        if is_float_dtype(left.dtype) and is_integer_dtype(right.dtype):
+            right = right.astype(left.dtype)
+        elif is_float_dtype(right.dtype) and is_integer_dtype(left.dtype):
+            left = left.astype(right.dtype)
+
+        if type(left) != type(right):
+            msg = (
+                f"must not have differing left [{type(left).__name__}] and "
+                f"right [{type(right).__name__}] types"
+            )
+            raise ValueError(msg)
+        if isinstance(left.dtype, CategoricalDtype) or is_string_dtype(left.dtype):
+            # GH 19016
+            msg = (
+                "category, object, and string subtypes are not supported "
+                "for IntervalArray"
+            )
+            raise TypeError(msg)
+        if isinstance(left, ABCPeriodIndex):
+            msg = "Period dtypes are not supported, use a PeriodIndex instead"
+            raise ValueError(msg)
+        if isinstance(left, ABCDatetimeIndex) and str(left.tz) != str(right.tz):
+            msg = (
+                "left and right must have the same time zone, got "
+                f"'{left.tz}' and '{right.tz}'"
+            )
+            raise ValueError(msg)
+        elif needs_i8_conversion(left.dtype) and left.unit != right.unit:
+            # e.g. m8[s] vs m8[ms], try to cast to a common dtype GH#55714
+            left_arr, right_arr = left._data._ensure_matching_resos(right._data)
+            left = ensure_index(left_arr)
+            right = ensure_index(right_arr)
+
+        # For dt64/td64 we want DatetimeArray/TimedeltaArray instead of ndarray
+        left = ensure_wrapped_if_datetimelike(left)
+        left = extract_array(left, extract_numpy=True)
+        right = ensure_wrapped_if_datetimelike(right)
+        right = extract_array(right, extract_numpy=True)
+
+        if isinstance(left, ArrowExtensionArray) or isinstance(
+            right, ArrowExtensionArray
+        ):
+            pass
+        else:
+            lbase = getattr(left, "_ndarray", left)
+            lbase = getattr(lbase, "_data", lbase).base
+            rbase = getattr(right, "_ndarray", right)
+            rbase = getattr(rbase, "_data", rbase).base
+            if lbase is not None and lbase is rbase:
+                # If these share data, then setitem could corrupt our IA
+                right = right.copy()
+
+        dtype = IntervalDtype(left.dtype, closed=closed)
+
+        return left, right, dtype
+
+    @classmethod
+    def _from_sequence(
+        cls,
+        scalars,
+        *,
+        dtype: Dtype | None = None,
+        copy: bool = False,
+    ) -> Self:
+        return cls(scalars, dtype=dtype, copy=copy)
+
+    @classmethod
+    def _from_factorized(cls, values: np.ndarray, original: IntervalArray) -> Self:
+        return cls._from_sequence(values, dtype=original.dtype)
+
+    _interval_shared_docs["from_breaks"] = textwrap.dedent(
+        """
+        Construct an %(klass)s from an array of splits.
+
+        Parameters
+        ----------
+        breaks : array-like (1-dimensional)
+            Left and right bounds for each interval.
+        closed : {'left', 'right', 'both', 'neither'}, default 'right'
+            Whether the intervals are closed on the left-side, right-side, both
+            or neither.\
+        %(name)s
+        copy : bool, default False
+            Copy the data.
+        dtype : dtype or None, default None
+            If None, dtype will be inferred.
+
+        Returns
+        -------
+        %(klass)s
+
+        See Also
+        --------
+        interval_range : Function to create a fixed frequency IntervalIndex.
+        %(klass)s.from_arrays : Construct from a left and right array.
+        %(klass)s.from_tuples : Construct from a sequence of tuples.
+
+        %(examples)s\
+        """
+    )
+
+    @classmethod
+    @Appender(
+        _interval_shared_docs["from_breaks"]
+        % {
+            "klass": "IntervalArray",
+            "name": "",
+            "examples": textwrap.dedent(
+                """\
+        Examples
+        --------
+        >>> pd.arrays.IntervalArray.from_breaks([0, 1, 2, 3])
+        
+        [(0, 1], (1, 2], (2, 3]]
+        Length: 3, dtype: interval[int64, right]
+        """
+            ),
+        }
+    )
+    def from_breaks(
+        cls,
+        breaks,
+        closed: IntervalClosedType | None = "right",
+        copy: bool = False,
+        dtype: Dtype | None = None,
+    ) -> Self:
+        breaks = _maybe_convert_platform_interval(breaks)
+
+        return cls.from_arrays(breaks[:-1], breaks[1:], closed, copy=copy, dtype=dtype)
+
+    _interval_shared_docs["from_arrays"] = textwrap.dedent(
+        """
+        Construct from two arrays defining the left and right bounds.
+
+        Parameters
+        ----------
+        left : array-like (1-dimensional)
+            Left bounds for each interval.
+        right : array-like (1-dimensional)
+            Right bounds for each interval.
+        closed : {'left', 'right', 'both', 'neither'}, default 'right'
+            Whether the intervals are closed on the left-side, right-side, both
+            or neither.\
+        %(name)s
+        copy : bool, default False
+            Copy the data.
+        dtype : dtype, optional
+            If None, dtype will be inferred.
+
+        Returns
+        -------
+        %(klass)s
+
+        Raises
+        ------
+        ValueError
+            When a value is missing in only one of `left` or `right`.
+            When a value in `left` is greater than the corresponding value
+            in `right`.
+
+        See Also
+        --------
+        interval_range : Function to create a fixed frequency IntervalIndex.
+        %(klass)s.from_breaks : Construct an %(klass)s from an array of
+            splits.
+        %(klass)s.from_tuples : Construct an %(klass)s from an
+            array-like of tuples.
+
+        Notes
+        -----
+        Each element of `left` must be less than or equal to the `right`
+        element at the same position. If an element is missing, it must be
+        missing in both `left` and `right`. A TypeError is raised when
+        using an unsupported type for `left` or `right`. At the moment,
+        'category', 'object', and 'string' subtypes are not supported.
+
+        %(examples)s\
+        """
+    )
+
+    @classmethod
+    @Appender(
+        _interval_shared_docs["from_arrays"]
+        % {
+            "klass": "IntervalArray",
+            "name": "",
+            "examples": textwrap.dedent(
+                """\
+        Examples
+        --------
+        >>> pd.arrays.IntervalArray.from_arrays([0, 1, 2], [1, 2, 3])
+        
+        [(0, 1], (1, 2], (2, 3]]
+        Length: 3, dtype: interval[int64, right]
+        """
+            ),
+        }
+    )
+    def from_arrays(
+        cls,
+        left,
+        right,
+        closed: IntervalClosedType | None = "right",
+        copy: bool = False,
+        dtype: Dtype | None = None,
+    ) -> Self:
+        left = _maybe_convert_platform_interval(left)
+        right = _maybe_convert_platform_interval(right)
+
+        left, right, dtype = cls._ensure_simple_new_inputs(
+            left,
+            right,
+            closed=closed,
+            copy=copy,
+            dtype=dtype,
+        )
+        cls._validate(left, right, dtype=dtype)
+
+        return cls._simple_new(left, right, dtype=dtype)
+
+    _interval_shared_docs["from_tuples"] = textwrap.dedent(
+        """
+        Construct an %(klass)s from an array-like of tuples.
+
+        Parameters
+        ----------
+        data : array-like (1-dimensional)
+            Array of tuples.
+        closed : {'left', 'right', 'both', 'neither'}, default 'right'
+            Whether the intervals are closed on the left-side, right-side, both
+            or neither.\
+        %(name)s
+        copy : bool, default False
+            By-default copy the data, this is compat only and ignored.
+        dtype : dtype or None, default None
+            If None, dtype will be inferred.
+
+        Returns
+        -------
+        %(klass)s
+
+        See Also
+        --------
+        interval_range : Function to create a fixed frequency IntervalIndex.
+        %(klass)s.from_arrays : Construct an %(klass)s from a left and
+                                    right array.
+        %(klass)s.from_breaks : Construct an %(klass)s from an array of
+                                    splits.
+
+        %(examples)s\
+        """
+    )
+
+    @classmethod
+    @Appender(
+        _interval_shared_docs["from_tuples"]
+        % {
+            "klass": "IntervalArray",
+            "name": "",
+            "examples": textwrap.dedent(
+                """\
+        Examples
+        --------
+        >>> pd.arrays.IntervalArray.from_tuples([(0, 1), (1, 2)])
+        
+        [(0, 1], (1, 2]]
+        Length: 2, dtype: interval[int64, right]
+        """
+            ),
+        }
+    )
+    def from_tuples(
+        cls,
+        data,
+        closed: IntervalClosedType | None = "right",
+        copy: bool = False,
+        dtype: Dtype | None = None,
+    ) -> Self:
+        if len(data):
+            left, right = [], []
+        else:
+            # ensure that empty data keeps input dtype
+            left = right = data
+
+        for d in data:
+            if not isinstance(d, tuple) and isna(d):
+                lhs = rhs = np.nan
+            else:
+                name = cls.__name__
+                try:
+                    # need list of length 2 tuples, e.g. [(0, 1), (1, 2), ...]
+                    lhs, rhs = d
+                except ValueError as err:
+                    msg = f"{name}.from_tuples requires tuples of length 2, got {d}"
+                    raise ValueError(msg) from err
+                except TypeError as err:
+                    msg = f"{name}.from_tuples received an invalid item, {d}"
+                    raise TypeError(msg) from err
+            left.append(lhs)
+            right.append(rhs)
+
+        return cls.from_arrays(left, right, closed, copy=False, dtype=dtype)
+
+    @classmethod
+    def _validate(cls, left, right, dtype: IntervalDtype) -> None:
+        """
+        Verify that the IntervalArray is valid.
+
+        Checks that
+
+        * dtype is correct
+        * left and right match lengths
+        * left and right have the same missing values
+        * left is always below right
+        """
+        if not isinstance(dtype, IntervalDtype):
+            msg = f"invalid dtype: {dtype}"
+            raise ValueError(msg)
+        if len(left) != len(right):
+            msg = "left and right must have the same length"
+            raise ValueError(msg)
+        left_mask = notna(left)
+        right_mask = notna(right)
+        if not (left_mask == right_mask).all():
+            msg = (
+                "missing values must be missing in the same "
+                "location both left and right sides"
+            )
+            raise ValueError(msg)
+        if not (left[left_mask] <= right[left_mask]).all():
+            msg = "left side of interval must be <= right side"
+            raise ValueError(msg)
+
+    def _shallow_copy(self, left, right) -> Self:
+        """
+        Return a new IntervalArray with the replacement attributes
+
+        Parameters
+        ----------
+        left : Index
+            Values to be used for the left-side of the intervals.
+        right : Index
+            Values to be used for the right-side of the intervals.
+        """
+        dtype = IntervalDtype(left.dtype, closed=self.closed)
+        left, right, dtype = self._ensure_simple_new_inputs(left, right, dtype=dtype)
+
+        return self._simple_new(left, right, dtype=dtype)
+
+    # ---------------------------------------------------------------------
+    # Descriptive
+
+    @property
+    def dtype(self) -> IntervalDtype:
+        return self._dtype
+
+    @property
+    def nbytes(self) -> int:
+        return self.left.nbytes + self.right.nbytes
+
+    @property
+    def size(self) -> int:
+        # Avoid materializing self.values
+        return self.left.size
+
+    # ---------------------------------------------------------------------
+    # EA Interface
+
+    def __iter__(self) -> Iterator:
+        return iter(np.asarray(self))
+
+    def __len__(self) -> int:
+        return len(self._left)
+
+    @overload
+    def __getitem__(self, key: ScalarIndexer) -> IntervalOrNA:
+        ...
+
+    @overload
+    def __getitem__(self, key: SequenceIndexer) -> Self:
+        ...
+
+    def __getitem__(self, key: PositionalIndexer) -> Self | IntervalOrNA:
+        key = check_array_indexer(self, key)
+        left = self._left[key]
+        right = self._right[key]
+
+        if not isinstance(left, (np.ndarray, ExtensionArray)):
+            # scalar
+            if is_scalar(left) and isna(left):
+                return self._fill_value
+            return Interval(left, right, self.closed)
+        if np.ndim(left) > 1:
+            # GH#30588 multi-dimensional indexer disallowed
+            raise ValueError("multi-dimensional indexing not allowed")
+        # Argument 2 to "_simple_new" of "IntervalArray" has incompatible type
+        # "Union[Period, Timestamp, Timedelta, NaTType, DatetimeArray, TimedeltaArray,
+        # ndarray[Any, Any]]"; expected "Union[Union[DatetimeArray, TimedeltaArray],
+        # ndarray[Any, Any]]"
+        return self._simple_new(left, right, dtype=self.dtype)  # type: ignore[arg-type]
+
+    def __setitem__(self, key, value) -> None:
+        value_left, value_right = self._validate_setitem_value(value)
+        key = check_array_indexer(self, key)
+
+        self._left[key] = value_left
+        self._right[key] = value_right
+
+    def _cmp_method(self, other, op):
+        # ensure pandas array for list-like and eliminate non-interval scalars
+        if is_list_like(other):
+            if len(self) != len(other):
+                raise ValueError("Lengths must match to compare")
+            other = pd_array(other)
+        elif not isinstance(other, Interval):
+            # non-interval scalar -> no matches
+            if other is NA:
+                # GH#31882
+                from pandas.core.arrays import BooleanArray
+
+                arr = np.empty(self.shape, dtype=bool)
+                mask = np.ones(self.shape, dtype=bool)
+                return BooleanArray(arr, mask)
+            return invalid_comparison(self, other, op)
+
+        # determine the dtype of the elements we want to compare
+        if isinstance(other, Interval):
+            other_dtype = pandas_dtype("interval")
+        elif not isinstance(other.dtype, CategoricalDtype):
+            other_dtype = other.dtype
+        else:
+            # for categorical defer to categories for dtype
+            other_dtype = other.categories.dtype
+
+            # extract intervals if we have interval categories with matching closed
+            if isinstance(other_dtype, IntervalDtype):
+                if self.closed != other.categories.closed:
+                    return invalid_comparison(self, other, op)
+
+                other = other.categories._values.take(
+                    other.codes, allow_fill=True, fill_value=other.categories._na_value
+                )
+
+        # interval-like -> need same closed and matching endpoints
+        if isinstance(other_dtype, IntervalDtype):
+            if self.closed != other.closed:
+                return invalid_comparison(self, other, op)
+            elif not isinstance(other, Interval):
+                other = type(self)(other)
+
+            if op is operator.eq:
+                return (self._left == other.left) & (self._right == other.right)
+            elif op is operator.ne:
+                return (self._left != other.left) | (self._right != other.right)
+            elif op is operator.gt:
+                return (self._left > other.left) | (
+                    (self._left == other.left) & (self._right > other.right)
+                )
+            elif op is operator.ge:
+                return (self == other) | (self > other)
+            elif op is operator.lt:
+                return (self._left < other.left) | (
+                    (self._left == other.left) & (self._right < other.right)
+                )
+            else:
+                # operator.lt
+                return (self == other) | (self < other)
+
+        # non-interval/non-object dtype -> no matches
+        if not is_object_dtype(other_dtype):
+            return invalid_comparison(self, other, op)
+
+        # object dtype -> iteratively check for intervals
+        result = np.zeros(len(self), dtype=bool)
+        for i, obj in enumerate(other):
+            try:
+                result[i] = op(self[i], obj)
+            except TypeError:
+                if obj is NA:
+                    # comparison with np.nan returns NA
+                    # github.com/pandas-dev/pandas/pull/37124#discussion_r509095092
+                    result = result.astype(object)
+                    result[i] = NA
+                else:
+                    raise
+        return result
+
+    @unpack_zerodim_and_defer("__eq__")
+    def __eq__(self, other):
+        return self._cmp_method(other, operator.eq)
+
+    @unpack_zerodim_and_defer("__ne__")
+    def __ne__(self, other):
+        return self._cmp_method(other, operator.ne)
+
+    @unpack_zerodim_and_defer("__gt__")
+    def __gt__(self, other):
+        return self._cmp_method(other, operator.gt)
+
+    @unpack_zerodim_and_defer("__ge__")
+    def __ge__(self, other):
+        return self._cmp_method(other, operator.ge)
+
+    @unpack_zerodim_and_defer("__lt__")
+    def __lt__(self, other):
+        return self._cmp_method(other, operator.lt)
+
+    @unpack_zerodim_and_defer("__le__")
+    def __le__(self, other):
+        return self._cmp_method(other, operator.le)
+
+    def argsort(
+        self,
+        *,
+        ascending: bool = True,
+        kind: SortKind = "quicksort",
+        na_position: str = "last",
+        **kwargs,
+    ) -> np.ndarray:
+        ascending = nv.validate_argsort_with_ascending(ascending, (), kwargs)
+
+        if ascending and kind == "quicksort" and na_position == "last":
+            # TODO: in an IntervalIndex we can reuse the cached
+            #  IntervalTree.left_sorter
+            return np.lexsort((self.right, self.left))
+
+        # TODO: other cases we can use lexsort for?  much more performant.
+        return super().argsort(
+            ascending=ascending, kind=kind, na_position=na_position, **kwargs
+        )
+
+    def min(self, *, axis: AxisInt | None = None, skipna: bool = True) -> IntervalOrNA:
+        nv.validate_minmax_axis(axis, self.ndim)
+
+        if not len(self):
+            return self._na_value
+
+        mask = self.isna()
+        if mask.any():
+            if not skipna:
+                return self._na_value
+            obj = self[~mask]
+        else:
+            obj = self
+
+        indexer = obj.argsort()[0]
+        return obj[indexer]
+
+    def max(self, *, axis: AxisInt | None = None, skipna: bool = True) -> IntervalOrNA:
+        nv.validate_minmax_axis(axis, self.ndim)
+
+        if not len(self):
+            return self._na_value
+
+        mask = self.isna()
+        if mask.any():
+            if not skipna:
+                return self._na_value
+            obj = self[~mask]
+        else:
+            obj = self
+
+        indexer = obj.argsort()[-1]
+        return obj[indexer]
+
+    def _pad_or_backfill(  # pylint: disable=useless-parent-delegation
+        self,
+        *,
+        method: FillnaOptions,
+        limit: int | None = None,
+        limit_area: Literal["inside", "outside"] | None = None,
+        copy: bool = True,
+    ) -> Self:
+        # TODO(3.0): after EA.fillna 'method' deprecation is enforced, we can remove
+        #  this method entirely.
+        return super()._pad_or_backfill(
+            method=method, limit=limit, limit_area=limit_area, copy=copy
+        )
+
+    def fillna(
+        self, value=None, method=None, limit: int | None = None, copy: bool = True
+    ) -> Self:
+        """
+        Fill NA/NaN values using the specified method.
+
+        Parameters
+        ----------
+        value : scalar, dict, Series
+            If a scalar value is passed it is used to fill all missing values.
+            Alternatively, a Series or dict can be used to fill in different
+            values for each index. The value should not be a list. The
+            value(s) passed should be either Interval objects or NA/NaN.
+        method : {'backfill', 'bfill', 'pad', 'ffill', None}, default None
+            (Not implemented yet for IntervalArray)
+            Method to use for filling holes in reindexed Series
+        limit : int, default None
+            (Not implemented yet for IntervalArray)
+            If method is specified, this is the maximum number of consecutive
+            NaN values to forward/backward fill. In other words, if there is
+            a gap with more than this number of consecutive NaNs, it will only
+            be partially filled. If method is not specified, this is the
+            maximum number of entries along the entire axis where NaNs will be
+            filled.
+        copy : bool, default True
+            Whether to make a copy of the data before filling. If False, then
+            the original should be modified and no new memory should be allocated.
+            For ExtensionArray subclasses that cannot do this, it is at the
+            author's discretion whether to ignore "copy=False" or to raise.
+
+        Returns
+        -------
+        filled : IntervalArray with NA/NaN filled
+        """
+        if copy is False:
+            raise NotImplementedError
+        if method is not None:
+            return super().fillna(value=value, method=method, limit=limit)
+
+        value_left, value_right = self._validate_scalar(value)
+
+        left = self.left.fillna(value=value_left)
+        right = self.right.fillna(value=value_right)
+        return self._shallow_copy(left, right)
+
+    def astype(self, dtype, copy: bool = True):
+        """
+        Cast to an ExtensionArray or NumPy array with dtype 'dtype'.
+
+        Parameters
+        ----------
+        dtype : str or dtype
+            Typecode or data-type to which the array is cast.
+
+        copy : bool, default True
+            Whether to copy the data, even if not necessary. If False,
+            a copy is made only if the old dtype does not match the
+            new dtype.
+
+        Returns
+        -------
+        array : ExtensionArray or ndarray
+            ExtensionArray or NumPy ndarray with 'dtype' for its dtype.
+        """
+        from pandas import Index
+
+        if dtype is not None:
+            dtype = pandas_dtype(dtype)
+
+        if isinstance(dtype, IntervalDtype):
+            if dtype == self.dtype:
+                return self.copy() if copy else self
+
+            if is_float_dtype(self.dtype.subtype) and needs_i8_conversion(
+                dtype.subtype
+            ):
+                # This is allowed on the Index.astype but we disallow it here
+                msg = (
+                    f"Cannot convert {self.dtype} to {dtype}; subtypes are incompatible"
+                )
+                raise TypeError(msg)
+
+            # need to cast to different subtype
+            try:
+                # We need to use Index rules for astype to prevent casting
+                #  np.nan entries to int subtypes
+                new_left = Index(self._left, copy=False).astype(dtype.subtype)
+                new_right = Index(self._right, copy=False).astype(dtype.subtype)
+            except IntCastingNaNError:
+                # e.g test_subtype_integer
+                raise
+            except (TypeError, ValueError) as err:
+                # e.g. test_subtype_integer_errors f8->u8 can be lossy
+                #  and raises ValueError
+                msg = (
+                    f"Cannot convert {self.dtype} to {dtype}; subtypes are incompatible"
+                )
+                raise TypeError(msg) from err
+            return self._shallow_copy(new_left, new_right)
+        else:
+            try:
+                return super().astype(dtype, copy=copy)
+            except (TypeError, ValueError) as err:
+                msg = f"Cannot cast {type(self).__name__} to dtype {dtype}"
+                raise TypeError(msg) from err
+
+    def equals(self, other) -> bool:
+        if type(self) != type(other):
+            return False
+
+        return bool(
+            self.closed == other.closed
+            and self.left.equals(other.left)
+            and self.right.equals(other.right)
+        )
+
+    @classmethod
+    def _concat_same_type(cls, to_concat: Sequence[IntervalArray]) -> Self:
+        """
+        Concatenate multiple IntervalArray
+
+        Parameters
+        ----------
+        to_concat : sequence of IntervalArray
+
+        Returns
+        -------
+        IntervalArray
+        """
+        closed_set = {interval.closed for interval in to_concat}
+        if len(closed_set) != 1:
+            raise ValueError("Intervals must all be closed on the same side.")
+        closed = closed_set.pop()
+
+        left: IntervalSide = np.concatenate([interval.left for interval in to_concat])
+        right: IntervalSide = np.concatenate([interval.right for interval in to_concat])
+
+        left, right, dtype = cls._ensure_simple_new_inputs(left, right, closed=closed)
+
+        return cls._simple_new(left, right, dtype=dtype)
+
+    def copy(self) -> Self:
+        """
+        Return a copy of the array.
+
+        Returns
+        -------
+        IntervalArray
+        """
+        left = self._left.copy()
+        right = self._right.copy()
+        dtype = self.dtype
+        return self._simple_new(left, right, dtype=dtype)
+
+    def isna(self) -> np.ndarray:
+        return isna(self._left)
+
+    def shift(self, periods: int = 1, fill_value: object = None) -> IntervalArray:
+        if not len(self) or periods == 0:
+            return self.copy()
+
+        self._validate_scalar(fill_value)
+
+        # ExtensionArray.shift doesn't work for two reasons
+        # 1. IntervalArray.dtype.na_value may not be correct for the dtype.
+        # 2. IntervalArray._from_sequence only accepts NaN for missing values,
+        #    not other values like NaT
+
+        empty_len = min(abs(periods), len(self))
+        if isna(fill_value):
+            from pandas import Index
+
+            fill_value = Index(self._left, copy=False)._na_value
+            empty = IntervalArray.from_breaks([fill_value] * (empty_len + 1))
+        else:
+            empty = self._from_sequence([fill_value] * empty_len, dtype=self.dtype)
+
+        if periods > 0:
+            a = empty
+            b = self[:-periods]
+        else:
+            a = self[abs(periods) :]
+            b = empty
+        return self._concat_same_type([a, b])
+
+    def take(
+        self,
+        indices,
+        *,
+        allow_fill: bool = False,
+        fill_value=None,
+        axis=None,
+        **kwargs,
+    ) -> Self:
+        """
+        Take elements from the IntervalArray.
+
+        Parameters
+        ----------
+        indices : sequence of integers
+            Indices to be taken.
+
+        allow_fill : bool, default False
+            How to handle negative values in `indices`.
+
+            * False: negative values in `indices` indicate positional indices
+              from the right (the default). This is similar to
+              :func:`numpy.take`.
+
+            * True: negative values in `indices` indicate
+              missing values. These values are set to `fill_value`. Any other
+              other negative values raise a ``ValueError``.
+
+        fill_value : Interval or NA, optional
+            Fill value to use for NA-indices when `allow_fill` is True.
+            This may be ``None``, in which case the default NA value for
+            the type, ``self.dtype.na_value``, is used.
+
+            For many ExtensionArrays, there will be two representations of
+            `fill_value`: a user-facing "boxed" scalar, and a low-level
+            physical NA value. `fill_value` should be the user-facing version,
+            and the implementation should handle translating that to the
+            physical version for processing the take if necessary.
+
+        axis : any, default None
+            Present for compat with IntervalIndex; does nothing.
+
+        Returns
+        -------
+        IntervalArray
+
+        Raises
+        ------
+        IndexError
+            When the indices are out of bounds for the array.
+        ValueError
+            When `indices` contains negative values other than ``-1``
+            and `allow_fill` is True.
+        """
+        nv.validate_take((), kwargs)
+
+        fill_left = fill_right = fill_value
+        if allow_fill:
+            fill_left, fill_right = self._validate_scalar(fill_value)
+
+        left_take = take(
+            self._left, indices, allow_fill=allow_fill, fill_value=fill_left
+        )
+        right_take = take(
+            self._right, indices, allow_fill=allow_fill, fill_value=fill_right
+        )
+
+        return self._shallow_copy(left_take, right_take)
+
+    def _validate_listlike(self, value):
+        # list-like of intervals
+        try:
+            array = IntervalArray(value)
+            self._check_closed_matches(array, name="value")
+            value_left, value_right = array.left, array.right
+        except TypeError as err:
+            # wrong type: not interval or NA
+            msg = f"'value' should be an interval type, got {type(value)} instead."
+            raise TypeError(msg) from err
+
+        try:
+            self.left._validate_fill_value(value_left)
+        except (LossySetitemError, TypeError) as err:
+            msg = (
+                "'value' should be a compatible interval type, "
+                f"got {type(value)} instead."
+            )
+            raise TypeError(msg) from err
+
+        return value_left, value_right
+
+    def _validate_scalar(self, value):
+        if isinstance(value, Interval):
+            self._check_closed_matches(value, name="value")
+            left, right = value.left, value.right
+            # TODO: check subdtype match like _validate_setitem_value?
+        elif is_valid_na_for_dtype(value, self.left.dtype):
+            # GH#18295
+            left = right = self.left._na_value
+        else:
+            raise TypeError(
+                "can only insert Interval objects and NA into an IntervalArray"
+            )
+        return left, right
+
+    def _validate_setitem_value(self, value):
+        if is_valid_na_for_dtype(value, self.left.dtype):
+            # na value: need special casing to set directly on numpy arrays
+            value = self.left._na_value
+            if is_integer_dtype(self.dtype.subtype):
+                # can't set NaN on a numpy integer array
+                # GH#45484 TypeError, not ValueError, matches what we get with
+                #  non-NA un-holdable value.
+                raise TypeError("Cannot set float NaN to integer-backed IntervalArray")
+            value_left, value_right = value, value
+
+        elif isinstance(value, Interval):
+            # scalar interval
+            self._check_closed_matches(value, name="value")
+            value_left, value_right = value.left, value.right
+            self.left._validate_fill_value(value_left)
+            self.left._validate_fill_value(value_right)
+
+        else:
+            return self._validate_listlike(value)
+
+        return value_left, value_right
+
+    def value_counts(self, dropna: bool = True) -> Series:
+        """
+        Returns a Series containing counts of each interval.
+
+        Parameters
+        ----------
+        dropna : bool, default True
+            Don't include counts of NaN.
+
+        Returns
+        -------
+        counts : Series
+
+        See Also
+        --------
+        Series.value_counts
+        """
+        # TODO: implement this is a non-naive way!
+        with warnings.catch_warnings():
+            warnings.filterwarnings(
+                "ignore",
+                "The behavior of value_counts with object-dtype is deprecated",
+                category=FutureWarning,
+            )
+            result = value_counts(np.asarray(self), dropna=dropna)
+            # Once the deprecation is enforced, we will need to do
+            #  `result.index = result.index.astype(self.dtype)`
+        return result
+
+    # ---------------------------------------------------------------------
+    # Rendering Methods
+
+    def _formatter(self, boxed: bool = False):
+        # returning 'str' here causes us to render as e.g. "(0, 1]" instead of
+        #  "Interval(0, 1, closed='right')"
+        return str
+
+    # ---------------------------------------------------------------------
+    # Vectorized Interval Properties/Attributes
+
+    @property
+    def left(self) -> Index:
+        """
+        Return the left endpoints of each Interval in the IntervalArray as an Index.
+
+        Examples
+        --------
+
+        >>> interv_arr = pd.arrays.IntervalArray([pd.Interval(0, 1), pd.Interval(2, 5)])
+        >>> interv_arr
+        
+        [(0, 1], (2, 5]]
+        Length: 2, dtype: interval[int64, right]
+        >>> interv_arr.left
+        Index([0, 2], dtype='int64')
+        """
+        from pandas import Index
+
+        return Index(self._left, copy=False)
+
+    @property
+    def right(self) -> Index:
+        """
+        Return the right endpoints of each Interval in the IntervalArray as an Index.
+
+        Examples
+        --------
+
+        >>> interv_arr = pd.arrays.IntervalArray([pd.Interval(0, 1), pd.Interval(2, 5)])
+        >>> interv_arr
+        
+        [(0, 1], (2, 5]]
+        Length: 2, dtype: interval[int64, right]
+        >>> interv_arr.right
+        Index([1, 5], dtype='int64')
+        """
+        from pandas import Index
+
+        return Index(self._right, copy=False)
+
+    @property
+    def length(self) -> Index:
+        """
+        Return an Index with entries denoting the length of each Interval.
+
+        Examples
+        --------
+
+        >>> interv_arr = pd.arrays.IntervalArray([pd.Interval(0, 1), pd.Interval(1, 5)])
+        >>> interv_arr
+        
+        [(0, 1], (1, 5]]
+        Length: 2, dtype: interval[int64, right]
+        >>> interv_arr.length
+        Index([1, 4], dtype='int64')
+        """
+        return self.right - self.left
+
+    @property
+    def mid(self) -> Index:
+        """
+        Return the midpoint of each Interval in the IntervalArray as an Index.
+
+        Examples
+        --------
+
+        >>> interv_arr = pd.arrays.IntervalArray([pd.Interval(0, 1), pd.Interval(1, 5)])
+        >>> interv_arr
+        
+        [(0, 1], (1, 5]]
+        Length: 2, dtype: interval[int64, right]
+        >>> interv_arr.mid
+        Index([0.5, 3.0], dtype='float64')
+        """
+        try:
+            return 0.5 * (self.left + self.right)
+        except TypeError:
+            # datetime safe version
+            return self.left + 0.5 * self.length
+
+    _interval_shared_docs["overlaps"] = textwrap.dedent(
+        """
+        Check elementwise if an Interval overlaps the values in the %(klass)s.
+
+        Two intervals overlap if they share a common point, including closed
+        endpoints. Intervals that only have an open endpoint in common do not
+        overlap.
+
+        Parameters
+        ----------
+        other : %(klass)s
+            Interval to check against for an overlap.
+
+        Returns
+        -------
+        ndarray
+            Boolean array positionally indicating where an overlap occurs.
+
+        See Also
+        --------
+        Interval.overlaps : Check whether two Interval objects overlap.
+
+        Examples
+        --------
+        %(examples)s
+        >>> intervals.overlaps(pd.Interval(0.5, 1.5))
+        array([ True,  True, False])
+
+        Intervals that share closed endpoints overlap:
+
+        >>> intervals.overlaps(pd.Interval(1, 3, closed='left'))
+        array([ True,  True, True])
+
+        Intervals that only have an open endpoint in common do not overlap:
+
+        >>> intervals.overlaps(pd.Interval(1, 2, closed='right'))
+        array([False,  True, False])
+        """
+    )
+
+    @Appender(
+        _interval_shared_docs["overlaps"]
+        % {
+            "klass": "IntervalArray",
+            "examples": textwrap.dedent(
+                """\
+        >>> data = [(0, 1), (1, 3), (2, 4)]
+        >>> intervals = pd.arrays.IntervalArray.from_tuples(data)
+        >>> intervals
+        
+        [(0, 1], (1, 3], (2, 4]]
+        Length: 3, dtype: interval[int64, right]
+        """
+            ),
+        }
+    )
+    def overlaps(self, other):
+        if isinstance(other, (IntervalArray, ABCIntervalIndex)):
+            raise NotImplementedError
+        if not isinstance(other, Interval):
+            msg = f"`other` must be Interval-like, got {type(other).__name__}"
+            raise TypeError(msg)
+
+        # equality is okay if both endpoints are closed (overlap at a point)
+        op1 = le if (self.closed_left and other.closed_right) else lt
+        op2 = le if (other.closed_left and self.closed_right) else lt
+
+        # overlaps is equivalent negation of two interval being disjoint:
+        # disjoint = (A.left > B.right) or (B.left > A.right)
+        # (simplifying the negation allows this to be done in less operations)
+        return op1(self.left, other.right) & op2(other.left, self.right)
+
+    # ---------------------------------------------------------------------
+
+    @property
+    def closed(self) -> IntervalClosedType:
+        """
+        String describing the inclusive side the intervals.
+
+        Either ``left``, ``right``, ``both`` or ``neither``.
+
+        Examples
+        --------
+
+        For arrays:
+
+        >>> interv_arr = pd.arrays.IntervalArray([pd.Interval(0, 1), pd.Interval(1, 5)])
+        >>> interv_arr
+        
+        [(0, 1], (1, 5]]
+        Length: 2, dtype: interval[int64, right]
+        >>> interv_arr.closed
+        'right'
+
+        For Interval Index:
+
+        >>> interv_idx = pd.interval_range(start=0, end=2)
+        >>> interv_idx
+        IntervalIndex([(0, 1], (1, 2]], dtype='interval[int64, right]')
+        >>> interv_idx.closed
+        'right'
+        """
+        return self.dtype.closed
+
+    _interval_shared_docs["set_closed"] = textwrap.dedent(
+        """
+        Return an identical %(klass)s closed on the specified side.
+
+        Parameters
+        ----------
+        closed : {'left', 'right', 'both', 'neither'}
+            Whether the intervals are closed on the left-side, right-side, both
+            or neither.
+
+        Returns
+        -------
+        %(klass)s
+
+        %(examples)s\
+        """
+    )
+
+    @Appender(
+        _interval_shared_docs["set_closed"]
+        % {
+            "klass": "IntervalArray",
+            "examples": textwrap.dedent(
+                """\
+        Examples
+        --------
+        >>> index = pd.arrays.IntervalArray.from_breaks(range(4))
+        >>> index
+        
+        [(0, 1], (1, 2], (2, 3]]
+        Length: 3, dtype: interval[int64, right]
+        >>> index.set_closed('both')
+        
+        [[0, 1], [1, 2], [2, 3]]
+        Length: 3, dtype: interval[int64, both]
+        """
+            ),
+        }
+    )
+    def set_closed(self, closed: IntervalClosedType) -> Self:
+        if closed not in VALID_CLOSED:
+            msg = f"invalid option for 'closed': {closed}"
+            raise ValueError(msg)
+
+        left, right = self._left, self._right
+        dtype = IntervalDtype(left.dtype, closed=closed)
+        return self._simple_new(left, right, dtype=dtype)
+
+    _interval_shared_docs[
+        "is_non_overlapping_monotonic"
+    ] = """
+        Return a boolean whether the %(klass)s is non-overlapping and monotonic.
+
+        Non-overlapping means (no Intervals share points), and monotonic means
+        either monotonic increasing or monotonic decreasing.
+
+        Examples
+        --------
+        For arrays:
+
+        >>> interv_arr = pd.arrays.IntervalArray([pd.Interval(0, 1), pd.Interval(1, 5)])
+        >>> interv_arr
+        
+        [(0, 1], (1, 5]]
+        Length: 2, dtype: interval[int64, right]
+        >>> interv_arr.is_non_overlapping_monotonic
+        True
+
+        >>> interv_arr = pd.arrays.IntervalArray([pd.Interval(0, 1),
+        ...                                       pd.Interval(-1, 0.1)])
+        >>> interv_arr
+        
+        [(0.0, 1.0], (-1.0, 0.1]]
+        Length: 2, dtype: interval[float64, right]
+        >>> interv_arr.is_non_overlapping_monotonic
+        False
+
+        For Interval Index:
+
+        >>> interv_idx = pd.interval_range(start=0, end=2)
+        >>> interv_idx
+        IntervalIndex([(0, 1], (1, 2]], dtype='interval[int64, right]')
+        >>> interv_idx.is_non_overlapping_monotonic
+        True
+
+        >>> interv_idx = pd.interval_range(start=0, end=2, closed='both')
+        >>> interv_idx
+        IntervalIndex([[0, 1], [1, 2]], dtype='interval[int64, both]')
+        >>> interv_idx.is_non_overlapping_monotonic
+        False
+        """
+
+    @property
+    @Appender(
+        _interval_shared_docs["is_non_overlapping_monotonic"] % _shared_docs_kwargs
+    )
+    def is_non_overlapping_monotonic(self) -> bool:
+        # must be increasing  (e.g., [0, 1), [1, 2), [2, 3), ... )
+        # or decreasing (e.g., [-1, 0), [-2, -1), [-3, -2), ...)
+        # we already require left <= right
+
+        # strict inequality for closed == 'both'; equality implies overlapping
+        # at a point when both sides of intervals are included
+        if self.closed == "both":
+            return bool(
+                (self._right[:-1] < self._left[1:]).all()
+                or (self._left[:-1] > self._right[1:]).all()
+            )
+
+        # non-strict inequality when closed != 'both'; at least one side is
+        # not included in the intervals, so equality does not imply overlapping
+        return bool(
+            (self._right[:-1] <= self._left[1:]).all()
+            or (self._left[:-1] >= self._right[1:]).all()
+        )
+
+    # ---------------------------------------------------------------------
+    # Conversion
+
+    def __array__(
+        self, dtype: NpDtype | None = None, copy: bool | None = None
+    ) -> np.ndarray:
+        """
+        Return the IntervalArray's data as a numpy array of Interval
+        objects (with dtype='object')
+        """
+        if copy is False:
+            warnings.warn(
+                "Starting with NumPy 2.0, the behavior of the 'copy' keyword has "
+                "changed and passing 'copy=False' raises an error when returning "
+                "a zero-copy NumPy array is not possible. pandas will follow "
+                "this behavior starting with pandas 3.0.\nThis conversion to "
+                "NumPy requires a copy, but 'copy=False' was passed. Consider "
+                "using 'np.asarray(..)' instead.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+
+        left = self._left
+        right = self._right
+        mask = self.isna()
+        closed = self.closed
+
+        result = np.empty(len(left), dtype=object)
+        for i, left_value in enumerate(left):
+            if mask[i]:
+                result[i] = np.nan
+            else:
+                result[i] = Interval(left_value, right[i], closed)
+        return result
+
+    def __arrow_array__(self, type=None):
+        """
+        Convert myself into a pyarrow Array.
+        """
+        import pyarrow
+
+        from pandas.core.arrays.arrow.extension_types import ArrowIntervalType
+
+        try:
+            subtype = pyarrow.from_numpy_dtype(self.dtype.subtype)
+        except TypeError as err:
+            raise TypeError(
+                f"Conversion to arrow with subtype '{self.dtype.subtype}' "
+                "is not supported"
+            ) from err
+        interval_type = ArrowIntervalType(subtype, self.closed)
+        storage_array = pyarrow.StructArray.from_arrays(
+            [
+                pyarrow.array(self._left, type=subtype, from_pandas=True),
+                pyarrow.array(self._right, type=subtype, from_pandas=True),
+            ],
+            names=["left", "right"],
+        )
+        mask = self.isna()
+        if mask.any():
+            # if there are missing values, set validity bitmap also on the array level
+            null_bitmap = pyarrow.array(~mask).buffers()[1]
+            storage_array = pyarrow.StructArray.from_buffers(
+                storage_array.type,
+                len(storage_array),
+                [null_bitmap],
+                children=[storage_array.field(0), storage_array.field(1)],
+            )
+
+        if type is not None:
+            if type.equals(interval_type.storage_type):
+                return storage_array
+            elif isinstance(type, ArrowIntervalType):
+                # ensure we have the same subtype and closed attributes
+                if not type.equals(interval_type):
+                    raise TypeError(
+                        "Not supported to convert IntervalArray to type with "
+                        f"different 'subtype' ({self.dtype.subtype} vs {type.subtype}) "
+                        f"and 'closed' ({self.closed} vs {type.closed}) attributes"
+                    )
+            else:
+                raise TypeError(
+                    f"Not supported to convert IntervalArray to '{type}' type"
+                )
+
+        return pyarrow.ExtensionArray.from_storage(interval_type, storage_array)
+
+    _interval_shared_docs["to_tuples"] = textwrap.dedent(
+        """
+        Return an %(return_type)s of tuples of the form (left, right).
+
+        Parameters
+        ----------
+        na_tuple : bool, default True
+            If ``True``, return ``NA`` as a tuple ``(nan, nan)``. If ``False``,
+            just return ``NA`` as ``nan``.
+
+        Returns
+        -------
+        tuples: %(return_type)s
+        %(examples)s\
+        """
+    )
+
+    @Appender(
+        _interval_shared_docs["to_tuples"]
+        % {
+            "return_type": (
+                "ndarray (if self is IntervalArray) or Index (if self is IntervalIndex)"
+            ),
+            "examples": textwrap.dedent(
+                """\
+
+         Examples
+         --------
+         For :class:`pandas.IntervalArray`:
+
+         >>> idx = pd.arrays.IntervalArray.from_tuples([(0, 1), (1, 2)])
+         >>> idx
+         
+         [(0, 1], (1, 2]]
+         Length: 2, dtype: interval[int64, right]
+         >>> idx.to_tuples()
+         array([(0, 1), (1, 2)], dtype=object)
+
+         For :class:`pandas.IntervalIndex`:
+
+         >>> idx = pd.interval_range(start=0, end=2)
+         >>> idx
+         IntervalIndex([(0, 1], (1, 2]], dtype='interval[int64, right]')
+         >>> idx.to_tuples()
+         Index([(0, 1), (1, 2)], dtype='object')
+         """
+            ),
+        }
+    )
+    def to_tuples(self, na_tuple: bool = True) -> np.ndarray:
+        tuples = com.asarray_tuplesafe(zip(self._left, self._right))
+        if not na_tuple:
+            # GH 18756
+            tuples = np.where(~self.isna(), tuples, np.nan)
+        return tuples
+
+    # ---------------------------------------------------------------------
+
+    def _putmask(self, mask: npt.NDArray[np.bool_], value) -> None:
+        value_left, value_right = self._validate_setitem_value(value)
+
+        if isinstance(self._left, np.ndarray):
+            np.putmask(self._left, mask, value_left)
+            assert isinstance(self._right, np.ndarray)
+            np.putmask(self._right, mask, value_right)
+        else:
+            self._left._putmask(mask, value_left)
+            assert not isinstance(self._right, np.ndarray)
+            self._right._putmask(mask, value_right)
+
+    def insert(self, loc: int, item: Interval) -> Self:
+        """
+        Return a new IntervalArray inserting new item at location. Follows
+        Python numpy.insert semantics for negative values.  Only Interval
+        objects and NA can be inserted into an IntervalIndex
+
+        Parameters
+        ----------
+        loc : int
+        item : Interval
+
+        Returns
+        -------
+        IntervalArray
+        """
+        left_insert, right_insert = self._validate_scalar(item)
+
+        new_left = self.left.insert(loc, left_insert)
+        new_right = self.right.insert(loc, right_insert)
+
+        return self._shallow_copy(new_left, new_right)
+
+    def delete(self, loc) -> Self:
+        if isinstance(self._left, np.ndarray):
+            new_left = np.delete(self._left, loc)
+            assert isinstance(self._right, np.ndarray)
+            new_right = np.delete(self._right, loc)
+        else:
+            new_left = self._left.delete(loc)
+            assert not isinstance(self._right, np.ndarray)
+            new_right = self._right.delete(loc)
+        return self._shallow_copy(left=new_left, right=new_right)
+
+    @Appender(_extension_array_shared_docs["repeat"] % _shared_docs_kwargs)
+    def repeat(
+        self,
+        repeats: int | Sequence[int],
+        axis: AxisInt | None = None,
+    ) -> Self:
+        nv.validate_repeat((), {"axis": axis})
+        left_repeat = self.left.repeat(repeats)
+        right_repeat = self.right.repeat(repeats)
+        return self._shallow_copy(left=left_repeat, right=right_repeat)
+
+    _interval_shared_docs["contains"] = textwrap.dedent(
+        """
+        Check elementwise if the Intervals contain the value.
+
+        Return a boolean mask whether the value is contained in the Intervals
+        of the %(klass)s.
+
+        Parameters
+        ----------
+        other : scalar
+            The value to check whether it is contained in the Intervals.
+
+        Returns
+        -------
+        boolean array
+
+        See Also
+        --------
+        Interval.contains : Check whether Interval object contains value.
+        %(klass)s.overlaps : Check if an Interval overlaps the values in the
+            %(klass)s.
+
+        Examples
+        --------
+        %(examples)s
+        >>> intervals.contains(0.5)
+        array([ True, False, False])
+    """
+    )
+
+    @Appender(
+        _interval_shared_docs["contains"]
+        % {
+            "klass": "IntervalArray",
+            "examples": textwrap.dedent(
+                """\
+        >>> intervals = pd.arrays.IntervalArray.from_tuples([(0, 1), (1, 3), (2, 4)])
+        >>> intervals
+        
+        [(0, 1], (1, 3], (2, 4]]
+        Length: 3, dtype: interval[int64, right]
+        """
+            ),
+        }
+    )
+    def contains(self, other):
+        if isinstance(other, Interval):
+            raise NotImplementedError("contains not implemented for two intervals")
+
+        return (self._left < other if self.open_left else self._left <= other) & (
+            other < self._right if self.open_right else other <= self._right
+        )
+
+    def isin(self, values: ArrayLike) -> npt.NDArray[np.bool_]:
+        if isinstance(values, IntervalArray):
+            if self.closed != values.closed:
+                # not comparable -> no overlap
+                return np.zeros(self.shape, dtype=bool)
+
+            if self.dtype == values.dtype:
+                # GH#38353 instead of casting to object, operating on a
+                #  complex128 ndarray is much more performant.
+                left = self._combined.view("complex128")
+                right = values._combined.view("complex128")
+                # error: Argument 1 to "isin" has incompatible type
+                # "Union[ExtensionArray, ndarray[Any, Any],
+                # ndarray[Any, dtype[Any]]]"; expected
+                # "Union[_SupportsArray[dtype[Any]],
+                # _NestedSequence[_SupportsArray[dtype[Any]]], bool,
+                # int, float, complex, str, bytes, _NestedSequence[
+                # Union[bool, int, float, complex, str, bytes]]]"
+                return np.isin(left, right).ravel()  # type: ignore[arg-type]
+
+            elif needs_i8_conversion(self.left.dtype) ^ needs_i8_conversion(
+                values.left.dtype
+            ):
+                # not comparable -> no overlap
+                return np.zeros(self.shape, dtype=bool)
+
+        return isin(self.astype(object), values.astype(object))
+
+    @property
+    def _combined(self) -> IntervalSide:
+        # error: Item "ExtensionArray" of "ExtensionArray | ndarray[Any, Any]"
+        # has no attribute "reshape"  [union-attr]
+        left = self.left._values.reshape(-1, 1)  # type: ignore[union-attr]
+        right = self.right._values.reshape(-1, 1)  # type: ignore[union-attr]
+        if needs_i8_conversion(left.dtype):
+            # error: Item "ndarray[Any, Any]" of "Any | ndarray[Any, Any]" has
+            # no attribute "_concat_same_type"
+            comb = left._concat_same_type(  # type: ignore[union-attr]
+                [left, right], axis=1
+            )
+        else:
+            comb = np.concatenate([left, right], axis=1)
+        return comb
+
+    def _from_combined(self, combined: np.ndarray) -> IntervalArray:
+        """
+        Create a new IntervalArray with our dtype from a 1D complex128 ndarray.
+        """
+        nc = combined.view("i8").reshape(-1, 2)
+
+        dtype = self._left.dtype
+        if needs_i8_conversion(dtype):
+            assert isinstance(self._left, (DatetimeArray, TimedeltaArray))
+            new_left = type(self._left)._from_sequence(nc[:, 0], dtype=dtype)
+            assert isinstance(self._right, (DatetimeArray, TimedeltaArray))
+            new_right = type(self._right)._from_sequence(nc[:, 1], dtype=dtype)
+        else:
+            assert isinstance(dtype, np.dtype)
+            new_left = nc[:, 0].view(dtype)
+            new_right = nc[:, 1].view(dtype)
+        return self._shallow_copy(left=new_left, right=new_right)
+
+    def unique(self) -> IntervalArray:
+        # No overload variant of "__getitem__" of "ExtensionArray" matches argument
+        # type "Tuple[slice, int]"
+        nc = unique(
+            self._combined.view("complex128")[:, 0]  # type: ignore[call-overload]
+        )
+        nc = nc[:, None]
+        return self._from_combined(nc)
+
+
+def _maybe_convert_platform_interval(values) -> ArrayLike:
+    """
+    Try to do platform conversion, with special casing for IntervalArray.
+    Wrapper around maybe_convert_platform that alters the default return
+    dtype in certain cases to be compatible with IntervalArray.  For example,
+    empty lists return with integer dtype instead of object dtype, which is
+    prohibited for IntervalArray.
+
+    Parameters
+    ----------
+    values : array-like
+
+    Returns
+    -------
+    array
+    """
+    if isinstance(values, (list, tuple)) and len(values) == 0:
+        # GH 19016
+        # empty lists/tuples get object dtype by default, but this is
+        # prohibited for IntervalArray, so coerce to integer instead
+        return np.array([], dtype=np.int64)
+    elif not is_list_like(values) or isinstance(values, ABCDataFrame):
+        # This will raise later, but we avoid passing to maybe_convert_platform
+        return values
+    elif isinstance(getattr(values, "dtype", None), CategoricalDtype):
+        values = np.asarray(values)
+    elif not hasattr(values, "dtype") and not isinstance(values, (list, tuple, range)):
+        # TODO: should we just cast these to list?
+        return values
+    else:
+        values = extract_array(values, extract_numpy=True)
+
+    if not hasattr(values, "dtype"):
+        values = np.asarray(values)
+        if values.dtype.kind in "iu" and values.dtype != np.int64:
+            values = values.astype(np.int64)
+    return values
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/masked.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/masked.py
new file mode 100644
index 0000000000000000000000000000000000000000..da656a2768901b572abd8b43810f0f496a293e1c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/masked.py
@@ -0,0 +1,1669 @@
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Literal,
+    overload,
+)
+import warnings
+
+import numpy as np
+
+from pandas._libs import (
+    lib,
+    missing as libmissing,
+)
+from pandas._libs.tslibs import is_supported_dtype
+from pandas._typing import (
+    ArrayLike,
+    AstypeArg,
+    AxisInt,
+    DtypeObj,
+    FillnaOptions,
+    InterpolateOptions,
+    NpDtype,
+    PositionalIndexer,
+    Scalar,
+    ScalarIndexer,
+    Self,
+    SequenceIndexer,
+    Shape,
+    npt,
+)
+from pandas.compat import (
+    IS64,
+    is_platform_windows,
+)
+from pandas.errors import AbstractMethodError
+from pandas.util._decorators import doc
+from pandas.util._exceptions import find_stack_level
+from pandas.util._validators import validate_fillna_kwargs
+
+from pandas.core.dtypes.base import ExtensionDtype
+from pandas.core.dtypes.common import (
+    is_bool,
+    is_integer_dtype,
+    is_list_like,
+    is_scalar,
+    is_string_dtype,
+    pandas_dtype,
+)
+from pandas.core.dtypes.dtypes import BaseMaskedDtype
+from pandas.core.dtypes.missing import (
+    array_equivalent,
+    is_valid_na_for_dtype,
+    isna,
+    notna,
+)
+
+from pandas.core import (
+    algorithms as algos,
+    arraylike,
+    missing,
+    nanops,
+    ops,
+)
+from pandas.core.algorithms import (
+    factorize_array,
+    isin,
+    map_array,
+    mode,
+    take,
+)
+from pandas.core.array_algos import (
+    masked_accumulations,
+    masked_reductions,
+)
+from pandas.core.array_algos.quantile import quantile_with_mask
+from pandas.core.arraylike import OpsMixin
+from pandas.core.arrays._utils import to_numpy_dtype_inference
+from pandas.core.arrays.base import ExtensionArray
+from pandas.core.construction import (
+    array as pd_array,
+    ensure_wrapped_if_datetimelike,
+    extract_array,
+)
+from pandas.core.indexers import check_array_indexer
+from pandas.core.ops import invalid_comparison
+from pandas.core.util.hashing import hash_array
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Iterator,
+        Sequence,
+    )
+    from pandas import Series
+    from pandas.core.arrays import BooleanArray
+    from pandas._typing import (
+        NumpySorter,
+        NumpyValueArrayLike,
+    )
+    from pandas.core.arrays import FloatingArray
+
+from pandas.compat.numpy import function as nv
+
+
+class BaseMaskedArray(OpsMixin, ExtensionArray):
+    """
+    Base class for masked arrays (which use _data and _mask to store the data).
+
+    numpy based
+    """
+
+    # The value used to fill '_data' to avoid upcasting
+    _internal_fill_value: Scalar
+    # our underlying data and mask are each ndarrays
+    _data: np.ndarray
+    _mask: npt.NDArray[np.bool_]
+
+    # Fill values used for any/all
+    _truthy_value = Scalar  # bool(_truthy_value) = True
+    _falsey_value = Scalar  # bool(_falsey_value) = False
+
+    @classmethod
+    def _simple_new(cls, values: np.ndarray, mask: npt.NDArray[np.bool_]) -> Self:
+        result = BaseMaskedArray.__new__(cls)
+        result._data = values
+        result._mask = mask
+        return result
+
+    def __init__(
+        self, values: np.ndarray, mask: npt.NDArray[np.bool_], copy: bool = False
+    ) -> None:
+        # values is supposed to already be validated in the subclass
+        if not (isinstance(mask, np.ndarray) and mask.dtype == np.bool_):
+            raise TypeError(
+                "mask should be boolean numpy array. Use "
+                "the 'pd.array' function instead"
+            )
+        if values.shape != mask.shape:
+            raise ValueError("values.shape must match mask.shape")
+
+        if copy:
+            values = values.copy()
+            mask = mask.copy()
+
+        self._data = values
+        self._mask = mask
+
+    @classmethod
+    def _from_sequence(cls, scalars, *, dtype=None, copy: bool = False) -> Self:
+        values, mask = cls._coerce_to_array(scalars, dtype=dtype, copy=copy)
+        return cls(values, mask)
+
+    @classmethod
+    @doc(ExtensionArray._empty)
+    def _empty(cls, shape: Shape, dtype: ExtensionDtype):
+        values = np.empty(shape, dtype=dtype.type)
+        values.fill(cls._internal_fill_value)
+        mask = np.ones(shape, dtype=bool)
+        result = cls(values, mask)
+        if not isinstance(result, cls) or dtype != result.dtype:
+            raise NotImplementedError(
+                f"Default 'empty' implementation is invalid for dtype='{dtype}'"
+            )
+        return result
+
+    def _formatter(self, boxed: bool = False) -> Callable[[Any], str | None]:
+        # NEP 51: https://github.com/numpy/numpy/pull/22449
+        return str
+
+    @property
+    def dtype(self) -> BaseMaskedDtype:
+        raise AbstractMethodError(self)
+
+    @overload
+    def __getitem__(self, item: ScalarIndexer) -> Any:
+        ...
+
+    @overload
+    def __getitem__(self, item: SequenceIndexer) -> Self:
+        ...
+
+    def __getitem__(self, item: PositionalIndexer) -> Self | Any:
+        item = check_array_indexer(self, item)
+
+        newmask = self._mask[item]
+        if is_bool(newmask):
+            # This is a scalar indexing
+            if newmask:
+                return self.dtype.na_value
+            return self._data[item]
+
+        return self._simple_new(self._data[item], newmask)
+
+    def _pad_or_backfill(
+        self,
+        *,
+        method: FillnaOptions,
+        limit: int | None = None,
+        limit_area: Literal["inside", "outside"] | None = None,
+        copy: bool = True,
+    ) -> Self:
+        mask = self._mask
+
+        if mask.any():
+            func = missing.get_fill_func(method, ndim=self.ndim)
+
+            npvalues = self._data.T
+            new_mask = mask.T
+            if copy:
+                npvalues = npvalues.copy()
+                new_mask = new_mask.copy()
+            elif limit_area is not None:
+                mask = mask.copy()
+            func(npvalues, limit=limit, mask=new_mask)
+
+            if limit_area is not None and not mask.all():
+                mask = mask.T
+                neg_mask = ~mask
+                first = neg_mask.argmax()
+                last = len(neg_mask) - neg_mask[::-1].argmax() - 1
+                if limit_area == "inside":
+                    new_mask[:first] |= mask[:first]
+                    new_mask[last + 1 :] |= mask[last + 1 :]
+                elif limit_area == "outside":
+                    new_mask[first + 1 : last] |= mask[first + 1 : last]
+
+            if copy:
+                return self._simple_new(npvalues.T, new_mask.T)
+            else:
+                return self
+        else:
+            if copy:
+                new_values = self.copy()
+            else:
+                new_values = self
+        return new_values
+
+    @doc(ExtensionArray.fillna)
+    def fillna(
+        self, value=None, method=None, limit: int | None = None, copy: bool = True
+    ) -> Self:
+        value, method = validate_fillna_kwargs(value, method)
+
+        mask = self._mask
+
+        value = missing.check_value_size(value, mask, len(self))
+
+        if mask.any():
+            if method is not None:
+                func = missing.get_fill_func(method, ndim=self.ndim)
+                npvalues = self._data.T
+                new_mask = mask.T
+                if copy:
+                    npvalues = npvalues.copy()
+                    new_mask = new_mask.copy()
+                func(npvalues, limit=limit, mask=new_mask)
+                return self._simple_new(npvalues.T, new_mask.T)
+            else:
+                # fill with value
+                if copy:
+                    new_values = self.copy()
+                else:
+                    new_values = self[:]
+                new_values[mask] = value
+        else:
+            if copy:
+                new_values = self.copy()
+            else:
+                new_values = self[:]
+        return new_values
+
+    @classmethod
+    def _coerce_to_array(
+        cls, values, *, dtype: DtypeObj, copy: bool = False
+    ) -> tuple[np.ndarray, np.ndarray]:
+        raise AbstractMethodError(cls)
+
+    def _validate_setitem_value(self, value):
+        """
+        Check if we have a scalar that we can cast losslessly.
+
+        Raises
+        ------
+        TypeError
+        """
+        kind = self.dtype.kind
+        # TODO: get this all from np_can_hold_element?
+        if kind == "b":
+            if lib.is_bool(value):
+                return value
+
+        elif kind == "f":
+            if lib.is_integer(value) or lib.is_float(value):
+                return value
+
+        else:
+            if lib.is_integer(value) or (lib.is_float(value) and value.is_integer()):
+                return value
+            # TODO: unsigned checks
+
+        # Note: without the "str" here, the f-string rendering raises in
+        #  py38 builds.
+        raise TypeError(f"Invalid value '{value!s}' for dtype '{self.dtype}'")
+
+    def __setitem__(self, key, value) -> None:
+        key = check_array_indexer(self, key)
+
+        if is_scalar(value):
+            if is_valid_na_for_dtype(value, self.dtype):
+                self._mask[key] = True
+            else:
+                value = self._validate_setitem_value(value)
+                self._data[key] = value
+                self._mask[key] = False
+            return
+
+        value, mask = self._coerce_to_array(value, dtype=self.dtype)
+
+        self._data[key] = value
+        self._mask[key] = mask
+
+    def __contains__(self, key) -> bool:
+        if isna(key) and key is not self.dtype.na_value:
+            # GH#52840
+            if self._data.dtype.kind == "f" and lib.is_float(key):
+                return bool((np.isnan(self._data) & ~self._mask).any())
+
+        return bool(super().__contains__(key))
+
+    def __iter__(self) -> Iterator:
+        if self.ndim == 1:
+            if not self._hasna:
+                for val in self._data:
+                    yield val
+            else:
+                na_value = self.dtype.na_value
+                for isna_, val in zip(self._mask, self._data):
+                    if isna_:
+                        yield na_value
+                    else:
+                        yield val
+        else:
+            for i in range(len(self)):
+                yield self[i]
+
+    def __len__(self) -> int:
+        return len(self._data)
+
+    @property
+    def shape(self) -> Shape:
+        return self._data.shape
+
+    @property
+    def ndim(self) -> int:
+        return self._data.ndim
+
+    def swapaxes(self, axis1, axis2) -> Self:
+        data = self._data.swapaxes(axis1, axis2)
+        mask = self._mask.swapaxes(axis1, axis2)
+        return self._simple_new(data, mask)
+
+    def delete(self, loc, axis: AxisInt = 0) -> Self:
+        data = np.delete(self._data, loc, axis=axis)
+        mask = np.delete(self._mask, loc, axis=axis)
+        return self._simple_new(data, mask)
+
+    def reshape(self, *args, **kwargs) -> Self:
+        data = self._data.reshape(*args, **kwargs)
+        mask = self._mask.reshape(*args, **kwargs)
+        return self._simple_new(data, mask)
+
+    def ravel(self, *args, **kwargs) -> Self:
+        # TODO: need to make sure we have the same order for data/mask
+        data = self._data.ravel(*args, **kwargs)
+        mask = self._mask.ravel(*args, **kwargs)
+        return type(self)(data, mask)
+
+    @property
+    def T(self) -> Self:
+        return self._simple_new(self._data.T, self._mask.T)
+
+    def round(self, decimals: int = 0, *args, **kwargs):
+        """
+        Round each value in the array a to the given number of decimals.
+
+        Parameters
+        ----------
+        decimals : int, default 0
+            Number of decimal places to round to. If decimals is negative,
+            it specifies the number of positions to the left of the decimal point.
+        *args, **kwargs
+            Additional arguments and keywords have no effect but might be
+            accepted for compatibility with NumPy.
+
+        Returns
+        -------
+        NumericArray
+            Rounded values of the NumericArray.
+
+        See Also
+        --------
+        numpy.around : Round values of an np.array.
+        DataFrame.round : Round values of a DataFrame.
+        Series.round : Round values of a Series.
+        """
+        if self.dtype.kind == "b":
+            return self
+        nv.validate_round(args, kwargs)
+        values = np.round(self._data, decimals=decimals, **kwargs)
+
+        # Usually we'll get same type as self, but ndarray[bool] casts to float
+        return self._maybe_mask_result(values, self._mask.copy())
+
+    # ------------------------------------------------------------------
+    # Unary Methods
+
+    def __invert__(self) -> Self:
+        return self._simple_new(~self._data, self._mask.copy())
+
+    def __neg__(self) -> Self:
+        return self._simple_new(-self._data, self._mask.copy())
+
+    def __pos__(self) -> Self:
+        return self.copy()
+
+    def __abs__(self) -> Self:
+        return self._simple_new(abs(self._data), self._mask.copy())
+
+    # ------------------------------------------------------------------
+
+    def _values_for_json(self) -> np.ndarray:
+        return np.asarray(self, dtype=object)
+
+    def to_numpy(
+        self,
+        dtype: npt.DTypeLike | None = None,
+        copy: bool = False,
+        na_value: object = lib.no_default,
+    ) -> np.ndarray:
+        """
+        Convert to a NumPy Array.
+
+        By default converts to an object-dtype NumPy array. Specify the `dtype` and
+        `na_value` keywords to customize the conversion.
+
+        Parameters
+        ----------
+        dtype : dtype, default object
+            The numpy dtype to convert to.
+        copy : bool, default False
+            Whether to ensure that the returned value is a not a view on
+            the array. Note that ``copy=False`` does not *ensure* that
+            ``to_numpy()`` is no-copy. Rather, ``copy=True`` ensure that
+            a copy is made, even if not strictly necessary. This is typically
+            only possible when no missing values are present and `dtype`
+            is the equivalent numpy dtype.
+        na_value : scalar, optional
+             Scalar missing value indicator to use in numpy array. Defaults
+             to the native missing value indicator of this array (pd.NA).
+
+        Returns
+        -------
+        numpy.ndarray
+
+        Examples
+        --------
+        An object-dtype is the default result
+
+        >>> a = pd.array([True, False, pd.NA], dtype="boolean")
+        >>> a.to_numpy()
+        array([True, False, ], dtype=object)
+
+        When no missing values are present, an equivalent dtype can be used.
+
+        >>> pd.array([True, False], dtype="boolean").to_numpy(dtype="bool")
+        array([ True, False])
+        >>> pd.array([1, 2], dtype="Int64").to_numpy("int64")
+        array([1, 2])
+
+        However, requesting such dtype will raise a ValueError if
+        missing values are present and the default missing value :attr:`NA`
+        is used.
+
+        >>> a = pd.array([True, False, pd.NA], dtype="boolean")
+        >>> a
+        
+        [True, False, ]
+        Length: 3, dtype: boolean
+
+        >>> a.to_numpy(dtype="bool")
+        Traceback (most recent call last):
+        ...
+        ValueError: cannot convert to bool numpy array in presence of missing values
+
+        Specify a valid `na_value` instead
+
+        >>> a.to_numpy(dtype="bool", na_value=False)
+        array([ True, False, False])
+        """
+        hasna = self._hasna
+        dtype, na_value = to_numpy_dtype_inference(self, dtype, na_value, hasna)
+        if dtype is None:
+            dtype = object
+
+        if hasna:
+            if (
+                dtype != object
+                and not is_string_dtype(dtype)
+                and na_value is libmissing.NA
+            ):
+                raise ValueError(
+                    f"cannot convert to '{dtype}'-dtype NumPy array "
+                    "with missing values. Specify an appropriate 'na_value' "
+                    "for this dtype."
+                )
+            # don't pass copy to astype -> always need a copy since we are mutating
+            with warnings.catch_warnings():
+                warnings.filterwarnings("ignore", category=RuntimeWarning)
+                data = self._data.astype(dtype)
+            data[self._mask] = na_value
+        else:
+            with warnings.catch_warnings():
+                warnings.filterwarnings("ignore", category=RuntimeWarning)
+                data = self._data.astype(dtype, copy=copy)
+        return data
+
+    @doc(ExtensionArray.tolist)
+    def tolist(self):
+        if self.ndim > 1:
+            return [x.tolist() for x in self]
+        dtype = None if self._hasna else self._data.dtype
+        return self.to_numpy(dtype=dtype, na_value=libmissing.NA).tolist()
+
+    @overload
+    def astype(self, dtype: npt.DTypeLike, copy: bool = ...) -> np.ndarray:
+        ...
+
+    @overload
+    def astype(self, dtype: ExtensionDtype, copy: bool = ...) -> ExtensionArray:
+        ...
+
+    @overload
+    def astype(self, dtype: AstypeArg, copy: bool = ...) -> ArrayLike:
+        ...
+
+    def astype(self, dtype: AstypeArg, copy: bool = True) -> ArrayLike:
+        dtype = pandas_dtype(dtype)
+
+        if dtype == self.dtype:
+            if copy:
+                return self.copy()
+            return self
+
+        # if we are astyping to another nullable masked dtype, we can fastpath
+        if isinstance(dtype, BaseMaskedDtype):
+            # TODO deal with NaNs for FloatingArray case
+            with warnings.catch_warnings():
+                warnings.filterwarnings("ignore", category=RuntimeWarning)
+                # TODO: Is rounding what we want long term?
+                data = self._data.astype(dtype.numpy_dtype, copy=copy)
+            # mask is copied depending on whether the data was copied, and
+            # not directly depending on the `copy` keyword
+            mask = self._mask if data is self._data else self._mask.copy()
+            cls = dtype.construct_array_type()
+            return cls(data, mask, copy=False)
+
+        if isinstance(dtype, ExtensionDtype):
+            eacls = dtype.construct_array_type()
+            return eacls._from_sequence(self, dtype=dtype, copy=copy)
+
+        na_value: float | np.datetime64 | lib.NoDefault
+
+        # coerce
+        if dtype.kind == "f":
+            # In astype, we consider dtype=float to also mean na_value=np.nan
+            na_value = np.nan
+        elif dtype.kind == "M":
+            na_value = np.datetime64("NaT")
+        else:
+            na_value = lib.no_default
+
+        # to_numpy will also raise, but we get somewhat nicer exception messages here
+        if dtype.kind in "iu" and self._hasna:
+            raise ValueError("cannot convert NA to integer")
+        if dtype.kind == "b" and self._hasna:
+            # careful: astype_nansafe converts np.nan to True
+            raise ValueError("cannot convert float NaN to bool")
+
+        data = self.to_numpy(dtype=dtype, na_value=na_value, copy=copy)
+        return data
+
+    __array_priority__ = 1000  # higher than ndarray so ops dispatch to us
+
+    def __array__(
+        self, dtype: NpDtype | None = None, copy: bool | None = None
+    ) -> np.ndarray:
+        """
+        the array interface, return my values
+        We return an object array here to preserve our scalar values
+        """
+        if copy is False:
+            if not self._hasna:
+                # special case, here we can simply return the underlying data
+                return np.array(self._data, dtype=dtype, copy=copy)
+
+            warnings.warn(
+                "Starting with NumPy 2.0, the behavior of the 'copy' keyword has "
+                "changed and passing 'copy=False' raises an error when returning "
+                "a zero-copy NumPy array is not possible. pandas will follow "
+                "this behavior starting with pandas 3.0.\nThis conversion to "
+                "NumPy requires a copy, but 'copy=False' was passed. Consider "
+                "using 'np.asarray(..)' instead.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+
+        if copy is None:
+            copy = False  # The NumPy copy=False meaning is different here.
+        return self.to_numpy(dtype=dtype, copy=copy)
+
+    _HANDLED_TYPES: tuple[type, ...]
+
+    def __array_ufunc__(self, ufunc: np.ufunc, method: str, *inputs, **kwargs):
+        # For MaskedArray inputs, we apply the ufunc to ._data
+        # and mask the result.
+
+        out = kwargs.get("out", ())
+
+        for x in inputs + out:
+            if not isinstance(x, self._HANDLED_TYPES + (BaseMaskedArray,)):
+                return NotImplemented
+
+        # for binary ops, use our custom dunder methods
+        result = arraylike.maybe_dispatch_ufunc_to_dunder_op(
+            self, ufunc, method, *inputs, **kwargs
+        )
+        if result is not NotImplemented:
+            return result
+
+        if "out" in kwargs:
+            # e.g. test_ufunc_with_out
+            return arraylike.dispatch_ufunc_with_out(
+                self, ufunc, method, *inputs, **kwargs
+            )
+
+        if method == "reduce":
+            result = arraylike.dispatch_reduction_ufunc(
+                self, ufunc, method, *inputs, **kwargs
+            )
+            if result is not NotImplemented:
+                return result
+
+        mask = np.zeros(len(self), dtype=bool)
+        inputs2 = []
+        for x in inputs:
+            if isinstance(x, BaseMaskedArray):
+                mask |= x._mask
+                inputs2.append(x._data)
+            else:
+                inputs2.append(x)
+
+        def reconstruct(x: np.ndarray):
+            # we don't worry about scalar `x` here, since we
+            # raise for reduce up above.
+            from pandas.core.arrays import (
+                BooleanArray,
+                FloatingArray,
+                IntegerArray,
+            )
+
+            if x.dtype.kind == "b":
+                m = mask.copy()
+                return BooleanArray(x, m)
+            elif x.dtype.kind in "iu":
+                m = mask.copy()
+                return IntegerArray(x, m)
+            elif x.dtype.kind == "f":
+                m = mask.copy()
+                if x.dtype == np.float16:
+                    # reached in e.g. np.sqrt on BooleanArray
+                    # we don't support float16
+                    x = x.astype(np.float32)
+                return FloatingArray(x, m)
+            else:
+                x[mask] = np.nan
+            return x
+
+        result = getattr(ufunc, method)(*inputs2, **kwargs)
+        if ufunc.nout > 1:
+            # e.g. np.divmod
+            return tuple(reconstruct(x) for x in result)
+        elif method == "reduce":
+            # e.g. np.add.reduce; test_ufunc_reduce_raises
+            if self._mask.any():
+                return self._na_value
+            return result
+        else:
+            return reconstruct(result)
+
+    def __arrow_array__(self, type=None):
+        """
+        Convert myself into a pyarrow Array.
+        """
+        import pyarrow as pa
+
+        return pa.array(self._data, mask=self._mask, type=type)
+
+    @property
+    def _hasna(self) -> bool:
+        # Note: this is expensive right now! The hope is that we can
+        # make this faster by having an optional mask, but not have to change
+        # source code using it..
+
+        # error: Incompatible return value type (got "bool_", expected "bool")
+        return self._mask.any()  # type: ignore[return-value]
+
+    def _propagate_mask(
+        self, mask: npt.NDArray[np.bool_] | None, other
+    ) -> npt.NDArray[np.bool_]:
+        if mask is None:
+            mask = self._mask.copy()  # TODO: need test for BooleanArray needing a copy
+            if other is libmissing.NA:
+                # GH#45421 don't alter inplace
+                mask = mask | True
+            elif is_list_like(other) and len(other) == len(mask):
+                mask = mask | isna(other)
+        else:
+            mask = self._mask | mask
+        # Incompatible return value type (got "Optional[ndarray[Any, dtype[bool_]]]",
+        # expected "ndarray[Any, dtype[bool_]]")
+        return mask  # type: ignore[return-value]
+
+    def _arith_method(self, other, op):
+        op_name = op.__name__
+        omask = None
+
+        if (
+            not hasattr(other, "dtype")
+            and is_list_like(other)
+            and len(other) == len(self)
+        ):
+            # Try inferring masked dtype instead of casting to object
+            other = pd_array(other)
+            other = extract_array(other, extract_numpy=True)
+
+        if isinstance(other, BaseMaskedArray):
+            other, omask = other._data, other._mask
+
+        elif is_list_like(other):
+            if not isinstance(other, ExtensionArray):
+                other = np.asarray(other)
+            if other.ndim > 1:
+                raise NotImplementedError("can only perform ops with 1-d structures")
+
+        # We wrap the non-masked arithmetic logic used for numpy dtypes
+        #  in Series/Index arithmetic ops.
+        other = ops.maybe_prepare_scalar_for_op(other, (len(self),))
+        pd_op = ops.get_array_op(op)
+        other = ensure_wrapped_if_datetimelike(other)
+
+        if op_name in {"pow", "rpow"} and isinstance(other, np.bool_):
+            # Avoid DeprecationWarning: In future, it will be an error
+            #  for 'np.bool_' scalars to be interpreted as an index
+            #  e.g. test_array_scalar_like_equivalence
+            other = bool(other)
+
+        mask = self._propagate_mask(omask, other)
+
+        if other is libmissing.NA:
+            result = np.ones_like(self._data)
+            if self.dtype.kind == "b":
+                if op_name in {
+                    "floordiv",
+                    "rfloordiv",
+                    "pow",
+                    "rpow",
+                    "truediv",
+                    "rtruediv",
+                }:
+                    # GH#41165 Try to match non-masked Series behavior
+                    #  This is still imperfect GH#46043
+                    raise NotImplementedError(
+                        f"operator '{op_name}' not implemented for bool dtypes"
+                    )
+                if op_name in {"mod", "rmod"}:
+                    dtype = "int8"
+                else:
+                    dtype = "bool"
+                result = result.astype(dtype)
+            elif "truediv" in op_name and self.dtype.kind != "f":
+                # The actual data here doesn't matter since the mask
+                #  will be all-True, but since this is division, we want
+                #  to end up with floating dtype.
+                result = result.astype(np.float64)
+        else:
+            # Make sure we do this before the "pow" mask checks
+            #  to get an expected exception message on shape mismatch.
+            if self.dtype.kind in "iu" and op_name in ["floordiv", "mod"]:
+                # TODO(GH#30188) ATM we don't match the behavior of non-masked
+                #  types with respect to floordiv-by-zero
+                pd_op = op
+
+            with np.errstate(all="ignore"):
+                result = pd_op(self._data, other)
+
+        if op_name == "pow":
+            # 1 ** x is 1.
+            mask = np.where((self._data == 1) & ~self._mask, False, mask)
+            # x ** 0 is 1.
+            if omask is not None:
+                mask = np.where((other == 0) & ~omask, False, mask)
+            elif other is not libmissing.NA:
+                mask = np.where(other == 0, False, mask)
+
+        elif op_name == "rpow":
+            # 1 ** x is 1.
+            if omask is not None:
+                mask = np.where((other == 1) & ~omask, False, mask)
+            elif other is not libmissing.NA:
+                mask = np.where(other == 1, False, mask)
+            # x ** 0 is 1.
+            mask = np.where((self._data == 0) & ~self._mask, False, mask)
+
+        return self._maybe_mask_result(result, mask)
+
+    _logical_method = _arith_method
+
+    def _cmp_method(self, other, op) -> BooleanArray:
+        from pandas.core.arrays import BooleanArray
+
+        mask = None
+
+        if isinstance(other, BaseMaskedArray):
+            other, mask = other._data, other._mask
+
+        elif is_list_like(other):
+            other = np.asarray(other)
+            if other.ndim > 1:
+                raise NotImplementedError("can only perform ops with 1-d structures")
+            if len(self) != len(other):
+                raise ValueError("Lengths must match to compare")
+
+        if other is libmissing.NA:
+            # numpy does not handle pd.NA well as "other" scalar (it returns
+            # a scalar False instead of an array)
+            # This may be fixed by NA.__array_ufunc__. Revisit this check
+            # once that's implemented.
+            result = np.zeros(self._data.shape, dtype="bool")
+            mask = np.ones(self._data.shape, dtype="bool")
+        else:
+            with warnings.catch_warnings():
+                # numpy may show a FutureWarning or DeprecationWarning:
+                #     elementwise comparison failed; returning scalar instead,
+                #     but in the future will perform elementwise comparison
+                # before returning NotImplemented. We fall back to the correct
+                # behavior today, so that should be fine to ignore.
+                warnings.filterwarnings("ignore", "elementwise", FutureWarning)
+                warnings.filterwarnings("ignore", "elementwise", DeprecationWarning)
+                method = getattr(self._data, f"__{op.__name__}__")
+                result = method(other)
+
+                if result is NotImplemented:
+                    result = invalid_comparison(self._data, other, op)
+
+        mask = self._propagate_mask(mask, other)
+        return BooleanArray(result, mask, copy=False)
+
+    def _maybe_mask_result(
+        self, result: np.ndarray | tuple[np.ndarray, np.ndarray], mask: np.ndarray
+    ):
+        """
+        Parameters
+        ----------
+        result : array-like or tuple[array-like]
+        mask : array-like bool
+        """
+        if isinstance(result, tuple):
+            # i.e. divmod
+            div, mod = result
+            return (
+                self._maybe_mask_result(div, mask),
+                self._maybe_mask_result(mod, mask),
+            )
+
+        if result.dtype.kind == "f":
+            from pandas.core.arrays import FloatingArray
+
+            return FloatingArray(result, mask, copy=False)
+
+        elif result.dtype.kind == "b":
+            from pandas.core.arrays import BooleanArray
+
+            return BooleanArray(result, mask, copy=False)
+
+        elif lib.is_np_dtype(result.dtype, "m") and is_supported_dtype(result.dtype):
+            # e.g. test_numeric_arr_mul_tdscalar_numexpr_path
+            from pandas.core.arrays import TimedeltaArray
+
+            result[mask] = result.dtype.type("NaT")
+
+            if not isinstance(result, TimedeltaArray):
+                return TimedeltaArray._simple_new(result, dtype=result.dtype)
+
+            return result
+
+        elif result.dtype.kind in "iu":
+            from pandas.core.arrays import IntegerArray
+
+            return IntegerArray(result, mask, copy=False)
+
+        else:
+            result[mask] = np.nan
+            return result
+
+    def isna(self) -> np.ndarray:
+        return self._mask.copy()
+
+    @property
+    def _na_value(self):
+        return self.dtype.na_value
+
+    @property
+    def nbytes(self) -> int:
+        return self._data.nbytes + self._mask.nbytes
+
+    @classmethod
+    def _concat_same_type(
+        cls,
+        to_concat: Sequence[Self],
+        axis: AxisInt = 0,
+    ) -> Self:
+        data = np.concatenate([x._data for x in to_concat], axis=axis)
+        mask = np.concatenate([x._mask for x in to_concat], axis=axis)
+        return cls(data, mask)
+
+    def _hash_pandas_object(
+        self, *, encoding: str, hash_key: str, categorize: bool
+    ) -> npt.NDArray[np.uint64]:
+        hashed_array = hash_array(
+            self._data, encoding=encoding, hash_key=hash_key, categorize=categorize
+        )
+        hashed_array[self.isna()] = hash(self.dtype.na_value)
+        return hashed_array
+
+    def take(
+        self,
+        indexer,
+        *,
+        allow_fill: bool = False,
+        fill_value: Scalar | None = None,
+        axis: AxisInt = 0,
+    ) -> Self:
+        # we always fill with 1 internally
+        # to avoid upcasting
+        data_fill_value = self._internal_fill_value if isna(fill_value) else fill_value
+        result = take(
+            self._data,
+            indexer,
+            fill_value=data_fill_value,
+            allow_fill=allow_fill,
+            axis=axis,
+        )
+
+        mask = take(
+            self._mask, indexer, fill_value=True, allow_fill=allow_fill, axis=axis
+        )
+
+        # if we are filling
+        # we only fill where the indexer is null
+        # not existing missing values
+        # TODO(jreback) what if we have a non-na float as a fill value?
+        if allow_fill and notna(fill_value):
+            fill_mask = np.asarray(indexer) == -1
+            result[fill_mask] = fill_value
+            mask = mask ^ fill_mask
+
+        return self._simple_new(result, mask)
+
+    # error: Return type "BooleanArray" of "isin" incompatible with return type
+    # "ndarray" in supertype "ExtensionArray"
+    def isin(self, values: ArrayLike) -> BooleanArray:  # type: ignore[override]
+        from pandas.core.arrays import BooleanArray
+
+        # algorithms.isin will eventually convert values to an ndarray, so no extra
+        # cost to doing it here first
+        values_arr = np.asarray(values)
+        result = isin(self._data, values_arr)
+
+        if self._hasna:
+            values_have_NA = values_arr.dtype == object and any(
+                val is self.dtype.na_value for val in values_arr
+            )
+
+            # For now, NA does not propagate so set result according to presence of NA,
+            # see https://github.com/pandas-dev/pandas/pull/38379 for some discussion
+            result[self._mask] = values_have_NA
+
+        mask = np.zeros(self._data.shape, dtype=bool)
+        return BooleanArray(result, mask, copy=False)
+
+    def copy(self) -> Self:
+        data = self._data.copy()
+        mask = self._mask.copy()
+        return self._simple_new(data, mask)
+
+    @doc(ExtensionArray.duplicated)
+    def duplicated(
+        self, keep: Literal["first", "last", False] = "first"
+    ) -> npt.NDArray[np.bool_]:
+        values = self._data
+        mask = self._mask
+        return algos.duplicated(values, keep=keep, mask=mask)
+
+    def unique(self) -> Self:
+        """
+        Compute the BaseMaskedArray of unique values.
+
+        Returns
+        -------
+        uniques : BaseMaskedArray
+        """
+        uniques, mask = algos.unique_with_mask(self._data, self._mask)
+        return self._simple_new(uniques, mask)
+
+    @doc(ExtensionArray.searchsorted)
+    def searchsorted(
+        self,
+        value: NumpyValueArrayLike | ExtensionArray,
+        side: Literal["left", "right"] = "left",
+        sorter: NumpySorter | None = None,
+    ) -> npt.NDArray[np.intp] | np.intp:
+        if self._hasna:
+            raise ValueError(
+                "searchsorted requires array to be sorted, which is impossible "
+                "with NAs present."
+            )
+        if isinstance(value, ExtensionArray):
+            value = value.astype(object)
+        # Base class searchsorted would cast to object, which is *much* slower.
+        return self._data.searchsorted(value, side=side, sorter=sorter)
+
+    @doc(ExtensionArray.factorize)
+    def factorize(
+        self,
+        use_na_sentinel: bool = True,
+    ) -> tuple[np.ndarray, ExtensionArray]:
+        arr = self._data
+        mask = self._mask
+
+        # Use a sentinel for na; recode and add NA to uniques if necessary below
+        codes, uniques = factorize_array(arr, use_na_sentinel=True, mask=mask)
+
+        # check that factorize_array correctly preserves dtype.
+        assert uniques.dtype == self.dtype.numpy_dtype, (uniques.dtype, self.dtype)
+
+        has_na = mask.any()
+        if use_na_sentinel or not has_na:
+            size = len(uniques)
+        else:
+            # Make room for an NA value
+            size = len(uniques) + 1
+        uniques_mask = np.zeros(size, dtype=bool)
+        if not use_na_sentinel and has_na:
+            na_index = mask.argmax()
+            # Insert na with the proper code
+            if na_index == 0:
+                na_code = np.intp(0)
+            else:
+                na_code = codes[:na_index].max() + 1
+            codes[codes >= na_code] += 1
+            codes[codes == -1] = na_code
+            # dummy value for uniques; not used since uniques_mask will be True
+            uniques = np.insert(uniques, na_code, 0)
+            uniques_mask[na_code] = True
+        uniques_ea = self._simple_new(uniques, uniques_mask)
+
+        return codes, uniques_ea
+
+    @doc(ExtensionArray._values_for_argsort)
+    def _values_for_argsort(self) -> np.ndarray:
+        return self._data
+
+    def value_counts(self, dropna: bool = True) -> Series:
+        """
+        Returns a Series containing counts of each unique value.
+
+        Parameters
+        ----------
+        dropna : bool, default True
+            Don't include counts of missing values.
+
+        Returns
+        -------
+        counts : Series
+
+        See Also
+        --------
+        Series.value_counts
+        """
+        from pandas import (
+            Index,
+            Series,
+        )
+        from pandas.arrays import IntegerArray
+
+        keys, value_counts, na_counter = algos.value_counts_arraylike(
+            self._data, dropna=dropna, mask=self._mask
+        )
+        mask_index = np.zeros((len(value_counts),), dtype=np.bool_)
+        mask = mask_index.copy()
+
+        if na_counter > 0:
+            mask_index[-1] = True
+
+        arr = IntegerArray(value_counts, mask)
+        index = Index(
+            self.dtype.construct_array_type()(
+                keys, mask_index  # type: ignore[arg-type]
+            )
+        )
+        return Series(arr, index=index, name="count", copy=False)
+
+    def _mode(self, dropna: bool = True) -> Self:
+        if dropna:
+            result = mode(self._data, dropna=dropna, mask=self._mask)
+            res_mask = np.zeros(result.shape, dtype=np.bool_)
+        else:
+            result, res_mask = mode(self._data, dropna=dropna, mask=self._mask)
+        result = type(self)(result, res_mask)  # type: ignore[arg-type]
+        return result[result.argsort()]
+
+    @doc(ExtensionArray.equals)
+    def equals(self, other) -> bool:
+        if type(self) != type(other):
+            return False
+        if other.dtype != self.dtype:
+            return False
+
+        # GH#44382 if e.g. self[1] is np.nan and other[1] is pd.NA, we are NOT
+        #  equal.
+        if not np.array_equal(self._mask, other._mask):
+            return False
+
+        left = self._data[~self._mask]
+        right = other._data[~other._mask]
+        return array_equivalent(left, right, strict_nan=True, dtype_equal=True)
+
+    def _quantile(
+        self, qs: npt.NDArray[np.float64], interpolation: str
+    ) -> BaseMaskedArray:
+        """
+        Dispatch to quantile_with_mask, needed because we do not have
+        _from_factorized.
+
+        Notes
+        -----
+        We assume that all impacted cases are 1D-only.
+        """
+        res = quantile_with_mask(
+            self._data,
+            mask=self._mask,
+            # TODO(GH#40932): na_value_for_dtype(self.dtype.numpy_dtype)
+            #  instead of np.nan
+            fill_value=np.nan,
+            qs=qs,
+            interpolation=interpolation,
+        )
+
+        if self._hasna:
+            # Our result mask is all-False unless we are all-NA, in which
+            #  case it is all-True.
+            if self.ndim == 2:
+                # I think this should be out_mask=self.isna().all(axis=1)
+                #  but am holding off until we have tests
+                raise NotImplementedError
+            if self.isna().all():
+                out_mask = np.ones(res.shape, dtype=bool)
+
+                if is_integer_dtype(self.dtype):
+                    # We try to maintain int dtype if possible for not all-na case
+                    # as well
+                    res = np.zeros(res.shape, dtype=self.dtype.numpy_dtype)
+            else:
+                out_mask = np.zeros(res.shape, dtype=bool)
+        else:
+            out_mask = np.zeros(res.shape, dtype=bool)
+        return self._maybe_mask_result(res, mask=out_mask)
+
+    # ------------------------------------------------------------------
+    # Reductions
+
+    def _reduce(
+        self, name: str, *, skipna: bool = True, keepdims: bool = False, **kwargs
+    ):
+        if name in {"any", "all", "min", "max", "sum", "prod", "mean", "var", "std"}:
+            result = getattr(self, name)(skipna=skipna, **kwargs)
+        else:
+            # median, skew, kurt, sem
+            data = self._data
+            mask = self._mask
+            op = getattr(nanops, f"nan{name}")
+            axis = kwargs.pop("axis", None)
+            result = op(data, axis=axis, skipna=skipna, mask=mask, **kwargs)
+
+        if keepdims:
+            if isna(result):
+                return self._wrap_na_result(name=name, axis=0, mask_size=(1,))
+            else:
+                result = result.reshape(1)
+                mask = np.zeros(1, dtype=bool)
+                return self._maybe_mask_result(result, mask)
+
+        if isna(result):
+            return libmissing.NA
+        else:
+            return result
+
+    def _wrap_reduction_result(self, name: str, result, *, skipna, axis):
+        if isinstance(result, np.ndarray):
+            if skipna:
+                # we only retain mask for all-NA rows/columns
+                mask = self._mask.all(axis=axis)
+            else:
+                mask = self._mask.any(axis=axis)
+
+            return self._maybe_mask_result(result, mask)
+        return result
+
+    def _wrap_na_result(self, *, name, axis, mask_size):
+        mask = np.ones(mask_size, dtype=bool)
+
+        float_dtyp = "float32" if self.dtype == "Float32" else "float64"
+        if name in ["mean", "median", "var", "std", "skew", "kurt"]:
+            np_dtype = float_dtyp
+        elif name in ["min", "max"] or self.dtype.itemsize == 8:
+            np_dtype = self.dtype.numpy_dtype.name
+        else:
+            is_windows_or_32bit = is_platform_windows() or not IS64
+            int_dtyp = "int32" if is_windows_or_32bit else "int64"
+            uint_dtyp = "uint32" if is_windows_or_32bit else "uint64"
+            np_dtype = {"b": int_dtyp, "i": int_dtyp, "u": uint_dtyp, "f": float_dtyp}[
+                self.dtype.kind
+            ]
+
+        value = np.array([1], dtype=np_dtype)
+        return self._maybe_mask_result(value, mask=mask)
+
+    def _wrap_min_count_reduction_result(
+        self, name: str, result, *, skipna, min_count, axis
+    ):
+        if min_count == 0 and isinstance(result, np.ndarray):
+            return self._maybe_mask_result(result, np.zeros(result.shape, dtype=bool))
+        return self._wrap_reduction_result(name, result, skipna=skipna, axis=axis)
+
+    def sum(
+        self,
+        *,
+        skipna: bool = True,
+        min_count: int = 0,
+        axis: AxisInt | None = 0,
+        **kwargs,
+    ):
+        nv.validate_sum((), kwargs)
+
+        result = masked_reductions.sum(
+            self._data,
+            self._mask,
+            skipna=skipna,
+            min_count=min_count,
+            axis=axis,
+        )
+        return self._wrap_min_count_reduction_result(
+            "sum", result, skipna=skipna, min_count=min_count, axis=axis
+        )
+
+    def prod(
+        self,
+        *,
+        skipna: bool = True,
+        min_count: int = 0,
+        axis: AxisInt | None = 0,
+        **kwargs,
+    ):
+        nv.validate_prod((), kwargs)
+
+        result = masked_reductions.prod(
+            self._data,
+            self._mask,
+            skipna=skipna,
+            min_count=min_count,
+            axis=axis,
+        )
+        return self._wrap_min_count_reduction_result(
+            "prod", result, skipna=skipna, min_count=min_count, axis=axis
+        )
+
+    def mean(self, *, skipna: bool = True, axis: AxisInt | None = 0, **kwargs):
+        nv.validate_mean((), kwargs)
+        result = masked_reductions.mean(
+            self._data,
+            self._mask,
+            skipna=skipna,
+            axis=axis,
+        )
+        return self._wrap_reduction_result("mean", result, skipna=skipna, axis=axis)
+
+    def var(
+        self, *, skipna: bool = True, axis: AxisInt | None = 0, ddof: int = 1, **kwargs
+    ):
+        nv.validate_stat_ddof_func((), kwargs, fname="var")
+        result = masked_reductions.var(
+            self._data,
+            self._mask,
+            skipna=skipna,
+            axis=axis,
+            ddof=ddof,
+        )
+        return self._wrap_reduction_result("var", result, skipna=skipna, axis=axis)
+
+    def std(
+        self, *, skipna: bool = True, axis: AxisInt | None = 0, ddof: int = 1, **kwargs
+    ):
+        nv.validate_stat_ddof_func((), kwargs, fname="std")
+        result = masked_reductions.std(
+            self._data,
+            self._mask,
+            skipna=skipna,
+            axis=axis,
+            ddof=ddof,
+        )
+        return self._wrap_reduction_result("std", result, skipna=skipna, axis=axis)
+
+    def min(self, *, skipna: bool = True, axis: AxisInt | None = 0, **kwargs):
+        nv.validate_min((), kwargs)
+        result = masked_reductions.min(
+            self._data,
+            self._mask,
+            skipna=skipna,
+            axis=axis,
+        )
+        return self._wrap_reduction_result("min", result, skipna=skipna, axis=axis)
+
+    def max(self, *, skipna: bool = True, axis: AxisInt | None = 0, **kwargs):
+        nv.validate_max((), kwargs)
+        result = masked_reductions.max(
+            self._data,
+            self._mask,
+            skipna=skipna,
+            axis=axis,
+        )
+        return self._wrap_reduction_result("max", result, skipna=skipna, axis=axis)
+
+    def map(self, mapper, na_action=None):
+        return map_array(self.to_numpy(), mapper, na_action=na_action)
+
+    def any(self, *, skipna: bool = True, axis: AxisInt | None = 0, **kwargs):
+        """
+        Return whether any element is truthy.
+
+        Returns False unless there is at least one element that is truthy.
+        By default, NAs are skipped. If ``skipna=False`` is specified and
+        missing values are present, similar :ref:`Kleene logic `
+        is used as for logical operations.
+
+        .. versionchanged:: 1.4.0
+
+        Parameters
+        ----------
+        skipna : bool, default True
+            Exclude NA values. If the entire array is NA and `skipna` is
+            True, then the result will be False, as for an empty array.
+            If `skipna` is False, the result will still be True if there is
+            at least one element that is truthy, otherwise NA will be returned
+            if there are NA's present.
+        axis : int, optional, default 0
+        **kwargs : any, default None
+            Additional keywords have no effect but might be accepted for
+            compatibility with NumPy.
+
+        Returns
+        -------
+        bool or :attr:`pandas.NA`
+
+        See Also
+        --------
+        numpy.any : Numpy version of this method.
+        BaseMaskedArray.all : Return whether all elements are truthy.
+
+        Examples
+        --------
+        The result indicates whether any element is truthy (and by default
+        skips NAs):
+
+        >>> pd.array([True, False, True]).any()
+        True
+        >>> pd.array([True, False, pd.NA]).any()
+        True
+        >>> pd.array([False, False, pd.NA]).any()
+        False
+        >>> pd.array([], dtype="boolean").any()
+        False
+        >>> pd.array([pd.NA], dtype="boolean").any()
+        False
+        >>> pd.array([pd.NA], dtype="Float64").any()
+        False
+
+        With ``skipna=False``, the result can be NA if this is logically
+        required (whether ``pd.NA`` is True or False influences the result):
+
+        >>> pd.array([True, False, pd.NA]).any(skipna=False)
+        True
+        >>> pd.array([1, 0, pd.NA]).any(skipna=False)
+        True
+        >>> pd.array([False, False, pd.NA]).any(skipna=False)
+        
+        >>> pd.array([0, 0, pd.NA]).any(skipna=False)
+        
+        """
+        nv.validate_any((), kwargs)
+
+        values = self._data.copy()
+        # error: Argument 3 to "putmask" has incompatible type "object";
+        # expected "Union[_SupportsArray[dtype[Any]],
+        # _NestedSequence[_SupportsArray[dtype[Any]]],
+        # bool, int, float, complex, str, bytes,
+        # _NestedSequence[Union[bool, int, float, complex, str, bytes]]]"
+        np.putmask(values, self._mask, self._falsey_value)  # type: ignore[arg-type]
+        result = values.any()
+        if skipna:
+            return result
+        else:
+            if result or len(self) == 0 or not self._mask.any():
+                return result
+            else:
+                return self.dtype.na_value
+
+    def all(self, *, skipna: bool = True, axis: AxisInt | None = 0, **kwargs):
+        """
+        Return whether all elements are truthy.
+
+        Returns True unless there is at least one element that is falsey.
+        By default, NAs are skipped. If ``skipna=False`` is specified and
+        missing values are present, similar :ref:`Kleene logic `
+        is used as for logical operations.
+
+        .. versionchanged:: 1.4.0
+
+        Parameters
+        ----------
+        skipna : bool, default True
+            Exclude NA values. If the entire array is NA and `skipna` is
+            True, then the result will be True, as for an empty array.
+            If `skipna` is False, the result will still be False if there is
+            at least one element that is falsey, otherwise NA will be returned
+            if there are NA's present.
+        axis : int, optional, default 0
+        **kwargs : any, default None
+            Additional keywords have no effect but might be accepted for
+            compatibility with NumPy.
+
+        Returns
+        -------
+        bool or :attr:`pandas.NA`
+
+        See Also
+        --------
+        numpy.all : Numpy version of this method.
+        BooleanArray.any : Return whether any element is truthy.
+
+        Examples
+        --------
+        The result indicates whether all elements are truthy (and by default
+        skips NAs):
+
+        >>> pd.array([True, True, pd.NA]).all()
+        True
+        >>> pd.array([1, 1, pd.NA]).all()
+        True
+        >>> pd.array([True, False, pd.NA]).all()
+        False
+        >>> pd.array([], dtype="boolean").all()
+        True
+        >>> pd.array([pd.NA], dtype="boolean").all()
+        True
+        >>> pd.array([pd.NA], dtype="Float64").all()
+        True
+
+        With ``skipna=False``, the result can be NA if this is logically
+        required (whether ``pd.NA`` is True or False influences the result):
+
+        >>> pd.array([True, True, pd.NA]).all(skipna=False)
+        
+        >>> pd.array([1, 1, pd.NA]).all(skipna=False)
+        
+        >>> pd.array([True, False, pd.NA]).all(skipna=False)
+        False
+        >>> pd.array([1, 0, pd.NA]).all(skipna=False)
+        False
+        """
+        nv.validate_all((), kwargs)
+
+        values = self._data.copy()
+        # error: Argument 3 to "putmask" has incompatible type "object";
+        # expected "Union[_SupportsArray[dtype[Any]],
+        # _NestedSequence[_SupportsArray[dtype[Any]]],
+        # bool, int, float, complex, str, bytes,
+        # _NestedSequence[Union[bool, int, float, complex, str, bytes]]]"
+        np.putmask(values, self._mask, self._truthy_value)  # type: ignore[arg-type]
+        result = values.all(axis=axis)
+
+        if skipna:
+            return result
+        else:
+            if not result or len(self) == 0 or not self._mask.any():
+                return result
+            else:
+                return self.dtype.na_value
+
+    def interpolate(
+        self,
+        *,
+        method: InterpolateOptions,
+        axis: int,
+        index,
+        limit,
+        limit_direction,
+        limit_area,
+        copy: bool,
+        **kwargs,
+    ) -> FloatingArray:
+        """
+        See NDFrame.interpolate.__doc__.
+        """
+        # NB: we return type(self) even if copy=False
+        if self.dtype.kind == "f":
+            if copy:
+                data = self._data.copy()
+                mask = self._mask.copy()
+            else:
+                data = self._data
+                mask = self._mask
+        elif self.dtype.kind in "iu":
+            copy = True
+            data = self._data.astype("f8")
+            mask = self._mask.copy()
+        else:
+            raise NotImplementedError(
+                f"interpolate is not implemented for dtype={self.dtype}"
+            )
+
+        missing.interpolate_2d_inplace(
+            data,
+            method=method,
+            axis=0,
+            index=index,
+            limit=limit,
+            limit_direction=limit_direction,
+            limit_area=limit_area,
+            mask=mask,
+            **kwargs,
+        )
+        if not copy:
+            return self  # type: ignore[return-value]
+        if self.dtype.kind == "f":
+            return type(self)._simple_new(data, mask)  # type: ignore[return-value]
+        else:
+            from pandas.core.arrays import FloatingArray
+
+            return FloatingArray._simple_new(data, mask)
+
+    def _accumulate(
+        self, name: str, *, skipna: bool = True, **kwargs
+    ) -> BaseMaskedArray:
+        data = self._data
+        mask = self._mask
+
+        op = getattr(masked_accumulations, name)
+        data, mask = op(data, mask, skipna=skipna, **kwargs)
+
+        return self._simple_new(data, mask)
+
+    # ------------------------------------------------------------------
+    # GroupBy Methods
+
+    def _groupby_op(
+        self,
+        *,
+        how: str,
+        has_dropped_na: bool,
+        min_count: int,
+        ngroups: int,
+        ids: npt.NDArray[np.intp],
+        **kwargs,
+    ):
+        from pandas.core.groupby.ops import WrappedCythonOp
+
+        kind = WrappedCythonOp.get_kind_from_how(how)
+        op = WrappedCythonOp(how=how, kind=kind, has_dropped_na=has_dropped_na)
+
+        # libgroupby functions are responsible for NOT altering mask
+        mask = self._mask
+        if op.kind != "aggregate":
+            result_mask = mask.copy()
+        else:
+            result_mask = np.zeros(ngroups, dtype=bool)
+
+        if how == "rank" and kwargs.get("na_option") in ["top", "bottom"]:
+            result_mask[:] = False
+
+        res_values = op._cython_op_ndim_compat(
+            self._data,
+            min_count=min_count,
+            ngroups=ngroups,
+            comp_ids=ids,
+            mask=mask,
+            result_mask=result_mask,
+            **kwargs,
+        )
+
+        if op.how == "ohlc":
+            arity = op._cython_arity.get(op.how, 1)
+            result_mask = np.tile(result_mask, (arity, 1)).T
+
+        if op.how in ["idxmin", "idxmax"]:
+            # Result values are indexes to take, keep as ndarray
+            return res_values
+        else:
+            # res_values should already have the correct dtype, we just need to
+            #  wrap in a MaskedArray
+            return self._maybe_mask_result(res_values, result_mask)
+
+
+def transpose_homogeneous_masked_arrays(
+    masked_arrays: Sequence[BaseMaskedArray],
+) -> list[BaseMaskedArray]:
+    """Transpose masked arrays in a list, but faster.
+
+    Input should be a list of 1-dim masked arrays of equal length and all have the
+    same dtype. The caller is responsible for ensuring validity of input data.
+    """
+    masked_arrays = list(masked_arrays)
+    dtype = masked_arrays[0].dtype
+
+    values = [arr._data.reshape(1, -1) for arr in masked_arrays]
+    transposed_values = np.concatenate(
+        values,
+        axis=0,
+        out=np.empty(
+            (len(masked_arrays), len(masked_arrays[0])),
+            order="F",
+            dtype=dtype.numpy_dtype,
+        ),
+    )
+
+    masks = [arr._mask.reshape(1, -1) for arr in masked_arrays]
+    transposed_masks = np.concatenate(
+        masks, axis=0, out=np.empty_like(transposed_values, dtype=bool)
+    )
+
+    arr_type = dtype.construct_array_type()
+    transposed_arrays: list[BaseMaskedArray] = []
+    for i in range(transposed_values.shape[1]):
+        transposed_arr = arr_type(transposed_values[:, i], mask=transposed_masks[:, i])
+        transposed_arrays.append(transposed_arr)
+
+    return transposed_arrays
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/numeric.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/numeric.py
new file mode 100644
index 0000000000000000000000000000000000000000..68fa7fcb6573c6b5ec754ca65263f8ddd6a6ba74
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/numeric.py
@@ -0,0 +1,286 @@
+from __future__ import annotations
+
+import numbers
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+)
+
+import numpy as np
+
+from pandas._libs import (
+    lib,
+    missing as libmissing,
+)
+from pandas.errors import AbstractMethodError
+from pandas.util._decorators import cache_readonly
+
+from pandas.core.dtypes.common import (
+    is_integer_dtype,
+    is_string_dtype,
+    pandas_dtype,
+)
+
+from pandas.core.arrays.masked import (
+    BaseMaskedArray,
+    BaseMaskedDtype,
+)
+
+if TYPE_CHECKING:
+    from collections.abc import Mapping
+
+    import pyarrow
+
+    from pandas._typing import (
+        Dtype,
+        DtypeObj,
+        Self,
+        npt,
+    )
+
+
+class NumericDtype(BaseMaskedDtype):
+    _default_np_dtype: np.dtype
+    _checker: Callable[[Any], bool]  # is_foo_dtype
+
+    def __repr__(self) -> str:
+        return f"{self.name}Dtype()"
+
+    @cache_readonly
+    def is_signed_integer(self) -> bool:
+        return self.kind == "i"
+
+    @cache_readonly
+    def is_unsigned_integer(self) -> bool:
+        return self.kind == "u"
+
+    @property
+    def _is_numeric(self) -> bool:
+        return True
+
+    def __from_arrow__(
+        self, array: pyarrow.Array | pyarrow.ChunkedArray
+    ) -> BaseMaskedArray:
+        """
+        Construct IntegerArray/FloatingArray from pyarrow Array/ChunkedArray.
+        """
+        import pyarrow
+
+        from pandas.core.arrays.arrow._arrow_utils import (
+            pyarrow_array_to_numpy_and_mask,
+        )
+
+        array_class = self.construct_array_type()
+
+        pyarrow_type = pyarrow.from_numpy_dtype(self.type)
+        if not array.type.equals(pyarrow_type) and not pyarrow.types.is_null(
+            array.type
+        ):
+            # test_from_arrow_type_error raise for string, but allow
+            #  through itemsize conversion GH#31896
+            rt_dtype = pandas_dtype(array.type.to_pandas_dtype())
+            if rt_dtype.kind not in "iuf":
+                # Could allow "c" or potentially disallow float<->int conversion,
+                #  but at the moment we specifically test that uint<->int works
+                raise TypeError(
+                    f"Expected array of {self} type, got {array.type} instead"
+                )
+
+            array = array.cast(pyarrow_type)
+
+        if isinstance(array, pyarrow.ChunkedArray):
+            # TODO this "if" can be removed when requiring pyarrow >= 10.0, which fixed
+            # combine_chunks for empty arrays https://github.com/apache/arrow/pull/13757
+            if array.num_chunks == 0:
+                array = pyarrow.array([], type=array.type)
+            else:
+                array = array.combine_chunks()
+
+        data, mask = pyarrow_array_to_numpy_and_mask(array, dtype=self.numpy_dtype)
+        return array_class(data.copy(), ~mask, copy=False)
+
+    @classmethod
+    def _get_dtype_mapping(cls) -> Mapping[np.dtype, NumericDtype]:
+        raise AbstractMethodError(cls)
+
+    @classmethod
+    def _standardize_dtype(cls, dtype: NumericDtype | str | np.dtype) -> NumericDtype:
+        """
+        Convert a string representation or a numpy dtype to NumericDtype.
+        """
+        if isinstance(dtype, str) and (dtype.startswith(("Int", "UInt", "Float"))):
+            # Avoid DeprecationWarning from NumPy about np.dtype("Int64")
+            # https://github.com/numpy/numpy/pull/7476
+            dtype = dtype.lower()
+
+        if not isinstance(dtype, NumericDtype):
+            mapping = cls._get_dtype_mapping()
+            try:
+                dtype = mapping[np.dtype(dtype)]
+            except KeyError as err:
+                raise ValueError(f"invalid dtype specified {dtype}") from err
+        return dtype
+
+    @classmethod
+    def _safe_cast(cls, values: np.ndarray, dtype: np.dtype, copy: bool) -> np.ndarray:
+        """
+        Safely cast the values to the given dtype.
+
+        "safe" in this context means the casting is lossless.
+        """
+        raise AbstractMethodError(cls)
+
+
+def _coerce_to_data_and_mask(
+    values, dtype, copy: bool, dtype_cls: type[NumericDtype], default_dtype: np.dtype
+):
+    checker = dtype_cls._checker
+
+    mask = None
+    inferred_type = None
+
+    if dtype is None and hasattr(values, "dtype"):
+        if checker(values.dtype):
+            dtype = values.dtype
+
+    if dtype is not None:
+        dtype = dtype_cls._standardize_dtype(dtype)
+
+    cls = dtype_cls.construct_array_type()
+    if isinstance(values, cls):
+        values, mask = values._data, values._mask
+        if dtype is not None:
+            values = values.astype(dtype.numpy_dtype, copy=False)
+
+        if copy:
+            values = values.copy()
+            mask = mask.copy()
+        return values, mask, dtype, inferred_type
+
+    original = values
+    if not copy:
+        values = np.asarray(values)
+    else:
+        values = np.array(values, copy=copy)
+    inferred_type = None
+    if values.dtype == object or is_string_dtype(values.dtype):
+        inferred_type = lib.infer_dtype(values, skipna=True)
+        if inferred_type == "boolean" and dtype is None:
+            name = dtype_cls.__name__.strip("_")
+            raise TypeError(f"{values.dtype} cannot be converted to {name}")
+
+    elif values.dtype.kind == "b" and checker(dtype):
+        if not copy:
+            values = np.asarray(values, dtype=default_dtype)
+        else:
+            values = np.array(values, dtype=default_dtype, copy=copy)
+
+    elif values.dtype.kind not in "iuf":
+        name = dtype_cls.__name__.strip("_")
+        raise TypeError(f"{values.dtype} cannot be converted to {name}")
+
+    if values.ndim != 1:
+        raise TypeError("values must be a 1D list-like")
+
+    if mask is None:
+        if values.dtype.kind in "iu":
+            # fastpath
+            mask = np.zeros(len(values), dtype=np.bool_)
+        else:
+            mask = libmissing.is_numeric_na(values)
+    else:
+        assert len(mask) == len(values)
+
+    if mask.ndim != 1:
+        raise TypeError("mask must be a 1D list-like")
+
+    # infer dtype if needed
+    if dtype is None:
+        dtype = default_dtype
+    else:
+        dtype = dtype.numpy_dtype
+
+    if is_integer_dtype(dtype) and values.dtype.kind == "f" and len(values) > 0:
+        if mask.all():
+            values = np.ones(values.shape, dtype=dtype)
+        else:
+            idx = np.nanargmax(values)
+            if int(values[idx]) != original[idx]:
+                # We have ints that lost precision during the cast.
+                inferred_type = lib.infer_dtype(original, skipna=True)
+                if (
+                    inferred_type not in ["floating", "mixed-integer-float"]
+                    and not mask.any()
+                ):
+                    values = np.asarray(original, dtype=dtype)
+                else:
+                    values = np.asarray(original, dtype="object")
+
+    # we copy as need to coerce here
+    if mask.any():
+        values = values.copy()
+        values[mask] = cls._internal_fill_value
+    if inferred_type in ("string", "unicode"):
+        # casts from str are always safe since they raise
+        # a ValueError if the str cannot be parsed into a float
+        values = values.astype(dtype, copy=copy)
+    else:
+        values = dtype_cls._safe_cast(values, dtype, copy=False)
+
+    return values, mask, dtype, inferred_type
+
+
+class NumericArray(BaseMaskedArray):
+    """
+    Base class for IntegerArray and FloatingArray.
+    """
+
+    _dtype_cls: type[NumericDtype]
+
+    def __init__(
+        self, values: np.ndarray, mask: npt.NDArray[np.bool_], copy: bool = False
+    ) -> None:
+        checker = self._dtype_cls._checker
+        if not (isinstance(values, np.ndarray) and checker(values.dtype)):
+            descr = (
+                "floating"
+                if self._dtype_cls.kind == "f"  # type: ignore[comparison-overlap]
+                else "integer"
+            )
+            raise TypeError(
+                f"values should be {descr} numpy array. Use "
+                "the 'pd.array' function instead"
+            )
+        if values.dtype == np.float16:
+            # If we don't raise here, then accessing self.dtype would raise
+            raise TypeError("FloatingArray does not support np.float16 dtype.")
+
+        super().__init__(values, mask, copy=copy)
+
+    @cache_readonly
+    def dtype(self) -> NumericDtype:
+        mapping = self._dtype_cls._get_dtype_mapping()
+        return mapping[self._data.dtype]
+
+    @classmethod
+    def _coerce_to_array(
+        cls, value, *, dtype: DtypeObj, copy: bool = False
+    ) -> tuple[np.ndarray, np.ndarray]:
+        dtype_cls = cls._dtype_cls
+        default_dtype = dtype_cls._default_np_dtype
+        values, mask, _, _ = _coerce_to_data_and_mask(
+            value, dtype, copy, dtype_cls, default_dtype
+        )
+        return values, mask
+
+    @classmethod
+    def _from_sequence_of_strings(
+        cls, strings, *, dtype: Dtype | None = None, copy: bool = False
+    ) -> Self:
+        from pandas.core.tools.numeric import to_numeric
+
+        scalars = to_numeric(strings, errors="raise", dtype_backend="numpy_nullable")
+        return cls._from_sequence(scalars, dtype=dtype, copy=copy)
+
+    _HANDLED_TYPES = (np.ndarray, numbers.Number)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/numpy_.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/numpy_.py
new file mode 100644
index 0000000000000000000000000000000000000000..07fd207933fc15bc26d0b001fff434f45bf0c3f6
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/numpy_.py
@@ -0,0 +1,582 @@
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Literal,
+)
+
+import numpy as np
+
+from pandas._libs import lib
+from pandas._libs.tslibs import is_supported_dtype
+from pandas.compat.numpy import function as nv
+
+from pandas.core.dtypes.astype import astype_array
+from pandas.core.dtypes.cast import construct_1d_object_array_from_listlike
+from pandas.core.dtypes.common import pandas_dtype
+from pandas.core.dtypes.dtypes import NumpyEADtype
+from pandas.core.dtypes.missing import isna
+
+from pandas.core import (
+    arraylike,
+    missing,
+    nanops,
+    ops,
+)
+from pandas.core.arraylike import OpsMixin
+from pandas.core.arrays._mixins import NDArrayBackedExtensionArray
+from pandas.core.construction import ensure_wrapped_if_datetimelike
+from pandas.core.strings.object_array import ObjectStringArrayMixin
+
+if TYPE_CHECKING:
+    from collections.abc import Callable
+
+    from pandas._typing import (
+        AxisInt,
+        Dtype,
+        FillnaOptions,
+        InterpolateOptions,
+        NpDtype,
+        Scalar,
+        Self,
+        npt,
+    )
+
+    from pandas import Index
+
+
+# error: Definition of "_concat_same_type" in base class "NDArrayBacked" is
+# incompatible with definition in base class "ExtensionArray"
+class NumpyExtensionArray(  # type: ignore[misc]
+    OpsMixin,
+    NDArrayBackedExtensionArray,
+    ObjectStringArrayMixin,
+):
+    """
+    A pandas ExtensionArray for NumPy data.
+
+    This is mostly for internal compatibility, and is not especially
+    useful on its own.
+
+    Parameters
+    ----------
+    values : ndarray
+        The NumPy ndarray to wrap. Must be 1-dimensional.
+    copy : bool, default False
+        Whether to copy `values`.
+
+    Attributes
+    ----------
+    None
+
+    Methods
+    -------
+    None
+
+    Examples
+    --------
+    >>> pd.arrays.NumpyExtensionArray(np.array([0, 1, 2, 3]))
+    
+    [0, 1, 2, 3]
+    Length: 4, dtype: int64
+    """
+
+    # If you're wondering why pd.Series(cls) doesn't put the array in an
+    # ExtensionBlock, search for `ABCNumpyExtensionArray`. We check for
+    # that _typ to ensure that users don't unnecessarily use EAs inside
+    # pandas internals, which turns off things like block consolidation.
+    _typ = "npy_extension"
+    __array_priority__ = 1000
+    _ndarray: np.ndarray
+    _dtype: NumpyEADtype
+    _internal_fill_value = np.nan
+
+    # ------------------------------------------------------------------------
+    # Constructors
+
+    def __init__(
+        self, values: np.ndarray | NumpyExtensionArray, copy: bool = False
+    ) -> None:
+        if isinstance(values, type(self)):
+            values = values._ndarray
+        if not isinstance(values, np.ndarray):
+            raise ValueError(
+                f"'values' must be a NumPy array, not {type(values).__name__}"
+            )
+
+        if values.ndim == 0:
+            # Technically we support 2, but do not advertise that fact.
+            raise ValueError("NumpyExtensionArray must be 1-dimensional.")
+
+        if copy:
+            values = values.copy()
+
+        dtype = NumpyEADtype(values.dtype)
+        super().__init__(values, dtype)
+
+    @classmethod
+    def _from_sequence(
+        cls, scalars, *, dtype: Dtype | None = None, copy: bool = False
+    ) -> NumpyExtensionArray:
+        if isinstance(dtype, NumpyEADtype):
+            dtype = dtype._dtype
+
+        # error: Argument "dtype" to "asarray" has incompatible type
+        # "Union[ExtensionDtype, str, dtype[Any], dtype[floating[_64Bit]], Type[object],
+        # None]"; expected "Union[dtype[Any], None, type, _SupportsDType, str,
+        # Union[Tuple[Any, int], Tuple[Any, Union[int, Sequence[int]]], List[Any],
+        # _DTypeDict, Tuple[Any, Any]]]"
+        result = np.asarray(scalars, dtype=dtype)  # type: ignore[arg-type]
+        if (
+            result.ndim > 1
+            and not hasattr(scalars, "dtype")
+            and (dtype is None or dtype == object)
+        ):
+            # e.g. list-of-tuples
+            result = construct_1d_object_array_from_listlike(scalars)
+
+        if copy and result is scalars:
+            result = result.copy()
+        return cls(result)
+
+    # ------------------------------------------------------------------------
+    # Data
+
+    @property
+    def dtype(self) -> NumpyEADtype:
+        return self._dtype
+
+    # ------------------------------------------------------------------------
+    # NumPy Array Interface
+
+    def __array__(
+        self, dtype: NpDtype | None = None, copy: bool | None = None
+    ) -> np.ndarray:
+        if copy is not None:
+            # Note: branch avoids `copy=None` for NumPy 1.x support
+            return np.array(self._ndarray, dtype=dtype, copy=copy)
+        return np.asarray(self._ndarray, dtype=dtype)
+
+    def __array_ufunc__(self, ufunc: np.ufunc, method: str, *inputs, **kwargs):
+        # Lightly modified version of
+        # https://numpy.org/doc/stable/reference/generated/numpy.lib.mixins.NDArrayOperatorsMixin.html
+        # The primary modification is not boxing scalar return values
+        # in NumpyExtensionArray, since pandas' ExtensionArrays are 1-d.
+        out = kwargs.get("out", ())
+
+        result = arraylike.maybe_dispatch_ufunc_to_dunder_op(
+            self, ufunc, method, *inputs, **kwargs
+        )
+        if result is not NotImplemented:
+            return result
+
+        if "out" in kwargs:
+            # e.g. test_ufunc_unary
+            return arraylike.dispatch_ufunc_with_out(
+                self, ufunc, method, *inputs, **kwargs
+            )
+
+        if method == "reduce":
+            result = arraylike.dispatch_reduction_ufunc(
+                self, ufunc, method, *inputs, **kwargs
+            )
+            if result is not NotImplemented:
+                # e.g. tests.series.test_ufunc.TestNumpyReductions
+                return result
+
+        # Defer to the implementation of the ufunc on unwrapped values.
+        inputs = tuple(
+            x._ndarray if isinstance(x, NumpyExtensionArray) else x for x in inputs
+        )
+        if out:
+            kwargs["out"] = tuple(
+                x._ndarray if isinstance(x, NumpyExtensionArray) else x for x in out
+            )
+        result = getattr(ufunc, method)(*inputs, **kwargs)
+
+        if ufunc.nout > 1:
+            # multiple return values; re-box array-like results
+            return tuple(type(self)(x) for x in result)
+        elif method == "at":
+            # no return value
+            return None
+        elif method == "reduce":
+            if isinstance(result, np.ndarray):
+                # e.g. test_np_reduce_2d
+                return type(self)(result)
+
+            # e.g. test_np_max_nested_tuples
+            return result
+        else:
+            if self.dtype.type is str:  # type: ignore[comparison-overlap]
+                # StringDtype
+                try:
+                    return type(self)(result)
+                except ValueError:
+                    # if validation of input fails (no strings)
+                    # -> fallback to returning raw numpy array
+                    return result
+            # one return value; re-box array-like results
+            return type(self)(result)
+
+    # ------------------------------------------------------------------------
+    # Pandas ExtensionArray Interface
+
+    def astype(self, dtype, copy: bool = True):
+        dtype = pandas_dtype(dtype)
+
+        if dtype == self.dtype:
+            if copy:
+                return self.copy()
+            return self
+
+        result = astype_array(self._ndarray, dtype=dtype, copy=copy)
+        return result
+
+    def isna(self) -> np.ndarray:
+        return isna(self._ndarray)
+
+    def _validate_scalar(self, fill_value):
+        if fill_value is None:
+            # Primarily for subclasses
+            fill_value = self.dtype.na_value
+        return fill_value
+
+    def _values_for_factorize(self) -> tuple[np.ndarray, float | None]:
+        if self.dtype.kind in "iub":
+            fv = None
+        else:
+            fv = np.nan
+        return self._ndarray, fv
+
+    # Base EA class (and all other EA classes) don't have limit_area keyword
+    # This can be removed here as well when the interpolate ffill/bfill method
+    # deprecation is enforced
+    def _pad_or_backfill(
+        self,
+        *,
+        method: FillnaOptions,
+        limit: int | None = None,
+        limit_area: Literal["inside", "outside"] | None = None,
+        copy: bool = True,
+    ) -> Self:
+        """
+        ffill or bfill along axis=0.
+        """
+        if copy:
+            out_data = self._ndarray.copy()
+        else:
+            out_data = self._ndarray
+
+        meth = missing.clean_fill_method(method)
+        missing.pad_or_backfill_inplace(
+            out_data.T,
+            method=meth,
+            axis=0,
+            limit=limit,
+            limit_area=limit_area,
+        )
+
+        if not copy:
+            return self
+        return type(self)._simple_new(out_data, dtype=self.dtype)
+
+    def interpolate(
+        self,
+        *,
+        method: InterpolateOptions,
+        axis: int,
+        index: Index,
+        limit,
+        limit_direction,
+        limit_area,
+        copy: bool,
+        **kwargs,
+    ) -> Self:
+        """
+        See NDFrame.interpolate.__doc__.
+        """
+        # NB: we return type(self) even if copy=False
+        if not self.dtype._is_numeric:
+            raise TypeError(f"Cannot interpolate with {self.dtype} dtype")
+
+        if not copy:
+            out_data = self._ndarray
+        else:
+            out_data = self._ndarray.copy()
+
+        # TODO: assert we have floating dtype?
+        missing.interpolate_2d_inplace(
+            out_data,
+            method=method,
+            axis=axis,
+            index=index,
+            limit=limit,
+            limit_direction=limit_direction,
+            limit_area=limit_area,
+            **kwargs,
+        )
+        if not copy:
+            return self
+        return type(self)._simple_new(out_data, dtype=self.dtype)
+
+    # ------------------------------------------------------------------------
+    # Reductions
+
+    def any(
+        self,
+        *,
+        axis: AxisInt | None = None,
+        out=None,
+        keepdims: bool = False,
+        skipna: bool = True,
+    ):
+        nv.validate_any((), {"out": out, "keepdims": keepdims})
+        result = nanops.nanany(self._ndarray, axis=axis, skipna=skipna)
+        return self._wrap_reduction_result(axis, result)
+
+    def all(
+        self,
+        *,
+        axis: AxisInt | None = None,
+        out=None,
+        keepdims: bool = False,
+        skipna: bool = True,
+    ):
+        nv.validate_all((), {"out": out, "keepdims": keepdims})
+        result = nanops.nanall(self._ndarray, axis=axis, skipna=skipna)
+        return self._wrap_reduction_result(axis, result)
+
+    def min(
+        self, *, axis: AxisInt | None = None, skipna: bool = True, **kwargs
+    ) -> Scalar:
+        nv.validate_min((), kwargs)
+        result = nanops.nanmin(
+            values=self._ndarray, axis=axis, mask=self.isna(), skipna=skipna
+        )
+        return self._wrap_reduction_result(axis, result)
+
+    def max(
+        self, *, axis: AxisInt | None = None, skipna: bool = True, **kwargs
+    ) -> Scalar:
+        nv.validate_max((), kwargs)
+        result = nanops.nanmax(
+            values=self._ndarray, axis=axis, mask=self.isna(), skipna=skipna
+        )
+        return self._wrap_reduction_result(axis, result)
+
+    def sum(
+        self,
+        *,
+        axis: AxisInt | None = None,
+        skipna: bool = True,
+        min_count: int = 0,
+        **kwargs,
+    ) -> Scalar:
+        nv.validate_sum((), kwargs)
+        result = nanops.nansum(
+            self._ndarray, axis=axis, skipna=skipna, min_count=min_count
+        )
+        return self._wrap_reduction_result(axis, result)
+
+    def prod(
+        self,
+        *,
+        axis: AxisInt | None = None,
+        skipna: bool = True,
+        min_count: int = 0,
+        **kwargs,
+    ) -> Scalar:
+        nv.validate_prod((), kwargs)
+        result = nanops.nanprod(
+            self._ndarray, axis=axis, skipna=skipna, min_count=min_count
+        )
+        return self._wrap_reduction_result(axis, result)
+
+    def mean(
+        self,
+        *,
+        axis: AxisInt | None = None,
+        dtype: NpDtype | None = None,
+        out=None,
+        keepdims: bool = False,
+        skipna: bool = True,
+    ):
+        nv.validate_mean((), {"dtype": dtype, "out": out, "keepdims": keepdims})
+        result = nanops.nanmean(self._ndarray, axis=axis, skipna=skipna)
+        return self._wrap_reduction_result(axis, result)
+
+    def median(
+        self,
+        *,
+        axis: AxisInt | None = None,
+        out=None,
+        overwrite_input: bool = False,
+        keepdims: bool = False,
+        skipna: bool = True,
+    ):
+        nv.validate_median(
+            (), {"out": out, "overwrite_input": overwrite_input, "keepdims": keepdims}
+        )
+        result = nanops.nanmedian(self._ndarray, axis=axis, skipna=skipna)
+        return self._wrap_reduction_result(axis, result)
+
+    def std(
+        self,
+        *,
+        axis: AxisInt | None = None,
+        dtype: NpDtype | None = None,
+        out=None,
+        ddof: int = 1,
+        keepdims: bool = False,
+        skipna: bool = True,
+    ):
+        nv.validate_stat_ddof_func(
+            (), {"dtype": dtype, "out": out, "keepdims": keepdims}, fname="std"
+        )
+        result = nanops.nanstd(self._ndarray, axis=axis, skipna=skipna, ddof=ddof)
+        return self._wrap_reduction_result(axis, result)
+
+    def var(
+        self,
+        *,
+        axis: AxisInt | None = None,
+        dtype: NpDtype | None = None,
+        out=None,
+        ddof: int = 1,
+        keepdims: bool = False,
+        skipna: bool = True,
+    ):
+        nv.validate_stat_ddof_func(
+            (), {"dtype": dtype, "out": out, "keepdims": keepdims}, fname="var"
+        )
+        result = nanops.nanvar(self._ndarray, axis=axis, skipna=skipna, ddof=ddof)
+        return self._wrap_reduction_result(axis, result)
+
+    def sem(
+        self,
+        *,
+        axis: AxisInt | None = None,
+        dtype: NpDtype | None = None,
+        out=None,
+        ddof: int = 1,
+        keepdims: bool = False,
+        skipna: bool = True,
+    ):
+        nv.validate_stat_ddof_func(
+            (), {"dtype": dtype, "out": out, "keepdims": keepdims}, fname="sem"
+        )
+        result = nanops.nansem(self._ndarray, axis=axis, skipna=skipna, ddof=ddof)
+        return self._wrap_reduction_result(axis, result)
+
+    def kurt(
+        self,
+        *,
+        axis: AxisInt | None = None,
+        dtype: NpDtype | None = None,
+        out=None,
+        keepdims: bool = False,
+        skipna: bool = True,
+    ):
+        nv.validate_stat_ddof_func(
+            (), {"dtype": dtype, "out": out, "keepdims": keepdims}, fname="kurt"
+        )
+        result = nanops.nankurt(self._ndarray, axis=axis, skipna=skipna)
+        return self._wrap_reduction_result(axis, result)
+
+    def skew(
+        self,
+        *,
+        axis: AxisInt | None = None,
+        dtype: NpDtype | None = None,
+        out=None,
+        keepdims: bool = False,
+        skipna: bool = True,
+    ):
+        nv.validate_stat_ddof_func(
+            (), {"dtype": dtype, "out": out, "keepdims": keepdims}, fname="skew"
+        )
+        result = nanops.nanskew(self._ndarray, axis=axis, skipna=skipna)
+        return self._wrap_reduction_result(axis, result)
+
+    # ------------------------------------------------------------------------
+    # Additional Methods
+
+    def to_numpy(
+        self,
+        dtype: npt.DTypeLike | None = None,
+        copy: bool = False,
+        na_value: object = lib.no_default,
+    ) -> np.ndarray:
+        mask = self.isna()
+        if na_value is not lib.no_default and mask.any():
+            result = self._ndarray.copy()
+            result[mask] = na_value
+        else:
+            result = self._ndarray
+
+        result = np.asarray(result, dtype=dtype)
+
+        if copy and result is self._ndarray:
+            result = result.copy()
+
+        return result
+
+    # ------------------------------------------------------------------------
+    # Ops
+
+    def __invert__(self) -> NumpyExtensionArray:
+        return type(self)(~self._ndarray)
+
+    def __neg__(self) -> NumpyExtensionArray:
+        return type(self)(-self._ndarray)
+
+    def __pos__(self) -> NumpyExtensionArray:
+        return type(self)(+self._ndarray)
+
+    def __abs__(self) -> NumpyExtensionArray:
+        return type(self)(abs(self._ndarray))
+
+    def _cmp_method(self, other, op):
+        if isinstance(other, NumpyExtensionArray):
+            other = other._ndarray
+
+        other = ops.maybe_prepare_scalar_for_op(other, (len(self),))
+        pd_op = ops.get_array_op(op)
+        other = ensure_wrapped_if_datetimelike(other)
+        result = pd_op(self._ndarray, other)
+
+        if op is divmod or op is ops.rdivmod:
+            a, b = result
+            if isinstance(a, np.ndarray):
+                # for e.g. op vs TimedeltaArray, we may already
+                #  have an ExtensionArray, in which case we do not wrap
+                return self._wrap_ndarray_result(a), self._wrap_ndarray_result(b)
+            return a, b
+
+        if isinstance(result, np.ndarray):
+            # for e.g. multiplication vs TimedeltaArray, we may already
+            #  have an ExtensionArray, in which case we do not wrap
+            return self._wrap_ndarray_result(result)
+        return result
+
+    _arith_method = _cmp_method
+
+    def _wrap_ndarray_result(self, result: np.ndarray):
+        # If we have timedelta64[ns] result, return a TimedeltaArray instead
+        #  of a NumpyExtensionArray
+        if result.dtype.kind == "m" and is_supported_dtype(result.dtype):
+            from pandas.core.arrays import TimedeltaArray
+
+            return TimedeltaArray._simple_new(result, dtype=result.dtype)
+        return type(self)(result)
+
+    def _formatter(self, boxed: bool = False) -> Callable[[Any], str | None]:
+        # NEP 51: https://github.com/numpy/numpy/pull/22449
+        if self.dtype.kind in "SU":
+            return "'{}'".format
+        elif self.dtype == "object":
+            return repr
+        else:
+            return str
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/period.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/period.py
new file mode 100644
index 0000000000000000000000000000000000000000..2947ba7b8c72ac09497f796aaaec8edfb133a948
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/period.py
@@ -0,0 +1,1331 @@
+from __future__ import annotations
+
+from datetime import timedelta
+import operator
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Literal,
+    TypeVar,
+    cast,
+    overload,
+)
+import warnings
+
+import numpy as np
+
+from pandas._libs import (
+    algos as libalgos,
+    lib,
+)
+from pandas._libs.arrays import NDArrayBacked
+from pandas._libs.tslibs import (
+    BaseOffset,
+    NaT,
+    NaTType,
+    Timedelta,
+    add_overflowsafe,
+    astype_overflowsafe,
+    dt64arr_to_periodarr as c_dt64arr_to_periodarr,
+    get_unit_from_dtype,
+    iNaT,
+    parsing,
+    period as libperiod,
+    to_offset,
+)
+from pandas._libs.tslibs.dtypes import (
+    FreqGroup,
+    PeriodDtypeBase,
+    freq_to_period_freqstr,
+)
+from pandas._libs.tslibs.fields import isleapyear_arr
+from pandas._libs.tslibs.offsets import (
+    Tick,
+    delta_to_tick,
+)
+from pandas._libs.tslibs.period import (
+    DIFFERENT_FREQ,
+    IncompatibleFrequency,
+    Period,
+    get_period_field_arr,
+    period_asfreq_arr,
+)
+from pandas.util._decorators import (
+    cache_readonly,
+    doc,
+)
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.common import (
+    ensure_object,
+    pandas_dtype,
+)
+from pandas.core.dtypes.dtypes import (
+    DatetimeTZDtype,
+    PeriodDtype,
+)
+from pandas.core.dtypes.generic import (
+    ABCIndex,
+    ABCPeriodIndex,
+    ABCSeries,
+    ABCTimedeltaArray,
+)
+from pandas.core.dtypes.missing import isna
+
+from pandas.core.arrays import datetimelike as dtl
+import pandas.core.common as com
+
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+
+    from pandas._typing import (
+        AnyArrayLike,
+        Dtype,
+        FillnaOptions,
+        NpDtype,
+        NumpySorter,
+        NumpyValueArrayLike,
+        Self,
+        npt,
+    )
+
+    from pandas.core.arrays import (
+        DatetimeArray,
+        TimedeltaArray,
+    )
+    from pandas.core.arrays.base import ExtensionArray
+
+
+BaseOffsetT = TypeVar("BaseOffsetT", bound=BaseOffset)
+
+
+_shared_doc_kwargs = {
+    "klass": "PeriodArray",
+}
+
+
+def _field_accessor(name: str, docstring: str | None = None):
+    def f(self):
+        base = self.dtype._dtype_code
+        result = get_period_field_arr(name, self.asi8, base)
+        return result
+
+    f.__name__ = name
+    f.__doc__ = docstring
+    return property(f)
+
+
+# error: Definition of "_concat_same_type" in base class "NDArrayBacked" is
+# incompatible with definition in base class "ExtensionArray"
+class PeriodArray(dtl.DatelikeOps, libperiod.PeriodMixin):  # type: ignore[misc]
+    """
+    Pandas ExtensionArray for storing Period data.
+
+    Users should use :func:`~pandas.array` to create new instances.
+
+    Parameters
+    ----------
+    values : Union[PeriodArray, Series[period], ndarray[int], PeriodIndex]
+        The data to store. These should be arrays that can be directly
+        converted to ordinals without inference or copy (PeriodArray,
+        ndarray[int64]), or a box around such an array (Series[period],
+        PeriodIndex).
+    dtype : PeriodDtype, optional
+        A PeriodDtype instance from which to extract a `freq`. If both
+        `freq` and `dtype` are specified, then the frequencies must match.
+    freq : str or DateOffset
+        The `freq` to use for the array. Mostly applicable when `values`
+        is an ndarray of integers, when `freq` is required. When `values`
+        is a PeriodArray (or box around), it's checked that ``values.freq``
+        matches `freq`.
+    copy : bool, default False
+        Whether to copy the ordinals before storing.
+
+    Attributes
+    ----------
+    None
+
+    Methods
+    -------
+    None
+
+    See Also
+    --------
+    Period: Represents a period of time.
+    PeriodIndex : Immutable Index for period data.
+    period_range: Create a fixed-frequency PeriodArray.
+    array: Construct a pandas array.
+
+    Notes
+    -----
+    There are two components to a PeriodArray
+
+    - ordinals : integer ndarray
+    - freq : pd.tseries.offsets.Offset
+
+    The values are physically stored as a 1-D ndarray of integers. These are
+    called "ordinals" and represent some kind of offset from a base.
+
+    The `freq` indicates the span covered by each element of the array.
+    All elements in the PeriodArray have the same `freq`.
+
+    Examples
+    --------
+    >>> pd.arrays.PeriodArray(pd.PeriodIndex(['2023-01-01',
+    ...                                       '2023-01-02'], freq='D'))
+    
+    ['2023-01-01', '2023-01-02']
+    Length: 2, dtype: period[D]
+    """
+
+    # array priority higher than numpy scalars
+    __array_priority__ = 1000
+    _typ = "periodarray"  # ABCPeriodArray
+    _internal_fill_value = np.int64(iNaT)
+    _recognized_scalars = (Period,)
+    _is_recognized_dtype = lambda x: isinstance(
+        x, PeriodDtype
+    )  # check_compatible_with checks freq match
+    _infer_matches = ("period",)
+
+    @property
+    def _scalar_type(self) -> type[Period]:
+        return Period
+
+    # Names others delegate to us
+    _other_ops: list[str] = []
+    _bool_ops: list[str] = ["is_leap_year"]
+    _object_ops: list[str] = ["start_time", "end_time", "freq"]
+    _field_ops: list[str] = [
+        "year",
+        "month",
+        "day",
+        "hour",
+        "minute",
+        "second",
+        "weekofyear",
+        "weekday",
+        "week",
+        "dayofweek",
+        "day_of_week",
+        "dayofyear",
+        "day_of_year",
+        "quarter",
+        "qyear",
+        "days_in_month",
+        "daysinmonth",
+    ]
+    _datetimelike_ops: list[str] = _field_ops + _object_ops + _bool_ops
+    _datetimelike_methods: list[str] = ["strftime", "to_timestamp", "asfreq"]
+
+    _dtype: PeriodDtype
+
+    # --------------------------------------------------------------------
+    # Constructors
+
+    def __init__(
+        self, values, dtype: Dtype | None = None, freq=None, copy: bool = False
+    ) -> None:
+        if freq is not None:
+            # GH#52462
+            warnings.warn(
+                "The 'freq' keyword in the PeriodArray constructor is deprecated "
+                "and will be removed in a future version. Pass 'dtype' instead",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+            freq = validate_dtype_freq(dtype, freq)
+            dtype = PeriodDtype(freq)
+
+        if dtype is not None:
+            dtype = pandas_dtype(dtype)
+            if not isinstance(dtype, PeriodDtype):
+                raise ValueError(f"Invalid dtype {dtype} for PeriodArray")
+
+        if isinstance(values, ABCSeries):
+            values = values._values
+            if not isinstance(values, type(self)):
+                raise TypeError("Incorrect dtype")
+
+        elif isinstance(values, ABCPeriodIndex):
+            values = values._values
+
+        if isinstance(values, type(self)):
+            if dtype is not None and dtype != values.dtype:
+                raise raise_on_incompatible(values, dtype.freq)
+            values, dtype = values._ndarray, values.dtype
+
+        if not copy:
+            values = np.asarray(values, dtype="int64")
+        else:
+            values = np.array(values, dtype="int64", copy=copy)
+        if dtype is None:
+            raise ValueError("dtype is not specified and cannot be inferred")
+        dtype = cast(PeriodDtype, dtype)
+        NDArrayBacked.__init__(self, values, dtype)
+
+    # error: Signature of "_simple_new" incompatible with supertype "NDArrayBacked"
+    @classmethod
+    def _simple_new(  # type: ignore[override]
+        cls,
+        values: npt.NDArray[np.int64],
+        dtype: PeriodDtype,
+    ) -> Self:
+        # alias for PeriodArray.__init__
+        assertion_msg = "Should be numpy array of type i8"
+        assert isinstance(values, np.ndarray) and values.dtype == "i8", assertion_msg
+        return cls(values, dtype=dtype)
+
+    @classmethod
+    def _from_sequence(
+        cls,
+        scalars,
+        *,
+        dtype: Dtype | None = None,
+        copy: bool = False,
+    ) -> Self:
+        if dtype is not None:
+            dtype = pandas_dtype(dtype)
+        if dtype and isinstance(dtype, PeriodDtype):
+            freq = dtype.freq
+        else:
+            freq = None
+
+        if isinstance(scalars, cls):
+            validate_dtype_freq(scalars.dtype, freq)
+            if copy:
+                scalars = scalars.copy()
+            return scalars
+
+        periods = np.asarray(scalars, dtype=object)
+
+        freq = freq or libperiod.extract_freq(periods)
+        ordinals = libperiod.extract_ordinals(periods, freq)
+        dtype = PeriodDtype(freq)
+        return cls(ordinals, dtype=dtype)
+
+    @classmethod
+    def _from_sequence_of_strings(
+        cls, strings, *, dtype: Dtype | None = None, copy: bool = False
+    ) -> Self:
+        return cls._from_sequence(strings, dtype=dtype, copy=copy)
+
+    @classmethod
+    def _from_datetime64(cls, data, freq, tz=None) -> Self:
+        """
+        Construct a PeriodArray from a datetime64 array
+
+        Parameters
+        ----------
+        data : ndarray[datetime64[ns], datetime64[ns, tz]]
+        freq : str or Tick
+        tz : tzinfo, optional
+
+        Returns
+        -------
+        PeriodArray[freq]
+        """
+        if isinstance(freq, BaseOffset):
+            freq = freq_to_period_freqstr(freq.n, freq.name)
+        data, freq = dt64arr_to_periodarr(data, freq, tz)
+        dtype = PeriodDtype(freq)
+        return cls(data, dtype=dtype)
+
+    @classmethod
+    def _generate_range(cls, start, end, periods, freq):
+        periods = dtl.validate_periods(periods)
+
+        if freq is not None:
+            freq = Period._maybe_convert_freq(freq)
+
+        if start is not None or end is not None:
+            subarr, freq = _get_ordinal_range(start, end, periods, freq)
+        else:
+            raise ValueError("Not enough parameters to construct Period range")
+
+        return subarr, freq
+
+    @classmethod
+    def _from_fields(cls, *, fields: dict, freq) -> Self:
+        subarr, freq = _range_from_fields(freq=freq, **fields)
+        dtype = PeriodDtype(freq)
+        return cls._simple_new(subarr, dtype=dtype)
+
+    # -----------------------------------------------------------------
+    # DatetimeLike Interface
+
+    # error: Argument 1 of "_unbox_scalar" is incompatible with supertype
+    # "DatetimeLikeArrayMixin"; supertype defines the argument type as
+    # "Union[Union[Period, Any, Timedelta], NaTType]"
+    def _unbox_scalar(  # type: ignore[override]
+        self,
+        value: Period | NaTType,
+    ) -> np.int64:
+        if value is NaT:
+            # error: Item "Period" of "Union[Period, NaTType]" has no attribute "value"
+            return np.int64(value._value)  # type: ignore[union-attr]
+        elif isinstance(value, self._scalar_type):
+            self._check_compatible_with(value)
+            return np.int64(value.ordinal)
+        else:
+            raise ValueError(f"'value' should be a Period. Got '{value}' instead.")
+
+    def _scalar_from_string(self, value: str) -> Period:
+        return Period(value, freq=self.freq)
+
+    # error: Argument 1 of "_check_compatible_with" is incompatible with
+    # supertype "DatetimeLikeArrayMixin"; supertype defines the argument type
+    # as "Period | Timestamp | Timedelta | NaTType"
+    def _check_compatible_with(self, other: Period | NaTType | PeriodArray) -> None:  # type: ignore[override]
+        if other is NaT:
+            return
+        # error: Item "NaTType" of "Period | NaTType | PeriodArray" has no
+        # attribute "freq"
+        self._require_matching_freq(other.freq)  # type: ignore[union-attr]
+
+    # --------------------------------------------------------------------
+    # Data / Attributes
+
+    @cache_readonly
+    def dtype(self) -> PeriodDtype:
+        return self._dtype
+
+    # error: Cannot override writeable attribute with read-only property
+    @property  # type: ignore[override]
+    def freq(self) -> BaseOffset:
+        """
+        Return the frequency object for this PeriodArray.
+        """
+        return self.dtype.freq
+
+    @property
+    def freqstr(self) -> str:
+        return freq_to_period_freqstr(self.freq.n, self.freq.name)
+
+    def __array__(
+        self, dtype: NpDtype | None = None, copy: bool | None = None
+    ) -> np.ndarray:
+        if dtype == "i8":
+            # For NumPy 1.x compatibility we cannot use copy=None.  And
+            # `copy=False` has the meaning of `copy=None` here:
+            if not copy:
+                return np.asarray(self.asi8, dtype=dtype)
+            else:
+                return np.array(self.asi8, dtype=dtype)
+
+        if copy is False:
+            warnings.warn(
+                "Starting with NumPy 2.0, the behavior of the 'copy' keyword has "
+                "changed and passing 'copy=False' raises an error when returning "
+                "a zero-copy NumPy array is not possible. pandas will follow "
+                "this behavior starting with pandas 3.0.\nThis conversion to "
+                "NumPy requires a copy, but 'copy=False' was passed. Consider "
+                "using 'np.asarray(..)' instead.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+
+        if dtype == bool:
+            return ~self._isnan
+
+        # This will raise TypeError for non-object dtypes
+        return np.array(list(self), dtype=object)
+
+    def __arrow_array__(self, type=None):
+        """
+        Convert myself into a pyarrow Array.
+        """
+        import pyarrow
+
+        from pandas.core.arrays.arrow.extension_types import ArrowPeriodType
+
+        if type is not None:
+            if pyarrow.types.is_integer(type):
+                return pyarrow.array(self._ndarray, mask=self.isna(), type=type)
+            elif isinstance(type, ArrowPeriodType):
+                # ensure we have the same freq
+                if self.freqstr != type.freq:
+                    raise TypeError(
+                        "Not supported to convert PeriodArray to array with different "
+                        f"'freq' ({self.freqstr} vs {type.freq})"
+                    )
+            else:
+                raise TypeError(
+                    f"Not supported to convert PeriodArray to '{type}' type"
+                )
+
+        period_type = ArrowPeriodType(self.freqstr)
+        storage_array = pyarrow.array(self._ndarray, mask=self.isna(), type="int64")
+        return pyarrow.ExtensionArray.from_storage(period_type, storage_array)
+
+    # --------------------------------------------------------------------
+    # Vectorized analogues of Period properties
+
+    year = _field_accessor(
+        "year",
+        """
+        The year of the period.
+
+        Examples
+        --------
+        >>> idx = pd.PeriodIndex(["2023", "2024", "2025"], freq="Y")
+        >>> idx.year
+        Index([2023, 2024, 2025], dtype='int64')
+        """,
+    )
+    month = _field_accessor(
+        "month",
+        """
+        The month as January=1, December=12.
+
+        Examples
+        --------
+        >>> idx = pd.PeriodIndex(["2023-01", "2023-02", "2023-03"], freq="M")
+        >>> idx.month
+        Index([1, 2, 3], dtype='int64')
+        """,
+    )
+    day = _field_accessor(
+        "day",
+        """
+        The days of the period.
+
+        Examples
+        --------
+        >>> idx = pd.PeriodIndex(['2020-01-31', '2020-02-28'], freq='D')
+        >>> idx.day
+        Index([31, 28], dtype='int64')
+        """,
+    )
+    hour = _field_accessor(
+        "hour",
+        """
+        The hour of the period.
+
+        Examples
+        --------
+        >>> idx = pd.PeriodIndex(["2023-01-01 10:00", "2023-01-01 11:00"], freq='h')
+        >>> idx.hour
+        Index([10, 11], dtype='int64')
+        """,
+    )
+    minute = _field_accessor(
+        "minute",
+        """
+        The minute of the period.
+
+        Examples
+        --------
+        >>> idx = pd.PeriodIndex(["2023-01-01 10:30:00",
+        ...                       "2023-01-01 11:50:00"], freq='min')
+        >>> idx.minute
+        Index([30, 50], dtype='int64')
+        """,
+    )
+    second = _field_accessor(
+        "second",
+        """
+        The second of the period.
+
+        Examples
+        --------
+        >>> idx = pd.PeriodIndex(["2023-01-01 10:00:30",
+        ...                       "2023-01-01 10:00:31"], freq='s')
+        >>> idx.second
+        Index([30, 31], dtype='int64')
+        """,
+    )
+    weekofyear = _field_accessor(
+        "week",
+        """
+        The week ordinal of the year.
+
+        Examples
+        --------
+        >>> idx = pd.PeriodIndex(["2023-01", "2023-02", "2023-03"], freq="M")
+        >>> idx.week  # It can be written `weekofyear`
+        Index([5, 9, 13], dtype='int64')
+        """,
+    )
+    week = weekofyear
+    day_of_week = _field_accessor(
+        "day_of_week",
+        """
+        The day of the week with Monday=0, Sunday=6.
+
+        Examples
+        --------
+        >>> idx = pd.PeriodIndex(["2023-01-01", "2023-01-02", "2023-01-03"], freq="D")
+        >>> idx.weekday
+        Index([6, 0, 1], dtype='int64')
+        """,
+    )
+    dayofweek = day_of_week
+    weekday = dayofweek
+    dayofyear = day_of_year = _field_accessor(
+        "day_of_year",
+        """
+        The ordinal day of the year.
+
+        Examples
+        --------
+        >>> idx = pd.PeriodIndex(["2023-01-10", "2023-02-01", "2023-03-01"], freq="D")
+        >>> idx.dayofyear
+        Index([10, 32, 60], dtype='int64')
+
+        >>> idx = pd.PeriodIndex(["2023", "2024", "2025"], freq="Y")
+        >>> idx
+        PeriodIndex(['2023', '2024', '2025'], dtype='period[Y-DEC]')
+        >>> idx.dayofyear
+        Index([365, 366, 365], dtype='int64')
+        """,
+    )
+    quarter = _field_accessor(
+        "quarter",
+        """
+        The quarter of the date.
+
+        Examples
+        --------
+        >>> idx = pd.PeriodIndex(["2023-01", "2023-02", "2023-03"], freq="M")
+        >>> idx.quarter
+        Index([1, 1, 1], dtype='int64')
+        """,
+    )
+    qyear = _field_accessor("qyear")
+    days_in_month = _field_accessor(
+        "days_in_month",
+        """
+        The number of days in the month.
+
+        Examples
+        --------
+        For Series:
+
+        >>> period = pd.period_range('2020-1-1 00:00', '2020-3-1 00:00', freq='M')
+        >>> s = pd.Series(period)
+        >>> s
+        0   2020-01
+        1   2020-02
+        2   2020-03
+        dtype: period[M]
+        >>> s.dt.days_in_month
+        0    31
+        1    29
+        2    31
+        dtype: int64
+
+        For PeriodIndex:
+
+        >>> idx = pd.PeriodIndex(["2023-01", "2023-02", "2023-03"], freq="M")
+        >>> idx.days_in_month   # It can be also entered as `daysinmonth`
+        Index([31, 28, 31], dtype='int64')
+        """,
+    )
+    daysinmonth = days_in_month
+
+    @property
+    def is_leap_year(self) -> npt.NDArray[np.bool_]:
+        """
+        Logical indicating if the date belongs to a leap year.
+
+        Examples
+        --------
+        >>> idx = pd.PeriodIndex(["2023", "2024", "2025"], freq="Y")
+        >>> idx.is_leap_year
+        array([False,  True, False])
+        """
+        return isleapyear_arr(np.asarray(self.year))
+
+    def to_timestamp(self, freq=None, how: str = "start") -> DatetimeArray:
+        """
+        Cast to DatetimeArray/Index.
+
+        Parameters
+        ----------
+        freq : str or DateOffset, optional
+            Target frequency. The default is 'D' for week or longer,
+            's' otherwise.
+        how : {'s', 'e', 'start', 'end'}
+            Whether to use the start or end of the time period being converted.
+
+        Returns
+        -------
+        DatetimeArray/Index
+
+        Examples
+        --------
+        >>> idx = pd.PeriodIndex(["2023-01", "2023-02", "2023-03"], freq="M")
+        >>> idx.to_timestamp()
+        DatetimeIndex(['2023-01-01', '2023-02-01', '2023-03-01'],
+        dtype='datetime64[ns]', freq='MS')
+        """
+        from pandas.core.arrays import DatetimeArray
+
+        how = libperiod.validate_end_alias(how)
+
+        end = how == "E"
+        if end:
+            if freq == "B" or self.freq == "B":
+                # roll forward to ensure we land on B date
+                adjust = Timedelta(1, "D") - Timedelta(1, "ns")
+                return self.to_timestamp(how="start") + adjust
+            else:
+                adjust = Timedelta(1, "ns")
+                return (self + self.freq).to_timestamp(how="start") - adjust
+
+        if freq is None:
+            freq_code = self._dtype._get_to_timestamp_base()
+            dtype = PeriodDtypeBase(freq_code, 1)
+            freq = dtype._freqstr
+            base = freq_code
+        else:
+            freq = Period._maybe_convert_freq(freq)
+            base = freq._period_dtype_code
+
+        new_parr = self.asfreq(freq, how=how)
+
+        new_data = libperiod.periodarr_to_dt64arr(new_parr.asi8, base)
+        dta = DatetimeArray._from_sequence(new_data)
+
+        if self.freq.name == "B":
+            # See if we can retain BDay instead of Day in cases where
+            #  len(self) is too small for infer_freq to distinguish between them
+            diffs = libalgos.unique_deltas(self.asi8)
+            if len(diffs) == 1:
+                diff = diffs[0]
+                if diff == self.dtype._n:
+                    dta._freq = self.freq
+                elif diff == 1:
+                    dta._freq = self.freq.base
+                # TODO: other cases?
+            return dta
+        else:
+            return dta._with_freq("infer")
+
+    # --------------------------------------------------------------------
+
+    def _box_func(self, x) -> Period | NaTType:
+        return Period._from_ordinal(ordinal=x, freq=self.freq)
+
+    @doc(**_shared_doc_kwargs, other="PeriodIndex", other_name="PeriodIndex")
+    def asfreq(self, freq=None, how: str = "E") -> Self:
+        """
+        Convert the {klass} to the specified frequency `freq`.
+
+        Equivalent to applying :meth:`pandas.Period.asfreq` with the given arguments
+        to each :class:`~pandas.Period` in this {klass}.
+
+        Parameters
+        ----------
+        freq : str
+            A frequency.
+        how : str {{'E', 'S'}}, default 'E'
+            Whether the elements should be aligned to the end
+            or start within pa period.
+
+            * 'E', 'END', or 'FINISH' for end,
+            * 'S', 'START', or 'BEGIN' for start.
+
+            January 31st ('END') vs. January 1st ('START') for example.
+
+        Returns
+        -------
+        {klass}
+            The transformed {klass} with the new frequency.
+
+        See Also
+        --------
+        {other}.asfreq: Convert each Period in a {other_name} to the given frequency.
+        Period.asfreq : Convert a :class:`~pandas.Period` object to the given frequency.
+
+        Examples
+        --------
+        >>> pidx = pd.period_range('2010-01-01', '2015-01-01', freq='Y')
+        >>> pidx
+        PeriodIndex(['2010', '2011', '2012', '2013', '2014', '2015'],
+        dtype='period[Y-DEC]')
+
+        >>> pidx.asfreq('M')
+        PeriodIndex(['2010-12', '2011-12', '2012-12', '2013-12', '2014-12',
+        '2015-12'], dtype='period[M]')
+
+        >>> pidx.asfreq('M', how='S')
+        PeriodIndex(['2010-01', '2011-01', '2012-01', '2013-01', '2014-01',
+        '2015-01'], dtype='period[M]')
+        """
+        how = libperiod.validate_end_alias(how)
+        if isinstance(freq, BaseOffset) and hasattr(freq, "_period_dtype_code"):
+            freq = PeriodDtype(freq)._freqstr
+        freq = Period._maybe_convert_freq(freq)
+
+        base1 = self._dtype._dtype_code
+        base2 = freq._period_dtype_code
+
+        asi8 = self.asi8
+        # self.freq.n can't be negative or 0
+        end = how == "E"
+        if end:
+            ordinal = asi8 + self.dtype._n - 1
+        else:
+            ordinal = asi8
+
+        new_data = period_asfreq_arr(ordinal, base1, base2, end)
+
+        if self._hasna:
+            new_data[self._isnan] = iNaT
+
+        dtype = PeriodDtype(freq)
+        return type(self)(new_data, dtype=dtype)
+
+    # ------------------------------------------------------------------
+    # Rendering Methods
+
+    def _formatter(self, boxed: bool = False):
+        if boxed:
+            return str
+        return "'{}'".format
+
+    def _format_native_types(
+        self, *, na_rep: str | float = "NaT", date_format=None, **kwargs
+    ) -> npt.NDArray[np.object_]:
+        """
+        actually format my specific types
+        """
+        return libperiod.period_array_strftime(
+            self.asi8, self.dtype._dtype_code, na_rep, date_format
+        )
+
+    # ------------------------------------------------------------------
+
+    def astype(self, dtype, copy: bool = True):
+        # We handle Period[T] -> Period[U]
+        # Our parent handles everything else.
+        dtype = pandas_dtype(dtype)
+        if dtype == self._dtype:
+            if not copy:
+                return self
+            else:
+                return self.copy()
+        if isinstance(dtype, PeriodDtype):
+            return self.asfreq(dtype.freq)
+
+        if lib.is_np_dtype(dtype, "M") or isinstance(dtype, DatetimeTZDtype):
+            # GH#45038 match PeriodIndex behavior.
+            tz = getattr(dtype, "tz", None)
+            unit = dtl.dtype_to_unit(dtype)
+            return self.to_timestamp().tz_localize(tz).as_unit(unit)
+
+        return super().astype(dtype, copy=copy)
+
+    def searchsorted(
+        self,
+        value: NumpyValueArrayLike | ExtensionArray,
+        side: Literal["left", "right"] = "left",
+        sorter: NumpySorter | None = None,
+    ) -> npt.NDArray[np.intp] | np.intp:
+        npvalue = self._validate_setitem_value(value).view("M8[ns]")
+
+        # Cast to M8 to get datetime-like NaT placement,
+        #  similar to dtl._period_dispatch
+        m8arr = self._ndarray.view("M8[ns]")
+        return m8arr.searchsorted(npvalue, side=side, sorter=sorter)
+
+    def _pad_or_backfill(
+        self,
+        *,
+        method: FillnaOptions,
+        limit: int | None = None,
+        limit_area: Literal["inside", "outside"] | None = None,
+        copy: bool = True,
+    ) -> Self:
+        # view as dt64 so we get treated as timelike in core.missing,
+        #  similar to dtl._period_dispatch
+        dta = self.view("M8[ns]")
+        result = dta._pad_or_backfill(
+            method=method, limit=limit, limit_area=limit_area, copy=copy
+        )
+        if copy:
+            return cast("Self", result.view(self.dtype))
+        else:
+            return self
+
+    def fillna(
+        self, value=None, method=None, limit: int | None = None, copy: bool = True
+    ) -> Self:
+        if method is not None:
+            # view as dt64 so we get treated as timelike in core.missing,
+            #  similar to dtl._period_dispatch
+            dta = self.view("M8[ns]")
+            result = dta.fillna(value=value, method=method, limit=limit, copy=copy)
+            # error: Incompatible return value type (got "Union[ExtensionArray,
+            # ndarray[Any, Any]]", expected "PeriodArray")
+            return result.view(self.dtype)  # type: ignore[return-value]
+        return super().fillna(value=value, method=method, limit=limit, copy=copy)
+
+    # ------------------------------------------------------------------
+    # Arithmetic Methods
+
+    def _addsub_int_array_or_scalar(
+        self, other: np.ndarray | int, op: Callable[[Any, Any], Any]
+    ) -> Self:
+        """
+        Add or subtract array of integers.
+
+        Parameters
+        ----------
+        other : np.ndarray[int64] or int
+        op : {operator.add, operator.sub}
+
+        Returns
+        -------
+        result : PeriodArray
+        """
+        assert op in [operator.add, operator.sub]
+        if op is operator.sub:
+            other = -other
+        res_values = add_overflowsafe(self.asi8, np.asarray(other, dtype="i8"))
+        return type(self)(res_values, dtype=self.dtype)
+
+    def _add_offset(self, other: BaseOffset):
+        assert not isinstance(other, Tick)
+
+        self._require_matching_freq(other, base=True)
+        return self._addsub_int_array_or_scalar(other.n, operator.add)
+
+    # TODO: can we de-duplicate with Period._add_timedeltalike_scalar?
+    def _add_timedeltalike_scalar(self, other):
+        """
+        Parameters
+        ----------
+        other : timedelta, Tick, np.timedelta64
+
+        Returns
+        -------
+        PeriodArray
+        """
+        if not isinstance(self.freq, Tick):
+            # We cannot add timedelta-like to non-tick PeriodArray
+            raise raise_on_incompatible(self, other)
+
+        if isna(other):
+            # i.e. np.timedelta64("NaT")
+            return super()._add_timedeltalike_scalar(other)
+
+        td = np.asarray(Timedelta(other).asm8)
+        return self._add_timedelta_arraylike(td)
+
+    def _add_timedelta_arraylike(
+        self, other: TimedeltaArray | npt.NDArray[np.timedelta64]
+    ) -> Self:
+        """
+        Parameters
+        ----------
+        other : TimedeltaArray or ndarray[timedelta64]
+
+        Returns
+        -------
+        PeriodArray
+        """
+        if not self.dtype._is_tick_like():
+            # We cannot add timedelta-like to non-tick PeriodArray
+            raise TypeError(
+                f"Cannot add or subtract timedelta64[ns] dtype from {self.dtype}"
+            )
+
+        dtype = np.dtype(f"m8[{self.dtype._td64_unit}]")
+
+        # Similar to _check_timedeltalike_freq_compat, but we raise with a
+        #  more specific exception message if necessary.
+        try:
+            delta = astype_overflowsafe(
+                np.asarray(other), dtype=dtype, copy=False, round_ok=False
+            )
+        except ValueError as err:
+            # e.g. if we have minutes freq and try to add 30s
+            # "Cannot losslessly convert units"
+            raise IncompatibleFrequency(
+                "Cannot add/subtract timedelta-like from PeriodArray that is "
+                "not an integer multiple of the PeriodArray's freq."
+            ) from err
+
+        res_values = add_overflowsafe(self.asi8, np.asarray(delta.view("i8")))
+        return type(self)(res_values, dtype=self.dtype)
+
+    def _check_timedeltalike_freq_compat(self, other):
+        """
+        Arithmetic operations with timedelta-like scalars or array `other`
+        are only valid if `other` is an integer multiple of `self.freq`.
+        If the operation is valid, find that integer multiple.  Otherwise,
+        raise because the operation is invalid.
+
+        Parameters
+        ----------
+        other : timedelta, np.timedelta64, Tick,
+                ndarray[timedelta64], TimedeltaArray, TimedeltaIndex
+
+        Returns
+        -------
+        multiple : int or ndarray[int64]
+
+        Raises
+        ------
+        IncompatibleFrequency
+        """
+        assert self.dtype._is_tick_like()  # checked by calling function
+
+        dtype = np.dtype(f"m8[{self.dtype._td64_unit}]")
+
+        if isinstance(other, (timedelta, np.timedelta64, Tick)):
+            td = np.asarray(Timedelta(other).asm8)
+        else:
+            td = np.asarray(other)
+
+        try:
+            delta = astype_overflowsafe(td, dtype=dtype, copy=False, round_ok=False)
+        except ValueError as err:
+            raise raise_on_incompatible(self, other) from err
+
+        delta = delta.view("i8")
+        return lib.item_from_zerodim(delta)
+
+
+def raise_on_incompatible(left, right) -> IncompatibleFrequency:
+    """
+    Helper function to render a consistent error message when raising
+    IncompatibleFrequency.
+
+    Parameters
+    ----------
+    left : PeriodArray
+    right : None, DateOffset, Period, ndarray, or timedelta-like
+
+    Returns
+    -------
+    IncompatibleFrequency
+        Exception to be raised by the caller.
+    """
+    # GH#24283 error message format depends on whether right is scalar
+    if isinstance(right, (np.ndarray, ABCTimedeltaArray)) or right is None:
+        other_freq = None
+    elif isinstance(right, BaseOffset):
+        other_freq = freq_to_period_freqstr(right.n, right.name)
+    elif isinstance(right, (ABCPeriodIndex, PeriodArray, Period)):
+        other_freq = right.freqstr
+    else:
+        other_freq = delta_to_tick(Timedelta(right)).freqstr
+
+    own_freq = freq_to_period_freqstr(left.freq.n, left.freq.name)
+    msg = DIFFERENT_FREQ.format(
+        cls=type(left).__name__, own_freq=own_freq, other_freq=other_freq
+    )
+    return IncompatibleFrequency(msg)
+
+
+# -------------------------------------------------------------------
+# Constructor Helpers
+
+
+def period_array(
+    data: Sequence[Period | str | None] | AnyArrayLike,
+    freq: str | Tick | BaseOffset | None = None,
+    copy: bool = False,
+) -> PeriodArray:
+    """
+    Construct a new PeriodArray from a sequence of Period scalars.
+
+    Parameters
+    ----------
+    data : Sequence of Period objects
+        A sequence of Period objects. These are required to all have
+        the same ``freq.`` Missing values can be indicated by ``None``
+        or ``pandas.NaT``.
+    freq : str, Tick, or Offset
+        The frequency of every element of the array. This can be specified
+        to avoid inferring the `freq` from `data`.
+    copy : bool, default False
+        Whether to ensure a copy of the data is made.
+
+    Returns
+    -------
+    PeriodArray
+
+    See Also
+    --------
+    PeriodArray
+    pandas.PeriodIndex
+
+    Examples
+    --------
+    >>> period_array([pd.Period('2017', freq='Y'),
+    ...               pd.Period('2018', freq='Y')])
+    
+    ['2017', '2018']
+    Length: 2, dtype: period[Y-DEC]
+
+    >>> period_array([pd.Period('2017', freq='Y'),
+    ...               pd.Period('2018', freq='Y'),
+    ...               pd.NaT])
+    
+    ['2017', '2018', 'NaT']
+    Length: 3, dtype: period[Y-DEC]
+
+    Integers that look like years are handled
+
+    >>> period_array([2000, 2001, 2002], freq='D')
+    
+    ['2000-01-01', '2001-01-01', '2002-01-01']
+    Length: 3, dtype: period[D]
+
+    Datetime-like strings may also be passed
+
+    >>> period_array(['2000-Q1', '2000-Q2', '2000-Q3', '2000-Q4'], freq='Q')
+    
+    ['2000Q1', '2000Q2', '2000Q3', '2000Q4']
+    Length: 4, dtype: period[Q-DEC]
+    """
+    data_dtype = getattr(data, "dtype", None)
+
+    if lib.is_np_dtype(data_dtype, "M"):
+        return PeriodArray._from_datetime64(data, freq)
+    if isinstance(data_dtype, PeriodDtype):
+        out = PeriodArray(data)
+        if freq is not None:
+            if freq == data_dtype.freq:
+                return out
+            return out.asfreq(freq)
+        return out
+
+    # other iterable of some kind
+    if not isinstance(data, (np.ndarray, list, tuple, ABCSeries)):
+        data = list(data)
+
+    arrdata = np.asarray(data)
+
+    dtype: PeriodDtype | None
+    if freq:
+        dtype = PeriodDtype(freq)
+    else:
+        dtype = None
+
+    if arrdata.dtype.kind == "f" and len(arrdata) > 0:
+        raise TypeError("PeriodIndex does not allow floating point in construction")
+
+    if arrdata.dtype.kind in "iu":
+        arr = arrdata.astype(np.int64, copy=False)
+        # error: Argument 2 to "from_ordinals" has incompatible type "Union[str,
+        # Tick, None]"; expected "Union[timedelta, BaseOffset, str]"
+        ordinals = libperiod.from_ordinals(arr, freq)  # type: ignore[arg-type]
+        return PeriodArray(ordinals, dtype=dtype)
+
+    data = ensure_object(arrdata)
+    if freq is None:
+        freq = libperiod.extract_freq(data)
+    dtype = PeriodDtype(freq)
+    return PeriodArray._from_sequence(data, dtype=dtype)
+
+
+@overload
+def validate_dtype_freq(dtype, freq: BaseOffsetT) -> BaseOffsetT:
+    ...
+
+
+@overload
+def validate_dtype_freq(dtype, freq: timedelta | str | None) -> BaseOffset:
+    ...
+
+
+def validate_dtype_freq(
+    dtype, freq: BaseOffsetT | BaseOffset | timedelta | str | None
+) -> BaseOffsetT:
+    """
+    If both a dtype and a freq are available, ensure they match.  If only
+    dtype is available, extract the implied freq.
+
+    Parameters
+    ----------
+    dtype : dtype
+    freq : DateOffset or None
+
+    Returns
+    -------
+    freq : DateOffset
+
+    Raises
+    ------
+    ValueError : non-period dtype
+    IncompatibleFrequency : mismatch between dtype and freq
+    """
+    if freq is not None:
+        freq = to_offset(freq, is_period=True)
+
+    if dtype is not None:
+        dtype = pandas_dtype(dtype)
+        if not isinstance(dtype, PeriodDtype):
+            raise ValueError("dtype must be PeriodDtype")
+        if freq is None:
+            freq = dtype.freq
+        elif freq != dtype.freq:
+            raise IncompatibleFrequency("specified freq and dtype are different")
+    # error: Incompatible return value type (got "Union[BaseOffset, Any, None]",
+    # expected "BaseOffset")
+    return freq  # type: ignore[return-value]
+
+
+def dt64arr_to_periodarr(
+    data, freq, tz=None
+) -> tuple[npt.NDArray[np.int64], BaseOffset]:
+    """
+    Convert an datetime-like array to values Period ordinals.
+
+    Parameters
+    ----------
+    data : Union[Series[datetime64[ns]], DatetimeIndex, ndarray[datetime64ns]]
+    freq : Optional[Union[str, Tick]]
+        Must match the `freq` on the `data` if `data` is a DatetimeIndex
+        or Series.
+    tz : Optional[tzinfo]
+
+    Returns
+    -------
+    ordinals : ndarray[int64]
+    freq : Tick
+        The frequency extracted from the Series or DatetimeIndex if that's
+        used.
+
+    """
+    if not isinstance(data.dtype, np.dtype) or data.dtype.kind != "M":
+        raise ValueError(f"Wrong dtype: {data.dtype}")
+
+    if freq is None:
+        if isinstance(data, ABCIndex):
+            data, freq = data._values, data.freq
+        elif isinstance(data, ABCSeries):
+            data, freq = data._values, data.dt.freq
+
+    elif isinstance(data, (ABCIndex, ABCSeries)):
+        data = data._values
+
+    reso = get_unit_from_dtype(data.dtype)
+    freq = Period._maybe_convert_freq(freq)
+    base = freq._period_dtype_code
+    return c_dt64arr_to_periodarr(data.view("i8"), base, tz, reso=reso), freq
+
+
+def _get_ordinal_range(start, end, periods, freq, mult: int = 1):
+    if com.count_not_none(start, end, periods) != 2:
+        raise ValueError(
+            "Of the three parameters: start, end, and periods, "
+            "exactly two must be specified"
+        )
+
+    if freq is not None:
+        freq = to_offset(freq, is_period=True)
+        mult = freq.n
+
+    if start is not None:
+        start = Period(start, freq)
+    if end is not None:
+        end = Period(end, freq)
+
+    is_start_per = isinstance(start, Period)
+    is_end_per = isinstance(end, Period)
+
+    if is_start_per and is_end_per and start.freq != end.freq:
+        raise ValueError("start and end must have same freq")
+    if start is NaT or end is NaT:
+        raise ValueError("start and end must not be NaT")
+
+    if freq is None:
+        if is_start_per:
+            freq = start.freq
+        elif is_end_per:
+            freq = end.freq
+        else:  # pragma: no cover
+            raise ValueError("Could not infer freq from start/end")
+        mult = freq.n
+
+    if periods is not None:
+        periods = periods * mult
+        if start is None:
+            data = np.arange(
+                end.ordinal - periods + mult, end.ordinal + 1, mult, dtype=np.int64
+            )
+        else:
+            data = np.arange(
+                start.ordinal, start.ordinal + periods, mult, dtype=np.int64
+            )
+    else:
+        data = np.arange(start.ordinal, end.ordinal + 1, mult, dtype=np.int64)
+
+    return data, freq
+
+
+def _range_from_fields(
+    year=None,
+    month=None,
+    quarter=None,
+    day=None,
+    hour=None,
+    minute=None,
+    second=None,
+    freq=None,
+) -> tuple[np.ndarray, BaseOffset]:
+    if hour is None:
+        hour = 0
+    if minute is None:
+        minute = 0
+    if second is None:
+        second = 0
+    if day is None:
+        day = 1
+
+    ordinals = []
+
+    if quarter is not None:
+        if freq is None:
+            freq = to_offset("Q", is_period=True)
+            base = FreqGroup.FR_QTR.value
+        else:
+            freq = to_offset(freq, is_period=True)
+            base = libperiod.freq_to_dtype_code(freq)
+            if base != FreqGroup.FR_QTR.value:
+                raise AssertionError("base must equal FR_QTR")
+
+        freqstr = freq.freqstr
+        year, quarter = _make_field_arrays(year, quarter)
+        for y, q in zip(year, quarter):
+            calendar_year, calendar_month = parsing.quarter_to_myear(y, q, freqstr)
+            val = libperiod.period_ordinal(
+                calendar_year, calendar_month, 1, 1, 1, 1, 0, 0, base
+            )
+            ordinals.append(val)
+    else:
+        freq = to_offset(freq, is_period=True)
+        base = libperiod.freq_to_dtype_code(freq)
+        arrays = _make_field_arrays(year, month, day, hour, minute, second)
+        for y, mth, d, h, mn, s in zip(*arrays):
+            ordinals.append(libperiod.period_ordinal(y, mth, d, h, mn, s, 0, 0, base))
+
+    return np.array(ordinals, dtype=np.int64), freq
+
+
+def _make_field_arrays(*fields) -> list[np.ndarray]:
+    length = None
+    for x in fields:
+        if isinstance(x, (list, np.ndarray, ABCSeries)):
+            if length is not None and len(x) != length:
+                raise ValueError("Mismatched Period array lengths")
+            if length is None:
+                length = len(x)
+
+    # error: Argument 2 to "repeat" has incompatible type "Optional[int]"; expected
+    # "Union[Union[int, integer[Any]], Union[bool, bool_], ndarray, Sequence[Union[int,
+    # integer[Any]]], Sequence[Union[bool, bool_]], Sequence[Sequence[Any]]]"
+    return [
+        np.asarray(x)
+        if isinstance(x, (np.ndarray, list, ABCSeries))
+        else np.repeat(x, length)  # type: ignore[arg-type]
+        for x in fields
+    ]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/sparse/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/sparse/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..adf83963aca39e7d2ec2da55d21fc69aaca48977
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/sparse/__init__.py
@@ -0,0 +1,19 @@
+from pandas.core.arrays.sparse.accessor import (
+    SparseAccessor,
+    SparseFrameAccessor,
+)
+from pandas.core.arrays.sparse.array import (
+    BlockIndex,
+    IntIndex,
+    SparseArray,
+    make_sparse_index,
+)
+
+__all__ = [
+    "BlockIndex",
+    "IntIndex",
+    "make_sparse_index",
+    "SparseAccessor",
+    "SparseArray",
+    "SparseFrameAccessor",
+]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/sparse/accessor.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/sparse/accessor.py
new file mode 100644
index 0000000000000000000000000000000000000000..67bb41786547501c0cd7187e5ca6be8393b85ced
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/sparse/accessor.py
@@ -0,0 +1,414 @@
+"""Sparse accessor"""
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import numpy as np
+
+from pandas.compat._optional import import_optional_dependency
+
+from pandas.core.dtypes.cast import find_common_type
+from pandas.core.dtypes.dtypes import SparseDtype
+
+from pandas.core.accessor import (
+    PandasDelegate,
+    delegate_names,
+)
+from pandas.core.arrays.sparse.array import SparseArray
+
+if TYPE_CHECKING:
+    from pandas import (
+        DataFrame,
+        Series,
+    )
+
+
+class BaseAccessor:
+    _validation_msg = "Can only use the '.sparse' accessor with Sparse data."
+
+    def __init__(self, data=None) -> None:
+        self._parent = data
+        self._validate(data)
+
+    def _validate(self, data):
+        raise NotImplementedError
+
+
+@delegate_names(
+    SparseArray, ["npoints", "density", "fill_value", "sp_values"], typ="property"
+)
+class SparseAccessor(BaseAccessor, PandasDelegate):
+    """
+    Accessor for SparseSparse from other sparse matrix data types.
+
+    Examples
+    --------
+    >>> ser = pd.Series([0, 0, 2, 2, 2], dtype="Sparse[int]")
+    >>> ser.sparse.density
+    0.6
+    >>> ser.sparse.sp_values
+    array([2, 2, 2])
+    """
+
+    def _validate(self, data):
+        if not isinstance(data.dtype, SparseDtype):
+            raise AttributeError(self._validation_msg)
+
+    def _delegate_property_get(self, name: str, *args, **kwargs):
+        return getattr(self._parent.array, name)
+
+    def _delegate_method(self, name: str, *args, **kwargs):
+        if name == "from_coo":
+            return self.from_coo(*args, **kwargs)
+        elif name == "to_coo":
+            return self.to_coo(*args, **kwargs)
+        else:
+            raise ValueError
+
+    @classmethod
+    def from_coo(cls, A, dense_index: bool = False) -> Series:
+        """
+        Create a Series with sparse values from a scipy.sparse.coo_matrix.
+
+        Parameters
+        ----------
+        A : scipy.sparse.coo_matrix
+        dense_index : bool, default False
+            If False (default), the index consists of only the
+            coords of the non-null entries of the original coo_matrix.
+            If True, the index consists of the full sorted
+            (row, col) coordinates of the coo_matrix.
+
+        Returns
+        -------
+        s : Series
+            A Series with sparse values.
+
+        Examples
+        --------
+        >>> from scipy import sparse
+
+        >>> A = sparse.coo_matrix(
+        ...     ([3.0, 1.0, 2.0], ([1, 0, 0], [0, 2, 3])), shape=(3, 4)
+        ... )
+        >>> A
+        
+
+        >>> A.todense()
+        matrix([[0., 0., 1., 2.],
+        [3., 0., 0., 0.],
+        [0., 0., 0., 0.]])
+
+        >>> ss = pd.Series.sparse.from_coo(A)
+        >>> ss
+        0  2    1.0
+           3    2.0
+        1  0    3.0
+        dtype: Sparse[float64, nan]
+        """
+        from pandas import Series
+        from pandas.core.arrays.sparse.scipy_sparse import coo_to_sparse_series
+
+        result = coo_to_sparse_series(A, dense_index=dense_index)
+        result = Series(result.array, index=result.index, copy=False)
+
+        return result
+
+    def to_coo(self, row_levels=(0,), column_levels=(1,), sort_labels: bool = False):
+        """
+        Create a scipy.sparse.coo_matrix from a Series with MultiIndex.
+
+        Use row_levels and column_levels to determine the row and column
+        coordinates respectively. row_levels and column_levels are the names
+        (labels) or numbers of the levels. {row_levels, column_levels} must be
+        a partition of the MultiIndex level names (or numbers).
+
+        Parameters
+        ----------
+        row_levels : tuple/list
+        column_levels : tuple/list
+        sort_labels : bool, default False
+            Sort the row and column labels before forming the sparse matrix.
+            When `row_levels` and/or `column_levels` refer to a single level,
+            set to `True` for a faster execution.
+
+        Returns
+        -------
+        y : scipy.sparse.coo_matrix
+        rows : list (row labels)
+        columns : list (column labels)
+
+        Examples
+        --------
+        >>> s = pd.Series([3.0, np.nan, 1.0, 3.0, np.nan, np.nan])
+        >>> s.index = pd.MultiIndex.from_tuples(
+        ...     [
+        ...         (1, 2, "a", 0),
+        ...         (1, 2, "a", 1),
+        ...         (1, 1, "b", 0),
+        ...         (1, 1, "b", 1),
+        ...         (2, 1, "b", 0),
+        ...         (2, 1, "b", 1)
+        ...     ],
+        ...     names=["A", "B", "C", "D"],
+        ... )
+        >>> s
+        A  B  C  D
+        1  2  a  0    3.0
+                 1    NaN
+           1  b  0    1.0
+                 1    3.0
+        2  1  b  0    NaN
+                 1    NaN
+        dtype: float64
+
+        >>> ss = s.astype("Sparse")
+        >>> ss
+        A  B  C  D
+        1  2  a  0    3.0
+                 1    NaN
+           1  b  0    1.0
+                 1    3.0
+        2  1  b  0    NaN
+                 1    NaN
+        dtype: Sparse[float64, nan]
+
+        >>> A, rows, columns = ss.sparse.to_coo(
+        ...     row_levels=["A", "B"], column_levels=["C", "D"], sort_labels=True
+        ... )
+        >>> A
+        
+        >>> A.todense()
+        matrix([[0., 0., 1., 3.],
+        [3., 0., 0., 0.],
+        [0., 0., 0., 0.]])
+
+        >>> rows
+        [(1, 1), (1, 2), (2, 1)]
+        >>> columns
+        [('a', 0), ('a', 1), ('b', 0), ('b', 1)]
+        """
+        from pandas.core.arrays.sparse.scipy_sparse import sparse_series_to_coo
+
+        A, rows, columns = sparse_series_to_coo(
+            self._parent, row_levels, column_levels, sort_labels=sort_labels
+        )
+        return A, rows, columns
+
+    def to_dense(self) -> Series:
+        """
+        Convert a Series from sparse values to dense.
+
+        Returns
+        -------
+        Series:
+            A Series with the same values, stored as a dense array.
+
+        Examples
+        --------
+        >>> series = pd.Series(pd.arrays.SparseArray([0, 1, 0]))
+        >>> series
+        0    0
+        1    1
+        2    0
+        dtype: Sparse[int64, 0]
+
+        >>> series.sparse.to_dense()
+        0    0
+        1    1
+        2    0
+        dtype: int64
+        """
+        from pandas import Series
+
+        return Series(
+            self._parent.array.to_dense(),
+            index=self._parent.index,
+            name=self._parent.name,
+            copy=False,
+        )
+
+
+class SparseFrameAccessor(BaseAccessor, PandasDelegate):
+    """
+    DataFrame accessor for sparse data.
+
+    Examples
+    --------
+    >>> df = pd.DataFrame({"a": [1, 2, 0, 0],
+    ...                   "b": [3, 0, 0, 4]}, dtype="Sparse[int]")
+    >>> df.sparse.density
+    0.5
+    """
+
+    def _validate(self, data):
+        dtypes = data.dtypes
+        if not all(isinstance(t, SparseDtype) for t in dtypes):
+            raise AttributeError(self._validation_msg)
+
+    @classmethod
+    def from_spmatrix(cls, data, index=None, columns=None) -> DataFrame:
+        """
+        Create a new DataFrame from a scipy sparse matrix.
+
+        Parameters
+        ----------
+        data : scipy.sparse.spmatrix
+            Must be convertible to csc format.
+        index, columns : Index, optional
+            Row and column labels to use for the resulting DataFrame.
+            Defaults to a RangeIndex.
+
+        Returns
+        -------
+        DataFrame
+            Each column of the DataFrame is stored as a
+            :class:`arrays.SparseArray`.
+
+        Examples
+        --------
+        >>> import scipy.sparse
+        >>> mat = scipy.sparse.eye(3, dtype=float)
+        >>> pd.DataFrame.sparse.from_spmatrix(mat)
+             0    1    2
+        0  1.0    0    0
+        1    0  1.0    0
+        2    0    0  1.0
+        """
+        from pandas._libs.sparse import IntIndex
+
+        from pandas import DataFrame
+
+        data = data.tocsc()
+        index, columns = cls._prep_index(data, index, columns)
+        n_rows, n_columns = data.shape
+        # We need to make sure indices are sorted, as we create
+        # IntIndex with no input validation (i.e. check_integrity=False ).
+        # Indices may already be sorted in scipy in which case this adds
+        # a small overhead.
+        data.sort_indices()
+        indices = data.indices
+        indptr = data.indptr
+        array_data = data.data
+        dtype = SparseDtype(array_data.dtype, 0)
+        arrays = []
+        for i in range(n_columns):
+            sl = slice(indptr[i], indptr[i + 1])
+            idx = IntIndex(n_rows, indices[sl], check_integrity=False)
+            arr = SparseArray._simple_new(array_data[sl], idx, dtype)
+            arrays.append(arr)
+        return DataFrame._from_arrays(
+            arrays, columns=columns, index=index, verify_integrity=False
+        )
+
+    def to_dense(self) -> DataFrame:
+        """
+        Convert a DataFrame with sparse values to dense.
+
+        Returns
+        -------
+        DataFrame
+            A DataFrame with the same values stored as dense arrays.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({"A": pd.arrays.SparseArray([0, 1, 0])})
+        >>> df.sparse.to_dense()
+           A
+        0  0
+        1  1
+        2  0
+        """
+        from pandas import DataFrame
+
+        data = {k: v.array.to_dense() for k, v in self._parent.items()}
+        return DataFrame(data, index=self._parent.index, columns=self._parent.columns)
+
+    def to_coo(self):
+        """
+        Return the contents of the frame as a sparse SciPy COO matrix.
+
+        Returns
+        -------
+        scipy.sparse.spmatrix
+            If the caller is heterogeneous and contains booleans or objects,
+            the result will be of dtype=object. See Notes.
+
+        Notes
+        -----
+        The dtype will be the lowest-common-denominator type (implicit
+        upcasting); that is to say if the dtypes (even of numeric types)
+        are mixed, the one that accommodates all will be chosen.
+
+        e.g. If the dtypes are float16 and float32, dtype will be upcast to
+        float32. By numpy.find_common_type convention, mixing int64 and
+        and uint64 will result in a float64 dtype.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({"A": pd.arrays.SparseArray([0, 1, 0, 1])})
+        >>> df.sparse.to_coo()
+        
+        """
+        import_optional_dependency("scipy")
+        from scipy.sparse import coo_matrix
+
+        dtype = find_common_type(self._parent.dtypes.to_list())
+        if isinstance(dtype, SparseDtype):
+            dtype = dtype.subtype
+
+        cols, rows, data = [], [], []
+        for col, (_, ser) in enumerate(self._parent.items()):
+            sp_arr = ser.array
+            if sp_arr.fill_value != 0:
+                raise ValueError("fill value must be 0 when converting to COO matrix")
+
+            row = sp_arr.sp_index.indices
+            cols.append(np.repeat(col, len(row)))
+            rows.append(row)
+            data.append(sp_arr.sp_values.astype(dtype, copy=False))
+
+        cols = np.concatenate(cols)
+        rows = np.concatenate(rows)
+        data = np.concatenate(data)
+        return coo_matrix((data, (rows, cols)), shape=self._parent.shape)
+
+    @property
+    def density(self) -> float:
+        """
+        Ratio of non-sparse points to total (dense) data points.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({"A": pd.arrays.SparseArray([0, 1, 0, 1])})
+        >>> df.sparse.density
+        0.5
+        """
+        tmp = np.mean([column.array.density for _, column in self._parent.items()])
+        return tmp
+
+    @staticmethod
+    def _prep_index(data, index, columns):
+        from pandas.core.indexes.api import (
+            default_index,
+            ensure_index,
+        )
+
+        N, K = data.shape
+        if index is None:
+            index = default_index(N)
+        else:
+            index = ensure_index(index)
+        if columns is None:
+            columns = default_index(K)
+        else:
+            columns = ensure_index(columns)
+
+        if len(columns) != K:
+            raise ValueError(f"Column length mismatch: {len(columns)} vs. {K}")
+        if len(index) != N:
+            raise ValueError(f"Index length mismatch: {len(index)} vs. {N}")
+        return index, columns
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/sparse/array.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/sparse/array.py
new file mode 100644
index 0000000000000000000000000000000000000000..07ff592f491a82bebef7b158ec00aaee6c8df419
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/sparse/array.py
@@ -0,0 +1,1945 @@
+"""
+SparseArray data structure
+"""
+from __future__ import annotations
+
+from collections import abc
+import numbers
+import operator
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Literal,
+    cast,
+    overload,
+)
+import warnings
+
+import numpy as np
+
+from pandas._libs import lib
+import pandas._libs.sparse as splib
+from pandas._libs.sparse import (
+    BlockIndex,
+    IntIndex,
+    SparseIndex,
+)
+from pandas._libs.tslibs import NaT
+from pandas.compat.numpy import function as nv
+from pandas.errors import PerformanceWarning
+from pandas.util._decorators import doc
+from pandas.util._exceptions import find_stack_level
+from pandas.util._validators import (
+    validate_bool_kwarg,
+    validate_insert_loc,
+)
+
+from pandas.core.dtypes.astype import astype_array
+from pandas.core.dtypes.cast import (
+    construct_1d_arraylike_from_scalar,
+    find_common_type,
+    maybe_box_datetimelike,
+)
+from pandas.core.dtypes.common import (
+    is_bool_dtype,
+    is_integer,
+    is_list_like,
+    is_object_dtype,
+    is_scalar,
+    is_string_dtype,
+    pandas_dtype,
+)
+from pandas.core.dtypes.dtypes import (
+    DatetimeTZDtype,
+    SparseDtype,
+)
+from pandas.core.dtypes.generic import (
+    ABCIndex,
+    ABCSeries,
+)
+from pandas.core.dtypes.missing import (
+    isna,
+    na_value_for_dtype,
+    notna,
+)
+
+from pandas.core import arraylike
+import pandas.core.algorithms as algos
+from pandas.core.arraylike import OpsMixin
+from pandas.core.arrays import ExtensionArray
+from pandas.core.base import PandasObject
+import pandas.core.common as com
+from pandas.core.construction import (
+    ensure_wrapped_if_datetimelike,
+    extract_array,
+    sanitize_array,
+)
+from pandas.core.indexers import (
+    check_array_indexer,
+    unpack_tuple_and_ellipses,
+)
+from pandas.core.nanops import check_below_min_count
+
+from pandas.io.formats import printing
+
+# See https://github.com/python/typing/issues/684
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+    from enum import Enum
+
+    class ellipsis(Enum):
+        Ellipsis = "..."
+
+    Ellipsis = ellipsis.Ellipsis
+
+    from scipy.sparse import spmatrix
+
+    from pandas._typing import (
+        FillnaOptions,
+        NumpySorter,
+    )
+
+    SparseIndexKind = Literal["integer", "block"]
+
+    from pandas._typing import (
+        ArrayLike,
+        AstypeArg,
+        Axis,
+        AxisInt,
+        Dtype,
+        NpDtype,
+        PositionalIndexer,
+        Scalar,
+        ScalarIndexer,
+        Self,
+        SequenceIndexer,
+        npt,
+    )
+
+    from pandas import Series
+
+else:
+    ellipsis = type(Ellipsis)
+
+
+# ----------------------------------------------------------------------------
+# Array
+
+_sparray_doc_kwargs = {"klass": "SparseArray"}
+
+
+def _get_fill(arr: SparseArray) -> np.ndarray:
+    """
+    Create a 0-dim ndarray containing the fill value
+
+    Parameters
+    ----------
+    arr : SparseArray
+
+    Returns
+    -------
+    fill_value : ndarray
+        0-dim ndarray with just the fill value.
+
+    Notes
+    -----
+    coerce fill_value to arr dtype if possible
+    int64 SparseArray can have NaN as fill_value if there is no missing
+    """
+    try:
+        return np.asarray(arr.fill_value, dtype=arr.dtype.subtype)
+    except ValueError:
+        return np.asarray(arr.fill_value)
+
+
+def _sparse_array_op(
+    left: SparseArray, right: SparseArray, op: Callable, name: str
+) -> SparseArray:
+    """
+    Perform a binary operation between two arrays.
+
+    Parameters
+    ----------
+    left : Union[SparseArray, ndarray]
+    right : Union[SparseArray, ndarray]
+    op : Callable
+        The binary operation to perform
+    name str
+        Name of the callable.
+
+    Returns
+    -------
+    SparseArray
+    """
+    if name.startswith("__"):
+        # For lookups in _libs.sparse we need non-dunder op name
+        name = name[2:-2]
+
+    # dtype used to find corresponding sparse method
+    ltype = left.dtype.subtype
+    rtype = right.dtype.subtype
+
+    if ltype != rtype:
+        subtype = find_common_type([ltype, rtype])
+        ltype = SparseDtype(subtype, left.fill_value)
+        rtype = SparseDtype(subtype, right.fill_value)
+
+        left = left.astype(ltype, copy=False)
+        right = right.astype(rtype, copy=False)
+        dtype = ltype.subtype
+    else:
+        dtype = ltype
+
+    # dtype the result must have
+    result_dtype = None
+
+    if left.sp_index.ngaps == 0 or right.sp_index.ngaps == 0:
+        with np.errstate(all="ignore"):
+            result = op(left.to_dense(), right.to_dense())
+            fill = op(_get_fill(left), _get_fill(right))
+
+        if left.sp_index.ngaps == 0:
+            index = left.sp_index
+        else:
+            index = right.sp_index
+    elif left.sp_index.equals(right.sp_index):
+        with np.errstate(all="ignore"):
+            result = op(left.sp_values, right.sp_values)
+            fill = op(_get_fill(left), _get_fill(right))
+        index = left.sp_index
+    else:
+        if name[0] == "r":
+            left, right = right, left
+            name = name[1:]
+
+        if name in ("and", "or", "xor") and dtype == "bool":
+            opname = f"sparse_{name}_uint8"
+            # to make template simple, cast here
+            left_sp_values = left.sp_values.view(np.uint8)
+            right_sp_values = right.sp_values.view(np.uint8)
+            result_dtype = bool
+        else:
+            opname = f"sparse_{name}_{dtype}"
+            left_sp_values = left.sp_values
+            right_sp_values = right.sp_values
+
+        if (
+            name in ["floordiv", "mod"]
+            and (right == 0).any()
+            and left.dtype.kind in "iu"
+        ):
+            # Match the non-Sparse Series behavior
+            opname = f"sparse_{name}_float64"
+            left_sp_values = left_sp_values.astype("float64")
+            right_sp_values = right_sp_values.astype("float64")
+
+        sparse_op = getattr(splib, opname)
+
+        with np.errstate(all="ignore"):
+            result, index, fill = sparse_op(
+                left_sp_values,
+                left.sp_index,
+                left.fill_value,
+                right_sp_values,
+                right.sp_index,
+                right.fill_value,
+            )
+
+    if name == "divmod":
+        # result is a 2-tuple
+        # error: Incompatible return value type (got "Tuple[SparseArray,
+        # SparseArray]", expected "SparseArray")
+        return (  # type: ignore[return-value]
+            _wrap_result(name, result[0], index, fill[0], dtype=result_dtype),
+            _wrap_result(name, result[1], index, fill[1], dtype=result_dtype),
+        )
+
+    if result_dtype is None:
+        result_dtype = result.dtype
+
+    return _wrap_result(name, result, index, fill, dtype=result_dtype)
+
+
+def _wrap_result(
+    name: str, data, sparse_index, fill_value, dtype: Dtype | None = None
+) -> SparseArray:
+    """
+    wrap op result to have correct dtype
+    """
+    if name.startswith("__"):
+        # e.g. __eq__ --> eq
+        name = name[2:-2]
+
+    if name in ("eq", "ne", "lt", "gt", "le", "ge"):
+        dtype = bool
+
+    fill_value = lib.item_from_zerodim(fill_value)
+
+    if is_bool_dtype(dtype):
+        # fill_value may be np.bool_
+        fill_value = bool(fill_value)
+    return SparseArray(
+        data, sparse_index=sparse_index, fill_value=fill_value, dtype=dtype
+    )
+
+
+class SparseArray(OpsMixin, PandasObject, ExtensionArray):
+    """
+    An ExtensionArray for storing sparse data.
+
+    Parameters
+    ----------
+    data : array-like or scalar
+        A dense array of values to store in the SparseArray. This may contain
+        `fill_value`.
+    sparse_index : SparseIndex, optional
+    fill_value : scalar, optional
+        Elements in data that are ``fill_value`` are not stored in the
+        SparseArray. For memory savings, this should be the most common value
+        in `data`. By default, `fill_value` depends on the dtype of `data`:
+
+        =========== ==========
+        data.dtype  na_value
+        =========== ==========
+        float       ``np.nan``
+        int         ``0``
+        bool        False
+        datetime64  ``pd.NaT``
+        timedelta64 ``pd.NaT``
+        =========== ==========
+
+        The fill value is potentially specified in three ways. In order of
+        precedence, these are
+
+        1. The `fill_value` argument
+        2. ``dtype.fill_value`` if `fill_value` is None and `dtype` is
+           a ``SparseDtype``
+        3. ``data.dtype.fill_value`` if `fill_value` is None and `dtype`
+           is not a ``SparseDtype`` and `data` is a ``SparseArray``.
+
+    kind : str
+        Can be 'integer' or 'block', default is 'integer'.
+        The type of storage for sparse locations.
+
+        * 'block': Stores a `block` and `block_length` for each
+          contiguous *span* of sparse values. This is best when
+          sparse data tends to be clumped together, with large
+          regions of ``fill-value`` values between sparse values.
+        * 'integer': uses an integer to store the location of
+          each sparse value.
+
+    dtype : np.dtype or SparseDtype, optional
+        The dtype to use for the SparseArray. For numpy dtypes, this
+        determines the dtype of ``self.sp_values``. For SparseDtype,
+        this determines ``self.sp_values`` and ``self.fill_value``.
+    copy : bool, default False
+        Whether to explicitly copy the incoming `data` array.
+
+    Attributes
+    ----------
+    None
+
+    Methods
+    -------
+    None
+
+    Examples
+    --------
+    >>> from pandas.arrays import SparseArray
+    >>> arr = SparseArray([0, 0, 1, 2])
+    >>> arr
+    [0, 0, 1, 2]
+    Fill: 0
+    IntIndex
+    Indices: array([2, 3], dtype=int32)
+    """
+
+    _subtyp = "sparse_array"  # register ABCSparseArray
+    _hidden_attrs = PandasObject._hidden_attrs | frozenset([])
+    _sparse_index: SparseIndex
+    _sparse_values: np.ndarray
+    _dtype: SparseDtype
+
+    def __init__(
+        self,
+        data,
+        sparse_index=None,
+        fill_value=None,
+        kind: SparseIndexKind = "integer",
+        dtype: Dtype | None = None,
+        copy: bool = False,
+    ) -> None:
+        if fill_value is None and isinstance(dtype, SparseDtype):
+            fill_value = dtype.fill_value
+
+        if isinstance(data, type(self)):
+            # disable normal inference on dtype, sparse_index, & fill_value
+            if sparse_index is None:
+                sparse_index = data.sp_index
+            if fill_value is None:
+                fill_value = data.fill_value
+            if dtype is None:
+                dtype = data.dtype
+            # TODO: make kind=None, and use data.kind?
+            data = data.sp_values
+
+        # Handle use-provided dtype
+        if isinstance(dtype, str):
+            # Two options: dtype='int', regular numpy dtype
+            # or dtype='Sparse[int]', a sparse dtype
+            try:
+                dtype = SparseDtype.construct_from_string(dtype)
+            except TypeError:
+                dtype = pandas_dtype(dtype)
+
+        if isinstance(dtype, SparseDtype):
+            if fill_value is None:
+                fill_value = dtype.fill_value
+            dtype = dtype.subtype
+
+        if is_scalar(data):
+            warnings.warn(
+                f"Constructing {type(self).__name__} with scalar data is deprecated "
+                "and will raise in a future version. Pass a sequence instead.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+            if sparse_index is None:
+                npoints = 1
+            else:
+                npoints = sparse_index.length
+
+            data = construct_1d_arraylike_from_scalar(data, npoints, dtype=None)
+            dtype = data.dtype
+
+        if dtype is not None:
+            dtype = pandas_dtype(dtype)
+
+        # TODO: disentangle the fill_value dtype inference from
+        # dtype inference
+        if data is None:
+            # TODO: What should the empty dtype be? Object or float?
+
+            # error: Argument "dtype" to "array" has incompatible type
+            # "Union[ExtensionDtype, dtype[Any], None]"; expected "Union[dtype[Any],
+            # None, type, _SupportsDType, str, Union[Tuple[Any, int], Tuple[Any,
+            # Union[int, Sequence[int]]], List[Any], _DTypeDict, Tuple[Any, Any]]]"
+            data = np.array([], dtype=dtype)  # type: ignore[arg-type]
+
+        try:
+            data = sanitize_array(data, index=None)
+        except ValueError:
+            # NumPy may raise a ValueError on data like [1, []]
+            # we retry with object dtype here.
+            if dtype is None:
+                dtype = np.dtype(object)
+                data = np.atleast_1d(np.asarray(data, dtype=dtype))
+            else:
+                raise
+
+        if copy:
+            # TODO: avoid double copy when dtype forces cast.
+            data = data.copy()
+
+        if fill_value is None:
+            fill_value_dtype = data.dtype if dtype is None else dtype
+            if fill_value_dtype is None:
+                fill_value = np.nan
+            else:
+                fill_value = na_value_for_dtype(fill_value_dtype)
+
+        if isinstance(data, type(self)) and sparse_index is None:
+            sparse_index = data._sparse_index
+            # error: Argument "dtype" to "asarray" has incompatible type
+            # "Union[ExtensionDtype, dtype[Any], None]"; expected "None"
+            sparse_values = np.asarray(
+                data.sp_values, dtype=dtype  # type: ignore[arg-type]
+            )
+        elif sparse_index is None:
+            data = extract_array(data, extract_numpy=True)
+            if not isinstance(data, np.ndarray):
+                # EA
+                if isinstance(data.dtype, DatetimeTZDtype):
+                    warnings.warn(
+                        f"Creating SparseArray from {data.dtype} data "
+                        "loses timezone information. Cast to object before "
+                        "sparse to retain timezone information.",
+                        UserWarning,
+                        stacklevel=find_stack_level(),
+                    )
+                    data = np.asarray(data, dtype="datetime64[ns]")
+                    if fill_value is NaT:
+                        fill_value = np.datetime64("NaT", "ns")
+                data = np.asarray(data)
+            sparse_values, sparse_index, fill_value = _make_sparse(
+                # error: Argument "dtype" to "_make_sparse" has incompatible type
+                # "Union[ExtensionDtype, dtype[Any], None]"; expected
+                # "Optional[dtype[Any]]"
+                data,
+                kind=kind,
+                fill_value=fill_value,
+                dtype=dtype,  # type: ignore[arg-type]
+            )
+        else:
+            # error: Argument "dtype" to "asarray" has incompatible type
+            # "Union[ExtensionDtype, dtype[Any], None]"; expected "None"
+            sparse_values = np.asarray(data, dtype=dtype)  # type: ignore[arg-type]
+            if len(sparse_values) != sparse_index.npoints:
+                raise AssertionError(
+                    f"Non array-like type {type(sparse_values)} must "
+                    "have the same length as the index"
+                )
+        self._sparse_index = sparse_index
+        self._sparse_values = sparse_values
+        self._dtype = SparseDtype(sparse_values.dtype, fill_value)
+
+    @classmethod
+    def _simple_new(
+        cls,
+        sparse_array: np.ndarray,
+        sparse_index: SparseIndex,
+        dtype: SparseDtype,
+    ) -> Self:
+        new = object.__new__(cls)
+        new._sparse_index = sparse_index
+        new._sparse_values = sparse_array
+        new._dtype = dtype
+        return new
+
+    @classmethod
+    def from_spmatrix(cls, data: spmatrix) -> Self:
+        """
+        Create a SparseArray from a scipy.sparse matrix.
+
+        Parameters
+        ----------
+        data : scipy.sparse.sp_matrix
+            This should be a SciPy sparse matrix where the size
+            of the second dimension is 1. In other words, a
+            sparse matrix with a single column.
+
+        Returns
+        -------
+        SparseArray
+
+        Examples
+        --------
+        >>> import scipy.sparse
+        >>> mat = scipy.sparse.coo_matrix((4, 1))
+        >>> pd.arrays.SparseArray.from_spmatrix(mat)
+        [0.0, 0.0, 0.0, 0.0]
+        Fill: 0.0
+        IntIndex
+        Indices: array([], dtype=int32)
+        """
+        length, ncol = data.shape
+
+        if ncol != 1:
+            raise ValueError(f"'data' must have a single column, not '{ncol}'")
+
+        # our sparse index classes require that the positions be strictly
+        # increasing. So we need to sort loc, and arr accordingly.
+        data = data.tocsc()
+        data.sort_indices()
+        arr = data.data
+        idx = data.indices
+
+        zero = np.array(0, dtype=arr.dtype).item()
+        dtype = SparseDtype(arr.dtype, zero)
+        index = IntIndex(length, idx)
+
+        return cls._simple_new(arr, index, dtype)
+
+    def __array__(
+        self, dtype: NpDtype | None = None, copy: bool | None = None
+    ) -> np.ndarray:
+        if self.sp_index.ngaps == 0:
+            # Compat for na dtype and int values.
+            if copy is True:
+                return np.array(self.sp_values)
+            else:
+                return self.sp_values
+
+        if copy is False:
+            warnings.warn(
+                "Starting with NumPy 2.0, the behavior of the 'copy' keyword has "
+                "changed and passing 'copy=False' raises an error when returning "
+                "a zero-copy NumPy array is not possible. pandas will follow "
+                "this behavior starting with pandas 3.0.\nThis conversion to "
+                "NumPy requires a copy, but 'copy=False' was passed. Consider "
+                "using 'np.asarray(..)' instead.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+
+        fill_value = self.fill_value
+
+        if dtype is None:
+            # Can NumPy represent this type?
+            # If not, `np.result_type` will raise. We catch that
+            # and return object.
+            if self.sp_values.dtype.kind == "M":
+                # However, we *do* special-case the common case of
+                # a datetime64 with pandas NaT.
+                if fill_value is NaT:
+                    # Can't put pd.NaT in a datetime64[ns]
+                    fill_value = np.datetime64("NaT")
+            try:
+                dtype = np.result_type(self.sp_values.dtype, type(fill_value))
+            except TypeError:
+                dtype = object
+
+        out = np.full(self.shape, fill_value, dtype=dtype)
+        out[self.sp_index.indices] = self.sp_values
+        return out
+
+    def __setitem__(self, key, value) -> None:
+        # I suppose we could allow setting of non-fill_value elements.
+        # TODO(SparseArray.__setitem__): remove special cases in
+        # ExtensionBlock.where
+        msg = "SparseArray does not support item assignment via setitem"
+        raise TypeError(msg)
+
+    @classmethod
+    def _from_sequence(cls, scalars, *, dtype: Dtype | None = None, copy: bool = False):
+        return cls(scalars, dtype=dtype)
+
+    @classmethod
+    def _from_factorized(cls, values, original):
+        return cls(values, dtype=original.dtype)
+
+    # ------------------------------------------------------------------------
+    # Data
+    # ------------------------------------------------------------------------
+    @property
+    def sp_index(self) -> SparseIndex:
+        """
+        The SparseIndex containing the location of non- ``fill_value`` points.
+        """
+        return self._sparse_index
+
+    @property
+    def sp_values(self) -> np.ndarray:
+        """
+        An ndarray containing the non- ``fill_value`` values.
+
+        Examples
+        --------
+        >>> from pandas.arrays import SparseArray
+        >>> s = SparseArray([0, 0, 1, 0, 2], fill_value=0)
+        >>> s.sp_values
+        array([1, 2])
+        """
+        return self._sparse_values
+
+    @property
+    def dtype(self) -> SparseDtype:
+        return self._dtype
+
+    @property
+    def fill_value(self):
+        """
+        Elements in `data` that are `fill_value` are not stored.
+
+        For memory savings, this should be the most common value in the array.
+
+        Examples
+        --------
+        >>> ser = pd.Series([0, 0, 2, 2, 2], dtype="Sparse[int]")
+        >>> ser.sparse.fill_value
+        0
+        >>> spa_dtype = pd.SparseDtype(dtype=np.int32, fill_value=2)
+        >>> ser = pd.Series([0, 0, 2, 2, 2], dtype=spa_dtype)
+        >>> ser.sparse.fill_value
+        2
+        """
+        return self.dtype.fill_value
+
+    @fill_value.setter
+    def fill_value(self, value) -> None:
+        self._dtype = SparseDtype(self.dtype.subtype, value)
+
+    @property
+    def kind(self) -> SparseIndexKind:
+        """
+        The kind of sparse index for this array. One of {'integer', 'block'}.
+        """
+        if isinstance(self.sp_index, IntIndex):
+            return "integer"
+        else:
+            return "block"
+
+    @property
+    def _valid_sp_values(self) -> np.ndarray:
+        sp_vals = self.sp_values
+        mask = notna(sp_vals)
+        return sp_vals[mask]
+
+    def __len__(self) -> int:
+        return self.sp_index.length
+
+    @property
+    def _null_fill_value(self) -> bool:
+        return self._dtype._is_na_fill_value
+
+    def _fill_value_matches(self, fill_value) -> bool:
+        if self._null_fill_value:
+            return isna(fill_value)
+        else:
+            return self.fill_value == fill_value
+
+    @property
+    def nbytes(self) -> int:
+        return self.sp_values.nbytes + self.sp_index.nbytes
+
+    @property
+    def density(self) -> float:
+        """
+        The percent of non- ``fill_value`` points, as decimal.
+
+        Examples
+        --------
+        >>> from pandas.arrays import SparseArray
+        >>> s = SparseArray([0, 0, 1, 1, 1], fill_value=0)
+        >>> s.density
+        0.6
+        """
+        return self.sp_index.npoints / self.sp_index.length
+
+    @property
+    def npoints(self) -> int:
+        """
+        The number of non- ``fill_value`` points.
+
+        Examples
+        --------
+        >>> from pandas.arrays import SparseArray
+        >>> s = SparseArray([0, 0, 1, 1, 1], fill_value=0)
+        >>> s.npoints
+        3
+        """
+        return self.sp_index.npoints
+
+    # error: Return type "SparseArray" of "isna" incompatible with return type
+    # "ndarray[Any, Any] | ExtensionArraySupportsAnyAll" in supertype "ExtensionArray"
+    def isna(self) -> Self:  # type: ignore[override]
+        # If null fill value, we want SparseDtype[bool, true]
+        # to preserve the same memory usage.
+        dtype = SparseDtype(bool, self._null_fill_value)
+        if self._null_fill_value:
+            return type(self)._simple_new(isna(self.sp_values), self.sp_index, dtype)
+        mask = np.full(len(self), False, dtype=np.bool_)
+        mask[self.sp_index.indices] = isna(self.sp_values)
+        return type(self)(mask, fill_value=False, dtype=dtype)
+
+    def _pad_or_backfill(  # pylint: disable=useless-parent-delegation
+        self,
+        *,
+        method: FillnaOptions,
+        limit: int | None = None,
+        limit_area: Literal["inside", "outside"] | None = None,
+        copy: bool = True,
+    ) -> Self:
+        # TODO(3.0): We can remove this method once deprecation for fillna method
+        #  keyword is enforced.
+        return super()._pad_or_backfill(
+            method=method, limit=limit, limit_area=limit_area, copy=copy
+        )
+
+    def fillna(
+        self,
+        value=None,
+        method: FillnaOptions | None = None,
+        limit: int | None = None,
+        copy: bool = True,
+    ) -> Self:
+        """
+        Fill missing values with `value`.
+
+        Parameters
+        ----------
+        value : scalar, optional
+        method : str, optional
+
+            .. warning::
+
+               Using 'method' will result in high memory use,
+               as all `fill_value` methods will be converted to
+               an in-memory ndarray
+
+        limit : int, optional
+
+        copy: bool, default True
+            Ignored for SparseArray.
+
+        Returns
+        -------
+        SparseArray
+
+        Notes
+        -----
+        When `value` is specified, the result's ``fill_value`` depends on
+        ``self.fill_value``. The goal is to maintain low-memory use.
+
+        If ``self.fill_value`` is NA, the result dtype will be
+        ``SparseDtype(self.dtype, fill_value=value)``. This will preserve
+        amount of memory used before and after filling.
+
+        When ``self.fill_value`` is not NA, the result dtype will be
+        ``self.dtype``. Again, this preserves the amount of memory used.
+        """
+        if (method is None and value is None) or (
+            method is not None and value is not None
+        ):
+            raise ValueError("Must specify one of 'method' or 'value'.")
+
+        if method is not None:
+            return super().fillna(method=method, limit=limit)
+
+        else:
+            new_values = np.where(isna(self.sp_values), value, self.sp_values)
+
+            if self._null_fill_value:
+                # This is essentially just updating the dtype.
+                new_dtype = SparseDtype(self.dtype.subtype, fill_value=value)
+            else:
+                new_dtype = self.dtype
+
+        return self._simple_new(new_values, self._sparse_index, new_dtype)
+
+    def shift(self, periods: int = 1, fill_value=None) -> Self:
+        if not len(self) or periods == 0:
+            return self.copy()
+
+        if isna(fill_value):
+            fill_value = self.dtype.na_value
+
+        subtype = np.result_type(fill_value, self.dtype.subtype)
+
+        if subtype != self.dtype.subtype:
+            # just coerce up front
+            arr = self.astype(SparseDtype(subtype, self.fill_value))
+        else:
+            arr = self
+
+        empty = self._from_sequence(
+            [fill_value] * min(abs(periods), len(self)), dtype=arr.dtype
+        )
+
+        if periods > 0:
+            a = empty
+            b = arr[:-periods]
+        else:
+            a = arr[abs(periods) :]
+            b = empty
+        return arr._concat_same_type([a, b])
+
+    def _first_fill_value_loc(self):
+        """
+        Get the location of the first fill value.
+
+        Returns
+        -------
+        int
+        """
+        if len(self) == 0 or self.sp_index.npoints == len(self):
+            return -1
+
+        indices = self.sp_index.indices
+        if not len(indices) or indices[0] > 0:
+            return 0
+
+        # a number larger than 1 should be appended to
+        # the last in case of fill value only appears
+        # in the tail of array
+        diff = np.r_[np.diff(indices), 2]
+        return indices[(diff > 1).argmax()] + 1
+
+    @doc(ExtensionArray.duplicated)
+    def duplicated(
+        self, keep: Literal["first", "last", False] = "first"
+    ) -> npt.NDArray[np.bool_]:
+        values = np.asarray(self)
+        mask = np.asarray(self.isna())
+        return algos.duplicated(values, keep=keep, mask=mask)
+
+    def unique(self) -> Self:
+        uniques = algos.unique(self.sp_values)
+        if len(self.sp_values) != len(self):
+            fill_loc = self._first_fill_value_loc()
+            # Inorder to align the behavior of pd.unique or
+            # pd.Series.unique, we should keep the original
+            # order, here we use unique again to find the
+            # insertion place. Since the length of sp_values
+            # is not large, maybe minor performance hurt
+            # is worthwhile to the correctness.
+            insert_loc = len(algos.unique(self.sp_values[:fill_loc]))
+            uniques = np.insert(uniques, insert_loc, self.fill_value)
+        return type(self)._from_sequence(uniques, dtype=self.dtype)
+
+    def _values_for_factorize(self):
+        # Still override this for hash_pandas_object
+        return np.asarray(self), self.fill_value
+
+    def factorize(
+        self,
+        use_na_sentinel: bool = True,
+    ) -> tuple[np.ndarray, SparseArray]:
+        # Currently, ExtensionArray.factorize -> Tuple[ndarray, EA]
+        # The sparsity on this is backwards from what Sparse would want. Want
+        # ExtensionArray.factorize -> Tuple[EA, EA]
+        # Given that we have to return a dense array of codes, why bother
+        # implementing an efficient factorize?
+        codes, uniques = algos.factorize(
+            np.asarray(self), use_na_sentinel=use_na_sentinel
+        )
+        uniques_sp = SparseArray(uniques, dtype=self.dtype)
+        return codes, uniques_sp
+
+    def value_counts(self, dropna: bool = True) -> Series:
+        """
+        Returns a Series containing counts of unique values.
+
+        Parameters
+        ----------
+        dropna : bool, default True
+            Don't include counts of NaN, even if NaN is in sp_values.
+
+        Returns
+        -------
+        counts : Series
+        """
+        from pandas import (
+            Index,
+            Series,
+        )
+
+        keys, counts, _ = algos.value_counts_arraylike(self.sp_values, dropna=dropna)
+        fcounts = self.sp_index.ngaps
+        if fcounts > 0 and (not self._null_fill_value or not dropna):
+            mask = isna(keys) if self._null_fill_value else keys == self.fill_value
+            if mask.any():
+                counts[mask] += fcounts
+            else:
+                # error: Argument 1 to "insert" has incompatible type "Union[
+                # ExtensionArray,ndarray[Any, Any]]"; expected "Union[
+                # _SupportsArray[dtype[Any]], Sequence[_SupportsArray[dtype
+                # [Any]]], Sequence[Sequence[_SupportsArray[dtype[Any]]]],
+                # Sequence[Sequence[Sequence[_SupportsArray[dtype[Any]]]]], Sequence
+                # [Sequence[Sequence[Sequence[_SupportsArray[dtype[Any]]]]]]]"
+                keys = np.insert(keys, 0, self.fill_value)  # type: ignore[arg-type]
+                counts = np.insert(counts, 0, fcounts)
+
+        if not isinstance(keys, ABCIndex):
+            index = Index(keys)
+        else:
+            index = keys
+        return Series(counts, index=index, copy=False)
+
+    # --------
+    # Indexing
+    # --------
+    @overload
+    def __getitem__(self, key: ScalarIndexer) -> Any:
+        ...
+
+    @overload
+    def __getitem__(
+        self,
+        key: SequenceIndexer | tuple[int | ellipsis, ...],
+    ) -> Self:
+        ...
+
+    def __getitem__(
+        self,
+        key: PositionalIndexer | tuple[int | ellipsis, ...],
+    ) -> Self | Any:
+        if isinstance(key, tuple):
+            key = unpack_tuple_and_ellipses(key)
+            if key is Ellipsis:
+                raise ValueError("Cannot slice with Ellipsis")
+
+        if is_integer(key):
+            return self._get_val_at(key)
+        elif isinstance(key, tuple):
+            # error: Invalid index type "Tuple[Union[int, ellipsis], ...]"
+            # for "ndarray[Any, Any]"; expected type
+            # "Union[SupportsIndex, _SupportsArray[dtype[Union[bool_,
+            # integer[Any]]]], _NestedSequence[_SupportsArray[dtype[
+            # Union[bool_, integer[Any]]]]], _NestedSequence[Union[
+            # bool, int]], Tuple[Union[SupportsIndex, _SupportsArray[
+            # dtype[Union[bool_, integer[Any]]]], _NestedSequence[
+            # _SupportsArray[dtype[Union[bool_, integer[Any]]]]],
+            # _NestedSequence[Union[bool, int]]], ...]]"
+            data_slice = self.to_dense()[key]  # type: ignore[index]
+        elif isinstance(key, slice):
+            # Avoid densifying when handling contiguous slices
+            if key.step is None or key.step == 1:
+                start = 0 if key.start is None else key.start
+                if start < 0:
+                    start += len(self)
+
+                end = len(self) if key.stop is None else key.stop
+                if end < 0:
+                    end += len(self)
+
+                indices = self.sp_index.indices
+                keep_inds = np.flatnonzero((indices >= start) & (indices < end))
+                sp_vals = self.sp_values[keep_inds]
+
+                sp_index = indices[keep_inds].copy()
+
+                # If we've sliced to not include the start of the array, all our indices
+                # should be shifted. NB: here we are careful to also not shift by a
+                # negative value for a case like [0, 1][-100:] where the start index
+                # should be treated like 0
+                if start > 0:
+                    sp_index -= start
+
+                # Length of our result should match applying this slice to a range
+                # of the length of our original array
+                new_len = len(range(len(self))[key])
+                new_sp_index = make_sparse_index(new_len, sp_index, self.kind)
+                return type(self)._simple_new(sp_vals, new_sp_index, self.dtype)
+            else:
+                indices = np.arange(len(self), dtype=np.int32)[key]
+                return self.take(indices)
+
+        elif not is_list_like(key):
+            # e.g. "foo" or 2.5
+            # exception message copied from numpy
+            raise IndexError(
+                r"only integers, slices (`:`), ellipsis (`...`), numpy.newaxis "
+                r"(`None`) and integer or boolean arrays are valid indices"
+            )
+
+        else:
+            if isinstance(key, SparseArray):
+                # NOTE: If we guarantee that SparseDType(bool)
+                # has only fill_value - true, false or nan
+                # (see GH PR 44955)
+                # we can apply mask very fast:
+                if is_bool_dtype(key):
+                    if isna(key.fill_value):
+                        return self.take(key.sp_index.indices[key.sp_values])
+                    if not key.fill_value:
+                        return self.take(key.sp_index.indices)
+                    n = len(self)
+                    mask = np.full(n, True, dtype=np.bool_)
+                    mask[key.sp_index.indices] = False
+                    return self.take(np.arange(n)[mask])
+                else:
+                    key = np.asarray(key)
+
+            key = check_array_indexer(self, key)
+
+            if com.is_bool_indexer(key):
+                # mypy doesn't know we have an array here
+                key = cast(np.ndarray, key)
+                return self.take(np.arange(len(key), dtype=np.int32)[key])
+            elif hasattr(key, "__len__"):
+                return self.take(key)
+            else:
+                raise ValueError(f"Cannot slice with '{key}'")
+
+        return type(self)(data_slice, kind=self.kind)
+
+    def _get_val_at(self, loc):
+        loc = validate_insert_loc(loc, len(self))
+
+        sp_loc = self.sp_index.lookup(loc)
+        if sp_loc == -1:
+            return self.fill_value
+        else:
+            val = self.sp_values[sp_loc]
+            val = maybe_box_datetimelike(val, self.sp_values.dtype)
+            return val
+
+    def take(self, indices, *, allow_fill: bool = False, fill_value=None) -> Self:
+        if is_scalar(indices):
+            raise ValueError(f"'indices' must be an array, not a scalar '{indices}'.")
+        indices = np.asarray(indices, dtype=np.int32)
+
+        dtype = None
+        if indices.size == 0:
+            result = np.array([], dtype="object")
+            dtype = self.dtype
+        elif allow_fill:
+            result = self._take_with_fill(indices, fill_value=fill_value)
+        else:
+            return self._take_without_fill(indices)
+
+        return type(self)(
+            result, fill_value=self.fill_value, kind=self.kind, dtype=dtype
+        )
+
+    def _take_with_fill(self, indices, fill_value=None) -> np.ndarray:
+        if fill_value is None:
+            fill_value = self.dtype.na_value
+
+        if indices.min() < -1:
+            raise ValueError(
+                "Invalid value in 'indices'. Must be between -1 "
+                "and the length of the array."
+            )
+
+        if indices.max() >= len(self):
+            raise IndexError("out of bounds value in 'indices'.")
+
+        if len(self) == 0:
+            # Empty... Allow taking only if all empty
+            if (indices == -1).all():
+                dtype = np.result_type(self.sp_values, type(fill_value))
+                taken = np.empty_like(indices, dtype=dtype)
+                taken.fill(fill_value)
+                return taken
+            else:
+                raise IndexError("cannot do a non-empty take from an empty axes.")
+
+        # sp_indexer may be -1 for two reasons
+        # 1.) we took for an index of -1 (new)
+        # 2.) we took a value that was self.fill_value (old)
+        sp_indexer = self.sp_index.lookup_array(indices)
+        new_fill_indices = indices == -1
+        old_fill_indices = (sp_indexer == -1) & ~new_fill_indices
+
+        if self.sp_index.npoints == 0 and old_fill_indices.all():
+            # We've looked up all valid points on an all-sparse array.
+            taken = np.full(
+                sp_indexer.shape, fill_value=self.fill_value, dtype=self.dtype.subtype
+            )
+
+        elif self.sp_index.npoints == 0:
+            # Use the old fill_value unless we took for an index of -1
+            _dtype = np.result_type(self.dtype.subtype, type(fill_value))
+            taken = np.full(sp_indexer.shape, fill_value=fill_value, dtype=_dtype)
+            taken[old_fill_indices] = self.fill_value
+        else:
+            taken = self.sp_values.take(sp_indexer)
+
+            # Fill in two steps.
+            # Old fill values
+            # New fill values
+            # potentially coercing to a new dtype at each stage.
+
+            m0 = sp_indexer[old_fill_indices] < 0
+            m1 = sp_indexer[new_fill_indices] < 0
+
+            result_type = taken.dtype
+
+            if m0.any():
+                result_type = np.result_type(result_type, type(self.fill_value))
+                taken = taken.astype(result_type)
+                taken[old_fill_indices] = self.fill_value
+
+            if m1.any():
+                result_type = np.result_type(result_type, type(fill_value))
+                taken = taken.astype(result_type)
+                taken[new_fill_indices] = fill_value
+
+        return taken
+
+    def _take_without_fill(self, indices) -> Self:
+        to_shift = indices < 0
+
+        n = len(self)
+
+        if (indices.max() >= n) or (indices.min() < -n):
+            if n == 0:
+                raise IndexError("cannot do a non-empty take from an empty axes.")
+            raise IndexError("out of bounds value in 'indices'.")
+
+        if to_shift.any():
+            indices = indices.copy()
+            indices[to_shift] += n
+
+        sp_indexer = self.sp_index.lookup_array(indices)
+        value_mask = sp_indexer != -1
+        new_sp_values = self.sp_values[sp_indexer[value_mask]]
+
+        value_indices = np.flatnonzero(value_mask).astype(np.int32, copy=False)
+
+        new_sp_index = make_sparse_index(len(indices), value_indices, kind=self.kind)
+        return type(self)._simple_new(new_sp_values, new_sp_index, dtype=self.dtype)
+
+    def searchsorted(
+        self,
+        v: ArrayLike | object,
+        side: Literal["left", "right"] = "left",
+        sorter: NumpySorter | None = None,
+    ) -> npt.NDArray[np.intp] | np.intp:
+        msg = "searchsorted requires high memory usage."
+        warnings.warn(msg, PerformanceWarning, stacklevel=find_stack_level())
+        v = np.asarray(v)
+        return np.asarray(self, dtype=self.dtype.subtype).searchsorted(v, side, sorter)
+
+    def copy(self) -> Self:
+        values = self.sp_values.copy()
+        return self._simple_new(values, self.sp_index, self.dtype)
+
+    @classmethod
+    def _concat_same_type(cls, to_concat: Sequence[Self]) -> Self:
+        fill_value = to_concat[0].fill_value
+
+        values = []
+        length = 0
+
+        if to_concat:
+            sp_kind = to_concat[0].kind
+        else:
+            sp_kind = "integer"
+
+        sp_index: SparseIndex
+        if sp_kind == "integer":
+            indices = []
+
+            for arr in to_concat:
+                int_idx = arr.sp_index.indices.copy()
+                int_idx += length  # TODO: wraparound
+                length += arr.sp_index.length
+
+                values.append(arr.sp_values)
+                indices.append(int_idx)
+
+            data = np.concatenate(values)
+            indices_arr = np.concatenate(indices)
+            # error: Argument 2 to "IntIndex" has incompatible type
+            # "ndarray[Any, dtype[signedinteger[_32Bit]]]";
+            # expected "Sequence[int]"
+            sp_index = IntIndex(length, indices_arr)  # type: ignore[arg-type]
+
+        else:
+            # when concatenating block indices, we don't claim that you'll
+            # get an identical index as concatenating the values and then
+            # creating a new index. We don't want to spend the time trying
+            # to merge blocks across arrays in `to_concat`, so the resulting
+            # BlockIndex may have more blocks.
+            blengths = []
+            blocs = []
+
+            for arr in to_concat:
+                block_idx = arr.sp_index.to_block_index()
+
+                values.append(arr.sp_values)
+                blocs.append(block_idx.blocs.copy() + length)
+                blengths.append(block_idx.blengths)
+                length += arr.sp_index.length
+
+            data = np.concatenate(values)
+            blocs_arr = np.concatenate(blocs)
+            blengths_arr = np.concatenate(blengths)
+
+            sp_index = BlockIndex(length, blocs_arr, blengths_arr)
+
+        return cls(data, sparse_index=sp_index, fill_value=fill_value)
+
+    def astype(self, dtype: AstypeArg | None = None, copy: bool = True):
+        """
+        Change the dtype of a SparseArray.
+
+        The output will always be a SparseArray. To convert to a dense
+        ndarray with a certain dtype, use :meth:`numpy.asarray`.
+
+        Parameters
+        ----------
+        dtype : np.dtype or ExtensionDtype
+            For SparseDtype, this changes the dtype of
+            ``self.sp_values`` and the ``self.fill_value``.
+
+            For other dtypes, this only changes the dtype of
+            ``self.sp_values``.
+
+        copy : bool, default True
+            Whether to ensure a copy is made, even if not necessary.
+
+        Returns
+        -------
+        SparseArray
+
+        Examples
+        --------
+        >>> arr = pd.arrays.SparseArray([0, 0, 1, 2])
+        >>> arr
+        [0, 0, 1, 2]
+        Fill: 0
+        IntIndex
+        Indices: array([2, 3], dtype=int32)
+
+        >>> arr.astype(SparseDtype(np.dtype('int32')))
+        [0, 0, 1, 2]
+        Fill: 0
+        IntIndex
+        Indices: array([2, 3], dtype=int32)
+
+        Using a NumPy dtype with a different kind (e.g. float) will coerce
+        just ``self.sp_values``.
+
+        >>> arr.astype(SparseDtype(np.dtype('float64')))
+        ... # doctest: +NORMALIZE_WHITESPACE
+        [nan, nan, 1.0, 2.0]
+        Fill: nan
+        IntIndex
+        Indices: array([2, 3], dtype=int32)
+
+        Using a SparseDtype, you can also change the fill value as well.
+
+        >>> arr.astype(SparseDtype("float64", fill_value=0.0))
+        ... # doctest: +NORMALIZE_WHITESPACE
+        [0.0, 0.0, 1.0, 2.0]
+        Fill: 0.0
+        IntIndex
+        Indices: array([2, 3], dtype=int32)
+        """
+        if dtype == self._dtype:
+            if not copy:
+                return self
+            else:
+                return self.copy()
+
+        future_dtype = pandas_dtype(dtype)
+        if not isinstance(future_dtype, SparseDtype):
+            # GH#34457
+            values = np.asarray(self)
+            values = ensure_wrapped_if_datetimelike(values)
+            return astype_array(values, dtype=future_dtype, copy=False)
+
+        dtype = self.dtype.update_dtype(dtype)
+        subtype = pandas_dtype(dtype._subtype_with_str)
+        subtype = cast(np.dtype, subtype)  # ensured by update_dtype
+        values = ensure_wrapped_if_datetimelike(self.sp_values)
+        sp_values = astype_array(values, subtype, copy=copy)
+        sp_values = np.asarray(sp_values)
+
+        return self._simple_new(sp_values, self.sp_index, dtype)
+
+    def map(self, mapper, na_action=None) -> Self:
+        """
+        Map categories using an input mapping or function.
+
+        Parameters
+        ----------
+        mapper : dict, Series, callable
+            The correspondence from old values to new.
+        na_action : {None, 'ignore'}, default None
+            If 'ignore', propagate NA values, without passing them to the
+            mapping correspondence.
+
+        Returns
+        -------
+        SparseArray
+            The output array will have the same density as the input.
+            The output fill value will be the result of applying the
+            mapping to ``self.fill_value``
+
+        Examples
+        --------
+        >>> arr = pd.arrays.SparseArray([0, 1, 2])
+        >>> arr.map(lambda x: x + 10)
+        [10, 11, 12]
+        Fill: 10
+        IntIndex
+        Indices: array([1, 2], dtype=int32)
+
+        >>> arr.map({0: 10, 1: 11, 2: 12})
+        [10, 11, 12]
+        Fill: 10
+        IntIndex
+        Indices: array([1, 2], dtype=int32)
+
+        >>> arr.map(pd.Series([10, 11, 12], index=[0, 1, 2]))
+        [10, 11, 12]
+        Fill: 10
+        IntIndex
+        Indices: array([1, 2], dtype=int32)
+        """
+        is_map = isinstance(mapper, (abc.Mapping, ABCSeries))
+
+        fill_val = self.fill_value
+
+        if na_action is None or notna(fill_val):
+            fill_val = mapper.get(fill_val, fill_val) if is_map else mapper(fill_val)
+
+        def func(sp_val):
+            new_sp_val = mapper.get(sp_val, None) if is_map else mapper(sp_val)
+            # check identity and equality because nans are not equal to each other
+            if new_sp_val is fill_val or new_sp_val == fill_val:
+                msg = "fill value in the sparse values not supported"
+                raise ValueError(msg)
+            return new_sp_val
+
+        sp_values = [func(x) for x in self.sp_values]
+
+        return type(self)(sp_values, sparse_index=self.sp_index, fill_value=fill_val)
+
+    def to_dense(self) -> np.ndarray:
+        """
+        Convert SparseArray to a NumPy array.
+
+        Returns
+        -------
+        arr : NumPy array
+        """
+        return np.asarray(self, dtype=self.sp_values.dtype)
+
+    def _where(self, mask, value):
+        # NB: may not preserve dtype, e.g. result may be Sparse[float64]
+        #  while self is Sparse[int64]
+        naive_implementation = np.where(mask, self, value)
+        dtype = SparseDtype(naive_implementation.dtype, fill_value=self.fill_value)
+        result = type(self)._from_sequence(naive_implementation, dtype=dtype)
+        return result
+
+    # ------------------------------------------------------------------------
+    # IO
+    # ------------------------------------------------------------------------
+    def __setstate__(self, state) -> None:
+        """Necessary for making this object picklable"""
+        if isinstance(state, tuple):
+            # Compat for pandas < 0.24.0
+            nd_state, (fill_value, sp_index) = state
+            sparse_values = np.array([])
+            sparse_values.__setstate__(nd_state)
+
+            self._sparse_values = sparse_values
+            self._sparse_index = sp_index
+            self._dtype = SparseDtype(sparse_values.dtype, fill_value)
+        else:
+            self.__dict__.update(state)
+
+    def nonzero(self) -> tuple[npt.NDArray[np.int32]]:
+        if self.fill_value == 0:
+            return (self.sp_index.indices,)
+        else:
+            return (self.sp_index.indices[self.sp_values != 0],)
+
+    # ------------------------------------------------------------------------
+    # Reductions
+    # ------------------------------------------------------------------------
+
+    def _reduce(
+        self, name: str, *, skipna: bool = True, keepdims: bool = False, **kwargs
+    ):
+        method = getattr(self, name, None)
+
+        if method is None:
+            raise TypeError(f"cannot perform {name} with type {self.dtype}")
+
+        if skipna:
+            arr = self
+        else:
+            arr = self.dropna()
+
+        result = getattr(arr, name)(**kwargs)
+
+        if keepdims:
+            return type(self)([result], dtype=self.dtype)
+        else:
+            return result
+
+    def all(self, axis=None, *args, **kwargs):
+        """
+        Tests whether all elements evaluate True
+
+        Returns
+        -------
+        all : bool
+
+        See Also
+        --------
+        numpy.all
+        """
+        nv.validate_all(args, kwargs)
+
+        values = self.sp_values
+
+        if len(values) != len(self) and not np.all(self.fill_value):
+            return False
+
+        return values.all()
+
+    def any(self, axis: AxisInt = 0, *args, **kwargs) -> bool:
+        """
+        Tests whether at least one of elements evaluate True
+
+        Returns
+        -------
+        any : bool
+
+        See Also
+        --------
+        numpy.any
+        """
+        nv.validate_any(args, kwargs)
+
+        values = self.sp_values
+
+        if len(values) != len(self) and np.any(self.fill_value):
+            return True
+
+        return values.any().item()
+
+    def sum(
+        self,
+        axis: AxisInt = 0,
+        min_count: int = 0,
+        skipna: bool = True,
+        *args,
+        **kwargs,
+    ) -> Scalar:
+        """
+        Sum of non-NA/null values
+
+        Parameters
+        ----------
+        axis : int, default 0
+            Not Used. NumPy compatibility.
+        min_count : int, default 0
+            The required number of valid values to perform the summation. If fewer
+            than ``min_count`` valid values are present, the result will be the missing
+            value indicator for subarray type.
+        *args, **kwargs
+            Not Used. NumPy compatibility.
+
+        Returns
+        -------
+        scalar
+        """
+        nv.validate_sum(args, kwargs)
+        valid_vals = self._valid_sp_values
+        sp_sum = valid_vals.sum()
+        has_na = self.sp_index.ngaps > 0 and not self._null_fill_value
+
+        if has_na and not skipna:
+            return na_value_for_dtype(self.dtype.subtype, compat=False)
+
+        if self._null_fill_value:
+            if check_below_min_count(valid_vals.shape, None, min_count):
+                return na_value_for_dtype(self.dtype.subtype, compat=False)
+            return sp_sum
+        else:
+            nsparse = self.sp_index.ngaps
+            if check_below_min_count(valid_vals.shape, None, min_count - nsparse):
+                return na_value_for_dtype(self.dtype.subtype, compat=False)
+            return sp_sum + self.fill_value * nsparse
+
+    def cumsum(self, axis: AxisInt = 0, *args, **kwargs) -> SparseArray:
+        """
+        Cumulative sum of non-NA/null values.
+
+        When performing the cumulative summation, any non-NA/null values will
+        be skipped. The resulting SparseArray will preserve the locations of
+        NaN values, but the fill value will be `np.nan` regardless.
+
+        Parameters
+        ----------
+        axis : int or None
+            Axis over which to perform the cumulative summation. If None,
+            perform cumulative summation over flattened array.
+
+        Returns
+        -------
+        cumsum : SparseArray
+        """
+        nv.validate_cumsum(args, kwargs)
+
+        if axis is not None and axis >= self.ndim:  # Mimic ndarray behaviour.
+            raise ValueError(f"axis(={axis}) out of bounds")
+
+        if not self._null_fill_value:
+            return SparseArray(self.to_dense()).cumsum()
+
+        return SparseArray(
+            self.sp_values.cumsum(),
+            sparse_index=self.sp_index,
+            fill_value=self.fill_value,
+        )
+
+    def mean(self, axis: Axis = 0, *args, **kwargs):
+        """
+        Mean of non-NA/null values
+
+        Returns
+        -------
+        mean : float
+        """
+        nv.validate_mean(args, kwargs)
+        valid_vals = self._valid_sp_values
+        sp_sum = valid_vals.sum()
+        ct = len(valid_vals)
+
+        if self._null_fill_value:
+            return sp_sum / ct
+        else:
+            nsparse = self.sp_index.ngaps
+            return (sp_sum + self.fill_value * nsparse) / (ct + nsparse)
+
+    def max(self, *, axis: AxisInt | None = None, skipna: bool = True):
+        """
+        Max of array values, ignoring NA values if specified.
+
+        Parameters
+        ----------
+        axis : int, default 0
+            Not Used. NumPy compatibility.
+        skipna : bool, default True
+            Whether to ignore NA values.
+
+        Returns
+        -------
+        scalar
+        """
+        nv.validate_minmax_axis(axis, self.ndim)
+        return self._min_max("max", skipna=skipna)
+
+    def min(self, *, axis: AxisInt | None = None, skipna: bool = True):
+        """
+        Min of array values, ignoring NA values if specified.
+
+        Parameters
+        ----------
+        axis : int, default 0
+            Not Used. NumPy compatibility.
+        skipna : bool, default True
+            Whether to ignore NA values.
+
+        Returns
+        -------
+        scalar
+        """
+        nv.validate_minmax_axis(axis, self.ndim)
+        return self._min_max("min", skipna=skipna)
+
+    def _min_max(self, kind: Literal["min", "max"], skipna: bool) -> Scalar:
+        """
+        Min/max of non-NA/null values
+
+        Parameters
+        ----------
+        kind : {"min", "max"}
+        skipna : bool
+
+        Returns
+        -------
+        scalar
+        """
+        valid_vals = self._valid_sp_values
+        has_nonnull_fill_vals = not self._null_fill_value and self.sp_index.ngaps > 0
+
+        if len(valid_vals) > 0:
+            sp_min_max = getattr(valid_vals, kind)()
+
+            # If a non-null fill value is currently present, it might be the min/max
+            if has_nonnull_fill_vals:
+                func = max if kind == "max" else min
+                return func(sp_min_max, self.fill_value)
+            elif skipna:
+                return sp_min_max
+            elif self.sp_index.ngaps == 0:
+                # No NAs present
+                return sp_min_max
+            else:
+                return na_value_for_dtype(self.dtype.subtype, compat=False)
+        elif has_nonnull_fill_vals:
+            return self.fill_value
+        else:
+            return na_value_for_dtype(self.dtype.subtype, compat=False)
+
+    def _argmin_argmax(self, kind: Literal["argmin", "argmax"]) -> int:
+        values = self._sparse_values
+        index = self._sparse_index.indices
+        mask = np.asarray(isna(values))
+        func = np.argmax if kind == "argmax" else np.argmin
+
+        idx = np.arange(values.shape[0])
+        non_nans = values[~mask]
+        non_nan_idx = idx[~mask]
+
+        _candidate = non_nan_idx[func(non_nans)]
+        candidate = index[_candidate]
+
+        if isna(self.fill_value):
+            return candidate
+        if kind == "argmin" and self[candidate] < self.fill_value:
+            return candidate
+        if kind == "argmax" and self[candidate] > self.fill_value:
+            return candidate
+        _loc = self._first_fill_value_loc()
+        if _loc == -1:
+            # fill_value doesn't exist
+            return candidate
+        else:
+            return _loc
+
+    def argmax(self, skipna: bool = True) -> int:
+        validate_bool_kwarg(skipna, "skipna")
+        if not skipna and self._hasna:
+            raise NotImplementedError
+        return self._argmin_argmax("argmax")
+
+    def argmin(self, skipna: bool = True) -> int:
+        validate_bool_kwarg(skipna, "skipna")
+        if not skipna and self._hasna:
+            raise NotImplementedError
+        return self._argmin_argmax("argmin")
+
+    # ------------------------------------------------------------------------
+    # Ufuncs
+    # ------------------------------------------------------------------------
+
+    _HANDLED_TYPES = (np.ndarray, numbers.Number)
+
+    def __array_ufunc__(self, ufunc: np.ufunc, method: str, *inputs, **kwargs):
+        out = kwargs.get("out", ())
+
+        for x in inputs + out:
+            if not isinstance(x, self._HANDLED_TYPES + (SparseArray,)):
+                return NotImplemented
+
+        # for binary ops, use our custom dunder methods
+        result = arraylike.maybe_dispatch_ufunc_to_dunder_op(
+            self, ufunc, method, *inputs, **kwargs
+        )
+        if result is not NotImplemented:
+            return result
+
+        if "out" in kwargs:
+            # e.g. tests.arrays.sparse.test_arithmetics.test_ndarray_inplace
+            res = arraylike.dispatch_ufunc_with_out(
+                self, ufunc, method, *inputs, **kwargs
+            )
+            return res
+
+        if method == "reduce":
+            result = arraylike.dispatch_reduction_ufunc(
+                self, ufunc, method, *inputs, **kwargs
+            )
+            if result is not NotImplemented:
+                # e.g. tests.series.test_ufunc.TestNumpyReductions
+                return result
+
+        if len(inputs) == 1:
+            # No alignment necessary.
+            sp_values = getattr(ufunc, method)(self.sp_values, **kwargs)
+            fill_value = getattr(ufunc, method)(self.fill_value, **kwargs)
+
+            if ufunc.nout > 1:
+                # multiple outputs. e.g. modf
+                arrays = tuple(
+                    self._simple_new(
+                        sp_value, self.sp_index, SparseDtype(sp_value.dtype, fv)
+                    )
+                    for sp_value, fv in zip(sp_values, fill_value)
+                )
+                return arrays
+            elif method == "reduce":
+                # e.g. reductions
+                return sp_values
+
+            return self._simple_new(
+                sp_values, self.sp_index, SparseDtype(sp_values.dtype, fill_value)
+            )
+
+        new_inputs = tuple(np.asarray(x) for x in inputs)
+        result = getattr(ufunc, method)(*new_inputs, **kwargs)
+        if out:
+            if len(out) == 1:
+                out = out[0]
+            return out
+
+        if ufunc.nout > 1:
+            return tuple(type(self)(x) for x in result)
+        elif method == "at":
+            # no return value
+            return None
+        else:
+            return type(self)(result)
+
+    # ------------------------------------------------------------------------
+    # Ops
+    # ------------------------------------------------------------------------
+
+    def _arith_method(self, other, op):
+        op_name = op.__name__
+
+        if isinstance(other, SparseArray):
+            return _sparse_array_op(self, other, op, op_name)
+
+        elif is_scalar(other):
+            with np.errstate(all="ignore"):
+                fill = op(_get_fill(self), np.asarray(other))
+                result = op(self.sp_values, other)
+
+            if op_name == "divmod":
+                left, right = result
+                lfill, rfill = fill
+                return (
+                    _wrap_result(op_name, left, self.sp_index, lfill),
+                    _wrap_result(op_name, right, self.sp_index, rfill),
+                )
+
+            return _wrap_result(op_name, result, self.sp_index, fill)
+
+        else:
+            other = np.asarray(other)
+            with np.errstate(all="ignore"):
+                if len(self) != len(other):
+                    raise AssertionError(
+                        f"length mismatch: {len(self)} vs. {len(other)}"
+                    )
+                if not isinstance(other, SparseArray):
+                    dtype = getattr(other, "dtype", None)
+                    other = SparseArray(other, fill_value=self.fill_value, dtype=dtype)
+                return _sparse_array_op(self, other, op, op_name)
+
+    def _cmp_method(self, other, op) -> SparseArray:
+        if not is_scalar(other) and not isinstance(other, type(self)):
+            # convert list-like to ndarray
+            other = np.asarray(other)
+
+        if isinstance(other, np.ndarray):
+            # TODO: make this more flexible than just ndarray...
+            other = SparseArray(other, fill_value=self.fill_value)
+
+        if isinstance(other, SparseArray):
+            if len(self) != len(other):
+                raise ValueError(
+                    f"operands have mismatched length {len(self)} and {len(other)}"
+                )
+
+            op_name = op.__name__.strip("_")
+            return _sparse_array_op(self, other, op, op_name)
+        else:
+            # scalar
+            fill_value = op(self.fill_value, other)
+            result = np.full(len(self), fill_value, dtype=np.bool_)
+            result[self.sp_index.indices] = op(self.sp_values, other)
+
+            return type(self)(
+                result,
+                fill_value=fill_value,
+                dtype=np.bool_,
+            )
+
+    _logical_method = _cmp_method
+
+    def _unary_method(self, op) -> SparseArray:
+        fill_value = op(np.array(self.fill_value)).item()
+        dtype = SparseDtype(self.dtype.subtype, fill_value)
+        # NOTE: if fill_value doesn't change
+        # we just have to apply op to sp_values
+        if isna(self.fill_value) or fill_value == self.fill_value:
+            values = op(self.sp_values)
+            return type(self)._simple_new(values, self.sp_index, self.dtype)
+        # In the other case we have to recalc indexes
+        return type(self)(op(self.to_dense()), dtype=dtype)
+
+    def __pos__(self) -> SparseArray:
+        return self._unary_method(operator.pos)
+
+    def __neg__(self) -> SparseArray:
+        return self._unary_method(operator.neg)
+
+    def __invert__(self) -> SparseArray:
+        return self._unary_method(operator.invert)
+
+    def __abs__(self) -> SparseArray:
+        return self._unary_method(operator.abs)
+
+    # ----------
+    # Formatting
+    # -----------
+    def __repr__(self) -> str:
+        pp_str = printing.pprint_thing(self)
+        pp_fill = printing.pprint_thing(self.fill_value)
+        pp_index = printing.pprint_thing(self.sp_index)
+        return f"{pp_str}\nFill: {pp_fill}\n{pp_index}"
+
+    def _formatter(self, boxed: bool = False):
+        # Defer to the formatter from the GenericArrayFormatter calling us.
+        # This will infer the correct formatter from the dtype of the values.
+        return None
+
+
+def _make_sparse(
+    arr: np.ndarray,
+    kind: SparseIndexKind = "block",
+    fill_value=None,
+    dtype: np.dtype | None = None,
+):
+    """
+    Convert ndarray to sparse format
+
+    Parameters
+    ----------
+    arr : ndarray
+    kind : {'block', 'integer'}
+    fill_value : NaN or another value
+    dtype : np.dtype, optional
+    copy : bool, default False
+
+    Returns
+    -------
+    (sparse_values, index, fill_value) : (ndarray, SparseIndex, Scalar)
+    """
+    assert isinstance(arr, np.ndarray)
+
+    if arr.ndim > 1:
+        raise TypeError("expected dimension <= 1 data")
+
+    if fill_value is None:
+        fill_value = na_value_for_dtype(arr.dtype)
+
+    if isna(fill_value):
+        mask = notna(arr)
+    else:
+        # cast to object comparison to be safe
+        if is_string_dtype(arr.dtype):
+            arr = arr.astype(object)
+
+        if is_object_dtype(arr.dtype):
+            # element-wise equality check method in numpy doesn't treat
+            # each element type, eg. 0, 0.0, and False are treated as
+            # same. So we have to check the both of its type and value.
+            mask = splib.make_mask_object_ndarray(arr, fill_value)
+        else:
+            mask = arr != fill_value
+
+    length = len(arr)
+    if length != len(mask):
+        # the arr is a SparseArray
+        indices = mask.sp_index.indices
+    else:
+        indices = mask.nonzero()[0].astype(np.int32)
+
+    index = make_sparse_index(length, indices, kind)
+    sparsified_values = arr[mask]
+    if dtype is not None:
+        sparsified_values = ensure_wrapped_if_datetimelike(sparsified_values)
+        sparsified_values = astype_array(sparsified_values, dtype=dtype)
+        sparsified_values = np.asarray(sparsified_values)
+
+    # TODO: copy
+    return sparsified_values, index, fill_value
+
+
+@overload
+def make_sparse_index(length: int, indices, kind: Literal["block"]) -> BlockIndex:
+    ...
+
+
+@overload
+def make_sparse_index(length: int, indices, kind: Literal["integer"]) -> IntIndex:
+    ...
+
+
+def make_sparse_index(length: int, indices, kind: SparseIndexKind) -> SparseIndex:
+    index: SparseIndex
+    if kind == "block":
+        locs, lens = splib.get_blocks(indices)
+        index = BlockIndex(length, locs, lens)
+    elif kind == "integer":
+        index = IntIndex(length, indices)
+    else:  # pragma: no cover
+        raise ValueError("must be block or integer type")
+    return index
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/sparse/scipy_sparse.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/sparse/scipy_sparse.py
new file mode 100644
index 0000000000000000000000000000000000000000..71b71a9779da5c1a584e0ef98bc8320d81bc2a35
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/sparse/scipy_sparse.py
@@ -0,0 +1,207 @@
+"""
+Interaction with scipy.sparse matrices.
+
+Currently only includes to_coo helpers.
+"""
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+from pandas._libs import lib
+
+from pandas.core.dtypes.missing import notna
+
+from pandas.core.algorithms import factorize
+from pandas.core.indexes.api import MultiIndex
+from pandas.core.series import Series
+
+if TYPE_CHECKING:
+    from collections.abc import Iterable
+
+    import numpy as np
+    import scipy.sparse
+
+    from pandas._typing import (
+        IndexLabel,
+        npt,
+    )
+
+
+def _check_is_partition(parts: Iterable, whole: Iterable):
+    whole = set(whole)
+    parts = [set(x) for x in parts]
+    if set.intersection(*parts) != set():
+        raise ValueError("Is not a partition because intersection is not null.")
+    if set.union(*parts) != whole:
+        raise ValueError("Is not a partition because union is not the whole.")
+
+
+def _levels_to_axis(
+    ss,
+    levels: tuple[int] | list[int],
+    valid_ilocs: npt.NDArray[np.intp],
+    sort_labels: bool = False,
+) -> tuple[npt.NDArray[np.intp], list[IndexLabel]]:
+    """
+    For a MultiIndexed sparse Series `ss`, return `ax_coords` and `ax_labels`,
+    where `ax_coords` are the coordinates along one of the two axes of the
+    destination sparse matrix, and `ax_labels` are the labels from `ss`' Index
+    which correspond to these coordinates.
+
+    Parameters
+    ----------
+    ss : Series
+    levels : tuple/list
+    valid_ilocs : numpy.ndarray
+        Array of integer positions of valid values for the sparse matrix in ss.
+    sort_labels : bool, default False
+        Sort the axis labels before forming the sparse matrix. When `levels`
+        refers to a single level, set to True for a faster execution.
+
+    Returns
+    -------
+    ax_coords : numpy.ndarray (axis coordinates)
+    ax_labels : list (axis labels)
+    """
+    # Since the labels are sorted in `Index.levels`, when we wish to sort and
+    # there is only one level of the MultiIndex for this axis, the desired
+    # output can be obtained in the following simpler, more efficient way.
+    if sort_labels and len(levels) == 1:
+        ax_coords = ss.index.codes[levels[0]][valid_ilocs]
+        ax_labels = ss.index.levels[levels[0]]
+
+    else:
+        levels_values = lib.fast_zip(
+            [ss.index.get_level_values(lvl).to_numpy() for lvl in levels]
+        )
+        codes, ax_labels = factorize(levels_values, sort=sort_labels)
+        ax_coords = codes[valid_ilocs]
+
+    ax_labels = ax_labels.tolist()
+    return ax_coords, ax_labels
+
+
+def _to_ijv(
+    ss,
+    row_levels: tuple[int] | list[int] = (0,),
+    column_levels: tuple[int] | list[int] = (1,),
+    sort_labels: bool = False,
+) -> tuple[
+    np.ndarray,
+    npt.NDArray[np.intp],
+    npt.NDArray[np.intp],
+    list[IndexLabel],
+    list[IndexLabel],
+]:
+    """
+    For an arbitrary MultiIndexed sparse Series return (v, i, j, ilabels,
+    jlabels) where (v, (i, j)) is suitable for passing to scipy.sparse.coo
+    constructor, and ilabels and jlabels are the row and column labels
+    respectively.
+
+    Parameters
+    ----------
+    ss : Series
+    row_levels : tuple/list
+    column_levels : tuple/list
+    sort_labels : bool, default False
+        Sort the row and column labels before forming the sparse matrix.
+        When `row_levels` and/or `column_levels` refer to a single level,
+        set to `True` for a faster execution.
+
+    Returns
+    -------
+    values : numpy.ndarray
+        Valid values to populate a sparse matrix, extracted from
+        ss.
+    i_coords : numpy.ndarray (row coordinates of the values)
+    j_coords : numpy.ndarray (column coordinates of the values)
+    i_labels : list (row labels)
+    j_labels : list (column labels)
+    """
+    # index and column levels must be a partition of the index
+    _check_is_partition([row_levels, column_levels], range(ss.index.nlevels))
+    # From the sparse Series, get the integer indices and data for valid sparse
+    # entries.
+    sp_vals = ss.array.sp_values
+    na_mask = notna(sp_vals)
+    values = sp_vals[na_mask]
+    valid_ilocs = ss.array.sp_index.indices[na_mask]
+
+    i_coords, i_labels = _levels_to_axis(
+        ss, row_levels, valid_ilocs, sort_labels=sort_labels
+    )
+
+    j_coords, j_labels = _levels_to_axis(
+        ss, column_levels, valid_ilocs, sort_labels=sort_labels
+    )
+
+    return values, i_coords, j_coords, i_labels, j_labels
+
+
+def sparse_series_to_coo(
+    ss: Series,
+    row_levels: Iterable[int] = (0,),
+    column_levels: Iterable[int] = (1,),
+    sort_labels: bool = False,
+) -> tuple[scipy.sparse.coo_matrix, list[IndexLabel], list[IndexLabel]]:
+    """
+    Convert a sparse Series to a scipy.sparse.coo_matrix using index
+    levels row_levels, column_levels as the row and column
+    labels respectively. Returns the sparse_matrix, row and column labels.
+    """
+    import scipy.sparse
+
+    if ss.index.nlevels < 2:
+        raise ValueError("to_coo requires MultiIndex with nlevels >= 2.")
+    if not ss.index.is_unique:
+        raise ValueError(
+            "Duplicate index entries are not allowed in to_coo transformation."
+        )
+
+    # to keep things simple, only rely on integer indexing (not labels)
+    row_levels = [ss.index._get_level_number(x) for x in row_levels]
+    column_levels = [ss.index._get_level_number(x) for x in column_levels]
+
+    v, i, j, rows, columns = _to_ijv(
+        ss, row_levels=row_levels, column_levels=column_levels, sort_labels=sort_labels
+    )
+    sparse_matrix = scipy.sparse.coo_matrix(
+        (v, (i, j)), shape=(len(rows), len(columns))
+    )
+    return sparse_matrix, rows, columns
+
+
+def coo_to_sparse_series(
+    A: scipy.sparse.coo_matrix, dense_index: bool = False
+) -> Series:
+    """
+    Convert a scipy.sparse.coo_matrix to a Series with type sparse.
+
+    Parameters
+    ----------
+    A : scipy.sparse.coo_matrix
+    dense_index : bool, default False
+
+    Returns
+    -------
+    Series
+
+    Raises
+    ------
+    TypeError if A is not a coo_matrix
+    """
+    from pandas import SparseDtype
+
+    try:
+        ser = Series(A.data, MultiIndex.from_arrays((A.row, A.col)), copy=False)
+    except AttributeError as err:
+        raise TypeError(
+            f"Expected coo_matrix. Got {type(A).__name__} instead."
+        ) from err
+    ser = ser.sort_index()
+    ser = ser.astype(SparseDtype(ser.dtype))
+    if dense_index:
+        ind = MultiIndex.from_product([A.row, A.col])
+        ser = ser.reindex(ind)
+    return ser
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/string_.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/string_.py
new file mode 100644
index 0000000000000000000000000000000000000000..edc5529d0d78c78b4981ec953c4f202c23988914
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/string_.py
@@ -0,0 +1,1135 @@
+from __future__ import annotations
+
+from functools import partial
+import operator
+from pathlib import Path
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Literal,
+    cast,
+)
+import warnings
+
+import numpy as np
+
+from pandas._config import (
+    get_option,
+    using_string_dtype,
+)
+
+from pandas._libs import (
+    lib,
+    missing as libmissing,
+)
+from pandas._libs.arrays import NDArrayBacked
+from pandas._libs.lib import ensure_string_array
+from pandas.compat import (
+    HAS_PYARROW,
+    pa_version_under10p1,
+)
+from pandas.compat.numpy import function as nv
+from pandas.util._decorators import doc
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.base import (
+    ExtensionDtype,
+    StorageExtensionDtype,
+    register_extension_dtype,
+)
+from pandas.core.dtypes.common import (
+    is_array_like,
+    is_bool_dtype,
+    is_integer_dtype,
+    is_object_dtype,
+    is_string_dtype,
+    pandas_dtype,
+)
+
+from pandas.core import (
+    missing,
+    nanops,
+    ops,
+    roperator,
+)
+from pandas.core.algorithms import isin
+from pandas.core.array_algos import masked_reductions
+from pandas.core.arrays.base import ExtensionArray
+from pandas.core.arrays.floating import (
+    FloatingArray,
+    FloatingDtype,
+)
+from pandas.core.arrays.integer import (
+    IntegerArray,
+    IntegerDtype,
+)
+from pandas.core.arrays.numpy_ import NumpyExtensionArray
+from pandas.core.construction import extract_array
+from pandas.core.indexers import check_array_indexer
+from pandas.core.missing import isna
+
+from pandas.io.formats import printing
+
+if TYPE_CHECKING:
+    from collections.abc import MutableMapping
+
+    import pyarrow
+
+    from pandas._typing import (
+        ArrayLike,
+        AxisInt,
+        Dtype,
+        DtypeObj,
+        NumpySorter,
+        NumpyValueArrayLike,
+        Scalar,
+        Self,
+        npt,
+        type_t,
+    )
+
+    from pandas import Series
+
+
+@register_extension_dtype
+class StringDtype(StorageExtensionDtype):
+    """
+    Extension dtype for string data.
+
+    .. warning::
+
+       StringDtype is considered experimental. The implementation and
+       parts of the API may change without warning.
+
+    Parameters
+    ----------
+    storage : {"python", "pyarrow"}, optional
+        If not given, the value of ``pd.options.mode.string_storage``.
+    na_value : {np.nan, pd.NA}, default pd.NA
+        Whether the dtype follows NaN or NA missing value semantics.
+
+    Attributes
+    ----------
+    None
+
+    Methods
+    -------
+    None
+
+    Examples
+    --------
+    >>> pd.StringDtype()
+    string[python]
+
+    >>> pd.StringDtype(storage="pyarrow")
+    string[pyarrow]
+    """
+
+    @property
+    def name(self) -> str:  # type: ignore[override]
+        if self._na_value is libmissing.NA:
+            return "string"
+        else:
+            return "str"
+
+    #: StringDtype().na_value uses pandas.NA except the implementation that
+    # follows NumPy semantics, which uses nan.
+    @property
+    def na_value(self) -> libmissing.NAType | float:  # type: ignore[override]
+        return self._na_value
+
+    _metadata = ("storage", "_na_value")  # type: ignore[assignment]
+
+    def __init__(
+        self,
+        storage: str | None = None,
+        na_value: libmissing.NAType | float = libmissing.NA,
+    ) -> None:
+        # infer defaults
+        if storage is None:
+            if na_value is not libmissing.NA:
+                storage = get_option("mode.string_storage")
+                if storage == "auto":
+                    if HAS_PYARROW:
+                        storage = "pyarrow"
+                    else:
+                        storage = "python"
+            else:
+                storage = get_option("mode.string_storage")
+                if storage == "auto":
+                    storage = "python"
+
+        if storage == "pyarrow_numpy":
+            warnings.warn(
+                "The 'pyarrow_numpy' storage option name is deprecated and will be "
+                'removed in pandas 3.0. Use \'pd.StringDtype(storage="pyarrow", '
+                "na_value-np.nan)' to construct the same dtype.\nOr enable the "
+                "'pd.options.future.infer_string = True' option globally and use "
+                'the "str" alias as a shorthand notation to specify a dtype '
+                '(instead of "string[pyarrow_numpy]").',
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+            storage = "pyarrow"
+            na_value = np.nan
+
+        # validate options
+        if storage not in {"python", "pyarrow"}:
+            raise ValueError(
+                f"Storage must be 'python' or 'pyarrow'. Got {storage} instead."
+            )
+        if storage == "pyarrow" and pa_version_under10p1:
+            raise ImportError(
+                "pyarrow>=10.0.1 is required for PyArrow backed StringArray."
+            )
+
+        if isinstance(na_value, float) and np.isnan(na_value):
+            # when passed a NaN value, always set to np.nan to ensure we use
+            # a consistent NaN value (and we can use `dtype.na_value is np.nan`)
+            na_value = np.nan
+        elif na_value is not libmissing.NA:
+            raise ValueError(f"'na_value' must be np.nan or pd.NA, got {na_value}")
+
+        self.storage = cast(str, storage)
+        self._na_value = na_value
+
+    def __repr__(self) -> str:
+        if self._na_value is libmissing.NA:
+            return f"{self.name}[{self.storage}]"
+        else:
+            storage = "" if self.storage == "pyarrow" else "storage='python', "
+            return f""
+
+    def __eq__(self, other: object) -> bool:
+        # we need to override the base class __eq__ because na_value (NA or NaN)
+        # cannot be checked with normal `==`
+        if isinstance(other, str):
+            # TODO should dtype == "string" work for the NaN variant?
+            if other == "string" or other == self.name:  # noqa: PLR1714
+                return True
+            try:
+                other = self.construct_from_string(other)
+            except (TypeError, ImportError):
+                # TypeError if `other` is not a valid string for StringDtype
+                # ImportError if pyarrow is not installed for "string[pyarrow]"
+                return False
+        if isinstance(other, type(self)):
+            return self.storage == other.storage and self.na_value is other.na_value
+        return False
+
+    def __setstate__(self, state: MutableMapping[str, Any]) -> None:
+        # back-compat for pandas < 2.3, where na_value did not yet exist
+        self.storage = state.pop("storage", "python")
+        self._na_value = state.pop("_na_value", libmissing.NA)
+
+    def __hash__(self) -> int:
+        # need to override __hash__ as well because of overriding __eq__
+        return super().__hash__()
+
+    def __reduce__(self):
+        return StringDtype, (self.storage, self.na_value)
+
+    @property
+    def type(self) -> type[str]:
+        return str
+
+    @classmethod
+    def construct_from_string(cls, string) -> Self:
+        """
+        Construct a StringDtype from a string.
+
+        Parameters
+        ----------
+        string : str
+            The type of the name. The storage type will be taking from `string`.
+            Valid options and their storage types are
+
+            ========================== ==============================================
+            string                     result storage
+            ========================== ==============================================
+            ``'string'``               pd.options.mode.string_storage, default python
+            ``'string[python]'``       python
+            ``'string[pyarrow]'``      pyarrow
+            ========================== ==============================================
+
+        Returns
+        -------
+        StringDtype
+
+        Raise
+        -----
+        TypeError
+            If the string is not a valid option.
+        """
+        if not isinstance(string, str):
+            raise TypeError(
+                f"'construct_from_string' expects a string, got {type(string)}"
+            )
+        if string == "string":
+            return cls()
+        elif string == "str" and using_string_dtype():
+            return cls(na_value=np.nan)
+        elif string == "string[python]":
+            return cls(storage="python")
+        elif string == "string[pyarrow]":
+            return cls(storage="pyarrow")
+        elif string == "string[pyarrow_numpy]":
+            # this is deprecated in the dtype __init__, remove this in pandas 3.0
+            return cls(storage="pyarrow_numpy")
+        else:
+            raise TypeError(f"Cannot construct a '{cls.__name__}' from '{string}'")
+
+    # https://github.com/pandas-dev/pandas/issues/36126
+    # error: Signature of "construct_array_type" incompatible with supertype
+    # "ExtensionDtype"
+    def construct_array_type(  # type: ignore[override]
+        self,
+    ) -> type_t[BaseStringArray]:
+        """
+        Return the array type associated with this dtype.
+
+        Returns
+        -------
+        type
+        """
+        from pandas.core.arrays.string_arrow import (
+            ArrowStringArray,
+            ArrowStringArrayNumpySemantics,
+        )
+
+        if self.storage == "python" and self._na_value is libmissing.NA:
+            return StringArray
+        elif self.storage == "pyarrow" and self._na_value is libmissing.NA:
+            return ArrowStringArray
+        elif self.storage == "python":
+            return StringArrayNumpySemantics
+        else:
+            return ArrowStringArrayNumpySemantics
+
+    def _get_common_dtype(self, dtypes: list[DtypeObj]) -> DtypeObj | None:
+        storages = set()
+        na_values = set()
+
+        for dtype in dtypes:
+            if isinstance(dtype, StringDtype):
+                storages.add(dtype.storage)
+                na_values.add(dtype.na_value)
+            elif isinstance(dtype, np.dtype) and dtype.kind in ("U", "T"):
+                continue
+            else:
+                return None
+
+        if len(storages) == 2:
+            # if both python and pyarrow storage -> priority to pyarrow
+            storage = "pyarrow"
+        else:
+            storage = next(iter(storages))  # type: ignore[assignment]
+
+        na_value: libmissing.NAType | float
+        if len(na_values) == 2:
+            # if both NaN and NA -> priority to NA
+            na_value = libmissing.NA
+        else:
+            na_value = next(iter(na_values))
+
+        return StringDtype(storage=storage, na_value=na_value)
+
+    def __from_arrow__(
+        self, array: pyarrow.Array | pyarrow.ChunkedArray
+    ) -> BaseStringArray:
+        """
+        Construct StringArray from pyarrow Array/ChunkedArray.
+        """
+        if self.storage == "pyarrow":
+            if self._na_value is libmissing.NA:
+                from pandas.core.arrays.string_arrow import ArrowStringArray
+
+                return ArrowStringArray(array)
+            else:
+                from pandas.core.arrays.string_arrow import (
+                    ArrowStringArrayNumpySemantics,
+                )
+
+                return ArrowStringArrayNumpySemantics(array)
+
+        else:
+            import pyarrow
+
+            if isinstance(array, pyarrow.Array):
+                chunks = [array]
+            else:
+                # pyarrow.ChunkedArray
+                chunks = array.chunks
+
+            results = []
+            for arr in chunks:
+                # convert chunk by chunk to numpy and concatenate then, to avoid
+                # overflow for large string data when concatenating the pyarrow arrays
+                arr = arr.to_numpy(zero_copy_only=False)
+                arr = ensure_string_array(arr, na_value=self.na_value)
+                results.append(arr)
+
+        if len(chunks) == 0:
+            arr = np.array([], dtype=object)
+        else:
+            arr = np.concatenate(results)
+
+        # Bypass validation inside StringArray constructor, see GH#47781
+        new_string_array = StringArray.__new__(StringArray)
+        NDArrayBacked.__init__(new_string_array, arr, self)
+        return new_string_array
+
+
+class BaseStringArray(ExtensionArray):
+    """
+    Mixin class for StringArray, ArrowStringArray.
+    """
+
+    dtype: StringDtype
+
+    # TODO(4.0): Once the deprecation here is enforced, this method can be
+    #  removed and we use the parent class method instead.
+    def _logical_method(self, other, op):
+        if (
+            op in (roperator.ror_, roperator.rand_, roperator.rxor)
+            and isinstance(other, np.ndarray)
+            and other.dtype == bool
+        ):
+            # GH#60234 backward compatibility for the move to StringDtype in 3.0
+            op_name = op.__name__[1:].strip("_")
+            warnings.warn(
+                f"'{op_name}' operations between boolean dtype and {self.dtype} are "
+                "deprecated and will raise in a future version. Explicitly "
+                "cast the strings to a boolean dtype before operating instead.",
+                DeprecationWarning,
+                stacklevel=find_stack_level(),
+            )
+            return op(other, self.astype(bool))
+        return NotImplemented
+
+    @doc(ExtensionArray.tolist)
+    def tolist(self):
+        if self.ndim > 1:
+            return [x.tolist() for x in self]
+        return list(self.to_numpy())
+
+    @classmethod
+    def _from_scalars(cls, scalars, dtype: DtypeObj) -> Self:
+        if lib.infer_dtype(scalars, skipna=True) not in ["string", "empty"]:
+            # TODO: require any NAs be valid-for-string
+            raise ValueError
+        return cls._from_sequence(scalars, dtype=dtype)
+
+    def _formatter(self, boxed: bool = False):
+        formatter = partial(
+            printing.pprint_thing,
+            escape_chars=("\t", "\r", "\n"),
+            quote_strings=not boxed,
+        )
+        return formatter
+
+    def _str_map(
+        self,
+        f,
+        na_value=lib.no_default,
+        dtype: Dtype | None = None,
+        convert: bool = True,
+    ):
+        if self.dtype.na_value is np.nan:
+            return self._str_map_nan_semantics(
+                f, na_value=na_value, dtype=dtype, convert=convert
+            )
+
+        from pandas.arrays import BooleanArray
+
+        if dtype is None:
+            dtype = self.dtype
+        if na_value is lib.no_default:
+            na_value = self.dtype.na_value
+
+        mask = isna(self)
+        arr = np.asarray(self)
+
+        if is_integer_dtype(dtype) or is_bool_dtype(dtype):
+            constructor: type[IntegerArray | BooleanArray]
+            if is_integer_dtype(dtype):
+                constructor = IntegerArray
+            else:
+                constructor = BooleanArray
+
+            na_value_is_na = isna(na_value)
+            if na_value_is_na:
+                na_value = 1
+            elif dtype == np.dtype("bool"):
+                # GH#55736
+                na_value = bool(na_value)
+            result = lib.map_infer_mask(
+                arr,
+                f,
+                mask.view("uint8"),
+                convert=False,
+                na_value=na_value,
+                # error: Argument 1 to "dtype" has incompatible type
+                # "Union[ExtensionDtype, str, dtype[Any], Type[object]]"; expected
+                # "Type[object]"
+                dtype=np.dtype(cast(type, dtype)),
+            )
+
+            if not na_value_is_na:
+                mask[:] = False
+
+            return constructor(result, mask)
+
+        else:
+            return self._str_map_str_or_object(dtype, na_value, arr, f, mask)
+
+    def _str_map_str_or_object(
+        self,
+        dtype,
+        na_value,
+        arr: np.ndarray,
+        f,
+        mask: npt.NDArray[np.bool_],
+    ):
+        # _str_map helper for case where dtype is either string dtype or object
+        if is_string_dtype(dtype) and not is_object_dtype(dtype):
+            # i.e. StringDtype
+            result = lib.map_infer_mask(
+                arr, f, mask.view("uint8"), convert=False, na_value=na_value
+            )
+            if self.dtype.storage == "pyarrow":
+                import pyarrow as pa
+
+                result = pa.array(
+                    result, mask=mask, type=pa.large_string(), from_pandas=True
+                )
+            # error: Too many arguments for "BaseStringArray"
+            return type(self)(result)  # type: ignore[call-arg]
+
+        else:
+            # This is when the result type is object. We reach this when
+            # -> We know the result type is truly object (e.g. .encode returns bytes
+            #    or .findall returns a list).
+            # -> We don't know the result type. E.g. `.get` can return anything.
+            return lib.map_infer_mask(arr, f, mask.view("uint8"))
+
+    def _str_map_nan_semantics(
+        self,
+        f,
+        na_value=lib.no_default,
+        dtype: Dtype | None = None,
+        convert: bool = True,
+    ):
+        if dtype is None:
+            dtype = self.dtype
+        if na_value is lib.no_default:
+            if is_bool_dtype(dtype):
+                # NaN propagates as False
+                na_value = False
+            else:
+                na_value = self.dtype.na_value
+
+        mask = isna(self)
+        arr = np.asarray(self)
+
+        if is_integer_dtype(dtype) or is_bool_dtype(dtype):
+            na_value_is_na = isna(na_value)
+            if na_value_is_na:
+                if is_integer_dtype(dtype):
+                    na_value = 0
+                else:
+                    # NaN propagates as False
+                    na_value = False
+
+            result = lib.map_infer_mask(
+                arr,
+                f,
+                mask.view("uint8"),
+                convert=False,
+                na_value=na_value,
+                dtype=np.dtype(cast(type, dtype)),
+            )
+            if na_value_is_na and is_integer_dtype(dtype) and mask.any():
+                # TODO: we could alternatively do this check before map_infer_mask
+                #  and adjust the dtype/na_value we pass there. Which is more
+                #  performant?
+                result = result.astype("float64")
+                result[mask] = np.nan
+
+            return result
+
+        else:
+            return self._str_map_str_or_object(dtype, na_value, arr, f, mask)
+
+    def view(self, dtype: Dtype | None = None) -> ArrayLike:
+        if dtype is not None:
+            raise TypeError("Cannot change data-type for string array.")
+        return super().view(dtype=dtype)
+
+
+# error: Definition of "_concat_same_type" in base class "NDArrayBacked" is
+# incompatible with definition in base class "ExtensionArray"
+class StringArray(BaseStringArray, NumpyExtensionArray):  # type: ignore[misc]
+    """
+    Extension array for string data.
+
+    .. warning::
+
+       StringArray is considered experimental. The implementation and
+       parts of the API may change without warning.
+
+    Parameters
+    ----------
+    values : array-like
+        The array of data.
+
+        .. warning::
+
+           Currently, this expects an object-dtype ndarray
+           where the elements are Python strings
+           or nan-likes (``None``, ``np.nan``, ``NA``).
+           This may change without warning in the future. Use
+           :meth:`pandas.array` with ``dtype="string"`` for a stable way of
+           creating a `StringArray` from any sequence.
+
+        .. versionchanged:: 1.5.0
+
+           StringArray now accepts array-likes containing
+           nan-likes(``None``, ``np.nan``) for the ``values`` parameter
+           in addition to strings and :attr:`pandas.NA`
+
+    copy : bool, default False
+        Whether to copy the array of data.
+
+    Attributes
+    ----------
+    None
+
+    Methods
+    -------
+    None
+
+    See Also
+    --------
+    :func:`pandas.array`
+        The recommended function for creating a StringArray.
+    Series.str
+        The string methods are available on Series backed by
+        a StringArray.
+
+    Notes
+    -----
+    StringArray returns a BooleanArray for comparison methods.
+
+    Examples
+    --------
+    >>> pd.array(['This is', 'some text', None, 'data.'], dtype="string")
+    
+    ['This is', 'some text', , 'data.']
+    Length: 4, dtype: string
+
+    Unlike arrays instantiated with ``dtype="object"``, ``StringArray``
+    will convert the values to strings.
+
+    >>> pd.array(['1', 1], dtype="object")
+    
+    ['1', 1]
+    Length: 2, dtype: object
+    >>> pd.array(['1', 1], dtype="string")
+    
+    ['1', '1']
+    Length: 2, dtype: string
+
+    However, instantiating StringArrays directly with non-strings will raise an error.
+
+    For comparison methods, `StringArray` returns a :class:`pandas.BooleanArray`:
+
+    >>> pd.array(["a", None, "c"], dtype="string") == "a"
+    
+    [True, , False]
+    Length: 3, dtype: boolean
+    """
+
+    # undo the NumpyExtensionArray hack
+    _typ = "extension"
+    _storage = "python"
+    _na_value: libmissing.NAType | float = libmissing.NA
+
+    def __init__(self, values, copy: bool = False) -> None:
+        values = extract_array(values)
+
+        super().__init__(values, copy=copy)
+        if not isinstance(values, type(self)):
+            self._validate()
+        NDArrayBacked.__init__(
+            self,
+            self._ndarray,
+            StringDtype(storage=self._storage, na_value=self._na_value),
+        )
+
+    def _validate(self):
+        """Validate that we only store NA or strings."""
+        if len(self._ndarray) and not lib.is_string_array(self._ndarray, skipna=True):
+            raise ValueError("StringArray requires a sequence of strings or pandas.NA")
+        if self._ndarray.dtype != "object":
+            raise ValueError(
+                "StringArray requires a sequence of strings or pandas.NA. Got "
+                f"'{self._ndarray.dtype}' dtype instead."
+            )
+        # Check to see if need to convert Na values to pd.NA
+        if self._ndarray.ndim > 2:
+            # Ravel if ndims > 2 b/c no cythonized version available
+            lib.convert_nans_to_NA(self._ndarray.ravel("K"))
+        else:
+            lib.convert_nans_to_NA(self._ndarray)
+
+    def _validate_scalar(self, value):
+        # used by NDArrayBackedExtensionIndex.insert
+        if isna(value):
+            return self.dtype.na_value
+        elif not isinstance(value, str):
+            raise TypeError(
+                f"Invalid value '{value}' for dtype '{self.dtype}'. Value should be a "
+                f"string or missing value, got '{type(value).__name__}' instead."
+            )
+        return value
+
+    @classmethod
+    def _from_sequence(cls, scalars, *, dtype: Dtype | None = None, copy: bool = False):
+        if dtype and not (isinstance(dtype, str) and dtype == "string"):
+            dtype = pandas_dtype(dtype)
+            assert isinstance(dtype, StringDtype) and dtype.storage == "python"
+        else:
+            if using_string_dtype():
+                dtype = StringDtype(storage="python", na_value=np.nan)
+            else:
+                dtype = StringDtype(storage="python")
+
+        from pandas.core.arrays.masked import BaseMaskedArray
+
+        na_value = dtype.na_value
+        if isinstance(scalars, BaseMaskedArray):
+            # avoid costly conversion to object dtype
+            na_values = scalars._mask
+            result = scalars._data
+            result = lib.ensure_string_array(result, copy=copy, convert_na_value=False)
+            result[na_values] = na_value
+
+        else:
+            if lib.is_pyarrow_array(scalars):
+                # pyarrow array; we cannot rely on the "to_numpy" check in
+                #  ensure_string_array because calling scalars.to_numpy would set
+                #  zero_copy_only to True which caused problems see GH#52076
+                scalars = np.array(scalars)
+            # convert non-na-likes to str, and nan-likes to StringDtype().na_value
+            result = lib.ensure_string_array(scalars, na_value=na_value, copy=copy)
+
+        # Manually creating new array avoids the validation step in the __init__, so is
+        # faster. Refactor need for validation?
+        new_string_array = cls.__new__(cls)
+        NDArrayBacked.__init__(new_string_array, result, dtype)
+
+        return new_string_array
+
+    @classmethod
+    def _from_sequence_of_strings(
+        cls, strings, *, dtype: Dtype | None = None, copy: bool = False
+    ):
+        return cls._from_sequence(strings, dtype=dtype, copy=copy)
+
+    @classmethod
+    def _empty(cls, shape, dtype) -> StringArray:
+        values = np.empty(shape, dtype=object)
+        values[:] = libmissing.NA
+        return cls(values).astype(dtype, copy=False)
+
+    def __arrow_array__(self, type=None):
+        """
+        Convert myself into a pyarrow Array.
+        """
+        import pyarrow as pa
+
+        if type is None:
+            type = pa.string()
+
+        values = self._ndarray.copy()
+        values[self.isna()] = None
+        return pa.array(values, type=type, from_pandas=True)
+
+    def _values_for_factorize(self) -> tuple[np.ndarray, libmissing.NAType | float]:  # type: ignore[override]
+        arr = self._ndarray.copy()
+
+        return arr, self.dtype.na_value
+
+    def _maybe_convert_setitem_value(self, value):
+        """Maybe convert value to be pyarrow compatible."""
+        if lib.is_scalar(value):
+            if isna(value):
+                value = self.dtype.na_value
+            elif not isinstance(value, str):
+                raise TypeError(
+                    f"Invalid value '{value}' for dtype '{self.dtype}'. Value should "
+                    f"be a string or missing value, got '{type(value).__name__}' "
+                    "instead."
+                )
+        else:
+            value = extract_array(value, extract_numpy=True)
+            if not is_array_like(value):
+                value = np.asarray(value, dtype=object)
+            elif isinstance(value.dtype, type(self.dtype)):
+                return value
+            else:
+                # cast categories and friends to arrays to see if values are
+                # compatible, compatibility with arrow backed strings
+                value = np.asarray(value)
+            if len(value) and not lib.is_string_array(value, skipna=True):
+                raise TypeError(
+                    "Invalid value for dtype 'str'. Value should be a "
+                    "string or missing value (or array of those)."
+                )
+        return value
+
+    def __setitem__(self, key, value) -> None:
+        value = self._maybe_convert_setitem_value(value)
+
+        key = check_array_indexer(self, key)
+        scalar_key = lib.is_scalar(key)
+        scalar_value = lib.is_scalar(value)
+        if scalar_key and not scalar_value:
+            raise ValueError("setting an array element with a sequence.")
+
+        if not scalar_value:
+            if value.dtype == self.dtype:
+                value = value._ndarray
+            else:
+                value = np.asarray(value)
+                mask = isna(value)
+                if mask.any():
+                    value = value.copy()
+                    value[isna(value)] = self.dtype.na_value
+
+        super().__setitem__(key, value)
+
+    def _putmask(self, mask: npt.NDArray[np.bool_], value) -> None:
+        # the super() method NDArrayBackedExtensionArray._putmask uses
+        # np.putmask which doesn't properly handle None/pd.NA, so using the
+        # base class implementation that uses __setitem__
+        ExtensionArray._putmask(self, mask, value)
+
+    def _where(self, mask: npt.NDArray[np.bool_], value) -> Self:
+        # the super() method NDArrayBackedExtensionArray._where uses
+        # np.putmask which doesn't properly handle None/pd.NA, so using the
+        # base class implementation that uses __setitem__
+        return ExtensionArray._where(self, mask, value)
+
+    def isin(self, values: ArrayLike) -> npt.NDArray[np.bool_]:
+        if isinstance(values, BaseStringArray) or (
+            isinstance(values, ExtensionArray) and is_string_dtype(values.dtype)
+        ):
+            values = values.astype(self.dtype, copy=False)
+        else:
+            if not lib.is_string_array(np.asarray(values), skipna=True):
+                values = np.array(
+                    [val for val in values if isinstance(val, str) or isna(val)],
+                    dtype=object,
+                )
+                if not len(values):
+                    return np.zeros(self.shape, dtype=bool)
+
+            values = self._from_sequence(values, dtype=self.dtype)
+
+        return isin(np.asarray(self), np.asarray(values))
+
+    def astype(self, dtype, copy: bool = True):
+        dtype = pandas_dtype(dtype)
+
+        if dtype == self.dtype:
+            if copy:
+                return self.copy()
+            return self
+
+        elif isinstance(dtype, IntegerDtype):
+            arr = self._ndarray.copy()
+            mask = self.isna()
+            arr[mask] = 0
+            values = arr.astype(dtype.numpy_dtype)
+            return IntegerArray(values, mask, copy=False)
+        elif isinstance(dtype, FloatingDtype):
+            arr = self.copy()
+            mask = self.isna()
+            arr[mask] = "0"
+            values = arr.astype(dtype.numpy_dtype)
+            return FloatingArray(values, mask, copy=False)
+        elif isinstance(dtype, ExtensionDtype):
+            # Skip the NumpyExtensionArray.astype method
+            return ExtensionArray.astype(self, dtype, copy)
+        elif np.issubdtype(dtype, np.floating):
+            arr = self._ndarray.copy()
+            mask = self.isna()
+            arr[mask] = 0
+            values = arr.astype(dtype)
+            values[mask] = np.nan
+            return values
+
+        return super().astype(dtype, copy)
+
+    def _reduce(
+        self,
+        name: str,
+        *,
+        skipna: bool = True,
+        keepdims: bool = False,
+        axis: AxisInt | None = 0,
+        **kwargs,
+    ):
+        if self.dtype.na_value is np.nan and name in ["any", "all"]:
+            if name == "any":
+                return nanops.nanany(self._ndarray, skipna=skipna)
+            else:
+                return nanops.nanall(self._ndarray, skipna=skipna)
+
+        if name in ["min", "max", "argmin", "argmax", "sum"]:
+            result = getattr(self, name)(skipna=skipna, axis=axis, **kwargs)
+            if keepdims:
+                return self._from_sequence([result], dtype=self.dtype)
+            return result
+        raise TypeError(f"Cannot perform reduction '{name}' with string dtype")
+
+    def _accumulate(self, name: str, *, skipna: bool = True, **kwargs) -> StringArray:
+        """
+        Return an ExtensionArray performing an accumulation operation.
+
+        The underlying data type might change.
+
+        Parameters
+        ----------
+        name : str
+            Name of the function, supported values are:
+            - cummin
+            - cummax
+            - cumsum
+            - cumprod
+        skipna : bool, default True
+            If True, skip NA values.
+        **kwargs
+            Additional keyword arguments passed to the accumulation function.
+            Currently, there is no supported kwarg.
+
+        Returns
+        -------
+        array
+
+        Raises
+        ------
+        NotImplementedError : subclass does not define accumulations
+        """
+        if name == "cumprod":
+            msg = f"operation '{name}' not supported for dtype '{self.dtype}'"
+            raise TypeError(msg)
+
+        # We may need to strip out trailing NA values
+        tail: np.ndarray | None = None
+        na_mask: np.ndarray | None = None
+        ndarray = self._ndarray
+        np_func = {
+            "cumsum": np.cumsum,
+            "cummin": np.minimum.accumulate,
+            "cummax": np.maximum.accumulate,
+        }[name]
+
+        if self._hasna:
+            na_mask = cast("npt.NDArray[np.bool_]", isna(ndarray))
+            if np.all(na_mask):
+                return type(self)(ndarray)
+            if skipna:
+                if name == "cumsum":
+                    ndarray = np.where(na_mask, "", ndarray)
+                else:
+                    # We can retain the running min/max by forward/backward filling.
+                    ndarray = ndarray.copy()
+                    missing.pad_or_backfill_inplace(
+                        ndarray,
+                        method="pad",
+                        axis=0,
+                    )
+                    missing.pad_or_backfill_inplace(
+                        ndarray,
+                        method="backfill",
+                        axis=0,
+                    )
+            else:
+                # When not skipping NA values, the result should be null from
+                # the first NA value onward.
+                idx = np.argmax(na_mask)
+                tail = np.empty(len(ndarray) - idx, dtype="object")
+                tail[:] = self.dtype.na_value
+                ndarray = ndarray[:idx]
+
+        # mypy: Cannot call function of unknown type
+        np_result = np_func(ndarray)  # type: ignore[operator]
+
+        if tail is not None:
+            np_result = np.hstack((np_result, tail))
+        elif na_mask is not None:
+            # Argument 2 to "where" has incompatible type "NAType | float"
+            np_result = np.where(na_mask, self.dtype.na_value, np_result)  # type: ignore[arg-type]
+
+        result = type(self)(np_result)
+        return result
+
+    def _wrap_reduction_result(self, axis: AxisInt | None, result) -> Any:
+        if self.dtype.na_value is np.nan and result is libmissing.NA:
+            # the masked_reductions use pd.NA -> convert to np.nan
+            return np.nan
+        return super()._wrap_reduction_result(axis, result)
+
+    def min(self, axis=None, skipna: bool = True, **kwargs) -> Scalar:
+        nv.validate_min((), kwargs)
+        result = masked_reductions.min(
+            values=self.to_numpy(), mask=self.isna(), skipna=skipna
+        )
+        return self._wrap_reduction_result(axis, result)
+
+    def max(self, axis=None, skipna: bool = True, **kwargs) -> Scalar:
+        nv.validate_max((), kwargs)
+        result = masked_reductions.max(
+            values=self.to_numpy(), mask=self.isna(), skipna=skipna
+        )
+        return self._wrap_reduction_result(axis, result)
+
+    def sum(
+        self,
+        *,
+        axis: AxisInt | None = None,
+        skipna: bool = True,
+        min_count: int = 0,
+        **kwargs,
+    ) -> Scalar:
+        nv.validate_sum((), kwargs)
+        result = masked_reductions.sum(
+            values=self._ndarray, mask=self.isna(), skipna=skipna
+        )
+        return self._wrap_reduction_result(axis, result)
+
+    def value_counts(self, dropna: bool = True) -> Series:
+        from pandas.core.algorithms import value_counts_internal as value_counts
+
+        result = value_counts(self._ndarray, dropna=dropna).astype("Int64")
+        result = value_counts(self._ndarray, sort=False, dropna=dropna)
+        result.index = result.index.astype(self.dtype)
+
+        if self.dtype.na_value is libmissing.NA:
+            result = result.astype("Int64")
+        return result
+
+    def memory_usage(self, deep: bool = False) -> int:
+        result = self._ndarray.nbytes
+        if deep:
+            return result + lib.memory_usage_of_objects(self._ndarray)
+        return result
+
+    @doc(ExtensionArray.searchsorted)
+    def searchsorted(
+        self,
+        value: NumpyValueArrayLike | ExtensionArray,
+        side: Literal["left", "right"] = "left",
+        sorter: NumpySorter | None = None,
+    ) -> npt.NDArray[np.intp] | np.intp:
+        if self._hasna:
+            raise ValueError(
+                "searchsorted requires array to be sorted, which is impossible "
+                "with NAs present."
+            )
+        return super().searchsorted(value=value, side=side, sorter=sorter)
+
+    def _cmp_method(self, other, op):
+        from pandas.arrays import (
+            ArrowExtensionArray,
+            BooleanArray,
+        )
+
+        if (
+            isinstance(other, BaseStringArray)
+            and self.dtype.na_value is not libmissing.NA
+            and other.dtype.na_value is libmissing.NA
+        ):
+            # NA has priority of NaN semantics
+            return NotImplemented
+
+        if isinstance(other, ArrowExtensionArray):
+            if isinstance(other, BaseStringArray):
+                # pyarrow storage has priority over python storage
+                # (except if we have NA semantics and other not)
+                if not (
+                    self.dtype.na_value is libmissing.NA
+                    and other.dtype.na_value is not libmissing.NA
+                ):
+                    return NotImplemented
+            else:
+                return NotImplemented
+
+        if isinstance(other, StringArray):
+            other = other._ndarray
+
+        mask = isna(self) | isna(other)
+        valid = ~mask
+
+        if lib.is_list_like(other):
+            if len(other) != len(self):
+                # prevent improper broadcasting when other is 2D
+                raise ValueError(
+                    f"Lengths of operands do not match: {len(self)} != {len(other)}"
+                )
+
+            # for array-likes, first filter out NAs before converting to numpy
+            if not is_array_like(other):
+                other = np.asarray(other)
+            other = other[valid]
+
+        if op.__name__ in ops.ARITHMETIC_BINOPS:
+            result = np.empty_like(self._ndarray, dtype="object")
+            result[mask] = self.dtype.na_value
+            result[valid] = op(self._ndarray[valid], other)
+            if isinstance(other, Path):
+                # GH#61940
+                return result
+            return self._from_backing_data(result)
+        else:
+            # logical
+            result = np.zeros(len(self._ndarray), dtype="bool")
+            result[valid] = op(self._ndarray[valid], other)
+            res_arr = BooleanArray(result, mask)
+            if self.dtype.na_value is np.nan:
+                if op == operator.ne:
+                    return res_arr.to_numpy(np.bool_, na_value=True)
+                else:
+                    return res_arr.to_numpy(np.bool_, na_value=False)
+            return res_arr
+
+    _arith_method = _cmp_method
+
+
+class StringArrayNumpySemantics(StringArray):
+    _storage = "python"
+    _na_value = np.nan
+
+    def _validate(self) -> None:
+        """Validate that we only store NaN or strings."""
+        if len(self._ndarray) and not lib.is_string_array(self._ndarray, skipna=True):
+            raise ValueError(
+                "StringArrayNumpySemantics requires a sequence of strings or NaN"
+            )
+        if self._ndarray.dtype != "object":
+            raise ValueError(
+                "StringArrayNumpySemantics requires a sequence of strings or NaN. Got "
+                f"'{self._ndarray.dtype}' dtype instead."
+            )
+        # TODO validate or force NA/None to NaN
+
+    @classmethod
+    def _from_sequence(
+        cls, scalars, *, dtype: Dtype | None = None, copy: bool = False
+    ) -> Self:
+        if dtype is None:
+            dtype = StringDtype(storage="python", na_value=np.nan)
+        return super()._from_sequence(scalars, dtype=dtype, copy=copy)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/string_arrow.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/string_arrow.py
new file mode 100644
index 0000000000000000000000000000000000000000..f9fd74cbd76b1262f6cc13f04486ba89026ab959
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/string_arrow.py
@@ -0,0 +1,555 @@
+from __future__ import annotations
+
+import operator
+import re
+from typing import (
+    TYPE_CHECKING,
+    Callable,
+    Union,
+)
+import warnings
+
+import numpy as np
+
+from pandas._libs import (
+    lib,
+    missing as libmissing,
+)
+from pandas.compat import (
+    pa_version_under10p1,
+    pa_version_under13p0,
+    pa_version_under16p0,
+)
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.common import (
+    is_scalar,
+    pandas_dtype,
+)
+from pandas.core.dtypes.missing import isna
+
+from pandas.core.arrays._arrow_string_mixins import ArrowStringArrayMixin
+from pandas.core.arrays.arrow import ArrowExtensionArray
+from pandas.core.arrays.boolean import BooleanDtype
+from pandas.core.arrays.floating import Float64Dtype
+from pandas.core.arrays.integer import Int64Dtype
+from pandas.core.arrays.numeric import NumericDtype
+from pandas.core.arrays.string_ import (
+    BaseStringArray,
+    StringDtype,
+)
+from pandas.core.strings.object_array import ObjectStringArrayMixin
+
+if not pa_version_under10p1:
+    import pyarrow as pa
+    import pyarrow.compute as pc
+
+
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+
+    from pandas._typing import (
+        ArrayLike,
+        Dtype,
+        Scalar,
+        Self,
+        npt,
+    )
+
+    from pandas import Series
+
+
+ArrowStringScalarOrNAT = Union[str, libmissing.NAType]
+
+
+def _chk_pyarrow_available() -> None:
+    if pa_version_under10p1:
+        msg = "pyarrow>=10.0.1 is required for PyArrow backed ArrowExtensionArray."
+        raise ImportError(msg)
+
+
+def _is_string_view(typ):
+    return not pa_version_under16p0 and pa.types.is_string_view(typ)
+
+
+# TODO: Inherit directly from BaseStringArrayMethods. Currently we inherit from
+# ObjectStringArrayMixin because we want to have the object-dtype based methods as
+# fallback for the ones that pyarrow doesn't yet support
+
+
+class ArrowStringArray(ObjectStringArrayMixin, ArrowExtensionArray, BaseStringArray):
+    """
+    Extension array for string data in a ``pyarrow.ChunkedArray``.
+
+    .. warning::
+
+       ArrowStringArray is considered experimental. The implementation and
+       parts of the API may change without warning.
+
+    Parameters
+    ----------
+    values : pyarrow.Array or pyarrow.ChunkedArray
+        The array of data.
+
+    Attributes
+    ----------
+    None
+
+    Methods
+    -------
+    None
+
+    See Also
+    --------
+    :func:`pandas.array`
+        The recommended function for creating a ArrowStringArray.
+    Series.str
+        The string methods are available on Series backed by
+        a ArrowStringArray.
+
+    Notes
+    -----
+    ArrowStringArray returns a BooleanArray for comparison methods.
+
+    Examples
+    --------
+    >>> pd.array(['This is', 'some text', None, 'data.'], dtype="string[pyarrow]")
+    
+    ['This is', 'some text', , 'data.']
+    Length: 4, dtype: string
+    """
+
+    # error: Incompatible types in assignment (expression has type "StringDtype",
+    # base class "ArrowExtensionArray" defined the type as "ArrowDtype")
+    _dtype: StringDtype  # type: ignore[assignment]
+    _storage = "pyarrow"
+    _na_value: libmissing.NAType | float = libmissing.NA
+
+    def __init__(self, values) -> None:
+        _chk_pyarrow_available()
+        if isinstance(values, (pa.Array, pa.ChunkedArray)) and (
+            pa.types.is_string(values.type)
+            or _is_string_view(values.type)
+            or (
+                pa.types.is_dictionary(values.type)
+                and (
+                    pa.types.is_string(values.type.value_type)
+                    or pa.types.is_large_string(values.type.value_type)
+                    or _is_string_view(values.type.value_type)
+                )
+            )
+        ):
+            values = pc.cast(values, pa.large_string())
+
+        super().__init__(values)
+        self._dtype = StringDtype(storage=self._storage, na_value=self._na_value)
+
+        if not pa.types.is_large_string(self._pa_array.type):
+            raise ValueError(
+                "ArrowStringArray requires a PyArrow (chunked) array of "
+                "large_string type"
+            )
+
+    @classmethod
+    def _box_pa_scalar(cls, value, pa_type: pa.DataType | None = None) -> pa.Scalar:
+        pa_scalar = super()._box_pa_scalar(value, pa_type)
+        if pa.types.is_string(pa_scalar.type) and pa_type is None:
+            pa_scalar = pc.cast(pa_scalar, pa.large_string())
+        return pa_scalar
+
+    @classmethod
+    def _box_pa_array(
+        cls, value, pa_type: pa.DataType | None = None, copy: bool = False
+    ) -> pa.Array | pa.ChunkedArray:
+        pa_array = super()._box_pa_array(value, pa_type)
+        if pa.types.is_string(pa_array.type) and pa_type is None:
+            pa_array = pc.cast(pa_array, pa.large_string())
+        return pa_array
+
+    def __len__(self) -> int:
+        """
+        Length of this array.
+
+        Returns
+        -------
+        length : int
+        """
+        return len(self._pa_array)
+
+    @classmethod
+    def _from_sequence(cls, scalars, *, dtype: Dtype | None = None, copy: bool = False):
+        from pandas.core.arrays.masked import BaseMaskedArray
+
+        _chk_pyarrow_available()
+
+        if dtype and not (isinstance(dtype, str) and dtype == "string"):
+            dtype = pandas_dtype(dtype)
+            assert isinstance(dtype, StringDtype) and dtype.storage == "pyarrow"
+
+        if isinstance(scalars, BaseMaskedArray):
+            # avoid costly conversion to object dtype in ensure_string_array and
+            # numerical issues with Float32Dtype
+            na_values = scalars._mask
+            result = scalars._data
+            result = lib.ensure_string_array(result, copy=copy, convert_na_value=False)
+            return cls(pa.array(result, mask=na_values, type=pa.large_string()))
+        elif isinstance(scalars, (pa.Array, pa.ChunkedArray)):
+            return cls(pc.cast(scalars, pa.large_string()))
+
+        # convert non-na-likes to str
+        result = lib.ensure_string_array(scalars, copy=copy)
+        return cls(pa.array(result, type=pa.large_string(), from_pandas=True))
+
+    @classmethod
+    def _from_sequence_of_strings(
+        cls, strings, dtype: Dtype | None = None, copy: bool = False
+    ):
+        return cls._from_sequence(strings, dtype=dtype, copy=copy)
+
+    @property
+    def dtype(self) -> StringDtype:  # type: ignore[override]
+        """
+        An instance of 'string[pyarrow]'.
+        """
+        return self._dtype
+
+    def insert(self, loc: int, item) -> ArrowStringArray:
+        if self.dtype.na_value is np.nan and item is np.nan:
+            item = libmissing.NA
+        if not isinstance(item, str) and item is not libmissing.NA:
+            raise TypeError(
+                f"Invalid value '{item}' for dtype 'str'. Value should be a "
+                f"string or missing value, got '{type(item).__name__}' instead."
+            )
+        return super().insert(loc, item)
+
+    def _convert_bool_result(self, values, na=lib.no_default, method_name=None):
+        if na is not lib.no_default and not isna(na) and not isinstance(na, bool):
+            # GH#59561
+            warnings.warn(
+                f"Allowing a non-bool 'na' in obj.str.{method_name} is deprecated "
+                "and will raise in a future version.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+            na = bool(na)
+
+        if self.dtype.na_value is np.nan:
+            if na is lib.no_default or isna(na):
+                # NaN propagates as False
+                values = values.fill_null(False)
+            else:
+                values = values.fill_null(na)
+            return values.to_numpy()
+        else:
+            if na is not lib.no_default and not isna(
+                na
+            ):  # pyright: ignore [reportGeneralTypeIssues]
+                values = values.fill_null(na)
+        return BooleanDtype().__from_arrow__(values)
+
+    def _maybe_convert_setitem_value(self, value):
+        """Maybe convert value to be pyarrow compatible."""
+        if is_scalar(value):
+            if isna(value):
+                value = None
+            elif not isinstance(value, str):
+                raise TypeError(
+                    f"Invalid value '{value}' for dtype 'str'. Value should be a "
+                    f"string or missing value, got '{type(value).__name__}' instead."
+                )
+        else:
+            value = np.array(value, dtype=object, copy=True)
+            value[isna(value)] = None
+            for v in value:
+                if not (v is None or isinstance(v, str)):
+                    raise TypeError(
+                        "Invalid value for dtype 'str'. Value should be a "
+                        "string or missing value (or array of those)."
+                    )
+        return super()._maybe_convert_setitem_value(value)
+
+    def isin(self, values: ArrayLike) -> npt.NDArray[np.bool_]:
+        value_set = [
+            pa_scalar.as_py()
+            for pa_scalar in [pa.scalar(value, from_pandas=True) for value in values]
+            if pa_scalar.type in (pa.string(), pa.null(), pa.large_string())
+        ]
+
+        # short-circuit to return all False array.
+        if not len(value_set):
+            return np.zeros(len(self), dtype=bool)
+
+        result = pc.is_in(
+            self._pa_array, value_set=pa.array(value_set, type=self._pa_array.type)
+        )
+        # pyarrow 2.0.0 returned nulls, so we explicily specify dtype to convert nulls
+        # to False
+        return np.array(result, dtype=np.bool_)
+
+    def astype(self, dtype, copy: bool = True):
+        dtype = pandas_dtype(dtype)
+
+        if dtype == self.dtype:
+            if copy:
+                return self.copy()
+            return self
+        elif isinstance(dtype, NumericDtype):
+            data = self._pa_array.cast(pa.from_numpy_dtype(dtype.numpy_dtype))
+            return dtype.__from_arrow__(data)
+        elif isinstance(dtype, np.dtype) and np.issubdtype(dtype, np.floating):
+            return self.to_numpy(dtype=dtype, na_value=np.nan)
+
+        return super().astype(dtype, copy=copy)
+
+    @property
+    def _data(self):
+        # dask accesses ._data directlys
+        warnings.warn(
+            f"{type(self).__name__}._data is a deprecated and will be removed "
+            "in a future version, use ._pa_array instead",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        return self._pa_array
+
+    # ------------------------------------------------------------------------
+    # String methods interface
+
+    _str_isalnum = ArrowStringArrayMixin._str_isalnum
+    _str_isalpha = ArrowStringArrayMixin._str_isalpha
+    _str_isdecimal = ArrowStringArrayMixin._str_isdecimal
+    _str_isdigit = ArrowStringArrayMixin._str_isdigit
+    _str_islower = ArrowStringArrayMixin._str_islower
+    _str_isnumeric = ArrowStringArrayMixin._str_isnumeric
+    _str_isspace = ArrowStringArrayMixin._str_isspace
+    _str_istitle = ArrowStringArrayMixin._str_istitle
+    _str_isupper = ArrowStringArrayMixin._str_isupper
+
+    _str_map = BaseStringArray._str_map
+    _str_startswith = ArrowStringArrayMixin._str_startswith
+    _str_endswith = ArrowStringArrayMixin._str_endswith
+    _str_pad = ArrowStringArrayMixin._str_pad
+    _str_lower = ArrowStringArrayMixin._str_lower
+    _str_upper = ArrowStringArrayMixin._str_upper
+    _str_strip = ArrowStringArrayMixin._str_strip
+    _str_lstrip = ArrowStringArrayMixin._str_lstrip
+    _str_rstrip = ArrowStringArrayMixin._str_rstrip
+    _str_removesuffix = ArrowStringArrayMixin._str_removesuffix
+    _str_get = ArrowStringArrayMixin._str_get
+    _str_capitalize = ArrowStringArrayMixin._str_capitalize
+    _str_title = ArrowStringArrayMixin._str_title
+    _str_swapcase = ArrowStringArrayMixin._str_swapcase
+    _str_slice_replace = ArrowStringArrayMixin._str_slice_replace
+    _str_len = ArrowStringArrayMixin._str_len
+    _str_slice = ArrowStringArrayMixin._str_slice
+
+    @staticmethod
+    def _is_re_pattern_with_flags(pat: str | re.Pattern) -> bool:
+        # check if `pat` is a compiled regex pattern with flags that are not
+        # supported by pyarrow
+        return (
+            isinstance(pat, re.Pattern)
+            and (pat.flags & ~(re.IGNORECASE | re.UNICODE)) != 0
+        )
+
+    @staticmethod
+    def _preprocess_re_pattern(pat: re.Pattern, case: bool) -> tuple[str, bool, int]:
+        pattern = pat.pattern
+        flags = pat.flags
+        # flags is not supported by pyarrow, but `case` is -> extract and remove
+        if flags & re.IGNORECASE:
+            case = False
+            flags = flags & ~re.IGNORECASE
+        # when creating a pattern with re.compile and a string, it automatically
+        # gets a UNICODE flag, while pyarrow assumes unicode for strings anyway
+        flags = flags & ~re.UNICODE
+        return pattern, case, flags
+
+    def _str_contains(
+        self,
+        pat,
+        case: bool = True,
+        flags: int = 0,
+        na=lib.no_default,
+        regex: bool = True,
+    ):
+        if flags or self._is_re_pattern_with_flags(pat):
+            return super()._str_contains(pat, case, flags, na, regex)
+        if isinstance(pat, re.Pattern):
+            # TODO flags passed separately by user are ignored
+            pat, case, flags = self._preprocess_re_pattern(pat, case)
+
+        return ArrowStringArrayMixin._str_contains(self, pat, case, flags, na, regex)
+
+    def _str_match(
+        self,
+        pat: str | re.Pattern,
+        case: bool = True,
+        flags: int = 0,
+        na: Scalar | lib.NoDefault = lib.no_default,
+    ):
+        if flags or self._is_re_pattern_with_flags(pat):
+            return super()._str_match(pat, case, flags, na)
+        if isinstance(pat, re.Pattern):
+            pat, case, flags = self._preprocess_re_pattern(pat, case)
+
+        return ArrowStringArrayMixin._str_match(self, pat, case, flags, na)
+
+    def _str_fullmatch(
+        self,
+        pat: str | re.Pattern,
+        case: bool = True,
+        flags: int = 0,
+        na: Scalar | lib.NoDefault = lib.no_default,
+    ):
+        if flags or self._is_re_pattern_with_flags(pat):
+            return super()._str_fullmatch(pat, case, flags, na)
+        if isinstance(pat, re.Pattern):
+            pat, case, flags = self._preprocess_re_pattern(pat, case)
+
+        return ArrowStringArrayMixin._str_fullmatch(self, pat, case, flags, na)
+
+    def _str_replace(
+        self,
+        pat: str | re.Pattern,
+        repl: str | Callable,
+        n: int = -1,
+        case: bool = True,
+        flags: int = 0,
+        regex: bool = True,
+    ):
+        if (
+            isinstance(pat, re.Pattern)
+            or callable(repl)
+            or not case
+            or flags
+            or (  # substitution contains a named group pattern
+                # https://docs.python.org/3/library/re.html
+                isinstance(repl, str)
+                and (r"\g<" in repl or re.search(r"\\\d", repl) is not None)
+            )
+        ):
+            return super()._str_replace(pat, repl, n, case, flags, regex)
+
+        return ArrowStringArrayMixin._str_replace(
+            self, pat, repl, n, case, flags, regex
+        )
+
+    def _str_repeat(self, repeats: int | Sequence[int]):
+        if not isinstance(repeats, int):
+            return super()._str_repeat(repeats)
+        else:
+            return ArrowExtensionArray._str_repeat(self, repeats=repeats)
+
+    def _str_removeprefix(self, prefix: str):
+        if not pa_version_under13p0:
+            return ArrowStringArrayMixin._str_removeprefix(self, prefix)
+        return super()._str_removeprefix(prefix)
+
+    def _str_count(self, pat: str, flags: int = 0):
+        if flags:
+            return super()._str_count(pat, flags)
+        result = pc.count_substring_regex(self._pa_array, pat)
+        return self._convert_int_result(result)
+
+    def _str_find(self, sub: str, start: int = 0, end: int | None = None):
+        if (
+            pa_version_under13p0
+            and not (start != 0 and end is not None)
+            and not (start == 0 and end is None)
+        ):
+            # GH#59562
+            return super()._str_find(sub, start, end)
+        return ArrowStringArrayMixin._str_find(self, sub, start, end)
+
+    def _str_get_dummies(self, sep: str = "|"):
+        dummies_pa, labels = ArrowExtensionArray(self._pa_array)._str_get_dummies(sep)
+        if len(labels) == 0:
+            return np.empty(shape=(0, 0), dtype=np.int64), labels
+        dummies = np.vstack(dummies_pa.to_numpy())
+        return dummies.astype(np.int64, copy=False), labels
+
+    def _convert_int_result(self, result):
+        if self.dtype.na_value is np.nan:
+            if isinstance(result, pa.Array):
+                result = result.to_numpy(zero_copy_only=False)
+            else:
+                result = result.to_numpy()
+            if result.dtype == np.int32:
+                result = result.astype(np.int64)
+            return result
+
+        return Int64Dtype().__from_arrow__(result)
+
+    def _convert_rank_result(self, result):
+        if self.dtype.na_value is np.nan:
+            if isinstance(result, pa.Array):
+                result = result.to_numpy(zero_copy_only=False)
+            else:
+                result = result.to_numpy()
+            return result.astype("float64", copy=False)
+
+        return Float64Dtype().__from_arrow__(result)
+
+    def _reduce(
+        self, name: str, *, skipna: bool = True, keepdims: bool = False, **kwargs
+    ):
+        if self.dtype.na_value is np.nan and name in ["any", "all"]:
+            if not skipna:
+                nas = pc.is_null(self._pa_array)
+                arr = pc.or_kleene(nas, pc.not_equal(self._pa_array, ""))
+            else:
+                arr = pc.not_equal(self._pa_array, "")
+            result = ArrowExtensionArray(arr)._reduce(
+                name, skipna=skipna, keepdims=keepdims, **kwargs
+            )
+            if keepdims:
+                # ArrowExtensionArray will return a length-1 bool[pyarrow] array
+                return result.astype(np.bool_)
+            return result
+
+        if name in ("min", "max", "sum", "argmin", "argmax"):
+            result = self._reduce_calc(name, skipna=skipna, keepdims=keepdims, **kwargs)
+        else:
+            raise TypeError(f"Cannot perform reduction '{name}' with string dtype")
+
+        if name in ("argmin", "argmax") and isinstance(result, pa.Array):
+            return self._convert_int_result(result)
+        elif isinstance(result, pa.Array):
+            return type(self)(result)
+        else:
+            return result
+
+    def value_counts(self, dropna: bool = True) -> Series:
+        result = super().value_counts(dropna=dropna)
+        if self.dtype.na_value is np.nan:
+            res_values = result._values.to_numpy()
+            return result._constructor(
+                res_values, index=result.index, name=result.name, copy=False
+            )
+        return result
+
+    def _cmp_method(self, other, op):
+        if (
+            isinstance(other, (BaseStringArray, ArrowExtensionArray))
+            and self.dtype.na_value is not libmissing.NA
+            and other.dtype.na_value is libmissing.NA
+        ):
+            # NA has priority of NaN semantics
+            return NotImplemented
+
+        result = super()._cmp_method(other, op)
+        if self.dtype.na_value is np.nan:
+            if op == operator.ne:
+                return result.to_numpy(np.bool_, na_value=True)
+            else:
+                return result.to_numpy(np.bool_, na_value=False)
+        return result
+
+    def __pos__(self) -> Self:
+        raise TypeError(f"bad operand type for unary +: '{self.dtype}'")
+
+
+class ArrowStringArrayNumpySemantics(ArrowStringArray):
+    _na_value = np.nan
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/timedeltas.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/timedeltas.py
new file mode 100644
index 0000000000000000000000000000000000000000..d4caec4bfd58a653c3d4af9e550dbda3dc50264a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/arrays/timedeltas.py
@@ -0,0 +1,1185 @@
+from __future__ import annotations
+
+from datetime import timedelta
+import operator
+from typing import (
+    TYPE_CHECKING,
+    cast,
+)
+
+import numpy as np
+
+from pandas._libs import (
+    lib,
+    tslibs,
+)
+from pandas._libs.tslibs import (
+    NaT,
+    NaTType,
+    Tick,
+    Timedelta,
+    astype_overflowsafe,
+    get_supported_dtype,
+    iNaT,
+    is_supported_dtype,
+    periods_per_second,
+)
+from pandas._libs.tslibs.conversion import cast_from_unit_vectorized
+from pandas._libs.tslibs.fields import (
+    get_timedelta_days,
+    get_timedelta_field,
+)
+from pandas._libs.tslibs.timedeltas import (
+    array_to_timedelta64,
+    floordiv_object_array,
+    ints_to_pytimedelta,
+    parse_timedelta_unit,
+    truediv_object_array,
+)
+from pandas.compat.numpy import function as nv
+from pandas.util._validators import validate_endpoints
+
+from pandas.core.dtypes.common import (
+    TD64NS_DTYPE,
+    is_float_dtype,
+    is_integer_dtype,
+    is_object_dtype,
+    is_scalar,
+    is_string_dtype,
+    pandas_dtype,
+)
+from pandas.core.dtypes.dtypes import ExtensionDtype
+from pandas.core.dtypes.missing import isna
+
+from pandas.core import (
+    nanops,
+    roperator,
+)
+from pandas.core.array_algos import datetimelike_accumulations
+from pandas.core.arrays import datetimelike as dtl
+from pandas.core.arrays._ranges import generate_regular_range
+import pandas.core.common as com
+from pandas.core.ops.common import unpack_zerodim_and_defer
+
+if TYPE_CHECKING:
+    from collections.abc import Iterator
+
+    from pandas._typing import (
+        AxisInt,
+        DateTimeErrorChoices,
+        DtypeObj,
+        NpDtype,
+        Self,
+        npt,
+    )
+
+    from pandas import DataFrame
+
+import textwrap
+
+
+def _field_accessor(name: str, alias: str, docstring: str):
+    def f(self) -> np.ndarray:
+        values = self.asi8
+        if alias == "days":
+            result = get_timedelta_days(values, reso=self._creso)
+        else:
+            # error: Incompatible types in assignment (
+            # expression has type "ndarray[Any, dtype[signedinteger[_32Bit]]]",
+            # variable has type "ndarray[Any, dtype[signedinteger[_64Bit]]]
+            result = get_timedelta_field(values, alias, reso=self._creso)  # type: ignore[assignment]
+        if self._hasna:
+            result = self._maybe_mask_results(
+                result, fill_value=None, convert="float64"
+            )
+
+        return result
+
+    f.__name__ = name
+    f.__doc__ = f"\n{docstring}\n"
+    return property(f)
+
+
+class TimedeltaArray(dtl.TimelikeOps):
+    """
+    Pandas ExtensionArray for timedelta data.
+
+    .. warning::
+
+       TimedeltaArray is currently experimental, and its API may change
+       without warning. In particular, :attr:`TimedeltaArray.dtype` is
+       expected to change to be an instance of an ``ExtensionDtype``
+       subclass.
+
+    Parameters
+    ----------
+    values : array-like
+        The timedelta data.
+
+    dtype : numpy.dtype
+        Currently, only ``numpy.dtype("timedelta64[ns]")`` is accepted.
+    freq : Offset, optional
+    copy : bool, default False
+        Whether to copy the underlying array of data.
+
+    Attributes
+    ----------
+    None
+
+    Methods
+    -------
+    None
+
+    Examples
+    --------
+    >>> pd.arrays.TimedeltaArray._from_sequence(pd.TimedeltaIndex(['1h', '2h']))
+    
+    ['0 days 01:00:00', '0 days 02:00:00']
+    Length: 2, dtype: timedelta64[ns]
+    """
+
+    _typ = "timedeltaarray"
+    _internal_fill_value = np.timedelta64("NaT", "ns")
+    _recognized_scalars = (timedelta, np.timedelta64, Tick)
+    _is_recognized_dtype = lambda x: lib.is_np_dtype(x, "m")
+    _infer_matches = ("timedelta", "timedelta64")
+
+    @property
+    def _scalar_type(self) -> type[Timedelta]:
+        return Timedelta
+
+    __array_priority__ = 1000
+    # define my properties & methods for delegation
+    _other_ops: list[str] = []
+    _bool_ops: list[str] = []
+    _object_ops: list[str] = ["freq"]
+    _field_ops: list[str] = ["days", "seconds", "microseconds", "nanoseconds"]
+    _datetimelike_ops: list[str] = _field_ops + _object_ops + _bool_ops + ["unit"]
+    _datetimelike_methods: list[str] = [
+        "to_pytimedelta",
+        "total_seconds",
+        "round",
+        "floor",
+        "ceil",
+        "as_unit",
+    ]
+
+    # Note: ndim must be defined to ensure NaT.__richcmp__(TimedeltaArray)
+    #  operates pointwise.
+
+    def _box_func(self, x: np.timedelta64) -> Timedelta | NaTType:
+        y = x.view("i8")
+        if y == NaT._value:
+            return NaT
+        return Timedelta._from_value_and_reso(y, reso=self._creso)
+
+    @property
+    # error: Return type "dtype" of "dtype" incompatible with return type
+    # "ExtensionDtype" in supertype "ExtensionArray"
+    def dtype(self) -> np.dtype[np.timedelta64]:  # type: ignore[override]
+        """
+        The dtype for the TimedeltaArray.
+
+        .. warning::
+
+           A future version of pandas will change dtype to be an instance
+           of a :class:`pandas.api.extensions.ExtensionDtype` subclass,
+           not a ``numpy.dtype``.
+
+        Returns
+        -------
+        numpy.dtype
+        """
+        return self._ndarray.dtype
+
+    # ----------------------------------------------------------------
+    # Constructors
+
+    _freq = None
+    _default_dtype = TD64NS_DTYPE  # used in TimeLikeOps.__init__
+
+    @classmethod
+    def _validate_dtype(cls, values, dtype):
+        # used in TimeLikeOps.__init__
+        dtype = _validate_td64_dtype(dtype)
+        _validate_td64_dtype(values.dtype)
+        if dtype != values.dtype:
+            raise ValueError("Values resolution does not match dtype.")
+        return dtype
+
+    # error: Signature of "_simple_new" incompatible with supertype "NDArrayBacked"
+    @classmethod
+    def _simple_new(  # type: ignore[override]
+        cls,
+        values: npt.NDArray[np.timedelta64],
+        freq: Tick | None = None,
+        dtype: np.dtype[np.timedelta64] = TD64NS_DTYPE,
+    ) -> Self:
+        # Require td64 dtype, not unit-less, matching values.dtype
+        assert lib.is_np_dtype(dtype, "m")
+        assert not tslibs.is_unitless(dtype)
+        assert isinstance(values, np.ndarray), type(values)
+        assert dtype == values.dtype
+        assert freq is None or isinstance(freq, Tick)
+
+        result = super()._simple_new(values=values, dtype=dtype)
+        result._freq = freq
+        return result
+
+    @classmethod
+    def _from_sequence(cls, data, *, dtype=None, copy: bool = False) -> Self:
+        if dtype:
+            dtype = _validate_td64_dtype(dtype)
+
+        data, freq = sequence_to_td64ns(data, copy=copy, unit=None)
+
+        if dtype is not None:
+            data = astype_overflowsafe(data, dtype=dtype, copy=False)
+
+        return cls._simple_new(data, dtype=data.dtype, freq=freq)
+
+    @classmethod
+    def _from_sequence_not_strict(
+        cls,
+        data,
+        *,
+        dtype=None,
+        copy: bool = False,
+        freq=lib.no_default,
+        unit=None,
+    ) -> Self:
+        """
+        _from_sequence_not_strict but without responsibility for finding the
+        result's `freq`.
+        """
+        if dtype:
+            dtype = _validate_td64_dtype(dtype)
+
+        assert unit not in ["Y", "y", "M"]  # caller is responsible for checking
+
+        data, inferred_freq = sequence_to_td64ns(data, copy=copy, unit=unit)
+
+        if dtype is not None:
+            data = astype_overflowsafe(data, dtype=dtype, copy=False)
+
+        result = cls._simple_new(data, dtype=data.dtype, freq=inferred_freq)
+
+        result._maybe_pin_freq(freq, {})
+        return result
+
+    @classmethod
+    def _generate_range(
+        cls, start, end, periods, freq, closed=None, *, unit: str | None = None
+    ) -> Self:
+        periods = dtl.validate_periods(periods)
+        if freq is None and any(x is None for x in [periods, start, end]):
+            raise ValueError("Must provide freq argument if no data is supplied")
+
+        if com.count_not_none(start, end, periods, freq) != 3:
+            raise ValueError(
+                "Of the four parameters: start, end, periods, "
+                "and freq, exactly three must be specified"
+            )
+
+        if start is not None:
+            start = Timedelta(start).as_unit("ns")
+
+        if end is not None:
+            end = Timedelta(end).as_unit("ns")
+
+        if unit is not None:
+            if unit not in ["s", "ms", "us", "ns"]:
+                raise ValueError("'unit' must be one of 's', 'ms', 'us', 'ns'")
+        else:
+            unit = "ns"
+
+        if start is not None and unit is not None:
+            start = start.as_unit(unit, round_ok=False)
+        if end is not None and unit is not None:
+            end = end.as_unit(unit, round_ok=False)
+
+        left_closed, right_closed = validate_endpoints(closed)
+
+        if freq is not None:
+            index = generate_regular_range(start, end, periods, freq, unit=unit)
+        else:
+            index = np.linspace(start._value, end._value, periods).astype("i8")
+
+        if not left_closed:
+            index = index[1:]
+        if not right_closed:
+            index = index[:-1]
+
+        td64values = index.view(f"m8[{unit}]")
+        return cls._simple_new(td64values, dtype=td64values.dtype, freq=freq)
+
+    # ----------------------------------------------------------------
+    # DatetimeLike Interface
+
+    def _unbox_scalar(self, value) -> np.timedelta64:
+        if not isinstance(value, self._scalar_type) and value is not NaT:
+            raise ValueError("'value' should be a Timedelta.")
+        self._check_compatible_with(value)
+        if value is NaT:
+            return np.timedelta64(value._value, self.unit)
+        else:
+            return value.as_unit(self.unit).asm8
+
+    def _scalar_from_string(self, value) -> Timedelta | NaTType:
+        return Timedelta(value)
+
+    def _check_compatible_with(self, other) -> None:
+        # we don't have anything to validate.
+        pass
+
+    # ----------------------------------------------------------------
+    # Array-Like / EA-Interface Methods
+
+    def astype(self, dtype, copy: bool = True):
+        # We handle
+        #   --> timedelta64[ns]
+        #   --> timedelta64
+        # DatetimeLikeArrayMixin super call handles other cases
+        dtype = pandas_dtype(dtype)
+
+        if lib.is_np_dtype(dtype, "m"):
+            if dtype == self.dtype:
+                if copy:
+                    return self.copy()
+                return self
+
+            if is_supported_dtype(dtype):
+                # unit conversion e.g. timedelta64[s]
+                res_values = astype_overflowsafe(self._ndarray, dtype, copy=False)
+                return type(self)._simple_new(
+                    res_values, dtype=res_values.dtype, freq=self.freq
+                )
+            else:
+                raise ValueError(
+                    f"Cannot convert from {self.dtype} to {dtype}. "
+                    "Supported resolutions are 's', 'ms', 'us', 'ns'"
+                )
+
+        return dtl.DatetimeLikeArrayMixin.astype(self, dtype, copy=copy)
+
+    def __iter__(self) -> Iterator:
+        if self.ndim > 1:
+            for i in range(len(self)):
+                yield self[i]
+        else:
+            # convert in chunks of 10k for efficiency
+            data = self._ndarray
+            length = len(self)
+            chunksize = 10000
+            chunks = (length // chunksize) + 1
+            for i in range(chunks):
+                start_i = i * chunksize
+                end_i = min((i + 1) * chunksize, length)
+                converted = ints_to_pytimedelta(data[start_i:end_i], box=True)
+                yield from converted
+
+    # ----------------------------------------------------------------
+    # Reductions
+
+    def sum(
+        self,
+        *,
+        axis: AxisInt | None = None,
+        dtype: NpDtype | None = None,
+        out=None,
+        keepdims: bool = False,
+        initial=None,
+        skipna: bool = True,
+        min_count: int = 0,
+    ):
+        nv.validate_sum(
+            (), {"dtype": dtype, "out": out, "keepdims": keepdims, "initial": initial}
+        )
+
+        result = nanops.nansum(
+            self._ndarray, axis=axis, skipna=skipna, min_count=min_count
+        )
+        return self._wrap_reduction_result(axis, result)
+
+    def std(
+        self,
+        *,
+        axis: AxisInt | None = None,
+        dtype: NpDtype | None = None,
+        out=None,
+        ddof: int = 1,
+        keepdims: bool = False,
+        skipna: bool = True,
+    ):
+        nv.validate_stat_ddof_func(
+            (), {"dtype": dtype, "out": out, "keepdims": keepdims}, fname="std"
+        )
+
+        result = nanops.nanstd(self._ndarray, axis=axis, skipna=skipna, ddof=ddof)
+        if axis is None or self.ndim == 1:
+            return self._box_func(result)
+        return self._from_backing_data(result)
+
+    # ----------------------------------------------------------------
+    # Accumulations
+
+    def _accumulate(self, name: str, *, skipna: bool = True, **kwargs):
+        if name == "cumsum":
+            op = getattr(datetimelike_accumulations, name)
+            result = op(self._ndarray.copy(), skipna=skipna, **kwargs)
+
+            return type(self)._simple_new(result, freq=None, dtype=self.dtype)
+        elif name == "cumprod":
+            raise TypeError("cumprod not supported for Timedelta.")
+
+        else:
+            return super()._accumulate(name, skipna=skipna, **kwargs)
+
+    # ----------------------------------------------------------------
+    # Rendering Methods
+
+    def _formatter(self, boxed: bool = False):
+        from pandas.io.formats.format import get_format_timedelta64
+
+        return get_format_timedelta64(self, box=True)
+
+    def _format_native_types(
+        self, *, na_rep: str | float = "NaT", date_format=None, **kwargs
+    ) -> npt.NDArray[np.object_]:
+        from pandas.io.formats.format import get_format_timedelta64
+
+        # Relies on TimeDelta._repr_base
+        formatter = get_format_timedelta64(self, na_rep)
+        # equiv: np.array([formatter(x) for x in self._ndarray])
+        #  but independent of dimension
+        return np.frompyfunc(formatter, 1, 1)(self._ndarray)
+
+    # ----------------------------------------------------------------
+    # Arithmetic Methods
+
+    def _add_offset(self, other):
+        assert not isinstance(other, Tick)
+        raise TypeError(
+            f"cannot add the type {type(other).__name__} to a {type(self).__name__}"
+        )
+
+    @unpack_zerodim_and_defer("__mul__")
+    def __mul__(self, other) -> Self:
+        if is_scalar(other):
+            # numpy will accept float and int, raise TypeError for others
+            result = self._ndarray * other
+            if result.dtype.kind != "m":
+                # numpy >= 2.1 may not raise a TypeError
+                # and seems to dispatch to others.__rmul__?
+                raise TypeError(f"Cannot multiply with {type(other).__name__}")
+            freq = None
+            if self.freq is not None and not isna(other):
+                freq = self.freq * other
+                if freq.n == 0:
+                    # GH#51575 Better to have no freq than an incorrect one
+                    freq = None
+            return type(self)._simple_new(result, dtype=result.dtype, freq=freq)
+
+        if not hasattr(other, "dtype"):
+            # list, tuple
+            other = np.array(other)
+        if len(other) != len(self) and not lib.is_np_dtype(other.dtype, "m"):
+            # Exclude timedelta64 here so we correctly raise TypeError
+            #  for that instead of ValueError
+            raise ValueError("Cannot multiply with unequal lengths")
+
+        if is_object_dtype(other.dtype):
+            # this multiplication will succeed only if all elements of other
+            #  are int or float scalars, so we will end up with
+            #  timedelta64[ns]-dtyped result
+            arr = self._ndarray
+            result = [arr[n] * other[n] for n in range(len(self))]
+            result = np.array(result)
+            return type(self)._simple_new(result, dtype=result.dtype)
+
+        # numpy will accept float or int dtype, raise TypeError for others
+        result = self._ndarray * other
+        if result.dtype.kind != "m":
+            # numpy >= 2.1 may not raise a TypeError
+            # and seems to dispatch to others.__rmul__?
+            raise TypeError(f"Cannot multiply with {type(other).__name__}")
+        return type(self)._simple_new(result, dtype=result.dtype)
+
+    __rmul__ = __mul__
+
+    def _scalar_divlike_op(self, other, op):
+        """
+        Shared logic for __truediv__, __rtruediv__, __floordiv__, __rfloordiv__
+        with scalar 'other'.
+        """
+        if isinstance(other, self._recognized_scalars):
+            other = Timedelta(other)
+            # mypy assumes that __new__ returns an instance of the class
+            # github.com/python/mypy/issues/1020
+            if cast("Timedelta | NaTType", other) is NaT:
+                # specifically timedelta64-NaT
+                res = np.empty(self.shape, dtype=np.float64)
+                res.fill(np.nan)
+                return res
+
+            # otherwise, dispatch to Timedelta implementation
+            return op(self._ndarray, other)
+
+        else:
+            # caller is responsible for checking lib.is_scalar(other)
+            # assume other is numeric, otherwise numpy will raise
+
+            if op in [roperator.rtruediv, roperator.rfloordiv]:
+                raise TypeError(
+                    f"Cannot divide {type(other).__name__} by {type(self).__name__}"
+                )
+
+            result = op(self._ndarray, other)
+            freq = None
+
+            if self.freq is not None:
+                # Note: freq gets division, not floor-division, even if op
+                #  is floordiv.
+                freq = self.freq / other
+                if freq.nanos == 0 and self.freq.nanos != 0:
+                    # e.g. if self.freq is Nano(1) then dividing by 2
+                    #  rounds down to zero
+                    freq = None
+
+            return type(self)._simple_new(result, dtype=result.dtype, freq=freq)
+
+    def _cast_divlike_op(self, other):
+        if not hasattr(other, "dtype"):
+            # e.g. list, tuple
+            other = np.array(other)
+
+        if len(other) != len(self):
+            raise ValueError("Cannot divide vectors with unequal lengths")
+        return other
+
+    def _vector_divlike_op(self, other, op) -> np.ndarray | Self:
+        """
+        Shared logic for __truediv__, __floordiv__, and their reversed versions
+        with timedelta64-dtype ndarray other.
+        """
+        # Let numpy handle it
+        result = op(self._ndarray, np.asarray(other))
+
+        if (is_integer_dtype(other.dtype) or is_float_dtype(other.dtype)) and op in [
+            operator.truediv,
+            operator.floordiv,
+        ]:
+            return type(self)._simple_new(result, dtype=result.dtype)
+
+        if op in [operator.floordiv, roperator.rfloordiv]:
+            mask = self.isna() | isna(other)
+            if mask.any():
+                result = result.astype(np.float64)
+                np.putmask(result, mask, np.nan)
+
+        return result
+
+    @unpack_zerodim_and_defer("__truediv__")
+    def __truediv__(self, other):
+        # timedelta / X is well-defined for timedelta-like or numeric X
+        op = operator.truediv
+        if is_scalar(other):
+            return self._scalar_divlike_op(other, op)
+
+        other = self._cast_divlike_op(other)
+        if (
+            lib.is_np_dtype(other.dtype, "m")
+            or is_integer_dtype(other.dtype)
+            or is_float_dtype(other.dtype)
+        ):
+            return self._vector_divlike_op(other, op)
+
+        if is_object_dtype(other.dtype):
+            other = np.asarray(other)
+            if self.ndim > 1:
+                res_cols = [left / right for left, right in zip(self, other)]
+                res_cols2 = [x.reshape(1, -1) for x in res_cols]
+                result = np.concatenate(res_cols2, axis=0)
+            else:
+                result = truediv_object_array(self._ndarray, other)
+
+            return result
+
+        else:
+            return NotImplemented
+
+    @unpack_zerodim_and_defer("__rtruediv__")
+    def __rtruediv__(self, other):
+        # X / timedelta is defined only for timedelta-like X
+        op = roperator.rtruediv
+        if is_scalar(other):
+            return self._scalar_divlike_op(other, op)
+
+        other = self._cast_divlike_op(other)
+        if lib.is_np_dtype(other.dtype, "m"):
+            return self._vector_divlike_op(other, op)
+
+        elif is_object_dtype(other.dtype):
+            # Note: unlike in __truediv__, we do not _need_ to do type
+            #  inference on the result.  It does not raise, a numeric array
+            #  is returned.  GH#23829
+            result_list = [other[n] / self[n] for n in range(len(self))]
+            return np.array(result_list)
+
+        else:
+            return NotImplemented
+
+    @unpack_zerodim_and_defer("__floordiv__")
+    def __floordiv__(self, other):
+        op = operator.floordiv
+        if is_scalar(other):
+            return self._scalar_divlike_op(other, op)
+
+        other = self._cast_divlike_op(other)
+        if (
+            lib.is_np_dtype(other.dtype, "m")
+            or is_integer_dtype(other.dtype)
+            or is_float_dtype(other.dtype)
+        ):
+            return self._vector_divlike_op(other, op)
+
+        elif is_object_dtype(other.dtype):
+            other = np.asarray(other)
+            if self.ndim > 1:
+                res_cols = [left // right for left, right in zip(self, other)]
+                res_cols2 = [x.reshape(1, -1) for x in res_cols]
+                result = np.concatenate(res_cols2, axis=0)
+            else:
+                result = floordiv_object_array(self._ndarray, other)
+
+            assert result.dtype == object
+            return result
+
+        else:
+            return NotImplemented
+
+    @unpack_zerodim_and_defer("__rfloordiv__")
+    def __rfloordiv__(self, other):
+        op = roperator.rfloordiv
+        if is_scalar(other):
+            return self._scalar_divlike_op(other, op)
+
+        other = self._cast_divlike_op(other)
+        if lib.is_np_dtype(other.dtype, "m"):
+            return self._vector_divlike_op(other, op)
+
+        elif is_object_dtype(other.dtype):
+            result_list = [other[n] // self[n] for n in range(len(self))]
+            result = np.array(result_list)
+            return result
+
+        else:
+            return NotImplemented
+
+    @unpack_zerodim_and_defer("__mod__")
+    def __mod__(self, other):
+        # Note: This is a naive implementation, can likely be optimized
+        if isinstance(other, self._recognized_scalars):
+            other = Timedelta(other)
+        return self - (self // other) * other
+
+    @unpack_zerodim_and_defer("__rmod__")
+    def __rmod__(self, other):
+        # Note: This is a naive implementation, can likely be optimized
+        if isinstance(other, self._recognized_scalars):
+            other = Timedelta(other)
+        return other - (other // self) * self
+
+    @unpack_zerodim_and_defer("__divmod__")
+    def __divmod__(self, other):
+        # Note: This is a naive implementation, can likely be optimized
+        if isinstance(other, self._recognized_scalars):
+            other = Timedelta(other)
+
+        res1 = self // other
+        res2 = self - res1 * other
+        return res1, res2
+
+    @unpack_zerodim_and_defer("__rdivmod__")
+    def __rdivmod__(self, other):
+        # Note: This is a naive implementation, can likely be optimized
+        if isinstance(other, self._recognized_scalars):
+            other = Timedelta(other)
+
+        res1 = other // self
+        res2 = other - res1 * self
+        return res1, res2
+
+    def __neg__(self) -> TimedeltaArray:
+        freq = None
+        if self.freq is not None:
+            freq = -self.freq
+        return type(self)._simple_new(-self._ndarray, dtype=self.dtype, freq=freq)
+
+    def __pos__(self) -> TimedeltaArray:
+        return type(self)._simple_new(
+            self._ndarray.copy(), dtype=self.dtype, freq=self.freq
+        )
+
+    def __abs__(self) -> TimedeltaArray:
+        # Note: freq is not preserved
+        return type(self)._simple_new(np.abs(self._ndarray), dtype=self.dtype)
+
+    # ----------------------------------------------------------------
+    # Conversion Methods - Vectorized analogues of Timedelta methods
+
+    def total_seconds(self) -> npt.NDArray[np.float64]:
+        """
+        Return total duration of each element expressed in seconds.
+
+        This method is available directly on TimedeltaArray, TimedeltaIndex
+        and on Series containing timedelta values under the ``.dt`` namespace.
+
+        Returns
+        -------
+        ndarray, Index or Series
+            When the calling object is a TimedeltaArray, the return type
+            is ndarray.  When the calling object is a TimedeltaIndex,
+            the return type is an Index with a float64 dtype. When the calling object
+            is a Series, the return type is Series of type `float64` whose
+            index is the same as the original.
+
+        See Also
+        --------
+        datetime.timedelta.total_seconds : Standard library version
+            of this method.
+        TimedeltaIndex.components : Return a DataFrame with components of
+            each Timedelta.
+
+        Examples
+        --------
+        **Series**
+
+        >>> s = pd.Series(pd.to_timedelta(np.arange(5), unit='d'))
+        >>> s
+        0   0 days
+        1   1 days
+        2   2 days
+        3   3 days
+        4   4 days
+        dtype: timedelta64[ns]
+
+        >>> s.dt.total_seconds()
+        0         0.0
+        1     86400.0
+        2    172800.0
+        3    259200.0
+        4    345600.0
+        dtype: float64
+
+        **TimedeltaIndex**
+
+        >>> idx = pd.to_timedelta(np.arange(5), unit='d')
+        >>> idx
+        TimedeltaIndex(['0 days', '1 days', '2 days', '3 days', '4 days'],
+                       dtype='timedelta64[ns]', freq=None)
+
+        >>> idx.total_seconds()
+        Index([0.0, 86400.0, 172800.0, 259200.0, 345600.0], dtype='float64')
+        """
+        pps = periods_per_second(self._creso)
+        return self._maybe_mask_results(self.asi8 / pps, fill_value=None)
+
+    def to_pytimedelta(self) -> npt.NDArray[np.object_]:
+        """
+        Return an ndarray of datetime.timedelta objects.
+
+        Returns
+        -------
+        numpy.ndarray
+
+        Examples
+        --------
+        >>> tdelta_idx = pd.to_timedelta([1, 2, 3], unit='D')
+        >>> tdelta_idx
+        TimedeltaIndex(['1 days', '2 days', '3 days'],
+                        dtype='timedelta64[ns]', freq=None)
+        >>> tdelta_idx.to_pytimedelta()
+        array([datetime.timedelta(days=1), datetime.timedelta(days=2),
+               datetime.timedelta(days=3)], dtype=object)
+        """
+        return ints_to_pytimedelta(self._ndarray)
+
+    days_docstring = textwrap.dedent(
+        """Number of days for each element.
+
+    Examples
+    --------
+    For Series:
+
+    >>> ser = pd.Series(pd.to_timedelta([1, 2, 3], unit='d'))
+    >>> ser
+    0   1 days
+    1   2 days
+    2   3 days
+    dtype: timedelta64[ns]
+    >>> ser.dt.days
+    0    1
+    1    2
+    2    3
+    dtype: int64
+
+    For TimedeltaIndex:
+
+    >>> tdelta_idx = pd.to_timedelta(["0 days", "10 days", "20 days"])
+    >>> tdelta_idx
+    TimedeltaIndex(['0 days', '10 days', '20 days'],
+                    dtype='timedelta64[ns]', freq=None)
+    >>> tdelta_idx.days
+    Index([0, 10, 20], dtype='int64')"""
+    )
+    days = _field_accessor("days", "days", days_docstring)
+
+    seconds_docstring = textwrap.dedent(
+        """Number of seconds (>= 0 and less than 1 day) for each element.
+
+    Examples
+    --------
+    For Series:
+
+    >>> ser = pd.Series(pd.to_timedelta([1, 2, 3], unit='s'))
+    >>> ser
+    0   0 days 00:00:01
+    1   0 days 00:00:02
+    2   0 days 00:00:03
+    dtype: timedelta64[ns]
+    >>> ser.dt.seconds
+    0    1
+    1    2
+    2    3
+    dtype: int32
+
+    For TimedeltaIndex:
+
+    >>> tdelta_idx = pd.to_timedelta([1, 2, 3], unit='s')
+    >>> tdelta_idx
+    TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:02', '0 days 00:00:03'],
+                   dtype='timedelta64[ns]', freq=None)
+    >>> tdelta_idx.seconds
+    Index([1, 2, 3], dtype='int32')"""
+    )
+    seconds = _field_accessor(
+        "seconds",
+        "seconds",
+        seconds_docstring,
+    )
+
+    microseconds_docstring = textwrap.dedent(
+        """Number of microseconds (>= 0 and less than 1 second) for each element.
+
+    Examples
+    --------
+    For Series:
+
+    >>> ser = pd.Series(pd.to_timedelta([1, 2, 3], unit='us'))
+    >>> ser
+    0   0 days 00:00:00.000001
+    1   0 days 00:00:00.000002
+    2   0 days 00:00:00.000003
+    dtype: timedelta64[ns]
+    >>> ser.dt.microseconds
+    0    1
+    1    2
+    2    3
+    dtype: int32
+
+    For TimedeltaIndex:
+
+    >>> tdelta_idx = pd.to_timedelta([1, 2, 3], unit='us')
+    >>> tdelta_idx
+    TimedeltaIndex(['0 days 00:00:00.000001', '0 days 00:00:00.000002',
+                    '0 days 00:00:00.000003'],
+                   dtype='timedelta64[ns]', freq=None)
+    >>> tdelta_idx.microseconds
+    Index([1, 2, 3], dtype='int32')"""
+    )
+    microseconds = _field_accessor(
+        "microseconds",
+        "microseconds",
+        microseconds_docstring,
+    )
+
+    nanoseconds_docstring = textwrap.dedent(
+        """Number of nanoseconds (>= 0 and less than 1 microsecond) for each element.
+
+    Examples
+    --------
+    For Series:
+
+    >>> ser = pd.Series(pd.to_timedelta([1, 2, 3], unit='ns'))
+    >>> ser
+    0   0 days 00:00:00.000000001
+    1   0 days 00:00:00.000000002
+    2   0 days 00:00:00.000000003
+    dtype: timedelta64[ns]
+    >>> ser.dt.nanoseconds
+    0    1
+    1    2
+    2    3
+    dtype: int32
+
+    For TimedeltaIndex:
+
+    >>> tdelta_idx = pd.to_timedelta([1, 2, 3], unit='ns')
+    >>> tdelta_idx
+    TimedeltaIndex(['0 days 00:00:00.000000001', '0 days 00:00:00.000000002',
+                    '0 days 00:00:00.000000003'],
+                   dtype='timedelta64[ns]', freq=None)
+    >>> tdelta_idx.nanoseconds
+    Index([1, 2, 3], dtype='int32')"""
+    )
+    nanoseconds = _field_accessor(
+        "nanoseconds",
+        "nanoseconds",
+        nanoseconds_docstring,
+    )
+
+    @property
+    def components(self) -> DataFrame:
+        """
+        Return a DataFrame of the individual resolution components of the Timedeltas.
+
+        The components (days, hours, minutes seconds, milliseconds, microseconds,
+        nanoseconds) are returned as columns in a DataFrame.
+
+        Returns
+        -------
+        DataFrame
+
+        Examples
+        --------
+        >>> tdelta_idx = pd.to_timedelta(['1 day 3 min 2 us 42 ns'])
+        >>> tdelta_idx
+        TimedeltaIndex(['1 days 00:03:00.000002042'],
+                       dtype='timedelta64[ns]', freq=None)
+        >>> tdelta_idx.components
+           days  hours  minutes  seconds  milliseconds  microseconds  nanoseconds
+        0     1      0        3        0             0             2           42
+        """
+        from pandas import DataFrame
+
+        columns = [
+            "days",
+            "hours",
+            "minutes",
+            "seconds",
+            "milliseconds",
+            "microseconds",
+            "nanoseconds",
+        ]
+        hasnans = self._hasna
+        if hasnans:
+
+            def f(x):
+                if isna(x):
+                    return [np.nan] * len(columns)
+                return x.components
+
+        else:
+
+            def f(x):
+                return x.components
+
+        result = DataFrame([f(x) for x in self], columns=columns)
+        if not hasnans:
+            result = result.astype("int64")
+        return result
+
+
+# ---------------------------------------------------------------------
+# Constructor Helpers
+
+
+def sequence_to_td64ns(
+    data,
+    copy: bool = False,
+    unit=None,
+    errors: DateTimeErrorChoices = "raise",
+) -> tuple[np.ndarray, Tick | None]:
+    """
+    Parameters
+    ----------
+    data : list-like
+    copy : bool, default False
+    unit : str, optional
+        The timedelta unit to treat integers as multiples of. For numeric
+        data this defaults to ``'ns'``.
+        Must be un-specified if the data contains a str and ``errors=="raise"``.
+    errors : {"raise", "coerce", "ignore"}, default "raise"
+        How to handle elements that cannot be converted to timedelta64[ns].
+        See ``pandas.to_timedelta`` for details.
+
+    Returns
+    -------
+    converted : numpy.ndarray
+        The sequence converted to a numpy array with dtype ``timedelta64[ns]``.
+    inferred_freq : Tick or None
+        The inferred frequency of the sequence.
+
+    Raises
+    ------
+    ValueError : Data cannot be converted to timedelta64[ns].
+
+    Notes
+    -----
+    Unlike `pandas.to_timedelta`, if setting ``errors=ignore`` will not cause
+    errors to be ignored; they are caught and subsequently ignored at a
+    higher level.
+    """
+    assert unit not in ["Y", "y", "M"]  # caller is responsible for checking
+
+    inferred_freq = None
+    if unit is not None:
+        unit = parse_timedelta_unit(unit)
+
+    data, copy = dtl.ensure_arraylike_for_datetimelike(
+        data, copy, cls_name="TimedeltaArray"
+    )
+
+    if isinstance(data, TimedeltaArray):
+        inferred_freq = data.freq
+
+    # Convert whatever we have into timedelta64[ns] dtype
+    if data.dtype == object or is_string_dtype(data.dtype):
+        # no need to make a copy, need to convert if string-dtyped
+        data = _objects_to_td64ns(data, unit=unit, errors=errors)
+        copy = False
+
+    elif is_integer_dtype(data.dtype):
+        # treat as multiples of the given unit
+        data, copy_made = _ints_to_td64ns(data, unit=unit)
+        copy = copy and not copy_made
+
+    elif is_float_dtype(data.dtype):
+        # cast the unit, multiply base/frac separately
+        # to avoid precision issues from float -> int
+        if isinstance(data.dtype, ExtensionDtype):
+            mask = data._mask
+            data = data._data
+        else:
+            mask = np.isnan(data)
+
+        data = cast_from_unit_vectorized(data, unit or "ns")
+        data[mask] = iNaT
+        data = data.view("m8[ns]")
+        copy = False
+
+    elif lib.is_np_dtype(data.dtype, "m"):
+        if not is_supported_dtype(data.dtype):
+            # cast to closest supported unit, i.e. s or ns
+            new_dtype = get_supported_dtype(data.dtype)
+            data = astype_overflowsafe(data, dtype=new_dtype, copy=False)
+            copy = False
+
+    else:
+        # This includes datetime64-dtype, see GH#23539, GH#29794
+        raise TypeError(f"dtype {data.dtype} cannot be converted to timedelta64[ns]")
+
+    if not copy:
+        data = np.asarray(data)
+    else:
+        data = np.array(data, copy=copy)
+
+    assert data.dtype.kind == "m"
+    assert data.dtype != "m8"  # i.e. not unit-less
+
+    return data, inferred_freq
+
+
+def _ints_to_td64ns(data, unit: str = "ns"):
+    """
+    Convert an ndarray with integer-dtype to timedelta64[ns] dtype, treating
+    the integers as multiples of the given timedelta unit.
+
+    Parameters
+    ----------
+    data : numpy.ndarray with integer-dtype
+    unit : str, default "ns"
+        The timedelta unit to treat integers as multiples of.
+
+    Returns
+    -------
+    numpy.ndarray : timedelta64[ns] array converted from data
+    bool : whether a copy was made
+    """
+    copy_made = False
+    unit = unit if unit is not None else "ns"
+
+    if data.dtype != np.int64:
+        # converting to int64 makes a copy, so we can avoid
+        # re-copying later
+        data = data.astype(np.int64)
+        copy_made = True
+
+    if unit != "ns":
+        dtype_str = f"timedelta64[{unit}]"
+        data = data.view(dtype_str)
+
+        data = astype_overflowsafe(data, dtype=TD64NS_DTYPE)
+
+        # the astype conversion makes a copy, so we can avoid re-copying later
+        copy_made = True
+
+    else:
+        data = data.view("timedelta64[ns]")
+
+    return data, copy_made
+
+
+def _objects_to_td64ns(data, unit=None, errors: DateTimeErrorChoices = "raise"):
+    """
+    Convert a object-dtyped or string-dtyped array into an
+    timedelta64[ns]-dtyped array.
+
+    Parameters
+    ----------
+    data : ndarray or Index
+    unit : str, default "ns"
+        The timedelta unit to treat integers as multiples of.
+        Must not be specified if the data contains a str.
+    errors : {"raise", "coerce", "ignore"}, default "raise"
+        How to handle elements that cannot be converted to timedelta64[ns].
+        See ``pandas.to_timedelta`` for details.
+
+    Returns
+    -------
+    numpy.ndarray : timedelta64[ns] array converted from data
+
+    Raises
+    ------
+    ValueError : Data cannot be converted to timedelta64[ns].
+
+    Notes
+    -----
+    Unlike `pandas.to_timedelta`, if setting `errors=ignore` will not cause
+    errors to be ignored; they are caught and subsequently ignored at a
+    higher level.
+    """
+    # coerce Index to np.ndarray, converting string-dtype if necessary
+    values = np.asarray(data, dtype=np.object_)
+
+    result = array_to_timedelta64(values, unit=unit, errors=errors)
+    return result.view("timedelta64[ns]")
+
+
+def _validate_td64_dtype(dtype) -> DtypeObj:
+    dtype = pandas_dtype(dtype)
+    if dtype == np.dtype("m8"):
+        # no precision disallowed GH#24806
+        msg = (
+            "Passing in 'timedelta' dtype with no precision is not allowed. "
+            "Please pass in 'timedelta64[ns]' instead."
+        )
+        raise ValueError(msg)
+
+    if not lib.is_np_dtype(dtype, "m"):
+        raise ValueError(f"dtype '{dtype}' is invalid, should be np.timedelta64 dtype")
+    elif not is_supported_dtype(dtype):
+        raise ValueError("Supported timedelta64 resolutions are 's', 'ms', 'us', 'ns'")
+
+    return dtype
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/base.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/base.py
new file mode 100644
index 0000000000000000000000000000000000000000..af8f80db6a34741b761991a4256e395100080171
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/base.py
@@ -0,0 +1,1400 @@
+"""
+Base and utility classes for pandas objects.
+"""
+
+from __future__ import annotations
+
+import textwrap
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Generic,
+    Literal,
+    cast,
+    final,
+    overload,
+)
+import warnings
+
+import numpy as np
+
+from pandas._config import using_copy_on_write
+
+from pandas._libs import lib
+from pandas._typing import (
+    AxisInt,
+    DtypeObj,
+    IndexLabel,
+    NDFrameT,
+    Self,
+    Shape,
+    npt,
+)
+from pandas.compat import PYPY
+from pandas.compat.numpy import function as nv
+from pandas.errors import AbstractMethodError
+from pandas.util._decorators import (
+    cache_readonly,
+    doc,
+)
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.cast import can_hold_element
+from pandas.core.dtypes.common import (
+    is_object_dtype,
+    is_scalar,
+)
+from pandas.core.dtypes.dtypes import ExtensionDtype
+from pandas.core.dtypes.generic import (
+    ABCDataFrame,
+    ABCIndex,
+    ABCMultiIndex,
+    ABCSeries,
+)
+from pandas.core.dtypes.missing import (
+    isna,
+    remove_na_arraylike,
+)
+
+from pandas.core import (
+    algorithms,
+    nanops,
+    ops,
+)
+from pandas.core.accessor import DirNamesMixin
+from pandas.core.arraylike import OpsMixin
+from pandas.core.arrays import ExtensionArray
+from pandas.core.construction import (
+    ensure_wrapped_if_datetimelike,
+    extract_array,
+)
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Hashable,
+        Iterator,
+    )
+
+    from pandas._typing import (
+        DropKeep,
+        NumpySorter,
+        NumpyValueArrayLike,
+        ScalarLike_co,
+    )
+
+    from pandas import (
+        DataFrame,
+        Index,
+        Series,
+    )
+
+
+_shared_docs: dict[str, str] = {}
+_indexops_doc_kwargs = {
+    "klass": "IndexOpsMixin",
+    "inplace": "",
+    "unique": "IndexOpsMixin",
+    "duplicated": "IndexOpsMixin",
+}
+
+
+class PandasObject(DirNamesMixin):
+    """
+    Baseclass for various pandas objects.
+    """
+
+    # results from calls to methods decorated with cache_readonly get added to _cache
+    _cache: dict[str, Any]
+
+    @property
+    def _constructor(self):
+        """
+        Class constructor (for this class it's just `__class__`).
+        """
+        return type(self)
+
+    def __repr__(self) -> str:
+        """
+        Return a string representation for a particular object.
+        """
+        # Should be overwritten by base classes
+        return object.__repr__(self)
+
+    def _reset_cache(self, key: str | None = None) -> None:
+        """
+        Reset cached properties. If ``key`` is passed, only clears that key.
+        """
+        if not hasattr(self, "_cache"):
+            return
+        if key is None:
+            self._cache.clear()
+        else:
+            self._cache.pop(key, None)
+
+    def __sizeof__(self) -> int:
+        """
+        Generates the total memory usage for an object that returns
+        either a value or Series of values
+        """
+        memory_usage = getattr(self, "memory_usage", None)
+        if memory_usage:
+            mem = memory_usage(deep=True)  # pylint: disable=not-callable
+            return int(mem if is_scalar(mem) else mem.sum())
+
+        # no memory_usage attribute, so fall back to object's 'sizeof'
+        return super().__sizeof__()
+
+
+class NoNewAttributesMixin:
+    """
+    Mixin which prevents adding new attributes.
+
+    Prevents additional attributes via xxx.attribute = "something" after a
+    call to `self.__freeze()`. Mainly used to prevent the user from using
+    wrong attributes on an accessor (`Series.cat/.str/.dt`).
+
+    If you really want to add a new attribute at a later time, you need to use
+    `object.__setattr__(self, key, value)`.
+    """
+
+    def _freeze(self) -> None:
+        """
+        Prevents setting additional attributes.
+        """
+        object.__setattr__(self, "__frozen", True)
+
+    # prevent adding any attribute via s.xxx.new_attribute = ...
+    def __setattr__(self, key: str, value) -> None:
+        # _cache is used by a decorator
+        # We need to check both 1.) cls.__dict__ and 2.) getattr(self, key)
+        # because
+        # 1.) getattr is false for attributes that raise errors
+        # 2.) cls.__dict__ doesn't traverse into base classes
+        if getattr(self, "__frozen", False) and not (
+            key == "_cache"
+            or key in type(self).__dict__
+            or getattr(self, key, None) is not None
+        ):
+            raise AttributeError(f"You cannot add any new attribute '{key}'")
+        object.__setattr__(self, key, value)
+
+
+class SelectionMixin(Generic[NDFrameT]):
+    """
+    mixin implementing the selection & aggregation interface on a group-like
+    object sub-classes need to define: obj, exclusions
+    """
+
+    obj: NDFrameT
+    _selection: IndexLabel | None = None
+    exclusions: frozenset[Hashable]
+    _internal_names = ["_cache", "__setstate__"]
+    _internal_names_set = set(_internal_names)
+
+    @final
+    @property
+    def _selection_list(self):
+        if not isinstance(
+            self._selection, (list, tuple, ABCSeries, ABCIndex, np.ndarray)
+        ):
+            return [self._selection]
+        return self._selection
+
+    @cache_readonly
+    def _selected_obj(self):
+        if self._selection is None or isinstance(self.obj, ABCSeries):
+            return self.obj
+        else:
+            return self.obj[self._selection]
+
+    @final
+    @cache_readonly
+    def ndim(self) -> int:
+        return self._selected_obj.ndim
+
+    @final
+    @cache_readonly
+    def _obj_with_exclusions(self):
+        if isinstance(self.obj, ABCSeries):
+            return self.obj
+
+        if self._selection is not None:
+            return self.obj._getitem_nocopy(self._selection_list)
+
+        if len(self.exclusions) > 0:
+            # equivalent to `self.obj.drop(self.exclusions, axis=1)
+            #  but this avoids consolidating and making a copy
+            # TODO: following GH#45287 can we now use .drop directly without
+            #  making a copy?
+            return self.obj._drop_axis(self.exclusions, axis=1, only_slice=True)
+        else:
+            return self.obj
+
+    def __getitem__(self, key):
+        if self._selection is not None:
+            raise IndexError(f"Column(s) {self._selection} already selected")
+
+        if isinstance(key, (list, tuple, ABCSeries, ABCIndex, np.ndarray)):
+            if len(self.obj.columns.intersection(key)) != len(set(key)):
+                bad_keys = list(set(key).difference(self.obj.columns))
+                raise KeyError(f"Columns not found: {str(bad_keys)[1:-1]}")
+            return self._gotitem(list(key), ndim=2)
+
+        else:
+            if key not in self.obj:
+                raise KeyError(f"Column not found: {key}")
+            ndim = self.obj[key].ndim
+            return self._gotitem(key, ndim=ndim)
+
+    def _gotitem(self, key, ndim: int, subset=None):
+        """
+        sub-classes to define
+        return a sliced object
+
+        Parameters
+        ----------
+        key : str / list of selections
+        ndim : {1, 2}
+            requested ndim of result
+        subset : object, default None
+            subset to act on
+        """
+        raise AbstractMethodError(self)
+
+    @final
+    def _infer_selection(self, key, subset: Series | DataFrame):
+        """
+        Infer the `selection` to pass to our constructor in _gotitem.
+        """
+        # Shared by Rolling and Resample
+        selection = None
+        if subset.ndim == 2 and (
+            (lib.is_scalar(key) and key in subset) or lib.is_list_like(key)
+        ):
+            selection = key
+        elif subset.ndim == 1 and lib.is_scalar(key) and key == subset.name:
+            selection = key
+        return selection
+
+    def aggregate(self, func, *args, **kwargs):
+        raise AbstractMethodError(self)
+
+    agg = aggregate
+
+
+class IndexOpsMixin(OpsMixin):
+    """
+    Common ops mixin to support a unified interface / docs for Series / Index
+    """
+
+    # ndarray compatibility
+    __array_priority__ = 1000
+    _hidden_attrs: frozenset[str] = frozenset(
+        ["tolist"]  # tolist is not deprecated, just suppressed in the __dir__
+    )
+
+    @property
+    def dtype(self) -> DtypeObj:
+        # must be defined here as a property for mypy
+        raise AbstractMethodError(self)
+
+    @property
+    def _values(self) -> ExtensionArray | np.ndarray:
+        # must be defined here as a property for mypy
+        raise AbstractMethodError(self)
+
+    @final
+    def transpose(self, *args, **kwargs) -> Self:
+        """
+        Return the transpose, which is by definition self.
+
+        Returns
+        -------
+        %(klass)s
+        """
+        nv.validate_transpose(args, kwargs)
+        return self
+
+    T = property(
+        transpose,
+        doc="""
+        Return the transpose, which is by definition self.
+
+        Examples
+        --------
+        For Series:
+
+        >>> s = pd.Series(['Ant', 'Bear', 'Cow'])
+        >>> s
+        0     Ant
+        1    Bear
+        2     Cow
+        dtype: object
+        >>> s.T
+        0     Ant
+        1    Bear
+        2     Cow
+        dtype: object
+
+        For Index:
+
+        >>> idx = pd.Index([1, 2, 3])
+        >>> idx.T
+        Index([1, 2, 3], dtype='int64')
+        """,
+    )
+
+    @property
+    def shape(self) -> Shape:
+        """
+        Return a tuple of the shape of the underlying data.
+
+        Examples
+        --------
+        >>> s = pd.Series([1, 2, 3])
+        >>> s.shape
+        (3,)
+        """
+        return self._values.shape
+
+    def __len__(self) -> int:
+        # We need this defined here for mypy
+        raise AbstractMethodError(self)
+
+    # Temporarily avoid using `-> Literal[1]:` because of an IPython (jedi) bug
+    # https://github.com/ipython/ipython/issues/14412
+    # https://github.com/davidhalter/jedi/issues/1990
+    @property
+    def ndim(self) -> int:
+        """
+        Number of dimensions of the underlying data, by definition 1.
+
+        Examples
+        --------
+        >>> s = pd.Series(['Ant', 'Bear', 'Cow'])
+        >>> s
+        0     Ant
+        1    Bear
+        2     Cow
+        dtype: object
+        >>> s.ndim
+        1
+
+        For Index:
+
+        >>> idx = pd.Index([1, 2, 3])
+        >>> idx
+        Index([1, 2, 3], dtype='int64')
+        >>> idx.ndim
+        1
+        """
+        return 1
+
+    @final
+    def item(self):
+        """
+        Return the first element of the underlying data as a Python scalar.
+
+        Returns
+        -------
+        scalar
+            The first element of Series or Index.
+
+        Raises
+        ------
+        ValueError
+            If the data is not length = 1.
+
+        Examples
+        --------
+        >>> s = pd.Series([1])
+        >>> s.item()
+        1
+
+        For an index:
+
+        >>> s = pd.Series([1], index=['a'])
+        >>> s.index.item()
+        'a'
+        """
+        if len(self) == 1:
+            return next(iter(self))
+        raise ValueError("can only convert an array of size 1 to a Python scalar")
+
+    @property
+    def nbytes(self) -> int:
+        """
+        Return the number of bytes in the underlying data.
+
+        Examples
+        --------
+        For Series:
+
+        >>> s = pd.Series(['Ant', 'Bear', 'Cow'])
+        >>> s
+        0     Ant
+        1    Bear
+        2     Cow
+        dtype: object
+        >>> s.nbytes
+        24
+
+        For Index:
+
+        >>> idx = pd.Index([1, 2, 3])
+        >>> idx
+        Index([1, 2, 3], dtype='int64')
+        >>> idx.nbytes
+        24
+        """
+        return self._values.nbytes
+
+    @property
+    def size(self) -> int:
+        """
+        Return the number of elements in the underlying data.
+
+        Examples
+        --------
+        For Series:
+
+        >>> s = pd.Series(['Ant', 'Bear', 'Cow'])
+        >>> s
+        0     Ant
+        1    Bear
+        2     Cow
+        dtype: object
+        >>> s.size
+        3
+
+        For Index:
+
+        >>> idx = pd.Index([1, 2, 3])
+        >>> idx
+        Index([1, 2, 3], dtype='int64')
+        >>> idx.size
+        3
+        """
+        return len(self._values)
+
+    @property
+    def array(self) -> ExtensionArray:
+        """
+        The ExtensionArray of the data backing this Series or Index.
+
+        Returns
+        -------
+        ExtensionArray
+            An ExtensionArray of the values stored within. For extension
+            types, this is the actual array. For NumPy native types, this
+            is a thin (no copy) wrapper around :class:`numpy.ndarray`.
+
+            ``.array`` differs from ``.values``, which may require converting
+            the data to a different form.
+
+        See Also
+        --------
+        Index.to_numpy : Similar method that always returns a NumPy array.
+        Series.to_numpy : Similar method that always returns a NumPy array.
+
+        Notes
+        -----
+        This table lays out the different array types for each extension
+        dtype within pandas.
+
+        ================== =============================
+        dtype              array type
+        ================== =============================
+        category           Categorical
+        period             PeriodArray
+        interval           IntervalArray
+        IntegerNA          IntegerArray
+        string             StringArray
+        boolean            BooleanArray
+        datetime64[ns, tz] DatetimeArray
+        ================== =============================
+
+        For any 3rd-party extension types, the array type will be an
+        ExtensionArray.
+
+        For all remaining dtypes ``.array`` will be a
+        :class:`arrays.NumpyExtensionArray` wrapping the actual ndarray
+        stored within. If you absolutely need a NumPy array (possibly with
+        copying / coercing data), then use :meth:`Series.to_numpy` instead.
+
+        Examples
+        --------
+        For regular NumPy types like int, and float, a NumpyExtensionArray
+        is returned.
+
+        >>> pd.Series([1, 2, 3]).array
+        
+        [1, 2, 3]
+        Length: 3, dtype: int64
+
+        For extension types, like Categorical, the actual ExtensionArray
+        is returned
+
+        >>> ser = pd.Series(pd.Categorical(['a', 'b', 'a']))
+        >>> ser.array
+        ['a', 'b', 'a']
+        Categories (2, object): ['a', 'b']
+        """
+        raise AbstractMethodError(self)
+
+    @final
+    def to_numpy(
+        self,
+        dtype: npt.DTypeLike | None = None,
+        copy: bool = False,
+        na_value: object = lib.no_default,
+        **kwargs,
+    ) -> np.ndarray:
+        """
+        A NumPy ndarray representing the values in this Series or Index.
+
+        Parameters
+        ----------
+        dtype : str or numpy.dtype, optional
+            The dtype to pass to :meth:`numpy.asarray`.
+        copy : bool, default False
+            Whether to ensure that the returned value is not a view on
+            another array. Note that ``copy=False`` does not *ensure* that
+            ``to_numpy()`` is no-copy. Rather, ``copy=True`` ensure that
+            a copy is made, even if not strictly necessary.
+        na_value : Any, optional
+            The value to use for missing values. The default value depends
+            on `dtype` and the type of the array.
+        **kwargs
+            Additional keywords passed through to the ``to_numpy`` method
+            of the underlying array (for extension arrays).
+
+        Returns
+        -------
+        numpy.ndarray
+
+        See Also
+        --------
+        Series.array : Get the actual data stored within.
+        Index.array : Get the actual data stored within.
+        DataFrame.to_numpy : Similar method for DataFrame.
+
+        Notes
+        -----
+        The returned array will be the same up to equality (values equal
+        in `self` will be equal in the returned array; likewise for values
+        that are not equal). When `self` contains an ExtensionArray, the
+        dtype may be different. For example, for a category-dtype Series,
+        ``to_numpy()`` will return a NumPy array and the categorical dtype
+        will be lost.
+
+        For NumPy dtypes, this will be a reference to the actual data stored
+        in this Series or Index (assuming ``copy=False``). Modifying the result
+        in place will modify the data stored in the Series or Index (not that
+        we recommend doing that).
+
+        For extension types, ``to_numpy()`` *may* require copying data and
+        coercing the result to a NumPy type (possibly object), which may be
+        expensive. When you need a no-copy reference to the underlying data,
+        :attr:`Series.array` should be used instead.
+
+        This table lays out the different dtypes and default return types of
+        ``to_numpy()`` for various dtypes within pandas.
+
+        ================== ================================
+        dtype              array type
+        ================== ================================
+        category[T]        ndarray[T] (same dtype as input)
+        period             ndarray[object] (Periods)
+        interval           ndarray[object] (Intervals)
+        IntegerNA          ndarray[object]
+        datetime64[ns]     datetime64[ns]
+        datetime64[ns, tz] ndarray[object] (Timestamps)
+        ================== ================================
+
+        Examples
+        --------
+        >>> ser = pd.Series(pd.Categorical(['a', 'b', 'a']))
+        >>> ser.to_numpy()
+        array(['a', 'b', 'a'], dtype=object)
+
+        Specify the `dtype` to control how datetime-aware data is represented.
+        Use ``dtype=object`` to return an ndarray of pandas :class:`Timestamp`
+        objects, each with the correct ``tz``.
+
+        >>> ser = pd.Series(pd.date_range('2000', periods=2, tz="CET"))
+        >>> ser.to_numpy(dtype=object)
+        array([Timestamp('2000-01-01 00:00:00+0100', tz='CET'),
+               Timestamp('2000-01-02 00:00:00+0100', tz='CET')],
+              dtype=object)
+
+        Or ``dtype='datetime64[ns]'`` to return an ndarray of native
+        datetime64 values. The values are converted to UTC and the timezone
+        info is dropped.
+
+        >>> ser.to_numpy(dtype="datetime64[ns]")
+        ... # doctest: +ELLIPSIS
+        array(['1999-12-31T23:00:00.000000000', '2000-01-01T23:00:00...'],
+              dtype='datetime64[ns]')
+        """
+        if isinstance(self.dtype, ExtensionDtype):
+            return self.array.to_numpy(dtype, copy=copy, na_value=na_value, **kwargs)
+        elif kwargs:
+            bad_keys = next(iter(kwargs.keys()))
+            raise TypeError(
+                f"to_numpy() got an unexpected keyword argument '{bad_keys}'"
+            )
+
+        fillna = (
+            na_value is not lib.no_default
+            # no need to fillna with np.nan if we already have a float dtype
+            and not (na_value is np.nan and np.issubdtype(self.dtype, np.floating))
+        )
+
+        values = self._values
+        if fillna:
+            if not can_hold_element(values, na_value):
+                # if we can't hold the na_value asarray either makes a copy or we
+                # error before modifying values. The asarray later on thus won't make
+                # another copy
+                values = np.asarray(values, dtype=dtype)
+            else:
+                values = values.copy()
+
+            values[np.asanyarray(isna(self))] = na_value
+
+        result = np.asarray(values, dtype=dtype)
+
+        if (copy and not fillna) or (not copy and using_copy_on_write()):
+            if np.shares_memory(self._values[:2], result[:2]):
+                # Take slices to improve performance of check
+                if using_copy_on_write() and not copy:
+                    result = result.view()
+                    result.flags.writeable = False
+                else:
+                    result = result.copy()
+
+        return result
+
+    @final
+    @property
+    def empty(self) -> bool:
+        return not self.size
+
+    @doc(op="max", oppose="min", value="largest")
+    def argmax(
+        self, axis: AxisInt | None = None, skipna: bool = True, *args, **kwargs
+    ) -> int:
+        """
+        Return int position of the {value} value in the Series.
+
+        If the {op}imum is achieved in multiple locations,
+        the first row position is returned.
+
+        Parameters
+        ----------
+        axis : {{None}}
+            Unused. Parameter needed for compatibility with DataFrame.
+        skipna : bool, default True
+            Exclude NA/null values when showing the result.
+        *args, **kwargs
+            Additional arguments and keywords for compatibility with NumPy.
+
+        Returns
+        -------
+        int
+            Row position of the {op}imum value.
+
+        See Also
+        --------
+        Series.arg{op} : Return position of the {op}imum value.
+        Series.arg{oppose} : Return position of the {oppose}imum value.
+        numpy.ndarray.arg{op} : Equivalent method for numpy arrays.
+        Series.idxmax : Return index label of the maximum values.
+        Series.idxmin : Return index label of the minimum values.
+
+        Examples
+        --------
+        Consider dataset containing cereal calories
+
+        >>> s = pd.Series({{'Corn Flakes': 100.0, 'Almond Delight': 110.0,
+        ...                'Cinnamon Toast Crunch': 120.0, 'Cocoa Puff': 110.0}})
+        >>> s
+        Corn Flakes              100.0
+        Almond Delight           110.0
+        Cinnamon Toast Crunch    120.0
+        Cocoa Puff               110.0
+        dtype: float64
+
+        >>> s.argmax()
+        2
+        >>> s.argmin()
+        0
+
+        The maximum cereal calories is the third element and
+        the minimum cereal calories is the first element,
+        since series is zero-indexed.
+        """
+        delegate = self._values
+        nv.validate_minmax_axis(axis)
+        skipna = nv.validate_argmax_with_skipna(skipna, args, kwargs)
+
+        if isinstance(delegate, ExtensionArray):
+            if not skipna and delegate.isna().any():
+                warnings.warn(
+                    f"The behavior of {type(self).__name__}.argmax/argmin "
+                    "with skipna=False and NAs, or with all-NAs is deprecated. "
+                    "In a future version this will raise ValueError.",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+                return -1
+            else:
+                return delegate.argmax()
+        else:
+            result = nanops.nanargmax(delegate, skipna=skipna)
+            if result == -1:
+                warnings.warn(
+                    f"The behavior of {type(self).__name__}.argmax/argmin "
+                    "with skipna=False and NAs, or with all-NAs is deprecated. "
+                    "In a future version this will raise ValueError.",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+            # error: Incompatible return value type (got "Union[int, ndarray]", expected
+            # "int")
+            return result  # type: ignore[return-value]
+
+    @doc(argmax, op="min", oppose="max", value="smallest")
+    def argmin(
+        self, axis: AxisInt | None = None, skipna: bool = True, *args, **kwargs
+    ) -> int:
+        delegate = self._values
+        nv.validate_minmax_axis(axis)
+        skipna = nv.validate_argmin_with_skipna(skipna, args, kwargs)
+
+        if isinstance(delegate, ExtensionArray):
+            if not skipna and delegate.isna().any():
+                warnings.warn(
+                    f"The behavior of {type(self).__name__}.argmax/argmin "
+                    "with skipna=False and NAs, or with all-NAs is deprecated. "
+                    "In a future version this will raise ValueError.",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+                return -1
+            else:
+                return delegate.argmin()
+        else:
+            result = nanops.nanargmin(delegate, skipna=skipna)
+            if result == -1:
+                warnings.warn(
+                    f"The behavior of {type(self).__name__}.argmax/argmin "
+                    "with skipna=False and NAs, or with all-NAs is deprecated. "
+                    "In a future version this will raise ValueError.",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+            # error: Incompatible return value type (got "Union[int, ndarray]", expected
+            # "int")
+            return result  # type: ignore[return-value]
+
+    def tolist(self):
+        """
+        Return a list of the values.
+
+        These are each a scalar type, which is a Python scalar
+        (for str, int, float) or a pandas scalar
+        (for Timestamp/Timedelta/Interval/Period)
+
+        Returns
+        -------
+        list
+
+        See Also
+        --------
+        numpy.ndarray.tolist : Return the array as an a.ndim-levels deep
+            nested list of Python scalars.
+
+        Examples
+        --------
+        For Series
+
+        >>> s = pd.Series([1, 2, 3])
+        >>> s.to_list()
+        [1, 2, 3]
+
+        For Index:
+
+        >>> idx = pd.Index([1, 2, 3])
+        >>> idx
+        Index([1, 2, 3], dtype='int64')
+
+        >>> idx.to_list()
+        [1, 2, 3]
+        """
+        return self._values.tolist()
+
+    to_list = tolist
+
+    def __iter__(self) -> Iterator:
+        """
+        Return an iterator of the values.
+
+        These are each a scalar type, which is a Python scalar
+        (for str, int, float) or a pandas scalar
+        (for Timestamp/Timedelta/Interval/Period)
+
+        Returns
+        -------
+        iterator
+
+        Examples
+        --------
+        >>> s = pd.Series([1, 2, 3])
+        >>> for x in s:
+        ...     print(x)
+        1
+        2
+        3
+        """
+        # We are explicitly making element iterators.
+        if not isinstance(self._values, np.ndarray):
+            # Check type instead of dtype to catch DTA/TDA
+            return iter(self._values)
+        else:
+            return map(self._values.item, range(self._values.size))
+
+    @cache_readonly
+    def hasnans(self) -> bool:
+        """
+        Return True if there are any NaNs.
+
+        Enables various performance speedups.
+
+        Returns
+        -------
+        bool
+
+        Examples
+        --------
+        >>> s = pd.Series([1, 2, 3, None])
+        >>> s
+        0    1.0
+        1    2.0
+        2    3.0
+        3    NaN
+        dtype: float64
+        >>> s.hasnans
+        True
+        """
+        # error: Item "bool" of "Union[bool, ndarray[Any, dtype[bool_]], NDFrame]"
+        # has no attribute "any"
+        return bool(isna(self).any())  # type: ignore[union-attr]
+
+    @final
+    def _map_values(self, mapper, na_action=None, convert: bool = True):
+        """
+        An internal function that maps values using the input
+        correspondence (which can be a dict, Series, or function).
+
+        Parameters
+        ----------
+        mapper : function, dict, or Series
+            The input correspondence object
+        na_action : {None, 'ignore'}
+            If 'ignore', propagate NA values, without passing them to the
+            mapping function
+        convert : bool, default True
+            Try to find better dtype for elementwise function results. If
+            False, leave as dtype=object. Note that the dtype is always
+            preserved for some extension array dtypes, such as Categorical.
+
+        Returns
+        -------
+        Union[Index, MultiIndex], inferred
+            The output of the mapping function applied to the index.
+            If the function returns a tuple with more than one element
+            a MultiIndex will be returned.
+        """
+        arr = self._values
+
+        if isinstance(arr, ExtensionArray):
+            return arr.map(mapper, na_action=na_action)
+
+        return algorithms.map_array(arr, mapper, na_action=na_action, convert=convert)
+
+    @final
+    def value_counts(
+        self,
+        normalize: bool = False,
+        sort: bool = True,
+        ascending: bool = False,
+        bins=None,
+        dropna: bool = True,
+    ) -> Series:
+        """
+        Return a Series containing counts of unique values.
+
+        The resulting object will be in descending order so that the
+        first element is the most frequently-occurring element.
+        Excludes NA values by default.
+
+        Parameters
+        ----------
+        normalize : bool, default False
+            If True then the object returned will contain the relative
+            frequencies of the unique values.
+        sort : bool, default True
+            Sort by frequencies when True. Preserve the order of the data when False.
+        ascending : bool, default False
+            Sort in ascending order.
+        bins : int, optional
+            Rather than count values, group them into half-open bins,
+            a convenience for ``pd.cut``, only works with numeric data.
+        dropna : bool, default True
+            Don't include counts of NaN.
+
+        Returns
+        -------
+        Series
+
+        See Also
+        --------
+        Series.count: Number of non-NA elements in a Series.
+        DataFrame.count: Number of non-NA elements in a DataFrame.
+        DataFrame.value_counts: Equivalent method on DataFrames.
+
+        Examples
+        --------
+        >>> index = pd.Index([3, 1, 2, 3, 4, np.nan])
+        >>> index.value_counts()
+        3.0    2
+        1.0    1
+        2.0    1
+        4.0    1
+        Name: count, dtype: int64
+
+        With `normalize` set to `True`, returns the relative frequency by
+        dividing all values by the sum of values.
+
+        >>> s = pd.Series([3, 1, 2, 3, 4, np.nan])
+        >>> s.value_counts(normalize=True)
+        3.0    0.4
+        1.0    0.2
+        2.0    0.2
+        4.0    0.2
+        Name: proportion, dtype: float64
+
+        **bins**
+
+        Bins can be useful for going from a continuous variable to a
+        categorical variable; instead of counting unique
+        apparitions of values, divide the index in the specified
+        number of half-open bins.
+
+        >>> s.value_counts(bins=3)
+        (0.996, 2.0]    2
+        (2.0, 3.0]      2
+        (3.0, 4.0]      1
+        Name: count, dtype: int64
+
+        **dropna**
+
+        With `dropna` set to `False` we can also see NaN index values.
+
+        >>> s.value_counts(dropna=False)
+        3.0    2
+        1.0    1
+        2.0    1
+        4.0    1
+        NaN    1
+        Name: count, dtype: int64
+        """
+        return algorithms.value_counts_internal(
+            self,
+            sort=sort,
+            ascending=ascending,
+            normalize=normalize,
+            bins=bins,
+            dropna=dropna,
+        )
+
+    def unique(self):
+        values = self._values
+        if not isinstance(values, np.ndarray):
+            # i.e. ExtensionArray
+            result = values.unique()
+        else:
+            result = algorithms.unique1d(values)
+        return result
+
+    @final
+    def nunique(self, dropna: bool = True) -> int:
+        """
+        Return number of unique elements in the object.
+
+        Excludes NA values by default.
+
+        Parameters
+        ----------
+        dropna : bool, default True
+            Don't include NaN in the count.
+
+        Returns
+        -------
+        int
+
+        See Also
+        --------
+        DataFrame.nunique: Method nunique for DataFrame.
+        Series.count: Count non-NA/null observations in the Series.
+
+        Examples
+        --------
+        >>> s = pd.Series([1, 3, 5, 7, 7])
+        >>> s
+        0    1
+        1    3
+        2    5
+        3    7
+        4    7
+        dtype: int64
+
+        >>> s.nunique()
+        4
+        """
+        uniqs = self.unique()
+        if dropna:
+            uniqs = remove_na_arraylike(uniqs)
+        return len(uniqs)
+
+    @property
+    def is_unique(self) -> bool:
+        """
+        Return boolean if values in the object are unique.
+
+        Returns
+        -------
+        bool
+
+        Examples
+        --------
+        >>> s = pd.Series([1, 2, 3])
+        >>> s.is_unique
+        True
+
+        >>> s = pd.Series([1, 2, 3, 1])
+        >>> s.is_unique
+        False
+        """
+        return self.nunique(dropna=False) == len(self)
+
+    @property
+    def is_monotonic_increasing(self) -> bool:
+        """
+        Return boolean if values in the object are monotonically increasing.
+
+        Returns
+        -------
+        bool
+
+        Examples
+        --------
+        >>> s = pd.Series([1, 2, 2])
+        >>> s.is_monotonic_increasing
+        True
+
+        >>> s = pd.Series([3, 2, 1])
+        >>> s.is_monotonic_increasing
+        False
+        """
+        from pandas import Index
+
+        return Index(self).is_monotonic_increasing
+
+    @property
+    def is_monotonic_decreasing(self) -> bool:
+        """
+        Return boolean if values in the object are monotonically decreasing.
+
+        Returns
+        -------
+        bool
+
+        Examples
+        --------
+        >>> s = pd.Series([3, 2, 2, 1])
+        >>> s.is_monotonic_decreasing
+        True
+
+        >>> s = pd.Series([1, 2, 3])
+        >>> s.is_monotonic_decreasing
+        False
+        """
+        from pandas import Index
+
+        return Index(self).is_monotonic_decreasing
+
+    @final
+    def _memory_usage(self, deep: bool = False) -> int:
+        """
+        Memory usage of the values.
+
+        Parameters
+        ----------
+        deep : bool, default False
+            Introspect the data deeply, interrogate
+            `object` dtypes for system-level memory consumption.
+
+        Returns
+        -------
+        bytes used
+
+        See Also
+        --------
+        numpy.ndarray.nbytes : Total bytes consumed by the elements of the
+            array.
+
+        Notes
+        -----
+        Memory usage does not include memory consumed by elements that
+        are not components of the array if deep=False or if used on PyPy
+
+        Examples
+        --------
+        >>> idx = pd.Index([1, 2, 3])
+        >>> idx.memory_usage()
+        24
+        """
+        if hasattr(self.array, "memory_usage"):
+            return self.array.memory_usage(  # pyright: ignore[reportGeneralTypeIssues]
+                deep=deep,
+            )
+
+        v = self.array.nbytes
+        if deep and is_object_dtype(self.dtype) and not PYPY:
+            values = cast(np.ndarray, self._values)
+            v += lib.memory_usage_of_objects(values)
+        return v
+
+    @doc(
+        algorithms.factorize,
+        values="",
+        order="",
+        size_hint="",
+        sort=textwrap.dedent(
+            """\
+            sort : bool, default False
+                Sort `uniques` and shuffle `codes` to maintain the
+                relationship.
+            """
+        ),
+    )
+    def factorize(
+        self,
+        sort: bool = False,
+        use_na_sentinel: bool = True,
+    ) -> tuple[npt.NDArray[np.intp], Index]:
+        codes, uniques = algorithms.factorize(
+            self._values, sort=sort, use_na_sentinel=use_na_sentinel
+        )
+        if uniques.dtype == np.float16:
+            uniques = uniques.astype(np.float32)
+
+        if isinstance(self, ABCMultiIndex):
+            # preserve MultiIndex
+            uniques = self._constructor(uniques)
+        else:
+            from pandas import Index
+
+            try:
+                uniques = Index(uniques, dtype=self.dtype)
+            except NotImplementedError:
+                # not all dtypes are supported in Index that are allowed for Series
+                # e.g. float16 or bytes
+                uniques = Index(uniques)
+        return codes, uniques
+
+    _shared_docs[
+        "searchsorted"
+    ] = """
+        Find indices where elements should be inserted to maintain order.
+
+        Find the indices into a sorted {klass} `self` such that, if the
+        corresponding elements in `value` were inserted before the indices,
+        the order of `self` would be preserved.
+
+        .. note::
+
+            The {klass} *must* be monotonically sorted, otherwise
+            wrong locations will likely be returned. Pandas does *not*
+            check this for you.
+
+        Parameters
+        ----------
+        value : array-like or scalar
+            Values to insert into `self`.
+        side : {{'left', 'right'}}, optional
+            If 'left', the index of the first suitable location found is given.
+            If 'right', return the last such index.  If there is no suitable
+            index, return either 0 or N (where N is the length of `self`).
+        sorter : 1-D array-like, optional
+            Optional array of integer indices that sort `self` into ascending
+            order. They are typically the result of ``np.argsort``.
+
+        Returns
+        -------
+        int or array of int
+            A scalar or array of insertion points with the
+            same shape as `value`.
+
+        See Also
+        --------
+        sort_values : Sort by the values along either axis.
+        numpy.searchsorted : Similar method from NumPy.
+
+        Notes
+        -----
+        Binary search is used to find the required insertion points.
+
+        Examples
+        --------
+        >>> ser = pd.Series([1, 2, 3])
+        >>> ser
+        0    1
+        1    2
+        2    3
+        dtype: int64
+
+        >>> ser.searchsorted(4)
+        3
+
+        >>> ser.searchsorted([0, 4])
+        array([0, 3])
+
+        >>> ser.searchsorted([1, 3], side='left')
+        array([0, 2])
+
+        >>> ser.searchsorted([1, 3], side='right')
+        array([1, 3])
+
+        >>> ser = pd.Series(pd.to_datetime(['3/11/2000', '3/12/2000', '3/13/2000']))
+        >>> ser
+        0   2000-03-11
+        1   2000-03-12
+        2   2000-03-13
+        dtype: datetime64[ns]
+
+        >>> ser.searchsorted('3/14/2000')
+        3
+
+        >>> ser = pd.Categorical(
+        ...     ['apple', 'bread', 'bread', 'cheese', 'milk'], ordered=True
+        ... )
+        >>> ser
+        ['apple', 'bread', 'bread', 'cheese', 'milk']
+        Categories (4, object): ['apple' < 'bread' < 'cheese' < 'milk']
+
+        >>> ser.searchsorted('bread')
+        1
+
+        >>> ser.searchsorted(['bread'], side='right')
+        array([3])
+
+        If the values are not monotonically sorted, wrong locations
+        may be returned:
+
+        >>> ser = pd.Series([2, 1, 3])
+        >>> ser
+        0    2
+        1    1
+        2    3
+        dtype: int64
+
+        >>> ser.searchsorted(1)  # doctest: +SKIP
+        0  # wrong result, correct would be 1
+        """
+
+    # This overload is needed so that the call to searchsorted in
+    # pandas.core.resample.TimeGrouper._get_period_bins picks the correct result
+
+    # error: Overloaded function signatures 1 and 2 overlap with incompatible
+    # return types
+    @overload
+    def searchsorted(  # type: ignore[overload-overlap]
+        self,
+        value: ScalarLike_co,
+        side: Literal["left", "right"] = ...,
+        sorter: NumpySorter = ...,
+    ) -> np.intp:
+        ...
+
+    @overload
+    def searchsorted(
+        self,
+        value: npt.ArrayLike | ExtensionArray,
+        side: Literal["left", "right"] = ...,
+        sorter: NumpySorter = ...,
+    ) -> npt.NDArray[np.intp]:
+        ...
+
+    @doc(_shared_docs["searchsorted"], klass="Index")
+    def searchsorted(
+        self,
+        value: NumpyValueArrayLike | ExtensionArray,
+        side: Literal["left", "right"] = "left",
+        sorter: NumpySorter | None = None,
+    ) -> npt.NDArray[np.intp] | np.intp:
+        if isinstance(value, ABCDataFrame):
+            msg = (
+                "Value must be 1-D array-like or scalar, "
+                f"{type(value).__name__} is not supported"
+            )
+            raise ValueError(msg)
+
+        values = self._values
+        if not isinstance(values, np.ndarray):
+            # Going through EA.searchsorted directly improves performance GH#38083
+            return values.searchsorted(value, side=side, sorter=sorter)
+
+        return algorithms.searchsorted(
+            values,
+            value,
+            side=side,
+            sorter=sorter,
+        )
+
+    def drop_duplicates(self, *, keep: DropKeep = "first"):
+        duplicated = self._duplicated(keep=keep)
+        # error: Value of type "IndexOpsMixin" is not indexable
+        return self[~duplicated]  # type: ignore[index]
+
+    @final
+    def _duplicated(self, keep: DropKeep = "first") -> npt.NDArray[np.bool_]:
+        arr = self._values
+        if isinstance(arr, ExtensionArray):
+            return arr.duplicated(keep=keep)
+        return algorithms.duplicated(arr, keep=keep)
+
+    def _arith_method(self, other, op):
+        res_name = ops.get_op_result_name(self, other)
+
+        lvalues = self._values
+        rvalues = extract_array(other, extract_numpy=True, extract_range=True)
+        rvalues = ops.maybe_prepare_scalar_for_op(rvalues, lvalues.shape)
+        rvalues = ensure_wrapped_if_datetimelike(rvalues)
+        if isinstance(rvalues, range):
+            rvalues = np.arange(rvalues.start, rvalues.stop, rvalues.step)
+
+        with np.errstate(all="ignore"):
+            result = ops.arithmetic_op(lvalues, rvalues, op)
+
+        return self._construct_result(result, name=res_name)
+
+    def _construct_result(self, result, name):
+        """
+        Construct an appropriately-wrapped result from the ArrayLike result
+        of an arithmetic-like operation.
+        """
+        raise AbstractMethodError(self)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/common.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/common.py
new file mode 100644
index 0000000000000000000000000000000000000000..9f024498d66edfe9cab7b6fb2e0802da79163786
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/common.py
@@ -0,0 +1,657 @@
+"""
+Misc tools for implementing data structures
+
+Note: pandas.core.common is *not* part of the public API.
+"""
+from __future__ import annotations
+
+import builtins
+from collections import (
+    abc,
+    defaultdict,
+)
+from collections.abc import (
+    Collection,
+    Generator,
+    Hashable,
+    Iterable,
+    Sequence,
+)
+import contextlib
+from functools import partial
+import inspect
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    cast,
+    overload,
+)
+import warnings
+
+import numpy as np
+
+from pandas._libs import lib
+from pandas.compat.numpy import np_version_gte1p24
+
+from pandas.core.dtypes.cast import construct_1d_object_array_from_listlike
+from pandas.core.dtypes.common import (
+    is_bool_dtype,
+    is_integer,
+)
+from pandas.core.dtypes.generic import (
+    ABCExtensionArray,
+    ABCIndex,
+    ABCMultiIndex,
+    ABCSeries,
+)
+from pandas.core.dtypes.inference import iterable_not_string
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        AnyArrayLike,
+        ArrayLike,
+        NpDtype,
+        RandomState,
+        T,
+    )
+
+    from pandas import Index
+
+
+def flatten(line):
+    """
+    Flatten an arbitrarily nested sequence.
+
+    Parameters
+    ----------
+    line : sequence
+        The non string sequence to flatten
+
+    Notes
+    -----
+    This doesn't consider strings sequences.
+
+    Returns
+    -------
+    flattened : generator
+    """
+    for element in line:
+        if iterable_not_string(element):
+            yield from flatten(element)
+        else:
+            yield element
+
+
+def consensus_name_attr(objs):
+    name = objs[0].name
+    for obj in objs[1:]:
+        try:
+            if obj.name != name:
+                name = None
+        except ValueError:
+            name = None
+    return name
+
+
+def is_bool_indexer(key: Any) -> bool:
+    """
+    Check whether `key` is a valid boolean indexer.
+
+    Parameters
+    ----------
+    key : Any
+        Only list-likes may be considered boolean indexers.
+        All other types are not considered a boolean indexer.
+        For array-like input, boolean ndarrays or ExtensionArrays
+        with ``_is_boolean`` set are considered boolean indexers.
+
+    Returns
+    -------
+    bool
+        Whether `key` is a valid boolean indexer.
+
+    Raises
+    ------
+    ValueError
+        When the array is an object-dtype ndarray or ExtensionArray
+        and contains missing values.
+
+    See Also
+    --------
+    check_array_indexer : Check that `key` is a valid array to index,
+        and convert to an ndarray.
+    """
+    if isinstance(
+        key, (ABCSeries, np.ndarray, ABCIndex, ABCExtensionArray)
+    ) and not isinstance(key, ABCMultiIndex):
+        if key.dtype == np.object_:
+            key_array = np.asarray(key)
+
+            if not lib.is_bool_array(key_array):
+                na_msg = "Cannot mask with non-boolean array containing NA / NaN values"
+                if lib.is_bool_array(key_array, skipna=True):
+                    # Don't raise on e.g. ["A", "B", np.nan], see
+                    #  test_loc_getitem_list_of_labels_categoricalindex_with_na
+                    raise ValueError(na_msg)
+                return False
+            return True
+        elif is_bool_dtype(key.dtype):
+            return True
+    elif isinstance(key, list):
+        # check if np.array(key).dtype would be bool
+        if len(key) > 0:
+            if type(key) is not list:  # noqa: E721
+                # GH#42461 cython will raise TypeError if we pass a subclass
+                key = list(key)
+            return lib.is_bool_list(key)
+
+    return False
+
+
+def cast_scalar_indexer(val):
+    """
+    Disallow indexing with a float key, even if that key is a round number.
+
+    Parameters
+    ----------
+    val : scalar
+
+    Returns
+    -------
+    outval : scalar
+    """
+    # assumes lib.is_scalar(val)
+    if lib.is_float(val) and val.is_integer():
+        raise IndexError(
+            # GH#34193
+            "Indexing with a float is no longer supported. Manually convert "
+            "to an integer key instead."
+        )
+    return val
+
+
+def not_none(*args):
+    """
+    Returns a generator consisting of the arguments that are not None.
+    """
+    return (arg for arg in args if arg is not None)
+
+
+def any_none(*args) -> bool:
+    """
+    Returns a boolean indicating if any argument is None.
+    """
+    return any(arg is None for arg in args)
+
+
+def all_none(*args) -> bool:
+    """
+    Returns a boolean indicating if all arguments are None.
+    """
+    return all(arg is None for arg in args)
+
+
+def any_not_none(*args) -> bool:
+    """
+    Returns a boolean indicating if any argument is not None.
+    """
+    return any(arg is not None for arg in args)
+
+
+def all_not_none(*args) -> bool:
+    """
+    Returns a boolean indicating if all arguments are not None.
+    """
+    return all(arg is not None for arg in args)
+
+
+def count_not_none(*args) -> int:
+    """
+    Returns the count of arguments that are not None.
+    """
+    return sum(x is not None for x in args)
+
+
+@overload
+def asarray_tuplesafe(
+    values: ArrayLike | list | tuple | zip, dtype: NpDtype | None = ...
+) -> np.ndarray:
+    # ExtensionArray can only be returned when values is an Index, all other iterables
+    # will return np.ndarray. Unfortunately "all other" cannot be encoded in a type
+    # signature, so instead we special-case some common types.
+    ...
+
+
+@overload
+def asarray_tuplesafe(values: Iterable, dtype: NpDtype | None = ...) -> ArrayLike:
+    ...
+
+
+def asarray_tuplesafe(values: Iterable, dtype: NpDtype | None = None) -> ArrayLike:
+    if not (isinstance(values, (list, tuple)) or hasattr(values, "__array__")):
+        values = list(values)
+    elif isinstance(values, ABCIndex):
+        return values._values
+    elif isinstance(values, ABCSeries):
+        return values._values
+
+    if isinstance(values, list) and dtype in [np.object_, object]:
+        return construct_1d_object_array_from_listlike(values)
+
+    try:
+        with warnings.catch_warnings():
+            # Can remove warning filter once NumPy 1.24 is min version
+            if not np_version_gte1p24:
+                warnings.simplefilter("ignore", np.VisibleDeprecationWarning)
+            result = np.asarray(values, dtype=dtype)
+    except ValueError:
+        # Using try/except since it's more performant than checking is_list_like
+        # over each element
+        # error: Argument 1 to "construct_1d_object_array_from_listlike"
+        # has incompatible type "Iterable[Any]"; expected "Sized"
+        return construct_1d_object_array_from_listlike(values)  # type: ignore[arg-type]
+
+    if issubclass(result.dtype.type, str):
+        result = np.asarray(values, dtype=object)
+
+    if result.ndim == 2:
+        # Avoid building an array of arrays:
+        values = [tuple(x) for x in values]
+        result = construct_1d_object_array_from_listlike(values)
+
+    return result
+
+
+def index_labels_to_array(
+    labels: np.ndarray | Iterable, dtype: NpDtype | None = None
+) -> np.ndarray:
+    """
+    Transform label or iterable of labels to array, for use in Index.
+
+    Parameters
+    ----------
+    dtype : dtype
+        If specified, use as dtype of the resulting array, otherwise infer.
+
+    Returns
+    -------
+    array
+    """
+    if isinstance(labels, (str, tuple)):
+        labels = [labels]
+
+    if not isinstance(labels, (list, np.ndarray)):
+        try:
+            labels = list(labels)
+        except TypeError:  # non-iterable
+            labels = [labels]
+
+    labels = asarray_tuplesafe(labels, dtype=dtype)
+
+    return labels
+
+
+def maybe_make_list(obj):
+    if obj is not None and not isinstance(obj, (tuple, list)):
+        return [obj]
+    return obj
+
+
+def maybe_iterable_to_list(obj: Iterable[T] | T) -> Collection[T] | T:
+    """
+    If obj is Iterable but not list-like, consume into list.
+    """
+    if isinstance(obj, abc.Iterable) and not isinstance(obj, abc.Sized):
+        return list(obj)
+    obj = cast(Collection, obj)
+    return obj
+
+
+def is_null_slice(obj) -> bool:
+    """
+    We have a null slice.
+    """
+    return (
+        isinstance(obj, slice)
+        and obj.start is None
+        and obj.stop is None
+        and obj.step is None
+    )
+
+
+def is_empty_slice(obj) -> bool:
+    """
+    We have an empty slice, e.g. no values are selected.
+    """
+    return (
+        isinstance(obj, slice)
+        and obj.start is not None
+        and obj.stop is not None
+        and obj.start == obj.stop
+    )
+
+
+def is_true_slices(line) -> list[bool]:
+    """
+    Find non-trivial slices in "line": return a list of booleans with same length.
+    """
+    return [isinstance(k, slice) and not is_null_slice(k) for k in line]
+
+
+# TODO: used only once in indexing; belongs elsewhere?
+def is_full_slice(obj, line: int) -> bool:
+    """
+    We have a full length slice.
+    """
+    return (
+        isinstance(obj, slice)
+        and obj.start == 0
+        and obj.stop == line
+        and obj.step is None
+    )
+
+
+def get_callable_name(obj):
+    # typical case has name
+    if hasattr(obj, "__name__"):
+        return getattr(obj, "__name__")
+    # some objects don't; could recurse
+    if isinstance(obj, partial):
+        return get_callable_name(obj.func)
+    # fall back to class name
+    if callable(obj):
+        return type(obj).__name__
+    # everything failed (probably because the argument
+    # wasn't actually callable); we return None
+    # instead of the empty string in this case to allow
+    # distinguishing between no name and a name of ''
+    return None
+
+
+def apply_if_callable(maybe_callable, obj, **kwargs):
+    """
+    Evaluate possibly callable input using obj and kwargs if it is callable,
+    otherwise return as it is.
+
+    Parameters
+    ----------
+    maybe_callable : possibly a callable
+    obj : NDFrame
+    **kwargs
+    """
+    if callable(maybe_callable):
+        return maybe_callable(obj, **kwargs)
+
+    return maybe_callable
+
+
+def standardize_mapping(into):
+    """
+    Helper function to standardize a supplied mapping.
+
+    Parameters
+    ----------
+    into : instance or subclass of collections.abc.Mapping
+        Must be a class, an initialized collections.defaultdict,
+        or an instance of a collections.abc.Mapping subclass.
+
+    Returns
+    -------
+    mapping : a collections.abc.Mapping subclass or other constructor
+        a callable object that can accept an iterator to create
+        the desired Mapping.
+
+    See Also
+    --------
+    DataFrame.to_dict
+    Series.to_dict
+    """
+    if not inspect.isclass(into):
+        if isinstance(into, defaultdict):
+            return partial(defaultdict, into.default_factory)
+        into = type(into)
+    if not issubclass(into, abc.Mapping):
+        raise TypeError(f"unsupported type: {into}")
+    if into == defaultdict:
+        raise TypeError("to_dict() only accepts initialized defaultdicts")
+    return into
+
+
+@overload
+def random_state(state: np.random.Generator) -> np.random.Generator:
+    ...
+
+
+@overload
+def random_state(
+    state: int | np.ndarray | np.random.BitGenerator | np.random.RandomState | None,
+) -> np.random.RandomState:
+    ...
+
+
+def random_state(state: RandomState | None = None):
+    """
+    Helper function for processing random_state arguments.
+
+    Parameters
+    ----------
+    state : int, array-like, BitGenerator, Generator, np.random.RandomState, None.
+        If receives an int, array-like, or BitGenerator, passes to
+        np.random.RandomState() as seed.
+        If receives an np.random RandomState or Generator, just returns that unchanged.
+        If receives `None`, returns np.random.
+        If receives anything else, raises an informative ValueError.
+
+        Default None.
+
+    Returns
+    -------
+    np.random.RandomState or np.random.Generator. If state is None, returns np.random
+
+    """
+    if is_integer(state) or isinstance(state, (np.ndarray, np.random.BitGenerator)):
+        return np.random.RandomState(state)
+    elif isinstance(state, np.random.RandomState):
+        return state
+    elif isinstance(state, np.random.Generator):
+        return state
+    elif state is None:
+        return np.random
+    else:
+        raise ValueError(
+            "random_state must be an integer, array-like, a BitGenerator, Generator, "
+            "a numpy RandomState, or None"
+        )
+
+
+def pipe(
+    obj, func: Callable[..., T] | tuple[Callable[..., T], str], *args, **kwargs
+) -> T:
+    """
+    Apply a function ``func`` to object ``obj`` either by passing obj as the
+    first argument to the function or, in the case that the func is a tuple,
+    interpret the first element of the tuple as a function and pass the obj to
+    that function as a keyword argument whose key is the value of the second
+    element of the tuple.
+
+    Parameters
+    ----------
+    func : callable or tuple of (callable, str)
+        Function to apply to this object or, alternatively, a
+        ``(callable, data_keyword)`` tuple where ``data_keyword`` is a
+        string indicating the keyword of ``callable`` that expects the
+        object.
+    *args : iterable, optional
+        Positional arguments passed into ``func``.
+    **kwargs : dict, optional
+        A dictionary of keyword arguments passed into ``func``.
+
+    Returns
+    -------
+    object : the return type of ``func``.
+    """
+    if isinstance(func, tuple):
+        func, target = func
+        if target in kwargs:
+            msg = f"{target} is both the pipe target and a keyword argument"
+            raise ValueError(msg)
+        kwargs[target] = obj
+        return func(*args, **kwargs)
+    else:
+        return func(obj, *args, **kwargs)
+
+
+def get_rename_function(mapper):
+    """
+    Returns a function that will map names/labels, dependent if mapper
+    is a dict, Series or just a function.
+    """
+
+    def f(x):
+        if x in mapper:
+            return mapper[x]
+        else:
+            return x
+
+    return f if isinstance(mapper, (abc.Mapping, ABCSeries)) else mapper
+
+
+def convert_to_list_like(
+    values: Hashable | Iterable | AnyArrayLike,
+) -> list | AnyArrayLike:
+    """
+    Convert list-like or scalar input to list-like. List, numpy and pandas array-like
+    inputs are returned unmodified whereas others are converted to list.
+    """
+    if isinstance(values, (list, np.ndarray, ABCIndex, ABCSeries, ABCExtensionArray)):
+        return values
+    elif isinstance(values, abc.Iterable) and not isinstance(values, str):
+        return list(values)
+
+    return [values]
+
+
+@contextlib.contextmanager
+def temp_setattr(
+    obj, attr: str, value, condition: bool = True
+) -> Generator[None, None, None]:
+    """
+    Temporarily set attribute on an object.
+
+    Parameters
+    ----------
+    obj : object
+        Object whose attribute will be modified.
+    attr : str
+        Attribute to modify.
+    value : Any
+        Value to temporarily set attribute to.
+    condition : bool, default True
+        Whether to set the attribute. Provided in order to not have to
+        conditionally use this context manager.
+
+    Yields
+    ------
+    object : obj with modified attribute.
+    """
+    if condition:
+        old_value = getattr(obj, attr)
+        setattr(obj, attr, value)
+    try:
+        yield obj
+    finally:
+        if condition:
+            setattr(obj, attr, old_value)
+
+
+def require_length_match(data, index: Index) -> None:
+    """
+    Check the length of data matches the length of the index.
+    """
+    if len(data) != len(index):
+        raise ValueError(
+            "Length of values "
+            f"({len(data)}) "
+            "does not match length of index "
+            f"({len(index)})"
+        )
+
+
+# the ufuncs np.maximum.reduce and np.minimum.reduce default to axis=0,
+#  whereas np.min and np.max (which directly call obj.min and obj.max)
+#  default to axis=None.
+_builtin_table = {
+    builtins.sum: np.sum,
+    builtins.max: np.maximum.reduce,
+    builtins.min: np.minimum.reduce,
+}
+
+# GH#53425: Only for deprecation
+_builtin_table_alias = {
+    builtins.sum: "np.sum",
+    builtins.max: "np.maximum.reduce",
+    builtins.min: "np.minimum.reduce",
+}
+
+_cython_table = {
+    builtins.sum: "sum",
+    builtins.max: "max",
+    builtins.min: "min",
+    np.all: "all",
+    np.any: "any",
+    np.sum: "sum",
+    np.nansum: "sum",
+    np.mean: "mean",
+    np.nanmean: "mean",
+    np.prod: "prod",
+    np.nanprod: "prod",
+    np.std: "std",
+    np.nanstd: "std",
+    np.var: "var",
+    np.nanvar: "var",
+    np.median: "median",
+    np.nanmedian: "median",
+    np.max: "max",
+    np.nanmax: "max",
+    np.min: "min",
+    np.nanmin: "min",
+    np.cumprod: "cumprod",
+    np.nancumprod: "cumprod",
+    np.cumsum: "cumsum",
+    np.nancumsum: "cumsum",
+}
+
+
+def get_cython_func(arg: Callable) -> str | None:
+    """
+    if we define an internal function for this argument, return it
+    """
+    return _cython_table.get(arg)
+
+
+def is_builtin_func(arg):
+    """
+    if we define a builtin function for this argument, return it,
+    otherwise return the arg
+    """
+    return _builtin_table.get(arg, arg)
+
+
+def fill_missing_names(names: Sequence[Hashable | None]) -> list[Hashable]:
+    """
+    If a name is missing then replace it by level_n, where n is the count
+
+    .. versionadded:: 1.4.0
+
+    Parameters
+    ----------
+    names : list-like
+        list of column names or None values.
+
+    Returns
+    -------
+    list
+        list of column names with the None values replaced.
+    """
+    return [f"level_{i}" if name is None else name for i, name in enumerate(names)]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/computation/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/computation/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/computation/align.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/computation/align.py
new file mode 100644
index 0000000000000000000000000000000000000000..cd852ba9249cf26ea39b7e51bbfa754ffe9c10ce
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/computation/align.py
@@ -0,0 +1,213 @@
+"""
+Core eval alignment algorithms.
+"""
+from __future__ import annotations
+
+from functools import (
+    partial,
+    wraps,
+)
+from typing import (
+    TYPE_CHECKING,
+    Callable,
+)
+import warnings
+
+import numpy as np
+
+from pandas.errors import PerformanceWarning
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.generic import (
+    ABCDataFrame,
+    ABCSeries,
+)
+
+from pandas.core.base import PandasObject
+import pandas.core.common as com
+from pandas.core.computation.common import result_type_many
+
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+
+    from pandas._typing import F
+
+    from pandas.core.generic import NDFrame
+    from pandas.core.indexes.api import Index
+
+
+def _align_core_single_unary_op(
+    term,
+) -> tuple[partial | type[NDFrame], dict[str, Index] | None]:
+    typ: partial | type[NDFrame]
+    axes: dict[str, Index] | None = None
+
+    if isinstance(term.value, np.ndarray):
+        typ = partial(np.asanyarray, dtype=term.value.dtype)
+    else:
+        typ = type(term.value)
+        if hasattr(term.value, "axes"):
+            axes = _zip_axes_from_type(typ, term.value.axes)
+
+    return typ, axes
+
+
+def _zip_axes_from_type(
+    typ: type[NDFrame], new_axes: Sequence[Index]
+) -> dict[str, Index]:
+    return {name: new_axes[i] for i, name in enumerate(typ._AXIS_ORDERS)}
+
+
+def _any_pandas_objects(terms) -> bool:
+    """
+    Check a sequence of terms for instances of PandasObject.
+    """
+    return any(isinstance(term.value, PandasObject) for term in terms)
+
+
+def _filter_special_cases(f) -> Callable[[F], F]:
+    @wraps(f)
+    def wrapper(terms):
+        # single unary operand
+        if len(terms) == 1:
+            return _align_core_single_unary_op(terms[0])
+
+        term_values = (term.value for term in terms)
+
+        # we don't have any pandas objects
+        if not _any_pandas_objects(terms):
+            return result_type_many(*term_values), None
+
+        return f(terms)
+
+    return wrapper
+
+
+@_filter_special_cases
+def _align_core(terms):
+    term_index = [i for i, term in enumerate(terms) if hasattr(term.value, "axes")]
+    term_dims = [terms[i].value.ndim for i in term_index]
+
+    from pandas import Series
+
+    ndims = Series(dict(zip(term_index, term_dims)))
+
+    # initial axes are the axes of the largest-axis'd term
+    biggest = terms[ndims.idxmax()].value
+    typ = biggest._constructor
+    axes = biggest.axes
+    naxes = len(axes)
+    gt_than_one_axis = naxes > 1
+
+    for value in (terms[i].value for i in term_index):
+        is_series = isinstance(value, ABCSeries)
+        is_series_and_gt_one_axis = is_series and gt_than_one_axis
+
+        for axis, items in enumerate(value.axes):
+            if is_series_and_gt_one_axis:
+                ax, itm = naxes - 1, value.index
+            else:
+                ax, itm = axis, items
+
+            if not axes[ax].is_(itm):
+                axes[ax] = axes[ax].union(itm)
+
+    for i, ndim in ndims.items():
+        for axis, items in zip(range(ndim), axes):
+            ti = terms[i].value
+
+            if hasattr(ti, "reindex"):
+                transpose = isinstance(ti, ABCSeries) and naxes > 1
+                reindexer = axes[naxes - 1] if transpose else items
+
+                term_axis_size = len(ti.axes[axis])
+                reindexer_size = len(reindexer)
+
+                ordm = np.log10(max(1, abs(reindexer_size - term_axis_size)))
+                if ordm >= 1 and reindexer_size >= 10000:
+                    w = (
+                        f"Alignment difference on axis {axis} is larger "
+                        f"than an order of magnitude on term {repr(terms[i].name)}, "
+                        f"by more than {ordm:.4g}; performance may suffer."
+                    )
+                    warnings.warn(
+                        w, category=PerformanceWarning, stacklevel=find_stack_level()
+                    )
+
+                obj = ti.reindex(reindexer, axis=axis, copy=False)
+                terms[i].update(obj)
+
+        terms[i].update(terms[i].value.values)
+
+    return typ, _zip_axes_from_type(typ, axes)
+
+
+def align_terms(terms):
+    """
+    Align a set of terms.
+    """
+    try:
+        # flatten the parse tree (a nested list, really)
+        terms = list(com.flatten(terms))
+    except TypeError:
+        # can't iterate so it must just be a constant or single variable
+        if isinstance(terms.value, (ABCSeries, ABCDataFrame)):
+            typ = type(terms.value)
+            return typ, _zip_axes_from_type(typ, terms.value.axes)
+        return np.result_type(terms.type), None
+
+    # if all resolved variables are numeric scalars
+    if all(term.is_scalar for term in terms):
+        return result_type_many(*(term.value for term in terms)).type, None
+
+    # perform the main alignment
+    typ, axes = _align_core(terms)
+    return typ, axes
+
+
+def reconstruct_object(typ, obj, axes, dtype):
+    """
+    Reconstruct an object given its type, raw value, and possibly empty
+    (None) axes.
+
+    Parameters
+    ----------
+    typ : object
+        A type
+    obj : object
+        The value to use in the type constructor
+    axes : dict
+        The axes to use to construct the resulting pandas object
+
+    Returns
+    -------
+    ret : typ
+        An object of type ``typ`` with the value `obj` and possible axes
+        `axes`.
+    """
+    try:
+        typ = typ.type
+    except AttributeError:
+        pass
+
+    res_t = np.result_type(obj.dtype, dtype)
+
+    if not isinstance(typ, partial) and issubclass(typ, PandasObject):
+        return typ(obj, dtype=res_t, **axes)
+
+    # special case for pathological things like ~True/~False
+    if hasattr(res_t, "type") and typ == np.bool_ and res_t != np.bool_:
+        ret_value = res_t.type(obj)
+    else:
+        ret_value = typ(obj).astype(res_t)
+        # The condition is to distinguish 0-dim array (returned in case of
+        # scalar) and 1 element array
+        # e.g. np.array(0) and np.array([0])
+        if (
+            len(obj.shape) == 1
+            and len(obj) == 1
+            and not isinstance(ret_value, np.ndarray)
+        ):
+            ret_value = np.array([ret_value]).astype(res_t)
+
+    return ret_value
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/computation/api.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/computation/api.py
new file mode 100644
index 0000000000000000000000000000000000000000..bd3be5b3f8c42267c8a61421b7f0877a01b33d34
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/computation/api.py
@@ -0,0 +1,2 @@
+__all__ = ["eval"]
+from pandas.core.computation.eval import eval
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/computation/check.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/computation/check.py
new file mode 100644
index 0000000000000000000000000000000000000000..caccf34f811112abbe04d965d6f6be1e21527e8b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/computation/check.py
@@ -0,0 +1,8 @@
+from __future__ import annotations
+
+from pandas.compat._optional import import_optional_dependency
+
+ne = import_optional_dependency("numexpr", errors="warn")
+NUMEXPR_INSTALLED = ne is not None
+
+__all__ = ["NUMEXPR_INSTALLED"]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/computation/common.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/computation/common.py
new file mode 100644
index 0000000000000000000000000000000000000000..115191829f044a7d6d7f17c279025ccc26d44d04
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/computation/common.py
@@ -0,0 +1,48 @@
+from __future__ import annotations
+
+from functools import reduce
+
+import numpy as np
+
+from pandas._config import get_option
+
+
+def ensure_decoded(s) -> str:
+    """
+    If we have bytes, decode them to unicode.
+    """
+    if isinstance(s, (np.bytes_, bytes)):
+        s = s.decode(get_option("display.encoding"))
+    return s
+
+
+def result_type_many(*arrays_and_dtypes):
+    """
+    Wrapper around numpy.result_type which overcomes the NPY_MAXARGS (32)
+    argument limit.
+    """
+    try:
+        return np.result_type(*arrays_and_dtypes)
+    except ValueError:
+        # we have > NPY_MAXARGS terms in our expression
+        return reduce(np.result_type, arrays_and_dtypes)
+    except TypeError:
+        from pandas.core.dtypes.cast import find_common_type
+        from pandas.core.dtypes.common import is_extension_array_dtype
+
+        arr_and_dtypes = list(arrays_and_dtypes)
+        ea_dtypes, non_ea_dtypes = [], []
+        for arr_or_dtype in arr_and_dtypes:
+            if is_extension_array_dtype(arr_or_dtype):
+                ea_dtypes.append(arr_or_dtype)
+            else:
+                non_ea_dtypes.append(arr_or_dtype)
+
+        if non_ea_dtypes:
+            try:
+                np_dtype = np.result_type(*non_ea_dtypes)
+            except ValueError:
+                np_dtype = reduce(np.result_type, arrays_and_dtypes)
+            return find_common_type(ea_dtypes + [np_dtype])
+
+        return find_common_type(ea_dtypes)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/computation/engines.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/computation/engines.py
new file mode 100644
index 0000000000000000000000000000000000000000..a3a05a9d75c6ed6b80564a69ff5b6cf5a648c1b3
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/computation/engines.py
@@ -0,0 +1,143 @@
+"""
+Engine classes for :func:`~pandas.eval`
+"""
+from __future__ import annotations
+
+import abc
+from typing import TYPE_CHECKING
+
+from pandas.errors import NumExprClobberingError
+
+from pandas.core.computation.align import (
+    align_terms,
+    reconstruct_object,
+)
+from pandas.core.computation.ops import (
+    MATHOPS,
+    REDUCTIONS,
+)
+
+from pandas.io.formats import printing
+
+if TYPE_CHECKING:
+    from pandas.core.computation.expr import Expr
+
+_ne_builtins = frozenset(MATHOPS + REDUCTIONS)
+
+
+def _check_ne_builtin_clash(expr: Expr) -> None:
+    """
+    Attempt to prevent foot-shooting in a helpful way.
+
+    Parameters
+    ----------
+    expr : Expr
+        Terms can contain
+    """
+    names = expr.names
+    overlap = names & _ne_builtins
+
+    if overlap:
+        s = ", ".join([repr(x) for x in overlap])
+        raise NumExprClobberingError(
+            f'Variables in expression "{expr}" overlap with builtins: ({s})'
+        )
+
+
+class AbstractEngine(metaclass=abc.ABCMeta):
+    """Object serving as a base class for all engines."""
+
+    has_neg_frac = False
+
+    def __init__(self, expr) -> None:
+        self.expr = expr
+        self.aligned_axes = None
+        self.result_type = None
+
+    def convert(self) -> str:
+        """
+        Convert an expression for evaluation.
+
+        Defaults to return the expression as a string.
+        """
+        return printing.pprint_thing(self.expr)
+
+    def evaluate(self) -> object:
+        """
+        Run the engine on the expression.
+
+        This method performs alignment which is necessary no matter what engine
+        is being used, thus its implementation is in the base class.
+
+        Returns
+        -------
+        object
+            The result of the passed expression.
+        """
+        if not self._is_aligned:
+            self.result_type, self.aligned_axes = align_terms(self.expr.terms)
+
+        # make sure no names in resolvers and locals/globals clash
+        res = self._evaluate()
+        return reconstruct_object(
+            self.result_type, res, self.aligned_axes, self.expr.terms.return_type
+        )
+
+    @property
+    def _is_aligned(self) -> bool:
+        return self.aligned_axes is not None and self.result_type is not None
+
+    @abc.abstractmethod
+    def _evaluate(self):
+        """
+        Return an evaluated expression.
+
+        Parameters
+        ----------
+        env : Scope
+            The local and global environment in which to evaluate an
+            expression.
+
+        Notes
+        -----
+        Must be implemented by subclasses.
+        """
+
+
+class NumExprEngine(AbstractEngine):
+    """NumExpr engine class"""
+
+    has_neg_frac = True
+
+    def _evaluate(self):
+        import numexpr as ne
+
+        # convert the expression to a valid numexpr expression
+        s = self.convert()
+
+        env = self.expr.env
+        scope = env.full_scope
+        _check_ne_builtin_clash(self.expr)
+        return ne.evaluate(s, local_dict=scope)
+
+
+class PythonEngine(AbstractEngine):
+    """
+    Evaluate an expression in Python space.
+
+    Mostly for testing purposes.
+    """
+
+    has_neg_frac = False
+
+    def evaluate(self):
+        return self.expr()
+
+    def _evaluate(self) -> None:
+        pass
+
+
+ENGINES: dict[str, type[AbstractEngine]] = {
+    "numexpr": NumExprEngine,
+    "python": PythonEngine,
+}
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/computation/eval.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/computation/eval.py
new file mode 100644
index 0000000000000000000000000000000000000000..7bb623cba375539072e462cb1180f4ac85221ee1
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/computation/eval.py
@@ -0,0 +1,421 @@
+"""
+Top level ``eval`` module.
+"""
+from __future__ import annotations
+
+import tokenize
+from typing import TYPE_CHECKING
+import warnings
+
+from pandas.util._exceptions import find_stack_level
+from pandas.util._validators import validate_bool_kwarg
+
+from pandas.core.dtypes.common import (
+    is_extension_array_dtype,
+    is_string_dtype,
+)
+
+from pandas.core.computation.engines import ENGINES
+from pandas.core.computation.expr import (
+    PARSERS,
+    Expr,
+)
+from pandas.core.computation.parsing import tokenize_string
+from pandas.core.computation.scope import ensure_scope
+from pandas.core.generic import NDFrame
+
+from pandas.io.formats.printing import pprint_thing
+
+if TYPE_CHECKING:
+    from pandas.core.computation.ops import BinOp
+
+
+def _check_engine(engine: str | None) -> str:
+    """
+    Make sure a valid engine is passed.
+
+    Parameters
+    ----------
+    engine : str
+        String to validate.
+
+    Raises
+    ------
+    KeyError
+      * If an invalid engine is passed.
+    ImportError
+      * If numexpr was requested but doesn't exist.
+
+    Returns
+    -------
+    str
+        Engine name.
+    """
+    from pandas.core.computation.check import NUMEXPR_INSTALLED
+    from pandas.core.computation.expressions import USE_NUMEXPR
+
+    if engine is None:
+        engine = "numexpr" if USE_NUMEXPR else "python"
+
+    if engine not in ENGINES:
+        valid_engines = list(ENGINES.keys())
+        raise KeyError(
+            f"Invalid engine '{engine}' passed, valid engines are {valid_engines}"
+        )
+
+    # TODO: validate this in a more general way (thinking of future engines
+    # that won't necessarily be import-able)
+    # Could potentially be done on engine instantiation
+    if engine == "numexpr" and not NUMEXPR_INSTALLED:
+        raise ImportError(
+            "'numexpr' is not installed or an unsupported version. Cannot use "
+            "engine='numexpr' for query/eval if 'numexpr' is not installed"
+        )
+
+    return engine
+
+
+def _check_parser(parser: str):
+    """
+    Make sure a valid parser is passed.
+
+    Parameters
+    ----------
+    parser : str
+
+    Raises
+    ------
+    KeyError
+      * If an invalid parser is passed
+    """
+    if parser not in PARSERS:
+        raise KeyError(
+            f"Invalid parser '{parser}' passed, valid parsers are {PARSERS.keys()}"
+        )
+
+
+def _check_resolvers(resolvers):
+    if resolvers is not None:
+        for resolver in resolvers:
+            if not hasattr(resolver, "__getitem__"):
+                name = type(resolver).__name__
+                raise TypeError(
+                    f"Resolver of type '{name}' does not "
+                    "implement the __getitem__ method"
+                )
+
+
+def _check_expression(expr):
+    """
+    Make sure an expression is not an empty string
+
+    Parameters
+    ----------
+    expr : object
+        An object that can be converted to a string
+
+    Raises
+    ------
+    ValueError
+      * If expr is an empty string
+    """
+    if not expr:
+        raise ValueError("expr cannot be an empty string")
+
+
+def _convert_expression(expr) -> str:
+    """
+    Convert an object to an expression.
+
+    This function converts an object to an expression (a unicode string) and
+    checks to make sure it isn't empty after conversion. This is used to
+    convert operators to their string representation for recursive calls to
+    :func:`~pandas.eval`.
+
+    Parameters
+    ----------
+    expr : object
+        The object to be converted to a string.
+
+    Returns
+    -------
+    str
+        The string representation of an object.
+
+    Raises
+    ------
+    ValueError
+      * If the expression is empty.
+    """
+    s = pprint_thing(expr)
+    _check_expression(s)
+    return s
+
+
+def _check_for_locals(expr: str, stack_level: int, parser: str):
+    at_top_of_stack = stack_level == 0
+    not_pandas_parser = parser != "pandas"
+
+    if not_pandas_parser:
+        msg = "The '@' prefix is only supported by the pandas parser"
+    elif at_top_of_stack:
+        msg = (
+            "The '@' prefix is not allowed in top-level eval calls.\n"
+            "please refer to your variables by name without the '@' prefix."
+        )
+
+    if at_top_of_stack or not_pandas_parser:
+        for toknum, tokval in tokenize_string(expr):
+            if toknum == tokenize.OP and tokval == "@":
+                raise SyntaxError(msg)
+
+
+def eval(
+    expr: str | BinOp,  # we leave BinOp out of the docstr bc it isn't for users
+    parser: str = "pandas",
+    engine: str | None = None,
+    local_dict=None,
+    global_dict=None,
+    resolvers=(),
+    level: int = 0,
+    target=None,
+    inplace: bool = False,
+):
+    """
+    Evaluate a Python expression as a string using various backends.
+
+    The following arithmetic operations are supported: ``+``, ``-``, ``*``,
+    ``/``, ``**``, ``%``, ``//`` (python engine only) along with the following
+    boolean operations: ``|`` (or), ``&`` (and), and ``~`` (not).
+    Additionally, the ``'pandas'`` parser allows the use of :keyword:`and`,
+    :keyword:`or`, and :keyword:`not` with the same semantics as the
+    corresponding bitwise operators.  :class:`~pandas.Series` and
+    :class:`~pandas.DataFrame` objects are supported and behave as they would
+    with plain ol' Python evaluation.
+
+    Parameters
+    ----------
+    expr : str
+        The expression to evaluate. This string cannot contain any Python
+        `statements
+        `__,
+        only Python `expressions
+        `__.
+    parser : {'pandas', 'python'}, default 'pandas'
+        The parser to use to construct the syntax tree from the expression. The
+        default of ``'pandas'`` parses code slightly different than standard
+        Python. Alternatively, you can parse an expression using the
+        ``'python'`` parser to retain strict Python semantics.  See the
+        :ref:`enhancing performance ` documentation for
+        more details.
+    engine : {'python', 'numexpr'}, default 'numexpr'
+
+        The engine used to evaluate the expression. Supported engines are
+
+        - None : tries to use ``numexpr``, falls back to ``python``
+        - ``'numexpr'`` : This default engine evaluates pandas objects using
+          numexpr for large speed ups in complex expressions with large frames.
+        - ``'python'`` : Performs operations as if you had ``eval``'d in top
+          level python. This engine is generally not that useful.
+
+        More backends may be available in the future.
+    local_dict : dict or None, optional
+        A dictionary of local variables, taken from locals() by default.
+    global_dict : dict or None, optional
+        A dictionary of global variables, taken from globals() by default.
+    resolvers : list of dict-like or None, optional
+        A list of objects implementing the ``__getitem__`` special method that
+        you can use to inject an additional collection of namespaces to use for
+        variable lookup. For example, this is used in the
+        :meth:`~DataFrame.query` method to inject the
+        ``DataFrame.index`` and ``DataFrame.columns``
+        variables that refer to their respective :class:`~pandas.DataFrame`
+        instance attributes.
+    level : int, optional
+        The number of prior stack frames to traverse and add to the current
+        scope. Most users will **not** need to change this parameter.
+    target : object, optional, default None
+        This is the target object for assignment. It is used when there is
+        variable assignment in the expression. If so, then `target` must
+        support item assignment with string keys, and if a copy is being
+        returned, it must also support `.copy()`.
+    inplace : bool, default False
+        If `target` is provided, and the expression mutates `target`, whether
+        to modify `target` inplace. Otherwise, return a copy of `target` with
+        the mutation.
+
+    Returns
+    -------
+    ndarray, numeric scalar, DataFrame, Series, or None
+        The completion value of evaluating the given code or None if ``inplace=True``.
+
+    Raises
+    ------
+    ValueError
+        There are many instances where such an error can be raised:
+
+        - `target=None`, but the expression is multiline.
+        - The expression is multiline, but not all them have item assignment.
+          An example of such an arrangement is this:
+
+          a = b + 1
+          a + 2
+
+          Here, there are expressions on different lines, making it multiline,
+          but the last line has no variable assigned to the output of `a + 2`.
+        - `inplace=True`, but the expression is missing item assignment.
+        - Item assignment is provided, but the `target` does not support
+          string item assignment.
+        - Item assignment is provided and `inplace=False`, but the `target`
+          does not support the `.copy()` method
+
+    See Also
+    --------
+    DataFrame.query : Evaluates a boolean expression to query the columns
+            of a frame.
+    DataFrame.eval : Evaluate a string describing operations on
+            DataFrame columns.
+
+    Notes
+    -----
+    The ``dtype`` of any objects involved in an arithmetic ``%`` operation are
+    recursively cast to ``float64``.
+
+    See the :ref:`enhancing performance ` documentation for
+    more details.
+
+    Examples
+    --------
+    >>> df = pd.DataFrame({"animal": ["dog", "pig"], "age": [10, 20]})
+    >>> df
+      animal  age
+    0    dog   10
+    1    pig   20
+
+    We can add a new column using ``pd.eval``:
+
+    >>> pd.eval("double_age = df.age * 2", target=df)
+      animal  age  double_age
+    0    dog   10          20
+    1    pig   20          40
+    """
+    inplace = validate_bool_kwarg(inplace, "inplace")
+
+    exprs: list[str | BinOp]
+    if isinstance(expr, str):
+        _check_expression(expr)
+        exprs = [e.strip() for e in expr.splitlines() if e.strip() != ""]
+    else:
+        # ops.BinOp; for internal compat, not intended to be passed by users
+        exprs = [expr]
+    multi_line = len(exprs) > 1
+
+    if multi_line and target is None:
+        raise ValueError(
+            "multi-line expressions are only valid in the "
+            "context of data, use DataFrame.eval"
+        )
+    engine = _check_engine(engine)
+    _check_parser(parser)
+    _check_resolvers(resolvers)
+
+    ret = None
+    first_expr = True
+    target_modified = False
+
+    for expr in exprs:
+        expr = _convert_expression(expr)
+        _check_for_locals(expr, level, parser)
+
+        # get our (possibly passed-in) scope
+        env = ensure_scope(
+            level + 1,
+            global_dict=global_dict,
+            local_dict=local_dict,
+            resolvers=resolvers,
+            target=target,
+        )
+
+        parsed_expr = Expr(expr, engine=engine, parser=parser, env=env)
+
+        if engine == "numexpr" and (
+            (
+                is_extension_array_dtype(parsed_expr.terms.return_type)
+                and not is_string_dtype(parsed_expr.terms.return_type)
+            )
+            or getattr(parsed_expr.terms, "operand_types", None) is not None
+            and any(
+                (is_extension_array_dtype(elem) and not is_string_dtype(elem))
+                for elem in parsed_expr.terms.operand_types
+            )
+        ):
+            warnings.warn(
+                "Engine has switched to 'python' because numexpr does not support "
+                "extension array dtypes. Please set your engine to python manually.",
+                RuntimeWarning,
+                stacklevel=find_stack_level(),
+            )
+            engine = "python"
+
+        # construct the engine and evaluate the parsed expression
+        eng = ENGINES[engine]
+        eng_inst = eng(parsed_expr)
+        ret = eng_inst.evaluate()
+
+        if parsed_expr.assigner is None:
+            if multi_line:
+                raise ValueError(
+                    "Multi-line expressions are only valid "
+                    "if all expressions contain an assignment"
+                )
+            if inplace:
+                raise ValueError("Cannot operate inplace if there is no assignment")
+
+        # assign if needed
+        assigner = parsed_expr.assigner
+        if env.target is not None and assigner is not None:
+            target_modified = True
+
+            # if returning a copy, copy only on the first assignment
+            if not inplace and first_expr:
+                try:
+                    target = env.target
+                    if isinstance(target, NDFrame):
+                        target = target.copy(deep=None)
+                    else:
+                        target = target.copy()
+                except AttributeError as err:
+                    raise ValueError("Cannot return a copy of the target") from err
+            else:
+                target = env.target
+
+            # TypeError is most commonly raised (e.g. int, list), but you
+            # get IndexError if you try to do this assignment on np.ndarray.
+            # we will ignore numpy warnings here; e.g. if trying
+            # to use a non-numeric indexer
+            try:
+                if inplace and isinstance(target, NDFrame):
+                    target.loc[:, assigner] = ret
+                else:
+                    target[assigner] = ret  # pyright: ignore[reportGeneralTypeIssues]
+            except (TypeError, IndexError) as err:
+                raise ValueError("Cannot assign expression output to target") from err
+
+            if not resolvers:
+                resolvers = ({assigner: ret},)
+            else:
+                # existing resolver needs updated to handle
+                # case of mutating existing column in copy
+                for resolver in resolvers:
+                    if assigner in resolver:
+                        resolver[assigner] = ret
+                        break
+                else:
+                    resolvers += ({assigner: ret},)
+
+            ret = None
+            first_expr = False
+
+    # We want to exclude `inplace=None` as being False.
+    if inplace is False:
+        return target if target_modified else ret
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/computation/expr.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/computation/expr.py
new file mode 100644
index 0000000000000000000000000000000000000000..34055d2177626dad7415c2109dedbe6a205b626b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/computation/expr.py
@@ -0,0 +1,840 @@
+"""
+:func:`~pandas.eval` parsers.
+"""
+from __future__ import annotations
+
+import ast
+from functools import (
+    partial,
+    reduce,
+)
+from keyword import iskeyword
+import tokenize
+from typing import (
+    Callable,
+    ClassVar,
+    TypeVar,
+)
+
+import numpy as np
+
+from pandas.errors import UndefinedVariableError
+
+from pandas.core.dtypes.common import is_string_dtype
+
+import pandas.core.common as com
+from pandas.core.computation.ops import (
+    ARITH_OPS_SYMS,
+    BOOL_OPS_SYMS,
+    CMP_OPS_SYMS,
+    LOCAL_TAG,
+    MATHOPS,
+    REDUCTIONS,
+    UNARY_OPS_SYMS,
+    BinOp,
+    Constant,
+    FuncNode,
+    Op,
+    Term,
+    UnaryOp,
+    is_term,
+)
+from pandas.core.computation.parsing import (
+    clean_backtick_quoted_toks,
+    tokenize_string,
+)
+from pandas.core.computation.scope import Scope
+
+from pandas.io.formats import printing
+
+
+def _rewrite_assign(tok: tuple[int, str]) -> tuple[int, str]:
+    """
+    Rewrite the assignment operator for PyTables expressions that use ``=``
+    as a substitute for ``==``.
+
+    Parameters
+    ----------
+    tok : tuple of int, str
+        ints correspond to the all caps constants in the tokenize module
+
+    Returns
+    -------
+    tuple of int, str
+        Either the input or token or the replacement values
+    """
+    toknum, tokval = tok
+    return toknum, "==" if tokval == "=" else tokval
+
+
+def _replace_booleans(tok: tuple[int, str]) -> tuple[int, str]:
+    """
+    Replace ``&`` with ``and`` and ``|`` with ``or`` so that bitwise
+    precedence is changed to boolean precedence.
+
+    Parameters
+    ----------
+    tok : tuple of int, str
+        ints correspond to the all caps constants in the tokenize module
+
+    Returns
+    -------
+    tuple of int, str
+        Either the input or token or the replacement values
+    """
+    toknum, tokval = tok
+    if toknum == tokenize.OP:
+        if tokval == "&":
+            return tokenize.NAME, "and"
+        elif tokval == "|":
+            return tokenize.NAME, "or"
+        return toknum, tokval
+    return toknum, tokval
+
+
+def _replace_locals(tok: tuple[int, str]) -> tuple[int, str]:
+    """
+    Replace local variables with a syntactically valid name.
+
+    Parameters
+    ----------
+    tok : tuple of int, str
+        ints correspond to the all caps constants in the tokenize module
+
+    Returns
+    -------
+    tuple of int, str
+        Either the input or token or the replacement values
+
+    Notes
+    -----
+    This is somewhat of a hack in that we rewrite a string such as ``'@a'`` as
+    ``'__pd_eval_local_a'`` by telling the tokenizer that ``__pd_eval_local_``
+    is a ``tokenize.OP`` and to replace the ``'@'`` symbol with it.
+    """
+    toknum, tokval = tok
+    if toknum == tokenize.OP and tokval == "@":
+        return tokenize.OP, LOCAL_TAG
+    return toknum, tokval
+
+
+def _compose2(f, g):
+    """
+    Compose 2 callables.
+    """
+    return lambda *args, **kwargs: f(g(*args, **kwargs))
+
+
+def _compose(*funcs):
+    """
+    Compose 2 or more callables.
+    """
+    assert len(funcs) > 1, "At least 2 callables must be passed to compose"
+    return reduce(_compose2, funcs)
+
+
+def _preparse(
+    source: str,
+    f=_compose(
+        _replace_locals, _replace_booleans, _rewrite_assign, clean_backtick_quoted_toks
+    ),
+) -> str:
+    """
+    Compose a collection of tokenization functions.
+
+    Parameters
+    ----------
+    source : str
+        A Python source code string
+    f : callable
+        This takes a tuple of (toknum, tokval) as its argument and returns a
+        tuple with the same structure but possibly different elements. Defaults
+        to the composition of ``_rewrite_assign``, ``_replace_booleans``, and
+        ``_replace_locals``.
+
+    Returns
+    -------
+    str
+        Valid Python source code
+
+    Notes
+    -----
+    The `f` parameter can be any callable that takes *and* returns input of the
+    form ``(toknum, tokval)``, where ``toknum`` is one of the constants from
+    the ``tokenize`` module and ``tokval`` is a string.
+    """
+    assert callable(f), "f must be callable"
+    return tokenize.untokenize(f(x) for x in tokenize_string(source))
+
+
+def _is_type(t):
+    """
+    Factory for a type checking function of type ``t`` or tuple of types.
+    """
+    return lambda x: isinstance(x.value, t)
+
+
+_is_list = _is_type(list)
+_is_str = _is_type(str)
+
+
+# partition all AST nodes
+_all_nodes = frozenset(
+    node
+    for node in (getattr(ast, name) for name in dir(ast))
+    if isinstance(node, type) and issubclass(node, ast.AST)
+)
+
+
+def _filter_nodes(superclass, all_nodes=_all_nodes):
+    """
+    Filter out AST nodes that are subclasses of ``superclass``.
+    """
+    node_names = (node.__name__ for node in all_nodes if issubclass(node, superclass))
+    return frozenset(node_names)
+
+
+_all_node_names = frozenset(x.__name__ for x in _all_nodes)
+_mod_nodes = _filter_nodes(ast.mod)
+_stmt_nodes = _filter_nodes(ast.stmt)
+_expr_nodes = _filter_nodes(ast.expr)
+_expr_context_nodes = _filter_nodes(ast.expr_context)
+_boolop_nodes = _filter_nodes(ast.boolop)
+_operator_nodes = _filter_nodes(ast.operator)
+_unary_op_nodes = _filter_nodes(ast.unaryop)
+_cmp_op_nodes = _filter_nodes(ast.cmpop)
+_comprehension_nodes = _filter_nodes(ast.comprehension)
+_handler_nodes = _filter_nodes(ast.excepthandler)
+_arguments_nodes = _filter_nodes(ast.arguments)
+_keyword_nodes = _filter_nodes(ast.keyword)
+_alias_nodes = _filter_nodes(ast.alias)
+
+
+# nodes that we don't support directly but are needed for parsing
+_hacked_nodes = frozenset(["Assign", "Module", "Expr"])
+
+
+_unsupported_expr_nodes = frozenset(
+    [
+        "Yield",
+        "GeneratorExp",
+        "IfExp",
+        "DictComp",
+        "SetComp",
+        "Repr",
+        "Lambda",
+        "Set",
+        "AST",
+        "Is",
+        "IsNot",
+    ]
+)
+
+# these nodes are low priority or won't ever be supported (e.g., AST)
+_unsupported_nodes = (
+    _stmt_nodes
+    | _mod_nodes
+    | _handler_nodes
+    | _arguments_nodes
+    | _keyword_nodes
+    | _alias_nodes
+    | _expr_context_nodes
+    | _unsupported_expr_nodes
+) - _hacked_nodes
+
+# we're adding a different assignment in some cases to be equality comparison
+# and we don't want `stmt` and friends in their so get only the class whose
+# names are capitalized
+_base_supported_nodes = (_all_node_names - _unsupported_nodes) | _hacked_nodes
+intersection = _unsupported_nodes & _base_supported_nodes
+_msg = f"cannot both support and not support {intersection}"
+assert not intersection, _msg
+
+
+def _node_not_implemented(node_name: str) -> Callable[..., None]:
+    """
+    Return a function that raises a NotImplementedError with a passed node name.
+    """
+
+    def f(self, *args, **kwargs):
+        raise NotImplementedError(f"'{node_name}' nodes are not implemented")
+
+    return f
+
+
+# should be bound by BaseExprVisitor but that creates a circular dependency:
+# _T is used in disallow, but disallow is used to define BaseExprVisitor
+# https://github.com/microsoft/pyright/issues/2315
+_T = TypeVar("_T")
+
+
+def disallow(nodes: set[str]) -> Callable[[type[_T]], type[_T]]:
+    """
+    Decorator to disallow certain nodes from parsing. Raises a
+    NotImplementedError instead.
+
+    Returns
+    -------
+    callable
+    """
+
+    def disallowed(cls: type[_T]) -> type[_T]:
+        # error: "Type[_T]" has no attribute "unsupported_nodes"
+        cls.unsupported_nodes = ()  # type: ignore[attr-defined]
+        for node in nodes:
+            new_method = _node_not_implemented(node)
+            name = f"visit_{node}"
+            # error: "Type[_T]" has no attribute "unsupported_nodes"
+            cls.unsupported_nodes += (name,)  # type: ignore[attr-defined]
+            setattr(cls, name, new_method)
+        return cls
+
+    return disallowed
+
+
+def _op_maker(op_class, op_symbol):
+    """
+    Return a function to create an op class with its symbol already passed.
+
+    Returns
+    -------
+    callable
+    """
+
+    def f(self, node, *args, **kwargs):
+        """
+        Return a partial function with an Op subclass with an operator already passed.
+
+        Returns
+        -------
+        callable
+        """
+        return partial(op_class, op_symbol, *args, **kwargs)
+
+    return f
+
+
+_op_classes = {"binary": BinOp, "unary": UnaryOp}
+
+
+def add_ops(op_classes):
+    """
+    Decorator to add default implementation of ops.
+    """
+
+    def f(cls):
+        for op_attr_name, op_class in op_classes.items():
+            ops = getattr(cls, f"{op_attr_name}_ops")
+            ops_map = getattr(cls, f"{op_attr_name}_op_nodes_map")
+            for op in ops:
+                op_node = ops_map[op]
+                if op_node is not None:
+                    made_op = _op_maker(op_class, op)
+                    setattr(cls, f"visit_{op_node}", made_op)
+        return cls
+
+    return f
+
+
+@disallow(_unsupported_nodes)
+@add_ops(_op_classes)
+class BaseExprVisitor(ast.NodeVisitor):
+    """
+    Custom ast walker. Parsers of other engines should subclass this class
+    if necessary.
+
+    Parameters
+    ----------
+    env : Scope
+    engine : str
+    parser : str
+    preparser : callable
+    """
+
+    const_type: ClassVar[type[Term]] = Constant
+    term_type: ClassVar[type[Term]] = Term
+
+    binary_ops = CMP_OPS_SYMS + BOOL_OPS_SYMS + ARITH_OPS_SYMS
+    binary_op_nodes = (
+        "Gt",
+        "Lt",
+        "GtE",
+        "LtE",
+        "Eq",
+        "NotEq",
+        "In",
+        "NotIn",
+        "BitAnd",
+        "BitOr",
+        "And",
+        "Or",
+        "Add",
+        "Sub",
+        "Mult",
+        "Div",
+        "Pow",
+        "FloorDiv",
+        "Mod",
+    )
+    binary_op_nodes_map = dict(zip(binary_ops, binary_op_nodes))
+
+    unary_ops = UNARY_OPS_SYMS
+    unary_op_nodes = "UAdd", "USub", "Invert", "Not"
+    unary_op_nodes_map = dict(zip(unary_ops, unary_op_nodes))
+
+    rewrite_map = {
+        ast.Eq: ast.In,
+        ast.NotEq: ast.NotIn,
+        ast.In: ast.In,
+        ast.NotIn: ast.NotIn,
+    }
+
+    unsupported_nodes: tuple[str, ...]
+
+    def __init__(self, env, engine, parser, preparser=_preparse) -> None:
+        self.env = env
+        self.engine = engine
+        self.parser = parser
+        self.preparser = preparser
+        self.assigner = None
+
+    def visit(self, node, **kwargs):
+        if isinstance(node, str):
+            clean = self.preparser(node)
+            try:
+                node = ast.fix_missing_locations(ast.parse(clean))
+            except SyntaxError as e:
+                if any(iskeyword(x) for x in clean.split()):
+                    e.msg = "Python keyword not valid identifier in numexpr query"
+                raise e
+
+        method = f"visit_{type(node).__name__}"
+        visitor = getattr(self, method)
+        return visitor(node, **kwargs)
+
+    def visit_Module(self, node, **kwargs):
+        if len(node.body) != 1:
+            raise SyntaxError("only a single expression is allowed")
+        expr = node.body[0]
+        return self.visit(expr, **kwargs)
+
+    def visit_Expr(self, node, **kwargs):
+        return self.visit(node.value, **kwargs)
+
+    def _rewrite_membership_op(self, node, left, right):
+        # the kind of the operator (is actually an instance)
+        op_instance = node.op
+        op_type = type(op_instance)
+
+        # must be two terms and the comparison operator must be ==/!=/in/not in
+        if is_term(left) and is_term(right) and op_type in self.rewrite_map:
+            left_list, right_list = map(_is_list, (left, right))
+            left_str, right_str = map(_is_str, (left, right))
+
+            # if there are any strings or lists in the expression
+            if left_list or right_list or left_str or right_str:
+                op_instance = self.rewrite_map[op_type]()
+
+            # pop the string variable out of locals and replace it with a list
+            # of one string, kind of a hack
+            if right_str:
+                name = self.env.add_tmp([right.value])
+                right = self.term_type(name, self.env)
+
+            if left_str:
+                name = self.env.add_tmp([left.value])
+                left = self.term_type(name, self.env)
+
+        op = self.visit(op_instance)
+        return op, op_instance, left, right
+
+    def _maybe_transform_eq_ne(self, node, left=None, right=None):
+        if left is None:
+            left = self.visit(node.left, side="left")
+        if right is None:
+            right = self.visit(node.right, side="right")
+        op, op_class, left, right = self._rewrite_membership_op(node, left, right)
+        return op, op_class, left, right
+
+    def _maybe_downcast_constants(self, left, right):
+        f32 = np.dtype(np.float32)
+        if (
+            left.is_scalar
+            and hasattr(left, "value")
+            and not right.is_scalar
+            and right.return_type == f32
+        ):
+            # right is a float32 array, left is a scalar
+            name = self.env.add_tmp(np.float32(left.value))
+            left = self.term_type(name, self.env)
+        if (
+            right.is_scalar
+            and hasattr(right, "value")
+            and not left.is_scalar
+            and left.return_type == f32
+        ):
+            # left is a float32 array, right is a scalar
+            name = self.env.add_tmp(np.float32(right.value))
+            right = self.term_type(name, self.env)
+
+        return left, right
+
+    def _maybe_eval(self, binop, eval_in_python):
+        # eval `in` and `not in` (for now) in "partial" python space
+        # things that can be evaluated in "eval" space will be turned into
+        # temporary variables. for example,
+        # [1,2] in a + 2 * b
+        # in that case a + 2 * b will be evaluated using numexpr, and the "in"
+        # call will be evaluated using isin (in python space)
+        return binop.evaluate(
+            self.env, self.engine, self.parser, self.term_type, eval_in_python
+        )
+
+    def _maybe_evaluate_binop(
+        self,
+        op,
+        op_class,
+        lhs,
+        rhs,
+        eval_in_python=("in", "not in"),
+        maybe_eval_in_python=("==", "!=", "<", ">", "<=", ">="),
+    ):
+        res = op(lhs, rhs)
+
+        if res.has_invalid_return_type:
+            raise TypeError(
+                f"unsupported operand type(s) for {res.op}: "
+                f"'{lhs.type}' and '{rhs.type}'"
+            )
+
+        if self.engine != "pytables" and (
+            res.op in CMP_OPS_SYMS
+            and getattr(lhs, "is_datetime", False)
+            or getattr(rhs, "is_datetime", False)
+        ):
+            # all date ops must be done in python bc numexpr doesn't work
+            # well with NaT
+            return self._maybe_eval(res, self.binary_ops)
+
+        if res.op in eval_in_python:
+            # "in"/"not in" ops are always evaluated in python
+            return self._maybe_eval(res, eval_in_python)
+        elif self.engine != "pytables":
+            if (
+                getattr(lhs, "return_type", None) == object
+                or is_string_dtype(getattr(lhs, "return_type", None))
+                or getattr(rhs, "return_type", None) == object
+                or is_string_dtype(getattr(rhs, "return_type", None))
+            ):
+                # evaluate "==" and "!=" in python if either of our operands
+                # has an object or string return type
+                return self._maybe_eval(res, eval_in_python + maybe_eval_in_python)
+        return res
+
+    def visit_BinOp(self, node, **kwargs):
+        op, op_class, left, right = self._maybe_transform_eq_ne(node)
+        left, right = self._maybe_downcast_constants(left, right)
+        return self._maybe_evaluate_binop(op, op_class, left, right)
+
+    def visit_UnaryOp(self, node, **kwargs):
+        op = self.visit(node.op)
+        operand = self.visit(node.operand)
+        return op(operand)
+
+    def visit_Name(self, node, **kwargs) -> Term:
+        return self.term_type(node.id, self.env, **kwargs)
+
+    # TODO(py314): deprecated since Python 3.8. Remove after Python 3.14 is min
+    def visit_NameConstant(self, node, **kwargs) -> Term:
+        return self.const_type(node.value, self.env)
+
+    # TODO(py314): deprecated since Python 3.8. Remove after Python 3.14 is min
+    def visit_Num(self, node, **kwargs) -> Term:
+        return self.const_type(node.value, self.env)
+
+    def visit_Constant(self, node, **kwargs) -> Term:
+        return self.const_type(node.value, self.env)
+
+    # TODO(py314): deprecated since Python 3.8. Remove after Python 3.14 is min
+    def visit_Str(self, node, **kwargs) -> Term:
+        name = self.env.add_tmp(node.s)
+        return self.term_type(name, self.env)
+
+    def visit_List(self, node, **kwargs) -> Term:
+        name = self.env.add_tmp([self.visit(e)(self.env) for e in node.elts])
+        return self.term_type(name, self.env)
+
+    visit_Tuple = visit_List
+
+    def visit_Index(self, node, **kwargs):
+        """df.index[4]"""
+        return self.visit(node.value)
+
+    def visit_Subscript(self, node, **kwargs) -> Term:
+        from pandas import eval as pd_eval
+
+        value = self.visit(node.value)
+        slobj = self.visit(node.slice)
+        result = pd_eval(
+            slobj, local_dict=self.env, engine=self.engine, parser=self.parser
+        )
+        try:
+            # a Term instance
+            v = value.value[result]
+        except AttributeError:
+            # an Op instance
+            lhs = pd_eval(
+                value, local_dict=self.env, engine=self.engine, parser=self.parser
+            )
+            v = lhs[result]
+        name = self.env.add_tmp(v)
+        return self.term_type(name, env=self.env)
+
+    def visit_Slice(self, node, **kwargs) -> slice:
+        """df.index[slice(4,6)]"""
+        lower = node.lower
+        if lower is not None:
+            lower = self.visit(lower).value
+        upper = node.upper
+        if upper is not None:
+            upper = self.visit(upper).value
+        step = node.step
+        if step is not None:
+            step = self.visit(step).value
+
+        return slice(lower, upper, step)
+
+    def visit_Assign(self, node, **kwargs):
+        """
+        support a single assignment node, like
+
+        c = a + b
+
+        set the assigner at the top level, must be a Name node which
+        might or might not exist in the resolvers
+
+        """
+        if len(node.targets) != 1:
+            raise SyntaxError("can only assign a single expression")
+        if not isinstance(node.targets[0], ast.Name):
+            raise SyntaxError("left hand side of an assignment must be a single name")
+        if self.env.target is None:
+            raise ValueError("cannot assign without a target object")
+
+        try:
+            assigner = self.visit(node.targets[0], **kwargs)
+        except UndefinedVariableError:
+            assigner = node.targets[0].id
+
+        self.assigner = getattr(assigner, "name", assigner)
+        if self.assigner is None:
+            raise SyntaxError(
+                "left hand side of an assignment must be a single resolvable name"
+            )
+
+        return self.visit(node.value, **kwargs)
+
+    def visit_Attribute(self, node, **kwargs):
+        attr = node.attr
+        value = node.value
+
+        ctx = node.ctx
+        if isinstance(ctx, ast.Load):
+            # resolve the value
+            resolved = self.visit(value).value
+            try:
+                v = getattr(resolved, attr)
+                name = self.env.add_tmp(v)
+                return self.term_type(name, self.env)
+            except AttributeError:
+                # something like datetime.datetime where scope is overridden
+                if isinstance(value, ast.Name) and value.id == attr:
+                    return resolved
+                raise
+
+        raise ValueError(f"Invalid Attribute context {type(ctx).__name__}")
+
+    def visit_Call(self, node, side=None, **kwargs):
+        if isinstance(node.func, ast.Attribute) and node.func.attr != "__call__":
+            res = self.visit_Attribute(node.func)
+        elif not isinstance(node.func, ast.Name):
+            raise TypeError("Only named functions are supported")
+        else:
+            try:
+                res = self.visit(node.func)
+            except UndefinedVariableError:
+                # Check if this is a supported function name
+                try:
+                    res = FuncNode(node.func.id)
+                except ValueError:
+                    # Raise original error
+                    raise
+
+        if res is None:
+            # error: "expr" has no attribute "id"
+            raise ValueError(
+                f"Invalid function call {node.func.id}"  # type: ignore[attr-defined]
+            )
+        if hasattr(res, "value"):
+            res = res.value
+
+        if isinstance(res, FuncNode):
+            new_args = [self.visit(arg) for arg in node.args]
+
+            if node.keywords:
+                raise TypeError(
+                    f'Function "{res.name}" does not support keyword arguments'
+                )
+
+            return res(*new_args)
+
+        else:
+            new_args = [self.visit(arg)(self.env) for arg in node.args]
+
+            for key in node.keywords:
+                if not isinstance(key, ast.keyword):
+                    # error: "expr" has no attribute "id"
+                    raise ValueError(
+                        "keyword error in function call "
+                        f"'{node.func.id}'"  # type: ignore[attr-defined]
+                    )
+
+                if key.arg:
+                    kwargs[key.arg] = self.visit(key.value)(self.env)
+
+            name = self.env.add_tmp(res(*new_args, **kwargs))
+            return self.term_type(name=name, env=self.env)
+
+    def translate_In(self, op):
+        return op
+
+    def visit_Compare(self, node, **kwargs):
+        ops = node.ops
+        comps = node.comparators
+
+        # base case: we have something like a CMP b
+        if len(comps) == 1:
+            op = self.translate_In(ops[0])
+            binop = ast.BinOp(op=op, left=node.left, right=comps[0])
+            return self.visit(binop)
+
+        # recursive case: we have a chained comparison, a CMP b CMP c, etc.
+        left = node.left
+        values = []
+        for op, comp in zip(ops, comps):
+            new_node = self.visit(
+                ast.Compare(comparators=[comp], left=left, ops=[self.translate_In(op)])
+            )
+            left = comp
+            values.append(new_node)
+        return self.visit(ast.BoolOp(op=ast.And(), values=values))
+
+    def _try_visit_binop(self, bop):
+        if isinstance(bop, (Op, Term)):
+            return bop
+        return self.visit(bop)
+
+    def visit_BoolOp(self, node, **kwargs):
+        def visitor(x, y):
+            lhs = self._try_visit_binop(x)
+            rhs = self._try_visit_binop(y)
+
+            op, op_class, lhs, rhs = self._maybe_transform_eq_ne(node, lhs, rhs)
+            return self._maybe_evaluate_binop(op, node.op, lhs, rhs)
+
+        operands = node.values
+        return reduce(visitor, operands)
+
+
+_python_not_supported = frozenset(["Dict", "BoolOp", "In", "NotIn"])
+_numexpr_supported_calls = frozenset(REDUCTIONS + MATHOPS)
+
+
+@disallow(
+    (_unsupported_nodes | _python_not_supported)
+    - (_boolop_nodes | frozenset(["BoolOp", "Attribute", "In", "NotIn", "Tuple"]))
+)
+class PandasExprVisitor(BaseExprVisitor):
+    def __init__(
+        self,
+        env,
+        engine,
+        parser,
+        preparser=partial(
+            _preparse,
+            f=_compose(_replace_locals, _replace_booleans, clean_backtick_quoted_toks),
+        ),
+    ) -> None:
+        super().__init__(env, engine, parser, preparser)
+
+
+@disallow(_unsupported_nodes | _python_not_supported | frozenset(["Not"]))
+class PythonExprVisitor(BaseExprVisitor):
+    def __init__(
+        self, env, engine, parser, preparser=lambda source, f=None: source
+    ) -> None:
+        super().__init__(env, engine, parser, preparser=preparser)
+
+
+class Expr:
+    """
+    Object encapsulating an expression.
+
+    Parameters
+    ----------
+    expr : str
+    engine : str, optional, default 'numexpr'
+    parser : str, optional, default 'pandas'
+    env : Scope, optional, default None
+    level : int, optional, default 2
+    """
+
+    env: Scope
+    engine: str
+    parser: str
+
+    def __init__(
+        self,
+        expr,
+        engine: str = "numexpr",
+        parser: str = "pandas",
+        env: Scope | None = None,
+        level: int = 0,
+    ) -> None:
+        self.expr = expr
+        self.env = env or Scope(level=level + 1)
+        self.engine = engine
+        self.parser = parser
+        self._visitor = PARSERS[parser](self.env, self.engine, self.parser)
+        self.terms = self.parse()
+
+    @property
+    def assigner(self):
+        return getattr(self._visitor, "assigner", None)
+
+    def __call__(self):
+        return self.terms(self.env)
+
+    def __repr__(self) -> str:
+        return printing.pprint_thing(self.terms)
+
+    def __len__(self) -> int:
+        return len(self.expr)
+
+    def parse(self):
+        """
+        Parse an expression.
+        """
+        return self._visitor.visit(self.expr)
+
+    @property
+    def names(self):
+        """
+        Get the names in an expression.
+        """
+        if is_term(self.terms):
+            return frozenset([self.terms.name])
+        return frozenset(term.name for term in com.flatten(self.terms))
+
+
+PARSERS = {"python": PythonExprVisitor, "pandas": PandasExprVisitor}
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/computation/expressions.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/computation/expressions.py
new file mode 100644
index 0000000000000000000000000000000000000000..6219cac4aeb16ee019551f95a03af59da44c9d06
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/computation/expressions.py
@@ -0,0 +1,286 @@
+"""
+Expressions
+-----------
+
+Offer fast expression evaluation through numexpr
+
+"""
+from __future__ import annotations
+
+import operator
+from typing import TYPE_CHECKING
+import warnings
+
+import numpy as np
+
+from pandas._config import get_option
+
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core import roperator
+from pandas.core.computation.check import NUMEXPR_INSTALLED
+
+if NUMEXPR_INSTALLED:
+    import numexpr as ne
+
+if TYPE_CHECKING:
+    from pandas._typing import FuncType
+
+_TEST_MODE: bool | None = None
+_TEST_RESULT: list[bool] = []
+USE_NUMEXPR = NUMEXPR_INSTALLED
+_evaluate: FuncType | None = None
+_where: FuncType | None = None
+
+# the set of dtypes that we will allow pass to numexpr
+_ALLOWED_DTYPES = {
+    "evaluate": {"int64", "int32", "float64", "float32", "bool"},
+    "where": {"int64", "float64", "bool"},
+}
+
+# the minimum prod shape that we will use numexpr
+_MIN_ELEMENTS = 1_000_000
+
+
+def set_use_numexpr(v: bool = True) -> None:
+    # set/unset to use numexpr
+    global USE_NUMEXPR
+    if NUMEXPR_INSTALLED:
+        USE_NUMEXPR = v
+
+    # choose what we are going to do
+    global _evaluate, _where
+
+    _evaluate = _evaluate_numexpr if USE_NUMEXPR else _evaluate_standard
+    _where = _where_numexpr if USE_NUMEXPR else _where_standard
+
+
+def set_numexpr_threads(n=None) -> None:
+    # if we are using numexpr, set the threads to n
+    # otherwise reset
+    if NUMEXPR_INSTALLED and USE_NUMEXPR:
+        if n is None:
+            n = ne.detect_number_of_cores()
+        ne.set_num_threads(n)
+
+
+def _evaluate_standard(op, op_str, a, b):
+    """
+    Standard evaluation.
+    """
+    if _TEST_MODE:
+        _store_test_result(False)
+    return op(a, b)
+
+
+def _can_use_numexpr(op, op_str, a, b, dtype_check) -> bool:
+    """return a boolean if we WILL be using numexpr"""
+    if op_str is not None:
+        # required min elements (otherwise we are adding overhead)
+        if a.size > _MIN_ELEMENTS:
+            # check for dtype compatibility
+            dtypes: set[str] = set()
+            for o in [a, b]:
+                # ndarray and Series Case
+                if hasattr(o, "dtype"):
+                    dtypes |= {o.dtype.name}
+
+            # allowed are a superset
+            if not len(dtypes) or _ALLOWED_DTYPES[dtype_check] >= dtypes:
+                return True
+
+    return False
+
+
+def _evaluate_numexpr(op, op_str, a, b):
+    result = None
+
+    if _can_use_numexpr(op, op_str, a, b, "evaluate"):
+        is_reversed = op.__name__.strip("_").startswith("r")
+        if is_reversed:
+            # we were originally called by a reversed op method
+            a, b = b, a
+
+        a_value = a
+        b_value = b
+
+        try:
+            result = ne.evaluate(
+                f"a_value {op_str} b_value",
+                local_dict={"a_value": a_value, "b_value": b_value},
+                casting="safe",
+            )
+        except TypeError:
+            # numexpr raises eg for array ** array with integers
+            # (https://github.com/pydata/numexpr/issues/379)
+            pass
+        except NotImplementedError:
+            if _bool_arith_fallback(op_str, a, b):
+                pass
+            else:
+                raise
+
+        if is_reversed:
+            # reverse order to original for fallback
+            a, b = b, a
+
+    if _TEST_MODE:
+        _store_test_result(result is not None)
+
+    if result is None:
+        result = _evaluate_standard(op, op_str, a, b)
+
+    return result
+
+
+_op_str_mapping = {
+    operator.add: "+",
+    roperator.radd: "+",
+    operator.mul: "*",
+    roperator.rmul: "*",
+    operator.sub: "-",
+    roperator.rsub: "-",
+    operator.truediv: "/",
+    roperator.rtruediv: "/",
+    # floordiv not supported by numexpr 2.x
+    operator.floordiv: None,
+    roperator.rfloordiv: None,
+    # we require Python semantics for mod of negative for backwards compatibility
+    # see https://github.com/pydata/numexpr/issues/365
+    # so sticking with unaccelerated for now GH#36552
+    operator.mod: None,
+    roperator.rmod: None,
+    operator.pow: "**",
+    roperator.rpow: "**",
+    operator.eq: "==",
+    operator.ne: "!=",
+    operator.le: "<=",
+    operator.lt: "<",
+    operator.ge: ">=",
+    operator.gt: ">",
+    operator.and_: "&",
+    roperator.rand_: "&",
+    operator.or_: "|",
+    roperator.ror_: "|",
+    operator.xor: "^",
+    roperator.rxor: "^",
+    divmod: None,
+    roperator.rdivmod: None,
+}
+
+
+def _where_standard(cond, a, b):
+    # Caller is responsible for extracting ndarray if necessary
+    return np.where(cond, a, b)
+
+
+def _where_numexpr(cond, a, b):
+    # Caller is responsible for extracting ndarray if necessary
+    result = None
+
+    if _can_use_numexpr(None, "where", a, b, "where"):
+        result = ne.evaluate(
+            "where(cond_value, a_value, b_value)",
+            local_dict={"cond_value": cond, "a_value": a, "b_value": b},
+            casting="safe",
+        )
+
+    if result is None:
+        result = _where_standard(cond, a, b)
+
+    return result
+
+
+# turn myself on
+set_use_numexpr(get_option("compute.use_numexpr"))
+
+
+def _has_bool_dtype(x):
+    try:
+        return x.dtype == bool
+    except AttributeError:
+        return isinstance(x, (bool, np.bool_))
+
+
+_BOOL_OP_UNSUPPORTED = {"+": "|", "*": "&", "-": "^"}
+
+
+def _bool_arith_fallback(op_str, a, b) -> bool:
+    """
+    Check if we should fallback to the python `_evaluate_standard` in case
+    of an unsupported operation by numexpr, which is the case for some
+    boolean ops.
+    """
+    if _has_bool_dtype(a) and _has_bool_dtype(b):
+        if op_str in _BOOL_OP_UNSUPPORTED:
+            warnings.warn(
+                f"evaluating in Python space because the {repr(op_str)} "
+                "operator is not supported by numexpr for the bool dtype, "
+                f"use {repr(_BOOL_OP_UNSUPPORTED[op_str])} instead.",
+                stacklevel=find_stack_level(),
+            )
+            return True
+    return False
+
+
+def evaluate(op, a, b, use_numexpr: bool = True):
+    """
+    Evaluate and return the expression of the op on a and b.
+
+    Parameters
+    ----------
+    op : the actual operand
+    a : left operand
+    b : right operand
+    use_numexpr : bool, default True
+        Whether to try to use numexpr.
+    """
+    op_str = _op_str_mapping[op]
+    if op_str is not None:
+        if use_numexpr:
+            # error: "None" not callable
+            return _evaluate(op, op_str, a, b)  # type: ignore[misc]
+    return _evaluate_standard(op, op_str, a, b)
+
+
+def where(cond, a, b, use_numexpr: bool = True):
+    """
+    Evaluate the where condition cond on a and b.
+
+    Parameters
+    ----------
+    cond : np.ndarray[bool]
+    a : return if cond is True
+    b : return if cond is False
+    use_numexpr : bool, default True
+        Whether to try to use numexpr.
+    """
+    assert _where is not None
+    return _where(cond, a, b) if use_numexpr else _where_standard(cond, a, b)
+
+
+def set_test_mode(v: bool = True) -> None:
+    """
+    Keeps track of whether numexpr was used.
+
+    Stores an additional ``True`` for every successful use of evaluate with
+    numexpr since the last ``get_test_result``.
+    """
+    global _TEST_MODE, _TEST_RESULT
+    _TEST_MODE = v
+    _TEST_RESULT = []
+
+
+def _store_test_result(used_numexpr: bool) -> None:
+    if used_numexpr:
+        _TEST_RESULT.append(used_numexpr)
+
+
+def get_test_result() -> list[bool]:
+    """
+    Get test result and reset test_results.
+    """
+    global _TEST_RESULT
+    res = _TEST_RESULT
+    _TEST_RESULT = []
+    return res
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/computation/ops.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/computation/ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..d8265456dfcedb5eb398cbf0517d78a1d976cd2a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/computation/ops.py
@@ -0,0 +1,572 @@
+"""
+Operator classes for eval.
+"""
+
+from __future__ import annotations
+
+from datetime import datetime
+from functools import partial
+import operator
+from typing import (
+    TYPE_CHECKING,
+    Callable,
+    Literal,
+)
+
+import numpy as np
+
+from pandas._libs.tslibs import Timestamp
+
+from pandas.core.dtypes.common import (
+    is_list_like,
+    is_scalar,
+)
+
+import pandas.core.common as com
+from pandas.core.computation.common import (
+    ensure_decoded,
+    result_type_many,
+)
+from pandas.core.computation.scope import DEFAULT_GLOBALS
+
+from pandas.io.formats.printing import (
+    pprint_thing,
+    pprint_thing_encoded,
+)
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Iterable,
+        Iterator,
+    )
+
+REDUCTIONS = ("sum", "prod", "min", "max")
+
+_unary_math_ops = (
+    "sin",
+    "cos",
+    "exp",
+    "log",
+    "expm1",
+    "log1p",
+    "sqrt",
+    "sinh",
+    "cosh",
+    "tanh",
+    "arcsin",
+    "arccos",
+    "arctan",
+    "arccosh",
+    "arcsinh",
+    "arctanh",
+    "abs",
+    "log10",
+    "floor",
+    "ceil",
+)
+_binary_math_ops = ("arctan2",)
+
+MATHOPS = _unary_math_ops + _binary_math_ops
+
+
+LOCAL_TAG = "__pd_eval_local_"
+
+
+class Term:
+    def __new__(cls, name, env, side=None, encoding=None):
+        klass = Constant if not isinstance(name, str) else cls
+        # error: Argument 2 for "super" not an instance of argument 1
+        supr_new = super(Term, klass).__new__  # type: ignore[misc]
+        return supr_new(klass)
+
+    is_local: bool
+
+    def __init__(self, name, env, side=None, encoding=None) -> None:
+        # name is a str for Term, but may be something else for subclasses
+        self._name = name
+        self.env = env
+        self.side = side
+        tname = str(name)
+        self.is_local = tname.startswith(LOCAL_TAG) or tname in DEFAULT_GLOBALS
+        self._value = self._resolve_name()
+        self.encoding = encoding
+
+    @property
+    def local_name(self) -> str:
+        return self.name.replace(LOCAL_TAG, "")
+
+    def __repr__(self) -> str:
+        return pprint_thing(self.name)
+
+    def __call__(self, *args, **kwargs):
+        return self.value
+
+    def evaluate(self, *args, **kwargs) -> Term:
+        return self
+
+    def _resolve_name(self):
+        local_name = str(self.local_name)
+        is_local = self.is_local
+        if local_name in self.env.scope and isinstance(
+            self.env.scope[local_name], type
+        ):
+            is_local = False
+
+        res = self.env.resolve(local_name, is_local=is_local)
+        self.update(res)
+
+        if hasattr(res, "ndim") and res.ndim > 2:
+            raise NotImplementedError(
+                "N-dimensional objects, where N > 2, are not supported with eval"
+            )
+        return res
+
+    def update(self, value) -> None:
+        """
+        search order for local (i.e., @variable) variables:
+
+        scope, key_variable
+        [('locals', 'local_name'),
+         ('globals', 'local_name'),
+         ('locals', 'key'),
+         ('globals', 'key')]
+        """
+        key = self.name
+
+        # if it's a variable name (otherwise a constant)
+        if isinstance(key, str):
+            self.env.swapkey(self.local_name, key, new_value=value)
+
+        self.value = value
+
+    @property
+    def is_scalar(self) -> bool:
+        return is_scalar(self._value)
+
+    @property
+    def type(self):
+        try:
+            # potentially very slow for large, mixed dtype frames
+            return self._value.values.dtype
+        except AttributeError:
+            try:
+                # ndarray
+                return self._value.dtype
+            except AttributeError:
+                # scalar
+                return type(self._value)
+
+    return_type = type
+
+    @property
+    def raw(self) -> str:
+        return f"{type(self).__name__}(name={repr(self.name)}, type={self.type})"
+
+    @property
+    def is_datetime(self) -> bool:
+        try:
+            t = self.type.type
+        except AttributeError:
+            t = self.type
+
+        return issubclass(t, (datetime, np.datetime64))
+
+    @property
+    def value(self):
+        return self._value
+
+    @value.setter
+    def value(self, new_value) -> None:
+        self._value = new_value
+
+    @property
+    def name(self):
+        return self._name
+
+    @property
+    def ndim(self) -> int:
+        return self._value.ndim
+
+
+class Constant(Term):
+    def _resolve_name(self):
+        return self._name
+
+    @property
+    def name(self):
+        return self.value
+
+    def __repr__(self) -> str:
+        # in python 2 str() of float
+        # can truncate shorter than repr()
+        return repr(self.name)
+
+
+_bool_op_map = {"not": "~", "and": "&", "or": "|"}
+
+
+class Op:
+    """
+    Hold an operator of arbitrary arity.
+    """
+
+    op: str
+
+    def __init__(self, op: str, operands: Iterable[Term | Op], encoding=None) -> None:
+        self.op = _bool_op_map.get(op, op)
+        self.operands = operands
+        self.encoding = encoding
+
+    def __iter__(self) -> Iterator:
+        return iter(self.operands)
+
+    def __repr__(self) -> str:
+        """
+        Print a generic n-ary operator and its operands using infix notation.
+        """
+        # recurse over the operands
+        parened = (f"({pprint_thing(opr)})" for opr in self.operands)
+        return pprint_thing(f" {self.op} ".join(parened))
+
+    @property
+    def return_type(self):
+        # clobber types to bool if the op is a boolean operator
+        if self.op in (CMP_OPS_SYMS + BOOL_OPS_SYMS):
+            return np.bool_
+        return result_type_many(*(term.type for term in com.flatten(self)))
+
+    @property
+    def has_invalid_return_type(self) -> bool:
+        types = self.operand_types
+        obj_dtype_set = frozenset([np.dtype("object")])
+        return self.return_type == object and types - obj_dtype_set
+
+    @property
+    def operand_types(self):
+        return frozenset(term.type for term in com.flatten(self))
+
+    @property
+    def is_scalar(self) -> bool:
+        return all(operand.is_scalar for operand in self.operands)
+
+    @property
+    def is_datetime(self) -> bool:
+        try:
+            t = self.return_type.type
+        except AttributeError:
+            t = self.return_type
+
+        return issubclass(t, (datetime, np.datetime64))
+
+
+def _in(x, y):
+    """
+    Compute the vectorized membership of ``x in y`` if possible, otherwise
+    use Python.
+    """
+    try:
+        return x.isin(y)
+    except AttributeError:
+        if is_list_like(x):
+            try:
+                return y.isin(x)
+            except AttributeError:
+                pass
+        return x in y
+
+
+def _not_in(x, y):
+    """
+    Compute the vectorized membership of ``x not in y`` if possible,
+    otherwise use Python.
+    """
+    try:
+        return ~x.isin(y)
+    except AttributeError:
+        if is_list_like(x):
+            try:
+                return ~y.isin(x)
+            except AttributeError:
+                pass
+        return x not in y
+
+
+CMP_OPS_SYMS = (">", "<", ">=", "<=", "==", "!=", "in", "not in")
+_cmp_ops_funcs = (
+    operator.gt,
+    operator.lt,
+    operator.ge,
+    operator.le,
+    operator.eq,
+    operator.ne,
+    _in,
+    _not_in,
+)
+_cmp_ops_dict = dict(zip(CMP_OPS_SYMS, _cmp_ops_funcs))
+
+BOOL_OPS_SYMS = ("&", "|", "and", "or")
+_bool_ops_funcs = (operator.and_, operator.or_, operator.and_, operator.or_)
+_bool_ops_dict = dict(zip(BOOL_OPS_SYMS, _bool_ops_funcs))
+
+ARITH_OPS_SYMS = ("+", "-", "*", "/", "**", "//", "%")
+_arith_ops_funcs = (
+    operator.add,
+    operator.sub,
+    operator.mul,
+    operator.truediv,
+    operator.pow,
+    operator.floordiv,
+    operator.mod,
+)
+_arith_ops_dict = dict(zip(ARITH_OPS_SYMS, _arith_ops_funcs))
+
+SPECIAL_CASE_ARITH_OPS_SYMS = ("**", "//", "%")
+_special_case_arith_ops_funcs = (operator.pow, operator.floordiv, operator.mod)
+_special_case_arith_ops_dict = dict(
+    zip(SPECIAL_CASE_ARITH_OPS_SYMS, _special_case_arith_ops_funcs)
+)
+
+_binary_ops_dict = {}
+
+for d in (_cmp_ops_dict, _bool_ops_dict, _arith_ops_dict):
+    _binary_ops_dict.update(d)
+
+
+def is_term(obj) -> bool:
+    return isinstance(obj, Term)
+
+
+class BinOp(Op):
+    """
+    Hold a binary operator and its operands.
+
+    Parameters
+    ----------
+    op : str
+    lhs : Term or Op
+    rhs : Term or Op
+    """
+
+    def __init__(self, op: str, lhs, rhs) -> None:
+        super().__init__(op, (lhs, rhs))
+        self.lhs = lhs
+        self.rhs = rhs
+
+        self._disallow_scalar_only_bool_ops()
+
+        self.convert_values()
+
+        try:
+            self.func = _binary_ops_dict[op]
+        except KeyError as err:
+            # has to be made a list for python3
+            keys = list(_binary_ops_dict.keys())
+            raise ValueError(
+                f"Invalid binary operator {repr(op)}, valid operators are {keys}"
+            ) from err
+
+    def __call__(self, env):
+        """
+        Recursively evaluate an expression in Python space.
+
+        Parameters
+        ----------
+        env : Scope
+
+        Returns
+        -------
+        object
+            The result of an evaluated expression.
+        """
+        # recurse over the left/right nodes
+        left = self.lhs(env)
+        right = self.rhs(env)
+
+        return self.func(left, right)
+
+    def evaluate(self, env, engine: str, parser, term_type, eval_in_python):
+        """
+        Evaluate a binary operation *before* being passed to the engine.
+
+        Parameters
+        ----------
+        env : Scope
+        engine : str
+        parser : str
+        term_type : type
+        eval_in_python : list
+
+        Returns
+        -------
+        term_type
+            The "pre-evaluated" expression as an instance of ``term_type``
+        """
+        if engine == "python":
+            res = self(env)
+        else:
+            # recurse over the left/right nodes
+
+            left = self.lhs.evaluate(
+                env,
+                engine=engine,
+                parser=parser,
+                term_type=term_type,
+                eval_in_python=eval_in_python,
+            )
+
+            right = self.rhs.evaluate(
+                env,
+                engine=engine,
+                parser=parser,
+                term_type=term_type,
+                eval_in_python=eval_in_python,
+            )
+
+            # base cases
+            if self.op in eval_in_python:
+                res = self.func(left.value, right.value)
+            else:
+                from pandas.core.computation.eval import eval
+
+                res = eval(self, local_dict=env, engine=engine, parser=parser)
+
+        name = env.add_tmp(res)
+        return term_type(name, env=env)
+
+    def convert_values(self) -> None:
+        """
+        Convert datetimes to a comparable value in an expression.
+        """
+
+        def stringify(value):
+            encoder: Callable
+            if self.encoding is not None:
+                encoder = partial(pprint_thing_encoded, encoding=self.encoding)
+            else:
+                encoder = pprint_thing
+            return encoder(value)
+
+        lhs, rhs = self.lhs, self.rhs
+
+        if is_term(lhs) and lhs.is_datetime and is_term(rhs) and rhs.is_scalar:
+            v = rhs.value
+            if isinstance(v, (int, float)):
+                v = stringify(v)
+            v = Timestamp(ensure_decoded(v))
+            if v.tz is not None:
+                v = v.tz_convert("UTC")
+            self.rhs.update(v)
+
+        if is_term(rhs) and rhs.is_datetime and is_term(lhs) and lhs.is_scalar:
+            v = lhs.value
+            if isinstance(v, (int, float)):
+                v = stringify(v)
+            v = Timestamp(ensure_decoded(v))
+            if v.tz is not None:
+                v = v.tz_convert("UTC")
+            self.lhs.update(v)
+
+    def _disallow_scalar_only_bool_ops(self):
+        rhs = self.rhs
+        lhs = self.lhs
+
+        # GH#24883 unwrap dtype if necessary to ensure we have a type object
+        rhs_rt = rhs.return_type
+        rhs_rt = getattr(rhs_rt, "type", rhs_rt)
+        lhs_rt = lhs.return_type
+        lhs_rt = getattr(lhs_rt, "type", lhs_rt)
+        if (
+            (lhs.is_scalar or rhs.is_scalar)
+            and self.op in _bool_ops_dict
+            and (
+                not (
+                    issubclass(rhs_rt, (bool, np.bool_))
+                    and issubclass(lhs_rt, (bool, np.bool_))
+                )
+            )
+        ):
+            raise NotImplementedError("cannot evaluate scalar only bool ops")
+
+
+def isnumeric(dtype) -> bool:
+    return issubclass(np.dtype(dtype).type, np.number)
+
+
+UNARY_OPS_SYMS = ("+", "-", "~", "not")
+_unary_ops_funcs = (operator.pos, operator.neg, operator.invert, operator.invert)
+_unary_ops_dict = dict(zip(UNARY_OPS_SYMS, _unary_ops_funcs))
+
+
+class UnaryOp(Op):
+    """
+    Hold a unary operator and its operands.
+
+    Parameters
+    ----------
+    op : str
+        The token used to represent the operator.
+    operand : Term or Op
+        The Term or Op operand to the operator.
+
+    Raises
+    ------
+    ValueError
+        * If no function associated with the passed operator token is found.
+    """
+
+    def __init__(self, op: Literal["+", "-", "~", "not"], operand) -> None:
+        super().__init__(op, (operand,))
+        self.operand = operand
+
+        try:
+            self.func = _unary_ops_dict[op]
+        except KeyError as err:
+            raise ValueError(
+                f"Invalid unary operator {repr(op)}, "
+                f"valid operators are {UNARY_OPS_SYMS}"
+            ) from err
+
+    def __call__(self, env) -> MathCall:
+        operand = self.operand(env)
+        # error: Cannot call function of unknown type
+        return self.func(operand)  # type: ignore[operator]
+
+    def __repr__(self) -> str:
+        return pprint_thing(f"{self.op}({self.operand})")
+
+    @property
+    def return_type(self) -> np.dtype:
+        operand = self.operand
+        if operand.return_type == np.dtype("bool"):
+            return np.dtype("bool")
+        if isinstance(operand, Op) and (
+            operand.op in _cmp_ops_dict or operand.op in _bool_ops_dict
+        ):
+            return np.dtype("bool")
+        return np.dtype("int")
+
+
+class MathCall(Op):
+    def __init__(self, func, args) -> None:
+        super().__init__(func.name, args)
+        self.func = func
+
+    def __call__(self, env):
+        # error: "Op" not callable
+        operands = [op(env) for op in self.operands]  # type: ignore[operator]
+        return self.func.func(*operands)
+
+    def __repr__(self) -> str:
+        operands = map(str, self.operands)
+        return pprint_thing(f"{self.op}({','.join(operands)})")
+
+
+class FuncNode:
+    def __init__(self, name: str) -> None:
+        if name not in MATHOPS:
+            raise ValueError(f'"{name}" is not a supported function')
+        self.name = name
+        self.func = getattr(np, name)
+
+    def __call__(self, *args) -> MathCall:
+        return MathCall(self, args)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/computation/parsing.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/computation/parsing.py
new file mode 100644
index 0000000000000000000000000000000000000000..4cfa0f2baffd5ed45db19242c2afd00b6e5e23dc
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/computation/parsing.py
@@ -0,0 +1,198 @@
+"""
+:func:`~pandas.eval` source string parsing functions
+"""
+from __future__ import annotations
+
+from io import StringIO
+from keyword import iskeyword
+import token
+import tokenize
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Hashable,
+        Iterator,
+    )
+
+# A token value Python's tokenizer probably will never use.
+BACKTICK_QUOTED_STRING = 100
+
+
+def create_valid_python_identifier(name: str) -> str:
+    """
+    Create valid Python identifiers from any string.
+
+    Check if name contains any special characters. If it contains any
+    special characters, the special characters will be replaced by
+    a special string and a prefix is added.
+
+    Raises
+    ------
+    SyntaxError
+        If the returned name is not a Python valid identifier, raise an exception.
+        This can happen if there is a hashtag in the name, as the tokenizer will
+        than terminate and not find the backtick.
+        But also for characters that fall out of the range of (U+0001..U+007F).
+    """
+    if name.isidentifier() and not iskeyword(name):
+        return name
+
+    # Create a dict with the special characters and their replacement string.
+    # EXACT_TOKEN_TYPES contains these special characters
+    # token.tok_name contains a readable description of the replacement string.
+    special_characters_replacements = {
+        char: f"_{token.tok_name[tokval]}_"
+        for char, tokval in (tokenize.EXACT_TOKEN_TYPES.items())
+    }
+    special_characters_replacements.update(
+        {
+            " ": "_",
+            "?": "_QUESTIONMARK_",
+            "!": "_EXCLAMATIONMARK_",
+            "$": "_DOLLARSIGN_",
+            "€": "_EUROSIGN_",
+            "°": "_DEGREESIGN_",
+            # Including quotes works, but there are exceptions.
+            "'": "_SINGLEQUOTE_",
+            '"': "_DOUBLEQUOTE_",
+            # Currently not possible. Terminates parser and won't find backtick.
+            # "#": "_HASH_",
+        }
+    )
+
+    name = "".join([special_characters_replacements.get(char, char) for char in name])
+    name = f"BACKTICK_QUOTED_STRING_{name}"
+
+    if not name.isidentifier():
+        raise SyntaxError(f"Could not convert '{name}' to a valid Python identifier.")
+
+    return name
+
+
+def clean_backtick_quoted_toks(tok: tuple[int, str]) -> tuple[int, str]:
+    """
+    Clean up a column name if surrounded by backticks.
+
+    Backtick quoted string are indicated by a certain tokval value. If a string
+    is a backtick quoted token it will processed by
+    :func:`_create_valid_python_identifier` so that the parser can find this
+    string when the query is executed.
+    In this case the tok will get the NAME tokval.
+
+    Parameters
+    ----------
+    tok : tuple of int, str
+        ints correspond to the all caps constants in the tokenize module
+
+    Returns
+    -------
+    tok : Tuple[int, str]
+        Either the input or token or the replacement values
+    """
+    toknum, tokval = tok
+    if toknum == BACKTICK_QUOTED_STRING:
+        return tokenize.NAME, create_valid_python_identifier(tokval)
+    return toknum, tokval
+
+
+def clean_column_name(name: Hashable) -> Hashable:
+    """
+    Function to emulate the cleaning of a backtick quoted name.
+
+    The purpose for this function is to see what happens to the name of
+    identifier if it goes to the process of being parsed a Python code
+    inside a backtick quoted string and than being cleaned
+    (removed of any special characters).
+
+    Parameters
+    ----------
+    name : hashable
+        Name to be cleaned.
+
+    Returns
+    -------
+    name : hashable
+        Returns the name after tokenizing and cleaning.
+
+    Notes
+    -----
+        For some cases, a name cannot be converted to a valid Python identifier.
+        In that case :func:`tokenize_string` raises a SyntaxError.
+        In that case, we just return the name unmodified.
+
+        If this name was used in the query string (this makes the query call impossible)
+        an error will be raised by :func:`tokenize_backtick_quoted_string` instead,
+        which is not caught and propagates to the user level.
+    """
+    try:
+        tokenized = tokenize_string(f"`{name}`")
+        tokval = next(tokenized)[1]
+        return create_valid_python_identifier(tokval)
+    except SyntaxError:
+        return name
+
+
+def tokenize_backtick_quoted_string(
+    token_generator: Iterator[tokenize.TokenInfo], source: str, string_start: int
+) -> tuple[int, str]:
+    """
+    Creates a token from a backtick quoted string.
+
+    Moves the token_generator forwards till right after the next backtick.
+
+    Parameters
+    ----------
+    token_generator : Iterator[tokenize.TokenInfo]
+        The generator that yields the tokens of the source string (Tuple[int, str]).
+        The generator is at the first token after the backtick (`)
+
+    source : str
+        The Python source code string.
+
+    string_start : int
+        This is the start of backtick quoted string inside the source string.
+
+    Returns
+    -------
+    tok: Tuple[int, str]
+        The token that represents the backtick quoted string.
+        The integer is equal to BACKTICK_QUOTED_STRING (100).
+    """
+    for _, tokval, start, _, _ in token_generator:
+        if tokval == "`":
+            string_end = start[1]
+            break
+
+    return BACKTICK_QUOTED_STRING, source[string_start:string_end]
+
+
+def tokenize_string(source: str) -> Iterator[tuple[int, str]]:
+    """
+    Tokenize a Python source code string.
+
+    Parameters
+    ----------
+    source : str
+        The Python source code string.
+
+    Returns
+    -------
+    tok_generator : Iterator[Tuple[int, str]]
+        An iterator yielding all tokens with only toknum and tokval (Tuple[ing, str]).
+    """
+    line_reader = StringIO(source).readline
+    token_generator = tokenize.generate_tokens(line_reader)
+
+    # Loop over all tokens till a backtick (`) is found.
+    # Then, take all tokens till the next backtick to form a backtick quoted string
+    for toknum, tokval, start, _, _ in token_generator:
+        if tokval == "`":
+            try:
+                yield tokenize_backtick_quoted_string(
+                    token_generator, source, string_start=start[1] + 1
+                )
+            except Exception as err:
+                raise SyntaxError(f"Failed to parse backticks in '{source}'.") from err
+        else:
+            yield toknum, tokval
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/computation/pytables.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/computation/pytables.py
new file mode 100644
index 0000000000000000000000000000000000000000..04a8ad7ef0be6b044baf65b80cbf4161d45f8cac
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/computation/pytables.py
@@ -0,0 +1,666 @@
+""" manage PyTables query interface via Expressions """
+from __future__ import annotations
+
+import ast
+from decimal import (
+    Decimal,
+    InvalidOperation,
+)
+from functools import partial
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    ClassVar,
+)
+
+import numpy as np
+
+from pandas._libs.tslibs import (
+    Timedelta,
+    Timestamp,
+)
+from pandas.errors import UndefinedVariableError
+
+from pandas.core.dtypes.common import is_list_like
+
+import pandas.core.common as com
+from pandas.core.computation import (
+    expr,
+    ops,
+    scope as _scope,
+)
+from pandas.core.computation.common import ensure_decoded
+from pandas.core.computation.expr import BaseExprVisitor
+from pandas.core.computation.ops import is_term
+from pandas.core.construction import extract_array
+from pandas.core.indexes.base import Index
+
+from pandas.io.formats.printing import (
+    pprint_thing,
+    pprint_thing_encoded,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        Self,
+        npt,
+    )
+
+
+class PyTablesScope(_scope.Scope):
+    __slots__ = ("queryables",)
+
+    queryables: dict[str, Any]
+
+    def __init__(
+        self,
+        level: int,
+        global_dict=None,
+        local_dict=None,
+        queryables: dict[str, Any] | None = None,
+    ) -> None:
+        super().__init__(level + 1, global_dict=global_dict, local_dict=local_dict)
+        self.queryables = queryables or {}
+
+
+class Term(ops.Term):
+    env: PyTablesScope
+
+    def __new__(cls, name, env, side=None, encoding=None):
+        if isinstance(name, str):
+            klass = cls
+        else:
+            klass = Constant
+        return object.__new__(klass)
+
+    def __init__(self, name, env: PyTablesScope, side=None, encoding=None) -> None:
+        super().__init__(name, env, side=side, encoding=encoding)
+
+    def _resolve_name(self):
+        # must be a queryables
+        if self.side == "left":
+            # Note: The behavior of __new__ ensures that self.name is a str here
+            if self.name not in self.env.queryables:
+                raise NameError(f"name {repr(self.name)} is not defined")
+            return self.name
+
+        # resolve the rhs (and allow it to be None)
+        try:
+            return self.env.resolve(self.name, is_local=False)
+        except UndefinedVariableError:
+            return self.name
+
+    # read-only property overwriting read/write property
+    @property  # type: ignore[misc]
+    def value(self):
+        return self._value
+
+
+class Constant(Term):
+    def __init__(self, name, env: PyTablesScope, side=None, encoding=None) -> None:
+        assert isinstance(env, PyTablesScope), type(env)
+        super().__init__(name, env, side=side, encoding=encoding)
+
+    def _resolve_name(self):
+        return self._name
+
+
+class BinOp(ops.BinOp):
+    _max_selectors = 31
+
+    op: str
+    queryables: dict[str, Any]
+    condition: str | None
+
+    def __init__(self, op: str, lhs, rhs, queryables: dict[str, Any], encoding) -> None:
+        super().__init__(op, lhs, rhs)
+        self.queryables = queryables
+        self.encoding = encoding
+        self.condition = None
+
+    def _disallow_scalar_only_bool_ops(self) -> None:
+        pass
+
+    def prune(self, klass):
+        def pr(left, right):
+            """create and return a new specialized BinOp from myself"""
+            if left is None:
+                return right
+            elif right is None:
+                return left
+
+            k = klass
+            if isinstance(left, ConditionBinOp):
+                if isinstance(right, ConditionBinOp):
+                    k = JointConditionBinOp
+                elif isinstance(left, k):
+                    return left
+                elif isinstance(right, k):
+                    return right
+
+            elif isinstance(left, FilterBinOp):
+                if isinstance(right, FilterBinOp):
+                    k = JointFilterBinOp
+                elif isinstance(left, k):
+                    return left
+                elif isinstance(right, k):
+                    return right
+
+            return k(
+                self.op, left, right, queryables=self.queryables, encoding=self.encoding
+            ).evaluate()
+
+        left, right = self.lhs, self.rhs
+
+        if is_term(left) and is_term(right):
+            res = pr(left.value, right.value)
+        elif not is_term(left) and is_term(right):
+            res = pr(left.prune(klass), right.value)
+        elif is_term(left) and not is_term(right):
+            res = pr(left.value, right.prune(klass))
+        elif not (is_term(left) or is_term(right)):
+            res = pr(left.prune(klass), right.prune(klass))
+
+        return res
+
+    def conform(self, rhs):
+        """inplace conform rhs"""
+        if not is_list_like(rhs):
+            rhs = [rhs]
+        if isinstance(rhs, np.ndarray):
+            rhs = rhs.ravel()
+        return rhs
+
+    @property
+    def is_valid(self) -> bool:
+        """return True if this is a valid field"""
+        return self.lhs in self.queryables
+
+    @property
+    def is_in_table(self) -> bool:
+        """
+        return True if this is a valid column name for generation (e.g. an
+        actual column in the table)
+        """
+        return self.queryables.get(self.lhs) is not None
+
+    @property
+    def kind(self):
+        """the kind of my field"""
+        return getattr(self.queryables.get(self.lhs), "kind", None)
+
+    @property
+    def meta(self):
+        """the meta of my field"""
+        return getattr(self.queryables.get(self.lhs), "meta", None)
+
+    @property
+    def metadata(self):
+        """the metadata of my field"""
+        return getattr(self.queryables.get(self.lhs), "metadata", None)
+
+    def generate(self, v) -> str:
+        """create and return the op string for this TermValue"""
+        val = v.tostring(self.encoding)
+        return f"({self.lhs} {self.op} {val})"
+
+    def convert_value(self, v) -> TermValue:
+        """
+        convert the expression that is in the term to something that is
+        accepted by pytables
+        """
+
+        def stringify(value):
+            if self.encoding is not None:
+                return pprint_thing_encoded(value, encoding=self.encoding)
+            return pprint_thing(value)
+
+        kind = ensure_decoded(self.kind)
+        meta = ensure_decoded(self.meta)
+        if kind == "datetime" or (kind and kind.startswith("datetime64")):
+            if isinstance(v, (int, float)):
+                v = stringify(v)
+            v = ensure_decoded(v)
+            v = Timestamp(v).as_unit("ns")
+            if v.tz is not None:
+                v = v.tz_convert("UTC")
+            return TermValue(v, v._value, kind)
+        elif kind in ("timedelta64", "timedelta"):
+            if isinstance(v, str):
+                v = Timedelta(v)
+            else:
+                v = Timedelta(v, unit="s")
+            v = v.as_unit("ns")._value
+            return TermValue(int(v), v, kind)
+        elif meta == "category":
+            metadata = extract_array(self.metadata, extract_numpy=True)
+            result: npt.NDArray[np.intp] | np.intp | int
+            if v not in metadata:
+                result = -1
+            else:
+                result = metadata.searchsorted(v, side="left")
+            return TermValue(result, result, "integer")
+        elif kind == "integer":
+            try:
+                v_dec = Decimal(v)
+            except InvalidOperation:
+                # GH 54186
+                # convert v to float to raise float's ValueError
+                float(v)
+            else:
+                v = int(v_dec.to_integral_exact(rounding="ROUND_HALF_EVEN"))
+            return TermValue(v, v, kind)
+        elif kind == "float":
+            v = float(v)
+            return TermValue(v, v, kind)
+        elif kind == "bool":
+            if isinstance(v, str):
+                v = v.strip().lower() not in [
+                    "false",
+                    "f",
+                    "no",
+                    "n",
+                    "none",
+                    "0",
+                    "[]",
+                    "{}",
+                    "",
+                ]
+            else:
+                v = bool(v)
+            return TermValue(v, v, kind)
+        elif isinstance(v, str):
+            # string quoting
+            return TermValue(v, stringify(v), "string")
+        else:
+            raise TypeError(f"Cannot compare {v} of type {type(v)} to {kind} column")
+
+    def convert_values(self) -> None:
+        pass
+
+
+class FilterBinOp(BinOp):
+    filter: tuple[Any, Any, Index] | None = None
+
+    def __repr__(self) -> str:
+        if self.filter is None:
+            return "Filter: Not Initialized"
+        return pprint_thing(f"[Filter : [{self.filter[0]}] -> [{self.filter[1]}]")
+
+    def invert(self) -> Self:
+        """invert the filter"""
+        if self.filter is not None:
+            self.filter = (
+                self.filter[0],
+                self.generate_filter_op(invert=True),
+                self.filter[2],
+            )
+        return self
+
+    def format(self):
+        """return the actual filter format"""
+        return [self.filter]
+
+    # error: Signature of "evaluate" incompatible with supertype "BinOp"
+    def evaluate(self) -> Self | None:  # type: ignore[override]
+        if not self.is_valid:
+            raise ValueError(f"query term is not valid [{self}]")
+
+        rhs = self.conform(self.rhs)
+        values = list(rhs)
+
+        if self.is_in_table:
+            # if too many values to create the expression, use a filter instead
+            if self.op in ["==", "!="] and len(values) > self._max_selectors:
+                filter_op = self.generate_filter_op()
+                self.filter = (self.lhs, filter_op, Index(values))
+
+                return self
+            return None
+
+        # equality conditions
+        if self.op in ["==", "!="]:
+            filter_op = self.generate_filter_op()
+            self.filter = (self.lhs, filter_op, Index(values))
+
+        else:
+            raise TypeError(
+                f"passing a filterable condition to a non-table indexer [{self}]"
+            )
+
+        return self
+
+    def generate_filter_op(self, invert: bool = False):
+        if (self.op == "!=" and not invert) or (self.op == "==" and invert):
+            return lambda axis, vals: ~axis.isin(vals)
+        else:
+            return lambda axis, vals: axis.isin(vals)
+
+
+class JointFilterBinOp(FilterBinOp):
+    def format(self):
+        raise NotImplementedError("unable to collapse Joint Filters")
+
+    # error: Signature of "evaluate" incompatible with supertype "BinOp"
+    def evaluate(self) -> Self:  # type: ignore[override]
+        return self
+
+
+class ConditionBinOp(BinOp):
+    def __repr__(self) -> str:
+        return pprint_thing(f"[Condition : [{self.condition}]]")
+
+    def invert(self):
+        """invert the condition"""
+        # if self.condition is not None:
+        #    self.condition = "~(%s)" % self.condition
+        # return self
+        raise NotImplementedError(
+            "cannot use an invert condition when passing to numexpr"
+        )
+
+    def format(self):
+        """return the actual ne format"""
+        return self.condition
+
+    # error: Signature of "evaluate" incompatible with supertype "BinOp"
+    def evaluate(self) -> Self | None:  # type: ignore[override]
+        if not self.is_valid:
+            raise ValueError(f"query term is not valid [{self}]")
+
+        # convert values if we are in the table
+        if not self.is_in_table:
+            return None
+
+        rhs = self.conform(self.rhs)
+        values = [self.convert_value(v) for v in rhs]
+
+        # equality conditions
+        if self.op in ["==", "!="]:
+            # too many values to create the expression?
+            if len(values) <= self._max_selectors:
+                vs = [self.generate(v) for v in values]
+                self.condition = f"({' | '.join(vs)})"
+
+            # use a filter after reading
+            else:
+                return None
+        else:
+            self.condition = self.generate(values[0])
+
+        return self
+
+
+class JointConditionBinOp(ConditionBinOp):
+    # error: Signature of "evaluate" incompatible with supertype "BinOp"
+    def evaluate(self) -> Self:  # type: ignore[override]
+        self.condition = f"({self.lhs.condition} {self.op} {self.rhs.condition})"
+        return self
+
+
+class UnaryOp(ops.UnaryOp):
+    def prune(self, klass):
+        if self.op != "~":
+            raise NotImplementedError("UnaryOp only support invert type ops")
+
+        operand = self.operand
+        operand = operand.prune(klass)
+
+        if operand is not None and (
+            issubclass(klass, ConditionBinOp)
+            and operand.condition is not None
+            or not issubclass(klass, ConditionBinOp)
+            and issubclass(klass, FilterBinOp)
+            and operand.filter is not None
+        ):
+            return operand.invert()
+        return None
+
+
+class PyTablesExprVisitor(BaseExprVisitor):
+    const_type: ClassVar[type[ops.Term]] = Constant
+    term_type: ClassVar[type[Term]] = Term
+
+    def __init__(self, env, engine, parser, **kwargs) -> None:
+        super().__init__(env, engine, parser)
+        for bin_op in self.binary_ops:
+            bin_node = self.binary_op_nodes_map[bin_op]
+            setattr(
+                self,
+                f"visit_{bin_node}",
+                lambda node, bin_op=bin_op: partial(BinOp, bin_op, **kwargs),
+            )
+
+    def visit_UnaryOp(self, node, **kwargs) -> ops.Term | UnaryOp | None:
+        if isinstance(node.op, (ast.Not, ast.Invert)):
+            return UnaryOp("~", self.visit(node.operand))
+        elif isinstance(node.op, ast.USub):
+            return self.const_type(-self.visit(node.operand).value, self.env)
+        elif isinstance(node.op, ast.UAdd):
+            raise NotImplementedError("Unary addition not supported")
+        # TODO: return None might never be reached
+        return None
+
+    def visit_Index(self, node, **kwargs):
+        return self.visit(node.value).value
+
+    def visit_Assign(self, node, **kwargs):
+        cmpr = ast.Compare(
+            ops=[ast.Eq()], left=node.targets[0], comparators=[node.value]
+        )
+        return self.visit(cmpr)
+
+    def visit_Subscript(self, node, **kwargs) -> ops.Term:
+        # only allow simple subscripts
+
+        value = self.visit(node.value)
+        slobj = self.visit(node.slice)
+        try:
+            value = value.value
+        except AttributeError:
+            pass
+
+        if isinstance(slobj, Term):
+            # In py39 np.ndarray lookups with Term containing int raise
+            slobj = slobj.value
+
+        try:
+            return self.const_type(value[slobj], self.env)
+        except TypeError as err:
+            raise ValueError(
+                f"cannot subscript {repr(value)} with {repr(slobj)}"
+            ) from err
+
+    def visit_Attribute(self, node, **kwargs):
+        attr = node.attr
+        value = node.value
+
+        ctx = type(node.ctx)
+        if ctx == ast.Load:
+            # resolve the value
+            resolved = self.visit(value)
+
+            # try to get the value to see if we are another expression
+            try:
+                resolved = resolved.value
+            except AttributeError:
+                pass
+
+            try:
+                return self.term_type(getattr(resolved, attr), self.env)
+            except AttributeError:
+                # something like datetime.datetime where scope is overridden
+                if isinstance(value, ast.Name) and value.id == attr:
+                    return resolved
+
+        raise ValueError(f"Invalid Attribute context {ctx.__name__}")
+
+    def translate_In(self, op):
+        return ast.Eq() if isinstance(op, ast.In) else op
+
+    def _rewrite_membership_op(self, node, left, right):
+        return self.visit(node.op), node.op, left, right
+
+
+def _validate_where(w):
+    """
+    Validate that the where statement is of the right type.
+
+    The type may either be String, Expr, or list-like of Exprs.
+
+    Parameters
+    ----------
+    w : String term expression, Expr, or list-like of Exprs.
+
+    Returns
+    -------
+    where : The original where clause if the check was successful.
+
+    Raises
+    ------
+    TypeError : An invalid data type was passed in for w (e.g. dict).
+    """
+    if not (isinstance(w, (PyTablesExpr, str)) or is_list_like(w)):
+        raise TypeError(
+            "where must be passed as a string, PyTablesExpr, "
+            "or list-like of PyTablesExpr"
+        )
+
+    return w
+
+
+class PyTablesExpr(expr.Expr):
+    """
+    Hold a pytables-like expression, comprised of possibly multiple 'terms'.
+
+    Parameters
+    ----------
+    where : string term expression, PyTablesExpr, or list-like of PyTablesExprs
+    queryables : a "kinds" map (dict of column name -> kind), or None if column
+        is non-indexable
+    encoding : an encoding that will encode the query terms
+
+    Returns
+    -------
+    a PyTablesExpr object
+
+    Examples
+    --------
+    'index>=date'
+    "columns=['A', 'D']"
+    'columns=A'
+    'columns==A'
+    "~(columns=['A','B'])"
+    'index>df.index[3] & string="bar"'
+    '(index>df.index[3] & index<=df.index[6]) | string="bar"'
+    "ts>=Timestamp('2012-02-01')"
+    "major_axis>=20130101"
+    """
+
+    _visitor: PyTablesExprVisitor | None
+    env: PyTablesScope
+    expr: str
+
+    def __init__(
+        self,
+        where,
+        queryables: dict[str, Any] | None = None,
+        encoding=None,
+        scope_level: int = 0,
+    ) -> None:
+        where = _validate_where(where)
+
+        self.encoding = encoding
+        self.condition = None
+        self.filter = None
+        self.terms = None
+        self._visitor = None
+
+        # capture the environment if needed
+        local_dict: _scope.DeepChainMap[Any, Any] | None = None
+
+        if isinstance(where, PyTablesExpr):
+            local_dict = where.env.scope
+            _where = where.expr
+
+        elif is_list_like(where):
+            where = list(where)
+            for idx, w in enumerate(where):
+                if isinstance(w, PyTablesExpr):
+                    local_dict = w.env.scope
+                else:
+                    where[idx] = _validate_where(w)
+            _where = " & ".join([f"({w})" for w in com.flatten(where)])
+        else:
+            # _validate_where ensures we otherwise have a string
+            _where = where
+
+        self.expr = _where
+        self.env = PyTablesScope(scope_level + 1, local_dict=local_dict)
+
+        if queryables is not None and isinstance(self.expr, str):
+            self.env.queryables.update(queryables)
+            self._visitor = PyTablesExprVisitor(
+                self.env,
+                queryables=queryables,
+                parser="pytables",
+                engine="pytables",
+                encoding=encoding,
+            )
+            self.terms = self.parse()
+
+    def __repr__(self) -> str:
+        if self.terms is not None:
+            return pprint_thing(self.terms)
+        return pprint_thing(self.expr)
+
+    def evaluate(self):
+        """create and return the numexpr condition and filter"""
+        try:
+            self.condition = self.terms.prune(ConditionBinOp)
+        except AttributeError as err:
+            raise ValueError(
+                f"cannot process expression [{self.expr}], [{self}] "
+                "is not a valid condition"
+            ) from err
+        try:
+            self.filter = self.terms.prune(FilterBinOp)
+        except AttributeError as err:
+            raise ValueError(
+                f"cannot process expression [{self.expr}], [{self}] "
+                "is not a valid filter"
+            ) from err
+
+        return self.condition, self.filter
+
+
+class TermValue:
+    """hold a term value the we use to construct a condition/filter"""
+
+    def __init__(self, value, converted, kind: str) -> None:
+        assert isinstance(kind, str), kind
+        self.value = value
+        self.converted = converted
+        self.kind = kind
+
+    def tostring(self, encoding) -> str:
+        """quote the string if not encoded else encode and return"""
+        if self.kind == "string":
+            if encoding is not None:
+                return str(self.converted)
+            return f'"{self.converted}"'
+        elif self.kind == "float":
+            # python 2 str(float) is not always
+            # round-trippable so use repr()
+            return repr(self.converted)
+        return str(self.converted)
+
+
+def maybe_expression(s) -> bool:
+    """loose checking if s is a pytables-acceptable expression"""
+    if not isinstance(s, str):
+        return False
+    operations = PyTablesExprVisitor.binary_ops + PyTablesExprVisitor.unary_ops + ("=",)
+
+    # make sure we have an op at least
+    return any(op in s for op in operations)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/computation/scope.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/computation/scope.py
new file mode 100644
index 0000000000000000000000000000000000000000..7e553ca448218435eafe1fd7ca97dce6f739e2a3
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/computation/scope.py
@@ -0,0 +1,355 @@
+"""
+Module for scope operations
+"""
+from __future__ import annotations
+
+from collections import ChainMap
+import datetime
+import inspect
+from io import StringIO
+import itertools
+import pprint
+import struct
+import sys
+from typing import TypeVar
+
+import numpy as np
+
+from pandas._libs.tslibs import Timestamp
+from pandas.errors import UndefinedVariableError
+
+_KT = TypeVar("_KT")
+_VT = TypeVar("_VT")
+
+
+# https://docs.python.org/3/library/collections.html#chainmap-examples-and-recipes
+class DeepChainMap(ChainMap[_KT, _VT]):
+    """
+    Variant of ChainMap that allows direct updates to inner scopes.
+
+    Only works when all passed mapping are mutable.
+    """
+
+    def __setitem__(self, key: _KT, value: _VT) -> None:
+        for mapping in self.maps:
+            if key in mapping:
+                mapping[key] = value
+                return
+        self.maps[0][key] = value
+
+    def __delitem__(self, key: _KT) -> None:
+        """
+        Raises
+        ------
+        KeyError
+            If `key` doesn't exist.
+        """
+        for mapping in self.maps:
+            if key in mapping:
+                del mapping[key]
+                return
+        raise KeyError(key)
+
+
+def ensure_scope(
+    level: int, global_dict=None, local_dict=None, resolvers=(), target=None
+) -> Scope:
+    """Ensure that we are grabbing the correct scope."""
+    return Scope(
+        level + 1,
+        global_dict=global_dict,
+        local_dict=local_dict,
+        resolvers=resolvers,
+        target=target,
+    )
+
+
+def _replacer(x) -> str:
+    """
+    Replace a number with its hexadecimal representation. Used to tag
+    temporary variables with their calling scope's id.
+    """
+    # get the hex repr of the binary char and remove 0x and pad by pad_size
+    # zeros
+    try:
+        hexin = ord(x)
+    except TypeError:
+        # bytes literals masquerade as ints when iterating in py3
+        hexin = x
+
+    return hex(hexin)
+
+
+def _raw_hex_id(obj) -> str:
+    """Return the padded hexadecimal id of ``obj``."""
+    # interpret as a pointer since that's what really what id returns
+    packed = struct.pack("@P", id(obj))
+    return "".join([_replacer(x) for x in packed])
+
+
+DEFAULT_GLOBALS = {
+    "Timestamp": Timestamp,
+    "datetime": datetime.datetime,
+    "True": True,
+    "False": False,
+    "list": list,
+    "tuple": tuple,
+    "inf": np.inf,
+    "Inf": np.inf,
+}
+
+
+def _get_pretty_string(obj) -> str:
+    """
+    Return a prettier version of obj.
+
+    Parameters
+    ----------
+    obj : object
+        Object to pretty print
+
+    Returns
+    -------
+    str
+        Pretty print object repr
+    """
+    sio = StringIO()
+    pprint.pprint(obj, stream=sio)
+    return sio.getvalue()
+
+
+class Scope:
+    """
+    Object to hold scope, with a few bells to deal with some custom syntax
+    and contexts added by pandas.
+
+    Parameters
+    ----------
+    level : int
+    global_dict : dict or None, optional, default None
+    local_dict : dict or Scope or None, optional, default None
+    resolvers : list-like or None, optional, default None
+    target : object
+
+    Attributes
+    ----------
+    level : int
+    scope : DeepChainMap
+    target : object
+    temps : dict
+    """
+
+    __slots__ = ["level", "scope", "target", "resolvers", "temps"]
+    level: int
+    scope: DeepChainMap
+    resolvers: DeepChainMap
+    temps: dict
+
+    def __init__(
+        self, level: int, global_dict=None, local_dict=None, resolvers=(), target=None
+    ) -> None:
+        self.level = level + 1
+
+        # shallow copy because we don't want to keep filling this up with what
+        # was there before if there are multiple calls to Scope/_ensure_scope
+        self.scope = DeepChainMap(DEFAULT_GLOBALS.copy())
+        self.target = target
+
+        if isinstance(local_dict, Scope):
+            self.scope.update(local_dict.scope)
+            if local_dict.target is not None:
+                self.target = local_dict.target
+            self._update(local_dict.level)
+
+        frame = sys._getframe(self.level)
+
+        try:
+            # shallow copy here because we don't want to replace what's in
+            # scope when we align terms (alignment accesses the underlying
+            # numpy array of pandas objects)
+            scope_global = self.scope.new_child(
+                (global_dict if global_dict is not None else frame.f_globals).copy()
+            )
+            self.scope = DeepChainMap(scope_global)
+            if not isinstance(local_dict, Scope):
+                scope_local = self.scope.new_child(
+                    (local_dict if local_dict is not None else frame.f_locals).copy()
+                )
+                self.scope = DeepChainMap(scope_local)
+        finally:
+            del frame
+
+        # assumes that resolvers are going from outermost scope to inner
+        if isinstance(local_dict, Scope):
+            resolvers += tuple(local_dict.resolvers.maps)
+        self.resolvers = DeepChainMap(*resolvers)
+        self.temps = {}
+
+    def __repr__(self) -> str:
+        scope_keys = _get_pretty_string(list(self.scope.keys()))
+        res_keys = _get_pretty_string(list(self.resolvers.keys()))
+        return f"{type(self).__name__}(scope={scope_keys}, resolvers={res_keys})"
+
+    @property
+    def has_resolvers(self) -> bool:
+        """
+        Return whether we have any extra scope.
+
+        For example, DataFrames pass Their columns as resolvers during calls to
+        ``DataFrame.eval()`` and ``DataFrame.query()``.
+
+        Returns
+        -------
+        hr : bool
+        """
+        return bool(len(self.resolvers))
+
+    def resolve(self, key: str, is_local: bool):
+        """
+        Resolve a variable name in a possibly local context.
+
+        Parameters
+        ----------
+        key : str
+            A variable name
+        is_local : bool
+            Flag indicating whether the variable is local or not (prefixed with
+            the '@' symbol)
+
+        Returns
+        -------
+        value : object
+            The value of a particular variable
+        """
+        try:
+            # only look for locals in outer scope
+            if is_local:
+                return self.scope[key]
+
+            # not a local variable so check in resolvers if we have them
+            if self.has_resolvers:
+                return self.resolvers[key]
+
+            # if we're here that means that we have no locals and we also have
+            # no resolvers
+            assert not is_local and not self.has_resolvers
+            return self.scope[key]
+        except KeyError:
+            try:
+                # last ditch effort we look in temporaries
+                # these are created when parsing indexing expressions
+                # e.g., df[df > 0]
+                return self.temps[key]
+            except KeyError as err:
+                raise UndefinedVariableError(key, is_local) from err
+
+    def swapkey(self, old_key: str, new_key: str, new_value=None) -> None:
+        """
+        Replace a variable name, with a potentially new value.
+
+        Parameters
+        ----------
+        old_key : str
+            Current variable name to replace
+        new_key : str
+            New variable name to replace `old_key` with
+        new_value : object
+            Value to be replaced along with the possible renaming
+        """
+        if self.has_resolvers:
+            maps = self.resolvers.maps + self.scope.maps
+        else:
+            maps = self.scope.maps
+
+        maps.append(self.temps)
+
+        for mapping in maps:
+            if old_key in mapping:
+                mapping[new_key] = new_value
+                return
+
+    def _get_vars(self, stack, scopes: list[str]) -> None:
+        """
+        Get specifically scoped variables from a list of stack frames.
+
+        Parameters
+        ----------
+        stack : list
+            A list of stack frames as returned by ``inspect.stack()``
+        scopes : sequence of strings
+            A sequence containing valid stack frame attribute names that
+            evaluate to a dictionary. For example, ('locals', 'globals')
+        """
+        variables = itertools.product(scopes, stack)
+        for scope, (frame, _, _, _, _, _) in variables:
+            try:
+                d = getattr(frame, f"f_{scope}")
+                self.scope = DeepChainMap(self.scope.new_child(d))
+            finally:
+                # won't remove it, but DECREF it
+                # in Py3 this probably isn't necessary since frame won't be
+                # scope after the loop
+                del frame
+
+    def _update(self, level: int) -> None:
+        """
+        Update the current scope by going back `level` levels.
+
+        Parameters
+        ----------
+        level : int
+        """
+        sl = level + 1
+
+        # add sl frames to the scope starting with the
+        # most distant and overwriting with more current
+        # makes sure that we can capture variable scope
+        stack = inspect.stack()
+
+        try:
+            self._get_vars(stack[:sl], scopes=["locals"])
+        finally:
+            del stack[:], stack
+
+    def add_tmp(self, value) -> str:
+        """
+        Add a temporary variable to the scope.
+
+        Parameters
+        ----------
+        value : object
+            An arbitrary object to be assigned to a temporary variable.
+
+        Returns
+        -------
+        str
+            The name of the temporary variable created.
+        """
+        name = f"{type(value).__name__}_{self.ntemps}_{_raw_hex_id(self)}"
+
+        # add to inner most scope
+        assert name not in self.temps
+        self.temps[name] = value
+        assert name in self.temps
+
+        # only increment if the variable gets put in the scope
+        return name
+
+    @property
+    def ntemps(self) -> int:
+        """The number of temporary variables in this scope"""
+        return len(self.temps)
+
+    @property
+    def full_scope(self) -> DeepChainMap:
+        """
+        Return the full scope for use with passing to engines transparently
+        as a mapping.
+
+        Returns
+        -------
+        vars : DeepChainMap
+            All variables in this scope.
+        """
+        maps = [self.temps] + self.resolvers.maps + self.scope.maps
+        return DeepChainMap(*maps)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/config_init.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/config_init.py
new file mode 100644
index 0000000000000000000000000000000000000000..a1df455eebacf8868384ad383ee34c934d059df3
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/config_init.py
@@ -0,0 +1,941 @@
+"""
+This module is imported from the pandas package __init__.py file
+in order to ensure that the core.config options registered here will
+be available as soon as the user loads the package. if register_option
+is invoked inside specific modules, they will not be registered until that
+module is imported, which may or may not be a problem.
+
+If you need to make sure options are available even before a certain
+module is imported, register them here rather than in the module.
+
+"""
+from __future__ import annotations
+
+import os
+from typing import (
+    Any,
+    Callable,
+)
+
+import pandas._config.config as cf
+from pandas._config.config import (
+    is_bool,
+    is_callable,
+    is_instance_factory,
+    is_int,
+    is_nonnegative_int,
+    is_one_of_factory,
+    is_str,
+    is_text,
+)
+
+# compute
+
+use_bottleneck_doc = """
+: bool
+    Use the bottleneck library to accelerate if it is installed,
+    the default is True
+    Valid values: False,True
+"""
+
+
+def use_bottleneck_cb(key) -> None:
+    from pandas.core import nanops
+
+    nanops.set_use_bottleneck(cf.get_option(key))
+
+
+use_numexpr_doc = """
+: bool
+    Use the numexpr library to accelerate computation if it is installed,
+    the default is True
+    Valid values: False,True
+"""
+
+
+def use_numexpr_cb(key) -> None:
+    from pandas.core.computation import expressions
+
+    expressions.set_use_numexpr(cf.get_option(key))
+
+
+use_numba_doc = """
+: bool
+    Use the numba engine option for select operations if it is installed,
+    the default is False
+    Valid values: False,True
+"""
+
+
+def use_numba_cb(key) -> None:
+    from pandas.core.util import numba_
+
+    numba_.set_use_numba(cf.get_option(key))
+
+
+with cf.config_prefix("compute"):
+    cf.register_option(
+        "use_bottleneck",
+        True,
+        use_bottleneck_doc,
+        validator=is_bool,
+        cb=use_bottleneck_cb,
+    )
+    cf.register_option(
+        "use_numexpr", True, use_numexpr_doc, validator=is_bool, cb=use_numexpr_cb
+    )
+    cf.register_option(
+        "use_numba", False, use_numba_doc, validator=is_bool, cb=use_numba_cb
+    )
+#
+# options from the "display" namespace
+
+pc_precision_doc = """
+: int
+    Floating point output precision in terms of number of places after the
+    decimal, for regular formatting as well as scientific notation. Similar
+    to ``precision`` in :meth:`numpy.set_printoptions`.
+"""
+
+pc_colspace_doc = """
+: int
+    Default space for DataFrame columns.
+"""
+
+pc_max_rows_doc = """
+: int
+    If max_rows is exceeded, switch to truncate view. Depending on
+    `large_repr`, objects are either centrally truncated or printed as
+    a summary view. 'None' value means unlimited.
+
+    In case python/IPython is running in a terminal and `large_repr`
+    equals 'truncate' this can be set to 0 and pandas will auto-detect
+    the height of the terminal and print a truncated object which fits
+    the screen height. The IPython notebook, IPython qtconsole, or
+    IDLE do not run in a terminal and hence it is not possible to do
+    correct auto-detection.
+"""
+
+pc_min_rows_doc = """
+: int
+    The numbers of rows to show in a truncated view (when `max_rows` is
+    exceeded). Ignored when `max_rows` is set to None or 0. When set to
+    None, follows the value of `max_rows`.
+"""
+
+pc_max_cols_doc = """
+: int
+    If max_cols is exceeded, switch to truncate view. Depending on
+    `large_repr`, objects are either centrally truncated or printed as
+    a summary view. 'None' value means unlimited.
+
+    In case python/IPython is running in a terminal and `large_repr`
+    equals 'truncate' this can be set to 0 or None and pandas will auto-detect
+    the width of the terminal and print a truncated object which fits
+    the screen width. The IPython notebook, IPython qtconsole, or IDLE
+    do not run in a terminal and hence it is not possible to do
+    correct auto-detection and defaults to 20.
+"""
+
+pc_max_categories_doc = """
+: int
+    This sets the maximum number of categories pandas should output when
+    printing out a `Categorical` or a Series of dtype "category".
+"""
+
+pc_max_info_cols_doc = """
+: int
+    max_info_columns is used in DataFrame.info method to decide if
+    per column information will be printed.
+"""
+
+pc_nb_repr_h_doc = """
+: boolean
+    When True, IPython notebook will use html representation for
+    pandas objects (if it is available).
+"""
+
+pc_pprint_nest_depth = """
+: int
+    Controls the number of nested levels to process when pretty-printing
+"""
+
+pc_multi_sparse_doc = """
+: boolean
+    "sparsify" MultiIndex display (don't display repeated
+    elements in outer levels within groups)
+"""
+
+float_format_doc = """
+: callable
+    The callable should accept a floating point number and return
+    a string with the desired format of the number. This is used
+    in some places like SeriesFormatter.
+    See formats.format.EngFormatter for an example.
+"""
+
+max_colwidth_doc = """
+: int or None
+    The maximum width in characters of a column in the repr of
+    a pandas data structure. When the column overflows, a "..."
+    placeholder is embedded in the output. A 'None' value means unlimited.
+"""
+
+colheader_justify_doc = """
+: 'left'/'right'
+    Controls the justification of column headers. used by DataFrameFormatter.
+"""
+
+pc_expand_repr_doc = """
+: boolean
+    Whether to print out the full DataFrame repr for wide DataFrames across
+    multiple lines, `max_columns` is still respected, but the output will
+    wrap-around across multiple "pages" if its width exceeds `display.width`.
+"""
+
+pc_show_dimensions_doc = """
+: boolean or 'truncate'
+    Whether to print out dimensions at the end of DataFrame repr.
+    If 'truncate' is specified, only print out the dimensions if the
+    frame is truncated (e.g. not display all rows and/or columns)
+"""
+
+pc_east_asian_width_doc = """
+: boolean
+    Whether to use the Unicode East Asian Width to calculate the display text
+    width.
+    Enabling this may affect to the performance (default: False)
+"""
+
+pc_ambiguous_as_wide_doc = """
+: boolean
+    Whether to handle Unicode characters belong to Ambiguous as Wide (width=2)
+    (default: False)
+"""
+
+pc_table_schema_doc = """
+: boolean
+    Whether to publish a Table Schema representation for frontends
+    that support it.
+    (default: False)
+"""
+
+pc_html_border_doc = """
+: int
+    A ``border=value`` attribute is inserted in the ```` tag
+    for the DataFrame HTML repr.
+"""
+
+pc_html_use_mathjax_doc = """\
+: boolean
+    When True, Jupyter notebook will process table contents using MathJax,
+    rendering mathematical expressions enclosed by the dollar symbol.
+    (default: True)
+"""
+
+pc_max_dir_items = """\
+: int
+    The number of items that will be added to `dir(...)`. 'None' value means
+    unlimited. Because dir is cached, changing this option will not immediately
+    affect already existing dataframes until a column is deleted or added.
+
+    This is for instance used to suggest columns from a dataframe to tab
+    completion.
+"""
+
+pc_width_doc = """
+: int
+    Width of the display in characters. In case python/IPython is running in
+    a terminal this can be set to None and pandas will correctly auto-detect
+    the width.
+    Note that the IPython notebook, IPython qtconsole, or IDLE do not run in a
+    terminal and hence it is not possible to correctly detect the width.
+"""
+
+pc_chop_threshold_doc = """
+: float or None
+    if set to a float value, all float values smaller than the given threshold
+    will be displayed as exactly 0 by repr and friends.
+"""
+
+pc_max_seq_items = """
+: int or None
+    When pretty-printing a long sequence, no more then `max_seq_items`
+    will be printed. If items are omitted, they will be denoted by the
+    addition of "..." to the resulting string.
+
+    If set to None, the number of items to be printed is unlimited.
+"""
+
+pc_max_info_rows_doc = """
+: int
+    df.info() will usually show null-counts for each column.
+    For large frames this can be quite slow. max_info_rows and max_info_cols
+    limit this null check only to frames with smaller dimensions than
+    specified.
+"""
+
+pc_large_repr_doc = """
+: 'truncate'/'info'
+    For DataFrames exceeding max_rows/max_cols, the repr (and HTML repr) can
+    show a truncated table, or switch to the view from
+    df.info() (the behaviour in earlier versions of pandas).
+"""
+
+pc_memory_usage_doc = """
+: bool, string or None
+    This specifies if the memory usage of a DataFrame should be displayed when
+    df.info() is called. Valid values True,False,'deep'
+"""
+
+
+def table_schema_cb(key) -> None:
+    from pandas.io.formats.printing import enable_data_resource_formatter
+
+    enable_data_resource_formatter(cf.get_option(key))
+
+
+def is_terminal() -> bool:
+    """
+    Detect if Python is running in a terminal.
+
+    Returns True if Python is running in a terminal or False if not.
+    """
+    try:
+        # error: Name 'get_ipython' is not defined
+        ip = get_ipython()  # type: ignore[name-defined]
+    except NameError:  # assume standard Python interpreter in a terminal
+        return True
+    else:
+        if hasattr(ip, "kernel"):  # IPython as a Jupyter kernel
+            return False
+        else:  # IPython in a terminal
+            return True
+
+
+with cf.config_prefix("display"):
+    cf.register_option("precision", 6, pc_precision_doc, validator=is_nonnegative_int)
+    cf.register_option(
+        "float_format",
+        None,
+        float_format_doc,
+        validator=is_one_of_factory([None, is_callable]),
+    )
+    cf.register_option(
+        "max_info_rows",
+        1690785,
+        pc_max_info_rows_doc,
+        validator=is_int,
+    )
+    cf.register_option("max_rows", 60, pc_max_rows_doc, validator=is_nonnegative_int)
+    cf.register_option(
+        "min_rows",
+        10,
+        pc_min_rows_doc,
+        validator=is_instance_factory([type(None), int]),
+    )
+    cf.register_option("max_categories", 8, pc_max_categories_doc, validator=is_int)
+
+    cf.register_option(
+        "max_colwidth",
+        50,
+        max_colwidth_doc,
+        validator=is_nonnegative_int,
+    )
+    if is_terminal():
+        max_cols = 0  # automatically determine optimal number of columns
+    else:
+        max_cols = 20  # cannot determine optimal number of columns
+    cf.register_option(
+        "max_columns", max_cols, pc_max_cols_doc, validator=is_nonnegative_int
+    )
+    cf.register_option(
+        "large_repr",
+        "truncate",
+        pc_large_repr_doc,
+        validator=is_one_of_factory(["truncate", "info"]),
+    )
+    cf.register_option("max_info_columns", 100, pc_max_info_cols_doc, validator=is_int)
+    cf.register_option(
+        "colheader_justify", "right", colheader_justify_doc, validator=is_text
+    )
+    cf.register_option("notebook_repr_html", True, pc_nb_repr_h_doc, validator=is_bool)
+    cf.register_option("pprint_nest_depth", 3, pc_pprint_nest_depth, validator=is_int)
+    cf.register_option("multi_sparse", True, pc_multi_sparse_doc, validator=is_bool)
+    cf.register_option("expand_frame_repr", True, pc_expand_repr_doc)
+    cf.register_option(
+        "show_dimensions",
+        "truncate",
+        pc_show_dimensions_doc,
+        validator=is_one_of_factory([True, False, "truncate"]),
+    )
+    cf.register_option("chop_threshold", None, pc_chop_threshold_doc)
+    cf.register_option("max_seq_items", 100, pc_max_seq_items)
+    cf.register_option(
+        "width", 80, pc_width_doc, validator=is_instance_factory([type(None), int])
+    )
+    cf.register_option(
+        "memory_usage",
+        True,
+        pc_memory_usage_doc,
+        validator=is_one_of_factory([None, True, False, "deep"]),
+    )
+    cf.register_option(
+        "unicode.east_asian_width", False, pc_east_asian_width_doc, validator=is_bool
+    )
+    cf.register_option(
+        "unicode.ambiguous_as_wide", False, pc_east_asian_width_doc, validator=is_bool
+    )
+    cf.register_option(
+        "html.table_schema",
+        False,
+        pc_table_schema_doc,
+        validator=is_bool,
+        cb=table_schema_cb,
+    )
+    cf.register_option("html.border", 1, pc_html_border_doc, validator=is_int)
+    cf.register_option(
+        "html.use_mathjax", True, pc_html_use_mathjax_doc, validator=is_bool
+    )
+    cf.register_option(
+        "max_dir_items", 100, pc_max_dir_items, validator=is_nonnegative_int
+    )
+
+tc_sim_interactive_doc = """
+: boolean
+    Whether to simulate interactive mode for purposes of testing
+"""
+
+with cf.config_prefix("mode"):
+    cf.register_option("sim_interactive", False, tc_sim_interactive_doc)
+
+use_inf_as_na_doc = """
+: boolean
+    True means treat None, NaN, INF, -INF as NA (old way),
+    False means None and NaN are null, but INF, -INF are not NA
+    (new way).
+
+    This option is deprecated in pandas 2.1.0 and will be removed in 3.0.
+"""
+
+# We don't want to start importing everything at the global context level
+# or we'll hit circular deps.
+
+
+def use_inf_as_na_cb(key) -> None:
+    # TODO(3.0): enforcing this deprecation will close GH#52501
+    from pandas.core.dtypes.missing import _use_inf_as_na
+
+    _use_inf_as_na(key)
+
+
+with cf.config_prefix("mode"):
+    cf.register_option("use_inf_as_na", False, use_inf_as_na_doc, cb=use_inf_as_na_cb)
+
+cf.deprecate_option(
+    # GH#51684
+    "mode.use_inf_as_na",
+    "use_inf_as_na option is deprecated and will be removed in a future "
+    "version. Convert inf values to NaN before operating instead.",
+)
+
+data_manager_doc = """
+: string
+    Internal data manager type; can be "block" or "array". Defaults to "block",
+    unless overridden by the 'PANDAS_DATA_MANAGER' environment variable (needs
+    to be set before pandas is imported).
+"""
+
+
+with cf.config_prefix("mode"):
+    cf.register_option(
+        "data_manager",
+        # Get the default from an environment variable, if set, otherwise defaults
+        # to "block". This environment variable can be set for testing.
+        os.environ.get("PANDAS_DATA_MANAGER", "block"),
+        data_manager_doc,
+        validator=is_one_of_factory(["block", "array"]),
+    )
+
+cf.deprecate_option(
+    # GH#55043
+    "mode.data_manager",
+    "data_manager option is deprecated and will be removed in a future "
+    "version. Only the BlockManager will be available.",
+)
+
+
+# TODO better name?
+copy_on_write_doc = """
+: bool
+    Use new copy-view behaviour using Copy-on-Write. Defaults to False,
+    unless overridden by the 'PANDAS_COPY_ON_WRITE' environment variable
+    (if set to "1" for True, needs to be set before pandas is imported).
+"""
+
+
+with cf.config_prefix("mode"):
+    cf.register_option(
+        "copy_on_write",
+        # Get the default from an environment variable, if set, otherwise defaults
+        # to False. This environment variable can be set for testing.
+        "warn"
+        if os.environ.get("PANDAS_COPY_ON_WRITE", "0") == "warn"
+        else os.environ.get("PANDAS_COPY_ON_WRITE", "0") == "1",
+        copy_on_write_doc,
+        validator=is_one_of_factory([True, False, "warn"]),
+    )
+
+
+# user warnings
+chained_assignment = """
+: string
+    Raise an exception, warn, or no action if trying to use chained assignment,
+    The default is warn
+"""
+
+with cf.config_prefix("mode"):
+    cf.register_option(
+        "chained_assignment",
+        "warn",
+        chained_assignment,
+        validator=is_one_of_factory([None, "warn", "raise"]),
+    )
+
+
+string_storage_doc = """
+: string
+    The default storage for StringDtype.
+"""
+
+
+def is_valid_string_storage(value: Any) -> None:
+    legal_values = ["auto", "python", "pyarrow"]
+    if value not in legal_values:
+        msg = "Value must be one of python|pyarrow"
+        if value == "pyarrow_numpy":
+            # TODO: we can remove extra message after 3.0
+            msg += (
+                ". 'pyarrow_numpy' was specified, but this option should be "
+                "enabled using pandas.options.future.infer_string instead"
+            )
+        raise ValueError(msg)
+
+
+with cf.config_prefix("mode"):
+    cf.register_option(
+        "string_storage",
+        "auto",
+        string_storage_doc,
+        # validator=is_one_of_factory(["python", "pyarrow"]),
+        validator=is_valid_string_storage,
+    )
+
+
+# Set up the io.excel specific reader configuration.
+reader_engine_doc = """
+: string
+    The default Excel reader engine for '{ext}' files. Available options:
+    auto, {others}.
+"""
+
+_xls_options = ["xlrd", "calamine"]
+_xlsm_options = ["xlrd", "openpyxl", "calamine"]
+_xlsx_options = ["xlrd", "openpyxl", "calamine"]
+_ods_options = ["odf", "calamine"]
+_xlsb_options = ["pyxlsb", "calamine"]
+
+
+with cf.config_prefix("io.excel.xls"):
+    cf.register_option(
+        "reader",
+        "auto",
+        reader_engine_doc.format(ext="xls", others=", ".join(_xls_options)),
+        validator=is_one_of_factory(_xls_options + ["auto"]),
+    )
+
+with cf.config_prefix("io.excel.xlsm"):
+    cf.register_option(
+        "reader",
+        "auto",
+        reader_engine_doc.format(ext="xlsm", others=", ".join(_xlsm_options)),
+        validator=is_one_of_factory(_xlsm_options + ["auto"]),
+    )
+
+
+with cf.config_prefix("io.excel.xlsx"):
+    cf.register_option(
+        "reader",
+        "auto",
+        reader_engine_doc.format(ext="xlsx", others=", ".join(_xlsx_options)),
+        validator=is_one_of_factory(_xlsx_options + ["auto"]),
+    )
+
+
+with cf.config_prefix("io.excel.ods"):
+    cf.register_option(
+        "reader",
+        "auto",
+        reader_engine_doc.format(ext="ods", others=", ".join(_ods_options)),
+        validator=is_one_of_factory(_ods_options + ["auto"]),
+    )
+
+with cf.config_prefix("io.excel.xlsb"):
+    cf.register_option(
+        "reader",
+        "auto",
+        reader_engine_doc.format(ext="xlsb", others=", ".join(_xlsb_options)),
+        validator=is_one_of_factory(_xlsb_options + ["auto"]),
+    )
+
+# Set up the io.excel specific writer configuration.
+writer_engine_doc = """
+: string
+    The default Excel writer engine for '{ext}' files. Available options:
+    auto, {others}.
+"""
+
+_xlsm_options = ["openpyxl"]
+_xlsx_options = ["openpyxl", "xlsxwriter"]
+_ods_options = ["odf"]
+
+
+with cf.config_prefix("io.excel.xlsm"):
+    cf.register_option(
+        "writer",
+        "auto",
+        writer_engine_doc.format(ext="xlsm", others=", ".join(_xlsm_options)),
+        validator=str,
+    )
+
+
+with cf.config_prefix("io.excel.xlsx"):
+    cf.register_option(
+        "writer",
+        "auto",
+        writer_engine_doc.format(ext="xlsx", others=", ".join(_xlsx_options)),
+        validator=str,
+    )
+
+
+with cf.config_prefix("io.excel.ods"):
+    cf.register_option(
+        "writer",
+        "auto",
+        writer_engine_doc.format(ext="ods", others=", ".join(_ods_options)),
+        validator=str,
+    )
+
+
+# Set up the io.parquet specific configuration.
+parquet_engine_doc = """
+: string
+    The default parquet reader/writer engine. Available options:
+    'auto', 'pyarrow', 'fastparquet', the default is 'auto'
+"""
+
+with cf.config_prefix("io.parquet"):
+    cf.register_option(
+        "engine",
+        "auto",
+        parquet_engine_doc,
+        validator=is_one_of_factory(["auto", "pyarrow", "fastparquet"]),
+    )
+
+
+# Set up the io.sql specific configuration.
+sql_engine_doc = """
+: string
+    The default sql reader/writer engine. Available options:
+    'auto', 'sqlalchemy', the default is 'auto'
+"""
+
+with cf.config_prefix("io.sql"):
+    cf.register_option(
+        "engine",
+        "auto",
+        sql_engine_doc,
+        validator=is_one_of_factory(["auto", "sqlalchemy"]),
+    )
+
+# --------
+# Plotting
+# ---------
+
+plotting_backend_doc = """
+: str
+    The plotting backend to use. The default value is "matplotlib", the
+    backend provided with pandas. Other backends can be specified by
+    providing the name of the module that implements the backend.
+"""
+
+
+def register_plotting_backend_cb(key) -> None:
+    if key == "matplotlib":
+        # We defer matplotlib validation, since it's the default
+        return
+    from pandas.plotting._core import _get_plot_backend
+
+    _get_plot_backend(key)
+
+
+with cf.config_prefix("plotting"):
+    cf.register_option(
+        "backend",
+        defval="matplotlib",
+        doc=plotting_backend_doc,
+        validator=register_plotting_backend_cb,
+    )
+
+
+register_converter_doc = """
+: bool or 'auto'.
+    Whether to register converters with matplotlib's units registry for
+    dates, times, datetimes, and Periods. Toggling to False will remove
+    the converters, restoring any converters that pandas overwrote.
+"""
+
+
+def register_converter_cb(key) -> None:
+    from pandas.plotting import (
+        deregister_matplotlib_converters,
+        register_matplotlib_converters,
+    )
+
+    if cf.get_option(key):
+        register_matplotlib_converters()
+    else:
+        deregister_matplotlib_converters()
+
+
+with cf.config_prefix("plotting.matplotlib"):
+    cf.register_option(
+        "register_converters",
+        "auto",
+        register_converter_doc,
+        validator=is_one_of_factory(["auto", True, False]),
+        cb=register_converter_cb,
+    )
+
+# ------
+# Styler
+# ------
+
+styler_sparse_index_doc = """
+: bool
+    Whether to sparsify the display of a hierarchical index. Setting to False will
+    display each explicit level element in a hierarchical key for each row.
+"""
+
+styler_sparse_columns_doc = """
+: bool
+    Whether to sparsify the display of hierarchical columns. Setting to False will
+    display each explicit level element in a hierarchical key for each column.
+"""
+
+styler_render_repr = """
+: str
+    Determine which output to use in Jupyter Notebook in {"html", "latex"}.
+"""
+
+styler_max_elements = """
+: int
+    The maximum number of data-cell (", indent)
+
+        if self.fmt.header:
+            self._write_col_header(indent + self.indent_delta)
+
+        if self.show_row_idx_names:
+            self._write_row_header(indent + self.indent_delta)
+
+        self.write("", indent)
+
+    def _get_formatted_values(self) -> dict[int, list[str]]:
+        with option_context("display.max_colwidth", None):
+            fmt_values = {i: self.fmt.format_col(i) for i in range(self.ncols)}
+        return fmt_values
+
+    def _write_body(self, indent: int) -> None:
+        self.write("", indent)
+        fmt_values = self._get_formatted_values()
+
+        # write values
+        if self.fmt.index and isinstance(self.frame.index, MultiIndex):
+            self._write_hierarchical_rows(fmt_values, indent + self.indent_delta)
+        else:
+            self._write_regular_rows(fmt_values, indent + self.indent_delta)
+
+        self.write("", indent)
+
+    def _write_regular_rows(
+        self, fmt_values: Mapping[int, list[str]], indent: int
+    ) -> None:
+        is_truncated_horizontally = self.fmt.is_truncated_horizontally
+        is_truncated_vertically = self.fmt.is_truncated_vertically
+
+        nrows = len(self.fmt.tr_frame)
+
+        if self.fmt.index:
+            fmt = self.fmt._get_formatter("__index__")
+            if fmt is not None:
+                index_values = self.fmt.tr_frame.index.map(fmt)
+            else:
+                # only reached with non-Multi index
+                index_values = self.fmt.tr_frame.index._format_flat(include_name=False)
+
+        row: list[str] = []
+        for i in range(nrows):
+            if is_truncated_vertically and i == (self.fmt.tr_row_num):
+                str_sep_row = ["..."] * len(row)
+                self.write_tr(
+                    str_sep_row,
+                    indent,
+                    self.indent_delta,
+                    tags=None,
+                    nindex_levels=self.row_levels,
+                )
+
+            row = []
+            if self.fmt.index:
+                row.append(index_values[i])
+            # see gh-22579
+            # Column misalignment also occurs for
+            # a standard index when the columns index is named.
+            # Add blank cell before data cells.
+            elif self.show_col_idx_names:
+                row.append("")
+            row.extend(fmt_values[j][i] for j in range(self.ncols))
+
+            if is_truncated_horizontally:
+                dot_col_ix = self.fmt.tr_col_num + self.row_levels
+                row.insert(dot_col_ix, "...")
+            self.write_tr(
+                row, indent, self.indent_delta, tags=None, nindex_levels=self.row_levels
+            )
+
+    def _write_hierarchical_rows(
+        self, fmt_values: Mapping[int, list[str]], indent: int
+    ) -> None:
+        template = 'rowspan="{span}" valign="top"'
+
+        is_truncated_horizontally = self.fmt.is_truncated_horizontally
+        is_truncated_vertically = self.fmt.is_truncated_vertically
+        frame = self.fmt.tr_frame
+        nrows = len(frame)
+
+        assert isinstance(frame.index, MultiIndex)
+        idx_values = frame.index._format_multi(sparsify=False, include_names=False)
+        idx_values = list(zip(*idx_values))
+
+        if self.fmt.sparsify:
+            # GH3547
+            sentinel = lib.no_default
+            levels = frame.index._format_multi(sparsify=sentinel, include_names=False)
+
+            level_lengths = get_level_lengths(levels, sentinel)
+            inner_lvl = len(level_lengths) - 1
+            if is_truncated_vertically:
+                # Insert ... row and adjust idx_values and
+                # level_lengths to take this into account.
+                ins_row = self.fmt.tr_row_num
+                inserted = False
+                for lnum, records in enumerate(level_lengths):
+                    rec_new = {}
+                    for tag, span in list(records.items()):
+                        if tag >= ins_row:
+                            rec_new[tag + 1] = span
+                        elif tag + span > ins_row:
+                            rec_new[tag] = span + 1
+
+                            # GH 14882 - Make sure insertion done once
+                            if not inserted:
+                                dot_row = list(idx_values[ins_row - 1])
+                                dot_row[-1] = "..."
+                                idx_values.insert(ins_row, tuple(dot_row))
+                                inserted = True
+                            else:
+                                dot_row = list(idx_values[ins_row])
+                                dot_row[inner_lvl - lnum] = "..."
+                                idx_values[ins_row] = tuple(dot_row)
+                        else:
+                            rec_new[tag] = span
+                        # If ins_row lies between tags, all cols idx cols
+                        # receive ...
+                        if tag + span == ins_row:
+                            rec_new[ins_row] = 1
+                            if lnum == 0:
+                                idx_values.insert(
+                                    ins_row, tuple(["..."] * len(level_lengths))
+                                )
+
+                            # GH 14882 - Place ... in correct level
+                            elif inserted:
+                                dot_row = list(idx_values[ins_row])
+                                dot_row[inner_lvl - lnum] = "..."
+                                idx_values[ins_row] = tuple(dot_row)
+                    level_lengths[lnum] = rec_new
+
+                level_lengths[inner_lvl][ins_row] = 1
+                for ix_col in fmt_values:
+                    fmt_values[ix_col].insert(ins_row, "...")
+                nrows += 1
+
+            for i in range(nrows):
+                row = []
+                tags = {}
+
+                sparse_offset = 0
+                j = 0
+                for records, v in zip(level_lengths, idx_values[i]):
+                    if i in records:
+                        if records[i] > 1:
+                            tags[j] = template.format(span=records[i])
+                    else:
+                        sparse_offset += 1
+                        continue
+
+                    j += 1
+                    row.append(v)
+
+                row.extend(fmt_values[j][i] for j in range(self.ncols))
+                if is_truncated_horizontally:
+                    row.insert(
+                        self.row_levels - sparse_offset + self.fmt.tr_col_num, "..."
+                    )
+                self.write_tr(
+                    row,
+                    indent,
+                    self.indent_delta,
+                    tags=tags,
+                    nindex_levels=len(levels) - sparse_offset,
+                )
+        else:
+            row = []
+            for i in range(len(frame)):
+                if is_truncated_vertically and i == (self.fmt.tr_row_num):
+                    str_sep_row = ["..."] * len(row)
+                    self.write_tr(
+                        str_sep_row,
+                        indent,
+                        self.indent_delta,
+                        tags=None,
+                        nindex_levels=self.row_levels,
+                    )
+
+                idx_values = list(
+                    zip(*frame.index._format_multi(sparsify=False, include_names=False))
+                )
+                row = []
+                row.extend(idx_values[i])
+                row.extend(fmt_values[j][i] for j in range(self.ncols))
+                if is_truncated_horizontally:
+                    row.insert(self.row_levels + self.fmt.tr_col_num, "...")
+                self.write_tr(
+                    row,
+                    indent,
+                    self.indent_delta,
+                    tags=None,
+                    nindex_levels=frame.index.nlevels,
+                )
+
+
+class NotebookFormatter(HTMLFormatter):
+    """
+    Internal class for formatting output data in html for display in Jupyter
+    Notebooks. This class is intended for functionality specific to
+    DataFrame._repr_html_() and DataFrame.to_html(notebook=True)
+    """
+
+    def _get_formatted_values(self) -> dict[int, list[str]]:
+        return {i: self.fmt.format_col(i) for i in range(self.ncols)}
+
+    def _get_columns_formatted_values(self) -> list[str]:
+        # only reached with non-Multi Index
+        return self.columns._format_flat(include_name=False)
+
+    def write_style(self) -> None:
+        # We use the "scoped" attribute here so that the desired
+        # style properties for the data frame are not then applied
+        # throughout the entire notebook.
+        template_first = """\
+            """
+        template_select = """\
+                .dataframe %s {
+                    %s: %s;
+                }"""
+        element_props = [
+            ("tbody tr th:only-of-type", "vertical-align", "middle"),
+            ("tbody tr th", "vertical-align", "top"),
+        ]
+        if isinstance(self.columns, MultiIndex):
+            element_props.append(("thead tr th", "text-align", "left"))
+            if self.show_row_idx_names:
+                element_props.append(
+                    ("thead tr:last-of-type th", "text-align", "right")
+                )
+        else:
+            element_props.append(("thead th", "text-align", "right"))
+        template_mid = "\n\n".join(template_select % t for t in element_props)
+        template = dedent(f"{template_first}\n{template_mid}\n{template_last}")
+        self.write(template)
+
+    def render(self) -> list[str]:
+        self.write("
")
+        self.write_style()
+        super().render()
+        self.write("
")
+        return self.elements
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/formats/info.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/formats/info.py
new file mode 100644
index 0000000000000000000000000000000000000000..552affbd053f2bed3f4d5f678ddf8eb293f65b01
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/formats/info.py
@@ -0,0 +1,1101 @@
+from __future__ import annotations
+
+from abc import (
+    ABC,
+    abstractmethod,
+)
+import sys
+from textwrap import dedent
+from typing import TYPE_CHECKING
+
+from pandas._config import get_option
+
+from pandas.io.formats import format as fmt
+from pandas.io.formats.printing import pprint_thing
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Iterable,
+        Iterator,
+        Mapping,
+        Sequence,
+    )
+
+    from pandas._typing import (
+        Dtype,
+        WriteBuffer,
+    )
+
+    from pandas import (
+        DataFrame,
+        Index,
+        Series,
+    )
+
+
+frame_max_cols_sub = dedent(
+    """\
+    max_cols : int, optional
+        When to switch from the verbose to the truncated output. If the
+        DataFrame has more than `max_cols` columns, the truncated output
+        is used. By default, the setting in
+        ``pandas.options.display.max_info_columns`` is used."""
+)
+
+
+show_counts_sub = dedent(
+    """\
+    show_counts : bool, optional
+        Whether to show the non-null counts. By default, this is shown
+        only if the DataFrame is smaller than
+        ``pandas.options.display.max_info_rows`` and
+        ``pandas.options.display.max_info_columns``. A value of True always
+        shows the counts, and False never shows the counts."""
+)
+
+
+frame_examples_sub = dedent(
+    """\
+    >>> int_values = [1, 2, 3, 4, 5]
+    >>> text_values = ['alpha', 'beta', 'gamma', 'delta', 'epsilon']
+    >>> float_values = [0.0, 0.25, 0.5, 0.75, 1.0]
+    >>> df = pd.DataFrame({"int_col": int_values, "text_col": text_values,
+    ...                   "float_col": float_values})
+    >>> df
+        int_col text_col  float_col
+    0        1    alpha       0.00
+    1        2     beta       0.25
+    2        3    gamma       0.50
+    3        4    delta       0.75
+    4        5  epsilon       1.00
+
+    Prints information of all columns:
+
+    >>> df.info(verbose=True)
+    
+    RangeIndex: 5 entries, 0 to 4
+    Data columns (total 3 columns):
+     #   Column     Non-Null Count  Dtype
+    ---  ------     --------------  -----
+     0   int_col    5 non-null      int64
+     1   text_col   5 non-null      object
+     2   float_col  5 non-null      float64
+    dtypes: float64(1), int64(1), object(1)
+    memory usage: 248.0+ bytes
+
+    Prints a summary of columns count and its dtypes but not per column
+    information:
+
+    >>> df.info(verbose=False)
+    
+    RangeIndex: 5 entries, 0 to 4
+    Columns: 3 entries, int_col to float_col
+    dtypes: float64(1), int64(1), object(1)
+    memory usage: 248.0+ bytes
+
+    Pipe output of DataFrame.info to buffer instead of sys.stdout, get
+    buffer content and writes to a text file:
+
+    >>> import io
+    >>> buffer = io.StringIO()
+    >>> df.info(buf=buffer)
+    >>> s = buffer.getvalue()
+    >>> with open("df_info.txt", "w",
+    ...           encoding="utf-8") as f:  # doctest: +SKIP
+    ...     f.write(s)
+    260
+
+    The `memory_usage` parameter allows deep introspection mode, specially
+    useful for big DataFrames and fine-tune memory optimization:
+
+    >>> random_strings_array = np.random.choice(['a', 'b', 'c'], 10 ** 6)
+    >>> df = pd.DataFrame({
+    ...     'column_1': np.random.choice(['a', 'b', 'c'], 10 ** 6),
+    ...     'column_2': np.random.choice(['a', 'b', 'c'], 10 ** 6),
+    ...     'column_3': np.random.choice(['a', 'b', 'c'], 10 ** 6)
+    ... })
+    >>> df.info()
+    
+    RangeIndex: 1000000 entries, 0 to 999999
+    Data columns (total 3 columns):
+     #   Column    Non-Null Count    Dtype
+    ---  ------    --------------    -----
+     0   column_1  1000000 non-null  object
+     1   column_2  1000000 non-null  object
+     2   column_3  1000000 non-null  object
+    dtypes: object(3)
+    memory usage: 22.9+ MB
+
+    >>> df.info(memory_usage='deep')
+    
+    RangeIndex: 1000000 entries, 0 to 999999
+    Data columns (total 3 columns):
+     #   Column    Non-Null Count    Dtype
+    ---  ------    --------------    -----
+     0   column_1  1000000 non-null  object
+     1   column_2  1000000 non-null  object
+     2   column_3  1000000 non-null  object
+    dtypes: object(3)
+    memory usage: 165.9 MB"""
+)
+
+
+frame_see_also_sub = dedent(
+    """\
+    DataFrame.describe: Generate descriptive statistics of DataFrame
+        columns.
+    DataFrame.memory_usage: Memory usage of DataFrame columns."""
+)
+
+
+frame_sub_kwargs = {
+    "klass": "DataFrame",
+    "type_sub": " and columns",
+    "max_cols_sub": frame_max_cols_sub,
+    "show_counts_sub": show_counts_sub,
+    "examples_sub": frame_examples_sub,
+    "see_also_sub": frame_see_also_sub,
+    "version_added_sub": "",
+}
+
+
+series_examples_sub = dedent(
+    """\
+    >>> int_values = [1, 2, 3, 4, 5]
+    >>> text_values = ['alpha', 'beta', 'gamma', 'delta', 'epsilon']
+    >>> s = pd.Series(text_values, index=int_values)
+    >>> s.info()
+    
+    Index: 5 entries, 1 to 5
+    Series name: None
+    Non-Null Count  Dtype
+    --------------  -----
+    5 non-null      object
+    dtypes: object(1)
+    memory usage: 80.0+ bytes
+
+    Prints a summary excluding information about its values:
+
+    >>> s.info(verbose=False)
+    
+    Index: 5 entries, 1 to 5
+    dtypes: object(1)
+    memory usage: 80.0+ bytes
+
+    Pipe output of Series.info to buffer instead of sys.stdout, get
+    buffer content and writes to a text file:
+
+    >>> import io
+    >>> buffer = io.StringIO()
+    >>> s.info(buf=buffer)
+    >>> s = buffer.getvalue()
+    >>> with open("df_info.txt", "w",
+    ...           encoding="utf-8") as f:  # doctest: +SKIP
+    ...     f.write(s)
+    260
+
+    The `memory_usage` parameter allows deep introspection mode, specially
+    useful for big Series and fine-tune memory optimization:
+
+    >>> random_strings_array = np.random.choice(['a', 'b', 'c'], 10 ** 6)
+    >>> s = pd.Series(np.random.choice(['a', 'b', 'c'], 10 ** 6))
+    >>> s.info()
+    
+    RangeIndex: 1000000 entries, 0 to 999999
+    Series name: None
+    Non-Null Count    Dtype
+    --------------    -----
+    1000000 non-null  object
+    dtypes: object(1)
+    memory usage: 7.6+ MB
+
+    >>> s.info(memory_usage='deep')
+    
+    RangeIndex: 1000000 entries, 0 to 999999
+    Series name: None
+    Non-Null Count    Dtype
+    --------------    -----
+    1000000 non-null  object
+    dtypes: object(1)
+    memory usage: 55.3 MB"""
+)
+
+
+series_see_also_sub = dedent(
+    """\
+    Series.describe: Generate descriptive statistics of Series.
+    Series.memory_usage: Memory usage of Series."""
+)
+
+
+series_sub_kwargs = {
+    "klass": "Series",
+    "type_sub": "",
+    "max_cols_sub": "",
+    "show_counts_sub": show_counts_sub,
+    "examples_sub": series_examples_sub,
+    "see_also_sub": series_see_also_sub,
+    "version_added_sub": "\n.. versionadded:: 1.4.0\n",
+}
+
+
+INFO_DOCSTRING = dedent(
+    """
+    Print a concise summary of a {klass}.
+
+    This method prints information about a {klass} including
+    the index dtype{type_sub}, non-null values and memory usage.
+    {version_added_sub}\
+
+    Parameters
+    ----------
+    verbose : bool, optional
+        Whether to print the full summary. By default, the setting in
+        ``pandas.options.display.max_info_columns`` is followed.
+    buf : writable buffer, defaults to sys.stdout
+        Where to send the output. By default, the output is printed to
+        sys.stdout. Pass a writable buffer if you need to further process
+        the output.
+    {max_cols_sub}
+    memory_usage : bool, str, optional
+        Specifies whether total memory usage of the {klass}
+        elements (including the index) should be displayed. By default,
+        this follows the ``pandas.options.display.memory_usage`` setting.
+
+        True always show memory usage. False never shows memory usage.
+        A value of 'deep' is equivalent to "True with deep introspection".
+        Memory usage is shown in human-readable units (base-2
+        representation). Without deep introspection a memory estimation is
+        made based in column dtype and number of rows assuming values
+        consume the same memory amount for corresponding dtypes. With deep
+        memory introspection, a real memory usage calculation is performed
+        at the cost of computational resources. See the
+        :ref:`Frequently Asked Questions ` for more
+        details.
+    {show_counts_sub}
+
+    Returns
+    -------
+    None
+        This method prints a summary of a {klass} and returns None.
+
+    See Also
+    --------
+    {see_also_sub}
+
+    Examples
+    --------
+    {examples_sub}
+    """
+)
+
+
+def _put_str(s: str | Dtype, space: int) -> str:
+    """
+    Make string of specified length, padding to the right if necessary.
+
+    Parameters
+    ----------
+    s : Union[str, Dtype]
+        String to be formatted.
+    space : int
+        Length to force string to be of.
+
+    Returns
+    -------
+    str
+        String coerced to given length.
+
+    Examples
+    --------
+    >>> pd.io.formats.info._put_str("panda", 6)
+    'panda '
+    >>> pd.io.formats.info._put_str("panda", 4)
+    'pand'
+    """
+    return str(s)[:space].ljust(space)
+
+
+def _sizeof_fmt(num: float, size_qualifier: str) -> str:
+    """
+    Return size in human readable format.
+
+    Parameters
+    ----------
+    num : int
+        Size in bytes.
+    size_qualifier : str
+        Either empty, or '+' (if lower bound).
+
+    Returns
+    -------
+    str
+        Size in human readable format.
+
+    Examples
+    --------
+    >>> _sizeof_fmt(23028, '')
+    '22.5 KB'
+
+    >>> _sizeof_fmt(23028, '+')
+    '22.5+ KB'
+    """
+    for x in ["bytes", "KB", "MB", "GB", "TB"]:
+        if num < 1024.0:
+            return f"{num:3.1f}{size_qualifier} {x}"
+        num /= 1024.0
+    return f"{num:3.1f}{size_qualifier} PB"
+
+
+def _initialize_memory_usage(
+    memory_usage: bool | str | None = None,
+) -> bool | str:
+    """Get memory usage based on inputs and display options."""
+    if memory_usage is None:
+        memory_usage = get_option("display.memory_usage")
+    return memory_usage
+
+
+class _BaseInfo(ABC):
+    """
+    Base class for DataFrameInfo and SeriesInfo.
+
+    Parameters
+    ----------
+    data : DataFrame or Series
+        Either dataframe or series.
+    memory_usage : bool or str, optional
+        If "deep", introspect the data deeply by interrogating object dtypes
+        for system-level memory consumption, and include it in the returned
+        values.
+    """
+
+    data: DataFrame | Series
+    memory_usage: bool | str
+
+    @property
+    @abstractmethod
+    def dtypes(self) -> Iterable[Dtype]:
+        """
+        Dtypes.
+
+        Returns
+        -------
+        dtypes : sequence
+            Dtype of each of the DataFrame's columns (or one series column).
+        """
+
+    @property
+    @abstractmethod
+    def dtype_counts(self) -> Mapping[str, int]:
+        """Mapping dtype - number of counts."""
+
+    @property
+    @abstractmethod
+    def non_null_counts(self) -> Sequence[int]:
+        """Sequence of non-null counts for all columns or column (if series)."""
+
+    @property
+    @abstractmethod
+    def memory_usage_bytes(self) -> int:
+        """
+        Memory usage in bytes.
+
+        Returns
+        -------
+        memory_usage_bytes : int
+            Object's total memory usage in bytes.
+        """
+
+    @property
+    def memory_usage_string(self) -> str:
+        """Memory usage in a form of human readable string."""
+        return f"{_sizeof_fmt(self.memory_usage_bytes, self.size_qualifier)}\n"
+
+    @property
+    def size_qualifier(self) -> str:
+        size_qualifier = ""
+        if self.memory_usage:
+            if self.memory_usage != "deep":
+                # size_qualifier is just a best effort; not guaranteed to catch
+                # all cases (e.g., it misses categorical data even with object
+                # categories)
+                if (
+                    "object" in self.dtype_counts
+                    or self.data.index._is_memory_usage_qualified()
+                ):
+                    size_qualifier = "+"
+        return size_qualifier
+
+    @abstractmethod
+    def render(
+        self,
+        *,
+        buf: WriteBuffer[str] | None,
+        max_cols: int | None,
+        verbose: bool | None,
+        show_counts: bool | None,
+    ) -> None:
+        pass
+
+
+class DataFrameInfo(_BaseInfo):
+    """
+    Class storing dataframe-specific info.
+    """
+
+    def __init__(
+        self,
+        data: DataFrame,
+        memory_usage: bool | str | None = None,
+    ) -> None:
+        self.data: DataFrame = data
+        self.memory_usage = _initialize_memory_usage(memory_usage)
+
+    @property
+    def dtype_counts(self) -> Mapping[str, int]:
+        return _get_dataframe_dtype_counts(self.data)
+
+    @property
+    def dtypes(self) -> Iterable[Dtype]:
+        """
+        Dtypes.
+
+        Returns
+        -------
+        dtypes
+            Dtype of each of the DataFrame's columns.
+        """
+        return self.data.dtypes
+
+    @property
+    def ids(self) -> Index:
+        """
+        Column names.
+
+        Returns
+        -------
+        ids : Index
+            DataFrame's column names.
+        """
+        return self.data.columns
+
+    @property
+    def col_count(self) -> int:
+        """Number of columns to be summarized."""
+        return len(self.ids)
+
+    @property
+    def non_null_counts(self) -> Sequence[int]:
+        """Sequence of non-null counts for all columns or column (if series)."""
+        return self.data.count()
+
+    @property
+    def memory_usage_bytes(self) -> int:
+        deep = self.memory_usage == "deep"
+        return self.data.memory_usage(index=True, deep=deep).sum()
+
+    def render(
+        self,
+        *,
+        buf: WriteBuffer[str] | None,
+        max_cols: int | None,
+        verbose: bool | None,
+        show_counts: bool | None,
+    ) -> None:
+        printer = _DataFrameInfoPrinter(
+            info=self,
+            max_cols=max_cols,
+            verbose=verbose,
+            show_counts=show_counts,
+        )
+        printer.to_buffer(buf)
+
+
+class SeriesInfo(_BaseInfo):
+    """
+    Class storing series-specific info.
+    """
+
+    def __init__(
+        self,
+        data: Series,
+        memory_usage: bool | str | None = None,
+    ) -> None:
+        self.data: Series = data
+        self.memory_usage = _initialize_memory_usage(memory_usage)
+
+    def render(
+        self,
+        *,
+        buf: WriteBuffer[str] | None = None,
+        max_cols: int | None = None,
+        verbose: bool | None = None,
+        show_counts: bool | None = None,
+    ) -> None:
+        if max_cols is not None:
+            raise ValueError(
+                "Argument `max_cols` can only be passed "
+                "in DataFrame.info, not Series.info"
+            )
+        printer = _SeriesInfoPrinter(
+            info=self,
+            verbose=verbose,
+            show_counts=show_counts,
+        )
+        printer.to_buffer(buf)
+
+    @property
+    def non_null_counts(self) -> Sequence[int]:
+        return [self.data.count()]
+
+    @property
+    def dtypes(self) -> Iterable[Dtype]:
+        return [self.data.dtypes]
+
+    @property
+    def dtype_counts(self) -> Mapping[str, int]:
+        from pandas.core.frame import DataFrame
+
+        return _get_dataframe_dtype_counts(DataFrame(self.data))
+
+    @property
+    def memory_usage_bytes(self) -> int:
+        """Memory usage in bytes.
+
+        Returns
+        -------
+        memory_usage_bytes : int
+            Object's total memory usage in bytes.
+        """
+        deep = self.memory_usage == "deep"
+        return self.data.memory_usage(index=True, deep=deep)
+
+
+class _InfoPrinterAbstract:
+    """
+    Class for printing dataframe or series info.
+    """
+
+    def to_buffer(self, buf: WriteBuffer[str] | None = None) -> None:
+        """Save dataframe info into buffer."""
+        table_builder = self._create_table_builder()
+        lines = table_builder.get_lines()
+        if buf is None:  # pragma: no cover
+            buf = sys.stdout
+        fmt.buffer_put_lines(buf, lines)
+
+    @abstractmethod
+    def _create_table_builder(self) -> _TableBuilderAbstract:
+        """Create instance of table builder."""
+
+
+class _DataFrameInfoPrinter(_InfoPrinterAbstract):
+    """
+    Class for printing dataframe info.
+
+    Parameters
+    ----------
+    info : DataFrameInfo
+        Instance of DataFrameInfo.
+    max_cols : int, optional
+        When to switch from the verbose to the truncated output.
+    verbose : bool, optional
+        Whether to print the full summary.
+    show_counts : bool, optional
+        Whether to show the non-null counts.
+    """
+
+    def __init__(
+        self,
+        info: DataFrameInfo,
+        max_cols: int | None = None,
+        verbose: bool | None = None,
+        show_counts: bool | None = None,
+    ) -> None:
+        self.info = info
+        self.data = info.data
+        self.verbose = verbose
+        self.max_cols = self._initialize_max_cols(max_cols)
+        self.show_counts = self._initialize_show_counts(show_counts)
+
+    @property
+    def max_rows(self) -> int:
+        """Maximum info rows to be displayed."""
+        return get_option("display.max_info_rows", len(self.data) + 1)
+
+    @property
+    def exceeds_info_cols(self) -> bool:
+        """Check if number of columns to be summarized does not exceed maximum."""
+        return bool(self.col_count > self.max_cols)
+
+    @property
+    def exceeds_info_rows(self) -> bool:
+        """Check if number of rows to be summarized does not exceed maximum."""
+        return bool(len(self.data) > self.max_rows)
+
+    @property
+    def col_count(self) -> int:
+        """Number of columns to be summarized."""
+        return self.info.col_count
+
+    def _initialize_max_cols(self, max_cols: int | None) -> int:
+        if max_cols is None:
+            return get_option("display.max_info_columns", self.col_count + 1)
+        return max_cols
+
+    def _initialize_show_counts(self, show_counts: bool | None) -> bool:
+        if show_counts is None:
+            return bool(not self.exceeds_info_cols and not self.exceeds_info_rows)
+        else:
+            return show_counts
+
+    def _create_table_builder(self) -> _DataFrameTableBuilder:
+        """
+        Create instance of table builder based on verbosity and display settings.
+        """
+        if self.verbose:
+            return _DataFrameTableBuilderVerbose(
+                info=self.info,
+                with_counts=self.show_counts,
+            )
+        elif self.verbose is False:  # specifically set to False, not necessarily None
+            return _DataFrameTableBuilderNonVerbose(info=self.info)
+        elif self.exceeds_info_cols:
+            return _DataFrameTableBuilderNonVerbose(info=self.info)
+        else:
+            return _DataFrameTableBuilderVerbose(
+                info=self.info,
+                with_counts=self.show_counts,
+            )
+
+
+class _SeriesInfoPrinter(_InfoPrinterAbstract):
+    """Class for printing series info.
+
+    Parameters
+    ----------
+    info : SeriesInfo
+        Instance of SeriesInfo.
+    verbose : bool, optional
+        Whether to print the full summary.
+    show_counts : bool, optional
+        Whether to show the non-null counts.
+    """
+
+    def __init__(
+        self,
+        info: SeriesInfo,
+        verbose: bool | None = None,
+        show_counts: bool | None = None,
+    ) -> None:
+        self.info = info
+        self.data = info.data
+        self.verbose = verbose
+        self.show_counts = self._initialize_show_counts(show_counts)
+
+    def _create_table_builder(self) -> _SeriesTableBuilder:
+        """
+        Create instance of table builder based on verbosity.
+        """
+        if self.verbose or self.verbose is None:
+            return _SeriesTableBuilderVerbose(
+                info=self.info,
+                with_counts=self.show_counts,
+            )
+        else:
+            return _SeriesTableBuilderNonVerbose(info=self.info)
+
+    def _initialize_show_counts(self, show_counts: bool | None) -> bool:
+        if show_counts is None:
+            return True
+        else:
+            return show_counts
+
+
+class _TableBuilderAbstract(ABC):
+    """
+    Abstract builder for info table.
+    """
+
+    _lines: list[str]
+    info: _BaseInfo
+
+    @abstractmethod
+    def get_lines(self) -> list[str]:
+        """Product in a form of list of lines (strings)."""
+
+    @property
+    def data(self) -> DataFrame | Series:
+        return self.info.data
+
+    @property
+    def dtypes(self) -> Iterable[Dtype]:
+        """Dtypes of each of the DataFrame's columns."""
+        return self.info.dtypes
+
+    @property
+    def dtype_counts(self) -> Mapping[str, int]:
+        """Mapping dtype - number of counts."""
+        return self.info.dtype_counts
+
+    @property
+    def display_memory_usage(self) -> bool:
+        """Whether to display memory usage."""
+        return bool(self.info.memory_usage)
+
+    @property
+    def memory_usage_string(self) -> str:
+        """Memory usage string with proper size qualifier."""
+        return self.info.memory_usage_string
+
+    @property
+    def non_null_counts(self) -> Sequence[int]:
+        return self.info.non_null_counts
+
+    def add_object_type_line(self) -> None:
+        """Add line with string representation of dataframe to the table."""
+        self._lines.append(str(type(self.data)))
+
+    def add_index_range_line(self) -> None:
+        """Add line with range of indices to the table."""
+        self._lines.append(self.data.index._summary())
+
+    def add_dtypes_line(self) -> None:
+        """Add summary line with dtypes present in dataframe."""
+        collected_dtypes = [
+            f"{key}({val:d})" for key, val in sorted(self.dtype_counts.items())
+        ]
+        self._lines.append(f"dtypes: {', '.join(collected_dtypes)}")
+
+
+class _DataFrameTableBuilder(_TableBuilderAbstract):
+    """
+    Abstract builder for dataframe info table.
+
+    Parameters
+    ----------
+    info : DataFrameInfo.
+        Instance of DataFrameInfo.
+    """
+
+    def __init__(self, *, info: DataFrameInfo) -> None:
+        self.info: DataFrameInfo = info
+
+    def get_lines(self) -> list[str]:
+        self._lines = []
+        if self.col_count == 0:
+            self._fill_empty_info()
+        else:
+            self._fill_non_empty_info()
+        return self._lines
+
+    def _fill_empty_info(self) -> None:
+        """Add lines to the info table, pertaining to empty dataframe."""
+        self.add_object_type_line()
+        self.add_index_range_line()
+        self._lines.append(f"Empty {type(self.data).__name__}\n")
+
+    @abstractmethod
+    def _fill_non_empty_info(self) -> None:
+        """Add lines to the info table, pertaining to non-empty dataframe."""
+
+    @property
+    def data(self) -> DataFrame:
+        """DataFrame."""
+        return self.info.data
+
+    @property
+    def ids(self) -> Index:
+        """Dataframe columns."""
+        return self.info.ids
+
+    @property
+    def col_count(self) -> int:
+        """Number of dataframe columns to be summarized."""
+        return self.info.col_count
+
+    def add_memory_usage_line(self) -> None:
+        """Add line containing memory usage."""
+        self._lines.append(f"memory usage: {self.memory_usage_string}")
+
+
+class _DataFrameTableBuilderNonVerbose(_DataFrameTableBuilder):
+    """
+    Dataframe info table builder for non-verbose output.
+    """
+
+    def _fill_non_empty_info(self) -> None:
+        """Add lines to the info table, pertaining to non-empty dataframe."""
+        self.add_object_type_line()
+        self.add_index_range_line()
+        self.add_columns_summary_line()
+        self.add_dtypes_line()
+        if self.display_memory_usage:
+            self.add_memory_usage_line()
+
+    def add_columns_summary_line(self) -> None:
+        self._lines.append(self.ids._summary(name="Columns"))
+
+
+class _TableBuilderVerboseMixin(_TableBuilderAbstract):
+    """
+    Mixin for verbose info output.
+    """
+
+    SPACING: str = " " * 2
+    strrows: Sequence[Sequence[str]]
+    gross_column_widths: Sequence[int]
+    with_counts: bool
+
+    @property
+    @abstractmethod
+    def headers(self) -> Sequence[str]:
+        """Headers names of the columns in verbose table."""
+
+    @property
+    def header_column_widths(self) -> Sequence[int]:
+        """Widths of header columns (only titles)."""
+        return [len(col) for col in self.headers]
+
+    def _get_gross_column_widths(self) -> Sequence[int]:
+        """Get widths of columns containing both headers and actual content."""
+        body_column_widths = self._get_body_column_widths()
+        return [
+            max(*widths)
+            for widths in zip(self.header_column_widths, body_column_widths)
+        ]
+
+    def _get_body_column_widths(self) -> Sequence[int]:
+        """Get widths of table content columns."""
+        strcols: Sequence[Sequence[str]] = list(zip(*self.strrows))
+        return [max(len(x) for x in col) for col in strcols]
+
+    def _gen_rows(self) -> Iterator[Sequence[str]]:
+        """
+        Generator function yielding rows content.
+
+        Each element represents a row comprising a sequence of strings.
+        """
+        if self.with_counts:
+            return self._gen_rows_with_counts()
+        else:
+            return self._gen_rows_without_counts()
+
+    @abstractmethod
+    def _gen_rows_with_counts(self) -> Iterator[Sequence[str]]:
+        """Iterator with string representation of body data with counts."""
+
+    @abstractmethod
+    def _gen_rows_without_counts(self) -> Iterator[Sequence[str]]:
+        """Iterator with string representation of body data without counts."""
+
+    def add_header_line(self) -> None:
+        header_line = self.SPACING.join(
+            [
+                _put_str(header, col_width)
+                for header, col_width in zip(self.headers, self.gross_column_widths)
+            ]
+        )
+        self._lines.append(header_line)
+
+    def add_separator_line(self) -> None:
+        separator_line = self.SPACING.join(
+            [
+                _put_str("-" * header_colwidth, gross_colwidth)
+                for header_colwidth, gross_colwidth in zip(
+                    self.header_column_widths, self.gross_column_widths
+                )
+            ]
+        )
+        self._lines.append(separator_line)
+
+    def add_body_lines(self) -> None:
+        for row in self.strrows:
+            body_line = self.SPACING.join(
+                [
+                    _put_str(col, gross_colwidth)
+                    for col, gross_colwidth in zip(row, self.gross_column_widths)
+                ]
+            )
+            self._lines.append(body_line)
+
+    def _gen_non_null_counts(self) -> Iterator[str]:
+        """Iterator with string representation of non-null counts."""
+        for count in self.non_null_counts:
+            yield f"{count} non-null"
+
+    def _gen_dtypes(self) -> Iterator[str]:
+        """Iterator with string representation of column dtypes."""
+        for dtype in self.dtypes:
+            yield pprint_thing(dtype)
+
+
+class _DataFrameTableBuilderVerbose(_DataFrameTableBuilder, _TableBuilderVerboseMixin):
+    """
+    Dataframe info table builder for verbose output.
+    """
+
+    def __init__(
+        self,
+        *,
+        info: DataFrameInfo,
+        with_counts: bool,
+    ) -> None:
+        self.info = info
+        self.with_counts = with_counts
+        self.strrows: Sequence[Sequence[str]] = list(self._gen_rows())
+        self.gross_column_widths: Sequence[int] = self._get_gross_column_widths()
+
+    def _fill_non_empty_info(self) -> None:
+        """Add lines to the info table, pertaining to non-empty dataframe."""
+        self.add_object_type_line()
+        self.add_index_range_line()
+        self.add_columns_summary_line()
+        self.add_header_line()
+        self.add_separator_line()
+        self.add_body_lines()
+        self.add_dtypes_line()
+        if self.display_memory_usage:
+            self.add_memory_usage_line()
+
+    @property
+    def headers(self) -> Sequence[str]:
+        """Headers names of the columns in verbose table."""
+        if self.with_counts:
+            return [" # ", "Column", "Non-Null Count", "Dtype"]
+        return [" # ", "Column", "Dtype"]
+
+    def add_columns_summary_line(self) -> None:
+        self._lines.append(f"Data columns (total {self.col_count} columns):")
+
+    def _gen_rows_without_counts(self) -> Iterator[Sequence[str]]:
+        """Iterator with string representation of body data without counts."""
+        yield from zip(
+            self._gen_line_numbers(),
+            self._gen_columns(),
+            self._gen_dtypes(),
+        )
+
+    def _gen_rows_with_counts(self) -> Iterator[Sequence[str]]:
+        """Iterator with string representation of body data with counts."""
+        yield from zip(
+            self._gen_line_numbers(),
+            self._gen_columns(),
+            self._gen_non_null_counts(),
+            self._gen_dtypes(),
+        )
+
+    def _gen_line_numbers(self) -> Iterator[str]:
+        """Iterator with string representation of column numbers."""
+        for i, _ in enumerate(self.ids):
+            yield f" {i}"
+
+    def _gen_columns(self) -> Iterator[str]:
+        """Iterator with string representation of column names."""
+        for col in self.ids:
+            yield pprint_thing(col)
+
+
+class _SeriesTableBuilder(_TableBuilderAbstract):
+    """
+    Abstract builder for series info table.
+
+    Parameters
+    ----------
+    info : SeriesInfo.
+        Instance of SeriesInfo.
+    """
+
+    def __init__(self, *, info: SeriesInfo) -> None:
+        self.info: SeriesInfo = info
+
+    def get_lines(self) -> list[str]:
+        self._lines = []
+        self._fill_non_empty_info()
+        return self._lines
+
+    @property
+    def data(self) -> Series:
+        """Series."""
+        return self.info.data
+
+    def add_memory_usage_line(self) -> None:
+        """Add line containing memory usage."""
+        self._lines.append(f"memory usage: {self.memory_usage_string}")
+
+    @abstractmethod
+    def _fill_non_empty_info(self) -> None:
+        """Add lines to the info table, pertaining to non-empty series."""
+
+
+class _SeriesTableBuilderNonVerbose(_SeriesTableBuilder):
+    """
+    Series info table builder for non-verbose output.
+    """
+
+    def _fill_non_empty_info(self) -> None:
+        """Add lines to the info table, pertaining to non-empty series."""
+        self.add_object_type_line()
+        self.add_index_range_line()
+        self.add_dtypes_line()
+        if self.display_memory_usage:
+            self.add_memory_usage_line()
+
+
+class _SeriesTableBuilderVerbose(_SeriesTableBuilder, _TableBuilderVerboseMixin):
+    """
+    Series info table builder for verbose output.
+    """
+
+    def __init__(
+        self,
+        *,
+        info: SeriesInfo,
+        with_counts: bool,
+    ) -> None:
+        self.info = info
+        self.with_counts = with_counts
+        self.strrows: Sequence[Sequence[str]] = list(self._gen_rows())
+        self.gross_column_widths: Sequence[int] = self._get_gross_column_widths()
+
+    def _fill_non_empty_info(self) -> None:
+        """Add lines to the info table, pertaining to non-empty series."""
+        self.add_object_type_line()
+        self.add_index_range_line()
+        self.add_series_name_line()
+        self.add_header_line()
+        self.add_separator_line()
+        self.add_body_lines()
+        self.add_dtypes_line()
+        if self.display_memory_usage:
+            self.add_memory_usage_line()
+
+    def add_series_name_line(self) -> None:
+        self._lines.append(f"Series name: {self.data.name}")
+
+    @property
+    def headers(self) -> Sequence[str]:
+        """Headers names of the columns in verbose table."""
+        if self.with_counts:
+            return ["Non-Null Count", "Dtype"]
+        return ["Dtype"]
+
+    def _gen_rows_without_counts(self) -> Iterator[Sequence[str]]:
+        """Iterator with string representation of body data without counts."""
+        yield from self._gen_dtypes()
+
+    def _gen_rows_with_counts(self) -> Iterator[Sequence[str]]:
+        """Iterator with string representation of body data with counts."""
+        yield from zip(
+            self._gen_non_null_counts(),
+            self._gen_dtypes(),
+        )
+
+
+def _get_dataframe_dtype_counts(df: DataFrame) -> Mapping[str, int]:
+    """
+    Create mapping between datatypes and their number of occurrences.
+    """
+    # groupby dtype.name to collect e.g. Categorical columns
+    return df.dtypes.value_counts().groupby(lambda x: x.name).sum()
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/formats/printing.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/formats/printing.py
new file mode 100644
index 0000000000000000000000000000000000000000..2cc9368f8846a6423655040673df283d111efeda
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/formats/printing.py
@@ -0,0 +1,572 @@
+"""
+Printing tools.
+"""
+from __future__ import annotations
+
+from collections.abc import (
+    Iterable,
+    Mapping,
+    Sequence,
+)
+import sys
+from typing import (
+    Any,
+    Callable,
+    TypeVar,
+    Union,
+)
+from unicodedata import east_asian_width
+
+from pandas._config import get_option
+
+from pandas.core.dtypes.inference import is_sequence
+
+from pandas.io.formats.console import get_console_size
+
+EscapeChars = Union[Mapping[str, str], Iterable[str]]
+_KT = TypeVar("_KT")
+_VT = TypeVar("_VT")
+
+
+def adjoin(space: int, *lists: list[str], **kwargs) -> str:
+    """
+    Glues together two sets of strings using the amount of space requested.
+    The idea is to prettify.
+
+    ----------
+    space : int
+        number of spaces for padding
+    lists : str
+        list of str which being joined
+    strlen : callable
+        function used to calculate the length of each str. Needed for unicode
+        handling.
+    justfunc : callable
+        function used to justify str. Needed for unicode handling.
+    """
+    strlen = kwargs.pop("strlen", len)
+    justfunc = kwargs.pop("justfunc", _adj_justify)
+
+    newLists = []
+    lengths = [max(map(strlen, x)) + space for x in lists[:-1]]
+    # not the last one
+    lengths.append(max(map(len, lists[-1])))
+    maxLen = max(map(len, lists))
+    for i, lst in enumerate(lists):
+        nl = justfunc(lst, lengths[i], mode="left")
+        nl = ([" " * lengths[i]] * (maxLen - len(lst))) + nl
+        newLists.append(nl)
+    toJoin = zip(*newLists)
+    return "\n".join("".join(lines) for lines in toJoin)
+
+
+def _adj_justify(texts: Iterable[str], max_len: int, mode: str = "right") -> list[str]:
+    """
+    Perform ljust, center, rjust against string or list-like
+    """
+    if mode == "left":
+        return [x.ljust(max_len) for x in texts]
+    elif mode == "center":
+        return [x.center(max_len) for x in texts]
+    else:
+        return [x.rjust(max_len) for x in texts]
+
+
+# Unicode consolidation
+# ---------------------
+#
+# pprinting utility functions for generating Unicode text or
+# bytes(3.x)/str(2.x) representations of objects.
+# Try to use these as much as possible rather than rolling your own.
+#
+# When to use
+# -----------
+#
+# 1) If you're writing code internal to pandas (no I/O directly involved),
+#    use pprint_thing().
+#
+#    It will always return unicode text which can handled by other
+#    parts of the package without breakage.
+#
+# 2) if you need to write something out to file, use
+#    pprint_thing_encoded(encoding).
+#
+#    If no encoding is specified, it defaults to utf-8. Since encoding pure
+#    ascii with utf-8 is a no-op you can safely use the default utf-8 if you're
+#    working with straight ascii.
+
+
+def _pprint_seq(
+    seq: Sequence, _nest_lvl: int = 0, max_seq_items: int | None = None, **kwds
+) -> str:
+    """
+    internal. pprinter for iterables. you should probably use pprint_thing()
+    rather than calling this directly.
+
+    bounds length of printed sequence, depending on options
+    """
+    if isinstance(seq, set):
+        fmt = "{{{body}}}"
+    else:
+        fmt = "[{body}]" if hasattr(seq, "__setitem__") else "({body})"
+
+    if max_seq_items is False:
+        nitems = len(seq)
+    else:
+        nitems = max_seq_items or get_option("max_seq_items") or len(seq)
+
+    s = iter(seq)
+    # handle sets, no slicing
+    r = [
+        pprint_thing(next(s), _nest_lvl + 1, max_seq_items=max_seq_items, **kwds)
+        for i in range(min(nitems, len(seq)))
+    ]
+    body = ", ".join(r)
+
+    if nitems < len(seq):
+        body += ", ..."
+    elif isinstance(seq, tuple) and len(seq) == 1:
+        body += ","
+
+    return fmt.format(body=body)
+
+
+def _pprint_dict(
+    seq: Mapping, _nest_lvl: int = 0, max_seq_items: int | None = None, **kwds
+) -> str:
+    """
+    internal. pprinter for iterables. you should probably use pprint_thing()
+    rather than calling this directly.
+    """
+    fmt = "{{{things}}}"
+    pairs = []
+
+    pfmt = "{key}: {val}"
+
+    if max_seq_items is False:
+        nitems = len(seq)
+    else:
+        nitems = max_seq_items or get_option("max_seq_items") or len(seq)
+
+    for k, v in list(seq.items())[:nitems]:
+        pairs.append(
+            pfmt.format(
+                key=pprint_thing(k, _nest_lvl + 1, max_seq_items=max_seq_items, **kwds),
+                val=pprint_thing(v, _nest_lvl + 1, max_seq_items=max_seq_items, **kwds),
+            )
+        )
+
+    if nitems < len(seq):
+        return fmt.format(things=", ".join(pairs) + ", ...")
+    else:
+        return fmt.format(things=", ".join(pairs))
+
+
+def pprint_thing(
+    thing: Any,
+    _nest_lvl: int = 0,
+    escape_chars: EscapeChars | None = None,
+    default_escapes: bool = False,
+    quote_strings: bool = False,
+    max_seq_items: int | None = None,
+) -> str:
+    """
+    This function is the sanctioned way of converting objects
+    to a string representation and properly handles nested sequences.
+
+    Parameters
+    ----------
+    thing : anything to be formatted
+    _nest_lvl : internal use only. pprint_thing() is mutually-recursive
+        with pprint_sequence, this argument is used to keep track of the
+        current nesting level, and limit it.
+    escape_chars : list or dict, optional
+        Characters to escape. If a dict is passed the values are the
+        replacements
+    default_escapes : bool, default False
+        Whether the input escape characters replaces or adds to the defaults
+    max_seq_items : int or None, default None
+        Pass through to other pretty printers to limit sequence printing
+
+    Returns
+    -------
+    str
+    """
+
+    def as_escaped_string(
+        thing: Any, escape_chars: EscapeChars | None = escape_chars
+    ) -> str:
+        translate = {"\t": r"\t", "\n": r"\n", "\r": r"\r"}
+        if isinstance(escape_chars, dict):
+            if default_escapes:
+                translate.update(escape_chars)
+            else:
+                translate = escape_chars
+            escape_chars = list(escape_chars.keys())
+        else:
+            escape_chars = escape_chars or ()
+
+        result = str(thing)
+        for c in escape_chars:
+            result = result.replace(c, translate[c])
+        return result
+
+    if hasattr(thing, "__next__"):
+        return str(thing)
+    elif isinstance(thing, dict) and _nest_lvl < get_option(
+        "display.pprint_nest_depth"
+    ):
+        result = _pprint_dict(
+            thing, _nest_lvl, quote_strings=True, max_seq_items=max_seq_items
+        )
+    elif is_sequence(thing) and _nest_lvl < get_option("display.pprint_nest_depth"):
+        result = _pprint_seq(
+            thing,
+            _nest_lvl,
+            escape_chars=escape_chars,
+            quote_strings=quote_strings,
+            max_seq_items=max_seq_items,
+        )
+    elif isinstance(thing, str) and quote_strings:
+        result = f"'{as_escaped_string(thing)}'"
+    else:
+        result = as_escaped_string(thing)
+
+    return result
+
+
+def pprint_thing_encoded(
+    object, encoding: str = "utf-8", errors: str = "replace"
+) -> bytes:
+    value = pprint_thing(object)  # get unicode representation of object
+    return value.encode(encoding, errors)
+
+
+def enable_data_resource_formatter(enable: bool) -> None:
+    if "IPython" not in sys.modules:
+        # definitely not in IPython
+        return
+    from IPython import get_ipython
+
+    ip = get_ipython()
+    if ip is None:
+        # still not in IPython
+        return
+
+    formatters = ip.display_formatter.formatters
+    mimetype = "application/vnd.dataresource+json"
+
+    if enable:
+        if mimetype not in formatters:
+            # define tableschema formatter
+            from IPython.core.formatters import BaseFormatter
+            from traitlets import ObjectName
+
+            class TableSchemaFormatter(BaseFormatter):
+                print_method = ObjectName("_repr_data_resource_")
+                _return_type = (dict,)
+
+            # register it:
+            formatters[mimetype] = TableSchemaFormatter()
+        # enable it if it's been disabled:
+        formatters[mimetype].enabled = True
+    # unregister tableschema mime-type
+    elif mimetype in formatters:
+        formatters[mimetype].enabled = False
+
+
+def default_pprint(thing: Any, max_seq_items: int | None = None) -> str:
+    return pprint_thing(
+        thing,
+        escape_chars=("\t", "\r", "\n"),
+        quote_strings=True,
+        max_seq_items=max_seq_items,
+    )
+
+
+def format_object_summary(
+    obj,
+    formatter: Callable,
+    is_justify: bool = True,
+    name: str | None = None,
+    indent_for_name: bool = True,
+    line_break_each_value: bool = False,
+) -> str:
+    """
+    Return the formatted obj as a unicode string
+
+    Parameters
+    ----------
+    obj : object
+        must be iterable and support __getitem__
+    formatter : callable
+        string formatter for an element
+    is_justify : bool
+        should justify the display
+    name : name, optional
+        defaults to the class name of the obj
+    indent_for_name : bool, default True
+        Whether subsequent lines should be indented to
+        align with the name.
+    line_break_each_value : bool, default False
+        If True, inserts a line break for each value of ``obj``.
+        If False, only break lines when the a line of values gets wider
+        than the display width.
+
+    Returns
+    -------
+    summary string
+    """
+    display_width, _ = get_console_size()
+    if display_width is None:
+        display_width = get_option("display.width") or 80
+    if name is None:
+        name = type(obj).__name__
+
+    if indent_for_name:
+        name_len = len(name)
+        space1 = f'\n{(" " * (name_len + 1))}'
+        space2 = f'\n{(" " * (name_len + 2))}'
+    else:
+        space1 = "\n"
+        space2 = "\n "  # space for the opening '['
+
+    n = len(obj)
+    if line_break_each_value:
+        # If we want to vertically align on each value of obj, we need to
+        # separate values by a line break and indent the values
+        sep = ",\n " + " " * len(name)
+    else:
+        sep = ","
+    max_seq_items = get_option("display.max_seq_items") or n
+
+    # are we a truncated display
+    is_truncated = n > max_seq_items
+
+    # adj can optionally handle unicode eastern asian width
+    adj = get_adjustment()
+
+    def _extend_line(
+        s: str, line: str, value: str, display_width: int, next_line_prefix: str
+    ) -> tuple[str, str]:
+        if adj.len(line.rstrip()) + adj.len(value.rstrip()) >= display_width:
+            s += line.rstrip()
+            line = next_line_prefix
+        line += value
+        return s, line
+
+    def best_len(values: list[str]) -> int:
+        if values:
+            return max(adj.len(x) for x in values)
+        else:
+            return 0
+
+    close = ", "
+
+    if n == 0:
+        summary = f"[]{close}"
+    elif n == 1 and not line_break_each_value:
+        first = formatter(obj[0])
+        summary = f"[{first}]{close}"
+    elif n == 2 and not line_break_each_value:
+        first = formatter(obj[0])
+        last = formatter(obj[-1])
+        summary = f"[{first}, {last}]{close}"
+    else:
+        if max_seq_items == 1:
+            # If max_seq_items=1 show only last element
+            head = []
+            tail = [formatter(x) for x in obj[-1:]]
+        elif n > max_seq_items:
+            n = min(max_seq_items // 2, 10)
+            head = [formatter(x) for x in obj[:n]]
+            tail = [formatter(x) for x in obj[-n:]]
+        else:
+            head = []
+            tail = [formatter(x) for x in obj]
+
+        # adjust all values to max length if needed
+        if is_justify:
+            if line_break_each_value:
+                # Justify each string in the values of head and tail, so the
+                # strings will right align when head and tail are stacked
+                # vertically.
+                head, tail = _justify(head, tail)
+            elif is_truncated or not (
+                len(", ".join(head)) < display_width
+                and len(", ".join(tail)) < display_width
+            ):
+                # Each string in head and tail should align with each other
+                max_length = max(best_len(head), best_len(tail))
+                head = [x.rjust(max_length) for x in head]
+                tail = [x.rjust(max_length) for x in tail]
+            # If we are not truncated and we are only a single
+            # line, then don't justify
+
+        if line_break_each_value:
+            # Now head and tail are of type List[Tuple[str]]. Below we
+            # convert them into List[str], so there will be one string per
+            # value. Also truncate items horizontally if wider than
+            # max_space
+            max_space = display_width - len(space2)
+            value = tail[0]
+            max_items = 1
+            for num_items in reversed(range(1, len(value) + 1)):
+                pprinted_seq = _pprint_seq(value, max_seq_items=num_items)
+                if len(pprinted_seq) < max_space:
+                    max_items = num_items
+                    break
+            head = [_pprint_seq(x, max_seq_items=max_items) for x in head]
+            tail = [_pprint_seq(x, max_seq_items=max_items) for x in tail]
+
+        summary = ""
+        line = space2
+
+        for head_value in head:
+            word = head_value + sep + " "
+            summary, line = _extend_line(summary, line, word, display_width, space2)
+
+        if is_truncated:
+            # remove trailing space of last line
+            summary += line.rstrip() + space2 + "..."
+            line = space2
+
+        for tail_item in tail[:-1]:
+            word = tail_item + sep + " "
+            summary, line = _extend_line(summary, line, word, display_width, space2)
+
+        # last value: no sep added + 1 space of width used for trailing ','
+        summary, line = _extend_line(summary, line, tail[-1], display_width - 2, space2)
+        summary += line
+
+        # right now close is either '' or ', '
+        # Now we want to include the ']', but not the maybe space.
+        close = "]" + close.rstrip(" ")
+        summary += close
+
+        if len(summary) > (display_width) or line_break_each_value:
+            summary += space1
+        else:  # one row
+            summary += " "
+
+        # remove initial space
+        summary = "[" + summary[len(space2) :]
+
+    return summary
+
+
+def _justify(
+    head: list[Sequence[str]], tail: list[Sequence[str]]
+) -> tuple[list[tuple[str, ...]], list[tuple[str, ...]]]:
+    """
+    Justify items in head and tail, so they are right-aligned when stacked.
+
+    Parameters
+    ----------
+    head : list-like of list-likes of strings
+    tail : list-like of list-likes of strings
+
+    Returns
+    -------
+    tuple of list of tuples of strings
+        Same as head and tail, but items are right aligned when stacked
+        vertically.
+
+    Examples
+    --------
+    >>> _justify([['a', 'b']], [['abc', 'abcd']])
+    ([('  a', '   b')], [('abc', 'abcd')])
+    """
+    combined = head + tail
+
+    # For each position for the sequences in ``combined``,
+    # find the length of the largest string.
+    max_length = [0] * len(combined[0])
+    for inner_seq in combined:
+        length = [len(item) for item in inner_seq]
+        max_length = [max(x, y) for x, y in zip(max_length, length)]
+
+    # justify each item in each list-like in head and tail using max_length
+    head_tuples = [
+        tuple(x.rjust(max_len) for x, max_len in zip(seq, max_length)) for seq in head
+    ]
+    tail_tuples = [
+        tuple(x.rjust(max_len) for x, max_len in zip(seq, max_length)) for seq in tail
+    ]
+    return head_tuples, tail_tuples
+
+
+class PrettyDict(dict[_KT, _VT]):
+    """Dict extension to support abbreviated __repr__"""
+
+    def __repr__(self) -> str:
+        return pprint_thing(self)
+
+
+class _TextAdjustment:
+    def __init__(self) -> None:
+        self.encoding = get_option("display.encoding")
+
+    def len(self, text: str) -> int:
+        return len(text)
+
+    def justify(self, texts: Any, max_len: int, mode: str = "right") -> list[str]:
+        """
+        Perform ljust, center, rjust against string or list-like
+        """
+        if mode == "left":
+            return [x.ljust(max_len) for x in texts]
+        elif mode == "center":
+            return [x.center(max_len) for x in texts]
+        else:
+            return [x.rjust(max_len) for x in texts]
+
+    def adjoin(self, space: int, *lists, **kwargs) -> str:
+        return adjoin(space, *lists, strlen=self.len, justfunc=self.justify, **kwargs)
+
+
+class _EastAsianTextAdjustment(_TextAdjustment):
+    def __init__(self) -> None:
+        super().__init__()
+        if get_option("display.unicode.ambiguous_as_wide"):
+            self.ambiguous_width = 2
+        else:
+            self.ambiguous_width = 1
+
+        # Definition of East Asian Width
+        # https://unicode.org/reports/tr11/
+        # Ambiguous width can be changed by option
+        self._EAW_MAP = {"Na": 1, "N": 1, "W": 2, "F": 2, "H": 1}
+
+    def len(self, text: str) -> int:
+        """
+        Calculate display width considering unicode East Asian Width
+        """
+        if not isinstance(text, str):
+            return len(text)
+
+        return sum(
+            self._EAW_MAP.get(east_asian_width(c), self.ambiguous_width) for c in text
+        )
+
+    def justify(
+        self, texts: Iterable[str], max_len: int, mode: str = "right"
+    ) -> list[str]:
+        # re-calculate padding space per str considering East Asian Width
+        def _get_pad(t):
+            return max_len - self.len(t) + len(t)
+
+        if mode == "left":
+            return [x.ljust(_get_pad(x)) for x in texts]
+        elif mode == "center":
+            return [x.center(_get_pad(x)) for x in texts]
+        else:
+            return [x.rjust(_get_pad(x)) for x in texts]
+
+
+def get_adjustment() -> _TextAdjustment:
+    use_east_asian_width = get_option("display.unicode.east_asian_width")
+    if use_east_asian_width:
+        return _EastAsianTextAdjustment()
+    else:
+        return _TextAdjustment()
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/formats/string.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/formats/string.py
new file mode 100644
index 0000000000000000000000000000000000000000..cdad388592717dff79fde61bf35a12c0635034c1
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/formats/string.py
@@ -0,0 +1,206 @@
+"""
+Module for formatting output data in console (to string).
+"""
+from __future__ import annotations
+
+from shutil import get_terminal_size
+from typing import TYPE_CHECKING
+
+import numpy as np
+
+from pandas.io.formats.printing import pprint_thing
+
+if TYPE_CHECKING:
+    from collections.abc import Iterable
+
+    from pandas.io.formats.format import DataFrameFormatter
+
+
+class StringFormatter:
+    """Formatter for string representation of a dataframe."""
+
+    def __init__(self, fmt: DataFrameFormatter, line_width: int | None = None) -> None:
+        self.fmt = fmt
+        self.adj = fmt.adj
+        self.frame = fmt.frame
+        self.line_width = line_width
+
+    def to_string(self) -> str:
+        text = self._get_string_representation()
+        if self.fmt.should_show_dimensions:
+            text = f"{text}{self.fmt.dimensions_info}"
+        return text
+
+    def _get_strcols(self) -> list[list[str]]:
+        strcols = self.fmt.get_strcols()
+        if self.fmt.is_truncated:
+            strcols = self._insert_dot_separators(strcols)
+        return strcols
+
+    def _get_string_representation(self) -> str:
+        if self.fmt.frame.empty:
+            return self._empty_info_line
+
+        strcols = self._get_strcols()
+
+        if self.line_width is None:
+            # no need to wrap around just print the whole frame
+            return self.adj.adjoin(1, *strcols)
+
+        if self._need_to_wrap_around:
+            return self._join_multiline(strcols)
+
+        return self._fit_strcols_to_terminal_width(strcols)
+
+    @property
+    def _empty_info_line(self) -> str:
+        return (
+            f"Empty {type(self.frame).__name__}\n"
+            f"Columns: {pprint_thing(self.frame.columns)}\n"
+            f"Index: {pprint_thing(self.frame.index)}"
+        )
+
+    @property
+    def _need_to_wrap_around(self) -> bool:
+        return bool(self.fmt.max_cols is None or self.fmt.max_cols > 0)
+
+    def _insert_dot_separators(self, strcols: list[list[str]]) -> list[list[str]]:
+        str_index = self.fmt._get_formatted_index(self.fmt.tr_frame)
+        index_length = len(str_index)
+
+        if self.fmt.is_truncated_horizontally:
+            strcols = self._insert_dot_separator_horizontal(strcols, index_length)
+
+        if self.fmt.is_truncated_vertically:
+            strcols = self._insert_dot_separator_vertical(strcols, index_length)
+
+        return strcols
+
+    @property
+    def _adjusted_tr_col_num(self) -> int:
+        return self.fmt.tr_col_num + 1 if self.fmt.index else self.fmt.tr_col_num
+
+    def _insert_dot_separator_horizontal(
+        self, strcols: list[list[str]], index_length: int
+    ) -> list[list[str]]:
+        strcols.insert(self._adjusted_tr_col_num, [" ..."] * index_length)
+        return strcols
+
+    def _insert_dot_separator_vertical(
+        self, strcols: list[list[str]], index_length: int
+    ) -> list[list[str]]:
+        n_header_rows = index_length - len(self.fmt.tr_frame)
+        row_num = self.fmt.tr_row_num
+        for ix, col in enumerate(strcols):
+            cwidth = self.adj.len(col[row_num])
+
+            if self.fmt.is_truncated_horizontally:
+                is_dot_col = ix == self._adjusted_tr_col_num
+            else:
+                is_dot_col = False
+
+            if cwidth > 3 or is_dot_col:
+                dots = "..."
+            else:
+                dots = ".."
+
+            if ix == 0 and self.fmt.index:
+                dot_mode = "left"
+            elif is_dot_col:
+                cwidth = 4
+                dot_mode = "right"
+            else:
+                dot_mode = "right"
+
+            dot_str = self.adj.justify([dots], cwidth, mode=dot_mode)[0]
+            col.insert(row_num + n_header_rows, dot_str)
+        return strcols
+
+    def _join_multiline(self, strcols_input: Iterable[list[str]]) -> str:
+        lwidth = self.line_width
+        adjoin_width = 1
+        strcols = list(strcols_input)
+
+        if self.fmt.index:
+            idx = strcols.pop(0)
+            lwidth -= np.array([self.adj.len(x) for x in idx]).max() + adjoin_width
+
+        col_widths = [
+            np.array([self.adj.len(x) for x in col]).max() if len(col) > 0 else 0
+            for col in strcols
+        ]
+
+        assert lwidth is not None
+        col_bins = _binify(col_widths, lwidth)
+        nbins = len(col_bins)
+
+        str_lst = []
+        start = 0
+        for i, end in enumerate(col_bins):
+            row = strcols[start:end]
+            if self.fmt.index:
+                row.insert(0, idx)
+            if nbins > 1:
+                nrows = len(row[-1])
+                if end <= len(strcols) and i < nbins - 1:
+                    row.append([" \\"] + ["  "] * (nrows - 1))
+                else:
+                    row.append([" "] * nrows)
+            str_lst.append(self.adj.adjoin(adjoin_width, *row))
+            start = end
+        return "\n\n".join(str_lst)
+
+    def _fit_strcols_to_terminal_width(self, strcols: list[list[str]]) -> str:
+        from pandas import Series
+
+        lines = self.adj.adjoin(1, *strcols).split("\n")
+        max_len = Series(lines).str.len().max()
+        # plus truncate dot col
+        width, _ = get_terminal_size()
+        dif = max_len - width
+        # '+ 1' to avoid too wide repr (GH PR #17023)
+        adj_dif = dif + 1
+        col_lens = Series([Series(ele).str.len().max() for ele in strcols])
+        n_cols = len(col_lens)
+        counter = 0
+        while adj_dif > 0 and n_cols > 1:
+            counter += 1
+            mid = round(n_cols / 2)
+            mid_ix = col_lens.index[mid]
+            col_len = col_lens[mid_ix]
+            # adjoin adds one
+            adj_dif -= col_len + 1
+            col_lens = col_lens.drop(mid_ix)
+            n_cols = len(col_lens)
+
+        # subtract index column
+        max_cols_fitted = n_cols - self.fmt.index
+        # GH-21180. Ensure that we print at least two.
+        max_cols_fitted = max(max_cols_fitted, 2)
+        self.fmt.max_cols_fitted = max_cols_fitted
+
+        # Call again _truncate to cut frame appropriately
+        # and then generate string representation
+        self.fmt.truncate()
+        strcols = self._get_strcols()
+        return self.adj.adjoin(1, *strcols)
+
+
+def _binify(cols: list[int], line_width: int) -> list[int]:
+    adjoin_width = 1
+    bins = []
+    curr_width = 0
+    i_last_column = len(cols) - 1
+    for i, w in enumerate(cols):
+        w_adjoined = w + adjoin_width
+        curr_width += w_adjoined
+        if i_last_column == i:
+            wrap = curr_width + 1 > line_width and i > 0
+        else:
+            wrap = curr_width + 2 > line_width and i > 0
+        if wrap:
+            bins.append(i)
+            curr_width = w_adjoined
+
+    bins.append(len(cols))
+    return bins
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/formats/style.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/formats/style.py
new file mode 100644
index 0000000000000000000000000000000000000000..987577057e058e7dcc5f37ebdd0a8f6f4a302c20
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/formats/style.py
@@ -0,0 +1,4136 @@
+"""
+Module for applying conditional formatting to DataFrames and Series.
+"""
+from __future__ import annotations
+
+from contextlib import contextmanager
+import copy
+from functools import partial
+import operator
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    overload,
+)
+import warnings
+
+import numpy as np
+
+from pandas._config import get_option
+
+from pandas.compat._optional import import_optional_dependency
+from pandas.util._decorators import (
+    Substitution,
+    doc,
+)
+from pandas.util._exceptions import find_stack_level
+
+import pandas as pd
+from pandas import (
+    IndexSlice,
+    RangeIndex,
+)
+import pandas.core.common as com
+from pandas.core.frame import (
+    DataFrame,
+    Series,
+)
+from pandas.core.generic import NDFrame
+from pandas.core.shared_docs import _shared_docs
+
+from pandas.io.formats.format import save_to_buffer
+
+jinja2 = import_optional_dependency("jinja2", extra="DataFrame.style requires jinja2.")
+
+from pandas.io.formats.style_render import (
+    CSSProperties,
+    CSSStyles,
+    ExtFormatter,
+    StylerRenderer,
+    Subset,
+    Tooltips,
+    format_table_styles,
+    maybe_convert_css_to_tuples,
+    non_reducing_slice,
+    refactor_levels,
+)
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Generator,
+        Hashable,
+        Sequence,
+    )
+
+    from matplotlib.colors import Colormap
+
+    from pandas._typing import (
+        Axis,
+        AxisInt,
+        FilePath,
+        IndexLabel,
+        IntervalClosedType,
+        Level,
+        QuantileInterpolation,
+        Scalar,
+        StorageOptions,
+        WriteBuffer,
+        WriteExcelBuffer,
+    )
+
+    from pandas import ExcelWriter
+
+try:
+    import matplotlib as mpl
+    import matplotlib.pyplot as plt
+
+    has_mpl = True
+except ImportError:
+    has_mpl = False
+
+
+@contextmanager
+def _mpl(func: Callable) -> Generator[tuple[Any, Any], None, None]:
+    if has_mpl:
+        yield plt, mpl
+    else:
+        raise ImportError(f"{func.__name__} requires matplotlib.")
+
+
+####
+# Shared Doc Strings
+
+subset_args = """subset : label, array-like, IndexSlice, optional
+            A valid 2d input to `DataFrame.loc[]`, or, in the case of a 1d input
+            or single key, to `DataFrame.loc[:, ]` where the columns are
+            prioritised, to limit ``data`` to *before* applying the function."""
+
+properties_args = """props : str, default None
+           CSS properties to use for highlighting. If ``props`` is given, ``color``
+           is not used."""
+
+coloring_args = """color : str, default '{default}'
+           Background color to use for highlighting."""
+
+buffering_args = """buf : str, path object, file-like object, optional
+         String, path object (implementing ``os.PathLike[str]``), or file-like
+         object implementing a string ``write()`` function. If ``None``, the result is
+         returned as a string."""
+
+encoding_args = """encoding : str, optional
+              Character encoding setting for file output (and meta tags if available).
+              Defaults to ``pandas.options.styler.render.encoding`` value of "utf-8"."""
+
+#
+###
+
+
+class Styler(StylerRenderer):
+    r"""
+    Helps style a DataFrame or Series according to the data with HTML and CSS.
+
+    Parameters
+    ----------
+    data : Series or DataFrame
+        Data to be styled - either a Series or DataFrame.
+    precision : int, optional
+        Precision to round floats to. If not given defaults to
+        ``pandas.options.styler.format.precision``.
+
+        .. versionchanged:: 1.4.0
+    table_styles : list-like, default None
+        List of {selector: (attr, value)} dicts; see Notes.
+    uuid : str, default None
+        A unique identifier to avoid CSS collisions; generated automatically.
+    caption : str, tuple, default None
+        String caption to attach to the table. Tuple only used for LaTeX dual captions.
+    table_attributes : str, default None
+        Items that show up in the opening ``
) elements that will be rendered before
+    trimming will occur over columns, rows or both if needed.
+"""
+
+styler_max_rows = """
+: int, optional
+    The maximum number of rows that will be rendered. May still be reduced to
+    satisfy ``max_elements``, which takes precedence.
+"""
+
+styler_max_columns = """
+: int, optional
+    The maximum number of columns that will be rendered. May still be reduced to
+    satisfy ``max_elements``, which takes precedence.
+"""
+
+styler_precision = """
+: int
+    The precision for floats and complex numbers.
+"""
+
+styler_decimal = """
+: str
+    The character representation for the decimal separator for floats and complex.
+"""
+
+styler_thousands = """
+: str, optional
+    The character representation for thousands separator for floats, int and complex.
+"""
+
+styler_na_rep = """
+: str, optional
+    The string representation for values identified as missing.
+"""
+
+styler_escape = """
+: str, optional
+    Whether to escape certain characters according to the given context; html or latex.
+"""
+
+styler_formatter = """
+: str, callable, dict, optional
+    A formatter object to be used as default within ``Styler.format``.
+"""
+
+styler_multirow_align = """
+: {"c", "t", "b"}
+    The specifier for vertical alignment of sparsified LaTeX multirows.
+"""
+
+styler_multicol_align = r"""
+: {"r", "c", "l", "naive-l", "naive-r"}
+    The specifier for horizontal alignment of sparsified LaTeX multicolumns. Pipe
+    decorators can also be added to non-naive values to draw vertical
+    rules, e.g. "\|r" will draw a rule on the left side of right aligned merged cells.
+"""
+
+styler_hrules = """
+: bool
+    Whether to add horizontal rules on top and bottom and below the headers.
+"""
+
+styler_environment = """
+: str
+    The environment to replace ``\\begin{table}``. If "longtable" is used results
+    in a specific longtable environment format.
+"""
+
+styler_encoding = """
+: str
+    The encoding used for output HTML and LaTeX files.
+"""
+
+styler_mathjax = """
+: bool
+    If False will render special CSS classes to table attributes that indicate Mathjax
+    will not be used in Jupyter Notebook.
+"""
+
+with cf.config_prefix("styler"):
+    cf.register_option("sparse.index", True, styler_sparse_index_doc, validator=is_bool)
+
+    cf.register_option(
+        "sparse.columns", True, styler_sparse_columns_doc, validator=is_bool
+    )
+
+    cf.register_option(
+        "render.repr",
+        "html",
+        styler_render_repr,
+        validator=is_one_of_factory(["html", "latex"]),
+    )
+
+    cf.register_option(
+        "render.max_elements",
+        2**18,
+        styler_max_elements,
+        validator=is_nonnegative_int,
+    )
+
+    cf.register_option(
+        "render.max_rows",
+        None,
+        styler_max_rows,
+        validator=is_nonnegative_int,
+    )
+
+    cf.register_option(
+        "render.max_columns",
+        None,
+        styler_max_columns,
+        validator=is_nonnegative_int,
+    )
+
+    cf.register_option("render.encoding", "utf-8", styler_encoding, validator=is_str)
+
+    cf.register_option("format.decimal", ".", styler_decimal, validator=is_str)
+
+    cf.register_option(
+        "format.precision", 6, styler_precision, validator=is_nonnegative_int
+    )
+
+    cf.register_option(
+        "format.thousands",
+        None,
+        styler_thousands,
+        validator=is_instance_factory([type(None), str]),
+    )
+
+    cf.register_option(
+        "format.na_rep",
+        None,
+        styler_na_rep,
+        validator=is_instance_factory([type(None), str]),
+    )
+
+    cf.register_option(
+        "format.escape",
+        None,
+        styler_escape,
+        validator=is_one_of_factory([None, "html", "latex", "latex-math"]),
+    )
+
+    cf.register_option(
+        "format.formatter",
+        None,
+        styler_formatter,
+        validator=is_instance_factory([type(None), dict, Callable, str]),
+    )
+
+    cf.register_option("html.mathjax", True, styler_mathjax, validator=is_bool)
+
+    cf.register_option(
+        "latex.multirow_align",
+        "c",
+        styler_multirow_align,
+        validator=is_one_of_factory(["c", "t", "b", "naive"]),
+    )
+
+    val_mca = ["r", "|r|", "|r", "r|", "c", "|c|", "|c", "c|", "l", "|l|", "|l", "l|"]
+    val_mca += ["naive-l", "naive-r"]
+    cf.register_option(
+        "latex.multicol_align",
+        "r",
+        styler_multicol_align,
+        validator=is_one_of_factory(val_mca),
+    )
+
+    cf.register_option("latex.hrules", False, styler_hrules, validator=is_bool)
+
+    cf.register_option(
+        "latex.environment",
+        None,
+        styler_environment,
+        validator=is_instance_factory([type(None), str]),
+    )
+
+
+with cf.config_prefix("future"):
+    cf.register_option(
+        "infer_string",
+        True if os.environ.get("PANDAS_FUTURE_INFER_STRING", "0") == "1" else False,
+        "Whether to infer sequence of str objects as pyarrow string "
+        "dtype, which will be the default in pandas 3.0 "
+        "(at which point this option will be deprecated).",
+        validator=is_one_of_factory([True, False]),
+    )
+
+    cf.register_option(
+        "no_silent_downcasting",
+        False,
+        "Whether to opt-in to the future behavior which will *not* silently "
+        "downcast results from Series and DataFrame `where`, `mask`, and `clip` "
+        "methods. "
+        "Silent downcasting will be removed in pandas 3.0 "
+        "(at which point this option will be deprecated).",
+        validator=is_one_of_factory([True, False]),
+    )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/construction.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/construction.py
new file mode 100644
index 0000000000000000000000000000000000000000..59e87f28a3dce82395abc16d3bb3ef63d155c820
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/construction.py
@@ -0,0 +1,821 @@
+"""
+Constructor functions intended to be shared by pd.array, Series.__init__,
+and Index.__new__.
+
+These should not depend on core.internals.
+"""
+from __future__ import annotations
+
+from collections.abc import Sequence
+from typing import (
+    TYPE_CHECKING,
+    Optional,
+    Union,
+    cast,
+    overload,
+)
+import warnings
+
+import numpy as np
+from numpy import ma
+
+from pandas._config import using_string_dtype
+
+from pandas._libs import lib
+from pandas._libs.tslibs import (
+    Period,
+    get_supported_dtype,
+    is_supported_dtype,
+)
+from pandas._typing import (
+    AnyArrayLike,
+    ArrayLike,
+    Dtype,
+    DtypeObj,
+    T,
+)
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.base import ExtensionDtype
+from pandas.core.dtypes.cast import (
+    construct_1d_arraylike_from_scalar,
+    construct_1d_object_array_from_listlike,
+    maybe_cast_to_datetime,
+    maybe_cast_to_integer_array,
+    maybe_convert_platform,
+    maybe_infer_to_datetimelike,
+    maybe_promote,
+)
+from pandas.core.dtypes.common import (
+    is_list_like,
+    is_object_dtype,
+    is_string_dtype,
+    pandas_dtype,
+)
+from pandas.core.dtypes.dtypes import NumpyEADtype
+from pandas.core.dtypes.generic import (
+    ABCDataFrame,
+    ABCExtensionArray,
+    ABCIndex,
+    ABCSeries,
+)
+from pandas.core.dtypes.missing import isna
+
+import pandas.core.common as com
+
+if TYPE_CHECKING:
+    from pandas import (
+        Index,
+        Series,
+    )
+    from pandas.core.arrays.base import ExtensionArray
+
+
+def array(
+    data: Sequence[object] | AnyArrayLike,
+    dtype: Dtype | None = None,
+    copy: bool = True,
+) -> ExtensionArray:
+    """
+    Create an array.
+
+    Parameters
+    ----------
+    data : Sequence of objects
+        The scalars inside `data` should be instances of the
+        scalar type for `dtype`. It's expected that `data`
+        represents a 1-dimensional array of data.
+
+        When `data` is an Index or Series, the underlying array
+        will be extracted from `data`.
+
+    dtype : str, np.dtype, or ExtensionDtype, optional
+        The dtype to use for the array. This may be a NumPy
+        dtype or an extension type registered with pandas using
+        :meth:`pandas.api.extensions.register_extension_dtype`.
+
+        If not specified, there are two possibilities:
+
+        1. When `data` is a :class:`Series`, :class:`Index`, or
+           :class:`ExtensionArray`, the `dtype` will be taken
+           from the data.
+        2. Otherwise, pandas will attempt to infer the `dtype`
+           from the data.
+
+        Note that when `data` is a NumPy array, ``data.dtype`` is
+        *not* used for inferring the array type. This is because
+        NumPy cannot represent all the types of data that can be
+        held in extension arrays.
+
+        Currently, pandas will infer an extension dtype for sequences of
+
+        ============================== =======================================
+        Scalar Type                    Array Type
+        ============================== =======================================
+        :class:`pandas.Interval`       :class:`pandas.arrays.IntervalArray`
+        :class:`pandas.Period`         :class:`pandas.arrays.PeriodArray`
+        :class:`datetime.datetime`     :class:`pandas.arrays.DatetimeArray`
+        :class:`datetime.timedelta`    :class:`pandas.arrays.TimedeltaArray`
+        :class:`int`                   :class:`pandas.arrays.IntegerArray`
+        :class:`float`                 :class:`pandas.arrays.FloatingArray`
+        :class:`str`                   :class:`pandas.arrays.StringArray` or
+                                       :class:`pandas.arrays.ArrowStringArray`
+        :class:`bool`                  :class:`pandas.arrays.BooleanArray`
+        ============================== =======================================
+
+        The ExtensionArray created when the scalar type is :class:`str` is determined by
+        ``pd.options.mode.string_storage`` if the dtype is not explicitly given.
+
+        For all other cases, NumPy's usual inference rules will be used.
+    copy : bool, default True
+        Whether to copy the data, even if not necessary. Depending
+        on the type of `data`, creating the new array may require
+        copying data, even if ``copy=False``.
+
+    Returns
+    -------
+    ExtensionArray
+        The newly created array.
+
+    Raises
+    ------
+    ValueError
+        When `data` is not 1-dimensional.
+
+    See Also
+    --------
+    numpy.array : Construct a NumPy array.
+    Series : Construct a pandas Series.
+    Index : Construct a pandas Index.
+    arrays.NumpyExtensionArray : ExtensionArray wrapping a NumPy array.
+    Series.array : Extract the array stored within a Series.
+
+    Notes
+    -----
+    Omitting the `dtype` argument means pandas will attempt to infer the
+    best array type from the values in the data. As new array types are
+    added by pandas and 3rd party libraries, the "best" array type may
+    change. We recommend specifying `dtype` to ensure that
+
+    1. the correct array type for the data is returned
+    2. the returned array type doesn't change as new extension types
+       are added by pandas and third-party libraries
+
+    Additionally, if the underlying memory representation of the returned
+    array matters, we recommend specifying the `dtype` as a concrete object
+    rather than a string alias or allowing it to be inferred. For example,
+    a future version of pandas or a 3rd-party library may include a
+    dedicated ExtensionArray for string data. In this event, the following
+    would no longer return a :class:`arrays.NumpyExtensionArray` backed by a
+    NumPy array.
+
+    >>> pd.array(['a', 'b'], dtype=str)
+    
+    ['a', 'b']
+    Length: 2, dtype: str32
+
+    This would instead return the new ExtensionArray dedicated for string
+    data. If you really need the new array to be backed by a  NumPy array,
+    specify that in the dtype.
+
+    >>> pd.array(['a', 'b'], dtype=np.dtype("
+    ['a', 'b']
+    Length: 2, dtype: str32
+
+    Finally, Pandas has arrays that mostly overlap with NumPy
+
+      * :class:`arrays.DatetimeArray`
+      * :class:`arrays.TimedeltaArray`
+
+    When data with a ``datetime64[ns]`` or ``timedelta64[ns]`` dtype is
+    passed, pandas will always return a ``DatetimeArray`` or ``TimedeltaArray``
+    rather than a ``NumpyExtensionArray``. This is for symmetry with the case of
+    timezone-aware data, which NumPy does not natively support.
+
+    >>> pd.array(['2015', '2016'], dtype='datetime64[ns]')
+    
+    ['2015-01-01 00:00:00', '2016-01-01 00:00:00']
+    Length: 2, dtype: datetime64[ns]
+
+    >>> pd.array(["1h", "2h"], dtype='timedelta64[ns]')
+    
+    ['0 days 01:00:00', '0 days 02:00:00']
+    Length: 2, dtype: timedelta64[ns]
+
+    Examples
+    --------
+    If a dtype is not specified, pandas will infer the best dtype from the values.
+    See the description of `dtype` for the types pandas infers for.
+
+    >>> pd.array([1, 2])
+    
+    [1, 2]
+    Length: 2, dtype: Int64
+
+    >>> pd.array([1, 2, np.nan])
+    
+    [1, 2, ]
+    Length: 3, dtype: Int64
+
+    >>> pd.array([1.1, 2.2])
+    
+    [1.1, 2.2]
+    Length: 2, dtype: Float64
+
+    >>> pd.array(["a", None, "c"])
+    
+    ['a', , 'c']
+    Length: 3, dtype: string
+
+    >>> with pd.option_context("string_storage", "pyarrow"):
+    ...     arr = pd.array(["a", None, "c"])
+    ...
+    >>> arr
+    
+    ['a', , 'c']
+    Length: 3, dtype: string
+
+    >>> pd.array([pd.Period('2000', freq="D"), pd.Period("2000", freq="D")])
+    
+    ['2000-01-01', '2000-01-01']
+    Length: 2, dtype: period[D]
+
+    You can use the string alias for `dtype`
+
+    >>> pd.array(['a', 'b', 'a'], dtype='category')
+    ['a', 'b', 'a']
+    Categories (2, object): ['a', 'b']
+
+    Or specify the actual dtype
+
+    >>> pd.array(['a', 'b', 'a'],
+    ...          dtype=pd.CategoricalDtype(['a', 'b', 'c'], ordered=True))
+    ['a', 'b', 'a']
+    Categories (3, object): ['a' < 'b' < 'c']
+
+    If pandas does not infer a dedicated extension type a
+    :class:`arrays.NumpyExtensionArray` is returned.
+
+    >>> pd.array([1 + 1j, 3 + 2j])
+    
+    [(1+1j), (3+2j)]
+    Length: 2, dtype: complex128
+
+    As mentioned in the "Notes" section, new extension types may be added
+    in the future (by pandas or 3rd party libraries), causing the return
+    value to no longer be a :class:`arrays.NumpyExtensionArray`. Specify the
+    `dtype` as a NumPy dtype if you need to ensure there's no future change in
+    behavior.
+
+    >>> pd.array([1, 2], dtype=np.dtype("int32"))
+    
+    [1, 2]
+    Length: 2, dtype: int32
+
+    `data` must be 1-dimensional. A ValueError is raised when the input
+    has the wrong dimensionality.
+
+    >>> pd.array(1)
+    Traceback (most recent call last):
+      ...
+    ValueError: Cannot pass scalar '1' to 'pandas.array'.
+    """
+    from pandas.core.arrays import (
+        BooleanArray,
+        DatetimeArray,
+        ExtensionArray,
+        FloatingArray,
+        IntegerArray,
+        IntervalArray,
+        NumpyExtensionArray,
+        PeriodArray,
+        TimedeltaArray,
+    )
+    from pandas.core.arrays.string_ import StringDtype
+
+    if lib.is_scalar(data):
+        msg = f"Cannot pass scalar '{data}' to 'pandas.array'."
+        raise ValueError(msg)
+    elif isinstance(data, ABCDataFrame):
+        raise TypeError("Cannot pass DataFrame to 'pandas.array'")
+
+    if dtype is None and isinstance(data, (ABCSeries, ABCIndex, ExtensionArray)):
+        # Note: we exclude np.ndarray here, will do type inference on it
+        dtype = data.dtype
+
+    data = extract_array(data, extract_numpy=True)
+
+    # this returns None for not-found dtypes.
+    if dtype is not None:
+        dtype = pandas_dtype(dtype)
+
+    if isinstance(data, ExtensionArray) and (dtype is None or data.dtype == dtype):
+        # e.g. TimedeltaArray[s], avoid casting to NumpyExtensionArray
+        if copy:
+            return data.copy()
+        return data
+
+    if isinstance(dtype, ExtensionDtype):
+        cls = dtype.construct_array_type()
+        return cls._from_sequence(data, dtype=dtype, copy=copy)
+
+    if dtype is None:
+        inferred_dtype = lib.infer_dtype(data, skipna=True)
+        if inferred_dtype == "period":
+            period_data = cast(Union[Sequence[Optional[Period]], AnyArrayLike], data)
+            return PeriodArray._from_sequence(period_data, copy=copy)
+
+        elif inferred_dtype == "interval":
+            return IntervalArray(data, copy=copy)
+
+        elif inferred_dtype.startswith("datetime"):
+            # datetime, datetime64
+            try:
+                return DatetimeArray._from_sequence(data, copy=copy)
+            except ValueError:
+                # Mixture of timezones, fall back to NumpyExtensionArray
+                pass
+
+        elif inferred_dtype.startswith("timedelta"):
+            # timedelta, timedelta64
+            return TimedeltaArray._from_sequence(data, copy=copy)
+
+        elif inferred_dtype == "string":
+            # StringArray/ArrowStringArray depending on pd.options.mode.string_storage
+            dtype = StringDtype()
+            cls = dtype.construct_array_type()
+            return cls._from_sequence(data, dtype=dtype, copy=copy)
+
+        elif inferred_dtype == "integer":
+            return IntegerArray._from_sequence(data, copy=copy)
+        elif inferred_dtype == "empty" and not hasattr(data, "dtype") and not len(data):
+            return FloatingArray._from_sequence(data, copy=copy)
+        elif (
+            inferred_dtype in ("floating", "mixed-integer-float")
+            and getattr(data, "dtype", None) != np.float16
+        ):
+            # GH#44715 Exclude np.float16 bc FloatingArray does not support it;
+            #  we will fall back to NumpyExtensionArray.
+            return FloatingArray._from_sequence(data, copy=copy)
+
+        elif inferred_dtype == "boolean":
+            return BooleanArray._from_sequence(data, dtype="boolean", copy=copy)
+
+    # Pandas overrides NumPy for
+    #   1. datetime64[ns,us,ms,s]
+    #   2. timedelta64[ns,us,ms,s]
+    # so that a DatetimeArray is returned.
+    if lib.is_np_dtype(dtype, "M") and is_supported_dtype(dtype):
+        return DatetimeArray._from_sequence(data, dtype=dtype, copy=copy)
+    if lib.is_np_dtype(dtype, "m") and is_supported_dtype(dtype):
+        return TimedeltaArray._from_sequence(data, dtype=dtype, copy=copy)
+
+    elif lib.is_np_dtype(dtype, "mM"):
+        warnings.warn(
+            r"datetime64 and timedelta64 dtype resolutions other than "
+            r"'s', 'ms', 'us', and 'ns' are deprecated. "
+            r"In future releases passing unsupported resolutions will "
+            r"raise an exception.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+
+    return NumpyExtensionArray._from_sequence(data, dtype=dtype, copy=copy)
+
+
+_typs = frozenset(
+    {
+        "index",
+        "rangeindex",
+        "multiindex",
+        "datetimeindex",
+        "timedeltaindex",
+        "periodindex",
+        "categoricalindex",
+        "intervalindex",
+        "series",
+    }
+)
+
+
+@overload
+def extract_array(
+    obj: Series | Index, extract_numpy: bool = ..., extract_range: bool = ...
+) -> ArrayLike:
+    ...
+
+
+@overload
+def extract_array(
+    obj: T, extract_numpy: bool = ..., extract_range: bool = ...
+) -> T | ArrayLike:
+    ...
+
+
+def extract_array(
+    obj: T, extract_numpy: bool = False, extract_range: bool = False
+) -> T | ArrayLike:
+    """
+    Extract the ndarray or ExtensionArray from a Series or Index.
+
+    For all other types, `obj` is just returned as is.
+
+    Parameters
+    ----------
+    obj : object
+        For Series / Index, the underlying ExtensionArray is unboxed.
+
+    extract_numpy : bool, default False
+        Whether to extract the ndarray from a NumpyExtensionArray.
+
+    extract_range : bool, default False
+        If we have a RangeIndex, return range._values if True
+        (which is a materialized integer ndarray), otherwise return unchanged.
+
+    Returns
+    -------
+    arr : object
+
+    Examples
+    --------
+    >>> extract_array(pd.Series(['a', 'b', 'c'], dtype='category'))
+    ['a', 'b', 'c']
+    Categories (3, object): ['a', 'b', 'c']
+
+    Other objects like lists, arrays, and DataFrames are just passed through.
+
+    >>> extract_array([1, 2, 3])
+    [1, 2, 3]
+
+    For an ndarray-backed Series / Index the ndarray is returned.
+
+    >>> extract_array(pd.Series([1, 2, 3]))
+    array([1, 2, 3])
+
+    To extract all the way down to the ndarray, pass ``extract_numpy=True``.
+
+    >>> extract_array(pd.Series([1, 2, 3]), extract_numpy=True)
+    array([1, 2, 3])
+    """
+    typ = getattr(obj, "_typ", None)
+    if typ in _typs:
+        # i.e. isinstance(obj, (ABCIndex, ABCSeries))
+        if typ == "rangeindex":
+            if extract_range:
+                # error: "T" has no attribute "_values"
+                return obj._values  # type: ignore[attr-defined]
+            return obj
+
+        # error: "T" has no attribute "_values"
+        return obj._values  # type: ignore[attr-defined]
+
+    elif extract_numpy and typ == "npy_extension":
+        # i.e. isinstance(obj, ABCNumpyExtensionArray)
+        # error: "T" has no attribute "to_numpy"
+        return obj.to_numpy()  # type: ignore[attr-defined]
+
+    return obj
+
+
+def ensure_wrapped_if_datetimelike(arr):
+    """
+    Wrap datetime64 and timedelta64 ndarrays in DatetimeArray/TimedeltaArray.
+    """
+    if isinstance(arr, np.ndarray):
+        if arr.dtype.kind == "M":
+            from pandas.core.arrays import DatetimeArray
+
+            dtype = get_supported_dtype(arr.dtype)
+            return DatetimeArray._from_sequence(arr, dtype=dtype)
+
+        elif arr.dtype.kind == "m":
+            from pandas.core.arrays import TimedeltaArray
+
+            dtype = get_supported_dtype(arr.dtype)
+            return TimedeltaArray._from_sequence(arr, dtype=dtype)
+
+    return arr
+
+
+def sanitize_masked_array(data: ma.MaskedArray) -> np.ndarray:
+    """
+    Convert numpy MaskedArray to ensure mask is softened.
+    """
+    mask = ma.getmaskarray(data)
+    if mask.any():
+        dtype, fill_value = maybe_promote(data.dtype, np.nan)
+        dtype = cast(np.dtype, dtype)
+        data = ma.asarray(data.astype(dtype, copy=True))
+        data.soften_mask()  # set hardmask False if it was True
+        data[mask] = fill_value
+    else:
+        data = data.copy()
+    return data
+
+
+def sanitize_array(
+    data,
+    index: Index | None,
+    dtype: DtypeObj | None = None,
+    copy: bool = False,
+    *,
+    allow_2d: bool = False,
+) -> ArrayLike:
+    """
+    Sanitize input data to an ndarray or ExtensionArray, copy if specified,
+    coerce to the dtype if specified.
+
+    Parameters
+    ----------
+    data : Any
+    index : Index or None, default None
+    dtype : np.dtype, ExtensionDtype, or None, default None
+    copy : bool, default False
+    allow_2d : bool, default False
+        If False, raise if we have a 2D Arraylike.
+
+    Returns
+    -------
+    np.ndarray or ExtensionArray
+    """
+    original_dtype = dtype
+    if isinstance(data, ma.MaskedArray):
+        data = sanitize_masked_array(data)
+
+    if isinstance(dtype, NumpyEADtype):
+        # Avoid ending up with a NumpyExtensionArray
+        dtype = dtype.numpy_dtype
+
+    object_index = False
+    if isinstance(data, ABCIndex) and data.dtype == object and dtype is None:
+        object_index = True
+
+    # extract ndarray or ExtensionArray, ensure we have no NumpyExtensionArray
+    data = extract_array(data, extract_numpy=True, extract_range=True)
+
+    if isinstance(data, np.ndarray) and data.ndim == 0:
+        if dtype is None:
+            dtype = data.dtype
+        data = lib.item_from_zerodim(data)
+    elif isinstance(data, range):
+        # GH#16804
+        data = range_to_ndarray(data)
+        copy = False
+
+    if not is_list_like(data):
+        if index is None:
+            raise ValueError("index must be specified when data is not list-like")
+        if isinstance(data, str) and using_string_dtype() and original_dtype is None:
+            from pandas.core.arrays.string_ import StringDtype
+
+            dtype = StringDtype(na_value=np.nan)
+        data = construct_1d_arraylike_from_scalar(data, len(index), dtype)
+
+        return data
+
+    elif isinstance(data, ABCExtensionArray):
+        # it is already ensured above this is not a NumpyExtensionArray
+        # Until GH#49309 is fixed this check needs to come before the
+        #  ExtensionDtype check
+        if dtype is not None:
+            subarr = data.astype(dtype, copy=copy)
+        elif copy:
+            subarr = data.copy()
+        else:
+            subarr = data
+
+    elif isinstance(dtype, ExtensionDtype):
+        # create an extension array from its dtype
+        _sanitize_non_ordered(data)
+        cls = dtype.construct_array_type()
+        if not hasattr(data, "__array__"):
+            data = list(data)
+        subarr = cls._from_sequence(data, dtype=dtype, copy=copy)
+
+    # GH#846
+    elif isinstance(data, np.ndarray):
+        if isinstance(data, np.matrix):
+            data = data.A
+
+        if dtype is None:
+            subarr = data
+            if data.dtype == object:
+                subarr = maybe_infer_to_datetimelike(data)
+                if object_index and using_string_dtype() and is_string_dtype(subarr):
+                    # Avoid inference when string option is set
+                    subarr = data
+            elif data.dtype.kind == "U" and using_string_dtype():
+                from pandas.core.arrays.string_ import StringDtype
+
+                dtype = StringDtype(na_value=np.nan)
+                subarr = dtype.construct_array_type()._from_sequence(data, dtype=dtype)
+
+            if (
+                subarr is data
+                or (subarr.dtype == "str" and subarr.dtype.storage == "python")  # type: ignore[union-attr]
+            ) and copy:
+                subarr = subarr.copy()
+
+        else:
+            # we will try to copy by-definition here
+            subarr = _try_cast(data, dtype, copy)
+
+    elif hasattr(data, "__array__"):
+        # e.g. dask array GH#38645
+        if not copy:
+            data = np.asarray(data)
+        else:
+            data = np.array(data, copy=copy)
+        return sanitize_array(
+            data,
+            index=index,
+            dtype=dtype,
+            copy=False,
+            allow_2d=allow_2d,
+        )
+
+    else:
+        _sanitize_non_ordered(data)
+        # materialize e.g. generators, convert e.g. tuples, abc.ValueView
+        data = list(data)
+
+        if len(data) == 0 and dtype is None:
+            # We default to float64, matching numpy
+            subarr = np.array([], dtype=np.float64)
+
+        elif dtype is not None:
+            subarr = _try_cast(data, dtype, copy)
+
+        else:
+            subarr = maybe_convert_platform(data)
+            if subarr.dtype == object:
+                subarr = cast(np.ndarray, subarr)
+                subarr = maybe_infer_to_datetimelike(subarr)
+
+    subarr = _sanitize_ndim(subarr, data, dtype, index, allow_2d=allow_2d)
+
+    if isinstance(subarr, np.ndarray):
+        # at this point we should have dtype be None or subarr.dtype == dtype
+        dtype = cast(np.dtype, dtype)
+        subarr = _sanitize_str_dtypes(subarr, data, dtype, copy)
+
+    return subarr
+
+
+def range_to_ndarray(rng: range) -> np.ndarray:
+    """
+    Cast a range object to ndarray.
+    """
+    # GH#30171 perf avoid realizing range as a list in np.array
+    try:
+        arr = np.arange(rng.start, rng.stop, rng.step, dtype="int64")
+    except OverflowError:
+        # GH#30173 handling for ranges that overflow int64
+        if (rng.start >= 0 and rng.step > 0) or (rng.step < 0 <= rng.stop):
+            try:
+                arr = np.arange(rng.start, rng.stop, rng.step, dtype="uint64")
+            except OverflowError:
+                arr = construct_1d_object_array_from_listlike(list(rng))
+        else:
+            arr = construct_1d_object_array_from_listlike(list(rng))
+    return arr
+
+
+def _sanitize_non_ordered(data) -> None:
+    """
+    Raise only for unordered sets, e.g., not for dict_keys
+    """
+    if isinstance(data, (set, frozenset)):
+        raise TypeError(f"'{type(data).__name__}' type is unordered")
+
+
+def _sanitize_ndim(
+    result: ArrayLike,
+    data,
+    dtype: DtypeObj | None,
+    index: Index | None,
+    *,
+    allow_2d: bool = False,
+) -> ArrayLike:
+    """
+    Ensure we have a 1-dimensional result array.
+    """
+    if getattr(result, "ndim", 0) == 0:
+        raise ValueError("result should be arraylike with ndim > 0")
+
+    if result.ndim == 1:
+        # the result that we want
+        result = _maybe_repeat(result, index)
+
+    elif result.ndim > 1:
+        if isinstance(data, np.ndarray):
+            if allow_2d:
+                return result
+            raise ValueError(
+                f"Data must be 1-dimensional, got ndarray of shape {data.shape} instead"
+            )
+        if is_object_dtype(dtype) and isinstance(dtype, ExtensionDtype):
+            # i.e. NumpyEADtype("O")
+
+            result = com.asarray_tuplesafe(data, dtype=np.dtype("object"))
+            cls = dtype.construct_array_type()
+            result = cls._from_sequence(result, dtype=dtype)
+        else:
+            # error: Argument "dtype" to "asarray_tuplesafe" has incompatible type
+            # "Union[dtype[Any], ExtensionDtype, None]"; expected "Union[str,
+            # dtype[Any], None]"
+            result = com.asarray_tuplesafe(data, dtype=dtype)  # type: ignore[arg-type]
+    return result
+
+
+def _sanitize_str_dtypes(
+    result: np.ndarray, data, dtype: np.dtype | None, copy: bool
+) -> np.ndarray:
+    """
+    Ensure we have a dtype that is supported by pandas.
+    """
+
+    # This is to prevent mixed-type Series getting all casted to
+    # NumPy string type, e.g. NaN --> '-1#IND'.
+    if issubclass(result.dtype.type, str):
+        # GH#16605
+        # If not empty convert the data to dtype
+        # GH#19853: If data is a scalar, result has already the result
+        if not lib.is_scalar(data):
+            if not np.all(isna(data)):
+                data = np.asarray(data, dtype=dtype)
+            if not copy:
+                result = np.asarray(data, dtype=object)
+            else:
+                result = np.array(data, dtype=object, copy=copy)
+    return result
+
+
+def _maybe_repeat(arr: ArrayLike, index: Index | None) -> ArrayLike:
+    """
+    If we have a length-1 array and an index describing how long we expect
+    the result to be, repeat the array.
+    """
+    if index is not None:
+        if 1 == len(arr) != len(index):
+            arr = arr.repeat(len(index))
+    return arr
+
+
+def _try_cast(
+    arr: list | np.ndarray,
+    dtype: np.dtype,
+    copy: bool,
+) -> ArrayLike:
+    """
+    Convert input to numpy ndarray and optionally cast to a given dtype.
+
+    Parameters
+    ----------
+    arr : ndarray or list
+        Excludes: ExtensionArray, Series, Index.
+    dtype : np.dtype
+    copy : bool
+        If False, don't copy the data if not needed.
+
+    Returns
+    -------
+    np.ndarray or ExtensionArray
+    """
+    is_ndarray = isinstance(arr, np.ndarray)
+
+    if dtype == object:
+        if not is_ndarray:
+            subarr = construct_1d_object_array_from_listlike(arr)
+            return subarr
+        return ensure_wrapped_if_datetimelike(arr).astype(dtype, copy=copy)
+
+    elif dtype.kind == "U":
+        # TODO: test cases with arr.dtype.kind in "mM"
+        if is_ndarray:
+            arr = cast(np.ndarray, arr)
+            shape = arr.shape
+            if arr.ndim > 1:
+                arr = arr.ravel()
+        else:
+            shape = (len(arr),)
+        return lib.ensure_string_array(arr, convert_na_value=False, copy=copy).reshape(
+            shape
+        )
+
+    elif dtype.kind in "mM":
+        return maybe_cast_to_datetime(arr, dtype)
+
+    # GH#15832: Check if we are requesting a numeric dtype and
+    # that we can convert the data to the requested dtype.
+    elif dtype.kind in "iu":
+        # this will raise if we have e.g. floats
+
+        subarr = maybe_cast_to_integer_array(arr, dtype)
+    elif not copy:
+        subarr = np.asarray(arr, dtype=dtype)
+    else:
+        subarr = np.array(arr, dtype=dtype, copy=copy)
+
+    return subarr
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/dtypes/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/dtypes/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/dtypes/api.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/dtypes/api.py
new file mode 100644
index 0000000000000000000000000000000000000000..254abe330b8e7229d0c2a27519e82efbe902c537
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/dtypes/api.py
@@ -0,0 +1,85 @@
+from pandas.core.dtypes.common import (
+    is_any_real_numeric_dtype,
+    is_array_like,
+    is_bool,
+    is_bool_dtype,
+    is_categorical_dtype,
+    is_complex,
+    is_complex_dtype,
+    is_datetime64_any_dtype,
+    is_datetime64_dtype,
+    is_datetime64_ns_dtype,
+    is_datetime64tz_dtype,
+    is_dict_like,
+    is_dtype_equal,
+    is_extension_array_dtype,
+    is_file_like,
+    is_float,
+    is_float_dtype,
+    is_hashable,
+    is_int64_dtype,
+    is_integer,
+    is_integer_dtype,
+    is_interval,
+    is_interval_dtype,
+    is_iterator,
+    is_list_like,
+    is_named_tuple,
+    is_number,
+    is_numeric_dtype,
+    is_object_dtype,
+    is_period_dtype,
+    is_re,
+    is_re_compilable,
+    is_scalar,
+    is_signed_integer_dtype,
+    is_sparse,
+    is_string_dtype,
+    is_timedelta64_dtype,
+    is_timedelta64_ns_dtype,
+    is_unsigned_integer_dtype,
+    pandas_dtype,
+)
+
+__all__ = [
+    "is_any_real_numeric_dtype",
+    "is_array_like",
+    "is_bool",
+    "is_bool_dtype",
+    "is_categorical_dtype",
+    "is_complex",
+    "is_complex_dtype",
+    "is_datetime64_any_dtype",
+    "is_datetime64_dtype",
+    "is_datetime64_ns_dtype",
+    "is_datetime64tz_dtype",
+    "is_dict_like",
+    "is_dtype_equal",
+    "is_extension_array_dtype",
+    "is_file_like",
+    "is_float",
+    "is_float_dtype",
+    "is_hashable",
+    "is_int64_dtype",
+    "is_integer",
+    "is_integer_dtype",
+    "is_interval",
+    "is_interval_dtype",
+    "is_iterator",
+    "is_list_like",
+    "is_named_tuple",
+    "is_number",
+    "is_numeric_dtype",
+    "is_object_dtype",
+    "is_period_dtype",
+    "is_re",
+    "is_re_compilable",
+    "is_scalar",
+    "is_signed_integer_dtype",
+    "is_sparse",
+    "is_string_dtype",
+    "is_timedelta64_dtype",
+    "is_timedelta64_ns_dtype",
+    "is_unsigned_integer_dtype",
+    "pandas_dtype",
+]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/dtypes/astype.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/dtypes/astype.py
new file mode 100644
index 0000000000000000000000000000000000000000..f5579082c679bf131c056f3f2029b2485e88bd0d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/dtypes/astype.py
@@ -0,0 +1,301 @@
+"""
+Functions for implementing 'astype' methods according to pandas conventions,
+particularly ones that differ from numpy.
+"""
+from __future__ import annotations
+
+import inspect
+from typing import (
+    TYPE_CHECKING,
+    overload,
+)
+import warnings
+
+import numpy as np
+
+from pandas._libs import lib
+from pandas._libs.tslibs.timedeltas import array_to_timedelta64
+from pandas.errors import IntCastingNaNError
+
+from pandas.core.dtypes.common import (
+    is_object_dtype,
+    is_string_dtype,
+    pandas_dtype,
+)
+from pandas.core.dtypes.dtypes import (
+    ExtensionDtype,
+    NumpyEADtype,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        ArrayLike,
+        DtypeObj,
+        IgnoreRaise,
+    )
+
+    from pandas.core.arrays import ExtensionArray
+
+_dtype_obj = np.dtype(object)
+
+
+@overload
+def _astype_nansafe(
+    arr: np.ndarray, dtype: np.dtype, copy: bool = ..., skipna: bool = ...
+) -> np.ndarray:
+    ...
+
+
+@overload
+def _astype_nansafe(
+    arr: np.ndarray, dtype: ExtensionDtype, copy: bool = ..., skipna: bool = ...
+) -> ExtensionArray:
+    ...
+
+
+def _astype_nansafe(
+    arr: np.ndarray, dtype: DtypeObj, copy: bool = True, skipna: bool = False
+) -> ArrayLike:
+    """
+    Cast the elements of an array to a given dtype a nan-safe manner.
+
+    Parameters
+    ----------
+    arr : ndarray
+    dtype : np.dtype or ExtensionDtype
+    copy : bool, default True
+        If False, a view will be attempted but may fail, if
+        e.g. the item sizes don't align.
+    skipna: bool, default False
+        Whether or not we should skip NaN when casting as a string-type.
+
+    Raises
+    ------
+    ValueError
+        The dtype was a datetime64/timedelta64 dtype, but it had no unit.
+    """
+
+    # dispatch on extension dtype if needed
+    if isinstance(dtype, ExtensionDtype):
+        return dtype.construct_array_type()._from_sequence(arr, dtype=dtype, copy=copy)
+
+    elif not isinstance(dtype, np.dtype):  # pragma: no cover
+        raise ValueError("dtype must be np.dtype or ExtensionDtype")
+
+    if arr.dtype.kind in "mM":
+        from pandas.core.construction import ensure_wrapped_if_datetimelike
+
+        arr = ensure_wrapped_if_datetimelike(arr)
+        res = arr.astype(dtype, copy=copy)
+        return np.asarray(res)
+
+    if issubclass(dtype.type, str):
+        shape = arr.shape
+        if arr.ndim > 1:
+            arr = arr.ravel()
+        return lib.ensure_string_array(
+            arr, skipna=skipna, convert_na_value=False
+        ).reshape(shape)
+
+    elif np.issubdtype(arr.dtype, np.floating) and dtype.kind in "iu":
+        return _astype_float_to_int_nansafe(arr, dtype, copy)
+
+    elif arr.dtype == object:
+        # if we have a datetime/timedelta array of objects
+        # then coerce to datetime64[ns] and use DatetimeArray.astype
+
+        if lib.is_np_dtype(dtype, "M"):
+            from pandas.core.arrays import DatetimeArray
+
+            dta = DatetimeArray._from_sequence(arr, dtype=dtype)
+            return dta._ndarray
+
+        elif lib.is_np_dtype(dtype, "m"):
+            from pandas.core.construction import ensure_wrapped_if_datetimelike
+
+            # bc we know arr.dtype == object, this is equivalent to
+            #  `np.asarray(to_timedelta(arr))`, but using a lower-level API that
+            #  does not require a circular import.
+            tdvals = array_to_timedelta64(arr).view("m8[ns]")
+
+            tda = ensure_wrapped_if_datetimelike(tdvals)
+            return tda.astype(dtype, copy=False)._ndarray
+
+    if dtype.name in ("datetime64", "timedelta64"):
+        msg = (
+            f"The '{dtype.name}' dtype has no unit. Please pass in "
+            f"'{dtype.name}[ns]' instead."
+        )
+        raise ValueError(msg)
+
+    if copy or arr.dtype == object or dtype == object:
+        # Explicit copy, or required since NumPy can't view from / to object.
+        return arr.astype(dtype, copy=True)
+
+    return arr.astype(dtype, copy=copy)
+
+
+def _astype_float_to_int_nansafe(
+    values: np.ndarray, dtype: np.dtype, copy: bool
+) -> np.ndarray:
+    """
+    astype with a check preventing converting NaN to an meaningless integer value.
+    """
+    if not np.isfinite(values).all():
+        raise IntCastingNaNError(
+            "Cannot convert non-finite values (NA or inf) to integer"
+        )
+    if dtype.kind == "u":
+        # GH#45151
+        if not (values >= 0).all():
+            raise ValueError(f"Cannot losslessly cast from {values.dtype} to {dtype}")
+    with warnings.catch_warnings():
+        warnings.filterwarnings("ignore", category=RuntimeWarning)
+        return values.astype(dtype, copy=copy)
+
+
+def astype_array(values: ArrayLike, dtype: DtypeObj, copy: bool = False) -> ArrayLike:
+    """
+    Cast array (ndarray or ExtensionArray) to the new dtype.
+
+    Parameters
+    ----------
+    values : ndarray or ExtensionArray
+    dtype : dtype object
+    copy : bool, default False
+        copy if indicated
+
+    Returns
+    -------
+    ndarray or ExtensionArray
+    """
+    if values.dtype == dtype:
+        if copy:
+            return values.copy()
+        return values
+
+    if not isinstance(values, np.ndarray):
+        # i.e. ExtensionArray
+        values = values.astype(dtype, copy=copy)
+
+    else:
+        values = _astype_nansafe(values, dtype, copy=copy)
+
+    # in pandas we don't store numpy str dtypes, so convert to object
+    if isinstance(dtype, np.dtype) and issubclass(values.dtype.type, str):
+        values = np.array(values, dtype=object)
+
+    return values
+
+
+def astype_array_safe(
+    values: ArrayLike, dtype, copy: bool = False, errors: IgnoreRaise = "raise"
+) -> ArrayLike:
+    """
+    Cast array (ndarray or ExtensionArray) to the new dtype.
+
+    This basically is the implementation for DataFrame/Series.astype and
+    includes all custom logic for pandas (NaN-safety, converting str to object,
+    not allowing )
+
+    Parameters
+    ----------
+    values : ndarray or ExtensionArray
+    dtype : str, dtype convertible
+    copy : bool, default False
+        copy if indicated
+    errors : str, {'raise', 'ignore'}, default 'raise'
+        - ``raise`` : allow exceptions to be raised
+        - ``ignore`` : suppress exceptions. On error return original object
+
+    Returns
+    -------
+    ndarray or ExtensionArray
+    """
+    errors_legal_values = ("raise", "ignore")
+
+    if errors not in errors_legal_values:
+        invalid_arg = (
+            "Expected value of kwarg 'errors' to be one of "
+            f"{list(errors_legal_values)}. Supplied value is '{errors}'"
+        )
+        raise ValueError(invalid_arg)
+
+    if inspect.isclass(dtype) and issubclass(dtype, ExtensionDtype):
+        msg = (
+            f"Expected an instance of {dtype.__name__}, "
+            "but got the class instead. Try instantiating 'dtype'."
+        )
+        raise TypeError(msg)
+
+    dtype = pandas_dtype(dtype)
+    if isinstance(dtype, NumpyEADtype):
+        # Ensure we don't end up with a NumpyExtensionArray
+        dtype = dtype.numpy_dtype
+
+    try:
+        new_values = astype_array(values, dtype, copy=copy)
+    except (ValueError, TypeError):
+        # e.g. _astype_nansafe can fail on object-dtype of strings
+        #  trying to convert to float
+        if errors == "ignore":
+            new_values = values
+        else:
+            raise
+
+    return new_values
+
+
+def astype_is_view(dtype: DtypeObj, new_dtype: DtypeObj) -> bool:
+    """Checks if astype avoided copying the data.
+
+    Parameters
+    ----------
+    dtype : Original dtype
+    new_dtype : target dtype
+
+    Returns
+    -------
+    True if new data is a view or not guaranteed to be a copy, False otherwise
+    """
+    if isinstance(dtype, np.dtype) and not isinstance(new_dtype, np.dtype):
+        new_dtype, dtype = dtype, new_dtype
+
+    if dtype == new_dtype:
+        return True
+
+    elif isinstance(dtype, np.dtype) and isinstance(new_dtype, np.dtype):
+        # Only equal numpy dtypes avoid a copy
+        return False
+
+    elif is_string_dtype(dtype) and is_string_dtype(new_dtype):
+        # Potentially! a view when converting from object to string
+        return True
+
+    elif is_object_dtype(dtype) and new_dtype.kind == "O":
+        # When the underlying array has dtype object, we don't have to make a copy
+        return True
+
+    elif dtype.kind in "mM" and new_dtype.kind in "mM":
+        dtype = getattr(dtype, "numpy_dtype", dtype)
+        new_dtype = getattr(new_dtype, "numpy_dtype", new_dtype)
+        return getattr(dtype, "unit", None) == getattr(new_dtype, "unit", None)
+
+    numpy_dtype = getattr(dtype, "numpy_dtype", None)
+    new_numpy_dtype = getattr(new_dtype, "numpy_dtype", None)
+
+    if numpy_dtype is None and isinstance(dtype, np.dtype):
+        numpy_dtype = dtype
+
+    if new_numpy_dtype is None and isinstance(new_dtype, np.dtype):
+        new_numpy_dtype = new_dtype
+
+    if numpy_dtype is not None and new_numpy_dtype is not None:
+        # if both have NumPy dtype or one of them is a numpy dtype
+        # they are only a view when the numpy dtypes are equal, e.g.
+        # int64 -> Int64 or int64[pyarrow]
+        # int64 -> Int32 copies
+        return numpy_dtype == new_numpy_dtype
+
+    # Assume this is a view since we don't know for sure if a copy was made
+    return True
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/dtypes/base.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/dtypes/base.py
new file mode 100644
index 0000000000000000000000000000000000000000..6b00a5284ec5b18809e233e9ef89e31771ac651e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/dtypes/base.py
@@ -0,0 +1,583 @@
+"""
+Extend pandas with custom array types.
+"""
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    TypeVar,
+    cast,
+    overload,
+)
+
+import numpy as np
+
+from pandas._libs import missing as libmissing
+from pandas._libs.hashtable import object_hash
+from pandas._libs.properties import cache_readonly
+from pandas.errors import AbstractMethodError
+
+from pandas.core.dtypes.generic import (
+    ABCDataFrame,
+    ABCIndex,
+    ABCSeries,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        DtypeObj,
+        Self,
+        Shape,
+        npt,
+        type_t,
+    )
+
+    from pandas import Index
+    from pandas.core.arrays import ExtensionArray
+
+    # To parameterize on same ExtensionDtype
+    ExtensionDtypeT = TypeVar("ExtensionDtypeT", bound="ExtensionDtype")
+
+
+class ExtensionDtype:
+    """
+    A custom data type, to be paired with an ExtensionArray.
+
+    See Also
+    --------
+    extensions.register_extension_dtype: Register an ExtensionType
+        with pandas as class decorator.
+    extensions.ExtensionArray: Abstract base class for custom 1-D array types.
+
+    Notes
+    -----
+    The interface includes the following abstract methods that must
+    be implemented by subclasses:
+
+    * type
+    * name
+    * construct_array_type
+
+    The following attributes and methods influence the behavior of the dtype in
+    pandas operations
+
+    * _is_numeric
+    * _is_boolean
+    * _get_common_dtype
+
+    The `na_value` class attribute can be used to set the default NA value
+    for this type. :attr:`numpy.nan` is used by default.
+
+    ExtensionDtypes are required to be hashable. The base class provides
+    a default implementation, which relies on the ``_metadata`` class
+    attribute. ``_metadata`` should be a tuple containing the strings
+    that define your data type. For example, with ``PeriodDtype`` that's
+    the ``freq`` attribute.
+
+    **If you have a parametrized dtype you should set the ``_metadata``
+    class property**.
+
+    Ideally, the attributes in ``_metadata`` will match the
+    parameters to your ``ExtensionDtype.__init__`` (if any). If any of
+    the attributes in ``_metadata`` don't implement the standard
+    ``__eq__`` or ``__hash__``, the default implementations here will not
+    work.
+
+    Examples
+    --------
+
+    For interaction with Apache Arrow (pyarrow), a ``__from_arrow__`` method
+    can be implemented: this method receives a pyarrow Array or ChunkedArray
+    as only argument and is expected to return the appropriate pandas
+    ExtensionArray for this dtype and the passed values:
+
+    >>> import pyarrow
+    >>> from pandas.api.extensions import ExtensionArray
+    >>> class ExtensionDtype:
+    ...     def __from_arrow__(
+    ...         self,
+    ...         array: pyarrow.Array | pyarrow.ChunkedArray
+    ...     ) -> ExtensionArray:
+    ...         ...
+
+    This class does not inherit from 'abc.ABCMeta' for performance reasons.
+    Methods and properties required by the interface raise
+    ``pandas.errors.AbstractMethodError`` and no ``register`` method is
+    provided for registering virtual subclasses.
+    """
+
+    _metadata: tuple[str, ...] = ()
+
+    def __str__(self) -> str:
+        return self.name
+
+    def __eq__(self, other: object) -> bool:
+        """
+        Check whether 'other' is equal to self.
+
+        By default, 'other' is considered equal if either
+
+        * it's a string matching 'self.name'.
+        * it's an instance of this type and all of the attributes
+          in ``self._metadata`` are equal between `self` and `other`.
+
+        Parameters
+        ----------
+        other : Any
+
+        Returns
+        -------
+        bool
+        """
+        if isinstance(other, str):
+            try:
+                other = self.construct_from_string(other)
+            except TypeError:
+                return False
+        if isinstance(other, type(self)):
+            return all(
+                getattr(self, attr) == getattr(other, attr) for attr in self._metadata
+            )
+        return False
+
+    def __hash__(self) -> int:
+        # for python>=3.10, different nan objects have different hashes
+        # we need to avoid that and thus use hash function with old behavior
+        return object_hash(tuple(getattr(self, attr) for attr in self._metadata))
+
+    def __ne__(self, other: object) -> bool:
+        return not self.__eq__(other)
+
+    @property
+    def na_value(self) -> object:
+        """
+        Default NA value to use for this type.
+
+        This is used in e.g. ExtensionArray.take. This should be the
+        user-facing "boxed" version of the NA value, not the physical NA value
+        for storage.  e.g. for JSONArray, this is an empty dictionary.
+        """
+        return np.nan
+
+    @property
+    def type(self) -> type_t[Any]:
+        """
+        The scalar type for the array, e.g. ``int``
+
+        It's expected ``ExtensionArray[item]`` returns an instance
+        of ``ExtensionDtype.type`` for scalar ``item``, assuming
+        that value is valid (not NA). NA values do not need to be
+        instances of `type`.
+        """
+        raise AbstractMethodError(self)
+
+    @property
+    def kind(self) -> str:
+        """
+        A character code (one of 'biufcmMOSUV'), default 'O'
+
+        This should match the NumPy dtype used when the array is
+        converted to an ndarray, which is probably 'O' for object if
+        the extension type cannot be represented as a built-in NumPy
+        type.
+
+        See Also
+        --------
+        numpy.dtype.kind
+        """
+        return "O"
+
+    @property
+    def name(self) -> str:
+        """
+        A string identifying the data type.
+
+        Will be used for display in, e.g. ``Series.dtype``
+        """
+        raise AbstractMethodError(self)
+
+    @property
+    def names(self) -> list[str] | None:
+        """
+        Ordered list of field names, or None if there are no fields.
+
+        This is for compatibility with NumPy arrays, and may be removed in the
+        future.
+        """
+        return None
+
+    @classmethod
+    def construct_array_type(cls) -> type_t[ExtensionArray]:
+        """
+        Return the array type associated with this dtype.
+
+        Returns
+        -------
+        type
+        """
+        raise AbstractMethodError(cls)
+
+    def empty(self, shape: Shape) -> ExtensionArray:
+        """
+        Construct an ExtensionArray of this dtype with the given shape.
+
+        Analogous to numpy.empty.
+
+        Parameters
+        ----------
+        shape : int or tuple[int]
+
+        Returns
+        -------
+        ExtensionArray
+        """
+        cls = self.construct_array_type()
+        return cls._empty(shape, dtype=self)
+
+    @classmethod
+    def construct_from_string(cls, string: str) -> Self:
+        r"""
+        Construct this type from a string.
+
+        This is useful mainly for data types that accept parameters.
+        For example, a period dtype accepts a frequency parameter that
+        can be set as ``period[h]`` (where H means hourly frequency).
+
+        By default, in the abstract class, just the name of the type is
+        expected. But subclasses can overwrite this method to accept
+        parameters.
+
+        Parameters
+        ----------
+        string : str
+            The name of the type, for example ``category``.
+
+        Returns
+        -------
+        ExtensionDtype
+            Instance of the dtype.
+
+        Raises
+        ------
+        TypeError
+            If a class cannot be constructed from this 'string'.
+
+        Examples
+        --------
+        For extension dtypes with arguments the following may be an
+        adequate implementation.
+
+        >>> import re
+        >>> @classmethod
+        ... def construct_from_string(cls, string):
+        ...     pattern = re.compile(r"^my_type\[(?P.+)\]$")
+        ...     match = pattern.match(string)
+        ...     if match:
+        ...         return cls(**match.groupdict())
+        ...     else:
+        ...         raise TypeError(
+        ...             f"Cannot construct a '{cls.__name__}' from '{string}'"
+        ...         )
+        """
+        if not isinstance(string, str):
+            raise TypeError(
+                f"'construct_from_string' expects a string, got {type(string)}"
+            )
+        # error: Non-overlapping equality check (left operand type: "str", right
+        #  operand type: "Callable[[ExtensionDtype], str]")  [comparison-overlap]
+        assert isinstance(cls.name, str), (cls, type(cls.name))
+        if string != cls.name:
+            raise TypeError(f"Cannot construct a '{cls.__name__}' from '{string}'")
+        return cls()
+
+    @classmethod
+    def is_dtype(cls, dtype: object) -> bool:
+        """
+        Check if we match 'dtype'.
+
+        Parameters
+        ----------
+        dtype : object
+            The object to check.
+
+        Returns
+        -------
+        bool
+
+        Notes
+        -----
+        The default implementation is True if
+
+        1. ``cls.construct_from_string(dtype)`` is an instance
+           of ``cls``.
+        2. ``dtype`` is an object and is an instance of ``cls``
+        3. ``dtype`` has a ``dtype`` attribute, and any of the above
+           conditions is true for ``dtype.dtype``.
+        """
+        dtype = getattr(dtype, "dtype", dtype)
+
+        if isinstance(dtype, (ABCSeries, ABCIndex, ABCDataFrame, np.dtype)):
+            # https://github.com/pandas-dev/pandas/issues/22960
+            # avoid passing data to `construct_from_string`. This could
+            # cause a FutureWarning from numpy about failing elementwise
+            # comparison from, e.g., comparing DataFrame == 'category'.
+            return False
+        elif dtype is None:
+            return False
+        elif isinstance(dtype, cls):
+            return True
+        if isinstance(dtype, str):
+            try:
+                return cls.construct_from_string(dtype) is not None
+            except TypeError:
+                return False
+        return False
+
+    @property
+    def _is_numeric(self) -> bool:
+        """
+        Whether columns with this dtype should be considered numeric.
+
+        By default ExtensionDtypes are assumed to be non-numeric.
+        They'll be excluded from operations that exclude non-numeric
+        columns, like (groupby) reductions, plotting, etc.
+        """
+        return False
+
+    @property
+    def _is_boolean(self) -> bool:
+        """
+        Whether this dtype should be considered boolean.
+
+        By default, ExtensionDtypes are assumed to be non-numeric.
+        Setting this to True will affect the behavior of several places,
+        e.g.
+
+        * is_bool
+        * boolean indexing
+
+        Returns
+        -------
+        bool
+        """
+        return False
+
+    def _get_common_dtype(self, dtypes: list[DtypeObj]) -> DtypeObj | None:
+        """
+        Return the common dtype, if one exists.
+
+        Used in `find_common_type` implementation. This is for example used
+        to determine the resulting dtype in a concat operation.
+
+        If no common dtype exists, return None (which gives the other dtypes
+        the chance to determine a common dtype). If all dtypes in the list
+        return None, then the common dtype will be "object" dtype (this means
+        it is never needed to return "object" dtype from this method itself).
+
+        Parameters
+        ----------
+        dtypes : list of dtypes
+            The dtypes for which to determine a common dtype. This is a list
+            of np.dtype or ExtensionDtype instances.
+
+        Returns
+        -------
+        Common dtype (np.dtype or ExtensionDtype) or None
+        """
+        if len(set(dtypes)) == 1:
+            # only itself
+            return self
+        else:
+            return None
+
+    @property
+    def _can_hold_na(self) -> bool:
+        """
+        Can arrays of this dtype hold NA values?
+        """
+        return True
+
+    @property
+    def _is_immutable(self) -> bool:
+        """
+        Can arrays with this dtype be modified with __setitem__? If not, return
+        True.
+
+        Immutable arrays are expected to raise TypeError on __setitem__ calls.
+        """
+        return False
+
+    @cache_readonly
+    def index_class(self) -> type_t[Index]:
+        """
+        The Index subclass to return from Index.__new__ when this dtype is
+        encountered.
+        """
+        from pandas import Index
+
+        return Index
+
+    @property
+    def _supports_2d(self) -> bool:
+        """
+        Do ExtensionArrays with this dtype support 2D arrays?
+
+        Historically ExtensionArrays were limited to 1D. By returning True here,
+        authors can indicate that their arrays support 2D instances. This can
+        improve performance in some cases, particularly operations with `axis=1`.
+
+        Arrays that support 2D values should:
+
+            - implement Array.reshape
+            - subclass the Dim2CompatTests in tests.extension.base
+            - _concat_same_type should support `axis` keyword
+            - _reduce and reductions should support `axis` keyword
+        """
+        return False
+
+    @property
+    def _can_fast_transpose(self) -> bool:
+        """
+        Is transposing an array with this dtype zero-copy?
+
+        Only relevant for cases where _supports_2d is True.
+        """
+        return False
+
+
+class StorageExtensionDtype(ExtensionDtype):
+    """ExtensionDtype that may be backed by more than one implementation."""
+
+    name: str
+    _metadata = ("storage",)
+
+    def __init__(self, storage: str | None = None) -> None:
+        self.storage = storage
+
+    def __repr__(self) -> str:
+        return f"{self.name}[{self.storage}]"
+
+    def __str__(self) -> str:
+        return self.name
+
+    def __eq__(self, other: object) -> bool:
+        if isinstance(other, str) and other == self.name:
+            return True
+        return super().__eq__(other)
+
+    def __hash__(self) -> int:
+        # custom __eq__ so have to override __hash__
+        return super().__hash__()
+
+    @property
+    def na_value(self) -> libmissing.NAType:
+        return libmissing.NA
+
+
+def register_extension_dtype(cls: type_t[ExtensionDtypeT]) -> type_t[ExtensionDtypeT]:
+    """
+    Register an ExtensionType with pandas as class decorator.
+
+    This enables operations like ``.astype(name)`` for the name
+    of the ExtensionDtype.
+
+    Returns
+    -------
+    callable
+        A class decorator.
+
+    Examples
+    --------
+    >>> from pandas.api.extensions import register_extension_dtype, ExtensionDtype
+    >>> @register_extension_dtype
+    ... class MyExtensionDtype(ExtensionDtype):
+    ...     name = "myextension"
+    """
+    _registry.register(cls)
+    return cls
+
+
+class Registry:
+    """
+    Registry for dtype inference.
+
+    The registry allows one to map a string repr of a extension
+    dtype to an extension dtype. The string alias can be used in several
+    places, including
+
+    * Series and Index constructors
+    * :meth:`pandas.array`
+    * :meth:`pandas.Series.astype`
+
+    Multiple extension types can be registered.
+    These are tried in order.
+    """
+
+    def __init__(self) -> None:
+        self.dtypes: list[type_t[ExtensionDtype]] = []
+
+    def register(self, dtype: type_t[ExtensionDtype]) -> None:
+        """
+        Parameters
+        ----------
+        dtype : ExtensionDtype class
+        """
+        if not issubclass(dtype, ExtensionDtype):
+            raise ValueError("can only register pandas extension dtypes")
+
+        self.dtypes.append(dtype)
+
+    @overload
+    def find(self, dtype: type_t[ExtensionDtypeT]) -> type_t[ExtensionDtypeT]:
+        ...
+
+    @overload
+    def find(self, dtype: ExtensionDtypeT) -> ExtensionDtypeT:
+        ...
+
+    @overload
+    def find(self, dtype: str) -> ExtensionDtype | None:
+        ...
+
+    @overload
+    def find(
+        self, dtype: npt.DTypeLike
+    ) -> type_t[ExtensionDtype] | ExtensionDtype | None:
+        ...
+
+    def find(
+        self, dtype: type_t[ExtensionDtype] | ExtensionDtype | npt.DTypeLike
+    ) -> type_t[ExtensionDtype] | ExtensionDtype | None:
+        """
+        Parameters
+        ----------
+        dtype : ExtensionDtype class or instance or str or numpy dtype or python type
+
+        Returns
+        -------
+        return the first matching dtype, otherwise return None
+        """
+        if not isinstance(dtype, str):
+            dtype_type: type_t
+            if not isinstance(dtype, type):
+                dtype_type = type(dtype)
+            else:
+                dtype_type = dtype
+            if issubclass(dtype_type, ExtensionDtype):
+                # cast needed here as mypy doesn't know we have figured
+                # out it is an ExtensionDtype or type_t[ExtensionDtype]
+                return cast("ExtensionDtype | type_t[ExtensionDtype]", dtype)
+
+            return None
+
+        for dtype_type in self.dtypes:
+            try:
+                return dtype_type.construct_from_string(dtype)
+            except TypeError:
+                pass
+
+        return None
+
+
+_registry = Registry()
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/dtypes/cast.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/dtypes/cast.py
new file mode 100644
index 0000000000000000000000000000000000000000..9a7cfc0dec84ee09e1948ee5af885ae6170aae31
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/dtypes/cast.py
@@ -0,0 +1,1990 @@
+"""
+Routines for casting.
+"""
+
+from __future__ import annotations
+
+import datetime as dt
+import functools
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Literal,
+    TypeVar,
+    cast,
+    overload,
+)
+import warnings
+
+import numpy as np
+
+from pandas._config import using_string_dtype
+
+from pandas._libs import (
+    Interval,
+    Period,
+    lib,
+)
+from pandas._libs.missing import (
+    NA,
+    NAType,
+    checknull,
+)
+from pandas._libs.tslibs import (
+    NaT,
+    OutOfBoundsDatetime,
+    OutOfBoundsTimedelta,
+    Timedelta,
+    Timestamp,
+    is_supported_dtype,
+)
+from pandas._libs.tslibs.timedeltas import array_to_timedelta64
+from pandas.errors import (
+    IntCastingNaNError,
+    LossySetitemError,
+)
+
+from pandas.core.dtypes.common import (
+    ensure_int8,
+    ensure_int16,
+    ensure_int32,
+    ensure_int64,
+    ensure_object,
+    ensure_str,
+    is_bool,
+    is_complex,
+    is_float,
+    is_integer,
+    is_object_dtype,
+    is_scalar,
+    is_string_dtype,
+    pandas_dtype as pandas_dtype_func,
+)
+from pandas.core.dtypes.dtypes import (
+    ArrowDtype,
+    BaseMaskedDtype,
+    CategoricalDtype,
+    DatetimeTZDtype,
+    ExtensionDtype,
+    IntervalDtype,
+    PandasExtensionDtype,
+    PeriodDtype,
+)
+from pandas.core.dtypes.generic import (
+    ABCExtensionArray,
+    ABCIndex,
+    ABCSeries,
+)
+from pandas.core.dtypes.inference import is_list_like
+from pandas.core.dtypes.missing import (
+    is_valid_na_for_dtype,
+    isna,
+    na_value_for_dtype,
+    notna,
+)
+
+from pandas.io._util import _arrow_dtype_mapping
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Collection,
+        Sequence,
+    )
+
+    from pandas._typing import (
+        ArrayLike,
+        Dtype,
+        DtypeObj,
+        NumpyIndexT,
+        Scalar,
+        npt,
+    )
+
+    from pandas import Index
+    from pandas.core.arrays import (
+        Categorical,
+        DatetimeArray,
+        ExtensionArray,
+        IntervalArray,
+        PeriodArray,
+        TimedeltaArray,
+    )
+
+
+_int8_max = np.iinfo(np.int8).max
+_int16_max = np.iinfo(np.int16).max
+_int32_max = np.iinfo(np.int32).max
+
+_dtype_obj = np.dtype(object)
+
+NumpyArrayT = TypeVar("NumpyArrayT", bound=np.ndarray)
+
+
+def maybe_convert_platform(
+    values: list | tuple | range | np.ndarray | ExtensionArray,
+) -> ArrayLike:
+    """try to do platform conversion, allow ndarray or list here"""
+    arr: ArrayLike
+
+    if isinstance(values, (list, tuple, range)):
+        arr = construct_1d_object_array_from_listlike(values)
+    else:
+        # The caller is responsible for ensuring that we have np.ndarray
+        #  or ExtensionArray here.
+        arr = values
+
+    if arr.dtype == _dtype_obj:
+        arr = cast(np.ndarray, arr)
+        arr = lib.maybe_convert_objects(arr)
+
+    return arr
+
+
+def is_nested_object(obj) -> bool:
+    """
+    return a boolean if we have a nested object, e.g. a Series with 1 or
+    more Series elements
+
+    This may not be necessarily be performant.
+
+    """
+    return bool(
+        isinstance(obj, ABCSeries)
+        and is_object_dtype(obj.dtype)
+        and any(isinstance(v, ABCSeries) for v in obj._values)
+    )
+
+
+def maybe_box_datetimelike(value: Scalar, dtype: Dtype | None = None) -> Scalar:
+    """
+    Cast scalar to Timestamp or Timedelta if scalar is datetime-like
+    and dtype is not object.
+
+    Parameters
+    ----------
+    value : scalar
+    dtype : Dtype, optional
+
+    Returns
+    -------
+    scalar
+    """
+    if dtype == _dtype_obj:
+        pass
+    elif isinstance(value, (np.datetime64, dt.datetime)):
+        value = Timestamp(value)
+    elif isinstance(value, (np.timedelta64, dt.timedelta)):
+        value = Timedelta(value)
+
+    return value
+
+
+def maybe_box_native(value: Scalar | None | NAType) -> Scalar | None | NAType:
+    """
+    If passed a scalar cast the scalar to a python native type.
+
+    Parameters
+    ----------
+    value : scalar or Series
+
+    Returns
+    -------
+    scalar or Series
+    """
+    if is_float(value):
+        value = float(value)
+    elif is_integer(value):
+        value = int(value)
+    elif is_bool(value):
+        value = bool(value)
+    elif isinstance(value, (np.datetime64, np.timedelta64)):
+        value = maybe_box_datetimelike(value)
+    elif value is NA:
+        value = None
+    return value
+
+
+def _maybe_unbox_datetimelike(value: Scalar, dtype: DtypeObj) -> Scalar:
+    """
+    Convert a Timedelta or Timestamp to timedelta64 or datetime64 for setting
+    into a numpy array.  Failing to unbox would risk dropping nanoseconds.
+
+    Notes
+    -----
+    Caller is responsible for checking dtype.kind in "mM"
+    """
+    if is_valid_na_for_dtype(value, dtype):
+        # GH#36541: can't fill array directly with pd.NaT
+        # > np.empty(10, dtype="datetime64[ns]").fill(pd.NaT)
+        # ValueError: cannot convert float NaN to integer
+        value = dtype.type("NaT", "ns")
+    elif isinstance(value, Timestamp):
+        if value.tz is None:
+            value = value.to_datetime64()
+        elif not isinstance(dtype, DatetimeTZDtype):
+            raise TypeError("Cannot unbox tzaware Timestamp to tznaive dtype")
+    elif isinstance(value, Timedelta):
+        value = value.to_timedelta64()
+
+    _disallow_mismatched_datetimelike(value, dtype)
+    return value
+
+
+def _disallow_mismatched_datetimelike(value, dtype: DtypeObj):
+    """
+    numpy allows np.array(dt64values, dtype="timedelta64[ns]") and
+    vice-versa, but we do not want to allow this, so we need to
+    check explicitly
+    """
+    vdtype = getattr(value, "dtype", None)
+    if vdtype is None:
+        return
+    elif (vdtype.kind == "m" and dtype.kind == "M") or (
+        vdtype.kind == "M" and dtype.kind == "m"
+    ):
+        raise TypeError(f"Cannot cast {repr(value)} to {dtype}")
+
+
+@overload
+def maybe_downcast_to_dtype(result: np.ndarray, dtype: str | np.dtype) -> np.ndarray:
+    ...
+
+
+@overload
+def maybe_downcast_to_dtype(result: ExtensionArray, dtype: str | np.dtype) -> ArrayLike:
+    ...
+
+
+def maybe_downcast_to_dtype(result: ArrayLike, dtype: str | np.dtype) -> ArrayLike:
+    """
+    try to cast to the specified dtype (e.g. convert back to bool/int
+    or could be an astype of float64->float32
+    """
+    if isinstance(result, ABCSeries):
+        result = result._values
+    do_round = False
+
+    if isinstance(dtype, str):
+        if dtype == "infer":
+            inferred_type = lib.infer_dtype(result, skipna=False)
+            if inferred_type == "boolean":
+                dtype = "bool"
+            elif inferred_type == "integer":
+                dtype = "int64"
+            elif inferred_type == "datetime64":
+                dtype = "datetime64[ns]"
+            elif inferred_type in ["timedelta", "timedelta64"]:
+                dtype = "timedelta64[ns]"
+
+            # try to upcast here
+            elif inferred_type == "floating":
+                dtype = "int64"
+                if issubclass(result.dtype.type, np.number):
+                    do_round = True
+
+            else:
+                # TODO: complex?  what if result is already non-object?
+                dtype = "object"
+
+        dtype = np.dtype(dtype)
+
+    if not isinstance(dtype, np.dtype):
+        # enforce our signature annotation
+        raise TypeError(dtype)  # pragma: no cover
+
+    converted = maybe_downcast_numeric(result, dtype, do_round)
+    if converted is not result:
+        return converted
+
+    # a datetimelike
+    # GH12821, iNaT is cast to float
+    if dtype.kind in "mM" and result.dtype.kind in "if":
+        result = result.astype(dtype)
+
+    elif dtype.kind == "m" and result.dtype == _dtype_obj:
+        # test_where_downcast_to_td64
+        result = cast(np.ndarray, result)
+        result = array_to_timedelta64(result)
+
+    elif dtype == np.dtype("M8[ns]") and result.dtype == _dtype_obj:
+        result = cast(np.ndarray, result)
+        return np.asarray(maybe_cast_to_datetime(result, dtype=dtype))
+
+    return result
+
+
+@overload
+def maybe_downcast_numeric(
+    result: np.ndarray, dtype: np.dtype, do_round: bool = False
+) -> np.ndarray:
+    ...
+
+
+@overload
+def maybe_downcast_numeric(
+    result: ExtensionArray, dtype: DtypeObj, do_round: bool = False
+) -> ArrayLike:
+    ...
+
+
+def maybe_downcast_numeric(
+    result: ArrayLike, dtype: DtypeObj, do_round: bool = False
+) -> ArrayLike:
+    """
+    Subset of maybe_downcast_to_dtype restricted to numeric dtypes.
+
+    Parameters
+    ----------
+    result : ndarray or ExtensionArray
+    dtype : np.dtype or ExtensionDtype
+    do_round : bool
+
+    Returns
+    -------
+    ndarray or ExtensionArray
+    """
+    if not isinstance(dtype, np.dtype) or not isinstance(result.dtype, np.dtype):
+        # e.g. SparseDtype has no itemsize attr
+        return result
+
+    def trans(x):
+        if do_round:
+            return x.round()
+        return x
+
+    if dtype.kind == result.dtype.kind:
+        # don't allow upcasts here (except if empty)
+        if result.dtype.itemsize <= dtype.itemsize and result.size:
+            return result
+
+    if dtype.kind in "biu":
+        if not result.size:
+            # if we don't have any elements, just astype it
+            return trans(result).astype(dtype)
+
+        if isinstance(result, np.ndarray):
+            element = result.item(0)
+        else:
+            element = result.iloc[0]
+        if not isinstance(element, (np.integer, np.floating, int, float, bool)):
+            # a comparable, e.g. a Decimal may slip in here
+            return result
+
+        if (
+            issubclass(result.dtype.type, (np.object_, np.number))
+            and notna(result).all()
+        ):
+            new_result = trans(result).astype(dtype)
+            if new_result.dtype.kind == "O" or result.dtype.kind == "O":
+                # np.allclose may raise TypeError on object-dtype
+                if (new_result == result).all():
+                    return new_result
+            else:
+                if np.allclose(new_result, result, rtol=0):
+                    return new_result
+
+    elif (
+        issubclass(dtype.type, np.floating)
+        and result.dtype.kind != "b"
+        and not is_string_dtype(result.dtype)
+    ):
+        with warnings.catch_warnings():
+            warnings.filterwarnings(
+                "ignore", "overflow encountered in cast", RuntimeWarning
+            )
+            new_result = result.astype(dtype)
+
+        # Adjust tolerances based on floating point size
+        size_tols = {4: 5e-4, 8: 5e-8, 16: 5e-16}
+
+        atol = size_tols.get(new_result.dtype.itemsize, 0.0)
+
+        # Check downcast float values are still equal within 7 digits when
+        # converting from float64 to float32
+        if np.allclose(new_result, result, equal_nan=True, rtol=0.0, atol=atol):
+            return new_result
+
+    elif dtype.kind == result.dtype.kind == "c":
+        new_result = result.astype(dtype)
+
+        if np.array_equal(new_result, result, equal_nan=True):
+            # TODO: use tolerance like we do for float?
+            return new_result
+
+    return result
+
+
+def maybe_upcast_numeric_to_64bit(arr: NumpyIndexT) -> NumpyIndexT:
+    """
+    If array is a int/uint/float bit size lower than 64 bit, upcast it to 64 bit.
+
+    Parameters
+    ----------
+    arr : ndarray or ExtensionArray
+
+    Returns
+    -------
+    ndarray or ExtensionArray
+    """
+    dtype = arr.dtype
+    if dtype.kind == "i" and dtype != np.int64:
+        return arr.astype(np.int64)
+    elif dtype.kind == "u" and dtype != np.uint64:
+        return arr.astype(np.uint64)
+    elif dtype.kind == "f" and dtype != np.float64:
+        return arr.astype(np.float64)
+    else:
+        return arr
+
+
+def maybe_cast_pointwise_result(
+    result: ArrayLike,
+    dtype: DtypeObj,
+    numeric_only: bool = False,
+    same_dtype: bool = True,
+) -> ArrayLike:
+    """
+    Try casting result of a pointwise operation back to the original dtype if
+    appropriate.
+
+    Parameters
+    ----------
+    result : array-like
+        Result to cast.
+    dtype : np.dtype or ExtensionDtype
+        Input Series from which result was calculated.
+    numeric_only : bool, default False
+        Whether to cast only numerics or datetimes as well.
+    same_dtype : bool, default True
+        Specify dtype when calling _from_sequence
+
+    Returns
+    -------
+    result : array-like
+        result maybe casted to the dtype.
+    """
+
+    if isinstance(dtype, ExtensionDtype):
+        cls = dtype.construct_array_type()
+        if same_dtype:
+            result = _maybe_cast_to_extension_array(cls, result, dtype=dtype)
+        else:
+            result = _maybe_cast_to_extension_array(cls, result)
+
+    elif (numeric_only and dtype.kind in "iufcb") or not numeric_only:
+        result = maybe_downcast_to_dtype(result, dtype)
+
+    return result
+
+
+def _maybe_cast_to_extension_array(
+    cls: type[ExtensionArray], obj: ArrayLike, dtype: ExtensionDtype | None = None
+) -> ArrayLike:
+    """
+    Call to `_from_sequence` that returns the object unchanged on Exception.
+
+    Parameters
+    ----------
+    cls : class, subclass of ExtensionArray
+    obj : arraylike
+        Values to pass to cls._from_sequence
+    dtype : ExtensionDtype, optional
+
+    Returns
+    -------
+    ExtensionArray or obj
+    """
+    result: ArrayLike
+
+    if dtype is not None:
+        try:
+            result = cls._from_scalars(obj, dtype=dtype)
+        except (TypeError, ValueError):
+            return obj
+        return result
+
+    try:
+        result = cls._from_sequence(obj, dtype=dtype)
+    except Exception:
+        # We can't predict what downstream EA constructors may raise
+        result = obj
+    return result
+
+
+@overload
+def ensure_dtype_can_hold_na(dtype: np.dtype) -> np.dtype:
+    ...
+
+
+@overload
+def ensure_dtype_can_hold_na(dtype: ExtensionDtype) -> ExtensionDtype:
+    ...
+
+
+def ensure_dtype_can_hold_na(dtype: DtypeObj) -> DtypeObj:
+    """
+    If we have a dtype that cannot hold NA values, find the best match that can.
+    """
+    if isinstance(dtype, ExtensionDtype):
+        if dtype._can_hold_na:
+            return dtype
+        elif isinstance(dtype, IntervalDtype):
+            # TODO(GH#45349): don't special-case IntervalDtype, allow
+            #  overriding instead of returning object below.
+            return IntervalDtype(np.float64, closed=dtype.closed)
+        return _dtype_obj
+    elif dtype.kind == "b":
+        return _dtype_obj
+    elif dtype.kind in "iu":
+        return np.dtype(np.float64)
+    return dtype
+
+
+_canonical_nans = {
+    np.datetime64: np.datetime64("NaT", "ns"),
+    np.timedelta64: np.timedelta64("NaT", "ns"),
+    type(np.nan): np.nan,
+}
+
+
+def maybe_promote(dtype: np.dtype, fill_value=np.nan):
+    """
+    Find the minimal dtype that can hold both the given dtype and fill_value.
+
+    Parameters
+    ----------
+    dtype : np.dtype
+    fill_value : scalar, default np.nan
+
+    Returns
+    -------
+    dtype
+        Upcasted from dtype argument if necessary.
+    fill_value
+        Upcasted from fill_value argument if necessary.
+
+    Raises
+    ------
+    ValueError
+        If fill_value is a non-scalar and dtype is not object.
+    """
+    orig = fill_value
+    orig_is_nat = False
+    if checknull(fill_value):
+        # https://github.com/pandas-dev/pandas/pull/39692#issuecomment-1441051740
+        #  avoid cache misses with NaN/NaT values that are not singletons
+        if fill_value is not NA:
+            try:
+                orig_is_nat = np.isnat(fill_value)
+            except TypeError:
+                pass
+
+        fill_value = _canonical_nans.get(type(fill_value), fill_value)
+
+    # for performance, we are using a cached version of the actual implementation
+    # of the function in _maybe_promote. However, this doesn't always work (in case
+    # of non-hashable arguments), so we fallback to the actual implementation if needed
+    try:
+        # error: Argument 3 to "__call__" of "_lru_cache_wrapper" has incompatible type
+        # "Type[Any]"; expected "Hashable"  [arg-type]
+        dtype, fill_value = _maybe_promote_cached(
+            dtype, fill_value, type(fill_value)  # type: ignore[arg-type]
+        )
+    except TypeError:
+        # if fill_value is not hashable (required for caching)
+        dtype, fill_value = _maybe_promote(dtype, fill_value)
+
+    if (dtype == _dtype_obj and orig is not None) or (
+        orig_is_nat and np.datetime_data(orig)[0] != "ns"
+    ):
+        # GH#51592,53497 restore our potentially non-canonical fill_value
+        fill_value = orig
+    return dtype, fill_value
+
+
+@functools.lru_cache
+def _maybe_promote_cached(dtype, fill_value, fill_value_type):
+    # The cached version of _maybe_promote below
+    # This also use fill_value_type as (unused) argument to use this in the
+    # cache lookup -> to differentiate 1 and True
+    return _maybe_promote(dtype, fill_value)
+
+
+def _maybe_promote(dtype: np.dtype, fill_value=np.nan):
+    # The actual implementation of the function, use `maybe_promote` above for
+    # a cached version.
+    if not is_scalar(fill_value):
+        # with object dtype there is nothing to promote, and the user can
+        #  pass pretty much any weird fill_value they like
+        if dtype != object:
+            # with object dtype there is nothing to promote, and the user can
+            #  pass pretty much any weird fill_value they like
+            raise ValueError("fill_value must be a scalar")
+        dtype = _dtype_obj
+        return dtype, fill_value
+
+    if is_valid_na_for_dtype(fill_value, dtype) and dtype.kind in "iufcmM":
+        dtype = ensure_dtype_can_hold_na(dtype)
+        fv = na_value_for_dtype(dtype)
+        return dtype, fv
+
+    elif isinstance(dtype, CategoricalDtype):
+        if fill_value in dtype.categories or isna(fill_value):
+            return dtype, fill_value
+        else:
+            return object, ensure_object(fill_value)
+
+    elif isna(fill_value):
+        dtype = _dtype_obj
+        if fill_value is None:
+            # but we retain e.g. pd.NA
+            fill_value = np.nan
+        return dtype, fill_value
+
+    # returns tuple of (dtype, fill_value)
+    if issubclass(dtype.type, np.datetime64):
+        inferred, fv = infer_dtype_from_scalar(fill_value)
+        if inferred == dtype:
+            return dtype, fv
+
+        from pandas.core.arrays import DatetimeArray
+
+        dta = DatetimeArray._from_sequence([], dtype="M8[ns]")
+        try:
+            fv = dta._validate_setitem_value(fill_value)
+            return dta.dtype, fv
+        except (ValueError, TypeError):
+            return _dtype_obj, fill_value
+
+    elif issubclass(dtype.type, np.timedelta64):
+        inferred, fv = infer_dtype_from_scalar(fill_value)
+        if inferred == dtype:
+            return dtype, fv
+
+        elif inferred.kind == "m":
+            # different unit, e.g. passed np.timedelta64(24, "h") with dtype=m8[ns]
+            # see if we can losslessly cast it to our dtype
+            unit = np.datetime_data(dtype)[0]
+            try:
+                td = Timedelta(fill_value).as_unit(unit, round_ok=False)
+            except OutOfBoundsTimedelta:
+                return _dtype_obj, fill_value
+            else:
+                return dtype, td.asm8
+
+        return _dtype_obj, fill_value
+
+    elif is_float(fill_value):
+        if issubclass(dtype.type, np.bool_):
+            dtype = np.dtype(np.object_)
+
+        elif issubclass(dtype.type, np.integer):
+            dtype = np.dtype(np.float64)
+
+        elif dtype.kind == "f":
+            mst = np.min_scalar_type(fill_value)
+            if mst > dtype:
+                # e.g. mst is np.float64 and dtype is np.float32
+                dtype = mst
+
+        elif dtype.kind == "c":
+            mst = np.min_scalar_type(fill_value)
+            dtype = np.promote_types(dtype, mst)
+
+    elif is_bool(fill_value):
+        if not issubclass(dtype.type, np.bool_):
+            dtype = np.dtype(np.object_)
+
+    elif is_integer(fill_value):
+        if issubclass(dtype.type, np.bool_):
+            dtype = np.dtype(np.object_)
+
+        elif issubclass(dtype.type, np.integer):
+            if not np_can_cast_scalar(fill_value, dtype):  # type: ignore[arg-type]
+                # upcast to prevent overflow
+                mst = np.min_scalar_type(fill_value)
+                dtype = np.promote_types(dtype, mst)
+                if dtype.kind == "f":
+                    # Case where we disagree with numpy
+                    dtype = np.dtype(np.object_)
+
+    elif is_complex(fill_value):
+        if issubclass(dtype.type, np.bool_):
+            dtype = np.dtype(np.object_)
+
+        elif issubclass(dtype.type, (np.integer, np.floating)):
+            mst = np.min_scalar_type(fill_value)
+            dtype = np.promote_types(dtype, mst)
+
+        elif dtype.kind == "c":
+            mst = np.min_scalar_type(fill_value)
+            if mst > dtype:
+                # e.g. mst is np.complex128 and dtype is np.complex64
+                dtype = mst
+
+    else:
+        dtype = np.dtype(np.object_)
+
+    # in case we have a string that looked like a number
+    if issubclass(dtype.type, (bytes, str)):
+        dtype = np.dtype(np.object_)
+
+    fill_value = _ensure_dtype_type(fill_value, dtype)
+    return dtype, fill_value
+
+
+def _ensure_dtype_type(value, dtype: np.dtype):
+    """
+    Ensure that the given value is an instance of the given dtype.
+
+    e.g. if out dtype is np.complex64_, we should have an instance of that
+    as opposed to a python complex object.
+
+    Parameters
+    ----------
+    value : object
+    dtype : np.dtype
+
+    Returns
+    -------
+    object
+    """
+    # Start with exceptions in which we do _not_ cast to numpy types
+
+    if dtype == _dtype_obj:
+        return value
+
+    # Note: before we get here we have already excluded isna(value)
+    return dtype.type(value)
+
+
+def infer_dtype_from(val) -> tuple[DtypeObj, Any]:
+    """
+    Interpret the dtype from a scalar or array.
+
+    Parameters
+    ----------
+    val : object
+    """
+    if not is_list_like(val):
+        return infer_dtype_from_scalar(val)
+    return infer_dtype_from_array(val)
+
+
+def infer_dtype_from_scalar(val) -> tuple[DtypeObj, Any]:
+    """
+    Interpret the dtype from a scalar.
+
+    Parameters
+    ----------
+    val : object
+    """
+    dtype: DtypeObj = _dtype_obj
+
+    # a 1-element ndarray
+    if isinstance(val, np.ndarray):
+        if val.ndim != 0:
+            msg = "invalid ndarray passed to infer_dtype_from_scalar"
+            raise ValueError(msg)
+
+        dtype = val.dtype
+        val = lib.item_from_zerodim(val)
+
+    elif isinstance(val, str):
+        # If we create an empty array using a string to infer
+        # the dtype, NumPy will only allocate one character per entry
+        # so this is kind of bad. Alternately we could use np.repeat
+        # instead of np.empty (but then you still don't want things
+        # coming out as np.str_!
+
+        dtype = _dtype_obj
+        if using_string_dtype():
+            from pandas.core.arrays.string_ import StringDtype
+
+            dtype = StringDtype(na_value=np.nan)
+
+    elif isinstance(val, (np.datetime64, dt.datetime)):
+        try:
+            val = Timestamp(val)
+        except OutOfBoundsDatetime:
+            return _dtype_obj, val
+
+        if val is NaT or val.tz is None:
+            val = val.to_datetime64()
+            dtype = val.dtype
+            # TODO: test with datetime(2920, 10, 1) based on test_replace_dtypes
+        else:
+            dtype = DatetimeTZDtype(unit=val.unit, tz=val.tz)
+
+    elif isinstance(val, (np.timedelta64, dt.timedelta)):
+        try:
+            val = Timedelta(val)
+        except (OutOfBoundsTimedelta, OverflowError):
+            dtype = _dtype_obj
+        else:
+            if val is NaT:
+                val = np.timedelta64("NaT", "ns")
+            else:
+                val = val.asm8
+            dtype = val.dtype
+
+    elif is_bool(val):
+        dtype = np.dtype(np.bool_)
+
+    elif is_integer(val):
+        if isinstance(val, np.integer):
+            dtype = np.dtype(type(val))
+        else:
+            dtype = np.dtype(np.int64)
+
+        try:
+            np.array(val, dtype=dtype)
+        except OverflowError:
+            dtype = np.array(val).dtype
+
+    elif is_float(val):
+        if isinstance(val, np.floating):
+            dtype = np.dtype(type(val))
+        else:
+            dtype = np.dtype(np.float64)
+
+    elif is_complex(val):
+        dtype = np.dtype(np.complex128)
+
+    if isinstance(val, Period):
+        dtype = PeriodDtype(freq=val.freq)
+    elif isinstance(val, Interval):
+        subtype = infer_dtype_from_scalar(val.left)[0]
+        dtype = IntervalDtype(subtype=subtype, closed=val.closed)
+
+    return dtype, val
+
+
+def dict_compat(d: dict[Scalar, Scalar]) -> dict[Scalar, Scalar]:
+    """
+    Convert datetimelike-keyed dicts to a Timestamp-keyed dict.
+
+    Parameters
+    ----------
+    d: dict-like object
+
+    Returns
+    -------
+    dict
+    """
+    return {maybe_box_datetimelike(key): value for key, value in d.items()}
+
+
+def infer_dtype_from_array(arr) -> tuple[DtypeObj, ArrayLike]:
+    """
+    Infer the dtype from an array.
+
+    Parameters
+    ----------
+    arr : array
+
+    Returns
+    -------
+    tuple (pandas-compat dtype, array)
+
+
+    Examples
+    --------
+    >>> np.asarray([1, '1'])
+    array(['1', '1'], dtype='>> infer_dtype_from_array([1, '1'])
+    (dtype('O'), [1, '1'])
+    """
+    if isinstance(arr, np.ndarray):
+        return arr.dtype, arr
+
+    if not is_list_like(arr):
+        raise TypeError("'arr' must be list-like")
+
+    arr_dtype = getattr(arr, "dtype", None)
+    if isinstance(arr_dtype, ExtensionDtype):
+        return arr.dtype, arr
+
+    elif isinstance(arr, ABCSeries):
+        return arr.dtype, np.asarray(arr)
+
+    # don't force numpy coerce with nan's
+    inferred = lib.infer_dtype(arr, skipna=False)
+    if inferred in ["string", "bytes", "mixed", "mixed-integer"]:
+        return (np.dtype(np.object_), arr)
+
+    arr = np.asarray(arr)
+    return arr.dtype, arr
+
+
+def _maybe_infer_dtype_type(element):
+    """
+    Try to infer an object's dtype, for use in arithmetic ops.
+
+    Uses `element.dtype` if that's available.
+    Objects implementing the iterator protocol are cast to a NumPy array,
+    and from there the array's type is used.
+
+    Parameters
+    ----------
+    element : object
+        Possibly has a `.dtype` attribute, and possibly the iterator
+        protocol.
+
+    Returns
+    -------
+    tipo : type
+
+    Examples
+    --------
+    >>> from collections import namedtuple
+    >>> Foo = namedtuple("Foo", "dtype")
+    >>> _maybe_infer_dtype_type(Foo(np.dtype("i8")))
+    dtype('int64')
+    """
+    tipo = None
+    if hasattr(element, "dtype"):
+        tipo = element.dtype
+    elif is_list_like(element):
+        element = np.asarray(element)
+        tipo = element.dtype
+    return tipo
+
+
+def invalidate_string_dtypes(dtype_set: set[DtypeObj]) -> None:
+    """
+    Change string like dtypes to object for
+    ``DataFrame.select_dtypes()``.
+    """
+    # error: Argument 1 to  has incompatible type "Type[generic]"; expected
+    # "Union[dtype[Any], ExtensionDtype, None]"
+    # error: Argument 2 to  has incompatible type "Type[generic]"; expected
+    # "Union[dtype[Any], ExtensionDtype, None]"
+    non_string_dtypes = dtype_set - {
+        np.dtype("S").type,  # type: ignore[arg-type]
+        np.dtype(" np.ndarray:
+    """coerce the indexer input array to the smallest dtype possible"""
+    length = len(categories)
+    if length < _int8_max:
+        return ensure_int8(indexer)
+    elif length < _int16_max:
+        return ensure_int16(indexer)
+    elif length < _int32_max:
+        return ensure_int32(indexer)
+    return ensure_int64(indexer)
+
+
+def convert_dtypes(
+    input_array: ArrayLike,
+    convert_string: bool = True,
+    convert_integer: bool = True,
+    convert_boolean: bool = True,
+    convert_floating: bool = True,
+    infer_objects: bool = False,
+    dtype_backend: Literal["numpy_nullable", "pyarrow"] = "numpy_nullable",
+) -> DtypeObj:
+    """
+    Convert objects to best possible type, and optionally,
+    to types supporting ``pd.NA``.
+
+    Parameters
+    ----------
+    input_array : ExtensionArray or np.ndarray
+    convert_string : bool, default True
+        Whether object dtypes should be converted to ``StringDtype()``.
+    convert_integer : bool, default True
+        Whether, if possible, conversion can be done to integer extension types.
+    convert_boolean : bool, defaults True
+        Whether object dtypes should be converted to ``BooleanDtypes()``.
+    convert_floating : bool, defaults True
+        Whether, if possible, conversion can be done to floating extension types.
+        If `convert_integer` is also True, preference will be give to integer
+        dtypes if the floats can be faithfully casted to integers.
+    infer_objects : bool, defaults False
+        Whether to also infer objects to float/int if possible. Is only hit if the
+        object array contains pd.NA.
+    dtype_backend : {'numpy_nullable', 'pyarrow'}, default 'numpy_nullable'
+        Back-end data type applied to the resultant :class:`DataFrame`
+        (still experimental). Behaviour is as follows:
+
+        * ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame`
+          (default).
+        * ``"pyarrow"``: returns pyarrow-backed nullable :class:`ArrowDtype`
+          DataFrame.
+
+        .. versionadded:: 2.0
+
+    Returns
+    -------
+    np.dtype, or ExtensionDtype
+    """
+    from pandas.core.arrays.string_ import StringDtype
+
+    inferred_dtype: str | DtypeObj
+
+    if (
+        convert_string or convert_integer or convert_boolean or convert_floating
+    ) and isinstance(input_array, np.ndarray):
+        if input_array.dtype == object:
+            inferred_dtype = lib.infer_dtype(input_array)
+        else:
+            inferred_dtype = input_array.dtype
+
+        if is_string_dtype(inferred_dtype):
+            if not convert_string or inferred_dtype == "bytes":
+                inferred_dtype = input_array.dtype
+            else:
+                inferred_dtype = pandas_dtype_func("string")
+
+        if convert_integer:
+            target_int_dtype = pandas_dtype_func("Int64")
+
+            if input_array.dtype.kind in "iu":
+                from pandas.core.arrays.integer import NUMPY_INT_TO_DTYPE
+
+                inferred_dtype = NUMPY_INT_TO_DTYPE.get(
+                    input_array.dtype, target_int_dtype
+                )
+            elif input_array.dtype.kind in "fcb":
+                # TODO: de-dup with maybe_cast_to_integer_array?
+                arr = input_array[notna(input_array)]
+                if (arr.astype(int) == arr).all():
+                    inferred_dtype = target_int_dtype
+                else:
+                    inferred_dtype = input_array.dtype
+            elif (
+                infer_objects
+                and input_array.dtype == object
+                and (isinstance(inferred_dtype, str) and inferred_dtype == "integer")
+            ):
+                inferred_dtype = target_int_dtype
+
+        if convert_floating:
+            if input_array.dtype.kind in "fcb":
+                # i.e. numeric but not integer
+                from pandas.core.arrays.floating import NUMPY_FLOAT_TO_DTYPE
+
+                inferred_float_dtype: DtypeObj = NUMPY_FLOAT_TO_DTYPE.get(
+                    input_array.dtype, pandas_dtype_func("Float64")
+                )
+                # if we could also convert to integer, check if all floats
+                # are actually integers
+                if convert_integer:
+                    # TODO: de-dup with maybe_cast_to_integer_array?
+                    arr = input_array[notna(input_array)]
+                    if (arr.astype(int) == arr).all():
+                        inferred_dtype = pandas_dtype_func("Int64")
+                    else:
+                        inferred_dtype = inferred_float_dtype
+                else:
+                    inferred_dtype = inferred_float_dtype
+            elif (
+                infer_objects
+                and input_array.dtype == object
+                and (
+                    isinstance(inferred_dtype, str)
+                    and inferred_dtype == "mixed-integer-float"
+                )
+            ):
+                inferred_dtype = pandas_dtype_func("Float64")
+
+        if convert_boolean:
+            if input_array.dtype.kind == "b":
+                inferred_dtype = pandas_dtype_func("boolean")
+            elif isinstance(inferred_dtype, str) and inferred_dtype == "boolean":
+                inferred_dtype = pandas_dtype_func("boolean")
+
+        if isinstance(inferred_dtype, str):
+            # If we couldn't do anything else, then we retain the dtype
+            inferred_dtype = input_array.dtype
+
+    elif (
+        convert_string
+        and isinstance(input_array.dtype, StringDtype)
+        and input_array.dtype.na_value is np.nan
+    ):
+        inferred_dtype = pandas_dtype_func("string")
+
+    else:
+        inferred_dtype = input_array.dtype
+
+    if dtype_backend == "pyarrow":
+        from pandas.core.arrays.arrow.array import to_pyarrow_type
+
+        assert not isinstance(inferred_dtype, str)
+
+        if (
+            (convert_integer and inferred_dtype.kind in "iu")
+            or (convert_floating and inferred_dtype.kind in "fc")
+            or (convert_boolean and inferred_dtype.kind == "b")
+            or (convert_string and isinstance(inferred_dtype, StringDtype))
+            or (
+                inferred_dtype.kind not in "iufcb"
+                and not isinstance(inferred_dtype, StringDtype)
+            )
+        ):
+            if isinstance(inferred_dtype, PandasExtensionDtype) and not isinstance(
+                inferred_dtype, DatetimeTZDtype
+            ):
+                base_dtype = inferred_dtype.base
+            elif isinstance(inferred_dtype, (BaseMaskedDtype, ArrowDtype)):
+                base_dtype = inferred_dtype.numpy_dtype
+            elif isinstance(inferred_dtype, StringDtype):
+                base_dtype = np.dtype(str)
+            else:
+                base_dtype = inferred_dtype
+            if (
+                base_dtype.kind == "O"  # type: ignore[union-attr]
+                and input_array.size > 0
+                and isna(input_array).all()
+            ):
+                import pyarrow as pa
+
+                pa_type = pa.null()
+            else:
+                pa_type = to_pyarrow_type(base_dtype)
+            if pa_type is not None:
+                inferred_dtype = ArrowDtype(pa_type)
+    elif dtype_backend == "numpy_nullable" and isinstance(inferred_dtype, ArrowDtype):
+        # GH 53648
+        inferred_dtype = _arrow_dtype_mapping()[inferred_dtype.pyarrow_dtype]
+
+    # error: Incompatible return value type (got "Union[str, Union[dtype[Any],
+    # ExtensionDtype]]", expected "Union[dtype[Any], ExtensionDtype]")
+    return inferred_dtype  # type: ignore[return-value]
+
+
+def maybe_infer_to_datetimelike(
+    value: npt.NDArray[np.object_],
+    convert_to_nullable_dtype: bool = False,
+) -> np.ndarray | DatetimeArray | TimedeltaArray | PeriodArray | IntervalArray:
+    """
+    we might have a array (or single object) that is datetime like,
+    and no dtype is passed don't change the value unless we find a
+    datetime/timedelta set
+
+    this is pretty strict in that a datetime/timedelta is REQUIRED
+    in addition to possible nulls/string likes
+
+    Parameters
+    ----------
+    value : np.ndarray[object]
+
+    Returns
+    -------
+    np.ndarray, DatetimeArray, TimedeltaArray, PeriodArray, or IntervalArray
+
+    """
+    if not isinstance(value, np.ndarray) or value.dtype != object:
+        # Caller is responsible for passing only ndarray[object]
+        raise TypeError(type(value))  # pragma: no cover
+    if value.ndim != 1:
+        # Caller is responsible
+        raise ValueError(value.ndim)  # pragma: no cover
+
+    if not len(value):
+        return value
+
+    # error: Incompatible return value type (got "Union[ExtensionArray,
+    # ndarray[Any, Any]]", expected "Union[ndarray[Any, Any], DatetimeArray,
+    # TimedeltaArray, PeriodArray, IntervalArray]")
+    return lib.maybe_convert_objects(  # type: ignore[return-value]
+        value,
+        # Here we do not convert numeric dtypes, as if we wanted that,
+        #  numpy would have done it for us.
+        convert_numeric=False,
+        convert_non_numeric=True,
+        convert_to_nullable_dtype=convert_to_nullable_dtype,
+        dtype_if_all_nat=np.dtype("M8[ns]"),
+    )
+
+
+def maybe_cast_to_datetime(
+    value: np.ndarray | list, dtype: np.dtype
+) -> ExtensionArray | np.ndarray:
+    """
+    try to cast the array/value to a datetimelike dtype, converting float
+    nan to iNaT
+
+    Caller is responsible for handling ExtensionDtype cases and non dt64/td64
+    cases.
+    """
+    from pandas.core.arrays.datetimes import DatetimeArray
+    from pandas.core.arrays.timedeltas import TimedeltaArray
+
+    assert dtype.kind in "mM"
+    if not is_list_like(value):
+        raise TypeError("value must be listlike")
+
+    # TODO: _from_sequence would raise ValueError in cases where
+    #  _ensure_nanosecond_dtype raises TypeError
+    _ensure_nanosecond_dtype(dtype)
+
+    if lib.is_np_dtype(dtype, "m"):
+        res = TimedeltaArray._from_sequence(value, dtype=dtype)
+        return res
+    else:
+        try:
+            dta = DatetimeArray._from_sequence(value, dtype=dtype)
+        except ValueError as err:
+            # We can give a Series-specific exception message.
+            if "cannot supply both a tz and a timezone-naive dtype" in str(err):
+                raise ValueError(
+                    "Cannot convert timezone-aware data to "
+                    "timezone-naive dtype. Use "
+                    "pd.Series(values).dt.tz_localize(None) instead."
+                ) from err
+            raise
+
+        return dta
+
+
+def _ensure_nanosecond_dtype(dtype: DtypeObj) -> None:
+    """
+    Convert dtypes with granularity less than nanosecond to nanosecond
+
+    >>> _ensure_nanosecond_dtype(np.dtype("M8[us]"))
+
+    >>> _ensure_nanosecond_dtype(np.dtype("M8[D]"))
+    Traceback (most recent call last):
+        ...
+    TypeError: dtype=datetime64[D] is not supported. Supported resolutions are 's', 'ms', 'us', and 'ns'
+
+    >>> _ensure_nanosecond_dtype(np.dtype("m8[ps]"))
+    Traceback (most recent call last):
+        ...
+    TypeError: dtype=timedelta64[ps] is not supported. Supported resolutions are 's', 'ms', 'us', and 'ns'
+    """  # noqa: E501
+    msg = (
+        f"The '{dtype.name}' dtype has no unit. "
+        f"Please pass in '{dtype.name}[ns]' instead."
+    )
+
+    # unpack e.g. SparseDtype
+    dtype = getattr(dtype, "subtype", dtype)
+
+    if not isinstance(dtype, np.dtype):
+        # i.e. datetime64tz
+        pass
+
+    elif dtype.kind in "mM":
+        if not is_supported_dtype(dtype):
+            # pre-2.0 we would silently swap in nanos for lower-resolutions,
+            #  raise for above-nano resolutions
+            if dtype.name in ["datetime64", "timedelta64"]:
+                raise ValueError(msg)
+            # TODO: ValueError or TypeError? existing test
+            #  test_constructor_generic_timestamp_bad_frequency expects TypeError
+            raise TypeError(
+                f"dtype={dtype} is not supported. Supported resolutions are 's', "
+                "'ms', 'us', and 'ns'"
+            )
+
+
+# TODO: other value-dependent functions to standardize here include
+#  Index._find_common_type_compat
+def find_result_type(left_dtype: DtypeObj, right: Any) -> DtypeObj:
+    """
+    Find the type/dtype for the result of an operation between objects.
+
+    This is similar to find_common_type, but looks at the right object instead
+    of just its dtype. This can be useful in particular when the right
+    object does not have a `dtype`.
+
+    Parameters
+    ----------
+    left_dtype : np.dtype or ExtensionDtype
+    right : Any
+
+    Returns
+    -------
+    np.dtype or ExtensionDtype
+
+    See also
+    --------
+    find_common_type
+    numpy.result_type
+    """
+    new_dtype: DtypeObj
+
+    if (
+        isinstance(left_dtype, np.dtype)
+        and left_dtype.kind in "iuc"
+        and (lib.is_integer(right) or lib.is_float(right))
+    ):
+        # e.g. with int8 dtype and right=512, we want to end up with
+        # np.int16, whereas infer_dtype_from(512) gives np.int64,
+        #  which will make us upcast too far.
+        if lib.is_float(right) and right.is_integer() and left_dtype.kind != "f":
+            right = int(right)
+        # After NEP 50, numpy won't inspect Python scalars
+        # TODO: do we need to recreate numpy's inspection logic for floats too
+        # (this breaks some tests)
+        if isinstance(right, int) and not isinstance(right, np.integer):
+            # This gives an unsigned type by default
+            # (if our number is positive)
+
+            # If our left dtype is signed, we might not want this since
+            # this might give us 1 dtype too big
+            # We should check if the corresponding int dtype (e.g. int64 for uint64)
+            # can hold the number
+            right_dtype = np.min_scalar_type(right)
+            if right == 0:
+                # Special case 0
+                right = left_dtype
+            elif (
+                not np.issubdtype(left_dtype, np.unsignedinteger)
+                and 0 < right <= np.iinfo(right_dtype).max
+            ):
+                # If left dtype isn't unsigned, check if it fits in the signed dtype
+                right = np.dtype(f"i{right_dtype.itemsize}")
+            else:
+                right = right_dtype
+
+        new_dtype = np.result_type(left_dtype, right)
+
+    elif is_valid_na_for_dtype(right, left_dtype):
+        # e.g. IntervalDtype[int] and None/np.nan
+        new_dtype = ensure_dtype_can_hold_na(left_dtype)
+
+    else:
+        dtype, _ = infer_dtype_from(right)
+        new_dtype = find_common_type([left_dtype, dtype])
+
+    return new_dtype
+
+
+def common_dtype_categorical_compat(
+    objs: Sequence[Index | ArrayLike], dtype: DtypeObj
+) -> DtypeObj:
+    """
+    Update the result of find_common_type to account for NAs in a Categorical.
+
+    Parameters
+    ----------
+    objs : list[np.ndarray | ExtensionArray | Index]
+    dtype : np.dtype or ExtensionDtype
+
+    Returns
+    -------
+    np.dtype or ExtensionDtype
+    """
+    # GH#38240
+
+    # TODO: more generally, could do `not can_hold_na(dtype)`
+    if lib.is_np_dtype(dtype, "iu"):
+        for obj in objs:
+            # We don't want to accientally allow e.g. "categorical" str here
+            obj_dtype = getattr(obj, "dtype", None)
+            if isinstance(obj_dtype, CategoricalDtype):
+                if isinstance(obj, ABCIndex):
+                    # This check may already be cached
+                    hasnas = obj.hasnans
+                else:
+                    # Categorical
+                    hasnas = cast("Categorical", obj)._hasna
+
+                if hasnas:
+                    # see test_union_int_categorical_with_nan
+                    dtype = np.dtype(np.float64)
+                    break
+    return dtype
+
+
+def np_find_common_type(*dtypes: np.dtype) -> np.dtype:
+    """
+    np.find_common_type implementation pre-1.25 deprecation using np.result_type
+    https://github.com/pandas-dev/pandas/pull/49569#issuecomment-1308300065
+
+    Parameters
+    ----------
+    dtypes : np.dtypes
+
+    Returns
+    -------
+    np.dtype
+    """
+    try:
+        common_dtype = np.result_type(*dtypes)
+        if common_dtype.kind in "mMSU":
+            # NumPy promotion currently (1.25) misbehaves for for times and strings,
+            # so fall back to object (find_common_dtype did unless there
+            # was only one dtype)
+            common_dtype = np.dtype("O")
+
+    except TypeError:
+        common_dtype = np.dtype("O")
+    return common_dtype
+
+
+@overload
+def find_common_type(types: list[np.dtype]) -> np.dtype:
+    ...
+
+
+@overload
+def find_common_type(types: list[ExtensionDtype]) -> DtypeObj:
+    ...
+
+
+@overload
+def find_common_type(types: list[DtypeObj]) -> DtypeObj:
+    ...
+
+
+def find_common_type(types):
+    """
+    Find a common data type among the given dtypes.
+
+    Parameters
+    ----------
+    types : list of dtypes
+
+    Returns
+    -------
+    pandas extension or numpy dtype
+
+    See Also
+    --------
+    numpy.find_common_type
+
+    """
+    if not types:
+        raise ValueError("no types given")
+
+    first = types[0]
+
+    # workaround for find_common_type([np.dtype('datetime64[ns]')] * 2)
+    # => object
+    if lib.dtypes_all_equal(list(types)):
+        return first
+
+    # get unique types (dict.fromkeys is used as order-preserving set())
+    types = list(dict.fromkeys(types).keys())
+
+    if any(isinstance(t, ExtensionDtype) for t in types):
+        for t in types:
+            if isinstance(t, ExtensionDtype):
+                res = t._get_common_dtype(types)
+                if res is not None:
+                    return res
+        return np.dtype("object")
+
+    # take lowest unit
+    if all(lib.is_np_dtype(t, "M") for t in types):
+        return np.dtype(max(types))
+    if all(lib.is_np_dtype(t, "m") for t in types):
+        return np.dtype(max(types))
+
+    # don't mix bool / int or float or complex
+    # this is different from numpy, which casts bool with float/int as int
+    has_bools = any(t.kind == "b" for t in types)
+    if has_bools:
+        for t in types:
+            if t.kind in "iufc":
+                return np.dtype("object")
+
+    return np_find_common_type(*types)
+
+
+def construct_2d_arraylike_from_scalar(
+    value: Scalar, length: int, width: int, dtype: np.dtype, copy: bool
+) -> np.ndarray:
+    shape = (length, width)
+
+    if dtype.kind in "mM":
+        value = _maybe_box_and_unbox_datetimelike(value, dtype)
+    elif dtype == _dtype_obj:
+        if isinstance(value, (np.timedelta64, np.datetime64)):
+            # calling np.array below would cast to pytimedelta/pydatetime
+            out = np.empty(shape, dtype=object)
+            out.fill(value)
+            return out
+
+    # Attempt to coerce to a numpy array
+    try:
+        if not copy:
+            arr = np.asarray(value, dtype=dtype)
+        else:
+            arr = np.array(value, dtype=dtype, copy=copy)
+    except (ValueError, TypeError) as err:
+        raise TypeError(
+            f"DataFrame constructor called with incompatible data and dtype: {err}"
+        ) from err
+
+    if arr.ndim != 0:
+        raise ValueError("DataFrame constructor not properly called!")
+
+    return np.full(shape, arr)
+
+
+def construct_1d_arraylike_from_scalar(
+    value: Scalar, length: int, dtype: DtypeObj | None
+) -> ArrayLike:
+    """
+    create a np.ndarray / pandas type of specified shape and dtype
+    filled with values
+
+    Parameters
+    ----------
+    value : scalar value
+    length : int
+    dtype : pandas_dtype or np.dtype
+
+    Returns
+    -------
+    np.ndarray / pandas type of length, filled with value
+
+    """
+
+    if dtype is None:
+        try:
+            dtype, value = infer_dtype_from_scalar(value)
+        except OutOfBoundsDatetime:
+            dtype = _dtype_obj
+
+    if isinstance(dtype, ExtensionDtype):
+        cls = dtype.construct_array_type()
+        seq = [] if length == 0 else [value]
+        subarr = cls._from_sequence(seq, dtype=dtype).repeat(length)
+
+    else:
+        if length and dtype.kind in "iu" and isna(value):
+            # coerce if we have nan for an integer dtype
+            dtype = np.dtype("float64")
+        elif lib.is_np_dtype(dtype, "US"):
+            # we need to coerce to object dtype to avoid
+            # to allow numpy to take our string as a scalar value
+            dtype = np.dtype("object")
+            if not isna(value):
+                value = ensure_str(value)
+        elif dtype.kind in "mM":
+            value = _maybe_box_and_unbox_datetimelike(value, dtype)
+
+        subarr = np.empty(length, dtype=dtype)
+        if length:
+            # GH 47391: numpy > 1.24 will raise filling np.nan into int dtypes
+            subarr.fill(value)
+
+    return subarr
+
+
+def _maybe_box_and_unbox_datetimelike(value: Scalar, dtype: DtypeObj):
+    # Caller is responsible for checking dtype.kind in "mM"
+
+    if isinstance(value, dt.datetime):
+        # we dont want to box dt64, in particular datetime64("NaT")
+        value = maybe_box_datetimelike(value, dtype)
+
+    return _maybe_unbox_datetimelike(value, dtype)
+
+
+def construct_1d_object_array_from_listlike(values: Collection) -> np.ndarray:
+    """
+    Transform any list-like object in a 1-dimensional numpy array of object
+    dtype.
+
+    Parameters
+    ----------
+    values : any iterable which has a len()
+
+    Raises
+    ------
+    TypeError
+        * If `values` does not have a len()
+
+    Returns
+    -------
+    1-dimensional numpy array of dtype object
+    """
+    # numpy will try to interpret nested lists as further dimensions in np.array(),
+    # hence explicitly making a 1D array using np.fromiter
+    result = np.empty(len(values), dtype="object")
+    for i, obj in enumerate(values):
+        result[i] = obj
+    return result
+
+
+def maybe_cast_to_integer_array(arr: list | np.ndarray, dtype: np.dtype) -> np.ndarray:
+    """
+    Takes any dtype and returns the casted version, raising for when data is
+    incompatible with integer/unsigned integer dtypes.
+
+    Parameters
+    ----------
+    arr : np.ndarray or list
+        The array to cast.
+    dtype : np.dtype
+        The integer dtype to cast the array to.
+
+    Returns
+    -------
+    ndarray
+        Array of integer or unsigned integer dtype.
+
+    Raises
+    ------
+    OverflowError : the dtype is incompatible with the data
+    ValueError : loss of precision has occurred during casting
+
+    Examples
+    --------
+    If you try to coerce negative values to unsigned integers, it raises:
+
+    >>> pd.Series([-1], dtype="uint64")
+    Traceback (most recent call last):
+        ...
+    OverflowError: Trying to coerce negative values to unsigned integers
+
+    Also, if you try to coerce float values to integers, it raises:
+
+    >>> maybe_cast_to_integer_array([1, 2, 3.5], dtype=np.dtype("int64"))
+    Traceback (most recent call last):
+        ...
+    ValueError: Trying to coerce float values to integers
+    """
+    assert dtype.kind in "iu"
+
+    try:
+        if not isinstance(arr, np.ndarray):
+            with warnings.catch_warnings():
+                # We already disallow dtype=uint w/ negative numbers
+                # (test_constructor_coercion_signed_to_unsigned) so safe to ignore.
+                warnings.filterwarnings(
+                    "ignore",
+                    "NumPy will stop allowing conversion of out-of-bound Python int",
+                    DeprecationWarning,
+                )
+                casted = np.asarray(arr, dtype=dtype)
+        else:
+            with warnings.catch_warnings():
+                warnings.filterwarnings("ignore", category=RuntimeWarning)
+                casted = arr.astype(dtype, copy=False)
+    except OverflowError as err:
+        raise OverflowError(
+            "The elements provided in the data cannot all be "
+            f"casted to the dtype {dtype}"
+        ) from err
+
+    if isinstance(arr, np.ndarray) and arr.dtype == dtype:
+        # avoid expensive array_equal check
+        return casted
+
+    with warnings.catch_warnings():
+        warnings.filterwarnings("ignore", category=RuntimeWarning)
+        warnings.filterwarnings(
+            "ignore", "elementwise comparison failed", FutureWarning
+        )
+        if np.array_equal(arr, casted):
+            return casted
+
+    # We do this casting to allow for proper
+    # data and dtype checking.
+    #
+    # We didn't do this earlier because NumPy
+    # doesn't handle `uint64` correctly.
+    arr = np.asarray(arr)
+
+    if np.issubdtype(arr.dtype, str):
+        # TODO(numpy-2.0 min): This case will raise an OverflowError above
+        if (casted.astype(str) == arr).all():
+            return casted
+        raise ValueError(f"string values cannot be losslessly cast to {dtype}")
+
+    if dtype.kind == "u" and (arr < 0).any():
+        # TODO: can this be hit anymore after numpy 2.0?
+        raise OverflowError("Trying to coerce negative values to unsigned integers")
+
+    if arr.dtype.kind == "f":
+        if not np.isfinite(arr).all():
+            raise IntCastingNaNError(
+                "Cannot convert non-finite values (NA or inf) to integer"
+            )
+        raise ValueError("Trying to coerce float values to integers")
+    if arr.dtype == object:
+        raise ValueError("Trying to coerce float values to integers")
+
+    if casted.dtype < arr.dtype:
+        # TODO: Can this path be hit anymore with numpy > 2
+        # GH#41734 e.g. [1, 200, 923442] and dtype="int8" -> overflows
+        raise ValueError(
+            f"Values are too large to be losslessly converted to {dtype}. "
+            f"To cast anyway, use pd.Series(values).astype({dtype})"
+        )
+
+    if arr.dtype.kind in "mM":
+        # test_constructor_maskedarray_nonfloat
+        raise TypeError(
+            f"Constructing a Series or DataFrame from {arr.dtype} values and "
+            f"dtype={dtype} is not supported. Use values.view({dtype}) instead."
+        )
+
+    # No known cases that get here, but raising explicitly to cover our bases.
+    raise ValueError(f"values cannot be losslessly cast to {dtype}")
+
+
+def can_hold_element(arr: ArrayLike, element: Any) -> bool:
+    """
+    Can we do an inplace setitem with this element in an array with this dtype?
+
+    Parameters
+    ----------
+    arr : np.ndarray or ExtensionArray
+    element : Any
+
+    Returns
+    -------
+    bool
+    """
+    dtype = arr.dtype
+    if not isinstance(dtype, np.dtype) or dtype.kind in "mM":
+        if isinstance(dtype, (PeriodDtype, IntervalDtype, DatetimeTZDtype, np.dtype)):
+            # np.dtype here catches datetime64ns and timedelta64ns; we assume
+            #  in this case that we have DatetimeArray/TimedeltaArray
+            arr = cast(
+                "PeriodArray | DatetimeArray | TimedeltaArray | IntervalArray", arr
+            )
+            try:
+                arr._validate_setitem_value(element)
+                return True
+            except (ValueError, TypeError):
+                return False
+
+        if dtype == "string":
+            try:
+                arr._maybe_convert_setitem_value(element)  # type: ignore[union-attr]
+                return True
+            except (ValueError, TypeError):
+                return False
+
+        # This is technically incorrect, but maintains the behavior of
+        # ExtensionBlock._can_hold_element
+        return True
+
+    try:
+        np_can_hold_element(dtype, element)
+        return True
+    except (TypeError, LossySetitemError):
+        return False
+
+
+def np_can_hold_element(dtype: np.dtype, element: Any) -> Any:
+    """
+    Raise if we cannot losslessly set this element into an ndarray with this dtype.
+
+    Specifically about places where we disagree with numpy.  i.e. there are
+    cases where numpy will raise in doing the setitem that we do not check
+    for here, e.g. setting str "X" into a numeric ndarray.
+
+    Returns
+    -------
+    Any
+        The element, potentially cast to the dtype.
+
+    Raises
+    ------
+    ValueError : If we cannot losslessly store this element with this dtype.
+    """
+    if dtype == _dtype_obj:
+        return element
+
+    tipo = _maybe_infer_dtype_type(element)
+
+    if dtype.kind in "iu":
+        if isinstance(element, range):
+            if _dtype_can_hold_range(element, dtype):
+                return element
+            raise LossySetitemError
+
+        if is_integer(element) or (is_float(element) and element.is_integer()):
+            # e.g. test_setitem_series_int8 if we have a python int 1
+            #  tipo may be np.int32, despite the fact that it will fit
+            #  in smaller int dtypes.
+            info = np.iinfo(dtype)
+            if info.min <= element <= info.max:
+                return dtype.type(element)
+            raise LossySetitemError
+
+        if tipo is not None:
+            if tipo.kind not in "iu":
+                if isinstance(element, np.ndarray) and element.dtype.kind == "f":
+                    # If all can be losslessly cast to integers, then we can hold them
+                    with np.errstate(invalid="ignore"):
+                        # We check afterwards if cast was losslessly, so no need to show
+                        # the warning
+                        casted = element.astype(dtype)
+                    comp = casted == element
+                    if comp.all():
+                        # Return the casted values bc they can be passed to
+                        #  np.putmask, whereas the raw values cannot.
+                        #  see TestSetitemFloatNDarrayIntoIntegerSeries
+                        return casted
+                    raise LossySetitemError
+
+                elif isinstance(element, ABCExtensionArray) and isinstance(
+                    element.dtype, CategoricalDtype
+                ):
+                    # GH#52927 setting Categorical value into non-EA frame
+                    # TODO: general-case for EAs?
+                    try:
+                        casted = element.astype(dtype)
+                    except (ValueError, TypeError):
+                        raise LossySetitemError
+                    # Check for cases of either
+                    #  a) lossy overflow/rounding or
+                    #  b) semantic changes like dt64->int64
+                    comp = casted == element
+                    if not comp.all():
+                        raise LossySetitemError
+                    return casted
+
+                # Anything other than integer we cannot hold
+                raise LossySetitemError
+            if (
+                dtype.kind == "u"
+                and isinstance(element, np.ndarray)
+                and element.dtype.kind == "i"
+            ):
+                # see test_where_uint64
+                casted = element.astype(dtype)
+                if (casted == element).all():
+                    # TODO: faster to check (element >=0).all()?  potential
+                    #  itemsize issues there?
+                    return casted
+                raise LossySetitemError
+            if dtype.itemsize < tipo.itemsize:
+                raise LossySetitemError
+            if not isinstance(tipo, np.dtype):
+                # i.e. nullable IntegerDtype; we can put this into an ndarray
+                #  losslessly iff it has no NAs
+                arr = element._values if isinstance(element, ABCSeries) else element
+                if arr._hasna:
+                    raise LossySetitemError
+                return element
+
+            return element
+
+        raise LossySetitemError
+
+    if dtype.kind == "f":
+        if lib.is_integer(element) or lib.is_float(element):
+            casted = dtype.type(element)
+            if np.isnan(casted) or casted == element:
+                return casted
+            # otherwise e.g. overflow see TestCoercionFloat32
+            raise LossySetitemError
+
+        if tipo is not None:
+            # TODO: itemsize check?
+            if tipo.kind not in "iuf":
+                # Anything other than float/integer we cannot hold
+                raise LossySetitemError
+            if not isinstance(tipo, np.dtype):
+                # i.e. nullable IntegerDtype or FloatingDtype;
+                #  we can put this into an ndarray losslessly iff it has no NAs
+                if element._hasna:
+                    raise LossySetitemError
+                return element
+            elif tipo.itemsize > dtype.itemsize or tipo.kind != dtype.kind:
+                if isinstance(element, np.ndarray):
+                    # e.g. TestDataFrameIndexingWhere::test_where_alignment
+                    casted = element.astype(dtype)
+                    if np.array_equal(casted, element, equal_nan=True):
+                        return casted
+                    raise LossySetitemError
+
+            return element
+
+        raise LossySetitemError
+
+    if dtype.kind == "c":
+        if lib.is_integer(element) or lib.is_complex(element) or lib.is_float(element):
+            if np.isnan(element):
+                # see test_where_complex GH#6345
+                return dtype.type(element)
+
+            with warnings.catch_warnings():
+                warnings.filterwarnings("ignore")
+                casted = dtype.type(element)
+            if casted == element:
+                return casted
+            # otherwise e.g. overflow see test_32878_complex_itemsize
+            raise LossySetitemError
+
+        if tipo is not None:
+            if tipo.kind in "iufc":
+                return element
+            raise LossySetitemError
+        raise LossySetitemError
+
+    if dtype.kind == "b":
+        if tipo is not None:
+            if tipo.kind == "b":
+                if not isinstance(tipo, np.dtype):
+                    # i.e. we have a BooleanArray
+                    if element._hasna:
+                        # i.e. there are pd.NA elements
+                        raise LossySetitemError
+                return element
+            raise LossySetitemError
+        if lib.is_bool(element):
+            return element
+        raise LossySetitemError
+
+    if dtype.kind == "S":
+        # TODO: test tests.frame.methods.test_replace tests get here,
+        #  need more targeted tests.  xref phofl has a PR about this
+        if tipo is not None:
+            if tipo.kind == "S" and tipo.itemsize <= dtype.itemsize:
+                return element
+            raise LossySetitemError
+        if isinstance(element, bytes) and len(element) <= dtype.itemsize:
+            return element
+        raise LossySetitemError
+
+    if dtype.kind == "V":
+        # i.e. np.void, which cannot hold _anything_
+        raise LossySetitemError
+
+    raise NotImplementedError(dtype)
+
+
+def _dtype_can_hold_range(rng: range, dtype: np.dtype) -> bool:
+    """
+    _maybe_infer_dtype_type infers to int64 (and float64 for very large endpoints),
+    but in many cases a range can be held by a smaller integer dtype.
+    Check if this is one of those cases.
+    """
+    if not len(rng):
+        return True
+    return np_can_cast_scalar(rng.start, dtype) and np_can_cast_scalar(rng.stop, dtype)
+
+
+def np_can_cast_scalar(element: Scalar, dtype: np.dtype) -> bool:
+    """
+    np.can_cast pandas-equivalent for pre 2-0 behavior that allowed scalar
+    inference
+
+    Parameters
+    ----------
+    element : Scalar
+    dtype : np.dtype
+
+    Returns
+    -------
+    bool
+    """
+    try:
+        np_can_hold_element(dtype, element)
+        return True
+    except (LossySetitemError, NotImplementedError):
+        return False
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/dtypes/common.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/dtypes/common.py
new file mode 100644
index 0000000000000000000000000000000000000000..6dea15ac0bc2474b222762505b50efc0dbe680e5
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/dtypes/common.py
@@ -0,0 +1,1766 @@
+"""
+Common type operations.
+"""
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+)
+import warnings
+
+import numpy as np
+
+from pandas._config import using_string_dtype
+
+from pandas._libs import (
+    Interval,
+    Period,
+    algos,
+    lib,
+)
+from pandas._libs.tslibs import conversion
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.base import _registry as registry
+from pandas.core.dtypes.dtypes import (
+    CategoricalDtype,
+    DatetimeTZDtype,
+    ExtensionDtype,
+    IntervalDtype,
+    PeriodDtype,
+    SparseDtype,
+)
+from pandas.core.dtypes.generic import ABCIndex
+from pandas.core.dtypes.inference import (
+    is_array_like,
+    is_bool,
+    is_complex,
+    is_dataclass,
+    is_decimal,
+    is_dict_like,
+    is_file_like,
+    is_float,
+    is_hashable,
+    is_integer,
+    is_interval,
+    is_iterator,
+    is_list_like,
+    is_named_tuple,
+    is_nested_list_like,
+    is_number,
+    is_re,
+    is_re_compilable,
+    is_scalar,
+    is_sequence,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        ArrayLike,
+        DtypeObj,
+    )
+
+DT64NS_DTYPE = conversion.DT64NS_DTYPE
+TD64NS_DTYPE = conversion.TD64NS_DTYPE
+INT64_DTYPE = np.dtype(np.int64)
+
+# oh the troubles to reduce import time
+_is_scipy_sparse = None
+
+ensure_float64 = algos.ensure_float64
+ensure_int64 = algos.ensure_int64
+ensure_int32 = algos.ensure_int32
+ensure_int16 = algos.ensure_int16
+ensure_int8 = algos.ensure_int8
+ensure_platform_int = algos.ensure_platform_int
+ensure_object = algos.ensure_object
+ensure_uint64 = algos.ensure_uint64
+
+
+def ensure_str(value: bytes | Any) -> str:
+    """
+    Ensure that bytes and non-strings get converted into ``str`` objects.
+    """
+    if isinstance(value, bytes):
+        value = value.decode("utf-8")
+    elif not isinstance(value, str):
+        value = str(value)
+    return value
+
+
+def ensure_python_int(value: int | np.integer) -> int:
+    """
+    Ensure that a value is a python int.
+
+    Parameters
+    ----------
+    value: int or numpy.integer
+
+    Returns
+    -------
+    int
+
+    Raises
+    ------
+    TypeError: if the value isn't an int or can't be converted to one.
+    """
+    if not (is_integer(value) or is_float(value)):
+        if not is_scalar(value):
+            raise TypeError(
+                f"Value needs to be a scalar value, was type {type(value).__name__}"
+            )
+        raise TypeError(f"Wrong type {type(value)} for value {value}")
+    try:
+        new_value = int(value)
+        assert new_value == value
+    except (TypeError, ValueError, AssertionError) as err:
+        raise TypeError(f"Wrong type {type(value)} for value {value}") from err
+    return new_value
+
+
+def classes(*klasses) -> Callable:
+    """Evaluate if the tipo is a subclass of the klasses."""
+    return lambda tipo: issubclass(tipo, klasses)
+
+
+def _classes_and_not_datetimelike(*klasses) -> Callable:
+    """
+    Evaluate if the tipo is a subclass of the klasses
+    and not a datetimelike.
+    """
+    return lambda tipo: (
+        issubclass(tipo, klasses)
+        and not issubclass(tipo, (np.datetime64, np.timedelta64))
+    )
+
+
+def is_object_dtype(arr_or_dtype) -> bool:
+    """
+    Check whether an array-like or dtype is of the object dtype.
+
+    Parameters
+    ----------
+    arr_or_dtype : array-like or dtype
+        The array-like or dtype to check.
+
+    Returns
+    -------
+    boolean
+        Whether or not the array-like or dtype is of the object dtype.
+
+    Examples
+    --------
+    >>> from pandas.api.types import is_object_dtype
+    >>> is_object_dtype(object)
+    True
+    >>> is_object_dtype(int)
+    False
+    >>> is_object_dtype(np.array([], dtype=object))
+    True
+    >>> is_object_dtype(np.array([], dtype=int))
+    False
+    >>> is_object_dtype([1, 2, 3])
+    False
+    """
+    return _is_dtype_type(arr_or_dtype, classes(np.object_))
+
+
+def is_sparse(arr) -> bool:
+    """
+    Check whether an array-like is a 1-D pandas sparse array.
+
+    .. deprecated:: 2.1.0
+        Use isinstance(dtype, pd.SparseDtype) instead.
+
+    Check that the one-dimensional array-like is a pandas sparse array.
+    Returns True if it is a pandas sparse array, not another type of
+    sparse array.
+
+    Parameters
+    ----------
+    arr : array-like
+        Array-like to check.
+
+    Returns
+    -------
+    bool
+        Whether or not the array-like is a pandas sparse array.
+
+    Examples
+    --------
+    Returns `True` if the parameter is a 1-D pandas sparse array.
+
+    >>> from pandas.api.types import is_sparse
+    >>> is_sparse(pd.arrays.SparseArray([0, 0, 1, 0]))
+    True
+    >>> is_sparse(pd.Series(pd.arrays.SparseArray([0, 0, 1, 0])))
+    True
+
+    Returns `False` if the parameter is not sparse.
+
+    >>> is_sparse(np.array([0, 0, 1, 0]))
+    False
+    >>> is_sparse(pd.Series([0, 1, 0, 0]))
+    False
+
+    Returns `False` if the parameter is not a pandas sparse array.
+
+    >>> from scipy.sparse import bsr_matrix
+    >>> is_sparse(bsr_matrix([0, 1, 0, 0]))
+    False
+
+    Returns `False` if the parameter has more than one dimension.
+    """
+    warnings.warn(
+        "is_sparse is deprecated and will be removed in a future "
+        "version. Check `isinstance(dtype, pd.SparseDtype)` instead.",
+        DeprecationWarning,
+        stacklevel=2,
+    )
+
+    dtype = getattr(arr, "dtype", arr)
+    return isinstance(dtype, SparseDtype)
+
+
+def is_scipy_sparse(arr) -> bool:
+    """
+    Check whether an array-like is a scipy.sparse.spmatrix instance.
+
+    Parameters
+    ----------
+    arr : array-like
+        The array-like to check.
+
+    Returns
+    -------
+    boolean
+        Whether or not the array-like is a scipy.sparse.spmatrix instance.
+
+    Notes
+    -----
+    If scipy is not installed, this function will always return False.
+
+    Examples
+    --------
+    >>> from scipy.sparse import bsr_matrix
+    >>> is_scipy_sparse(bsr_matrix([1, 2, 3]))
+    True
+    >>> is_scipy_sparse(pd.arrays.SparseArray([1, 2, 3]))
+    False
+    """
+    global _is_scipy_sparse
+
+    if _is_scipy_sparse is None:  # pylint: disable=used-before-assignment
+        try:
+            from scipy.sparse import issparse as _is_scipy_sparse
+        except ImportError:
+            _is_scipy_sparse = lambda _: False
+
+    assert _is_scipy_sparse is not None
+    return _is_scipy_sparse(arr)
+
+
+def is_datetime64_dtype(arr_or_dtype) -> bool:
+    """
+    Check whether an array-like or dtype is of the datetime64 dtype.
+
+    Parameters
+    ----------
+    arr_or_dtype : array-like or dtype
+        The array-like or dtype to check.
+
+    Returns
+    -------
+    boolean
+        Whether or not the array-like or dtype is of the datetime64 dtype.
+
+    Examples
+    --------
+    >>> from pandas.api.types import is_datetime64_dtype
+    >>> is_datetime64_dtype(object)
+    False
+    >>> is_datetime64_dtype(np.datetime64)
+    True
+    >>> is_datetime64_dtype(np.array([], dtype=int))
+    False
+    >>> is_datetime64_dtype(np.array([], dtype=np.datetime64))
+    True
+    >>> is_datetime64_dtype([1, 2, 3])
+    False
+    """
+    if isinstance(arr_or_dtype, np.dtype):
+        # GH#33400 fastpath for dtype object
+        return arr_or_dtype.kind == "M"
+    return _is_dtype_type(arr_or_dtype, classes(np.datetime64))
+
+
+def is_datetime64tz_dtype(arr_or_dtype) -> bool:
+    """
+    Check whether an array-like or dtype is of a DatetimeTZDtype dtype.
+
+    .. deprecated:: 2.1.0
+        Use isinstance(dtype, pd.DatetimeTZDtype) instead.
+
+    Parameters
+    ----------
+    arr_or_dtype : array-like or dtype
+        The array-like or dtype to check.
+
+    Returns
+    -------
+    boolean
+        Whether or not the array-like or dtype is of a DatetimeTZDtype dtype.
+
+    Examples
+    --------
+    >>> from pandas.api.types import is_datetime64tz_dtype
+    >>> is_datetime64tz_dtype(object)
+    False
+    >>> is_datetime64tz_dtype([1, 2, 3])
+    False
+    >>> is_datetime64tz_dtype(pd.DatetimeIndex([1, 2, 3]))  # tz-naive
+    False
+    >>> is_datetime64tz_dtype(pd.DatetimeIndex([1, 2, 3], tz="US/Eastern"))
+    True
+
+    >>> from pandas.core.dtypes.dtypes import DatetimeTZDtype
+    >>> dtype = DatetimeTZDtype("ns", tz="US/Eastern")
+    >>> s = pd.Series([], dtype=dtype)
+    >>> is_datetime64tz_dtype(dtype)
+    True
+    >>> is_datetime64tz_dtype(s)
+    True
+    """
+    # GH#52607
+    warnings.warn(
+        "is_datetime64tz_dtype is deprecated and will be removed in a future "
+        "version. Check `isinstance(dtype, pd.DatetimeTZDtype)` instead.",
+        DeprecationWarning,
+        stacklevel=2,
+    )
+    if isinstance(arr_or_dtype, DatetimeTZDtype):
+        # GH#33400 fastpath for dtype object
+        # GH 34986
+        return True
+
+    if arr_or_dtype is None:
+        return False
+    return DatetimeTZDtype.is_dtype(arr_or_dtype)
+
+
+def is_timedelta64_dtype(arr_or_dtype) -> bool:
+    """
+    Check whether an array-like or dtype is of the timedelta64 dtype.
+
+    Parameters
+    ----------
+    arr_or_dtype : array-like or dtype
+        The array-like or dtype to check.
+
+    Returns
+    -------
+    boolean
+        Whether or not the array-like or dtype is of the timedelta64 dtype.
+
+    Examples
+    --------
+    >>> from pandas.core.dtypes.common import is_timedelta64_dtype
+    >>> is_timedelta64_dtype(object)
+    False
+    >>> is_timedelta64_dtype(np.timedelta64)
+    True
+    >>> is_timedelta64_dtype([1, 2, 3])
+    False
+    >>> is_timedelta64_dtype(pd.Series([], dtype="timedelta64[ns]"))
+    True
+    >>> is_timedelta64_dtype('0 days')
+    False
+    """
+    if isinstance(arr_or_dtype, np.dtype):
+        # GH#33400 fastpath for dtype object
+        return arr_or_dtype.kind == "m"
+
+    return _is_dtype_type(arr_or_dtype, classes(np.timedelta64))
+
+
+def is_period_dtype(arr_or_dtype) -> bool:
+    """
+    Check whether an array-like or dtype is of the Period dtype.
+
+    .. deprecated:: 2.2.0
+        Use isinstance(dtype, pd.Period) instead.
+
+    Parameters
+    ----------
+    arr_or_dtype : array-like or dtype
+        The array-like or dtype to check.
+
+    Returns
+    -------
+    boolean
+        Whether or not the array-like or dtype is of the Period dtype.
+
+    Examples
+    --------
+    >>> from pandas.core.dtypes.common import is_period_dtype
+    >>> is_period_dtype(object)
+    False
+    >>> is_period_dtype(pd.PeriodDtype(freq="D"))
+    True
+    >>> is_period_dtype([1, 2, 3])
+    False
+    >>> is_period_dtype(pd.Period("2017-01-01"))
+    False
+    >>> is_period_dtype(pd.PeriodIndex([], freq="Y"))
+    True
+    """
+    warnings.warn(
+        "is_period_dtype is deprecated and will be removed in a future version. "
+        "Use `isinstance(dtype, pd.PeriodDtype)` instead",
+        DeprecationWarning,
+        stacklevel=2,
+    )
+    if isinstance(arr_or_dtype, ExtensionDtype):
+        # GH#33400 fastpath for dtype object
+        return arr_or_dtype.type is Period
+
+    if arr_or_dtype is None:
+        return False
+    return PeriodDtype.is_dtype(arr_or_dtype)
+
+
+def is_interval_dtype(arr_or_dtype) -> bool:
+    """
+    Check whether an array-like or dtype is of the Interval dtype.
+
+    .. deprecated:: 2.2.0
+        Use isinstance(dtype, pd.IntervalDtype) instead.
+
+    Parameters
+    ----------
+    arr_or_dtype : array-like or dtype
+        The array-like or dtype to check.
+
+    Returns
+    -------
+    boolean
+        Whether or not the array-like or dtype is of the Interval dtype.
+
+    Examples
+    --------
+    >>> from pandas.core.dtypes.common import is_interval_dtype
+    >>> is_interval_dtype(object)
+    False
+    >>> is_interval_dtype(pd.IntervalDtype())
+    True
+    >>> is_interval_dtype([1, 2, 3])
+    False
+    >>>
+    >>> interval = pd.Interval(1, 2, closed="right")
+    >>> is_interval_dtype(interval)
+    False
+    >>> is_interval_dtype(pd.IntervalIndex([interval]))
+    True
+    """
+    # GH#52607
+    warnings.warn(
+        "is_interval_dtype is deprecated and will be removed in a future version. "
+        "Use `isinstance(dtype, pd.IntervalDtype)` instead",
+        DeprecationWarning,
+        stacklevel=2,
+    )
+    if isinstance(arr_or_dtype, ExtensionDtype):
+        # GH#33400 fastpath for dtype object
+        return arr_or_dtype.type is Interval
+
+    if arr_or_dtype is None:
+        return False
+    return IntervalDtype.is_dtype(arr_or_dtype)
+
+
+def is_categorical_dtype(arr_or_dtype) -> bool:
+    """
+    Check whether an array-like or dtype is of the Categorical dtype.
+
+    .. deprecated:: 2.2.0
+        Use isinstance(dtype, pd.CategoricalDtype) instead.
+
+    Parameters
+    ----------
+    arr_or_dtype : array-like or dtype
+        The array-like or dtype to check.
+
+    Returns
+    -------
+    boolean
+        Whether or not the array-like or dtype is of the Categorical dtype.
+
+    Examples
+    --------
+    >>> from pandas.api.types import is_categorical_dtype
+    >>> from pandas import CategoricalDtype
+    >>> is_categorical_dtype(object)
+    False
+    >>> is_categorical_dtype(CategoricalDtype())
+    True
+    >>> is_categorical_dtype([1, 2, 3])
+    False
+    >>> is_categorical_dtype(pd.Categorical([1, 2, 3]))
+    True
+    >>> is_categorical_dtype(pd.CategoricalIndex([1, 2, 3]))
+    True
+    """
+    # GH#52527
+    warnings.warn(
+        "is_categorical_dtype is deprecated and will be removed in a future "
+        "version. Use isinstance(dtype, pd.CategoricalDtype) instead",
+        DeprecationWarning,
+        stacklevel=2,
+    )
+    if isinstance(arr_or_dtype, ExtensionDtype):
+        # GH#33400 fastpath for dtype object
+        return arr_or_dtype.name == "category"
+
+    if arr_or_dtype is None:
+        return False
+    return CategoricalDtype.is_dtype(arr_or_dtype)
+
+
+def is_string_or_object_np_dtype(dtype: np.dtype) -> bool:
+    """
+    Faster alternative to is_string_dtype, assumes we have a np.dtype object.
+    """
+    return dtype == object or dtype.kind in "SU"
+
+
+def is_string_dtype(arr_or_dtype) -> bool:
+    """
+    Check whether the provided array or dtype is of the string dtype.
+
+    If an array is passed with an object dtype, the elements must be
+    inferred as strings.
+
+    Parameters
+    ----------
+    arr_or_dtype : array-like or dtype
+        The array or dtype to check.
+
+    Returns
+    -------
+    boolean
+        Whether or not the array or dtype is of the string dtype.
+
+    Examples
+    --------
+    >>> from pandas.api.types import is_string_dtype
+    >>> is_string_dtype(str)
+    True
+    >>> is_string_dtype(object)
+    True
+    >>> is_string_dtype(int)
+    False
+    >>> is_string_dtype(np.array(['a', 'b']))
+    True
+    >>> is_string_dtype(pd.Series([1, 2]))
+    False
+    >>> is_string_dtype(pd.Series([1, 2], dtype=object))
+    False
+    """
+    if hasattr(arr_or_dtype, "dtype") and _get_dtype(arr_or_dtype).kind == "O":
+        return is_all_strings(arr_or_dtype)
+
+    def condition(dtype) -> bool:
+        if is_string_or_object_np_dtype(dtype):
+            return True
+        try:
+            return dtype == "string"
+        except TypeError:
+            return False
+
+    return _is_dtype(arr_or_dtype, condition)
+
+
+def is_dtype_equal(source, target) -> bool:
+    """
+    Check if two dtypes are equal.
+
+    Parameters
+    ----------
+    source : The first dtype to compare
+    target : The second dtype to compare
+
+    Returns
+    -------
+    boolean
+        Whether or not the two dtypes are equal.
+
+    Examples
+    --------
+    >>> is_dtype_equal(int, float)
+    False
+    >>> is_dtype_equal("int", int)
+    True
+    >>> is_dtype_equal(object, "category")
+    False
+    >>> is_dtype_equal(CategoricalDtype(), "category")
+    True
+    >>> is_dtype_equal(DatetimeTZDtype(tz="UTC"), "datetime64")
+    False
+    """
+    if isinstance(target, str):
+        if not isinstance(source, str):
+            # GH#38516 ensure we get the same behavior from
+            #  is_dtype_equal(CDT, "category") and CDT == "category"
+            try:
+                src = _get_dtype(source)
+                if isinstance(src, ExtensionDtype):
+                    return src == target
+            except (TypeError, AttributeError, ImportError):
+                return False
+    elif isinstance(source, str):
+        return is_dtype_equal(target, source)
+
+    try:
+        source = _get_dtype(source)
+        target = _get_dtype(target)
+        return source == target
+    except (TypeError, AttributeError, ImportError):
+        # invalid comparison
+        # object == category will hit this
+        return False
+
+
+def is_integer_dtype(arr_or_dtype) -> bool:
+    """
+    Check whether the provided array or dtype is of an integer dtype.
+
+    Unlike in `is_any_int_dtype`, timedelta64 instances will return False.
+
+    The nullable Integer dtypes (e.g. pandas.Int64Dtype) are also considered
+    as integer by this function.
+
+    Parameters
+    ----------
+    arr_or_dtype : array-like or dtype
+        The array or dtype to check.
+
+    Returns
+    -------
+    boolean
+        Whether or not the array or dtype is of an integer dtype and
+        not an instance of timedelta64.
+
+    Examples
+    --------
+    >>> from pandas.api.types import is_integer_dtype
+    >>> is_integer_dtype(str)
+    False
+    >>> is_integer_dtype(int)
+    True
+    >>> is_integer_dtype(float)
+    False
+    >>> is_integer_dtype(np.uint64)
+    True
+    >>> is_integer_dtype('int8')
+    True
+    >>> is_integer_dtype('Int8')
+    True
+    >>> is_integer_dtype(pd.Int8Dtype)
+    True
+    >>> is_integer_dtype(np.datetime64)
+    False
+    >>> is_integer_dtype(np.timedelta64)
+    False
+    >>> is_integer_dtype(np.array(['a', 'b']))
+    False
+    >>> is_integer_dtype(pd.Series([1, 2]))
+    True
+    >>> is_integer_dtype(np.array([], dtype=np.timedelta64))
+    False
+    >>> is_integer_dtype(pd.Index([1, 2.]))  # float
+    False
+    """
+    return _is_dtype_type(
+        arr_or_dtype, _classes_and_not_datetimelike(np.integer)
+    ) or _is_dtype(
+        arr_or_dtype, lambda typ: isinstance(typ, ExtensionDtype) and typ.kind in "iu"
+    )
+
+
+def is_signed_integer_dtype(arr_or_dtype) -> bool:
+    """
+    Check whether the provided array or dtype is of a signed integer dtype.
+
+    Unlike in `is_any_int_dtype`, timedelta64 instances will return False.
+
+    The nullable Integer dtypes (e.g. pandas.Int64Dtype) are also considered
+    as integer by this function.
+
+    Parameters
+    ----------
+    arr_or_dtype : array-like or dtype
+        The array or dtype to check.
+
+    Returns
+    -------
+    boolean
+        Whether or not the array or dtype is of a signed integer dtype
+        and not an instance of timedelta64.
+
+    Examples
+    --------
+    >>> from pandas.core.dtypes.common import is_signed_integer_dtype
+    >>> is_signed_integer_dtype(str)
+    False
+    >>> is_signed_integer_dtype(int)
+    True
+    >>> is_signed_integer_dtype(float)
+    False
+    >>> is_signed_integer_dtype(np.uint64)  # unsigned
+    False
+    >>> is_signed_integer_dtype('int8')
+    True
+    >>> is_signed_integer_dtype('Int8')
+    True
+    >>> is_signed_integer_dtype(pd.Int8Dtype)
+    True
+    >>> is_signed_integer_dtype(np.datetime64)
+    False
+    >>> is_signed_integer_dtype(np.timedelta64)
+    False
+    >>> is_signed_integer_dtype(np.array(['a', 'b']))
+    False
+    >>> is_signed_integer_dtype(pd.Series([1, 2]))
+    True
+    >>> is_signed_integer_dtype(np.array([], dtype=np.timedelta64))
+    False
+    >>> is_signed_integer_dtype(pd.Index([1, 2.]))  # float
+    False
+    >>> is_signed_integer_dtype(np.array([1, 2], dtype=np.uint32))  # unsigned
+    False
+    """
+    return _is_dtype_type(
+        arr_or_dtype, _classes_and_not_datetimelike(np.signedinteger)
+    ) or _is_dtype(
+        arr_or_dtype, lambda typ: isinstance(typ, ExtensionDtype) and typ.kind == "i"
+    )
+
+
+def is_unsigned_integer_dtype(arr_or_dtype) -> bool:
+    """
+    Check whether the provided array or dtype is of an unsigned integer dtype.
+
+    The nullable Integer dtypes (e.g. pandas.UInt64Dtype) are also
+    considered as integer by this function.
+
+    Parameters
+    ----------
+    arr_or_dtype : array-like or dtype
+        The array or dtype to check.
+
+    Returns
+    -------
+    boolean
+        Whether or not the array or dtype is of an unsigned integer dtype.
+
+    Examples
+    --------
+    >>> from pandas.api.types import is_unsigned_integer_dtype
+    >>> is_unsigned_integer_dtype(str)
+    False
+    >>> is_unsigned_integer_dtype(int)  # signed
+    False
+    >>> is_unsigned_integer_dtype(float)
+    False
+    >>> is_unsigned_integer_dtype(np.uint64)
+    True
+    >>> is_unsigned_integer_dtype('uint8')
+    True
+    >>> is_unsigned_integer_dtype('UInt8')
+    True
+    >>> is_unsigned_integer_dtype(pd.UInt8Dtype)
+    True
+    >>> is_unsigned_integer_dtype(np.array(['a', 'b']))
+    False
+    >>> is_unsigned_integer_dtype(pd.Series([1, 2]))  # signed
+    False
+    >>> is_unsigned_integer_dtype(pd.Index([1, 2.]))  # float
+    False
+    >>> is_unsigned_integer_dtype(np.array([1, 2], dtype=np.uint32))
+    True
+    """
+    return _is_dtype_type(
+        arr_or_dtype, _classes_and_not_datetimelike(np.unsignedinteger)
+    ) or _is_dtype(
+        arr_or_dtype, lambda typ: isinstance(typ, ExtensionDtype) and typ.kind == "u"
+    )
+
+
+def is_int64_dtype(arr_or_dtype) -> bool:
+    """
+    Check whether the provided array or dtype is of the int64 dtype.
+
+    .. deprecated:: 2.1.0
+
+       is_int64_dtype is deprecated and will be removed in a future
+       version. Use dtype == np.int64 instead.
+
+    Parameters
+    ----------
+    arr_or_dtype : array-like or dtype
+        The array or dtype to check.
+
+    Returns
+    -------
+    boolean
+        Whether or not the array or dtype is of the int64 dtype.
+
+    Notes
+    -----
+    Depending on system architecture, the return value of `is_int64_dtype(
+    int)` will be True if the OS uses 64-bit integers and False if the OS
+    uses 32-bit integers.
+
+    Examples
+    --------
+    >>> from pandas.api.types import is_int64_dtype
+    >>> is_int64_dtype(str)  # doctest: +SKIP
+    False
+    >>> is_int64_dtype(np.int32)  # doctest: +SKIP
+    False
+    >>> is_int64_dtype(np.int64)  # doctest: +SKIP
+    True
+    >>> is_int64_dtype('int8')  # doctest: +SKIP
+    False
+    >>> is_int64_dtype('Int8')  # doctest: +SKIP
+    False
+    >>> is_int64_dtype(pd.Int64Dtype)  # doctest: +SKIP
+    True
+    >>> is_int64_dtype(float)  # doctest: +SKIP
+    False
+    >>> is_int64_dtype(np.uint64)  # unsigned  # doctest: +SKIP
+    False
+    >>> is_int64_dtype(np.array(['a', 'b']))  # doctest: +SKIP
+    False
+    >>> is_int64_dtype(np.array([1, 2], dtype=np.int64))  # doctest: +SKIP
+    True
+    >>> is_int64_dtype(pd.Index([1, 2.]))  # float  # doctest: +SKIP
+    False
+    >>> is_int64_dtype(np.array([1, 2], dtype=np.uint32))  # unsigned  # doctest: +SKIP
+    False
+    """
+    # GH#52564
+    warnings.warn(
+        "is_int64_dtype is deprecated and will be removed in a future "
+        "version. Use dtype == np.int64 instead.",
+        DeprecationWarning,
+        stacklevel=2,
+    )
+    return _is_dtype_type(arr_or_dtype, classes(np.int64))
+
+
+def is_datetime64_any_dtype(arr_or_dtype) -> bool:
+    """
+    Check whether the provided array or dtype is of the datetime64 dtype.
+
+    Parameters
+    ----------
+    arr_or_dtype : array-like or dtype
+        The array or dtype to check.
+
+    Returns
+    -------
+    bool
+        Whether or not the array or dtype is of the datetime64 dtype.
+
+    Examples
+    --------
+    >>> from pandas.api.types import is_datetime64_any_dtype
+    >>> from pandas.core.dtypes.dtypes import DatetimeTZDtype
+    >>> is_datetime64_any_dtype(str)
+    False
+    >>> is_datetime64_any_dtype(int)
+    False
+    >>> is_datetime64_any_dtype(np.datetime64)  # can be tz-naive
+    True
+    >>> is_datetime64_any_dtype(DatetimeTZDtype("ns", "US/Eastern"))
+    True
+    >>> is_datetime64_any_dtype(np.array(['a', 'b']))
+    False
+    >>> is_datetime64_any_dtype(np.array([1, 2]))
+    False
+    >>> is_datetime64_any_dtype(np.array([], dtype="datetime64[ns]"))
+    True
+    >>> is_datetime64_any_dtype(pd.DatetimeIndex([1, 2, 3], dtype="datetime64[ns]"))
+    True
+    """
+    if isinstance(arr_or_dtype, (np.dtype, ExtensionDtype)):
+        # GH#33400 fastpath for dtype object
+        return arr_or_dtype.kind == "M"
+
+    if arr_or_dtype is None:
+        return False
+
+    try:
+        tipo = _get_dtype(arr_or_dtype)
+    except TypeError:
+        return False
+    return lib.is_np_dtype(tipo, "M") or isinstance(tipo, DatetimeTZDtype)
+
+
+def is_datetime64_ns_dtype(arr_or_dtype) -> bool:
+    """
+    Check whether the provided array or dtype is of the datetime64[ns] dtype.
+
+    Parameters
+    ----------
+    arr_or_dtype : array-like or dtype
+        The array or dtype to check.
+
+    Returns
+    -------
+    bool
+        Whether or not the array or dtype is of the datetime64[ns] dtype.
+
+    Examples
+    --------
+    >>> from pandas.api.types import is_datetime64_ns_dtype
+    >>> from pandas.core.dtypes.dtypes import DatetimeTZDtype
+    >>> is_datetime64_ns_dtype(str)
+    False
+    >>> is_datetime64_ns_dtype(int)
+    False
+    >>> is_datetime64_ns_dtype(np.datetime64)  # no unit
+    False
+    >>> is_datetime64_ns_dtype(DatetimeTZDtype("ns", "US/Eastern"))
+    True
+    >>> is_datetime64_ns_dtype(np.array(['a', 'b']))
+    False
+    >>> is_datetime64_ns_dtype(np.array([1, 2]))
+    False
+    >>> is_datetime64_ns_dtype(np.array([], dtype="datetime64"))  # no unit
+    False
+    >>> is_datetime64_ns_dtype(np.array([], dtype="datetime64[ps]"))  # wrong unit
+    False
+    >>> is_datetime64_ns_dtype(pd.DatetimeIndex([1, 2, 3], dtype="datetime64[ns]"))
+    True
+    """
+    if arr_or_dtype is None:
+        return False
+    try:
+        tipo = _get_dtype(arr_or_dtype)
+    except TypeError:
+        return False
+    return tipo == DT64NS_DTYPE or (
+        isinstance(tipo, DatetimeTZDtype) and tipo.unit == "ns"
+    )
+
+
+def is_timedelta64_ns_dtype(arr_or_dtype) -> bool:
+    """
+    Check whether the provided array or dtype is of the timedelta64[ns] dtype.
+
+    This is a very specific dtype, so generic ones like `np.timedelta64`
+    will return False if passed into this function.
+
+    Parameters
+    ----------
+    arr_or_dtype : array-like or dtype
+        The array or dtype to check.
+
+    Returns
+    -------
+    boolean
+        Whether or not the array or dtype is of the timedelta64[ns] dtype.
+
+    Examples
+    --------
+    >>> from pandas.core.dtypes.common import is_timedelta64_ns_dtype
+    >>> is_timedelta64_ns_dtype(np.dtype('m8[ns]'))
+    True
+    >>> is_timedelta64_ns_dtype(np.dtype('m8[ps]'))  # Wrong frequency
+    False
+    >>> is_timedelta64_ns_dtype(np.array([1, 2], dtype='m8[ns]'))
+    True
+    >>> is_timedelta64_ns_dtype(np.array([1, 2], dtype=np.timedelta64))
+    False
+    """
+    return _is_dtype(arr_or_dtype, lambda dtype: dtype == TD64NS_DTYPE)
+
+
+# This exists to silence numpy deprecation warnings, see GH#29553
+def is_numeric_v_string_like(a: ArrayLike, b) -> bool:
+    """
+    Check if we are comparing a string-like object to a numeric ndarray.
+    NumPy doesn't like to compare such objects, especially numeric arrays
+    and scalar string-likes.
+
+    Parameters
+    ----------
+    a : array-like, scalar
+        The first object to check.
+    b : array-like, scalar
+        The second object to check.
+
+    Returns
+    -------
+    boolean
+        Whether we return a comparing a string-like object to a numeric array.
+
+    Examples
+    --------
+    >>> is_numeric_v_string_like(np.array([1]), "foo")
+    True
+    >>> is_numeric_v_string_like(np.array([1, 2]), np.array(["foo"]))
+    True
+    >>> is_numeric_v_string_like(np.array(["foo"]), np.array([1, 2]))
+    True
+    >>> is_numeric_v_string_like(np.array([1]), np.array([2]))
+    False
+    >>> is_numeric_v_string_like(np.array(["foo"]), np.array(["foo"]))
+    False
+    """
+    is_a_array = isinstance(a, np.ndarray)
+    is_b_array = isinstance(b, np.ndarray)
+
+    is_a_numeric_array = is_a_array and a.dtype.kind in ("u", "i", "f", "c", "b")
+    is_b_numeric_array = is_b_array and b.dtype.kind in ("u", "i", "f", "c", "b")
+    is_a_string_array = is_a_array and a.dtype.kind in ("S", "U")
+    is_b_string_array = is_b_array and b.dtype.kind in ("S", "U")
+
+    is_b_scalar_string_like = not is_b_array and isinstance(b, str)
+
+    return (
+        (is_a_numeric_array and is_b_scalar_string_like)
+        or (is_a_numeric_array and is_b_string_array)
+        or (is_b_numeric_array and is_a_string_array)
+    )
+
+
+def needs_i8_conversion(dtype: DtypeObj | None) -> bool:
+    """
+    Check whether the dtype should be converted to int64.
+
+    Dtype "needs" such a conversion if the dtype is of a datetime-like dtype
+
+    Parameters
+    ----------
+    dtype : np.dtype, ExtensionDtype, or None
+
+    Returns
+    -------
+    boolean
+        Whether or not the dtype should be converted to int64.
+
+    Examples
+    --------
+    >>> needs_i8_conversion(str)
+    False
+    >>> needs_i8_conversion(np.int64)
+    False
+    >>> needs_i8_conversion(np.datetime64)
+    False
+    >>> needs_i8_conversion(np.dtype(np.datetime64))
+    True
+    >>> needs_i8_conversion(np.array(['a', 'b']))
+    False
+    >>> needs_i8_conversion(pd.Series([1, 2]))
+    False
+    >>> needs_i8_conversion(pd.Series([], dtype="timedelta64[ns]"))
+    False
+    >>> needs_i8_conversion(pd.DatetimeIndex([1, 2, 3], tz="US/Eastern"))
+    False
+    >>> needs_i8_conversion(pd.DatetimeIndex([1, 2, 3], tz="US/Eastern").dtype)
+    True
+    """
+    if isinstance(dtype, np.dtype):
+        return dtype.kind in "mM"
+    return isinstance(dtype, (PeriodDtype, DatetimeTZDtype))
+
+
+def is_numeric_dtype(arr_or_dtype) -> bool:
+    """
+    Check whether the provided array or dtype is of a numeric dtype.
+
+    Parameters
+    ----------
+    arr_or_dtype : array-like or dtype
+        The array or dtype to check.
+
+    Returns
+    -------
+    boolean
+        Whether or not the array or dtype is of a numeric dtype.
+
+    Examples
+    --------
+    >>> from pandas.api.types import is_numeric_dtype
+    >>> is_numeric_dtype(str)
+    False
+    >>> is_numeric_dtype(int)
+    True
+    >>> is_numeric_dtype(float)
+    True
+    >>> is_numeric_dtype(np.uint64)
+    True
+    >>> is_numeric_dtype(np.datetime64)
+    False
+    >>> is_numeric_dtype(np.timedelta64)
+    False
+    >>> is_numeric_dtype(np.array(['a', 'b']))
+    False
+    >>> is_numeric_dtype(pd.Series([1, 2]))
+    True
+    >>> is_numeric_dtype(pd.Index([1, 2.]))
+    True
+    >>> is_numeric_dtype(np.array([], dtype=np.timedelta64))
+    False
+    """
+    return _is_dtype_type(
+        arr_or_dtype, _classes_and_not_datetimelike(np.number, np.bool_)
+    ) or _is_dtype(
+        arr_or_dtype, lambda typ: isinstance(typ, ExtensionDtype) and typ._is_numeric
+    )
+
+
+def is_any_real_numeric_dtype(arr_or_dtype) -> bool:
+    """
+    Check whether the provided array or dtype is of a real number dtype.
+
+    Parameters
+    ----------
+    arr_or_dtype : array-like or dtype
+        The array or dtype to check.
+
+    Returns
+    -------
+    boolean
+        Whether or not the array or dtype is of a real number dtype.
+
+    Examples
+    --------
+    >>> from pandas.api.types import is_any_real_numeric_dtype
+    >>> is_any_real_numeric_dtype(int)
+    True
+    >>> is_any_real_numeric_dtype(float)
+    True
+    >>> is_any_real_numeric_dtype(object)
+    False
+    >>> is_any_real_numeric_dtype(str)
+    False
+    >>> is_any_real_numeric_dtype(complex(1, 2))
+    False
+    >>> is_any_real_numeric_dtype(bool)
+    False
+    """
+    return (
+        is_numeric_dtype(arr_or_dtype)
+        and not is_complex_dtype(arr_or_dtype)
+        and not is_bool_dtype(arr_or_dtype)
+    )
+
+
+def is_float_dtype(arr_or_dtype) -> bool:
+    """
+    Check whether the provided array or dtype is of a float dtype.
+
+    Parameters
+    ----------
+    arr_or_dtype : array-like or dtype
+        The array or dtype to check.
+
+    Returns
+    -------
+    boolean
+        Whether or not the array or dtype is of a float dtype.
+
+    Examples
+    --------
+    >>> from pandas.api.types import is_float_dtype
+    >>> is_float_dtype(str)
+    False
+    >>> is_float_dtype(int)
+    False
+    >>> is_float_dtype(float)
+    True
+    >>> is_float_dtype(np.array(['a', 'b']))
+    False
+    >>> is_float_dtype(pd.Series([1, 2]))
+    False
+    >>> is_float_dtype(pd.Index([1, 2.]))
+    True
+    """
+    return _is_dtype_type(arr_or_dtype, classes(np.floating)) or _is_dtype(
+        arr_or_dtype, lambda typ: isinstance(typ, ExtensionDtype) and typ.kind in "f"
+    )
+
+
+def is_bool_dtype(arr_or_dtype) -> bool:
+    """
+    Check whether the provided array or dtype is of a boolean dtype.
+
+    Parameters
+    ----------
+    arr_or_dtype : array-like or dtype
+        The array or dtype to check.
+
+    Returns
+    -------
+    boolean
+        Whether or not the array or dtype is of a boolean dtype.
+
+    Notes
+    -----
+    An ExtensionArray is considered boolean when the ``_is_boolean``
+    attribute is set to True.
+
+    Examples
+    --------
+    >>> from pandas.api.types import is_bool_dtype
+    >>> is_bool_dtype(str)
+    False
+    >>> is_bool_dtype(int)
+    False
+    >>> is_bool_dtype(bool)
+    True
+    >>> is_bool_dtype(np.bool_)
+    True
+    >>> is_bool_dtype(np.array(['a', 'b']))
+    False
+    >>> is_bool_dtype(pd.Series([1, 2]))
+    False
+    >>> is_bool_dtype(np.array([True, False]))
+    True
+    >>> is_bool_dtype(pd.Categorical([True, False]))
+    True
+    >>> is_bool_dtype(pd.arrays.SparseArray([True, False]))
+    True
+    """
+    if arr_or_dtype is None:
+        return False
+    try:
+        dtype = _get_dtype(arr_or_dtype)
+    except (TypeError, ValueError):
+        return False
+
+    if isinstance(dtype, CategoricalDtype):
+        arr_or_dtype = dtype.categories
+        # now we use the special definition for Index
+
+    if isinstance(arr_or_dtype, ABCIndex):
+        # Allow Index[object] that is all-bools or Index["boolean"]
+        if arr_or_dtype.inferred_type == "boolean":
+            if not is_bool_dtype(arr_or_dtype.dtype):
+                # GH#52680
+                warnings.warn(
+                    "The behavior of is_bool_dtype with an object-dtype Index "
+                    "of bool objects is deprecated. In a future version, "
+                    "this will return False. Cast the Index to a bool dtype instead.",
+                    DeprecationWarning,
+                    stacklevel=2,
+                )
+            return True
+        return False
+    elif isinstance(dtype, ExtensionDtype):
+        return getattr(dtype, "_is_boolean", False)
+
+    return issubclass(dtype.type, np.bool_)
+
+
+def is_1d_only_ea_dtype(dtype: DtypeObj | None) -> bool:
+    """
+    Analogue to is_extension_array_dtype but excluding DatetimeTZDtype.
+    """
+    return isinstance(dtype, ExtensionDtype) and not dtype._supports_2d
+
+
+def is_extension_array_dtype(arr_or_dtype) -> bool:
+    """
+    Check if an object is a pandas extension array type.
+
+    See the :ref:`Use Guide ` for more.
+
+    Parameters
+    ----------
+    arr_or_dtype : object
+        For array-like input, the ``.dtype`` attribute will
+        be extracted.
+
+    Returns
+    -------
+    bool
+        Whether the `arr_or_dtype` is an extension array type.
+
+    Notes
+    -----
+    This checks whether an object implements the pandas extension
+    array interface. In pandas, this includes:
+
+    * Categorical
+    * Sparse
+    * Interval
+    * Period
+    * DatetimeArray
+    * TimedeltaArray
+
+    Third-party libraries may implement arrays or types satisfying
+    this interface as well.
+
+    Examples
+    --------
+    >>> from pandas.api.types import is_extension_array_dtype
+    >>> arr = pd.Categorical(['a', 'b'])
+    >>> is_extension_array_dtype(arr)
+    True
+    >>> is_extension_array_dtype(arr.dtype)
+    True
+
+    >>> arr = np.array(['a', 'b'])
+    >>> is_extension_array_dtype(arr.dtype)
+    False
+    """
+    dtype = getattr(arr_or_dtype, "dtype", arr_or_dtype)
+    if isinstance(dtype, ExtensionDtype):
+        return True
+    elif isinstance(dtype, np.dtype):
+        return False
+    else:
+        try:
+            with warnings.catch_warnings():
+                # pandas_dtype(..) can raise UserWarning for class input
+                warnings.simplefilter("ignore", UserWarning)
+                dtype = pandas_dtype(dtype)
+        except (TypeError, ValueError):
+            # np.dtype(..) can raise ValueError
+            return False
+        return isinstance(dtype, ExtensionDtype)
+
+
+def is_ea_or_datetimelike_dtype(dtype: DtypeObj | None) -> bool:
+    """
+    Check for ExtensionDtype, datetime64 dtype, or timedelta64 dtype.
+
+    Notes
+    -----
+    Checks only for dtype objects, not dtype-castable strings or types.
+    """
+    return isinstance(dtype, ExtensionDtype) or (lib.is_np_dtype(dtype, "mM"))
+
+
+def is_complex_dtype(arr_or_dtype) -> bool:
+    """
+    Check whether the provided array or dtype is of a complex dtype.
+
+    Parameters
+    ----------
+    arr_or_dtype : array-like or dtype
+        The array or dtype to check.
+
+    Returns
+    -------
+    boolean
+        Whether or not the array or dtype is of a complex dtype.
+
+    Examples
+    --------
+    >>> from pandas.api.types import is_complex_dtype
+    >>> is_complex_dtype(str)
+    False
+    >>> is_complex_dtype(int)
+    False
+    >>> is_complex_dtype(np.complex128)
+    True
+    >>> is_complex_dtype(np.array(['a', 'b']))
+    False
+    >>> is_complex_dtype(pd.Series([1, 2]))
+    False
+    >>> is_complex_dtype(np.array([1 + 1j, 5]))
+    True
+    """
+    return _is_dtype_type(arr_or_dtype, classes(np.complexfloating))
+
+
+def _is_dtype(arr_or_dtype, condition) -> bool:
+    """
+    Return true if the condition is satisfied for the arr_or_dtype.
+
+    Parameters
+    ----------
+    arr_or_dtype : array-like, str, np.dtype, or ExtensionArrayType
+        The array-like or dtype object whose dtype we want to extract.
+    condition : callable[Union[np.dtype, ExtensionDtype]]
+
+    Returns
+    -------
+    bool
+
+    """
+    if arr_or_dtype is None:
+        return False
+    try:
+        dtype = _get_dtype(arr_or_dtype)
+    except (TypeError, ValueError):
+        return False
+    return condition(dtype)
+
+
+def _get_dtype(arr_or_dtype) -> DtypeObj:
+    """
+    Get the dtype instance associated with an array
+    or dtype object.
+
+    Parameters
+    ----------
+    arr_or_dtype : array-like or dtype
+        The array-like or dtype object whose dtype we want to extract.
+
+    Returns
+    -------
+    obj_dtype : The extract dtype instance from the
+                passed in array or dtype object.
+
+    Raises
+    ------
+    TypeError : The passed in object is None.
+    """
+    if arr_or_dtype is None:
+        raise TypeError("Cannot deduce dtype from null object")
+
+    # fastpath
+    if isinstance(arr_or_dtype, np.dtype):
+        return arr_or_dtype
+    elif isinstance(arr_or_dtype, type):
+        return np.dtype(arr_or_dtype)
+
+    # if we have an array-like
+    elif hasattr(arr_or_dtype, "dtype"):
+        arr_or_dtype = arr_or_dtype.dtype
+
+    return pandas_dtype(arr_or_dtype)
+
+
+def _is_dtype_type(arr_or_dtype, condition) -> bool:
+    """
+    Return true if the condition is satisfied for the arr_or_dtype.
+
+    Parameters
+    ----------
+    arr_or_dtype : array-like or dtype
+        The array-like or dtype object whose dtype we want to extract.
+    condition : callable[Union[np.dtype, ExtensionDtypeType]]
+
+    Returns
+    -------
+    bool : if the condition is satisfied for the arr_or_dtype
+    """
+    if arr_or_dtype is None:
+        return condition(type(None))
+
+    # fastpath
+    if isinstance(arr_or_dtype, np.dtype):
+        return condition(arr_or_dtype.type)
+    elif isinstance(arr_or_dtype, type):
+        if issubclass(arr_or_dtype, ExtensionDtype):
+            arr_or_dtype = arr_or_dtype.type
+        return condition(np.dtype(arr_or_dtype).type)
+
+    # if we have an array-like
+    if hasattr(arr_or_dtype, "dtype"):
+        arr_or_dtype = arr_or_dtype.dtype
+
+    # we are not possibly a dtype
+    elif is_list_like(arr_or_dtype):
+        return condition(type(None))
+
+    try:
+        tipo = pandas_dtype(arr_or_dtype).type
+    except (TypeError, ValueError):
+        if is_scalar(arr_or_dtype):
+            return condition(type(None))
+
+        return False
+
+    return condition(tipo)
+
+
+def infer_dtype_from_object(dtype) -> type:
+    """
+    Get a numpy dtype.type-style object for a dtype object.
+
+    This methods also includes handling of the datetime64[ns] and
+    datetime64[ns, TZ] objects.
+
+    If no dtype can be found, we return ``object``.
+
+    Parameters
+    ----------
+    dtype : dtype, type
+        The dtype object whose numpy dtype.type-style
+        object we want to extract.
+
+    Returns
+    -------
+    type
+    """
+    if isinstance(dtype, type) and issubclass(dtype, np.generic):
+        # Type object from a dtype
+
+        return dtype
+    elif isinstance(dtype, (np.dtype, ExtensionDtype)):
+        # dtype object
+        try:
+            _validate_date_like_dtype(dtype)
+        except TypeError:
+            # Should still pass if we don't have a date-like
+            pass
+        if hasattr(dtype, "numpy_dtype"):
+            # TODO: Implement this properly
+            # https://github.com/pandas-dev/pandas/issues/52576
+            return dtype.numpy_dtype.type
+        return dtype.type
+
+    try:
+        dtype = pandas_dtype(dtype)
+    except TypeError:
+        pass
+
+    if isinstance(dtype, ExtensionDtype):
+        return dtype.type
+    elif isinstance(dtype, str):
+        # TODO(jreback)
+        # should deprecate these
+        if dtype in ["datetimetz", "datetime64tz"]:
+            return DatetimeTZDtype.type
+        elif dtype in ["period"]:
+            raise NotImplementedError
+
+        if dtype in ["datetime", "timedelta"]:
+            dtype += "64"
+        try:
+            return infer_dtype_from_object(getattr(np, dtype))
+        except (AttributeError, TypeError):
+            # Handles cases like _get_dtype(int) i.e.,
+            # Python objects that are valid dtypes
+            # (unlike user-defined types, in general)
+            #
+            # TypeError handles the float16 type code of 'e'
+            # further handle internal types
+            pass
+
+    return infer_dtype_from_object(np.dtype(dtype))
+
+
+def _validate_date_like_dtype(dtype) -> None:
+    """
+    Check whether the dtype is a date-like dtype. Raises an error if invalid.
+
+    Parameters
+    ----------
+    dtype : dtype, type
+        The dtype to check.
+
+    Raises
+    ------
+    TypeError : The dtype could not be casted to a date-like dtype.
+    ValueError : The dtype is an illegal date-like dtype (e.g. the
+                 frequency provided is too specific)
+    """
+    try:
+        typ = np.datetime_data(dtype)[0]
+    except ValueError as e:
+        raise TypeError(e) from e
+    if typ not in ["generic", "ns"]:
+        raise ValueError(
+            f"{repr(dtype.name)} is too specific of a frequency, "
+            f"try passing {repr(dtype.type.__name__)}"
+        )
+
+
+def validate_all_hashable(*args, error_name: str | None = None) -> None:
+    """
+    Return None if all args are hashable, else raise a TypeError.
+
+    Parameters
+    ----------
+    *args
+        Arguments to validate.
+    error_name : str, optional
+        The name to use if error
+
+    Raises
+    ------
+    TypeError : If an argument is not hashable
+
+    Returns
+    -------
+    None
+    """
+    if not all(is_hashable(arg) for arg in args):
+        if error_name:
+            raise TypeError(f"{error_name} must be a hashable type")
+        raise TypeError("All elements must be hashable")
+
+
+def pandas_dtype(dtype) -> DtypeObj:
+    """
+    Convert input into a pandas only dtype object or a numpy dtype object.
+
+    Parameters
+    ----------
+    dtype : object to be converted
+
+    Returns
+    -------
+    np.dtype or a pandas dtype
+
+    Raises
+    ------
+    TypeError if not a dtype
+
+    Examples
+    --------
+    >>> pd.api.types.pandas_dtype(int)
+    dtype('int64')
+    """
+    # short-circuit
+    if isinstance(dtype, np.ndarray):
+        return dtype.dtype
+    elif isinstance(dtype, (np.dtype, ExtensionDtype)):
+        return dtype
+
+    # builtin aliases
+    if dtype is str and using_string_dtype():
+        from pandas.core.arrays.string_ import StringDtype
+
+        return StringDtype(na_value=np.nan)
+
+    # registered extension types
+    result = registry.find(dtype)
+    if result is not None:
+        if isinstance(result, type):
+            # GH 31356, GH 54592
+            warnings.warn(
+                f"Instantiating {result.__name__} without any arguments."
+                f"Pass a {result.__name__} instance to silence this warning.",
+                UserWarning,
+                stacklevel=find_stack_level(),
+            )
+            result = result()
+        return result
+
+    # try a numpy dtype
+    # raise a consistent TypeError if failed
+    try:
+        with warnings.catch_warnings():
+            # TODO: warnings.catch_warnings can be removed when numpy>2.3.0
+            # is the minimum version
+            # GH#51523 - Series.astype(np.integer) doesn't show
+            # numpy deprecation warning of np.integer
+            # Hence enabling DeprecationWarning
+            warnings.simplefilter("always", DeprecationWarning)
+            npdtype = np.dtype(dtype)
+    except SyntaxError as err:
+        # np.dtype uses `eval` which can raise SyntaxError
+        raise TypeError(f"data type '{dtype}' not understood") from err
+
+    # Any invalid dtype (such as pd.Timestamp) should raise an error.
+    # np.dtype(invalid_type).kind = 0 for such objects. However, this will
+    # also catch some valid dtypes such as object, np.object_ and 'object'
+    # which we safeguard against by catching them earlier and returning
+    # np.dtype(valid_dtype) before this condition is evaluated.
+    if is_hashable(dtype) and dtype in [
+        object,
+        np.object_,
+        "object",
+        "O",
+        "object_",
+    ]:
+        # check hashability to avoid errors/DeprecationWarning when we get
+        # here and `dtype` is an array
+        return npdtype
+    elif npdtype.kind == "O":
+        raise TypeError(f"dtype '{dtype}' not understood")
+
+    return npdtype
+
+
+def is_all_strings(value: ArrayLike) -> bool:
+    """
+    Check if this is an array of strings that we should try parsing.
+
+    Includes object-dtype ndarray containing all-strings, StringArray,
+    and Categorical with all-string categories.
+    Does not include numpy string dtypes.
+    """
+    dtype = value.dtype
+
+    if isinstance(dtype, np.dtype):
+        if len(value) == 0:
+            return dtype == np.dtype("object")
+        else:
+            return dtype == np.dtype("object") and lib.is_string_array(
+                np.asarray(value), skipna=False
+            )
+    elif isinstance(dtype, CategoricalDtype):
+        return dtype.categories.inferred_type == "string"
+    return dtype == "string"
+
+
+__all__ = [
+    "classes",
+    "DT64NS_DTYPE",
+    "ensure_float64",
+    "ensure_python_int",
+    "ensure_str",
+    "infer_dtype_from_object",
+    "INT64_DTYPE",
+    "is_1d_only_ea_dtype",
+    "is_all_strings",
+    "is_any_real_numeric_dtype",
+    "is_array_like",
+    "is_bool",
+    "is_bool_dtype",
+    "is_categorical_dtype",
+    "is_complex",
+    "is_complex_dtype",
+    "is_dataclass",
+    "is_datetime64_any_dtype",
+    "is_datetime64_dtype",
+    "is_datetime64_ns_dtype",
+    "is_datetime64tz_dtype",
+    "is_decimal",
+    "is_dict_like",
+    "is_dtype_equal",
+    "is_ea_or_datetimelike_dtype",
+    "is_extension_array_dtype",
+    "is_file_like",
+    "is_float_dtype",
+    "is_int64_dtype",
+    "is_integer_dtype",
+    "is_interval",
+    "is_interval_dtype",
+    "is_iterator",
+    "is_named_tuple",
+    "is_nested_list_like",
+    "is_number",
+    "is_numeric_dtype",
+    "is_object_dtype",
+    "is_period_dtype",
+    "is_re",
+    "is_re_compilable",
+    "is_scipy_sparse",
+    "is_sequence",
+    "is_signed_integer_dtype",
+    "is_sparse",
+    "is_string_dtype",
+    "is_string_or_object_np_dtype",
+    "is_timedelta64_dtype",
+    "is_timedelta64_ns_dtype",
+    "is_unsigned_integer_dtype",
+    "needs_i8_conversion",
+    "pandas_dtype",
+    "TD64NS_DTYPE",
+    "validate_all_hashable",
+]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/dtypes/concat.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/dtypes/concat.py
new file mode 100644
index 0000000000000000000000000000000000000000..9ec662a6cd3520aaa49fdc96142ad1b02bb518d8
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/dtypes/concat.py
@@ -0,0 +1,348 @@
+"""
+Utility functions related to concat.
+"""
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    cast,
+)
+import warnings
+
+import numpy as np
+
+from pandas._libs import lib
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.astype import astype_array
+from pandas.core.dtypes.cast import (
+    common_dtype_categorical_compat,
+    find_common_type,
+    np_find_common_type,
+)
+from pandas.core.dtypes.dtypes import CategoricalDtype
+from pandas.core.dtypes.generic import (
+    ABCCategoricalIndex,
+    ABCSeries,
+)
+
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+
+    from pandas._typing import (
+        ArrayLike,
+        AxisInt,
+        DtypeObj,
+    )
+
+    from pandas.core.arrays import (
+        Categorical,
+        ExtensionArray,
+    )
+
+
+def _is_nonempty(x, axis) -> bool:
+    # filter empty arrays
+    # 1-d dtypes always are included here
+    if x.ndim <= axis:
+        return True
+    return x.shape[axis] > 0
+
+
+def concat_compat(
+    to_concat: Sequence[ArrayLike], axis: AxisInt = 0, ea_compat_axis: bool = False
+) -> ArrayLike:
+    """
+    provide concatenation of an array of arrays each of which is a single
+    'normalized' dtypes (in that for example, if it's object, then it is a
+    non-datetimelike and provide a combined dtype for the resulting array that
+    preserves the overall dtype if possible)
+
+    Parameters
+    ----------
+    to_concat : sequence of arrays
+    axis : axis to provide concatenation
+    ea_compat_axis : bool, default False
+        For ExtensionArray compat, behave as if axis == 1 when determining
+        whether to drop empty arrays.
+
+    Returns
+    -------
+    a single array, preserving the combined dtypes
+    """
+    if len(to_concat) and lib.dtypes_all_equal([obj.dtype for obj in to_concat]):
+        # fastpath!
+        obj = to_concat[0]
+        if isinstance(obj, np.ndarray):
+            to_concat_arrs = cast("Sequence[np.ndarray]", to_concat)
+            return np.concatenate(to_concat_arrs, axis=axis)
+
+        to_concat_eas = cast("Sequence[ExtensionArray]", to_concat)
+        if ea_compat_axis:
+            # We have 1D objects, that don't support axis keyword
+            return obj._concat_same_type(to_concat_eas)
+        elif axis == 0:
+            return obj._concat_same_type(to_concat_eas)
+        else:
+            # e.g. DatetimeArray
+            # NB: We are assuming here that ensure_wrapped_if_arraylike has
+            #  been called where relevant.
+            return obj._concat_same_type(
+                # error: Unexpected keyword argument "axis" for "_concat_same_type"
+                # of "ExtensionArray"
+                to_concat_eas,
+                axis=axis,  # type: ignore[call-arg]
+            )
+
+    # If all arrays are empty, there's nothing to convert, just short-cut to
+    # the concatenation, #3121.
+    #
+    # Creating an empty array directly is tempting, but the winnings would be
+    # marginal given that it would still require shape & dtype calculation and
+    # np.concatenate which has them both implemented is compiled.
+    orig = to_concat
+    non_empties = [x for x in to_concat if _is_nonempty(x, axis)]
+    if non_empties and axis == 0 and not ea_compat_axis:
+        # ea_compat_axis see GH#39574
+        to_concat = non_empties
+
+    any_ea, kinds, target_dtype = _get_result_dtype(to_concat, non_empties)
+
+    if len(to_concat) < len(orig):
+        _, _, alt_dtype = _get_result_dtype(orig, non_empties)
+        if alt_dtype != target_dtype:
+            # GH#39122
+            warnings.warn(
+                "The behavior of array concatenation with empty entries is "
+                "deprecated. In a future version, this will no longer exclude "
+                "empty items when determining the result dtype. "
+                "To retain the old behavior, exclude the empty entries before "
+                "the concat operation.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+
+    if target_dtype is not None:
+        to_concat = [astype_array(arr, target_dtype, copy=False) for arr in to_concat]
+
+    if not isinstance(to_concat[0], np.ndarray):
+        # i.e. isinstance(to_concat[0], ExtensionArray)
+        to_concat_eas = cast("Sequence[ExtensionArray]", to_concat)
+        cls = type(to_concat[0])
+        # GH#53640: eg. for datetime array, axis=1 but 0 is default
+        # However, class method `_concat_same_type()` for some classes
+        # may not support the `axis` keyword
+        if ea_compat_axis or axis == 0:
+            return cls._concat_same_type(to_concat_eas)
+        else:
+            return cls._concat_same_type(
+                to_concat_eas,
+                axis=axis,  # type: ignore[call-arg]
+            )
+    else:
+        to_concat_arrs = cast("Sequence[np.ndarray]", to_concat)
+        result = np.concatenate(to_concat_arrs, axis=axis)
+
+        if not any_ea and "b" in kinds and result.dtype.kind in "iuf":
+            # GH#39817 cast to object instead of casting bools to numeric
+            result = result.astype(object, copy=False)
+    return result
+
+
+def _get_result_dtype(
+    to_concat: Sequence[ArrayLike], non_empties: Sequence[ArrayLike]
+) -> tuple[bool, set[str], DtypeObj | None]:
+    target_dtype = None
+
+    dtypes = {obj.dtype for obj in to_concat}
+    kinds = {obj.dtype.kind for obj in to_concat}
+
+    any_ea = any(not isinstance(x, np.ndarray) for x in to_concat)
+    if any_ea:
+        # i.e. any ExtensionArrays
+
+        # we ignore axis here, as internally concatting with EAs is always
+        # for axis=0
+        if len(dtypes) != 1:
+            target_dtype = find_common_type([x.dtype for x in to_concat])
+            target_dtype = common_dtype_categorical_compat(to_concat, target_dtype)
+
+    elif not len(non_empties):
+        # we have all empties, but may need to coerce the result dtype to
+        # object if we have non-numeric type operands (numpy would otherwise
+        # cast this to float)
+        if len(kinds) != 1:
+            if not len(kinds - {"i", "u", "f"}) or not len(kinds - {"b", "i", "u"}):
+                # let numpy coerce
+                pass
+            else:
+                # coerce to object
+                target_dtype = np.dtype(object)
+                kinds = {"o"}
+    else:
+        # error: Argument 1 to "np_find_common_type" has incompatible type
+        # "*Set[Union[ExtensionDtype, Any]]"; expected "dtype[Any]"
+        target_dtype = np_find_common_type(*dtypes)  # type: ignore[arg-type]
+
+    return any_ea, kinds, target_dtype
+
+
+def union_categoricals(
+    to_union, sort_categories: bool = False, ignore_order: bool = False
+) -> Categorical:
+    """
+    Combine list-like of Categorical-like, unioning categories.
+
+    All categories must have the same dtype.
+
+    Parameters
+    ----------
+    to_union : list-like
+        Categorical, CategoricalIndex, or Series with dtype='category'.
+    sort_categories : bool, default False
+        If true, resulting categories will be lexsorted, otherwise
+        they will be ordered as they appear in the data.
+    ignore_order : bool, default False
+        If true, the ordered attribute of the Categoricals will be ignored.
+        Results in an unordered categorical.
+
+    Returns
+    -------
+    Categorical
+
+    Raises
+    ------
+    TypeError
+        - all inputs do not have the same dtype
+        - all inputs do not have the same ordered property
+        - all inputs are ordered and their categories are not identical
+        - sort_categories=True and Categoricals are ordered
+    ValueError
+        Empty list of categoricals passed
+
+    Notes
+    -----
+    To learn more about categories, see `link
+    `__
+
+    Examples
+    --------
+    If you want to combine categoricals that do not necessarily have
+    the same categories, `union_categoricals` will combine a list-like
+    of categoricals. The new categories will be the union of the
+    categories being combined.
+
+    >>> a = pd.Categorical(["b", "c"])
+    >>> b = pd.Categorical(["a", "b"])
+    >>> pd.api.types.union_categoricals([a, b])
+    ['b', 'c', 'a', 'b']
+    Categories (3, object): ['b', 'c', 'a']
+
+    By default, the resulting categories will be ordered as they appear
+    in the `categories` of the data. If you want the categories to be
+    lexsorted, use `sort_categories=True` argument.
+
+    >>> pd.api.types.union_categoricals([a, b], sort_categories=True)
+    ['b', 'c', 'a', 'b']
+    Categories (3, object): ['a', 'b', 'c']
+
+    `union_categoricals` also works with the case of combining two
+    categoricals of the same categories and order information (e.g. what
+    you could also `append` for).
+
+    >>> a = pd.Categorical(["a", "b"], ordered=True)
+    >>> b = pd.Categorical(["a", "b", "a"], ordered=True)
+    >>> pd.api.types.union_categoricals([a, b])
+    ['a', 'b', 'a', 'b', 'a']
+    Categories (2, object): ['a' < 'b']
+
+    Raises `TypeError` because the categories are ordered and not identical.
+
+    >>> a = pd.Categorical(["a", "b"], ordered=True)
+    >>> b = pd.Categorical(["a", "b", "c"], ordered=True)
+    >>> pd.api.types.union_categoricals([a, b])
+    Traceback (most recent call last):
+        ...
+    TypeError: to union ordered Categoricals, all categories must be the same
+
+    Ordered categoricals with different categories or orderings can be
+    combined by using the `ignore_ordered=True` argument.
+
+    >>> a = pd.Categorical(["a", "b", "c"], ordered=True)
+    >>> b = pd.Categorical(["c", "b", "a"], ordered=True)
+    >>> pd.api.types.union_categoricals([a, b], ignore_order=True)
+    ['a', 'b', 'c', 'c', 'b', 'a']
+    Categories (3, object): ['a', 'b', 'c']
+
+    `union_categoricals` also works with a `CategoricalIndex`, or `Series`
+    containing categorical data, but note that the resulting array will
+    always be a plain `Categorical`
+
+    >>> a = pd.Series(["b", "c"], dtype='category')
+    >>> b = pd.Series(["a", "b"], dtype='category')
+    >>> pd.api.types.union_categoricals([a, b])
+    ['b', 'c', 'a', 'b']
+    Categories (3, object): ['b', 'c', 'a']
+    """
+    from pandas import Categorical
+    from pandas.core.arrays.categorical import recode_for_categories
+
+    if len(to_union) == 0:
+        raise ValueError("No Categoricals to union")
+
+    def _maybe_unwrap(x):
+        if isinstance(x, (ABCCategoricalIndex, ABCSeries)):
+            return x._values
+        elif isinstance(x, Categorical):
+            return x
+        else:
+            raise TypeError("all components to combine must be Categorical")
+
+    to_union = [_maybe_unwrap(x) for x in to_union]
+    first = to_union[0]
+
+    if not lib.dtypes_all_equal([obj.categories.dtype for obj in to_union]):
+        raise TypeError("dtype of categories must be the same")
+
+    ordered = False
+    if all(first._categories_match_up_to_permutation(other) for other in to_union[1:]):
+        # identical categories - fastpath
+        categories = first.categories
+        ordered = first.ordered
+
+        all_codes = [first._encode_with_my_categories(x)._codes for x in to_union]
+        new_codes = np.concatenate(all_codes)
+
+        if sort_categories and not ignore_order and ordered:
+            raise TypeError("Cannot use sort_categories=True with ordered Categoricals")
+
+        if sort_categories and not categories.is_monotonic_increasing:
+            categories = categories.sort_values()
+            indexer = categories.get_indexer(first.categories)
+
+            from pandas.core.algorithms import take_nd
+
+            new_codes = take_nd(indexer, new_codes, fill_value=-1)
+    elif ignore_order or all(not c.ordered for c in to_union):
+        # different categories - union and recode
+        cats = first.categories.append([c.categories for c in to_union[1:]])
+        categories = cats.unique()
+        if sort_categories:
+            categories = categories.sort_values()
+
+        new_codes = [
+            recode_for_categories(c.codes, c.categories, categories) for c in to_union
+        ]
+        new_codes = np.concatenate(new_codes)
+    else:
+        # ordered - to show a proper error message
+        if all(c.ordered for c in to_union):
+            msg = "to union ordered Categoricals, all categories must be the same"
+            raise TypeError(msg)
+        raise TypeError("Categorical.ordered must be the same")
+
+    if ignore_order:
+        ordered = False
+
+    dtype = CategoricalDtype(categories=categories, ordered=ordered)
+    return Categorical._simple_new(new_codes, dtype=dtype)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/dtypes/dtypes.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/dtypes/dtypes.py
new file mode 100644
index 0000000000000000000000000000000000000000..542bc85110cadfc777f82a6859899c129ef1d47f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/dtypes/dtypes.py
@@ -0,0 +1,2348 @@
+"""
+Define extension dtypes.
+"""
+from __future__ import annotations
+
+from datetime import (
+    date,
+    datetime,
+    time,
+    timedelta,
+)
+from decimal import Decimal
+import re
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    cast,
+)
+import warnings
+
+import numpy as np
+import pytz
+
+from pandas._libs import (
+    lib,
+    missing as libmissing,
+)
+from pandas._libs.interval import Interval
+from pandas._libs.properties import cache_readonly
+from pandas._libs.tslibs import (
+    BaseOffset,
+    NaT,
+    NaTType,
+    Period,
+    Timedelta,
+    Timestamp,
+    timezones,
+    to_offset,
+    tz_compare,
+)
+from pandas._libs.tslibs.dtypes import (
+    PeriodDtypeBase,
+    abbrev_to_npy_unit,
+)
+from pandas._libs.tslibs.offsets import BDay
+from pandas.compat import pa_version_under10p1
+from pandas.errors import PerformanceWarning
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.base import (
+    ExtensionDtype,
+    StorageExtensionDtype,
+    register_extension_dtype,
+)
+from pandas.core.dtypes.generic import (
+    ABCCategoricalIndex,
+    ABCIndex,
+    ABCRangeIndex,
+)
+from pandas.core.dtypes.inference import (
+    is_bool,
+    is_list_like,
+)
+
+from pandas.util import capitalize_first_letter
+
+if not pa_version_under10p1:
+    import pyarrow as pa
+
+if TYPE_CHECKING:
+    from collections.abc import MutableMapping
+    from datetime import tzinfo
+
+    import pyarrow as pa  # noqa: TCH004
+
+    from pandas._typing import (
+        Dtype,
+        DtypeObj,
+        IntervalClosedType,
+        Ordered,
+        Self,
+        npt,
+        type_t,
+    )
+
+    from pandas import (
+        Categorical,
+        CategoricalIndex,
+        DatetimeIndex,
+        Index,
+        IntervalIndex,
+        PeriodIndex,
+    )
+    from pandas.core.arrays import (
+        BaseMaskedArray,
+        DatetimeArray,
+        IntervalArray,
+        NumpyExtensionArray,
+        PeriodArray,
+        SparseArray,
+    )
+    from pandas.core.arrays.arrow import ArrowExtensionArray
+
+str_type = str
+
+
+class PandasExtensionDtype(ExtensionDtype):
+    """
+    A np.dtype duck-typed class, suitable for holding a custom dtype.
+
+    THIS IS NOT A REAL NUMPY DTYPE
+    """
+
+    type: Any
+    kind: Any
+    # The Any type annotations above are here only because mypy seems to have a
+    # problem dealing with multiple inheritance from PandasExtensionDtype
+    # and ExtensionDtype's @properties in the subclasses below. The kind and
+    # type variables in those subclasses are explicitly typed below.
+    subdtype = None
+    str: str_type
+    num = 100
+    shape: tuple[int, ...] = ()
+    itemsize = 8
+    base: DtypeObj | None = None
+    isbuiltin = 0
+    isnative = 0
+    _cache_dtypes: dict[str_type, PandasExtensionDtype] = {}
+
+    def __repr__(self) -> str_type:
+        """
+        Return a string representation for a particular object.
+        """
+        return str(self)
+
+    def __hash__(self) -> int:
+        raise NotImplementedError("sub-classes should implement an __hash__ method")
+
+    def __getstate__(self) -> dict[str_type, Any]:
+        # pickle support; we don't want to pickle the cache
+        return {k: getattr(self, k, None) for k in self._metadata}
+
+    @classmethod
+    def reset_cache(cls) -> None:
+        """clear the cache"""
+        cls._cache_dtypes = {}
+
+
+class CategoricalDtypeType(type):
+    """
+    the type of CategoricalDtype, this metaclass determines subclass ability
+    """
+
+
+@register_extension_dtype
+class CategoricalDtype(PandasExtensionDtype, ExtensionDtype):
+    """
+    Type for categorical data with the categories and orderedness.
+
+    Parameters
+    ----------
+    categories : sequence, optional
+        Must be unique, and must not contain any nulls.
+        The categories are stored in an Index,
+        and if an index is provided the dtype of that index will be used.
+    ordered : bool or None, default False
+        Whether or not this categorical is treated as a ordered categorical.
+        None can be used to maintain the ordered value of existing categoricals when
+        used in operations that combine categoricals, e.g. astype, and will resolve to
+        False if there is no existing ordered to maintain.
+
+    Attributes
+    ----------
+    categories
+    ordered
+
+    Methods
+    -------
+    None
+
+    See Also
+    --------
+    Categorical : Represent a categorical variable in classic R / S-plus fashion.
+
+    Notes
+    -----
+    This class is useful for specifying the type of a ``Categorical``
+    independent of the values. See :ref:`categorical.categoricaldtype`
+    for more.
+
+    Examples
+    --------
+    >>> t = pd.CategoricalDtype(categories=['b', 'a'], ordered=True)
+    >>> pd.Series(['a', 'b', 'a', 'c'], dtype=t)
+    0      a
+    1      b
+    2      a
+    3    NaN
+    dtype: category
+    Categories (2, object): ['b' < 'a']
+
+    An empty CategoricalDtype with a specific dtype can be created
+    by providing an empty index. As follows,
+
+    >>> pd.CategoricalDtype(pd.DatetimeIndex([])).categories.dtype
+    dtype(' None:
+        self._finalize(categories, ordered, fastpath=False)
+
+    @classmethod
+    def _from_fastpath(
+        cls, categories=None, ordered: bool | None = None
+    ) -> CategoricalDtype:
+        self = cls.__new__(cls)
+        self._finalize(categories, ordered, fastpath=True)
+        return self
+
+    @classmethod
+    def _from_categorical_dtype(
+        cls, dtype: CategoricalDtype, categories=None, ordered: Ordered | None = None
+    ) -> CategoricalDtype:
+        if categories is ordered is None:
+            return dtype
+        if categories is None:
+            categories = dtype.categories
+        if ordered is None:
+            ordered = dtype.ordered
+        return cls(categories, ordered)
+
+    @classmethod
+    def _from_values_or_dtype(
+        cls,
+        values=None,
+        categories=None,
+        ordered: bool | None = None,
+        dtype: Dtype | None = None,
+    ) -> CategoricalDtype:
+        """
+        Construct dtype from the input parameters used in :class:`Categorical`.
+
+        This constructor method specifically does not do the factorization
+        step, if that is needed to find the categories. This constructor may
+        therefore return ``CategoricalDtype(categories=None, ordered=None)``,
+        which may not be useful. Additional steps may therefore have to be
+        taken to create the final dtype.
+
+        The return dtype is specified from the inputs in this prioritized
+        order:
+        1. if dtype is a CategoricalDtype, return dtype
+        2. if dtype is the string 'category', create a CategoricalDtype from
+           the supplied categories and ordered parameters, and return that.
+        3. if values is a categorical, use value.dtype, but override it with
+           categories and ordered if either/both of those are not None.
+        4. if dtype is None and values is not a categorical, construct the
+           dtype from categories and ordered, even if either of those is None.
+
+        Parameters
+        ----------
+        values : list-like, optional
+            The list-like must be 1-dimensional.
+        categories : list-like, optional
+            Categories for the CategoricalDtype.
+        ordered : bool, optional
+            Designating if the categories are ordered.
+        dtype : CategoricalDtype or the string "category", optional
+            If ``CategoricalDtype``, cannot be used together with
+            `categories` or `ordered`.
+
+        Returns
+        -------
+        CategoricalDtype
+
+        Examples
+        --------
+        >>> pd.CategoricalDtype._from_values_or_dtype()
+        CategoricalDtype(categories=None, ordered=None, categories_dtype=None)
+        >>> pd.CategoricalDtype._from_values_or_dtype(
+        ...     categories=['a', 'b'], ordered=True
+        ... )
+        CategoricalDtype(categories=['a', 'b'], ordered=True, categories_dtype=object)
+        >>> dtype1 = pd.CategoricalDtype(['a', 'b'], ordered=True)
+        >>> dtype2 = pd.CategoricalDtype(['x', 'y'], ordered=False)
+        >>> c = pd.Categorical([0, 1], dtype=dtype1)
+        >>> pd.CategoricalDtype._from_values_or_dtype(
+        ...     c, ['x', 'y'], ordered=True, dtype=dtype2
+        ... )
+        Traceback (most recent call last):
+            ...
+        ValueError: Cannot specify `categories` or `ordered` together with
+        `dtype`.
+
+        The supplied dtype takes precedence over values' dtype:
+
+        >>> pd.CategoricalDtype._from_values_or_dtype(c, dtype=dtype2)
+        CategoricalDtype(categories=['x', 'y'], ordered=False, categories_dtype=object)
+        """
+
+        if dtype is not None:
+            # The dtype argument takes precedence over values.dtype (if any)
+            if isinstance(dtype, str):
+                if dtype == "category":
+                    if ordered is None and cls.is_dtype(values):
+                        # GH#49309 preserve orderedness
+                        ordered = values.dtype.ordered
+
+                    dtype = CategoricalDtype(categories, ordered)
+                else:
+                    raise ValueError(f"Unknown dtype {repr(dtype)}")
+            elif categories is not None or ordered is not None:
+                raise ValueError(
+                    "Cannot specify `categories` or `ordered` together with `dtype`."
+                )
+            elif not isinstance(dtype, CategoricalDtype):
+                raise ValueError(f"Cannot not construct CategoricalDtype from {dtype}")
+        elif cls.is_dtype(values):
+            # If no "dtype" was passed, use the one from "values", but honor
+            # the "ordered" and "categories" arguments
+            dtype = values.dtype._from_categorical_dtype(
+                values.dtype, categories, ordered
+            )
+        else:
+            # If dtype=None and values is not categorical, create a new dtype.
+            # Note: This could potentially have categories=None and
+            # ordered=None.
+            dtype = CategoricalDtype(categories, ordered)
+
+        return cast(CategoricalDtype, dtype)
+
+    @classmethod
+    def construct_from_string(cls, string: str_type) -> CategoricalDtype:
+        """
+        Construct a CategoricalDtype from a string.
+
+        Parameters
+        ----------
+        string : str
+            Must be the string "category" in order to be successfully constructed.
+
+        Returns
+        -------
+        CategoricalDtype
+            Instance of the dtype.
+
+        Raises
+        ------
+        TypeError
+            If a CategoricalDtype cannot be constructed from the input.
+        """
+        if not isinstance(string, str):
+            raise TypeError(
+                f"'construct_from_string' expects a string, got {type(string)}"
+            )
+        if string != cls.name:
+            raise TypeError(f"Cannot construct a 'CategoricalDtype' from '{string}'")
+
+        # need ordered=None to ensure that operations specifying dtype="category" don't
+        # override the ordered value for existing categoricals
+        return cls(ordered=None)
+
+    def _finalize(self, categories, ordered: Ordered, fastpath: bool = False) -> None:
+        if ordered is not None:
+            self.validate_ordered(ordered)
+
+        if categories is not None:
+            categories = self.validate_categories(categories, fastpath=fastpath)
+
+        self._categories = categories
+        self._ordered = ordered
+
+    def __setstate__(self, state: MutableMapping[str_type, Any]) -> None:
+        # for pickle compat. __get_state__ is defined in the
+        # PandasExtensionDtype superclass and uses the public properties to
+        # pickle -> need to set the settable private ones here (see GH26067)
+        self._categories = state.pop("categories", None)
+        self._ordered = state.pop("ordered", False)
+
+    def __hash__(self) -> int:
+        # _hash_categories returns a uint64, so use the negative
+        # space for when we have unknown categories to avoid a conflict
+        if self.categories is None:
+            if self.ordered:
+                return -1
+            else:
+                return -2
+        # We *do* want to include the real self.ordered here
+        return int(self._hash_categories)
+
+    def __eq__(self, other: object) -> bool:
+        """
+        Rules for CDT equality:
+        1) Any CDT is equal to the string 'category'
+        2) Any CDT is equal to itself
+        3) Any CDT is equal to a CDT with categories=None regardless of ordered
+        4) A CDT with ordered=True is only equal to another CDT with
+           ordered=True and identical categories in the same order
+        5) A CDT with ordered={False, None} is only equal to another CDT with
+           ordered={False, None} and identical categories, but same order is
+           not required. There is no distinction between False/None.
+        6) Any other comparison returns False
+        """
+        if isinstance(other, str):
+            return other == self.name
+        elif other is self:
+            return True
+        elif not (hasattr(other, "ordered") and hasattr(other, "categories")):
+            return False
+        elif self.categories is None or other.categories is None:
+            # For non-fully-initialized dtypes, these are only equal to
+            #  - the string "category" (handled above)
+            #  - other CategoricalDtype with categories=None
+            return self.categories is other.categories
+        elif self.ordered or other.ordered:
+            # At least one has ordered=True; equal if both have ordered=True
+            # and the same values for categories in the same order.
+            return (self.ordered == other.ordered) and self.categories.equals(
+                other.categories
+            )
+        else:
+            # Neither has ordered=True; equal if both have the same categories,
+            # but same order is not necessary.  There is no distinction between
+            # ordered=False and ordered=None: CDT(., False) and CDT(., None)
+            # will be equal if they have the same categories.
+            left = self.categories
+            right = other.categories
+
+            # GH#36280 the ordering of checks here is for performance
+            if not left.dtype == right.dtype:
+                return False
+
+            if len(left) != len(right):
+                return False
+
+            if self.categories.equals(other.categories):
+                # Check and see if they happen to be identical categories
+                return True
+
+            if left.dtype != object:
+                # Faster than calculating hash
+                indexer = left.get_indexer(right)
+                # Because left and right have the same length and are unique,
+                #  `indexer` not having any -1s implies that there is a
+                #  bijection between `left` and `right`.
+                return bool((indexer != -1).all())
+
+            # With object-dtype we need a comparison that identifies
+            #  e.g. int(2) as distinct from float(2)
+            return set(left) == set(right)
+
+    def __repr__(self) -> str_type:
+        if self.categories is None:
+            data = "None"
+            dtype = "None"
+        else:
+            data = self.categories._format_data(name=type(self).__name__)
+            if isinstance(self.categories, ABCRangeIndex):
+                data = str(self.categories._range)
+            data = data.rstrip(", ")
+            dtype = self.categories.dtype
+
+        return (
+            f"CategoricalDtype(categories={data}, ordered={self.ordered}, "
+            f"categories_dtype={dtype})"
+        )
+
+    @cache_readonly
+    def _hash_categories(self) -> int:
+        from pandas.core.util.hashing import (
+            combine_hash_arrays,
+            hash_array,
+            hash_tuples,
+        )
+
+        categories = self.categories
+        ordered = self.ordered
+
+        if len(categories) and isinstance(categories[0], tuple):
+            # assumes if any individual category is a tuple, then all our. ATM
+            # I don't really want to support just some of the categories being
+            # tuples.
+            cat_list = list(categories)  # breaks if a np.array of categories
+            cat_array = hash_tuples(cat_list)
+        else:
+            if categories.dtype == "O" and len({type(x) for x in categories}) != 1:
+                # TODO: hash_array doesn't handle mixed types. It casts
+                # everything to a str first, which means we treat
+                # {'1', '2'} the same as {'1', 2}
+                # find a better solution
+                hashed = hash((tuple(categories), ordered))
+                return hashed
+
+            if DatetimeTZDtype.is_dtype(categories.dtype):
+                # Avoid future warning.
+                categories = categories.view("datetime64[ns]")
+
+            cat_array = hash_array(np.asarray(categories), categorize=False)
+        if ordered:
+            cat_array = np.vstack(
+                [cat_array, np.arange(len(cat_array), dtype=cat_array.dtype)]
+            )
+        else:
+            cat_array = np.array([cat_array])
+        combined_hashed = combine_hash_arrays(iter(cat_array), num_items=len(cat_array))
+        return np.bitwise_xor.reduce(combined_hashed)
+
+    @classmethod
+    def construct_array_type(cls) -> type_t[Categorical]:
+        """
+        Return the array type associated with this dtype.
+
+        Returns
+        -------
+        type
+        """
+        from pandas import Categorical
+
+        return Categorical
+
+    @staticmethod
+    def validate_ordered(ordered: Ordered) -> None:
+        """
+        Validates that we have a valid ordered parameter. If
+        it is not a boolean, a TypeError will be raised.
+
+        Parameters
+        ----------
+        ordered : object
+            The parameter to be verified.
+
+        Raises
+        ------
+        TypeError
+            If 'ordered' is not a boolean.
+        """
+        if not is_bool(ordered):
+            raise TypeError("'ordered' must either be 'True' or 'False'")
+
+    @staticmethod
+    def validate_categories(categories, fastpath: bool = False) -> Index:
+        """
+        Validates that we have good categories
+
+        Parameters
+        ----------
+        categories : array-like
+        fastpath : bool
+            Whether to skip nan and uniqueness checks
+
+        Returns
+        -------
+        categories : Index
+        """
+        from pandas.core.indexes.base import Index
+
+        if not fastpath and not is_list_like(categories):
+            raise TypeError(
+                f"Parameter 'categories' must be list-like, was {repr(categories)}"
+            )
+        if not isinstance(categories, ABCIndex):
+            categories = Index._with_infer(categories, tupleize_cols=False)
+
+        if not fastpath:
+            if categories.hasnans:
+                raise ValueError("Categorical categories cannot be null")
+
+            if not categories.is_unique:
+                raise ValueError("Categorical categories must be unique")
+
+        if isinstance(categories, ABCCategoricalIndex):
+            categories = categories.categories
+
+        return categories
+
+    def update_dtype(self, dtype: str_type | CategoricalDtype) -> CategoricalDtype:
+        """
+        Returns a CategoricalDtype with categories and ordered taken from dtype
+        if specified, otherwise falling back to self if unspecified
+
+        Parameters
+        ----------
+        dtype : CategoricalDtype
+
+        Returns
+        -------
+        new_dtype : CategoricalDtype
+        """
+        if isinstance(dtype, str) and dtype == "category":
+            # dtype='category' should not change anything
+            return self
+        elif not self.is_dtype(dtype):
+            raise ValueError(
+                f"a CategoricalDtype must be passed to perform an update, "
+                f"got {repr(dtype)}"
+            )
+        else:
+            # from here on, dtype is a CategoricalDtype
+            dtype = cast(CategoricalDtype, dtype)
+
+        # update categories/ordered unless they've been explicitly passed as None
+        new_categories = (
+            dtype.categories if dtype.categories is not None else self.categories
+        )
+        new_ordered = dtype.ordered if dtype.ordered is not None else self.ordered
+
+        return CategoricalDtype(new_categories, new_ordered)
+
+    @property
+    def categories(self) -> Index:
+        """
+        An ``Index`` containing the unique categories allowed.
+
+        Examples
+        --------
+        >>> cat_type = pd.CategoricalDtype(categories=['a', 'b'], ordered=True)
+        >>> cat_type.categories
+        Index(['a', 'b'], dtype='object')
+        """
+        return self._categories
+
+    @property
+    def ordered(self) -> Ordered:
+        """
+        Whether the categories have an ordered relationship.
+
+        Examples
+        --------
+        >>> cat_type = pd.CategoricalDtype(categories=['a', 'b'], ordered=True)
+        >>> cat_type.ordered
+        True
+
+        >>> cat_type = pd.CategoricalDtype(categories=['a', 'b'], ordered=False)
+        >>> cat_type.ordered
+        False
+        """
+        return self._ordered
+
+    @property
+    def _is_boolean(self) -> bool:
+        from pandas.core.dtypes.common import is_bool_dtype
+
+        return is_bool_dtype(self.categories)
+
+    def _get_common_dtype(self, dtypes: list[DtypeObj]) -> DtypeObj | None:
+        # check if we have all categorical dtype with identical categories
+        if all(isinstance(x, CategoricalDtype) for x in dtypes):
+            first = dtypes[0]
+            if all(first == other for other in dtypes[1:]):
+                return first
+
+        # special case non-initialized categorical
+        # TODO we should figure out the expected return value in general
+        non_init_cats = [
+            isinstance(x, CategoricalDtype) and x.categories is None for x in dtypes
+        ]
+        if all(non_init_cats):
+            return self
+        elif any(non_init_cats):
+            return None
+
+        # categorical is aware of Sparse -> extract sparse subdtypes
+        dtypes = [x.subtype if isinstance(x, SparseDtype) else x for x in dtypes]
+        # extract the categories' dtype
+        non_cat_dtypes = [
+            x.categories.dtype if isinstance(x, CategoricalDtype) else x for x in dtypes
+        ]
+        # TODO should categorical always give an answer?
+        from pandas.core.dtypes.cast import find_common_type
+
+        return find_common_type(non_cat_dtypes)
+
+    @cache_readonly
+    def index_class(self) -> type_t[CategoricalIndex]:
+        from pandas import CategoricalIndex
+
+        return CategoricalIndex
+
+
+@register_extension_dtype
+class DatetimeTZDtype(PandasExtensionDtype):
+    """
+    An ExtensionDtype for timezone-aware datetime data.
+
+    **This is not an actual numpy dtype**, but a duck type.
+
+    Parameters
+    ----------
+    unit : str, default "ns"
+        The precision of the datetime data. Currently limited
+        to ``"ns"``.
+    tz : str, int, or datetime.tzinfo
+        The timezone.
+
+    Attributes
+    ----------
+    unit
+    tz
+
+    Methods
+    -------
+    None
+
+    Raises
+    ------
+    ZoneInfoNotFoundError
+        When the requested timezone cannot be found.
+
+    Examples
+    --------
+    >>> from zoneinfo import ZoneInfo
+    >>> pd.DatetimeTZDtype(tz=ZoneInfo('UTC'))
+    datetime64[ns, UTC]
+
+    >>> pd.DatetimeTZDtype(tz=ZoneInfo('Europe/Paris'))
+    datetime64[ns, Europe/Paris]
+    """
+
+    type: type[Timestamp] = Timestamp
+    kind: str_type = "M"
+    num = 101
+    _metadata = ("unit", "tz")
+    _match = re.compile(r"(datetime64|M8)\[(?P.+), (?P.+)\]")
+    _cache_dtypes: dict[str_type, PandasExtensionDtype] = {}
+    _supports_2d = True
+    _can_fast_transpose = True
+
+    @property
+    def na_value(self) -> NaTType:
+        return NaT
+
+    @cache_readonly
+    def base(self) -> DtypeObj:  # type: ignore[override]
+        return np.dtype(f"M8[{self.unit}]")
+
+    # error: Signature of "str" incompatible with supertype "PandasExtensionDtype"
+    @cache_readonly
+    def str(self) -> str:  # type: ignore[override]
+        return f"|M8[{self.unit}]"
+
+    def __init__(self, unit: str_type | DatetimeTZDtype = "ns", tz=None) -> None:
+        if isinstance(unit, DatetimeTZDtype):
+            # error: "str" has no attribute "tz"
+            unit, tz = unit.unit, unit.tz  # type: ignore[attr-defined]
+
+        if unit != "ns":
+            if isinstance(unit, str) and tz is None:
+                # maybe a string like datetime64[ns, tz], which we support for
+                # now.
+                result = type(self).construct_from_string(unit)
+                unit = result.unit
+                tz = result.tz
+                msg = (
+                    f"Passing a dtype alias like 'datetime64[ns, {tz}]' "
+                    "to DatetimeTZDtype is no longer supported. Use "
+                    "'DatetimeTZDtype.construct_from_string()' instead."
+                )
+                raise ValueError(msg)
+            if unit not in ["s", "ms", "us", "ns"]:
+                raise ValueError("DatetimeTZDtype only supports s, ms, us, ns units")
+
+        if tz:
+            tz = timezones.maybe_get_tz(tz)
+            tz = timezones.tz_standardize(tz)
+        elif tz is not None:
+            raise pytz.UnknownTimeZoneError(tz)
+        if tz is None:
+            raise TypeError("A 'tz' is required.")
+
+        self._unit = unit
+        self._tz = tz
+
+    @cache_readonly
+    def _creso(self) -> int:
+        """
+        The NPY_DATETIMEUNIT corresponding to this dtype's resolution.
+        """
+        return abbrev_to_npy_unit(self.unit)
+
+    @property
+    def unit(self) -> str_type:
+        """
+        The precision of the datetime data.
+
+        Examples
+        --------
+        >>> from zoneinfo import ZoneInfo
+        >>> dtype = pd.DatetimeTZDtype(tz=ZoneInfo('America/Los_Angeles'))
+        >>> dtype.unit
+        'ns'
+        """
+        return self._unit
+
+    @property
+    def tz(self) -> tzinfo:
+        """
+        The timezone.
+
+        Examples
+        --------
+        >>> from zoneinfo import ZoneInfo
+        >>> dtype = pd.DatetimeTZDtype(tz=ZoneInfo('America/Los_Angeles'))
+        >>> dtype.tz
+        zoneinfo.ZoneInfo(key='America/Los_Angeles')
+        """
+        return self._tz
+
+    @classmethod
+    def construct_array_type(cls) -> type_t[DatetimeArray]:
+        """
+        Return the array type associated with this dtype.
+
+        Returns
+        -------
+        type
+        """
+        from pandas.core.arrays import DatetimeArray
+
+        return DatetimeArray
+
+    @classmethod
+    def construct_from_string(cls, string: str_type) -> DatetimeTZDtype:
+        """
+        Construct a DatetimeTZDtype from a string.
+
+        Parameters
+        ----------
+        string : str
+            The string alias for this DatetimeTZDtype.
+            Should be formatted like ``datetime64[ns, ]``,
+            where ```` is the timezone name.
+
+        Examples
+        --------
+        >>> DatetimeTZDtype.construct_from_string('datetime64[ns, UTC]')
+        datetime64[ns, UTC]
+        """
+        if not isinstance(string, str):
+            raise TypeError(
+                f"'construct_from_string' expects a string, got {type(string)}"
+            )
+
+        msg = f"Cannot construct a 'DatetimeTZDtype' from '{string}'"
+        match = cls._match.match(string)
+        if match:
+            d = match.groupdict()
+            try:
+                return cls(unit=d["unit"], tz=d["tz"])
+            except (KeyError, TypeError, ValueError) as err:
+                # KeyError if maybe_get_tz tries and fails to get a
+                #  pytz timezone (actually pytz.UnknownTimeZoneError).
+                # TypeError if we pass a nonsense tz;
+                # ValueError if we pass a unit other than "ns"
+                raise TypeError(msg) from err
+        raise TypeError(msg)
+
+    def __str__(self) -> str_type:
+        return f"datetime64[{self.unit}, {self.tz}]"
+
+    @property
+    def name(self) -> str_type:
+        """A string representation of the dtype."""
+        return str(self)
+
+    def __hash__(self) -> int:
+        # make myself hashable
+        # TODO: update this.
+        return hash(str(self))
+
+    def __eq__(self, other: object) -> bool:
+        if isinstance(other, str):
+            if other.startswith("M8["):
+                other = f"datetime64[{other[3:]}"
+            return other == self.name
+
+        return (
+            isinstance(other, DatetimeTZDtype)
+            and self.unit == other.unit
+            and tz_compare(self.tz, other.tz)
+        )
+
+    def __from_arrow__(self, array: pa.Array | pa.ChunkedArray) -> DatetimeArray:
+        """
+        Construct DatetimeArray from pyarrow Array/ChunkedArray.
+
+        Note: If the units in the pyarrow Array are the same as this
+        DatetimeDtype, then values corresponding to the integer representation
+        of ``NaT`` (e.g. one nanosecond before :attr:`pandas.Timestamp.min`)
+        are converted to ``NaT``, regardless of the null indicator in the
+        pyarrow array.
+
+        Parameters
+        ----------
+        array : pyarrow.Array or pyarrow.ChunkedArray
+            The Arrow array to convert to DatetimeArray.
+
+        Returns
+        -------
+        extension array : DatetimeArray
+        """
+        import pyarrow
+
+        from pandas.core.arrays import DatetimeArray
+
+        array = array.cast(pyarrow.timestamp(unit=self._unit), safe=True)
+
+        if isinstance(array, pyarrow.Array):
+            np_arr = array.to_numpy(zero_copy_only=False)
+        else:
+            np_arr = array.to_numpy()
+
+        return DatetimeArray._simple_new(np_arr, dtype=self)
+
+    def __setstate__(self, state) -> None:
+        # for pickle compat. __get_state__ is defined in the
+        # PandasExtensionDtype superclass and uses the public properties to
+        # pickle -> need to set the settable private ones here (see GH26067)
+        self._tz = state["tz"]
+        self._unit = state["unit"]
+
+    def _get_common_dtype(self, dtypes: list[DtypeObj]) -> DtypeObj | None:
+        if all(isinstance(t, DatetimeTZDtype) and t.tz == self.tz for t in dtypes):
+            np_dtype = np.max([cast(DatetimeTZDtype, t).base for t in [self, *dtypes]])
+            unit = np.datetime_data(np_dtype)[0]
+            return type(self)(unit=unit, tz=self.tz)
+        return super()._get_common_dtype(dtypes)
+
+    @cache_readonly
+    def index_class(self) -> type_t[DatetimeIndex]:
+        from pandas import DatetimeIndex
+
+        return DatetimeIndex
+
+
+@register_extension_dtype
+class PeriodDtype(PeriodDtypeBase, PandasExtensionDtype):
+    """
+    An ExtensionDtype for Period data.
+
+    **This is not an actual numpy dtype**, but a duck type.
+
+    Parameters
+    ----------
+    freq : str or DateOffset
+        The frequency of this PeriodDtype.
+
+    Attributes
+    ----------
+    freq
+
+    Methods
+    -------
+    None
+
+    Examples
+    --------
+    >>> pd.PeriodDtype(freq='D')
+    period[D]
+
+    >>> pd.PeriodDtype(freq=pd.offsets.MonthEnd())
+    period[M]
+    """
+
+    type: type[Period] = Period
+    kind: str_type = "O"
+    str = "|O08"
+    base = np.dtype("O")
+    num = 102
+    _metadata = ("freq",)
+    _match = re.compile(r"(P|p)eriod\[(?P.+)\]")
+    # error: Incompatible types in assignment (expression has type
+    # "Dict[int, PandasExtensionDtype]", base class "PandasExtensionDtype"
+    # defined the type as "Dict[str, PandasExtensionDtype]")  [assignment]
+    _cache_dtypes: dict[BaseOffset, int] = {}  # type: ignore[assignment]
+    __hash__ = PeriodDtypeBase.__hash__
+    _freq: BaseOffset
+    _supports_2d = True
+    _can_fast_transpose = True
+
+    def __new__(cls, freq) -> PeriodDtype:  # noqa: PYI034
+        """
+        Parameters
+        ----------
+        freq : PeriodDtype, BaseOffset, or string
+        """
+        if isinstance(freq, PeriodDtype):
+            return freq
+
+        if not isinstance(freq, BaseOffset):
+            freq = cls._parse_dtype_strict(freq)
+
+        if isinstance(freq, BDay):
+            # GH#53446
+            # TODO(3.0): enforcing this will close GH#10575
+            warnings.warn(
+                "PeriodDtype[B] is deprecated and will be removed in a future "
+                "version. Use a DatetimeIndex with freq='B' instead",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+
+        try:
+            dtype_code = cls._cache_dtypes[freq]
+        except KeyError:
+            dtype_code = freq._period_dtype_code
+            cls._cache_dtypes[freq] = dtype_code
+        u = PeriodDtypeBase.__new__(cls, dtype_code, freq.n)
+        u._freq = freq
+        return u
+
+    def __reduce__(self) -> tuple[type_t[Self], tuple[str_type]]:
+        return type(self), (self.name,)
+
+    @property
+    def freq(self) -> BaseOffset:
+        """
+        The frequency object of this PeriodDtype.
+
+        Examples
+        --------
+        >>> dtype = pd.PeriodDtype(freq='D')
+        >>> dtype.freq
+        
+        """
+        return self._freq
+
+    @classmethod
+    def _parse_dtype_strict(cls, freq: str_type) -> BaseOffset:
+        if isinstance(freq, str):  # note: freq is already of type str!
+            if freq.startswith(("Period[", "period[")):
+                m = cls._match.search(freq)
+                if m is not None:
+                    freq = m.group("freq")
+
+            freq_offset = to_offset(freq, is_period=True)
+            if freq_offset is not None:
+                return freq_offset
+
+        raise TypeError(
+            "PeriodDtype argument should be string or BaseOffset, "
+            f"got {type(freq).__name__}"
+        )
+
+    @classmethod
+    def construct_from_string(cls, string: str_type) -> PeriodDtype:
+        """
+        Strict construction from a string, raise a TypeError if not
+        possible
+        """
+        if (
+            isinstance(string, str)
+            and (string.startswith(("period[", "Period[")))
+            or isinstance(string, BaseOffset)
+        ):
+            # do not parse string like U as period[U]
+            # avoid tuple to be regarded as freq
+            try:
+                return cls(freq=string)
+            except ValueError:
+                pass
+        if isinstance(string, str):
+            msg = f"Cannot construct a 'PeriodDtype' from '{string}'"
+        else:
+            msg = f"'construct_from_string' expects a string, got {type(string)}"
+        raise TypeError(msg)
+
+    def __str__(self) -> str_type:
+        return self.name
+
+    @property
+    def name(self) -> str_type:
+        return f"period[{self._freqstr}]"
+
+    @property
+    def na_value(self) -> NaTType:
+        return NaT
+
+    def __eq__(self, other: object) -> bool:
+        if isinstance(other, str):
+            return other in [self.name, capitalize_first_letter(self.name)]
+
+        return super().__eq__(other)
+
+    def __ne__(self, other: object) -> bool:
+        return not self.__eq__(other)
+
+    @classmethod
+    def is_dtype(cls, dtype: object) -> bool:
+        """
+        Return a boolean if we if the passed type is an actual dtype that we
+        can match (via string or type)
+        """
+        if isinstance(dtype, str):
+            # PeriodDtype can be instantiated from freq string like "U",
+            # but doesn't regard freq str like "U" as dtype.
+            if dtype.startswith(("period[", "Period[")):
+                try:
+                    return cls._parse_dtype_strict(dtype) is not None
+                except ValueError:
+                    return False
+            else:
+                return False
+        return super().is_dtype(dtype)
+
+    @classmethod
+    def construct_array_type(cls) -> type_t[PeriodArray]:
+        """
+        Return the array type associated with this dtype.
+
+        Returns
+        -------
+        type
+        """
+        from pandas.core.arrays import PeriodArray
+
+        return PeriodArray
+
+    def __from_arrow__(self, array: pa.Array | pa.ChunkedArray) -> PeriodArray:
+        """
+        Construct PeriodArray from pyarrow Array/ChunkedArray.
+        """
+        import pyarrow
+
+        from pandas.core.arrays import PeriodArray
+        from pandas.core.arrays.arrow._arrow_utils import (
+            pyarrow_array_to_numpy_and_mask,
+        )
+
+        if isinstance(array, pyarrow.Array):
+            chunks = [array]
+        else:
+            chunks = array.chunks
+
+        results = []
+        for arr in chunks:
+            data, mask = pyarrow_array_to_numpy_and_mask(arr, dtype=np.dtype(np.int64))
+            parr = PeriodArray(data.copy(), dtype=self, copy=False)
+            # error: Invalid index type "ndarray[Any, dtype[bool_]]" for "PeriodArray";
+            # expected type "Union[int, Sequence[int], Sequence[bool], slice]"
+            parr[~mask] = NaT  # type: ignore[index]
+            results.append(parr)
+
+        if not results:
+            return PeriodArray(np.array([], dtype="int64"), dtype=self, copy=False)
+        return PeriodArray._concat_same_type(results)
+
+    @cache_readonly
+    def index_class(self) -> type_t[PeriodIndex]:
+        from pandas import PeriodIndex
+
+        return PeriodIndex
+
+
+@register_extension_dtype
+class IntervalDtype(PandasExtensionDtype):
+    """
+    An ExtensionDtype for Interval data.
+
+    **This is not an actual numpy dtype**, but a duck type.
+
+    Parameters
+    ----------
+    subtype : str, np.dtype
+        The dtype of the Interval bounds.
+
+    Attributes
+    ----------
+    subtype
+
+    Methods
+    -------
+    None
+
+    Examples
+    --------
+    >>> pd.IntervalDtype(subtype='int64', closed='both')
+    interval[int64, both]
+    """
+
+    name = "interval"
+    kind: str_type = "O"
+    str = "|O08"
+    base = np.dtype("O")
+    num = 103
+    _metadata = (
+        "subtype",
+        "closed",
+    )
+
+    _match = re.compile(
+        r"(I|i)nterval\[(?P[^,]+(\[.+\])?)"
+        r"(, (?P(right|left|both|neither)))?\]"
+    )
+
+    _cache_dtypes: dict[str_type, PandasExtensionDtype] = {}
+    _subtype: None | np.dtype
+    _closed: IntervalClosedType | None
+
+    def __init__(self, subtype=None, closed: IntervalClosedType | None = None) -> None:
+        from pandas.core.dtypes.common import (
+            is_string_dtype,
+            pandas_dtype,
+        )
+
+        if closed is not None and closed not in {"right", "left", "both", "neither"}:
+            raise ValueError("closed must be one of 'right', 'left', 'both', 'neither'")
+
+        if isinstance(subtype, IntervalDtype):
+            if closed is not None and closed != subtype.closed:
+                raise ValueError(
+                    "dtype.closed and 'closed' do not match. "
+                    "Try IntervalDtype(dtype.subtype, closed) instead."
+                )
+            self._subtype = subtype._subtype
+            self._closed = subtype._closed
+        elif subtype is None:
+            # we are called as an empty constructor
+            # generally for pickle compat
+            self._subtype = None
+            self._closed = closed
+        elif isinstance(subtype, str) and subtype.lower() == "interval":
+            self._subtype = None
+            self._closed = closed
+        else:
+            if isinstance(subtype, str):
+                m = IntervalDtype._match.search(subtype)
+                if m is not None:
+                    gd = m.groupdict()
+                    subtype = gd["subtype"]
+                    if gd.get("closed", None) is not None:
+                        if closed is not None:
+                            if closed != gd["closed"]:
+                                raise ValueError(
+                                    "'closed' keyword does not match value "
+                                    "specified in dtype string"
+                                )
+                        closed = gd["closed"]  # type: ignore[assignment]
+
+            try:
+                subtype = pandas_dtype(subtype)
+            except TypeError as err:
+                raise TypeError("could not construct IntervalDtype") from err
+            if CategoricalDtype.is_dtype(subtype) or is_string_dtype(subtype):
+                # GH 19016
+                msg = (
+                    "category, object, and string subtypes are not supported "
+                    "for IntervalDtype"
+                )
+                raise TypeError(msg)
+            self._subtype = subtype
+            self._closed = closed
+
+    @cache_readonly
+    def _can_hold_na(self) -> bool:
+        subtype = self._subtype
+        if subtype is None:
+            # partially-initialized
+            raise NotImplementedError(
+                "_can_hold_na is not defined for partially-initialized IntervalDtype"
+            )
+        if subtype.kind in "iu":
+            return False
+        return True
+
+    @property
+    def closed(self) -> IntervalClosedType:
+        return self._closed  # type: ignore[return-value]
+
+    @property
+    def subtype(self):
+        """
+        The dtype of the Interval bounds.
+
+        Examples
+        --------
+        >>> dtype = pd.IntervalDtype(subtype='int64', closed='both')
+        >>> dtype.subtype
+        dtype('int64')
+        """
+        return self._subtype
+
+    @classmethod
+    def construct_array_type(cls) -> type[IntervalArray]:
+        """
+        Return the array type associated with this dtype.
+
+        Returns
+        -------
+        type
+        """
+        from pandas.core.arrays import IntervalArray
+
+        return IntervalArray
+
+    @classmethod
+    def construct_from_string(cls, string: str_type) -> IntervalDtype:
+        """
+        attempt to construct this type from a string, raise a TypeError
+        if its not possible
+        """
+        if not isinstance(string, str):
+            raise TypeError(
+                f"'construct_from_string' expects a string, got {type(string)}"
+            )
+
+        if string.lower() == "interval" or cls._match.search(string) is not None:
+            return cls(string)
+
+        msg = (
+            f"Cannot construct a 'IntervalDtype' from '{string}'.\n\n"
+            "Incorrectly formatted string passed to constructor. "
+            "Valid formats include Interval or Interval[dtype] "
+            "where dtype is numeric, datetime, or timedelta"
+        )
+        raise TypeError(msg)
+
+    @property
+    def type(self) -> type[Interval]:
+        return Interval
+
+    def __str__(self) -> str_type:
+        if self.subtype is None:
+            return "interval"
+        if self.closed is None:
+            # Only partially initialized GH#38394
+            return f"interval[{self.subtype}]"
+        return f"interval[{self.subtype}, {self.closed}]"
+
+    def __hash__(self) -> int:
+        # make myself hashable
+        return hash(str(self))
+
+    def __eq__(self, other: object) -> bool:
+        if isinstance(other, str):
+            return other.lower() in (self.name.lower(), str(self).lower())
+        elif not isinstance(other, IntervalDtype):
+            return False
+        elif self.subtype is None or other.subtype is None:
+            # None should match any subtype
+            return True
+        elif self.closed != other.closed:
+            return False
+        else:
+            return self.subtype == other.subtype
+
+    def __setstate__(self, state) -> None:
+        # for pickle compat. __get_state__ is defined in the
+        # PandasExtensionDtype superclass and uses the public properties to
+        # pickle -> need to set the settable private ones here (see GH26067)
+        self._subtype = state["subtype"]
+
+        # backward-compat older pickles won't have "closed" key
+        self._closed = state.pop("closed", None)
+
+    @classmethod
+    def is_dtype(cls, dtype: object) -> bool:
+        """
+        Return a boolean if we if the passed type is an actual dtype that we
+        can match (via string or type)
+        """
+        if isinstance(dtype, str):
+            if dtype.lower().startswith("interval"):
+                try:
+                    return cls.construct_from_string(dtype) is not None
+                except (ValueError, TypeError):
+                    return False
+            else:
+                return False
+        return super().is_dtype(dtype)
+
+    def __from_arrow__(self, array: pa.Array | pa.ChunkedArray) -> IntervalArray:
+        """
+        Construct IntervalArray from pyarrow Array/ChunkedArray.
+        """
+        import pyarrow
+
+        from pandas.core.arrays import IntervalArray
+
+        if isinstance(array, pyarrow.Array):
+            chunks = [array]
+        else:
+            chunks = array.chunks
+
+        results = []
+        for arr in chunks:
+            if isinstance(arr, pyarrow.ExtensionArray):
+                arr = arr.storage
+            left = np.asarray(arr.field("left"), dtype=self.subtype)
+            right = np.asarray(arr.field("right"), dtype=self.subtype)
+            iarr = IntervalArray.from_arrays(left, right, closed=self.closed)
+            results.append(iarr)
+
+        if not results:
+            return IntervalArray.from_arrays(
+                np.array([], dtype=self.subtype),
+                np.array([], dtype=self.subtype),
+                closed=self.closed,
+            )
+        return IntervalArray._concat_same_type(results)
+
+    def _get_common_dtype(self, dtypes: list[DtypeObj]) -> DtypeObj | None:
+        if not all(isinstance(x, IntervalDtype) for x in dtypes):
+            return None
+
+        closed = cast("IntervalDtype", dtypes[0]).closed
+        if not all(cast("IntervalDtype", x).closed == closed for x in dtypes):
+            return np.dtype(object)
+
+        from pandas.core.dtypes.cast import find_common_type
+
+        common = find_common_type([cast("IntervalDtype", x).subtype for x in dtypes])
+        if common == object:
+            return np.dtype(object)
+        return IntervalDtype(common, closed=closed)
+
+    @cache_readonly
+    def index_class(self) -> type_t[IntervalIndex]:
+        from pandas import IntervalIndex
+
+        return IntervalIndex
+
+
+class NumpyEADtype(ExtensionDtype):
+    """
+    A Pandas ExtensionDtype for NumPy dtypes.
+
+    This is mostly for internal compatibility, and is not especially
+    useful on its own.
+
+    Parameters
+    ----------
+    dtype : object
+        Object to be converted to a NumPy data type object.
+
+    See Also
+    --------
+    numpy.dtype
+    """
+
+    _metadata = ("_dtype",)
+    _supports_2d = False
+    _can_fast_transpose = False
+
+    def __init__(self, dtype: npt.DTypeLike | NumpyEADtype | None) -> None:
+        if isinstance(dtype, NumpyEADtype):
+            # make constructor idempotent
+            dtype = dtype.numpy_dtype
+        self._dtype = np.dtype(dtype)
+
+    def __repr__(self) -> str:
+        return f"NumpyEADtype({repr(self.name)})"
+
+    @property
+    def numpy_dtype(self) -> np.dtype:
+        """
+        The NumPy dtype this NumpyEADtype wraps.
+        """
+        return self._dtype
+
+    @property
+    def name(self) -> str:
+        """
+        A bit-width name for this data-type.
+        """
+        return self._dtype.name
+
+    @property
+    def type(self) -> type[np.generic]:
+        """
+        The type object used to instantiate a scalar of this NumPy data-type.
+        """
+        return self._dtype.type
+
+    @property
+    def _is_numeric(self) -> bool:
+        # exclude object, str, unicode, void.
+        return self.kind in set("biufc")
+
+    @property
+    def _is_boolean(self) -> bool:
+        return self.kind == "b"
+
+    @classmethod
+    def construct_from_string(cls, string: str) -> NumpyEADtype:
+        try:
+            dtype = np.dtype(string)
+        except TypeError as err:
+            if not isinstance(string, str):
+                msg = f"'construct_from_string' expects a string, got {type(string)}"
+            else:
+                msg = f"Cannot construct a 'NumpyEADtype' from '{string}'"
+            raise TypeError(msg) from err
+        return cls(dtype)
+
+    @classmethod
+    def construct_array_type(cls) -> type_t[NumpyExtensionArray]:
+        """
+        Return the array type associated with this dtype.
+
+        Returns
+        -------
+        type
+        """
+        from pandas.core.arrays import NumpyExtensionArray
+
+        return NumpyExtensionArray
+
+    @property
+    def kind(self) -> str:
+        """
+        A character code (one of 'biufcmMOSUV') identifying the general kind of data.
+        """
+        return self._dtype.kind
+
+    @property
+    def itemsize(self) -> int:
+        """
+        The element size of this data-type object.
+        """
+        return self._dtype.itemsize
+
+
+class BaseMaskedDtype(ExtensionDtype):
+    """
+    Base class for dtypes for BaseMaskedArray subclasses.
+    """
+
+    base = None
+    type: type
+
+    @property
+    def na_value(self) -> libmissing.NAType:
+        return libmissing.NA
+
+    @cache_readonly
+    def numpy_dtype(self) -> np.dtype:
+        """Return an instance of our numpy dtype"""
+        return np.dtype(self.type)
+
+    @cache_readonly
+    def kind(self) -> str:
+        return self.numpy_dtype.kind
+
+    @cache_readonly
+    def itemsize(self) -> int:
+        """Return the number of bytes in this dtype"""
+        return self.numpy_dtype.itemsize
+
+    @classmethod
+    def construct_array_type(cls) -> type_t[BaseMaskedArray]:
+        """
+        Return the array type associated with this dtype.
+
+        Returns
+        -------
+        type
+        """
+        raise NotImplementedError
+
+    @classmethod
+    def from_numpy_dtype(cls, dtype: np.dtype) -> BaseMaskedDtype:
+        """
+        Construct the MaskedDtype corresponding to the given numpy dtype.
+        """
+        if dtype.kind == "b":
+            from pandas.core.arrays.boolean import BooleanDtype
+
+            return BooleanDtype()
+        elif dtype.kind in "iu":
+            from pandas.core.arrays.integer import NUMPY_INT_TO_DTYPE
+
+            return NUMPY_INT_TO_DTYPE[dtype]
+        elif dtype.kind == "f":
+            from pandas.core.arrays.floating import NUMPY_FLOAT_TO_DTYPE
+
+            return NUMPY_FLOAT_TO_DTYPE[dtype]
+        else:
+            raise NotImplementedError(dtype)
+
+    def _get_common_dtype(self, dtypes: list[DtypeObj]) -> DtypeObj | None:
+        # We unwrap any masked dtypes, find the common dtype we would use
+        #  for that, then re-mask the result.
+        from pandas.core.dtypes.cast import find_common_type
+
+        new_dtype = find_common_type(
+            [
+                dtype.numpy_dtype if isinstance(dtype, BaseMaskedDtype) else dtype
+                for dtype in dtypes
+            ]
+        )
+        if not isinstance(new_dtype, np.dtype):
+            # If we ever support e.g. Masked[DatetimeArray] then this will change
+            return None
+        try:
+            return type(self).from_numpy_dtype(new_dtype)
+        except (KeyError, NotImplementedError):
+            return None
+
+
+@register_extension_dtype
+class SparseDtype(ExtensionDtype):
+    """
+    Dtype for data stored in :class:`SparseArray`.
+
+    This dtype implements the pandas ExtensionDtype interface.
+
+    Parameters
+    ----------
+    dtype : str, ExtensionDtype, numpy.dtype, type, default numpy.float64
+        The dtype of the underlying array storing the non-fill value values.
+    fill_value : scalar, optional
+        The scalar value not stored in the SparseArray. By default, this
+        depends on `dtype`.
+
+        =========== ==========
+        dtype       na_value
+        =========== ==========
+        float       ``np.nan``
+        int         ``0``
+        bool        ``False``
+        datetime64  ``pd.NaT``
+        timedelta64 ``pd.NaT``
+        =========== ==========
+
+        The default value may be overridden by specifying a `fill_value`.
+
+    Attributes
+    ----------
+    None
+
+    Methods
+    -------
+    None
+
+    Examples
+    --------
+    >>> ser = pd.Series([1, 0, 0], dtype=pd.SparseDtype(dtype=int, fill_value=0))
+    >>> ser
+    0    1
+    1    0
+    2    0
+    dtype: Sparse[int64, 0]
+    >>> ser.sparse.density
+    0.3333333333333333
+    """
+
+    _is_immutable = True
+
+    # We include `_is_na_fill_value` in the metadata to avoid hash collisions
+    # between SparseDtype(float, 0.0) and SparseDtype(float, nan).
+    # Without is_na_fill_value in the comparison, those would be equal since
+    # hash(nan) is (sometimes?) 0.
+    _metadata = ("_dtype", "_fill_value", "_is_na_fill_value")
+
+    def __init__(self, dtype: Dtype = np.float64, fill_value: Any = None) -> None:
+        if isinstance(dtype, type(self)):
+            if fill_value is None:
+                fill_value = dtype.fill_value
+            dtype = dtype.subtype
+
+        from pandas.core.dtypes.common import (
+            is_string_dtype,
+            pandas_dtype,
+        )
+        from pandas.core.dtypes.missing import na_value_for_dtype
+
+        dtype = pandas_dtype(dtype)
+        if is_string_dtype(dtype):
+            dtype = np.dtype("object")
+        if not isinstance(dtype, np.dtype):
+            # GH#53160
+            raise TypeError("SparseDtype subtype must be a numpy dtype")
+
+        if fill_value is None:
+            fill_value = na_value_for_dtype(dtype)
+
+        self._dtype = dtype
+        self._fill_value = fill_value
+        self._check_fill_value()
+
+    def __hash__(self) -> int:
+        # Python3 doesn't inherit __hash__ when a base class overrides
+        # __eq__, so we explicitly do it here.
+        return super().__hash__()
+
+    def __eq__(self, other: object) -> bool:
+        # We have to override __eq__ to handle NA values in _metadata.
+        # The base class does simple == checks, which fail for NA.
+        if isinstance(other, str):
+            try:
+                other = self.construct_from_string(other)
+            except TypeError:
+                return False
+
+        if isinstance(other, type(self)):
+            subtype = self.subtype == other.subtype
+            if self._is_na_fill_value:
+                # this case is complicated by two things:
+                # SparseDtype(float, float(nan)) == SparseDtype(float, np.nan)
+                # SparseDtype(float, np.nan)     != SparseDtype(float, pd.NaT)
+                # i.e. we want to treat any floating-point NaN as equal, but
+                # not a floating-point NaN and a datetime NaT.
+                fill_value = (
+                    other._is_na_fill_value
+                    and isinstance(self.fill_value, type(other.fill_value))
+                    or isinstance(other.fill_value, type(self.fill_value))
+                )
+            else:
+                with warnings.catch_warnings():
+                    # Ignore spurious numpy warning
+                    warnings.filterwarnings(
+                        "ignore",
+                        "elementwise comparison failed",
+                        category=DeprecationWarning,
+                    )
+
+                    fill_value = self.fill_value == other.fill_value
+
+            return subtype and fill_value
+        return False
+
+    @property
+    def fill_value(self):
+        """
+        The fill value of the array.
+
+        Converting the SparseArray to a dense ndarray will fill the
+        array with this value.
+
+        .. warning::
+
+           It's possible to end up with a SparseArray that has ``fill_value``
+           values in ``sp_values``. This can occur, for example, when setting
+           ``SparseArray.fill_value`` directly.
+        """
+        return self._fill_value
+
+    def _check_fill_value(self) -> None:
+        if not lib.is_scalar(self._fill_value):
+            raise ValueError(
+                f"fill_value must be a scalar. Got {self._fill_value} instead"
+            )
+
+        from pandas.core.dtypes.cast import can_hold_element
+        from pandas.core.dtypes.missing import (
+            is_valid_na_for_dtype,
+            isna,
+        )
+
+        from pandas.core.construction import ensure_wrapped_if_datetimelike
+
+        # GH#23124 require fill_value and subtype to match
+        val = self._fill_value
+        if isna(val):
+            if not is_valid_na_for_dtype(val, self.subtype):
+                warnings.warn(
+                    "Allowing arbitrary scalar fill_value in SparseDtype is "
+                    "deprecated. In a future version, the fill_value must be "
+                    "a valid value for the SparseDtype.subtype.",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+        else:
+            dummy = np.empty(0, dtype=self.subtype)
+            dummy = ensure_wrapped_if_datetimelike(dummy)
+
+            if not can_hold_element(dummy, val):
+                warnings.warn(
+                    "Allowing arbitrary scalar fill_value in SparseDtype is "
+                    "deprecated. In a future version, the fill_value must be "
+                    "a valid value for the SparseDtype.subtype.",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+
+    @property
+    def _is_na_fill_value(self) -> bool:
+        from pandas import isna
+
+        return isna(self.fill_value)
+
+    @property
+    def _is_numeric(self) -> bool:
+        return self.subtype != object
+
+    @property
+    def _is_boolean(self) -> bool:
+        return self.subtype.kind == "b"
+
+    @property
+    def kind(self) -> str:
+        """
+        The sparse kind. Either 'integer', or 'block'.
+        """
+        return self.subtype.kind
+
+    @property
+    def type(self):
+        return self.subtype.type
+
+    @property
+    def subtype(self):
+        return self._dtype
+
+    @property
+    def name(self) -> str:
+        return f"Sparse[{self.subtype.name}, {repr(self.fill_value)}]"
+
+    def __repr__(self) -> str:
+        return self.name
+
+    @classmethod
+    def construct_array_type(cls) -> type_t[SparseArray]:
+        """
+        Return the array type associated with this dtype.
+
+        Returns
+        -------
+        type
+        """
+        from pandas.core.arrays.sparse.array import SparseArray
+
+        return SparseArray
+
+    @classmethod
+    def construct_from_string(cls, string: str) -> SparseDtype:
+        """
+        Construct a SparseDtype from a string form.
+
+        Parameters
+        ----------
+        string : str
+            Can take the following forms.
+
+            string           dtype
+            ================ ============================
+            'int'            SparseDtype[np.int64, 0]
+            'Sparse'         SparseDtype[np.float64, nan]
+            'Sparse[int]'    SparseDtype[np.int64, 0]
+            'Sparse[int, 0]' SparseDtype[np.int64, 0]
+            ================ ============================
+
+            It is not possible to specify non-default fill values
+            with a string. An argument like ``'Sparse[int, 1]'``
+            will raise a ``TypeError`` because the default fill value
+            for integers is 0.
+
+        Returns
+        -------
+        SparseDtype
+        """
+        if not isinstance(string, str):
+            raise TypeError(
+                f"'construct_from_string' expects a string, got {type(string)}"
+            )
+        msg = f"Cannot construct a 'SparseDtype' from '{string}'"
+        if string.startswith("Sparse"):
+            try:
+                sub_type, has_fill_value = cls._parse_subtype(string)
+            except ValueError as err:
+                raise TypeError(msg) from err
+            else:
+                result = SparseDtype(sub_type)
+                msg = (
+                    f"Cannot construct a 'SparseDtype' from '{string}'.\n\nIt "
+                    "looks like the fill_value in the string is not "
+                    "the default for the dtype. Non-default fill_values "
+                    "are not supported. Use the 'SparseDtype()' "
+                    "constructor instead."
+                )
+                if has_fill_value and str(result) != string:
+                    raise TypeError(msg)
+                return result
+        else:
+            raise TypeError(msg)
+
+    @staticmethod
+    def _parse_subtype(dtype: str) -> tuple[str, bool]:
+        """
+        Parse a string to get the subtype
+
+        Parameters
+        ----------
+        dtype : str
+            A string like
+
+            * Sparse[subtype]
+            * Sparse[subtype, fill_value]
+
+        Returns
+        -------
+        subtype : str
+
+        Raises
+        ------
+        ValueError
+            When the subtype cannot be extracted.
+        """
+        xpr = re.compile(r"Sparse\[(?P[^,]*)(, )?(?P.*?)?\]$")
+        m = xpr.match(dtype)
+        has_fill_value = False
+        if m:
+            subtype = m.groupdict()["subtype"]
+            has_fill_value = bool(m.groupdict()["fill_value"])
+        elif dtype == "Sparse":
+            subtype = "float64"
+        else:
+            raise ValueError(f"Cannot parse {dtype}")
+        return subtype, has_fill_value
+
+    @classmethod
+    def is_dtype(cls, dtype: object) -> bool:
+        dtype = getattr(dtype, "dtype", dtype)
+        if isinstance(dtype, str) and dtype.startswith("Sparse"):
+            sub_type, _ = cls._parse_subtype(dtype)
+            dtype = np.dtype(sub_type)
+        elif isinstance(dtype, cls):
+            return True
+        return isinstance(dtype, np.dtype) or dtype == "Sparse"
+
+    def update_dtype(self, dtype) -> SparseDtype:
+        """
+        Convert the SparseDtype to a new dtype.
+
+        This takes care of converting the ``fill_value``.
+
+        Parameters
+        ----------
+        dtype : Union[str, numpy.dtype, SparseDtype]
+            The new dtype to use.
+
+            * For a SparseDtype, it is simply returned
+            * For a NumPy dtype (or str), the current fill value
+              is converted to the new dtype, and a SparseDtype
+              with `dtype` and the new fill value is returned.
+
+        Returns
+        -------
+        SparseDtype
+            A new SparseDtype with the correct `dtype` and fill value
+            for that `dtype`.
+
+        Raises
+        ------
+        ValueError
+            When the current fill value cannot be converted to the
+            new `dtype` (e.g. trying to convert ``np.nan`` to an
+            integer dtype).
+
+
+        Examples
+        --------
+        >>> SparseDtype(int, 0).update_dtype(float)
+        Sparse[float64, 0.0]
+
+        >>> SparseDtype(int, 1).update_dtype(SparseDtype(float, np.nan))
+        Sparse[float64, nan]
+        """
+        from pandas.core.dtypes.astype import astype_array
+        from pandas.core.dtypes.common import pandas_dtype
+
+        cls = type(self)
+        dtype = pandas_dtype(dtype)
+
+        if not isinstance(dtype, cls):
+            if not isinstance(dtype, np.dtype):
+                raise TypeError("sparse arrays of extension dtypes not supported")
+
+            fv_asarray = np.atleast_1d(np.array(self.fill_value))
+            fvarr = astype_array(fv_asarray, dtype)
+            # NB: not fv_0d.item(), as that casts dt64->int
+            fill_value = fvarr[0]
+            dtype = cls(dtype, fill_value=fill_value)
+
+        return dtype
+
+    @property
+    def _subtype_with_str(self):
+        """
+        Whether the SparseDtype's subtype should be considered ``str``.
+
+        Typically, pandas will store string data in an object-dtype array.
+        When converting values to a dtype, e.g. in ``.astype``, we need to
+        be more specific, we need the actual underlying type.
+
+        Returns
+        -------
+        >>> SparseDtype(int, 1)._subtype_with_str
+        dtype('int64')
+
+        >>> SparseDtype(object, 1)._subtype_with_str
+        dtype('O')
+
+        >>> dtype = SparseDtype(str, '')
+        >>> dtype.subtype
+        dtype('O')
+
+        >>> dtype._subtype_with_str
+        
+        """
+        if isinstance(self.fill_value, str):
+            return type(self.fill_value)
+        return self.subtype
+
+    def _get_common_dtype(self, dtypes: list[DtypeObj]) -> DtypeObj | None:
+        # TODO for now only handle SparseDtypes and numpy dtypes => extend
+        # with other compatible extension dtypes
+        from pandas.core.dtypes.cast import np_find_common_type
+
+        if any(
+            isinstance(x, ExtensionDtype) and not isinstance(x, SparseDtype)
+            for x in dtypes
+        ):
+            return None
+
+        fill_values = [x.fill_value for x in dtypes if isinstance(x, SparseDtype)]
+        fill_value = fill_values[0]
+
+        from pandas import isna
+
+        # np.nan isn't a singleton, so we may end up with multiple
+        # NaNs here, so we ignore the all NA case too.
+        if not (len(set(fill_values)) == 1 or isna(fill_values).all()):
+            warnings.warn(
+                "Concatenating sparse arrays with multiple fill "
+                f"values: '{fill_values}'. Picking the first and "
+                "converting the rest.",
+                PerformanceWarning,
+                stacklevel=find_stack_level(),
+            )
+
+        np_dtypes = (x.subtype if isinstance(x, SparseDtype) else x for x in dtypes)
+        return SparseDtype(np_find_common_type(*np_dtypes), fill_value=fill_value)
+
+
+@register_extension_dtype
+class ArrowDtype(StorageExtensionDtype):
+    """
+    An ExtensionDtype for PyArrow data types.
+
+    .. warning::
+
+       ArrowDtype is considered experimental. The implementation and
+       parts of the API may change without warning.
+
+    While most ``dtype`` arguments can accept the "string"
+    constructor, e.g. ``"int64[pyarrow]"``, ArrowDtype is useful
+    if the data type contains parameters like ``pyarrow.timestamp``.
+
+    Parameters
+    ----------
+    pyarrow_dtype : pa.DataType
+        An instance of a `pyarrow.DataType `__.
+
+    Attributes
+    ----------
+    pyarrow_dtype
+
+    Methods
+    -------
+    None
+
+    Returns
+    -------
+    ArrowDtype
+
+    Examples
+    --------
+    >>> import pyarrow as pa
+    >>> pd.ArrowDtype(pa.int64())
+    int64[pyarrow]
+
+    Types with parameters must be constructed with ArrowDtype.
+
+    >>> pd.ArrowDtype(pa.timestamp("s", tz="America/New_York"))
+    timestamp[s, tz=America/New_York][pyarrow]
+    >>> pd.ArrowDtype(pa.list_(pa.int64()))
+    list[pyarrow]
+    """
+
+    _metadata = ("storage", "pyarrow_dtype")  # type: ignore[assignment]
+
+    def __init__(self, pyarrow_dtype: pa.DataType) -> None:
+        super().__init__("pyarrow")
+        if pa_version_under10p1:
+            raise ImportError("pyarrow>=10.0.1 is required for ArrowDtype")
+        if not isinstance(pyarrow_dtype, pa.DataType):
+            raise ValueError(
+                f"pyarrow_dtype ({pyarrow_dtype}) must be an instance "
+                f"of a pyarrow.DataType. Got {type(pyarrow_dtype)} instead."
+            )
+        self.pyarrow_dtype = pyarrow_dtype
+
+    def __repr__(self) -> str:
+        return self.name
+
+    def __hash__(self) -> int:
+        # make myself hashable
+        return hash(str(self))
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, type(self)):
+            return super().__eq__(other)
+        return self.pyarrow_dtype == other.pyarrow_dtype
+
+    @property
+    def type(self):
+        """
+        Returns associated scalar type.
+        """
+        pa_type = self.pyarrow_dtype
+        if pa.types.is_integer(pa_type):
+            return int
+        elif pa.types.is_floating(pa_type):
+            return float
+        elif pa.types.is_string(pa_type) or pa.types.is_large_string(pa_type):
+            return str
+        elif (
+            pa.types.is_binary(pa_type)
+            or pa.types.is_fixed_size_binary(pa_type)
+            or pa.types.is_large_binary(pa_type)
+        ):
+            return bytes
+        elif pa.types.is_boolean(pa_type):
+            return bool
+        elif pa.types.is_duration(pa_type):
+            if pa_type.unit == "ns":
+                return Timedelta
+            else:
+                return timedelta
+        elif pa.types.is_timestamp(pa_type):
+            if pa_type.unit == "ns":
+                return Timestamp
+            else:
+                return datetime
+        elif pa.types.is_date(pa_type):
+            return date
+        elif pa.types.is_time(pa_type):
+            return time
+        elif pa.types.is_decimal(pa_type):
+            return Decimal
+        elif pa.types.is_dictionary(pa_type):
+            # TODO: Potentially change this & CategoricalDtype.type to
+            #  something more representative of the scalar
+            return CategoricalDtypeType
+        elif pa.types.is_list(pa_type) or pa.types.is_large_list(pa_type):
+            return list
+        elif pa.types.is_fixed_size_list(pa_type):
+            return list
+        elif pa.types.is_map(pa_type):
+            return list
+        elif pa.types.is_struct(pa_type):
+            return dict
+        elif pa.types.is_null(pa_type):
+            # TODO: None? pd.NA? pa.null?
+            return type(pa_type)
+        elif isinstance(pa_type, pa.ExtensionType):
+            return type(self)(pa_type.storage_type).type
+        raise NotImplementedError(pa_type)
+
+    @property
+    def name(self) -> str:  # type: ignore[override]
+        """
+        A string identifying the data type.
+        """
+        return f"{str(self.pyarrow_dtype)}[{self.storage}]"
+
+    @cache_readonly
+    def numpy_dtype(self) -> np.dtype:
+        """Return an instance of the related numpy dtype"""
+        if pa.types.is_timestamp(self.pyarrow_dtype):
+            # pa.timestamp(unit).to_pandas_dtype() returns ns units
+            # regardless of the pyarrow timestamp units.
+            # This can be removed if/when pyarrow addresses it:
+            # https://github.com/apache/arrow/issues/34462
+            return np.dtype(f"datetime64[{self.pyarrow_dtype.unit}]")
+        if pa.types.is_duration(self.pyarrow_dtype):
+            # pa.duration(unit).to_pandas_dtype() returns ns units
+            # regardless of the pyarrow duration units
+            # This can be removed if/when pyarrow addresses it:
+            # https://github.com/apache/arrow/issues/34462
+            return np.dtype(f"timedelta64[{self.pyarrow_dtype.unit}]")
+        if pa.types.is_string(self.pyarrow_dtype) or pa.types.is_large_string(
+            self.pyarrow_dtype
+        ):
+            # pa.string().to_pandas_dtype() = object which we don't want
+            return np.dtype(str)
+        try:
+            return np.dtype(self.pyarrow_dtype.to_pandas_dtype())
+        except (NotImplementedError, TypeError):
+            return np.dtype(object)
+
+    @cache_readonly
+    def kind(self) -> str:
+        if pa.types.is_timestamp(self.pyarrow_dtype):
+            # To mirror DatetimeTZDtype
+            return "M"
+        return self.numpy_dtype.kind
+
+    @cache_readonly
+    def itemsize(self) -> int:
+        """Return the number of bytes in this dtype"""
+        return self.numpy_dtype.itemsize
+
+    @classmethod
+    def construct_array_type(cls) -> type_t[ArrowExtensionArray]:
+        """
+        Return the array type associated with this dtype.
+
+        Returns
+        -------
+        type
+        """
+        from pandas.core.arrays.arrow import ArrowExtensionArray
+
+        return ArrowExtensionArray
+
+    @classmethod
+    def construct_from_string(cls, string: str) -> ArrowDtype:
+        """
+        Construct this type from a string.
+
+        Parameters
+        ----------
+        string : str
+            string should follow the format f"{pyarrow_type}[pyarrow]"
+            e.g. int64[pyarrow]
+        """
+        if not isinstance(string, str):
+            raise TypeError(
+                f"'construct_from_string' expects a string, got {type(string)}"
+            )
+        if not string.endswith("[pyarrow]"):
+            raise TypeError(f"'{string}' must end with '[pyarrow]'")
+        if string in ("string[pyarrow]", "str[pyarrow]"):
+            # Ensure Registry.find skips ArrowDtype to use StringDtype instead
+            raise TypeError("string[pyarrow] should be constructed by StringDtype")
+
+        base_type = string[:-9]  # get rid of "[pyarrow]"
+        try:
+            pa_dtype = pa.type_for_alias(base_type)
+        except ValueError as err:
+            has_parameters = re.search(r"[\[\(].*[\]\)]", base_type)
+            if has_parameters:
+                # Fallback to try common temporal types
+                try:
+                    return cls._parse_temporal_dtype_string(base_type)
+                except (NotImplementedError, ValueError):
+                    # Fall through to raise with nice exception message below
+                    pass
+
+                raise NotImplementedError(
+                    "Passing pyarrow type specific parameters "
+                    f"({has_parameters.group()}) in the string is not supported. "
+                    "Please construct an ArrowDtype object with a pyarrow_dtype "
+                    "instance with specific parameters."
+                ) from err
+            raise TypeError(f"'{base_type}' is not a valid pyarrow data type.") from err
+        return cls(pa_dtype)
+
+    # TODO(arrow#33642): This can be removed once supported by pyarrow
+    @classmethod
+    def _parse_temporal_dtype_string(cls, string: str) -> ArrowDtype:
+        """
+        Construct a temporal ArrowDtype from string.
+        """
+        # we assume
+        #  1) "[pyarrow]" has already been stripped from the end of our string.
+        #  2) we know "[" is present
+        head, tail = string.split("[", 1)
+
+        if not tail.endswith("]"):
+            raise ValueError
+        tail = tail[:-1]
+
+        if head == "timestamp":
+            assert "," in tail  # otherwise type_for_alias should work
+            unit, tz = tail.split(",", 1)
+            unit = unit.strip()
+            tz = tz.strip()
+            if tz.startswith("tz="):
+                tz = tz[3:]
+
+            pa_type = pa.timestamp(unit, tz=tz)
+            dtype = cls(pa_type)
+            return dtype
+
+        raise NotImplementedError(string)
+
+    @property
+    def _is_numeric(self) -> bool:
+        """
+        Whether columns with this dtype should be considered numeric.
+        """
+        # TODO: pa.types.is_boolean?
+        return (
+            pa.types.is_integer(self.pyarrow_dtype)
+            or pa.types.is_floating(self.pyarrow_dtype)
+            or pa.types.is_decimal(self.pyarrow_dtype)
+        )
+
+    @property
+    def _is_boolean(self) -> bool:
+        """
+        Whether this dtype should be considered boolean.
+        """
+        return pa.types.is_boolean(self.pyarrow_dtype)
+
+    def _get_common_dtype(self, dtypes: list[DtypeObj]) -> DtypeObj | None:
+        # We unwrap any masked dtypes, find the common dtype we would use
+        #  for that, then re-mask the result.
+        # Mirrors BaseMaskedDtype
+        from pandas.core.dtypes.cast import find_common_type
+
+        null_dtype = type(self)(pa.null())
+
+        new_dtype = find_common_type(
+            [
+                dtype.numpy_dtype if isinstance(dtype, ArrowDtype) else dtype
+                for dtype in dtypes
+                if dtype != null_dtype
+            ]
+        )
+        if not isinstance(new_dtype, np.dtype):
+            return None
+        try:
+            pa_dtype = pa.from_numpy_dtype(new_dtype)
+            return type(self)(pa_dtype)
+        except NotImplementedError:
+            return None
+
+    def __from_arrow__(self, array: pa.Array | pa.ChunkedArray):
+        """
+        Construct IntegerArray/FloatingArray from pyarrow Array/ChunkedArray.
+        """
+        array_class = self.construct_array_type()
+        arr = array.cast(self.pyarrow_dtype, safe=True)
+        return array_class(arr)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/dtypes/generic.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/dtypes/generic.py
new file mode 100644
index 0000000000000000000000000000000000000000..9718ad600cb80b6e38f069a83aaf35ddb376fb00
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/dtypes/generic.py
@@ -0,0 +1,147 @@
+""" define generic base classes for pandas objects """
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    Type,
+    cast,
+)
+
+if TYPE_CHECKING:
+    from pandas import (
+        Categorical,
+        CategoricalIndex,
+        DataFrame,
+        DatetimeIndex,
+        Index,
+        IntervalIndex,
+        MultiIndex,
+        PeriodIndex,
+        RangeIndex,
+        Series,
+        TimedeltaIndex,
+    )
+    from pandas.core.arrays import (
+        DatetimeArray,
+        ExtensionArray,
+        NumpyExtensionArray,
+        PeriodArray,
+        TimedeltaArray,
+    )
+    from pandas.core.generic import NDFrame
+
+
+# define abstract base classes to enable isinstance type checking on our
+# objects
+def create_pandas_abc_type(name, attr, comp):
+    def _check(inst) -> bool:
+        return getattr(inst, attr, "_typ") in comp
+
+    # https://github.com/python/mypy/issues/1006
+    # error: 'classmethod' used with a non-method
+    @classmethod  # type: ignore[misc]
+    def _instancecheck(cls, inst) -> bool:
+        return _check(inst) and not isinstance(inst, type)
+
+    @classmethod  # type: ignore[misc]
+    def _subclasscheck(cls, inst) -> bool:
+        # Raise instead of returning False
+        # This is consistent with default __subclasscheck__ behavior
+        if not isinstance(inst, type):
+            raise TypeError("issubclass() arg 1 must be a class")
+
+        return _check(inst)
+
+    dct = {"__instancecheck__": _instancecheck, "__subclasscheck__": _subclasscheck}
+    meta = type("ABCBase", (type,), dct)
+    return meta(name, (), dct)
+
+
+ABCRangeIndex = cast(
+    "Type[RangeIndex]",
+    create_pandas_abc_type("ABCRangeIndex", "_typ", ("rangeindex",)),
+)
+ABCMultiIndex = cast(
+    "Type[MultiIndex]",
+    create_pandas_abc_type("ABCMultiIndex", "_typ", ("multiindex",)),
+)
+ABCDatetimeIndex = cast(
+    "Type[DatetimeIndex]",
+    create_pandas_abc_type("ABCDatetimeIndex", "_typ", ("datetimeindex",)),
+)
+ABCTimedeltaIndex = cast(
+    "Type[TimedeltaIndex]",
+    create_pandas_abc_type("ABCTimedeltaIndex", "_typ", ("timedeltaindex",)),
+)
+ABCPeriodIndex = cast(
+    "Type[PeriodIndex]",
+    create_pandas_abc_type("ABCPeriodIndex", "_typ", ("periodindex",)),
+)
+ABCCategoricalIndex = cast(
+    "Type[CategoricalIndex]",
+    create_pandas_abc_type("ABCCategoricalIndex", "_typ", ("categoricalindex",)),
+)
+ABCIntervalIndex = cast(
+    "Type[IntervalIndex]",
+    create_pandas_abc_type("ABCIntervalIndex", "_typ", ("intervalindex",)),
+)
+ABCIndex = cast(
+    "Type[Index]",
+    create_pandas_abc_type(
+        "ABCIndex",
+        "_typ",
+        {
+            "index",
+            "rangeindex",
+            "multiindex",
+            "datetimeindex",
+            "timedeltaindex",
+            "periodindex",
+            "categoricalindex",
+            "intervalindex",
+        },
+    ),
+)
+
+
+ABCNDFrame = cast(
+    "Type[NDFrame]",
+    create_pandas_abc_type("ABCNDFrame", "_typ", ("series", "dataframe")),
+)
+ABCSeries = cast(
+    "Type[Series]",
+    create_pandas_abc_type("ABCSeries", "_typ", ("series",)),
+)
+ABCDataFrame = cast(
+    "Type[DataFrame]", create_pandas_abc_type("ABCDataFrame", "_typ", ("dataframe",))
+)
+
+ABCCategorical = cast(
+    "Type[Categorical]",
+    create_pandas_abc_type("ABCCategorical", "_typ", ("categorical")),
+)
+ABCDatetimeArray = cast(
+    "Type[DatetimeArray]",
+    create_pandas_abc_type("ABCDatetimeArray", "_typ", ("datetimearray")),
+)
+ABCTimedeltaArray = cast(
+    "Type[TimedeltaArray]",
+    create_pandas_abc_type("ABCTimedeltaArray", "_typ", ("timedeltaarray")),
+)
+ABCPeriodArray = cast(
+    "Type[PeriodArray]",
+    create_pandas_abc_type("ABCPeriodArray", "_typ", ("periodarray",)),
+)
+ABCExtensionArray = cast(
+    "Type[ExtensionArray]",
+    create_pandas_abc_type(
+        "ABCExtensionArray",
+        "_typ",
+        # Note: IntervalArray and SparseArray are included bc they have _typ="extension"
+        {"extension", "categorical", "periodarray", "datetimearray", "timedeltaarray"},
+    ),
+)
+ABCNumpyExtensionArray = cast(
+    "Type[NumpyExtensionArray]",
+    create_pandas_abc_type("ABCNumpyExtensionArray", "_typ", ("npy_extension",)),
+)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/dtypes/inference.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/dtypes/inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..f551716772f61455a330bdb308cee830bd54fb03
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/dtypes/inference.py
@@ -0,0 +1,437 @@
+""" basic inference routines """
+
+from __future__ import annotations
+
+from collections import abc
+from numbers import Number
+import re
+from re import Pattern
+from typing import TYPE_CHECKING
+
+import numpy as np
+
+from pandas._libs import lib
+
+if TYPE_CHECKING:
+    from collections.abc import Hashable
+
+    from pandas._typing import TypeGuard
+
+is_bool = lib.is_bool
+
+is_integer = lib.is_integer
+
+is_float = lib.is_float
+
+is_complex = lib.is_complex
+
+is_scalar = lib.is_scalar
+
+is_decimal = lib.is_decimal
+
+is_interval = lib.is_interval
+
+is_list_like = lib.is_list_like
+
+is_iterator = lib.is_iterator
+
+
+def is_number(obj) -> TypeGuard[Number | np.number]:
+    """
+    Check if the object is a number.
+
+    Returns True when the object is a number, and False if is not.
+
+    Parameters
+    ----------
+    obj : any type
+        The object to check if is a number.
+
+    Returns
+    -------
+    bool
+        Whether `obj` is a number or not.
+
+    See Also
+    --------
+    api.types.is_integer: Checks a subgroup of numbers.
+
+    Examples
+    --------
+    >>> from pandas.api.types import is_number
+    >>> is_number(1)
+    True
+    >>> is_number(7.15)
+    True
+
+    Booleans are valid because they are int subclass.
+
+    >>> is_number(False)
+    True
+
+    >>> is_number("foo")
+    False
+    >>> is_number("5")
+    False
+    """
+    return isinstance(obj, (Number, np.number))
+
+
+def iterable_not_string(obj) -> bool:
+    """
+    Check if the object is an iterable but not a string.
+
+    Parameters
+    ----------
+    obj : The object to check.
+
+    Returns
+    -------
+    is_iter_not_string : bool
+        Whether `obj` is a non-string iterable.
+
+    Examples
+    --------
+    >>> iterable_not_string([1, 2, 3])
+    True
+    >>> iterable_not_string("foo")
+    False
+    >>> iterable_not_string(1)
+    False
+    """
+    return isinstance(obj, abc.Iterable) and not isinstance(obj, str)
+
+
+def is_file_like(obj) -> bool:
+    """
+    Check if the object is a file-like object.
+
+    For objects to be considered file-like, they must
+    be an iterator AND have either a `read` and/or `write`
+    method as an attribute.
+
+    Note: file-like objects must be iterable, but
+    iterable objects need not be file-like.
+
+    Parameters
+    ----------
+    obj : The object to check
+
+    Returns
+    -------
+    bool
+        Whether `obj` has file-like properties.
+
+    Examples
+    --------
+    >>> import io
+    >>> from pandas.api.types import is_file_like
+    >>> buffer = io.StringIO("data")
+    >>> is_file_like(buffer)
+    True
+    >>> is_file_like([1, 2, 3])
+    False
+    """
+    if not (hasattr(obj, "read") or hasattr(obj, "write")):
+        return False
+
+    return bool(hasattr(obj, "__iter__"))
+
+
+def is_re(obj) -> TypeGuard[Pattern]:
+    """
+    Check if the object is a regex pattern instance.
+
+    Parameters
+    ----------
+    obj : The object to check
+
+    Returns
+    -------
+    bool
+        Whether `obj` is a regex pattern.
+
+    Examples
+    --------
+    >>> from pandas.api.types import is_re
+    >>> import re
+    >>> is_re(re.compile(".*"))
+    True
+    >>> is_re("foo")
+    False
+    """
+    return isinstance(obj, Pattern)
+
+
+def is_re_compilable(obj) -> bool:
+    """
+    Check if the object can be compiled into a regex pattern instance.
+
+    Parameters
+    ----------
+    obj : The object to check
+
+    Returns
+    -------
+    bool
+        Whether `obj` can be compiled as a regex pattern.
+
+    Examples
+    --------
+    >>> from pandas.api.types import is_re_compilable
+    >>> is_re_compilable(".*")
+    True
+    >>> is_re_compilable(1)
+    False
+    """
+    try:
+        re.compile(obj)
+    except TypeError:
+        return False
+    else:
+        return True
+
+
+def is_array_like(obj) -> bool:
+    """
+    Check if the object is array-like.
+
+    For an object to be considered array-like, it must be list-like and
+    have a `dtype` attribute.
+
+    Parameters
+    ----------
+    obj : The object to check
+
+    Returns
+    -------
+    is_array_like : bool
+        Whether `obj` has array-like properties.
+
+    Examples
+    --------
+    >>> is_array_like(np.array([1, 2, 3]))
+    True
+    >>> is_array_like(pd.Series(["a", "b"]))
+    True
+    >>> is_array_like(pd.Index(["2016-01-01"]))
+    True
+    >>> is_array_like([1, 2, 3])
+    False
+    >>> is_array_like(("a", "b"))
+    False
+    """
+    return is_list_like(obj) and hasattr(obj, "dtype")
+
+
+def is_nested_list_like(obj) -> bool:
+    """
+    Check if the object is list-like, and that all of its elements
+    are also list-like.
+
+    Parameters
+    ----------
+    obj : The object to check
+
+    Returns
+    -------
+    is_list_like : bool
+        Whether `obj` has list-like properties.
+
+    Examples
+    --------
+    >>> is_nested_list_like([[1, 2, 3]])
+    True
+    >>> is_nested_list_like([{1, 2, 3}, {1, 2, 3}])
+    True
+    >>> is_nested_list_like(["foo"])
+    False
+    >>> is_nested_list_like([])
+    False
+    >>> is_nested_list_like([[1, 2, 3], 1])
+    False
+
+    Notes
+    -----
+    This won't reliably detect whether a consumable iterator (e. g.
+    a generator) is a nested-list-like without consuming the iterator.
+    To avoid consuming it, we always return False if the outer container
+    doesn't define `__len__`.
+
+    See Also
+    --------
+    is_list_like
+    """
+    return (
+        is_list_like(obj)
+        and hasattr(obj, "__len__")
+        and len(obj) > 0
+        and all(is_list_like(item) for item in obj)
+    )
+
+
+def is_dict_like(obj) -> bool:
+    """
+    Check if the object is dict-like.
+
+    Parameters
+    ----------
+    obj : The object to check
+
+    Returns
+    -------
+    bool
+        Whether `obj` has dict-like properties.
+
+    Examples
+    --------
+    >>> from pandas.api.types import is_dict_like
+    >>> is_dict_like({1: 2})
+    True
+    >>> is_dict_like([1, 2, 3])
+    False
+    >>> is_dict_like(dict)
+    False
+    >>> is_dict_like(dict())
+    True
+    """
+    dict_like_attrs = ("__getitem__", "keys", "__contains__")
+    return (
+        all(hasattr(obj, attr) for attr in dict_like_attrs)
+        # [GH 25196] exclude classes
+        and not isinstance(obj, type)
+    )
+
+
+def is_named_tuple(obj) -> bool:
+    """
+    Check if the object is a named tuple.
+
+    Parameters
+    ----------
+    obj : The object to check
+
+    Returns
+    -------
+    bool
+        Whether `obj` is a named tuple.
+
+    Examples
+    --------
+    >>> from collections import namedtuple
+    >>> from pandas.api.types import is_named_tuple
+    >>> Point = namedtuple("Point", ["x", "y"])
+    >>> p = Point(1, 2)
+    >>>
+    >>> is_named_tuple(p)
+    True
+    >>> is_named_tuple((1, 2))
+    False
+    """
+    return isinstance(obj, abc.Sequence) and hasattr(obj, "_fields")
+
+
+def is_hashable(obj) -> TypeGuard[Hashable]:
+    """
+    Return True if hash(obj) will succeed, False otherwise.
+
+    Some types will pass a test against collections.abc.Hashable but fail when
+    they are actually hashed with hash().
+
+    Distinguish between these and other types by trying the call to hash() and
+    seeing if they raise TypeError.
+
+    Returns
+    -------
+    bool
+
+    Examples
+    --------
+    >>> import collections
+    >>> from pandas.api.types import is_hashable
+    >>> a = ([],)
+    >>> isinstance(a, collections.abc.Hashable)
+    True
+    >>> is_hashable(a)
+    False
+    """
+    # Unfortunately, we can't use isinstance(obj, collections.abc.Hashable),
+    # which can be faster than calling hash. That is because numpy scalars
+    # fail this test.
+
+    # Reconsider this decision once this numpy bug is fixed:
+    # https://github.com/numpy/numpy/issues/5562
+
+    try:
+        hash(obj)
+    except TypeError:
+        return False
+    else:
+        return True
+
+
+def is_sequence(obj) -> bool:
+    """
+    Check if the object is a sequence of objects.
+    String types are not included as sequences here.
+
+    Parameters
+    ----------
+    obj : The object to check
+
+    Returns
+    -------
+    is_sequence : bool
+        Whether `obj` is a sequence of objects.
+
+    Examples
+    --------
+    >>> l = [1, 2, 3]
+    >>>
+    >>> is_sequence(l)
+    True
+    >>> is_sequence(iter(l))
+    False
+    """
+    try:
+        iter(obj)  # Can iterate over it.
+        len(obj)  # Has a length associated with it.
+        return not isinstance(obj, (str, bytes))
+    except (TypeError, AttributeError):
+        return False
+
+
+def is_dataclass(item) -> bool:
+    """
+    Checks if the object is a data-class instance
+
+    Parameters
+    ----------
+    item : object
+
+    Returns
+    --------
+    is_dataclass : bool
+        True if the item is an instance of a data-class,
+        will return false if you pass the data class itself
+
+    Examples
+    --------
+    >>> from dataclasses import dataclass
+    >>> @dataclass
+    ... class Point:
+    ...     x: int
+    ...     y: int
+
+    >>> is_dataclass(Point)
+    False
+    >>> is_dataclass(Point(0,2))
+    True
+
+    """
+    try:
+        import dataclasses
+
+        return dataclasses.is_dataclass(item) and not isinstance(item, type)
+    except ImportError:
+        return False
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/dtypes/missing.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/dtypes/missing.py
new file mode 100644
index 0000000000000000000000000000000000000000..c341ff9dff7e613d8db2209efb5c10f170a9cd47
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/dtypes/missing.py
@@ -0,0 +1,810 @@
+"""
+missing types & inference
+"""
+from __future__ import annotations
+
+from decimal import Decimal
+from functools import partial
+from typing import (
+    TYPE_CHECKING,
+    overload,
+)
+import warnings
+
+import numpy as np
+
+from pandas._config import get_option
+
+from pandas._libs import lib
+import pandas._libs.missing as libmissing
+from pandas._libs.tslibs import (
+    NaT,
+    iNaT,
+)
+
+from pandas.core.dtypes.common import (
+    DT64NS_DTYPE,
+    TD64NS_DTYPE,
+    ensure_object,
+    is_scalar,
+    is_string_or_object_np_dtype,
+)
+from pandas.core.dtypes.dtypes import (
+    CategoricalDtype,
+    DatetimeTZDtype,
+    ExtensionDtype,
+    IntervalDtype,
+    PeriodDtype,
+)
+from pandas.core.dtypes.generic import (
+    ABCDataFrame,
+    ABCExtensionArray,
+    ABCIndex,
+    ABCMultiIndex,
+    ABCSeries,
+)
+from pandas.core.dtypes.inference import is_list_like
+
+if TYPE_CHECKING:
+    from re import Pattern
+
+    from pandas._typing import (
+        ArrayLike,
+        DtypeObj,
+        NDFrame,
+        NDFrameT,
+        Scalar,
+        npt,
+    )
+
+    from pandas import Series
+    from pandas.core.indexes.base import Index
+
+
+isposinf_scalar = libmissing.isposinf_scalar
+isneginf_scalar = libmissing.isneginf_scalar
+
+nan_checker = np.isnan
+INF_AS_NA = False
+_dtype_object = np.dtype("object")
+_dtype_str = np.dtype(str)
+
+
+@overload
+def isna(obj: Scalar | Pattern) -> bool:
+    ...
+
+
+@overload
+def isna(
+    obj: ArrayLike | Index | list,
+) -> npt.NDArray[np.bool_]:
+    ...
+
+
+@overload
+def isna(obj: NDFrameT) -> NDFrameT:
+    ...
+
+
+# handle unions
+@overload
+def isna(obj: NDFrameT | ArrayLike | Index | list) -> NDFrameT | npt.NDArray[np.bool_]:
+    ...
+
+
+@overload
+def isna(obj: object) -> bool | npt.NDArray[np.bool_] | NDFrame:
+    ...
+
+
+def isna(obj: object) -> bool | npt.NDArray[np.bool_] | NDFrame:
+    """
+    Detect missing values for an array-like object.
+
+    This function takes a scalar or array-like object and indicates
+    whether values are missing (``NaN`` in numeric arrays, ``None`` or ``NaN``
+    in object arrays, ``NaT`` in datetimelike).
+
+    Parameters
+    ----------
+    obj : scalar or array-like
+        Object to check for null or missing values.
+
+    Returns
+    -------
+    bool or array-like of bool
+        For scalar input, returns a scalar boolean.
+        For array input, returns an array of boolean indicating whether each
+        corresponding element is missing.
+
+    See Also
+    --------
+    notna : Boolean inverse of pandas.isna.
+    Series.isna : Detect missing values in a Series.
+    DataFrame.isna : Detect missing values in a DataFrame.
+    Index.isna : Detect missing values in an Index.
+
+    Examples
+    --------
+    Scalar arguments (including strings) result in a scalar boolean.
+
+    >>> pd.isna('dog')
+    False
+
+    >>> pd.isna(pd.NA)
+    True
+
+    >>> pd.isna(np.nan)
+    True
+
+    ndarrays result in an ndarray of booleans.
+
+    >>> array = np.array([[1, np.nan, 3], [4, 5, np.nan]])
+    >>> array
+    array([[ 1., nan,  3.],
+           [ 4.,  5., nan]])
+    >>> pd.isna(array)
+    array([[False,  True, False],
+           [False, False,  True]])
+
+    For indexes, an ndarray of booleans is returned.
+
+    >>> index = pd.DatetimeIndex(["2017-07-05", "2017-07-06", None,
+    ...                           "2017-07-08"])
+    >>> index
+    DatetimeIndex(['2017-07-05', '2017-07-06', 'NaT', '2017-07-08'],
+                  dtype='datetime64[ns]', freq=None)
+    >>> pd.isna(index)
+    array([False, False,  True, False])
+
+    For Series and DataFrame, the same type is returned, containing booleans.
+
+    >>> df = pd.DataFrame([['ant', 'bee', 'cat'], ['dog', None, 'fly']])
+    >>> df
+         0     1    2
+    0  ant   bee  cat
+    1  dog  None  fly
+    >>> pd.isna(df)
+           0      1      2
+    0  False  False  False
+    1  False   True  False
+
+    >>> pd.isna(df[1])
+    0    False
+    1     True
+    Name: 1, dtype: bool
+    """
+    return _isna(obj)
+
+
+isnull = isna
+
+
+def _isna(obj, inf_as_na: bool = False):
+    """
+    Detect missing values, treating None, NaN or NA as null. Infinite
+    values will also be treated as null if inf_as_na is True.
+
+    Parameters
+    ----------
+    obj: ndarray or object value
+        Input array or scalar value.
+    inf_as_na: bool
+        Whether to treat infinity as null.
+
+    Returns
+    -------
+    boolean ndarray or boolean
+    """
+    if is_scalar(obj):
+        return libmissing.checknull(obj, inf_as_na=inf_as_na)
+    elif isinstance(obj, ABCMultiIndex):
+        raise NotImplementedError("isna is not defined for MultiIndex")
+    elif isinstance(obj, type):
+        return False
+    elif isinstance(obj, (np.ndarray, ABCExtensionArray)):
+        return _isna_array(obj, inf_as_na=inf_as_na)
+    elif isinstance(obj, ABCIndex):
+        # Try to use cached isna, which also short-circuits for integer dtypes
+        #  and avoids materializing RangeIndex._values
+        if not obj._can_hold_na:
+            return obj.isna()
+        return _isna_array(obj._values, inf_as_na=inf_as_na)
+
+    elif isinstance(obj, ABCSeries):
+        result = _isna_array(obj._values, inf_as_na=inf_as_na)
+        # box
+        result = obj._constructor(result, index=obj.index, name=obj.name, copy=False)
+        return result
+    elif isinstance(obj, ABCDataFrame):
+        return obj.isna()
+    elif isinstance(obj, list):
+        return _isna_array(np.asarray(obj, dtype=object), inf_as_na=inf_as_na)
+    elif hasattr(obj, "__array__"):
+        return _isna_array(np.asarray(obj), inf_as_na=inf_as_na)
+    else:
+        return False
+
+
+def _use_inf_as_na(key) -> None:
+    """
+    Option change callback for na/inf behaviour.
+
+    Choose which replacement for numpy.isnan / -numpy.isfinite is used.
+
+    Parameters
+    ----------
+    flag: bool
+        True means treat None, NaN, INF, -INF as null (old way),
+        False means None and NaN are null, but INF, -INF are not null
+        (new way).
+
+    Notes
+    -----
+    This approach to setting global module values is discussed and
+    approved here:
+
+    * https://stackoverflow.com/questions/4859217/
+      programmatically-creating-variables-in-python/4859312#4859312
+    """
+    inf_as_na = get_option(key)
+    globals()["_isna"] = partial(_isna, inf_as_na=inf_as_na)
+    if inf_as_na:
+        globals()["nan_checker"] = lambda x: ~np.isfinite(x)
+        globals()["INF_AS_NA"] = True
+    else:
+        globals()["nan_checker"] = np.isnan
+        globals()["INF_AS_NA"] = False
+
+
+def _isna_array(values: ArrayLike, inf_as_na: bool = False):
+    """
+    Return an array indicating which values of the input array are NaN / NA.
+
+    Parameters
+    ----------
+    obj: ndarray or ExtensionArray
+        The input array whose elements are to be checked.
+    inf_as_na: bool
+        Whether or not to treat infinite values as NA.
+
+    Returns
+    -------
+    array-like
+        Array of boolean values denoting the NA status of each element.
+    """
+    dtype = values.dtype
+
+    if not isinstance(values, np.ndarray):
+        # i.e. ExtensionArray
+        if inf_as_na and isinstance(dtype, CategoricalDtype):
+            result = libmissing.isnaobj(values.to_numpy(), inf_as_na=inf_as_na)
+        else:
+            # error: Incompatible types in assignment (expression has type
+            # "Union[ndarray[Any, Any], ExtensionArraySupportsAnyAll]", variable has
+            # type "ndarray[Any, dtype[bool_]]")
+            result = values.isna()  # type: ignore[assignment]
+    elif isinstance(values, np.rec.recarray):
+        # GH 48526
+        result = _isna_recarray_dtype(values, inf_as_na=inf_as_na)
+    elif is_string_or_object_np_dtype(values.dtype):
+        result = _isna_string_dtype(values, inf_as_na=inf_as_na)
+    elif dtype.kind in "mM":
+        # this is the NaT pattern
+        result = values.view("i8") == iNaT
+    else:
+        if inf_as_na:
+            result = ~np.isfinite(values)
+        else:
+            result = np.isnan(values)
+
+    return result
+
+
+def _isna_string_dtype(values: np.ndarray, inf_as_na: bool) -> npt.NDArray[np.bool_]:
+    # Working around NumPy ticket 1542
+    dtype = values.dtype
+
+    if dtype.kind in ("S", "U"):
+        result = np.zeros(values.shape, dtype=bool)
+    else:
+        if values.ndim in {1, 2}:
+            result = libmissing.isnaobj(values, inf_as_na=inf_as_na)
+        else:
+            # 0-D, reached via e.g. mask_missing
+            result = libmissing.isnaobj(values.ravel(), inf_as_na=inf_as_na)
+            result = result.reshape(values.shape)
+
+    return result
+
+
+def _has_record_inf_value(record_as_array: np.ndarray) -> np.bool_:
+    is_inf_in_record = np.zeros(len(record_as_array), dtype=bool)
+    for i, value in enumerate(record_as_array):
+        is_element_inf = False
+        try:
+            is_element_inf = np.isinf(value)
+        except TypeError:
+            is_element_inf = False
+        is_inf_in_record[i] = is_element_inf
+
+    return np.any(is_inf_in_record)
+
+
+def _isna_recarray_dtype(
+    values: np.rec.recarray, inf_as_na: bool
+) -> npt.NDArray[np.bool_]:
+    result = np.zeros(values.shape, dtype=bool)
+    for i, record in enumerate(values):
+        record_as_array = np.array(record.tolist())
+        does_record_contain_nan = isna_all(record_as_array)
+        does_record_contain_inf = False
+        if inf_as_na:
+            does_record_contain_inf = bool(_has_record_inf_value(record_as_array))
+        result[i] = np.any(
+            np.logical_or(does_record_contain_nan, does_record_contain_inf)
+        )
+
+    return result
+
+
+@overload
+def notna(obj: Scalar) -> bool:
+    ...
+
+
+@overload
+def notna(
+    obj: ArrayLike | Index | list,
+) -> npt.NDArray[np.bool_]:
+    ...
+
+
+@overload
+def notna(obj: NDFrameT) -> NDFrameT:
+    ...
+
+
+# handle unions
+@overload
+def notna(obj: NDFrameT | ArrayLike | Index | list) -> NDFrameT | npt.NDArray[np.bool_]:
+    ...
+
+
+@overload
+def notna(obj: object) -> bool | npt.NDArray[np.bool_] | NDFrame:
+    ...
+
+
+def notna(obj: object) -> bool | npt.NDArray[np.bool_] | NDFrame:
+    """
+    Detect non-missing values for an array-like object.
+
+    This function takes a scalar or array-like object and indicates
+    whether values are valid (not missing, which is ``NaN`` in numeric
+    arrays, ``None`` or ``NaN`` in object arrays, ``NaT`` in datetimelike).
+
+    Parameters
+    ----------
+    obj : array-like or object value
+        Object to check for *not* null or *non*-missing values.
+
+    Returns
+    -------
+    bool or array-like of bool
+        For scalar input, returns a scalar boolean.
+        For array input, returns an array of boolean indicating whether each
+        corresponding element is valid.
+
+    See Also
+    --------
+    isna : Boolean inverse of pandas.notna.
+    Series.notna : Detect valid values in a Series.
+    DataFrame.notna : Detect valid values in a DataFrame.
+    Index.notna : Detect valid values in an Index.
+
+    Examples
+    --------
+    Scalar arguments (including strings) result in a scalar boolean.
+
+    >>> pd.notna('dog')
+    True
+
+    >>> pd.notna(pd.NA)
+    False
+
+    >>> pd.notna(np.nan)
+    False
+
+    ndarrays result in an ndarray of booleans.
+
+    >>> array = np.array([[1, np.nan, 3], [4, 5, np.nan]])
+    >>> array
+    array([[ 1., nan,  3.],
+           [ 4.,  5., nan]])
+    >>> pd.notna(array)
+    array([[ True, False,  True],
+           [ True,  True, False]])
+
+    For indexes, an ndarray of booleans is returned.
+
+    >>> index = pd.DatetimeIndex(["2017-07-05", "2017-07-06", None,
+    ...                          "2017-07-08"])
+    >>> index
+    DatetimeIndex(['2017-07-05', '2017-07-06', 'NaT', '2017-07-08'],
+                  dtype='datetime64[ns]', freq=None)
+    >>> pd.notna(index)
+    array([ True,  True, False,  True])
+
+    For Series and DataFrame, the same type is returned, containing booleans.
+
+    >>> df = pd.DataFrame([['ant', 'bee', 'cat'], ['dog', None, 'fly']])
+    >>> df
+         0     1    2
+    0  ant   bee  cat
+    1  dog  None  fly
+    >>> pd.notna(df)
+          0      1     2
+    0  True   True  True
+    1  True  False  True
+
+    >>> pd.notna(df[1])
+    0     True
+    1    False
+    Name: 1, dtype: bool
+    """
+    res = isna(obj)
+    if isinstance(res, bool):
+        return not res
+    return ~res
+
+
+notnull = notna
+
+
+def array_equivalent(
+    left,
+    right,
+    strict_nan: bool = False,
+    dtype_equal: bool = False,
+) -> bool:
+    """
+    True if two arrays, left and right, have equal non-NaN elements, and NaNs
+    in corresponding locations.  False otherwise. It is assumed that left and
+    right are NumPy arrays of the same dtype. The behavior of this function
+    (particularly with respect to NaNs) is not defined if the dtypes are
+    different.
+
+    Parameters
+    ----------
+    left, right : ndarrays
+    strict_nan : bool, default False
+        If True, consider NaN and None to be different.
+    dtype_equal : bool, default False
+        Whether `left` and `right` are known to have the same dtype
+        according to `is_dtype_equal`. Some methods like `BlockManager.equals`.
+        require that the dtypes match. Setting this to ``True`` can improve
+        performance, but will give different results for arrays that are
+        equal but different dtypes.
+
+    Returns
+    -------
+    b : bool
+        Returns True if the arrays are equivalent.
+
+    Examples
+    --------
+    >>> array_equivalent(
+    ...     np.array([1, 2, np.nan]),
+    ...     np.array([1, 2, np.nan]))
+    True
+    >>> array_equivalent(
+    ...     np.array([1, np.nan, 2]),
+    ...     np.array([1, 2, np.nan]))
+    False
+    """
+    left, right = np.asarray(left), np.asarray(right)
+
+    # shape compat
+    if left.shape != right.shape:
+        return False
+
+    if dtype_equal:
+        # fastpath when we require that the dtypes match (Block.equals)
+        if left.dtype.kind in "fc":
+            return _array_equivalent_float(left, right)
+        elif left.dtype.kind in "mM":
+            return _array_equivalent_datetimelike(left, right)
+        elif is_string_or_object_np_dtype(left.dtype):
+            # TODO: fastpath for pandas' StringDtype
+            return _array_equivalent_object(left, right, strict_nan)
+        else:
+            return np.array_equal(left, right)
+
+    # Slow path when we allow comparing different dtypes.
+    # Object arrays can contain None, NaN and NaT.
+    # string dtypes must be come to this path for NumPy 1.7.1 compat
+    if left.dtype.kind in "OSU" or right.dtype.kind in "OSU":
+        # Note: `in "OSU"` is non-trivially faster than `in ["O", "S", "U"]`
+        #  or `in ("O", "S", "U")`
+        return _array_equivalent_object(left, right, strict_nan)
+
+    # NaNs can occur in float and complex arrays.
+    if left.dtype.kind in "fc":
+        if not (left.size and right.size):
+            return True
+        return ((left == right) | (isna(left) & isna(right))).all()
+
+    elif left.dtype.kind in "mM" or right.dtype.kind in "mM":
+        # datetime64, timedelta64, Period
+        if left.dtype != right.dtype:
+            return False
+
+        left = left.view("i8")
+        right = right.view("i8")
+
+    # if we have structured dtypes, compare first
+    if (
+        left.dtype.type is np.void or right.dtype.type is np.void
+    ) and left.dtype != right.dtype:
+        return False
+
+    return np.array_equal(left, right)
+
+
+def _array_equivalent_float(left: np.ndarray, right: np.ndarray) -> bool:
+    return bool(((left == right) | (np.isnan(left) & np.isnan(right))).all())
+
+
+def _array_equivalent_datetimelike(left: np.ndarray, right: np.ndarray):
+    return np.array_equal(left.view("i8"), right.view("i8"))
+
+
+def _array_equivalent_object(left: np.ndarray, right: np.ndarray, strict_nan: bool):
+    left = ensure_object(left)
+    right = ensure_object(right)
+
+    mask: npt.NDArray[np.bool_] | None = None
+    if strict_nan:
+        mask = isna(left) & isna(right)
+        if not mask.any():
+            mask = None
+
+    try:
+        if mask is None:
+            return lib.array_equivalent_object(left, right)
+        if not lib.array_equivalent_object(left[~mask], right[~mask]):
+            return False
+        left_remaining = left[mask]
+        right_remaining = right[mask]
+    except ValueError:
+        # can raise a ValueError if left and right cannot be
+        # compared (e.g. nested arrays)
+        left_remaining = left
+        right_remaining = right
+
+    for left_value, right_value in zip(left_remaining, right_remaining):
+        if left_value is NaT and right_value is not NaT:
+            return False
+
+        elif left_value is libmissing.NA and right_value is not libmissing.NA:
+            return False
+
+        elif isinstance(left_value, float) and np.isnan(left_value):
+            if not isinstance(right_value, float) or not np.isnan(right_value):
+                return False
+        else:
+            with warnings.catch_warnings():
+                # suppress numpy's "elementwise comparison failed"
+                warnings.simplefilter("ignore", DeprecationWarning)
+                try:
+                    if np.any(np.asarray(left_value != right_value)):
+                        return False
+                except TypeError as err:
+                    if "boolean value of NA is ambiguous" in str(err):
+                        return False
+                    raise
+                except ValueError:
+                    # numpy can raise a ValueError if left and right cannot be
+                    # compared (e.g. nested arrays)
+                    return False
+    return True
+
+
+def array_equals(left: ArrayLike, right: ArrayLike) -> bool:
+    """
+    ExtensionArray-compatible implementation of array_equivalent.
+    """
+    if left.dtype != right.dtype:
+        return False
+    elif isinstance(left, ABCExtensionArray):
+        return left.equals(right)
+    else:
+        return array_equivalent(left, right, dtype_equal=True)
+
+
+def infer_fill_value(val):
+    """
+    infer the fill value for the nan/NaT from the provided
+    scalar/ndarray/list-like if we are a NaT, return the correct dtyped
+    element to provide proper block construction
+    """
+    if not is_list_like(val):
+        val = [val]
+    val = np.asarray(val)
+    if val.dtype.kind in "mM":
+        return np.array("NaT", dtype=val.dtype)
+    elif val.dtype == object:
+        dtype = lib.infer_dtype(ensure_object(val), skipna=False)
+        if dtype in ["datetime", "datetime64"]:
+            return np.array("NaT", dtype=DT64NS_DTYPE)
+        elif dtype in ["timedelta", "timedelta64"]:
+            return np.array("NaT", dtype=TD64NS_DTYPE)
+        return np.array(np.nan, dtype=object)
+    elif val.dtype.kind == "U":
+        return np.array(np.nan, dtype=val.dtype)
+    return np.nan
+
+
+def construct_1d_array_from_inferred_fill_value(
+    value: object, length: int
+) -> ArrayLike:
+    # Find our empty_value dtype by constructing an array
+    #  from our value and doing a .take on it
+    from pandas.core.algorithms import take_nd
+    from pandas.core.construction import sanitize_array
+    from pandas.core.indexes.base import Index
+
+    arr = sanitize_array(value, Index(range(1)), copy=False)
+    taker = -1 * np.ones(length, dtype=np.intp)
+    return take_nd(arr, taker)
+
+
+def maybe_fill(arr: np.ndarray) -> np.ndarray:
+    """
+    Fill numpy.ndarray with NaN, unless we have a integer or boolean dtype.
+    """
+    if arr.dtype.kind not in "iub":
+        arr.fill(np.nan)
+    return arr
+
+
+def na_value_for_dtype(dtype: DtypeObj, compat: bool = True):
+    """
+    Return a dtype compat na value
+
+    Parameters
+    ----------
+    dtype : string / dtype
+    compat : bool, default True
+
+    Returns
+    -------
+    np.dtype or a pandas dtype
+
+    Examples
+    --------
+    >>> na_value_for_dtype(np.dtype('int64'))
+    0
+    >>> na_value_for_dtype(np.dtype('int64'), compat=False)
+    nan
+    >>> na_value_for_dtype(np.dtype('float64'))
+    nan
+    >>> na_value_for_dtype(np.dtype('bool'))
+    False
+    >>> na_value_for_dtype(np.dtype('datetime64[ns]'))
+    numpy.datetime64('NaT')
+    """
+
+    if isinstance(dtype, ExtensionDtype):
+        return dtype.na_value
+    elif dtype.kind in "mM":
+        unit = np.datetime_data(dtype)[0]
+        return dtype.type("NaT", unit)
+    elif dtype.kind == "f":
+        return np.nan
+    elif dtype.kind in "iu":
+        if compat:
+            return 0
+        return np.nan
+    elif dtype.kind == "b":
+        if compat:
+            return False
+        return np.nan
+    return np.nan
+
+
+def remove_na_arraylike(arr: Series | Index | np.ndarray):
+    """
+    Return array-like containing only true/non-NaN values, possibly empty.
+    """
+    if isinstance(arr.dtype, ExtensionDtype):
+        return arr[notna(arr)]
+    else:
+        return arr[notna(np.asarray(arr))]
+
+
+def is_valid_na_for_dtype(obj, dtype: DtypeObj) -> bool:
+    """
+    isna check that excludes incompatible dtypes
+
+    Parameters
+    ----------
+    obj : object
+    dtype : np.datetime64, np.timedelta64, DatetimeTZDtype, or PeriodDtype
+
+    Returns
+    -------
+    bool
+    """
+    if not lib.is_scalar(obj) or not isna(obj):
+        return False
+    elif dtype.kind == "M":
+        if isinstance(dtype, np.dtype):
+            # i.e. not tzaware
+            return not isinstance(obj, (np.timedelta64, Decimal))
+        # we have to rule out tznaive dt64("NaT")
+        return not isinstance(obj, (np.timedelta64, np.datetime64, Decimal))
+    elif dtype.kind == "m":
+        return not isinstance(obj, (np.datetime64, Decimal))
+    elif dtype.kind in "iufc":
+        # Numeric
+        return obj is not NaT and not isinstance(obj, (np.datetime64, np.timedelta64))
+    elif dtype.kind == "b":
+        # We allow pd.NA, None, np.nan in BooleanArray (same as IntervalDtype)
+        return lib.is_float(obj) or obj is None or obj is libmissing.NA
+
+    elif dtype == _dtype_str:
+        # numpy string dtypes to avoid float np.nan
+        return not isinstance(obj, (np.datetime64, np.timedelta64, Decimal, float))
+
+    elif dtype == _dtype_object:
+        # This is needed for Categorical, but is kind of weird
+        return True
+
+    elif isinstance(dtype, PeriodDtype):
+        return not isinstance(obj, (np.datetime64, np.timedelta64, Decimal))
+
+    elif isinstance(dtype, IntervalDtype):
+        return lib.is_float(obj) or obj is None or obj is libmissing.NA
+
+    elif isinstance(dtype, CategoricalDtype):
+        return is_valid_na_for_dtype(obj, dtype.categories.dtype)
+
+    # fallback, default to allowing NaN, None, NA, NaT
+    return not isinstance(obj, (np.datetime64, np.timedelta64, Decimal))
+
+
+def isna_all(arr: ArrayLike) -> bool:
+    """
+    Optimized equivalent to isna(arr).all()
+    """
+    total_len = len(arr)
+
+    # Usually it's enough to check but a small fraction of values to see if
+    #  a block is NOT null, chunks should help in such cases.
+    #  parameters 1000 and 40 were chosen arbitrarily
+    chunk_len = max(total_len // 40, 1000)
+
+    dtype = arr.dtype
+    if lib.is_np_dtype(dtype, "f"):
+        checker = nan_checker
+
+    elif (lib.is_np_dtype(dtype, "mM")) or isinstance(
+        dtype, (DatetimeTZDtype, PeriodDtype)
+    ):
+        # error: Incompatible types in assignment (expression has type
+        # "Callable[[Any], Any]", variable has type "ufunc")
+        checker = lambda x: np.asarray(x.view("i8")) == iNaT  # type: ignore[assignment]
+
+    else:
+        # error: Incompatible types in assignment (expression has type "Callable[[Any],
+        # Any]", variable has type "ufunc")
+        checker = lambda x: _isna_array(  # type: ignore[assignment]
+            x, inf_as_na=INF_AS_NA
+        )
+
+    return all(
+        checker(arr[i : i + chunk_len]).all() for i in range(0, total_len, chunk_len)
+    )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/flags.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/flags.py
new file mode 100644
index 0000000000000000000000000000000000000000..aff7a15f283bafe1459173070e64df4caef0d45d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/flags.py
@@ -0,0 +1,117 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+import weakref
+
+if TYPE_CHECKING:
+    from pandas.core.generic import NDFrame
+
+
+class Flags:
+    """
+    Flags that apply to pandas objects.
+
+    Parameters
+    ----------
+    obj : Series or DataFrame
+        The object these flags are associated with.
+    allows_duplicate_labels : bool, default True
+        Whether to allow duplicate labels in this object. By default,
+        duplicate labels are permitted. Setting this to ``False`` will
+        cause an :class:`errors.DuplicateLabelError` to be raised when
+        `index` (or columns for DataFrame) is not unique, or any
+        subsequent operation on introduces duplicates.
+        See :ref:`duplicates.disallow` for more.
+
+        .. warning::
+
+           This is an experimental feature. Currently, many methods fail to
+           propagate the ``allows_duplicate_labels`` value. In future versions
+           it is expected that every method taking or returning one or more
+           DataFrame or Series objects will propagate ``allows_duplicate_labels``.
+
+    Examples
+    --------
+    Attributes can be set in two ways:
+
+    >>> df = pd.DataFrame()
+    >>> df.flags
+    
+    >>> df.flags.allows_duplicate_labels = False
+    >>> df.flags
+    
+
+    >>> df.flags['allows_duplicate_labels'] = True
+    >>> df.flags
+    
+    """
+
+    _keys: set[str] = {"allows_duplicate_labels"}
+
+    def __init__(self, obj: NDFrame, *, allows_duplicate_labels: bool) -> None:
+        self._allows_duplicate_labels = allows_duplicate_labels
+        self._obj = weakref.ref(obj)
+
+    @property
+    def allows_duplicate_labels(self) -> bool:
+        """
+        Whether this object allows duplicate labels.
+
+        Setting ``allows_duplicate_labels=False`` ensures that the
+        index (and columns of a DataFrame) are unique. Most methods
+        that accept and return a Series or DataFrame will propagate
+        the value of ``allows_duplicate_labels``.
+
+        See :ref:`duplicates` for more.
+
+        See Also
+        --------
+        DataFrame.attrs : Set global metadata on this object.
+        DataFrame.set_flags : Set global flags on this object.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({"A": [1, 2]}, index=['a', 'a'])
+        >>> df.flags.allows_duplicate_labels
+        True
+        >>> df.flags.allows_duplicate_labels = False
+        Traceback (most recent call last):
+            ...
+        pandas.errors.DuplicateLabelError: Index has duplicates.
+              positions
+        label
+        a        [0, 1]
+        """
+        return self._allows_duplicate_labels
+
+    @allows_duplicate_labels.setter
+    def allows_duplicate_labels(self, value: bool) -> None:
+        value = bool(value)
+        obj = self._obj()
+        if obj is None:
+            raise ValueError("This flag's object has been deleted.")
+
+        if not value:
+            for ax in obj.axes:
+                ax._maybe_check_unique()
+
+        self._allows_duplicate_labels = value
+
+    def __getitem__(self, key: str):
+        if key not in self._keys:
+            raise KeyError(key)
+
+        return getattr(self, key)
+
+    def __setitem__(self, key: str, value) -> None:
+        if key not in self._keys:
+            raise ValueError(f"Unknown flag {key}. Must be one of {self._keys}")
+        setattr(self, key, value)
+
+    def __repr__(self) -> str:
+        return f""
+
+    def __eq__(self, other) -> bool:
+        if isinstance(other, type(self)):
+            return self.allows_duplicate_labels == other.allows_duplicate_labels
+        return False
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/frame.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/frame.py
new file mode 100644
index 0000000000000000000000000000000000000000..cc260770cbd78ffc0bf46d7d004cde56409df300
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/frame.py
@@ -0,0 +1,12724 @@
+"""
+DataFrame
+---------
+An efficient 2D container for potentially mixed-type time series or other
+labeled data series.
+
+Similar to its R counterpart, data.frame, except providing automatic data
+alignment and a host of useful data manipulation methods having to do with the
+labeling information
+"""
+from __future__ import annotations
+
+import collections
+from collections import abc
+from collections.abc import (
+    Hashable,
+    Iterable,
+    Iterator,
+    Mapping,
+    Sequence,
+)
+import functools
+from inspect import signature
+from io import StringIO
+import itertools
+import operator
+import sys
+from textwrap import dedent
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Literal,
+    cast,
+    overload,
+)
+import warnings
+
+import numpy as np
+from numpy import ma
+
+from pandas._config import (
+    get_option,
+    using_copy_on_write,
+    warn_copy_on_write,
+)
+from pandas._config.config import _get_option
+
+from pandas._libs import (
+    algos as libalgos,
+    lib,
+    properties,
+)
+from pandas._libs.hashtable import duplicated
+from pandas._libs.lib import is_range_indexer
+from pandas.compat import PYPY
+from pandas.compat._constants import (
+    REF_COUNT,
+    WARNING_CHECK_DISABLED,
+)
+from pandas.compat._optional import import_optional_dependency
+from pandas.compat.numpy import function as nv
+from pandas.errors import (
+    ChainedAssignmentError,
+    InvalidIndexError,
+    _chained_assignment_method_msg,
+    _chained_assignment_msg,
+    _chained_assignment_warning_method_msg,
+    _chained_assignment_warning_msg,
+)
+from pandas.util._decorators import (
+    Appender,
+    Substitution,
+    deprecate_nonkeyword_arguments,
+    doc,
+)
+from pandas.util._exceptions import (
+    find_stack_level,
+    rewrite_warning,
+)
+from pandas.util._validators import (
+    validate_ascending,
+    validate_bool_kwarg,
+    validate_percentile,
+)
+
+from pandas.core.dtypes.cast import (
+    LossySetitemError,
+    can_hold_element,
+    construct_1d_arraylike_from_scalar,
+    construct_2d_arraylike_from_scalar,
+    find_common_type,
+    infer_dtype_from_scalar,
+    invalidate_string_dtypes,
+    maybe_box_native,
+    maybe_downcast_to_dtype,
+)
+from pandas.core.dtypes.common import (
+    infer_dtype_from_object,
+    is_1d_only_ea_dtype,
+    is_array_like,
+    is_bool_dtype,
+    is_dataclass,
+    is_dict_like,
+    is_float,
+    is_float_dtype,
+    is_hashable,
+    is_integer,
+    is_integer_dtype,
+    is_iterator,
+    is_list_like,
+    is_scalar,
+    is_sequence,
+    needs_i8_conversion,
+    pandas_dtype,
+)
+from pandas.core.dtypes.concat import concat_compat
+from pandas.core.dtypes.dtypes import (
+    ArrowDtype,
+    BaseMaskedDtype,
+    ExtensionDtype,
+)
+from pandas.core.dtypes.missing import (
+    isna,
+    notna,
+)
+
+from pandas.core import (
+    algorithms,
+    common as com,
+    nanops,
+    ops,
+    roperator,
+)
+from pandas.core.accessor import CachedAccessor
+from pandas.core.apply import reconstruct_and_relabel_result
+from pandas.core.array_algos.take import take_2d_multi
+from pandas.core.arraylike import OpsMixin
+from pandas.core.arrays import (
+    BaseMaskedArray,
+    DatetimeArray,
+    ExtensionArray,
+    PeriodArray,
+    TimedeltaArray,
+)
+from pandas.core.arrays.sparse import SparseFrameAccessor
+from pandas.core.arrays.string_ import StringDtype
+from pandas.core.construction import (
+    ensure_wrapped_if_datetimelike,
+    sanitize_array,
+    sanitize_masked_array,
+)
+from pandas.core.generic import (
+    NDFrame,
+    make_doc,
+)
+from pandas.core.indexers import check_key_length
+from pandas.core.indexes.api import (
+    DatetimeIndex,
+    Index,
+    PeriodIndex,
+    default_index,
+    ensure_index,
+    ensure_index_from_sequences,
+)
+from pandas.core.indexes.multi import (
+    MultiIndex,
+    maybe_droplevels,
+)
+from pandas.core.indexing import (
+    check_bool_indexer,
+    check_dict_or_set_indexers,
+)
+from pandas.core.internals import (
+    ArrayManager,
+    BlockManager,
+)
+from pandas.core.internals.construction import (
+    arrays_to_mgr,
+    dataclasses_to_dicts,
+    dict_to_mgr,
+    mgr_to_mgr,
+    ndarray_to_mgr,
+    nested_data_to_arrays,
+    rec_array_to_mgr,
+    reorder_arrays,
+    to_arrays,
+    treat_as_nested,
+)
+from pandas.core.methods import selectn
+from pandas.core.reshape.melt import melt
+from pandas.core.series import Series
+from pandas.core.shared_docs import _shared_docs
+from pandas.core.sorting import (
+    get_group_index,
+    lexsort_indexer,
+    nargsort,
+)
+
+from pandas.io.common import get_handle
+from pandas.io.formats import (
+    console,
+    format as fmt,
+)
+from pandas.io.formats.info import (
+    INFO_DOCSTRING,
+    DataFrameInfo,
+    frame_sub_kwargs,
+)
+import pandas.plotting
+
+if TYPE_CHECKING:
+    import datetime
+
+    from pandas._libs.internals import BlockValuesRefs
+    from pandas._typing import (
+        AggFuncType,
+        AnyAll,
+        AnyArrayLike,
+        ArrayLike,
+        Axes,
+        Axis,
+        AxisInt,
+        ColspaceArgType,
+        CompressionOptions,
+        CorrelationMethod,
+        DropKeep,
+        Dtype,
+        DtypeObj,
+        FilePath,
+        FloatFormatType,
+        FormattersType,
+        Frequency,
+        FromDictOrient,
+        IgnoreRaise,
+        IndexKeyFunc,
+        IndexLabel,
+        JoinValidate,
+        Level,
+        MergeHow,
+        MergeValidate,
+        MutableMappingT,
+        NaAction,
+        NaPosition,
+        NsmallestNlargestKeep,
+        PythonFuncType,
+        QuantileInterpolation,
+        ReadBuffer,
+        ReindexMethod,
+        Renamer,
+        Scalar,
+        Self,
+        SequenceNotStr,
+        SortKind,
+        StorageOptions,
+        Suffixes,
+        ToGbqIfexist,
+        ToStataByteorder,
+        ToTimestampHow,
+        UpdateJoin,
+        ValueKeyFunc,
+        WriteBuffer,
+        XMLParsers,
+        npt,
+    )
+
+    from pandas.core.groupby.generic import DataFrameGroupBy
+    from pandas.core.interchange.dataframe_protocol import DataFrame as DataFrameXchg
+    from pandas.core.internals import SingleDataManager
+
+    from pandas.io.formats.style import Styler
+
+# ---------------------------------------------------------------------
+# Docstring templates
+
+_shared_doc_kwargs = {
+    "axes": "index, columns",
+    "klass": "DataFrame",
+    "axes_single_arg": "{0 or 'index', 1 or 'columns'}",
+    "axis": """axis : {0 or 'index', 1 or 'columns'}, default 0
+        If 0 or 'index': apply function to each column.
+        If 1 or 'columns': apply function to each row.""",
+    "inplace": """
+    inplace : bool, default False
+        Whether to modify the DataFrame rather than creating a new one.""",
+    "optional_by": """
+by : str or list of str
+    Name or list of names to sort by.
+
+    - if `axis` is 0 or `'index'` then `by` may contain index
+      levels and/or column labels.
+    - if `axis` is 1 or `'columns'` then `by` may contain column
+      levels and/or index labels.""",
+    "optional_reindex": """
+labels : array-like, optional
+    New labels / index to conform the axis specified by 'axis' to.
+index : array-like, optional
+    New labels for the index. Preferably an Index object to avoid
+    duplicating data.
+columns : array-like, optional
+    New labels for the columns. Preferably an Index object to avoid
+    duplicating data.
+axis : int or str, optional
+    Axis to target. Can be either the axis name ('index', 'columns')
+    or number (0, 1).""",
+}
+
+_merge_doc = """
+Merge DataFrame or named Series objects with a database-style join.
+
+A named Series object is treated as a DataFrame with a single named column.
+
+The join is done on columns or indexes. If joining columns on
+columns, the DataFrame indexes *will be ignored*. Otherwise if joining indexes
+on indexes or indexes on a column or columns, the index will be passed on.
+When performing a cross merge, no column specifications to merge on are
+allowed.
+
+.. warning::
+
+    If both key columns contain rows where the key is a null value, those
+    rows will be matched against each other. This is different from usual SQL
+    join behaviour and can lead to unexpected results.
+
+Parameters
+----------%s
+right : DataFrame or named Series
+    Object to merge with.
+how : {'left', 'right', 'outer', 'inner', 'cross'}, default 'inner'
+    Type of merge to be performed.
+
+    * left: use only keys from left frame, similar to a SQL left outer join;
+      preserve key order.
+    * right: use only keys from right frame, similar to a SQL right outer join;
+      preserve key order.
+    * outer: use union of keys from both frames, similar to a SQL full outer
+      join; sort keys lexicographically.
+    * inner: use intersection of keys from both frames, similar to a SQL inner
+      join; preserve the order of the left keys.
+    * cross: creates the cartesian product from both frames, preserves the order
+      of the left keys.
+on : label or list
+    Column or index level names to join on. These must be found in both
+    DataFrames. If `on` is None and not merging on indexes then this defaults
+    to the intersection of the columns in both DataFrames.
+left_on : label or list, or array-like
+    Column or index level names to join on in the left DataFrame. Can also
+    be an array or list of arrays of the length of the left DataFrame.
+    These arrays are treated as if they are columns.
+right_on : label or list, or array-like
+    Column or index level names to join on in the right DataFrame. Can also
+    be an array or list of arrays of the length of the right DataFrame.
+    These arrays are treated as if they are columns.
+left_index : bool, default False
+    Use the index from the left DataFrame as the join key(s). If it is a
+    MultiIndex, the number of keys in the other DataFrame (either the index
+    or a number of columns) must match the number of levels.
+right_index : bool, default False
+    Use the index from the right DataFrame as the join key. Same caveats as
+    left_index.
+sort : bool, default False
+    Sort the join keys lexicographically in the result DataFrame. If False,
+    the order of the join keys depends on the join type (how keyword).
+suffixes : list-like, default is ("_x", "_y")
+    A length-2 sequence where each element is optionally a string
+    indicating the suffix to add to overlapping column names in
+    `left` and `right` respectively. Pass a value of `None` instead
+    of a string to indicate that the column name from `left` or
+    `right` should be left as-is, with no suffix. At least one of the
+    values must not be None.
+copy : bool, default True
+    If False, avoid copy if possible.
+
+    .. note::
+        The `copy` keyword will change behavior in pandas 3.0.
+        `Copy-on-Write
+        `__
+        will be enabled by default, which means that all methods with a
+        `copy` keyword will use a lazy copy mechanism to defer the copy and
+        ignore the `copy` keyword. The `copy` keyword will be removed in a
+        future version of pandas.
+
+        You can already get the future behavior and improvements through
+        enabling copy on write ``pd.options.mode.copy_on_write = True``
+indicator : bool or str, default False
+    If True, adds a column to the output DataFrame called "_merge" with
+    information on the source of each row. The column can be given a different
+    name by providing a string argument. The column will have a Categorical
+    type with the value of "left_only" for observations whose merge key only
+    appears in the left DataFrame, "right_only" for observations
+    whose merge key only appears in the right DataFrame, and "both"
+    if the observation's merge key is found in both DataFrames.
+
+validate : str, optional
+    If specified, checks if merge is of specified type.
+
+    * "one_to_one" or "1:1": check if merge keys are unique in both
+      left and right datasets.
+    * "one_to_many" or "1:m": check if merge keys are unique in left
+      dataset.
+    * "many_to_one" or "m:1": check if merge keys are unique in right
+      dataset.
+    * "many_to_many" or "m:m": allowed, but does not result in checks.
+
+Returns
+-------
+DataFrame
+    A DataFrame of the two merged objects.
+
+See Also
+--------
+merge_ordered : Merge with optional filling/interpolation.
+merge_asof : Merge on nearest keys.
+DataFrame.join : Similar method using indices.
+
+Examples
+--------
+>>> df1 = pd.DataFrame({'lkey': ['foo', 'bar', 'baz', 'foo'],
+...                     'value': [1, 2, 3, 5]})
+>>> df2 = pd.DataFrame({'rkey': ['foo', 'bar', 'baz', 'foo'],
+...                     'value': [5, 6, 7, 8]})
+>>> df1
+    lkey value
+0   foo      1
+1   bar      2
+2   baz      3
+3   foo      5
+>>> df2
+    rkey value
+0   foo      5
+1   bar      6
+2   baz      7
+3   foo      8
+
+Merge df1 and df2 on the lkey and rkey columns. The value columns have
+the default suffixes, _x and _y, appended.
+
+>>> df1.merge(df2, left_on='lkey', right_on='rkey')
+  lkey  value_x rkey  value_y
+0  foo        1  foo        5
+1  foo        1  foo        8
+2  bar        2  bar        6
+3  baz        3  baz        7
+4  foo        5  foo        5
+5  foo        5  foo        8
+
+Merge DataFrames df1 and df2 with specified left and right suffixes
+appended to any overlapping columns.
+
+>>> df1.merge(df2, left_on='lkey', right_on='rkey',
+...           suffixes=('_left', '_right'))
+  lkey  value_left rkey  value_right
+0  foo           1  foo            5
+1  foo           1  foo            8
+2  bar           2  bar            6
+3  baz           3  baz            7
+4  foo           5  foo            5
+5  foo           5  foo            8
+
+Merge DataFrames df1 and df2, but raise an exception if the DataFrames have
+any overlapping columns.
+
+>>> df1.merge(df2, left_on='lkey', right_on='rkey', suffixes=(False, False))
+Traceback (most recent call last):
+...
+ValueError: columns overlap but no suffix specified:
+    Index(['value'], dtype='object')
+
+>>> df1 = pd.DataFrame({'a': ['foo', 'bar'], 'b': [1, 2]})
+>>> df2 = pd.DataFrame({'a': ['foo', 'baz'], 'c': [3, 4]})
+>>> df1
+      a  b
+0   foo  1
+1   bar  2
+>>> df2
+      a  c
+0   foo  3
+1   baz  4
+
+>>> df1.merge(df2, how='inner', on='a')
+      a  b  c
+0   foo  1  3
+
+>>> df1.merge(df2, how='left', on='a')
+      a  b  c
+0   foo  1  3.0
+1   bar  2  NaN
+
+>>> df1 = pd.DataFrame({'left': ['foo', 'bar']})
+>>> df2 = pd.DataFrame({'right': [7, 8]})
+>>> df1
+    left
+0   foo
+1   bar
+>>> df2
+    right
+0   7
+1   8
+
+>>> df1.merge(df2, how='cross')
+   left  right
+0   foo      7
+1   foo      8
+2   bar      7
+3   bar      8
+"""
+
+
+# -----------------------------------------------------------------------
+# DataFrame class
+
+
+class DataFrame(NDFrame, OpsMixin):
+    """
+    Two-dimensional, size-mutable, potentially heterogeneous tabular data.
+
+    Data structure also contains labeled axes (rows and columns).
+    Arithmetic operations align on both row and column labels. Can be
+    thought of as a dict-like container for Series objects. The primary
+    pandas data structure.
+
+    Parameters
+    ----------
+    data : ndarray (structured or homogeneous), Iterable, dict, or DataFrame
+        Dict can contain Series, arrays, constants, dataclass or list-like objects. If
+        data is a dict, column order follows insertion-order. If a dict contains Series
+        which have an index defined, it is aligned by its index. This alignment also
+        occurs if data is a Series or a DataFrame itself. Alignment is done on
+        Series/DataFrame inputs.
+
+        If data is a list of dicts, column order follows insertion-order.
+
+    index : Index or array-like
+        Index to use for resulting frame. Will default to RangeIndex if
+        no indexing information part of input data and no index provided.
+    columns : Index or array-like
+        Column labels to use for resulting frame when data does not have them,
+        defaulting to RangeIndex(0, 1, 2, ..., n). If data contains column labels,
+        will perform column selection instead.
+    dtype : dtype, default None
+        Data type to force. Only a single dtype is allowed. If None, infer.
+    copy : bool or None, default None
+        Copy data from inputs.
+        For dict data, the default of None behaves like ``copy=True``.  For DataFrame
+        or 2d ndarray input, the default of None behaves like ``copy=False``.
+        If data is a dict containing one or more Series (possibly of different dtypes),
+        ``copy=False`` will ensure that these inputs are not copied.
+
+        .. versionchanged:: 1.3.0
+
+    See Also
+    --------
+    DataFrame.from_records : Constructor from tuples, also record arrays.
+    DataFrame.from_dict : From dicts of Series, arrays, or dicts.
+    read_csv : Read a comma-separated values (csv) file into DataFrame.
+    read_table : Read general delimited file into DataFrame.
+    read_clipboard : Read text from clipboard into DataFrame.
+
+    Notes
+    -----
+    Please reference the :ref:`User Guide ` for more information.
+
+    Examples
+    --------
+    Constructing DataFrame from a dictionary.
+
+    >>> d = {'col1': [1, 2], 'col2': [3, 4]}
+    >>> df = pd.DataFrame(data=d)
+    >>> df
+       col1  col2
+    0     1     3
+    1     2     4
+
+    Notice that the inferred dtype is int64.
+
+    >>> df.dtypes
+    col1    int64
+    col2    int64
+    dtype: object
+
+    To enforce a single dtype:
+
+    >>> df = pd.DataFrame(data=d, dtype=np.int8)
+    >>> df.dtypes
+    col1    int8
+    col2    int8
+    dtype: object
+
+    Constructing DataFrame from a dictionary including Series:
+
+    >>> d = {'col1': [0, 1, 2, 3], 'col2': pd.Series([2, 3], index=[2, 3])}
+    >>> pd.DataFrame(data=d, index=[0, 1, 2, 3])
+       col1  col2
+    0     0   NaN
+    1     1   NaN
+    2     2   2.0
+    3     3   3.0
+
+    Constructing DataFrame from numpy ndarray:
+
+    >>> df2 = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]),
+    ...                    columns=['a', 'b', 'c'])
+    >>> df2
+       a  b  c
+    0  1  2  3
+    1  4  5  6
+    2  7  8  9
+
+    Constructing DataFrame from a numpy ndarray that has labeled columns:
+
+    >>> data = np.array([(1, 2, 3), (4, 5, 6), (7, 8, 9)],
+    ...                 dtype=[("a", "i4"), ("b", "i4"), ("c", "i4")])
+    >>> df3 = pd.DataFrame(data, columns=['c', 'a'])
+    ...
+    >>> df3
+       c  a
+    0  3  1
+    1  6  4
+    2  9  7
+
+    Constructing DataFrame from dataclass:
+
+    >>> from dataclasses import make_dataclass
+    >>> Point = make_dataclass("Point", [("x", int), ("y", int)])
+    >>> pd.DataFrame([Point(0, 0), Point(0, 3), Point(2, 3)])
+       x  y
+    0  0  0
+    1  0  3
+    2  2  3
+
+    Constructing DataFrame from Series/DataFrame:
+
+    >>> ser = pd.Series([1, 2, 3], index=["a", "b", "c"])
+    >>> df = pd.DataFrame(data=ser, index=["a", "c"])
+    >>> df
+       0
+    a  1
+    c  3
+
+    >>> df1 = pd.DataFrame([1, 2, 3], index=["a", "b", "c"], columns=["x"])
+    >>> df2 = pd.DataFrame(data=df1, index=["a", "c"])
+    >>> df2
+       x
+    a  1
+    c  3
+    """
+
+    _internal_names_set = {"columns", "index"} | NDFrame._internal_names_set
+    _typ = "dataframe"
+    _HANDLED_TYPES = (Series, Index, ExtensionArray, np.ndarray)
+    _accessors: set[str] = {"sparse"}
+    _hidden_attrs: frozenset[str] = NDFrame._hidden_attrs | frozenset([])
+    _mgr: BlockManager | ArrayManager
+
+    # similar to __array_priority__, positions DataFrame before Series, Index,
+    #  and ExtensionArray.  Should NOT be overridden by subclasses.
+    __pandas_priority__ = 4000
+
+    @property
+    def _constructor(self) -> Callable[..., DataFrame]:
+        return DataFrame
+
+    def _constructor_from_mgr(self, mgr, axes) -> DataFrame:
+        df = DataFrame._from_mgr(mgr, axes=axes)
+
+        if type(self) is DataFrame:
+            # This would also work `if self._constructor is DataFrame`, but
+            #  this check is slightly faster, benefiting the most-common case.
+            return df
+
+        elif type(self).__name__ == "GeoDataFrame":
+            # Shim until geopandas can override their _constructor_from_mgr
+            #  bc they have different behavior for Managers than for DataFrames
+            return self._constructor(mgr)
+
+        # We assume that the subclass __init__ knows how to handle a
+        #  pd.DataFrame object.
+        return self._constructor(df)
+
+    _constructor_sliced: Callable[..., Series] = Series
+
+    def _constructor_sliced_from_mgr(self, mgr, axes) -> Series:
+        ser = Series._from_mgr(mgr, axes)
+        ser._name = None  # caller is responsible for setting real name
+
+        if type(self) is DataFrame:
+            # This would also work `if self._constructor_sliced is Series`, but
+            #  this check is slightly faster, benefiting the most-common case.
+            return ser
+
+        # We assume that the subclass __init__ knows how to handle a
+        #  pd.Series object.
+        return self._constructor_sliced(ser)
+
+    # ----------------------------------------------------------------------
+    # Constructors
+
+    def __init__(
+        self,
+        data=None,
+        index: Axes | None = None,
+        columns: Axes | None = None,
+        dtype: Dtype | None = None,
+        copy: bool | None = None,
+    ) -> None:
+        allow_mgr = False
+        if dtype is not None:
+            dtype = self._validate_dtype(dtype)
+
+        if isinstance(data, DataFrame):
+            data = data._mgr
+            allow_mgr = True
+            if not copy:
+                # if not copying data, ensure to still return a shallow copy
+                # to avoid the result sharing the same Manager
+                data = data.copy(deep=False)
+
+        if isinstance(data, (BlockManager, ArrayManager)):
+            if not allow_mgr:
+                # GH#52419
+                warnings.warn(
+                    f"Passing a {type(data).__name__} to {type(self).__name__} "
+                    "is deprecated and will raise in a future version. "
+                    "Use public APIs instead.",
+                    DeprecationWarning,
+                    stacklevel=1,  # bump to 2 once pyarrow 15.0 is released with fix
+                )
+
+            if using_copy_on_write():
+                data = data.copy(deep=False)
+            # first check if a Manager is passed without any other arguments
+            # -> use fastpath (without checking Manager type)
+            if index is None and columns is None and dtype is None and not copy:
+                # GH#33357 fastpath
+                NDFrame.__init__(self, data)
+                return
+
+        manager = _get_option("mode.data_manager", silent=True)
+
+        is_pandas_object = isinstance(data, (Series, Index, ExtensionArray))
+        data_dtype = getattr(data, "dtype", None)
+        original_dtype = dtype
+
+        # GH47215
+        if isinstance(index, set):
+            raise ValueError("index cannot be a set")
+        if isinstance(columns, set):
+            raise ValueError("columns cannot be a set")
+
+        if copy is None:
+            if isinstance(data, dict):
+                # retain pre-GH#38939 default behavior
+                copy = True
+            elif (
+                manager == "array"
+                and isinstance(data, (np.ndarray, ExtensionArray))
+                and data.ndim == 2
+            ):
+                # INFO(ArrayManager) by default copy the 2D input array to get
+                # contiguous 1D arrays
+                copy = True
+            elif using_copy_on_write() and not isinstance(
+                data, (Index, DataFrame, Series)
+            ):
+                copy = True
+            else:
+                copy = False
+
+        if data is None:
+            index = index if index is not None else default_index(0)
+            columns = columns if columns is not None else default_index(0)
+            dtype = dtype if dtype is not None else pandas_dtype(object)
+            data = []
+
+        if isinstance(data, (BlockManager, ArrayManager)):
+            mgr = self._init_mgr(
+                data, axes={"index": index, "columns": columns}, dtype=dtype, copy=copy
+            )
+
+        elif isinstance(data, dict):
+            # GH#38939 de facto copy defaults to False only in non-dict cases
+            mgr = dict_to_mgr(data, index, columns, dtype=dtype, copy=copy, typ=manager)
+        elif isinstance(data, ma.MaskedArray):
+            from numpy.ma import mrecords
+
+            # masked recarray
+            if isinstance(data, mrecords.MaskedRecords):
+                raise TypeError(
+                    "MaskedRecords are not supported. Pass "
+                    "{name: data[name] for name in data.dtype.names} "
+                    "instead"
+                )
+
+            # a masked array
+            data = sanitize_masked_array(data)
+            mgr = ndarray_to_mgr(
+                data,
+                index,
+                columns,
+                dtype=dtype,
+                copy=copy,
+                typ=manager,
+            )
+
+        elif isinstance(data, (np.ndarray, Series, Index, ExtensionArray)):
+            if data.dtype.names:
+                # i.e. numpy structured array
+                data = cast(np.ndarray, data)
+                mgr = rec_array_to_mgr(
+                    data,
+                    index,
+                    columns,
+                    dtype,
+                    copy,
+                    typ=manager,
+                )
+            elif getattr(data, "name", None) is not None:
+                # i.e. Series/Index with non-None name
+                _copy = copy if using_copy_on_write() else True
+                mgr = dict_to_mgr(
+                    # error: Item "ndarray" of "Union[ndarray, Series, Index]" has no
+                    # attribute "name"
+                    {data.name: data},  # type: ignore[union-attr]
+                    index,
+                    columns,
+                    dtype=dtype,
+                    typ=manager,
+                    copy=_copy,
+                )
+            else:
+                mgr = ndarray_to_mgr(
+                    data,
+                    index,
+                    columns,
+                    dtype=dtype,
+                    copy=copy,
+                    typ=manager,
+                )
+
+        # For data is list-like, or Iterable (will consume into list)
+        elif is_list_like(data):
+            if not isinstance(data, abc.Sequence):
+                if hasattr(data, "__array__"):
+                    # GH#44616 big perf improvement for e.g. pytorch tensor
+                    data = np.asarray(data)
+                else:
+                    data = list(data)
+            if len(data) > 0:
+                if is_dataclass(data[0]):
+                    data = dataclasses_to_dicts(data)
+                if not isinstance(data, np.ndarray) and treat_as_nested(data):
+                    # exclude ndarray as we may have cast it a few lines above
+                    if columns is not None:
+                        columns = ensure_index(columns)
+                    arrays, columns, index = nested_data_to_arrays(
+                        # error: Argument 3 to "nested_data_to_arrays" has incompatible
+                        # type "Optional[Collection[Any]]"; expected "Optional[Index]"
+                        data,
+                        columns,
+                        index,  # type: ignore[arg-type]
+                        dtype,
+                    )
+                    mgr = arrays_to_mgr(
+                        arrays,
+                        columns,
+                        index,
+                        dtype=dtype,
+                        typ=manager,
+                    )
+                else:
+                    mgr = ndarray_to_mgr(
+                        data,
+                        index,
+                        columns,
+                        dtype=dtype,
+                        copy=copy,
+                        typ=manager,
+                    )
+            else:
+                mgr = dict_to_mgr(
+                    {},
+                    index,
+                    columns if columns is not None else default_index(0),
+                    dtype=dtype,
+                    typ=manager,
+                )
+        # For data is scalar
+        else:
+            if index is None or columns is None:
+                raise ValueError("DataFrame constructor not properly called!")
+
+            index = ensure_index(index)
+            columns = ensure_index(columns)
+
+            if not dtype:
+                dtype, _ = infer_dtype_from_scalar(data)
+
+            # For data is a scalar extension dtype
+            if isinstance(dtype, ExtensionDtype):
+                # TODO(EA2D): special case not needed with 2D EAs
+
+                values = [
+                    construct_1d_arraylike_from_scalar(data, len(index), dtype)
+                    for _ in range(len(columns))
+                ]
+                mgr = arrays_to_mgr(values, columns, index, dtype=None, typ=manager)
+            else:
+                arr2d = construct_2d_arraylike_from_scalar(
+                    data,
+                    len(index),
+                    len(columns),
+                    dtype,
+                    copy,
+                )
+
+                mgr = ndarray_to_mgr(
+                    arr2d,
+                    index,
+                    columns,
+                    dtype=arr2d.dtype,
+                    copy=False,
+                    typ=manager,
+                )
+
+        # ensure correct Manager type according to settings
+        mgr = mgr_to_mgr(mgr, typ=manager)
+
+        NDFrame.__init__(self, mgr)
+
+        if original_dtype is None and is_pandas_object and data_dtype == np.object_:
+            if self.dtypes.iloc[0] != data_dtype:
+                warnings.warn(
+                    "Dtype inference on a pandas object "
+                    "(Series, Index, ExtensionArray) is deprecated. The DataFrame "
+                    "constructor will keep the original dtype in the future. "
+                    "Call `infer_objects` on the result to get the old "
+                    "behavior.",
+                    FutureWarning,
+                    stacklevel=2,
+                )
+
+    # ----------------------------------------------------------------------
+
+    def __dataframe__(
+        self, nan_as_null: bool = False, allow_copy: bool = True
+    ) -> DataFrameXchg:
+        """
+        Return the dataframe interchange object implementing the interchange protocol.
+
+        Parameters
+        ----------
+        nan_as_null : bool, default False
+            `nan_as_null` is DEPRECATED and has no effect. Please avoid using
+            it; it will be removed in a future release.
+        allow_copy : bool, default True
+            Whether to allow memory copying when exporting. If set to False
+            it would cause non-zero-copy exports to fail.
+
+        Returns
+        -------
+        DataFrame interchange object
+            The object which consuming library can use to ingress the dataframe.
+
+        Notes
+        -----
+        Details on the interchange protocol:
+        https://data-apis.org/dataframe-protocol/latest/index.html
+
+        Examples
+        --------
+        >>> df_not_necessarily_pandas = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
+        >>> interchange_object = df_not_necessarily_pandas.__dataframe__()
+        >>> interchange_object.column_names()
+        Index(['A', 'B'], dtype='object')
+        >>> df_pandas = (pd.api.interchange.from_dataframe
+        ...              (interchange_object.select_columns_by_name(['A'])))
+        >>> df_pandas
+             A
+        0    1
+        1    2
+
+        These methods (``column_names``, ``select_columns_by_name``) should work
+        for any dataframe library which implements the interchange protocol.
+        """
+
+        from pandas.core.interchange.dataframe import PandasDataFrameXchg
+
+        return PandasDataFrameXchg(self, allow_copy=allow_copy)
+
+    def __dataframe_consortium_standard__(
+        self, *, api_version: str | None = None
+    ) -> Any:
+        """
+        Provide entry point to the Consortium DataFrame Standard API.
+
+        This is developed and maintained outside of pandas.
+        Please report any issues to https://github.com/data-apis/dataframe-api-compat.
+        """
+        dataframe_api_compat = import_optional_dependency("dataframe_api_compat")
+        convert_to_standard_compliant_dataframe = (
+            dataframe_api_compat.pandas_standard.convert_to_standard_compliant_dataframe
+        )
+        return convert_to_standard_compliant_dataframe(self, api_version=api_version)
+
+    def __arrow_c_stream__(self, requested_schema=None):
+        """
+        Export the pandas DataFrame as an Arrow C stream PyCapsule.
+
+        This relies on pyarrow to convert the pandas DataFrame to the Arrow
+        format (and follows the default behaviour of ``pyarrow.Table.from_pandas``
+        in its handling of the index, i.e. store the index as a column except
+        for RangeIndex).
+        This conversion is not necessarily zero-copy.
+
+        Parameters
+        ----------
+        requested_schema : PyCapsule, default None
+            The schema to which the dataframe should be casted, passed as a
+            PyCapsule containing a C ArrowSchema representation of the
+            requested schema.
+
+        Returns
+        -------
+        PyCapsule
+        """
+        pa = import_optional_dependency("pyarrow", min_version="14.0.0")
+        if requested_schema is not None:
+            requested_schema = pa.Schema._import_from_c_capsule(requested_schema)
+        table = pa.Table.from_pandas(self, schema=requested_schema)
+        return table.__arrow_c_stream__()
+
+    # ----------------------------------------------------------------------
+
+    @property
+    def axes(self) -> list[Index]:
+        """
+        Return a list representing the axes of the DataFrame.
+
+        It has the row axis labels and column axis labels as the only members.
+        They are returned in that order.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
+        >>> df.axes
+        [RangeIndex(start=0, stop=2, step=1), Index(['col1', 'col2'],
+        dtype='object')]
+        """
+        return [self.index, self.columns]
+
+    @property
+    def shape(self) -> tuple[int, int]:
+        """
+        Return a tuple representing the dimensionality of the DataFrame.
+
+        See Also
+        --------
+        ndarray.shape : Tuple of array dimensions.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
+        >>> df.shape
+        (2, 2)
+
+        >>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4],
+        ...                    'col3': [5, 6]})
+        >>> df.shape
+        (2, 3)
+        """
+        return len(self.index), len(self.columns)
+
+    @property
+    def _is_homogeneous_type(self) -> bool:
+        """
+        Whether all the columns in a DataFrame have the same type.
+
+        Returns
+        -------
+        bool
+
+        Examples
+        --------
+        >>> DataFrame({"A": [1, 2], "B": [3, 4]})._is_homogeneous_type
+        True
+        >>> DataFrame({"A": [1, 2], "B": [3.0, 4.0]})._is_homogeneous_type
+        False
+
+        Items with the same type but different sizes are considered
+        different types.
+
+        >>> DataFrame({
+        ...    "A": np.array([1, 2], dtype=np.int32),
+        ...    "B": np.array([1, 2], dtype=np.int64)})._is_homogeneous_type
+        False
+        """
+        # The "<" part of "<=" here is for empty DataFrame cases
+        return len({arr.dtype for arr in self._mgr.arrays}) <= 1
+
+    @property
+    def _can_fast_transpose(self) -> bool:
+        """
+        Can we transpose this DataFrame without creating any new array objects.
+        """
+        if isinstance(self._mgr, ArrayManager):
+            return False
+        blocks = self._mgr.blocks
+        if len(blocks) != 1:
+            return False
+
+        dtype = blocks[0].dtype
+        # TODO(EA2D) special case would be unnecessary with 2D EAs
+        return not is_1d_only_ea_dtype(dtype)
+
+    @property
+    def _values(self) -> np.ndarray | DatetimeArray | TimedeltaArray | PeriodArray:
+        """
+        Analogue to ._values that may return a 2D ExtensionArray.
+        """
+        mgr = self._mgr
+
+        if isinstance(mgr, ArrayManager):
+            if len(mgr.arrays) == 1 and not is_1d_only_ea_dtype(mgr.arrays[0].dtype):
+                # error: Item "ExtensionArray" of "Union[ndarray, ExtensionArray]"
+                # has no attribute "reshape"
+                return mgr.arrays[0].reshape(-1, 1)  # type: ignore[union-attr]
+            return ensure_wrapped_if_datetimelike(self.values)
+
+        blocks = mgr.blocks
+        if len(blocks) != 1:
+            return ensure_wrapped_if_datetimelike(self.values)
+
+        arr = blocks[0].values
+        if arr.ndim == 1:
+            # non-2D ExtensionArray
+            return self.values
+
+        # more generally, whatever we allow in NDArrayBackedExtensionBlock
+        arr = cast("np.ndarray | DatetimeArray | TimedeltaArray | PeriodArray", arr)
+        return arr.T
+
+    # ----------------------------------------------------------------------
+    # Rendering Methods
+
+    def _repr_fits_vertical_(self) -> bool:
+        """
+        Check length against max_rows.
+        """
+        max_rows = get_option("display.max_rows")
+        return len(self) <= max_rows
+
+    def _repr_fits_horizontal_(self) -> bool:
+        """
+        Check if full repr fits in horizontal boundaries imposed by the display
+        options width and max_columns.
+        """
+        width, height = console.get_console_size()
+        max_columns = get_option("display.max_columns")
+        nb_columns = len(self.columns)
+
+        # exceed max columns
+        if (max_columns and nb_columns > max_columns) or (
+            width and nb_columns > (width // 2)
+        ):
+            return False
+
+        # used by repr_html under IPython notebook or scripts ignore terminal
+        # dims
+        if width is None or not console.in_interactive_session():
+            return True
+
+        if get_option("display.width") is not None or console.in_ipython_frontend():
+            # check at least the column row for excessive width
+            max_rows = 1
+        else:
+            max_rows = get_option("display.max_rows")
+
+        # when auto-detecting, so width=None and not in ipython front end
+        # check whether repr fits horizontal by actually checking
+        # the width of the rendered repr
+        buf = StringIO()
+
+        # only care about the stuff we'll actually print out
+        # and to_string on entire frame may be expensive
+        d = self
+
+        if max_rows is not None:  # unlimited rows
+            # min of two, where one may be None
+            d = d.iloc[: min(max_rows, len(d))]
+        else:
+            return True
+
+        d.to_string(buf=buf)
+        value = buf.getvalue()
+        repr_width = max(len(line) for line in value.split("\n"))
+
+        return repr_width < width
+
+    def _info_repr(self) -> bool:
+        """
+        True if the repr should show the info view.
+        """
+        info_repr_option = get_option("display.large_repr") == "info"
+        return info_repr_option and not (
+            self._repr_fits_horizontal_() and self._repr_fits_vertical_()
+        )
+
+    def __repr__(self) -> str:
+        """
+        Return a string representation for a particular DataFrame.
+        """
+        if self._info_repr():
+            buf = StringIO()
+            self.info(buf=buf)
+            return buf.getvalue()
+
+        repr_params = fmt.get_dataframe_repr_params()
+        return self.to_string(**repr_params)
+
+    def _repr_html_(self) -> str | None:
+        """
+        Return a html representation for a particular DataFrame.
+
+        Mainly for IPython notebook.
+        """
+        if self._info_repr():
+            buf = StringIO()
+            self.info(buf=buf)
+            # need to escape the , should be the first line.
+            val = buf.getvalue().replace("<", r"<", 1)
+            val = val.replace(">", r">", 1)
+            return f"
{val}
"
+
+        if get_option("display.notebook_repr_html"):
+            max_rows = get_option("display.max_rows")
+            min_rows = get_option("display.min_rows")
+            max_cols = get_option("display.max_columns")
+            show_dimensions = get_option("display.show_dimensions")
+
+            formatter = fmt.DataFrameFormatter(
+                self,
+                columns=None,
+                col_space=None,
+                na_rep="NaN",
+                formatters=None,
+                float_format=None,
+                sparsify=None,
+                justify=None,
+                index_names=True,
+                header=True,
+                index=True,
+                bold_rows=True,
+                escape=True,
+                max_rows=max_rows,
+                min_rows=min_rows,
+                max_cols=max_cols,
+                show_dimensions=show_dimensions,
+                decimal=".",
+            )
+            return fmt.DataFrameRenderer(formatter).to_html(notebook=True)
+        else:
+            return None
+
+    @overload
+    def to_string(
+        self,
+        buf: None = ...,
+        columns: Axes | None = ...,
+        col_space: int | list[int] | dict[Hashable, int] | None = ...,
+        header: bool | SequenceNotStr[str] = ...,
+        index: bool = ...,
+        na_rep: str = ...,
+        formatters: fmt.FormattersType | None = ...,
+        float_format: fmt.FloatFormatType | None = ...,
+        sparsify: bool | None = ...,
+        index_names: bool = ...,
+        justify: str | None = ...,
+        max_rows: int | None = ...,
+        max_cols: int | None = ...,
+        show_dimensions: bool = ...,
+        decimal: str = ...,
+        line_width: int | None = ...,
+        min_rows: int | None = ...,
+        max_colwidth: int | None = ...,
+        encoding: str | None = ...,
+    ) -> str:
+        ...
+
+    @overload
+    def to_string(
+        self,
+        buf: FilePath | WriteBuffer[str],
+        columns: Axes | None = ...,
+        col_space: int | list[int] | dict[Hashable, int] | None = ...,
+        header: bool | SequenceNotStr[str] = ...,
+        index: bool = ...,
+        na_rep: str = ...,
+        formatters: fmt.FormattersType | None = ...,
+        float_format: fmt.FloatFormatType | None = ...,
+        sparsify: bool | None = ...,
+        index_names: bool = ...,
+        justify: str | None = ...,
+        max_rows: int | None = ...,
+        max_cols: int | None = ...,
+        show_dimensions: bool = ...,
+        decimal: str = ...,
+        line_width: int | None = ...,
+        min_rows: int | None = ...,
+        max_colwidth: int | None = ...,
+        encoding: str | None = ...,
+    ) -> None:
+        ...
+
+    @deprecate_nonkeyword_arguments(
+        version="3.0", allowed_args=["self", "buf"], name="to_string"
+    )
+    @Substitution(
+        header_type="bool or list of str",
+        header="Write out the column names. If a list of columns "
+        "is given, it is assumed to be aliases for the "
+        "column names",
+        col_space_type="int, list or dict of int",
+        col_space="The minimum width of each column. If a list of ints is given "
+        "every integers corresponds with one column. If a dict is given, the key "
+        "references the column, while the value defines the space to use.",
+    )
+    @Substitution(shared_params=fmt.common_docstring, returns=fmt.return_docstring)
+    def to_string(
+        self,
+        buf: FilePath | WriteBuffer[str] | None = None,
+        columns: Axes | None = None,
+        col_space: int | list[int] | dict[Hashable, int] | None = None,
+        header: bool | SequenceNotStr[str] = True,
+        index: bool = True,
+        na_rep: str = "NaN",
+        formatters: fmt.FormattersType | None = None,
+        float_format: fmt.FloatFormatType | None = None,
+        sparsify: bool | None = None,
+        index_names: bool = True,
+        justify: str | None = None,
+        max_rows: int | None = None,
+        max_cols: int | None = None,
+        show_dimensions: bool = False,
+        decimal: str = ".",
+        line_width: int | None = None,
+        min_rows: int | None = None,
+        max_colwidth: int | None = None,
+        encoding: str | None = None,
+    ) -> str | None:
+        """
+        Render a DataFrame to a console-friendly tabular output.
+        %(shared_params)s
+        line_width : int, optional
+            Width to wrap a line in characters.
+        min_rows : int, optional
+            The number of rows to display in the console in a truncated repr
+            (when number of rows is above `max_rows`).
+        max_colwidth : int, optional
+            Max width to truncate each column in characters. By default, no limit.
+        encoding : str, default "utf-8"
+            Set character encoding.
+        %(returns)s
+        See Also
+        --------
+        to_html : Convert DataFrame to HTML.
+
+        Examples
+        --------
+        >>> d = {'col1': [1, 2, 3], 'col2': [4, 5, 6]}
+        >>> df = pd.DataFrame(d)
+        >>> print(df.to_string())
+           col1  col2
+        0     1     4
+        1     2     5
+        2     3     6
+        """
+        from pandas import option_context
+
+        with option_context("display.max_colwidth", max_colwidth):
+            formatter = fmt.DataFrameFormatter(
+                self,
+                columns=columns,
+                col_space=col_space,
+                na_rep=na_rep,
+                formatters=formatters,
+                float_format=float_format,
+                sparsify=sparsify,
+                justify=justify,
+                index_names=index_names,
+                header=header,
+                index=index,
+                min_rows=min_rows,
+                max_rows=max_rows,
+                max_cols=max_cols,
+                show_dimensions=show_dimensions,
+                decimal=decimal,
+            )
+            return fmt.DataFrameRenderer(formatter).to_string(
+                buf=buf,
+                encoding=encoding,
+                line_width=line_width,
+            )
+
+    def _get_values_for_csv(
+        self,
+        *,
+        float_format: FloatFormatType | None,
+        date_format: str | None,
+        decimal: str,
+        na_rep: str,
+        quoting,  # int csv.QUOTE_FOO from stdlib
+    ) -> Self:
+        # helper used by to_csv
+        mgr = self._mgr.get_values_for_csv(
+            float_format=float_format,
+            date_format=date_format,
+            decimal=decimal,
+            na_rep=na_rep,
+            quoting=quoting,
+        )
+        # error: Incompatible return value type (got "DataFrame", expected "Self")
+        return self._constructor_from_mgr(mgr, axes=mgr.axes)  # type: ignore[return-value]
+
+    # ----------------------------------------------------------------------
+
+    @property
+    def style(self) -> Styler:
+        """
+        Returns a Styler object.
+
+        Contains methods for building a styled HTML representation of the DataFrame.
+
+        See Also
+        --------
+        io.formats.style.Styler : Helps style a DataFrame or Series according to the
+            data with HTML and CSS.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'A': [1, 2, 3]})
+        >>> df.style  # doctest: +SKIP
+
+        Please see
+        `Table Visualization <../../user_guide/style.ipynb>`_ for more examples.
+        """
+        # Raise AttributeError so that inspect works even if jinja2 is not installed.
+        has_jinja2 = import_optional_dependency("jinja2", errors="ignore")
+        if not has_jinja2:
+            raise AttributeError("The '.style' accessor requires jinja2")
+
+        from pandas.io.formats.style import Styler
+
+        return Styler(self)
+
+    _shared_docs[
+        "items"
+    ] = r"""
+        Iterate over (column name, Series) pairs.
+
+        Iterates over the DataFrame columns, returning a tuple with
+        the column name and the content as a Series.
+
+        Yields
+        ------
+        label : object
+            The column names for the DataFrame being iterated over.
+        content : Series
+            The column entries belonging to each label, as a Series.
+
+        See Also
+        --------
+        DataFrame.iterrows : Iterate over DataFrame rows as
+            (index, Series) pairs.
+        DataFrame.itertuples : Iterate over DataFrame rows as namedtuples
+            of the values.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'species': ['bear', 'bear', 'marsupial'],
+        ...                   'population': [1864, 22000, 80000]},
+        ...                   index=['panda', 'polar', 'koala'])
+        >>> df
+                species   population
+        panda   bear      1864
+        polar   bear      22000
+        koala   marsupial 80000
+        >>> for label, content in df.items():
+        ...     print(f'label: {label}')
+        ...     print(f'content: {content}', sep='\n')
+        ...
+        label: species
+        content:
+        panda         bear
+        polar         bear
+        koala    marsupial
+        Name: species, dtype: object
+        label: population
+        content:
+        panda     1864
+        polar    22000
+        koala    80000
+        Name: population, dtype: int64
+        """
+
+    @Appender(_shared_docs["items"])
+    def items(self) -> Iterable[tuple[Hashable, Series]]:
+        if self.columns.is_unique and hasattr(self, "_item_cache"):
+            for k in self.columns:
+                yield k, self._get_item_cache(k)
+        else:
+            for i, k in enumerate(self.columns):
+                yield k, self._ixs(i, axis=1)
+
+    def iterrows(self) -> Iterable[tuple[Hashable, Series]]:
+        """
+        Iterate over DataFrame rows as (index, Series) pairs.
+
+        Yields
+        ------
+        index : label or tuple of label
+            The index of the row. A tuple for a `MultiIndex`.
+        data : Series
+            The data of the row as a Series.
+
+        See Also
+        --------
+        DataFrame.itertuples : Iterate over DataFrame rows as namedtuples of the values.
+        DataFrame.items : Iterate over (column name, Series) pairs.
+
+        Notes
+        -----
+        1. Because ``iterrows`` returns a Series for each row,
+           it does **not** preserve dtypes across the rows (dtypes are
+           preserved across columns for DataFrames).
+
+           To preserve dtypes while iterating over the rows, it is better
+           to use :meth:`itertuples` which returns namedtuples of the values
+           and which is generally faster than ``iterrows``.
+
+        2. You should **never modify** something you are iterating over.
+           This is not guaranteed to work in all cases. Depending on the
+           data types, the iterator returns a copy and not a view, and writing
+           to it will have no effect.
+
+        Examples
+        --------
+
+        >>> df = pd.DataFrame([[1, 1.5]], columns=['int', 'float'])
+        >>> row = next(df.iterrows())[1]
+        >>> row
+        int      1.0
+        float    1.5
+        Name: 0, dtype: float64
+        >>> print(row['int'].dtype)
+        float64
+        >>> print(df['int'].dtype)
+        int64
+        """
+        columns = self.columns
+        klass = self._constructor_sliced
+        using_cow = using_copy_on_write()
+        for k, v in zip(self.index, self.values):
+            s = klass(v, index=columns, name=k).__finalize__(self)
+            if using_cow and self._mgr.is_single_block:
+                s._mgr.add_references(self._mgr)  # type: ignore[arg-type]
+            yield k, s
+
+    def itertuples(
+        self, index: bool = True, name: str | None = "Pandas"
+    ) -> Iterable[tuple[Any, ...]]:
+        """
+        Iterate over DataFrame rows as namedtuples.
+
+        Parameters
+        ----------
+        index : bool, default True
+            If True, return the index as the first element of the tuple.
+        name : str or None, default "Pandas"
+            The name of the returned namedtuples or None to return regular
+            tuples.
+
+        Returns
+        -------
+        iterator
+            An object to iterate over namedtuples for each row in the
+            DataFrame with the first field possibly being the index and
+            following fields being the column values.
+
+        See Also
+        --------
+        DataFrame.iterrows : Iterate over DataFrame rows as (index, Series)
+            pairs.
+        DataFrame.items : Iterate over (column name, Series) pairs.
+
+        Notes
+        -----
+        The column names will be renamed to positional names if they are
+        invalid Python identifiers, repeated, or start with an underscore.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'num_legs': [4, 2], 'num_wings': [0, 2]},
+        ...                   index=['dog', 'hawk'])
+        >>> df
+              num_legs  num_wings
+        dog          4          0
+        hawk         2          2
+        >>> for row in df.itertuples():
+        ...     print(row)
+        ...
+        Pandas(Index='dog', num_legs=4, num_wings=0)
+        Pandas(Index='hawk', num_legs=2, num_wings=2)
+
+        By setting the `index` parameter to False we can remove the index
+        as the first element of the tuple:
+
+        >>> for row in df.itertuples(index=False):
+        ...     print(row)
+        ...
+        Pandas(num_legs=4, num_wings=0)
+        Pandas(num_legs=2, num_wings=2)
+
+        With the `name` parameter set we set a custom name for the yielded
+        namedtuples:
+
+        >>> for row in df.itertuples(name='Animal'):
+        ...     print(row)
+        ...
+        Animal(Index='dog', num_legs=4, num_wings=0)
+        Animal(Index='hawk', num_legs=2, num_wings=2)
+        """
+        arrays = []
+        fields = list(self.columns)
+        if index:
+            arrays.append(self.index)
+            fields.insert(0, "Index")
+
+        # use integer indexing because of possible duplicate column names
+        arrays.extend(self.iloc[:, k] for k in range(len(self.columns)))
+
+        if name is not None:
+            # https://github.com/python/mypy/issues/9046
+            # error: namedtuple() expects a string literal as the first argument
+            itertuple = collections.namedtuple(  # type: ignore[misc]
+                name, fields, rename=True
+            )
+            return map(itertuple._make, zip(*arrays))
+
+        # fallback to regular tuples
+        return zip(*arrays)
+
+    def __len__(self) -> int:
+        """
+        Returns length of info axis, but here we use the index.
+        """
+        return len(self.index)
+
+    @overload
+    def dot(self, other: Series) -> Series:
+        ...
+
+    @overload
+    def dot(self, other: DataFrame | Index | ArrayLike) -> DataFrame:
+        ...
+
+    def dot(self, other: AnyArrayLike | DataFrame) -> DataFrame | Series:
+        """
+        Compute the matrix multiplication between the DataFrame and other.
+
+        This method computes the matrix product between the DataFrame and the
+        values of an other Series, DataFrame or a numpy array.
+
+        It can also be called using ``self @ other``.
+
+        Parameters
+        ----------
+        other : Series, DataFrame or array-like
+            The other object to compute the matrix product with.
+
+        Returns
+        -------
+        Series or DataFrame
+            If other is a Series, return the matrix product between self and
+            other as a Series. If other is a DataFrame or a numpy.array, return
+            the matrix product of self and other in a DataFrame of a np.array.
+
+        See Also
+        --------
+        Series.dot: Similar method for Series.
+
+        Notes
+        -----
+        The dimensions of DataFrame and other must be compatible in order to
+        compute the matrix multiplication. In addition, the column names of
+        DataFrame and the index of other must contain the same values, as they
+        will be aligned prior to the multiplication.
+
+        The dot method for Series computes the inner product, instead of the
+        matrix product here.
+
+        Examples
+        --------
+        Here we multiply a DataFrame with a Series.
+
+        >>> df = pd.DataFrame([[0, 1, -2, -1], [1, 1, 1, 1]])
+        >>> s = pd.Series([1, 1, 2, 1])
+        >>> df.dot(s)
+        0    -4
+        1     5
+        dtype: int64
+
+        Here we multiply a DataFrame with another DataFrame.
+
+        >>> other = pd.DataFrame([[0, 1], [1, 2], [-1, -1], [2, 0]])
+        >>> df.dot(other)
+            0   1
+        0   1   4
+        1   2   2
+
+        Note that the dot method give the same result as @
+
+        >>> df @ other
+            0   1
+        0   1   4
+        1   2   2
+
+        The dot method works also if other is an np.array.
+
+        >>> arr = np.array([[0, 1], [1, 2], [-1, -1], [2, 0]])
+        >>> df.dot(arr)
+            0   1
+        0   1   4
+        1   2   2
+
+        Note how shuffling of the objects does not change the result.
+
+        >>> s2 = s.reindex([1, 0, 2, 3])
+        >>> df.dot(s2)
+        0    -4
+        1     5
+        dtype: int64
+        """
+        if isinstance(other, (Series, DataFrame)):
+            common = self.columns.union(other.index)
+            if len(common) > len(self.columns) or len(common) > len(other.index):
+                raise ValueError("matrices are not aligned")
+
+            left = self.reindex(columns=common, copy=False)
+            right = other.reindex(index=common, copy=False)
+            lvals = left.values
+            rvals = right._values
+        else:
+            left = self
+            lvals = self.values
+            rvals = np.asarray(other)
+            if lvals.shape[1] != rvals.shape[0]:
+                raise ValueError(
+                    f"Dot product shape mismatch, {lvals.shape} vs {rvals.shape}"
+                )
+
+        if isinstance(other, DataFrame):
+            common_type = find_common_type(list(self.dtypes) + list(other.dtypes))
+            return self._constructor(
+                np.dot(lvals, rvals),
+                index=left.index,
+                columns=other.columns,
+                copy=False,
+                dtype=common_type,
+            )
+        elif isinstance(other, Series):
+            common_type = find_common_type(list(self.dtypes) + [other.dtypes])
+            return self._constructor_sliced(
+                np.dot(lvals, rvals), index=left.index, copy=False, dtype=common_type
+            )
+        elif isinstance(rvals, (np.ndarray, Index)):
+            result = np.dot(lvals, rvals)
+            if result.ndim == 2:
+                return self._constructor(result, index=left.index, copy=False)
+            else:
+                return self._constructor_sliced(result, index=left.index, copy=False)
+        else:  # pragma: no cover
+            raise TypeError(f"unsupported type: {type(other)}")
+
+    @overload
+    def __matmul__(self, other: Series) -> Series:
+        ...
+
+    @overload
+    def __matmul__(self, other: AnyArrayLike | DataFrame) -> DataFrame | Series:
+        ...
+
+    def __matmul__(self, other: AnyArrayLike | DataFrame) -> DataFrame | Series:
+        """
+        Matrix multiplication using binary `@` operator.
+        """
+        return self.dot(other)
+
+    def __rmatmul__(self, other) -> DataFrame:
+        """
+        Matrix multiplication using binary `@` operator.
+        """
+        try:
+            return self.T.dot(np.transpose(other)).T
+        except ValueError as err:
+            if "shape mismatch" not in str(err):
+                raise
+            # GH#21581 give exception message for original shapes
+            msg = f"shapes {np.shape(other)} and {self.shape} not aligned"
+            raise ValueError(msg) from err
+
+    # ----------------------------------------------------------------------
+    # IO methods (to / from other formats)
+
+    @classmethod
+    def from_dict(
+        cls,
+        data: dict,
+        orient: FromDictOrient = "columns",
+        dtype: Dtype | None = None,
+        columns: Axes | None = None,
+    ) -> DataFrame:
+        """
+        Construct DataFrame from dict of array-like or dicts.
+
+        Creates DataFrame object from dictionary by columns or by index
+        allowing dtype specification.
+
+        Parameters
+        ----------
+        data : dict
+            Of the form {field : array-like} or {field : dict}.
+        orient : {'columns', 'index', 'tight'}, default 'columns'
+            The "orientation" of the data. If the keys of the passed dict
+            should be the columns of the resulting DataFrame, pass 'columns'
+            (default). Otherwise if the keys should be rows, pass 'index'.
+            If 'tight', assume a dict with keys ['index', 'columns', 'data',
+            'index_names', 'column_names'].
+
+            .. versionadded:: 1.4.0
+               'tight' as an allowed value for the ``orient`` argument
+
+        dtype : dtype, default None
+            Data type to force after DataFrame construction, otherwise infer.
+        columns : list, default None
+            Column labels to use when ``orient='index'``. Raises a ValueError
+            if used with ``orient='columns'`` or ``orient='tight'``.
+
+        Returns
+        -------
+        DataFrame
+
+        See Also
+        --------
+        DataFrame.from_records : DataFrame from structured ndarray, sequence
+            of tuples or dicts, or DataFrame.
+        DataFrame : DataFrame object creation using constructor.
+        DataFrame.to_dict : Convert the DataFrame to a dictionary.
+
+        Examples
+        --------
+        By default the keys of the dict become the DataFrame columns:
+
+        >>> data = {'col_1': [3, 2, 1, 0], 'col_2': ['a', 'b', 'c', 'd']}
+        >>> pd.DataFrame.from_dict(data)
+           col_1 col_2
+        0      3     a
+        1      2     b
+        2      1     c
+        3      0     d
+
+        Specify ``orient='index'`` to create the DataFrame using dictionary
+        keys as rows:
+
+        >>> data = {'row_1': [3, 2, 1, 0], 'row_2': ['a', 'b', 'c', 'd']}
+        >>> pd.DataFrame.from_dict(data, orient='index')
+               0  1  2  3
+        row_1  3  2  1  0
+        row_2  a  b  c  d
+
+        When using the 'index' orientation, the column names can be
+        specified manually:
+
+        >>> pd.DataFrame.from_dict(data, orient='index',
+        ...                        columns=['A', 'B', 'C', 'D'])
+               A  B  C  D
+        row_1  3  2  1  0
+        row_2  a  b  c  d
+
+        Specify ``orient='tight'`` to create the DataFrame using a 'tight'
+        format:
+
+        >>> data = {'index': [('a', 'b'), ('a', 'c')],
+        ...         'columns': [('x', 1), ('y', 2)],
+        ...         'data': [[1, 3], [2, 4]],
+        ...         'index_names': ['n1', 'n2'],
+        ...         'column_names': ['z1', 'z2']}
+        >>> pd.DataFrame.from_dict(data, orient='tight')
+        z1     x  y
+        z2     1  2
+        n1 n2
+        a  b   1  3
+           c   2  4
+        """
+        index = None
+        orient = orient.lower()  # type: ignore[assignment]
+        if orient == "index":
+            if len(data) > 0:
+                # TODO speed up Series case
+                if isinstance(next(iter(data.values())), (Series, dict)):
+                    data = _from_nested_dict(data)
+                else:
+                    index = list(data.keys())
+                    # error: Incompatible types in assignment (expression has type
+                    # "List[Any]", variable has type "Dict[Any, Any]")
+                    data = list(data.values())  # type: ignore[assignment]
+        elif orient in ("columns", "tight"):
+            if columns is not None:
+                raise ValueError(f"cannot use columns parameter with orient='{orient}'")
+        else:  # pragma: no cover
+            raise ValueError(
+                f"Expected 'index', 'columns' or 'tight' for orient parameter. "
+                f"Got '{orient}' instead"
+            )
+
+        if orient != "tight":
+            return cls(data, index=index, columns=columns, dtype=dtype)
+        else:
+            realdata = data["data"]
+
+            def create_index(indexlist, namelist):
+                index: Index
+                if len(namelist) > 1:
+                    index = MultiIndex.from_tuples(indexlist, names=namelist)
+                else:
+                    index = Index(indexlist, name=namelist[0])
+                return index
+
+            index = create_index(data["index"], data["index_names"])
+            columns = create_index(data["columns"], data["column_names"])
+            return cls(realdata, index=index, columns=columns, dtype=dtype)
+
+    def to_numpy(
+        self,
+        dtype: npt.DTypeLike | None = None,
+        copy: bool = False,
+        na_value: object = lib.no_default,
+    ) -> np.ndarray:
+        """
+        Convert the DataFrame to a NumPy array.
+
+        By default, the dtype of the returned array will be the common NumPy
+        dtype of all types in the DataFrame. For example, if the dtypes are
+        ``float16`` and ``float32``, the results dtype will be ``float32``.
+        This may require copying data and coercing values, which may be
+        expensive.
+
+        Parameters
+        ----------
+        dtype : str or numpy.dtype, optional
+            The dtype to pass to :meth:`numpy.asarray`.
+        copy : bool, default False
+            Whether to ensure that the returned value is not a view on
+            another array. Note that ``copy=False`` does not *ensure* that
+            ``to_numpy()`` is no-copy. Rather, ``copy=True`` ensure that
+            a copy is made, even if not strictly necessary.
+        na_value : Any, optional
+            The value to use for missing values. The default value depends
+            on `dtype` and the dtypes of the DataFrame columns.
+
+        Returns
+        -------
+        numpy.ndarray
+
+        See Also
+        --------
+        Series.to_numpy : Similar method for Series.
+
+        Examples
+        --------
+        >>> pd.DataFrame({"A": [1, 2], "B": [3, 4]}).to_numpy()
+        array([[1, 3],
+               [2, 4]])
+
+        With heterogeneous data, the lowest common type will have to
+        be used.
+
+        >>> df = pd.DataFrame({"A": [1, 2], "B": [3.0, 4.5]})
+        >>> df.to_numpy()
+        array([[1. , 3. ],
+               [2. , 4.5]])
+
+        For a mix of numeric and non-numeric types, the output array will
+        have object dtype.
+
+        >>> df['C'] = pd.date_range('2000', periods=2)
+        >>> df.to_numpy()
+        array([[1, 3.0, Timestamp('2000-01-01 00:00:00')],
+               [2, 4.5, Timestamp('2000-01-02 00:00:00')]], dtype=object)
+        """
+        if dtype is not None:
+            dtype = np.dtype(dtype)
+        result = self._mgr.as_array(dtype=dtype, copy=copy, na_value=na_value)
+        if result.dtype is not dtype:
+            result = np.asarray(result, dtype=dtype)
+
+        return result
+
+    def _create_data_for_split_and_tight_to_dict(
+        self, are_all_object_dtype_cols: bool, object_dtype_indices: list[int]
+    ) -> list:
+        """
+        Simple helper method to create data for to ``to_dict(orient="split")`` and
+        ``to_dict(orient="tight")`` to create the main output data
+        """
+        if are_all_object_dtype_cols:
+            data = [
+                list(map(maybe_box_native, t))
+                for t in self.itertuples(index=False, name=None)
+            ]
+        else:
+            data = [list(t) for t in self.itertuples(index=False, name=None)]
+            if object_dtype_indices:
+                # If we have object_dtype_cols, apply maybe_box_naive after list
+                # comprehension for perf
+                for row in data:
+                    for i in object_dtype_indices:
+                        row[i] = maybe_box_native(row[i])
+        return data
+
+    @overload
+    def to_dict(
+        self,
+        orient: Literal["dict", "list", "series", "split", "tight", "index"] = ...,
+        *,
+        into: type[MutableMappingT] | MutableMappingT,
+        index: bool = ...,
+    ) -> MutableMappingT:
+        ...
+
+    @overload
+    def to_dict(
+        self,
+        orient: Literal["records"],
+        *,
+        into: type[MutableMappingT] | MutableMappingT,
+        index: bool = ...,
+    ) -> list[MutableMappingT]:
+        ...
+
+    @overload
+    def to_dict(
+        self,
+        orient: Literal["dict", "list", "series", "split", "tight", "index"] = ...,
+        *,
+        into: type[dict] = ...,
+        index: bool = ...,
+    ) -> dict:
+        ...
+
+    @overload
+    def to_dict(
+        self,
+        orient: Literal["records"],
+        *,
+        into: type[dict] = ...,
+        index: bool = ...,
+    ) -> list[dict]:
+        ...
+
+    # error: Incompatible default for argument "into" (default has type "type
+    # [dict[Any, Any]]", argument has type "type[MutableMappingT] | MutableMappingT")
+    @deprecate_nonkeyword_arguments(
+        version="3.0", allowed_args=["self", "orient"], name="to_dict"
+    )
+    def to_dict(
+        self,
+        orient: Literal[
+            "dict", "list", "series", "split", "tight", "records", "index"
+        ] = "dict",
+        into: type[MutableMappingT]
+        | MutableMappingT = dict,  # type: ignore[assignment]
+        index: bool = True,
+    ) -> MutableMappingT | list[MutableMappingT]:
+        """
+        Convert the DataFrame to a dictionary.
+
+        The type of the key-value pairs can be customized with the parameters
+        (see below).
+
+        Parameters
+        ----------
+        orient : str {'dict', 'list', 'series', 'split', 'tight', 'records', 'index'}
+            Determines the type of the values of the dictionary.
+
+            - 'dict' (default) : dict like {column -> {index -> value}}
+            - 'list' : dict like {column -> [values]}
+            - 'series' : dict like {column -> Series(values)}
+            - 'split' : dict like
+              {'index' -> [index], 'columns' -> [columns], 'data' -> [values]}
+            - 'tight' : dict like
+              {'index' -> [index], 'columns' -> [columns], 'data' -> [values],
+              'index_names' -> [index.names], 'column_names' -> [column.names]}
+            - 'records' : list like
+              [{column -> value}, ... , {column -> value}]
+            - 'index' : dict like {index -> {column -> value}}
+
+            .. versionadded:: 1.4.0
+                'tight' as an allowed value for the ``orient`` argument
+
+        into : class, default dict
+            The collections.abc.MutableMapping subclass used for all Mappings
+            in the return value.  Can be the actual class or an empty
+            instance of the mapping type you want.  If you want a
+            collections.defaultdict, you must pass it initialized.
+
+        index : bool, default True
+            Whether to include the index item (and index_names item if `orient`
+            is 'tight') in the returned dictionary. Can only be ``False``
+            when `orient` is 'split' or 'tight'.
+
+            .. versionadded:: 2.0.0
+
+        Returns
+        -------
+        dict, list or collections.abc.MutableMapping
+            Return a collections.abc.MutableMapping object representing the
+            DataFrame. The resulting transformation depends on the `orient`
+            parameter.
+
+        See Also
+        --------
+        DataFrame.from_dict: Create a DataFrame from a dictionary.
+        DataFrame.to_json: Convert a DataFrame to JSON format.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'col1': [1, 2],
+        ...                    'col2': [0.5, 0.75]},
+        ...                   index=['row1', 'row2'])
+        >>> df
+              col1  col2
+        row1     1  0.50
+        row2     2  0.75
+        >>> df.to_dict()
+        {'col1': {'row1': 1, 'row2': 2}, 'col2': {'row1': 0.5, 'row2': 0.75}}
+
+        You can specify the return orientation.
+
+        >>> df.to_dict('series')
+        {'col1': row1    1
+                 row2    2
+        Name: col1, dtype: int64,
+        'col2': row1    0.50
+                row2    0.75
+        Name: col2, dtype: float64}
+
+        >>> df.to_dict('split')
+        {'index': ['row1', 'row2'], 'columns': ['col1', 'col2'],
+         'data': [[1, 0.5], [2, 0.75]]}
+
+        >>> df.to_dict('records')
+        [{'col1': 1, 'col2': 0.5}, {'col1': 2, 'col2': 0.75}]
+
+        >>> df.to_dict('index')
+        {'row1': {'col1': 1, 'col2': 0.5}, 'row2': {'col1': 2, 'col2': 0.75}}
+
+        >>> df.to_dict('tight')
+        {'index': ['row1', 'row2'], 'columns': ['col1', 'col2'],
+         'data': [[1, 0.5], [2, 0.75]], 'index_names': [None], 'column_names': [None]}
+
+        You can also specify the mapping type.
+
+        >>> from collections import OrderedDict, defaultdict
+        >>> df.to_dict(into=OrderedDict)
+        OrderedDict([('col1', OrderedDict([('row1', 1), ('row2', 2)])),
+                     ('col2', OrderedDict([('row1', 0.5), ('row2', 0.75)]))])
+
+        If you want a `defaultdict`, you need to initialize it:
+
+        >>> dd = defaultdict(list)
+        >>> df.to_dict('records', into=dd)
+        [defaultdict(, {'col1': 1, 'col2': 0.5}),
+         defaultdict(, {'col1': 2, 'col2': 0.75})]
+        """
+        from pandas.core.methods.to_dict import to_dict
+
+        return to_dict(self, orient, into=into, index=index)
+
+    @deprecate_nonkeyword_arguments(
+        version="3.0", allowed_args=["self", "destination_table"], name="to_gbq"
+    )
+    def to_gbq(
+        self,
+        destination_table: str,
+        project_id: str | None = None,
+        chunksize: int | None = None,
+        reauth: bool = False,
+        if_exists: ToGbqIfexist = "fail",
+        auth_local_webserver: bool = True,
+        table_schema: list[dict[str, str]] | None = None,
+        location: str | None = None,
+        progress_bar: bool = True,
+        credentials=None,
+    ) -> None:
+        """
+        Write a DataFrame to a Google BigQuery table.
+
+        .. deprecated:: 2.2.0
+
+           Please use ``pandas_gbq.to_gbq`` instead.
+
+        This function requires the `pandas-gbq package
+        `__.
+
+        See the `How to authenticate with Google BigQuery
+        `__
+        guide for authentication instructions.
+
+        Parameters
+        ----------
+        destination_table : str
+            Name of table to be written, in the form ``dataset.tablename``.
+        project_id : str, optional
+            Google BigQuery Account project ID. Optional when available from
+            the environment.
+        chunksize : int, optional
+            Number of rows to be inserted in each chunk from the dataframe.
+            Set to ``None`` to load the whole dataframe at once.
+        reauth : bool, default False
+            Force Google BigQuery to re-authenticate the user. This is useful
+            if multiple accounts are used.
+        if_exists : str, default 'fail'
+            Behavior when the destination table exists. Value can be one of:
+
+            ``'fail'``
+                If table exists raise pandas_gbq.gbq.TableCreationError.
+            ``'replace'``
+                If table exists, drop it, recreate it, and insert data.
+            ``'append'``
+                If table exists, insert data. Create if does not exist.
+        auth_local_webserver : bool, default True
+            Use the `local webserver flow`_ instead of the `console flow`_
+            when getting user credentials.
+
+            .. _local webserver flow:
+                https://google-auth-oauthlib.readthedocs.io/en/latest/reference/google_auth_oauthlib.flow.html#google_auth_oauthlib.flow.InstalledAppFlow.run_local_server
+            .. _console flow:
+                https://google-auth-oauthlib.readthedocs.io/en/latest/reference/google_auth_oauthlib.flow.html#google_auth_oauthlib.flow.InstalledAppFlow.run_console
+
+            *New in version 0.2.0 of pandas-gbq*.
+
+            .. versionchanged:: 1.5.0
+               Default value is changed to ``True``. Google has deprecated the
+               ``auth_local_webserver = False`` `"out of band" (copy-paste)
+               flow
+               `_.
+        table_schema : list of dicts, optional
+            List of BigQuery table fields to which according DataFrame
+            columns conform to, e.g. ``[{'name': 'col1', 'type':
+            'STRING'},...]``. If schema is not provided, it will be
+            generated according to dtypes of DataFrame columns. See
+            BigQuery API documentation on available names of a field.
+
+            *New in version 0.3.1 of pandas-gbq*.
+        location : str, optional
+            Location where the load job should run. See the `BigQuery locations
+            documentation
+            `__ for a
+            list of available locations. The location must match that of the
+            target dataset.
+
+            *New in version 0.5.0 of pandas-gbq*.
+        progress_bar : bool, default True
+            Use the library `tqdm` to show the progress bar for the upload,
+            chunk by chunk.
+
+            *New in version 0.5.0 of pandas-gbq*.
+        credentials : google.auth.credentials.Credentials, optional
+            Credentials for accessing Google APIs. Use this parameter to
+            override default credentials, such as to use Compute Engine
+            :class:`google.auth.compute_engine.Credentials` or Service
+            Account :class:`google.oauth2.service_account.Credentials`
+            directly.
+
+            *New in version 0.8.0 of pandas-gbq*.
+
+        See Also
+        --------
+        pandas_gbq.to_gbq : This function in the pandas-gbq library.
+        read_gbq : Read a DataFrame from Google BigQuery.
+
+        Examples
+        --------
+        Example taken from `Google BigQuery documentation
+        `_
+
+        >>> project_id = "my-project"
+        >>> table_id = 'my_dataset.my_table'
+        >>> df = pd.DataFrame({
+        ...                   "my_string": ["a", "b", "c"],
+        ...                   "my_int64": [1, 2, 3],
+        ...                   "my_float64": [4.0, 5.0, 6.0],
+        ...                   "my_bool1": [True, False, True],
+        ...                   "my_bool2": [False, True, False],
+        ...                   "my_dates": pd.date_range("now", periods=3),
+        ...                   }
+        ...                   )
+
+        >>> df.to_gbq(table_id, project_id=project_id)  # doctest: +SKIP
+        """
+        from pandas.io import gbq
+
+        gbq.to_gbq(
+            self,
+            destination_table,
+            project_id=project_id,
+            chunksize=chunksize,
+            reauth=reauth,
+            if_exists=if_exists,
+            auth_local_webserver=auth_local_webserver,
+            table_schema=table_schema,
+            location=location,
+            progress_bar=progress_bar,
+            credentials=credentials,
+        )
+
+    @classmethod
+    def from_records(
+        cls,
+        data,
+        index=None,
+        exclude=None,
+        columns=None,
+        coerce_float: bool = False,
+        nrows: int | None = None,
+    ) -> DataFrame:
+        """
+        Convert structured or record ndarray to DataFrame.
+
+        Creates a DataFrame object from a structured ndarray, sequence of
+        tuples or dicts, or DataFrame.
+
+        Parameters
+        ----------
+        data : structured ndarray, sequence of tuples or dicts, or DataFrame
+            Structured input data.
+
+            .. deprecated:: 2.1.0
+                Passing a DataFrame is deprecated.
+        index : str, list of fields, array-like
+            Field of array to use as the index, alternately a specific set of
+            input labels to use.
+        exclude : sequence, default None
+            Columns or fields to exclude.
+        columns : sequence, default None
+            Column names to use. If the passed data do not have names
+            associated with them, this argument provides names for the
+            columns. Otherwise this argument indicates the order of the columns
+            in the result (any names not found in the data will become all-NA
+            columns).
+        coerce_float : bool, default False
+            Attempt to convert values of non-string, non-numeric objects (like
+            decimal.Decimal) to floating point, useful for SQL result sets.
+        nrows : int, default None
+            Number of rows to read if data is an iterator.
+
+        Returns
+        -------
+        DataFrame
+
+        See Also
+        --------
+        DataFrame.from_dict : DataFrame from dict of array-like or dicts.
+        DataFrame : DataFrame object creation using constructor.
+
+        Examples
+        --------
+        Data can be provided as a structured ndarray:
+
+        >>> data = np.array([(3, 'a'), (2, 'b'), (1, 'c'), (0, 'd')],
+        ...                 dtype=[('col_1', 'i4'), ('col_2', 'U1')])
+        >>> pd.DataFrame.from_records(data)
+           col_1 col_2
+        0      3     a
+        1      2     b
+        2      1     c
+        3      0     d
+
+        Data can be provided as a list of dicts:
+
+        >>> data = [{'col_1': 3, 'col_2': 'a'},
+        ...         {'col_1': 2, 'col_2': 'b'},
+        ...         {'col_1': 1, 'col_2': 'c'},
+        ...         {'col_1': 0, 'col_2': 'd'}]
+        >>> pd.DataFrame.from_records(data)
+           col_1 col_2
+        0      3     a
+        1      2     b
+        2      1     c
+        3      0     d
+
+        Data can be provided as a list of tuples with corresponding columns:
+
+        >>> data = [(3, 'a'), (2, 'b'), (1, 'c'), (0, 'd')]
+        >>> pd.DataFrame.from_records(data, columns=['col_1', 'col_2'])
+           col_1 col_2
+        0      3     a
+        1      2     b
+        2      1     c
+        3      0     d
+        """
+        if isinstance(data, DataFrame):
+            warnings.warn(
+                "Passing a DataFrame to DataFrame.from_records is deprecated. Use "
+                "set_index and/or drop to modify the DataFrame instead.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+            if columns is not None:
+                if is_scalar(columns):
+                    columns = [columns]
+                data = data[columns]
+            if index is not None:
+                data = data.set_index(index)
+            if exclude is not None:
+                data = data.drop(columns=exclude)
+            return data.copy(deep=False)
+
+        result_index = None
+
+        # Make a copy of the input columns so we can modify it
+        if columns is not None:
+            columns = ensure_index(columns)
+
+        def maybe_reorder(
+            arrays: list[ArrayLike], arr_columns: Index, columns: Index, index
+        ) -> tuple[list[ArrayLike], Index, Index | None]:
+            """
+            If our desired 'columns' do not match the data's pre-existing 'arr_columns',
+            we re-order our arrays.  This is like a pre-emptive (cheap) reindex.
+            """
+            if len(arrays):
+                length = len(arrays[0])
+            else:
+                length = 0
+
+            result_index = None
+            if len(arrays) == 0 and index is None and length == 0:
+                result_index = default_index(0)
+
+            arrays, arr_columns = reorder_arrays(arrays, arr_columns, columns, length)
+            return arrays, arr_columns, result_index
+
+        if is_iterator(data):
+            if nrows == 0:
+                return cls()
+
+            try:
+                first_row = next(data)
+            except StopIteration:
+                return cls(index=index, columns=columns)
+
+            dtype = None
+            if hasattr(first_row, "dtype") and first_row.dtype.names:
+                dtype = first_row.dtype
+
+            values = [first_row]
+
+            if nrows is None:
+                values += data
+            else:
+                values.extend(itertools.islice(data, nrows - 1))
+
+            if dtype is not None:
+                data = np.array(values, dtype=dtype)
+            else:
+                data = values
+
+        if isinstance(data, dict):
+            if columns is None:
+                columns = arr_columns = ensure_index(sorted(data))
+                arrays = [data[k] for k in columns]
+            else:
+                arrays = []
+                arr_columns_list = []
+                for k, v in data.items():
+                    if k in columns:
+                        arr_columns_list.append(k)
+                        arrays.append(v)
+
+                arr_columns = Index(arr_columns_list)
+                arrays, arr_columns, result_index = maybe_reorder(
+                    arrays, arr_columns, columns, index
+                )
+
+        elif isinstance(data, np.ndarray):
+            arrays, columns = to_arrays(data, columns)
+            arr_columns = columns
+        else:
+            arrays, arr_columns = to_arrays(data, columns)
+            if coerce_float:
+                for i, arr in enumerate(arrays):
+                    if arr.dtype == object:
+                        # error: Argument 1 to "maybe_convert_objects" has
+                        # incompatible type "Union[ExtensionArray, ndarray]";
+                        # expected "ndarray"
+                        arrays[i] = lib.maybe_convert_objects(
+                            arr,  # type: ignore[arg-type]
+                            try_float=True,
+                        )
+
+            arr_columns = ensure_index(arr_columns)
+            if columns is None:
+                columns = arr_columns
+            else:
+                arrays, arr_columns, result_index = maybe_reorder(
+                    arrays, arr_columns, columns, index
+                )
+
+        if exclude is None:
+            exclude = set()
+        else:
+            exclude = set(exclude)
+
+        if index is not None:
+            if isinstance(index, str) or not hasattr(index, "__iter__"):
+                i = columns.get_loc(index)
+                exclude.add(index)
+                if len(arrays) > 0:
+                    result_index = Index(arrays[i], name=index)
+                else:
+                    result_index = Index([], name=index)
+            else:
+                try:
+                    index_data = [arrays[arr_columns.get_loc(field)] for field in index]
+                except (KeyError, TypeError):
+                    # raised by get_loc, see GH#29258
+                    result_index = index
+                else:
+                    result_index = ensure_index_from_sequences(index_data, names=index)
+                    exclude.update(index)
+
+        if any(exclude):
+            arr_exclude = [x for x in exclude if x in arr_columns]
+            to_remove = [arr_columns.get_loc(col) for col in arr_exclude]
+            arrays = [v for i, v in enumerate(arrays) if i not in to_remove]
+
+            columns = columns.drop(exclude)
+
+        manager = _get_option("mode.data_manager", silent=True)
+        mgr = arrays_to_mgr(arrays, columns, result_index, typ=manager)
+        df = DataFrame._from_mgr(mgr, axes=mgr.axes)
+        if cls is not DataFrame:
+            return cls(df, copy=False)
+        return df
+
+    def to_records(
+        self, index: bool = True, column_dtypes=None, index_dtypes=None
+    ) -> np.rec.recarray:
+        """
+        Convert DataFrame to a NumPy record array.
+
+        Index will be included as the first field of the record array if
+        requested.
+
+        Parameters
+        ----------
+        index : bool, default True
+            Include index in resulting record array, stored in 'index'
+            field or using the index label, if set.
+        column_dtypes : str, type, dict, default None
+            If a string or type, the data type to store all columns. If
+            a dictionary, a mapping of column names and indices (zero-indexed)
+            to specific data types.
+        index_dtypes : str, type, dict, default None
+            If a string or type, the data type to store all index levels. If
+            a dictionary, a mapping of index level names and indices
+            (zero-indexed) to specific data types.
+
+            This mapping is applied only if `index=True`.
+
+        Returns
+        -------
+        numpy.rec.recarray
+            NumPy ndarray with the DataFrame labels as fields and each row
+            of the DataFrame as entries.
+
+        See Also
+        --------
+        DataFrame.from_records: Convert structured or record ndarray
+            to DataFrame.
+        numpy.rec.recarray: An ndarray that allows field access using
+            attributes, analogous to typed columns in a
+            spreadsheet.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'A': [1, 2], 'B': [0.5, 0.75]},
+        ...                   index=['a', 'b'])
+        >>> df
+           A     B
+        a  1  0.50
+        b  2  0.75
+        >>> df.to_records()
+        rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],
+                  dtype=[('index', 'O'), ('A', '>> df.index = df.index.rename("I")
+        >>> df.to_records()
+        rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],
+                  dtype=[('I', 'O'), ('A', '>> df.to_records(index=False)
+        rec.array([(1, 0.5 ), (2, 0.75)],
+                  dtype=[('A', '>> df.to_records(column_dtypes={"A": "int32"})
+        rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],
+                  dtype=[('I', 'O'), ('A', '>> df.to_records(index_dtypes=">> index_dtypes = f">> df.to_records(index_dtypes=index_dtypes)
+        rec.array([(b'a', 1, 0.5 ), (b'b', 2, 0.75)],
+                  dtype=[('I', 'S1'), ('A', ' Self:
+        """
+        Create DataFrame from a list of arrays corresponding to the columns.
+
+        Parameters
+        ----------
+        arrays : list-like of arrays
+            Each array in the list corresponds to one column, in order.
+        columns : list-like, Index
+            The column names for the resulting DataFrame.
+        index : list-like, Index
+            The rows labels for the resulting DataFrame.
+        dtype : dtype, optional
+            Optional dtype to enforce for all arrays.
+        verify_integrity : bool, default True
+            Validate and homogenize all input. If set to False, it is assumed
+            that all elements of `arrays` are actual arrays how they will be
+            stored in a block (numpy ndarray or ExtensionArray), have the same
+            length as and are aligned with the index, and that `columns` and
+            `index` are ensured to be an Index object.
+
+        Returns
+        -------
+        DataFrame
+        """
+        if dtype is not None:
+            dtype = pandas_dtype(dtype)
+
+        manager = _get_option("mode.data_manager", silent=True)
+        columns = ensure_index(columns)
+        if len(columns) != len(arrays):
+            raise ValueError("len(columns) must match len(arrays)")
+        mgr = arrays_to_mgr(
+            arrays,
+            columns,
+            index,
+            dtype=dtype,
+            verify_integrity=verify_integrity,
+            typ=manager,
+        )
+        return cls._from_mgr(mgr, axes=mgr.axes)
+
+    @doc(
+        storage_options=_shared_docs["storage_options"],
+        compression_options=_shared_docs["compression_options"] % "path",
+    )
+    def to_stata(
+        self,
+        path: FilePath | WriteBuffer[bytes],
+        *,
+        convert_dates: dict[Hashable, str] | None = None,
+        write_index: bool = True,
+        byteorder: ToStataByteorder | None = None,
+        time_stamp: datetime.datetime | None = None,
+        data_label: str | None = None,
+        variable_labels: dict[Hashable, str] | None = None,
+        version: int | None = 114,
+        convert_strl: Sequence[Hashable] | None = None,
+        compression: CompressionOptions = "infer",
+        storage_options: StorageOptions | None = None,
+        value_labels: dict[Hashable, dict[float, str]] | None = None,
+    ) -> None:
+        """
+        Export DataFrame object to Stata dta format.
+
+        Writes the DataFrame to a Stata dataset file.
+        "dta" files contain a Stata dataset.
+
+        Parameters
+        ----------
+        path : str, path object, or buffer
+            String, path object (implementing ``os.PathLike[str]``), or file-like
+            object implementing a binary ``write()`` function.
+
+        convert_dates : dict
+            Dictionary mapping columns containing datetime types to stata
+            internal format to use when writing the dates. Options are 'tc',
+            'td', 'tm', 'tw', 'th', 'tq', 'ty'. Column can be either an integer
+            or a name. Datetime columns that do not have a conversion type
+            specified will be converted to 'tc'. Raises NotImplementedError if
+            a datetime column has timezone information.
+        write_index : bool
+            Write the index to Stata dataset.
+        byteorder : str
+            Can be ">", "<", "little", or "big". default is `sys.byteorder`.
+        time_stamp : datetime
+            A datetime to use as file creation date.  Default is the current
+            time.
+        data_label : str, optional
+            A label for the data set.  Must be 80 characters or smaller.
+        variable_labels : dict
+            Dictionary containing columns as keys and variable labels as
+            values. Each label must be 80 characters or smaller.
+        version : {{114, 117, 118, 119, None}}, default 114
+            Version to use in the output dta file. Set to None to let pandas
+            decide between 118 or 119 formats depending on the number of
+            columns in the frame. Version 114 can be read by Stata 10 and
+            later. Version 117 can be read by Stata 13 or later. Version 118
+            is supported in Stata 14 and later. Version 119 is supported in
+            Stata 15 and later. Version 114 limits string variables to 244
+            characters or fewer while versions 117 and later allow strings
+            with lengths up to 2,000,000 characters. Versions 118 and 119
+            support Unicode characters, and version 119 supports more than
+            32,767 variables.
+
+            Version 119 should usually only be used when the number of
+            variables exceeds the capacity of dta format 118. Exporting
+            smaller datasets in format 119 may have unintended consequences,
+            and, as of November 2020, Stata SE cannot read version 119 files.
+
+        convert_strl : list, optional
+            List of column names to convert to string columns to Stata StrL
+            format. Only available if version is 117.  Storing strings in the
+            StrL format can produce smaller dta files if strings have more than
+            8 characters and values are repeated.
+        {compression_options}
+
+            .. versionchanged:: 1.4.0 Zstandard support.
+
+        {storage_options}
+
+        value_labels : dict of dicts
+            Dictionary containing columns as keys and dictionaries of column value
+            to labels as values. Labels for a single variable must be 32,000
+            characters or smaller.
+
+            .. versionadded:: 1.4.0
+
+        Raises
+        ------
+        NotImplementedError
+            * If datetimes contain timezone information
+            * Column dtype is not representable in Stata
+        ValueError
+            * Columns listed in convert_dates are neither datetime64[ns]
+              or datetime.datetime
+            * Column listed in convert_dates is not in DataFrame
+            * Categorical label contains more than 32,000 characters
+
+        See Also
+        --------
+        read_stata : Import Stata data files.
+        io.stata.StataWriter : Low-level writer for Stata data files.
+        io.stata.StataWriter117 : Low-level writer for version 117 files.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({{'animal': ['falcon', 'parrot', 'falcon',
+        ...                               'parrot'],
+        ...                    'speed': [350, 18, 361, 15]}})
+        >>> df.to_stata('animals.dta')  # doctest: +SKIP
+        """
+        if version not in (114, 117, 118, 119, None):
+            raise ValueError("Only formats 114, 117, 118 and 119 are supported.")
+        if version == 114:
+            if convert_strl is not None:
+                raise ValueError("strl is not supported in format 114")
+            from pandas.io.stata import StataWriter as statawriter
+        elif version == 117:
+            # Incompatible import of "statawriter" (imported name has type
+            # "Type[StataWriter117]", local name has type "Type[StataWriter]")
+            from pandas.io.stata import (  # type: ignore[assignment]
+                StataWriter117 as statawriter,
+            )
+        else:  # versions 118 and 119
+            # Incompatible import of "statawriter" (imported name has type
+            # "Type[StataWriter117]", local name has type "Type[StataWriter]")
+            from pandas.io.stata import (  # type: ignore[assignment]
+                StataWriterUTF8 as statawriter,
+            )
+
+        kwargs: dict[str, Any] = {}
+        if version is None or version >= 117:
+            # strl conversion is only supported >= 117
+            kwargs["convert_strl"] = convert_strl
+        if version is None or version >= 118:
+            # Specifying the version is only supported for UTF8 (118 or 119)
+            kwargs["version"] = version
+
+        writer = statawriter(
+            path,
+            self,
+            convert_dates=convert_dates,
+            byteorder=byteorder,
+            time_stamp=time_stamp,
+            data_label=data_label,
+            write_index=write_index,
+            variable_labels=variable_labels,
+            compression=compression,
+            storage_options=storage_options,
+            value_labels=value_labels,
+            **kwargs,
+        )
+        writer.write_file()
+
+    def to_feather(self, path: FilePath | WriteBuffer[bytes], **kwargs) -> None:
+        """
+        Write a DataFrame to the binary Feather format.
+
+        Parameters
+        ----------
+        path : str, path object, file-like object
+            String, path object (implementing ``os.PathLike[str]``), or file-like
+            object implementing a binary ``write()`` function. If a string or a path,
+            it will be used as Root Directory path when writing a partitioned dataset.
+        **kwargs :
+            Additional keywords passed to :func:`pyarrow.feather.write_feather`.
+            This includes the `compression`, `compression_level`, `chunksize`
+            and `version` keywords.
+
+        Notes
+        -----
+        This function writes the dataframe as a `feather file
+        `_. Requires a default
+        index. For saving the DataFrame with your custom index use a method that
+        supports custom indices e.g. `to_parquet`.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([[1, 2, 3], [4, 5, 6]])
+        >>> df.to_feather("file.feather")  # doctest: +SKIP
+        """
+        from pandas.io.feather_format import to_feather
+
+        to_feather(self, path, **kwargs)
+
+    @deprecate_nonkeyword_arguments(
+        version="3.0", allowed_args=["self", "buf"], name="to_markdown"
+    )
+    @doc(
+        Series.to_markdown,
+        klass=_shared_doc_kwargs["klass"],
+        storage_options=_shared_docs["storage_options"],
+        examples="""Examples
+        --------
+        >>> df = pd.DataFrame(
+        ...     data={"animal_1": ["elk", "pig"], "animal_2": ["dog", "quetzal"]}
+        ... )
+        >>> print(df.to_markdown())
+        |    | animal_1   | animal_2   |
+        |---:|:-----------|:-----------|
+        |  0 | elk        | dog        |
+        |  1 | pig        | quetzal    |
+
+        Output markdown with a tabulate option.
+
+        >>> print(df.to_markdown(tablefmt="grid"))
+        +----+------------+------------+
+        |    | animal_1   | animal_2   |
+        +====+============+============+
+        |  0 | elk        | dog        |
+        +----+------------+------------+
+        |  1 | pig        | quetzal    |
+        +----+------------+------------+""",
+    )
+    def to_markdown(
+        self,
+        buf: FilePath | WriteBuffer[str] | None = None,
+        mode: str = "wt",
+        index: bool = True,
+        storage_options: StorageOptions | None = None,
+        **kwargs,
+    ) -> str | None:
+        if "showindex" in kwargs:
+            raise ValueError("Pass 'index' instead of 'showindex")
+
+        kwargs.setdefault("headers", "keys")
+        kwargs.setdefault("tablefmt", "pipe")
+        kwargs.setdefault("showindex", index)
+        tabulate = import_optional_dependency("tabulate")
+        result = tabulate.tabulate(self, **kwargs)
+        if buf is None:
+            return result
+
+        with get_handle(buf, mode, storage_options=storage_options) as handles:
+            handles.handle.write(result)
+        return None
+
+    @overload
+    def to_parquet(
+        self,
+        path: None = ...,
+        engine: Literal["auto", "pyarrow", "fastparquet"] = ...,
+        compression: str | None = ...,
+        index: bool | None = ...,
+        partition_cols: list[str] | None = ...,
+        storage_options: StorageOptions = ...,
+        **kwargs,
+    ) -> bytes:
+        ...
+
+    @overload
+    def to_parquet(
+        self,
+        path: FilePath | WriteBuffer[bytes],
+        engine: Literal["auto", "pyarrow", "fastparquet"] = ...,
+        compression: str | None = ...,
+        index: bool | None = ...,
+        partition_cols: list[str] | None = ...,
+        storage_options: StorageOptions = ...,
+        **kwargs,
+    ) -> None:
+        ...
+
+    @deprecate_nonkeyword_arguments(
+        version="3.0", allowed_args=["self", "path"], name="to_parquet"
+    )
+    @doc(storage_options=_shared_docs["storage_options"])
+    def to_parquet(
+        self,
+        path: FilePath | WriteBuffer[bytes] | None = None,
+        engine: Literal["auto", "pyarrow", "fastparquet"] = "auto",
+        compression: str | None = "snappy",
+        index: bool | None = None,
+        partition_cols: list[str] | None = None,
+        storage_options: StorageOptions | None = None,
+        **kwargs,
+    ) -> bytes | None:
+        """
+        Write a DataFrame to the binary parquet format.
+
+        This function writes the dataframe as a `parquet file
+        `_. You can choose different parquet
+        backends, and have the option of compression. See
+        :ref:`the user guide ` for more details.
+
+        Parameters
+        ----------
+        path : str, path object, file-like object, or None, default None
+            String, path object (implementing ``os.PathLike[str]``), or file-like
+            object implementing a binary ``write()`` function. If None, the result is
+            returned as bytes. If a string or path, it will be used as Root Directory
+            path when writing a partitioned dataset.
+        engine : {{'auto', 'pyarrow', 'fastparquet'}}, default 'auto'
+            Parquet library to use. If 'auto', then the option
+            ``io.parquet.engine`` is used. The default ``io.parquet.engine``
+            behavior is to try 'pyarrow', falling back to 'fastparquet' if
+            'pyarrow' is unavailable.
+        compression : str or None, default 'snappy'
+            Name of the compression to use. Use ``None`` for no compression.
+            Supported options: 'snappy', 'gzip', 'brotli', 'lz4', 'zstd'.
+        index : bool, default None
+            If ``True``, include the dataframe's index(es) in the file output.
+            If ``False``, they will not be written to the file.
+            If ``None``, similar to ``True`` the dataframe's index(es)
+            will be saved. However, instead of being saved as values,
+            the RangeIndex will be stored as a range in the metadata so it
+            doesn't require much space and is faster. Other indexes will
+            be included as columns in the file output.
+        partition_cols : list, optional, default None
+            Column names by which to partition the dataset.
+            Columns are partitioned in the order they are given.
+            Must be None if path is not a string.
+        {storage_options}
+
+        **kwargs
+            Additional arguments passed to the parquet library. See
+            :ref:`pandas io ` for more details.
+
+        Returns
+        -------
+        bytes if no path argument is provided else None
+
+        See Also
+        --------
+        read_parquet : Read a parquet file.
+        DataFrame.to_orc : Write an orc file.
+        DataFrame.to_csv : Write a csv file.
+        DataFrame.to_sql : Write to a sql table.
+        DataFrame.to_hdf : Write to hdf.
+
+        Notes
+        -----
+        This function requires either the `fastparquet
+        `_ or `pyarrow
+        `_ library.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame(data={{'col1': [1, 2], 'col2': [3, 4]}})
+        >>> df.to_parquet('df.parquet.gzip',
+        ...               compression='gzip')  # doctest: +SKIP
+        >>> pd.read_parquet('df.parquet.gzip')  # doctest: +SKIP
+           col1  col2
+        0     1     3
+        1     2     4
+
+        If you want to get a buffer to the parquet content you can use a io.BytesIO
+        object, as long as you don't use partition_cols, which creates multiple files.
+
+        >>> import io
+        >>> f = io.BytesIO()
+        >>> df.to_parquet(f)
+        >>> f.seek(0)
+        0
+        >>> content = f.read()
+        """
+        from pandas.io.parquet import to_parquet
+
+        return to_parquet(
+            self,
+            path,
+            engine,
+            compression=compression,
+            index=index,
+            partition_cols=partition_cols,
+            storage_options=storage_options,
+            **kwargs,
+        )
+
+    def to_orc(
+        self,
+        path: FilePath | WriteBuffer[bytes] | None = None,
+        *,
+        engine: Literal["pyarrow"] = "pyarrow",
+        index: bool | None = None,
+        engine_kwargs: dict[str, Any] | None = None,
+    ) -> bytes | None:
+        """
+        Write a DataFrame to the ORC format.
+
+        .. versionadded:: 1.5.0
+
+        Parameters
+        ----------
+        path : str, file-like object or None, default None
+            If a string, it will be used as Root Directory path
+            when writing a partitioned dataset. By file-like object,
+            we refer to objects with a write() method, such as a file handle
+            (e.g. via builtin open function). If path is None,
+            a bytes object is returned.
+        engine : {'pyarrow'}, default 'pyarrow'
+            ORC library to use.
+        index : bool, optional
+            If ``True``, include the dataframe's index(es) in the file output.
+            If ``False``, they will not be written to the file.
+            If ``None``, similar to ``infer`` the dataframe's index(es)
+            will be saved. However, instead of being saved as values,
+            the RangeIndex will be stored as a range in the metadata so it
+            doesn't require much space and is faster. Other indexes will
+            be included as columns in the file output.
+        engine_kwargs : dict[str, Any] or None, default None
+            Additional keyword arguments passed to :func:`pyarrow.orc.write_table`.
+
+        Returns
+        -------
+        bytes if no path argument is provided else None
+
+        Raises
+        ------
+        NotImplementedError
+            Dtype of one or more columns is category, unsigned integers, interval,
+            period or sparse.
+        ValueError
+            engine is not pyarrow.
+
+        See Also
+        --------
+        read_orc : Read a ORC file.
+        DataFrame.to_parquet : Write a parquet file.
+        DataFrame.to_csv : Write a csv file.
+        DataFrame.to_sql : Write to a sql table.
+        DataFrame.to_hdf : Write to hdf.
+
+        Notes
+        -----
+        * Before using this function you should read the :ref:`user guide about
+          ORC ` and :ref:`install optional dependencies `.
+        * This function requires `pyarrow `_
+          library.
+        * For supported dtypes please refer to `supported ORC features in Arrow
+          `__.
+        * Currently timezones in datetime columns are not preserved when a
+          dataframe is converted into ORC files.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame(data={'col1': [1, 2], 'col2': [4, 3]})
+        >>> df.to_orc('df.orc')  # doctest: +SKIP
+        >>> pd.read_orc('df.orc')  # doctest: +SKIP
+           col1  col2
+        0     1     4
+        1     2     3
+
+        If you want to get a buffer to the orc content you can write it to io.BytesIO
+
+        >>> import io
+        >>> b = io.BytesIO(df.to_orc())  # doctest: +SKIP
+        >>> b.seek(0)  # doctest: +SKIP
+        0
+        >>> content = b.read()  # doctest: +SKIP
+        """
+        from pandas.io.orc import to_orc
+
+        return to_orc(
+            self, path, engine=engine, index=index, engine_kwargs=engine_kwargs
+        )
+
+    @overload
+    def to_html(
+        self,
+        buf: FilePath | WriteBuffer[str],
+        columns: Axes | None = ...,
+        col_space: ColspaceArgType | None = ...,
+        header: bool = ...,
+        index: bool = ...,
+        na_rep: str = ...,
+        formatters: FormattersType | None = ...,
+        float_format: FloatFormatType | None = ...,
+        sparsify: bool | None = ...,
+        index_names: bool = ...,
+        justify: str | None = ...,
+        max_rows: int | None = ...,
+        max_cols: int | None = ...,
+        show_dimensions: bool | str = ...,
+        decimal: str = ...,
+        bold_rows: bool = ...,
+        classes: str | list | tuple | None = ...,
+        escape: bool = ...,
+        notebook: bool = ...,
+        border: int | bool | None = ...,
+        table_id: str | None = ...,
+        render_links: bool = ...,
+        encoding: str | None = ...,
+    ) -> None:
+        ...
+
+    @overload
+    def to_html(
+        self,
+        buf: None = ...,
+        columns: Axes | None = ...,
+        col_space: ColspaceArgType | None = ...,
+        header: bool = ...,
+        index: bool = ...,
+        na_rep: str = ...,
+        formatters: FormattersType | None = ...,
+        float_format: FloatFormatType | None = ...,
+        sparsify: bool | None = ...,
+        index_names: bool = ...,
+        justify: str | None = ...,
+        max_rows: int | None = ...,
+        max_cols: int | None = ...,
+        show_dimensions: bool | str = ...,
+        decimal: str = ...,
+        bold_rows: bool = ...,
+        classes: str | list | tuple | None = ...,
+        escape: bool = ...,
+        notebook: bool = ...,
+        border: int | bool | None = ...,
+        table_id: str | None = ...,
+        render_links: bool = ...,
+        encoding: str | None = ...,
+    ) -> str:
+        ...
+
+    @deprecate_nonkeyword_arguments(
+        version="3.0", allowed_args=["self", "buf"], name="to_html"
+    )
+    @Substitution(
+        header_type="bool",
+        header="Whether to print column labels, default True",
+        col_space_type="str or int, list or dict of int or str",
+        col_space="The minimum width of each column in CSS length "
+        "units.  An int is assumed to be px units.",
+    )
+    @Substitution(shared_params=fmt.common_docstring, returns=fmt.return_docstring)
+    def to_html(
+        self,
+        buf: FilePath | WriteBuffer[str] | None = None,
+        columns: Axes | None = None,
+        col_space: ColspaceArgType | None = None,
+        header: bool = True,
+        index: bool = True,
+        na_rep: str = "NaN",
+        formatters: FormattersType | None = None,
+        float_format: FloatFormatType | None = None,
+        sparsify: bool | None = None,
+        index_names: bool = True,
+        justify: str | None = None,
+        max_rows: int | None = None,
+        max_cols: int | None = None,
+        show_dimensions: bool | str = False,
+        decimal: str = ".",
+        bold_rows: bool = True,
+        classes: str | list | tuple | None = None,
+        escape: bool = True,
+        notebook: bool = False,
+        border: int | bool | None = None,
+        table_id: str | None = None,
+        render_links: bool = False,
+        encoding: str | None = None,
+    ) -> str | None:
+        """
+        Render a DataFrame as an HTML table.
+        %(shared_params)s
+        bold_rows : bool, default True
+            Make the row labels bold in the output.
+        classes : str or list or tuple, default None
+            CSS class(es) to apply to the resulting html table.
+        escape : bool, default True
+            Convert the characters <, >, and & to HTML-safe sequences.
+        notebook : {True, False}, default False
+            Whether the generated HTML is for IPython Notebook.
+        border : int
+            A ``border=border`` attribute is included in the opening
+            `` tag. Default ``pd.options.display.html.border``.
+        table_id : str, optional
+            A css id is included in the opening `
` tag if specified.
+        render_links : bool, default False
+            Convert URLs to HTML links.
+        encoding : str, default "utf-8"
+            Set character encoding.
+        %(returns)s
+        See Also
+        --------
+        to_string : Convert DataFrame to a string.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame(data={'col1': [1, 2], 'col2': [4, 3]})
+        >>> html_string = '''

+        ...   
+        ...     
+        ...       
+        ...       
+        ...       
+        ...     
+        ...   
+        ...   
+        ...     
+        ...       
+        ...       
+        ...       
+        ...     
+        ...     
+        ...       
+        ...       
+        ...       
+        ...     
+        ...   
+        ... 
col1col2
014
123
'''
+        >>> assert html_string == df.to_html()
+        """
+        if justify is not None and justify not in fmt.VALID_JUSTIFY_PARAMETERS:
+            raise ValueError("Invalid value for justify parameter")
+
+        formatter = fmt.DataFrameFormatter(
+            self,
+            columns=columns,
+            col_space=col_space,
+            na_rep=na_rep,
+            header=header,
+            index=index,
+            formatters=formatters,
+            float_format=float_format,
+            bold_rows=bold_rows,
+            sparsify=sparsify,
+            justify=justify,
+            index_names=index_names,
+            escape=escape,
+            decimal=decimal,
+            max_rows=max_rows,
+            max_cols=max_cols,
+            show_dimensions=show_dimensions,
+        )
+        # TODO: a generic formatter wld b in DataFrameFormatter
+        return fmt.DataFrameRenderer(formatter).to_html(
+            buf=buf,
+            classes=classes,
+            notebook=notebook,
+            border=border,
+            encoding=encoding,
+            table_id=table_id,
+            render_links=render_links,
+        )
+
+    @overload
+    def to_xml(
+        self,
+        path_or_buffer: None = ...,
+        *,
+        index: bool = ...,
+        root_name: str | None = ...,
+        row_name: str | None = ...,
+        na_rep: str | None = ...,
+        attr_cols: list[str] | None = ...,
+        elem_cols: list[str] | None = ...,
+        namespaces: dict[str | None, str] | None = ...,
+        prefix: str | None = ...,
+        encoding: str = ...,
+        xml_declaration: bool | None = ...,
+        pretty_print: bool | None = ...,
+        parser: XMLParsers | None = ...,
+        stylesheet: FilePath | ReadBuffer[str] | ReadBuffer[bytes] | None = ...,
+        compression: CompressionOptions = ...,
+        storage_options: StorageOptions | None = ...,
+    ) -> str:
+        ...
+
+    @overload
+    def to_xml(
+        self,
+        path_or_buffer: FilePath | WriteBuffer[bytes] | WriteBuffer[str],
+        *,
+        index: bool = ...,
+        root_name: str | None = ...,
+        row_name: str | None = ...,
+        na_rep: str | None = ...,
+        attr_cols: list[str] | None = ...,
+        elem_cols: list[str] | None = ...,
+        namespaces: dict[str | None, str] | None = ...,
+        prefix: str | None = ...,
+        encoding: str = ...,
+        xml_declaration: bool | None = ...,
+        pretty_print: bool | None = ...,
+        parser: XMLParsers | None = ...,
+        stylesheet: FilePath | ReadBuffer[str] | ReadBuffer[bytes] | None = ...,
+        compression: CompressionOptions = ...,
+        storage_options: StorageOptions | None = ...,
+    ) -> None:
+        ...
+
+    @deprecate_nonkeyword_arguments(
+        version="3.0", allowed_args=["self", "path_or_buffer"], name="to_xml"
+    )
+    @doc(
+        storage_options=_shared_docs["storage_options"],
+        compression_options=_shared_docs["compression_options"] % "path_or_buffer",
+    )
+    def to_xml(
+        self,
+        path_or_buffer: FilePath | WriteBuffer[bytes] | WriteBuffer[str] | None = None,
+        index: bool = True,
+        root_name: str | None = "data",
+        row_name: str | None = "row",
+        na_rep: str | None = None,
+        attr_cols: list[str] | None = None,
+        elem_cols: list[str] | None = None,
+        namespaces: dict[str | None, str] | None = None,
+        prefix: str | None = None,
+        encoding: str = "utf-8",
+        xml_declaration: bool | None = True,
+        pretty_print: bool | None = True,
+        parser: XMLParsers | None = "lxml",
+        stylesheet: FilePath | ReadBuffer[str] | ReadBuffer[bytes] | None = None,
+        compression: CompressionOptions = "infer",
+        storage_options: StorageOptions | None = None,
+    ) -> str | None:
+        """
+        Render a DataFrame to an XML document.
+
+        .. versionadded:: 1.3.0
+
+        Parameters
+        ----------
+        path_or_buffer : str, path object, file-like object, or None, default None
+            String, path object (implementing ``os.PathLike[str]``), or file-like
+            object implementing a ``write()`` function. If None, the result is returned
+            as a string.
+        index : bool, default True
+            Whether to include index in XML document.
+        root_name : str, default 'data'
+            The name of root element in XML document.
+        row_name : str, default 'row'
+            The name of row element in XML document.
+        na_rep : str, optional
+            Missing data representation.
+        attr_cols : list-like, optional
+            List of columns to write as attributes in row element.
+            Hierarchical columns will be flattened with underscore
+            delimiting the different levels.
+        elem_cols : list-like, optional
+            List of columns to write as children in row element. By default,
+            all columns output as children of row element. Hierarchical
+            columns will be flattened with underscore delimiting the
+            different levels.
+        namespaces : dict, optional
+            All namespaces to be defined in root element. Keys of dict
+            should be prefix names and values of dict corresponding URIs.
+            Default namespaces should be given empty string key. For
+            example, ::
+
+                namespaces = {{"": "https://example.com"}}
+
+        prefix : str, optional
+            Namespace prefix to be used for every element and/or attribute
+            in document. This should be one of the keys in ``namespaces``
+            dict.
+        encoding : str, default 'utf-8'
+            Encoding of the resulting document.
+        xml_declaration : bool, default True
+            Whether to include the XML declaration at start of document.
+        pretty_print : bool, default True
+            Whether output should be pretty printed with indentation and
+            line breaks.
+        parser : {{'lxml','etree'}}, default 'lxml'
+            Parser module to use for building of tree. Only 'lxml' and
+            'etree' are supported. With 'lxml', the ability to use XSLT
+            stylesheet is supported.
+        stylesheet : str, path object or file-like object, optional
+            A URL, file-like object, or a raw string containing an XSLT
+            script used to transform the raw XML output. Script should use
+            layout of elements and attributes from original output. This
+            argument requires ``lxml`` to be installed. Only XSLT 1.0
+            scripts and not later versions is currently supported.
+        {compression_options}
+
+            .. versionchanged:: 1.4.0 Zstandard support.
+
+        {storage_options}
+
+        Returns
+        -------
+        None or str
+            If ``io`` is None, returns the resulting XML format as a
+            string. Otherwise returns None.
+
+        See Also
+        --------
+        to_json : Convert the pandas object to a JSON string.
+        to_html : Convert DataFrame to a html.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({{'shape': ['square', 'circle', 'triangle'],
+        ...                    'degrees': [360, 360, 180],
+        ...                    'sides': [4, np.nan, 3]}})
+
+        >>> df.to_xml()  # doctest: +SKIP
+        
+        
+          
+            0
+            square
+            360
+            4.0
+          
+          
+            1
+            circle
+            360
+            
+          
+          
+            2
+            triangle
+            180
+            3.0
+          
+        
+
+        >>> df.to_xml(attr_cols=[
+        ...           'index', 'shape', 'degrees', 'sides'
+        ...           ])  # doctest: +SKIP
+        
+        
+          
+          
+          
+        
+
+        >>> df.to_xml(namespaces={{"doc": "https://example.com"}},
+        ...           prefix="doc")  # doctest: +SKIP
+        
+        
+          
+            0
+            square
+            360
+            4.0
+          
+          
+            1
+            circle
+            360
+            
+          
+          
+            2
+            triangle
+            180
+            3.0
+          
+        
+        """
+
+        from pandas.io.formats.xml import (
+            EtreeXMLFormatter,
+            LxmlXMLFormatter,
+        )
+
+        lxml = import_optional_dependency("lxml.etree", errors="ignore")
+
+        TreeBuilder: type[EtreeXMLFormatter | LxmlXMLFormatter]
+
+        if parser == "lxml":
+            if lxml is not None:
+                TreeBuilder = LxmlXMLFormatter
+            else:
+                raise ImportError(
+                    "lxml not found, please install or use the etree parser."
+                )
+
+        elif parser == "etree":
+            TreeBuilder = EtreeXMLFormatter
+
+        else:
+            raise ValueError("Values for parser can only be lxml or etree.")
+
+        xml_formatter = TreeBuilder(
+            self,
+            path_or_buffer=path_or_buffer,
+            index=index,
+            root_name=root_name,
+            row_name=row_name,
+            na_rep=na_rep,
+            attr_cols=attr_cols,
+            elem_cols=elem_cols,
+            namespaces=namespaces,
+            prefix=prefix,
+            encoding=encoding,
+            xml_declaration=xml_declaration,
+            pretty_print=pretty_print,
+            stylesheet=stylesheet,
+            compression=compression,
+            storage_options=storage_options,
+        )
+
+        return xml_formatter.write_output()
+
+    # ----------------------------------------------------------------------
+    @doc(INFO_DOCSTRING, **frame_sub_kwargs)
+    def info(
+        self,
+        verbose: bool | None = None,
+        buf: WriteBuffer[str] | None = None,
+        max_cols: int | None = None,
+        memory_usage: bool | str | None = None,
+        show_counts: bool | None = None,
+    ) -> None:
+        info = DataFrameInfo(
+            data=self,
+            memory_usage=memory_usage,
+        )
+        info.render(
+            buf=buf,
+            max_cols=max_cols,
+            verbose=verbose,
+            show_counts=show_counts,
+        )
+
+    def memory_usage(self, index: bool = True, deep: bool = False) -> Series:
+        """
+        Return the memory usage of each column in bytes.
+
+        The memory usage can optionally include the contribution of
+        the index and elements of `object` dtype.
+
+        This value is displayed in `DataFrame.info` by default. This can be
+        suppressed by setting ``pandas.options.display.memory_usage`` to False.
+
+        Parameters
+        ----------
+        index : bool, default True
+            Specifies whether to include the memory usage of the DataFrame's
+            index in returned Series. If ``index=True``, the memory usage of
+            the index is the first item in the output.
+        deep : bool, default False
+            If True, introspect the data deeply by interrogating
+            `object` dtypes for system-level memory consumption, and include
+            it in the returned values.
+
+        Returns
+        -------
+        Series
+            A Series whose index is the original column names and whose values
+            is the memory usage of each column in bytes.
+
+        See Also
+        --------
+        numpy.ndarray.nbytes : Total bytes consumed by the elements of an
+            ndarray.
+        Series.memory_usage : Bytes consumed by a Series.
+        Categorical : Memory-efficient array for string values with
+            many repeated values.
+        DataFrame.info : Concise summary of a DataFrame.
+
+        Notes
+        -----
+        See the :ref:`Frequently Asked Questions ` for more
+        details.
+
+        Examples
+        --------
+        >>> dtypes = ['int64', 'float64', 'complex128', 'object', 'bool']
+        >>> data = dict([(t, np.ones(shape=5000, dtype=int).astype(t))
+        ...              for t in dtypes])
+        >>> df = pd.DataFrame(data)
+        >>> df.head()
+           int64  float64            complex128  object  bool
+        0      1      1.0              1.0+0.0j       1  True
+        1      1      1.0              1.0+0.0j       1  True
+        2      1      1.0              1.0+0.0j       1  True
+        3      1      1.0              1.0+0.0j       1  True
+        4      1      1.0              1.0+0.0j       1  True
+
+        >>> df.memory_usage()
+        Index           128
+        int64         40000
+        float64       40000
+        complex128    80000
+        object        40000
+        bool           5000
+        dtype: int64
+
+        >>> df.memory_usage(index=False)
+        int64         40000
+        float64       40000
+        complex128    80000
+        object        40000
+        bool           5000
+        dtype: int64
+
+        The memory footprint of `object` dtype columns is ignored by default:
+
+        >>> df.memory_usage(deep=True)
+        Index            128
+        int64          40000
+        float64        40000
+        complex128     80000
+        object        180000
+        bool            5000
+        dtype: int64
+
+        Use a Categorical for efficient storage of an object-dtype column with
+        many repeated values.
+
+        >>> df['object'].astype('category').memory_usage(deep=True)
+        5244
+        """
+        result = self._constructor_sliced(
+            [c.memory_usage(index=False, deep=deep) for col, c in self.items()],
+            index=self.columns,
+            dtype=np.intp,
+        )
+        if index:
+            index_memory_usage = self._constructor_sliced(
+                self.index.memory_usage(deep=deep), index=["Index"]
+            )
+            result = index_memory_usage._append(result)
+        return result
+
+    def transpose(self, *args, copy: bool = False) -> DataFrame:
+        """
+        Transpose index and columns.
+
+        Reflect the DataFrame over its main diagonal by writing rows as columns
+        and vice-versa. The property :attr:`.T` is an accessor to the method
+        :meth:`transpose`.
+
+        Parameters
+        ----------
+        *args : tuple, optional
+            Accepted for compatibility with NumPy.
+        copy : bool, default False
+            Whether to copy the data after transposing, even for DataFrames
+            with a single dtype.
+
+            Note that a copy is always required for mixed dtype DataFrames,
+            or for DataFrames with any extension types.
+
+            .. note::
+                The `copy` keyword will change behavior in pandas 3.0.
+                `Copy-on-Write
+                `__
+                will be enabled by default, which means that all methods with a
+                `copy` keyword will use a lazy copy mechanism to defer the copy and
+                ignore the `copy` keyword. The `copy` keyword will be removed in a
+                future version of pandas.
+
+                You can already get the future behavior and improvements through
+                enabling copy on write ``pd.options.mode.copy_on_write = True``
+
+        Returns
+        -------
+        DataFrame
+            The transposed DataFrame.
+
+        See Also
+        --------
+        numpy.transpose : Permute the dimensions of a given array.
+
+        Notes
+        -----
+        Transposing a DataFrame with mixed dtypes will result in a homogeneous
+        DataFrame with the `object` dtype. In such a case, a copy of the data
+        is always made.
+
+        Examples
+        --------
+        **Square DataFrame with homogeneous dtype**
+
+        >>> d1 = {'col1': [1, 2], 'col2': [3, 4]}
+        >>> df1 = pd.DataFrame(data=d1)
+        >>> df1
+           col1  col2
+        0     1     3
+        1     2     4
+
+        >>> df1_transposed = df1.T  # or df1.transpose()
+        >>> df1_transposed
+              0  1
+        col1  1  2
+        col2  3  4
+
+        When the dtype is homogeneous in the original DataFrame, we get a
+        transposed DataFrame with the same dtype:
+
+        >>> df1.dtypes
+        col1    int64
+        col2    int64
+        dtype: object
+        >>> df1_transposed.dtypes
+        0    int64
+        1    int64
+        dtype: object
+
+        **Non-square DataFrame with mixed dtypes**
+
+        >>> d2 = {'name': ['Alice', 'Bob'],
+        ...       'score': [9.5, 8],
+        ...       'employed': [False, True],
+        ...       'kids': [0, 0]}
+        >>> df2 = pd.DataFrame(data=d2)
+        >>> df2
+            name  score  employed  kids
+        0  Alice    9.5     False     0
+        1    Bob    8.0      True     0
+
+        >>> df2_transposed = df2.T  # or df2.transpose()
+        >>> df2_transposed
+                      0     1
+        name      Alice   Bob
+        score       9.5   8.0
+        employed  False  True
+        kids          0     0
+
+        When the DataFrame has mixed dtypes, we get a transposed DataFrame with
+        the `object` dtype:
+
+        >>> df2.dtypes
+        name         object
+        score       float64
+        employed       bool
+        kids          int64
+        dtype: object
+        >>> df2_transposed.dtypes
+        0    object
+        1    object
+        dtype: object
+        """
+        nv.validate_transpose(args, {})
+        # construct the args
+
+        dtypes = list(self.dtypes)
+
+        if self._can_fast_transpose:
+            # Note: tests pass without this, but this improves perf quite a bit.
+            new_vals = self._values.T
+            if copy and not using_copy_on_write():
+                new_vals = new_vals.copy()
+
+            result = self._constructor(
+                new_vals,
+                index=self.columns,
+                columns=self.index,
+                copy=False,
+                dtype=new_vals.dtype,
+            )
+            if using_copy_on_write() and len(self) > 0:
+                result._mgr.add_references(self._mgr)  # type: ignore[arg-type]
+
+        elif (
+            self._is_homogeneous_type
+            and dtypes
+            and isinstance(dtypes[0], ExtensionDtype)
+        ):
+            new_values: list
+            if isinstance(dtypes[0], BaseMaskedDtype):
+                # We have masked arrays with the same dtype. We can transpose faster.
+                from pandas.core.arrays.masked import (
+                    transpose_homogeneous_masked_arrays,
+                )
+
+                new_values = transpose_homogeneous_masked_arrays(
+                    cast(Sequence[BaseMaskedArray], self._iter_column_arrays())
+                )
+            elif isinstance(dtypes[0], ArrowDtype):
+                # We have arrow EAs with the same dtype. We can transpose faster.
+                from pandas.core.arrays.arrow.array import (
+                    ArrowExtensionArray,
+                    transpose_homogeneous_pyarrow,
+                )
+
+                new_values = transpose_homogeneous_pyarrow(
+                    cast(Sequence[ArrowExtensionArray], self._iter_column_arrays())
+                )
+            else:
+                # We have other EAs with the same dtype. We preserve dtype in transpose.
+                dtyp = dtypes[0]
+                arr_typ = dtyp.construct_array_type()
+                values = self.values
+                new_values = [arr_typ._from_sequence(row, dtype=dtyp) for row in values]
+
+            result = type(self)._from_arrays(
+                new_values,
+                index=self.columns,
+                columns=self.index,
+                verify_integrity=False,
+            )
+
+        else:
+            new_arr = self.values.T
+            if copy and not using_copy_on_write():
+                new_arr = new_arr.copy()
+            result = self._constructor(
+                new_arr,
+                index=self.columns,
+                columns=self.index,
+                dtype=new_arr.dtype,
+                # We already made a copy (more than one block)
+                copy=False,
+            )
+
+        return result.__finalize__(self, method="transpose")
+
+    @property
+    def T(self) -> DataFrame:
+        """
+        The transpose of the DataFrame.
+
+        Returns
+        -------
+        DataFrame
+            The transposed DataFrame.
+
+        See Also
+        --------
+        DataFrame.transpose : Transpose index and columns.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
+        >>> df
+           col1  col2
+        0     1     3
+        1     2     4
+
+        >>> df.T
+              0  1
+        col1  1  2
+        col2  3  4
+        """
+        return self.transpose()
+
+    # ----------------------------------------------------------------------
+    # Indexing Methods
+
+    def _ixs(self, i: int, axis: AxisInt = 0) -> Series:
+        """
+        Parameters
+        ----------
+        i : int
+        axis : int
+
+        Returns
+        -------
+        Series
+        """
+        # irow
+        if axis == 0:
+            new_mgr = self._mgr.fast_xs(i)
+
+            # if we are a copy, mark as such
+            copy = isinstance(new_mgr.array, np.ndarray) and new_mgr.array.base is None
+            result = self._constructor_sliced_from_mgr(new_mgr, axes=new_mgr.axes)
+            result._name = self.index[i]
+            result = result.__finalize__(self)
+            result._set_is_copy(self, copy=copy)
+            return result
+
+        # icol
+        else:
+            label = self.columns[i]
+
+            col_mgr = self._mgr.iget(i)
+            result = self._box_col_values(col_mgr, i)
+
+            # this is a cached value, mark it so
+            result._set_as_cached(label, self)
+            return result
+
+    def _get_column_array(self, i: int) -> ArrayLike:
+        """
+        Get the values of the i'th column (ndarray or ExtensionArray, as stored
+        in the Block)
+
+        Warning! The returned array is a view but doesn't handle Copy-on-Write,
+        so this should be used with caution (for read-only purposes).
+        """
+        return self._mgr.iget_values(i)
+
+    def _iter_column_arrays(self) -> Iterator[ArrayLike]:
+        """
+        Iterate over the arrays of all columns in order.
+        This returns the values as stored in the Block (ndarray or ExtensionArray).
+
+        Warning! The returned array is a view but doesn't handle Copy-on-Write,
+        so this should be used with caution (for read-only purposes).
+        """
+        if isinstance(self._mgr, ArrayManager):
+            yield from self._mgr.arrays
+        else:
+            for i in range(len(self.columns)):
+                yield self._get_column_array(i)
+
+    def _getitem_nocopy(self, key: list):
+        """
+        Behaves like __getitem__, but returns a view in cases where __getitem__
+        would make a copy.
+        """
+        # TODO(CoW): can be removed if/when we are always Copy-on-Write
+        indexer = self.columns._get_indexer_strict(key, "columns")[1]
+        new_axis = self.columns[indexer]
+
+        new_mgr = self._mgr.reindex_indexer(
+            new_axis,
+            indexer,
+            axis=0,
+            allow_dups=True,
+            copy=False,
+            only_slice=True,
+        )
+        result = self._constructor_from_mgr(new_mgr, axes=new_mgr.axes)
+        result = result.__finalize__(self)
+        return result
+
+    def __getitem__(self, key):
+        check_dict_or_set_indexers(key)
+        key = lib.item_from_zerodim(key)
+        key = com.apply_if_callable(key, self)
+
+        if is_hashable(key) and not is_iterator(key):
+            # is_iterator to exclude generator e.g. test_getitem_listlike
+            # shortcut if the key is in columns
+            is_mi = isinstance(self.columns, MultiIndex)
+            # GH#45316 Return view if key is not duplicated
+            # Only use drop_duplicates with duplicates for performance
+            if not is_mi and (
+                self.columns.is_unique
+                and key in self.columns
+                or key in self.columns.drop_duplicates(keep=False)
+            ):
+                return self._get_item_cache(key)
+
+            elif is_mi and self.columns.is_unique and key in self.columns:
+                return self._getitem_multilevel(key)
+
+        # Do we have a slicer (on rows)?
+        if isinstance(key, slice):
+            return self._getitem_slice(key)
+
+        # Do we have a (boolean) DataFrame?
+        if isinstance(key, DataFrame):
+            return self.where(key)
+
+        # Do we have a (boolean) 1d indexer?
+        if com.is_bool_indexer(key):
+            return self._getitem_bool_array(key)
+
+        # We are left with two options: a single key, and a collection of keys,
+        # We interpret tuples as collections only for non-MultiIndex
+        is_single_key = isinstance(key, tuple) or not is_list_like(key)
+
+        if is_single_key:
+            if self.columns.nlevels > 1:
+                return self._getitem_multilevel(key)
+            indexer = self.columns.get_loc(key)
+            if is_integer(indexer):
+                indexer = [indexer]
+        else:
+            if is_iterator(key):
+                key = list(key)
+            indexer = self.columns._get_indexer_strict(key, "columns")[1]
+
+        # take() does not accept boolean indexers
+        if getattr(indexer, "dtype", None) == bool:
+            indexer = np.where(indexer)[0]
+
+        if isinstance(indexer, slice):
+            return self._slice(indexer, axis=1)
+
+        data = self._take_with_is_copy(indexer, axis=1)
+
+        if is_single_key:
+            # What does looking for a single key in a non-unique index return?
+            # The behavior is inconsistent. It returns a Series, except when
+            # - the key itself is repeated (test on data.shape, #9519), or
+            # - we have a MultiIndex on columns (test on self.columns, #21309)
+            if data.shape[1] == 1 and not isinstance(self.columns, MultiIndex):
+                # GH#26490 using data[key] can cause RecursionError
+                return data._get_item_cache(key)
+
+        return data
+
+    def _getitem_bool_array(self, key):
+        # also raises Exception if object array with NA values
+        # warning here just in case -- previously __setitem__ was
+        # reindexing but __getitem__ was not; it seems more reasonable to
+        # go with the __setitem__ behavior since that is more consistent
+        # with all other indexing behavior
+        if isinstance(key, Series) and not key.index.equals(self.index):
+            warnings.warn(
+                "Boolean Series key will be reindexed to match DataFrame index.",
+                UserWarning,
+                stacklevel=find_stack_level(),
+            )
+        elif len(key) != len(self.index):
+            raise ValueError(
+                f"Item wrong length {len(key)} instead of {len(self.index)}."
+            )
+
+        # check_bool_indexer will throw exception if Series key cannot
+        # be reindexed to match DataFrame rows
+        key = check_bool_indexer(self.index, key)
+
+        if key.all():
+            return self.copy(deep=None)
+
+        indexer = key.nonzero()[0]
+        return self._take_with_is_copy(indexer, axis=0)
+
+    def _getitem_multilevel(self, key):
+        # self.columns is a MultiIndex
+        loc = self.columns.get_loc(key)
+        if isinstance(loc, (slice, np.ndarray)):
+            new_columns = self.columns[loc]
+            result_columns = maybe_droplevels(new_columns, key)
+            result = self.iloc[:, loc]
+            result.columns = result_columns
+
+            # If there is only one column being returned, and its name is
+            # either an empty string, or a tuple with an empty string as its
+            # first element, then treat the empty string as a placeholder
+            # and return the column as if the user had provided that empty
+            # string in the key. If the result is a Series, exclude the
+            # implied empty string from its name.
+            if len(result.columns) == 1:
+                # e.g. test_frame_getitem_multicolumn_empty_level,
+                #  test_frame_mixed_depth_get, test_loc_setitem_single_column_slice
+                top = result.columns[0]
+                if isinstance(top, tuple):
+                    top = top[0]
+                if top == "":
+                    result = result[""]
+                    if isinstance(result, Series):
+                        result = self._constructor_sliced(
+                            result, index=self.index, name=key
+                        )
+
+            result._set_is_copy(self)
+            return result
+        else:
+            # loc is neither a slice nor ndarray, so must be an int
+            return self._ixs(loc, axis=1)
+
+    def _get_value(self, index, col, takeable: bool = False) -> Scalar:
+        """
+        Quickly retrieve single value at passed column and index.
+
+        Parameters
+        ----------
+        index : row label
+        col : column label
+        takeable : interpret the index/col as indexers, default False
+
+        Returns
+        -------
+        scalar
+
+        Notes
+        -----
+        Assumes that both `self.index._index_as_unique` and
+        `self.columns._index_as_unique`; Caller is responsible for checking.
+        """
+        if takeable:
+            series = self._ixs(col, axis=1)
+            return series._values[index]
+
+        series = self._get_item_cache(col)
+        engine = self.index._engine
+
+        if not isinstance(self.index, MultiIndex):
+            # CategoricalIndex: Trying to use the engine fastpath may give incorrect
+            #  results if our categories are integers that dont match our codes
+            # IntervalIndex: IntervalTree has no get_loc
+            row = self.index.get_loc(index)
+            return series._values[row]
+
+        # For MultiIndex going through engine effectively restricts us to
+        #  same-length tuples; see test_get_set_value_no_partial_indexing
+        loc = engine.get_loc(index)
+        return series._values[loc]
+
+    def isetitem(self, loc, value) -> None:
+        """
+        Set the given value in the column with position `loc`.
+
+        This is a positional analogue to ``__setitem__``.
+
+        Parameters
+        ----------
+        loc : int or sequence of ints
+            Index position for the column.
+        value : scalar or arraylike
+            Value(s) for the column.
+
+        Notes
+        -----
+        ``frame.isetitem(loc, value)`` is an in-place method as it will
+        modify the DataFrame in place (not returning a new object). In contrast to
+        ``frame.iloc[:, i] = value`` which will try to update the existing values in
+        place, ``frame.isetitem(loc, value)`` will not update the values of the column
+        itself in place, it will instead insert a new array.
+
+        In cases where ``frame.columns`` is unique, this is equivalent to
+        ``frame[frame.columns[i]] = value``.
+        """
+        if isinstance(value, DataFrame):
+            if is_integer(loc):
+                loc = [loc]
+
+            if len(loc) != len(value.columns):
+                raise ValueError(
+                    f"Got {len(loc)} positions but value has {len(value.columns)} "
+                    f"columns."
+                )
+
+            for i, idx in enumerate(loc):
+                arraylike, refs = self._sanitize_column(value.iloc[:, i])
+                self._iset_item_mgr(idx, arraylike, inplace=False, refs=refs)
+            return
+
+        arraylike, refs = self._sanitize_column(value)
+        self._iset_item_mgr(loc, arraylike, inplace=False, refs=refs)
+
+    def __setitem__(self, key, value) -> None:
+        if not PYPY and not WARNING_CHECK_DISABLED and using_copy_on_write():
+            if sys.getrefcount(self) <= 3:
+                warnings.warn(
+                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
+                )
+        elif not PYPY and not WARNING_CHECK_DISABLED and not using_copy_on_write():
+            if sys.getrefcount(self) <= 3 and (
+                warn_copy_on_write()
+                or (
+                    not warn_copy_on_write()
+                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
+                )
+            ):
+                warnings.warn(
+                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
+                )
+
+        key = com.apply_if_callable(key, self)
+
+        # see if we can slice the rows
+        if isinstance(key, slice):
+            slc = self.index._convert_slice_indexer(key, kind="getitem")
+            return self._setitem_slice(slc, value)
+
+        if isinstance(key, DataFrame) or getattr(key, "ndim", None) == 2:
+            self._setitem_frame(key, value)
+        elif isinstance(key, (Series, np.ndarray, list, Index)):
+            self._setitem_array(key, value)
+        elif isinstance(value, DataFrame):
+            self._set_item_frame_value(key, value)
+        elif (
+            is_list_like(value)
+            and not self.columns.is_unique
+            and 1 < len(self.columns.get_indexer_for([key])) == len(value)
+        ):
+            # Column to set is duplicated
+            self._setitem_array([key], value)
+        else:
+            # set column
+            self._set_item(key, value)
+
+    def _setitem_slice(self, key: slice, value) -> None:
+        # NB: we can't just use self.loc[key] = value because that
+        #  operates on labels and we need to operate positional for
+        #  backwards-compat, xref GH#31469
+        self._check_setitem_copy()
+        self.iloc[key] = value
+
+    def _setitem_array(self, key, value):
+        # also raises Exception if object array with NA values
+        if com.is_bool_indexer(key):
+            # bool indexer is indexing along rows
+            if len(key) != len(self.index):
+                raise ValueError(
+                    f"Item wrong length {len(key)} instead of {len(self.index)}!"
+                )
+            key = check_bool_indexer(self.index, key)
+            indexer = key.nonzero()[0]
+            self._check_setitem_copy()
+            if isinstance(value, DataFrame):
+                # GH#39931 reindex since iloc does not align
+                value = value.reindex(self.index.take(indexer))
+            self.iloc[indexer] = value
+
+        else:
+            # Note: unlike self.iloc[:, indexer] = value, this will
+            #  never try to overwrite values inplace
+
+            if isinstance(value, DataFrame):
+                check_key_length(self.columns, key, value)
+                for k1, k2 in zip(key, value.columns):
+                    self[k1] = value[k2]
+
+            elif not is_list_like(value):
+                for col in key:
+                    self[col] = value
+
+            elif isinstance(value, np.ndarray) and value.ndim == 2:
+                self._iset_not_inplace(key, value)
+
+            elif np.ndim(value) > 1:
+                # list of lists
+                value = DataFrame(value).values
+                return self._setitem_array(key, value)
+
+            else:
+                self._iset_not_inplace(key, value)
+
+    def _iset_not_inplace(self, key, value):
+        # GH#39510 when setting with df[key] = obj with a list-like key and
+        #  list-like value, we iterate over those listlikes and set columns
+        #  one at a time.  This is different from dispatching to
+        #  `self.loc[:, key]= value`  because loc.__setitem__ may overwrite
+        #  data inplace, whereas this will insert new arrays.
+
+        def igetitem(obj, i: int):
+            # Note: we catch DataFrame obj before getting here, but
+            #  hypothetically would return obj.iloc[:, i]
+            if isinstance(obj, np.ndarray):
+                return obj[..., i]
+            else:
+                return obj[i]
+
+        if self.columns.is_unique:
+            if np.shape(value)[-1] != len(key):
+                raise ValueError("Columns must be same length as key")
+
+            for i, col in enumerate(key):
+                self[col] = igetitem(value, i)
+
+        else:
+            ilocs = self.columns.get_indexer_non_unique(key)[0]
+            if (ilocs < 0).any():
+                # key entries not in self.columns
+                raise NotImplementedError
+
+            if np.shape(value)[-1] != len(ilocs):
+                raise ValueError("Columns must be same length as key")
+
+            assert np.ndim(value) <= 2
+
+            orig_columns = self.columns
+
+            # Using self.iloc[:, i] = ... may set values inplace, which
+            #  by convention we do not do in __setitem__
+            try:
+                self.columns = Index(range(len(self.columns)))
+                for i, iloc in enumerate(ilocs):
+                    self[iloc] = igetitem(value, i)
+            finally:
+                self.columns = orig_columns
+
+    def _setitem_frame(self, key, value):
+        # support boolean setting with DataFrame input, e.g.
+        # df[df > df2] = 0
+        if isinstance(key, np.ndarray):
+            if key.shape != self.shape:
+                raise ValueError("Array conditional must be same shape as self")
+            key = self._constructor(key, **self._construct_axes_dict(), copy=False)
+
+        if key.size and not all(is_bool_dtype(dtype) for dtype in key.dtypes):
+            raise TypeError(
+                "Must pass DataFrame or 2-d ndarray with boolean values only"
+            )
+
+        self._check_setitem_copy()
+        self._where(-key, value, inplace=True)
+
+    def _set_item_frame_value(self, key, value: DataFrame) -> None:
+        self._ensure_valid_index(value)
+
+        # align columns
+        if key in self.columns:
+            loc = self.columns.get_loc(key)
+            cols = self.columns[loc]
+            len_cols = 1 if is_scalar(cols) or isinstance(cols, tuple) else len(cols)
+            if len_cols != len(value.columns):
+                raise ValueError("Columns must be same length as key")
+
+            # align right-hand-side columns if self.columns
+            # is multi-index and self[key] is a sub-frame
+            if isinstance(self.columns, MultiIndex) and isinstance(
+                loc, (slice, Series, np.ndarray, Index)
+            ):
+                cols_droplevel = maybe_droplevels(cols, key)
+                if len(cols_droplevel) and not cols_droplevel.equals(value.columns):
+                    value = value.reindex(cols_droplevel, axis=1)
+
+                for col, col_droplevel in zip(cols, cols_droplevel):
+                    self[col] = value[col_droplevel]
+                return
+
+            if is_scalar(cols):
+                self[cols] = value[value.columns[0]]
+                return
+
+            locs: np.ndarray | list
+            if isinstance(loc, slice):
+                locs = np.arange(loc.start, loc.stop, loc.step)
+            elif is_scalar(loc):
+                locs = [loc]
+            else:
+                locs = loc.nonzero()[0]
+
+            return self.isetitem(locs, value)
+
+        if len(value.columns) > 1:
+            raise ValueError(
+                "Cannot set a DataFrame with multiple columns to the single "
+                f"column {key}"
+            )
+        elif len(value.columns) == 0:
+            raise ValueError(
+                f"Cannot set a DataFrame without columns to the column {key}"
+            )
+
+        self[key] = value[value.columns[0]]
+
+    def _iset_item_mgr(
+        self,
+        loc: int | slice | np.ndarray,
+        value,
+        inplace: bool = False,
+        refs: BlockValuesRefs | None = None,
+    ) -> None:
+        # when called from _set_item_mgr loc can be anything returned from get_loc
+        self._mgr.iset(loc, value, inplace=inplace, refs=refs)
+        self._clear_item_cache()
+
+    def _set_item_mgr(
+        self, key, value: ArrayLike, refs: BlockValuesRefs | None = None
+    ) -> None:
+        try:
+            loc = self._info_axis.get_loc(key)
+        except KeyError:
+            # This item wasn't present, just insert at end
+            self._mgr.insert(len(self._info_axis), key, value, refs)
+        else:
+            self._iset_item_mgr(loc, value, refs=refs)
+
+        # check if we are modifying a copy
+        # try to set first as we want an invalid
+        # value exception to occur first
+        if len(self):
+            self._check_setitem_copy()
+
+    def _iset_item(self, loc: int, value: Series, inplace: bool = True) -> None:
+        # We are only called from _replace_columnwise which guarantees that
+        # no reindex is necessary
+        if using_copy_on_write():
+            self._iset_item_mgr(
+                loc, value._values, inplace=inplace, refs=value._references
+            )
+        else:
+            self._iset_item_mgr(loc, value._values.copy(), inplace=True)
+
+        # check if we are modifying a copy
+        # try to set first as we want an invalid
+        # value exception to occur first
+        if len(self):
+            self._check_setitem_copy()
+
+    def _set_item(self, key, value) -> None:
+        """
+        Add series to DataFrame in specified column.
+
+        If series is a numpy-array (not a Series/TimeSeries), it must be the
+        same length as the DataFrames index or an error will be thrown.
+
+        Series/TimeSeries will be conformed to the DataFrames index to
+        ensure homogeneity.
+        """
+        value, refs = self._sanitize_column(value)
+
+        if (
+            key in self.columns
+            and value.ndim == 1
+            and not isinstance(value.dtype, ExtensionDtype)
+        ):
+            # broadcast across multiple columns if necessary
+            if not self.columns.is_unique or isinstance(self.columns, MultiIndex):
+                existing_piece = self[key]
+                if isinstance(existing_piece, DataFrame):
+                    value = np.tile(value, (len(existing_piece.columns), 1)).T
+                    refs = None
+
+        self._set_item_mgr(key, value, refs)
+
+    def _set_value(
+        self, index: IndexLabel, col, value: Scalar, takeable: bool = False
+    ) -> None:
+        """
+        Put single value at passed column and index.
+
+        Parameters
+        ----------
+        index : Label
+            row label
+        col : Label
+            column label
+        value : scalar
+        takeable : bool, default False
+            Sets whether or not index/col interpreted as indexers
+        """
+        try:
+            if takeable:
+                icol = col
+                iindex = cast(int, index)
+            else:
+                icol = self.columns.get_loc(col)
+                iindex = self.index.get_loc(index)
+            self._mgr.column_setitem(icol, iindex, value, inplace_only=True)
+            self._clear_item_cache()
+
+        except (KeyError, TypeError, ValueError, LossySetitemError):
+            # get_loc might raise a KeyError for missing labels (falling back
+            #  to (i)loc will do expansion of the index)
+            # column_setitem will do validation that may raise TypeError,
+            #  ValueError, or LossySetitemError
+            # set using a non-recursive method & reset the cache
+            if takeable:
+                self.iloc[index, col] = value
+            else:
+                self.loc[index, col] = value
+            self._item_cache.pop(col, None)
+
+        except InvalidIndexError as ii_err:
+            # GH48729: Seems like you are trying to assign a value to a
+            # row when only scalar options are permitted
+            raise InvalidIndexError(
+                f"You can only assign a scalar value not a {type(value)}"
+            ) from ii_err
+
+    def _ensure_valid_index(self, value) -> None:
+        """
+        Ensure that if we don't have an index, that we can create one from the
+        passed value.
+        """
+        # GH5632, make sure that we are a Series convertible
+        if not len(self.index) and is_list_like(value) and len(value):
+            if not isinstance(value, DataFrame):
+                try:
+                    value = Series(value)
+                except (ValueError, NotImplementedError, TypeError) as err:
+                    raise ValueError(
+                        "Cannot set a frame with no defined index "
+                        "and a value that cannot be converted to a Series"
+                    ) from err
+
+            # GH31368 preserve name of index
+            index_copy = value.index.copy()
+            if self.index.name is not None:
+                index_copy.name = self.index.name
+
+            self._mgr = self._mgr.reindex_axis(index_copy, axis=1, fill_value=np.nan)
+
+    def _box_col_values(self, values: SingleDataManager, loc: int) -> Series:
+        """
+        Provide boxed values for a column.
+        """
+        # Lookup in columns so that if e.g. a str datetime was passed
+        #  we attach the Timestamp object as the name.
+        name = self.columns[loc]
+        # We get index=self.index bc values is a SingleDataManager
+        obj = self._constructor_sliced_from_mgr(values, axes=values.axes)
+        obj._name = name
+        return obj.__finalize__(self)
+
+    # ----------------------------------------------------------------------
+    # Lookup Caching
+
+    def _clear_item_cache(self) -> None:
+        self._item_cache.clear()
+
+    def _get_item_cache(self, item: Hashable) -> Series:
+        """Return the cached item, item represents a label indexer."""
+        if using_copy_on_write() or warn_copy_on_write():
+            loc = self.columns.get_loc(item)
+            return self._ixs(loc, axis=1)
+
+        cache = self._item_cache
+        res = cache.get(item)
+        if res is None:
+            # All places that call _get_item_cache have unique columns,
+            #  pending resolution of GH#33047
+
+            loc = self.columns.get_loc(item)
+            res = self._ixs(loc, axis=1)
+
+            cache[item] = res
+
+            # for a chain
+            res._is_copy = self._is_copy
+        return res
+
+    def _reset_cacher(self) -> None:
+        # no-op for DataFrame
+        pass
+
+    def _maybe_cache_changed(self, item, value: Series, inplace: bool) -> None:
+        """
+        The object has called back to us saying maybe it has changed.
+        """
+        loc = self._info_axis.get_loc(item)
+        arraylike = value._values
+
+        old = self._ixs(loc, axis=1)
+        if old._values is value._values and inplace:
+            # GH#46149 avoid making unnecessary copies/block-splitting
+            return
+
+        self._mgr.iset(loc, arraylike, inplace=inplace)
+
+    # ----------------------------------------------------------------------
+    # Unsorted
+
+    @overload
+    def query(self, expr: str, *, inplace: Literal[False] = ..., **kwargs) -> DataFrame:
+        ...
+
+    @overload
+    def query(self, expr: str, *, inplace: Literal[True], **kwargs) -> None:
+        ...
+
+    @overload
+    def query(self, expr: str, *, inplace: bool = ..., **kwargs) -> DataFrame | None:
+        ...
+
+    def query(self, expr: str, *, inplace: bool = False, **kwargs) -> DataFrame | None:
+        """
+        Query the columns of a DataFrame with a boolean expression.
+
+        Parameters
+        ----------
+        expr : str
+            The query string to evaluate.
+
+            You can refer to variables
+            in the environment by prefixing them with an '@' character like
+            ``@a + b``.
+
+            You can refer to column names that are not valid Python variable names
+            by surrounding them in backticks. Thus, column names containing spaces
+            or punctuations (besides underscores) or starting with digits must be
+            surrounded by backticks. (For example, a column named "Area (cm^2)" would
+            be referenced as ```Area (cm^2)```). Column names which are Python keywords
+            (like "list", "for", "import", etc) cannot be used.
+
+            For example, if one of your columns is called ``a a`` and you want
+            to sum it with ``b``, your query should be ```a a` + b``.
+
+        inplace : bool
+            Whether to modify the DataFrame rather than creating a new one.
+        **kwargs
+            See the documentation for :func:`eval` for complete details
+            on the keyword arguments accepted by :meth:`DataFrame.query`.
+
+        Returns
+        -------
+        DataFrame or None
+            DataFrame resulting from the provided query expression or
+            None if ``inplace=True``.
+
+        See Also
+        --------
+        eval : Evaluate a string describing operations on
+            DataFrame columns.
+        DataFrame.eval : Evaluate a string describing operations on
+            DataFrame columns.
+
+        Notes
+        -----
+        The result of the evaluation of this expression is first passed to
+        :attr:`DataFrame.loc` and if that fails because of a
+        multidimensional key (e.g., a DataFrame) then the result will be passed
+        to :meth:`DataFrame.__getitem__`.
+
+        This method uses the top-level :func:`eval` function to
+        evaluate the passed query.
+
+        The :meth:`~pandas.DataFrame.query` method uses a slightly
+        modified Python syntax by default. For example, the ``&`` and ``|``
+        (bitwise) operators have the precedence of their boolean cousins,
+        :keyword:`and` and :keyword:`or`. This *is* syntactically valid Python,
+        however the semantics are different.
+
+        You can change the semantics of the expression by passing the keyword
+        argument ``parser='python'``. This enforces the same semantics as
+        evaluation in Python space. Likewise, you can pass ``engine='python'``
+        to evaluate an expression using Python itself as a backend. This is not
+        recommended as it is inefficient compared to using ``numexpr`` as the
+        engine.
+
+        The :attr:`DataFrame.index` and
+        :attr:`DataFrame.columns` attributes of the
+        :class:`~pandas.DataFrame` instance are placed in the query namespace
+        by default, which allows you to treat both the index and columns of the
+        frame as a column in the frame.
+        The identifier ``index`` is used for the frame index; you can also
+        use the name of the index to identify it in a query. Please note that
+        Python keywords may not be used as identifiers.
+
+        For further details and examples see the ``query`` documentation in
+        :ref:`indexing `.
+
+        *Backtick quoted variables*
+
+        Backtick quoted variables are parsed as literal Python code and
+        are converted internally to a Python valid identifier.
+        This can lead to the following problems.
+
+        During parsing a number of disallowed characters inside the backtick
+        quoted string are replaced by strings that are allowed as a Python identifier.
+        These characters include all operators in Python, the space character, the
+        question mark, the exclamation mark, the dollar sign, and the euro sign.
+        For other characters that fall outside the ASCII range (U+0001..U+007F)
+        and those that are not further specified in PEP 3131,
+        the query parser will raise an error.
+        This excludes whitespace different than the space character,
+        but also the hashtag (as it is used for comments) and the backtick
+        itself (backtick can also not be escaped).
+
+        In a special case, quotes that make a pair around a backtick can
+        confuse the parser.
+        For example, ```it's` > `that's``` will raise an error,
+        as it forms a quoted string (``'s > `that'``) with a backtick inside.
+
+        See also the Python documentation about lexical analysis
+        (https://docs.python.org/3/reference/lexical_analysis.html)
+        in combination with the source code in :mod:`pandas.core.computation.parsing`.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'A': range(1, 6),
+        ...                    'B': range(10, 0, -2),
+        ...                    'C C': range(10, 5, -1)})
+        >>> df
+           A   B  C C
+        0  1  10   10
+        1  2   8    9
+        2  3   6    8
+        3  4   4    7
+        4  5   2    6
+        >>> df.query('A > B')
+           A  B  C C
+        4  5  2    6
+
+        The previous expression is equivalent to
+
+        >>> df[df.A > df.B]
+           A  B  C C
+        4  5  2    6
+
+        For columns with spaces in their name, you can use backtick quoting.
+
+        >>> df.query('B == `C C`')
+           A   B  C C
+        0  1  10   10
+
+        The previous expression is equivalent to
+
+        >>> df[df.B == df['C C']]
+           A   B  C C
+        0  1  10   10
+        """
+        inplace = validate_bool_kwarg(inplace, "inplace")
+        if not isinstance(expr, str):
+            msg = f"expr must be a string to be evaluated, {type(expr)} given"
+            raise ValueError(msg)
+        kwargs["level"] = kwargs.pop("level", 0) + 1
+        kwargs["target"] = None
+        res = self.eval(expr, **kwargs)
+
+        try:
+            result = self.loc[res]
+        except ValueError:
+            # when res is multi-dimensional loc raises, but this is sometimes a
+            # valid query
+            result = self[res]
+
+        if inplace:
+            self._update_inplace(result)
+            return None
+        else:
+            return result
+
+    @overload
+    def eval(self, expr: str, *, inplace: Literal[False] = ..., **kwargs) -> Any:
+        ...
+
+    @overload
+    def eval(self, expr: str, *, inplace: Literal[True], **kwargs) -> None:
+        ...
+
+    def eval(self, expr: str, *, inplace: bool = False, **kwargs) -> Any | None:
+        """
+        Evaluate a string describing operations on DataFrame columns.
+
+        Operates on columns only, not specific rows or elements.  This allows
+        `eval` to run arbitrary code, which can make you vulnerable to code
+        injection if you pass user input to this function.
+
+        Parameters
+        ----------
+        expr : str
+            The expression string to evaluate.
+        inplace : bool, default False
+            If the expression contains an assignment, whether to perform the
+            operation inplace and mutate the existing DataFrame. Otherwise,
+            a new DataFrame is returned.
+        **kwargs
+            See the documentation for :func:`eval` for complete details
+            on the keyword arguments accepted by
+            :meth:`~pandas.DataFrame.query`.
+
+        Returns
+        -------
+        ndarray, scalar, pandas object, or None
+            The result of the evaluation or None if ``inplace=True``.
+
+        See Also
+        --------
+        DataFrame.query : Evaluates a boolean expression to query the columns
+            of a frame.
+        DataFrame.assign : Can evaluate an expression or function to create new
+            values for a column.
+        eval : Evaluate a Python expression as a string using various
+            backends.
+
+        Notes
+        -----
+        For more details see the API documentation for :func:`~eval`.
+        For detailed examples see :ref:`enhancing performance with eval
+        `.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'A': range(1, 6), 'B': range(10, 0, -2)})
+        >>> df
+           A   B
+        0  1  10
+        1  2   8
+        2  3   6
+        3  4   4
+        4  5   2
+        >>> df.eval('A + B')
+        0    11
+        1    10
+        2     9
+        3     8
+        4     7
+        dtype: int64
+
+        Assignment is allowed though by default the original DataFrame is not
+        modified.
+
+        >>> df.eval('C = A + B')
+           A   B   C
+        0  1  10  11
+        1  2   8  10
+        2  3   6   9
+        3  4   4   8
+        4  5   2   7
+        >>> df
+           A   B
+        0  1  10
+        1  2   8
+        2  3   6
+        3  4   4
+        4  5   2
+
+        Multiple columns can be assigned to using multi-line expressions:
+
+        >>> df.eval(
+        ...     '''
+        ... C = A + B
+        ... D = A - B
+        ... '''
+        ... )
+           A   B   C  D
+        0  1  10  11 -9
+        1  2   8  10 -6
+        2  3   6   9 -3
+        3  4   4   8  0
+        4  5   2   7  3
+        """
+        from pandas.core.computation.eval import eval as _eval
+
+        inplace = validate_bool_kwarg(inplace, "inplace")
+        kwargs["level"] = kwargs.pop("level", 0) + 1
+        index_resolvers = self._get_index_resolvers()
+        column_resolvers = self._get_cleaned_column_resolvers()
+        resolvers = column_resolvers, index_resolvers
+        if "target" not in kwargs:
+            kwargs["target"] = self
+        kwargs["resolvers"] = tuple(kwargs.get("resolvers", ())) + resolvers
+
+        return _eval(expr, inplace=inplace, **kwargs)
+
+    def select_dtypes(self, include=None, exclude=None) -> Self:
+        """
+        Return a subset of the DataFrame's columns based on the column dtypes.
+
+        Parameters
+        ----------
+        include, exclude : scalar or list-like
+            A selection of dtypes or strings to be included/excluded. At least
+            one of these parameters must be supplied.
+
+        Returns
+        -------
+        DataFrame
+            The subset of the frame including the dtypes in ``include`` and
+            excluding the dtypes in ``exclude``.
+
+        Raises
+        ------
+        ValueError
+            * If both of ``include`` and ``exclude`` are empty
+            * If ``include`` and ``exclude`` have overlapping elements
+            * If any kind of string dtype is passed in.
+
+        See Also
+        --------
+        DataFrame.dtypes: Return Series with the data type of each column.
+
+        Notes
+        -----
+        * To select all *numeric* types, use ``np.number`` or ``'number'``
+        * To select strings you must use the ``object`` dtype, but note that
+          this will return *all* object dtype columns. With
+          ``pd.options.future.infer_string`` enabled, using ``"str"`` will
+          work to select all string columns.
+        * See the `numpy dtype hierarchy
+          `__
+        * To select datetimes, use ``np.datetime64``, ``'datetime'`` or
+          ``'datetime64'``
+        * To select timedeltas, use ``np.timedelta64``, ``'timedelta'`` or
+          ``'timedelta64'``
+        * To select Pandas categorical dtypes, use ``'category'``
+        * To select Pandas datetimetz dtypes, use ``'datetimetz'``
+          or ``'datetime64[ns, tz]'``
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'a': [1, 2] * 3,
+        ...                    'b': [True, False] * 3,
+        ...                    'c': [1.0, 2.0] * 3})
+        >>> df
+                a      b  c
+        0       1   True  1.0
+        1       2  False  2.0
+        2       1   True  1.0
+        3       2  False  2.0
+        4       1   True  1.0
+        5       2  False  2.0
+
+        >>> df.select_dtypes(include='bool')
+           b
+        0  True
+        1  False
+        2  True
+        3  False
+        4  True
+        5  False
+
+        >>> df.select_dtypes(include=['float64'])
+           c
+        0  1.0
+        1  2.0
+        2  1.0
+        3  2.0
+        4  1.0
+        5  2.0
+
+        >>> df.select_dtypes(exclude=['int64'])
+               b    c
+        0   True  1.0
+        1  False  2.0
+        2   True  1.0
+        3  False  2.0
+        4   True  1.0
+        5  False  2.0
+        """
+        if not is_list_like(include):
+            include = (include,) if include is not None else ()
+        if not is_list_like(exclude):
+            exclude = (exclude,) if exclude is not None else ()
+
+        selection = (frozenset(include), frozenset(exclude))
+
+        if not any(selection):
+            raise ValueError("at least one of include or exclude must be nonempty")
+
+        # convert the myriad valid dtypes object to a single representation
+        def check_int_infer_dtype(dtypes):
+            converted_dtypes: list[type] = []
+            for dtype in dtypes:
+                # Numpy maps int to different types (int32, in64) on Windows and Linux
+                # see https://github.com/numpy/numpy/issues/9464
+                if (isinstance(dtype, str) and dtype == "int") or (dtype is int):
+                    converted_dtypes.append(np.int32)
+                    converted_dtypes.append(np.int64)
+                elif dtype == "float" or dtype is float:
+                    # GH#42452 : np.dtype("float") coerces to np.float64 from Numpy 1.20
+                    converted_dtypes.extend([np.float64, np.float32])
+                else:
+                    converted_dtypes.append(infer_dtype_from_object(dtype))
+            return frozenset(converted_dtypes)
+
+        include = check_int_infer_dtype(include)
+        exclude = check_int_infer_dtype(exclude)
+
+        for dtypes in (include, exclude):
+            invalidate_string_dtypes(dtypes)
+
+        # can't both include AND exclude!
+        if not include.isdisjoint(exclude):
+            raise ValueError(f"include and exclude overlap on {(include & exclude)}")
+
+        def dtype_predicate(dtype: DtypeObj, dtypes_set) -> bool:
+            # GH 46870: BooleanDtype._is_numeric == True but should be excluded
+            dtype = dtype if not isinstance(dtype, ArrowDtype) else dtype.numpy_dtype
+            return (
+                issubclass(dtype.type, tuple(dtypes_set))
+                or (
+                    np.number in dtypes_set
+                    and getattr(dtype, "_is_numeric", False)
+                    and not is_bool_dtype(dtype)
+                )
+                # backwards compat for the default `str` dtype being selected by object
+                or (
+                    isinstance(dtype, StringDtype)
+                    and dtype.na_value is np.nan
+                    and np.object_ in dtypes_set
+                )
+            )
+
+        def predicate(arr: ArrayLike) -> bool:
+            dtype = arr.dtype
+            if include:
+                if not dtype_predicate(dtype, include):
+                    return False
+
+            if exclude:
+                if dtype_predicate(dtype, exclude):
+                    return False
+
+            return True
+
+        mgr = self._mgr._get_data_subset(predicate).copy(deep=None)
+        # error: Incompatible return value type (got "DataFrame", expected "Self")
+        return self._constructor_from_mgr(mgr, axes=mgr.axes).__finalize__(self)  # type: ignore[return-value]
+
+    def insert(
+        self,
+        loc: int,
+        column: Hashable,
+        value: Scalar | AnyArrayLike,
+        allow_duplicates: bool | lib.NoDefault = lib.no_default,
+    ) -> None:
+        """
+        Insert column into DataFrame at specified location.
+
+        Raises a ValueError if `column` is already contained in the DataFrame,
+        unless `allow_duplicates` is set to True.
+
+        Parameters
+        ----------
+        loc : int
+            Insertion index. Must verify 0 <= loc <= len(columns).
+        column : str, number, or hashable object
+            Label of the inserted column.
+        value : Scalar, Series, or array-like
+            Content of the inserted column.
+        allow_duplicates : bool, optional, default lib.no_default
+            Allow duplicate column labels to be created.
+
+        See Also
+        --------
+        Index.insert : Insert new item by index.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
+        >>> df
+           col1  col2
+        0     1     3
+        1     2     4
+        >>> df.insert(1, "newcol", [99, 99])
+        >>> df
+           col1  newcol  col2
+        0     1      99     3
+        1     2      99     4
+        >>> df.insert(0, "col1", [100, 100], allow_duplicates=True)
+        >>> df
+           col1  col1  newcol  col2
+        0   100     1      99     3
+        1   100     2      99     4
+
+        Notice that pandas uses index alignment in case of `value` from type `Series`:
+
+        >>> df.insert(0, "col0", pd.Series([5, 6], index=[1, 2]))
+        >>> df
+           col0  col1  col1  newcol  col2
+        0   NaN   100     1      99     3
+        1   5.0   100     2      99     4
+        """
+        if allow_duplicates is lib.no_default:
+            allow_duplicates = False
+        if allow_duplicates and not self.flags.allows_duplicate_labels:
+            raise ValueError(
+                "Cannot specify 'allow_duplicates=True' when "
+                "'self.flags.allows_duplicate_labels' is False."
+            )
+        if not allow_duplicates and column in self.columns:
+            # Should this be a different kind of error??
+            raise ValueError(f"cannot insert {column}, already exists")
+        if not is_integer(loc):
+            raise TypeError("loc must be int")
+        # convert non stdlib ints to satisfy typing checks
+        loc = int(loc)
+        if isinstance(value, DataFrame) and len(value.columns) > 1:
+            raise ValueError(
+                f"Expected a one-dimensional object, got a DataFrame with "
+                f"{len(value.columns)} columns instead."
+            )
+        elif isinstance(value, DataFrame):
+            value = value.iloc[:, 0]
+
+        value, refs = self._sanitize_column(value)
+        self._mgr.insert(loc, column, value, refs=refs)
+
+    def assign(self, **kwargs) -> DataFrame:
+        r"""
+        Assign new columns to a DataFrame.
+
+        Returns a new object with all original columns in addition to new ones.
+        Existing columns that are re-assigned will be overwritten.
+
+        Parameters
+        ----------
+        **kwargs : dict of {str: callable or Series}
+            The column names are keywords. If the values are
+            callable, they are computed on the DataFrame and
+            assigned to the new columns. The callable must not
+            change input DataFrame (though pandas doesn't check it).
+            If the values are not callable, (e.g. a Series, scalar, or array),
+            they are simply assigned.
+
+        Returns
+        -------
+        DataFrame
+            A new DataFrame with the new columns in addition to
+            all the existing columns.
+
+        Notes
+        -----
+        Assigning multiple columns within the same ``assign`` is possible.
+        Later items in '\*\*kwargs' may refer to newly created or modified
+        columns in 'df'; items are computed and assigned into 'df' in order.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'temp_c': [17.0, 25.0]},
+        ...                   index=['Portland', 'Berkeley'])
+        >>> df
+                  temp_c
+        Portland    17.0
+        Berkeley    25.0
+
+        Where the value is a callable, evaluated on `df`:
+
+        >>> df.assign(temp_f=lambda x: x.temp_c * 9 / 5 + 32)
+                  temp_c  temp_f
+        Portland    17.0    62.6
+        Berkeley    25.0    77.0
+
+        Alternatively, the same behavior can be achieved by directly
+        referencing an existing Series or sequence:
+
+        >>> df.assign(temp_f=df['temp_c'] * 9 / 5 + 32)
+                  temp_c  temp_f
+        Portland    17.0    62.6
+        Berkeley    25.0    77.0
+
+        You can create multiple columns within the same assign where one
+        of the columns depends on another one defined within the same assign:
+
+        >>> df.assign(temp_f=lambda x: x['temp_c'] * 9 / 5 + 32,
+        ...           temp_k=lambda x: (x['temp_f'] + 459.67) * 5 / 9)
+                  temp_c  temp_f  temp_k
+        Portland    17.0    62.6  290.15
+        Berkeley    25.0    77.0  298.15
+        """
+        data = self.copy(deep=None)
+
+        for k, v in kwargs.items():
+            data[k] = com.apply_if_callable(v, data)
+        return data
+
+    def _sanitize_column(self, value) -> tuple[ArrayLike, BlockValuesRefs | None]:
+        """
+        Ensures new columns (which go into the BlockManager as new blocks) are
+        always copied (or a reference is being tracked to them under CoW)
+        and converted into an array.
+
+        Parameters
+        ----------
+        value : scalar, Series, or array-like
+
+        Returns
+        -------
+        tuple of numpy.ndarray or ExtensionArray and optional BlockValuesRefs
+        """
+        self._ensure_valid_index(value)
+
+        # Using a DataFrame would mean coercing values to one dtype
+        assert not isinstance(value, DataFrame)
+        if is_dict_like(value):
+            if not isinstance(value, Series):
+                value = Series(value)
+            return _reindex_for_setitem(value, self.index)
+
+        if is_list_like(value):
+            com.require_length_match(value, self.index)
+        arr = sanitize_array(value, self.index, copy=True, allow_2d=True)
+        if (
+            isinstance(value, Index)
+            and value.dtype == "object"
+            and arr.dtype != value.dtype
+        ):  #
+            # TODO: Remove kludge in sanitize_array for string mode when enforcing
+            # this deprecation
+            warnings.warn(
+                "Setting an Index with object dtype into a DataFrame will stop "
+                "inferring another dtype in a future version. Cast the Index "
+                "explicitly before setting it into the DataFrame.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+        return arr, None
+
+    @property
+    def _series(self):
+        return {item: self._ixs(idx, axis=1) for idx, item in enumerate(self.columns)}
+
+    # ----------------------------------------------------------------------
+    # Reindexing and alignment
+
+    def _reindex_multi(
+        self, axes: dict[str, Index], copy: bool, fill_value
+    ) -> DataFrame:
+        """
+        We are guaranteed non-Nones in the axes.
+        """
+
+        new_index, row_indexer = self.index.reindex(axes["index"])
+        new_columns, col_indexer = self.columns.reindex(axes["columns"])
+
+        if row_indexer is not None and col_indexer is not None:
+            # Fastpath. By doing two 'take's at once we avoid making an
+            #  unnecessary copy.
+            # We only get here with `self._can_fast_transpose`, which (almost)
+            #  ensures that self.values is cheap. It may be worth making this
+            #  condition more specific.
+            indexer = row_indexer, col_indexer
+            new_values = take_2d_multi(self.values, indexer, fill_value=fill_value)
+            return self._constructor(
+                new_values, index=new_index, columns=new_columns, copy=False
+            )
+        else:
+            return self._reindex_with_indexers(
+                {0: [new_index, row_indexer], 1: [new_columns, col_indexer]},
+                copy=copy,
+                fill_value=fill_value,
+            )
+
+    @Appender(
+        """
+        Examples
+        --------
+        >>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
+
+        Change the row labels.
+
+        >>> df.set_axis(['a', 'b', 'c'], axis='index')
+           A  B
+        a  1  4
+        b  2  5
+        c  3  6
+
+        Change the column labels.
+
+        >>> df.set_axis(['I', 'II'], axis='columns')
+           I  II
+        0  1   4
+        1  2   5
+        2  3   6
+        """
+    )
+    @Substitution(
+        klass=_shared_doc_kwargs["klass"],
+        axes_single_arg=_shared_doc_kwargs["axes_single_arg"],
+        extended_summary_sub=" column or",
+        axis_description_sub=", and 1 identifies the columns",
+        see_also_sub=" or columns",
+    )
+    @Appender(NDFrame.set_axis.__doc__)
+    def set_axis(
+        self,
+        labels,
+        *,
+        axis: Axis = 0,
+        copy: bool | None = None,
+    ) -> DataFrame:
+        return super().set_axis(labels, axis=axis, copy=copy)
+
+    @doc(
+        NDFrame.reindex,
+        klass=_shared_doc_kwargs["klass"],
+        optional_reindex=_shared_doc_kwargs["optional_reindex"],
+    )
+    def reindex(
+        self,
+        labels=None,
+        *,
+        index=None,
+        columns=None,
+        axis: Axis | None = None,
+        method: ReindexMethod | None = None,
+        copy: bool | None = None,
+        level: Level | None = None,
+        fill_value: Scalar | None = np.nan,
+        limit: int | None = None,
+        tolerance=None,
+    ) -> DataFrame:
+        return super().reindex(
+            labels=labels,
+            index=index,
+            columns=columns,
+            axis=axis,
+            method=method,
+            copy=copy,
+            level=level,
+            fill_value=fill_value,
+            limit=limit,
+            tolerance=tolerance,
+        )
+
+    @overload
+    def drop(
+        self,
+        labels: IndexLabel = ...,
+        *,
+        axis: Axis = ...,
+        index: IndexLabel = ...,
+        columns: IndexLabel = ...,
+        level: Level = ...,
+        inplace: Literal[True],
+        errors: IgnoreRaise = ...,
+    ) -> None:
+        ...
+
+    @overload
+    def drop(
+        self,
+        labels: IndexLabel = ...,
+        *,
+        axis: Axis = ...,
+        index: IndexLabel = ...,
+        columns: IndexLabel = ...,
+        level: Level = ...,
+        inplace: Literal[False] = ...,
+        errors: IgnoreRaise = ...,
+    ) -> DataFrame:
+        ...
+
+    @overload
+    def drop(
+        self,
+        labels: IndexLabel = ...,
+        *,
+        axis: Axis = ...,
+        index: IndexLabel = ...,
+        columns: IndexLabel = ...,
+        level: Level = ...,
+        inplace: bool = ...,
+        errors: IgnoreRaise = ...,
+    ) -> DataFrame | None:
+        ...
+
+    def drop(
+        self,
+        labels: IndexLabel | None = None,
+        *,
+        axis: Axis = 0,
+        index: IndexLabel | None = None,
+        columns: IndexLabel | None = None,
+        level: Level | None = None,
+        inplace: bool = False,
+        errors: IgnoreRaise = "raise",
+    ) -> DataFrame | None:
+        """
+        Drop specified labels from rows or columns.
+
+        Remove rows or columns by specifying label names and corresponding
+        axis, or by directly specifying index or column names. When using a
+        multi-index, labels on different levels can be removed by specifying
+        the level. See the :ref:`user guide `
+        for more information about the now unused levels.
+
+        Parameters
+        ----------
+        labels : single label or list-like
+            Index or column labels to drop. A tuple will be used as a single
+            label and not treated as a list-like.
+        axis : {0 or 'index', 1 or 'columns'}, default 0
+            Whether to drop labels from the index (0 or 'index') or
+            columns (1 or 'columns').
+        index : single label or list-like
+            Alternative to specifying axis (``labels, axis=0``
+            is equivalent to ``index=labels``).
+        columns : single label or list-like
+            Alternative to specifying axis (``labels, axis=1``
+            is equivalent to ``columns=labels``).
+        level : int or level name, optional
+            For MultiIndex, level from which the labels will be removed.
+        inplace : bool, default False
+            If False, return a copy. Otherwise, do operation
+            in place and return None.
+        errors : {'ignore', 'raise'}, default 'raise'
+            If 'ignore', suppress error and only existing labels are
+            dropped.
+
+        Returns
+        -------
+        DataFrame or None
+            Returns DataFrame or None DataFrame with the specified
+            index or column labels removed or None if inplace=True.
+
+        Raises
+        ------
+        KeyError
+            If any of the labels is not found in the selected axis.
+
+        See Also
+        --------
+        DataFrame.loc : Label-location based indexer for selection by label.
+        DataFrame.dropna : Return DataFrame with labels on given axis omitted
+            where (all or any) data are missing.
+        DataFrame.drop_duplicates : Return DataFrame with duplicate rows
+            removed, optionally only considering certain columns.
+        Series.drop : Return Series with specified index labels removed.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame(np.arange(12).reshape(3, 4),
+        ...                   columns=['A', 'B', 'C', 'D'])
+        >>> df
+           A  B   C   D
+        0  0  1   2   3
+        1  4  5   6   7
+        2  8  9  10  11
+
+        Drop columns
+
+        >>> df.drop(['B', 'C'], axis=1)
+           A   D
+        0  0   3
+        1  4   7
+        2  8  11
+
+        >>> df.drop(columns=['B', 'C'])
+           A   D
+        0  0   3
+        1  4   7
+        2  8  11
+
+        Drop a row by index
+
+        >>> df.drop([0, 1])
+           A  B   C   D
+        2  8  9  10  11
+
+        Drop columns and/or rows of MultiIndex DataFrame
+
+        >>> midx = pd.MultiIndex(levels=[['llama', 'cow', 'falcon'],
+        ...                              ['speed', 'weight', 'length']],
+        ...                      codes=[[0, 0, 0, 1, 1, 1, 2, 2, 2],
+        ...                             [0, 1, 2, 0, 1, 2, 0, 1, 2]])
+        >>> df = pd.DataFrame(index=midx, columns=['big', 'small'],
+        ...                   data=[[45, 30], [200, 100], [1.5, 1], [30, 20],
+        ...                         [250, 150], [1.5, 0.8], [320, 250],
+        ...                         [1, 0.8], [0.3, 0.2]])
+        >>> df
+                        big     small
+        llama   speed   45.0    30.0
+                weight  200.0   100.0
+                length  1.5     1.0
+        cow     speed   30.0    20.0
+                weight  250.0   150.0
+                length  1.5     0.8
+        falcon  speed   320.0   250.0
+                weight  1.0     0.8
+                length  0.3     0.2
+
+        Drop a specific index combination from the MultiIndex
+        DataFrame, i.e., drop the combination ``'falcon'`` and
+        ``'weight'``, which deletes only the corresponding row
+
+        >>> df.drop(index=('falcon', 'weight'))
+                        big     small
+        llama   speed   45.0    30.0
+                weight  200.0   100.0
+                length  1.5     1.0
+        cow     speed   30.0    20.0
+                weight  250.0   150.0
+                length  1.5     0.8
+        falcon  speed   320.0   250.0
+                length  0.3     0.2
+
+        >>> df.drop(index='cow', columns='small')
+                        big
+        llama   speed   45.0
+                weight  200.0
+                length  1.5
+        falcon  speed   320.0
+                weight  1.0
+                length  0.3
+
+        >>> df.drop(index='length', level=1)
+                        big     small
+        llama   speed   45.0    30.0
+                weight  200.0   100.0
+        cow     speed   30.0    20.0
+                weight  250.0   150.0
+        falcon  speed   320.0   250.0
+                weight  1.0     0.8
+        """
+        return super().drop(
+            labels=labels,
+            axis=axis,
+            index=index,
+            columns=columns,
+            level=level,
+            inplace=inplace,
+            errors=errors,
+        )
+
+    @overload
+    def rename(
+        self,
+        mapper: Renamer | None = ...,
+        *,
+        index: Renamer | None = ...,
+        columns: Renamer | None = ...,
+        axis: Axis | None = ...,
+        copy: bool | None = ...,
+        inplace: Literal[True],
+        level: Level = ...,
+        errors: IgnoreRaise = ...,
+    ) -> None:
+        ...
+
+    @overload
+    def rename(
+        self,
+        mapper: Renamer | None = ...,
+        *,
+        index: Renamer | None = ...,
+        columns: Renamer | None = ...,
+        axis: Axis | None = ...,
+        copy: bool | None = ...,
+        inplace: Literal[False] = ...,
+        level: Level = ...,
+        errors: IgnoreRaise = ...,
+    ) -> DataFrame:
+        ...
+
+    @overload
+    def rename(
+        self,
+        mapper: Renamer | None = ...,
+        *,
+        index: Renamer | None = ...,
+        columns: Renamer | None = ...,
+        axis: Axis | None = ...,
+        copy: bool | None = ...,
+        inplace: bool = ...,
+        level: Level = ...,
+        errors: IgnoreRaise = ...,
+    ) -> DataFrame | None:
+        ...
+
+    def rename(
+        self,
+        mapper: Renamer | None = None,
+        *,
+        index: Renamer | None = None,
+        columns: Renamer | None = None,
+        axis: Axis | None = None,
+        copy: bool | None = None,
+        inplace: bool = False,
+        level: Level | None = None,
+        errors: IgnoreRaise = "ignore",
+    ) -> DataFrame | None:
+        """
+        Rename columns or index labels.
+
+        Function / dict values must be unique (1-to-1). Labels not contained in
+        a dict / Series will be left as-is. Extra labels listed don't throw an
+        error.
+
+        See the :ref:`user guide ` for more.
+
+        Parameters
+        ----------
+        mapper : dict-like or function
+            Dict-like or function transformations to apply to
+            that axis' values. Use either ``mapper`` and ``axis`` to
+            specify the axis to target with ``mapper``, or ``index`` and
+            ``columns``.
+        index : dict-like or function
+            Alternative to specifying axis (``mapper, axis=0``
+            is equivalent to ``index=mapper``).
+        columns : dict-like or function
+            Alternative to specifying axis (``mapper, axis=1``
+            is equivalent to ``columns=mapper``).
+        axis : {0 or 'index', 1 or 'columns'}, default 0
+            Axis to target with ``mapper``. Can be either the axis name
+            ('index', 'columns') or number (0, 1). The default is 'index'.
+        copy : bool, default True
+            Also copy underlying data.
+
+            .. note::
+                The `copy` keyword will change behavior in pandas 3.0.
+                `Copy-on-Write
+                `__
+                will be enabled by default, which means that all methods with a
+                `copy` keyword will use a lazy copy mechanism to defer the copy and
+                ignore the `copy` keyword. The `copy` keyword will be removed in a
+                future version of pandas.
+
+                You can already get the future behavior and improvements through
+                enabling copy on write ``pd.options.mode.copy_on_write = True``
+        inplace : bool, default False
+            Whether to modify the DataFrame rather than creating a new one.
+            If True then value of copy is ignored.
+        level : int or level name, default None
+            In case of a MultiIndex, only rename labels in the specified
+            level.
+        errors : {'ignore', 'raise'}, default 'ignore'
+            If 'raise', raise a `KeyError` when a dict-like `mapper`, `index`,
+            or `columns` contains labels that are not present in the Index
+            being transformed.
+            If 'ignore', existing keys will be renamed and extra keys will be
+            ignored.
+
+        Returns
+        -------
+        DataFrame or None
+            DataFrame with the renamed axis labels or None if ``inplace=True``.
+
+        Raises
+        ------
+        KeyError
+            If any of the labels is not found in the selected axis and
+            "errors='raise'".
+
+        See Also
+        --------
+        DataFrame.rename_axis : Set the name of the axis.
+
+        Examples
+        --------
+        ``DataFrame.rename`` supports two calling conventions
+
+        * ``(index=index_mapper, columns=columns_mapper, ...)``
+        * ``(mapper, axis={'index', 'columns'}, ...)``
+
+        We *highly* recommend using keyword arguments to clarify your
+        intent.
+
+        Rename columns using a mapping:
+
+        >>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
+        >>> df.rename(columns={"A": "a", "B": "c"})
+           a  c
+        0  1  4
+        1  2  5
+        2  3  6
+
+        Rename index using a mapping:
+
+        >>> df.rename(index={0: "x", 1: "y", 2: "z"})
+           A  B
+        x  1  4
+        y  2  5
+        z  3  6
+
+        Cast index labels to a different type:
+
+        >>> df.index
+        RangeIndex(start=0, stop=3, step=1)
+        >>> df.rename(index=str).index
+        Index(['0', '1', '2'], dtype='object')
+
+        >>> df.rename(columns={"A": "a", "B": "b", "C": "c"}, errors="raise")
+        Traceback (most recent call last):
+        KeyError: ['C'] not found in axis
+
+        Using axis-style parameters:
+
+        >>> df.rename(str.lower, axis='columns')
+           a  b
+        0  1  4
+        1  2  5
+        2  3  6
+
+        >>> df.rename({1: 2, 2: 4}, axis='index')
+           A  B
+        0  1  4
+        2  2  5
+        4  3  6
+        """
+        return super()._rename(
+            mapper=mapper,
+            index=index,
+            columns=columns,
+            axis=axis,
+            copy=copy,
+            inplace=inplace,
+            level=level,
+            errors=errors,
+        )
+
+    def pop(self, item: Hashable) -> Series:
+        """
+        Return item and drop from frame. Raise KeyError if not found.
+
+        Parameters
+        ----------
+        item : label
+            Label of column to be popped.
+
+        Returns
+        -------
+        Series
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([('falcon', 'bird', 389.0),
+        ...                    ('parrot', 'bird', 24.0),
+        ...                    ('lion', 'mammal', 80.5),
+        ...                    ('monkey', 'mammal', np.nan)],
+        ...                   columns=('name', 'class', 'max_speed'))
+        >>> df
+             name   class  max_speed
+        0  falcon    bird      389.0
+        1  parrot    bird       24.0
+        2    lion  mammal       80.5
+        3  monkey  mammal        NaN
+
+        >>> df.pop('class')
+        0      bird
+        1      bird
+        2    mammal
+        3    mammal
+        Name: class, dtype: object
+
+        >>> df
+             name  max_speed
+        0  falcon      389.0
+        1  parrot       24.0
+        2    lion       80.5
+        3  monkey        NaN
+        """
+        return super().pop(item=item)
+
+    def _replace_columnwise(
+        self, mapping: dict[Hashable, tuple[Any, Any]], inplace: bool, regex
+    ):
+        """
+        Dispatch to Series.replace column-wise.
+
+        Parameters
+        ----------
+        mapping : dict
+            of the form {col: (target, value)}
+        inplace : bool
+        regex : bool or same types as `to_replace` in DataFrame.replace
+
+        Returns
+        -------
+        DataFrame or None
+        """
+        # Operate column-wise
+        res = self if inplace else self.copy(deep=None)
+        ax = self.columns
+
+        for i, ax_value in enumerate(ax):
+            if ax_value in mapping:
+                ser = self.iloc[:, i]
+
+                target, value = mapping[ax_value]
+                newobj = ser.replace(target, value, regex=regex)
+
+                res._iset_item(i, newobj, inplace=inplace)
+
+        if inplace:
+            return
+        return res.__finalize__(self)
+
+    @doc(NDFrame.shift, klass=_shared_doc_kwargs["klass"])
+    def shift(
+        self,
+        periods: int | Sequence[int] = 1,
+        freq: Frequency | None = None,
+        axis: Axis = 0,
+        fill_value: Hashable = lib.no_default,
+        suffix: str | None = None,
+    ) -> DataFrame:
+        if freq is not None and fill_value is not lib.no_default:
+            # GH#53832
+            warnings.warn(
+                "Passing a 'freq' together with a 'fill_value' silently ignores "
+                "the fill_value and is deprecated. This will raise in a future "
+                "version.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+            fill_value = lib.no_default
+
+        if self.empty:
+            return self.copy()
+
+        axis = self._get_axis_number(axis)
+
+        if is_list_like(periods):
+            periods = cast(Sequence, periods)
+            if axis == 1:
+                raise ValueError(
+                    "If `periods` contains multiple shifts, `axis` cannot be 1."
+                )
+            if len(periods) == 0:
+                raise ValueError("If `periods` is an iterable, it cannot be empty.")
+            from pandas.core.reshape.concat import concat
+
+            shifted_dataframes = []
+            for period in periods:
+                if not is_integer(period):
+                    raise TypeError(
+                        f"Periods must be integer, but {period} is {type(period)}."
+                    )
+                period = cast(int, period)
+                shifted_dataframes.append(
+                    super()
+                    .shift(periods=period, freq=freq, axis=axis, fill_value=fill_value)
+                    .add_suffix(f"{suffix}_{period}" if suffix else f"_{period}")
+                )
+            return concat(shifted_dataframes, axis=1)
+        elif suffix:
+            raise ValueError("Cannot specify `suffix` if `periods` is an int.")
+        periods = cast(int, periods)
+
+        ncols = len(self.columns)
+        arrays = self._mgr.arrays
+        if axis == 1 and periods != 0 and ncols > 0 and freq is None:
+            if fill_value is lib.no_default:
+                # We will infer fill_value to match the closest column
+
+                # Use a column that we know is valid for our column's dtype GH#38434
+                label = self.columns[0]
+
+                if periods > 0:
+                    result = self.iloc[:, :-periods]
+                    for col in range(min(ncols, abs(periods))):
+                        # TODO(EA2D): doing this in a loop unnecessary with 2D EAs
+                        # Define filler inside loop so we get a copy
+                        filler = self.iloc[:, 0].shift(len(self))
+                        result.insert(0, label, filler, allow_duplicates=True)
+                else:
+                    result = self.iloc[:, -periods:]
+                    for col in range(min(ncols, abs(periods))):
+                        # Define filler inside loop so we get a copy
+                        filler = self.iloc[:, -1].shift(len(self))
+                        result.insert(
+                            len(result.columns), label, filler, allow_duplicates=True
+                        )
+
+                result.columns = self.columns.copy()
+                return result
+            elif len(arrays) > 1 or (
+                # If we only have one block and we know that we can't
+                #  keep the same dtype (i.e. the _can_hold_element check)
+                #  then we can go through the reindex_indexer path
+                #  (and avoid casting logic in the Block method).
+                not can_hold_element(arrays[0], fill_value)
+            ):
+                # GH#35488 we need to watch out for multi-block cases
+                # We only get here with fill_value not-lib.no_default
+                nper = abs(periods)
+                nper = min(nper, ncols)
+                if periods > 0:
+                    indexer = np.array(
+                        [-1] * nper + list(range(ncols - periods)), dtype=np.intp
+                    )
+                else:
+                    indexer = np.array(
+                        list(range(nper, ncols)) + [-1] * nper, dtype=np.intp
+                    )
+                mgr = self._mgr.reindex_indexer(
+                    self.columns,
+                    indexer,
+                    axis=0,
+                    fill_value=fill_value,
+                    allow_dups=True,
+                )
+                res_df = self._constructor_from_mgr(mgr, axes=mgr.axes)
+                return res_df.__finalize__(self, method="shift")
+            else:
+                return self.T.shift(periods=periods, fill_value=fill_value).T
+
+        return super().shift(
+            periods=periods, freq=freq, axis=axis, fill_value=fill_value
+        )
+
+    @overload
+    def set_index(
+        self,
+        keys,
+        *,
+        drop: bool = ...,
+        append: bool = ...,
+        inplace: Literal[False] = ...,
+        verify_integrity: bool = ...,
+    ) -> DataFrame:
+        ...
+
+    @overload
+    def set_index(
+        self,
+        keys,
+        *,
+        drop: bool = ...,
+        append: bool = ...,
+        inplace: Literal[True],
+        verify_integrity: bool = ...,
+    ) -> None:
+        ...
+
+    def set_index(
+        self,
+        keys,
+        *,
+        drop: bool = True,
+        append: bool = False,
+        inplace: bool = False,
+        verify_integrity: bool = False,
+    ) -> DataFrame | None:
+        """
+        Set the DataFrame index using existing columns.
+
+        Set the DataFrame index (row labels) using one or more existing
+        columns or arrays (of the correct length). The index can replace the
+        existing index or expand on it.
+
+        Parameters
+        ----------
+        keys : label or array-like or list of labels/arrays
+            This parameter can be either a single column key, a single array of
+            the same length as the calling DataFrame, or a list containing an
+            arbitrary combination of column keys and arrays. Here, "array"
+            encompasses :class:`Series`, :class:`Index`, ``np.ndarray``, and
+            instances of :class:`~collections.abc.Iterator`.
+        drop : bool, default True
+            Delete columns to be used as the new index.
+        append : bool, default False
+            Whether to append columns to existing index.
+        inplace : bool, default False
+            Whether to modify the DataFrame rather than creating a new one.
+        verify_integrity : bool, default False
+            Check the new index for duplicates. Otherwise defer the check until
+            necessary. Setting to False will improve the performance of this
+            method.
+
+        Returns
+        -------
+        DataFrame or None
+            Changed row labels or None if ``inplace=True``.
+
+        See Also
+        --------
+        DataFrame.reset_index : Opposite of set_index.
+        DataFrame.reindex : Change to new indices or expand indices.
+        DataFrame.reindex_like : Change to same indices as other DataFrame.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'month': [1, 4, 7, 10],
+        ...                    'year': [2012, 2014, 2013, 2014],
+        ...                    'sale': [55, 40, 84, 31]})
+        >>> df
+           month  year  sale
+        0      1  2012    55
+        1      4  2014    40
+        2      7  2013    84
+        3     10  2014    31
+
+        Set the index to become the 'month' column:
+
+        >>> df.set_index('month')
+               year  sale
+        month
+        1      2012    55
+        4      2014    40
+        7      2013    84
+        10     2014    31
+
+        Create a MultiIndex using columns 'year' and 'month':
+
+        >>> df.set_index(['year', 'month'])
+                    sale
+        year  month
+        2012  1     55
+        2014  4     40
+        2013  7     84
+        2014  10    31
+
+        Create a MultiIndex using an Index and a column:
+
+        >>> df.set_index([pd.Index([1, 2, 3, 4]), 'year'])
+                 month  sale
+           year
+        1  2012  1      55
+        2  2014  4      40
+        3  2013  7      84
+        4  2014  10     31
+
+        Create a MultiIndex using two Series:
+
+        >>> s = pd.Series([1, 2, 3, 4])
+        >>> df.set_index([s, s**2])
+              month  year  sale
+        1 1       1  2012    55
+        2 4       4  2014    40
+        3 9       7  2013    84
+        4 16     10  2014    31
+        """
+        inplace = validate_bool_kwarg(inplace, "inplace")
+        self._check_inplace_and_allows_duplicate_labels(inplace)
+        if not isinstance(keys, list):
+            keys = [keys]
+
+        err_msg = (
+            'The parameter "keys" may be a column key, one-dimensional '
+            "array, or a list containing only valid column keys and "
+            "one-dimensional arrays."
+        )
+
+        missing: list[Hashable] = []
+        for col in keys:
+            if isinstance(col, (Index, Series, np.ndarray, list, abc.Iterator)):
+                # arrays are fine as long as they are one-dimensional
+                # iterators get converted to list below
+                if getattr(col, "ndim", 1) != 1:
+                    raise ValueError(err_msg)
+            else:
+                # everything else gets tried as a key; see GH 24969
+                try:
+                    found = col in self.columns
+                except TypeError as err:
+                    raise TypeError(
+                        f"{err_msg}. Received column of type {type(col)}"
+                    ) from err
+                else:
+                    if not found:
+                        missing.append(col)
+
+        if missing:
+            raise KeyError(f"None of {missing} are in the columns")
+
+        if inplace:
+            frame = self
+        else:
+            # GH 49473 Use "lazy copy" with Copy-on-Write
+            frame = self.copy(deep=None)
+
+        arrays: list[Index] = []
+        names: list[Hashable] = []
+        if append:
+            names = list(self.index.names)
+            if isinstance(self.index, MultiIndex):
+                arrays.extend(
+                    self.index._get_level_values(i) for i in range(self.index.nlevels)
+                )
+            else:
+                arrays.append(self.index)
+
+        to_remove: list[Hashable] = []
+        for col in keys:
+            if isinstance(col, MultiIndex):
+                arrays.extend(col._get_level_values(n) for n in range(col.nlevels))
+                names.extend(col.names)
+            elif isinstance(col, (Index, Series)):
+                # if Index then not MultiIndex (treated above)
+
+                # error: Argument 1 to "append" of "list" has incompatible type
+                #  "Union[Index, Series]"; expected "Index"
+                arrays.append(col)  # type: ignore[arg-type]
+                names.append(col.name)
+            elif isinstance(col, (list, np.ndarray)):
+                # error: Argument 1 to "append" of "list" has incompatible type
+                # "Union[List[Any], ndarray]"; expected "Index"
+                arrays.append(col)  # type: ignore[arg-type]
+                names.append(None)
+            elif isinstance(col, abc.Iterator):
+                # error: Argument 1 to "append" of "list" has incompatible type
+                # "List[Any]"; expected "Index"
+                arrays.append(list(col))  # type: ignore[arg-type]
+                names.append(None)
+            # from here, col can only be a column label
+            else:
+                arrays.append(frame[col])
+                names.append(col)
+                if drop:
+                    to_remove.append(col)
+
+            if len(arrays[-1]) != len(self):
+                # check newest element against length of calling frame, since
+                # ensure_index_from_sequences would not raise for append=False.
+                raise ValueError(
+                    f"Length mismatch: Expected {len(self)} rows, "
+                    f"received array of length {len(arrays[-1])}"
+                )
+
+        index = ensure_index_from_sequences(arrays, names)
+
+        if verify_integrity and not index.is_unique:
+            duplicates = index[index.duplicated()].unique()
+            raise ValueError(f"Index has duplicate keys: {duplicates}")
+
+        # use set to handle duplicate column names gracefully in case of drop
+        for c in set(to_remove):
+            del frame[c]
+
+        # clear up memory usage
+        index._cleanup()
+
+        frame.index = index
+
+        if not inplace:
+            return frame
+        return None
+
+    @overload
+    def reset_index(
+        self,
+        level: IndexLabel = ...,
+        *,
+        drop: bool = ...,
+        inplace: Literal[False] = ...,
+        col_level: Hashable = ...,
+        col_fill: Hashable = ...,
+        allow_duplicates: bool | lib.NoDefault = ...,
+        names: Hashable | Sequence[Hashable] | None = None,
+    ) -> DataFrame:
+        ...
+
+    @overload
+    def reset_index(
+        self,
+        level: IndexLabel = ...,
+        *,
+        drop: bool = ...,
+        inplace: Literal[True],
+        col_level: Hashable = ...,
+        col_fill: Hashable = ...,
+        allow_duplicates: bool | lib.NoDefault = ...,
+        names: Hashable | Sequence[Hashable] | None = None,
+    ) -> None:
+        ...
+
+    @overload
+    def reset_index(
+        self,
+        level: IndexLabel = ...,
+        *,
+        drop: bool = ...,
+        inplace: bool = ...,
+        col_level: Hashable = ...,
+        col_fill: Hashable = ...,
+        allow_duplicates: bool | lib.NoDefault = ...,
+        names: Hashable | Sequence[Hashable] | None = None,
+    ) -> DataFrame | None:
+        ...
+
+    def reset_index(
+        self,
+        level: IndexLabel | None = None,
+        *,
+        drop: bool = False,
+        inplace: bool = False,
+        col_level: Hashable = 0,
+        col_fill: Hashable = "",
+        allow_duplicates: bool | lib.NoDefault = lib.no_default,
+        names: Hashable | Sequence[Hashable] | None = None,
+    ) -> DataFrame | None:
+        """
+        Reset the index, or a level of it.
+
+        Reset the index of the DataFrame, and use the default one instead.
+        If the DataFrame has a MultiIndex, this method can remove one or more
+        levels.
+
+        Parameters
+        ----------
+        level : int, str, tuple, or list, default None
+            Only remove the given levels from the index. Removes all levels by
+            default.
+        drop : bool, default False
+            Do not try to insert index into dataframe columns. This resets
+            the index to the default integer index.
+        inplace : bool, default False
+            Whether to modify the DataFrame rather than creating a new one.
+        col_level : int or str, default 0
+            If the columns have multiple levels, determines which level the
+            labels are inserted into. By default it is inserted into the first
+            level.
+        col_fill : object, default ''
+            If the columns have multiple levels, determines how the other
+            levels are named. If None then the index name is repeated.
+        allow_duplicates : bool, optional, default lib.no_default
+            Allow duplicate column labels to be created.
+
+            .. versionadded:: 1.5.0
+
+        names : int, str or 1-dimensional list, default None
+            Using the given string, rename the DataFrame column which contains the
+            index data. If the DataFrame has a MultiIndex, this has to be a list or
+            tuple with length equal to the number of levels.
+
+            .. versionadded:: 1.5.0
+
+        Returns
+        -------
+        DataFrame or None
+            DataFrame with the new index or None if ``inplace=True``.
+
+        See Also
+        --------
+        DataFrame.set_index : Opposite of reset_index.
+        DataFrame.reindex : Change to new indices or expand indices.
+        DataFrame.reindex_like : Change to same indices as other DataFrame.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([('bird', 389.0),
+        ...                    ('bird', 24.0),
+        ...                    ('mammal', 80.5),
+        ...                    ('mammal', np.nan)],
+        ...                   index=['falcon', 'parrot', 'lion', 'monkey'],
+        ...                   columns=('class', 'max_speed'))
+        >>> df
+                 class  max_speed
+        falcon    bird      389.0
+        parrot    bird       24.0
+        lion    mammal       80.5
+        monkey  mammal        NaN
+
+        When we reset the index, the old index is added as a column, and a
+        new sequential index is used:
+
+        >>> df.reset_index()
+            index   class  max_speed
+        0  falcon    bird      389.0
+        1  parrot    bird       24.0
+        2    lion  mammal       80.5
+        3  monkey  mammal        NaN
+
+        We can use the `drop` parameter to avoid the old index being added as
+        a column:
+
+        >>> df.reset_index(drop=True)
+            class  max_speed
+        0    bird      389.0
+        1    bird       24.0
+        2  mammal       80.5
+        3  mammal        NaN
+
+        You can also use `reset_index` with `MultiIndex`.
+
+        >>> index = pd.MultiIndex.from_tuples([('bird', 'falcon'),
+        ...                                    ('bird', 'parrot'),
+        ...                                    ('mammal', 'lion'),
+        ...                                    ('mammal', 'monkey')],
+        ...                                   names=['class', 'name'])
+        >>> columns = pd.MultiIndex.from_tuples([('speed', 'max'),
+        ...                                      ('species', 'type')])
+        >>> df = pd.DataFrame([(389.0, 'fly'),
+        ...                    (24.0, 'fly'),
+        ...                    (80.5, 'run'),
+        ...                    (np.nan, 'jump')],
+        ...                   index=index,
+        ...                   columns=columns)
+        >>> df
+                       speed species
+                         max    type
+        class  name
+        bird   falcon  389.0     fly
+               parrot   24.0     fly
+        mammal lion     80.5     run
+               monkey    NaN    jump
+
+        Using the `names` parameter, choose a name for the index column:
+
+        >>> df.reset_index(names=['classes', 'names'])
+          classes   names  speed species
+                             max    type
+        0    bird  falcon  389.0     fly
+        1    bird  parrot   24.0     fly
+        2  mammal    lion   80.5     run
+        3  mammal  monkey    NaN    jump
+
+        If the index has multiple levels, we can reset a subset of them:
+
+        >>> df.reset_index(level='class')
+                 class  speed species
+                          max    type
+        name
+        falcon    bird  389.0     fly
+        parrot    bird   24.0     fly
+        lion    mammal   80.5     run
+        monkey  mammal    NaN    jump
+
+        If we are not dropping the index, by default, it is placed in the top
+        level. We can place it in another level:
+
+        >>> df.reset_index(level='class', col_level=1)
+                        speed species
+                 class    max    type
+        name
+        falcon    bird  389.0     fly
+        parrot    bird   24.0     fly
+        lion    mammal   80.5     run
+        monkey  mammal    NaN    jump
+
+        When the index is inserted under another level, we can specify under
+        which one with the parameter `col_fill`:
+
+        >>> df.reset_index(level='class', col_level=1, col_fill='species')
+                      species  speed species
+                        class    max    type
+        name
+        falcon           bird  389.0     fly
+        parrot           bird   24.0     fly
+        lion           mammal   80.5     run
+        monkey         mammal    NaN    jump
+
+        If we specify a nonexistent level for `col_fill`, it is created:
+
+        >>> df.reset_index(level='class', col_level=1, col_fill='genus')
+                        genus  speed species
+                        class    max    type
+        name
+        falcon           bird  389.0     fly
+        parrot           bird   24.0     fly
+        lion           mammal   80.5     run
+        monkey         mammal    NaN    jump
+        """
+        inplace = validate_bool_kwarg(inplace, "inplace")
+        self._check_inplace_and_allows_duplicate_labels(inplace)
+        if inplace:
+            new_obj = self
+        else:
+            new_obj = self.copy(deep=None)
+        if allow_duplicates is not lib.no_default:
+            allow_duplicates = validate_bool_kwarg(allow_duplicates, "allow_duplicates")
+
+        new_index = default_index(len(new_obj))
+        if level is not None:
+            if not isinstance(level, (tuple, list)):
+                level = [level]
+            level = [self.index._get_level_number(lev) for lev in level]
+            if len(level) < self.index.nlevels:
+                new_index = self.index.droplevel(level)
+
+        if not drop:
+            to_insert: Iterable[tuple[Any, Any | None]]
+
+            default = "index" if "index" not in self else "level_0"
+            names = self.index._get_default_index_names(names, default)
+
+            if isinstance(self.index, MultiIndex):
+                to_insert = zip(self.index.levels, self.index.codes)
+            else:
+                to_insert = ((self.index, None),)
+
+            multi_col = isinstance(self.columns, MultiIndex)
+            for i, (lev, lab) in reversed(list(enumerate(to_insert))):
+                if level is not None and i not in level:
+                    continue
+                name = names[i]
+                if multi_col:
+                    col_name = list(name) if isinstance(name, tuple) else [name]
+                    if col_fill is None:
+                        if len(col_name) not in (1, self.columns.nlevels):
+                            raise ValueError(
+                                "col_fill=None is incompatible "
+                                f"with incomplete column name {name}"
+                            )
+                        col_fill = col_name[0]
+
+                    lev_num = self.columns._get_level_number(col_level)
+                    name_lst = [col_fill] * lev_num + col_name
+                    missing = self.columns.nlevels - len(name_lst)
+                    name_lst += [col_fill] * missing
+                    name = tuple(name_lst)
+
+                # to ndarray and maybe infer different dtype
+                level_values = lev._values
+                if level_values.dtype == np.object_:
+                    level_values = lib.maybe_convert_objects(level_values)
+
+                if lab is not None:
+                    # if we have the codes, extract the values with a mask
+                    level_values = algorithms.take(
+                        level_values, lab, allow_fill=True, fill_value=lev._na_value
+                    )
+
+                new_obj.insert(
+                    0,
+                    name,
+                    level_values,
+                    allow_duplicates=allow_duplicates,
+                )
+
+        new_obj.index = new_index
+        if not inplace:
+            return new_obj
+
+        return None
+
+    # ----------------------------------------------------------------------
+    # Reindex-based selection methods
+
+    @doc(NDFrame.isna, klass=_shared_doc_kwargs["klass"])
+    def isna(self) -> DataFrame:
+        res_mgr = self._mgr.isna(func=isna)
+        result = self._constructor_from_mgr(res_mgr, axes=res_mgr.axes)
+        return result.__finalize__(self, method="isna")
+
+    @doc(NDFrame.isna, klass=_shared_doc_kwargs["klass"])
+    def isnull(self) -> DataFrame:
+        """
+        DataFrame.isnull is an alias for DataFrame.isna.
+        """
+        return self.isna()
+
+    @doc(NDFrame.notna, klass=_shared_doc_kwargs["klass"])
+    def notna(self) -> DataFrame:
+        return ~self.isna()
+
+    @doc(NDFrame.notna, klass=_shared_doc_kwargs["klass"])
+    def notnull(self) -> DataFrame:
+        """
+        DataFrame.notnull is an alias for DataFrame.notna.
+        """
+        return ~self.isna()
+
+    @overload
+    def dropna(
+        self,
+        *,
+        axis: Axis = ...,
+        how: AnyAll | lib.NoDefault = ...,
+        thresh: int | lib.NoDefault = ...,
+        subset: IndexLabel = ...,
+        inplace: Literal[False] = ...,
+        ignore_index: bool = ...,
+    ) -> DataFrame:
+        ...
+
+    @overload
+    def dropna(
+        self,
+        *,
+        axis: Axis = ...,
+        how: AnyAll | lib.NoDefault = ...,
+        thresh: int | lib.NoDefault = ...,
+        subset: IndexLabel = ...,
+        inplace: Literal[True],
+        ignore_index: bool = ...,
+    ) -> None:
+        ...
+
+    def dropna(
+        self,
+        *,
+        axis: Axis = 0,
+        how: AnyAll | lib.NoDefault = lib.no_default,
+        thresh: int | lib.NoDefault = lib.no_default,
+        subset: IndexLabel | None = None,
+        inplace: bool = False,
+        ignore_index: bool = False,
+    ) -> DataFrame | None:
+        """
+        Remove missing values.
+
+        See the :ref:`User Guide ` for more on which values are
+        considered missing, and how to work with missing data.
+
+        Parameters
+        ----------
+        axis : {0 or 'index', 1 or 'columns'}, default 0
+            Determine if rows or columns which contain missing values are
+            removed.
+
+            * 0, or 'index' : Drop rows which contain missing values.
+            * 1, or 'columns' : Drop columns which contain missing value.
+
+            Only a single axis is allowed.
+
+        how : {'any', 'all'}, default 'any'
+            Determine if row or column is removed from DataFrame, when we have
+            at least one NA or all NA.
+
+            * 'any' : If any NA values are present, drop that row or column.
+            * 'all' : If all values are NA, drop that row or column.
+
+        thresh : int, optional
+            Require that many non-NA values. Cannot be combined with how.
+        subset : column label or sequence of labels, optional
+            Labels along other axis to consider, e.g. if you are dropping rows
+            these would be a list of columns to include.
+        inplace : bool, default False
+            Whether to modify the DataFrame rather than creating a new one.
+        ignore_index : bool, default ``False``
+            If ``True``, the resulting axis will be labeled 0, 1, …, n - 1.
+
+            .. versionadded:: 2.0.0
+
+        Returns
+        -------
+        DataFrame or None
+            DataFrame with NA entries dropped from it or None if ``inplace=True``.
+
+        See Also
+        --------
+        DataFrame.isna: Indicate missing values.
+        DataFrame.notna : Indicate existing (non-missing) values.
+        DataFrame.fillna : Replace missing values.
+        Series.dropna : Drop missing values.
+        Index.dropna : Drop missing indices.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({"name": ['Alfred', 'Batman', 'Catwoman'],
+        ...                    "toy": [np.nan, 'Batmobile', 'Bullwhip'],
+        ...                    "born": [pd.NaT, pd.Timestamp("1940-04-25"),
+        ...                             pd.NaT]})
+        >>> df
+               name        toy       born
+        0    Alfred        NaN        NaT
+        1    Batman  Batmobile 1940-04-25
+        2  Catwoman   Bullwhip        NaT
+
+        Drop the rows where at least one element is missing.
+
+        >>> df.dropna()
+             name        toy       born
+        1  Batman  Batmobile 1940-04-25
+
+        Drop the columns where at least one element is missing.
+
+        >>> df.dropna(axis='columns')
+               name
+        0    Alfred
+        1    Batman
+        2  Catwoman
+
+        Drop the rows where all elements are missing.
+
+        >>> df.dropna(how='all')
+               name        toy       born
+        0    Alfred        NaN        NaT
+        1    Batman  Batmobile 1940-04-25
+        2  Catwoman   Bullwhip        NaT
+
+        Keep only the rows with at least 2 non-NA values.
+
+        >>> df.dropna(thresh=2)
+               name        toy       born
+        1    Batman  Batmobile 1940-04-25
+        2  Catwoman   Bullwhip        NaT
+
+        Define in which columns to look for missing values.
+
+        >>> df.dropna(subset=['name', 'toy'])
+               name        toy       born
+        1    Batman  Batmobile 1940-04-25
+        2  Catwoman   Bullwhip        NaT
+        """
+        if (how is not lib.no_default) and (thresh is not lib.no_default):
+            raise TypeError(
+                "You cannot set both the how and thresh arguments at the same time."
+            )
+
+        if how is lib.no_default:
+            how = "any"
+
+        inplace = validate_bool_kwarg(inplace, "inplace")
+        if isinstance(axis, (tuple, list)):
+            # GH20987
+            raise TypeError("supplying multiple axes to axis is no longer supported.")
+
+        axis = self._get_axis_number(axis)
+        agg_axis = 1 - axis
+
+        agg_obj = self
+        if subset is not None:
+            # subset needs to be list
+            if not is_list_like(subset):
+                subset = [subset]
+            ax = self._get_axis(agg_axis)
+            indices = ax.get_indexer_for(subset)
+            check = indices == -1
+            if check.any():
+                raise KeyError(np.array(subset)[check].tolist())
+            agg_obj = self.take(indices, axis=agg_axis)
+
+        if thresh is not lib.no_default:
+            count = agg_obj.count(axis=agg_axis)
+            mask = count >= thresh
+        elif how == "any":
+            # faster equivalent to 'agg_obj.count(agg_axis) == self.shape[agg_axis]'
+            mask = notna(agg_obj).all(axis=agg_axis, bool_only=False)
+        elif how == "all":
+            # faster equivalent to 'agg_obj.count(agg_axis) > 0'
+            mask = notna(agg_obj).any(axis=agg_axis, bool_only=False)
+        else:
+            raise ValueError(f"invalid how option: {how}")
+
+        if np.all(mask):
+            result = self.copy(deep=None)
+        else:
+            result = self.loc(axis=axis)[mask]
+
+        if ignore_index:
+            result.index = default_index(len(result))
+
+        if not inplace:
+            return result
+        self._update_inplace(result)
+        return None
+
+    @overload
+    def drop_duplicates(
+        self,
+        subset: Hashable | Sequence[Hashable] | None = ...,
+        *,
+        keep: DropKeep = ...,
+        inplace: Literal[True],
+        ignore_index: bool = ...,
+    ) -> None:
+        ...
+
+    @overload
+    def drop_duplicates(
+        self,
+        subset: Hashable | Sequence[Hashable] | None = ...,
+        *,
+        keep: DropKeep = ...,
+        inplace: Literal[False] = ...,
+        ignore_index: bool = ...,
+    ) -> DataFrame:
+        ...
+
+    @overload
+    def drop_duplicates(
+        self,
+        subset: Hashable | Sequence[Hashable] | None = ...,
+        *,
+        keep: DropKeep = ...,
+        inplace: bool = ...,
+        ignore_index: bool = ...,
+    ) -> DataFrame | None:
+        ...
+
+    def drop_duplicates(
+        self,
+        subset: Hashable | Sequence[Hashable] | None = None,
+        *,
+        keep: DropKeep = "first",
+        inplace: bool = False,
+        ignore_index: bool = False,
+    ) -> DataFrame | None:
+        """
+        Return DataFrame with duplicate rows removed.
+
+        Considering certain columns is optional. Indexes, including time indexes
+        are ignored.
+
+        Parameters
+        ----------
+        subset : column label or sequence of labels, optional
+            Only consider certain columns for identifying duplicates, by
+            default use all of the columns.
+        keep : {'first', 'last', ``False``}, default 'first'
+            Determines which duplicates (if any) to keep.
+
+            - 'first' : Drop duplicates except for the first occurrence.
+            - 'last' : Drop duplicates except for the last occurrence.
+            - ``False`` : Drop all duplicates.
+
+        inplace : bool, default ``False``
+            Whether to modify the DataFrame rather than creating a new one.
+        ignore_index : bool, default ``False``
+            If ``True``, the resulting axis will be labeled 0, 1, …, n - 1.
+
+        Returns
+        -------
+        DataFrame or None
+            DataFrame with duplicates removed or None if ``inplace=True``.
+
+        See Also
+        --------
+        DataFrame.value_counts: Count unique combinations of columns.
+
+        Examples
+        --------
+        Consider dataset containing ramen rating.
+
+        >>> df = pd.DataFrame({
+        ...     'brand': ['Yum Yum', 'Yum Yum', 'Indomie', 'Indomie', 'Indomie'],
+        ...     'style': ['cup', 'cup', 'cup', 'pack', 'pack'],
+        ...     'rating': [4, 4, 3.5, 15, 5]
+        ... })
+        >>> df
+            brand style  rating
+        0  Yum Yum   cup     4.0
+        1  Yum Yum   cup     4.0
+        2  Indomie   cup     3.5
+        3  Indomie  pack    15.0
+        4  Indomie  pack     5.0
+
+        By default, it removes duplicate rows based on all columns.
+
+        >>> df.drop_duplicates()
+            brand style  rating
+        0  Yum Yum   cup     4.0
+        2  Indomie   cup     3.5
+        3  Indomie  pack    15.0
+        4  Indomie  pack     5.0
+
+        To remove duplicates on specific column(s), use ``subset``.
+
+        >>> df.drop_duplicates(subset=['brand'])
+            brand style  rating
+        0  Yum Yum   cup     4.0
+        2  Indomie   cup     3.5
+
+        To remove duplicates and keep last occurrences, use ``keep``.
+
+        >>> df.drop_duplicates(subset=['brand', 'style'], keep='last')
+            brand style  rating
+        1  Yum Yum   cup     4.0
+        2  Indomie   cup     3.5
+        4  Indomie  pack     5.0
+        """
+        if self.empty:
+            return self.copy(deep=None)
+
+        inplace = validate_bool_kwarg(inplace, "inplace")
+        ignore_index = validate_bool_kwarg(ignore_index, "ignore_index")
+
+        result = self[-self.duplicated(subset, keep=keep)]
+        if ignore_index:
+            result.index = default_index(len(result))
+
+        if inplace:
+            self._update_inplace(result)
+            return None
+        else:
+            return result
+
+    def duplicated(
+        self,
+        subset: Hashable | Sequence[Hashable] | None = None,
+        keep: DropKeep = "first",
+    ) -> Series:
+        """
+        Return boolean Series denoting duplicate rows.
+
+        Considering certain columns is optional.
+
+        Parameters
+        ----------
+        subset : column label or sequence of labels, optional
+            Only consider certain columns for identifying duplicates, by
+            default use all of the columns.
+        keep : {'first', 'last', False}, default 'first'
+            Determines which duplicates (if any) to mark.
+
+            - ``first`` : Mark duplicates as ``True`` except for the first occurrence.
+            - ``last`` : Mark duplicates as ``True`` except for the last occurrence.
+            - False : Mark all duplicates as ``True``.
+
+        Returns
+        -------
+        Series
+            Boolean series for each duplicated rows.
+
+        See Also
+        --------
+        Index.duplicated : Equivalent method on index.
+        Series.duplicated : Equivalent method on Series.
+        Series.drop_duplicates : Remove duplicate values from Series.
+        DataFrame.drop_duplicates : Remove duplicate values from DataFrame.
+
+        Examples
+        --------
+        Consider dataset containing ramen rating.
+
+        >>> df = pd.DataFrame({
+        ...     'brand': ['Yum Yum', 'Yum Yum', 'Indomie', 'Indomie', 'Indomie'],
+        ...     'style': ['cup', 'cup', 'cup', 'pack', 'pack'],
+        ...     'rating': [4, 4, 3.5, 15, 5]
+        ... })
+        >>> df
+            brand style  rating
+        0  Yum Yum   cup     4.0
+        1  Yum Yum   cup     4.0
+        2  Indomie   cup     3.5
+        3  Indomie  pack    15.0
+        4  Indomie  pack     5.0
+
+        By default, for each set of duplicated values, the first occurrence
+        is set on False and all others on True.
+
+        >>> df.duplicated()
+        0    False
+        1     True
+        2    False
+        3    False
+        4    False
+        dtype: bool
+
+        By using 'last', the last occurrence of each set of duplicated values
+        is set on False and all others on True.
+
+        >>> df.duplicated(keep='last')
+        0     True
+        1    False
+        2    False
+        3    False
+        4    False
+        dtype: bool
+
+        By setting ``keep`` on False, all duplicates are True.
+
+        >>> df.duplicated(keep=False)
+        0     True
+        1     True
+        2    False
+        3    False
+        4    False
+        dtype: bool
+
+        To find duplicates on specific column(s), use ``subset``.
+
+        >>> df.duplicated(subset=['brand'])
+        0    False
+        1     True
+        2    False
+        3     True
+        4     True
+        dtype: bool
+        """
+
+        if self.empty:
+            return self._constructor_sliced(dtype=bool)
+
+        def f(vals) -> tuple[np.ndarray, int]:
+            labels, shape = algorithms.factorize(vals, size_hint=len(self))
+            return labels.astype("i8", copy=False), len(shape)
+
+        if subset is None:
+            # https://github.com/pandas-dev/pandas/issues/28770
+            # Incompatible types in assignment (expression has type "Index", variable
+            # has type "Sequence[Any]")
+            subset = self.columns  # type: ignore[assignment]
+        elif (
+            not np.iterable(subset)
+            or isinstance(subset, str)
+            or isinstance(subset, tuple)
+            and subset in self.columns
+        ):
+            subset = (subset,)
+
+        #  needed for mypy since can't narrow types using np.iterable
+        subset = cast(Sequence, subset)
+
+        # Verify all columns in subset exist in the queried dataframe
+        # Otherwise, raise a KeyError, same as if you try to __getitem__ with a
+        # key that doesn't exist.
+        diff = set(subset) - set(self.columns)
+        if diff:
+            raise KeyError(Index(diff))
+
+        if len(subset) == 1 and self.columns.is_unique:
+            # GH#45236 This is faster than get_group_index below
+            result = self[subset[0]].duplicated(keep)
+            result.name = None
+        else:
+            vals = (col.values for name, col in self.items() if name in subset)
+            labels, shape = map(list, zip(*map(f, vals)))
+
+            ids = get_group_index(labels, tuple(shape), sort=False, xnull=False)
+            result = self._constructor_sliced(duplicated(ids, keep), index=self.index)
+        return result.__finalize__(self, method="duplicated")
+
+    # ----------------------------------------------------------------------
+    # Sorting
+    # error: Signature of "sort_values" incompatible with supertype "NDFrame"
+    @overload  # type: ignore[override]
+    def sort_values(
+        self,
+        by: IndexLabel,
+        *,
+        axis: Axis = ...,
+        ascending=...,
+        inplace: Literal[False] = ...,
+        kind: SortKind = ...,
+        na_position: NaPosition = ...,
+        ignore_index: bool = ...,
+        key: ValueKeyFunc = ...,
+    ) -> DataFrame:
+        ...
+
+    @overload
+    def sort_values(
+        self,
+        by: IndexLabel,
+        *,
+        axis: Axis = ...,
+        ascending=...,
+        inplace: Literal[True],
+        kind: SortKind = ...,
+        na_position: str = ...,
+        ignore_index: bool = ...,
+        key: ValueKeyFunc = ...,
+    ) -> None:
+        ...
+
+    def sort_values(
+        self,
+        by: IndexLabel,
+        *,
+        axis: Axis = 0,
+        ascending: bool | list[bool] | tuple[bool, ...] = True,
+        inplace: bool = False,
+        kind: SortKind = "quicksort",
+        na_position: str = "last",
+        ignore_index: bool = False,
+        key: ValueKeyFunc | None = None,
+    ) -> DataFrame | None:
+        """
+        Sort by the values along either axis.
+
+        Parameters
+        ----------
+        by : str or list of str
+            Name or list of names to sort by.
+
+            - if `axis` is 0 or `'index'` then `by` may contain index
+              levels and/or column labels.
+            - if `axis` is 1 or `'columns'` then `by` may contain column
+              levels and/or index labels.
+        axis : "{0 or 'index', 1 or 'columns'}", default 0
+             Axis to be sorted.
+        ascending : bool or list of bool, default True
+             Sort ascending vs. descending. Specify list for multiple sort
+             orders.  If this is a list of bools, must match the length of
+             the by.
+        inplace : bool, default False
+             If True, perform operation in-place.
+        kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, default 'quicksort'
+             Choice of sorting algorithm. See also :func:`numpy.sort` for more
+             information. `mergesort` and `stable` are the only stable algorithms. For
+             DataFrames, this option is only applied when sorting on a single
+             column or label.
+        na_position : {'first', 'last'}, default 'last'
+             Puts NaNs at the beginning if `first`; `last` puts NaNs at the
+             end.
+        ignore_index : bool, default False
+             If True, the resulting axis will be labeled 0, 1, …, n - 1.
+        key : callable, optional
+            Apply the key function to the values
+            before sorting. This is similar to the `key` argument in the
+            builtin :meth:`sorted` function, with the notable difference that
+            this `key` function should be *vectorized*. It should expect a
+            ``Series`` and return a Series with the same shape as the input.
+            It will be applied to each column in `by` independently.
+
+        Returns
+        -------
+        DataFrame or None
+            DataFrame with sorted values or None if ``inplace=True``.
+
+        See Also
+        --------
+        DataFrame.sort_index : Sort a DataFrame by the index.
+        Series.sort_values : Similar method for a Series.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({
+        ...     'col1': ['A', 'A', 'B', np.nan, 'D', 'C'],
+        ...     'col2': [2, 1, 9, 8, 7, 4],
+        ...     'col3': [0, 1, 9, 4, 2, 3],
+        ...     'col4': ['a', 'B', 'c', 'D', 'e', 'F']
+        ... })
+        >>> df
+          col1  col2  col3 col4
+        0    A     2     0    a
+        1    A     1     1    B
+        2    B     9     9    c
+        3  NaN     8     4    D
+        4    D     7     2    e
+        5    C     4     3    F
+
+        Sort by col1
+
+        >>> df.sort_values(by=['col1'])
+          col1  col2  col3 col4
+        0    A     2     0    a
+        1    A     1     1    B
+        2    B     9     9    c
+        5    C     4     3    F
+        4    D     7     2    e
+        3  NaN     8     4    D
+
+        Sort by multiple columns
+
+        >>> df.sort_values(by=['col1', 'col2'])
+          col1  col2  col3 col4
+        1    A     1     1    B
+        0    A     2     0    a
+        2    B     9     9    c
+        5    C     4     3    F
+        4    D     7     2    e
+        3  NaN     8     4    D
+
+        Sort Descending
+
+        >>> df.sort_values(by='col1', ascending=False)
+          col1  col2  col3 col4
+        4    D     7     2    e
+        5    C     4     3    F
+        2    B     9     9    c
+        0    A     2     0    a
+        1    A     1     1    B
+        3  NaN     8     4    D
+
+        Putting NAs first
+
+        >>> df.sort_values(by='col1', ascending=False, na_position='first')
+          col1  col2  col3 col4
+        3  NaN     8     4    D
+        4    D     7     2    e
+        5    C     4     3    F
+        2    B     9     9    c
+        0    A     2     0    a
+        1    A     1     1    B
+
+        Sorting with a key function
+
+        >>> df.sort_values(by='col4', key=lambda col: col.str.lower())
+           col1  col2  col3 col4
+        0    A     2     0    a
+        1    A     1     1    B
+        2    B     9     9    c
+        3  NaN     8     4    D
+        4    D     7     2    e
+        5    C     4     3    F
+
+        Natural sort with the key argument,
+        using the `natsort ` package.
+
+        >>> df = pd.DataFrame({
+        ...    "time": ['0hr', '128hr', '72hr', '48hr', '96hr'],
+        ...    "value": [10, 20, 30, 40, 50]
+        ... })
+        >>> df
+            time  value
+        0    0hr     10
+        1  128hr     20
+        2   72hr     30
+        3   48hr     40
+        4   96hr     50
+        >>> from natsort import index_natsorted
+        >>> df.sort_values(
+        ...     by="time",
+        ...     key=lambda x: np.argsort(index_natsorted(df["time"]))
+        ... )
+            time  value
+        0    0hr     10
+        3   48hr     40
+        2   72hr     30
+        4   96hr     50
+        1  128hr     20
+        """
+        inplace = validate_bool_kwarg(inplace, "inplace")
+        axis = self._get_axis_number(axis)
+        ascending = validate_ascending(ascending)
+        if not isinstance(by, list):
+            by = [by]
+        # error: Argument 1 to "len" has incompatible type "Union[bool, List[bool]]";
+        # expected "Sized"
+        if is_sequence(ascending) and (
+            len(by) != len(ascending)  # type: ignore[arg-type]
+        ):
+            # error: Argument 1 to "len" has incompatible type "Union[bool,
+            # List[bool]]"; expected "Sized"
+            raise ValueError(
+                f"Length of ascending ({len(ascending)})"  # type: ignore[arg-type]
+                f" != length of by ({len(by)})"
+            )
+        if len(by) > 1:
+            keys = [self._get_label_or_level_values(x, axis=axis) for x in by]
+
+            # need to rewrap columns in Series to apply key function
+            if key is not None:
+                # error: List comprehension has incompatible type List[Series];
+                # expected List[ndarray]
+                keys = [
+                    Series(k, name=name)  # type: ignore[misc]
+                    for (k, name) in zip(keys, by)
+                ]
+
+            indexer = lexsort_indexer(
+                keys, orders=ascending, na_position=na_position, key=key
+            )
+        elif len(by):
+            # len(by) == 1
+
+            k = self._get_label_or_level_values(by[0], axis=axis)
+
+            # need to rewrap column in Series to apply key function
+            if key is not None:
+                # error: Incompatible types in assignment (expression has type
+                # "Series", variable has type "ndarray")
+                k = Series(k, name=by[0])  # type: ignore[assignment]
+
+            if isinstance(ascending, (tuple, list)):
+                ascending = ascending[0]
+
+            indexer = nargsort(
+                k, kind=kind, ascending=ascending, na_position=na_position, key=key
+            )
+        else:
+            if inplace:
+                return self._update_inplace(self)
+            else:
+                return self.copy(deep=None)
+
+        if is_range_indexer(indexer, len(indexer)):
+            result = self.copy(deep=(not inplace and not using_copy_on_write()))
+            if ignore_index:
+                result.index = default_index(len(result))
+
+            if inplace:
+                return self._update_inplace(result)
+            else:
+                return result
+
+        new_data = self._mgr.take(
+            indexer, axis=self._get_block_manager_axis(axis), verify=False
+        )
+
+        if ignore_index:
+            new_data.set_axis(
+                self._get_block_manager_axis(axis), default_index(len(indexer))
+            )
+
+        result = self._constructor_from_mgr(new_data, axes=new_data.axes)
+        if inplace:
+            return self._update_inplace(result)
+        else:
+            return result.__finalize__(self, method="sort_values")
+
+    @overload
+    def sort_index(
+        self,
+        *,
+        axis: Axis = ...,
+        level: IndexLabel = ...,
+        ascending: bool | Sequence[bool] = ...,
+        inplace: Literal[True],
+        kind: SortKind = ...,
+        na_position: NaPosition = ...,
+        sort_remaining: bool = ...,
+        ignore_index: bool = ...,
+        key: IndexKeyFunc = ...,
+    ) -> None:
+        ...
+
+    @overload
+    def sort_index(
+        self,
+        *,
+        axis: Axis = ...,
+        level: IndexLabel = ...,
+        ascending: bool | Sequence[bool] = ...,
+        inplace: Literal[False] = ...,
+        kind: SortKind = ...,
+        na_position: NaPosition = ...,
+        sort_remaining: bool = ...,
+        ignore_index: bool = ...,
+        key: IndexKeyFunc = ...,
+    ) -> DataFrame:
+        ...
+
+    @overload
+    def sort_index(
+        self,
+        *,
+        axis: Axis = ...,
+        level: IndexLabel = ...,
+        ascending: bool | Sequence[bool] = ...,
+        inplace: bool = ...,
+        kind: SortKind = ...,
+        na_position: NaPosition = ...,
+        sort_remaining: bool = ...,
+        ignore_index: bool = ...,
+        key: IndexKeyFunc = ...,
+    ) -> DataFrame | None:
+        ...
+
+    def sort_index(
+        self,
+        *,
+        axis: Axis = 0,
+        level: IndexLabel | None = None,
+        ascending: bool | Sequence[bool] = True,
+        inplace: bool = False,
+        kind: SortKind = "quicksort",
+        na_position: NaPosition = "last",
+        sort_remaining: bool = True,
+        ignore_index: bool = False,
+        key: IndexKeyFunc | None = None,
+    ) -> DataFrame | None:
+        """
+        Sort object by labels (along an axis).
+
+        Returns a new DataFrame sorted by label if `inplace` argument is
+        ``False``, otherwise updates the original DataFrame and returns None.
+
+        Parameters
+        ----------
+        axis : {0 or 'index', 1 or 'columns'}, default 0
+            The axis along which to sort.  The value 0 identifies the rows,
+            and 1 identifies the columns.
+        level : int or level name or list of ints or list of level names
+            If not None, sort on values in specified index level(s).
+        ascending : bool or list-like of bools, default True
+            Sort ascending vs. descending. When the index is a MultiIndex the
+            sort direction can be controlled for each level individually.
+        inplace : bool, default False
+            Whether to modify the DataFrame rather than creating a new one.
+        kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, default 'quicksort'
+            Choice of sorting algorithm. See also :func:`numpy.sort` for more
+            information. `mergesort` and `stable` are the only stable algorithms. For
+            DataFrames, this option is only applied when sorting on a single
+            column or label.
+        na_position : {'first', 'last'}, default 'last'
+            Puts NaNs at the beginning if `first`; `last` puts NaNs at the end.
+            Not implemented for MultiIndex.
+        sort_remaining : bool, default True
+            If True and sorting by level and index is multilevel, sort by other
+            levels too (in order) after sorting by specified level.
+        ignore_index : bool, default False
+            If True, the resulting axis will be labeled 0, 1, …, n - 1.
+        key : callable, optional
+            If not None, apply the key function to the index values
+            before sorting. This is similar to the `key` argument in the
+            builtin :meth:`sorted` function, with the notable difference that
+            this `key` function should be *vectorized*. It should expect an
+            ``Index`` and return an ``Index`` of the same shape. For MultiIndex
+            inputs, the key is applied *per level*.
+
+        Returns
+        -------
+        DataFrame or None
+            The original DataFrame sorted by the labels or None if ``inplace=True``.
+
+        See Also
+        --------
+        Series.sort_index : Sort Series by the index.
+        DataFrame.sort_values : Sort DataFrame by the value.
+        Series.sort_values : Sort Series by the value.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([1, 2, 3, 4, 5], index=[100, 29, 234, 1, 150],
+        ...                   columns=['A'])
+        >>> df.sort_index()
+             A
+        1    4
+        29   2
+        100  1
+        150  5
+        234  3
+
+        By default, it sorts in ascending order, to sort in descending order,
+        use ``ascending=False``
+
+        >>> df.sort_index(ascending=False)
+             A
+        234  3
+        150  5
+        100  1
+        29   2
+        1    4
+
+        A key function can be specified which is applied to the index before
+        sorting. For a ``MultiIndex`` this is applied to each level separately.
+
+        >>> df = pd.DataFrame({"a": [1, 2, 3, 4]}, index=['A', 'b', 'C', 'd'])
+        >>> df.sort_index(key=lambda x: x.str.lower())
+           a
+        A  1
+        b  2
+        C  3
+        d  4
+        """
+        return super().sort_index(
+            axis=axis,
+            level=level,
+            ascending=ascending,
+            inplace=inplace,
+            kind=kind,
+            na_position=na_position,
+            sort_remaining=sort_remaining,
+            ignore_index=ignore_index,
+            key=key,
+        )
+
+    def value_counts(
+        self,
+        subset: IndexLabel | None = None,
+        normalize: bool = False,
+        sort: bool = True,
+        ascending: bool = False,
+        dropna: bool = True,
+    ) -> Series:
+        """
+        Return a Series containing the frequency of each distinct row in the Dataframe.
+
+        Parameters
+        ----------
+        subset : label or list of labels, optional
+            Columns to use when counting unique combinations.
+        normalize : bool, default False
+            Return proportions rather than frequencies.
+        sort : bool, default True
+            Sort by frequencies when True. Sort by DataFrame column values when False.
+        ascending : bool, default False
+            Sort in ascending order.
+        dropna : bool, default True
+            Don't include counts of rows that contain NA values.
+
+            .. versionadded:: 1.3.0
+
+        Returns
+        -------
+        Series
+
+        See Also
+        --------
+        Series.value_counts: Equivalent method on Series.
+
+        Notes
+        -----
+        The returned Series will have a MultiIndex with one level per input
+        column but an Index (non-multi) for a single label. By default, rows
+        that contain any NA values are omitted from the result. By default,
+        the resulting Series will be in descending order so that the first
+        element is the most frequently-occurring row.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'num_legs': [2, 4, 4, 6],
+        ...                    'num_wings': [2, 0, 0, 0]},
+        ...                   index=['falcon', 'dog', 'cat', 'ant'])
+        >>> df
+                num_legs  num_wings
+        falcon         2          2
+        dog            4          0
+        cat            4          0
+        ant            6          0
+
+        >>> df.value_counts()
+        num_legs  num_wings
+        4         0            2
+        2         2            1
+        6         0            1
+        Name: count, dtype: int64
+
+        >>> df.value_counts(sort=False)
+        num_legs  num_wings
+        2         2            1
+        4         0            2
+        6         0            1
+        Name: count, dtype: int64
+
+        >>> df.value_counts(ascending=True)
+        num_legs  num_wings
+        2         2            1
+        6         0            1
+        4         0            2
+        Name: count, dtype: int64
+
+        >>> df.value_counts(normalize=True)
+        num_legs  num_wings
+        4         0            0.50
+        2         2            0.25
+        6         0            0.25
+        Name: proportion, dtype: float64
+
+        With `dropna` set to `False` we can also count rows with NA values.
+
+        >>> df = pd.DataFrame({'first_name': ['John', 'Anne', 'John', 'Beth'],
+        ...                    'middle_name': ['Smith', pd.NA, pd.NA, 'Louise']})
+        >>> df
+          first_name middle_name
+        0       John       Smith
+        1       Anne        
+        2       John        
+        3       Beth      Louise
+
+        >>> df.value_counts()
+        first_name  middle_name
+        Beth        Louise         1
+        John        Smith          1
+        Name: count, dtype: int64
+
+        >>> df.value_counts(dropna=False)
+        first_name  middle_name
+        Anne        NaN            1
+        Beth        Louise         1
+        John        Smith          1
+                    NaN            1
+        Name: count, dtype: int64
+
+        >>> df.value_counts("first_name")
+        first_name
+        John    2
+        Anne    1
+        Beth    1
+        Name: count, dtype: int64
+        """
+        if subset is None:
+            subset = self.columns.tolist()
+
+        name = "proportion" if normalize else "count"
+        counts = self.groupby(subset, dropna=dropna, observed=False)._grouper.size()
+        counts.name = name
+
+        if sort:
+            counts = counts.sort_values(ascending=ascending)
+        if normalize:
+            counts /= counts.sum()
+
+        # Force MultiIndex for a list_like subset with a single column
+        if is_list_like(subset) and len(subset) == 1:  # type: ignore[arg-type]
+            counts.index = MultiIndex.from_arrays(
+                [counts.index], names=[counts.index.name]
+            )
+
+        return counts
+
+    def nlargest(
+        self, n: int, columns: IndexLabel, keep: NsmallestNlargestKeep = "first"
+    ) -> DataFrame:
+        """
+        Return the first `n` rows ordered by `columns` in descending order.
+
+        Return the first `n` rows with the largest values in `columns`, in
+        descending order. The columns that are not specified are returned as
+        well, but not used for ordering.
+
+        This method is equivalent to
+        ``df.sort_values(columns, ascending=False).head(n)``, but more
+        performant.
+
+        Parameters
+        ----------
+        n : int
+            Number of rows to return.
+        columns : label or list of labels
+            Column label(s) to order by.
+        keep : {'first', 'last', 'all'}, default 'first'
+            Where there are duplicate values:
+
+            - ``first`` : prioritize the first occurrence(s)
+            - ``last`` : prioritize the last occurrence(s)
+            - ``all`` : keep all the ties of the smallest item even if it means
+              selecting more than ``n`` items.
+
+        Returns
+        -------
+        DataFrame
+            The first `n` rows ordered by the given columns in descending
+            order.
+
+        See Also
+        --------
+        DataFrame.nsmallest : Return the first `n` rows ordered by `columns` in
+            ascending order.
+        DataFrame.sort_values : Sort DataFrame by the values.
+        DataFrame.head : Return the first `n` rows without re-ordering.
+
+        Notes
+        -----
+        This function cannot be used with all column types. For example, when
+        specifying columns with `object` or `category` dtypes, ``TypeError`` is
+        raised.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'population': [59000000, 65000000, 434000,
+        ...                                   434000, 434000, 337000, 11300,
+        ...                                   11300, 11300],
+        ...                    'GDP': [1937894, 2583560 , 12011, 4520, 12128,
+        ...                            17036, 182, 38, 311],
+        ...                    'alpha-2': ["IT", "FR", "MT", "MV", "BN",
+        ...                                "IS", "NR", "TV", "AI"]},
+        ...                   index=["Italy", "France", "Malta",
+        ...                          "Maldives", "Brunei", "Iceland",
+        ...                          "Nauru", "Tuvalu", "Anguilla"])
+        >>> df
+                  population      GDP alpha-2
+        Italy       59000000  1937894      IT
+        France      65000000  2583560      FR
+        Malta         434000    12011      MT
+        Maldives      434000     4520      MV
+        Brunei        434000    12128      BN
+        Iceland       337000    17036      IS
+        Nauru          11300      182      NR
+        Tuvalu         11300       38      TV
+        Anguilla       11300      311      AI
+
+        In the following example, we will use ``nlargest`` to select the three
+        rows having the largest values in column "population".
+
+        >>> df.nlargest(3, 'population')
+                population      GDP alpha-2
+        France    65000000  2583560      FR
+        Italy     59000000  1937894      IT
+        Malta       434000    12011      MT
+
+        When using ``keep='last'``, ties are resolved in reverse order:
+
+        >>> df.nlargest(3, 'population', keep='last')
+                population      GDP alpha-2
+        France    65000000  2583560      FR
+        Italy     59000000  1937894      IT
+        Brunei      434000    12128      BN
+
+        When using ``keep='all'``, the number of element kept can go beyond ``n``
+        if there are duplicate values for the smallest element, all the
+        ties are kept:
+
+        >>> df.nlargest(3, 'population', keep='all')
+                  population      GDP alpha-2
+        France      65000000  2583560      FR
+        Italy       59000000  1937894      IT
+        Malta         434000    12011      MT
+        Maldives      434000     4520      MV
+        Brunei        434000    12128      BN
+
+        However, ``nlargest`` does not keep ``n`` distinct largest elements:
+
+        >>> df.nlargest(5, 'population', keep='all')
+                  population      GDP alpha-2
+        France      65000000  2583560      FR
+        Italy       59000000  1937894      IT
+        Malta         434000    12011      MT
+        Maldives      434000     4520      MV
+        Brunei        434000    12128      BN
+
+        To order by the largest values in column "population" and then "GDP",
+        we can specify multiple columns like in the next example.
+
+        >>> df.nlargest(3, ['population', 'GDP'])
+                population      GDP alpha-2
+        France    65000000  2583560      FR
+        Italy     59000000  1937894      IT
+        Brunei      434000    12128      BN
+        """
+        return selectn.SelectNFrame(self, n=n, keep=keep, columns=columns).nlargest()
+
+    def nsmallest(
+        self, n: int, columns: IndexLabel, keep: NsmallestNlargestKeep = "first"
+    ) -> DataFrame:
+        """
+        Return the first `n` rows ordered by `columns` in ascending order.
+
+        Return the first `n` rows with the smallest values in `columns`, in
+        ascending order. The columns that are not specified are returned as
+        well, but not used for ordering.
+
+        This method is equivalent to
+        ``df.sort_values(columns, ascending=True).head(n)``, but more
+        performant.
+
+        Parameters
+        ----------
+        n : int
+            Number of items to retrieve.
+        columns : list or str
+            Column name or names to order by.
+        keep : {'first', 'last', 'all'}, default 'first'
+            Where there are duplicate values:
+
+            - ``first`` : take the first occurrence.
+            - ``last`` : take the last occurrence.
+            - ``all`` : keep all the ties of the largest item even if it means
+              selecting more than ``n`` items.
+
+        Returns
+        -------
+        DataFrame
+
+        See Also
+        --------
+        DataFrame.nlargest : Return the first `n` rows ordered by `columns` in
+            descending order.
+        DataFrame.sort_values : Sort DataFrame by the values.
+        DataFrame.head : Return the first `n` rows without re-ordering.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'population': [59000000, 65000000, 434000,
+        ...                                   434000, 434000, 337000, 337000,
+        ...                                   11300, 11300],
+        ...                    'GDP': [1937894, 2583560 , 12011, 4520, 12128,
+        ...                            17036, 182, 38, 311],
+        ...                    'alpha-2': ["IT", "FR", "MT", "MV", "BN",
+        ...                                "IS", "NR", "TV", "AI"]},
+        ...                   index=["Italy", "France", "Malta",
+        ...                          "Maldives", "Brunei", "Iceland",
+        ...                          "Nauru", "Tuvalu", "Anguilla"])
+        >>> df
+                  population      GDP alpha-2
+        Italy       59000000  1937894      IT
+        France      65000000  2583560      FR
+        Malta         434000    12011      MT
+        Maldives      434000     4520      MV
+        Brunei        434000    12128      BN
+        Iceland       337000    17036      IS
+        Nauru         337000      182      NR
+        Tuvalu         11300       38      TV
+        Anguilla       11300      311      AI
+
+        In the following example, we will use ``nsmallest`` to select the
+        three rows having the smallest values in column "population".
+
+        >>> df.nsmallest(3, 'population')
+                  population    GDP alpha-2
+        Tuvalu         11300     38      TV
+        Anguilla       11300    311      AI
+        Iceland       337000  17036      IS
+
+        When using ``keep='last'``, ties are resolved in reverse order:
+
+        >>> df.nsmallest(3, 'population', keep='last')
+                  population  GDP alpha-2
+        Anguilla       11300  311      AI
+        Tuvalu         11300   38      TV
+        Nauru         337000  182      NR
+
+        When using ``keep='all'``, the number of element kept can go beyond ``n``
+        if there are duplicate values for the largest element, all the
+        ties are kept.
+
+        >>> df.nsmallest(3, 'population', keep='all')
+                  population    GDP alpha-2
+        Tuvalu         11300     38      TV
+        Anguilla       11300    311      AI
+        Iceland       337000  17036      IS
+        Nauru         337000    182      NR
+
+        However, ``nsmallest`` does not keep ``n`` distinct
+        smallest elements:
+
+        >>> df.nsmallest(4, 'population', keep='all')
+                  population    GDP alpha-2
+        Tuvalu         11300     38      TV
+        Anguilla       11300    311      AI
+        Iceland       337000  17036      IS
+        Nauru         337000    182      NR
+
+        To order by the smallest values in column "population" and then "GDP", we can
+        specify multiple columns like in the next example.
+
+        >>> df.nsmallest(3, ['population', 'GDP'])
+                  population  GDP alpha-2
+        Tuvalu         11300   38      TV
+        Anguilla       11300  311      AI
+        Nauru         337000  182      NR
+        """
+        return selectn.SelectNFrame(self, n=n, keep=keep, columns=columns).nsmallest()
+
+    @doc(
+        Series.swaplevel,
+        klass=_shared_doc_kwargs["klass"],
+        extra_params=dedent(
+            """axis : {0 or 'index', 1 or 'columns'}, default 0
+            The axis to swap levels on. 0 or 'index' for row-wise, 1 or
+            'columns' for column-wise."""
+        ),
+        examples=dedent(
+            """\
+        Examples
+        --------
+        >>> df = pd.DataFrame(
+        ...     {"Grade": ["A", "B", "A", "C"]},
+        ...     index=[
+        ...         ["Final exam", "Final exam", "Coursework", "Coursework"],
+        ...         ["History", "Geography", "History", "Geography"],
+        ...         ["January", "February", "March", "April"],
+        ...     ],
+        ... )
+        >>> df
+                                            Grade
+        Final exam  History     January      A
+                    Geography   February     B
+        Coursework  History     March        A
+                    Geography   April        C
+
+        In the following example, we will swap the levels of the indices.
+        Here, we will swap the levels column-wise, but levels can be swapped row-wise
+        in a similar manner. Note that column-wise is the default behaviour.
+        By not supplying any arguments for i and j, we swap the last and second to
+        last indices.
+
+        >>> df.swaplevel()
+                                            Grade
+        Final exam  January     History         A
+                    February    Geography       B
+        Coursework  March       History         A
+                    April       Geography       C
+
+        By supplying one argument, we can choose which index to swap the last
+        index with. We can for example swap the first index with the last one as
+        follows.
+
+        >>> df.swaplevel(0)
+                                            Grade
+        January     History     Final exam      A
+        February    Geography   Final exam      B
+        March       History     Coursework      A
+        April       Geography   Coursework      C
+
+        We can also define explicitly which indices we want to swap by supplying values
+        for both i and j. Here, we for example swap the first and second indices.
+
+        >>> df.swaplevel(0, 1)
+                                            Grade
+        History     Final exam  January         A
+        Geography   Final exam  February        B
+        History     Coursework  March           A
+        Geography   Coursework  April           C"""
+        ),
+    )
+    def swaplevel(self, i: Axis = -2, j: Axis = -1, axis: Axis = 0) -> DataFrame:
+        result = self.copy(deep=None)
+
+        axis = self._get_axis_number(axis)
+
+        if not isinstance(result._get_axis(axis), MultiIndex):  # pragma: no cover
+            raise TypeError("Can only swap levels on a hierarchical axis.")
+
+        if axis == 0:
+            assert isinstance(result.index, MultiIndex)
+            result.index = result.index.swaplevel(i, j)
+        else:
+            assert isinstance(result.columns, MultiIndex)
+            result.columns = result.columns.swaplevel(i, j)
+        return result
+
+    def reorder_levels(self, order: Sequence[int | str], axis: Axis = 0) -> DataFrame:
+        """
+        Rearrange index levels using input order. May not drop or duplicate levels.
+
+        Parameters
+        ----------
+        order : list of int or list of str
+            List representing new level order. Reference level by number
+            (position) or by key (label).
+        axis : {0 or 'index', 1 or 'columns'}, default 0
+            Where to reorder levels.
+
+        Returns
+        -------
+        DataFrame
+
+        Examples
+        --------
+        >>> data = {
+        ...     "class": ["Mammals", "Mammals", "Reptiles"],
+        ...     "diet": ["Omnivore", "Carnivore", "Carnivore"],
+        ...     "species": ["Humans", "Dogs", "Snakes"],
+        ... }
+        >>> df = pd.DataFrame(data, columns=["class", "diet", "species"])
+        >>> df = df.set_index(["class", "diet"])
+        >>> df
+                                          species
+        class      diet
+        Mammals    Omnivore                Humans
+                   Carnivore                 Dogs
+        Reptiles   Carnivore               Snakes
+
+        Let's reorder the levels of the index:
+
+        >>> df.reorder_levels(["diet", "class"])
+                                          species
+        diet      class
+        Omnivore  Mammals                  Humans
+        Carnivore Mammals                    Dogs
+                  Reptiles                 Snakes
+        """
+        axis = self._get_axis_number(axis)
+        if not isinstance(self._get_axis(axis), MultiIndex):  # pragma: no cover
+            raise TypeError("Can only reorder levels on a hierarchical axis.")
+
+        result = self.copy(deep=None)
+
+        if axis == 0:
+            assert isinstance(result.index, MultiIndex)
+            result.index = result.index.reorder_levels(order)
+        else:
+            assert isinstance(result.columns, MultiIndex)
+            result.columns = result.columns.reorder_levels(order)
+        return result
+
+    # ----------------------------------------------------------------------
+    # Arithmetic Methods
+
+    def _cmp_method(self, other, op):
+        axis: Literal[1] = 1  # only relevant for Series other case
+
+        self, other = self._align_for_op(other, axis, flex=False, level=None)
+
+        # See GH#4537 for discussion of scalar op behavior
+        new_data = self._dispatch_frame_op(other, op, axis=axis)
+        return self._construct_result(new_data)
+
+    def _arith_method(self, other, op):
+        if self._should_reindex_frame_op(other, op, 1, None, None):
+            return self._arith_method_with_reindex(other, op)
+
+        axis: Literal[1] = 1  # only relevant for Series other case
+        other = ops.maybe_prepare_scalar_for_op(other, (self.shape[axis],))
+
+        self, other = self._align_for_op(other, axis, flex=True, level=None)
+
+        with np.errstate(all="ignore"):
+            new_data = self._dispatch_frame_op(other, op, axis=axis)
+        return self._construct_result(new_data)
+
+    _logical_method = _arith_method
+
+    def _dispatch_frame_op(
+        self, right, func: Callable, axis: AxisInt | None = None
+    ) -> DataFrame:
+        """
+        Evaluate the frame operation func(left, right) by evaluating
+        column-by-column, dispatching to the Series implementation.
+
+        Parameters
+        ----------
+        right : scalar, Series, or DataFrame
+        func : arithmetic or comparison operator
+        axis : {None, 0, 1}
+
+        Returns
+        -------
+        DataFrame
+
+        Notes
+        -----
+        Caller is responsible for setting np.errstate where relevant.
+        """
+        # Get the appropriate array-op to apply to each column/block's values.
+        array_op = ops.get_array_op(func)
+
+        right = lib.item_from_zerodim(right)
+        if not is_list_like(right):
+            # i.e. scalar, faster than checking np.ndim(right) == 0
+            bm = self._mgr.apply(array_op, right=right)
+            return self._constructor_from_mgr(bm, axes=bm.axes)
+
+        elif isinstance(right, DataFrame):
+            assert self.index.equals(right.index)
+            assert self.columns.equals(right.columns)
+            # TODO: The previous assertion `assert right._indexed_same(self)`
+            #  fails in cases with empty columns reached via
+            #  _frame_arith_method_with_reindex
+
+            # TODO operate_blockwise expects a manager of the same type
+            bm = self._mgr.operate_blockwise(
+                # error: Argument 1 to "operate_blockwise" of "ArrayManager" has
+                # incompatible type "Union[ArrayManager, BlockManager]"; expected
+                # "ArrayManager"
+                # error: Argument 1 to "operate_blockwise" of "BlockManager" has
+                # incompatible type "Union[ArrayManager, BlockManager]"; expected
+                # "BlockManager"
+                right._mgr,  # type: ignore[arg-type]
+                array_op,
+            )
+            return self._constructor_from_mgr(bm, axes=bm.axes)
+
+        elif isinstance(right, Series) and axis == 1:
+            # axis=1 means we want to operate row-by-row
+            assert right.index.equals(self.columns)
+
+            right = right._values
+            # maybe_align_as_frame ensures we do not have an ndarray here
+            assert not isinstance(right, np.ndarray)
+
+            arrays = [
+                array_op(_left, _right)
+                for _left, _right in zip(self._iter_column_arrays(), right)
+            ]
+
+        elif isinstance(right, Series):
+            assert right.index.equals(self.index)
+            right = right._values
+
+            arrays = [array_op(left, right) for left in self._iter_column_arrays()]
+
+        else:
+            raise NotImplementedError(right)
+
+        return type(self)._from_arrays(
+            arrays, self.columns, self.index, verify_integrity=False
+        )
+
+    def _combine_frame(self, other: DataFrame, func, fill_value=None):
+        # at this point we have `self._indexed_same(other)`
+
+        if fill_value is None:
+            # since _arith_op may be called in a loop, avoid function call
+            #  overhead if possible by doing this check once
+            _arith_op = func
+
+        else:
+
+            def _arith_op(left, right):
+                # for the mixed_type case where we iterate over columns,
+                # _arith_op(left, right) is equivalent to
+                # left._binop(right, func, fill_value=fill_value)
+                left, right = ops.fill_binop(left, right, fill_value)
+                return func(left, right)
+
+        new_data = self._dispatch_frame_op(other, _arith_op)
+        return new_data
+
+    def _arith_method_with_reindex(self, right: DataFrame, op) -> DataFrame:
+        """
+        For DataFrame-with-DataFrame operations that require reindexing,
+        operate only on shared columns, then reindex.
+
+        Parameters
+        ----------
+        right : DataFrame
+        op : binary operator
+
+        Returns
+        -------
+        DataFrame
+        """
+        left = self
+
+        # GH#31623, only operate on shared columns
+        cols, lcols, rcols = left.columns.join(
+            right.columns, how="inner", level=None, return_indexers=True
+        )
+
+        new_left = left.iloc[:, lcols]
+        new_right = right.iloc[:, rcols]
+        result = op(new_left, new_right)
+
+        # Do the join on the columns instead of using left._align_for_op
+        #  to avoid constructing two potentially large/sparse DataFrames
+        join_columns, _, _ = left.columns.join(
+            right.columns, how="outer", level=None, return_indexers=True
+        )
+
+        if result.columns.has_duplicates:
+            # Avoid reindexing with a duplicate axis.
+            # https://github.com/pandas-dev/pandas/issues/35194
+            indexer, _ = result.columns.get_indexer_non_unique(join_columns)
+            indexer = algorithms.unique1d(indexer)
+            result = result._reindex_with_indexers(
+                {1: [join_columns, indexer]}, allow_dups=True
+            )
+        else:
+            result = result.reindex(join_columns, axis=1)
+
+        return result
+
+    def _should_reindex_frame_op(self, right, op, axis: int, fill_value, level) -> bool:
+        """
+        Check if this is an operation between DataFrames that will need to reindex.
+        """
+        if op is operator.pow or op is roperator.rpow:
+            # GH#32685 pow has special semantics for operating with null values
+            return False
+
+        if not isinstance(right, DataFrame):
+            return False
+
+        if fill_value is None and level is None and axis == 1:
+            # TODO: any other cases we should handle here?
+
+            # Intersection is always unique so we have to check the unique columns
+            left_uniques = self.columns.unique()
+            right_uniques = right.columns.unique()
+            cols = left_uniques.intersection(right_uniques)
+            if len(cols) and not (
+                len(cols) == len(left_uniques) and len(cols) == len(right_uniques)
+            ):
+                # TODO: is there a shortcut available when len(cols) == 0?
+                return True
+
+        return False
+
+    def _align_for_op(
+        self,
+        other,
+        axis: AxisInt,
+        flex: bool | None = False,
+        level: Level | None = None,
+    ):
+        """
+        Convert rhs to meet lhs dims if input is list, tuple or np.ndarray.
+
+        Parameters
+        ----------
+        left : DataFrame
+        right : Any
+        axis : int
+        flex : bool or None, default False
+            Whether this is a flex op, in which case we reindex.
+            None indicates not to check for alignment.
+        level : int or level name, default None
+
+        Returns
+        -------
+        left : DataFrame
+        right : Any
+        """
+        left, right = self, other
+
+        def to_series(right):
+            msg = (
+                "Unable to coerce to Series, "
+                "length must be {req_len}: given {given_len}"
+            )
+
+            # pass dtype to avoid doing inference, which would break consistency
+            #  with Index/Series ops
+            dtype = None
+            if getattr(right, "dtype", None) == object:
+                # can't pass right.dtype unconditionally as that would break on e.g.
+                #  datetime64[h] ndarray
+                dtype = object
+
+            if axis == 0:
+                if len(left.index) != len(right):
+                    raise ValueError(
+                        msg.format(req_len=len(left.index), given_len=len(right))
+                    )
+                right = left._constructor_sliced(right, index=left.index, dtype=dtype)
+            else:
+                if len(left.columns) != len(right):
+                    raise ValueError(
+                        msg.format(req_len=len(left.columns), given_len=len(right))
+                    )
+                right = left._constructor_sliced(right, index=left.columns, dtype=dtype)
+            return right
+
+        if isinstance(right, np.ndarray):
+            if right.ndim == 1:
+                right = to_series(right)
+
+            elif right.ndim == 2:
+                # We need to pass dtype=right.dtype to retain object dtype
+                #  otherwise we lose consistency with Index and array ops
+                dtype = None
+                if right.dtype == object:
+                    # can't pass right.dtype unconditionally as that would break on e.g.
+                    #  datetime64[h] ndarray
+                    dtype = object
+
+                if right.shape == left.shape:
+                    right = left._constructor(
+                        right, index=left.index, columns=left.columns, dtype=dtype
+                    )
+
+                elif right.shape[0] == left.shape[0] and right.shape[1] == 1:
+                    # Broadcast across columns
+                    right = np.broadcast_to(right, left.shape)
+                    right = left._constructor(
+                        right, index=left.index, columns=left.columns, dtype=dtype
+                    )
+
+                elif right.shape[1] == left.shape[1] and right.shape[0] == 1:
+                    # Broadcast along rows
+                    right = to_series(right[0, :])
+
+                else:
+                    raise ValueError(
+                        "Unable to coerce to DataFrame, shape "
+                        f"must be {left.shape}: given {right.shape}"
+                    )
+
+            elif right.ndim > 2:
+                raise ValueError(
+                    "Unable to coerce to Series/DataFrame, "
+                    f"dimension must be <= 2: {right.shape}"
+                )
+
+        elif is_list_like(right) and not isinstance(right, (Series, DataFrame)):
+            # GH#36702. Raise when attempting arithmetic with list of array-like.
+            if any(is_array_like(el) for el in right):
+                raise ValueError(
+                    f"Unable to coerce list of {type(right[0])} to Series/DataFrame"
+                )
+            # GH#17901
+            right = to_series(right)
+
+        if flex is not None and isinstance(right, DataFrame):
+            if not left._indexed_same(right):
+                if flex:
+                    left, right = left.align(
+                        right, join="outer", level=level, copy=False
+                    )
+                else:
+                    raise ValueError(
+                        "Can only compare identically-labeled (both index and columns) "
+                        "DataFrame objects"
+                    )
+        elif isinstance(right, Series):
+            # axis=1 is default for DataFrame-with-Series op
+            axis = axis if axis is not None else 1
+            if not flex:
+                if not left.axes[axis].equals(right.index):
+                    raise ValueError(
+                        "Operands are not aligned. Do "
+                        "`left, right = left.align(right, axis=1, copy=False)` "
+                        "before operating."
+                    )
+
+            left, right = left.align(
+                right,
+                join="outer",
+                axis=axis,
+                level=level,
+                copy=False,
+            )
+            right = left._maybe_align_series_as_frame(right, axis)
+
+        return left, right
+
+    def _maybe_align_series_as_frame(self, series: Series, axis: AxisInt):
+        """
+        If the Series operand is not EA-dtype, we can broadcast to 2D and operate
+        blockwise.
+        """
+        rvalues = series._values
+        if not isinstance(rvalues, np.ndarray):
+            # TODO(EA2D): no need to special-case with 2D EAs
+            if rvalues.dtype in ("datetime64[ns]", "timedelta64[ns]"):
+                # We can losslessly+cheaply cast to ndarray
+                rvalues = np.asarray(rvalues)
+            else:
+                return series
+
+        if axis == 0:
+            rvalues = rvalues.reshape(-1, 1)
+        else:
+            rvalues = rvalues.reshape(1, -1)
+
+        rvalues = np.broadcast_to(rvalues, self.shape)
+        # pass dtype to avoid doing inference
+        return self._constructor(
+            rvalues,
+            index=self.index,
+            columns=self.columns,
+            dtype=rvalues.dtype,
+        )
+
+    def _flex_arith_method(
+        self, other, op, *, axis: Axis = "columns", level=None, fill_value=None
+    ):
+        axis = self._get_axis_number(axis) if axis is not None else 1
+
+        if self._should_reindex_frame_op(other, op, axis, fill_value, level):
+            return self._arith_method_with_reindex(other, op)
+
+        if isinstance(other, Series) and fill_value is not None:
+            # TODO: We could allow this in cases where we end up going
+            #  through the DataFrame path
+            raise NotImplementedError(f"fill_value {fill_value} not supported.")
+
+        other = ops.maybe_prepare_scalar_for_op(other, self.shape)
+        self, other = self._align_for_op(other, axis, flex=True, level=level)
+
+        with np.errstate(all="ignore"):
+            if isinstance(other, DataFrame):
+                # Another DataFrame
+                new_data = self._combine_frame(other, op, fill_value)
+
+            elif isinstance(other, Series):
+                new_data = self._dispatch_frame_op(other, op, axis=axis)
+            else:
+                # in this case we always have `np.ndim(other) == 0`
+                if fill_value is not None:
+                    self = self.fillna(fill_value)
+
+                new_data = self._dispatch_frame_op(other, op)
+
+        return self._construct_result(new_data)
+
+    def _construct_result(self, result) -> DataFrame:
+        """
+        Wrap the result of an arithmetic, comparison, or logical operation.
+
+        Parameters
+        ----------
+        result : DataFrame
+
+        Returns
+        -------
+        DataFrame
+        """
+        out = self._constructor(result, copy=False).__finalize__(self)
+        # Pin columns instead of passing to constructor for compat with
+        #  non-unique columns case
+        out.columns = self.columns
+        out.index = self.index
+        return out
+
+    def __divmod__(self, other) -> tuple[DataFrame, DataFrame]:
+        # Naive implementation, room for optimization
+        div = self // other
+        mod = self - div * other
+        return div, mod
+
+    def __rdivmod__(self, other) -> tuple[DataFrame, DataFrame]:
+        # Naive implementation, room for optimization
+        div = other // self
+        mod = other - div * self
+        return div, mod
+
+    def _flex_cmp_method(self, other, op, *, axis: Axis = "columns", level=None):
+        axis = self._get_axis_number(axis) if axis is not None else 1
+
+        self, other = self._align_for_op(other, axis, flex=True, level=level)
+
+        new_data = self._dispatch_frame_op(other, op, axis=axis)
+        return self._construct_result(new_data)
+
+    @Appender(ops.make_flex_doc("eq", "dataframe"))
+    def eq(self, other, axis: Axis = "columns", level=None) -> DataFrame:
+        return self._flex_cmp_method(other, operator.eq, axis=axis, level=level)
+
+    @Appender(ops.make_flex_doc("ne", "dataframe"))
+    def ne(self, other, axis: Axis = "columns", level=None) -> DataFrame:
+        return self._flex_cmp_method(other, operator.ne, axis=axis, level=level)
+
+    @Appender(ops.make_flex_doc("le", "dataframe"))
+    def le(self, other, axis: Axis = "columns", level=None) -> DataFrame:
+        return self._flex_cmp_method(other, operator.le, axis=axis, level=level)
+
+    @Appender(ops.make_flex_doc("lt", "dataframe"))
+    def lt(self, other, axis: Axis = "columns", level=None) -> DataFrame:
+        return self._flex_cmp_method(other, operator.lt, axis=axis, level=level)
+
+    @Appender(ops.make_flex_doc("ge", "dataframe"))
+    def ge(self, other, axis: Axis = "columns", level=None) -> DataFrame:
+        return self._flex_cmp_method(other, operator.ge, axis=axis, level=level)
+
+    @Appender(ops.make_flex_doc("gt", "dataframe"))
+    def gt(self, other, axis: Axis = "columns", level=None) -> DataFrame:
+        return self._flex_cmp_method(other, operator.gt, axis=axis, level=level)
+
+    @Appender(ops.make_flex_doc("add", "dataframe"))
+    def add(
+        self, other, axis: Axis = "columns", level=None, fill_value=None
+    ) -> DataFrame:
+        return self._flex_arith_method(
+            other, operator.add, level=level, fill_value=fill_value, axis=axis
+        )
+
+    @Appender(ops.make_flex_doc("radd", "dataframe"))
+    def radd(
+        self, other, axis: Axis = "columns", level=None, fill_value=None
+    ) -> DataFrame:
+        return self._flex_arith_method(
+            other, roperator.radd, level=level, fill_value=fill_value, axis=axis
+        )
+
+    @Appender(ops.make_flex_doc("sub", "dataframe"))
+    def sub(
+        self, other, axis: Axis = "columns", level=None, fill_value=None
+    ) -> DataFrame:
+        return self._flex_arith_method(
+            other, operator.sub, level=level, fill_value=fill_value, axis=axis
+        )
+
+    subtract = sub
+
+    @Appender(ops.make_flex_doc("rsub", "dataframe"))
+    def rsub(
+        self, other, axis: Axis = "columns", level=None, fill_value=None
+    ) -> DataFrame:
+        return self._flex_arith_method(
+            other, roperator.rsub, level=level, fill_value=fill_value, axis=axis
+        )
+
+    @Appender(ops.make_flex_doc("mul", "dataframe"))
+    def mul(
+        self, other, axis: Axis = "columns", level=None, fill_value=None
+    ) -> DataFrame:
+        return self._flex_arith_method(
+            other, operator.mul, level=level, fill_value=fill_value, axis=axis
+        )
+
+    multiply = mul
+
+    @Appender(ops.make_flex_doc("rmul", "dataframe"))
+    def rmul(
+        self, other, axis: Axis = "columns", level=None, fill_value=None
+    ) -> DataFrame:
+        return self._flex_arith_method(
+            other, roperator.rmul, level=level, fill_value=fill_value, axis=axis
+        )
+
+    @Appender(ops.make_flex_doc("truediv", "dataframe"))
+    def truediv(
+        self, other, axis: Axis = "columns", level=None, fill_value=None
+    ) -> DataFrame:
+        return self._flex_arith_method(
+            other, operator.truediv, level=level, fill_value=fill_value, axis=axis
+        )
+
+    div = truediv
+    divide = truediv
+
+    @Appender(ops.make_flex_doc("rtruediv", "dataframe"))
+    def rtruediv(
+        self, other, axis: Axis = "columns", level=None, fill_value=None
+    ) -> DataFrame:
+        return self._flex_arith_method(
+            other, roperator.rtruediv, level=level, fill_value=fill_value, axis=axis
+        )
+
+    rdiv = rtruediv
+
+    @Appender(ops.make_flex_doc("floordiv", "dataframe"))
+    def floordiv(
+        self, other, axis: Axis = "columns", level=None, fill_value=None
+    ) -> DataFrame:
+        return self._flex_arith_method(
+            other, operator.floordiv, level=level, fill_value=fill_value, axis=axis
+        )
+
+    @Appender(ops.make_flex_doc("rfloordiv", "dataframe"))
+    def rfloordiv(
+        self, other, axis: Axis = "columns", level=None, fill_value=None
+    ) -> DataFrame:
+        return self._flex_arith_method(
+            other, roperator.rfloordiv, level=level, fill_value=fill_value, axis=axis
+        )
+
+    @Appender(ops.make_flex_doc("mod", "dataframe"))
+    def mod(
+        self, other, axis: Axis = "columns", level=None, fill_value=None
+    ) -> DataFrame:
+        return self._flex_arith_method(
+            other, operator.mod, level=level, fill_value=fill_value, axis=axis
+        )
+
+    @Appender(ops.make_flex_doc("rmod", "dataframe"))
+    def rmod(
+        self, other, axis: Axis = "columns", level=None, fill_value=None
+    ) -> DataFrame:
+        return self._flex_arith_method(
+            other, roperator.rmod, level=level, fill_value=fill_value, axis=axis
+        )
+
+    @Appender(ops.make_flex_doc("pow", "dataframe"))
+    def pow(
+        self, other, axis: Axis = "columns", level=None, fill_value=None
+    ) -> DataFrame:
+        return self._flex_arith_method(
+            other, operator.pow, level=level, fill_value=fill_value, axis=axis
+        )
+
+    @Appender(ops.make_flex_doc("rpow", "dataframe"))
+    def rpow(
+        self, other, axis: Axis = "columns", level=None, fill_value=None
+    ) -> DataFrame:
+        return self._flex_arith_method(
+            other, roperator.rpow, level=level, fill_value=fill_value, axis=axis
+        )
+
+    # ----------------------------------------------------------------------
+    # Combination-Related
+
+    @doc(
+        _shared_docs["compare"],
+        dedent(
+            """
+        Returns
+        -------
+        DataFrame
+            DataFrame that shows the differences stacked side by side.
+
+            The resulting index will be a MultiIndex with 'self' and 'other'
+            stacked alternately at the inner level.
+
+        Raises
+        ------
+        ValueError
+            When the two DataFrames don't have identical labels or shape.
+
+        See Also
+        --------
+        Series.compare : Compare with another Series and show differences.
+        DataFrame.equals : Test whether two objects contain the same elements.
+
+        Notes
+        -----
+        Matching NaNs will not appear as a difference.
+
+        Can only compare identically-labeled
+        (i.e. same shape, identical row and column labels) DataFrames
+
+        Examples
+        --------
+        >>> df = pd.DataFrame(
+        ...     {{
+        ...         "col1": ["a", "a", "b", "b", "a"],
+        ...         "col2": [1.0, 2.0, 3.0, np.nan, 5.0],
+        ...         "col3": [1.0, 2.0, 3.0, 4.0, 5.0]
+        ...     }},
+        ...     columns=["col1", "col2", "col3"],
+        ... )
+        >>> df
+          col1  col2  col3
+        0    a   1.0   1.0
+        1    a   2.0   2.0
+        2    b   3.0   3.0
+        3    b   NaN   4.0
+        4    a   5.0   5.0
+
+        >>> df2 = df.copy()
+        >>> df2.loc[0, 'col1'] = 'c'
+        >>> df2.loc[2, 'col3'] = 4.0
+        >>> df2
+          col1  col2  col3
+        0    c   1.0   1.0
+        1    a   2.0   2.0
+        2    b   3.0   4.0
+        3    b   NaN   4.0
+        4    a   5.0   5.0
+
+        Align the differences on columns
+
+        >>> df.compare(df2)
+          col1       col3
+          self other self other
+        0    a     c  NaN   NaN
+        2  NaN   NaN  3.0   4.0
+
+        Assign result_names
+
+        >>> df.compare(df2, result_names=("left", "right"))
+          col1       col3
+          left right left right
+        0    a     c  NaN   NaN
+        2  NaN   NaN  3.0   4.0
+
+        Stack the differences on rows
+
+        >>> df.compare(df2, align_axis=0)
+                col1  col3
+        0 self     a   NaN
+          other    c   NaN
+        2 self   NaN   3.0
+          other  NaN   4.0
+
+        Keep the equal values
+
+        >>> df.compare(df2, keep_equal=True)
+          col1       col3
+          self other self other
+        0    a     c  1.0   1.0
+        2    b     b  3.0   4.0
+
+        Keep all original rows and columns
+
+        >>> df.compare(df2, keep_shape=True)
+          col1       col2       col3
+          self other self other self other
+        0    a     c  NaN   NaN  NaN   NaN
+        1  NaN   NaN  NaN   NaN  NaN   NaN
+        2  NaN   NaN  NaN   NaN  3.0   4.0
+        3  NaN   NaN  NaN   NaN  NaN   NaN
+        4  NaN   NaN  NaN   NaN  NaN   NaN
+
+        Keep all original rows and columns and also all original values
+
+        >>> df.compare(df2, keep_shape=True, keep_equal=True)
+          col1       col2       col3
+          self other self other self other
+        0    a     c  1.0   1.0  1.0   1.0
+        1    a     a  2.0   2.0  2.0   2.0
+        2    b     b  3.0   3.0  3.0   4.0
+        3    b     b  NaN   NaN  4.0   4.0
+        4    a     a  5.0   5.0  5.0   5.0
+        """
+        ),
+        klass=_shared_doc_kwargs["klass"],
+    )
+    def compare(
+        self,
+        other: DataFrame,
+        align_axis: Axis = 1,
+        keep_shape: bool = False,
+        keep_equal: bool = False,
+        result_names: Suffixes = ("self", "other"),
+    ) -> DataFrame:
+        return super().compare(
+            other=other,
+            align_axis=align_axis,
+            keep_shape=keep_shape,
+            keep_equal=keep_equal,
+            result_names=result_names,
+        )
+
+    def combine(
+        self,
+        other: DataFrame,
+        func: Callable[[Series, Series], Series | Hashable],
+        fill_value=None,
+        overwrite: bool = True,
+    ) -> DataFrame:
+        """
+        Perform column-wise combine with another DataFrame.
+
+        Combines a DataFrame with `other` DataFrame using `func`
+        to element-wise combine columns. The row and column indexes of the
+        resulting DataFrame will be the union of the two.
+
+        Parameters
+        ----------
+        other : DataFrame
+            The DataFrame to merge column-wise.
+        func : function
+            Function that takes two series as inputs and return a Series or a
+            scalar. Used to merge the two dataframes column by columns.
+        fill_value : scalar value, default None
+            The value to fill NaNs with prior to passing any column to the
+            merge func.
+        overwrite : bool, default True
+            If True, columns in `self` that do not exist in `other` will be
+            overwritten with NaNs.
+
+        Returns
+        -------
+        DataFrame
+            Combination of the provided DataFrames.
+
+        See Also
+        --------
+        DataFrame.combine_first : Combine two DataFrame objects and default to
+            non-null values in frame calling the method.
+
+        Examples
+        --------
+        Combine using a simple function that chooses the smaller column.
+
+        >>> df1 = pd.DataFrame({'A': [0, 0], 'B': [4, 4]})
+        >>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
+        >>> take_smaller = lambda s1, s2: s1 if s1.sum() < s2.sum() else s2
+        >>> df1.combine(df2, take_smaller)
+           A  B
+        0  0  3
+        1  0  3
+
+        Example using a true element-wise combine function.
+
+        >>> df1 = pd.DataFrame({'A': [5, 0], 'B': [2, 4]})
+        >>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
+        >>> df1.combine(df2, np.minimum)
+           A  B
+        0  1  2
+        1  0  3
+
+        Using `fill_value` fills Nones prior to passing the column to the
+        merge function.
+
+        >>> df1 = pd.DataFrame({'A': [0, 0], 'B': [None, 4]})
+        >>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
+        >>> df1.combine(df2, take_smaller, fill_value=-5)
+           A    B
+        0  0 -5.0
+        1  0  4.0
+
+        However, if the same element in both dataframes is None, that None
+        is preserved
+
+        >>> df1 = pd.DataFrame({'A': [0, 0], 'B': [None, 4]})
+        >>> df2 = pd.DataFrame({'A': [1, 1], 'B': [None, 3]})
+        >>> df1.combine(df2, take_smaller, fill_value=-5)
+            A    B
+        0  0 -5.0
+        1  0  3.0
+
+        Example that demonstrates the use of `overwrite` and behavior when
+        the axis differ between the dataframes.
+
+        >>> df1 = pd.DataFrame({'A': [0, 0], 'B': [4, 4]})
+        >>> df2 = pd.DataFrame({'B': [3, 3], 'C': [-10, 1], }, index=[1, 2])
+        >>> df1.combine(df2, take_smaller)
+             A    B     C
+        0  NaN  NaN   NaN
+        1  NaN  3.0 -10.0
+        2  NaN  3.0   1.0
+
+        >>> df1.combine(df2, take_smaller, overwrite=False)
+             A    B     C
+        0  0.0  NaN   NaN
+        1  0.0  3.0 -10.0
+        2  NaN  3.0   1.0
+
+        Demonstrating the preference of the passed in dataframe.
+
+        >>> df2 = pd.DataFrame({'B': [3, 3], 'C': [1, 1], }, index=[1, 2])
+        >>> df2.combine(df1, take_smaller)
+           A    B   C
+        0  0.0  NaN NaN
+        1  0.0  3.0 NaN
+        2  NaN  3.0 NaN
+
+        >>> df2.combine(df1, take_smaller, overwrite=False)
+             A    B   C
+        0  0.0  NaN NaN
+        1  0.0  3.0 1.0
+        2  NaN  3.0 1.0
+        """
+        other_idxlen = len(other.index)  # save for compare
+
+        this, other = self.align(other, copy=False)
+        new_index = this.index
+
+        if other.empty and len(new_index) == len(self.index):
+            return self.copy()
+
+        if self.empty and len(other) == other_idxlen:
+            return other.copy()
+
+        # sorts if possible; otherwise align above ensures that these are set-equal
+        new_columns = this.columns.union(other.columns)
+        do_fill = fill_value is not None
+        result = {}
+        for col in new_columns:
+            series = this[col]
+            other_series = other[col]
+
+            this_dtype = series.dtype
+            other_dtype = other_series.dtype
+
+            this_mask = isna(series)
+            other_mask = isna(other_series)
+
+            # don't overwrite columns unnecessarily
+            # DO propagate if this column is not in the intersection
+            if not overwrite and other_mask.all():
+                result[col] = this[col].copy()
+                continue
+
+            if do_fill:
+                series = series.copy()
+                other_series = other_series.copy()
+                series[this_mask] = fill_value
+                other_series[other_mask] = fill_value
+
+            if col not in self.columns:
+                # If self DataFrame does not have col in other DataFrame,
+                # try to promote series, which is all NaN, as other_dtype.
+                new_dtype = other_dtype
+                try:
+                    series = series.astype(new_dtype, copy=False)
+                except ValueError:
+                    # e.g. new_dtype is integer types
+                    pass
+            else:
+                # if we have different dtypes, possibly promote
+                new_dtype = find_common_type([this_dtype, other_dtype])
+                series = series.astype(new_dtype, copy=False)
+                other_series = other_series.astype(new_dtype, copy=False)
+
+            arr = func(series, other_series)
+            if isinstance(new_dtype, np.dtype):
+                # if new_dtype is an EA Dtype, then `func` is expected to return
+                # the correct dtype without any additional casting
+                # error: No overload variant of "maybe_downcast_to_dtype" matches
+                # argument types "Union[Series, Hashable]", "dtype[Any]"
+                arr = maybe_downcast_to_dtype(  # type: ignore[call-overload]
+                    arr, new_dtype
+                )
+
+            result[col] = arr
+
+        # convert_objects just in case
+        frame_result = self._constructor(result, index=new_index, columns=new_columns)
+        return frame_result.__finalize__(self, method="combine")
+
+    def combine_first(self, other: DataFrame) -> DataFrame:
+        """
+        Update null elements with value in the same location in `other`.
+
+        Combine two DataFrame objects by filling null values in one DataFrame
+        with non-null values from other DataFrame. The row and column indexes
+        of the resulting DataFrame will be the union of the two. The resulting
+        dataframe contains the 'first' dataframe values and overrides the
+        second one values where both first.loc[index, col] and
+        second.loc[index, col] are not missing values, upon calling
+        first.combine_first(second).
+
+        Parameters
+        ----------
+        other : DataFrame
+            Provided DataFrame to use to fill null values.
+
+        Returns
+        -------
+        DataFrame
+            The result of combining the provided DataFrame with the other object.
+
+        See Also
+        --------
+        DataFrame.combine : Perform series-wise operation on two DataFrames
+            using a given function.
+
+        Examples
+        --------
+        >>> df1 = pd.DataFrame({'A': [None, 0], 'B': [None, 4]})
+        >>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
+        >>> df1.combine_first(df2)
+             A    B
+        0  1.0  3.0
+        1  0.0  4.0
+
+        Null values still persist if the location of that null value
+        does not exist in `other`
+
+        >>> df1 = pd.DataFrame({'A': [None, 0], 'B': [4, None]})
+        >>> df2 = pd.DataFrame({'B': [3, 3], 'C': [1, 1]}, index=[1, 2])
+        >>> df1.combine_first(df2)
+             A    B    C
+        0  NaN  4.0  NaN
+        1  0.0  3.0  1.0
+        2  NaN  3.0  1.0
+        """
+        from pandas.core.computation import expressions
+
+        def combiner(x: Series, y: Series):
+            mask = x.isna()._values
+
+            x_values = x._values
+            y_values = y._values
+
+            # If the column y in other DataFrame is not in first DataFrame,
+            # just return y_values.
+            if y.name not in self.columns:
+                return y_values
+
+            return expressions.where(mask, y_values, x_values)
+
+        if len(other) == 0:
+            combined = self.reindex(
+                self.columns.append(other.columns.difference(self.columns)), axis=1
+            )
+            combined = combined.astype(other.dtypes)
+        else:
+            combined = self.combine(other, combiner, overwrite=False)
+
+        dtypes = {
+            col: find_common_type([self.dtypes[col], other.dtypes[col]])
+            for col in self.columns.intersection(other.columns)
+            if combined.dtypes[col] != self.dtypes[col]
+        }
+
+        if dtypes:
+            combined = combined.astype(dtypes)
+
+        return combined.__finalize__(self, method="combine_first")
+
+    def update(
+        self,
+        other,
+        join: UpdateJoin = "left",
+        overwrite: bool = True,
+        filter_func=None,
+        errors: IgnoreRaise = "ignore",
+    ) -> None:
+        """
+        Modify in place using non-NA values from another DataFrame.
+
+        Aligns on indices. There is no return value.
+
+        Parameters
+        ----------
+        other : DataFrame, or object coercible into a DataFrame
+            Should have at least one matching index/column label
+            with the original DataFrame. If a Series is passed,
+            its name attribute must be set, and that will be
+            used as the column name to align with the original DataFrame.
+        join : {'left'}, default 'left'
+            Only left join is implemented, keeping the index and columns of the
+            original object.
+        overwrite : bool, default True
+            How to handle non-NA values for overlapping keys:
+
+            * True: overwrite original DataFrame's values
+              with values from `other`.
+            * False: only update values that are NA in
+              the original DataFrame.
+
+        filter_func : callable(1d-array) -> bool 1d-array, optional
+            Can choose to replace values other than NA. Return True for values
+            that should be updated.
+        errors : {'raise', 'ignore'}, default 'ignore'
+            If 'raise', will raise a ValueError if the DataFrame and `other`
+            both contain non-NA data in the same place.
+
+        Returns
+        -------
+        None
+            This method directly changes calling object.
+
+        Raises
+        ------
+        ValueError
+            * When `errors='raise'` and there's overlapping non-NA data.
+            * When `errors` is not either `'ignore'` or `'raise'`
+        NotImplementedError
+            * If `join != 'left'`
+
+        See Also
+        --------
+        dict.update : Similar method for dictionaries.
+        DataFrame.merge : For column(s)-on-column(s) operations.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'A': [1, 2, 3],
+        ...                    'B': [400, 500, 600]})
+        >>> new_df = pd.DataFrame({'B': [4, 5, 6],
+        ...                        'C': [7, 8, 9]})
+        >>> df.update(new_df)
+        >>> df
+           A  B
+        0  1  4
+        1  2  5
+        2  3  6
+
+        The DataFrame's length does not increase as a result of the update,
+        only values at matching index/column labels are updated.
+
+        >>> df = pd.DataFrame({'A': ['a', 'b', 'c'],
+        ...                    'B': ['x', 'y', 'z']})
+        >>> new_df = pd.DataFrame({'B': ['d', 'e', 'f', 'g', 'h', 'i']})
+        >>> df.update(new_df)
+        >>> df
+           A  B
+        0  a  d
+        1  b  e
+        2  c  f
+
+        >>> df = pd.DataFrame({'A': ['a', 'b', 'c'],
+        ...                    'B': ['x', 'y', 'z']})
+        >>> new_df = pd.DataFrame({'B': ['d', 'f']}, index=[0, 2])
+        >>> df.update(new_df)
+        >>> df
+           A  B
+        0  a  d
+        1  b  y
+        2  c  f
+
+        For Series, its name attribute must be set.
+
+        >>> df = pd.DataFrame({'A': ['a', 'b', 'c'],
+        ...                    'B': ['x', 'y', 'z']})
+        >>> new_column = pd.Series(['d', 'e', 'f'], name='B')
+        >>> df.update(new_column)
+        >>> df
+           A  B
+        0  a  d
+        1  b  e
+        2  c  f
+
+        If `other` contains NaNs the corresponding values are not updated
+        in the original dataframe.
+
+        >>> df = pd.DataFrame({'A': [1, 2, 3],
+        ...                    'B': [400., 500., 600.]})
+        >>> new_df = pd.DataFrame({'B': [4, np.nan, 6]})
+        >>> df.update(new_df)
+        >>> df
+           A      B
+        0  1    4.0
+        1  2  500.0
+        2  3    6.0
+        """
+
+        if not PYPY and not WARNING_CHECK_DISABLED and using_copy_on_write():
+            if sys.getrefcount(self) <= REF_COUNT:
+                warnings.warn(
+                    _chained_assignment_method_msg,
+                    ChainedAssignmentError,
+                    stacklevel=2,
+                )
+        elif (
+            not PYPY
+            and not WARNING_CHECK_DISABLED
+            and not using_copy_on_write()
+            and self._is_view_after_cow_rules()
+        ):
+            if sys.getrefcount(self) <= REF_COUNT:
+                warnings.warn(
+                    _chained_assignment_warning_method_msg,
+                    FutureWarning,
+                    stacklevel=2,
+                )
+
+        # TODO: Support other joins
+        if join != "left":  # pragma: no cover
+            raise NotImplementedError("Only left join is supported")
+        if errors not in ["ignore", "raise"]:
+            raise ValueError("The parameter errors must be either 'ignore' or 'raise'")
+
+        if not isinstance(other, DataFrame):
+            other = DataFrame(other)
+
+        other = other.reindex(self.index)
+
+        for col in self.columns.intersection(other.columns):
+            this = self[col]._values
+            that = other[col]._values
+
+            if filter_func is not None:
+                mask = ~filter_func(this) | isna(that)
+            else:
+                if errors == "raise":
+                    mask_this = notna(that)
+                    mask_that = notna(this)
+                    if any(mask_this & mask_that):
+                        raise ValueError("Data overlaps.")
+
+                if overwrite:
+                    mask = isna(that)
+                else:
+                    mask = notna(this)
+
+            # don't overwrite columns unnecessarily
+            if mask.all():
+                continue
+
+            with warnings.catch_warnings():
+                warnings.filterwarnings(
+                    "ignore",
+                    message="Downcasting behavior",
+                    category=FutureWarning,
+                )
+                # GH#57124 - `that` might get upcasted because of NA values, and then
+                # downcasted in where because of the mask. Ignoring the warning
+                # is a stopgap, will replace with a new implementation of update
+                # in 3.0.
+                self.loc[:, col] = self[col].where(mask, that)
+
+    # ----------------------------------------------------------------------
+    # Data reshaping
+    @Appender(
+        dedent(
+            """
+        Examples
+        --------
+        >>> df = pd.DataFrame({'Animal': ['Falcon', 'Falcon',
+        ...                               'Parrot', 'Parrot'],
+        ...                    'Max Speed': [380., 370., 24., 26.]})
+        >>> df
+           Animal  Max Speed
+        0  Falcon      380.0
+        1  Falcon      370.0
+        2  Parrot       24.0
+        3  Parrot       26.0
+        >>> df.groupby(['Animal']).mean()
+                Max Speed
+        Animal
+        Falcon      375.0
+        Parrot       25.0
+
+        **Hierarchical Indexes**
+
+        We can groupby different levels of a hierarchical index
+        using the `level` parameter:
+
+        >>> arrays = [['Falcon', 'Falcon', 'Parrot', 'Parrot'],
+        ...           ['Captive', 'Wild', 'Captive', 'Wild']]
+        >>> index = pd.MultiIndex.from_arrays(arrays, names=('Animal', 'Type'))
+        >>> df = pd.DataFrame({'Max Speed': [390., 350., 30., 20.]},
+        ...                   index=index)
+        >>> df
+                        Max Speed
+        Animal Type
+        Falcon Captive      390.0
+               Wild         350.0
+        Parrot Captive       30.0
+               Wild          20.0
+        >>> df.groupby(level=0).mean()
+                Max Speed
+        Animal
+        Falcon      370.0
+        Parrot       25.0
+        >>> df.groupby(level="Type").mean()
+                 Max Speed
+        Type
+        Captive      210.0
+        Wild         185.0
+
+        We can also choose to include NA in group keys or not by setting
+        `dropna` parameter, the default setting is `True`.
+
+        >>> l = [[1, 2, 3], [1, None, 4], [2, 1, 3], [1, 2, 2]]
+        >>> df = pd.DataFrame(l, columns=["a", "b", "c"])
+
+        >>> df.groupby(by=["b"]).sum()
+            a   c
+        b
+        1.0 2   3
+        2.0 2   5
+
+        >>> df.groupby(by=["b"], dropna=False).sum()
+            a   c
+        b
+        1.0 2   3
+        2.0 2   5
+        NaN 1   4
+
+        >>> l = [["a", 12, 12], [None, 12.3, 33.], ["b", 12.3, 123], ["a", 1, 1]]
+        >>> df = pd.DataFrame(l, columns=["a", "b", "c"])
+
+        >>> df.groupby(by="a").sum()
+            b     c
+        a
+        a   13.0   13.0
+        b   12.3  123.0
+
+        >>> df.groupby(by="a", dropna=False).sum()
+            b     c
+        a
+        a   13.0   13.0
+        b   12.3  123.0
+        NaN 12.3   33.0
+
+        When using ``.apply()``, use ``group_keys`` to include or exclude the
+        group keys. The ``group_keys`` argument defaults to ``True`` (include).
+
+        >>> df = pd.DataFrame({'Animal': ['Falcon', 'Falcon',
+        ...                               'Parrot', 'Parrot'],
+        ...                    'Max Speed': [380., 370., 24., 26.]})
+        >>> df.groupby("Animal", group_keys=True)[['Max Speed']].apply(lambda x: x)
+                  Max Speed
+        Animal
+        Falcon 0      380.0
+               1      370.0
+        Parrot 2       24.0
+               3       26.0
+
+        >>> df.groupby("Animal", group_keys=False)[['Max Speed']].apply(lambda x: x)
+           Max Speed
+        0      380.0
+        1      370.0
+        2       24.0
+        3       26.0
+        """
+        )
+    )
+    @Appender(_shared_docs["groupby"] % _shared_doc_kwargs)
+    def groupby(
+        self,
+        by=None,
+        axis: Axis | lib.NoDefault = lib.no_default,
+        level: IndexLabel | None = None,
+        as_index: bool = True,
+        sort: bool = True,
+        group_keys: bool = True,
+        observed: bool | lib.NoDefault = lib.no_default,
+        dropna: bool = True,
+    ) -> DataFrameGroupBy:
+        if axis is not lib.no_default:
+            axis = self._get_axis_number(axis)
+            if axis == 1:
+                warnings.warn(
+                    "DataFrame.groupby with axis=1 is deprecated. Do "
+                    "`frame.T.groupby(...)` without axis instead.",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+            else:
+                warnings.warn(
+                    "The 'axis' keyword in DataFrame.groupby is deprecated and "
+                    "will be removed in a future version.",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+        else:
+            axis = 0
+
+        from pandas.core.groupby.generic import DataFrameGroupBy
+
+        if level is None and by is None:
+            raise TypeError("You have to supply one of 'by' and 'level'")
+
+        return DataFrameGroupBy(
+            obj=self,
+            keys=by,
+            axis=axis,
+            level=level,
+            as_index=as_index,
+            sort=sort,
+            group_keys=group_keys,
+            observed=observed,
+            dropna=dropna,
+        )
+
+    _shared_docs[
+        "pivot"
+    ] = """
+        Return reshaped DataFrame organized by given index / column values.
+
+        Reshape data (produce a "pivot" table) based on column values. Uses
+        unique values from specified `index` / `columns` to form axes of the
+        resulting DataFrame. This function does not support data
+        aggregation, multiple values will result in a MultiIndex in the
+        columns. See the :ref:`User Guide ` for more on reshaping.
+
+        Parameters
+        ----------%s
+        columns : str or object or a list of str
+            Column to use to make new frame's columns.
+        index : str or object or a list of str, optional
+            Column to use to make new frame's index. If not given, uses existing index.
+        values : str, object or a list of the previous, optional
+            Column(s) to use for populating new frame's values. If not
+            specified, all remaining columns will be used and the result will
+            have hierarchically indexed columns.
+
+        Returns
+        -------
+        DataFrame
+            Returns reshaped DataFrame.
+
+        Raises
+        ------
+        ValueError:
+            When there are any `index`, `columns` combinations with multiple
+            values. `DataFrame.pivot_table` when you need to aggregate.
+
+        See Also
+        --------
+        DataFrame.pivot_table : Generalization of pivot that can handle
+            duplicate values for one index/column pair.
+        DataFrame.unstack : Pivot based on the index values instead of a
+            column.
+        wide_to_long : Wide panel to long format. Less flexible but more
+            user-friendly than melt.
+
+        Notes
+        -----
+        For finer-tuned control, see hierarchical indexing documentation along
+        with the related stack/unstack methods.
+
+        Reference :ref:`the user guide ` for more examples.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'foo': ['one', 'one', 'one', 'two', 'two',
+        ...                            'two'],
+        ...                    'bar': ['A', 'B', 'C', 'A', 'B', 'C'],
+        ...                    'baz': [1, 2, 3, 4, 5, 6],
+        ...                    'zoo': ['x', 'y', 'z', 'q', 'w', 't']})
+        >>> df
+            foo   bar  baz  zoo
+        0   one   A    1    x
+        1   one   B    2    y
+        2   one   C    3    z
+        3   two   A    4    q
+        4   two   B    5    w
+        5   two   C    6    t
+
+        >>> df.pivot(index='foo', columns='bar', values='baz')
+        bar  A   B   C
+        foo
+        one  1   2   3
+        two  4   5   6
+
+        >>> df.pivot(index='foo', columns='bar')['baz']
+        bar  A   B   C
+        foo
+        one  1   2   3
+        two  4   5   6
+
+        >>> df.pivot(index='foo', columns='bar', values=['baz', 'zoo'])
+              baz       zoo
+        bar   A  B  C   A  B  C
+        foo
+        one   1  2  3   x  y  z
+        two   4  5  6   q  w  t
+
+        You could also assign a list of column names or a list of index names.
+
+        >>> df = pd.DataFrame({
+        ...        "lev1": [1, 1, 1, 2, 2, 2],
+        ...        "lev2": [1, 1, 2, 1, 1, 2],
+        ...        "lev3": [1, 2, 1, 2, 1, 2],
+        ...        "lev4": [1, 2, 3, 4, 5, 6],
+        ...        "values": [0, 1, 2, 3, 4, 5]})
+        >>> df
+            lev1 lev2 lev3 lev4 values
+        0   1    1    1    1    0
+        1   1    1    2    2    1
+        2   1    2    1    3    2
+        3   2    1    2    4    3
+        4   2    1    1    5    4
+        5   2    2    2    6    5
+
+        >>> df.pivot(index="lev1", columns=["lev2", "lev3"], values="values")
+        lev2    1         2
+        lev3    1    2    1    2
+        lev1
+        1     0.0  1.0  2.0  NaN
+        2     4.0  3.0  NaN  5.0
+
+        >>> df.pivot(index=["lev1", "lev2"], columns=["lev3"], values="values")
+              lev3    1    2
+        lev1  lev2
+           1     1  0.0  1.0
+                 2  2.0  NaN
+           2     1  4.0  3.0
+                 2  NaN  5.0
+
+        A ValueError is raised if there are any duplicates.
+
+        >>> df = pd.DataFrame({"foo": ['one', 'one', 'two', 'two'],
+        ...                    "bar": ['A', 'A', 'B', 'C'],
+        ...                    "baz": [1, 2, 3, 4]})
+        >>> df
+           foo bar  baz
+        0  one   A    1
+        1  one   A    2
+        2  two   B    3
+        3  two   C    4
+
+        Notice that the first two rows are the same for our `index`
+        and `columns` arguments.
+
+        >>> df.pivot(index='foo', columns='bar', values='baz')
+        Traceback (most recent call last):
+           ...
+        ValueError: Index contains duplicate entries, cannot reshape
+        """
+
+    @Substitution("")
+    @Appender(_shared_docs["pivot"])
+    def pivot(
+        self, *, columns, index=lib.no_default, values=lib.no_default
+    ) -> DataFrame:
+        from pandas.core.reshape.pivot import pivot
+
+        return pivot(self, index=index, columns=columns, values=values)
+
+    _shared_docs[
+        "pivot_table"
+    ] = """
+        Create a spreadsheet-style pivot table as a DataFrame.
+
+        The levels in the pivot table will be stored in MultiIndex objects
+        (hierarchical indexes) on the index and columns of the result DataFrame.
+
+        Parameters
+        ----------%s
+        values : list-like or scalar, optional
+            Column or columns to aggregate.
+        index : column, Grouper, array, or list of the previous
+            Keys to group by on the pivot table index. If a list is passed,
+            it can contain any of the other types (except list). If an array is
+            passed, it must be the same length as the data and will be used in
+            the same manner as column values.
+        columns : column, Grouper, array, or list of the previous
+            Keys to group by on the pivot table column. If a list is passed,
+            it can contain any of the other types (except list). If an array is
+            passed, it must be the same length as the data and will be used in
+            the same manner as column values.
+        aggfunc : function, list of functions, dict, default "mean"
+            If a list of functions is passed, the resulting pivot table will have
+            hierarchical columns whose top level are the function names
+            (inferred from the function objects themselves).
+            If a dict is passed, the key is column to aggregate and the value is
+            function or list of functions. If ``margin=True``, aggfunc will be
+            used to calculate the partial aggregates.
+        fill_value : scalar, default None
+            Value to replace missing values with (in the resulting pivot table,
+            after aggregation).
+        margins : bool, default False
+            If ``margins=True``, special ``All`` columns and rows
+            will be added with partial group aggregates across the categories
+            on the rows and columns.
+        dropna : bool, default True
+            Do not include columns whose entries are all NaN. If True,
+            rows with a NaN value in any column will be omitted before
+            computing margins.
+        margins_name : str, default 'All'
+            Name of the row / column that will contain the totals
+            when margins is True.
+        observed : bool, default False
+            This only applies if any of the groupers are Categoricals.
+            If True: only show observed values for categorical groupers.
+            If False: show all values for categorical groupers.
+
+            .. deprecated:: 2.2.0
+
+                The default value of ``False`` is deprecated and will change to
+                ``True`` in a future version of pandas.
+
+        sort : bool, default True
+            Specifies if the result should be sorted.
+
+            .. versionadded:: 1.3.0
+
+        Returns
+        -------
+        DataFrame
+            An Excel style pivot table.
+
+        See Also
+        --------
+        DataFrame.pivot : Pivot without aggregation that can handle
+            non-numeric data.
+        DataFrame.melt: Unpivot a DataFrame from wide to long format,
+            optionally leaving identifiers set.
+        wide_to_long : Wide panel to long format. Less flexible but more
+            user-friendly than melt.
+
+        Notes
+        -----
+        Reference :ref:`the user guide ` for more examples.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({"A": ["foo", "foo", "foo", "foo", "foo",
+        ...                          "bar", "bar", "bar", "bar"],
+        ...                    "B": ["one", "one", "one", "two", "two",
+        ...                          "one", "one", "two", "two"],
+        ...                    "C": ["small", "large", "large", "small",
+        ...                          "small", "large", "small", "small",
+        ...                          "large"],
+        ...                    "D": [1, 2, 2, 3, 3, 4, 5, 6, 7],
+        ...                    "E": [2, 4, 5, 5, 6, 6, 8, 9, 9]})
+        >>> df
+             A    B      C  D  E
+        0  foo  one  small  1  2
+        1  foo  one  large  2  4
+        2  foo  one  large  2  5
+        3  foo  two  small  3  5
+        4  foo  two  small  3  6
+        5  bar  one  large  4  6
+        6  bar  one  small  5  8
+        7  bar  two  small  6  9
+        8  bar  two  large  7  9
+
+        This first example aggregates values by taking the sum.
+
+        >>> table = pd.pivot_table(df, values='D', index=['A', 'B'],
+        ...                        columns=['C'], aggfunc="sum")
+        >>> table
+        C        large  small
+        A   B
+        bar one    4.0    5.0
+            two    7.0    6.0
+        foo one    4.0    1.0
+            two    NaN    6.0
+
+        We can also fill missing values using the `fill_value` parameter.
+
+        >>> table = pd.pivot_table(df, values='D', index=['A', 'B'],
+        ...                        columns=['C'], aggfunc="sum", fill_value=0)
+        >>> table
+        C        large  small
+        A   B
+        bar one      4      5
+            two      7      6
+        foo one      4      1
+            two      0      6
+
+        The next example aggregates by taking the mean across multiple columns.
+
+        >>> table = pd.pivot_table(df, values=['D', 'E'], index=['A', 'C'],
+        ...                        aggfunc={'D': "mean", 'E': "mean"})
+        >>> table
+                        D         E
+        A   C
+        bar large  5.500000  7.500000
+            small  5.500000  8.500000
+        foo large  2.000000  4.500000
+            small  2.333333  4.333333
+
+        We can also calculate multiple types of aggregations for any given
+        value column.
+
+        >>> table = pd.pivot_table(df, values=['D', 'E'], index=['A', 'C'],
+        ...                        aggfunc={'D': "mean",
+        ...                                 'E': ["min", "max", "mean"]})
+        >>> table
+                          D   E
+                       mean max      mean  min
+        A   C
+        bar large  5.500000   9  7.500000    6
+            small  5.500000   9  8.500000    8
+        foo large  2.000000   5  4.500000    4
+            small  2.333333   6  4.333333    2
+        """
+
+    @Substitution("")
+    @Appender(_shared_docs["pivot_table"])
+    def pivot_table(
+        self,
+        values=None,
+        index=None,
+        columns=None,
+        aggfunc: AggFuncType = "mean",
+        fill_value=None,
+        margins: bool = False,
+        dropna: bool = True,
+        margins_name: Level = "All",
+        observed: bool | lib.NoDefault = lib.no_default,
+        sort: bool = True,
+    ) -> DataFrame:
+        from pandas.core.reshape.pivot import pivot_table
+
+        return pivot_table(
+            self,
+            values=values,
+            index=index,
+            columns=columns,
+            aggfunc=aggfunc,
+            fill_value=fill_value,
+            margins=margins,
+            dropna=dropna,
+            margins_name=margins_name,
+            observed=observed,
+            sort=sort,
+        )
+
+    def stack(
+        self,
+        level: IndexLabel = -1,
+        dropna: bool | lib.NoDefault = lib.no_default,
+        sort: bool | lib.NoDefault = lib.no_default,
+        future_stack: bool = False,
+    ):
+        """
+        Stack the prescribed level(s) from columns to index.
+
+        Return a reshaped DataFrame or Series having a multi-level
+        index with one or more new inner-most levels compared to the current
+        DataFrame. The new inner-most levels are created by pivoting the
+        columns of the current dataframe:
+
+          - if the columns have a single level, the output is a Series;
+          - if the columns have multiple levels, the new index
+            level(s) is (are) taken from the prescribed level(s) and
+            the output is a DataFrame.
+
+        Parameters
+        ----------
+        level : int, str, list, default -1
+            Level(s) to stack from the column axis onto the index
+            axis, defined as one index or label, or a list of indices
+            or labels.
+        dropna : bool, default True
+            Whether to drop rows in the resulting Frame/Series with
+            missing values. Stacking a column level onto the index
+            axis can create combinations of index and column values
+            that are missing from the original dataframe. See Examples
+            section.
+        sort : bool, default True
+            Whether to sort the levels of the resulting MultiIndex.
+        future_stack : bool, default False
+            Whether to use the new implementation that will replace the current
+            implementation in pandas 3.0. When True, dropna and sort have no impact
+            on the result and must remain unspecified. See :ref:`pandas 2.1.0 Release
+            notes ` for more details.
+
+        Returns
+        -------
+        DataFrame or Series
+            Stacked dataframe or series.
+
+        See Also
+        --------
+        DataFrame.unstack : Unstack prescribed level(s) from index axis
+             onto column axis.
+        DataFrame.pivot : Reshape dataframe from long format to wide
+             format.
+        DataFrame.pivot_table : Create a spreadsheet-style pivot table
+             as a DataFrame.
+
+        Notes
+        -----
+        The function is named by analogy with a collection of books
+        being reorganized from being side by side on a horizontal
+        position (the columns of the dataframe) to being stacked
+        vertically on top of each other (in the index of the
+        dataframe).
+
+        Reference :ref:`the user guide ` for more examples.
+
+        Examples
+        --------
+        **Single level columns**
+
+        >>> df_single_level_cols = pd.DataFrame([[0, 1], [2, 3]],
+        ...                                     index=['cat', 'dog'],
+        ...                                     columns=['weight', 'height'])
+
+        Stacking a dataframe with a single level column axis returns a Series:
+
+        >>> df_single_level_cols
+             weight height
+        cat       0      1
+        dog       2      3
+        >>> df_single_level_cols.stack(future_stack=True)
+        cat  weight    0
+             height    1
+        dog  weight    2
+             height    3
+        dtype: int64
+
+        **Multi level columns: simple case**
+
+        >>> multicol1 = pd.MultiIndex.from_tuples([('weight', 'kg'),
+        ...                                        ('weight', 'pounds')])
+        >>> df_multi_level_cols1 = pd.DataFrame([[1, 2], [2, 4]],
+        ...                                     index=['cat', 'dog'],
+        ...                                     columns=multicol1)
+
+        Stacking a dataframe with a multi-level column axis:
+
+        >>> df_multi_level_cols1
+             weight
+                 kg    pounds
+        cat       1        2
+        dog       2        4
+        >>> df_multi_level_cols1.stack(future_stack=True)
+                    weight
+        cat kg           1
+            pounds       2
+        dog kg           2
+            pounds       4
+
+        **Missing values**
+
+        >>> multicol2 = pd.MultiIndex.from_tuples([('weight', 'kg'),
+        ...                                        ('height', 'm')])
+        >>> df_multi_level_cols2 = pd.DataFrame([[1.0, 2.0], [3.0, 4.0]],
+        ...                                     index=['cat', 'dog'],
+        ...                                     columns=multicol2)
+
+        It is common to have missing values when stacking a dataframe
+        with multi-level columns, as the stacked dataframe typically
+        has more values than the original dataframe. Missing values
+        are filled with NaNs:
+
+        >>> df_multi_level_cols2
+            weight height
+                kg      m
+        cat    1.0    2.0
+        dog    3.0    4.0
+        >>> df_multi_level_cols2.stack(future_stack=True)
+                weight  height
+        cat kg     1.0     NaN
+            m      NaN     2.0
+        dog kg     3.0     NaN
+            m      NaN     4.0
+
+        **Prescribing the level(s) to be stacked**
+
+        The first parameter controls which level or levels are stacked:
+
+        >>> df_multi_level_cols2.stack(0, future_stack=True)
+                     kg    m
+        cat weight  1.0  NaN
+            height  NaN  2.0
+        dog weight  3.0  NaN
+            height  NaN  4.0
+        >>> df_multi_level_cols2.stack([0, 1], future_stack=True)
+        cat  weight  kg    1.0
+             height  m     2.0
+        dog  weight  kg    3.0
+             height  m     4.0
+        dtype: float64
+        """
+        if not future_stack:
+            from pandas.core.reshape.reshape import (
+                stack,
+                stack_multiple,
+            )
+
+            if (
+                dropna is not lib.no_default
+                or sort is not lib.no_default
+                or self.columns.nlevels > 1
+            ):
+                warnings.warn(
+                    "The previous implementation of stack is deprecated and will be "
+                    "removed in a future version of pandas. See the What's New notes "
+                    "for pandas 2.1.0 for details. Specify future_stack=True to adopt "
+                    "the new implementation and silence this warning.",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+
+            if dropna is lib.no_default:
+                dropna = True
+            if sort is lib.no_default:
+                sort = True
+
+            if isinstance(level, (tuple, list)):
+                result = stack_multiple(self, level, dropna=dropna, sort=sort)
+            else:
+                result = stack(self, level, dropna=dropna, sort=sort)
+        else:
+            from pandas.core.reshape.reshape import stack_v3
+
+            if dropna is not lib.no_default:
+                raise ValueError(
+                    "dropna must be unspecified with future_stack=True as the new "
+                    "implementation does not introduce rows of NA values. This "
+                    "argument will be removed in a future version of pandas."
+                )
+
+            if sort is not lib.no_default:
+                raise ValueError(
+                    "Cannot specify sort with future_stack=True, this argument will be "
+                    "removed in a future version of pandas. Sort the result using "
+                    ".sort_index instead."
+                )
+
+            if (
+                isinstance(level, (tuple, list))
+                and not all(lev in self.columns.names for lev in level)
+                and not all(isinstance(lev, int) for lev in level)
+            ):
+                raise ValueError(
+                    "level should contain all level names or all level "
+                    "numbers, not a mixture of the two."
+                )
+
+            if not isinstance(level, (tuple, list)):
+                level = [level]
+            level = [self.columns._get_level_number(lev) for lev in level]
+            result = stack_v3(self, level)
+
+        return result.__finalize__(self, method="stack")
+
+    def explode(
+        self,
+        column: IndexLabel,
+        ignore_index: bool = False,
+    ) -> DataFrame:
+        """
+        Transform each element of a list-like to a row, replicating index values.
+
+        Parameters
+        ----------
+        column : IndexLabel
+            Column(s) to explode.
+            For multiple columns, specify a non-empty list with each element
+            be str or tuple, and all specified columns their list-like data
+            on same row of the frame must have matching length.
+
+            .. versionadded:: 1.3.0
+                Multi-column explode
+
+        ignore_index : bool, default False
+            If True, the resulting index will be labeled 0, 1, …, n - 1.
+
+        Returns
+        -------
+        DataFrame
+            Exploded lists to rows of the subset columns;
+            index will be duplicated for these rows.
+
+        Raises
+        ------
+        ValueError :
+            * If columns of the frame are not unique.
+            * If specified columns to explode is empty list.
+            * If specified columns to explode have not matching count of
+              elements rowwise in the frame.
+
+        See Also
+        --------
+        DataFrame.unstack : Pivot a level of the (necessarily hierarchical)
+            index labels.
+        DataFrame.melt : Unpivot a DataFrame from wide format to long format.
+        Series.explode : Explode a DataFrame from list-like columns to long format.
+
+        Notes
+        -----
+        This routine will explode list-likes including lists, tuples, sets,
+        Series, and np.ndarray. The result dtype of the subset rows will
+        be object. Scalars will be returned unchanged, and empty list-likes will
+        result in a np.nan for that row. In addition, the ordering of rows in the
+        output will be non-deterministic when exploding sets.
+
+        Reference :ref:`the user guide ` for more examples.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'A': [[0, 1, 2], 'foo', [], [3, 4]],
+        ...                    'B': 1,
+        ...                    'C': [['a', 'b', 'c'], np.nan, [], ['d', 'e']]})
+        >>> df
+                   A  B          C
+        0  [0, 1, 2]  1  [a, b, c]
+        1        foo  1        NaN
+        2         []  1         []
+        3     [3, 4]  1     [d, e]
+
+        Single-column explode.
+
+        >>> df.explode('A')
+             A  B          C
+        0    0  1  [a, b, c]
+        0    1  1  [a, b, c]
+        0    2  1  [a, b, c]
+        1  foo  1        NaN
+        2  NaN  1         []
+        3    3  1     [d, e]
+        3    4  1     [d, e]
+
+        Multi-column explode.
+
+        >>> df.explode(list('AC'))
+             A  B    C
+        0    0  1    a
+        0    1  1    b
+        0    2  1    c
+        1  foo  1  NaN
+        2  NaN  1  NaN
+        3    3  1    d
+        3    4  1    e
+        """
+        if not self.columns.is_unique:
+            duplicate_cols = self.columns[self.columns.duplicated()].tolist()
+            raise ValueError(
+                f"DataFrame columns must be unique. Duplicate columns: {duplicate_cols}"
+            )
+
+        columns: list[Hashable]
+        if is_scalar(column) or isinstance(column, tuple):
+            columns = [column]
+        elif isinstance(column, list) and all(
+            is_scalar(c) or isinstance(c, tuple) for c in column
+        ):
+            if not column:
+                raise ValueError("column must be nonempty")
+            if len(column) > len(set(column)):
+                raise ValueError("column must be unique")
+            columns = column
+        else:
+            raise ValueError("column must be a scalar, tuple, or list thereof")
+
+        df = self.reset_index(drop=True)
+        if len(columns) == 1:
+            result = df[columns[0]].explode()
+        else:
+            mylen = lambda x: len(x) if (is_list_like(x) and len(x) > 0) else 1
+            counts0 = self[columns[0]].apply(mylen)
+            for c in columns[1:]:
+                if not all(counts0 == self[c].apply(mylen)):
+                    raise ValueError("columns must have matching element counts")
+            result = DataFrame({c: df[c].explode() for c in columns})
+        result = df.drop(columns, axis=1).join(result)
+        if ignore_index:
+            result.index = default_index(len(result))
+        else:
+            result.index = self.index.take(result.index)
+        result = result.reindex(columns=self.columns, copy=False)
+
+        return result.__finalize__(self, method="explode")
+
+    def unstack(self, level: IndexLabel = -1, fill_value=None, sort: bool = True):
+        """
+        Pivot a level of the (necessarily hierarchical) index labels.
+
+        Returns a DataFrame having a new level of column labels whose inner-most level
+        consists of the pivoted index labels.
+
+        If the index is not a MultiIndex, the output will be a Series
+        (the analogue of stack when the columns are not a MultiIndex).
+
+        Parameters
+        ----------
+        level : int, str, or list of these, default -1 (last level)
+            Level(s) of index to unstack, can pass level name.
+        fill_value : int, str or dict
+            Replace NaN with this value if the unstack produces missing values.
+        sort : bool, default True
+            Sort the level(s) in the resulting MultiIndex columns.
+
+        Returns
+        -------
+        Series or DataFrame
+
+        See Also
+        --------
+        DataFrame.pivot : Pivot a table based on column values.
+        DataFrame.stack : Pivot a level of the column labels (inverse operation
+            from `unstack`).
+
+        Notes
+        -----
+        Reference :ref:`the user guide ` for more examples.
+
+        Examples
+        --------
+        >>> index = pd.MultiIndex.from_tuples([('one', 'a'), ('one', 'b'),
+        ...                                    ('two', 'a'), ('two', 'b')])
+        >>> s = pd.Series(np.arange(1.0, 5.0), index=index)
+        >>> s
+        one  a   1.0
+             b   2.0
+        two  a   3.0
+             b   4.0
+        dtype: float64
+
+        >>> s.unstack(level=-1)
+             a   b
+        one  1.0  2.0
+        two  3.0  4.0
+
+        >>> s.unstack(level=0)
+           one  two
+        a  1.0   3.0
+        b  2.0   4.0
+
+        >>> df = s.unstack(level=0)
+        >>> df.unstack()
+        one  a  1.0
+             b  2.0
+        two  a  3.0
+             b  4.0
+        dtype: float64
+        """
+        from pandas.core.reshape.reshape import unstack
+
+        result = unstack(self, level, fill_value, sort)
+
+        return result.__finalize__(self, method="unstack")
+
+    @Appender(_shared_docs["melt"] % {"caller": "df.melt(", "other": "melt"})
+    def melt(
+        self,
+        id_vars=None,
+        value_vars=None,
+        var_name=None,
+        value_name: Hashable = "value",
+        col_level: Level | None = None,
+        ignore_index: bool = True,
+    ) -> DataFrame:
+        return melt(
+            self,
+            id_vars=id_vars,
+            value_vars=value_vars,
+            var_name=var_name,
+            value_name=value_name,
+            col_level=col_level,
+            ignore_index=ignore_index,
+        ).__finalize__(self, method="melt")
+
+    # ----------------------------------------------------------------------
+    # Time series-related
+
+    @doc(
+        Series.diff,
+        klass="DataFrame",
+        extra_params="axis : {0 or 'index', 1 or 'columns'}, default 0\n    "
+        "Take difference over rows (0) or columns (1).\n",
+        other_klass="Series",
+        examples=dedent(
+            """
+        Difference with previous row
+
+        >>> df = pd.DataFrame({'a': [1, 2, 3, 4, 5, 6],
+        ...                    'b': [1, 1, 2, 3, 5, 8],
+        ...                    'c': [1, 4, 9, 16, 25, 36]})
+        >>> df
+           a  b   c
+        0  1  1   1
+        1  2  1   4
+        2  3  2   9
+        3  4  3  16
+        4  5  5  25
+        5  6  8  36
+
+        >>> df.diff()
+             a    b     c
+        0  NaN  NaN   NaN
+        1  1.0  0.0   3.0
+        2  1.0  1.0   5.0
+        3  1.0  1.0   7.0
+        4  1.0  2.0   9.0
+        5  1.0  3.0  11.0
+
+        Difference with previous column
+
+        >>> df.diff(axis=1)
+            a  b   c
+        0 NaN  0   0
+        1 NaN -1   3
+        2 NaN -1   7
+        3 NaN -1  13
+        4 NaN  0  20
+        5 NaN  2  28
+
+        Difference with 3rd previous row
+
+        >>> df.diff(periods=3)
+             a    b     c
+        0  NaN  NaN   NaN
+        1  NaN  NaN   NaN
+        2  NaN  NaN   NaN
+        3  3.0  2.0  15.0
+        4  3.0  4.0  21.0
+        5  3.0  6.0  27.0
+
+        Difference with following row
+
+        >>> df.diff(periods=-1)
+             a    b     c
+        0 -1.0  0.0  -3.0
+        1 -1.0 -1.0  -5.0
+        2 -1.0 -1.0  -7.0
+        3 -1.0 -2.0  -9.0
+        4 -1.0 -3.0 -11.0
+        5  NaN  NaN   NaN
+
+        Overflow in input dtype
+
+        >>> df = pd.DataFrame({'a': [1, 0]}, dtype=np.uint8)
+        >>> df.diff()
+               a
+        0    NaN
+        1  255.0"""
+        ),
+    )
+    def diff(self, periods: int = 1, axis: Axis = 0) -> DataFrame:
+        if not lib.is_integer(periods):
+            if not (is_float(periods) and periods.is_integer()):
+                raise ValueError("periods must be an integer")
+            periods = int(periods)
+
+        axis = self._get_axis_number(axis)
+        if axis == 1:
+            if periods != 0:
+                # in the periods == 0 case, this is equivalent diff of 0 periods
+                #  along axis=0, and the Manager method may be somewhat more
+                #  performant, so we dispatch in that case.
+                return self - self.shift(periods, axis=axis)
+            # With periods=0 this is equivalent to a diff with axis=0
+            axis = 0
+
+        new_data = self._mgr.diff(n=periods)
+        res_df = self._constructor_from_mgr(new_data, axes=new_data.axes)
+        return res_df.__finalize__(self, "diff")
+
+    # ----------------------------------------------------------------------
+    # Function application
+
+    def _gotitem(
+        self,
+        key: IndexLabel,
+        ndim: int,
+        subset: DataFrame | Series | None = None,
+    ) -> DataFrame | Series:
+        """
+        Sub-classes to define. Return a sliced object.
+
+        Parameters
+        ----------
+        key : string / list of selections
+        ndim : {1, 2}
+            requested ndim of result
+        subset : object, default None
+            subset to act on
+        """
+        if subset is None:
+            subset = self
+        elif subset.ndim == 1:  # is Series
+            return subset
+
+        # TODO: _shallow_copy(subset)?
+        return subset[key]
+
+    _agg_see_also_doc = dedent(
+        """
+    See Also
+    --------
+    DataFrame.apply : Perform any type of operations.
+    DataFrame.transform : Perform transformation type operations.
+    pandas.DataFrame.groupby : Perform operations over groups.
+    pandas.DataFrame.resample : Perform operations over resampled bins.
+    pandas.DataFrame.rolling : Perform operations over rolling window.
+    pandas.DataFrame.expanding : Perform operations over expanding window.
+    pandas.core.window.ewm.ExponentialMovingWindow : Perform operation over exponential
+        weighted window.
+    """
+    )
+
+    _agg_examples_doc = dedent(
+        """
+    Examples
+    --------
+    >>> df = pd.DataFrame([[1, 2, 3],
+    ...                    [4, 5, 6],
+    ...                    [7, 8, 9],
+    ...                    [np.nan, np.nan, np.nan]],
+    ...                   columns=['A', 'B', 'C'])
+
+    Aggregate these functions over the rows.
+
+    >>> df.agg(['sum', 'min'])
+            A     B     C
+    sum  12.0  15.0  18.0
+    min   1.0   2.0   3.0
+
+    Different aggregations per column.
+
+    >>> df.agg({'A' : ['sum', 'min'], 'B' : ['min', 'max']})
+            A    B
+    sum  12.0  NaN
+    min   1.0  2.0
+    max   NaN  8.0
+
+    Aggregate different functions over the columns and rename the index of the resulting
+    DataFrame.
+
+    >>> df.agg(x=('A', 'max'), y=('B', 'min'), z=('C', 'mean'))
+         A    B    C
+    x  7.0  NaN  NaN
+    y  NaN  2.0  NaN
+    z  NaN  NaN  6.0
+
+    Aggregate over the columns.
+
+    >>> df.agg("mean", axis="columns")
+    0    2.0
+    1    5.0
+    2    8.0
+    3    NaN
+    dtype: float64
+    """
+    )
+
+    @doc(
+        _shared_docs["aggregate"],
+        klass=_shared_doc_kwargs["klass"],
+        axis=_shared_doc_kwargs["axis"],
+        see_also=_agg_see_also_doc,
+        examples=_agg_examples_doc,
+    )
+    def aggregate(self, func=None, axis: Axis = 0, *args, **kwargs):
+        from pandas.core.apply import frame_apply
+
+        axis = self._get_axis_number(axis)
+
+        op = frame_apply(self, func=func, axis=axis, args=args, kwargs=kwargs)
+        result = op.agg()
+        result = reconstruct_and_relabel_result(result, func, **kwargs)
+        return result
+
+    agg = aggregate
+
+    @doc(
+        _shared_docs["transform"],
+        klass=_shared_doc_kwargs["klass"],
+        axis=_shared_doc_kwargs["axis"],
+    )
+    def transform(
+        self, func: AggFuncType, axis: Axis = 0, *args, **kwargs
+    ) -> DataFrame:
+        from pandas.core.apply import frame_apply
+
+        op = frame_apply(self, func=func, axis=axis, args=args, kwargs=kwargs)
+        result = op.transform()
+        assert isinstance(result, DataFrame)
+        return result
+
+    def apply(
+        self,
+        func: AggFuncType,
+        axis: Axis = 0,
+        raw: bool = False,
+        result_type: Literal["expand", "reduce", "broadcast"] | None = None,
+        args=(),
+        by_row: Literal[False, "compat"] = "compat",
+        engine: Literal["python", "numba"] = "python",
+        engine_kwargs: dict[str, bool] | None = None,
+        **kwargs,
+    ):
+        """
+        Apply a function along an axis of the DataFrame.
+
+        Objects passed to the function are Series objects whose index is
+        either the DataFrame's index (``axis=0``) or the DataFrame's columns
+        (``axis=1``). By default (``result_type=None``), the final return type
+        is inferred from the return type of the applied function. Otherwise,
+        it depends on the `result_type` argument.
+
+        Parameters
+        ----------
+        func : function
+            Function to apply to each column or row.
+        axis : {0 or 'index', 1 or 'columns'}, default 0
+            Axis along which the function is applied:
+
+            * 0 or 'index': apply function to each column.
+            * 1 or 'columns': apply function to each row.
+
+        raw : bool, default False
+            Determines if row or column is passed as a Series or ndarray object:
+
+            * ``False`` : passes each row or column as a Series to the
+              function.
+            * ``True`` : the passed function will receive ndarray objects
+              instead.
+              If you are just applying a NumPy reduction function this will
+              achieve much better performance.
+
+        result_type : {'expand', 'reduce', 'broadcast', None}, default None
+            These only act when ``axis=1`` (columns):
+
+            * 'expand' : list-like results will be turned into columns.
+            * 'reduce' : returns a Series if possible rather than expanding
+              list-like results. This is the opposite of 'expand'.
+            * 'broadcast' : results will be broadcast to the original shape
+              of the DataFrame, the original index and columns will be
+              retained.
+
+            The default behaviour (None) depends on the return value of the
+            applied function: list-like results will be returned as a Series
+            of those. However if the apply function returns a Series these
+            are expanded to columns.
+        args : tuple
+            Positional arguments to pass to `func` in addition to the
+            array/series.
+        by_row : False or "compat", default "compat"
+            Only has an effect when ``func`` is a listlike or dictlike of funcs
+            and the func isn't a string.
+            If "compat", will if possible first translate the func into pandas
+            methods (e.g. ``Series().apply(np.sum)`` will be translated to
+            ``Series().sum()``). If that doesn't work, will try call to apply again with
+            ``by_row=True`` and if that fails, will call apply again with
+            ``by_row=False`` (backward compatible).
+            If False, the funcs will be passed the whole Series at once.
+
+            .. versionadded:: 2.1.0
+
+        engine : {'python', 'numba'}, default 'python'
+            Choose between the python (default) engine or the numba engine in apply.
+
+            The numba engine will attempt to JIT compile the passed function,
+            which may result in speedups for large DataFrames.
+            It also supports the following engine_kwargs :
+
+            - nopython (compile the function in nopython mode)
+            - nogil (release the GIL inside the JIT compiled function)
+            - parallel (try to apply the function in parallel over the DataFrame)
+
+              Note: Due to limitations within numba/how pandas interfaces with numba,
+              you should only use this if raw=True
+
+            Note: The numba compiler only supports a subset of
+            valid Python/numpy operations.
+
+            Please read more about the `supported python features
+            `_
+            and `supported numpy features
+            `_
+            in numba to learn what you can or cannot use in the passed function.
+
+            .. versionadded:: 2.2.0
+
+        engine_kwargs : dict
+            Pass keyword arguments to the engine.
+            This is currently only used by the numba engine,
+            see the documentation for the engine argument for more information.
+        **kwargs
+            Additional keyword arguments to pass as keywords arguments to
+            `func`.
+
+        Returns
+        -------
+        Series or DataFrame
+            Result of applying ``func`` along the given axis of the
+            DataFrame.
+
+        See Also
+        --------
+        DataFrame.map: For elementwise operations.
+        DataFrame.aggregate: Only perform aggregating type operations.
+        DataFrame.transform: Only perform transforming type operations.
+
+        Notes
+        -----
+        Functions that mutate the passed object can produce unexpected
+        behavior or errors and are not supported. See :ref:`gotchas.udf-mutation`
+        for more details.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([[4, 9]] * 3, columns=['A', 'B'])
+        >>> df
+           A  B
+        0  4  9
+        1  4  9
+        2  4  9
+
+        Using a numpy universal function (in this case the same as
+        ``np.sqrt(df)``):
+
+        >>> df.apply(np.sqrt)
+             A    B
+        0  2.0  3.0
+        1  2.0  3.0
+        2  2.0  3.0
+
+        Using a reducing function on either axis
+
+        >>> df.apply(np.sum, axis=0)
+        A    12
+        B    27
+        dtype: int64
+
+        >>> df.apply(np.sum, axis=1)
+        0    13
+        1    13
+        2    13
+        dtype: int64
+
+        Returning a list-like will result in a Series
+
+        >>> df.apply(lambda x: [1, 2], axis=1)
+        0    [1, 2]
+        1    [1, 2]
+        2    [1, 2]
+        dtype: object
+
+        Passing ``result_type='expand'`` will expand list-like results
+        to columns of a Dataframe
+
+        >>> df.apply(lambda x: [1, 2], axis=1, result_type='expand')
+           0  1
+        0  1  2
+        1  1  2
+        2  1  2
+
+        Returning a Series inside the function is similar to passing
+        ``result_type='expand'``. The resulting column names
+        will be the Series index.
+
+        >>> df.apply(lambda x: pd.Series([1, 2], index=['foo', 'bar']), axis=1)
+           foo  bar
+        0    1    2
+        1    1    2
+        2    1    2
+
+        Passing ``result_type='broadcast'`` will ensure the same shape
+        result, whether list-like or scalar is returned by the function,
+        and broadcast it along the axis. The resulting column names will
+        be the originals.
+
+        >>> df.apply(lambda x: [1, 2], axis=1, result_type='broadcast')
+           A  B
+        0  1  2
+        1  1  2
+        2  1  2
+        """
+        from pandas.core.apply import frame_apply
+
+        op = frame_apply(
+            self,
+            func=func,
+            axis=axis,
+            raw=raw,
+            result_type=result_type,
+            by_row=by_row,
+            engine=engine,
+            engine_kwargs=engine_kwargs,
+            args=args,
+            kwargs=kwargs,
+        )
+        return op.apply().__finalize__(self, method="apply")
+
+    def map(
+        self, func: PythonFuncType, na_action: str | None = None, **kwargs
+    ) -> DataFrame:
+        """
+        Apply a function to a Dataframe elementwise.
+
+        .. versionadded:: 2.1.0
+
+           DataFrame.applymap was deprecated and renamed to DataFrame.map.
+
+        This method applies a function that accepts and returns a scalar
+        to every element of a DataFrame.
+
+        Parameters
+        ----------
+        func : callable
+            Python function, returns a single value from a single value.
+        na_action : {None, 'ignore'}, default None
+            If 'ignore', propagate NaN values, without passing them to func.
+        **kwargs
+            Additional keyword arguments to pass as keywords arguments to
+            `func`.
+
+        Returns
+        -------
+        DataFrame
+            Transformed DataFrame.
+
+        See Also
+        --------
+        DataFrame.apply : Apply a function along input axis of DataFrame.
+        DataFrame.replace: Replace values given in `to_replace` with `value`.
+        Series.map : Apply a function elementwise on a Series.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([[1, 2.12], [3.356, 4.567]])
+        >>> df
+               0      1
+        0  1.000  2.120
+        1  3.356  4.567
+
+        >>> df.map(lambda x: len(str(x)))
+           0  1
+        0  3  4
+        1  5  5
+
+        Like Series.map, NA values can be ignored:
+
+        >>> df_copy = df.copy()
+        >>> df_copy.iloc[0, 0] = pd.NA
+        >>> df_copy.map(lambda x: len(str(x)), na_action='ignore')
+             0  1
+        0  NaN  4
+        1  5.0  5
+
+        It is also possible to use `map` with functions that are not
+        `lambda` functions:
+
+        >>> df.map(round, ndigits=1)
+             0    1
+        0  1.0  2.1
+        1  3.4  4.6
+
+        Note that a vectorized version of `func` often exists, which will
+        be much faster. You could square each number elementwise.
+
+        >>> df.map(lambda x: x**2)
+                   0          1
+        0   1.000000   4.494400
+        1  11.262736  20.857489
+
+        But it's better to avoid map in that case.
+
+        >>> df ** 2
+                   0          1
+        0   1.000000   4.494400
+        1  11.262736  20.857489
+        """
+        if na_action not in {"ignore", None}:
+            raise ValueError(
+                f"na_action must be 'ignore' or None. Got {repr(na_action)}"
+            )
+
+        if self.empty:
+            return self.copy()
+
+        func = functools.partial(func, **kwargs)
+
+        def infer(x):
+            return x._map_values(func, na_action=na_action)
+
+        return self.apply(infer).__finalize__(self, "map")
+
+    def applymap(
+        self, func: PythonFuncType, na_action: NaAction | None = None, **kwargs
+    ) -> DataFrame:
+        """
+        Apply a function to a Dataframe elementwise.
+
+        .. deprecated:: 2.1.0
+
+           DataFrame.applymap has been deprecated. Use DataFrame.map instead.
+
+        This method applies a function that accepts and returns a scalar
+        to every element of a DataFrame.
+
+        Parameters
+        ----------
+        func : callable
+            Python function, returns a single value from a single value.
+        na_action : {None, 'ignore'}, default None
+            If 'ignore', propagate NaN values, without passing them to func.
+        **kwargs
+            Additional keyword arguments to pass as keywords arguments to
+            `func`.
+
+        Returns
+        -------
+        DataFrame
+            Transformed DataFrame.
+
+        See Also
+        --------
+        DataFrame.apply : Apply a function along input axis of DataFrame.
+        DataFrame.map : Apply a function along input axis of DataFrame.
+        DataFrame.replace: Replace values given in `to_replace` with `value`.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([[1, 2.12], [3.356, 4.567]])
+        >>> df
+               0      1
+        0  1.000  2.120
+        1  3.356  4.567
+
+        >>> df.map(lambda x: len(str(x)))
+           0  1
+        0  3  4
+        1  5  5
+        """
+        warnings.warn(
+            "DataFrame.applymap has been deprecated. Use DataFrame.map instead.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        return self.map(func, na_action=na_action, **kwargs)
+
+    # ----------------------------------------------------------------------
+    # Merging / joining methods
+
+    def _append(
+        self,
+        other,
+        ignore_index: bool = False,
+        verify_integrity: bool = False,
+        sort: bool = False,
+    ) -> DataFrame:
+        if isinstance(other, (Series, dict)):
+            if isinstance(other, dict):
+                if not ignore_index:
+                    raise TypeError("Can only append a dict if ignore_index=True")
+                other = Series(other)
+            if other.name is None and not ignore_index:
+                raise TypeError(
+                    "Can only append a Series if ignore_index=True "
+                    "or if the Series has a name"
+                )
+
+            index = Index(
+                [other.name],
+                name=self.index.names
+                if isinstance(self.index, MultiIndex)
+                else self.index.name,
+            )
+            row_df = other.to_frame().T
+            # infer_objects is needed for
+            #  test_append_empty_frame_to_series_with_dateutil_tz
+            other = row_df.infer_objects(copy=False).rename_axis(
+                index.names, copy=False
+            )
+        elif isinstance(other, list):
+            if not other:
+                pass
+            elif not isinstance(other[0], DataFrame):
+                other = DataFrame(other)
+                if self.index.name is not None and not ignore_index:
+                    other.index.name = self.index.name
+
+        from pandas.core.reshape.concat import concat
+
+        if isinstance(other, (list, tuple)):
+            to_concat = [self, *other]
+        else:
+            to_concat = [self, other]
+
+        result = concat(
+            to_concat,
+            ignore_index=ignore_index,
+            verify_integrity=verify_integrity,
+            sort=sort,
+        )
+        return result.__finalize__(self, method="append")
+
+    def join(
+        self,
+        other: DataFrame | Series | Iterable[DataFrame | Series],
+        on: IndexLabel | None = None,
+        how: MergeHow = "left",
+        lsuffix: str = "",
+        rsuffix: str = "",
+        sort: bool = False,
+        validate: JoinValidate | None = None,
+    ) -> DataFrame:
+        """
+        Join columns of another DataFrame.
+
+        Join columns with `other` DataFrame either on index or on a key
+        column. Efficiently join multiple DataFrame objects by index at once by
+        passing a list.
+
+        Parameters
+        ----------
+        other : DataFrame, Series, or a list containing any combination of them
+            Index should be similar to one of the columns in this one. If a
+            Series is passed, its name attribute must be set, and that will be
+            used as the column name in the resulting joined DataFrame.
+        on : str, list of str, or array-like, optional
+            Column or index level name(s) in the caller to join on the index
+            in `other`, otherwise joins index-on-index. If multiple
+            values given, the `other` DataFrame must have a MultiIndex. Can
+            pass an array as the join key if it is not already contained in
+            the calling DataFrame. Like an Excel VLOOKUP operation.
+        how : {'left', 'right', 'outer', 'inner', 'cross'}, default 'left'
+            How to handle the operation of the two objects.
+
+            * left: use calling frame's index (or column if on is specified)
+            * right: use `other`'s index.
+            * outer: form union of calling frame's index (or column if on is
+              specified) with `other`'s index, and sort it lexicographically.
+            * inner: form intersection of calling frame's index (or column if
+              on is specified) with `other`'s index, preserving the order
+              of the calling's one.
+            * cross: creates the cartesian product from both frames, preserves the order
+              of the left keys.
+        lsuffix : str, default ''
+            Suffix to use from left frame's overlapping columns.
+        rsuffix : str, default ''
+            Suffix to use from right frame's overlapping columns.
+        sort : bool, default False
+            Order result DataFrame lexicographically by the join key. If False,
+            the order of the join key depends on the join type (how keyword).
+        validate : str, optional
+            If specified, checks if join is of specified type.
+
+            * "one_to_one" or "1:1": check if join keys are unique in both left
+              and right datasets.
+            * "one_to_many" or "1:m": check if join keys are unique in left dataset.
+            * "many_to_one" or "m:1": check if join keys are unique in right dataset.
+            * "many_to_many" or "m:m": allowed, but does not result in checks.
+
+            .. versionadded:: 1.5.0
+
+        Returns
+        -------
+        DataFrame
+            A dataframe containing columns from both the caller and `other`.
+
+        See Also
+        --------
+        DataFrame.merge : For column(s)-on-column(s) operations.
+
+        Notes
+        -----
+        Parameters `on`, `lsuffix`, and `rsuffix` are not supported when
+        passing a list of `DataFrame` objects.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3', 'K4', 'K5'],
+        ...                    'A': ['A0', 'A1', 'A2', 'A3', 'A4', 'A5']})
+
+        >>> df
+          key   A
+        0  K0  A0
+        1  K1  A1
+        2  K2  A2
+        3  K3  A3
+        4  K4  A4
+        5  K5  A5
+
+        >>> other = pd.DataFrame({'key': ['K0', 'K1', 'K2'],
+        ...                       'B': ['B0', 'B1', 'B2']})
+
+        >>> other
+          key   B
+        0  K0  B0
+        1  K1  B1
+        2  K2  B2
+
+        Join DataFrames using their indexes.
+
+        >>> df.join(other, lsuffix='_caller', rsuffix='_other')
+          key_caller   A key_other    B
+        0         K0  A0        K0   B0
+        1         K1  A1        K1   B1
+        2         K2  A2        K2   B2
+        3         K3  A3       NaN  NaN
+        4         K4  A4       NaN  NaN
+        5         K5  A5       NaN  NaN
+
+        If we want to join using the key columns, we need to set key to be
+        the index in both `df` and `other`. The joined DataFrame will have
+        key as its index.
+
+        >>> df.set_index('key').join(other.set_index('key'))
+              A    B
+        key
+        K0   A0   B0
+        K1   A1   B1
+        K2   A2   B2
+        K3   A3  NaN
+        K4   A4  NaN
+        K5   A5  NaN
+
+        Another option to join using the key columns is to use the `on`
+        parameter. DataFrame.join always uses `other`'s index but we can use
+        any column in `df`. This method preserves the original DataFrame's
+        index in the result.
+
+        >>> df.join(other.set_index('key'), on='key')
+          key   A    B
+        0  K0  A0   B0
+        1  K1  A1   B1
+        2  K2  A2   B2
+        3  K3  A3  NaN
+        4  K4  A4  NaN
+        5  K5  A5  NaN
+
+        Using non-unique key values shows how they are matched.
+
+        >>> df = pd.DataFrame({'key': ['K0', 'K1', 'K1', 'K3', 'K0', 'K1'],
+        ...                    'A': ['A0', 'A1', 'A2', 'A3', 'A4', 'A5']})
+
+        >>> df
+          key   A
+        0  K0  A0
+        1  K1  A1
+        2  K1  A2
+        3  K3  A3
+        4  K0  A4
+        5  K1  A5
+
+        >>> df.join(other.set_index('key'), on='key', validate='m:1')
+          key   A    B
+        0  K0  A0   B0
+        1  K1  A1   B1
+        2  K1  A2   B1
+        3  K3  A3  NaN
+        4  K0  A4   B0
+        5  K1  A5   B1
+        """
+        from pandas.core.reshape.concat import concat
+        from pandas.core.reshape.merge import merge
+
+        if isinstance(other, Series):
+            if other.name is None:
+                raise ValueError("Other Series must have a name")
+            other = DataFrame({other.name: other})
+
+        if isinstance(other, DataFrame):
+            if how == "cross":
+                return merge(
+                    self,
+                    other,
+                    how=how,
+                    on=on,
+                    suffixes=(lsuffix, rsuffix),
+                    sort=sort,
+                    validate=validate,
+                )
+            return merge(
+                self,
+                other,
+                left_on=on,
+                how=how,
+                left_index=on is None,
+                right_index=True,
+                suffixes=(lsuffix, rsuffix),
+                sort=sort,
+                validate=validate,
+            )
+        else:
+            if on is not None:
+                raise ValueError(
+                    "Joining multiple DataFrames only supported for joining on index"
+                )
+
+            if rsuffix or lsuffix:
+                raise ValueError(
+                    "Suffixes not supported when joining multiple DataFrames"
+                )
+
+            # Mypy thinks the RHS is a
+            # "Union[DataFrame, Series, Iterable[Union[DataFrame, Series]]]" whereas
+            # the LHS is an "Iterable[DataFrame]", but in reality both types are
+            # "Iterable[Union[DataFrame, Series]]" due to the if statements
+            frames = [cast("DataFrame | Series", self)] + list(other)
+
+            can_concat = all(df.index.is_unique for df in frames)
+
+            # join indexes only using concat
+            if can_concat:
+                if how == "left":
+                    res = concat(
+                        frames, axis=1, join="outer", verify_integrity=True, sort=sort
+                    )
+                    return res.reindex(self.index, copy=False)
+                else:
+                    return concat(
+                        frames, axis=1, join=how, verify_integrity=True, sort=sort
+                    )
+
+            joined = frames[0]
+
+            for frame in frames[1:]:
+                joined = merge(
+                    joined,
+                    frame,
+                    how=how,
+                    left_index=True,
+                    right_index=True,
+                    validate=validate,
+                )
+
+            return joined
+
+    @Substitution("")
+    @Appender(_merge_doc, indents=2)
+    def merge(
+        self,
+        right: DataFrame | Series,
+        how: MergeHow = "inner",
+        on: IndexLabel | AnyArrayLike | None = None,
+        left_on: IndexLabel | AnyArrayLike | None = None,
+        right_on: IndexLabel | AnyArrayLike | None = None,
+        left_index: bool = False,
+        right_index: bool = False,
+        sort: bool = False,
+        suffixes: Suffixes = ("_x", "_y"),
+        copy: bool | None = None,
+        indicator: str | bool = False,
+        validate: MergeValidate | None = None,
+    ) -> DataFrame:
+        from pandas.core.reshape.merge import merge
+
+        return merge(
+            self,
+            right,
+            how=how,
+            on=on,
+            left_on=left_on,
+            right_on=right_on,
+            left_index=left_index,
+            right_index=right_index,
+            sort=sort,
+            suffixes=suffixes,
+            copy=copy,
+            indicator=indicator,
+            validate=validate,
+        )
+
+    def round(
+        self, decimals: int | dict[IndexLabel, int] | Series = 0, *args, **kwargs
+    ) -> DataFrame:
+        """
+        Round a DataFrame to a variable number of decimal places.
+
+        Parameters
+        ----------
+        decimals : int, dict, Series
+            Number of decimal places to round each column to. If an int is
+            given, round each column to the same number of places.
+            Otherwise dict and Series round to variable numbers of places.
+            Column names should be in the keys if `decimals` is a
+            dict-like, or in the index if `decimals` is a Series. Any
+            columns not included in `decimals` will be left as is. Elements
+            of `decimals` which are not columns of the input will be
+            ignored.
+        *args
+            Additional keywords have no effect but might be accepted for
+            compatibility with numpy.
+        **kwargs
+            Additional keywords have no effect but might be accepted for
+            compatibility with numpy.
+
+        Returns
+        -------
+        DataFrame
+            A DataFrame with the affected columns rounded to the specified
+            number of decimal places.
+
+        See Also
+        --------
+        numpy.around : Round a numpy array to the given number of decimals.
+        Series.round : Round a Series to the given number of decimals.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([(.21, .32), (.01, .67), (.66, .03), (.21, .18)],
+        ...                   columns=['dogs', 'cats'])
+        >>> df
+            dogs  cats
+        0  0.21  0.32
+        1  0.01  0.67
+        2  0.66  0.03
+        3  0.21  0.18
+
+        By providing an integer each column is rounded to the same number
+        of decimal places
+
+        >>> df.round(1)
+            dogs  cats
+        0   0.2   0.3
+        1   0.0   0.7
+        2   0.7   0.0
+        3   0.2   0.2
+
+        With a dict, the number of places for specific columns can be
+        specified with the column names as key and the number of decimal
+        places as value
+
+        >>> df.round({'dogs': 1, 'cats': 0})
+            dogs  cats
+        0   0.2   0.0
+        1   0.0   1.0
+        2   0.7   0.0
+        3   0.2   0.0
+
+        Using a Series, the number of places for specific columns can be
+        specified with the column names as index and the number of
+        decimal places as value
+
+        >>> decimals = pd.Series([0, 1], index=['cats', 'dogs'])
+        >>> df.round(decimals)
+            dogs  cats
+        0   0.2   0.0
+        1   0.0   1.0
+        2   0.7   0.0
+        3   0.2   0.0
+        """
+        from pandas.core.reshape.concat import concat
+
+        def _dict_round(df: DataFrame, decimals):
+            for col, vals in df.items():
+                try:
+                    yield _series_round(vals, decimals[col])
+                except KeyError:
+                    yield vals
+
+        def _series_round(ser: Series, decimals: int) -> Series:
+            if is_integer_dtype(ser.dtype) or is_float_dtype(ser.dtype):
+                return ser.round(decimals)
+            return ser
+
+        nv.validate_round(args, kwargs)
+
+        if isinstance(decimals, (dict, Series)):
+            if isinstance(decimals, Series) and not decimals.index.is_unique:
+                raise ValueError("Index of decimals must be unique")
+            if is_dict_like(decimals) and not all(
+                is_integer(value) for _, value in decimals.items()
+            ):
+                raise TypeError("Values in decimals must be integers")
+            new_cols = list(_dict_round(self, decimals))
+        elif is_integer(decimals):
+            # Dispatch to Block.round
+            # Argument "decimals" to "round" of "BaseBlockManager" has incompatible
+            # type "Union[int, integer[Any]]"; expected "int"
+            new_mgr = self._mgr.round(
+                decimals=decimals,  # type: ignore[arg-type]
+                using_cow=using_copy_on_write(),
+            )
+            return self._constructor_from_mgr(new_mgr, axes=new_mgr.axes).__finalize__(
+                self, method="round"
+            )
+        else:
+            raise TypeError("decimals must be an integer, a dict-like or a Series")
+
+        if new_cols is not None and len(new_cols) > 0:
+            return self._constructor(
+                concat(new_cols, axis=1), index=self.index, columns=self.columns
+            ).__finalize__(self, method="round")
+        else:
+            return self.copy(deep=False)
+
+    # ----------------------------------------------------------------------
+    # Statistical methods, etc.
+
+    def corr(
+        self,
+        method: CorrelationMethod = "pearson",
+        min_periods: int = 1,
+        numeric_only: bool = False,
+    ) -> DataFrame:
+        """
+        Compute pairwise correlation of columns, excluding NA/null values.
+
+        Parameters
+        ----------
+        method : {'pearson', 'kendall', 'spearman'} or callable
+            Method of correlation:
+
+            * pearson : standard correlation coefficient
+            * kendall : Kendall Tau correlation coefficient
+            * spearman : Spearman rank correlation
+            * callable: callable with input two 1d ndarrays
+                and returning a float. Note that the returned matrix from corr
+                will have 1 along the diagonals and will be symmetric
+                regardless of the callable's behavior.
+        min_periods : int, optional
+            Minimum number of observations required per pair of columns
+            to have a valid result. Currently only available for Pearson
+            and Spearman correlation.
+        numeric_only : bool, default False
+            Include only `float`, `int` or `boolean` data.
+
+            .. versionadded:: 1.5.0
+
+            .. versionchanged:: 2.0.0
+                The default value of ``numeric_only`` is now ``False``.
+
+        Returns
+        -------
+        DataFrame
+            Correlation matrix.
+
+        See Also
+        --------
+        DataFrame.corrwith : Compute pairwise correlation with another
+            DataFrame or Series.
+        Series.corr : Compute the correlation between two Series.
+
+        Notes
+        -----
+        Pearson, Kendall and Spearman correlation are currently computed using pairwise complete observations.
+
+        * `Pearson correlation coefficient `_
+        * `Kendall rank correlation coefficient `_
+        * `Spearman's rank correlation coefficient `_
+
+        Examples
+        --------
+        >>> def histogram_intersection(a, b):
+        ...     v = np.minimum(a, b).sum().round(decimals=1)
+        ...     return v
+        >>> df = pd.DataFrame([(.2, .3), (.0, .6), (.6, .0), (.2, .1)],
+        ...                   columns=['dogs', 'cats'])
+        >>> df.corr(method=histogram_intersection)
+              dogs  cats
+        dogs   1.0   0.3
+        cats   0.3   1.0
+
+        >>> df = pd.DataFrame([(1, 1), (2, np.nan), (np.nan, 3), (4, 4)],
+        ...                   columns=['dogs', 'cats'])
+        >>> df.corr(min_periods=3)
+              dogs  cats
+        dogs   1.0   NaN
+        cats   NaN   1.0
+        """  # noqa: E501
+        data = self._get_numeric_data() if numeric_only else self
+        cols = data.columns
+        idx = cols.copy()
+        mat = data.to_numpy(dtype=float, na_value=np.nan, copy=False)
+
+        if method == "pearson":
+            correl = libalgos.nancorr(mat, minp=min_periods)
+        elif method == "spearman":
+            correl = libalgos.nancorr_spearman(mat, minp=min_periods)
+        elif method == "kendall" or callable(method):
+            if min_periods is None:
+                min_periods = 1
+            mat = mat.T
+            corrf = nanops.get_corr_func(method)
+            K = len(cols)
+            correl = np.empty((K, K), dtype=float)
+            mask = np.isfinite(mat)
+            for i, ac in enumerate(mat):
+                for j, bc in enumerate(mat):
+                    if i > j:
+                        continue
+
+                    valid = mask[i] & mask[j]
+                    if valid.sum() < min_periods:
+                        c = np.nan
+                    elif i == j:
+                        c = 1.0
+                    elif not valid.all():
+                        c = corrf(ac[valid], bc[valid])
+                    else:
+                        c = corrf(ac, bc)
+                    correl[i, j] = c
+                    correl[j, i] = c
+        else:
+            raise ValueError(
+                "method must be either 'pearson', "
+                "'spearman', 'kendall', or a callable, "
+                f"'{method}' was supplied"
+            )
+
+        result = self._constructor(correl, index=idx, columns=cols, copy=False)
+        return result.__finalize__(self, method="corr")
+
+    def cov(
+        self,
+        min_periods: int | None = None,
+        ddof: int | None = 1,
+        numeric_only: bool = False,
+    ) -> DataFrame:
+        """
+        Compute pairwise covariance of columns, excluding NA/null values.
+
+        Compute the pairwise covariance among the series of a DataFrame.
+        The returned data frame is the `covariance matrix
+        `__ of the columns
+        of the DataFrame.
+
+        Both NA and null values are automatically excluded from the
+        calculation. (See the note below about bias from missing values.)
+        A threshold can be set for the minimum number of
+        observations for each value created. Comparisons with observations
+        below this threshold will be returned as ``NaN``.
+
+        This method is generally used for the analysis of time series data to
+        understand the relationship between different measures
+        across time.
+
+        Parameters
+        ----------
+        min_periods : int, optional
+            Minimum number of observations required per pair of columns
+            to have a valid result.
+
+        ddof : int, default 1
+            Delta degrees of freedom.  The divisor used in calculations
+            is ``N - ddof``, where ``N`` represents the number of elements.
+            This argument is applicable only when no ``nan`` is in the dataframe.
+
+        numeric_only : bool, default False
+            Include only `float`, `int` or `boolean` data.
+
+            .. versionadded:: 1.5.0
+
+            .. versionchanged:: 2.0.0
+                The default value of ``numeric_only`` is now ``False``.
+
+        Returns
+        -------
+        DataFrame
+            The covariance matrix of the series of the DataFrame.
+
+        See Also
+        --------
+        Series.cov : Compute covariance with another Series.
+        core.window.ewm.ExponentialMovingWindow.cov : Exponential weighted sample
+            covariance.
+        core.window.expanding.Expanding.cov : Expanding sample covariance.
+        core.window.rolling.Rolling.cov : Rolling sample covariance.
+
+        Notes
+        -----
+        Returns the covariance matrix of the DataFrame's time series.
+        The covariance is normalized by N-ddof.
+
+        For DataFrames that have Series that are missing data (assuming that
+        data is `missing at random
+        `__)
+        the returned covariance matrix will be an unbiased estimate
+        of the variance and covariance between the member Series.
+
+        However, for many applications this estimate may not be acceptable
+        because the estimate covariance matrix is not guaranteed to be positive
+        semi-definite. This could lead to estimate correlations having
+        absolute values which are greater than one, and/or a non-invertible
+        covariance matrix. See `Estimation of covariance matrices
+        `__ for more details.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([(1, 2), (0, 3), (2, 0), (1, 1)],
+        ...                   columns=['dogs', 'cats'])
+        >>> df.cov()
+                  dogs      cats
+        dogs  0.666667 -1.000000
+        cats -1.000000  1.666667
+
+        >>> np.random.seed(42)
+        >>> df = pd.DataFrame(np.random.randn(1000, 5),
+        ...                   columns=['a', 'b', 'c', 'd', 'e'])
+        >>> df.cov()
+                  a         b         c         d         e
+        a  0.998438 -0.020161  0.059277 -0.008943  0.014144
+        b -0.020161  1.059352 -0.008543 -0.024738  0.009826
+        c  0.059277 -0.008543  1.010670 -0.001486 -0.000271
+        d -0.008943 -0.024738 -0.001486  0.921297 -0.013692
+        e  0.014144  0.009826 -0.000271 -0.013692  0.977795
+
+        **Minimum number of periods**
+
+        This method also supports an optional ``min_periods`` keyword
+        that specifies the required minimum number of non-NA observations for
+        each column pair in order to have a valid result:
+
+        >>> np.random.seed(42)
+        >>> df = pd.DataFrame(np.random.randn(20, 3),
+        ...                   columns=['a', 'b', 'c'])
+        >>> df.loc[df.index[:5], 'a'] = np.nan
+        >>> df.loc[df.index[5:10], 'b'] = np.nan
+        >>> df.cov(min_periods=12)
+                  a         b         c
+        a  0.316741       NaN -0.150812
+        b       NaN  1.248003  0.191417
+        c -0.150812  0.191417  0.895202
+        """
+        data = self._get_numeric_data() if numeric_only else self
+        cols = data.columns
+        idx = cols.copy()
+        mat = data.to_numpy(dtype=float, na_value=np.nan, copy=False)
+
+        if notna(mat).all():
+            if min_periods is not None and min_periods > len(mat):
+                base_cov = np.empty((mat.shape[1], mat.shape[1]))
+                base_cov.fill(np.nan)
+            else:
+                base_cov = np.cov(mat.T, ddof=ddof)
+            base_cov = base_cov.reshape((len(cols), len(cols)))
+        else:
+            base_cov = libalgos.nancorr(mat, cov=True, minp=min_periods)
+
+        result = self._constructor(base_cov, index=idx, columns=cols, copy=False)
+        return result.__finalize__(self, method="cov")
+
+    def corrwith(
+        self,
+        other: DataFrame | Series,
+        axis: Axis = 0,
+        drop: bool = False,
+        method: CorrelationMethod = "pearson",
+        numeric_only: bool = False,
+    ) -> Series:
+        """
+        Compute pairwise correlation.
+
+        Pairwise correlation is computed between rows or columns of
+        DataFrame with rows or columns of Series or DataFrame. DataFrames
+        are first aligned along both axes before computing the
+        correlations.
+
+        Parameters
+        ----------
+        other : DataFrame, Series
+            Object with which to compute correlations.
+        axis : {0 or 'index', 1 or 'columns'}, default 0
+            The axis to use. 0 or 'index' to compute row-wise, 1 or 'columns' for
+            column-wise.
+        drop : bool, default False
+            Drop missing indices from result.
+        method : {'pearson', 'kendall', 'spearman'} or callable
+            Method of correlation:
+
+            * pearson : standard correlation coefficient
+            * kendall : Kendall Tau correlation coefficient
+            * spearman : Spearman rank correlation
+            * callable: callable with input two 1d ndarrays
+                and returning a float.
+
+        numeric_only : bool, default False
+            Include only `float`, `int` or `boolean` data.
+
+            .. versionadded:: 1.5.0
+
+            .. versionchanged:: 2.0.0
+                The default value of ``numeric_only`` is now ``False``.
+
+        Returns
+        -------
+        Series
+            Pairwise correlations.
+
+        See Also
+        --------
+        DataFrame.corr : Compute pairwise correlation of columns.
+
+        Examples
+        --------
+        >>> index = ["a", "b", "c", "d", "e"]
+        >>> columns = ["one", "two", "three", "four"]
+        >>> df1 = pd.DataFrame(np.arange(20).reshape(5, 4), index=index, columns=columns)
+        >>> df2 = pd.DataFrame(np.arange(16).reshape(4, 4), index=index[:4], columns=columns)
+        >>> df1.corrwith(df2)
+        one      1.0
+        two      1.0
+        three    1.0
+        four     1.0
+        dtype: float64
+
+        >>> df2.corrwith(df1, axis=1)
+        a    1.0
+        b    1.0
+        c    1.0
+        d    1.0
+        e    NaN
+        dtype: float64
+        """  # noqa: E501
+        axis = self._get_axis_number(axis)
+        this = self._get_numeric_data() if numeric_only else self
+
+        if isinstance(other, Series):
+            return this.apply(lambda x: other.corr(x, method=method), axis=axis)
+
+        if numeric_only:
+            other = other._get_numeric_data()
+        left, right = this.align(other, join="inner", copy=False)
+
+        if axis == 1:
+            left = left.T
+            right = right.T
+
+        if method == "pearson":
+            # mask missing values
+            left = left + right * 0
+            right = right + left * 0
+
+            # demeaned data
+            ldem = left - left.mean(numeric_only=numeric_only)
+            rdem = right - right.mean(numeric_only=numeric_only)
+
+            num = (ldem * rdem).sum()
+            dom = (
+                (left.count() - 1)
+                * left.std(numeric_only=numeric_only)
+                * right.std(numeric_only=numeric_only)
+            )
+
+            correl = num / dom
+
+        elif method in ["kendall", "spearman"] or callable(method):
+
+            def c(x):
+                return nanops.nancorr(x[0], x[1], method=method)
+
+            correl = self._constructor_sliced(
+                map(c, zip(left.values.T, right.values.T)),
+                index=left.columns,
+                copy=False,
+            )
+
+        else:
+            raise ValueError(
+                f"Invalid method {method} was passed, "
+                "valid methods are: 'pearson', 'kendall', "
+                "'spearman', or callable"
+            )
+
+        if not drop:
+            # Find non-matching labels along the given axis
+            # and append missing correlations (GH 22375)
+            raxis: AxisInt = 1 if axis == 0 else 0
+            result_index = this._get_axis(raxis).union(other._get_axis(raxis))
+            idx_diff = result_index.difference(correl.index)
+
+            if len(idx_diff) > 0:
+                correl = correl._append(
+                    Series([np.nan] * len(idx_diff), index=idx_diff)
+                )
+
+        return correl
+
+    # ----------------------------------------------------------------------
+    # ndarray-like stats methods
+
+    def count(self, axis: Axis = 0, numeric_only: bool = False):
+        """
+        Count non-NA cells for each column or row.
+
+        The values `None`, `NaN`, `NaT`, ``pandas.NA`` are considered NA.
+
+        Parameters
+        ----------
+        axis : {0 or 'index', 1 or 'columns'}, default 0
+            If 0 or 'index' counts are generated for each column.
+            If 1 or 'columns' counts are generated for each row.
+        numeric_only : bool, default False
+            Include only `float`, `int` or `boolean` data.
+
+        Returns
+        -------
+        Series
+            For each column/row the number of non-NA/null entries.
+
+        See Also
+        --------
+        Series.count: Number of non-NA elements in a Series.
+        DataFrame.value_counts: Count unique combinations of columns.
+        DataFrame.shape: Number of DataFrame rows and columns (including NA
+            elements).
+        DataFrame.isna: Boolean same-sized DataFrame showing places of NA
+            elements.
+
+        Examples
+        --------
+        Constructing DataFrame from a dictionary:
+
+        >>> df = pd.DataFrame({"Person":
+        ...                    ["John", "Myla", "Lewis", "John", "Myla"],
+        ...                    "Age": [24., np.nan, 21., 33, 26],
+        ...                    "Single": [False, True, True, True, False]})
+        >>> df
+           Person   Age  Single
+        0    John  24.0   False
+        1    Myla   NaN    True
+        2   Lewis  21.0    True
+        3    John  33.0    True
+        4    Myla  26.0   False
+
+        Notice the uncounted NA values:
+
+        >>> df.count()
+        Person    5
+        Age       4
+        Single    5
+        dtype: int64
+
+        Counts for each **row**:
+
+        >>> df.count(axis='columns')
+        0    3
+        1    2
+        2    3
+        3    3
+        4    3
+        dtype: int64
+        """
+        axis = self._get_axis_number(axis)
+
+        if numeric_only:
+            frame = self._get_numeric_data()
+        else:
+            frame = self
+
+        # GH #423
+        if len(frame._get_axis(axis)) == 0:
+            result = self._constructor_sliced(0, index=frame._get_agg_axis(axis))
+        else:
+            result = notna(frame).sum(axis=axis)
+
+        return result.astype("int64", copy=False).__finalize__(self, method="count")
+
+    def _reduce(
+        self,
+        op,
+        name: str,
+        *,
+        axis: Axis = 0,
+        skipna: bool = True,
+        numeric_only: bool = False,
+        filter_type=None,
+        **kwds,
+    ):
+        assert filter_type is None or filter_type == "bool", filter_type
+        out_dtype = "bool" if filter_type == "bool" else None
+
+        if axis is not None:
+            axis = self._get_axis_number(axis)
+
+        def func(values: np.ndarray):
+            # We only use this in the case that operates on self.values
+            return op(values, axis=axis, skipna=skipna, **kwds)
+
+        dtype_has_keepdims: dict[ExtensionDtype, bool] = {}
+
+        def blk_func(values, axis: Axis = 1):
+            if isinstance(values, ExtensionArray):
+                if not is_1d_only_ea_dtype(values.dtype) and not isinstance(
+                    self._mgr, ArrayManager
+                ):
+                    return values._reduce(name, axis=1, skipna=skipna, **kwds)
+                has_keepdims = dtype_has_keepdims.get(values.dtype)
+                if has_keepdims is None:
+                    sign = signature(values._reduce)
+                    has_keepdims = "keepdims" in sign.parameters
+                    dtype_has_keepdims[values.dtype] = has_keepdims
+                if has_keepdims:
+                    return values._reduce(name, skipna=skipna, keepdims=True, **kwds)
+                else:
+                    warnings.warn(
+                        f"{type(values)}._reduce will require a `keepdims` parameter "
+                        "in the future",
+                        FutureWarning,
+                        stacklevel=find_stack_level(),
+                    )
+                    result = values._reduce(name, skipna=skipna, **kwds)
+                    return np.array([result])
+            else:
+                return op(values, axis=axis, skipna=skipna, **kwds)
+
+        def _get_data() -> DataFrame:
+            if filter_type is None:
+                data = self._get_numeric_data()
+            else:
+                # GH#25101, GH#24434
+                assert filter_type == "bool"
+                data = self._get_bool_data()
+            return data
+
+        # Case with EAs see GH#35881
+        df = self
+        if numeric_only:
+            df = _get_data()
+        if axis is None:
+            dtype = find_common_type([arr.dtype for arr in df._mgr.arrays])
+            if isinstance(dtype, ExtensionDtype):
+                df = df.astype(dtype, copy=False)
+                arr = concat_compat(list(df._iter_column_arrays()))
+                return arr._reduce(name, skipna=skipna, keepdims=False, **kwds)
+            return func(df.values)
+        elif axis == 1:
+            if len(df.index) == 0:
+                # Taking a transpose would result in no columns, losing the dtype.
+                # In the empty case, reducing along axis 0 or 1 gives the same
+                # result dtype, so reduce with axis=0 and ignore values
+                result = df._reduce(
+                    op,
+                    name,
+                    axis=0,
+                    skipna=skipna,
+                    numeric_only=False,
+                    filter_type=filter_type,
+                    **kwds,
+                ).iloc[:0]
+                result.index = df.index
+                return result
+
+            # kurtosis excluded since groupby does not implement it
+            if df.shape[1] and name != "kurt":
+                dtype = find_common_type([arr.dtype for arr in df._mgr.arrays])
+                if isinstance(dtype, ExtensionDtype):
+                    # GH 54341: fastpath for EA-backed axis=1 reductions
+                    # This flattens the frame into a single 1D array while keeping
+                    # track of the row and column indices of the original frame. Once
+                    # flattened, grouping by the row indices and aggregating should
+                    # be equivalent to transposing the original frame and aggregating
+                    # with axis=0.
+                    name = {"argmax": "idxmax", "argmin": "idxmin"}.get(name, name)
+                    df = df.astype(dtype, copy=False)
+                    arr = concat_compat(list(df._iter_column_arrays()))
+                    nrows, ncols = df.shape
+                    row_index = np.tile(np.arange(nrows), ncols)
+                    col_index = np.repeat(np.arange(ncols), nrows)
+                    ser = Series(arr, index=col_index, copy=False)
+                    # GroupBy will raise a warning with SeriesGroupBy as the object,
+                    # likely confusing users
+                    with rewrite_warning(
+                        target_message=(
+                            f"The behavior of SeriesGroupBy.{name} with all-NA values"
+                        ),
+                        target_category=FutureWarning,
+                        new_message=(
+                            f"The behavior of {type(self).__name__}.{name} with all-NA "
+                            "values, or any-NA and skipna=False, is deprecated. In "
+                            "a future version this will raise ValueError"
+                        ),
+                    ):
+                        result = ser.groupby(row_index).agg(name, **kwds)
+                    result.index = df.index
+                    if not skipna and name not in ("any", "all"):
+                        mask = df.isna().to_numpy(dtype=np.bool_).any(axis=1)
+                        other = -1 if name in ("idxmax", "idxmin") else lib.no_default
+                        result = result.mask(mask, other)
+                    return result
+
+            df = df.T
+
+        # After possibly _get_data and transposing, we are now in the
+        #  simple case where we can use BlockManager.reduce
+        res = df._mgr.reduce(blk_func)
+        out = df._constructor_from_mgr(res, axes=res.axes).iloc[0]
+        if out_dtype is not None and out.dtype != "boolean":
+            out = out.astype(out_dtype)
+        elif (df._mgr.get_dtypes() == object).any() and name not in ["any", "all"]:
+            out = out.astype(object)
+        elif len(self) == 0 and out.dtype == object and name in ("sum", "prod"):
+            # Even if we are object dtype, follow numpy and return
+            #  float64, see test_apply_funcs_over_empty
+            out = out.astype(np.float64)
+
+        return out
+
+    def _reduce_axis1(self, name: str, func, skipna: bool) -> Series:
+        """
+        Special case for _reduce to try to avoid a potentially-expensive transpose.
+
+        Apply the reduction block-wise along axis=1 and then reduce the resulting
+        1D arrays.
+        """
+        if name == "all":
+            result = np.ones(len(self), dtype=bool)
+            ufunc = np.logical_and
+        elif name == "any":
+            result = np.zeros(len(self), dtype=bool)
+            # error: Incompatible types in assignment
+            # (expression has type "_UFunc_Nin2_Nout1[Literal['logical_or'],
+            # Literal[20], Literal[False]]", variable has type
+            # "_UFunc_Nin2_Nout1[Literal['logical_and'], Literal[20],
+            # Literal[True]]")
+            ufunc = np.logical_or  # type: ignore[assignment]
+        else:
+            raise NotImplementedError(name)
+
+        for arr in self._mgr.arrays:
+            middle = func(arr, axis=0, skipna=skipna)
+            result = ufunc(result, middle)
+
+        res_ser = self._constructor_sliced(result, index=self.index, copy=False)
+        return res_ser
+
+    @doc(make_doc("any", ndim=2))
+    # error: Signature of "any" incompatible with supertype "NDFrame"
+    def any(  # type: ignore[override]
+        self,
+        *,
+        axis: Axis | None = 0,
+        bool_only: bool = False,
+        skipna: bool = True,
+        **kwargs,
+    ) -> Series | bool:
+        result = self._logical_func(
+            "any", nanops.nanany, axis, bool_only, skipna, **kwargs
+        )
+        if isinstance(result, Series):
+            result = result.__finalize__(self, method="any")
+        return result
+
+    @doc(make_doc("all", ndim=2))
+    def all(
+        self,
+        axis: Axis | None = 0,
+        bool_only: bool = False,
+        skipna: bool = True,
+        **kwargs,
+    ) -> Series | bool:
+        result = self._logical_func(
+            "all", nanops.nanall, axis, bool_only, skipna, **kwargs
+        )
+        if isinstance(result, Series):
+            result = result.__finalize__(self, method="all")
+        return result
+
+    @doc(make_doc("min", ndim=2))
+    def min(
+        self,
+        axis: Axis | None = 0,
+        skipna: bool = True,
+        numeric_only: bool = False,
+        **kwargs,
+    ):
+        result = super().min(axis, skipna, numeric_only, **kwargs)
+        if isinstance(result, Series):
+            result = result.__finalize__(self, method="min")
+        return result
+
+    @doc(make_doc("max", ndim=2))
+    def max(
+        self,
+        axis: Axis | None = 0,
+        skipna: bool = True,
+        numeric_only: bool = False,
+        **kwargs,
+    ):
+        result = super().max(axis, skipna, numeric_only, **kwargs)
+        if isinstance(result, Series):
+            result = result.__finalize__(self, method="max")
+        return result
+
+    @doc(make_doc("sum", ndim=2))
+    def sum(
+        self,
+        axis: Axis | None = 0,
+        skipna: bool = True,
+        numeric_only: bool = False,
+        min_count: int = 0,
+        **kwargs,
+    ):
+        result = super().sum(axis, skipna, numeric_only, min_count, **kwargs)
+        return result.__finalize__(self, method="sum")
+
+    @doc(make_doc("prod", ndim=2))
+    def prod(
+        self,
+        axis: Axis | None = 0,
+        skipna: bool = True,
+        numeric_only: bool = False,
+        min_count: int = 0,
+        **kwargs,
+    ):
+        result = super().prod(axis, skipna, numeric_only, min_count, **kwargs)
+        return result.__finalize__(self, method="prod")
+
+    @doc(make_doc("mean", ndim=2))
+    def mean(
+        self,
+        axis: Axis | None = 0,
+        skipna: bool = True,
+        numeric_only: bool = False,
+        **kwargs,
+    ):
+        result = super().mean(axis, skipna, numeric_only, **kwargs)
+        if isinstance(result, Series):
+            result = result.__finalize__(self, method="mean")
+        return result
+
+    @doc(make_doc("median", ndim=2))
+    def median(
+        self,
+        axis: Axis | None = 0,
+        skipna: bool = True,
+        numeric_only: bool = False,
+        **kwargs,
+    ):
+        result = super().median(axis, skipna, numeric_only, **kwargs)
+        if isinstance(result, Series):
+            result = result.__finalize__(self, method="median")
+        return result
+
+    @doc(make_doc("sem", ndim=2))
+    def sem(
+        self,
+        axis: Axis | None = 0,
+        skipna: bool = True,
+        ddof: int = 1,
+        numeric_only: bool = False,
+        **kwargs,
+    ):
+        result = super().sem(axis, skipna, ddof, numeric_only, **kwargs)
+        if isinstance(result, Series):
+            result = result.__finalize__(self, method="sem")
+        return result
+
+    @doc(make_doc("var", ndim=2))
+    def var(
+        self,
+        axis: Axis | None = 0,
+        skipna: bool = True,
+        ddof: int = 1,
+        numeric_only: bool = False,
+        **kwargs,
+    ):
+        result = super().var(axis, skipna, ddof, numeric_only, **kwargs)
+        if isinstance(result, Series):
+            result = result.__finalize__(self, method="var")
+        return result
+
+    @doc(make_doc("std", ndim=2))
+    def std(
+        self,
+        axis: Axis | None = 0,
+        skipna: bool = True,
+        ddof: int = 1,
+        numeric_only: bool = False,
+        **kwargs,
+    ):
+        result = super().std(axis, skipna, ddof, numeric_only, **kwargs)
+        if isinstance(result, Series):
+            result = result.__finalize__(self, method="std")
+        return result
+
+    @doc(make_doc("skew", ndim=2))
+    def skew(
+        self,
+        axis: Axis | None = 0,
+        skipna: bool = True,
+        numeric_only: bool = False,
+        **kwargs,
+    ):
+        result = super().skew(axis, skipna, numeric_only, **kwargs)
+        if isinstance(result, Series):
+            result = result.__finalize__(self, method="skew")
+        return result
+
+    @doc(make_doc("kurt", ndim=2))
+    def kurt(
+        self,
+        axis: Axis | None = 0,
+        skipna: bool = True,
+        numeric_only: bool = False,
+        **kwargs,
+    ):
+        result = super().kurt(axis, skipna, numeric_only, **kwargs)
+        if isinstance(result, Series):
+            result = result.__finalize__(self, method="kurt")
+        return result
+
+    kurtosis = kurt
+    product = prod
+
+    @doc(make_doc("cummin", ndim=2))
+    def cummin(self, axis: Axis | None = None, skipna: bool = True, *args, **kwargs):
+        return NDFrame.cummin(self, axis, skipna, *args, **kwargs)
+
+    @doc(make_doc("cummax", ndim=2))
+    def cummax(self, axis: Axis | None = None, skipna: bool = True, *args, **kwargs):
+        return NDFrame.cummax(self, axis, skipna, *args, **kwargs)
+
+    @doc(make_doc("cumsum", ndim=2))
+    def cumsum(self, axis: Axis | None = None, skipna: bool = True, *args, **kwargs):
+        return NDFrame.cumsum(self, axis, skipna, *args, **kwargs)
+
+    @doc(make_doc("cumprod", 2))
+    def cumprod(self, axis: Axis | None = None, skipna: bool = True, *args, **kwargs):
+        return NDFrame.cumprod(self, axis, skipna, *args, **kwargs)
+
+    def nunique(self, axis: Axis = 0, dropna: bool = True) -> Series:
+        """
+        Count number of distinct elements in specified axis.
+
+        Return Series with number of distinct elements. Can ignore NaN
+        values.
+
+        Parameters
+        ----------
+        axis : {0 or 'index', 1 or 'columns'}, default 0
+            The axis to use. 0 or 'index' for row-wise, 1 or 'columns' for
+            column-wise.
+        dropna : bool, default True
+            Don't include NaN in the counts.
+
+        Returns
+        -------
+        Series
+
+        See Also
+        --------
+        Series.nunique: Method nunique for Series.
+        DataFrame.count: Count non-NA cells for each column or row.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'A': [4, 5, 6], 'B': [4, 1, 1]})
+        >>> df.nunique()
+        A    3
+        B    2
+        dtype: int64
+
+        >>> df.nunique(axis=1)
+        0    1
+        1    2
+        2    2
+        dtype: int64
+        """
+        return self.apply(Series.nunique, axis=axis, dropna=dropna)
+
+    @doc(_shared_docs["idxmin"], numeric_only_default="False")
+    def idxmin(
+        self, axis: Axis = 0, skipna: bool = True, numeric_only: bool = False
+    ) -> Series:
+        axis = self._get_axis_number(axis)
+
+        if self.empty and len(self.axes[axis]):
+            axis_dtype = self.axes[axis].dtype
+            return self._constructor_sliced(dtype=axis_dtype)
+
+        if numeric_only:
+            data = self._get_numeric_data()
+        else:
+            data = self
+
+        res = data._reduce(
+            nanops.nanargmin, "argmin", axis=axis, skipna=skipna, numeric_only=False
+        )
+        indices = res._values
+        # indices will always be np.ndarray since axis is not N
+
+        if (indices == -1).any():
+            warnings.warn(
+                f"The behavior of {type(self).__name__}.idxmin with all-NA "
+                "values, or any-NA and skipna=False, is deprecated. In a future "
+                "version this will raise ValueError",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+
+        index = data._get_axis(axis)
+        result = algorithms.take(
+            index._values, indices, allow_fill=True, fill_value=index._na_value
+        )
+        final_result = data._constructor_sliced(result, index=data._get_agg_axis(axis))
+        return final_result.__finalize__(self, method="idxmin")
+
+    @doc(_shared_docs["idxmax"], numeric_only_default="False")
+    def idxmax(
+        self, axis: Axis = 0, skipna: bool = True, numeric_only: bool = False
+    ) -> Series:
+        axis = self._get_axis_number(axis)
+
+        if self.empty and len(self.axes[axis]):
+            axis_dtype = self.axes[axis].dtype
+            return self._constructor_sliced(dtype=axis_dtype)
+
+        if numeric_only:
+            data = self._get_numeric_data()
+        else:
+            data = self
+
+        res = data._reduce(
+            nanops.nanargmax, "argmax", axis=axis, skipna=skipna, numeric_only=False
+        )
+        indices = res._values
+        # indices will always be 1d array since axis is not None
+
+        if (indices == -1).any():
+            warnings.warn(
+                f"The behavior of {type(self).__name__}.idxmax with all-NA "
+                "values, or any-NA and skipna=False, is deprecated. In a future "
+                "version this will raise ValueError",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+
+        index = data._get_axis(axis)
+        result = algorithms.take(
+            index._values, indices, allow_fill=True, fill_value=index._na_value
+        )
+        final_result = data._constructor_sliced(result, index=data._get_agg_axis(axis))
+        return final_result.__finalize__(self, method="idxmax")
+
+    def _get_agg_axis(self, axis_num: int) -> Index:
+        """
+        Let's be explicit about this.
+        """
+        if axis_num == 0:
+            return self.columns
+        elif axis_num == 1:
+            return self.index
+        else:
+            raise ValueError(f"Axis must be 0 or 1 (got {repr(axis_num)})")
+
+    def mode(
+        self, axis: Axis = 0, numeric_only: bool = False, dropna: bool = True
+    ) -> DataFrame:
+        """
+        Get the mode(s) of each element along the selected axis.
+
+        The mode of a set of values is the value that appears most often.
+        It can be multiple values.
+
+        Parameters
+        ----------
+        axis : {0 or 'index', 1 or 'columns'}, default 0
+            The axis to iterate over while searching for the mode:
+
+            * 0 or 'index' : get mode of each column
+            * 1 or 'columns' : get mode of each row.
+
+        numeric_only : bool, default False
+            If True, only apply to numeric columns.
+        dropna : bool, default True
+            Don't consider counts of NaN/NaT.
+
+        Returns
+        -------
+        DataFrame
+            The modes of each column or row.
+
+        See Also
+        --------
+        Series.mode : Return the highest frequency value in a Series.
+        Series.value_counts : Return the counts of values in a Series.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([('bird', 2, 2),
+        ...                    ('mammal', 4, np.nan),
+        ...                    ('arthropod', 8, 0),
+        ...                    ('bird', 2, np.nan)],
+        ...                   index=('falcon', 'horse', 'spider', 'ostrich'),
+        ...                   columns=('species', 'legs', 'wings'))
+        >>> df
+                   species  legs  wings
+        falcon        bird     2    2.0
+        horse       mammal     4    NaN
+        spider   arthropod     8    0.0
+        ostrich       bird     2    NaN
+
+        By default, missing values are not considered, and the mode of wings
+        are both 0 and 2. Because the resulting DataFrame has two rows,
+        the second row of ``species`` and ``legs`` contains ``NaN``.
+
+        >>> df.mode()
+          species  legs  wings
+        0    bird   2.0    0.0
+        1     NaN   NaN    2.0
+
+        Setting ``dropna=False`` ``NaN`` values are considered and they can be
+        the mode (like for wings).
+
+        >>> df.mode(dropna=False)
+          species  legs  wings
+        0    bird     2    NaN
+
+        Setting ``numeric_only=True``, only the mode of numeric columns is
+        computed, and columns of other types are ignored.
+
+        >>> df.mode(numeric_only=True)
+           legs  wings
+        0   2.0    0.0
+        1   NaN    2.0
+
+        To compute the mode over columns and not rows, use the axis parameter:
+
+        >>> df.mode(axis='columns', numeric_only=True)
+                   0    1
+        falcon   2.0  NaN
+        horse    4.0  NaN
+        spider   0.0  8.0
+        ostrich  2.0  NaN
+        """
+        data = self if not numeric_only else self._get_numeric_data()
+
+        def f(s):
+            return s.mode(dropna=dropna)
+
+        data = data.apply(f, axis=axis)
+        # Ensure index is type stable (should always use int index)
+        if data.empty:
+            data.index = default_index(0)
+
+        return data
+
+    @overload
+    def quantile(
+        self,
+        q: float = ...,
+        axis: Axis = ...,
+        numeric_only: bool = ...,
+        interpolation: QuantileInterpolation = ...,
+        method: Literal["single", "table"] = ...,
+    ) -> Series:
+        ...
+
+    @overload
+    def quantile(
+        self,
+        q: AnyArrayLike | Sequence[float],
+        axis: Axis = ...,
+        numeric_only: bool = ...,
+        interpolation: QuantileInterpolation = ...,
+        method: Literal["single", "table"] = ...,
+    ) -> Series | DataFrame:
+        ...
+
+    @overload
+    def quantile(
+        self,
+        q: float | AnyArrayLike | Sequence[float] = ...,
+        axis: Axis = ...,
+        numeric_only: bool = ...,
+        interpolation: QuantileInterpolation = ...,
+        method: Literal["single", "table"] = ...,
+    ) -> Series | DataFrame:
+        ...
+
+    def quantile(
+        self,
+        q: float | AnyArrayLike | Sequence[float] = 0.5,
+        axis: Axis = 0,
+        numeric_only: bool = False,
+        interpolation: QuantileInterpolation = "linear",
+        method: Literal["single", "table"] = "single",
+    ) -> Series | DataFrame:
+        """
+        Return values at the given quantile over requested axis.
+
+        Parameters
+        ----------
+        q : float or array-like, default 0.5 (50% quantile)
+            Value between 0 <= q <= 1, the quantile(s) to compute.
+        axis : {0 or 'index', 1 or 'columns'}, default 0
+            Equals 0 or 'index' for row-wise, 1 or 'columns' for column-wise.
+        numeric_only : bool, default False
+            Include only `float`, `int` or `boolean` data.
+
+            .. versionchanged:: 2.0.0
+                The default value of ``numeric_only`` is now ``False``.
+
+        interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'}
+            This optional parameter specifies the interpolation method to use,
+            when the desired quantile lies between two data points `i` and `j`:
+
+            * linear: `i + (j - i) * fraction`, where `fraction` is the
+              fractional part of the index surrounded by `i` and `j`.
+            * lower: `i`.
+            * higher: `j`.
+            * nearest: `i` or `j` whichever is nearest.
+            * midpoint: (`i` + `j`) / 2.
+        method : {'single', 'table'}, default 'single'
+            Whether to compute quantiles per-column ('single') or over all columns
+            ('table'). When 'table', the only allowed interpolation methods are
+            'nearest', 'lower', and 'higher'.
+
+        Returns
+        -------
+        Series or DataFrame
+
+            If ``q`` is an array, a DataFrame will be returned where the
+              index is ``q``, the columns are the columns of self, and the
+              values are the quantiles.
+            If ``q`` is a float, a Series will be returned where the
+              index is the columns of self and the values are the quantiles.
+
+        See Also
+        --------
+        core.window.rolling.Rolling.quantile: Rolling quantile.
+        numpy.percentile: Numpy function to compute the percentile.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame(np.array([[1, 1], [2, 10], [3, 100], [4, 100]]),
+        ...                   columns=['a', 'b'])
+        >>> df.quantile(.1)
+        a    1.3
+        b    3.7
+        Name: 0.1, dtype: float64
+        >>> df.quantile([.1, .5])
+               a     b
+        0.1  1.3   3.7
+        0.5  2.5  55.0
+
+        Specifying `method='table'` will compute the quantile over all columns.
+
+        >>> df.quantile(.1, method="table", interpolation="nearest")
+        a    1
+        b    1
+        Name: 0.1, dtype: int64
+        >>> df.quantile([.1, .5], method="table", interpolation="nearest")
+             a    b
+        0.1  1    1
+        0.5  3  100
+
+        Specifying `numeric_only=False` will also compute the quantile of
+        datetime and timedelta data.
+
+        >>> df = pd.DataFrame({'A': [1, 2],
+        ...                    'B': [pd.Timestamp('2010'),
+        ...                          pd.Timestamp('2011')],
+        ...                    'C': [pd.Timedelta('1 days'),
+        ...                          pd.Timedelta('2 days')]})
+        >>> df.quantile(0.5, numeric_only=False)
+        A                    1.5
+        B    2010-07-02 12:00:00
+        C        1 days 12:00:00
+        Name: 0.5, dtype: object
+        """
+        validate_percentile(q)
+        axis = self._get_axis_number(axis)
+
+        if not is_list_like(q):
+            # BlockManager.quantile expects listlike, so we wrap and unwrap here
+            # error: List item 0 has incompatible type "float | ExtensionArray |
+            # ndarray[Any, Any] | Index | Series | Sequence[float]"; expected "float"
+            res_df = self.quantile(
+                [q],  # type: ignore[list-item]
+                axis=axis,
+                numeric_only=numeric_only,
+                interpolation=interpolation,
+                method=method,
+            )
+            if method == "single":
+                res = res_df.iloc[0]
+            else:
+                # cannot directly iloc over sparse arrays
+                res = res_df.T.iloc[:, 0]
+            if axis == 1 and len(self) == 0:
+                # GH#41544 try to get an appropriate dtype
+                dtype = find_common_type(list(self.dtypes))
+                if needs_i8_conversion(dtype):
+                    return res.astype(dtype)
+            return res
+
+        q = Index(q, dtype=np.float64)
+        data = self._get_numeric_data() if numeric_only else self
+
+        if axis == 1:
+            data = data.T
+
+        if len(data.columns) == 0:
+            # GH#23925 _get_numeric_data may have dropped all columns
+            cols = Index([], name=self.columns.name)
+
+            dtype = np.float64
+            if axis == 1:
+                # GH#41544 try to get an appropriate dtype
+                cdtype = find_common_type(list(self.dtypes))
+                if needs_i8_conversion(cdtype):
+                    dtype = cdtype
+
+            res = self._constructor([], index=q, columns=cols, dtype=dtype)
+            return res.__finalize__(self, method="quantile")
+
+        valid_method = {"single", "table"}
+        if method not in valid_method:
+            raise ValueError(
+                f"Invalid method: {method}. Method must be in {valid_method}."
+            )
+        if method == "single":
+            res = data._mgr.quantile(qs=q, interpolation=interpolation)
+        elif method == "table":
+            valid_interpolation = {"nearest", "lower", "higher"}
+            if interpolation not in valid_interpolation:
+                raise ValueError(
+                    f"Invalid interpolation: {interpolation}. "
+                    f"Interpolation must be in {valid_interpolation}"
+                )
+            # handle degenerate case
+            if len(data) == 0:
+                if data.ndim == 2:
+                    dtype = find_common_type(list(self.dtypes))
+                else:
+                    dtype = self.dtype
+                return self._constructor([], index=q, columns=data.columns, dtype=dtype)
+
+            q_idx = np.quantile(np.arange(len(data)), q, method=interpolation)
+
+            by = data.columns
+            if len(by) > 1:
+                keys = [data._get_label_or_level_values(x) for x in by]
+                indexer = lexsort_indexer(keys)
+            else:
+                k = data._get_label_or_level_values(by[0])
+                indexer = nargsort(k)
+
+            res = data._mgr.take(indexer[q_idx], verify=False)
+            res.axes[1] = q
+
+        result = self._constructor_from_mgr(res, axes=res.axes)
+        return result.__finalize__(self, method="quantile")
+
+    def to_timestamp(
+        self,
+        freq: Frequency | None = None,
+        how: ToTimestampHow = "start",
+        axis: Axis = 0,
+        copy: bool | None = None,
+    ) -> DataFrame:
+        """
+        Cast to DatetimeIndex of timestamps, at *beginning* of period.
+
+        Parameters
+        ----------
+        freq : str, default frequency of PeriodIndex
+            Desired frequency.
+        how : {'s', 'e', 'start', 'end'}
+            Convention for converting period to timestamp; start of period
+            vs. end.
+        axis : {0 or 'index', 1 or 'columns'}, default 0
+            The axis to convert (the index by default).
+        copy : bool, default True
+            If False then underlying input data is not copied.
+
+            .. note::
+                The `copy` keyword will change behavior in pandas 3.0.
+                `Copy-on-Write
+                `__
+                will be enabled by default, which means that all methods with a
+                `copy` keyword will use a lazy copy mechanism to defer the copy and
+                ignore the `copy` keyword. The `copy` keyword will be removed in a
+                future version of pandas.
+
+                You can already get the future behavior and improvements through
+                enabling copy on write ``pd.options.mode.copy_on_write = True``
+
+        Returns
+        -------
+        DataFrame
+            The DataFrame has a DatetimeIndex.
+
+        Examples
+        --------
+        >>> idx = pd.PeriodIndex(['2023', '2024'], freq='Y')
+        >>> d = {'col1': [1, 2], 'col2': [3, 4]}
+        >>> df1 = pd.DataFrame(data=d, index=idx)
+        >>> df1
+              col1   col2
+        2023     1      3
+        2024	 2      4
+
+        The resulting timestamps will be at the beginning of the year in this case
+
+        >>> df1 = df1.to_timestamp()
+        >>> df1
+                    col1   col2
+        2023-01-01     1      3
+        2024-01-01     2      4
+        >>> df1.index
+        DatetimeIndex(['2023-01-01', '2024-01-01'], dtype='datetime64[ns]', freq=None)
+
+        Using `freq` which is the offset that the Timestamps will have
+
+        >>> df2 = pd.DataFrame(data=d, index=idx)
+        >>> df2 = df2.to_timestamp(freq='M')
+        >>> df2
+                    col1   col2
+        2023-01-31     1      3
+        2024-01-31     2      4
+        >>> df2.index
+        DatetimeIndex(['2023-01-31', '2024-01-31'], dtype='datetime64[ns]', freq=None)
+        """
+        new_obj = self.copy(deep=copy and not using_copy_on_write())
+
+        axis_name = self._get_axis_name(axis)
+        old_ax = getattr(self, axis_name)
+        if not isinstance(old_ax, PeriodIndex):
+            raise TypeError(f"unsupported Type {type(old_ax).__name__}")
+
+        new_ax = old_ax.to_timestamp(freq=freq, how=how)
+
+        setattr(new_obj, axis_name, new_ax)
+        return new_obj
+
+    def to_period(
+        self, freq: Frequency | None = None, axis: Axis = 0, copy: bool | None = None
+    ) -> DataFrame:
+        """
+        Convert DataFrame from DatetimeIndex to PeriodIndex.
+
+        Convert DataFrame from DatetimeIndex to PeriodIndex with desired
+        frequency (inferred from index if not passed).
+
+        Parameters
+        ----------
+        freq : str, default
+            Frequency of the PeriodIndex.
+        axis : {0 or 'index', 1 or 'columns'}, default 0
+            The axis to convert (the index by default).
+        copy : bool, default True
+            If False then underlying input data is not copied.
+
+            .. note::
+                The `copy` keyword will change behavior in pandas 3.0.
+                `Copy-on-Write
+                `__
+                will be enabled by default, which means that all methods with a
+                `copy` keyword will use a lazy copy mechanism to defer the copy and
+                ignore the `copy` keyword. The `copy` keyword will be removed in a
+                future version of pandas.
+
+                You can already get the future behavior and improvements through
+                enabling copy on write ``pd.options.mode.copy_on_write = True``
+
+        Returns
+        -------
+        DataFrame
+            The DataFrame has a PeriodIndex.
+
+        Examples
+        --------
+        >>> idx = pd.to_datetime(
+        ...     [
+        ...         "2001-03-31 00:00:00",
+        ...         "2002-05-31 00:00:00",
+        ...         "2003-08-31 00:00:00",
+        ...     ]
+        ... )
+
+        >>> idx
+        DatetimeIndex(['2001-03-31', '2002-05-31', '2003-08-31'],
+        dtype='datetime64[ns]', freq=None)
+
+        >>> idx.to_period("M")
+        PeriodIndex(['2001-03', '2002-05', '2003-08'], dtype='period[M]')
+
+        For the yearly frequency
+
+        >>> idx.to_period("Y")
+        PeriodIndex(['2001', '2002', '2003'], dtype='period[Y-DEC]')
+        """
+        new_obj = self.copy(deep=copy and not using_copy_on_write())
+
+        axis_name = self._get_axis_name(axis)
+        old_ax = getattr(self, axis_name)
+        if not isinstance(old_ax, DatetimeIndex):
+            raise TypeError(f"unsupported Type {type(old_ax).__name__}")
+
+        new_ax = old_ax.to_period(freq=freq)
+
+        setattr(new_obj, axis_name, new_ax)
+        return new_obj
+
+    def isin(self, values: Series | DataFrame | Sequence | Mapping) -> DataFrame:
+        """
+        Whether each element in the DataFrame is contained in values.
+
+        Parameters
+        ----------
+        values : iterable, Series, DataFrame or dict
+            The result will only be true at a location if all the
+            labels match. If `values` is a Series, that's the index. If
+            `values` is a dict, the keys must be the column names,
+            which must match. If `values` is a DataFrame,
+            then both the index and column labels must match.
+
+        Returns
+        -------
+        DataFrame
+            DataFrame of booleans showing whether each element in the DataFrame
+            is contained in values.
+
+        See Also
+        --------
+        DataFrame.eq: Equality test for DataFrame.
+        Series.isin: Equivalent method on Series.
+        Series.str.contains: Test if pattern or regex is contained within a
+            string of a Series or Index.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'num_legs': [2, 4], 'num_wings': [2, 0]},
+        ...                   index=['falcon', 'dog'])
+        >>> df
+                num_legs  num_wings
+        falcon         2          2
+        dog            4          0
+
+        When ``values`` is a list check whether every value in the DataFrame
+        is present in the list (which animals have 0 or 2 legs or wings)
+
+        >>> df.isin([0, 2])
+                num_legs  num_wings
+        falcon      True       True
+        dog        False       True
+
+        To check if ``values`` is *not* in the DataFrame, use the ``~`` operator:
+
+        >>> ~df.isin([0, 2])
+                num_legs  num_wings
+        falcon     False      False
+        dog         True      False
+
+        When ``values`` is a dict, we can pass values to check for each
+        column separately:
+
+        >>> df.isin({'num_wings': [0, 3]})
+                num_legs  num_wings
+        falcon     False      False
+        dog        False       True
+
+        When ``values`` is a Series or DataFrame the index and column must
+        match. Note that 'falcon' does not match based on the number of legs
+        in other.
+
+        >>> other = pd.DataFrame({'num_legs': [8, 3], 'num_wings': [0, 2]},
+        ...                      index=['spider', 'falcon'])
+        >>> df.isin(other)
+                num_legs  num_wings
+        falcon     False       True
+        dog        False      False
+        """
+        if isinstance(values, dict):
+            from pandas.core.reshape.concat import concat
+
+            values = collections.defaultdict(list, values)
+            result = concat(
+                (
+                    self.iloc[:, [i]].isin(values[col])
+                    for i, col in enumerate(self.columns)
+                ),
+                axis=1,
+            )
+        elif isinstance(values, Series):
+            if not values.index.is_unique:
+                raise ValueError("cannot compute isin with a duplicate axis.")
+            result = self.eq(values.reindex_like(self), axis="index")
+        elif isinstance(values, DataFrame):
+            if not (values.columns.is_unique and values.index.is_unique):
+                raise ValueError("cannot compute isin with a duplicate axis.")
+            result = self.eq(values.reindex_like(self))
+        else:
+            if not is_list_like(values):
+                raise TypeError(
+                    "only list-like or dict-like objects are allowed "
+                    "to be passed to DataFrame.isin(), "
+                    f"you passed a '{type(values).__name__}'"
+                )
+
+            def isin_(x):
+                # error: Argument 2 to "isin" has incompatible type "Union[Series,
+                # DataFrame, Sequence[Any], Mapping[Any, Any]]"; expected
+                # "Union[Union[Union[ExtensionArray, ndarray[Any, Any]], Index,
+                # Series], List[Any], range]"
+                result = algorithms.isin(
+                    x.ravel(),
+                    values,  # type: ignore[arg-type]
+                )
+                return result.reshape(x.shape)
+
+            res_mgr = self._mgr.apply(isin_)
+            result = self._constructor_from_mgr(
+                res_mgr,
+                axes=res_mgr.axes,
+            )
+        return result.__finalize__(self, method="isin")
+
+    # ----------------------------------------------------------------------
+    # Add index and columns
+    _AXIS_ORDERS: list[Literal["index", "columns"]] = ["index", "columns"]
+    _AXIS_TO_AXIS_NUMBER: dict[Axis, int] = {
+        **NDFrame._AXIS_TO_AXIS_NUMBER,
+        1: 1,
+        "columns": 1,
+    }
+    _AXIS_LEN = len(_AXIS_ORDERS)
+    _info_axis_number: Literal[1] = 1
+    _info_axis_name: Literal["columns"] = "columns"
+
+    index = properties.AxisProperty(
+        axis=1,
+        doc="""
+        The index (row labels) of the DataFrame.
+
+        The index of a DataFrame is a series of labels that identify each row.
+        The labels can be integers, strings, or any other hashable type. The index
+        is used for label-based access and alignment, and can be accessed or
+        modified using this attribute.
+
+        Returns
+        -------
+        pandas.Index
+            The index labels of the DataFrame.
+
+        See Also
+        --------
+        DataFrame.columns : The column labels of the DataFrame.
+        DataFrame.to_numpy : Convert the DataFrame to a NumPy array.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'Name': ['Alice', 'Bob', 'Aritra'],
+        ...                    'Age': [25, 30, 35],
+        ...                    'Location': ['Seattle', 'New York', 'Kona']},
+        ...                   index=([10, 20, 30]))
+        >>> df.index
+        Index([10, 20, 30], dtype='int64')
+
+        In this example, we create a DataFrame with 3 rows and 3 columns,
+        including Name, Age, and Location information. We set the index labels to
+        be the integers 10, 20, and 30. We then access the `index` attribute of the
+        DataFrame, which returns an `Index` object containing the index labels.
+
+        >>> df.index = [100, 200, 300]
+        >>> df
+            Name  Age Location
+        100  Alice   25  Seattle
+        200    Bob   30 New York
+        300  Aritra  35    Kona
+
+        In this example, we modify the index labels of the DataFrame by assigning
+        a new list of labels to the `index` attribute. The DataFrame is then
+        updated with the new labels, and the output shows the modified DataFrame.
+        """,
+    )
+    columns = properties.AxisProperty(
+        axis=0,
+        doc=dedent(
+            """
+                The column labels of the DataFrame.
+
+                Examples
+                --------
+                >>> df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
+                >>> df
+                     A  B
+                0    1  3
+                1    2  4
+                >>> df.columns
+                Index(['A', 'B'], dtype='object')
+                """
+        ),
+    )
+
+    # ----------------------------------------------------------------------
+    # Add plotting methods to DataFrame
+    plot = CachedAccessor("plot", pandas.plotting.PlotAccessor)
+    hist = pandas.plotting.hist_frame
+    boxplot = pandas.plotting.boxplot_frame
+    sparse = CachedAccessor("sparse", SparseFrameAccessor)
+
+    # ----------------------------------------------------------------------
+    # Internal Interface Methods
+
+    def _to_dict_of_blocks(self):
+        """
+        Return a dict of dtype -> Constructor Types that
+        each is a homogeneous dtype.
+
+        Internal ONLY - only works for BlockManager
+        """
+        mgr = self._mgr
+        # convert to BlockManager if needed -> this way support ArrayManager as well
+        mgr = cast(BlockManager, mgr_to_mgr(mgr, "block"))
+        return {
+            k: self._constructor_from_mgr(v, axes=v.axes).__finalize__(self)
+            for k, v, in mgr.to_dict().items()
+        }
+
+    @property
+    def values(self) -> np.ndarray:
+        """
+        Return a Numpy representation of the DataFrame.
+
+        .. warning::
+
+           We recommend using :meth:`DataFrame.to_numpy` instead.
+
+        Only the values in the DataFrame will be returned, the axes labels
+        will be removed.
+
+        Returns
+        -------
+        numpy.ndarray
+            The values of the DataFrame.
+
+        See Also
+        --------
+        DataFrame.to_numpy : Recommended alternative to this method.
+        DataFrame.index : Retrieve the index labels.
+        DataFrame.columns : Retrieving the column names.
+
+        Notes
+        -----
+        The dtype will be a lower-common-denominator dtype (implicit
+        upcasting); that is to say if the dtypes (even of numeric types)
+        are mixed, the one that accommodates all will be chosen. Use this
+        with care if you are not dealing with the blocks.
+
+        e.g. If the dtypes are float16 and float32, dtype will be upcast to
+        float32.  If dtypes are int32 and uint8, dtype will be upcast to
+        int32. By :func:`numpy.find_common_type` convention, mixing int64
+        and uint64 will result in a float64 dtype.
+
+        Examples
+        --------
+        A DataFrame where all columns are the same type (e.g., int64) results
+        in an array of the same type.
+
+        >>> df = pd.DataFrame({'age':    [ 3,  29],
+        ...                    'height': [94, 170],
+        ...                    'weight': [31, 115]})
+        >>> df
+           age  height  weight
+        0    3      94      31
+        1   29     170     115
+        >>> df.dtypes
+        age       int64
+        height    int64
+        weight    int64
+        dtype: object
+        >>> df.values
+        array([[  3,  94,  31],
+               [ 29, 170, 115]])
+
+        A DataFrame with mixed type columns(e.g., str/object, int64, float32)
+        results in an ndarray of the broadest type that accommodates these
+        mixed types (e.g., object).
+
+        >>> df2 = pd.DataFrame([('parrot',   24.0, 'second'),
+        ...                     ('lion',     80.5, 1),
+        ...                     ('monkey', np.nan, None)],
+        ...                   columns=('name', 'max_speed', 'rank'))
+        >>> df2.dtypes
+        name          object
+        max_speed    float64
+        rank          object
+        dtype: object
+        >>> df2.values
+        array([['parrot', 24.0, 'second'],
+               ['lion', 80.5, 1],
+               ['monkey', nan, None]], dtype=object)
+        """
+        return self._mgr.as_array()
+
+
+def _from_nested_dict(data) -> collections.defaultdict:
+    new_data: collections.defaultdict = collections.defaultdict(dict)
+    for index, s in data.items():
+        for col, v in s.items():
+            new_data[col][index] = v
+    return new_data
+
+
+def _reindex_for_setitem(
+    value: DataFrame | Series, index: Index
+) -> tuple[ArrayLike, BlockValuesRefs | None]:
+    # reindex if necessary
+
+    if value.index.equals(index) or not len(index):
+        if using_copy_on_write() and isinstance(value, Series):
+            return value._values, value._references
+        return value._values.copy(), None
+
+    # GH#4107
+    try:
+        reindexed_value = value.reindex(index)._values
+    except ValueError as err:
+        # raised in MultiIndex.from_tuples, see test_insert_error_msmgs
+        if not value.index.is_unique:
+            # duplicate axis
+            raise err
+
+        raise TypeError(
+            "incompatible index of inserted column with frame index"
+        ) from err
+    return reindexed_value, None
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/generic.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/generic.py
new file mode 100644
index 0000000000000000000000000000000000000000..dd8c1723be54a91bf87f72c72e20607452ad65db
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/generic.py
@@ -0,0 +1,14025 @@
+# pyright: reportPropertyTypeMismatch=false
+from __future__ import annotations
+
+import collections
+from copy import deepcopy
+import datetime as dt
+from functools import partial
+import gc
+from json import loads
+import operator
+import pickle
+import re
+import sys
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    ClassVar,
+    Literal,
+    NoReturn,
+    cast,
+    final,
+    overload,
+)
+import warnings
+import weakref
+
+import numpy as np
+
+from pandas._config import (
+    config,
+    using_copy_on_write,
+    warn_copy_on_write,
+)
+
+from pandas._libs import lib
+from pandas._libs.lib import is_range_indexer
+from pandas._libs.tslibs import (
+    Period,
+    Tick,
+    Timestamp,
+    to_offset,
+)
+from pandas._libs.tslibs.dtypes import freq_to_period_freqstr
+from pandas._typing import (
+    AlignJoin,
+    AnyArrayLike,
+    ArrayLike,
+    Axes,
+    Axis,
+    AxisInt,
+    CompressionOptions,
+    DtypeArg,
+    DtypeBackend,
+    DtypeObj,
+    FilePath,
+    FillnaOptions,
+    FloatFormatType,
+    FormattersType,
+    Frequency,
+    IgnoreRaise,
+    IndexKeyFunc,
+    IndexLabel,
+    InterpolateOptions,
+    IntervalClosedType,
+    JSONSerializable,
+    Level,
+    Manager,
+    NaPosition,
+    NDFrameT,
+    OpenFileErrors,
+    RandomState,
+    ReindexMethod,
+    Renamer,
+    Scalar,
+    Self,
+    SequenceNotStr,
+    SortKind,
+    StorageOptions,
+    Suffixes,
+    T,
+    TimeAmbiguous,
+    TimedeltaConvertibleTypes,
+    TimeNonexistent,
+    TimestampConvertibleTypes,
+    TimeUnit,
+    ValueKeyFunc,
+    WriteBuffer,
+    WriteExcelBuffer,
+    npt,
+)
+from pandas.compat import PYPY
+from pandas.compat._constants import (
+    REF_COUNT,
+    WARNING_CHECK_DISABLED,
+)
+from pandas.compat._optional import import_optional_dependency
+from pandas.compat.numpy import function as nv
+from pandas.errors import (
+    AbstractMethodError,
+    ChainedAssignmentError,
+    InvalidIndexError,
+    SettingWithCopyError,
+    SettingWithCopyWarning,
+    _chained_assignment_method_msg,
+    _chained_assignment_warning_method_msg,
+    _check_cacher,
+)
+from pandas.util._decorators import (
+    deprecate_nonkeyword_arguments,
+    doc,
+)
+from pandas.util._exceptions import find_stack_level
+from pandas.util._validators import (
+    check_dtype_backend,
+    validate_ascending,
+    validate_bool_kwarg,
+    validate_fillna_kwargs,
+    validate_inclusive,
+)
+
+from pandas.core.dtypes.astype import astype_is_view
+from pandas.core.dtypes.common import (
+    ensure_object,
+    ensure_platform_int,
+    ensure_str,
+    is_bool,
+    is_bool_dtype,
+    is_dict_like,
+    is_extension_array_dtype,
+    is_list_like,
+    is_number,
+    is_numeric_dtype,
+    is_re_compilable,
+    is_scalar,
+    pandas_dtype,
+)
+from pandas.core.dtypes.dtypes import (
+    DatetimeTZDtype,
+    ExtensionDtype,
+)
+from pandas.core.dtypes.generic import (
+    ABCDataFrame,
+    ABCSeries,
+)
+from pandas.core.dtypes.inference import (
+    is_hashable,
+    is_nested_list_like,
+)
+from pandas.core.dtypes.missing import (
+    isna,
+    notna,
+)
+
+from pandas.core import (
+    algorithms as algos,
+    arraylike,
+    common,
+    indexing,
+    missing,
+    nanops,
+    sample,
+)
+from pandas.core.array_algos.replace import should_use_regex
+from pandas.core.arrays import ExtensionArray
+from pandas.core.base import PandasObject
+from pandas.core.construction import extract_array
+from pandas.core.flags import Flags
+from pandas.core.indexes.api import (
+    DatetimeIndex,
+    Index,
+    MultiIndex,
+    PeriodIndex,
+    RangeIndex,
+    default_index,
+    ensure_index,
+)
+from pandas.core.internals import (
+    ArrayManager,
+    BlockManager,
+    SingleArrayManager,
+)
+from pandas.core.internals.construction import (
+    mgr_to_mgr,
+    ndarray_to_mgr,
+)
+from pandas.core.methods.describe import describe_ndframe
+from pandas.core.missing import (
+    clean_fill_method,
+    clean_reindex_fill_method,
+    find_valid_index,
+)
+from pandas.core.reshape.concat import concat
+from pandas.core.shared_docs import _shared_docs
+from pandas.core.sorting import get_indexer_indexer
+from pandas.core.window import (
+    Expanding,
+    ExponentialMovingWindow,
+    Rolling,
+    Window,
+)
+
+from pandas.io.formats.format import (
+    DataFrameFormatter,
+    DataFrameRenderer,
+)
+from pandas.io.formats.printing import pprint_thing
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Hashable,
+        Iterator,
+        Mapping,
+        Sequence,
+    )
+
+    from pandas._libs.tslibs import BaseOffset
+
+    from pandas import (
+        DataFrame,
+        ExcelWriter,
+        HDFStore,
+        Series,
+    )
+    from pandas.core.indexers.objects import BaseIndexer
+    from pandas.core.resample import Resampler
+
+# goal is to be able to define the docs close to function, while still being
+# able to share
+_shared_docs = {**_shared_docs}
+_shared_doc_kwargs = {
+    "axes": "keywords for axes",
+    "klass": "Series/DataFrame",
+    "axes_single_arg": "{0 or 'index'} for Series, {0 or 'index', 1 or 'columns'} for DataFrame",  # noqa: E501
+    "inplace": """
+    inplace : bool, default False
+        If True, performs operation inplace and returns None.""",
+    "optional_by": """
+        by : str or list of str
+            Name or list of names to sort by""",
+}
+
+
+bool_t = bool  # Need alias because NDFrame has def bool:
+
+
+class NDFrame(PandasObject, indexing.IndexingMixin):
+    """
+    N-dimensional analogue of DataFrame. Store multi-dimensional in a
+    size-mutable, labeled data structure
+
+    Parameters
+    ----------
+    data : BlockManager
+    axes : list
+    copy : bool, default False
+    """
+
+    _internal_names: list[str] = [
+        "_mgr",
+        "_cacher",
+        "_item_cache",
+        "_cache",
+        "_is_copy",
+        "_name",
+        "_metadata",
+        "_flags",
+    ]
+    _internal_names_set: set[str] = set(_internal_names)
+    _accessors: set[str] = set()
+    _hidden_attrs: frozenset[str] = frozenset([])
+    _metadata: list[str] = []
+    _is_copy: weakref.ReferenceType[NDFrame] | str | None = None
+    _mgr: Manager
+    _attrs: dict[Hashable, Any]
+    _typ: str
+
+    # ----------------------------------------------------------------------
+    # Constructors
+
+    def __init__(self, data: Manager) -> None:
+        object.__setattr__(self, "_is_copy", None)
+        object.__setattr__(self, "_mgr", data)
+        object.__setattr__(self, "_item_cache", {})
+        object.__setattr__(self, "_attrs", {})
+        object.__setattr__(self, "_flags", Flags(self, allows_duplicate_labels=True))
+
+    @final
+    @classmethod
+    def _init_mgr(
+        cls,
+        mgr: Manager,
+        axes: dict[Literal["index", "columns"], Axes | None],
+        dtype: DtypeObj | None = None,
+        copy: bool_t = False,
+    ) -> Manager:
+        """passed a manager and a axes dict"""
+        for a, axe in axes.items():
+            if axe is not None:
+                axe = ensure_index(axe)
+                bm_axis = cls._get_block_manager_axis(a)
+                mgr = mgr.reindex_axis(axe, axis=bm_axis)
+
+        # make a copy if explicitly requested
+        if copy:
+            mgr = mgr.copy()
+        if dtype is not None:
+            # avoid further copies if we can
+            if (
+                isinstance(mgr, BlockManager)
+                and len(mgr.blocks) == 1
+                and mgr.blocks[0].values.dtype == dtype
+            ):
+                pass
+            else:
+                mgr = mgr.astype(dtype=dtype)
+        return mgr
+
+    @final
+    def _as_manager(self, typ: str, copy: bool_t = True) -> Self:
+        """
+        Private helper function to create a DataFrame with specific manager.
+
+        Parameters
+        ----------
+        typ : {"block", "array"}
+        copy : bool, default True
+            Only controls whether the conversion from Block->ArrayManager
+            copies the 1D arrays (to ensure proper/contiguous memory layout).
+
+        Returns
+        -------
+        DataFrame
+            New DataFrame using specified manager type. Is not guaranteed
+            to be a copy or not.
+        """
+        new_mgr: Manager
+        new_mgr = mgr_to_mgr(self._mgr, typ=typ, copy=copy)
+        # fastpath of passing a manager doesn't check the option/manager class
+        return self._constructor_from_mgr(new_mgr, axes=new_mgr.axes).__finalize__(self)
+
+    @final
+    @classmethod
+    def _from_mgr(cls, mgr: Manager, axes: list[Index]) -> Self:
+        """
+        Construct a new object of this type from a Manager object and axes.
+
+        Parameters
+        ----------
+        mgr : Manager
+            Must have the same ndim as cls.
+        axes : list[Index]
+
+        Notes
+        -----
+        The axes must match mgr.axes, but are required for future-proofing
+        in the event that axes are refactored out of the Manager objects.
+        """
+        obj = cls.__new__(cls)
+        NDFrame.__init__(obj, mgr)
+        return obj
+
+    # ----------------------------------------------------------------------
+    # attrs and flags
+
+    @property
+    def attrs(self) -> dict[Hashable, Any]:
+        """
+        Dictionary of global attributes of this dataset.
+
+        .. warning::
+
+           attrs is experimental and may change without warning.
+
+        See Also
+        --------
+        DataFrame.flags : Global flags applying to this object.
+
+        Notes
+        -----
+        Many operations that create new datasets will copy ``attrs``. Copies
+        are always deep so that changing ``attrs`` will only affect the
+        present dataset. ``pandas.concat`` copies ``attrs`` only if all input
+        datasets have the same ``attrs``.
+
+        Examples
+        --------
+        For Series:
+
+        >>> ser = pd.Series([1, 2, 3])
+        >>> ser.attrs = {"A": [10, 20, 30]}
+        >>> ser.attrs
+        {'A': [10, 20, 30]}
+
+        For DataFrame:
+
+        >>> df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
+        >>> df.attrs = {"A": [10, 20, 30]}
+        >>> df.attrs
+        {'A': [10, 20, 30]}
+        """
+        return self._attrs
+
+    @attrs.setter
+    def attrs(self, value: Mapping[Hashable, Any]) -> None:
+        self._attrs = dict(value)
+
+    @final
+    @property
+    def flags(self) -> Flags:
+        """
+        Get the properties associated with this pandas object.
+
+        The available flags are
+
+        * :attr:`Flags.allows_duplicate_labels`
+
+        See Also
+        --------
+        Flags : Flags that apply to pandas objects.
+        DataFrame.attrs : Global metadata applying to this dataset.
+
+        Notes
+        -----
+        "Flags" differ from "metadata". Flags reflect properties of the
+        pandas object (the Series or DataFrame). Metadata refer to properties
+        of the dataset, and should be stored in :attr:`DataFrame.attrs`.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({"A": [1, 2]})
+        >>> df.flags
+        
+
+        Flags can be get or set using ``.``
+
+        >>> df.flags.allows_duplicate_labels
+        True
+        >>> df.flags.allows_duplicate_labels = False
+
+        Or by slicing with a key
+
+        >>> df.flags["allows_duplicate_labels"]
+        False
+        >>> df.flags["allows_duplicate_labels"] = True
+        """
+        return self._flags
+
+    @final
+    def set_flags(
+        self,
+        *,
+        copy: bool_t = False,
+        allows_duplicate_labels: bool_t | None = None,
+    ) -> Self:
+        """
+        Return a new object with updated flags.
+
+        Parameters
+        ----------
+        copy : bool, default False
+            Specify if a copy of the object should be made.
+
+            .. note::
+                The `copy` keyword will change behavior in pandas 3.0.
+                `Copy-on-Write
+                `__
+                will be enabled by default, which means that all methods with a
+                `copy` keyword will use a lazy copy mechanism to defer the copy and
+                ignore the `copy` keyword. The `copy` keyword will be removed in a
+                future version of pandas.
+
+                You can already get the future behavior and improvements through
+                enabling copy on write ``pd.options.mode.copy_on_write = True``
+        allows_duplicate_labels : bool, optional
+            Whether the returned object allows duplicate labels.
+
+        Returns
+        -------
+        Series or DataFrame
+            The same type as the caller.
+
+        See Also
+        --------
+        DataFrame.attrs : Global metadata applying to this dataset.
+        DataFrame.flags : Global flags applying to this object.
+
+        Notes
+        -----
+        This method returns a new object that's a view on the same data
+        as the input. Mutating the input or the output values will be reflected
+        in the other.
+
+        This method is intended to be used in method chains.
+
+        "Flags" differ from "metadata". Flags reflect properties of the
+        pandas object (the Series or DataFrame). Metadata refer to properties
+        of the dataset, and should be stored in :attr:`DataFrame.attrs`.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({"A": [1, 2]})
+        >>> df.flags.allows_duplicate_labels
+        True
+        >>> df2 = df.set_flags(allows_duplicate_labels=False)
+        >>> df2.flags.allows_duplicate_labels
+        False
+        """
+        df = self.copy(deep=copy and not using_copy_on_write())
+        if allows_duplicate_labels is not None:
+            df.flags["allows_duplicate_labels"] = allows_duplicate_labels
+        return df
+
+    @final
+    @classmethod
+    def _validate_dtype(cls, dtype) -> DtypeObj | None:
+        """validate the passed dtype"""
+        if dtype is not None:
+            dtype = pandas_dtype(dtype)
+
+            # a compound dtype
+            if dtype.kind == "V":
+                raise NotImplementedError(
+                    "compound dtypes are not implemented "
+                    f"in the {cls.__name__} constructor"
+                )
+
+        return dtype
+
+    # ----------------------------------------------------------------------
+    # Construction
+
+    @property
+    def _constructor(self) -> Callable[..., Self]:
+        """
+        Used when a manipulation result has the same dimensions as the
+        original.
+        """
+        raise AbstractMethodError(self)
+
+    # ----------------------------------------------------------------------
+    # Internals
+
+    @final
+    @property
+    def _data(self):
+        # GH#33054 retained because some downstream packages uses this,
+        #  e.g. fastparquet
+        # GH#33333
+        warnings.warn(
+            f"{type(self).__name__}._data is deprecated and will be removed in "
+            "a future version. Use public APIs instead.",
+            DeprecationWarning,
+            stacklevel=find_stack_level(),
+        )
+        return self._mgr
+
+    # ----------------------------------------------------------------------
+    # Axis
+    _AXIS_ORDERS: list[Literal["index", "columns"]]
+    _AXIS_TO_AXIS_NUMBER: dict[Axis, AxisInt] = {0: 0, "index": 0, "rows": 0}
+    _info_axis_number: int
+    _info_axis_name: Literal["index", "columns"]
+    _AXIS_LEN: int
+
+    @final
+    def _construct_axes_dict(self, axes: Sequence[Axis] | None = None, **kwargs):
+        """Return an axes dictionary for myself."""
+        d = {a: self._get_axis(a) for a in (axes or self._AXIS_ORDERS)}
+        # error: Argument 1 to "update" of "MutableMapping" has incompatible type
+        # "Dict[str, Any]"; expected "SupportsKeysAndGetItem[Union[int, str], Any]"
+        d.update(kwargs)  # type: ignore[arg-type]
+        return d
+
+    @final
+    @classmethod
+    def _get_axis_number(cls, axis: Axis) -> AxisInt:
+        try:
+            return cls._AXIS_TO_AXIS_NUMBER[axis]
+        except KeyError:
+            raise ValueError(f"No axis named {axis} for object type {cls.__name__}")
+
+    @final
+    @classmethod
+    def _get_axis_name(cls, axis: Axis) -> Literal["index", "columns"]:
+        axis_number = cls._get_axis_number(axis)
+        return cls._AXIS_ORDERS[axis_number]
+
+    @final
+    def _get_axis(self, axis: Axis) -> Index:
+        axis_number = self._get_axis_number(axis)
+        assert axis_number in {0, 1}
+        return self.index if axis_number == 0 else self.columns
+
+    @final
+    @classmethod
+    def _get_block_manager_axis(cls, axis: Axis) -> AxisInt:
+        """Map the axis to the block_manager axis."""
+        axis = cls._get_axis_number(axis)
+        ndim = cls._AXIS_LEN
+        if ndim == 2:
+            # i.e. DataFrame
+            return 1 - axis
+        return axis
+
+    @final
+    def _get_axis_resolvers(self, axis: str) -> dict[str, Series | MultiIndex]:
+        # index or columns
+        axis_index = getattr(self, axis)
+        d = {}
+        prefix = axis[0]
+
+        for i, name in enumerate(axis_index.names):
+            if name is not None:
+                key = level = name
+            else:
+                # prefix with 'i' or 'c' depending on the input axis
+                # e.g., you must do ilevel_0 for the 0th level of an unnamed
+                # multiiindex
+                key = f"{prefix}level_{i}"
+                level = i
+
+            level_values = axis_index.get_level_values(level)
+            s = level_values.to_series()
+            s.index = axis_index
+            d[key] = s
+
+        # put the index/columns itself in the dict
+        if isinstance(axis_index, MultiIndex):
+            dindex = axis_index
+        else:
+            dindex = axis_index.to_series()
+
+        d[axis] = dindex
+        return d
+
+    @final
+    def _get_index_resolvers(self) -> dict[Hashable, Series | MultiIndex]:
+        from pandas.core.computation.parsing import clean_column_name
+
+        d: dict[str, Series | MultiIndex] = {}
+        for axis_name in self._AXIS_ORDERS:
+            d.update(self._get_axis_resolvers(axis_name))
+
+        return {clean_column_name(k): v for k, v in d.items() if not isinstance(k, int)}
+
+    @final
+    def _get_cleaned_column_resolvers(self) -> dict[Hashable, Series]:
+        """
+        Return the special character free column resolvers of a dataframe.
+
+        Column names with special characters are 'cleaned up' so that they can
+        be referred to by backtick quoting.
+        Used in :meth:`DataFrame.eval`.
+        """
+        from pandas.core.computation.parsing import clean_column_name
+        from pandas.core.series import Series
+
+        if isinstance(self, ABCSeries):
+            return {clean_column_name(self.name): self}
+
+        return {
+            clean_column_name(k): Series(
+                v, copy=False, index=self.index, name=k, dtype=self.dtypes[k]
+            ).__finalize__(self)
+            for k, v in zip(self.columns, self._iter_column_arrays())
+            if not isinstance(k, int)
+        }
+
+    @final
+    @property
+    def _info_axis(self) -> Index:
+        return getattr(self, self._info_axis_name)
+
+    def _is_view_after_cow_rules(self):
+        # Only to be used in cases of chained assignment checks, this is a
+        # simplified check that assumes that either the whole object is a view
+        # or a copy
+        if len(self._mgr.blocks) == 0:  # type: ignore[union-attr]
+            return False
+        return self._mgr.blocks[0].refs.has_reference()  # type: ignore[union-attr]
+
+    @property
+    def shape(self) -> tuple[int, ...]:
+        """
+        Return a tuple of axis dimensions
+        """
+        return tuple(len(self._get_axis(a)) for a in self._AXIS_ORDERS)
+
+    @property
+    def axes(self) -> list[Index]:
+        """
+        Return index label(s) of the internal NDFrame
+        """
+        # we do it this way because if we have reversed axes, then
+        # the block manager shows then reversed
+        return [self._get_axis(a) for a in self._AXIS_ORDERS]
+
+    @final
+    @property
+    def ndim(self) -> int:
+        """
+        Return an int representing the number of axes / array dimensions.
+
+        Return 1 if Series. Otherwise return 2 if DataFrame.
+
+        See Also
+        --------
+        ndarray.ndim : Number of array dimensions.
+
+        Examples
+        --------
+        >>> s = pd.Series({'a': 1, 'b': 2, 'c': 3})
+        >>> s.ndim
+        1
+
+        >>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
+        >>> df.ndim
+        2
+        """
+        return self._mgr.ndim
+
+    @final
+    @property
+    def size(self) -> int:
+        """
+        Return an int representing the number of elements in this object.
+
+        Return the number of rows if Series. Otherwise return the number of
+        rows times number of columns if DataFrame.
+
+        See Also
+        --------
+        ndarray.size : Number of elements in the array.
+
+        Examples
+        --------
+        >>> s = pd.Series({'a': 1, 'b': 2, 'c': 3})
+        >>> s.size
+        3
+
+        >>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
+        >>> df.size
+        4
+        """
+
+        return int(np.prod(self.shape))
+
+    def set_axis(
+        self,
+        labels,
+        *,
+        axis: Axis = 0,
+        copy: bool_t | None = None,
+    ) -> Self:
+        """
+        Assign desired index to given axis.
+
+        Indexes for%(extended_summary_sub)s row labels can be changed by assigning
+        a list-like or Index.
+
+        Parameters
+        ----------
+        labels : list-like, Index
+            The values for the new index.
+
+        axis : %(axes_single_arg)s, default 0
+            The axis to update. The value 0 identifies the rows. For `Series`
+            this parameter is unused and defaults to 0.
+
+        copy : bool, default True
+            Whether to make a copy of the underlying data.
+
+            .. note::
+                The `copy` keyword will change behavior in pandas 3.0.
+                `Copy-on-Write
+                `__
+                will be enabled by default, which means that all methods with a
+                `copy` keyword will use a lazy copy mechanism to defer the copy and
+                ignore the `copy` keyword. The `copy` keyword will be removed in a
+                future version of pandas.
+
+                You can already get the future behavior and improvements through
+                enabling copy on write ``pd.options.mode.copy_on_write = True``
+
+        Returns
+        -------
+        %(klass)s
+            An object of type %(klass)s.
+
+        See Also
+        --------
+        %(klass)s.rename_axis : Alter the name of the index%(see_also_sub)s.
+        """
+        return self._set_axis_nocheck(labels, axis, inplace=False, copy=copy)
+
+    @final
+    def _set_axis_nocheck(
+        self, labels, axis: Axis, inplace: bool_t, copy: bool_t | None
+    ):
+        if inplace:
+            setattr(self, self._get_axis_name(axis), labels)
+        else:
+            # With copy=False, we create a new object but don't copy the
+            #  underlying data.
+            obj = self.copy(deep=copy and not using_copy_on_write())
+            setattr(obj, obj._get_axis_name(axis), labels)
+            return obj
+
+    @final
+    def _set_axis(self, axis: AxisInt, labels: AnyArrayLike | list) -> None:
+        """
+        This is called from the cython code when we set the `index` attribute
+        directly, e.g. `series.index = [1, 2, 3]`.
+        """
+        labels = ensure_index(labels)
+        self._mgr.set_axis(axis, labels)
+        self._clear_item_cache()
+
+    @final
+    def swapaxes(self, axis1: Axis, axis2: Axis, copy: bool_t | None = None) -> Self:
+        """
+        Interchange axes and swap values axes appropriately.
+
+        .. deprecated:: 2.1.0
+            ``swapaxes`` is deprecated and will be removed.
+            Please use ``transpose`` instead.
+
+        Returns
+        -------
+        same as input
+
+        Examples
+        --------
+        Please see examples for :meth:`DataFrame.transpose`.
+        """
+        warnings.warn(
+            # GH#51946
+            f"'{type(self).__name__}.swapaxes' is deprecated and "
+            "will be removed in a future version. "
+            f"Please use '{type(self).__name__}.transpose' instead.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+
+        i = self._get_axis_number(axis1)
+        j = self._get_axis_number(axis2)
+
+        if i == j:
+            return self.copy(deep=copy and not using_copy_on_write())
+
+        mapping = {i: j, j: i}
+
+        new_axes = [self._get_axis(mapping.get(k, k)) for k in range(self._AXIS_LEN)]
+        new_values = self._values.swapaxes(i, j)  # type: ignore[union-attr]
+        if self._mgr.is_single_block and isinstance(self._mgr, BlockManager):
+            # This should only get hit in case of having a single block, otherwise a
+            # copy is made, we don't have to set up references.
+            new_mgr = ndarray_to_mgr(
+                new_values,
+                new_axes[0],
+                new_axes[1],
+                dtype=None,
+                copy=False,
+                typ="block",
+            )
+            assert isinstance(new_mgr, BlockManager)
+            assert isinstance(self._mgr, BlockManager)
+            new_mgr.blocks[0].refs = self._mgr.blocks[0].refs
+            new_mgr.blocks[0].refs.add_reference(new_mgr.blocks[0])
+            if not using_copy_on_write() and copy is not False:
+                new_mgr = new_mgr.copy(deep=True)
+
+            out = self._constructor_from_mgr(new_mgr, axes=new_mgr.axes)
+            return out.__finalize__(self, method="swapaxes")
+
+        return self._constructor(
+            new_values,
+            *new_axes,
+            # The no-copy case for CoW is handled above
+            copy=False,
+        ).__finalize__(self, method="swapaxes")
+
+    @final
+    @doc(klass=_shared_doc_kwargs["klass"])
+    def droplevel(self, level: IndexLabel, axis: Axis = 0) -> Self:
+        """
+        Return {klass} with requested index / column level(s) removed.
+
+        Parameters
+        ----------
+        level : int, str, or list-like
+            If a string is given, must be the name of a level
+            If list-like, elements must be names or positional indexes
+            of levels.
+
+        axis : {{0 or 'index', 1 or 'columns'}}, default 0
+            Axis along which the level(s) is removed:
+
+            * 0 or 'index': remove level(s) in column.
+            * 1 or 'columns': remove level(s) in row.
+
+            For `Series` this parameter is unused and defaults to 0.
+
+        Returns
+        -------
+        {klass}
+            {klass} with requested index / column level(s) removed.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([
+        ...     [1, 2, 3, 4],
+        ...     [5, 6, 7, 8],
+        ...     [9, 10, 11, 12]
+        ... ]).set_index([0, 1]).rename_axis(['a', 'b'])
+
+        >>> df.columns = pd.MultiIndex.from_tuples([
+        ...     ('c', 'e'), ('d', 'f')
+        ... ], names=['level_1', 'level_2'])
+
+        >>> df
+        level_1   c   d
+        level_2   e   f
+        a b
+        1 2      3   4
+        5 6      7   8
+        9 10    11  12
+
+        >>> df.droplevel('a')
+        level_1   c   d
+        level_2   e   f
+        b
+        2        3   4
+        6        7   8
+        10      11  12
+
+        >>> df.droplevel('level_2', axis=1)
+        level_1   c   d
+        a b
+        1 2      3   4
+        5 6      7   8
+        9 10    11  12
+        """
+        labels = self._get_axis(axis)
+        new_labels = labels.droplevel(level)
+        return self.set_axis(new_labels, axis=axis, copy=None)
+
+    def pop(self, item: Hashable) -> Series | Any:
+        result = self[item]
+        del self[item]
+
+        return result
+
+    @final
+    def squeeze(self, axis: Axis | None = None):
+        """
+        Squeeze 1 dimensional axis objects into scalars.
+
+        Series or DataFrames with a single element are squeezed to a scalar.
+        DataFrames with a single column or a single row are squeezed to a
+        Series. Otherwise the object is unchanged.
+
+        This method is most useful when you don't know if your
+        object is a Series or DataFrame, but you do know it has just a single
+        column. In that case you can safely call `squeeze` to ensure you have a
+        Series.
+
+        Parameters
+        ----------
+        axis : {0 or 'index', 1 or 'columns', None}, default None
+            A specific axis to squeeze. By default, all length-1 axes are
+            squeezed. For `Series` this parameter is unused and defaults to `None`.
+
+        Returns
+        -------
+        DataFrame, Series, or scalar
+            The projection after squeezing `axis` or all the axes.
+
+        See Also
+        --------
+        Series.iloc : Integer-location based indexing for selecting scalars.
+        DataFrame.iloc : Integer-location based indexing for selecting Series.
+        Series.to_frame : Inverse of DataFrame.squeeze for a
+            single-column DataFrame.
+
+        Examples
+        --------
+        >>> primes = pd.Series([2, 3, 5, 7])
+
+        Slicing might produce a Series with a single value:
+
+        >>> even_primes = primes[primes % 2 == 0]
+        >>> even_primes
+        0    2
+        dtype: int64
+
+        >>> even_primes.squeeze()
+        2
+
+        Squeezing objects with more than one value in every axis does nothing:
+
+        >>> odd_primes = primes[primes % 2 == 1]
+        >>> odd_primes
+        1    3
+        2    5
+        3    7
+        dtype: int64
+
+        >>> odd_primes.squeeze()
+        1    3
+        2    5
+        3    7
+        dtype: int64
+
+        Squeezing is even more effective when used with DataFrames.
+
+        >>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['a', 'b'])
+        >>> df
+           a  b
+        0  1  2
+        1  3  4
+
+        Slicing a single column will produce a DataFrame with the columns
+        having only one value:
+
+        >>> df_a = df[['a']]
+        >>> df_a
+           a
+        0  1
+        1  3
+
+        So the columns can be squeezed down, resulting in a Series:
+
+        >>> df_a.squeeze('columns')
+        0    1
+        1    3
+        Name: a, dtype: int64
+
+        Slicing a single row from a single column will produce a single
+        scalar DataFrame:
+
+        >>> df_0a = df.loc[df.index < 1, ['a']]
+        >>> df_0a
+           a
+        0  1
+
+        Squeezing the rows produces a single scalar Series:
+
+        >>> df_0a.squeeze('rows')
+        a    1
+        Name: 0, dtype: int64
+
+        Squeezing all axes will project directly into a scalar:
+
+        >>> df_0a.squeeze()
+        1
+        """
+        axes = range(self._AXIS_LEN) if axis is None else (self._get_axis_number(axis),)
+        result = self.iloc[
+            tuple(
+                0 if i in axes and len(a) == 1 else slice(None)
+                for i, a in enumerate(self.axes)
+            )
+        ]
+        if isinstance(result, NDFrame):
+            result = result.__finalize__(self, method="squeeze")
+        return result
+
+    # ----------------------------------------------------------------------
+    # Rename
+
+    @final
+    def _rename(
+        self,
+        mapper: Renamer | None = None,
+        *,
+        index: Renamer | None = None,
+        columns: Renamer | None = None,
+        axis: Axis | None = None,
+        copy: bool_t | None = None,
+        inplace: bool_t = False,
+        level: Level | None = None,
+        errors: str = "ignore",
+    ) -> Self | None:
+        # called by Series.rename and DataFrame.rename
+
+        if mapper is None and index is None and columns is None:
+            raise TypeError("must pass an index to rename")
+
+        if index is not None or columns is not None:
+            if axis is not None:
+                raise TypeError(
+                    "Cannot specify both 'axis' and any of 'index' or 'columns'"
+                )
+            if mapper is not None:
+                raise TypeError(
+                    "Cannot specify both 'mapper' and any of 'index' or 'columns'"
+                )
+        else:
+            # use the mapper argument
+            if axis and self._get_axis_number(axis) == 1:
+                columns = mapper
+            else:
+                index = mapper
+
+        self._check_inplace_and_allows_duplicate_labels(inplace)
+        result = self if inplace else self.copy(deep=copy and not using_copy_on_write())
+
+        for axis_no, replacements in enumerate((index, columns)):
+            if replacements is None:
+                continue
+
+            ax = self._get_axis(axis_no)
+            f = common.get_rename_function(replacements)
+
+            if level is not None:
+                level = ax._get_level_number(level)
+
+            # GH 13473
+            if not callable(replacements):
+                if ax._is_multi and level is not None:
+                    indexer = ax.get_level_values(level).get_indexer_for(replacements)
+                else:
+                    indexer = ax.get_indexer_for(replacements)
+
+                if errors == "raise" and len(indexer[indexer == -1]):
+                    missing_labels = [
+                        label
+                        for index, label in enumerate(replacements)
+                        if indexer[index] == -1
+                    ]
+                    raise KeyError(f"{missing_labels} not found in axis")
+
+            new_index = ax._transform_index(f, level=level)
+            result._set_axis_nocheck(new_index, axis=axis_no, inplace=True, copy=False)
+            result._clear_item_cache()
+
+        if inplace:
+            self._update_inplace(result)
+            return None
+        else:
+            return result.__finalize__(self, method="rename")
+
+    @overload
+    def rename_axis(
+        self,
+        mapper: IndexLabel | lib.NoDefault = ...,
+        *,
+        index=...,
+        columns=...,
+        axis: Axis = ...,
+        copy: bool_t | None = ...,
+        inplace: Literal[False] = ...,
+    ) -> Self:
+        ...
+
+    @overload
+    def rename_axis(
+        self,
+        mapper: IndexLabel | lib.NoDefault = ...,
+        *,
+        index=...,
+        columns=...,
+        axis: Axis = ...,
+        copy: bool_t | None = ...,
+        inplace: Literal[True],
+    ) -> None:
+        ...
+
+    @overload
+    def rename_axis(
+        self,
+        mapper: IndexLabel | lib.NoDefault = ...,
+        *,
+        index=...,
+        columns=...,
+        axis: Axis = ...,
+        copy: bool_t | None = ...,
+        inplace: bool_t = ...,
+    ) -> Self | None:
+        ...
+
+    def rename_axis(
+        self,
+        mapper: IndexLabel | lib.NoDefault = lib.no_default,
+        *,
+        index=lib.no_default,
+        columns=lib.no_default,
+        axis: Axis = 0,
+        copy: bool_t | None = None,
+        inplace: bool_t = False,
+    ) -> Self | None:
+        """
+        Set the name of the axis for the index or columns.
+
+        Parameters
+        ----------
+        mapper : scalar, list-like, optional
+            Value to set the axis name attribute.
+        index, columns : scalar, list-like, dict-like or function, optional
+            A scalar, list-like, dict-like or functions transformations to
+            apply to that axis' values.
+            Note that the ``columns`` parameter is not allowed if the
+            object is a Series. This parameter only apply for DataFrame
+            type objects.
+
+            Use either ``mapper`` and ``axis`` to
+            specify the axis to target with ``mapper``, or ``index``
+            and/or ``columns``.
+        axis : {0 or 'index', 1 or 'columns'}, default 0
+            The axis to rename. For `Series` this parameter is unused and defaults to 0.
+        copy : bool, default None
+            Also copy underlying data.
+
+            .. note::
+                The `copy` keyword will change behavior in pandas 3.0.
+                `Copy-on-Write
+                `__
+                will be enabled by default, which means that all methods with a
+                `copy` keyword will use a lazy copy mechanism to defer the copy and
+                ignore the `copy` keyword. The `copy` keyword will be removed in a
+                future version of pandas.
+
+                You can already get the future behavior and improvements through
+                enabling copy on write ``pd.options.mode.copy_on_write = True``
+        inplace : bool, default False
+            Modifies the object directly, instead of creating a new Series
+            or DataFrame.
+
+        Returns
+        -------
+        Series, DataFrame, or None
+            The same type as the caller or None if ``inplace=True``.
+
+        See Also
+        --------
+        Series.rename : Alter Series index labels or name.
+        DataFrame.rename : Alter DataFrame index labels or name.
+        Index.rename : Set new names on index.
+
+        Notes
+        -----
+        ``DataFrame.rename_axis`` supports two calling conventions
+
+        * ``(index=index_mapper, columns=columns_mapper, ...)``
+        * ``(mapper, axis={'index', 'columns'}, ...)``
+
+        The first calling convention will only modify the names of
+        the index and/or the names of the Index object that is the columns.
+        In this case, the parameter ``copy`` is ignored.
+
+        The second calling convention will modify the names of the
+        corresponding index if mapper is a list or a scalar.
+        However, if mapper is dict-like or a function, it will use the
+        deprecated behavior of modifying the axis *labels*.
+
+        We *highly* recommend using keyword arguments to clarify your
+        intent.
+
+        Examples
+        --------
+        **Series**
+
+        >>> s = pd.Series(["dog", "cat", "monkey"])
+        >>> s
+        0       dog
+        1       cat
+        2    monkey
+        dtype: object
+        >>> s.rename_axis("animal")
+        animal
+        0    dog
+        1    cat
+        2    monkey
+        dtype: object
+
+        **DataFrame**
+
+        >>> df = pd.DataFrame({"num_legs": [4, 4, 2],
+        ...                    "num_arms": [0, 0, 2]},
+        ...                   ["dog", "cat", "monkey"])
+        >>> df
+                num_legs  num_arms
+        dog            4         0
+        cat            4         0
+        monkey         2         2
+        >>> df = df.rename_axis("animal")
+        >>> df
+                num_legs  num_arms
+        animal
+        dog            4         0
+        cat            4         0
+        monkey         2         2
+        >>> df = df.rename_axis("limbs", axis="columns")
+        >>> df
+        limbs   num_legs  num_arms
+        animal
+        dog            4         0
+        cat            4         0
+        monkey         2         2
+
+        **MultiIndex**
+
+        >>> df.index = pd.MultiIndex.from_product([['mammal'],
+        ...                                        ['dog', 'cat', 'monkey']],
+        ...                                       names=['type', 'name'])
+        >>> df
+        limbs          num_legs  num_arms
+        type   name
+        mammal dog            4         0
+               cat            4         0
+               monkey         2         2
+
+        >>> df.rename_axis(index={'type': 'class'})
+        limbs          num_legs  num_arms
+        class  name
+        mammal dog            4         0
+               cat            4         0
+               monkey         2         2
+
+        >>> df.rename_axis(columns=str.upper)
+        LIMBS          num_legs  num_arms
+        type   name
+        mammal dog            4         0
+               cat            4         0
+               monkey         2         2
+        """
+        axes = {"index": index, "columns": columns}
+
+        if axis is not None:
+            axis = self._get_axis_number(axis)
+
+        inplace = validate_bool_kwarg(inplace, "inplace")
+
+        if copy and using_copy_on_write():
+            copy = False
+
+        if mapper is not lib.no_default:
+            # Use v0.23 behavior if a scalar or list
+            non_mapper = is_scalar(mapper) or (
+                is_list_like(mapper) and not is_dict_like(mapper)
+            )
+            if non_mapper:
+                return self._set_axis_name(
+                    mapper, axis=axis, inplace=inplace, copy=copy
+                )
+            else:
+                raise ValueError("Use `.rename` to alter labels with a mapper.")
+        else:
+            # Use new behavior.  Means that index and/or columns
+            # is specified
+            result = self if inplace else self.copy(deep=copy)
+
+            for axis in range(self._AXIS_LEN):
+                v = axes.get(self._get_axis_name(axis))
+                if v is lib.no_default:
+                    continue
+                non_mapper = is_scalar(v) or (is_list_like(v) and not is_dict_like(v))
+                if non_mapper:
+                    newnames = v
+                else:
+                    f = common.get_rename_function(v)
+                    curnames = self._get_axis(axis).names
+                    newnames = [f(name) for name in curnames]
+                result._set_axis_name(newnames, axis=axis, inplace=True, copy=copy)
+            if not inplace:
+                return result
+            return None
+
+    @final
+    def _set_axis_name(
+        self, name, axis: Axis = 0, inplace: bool_t = False, copy: bool_t | None = True
+    ):
+        """
+        Set the name(s) of the axis.
+
+        Parameters
+        ----------
+        name : str or list of str
+            Name(s) to set.
+        axis : {0 or 'index', 1 or 'columns'}, default 0
+            The axis to set the label. The value 0 or 'index' specifies index,
+            and the value 1 or 'columns' specifies columns.
+        inplace : bool, default False
+            If `True`, do operation inplace and return None.
+        copy:
+            Whether to make a copy of the result.
+
+        Returns
+        -------
+        Series, DataFrame, or None
+            The same type as the caller or `None` if `inplace` is `True`.
+
+        See Also
+        --------
+        DataFrame.rename : Alter the axis labels of :class:`DataFrame`.
+        Series.rename : Alter the index labels or set the index name
+            of :class:`Series`.
+        Index.rename : Set the name of :class:`Index` or :class:`MultiIndex`.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({"num_legs": [4, 4, 2]},
+        ...                   ["dog", "cat", "monkey"])
+        >>> df
+                num_legs
+        dog            4
+        cat            4
+        monkey         2
+        >>> df._set_axis_name("animal")
+                num_legs
+        animal
+        dog            4
+        cat            4
+        monkey         2
+        >>> df.index = pd.MultiIndex.from_product(
+        ...                [["mammal"], ['dog', 'cat', 'monkey']])
+        >>> df._set_axis_name(["type", "name"])
+                       num_legs
+        type   name
+        mammal dog        4
+               cat        4
+               monkey     2
+        """
+        axis = self._get_axis_number(axis)
+        idx = self._get_axis(axis).set_names(name)
+
+        inplace = validate_bool_kwarg(inplace, "inplace")
+        renamed = self if inplace else self.copy(deep=copy)
+        if axis == 0:
+            renamed.index = idx
+        else:
+            renamed.columns = idx
+
+        if not inplace:
+            return renamed
+
+    # ----------------------------------------------------------------------
+    # Comparison Methods
+
+    @final
+    def _indexed_same(self, other) -> bool_t:
+        return all(
+            self._get_axis(a).equals(other._get_axis(a)) for a in self._AXIS_ORDERS
+        )
+
+    @final
+    def equals(self, other: object) -> bool_t:
+        """
+        Test whether two objects contain the same elements.
+
+        This function allows two Series or DataFrames to be compared against
+        each other to see if they have the same shape and elements. NaNs in
+        the same location are considered equal.
+
+        The row/column index do not need to have the same type, as long
+        as the values are considered equal. Corresponding columns and
+        index must be of the same dtype.
+
+        Parameters
+        ----------
+        other : Series or DataFrame
+            The other Series or DataFrame to be compared with the first.
+
+        Returns
+        -------
+        bool
+            True if all elements are the same in both objects, False
+            otherwise.
+
+        See Also
+        --------
+        Series.eq : Compare two Series objects of the same length
+            and return a Series where each element is True if the element
+            in each Series is equal, False otherwise.
+        DataFrame.eq : Compare two DataFrame objects of the same shape and
+            return a DataFrame where each element is True if the respective
+            element in each DataFrame is equal, False otherwise.
+        testing.assert_series_equal : Raises an AssertionError if left and
+            right are not equal. Provides an easy interface to ignore
+            inequality in dtypes, indexes and precision among others.
+        testing.assert_frame_equal : Like assert_series_equal, but targets
+            DataFrames.
+        numpy.array_equal : Return True if two arrays have the same shape
+            and elements, False otherwise.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({1: [10], 2: [20]})
+        >>> df
+            1   2
+        0  10  20
+
+        DataFrames df and exactly_equal have the same types and values for
+        their elements and column labels, which will return True.
+
+        >>> exactly_equal = pd.DataFrame({1: [10], 2: [20]})
+        >>> exactly_equal
+            1   2
+        0  10  20
+        >>> df.equals(exactly_equal)
+        True
+
+        DataFrames df and different_column_type have the same element
+        types and values, but have different types for the column labels,
+        which will still return True.
+
+        >>> different_column_type = pd.DataFrame({1.0: [10], 2.0: [20]})
+        >>> different_column_type
+           1.0  2.0
+        0   10   20
+        >>> df.equals(different_column_type)
+        True
+
+        DataFrames df and different_data_type have different types for the
+        same values for their elements, and will return False even though
+        their column labels are the same values and types.
+
+        >>> different_data_type = pd.DataFrame({1: [10.0], 2: [20.0]})
+        >>> different_data_type
+              1     2
+        0  10.0  20.0
+        >>> df.equals(different_data_type)
+        False
+        """
+        if not (isinstance(other, type(self)) or isinstance(self, type(other))):
+            return False
+        other = cast(NDFrame, other)
+        return self._mgr.equals(other._mgr)
+
+    # -------------------------------------------------------------------------
+    # Unary Methods
+
+    @final
+    def __neg__(self) -> Self:
+        def blk_func(values: ArrayLike):
+            if is_bool_dtype(values.dtype):
+                # error: Argument 1 to "inv" has incompatible type "Union
+                # [ExtensionArray, ndarray[Any, Any]]"; expected
+                # "_SupportsInversion[ndarray[Any, dtype[bool_]]]"
+                return operator.inv(values)  # type: ignore[arg-type]
+            else:
+                # error: Argument 1 to "neg" has incompatible type "Union
+                # [ExtensionArray, ndarray[Any, Any]]"; expected
+                # "_SupportsNeg[ndarray[Any, dtype[Any]]]"
+                return operator.neg(values)  # type: ignore[arg-type]
+
+        new_data = self._mgr.apply(blk_func)
+        res = self._constructor_from_mgr(new_data, axes=new_data.axes)
+        return res.__finalize__(self, method="__neg__")
+
+    @final
+    def __pos__(self) -> Self:
+        def blk_func(values: ArrayLike):
+            if is_bool_dtype(values.dtype):
+                return values.copy()
+            else:
+                # error: Argument 1 to "pos" has incompatible type "Union
+                # [ExtensionArray, ndarray[Any, Any]]"; expected
+                # "_SupportsPos[ndarray[Any, dtype[Any]]]"
+                return operator.pos(values)  # type: ignore[arg-type]
+
+        new_data = self._mgr.apply(blk_func)
+        res = self._constructor_from_mgr(new_data, axes=new_data.axes)
+        return res.__finalize__(self, method="__pos__")
+
+    @final
+    def __invert__(self) -> Self:
+        if not self.size:
+            # inv fails with 0 len
+            return self.copy(deep=False)
+
+        new_data = self._mgr.apply(operator.invert)
+        res = self._constructor_from_mgr(new_data, axes=new_data.axes)
+        return res.__finalize__(self, method="__invert__")
+
+    @final
+    def __nonzero__(self) -> NoReturn:
+        raise ValueError(
+            f"The truth value of a {type(self).__name__} is ambiguous. "
+            "Use a.empty, a.bool(), a.item(), a.any() or a.all()."
+        )
+
+    __bool__ = __nonzero__
+
+    @final
+    def bool(self) -> bool_t:
+        """
+        Return the bool of a single element Series or DataFrame.
+
+        .. deprecated:: 2.1.0
+
+           bool is deprecated and will be removed in future version of pandas.
+           For ``Series`` use ``pandas.Series.item``.
+
+        This must be a boolean scalar value, either True or False. It will raise a
+        ValueError if the Series or DataFrame does not have exactly 1 element, or that
+        element is not boolean (integer values 0 and 1 will also raise an exception).
+
+        Returns
+        -------
+        bool
+            The value in the Series or DataFrame.
+
+        See Also
+        --------
+        Series.astype : Change the data type of a Series, including to boolean.
+        DataFrame.astype : Change the data type of a DataFrame, including to boolean.
+        numpy.bool_ : NumPy boolean data type, used by pandas for boolean values.
+
+        Examples
+        --------
+        The method will only work for single element objects with a boolean value:
+
+        >>> pd.Series([True]).bool()  # doctest: +SKIP
+        True
+        >>> pd.Series([False]).bool()  # doctest: +SKIP
+        False
+
+        >>> pd.DataFrame({'col': [True]}).bool()  # doctest: +SKIP
+        True
+        >>> pd.DataFrame({'col': [False]}).bool()  # doctest: +SKIP
+        False
+
+        This is an alternative method and will only work
+        for single element objects with a boolean value:
+
+        >>> pd.Series([True]).item()  # doctest: +SKIP
+        True
+        >>> pd.Series([False]).item()  # doctest: +SKIP
+        False
+        """
+
+        warnings.warn(
+            f"{type(self).__name__}.bool is now deprecated and will be removed "
+            "in future version of pandas",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        v = self.squeeze()
+        if isinstance(v, (bool, np.bool_)):
+            return bool(v)
+        elif is_scalar(v):
+            raise ValueError(
+                "bool cannot act on a non-boolean single element "
+                f"{type(self).__name__}"
+            )
+
+        self.__nonzero__()
+        # for mypy (__nonzero__ raises)
+        return True
+
+    @final
+    def abs(self) -> Self:
+        """
+        Return a Series/DataFrame with absolute numeric value of each element.
+
+        This function only applies to elements that are all numeric.
+
+        Returns
+        -------
+        abs
+            Series/DataFrame containing the absolute value of each element.
+
+        See Also
+        --------
+        numpy.absolute : Calculate the absolute value element-wise.
+
+        Notes
+        -----
+        For ``complex`` inputs, ``1.2 + 1j``, the absolute value is
+        :math:`\\sqrt{ a^2 + b^2 }`.
+
+        Examples
+        --------
+        Absolute numeric values in a Series.
+
+        >>> s = pd.Series([-1.10, 2, -3.33, 4])
+        >>> s.abs()
+        0    1.10
+        1    2.00
+        2    3.33
+        3    4.00
+        dtype: float64
+
+        Absolute numeric values in a Series with complex numbers.
+
+        >>> s = pd.Series([1.2 + 1j])
+        >>> s.abs()
+        0    1.56205
+        dtype: float64
+
+        Absolute numeric values in a Series with a Timedelta element.
+
+        >>> s = pd.Series([pd.Timedelta('1 days')])
+        >>> s.abs()
+        0   1 days
+        dtype: timedelta64[ns]
+
+        Select rows with data closest to certain value using argsort (from
+        `StackOverflow `__).
+
+        >>> df = pd.DataFrame({
+        ...     'a': [4, 5, 6, 7],
+        ...     'b': [10, 20, 30, 40],
+        ...     'c': [100, 50, -30, -50]
+        ... })
+        >>> df
+             a    b    c
+        0    4   10  100
+        1    5   20   50
+        2    6   30  -30
+        3    7   40  -50
+        >>> df.loc[(df.c - 43).abs().argsort()]
+             a    b    c
+        1    5   20   50
+        0    4   10  100
+        2    6   30  -30
+        3    7   40  -50
+        """
+        res_mgr = self._mgr.apply(np.abs)
+        return self._constructor_from_mgr(res_mgr, axes=res_mgr.axes).__finalize__(
+            self, name="abs"
+        )
+
+    @final
+    def __abs__(self) -> Self:
+        return self.abs()
+
+    @final
+    def __round__(self, decimals: int = 0) -> Self:
+        return self.round(decimals).__finalize__(self, method="__round__")
+
+    # -------------------------------------------------------------------------
+    # Label or Level Combination Helpers
+    #
+    # A collection of helper methods for DataFrame/Series operations that
+    # accept a combination of column/index labels and levels.  All such
+    # operations should utilize/extend these methods when possible so that we
+    # have consistent precedence and validation logic throughout the library.
+
+    @final
+    def _is_level_reference(self, key: Level, axis: Axis = 0) -> bool_t:
+        """
+        Test whether a key is a level reference for a given axis.
+
+        To be considered a level reference, `key` must be a string that:
+          - (axis=0): Matches the name of an index level and does NOT match
+            a column label.
+          - (axis=1): Matches the name of a column level and does NOT match
+            an index label.
+
+        Parameters
+        ----------
+        key : Hashable
+            Potential level name for the given axis
+        axis : int, default 0
+            Axis that levels are associated with (0 for index, 1 for columns)
+
+        Returns
+        -------
+        is_level : bool
+        """
+        axis_int = self._get_axis_number(axis)
+
+        return (
+            key is not None
+            and is_hashable(key)
+            and key in self.axes[axis_int].names
+            and not self._is_label_reference(key, axis=axis_int)
+        )
+
+    @final
+    def _is_label_reference(self, key: Level, axis: Axis = 0) -> bool_t:
+        """
+        Test whether a key is a label reference for a given axis.
+
+        To be considered a label reference, `key` must be a string that:
+          - (axis=0): Matches a column label
+          - (axis=1): Matches an index label
+
+        Parameters
+        ----------
+        key : Hashable
+            Potential label name, i.e. Index entry.
+        axis : int, default 0
+            Axis perpendicular to the axis that labels are associated with
+            (0 means search for column labels, 1 means search for index labels)
+
+        Returns
+        -------
+        is_label: bool
+        """
+        axis_int = self._get_axis_number(axis)
+        other_axes = (ax for ax in range(self._AXIS_LEN) if ax != axis_int)
+
+        return (
+            key is not None
+            and is_hashable(key)
+            and any(key in self.axes[ax] for ax in other_axes)
+        )
+
+    @final
+    def _is_label_or_level_reference(self, key: Level, axis: AxisInt = 0) -> bool_t:
+        """
+        Test whether a key is a label or level reference for a given axis.
+
+        To be considered either a label or a level reference, `key` must be a
+        string that:
+          - (axis=0): Matches a column label or an index level
+          - (axis=1): Matches an index label or a column level
+
+        Parameters
+        ----------
+        key : Hashable
+            Potential label or level name
+        axis : int, default 0
+            Axis that levels are associated with (0 for index, 1 for columns)
+
+        Returns
+        -------
+        bool
+        """
+        return self._is_level_reference(key, axis=axis) or self._is_label_reference(
+            key, axis=axis
+        )
+
+    @final
+    def _check_label_or_level_ambiguity(self, key: Level, axis: Axis = 0) -> None:
+        """
+        Check whether `key` is ambiguous.
+
+        By ambiguous, we mean that it matches both a level of the input
+        `axis` and a label of the other axis.
+
+        Parameters
+        ----------
+        key : Hashable
+            Label or level name.
+        axis : int, default 0
+            Axis that levels are associated with (0 for index, 1 for columns).
+
+        Raises
+        ------
+        ValueError: `key` is ambiguous
+        """
+
+        axis_int = self._get_axis_number(axis)
+        other_axes = (ax for ax in range(self._AXIS_LEN) if ax != axis_int)
+
+        if (
+            key is not None
+            and is_hashable(key)
+            and key in self.axes[axis_int].names
+            and any(key in self.axes[ax] for ax in other_axes)
+        ):
+            # Build an informative and grammatical warning
+            level_article, level_type = (
+                ("an", "index") if axis_int == 0 else ("a", "column")
+            )
+
+            label_article, label_type = (
+                ("a", "column") if axis_int == 0 else ("an", "index")
+            )
+
+            msg = (
+                f"'{key}' is both {level_article} {level_type} level and "
+                f"{label_article} {label_type} label, which is ambiguous."
+            )
+            raise ValueError(msg)
+
+    @final
+    def _get_label_or_level_values(self, key: Level, axis: AxisInt = 0) -> ArrayLike:
+        """
+        Return a 1-D array of values associated with `key`, a label or level
+        from the given `axis`.
+
+        Retrieval logic:
+          - (axis=0): Return column values if `key` matches a column label.
+            Otherwise return index level values if `key` matches an index
+            level.
+          - (axis=1): Return row values if `key` matches an index label.
+            Otherwise return column level values if 'key' matches a column
+            level
+
+        Parameters
+        ----------
+        key : Hashable
+            Label or level name.
+        axis : int, default 0
+            Axis that levels are associated with (0 for index, 1 for columns)
+
+        Returns
+        -------
+        np.ndarray or ExtensionArray
+
+        Raises
+        ------
+        KeyError
+            if `key` matches neither a label nor a level
+        ValueError
+            if `key` matches multiple labels
+        """
+        axis = self._get_axis_number(axis)
+        other_axes = [ax for ax in range(self._AXIS_LEN) if ax != axis]
+
+        if self._is_label_reference(key, axis=axis):
+            self._check_label_or_level_ambiguity(key, axis=axis)
+            values = self.xs(key, axis=other_axes[0])._values
+        elif self._is_level_reference(key, axis=axis):
+            values = self.axes[axis].get_level_values(key)._values
+        else:
+            raise KeyError(key)
+
+        # Check for duplicates
+        if values.ndim > 1:
+            if other_axes and isinstance(self._get_axis(other_axes[0]), MultiIndex):
+                multi_message = (
+                    "\n"
+                    "For a multi-index, the label must be a "
+                    "tuple with elements corresponding to each level."
+                )
+            else:
+                multi_message = ""
+
+            label_axis_name = "column" if axis == 0 else "index"
+            raise ValueError(
+                f"The {label_axis_name} label '{key}' is not unique.{multi_message}"
+            )
+
+        return values
+
+    @final
+    def _drop_labels_or_levels(self, keys, axis: AxisInt = 0):
+        """
+        Drop labels and/or levels for the given `axis`.
+
+        For each key in `keys`:
+          - (axis=0): If key matches a column label then drop the column.
+            Otherwise if key matches an index level then drop the level.
+          - (axis=1): If key matches an index label then drop the row.
+            Otherwise if key matches a column level then drop the level.
+
+        Parameters
+        ----------
+        keys : str or list of str
+            labels or levels to drop
+        axis : int, default 0
+            Axis that levels are associated with (0 for index, 1 for columns)
+
+        Returns
+        -------
+        dropped: DataFrame
+
+        Raises
+        ------
+        ValueError
+            if any `keys` match neither a label nor a level
+        """
+        axis = self._get_axis_number(axis)
+
+        # Validate keys
+        keys = common.maybe_make_list(keys)
+        invalid_keys = [
+            k for k in keys if not self._is_label_or_level_reference(k, axis=axis)
+        ]
+
+        if invalid_keys:
+            raise ValueError(
+                "The following keys are not valid labels or "
+                f"levels for axis {axis}: {invalid_keys}"
+            )
+
+        # Compute levels and labels to drop
+        levels_to_drop = [k for k in keys if self._is_level_reference(k, axis=axis)]
+
+        labels_to_drop = [k for k in keys if not self._is_level_reference(k, axis=axis)]
+
+        # Perform copy upfront and then use inplace operations below.
+        # This ensures that we always perform exactly one copy.
+        # ``copy`` and/or ``inplace`` options could be added in the future.
+        dropped = self.copy(deep=False)
+
+        if axis == 0:
+            # Handle dropping index levels
+            if levels_to_drop:
+                dropped.reset_index(levels_to_drop, drop=True, inplace=True)
+
+            # Handle dropping columns labels
+            if labels_to_drop:
+                dropped.drop(labels_to_drop, axis=1, inplace=True)
+        else:
+            # Handle dropping column levels
+            if levels_to_drop:
+                if isinstance(dropped.columns, MultiIndex):
+                    # Drop the specified levels from the MultiIndex
+                    dropped.columns = dropped.columns.droplevel(levels_to_drop)
+                else:
+                    # Drop the last level of Index by replacing with
+                    # a RangeIndex
+                    dropped.columns = RangeIndex(dropped.columns.size)
+
+            # Handle dropping index labels
+            if labels_to_drop:
+                dropped.drop(labels_to_drop, axis=0, inplace=True)
+
+        return dropped
+
+    # ----------------------------------------------------------------------
+    # Iteration
+
+    # https://github.com/python/typeshed/issues/2148#issuecomment-520783318
+    # Incompatible types in assignment (expression has type "None", base class
+    # "object" defined the type as "Callable[[object], int]")
+    __hash__: ClassVar[None]  # type: ignore[assignment]
+
+    def __iter__(self) -> Iterator:
+        """
+        Iterate over info axis.
+
+        Returns
+        -------
+        iterator
+            Info axis as iterator.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
+        >>> for x in df:
+        ...     print(x)
+        A
+        B
+        """
+        return iter(self._info_axis)
+
+    # can we get a better explanation of this?
+    def keys(self) -> Index:
+        """
+        Get the 'info axis' (see Indexing for more).
+
+        This is index for Series, columns for DataFrame.
+
+        Returns
+        -------
+        Index
+            Info axis.
+
+        Examples
+        --------
+        >>> d = pd.DataFrame(data={'A': [1, 2, 3], 'B': [0, 4, 8]},
+        ...                  index=['a', 'b', 'c'])
+        >>> d
+           A  B
+        a  1  0
+        b  2  4
+        c  3  8
+        >>> d.keys()
+        Index(['A', 'B'], dtype='object')
+        """
+        return self._info_axis
+
+    def items(self):
+        """
+        Iterate over (label, values) on info axis
+
+        This is index for Series and columns for DataFrame.
+
+        Returns
+        -------
+        Generator
+        """
+        for h in self._info_axis:
+            yield h, self[h]
+
+    def __len__(self) -> int:
+        """Returns length of info axis"""
+        return len(self._info_axis)
+
+    @final
+    def __contains__(self, key) -> bool_t:
+        """True if the key is in the info axis"""
+        return key in self._info_axis
+
+    @property
+    def empty(self) -> bool_t:
+        """
+        Indicator whether Series/DataFrame is empty.
+
+        True if Series/DataFrame is entirely empty (no items), meaning any of the
+        axes are of length 0.
+
+        Returns
+        -------
+        bool
+            If Series/DataFrame is empty, return True, if not return False.
+
+        See Also
+        --------
+        Series.dropna : Return series without null values.
+        DataFrame.dropna : Return DataFrame with labels on given axis omitted
+            where (all or any) data are missing.
+
+        Notes
+        -----
+        If Series/DataFrame contains only NaNs, it is still not considered empty. See
+        the example below.
+
+        Examples
+        --------
+        An example of an actual empty DataFrame. Notice the index is empty:
+
+        >>> df_empty = pd.DataFrame({'A' : []})
+        >>> df_empty
+        Empty DataFrame
+        Columns: [A]
+        Index: []
+        >>> df_empty.empty
+        True
+
+        If we only have NaNs in our DataFrame, it is not considered empty! We
+        will need to drop the NaNs to make the DataFrame empty:
+
+        >>> df = pd.DataFrame({'A' : [np.nan]})
+        >>> df
+            A
+        0 NaN
+        >>> df.empty
+        False
+        >>> df.dropna().empty
+        True
+
+        >>> ser_empty = pd.Series({'A' : []})
+        >>> ser_empty
+        A    []
+        dtype: object
+        >>> ser_empty.empty
+        False
+        >>> ser_empty = pd.Series()
+        >>> ser_empty.empty
+        True
+        """
+        return any(len(self._get_axis(a)) == 0 for a in self._AXIS_ORDERS)
+
+    # ----------------------------------------------------------------------
+    # Array Interface
+
+    # This is also set in IndexOpsMixin
+    # GH#23114 Ensure ndarray.__op__(DataFrame) returns NotImplemented
+    __array_priority__: int = 1000
+
+    def __array__(
+        self, dtype: npt.DTypeLike | None = None, copy: bool_t | None = None
+    ) -> np.ndarray:
+        if copy is False and not self._mgr.is_single_block and not self.empty:
+            # check this manually, otherwise ._values will already return a copy
+            # and np.array(values, copy=False) will not raise a warning
+            warnings.warn(
+                "Starting with NumPy 2.0, the behavior of the 'copy' keyword has "
+                "changed and passing 'copy=False' raises an error when returning "
+                "a zero-copy NumPy array is not possible. pandas will follow "
+                "this behavior starting with pandas 3.0.\nThis conversion to "
+                "NumPy requires a copy, but 'copy=False' was passed. Consider "
+                "using 'np.asarray(..)' instead.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+        values = self._values
+        if copy is None:
+            # Note: branch avoids `copy=None` for NumPy 1.x support
+            arr = np.asarray(values, dtype=dtype)
+        else:
+            arr = np.array(values, dtype=dtype, copy=copy)
+
+        if (
+            copy is not True
+            and astype_is_view(values.dtype, arr.dtype)
+            and using_copy_on_write()
+            and self._mgr.is_single_block
+        ):
+            # Check if both conversions can be done without a copy
+            if astype_is_view(self.dtypes.iloc[0], values.dtype) and astype_is_view(
+                values.dtype, arr.dtype
+            ):
+                arr = arr.view()
+                arr.flags.writeable = False
+        return arr
+
+    @final
+    def __array_ufunc__(
+        self, ufunc: np.ufunc, method: str, *inputs: Any, **kwargs: Any
+    ):
+        return arraylike.array_ufunc(self, ufunc, method, *inputs, **kwargs)
+
+    # ----------------------------------------------------------------------
+    # Picklability
+
+    @final
+    def __getstate__(self) -> dict[str, Any]:
+        meta = {k: getattr(self, k, None) for k in self._metadata}
+        return {
+            "_mgr": self._mgr,
+            "_typ": self._typ,
+            "_metadata": self._metadata,
+            "attrs": self.attrs,
+            "_flags": {k: self.flags[k] for k in self.flags._keys},
+            **meta,
+        }
+
+    @final
+    def __setstate__(self, state) -> None:
+        if isinstance(state, BlockManager):
+            self._mgr = state
+        elif isinstance(state, dict):
+            if "_data" in state and "_mgr" not in state:
+                # compat for older pickles
+                state["_mgr"] = state.pop("_data")
+            typ = state.get("_typ")
+            if typ is not None:
+                attrs = state.get("_attrs", {})
+                if attrs is None:  # should not happen, but better be on the safe side
+                    attrs = {}
+                object.__setattr__(self, "_attrs", attrs)
+                flags = state.get("_flags", {"allows_duplicate_labels": True})
+                object.__setattr__(self, "_flags", Flags(self, **flags))
+
+                # set in the order of internal names
+                # to avoid definitional recursion
+                # e.g. say fill_value needing _mgr to be
+                # defined
+                meta = set(self._internal_names + self._metadata)
+                for k in list(meta):
+                    if k in state and k != "_flags":
+                        v = state[k]
+                        object.__setattr__(self, k, v)
+
+                for k, v in state.items():
+                    if k not in meta:
+                        object.__setattr__(self, k, v)
+
+            else:
+                raise NotImplementedError("Pre-0.12 pickles are no longer supported")
+        elif len(state) == 2:
+            raise NotImplementedError("Pre-0.12 pickles are no longer supported")
+
+        self._item_cache: dict[Hashable, Series] = {}
+
+    # ----------------------------------------------------------------------
+    # Rendering Methods
+
+    def __repr__(self) -> str:
+        # string representation based upon iterating over self
+        # (since, by definition, `PandasContainers` are iterable)
+        prepr = f"[{','.join(map(pprint_thing, self))}]"
+        return f"{type(self).__name__}({prepr})"
+
+    @final
+    def _repr_latex_(self):
+        """
+        Returns a LaTeX representation for a particular object.
+        Mainly for use with nbconvert (jupyter notebook conversion to pdf).
+        """
+        if config.get_option("styler.render.repr") == "latex":
+            return self.to_latex()
+        else:
+            return None
+
+    @final
+    def _repr_data_resource_(self):
+        """
+        Not a real Jupyter special repr method, but we use the same
+        naming convention.
+        """
+        if config.get_option("display.html.table_schema"):
+            data = self.head(config.get_option("display.max_rows"))
+
+            as_json = data.to_json(orient="table")
+            as_json = cast(str, as_json)
+            return loads(as_json, object_pairs_hook=collections.OrderedDict)
+
+    # ----------------------------------------------------------------------
+    # I/O Methods
+
+    @final
+    @deprecate_nonkeyword_arguments(
+        version="3.0", allowed_args=["self", "excel_writer"], name="to_excel"
+    )
+    @doc(
+        klass="object",
+        storage_options=_shared_docs["storage_options"],
+        storage_options_versionadded="1.2.0",
+    )
+    def to_excel(
+        self,
+        excel_writer: FilePath | WriteExcelBuffer | ExcelWriter,
+        sheet_name: str = "Sheet1",
+        na_rep: str = "",
+        float_format: str | None = None,
+        columns: Sequence[Hashable] | None = None,
+        header: Sequence[Hashable] | bool_t = True,
+        index: bool_t = True,
+        index_label: IndexLabel | None = None,
+        startrow: int = 0,
+        startcol: int = 0,
+        engine: Literal["openpyxl", "xlsxwriter"] | None = None,
+        merge_cells: bool_t = True,
+        inf_rep: str = "inf",
+        freeze_panes: tuple[int, int] | None = None,
+        storage_options: StorageOptions | None = None,
+        engine_kwargs: dict[str, Any] | None = None,
+    ) -> None:
+        """
+        Write {klass} to an Excel sheet.
+
+        To write a single {klass} to an Excel .xlsx file it is only necessary to
+        specify a target file name. To write to multiple sheets it is necessary to
+        create an `ExcelWriter` object with a target file name, and specify a sheet
+        in the file to write to.
+
+        Multiple sheets may be written to by specifying unique `sheet_name`.
+        With all data written to the file it is necessary to save the changes.
+        Note that creating an `ExcelWriter` object with a file name that already
+        exists will result in the contents of the existing file being erased.
+
+        Parameters
+        ----------
+        excel_writer : path-like, file-like, or ExcelWriter object
+            File path or existing ExcelWriter.
+        sheet_name : str, default 'Sheet1'
+            Name of sheet which will contain DataFrame.
+        na_rep : str, default ''
+            Missing data representation.
+        float_format : str, optional
+            Format string for floating point numbers. For example
+            ``float_format="%.2f"`` will format 0.1234 to 0.12.
+        columns : sequence or list of str, optional
+            Columns to write.
+        header : bool or list of str, default True
+            Write out the column names. If a list of string is given it is
+            assumed to be aliases for the column names.
+        index : bool, default True
+            Write row names (index).
+        index_label : str or sequence, optional
+            Column label for index column(s) if desired. If not specified, and
+            `header` and `index` are True, then the index names are used. A
+            sequence should be given if the DataFrame uses MultiIndex.
+        startrow : int, default 0
+            Upper left cell row to dump data frame.
+        startcol : int, default 0
+            Upper left cell column to dump data frame.
+        engine : str, optional
+            Write engine to use, 'openpyxl' or 'xlsxwriter'. You can also set this
+            via the options ``io.excel.xlsx.writer`` or
+            ``io.excel.xlsm.writer``.
+
+        merge_cells : bool, default True
+            Write MultiIndex and Hierarchical Rows as merged cells.
+        inf_rep : str, default 'inf'
+            Representation for infinity (there is no native representation for
+            infinity in Excel).
+        freeze_panes : tuple of int (length 2), optional
+            Specifies the one-based bottommost row and rightmost column that
+            is to be frozen.
+        {storage_options}
+
+            .. versionadded:: {storage_options_versionadded}
+        engine_kwargs : dict, optional
+            Arbitrary keyword arguments passed to excel engine.
+
+        See Also
+        --------
+        to_csv : Write DataFrame to a comma-separated values (csv) file.
+        ExcelWriter : Class for writing DataFrame objects into excel sheets.
+        read_excel : Read an Excel file into a pandas DataFrame.
+        read_csv : Read a comma-separated values (csv) file into DataFrame.
+        io.formats.style.Styler.to_excel : Add styles to Excel sheet.
+
+        Notes
+        -----
+        For compatibility with :meth:`~DataFrame.to_csv`,
+        to_excel serializes lists and dicts to strings before writing.
+
+        Once a workbook has been saved it is not possible to write further
+        data without rewriting the whole workbook.
+
+        Examples
+        --------
+
+        Create, write to and save a workbook:
+
+        >>> df1 = pd.DataFrame([['a', 'b'], ['c', 'd']],
+        ...                    index=['row 1', 'row 2'],
+        ...                    columns=['col 1', 'col 2'])
+        >>> df1.to_excel("output.xlsx")  # doctest: +SKIP
+
+        To specify the sheet name:
+
+        >>> df1.to_excel("output.xlsx",
+        ...              sheet_name='Sheet_name_1')  # doctest: +SKIP
+
+        If you wish to write to more than one sheet in the workbook, it is
+        necessary to specify an ExcelWriter object:
+
+        >>> df2 = df1.copy()
+        >>> with pd.ExcelWriter('output.xlsx') as writer:  # doctest: +SKIP
+        ...     df1.to_excel(writer, sheet_name='Sheet_name_1')
+        ...     df2.to_excel(writer, sheet_name='Sheet_name_2')
+
+        ExcelWriter can also be used to append to an existing Excel file:
+
+        >>> with pd.ExcelWriter('output.xlsx',
+        ...                     mode='a') as writer:  # doctest: +SKIP
+        ...     df1.to_excel(writer, sheet_name='Sheet_name_3')
+
+        To set the library that is used to write the Excel file,
+        you can pass the `engine` keyword (the default engine is
+        automatically chosen depending on the file extension):
+
+        >>> df1.to_excel('output1.xlsx', engine='xlsxwriter')  # doctest: +SKIP
+        """
+        if engine_kwargs is None:
+            engine_kwargs = {}
+
+        df = self if isinstance(self, ABCDataFrame) else self.to_frame()
+
+        from pandas.io.formats.excel import ExcelFormatter
+
+        formatter = ExcelFormatter(
+            df,
+            na_rep=na_rep,
+            cols=columns,
+            header=header,
+            float_format=float_format,
+            index=index,
+            index_label=index_label,
+            merge_cells=merge_cells,
+            inf_rep=inf_rep,
+        )
+        formatter.write(
+            excel_writer,
+            sheet_name=sheet_name,
+            startrow=startrow,
+            startcol=startcol,
+            freeze_panes=freeze_panes,
+            engine=engine,
+            storage_options=storage_options,
+            engine_kwargs=engine_kwargs,
+        )
+
+    @final
+    @deprecate_nonkeyword_arguments(
+        version="3.0", allowed_args=["self", "path_or_buf"], name="to_json"
+    )
+    @doc(
+        storage_options=_shared_docs["storage_options"],
+        compression_options=_shared_docs["compression_options"] % "path_or_buf",
+    )
+    def to_json(
+        self,
+        path_or_buf: FilePath | WriteBuffer[bytes] | WriteBuffer[str] | None = None,
+        orient: Literal["split", "records", "index", "table", "columns", "values"]
+        | None = None,
+        date_format: str | None = None,
+        double_precision: int = 10,
+        force_ascii: bool_t = True,
+        date_unit: TimeUnit = "ms",
+        default_handler: Callable[[Any], JSONSerializable] | None = None,
+        lines: bool_t = False,
+        compression: CompressionOptions = "infer",
+        index: bool_t | None = None,
+        indent: int | None = None,
+        storage_options: StorageOptions | None = None,
+        mode: Literal["a", "w"] = "w",
+    ) -> str | None:
+        """
+        Convert the object to a JSON string.
+
+        Note NaN's and None will be converted to null and datetime objects
+        will be converted to UNIX timestamps.
+
+        Parameters
+        ----------
+        path_or_buf : str, path object, file-like object, or None, default None
+            String, path object (implementing os.PathLike[str]), or file-like
+            object implementing a write() function. If None, the result is
+            returned as a string.
+        orient : str
+            Indication of expected JSON string format.
+
+            * Series:
+
+                - default is 'index'
+                - allowed values are: {{'split', 'records', 'index', 'table'}}.
+
+            * DataFrame:
+
+                - default is 'columns'
+                - allowed values are: {{'split', 'records', 'index', 'columns',
+                  'values', 'table'}}.
+
+            * The format of the JSON string:
+
+                - 'split' : dict like {{'index' -> [index], 'columns' -> [columns],
+                  'data' -> [values]}}
+                - 'records' : list like [{{column -> value}}, ... , {{column -> value}}]
+                - 'index' : dict like {{index -> {{column -> value}}}}
+                - 'columns' : dict like {{column -> {{index -> value}}}}
+                - 'values' : just the values array
+                - 'table' : dict like {{'schema': {{schema}}, 'data': {{data}}}}
+
+                Describing the data, where data component is like ``orient='records'``.
+
+        date_format : {{None, 'epoch', 'iso'}}
+            Type of date conversion. 'epoch' = epoch milliseconds,
+            'iso' = ISO8601. The default depends on the `orient`. For
+            ``orient='table'``, the default is 'iso'. For all other orients,
+            the default is 'epoch'.
+        double_precision : int, default 10
+            The number of decimal places to use when encoding
+            floating point values. The possible maximal value is 15.
+            Passing double_precision greater than 15 will raise a ValueError.
+        force_ascii : bool, default True
+            Force encoded string to be ASCII.
+        date_unit : str, default 'ms' (milliseconds)
+            The time unit to encode to, governs timestamp and ISO8601
+            precision.  One of 's', 'ms', 'us', 'ns' for second, millisecond,
+            microsecond, and nanosecond respectively.
+        default_handler : callable, default None
+            Handler to call if object cannot otherwise be converted to a
+            suitable format for JSON. Should receive a single argument which is
+            the object to convert and return a serialisable object.
+        lines : bool, default False
+            If 'orient' is 'records' write out line-delimited json format. Will
+            throw ValueError if incorrect 'orient' since others are not
+            list-like.
+        {compression_options}
+
+            .. versionchanged:: 1.4.0 Zstandard support.
+
+        index : bool or None, default None
+            The index is only used when 'orient' is 'split', 'index', 'column',
+            or 'table'. Of these, 'index' and 'column' do not support
+            `index=False`.
+
+        indent : int, optional
+           Length of whitespace used to indent each record.
+
+        {storage_options}
+
+        mode : str, default 'w' (writing)
+            Specify the IO mode for output when supplying a path_or_buf.
+            Accepted args are 'w' (writing) and 'a' (append) only.
+            mode='a' is only supported when lines is True and orient is 'records'.
+
+        Returns
+        -------
+        None or str
+            If path_or_buf is None, returns the resulting json format as a
+            string. Otherwise returns None.
+
+        See Also
+        --------
+        read_json : Convert a JSON string to pandas object.
+
+        Notes
+        -----
+        The behavior of ``indent=0`` varies from the stdlib, which does not
+        indent the output but does insert newlines. Currently, ``indent=0``
+        and the default ``indent=None`` are equivalent in pandas, though this
+        may change in a future release.
+
+        ``orient='table'`` contains a 'pandas_version' field under 'schema'.
+        This stores the version of `pandas` used in the latest revision of the
+        schema.
+
+        Examples
+        --------
+        >>> from json import loads, dumps
+        >>> df = pd.DataFrame(
+        ...     [["a", "b"], ["c", "d"]],
+        ...     index=["row 1", "row 2"],
+        ...     columns=["col 1", "col 2"],
+        ... )
+
+        >>> result = df.to_json(orient="split")
+        >>> parsed = loads(result)
+        >>> dumps(parsed, indent=4)  # doctest: +SKIP
+        {{
+            "columns": [
+                "col 1",
+                "col 2"
+            ],
+            "index": [
+                "row 1",
+                "row 2"
+            ],
+            "data": [
+                [
+                    "a",
+                    "b"
+                ],
+                [
+                    "c",
+                    "d"
+                ]
+            ]
+        }}
+
+        Encoding/decoding a Dataframe using ``'records'`` formatted JSON.
+        Note that index labels are not preserved with this encoding.
+
+        >>> result = df.to_json(orient="records")
+        >>> parsed = loads(result)
+        >>> dumps(parsed, indent=4)  # doctest: +SKIP
+        [
+            {{
+                "col 1": "a",
+                "col 2": "b"
+            }},
+            {{
+                "col 1": "c",
+                "col 2": "d"
+            }}
+        ]
+
+        Encoding/decoding a Dataframe using ``'index'`` formatted JSON:
+
+        >>> result = df.to_json(orient="index")
+        >>> parsed = loads(result)
+        >>> dumps(parsed, indent=4)  # doctest: +SKIP
+        {{
+            "row 1": {{
+                "col 1": "a",
+                "col 2": "b"
+            }},
+            "row 2": {{
+                "col 1": "c",
+                "col 2": "d"
+            }}
+        }}
+
+        Encoding/decoding a Dataframe using ``'columns'`` formatted JSON:
+
+        >>> result = df.to_json(orient="columns")
+        >>> parsed = loads(result)
+        >>> dumps(parsed, indent=4)  # doctest: +SKIP
+        {{
+            "col 1": {{
+                "row 1": "a",
+                "row 2": "c"
+            }},
+            "col 2": {{
+                "row 1": "b",
+                "row 2": "d"
+            }}
+        }}
+
+        Encoding/decoding a Dataframe using ``'values'`` formatted JSON:
+
+        >>> result = df.to_json(orient="values")
+        >>> parsed = loads(result)
+        >>> dumps(parsed, indent=4)  # doctest: +SKIP
+        [
+            [
+                "a",
+                "b"
+            ],
+            [
+                "c",
+                "d"
+            ]
+        ]
+
+        Encoding with Table Schema:
+
+        >>> result = df.to_json(orient="table")
+        >>> parsed = loads(result)
+        >>> dumps(parsed, indent=4)  # doctest: +SKIP
+        {{
+            "schema": {{
+                "fields": [
+                    {{
+                        "name": "index",
+                        "type": "string"
+                    }},
+                    {{
+                        "name": "col 1",
+                        "type": "string"
+                    }},
+                    {{
+                        "name": "col 2",
+                        "type": "string"
+                    }}
+                ],
+                "primaryKey": [
+                    "index"
+                ],
+                "pandas_version": "1.4.0"
+            }},
+            "data": [
+                {{
+                    "index": "row 1",
+                    "col 1": "a",
+                    "col 2": "b"
+                }},
+                {{
+                    "index": "row 2",
+                    "col 1": "c",
+                    "col 2": "d"
+                }}
+            ]
+        }}
+        """
+        from pandas.io import json
+
+        if date_format is None and orient == "table":
+            date_format = "iso"
+        elif date_format is None:
+            date_format = "epoch"
+
+        config.is_nonnegative_int(indent)
+        indent = indent or 0
+
+        return json.to_json(
+            path_or_buf=path_or_buf,
+            obj=self,
+            orient=orient,
+            date_format=date_format,
+            double_precision=double_precision,
+            force_ascii=force_ascii,
+            date_unit=date_unit,
+            default_handler=default_handler,
+            lines=lines,
+            compression=compression,
+            index=index,
+            indent=indent,
+            storage_options=storage_options,
+            mode=mode,
+        )
+
+    @final
+    @deprecate_nonkeyword_arguments(
+        version="3.0", allowed_args=["self", "path_or_buf"], name="to_hdf"
+    )
+    def to_hdf(
+        self,
+        path_or_buf: FilePath | HDFStore,
+        key: str,
+        mode: Literal["a", "w", "r+"] = "a",
+        complevel: int | None = None,
+        complib: Literal["zlib", "lzo", "bzip2", "blosc"] | None = None,
+        append: bool_t = False,
+        format: Literal["fixed", "table"] | None = None,
+        index: bool_t = True,
+        min_itemsize: int | dict[str, int] | None = None,
+        nan_rep=None,
+        dropna: bool_t | None = None,
+        data_columns: Literal[True] | list[str] | None = None,
+        errors: OpenFileErrors = "strict",
+        encoding: str = "UTF-8",
+    ) -> None:
+        """
+        Write the contained data to an HDF5 file using HDFStore.
+
+        Hierarchical Data Format (HDF) is self-describing, allowing an
+        application to interpret the structure and contents of a file with
+        no outside information. One HDF file can hold a mix of related objects
+        which can be accessed as a group or as individual objects.
+
+        In order to add another DataFrame or Series to an existing HDF file
+        please use append mode and a different a key.
+
+        .. warning::
+
+           One can store a subclass of ``DataFrame`` or ``Series`` to HDF5,
+           but the type of the subclass is lost upon storing.
+
+        For more information see the :ref:`user guide `.
+
+        Parameters
+        ----------
+        path_or_buf : str or pandas.HDFStore
+            File path or HDFStore object.
+        key : str
+            Identifier for the group in the store.
+        mode : {'a', 'w', 'r+'}, default 'a'
+            Mode to open file:
+
+            - 'w': write, a new file is created (an existing file with
+              the same name would be deleted).
+            - 'a': append, an existing file is opened for reading and
+              writing, and if the file does not exist it is created.
+            - 'r+': similar to 'a', but the file must already exist.
+        complevel : {0-9}, default None
+            Specifies a compression level for data.
+            A value of 0 or None disables compression.
+        complib : {'zlib', 'lzo', 'bzip2', 'blosc'}, default 'zlib'
+            Specifies the compression library to be used.
+            These additional compressors for Blosc are supported
+            (default if no compressor specified: 'blosc:blosclz'):
+            {'blosc:blosclz', 'blosc:lz4', 'blosc:lz4hc', 'blosc:snappy',
+            'blosc:zlib', 'blosc:zstd'}.
+            Specifying a compression library which is not available issues
+            a ValueError.
+        append : bool, default False
+            For Table formats, append the input data to the existing.
+        format : {'fixed', 'table', None}, default 'fixed'
+            Possible values:
+
+            - 'fixed': Fixed format. Fast writing/reading. Not-appendable,
+              nor searchable.
+            - 'table': Table format. Write as a PyTables Table structure
+              which may perform worse but allow more flexible operations
+              like searching / selecting subsets of the data.
+            - If None, pd.get_option('io.hdf.default_format') is checked,
+              followed by fallback to "fixed".
+        index : bool, default True
+            Write DataFrame index as a column.
+        min_itemsize : dict or int, optional
+            Map column names to minimum string sizes for columns.
+        nan_rep : Any, optional
+            How to represent null values as str.
+            Not allowed with append=True.
+        dropna : bool, default False, optional
+            Remove missing values.
+        data_columns : list of columns or True, optional
+            List of columns to create as indexed data columns for on-disk
+            queries, or True to use all columns. By default only the axes
+            of the object are indexed. See
+            :ref:`Query via data columns`. for
+            more information.
+            Applicable only to format='table'.
+        errors : str, default 'strict'
+            Specifies how encoding and decoding errors are to be handled.
+            See the errors argument for :func:`open` for a full list
+            of options.
+        encoding : str, default "UTF-8"
+
+        See Also
+        --------
+        read_hdf : Read from HDF file.
+        DataFrame.to_orc : Write a DataFrame to the binary orc format.
+        DataFrame.to_parquet : Write a DataFrame to the binary parquet format.
+        DataFrame.to_sql : Write to a SQL table.
+        DataFrame.to_feather : Write out feather-format for DataFrames.
+        DataFrame.to_csv : Write out to a csv file.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]},
+        ...                   index=['a', 'b', 'c'])  # doctest: +SKIP
+        >>> df.to_hdf('data.h5', key='df', mode='w')  # doctest: +SKIP
+
+        We can add another object to the same file:
+
+        >>> s = pd.Series([1, 2, 3, 4])  # doctest: +SKIP
+        >>> s.to_hdf('data.h5', key='s')  # doctest: +SKIP
+
+        Reading from HDF file:
+
+        >>> pd.read_hdf('data.h5', 'df')  # doctest: +SKIP
+        A  B
+        a  1  4
+        b  2  5
+        c  3  6
+        >>> pd.read_hdf('data.h5', 's')  # doctest: +SKIP
+        0    1
+        1    2
+        2    3
+        3    4
+        dtype: int64
+        """
+        from pandas.io import pytables
+
+        # Argument 3 to "to_hdf" has incompatible type "NDFrame"; expected
+        # "Union[DataFrame, Series]" [arg-type]
+        pytables.to_hdf(
+            path_or_buf,
+            key,
+            self,  # type: ignore[arg-type]
+            mode=mode,
+            complevel=complevel,
+            complib=complib,
+            append=append,
+            format=format,
+            index=index,
+            min_itemsize=min_itemsize,
+            nan_rep=nan_rep,
+            dropna=dropna,
+            data_columns=data_columns,
+            errors=errors,
+            encoding=encoding,
+        )
+
+    @final
+    @deprecate_nonkeyword_arguments(
+        version="3.0", allowed_args=["self", "name", "con"], name="to_sql"
+    )
+    def to_sql(
+        self,
+        name: str,
+        con,
+        schema: str | None = None,
+        if_exists: Literal["fail", "replace", "append"] = "fail",
+        index: bool_t = True,
+        index_label: IndexLabel | None = None,
+        chunksize: int | None = None,
+        dtype: DtypeArg | None = None,
+        method: Literal["multi"] | Callable | None = None,
+    ) -> int | None:
+        """
+        Write records stored in a DataFrame to a SQL database.
+
+        Databases supported by SQLAlchemy [1]_ are supported. Tables can be
+        newly created, appended to, or overwritten.
+
+        Parameters
+        ----------
+        name : str
+            Name of SQL table.
+        con : sqlalchemy.engine.(Engine or Connection) or sqlite3.Connection
+            Using SQLAlchemy makes it possible to use any DB supported by that
+            library. Legacy support is provided for sqlite3.Connection objects. The user
+            is responsible for engine disposal and connection closure for the SQLAlchemy
+            connectable. See `here \
+                `_.
+            If passing a sqlalchemy.engine.Connection which is already in a transaction,
+            the transaction will not be committed.  If passing a sqlite3.Connection,
+            it will not be possible to roll back the record insertion.
+
+        schema : str, optional
+            Specify the schema (if database flavor supports this). If None, use
+            default schema.
+        if_exists : {'fail', 'replace', 'append'}, default 'fail'
+            How to behave if the table already exists.
+
+            * fail: Raise a ValueError.
+            * replace: Drop the table before inserting new values.
+            * append: Insert new values to the existing table.
+
+        index : bool, default True
+            Write DataFrame index as a column. Uses `index_label` as the column
+            name in the table. Creates a table index for this column.
+        index_label : str or sequence, default None
+            Column label for index column(s). If None is given (default) and
+            `index` is True, then the index names are used.
+            A sequence should be given if the DataFrame uses MultiIndex.
+        chunksize : int, optional
+            Specify the number of rows in each batch to be written at a time.
+            By default, all rows will be written at once.
+        dtype : dict or scalar, optional
+            Specifying the datatype for columns. If a dictionary is used, the
+            keys should be the column names and the values should be the
+            SQLAlchemy types or strings for the sqlite3 legacy mode. If a
+            scalar is provided, it will be applied to all columns.
+        method : {None, 'multi', callable}, optional
+            Controls the SQL insertion clause used:
+
+            * None : Uses standard SQL ``INSERT`` clause (one per row).
+            * 'multi': Pass multiple values in a single ``INSERT`` clause.
+            * callable with signature ``(pd_table, conn, keys, data_iter)``.
+
+            Details and a sample callable implementation can be found in the
+            section :ref:`insert method `.
+
+        Returns
+        -------
+        None or int
+            Number of rows affected by to_sql. None is returned if the callable
+            passed into ``method`` does not return an integer number of rows.
+
+            The number of returned rows affected is the sum of the ``rowcount``
+            attribute of ``sqlite3.Cursor`` or SQLAlchemy connectable which may not
+            reflect the exact number of written rows as stipulated in the
+            `sqlite3 `__ or
+            `SQLAlchemy `__.
+
+            .. versionadded:: 1.4.0
+
+        Raises
+        ------
+        ValueError
+            When the table already exists and `if_exists` is 'fail' (the
+            default).
+
+        See Also
+        --------
+        read_sql : Read a DataFrame from a table.
+
+        Notes
+        -----
+        Timezone aware datetime columns will be written as
+        ``Timestamp with timezone`` type with SQLAlchemy if supported by the
+        database. Otherwise, the datetimes will be stored as timezone unaware
+        timestamps local to the original timezone.
+
+        Not all datastores support ``method="multi"``. Oracle, for example,
+        does not support multi-value insert.
+
+        References
+        ----------
+        .. [1] https://docs.sqlalchemy.org
+        .. [2] https://www.python.org/dev/peps/pep-0249/
+
+        Examples
+        --------
+        Create an in-memory SQLite database.
+
+        >>> from sqlalchemy import create_engine
+        >>> engine = create_engine('sqlite://', echo=False)
+
+        Create a table from scratch with 3 rows.
+
+        >>> df = pd.DataFrame({'name' : ['User 1', 'User 2', 'User 3']})
+        >>> df
+             name
+        0  User 1
+        1  User 2
+        2  User 3
+
+        >>> df.to_sql(name='users', con=engine)
+        3
+        >>> from sqlalchemy import text
+        >>> with engine.connect() as conn:
+        ...    conn.execute(text("SELECT * FROM users")).fetchall()
+        [(0, 'User 1'), (1, 'User 2'), (2, 'User 3')]
+
+        An `sqlalchemy.engine.Connection` can also be passed to `con`:
+
+        >>> with engine.begin() as connection:
+        ...     df1 = pd.DataFrame({'name' : ['User 4', 'User 5']})
+        ...     df1.to_sql(name='users', con=connection, if_exists='append')
+        2
+
+        This is allowed to support operations that require that the same
+        DBAPI connection is used for the entire operation.
+
+        >>> df2 = pd.DataFrame({'name' : ['User 6', 'User 7']})
+        >>> df2.to_sql(name='users', con=engine, if_exists='append')
+        2
+        >>> with engine.connect() as conn:
+        ...    conn.execute(text("SELECT * FROM users")).fetchall()
+        [(0, 'User 1'), (1, 'User 2'), (2, 'User 3'),
+         (0, 'User 4'), (1, 'User 5'), (0, 'User 6'),
+         (1, 'User 7')]
+
+        Overwrite the table with just ``df2``.
+
+        >>> df2.to_sql(name='users', con=engine, if_exists='replace',
+        ...            index_label='id')
+        2
+        >>> with engine.connect() as conn:
+        ...    conn.execute(text("SELECT * FROM users")).fetchall()
+        [(0, 'User 6'), (1, 'User 7')]
+
+        Use ``method`` to define a callable insertion method to do nothing
+        if there's a primary key conflict on a table in a PostgreSQL database.
+
+        >>> from sqlalchemy.dialects.postgresql import insert
+        >>> def insert_on_conflict_nothing(table, conn, keys, data_iter):
+        ...     # "a" is the primary key in "conflict_table"
+        ...     data = [dict(zip(keys, row)) for row in data_iter]
+        ...     stmt = insert(table.table).values(data).on_conflict_do_nothing(index_elements=["a"])
+        ...     result = conn.execute(stmt)
+        ...     return result.rowcount
+        >>> df_conflict.to_sql(name="conflict_table", con=conn, if_exists="append", method=insert_on_conflict_nothing)  # doctest: +SKIP
+        0
+
+        For MySQL, a callable to update columns ``b`` and ``c`` if there's a conflict
+        on a primary key.
+
+        >>> from sqlalchemy.dialects.mysql import insert
+        >>> def insert_on_conflict_update(table, conn, keys, data_iter):
+        ...     # update columns "b" and "c" on primary key conflict
+        ...     data = [dict(zip(keys, row)) for row in data_iter]
+        ...     stmt = (
+        ...         insert(table.table)
+        ...         .values(data)
+        ...     )
+        ...     stmt = stmt.on_duplicate_key_update(b=stmt.inserted.b, c=stmt.inserted.c)
+        ...     result = conn.execute(stmt)
+        ...     return result.rowcount
+        >>> df_conflict.to_sql(name="conflict_table", con=conn, if_exists="append", method=insert_on_conflict_update)  # doctest: +SKIP
+        2
+
+        Specify the dtype (especially useful for integers with missing values).
+        Notice that while pandas is forced to store the data as floating point,
+        the database supports nullable integers. When fetching the data with
+        Python, we get back integer scalars.
+
+        >>> df = pd.DataFrame({"A": [1, None, 2]})
+        >>> df
+             A
+        0  1.0
+        1  NaN
+        2  2.0
+
+        >>> from sqlalchemy.types import Integer
+        >>> df.to_sql(name='integers', con=engine, index=False,
+        ...           dtype={"A": Integer()})
+        3
+
+        >>> with engine.connect() as conn:
+        ...   conn.execute(text("SELECT * FROM integers")).fetchall()
+        [(1,), (None,), (2,)]
+        """  # noqa: E501
+        from pandas.io import sql
+
+        return sql.to_sql(
+            self,
+            name,
+            con,
+            schema=schema,
+            if_exists=if_exists,
+            index=index,
+            index_label=index_label,
+            chunksize=chunksize,
+            dtype=dtype,
+            method=method,
+        )
+
+    @final
+    @deprecate_nonkeyword_arguments(
+        version="3.0", allowed_args=["self", "path"], name="to_pickle"
+    )
+    @doc(
+        storage_options=_shared_docs["storage_options"],
+        compression_options=_shared_docs["compression_options"] % "path",
+    )
+    def to_pickle(
+        self,
+        path: FilePath | WriteBuffer[bytes],
+        compression: CompressionOptions = "infer",
+        protocol: int = pickle.HIGHEST_PROTOCOL,
+        storage_options: StorageOptions | None = None,
+    ) -> None:
+        """
+        Pickle (serialize) object to file.
+
+        Parameters
+        ----------
+        path : str, path object, or file-like object
+            String, path object (implementing ``os.PathLike[str]``), or file-like
+            object implementing a binary ``write()`` function. File path where
+            the pickled object will be stored.
+        {compression_options}
+        protocol : int
+            Int which indicates which protocol should be used by the pickler,
+            default HIGHEST_PROTOCOL (see [1]_ paragraph 12.1.2). The possible
+            values are 0, 1, 2, 3, 4, 5. A negative value for the protocol
+            parameter is equivalent to setting its value to HIGHEST_PROTOCOL.
+
+            .. [1] https://docs.python.org/3/library/pickle.html.
+
+        {storage_options}
+
+        See Also
+        --------
+        read_pickle : Load pickled pandas object (or any object) from file.
+        DataFrame.to_hdf : Write DataFrame to an HDF5 file.
+        DataFrame.to_sql : Write DataFrame to a SQL database.
+        DataFrame.to_parquet : Write a DataFrame to the binary parquet format.
+
+        Examples
+        --------
+        >>> original_df = pd.DataFrame({{"foo": range(5), "bar": range(5, 10)}})  # doctest: +SKIP
+        >>> original_df  # doctest: +SKIP
+           foo  bar
+        0    0    5
+        1    1    6
+        2    2    7
+        3    3    8
+        4    4    9
+        >>> original_df.to_pickle("./dummy.pkl")  # doctest: +SKIP
+
+        >>> unpickled_df = pd.read_pickle("./dummy.pkl")  # doctest: +SKIP
+        >>> unpickled_df  # doctest: +SKIP
+           foo  bar
+        0    0    5
+        1    1    6
+        2    2    7
+        3    3    8
+        4    4    9
+        """  # noqa: E501
+        from pandas.io.pickle import to_pickle
+
+        to_pickle(
+            self,
+            path,
+            compression=compression,
+            protocol=protocol,
+            storage_options=storage_options,
+        )
+
+    @final
+    @deprecate_nonkeyword_arguments(
+        version="3.0", allowed_args=["self"], name="to_clipboard"
+    )
+    def to_clipboard(
+        self, excel: bool_t = True, sep: str | None = None, **kwargs
+    ) -> None:
+        r"""
+        Copy object to the system clipboard.
+
+        Write a text representation of object to the system clipboard.
+        This can be pasted into Excel, for example.
+
+        Parameters
+        ----------
+        excel : bool, default True
+            Produce output in a csv format for easy pasting into excel.
+
+            - True, use the provided separator for csv pasting.
+            - False, write a string representation of the object to the clipboard.
+
+        sep : str, default ``'\t'``
+            Field delimiter.
+        **kwargs
+            These parameters will be passed to DataFrame.to_csv.
+
+        See Also
+        --------
+        DataFrame.to_csv : Write a DataFrame to a comma-separated values
+            (csv) file.
+        read_clipboard : Read text from clipboard and pass to read_csv.
+
+        Notes
+        -----
+        Requirements for your platform.
+
+          - Linux : `xclip`, or `xsel` (with `PyQt4` modules)
+          - Windows : none
+          - macOS : none
+
+        This method uses the processes developed for the package `pyperclip`. A
+        solution to render any output string format is given in the examples.
+
+        Examples
+        --------
+        Copy the contents of a DataFrame to the clipboard.
+
+        >>> df = pd.DataFrame([[1, 2, 3], [4, 5, 6]], columns=['A', 'B', 'C'])
+
+        >>> df.to_clipboard(sep=',')  # doctest: +SKIP
+        ... # Wrote the following to the system clipboard:
+        ... # ,A,B,C
+        ... # 0,1,2,3
+        ... # 1,4,5,6
+
+        We can omit the index by passing the keyword `index` and setting
+        it to false.
+
+        >>> df.to_clipboard(sep=',', index=False)  # doctest: +SKIP
+        ... # Wrote the following to the system clipboard:
+        ... # A,B,C
+        ... # 1,2,3
+        ... # 4,5,6
+
+        Using the original `pyperclip` package for any string output format.
+
+        .. code-block:: python
+
+           import pyperclip
+           html = df.style.to_html()
+           pyperclip.copy(html)
+        """
+        from pandas.io import clipboards
+
+        clipboards.to_clipboard(self, excel=excel, sep=sep, **kwargs)
+
+    @final
+    def to_xarray(self):
+        """
+        Return an xarray object from the pandas object.
+
+        Returns
+        -------
+        xarray.DataArray or xarray.Dataset
+            Data in the pandas structure converted to Dataset if the object is
+            a DataFrame, or a DataArray if the object is a Series.
+
+        See Also
+        --------
+        DataFrame.to_hdf : Write DataFrame to an HDF5 file.
+        DataFrame.to_parquet : Write a DataFrame to the binary parquet format.
+
+        Notes
+        -----
+        See the `xarray docs `__
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([('falcon', 'bird', 389.0, 2),
+        ...                    ('parrot', 'bird', 24.0, 2),
+        ...                    ('lion', 'mammal', 80.5, 4),
+        ...                    ('monkey', 'mammal', np.nan, 4)],
+        ...                   columns=['name', 'class', 'max_speed',
+        ...                            'num_legs'])
+        >>> df
+             name   class  max_speed  num_legs
+        0  falcon    bird      389.0         2
+        1  parrot    bird       24.0         2
+        2    lion  mammal       80.5         4
+        3  monkey  mammal        NaN         4
+
+        >>> df.to_xarray()  # doctest: +SKIP
+        
+        Dimensions:    (index: 4)
+        Coordinates:
+          * index      (index) int64 32B 0 1 2 3
+        Data variables:
+            name       (index) object 32B 'falcon' 'parrot' 'lion' 'monkey'
+            class      (index) object 32B 'bird' 'bird' 'mammal' 'mammal'
+            max_speed  (index) float64 32B 389.0 24.0 80.5 nan
+            num_legs   (index) int64 32B 2 2 4 4
+
+        >>> df['max_speed'].to_xarray()  # doctest: +SKIP
+        
+        array([389. ,  24. ,  80.5,   nan])
+        Coordinates:
+          * index    (index) int64 0 1 2 3
+
+        >>> dates = pd.to_datetime(['2018-01-01', '2018-01-01',
+        ...                         '2018-01-02', '2018-01-02'])
+        >>> df_multiindex = pd.DataFrame({'date': dates,
+        ...                               'animal': ['falcon', 'parrot',
+        ...                                          'falcon', 'parrot'],
+        ...                               'speed': [350, 18, 361, 15]})
+        >>> df_multiindex = df_multiindex.set_index(['date', 'animal'])
+
+        >>> df_multiindex
+                           speed
+        date       animal
+        2018-01-01 falcon    350
+                   parrot     18
+        2018-01-02 falcon    361
+                   parrot     15
+
+        >>> df_multiindex.to_xarray()  # doctest: +SKIP
+        
+        Dimensions:  (date: 2, animal: 2)
+        Coordinates:
+          * date     (date) datetime64[ns] 2018-01-01 2018-01-02
+          * animal   (animal) object 'falcon' 'parrot'
+        Data variables:
+            speed    (date, animal) int64 350 18 361 15
+        """
+        xarray = import_optional_dependency("xarray")
+
+        if self.ndim == 1:
+            return xarray.DataArray.from_series(self)
+        else:
+            return xarray.Dataset.from_dataframe(self)
+
+    @overload
+    def to_latex(
+        self,
+        buf: None = ...,
+        columns: Sequence[Hashable] | None = ...,
+        header: bool_t | SequenceNotStr[str] = ...,
+        index: bool_t = ...,
+        na_rep: str = ...,
+        formatters: FormattersType | None = ...,
+        float_format: FloatFormatType | None = ...,
+        sparsify: bool_t | None = ...,
+        index_names: bool_t = ...,
+        bold_rows: bool_t = ...,
+        column_format: str | None = ...,
+        longtable: bool_t | None = ...,
+        escape: bool_t | None = ...,
+        encoding: str | None = ...,
+        decimal: str = ...,
+        multicolumn: bool_t | None = ...,
+        multicolumn_format: str | None = ...,
+        multirow: bool_t | None = ...,
+        caption: str | tuple[str, str] | None = ...,
+        label: str | None = ...,
+        position: str | None = ...,
+    ) -> str:
+        ...
+
+    @overload
+    def to_latex(
+        self,
+        buf: FilePath | WriteBuffer[str],
+        columns: Sequence[Hashable] | None = ...,
+        header: bool_t | SequenceNotStr[str] = ...,
+        index: bool_t = ...,
+        na_rep: str = ...,
+        formatters: FormattersType | None = ...,
+        float_format: FloatFormatType | None = ...,
+        sparsify: bool_t | None = ...,
+        index_names: bool_t = ...,
+        bold_rows: bool_t = ...,
+        column_format: str | None = ...,
+        longtable: bool_t | None = ...,
+        escape: bool_t | None = ...,
+        encoding: str | None = ...,
+        decimal: str = ...,
+        multicolumn: bool_t | None = ...,
+        multicolumn_format: str | None = ...,
+        multirow: bool_t | None = ...,
+        caption: str | tuple[str, str] | None = ...,
+        label: str | None = ...,
+        position: str | None = ...,
+    ) -> None:
+        ...
+
+    @final
+    @deprecate_nonkeyword_arguments(
+        version="3.0", allowed_args=["self", "buf"], name="to_latex"
+    )
+    def to_latex(
+        self,
+        buf: FilePath | WriteBuffer[str] | None = None,
+        columns: Sequence[Hashable] | None = None,
+        header: bool_t | SequenceNotStr[str] = True,
+        index: bool_t = True,
+        na_rep: str = "NaN",
+        formatters: FormattersType | None = None,
+        float_format: FloatFormatType | None = None,
+        sparsify: bool_t | None = None,
+        index_names: bool_t = True,
+        bold_rows: bool_t = False,
+        column_format: str | None = None,
+        longtable: bool_t | None = None,
+        escape: bool_t | None = None,
+        encoding: str | None = None,
+        decimal: str = ".",
+        multicolumn: bool_t | None = None,
+        multicolumn_format: str | None = None,
+        multirow: bool_t | None = None,
+        caption: str | tuple[str, str] | None = None,
+        label: str | None = None,
+        position: str | None = None,
+    ) -> str | None:
+        r"""
+        Render object to a LaTeX tabular, longtable, or nested table.
+
+        Requires ``\usepackage{{booktabs}}``.  The output can be copy/pasted
+        into a main LaTeX document or read from an external file
+        with ``\input{{table.tex}}``.
+
+        .. versionchanged:: 2.0.0
+           Refactored to use the Styler implementation via jinja2 templating.
+
+        Parameters
+        ----------
+        buf : str, Path or StringIO-like, optional, default None
+            Buffer to write to. If None, the output is returned as a string.
+        columns : list of label, optional
+            The subset of columns to write. Writes all columns by default.
+        header : bool or list of str, default True
+            Write out the column names. If a list of strings is given,
+            it is assumed to be aliases for the column names.
+        index : bool, default True
+            Write row names (index).
+        na_rep : str, default 'NaN'
+            Missing data representation.
+        formatters : list of functions or dict of {{str: function}}, optional
+            Formatter functions to apply to columns' elements by position or
+            name. The result of each function must be a unicode string.
+            List must be of length equal to the number of columns.
+        float_format : one-parameter function or str, optional, default None
+            Formatter for floating point numbers. For example
+            ``float_format="%.2f"`` and ``float_format="{{:0.2f}}".format`` will
+            both result in 0.1234 being formatted as 0.12.
+        sparsify : bool, optional
+            Set to False for a DataFrame with a hierarchical index to print
+            every multiindex key at each row. By default, the value will be
+            read from the config module.
+        index_names : bool, default True
+            Prints the names of the indexes.
+        bold_rows : bool, default False
+            Make the row labels bold in the output.
+        column_format : str, optional
+            The columns format as specified in `LaTeX table format
+            `__ e.g. 'rcl' for 3
+            columns. By default, 'l' will be used for all columns except
+            columns of numbers, which default to 'r'.
+        longtable : bool, optional
+            Use a longtable environment instead of tabular. Requires
+            adding a \usepackage{{longtable}} to your LaTeX preamble.
+            By default, the value will be read from the pandas config
+            module, and set to `True` if the option ``styler.latex.environment`` is
+            `"longtable"`.
+
+            .. versionchanged:: 2.0.0
+               The pandas option affecting this argument has changed.
+        escape : bool, optional
+            By default, the value will be read from the pandas config
+            module and set to `True` if the option ``styler.format.escape`` is
+            `"latex"`. When set to False prevents from escaping latex special
+            characters in column names.
+
+            .. versionchanged:: 2.0.0
+               The pandas option affecting this argument has changed, as has the
+               default value to `False`.
+        encoding : str, optional
+            A string representing the encoding to use in the output file,
+            defaults to 'utf-8'.
+        decimal : str, default '.'
+            Character recognized as decimal separator, e.g. ',' in Europe.
+        multicolumn : bool, default True
+            Use \multicolumn to enhance MultiIndex columns.
+            The default will be read from the config module, and is set
+            as the option ``styler.sparse.columns``.
+
+            .. versionchanged:: 2.0.0
+               The pandas option affecting this argument has changed.
+        multicolumn_format : str, default 'r'
+            The alignment for multicolumns, similar to `column_format`
+            The default will be read from the config module, and is set as the option
+            ``styler.latex.multicol_align``.
+
+            .. versionchanged:: 2.0.0
+               The pandas option affecting this argument has changed, as has the
+               default value to "r".
+        multirow : bool, default True
+            Use \multirow to enhance MultiIndex rows. Requires adding a
+            \usepackage{{multirow}} to your LaTeX preamble. Will print
+            centered labels (instead of top-aligned) across the contained
+            rows, separating groups via clines. The default will be read
+            from the pandas config module, and is set as the option
+            ``styler.sparse.index``.
+
+            .. versionchanged:: 2.0.0
+               The pandas option affecting this argument has changed, as has the
+               default value to `True`.
+        caption : str or tuple, optional
+            Tuple (full_caption, short_caption),
+            which results in ``\caption[short_caption]{{full_caption}}``;
+            if a single string is passed, no short caption will be set.
+        label : str, optional
+            The LaTeX label to be placed inside ``\label{{}}`` in the output.
+            This is used with ``\ref{{}}`` in the main ``.tex`` file.
+
+        position : str, optional
+            The LaTeX positional argument for tables, to be placed after
+            ``\begin{{}}`` in the output.
+
+        Returns
+        -------
+        str or None
+            If buf is None, returns the result as a string. Otherwise returns None.
+
+        See Also
+        --------
+        io.formats.style.Styler.to_latex : Render a DataFrame to LaTeX
+            with conditional formatting.
+        DataFrame.to_string : Render a DataFrame to a console-friendly
+            tabular output.
+        DataFrame.to_html : Render a DataFrame as an HTML table.
+
+        Notes
+        -----
+        As of v2.0.0 this method has changed to use the Styler implementation as
+        part of :meth:`.Styler.to_latex` via ``jinja2`` templating. This means
+        that ``jinja2`` is a requirement, and needs to be installed, for this method
+        to function. It is advised that users switch to using Styler, since that
+        implementation is more frequently updated and contains much more
+        flexibility with the output.
+
+        Examples
+        --------
+        Convert a general DataFrame to LaTeX with formatting:
+
+        >>> df = pd.DataFrame(dict(name=['Raphael', 'Donatello'],
+        ...                        age=[26, 45],
+        ...                        height=[181.23, 177.65]))
+        >>> print(df.to_latex(index=False,
+        ...                   formatters={"name": str.upper},
+        ...                   float_format="{:.1f}".format,
+        ... ))  # doctest: +SKIP
+        \begin{tabular}{lrr}
+        \toprule
+        name & age & height \\
+        \midrule
+        RAPHAEL & 26 & 181.2 \\
+        DONATELLO & 45 & 177.7 \\
+        \bottomrule
+        \end{tabular}
+        """
+        # Get defaults from the pandas config
+        if self.ndim == 1:
+            self = self.to_frame()
+        if longtable is None:
+            longtable = config.get_option("styler.latex.environment") == "longtable"
+        if escape is None:
+            escape = config.get_option("styler.format.escape") == "latex"
+        if multicolumn is None:
+            multicolumn = config.get_option("styler.sparse.columns")
+        if multicolumn_format is None:
+            multicolumn_format = config.get_option("styler.latex.multicol_align")
+        if multirow is None:
+            multirow = config.get_option("styler.sparse.index")
+
+        if column_format is not None and not isinstance(column_format, str):
+            raise ValueError("`column_format` must be str or unicode")
+        length = len(self.columns) if columns is None else len(columns)
+        if isinstance(header, (list, tuple)) and len(header) != length:
+            raise ValueError(f"Writing {length} cols but got {len(header)} aliases")
+
+        # Refactor formatters/float_format/decimal/na_rep/escape to Styler structure
+        base_format_ = {
+            "na_rep": na_rep,
+            "escape": "latex" if escape else None,
+            "decimal": decimal,
+        }
+        index_format_: dict[str, Any] = {"axis": 0, **base_format_}
+        column_format_: dict[str, Any] = {"axis": 1, **base_format_}
+
+        if isinstance(float_format, str):
+            float_format_: Callable | None = lambda x: float_format % x
+        else:
+            float_format_ = float_format
+
+        def _wrap(x, alt_format_):
+            if isinstance(x, (float, complex)) and float_format_ is not None:
+                return float_format_(x)
+            else:
+                return alt_format_(x)
+
+        formatters_: list | tuple | dict | Callable | None = None
+        if isinstance(formatters, list):
+            formatters_ = {
+                c: partial(_wrap, alt_format_=formatters[i])
+                for i, c in enumerate(self.columns)
+            }
+        elif isinstance(formatters, dict):
+            index_formatter = formatters.pop("__index__", None)
+            column_formatter = formatters.pop("__columns__", None)
+            if index_formatter is not None:
+                index_format_.update({"formatter": index_formatter})
+            if column_formatter is not None:
+                column_format_.update({"formatter": column_formatter})
+
+            formatters_ = formatters
+            float_columns = self.select_dtypes(include="float").columns
+            for col in float_columns:
+                if col not in formatters.keys():
+                    formatters_.update({col: float_format_})
+        elif formatters is None and float_format is not None:
+            formatters_ = partial(_wrap, alt_format_=lambda v: v)
+        format_index_ = [index_format_, column_format_]
+
+        # Deal with hiding indexes and relabelling column names
+        hide_: list[dict] = []
+        relabel_index_: list[dict] = []
+        if columns:
+            hide_.append(
+                {
+                    "subset": [c for c in self.columns if c not in columns],
+                    "axis": "columns",
+                }
+            )
+        if header is False:
+            hide_.append({"axis": "columns"})
+        elif isinstance(header, (list, tuple)):
+            relabel_index_.append({"labels": header, "axis": "columns"})
+            format_index_ = [index_format_]  # column_format is overwritten
+
+        if index is False:
+            hide_.append({"axis": "index"})
+        if index_names is False:
+            hide_.append({"names": True, "axis": "index"})
+
+        render_kwargs_ = {
+            "hrules": True,
+            "sparse_index": sparsify,
+            "sparse_columns": sparsify,
+            "environment": "longtable" if longtable else None,
+            "multicol_align": multicolumn_format
+            if multicolumn
+            else f"naive-{multicolumn_format}",
+            "multirow_align": "t" if multirow else "naive",
+            "encoding": encoding,
+            "caption": caption,
+            "label": label,
+            "position": position,
+            "column_format": column_format,
+            "clines": "skip-last;data"
+            if (multirow and isinstance(self.index, MultiIndex))
+            else None,
+            "bold_rows": bold_rows,
+        }
+
+        return self._to_latex_via_styler(
+            buf,
+            hide=hide_,
+            relabel_index=relabel_index_,
+            format={"formatter": formatters_, **base_format_},
+            format_index=format_index_,
+            render_kwargs=render_kwargs_,
+        )
+
+    @final
+    def _to_latex_via_styler(
+        self,
+        buf=None,
+        *,
+        hide: dict | list[dict] | None = None,
+        relabel_index: dict | list[dict] | None = None,
+        format: dict | list[dict] | None = None,
+        format_index: dict | list[dict] | None = None,
+        render_kwargs: dict | None = None,
+    ):
+        """
+        Render object to a LaTeX tabular, longtable, or nested table.
+
+        Uses the ``Styler`` implementation with the following, ordered, method chaining:
+
+        .. code-block:: python
+           styler = Styler(DataFrame)
+           styler.hide(**hide)
+           styler.relabel_index(**relabel_index)
+           styler.format(**format)
+           styler.format_index(**format_index)
+           styler.to_latex(buf=buf, **render_kwargs)
+
+        Parameters
+        ----------
+        buf : str, Path or StringIO-like, optional, default None
+            Buffer to write to. If None, the output is returned as a string.
+        hide : dict, list of dict
+            Keyword args to pass to the method call of ``Styler.hide``. If a list will
+            call the method numerous times.
+        relabel_index : dict, list of dict
+            Keyword args to pass to the method of ``Styler.relabel_index``. If a list
+            will call the method numerous times.
+        format : dict, list of dict
+            Keyword args to pass to the method call of ``Styler.format``. If a list will
+            call the method numerous times.
+        format_index : dict, list of dict
+            Keyword args to pass to the method call of ``Styler.format_index``. If a
+            list will call the method numerous times.
+        render_kwargs : dict
+            Keyword args to pass to the method call of ``Styler.to_latex``.
+
+        Returns
+        -------
+        str or None
+            If buf is None, returns the result as a string. Otherwise returns None.
+        """
+        from pandas.io.formats.style import Styler
+
+        self = cast("DataFrame", self)
+        styler = Styler(self, uuid="")
+
+        for kw_name in ["hide", "relabel_index", "format", "format_index"]:
+            kw = vars()[kw_name]
+            if isinstance(kw, dict):
+                getattr(styler, kw_name)(**kw)
+            elif isinstance(kw, list):
+                for sub_kw in kw:
+                    getattr(styler, kw_name)(**sub_kw)
+
+        # bold_rows is not a direct kwarg of Styler.to_latex
+        render_kwargs = {} if render_kwargs is None else render_kwargs
+        if render_kwargs.pop("bold_rows"):
+            styler.map_index(lambda v: "textbf:--rwrap;")
+
+        return styler.to_latex(buf=buf, **render_kwargs)
+
+    @overload
+    def to_csv(
+        self,
+        path_or_buf: None = ...,
+        sep: str = ...,
+        na_rep: str = ...,
+        float_format: str | Callable | None = ...,
+        columns: Sequence[Hashable] | None = ...,
+        header: bool_t | list[str] = ...,
+        index: bool_t = ...,
+        index_label: IndexLabel | None = ...,
+        mode: str = ...,
+        encoding: str | None = ...,
+        compression: CompressionOptions = ...,
+        quoting: int | None = ...,
+        quotechar: str = ...,
+        lineterminator: str | None = ...,
+        chunksize: int | None = ...,
+        date_format: str | None = ...,
+        doublequote: bool_t = ...,
+        escapechar: str | None = ...,
+        decimal: str = ...,
+        errors: OpenFileErrors = ...,
+        storage_options: StorageOptions = ...,
+    ) -> str:
+        ...
+
+    @overload
+    def to_csv(
+        self,
+        path_or_buf: FilePath | WriteBuffer[bytes] | WriteBuffer[str],
+        sep: str = ...,
+        na_rep: str = ...,
+        float_format: str | Callable | None = ...,
+        columns: Sequence[Hashable] | None = ...,
+        header: bool_t | list[str] = ...,
+        index: bool_t = ...,
+        index_label: IndexLabel | None = ...,
+        mode: str = ...,
+        encoding: str | None = ...,
+        compression: CompressionOptions = ...,
+        quoting: int | None = ...,
+        quotechar: str = ...,
+        lineterminator: str | None = ...,
+        chunksize: int | None = ...,
+        date_format: str | None = ...,
+        doublequote: bool_t = ...,
+        escapechar: str | None = ...,
+        decimal: str = ...,
+        errors: OpenFileErrors = ...,
+        storage_options: StorageOptions = ...,
+    ) -> None:
+        ...
+
+    @final
+    @deprecate_nonkeyword_arguments(
+        version="3.0", allowed_args=["self", "path_or_buf"], name="to_csv"
+    )
+    @doc(
+        storage_options=_shared_docs["storage_options"],
+        compression_options=_shared_docs["compression_options"] % "path_or_buf",
+    )
+    def to_csv(
+        self,
+        path_or_buf: FilePath | WriteBuffer[bytes] | WriteBuffer[str] | None = None,
+        sep: str = ",",
+        na_rep: str = "",
+        float_format: str | Callable | None = None,
+        columns: Sequence[Hashable] | None = None,
+        header: bool_t | list[str] = True,
+        index: bool_t = True,
+        index_label: IndexLabel | None = None,
+        mode: str = "w",
+        encoding: str | None = None,
+        compression: CompressionOptions = "infer",
+        quoting: int | None = None,
+        quotechar: str = '"',
+        lineterminator: str | None = None,
+        chunksize: int | None = None,
+        date_format: str | None = None,
+        doublequote: bool_t = True,
+        escapechar: str | None = None,
+        decimal: str = ".",
+        errors: OpenFileErrors = "strict",
+        storage_options: StorageOptions | None = None,
+    ) -> str | None:
+        r"""
+        Write object to a comma-separated values (csv) file.
+
+        Parameters
+        ----------
+        path_or_buf : str, path object, file-like object, or None, default None
+            String, path object (implementing os.PathLike[str]), or file-like
+            object implementing a write() function. If None, the result is
+            returned as a string. If a non-binary file object is passed, it should
+            be opened with `newline=''`, disabling universal newlines. If a binary
+            file object is passed, `mode` might need to contain a `'b'`.
+        sep : str, default ','
+            String of length 1. Field delimiter for the output file.
+        na_rep : str, default ''
+            Missing data representation.
+        float_format : str, Callable, default None
+            Format string for floating point numbers. If a Callable is given, it takes
+            precedence over other numeric formatting parameters, like decimal.
+        columns : sequence, optional
+            Columns to write.
+        header : bool or list of str, default True
+            Write out the column names. If a list of strings is given it is
+            assumed to be aliases for the column names.
+        index : bool, default True
+            Write row names (index).
+        index_label : str or sequence, or False, default None
+            Column label for index column(s) if desired. If None is given, and
+            `header` and `index` are True, then the index names are used. A
+            sequence should be given if the object uses MultiIndex. If
+            False do not print fields for index names. Use index_label=False
+            for easier importing in R.
+        mode : {{'w', 'x', 'a'}}, default 'w'
+            Forwarded to either `open(mode=)` or `fsspec.open(mode=)` to control
+            the file opening. Typical values include:
+
+            - 'w', truncate the file first.
+            - 'x', exclusive creation, failing if the file already exists.
+            - 'a', append to the end of file if it exists.
+
+        encoding : str, optional
+            A string representing the encoding to use in the output file,
+            defaults to 'utf-8'. `encoding` is not supported if `path_or_buf`
+            is a non-binary file object.
+        {compression_options}
+
+               May be a dict with key 'method' as compression mode
+               and other entries as additional compression options if
+               compression mode is 'zip'.
+
+               Passing compression options as keys in dict is
+               supported for compression modes 'gzip', 'bz2', 'zstd', and 'zip'.
+        quoting : optional constant from csv module
+            Defaults to csv.QUOTE_MINIMAL. If you have set a `float_format`
+            then floats are converted to strings and thus csv.QUOTE_NONNUMERIC
+            will treat them as non-numeric.
+        quotechar : str, default '\"'
+            String of length 1. Character used to quote fields.
+        lineterminator : str, optional
+            The newline character or character sequence to use in the output
+            file. Defaults to `os.linesep`, which depends on the OS in which
+            this method is called ('\\n' for linux, '\\r\\n' for Windows, i.e.).
+
+            .. versionchanged:: 1.5.0
+
+                Previously was line_terminator, changed for consistency with
+                read_csv and the standard library 'csv' module.
+
+        chunksize : int or None
+            Rows to write at a time.
+        date_format : str, default None
+            Format string for datetime objects.
+        doublequote : bool, default True
+            Control quoting of `quotechar` inside a field.
+        escapechar : str, default None
+            String of length 1. Character used to escape `sep` and `quotechar`
+            when appropriate.
+        decimal : str, default '.'
+            Character recognized as decimal separator. E.g. use ',' for
+            European data.
+        errors : str, default 'strict'
+            Specifies how encoding and decoding errors are to be handled.
+            See the errors argument for :func:`open` for a full list
+            of options.
+
+        {storage_options}
+
+        Returns
+        -------
+        None or str
+            If path_or_buf is None, returns the resulting csv format as a
+            string. Otherwise returns None.
+
+        See Also
+        --------
+        read_csv : Load a CSV file into a DataFrame.
+        to_excel : Write DataFrame to an Excel file.
+
+        Examples
+        --------
+        Create 'out.csv' containing 'df' without indices
+
+        >>> df = pd.DataFrame({{'name': ['Raphael', 'Donatello'],
+        ...                    'mask': ['red', 'purple'],
+        ...                    'weapon': ['sai', 'bo staff']}})
+        >>> df.to_csv('out.csv', index=False)  # doctest: +SKIP
+
+        Create 'out.zip' containing 'out.csv'
+
+        >>> df.to_csv(index=False)
+        'name,mask,weapon\nRaphael,red,sai\nDonatello,purple,bo staff\n'
+        >>> compression_opts = dict(method='zip',
+        ...                         archive_name='out.csv')  # doctest: +SKIP
+        >>> df.to_csv('out.zip', index=False,
+        ...           compression=compression_opts)  # doctest: +SKIP
+
+        To write a csv file to a new folder or nested folder you will first
+        need to create it using either Pathlib or os:
+
+        >>> from pathlib import Path  # doctest: +SKIP
+        >>> filepath = Path('folder/subfolder/out.csv')  # doctest: +SKIP
+        >>> filepath.parent.mkdir(parents=True, exist_ok=True)  # doctest: +SKIP
+        >>> df.to_csv(filepath)  # doctest: +SKIP
+
+        >>> import os  # doctest: +SKIP
+        >>> os.makedirs('folder/subfolder', exist_ok=True)  # doctest: +SKIP
+        >>> df.to_csv('folder/subfolder/out.csv')  # doctest: +SKIP
+        """
+        df = self if isinstance(self, ABCDataFrame) else self.to_frame()
+
+        formatter = DataFrameFormatter(
+            frame=df,
+            header=header,
+            index=index,
+            na_rep=na_rep,
+            float_format=float_format,
+            decimal=decimal,
+        )
+
+        return DataFrameRenderer(formatter).to_csv(
+            path_or_buf,
+            lineterminator=lineterminator,
+            sep=sep,
+            encoding=encoding,
+            errors=errors,
+            compression=compression,
+            quoting=quoting,
+            columns=columns,
+            index_label=index_label,
+            mode=mode,
+            chunksize=chunksize,
+            quotechar=quotechar,
+            date_format=date_format,
+            doublequote=doublequote,
+            escapechar=escapechar,
+            storage_options=storage_options,
+        )
+
+    # ----------------------------------------------------------------------
+    # Lookup Caching
+
+    def _reset_cacher(self) -> None:
+        """
+        Reset the cacher.
+        """
+        raise AbstractMethodError(self)
+
+    def _maybe_update_cacher(
+        self,
+        clear: bool_t = False,
+        verify_is_copy: bool_t = True,
+        inplace: bool_t = False,
+    ) -> None:
+        """
+        See if we need to update our parent cacher if clear, then clear our
+        cache.
+
+        Parameters
+        ----------
+        clear : bool, default False
+            Clear the item cache.
+        verify_is_copy : bool, default True
+            Provide is_copy checks.
+        """
+        if using_copy_on_write():
+            return
+
+        if verify_is_copy:
+            self._check_setitem_copy(t="referent")
+
+        if clear:
+            self._clear_item_cache()
+
+    def _clear_item_cache(self) -> None:
+        raise AbstractMethodError(self)
+
+    # ----------------------------------------------------------------------
+    # Indexing Methods
+
+    @final
+    def take(self, indices, axis: Axis = 0, **kwargs) -> Self:
+        """
+        Return the elements in the given *positional* indices along an axis.
+
+        This means that we are not indexing according to actual values in
+        the index attribute of the object. We are indexing according to the
+        actual position of the element in the object.
+
+        Parameters
+        ----------
+        indices : array-like
+            An array of ints indicating which positions to take.
+        axis : {0 or 'index', 1 or 'columns', None}, default 0
+            The axis on which to select elements. ``0`` means that we are
+            selecting rows, ``1`` means that we are selecting columns.
+            For `Series` this parameter is unused and defaults to 0.
+        **kwargs
+            For compatibility with :meth:`numpy.take`. Has no effect on the
+            output.
+
+        Returns
+        -------
+        same type as caller
+            An array-like containing the elements taken from the object.
+
+        See Also
+        --------
+        DataFrame.loc : Select a subset of a DataFrame by labels.
+        DataFrame.iloc : Select a subset of a DataFrame by positions.
+        numpy.take : Take elements from an array along an axis.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([('falcon', 'bird', 389.0),
+        ...                    ('parrot', 'bird', 24.0),
+        ...                    ('lion', 'mammal', 80.5),
+        ...                    ('monkey', 'mammal', np.nan)],
+        ...                   columns=['name', 'class', 'max_speed'],
+        ...                   index=[0, 2, 3, 1])
+        >>> df
+             name   class  max_speed
+        0  falcon    bird      389.0
+        2  parrot    bird       24.0
+        3    lion  mammal       80.5
+        1  monkey  mammal        NaN
+
+        Take elements at positions 0 and 3 along the axis 0 (default).
+
+        Note how the actual indices selected (0 and 1) do not correspond to
+        our selected indices 0 and 3. That's because we are selecting the 0th
+        and 3rd rows, not rows whose indices equal 0 and 3.
+
+        >>> df.take([0, 3])
+             name   class  max_speed
+        0  falcon    bird      389.0
+        1  monkey  mammal        NaN
+
+        Take elements at indices 1 and 2 along the axis 1 (column selection).
+
+        >>> df.take([1, 2], axis=1)
+            class  max_speed
+        0    bird      389.0
+        2    bird       24.0
+        3  mammal       80.5
+        1  mammal        NaN
+
+        We may take elements using negative integers for positive indices,
+        starting from the end of the object, just like with Python lists.
+
+        >>> df.take([-1, -2])
+             name   class  max_speed
+        1  monkey  mammal        NaN
+        3    lion  mammal       80.5
+        """
+
+        nv.validate_take((), kwargs)
+
+        if not isinstance(indices, slice):
+            indices = np.asarray(indices, dtype=np.intp)
+            if (
+                axis == 0
+                and indices.ndim == 1
+                and using_copy_on_write()
+                and is_range_indexer(indices, len(self))
+            ):
+                return self.copy(deep=None)
+        elif self.ndim == 1:
+            raise TypeError(
+                f"{type(self).__name__}.take requires a sequence of integers, "
+                "not slice."
+            )
+        else:
+            warnings.warn(
+                # GH#51539
+                f"Passing a slice to {type(self).__name__}.take is deprecated "
+                "and will raise in a future version. Use `obj[slicer]` or pass "
+                "a sequence of integers instead.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+            # We can get here with a slice via DataFrame.__getitem__
+            indices = np.arange(
+                indices.start, indices.stop, indices.step, dtype=np.intp
+            )
+
+        new_data = self._mgr.take(
+            indices,
+            axis=self._get_block_manager_axis(axis),
+            verify=True,
+        )
+        return self._constructor_from_mgr(new_data, axes=new_data.axes).__finalize__(
+            self, method="take"
+        )
+
+    @final
+    def _take_with_is_copy(self, indices, axis: Axis = 0) -> Self:
+        """
+        Internal version of the `take` method that sets the `_is_copy`
+        attribute to keep track of the parent dataframe (using in indexing
+        for the SettingWithCopyWarning).
+
+        For Series this does the same as the public take (it never sets `_is_copy`).
+
+        See the docstring of `take` for full explanation of the parameters.
+        """
+        result = self.take(indices=indices, axis=axis)
+        # Maybe set copy if we didn't actually change the index.
+        if self.ndim == 2 and not result._get_axis(axis).equals(self._get_axis(axis)):
+            result._set_is_copy(self)
+        return result
+
+    @final
+    def xs(
+        self,
+        key: IndexLabel,
+        axis: Axis = 0,
+        level: IndexLabel | None = None,
+        drop_level: bool_t = True,
+    ) -> Self:
+        """
+        Return cross-section from the Series/DataFrame.
+
+        This method takes a `key` argument to select data at a particular
+        level of a MultiIndex.
+
+        Parameters
+        ----------
+        key : label or tuple of label
+            Label contained in the index, or partially in a MultiIndex.
+        axis : {0 or 'index', 1 or 'columns'}, default 0
+            Axis to retrieve cross-section on.
+        level : object, defaults to first n levels (n=1 or len(key))
+            In case of a key partially contained in a MultiIndex, indicate
+            which levels are used. Levels can be referred by label or position.
+        drop_level : bool, default True
+            If False, returns object with same levels as self.
+
+        Returns
+        -------
+        Series or DataFrame
+            Cross-section from the original Series or DataFrame
+            corresponding to the selected index levels.
+
+        See Also
+        --------
+        DataFrame.loc : Access a group of rows and columns
+            by label(s) or a boolean array.
+        DataFrame.iloc : Purely integer-location based indexing
+            for selection by position.
+
+        Notes
+        -----
+        `xs` can not be used to set values.
+
+        MultiIndex Slicers is a generic way to get/set values on
+        any level or levels.
+        It is a superset of `xs` functionality, see
+        :ref:`MultiIndex Slicers `.
+
+        Examples
+        --------
+        >>> d = {'num_legs': [4, 4, 2, 2],
+        ...      'num_wings': [0, 0, 2, 2],
+        ...      'class': ['mammal', 'mammal', 'mammal', 'bird'],
+        ...      'animal': ['cat', 'dog', 'bat', 'penguin'],
+        ...      'locomotion': ['walks', 'walks', 'flies', 'walks']}
+        >>> df = pd.DataFrame(data=d)
+        >>> df = df.set_index(['class', 'animal', 'locomotion'])
+        >>> df
+                                   num_legs  num_wings
+        class  animal  locomotion
+        mammal cat     walks              4          0
+               dog     walks              4          0
+               bat     flies              2          2
+        bird   penguin walks              2          2
+
+        Get values at specified index
+
+        >>> df.xs('mammal')
+                           num_legs  num_wings
+        animal locomotion
+        cat    walks              4          0
+        dog    walks              4          0
+        bat    flies              2          2
+
+        Get values at several indexes
+
+        >>> df.xs(('mammal', 'dog', 'walks'))
+        num_legs     4
+        num_wings    0
+        Name: (mammal, dog, walks), dtype: int64
+
+        Get values at specified index and level
+
+        >>> df.xs('cat', level=1)
+                           num_legs  num_wings
+        class  locomotion
+        mammal walks              4          0
+
+        Get values at several indexes and levels
+
+        >>> df.xs(('bird', 'walks'),
+        ...       level=[0, 'locomotion'])
+                 num_legs  num_wings
+        animal
+        penguin         2          2
+
+        Get values at specified column and axis
+
+        >>> df.xs('num_wings', axis=1)
+        class   animal   locomotion
+        mammal  cat      walks         0
+                dog      walks         0
+                bat      flies         2
+        bird    penguin  walks         2
+        Name: num_wings, dtype: int64
+        """
+        axis = self._get_axis_number(axis)
+        labels = self._get_axis(axis)
+
+        if isinstance(key, list):
+            raise TypeError("list keys are not supported in xs, pass a tuple instead")
+
+        if level is not None:
+            if not isinstance(labels, MultiIndex):
+                raise TypeError("Index must be a MultiIndex")
+            loc, new_ax = labels.get_loc_level(key, level=level, drop_level=drop_level)
+
+            # create the tuple of the indexer
+            _indexer = [slice(None)] * self.ndim
+            _indexer[axis] = loc
+            indexer = tuple(_indexer)
+
+            result = self.iloc[indexer]
+            setattr(result, result._get_axis_name(axis), new_ax)
+            return result
+
+        if axis == 1:
+            if drop_level:
+                return self[key]
+            index = self.columns
+        else:
+            index = self.index
+
+        if isinstance(index, MultiIndex):
+            loc, new_index = index._get_loc_level(key, level=0)
+            if not drop_level:
+                if lib.is_integer(loc):
+                    # Slice index must be an integer or None
+                    new_index = index[loc : loc + 1]
+                else:
+                    new_index = index[loc]
+        else:
+            loc = index.get_loc(key)
+
+            if isinstance(loc, np.ndarray):
+                if loc.dtype == np.bool_:
+                    (inds,) = loc.nonzero()
+                    return self._take_with_is_copy(inds, axis=axis)
+                else:
+                    return self._take_with_is_copy(loc, axis=axis)
+
+            if not is_scalar(loc):
+                new_index = index[loc]
+
+        if is_scalar(loc) and axis == 0:
+            # In this case loc should be an integer
+            if self.ndim == 1:
+                # if we encounter an array-like and we only have 1 dim
+                # that means that their are list/ndarrays inside the Series!
+                # so just return them (GH 6394)
+                return self._values[loc]
+
+            new_mgr = self._mgr.fast_xs(loc)
+
+            result = self._constructor_sliced_from_mgr(new_mgr, axes=new_mgr.axes)
+            result._name = self.index[loc]
+            result = result.__finalize__(self)
+        elif is_scalar(loc):
+            result = self.iloc[:, slice(loc, loc + 1)]
+        elif axis == 1:
+            result = self.iloc[:, loc]
+        else:
+            result = self.iloc[loc]
+            result.index = new_index
+
+        # this could be a view
+        # but only in a single-dtyped view sliceable case
+        result._set_is_copy(self, copy=not result._is_view)
+        return result
+
+    def __getitem__(self, item):
+        raise AbstractMethodError(self)
+
+    @final
+    def _getitem_slice(self, key: slice) -> Self:
+        """
+        __getitem__ for the case where the key is a slice object.
+        """
+        # _convert_slice_indexer to determine if this slice is positional
+        #  or label based, and if the latter, convert to positional
+        slobj = self.index._convert_slice_indexer(key, kind="getitem")
+        if isinstance(slobj, np.ndarray):
+            # reachable with DatetimeIndex
+            indexer = lib.maybe_indices_to_slice(
+                slobj.astype(np.intp, copy=False), len(self)
+            )
+            if isinstance(indexer, np.ndarray):
+                # GH#43223 If we can not convert, use take
+                return self.take(indexer, axis=0)
+            slobj = indexer
+        return self._slice(slobj)
+
+    def _slice(self, slobj: slice, axis: AxisInt = 0) -> Self:
+        """
+        Construct a slice of this container.
+
+        Slicing with this method is *always* positional.
+        """
+        assert isinstance(slobj, slice), type(slobj)
+        axis = self._get_block_manager_axis(axis)
+        new_mgr = self._mgr.get_slice(slobj, axis=axis)
+        result = self._constructor_from_mgr(new_mgr, axes=new_mgr.axes)
+        result = result.__finalize__(self)
+
+        # this could be a view
+        # but only in a single-dtyped view sliceable case
+        is_copy = axis != 0 or result._is_view
+        result._set_is_copy(self, copy=is_copy)
+        return result
+
+    @final
+    def _set_is_copy(self, ref: NDFrame, copy: bool_t = True) -> None:
+        if not copy:
+            self._is_copy = None
+        else:
+            assert ref is not None
+            self._is_copy = weakref.ref(ref)
+
+    def _check_is_chained_assignment_possible(self) -> bool_t:
+        """
+        Check if we are a view, have a cacher, and are of mixed type.
+        If so, then force a setitem_copy check.
+
+        Should be called just near setting a value
+
+        Will return a boolean if it we are a view and are cached, but a
+        single-dtype meaning that the cacher should be updated following
+        setting.
+        """
+        if self._is_copy:
+            self._check_setitem_copy(t="referent")
+        return False
+
+    @final
+    def _check_setitem_copy(self, t: str = "setting", force: bool_t = False):
+        """
+
+        Parameters
+        ----------
+        t : str, the type of setting error
+        force : bool, default False
+           If True, then force showing an error.
+
+        validate if we are doing a setitem on a chained copy.
+
+        It is technically possible to figure out that we are setting on
+        a copy even WITH a multi-dtyped pandas object. In other words, some
+        blocks may be views while other are not. Currently _is_view will ALWAYS
+        return False for multi-blocks to avoid having to handle this case.
+
+        df = DataFrame(np.arange(0,9), columns=['count'])
+        df['group'] = 'b'
+
+        # This technically need not raise SettingWithCopy if both are view
+        # (which is not generally guaranteed but is usually True.  However,
+        # this is in general not a good practice and we recommend using .loc.
+        df.iloc[0:5]['group'] = 'a'
+
+        """
+        if using_copy_on_write() or warn_copy_on_write():
+            return
+
+        # return early if the check is not needed
+        if not (force or self._is_copy):
+            return
+
+        value = config.get_option("mode.chained_assignment")
+        if value is None:
+            return
+
+        # see if the copy is not actually referred; if so, then dissolve
+        # the copy weakref
+        if self._is_copy is not None and not isinstance(self._is_copy, str):
+            r = self._is_copy()
+            if not gc.get_referents(r) or (r is not None and r.shape == self.shape):
+                self._is_copy = None
+                return
+
+        # a custom message
+        if isinstance(self._is_copy, str):
+            t = self._is_copy
+
+        elif t == "referent":
+            t = (
+                "\n"
+                "A value is trying to be set on a copy of a slice from a "
+                "DataFrame\n\n"
+                "See the caveats in the documentation: "
+                "https://pandas.pydata.org/pandas-docs/stable/user_guide/"
+                "indexing.html#returning-a-view-versus-a-copy"
+            )
+
+        else:
+            t = (
+                "\n"
+                "A value is trying to be set on a copy of a slice from a "
+                "DataFrame.\n"
+                "Try using .loc[row_indexer,col_indexer] = value "
+                "instead\n\nSee the caveats in the documentation: "
+                "https://pandas.pydata.org/pandas-docs/stable/user_guide/"
+                "indexing.html#returning-a-view-versus-a-copy"
+            )
+
+        if value == "raise":
+            raise SettingWithCopyError(t)
+        if value == "warn":
+            warnings.warn(t, SettingWithCopyWarning, stacklevel=find_stack_level())
+
+    @final
+    def __delitem__(self, key) -> None:
+        """
+        Delete item
+        """
+        deleted = False
+
+        maybe_shortcut = False
+        if self.ndim == 2 and isinstance(self.columns, MultiIndex):
+            try:
+                # By using engine's __contains__ we effectively
+                # restrict to same-length tuples
+                maybe_shortcut = key not in self.columns._engine
+            except TypeError:
+                pass
+
+        if maybe_shortcut:
+            # Allow shorthand to delete all columns whose first len(key)
+            # elements match key:
+            if not isinstance(key, tuple):
+                key = (key,)
+            for col in self.columns:
+                if isinstance(col, tuple) and col[: len(key)] == key:
+                    del self[col]
+                    deleted = True
+        if not deleted:
+            # If the above loop ran and didn't delete anything because
+            # there was no match, this call should raise the appropriate
+            # exception:
+            loc = self.axes[-1].get_loc(key)
+            self._mgr = self._mgr.idelete(loc)
+
+        # delete from the caches
+        try:
+            del self._item_cache[key]
+        except KeyError:
+            pass
+
+    # ----------------------------------------------------------------------
+    # Unsorted
+
+    @final
+    def _check_inplace_and_allows_duplicate_labels(self, inplace: bool_t):
+        if inplace and not self.flags.allows_duplicate_labels:
+            raise ValueError(
+                "Cannot specify 'inplace=True' when "
+                "'self.flags.allows_duplicate_labels' is False."
+            )
+
+    @final
+    def get(self, key, default=None):
+        """
+        Get item from object for given key (ex: DataFrame column).
+
+        Returns default value if not found.
+
+        Parameters
+        ----------
+        key : object
+
+        Returns
+        -------
+        same type as items contained in object
+
+        Examples
+        --------
+        >>> df = pd.DataFrame(
+        ...     [
+        ...         [24.3, 75.7, "high"],
+        ...         [31, 87.8, "high"],
+        ...         [22, 71.6, "medium"],
+        ...         [35, 95, "medium"],
+        ...     ],
+        ...     columns=["temp_celsius", "temp_fahrenheit", "windspeed"],
+        ...     index=pd.date_range(start="2014-02-12", end="2014-02-15", freq="D"),
+        ... )
+
+        >>> df
+                    temp_celsius  temp_fahrenheit windspeed
+        2014-02-12          24.3             75.7      high
+        2014-02-13          31.0             87.8      high
+        2014-02-14          22.0             71.6    medium
+        2014-02-15          35.0             95.0    medium
+
+        >>> df.get(["temp_celsius", "windspeed"])
+                    temp_celsius windspeed
+        2014-02-12          24.3      high
+        2014-02-13          31.0      high
+        2014-02-14          22.0    medium
+        2014-02-15          35.0    medium
+
+        >>> ser = df['windspeed']
+        >>> ser.get('2014-02-13')
+        'high'
+
+        If the key isn't found, the default value will be used.
+
+        >>> df.get(["temp_celsius", "temp_kelvin"], default="default_value")
+        'default_value'
+
+        >>> ser.get('2014-02-10', '[unknown]')
+        '[unknown]'
+        """
+        try:
+            return self[key]
+        except (KeyError, ValueError, IndexError):
+            return default
+
+    @final
+    @property
+    def _is_view(self) -> bool_t:
+        """Return boolean indicating if self is view of another array"""
+        return self._mgr.is_view
+
+    @final
+    def reindex_like(
+        self,
+        other,
+        method: Literal["backfill", "bfill", "pad", "ffill", "nearest"] | None = None,
+        copy: bool_t | None = None,
+        limit: int | None = None,
+        tolerance=None,
+    ) -> Self:
+        """
+        Return an object with matching indices as other object.
+
+        Conform the object to the same index on all axes. Optional
+        filling logic, placing NaN in locations having no value
+        in the previous index. A new object is produced unless the
+        new index is equivalent to the current one and copy=False.
+
+        Parameters
+        ----------
+        other : Object of the same data type
+            Its row and column indices are used to define the new indices
+            of this object.
+        method : {None, 'backfill'/'bfill', 'pad'/'ffill', 'nearest'}
+            Method to use for filling holes in reindexed DataFrame.
+            Please note: this is only applicable to DataFrames/Series with a
+            monotonically increasing/decreasing index.
+
+            * None (default): don't fill gaps
+            * pad / ffill: propagate last valid observation forward to next
+              valid
+            * backfill / bfill: use next valid observation to fill gap
+            * nearest: use nearest valid observations to fill gap.
+
+        copy : bool, default True
+            Return a new object, even if the passed indexes are the same.
+
+            .. note::
+                The `copy` keyword will change behavior in pandas 3.0.
+                `Copy-on-Write
+                `__
+                will be enabled by default, which means that all methods with a
+                `copy` keyword will use a lazy copy mechanism to defer the copy and
+                ignore the `copy` keyword. The `copy` keyword will be removed in a
+                future version of pandas.
+
+                You can already get the future behavior and improvements through
+                enabling copy on write ``pd.options.mode.copy_on_write = True``
+        limit : int, default None
+            Maximum number of consecutive labels to fill for inexact matches.
+        tolerance : optional
+            Maximum distance between original and new labels for inexact
+            matches. The values of the index at the matching locations must
+            satisfy the equation ``abs(index[indexer] - target) <= tolerance``.
+
+            Tolerance may be a scalar value, which applies the same tolerance
+            to all values, or list-like, which applies variable tolerance per
+            element. List-like includes list, tuple, array, Series, and must be
+            the same size as the index and its dtype must exactly match the
+            index's type.
+
+        Returns
+        -------
+        Series or DataFrame
+            Same type as caller, but with changed indices on each axis.
+
+        See Also
+        --------
+        DataFrame.set_index : Set row labels.
+        DataFrame.reset_index : Remove row labels or move them to new columns.
+        DataFrame.reindex : Change to new indices or expand indices.
+
+        Notes
+        -----
+        Same as calling
+        ``.reindex(index=other.index, columns=other.columns,...)``.
+
+        Examples
+        --------
+        >>> df1 = pd.DataFrame([[24.3, 75.7, 'high'],
+        ...                     [31, 87.8, 'high'],
+        ...                     [22, 71.6, 'medium'],
+        ...                     [35, 95, 'medium']],
+        ...                    columns=['temp_celsius', 'temp_fahrenheit',
+        ...                             'windspeed'],
+        ...                    index=pd.date_range(start='2014-02-12',
+        ...                                        end='2014-02-15', freq='D'))
+
+        >>> df1
+                    temp_celsius  temp_fahrenheit windspeed
+        2014-02-12          24.3             75.7      high
+        2014-02-13          31.0             87.8      high
+        2014-02-14          22.0             71.6    medium
+        2014-02-15          35.0             95.0    medium
+
+        >>> df2 = pd.DataFrame([[28, 'low'],
+        ...                     [30, 'low'],
+        ...                     [35.1, 'medium']],
+        ...                    columns=['temp_celsius', 'windspeed'],
+        ...                    index=pd.DatetimeIndex(['2014-02-12', '2014-02-13',
+        ...                                            '2014-02-15']))
+
+        >>> df2
+                    temp_celsius windspeed
+        2014-02-12          28.0       low
+        2014-02-13          30.0       low
+        2014-02-15          35.1    medium
+
+        >>> df2.reindex_like(df1)
+                    temp_celsius  temp_fahrenheit windspeed
+        2014-02-12          28.0              NaN       low
+        2014-02-13          30.0              NaN       low
+        2014-02-14           NaN              NaN       NaN
+        2014-02-15          35.1              NaN    medium
+        """
+        d = other._construct_axes_dict(
+            axes=self._AXIS_ORDERS,
+            method=method,
+            copy=copy,
+            limit=limit,
+            tolerance=tolerance,
+        )
+
+        return self.reindex(**d)
+
+    @overload
+    def drop(
+        self,
+        labels: IndexLabel = ...,
+        *,
+        axis: Axis = ...,
+        index: IndexLabel = ...,
+        columns: IndexLabel = ...,
+        level: Level | None = ...,
+        inplace: Literal[True],
+        errors: IgnoreRaise = ...,
+    ) -> None:
+        ...
+
+    @overload
+    def drop(
+        self,
+        labels: IndexLabel = ...,
+        *,
+        axis: Axis = ...,
+        index: IndexLabel = ...,
+        columns: IndexLabel = ...,
+        level: Level | None = ...,
+        inplace: Literal[False] = ...,
+        errors: IgnoreRaise = ...,
+    ) -> Self:
+        ...
+
+    @overload
+    def drop(
+        self,
+        labels: IndexLabel = ...,
+        *,
+        axis: Axis = ...,
+        index: IndexLabel = ...,
+        columns: IndexLabel = ...,
+        level: Level | None = ...,
+        inplace: bool_t = ...,
+        errors: IgnoreRaise = ...,
+    ) -> Self | None:
+        ...
+
+    def drop(
+        self,
+        labels: IndexLabel | None = None,
+        *,
+        axis: Axis = 0,
+        index: IndexLabel | None = None,
+        columns: IndexLabel | None = None,
+        level: Level | None = None,
+        inplace: bool_t = False,
+        errors: IgnoreRaise = "raise",
+    ) -> Self | None:
+        inplace = validate_bool_kwarg(inplace, "inplace")
+
+        if labels is not None:
+            if index is not None or columns is not None:
+                raise ValueError("Cannot specify both 'labels' and 'index'/'columns'")
+            axis_name = self._get_axis_name(axis)
+            axes = {axis_name: labels}
+        elif index is not None or columns is not None:
+            axes = {"index": index}
+            if self.ndim == 2:
+                axes["columns"] = columns
+        else:
+            raise ValueError(
+                "Need to specify at least one of 'labels', 'index' or 'columns'"
+            )
+
+        obj = self
+
+        for axis, labels in axes.items():
+            if labels is not None:
+                obj = obj._drop_axis(labels, axis, level=level, errors=errors)
+
+        if inplace:
+            self._update_inplace(obj)
+            return None
+        else:
+            return obj
+
+    @final
+    def _drop_axis(
+        self,
+        labels,
+        axis,
+        level=None,
+        errors: IgnoreRaise = "raise",
+        only_slice: bool_t = False,
+    ) -> Self:
+        """
+        Drop labels from specified axis. Used in the ``drop`` method
+        internally.
+
+        Parameters
+        ----------
+        labels : single label or list-like
+        axis : int or axis name
+        level : int or level name, default None
+            For MultiIndex
+        errors : {'ignore', 'raise'}, default 'raise'
+            If 'ignore', suppress error and existing labels are dropped.
+        only_slice : bool, default False
+            Whether indexing along columns should be view-only.
+
+        """
+        axis_num = self._get_axis_number(axis)
+        axis = self._get_axis(axis)
+
+        if axis.is_unique:
+            if level is not None:
+                if not isinstance(axis, MultiIndex):
+                    raise AssertionError("axis must be a MultiIndex")
+                new_axis = axis.drop(labels, level=level, errors=errors)
+            else:
+                new_axis = axis.drop(labels, errors=errors)
+            indexer = axis.get_indexer(new_axis)
+
+        # Case for non-unique axis
+        else:
+            is_tuple_labels = is_nested_list_like(labels) or isinstance(labels, tuple)
+            labels = ensure_object(common.index_labels_to_array(labels))
+            if level is not None:
+                if not isinstance(axis, MultiIndex):
+                    raise AssertionError("axis must be a MultiIndex")
+                mask = ~axis.get_level_values(level).isin(labels)
+
+                # GH 18561 MultiIndex.drop should raise if label is absent
+                if errors == "raise" and mask.all():
+                    raise KeyError(f"{labels} not found in axis")
+            elif (
+                isinstance(axis, MultiIndex)
+                and labels.dtype == "object"
+                and not is_tuple_labels
+            ):
+                # Set level to zero in case of MultiIndex and label is string,
+                #  because isin can't handle strings for MultiIndexes GH#36293
+                # In case of tuples we get dtype object but have to use isin GH#42771
+                mask = ~axis.get_level_values(0).isin(labels)
+            else:
+                mask = ~axis.isin(labels)
+                # Check if label doesn't exist along axis
+                labels_missing = (axis.get_indexer_for(labels) == -1).any()
+                if errors == "raise" and labels_missing:
+                    raise KeyError(f"{labels} not found in axis")
+
+            if isinstance(mask.dtype, ExtensionDtype):
+                # GH#45860
+                mask = mask.to_numpy(dtype=bool)
+
+            indexer = mask.nonzero()[0]
+            new_axis = axis.take(indexer)
+
+        bm_axis = self.ndim - axis_num - 1
+        new_mgr = self._mgr.reindex_indexer(
+            new_axis,
+            indexer,
+            axis=bm_axis,
+            allow_dups=True,
+            copy=None,
+            only_slice=only_slice,
+        )
+        result = self._constructor_from_mgr(new_mgr, axes=new_mgr.axes)
+        if self.ndim == 1:
+            result._name = self.name
+
+        return result.__finalize__(self)
+
+    @final
+    def _update_inplace(self, result, verify_is_copy: bool_t = True) -> None:
+        """
+        Replace self internals with result.
+
+        Parameters
+        ----------
+        result : same type as self
+        verify_is_copy : bool, default True
+            Provide is_copy checks.
+        """
+        # NOTE: This does *not* call __finalize__ and that's an explicit
+        # decision that we may revisit in the future.
+        self._reset_cache()
+        self._clear_item_cache()
+        self._mgr = result._mgr
+        self._maybe_update_cacher(verify_is_copy=verify_is_copy, inplace=True)
+
+    @final
+    def add_prefix(self, prefix: str, axis: Axis | None = None) -> Self:
+        """
+        Prefix labels with string `prefix`.
+
+        For Series, the row labels are prefixed.
+        For DataFrame, the column labels are prefixed.
+
+        Parameters
+        ----------
+        prefix : str
+            The string to add before each label.
+        axis : {0 or 'index', 1 or 'columns', None}, default None
+            Axis to add prefix on
+
+            .. versionadded:: 2.0.0
+
+        Returns
+        -------
+        Series or DataFrame
+            New Series or DataFrame with updated labels.
+
+        See Also
+        --------
+        Series.add_suffix: Suffix row labels with string `suffix`.
+        DataFrame.add_suffix: Suffix column labels with string `suffix`.
+
+        Examples
+        --------
+        >>> s = pd.Series([1, 2, 3, 4])
+        >>> s
+        0    1
+        1    2
+        2    3
+        3    4
+        dtype: int64
+
+        >>> s.add_prefix('item_')
+        item_0    1
+        item_1    2
+        item_2    3
+        item_3    4
+        dtype: int64
+
+        >>> df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [3, 4, 5, 6]})
+        >>> df
+           A  B
+        0  1  3
+        1  2  4
+        2  3  5
+        3  4  6
+
+        >>> df.add_prefix('col_')
+             col_A  col_B
+        0       1       3
+        1       2       4
+        2       3       5
+        3       4       6
+        """
+        f = lambda x: f"{prefix}{x}"
+
+        axis_name = self._info_axis_name
+        if axis is not None:
+            axis_name = self._get_axis_name(axis)
+
+        mapper = {axis_name: f}
+
+        # error: Incompatible return value type (got "Optional[Self]",
+        # expected "Self")
+        # error: Argument 1 to "rename" of "NDFrame" has incompatible type
+        # "**Dict[str, partial[str]]"; expected "Union[str, int, None]"
+        # error: Keywords must be strings
+        return self._rename(**mapper)  # type: ignore[return-value, arg-type, misc]
+
+    @final
+    def add_suffix(self, suffix: str, axis: Axis | None = None) -> Self:
+        """
+        Suffix labels with string `suffix`.
+
+        For Series, the row labels are suffixed.
+        For DataFrame, the column labels are suffixed.
+
+        Parameters
+        ----------
+        suffix : str
+            The string to add after each label.
+        axis : {0 or 'index', 1 or 'columns', None}, default None
+            Axis to add suffix on
+
+            .. versionadded:: 2.0.0
+
+        Returns
+        -------
+        Series or DataFrame
+            New Series or DataFrame with updated labels.
+
+        See Also
+        --------
+        Series.add_prefix: Prefix row labels with string `prefix`.
+        DataFrame.add_prefix: Prefix column labels with string `prefix`.
+
+        Examples
+        --------
+        >>> s = pd.Series([1, 2, 3, 4])
+        >>> s
+        0    1
+        1    2
+        2    3
+        3    4
+        dtype: int64
+
+        >>> s.add_suffix('_item')
+        0_item    1
+        1_item    2
+        2_item    3
+        3_item    4
+        dtype: int64
+
+        >>> df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [3, 4, 5, 6]})
+        >>> df
+           A  B
+        0  1  3
+        1  2  4
+        2  3  5
+        3  4  6
+
+        >>> df.add_suffix('_col')
+             A_col  B_col
+        0       1       3
+        1       2       4
+        2       3       5
+        3       4       6
+        """
+        f = lambda x: f"{x}{suffix}"
+
+        axis_name = self._info_axis_name
+        if axis is not None:
+            axis_name = self._get_axis_name(axis)
+
+        mapper = {axis_name: f}
+        # error: Incompatible return value type (got "Optional[Self]",
+        # expected "Self")
+        # error: Argument 1 to "rename" of "NDFrame" has incompatible type
+        # "**Dict[str, partial[str]]"; expected "Union[str, int, None]"
+        # error: Keywords must be strings
+        return self._rename(**mapper)  # type: ignore[return-value, arg-type, misc]
+
+    @overload
+    def sort_values(
+        self,
+        *,
+        axis: Axis = ...,
+        ascending: bool_t | Sequence[bool_t] = ...,
+        inplace: Literal[False] = ...,
+        kind: SortKind = ...,
+        na_position: NaPosition = ...,
+        ignore_index: bool_t = ...,
+        key: ValueKeyFunc = ...,
+    ) -> Self:
+        ...
+
+    @overload
+    def sort_values(
+        self,
+        *,
+        axis: Axis = ...,
+        ascending: bool_t | Sequence[bool_t] = ...,
+        inplace: Literal[True],
+        kind: SortKind = ...,
+        na_position: NaPosition = ...,
+        ignore_index: bool_t = ...,
+        key: ValueKeyFunc = ...,
+    ) -> None:
+        ...
+
+    @overload
+    def sort_values(
+        self,
+        *,
+        axis: Axis = ...,
+        ascending: bool_t | Sequence[bool_t] = ...,
+        inplace: bool_t = ...,
+        kind: SortKind = ...,
+        na_position: NaPosition = ...,
+        ignore_index: bool_t = ...,
+        key: ValueKeyFunc = ...,
+    ) -> Self | None:
+        ...
+
+    def sort_values(
+        self,
+        *,
+        axis: Axis = 0,
+        ascending: bool_t | Sequence[bool_t] = True,
+        inplace: bool_t = False,
+        kind: SortKind = "quicksort",
+        na_position: NaPosition = "last",
+        ignore_index: bool_t = False,
+        key: ValueKeyFunc | None = None,
+    ) -> Self | None:
+        """
+        Sort by the values along either axis.
+
+        Parameters
+        ----------%(optional_by)s
+        axis : %(axes_single_arg)s, default 0
+             Axis to be sorted.
+        ascending : bool or list of bool, default True
+             Sort ascending vs. descending. Specify list for multiple sort
+             orders.  If this is a list of bools, must match the length of
+             the by.
+        inplace : bool, default False
+             If True, perform operation in-place.
+        kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, default 'quicksort'
+             Choice of sorting algorithm. See also :func:`numpy.sort` for more
+             information. `mergesort` and `stable` are the only stable algorithms. For
+             DataFrames, this option is only applied when sorting on a single
+             column or label.
+        na_position : {'first', 'last'}, default 'last'
+             Puts NaNs at the beginning if `first`; `last` puts NaNs at the
+             end.
+        ignore_index : bool, default False
+             If True, the resulting axis will be labeled 0, 1, …, n - 1.
+        key : callable, optional
+            Apply the key function to the values
+            before sorting. This is similar to the `key` argument in the
+            builtin :meth:`sorted` function, with the notable difference that
+            this `key` function should be *vectorized*. It should expect a
+            ``Series`` and return a Series with the same shape as the input.
+            It will be applied to each column in `by` independently.
+
+        Returns
+        -------
+        DataFrame or None
+            DataFrame with sorted values or None if ``inplace=True``.
+
+        See Also
+        --------
+        DataFrame.sort_index : Sort a DataFrame by the index.
+        Series.sort_values : Similar method for a Series.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({
+        ...     'col1': ['A', 'A', 'B', np.nan, 'D', 'C'],
+        ...     'col2': [2, 1, 9, 8, 7, 4],
+        ...     'col3': [0, 1, 9, 4, 2, 3],
+        ...     'col4': ['a', 'B', 'c', 'D', 'e', 'F']
+        ... })
+        >>> df
+          col1  col2  col3 col4
+        0    A     2     0    a
+        1    A     1     1    B
+        2    B     9     9    c
+        3  NaN     8     4    D
+        4    D     7     2    e
+        5    C     4     3    F
+
+        Sort by col1
+
+        >>> df.sort_values(by=['col1'])
+          col1  col2  col3 col4
+        0    A     2     0    a
+        1    A     1     1    B
+        2    B     9     9    c
+        5    C     4     3    F
+        4    D     7     2    e
+        3  NaN     8     4    D
+
+        Sort by multiple columns
+
+        >>> df.sort_values(by=['col1', 'col2'])
+          col1  col2  col3 col4
+        1    A     1     1    B
+        0    A     2     0    a
+        2    B     9     9    c
+        5    C     4     3    F
+        4    D     7     2    e
+        3  NaN     8     4    D
+
+        Sort Descending
+
+        >>> df.sort_values(by='col1', ascending=False)
+          col1  col2  col3 col4
+        4    D     7     2    e
+        5    C     4     3    F
+        2    B     9     9    c
+        0    A     2     0    a
+        1    A     1     1    B
+        3  NaN     8     4    D
+
+        Putting NAs first
+
+        >>> df.sort_values(by='col1', ascending=False, na_position='first')
+          col1  col2  col3 col4
+        3  NaN     8     4    D
+        4    D     7     2    e
+        5    C     4     3    F
+        2    B     9     9    c
+        0    A     2     0    a
+        1    A     1     1    B
+
+        Sorting with a key function
+
+        >>> df.sort_values(by='col4', key=lambda col: col.str.lower())
+           col1  col2  col3 col4
+        0    A     2     0    a
+        1    A     1     1    B
+        2    B     9     9    c
+        3  NaN     8     4    D
+        4    D     7     2    e
+        5    C     4     3    F
+
+        Natural sort with the key argument,
+        using the `natsort ` package.
+
+        >>> df = pd.DataFrame({
+        ...    "time": ['0hr', '128hr', '72hr', '48hr', '96hr'],
+        ...    "value": [10, 20, 30, 40, 50]
+        ... })
+        >>> df
+            time  value
+        0    0hr     10
+        1  128hr     20
+        2   72hr     30
+        3   48hr     40
+        4   96hr     50
+        >>> from natsort import index_natsorted
+        >>> df.sort_values(
+        ...     by="time",
+        ...     key=lambda x: np.argsort(index_natsorted(df["time"]))
+        ... )
+            time  value
+        0    0hr     10
+        3   48hr     40
+        2   72hr     30
+        4   96hr     50
+        1  128hr     20
+        """
+        raise AbstractMethodError(self)
+
+    @overload
+    def sort_index(
+        self,
+        *,
+        axis: Axis = ...,
+        level: IndexLabel = ...,
+        ascending: bool_t | Sequence[bool_t] = ...,
+        inplace: Literal[True],
+        kind: SortKind = ...,
+        na_position: NaPosition = ...,
+        sort_remaining: bool_t = ...,
+        ignore_index: bool_t = ...,
+        key: IndexKeyFunc = ...,
+    ) -> None:
+        ...
+
+    @overload
+    def sort_index(
+        self,
+        *,
+        axis: Axis = ...,
+        level: IndexLabel = ...,
+        ascending: bool_t | Sequence[bool_t] = ...,
+        inplace: Literal[False] = ...,
+        kind: SortKind = ...,
+        na_position: NaPosition = ...,
+        sort_remaining: bool_t = ...,
+        ignore_index: bool_t = ...,
+        key: IndexKeyFunc = ...,
+    ) -> Self:
+        ...
+
+    @overload
+    def sort_index(
+        self,
+        *,
+        axis: Axis = ...,
+        level: IndexLabel = ...,
+        ascending: bool_t | Sequence[bool_t] = ...,
+        inplace: bool_t = ...,
+        kind: SortKind = ...,
+        na_position: NaPosition = ...,
+        sort_remaining: bool_t = ...,
+        ignore_index: bool_t = ...,
+        key: IndexKeyFunc = ...,
+    ) -> Self | None:
+        ...
+
+    def sort_index(
+        self,
+        *,
+        axis: Axis = 0,
+        level: IndexLabel | None = None,
+        ascending: bool_t | Sequence[bool_t] = True,
+        inplace: bool_t = False,
+        kind: SortKind = "quicksort",
+        na_position: NaPosition = "last",
+        sort_remaining: bool_t = True,
+        ignore_index: bool_t = False,
+        key: IndexKeyFunc | None = None,
+    ) -> Self | None:
+        inplace = validate_bool_kwarg(inplace, "inplace")
+        axis = self._get_axis_number(axis)
+        ascending = validate_ascending(ascending)
+
+        target = self._get_axis(axis)
+
+        indexer = get_indexer_indexer(
+            target, level, ascending, kind, na_position, sort_remaining, key
+        )
+
+        if indexer is None:
+            if inplace:
+                result = self
+            else:
+                result = self.copy(deep=None)
+
+            if ignore_index:
+                result.index = default_index(len(self))
+            if inplace:
+                return None
+            else:
+                return result
+
+        baxis = self._get_block_manager_axis(axis)
+        new_data = self._mgr.take(indexer, axis=baxis, verify=False)
+
+        # reconstruct axis if needed
+        if not ignore_index:
+            new_axis = new_data.axes[baxis]._sort_levels_monotonic()
+        else:
+            new_axis = default_index(len(indexer))
+        new_data.set_axis(baxis, new_axis)
+
+        result = self._constructor_from_mgr(new_data, axes=new_data.axes)
+
+        if inplace:
+            return self._update_inplace(result)
+        else:
+            return result.__finalize__(self, method="sort_index")
+
+    @doc(
+        klass=_shared_doc_kwargs["klass"],
+        optional_reindex="",
+    )
+    def reindex(
+        self,
+        labels=None,
+        *,
+        index=None,
+        columns=None,
+        axis: Axis | None = None,
+        method: ReindexMethod | None = None,
+        copy: bool_t | None = None,
+        level: Level | None = None,
+        fill_value: Scalar | None = np.nan,
+        limit: int | None = None,
+        tolerance=None,
+    ) -> Self:
+        """
+        Conform {klass} to new index with optional filling logic.
+
+        Places NA/NaN in locations having no value in the previous index. A new object
+        is produced unless the new index is equivalent to the current one and
+        ``copy=False``.
+
+        Parameters
+        ----------
+        {optional_reindex}
+        method : {{None, 'backfill'/'bfill', 'pad'/'ffill', 'nearest'}}
+            Method to use for filling holes in reindexed DataFrame.
+            Please note: this is only applicable to DataFrames/Series with a
+            monotonically increasing/decreasing index.
+
+            * None (default): don't fill gaps
+            * pad / ffill: Propagate last valid observation forward to next
+              valid.
+            * backfill / bfill: Use next valid observation to fill gap.
+            * nearest: Use nearest valid observations to fill gap.
+
+        copy : bool, default True
+            Return a new object, even if the passed indexes are the same.
+
+            .. note::
+                The `copy` keyword will change behavior in pandas 3.0.
+                `Copy-on-Write
+                `__
+                will be enabled by default, which means that all methods with a
+                `copy` keyword will use a lazy copy mechanism to defer the copy and
+                ignore the `copy` keyword. The `copy` keyword will be removed in a
+                future version of pandas.
+
+                You can already get the future behavior and improvements through
+                enabling copy on write ``pd.options.mode.copy_on_write = True``
+        level : int or name
+            Broadcast across a level, matching Index values on the
+            passed MultiIndex level.
+        fill_value : scalar, default np.nan
+            Value to use for missing values. Defaults to NaN, but can be any
+            "compatible" value.
+        limit : int, default None
+            Maximum number of consecutive elements to forward or backward fill.
+        tolerance : optional
+            Maximum distance between original and new labels for inexact
+            matches. The values of the index at the matching locations most
+            satisfy the equation ``abs(index[indexer] - target) <= tolerance``.
+
+            Tolerance may be a scalar value, which applies the same tolerance
+            to all values, or list-like, which applies variable tolerance per
+            element. List-like includes list, tuple, array, Series, and must be
+            the same size as the index and its dtype must exactly match the
+            index's type.
+
+        Returns
+        -------
+        {klass} with changed index.
+
+        See Also
+        --------
+        DataFrame.set_index : Set row labels.
+        DataFrame.reset_index : Remove row labels or move them to new columns.
+        DataFrame.reindex_like : Change to same indices as other DataFrame.
+
+        Examples
+        --------
+        ``DataFrame.reindex`` supports two calling conventions
+
+        * ``(index=index_labels, columns=column_labels, ...)``
+        * ``(labels, axis={{'index', 'columns'}}, ...)``
+
+        We *highly* recommend using keyword arguments to clarify your
+        intent.
+
+        Create a dataframe with some fictional data.
+
+        >>> index = ['Firefox', 'Chrome', 'Safari', 'IE10', 'Konqueror']
+        >>> df = pd.DataFrame({{'http_status': [200, 200, 404, 404, 301],
+        ...                   'response_time': [0.04, 0.02, 0.07, 0.08, 1.0]}},
+        ...                   index=index)
+        >>> df
+                   http_status  response_time
+        Firefox            200           0.04
+        Chrome             200           0.02
+        Safari             404           0.07
+        IE10               404           0.08
+        Konqueror          301           1.00
+
+        Create a new index and reindex the dataframe. By default
+        values in the new index that do not have corresponding
+        records in the dataframe are assigned ``NaN``.
+
+        >>> new_index = ['Safari', 'Iceweasel', 'Comodo Dragon', 'IE10',
+        ...              'Chrome']
+        >>> df.reindex(new_index)
+                       http_status  response_time
+        Safari               404.0           0.07
+        Iceweasel              NaN            NaN
+        Comodo Dragon          NaN            NaN
+        IE10                 404.0           0.08
+        Chrome               200.0           0.02
+
+        We can fill in the missing values by passing a value to
+        the keyword ``fill_value``. Because the index is not monotonically
+        increasing or decreasing, we cannot use arguments to the keyword
+        ``method`` to fill the ``NaN`` values.
+
+        >>> df.reindex(new_index, fill_value=0)
+                       http_status  response_time
+        Safari                 404           0.07
+        Iceweasel                0           0.00
+        Comodo Dragon            0           0.00
+        IE10                   404           0.08
+        Chrome                 200           0.02
+
+        >>> df.reindex(new_index, fill_value='missing')
+                      http_status response_time
+        Safari                404          0.07
+        Iceweasel         missing       missing
+        Comodo Dragon     missing       missing
+        IE10                  404          0.08
+        Chrome                200          0.02
+
+        We can also reindex the columns.
+
+        >>> df.reindex(columns=['http_status', 'user_agent'])
+                   http_status  user_agent
+        Firefox            200         NaN
+        Chrome             200         NaN
+        Safari             404         NaN
+        IE10               404         NaN
+        Konqueror          301         NaN
+
+        Or we can use "axis-style" keyword arguments
+
+        >>> df.reindex(['http_status', 'user_agent'], axis="columns")
+                   http_status  user_agent
+        Firefox            200         NaN
+        Chrome             200         NaN
+        Safari             404         NaN
+        IE10               404         NaN
+        Konqueror          301         NaN
+
+        To further illustrate the filling functionality in
+        ``reindex``, we will create a dataframe with a
+        monotonically increasing index (for example, a sequence
+        of dates).
+
+        >>> date_index = pd.date_range('1/1/2010', periods=6, freq='D')
+        >>> df2 = pd.DataFrame({{"prices": [100, 101, np.nan, 100, 89, 88]}},
+        ...                    index=date_index)
+        >>> df2
+                    prices
+        2010-01-01   100.0
+        2010-01-02   101.0
+        2010-01-03     NaN
+        2010-01-04   100.0
+        2010-01-05    89.0
+        2010-01-06    88.0
+
+        Suppose we decide to expand the dataframe to cover a wider
+        date range.
+
+        >>> date_index2 = pd.date_range('12/29/2009', periods=10, freq='D')
+        >>> df2.reindex(date_index2)
+                    prices
+        2009-12-29     NaN
+        2009-12-30     NaN
+        2009-12-31     NaN
+        2010-01-01   100.0
+        2010-01-02   101.0
+        2010-01-03     NaN
+        2010-01-04   100.0
+        2010-01-05    89.0
+        2010-01-06    88.0
+        2010-01-07     NaN
+
+        The index entries that did not have a value in the original data frame
+        (for example, '2009-12-29') are by default filled with ``NaN``.
+        If desired, we can fill in the missing values using one of several
+        options.
+
+        For example, to back-propagate the last valid value to fill the ``NaN``
+        values, pass ``bfill`` as an argument to the ``method`` keyword.
+
+        >>> df2.reindex(date_index2, method='bfill')
+                    prices
+        2009-12-29   100.0
+        2009-12-30   100.0
+        2009-12-31   100.0
+        2010-01-01   100.0
+        2010-01-02   101.0
+        2010-01-03     NaN
+        2010-01-04   100.0
+        2010-01-05    89.0
+        2010-01-06    88.0
+        2010-01-07     NaN
+
+        Please note that the ``NaN`` value present in the original dataframe
+        (at index value 2010-01-03) will not be filled by any of the
+        value propagation schemes. This is because filling while reindexing
+        does not look at dataframe values, but only compares the original and
+        desired indexes. If you do want to fill in the ``NaN`` values present
+        in the original dataframe, use the ``fillna()`` method.
+
+        See the :ref:`user guide ` for more.
+        """
+        # TODO: Decide if we care about having different examples for different
+        # kinds
+
+        if index is not None and columns is not None and labels is not None:
+            raise TypeError("Cannot specify all of 'labels', 'index', 'columns'.")
+        elif index is not None or columns is not None:
+            if axis is not None:
+                raise TypeError(
+                    "Cannot specify both 'axis' and any of 'index' or 'columns'"
+                )
+            if labels is not None:
+                if index is not None:
+                    columns = labels
+                else:
+                    index = labels
+        else:
+            if axis and self._get_axis_number(axis) == 1:
+                columns = labels
+            else:
+                index = labels
+        axes: dict[Literal["index", "columns"], Any] = {
+            "index": index,
+            "columns": columns,
+        }
+        method = clean_reindex_fill_method(method)
+
+        # if all axes that are requested to reindex are equal, then only copy
+        # if indicated must have index names equal here as well as values
+        if copy and using_copy_on_write():
+            copy = False
+        if all(
+            self._get_axis(axis_name).identical(ax)
+            for axis_name, ax in axes.items()
+            if ax is not None
+        ):
+            return self.copy(deep=copy)
+
+        # check if we are a multi reindex
+        if self._needs_reindex_multi(axes, method, level):
+            return self._reindex_multi(axes, copy, fill_value)
+
+        # perform the reindex on the axes
+        return self._reindex_axes(
+            axes, level, limit, tolerance, method, fill_value, copy
+        ).__finalize__(self, method="reindex")
+
+    @final
+    def _reindex_axes(
+        self,
+        axes,
+        level: Level | None,
+        limit: int | None,
+        tolerance,
+        method,
+        fill_value: Scalar | None,
+        copy: bool_t | None,
+    ) -> Self:
+        """Perform the reindex for all the axes."""
+        obj = self
+        for a in self._AXIS_ORDERS:
+            labels = axes[a]
+            if labels is None:
+                continue
+
+            ax = self._get_axis(a)
+            new_index, indexer = ax.reindex(
+                labels, level=level, limit=limit, tolerance=tolerance, method=method
+            )
+
+            axis = self._get_axis_number(a)
+            obj = obj._reindex_with_indexers(
+                {axis: [new_index, indexer]},
+                fill_value=fill_value,
+                copy=copy,
+                allow_dups=False,
+            )
+            # If we've made a copy once, no need to make another one
+            copy = False
+
+        return obj
+
+    def _needs_reindex_multi(self, axes, method, level: Level | None) -> bool_t:
+        """Check if we do need a multi reindex."""
+        return (
+            (common.count_not_none(*axes.values()) == self._AXIS_LEN)
+            and method is None
+            and level is None
+            # reindex_multi calls self.values, so we only want to go
+            #  down that path when doing so is cheap.
+            and self._can_fast_transpose
+        )
+
+    def _reindex_multi(self, axes, copy, fill_value):
+        raise AbstractMethodError(self)
+
+    @final
+    def _reindex_with_indexers(
+        self,
+        reindexers,
+        fill_value=None,
+        copy: bool_t | None = False,
+        allow_dups: bool_t = False,
+    ) -> Self:
+        """allow_dups indicates an internal call here"""
+        # reindex doing multiple operations on different axes if indicated
+        new_data = self._mgr
+        for axis in sorted(reindexers.keys()):
+            index, indexer = reindexers[axis]
+            baxis = self._get_block_manager_axis(axis)
+
+            if index is None:
+                continue
+
+            index = ensure_index(index)
+            if indexer is not None:
+                indexer = ensure_platform_int(indexer)
+
+            # TODO: speed up on homogeneous DataFrame objects (see _reindex_multi)
+            new_data = new_data.reindex_indexer(
+                index,
+                indexer,
+                axis=baxis,
+                fill_value=fill_value,
+                allow_dups=allow_dups,
+                copy=copy,
+            )
+            # If we've made a copy once, no need to make another one
+            copy = False
+
+        if (
+            (copy or copy is None)
+            and new_data is self._mgr
+            and not using_copy_on_write()
+        ):
+            new_data = new_data.copy(deep=copy)
+        elif using_copy_on_write() and new_data is self._mgr:
+            new_data = new_data.copy(deep=False)
+
+        return self._constructor_from_mgr(new_data, axes=new_data.axes).__finalize__(
+            self
+        )
+
+    def filter(
+        self,
+        items=None,
+        like: str | None = None,
+        regex: str | None = None,
+        axis: Axis | None = None,
+    ) -> Self:
+        """
+        Subset the dataframe rows or columns according to the specified index labels.
+
+        Note that this routine does not filter a dataframe on its
+        contents. The filter is applied to the labels of the index.
+
+        Parameters
+        ----------
+        items : list-like
+            Keep labels from axis which are in items.
+        like : str
+            Keep labels from axis for which "like in label == True".
+        regex : str (regular expression)
+            Keep labels from axis for which re.search(regex, label) == True.
+        axis : {0 or 'index', 1 or 'columns', None}, default None
+            The axis to filter on, expressed either as an index (int)
+            or axis name (str). By default this is the info axis, 'columns' for
+            DataFrame. For `Series` this parameter is unused and defaults to `None`.
+
+        Returns
+        -------
+        same type as input object
+
+        See Also
+        --------
+        DataFrame.loc : Access a group of rows and columns
+            by label(s) or a boolean array.
+
+        Notes
+        -----
+        The ``items``, ``like``, and ``regex`` parameters are
+        enforced to be mutually exclusive.
+
+        ``axis`` defaults to the info axis that is used when indexing
+        with ``[]``.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame(np.array(([1, 2, 3], [4, 5, 6])),
+        ...                   index=['mouse', 'rabbit'],
+        ...                   columns=['one', 'two', 'three'])
+        >>> df
+                one  two  three
+        mouse     1    2      3
+        rabbit    4    5      6
+
+        >>> # select columns by name
+        >>> df.filter(items=['one', 'three'])
+                 one  three
+        mouse     1      3
+        rabbit    4      6
+
+        >>> # select columns by regular expression
+        >>> df.filter(regex='e$', axis=1)
+                 one  three
+        mouse     1      3
+        rabbit    4      6
+
+        >>> # select rows containing 'bbi'
+        >>> df.filter(like='bbi', axis=0)
+                 one  two  three
+        rabbit    4    5      6
+        """
+        nkw = common.count_not_none(items, like, regex)
+        if nkw > 1:
+            raise TypeError(
+                "Keyword arguments `items`, `like`, or `regex` "
+                "are mutually exclusive"
+            )
+
+        if axis is None:
+            axis = self._info_axis_name
+        labels = self._get_axis(axis)
+
+        if items is not None:
+            name = self._get_axis_name(axis)
+            items = Index(items).intersection(labels)
+            if len(items) == 0:
+                # Keep the dtype of labels when we are empty
+                items = items.astype(labels.dtype)
+            # error: Keywords must be strings
+            return self.reindex(**{name: items})  # type: ignore[misc]
+        elif like:
+
+            def f(x) -> bool_t:
+                assert like is not None  # needed for mypy
+                return like in ensure_str(x)
+
+            values = labels.map(f)
+            return self.loc(axis=axis)[values]
+        elif regex:
+
+            def f(x) -> bool_t:
+                return matcher.search(ensure_str(x)) is not None
+
+            matcher = re.compile(regex)
+            values = labels.map(f)
+            return self.loc(axis=axis)[values]
+        else:
+            raise TypeError("Must pass either `items`, `like`, or `regex`")
+
+    @final
+    def head(self, n: int = 5) -> Self:
+        """
+        Return the first `n` rows.
+
+        This function returns the first `n` rows for the object based
+        on position. It is useful for quickly testing if your object
+        has the right type of data in it.
+
+        For negative values of `n`, this function returns all rows except
+        the last `|n|` rows, equivalent to ``df[:n]``.
+
+        If n is larger than the number of rows, this function returns all rows.
+
+        Parameters
+        ----------
+        n : int, default 5
+            Number of rows to select.
+
+        Returns
+        -------
+        same type as caller
+            The first `n` rows of the caller object.
+
+        See Also
+        --------
+        DataFrame.tail: Returns the last `n` rows.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'animal': ['alligator', 'bee', 'falcon', 'lion',
+        ...                    'monkey', 'parrot', 'shark', 'whale', 'zebra']})
+        >>> df
+              animal
+        0  alligator
+        1        bee
+        2     falcon
+        3       lion
+        4     monkey
+        5     parrot
+        6      shark
+        7      whale
+        8      zebra
+
+        Viewing the first 5 lines
+
+        >>> df.head()
+              animal
+        0  alligator
+        1        bee
+        2     falcon
+        3       lion
+        4     monkey
+
+        Viewing the first `n` lines (three in this case)
+
+        >>> df.head(3)
+              animal
+        0  alligator
+        1        bee
+        2     falcon
+
+        For negative values of `n`
+
+        >>> df.head(-3)
+              animal
+        0  alligator
+        1        bee
+        2     falcon
+        3       lion
+        4     monkey
+        5     parrot
+        """
+        if using_copy_on_write():
+            return self.iloc[:n].copy()
+        return self.iloc[:n]
+
+    @final
+    def tail(self, n: int = 5) -> Self:
+        """
+        Return the last `n` rows.
+
+        This function returns last `n` rows from the object based on
+        position. It is useful for quickly verifying data, for example,
+        after sorting or appending rows.
+
+        For negative values of `n`, this function returns all rows except
+        the first `|n|` rows, equivalent to ``df[|n|:]``.
+
+        If n is larger than the number of rows, this function returns all rows.
+
+        Parameters
+        ----------
+        n : int, default 5
+            Number of rows to select.
+
+        Returns
+        -------
+        type of caller
+            The last `n` rows of the caller object.
+
+        See Also
+        --------
+        DataFrame.head : The first `n` rows of the caller object.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'animal': ['alligator', 'bee', 'falcon', 'lion',
+        ...                    'monkey', 'parrot', 'shark', 'whale', 'zebra']})
+        >>> df
+              animal
+        0  alligator
+        1        bee
+        2     falcon
+        3       lion
+        4     monkey
+        5     parrot
+        6      shark
+        7      whale
+        8      zebra
+
+        Viewing the last 5 lines
+
+        >>> df.tail()
+           animal
+        4  monkey
+        5  parrot
+        6   shark
+        7   whale
+        8   zebra
+
+        Viewing the last `n` lines (three in this case)
+
+        >>> df.tail(3)
+          animal
+        6  shark
+        7  whale
+        8  zebra
+
+        For negative values of `n`
+
+        >>> df.tail(-3)
+           animal
+        3    lion
+        4  monkey
+        5  parrot
+        6   shark
+        7   whale
+        8   zebra
+        """
+        if using_copy_on_write():
+            if n == 0:
+                return self.iloc[0:0].copy()
+            return self.iloc[-n:].copy()
+        if n == 0:
+            return self.iloc[0:0]
+        return self.iloc[-n:]
+
+    @final
+    def sample(
+        self,
+        n: int | None = None,
+        frac: float | None = None,
+        replace: bool_t = False,
+        weights=None,
+        random_state: RandomState | None = None,
+        axis: Axis | None = None,
+        ignore_index: bool_t = False,
+    ) -> Self:
+        """
+        Return a random sample of items from an axis of object.
+
+        You can use `random_state` for reproducibility.
+
+        Parameters
+        ----------
+        n : int, optional
+            Number of items from axis to return. Cannot be used with `frac`.
+            Default = 1 if `frac` = None.
+        frac : float, optional
+            Fraction of axis items to return. Cannot be used with `n`.
+        replace : bool, default False
+            Allow or disallow sampling of the same row more than once.
+        weights : str or ndarray-like, optional
+            Default 'None' results in equal probability weighting.
+            If passed a Series, will align with target object on index. Index
+            values in weights not found in sampled object will be ignored and
+            index values in sampled object not in weights will be assigned
+            weights of zero.
+            If called on a DataFrame, will accept the name of a column
+            when axis = 0.
+            Unless weights are a Series, weights must be same length as axis
+            being sampled.
+            If weights do not sum to 1, they will be normalized to sum to 1.
+            Missing values in the weights column will be treated as zero.
+            Infinite values not allowed.
+        random_state : int, array-like, BitGenerator, np.random.RandomState, np.random.Generator, optional
+            If int, array-like, or BitGenerator, seed for random number generator.
+            If np.random.RandomState or np.random.Generator, use as given.
+
+            .. versionchanged:: 1.4.0
+
+                np.random.Generator objects now accepted
+
+        axis : {0 or 'index', 1 or 'columns', None}, default None
+            Axis to sample. Accepts axis number or name. Default is stat axis
+            for given data type. For `Series` this parameter is unused and defaults to `None`.
+        ignore_index : bool, default False
+            If True, the resulting index will be labeled 0, 1, …, n - 1.
+
+            .. versionadded:: 1.3.0
+
+        Returns
+        -------
+        Series or DataFrame
+            A new object of same type as caller containing `n` items randomly
+            sampled from the caller object.
+
+        See Also
+        --------
+        DataFrameGroupBy.sample: Generates random samples from each group of a
+            DataFrame object.
+        SeriesGroupBy.sample: Generates random samples from each group of a
+            Series object.
+        numpy.random.choice: Generates a random sample from a given 1-D numpy
+            array.
+
+        Notes
+        -----
+        If `frac` > 1, `replacement` should be set to `True`.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'num_legs': [2, 4, 8, 0],
+        ...                    'num_wings': [2, 0, 0, 0],
+        ...                    'num_specimen_seen': [10, 2, 1, 8]},
+        ...                   index=['falcon', 'dog', 'spider', 'fish'])
+        >>> df
+                num_legs  num_wings  num_specimen_seen
+        falcon         2          2                 10
+        dog            4          0                  2
+        spider         8          0                  1
+        fish           0          0                  8
+
+        Extract 3 random elements from the ``Series`` ``df['num_legs']``:
+        Note that we use `random_state` to ensure the reproducibility of
+        the examples.
+
+        >>> df['num_legs'].sample(n=3, random_state=1)
+        fish      0
+        spider    8
+        falcon    2
+        Name: num_legs, dtype: int64
+
+        A random 50% sample of the ``DataFrame`` with replacement:
+
+        >>> df.sample(frac=0.5, replace=True, random_state=1)
+              num_legs  num_wings  num_specimen_seen
+        dog          4          0                  2
+        fish         0          0                  8
+
+        An upsample sample of the ``DataFrame`` with replacement:
+        Note that `replace` parameter has to be `True` for `frac` parameter > 1.
+
+        >>> df.sample(frac=2, replace=True, random_state=1)
+                num_legs  num_wings  num_specimen_seen
+        dog            4          0                  2
+        fish           0          0                  8
+        falcon         2          2                 10
+        falcon         2          2                 10
+        fish           0          0                  8
+        dog            4          0                  2
+        fish           0          0                  8
+        dog            4          0                  2
+
+        Using a DataFrame column as weights. Rows with larger value in the
+        `num_specimen_seen` column are more likely to be sampled.
+
+        >>> df.sample(n=2, weights='num_specimen_seen', random_state=1)
+                num_legs  num_wings  num_specimen_seen
+        falcon         2          2                 10
+        fish           0          0                  8
+        """  # noqa: E501
+        if axis is None:
+            axis = 0
+
+        axis = self._get_axis_number(axis)
+        obj_len = self.shape[axis]
+
+        # Process random_state argument
+        rs = common.random_state(random_state)
+
+        size = sample.process_sampling_size(n, frac, replace)
+        if size is None:
+            assert frac is not None
+            size = round(frac * obj_len)
+
+        if weights is not None:
+            weights = sample.preprocess_weights(self, weights, axis)
+
+        sampled_indices = sample.sample(obj_len, size, replace, weights, rs)
+        result = self.take(sampled_indices, axis=axis)
+
+        if ignore_index:
+            result.index = default_index(len(result))
+
+        return result
+
+    @final
+    @doc(klass=_shared_doc_kwargs["klass"])
+    def pipe(
+        self,
+        func: Callable[..., T] | tuple[Callable[..., T], str],
+        *args,
+        **kwargs,
+    ) -> T:
+        r"""
+        Apply chainable functions that expect Series or DataFrames.
+
+        Parameters
+        ----------
+        func : function
+            Function to apply to the {klass}.
+            ``args``, and ``kwargs`` are passed into ``func``.
+            Alternatively a ``(callable, data_keyword)`` tuple where
+            ``data_keyword`` is a string indicating the keyword of
+            ``callable`` that expects the {klass}.
+        *args : iterable, optional
+            Positional arguments passed into ``func``.
+        **kwargs : mapping, optional
+            A dictionary of keyword arguments passed into ``func``.
+
+        Returns
+        -------
+        the return type of ``func``.
+
+        See Also
+        --------
+        DataFrame.apply : Apply a function along input axis of DataFrame.
+        DataFrame.map : Apply a function elementwise on a whole DataFrame.
+        Series.map : Apply a mapping correspondence on a
+            :class:`~pandas.Series`.
+
+        Notes
+        -----
+        Use ``.pipe`` when chaining together functions that expect
+        Series, DataFrames or GroupBy objects.
+
+        Examples
+        --------
+        Constructing a income DataFrame from a dictionary.
+
+        >>> data = [[8000, 1000], [9500, np.nan], [5000, 2000]]
+        >>> df = pd.DataFrame(data, columns=['Salary', 'Others'])
+        >>> df
+           Salary  Others
+        0    8000  1000.0
+        1    9500     NaN
+        2    5000  2000.0
+
+        Functions that perform tax reductions on an income DataFrame.
+
+        >>> def subtract_federal_tax(df):
+        ...     return df * 0.9
+        >>> def subtract_state_tax(df, rate):
+        ...     return df * (1 - rate)
+        >>> def subtract_national_insurance(df, rate, rate_increase):
+        ...     new_rate = rate + rate_increase
+        ...     return df * (1 - new_rate)
+
+        Instead of writing
+
+        >>> subtract_national_insurance(
+        ...     subtract_state_tax(subtract_federal_tax(df), rate=0.12),
+        ...     rate=0.05,
+        ...     rate_increase=0.02)  # doctest: +SKIP
+
+        You can write
+
+        >>> (
+        ...     df.pipe(subtract_federal_tax)
+        ...     .pipe(subtract_state_tax, rate=0.12)
+        ...     .pipe(subtract_national_insurance, rate=0.05, rate_increase=0.02)
+        ... )
+            Salary   Others
+        0  5892.48   736.56
+        1  6997.32      NaN
+        2  3682.80  1473.12
+
+        If you have a function that takes the data as (say) the second
+        argument, pass a tuple indicating which keyword expects the
+        data. For example, suppose ``national_insurance`` takes its data as ``df``
+        in the second argument:
+
+        >>> def subtract_national_insurance(rate, df, rate_increase):
+        ...     new_rate = rate + rate_increase
+        ...     return df * (1 - new_rate)
+        >>> (
+        ...     df.pipe(subtract_federal_tax)
+        ...     .pipe(subtract_state_tax, rate=0.12)
+        ...     .pipe(
+        ...         (subtract_national_insurance, 'df'),
+        ...         rate=0.05,
+        ...         rate_increase=0.02
+        ...     )
+        ... )
+            Salary   Others
+        0  5892.48   736.56
+        1  6997.32      NaN
+        2  3682.80  1473.12
+        """
+        if using_copy_on_write():
+            return common.pipe(self.copy(deep=None), func, *args, **kwargs)
+        return common.pipe(self, func, *args, **kwargs)
+
+    # ----------------------------------------------------------------------
+    # Attribute access
+
+    @final
+    def __finalize__(self, other, method: str | None = None, **kwargs) -> Self:
+        """
+        Propagate metadata from other to self.
+
+        Parameters
+        ----------
+        other : the object from which to get the attributes that we are going
+            to propagate
+        method : str, optional
+            A passed method name providing context on where ``__finalize__``
+            was called.
+
+            .. warning::
+
+               The value passed as `method` are not currently considered
+               stable across pandas releases.
+        """
+        if isinstance(other, NDFrame):
+            if other.attrs:
+                # We want attrs propagation to have minimal performance
+                # impact if attrs are not used; i.e. attrs is an empty dict.
+                # One could make the deepcopy unconditionally, but a deepcopy
+                # of an empty dict is 50x more expensive than the empty check.
+                self.attrs = deepcopy(other.attrs)
+
+            self.flags.allows_duplicate_labels = other.flags.allows_duplicate_labels
+            # For subclasses using _metadata.
+            for name in set(self._metadata) & set(other._metadata):
+                assert isinstance(name, str)
+                object.__setattr__(self, name, getattr(other, name, None))
+
+        if method == "concat":
+            # propagate attrs only if all concat arguments have the same attrs
+            if all(bool(obj.attrs) for obj in other.objs):
+                # all concatenate arguments have non-empty attrs
+                attrs = other.objs[0].attrs
+                have_same_attrs = all(obj.attrs == attrs for obj in other.objs[1:])
+                if have_same_attrs:
+                    self.attrs = deepcopy(attrs)
+
+            allows_duplicate_labels = all(
+                x.flags.allows_duplicate_labels for x in other.objs
+            )
+            self.flags.allows_duplicate_labels = allows_duplicate_labels
+
+        return self
+
+    @final
+    def __getattr__(self, name: str):
+        """
+        After regular attribute access, try looking up the name
+        This allows simpler access to columns for interactive use.
+        """
+        # Note: obj.x will always call obj.__getattribute__('x') prior to
+        # calling obj.__getattr__('x').
+        if (
+            name not in self._internal_names_set
+            and name not in self._metadata
+            and name not in self._accessors
+            and self._info_axis._can_hold_identifiers_and_holds_name(name)
+        ):
+            return self[name]
+        return object.__getattribute__(self, name)
+
+    @final
+    def __setattr__(self, name: str, value) -> None:
+        """
+        After regular attribute access, try setting the name
+        This allows simpler access to columns for interactive use.
+        """
+        # first try regular attribute access via __getattribute__, so that
+        # e.g. ``obj.x`` and ``obj.x = 4`` will always reference/modify
+        # the same attribute.
+
+        try:
+            object.__getattribute__(self, name)
+            return object.__setattr__(self, name, value)
+        except AttributeError:
+            pass
+
+        # if this fails, go on to more involved attribute setting
+        # (note that this matches __getattr__, above).
+        if name in self._internal_names_set:
+            object.__setattr__(self, name, value)
+        elif name in self._metadata:
+            object.__setattr__(self, name, value)
+        else:
+            try:
+                existing = getattr(self, name)
+                if isinstance(existing, Index):
+                    object.__setattr__(self, name, value)
+                elif name in self._info_axis:
+                    self[name] = value
+                else:
+                    object.__setattr__(self, name, value)
+            except (AttributeError, TypeError):
+                if isinstance(self, ABCDataFrame) and (is_list_like(value)):
+                    warnings.warn(
+                        "Pandas doesn't allow columns to be "
+                        "created via a new attribute name - see "
+                        "https://pandas.pydata.org/pandas-docs/"
+                        "stable/indexing.html#attribute-access",
+                        stacklevel=find_stack_level(),
+                    )
+                object.__setattr__(self, name, value)
+
+    @final
+    def _dir_additions(self) -> set[str]:
+        """
+        add the string-like attributes from the info_axis.
+        If info_axis is a MultiIndex, its first level values are used.
+        """
+        additions = super()._dir_additions()
+        if self._info_axis._can_hold_strings:
+            additions.update(self._info_axis._dir_additions_for_owner)
+        return additions
+
+    # ----------------------------------------------------------------------
+    # Consolidation of internals
+
+    @final
+    def _protect_consolidate(self, f):
+        """
+        Consolidate _mgr -- if the blocks have changed, then clear the
+        cache
+        """
+        if isinstance(self._mgr, (ArrayManager, SingleArrayManager)):
+            return f()
+        blocks_before = len(self._mgr.blocks)
+        result = f()
+        if len(self._mgr.blocks) != blocks_before:
+            self._clear_item_cache()
+        return result
+
+    @final
+    def _consolidate_inplace(self) -> None:
+        """Consolidate data in place and return None"""
+
+        def f() -> None:
+            self._mgr = self._mgr.consolidate()
+
+        self._protect_consolidate(f)
+
+    @final
+    def _consolidate(self):
+        """
+        Compute NDFrame with "consolidated" internals (data of each dtype
+        grouped together in a single ndarray).
+
+        Returns
+        -------
+        consolidated : same type as caller
+        """
+        f = lambda: self._mgr.consolidate()
+        cons_data = self._protect_consolidate(f)
+        return self._constructor_from_mgr(cons_data, axes=cons_data.axes).__finalize__(
+            self
+        )
+
+    @final
+    @property
+    def _is_mixed_type(self) -> bool_t:
+        if self._mgr.is_single_block:
+            # Includes all Series cases
+            return False
+
+        if self._mgr.any_extension_types:
+            # Even if they have the same dtype, we can't consolidate them,
+            #  so we pretend this is "mixed'"
+            return True
+
+        return self.dtypes.nunique() > 1
+
+    @final
+    def _get_numeric_data(self) -> Self:
+        new_mgr = self._mgr.get_numeric_data()
+        return self._constructor_from_mgr(new_mgr, axes=new_mgr.axes).__finalize__(self)
+
+    @final
+    def _get_bool_data(self):
+        new_mgr = self._mgr.get_bool_data()
+        return self._constructor_from_mgr(new_mgr, axes=new_mgr.axes).__finalize__(self)
+
+    # ----------------------------------------------------------------------
+    # Internal Interface Methods
+
+    @property
+    def values(self):
+        raise AbstractMethodError(self)
+
+    @property
+    def _values(self) -> ArrayLike:
+        """internal implementation"""
+        raise AbstractMethodError(self)
+
+    @property
+    def dtypes(self):
+        """
+        Return the dtypes in the DataFrame.
+
+        This returns a Series with the data type of each column.
+        The result's index is the original DataFrame's columns. Columns
+        with mixed types are stored with the ``object`` dtype. See
+        :ref:`the User Guide ` for more.
+
+        Returns
+        -------
+        pandas.Series
+            The data type of each column.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'float': [1.0],
+        ...                    'int': [1],
+        ...                    'datetime': [pd.Timestamp('20180310')],
+        ...                    'string': ['foo']})
+        >>> df.dtypes
+        float              float64
+        int                  int64
+        datetime    datetime64[ns]
+        string              object
+        dtype: object
+        """
+        data = self._mgr.get_dtypes()
+        return self._constructor_sliced(data, index=self._info_axis, dtype=np.object_)
+
+    @final
+    def astype(
+        self, dtype, copy: bool_t | None = None, errors: IgnoreRaise = "raise"
+    ) -> Self:
+        """
+        Cast a pandas object to a specified dtype ``dtype``.
+
+        Parameters
+        ----------
+        dtype : str, data type, Series or Mapping of column name -> data type
+            Use a str, numpy.dtype, pandas.ExtensionDtype or Python type to
+            cast entire pandas object to the same type. Alternatively, use a
+            mapping, e.g. {col: dtype, ...}, where col is a column label and dtype is
+            a numpy.dtype or Python type to cast one or more of the DataFrame's
+            columns to column-specific types.
+        copy : bool, default True
+            Return a copy when ``copy=True`` (be very careful setting
+            ``copy=False`` as changes to values then may propagate to other
+            pandas objects).
+
+            .. note::
+                The `copy` keyword will change behavior in pandas 3.0.
+                `Copy-on-Write
+                `__
+                will be enabled by default, which means that all methods with a
+                `copy` keyword will use a lazy copy mechanism to defer the copy and
+                ignore the `copy` keyword. The `copy` keyword will be removed in a
+                future version of pandas.
+
+                You can already get the future behavior and improvements through
+                enabling copy on write ``pd.options.mode.copy_on_write = True``
+        errors : {'raise', 'ignore'}, default 'raise'
+            Control raising of exceptions on invalid data for provided dtype.
+
+            - ``raise`` : allow exceptions to be raised
+            - ``ignore`` : suppress exceptions. On error return original object.
+
+        Returns
+        -------
+        same type as caller
+
+        See Also
+        --------
+        to_datetime : Convert argument to datetime.
+        to_timedelta : Convert argument to timedelta.
+        to_numeric : Convert argument to a numeric type.
+        numpy.ndarray.astype : Cast a numpy array to a specified type.
+
+        Notes
+        -----
+        .. versionchanged:: 2.0.0
+
+            Using ``astype`` to convert from timezone-naive dtype to
+            timezone-aware dtype will raise an exception.
+            Use :meth:`Series.dt.tz_localize` instead.
+
+        Examples
+        --------
+        Create a DataFrame:
+
+        >>> d = {'col1': [1, 2], 'col2': [3, 4]}
+        >>> df = pd.DataFrame(data=d)
+        >>> df.dtypes
+        col1    int64
+        col2    int64
+        dtype: object
+
+        Cast all columns to int32:
+
+        >>> df.astype('int32').dtypes
+        col1    int32
+        col2    int32
+        dtype: object
+
+        Cast col1 to int32 using a dictionary:
+
+        >>> df.astype({'col1': 'int32'}).dtypes
+        col1    int32
+        col2    int64
+        dtype: object
+
+        Create a series:
+
+        >>> ser = pd.Series([1, 2], dtype='int32')
+        >>> ser
+        0    1
+        1    2
+        dtype: int32
+        >>> ser.astype('int64')
+        0    1
+        1    2
+        dtype: int64
+
+        Convert to categorical type:
+
+        >>> ser.astype('category')
+        0    1
+        1    2
+        dtype: category
+        Categories (2, int32): [1, 2]
+
+        Convert to ordered categorical type with custom ordering:
+
+        >>> from pandas.api.types import CategoricalDtype
+        >>> cat_dtype = CategoricalDtype(
+        ...     categories=[2, 1], ordered=True)
+        >>> ser.astype(cat_dtype)
+        0    1
+        1    2
+        dtype: category
+        Categories (2, int64): [2 < 1]
+
+        Create a series of dates:
+
+        >>> ser_date = pd.Series(pd.date_range('20200101', periods=3))
+        >>> ser_date
+        0   2020-01-01
+        1   2020-01-02
+        2   2020-01-03
+        dtype: datetime64[ns]
+        """
+        if copy and using_copy_on_write():
+            copy = False
+
+        if is_dict_like(dtype):
+            if self.ndim == 1:  # i.e. Series
+                if len(dtype) > 1 or self.name not in dtype:
+                    raise KeyError(
+                        "Only the Series name can be used for "
+                        "the key in Series dtype mappings."
+                    )
+                new_type = dtype[self.name]
+                return self.astype(new_type, copy, errors)
+
+            # GH#44417 cast to Series so we can use .iat below, which will be
+            #  robust in case we
+            from pandas import Series
+
+            dtype_ser = Series(dtype, dtype=object)
+
+            for col_name in dtype_ser.index:
+                if col_name not in self:
+                    raise KeyError(
+                        "Only a column name can be used for the "
+                        "key in a dtype mappings argument. "
+                        f"'{col_name}' not found in columns."
+                    )
+
+            dtype_ser = dtype_ser.reindex(self.columns, fill_value=None, copy=False)
+
+            results = []
+            for i, (col_name, col) in enumerate(self.items()):
+                cdt = dtype_ser.iat[i]
+                if isna(cdt):
+                    res_col = col.copy(deep=copy)
+                else:
+                    try:
+                        res_col = col.astype(dtype=cdt, copy=copy, errors=errors)
+                    except ValueError as ex:
+                        ex.args = (
+                            f"{ex}: Error while type casting for column '{col_name}'",
+                        )
+                        raise
+                results.append(res_col)
+
+        elif is_extension_array_dtype(dtype) and self.ndim > 1:
+            # TODO(EA2D): special case not needed with 2D EAs
+            dtype = pandas_dtype(dtype)
+            if isinstance(dtype, ExtensionDtype) and all(
+                arr.dtype == dtype for arr in self._mgr.arrays
+            ):
+                return self.copy(deep=copy)
+            # GH 18099/22869: columnwise conversion to extension dtype
+            # GH 24704: self.items handles duplicate column names
+            results = [
+                ser.astype(dtype, copy=copy, errors=errors) for _, ser in self.items()
+            ]
+
+        else:
+            # else, only a single dtype is given
+            new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors)
+            res = self._constructor_from_mgr(new_data, axes=new_data.axes)
+            return res.__finalize__(self, method="astype")
+
+        # GH 33113: handle empty frame or series
+        if not results:
+            return self.copy(deep=None)
+
+        # GH 19920: retain column metadata after concat
+        result = concat(results, axis=1, copy=False)
+        # GH#40810 retain subclass
+        # error: Incompatible types in assignment
+        # (expression has type "Self", variable has type "DataFrame")
+        result = self._constructor(result)  # type: ignore[assignment]
+        result.columns = self.columns
+        result = result.__finalize__(self, method="astype")
+        # https://github.com/python/mypy/issues/8354
+        return cast(Self, result)
+
+    @final
+    def copy(self, deep: bool_t | None = True) -> Self:
+        """
+        Make a copy of this object's indices and data.
+
+        When ``deep=True`` (default), a new object will be created with a
+        copy of the calling object's data and indices. Modifications to
+        the data or indices of the copy will not be reflected in the
+        original object (see notes below).
+
+        When ``deep=False``, a new object will be created without copying
+        the calling object's data or index (only references to the data
+        and index are copied). Any changes to the data of the original
+        will be reflected in the shallow copy (and vice versa).
+
+        .. note::
+            The ``deep=False`` behaviour as described above will change
+            in pandas 3.0. `Copy-on-Write
+            `__
+            will be enabled by default, which means that the "shallow" copy
+            is that is returned with ``deep=False`` will still avoid making
+            an eager copy, but changes to the data of the original will *no*
+            longer be reflected in the shallow copy (or vice versa). Instead,
+            it makes use of a lazy (deferred) copy mechanism that will copy
+            the data only when any changes to the original or shallow copy is
+            made.
+
+            You can already get the future behavior and improvements through
+            enabling copy on write ``pd.options.mode.copy_on_write = True``
+
+        Parameters
+        ----------
+        deep : bool, default True
+            Make a deep copy, including a copy of the data and the indices.
+            With ``deep=False`` neither the indices nor the data are copied.
+
+        Returns
+        -------
+        Series or DataFrame
+            Object type matches caller.
+
+        Notes
+        -----
+        When ``deep=True``, data is copied but actual Python objects
+        will not be copied recursively, only the reference to the object.
+        This is in contrast to `copy.deepcopy` in the Standard Library,
+        which recursively copies object data (see examples below).
+
+        While ``Index`` objects are copied when ``deep=True``, the underlying
+        numpy array is not copied for performance reasons. Since ``Index`` is
+        immutable, the underlying data can be safely shared and a copy
+        is not needed.
+
+        Since pandas is not thread safe, see the
+        :ref:`gotchas ` when copying in a threading
+        environment.
+
+        When ``copy_on_write`` in pandas config is set to ``True``, the
+        ``copy_on_write`` config takes effect even when ``deep=False``.
+        This means that any changes to the copied data would make a new copy
+        of the data upon write (and vice versa). Changes made to either the
+        original or copied variable would not be reflected in the counterpart.
+        See :ref:`Copy_on_Write ` for more information.
+
+        Examples
+        --------
+        >>> s = pd.Series([1, 2], index=["a", "b"])
+        >>> s
+        a    1
+        b    2
+        dtype: int64
+
+        >>> s_copy = s.copy()
+        >>> s_copy
+        a    1
+        b    2
+        dtype: int64
+
+        **Shallow copy versus default (deep) copy:**
+
+        >>> s = pd.Series([1, 2], index=["a", "b"])
+        >>> deep = s.copy()
+        >>> shallow = s.copy(deep=False)
+
+        Shallow copy shares data and index with original.
+
+        >>> s is shallow
+        False
+        >>> s.values is shallow.values and s.index is shallow.index
+        True
+
+        Deep copy has own copy of data and index.
+
+        >>> s is deep
+        False
+        >>> s.values is deep.values or s.index is deep.index
+        False
+
+        Updates to the data shared by shallow copy and original is reflected
+        in both (NOTE: this will no longer be true for pandas >= 3.0);
+        deep copy remains unchanged.
+
+        >>> s.iloc[0] = 3
+        >>> shallow.iloc[1] = 4
+        >>> s
+        a    3
+        b    4
+        dtype: int64
+        >>> shallow
+        a    3
+        b    4
+        dtype: int64
+        >>> deep
+        a    1
+        b    2
+        dtype: int64
+
+        Note that when copying an object containing Python objects, a deep copy
+        will copy the data, but will not do so recursively. Updating a nested
+        data object will be reflected in the deep copy.
+
+        >>> s = pd.Series([[1, 2], [3, 4]])
+        >>> deep = s.copy()
+        >>> s[0][0] = 10
+        >>> s
+        0    [10, 2]
+        1     [3, 4]
+        dtype: object
+        >>> deep
+        0    [10, 2]
+        1     [3, 4]
+        dtype: object
+
+        **Copy-on-Write is set to true**, the shallow copy is not modified
+        when the original data is changed:
+
+        >>> with pd.option_context("mode.copy_on_write", True):
+        ...     s = pd.Series([1, 2], index=["a", "b"])
+        ...     copy = s.copy(deep=False)
+        ...     s.iloc[0] = 100
+        ...     s
+        a    100
+        b      2
+        dtype: int64
+        >>> copy
+        a    1
+        b    2
+        dtype: int64
+        """
+        data = self._mgr.copy(deep=deep)
+        self._clear_item_cache()
+        return self._constructor_from_mgr(data, axes=data.axes).__finalize__(
+            self, method="copy"
+        )
+
+    @final
+    def __copy__(self, deep: bool_t = True) -> Self:
+        return self.copy(deep=deep)
+
+    @final
+    def __deepcopy__(self, memo=None) -> Self:
+        """
+        Parameters
+        ----------
+        memo, default None
+            Standard signature. Unused
+        """
+        return self.copy(deep=True)
+
+    @final
+    def infer_objects(self, copy: bool_t | None = None) -> Self:
+        """
+        Attempt to infer better dtypes for object columns.
+
+        Attempts soft conversion of object-dtyped
+        columns, leaving non-object and unconvertible
+        columns unchanged. The inference rules are the
+        same as during normal Series/DataFrame construction.
+
+        Parameters
+        ----------
+        copy : bool, default True
+            Whether to make a copy for non-object or non-inferable columns
+            or Series.
+
+            .. note::
+                The `copy` keyword will change behavior in pandas 3.0.
+                `Copy-on-Write
+                `__
+                will be enabled by default, which means that all methods with a
+                `copy` keyword will use a lazy copy mechanism to defer the copy and
+                ignore the `copy` keyword. The `copy` keyword will be removed in a
+                future version of pandas.
+
+                You can already get the future behavior and improvements through
+                enabling copy on write ``pd.options.mode.copy_on_write = True``
+
+        Returns
+        -------
+        same type as input object
+
+        See Also
+        --------
+        to_datetime : Convert argument to datetime.
+        to_timedelta : Convert argument to timedelta.
+        to_numeric : Convert argument to numeric type.
+        convert_dtypes : Convert argument to best possible dtype.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({"A": ["a", 1, 2, 3]})
+        >>> df = df.iloc[1:]
+        >>> df
+           A
+        1  1
+        2  2
+        3  3
+
+        >>> df.dtypes
+        A    object
+        dtype: object
+
+        >>> df.infer_objects().dtypes
+        A    int64
+        dtype: object
+        """
+        new_mgr = self._mgr.convert(copy=copy)
+        res = self._constructor_from_mgr(new_mgr, axes=new_mgr.axes)
+        return res.__finalize__(self, method="infer_objects")
+
+    @final
+    def convert_dtypes(
+        self,
+        infer_objects: bool_t = True,
+        convert_string: bool_t = True,
+        convert_integer: bool_t = True,
+        convert_boolean: bool_t = True,
+        convert_floating: bool_t = True,
+        dtype_backend: DtypeBackend = "numpy_nullable",
+    ) -> Self:
+        """
+        Convert columns to the best possible dtypes using dtypes supporting ``pd.NA``.
+
+        Parameters
+        ----------
+        infer_objects : bool, default True
+            Whether object dtypes should be converted to the best possible types.
+        convert_string : bool, default True
+            Whether object dtypes should be converted to ``StringDtype()``.
+        convert_integer : bool, default True
+            Whether, if possible, conversion can be done to integer extension types.
+        convert_boolean : bool, defaults True
+            Whether object dtypes should be converted to ``BooleanDtypes()``.
+        convert_floating : bool, defaults True
+            Whether, if possible, conversion can be done to floating extension types.
+            If `convert_integer` is also True, preference will be give to integer
+            dtypes if the floats can be faithfully casted to integers.
+        dtype_backend : {'numpy_nullable', 'pyarrow'}, default 'numpy_nullable'
+            Back-end data type applied to the resultant :class:`DataFrame`
+            (still experimental). Behaviour is as follows:
+
+            * ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame`
+              (default).
+            * ``"pyarrow"``: returns pyarrow-backed nullable :class:`ArrowDtype`
+              DataFrame.
+
+            .. versionadded:: 2.0
+
+        Returns
+        -------
+        Series or DataFrame
+            Copy of input object with new dtype.
+
+        See Also
+        --------
+        infer_objects : Infer dtypes of objects.
+        to_datetime : Convert argument to datetime.
+        to_timedelta : Convert argument to timedelta.
+        to_numeric : Convert argument to a numeric type.
+
+        Notes
+        -----
+        By default, ``convert_dtypes`` will attempt to convert a Series (or each
+        Series in a DataFrame) to dtypes that support ``pd.NA``. By using the options
+        ``convert_string``, ``convert_integer``, ``convert_boolean`` and
+        ``convert_floating``, it is possible to turn off individual conversions
+        to ``StringDtype``, the integer extension types, ``BooleanDtype``
+        or floating extension types, respectively.
+
+        For object-dtyped columns, if ``infer_objects`` is ``True``, use the inference
+        rules as during normal Series/DataFrame construction.  Then, if possible,
+        convert to ``StringDtype``, ``BooleanDtype`` or an appropriate integer
+        or floating extension type, otherwise leave as ``object``.
+
+        If the dtype is integer, convert to an appropriate integer extension type.
+
+        If the dtype is numeric, and consists of all integers, convert to an
+        appropriate integer extension type. Otherwise, convert to an
+        appropriate floating extension type.
+
+        In the future, as new dtypes are added that support ``pd.NA``, the results
+        of this method will change to support those new dtypes.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame(
+        ...     {
+        ...         "a": pd.Series([1, 2, 3], dtype=np.dtype("int32")),
+        ...         "b": pd.Series(["x", "y", "z"], dtype=np.dtype("O")),
+        ...         "c": pd.Series([True, False, np.nan], dtype=np.dtype("O")),
+        ...         "d": pd.Series(["h", "i", np.nan], dtype=np.dtype("O")),
+        ...         "e": pd.Series([10, np.nan, 20], dtype=np.dtype("float")),
+        ...         "f": pd.Series([np.nan, 100.5, 200], dtype=np.dtype("float")),
+        ...     }
+        ... )
+
+        Start with a DataFrame with default dtypes.
+
+        >>> df
+           a  b      c    d     e      f
+        0  1  x   True    h  10.0    NaN
+        1  2  y  False    i   NaN  100.5
+        2  3  z    NaN  NaN  20.0  200.0
+
+        >>> df.dtypes
+        a      int32
+        b     object
+        c     object
+        d     object
+        e    float64
+        f    float64
+        dtype: object
+
+        Convert the DataFrame to use best possible dtypes.
+
+        >>> dfn = df.convert_dtypes()
+        >>> dfn
+           a  b      c     d     e      f
+        0  1  x   True     h    10   
+        1  2  y  False     i    100.5
+        2  3  z         20  200.0
+
+        >>> dfn.dtypes
+        a             Int32
+        b    string[python]
+        c           boolean
+        d    string[python]
+        e             Int64
+        f           Float64
+        dtype: object
+
+        Start with a Series of strings and missing data represented by ``np.nan``.
+
+        >>> s = pd.Series(["a", "b", np.nan])
+        >>> s
+        0      a
+        1      b
+        2    NaN
+        dtype: object
+
+        Obtain a Series with dtype ``StringDtype``.
+
+        >>> s.convert_dtypes()
+        0       a
+        1       b
+        2    
+        dtype: string
+        """
+        check_dtype_backend(dtype_backend)
+        new_mgr = self._mgr.convert_dtypes(  # type: ignore[union-attr]
+            infer_objects=infer_objects,
+            convert_string=convert_string,
+            convert_integer=convert_integer,
+            convert_boolean=convert_boolean,
+            convert_floating=convert_floating,
+            dtype_backend=dtype_backend,
+        )
+        res = self._constructor_from_mgr(new_mgr, axes=new_mgr.axes)
+        return res.__finalize__(self, method="convert_dtypes")
+
+    # ----------------------------------------------------------------------
+    # Filling NA's
+
+    def _deprecate_downcast(self, downcast, method_name: str):
+        # GH#40988
+        if downcast is not lib.no_default:
+            warnings.warn(
+                f"The 'downcast' keyword in {method_name} is deprecated and "
+                "will be removed in a future version. Use "
+                "res.infer_objects(copy=False) to infer non-object dtype, or "
+                "pd.to_numeric with the 'downcast' keyword to downcast numeric "
+                "results.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+        else:
+            downcast = None
+        return downcast
+
+    @final
+    def _pad_or_backfill(
+        self,
+        method: Literal["ffill", "bfill", "pad", "backfill"],
+        *,
+        axis: None | Axis = None,
+        inplace: bool_t = False,
+        limit: None | int = None,
+        limit_area: Literal["inside", "outside"] | None = None,
+        downcast: dict | None = None,
+    ):
+        if axis is None:
+            axis = 0
+        axis = self._get_axis_number(axis)
+        method = clean_fill_method(method)
+
+        if not self._mgr.is_single_block and axis == 1:
+            # e.g. test_align_fill_method
+            # TODO(3.0): once downcast is removed, we can do the .T
+            #  in all axis=1 cases, and remove axis kward from mgr.pad_or_backfill.
+            if inplace:
+                raise NotImplementedError()
+            result = self.T._pad_or_backfill(
+                method=method, limit=limit, limit_area=limit_area
+            ).T
+
+            return result
+
+        new_mgr = self._mgr.pad_or_backfill(
+            method=method,
+            axis=self._get_block_manager_axis(axis),
+            limit=limit,
+            limit_area=limit_area,
+            inplace=inplace,
+            downcast=downcast,
+        )
+        result = self._constructor_from_mgr(new_mgr, axes=new_mgr.axes)
+        if inplace:
+            return self._update_inplace(result)
+        else:
+            return result.__finalize__(self, method="fillna")
+
+    @overload
+    def fillna(
+        self,
+        value: Hashable | Mapping | Series | DataFrame = ...,
+        *,
+        method: FillnaOptions | None = ...,
+        axis: Axis | None = ...,
+        inplace: Literal[False] = ...,
+        limit: int | None = ...,
+        downcast: dict | None = ...,
+    ) -> Self:
+        ...
+
+    @overload
+    def fillna(
+        self,
+        value: Hashable | Mapping | Series | DataFrame = ...,
+        *,
+        method: FillnaOptions | None = ...,
+        axis: Axis | None = ...,
+        inplace: Literal[True],
+        limit: int | None = ...,
+        downcast: dict | None = ...,
+    ) -> None:
+        ...
+
+    @overload
+    def fillna(
+        self,
+        value: Hashable | Mapping | Series | DataFrame = ...,
+        *,
+        method: FillnaOptions | None = ...,
+        axis: Axis | None = ...,
+        inplace: bool_t = ...,
+        limit: int | None = ...,
+        downcast: dict | None = ...,
+    ) -> Self | None:
+        ...
+
+    @final
+    @doc(
+        klass=_shared_doc_kwargs["klass"],
+        axes_single_arg=_shared_doc_kwargs["axes_single_arg"],
+    )
+    def fillna(
+        self,
+        value: Hashable | Mapping | Series | DataFrame | None = None,
+        *,
+        method: FillnaOptions | None = None,
+        axis: Axis | None = None,
+        inplace: bool_t = False,
+        limit: int | None = None,
+        downcast: dict | None | lib.NoDefault = lib.no_default,
+    ) -> Self | None:
+        """
+        Fill NA/NaN values using the specified method.
+
+        Parameters
+        ----------
+        value : scalar, dict, Series, or DataFrame
+            Value to use to fill holes (e.g. 0), alternately a
+            dict/Series/DataFrame of values specifying which value to use for
+            each index (for a Series) or column (for a DataFrame).  Values not
+            in the dict/Series/DataFrame will not be filled. This value cannot
+            be a list.
+        method : {{'backfill', 'bfill', 'ffill', None}}, default None
+            Method to use for filling holes in reindexed Series:
+
+            * ffill: propagate last valid observation forward to next valid.
+            * backfill / bfill: use next valid observation to fill gap.
+
+            .. deprecated:: 2.1.0
+                Use ffill or bfill instead.
+
+        axis : {axes_single_arg}
+            Axis along which to fill missing values. For `Series`
+            this parameter is unused and defaults to 0.
+        inplace : bool, default False
+            If True, fill in-place. Note: this will modify any
+            other views on this object (e.g., a no-copy slice for a column in a
+            DataFrame).
+        limit : int, default None
+            If method is specified, this is the maximum number of consecutive
+            NaN values to forward/backward fill. In other words, if there is
+            a gap with more than this number of consecutive NaNs, it will only
+            be partially filled. If method is not specified, this is the
+            maximum number of entries along the entire axis where NaNs will be
+            filled. Must be greater than 0 if not None.
+        downcast : dict, default is None
+            A dict of item->dtype of what to downcast if possible,
+            or the string 'infer' which will try to downcast to an appropriate
+            equal type (e.g. float64 to int64 if possible).
+
+            .. deprecated:: 2.2.0
+
+        Returns
+        -------
+        {klass} or None
+            Object with missing values filled or None if ``inplace=True``.
+
+        See Also
+        --------
+        ffill : Fill values by propagating the last valid observation to next valid.
+        bfill : Fill values by using the next valid observation to fill the gap.
+        interpolate : Fill NaN values using interpolation.
+        reindex : Conform object to new index.
+        asfreq : Convert TimeSeries to specified frequency.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([[np.nan, 2, np.nan, 0],
+        ...                    [3, 4, np.nan, 1],
+        ...                    [np.nan, np.nan, np.nan, np.nan],
+        ...                    [np.nan, 3, np.nan, 4]],
+        ...                   columns=list("ABCD"))
+        >>> df
+             A    B   C    D
+        0  NaN  2.0 NaN  0.0
+        1  3.0  4.0 NaN  1.0
+        2  NaN  NaN NaN  NaN
+        3  NaN  3.0 NaN  4.0
+
+        Replace all NaN elements with 0s.
+
+        >>> df.fillna(0)
+             A    B    C    D
+        0  0.0  2.0  0.0  0.0
+        1  3.0  4.0  0.0  1.0
+        2  0.0  0.0  0.0  0.0
+        3  0.0  3.0  0.0  4.0
+
+        Replace all NaN elements in column 'A', 'B', 'C', and 'D', with 0, 1,
+        2, and 3 respectively.
+
+        >>> values = {{"A": 0, "B": 1, "C": 2, "D": 3}}
+        >>> df.fillna(value=values)
+             A    B    C    D
+        0  0.0  2.0  2.0  0.0
+        1  3.0  4.0  2.0  1.0
+        2  0.0  1.0  2.0  3.0
+        3  0.0  3.0  2.0  4.0
+
+        Only replace the first NaN element.
+
+        >>> df.fillna(value=values, limit=1)
+             A    B    C    D
+        0  0.0  2.0  2.0  0.0
+        1  3.0  4.0  NaN  1.0
+        2  NaN  1.0  NaN  3.0
+        3  NaN  3.0  NaN  4.0
+
+        When filling using a DataFrame, replacement happens along
+        the same column names and same indices
+
+        >>> df2 = pd.DataFrame(np.zeros((4, 4)), columns=list("ABCE"))
+        >>> df.fillna(df2)
+             A    B    C    D
+        0  0.0  2.0  0.0  0.0
+        1  3.0  4.0  0.0  1.0
+        2  0.0  0.0  0.0  NaN
+        3  0.0  3.0  0.0  4.0
+
+        Note that column D is not affected since it is not present in df2.
+        """
+        inplace = validate_bool_kwarg(inplace, "inplace")
+        if inplace:
+            if not PYPY and not WARNING_CHECK_DISABLED and using_copy_on_write():
+                if sys.getrefcount(self) <= REF_COUNT:
+                    warnings.warn(
+                        _chained_assignment_method_msg,
+                        ChainedAssignmentError,
+                        stacklevel=2,
+                    )
+            elif (
+                not PYPY
+                and not WARNING_CHECK_DISABLED
+                and not using_copy_on_write()
+                and self._is_view_after_cow_rules()
+            ):
+                ctr = sys.getrefcount(self)
+                ref_count = REF_COUNT
+                if isinstance(self, ABCSeries) and _check_cacher(self):
+                    # see https://github.com/pandas-dev/pandas/pull/56060#discussion_r1399245221
+                    ref_count += 1
+                if ctr <= ref_count:
+                    warnings.warn(
+                        _chained_assignment_warning_method_msg,
+                        FutureWarning,
+                        stacklevel=2,
+                    )
+
+        value, method = validate_fillna_kwargs(value, method)
+        if method is not None:
+            warnings.warn(
+                f"{type(self).__name__}.fillna with 'method' is deprecated and "
+                "will raise in a future version. Use obj.ffill() or obj.bfill() "
+                "instead.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+
+        was_no_default = downcast is lib.no_default
+        downcast = self._deprecate_downcast(downcast, "fillna")
+
+        # set the default here, so functions examining the signaure
+        # can detect if something was set (e.g. in groupby) (GH9221)
+        if axis is None:
+            axis = 0
+        axis = self._get_axis_number(axis)
+
+        if value is None:
+            return self._pad_or_backfill(
+                # error: Argument 1 to "_pad_or_backfill" of "NDFrame" has
+                # incompatible type "Optional[Literal['backfill', 'bfill', 'ffill',
+                # 'pad']]"; expected "Literal['ffill', 'bfill', 'pad', 'backfill']"
+                method,  # type: ignore[arg-type]
+                axis=axis,
+                limit=limit,
+                inplace=inplace,
+                # error: Argument "downcast" to "_fillna_with_method" of "NDFrame"
+                # has incompatible type "Union[Dict[Any, Any], None,
+                # Literal[_NoDefault.no_default]]"; expected
+                # "Optional[Dict[Any, Any]]"
+                downcast=downcast,  # type: ignore[arg-type]
+            )
+        else:
+            if self.ndim == 1:
+                if isinstance(value, (dict, ABCSeries)):
+                    if not len(value):
+                        # test_fillna_nonscalar
+                        if inplace:
+                            return None
+                        return self.copy(deep=None)
+                    from pandas import Series
+
+                    value = Series(value)
+                    value = value.reindex(self.index, copy=False)
+                    value = value._values
+                elif not is_list_like(value):
+                    pass
+                else:
+                    raise TypeError(
+                        '"value" parameter must be a scalar, dict '
+                        "or Series, but you passed a "
+                        f'"{type(value).__name__}"'
+                    )
+
+                new_data = self._mgr.fillna(
+                    value=value, limit=limit, inplace=inplace, downcast=downcast
+                )
+
+            elif isinstance(value, (dict, ABCSeries)):
+                if axis == 1:
+                    raise NotImplementedError(
+                        "Currently only can fill "
+                        "with dict/Series column "
+                        "by column"
+                    )
+                if using_copy_on_write():
+                    result = self.copy(deep=None)
+                else:
+                    result = self if inplace else self.copy()
+                is_dict = isinstance(downcast, dict)
+                for k, v in value.items():
+                    if k not in result:
+                        continue
+
+                    if was_no_default:
+                        downcast_k = lib.no_default
+                    else:
+                        downcast_k = (
+                            # error: Incompatible types in assignment (expression
+                            # has type "Union[Dict[Any, Any], None,
+                            # Literal[_NoDefault.no_default], Any]", variable has
+                            # type "_NoDefault")
+                            downcast  # type: ignore[assignment]
+                            if not is_dict
+                            # error: Item "None" of "Optional[Dict[Any, Any]]" has
+                            # no attribute "get"
+                            else downcast.get(k)  # type: ignore[union-attr]
+                        )
+
+                    res_k = result[k].fillna(v, limit=limit, downcast=downcast_k)
+
+                    if not inplace:
+                        result[k] = res_k
+                    else:
+                        # We can write into our existing column(s) iff dtype
+                        #  was preserved.
+                        if isinstance(res_k, ABCSeries):
+                            # i.e. 'k' only shows up once in self.columns
+                            if res_k.dtype == result[k].dtype:
+                                result.loc[:, k] = res_k
+                            else:
+                                # Different dtype -> no way to do inplace.
+                                result[k] = res_k
+                        else:
+                            # see test_fillna_dict_inplace_nonunique_columns
+                            locs = result.columns.get_loc(k)
+                            if isinstance(locs, slice):
+                                locs = np.arange(self.shape[1])[locs]
+                            elif (
+                                isinstance(locs, np.ndarray) and locs.dtype.kind == "b"
+                            ):
+                                locs = locs.nonzero()[0]
+                            elif not (
+                                isinstance(locs, np.ndarray) and locs.dtype.kind == "i"
+                            ):
+                                # Should never be reached, but let's cover our bases
+                                raise NotImplementedError(
+                                    "Unexpected get_loc result, please report a bug at "
+                                    "https://github.com/pandas-dev/pandas"
+                                )
+
+                            for i, loc in enumerate(locs):
+                                res_loc = res_k.iloc[:, i]
+                                target = self.iloc[:, loc]
+
+                                if res_loc.dtype == target.dtype:
+                                    result.iloc[:, loc] = res_loc
+                                else:
+                                    result.isetitem(loc, res_loc)
+                if inplace:
+                    return self._update_inplace(result)
+                else:
+                    return result
+
+            elif not is_list_like(value):
+                if axis == 1:
+                    result = self.T.fillna(value=value, limit=limit).T
+                    new_data = result._mgr
+                else:
+                    new_data = self._mgr.fillna(
+                        value=value, limit=limit, inplace=inplace, downcast=downcast
+                    )
+            elif isinstance(value, ABCDataFrame) and self.ndim == 2:
+                new_data = self.where(self.notna(), value)._mgr
+            else:
+                raise ValueError(f"invalid fill value with a {type(value)}")
+
+        result = self._constructor_from_mgr(new_data, axes=new_data.axes)
+        if inplace:
+            return self._update_inplace(result)
+        else:
+            return result.__finalize__(self, method="fillna")
+
+    @overload
+    def ffill(
+        self,
+        *,
+        axis: None | Axis = ...,
+        inplace: Literal[False] = ...,
+        limit: None | int = ...,
+        limit_area: Literal["inside", "outside"] | None = ...,
+        downcast: dict | None | lib.NoDefault = ...,
+    ) -> Self:
+        ...
+
+    @overload
+    def ffill(
+        self,
+        *,
+        axis: None | Axis = ...,
+        inplace: Literal[True],
+        limit: None | int = ...,
+        limit_area: Literal["inside", "outside"] | None = ...,
+        downcast: dict | None | lib.NoDefault = ...,
+    ) -> None:
+        ...
+
+    @overload
+    def ffill(
+        self,
+        *,
+        axis: None | Axis = ...,
+        inplace: bool_t = ...,
+        limit: None | int = ...,
+        limit_area: Literal["inside", "outside"] | None = ...,
+        downcast: dict | None | lib.NoDefault = ...,
+    ) -> Self | None:
+        ...
+
+    @final
+    @doc(
+        klass=_shared_doc_kwargs["klass"],
+        axes_single_arg=_shared_doc_kwargs["axes_single_arg"],
+    )
+    def ffill(
+        self,
+        *,
+        axis: None | Axis = None,
+        inplace: bool_t = False,
+        limit: None | int = None,
+        limit_area: Literal["inside", "outside"] | None = None,
+        downcast: dict | None | lib.NoDefault = lib.no_default,
+    ) -> Self | None:
+        """
+        Fill NA/NaN values by propagating the last valid observation to next valid.
+
+        Parameters
+        ----------
+        axis : {axes_single_arg}
+            Axis along which to fill missing values. For `Series`
+            this parameter is unused and defaults to 0.
+        inplace : bool, default False
+            If True, fill in-place. Note: this will modify any
+            other views on this object (e.g., a no-copy slice for a column in a
+            DataFrame).
+        limit : int, default None
+            If method is specified, this is the maximum number of consecutive
+            NaN values to forward/backward fill. In other words, if there is
+            a gap with more than this number of consecutive NaNs, it will only
+            be partially filled. If method is not specified, this is the
+            maximum number of entries along the entire axis where NaNs will be
+            filled. Must be greater than 0 if not None.
+        limit_area : {{`None`, 'inside', 'outside'}}, default None
+            If limit is specified, consecutive NaNs will be filled with this
+            restriction.
+
+            * ``None``: No fill restriction.
+            * 'inside': Only fill NaNs surrounded by valid values
+              (interpolate).
+            * 'outside': Only fill NaNs outside valid values (extrapolate).
+
+            .. versionadded:: 2.2.0
+
+        downcast : dict, default is None
+            A dict of item->dtype of what to downcast if possible,
+            or the string 'infer' which will try to downcast to an appropriate
+            equal type (e.g. float64 to int64 if possible).
+
+            .. deprecated:: 2.2.0
+
+        Returns
+        -------
+        {klass} or None
+            Object with missing values filled or None if ``inplace=True``.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([[np.nan, 2, np.nan, 0],
+        ...                    [3, 4, np.nan, 1],
+        ...                    [np.nan, np.nan, np.nan, np.nan],
+        ...                    [np.nan, 3, np.nan, 4]],
+        ...                   columns=list("ABCD"))
+        >>> df
+             A    B   C    D
+        0  NaN  2.0 NaN  0.0
+        1  3.0  4.0 NaN  1.0
+        2  NaN  NaN NaN  NaN
+        3  NaN  3.0 NaN  4.0
+
+        >>> df.ffill()
+             A    B   C    D
+        0  NaN  2.0 NaN  0.0
+        1  3.0  4.0 NaN  1.0
+        2  3.0  4.0 NaN  1.0
+        3  3.0  3.0 NaN  4.0
+
+        >>> ser = pd.Series([1, np.nan, 2, 3])
+        >>> ser.ffill()
+        0   1.0
+        1   1.0
+        2   2.0
+        3   3.0
+        dtype: float64
+        """
+        downcast = self._deprecate_downcast(downcast, "ffill")
+        inplace = validate_bool_kwarg(inplace, "inplace")
+        if inplace:
+            if not PYPY and not WARNING_CHECK_DISABLED and using_copy_on_write():
+                if sys.getrefcount(self) <= REF_COUNT:
+                    warnings.warn(
+                        _chained_assignment_method_msg,
+                        ChainedAssignmentError,
+                        stacklevel=2,
+                    )
+            elif (
+                not PYPY
+                and not WARNING_CHECK_DISABLED
+                and not using_copy_on_write()
+                and self._is_view_after_cow_rules()
+            ):
+                ctr = sys.getrefcount(self)
+                ref_count = REF_COUNT
+                if isinstance(self, ABCSeries) and _check_cacher(self):
+                    # see https://github.com/pandas-dev/pandas/pull/56060#discussion_r1399245221
+                    ref_count += 1
+                if ctr <= ref_count:
+                    warnings.warn(
+                        _chained_assignment_warning_method_msg,
+                        FutureWarning,
+                        stacklevel=2,
+                    )
+
+        return self._pad_or_backfill(
+            "ffill",
+            axis=axis,
+            inplace=inplace,
+            limit=limit,
+            limit_area=limit_area,
+            # error: Argument "downcast" to "_fillna_with_method" of "NDFrame"
+            # has incompatible type "Union[Dict[Any, Any], None,
+            # Literal[_NoDefault.no_default]]"; expected "Optional[Dict[Any, Any]]"
+            downcast=downcast,  # type: ignore[arg-type]
+        )
+
+    @final
+    @doc(klass=_shared_doc_kwargs["klass"])
+    def pad(
+        self,
+        *,
+        axis: None | Axis = None,
+        inplace: bool_t = False,
+        limit: None | int = None,
+        downcast: dict | None | lib.NoDefault = lib.no_default,
+    ) -> Self | None:
+        """
+        Fill NA/NaN values by propagating the last valid observation to next valid.
+
+        .. deprecated:: 2.0
+
+            {klass}.pad is deprecated. Use {klass}.ffill instead.
+
+        Returns
+        -------
+        {klass} or None
+            Object with missing values filled or None if ``inplace=True``.
+
+        Examples
+        --------
+        Please see examples for :meth:`DataFrame.ffill` or :meth:`Series.ffill`.
+        """
+        warnings.warn(
+            "DataFrame.pad/Series.pad is deprecated. Use "
+            "DataFrame.ffill/Series.ffill instead",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        return self.ffill(axis=axis, inplace=inplace, limit=limit, downcast=downcast)
+
+    @overload
+    def bfill(
+        self,
+        *,
+        axis: None | Axis = ...,
+        inplace: Literal[False] = ...,
+        limit: None | int = ...,
+        limit_area: Literal["inside", "outside"] | None = ...,
+        downcast: dict | None | lib.NoDefault = ...,
+    ) -> Self:
+        ...
+
+    @overload
+    def bfill(
+        self,
+        *,
+        axis: None | Axis = ...,
+        inplace: Literal[True],
+        limit: None | int = ...,
+        downcast: dict | None | lib.NoDefault = ...,
+    ) -> None:
+        ...
+
+    @overload
+    def bfill(
+        self,
+        *,
+        axis: None | Axis = ...,
+        inplace: bool_t = ...,
+        limit: None | int = ...,
+        limit_area: Literal["inside", "outside"] | None = ...,
+        downcast: dict | None | lib.NoDefault = ...,
+    ) -> Self | None:
+        ...
+
+    @final
+    @doc(
+        klass=_shared_doc_kwargs["klass"],
+        axes_single_arg=_shared_doc_kwargs["axes_single_arg"],
+    )
+    def bfill(
+        self,
+        *,
+        axis: None | Axis = None,
+        inplace: bool_t = False,
+        limit: None | int = None,
+        limit_area: Literal["inside", "outside"] | None = None,
+        downcast: dict | None | lib.NoDefault = lib.no_default,
+    ) -> Self | None:
+        """
+        Fill NA/NaN values by using the next valid observation to fill the gap.
+
+        Parameters
+        ----------
+        axis : {axes_single_arg}
+            Axis along which to fill missing values. For `Series`
+            this parameter is unused and defaults to 0.
+        inplace : bool, default False
+            If True, fill in-place. Note: this will modify any
+            other views on this object (e.g., a no-copy slice for a column in a
+            DataFrame).
+        limit : int, default None
+            If method is specified, this is the maximum number of consecutive
+            NaN values to forward/backward fill. In other words, if there is
+            a gap with more than this number of consecutive NaNs, it will only
+            be partially filled. If method is not specified, this is the
+            maximum number of entries along the entire axis where NaNs will be
+            filled. Must be greater than 0 if not None.
+        limit_area : {{`None`, 'inside', 'outside'}}, default None
+            If limit is specified, consecutive NaNs will be filled with this
+            restriction.
+
+            * ``None``: No fill restriction.
+            * 'inside': Only fill NaNs surrounded by valid values
+              (interpolate).
+            * 'outside': Only fill NaNs outside valid values (extrapolate).
+
+            .. versionadded:: 2.2.0
+
+        downcast : dict, default is None
+            A dict of item->dtype of what to downcast if possible,
+            or the string 'infer' which will try to downcast to an appropriate
+            equal type (e.g. float64 to int64 if possible).
+
+            .. deprecated:: 2.2.0
+
+        Returns
+        -------
+        {klass} or None
+            Object with missing values filled or None if ``inplace=True``.
+
+        Examples
+        --------
+        For Series:
+
+        >>> s = pd.Series([1, None, None, 2])
+        >>> s.bfill()
+        0    1.0
+        1    2.0
+        2    2.0
+        3    2.0
+        dtype: float64
+        >>> s.bfill(limit=1)
+        0    1.0
+        1    NaN
+        2    2.0
+        3    2.0
+        dtype: float64
+
+        With DataFrame:
+
+        >>> df = pd.DataFrame({{'A': [1, None, None, 4], 'B': [None, 5, None, 7]}})
+        >>> df
+              A     B
+        0   1.0	  NaN
+        1   NaN	  5.0
+        2   NaN   NaN
+        3   4.0   7.0
+        >>> df.bfill()
+              A     B
+        0   1.0   5.0
+        1   4.0   5.0
+        2   4.0   7.0
+        3   4.0   7.0
+        >>> df.bfill(limit=1)
+              A     B
+        0   1.0   5.0
+        1   NaN   5.0
+        2   4.0   7.0
+        3   4.0   7.0
+        """
+        downcast = self._deprecate_downcast(downcast, "bfill")
+        inplace = validate_bool_kwarg(inplace, "inplace")
+        if inplace:
+            if not PYPY and not WARNING_CHECK_DISABLED and using_copy_on_write():
+                if sys.getrefcount(self) <= REF_COUNT:
+                    warnings.warn(
+                        _chained_assignment_method_msg,
+                        ChainedAssignmentError,
+                        stacklevel=2,
+                    )
+            elif (
+                not PYPY
+                and not WARNING_CHECK_DISABLED
+                and not using_copy_on_write()
+                and self._is_view_after_cow_rules()
+            ):
+                ctr = sys.getrefcount(self)
+                ref_count = REF_COUNT
+                if isinstance(self, ABCSeries) and _check_cacher(self):
+                    # see https://github.com/pandas-dev/pandas/pull/56060#discussion_r1399245221
+                    ref_count += 1
+                if ctr <= ref_count:
+                    warnings.warn(
+                        _chained_assignment_warning_method_msg,
+                        FutureWarning,
+                        stacklevel=2,
+                    )
+
+        return self._pad_or_backfill(
+            "bfill",
+            axis=axis,
+            inplace=inplace,
+            limit=limit,
+            limit_area=limit_area,
+            # error: Argument "downcast" to "_fillna_with_method" of "NDFrame"
+            # has incompatible type "Union[Dict[Any, Any], None,
+            # Literal[_NoDefault.no_default]]"; expected "Optional[Dict[Any, Any]]"
+            downcast=downcast,  # type: ignore[arg-type]
+        )
+
+    @final
+    @doc(klass=_shared_doc_kwargs["klass"])
+    def backfill(
+        self,
+        *,
+        axis: None | Axis = None,
+        inplace: bool_t = False,
+        limit: None | int = None,
+        downcast: dict | None | lib.NoDefault = lib.no_default,
+    ) -> Self | None:
+        """
+        Fill NA/NaN values by using the next valid observation to fill the gap.
+
+        .. deprecated:: 2.0
+
+            {klass}.backfill is deprecated. Use {klass}.bfill instead.
+
+        Returns
+        -------
+        {klass} or None
+            Object with missing values filled or None if ``inplace=True``.
+
+        Examples
+        --------
+        Please see examples for :meth:`DataFrame.bfill` or :meth:`Series.bfill`.
+        """
+        warnings.warn(
+            "DataFrame.backfill/Series.backfill is deprecated. Use "
+            "DataFrame.bfill/Series.bfill instead",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        return self.bfill(axis=axis, inplace=inplace, limit=limit, downcast=downcast)
+
+    @overload
+    def replace(
+        self,
+        to_replace=...,
+        value=...,
+        *,
+        inplace: Literal[False] = ...,
+        limit: int | None = ...,
+        regex: bool_t = ...,
+        method: Literal["pad", "ffill", "bfill"] | lib.NoDefault = ...,
+    ) -> Self:
+        ...
+
+    @overload
+    def replace(
+        self,
+        to_replace=...,
+        value=...,
+        *,
+        inplace: Literal[True],
+        limit: int | None = ...,
+        regex: bool_t = ...,
+        method: Literal["pad", "ffill", "bfill"] | lib.NoDefault = ...,
+    ) -> None:
+        ...
+
+    @overload
+    def replace(
+        self,
+        to_replace=...,
+        value=...,
+        *,
+        inplace: bool_t = ...,
+        limit: int | None = ...,
+        regex: bool_t = ...,
+        method: Literal["pad", "ffill", "bfill"] | lib.NoDefault = ...,
+    ) -> Self | None:
+        ...
+
+    @final
+    @doc(
+        _shared_docs["replace"],
+        klass=_shared_doc_kwargs["klass"],
+        inplace=_shared_doc_kwargs["inplace"],
+    )
+    def replace(
+        self,
+        to_replace=None,
+        value=lib.no_default,
+        *,
+        inplace: bool_t = False,
+        limit: int | None = None,
+        regex: bool_t = False,
+        method: Literal["pad", "ffill", "bfill"] | lib.NoDefault = lib.no_default,
+    ) -> Self | None:
+        if method is not lib.no_default:
+            warnings.warn(
+                # GH#33302
+                f"The 'method' keyword in {type(self).__name__}.replace is "
+                "deprecated and will be removed in a future version.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+        elif limit is not None:
+            warnings.warn(
+                # GH#33302
+                f"The 'limit' keyword in {type(self).__name__}.replace is "
+                "deprecated and will be removed in a future version.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+        if (
+            value is lib.no_default
+            and method is lib.no_default
+            and not is_dict_like(to_replace)
+            and regex is False
+        ):
+            # case that goes through _replace_single and defaults to method="pad"
+            warnings.warn(
+                # GH#33302
+                f"{type(self).__name__}.replace without 'value' and with "
+                "non-dict-like 'to_replace' is deprecated "
+                "and will raise in a future version. "
+                "Explicitly specify the new values instead.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+
+        if not (
+            is_scalar(to_replace)
+            or is_re_compilable(to_replace)
+            or is_list_like(to_replace)
+        ):
+            raise TypeError(
+                "Expecting 'to_replace' to be either a scalar, array-like, "
+                "dict or None, got invalid type "
+                f"{repr(type(to_replace).__name__)}"
+            )
+
+        inplace = validate_bool_kwarg(inplace, "inplace")
+        if inplace:
+            if not PYPY and not WARNING_CHECK_DISABLED and using_copy_on_write():
+                if sys.getrefcount(self) <= REF_COUNT:
+                    warnings.warn(
+                        _chained_assignment_method_msg,
+                        ChainedAssignmentError,
+                        stacklevel=2,
+                    )
+            elif (
+                not PYPY
+                and not WARNING_CHECK_DISABLED
+                and not using_copy_on_write()
+                and self._is_view_after_cow_rules()
+            ):
+                ctr = sys.getrefcount(self)
+                ref_count = REF_COUNT
+                if isinstance(self, ABCSeries) and _check_cacher(self):
+                    # in non-CoW mode, chained Series access will populate the
+                    # `_item_cache` which results in an increased ref count not below
+                    # the threshold, while we still need to warn. We detect this case
+                    # of a Series derived from a DataFrame through the presence of
+                    # checking the `_cacher`
+                    ref_count += 1
+                if ctr <= ref_count:
+                    warnings.warn(
+                        _chained_assignment_warning_method_msg,
+                        FutureWarning,
+                        stacklevel=2,
+                    )
+
+        if not is_bool(regex) and to_replace is not None:
+            raise ValueError("'to_replace' must be 'None' if 'regex' is not a bool")
+
+        if value is lib.no_default or method is not lib.no_default:
+            # GH#36984 if the user explicitly passes value=None we want to
+            #  respect that. We have the corner case where the user explicitly
+            #  passes value=None *and* a method, which we interpret as meaning
+            #  they want the (documented) default behavior.
+            if method is lib.no_default:
+                # TODO: get this to show up as the default in the docs?
+                method = "pad"
+
+            # passing a single value that is scalar like
+            # when value is None (GH5319), for compat
+            if not is_dict_like(to_replace) and not is_dict_like(regex):
+                to_replace = [to_replace]
+
+            if isinstance(to_replace, (tuple, list)):
+                # TODO: Consider copy-on-write for non-replaced columns's here
+                if isinstance(self, ABCDataFrame):
+                    from pandas import Series
+
+                    result = self.apply(
+                        Series._replace_single,
+                        args=(to_replace, method, inplace, limit),
+                    )
+                    if inplace:
+                        return None
+                    return result
+                return self._replace_single(to_replace, method, inplace, limit)
+
+            if not is_dict_like(to_replace):
+                if not is_dict_like(regex):
+                    raise TypeError(
+                        'If "to_replace" and "value" are both None '
+                        'and "to_replace" is not a list, then '
+                        "regex must be a mapping"
+                    )
+                to_replace = regex
+                regex = True
+
+            items = list(to_replace.items())
+            if items:
+                keys, values = zip(*items)
+            else:
+                # error: Incompatible types in assignment (expression has type
+                # "list[Never]", variable has type "tuple[Any, ...]")
+                keys, values = ([], [])  # type: ignore[assignment]
+
+            are_mappings = [is_dict_like(v) for v in values]
+
+            if any(are_mappings):
+                if not all(are_mappings):
+                    raise TypeError(
+                        "If a nested mapping is passed, all values "
+                        "of the top level mapping must be mappings"
+                    )
+                # passed a nested dict/Series
+                to_rep_dict = {}
+                value_dict = {}
+
+                for k, v in items:
+                    # error: Incompatible types in assignment (expression has type
+                    # "list[Never]", variable has type "tuple[Any, ...]")
+                    keys, values = list(zip(*v.items())) or (  # type: ignore[assignment]
+                        [],
+                        [],
+                    )
+
+                    to_rep_dict[k] = list(keys)
+                    value_dict[k] = list(values)
+
+                to_replace, value = to_rep_dict, value_dict
+            else:
+                to_replace, value = keys, values
+
+            return self.replace(
+                to_replace, value, inplace=inplace, limit=limit, regex=regex
+            )
+        else:
+            # need a non-zero len on all axes
+            if not self.size:
+                if inplace:
+                    return None
+                return self.copy(deep=None)
+
+            if is_dict_like(to_replace):
+                if is_dict_like(value):  # {'A' : NA} -> {'A' : 0}
+                    # Note: Checking below for `in foo.keys()` instead of
+                    #  `in foo` is needed for when we have a Series and not dict
+                    mapping = {
+                        col: (to_replace[col], value[col])
+                        for col in to_replace.keys()
+                        if col in value.keys() and col in self
+                    }
+                    return self._replace_columnwise(mapping, inplace, regex)
+
+                # {'A': NA} -> 0
+                elif not is_list_like(value):
+                    # Operate column-wise
+                    if self.ndim == 1:
+                        raise ValueError(
+                            "Series.replace cannot use dict-like to_replace "
+                            "and non-None value"
+                        )
+                    mapping = {
+                        col: (to_rep, value) for col, to_rep in to_replace.items()
+                    }
+                    return self._replace_columnwise(mapping, inplace, regex)
+                else:
+                    raise TypeError("value argument must be scalar, dict, or Series")
+
+            elif is_list_like(to_replace):
+                if not is_list_like(value):
+                    # e.g. to_replace = [NA, ''] and value is 0,
+                    #  so we replace NA with 0 and then replace '' with 0
+                    value = [value] * len(to_replace)
+
+                # e.g. we have to_replace = [NA, ''] and value = [0, 'missing']
+                if len(to_replace) != len(value):
+                    raise ValueError(
+                        f"Replacement lists must match in length. "
+                        f"Expecting {len(to_replace)} got {len(value)} "
+                    )
+                new_data = self._mgr.replace_list(
+                    src_list=to_replace,
+                    dest_list=value,
+                    inplace=inplace,
+                    regex=regex,
+                )
+
+            elif to_replace is None:
+                if not (
+                    is_re_compilable(regex)
+                    or is_list_like(regex)
+                    or is_dict_like(regex)
+                ):
+                    raise TypeError(
+                        f"'regex' must be a string or a compiled regular expression "
+                        f"or a list or dict of strings or regular expressions, "
+                        f"you passed a {repr(type(regex).__name__)}"
+                    )
+                return self.replace(
+                    regex, value, inplace=inplace, limit=limit, regex=True
+                )
+            else:
+                # dest iterable dict-like
+                if is_dict_like(value):  # NA -> {'A' : 0, 'B' : -1}
+                    # Operate column-wise
+                    if self.ndim == 1:
+                        raise ValueError(
+                            "Series.replace cannot use dict-value and "
+                            "non-None to_replace"
+                        )
+                    mapping = {col: (to_replace, val) for col, val in value.items()}
+                    return self._replace_columnwise(mapping, inplace, regex)
+
+                elif not is_list_like(value):  # NA -> 0
+                    regex = should_use_regex(regex, to_replace)
+                    if regex:
+                        new_data = self._mgr.replace_regex(
+                            to_replace=to_replace,
+                            value=value,
+                            inplace=inplace,
+                        )
+                    else:
+                        new_data = self._mgr.replace(
+                            to_replace=to_replace, value=value, inplace=inplace
+                        )
+                else:
+                    raise TypeError(
+                        f'Invalid "to_replace" type: {repr(type(to_replace).__name__)}'
+                    )
+
+        result = self._constructor_from_mgr(new_data, axes=new_data.axes)
+        if inplace:
+            return self._update_inplace(result)
+        else:
+            return result.__finalize__(self, method="replace")
+
+    @overload
+    def interpolate(
+        self,
+        method: InterpolateOptions = ...,
+        *,
+        axis: Axis = ...,
+        limit: int | None = ...,
+        inplace: Literal[False] = ...,
+        limit_direction: Literal["forward", "backward", "both"] | None = ...,
+        limit_area: Literal["inside", "outside"] | None = ...,
+        downcast: Literal["infer"] | None | lib.NoDefault = ...,
+        **kwargs,
+    ) -> Self:
+        ...
+
+    @overload
+    def interpolate(
+        self,
+        method: InterpolateOptions = ...,
+        *,
+        axis: Axis = ...,
+        limit: int | None = ...,
+        inplace: Literal[True],
+        limit_direction: Literal["forward", "backward", "both"] | None = ...,
+        limit_area: Literal["inside", "outside"] | None = ...,
+        downcast: Literal["infer"] | None | lib.NoDefault = ...,
+        **kwargs,
+    ) -> None:
+        ...
+
+    @overload
+    def interpolate(
+        self,
+        method: InterpolateOptions = ...,
+        *,
+        axis: Axis = ...,
+        limit: int | None = ...,
+        inplace: bool_t = ...,
+        limit_direction: Literal["forward", "backward", "both"] | None = ...,
+        limit_area: Literal["inside", "outside"] | None = ...,
+        downcast: Literal["infer"] | None | lib.NoDefault = ...,
+        **kwargs,
+    ) -> Self | None:
+        ...
+
+    @final
+    def interpolate(
+        self,
+        method: InterpolateOptions = "linear",
+        *,
+        axis: Axis = 0,
+        limit: int | None = None,
+        inplace: bool_t = False,
+        limit_direction: Literal["forward", "backward", "both"] | None = None,
+        limit_area: Literal["inside", "outside"] | None = None,
+        downcast: Literal["infer"] | None | lib.NoDefault = lib.no_default,
+        **kwargs,
+    ) -> Self | None:
+        """
+        Fill NaN values using an interpolation method.
+
+        Please note that only ``method='linear'`` is supported for
+        DataFrame/Series with a MultiIndex.
+
+        Parameters
+        ----------
+        method : str, default 'linear'
+            Interpolation technique to use. One of:
+
+            * 'linear': Ignore the index and treat the values as equally
+              spaced. This is the only method supported on MultiIndexes.
+            * 'time': Works on daily and higher resolution data to interpolate
+              given length of interval.
+            * 'index', 'values': use the actual numerical values of the index.
+            * 'pad': Fill in NaNs using existing values.
+            * 'nearest', 'zero', 'slinear', 'quadratic', 'cubic',
+              'barycentric', 'polynomial': Passed to
+              `scipy.interpolate.interp1d`, whereas 'spline' is passed to
+              `scipy.interpolate.UnivariateSpline`. These methods use the numerical
+              values of the index.  Both 'polynomial' and 'spline' require that
+              you also specify an `order` (int), e.g.
+              ``df.interpolate(method='polynomial', order=5)``. Note that,
+              `slinear` method in Pandas refers to the Scipy first order `spline`
+              instead of Pandas first order `spline`.
+            * 'krogh', 'piecewise_polynomial', 'spline', 'pchip', 'akima',
+              'cubicspline': Wrappers around the SciPy interpolation methods of
+              similar names. See `Notes`.
+            * 'from_derivatives': Refers to
+              `scipy.interpolate.BPoly.from_derivatives`.
+
+        axis : {{0 or 'index', 1 or 'columns', None}}, default None
+            Axis to interpolate along. For `Series` this parameter is unused
+            and defaults to 0.
+        limit : int, optional
+            Maximum number of consecutive NaNs to fill. Must be greater than
+            0.
+        inplace : bool, default False
+            Update the data in place if possible.
+        limit_direction : {{'forward', 'backward', 'both'}}, Optional
+            Consecutive NaNs will be filled in this direction.
+
+            If limit is specified:
+                * If 'method' is 'pad' or 'ffill', 'limit_direction' must be 'forward'.
+                * If 'method' is 'backfill' or 'bfill', 'limit_direction' must be
+                  'backwards'.
+
+            If 'limit' is not specified:
+                * If 'method' is 'backfill' or 'bfill', the default is 'backward'
+                * else the default is 'forward'
+
+            raises ValueError if `limit_direction` is 'forward' or 'both' and
+                method is 'backfill' or 'bfill'.
+            raises ValueError if `limit_direction` is 'backward' or 'both' and
+                method is 'pad' or 'ffill'.
+
+        limit_area : {{`None`, 'inside', 'outside'}}, default None
+            If limit is specified, consecutive NaNs will be filled with this
+            restriction.
+
+            * ``None``: No fill restriction.
+            * 'inside': Only fill NaNs surrounded by valid values
+              (interpolate).
+            * 'outside': Only fill NaNs outside valid values (extrapolate).
+
+        downcast : optional, 'infer' or None, defaults to None
+            Downcast dtypes if possible.
+
+            .. deprecated:: 2.1.0
+
+        ``**kwargs`` : optional
+            Keyword arguments to pass on to the interpolating function.
+
+        Returns
+        -------
+        Series or DataFrame or None
+            Returns the same object type as the caller, interpolated at
+            some or all ``NaN`` values or None if ``inplace=True``.
+
+        See Also
+        --------
+        fillna : Fill missing values using different methods.
+        scipy.interpolate.Akima1DInterpolator : Piecewise cubic polynomials
+            (Akima interpolator).
+        scipy.interpolate.BPoly.from_derivatives : Piecewise polynomial in the
+            Bernstein basis.
+        scipy.interpolate.interp1d : Interpolate a 1-D function.
+        scipy.interpolate.KroghInterpolator : Interpolate polynomial (Krogh
+            interpolator).
+        scipy.interpolate.PchipInterpolator : PCHIP 1-d monotonic cubic
+            interpolation.
+        scipy.interpolate.CubicSpline : Cubic spline data interpolator.
+
+        Notes
+        -----
+        The 'krogh', 'piecewise_polynomial', 'spline', 'pchip' and 'akima'
+        methods are wrappers around the respective SciPy implementations of
+        similar names. These use the actual numerical values of the index.
+        For more information on their behavior, see the
+        `SciPy documentation
+        `__.
+
+        Examples
+        --------
+        Filling in ``NaN`` in a :class:`~pandas.Series` via linear
+        interpolation.
+
+        >>> s = pd.Series([0, 1, np.nan, 3])
+        >>> s
+        0    0.0
+        1    1.0
+        2    NaN
+        3    3.0
+        dtype: float64
+        >>> s.interpolate()
+        0    0.0
+        1    1.0
+        2    2.0
+        3    3.0
+        dtype: float64
+
+        Filling in ``NaN`` in a Series via polynomial interpolation or splines:
+        Both 'polynomial' and 'spline' methods require that you also specify
+        an ``order`` (int).
+
+        >>> s = pd.Series([0, 2, np.nan, 8])
+        >>> s.interpolate(method='polynomial', order=2)
+        0    0.000000
+        1    2.000000
+        2    4.666667
+        3    8.000000
+        dtype: float64
+
+        Fill the DataFrame forward (that is, going down) along each column
+        using linear interpolation.
+
+        Note how the last entry in column 'a' is interpolated differently,
+        because there is no entry after it to use for interpolation.
+        Note how the first entry in column 'b' remains ``NaN``, because there
+        is no entry before it to use for interpolation.
+
+        >>> df = pd.DataFrame([(0.0, np.nan, -1.0, 1.0),
+        ...                    (np.nan, 2.0, np.nan, np.nan),
+        ...                    (2.0, 3.0, np.nan, 9.0),
+        ...                    (np.nan, 4.0, -4.0, 16.0)],
+        ...                   columns=list('abcd'))
+        >>> df
+             a    b    c     d
+        0  0.0  NaN -1.0   1.0
+        1  NaN  2.0  NaN   NaN
+        2  2.0  3.0  NaN   9.0
+        3  NaN  4.0 -4.0  16.0
+        >>> df.interpolate(method='linear', limit_direction='forward', axis=0)
+             a    b    c     d
+        0  0.0  NaN -1.0   1.0
+        1  1.0  2.0 -2.0   5.0
+        2  2.0  3.0 -3.0   9.0
+        3  2.0  4.0 -4.0  16.0
+
+        Using polynomial interpolation.
+
+        >>> df['d'].interpolate(method='polynomial', order=2)
+        0     1.0
+        1     4.0
+        2     9.0
+        3    16.0
+        Name: d, dtype: float64
+        """
+        if downcast is not lib.no_default:
+            # GH#40988
+            warnings.warn(
+                f"The 'downcast' keyword in {type(self).__name__}.interpolate "
+                "is deprecated and will be removed in a future version. "
+                "Call result.infer_objects(copy=False) on the result instead.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+        else:
+            downcast = None
+        if downcast is not None and downcast != "infer":
+            raise ValueError("downcast must be either None or 'infer'")
+
+        inplace = validate_bool_kwarg(inplace, "inplace")
+
+        if inplace:
+            if not PYPY and not WARNING_CHECK_DISABLED and using_copy_on_write():
+                if sys.getrefcount(self) <= REF_COUNT:
+                    warnings.warn(
+                        _chained_assignment_method_msg,
+                        ChainedAssignmentError,
+                        stacklevel=2,
+                    )
+            elif (
+                not PYPY
+                and not WARNING_CHECK_DISABLED
+                and not using_copy_on_write()
+                and self._is_view_after_cow_rules()
+            ):
+                ctr = sys.getrefcount(self)
+                ref_count = REF_COUNT
+                if isinstance(self, ABCSeries) and _check_cacher(self):
+                    # see https://github.com/pandas-dev/pandas/pull/56060#discussion_r1399245221
+                    ref_count += 1
+                if ctr <= ref_count:
+                    warnings.warn(
+                        _chained_assignment_warning_method_msg,
+                        FutureWarning,
+                        stacklevel=2,
+                    )
+
+        axis = self._get_axis_number(axis)
+
+        if self.empty:
+            if inplace:
+                return None
+            return self.copy()
+
+        if not isinstance(method, str):
+            raise ValueError("'method' should be a string, not None.")
+
+        fillna_methods = ["ffill", "bfill", "pad", "backfill"]
+        if method.lower() in fillna_methods:
+            # GH#53581
+            warnings.warn(
+                f"{type(self).__name__}.interpolate with method={method} is "
+                "deprecated and will raise in a future version. "
+                "Use obj.ffill() or obj.bfill() instead.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+            obj, should_transpose = self, False
+        else:
+            obj, should_transpose = (self.T, True) if axis == 1 else (self, False)
+            if np.any(obj.dtypes == object):
+                # GH#53631
+                if not (obj.ndim == 2 and np.all(obj.dtypes == object)):
+                    # don't warn in cases that already raise
+                    warnings.warn(
+                        f"{type(self).__name__}.interpolate with object dtype is "
+                        "deprecated and will raise in a future version. Call "
+                        "obj.infer_objects(copy=False) before interpolating instead.",
+                        FutureWarning,
+                        stacklevel=find_stack_level(),
+                    )
+
+        if method in fillna_methods and "fill_value" in kwargs:
+            raise ValueError(
+                "'fill_value' is not a valid keyword for "
+                f"{type(self).__name__}.interpolate with method from "
+                f"{fillna_methods}"
+            )
+
+        if isinstance(obj.index, MultiIndex) and method != "linear":
+            raise ValueError(
+                "Only `method=linear` interpolation is supported on MultiIndexes."
+            )
+
+        limit_direction = missing.infer_limit_direction(limit_direction, method)
+
+        if obj.ndim == 2 and np.all(obj.dtypes == object):
+            raise TypeError(
+                "Cannot interpolate with all object-dtype columns "
+                "in the DataFrame. Try setting at least one "
+                "column to a numeric dtype."
+            )
+
+        if method.lower() in fillna_methods:
+            # TODO(3.0): remove this case
+            # TODO: warn/raise on limit_direction or kwargs which are ignored?
+            #  as of 2023-06-26 no tests get here with either
+            if not self._mgr.is_single_block and axis == 1:
+                # GH#53898
+                if inplace:
+                    raise NotImplementedError()
+                obj, axis, should_transpose = self.T, 1 - axis, True
+
+            new_data = obj._mgr.pad_or_backfill(
+                method=method,
+                axis=self._get_block_manager_axis(axis),
+                limit=limit,
+                limit_area=limit_area,
+                inplace=inplace,
+                downcast=downcast,
+            )
+        else:
+            index = missing.get_interp_index(method, obj.index)
+            new_data = obj._mgr.interpolate(
+                method=method,
+                index=index,
+                limit=limit,
+                limit_direction=limit_direction,
+                limit_area=limit_area,
+                inplace=inplace,
+                downcast=downcast,
+                **kwargs,
+            )
+
+        result = self._constructor_from_mgr(new_data, axes=new_data.axes)
+        if should_transpose:
+            result = result.T
+        if inplace:
+            return self._update_inplace(result)
+        else:
+            return result.__finalize__(self, method="interpolate")
+
+    # ----------------------------------------------------------------------
+    # Timeseries methods Methods
+
+    @final
+    def asof(self, where, subset=None):
+        """
+        Return the last row(s) without any NaNs before `where`.
+
+        The last row (for each element in `where`, if list) without any
+        NaN is taken.
+        In case of a :class:`~pandas.DataFrame`, the last row without NaN
+        considering only the subset of columns (if not `None`)
+
+        If there is no good value, NaN is returned for a Series or
+        a Series of NaN values for a DataFrame
+
+        Parameters
+        ----------
+        where : date or array-like of dates
+            Date(s) before which the last row(s) are returned.
+        subset : str or array-like of str, default `None`
+            For DataFrame, if not `None`, only use these columns to
+            check for NaNs.
+
+        Returns
+        -------
+        scalar, Series, or DataFrame
+
+            The return can be:
+
+            * scalar : when `self` is a Series and `where` is a scalar
+            * Series: when `self` is a Series and `where` is an array-like,
+              or when `self` is a DataFrame and `where` is a scalar
+            * DataFrame : when `self` is a DataFrame and `where` is an
+              array-like
+
+        See Also
+        --------
+        merge_asof : Perform an asof merge. Similar to left join.
+
+        Notes
+        -----
+        Dates are assumed to be sorted. Raises if this is not the case.
+
+        Examples
+        --------
+        A Series and a scalar `where`.
+
+        >>> s = pd.Series([1, 2, np.nan, 4], index=[10, 20, 30, 40])
+        >>> s
+        10    1.0
+        20    2.0
+        30    NaN
+        40    4.0
+        dtype: float64
+
+        >>> s.asof(20)
+        2.0
+
+        For a sequence `where`, a Series is returned. The first value is
+        NaN, because the first element of `where` is before the first
+        index value.
+
+        >>> s.asof([5, 20])
+        5     NaN
+        20    2.0
+        dtype: float64
+
+        Missing values are not considered. The following is ``2.0``, not
+        NaN, even though NaN is at the index location for ``30``.
+
+        >>> s.asof(30)
+        2.0
+
+        Take all columns into consideration
+
+        >>> df = pd.DataFrame({'a': [10., 20., 30., 40., 50.],
+        ...                    'b': [None, None, None, None, 500]},
+        ...                   index=pd.DatetimeIndex(['2018-02-27 09:01:00',
+        ...                                           '2018-02-27 09:02:00',
+        ...                                           '2018-02-27 09:03:00',
+        ...                                           '2018-02-27 09:04:00',
+        ...                                           '2018-02-27 09:05:00']))
+        >>> df.asof(pd.DatetimeIndex(['2018-02-27 09:03:30',
+        ...                           '2018-02-27 09:04:30']))
+                              a   b
+        2018-02-27 09:03:30 NaN NaN
+        2018-02-27 09:04:30 NaN NaN
+
+        Take a single column into consideration
+
+        >>> df.asof(pd.DatetimeIndex(['2018-02-27 09:03:30',
+        ...                           '2018-02-27 09:04:30']),
+        ...         subset=['a'])
+                                a   b
+        2018-02-27 09:03:30  30.0 NaN
+        2018-02-27 09:04:30  40.0 NaN
+        """
+        if isinstance(where, str):
+            where = Timestamp(where)
+
+        if not self.index.is_monotonic_increasing:
+            raise ValueError("asof requires a sorted index")
+
+        is_series = isinstance(self, ABCSeries)
+        if is_series:
+            if subset is not None:
+                raise ValueError("subset is not valid for Series")
+        else:
+            if subset is None:
+                subset = self.columns
+            if not is_list_like(subset):
+                subset = [subset]
+
+        is_list = is_list_like(where)
+        if not is_list:
+            start = self.index[0]
+            if isinstance(self.index, PeriodIndex):
+                where = Period(where, freq=self.index.freq)
+
+            if where < start:
+                if not is_series:
+                    return self._constructor_sliced(
+                        index=self.columns, name=where, dtype=np.float64
+                    )
+                return np.nan
+
+            # It's always much faster to use a *while* loop here for
+            # Series than pre-computing all the NAs. However a
+            # *while* loop is extremely expensive for DataFrame
+            # so we later pre-compute all the NAs and use the same
+            # code path whether *where* is a scalar or list.
+            # See PR: https://github.com/pandas-dev/pandas/pull/14476
+            if is_series:
+                loc = self.index.searchsorted(where, side="right")
+                if loc > 0:
+                    loc -= 1
+
+                values = self._values
+                while loc > 0 and isna(values[loc]):
+                    loc -= 1
+                return values[loc]
+
+        if not isinstance(where, Index):
+            where = Index(where) if is_list else Index([where])
+
+        nulls = self.isna() if is_series else self[subset].isna().any(axis=1)
+        if nulls.all():
+            if is_series:
+                self = cast("Series", self)
+                return self._constructor(np.nan, index=where, name=self.name)
+            elif is_list:
+                self = cast("DataFrame", self)
+                return self._constructor(np.nan, index=where, columns=self.columns)
+            else:
+                self = cast("DataFrame", self)
+                return self._constructor_sliced(
+                    np.nan, index=self.columns, name=where[0]
+                )
+
+        locs = self.index.asof_locs(where, ~(nulls._values))
+
+        # mask the missing
+        mask = locs == -1
+        data = self.take(locs)
+        data.index = where
+        if mask.any():
+            # GH#16063 only do this setting when necessary, otherwise
+            #  we'd cast e.g. bools to floats
+            data.loc[mask] = np.nan
+        return data if is_list else data.iloc[-1]
+
+    # ----------------------------------------------------------------------
+    # Action Methods
+
+    @doc(klass=_shared_doc_kwargs["klass"])
+    def isna(self) -> Self:
+        """
+        Detect missing values.
+
+        Return a boolean same-sized object indicating if the values are NA.
+        NA values, such as None or :attr:`numpy.NaN`, gets mapped to True
+        values.
+        Everything else gets mapped to False values. Characters such as empty
+        strings ``''`` or :attr:`numpy.inf` are not considered NA values
+        (unless you set ``pandas.options.mode.use_inf_as_na = True``).
+
+        Returns
+        -------
+        {klass}
+            Mask of bool values for each element in {klass} that
+            indicates whether an element is an NA value.
+
+        See Also
+        --------
+        {klass}.isnull : Alias of isna.
+        {klass}.notna : Boolean inverse of isna.
+        {klass}.dropna : Omit axes labels with missing values.
+        isna : Top-level isna.
+
+        Examples
+        --------
+        Show which entries in a DataFrame are NA.
+
+        >>> df = pd.DataFrame(dict(age=[5, 6, np.nan],
+        ...                        born=[pd.NaT, pd.Timestamp('1939-05-27'),
+        ...                              pd.Timestamp('1940-04-25')],
+        ...                        name=['Alfred', 'Batman', ''],
+        ...                        toy=[None, 'Batmobile', 'Joker']))
+        >>> df
+           age       born    name        toy
+        0  5.0        NaT  Alfred       None
+        1  6.0 1939-05-27  Batman  Batmobile
+        2  NaN 1940-04-25              Joker
+
+        >>> df.isna()
+             age   born   name    toy
+        0  False   True  False   True
+        1  False  False  False  False
+        2   True  False  False  False
+
+        Show which entries in a Series are NA.
+
+        >>> ser = pd.Series([5, 6, np.nan])
+        >>> ser
+        0    5.0
+        1    6.0
+        2    NaN
+        dtype: float64
+
+        >>> ser.isna()
+        0    False
+        1    False
+        2     True
+        dtype: bool
+        """
+        return isna(self).__finalize__(self, method="isna")
+
+    @doc(isna, klass=_shared_doc_kwargs["klass"])
+    def isnull(self) -> Self:
+        return isna(self).__finalize__(self, method="isnull")
+
+    @doc(klass=_shared_doc_kwargs["klass"])
+    def notna(self) -> Self:
+        """
+        Detect existing (non-missing) values.
+
+        Return a boolean same-sized object indicating if the values are not NA.
+        Non-missing values get mapped to True. Characters such as empty
+        strings ``''`` or :attr:`numpy.inf` are not considered NA values
+        (unless you set ``pandas.options.mode.use_inf_as_na = True``).
+        NA values, such as None or :attr:`numpy.NaN`, get mapped to False
+        values.
+
+        Returns
+        -------
+        {klass}
+            Mask of bool values for each element in {klass} that
+            indicates whether an element is not an NA value.
+
+        See Also
+        --------
+        {klass}.notnull : Alias of notna.
+        {klass}.isna : Boolean inverse of notna.
+        {klass}.dropna : Omit axes labels with missing values.
+        notna : Top-level notna.
+
+        Examples
+        --------
+        Show which entries in a DataFrame are not NA.
+
+        >>> df = pd.DataFrame(dict(age=[5, 6, np.nan],
+        ...                        born=[pd.NaT, pd.Timestamp('1939-05-27'),
+        ...                              pd.Timestamp('1940-04-25')],
+        ...                        name=['Alfred', 'Batman', ''],
+        ...                        toy=[None, 'Batmobile', 'Joker']))
+        >>> df
+           age       born    name        toy
+        0  5.0        NaT  Alfred       None
+        1  6.0 1939-05-27  Batman  Batmobile
+        2  NaN 1940-04-25              Joker
+
+        >>> df.notna()
+             age   born  name    toy
+        0   True  False  True  False
+        1   True   True  True   True
+        2  False   True  True   True
+
+        Show which entries in a Series are not NA.
+
+        >>> ser = pd.Series([5, 6, np.nan])
+        >>> ser
+        0    5.0
+        1    6.0
+        2    NaN
+        dtype: float64
+
+        >>> ser.notna()
+        0     True
+        1     True
+        2    False
+        dtype: bool
+        """
+        return notna(self).__finalize__(self, method="notna")
+
+    @doc(notna, klass=_shared_doc_kwargs["klass"])
+    def notnull(self) -> Self:
+        return notna(self).__finalize__(self, method="notnull")
+
+    @final
+    def _clip_with_scalar(self, lower, upper, inplace: bool_t = False):
+        if (lower is not None and np.any(isna(lower))) or (
+            upper is not None and np.any(isna(upper))
+        ):
+            raise ValueError("Cannot use an NA value as a clip threshold")
+
+        result = self
+        mask = self.isna()
+
+        if lower is not None:
+            cond = mask | (self >= lower)
+            result = result.where(
+                cond, lower, inplace=inplace
+            )  # type: ignore[assignment]
+        if upper is not None:
+            cond = mask | (self <= upper)
+            result = self if inplace else result
+            result = result.where(
+                cond, upper, inplace=inplace
+            )  # type: ignore[assignment]
+
+        return result
+
+    @final
+    def _clip_with_one_bound(self, threshold, method, axis, inplace):
+        if axis is not None:
+            axis = self._get_axis_number(axis)
+
+        # method is self.le for upper bound and self.ge for lower bound
+        if is_scalar(threshold) and is_number(threshold):
+            if method.__name__ == "le":
+                return self._clip_with_scalar(None, threshold, inplace=inplace)
+            return self._clip_with_scalar(threshold, None, inplace=inplace)
+
+        # GH #15390
+        # In order for where method to work, the threshold must
+        # be transformed to NDFrame from other array like structure.
+        if (not isinstance(threshold, ABCSeries)) and is_list_like(threshold):
+            if isinstance(self, ABCSeries):
+                threshold = self._constructor(threshold, index=self.index)
+            else:
+                threshold = self._align_for_op(threshold, axis, flex=None)[1]
+
+        # GH 40420
+        # Treat missing thresholds as no bounds, not clipping the values
+        if is_list_like(threshold):
+            fill_value = np.inf if method.__name__ == "le" else -np.inf
+            threshold_inf = threshold.fillna(fill_value)
+        else:
+            threshold_inf = threshold
+
+        subset = method(threshold_inf, axis=axis) | isna(self)
+
+        # GH 40420
+        return self.where(subset, threshold, axis=axis, inplace=inplace)
+
+    @overload
+    def clip(
+        self,
+        lower=...,
+        upper=...,
+        *,
+        axis: Axis | None = ...,
+        inplace: Literal[False] = ...,
+        **kwargs,
+    ) -> Self:
+        ...
+
+    @overload
+    def clip(
+        self,
+        lower=...,
+        upper=...,
+        *,
+        axis: Axis | None = ...,
+        inplace: Literal[True],
+        **kwargs,
+    ) -> None:
+        ...
+
+    @overload
+    def clip(
+        self,
+        lower=...,
+        upper=...,
+        *,
+        axis: Axis | None = ...,
+        inplace: bool_t = ...,
+        **kwargs,
+    ) -> Self | None:
+        ...
+
+    @final
+    def clip(
+        self,
+        lower=None,
+        upper=None,
+        *,
+        axis: Axis | None = None,
+        inplace: bool_t = False,
+        **kwargs,
+    ) -> Self | None:
+        """
+        Trim values at input threshold(s).
+
+        Assigns values outside boundary to boundary values. Thresholds
+        can be singular values or array like, and in the latter case
+        the clipping is performed element-wise in the specified axis.
+
+        Parameters
+        ----------
+        lower : float or array-like, default None
+            Minimum threshold value. All values below this
+            threshold will be set to it. A missing
+            threshold (e.g `NA`) will not clip the value.
+        upper : float or array-like, default None
+            Maximum threshold value. All values above this
+            threshold will be set to it. A missing
+            threshold (e.g `NA`) will not clip the value.
+        axis : {{0 or 'index', 1 or 'columns', None}}, default None
+            Align object with lower and upper along the given axis.
+            For `Series` this parameter is unused and defaults to `None`.
+        inplace : bool, default False
+            Whether to perform the operation in place on the data.
+        *args, **kwargs
+            Additional keywords have no effect but might be accepted
+            for compatibility with numpy.
+
+        Returns
+        -------
+        Series or DataFrame or None
+            Same type as calling object with the values outside the
+            clip boundaries replaced or None if ``inplace=True``.
+
+        See Also
+        --------
+        Series.clip : Trim values at input threshold in series.
+        DataFrame.clip : Trim values at input threshold in dataframe.
+        numpy.clip : Clip (limit) the values in an array.
+
+        Examples
+        --------
+        >>> data = {'col_0': [9, -3, 0, -1, 5], 'col_1': [-2, -7, 6, 8, -5]}
+        >>> df = pd.DataFrame(data)
+        >>> df
+           col_0  col_1
+        0      9     -2
+        1     -3     -7
+        2      0      6
+        3     -1      8
+        4      5     -5
+
+        Clips per column using lower and upper thresholds:
+
+        >>> df.clip(-4, 6)
+           col_0  col_1
+        0      6     -2
+        1     -3     -4
+        2      0      6
+        3     -1      6
+        4      5     -4
+
+        Clips using specific lower and upper thresholds per column:
+
+        >>> df.clip([-2, -1], [4, 5])
+            col_0  col_1
+        0      4     -1
+        1     -2     -1
+        2      0      5
+        3     -1      5
+        4      4     -1
+
+        Clips using specific lower and upper thresholds per column element:
+
+        >>> t = pd.Series([2, -4, -1, 6, 3])
+        >>> t
+        0    2
+        1   -4
+        2   -1
+        3    6
+        4    3
+        dtype: int64
+
+        >>> df.clip(t, t + 4, axis=0)
+           col_0  col_1
+        0      6      2
+        1     -3     -4
+        2      0      3
+        3      6      8
+        4      5      3
+
+        Clips using specific lower threshold per column element, with missing values:
+
+        >>> t = pd.Series([2, -4, np.nan, 6, 3])
+        >>> t
+        0    2.0
+        1   -4.0
+        2    NaN
+        3    6.0
+        4    3.0
+        dtype: float64
+
+        >>> df.clip(t, axis=0)
+        col_0  col_1
+        0      9      2
+        1     -3     -4
+        2      0      6
+        3      6      8
+        4      5      3
+        """
+        inplace = validate_bool_kwarg(inplace, "inplace")
+
+        if inplace:
+            if not PYPY and not WARNING_CHECK_DISABLED and using_copy_on_write():
+                if sys.getrefcount(self) <= REF_COUNT:
+                    warnings.warn(
+                        _chained_assignment_method_msg,
+                        ChainedAssignmentError,
+                        stacklevel=2,
+                    )
+            elif (
+                not PYPY
+                and not WARNING_CHECK_DISABLED
+                and not using_copy_on_write()
+                and self._is_view_after_cow_rules()
+            ):
+                ctr = sys.getrefcount(self)
+                ref_count = REF_COUNT
+                if isinstance(self, ABCSeries) and hasattr(self, "_cacher"):
+                    # see https://github.com/pandas-dev/pandas/pull/56060#discussion_r1399245221
+                    ref_count += 1
+                if ctr <= ref_count:
+                    warnings.warn(
+                        _chained_assignment_warning_method_msg,
+                        FutureWarning,
+                        stacklevel=2,
+                    )
+
+        axis = nv.validate_clip_with_axis(axis, (), kwargs)
+        if axis is not None:
+            axis = self._get_axis_number(axis)
+
+        # GH 17276
+        # numpy doesn't like NaN as a clip value
+        # so ignore
+        # GH 19992
+        # numpy doesn't drop a list-like bound containing NaN
+        isna_lower = isna(lower)
+        if not is_list_like(lower):
+            if np.any(isna_lower):
+                lower = None
+        elif np.all(isna_lower):
+            lower = None
+        isna_upper = isna(upper)
+        if not is_list_like(upper):
+            if np.any(isna_upper):
+                upper = None
+        elif np.all(isna_upper):
+            upper = None
+
+        # GH 2747 (arguments were reversed)
+        if (
+            lower is not None
+            and upper is not None
+            and is_scalar(lower)
+            and is_scalar(upper)
+        ):
+            lower, upper = min(lower, upper), max(lower, upper)
+
+        # fast-path for scalars
+        if (lower is None or is_number(lower)) and (upper is None or is_number(upper)):
+            return self._clip_with_scalar(lower, upper, inplace=inplace)
+
+        result = self
+        if lower is not None:
+            result = result._clip_with_one_bound(
+                lower, method=self.ge, axis=axis, inplace=inplace
+            )
+        if upper is not None:
+            if inplace:
+                result = self
+            result = result._clip_with_one_bound(
+                upper, method=self.le, axis=axis, inplace=inplace
+            )
+
+        return result
+
+    @final
+    @doc(klass=_shared_doc_kwargs["klass"])
+    def asfreq(
+        self,
+        freq: Frequency,
+        method: FillnaOptions | None = None,
+        how: Literal["start", "end"] | None = None,
+        normalize: bool_t = False,
+        fill_value: Hashable | None = None,
+    ) -> Self:
+        """
+        Convert time series to specified frequency.
+
+        Returns the original data conformed to a new index with the specified
+        frequency.
+
+        If the index of this {klass} is a :class:`~pandas.PeriodIndex`, the new index
+        is the result of transforming the original index with
+        :meth:`PeriodIndex.asfreq ` (so the original index
+        will map one-to-one to the new index).
+
+        Otherwise, the new index will be equivalent to ``pd.date_range(start, end,
+        freq=freq)`` where ``start`` and ``end`` are, respectively, the first and
+        last entries in the original index (see :func:`pandas.date_range`). The
+        values corresponding to any timesteps in the new index which were not present
+        in the original index will be null (``NaN``), unless a method for filling
+        such unknowns is provided (see the ``method`` parameter below).
+
+        The :meth:`resample` method is more appropriate if an operation on each group of
+        timesteps (such as an aggregate) is necessary to represent the data at the new
+        frequency.
+
+        Parameters
+        ----------
+        freq : DateOffset or str
+            Frequency DateOffset or string.
+        method : {{'backfill'/'bfill', 'pad'/'ffill'}}, default None
+            Method to use for filling holes in reindexed Series (note this
+            does not fill NaNs that already were present):
+
+            * 'pad' / 'ffill': propagate last valid observation forward to next
+              valid
+            * 'backfill' / 'bfill': use NEXT valid observation to fill.
+        how : {{'start', 'end'}}, default end
+            For PeriodIndex only (see PeriodIndex.asfreq).
+        normalize : bool, default False
+            Whether to reset output index to midnight.
+        fill_value : scalar, optional
+            Value to use for missing values, applied during upsampling (note
+            this does not fill NaNs that already were present).
+
+        Returns
+        -------
+        {klass}
+            {klass} object reindexed to the specified frequency.
+
+        See Also
+        --------
+        reindex : Conform DataFrame to new index with optional filling logic.
+
+        Notes
+        -----
+        To learn more about the frequency strings, please see `this link
+        `__.
+
+        Examples
+        --------
+        Start by creating a series with 4 one minute timestamps.
+
+        >>> index = pd.date_range('1/1/2000', periods=4, freq='min')
+        >>> series = pd.Series([0.0, None, 2.0, 3.0], index=index)
+        >>> df = pd.DataFrame({{'s': series}})
+        >>> df
+                               s
+        2000-01-01 00:00:00    0.0
+        2000-01-01 00:01:00    NaN
+        2000-01-01 00:02:00    2.0
+        2000-01-01 00:03:00    3.0
+
+        Upsample the series into 30 second bins.
+
+        >>> df.asfreq(freq='30s')
+                               s
+        2000-01-01 00:00:00    0.0
+        2000-01-01 00:00:30    NaN
+        2000-01-01 00:01:00    NaN
+        2000-01-01 00:01:30    NaN
+        2000-01-01 00:02:00    2.0
+        2000-01-01 00:02:30    NaN
+        2000-01-01 00:03:00    3.0
+
+        Upsample again, providing a ``fill value``.
+
+        >>> df.asfreq(freq='30s', fill_value=9.0)
+                               s
+        2000-01-01 00:00:00    0.0
+        2000-01-01 00:00:30    9.0
+        2000-01-01 00:01:00    NaN
+        2000-01-01 00:01:30    9.0
+        2000-01-01 00:02:00    2.0
+        2000-01-01 00:02:30    9.0
+        2000-01-01 00:03:00    3.0
+
+        Upsample again, providing a ``method``.
+
+        >>> df.asfreq(freq='30s', method='bfill')
+                               s
+        2000-01-01 00:00:00    0.0
+        2000-01-01 00:00:30    NaN
+        2000-01-01 00:01:00    NaN
+        2000-01-01 00:01:30    2.0
+        2000-01-01 00:02:00    2.0
+        2000-01-01 00:02:30    3.0
+        2000-01-01 00:03:00    3.0
+        """
+        from pandas.core.resample import asfreq
+
+        return asfreq(
+            self,
+            freq,
+            method=method,
+            how=how,
+            normalize=normalize,
+            fill_value=fill_value,
+        )
+
+    @final
+    def at_time(self, time, asof: bool_t = False, axis: Axis | None = None) -> Self:
+        """
+        Select values at particular time of day (e.g., 9:30AM).
+
+        Parameters
+        ----------
+        time : datetime.time or str
+            The values to select.
+        axis : {0 or 'index', 1 or 'columns'}, default 0
+            For `Series` this parameter is unused and defaults to 0.
+
+        Returns
+        -------
+        Series or DataFrame
+
+        Raises
+        ------
+        TypeError
+            If the index is not  a :class:`DatetimeIndex`
+
+        See Also
+        --------
+        between_time : Select values between particular times of the day.
+        first : Select initial periods of time series based on a date offset.
+        last : Select final periods of time series based on a date offset.
+        DatetimeIndex.indexer_at_time : Get just the index locations for
+            values at particular time of the day.
+
+        Examples
+        --------
+        >>> i = pd.date_range('2018-04-09', periods=4, freq='12h')
+        >>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i)
+        >>> ts
+                             A
+        2018-04-09 00:00:00  1
+        2018-04-09 12:00:00  2
+        2018-04-10 00:00:00  3
+        2018-04-10 12:00:00  4
+
+        >>> ts.at_time('12:00')
+                             A
+        2018-04-09 12:00:00  2
+        2018-04-10 12:00:00  4
+        """
+        if axis is None:
+            axis = 0
+        axis = self._get_axis_number(axis)
+
+        index = self._get_axis(axis)
+
+        if not isinstance(index, DatetimeIndex):
+            raise TypeError("Index must be DatetimeIndex")
+
+        indexer = index.indexer_at_time(time, asof=asof)
+        return self._take_with_is_copy(indexer, axis=axis)
+
+    @final
+    def between_time(
+        self,
+        start_time,
+        end_time,
+        inclusive: IntervalClosedType = "both",
+        axis: Axis | None = None,
+    ) -> Self:
+        """
+        Select values between particular times of the day (e.g., 9:00-9:30 AM).
+
+        By setting ``start_time`` to be later than ``end_time``,
+        you can get the times that are *not* between the two times.
+
+        Parameters
+        ----------
+        start_time : datetime.time or str
+            Initial time as a time filter limit.
+        end_time : datetime.time or str
+            End time as a time filter limit.
+        inclusive : {"both", "neither", "left", "right"}, default "both"
+            Include boundaries; whether to set each bound as closed or open.
+        axis : {0 or 'index', 1 or 'columns'}, default 0
+            Determine range time on index or columns value.
+            For `Series` this parameter is unused and defaults to 0.
+
+        Returns
+        -------
+        Series or DataFrame
+            Data from the original object filtered to the specified dates range.
+
+        Raises
+        ------
+        TypeError
+            If the index is not  a :class:`DatetimeIndex`
+
+        See Also
+        --------
+        at_time : Select values at a particular time of the day.
+        first : Select initial periods of time series based on a date offset.
+        last : Select final periods of time series based on a date offset.
+        DatetimeIndex.indexer_between_time : Get just the index locations for
+            values between particular times of the day.
+
+        Examples
+        --------
+        >>> i = pd.date_range('2018-04-09', periods=4, freq='1D20min')
+        >>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i)
+        >>> ts
+                             A
+        2018-04-09 00:00:00  1
+        2018-04-10 00:20:00  2
+        2018-04-11 00:40:00  3
+        2018-04-12 01:00:00  4
+
+        >>> ts.between_time('0:15', '0:45')
+                             A
+        2018-04-10 00:20:00  2
+        2018-04-11 00:40:00  3
+
+        You get the times that are *not* between two times by setting
+        ``start_time`` later than ``end_time``:
+
+        >>> ts.between_time('0:45', '0:15')
+                             A
+        2018-04-09 00:00:00  1
+        2018-04-12 01:00:00  4
+        """
+        if axis is None:
+            axis = 0
+        axis = self._get_axis_number(axis)
+
+        index = self._get_axis(axis)
+        if not isinstance(index, DatetimeIndex):
+            raise TypeError("Index must be DatetimeIndex")
+
+        left_inclusive, right_inclusive = validate_inclusive(inclusive)
+        indexer = index.indexer_between_time(
+            start_time,
+            end_time,
+            include_start=left_inclusive,
+            include_end=right_inclusive,
+        )
+        return self._take_with_is_copy(indexer, axis=axis)
+
+    @final
+    @doc(klass=_shared_doc_kwargs["klass"])
+    def resample(
+        self,
+        rule,
+        axis: Axis | lib.NoDefault = lib.no_default,
+        closed: Literal["right", "left"] | None = None,
+        label: Literal["right", "left"] | None = None,
+        convention: Literal["start", "end", "s", "e"] = "start",
+        kind: Literal["timestamp", "period"] | None | lib.NoDefault = lib.no_default,
+        on: Level | None = None,
+        level: Level | None = None,
+        origin: str | TimestampConvertibleTypes = "start_day",
+        offset: TimedeltaConvertibleTypes | None = None,
+        group_keys: bool_t = False,
+    ) -> Resampler:
+        """
+        Resample time-series data.
+
+        Convenience method for frequency conversion and resampling of time series.
+        The object must have a datetime-like index (`DatetimeIndex`, `PeriodIndex`,
+        or `TimedeltaIndex`), or the caller must pass the label of a datetime-like
+        series/index to the ``on``/``level`` keyword parameter.
+
+        Parameters
+        ----------
+        rule : DateOffset, Timedelta or str
+            The offset string or object representing target conversion.
+        axis : {{0 or 'index', 1 or 'columns'}}, default 0
+            Which axis to use for up- or down-sampling. For `Series` this parameter
+            is unused and defaults to 0. Must be
+            `DatetimeIndex`, `TimedeltaIndex` or `PeriodIndex`.
+
+            .. deprecated:: 2.0.0
+                Use frame.T.resample(...) instead.
+        closed : {{'right', 'left'}}, default None
+            Which side of bin interval is closed. The default is 'left'
+            for all frequency offsets except for 'ME', 'YE', 'QE', 'BME',
+            'BA', 'BQE', and 'W' which all have a default of 'right'.
+        label : {{'right', 'left'}}, default None
+            Which bin edge label to label bucket with. The default is 'left'
+            for all frequency offsets except for 'ME', 'YE', 'QE', 'BME',
+            'BA', 'BQE', and 'W' which all have a default of 'right'.
+        convention : {{'start', 'end', 's', 'e'}}, default 'start'
+            For `PeriodIndex` only, controls whether to use the start or
+            end of `rule`.
+
+        kind : {{'timestamp', 'period'}}, optional, default None
+            Pass 'timestamp' to convert the resulting index to a
+            `DateTimeIndex` or 'period' to convert it to a `PeriodIndex`.
+            By default the input representation is retained.
+
+            .. deprecated:: 2.2.0
+                Convert index to desired type explicitly instead.
+
+        on : str, optional
+            For a DataFrame, column to use instead of index for resampling.
+            Column must be datetime-like.
+        level : str or int, optional
+            For a MultiIndex, level (name or number) to use for
+            resampling. `level` must be datetime-like.
+        origin : Timestamp or str, default 'start_day'
+            The timestamp on which to adjust the grouping. The timezone of origin
+            must match the timezone of the index.
+            If string, must be one of the following:
+
+            - 'epoch': `origin` is 1970-01-01
+            - 'start': `origin` is the first value of the timeseries
+            - 'start_day': `origin` is the first day at midnight of the timeseries
+
+            - 'end': `origin` is the last value of the timeseries
+            - 'end_day': `origin` is the ceiling midnight of the last day
+
+            .. versionadded:: 1.3.0
+
+            .. note::
+
+                Only takes effect for Tick-frequencies (i.e. fixed frequencies like
+                days, hours, and minutes, rather than months or quarters).
+        offset : Timedelta or str, default is None
+            An offset timedelta added to the origin.
+
+        group_keys : bool, default False
+            Whether to include the group keys in the result index when using
+            ``.apply()`` on the resampled object.
+
+            .. versionadded:: 1.5.0
+
+                Not specifying ``group_keys`` will retain values-dependent behavior
+                from pandas 1.4 and earlier (see :ref:`pandas 1.5.0 Release notes
+                ` for examples).
+
+            .. versionchanged:: 2.0.0
+
+                ``group_keys`` now defaults to ``False``.
+
+        Returns
+        -------
+        pandas.api.typing.Resampler
+            :class:`~pandas.core.Resampler` object.
+
+        See Also
+        --------
+        Series.resample : Resample a Series.
+        DataFrame.resample : Resample a DataFrame.
+        groupby : Group {klass} by mapping, function, label, or list of labels.
+        asfreq : Reindex a {klass} with the given frequency without grouping.
+
+        Notes
+        -----
+        See the `user guide
+        `__
+        for more.
+
+        To learn more about the offset strings, please see `this link
+        `__.
+
+        Examples
+        --------
+        Start by creating a series with 9 one minute timestamps.
+
+        >>> index = pd.date_range('1/1/2000', periods=9, freq='min')
+        >>> series = pd.Series(range(9), index=index)
+        >>> series
+        2000-01-01 00:00:00    0
+        2000-01-01 00:01:00    1
+        2000-01-01 00:02:00    2
+        2000-01-01 00:03:00    3
+        2000-01-01 00:04:00    4
+        2000-01-01 00:05:00    5
+        2000-01-01 00:06:00    6
+        2000-01-01 00:07:00    7
+        2000-01-01 00:08:00    8
+        Freq: min, dtype: int64
+
+        Downsample the series into 3 minute bins and sum the values
+        of the timestamps falling into a bin.
+
+        >>> series.resample('3min').sum()
+        2000-01-01 00:00:00     3
+        2000-01-01 00:03:00    12
+        2000-01-01 00:06:00    21
+        Freq: 3min, dtype: int64
+
+        Downsample the series into 3 minute bins as above, but label each
+        bin using the right edge instead of the left. Please note that the
+        value in the bucket used as the label is not included in the bucket,
+        which it labels. For example, in the original series the
+        bucket ``2000-01-01 00:03:00`` contains the value 3, but the summed
+        value in the resampled bucket with the label ``2000-01-01 00:03:00``
+        does not include 3 (if it did, the summed value would be 6, not 3).
+
+        >>> series.resample('3min', label='right').sum()
+        2000-01-01 00:03:00     3
+        2000-01-01 00:06:00    12
+        2000-01-01 00:09:00    21
+        Freq: 3min, dtype: int64
+
+        To include this value close the right side of the bin interval,
+        as shown below.
+
+        >>> series.resample('3min', label='right', closed='right').sum()
+        2000-01-01 00:00:00     0
+        2000-01-01 00:03:00     6
+        2000-01-01 00:06:00    15
+        2000-01-01 00:09:00    15
+        Freq: 3min, dtype: int64
+
+        Upsample the series into 30 second bins.
+
+        >>> series.resample('30s').asfreq()[0:5]   # Select first 5 rows
+        2000-01-01 00:00:00   0.0
+        2000-01-01 00:00:30   NaN
+        2000-01-01 00:01:00   1.0
+        2000-01-01 00:01:30   NaN
+        2000-01-01 00:02:00   2.0
+        Freq: 30s, dtype: float64
+
+        Upsample the series into 30 second bins and fill the ``NaN``
+        values using the ``ffill`` method.
+
+        >>> series.resample('30s').ffill()[0:5]
+        2000-01-01 00:00:00    0
+        2000-01-01 00:00:30    0
+        2000-01-01 00:01:00    1
+        2000-01-01 00:01:30    1
+        2000-01-01 00:02:00    2
+        Freq: 30s, dtype: int64
+
+        Upsample the series into 30 second bins and fill the
+        ``NaN`` values using the ``bfill`` method.
+
+        >>> series.resample('30s').bfill()[0:5]
+        2000-01-01 00:00:00    0
+        2000-01-01 00:00:30    1
+        2000-01-01 00:01:00    1
+        2000-01-01 00:01:30    2
+        2000-01-01 00:02:00    2
+        Freq: 30s, dtype: int64
+
+        Pass a custom function via ``apply``
+
+        >>> def custom_resampler(arraylike):
+        ...     return np.sum(arraylike) + 5
+        ...
+        >>> series.resample('3min').apply(custom_resampler)
+        2000-01-01 00:00:00     8
+        2000-01-01 00:03:00    17
+        2000-01-01 00:06:00    26
+        Freq: 3min, dtype: int64
+
+        For a Series with a PeriodIndex, the keyword `convention` can be
+        used to control whether to use the start or end of `rule`.
+
+        Resample a year by quarter using 'start' `convention`. Values are
+        assigned to the first quarter of the period.
+
+        >>> s = pd.Series(
+        ...     [1, 2], index=pd.period_range("2012-01-01", freq="Y", periods=2)
+        ... )
+        >>> s
+        2012    1
+        2013    2
+        Freq: Y-DEC, dtype: int64
+        >>> s.resample("Q", convention="start").asfreq()
+        2012Q1    1.0
+        2012Q2    NaN
+        2012Q3    NaN
+        2012Q4    NaN
+        2013Q1    2.0
+        2013Q2    NaN
+        2013Q3    NaN
+        2013Q4    NaN
+        Freq: Q-DEC, dtype: float64
+
+        Resample quarters by month using 'end' `convention`. Values are
+        assigned to the last month of the period.
+
+        >>> q = pd.Series(
+        ...     [1, 2, 3, 4], index=pd.period_range("2018-01-01", freq="Q", periods=4)
+        ... )
+        >>> q
+        2018Q1    1
+        2018Q2    2
+        2018Q3    3
+        2018Q4    4
+        Freq: Q-DEC, dtype: int64
+        >>> q.resample("M", convention="end").asfreq()
+        2018-03    1.0
+        2018-04    NaN
+        2018-05    NaN
+        2018-06    2.0
+        2018-07    NaN
+        2018-08    NaN
+        2018-09    3.0
+        2018-10    NaN
+        2018-11    NaN
+        2018-12    4.0
+        Freq: M, dtype: float64
+
+        For DataFrame objects, the keyword `on` can be used to specify the
+        column instead of the index for resampling.
+
+        >>> d = {{'price': [10, 11, 9, 13, 14, 18, 17, 19],
+        ...      'volume': [50, 60, 40, 100, 50, 100, 40, 50]}}
+        >>> df = pd.DataFrame(d)
+        >>> df['week_starting'] = pd.date_range('01/01/2018',
+        ...                                     periods=8,
+        ...                                     freq='W')
+        >>> df
+           price  volume week_starting
+        0     10      50    2018-01-07
+        1     11      60    2018-01-14
+        2      9      40    2018-01-21
+        3     13     100    2018-01-28
+        4     14      50    2018-02-04
+        5     18     100    2018-02-11
+        6     17      40    2018-02-18
+        7     19      50    2018-02-25
+        >>> df.resample('ME', on='week_starting').mean()
+                       price  volume
+        week_starting
+        2018-01-31     10.75    62.5
+        2018-02-28     17.00    60.0
+
+        For a DataFrame with MultiIndex, the keyword `level` can be used to
+        specify on which level the resampling needs to take place.
+
+        >>> days = pd.date_range('1/1/2000', periods=4, freq='D')
+        >>> d2 = {{'price': [10, 11, 9, 13, 14, 18, 17, 19],
+        ...       'volume': [50, 60, 40, 100, 50, 100, 40, 50]}}
+        >>> df2 = pd.DataFrame(
+        ...     d2,
+        ...     index=pd.MultiIndex.from_product(
+        ...         [days, ['morning', 'afternoon']]
+        ...     )
+        ... )
+        >>> df2
+                              price  volume
+        2000-01-01 morning       10      50
+                   afternoon     11      60
+        2000-01-02 morning        9      40
+                   afternoon     13     100
+        2000-01-03 morning       14      50
+                   afternoon     18     100
+        2000-01-04 morning       17      40
+                   afternoon     19      50
+        >>> df2.resample('D', level=0).sum()
+                    price  volume
+        2000-01-01     21     110
+        2000-01-02     22     140
+        2000-01-03     32     150
+        2000-01-04     36      90
+
+        If you want to adjust the start of the bins based on a fixed timestamp:
+
+        >>> start, end = '2000-10-01 23:30:00', '2000-10-02 00:30:00'
+        >>> rng = pd.date_range(start, end, freq='7min')
+        >>> ts = pd.Series(np.arange(len(rng)) * 3, index=rng)
+        >>> ts
+        2000-10-01 23:30:00     0
+        2000-10-01 23:37:00     3
+        2000-10-01 23:44:00     6
+        2000-10-01 23:51:00     9
+        2000-10-01 23:58:00    12
+        2000-10-02 00:05:00    15
+        2000-10-02 00:12:00    18
+        2000-10-02 00:19:00    21
+        2000-10-02 00:26:00    24
+        Freq: 7min, dtype: int64
+
+        >>> ts.resample('17min').sum()
+        2000-10-01 23:14:00     0
+        2000-10-01 23:31:00     9
+        2000-10-01 23:48:00    21
+        2000-10-02 00:05:00    54
+        2000-10-02 00:22:00    24
+        Freq: 17min, dtype: int64
+
+        >>> ts.resample('17min', origin='epoch').sum()
+        2000-10-01 23:18:00     0
+        2000-10-01 23:35:00    18
+        2000-10-01 23:52:00    27
+        2000-10-02 00:09:00    39
+        2000-10-02 00:26:00    24
+        Freq: 17min, dtype: int64
+
+        >>> ts.resample('17min', origin='2000-01-01').sum()
+        2000-10-01 23:24:00     3
+        2000-10-01 23:41:00    15
+        2000-10-01 23:58:00    45
+        2000-10-02 00:15:00    45
+        Freq: 17min, dtype: int64
+
+        If you want to adjust the start of the bins with an `offset` Timedelta, the two
+        following lines are equivalent:
+
+        >>> ts.resample('17min', origin='start').sum()
+        2000-10-01 23:30:00     9
+        2000-10-01 23:47:00    21
+        2000-10-02 00:04:00    54
+        2000-10-02 00:21:00    24
+        Freq: 17min, dtype: int64
+
+        >>> ts.resample('17min', offset='23h30min').sum()
+        2000-10-01 23:30:00     9
+        2000-10-01 23:47:00    21
+        2000-10-02 00:04:00    54
+        2000-10-02 00:21:00    24
+        Freq: 17min, dtype: int64
+
+        If you want to take the largest Timestamp as the end of the bins:
+
+        >>> ts.resample('17min', origin='end').sum()
+        2000-10-01 23:35:00     0
+        2000-10-01 23:52:00    18
+        2000-10-02 00:09:00    27
+        2000-10-02 00:26:00    63
+        Freq: 17min, dtype: int64
+
+        In contrast with the `start_day`, you can use `end_day` to take the ceiling
+        midnight of the largest Timestamp as the end of the bins and drop the bins
+        not containing data:
+
+        >>> ts.resample('17min', origin='end_day').sum()
+        2000-10-01 23:38:00     3
+        2000-10-01 23:55:00    15
+        2000-10-02 00:12:00    45
+        2000-10-02 00:29:00    45
+        Freq: 17min, dtype: int64
+        """
+        from pandas.core.resample import get_resampler
+
+        if axis is not lib.no_default:
+            axis = self._get_axis_number(axis)
+            if axis == 1:
+                warnings.warn(
+                    "DataFrame.resample with axis=1 is deprecated. Do "
+                    "`frame.T.resample(...)` without axis instead.",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+            else:
+                warnings.warn(
+                    f"The 'axis' keyword in {type(self).__name__}.resample is "
+                    "deprecated and will be removed in a future version.",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+        else:
+            axis = 0
+
+        if kind is not lib.no_default:
+            # GH#55895
+            warnings.warn(
+                f"The 'kind' keyword in {type(self).__name__}.resample is "
+                "deprecated and will be removed in a future version. "
+                "Explicitly cast the index to the desired type instead",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+        else:
+            kind = None
+
+        return get_resampler(
+            cast("Series | DataFrame", self),
+            freq=rule,
+            label=label,
+            closed=closed,
+            axis=axis,
+            kind=kind,
+            convention=convention,
+            key=on,
+            level=level,
+            origin=origin,
+            offset=offset,
+            group_keys=group_keys,
+        )
+
+    @final
+    def first(self, offset) -> Self:
+        """
+        Select initial periods of time series data based on a date offset.
+
+        .. deprecated:: 2.1
+            :meth:`.first` is deprecated and will be removed in a future version.
+            Please create a mask and filter using `.loc` instead.
+
+        For a DataFrame with a sorted DatetimeIndex, this function can
+        select the first few rows based on a date offset.
+
+        Parameters
+        ----------
+        offset : str, DateOffset or dateutil.relativedelta
+            The offset length of the data that will be selected. For instance,
+            '1ME' will display all the rows having their index within the first month.
+
+        Returns
+        -------
+        Series or DataFrame
+            A subset of the caller.
+
+        Raises
+        ------
+        TypeError
+            If the index is not  a :class:`DatetimeIndex`
+
+        See Also
+        --------
+        last : Select final periods of time series based on a date offset.
+        at_time : Select values at a particular time of the day.
+        between_time : Select values between particular times of the day.
+
+        Examples
+        --------
+        >>> i = pd.date_range('2018-04-09', periods=4, freq='2D')
+        >>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i)
+        >>> ts
+                    A
+        2018-04-09  1
+        2018-04-11  2
+        2018-04-13  3
+        2018-04-15  4
+
+        Get the rows for the first 3 days:
+
+        >>> ts.first('3D')
+                    A
+        2018-04-09  1
+        2018-04-11  2
+
+        Notice the data for 3 first calendar days were returned, not the first
+        3 days observed in the dataset, and therefore data for 2018-04-13 was
+        not returned.
+        """
+        warnings.warn(
+            "first is deprecated and will be removed in a future version. "
+            "Please create a mask and filter using `.loc` instead",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        if not isinstance(self.index, DatetimeIndex):
+            raise TypeError("'first' only supports a DatetimeIndex index")
+
+        if len(self.index) == 0:
+            return self.copy(deep=False)
+
+        offset = to_offset(offset)
+        if not isinstance(offset, Tick) and offset.is_on_offset(self.index[0]):
+            # GH#29623 if first value is end of period, remove offset with n = 1
+            #  before adding the real offset
+            end_date = end = self.index[0] - offset.base + offset
+        else:
+            end_date = end = self.index[0] + offset
+
+        # Tick-like, e.g. 3 weeks
+        if isinstance(offset, Tick) and end_date in self.index:
+            end = self.index.searchsorted(end_date, side="left")
+            return self.iloc[:end]
+
+        return self.loc[:end]
+
+    @final
+    def last(self, offset) -> Self:
+        """
+        Select final periods of time series data based on a date offset.
+
+        .. deprecated:: 2.1
+            :meth:`.last` is deprecated and will be removed in a future version.
+            Please create a mask and filter using `.loc` instead.
+
+        For a DataFrame with a sorted DatetimeIndex, this function
+        selects the last few rows based on a date offset.
+
+        Parameters
+        ----------
+        offset : str, DateOffset, dateutil.relativedelta
+            The offset length of the data that will be selected. For instance,
+            '3D' will display all the rows having their index within the last 3 days.
+
+        Returns
+        -------
+        Series or DataFrame
+            A subset of the caller.
+
+        Raises
+        ------
+        TypeError
+            If the index is not  a :class:`DatetimeIndex`
+
+        See Also
+        --------
+        first : Select initial periods of time series based on a date offset.
+        at_time : Select values at a particular time of the day.
+        between_time : Select values between particular times of the day.
+
+        Notes
+        -----
+        .. deprecated:: 2.1.0
+            Please create a mask and filter using `.loc` instead
+
+        Examples
+        --------
+        >>> i = pd.date_range('2018-04-09', periods=4, freq='2D')
+        >>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i)
+        >>> ts
+                    A
+        2018-04-09  1
+        2018-04-11  2
+        2018-04-13  3
+        2018-04-15  4
+
+        Get the rows for the last 3 days:
+
+        >>> ts.last('3D')  # doctest: +SKIP
+                    A
+        2018-04-13  3
+        2018-04-15  4
+
+        Notice the data for 3 last calendar days were returned, not the last
+        3 observed days in the dataset, and therefore data for 2018-04-11 was
+        not returned.
+        """
+        warnings.warn(
+            "last is deprecated and will be removed in a future version. "
+            "Please create a mask and filter using `.loc` instead",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+
+        if not isinstance(self.index, DatetimeIndex):
+            raise TypeError("'last' only supports a DatetimeIndex index")
+
+        if len(self.index) == 0:
+            return self.copy(deep=False)
+
+        offset = to_offset(offset)
+
+        start_date = self.index[-1] - offset
+        start = self.index.searchsorted(start_date, side="right")
+        return self.iloc[start:]
+
+    @final
+    def rank(
+        self,
+        axis: Axis = 0,
+        method: Literal["average", "min", "max", "first", "dense"] = "average",
+        numeric_only: bool_t = False,
+        na_option: Literal["keep", "top", "bottom"] = "keep",
+        ascending: bool_t = True,
+        pct: bool_t = False,
+    ) -> Self:
+        """
+        Compute numerical data ranks (1 through n) along axis.
+
+        By default, equal values are assigned a rank that is the average of the
+        ranks of those values.
+
+        Parameters
+        ----------
+        axis : {0 or 'index', 1 or 'columns'}, default 0
+            Index to direct ranking.
+            For `Series` this parameter is unused and defaults to 0.
+        method : {'average', 'min', 'max', 'first', 'dense'}, default 'average'
+            How to rank the group of records that have the same value (i.e. ties):
+
+            * average: average rank of the group
+            * min: lowest rank in the group
+            * max: highest rank in the group
+            * first: ranks assigned in order they appear in the array
+            * dense: like 'min', but rank always increases by 1 between groups.
+
+        numeric_only : bool, default False
+            For DataFrame objects, rank only numeric columns if set to True.
+
+            .. versionchanged:: 2.0.0
+                The default value of ``numeric_only`` is now ``False``.
+
+        na_option : {'keep', 'top', 'bottom'}, default 'keep'
+            How to rank NaN values:
+
+            * keep: assign NaN rank to NaN values
+            * top: assign lowest rank to NaN values
+            * bottom: assign highest rank to NaN values
+
+        ascending : bool, default True
+            Whether or not the elements should be ranked in ascending order.
+        pct : bool, default False
+            Whether or not to display the returned rankings in percentile
+            form.
+
+        Returns
+        -------
+        same type as caller
+            Return a Series or DataFrame with data ranks as values.
+
+        See Also
+        --------
+        core.groupby.DataFrameGroupBy.rank : Rank of values within each group.
+        core.groupby.SeriesGroupBy.rank : Rank of values within each group.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame(data={'Animal': ['cat', 'penguin', 'dog',
+        ...                                    'spider', 'snake'],
+        ...                         'Number_legs': [4, 2, 4, 8, np.nan]})
+        >>> df
+            Animal  Number_legs
+        0      cat          4.0
+        1  penguin          2.0
+        2      dog          4.0
+        3   spider          8.0
+        4    snake          NaN
+
+        Ties are assigned the mean of the ranks (by default) for the group.
+
+        >>> s = pd.Series(range(5), index=list("abcde"))
+        >>> s["d"] = s["b"]
+        >>> s.rank()
+        a    1.0
+        b    2.5
+        c    4.0
+        d    2.5
+        e    5.0
+        dtype: float64
+
+        The following example shows how the method behaves with the above
+        parameters:
+
+        * default_rank: this is the default behaviour obtained without using
+          any parameter.
+        * max_rank: setting ``method = 'max'`` the records that have the
+          same values are ranked using the highest rank (e.g.: since 'cat'
+          and 'dog' are both in the 2nd and 3rd position, rank 3 is assigned.)
+        * NA_bottom: choosing ``na_option = 'bottom'``, if there are records
+          with NaN values they are placed at the bottom of the ranking.
+        * pct_rank: when setting ``pct = True``, the ranking is expressed as
+          percentile rank.
+
+        >>> df['default_rank'] = df['Number_legs'].rank()
+        >>> df['max_rank'] = df['Number_legs'].rank(method='max')
+        >>> df['NA_bottom'] = df['Number_legs'].rank(na_option='bottom')
+        >>> df['pct_rank'] = df['Number_legs'].rank(pct=True)
+        >>> df
+            Animal  Number_legs  default_rank  max_rank  NA_bottom  pct_rank
+        0      cat          4.0           2.5       3.0        2.5     0.625
+        1  penguin          2.0           1.0       1.0        1.0     0.250
+        2      dog          4.0           2.5       3.0        2.5     0.625
+        3   spider          8.0           4.0       4.0        4.0     1.000
+        4    snake          NaN           NaN       NaN        5.0       NaN
+        """
+        axis_int = self._get_axis_number(axis)
+
+        if na_option not in {"keep", "top", "bottom"}:
+            msg = "na_option must be one of 'keep', 'top', or 'bottom'"
+            raise ValueError(msg)
+
+        def ranker(data):
+            if data.ndim == 2:
+                # i.e. DataFrame, we cast to ndarray
+                values = data.values
+            else:
+                # i.e. Series, can dispatch to EA
+                values = data._values
+
+            if isinstance(values, ExtensionArray):
+                ranks = values._rank(
+                    axis=axis_int,
+                    method=method,
+                    ascending=ascending,
+                    na_option=na_option,
+                    pct=pct,
+                )
+            else:
+                ranks = algos.rank(
+                    values,
+                    axis=axis_int,
+                    method=method,
+                    ascending=ascending,
+                    na_option=na_option,
+                    pct=pct,
+                )
+
+            ranks_obj = self._constructor(ranks, **data._construct_axes_dict())
+            return ranks_obj.__finalize__(self, method="rank")
+
+        if numeric_only:
+            if self.ndim == 1 and not is_numeric_dtype(self.dtype):
+                # GH#47500
+                raise TypeError(
+                    "Series.rank does not allow numeric_only=True with "
+                    "non-numeric dtype."
+                )
+            data = self._get_numeric_data()
+        else:
+            data = self
+
+        return ranker(data)
+
+    @doc(_shared_docs["compare"], klass=_shared_doc_kwargs["klass"])
+    def compare(
+        self,
+        other,
+        align_axis: Axis = 1,
+        keep_shape: bool_t = False,
+        keep_equal: bool_t = False,
+        result_names: Suffixes = ("self", "other"),
+    ):
+        if type(self) is not type(other):
+            cls_self, cls_other = type(self).__name__, type(other).__name__
+            raise TypeError(
+                f"can only compare '{cls_self}' (not '{cls_other}') with '{cls_self}'"
+            )
+
+        mask = ~((self == other) | (self.isna() & other.isna()))
+        mask.fillna(True, inplace=True)
+
+        if not keep_equal:
+            self = self.where(mask)
+            other = other.where(mask)
+
+        if not keep_shape:
+            if isinstance(self, ABCDataFrame):
+                cmask = mask.any()
+                rmask = mask.any(axis=1)
+                self = self.loc[rmask, cmask]
+                other = other.loc[rmask, cmask]
+            else:
+                self = self[mask]
+                other = other[mask]
+        if not isinstance(result_names, tuple):
+            raise TypeError(
+                f"Passing 'result_names' as a {type(result_names)} is not "
+                "supported. Provide 'result_names' as a tuple instead."
+            )
+
+        if align_axis in (1, "columns"):  # This is needed for Series
+            axis = 1
+        else:
+            axis = self._get_axis_number(align_axis)
+
+        # error: List item 0 has incompatible type "NDFrame"; expected
+        #  "Union[Series, DataFrame]"
+        diff = concat(
+            [self, other],  # type: ignore[list-item]
+            axis=axis,
+            keys=result_names,
+        )
+
+        if axis >= self.ndim:
+            # No need to reorganize data if stacking on new axis
+            # This currently applies for stacking two Series on columns
+            return diff
+
+        ax = diff._get_axis(axis)
+        ax_names = np.array(ax.names)
+
+        # set index names to positions to avoid confusion
+        ax.names = np.arange(len(ax_names))
+
+        # bring self-other to inner level
+        order = list(range(1, ax.nlevels)) + [0]
+        if isinstance(diff, ABCDataFrame):
+            diff = diff.reorder_levels(order, axis=axis)
+        else:
+            diff = diff.reorder_levels(order)
+
+        # restore the index names in order
+        diff._get_axis(axis=axis).names = ax_names[order]
+
+        # reorder axis to keep things organized
+        indices = (
+            np.arange(diff.shape[axis]).reshape([2, diff.shape[axis] // 2]).T.flatten()
+        )
+        diff = diff.take(indices, axis=axis)
+
+        return diff
+
+    @final
+    @doc(
+        klass=_shared_doc_kwargs["klass"],
+        axes_single_arg=_shared_doc_kwargs["axes_single_arg"],
+    )
+    def align(
+        self,
+        other: NDFrameT,
+        join: AlignJoin = "outer",
+        axis: Axis | None = None,
+        level: Level | None = None,
+        copy: bool_t | None = None,
+        fill_value: Hashable | None = None,
+        method: FillnaOptions | None | lib.NoDefault = lib.no_default,
+        limit: int | None | lib.NoDefault = lib.no_default,
+        fill_axis: Axis | lib.NoDefault = lib.no_default,
+        broadcast_axis: Axis | None | lib.NoDefault = lib.no_default,
+    ) -> tuple[Self, NDFrameT]:
+        """
+        Align two objects on their axes with the specified join method.
+
+        Join method is specified for each axis Index.
+
+        Parameters
+        ----------
+        other : DataFrame or Series
+        join : {{'outer', 'inner', 'left', 'right'}}, default 'outer'
+            Type of alignment to be performed.
+
+            * left: use only keys from left frame, preserve key order.
+            * right: use only keys from right frame, preserve key order.
+            * outer: use union of keys from both frames, sort keys lexicographically.
+            * inner: use intersection of keys from both frames,
+              preserve the order of the left keys.
+
+        axis : allowed axis of the other object, default None
+            Align on index (0), columns (1), or both (None).
+        level : int or level name, default None
+            Broadcast across a level, matching Index values on the
+            passed MultiIndex level.
+        copy : bool, default True
+            Always returns new objects. If copy=False and no reindexing is
+            required then original objects are returned.
+
+            .. note::
+                The `copy` keyword will change behavior in pandas 3.0.
+                `Copy-on-Write
+                `__
+                will be enabled by default, which means that all methods with a
+                `copy` keyword will use a lazy copy mechanism to defer the copy and
+                ignore the `copy` keyword. The `copy` keyword will be removed in a
+                future version of pandas.
+
+                You can already get the future behavior and improvements through
+                enabling copy on write ``pd.options.mode.copy_on_write = True``
+        fill_value : scalar, default np.nan
+            Value to use for missing values. Defaults to NaN, but can be any
+            "compatible" value.
+        method : {{'backfill', 'bfill', 'pad', 'ffill', None}}, default None
+            Method to use for filling holes in reindexed Series:
+
+            - pad / ffill: propagate last valid observation forward to next valid.
+            - backfill / bfill: use NEXT valid observation to fill gap.
+
+            .. deprecated:: 2.1
+
+        limit : int, default None
+            If method is specified, this is the maximum number of consecutive
+            NaN values to forward/backward fill. In other words, if there is
+            a gap with more than this number of consecutive NaNs, it will only
+            be partially filled. If method is not specified, this is the
+            maximum number of entries along the entire axis where NaNs will be
+            filled. Must be greater than 0 if not None.
+
+            .. deprecated:: 2.1
+
+        fill_axis : {axes_single_arg}, default 0
+            Filling axis, method and limit.
+
+            .. deprecated:: 2.1
+
+        broadcast_axis : {axes_single_arg}, default None
+            Broadcast values along this axis, if aligning two objects of
+            different dimensions.
+
+            .. deprecated:: 2.1
+
+        Returns
+        -------
+        tuple of ({klass}, type of other)
+            Aligned objects.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame(
+        ...     [[1, 2, 3, 4], [6, 7, 8, 9]], columns=["D", "B", "E", "A"], index=[1, 2]
+        ... )
+        >>> other = pd.DataFrame(
+        ...     [[10, 20, 30, 40], [60, 70, 80, 90], [600, 700, 800, 900]],
+        ...     columns=["A", "B", "C", "D"],
+        ...     index=[2, 3, 4],
+        ... )
+        >>> df
+           D  B  E  A
+        1  1  2  3  4
+        2  6  7  8  9
+        >>> other
+            A    B    C    D
+        2   10   20   30   40
+        3   60   70   80   90
+        4  600  700  800  900
+
+        Align on columns:
+
+        >>> left, right = df.align(other, join="outer", axis=1)
+        >>> left
+           A  B   C  D  E
+        1  4  2 NaN  1  3
+        2  9  7 NaN  6  8
+        >>> right
+            A    B    C    D   E
+        2   10   20   30   40 NaN
+        3   60   70   80   90 NaN
+        4  600  700  800  900 NaN
+
+        We can also align on the index:
+
+        >>> left, right = df.align(other, join="outer", axis=0)
+        >>> left
+            D    B    E    A
+        1  1.0  2.0  3.0  4.0
+        2  6.0  7.0  8.0  9.0
+        3  NaN  NaN  NaN  NaN
+        4  NaN  NaN  NaN  NaN
+        >>> right
+            A      B      C      D
+        1    NaN    NaN    NaN    NaN
+        2   10.0   20.0   30.0   40.0
+        3   60.0   70.0   80.0   90.0
+        4  600.0  700.0  800.0  900.0
+
+        Finally, the default `axis=None` will align on both index and columns:
+
+        >>> left, right = df.align(other, join="outer", axis=None)
+        >>> left
+             A    B   C    D    E
+        1  4.0  2.0 NaN  1.0  3.0
+        2  9.0  7.0 NaN  6.0  8.0
+        3  NaN  NaN NaN  NaN  NaN
+        4  NaN  NaN NaN  NaN  NaN
+        >>> right
+               A      B      C      D   E
+        1    NaN    NaN    NaN    NaN NaN
+        2   10.0   20.0   30.0   40.0 NaN
+        3   60.0   70.0   80.0   90.0 NaN
+        4  600.0  700.0  800.0  900.0 NaN
+        """
+        if (
+            method is not lib.no_default
+            or limit is not lib.no_default
+            or fill_axis is not lib.no_default
+        ):
+            # GH#51856
+            warnings.warn(
+                "The 'method', 'limit', and 'fill_axis' keywords in "
+                f"{type(self).__name__}.align are deprecated and will be removed "
+                "in a future version. Call fillna directly on the returned objects "
+                "instead.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+        if fill_axis is lib.no_default:
+            fill_axis = 0
+        if method is lib.no_default:
+            method = None
+        if limit is lib.no_default:
+            limit = None
+
+        if method is not None:
+            method = clean_fill_method(method)
+
+        if broadcast_axis is not lib.no_default:
+            # GH#51856
+            # TODO(3.0): enforcing this deprecation will close GH#13194
+            msg = (
+                f"The 'broadcast_axis' keyword in {type(self).__name__}.align is "
+                "deprecated and will be removed in a future version."
+            )
+            if broadcast_axis is not None:
+                if self.ndim == 1 and other.ndim == 2:
+                    msg += (
+                        " Use left = DataFrame({col: left for col in right.columns}, "
+                        "index=right.index) before calling `left.align(right)` instead."
+                    )
+                elif self.ndim == 2 and other.ndim == 1:
+                    msg += (
+                        " Use right = DataFrame({col: right for col in left.columns}, "
+                        "index=left.index) before calling `left.align(right)` instead"
+                    )
+            warnings.warn(msg, FutureWarning, stacklevel=find_stack_level())
+        else:
+            broadcast_axis = None
+
+        if broadcast_axis == 1 and self.ndim != other.ndim:
+            if isinstance(self, ABCSeries):
+                # this means other is a DataFrame, and we need to broadcast
+                # self
+                cons = self._constructor_expanddim
+                df = cons(
+                    {c: self for c in other.columns}, **other._construct_axes_dict()
+                )
+                # error: Incompatible return value type (got "Tuple[DataFrame,
+                # DataFrame]", expected "Tuple[Self, NDFrameT]")
+                return df._align_frame(  # type: ignore[return-value]
+                    other,  # type: ignore[arg-type]
+                    join=join,
+                    axis=axis,
+                    level=level,
+                    copy=copy,
+                    fill_value=fill_value,
+                    method=method,
+                    limit=limit,
+                    fill_axis=fill_axis,
+                )[:2]
+            elif isinstance(other, ABCSeries):
+                # this means self is a DataFrame, and we need to broadcast
+                # other
+                cons = other._constructor_expanddim
+                df = cons(
+                    {c: other for c in self.columns}, **self._construct_axes_dict()
+                )
+                # error: Incompatible return value type (got "Tuple[NDFrameT,
+                # DataFrame]", expected "Tuple[Self, NDFrameT]")
+                return self._align_frame(  # type: ignore[return-value]
+                    df,
+                    join=join,
+                    axis=axis,
+                    level=level,
+                    copy=copy,
+                    fill_value=fill_value,
+                    method=method,
+                    limit=limit,
+                    fill_axis=fill_axis,
+                )[:2]
+
+        _right: DataFrame | Series
+        if axis is not None:
+            axis = self._get_axis_number(axis)
+        if isinstance(other, ABCDataFrame):
+            left, _right, join_index = self._align_frame(
+                other,
+                join=join,
+                axis=axis,
+                level=level,
+                copy=copy,
+                fill_value=fill_value,
+                method=method,
+                limit=limit,
+                fill_axis=fill_axis,
+            )
+
+        elif isinstance(other, ABCSeries):
+            left, _right, join_index = self._align_series(
+                other,
+                join=join,
+                axis=axis,
+                level=level,
+                copy=copy,
+                fill_value=fill_value,
+                method=method,
+                limit=limit,
+                fill_axis=fill_axis,
+            )
+        else:  # pragma: no cover
+            raise TypeError(f"unsupported type: {type(other)}")
+
+        right = cast(NDFrameT, _right)
+        if self.ndim == 1 or axis == 0:
+            # If we are aligning timezone-aware DatetimeIndexes and the timezones
+            #  do not match, convert both to UTC.
+            if isinstance(left.index.dtype, DatetimeTZDtype):
+                if left.index.tz != right.index.tz:
+                    if join_index is not None:
+                        # GH#33671 copy to ensure we don't change the index on
+                        #  our original Series
+                        left = left.copy(deep=False)
+                        right = right.copy(deep=False)
+                        left.index = join_index
+                        right.index = join_index
+
+        left = left.__finalize__(self)
+        right = right.__finalize__(other)
+        return left, right
+
+    @final
+    def _align_frame(
+        self,
+        other: DataFrame,
+        join: AlignJoin = "outer",
+        axis: Axis | None = None,
+        level=None,
+        copy: bool_t | None = None,
+        fill_value=None,
+        method=None,
+        limit: int | None = None,
+        fill_axis: Axis = 0,
+    ) -> tuple[Self, DataFrame, Index | None]:
+        # defaults
+        join_index, join_columns = None, None
+        ilidx, iridx = None, None
+        clidx, cridx = None, None
+
+        is_series = isinstance(self, ABCSeries)
+
+        if (axis is None or axis == 0) and not self.index.equals(other.index):
+            join_index, ilidx, iridx = self.index.join(
+                other.index, how=join, level=level, return_indexers=True
+            )
+
+        if (
+            (axis is None or axis == 1)
+            and not is_series
+            and not self.columns.equals(other.columns)
+        ):
+            join_columns, clidx, cridx = self.columns.join(
+                other.columns, how=join, level=level, return_indexers=True
+            )
+
+        if is_series:
+            reindexers = {0: [join_index, ilidx]}
+        else:
+            reindexers = {0: [join_index, ilidx], 1: [join_columns, clidx]}
+
+        left = self._reindex_with_indexers(
+            reindexers, copy=copy, fill_value=fill_value, allow_dups=True
+        )
+        # other must be always DataFrame
+        right = other._reindex_with_indexers(
+            {0: [join_index, iridx], 1: [join_columns, cridx]},
+            copy=copy,
+            fill_value=fill_value,
+            allow_dups=True,
+        )
+
+        if method is not None:
+            left = left._pad_or_backfill(method, axis=fill_axis, limit=limit)
+            right = right._pad_or_backfill(method, axis=fill_axis, limit=limit)
+
+        return left, right, join_index
+
+    @final
+    def _align_series(
+        self,
+        other: Series,
+        join: AlignJoin = "outer",
+        axis: Axis | None = None,
+        level=None,
+        copy: bool_t | None = None,
+        fill_value=None,
+        method=None,
+        limit: int | None = None,
+        fill_axis: Axis = 0,
+    ) -> tuple[Self, Series, Index | None]:
+        is_series = isinstance(self, ABCSeries)
+        if copy and using_copy_on_write():
+            copy = False
+
+        if (not is_series and axis is None) or axis not in [None, 0, 1]:
+            raise ValueError("Must specify axis=0 or 1")
+
+        if is_series and axis == 1:
+            raise ValueError("cannot align series to a series other than axis 0")
+
+        # series/series compat, other must always be a Series
+        if not axis:
+            # equal
+            if self.index.equals(other.index):
+                join_index, lidx, ridx = None, None, None
+            else:
+                join_index, lidx, ridx = self.index.join(
+                    other.index, how=join, level=level, return_indexers=True
+                )
+
+            if is_series:
+                left = self._reindex_indexer(join_index, lidx, copy)
+            elif lidx is None or join_index is None:
+                left = self.copy(deep=copy)
+            else:
+                new_mgr = self._mgr.reindex_indexer(join_index, lidx, axis=1, copy=copy)
+                left = self._constructor_from_mgr(new_mgr, axes=new_mgr.axes)
+
+            right = other._reindex_indexer(join_index, ridx, copy)
+
+        else:
+            # one has > 1 ndim
+            fdata = self._mgr
+            join_index = self.axes[1]
+            lidx, ridx = None, None
+            if not join_index.equals(other.index):
+                join_index, lidx, ridx = join_index.join(
+                    other.index, how=join, level=level, return_indexers=True
+                )
+
+            if lidx is not None:
+                bm_axis = self._get_block_manager_axis(1)
+                fdata = fdata.reindex_indexer(join_index, lidx, axis=bm_axis)
+
+            if copy and fdata is self._mgr:
+                fdata = fdata.copy()
+
+            left = self._constructor_from_mgr(fdata, axes=fdata.axes)
+
+            if ridx is None:
+                right = other.copy(deep=copy)
+            else:
+                right = other.reindex(join_index, level=level)
+
+        # fill
+        fill_na = notna(fill_value) or (method is not None)
+        if fill_na:
+            fill_value, method = validate_fillna_kwargs(fill_value, method)
+            if method is not None:
+                left = left._pad_or_backfill(method, limit=limit, axis=fill_axis)
+                right = right._pad_or_backfill(method, limit=limit)
+            else:
+                left = left.fillna(fill_value, limit=limit, axis=fill_axis)
+                right = right.fillna(fill_value, limit=limit)
+
+        return left, right, join_index
+
+    @final
+    def _where(
+        self,
+        cond,
+        other=lib.no_default,
+        inplace: bool_t = False,
+        axis: Axis | None = None,
+        level=None,
+        warn: bool_t = True,
+    ):
+        """
+        Equivalent to public method `where`, except that `other` is not
+        applied as a function even if callable. Used in __setitem__.
+        """
+        inplace = validate_bool_kwarg(inplace, "inplace")
+
+        if axis is not None:
+            axis = self._get_axis_number(axis)
+
+        # align the cond to same shape as myself
+        cond = common.apply_if_callable(cond, self)
+        if isinstance(cond, NDFrame):
+            # CoW: Make sure reference is not kept alive
+            if cond.ndim == 1 and self.ndim == 2:
+                cond = cond._constructor_expanddim(
+                    {i: cond for i in range(len(self.columns))},
+                    copy=False,
+                )
+                cond.columns = self.columns
+            cond = cond.align(self, join="right", copy=False)[0]
+        else:
+            if not hasattr(cond, "shape"):
+                cond = np.asanyarray(cond)
+            if cond.shape != self.shape:
+                raise ValueError("Array conditional must be same shape as self")
+            cond = self._constructor(cond, **self._construct_axes_dict(), copy=False)
+
+        # make sure we are boolean
+        fill_value = bool(inplace)
+        with warnings.catch_warnings():
+            warnings.filterwarnings(
+                "ignore",
+                "Downcasting object dtype arrays",
+                category=FutureWarning,
+            )
+            cond = cond.fillna(fill_value)
+        cond = cond.infer_objects(copy=False)
+
+        msg = "Boolean array expected for the condition, not {dtype}"
+
+        if not cond.empty:
+            if not isinstance(cond, ABCDataFrame):
+                # This is a single-dimensional object.
+                if not is_bool_dtype(cond):
+                    raise ValueError(msg.format(dtype=cond.dtype))
+            else:
+                for _dt in cond.dtypes:
+                    if not is_bool_dtype(_dt):
+                        raise ValueError(msg.format(dtype=_dt))
+                if cond._mgr.any_extension_types:
+                    # GH51574: avoid object ndarray conversion later on
+                    cond = cond._constructor(
+                        cond.to_numpy(dtype=bool, na_value=fill_value),
+                        **cond._construct_axes_dict(),
+                    )
+        else:
+            # GH#21947 we have an empty DataFrame/Series, could be object-dtype
+            cond = cond.astype(bool)
+
+        cond = -cond if inplace else cond
+        cond = cond.reindex(self._info_axis, axis=self._info_axis_number, copy=False)
+
+        # try to align with other
+        if isinstance(other, NDFrame):
+            # align with me
+            if other.ndim <= self.ndim:
+                # CoW: Make sure reference is not kept alive
+                other = self.align(
+                    other,
+                    join="left",
+                    axis=axis,
+                    level=level,
+                    fill_value=None,
+                    copy=False,
+                )[1]
+
+                # if we are NOT aligned, raise as we cannot where index
+                if axis is None and not other._indexed_same(self):
+                    raise InvalidIndexError
+
+                if other.ndim < self.ndim:
+                    # TODO(EA2D): avoid object-dtype cast in EA case GH#38729
+                    other = other._values
+                    if axis == 0:
+                        other = np.reshape(other, (-1, 1))
+                    elif axis == 1:
+                        other = np.reshape(other, (1, -1))
+
+                    other = np.broadcast_to(other, self.shape)
+
+            # slice me out of the other
+            else:
+                raise NotImplementedError(
+                    "cannot align with a higher dimensional NDFrame"
+                )
+
+        elif not isinstance(other, (MultiIndex, NDFrame)):
+            # mainly just catching Index here
+            other = extract_array(other, extract_numpy=True)
+
+        if isinstance(other, (np.ndarray, ExtensionArray)):
+            if other.shape != self.shape:
+                if self.ndim != 1:
+                    # In the ndim == 1 case we may have
+                    #  other length 1, which we treat as scalar (GH#2745, GH#4192)
+                    #  or len(other) == icond.sum(), which we treat like
+                    #  __setitem__ (GH#3235)
+                    raise ValueError(
+                        "other must be the same shape as self when an ndarray"
+                    )
+
+            # we are the same shape, so create an actual object for alignment
+            else:
+                other = self._constructor(
+                    other, **self._construct_axes_dict(), copy=False
+                )
+
+        if axis is None:
+            axis = 0
+
+        if self.ndim == getattr(other, "ndim", 0):
+            align = True
+        else:
+            align = self._get_axis_number(axis) == 1
+
+        if inplace:
+            # we may have different type blocks come out of putmask, so
+            # reconstruct the block manager
+
+            new_data = self._mgr.putmask(mask=cond, new=other, align=align, warn=warn)
+            result = self._constructor_from_mgr(new_data, axes=new_data.axes)
+            return self._update_inplace(result)
+
+        else:
+            new_data = self._mgr.where(
+                other=other,
+                cond=cond,
+                align=align,
+            )
+            result = self._constructor_from_mgr(new_data, axes=new_data.axes)
+            return result.__finalize__(self)
+
+    @overload
+    def where(
+        self,
+        cond,
+        other=...,
+        *,
+        inplace: Literal[False] = ...,
+        axis: Axis | None = ...,
+        level: Level = ...,
+    ) -> Self:
+        ...
+
+    @overload
+    def where(
+        self,
+        cond,
+        other=...,
+        *,
+        inplace: Literal[True],
+        axis: Axis | None = ...,
+        level: Level = ...,
+    ) -> None:
+        ...
+
+    @overload
+    def where(
+        self,
+        cond,
+        other=...,
+        *,
+        inplace: bool_t = ...,
+        axis: Axis | None = ...,
+        level: Level = ...,
+    ) -> Self | None:
+        ...
+
+    @final
+    @doc(
+        klass=_shared_doc_kwargs["klass"],
+        cond="True",
+        cond_rev="False",
+        name="where",
+        name_other="mask",
+    )
+    def where(
+        self,
+        cond,
+        other=np.nan,
+        *,
+        inplace: bool_t = False,
+        axis: Axis | None = None,
+        level: Level | None = None,
+    ) -> Self | None:
+        """
+        Replace values where the condition is {cond_rev}.
+
+        Parameters
+        ----------
+        cond : bool {klass}, array-like, or callable
+            Where `cond` is {cond}, keep the original value. Where
+            {cond_rev}, replace with corresponding value from `other`.
+            If `cond` is callable, it is computed on the {klass} and
+            should return boolean {klass} or array. The callable must
+            not change input {klass} (though pandas doesn't check it).
+        other : scalar, {klass}, or callable
+            Entries where `cond` is {cond_rev} are replaced with
+            corresponding value from `other`.
+            If other is callable, it is computed on the {klass} and
+            should return scalar or {klass}. The callable must not
+            change input {klass} (though pandas doesn't check it).
+            If not specified, entries will be filled with the corresponding
+            NULL value (``np.nan`` for numpy dtypes, ``pd.NA`` for extension
+            dtypes).
+        inplace : bool, default False
+            Whether to perform the operation in place on the data.
+        axis : int, default None
+            Alignment axis if needed. For `Series` this parameter is
+            unused and defaults to 0.
+        level : int, default None
+            Alignment level if needed.
+
+        Returns
+        -------
+        Same type as caller or None if ``inplace=True``.
+
+        See Also
+        --------
+        :func:`DataFrame.{name_other}` : Return an object of same shape as
+            self.
+
+        Notes
+        -----
+        The {name} method is an application of the if-then idiom. For each
+        element in the calling DataFrame, if ``cond`` is ``{cond}`` the
+        element is used; otherwise the corresponding element from the DataFrame
+        ``other`` is used. If the axis of ``other`` does not align with axis of
+        ``cond`` {klass}, the misaligned index positions will be filled with
+        {cond_rev}.
+
+        The signature for :func:`DataFrame.where` differs from
+        :func:`numpy.where`. Roughly ``df1.where(m, df2)`` is equivalent to
+        ``np.where(m, df1, df2)``.
+
+        For further details and examples see the ``{name}`` documentation in
+        :ref:`indexing `.
+
+        The dtype of the object takes precedence. The fill value is casted to
+        the object's dtype, if this can be done losslessly.
+
+        Examples
+        --------
+        >>> s = pd.Series(range(5))
+        >>> s.where(s > 0)
+        0    NaN
+        1    1.0
+        2    2.0
+        3    3.0
+        4    4.0
+        dtype: float64
+        >>> s.mask(s > 0)
+        0    0.0
+        1    NaN
+        2    NaN
+        3    NaN
+        4    NaN
+        dtype: float64
+
+        >>> s = pd.Series(range(5))
+        >>> t = pd.Series([True, False])
+        >>> s.where(t, 99)
+        0     0
+        1    99
+        2    99
+        3    99
+        4    99
+        dtype: int64
+        >>> s.mask(t, 99)
+        0    99
+        1     1
+        2    99
+        3    99
+        4    99
+        dtype: int64
+
+        >>> s.where(s > 1, 10)
+        0    10
+        1    10
+        2    2
+        3    3
+        4    4
+        dtype: int64
+        >>> s.mask(s > 1, 10)
+        0     0
+        1     1
+        2    10
+        3    10
+        4    10
+        dtype: int64
+
+        >>> df = pd.DataFrame(np.arange(10).reshape(-1, 2), columns=['A', 'B'])
+        >>> df
+           A  B
+        0  0  1
+        1  2  3
+        2  4  5
+        3  6  7
+        4  8  9
+        >>> m = df % 3 == 0
+        >>> df.where(m, -df)
+           A  B
+        0  0 -1
+        1 -2  3
+        2 -4 -5
+        3  6 -7
+        4 -8  9
+        >>> df.where(m, -df) == np.where(m, df, -df)
+              A     B
+        0  True  True
+        1  True  True
+        2  True  True
+        3  True  True
+        4  True  True
+        >>> df.where(m, -df) == df.mask(~m, -df)
+              A     B
+        0  True  True
+        1  True  True
+        2  True  True
+        3  True  True
+        4  True  True
+        """
+        inplace = validate_bool_kwarg(inplace, "inplace")
+        if inplace:
+            if not PYPY and not WARNING_CHECK_DISABLED and using_copy_on_write():
+                if sys.getrefcount(self) <= REF_COUNT:
+                    warnings.warn(
+                        _chained_assignment_method_msg,
+                        ChainedAssignmentError,
+                        stacklevel=2,
+                    )
+            elif (
+                not PYPY
+                and not WARNING_CHECK_DISABLED
+                and not using_copy_on_write()
+                and self._is_view_after_cow_rules()
+            ):
+                ctr = sys.getrefcount(self)
+                ref_count = REF_COUNT
+                if isinstance(self, ABCSeries) and hasattr(self, "_cacher"):
+                    # see https://github.com/pandas-dev/pandas/pull/56060#discussion_r1399245221
+                    ref_count += 1
+                if ctr <= ref_count:
+                    warnings.warn(
+                        _chained_assignment_warning_method_msg,
+                        FutureWarning,
+                        stacklevel=2,
+                    )
+
+        other = common.apply_if_callable(other, self)
+        return self._where(cond, other, inplace, axis, level)
+
+    @overload
+    def mask(
+        self,
+        cond,
+        other=...,
+        *,
+        inplace: Literal[False] = ...,
+        axis: Axis | None = ...,
+        level: Level = ...,
+    ) -> Self:
+        ...
+
+    @overload
+    def mask(
+        self,
+        cond,
+        other=...,
+        *,
+        inplace: Literal[True],
+        axis: Axis | None = ...,
+        level: Level = ...,
+    ) -> None:
+        ...
+
+    @overload
+    def mask(
+        self,
+        cond,
+        other=...,
+        *,
+        inplace: bool_t = ...,
+        axis: Axis | None = ...,
+        level: Level = ...,
+    ) -> Self | None:
+        ...
+
+    @final
+    @doc(
+        where,
+        klass=_shared_doc_kwargs["klass"],
+        cond="False",
+        cond_rev="True",
+        name="mask",
+        name_other="where",
+    )
+    def mask(
+        self,
+        cond,
+        other=lib.no_default,
+        *,
+        inplace: bool_t = False,
+        axis: Axis | None = None,
+        level: Level | None = None,
+    ) -> Self | None:
+        inplace = validate_bool_kwarg(inplace, "inplace")
+        if inplace:
+            if not PYPY and not WARNING_CHECK_DISABLED and using_copy_on_write():
+                if sys.getrefcount(self) <= REF_COUNT:
+                    warnings.warn(
+                        _chained_assignment_method_msg,
+                        ChainedAssignmentError,
+                        stacklevel=2,
+                    )
+            elif (
+                not PYPY
+                and not WARNING_CHECK_DISABLED
+                and not using_copy_on_write()
+                and self._is_view_after_cow_rules()
+            ):
+                ctr = sys.getrefcount(self)
+                ref_count = REF_COUNT
+                if isinstance(self, ABCSeries) and hasattr(self, "_cacher"):
+                    # see https://github.com/pandas-dev/pandas/pull/56060#discussion_r1399245221
+                    ref_count += 1
+                if ctr <= ref_count:
+                    warnings.warn(
+                        _chained_assignment_warning_method_msg,
+                        FutureWarning,
+                        stacklevel=2,
+                    )
+
+        cond = common.apply_if_callable(cond, self)
+        other = common.apply_if_callable(other, self)
+
+        # see gh-21891
+        if not hasattr(cond, "__invert__"):
+            cond = np.array(cond)
+
+        return self._where(
+            ~cond,
+            other=other,
+            inplace=inplace,
+            axis=axis,
+            level=level,
+        )
+
+    @doc(klass=_shared_doc_kwargs["klass"])
+    def shift(
+        self,
+        periods: int | Sequence[int] = 1,
+        freq=None,
+        axis: Axis = 0,
+        fill_value: Hashable = lib.no_default,
+        suffix: str | None = None,
+    ) -> Self | DataFrame:
+        """
+        Shift index by desired number of periods with an optional time `freq`.
+
+        When `freq` is not passed, shift the index without realigning the data.
+        If `freq` is passed (in this case, the index must be date or datetime,
+        or it will raise a `NotImplementedError`), the index will be
+        increased using the periods and the `freq`. `freq` can be inferred
+        when specified as "infer" as long as either freq or inferred_freq
+        attribute is set in the index.
+
+        Parameters
+        ----------
+        periods : int or Sequence
+            Number of periods to shift. Can be positive or negative.
+            If an iterable of ints, the data will be shifted once by each int.
+            This is equivalent to shifting by one value at a time and
+            concatenating all resulting frames. The resulting columns will have
+            the shift suffixed to their column names. For multiple periods,
+            axis must not be 1.
+        freq : DateOffset, tseries.offsets, timedelta, or str, optional
+            Offset to use from the tseries module or time rule (e.g. 'EOM').
+            If `freq` is specified then the index values are shifted but the
+            data is not realigned. That is, use `freq` if you would like to
+            extend the index when shifting and preserve the original data.
+            If `freq` is specified as "infer" then it will be inferred from
+            the freq or inferred_freq attributes of the index. If neither of
+            those attributes exist, a ValueError is thrown.
+        axis : {{0 or 'index', 1 or 'columns', None}}, default None
+            Shift direction. For `Series` this parameter is unused and defaults to 0.
+        fill_value : object, optional
+            The scalar value to use for newly introduced missing values.
+            the default depends on the dtype of `self`.
+            For numeric data, ``np.nan`` is used.
+            For datetime, timedelta, or period data, etc. :attr:`NaT` is used.
+            For extension dtypes, ``self.dtype.na_value`` is used.
+        suffix : str, optional
+            If str and periods is an iterable, this is added after the column
+            name and before the shift value for each shifted column name.
+
+        Returns
+        -------
+        {klass}
+            Copy of input object, shifted.
+
+        See Also
+        --------
+        Index.shift : Shift values of Index.
+        DatetimeIndex.shift : Shift values of DatetimeIndex.
+        PeriodIndex.shift : Shift values of PeriodIndex.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({{"Col1": [10, 20, 15, 30, 45],
+        ...                    "Col2": [13, 23, 18, 33, 48],
+        ...                    "Col3": [17, 27, 22, 37, 52]}},
+        ...                   index=pd.date_range("2020-01-01", "2020-01-05"))
+        >>> df
+                    Col1  Col2  Col3
+        2020-01-01    10    13    17
+        2020-01-02    20    23    27
+        2020-01-03    15    18    22
+        2020-01-04    30    33    37
+        2020-01-05    45    48    52
+
+        >>> df.shift(periods=3)
+                    Col1  Col2  Col3
+        2020-01-01   NaN   NaN   NaN
+        2020-01-02   NaN   NaN   NaN
+        2020-01-03   NaN   NaN   NaN
+        2020-01-04  10.0  13.0  17.0
+        2020-01-05  20.0  23.0  27.0
+
+        >>> df.shift(periods=1, axis="columns")
+                    Col1  Col2  Col3
+        2020-01-01   NaN    10    13
+        2020-01-02   NaN    20    23
+        2020-01-03   NaN    15    18
+        2020-01-04   NaN    30    33
+        2020-01-05   NaN    45    48
+
+        >>> df.shift(periods=3, fill_value=0)
+                    Col1  Col2  Col3
+        2020-01-01     0     0     0
+        2020-01-02     0     0     0
+        2020-01-03     0     0     0
+        2020-01-04    10    13    17
+        2020-01-05    20    23    27
+
+        >>> df.shift(periods=3, freq="D")
+                    Col1  Col2  Col3
+        2020-01-04    10    13    17
+        2020-01-05    20    23    27
+        2020-01-06    15    18    22
+        2020-01-07    30    33    37
+        2020-01-08    45    48    52
+
+        >>> df.shift(periods=3, freq="infer")
+                    Col1  Col2  Col3
+        2020-01-04    10    13    17
+        2020-01-05    20    23    27
+        2020-01-06    15    18    22
+        2020-01-07    30    33    37
+        2020-01-08    45    48    52
+
+        >>> df['Col1'].shift(periods=[0, 1, 2])
+                    Col1_0  Col1_1  Col1_2
+        2020-01-01      10     NaN     NaN
+        2020-01-02      20    10.0     NaN
+        2020-01-03      15    20.0    10.0
+        2020-01-04      30    15.0    20.0
+        2020-01-05      45    30.0    15.0
+        """
+        axis = self._get_axis_number(axis)
+
+        if freq is not None and fill_value is not lib.no_default:
+            # GH#53832
+            warnings.warn(
+                "Passing a 'freq' together with a 'fill_value' silently ignores "
+                "the fill_value and is deprecated. This will raise in a future "
+                "version.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+            fill_value = lib.no_default
+
+        if periods == 0:
+            return self.copy(deep=None)
+
+        if is_list_like(periods) and isinstance(self, ABCSeries):
+            return self.to_frame().shift(
+                periods=periods, freq=freq, axis=axis, fill_value=fill_value
+            )
+        periods = cast(int, periods)
+
+        if freq is None:
+            # when freq is None, data is shifted, index is not
+            axis = self._get_axis_number(axis)
+            assert axis == 0  # axis == 1 cases handled in DataFrame.shift
+            new_data = self._mgr.shift(periods=periods, fill_value=fill_value)
+            return self._constructor_from_mgr(
+                new_data, axes=new_data.axes
+            ).__finalize__(self, method="shift")
+
+        return self._shift_with_freq(periods, axis, freq)
+
+    @final
+    def _shift_with_freq(self, periods: int, axis: int, freq) -> Self:
+        # see shift.__doc__
+        # when freq is given, index is shifted, data is not
+        index = self._get_axis(axis)
+
+        if freq == "infer":
+            freq = getattr(index, "freq", None)
+
+            if freq is None:
+                freq = getattr(index, "inferred_freq", None)
+
+            if freq is None:
+                msg = "Freq was not set in the index hence cannot be inferred"
+                raise ValueError(msg)
+
+        elif isinstance(freq, str):
+            is_period = isinstance(index, PeriodIndex)
+            freq = to_offset(freq, is_period=is_period)
+
+        if isinstance(index, PeriodIndex):
+            orig_freq = to_offset(index.freq)
+            if freq != orig_freq:
+                assert orig_freq is not None  # for mypy
+                raise ValueError(
+                    f"Given freq {freq_to_period_freqstr(freq.n, freq.name)} "
+                    f"does not match PeriodIndex freq "
+                    f"{freq_to_period_freqstr(orig_freq.n, orig_freq.name)}"
+                )
+            new_ax = index.shift(periods)
+        else:
+            new_ax = index.shift(periods, freq)
+
+        result = self.set_axis(new_ax, axis=axis)
+        return result.__finalize__(self, method="shift")
+
+    @final
+    def truncate(
+        self,
+        before=None,
+        after=None,
+        axis: Axis | None = None,
+        copy: bool_t | None = None,
+    ) -> Self:
+        """
+        Truncate a Series or DataFrame before and after some index value.
+
+        This is a useful shorthand for boolean indexing based on index
+        values above or below certain thresholds.
+
+        Parameters
+        ----------
+        before : date, str, int
+            Truncate all rows before this index value.
+        after : date, str, int
+            Truncate all rows after this index value.
+        axis : {0 or 'index', 1 or 'columns'}, optional
+            Axis to truncate. Truncates the index (rows) by default.
+            For `Series` this parameter is unused and defaults to 0.
+        copy : bool, default is True,
+            Return a copy of the truncated section.
+
+            .. note::
+                The `copy` keyword will change behavior in pandas 3.0.
+                `Copy-on-Write
+                `__
+                will be enabled by default, which means that all methods with a
+                `copy` keyword will use a lazy copy mechanism to defer the copy and
+                ignore the `copy` keyword. The `copy` keyword will be removed in a
+                future version of pandas.
+
+                You can already get the future behavior and improvements through
+                enabling copy on write ``pd.options.mode.copy_on_write = True``
+
+        Returns
+        -------
+        type of caller
+            The truncated Series or DataFrame.
+
+        See Also
+        --------
+        DataFrame.loc : Select a subset of a DataFrame by label.
+        DataFrame.iloc : Select a subset of a DataFrame by position.
+
+        Notes
+        -----
+        If the index being truncated contains only datetime values,
+        `before` and `after` may be specified as strings instead of
+        Timestamps.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'A': ['a', 'b', 'c', 'd', 'e'],
+        ...                    'B': ['f', 'g', 'h', 'i', 'j'],
+        ...                    'C': ['k', 'l', 'm', 'n', 'o']},
+        ...                   index=[1, 2, 3, 4, 5])
+        >>> df
+           A  B  C
+        1  a  f  k
+        2  b  g  l
+        3  c  h  m
+        4  d  i  n
+        5  e  j  o
+
+        >>> df.truncate(before=2, after=4)
+           A  B  C
+        2  b  g  l
+        3  c  h  m
+        4  d  i  n
+
+        The columns of a DataFrame can be truncated.
+
+        >>> df.truncate(before="A", after="B", axis="columns")
+           A  B
+        1  a  f
+        2  b  g
+        3  c  h
+        4  d  i
+        5  e  j
+
+        For Series, only rows can be truncated.
+
+        >>> df['A'].truncate(before=2, after=4)
+        2    b
+        3    c
+        4    d
+        Name: A, dtype: object
+
+        The index values in ``truncate`` can be datetimes or string
+        dates.
+
+        >>> dates = pd.date_range('2016-01-01', '2016-02-01', freq='s')
+        >>> df = pd.DataFrame(index=dates, data={'A': 1})
+        >>> df.tail()
+                             A
+        2016-01-31 23:59:56  1
+        2016-01-31 23:59:57  1
+        2016-01-31 23:59:58  1
+        2016-01-31 23:59:59  1
+        2016-02-01 00:00:00  1
+
+        >>> df.truncate(before=pd.Timestamp('2016-01-05'),
+        ...             after=pd.Timestamp('2016-01-10')).tail()
+                             A
+        2016-01-09 23:59:56  1
+        2016-01-09 23:59:57  1
+        2016-01-09 23:59:58  1
+        2016-01-09 23:59:59  1
+        2016-01-10 00:00:00  1
+
+        Because the index is a DatetimeIndex containing only dates, we can
+        specify `before` and `after` as strings. They will be coerced to
+        Timestamps before truncation.
+
+        >>> df.truncate('2016-01-05', '2016-01-10').tail()
+                             A
+        2016-01-09 23:59:56  1
+        2016-01-09 23:59:57  1
+        2016-01-09 23:59:58  1
+        2016-01-09 23:59:59  1
+        2016-01-10 00:00:00  1
+
+        Note that ``truncate`` assumes a 0 value for any unspecified time
+        component (midnight). This differs from partial string slicing, which
+        returns any partially matching dates.
+
+        >>> df.loc['2016-01-05':'2016-01-10', :].tail()
+                             A
+        2016-01-10 23:59:55  1
+        2016-01-10 23:59:56  1
+        2016-01-10 23:59:57  1
+        2016-01-10 23:59:58  1
+        2016-01-10 23:59:59  1
+        """
+        if axis is None:
+            axis = 0
+        axis = self._get_axis_number(axis)
+        ax = self._get_axis(axis)
+
+        # GH 17935
+        # Check that index is sorted
+        if not ax.is_monotonic_increasing and not ax.is_monotonic_decreasing:
+            raise ValueError("truncate requires a sorted index")
+
+        # if we have a date index, convert to dates, otherwise
+        # treat like a slice
+        if ax._is_all_dates:
+            from pandas.core.tools.datetimes import to_datetime
+
+            before = to_datetime(before)
+            after = to_datetime(after)
+
+        if before is not None and after is not None and before > after:
+            raise ValueError(f"Truncate: {after} must be after {before}")
+
+        if len(ax) > 1 and ax.is_monotonic_decreasing and ax.nunique() > 1:
+            before, after = after, before
+
+        slicer = [slice(None, None)] * self._AXIS_LEN
+        slicer[axis] = slice(before, after)
+        result = self.loc[tuple(slicer)]
+
+        if isinstance(ax, MultiIndex):
+            setattr(result, self._get_axis_name(axis), ax.truncate(before, after))
+
+        result = result.copy(deep=copy and not using_copy_on_write())
+
+        return result
+
+    @final
+    @doc(klass=_shared_doc_kwargs["klass"])
+    def tz_convert(
+        self, tz, axis: Axis = 0, level=None, copy: bool_t | None = None
+    ) -> Self:
+        """
+        Convert tz-aware axis to target time zone.
+
+        Parameters
+        ----------
+        tz : str or tzinfo object or None
+            Target time zone. Passing ``None`` will convert to
+            UTC and remove the timezone information.
+        axis : {{0 or 'index', 1 or 'columns'}}, default 0
+            The axis to convert
+        level : int, str, default None
+            If axis is a MultiIndex, convert a specific level. Otherwise
+            must be None.
+        copy : bool, default True
+            Also make a copy of the underlying data.
+
+            .. note::
+                The `copy` keyword will change behavior in pandas 3.0.
+                `Copy-on-Write
+                `__
+                will be enabled by default, which means that all methods with a
+                `copy` keyword will use a lazy copy mechanism to defer the copy and
+                ignore the `copy` keyword. The `copy` keyword will be removed in a
+                future version of pandas.
+
+                You can already get the future behavior and improvements through
+                enabling copy on write ``pd.options.mode.copy_on_write = True``
+
+        Returns
+        -------
+        {klass}
+            Object with time zone converted axis.
+
+        Raises
+        ------
+        TypeError
+            If the axis is tz-naive.
+
+        Examples
+        --------
+        Change to another time zone:
+
+        >>> s = pd.Series(
+        ...     [1],
+        ...     index=pd.DatetimeIndex(['2018-09-15 01:30:00+02:00']),
+        ... )
+        >>> s.tz_convert('Asia/Shanghai')
+        2018-09-15 07:30:00+08:00    1
+        dtype: int64
+
+        Pass None to convert to UTC and get a tz-naive index:
+
+        >>> s = pd.Series([1],
+        ...               index=pd.DatetimeIndex(['2018-09-15 01:30:00+02:00']))
+        >>> s.tz_convert(None)
+        2018-09-14 23:30:00    1
+        dtype: int64
+        """
+        axis = self._get_axis_number(axis)
+        ax = self._get_axis(axis)
+
+        def _tz_convert(ax, tz):
+            if not hasattr(ax, "tz_convert"):
+                if len(ax) > 0:
+                    ax_name = self._get_axis_name(axis)
+                    raise TypeError(
+                        f"{ax_name} is not a valid DatetimeIndex or PeriodIndex"
+                    )
+                ax = DatetimeIndex([], tz=tz)
+            else:
+                ax = ax.tz_convert(tz)
+            return ax
+
+        # if a level is given it must be a MultiIndex level or
+        # equivalent to the axis name
+        if isinstance(ax, MultiIndex):
+            level = ax._get_level_number(level)
+            new_level = _tz_convert(ax.levels[level], tz)
+            ax = ax.set_levels(new_level, level=level)
+        else:
+            if level not in (None, 0, ax.name):
+                raise ValueError(f"The level {level} is not valid")
+            ax = _tz_convert(ax, tz)
+
+        result = self.copy(deep=copy and not using_copy_on_write())
+        result = result.set_axis(ax, axis=axis, copy=False)
+        return result.__finalize__(self, method="tz_convert")
+
+    @final
+    @doc(klass=_shared_doc_kwargs["klass"])
+    def tz_localize(
+        self,
+        tz,
+        axis: Axis = 0,
+        level=None,
+        copy: bool_t | None = None,
+        ambiguous: TimeAmbiguous = "raise",
+        nonexistent: TimeNonexistent = "raise",
+    ) -> Self:
+        """
+        Localize tz-naive index of a Series or DataFrame to target time zone.
+
+        This operation localizes the Index. To localize the values in a
+        timezone-naive Series, use :meth:`Series.dt.tz_localize`.
+
+        Parameters
+        ----------
+        tz : str or tzinfo or None
+            Time zone to localize. Passing ``None`` will remove the
+            time zone information and preserve local time.
+        axis : {{0 or 'index', 1 or 'columns'}}, default 0
+            The axis to localize
+        level : int, str, default None
+            If axis ia a MultiIndex, localize a specific level. Otherwise
+            must be None.
+        copy : bool, default True
+            Also make a copy of the underlying data.
+
+            .. note::
+                The `copy` keyword will change behavior in pandas 3.0.
+                `Copy-on-Write
+                `__
+                will be enabled by default, which means that all methods with a
+                `copy` keyword will use a lazy copy mechanism to defer the copy and
+                ignore the `copy` keyword. The `copy` keyword will be removed in a
+                future version of pandas.
+
+                You can already get the future behavior and improvements through
+                enabling copy on write ``pd.options.mode.copy_on_write = True``
+        ambiguous : 'infer', bool-ndarray, 'NaT', default 'raise'
+            When clocks moved backward due to DST, ambiguous times may arise.
+            For example in Central European Time (UTC+01), when going from
+            03:00 DST to 02:00 non-DST, 02:30:00 local time occurs both at
+            00:30:00 UTC and at 01:30:00 UTC. In such a situation, the
+            `ambiguous` parameter dictates how ambiguous times should be
+            handled.
+
+            - 'infer' will attempt to infer fall dst-transition hours based on
+              order
+            - bool-ndarray where True signifies a DST time, False designates
+              a non-DST time (note that this flag is only applicable for
+              ambiguous times)
+            - 'NaT' will return NaT where there are ambiguous times
+            - 'raise' will raise an AmbiguousTimeError if there are ambiguous
+              times.
+        nonexistent : str, default 'raise'
+            A nonexistent time does not exist in a particular timezone
+            where clocks moved forward due to DST. Valid values are:
+
+            - 'shift_forward' will shift the nonexistent time forward to the
+              closest existing time
+            - 'shift_backward' will shift the nonexistent time backward to the
+              closest existing time
+            - 'NaT' will return NaT where there are nonexistent times
+            - timedelta objects will shift nonexistent times by the timedelta
+            - 'raise' will raise an NonExistentTimeError if there are
+              nonexistent times.
+
+        Returns
+        -------
+        {klass}
+            Same type as the input.
+
+        Raises
+        ------
+        TypeError
+            If the TimeSeries is tz-aware and tz is not None.
+
+        Examples
+        --------
+        Localize local times:
+
+        >>> s = pd.Series(
+        ...     [1],
+        ...     index=pd.DatetimeIndex(['2018-09-15 01:30:00']),
+        ... )
+        >>> s.tz_localize('CET')
+        2018-09-15 01:30:00+02:00    1
+        dtype: int64
+
+        Pass None to convert to tz-naive index and preserve local time:
+
+        >>> s = pd.Series([1],
+        ...               index=pd.DatetimeIndex(['2018-09-15 01:30:00+02:00']))
+        >>> s.tz_localize(None)
+        2018-09-15 01:30:00    1
+        dtype: int64
+
+        Be careful with DST changes. When there is sequential data, pandas
+        can infer the DST time:
+
+        >>> s = pd.Series(range(7),
+        ...               index=pd.DatetimeIndex(['2018-10-28 01:30:00',
+        ...                                       '2018-10-28 02:00:00',
+        ...                                       '2018-10-28 02:30:00',
+        ...                                       '2018-10-28 02:00:00',
+        ...                                       '2018-10-28 02:30:00',
+        ...                                       '2018-10-28 03:00:00',
+        ...                                       '2018-10-28 03:30:00']))
+        >>> s.tz_localize('CET', ambiguous='infer')
+        2018-10-28 01:30:00+02:00    0
+        2018-10-28 02:00:00+02:00    1
+        2018-10-28 02:30:00+02:00    2
+        2018-10-28 02:00:00+01:00    3
+        2018-10-28 02:30:00+01:00    4
+        2018-10-28 03:00:00+01:00    5
+        2018-10-28 03:30:00+01:00    6
+        dtype: int64
+
+        In some cases, inferring the DST is impossible. In such cases, you can
+        pass an ndarray to the ambiguous parameter to set the DST explicitly
+
+        >>> s = pd.Series(range(3),
+        ...               index=pd.DatetimeIndex(['2018-10-28 01:20:00',
+        ...                                       '2018-10-28 02:36:00',
+        ...                                       '2018-10-28 03:46:00']))
+        >>> s.tz_localize('CET', ambiguous=np.array([True, True, False]))
+        2018-10-28 01:20:00+02:00    0
+        2018-10-28 02:36:00+02:00    1
+        2018-10-28 03:46:00+01:00    2
+        dtype: int64
+
+        If the DST transition causes nonexistent times, you can shift these
+        dates forward or backward with a timedelta object or `'shift_forward'`
+        or `'shift_backward'`.
+
+        >>> s = pd.Series(range(2),
+        ...               index=pd.DatetimeIndex(['2015-03-29 02:30:00',
+        ...                                       '2015-03-29 03:30:00']))
+        >>> s.tz_localize('Europe/Warsaw', nonexistent='shift_forward')
+        2015-03-29 03:00:00+02:00    0
+        2015-03-29 03:30:00+02:00    1
+        dtype: int64
+        >>> s.tz_localize('Europe/Warsaw', nonexistent='shift_backward')
+        2015-03-29 01:59:59.999999999+01:00    0
+        2015-03-29 03:30:00+02:00              1
+        dtype: int64
+        >>> s.tz_localize('Europe/Warsaw', nonexistent=pd.Timedelta('1h'))
+        2015-03-29 03:30:00+02:00    0
+        2015-03-29 03:30:00+02:00    1
+        dtype: int64
+        """
+        nonexistent_options = ("raise", "NaT", "shift_forward", "shift_backward")
+        if nonexistent not in nonexistent_options and not isinstance(
+            nonexistent, dt.timedelta
+        ):
+            raise ValueError(
+                "The nonexistent argument must be one of 'raise', "
+                "'NaT', 'shift_forward', 'shift_backward' or "
+                "a timedelta object"
+            )
+
+        axis = self._get_axis_number(axis)
+        ax = self._get_axis(axis)
+
+        def _tz_localize(ax, tz, ambiguous, nonexistent):
+            if not hasattr(ax, "tz_localize"):
+                if len(ax) > 0:
+                    ax_name = self._get_axis_name(axis)
+                    raise TypeError(
+                        f"{ax_name} is not a valid DatetimeIndex or PeriodIndex"
+                    )
+                ax = DatetimeIndex([], tz=tz)
+            else:
+                ax = ax.tz_localize(tz, ambiguous=ambiguous, nonexistent=nonexistent)
+            return ax
+
+        # if a level is given it must be a MultiIndex level or
+        # equivalent to the axis name
+        if isinstance(ax, MultiIndex):
+            level = ax._get_level_number(level)
+            new_level = _tz_localize(ax.levels[level], tz, ambiguous, nonexistent)
+            ax = ax.set_levels(new_level, level=level)
+        else:
+            if level not in (None, 0, ax.name):
+                raise ValueError(f"The level {level} is not valid")
+            ax = _tz_localize(ax, tz, ambiguous, nonexistent)
+
+        result = self.copy(deep=copy and not using_copy_on_write())
+        result = result.set_axis(ax, axis=axis, copy=False)
+        return result.__finalize__(self, method="tz_localize")
+
+    # ----------------------------------------------------------------------
+    # Numeric Methods
+
+    @final
+    def describe(
+        self,
+        percentiles=None,
+        include=None,
+        exclude=None,
+    ) -> Self:
+        """
+        Generate descriptive statistics.
+
+        Descriptive statistics include those that summarize the central
+        tendency, dispersion and shape of a
+        dataset's distribution, excluding ``NaN`` values.
+
+        Analyzes both numeric and object series, as well
+        as ``DataFrame`` column sets of mixed data types. The output
+        will vary depending on what is provided. Refer to the notes
+        below for more detail.
+
+        Parameters
+        ----------
+        percentiles : list-like of numbers, optional
+            The percentiles to include in the output. All should
+            fall between 0 and 1. The default is
+            ``[.25, .5, .75]``, which returns the 25th, 50th, and
+            75th percentiles.
+        include : 'all', list-like of dtypes or None (default), optional
+            A white list of data types to include in the result. Ignored
+            for ``Series``. Here are the options:
+
+            - 'all' : All columns of the input will be included in the output.
+            - A list-like of dtypes : Limits the results to the
+              provided data types.
+              To limit the result to numeric types submit
+              ``numpy.number``. To limit it instead to object columns submit
+              the ``numpy.object`` data type. Strings
+              can also be used in the style of
+              ``select_dtypes`` (e.g. ``df.describe(include=['O'])``). To
+              select pandas categorical columns, use ``'category'``
+            - None (default) : The result will include all numeric columns.
+        exclude : list-like of dtypes or None (default), optional,
+            A black list of data types to omit from the result. Ignored
+            for ``Series``. Here are the options:
+
+            - A list-like of dtypes : Excludes the provided data types
+              from the result. To exclude numeric types submit
+              ``numpy.number``. To exclude object columns submit the data
+              type ``numpy.object``. Strings can also be used in the style of
+              ``select_dtypes`` (e.g. ``df.describe(exclude=['O'])``). To
+              exclude pandas categorical columns, use ``'category'``
+            - None (default) : The result will exclude nothing.
+
+        Returns
+        -------
+        Series or DataFrame
+            Summary statistics of the Series or Dataframe provided.
+
+        See Also
+        --------
+        DataFrame.count: Count number of non-NA/null observations.
+        DataFrame.max: Maximum of the values in the object.
+        DataFrame.min: Minimum of the values in the object.
+        DataFrame.mean: Mean of the values.
+        DataFrame.std: Standard deviation of the observations.
+        DataFrame.select_dtypes: Subset of a DataFrame including/excluding
+            columns based on their dtype.
+
+        Notes
+        -----
+        For numeric data, the result's index will include ``count``,
+        ``mean``, ``std``, ``min``, ``max`` as well as lower, ``50`` and
+        upper percentiles. By default the lower percentile is ``25`` and the
+        upper percentile is ``75``. The ``50`` percentile is the
+        same as the median.
+
+        For object data (e.g. strings or timestamps), the result's index
+        will include ``count``, ``unique``, ``top``, and ``freq``. The ``top``
+        is the most common value. The ``freq`` is the most common value's
+        frequency. Timestamps also include the ``first`` and ``last`` items.
+
+        If multiple object values have the highest count, then the
+        ``count`` and ``top`` results will be arbitrarily chosen from
+        among those with the highest count.
+
+        For mixed data types provided via a ``DataFrame``, the default is to
+        return only an analysis of numeric columns. If the dataframe consists
+        only of object and categorical data without any numeric columns, the
+        default is to return an analysis of both the object and categorical
+        columns. If ``include='all'`` is provided as an option, the result
+        will include a union of attributes of each type.
+
+        The `include` and `exclude` parameters can be used to limit
+        which columns in a ``DataFrame`` are analyzed for the output.
+        The parameters are ignored when analyzing a ``Series``.
+
+        Examples
+        --------
+        Describing a numeric ``Series``.
+
+        >>> s = pd.Series([1, 2, 3])
+        >>> s.describe()
+        count    3.0
+        mean     2.0
+        std      1.0
+        min      1.0
+        25%      1.5
+        50%      2.0
+        75%      2.5
+        max      3.0
+        dtype: float64
+
+        Describing a categorical ``Series``.
+
+        >>> s = pd.Series(['a', 'a', 'b', 'c'])
+        >>> s.describe()
+        count     4
+        unique    3
+        top       a
+        freq      2
+        dtype: object
+
+        Describing a timestamp ``Series``.
+
+        >>> s = pd.Series([
+        ...     np.datetime64("2000-01-01"),
+        ...     np.datetime64("2010-01-01"),
+        ...     np.datetime64("2010-01-01")
+        ... ])
+        >>> s.describe()
+        count                      3
+        mean     2006-09-01 08:00:00
+        min      2000-01-01 00:00:00
+        25%      2004-12-31 12:00:00
+        50%      2010-01-01 00:00:00
+        75%      2010-01-01 00:00:00
+        max      2010-01-01 00:00:00
+        dtype: object
+
+        Describing a ``DataFrame``. By default only numeric fields
+        are returned.
+
+        >>> df = pd.DataFrame({'categorical': pd.Categorical(['d', 'e', 'f']),
+        ...                    'numeric': [1, 2, 3],
+        ...                    'object': ['a', 'b', 'c']
+        ...                    })
+        >>> df.describe()
+               numeric
+        count      3.0
+        mean       2.0
+        std        1.0
+        min        1.0
+        25%        1.5
+        50%        2.0
+        75%        2.5
+        max        3.0
+
+        Describing all columns of a ``DataFrame`` regardless of data type.
+
+        >>> df.describe(include='all')  # doctest: +SKIP
+               categorical  numeric object
+        count            3      3.0      3
+        unique           3      NaN      3
+        top              f      NaN      a
+        freq             1      NaN      1
+        mean           NaN      2.0    NaN
+        std            NaN      1.0    NaN
+        min            NaN      1.0    NaN
+        25%            NaN      1.5    NaN
+        50%            NaN      2.0    NaN
+        75%            NaN      2.5    NaN
+        max            NaN      3.0    NaN
+
+        Describing a column from a ``DataFrame`` by accessing it as
+        an attribute.
+
+        >>> df.numeric.describe()
+        count    3.0
+        mean     2.0
+        std      1.0
+        min      1.0
+        25%      1.5
+        50%      2.0
+        75%      2.5
+        max      3.0
+        Name: numeric, dtype: float64
+
+        Including only numeric columns in a ``DataFrame`` description.
+
+        >>> df.describe(include=[np.number])
+               numeric
+        count      3.0
+        mean       2.0
+        std        1.0
+        min        1.0
+        25%        1.5
+        50%        2.0
+        75%        2.5
+        max        3.0
+
+        Including only string columns in a ``DataFrame`` description.
+
+        >>> df.describe(include=[object])  # doctest: +SKIP
+               object
+        count       3
+        unique      3
+        top         a
+        freq        1
+
+        Including only categorical columns from a ``DataFrame`` description.
+
+        >>> df.describe(include=['category'])
+               categorical
+        count            3
+        unique           3
+        top              d
+        freq             1
+
+        Excluding numeric columns from a ``DataFrame`` description.
+
+        >>> df.describe(exclude=[np.number])  # doctest: +SKIP
+               categorical object
+        count            3      3
+        unique           3      3
+        top              f      a
+        freq             1      1
+
+        Excluding object columns from a ``DataFrame`` description.
+
+        >>> df.describe(exclude=[object])  # doctest: +SKIP
+               categorical  numeric
+        count            3      3.0
+        unique           3      NaN
+        top              f      NaN
+        freq             1      NaN
+        mean           NaN      2.0
+        std            NaN      1.0
+        min            NaN      1.0
+        25%            NaN      1.5
+        50%            NaN      2.0
+        75%            NaN      2.5
+        max            NaN      3.0
+        """
+        return describe_ndframe(
+            obj=self,
+            include=include,
+            exclude=exclude,
+            percentiles=percentiles,
+        ).__finalize__(self, method="describe")
+
+    @final
+    def pct_change(
+        self,
+        periods: int = 1,
+        fill_method: FillnaOptions | None | lib.NoDefault = lib.no_default,
+        limit: int | None | lib.NoDefault = lib.no_default,
+        freq=None,
+        **kwargs,
+    ) -> Self:
+        """
+        Fractional change between the current and a prior element.
+
+        Computes the fractional change from the immediately previous row by
+        default. This is useful in comparing the fraction of change in a time
+        series of elements.
+
+        .. note::
+
+            Despite the name of this method, it calculates fractional change
+            (also known as per unit change or relative change) and not
+            percentage change. If you need the percentage change, multiply
+            these values by 100.
+
+        Parameters
+        ----------
+        periods : int, default 1
+            Periods to shift for forming percent change.
+        fill_method : {'backfill', 'bfill', 'pad', 'ffill', None}, default 'pad'
+            How to handle NAs **before** computing percent changes.
+
+            .. deprecated:: 2.1
+                All options of `fill_method` are deprecated except `fill_method=None`.
+
+        limit : int, default None
+            The number of consecutive NAs to fill before stopping.
+
+            .. deprecated:: 2.1
+
+        freq : DateOffset, timedelta, or str, optional
+            Increment to use from time series API (e.g. 'ME' or BDay()).
+        **kwargs
+            Additional keyword arguments are passed into
+            `DataFrame.shift` or `Series.shift`.
+
+        Returns
+        -------
+        Series or DataFrame
+            The same type as the calling object.
+
+        See Also
+        --------
+        Series.diff : Compute the difference of two elements in a Series.
+        DataFrame.diff : Compute the difference of two elements in a DataFrame.
+        Series.shift : Shift the index by some number of periods.
+        DataFrame.shift : Shift the index by some number of periods.
+
+        Examples
+        --------
+        **Series**
+
+        >>> s = pd.Series([90, 91, 85])
+        >>> s
+        0    90
+        1    91
+        2    85
+        dtype: int64
+
+        >>> s.pct_change()
+        0         NaN
+        1    0.011111
+        2   -0.065934
+        dtype: float64
+
+        >>> s.pct_change(periods=2)
+        0         NaN
+        1         NaN
+        2   -0.055556
+        dtype: float64
+
+        See the percentage change in a Series where filling NAs with last
+        valid observation forward to next valid.
+
+        >>> s = pd.Series([90, 91, None, 85])
+        >>> s
+        0    90.0
+        1    91.0
+        2     NaN
+        3    85.0
+        dtype: float64
+
+        >>> s.ffill().pct_change()
+        0         NaN
+        1    0.011111
+        2    0.000000
+        3   -0.065934
+        dtype: float64
+
+        **DataFrame**
+
+        Percentage change in French franc, Deutsche Mark, and Italian lira from
+        1980-01-01 to 1980-03-01.
+
+        >>> df = pd.DataFrame({
+        ...     'FR': [4.0405, 4.0963, 4.3149],
+        ...     'GR': [1.7246, 1.7482, 1.8519],
+        ...     'IT': [804.74, 810.01, 860.13]},
+        ...     index=['1980-01-01', '1980-02-01', '1980-03-01'])
+        >>> df
+                        FR      GR      IT
+        1980-01-01  4.0405  1.7246  804.74
+        1980-02-01  4.0963  1.7482  810.01
+        1980-03-01  4.3149  1.8519  860.13
+
+        >>> df.pct_change()
+                          FR        GR        IT
+        1980-01-01       NaN       NaN       NaN
+        1980-02-01  0.013810  0.013684  0.006549
+        1980-03-01  0.053365  0.059318  0.061876
+
+        Percentage of change in GOOG and APPL stock volume. Shows computing
+        the percentage change between columns.
+
+        >>> df = pd.DataFrame({
+        ...     '2016': [1769950, 30586265],
+        ...     '2015': [1500923, 40912316],
+        ...     '2014': [1371819, 41403351]},
+        ...     index=['GOOG', 'APPL'])
+        >>> df
+                  2016      2015      2014
+        GOOG   1769950   1500923   1371819
+        APPL  30586265  40912316  41403351
+
+        >>> df.pct_change(axis='columns', periods=-1)
+                  2016      2015  2014
+        GOOG  0.179241  0.094112   NaN
+        APPL -0.252395 -0.011860   NaN
+        """
+        # GH#53491
+        if fill_method not in (lib.no_default, None) or limit is not lib.no_default:
+            warnings.warn(
+                "The 'fill_method' keyword being not None and the 'limit' keyword in "
+                f"{type(self).__name__}.pct_change are deprecated and will be removed "
+                "in a future version. Either fill in any non-leading NA values prior "
+                "to calling pct_change or specify 'fill_method=None' to not fill NA "
+                "values.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+        if fill_method is lib.no_default:
+            if limit is lib.no_default:
+                cols = self.items() if self.ndim == 2 else [(None, self)]
+                for _, col in cols:
+                    if len(col) > 0:
+                        mask = col.isna().values
+                        mask = mask[np.argmax(~mask) :]
+                        if mask.any():
+                            warnings.warn(
+                                "The default fill_method='pad' in "
+                                f"{type(self).__name__}.pct_change is deprecated and "
+                                "will be removed in a future version. Either fill in "
+                                "any non-leading NA values prior to calling pct_change "
+                                "or specify 'fill_method=None' to not fill NA values.",
+                                FutureWarning,
+                                stacklevel=find_stack_level(),
+                            )
+                            break
+            fill_method = "pad"
+        if limit is lib.no_default:
+            limit = None
+
+        axis = self._get_axis_number(kwargs.pop("axis", "index"))
+        if fill_method is None:
+            data = self
+        else:
+            data = self._pad_or_backfill(fill_method, axis=axis, limit=limit)
+
+        shifted = data.shift(periods=periods, freq=freq, axis=axis, **kwargs)
+        # Unsupported left operand type for / ("Self")
+        rs = data / shifted - 1  # type: ignore[operator]
+        if freq is not None:
+            # Shift method is implemented differently when freq is not None
+            # We want to restore the original index
+            rs = rs.loc[~rs.index.duplicated()]
+            rs = rs.reindex_like(data)
+        return rs.__finalize__(self, method="pct_change")
+
+    @final
+    def _logical_func(
+        self,
+        name: str,
+        func,
+        axis: Axis | None = 0,
+        bool_only: bool_t = False,
+        skipna: bool_t = True,
+        **kwargs,
+    ) -> Series | bool_t:
+        nv.validate_logical_func((), kwargs, fname=name)
+        validate_bool_kwarg(skipna, "skipna", none_allowed=False)
+
+        if self.ndim > 1 and axis is None:
+            # Reduce along one dimension then the other, to simplify DataFrame._reduce
+            res = self._logical_func(
+                name, func, axis=0, bool_only=bool_only, skipna=skipna, **kwargs
+            )
+            # error: Item "bool" of "Series | bool" has no attribute "_logical_func"
+            return res._logical_func(  # type: ignore[union-attr]
+                name, func, skipna=skipna, **kwargs
+            )
+        elif axis is None:
+            axis = 0
+
+        if (
+            self.ndim > 1
+            and axis == 1
+            and len(self._mgr.arrays) > 1
+            # TODO(EA2D): special-case not needed
+            and all(x.ndim == 2 for x in self._mgr.arrays)
+            and not kwargs
+        ):
+            # Fastpath avoiding potentially expensive transpose
+            obj = self
+            if bool_only:
+                obj = self._get_bool_data()
+            return obj._reduce_axis1(name, func, skipna=skipna)
+
+        return self._reduce(
+            func,
+            name=name,
+            axis=axis,
+            skipna=skipna,
+            numeric_only=bool_only,
+            filter_type="bool",
+        )
+
+    def any(
+        self,
+        axis: Axis | None = 0,
+        bool_only: bool_t = False,
+        skipna: bool_t = True,
+        **kwargs,
+    ) -> Series | bool_t:
+        return self._logical_func(
+            "any", nanops.nanany, axis, bool_only, skipna, **kwargs
+        )
+
+    def all(
+        self,
+        axis: Axis = 0,
+        bool_only: bool_t = False,
+        skipna: bool_t = True,
+        **kwargs,
+    ) -> Series | bool_t:
+        return self._logical_func(
+            "all", nanops.nanall, axis, bool_only, skipna, **kwargs
+        )
+
+    @final
+    def _accum_func(
+        self,
+        name: str,
+        func,
+        axis: Axis | None = None,
+        skipna: bool_t = True,
+        *args,
+        **kwargs,
+    ):
+        skipna = nv.validate_cum_func_with_skipna(skipna, args, kwargs, name)
+        if axis is None:
+            axis = 0
+        else:
+            axis = self._get_axis_number(axis)
+
+        if axis == 1:
+            return self.T._accum_func(
+                name, func, axis=0, skipna=skipna, *args, **kwargs  # noqa: B026
+            ).T
+
+        def block_accum_func(blk_values):
+            values = blk_values.T if hasattr(blk_values, "T") else blk_values
+
+            result: np.ndarray | ExtensionArray
+            if isinstance(values, ExtensionArray):
+                result = values._accumulate(name, skipna=skipna, **kwargs)
+            else:
+                result = nanops.na_accum_func(values, func, skipna=skipna)
+
+            result = result.T if hasattr(result, "T") else result
+            return result
+
+        result = self._mgr.apply(block_accum_func)
+
+        return self._constructor_from_mgr(result, axes=result.axes).__finalize__(
+            self, method=name
+        )
+
+    def cummax(self, axis: Axis | None = None, skipna: bool_t = True, *args, **kwargs):
+        return self._accum_func(
+            "cummax", np.maximum.accumulate, axis, skipna, *args, **kwargs
+        )
+
+    def cummin(self, axis: Axis | None = None, skipna: bool_t = True, *args, **kwargs):
+        return self._accum_func(
+            "cummin", np.minimum.accumulate, axis, skipna, *args, **kwargs
+        )
+
+    def cumsum(self, axis: Axis | None = None, skipna: bool_t = True, *args, **kwargs):
+        return self._accum_func("cumsum", np.cumsum, axis, skipna, *args, **kwargs)
+
+    def cumprod(self, axis: Axis | None = None, skipna: bool_t = True, *args, **kwargs):
+        return self._accum_func("cumprod", np.cumprod, axis, skipna, *args, **kwargs)
+
+    @final
+    def _stat_function_ddof(
+        self,
+        name: str,
+        func,
+        axis: Axis | None | lib.NoDefault = lib.no_default,
+        skipna: bool_t = True,
+        ddof: int = 1,
+        numeric_only: bool_t = False,
+        **kwargs,
+    ) -> Series | float:
+        nv.validate_stat_ddof_func((), kwargs, fname=name)
+        validate_bool_kwarg(skipna, "skipna", none_allowed=False)
+
+        if axis is None:
+            if self.ndim > 1:
+                warnings.warn(
+                    f"The behavior of {type(self).__name__}.{name} with axis=None "
+                    "is deprecated, in a future version this will reduce over both "
+                    "axes and return a scalar. To retain the old behavior, pass "
+                    "axis=0 (or do not pass axis)",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+            axis = 0
+        elif axis is lib.no_default:
+            axis = 0
+
+        return self._reduce(
+            func, name, axis=axis, numeric_only=numeric_only, skipna=skipna, ddof=ddof
+        )
+
+    def sem(
+        self,
+        axis: Axis | None = 0,
+        skipna: bool_t = True,
+        ddof: int = 1,
+        numeric_only: bool_t = False,
+        **kwargs,
+    ) -> Series | float:
+        return self._stat_function_ddof(
+            "sem", nanops.nansem, axis, skipna, ddof, numeric_only, **kwargs
+        )
+
+    def var(
+        self,
+        axis: Axis | None = 0,
+        skipna: bool_t = True,
+        ddof: int = 1,
+        numeric_only: bool_t = False,
+        **kwargs,
+    ) -> Series | float:
+        return self._stat_function_ddof(
+            "var", nanops.nanvar, axis, skipna, ddof, numeric_only, **kwargs
+        )
+
+    def std(
+        self,
+        axis: Axis | None = 0,
+        skipna: bool_t = True,
+        ddof: int = 1,
+        numeric_only: bool_t = False,
+        **kwargs,
+    ) -> Series | float:
+        return self._stat_function_ddof(
+            "std", nanops.nanstd, axis, skipna, ddof, numeric_only, **kwargs
+        )
+
+    @final
+    def _stat_function(
+        self,
+        name: str,
+        func,
+        axis: Axis | None = 0,
+        skipna: bool_t = True,
+        numeric_only: bool_t = False,
+        **kwargs,
+    ):
+        assert name in ["median", "mean", "min", "max", "kurt", "skew"], name
+        nv.validate_func(name, (), kwargs)
+
+        validate_bool_kwarg(skipna, "skipna", none_allowed=False)
+
+        return self._reduce(
+            func, name=name, axis=axis, skipna=skipna, numeric_only=numeric_only
+        )
+
+    def min(
+        self,
+        axis: Axis | None = 0,
+        skipna: bool_t = True,
+        numeric_only: bool_t = False,
+        **kwargs,
+    ):
+        return self._stat_function(
+            "min",
+            nanops.nanmin,
+            axis,
+            skipna,
+            numeric_only,
+            **kwargs,
+        )
+
+    def max(
+        self,
+        axis: Axis | None = 0,
+        skipna: bool_t = True,
+        numeric_only: bool_t = False,
+        **kwargs,
+    ):
+        return self._stat_function(
+            "max",
+            nanops.nanmax,
+            axis,
+            skipna,
+            numeric_only,
+            **kwargs,
+        )
+
+    def mean(
+        self,
+        axis: Axis | None = 0,
+        skipna: bool_t = True,
+        numeric_only: bool_t = False,
+        **kwargs,
+    ) -> Series | float:
+        return self._stat_function(
+            "mean", nanops.nanmean, axis, skipna, numeric_only, **kwargs
+        )
+
+    def median(
+        self,
+        axis: Axis | None = 0,
+        skipna: bool_t = True,
+        numeric_only: bool_t = False,
+        **kwargs,
+    ) -> Series | float:
+        return self._stat_function(
+            "median", nanops.nanmedian, axis, skipna, numeric_only, **kwargs
+        )
+
+    def skew(
+        self,
+        axis: Axis | None = 0,
+        skipna: bool_t = True,
+        numeric_only: bool_t = False,
+        **kwargs,
+    ) -> Series | float:
+        return self._stat_function(
+            "skew", nanops.nanskew, axis, skipna, numeric_only, **kwargs
+        )
+
+    def kurt(
+        self,
+        axis: Axis | None = 0,
+        skipna: bool_t = True,
+        numeric_only: bool_t = False,
+        **kwargs,
+    ) -> Series | float:
+        return self._stat_function(
+            "kurt", nanops.nankurt, axis, skipna, numeric_only, **kwargs
+        )
+
+    kurtosis = kurt
+
+    @final
+    def _min_count_stat_function(
+        self,
+        name: str,
+        func,
+        axis: Axis | None | lib.NoDefault = lib.no_default,
+        skipna: bool_t = True,
+        numeric_only: bool_t = False,
+        min_count: int = 0,
+        **kwargs,
+    ):
+        assert name in ["sum", "prod"], name
+        nv.validate_func(name, (), kwargs)
+
+        validate_bool_kwarg(skipna, "skipna", none_allowed=False)
+
+        if axis is None:
+            if self.ndim > 1:
+                warnings.warn(
+                    f"The behavior of {type(self).__name__}.{name} with axis=None "
+                    "is deprecated, in a future version this will reduce over both "
+                    "axes and return a scalar. To retain the old behavior, pass "
+                    "axis=0 (or do not pass axis)",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+            axis = 0
+        elif axis is lib.no_default:
+            axis = 0
+
+        return self._reduce(
+            func,
+            name=name,
+            axis=axis,
+            skipna=skipna,
+            numeric_only=numeric_only,
+            min_count=min_count,
+        )
+
+    def sum(
+        self,
+        axis: Axis | None = 0,
+        skipna: bool_t = True,
+        numeric_only: bool_t = False,
+        min_count: int = 0,
+        **kwargs,
+    ):
+        return self._min_count_stat_function(
+            "sum", nanops.nansum, axis, skipna, numeric_only, min_count, **kwargs
+        )
+
+    def prod(
+        self,
+        axis: Axis | None = 0,
+        skipna: bool_t = True,
+        numeric_only: bool_t = False,
+        min_count: int = 0,
+        **kwargs,
+    ):
+        return self._min_count_stat_function(
+            "prod",
+            nanops.nanprod,
+            axis,
+            skipna,
+            numeric_only,
+            min_count,
+            **kwargs,
+        )
+
+    product = prod
+
+    @final
+    @doc(Rolling)
+    def rolling(
+        self,
+        window: int | dt.timedelta | str | BaseOffset | BaseIndexer,
+        min_periods: int | None = None,
+        center: bool_t = False,
+        win_type: str | None = None,
+        on: str | None = None,
+        axis: Axis | lib.NoDefault = lib.no_default,
+        closed: IntervalClosedType | None = None,
+        step: int | None = None,
+        method: str = "single",
+    ) -> Window | Rolling:
+        if axis is not lib.no_default:
+            axis = self._get_axis_number(axis)
+            name = "rolling"
+            if axis == 1:
+                warnings.warn(
+                    f"Support for axis=1 in {type(self).__name__}.{name} is "
+                    "deprecated and will be removed in a future version. "
+                    f"Use obj.T.{name}(...) instead",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+            else:
+                warnings.warn(
+                    f"The 'axis' keyword in {type(self).__name__}.{name} is "
+                    "deprecated and will be removed in a future version. "
+                    "Call the method without the axis keyword instead.",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+        else:
+            axis = 0
+
+        if win_type is not None:
+            return Window(
+                self,
+                window=window,
+                min_periods=min_periods,
+                center=center,
+                win_type=win_type,
+                on=on,
+                axis=axis,
+                closed=closed,
+                step=step,
+                method=method,
+            )
+
+        return Rolling(
+            self,
+            window=window,
+            min_periods=min_periods,
+            center=center,
+            win_type=win_type,
+            on=on,
+            axis=axis,
+            closed=closed,
+            step=step,
+            method=method,
+        )
+
+    @final
+    @doc(Expanding)
+    def expanding(
+        self,
+        min_periods: int = 1,
+        axis: Axis | lib.NoDefault = lib.no_default,
+        method: Literal["single", "table"] = "single",
+    ) -> Expanding:
+        if axis is not lib.no_default:
+            axis = self._get_axis_number(axis)
+            name = "expanding"
+            if axis == 1:
+                warnings.warn(
+                    f"Support for axis=1 in {type(self).__name__}.{name} is "
+                    "deprecated and will be removed in a future version. "
+                    f"Use obj.T.{name}(...) instead",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+            else:
+                warnings.warn(
+                    f"The 'axis' keyword in {type(self).__name__}.{name} is "
+                    "deprecated and will be removed in a future version. "
+                    "Call the method without the axis keyword instead.",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+        else:
+            axis = 0
+        return Expanding(self, min_periods=min_periods, axis=axis, method=method)
+
+    @final
+    @doc(ExponentialMovingWindow)
+    def ewm(
+        self,
+        com: float | None = None,
+        span: float | None = None,
+        halflife: float | TimedeltaConvertibleTypes | None = None,
+        alpha: float | None = None,
+        min_periods: int | None = 0,
+        adjust: bool_t = True,
+        ignore_na: bool_t = False,
+        axis: Axis | lib.NoDefault = lib.no_default,
+        times: np.ndarray | DataFrame | Series | None = None,
+        method: Literal["single", "table"] = "single",
+    ) -> ExponentialMovingWindow:
+        if axis is not lib.no_default:
+            axis = self._get_axis_number(axis)
+            name = "ewm"
+            if axis == 1:
+                warnings.warn(
+                    f"Support for axis=1 in {type(self).__name__}.{name} is "
+                    "deprecated and will be removed in a future version. "
+                    f"Use obj.T.{name}(...) instead",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+            else:
+                warnings.warn(
+                    f"The 'axis' keyword in {type(self).__name__}.{name} is "
+                    "deprecated and will be removed in a future version. "
+                    "Call the method without the axis keyword instead.",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+        else:
+            axis = 0
+
+        return ExponentialMovingWindow(
+            self,
+            com=com,
+            span=span,
+            halflife=halflife,
+            alpha=alpha,
+            min_periods=min_periods,
+            adjust=adjust,
+            ignore_na=ignore_na,
+            axis=axis,
+            times=times,
+            method=method,
+        )
+
+    # ----------------------------------------------------------------------
+    # Arithmetic Methods
+
+    @final
+    def _inplace_method(self, other, op) -> Self:
+        """
+        Wrap arithmetic method to operate inplace.
+        """
+        warn = True
+        if not PYPY and warn_copy_on_write():
+            if sys.getrefcount(self) <= REF_COUNT + 2:
+                # we are probably in an inplace setitem context (e.g. df['a'] += 1)
+                warn = False
+
+        result = op(self, other)
+
+        if (
+            self.ndim == 1
+            and result._indexed_same(self)
+            and result.dtype == self.dtype
+            and not using_copy_on_write()
+            and not (warn_copy_on_write() and not warn)
+        ):
+            # GH#36498 this inplace op can _actually_ be inplace.
+            # Item "ArrayManager" of "Union[ArrayManager, SingleArrayManager,
+            # BlockManager, SingleBlockManager]" has no attribute "setitem_inplace"
+            self._mgr.setitem_inplace(  # type: ignore[union-attr]
+                slice(None), result._values, warn=warn
+            )
+            return self
+
+        # Delete cacher
+        self._reset_cacher()
+
+        # this makes sure that we are aligned like the input
+        # we are updating inplace so we want to ignore is_copy
+        self._update_inplace(
+            result.reindex_like(self, copy=False), verify_is_copy=False
+        )
+        return self
+
+    @final
+    def __iadd__(self, other) -> Self:
+        # error: Unsupported left operand type for + ("Type[NDFrame]")
+        return self._inplace_method(other, type(self).__add__)  # type: ignore[operator]
+
+    @final
+    def __isub__(self, other) -> Self:
+        # error: Unsupported left operand type for - ("Type[NDFrame]")
+        return self._inplace_method(other, type(self).__sub__)  # type: ignore[operator]
+
+    @final
+    def __imul__(self, other) -> Self:
+        # error: Unsupported left operand type for * ("Type[NDFrame]")
+        return self._inplace_method(other, type(self).__mul__)  # type: ignore[operator]
+
+    @final
+    def __itruediv__(self, other) -> Self:
+        # error: Unsupported left operand type for / ("Type[NDFrame]")
+        return self._inplace_method(
+            other, type(self).__truediv__  # type: ignore[operator]
+        )
+
+    @final
+    def __ifloordiv__(self, other) -> Self:
+        # error: Unsupported left operand type for // ("Type[NDFrame]")
+        return self._inplace_method(
+            other, type(self).__floordiv__  # type: ignore[operator]
+        )
+
+    @final
+    def __imod__(self, other) -> Self:
+        # error: Unsupported left operand type for % ("Type[NDFrame]")
+        return self._inplace_method(other, type(self).__mod__)  # type: ignore[operator]
+
+    @final
+    def __ipow__(self, other) -> Self:
+        # error: Unsupported left operand type for ** ("Type[NDFrame]")
+        return self._inplace_method(other, type(self).__pow__)  # type: ignore[operator]
+
+    @final
+    def __iand__(self, other) -> Self:
+        # error: Unsupported left operand type for & ("Type[NDFrame]")
+        return self._inplace_method(other, type(self).__and__)  # type: ignore[operator]
+
+    @final
+    def __ior__(self, other) -> Self:
+        return self._inplace_method(other, type(self).__or__)
+
+    @final
+    def __ixor__(self, other) -> Self:
+        # error: Unsupported left operand type for ^ ("Type[NDFrame]")
+        return self._inplace_method(other, type(self).__xor__)  # type: ignore[operator]
+
+    # ----------------------------------------------------------------------
+    # Misc methods
+
+    @final
+    def _find_valid_index(self, *, how: str) -> Hashable | None:
+        """
+        Retrieves the index of the first valid value.
+
+        Parameters
+        ----------
+        how : {'first', 'last'}
+            Use this parameter to change between the first or last valid index.
+
+        Returns
+        -------
+        idx_first_valid : type of index
+        """
+        is_valid = self.notna().values
+        idxpos = find_valid_index(how=how, is_valid=is_valid)
+        if idxpos is None:
+            return None
+        return self.index[idxpos]
+
+    @final
+    @doc(position="first", klass=_shared_doc_kwargs["klass"])
+    def first_valid_index(self) -> Hashable | None:
+        """
+        Return index for {position} non-NA value or None, if no non-NA value is found.
+
+        Returns
+        -------
+        type of index
+
+        Examples
+        --------
+        For Series:
+
+        >>> s = pd.Series([None, 3, 4])
+        >>> s.first_valid_index()
+        1
+        >>> s.last_valid_index()
+        2
+
+        >>> s = pd.Series([None, None])
+        >>> print(s.first_valid_index())
+        None
+        >>> print(s.last_valid_index())
+        None
+
+        If all elements in Series are NA/null, returns None.
+
+        >>> s = pd.Series()
+        >>> print(s.first_valid_index())
+        None
+        >>> print(s.last_valid_index())
+        None
+
+        If Series is empty, returns None.
+
+        For DataFrame:
+
+        >>> df = pd.DataFrame({{'A': [None, None, 2], 'B': [None, 3, 4]}})
+        >>> df
+             A      B
+        0  NaN    NaN
+        1  NaN    3.0
+        2  2.0    4.0
+        >>> df.first_valid_index()
+        1
+        >>> df.last_valid_index()
+        2
+
+        >>> df = pd.DataFrame({{'A': [None, None, None], 'B': [None, None, None]}})
+        >>> df
+             A      B
+        0  None   None
+        1  None   None
+        2  None   None
+        >>> print(df.first_valid_index())
+        None
+        >>> print(df.last_valid_index())
+        None
+
+        If all elements in DataFrame are NA/null, returns None.
+
+        >>> df = pd.DataFrame()
+        >>> df
+        Empty DataFrame
+        Columns: []
+        Index: []
+        >>> print(df.first_valid_index())
+        None
+        >>> print(df.last_valid_index())
+        None
+
+        If DataFrame is empty, returns None.
+        """
+        return self._find_valid_index(how="first")
+
+    @final
+    @doc(first_valid_index, position="last", klass=_shared_doc_kwargs["klass"])
+    def last_valid_index(self) -> Hashable | None:
+        return self._find_valid_index(how="last")
+
+
+_num_doc = """
+{desc}
+
+Parameters
+----------
+axis : {axis_descr}
+    Axis for the function to be applied on.
+    For `Series` this parameter is unused and defaults to 0.
+
+    For DataFrames, specifying ``axis=None`` will apply the aggregation
+    across both axes.
+
+    .. versionadded:: 2.0.0
+
+skipna : bool, default True
+    Exclude NA/null values when computing the result.
+numeric_only : bool, default False
+    Include only float, int, boolean columns. Not implemented for Series.
+
+{min_count}\
+**kwargs
+    Additional keyword arguments to be passed to the function.
+
+Returns
+-------
+{name1} or scalar\
+{see_also}\
+{examples}
+"""
+
+_sum_prod_doc = """
+{desc}
+
+Parameters
+----------
+axis : {axis_descr}
+    Axis for the function to be applied on.
+    For `Series` this parameter is unused and defaults to 0.
+
+    .. warning::
+
+        The behavior of DataFrame.{name} with ``axis=None`` is deprecated,
+        in a future version this will reduce over both axes and return a scalar
+        To retain the old behavior, pass axis=0 (or do not pass axis).
+
+    .. versionadded:: 2.0.0
+
+skipna : bool, default True
+    Exclude NA/null values when computing the result.
+numeric_only : bool, default False
+    Include only float, int, boolean columns. Not implemented for Series.
+
+{min_count}\
+**kwargs
+    Additional keyword arguments to be passed to the function.
+
+Returns
+-------
+{name1} or scalar\
+{see_also}\
+{examples}
+"""
+
+_num_ddof_doc = """
+{desc}
+
+Parameters
+----------
+axis : {axis_descr}
+    For `Series` this parameter is unused and defaults to 0.
+
+    .. warning::
+
+        The behavior of DataFrame.{name} with ``axis=None`` is deprecated,
+        in a future version this will reduce over both axes and return a scalar
+        To retain the old behavior, pass axis=0 (or do not pass axis).
+
+skipna : bool, default True
+    Exclude NA/null values. If an entire row/column is NA, the result
+    will be NA.
+ddof : int, default 1
+    Delta Degrees of Freedom. The divisor used in calculations is N - ddof,
+    where N represents the number of elements.
+numeric_only : bool, default False
+    Include only float, int, boolean columns. Not implemented for Series.
+
+Returns
+-------
+{name1} or {name2} (if level specified) \
+{notes}\
+{examples}
+"""
+
+_std_notes = """
+
+Notes
+-----
+To have the same behaviour as `numpy.std`, use `ddof=0` (instead of the
+default `ddof=1`)"""
+
+_std_examples = """
+
+Examples
+--------
+>>> df = pd.DataFrame({'person_id': [0, 1, 2, 3],
+...                    'age': [21, 25, 62, 43],
+...                    'height': [1.61, 1.87, 1.49, 2.01]}
+...                   ).set_index('person_id')
+>>> df
+           age  height
+person_id
+0           21    1.61
+1           25    1.87
+2           62    1.49
+3           43    2.01
+
+The standard deviation of the columns can be found as follows:
+
+>>> df.std()
+age       18.786076
+height     0.237417
+dtype: float64
+
+Alternatively, `ddof=0` can be set to normalize by N instead of N-1:
+
+>>> df.std(ddof=0)
+age       16.269219
+height     0.205609
+dtype: float64"""
+
+_var_examples = """
+
+Examples
+--------
+>>> df = pd.DataFrame({'person_id': [0, 1, 2, 3],
+...                    'age': [21, 25, 62, 43],
+...                    'height': [1.61, 1.87, 1.49, 2.01]}
+...                   ).set_index('person_id')
+>>> df
+           age  height
+person_id
+0           21    1.61
+1           25    1.87
+2           62    1.49
+3           43    2.01
+
+>>> df.var()
+age       352.916667
+height      0.056367
+dtype: float64
+
+Alternatively, ``ddof=0`` can be set to normalize by N instead of N-1:
+
+>>> df.var(ddof=0)
+age       264.687500
+height      0.042275
+dtype: float64"""
+
+_bool_doc = """
+{desc}
+
+Parameters
+----------
+axis : {{0 or 'index', 1 or 'columns', None}}, default 0
+    Indicate which axis or axes should be reduced. For `Series` this parameter
+    is unused and defaults to 0.
+
+    * 0 / 'index' : reduce the index, return a Series whose index is the
+      original column labels.
+    * 1 / 'columns' : reduce the columns, return a Series whose index is the
+      original index.
+    * None : reduce all axes, return a scalar.
+
+bool_only : bool, default False
+    Include only boolean columns. Not implemented for Series.
+skipna : bool, default True
+    Exclude NA/null values. If the entire row/column is NA and skipna is
+    True, then the result will be {empty_value}, as for an empty row/column.
+    If skipna is False, then NA are treated as True, because these are not
+    equal to zero.
+**kwargs : any, default None
+    Additional keywords have no effect but might be accepted for
+    compatibility with NumPy.
+
+Returns
+-------
+{name1} or {name2}
+    If level is specified, then, {name2} is returned; otherwise, {name1}
+    is returned.
+
+{see_also}
+{examples}"""
+
+_all_desc = """\
+Return whether all elements are True, potentially over an axis.
+
+Returns True unless there at least one element within a series or
+along a Dataframe axis that is False or equivalent (e.g. zero or
+empty)."""
+
+_all_examples = """\
+Examples
+--------
+**Series**
+
+>>> pd.Series([True, True]).all()
+True
+>>> pd.Series([True, False]).all()
+False
+>>> pd.Series([], dtype="float64").all()
+True
+>>> pd.Series([np.nan]).all()
+True
+>>> pd.Series([np.nan]).all(skipna=False)
+True
+
+**DataFrames**
+
+Create a dataframe from a dictionary.
+
+>>> df = pd.DataFrame({'col1': [True, True], 'col2': [True, False]})
+>>> df
+   col1   col2
+0  True   True
+1  True  False
+
+Default behaviour checks if values in each column all return True.
+
+>>> df.all()
+col1     True
+col2    False
+dtype: bool
+
+Specify ``axis='columns'`` to check if values in each row all return True.
+
+>>> df.all(axis='columns')
+0     True
+1    False
+dtype: bool
+
+Or ``axis=None`` for whether every value is True.
+
+>>> df.all(axis=None)
+False
+"""
+
+_all_see_also = """\
+See Also
+--------
+Series.all : Return True if all elements are True.
+DataFrame.any : Return True if one (or more) elements are True.
+"""
+
+_cnum_doc = """
+Return cumulative {desc} over a DataFrame or Series axis.
+
+Returns a DataFrame or Series of the same size containing the cumulative
+{desc}.
+
+Parameters
+----------
+axis : {{0 or 'index', 1 or 'columns'}}, default 0
+    The index or the name of the axis. 0 is equivalent to None or 'index'.
+    For `Series` this parameter is unused and defaults to 0.
+skipna : bool, default True
+    Exclude NA/null values. If an entire row/column is NA, the result
+    will be NA.
+*args, **kwargs
+    Additional keywords have no effect but might be accepted for
+    compatibility with NumPy.
+
+Returns
+-------
+{name1} or {name2}
+    Return cumulative {desc} of {name1} or {name2}.
+
+See Also
+--------
+core.window.expanding.Expanding.{accum_func_name} : Similar functionality
+    but ignores ``NaN`` values.
+{name2}.{accum_func_name} : Return the {desc} over
+    {name2} axis.
+{name2}.cummax : Return cumulative maximum over {name2} axis.
+{name2}.cummin : Return cumulative minimum over {name2} axis.
+{name2}.cumsum : Return cumulative sum over {name2} axis.
+{name2}.cumprod : Return cumulative product over {name2} axis.
+
+{examples}"""
+
+_cummin_examples = """\
+Examples
+--------
+**Series**
+
+>>> s = pd.Series([2, np.nan, 5, -1, 0])
+>>> s
+0    2.0
+1    NaN
+2    5.0
+3   -1.0
+4    0.0
+dtype: float64
+
+By default, NA values are ignored.
+
+>>> s.cummin()
+0    2.0
+1    NaN
+2    2.0
+3   -1.0
+4   -1.0
+dtype: float64
+
+To include NA values in the operation, use ``skipna=False``
+
+>>> s.cummin(skipna=False)
+0    2.0
+1    NaN
+2    NaN
+3    NaN
+4    NaN
+dtype: float64
+
+**DataFrame**
+
+>>> df = pd.DataFrame([[2.0, 1.0],
+...                    [3.0, np.nan],
+...                    [1.0, 0.0]],
+...                   columns=list('AB'))
+>>> df
+     A    B
+0  2.0  1.0
+1  3.0  NaN
+2  1.0  0.0
+
+By default, iterates over rows and finds the minimum
+in each column. This is equivalent to ``axis=None`` or ``axis='index'``.
+
+>>> df.cummin()
+     A    B
+0  2.0  1.0
+1  2.0  NaN
+2  1.0  0.0
+
+To iterate over columns and find the minimum in each row,
+use ``axis=1``
+
+>>> df.cummin(axis=1)
+     A    B
+0  2.0  1.0
+1  3.0  NaN
+2  1.0  0.0
+"""
+
+_cumsum_examples = """\
+Examples
+--------
+**Series**
+
+>>> s = pd.Series([2, np.nan, 5, -1, 0])
+>>> s
+0    2.0
+1    NaN
+2    5.0
+3   -1.0
+4    0.0
+dtype: float64
+
+By default, NA values are ignored.
+
+>>> s.cumsum()
+0    2.0
+1    NaN
+2    7.0
+3    6.0
+4    6.0
+dtype: float64
+
+To include NA values in the operation, use ``skipna=False``
+
+>>> s.cumsum(skipna=False)
+0    2.0
+1    NaN
+2    NaN
+3    NaN
+4    NaN
+dtype: float64
+
+**DataFrame**
+
+>>> df = pd.DataFrame([[2.0, 1.0],
+...                    [3.0, np.nan],
+...                    [1.0, 0.0]],
+...                   columns=list('AB'))
+>>> df
+     A    B
+0  2.0  1.0
+1  3.0  NaN
+2  1.0  0.0
+
+By default, iterates over rows and finds the sum
+in each column. This is equivalent to ``axis=None`` or ``axis='index'``.
+
+>>> df.cumsum()
+     A    B
+0  2.0  1.0
+1  5.0  NaN
+2  6.0  1.0
+
+To iterate over columns and find the sum in each row,
+use ``axis=1``
+
+>>> df.cumsum(axis=1)
+     A    B
+0  2.0  3.0
+1  3.0  NaN
+2  1.0  1.0
+"""
+
+_cumprod_examples = """\
+Examples
+--------
+**Series**
+
+>>> s = pd.Series([2, np.nan, 5, -1, 0])
+>>> s
+0    2.0
+1    NaN
+2    5.0
+3   -1.0
+4    0.0
+dtype: float64
+
+By default, NA values are ignored.
+
+>>> s.cumprod()
+0     2.0
+1     NaN
+2    10.0
+3   -10.0
+4    -0.0
+dtype: float64
+
+To include NA values in the operation, use ``skipna=False``
+
+>>> s.cumprod(skipna=False)
+0    2.0
+1    NaN
+2    NaN
+3    NaN
+4    NaN
+dtype: float64
+
+**DataFrame**
+
+>>> df = pd.DataFrame([[2.0, 1.0],
+...                    [3.0, np.nan],
+...                    [1.0, 0.0]],
+...                   columns=list('AB'))
+>>> df
+     A    B
+0  2.0  1.0
+1  3.0  NaN
+2  1.0  0.0
+
+By default, iterates over rows and finds the product
+in each column. This is equivalent to ``axis=None`` or ``axis='index'``.
+
+>>> df.cumprod()
+     A    B
+0  2.0  1.0
+1  6.0  NaN
+2  6.0  0.0
+
+To iterate over columns and find the product in each row,
+use ``axis=1``
+
+>>> df.cumprod(axis=1)
+     A    B
+0  2.0  2.0
+1  3.0  NaN
+2  1.0  0.0
+"""
+
+_cummax_examples = """\
+Examples
+--------
+**Series**
+
+>>> s = pd.Series([2, np.nan, 5, -1, 0])
+>>> s
+0    2.0
+1    NaN
+2    5.0
+3   -1.0
+4    0.0
+dtype: float64
+
+By default, NA values are ignored.
+
+>>> s.cummax()
+0    2.0
+1    NaN
+2    5.0
+3    5.0
+4    5.0
+dtype: float64
+
+To include NA values in the operation, use ``skipna=False``
+
+>>> s.cummax(skipna=False)
+0    2.0
+1    NaN
+2    NaN
+3    NaN
+4    NaN
+dtype: float64
+
+**DataFrame**
+
+>>> df = pd.DataFrame([[2.0, 1.0],
+...                    [3.0, np.nan],
+...                    [1.0, 0.0]],
+...                   columns=list('AB'))
+>>> df
+     A    B
+0  2.0  1.0
+1  3.0  NaN
+2  1.0  0.0
+
+By default, iterates over rows and finds the maximum
+in each column. This is equivalent to ``axis=None`` or ``axis='index'``.
+
+>>> df.cummax()
+     A    B
+0  2.0  1.0
+1  3.0  NaN
+2  3.0  1.0
+
+To iterate over columns and find the maximum in each row,
+use ``axis=1``
+
+>>> df.cummax(axis=1)
+     A    B
+0  2.0  2.0
+1  3.0  NaN
+2  1.0  1.0
+"""
+
+_any_see_also = """\
+See Also
+--------
+numpy.any : Numpy version of this method.
+Series.any : Return whether any element is True.
+Series.all : Return whether all elements are True.
+DataFrame.any : Return whether any element is True over requested axis.
+DataFrame.all : Return whether all elements are True over requested axis.
+"""
+
+_any_desc = """\
+Return whether any element is True, potentially over an axis.
+
+Returns False unless there is at least one element within a series or
+along a Dataframe axis that is True or equivalent (e.g. non-zero or
+non-empty)."""
+
+_any_examples = """\
+Examples
+--------
+**Series**
+
+For Series input, the output is a scalar indicating whether any element
+is True.
+
+>>> pd.Series([False, False]).any()
+False
+>>> pd.Series([True, False]).any()
+True
+>>> pd.Series([], dtype="float64").any()
+False
+>>> pd.Series([np.nan]).any()
+False
+>>> pd.Series([np.nan]).any(skipna=False)
+True
+
+**DataFrame**
+
+Whether each column contains at least one True element (the default).
+
+>>> df = pd.DataFrame({"A": [1, 2], "B": [0, 2], "C": [0, 0]})
+>>> df
+   A  B  C
+0  1  0  0
+1  2  2  0
+
+>>> df.any()
+A     True
+B     True
+C    False
+dtype: bool
+
+Aggregating over the columns.
+
+>>> df = pd.DataFrame({"A": [True, False], "B": [1, 2]})
+>>> df
+       A  B
+0   True  1
+1  False  2
+
+>>> df.any(axis='columns')
+0    True
+1    True
+dtype: bool
+
+>>> df = pd.DataFrame({"A": [True, False], "B": [1, 0]})
+>>> df
+       A  B
+0   True  1
+1  False  0
+
+>>> df.any(axis='columns')
+0    True
+1    False
+dtype: bool
+
+Aggregating over the entire DataFrame with ``axis=None``.
+
+>>> df.any(axis=None)
+True
+
+`any` for an empty DataFrame is an empty Series.
+
+>>> pd.DataFrame([]).any()
+Series([], dtype: bool)
+"""
+
+_shared_docs[
+    "stat_func_example"
+] = """
+
+Examples
+--------
+>>> idx = pd.MultiIndex.from_arrays([
+...     ['warm', 'warm', 'cold', 'cold'],
+...     ['dog', 'falcon', 'fish', 'spider']],
+...     names=['blooded', 'animal'])
+>>> s = pd.Series([4, 2, 0, 8], name='legs', index=idx)
+>>> s
+blooded  animal
+warm     dog       4
+         falcon    2
+cold     fish      0
+         spider    8
+Name: legs, dtype: int64
+
+>>> s.{stat_func}()
+{default_output}"""
+
+_sum_examples = _shared_docs["stat_func_example"].format(
+    stat_func="sum", verb="Sum", default_output=14, level_output_0=6, level_output_1=8
+)
+
+_sum_examples += """
+
+By default, the sum of an empty or all-NA Series is ``0``.
+
+>>> pd.Series([], dtype="float64").sum()  # min_count=0 is the default
+0.0
+
+This can be controlled with the ``min_count`` parameter. For example, if
+you'd like the sum of an empty series to be NaN, pass ``min_count=1``.
+
+>>> pd.Series([], dtype="float64").sum(min_count=1)
+nan
+
+Thanks to the ``skipna`` parameter, ``min_count`` handles all-NA and
+empty series identically.
+
+>>> pd.Series([np.nan]).sum()
+0.0
+
+>>> pd.Series([np.nan]).sum(min_count=1)
+nan"""
+
+_max_examples: str = _shared_docs["stat_func_example"].format(
+    stat_func="max", verb="Max", default_output=8, level_output_0=4, level_output_1=8
+)
+
+_min_examples: str = _shared_docs["stat_func_example"].format(
+    stat_func="min", verb="Min", default_output=0, level_output_0=2, level_output_1=0
+)
+
+_stat_func_see_also = """
+
+See Also
+--------
+Series.sum : Return the sum.
+Series.min : Return the minimum.
+Series.max : Return the maximum.
+Series.idxmin : Return the index of the minimum.
+Series.idxmax : Return the index of the maximum.
+DataFrame.sum : Return the sum over the requested axis.
+DataFrame.min : Return the minimum over the requested axis.
+DataFrame.max : Return the maximum over the requested axis.
+DataFrame.idxmin : Return the index of the minimum over the requested axis.
+DataFrame.idxmax : Return the index of the maximum over the requested axis."""
+
+_prod_examples = """
+
+Examples
+--------
+By default, the product of an empty or all-NA Series is ``1``
+
+>>> pd.Series([], dtype="float64").prod()
+1.0
+
+This can be controlled with the ``min_count`` parameter
+
+>>> pd.Series([], dtype="float64").prod(min_count=1)
+nan
+
+Thanks to the ``skipna`` parameter, ``min_count`` handles all-NA and
+empty series identically.
+
+>>> pd.Series([np.nan]).prod()
+1.0
+
+>>> pd.Series([np.nan]).prod(min_count=1)
+nan"""
+
+_min_count_stub = """\
+min_count : int, default 0
+    The required number of valid values to perform the operation. If fewer than
+    ``min_count`` non-NA values are present the result will be NA.
+"""
+
+
+def make_doc(name: str, ndim: int) -> str:
+    """
+    Generate the docstring for a Series/DataFrame reduction.
+    """
+    if ndim == 1:
+        name1 = "scalar"
+        name2 = "Series"
+        axis_descr = "{index (0)}"
+    else:
+        name1 = "Series"
+        name2 = "DataFrame"
+        axis_descr = "{index (0), columns (1)}"
+
+    if name == "any":
+        base_doc = _bool_doc
+        desc = _any_desc
+        see_also = _any_see_also
+        examples = _any_examples
+        kwargs = {"empty_value": "False"}
+    elif name == "all":
+        base_doc = _bool_doc
+        desc = _all_desc
+        see_also = _all_see_also
+        examples = _all_examples
+        kwargs = {"empty_value": "True"}
+    elif name == "min":
+        base_doc = _num_doc
+        desc = (
+            "Return the minimum of the values over the requested axis.\n\n"
+            "If you want the *index* of the minimum, use ``idxmin``. This is "
+            "the equivalent of the ``numpy.ndarray`` method ``argmin``."
+        )
+        see_also = _stat_func_see_also
+        examples = _min_examples
+        kwargs = {"min_count": ""}
+    elif name == "max":
+        base_doc = _num_doc
+        desc = (
+            "Return the maximum of the values over the requested axis.\n\n"
+            "If you want the *index* of the maximum, use ``idxmax``. This is "
+            "the equivalent of the ``numpy.ndarray`` method ``argmax``."
+        )
+        see_also = _stat_func_see_also
+        examples = _max_examples
+        kwargs = {"min_count": ""}
+
+    elif name == "sum":
+        base_doc = _sum_prod_doc
+        desc = (
+            "Return the sum of the values over the requested axis.\n\n"
+            "This is equivalent to the method ``numpy.sum``."
+        )
+        see_also = _stat_func_see_also
+        examples = _sum_examples
+        kwargs = {"min_count": _min_count_stub}
+
+    elif name == "prod":
+        base_doc = _sum_prod_doc
+        desc = "Return the product of the values over the requested axis."
+        see_also = _stat_func_see_also
+        examples = _prod_examples
+        kwargs = {"min_count": _min_count_stub}
+
+    elif name == "median":
+        base_doc = _num_doc
+        desc = "Return the median of the values over the requested axis."
+        see_also = ""
+        examples = """
+
+            Examples
+            --------
+            >>> s = pd.Series([1, 2, 3])
+            >>> s.median()
+            2.0
+
+            With a DataFrame
+
+            >>> df = pd.DataFrame({'a': [1, 2], 'b': [2, 3]}, index=['tiger', 'zebra'])
+            >>> df
+                   a   b
+            tiger  1   2
+            zebra  2   3
+            >>> df.median()
+            a   1.5
+            b   2.5
+            dtype: float64
+
+            Using axis=1
+
+            >>> df.median(axis=1)
+            tiger   1.5
+            zebra   2.5
+            dtype: float64
+
+            In this case, `numeric_only` should be set to `True`
+            to avoid getting an error.
+
+            >>> df = pd.DataFrame({'a': [1, 2], 'b': ['T', 'Z']},
+            ...                   index=['tiger', 'zebra'])
+            >>> df.median(numeric_only=True)
+            a   1.5
+            dtype: float64"""
+        kwargs = {"min_count": ""}
+
+    elif name == "mean":
+        base_doc = _num_doc
+        desc = "Return the mean of the values over the requested axis."
+        see_also = ""
+        examples = """
+
+            Examples
+            --------
+            >>> s = pd.Series([1, 2, 3])
+            >>> s.mean()
+            2.0
+
+            With a DataFrame
+
+            >>> df = pd.DataFrame({'a': [1, 2], 'b': [2, 3]}, index=['tiger', 'zebra'])
+            >>> df
+                   a   b
+            tiger  1   2
+            zebra  2   3
+            >>> df.mean()
+            a   1.5
+            b   2.5
+            dtype: float64
+
+            Using axis=1
+
+            >>> df.mean(axis=1)
+            tiger   1.5
+            zebra   2.5
+            dtype: float64
+
+            In this case, `numeric_only` should be set to `True` to avoid
+            getting an error.
+
+            >>> df = pd.DataFrame({'a': [1, 2], 'b': ['T', 'Z']},
+            ...                   index=['tiger', 'zebra'])
+            >>> df.mean(numeric_only=True)
+            a   1.5
+            dtype: float64"""
+        kwargs = {"min_count": ""}
+
+    elif name == "var":
+        base_doc = _num_ddof_doc
+        desc = (
+            "Return unbiased variance over requested axis.\n\nNormalized by "
+            "N-1 by default. This can be changed using the ddof argument."
+        )
+        examples = _var_examples
+        see_also = ""
+        kwargs = {"notes": ""}
+
+    elif name == "std":
+        base_doc = _num_ddof_doc
+        desc = (
+            "Return sample standard deviation over requested axis."
+            "\n\nNormalized by N-1 by default. This can be changed using the "
+            "ddof argument."
+        )
+        examples = _std_examples
+        see_also = ""
+        kwargs = {"notes": _std_notes}
+
+    elif name == "sem":
+        base_doc = _num_ddof_doc
+        desc = (
+            "Return unbiased standard error of the mean over requested "
+            "axis.\n\nNormalized by N-1 by default. This can be changed "
+            "using the ddof argument"
+        )
+        examples = """
+
+            Examples
+            --------
+            >>> s = pd.Series([1, 2, 3])
+            >>> s.sem().round(6)
+            0.57735
+
+            With a DataFrame
+
+            >>> df = pd.DataFrame({'a': [1, 2], 'b': [2, 3]}, index=['tiger', 'zebra'])
+            >>> df
+                   a   b
+            tiger  1   2
+            zebra  2   3
+            >>> df.sem()
+            a   0.5
+            b   0.5
+            dtype: float64
+
+            Using axis=1
+
+            >>> df.sem(axis=1)
+            tiger   0.5
+            zebra   0.5
+            dtype: float64
+
+            In this case, `numeric_only` should be set to `True`
+            to avoid getting an error.
+
+            >>> df = pd.DataFrame({'a': [1, 2], 'b': ['T', 'Z']},
+            ...                   index=['tiger', 'zebra'])
+            >>> df.sem(numeric_only=True)
+            a   0.5
+            dtype: float64"""
+        see_also = ""
+        kwargs = {"notes": ""}
+
+    elif name == "skew":
+        base_doc = _num_doc
+        desc = "Return unbiased skew over requested axis.\n\nNormalized by N-1."
+        see_also = ""
+        examples = """
+
+            Examples
+            --------
+            >>> s = pd.Series([1, 2, 3])
+            >>> s.skew()
+            0.0
+
+            With a DataFrame
+
+            >>> df = pd.DataFrame({'a': [1, 2, 3], 'b': [2, 3, 4], 'c': [1, 3, 5]},
+            ...                   index=['tiger', 'zebra', 'cow'])
+            >>> df
+                    a   b   c
+            tiger   1   2   1
+            zebra   2   3   3
+            cow     3   4   5
+            >>> df.skew()
+            a   0.0
+            b   0.0
+            c   0.0
+            dtype: float64
+
+            Using axis=1
+
+            >>> df.skew(axis=1)
+            tiger   1.732051
+            zebra  -1.732051
+            cow     0.000000
+            dtype: float64
+
+            In this case, `numeric_only` should be set to `True` to avoid
+            getting an error.
+
+            >>> df = pd.DataFrame({'a': [1, 2, 3], 'b': ['T', 'Z', 'X']},
+            ...                   index=['tiger', 'zebra', 'cow'])
+            >>> df.skew(numeric_only=True)
+            a   0.0
+            dtype: float64"""
+        kwargs = {"min_count": ""}
+    elif name == "kurt":
+        base_doc = _num_doc
+        desc = (
+            "Return unbiased kurtosis over requested axis.\n\n"
+            "Kurtosis obtained using Fisher's definition of\n"
+            "kurtosis (kurtosis of normal == 0.0). Normalized "
+            "by N-1."
+        )
+        see_also = ""
+        examples = """
+
+            Examples
+            --------
+            >>> s = pd.Series([1, 2, 2, 3], index=['cat', 'dog', 'dog', 'mouse'])
+            >>> s
+            cat    1
+            dog    2
+            dog    2
+            mouse  3
+            dtype: int64
+            >>> s.kurt()
+            1.5
+
+            With a DataFrame
+
+            >>> df = pd.DataFrame({'a': [1, 2, 2, 3], 'b': [3, 4, 4, 4]},
+            ...                   index=['cat', 'dog', 'dog', 'mouse'])
+            >>> df
+                   a   b
+              cat  1   3
+              dog  2   4
+              dog  2   4
+            mouse  3   4
+            >>> df.kurt()
+            a   1.5
+            b   4.0
+            dtype: float64
+
+            With axis=None
+
+            >>> df.kurt(axis=None).round(6)
+            -0.988693
+
+            Using axis=1
+
+            >>> df = pd.DataFrame({'a': [1, 2], 'b': [3, 4], 'c': [3, 4], 'd': [1, 2]},
+            ...                   index=['cat', 'dog'])
+            >>> df.kurt(axis=1)
+            cat   -6.0
+            dog   -6.0
+            dtype: float64"""
+        kwargs = {"min_count": ""}
+
+    elif name == "cumsum":
+        base_doc = _cnum_doc
+        desc = "sum"
+        see_also = ""
+        examples = _cumsum_examples
+        kwargs = {"accum_func_name": "sum"}
+
+    elif name == "cumprod":
+        base_doc = _cnum_doc
+        desc = "product"
+        see_also = ""
+        examples = _cumprod_examples
+        kwargs = {"accum_func_name": "prod"}
+
+    elif name == "cummin":
+        base_doc = _cnum_doc
+        desc = "minimum"
+        see_also = ""
+        examples = _cummin_examples
+        kwargs = {"accum_func_name": "min"}
+
+    elif name == "cummax":
+        base_doc = _cnum_doc
+        desc = "maximum"
+        see_also = ""
+        examples = _cummax_examples
+        kwargs = {"accum_func_name": "max"}
+
+    else:
+        raise NotImplementedError
+
+    docstr = base_doc.format(
+        desc=desc,
+        name=name,
+        name1=name1,
+        name2=name2,
+        axis_descr=axis_descr,
+        see_also=see_also,
+        examples=examples,
+        **kwargs,
+    )
+    return docstr
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/groupby/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/groupby/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..8248f378e2c1acea37bdc2d41065c591360b902a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/groupby/__init__.py
@@ -0,0 +1,15 @@
+from pandas.core.groupby.generic import (
+    DataFrameGroupBy,
+    NamedAgg,
+    SeriesGroupBy,
+)
+from pandas.core.groupby.groupby import GroupBy
+from pandas.core.groupby.grouper import Grouper
+
+__all__ = [
+    "DataFrameGroupBy",
+    "NamedAgg",
+    "SeriesGroupBy",
+    "GroupBy",
+    "Grouper",
+]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/groupby/base.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/groupby/base.py
new file mode 100644
index 0000000000000000000000000000000000000000..a443597347283887deb9cbd3eafb5f6d3bb6d9a6
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/groupby/base.py
@@ -0,0 +1,121 @@
+"""
+Provide basic components for groupby.
+"""
+from __future__ import annotations
+
+import dataclasses
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from collections.abc import Hashable
+
+
+@dataclasses.dataclass(order=True, frozen=True)
+class OutputKey:
+    label: Hashable
+    position: int
+
+
+# special case to prevent duplicate plots when catching exceptions when
+# forwarding methods from NDFrames
+plotting_methods = frozenset(["plot", "hist"])
+
+# cythonized transformations or canned "agg+broadcast", which do not
+# require postprocessing of the result by transform.
+cythonized_kernels = frozenset(["cumprod", "cumsum", "shift", "cummin", "cummax"])
+
+# List of aggregation/reduction functions.
+# These map each group to a single numeric value
+reduction_kernels = frozenset(
+    [
+        "all",
+        "any",
+        "corrwith",
+        "count",
+        "first",
+        "idxmax",
+        "idxmin",
+        "last",
+        "max",
+        "mean",
+        "median",
+        "min",
+        "nunique",
+        "prod",
+        # as long as `quantile`'s signature accepts only
+        # a single quantile value, it's a reduction.
+        # GH#27526 might change that.
+        "quantile",
+        "sem",
+        "size",
+        "skew",
+        "std",
+        "sum",
+        "var",
+    ]
+)
+
+# List of transformation functions.
+# a transformation is a function that, for each group,
+# produces a result that has the same shape as the group.
+
+
+transformation_kernels = frozenset(
+    [
+        "bfill",
+        "cumcount",
+        "cummax",
+        "cummin",
+        "cumprod",
+        "cumsum",
+        "diff",
+        "ffill",
+        "fillna",
+        "ngroup",
+        "pct_change",
+        "rank",
+        "shift",
+    ]
+)
+
+# these are all the public methods on Grouper which don't belong
+# in either of the above lists
+groupby_other_methods = frozenset(
+    [
+        "agg",
+        "aggregate",
+        "apply",
+        "boxplot",
+        # corr and cov return ngroups*ncolumns rows, so they
+        # are neither a transformation nor a reduction
+        "corr",
+        "cov",
+        "describe",
+        "dtypes",
+        "expanding",
+        "ewm",
+        "filter",
+        "get_group",
+        "groups",
+        "head",
+        "hist",
+        "indices",
+        "ndim",
+        "ngroups",
+        "nth",
+        "ohlc",
+        "pipe",
+        "plot",
+        "resample",
+        "rolling",
+        "tail",
+        "take",
+        "transform",
+        "sample",
+        "value_counts",
+    ]
+)
+# Valid values  of `name` for `groupby.transform(name)`
+# NOTE: do NOT edit this directly. New additions should be inserted
+# into the appropriate list above.
+transform_kernel_allowlist = reduction_kernels | transformation_kernels
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/groupby/categorical.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/groupby/categorical.py
new file mode 100644
index 0000000000000000000000000000000000000000..6ab98cf4fe55e9b064db99e61d1245cb83b63dc1
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/groupby/categorical.py
@@ -0,0 +1,87 @@
+from __future__ import annotations
+
+import numpy as np
+
+from pandas.core.algorithms import unique1d
+from pandas.core.arrays.categorical import (
+    Categorical,
+    CategoricalDtype,
+    recode_for_categories,
+)
+
+
+def recode_for_groupby(
+    c: Categorical, sort: bool, observed: bool
+) -> tuple[Categorical, Categorical | None]:
+    """
+    Code the categories to ensure we can groupby for categoricals.
+
+    If observed=True, we return a new Categorical with the observed
+    categories only.
+
+    If sort=False, return a copy of self, coded with categories as
+    returned by .unique(), followed by any categories not appearing in
+    the data. If sort=True, return self.
+
+    This method is needed solely to ensure the categorical index of the
+    GroupBy result has categories in the order of appearance in the data
+    (GH-8868).
+
+    Parameters
+    ----------
+    c : Categorical
+    sort : bool
+        The value of the sort parameter groupby was called with.
+    observed : bool
+        Account only for the observed values
+
+    Returns
+    -------
+    Categorical
+        If sort=False, the new categories are set to the order of
+        appearance in codes (unless ordered=True, in which case the
+        original order is preserved), followed by any unrepresented
+        categories in the original order.
+    Categorical or None
+        If we are observed, return the original categorical, otherwise None
+    """
+    # we only care about observed values
+    if observed:
+        # In cases with c.ordered, this is equivalent to
+        #  return c.remove_unused_categories(), c
+
+        unique_codes = unique1d(c.codes)
+
+        take_codes = unique_codes[unique_codes != -1]
+        if sort:
+            take_codes = np.sort(take_codes)
+
+        # we recode according to the uniques
+        categories = c.categories.take(take_codes)
+        codes = recode_for_categories(c.codes, c.categories, categories)
+
+        # return a new categorical that maps our new codes
+        # and categories
+        dtype = CategoricalDtype(categories, ordered=c.ordered)
+        return Categorical._simple_new(codes, dtype=dtype), c
+
+    # Already sorted according to c.categories; all is fine
+    if sort:
+        return c, None
+
+    # sort=False should order groups in as-encountered order (GH-8868)
+
+    # xref GH:46909: Re-ordering codes faster than using (set|add|reorder)_categories
+    all_codes = np.arange(c.categories.nunique())
+    # GH 38140: exclude nan from indexer for categories
+    unique_notnan_codes = unique1d(c.codes[c.codes != -1])
+    if sort:
+        unique_notnan_codes = np.sort(unique_notnan_codes)
+    if len(all_codes) > len(unique_notnan_codes):
+        # GH 13179: All categories need to be present, even if missing from the data
+        missing_codes = np.setdiff1d(all_codes, unique_notnan_codes, assume_unique=True)
+        take_codes = np.concatenate((unique_notnan_codes, missing_codes))
+    else:
+        take_codes = unique_notnan_codes
+
+    return Categorical(c, c.unique().categories.take(take_codes)), None
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/groupby/generic.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/groupby/generic.py
new file mode 100644
index 0000000000000000000000000000000000000000..f2e314046fb749e7d32bf7fc76ca22eb1194a328
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/groupby/generic.py
@@ -0,0 +1,2852 @@
+"""
+Define the SeriesGroupBy and DataFrameGroupBy
+classes that hold the groupby interfaces (and some implementations).
+
+These are user facing as the result of the ``df.groupby(...)`` operations,
+which here returns a DataFrameGroupBy object.
+"""
+from __future__ import annotations
+
+from collections import abc
+from functools import partial
+from textwrap import dedent
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Literal,
+    NamedTuple,
+    TypeVar,
+    Union,
+    cast,
+)
+import warnings
+
+import numpy as np
+
+from pandas._libs import (
+    Interval,
+    lib,
+)
+from pandas._libs.hashtable import duplicated
+from pandas.errors import SpecificationError
+from pandas.util._decorators import (
+    Appender,
+    Substitution,
+    doc,
+)
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.common import (
+    ensure_int64,
+    is_bool,
+    is_dict_like,
+    is_integer_dtype,
+    is_list_like,
+    is_numeric_dtype,
+    is_scalar,
+)
+from pandas.core.dtypes.dtypes import (
+    CategoricalDtype,
+    IntervalDtype,
+)
+from pandas.core.dtypes.inference import is_hashable
+from pandas.core.dtypes.missing import (
+    isna,
+    notna,
+)
+
+from pandas.core import algorithms
+from pandas.core.apply import (
+    GroupByApply,
+    maybe_mangle_lambdas,
+    reconstruct_func,
+    validate_func_kwargs,
+    warn_alias_replacement,
+)
+import pandas.core.common as com
+from pandas.core.frame import DataFrame
+from pandas.core.groupby import (
+    base,
+    ops,
+)
+from pandas.core.groupby.groupby import (
+    GroupBy,
+    GroupByPlot,
+    _agg_template_frame,
+    _agg_template_series,
+    _apply_docs,
+    _transform_template,
+)
+from pandas.core.indexes.api import (
+    Index,
+    MultiIndex,
+    all_indexes_same,
+    default_index,
+)
+from pandas.core.series import Series
+from pandas.core.sorting import get_group_index
+from pandas.core.util.numba_ import maybe_use_numba
+
+from pandas.plotting import boxplot_frame_groupby
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Hashable,
+        Mapping,
+        Sequence,
+    )
+
+    from pandas._typing import (
+        ArrayLike,
+        Axis,
+        AxisInt,
+        CorrelationMethod,
+        FillnaOptions,
+        IndexLabel,
+        Manager,
+        Manager2D,
+        SingleManager,
+        TakeIndexer,
+    )
+
+    from pandas import Categorical
+    from pandas.core.generic import NDFrame
+
+# TODO(typing) the return value on this callable should be any *scalar*.
+AggScalar = Union[str, Callable[..., Any]]
+# TODO: validate types on ScalarResult and move to _typing
+# Blocked from using by https://github.com/python/mypy/issues/1484
+# See note at _mangle_lambda_list
+ScalarResult = TypeVar("ScalarResult")
+
+
+class NamedAgg(NamedTuple):
+    """
+    Helper for column specific aggregation with control over output column names.
+
+    Subclass of typing.NamedTuple.
+
+    Parameters
+    ----------
+    column : Hashable
+        Column label in the DataFrame to apply aggfunc.
+    aggfunc : function or str
+        Function to apply to the provided column. If string, the name of a built-in
+        pandas function.
+
+    Examples
+    --------
+    >>> df = pd.DataFrame({"key": [1, 1, 2], "a": [-1, 0, 1], 1: [10, 11, 12]})
+    >>> agg_a = pd.NamedAgg(column="a", aggfunc="min")
+    >>> agg_1 = pd.NamedAgg(column=1, aggfunc=lambda x: np.mean(x))
+    >>> df.groupby("key").agg(result_a=agg_a, result_1=agg_1)
+         result_a  result_1
+    key
+    1          -1      10.5
+    2           1      12.0
+    """
+
+    column: Hashable
+    aggfunc: AggScalar
+
+
+class SeriesGroupBy(GroupBy[Series]):
+    def _wrap_agged_manager(self, mgr: Manager) -> Series:
+        out = self.obj._constructor_from_mgr(mgr, axes=mgr.axes)
+        out._name = self.obj.name
+        return out
+
+    def _get_data_to_aggregate(
+        self, *, numeric_only: bool = False, name: str | None = None
+    ) -> SingleManager:
+        ser = self._obj_with_exclusions
+        single = ser._mgr
+        if numeric_only and not is_numeric_dtype(ser.dtype):
+            # GH#41291 match Series behavior
+            kwd_name = "numeric_only"
+            raise TypeError(
+                f"Cannot use {kwd_name}=True with "
+                f"{type(self).__name__}.{name} and non-numeric dtypes."
+            )
+        return single
+
+    _agg_examples_doc = dedent(
+        """
+    Examples
+    --------
+    >>> s = pd.Series([1, 2, 3, 4])
+
+    >>> s
+    0    1
+    1    2
+    2    3
+    3    4
+    dtype: int64
+
+    >>> s.groupby([1, 1, 2, 2]).min()
+    1    1
+    2    3
+    dtype: int64
+
+    >>> s.groupby([1, 1, 2, 2]).agg('min')
+    1    1
+    2    3
+    dtype: int64
+
+    >>> s.groupby([1, 1, 2, 2]).agg(['min', 'max'])
+       min  max
+    1    1    2
+    2    3    4
+
+    The output column names can be controlled by passing
+    the desired column names and aggregations as keyword arguments.
+
+    >>> s.groupby([1, 1, 2, 2]).agg(
+    ...     minimum='min',
+    ...     maximum='max',
+    ... )
+       minimum  maximum
+    1        1        2
+    2        3        4
+
+    .. versionchanged:: 1.3.0
+
+        The resulting dtype will reflect the return value of the aggregating function.
+
+    >>> s.groupby([1, 1, 2, 2]).agg(lambda x: x.astype(float).min())
+    1    1.0
+    2    3.0
+    dtype: float64
+    """
+    )
+
+    @Appender(
+        _apply_docs["template"].format(
+            input="series", examples=_apply_docs["series_examples"]
+        )
+    )
+    def apply(self, func, *args, **kwargs) -> Series:
+        return super().apply(func, *args, **kwargs)
+
+    @doc(_agg_template_series, examples=_agg_examples_doc, klass="Series")
+    def aggregate(self, func=None, *args, engine=None, engine_kwargs=None, **kwargs):
+        relabeling = func is None
+        columns = None
+        if relabeling:
+            columns, func = validate_func_kwargs(kwargs)
+            kwargs = {}
+
+        if isinstance(func, str):
+            if maybe_use_numba(engine) and engine is not None:
+                # Not all agg functions support numba, only propagate numba kwargs
+                # if user asks for numba, and engine is not None
+                # (if engine is None, the called function will handle the case where
+                # numba is requested via the global option)
+                kwargs["engine"] = engine
+            if engine_kwargs is not None:
+                kwargs["engine_kwargs"] = engine_kwargs
+            return getattr(self, func)(*args, **kwargs)
+
+        elif isinstance(func, abc.Iterable):
+            # Catch instances of lists / tuples
+            # but not the class list / tuple itself.
+            func = maybe_mangle_lambdas(func)
+            kwargs["engine"] = engine
+            kwargs["engine_kwargs"] = engine_kwargs
+            ret = self._aggregate_multiple_funcs(func, *args, **kwargs)
+            if relabeling:
+                # columns is not narrowed by mypy from relabeling flag
+                assert columns is not None  # for mypy
+                ret.columns = columns
+            if not self.as_index:
+                ret = ret.reset_index()
+            return ret
+
+        else:
+            cyfunc = com.get_cython_func(func)
+            if cyfunc and not args and not kwargs:
+                warn_alias_replacement(self, func, cyfunc)
+                return getattr(self, cyfunc)()
+
+            if maybe_use_numba(engine):
+                return self._aggregate_with_numba(
+                    func, *args, engine_kwargs=engine_kwargs, **kwargs
+                )
+
+            if self.ngroups == 0:
+                # e.g. test_evaluate_with_empty_groups without any groups to
+                #  iterate over, we have no output on which to do dtype
+                #  inference. We default to using the existing dtype.
+                #  xref GH#51445
+                obj = self._obj_with_exclusions
+                return self.obj._constructor(
+                    [],
+                    name=self.obj.name,
+                    index=self._grouper.result_index,
+                    dtype=obj.dtype,
+                )
+
+            if self._grouper.nkeys > 1:
+                return self._python_agg_general(func, *args, **kwargs)
+
+            try:
+                return self._python_agg_general(func, *args, **kwargs)
+            except KeyError:
+                # KeyError raised in test_groupby.test_basic is bc the func does
+                #  a dictionary lookup on group.name, but group name is not
+                #  pinned in _python_agg_general, only in _aggregate_named
+                result = self._aggregate_named(func, *args, **kwargs)
+
+                warnings.warn(
+                    "Pinning the groupby key to each group in "
+                    f"{type(self).__name__}.agg is deprecated, and cases that "
+                    "relied on it will raise in a future version. "
+                    "If your operation requires utilizing the groupby keys, "
+                    "iterate over the groupby object instead.",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+
+                # result is a dict whose keys are the elements of result_index
+                result = Series(result, index=self._grouper.result_index)
+                result = self._wrap_aggregated_output(result)
+                return result
+
+    agg = aggregate
+
+    def _python_agg_general(self, func, *args, **kwargs):
+        orig_func = func
+        func = com.is_builtin_func(func)
+        if orig_func != func:
+            alias = com._builtin_table_alias[func]
+            warn_alias_replacement(self, orig_func, alias)
+        f = lambda x: func(x, *args, **kwargs)
+
+        obj = self._obj_with_exclusions
+        result = self._grouper.agg_series(obj, f)
+        res = obj._constructor(result, name=obj.name)
+        return self._wrap_aggregated_output(res)
+
+    def _aggregate_multiple_funcs(self, arg, *args, **kwargs) -> DataFrame:
+        if isinstance(arg, dict):
+            if self.as_index:
+                # GH 15931
+                raise SpecificationError("nested renamer is not supported")
+            else:
+                # GH#50684 - This accidentally worked in 1.x
+                msg = (
+                    "Passing a dictionary to SeriesGroupBy.agg is deprecated "
+                    "and will raise in a future version of pandas. Pass a list "
+                    "of aggregations instead."
+                )
+                warnings.warn(
+                    message=msg,
+                    category=FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+                arg = list(arg.items())
+        elif any(isinstance(x, (tuple, list)) for x in arg):
+            arg = [(x, x) if not isinstance(x, (tuple, list)) else x for x in arg]
+        else:
+            # list of functions / function names
+            columns = (com.get_callable_name(f) or f for f in arg)
+            arg = zip(columns, arg)
+
+        results: dict[base.OutputKey, DataFrame | Series] = {}
+        with com.temp_setattr(self, "as_index", True):
+            # Combine results using the index, need to adjust index after
+            # if as_index=False (GH#50724)
+            for idx, (name, func) in enumerate(arg):
+                key = base.OutputKey(label=name, position=idx)
+                results[key] = self.aggregate(func, *args, **kwargs)
+
+        if any(isinstance(x, DataFrame) for x in results.values()):
+            from pandas import concat
+
+            res_df = concat(
+                results.values(), axis=1, keys=[key.label for key in results]
+            )
+            return res_df
+
+        indexed_output = {key.position: val for key, val in results.items()}
+        output = self.obj._constructor_expanddim(indexed_output, index=None)
+        output.columns = Index(key.label for key in results)
+
+        return output
+
+    def _wrap_applied_output(
+        self,
+        data: Series,
+        values: list[Any],
+        not_indexed_same: bool = False,
+        is_transform: bool = False,
+    ) -> DataFrame | Series:
+        """
+        Wrap the output of SeriesGroupBy.apply into the expected result.
+
+        Parameters
+        ----------
+        data : Series
+            Input data for groupby operation.
+        values : List[Any]
+            Applied output for each group.
+        not_indexed_same : bool, default False
+            Whether the applied outputs are not indexed the same as the group axes.
+
+        Returns
+        -------
+        DataFrame or Series
+        """
+        if len(values) == 0:
+            # GH #6265
+            if is_transform:
+                # GH#47787 see test_group_on_empty_multiindex
+                res_index = data.index
+            else:
+                res_index = self._grouper.result_index
+
+            return self.obj._constructor(
+                [],
+                name=self.obj.name,
+                index=res_index,
+                dtype=data.dtype,
+            )
+        assert values is not None
+
+        if isinstance(values[0], dict):
+            # GH #823 #24880
+            index = self._grouper.result_index
+            res_df = self.obj._constructor_expanddim(values, index=index)
+            res_df = self._reindex_output(res_df)
+            # if self.observed is False,
+            # keep all-NaN rows created while re-indexing
+            res_ser = res_df.stack(future_stack=True)
+            res_ser.name = self.obj.name
+            return res_ser
+        elif isinstance(values[0], (Series, DataFrame)):
+            result = self._concat_objects(
+                values,
+                not_indexed_same=not_indexed_same,
+                is_transform=is_transform,
+            )
+            if isinstance(result, Series):
+                result.name = self.obj.name
+            if not self.as_index and not_indexed_same:
+                result = self._insert_inaxis_grouper(result)
+                result.index = default_index(len(result))
+            return result
+        else:
+            # GH #6265 #24880
+            result = self.obj._constructor(
+                data=values, index=self._grouper.result_index, name=self.obj.name
+            )
+            if not self.as_index:
+                result = self._insert_inaxis_grouper(result)
+                result.index = default_index(len(result))
+            return self._reindex_output(result)
+
+    def _aggregate_named(self, func, *args, **kwargs):
+        # Note: this is very similar to _aggregate_series_pure_python,
+        #  but that does not pin group.name
+        result = {}
+        initialized = False
+
+        for name, group in self._grouper.get_iterator(
+            self._obj_with_exclusions, axis=self.axis
+        ):
+            # needed for pandas/tests/groupby/test_groupby.py::test_basic_aggregations
+            object.__setattr__(group, "name", name)
+
+            output = func(group, *args, **kwargs)
+            output = ops.extract_result(output)
+            if not initialized:
+                # We only do this validation on the first iteration
+                ops.check_result_array(output, group.dtype)
+                initialized = True
+            result[name] = output
+
+        return result
+
+    __examples_series_doc = dedent(
+        """
+    >>> ser = pd.Series([390.0, 350.0, 30.0, 20.0],
+    ...                 index=["Falcon", "Falcon", "Parrot", "Parrot"],
+    ...                 name="Max Speed")
+    >>> grouped = ser.groupby([1, 1, 2, 2])
+    >>> grouped.transform(lambda x: (x - x.mean()) / x.std())
+        Falcon    0.707107
+        Falcon   -0.707107
+        Parrot    0.707107
+        Parrot   -0.707107
+        Name: Max Speed, dtype: float64
+
+    Broadcast result of the transformation
+
+    >>> grouped.transform(lambda x: x.max() - x.min())
+    Falcon    40.0
+    Falcon    40.0
+    Parrot    10.0
+    Parrot    10.0
+    Name: Max Speed, dtype: float64
+
+    >>> grouped.transform("mean")
+    Falcon    370.0
+    Falcon    370.0
+    Parrot     25.0
+    Parrot     25.0
+    Name: Max Speed, dtype: float64
+
+    .. versionchanged:: 1.3.0
+
+    The resulting dtype will reflect the return value of the passed ``func``,
+    for example:
+
+    >>> grouped.transform(lambda x: x.astype(int).max())
+    Falcon    390
+    Falcon    390
+    Parrot     30
+    Parrot     30
+    Name: Max Speed, dtype: int64
+    """
+    )
+
+    @Substitution(klass="Series", example=__examples_series_doc)
+    @Appender(_transform_template)
+    def transform(self, func, *args, engine=None, engine_kwargs=None, **kwargs):
+        return self._transform(
+            func, *args, engine=engine, engine_kwargs=engine_kwargs, **kwargs
+        )
+
+    def _cython_transform(
+        self, how: str, numeric_only: bool = False, axis: AxisInt = 0, **kwargs
+    ):
+        assert axis == 0  # handled by caller
+
+        obj = self._obj_with_exclusions
+
+        try:
+            result = self._grouper._cython_operation(
+                "transform", obj._values, how, axis, **kwargs
+            )
+        except NotImplementedError as err:
+            # e.g. test_groupby_raises_string
+            raise TypeError(f"{how} is not supported for {obj.dtype} dtype") from err
+
+        return obj._constructor(result, index=self.obj.index, name=obj.name)
+
+    def _transform_general(
+        self, func: Callable, engine, engine_kwargs, *args, **kwargs
+    ) -> Series:
+        """
+        Transform with a callable `func`.
+        """
+        if maybe_use_numba(engine):
+            return self._transform_with_numba(
+                func, *args, engine_kwargs=engine_kwargs, **kwargs
+            )
+        assert callable(func)
+        klass = type(self.obj)
+
+        results = []
+        for name, group in self._grouper.get_iterator(
+            self._obj_with_exclusions, axis=self.axis
+        ):
+            # this setattr is needed for test_transform_lambda_with_datetimetz
+            object.__setattr__(group, "name", name)
+            res = func(group, *args, **kwargs)
+
+            results.append(klass(res, index=group.index))
+
+        # check for empty "results" to avoid concat ValueError
+        if results:
+            from pandas.core.reshape.concat import concat
+
+            concatenated = concat(results)
+            result = self._set_result_index_ordered(concatenated)
+        else:
+            result = self.obj._constructor(dtype=np.float64)
+
+        result.name = self.obj.name
+        return result
+
+    def filter(self, func, dropna: bool = True, *args, **kwargs):
+        """
+        Filter elements from groups that don't satisfy a criterion.
+
+        Elements from groups are filtered if they do not satisfy the
+        boolean criterion specified by func.
+
+        Parameters
+        ----------
+        func : function
+            Criterion to apply to each group. Should return True or False.
+        dropna : bool
+            Drop groups that do not pass the filter. True by default; if False,
+            groups that evaluate False are filled with NaNs.
+
+        Returns
+        -------
+        Series
+
+        Notes
+        -----
+        Functions that mutate the passed object can produce unexpected
+        behavior or errors and are not supported. See :ref:`gotchas.udf-mutation`
+        for more details.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
+        ...                           'foo', 'bar'],
+        ...                    'B' : [1, 2, 3, 4, 5, 6],
+        ...                    'C' : [2.0, 5., 8., 1., 2., 9.]})
+        >>> grouped = df.groupby('A')
+        >>> df.groupby('A').B.filter(lambda x: x.mean() > 3.)
+        1    2
+        3    4
+        5    6
+        Name: B, dtype: int64
+        """
+        if isinstance(func, str):
+            wrapper = lambda x: getattr(x, func)(*args, **kwargs)
+        else:
+            wrapper = lambda x: func(x, *args, **kwargs)
+
+        # Interpret np.nan as False.
+        def true_and_notna(x) -> bool:
+            b = wrapper(x)
+            return notna(b) and b
+
+        try:
+            indices = [
+                self._get_index(name)
+                for name, group in self._grouper.get_iterator(
+                    self._obj_with_exclusions, axis=self.axis
+                )
+                if true_and_notna(group)
+            ]
+        except (ValueError, TypeError) as err:
+            raise TypeError("the filter must return a boolean result") from err
+
+        filtered = self._apply_filter(indices, dropna)
+        return filtered
+
+    def nunique(self, dropna: bool = True) -> Series | DataFrame:
+        """
+        Return number of unique elements in the group.
+
+        Returns
+        -------
+        Series
+            Number of unique values within each group.
+
+        Examples
+        --------
+        For SeriesGroupby:
+
+        >>> lst = ['a', 'a', 'b', 'b']
+        >>> ser = pd.Series([1, 2, 3, 3], index=lst)
+        >>> ser
+        a    1
+        a    2
+        b    3
+        b    3
+        dtype: int64
+        >>> ser.groupby(level=0).nunique()
+        a    2
+        b    1
+        dtype: int64
+
+        For Resampler:
+
+        >>> ser = pd.Series([1, 2, 3, 3], index=pd.DatetimeIndex(
+        ...                 ['2023-01-01', '2023-01-15', '2023-02-01', '2023-02-15']))
+        >>> ser
+        2023-01-01    1
+        2023-01-15    2
+        2023-02-01    3
+        2023-02-15    3
+        dtype: int64
+        >>> ser.resample('MS').nunique()
+        2023-01-01    2
+        2023-02-01    1
+        Freq: MS, dtype: int64
+        """
+        ids, _, ngroups = self._grouper.group_info
+        val = self.obj._values
+        codes, uniques = algorithms.factorize(val, use_na_sentinel=dropna, sort=False)
+
+        if self._grouper.has_dropped_na:
+            mask = ids >= 0
+            ids = ids[mask]
+            codes = codes[mask]
+
+        group_index = get_group_index(
+            labels=[ids, codes],
+            shape=(ngroups, len(uniques)),
+            sort=False,
+            xnull=dropna,
+        )
+
+        if dropna:
+            mask = group_index >= 0
+            if (~mask).any():
+                ids = ids[mask]
+                group_index = group_index[mask]
+
+        mask = duplicated(group_index, "first")
+        res = np.bincount(ids[~mask], minlength=ngroups)
+        res = ensure_int64(res)
+
+        ri = self._grouper.result_index
+        result: Series | DataFrame = self.obj._constructor(
+            res, index=ri, name=self.obj.name
+        )
+        if not self.as_index:
+            result = self._insert_inaxis_grouper(result)
+            result.index = default_index(len(result))
+        return self._reindex_output(result, fill_value=0)
+
+    @doc(Series.describe)
+    def describe(self, percentiles=None, include=None, exclude=None) -> Series:
+        return super().describe(
+            percentiles=percentiles, include=include, exclude=exclude
+        )
+
+    def value_counts(
+        self,
+        normalize: bool = False,
+        sort: bool = True,
+        ascending: bool = False,
+        bins=None,
+        dropna: bool = True,
+    ) -> Series | DataFrame:
+        name = "proportion" if normalize else "count"
+
+        if bins is None:
+            result = self._value_counts(
+                normalize=normalize, sort=sort, ascending=ascending, dropna=dropna
+            )
+            result.name = name
+            return result
+
+        from pandas.core.reshape.merge import get_join_indexers
+        from pandas.core.reshape.tile import cut
+
+        ids, _, _ = self._grouper.group_info
+        val = self.obj._values
+
+        index_names = self._grouper.names + [self.obj.name]
+
+        if isinstance(val.dtype, CategoricalDtype) or (
+            bins is not None and not np.iterable(bins)
+        ):
+            # scalar bins cannot be done at top level
+            # in a backward compatible way
+            # GH38672 relates to categorical dtype
+            ser = self.apply(
+                Series.value_counts,
+                normalize=normalize,
+                sort=sort,
+                ascending=ascending,
+                bins=bins,
+            )
+            ser.name = name
+            ser.index.names = index_names
+            return ser
+
+        # groupby removes null keys from groupings
+        mask = ids != -1
+        ids, val = ids[mask], val[mask]
+
+        lab: Index | np.ndarray
+        if bins is None:
+            lab, lev = algorithms.factorize(val, sort=True)
+            llab = lambda lab, inc: lab[inc]
+        else:
+            # lab is a Categorical with categories an IntervalIndex
+            cat_ser = cut(Series(val, copy=False), bins, include_lowest=True)
+            cat_obj = cast("Categorical", cat_ser._values)
+            lev = cat_obj.categories
+            lab = lev.take(
+                cat_obj.codes,
+                allow_fill=True,
+                fill_value=lev._na_value,
+            )
+            llab = lambda lab, inc: lab[inc]._multiindex.codes[-1]
+
+        if isinstance(lab.dtype, IntervalDtype):
+            # TODO: should we do this inside II?
+            lab_interval = cast(Interval, lab)
+
+            sorter = np.lexsort((lab_interval.left, lab_interval.right, ids))
+        else:
+            sorter = np.lexsort((lab, ids))
+
+        ids, lab = ids[sorter], lab[sorter]
+
+        # group boundaries are where group ids change
+        idchanges = 1 + np.nonzero(ids[1:] != ids[:-1])[0]
+        idx = np.r_[0, idchanges]
+        if not len(ids):
+            idx = idchanges
+
+        # new values are where sorted labels change
+        lchanges = llab(lab, slice(1, None)) != llab(lab, slice(None, -1))
+        inc = np.r_[True, lchanges]
+        if not len(val):
+            inc = lchanges
+        inc[idx] = True  # group boundaries are also new values
+        out = np.diff(np.nonzero(np.r_[inc, True])[0])  # value counts
+
+        # num. of times each group should be repeated
+        rep = partial(np.repeat, repeats=np.add.reduceat(inc, idx))
+
+        # multi-index components
+        codes = self._grouper.reconstructed_codes
+        codes = [rep(level_codes) for level_codes in codes] + [llab(lab, inc)]
+        levels = [ping._group_index for ping in self._grouper.groupings] + [lev]
+
+        if dropna:
+            mask = codes[-1] != -1
+            if mask.all():
+                dropna = False
+            else:
+                out, codes = out[mask], [level_codes[mask] for level_codes in codes]
+
+        if normalize:
+            out = out.astype("float")
+            d = np.diff(np.r_[idx, len(ids)])
+            if dropna:
+                m = ids[lab == -1]
+                np.add.at(d, m, -1)
+                acc = rep(d)[mask]
+            else:
+                acc = rep(d)
+            out /= acc
+
+        if sort and bins is None:
+            cat = ids[inc][mask] if dropna else ids[inc]
+            sorter = np.lexsort((out if ascending else -out, cat))
+            out, codes[-1] = out[sorter], codes[-1][sorter]
+
+        if bins is not None:
+            # for compat. with libgroupby.value_counts need to ensure every
+            # bin is present at every index level, null filled with zeros
+            diff = np.zeros(len(out), dtype="bool")
+            for level_codes in codes[:-1]:
+                diff |= np.r_[True, level_codes[1:] != level_codes[:-1]]
+
+            ncat, nbin = diff.sum(), len(levels[-1])
+
+            left = [np.repeat(np.arange(ncat), nbin), np.tile(np.arange(nbin), ncat)]
+
+            right = [diff.cumsum() - 1, codes[-1]]
+
+            # error: Argument 1 to "get_join_indexers" has incompatible type
+            # "List[ndarray[Any, Any]]"; expected "List[Union[Union[ExtensionArray,
+            # ndarray[Any, Any]], Index, Series]]
+            _, idx = get_join_indexers(
+                left, right, sort=False, how="left"  # type: ignore[arg-type]
+            )
+            if idx is not None:
+                out = np.where(idx != -1, out[idx], 0)
+
+            if sort:
+                sorter = np.lexsort((out if ascending else -out, left[0]))
+                out, left[-1] = out[sorter], left[-1][sorter]
+
+            # build the multi-index w/ full levels
+            def build_codes(lev_codes: np.ndarray) -> np.ndarray:
+                return np.repeat(lev_codes[diff], nbin)
+
+            codes = [build_codes(lev_codes) for lev_codes in codes[:-1]]
+            codes.append(left[-1])
+
+        mi = MultiIndex(
+            levels=levels, codes=codes, names=index_names, verify_integrity=False
+        )
+
+        if is_integer_dtype(out.dtype):
+            out = ensure_int64(out)
+        result = self.obj._constructor(out, index=mi, name=name)
+        if not self.as_index:
+            result = result.reset_index()
+        return result
+
+    def fillna(
+        self,
+        value: object | ArrayLike | None = None,
+        method: FillnaOptions | None = None,
+        axis: Axis | None | lib.NoDefault = lib.no_default,
+        inplace: bool = False,
+        limit: int | None = None,
+        downcast: dict | None | lib.NoDefault = lib.no_default,
+    ) -> Series | None:
+        """
+        Fill NA/NaN values using the specified method within groups.
+
+        .. deprecated:: 2.2.0
+            This method is deprecated and will be removed in a future version.
+            Use the :meth:`.SeriesGroupBy.ffill` or :meth:`.SeriesGroupBy.bfill`
+            for forward or backward filling instead. If you want to fill with a
+            single value, use :meth:`Series.fillna` instead.
+
+        Parameters
+        ----------
+        value : scalar, dict, Series, or DataFrame
+            Value to use to fill holes (e.g. 0), alternately a
+            dict/Series/DataFrame of values specifying which value to use for
+            each index (for a Series) or column (for a DataFrame).  Values not
+            in the dict/Series/DataFrame will not be filled. This value cannot
+            be a list. Users wanting to use the ``value`` argument and not ``method``
+            should prefer :meth:`.Series.fillna` as this
+            will produce the same result and be more performant.
+        method : {{'bfill', 'ffill', None}}, default None
+            Method to use for filling holes. ``'ffill'`` will propagate
+            the last valid observation forward within a group.
+            ``'bfill'`` will use next valid observation to fill the gap.
+        axis : {0 or 'index', 1 or 'columns'}
+            Unused, only for compatibility with :meth:`DataFrameGroupBy.fillna`.
+        inplace : bool, default False
+            Broken. Do not set to True.
+        limit : int, default None
+            If method is specified, this is the maximum number of consecutive
+            NaN values to forward/backward fill within a group. In other words,
+            if there is a gap with more than this number of consecutive NaNs,
+            it will only be partially filled. If method is not specified, this is the
+            maximum number of entries along the entire axis where NaNs will be
+            filled. Must be greater than 0 if not None.
+        downcast : dict, default is None
+            A dict of item->dtype of what to downcast if possible,
+            or the string 'infer' which will try to downcast to an appropriate
+            equal type (e.g. float64 to int64 if possible).
+
+        Returns
+        -------
+        Series
+            Object with missing values filled within groups.
+
+        See Also
+        --------
+        ffill : Forward fill values within a group.
+        bfill : Backward fill values within a group.
+
+        Examples
+        --------
+        For SeriesGroupBy:
+
+        >>> lst = ['cat', 'cat', 'cat', 'mouse', 'mouse']
+        >>> ser = pd.Series([1, None, None, 2, None], index=lst)
+        >>> ser
+        cat    1.0
+        cat    NaN
+        cat    NaN
+        mouse  2.0
+        mouse  NaN
+        dtype: float64
+        >>> ser.groupby(level=0).fillna(0, limit=1)
+        cat    1.0
+        cat    0.0
+        cat    NaN
+        mouse  2.0
+        mouse  0.0
+        dtype: float64
+        """
+        warnings.warn(
+            f"{type(self).__name__}.fillna is deprecated and "
+            "will be removed in a future version. Use obj.ffill() or obj.bfill() "
+            "for forward or backward filling instead. If you want to fill with a "
+            f"single value, use {type(self.obj).__name__}.fillna instead",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        result = self._op_via_apply(
+            "fillna",
+            value=value,
+            method=method,
+            axis=axis,
+            inplace=inplace,
+            limit=limit,
+            downcast=downcast,
+        )
+        return result
+
+    def take(
+        self,
+        indices: TakeIndexer,
+        axis: Axis | lib.NoDefault = lib.no_default,
+        **kwargs,
+    ) -> Series:
+        """
+        Return the elements in the given *positional* indices in each group.
+
+        This means that we are not indexing according to actual values in
+        the index attribute of the object. We are indexing according to the
+        actual position of the element in the object.
+
+        If a requested index does not exist for some group, this method will raise.
+        To get similar behavior that ignores indices that don't exist, see
+        :meth:`.SeriesGroupBy.nth`.
+
+        Parameters
+        ----------
+        indices : array-like
+            An array of ints indicating which positions to take in each group.
+        axis : {0 or 'index', 1 or 'columns', None}, default 0
+            The axis on which to select elements. ``0`` means that we are
+            selecting rows, ``1`` means that we are selecting columns.
+            For `SeriesGroupBy` this parameter is unused and defaults to 0.
+
+            .. deprecated:: 2.1.0
+                For axis=1, operate on the underlying object instead. Otherwise
+                the axis keyword is not necessary.
+
+        **kwargs
+            For compatibility with :meth:`numpy.take`. Has no effect on the
+            output.
+
+        Returns
+        -------
+        Series
+            A Series containing the elements taken from each group.
+
+        See Also
+        --------
+        Series.take : Take elements from a Series along an axis.
+        Series.loc : Select a subset of a DataFrame by labels.
+        Series.iloc : Select a subset of a DataFrame by positions.
+        numpy.take : Take elements from an array along an axis.
+        SeriesGroupBy.nth : Similar to take, won't raise if indices don't exist.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([('falcon', 'bird', 389.0),
+        ...                    ('parrot', 'bird', 24.0),
+        ...                    ('lion', 'mammal', 80.5),
+        ...                    ('monkey', 'mammal', np.nan),
+        ...                    ('rabbit', 'mammal', 15.0)],
+        ...                   columns=['name', 'class', 'max_speed'],
+        ...                   index=[4, 3, 2, 1, 0])
+        >>> df
+             name   class  max_speed
+        4  falcon    bird      389.0
+        3  parrot    bird       24.0
+        2    lion  mammal       80.5
+        1  monkey  mammal        NaN
+        0  rabbit  mammal       15.0
+        >>> gb = df["name"].groupby([1, 1, 2, 2, 2])
+
+        Take elements at positions 0 and 1 along the axis 0 in each group (default).
+
+        >>> gb.take([0, 1])
+        1  4    falcon
+           3    parrot
+        2  2      lion
+           1    monkey
+        Name: name, dtype: object
+
+        We may take elements using negative integers for positive indices,
+        starting from the end of the object, just like with Python lists.
+
+        >>> gb.take([-1, -2])
+        1  3    parrot
+           4    falcon
+        2  0    rabbit
+           1    monkey
+        Name: name, dtype: object
+        """
+        result = self._op_via_apply("take", indices=indices, axis=axis, **kwargs)
+        return result
+
+    def skew(
+        self,
+        axis: Axis | lib.NoDefault = lib.no_default,
+        skipna: bool = True,
+        numeric_only: bool = False,
+        **kwargs,
+    ) -> Series:
+        """
+        Return unbiased skew within groups.
+
+        Normalized by N-1.
+
+        Parameters
+        ----------
+        axis : {0 or 'index', 1 or 'columns', None}, default 0
+            Axis for the function to be applied on.
+            This parameter is only for compatibility with DataFrame and is unused.
+
+            .. deprecated:: 2.1.0
+                For axis=1, operate on the underlying object instead. Otherwise
+                the axis keyword is not necessary.
+
+        skipna : bool, default True
+            Exclude NA/null values when computing the result.
+
+        numeric_only : bool, default False
+            Include only float, int, boolean columns. Not implemented for Series.
+
+        **kwargs
+            Additional keyword arguments to be passed to the function.
+
+        Returns
+        -------
+        Series
+
+        See Also
+        --------
+        Series.skew : Return unbiased skew over requested axis.
+
+        Examples
+        --------
+        >>> ser = pd.Series([390., 350., 357., np.nan, 22., 20., 30.],
+        ...                 index=['Falcon', 'Falcon', 'Falcon', 'Falcon',
+        ...                        'Parrot', 'Parrot', 'Parrot'],
+        ...                 name="Max Speed")
+        >>> ser
+        Falcon    390.0
+        Falcon    350.0
+        Falcon    357.0
+        Falcon      NaN
+        Parrot     22.0
+        Parrot     20.0
+        Parrot     30.0
+        Name: Max Speed, dtype: float64
+        >>> ser.groupby(level=0).skew()
+        Falcon    1.525174
+        Parrot    1.457863
+        Name: Max Speed, dtype: float64
+        >>> ser.groupby(level=0).skew(skipna=False)
+        Falcon         NaN
+        Parrot    1.457863
+        Name: Max Speed, dtype: float64
+        """
+        if axis is lib.no_default:
+            axis = 0
+
+        if axis != 0:
+            result = self._op_via_apply(
+                "skew",
+                axis=axis,
+                skipna=skipna,
+                numeric_only=numeric_only,
+                **kwargs,
+            )
+            return result
+
+        def alt(obj):
+            # This should not be reached since the cython path should raise
+            #  TypeError and not NotImplementedError.
+            raise TypeError(f"'skew' is not supported for dtype={obj.dtype}")
+
+        return self._cython_agg_general(
+            "skew", alt=alt, skipna=skipna, numeric_only=numeric_only, **kwargs
+        )
+
+    @property
+    @doc(Series.plot.__doc__)
+    def plot(self) -> GroupByPlot:
+        result = GroupByPlot(self)
+        return result
+
+    @doc(Series.nlargest.__doc__)
+    def nlargest(
+        self, n: int = 5, keep: Literal["first", "last", "all"] = "first"
+    ) -> Series:
+        f = partial(Series.nlargest, n=n, keep=keep)
+        data = self._obj_with_exclusions
+        # Don't change behavior if result index happens to be the same, i.e.
+        # already ordered and n >= all group sizes.
+        result = self._python_apply_general(f, data, not_indexed_same=True)
+        return result
+
+    @doc(Series.nsmallest.__doc__)
+    def nsmallest(
+        self, n: int = 5, keep: Literal["first", "last", "all"] = "first"
+    ) -> Series:
+        f = partial(Series.nsmallest, n=n, keep=keep)
+        data = self._obj_with_exclusions
+        # Don't change behavior if result index happens to be the same, i.e.
+        # already ordered and n >= all group sizes.
+        result = self._python_apply_general(f, data, not_indexed_same=True)
+        return result
+
+    @doc(Series.idxmin.__doc__)
+    def idxmin(
+        self, axis: Axis | lib.NoDefault = lib.no_default, skipna: bool = True
+    ) -> Series:
+        return self._idxmax_idxmin("idxmin", axis=axis, skipna=skipna)
+
+    @doc(Series.idxmax.__doc__)
+    def idxmax(
+        self, axis: Axis | lib.NoDefault = lib.no_default, skipna: bool = True
+    ) -> Series:
+        return self._idxmax_idxmin("idxmax", axis=axis, skipna=skipna)
+
+    @doc(Series.corr.__doc__)
+    def corr(
+        self,
+        other: Series,
+        method: CorrelationMethod = "pearson",
+        min_periods: int | None = None,
+    ) -> Series:
+        result = self._op_via_apply(
+            "corr", other=other, method=method, min_periods=min_periods
+        )
+        return result
+
+    @doc(Series.cov.__doc__)
+    def cov(
+        self, other: Series, min_periods: int | None = None, ddof: int | None = 1
+    ) -> Series:
+        result = self._op_via_apply(
+            "cov", other=other, min_periods=min_periods, ddof=ddof
+        )
+        return result
+
+    @property
+    def is_monotonic_increasing(self) -> Series:
+        """
+        Return whether each group's values are monotonically increasing.
+
+        Returns
+        -------
+        Series
+
+        Examples
+        --------
+        >>> s = pd.Series([2, 1, 3, 4], index=['Falcon', 'Falcon', 'Parrot', 'Parrot'])
+        >>> s.groupby(level=0).is_monotonic_increasing
+        Falcon    False
+        Parrot     True
+        dtype: bool
+        """
+        return self.apply(lambda ser: ser.is_monotonic_increasing)
+
+    @property
+    def is_monotonic_decreasing(self) -> Series:
+        """
+        Return whether each group's values are monotonically decreasing.
+
+        Returns
+        -------
+        Series
+
+        Examples
+        --------
+        >>> s = pd.Series([2, 1, 3, 4], index=['Falcon', 'Falcon', 'Parrot', 'Parrot'])
+        >>> s.groupby(level=0).is_monotonic_decreasing
+        Falcon     True
+        Parrot    False
+        dtype: bool
+        """
+        return self.apply(lambda ser: ser.is_monotonic_decreasing)
+
+    @doc(Series.hist.__doc__)
+    def hist(
+        self,
+        by=None,
+        ax=None,
+        grid: bool = True,
+        xlabelsize: int | None = None,
+        xrot: float | None = None,
+        ylabelsize: int | None = None,
+        yrot: float | None = None,
+        figsize: tuple[int, int] | None = None,
+        bins: int | Sequence[int] = 10,
+        backend: str | None = None,
+        legend: bool = False,
+        **kwargs,
+    ):
+        result = self._op_via_apply(
+            "hist",
+            by=by,
+            ax=ax,
+            grid=grid,
+            xlabelsize=xlabelsize,
+            xrot=xrot,
+            ylabelsize=ylabelsize,
+            yrot=yrot,
+            figsize=figsize,
+            bins=bins,
+            backend=backend,
+            legend=legend,
+            **kwargs,
+        )
+        return result
+
+    @property
+    @doc(Series.dtype.__doc__)
+    def dtype(self) -> Series:
+        return self.apply(lambda ser: ser.dtype)
+
+    def unique(self) -> Series:
+        """
+        Return unique values for each group.
+
+        It returns unique values for each of the grouped values. Returned in
+        order of appearance. Hash table-based unique, therefore does NOT sort.
+
+        Returns
+        -------
+        Series
+            Unique values for each of the grouped values.
+
+        See Also
+        --------
+        Series.unique : Return unique values of Series object.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([('Chihuahua', 'dog', 6.1),
+        ...                    ('Beagle', 'dog', 15.2),
+        ...                    ('Chihuahua', 'dog', 6.9),
+        ...                    ('Persian', 'cat', 9.2),
+        ...                    ('Chihuahua', 'dog', 7),
+        ...                    ('Persian', 'cat', 8.8)],
+        ...                   columns=['breed', 'animal', 'height_in'])
+        >>> df
+               breed     animal   height_in
+        0  Chihuahua        dog         6.1
+        1     Beagle        dog        15.2
+        2  Chihuahua        dog         6.9
+        3    Persian        cat         9.2
+        4  Chihuahua        dog         7.0
+        5    Persian        cat         8.8
+        >>> ser = df.groupby('animal')['breed'].unique()
+        >>> ser
+        animal
+        cat              [Persian]
+        dog    [Chihuahua, Beagle]
+        Name: breed, dtype: object
+        """
+        result = self._op_via_apply("unique")
+        return result
+
+
+class DataFrameGroupBy(GroupBy[DataFrame]):
+    _agg_examples_doc = dedent(
+        """
+    Examples
+    --------
+    >>> data = {"A": [1, 1, 2, 2],
+    ...         "B": [1, 2, 3, 4],
+    ...         "C": [0.362838, 0.227877, 1.267767, -0.562860]}
+    >>> df = pd.DataFrame(data)
+    >>> df
+       A  B         C
+    0  1  1  0.362838
+    1  1  2  0.227877
+    2  2  3  1.267767
+    3  2  4 -0.562860
+
+    The aggregation is for each column.
+
+    >>> df.groupby('A').agg('min')
+       B         C
+    A
+    1  1  0.227877
+    2  3 -0.562860
+
+    Multiple aggregations
+
+    >>> df.groupby('A').agg(['min', 'max'])
+        B             C
+      min max       min       max
+    A
+    1   1   2  0.227877  0.362838
+    2   3   4 -0.562860  1.267767
+
+    Select a column for aggregation
+
+    >>> df.groupby('A').B.agg(['min', 'max'])
+       min  max
+    A
+    1    1    2
+    2    3    4
+
+    User-defined function for aggregation
+
+    >>> df.groupby('A').agg(lambda x: sum(x) + 2)
+        B	       C
+    A
+    1	5	2.590715
+    2	9	2.704907
+
+    Different aggregations per column
+
+    >>> df.groupby('A').agg({'B': ['min', 'max'], 'C': 'sum'})
+        B             C
+      min max       sum
+    A
+    1   1   2  0.590715
+    2   3   4  0.704907
+
+    To control the output names with different aggregations per column,
+    pandas supports "named aggregation"
+
+    >>> df.groupby("A").agg(
+    ...     b_min=pd.NamedAgg(column="B", aggfunc="min"),
+    ...     c_sum=pd.NamedAgg(column="C", aggfunc="sum")
+    ... )
+       b_min     c_sum
+    A
+    1      1  0.590715
+    2      3  0.704907
+
+    - The keywords are the *output* column names
+    - The values are tuples whose first element is the column to select
+      and the second element is the aggregation to apply to that column.
+      Pandas provides the ``pandas.NamedAgg`` namedtuple with the fields
+      ``['column', 'aggfunc']`` to make it clearer what the arguments are.
+      As usual, the aggregation can be a callable or a string alias.
+
+    See :ref:`groupby.aggregate.named` for more.
+
+    .. versionchanged:: 1.3.0
+
+        The resulting dtype will reflect the return value of the aggregating function.
+
+    >>> df.groupby("A")[["B"]].agg(lambda x: x.astype(float).min())
+          B
+    A
+    1   1.0
+    2   3.0
+    """
+    )
+
+    @doc(_agg_template_frame, examples=_agg_examples_doc, klass="DataFrame")
+    def aggregate(self, func=None, *args, engine=None, engine_kwargs=None, **kwargs):
+        relabeling, func, columns, order = reconstruct_func(func, **kwargs)
+        func = maybe_mangle_lambdas(func)
+
+        if maybe_use_numba(engine):
+            # Not all agg functions support numba, only propagate numba kwargs
+            # if user asks for numba
+            kwargs["engine"] = engine
+            kwargs["engine_kwargs"] = engine_kwargs
+
+        op = GroupByApply(self, func, args=args, kwargs=kwargs)
+        result = op.agg()
+        if not is_dict_like(func) and result is not None:
+            # GH #52849
+            if not self.as_index and is_list_like(func):
+                return result.reset_index()
+            else:
+                return result
+        elif relabeling:
+            # this should be the only (non-raising) case with relabeling
+            # used reordered index of columns
+            result = cast(DataFrame, result)
+            result = result.iloc[:, order]
+            result = cast(DataFrame, result)
+            # error: Incompatible types in assignment (expression has type
+            # "Optional[List[str]]", variable has type
+            # "Union[Union[Union[ExtensionArray, ndarray[Any, Any]],
+            # Index, Series], Sequence[Any]]")
+            result.columns = columns  # type: ignore[assignment]
+
+        if result is None:
+            # Remove the kwargs we inserted
+            # (already stored in engine, engine_kwargs arguments)
+            if "engine" in kwargs:
+                del kwargs["engine"]
+                del kwargs["engine_kwargs"]
+            # at this point func is not a str, list-like, dict-like,
+            # or a known callable(e.g. sum)
+            if maybe_use_numba(engine):
+                return self._aggregate_with_numba(
+                    func, *args, engine_kwargs=engine_kwargs, **kwargs
+                )
+            # grouper specific aggregations
+            if self._grouper.nkeys > 1:
+                # test_groupby_as_index_series_scalar gets here with 'not self.as_index'
+                return self._python_agg_general(func, *args, **kwargs)
+            elif args or kwargs:
+                # test_pass_args_kwargs gets here (with and without as_index)
+                # can't return early
+                result = self._aggregate_frame(func, *args, **kwargs)
+
+            elif self.axis == 1:
+                # _aggregate_multiple_funcs does not allow self.axis == 1
+                # Note: axis == 1 precludes 'not self.as_index', see __init__
+                result = self._aggregate_frame(func)
+                return result
+
+            else:
+                # try to treat as if we are passing a list
+                gba = GroupByApply(self, [func], args=(), kwargs={})
+                try:
+                    result = gba.agg()
+
+                except ValueError as err:
+                    if "No objects to concatenate" not in str(err):
+                        raise
+                    # _aggregate_frame can fail with e.g. func=Series.mode,
+                    # where it expects 1D values but would be getting 2D values
+                    # In other tests, using aggregate_frame instead of GroupByApply
+                    #  would give correct values but incorrect dtypes
+                    #  object vs float64 in test_cython_agg_empty_buckets
+                    #  float64 vs int64 in test_category_order_apply
+                    result = self._aggregate_frame(func)
+
+                else:
+                    # GH#32040, GH#35246
+                    # e.g. test_groupby_as_index_select_column_sum_empty_df
+                    result = cast(DataFrame, result)
+                    result.columns = self._obj_with_exclusions.columns.copy()
+
+        if not self.as_index:
+            result = self._insert_inaxis_grouper(result)
+            result.index = default_index(len(result))
+
+        return result
+
+    agg = aggregate
+
+    def _python_agg_general(self, func, *args, **kwargs):
+        orig_func = func
+        func = com.is_builtin_func(func)
+        if orig_func != func:
+            alias = com._builtin_table_alias[func]
+            warn_alias_replacement(self, orig_func, alias)
+        f = lambda x: func(x, *args, **kwargs)
+
+        if self.ngroups == 0:
+            # e.g. test_evaluate_with_empty_groups different path gets different
+            #  result dtype in empty case.
+            return self._python_apply_general(f, self._selected_obj, is_agg=True)
+
+        obj = self._obj_with_exclusions
+        if self.axis == 1:
+            obj = obj.T
+
+        if not len(obj.columns):
+            # e.g. test_margins_no_values_no_cols
+            return self._python_apply_general(f, self._selected_obj)
+
+        output: dict[int, ArrayLike] = {}
+        for idx, (name, ser) in enumerate(obj.items()):
+            result = self._grouper.agg_series(ser, f)
+            output[idx] = result
+
+        res = self.obj._constructor(output)
+        res.columns = obj.columns.copy(deep=False)
+        return self._wrap_aggregated_output(res)
+
+    def _aggregate_frame(self, func, *args, **kwargs) -> DataFrame:
+        if self._grouper.nkeys != 1:
+            raise AssertionError("Number of keys must be 1")
+
+        obj = self._obj_with_exclusions
+
+        result: dict[Hashable, NDFrame | np.ndarray] = {}
+        for name, grp_df in self._grouper.get_iterator(obj, self.axis):
+            fres = func(grp_df, *args, **kwargs)
+            result[name] = fres
+
+        result_index = self._grouper.result_index
+        other_ax = obj.axes[1 - self.axis]
+        out = self.obj._constructor(result, index=other_ax, columns=result_index)
+        if self.axis == 0:
+            out = out.T
+
+        return out
+
+    def _wrap_applied_output(
+        self,
+        data: DataFrame,
+        values: list,
+        not_indexed_same: bool = False,
+        is_transform: bool = False,
+    ):
+        if len(values) == 0:
+            if is_transform:
+                # GH#47787 see test_group_on_empty_multiindex
+                res_index = data.index
+            else:
+                res_index = self._grouper.result_index
+
+            result = self.obj._constructor(index=res_index, columns=data.columns)
+            result = result.astype(data.dtypes, copy=False)
+            return result
+
+        # GH12824
+        # using values[0] here breaks test_groupby_apply_none_first
+        first_not_none = next(com.not_none(*values), None)
+
+        if first_not_none is None:
+            # GH9684 - All values are None, return an empty frame.
+            return self.obj._constructor()
+        elif isinstance(first_not_none, DataFrame):
+            return self._concat_objects(
+                values,
+                not_indexed_same=not_indexed_same,
+                is_transform=is_transform,
+            )
+
+        key_index = self._grouper.result_index if self.as_index else None
+
+        if isinstance(first_not_none, (np.ndarray, Index)):
+            # GH#1738: values is list of arrays of unequal lengths
+            #  fall through to the outer else clause
+            # TODO: sure this is right?  we used to do this
+            #  after raising AttributeError above
+            # GH 18930
+            if not is_hashable(self._selection):
+                # error: Need type annotation for "name"
+                name = tuple(self._selection)  # type: ignore[var-annotated, arg-type]
+            else:
+                # error: Incompatible types in assignment
+                # (expression has type "Hashable", variable
+                # has type "Tuple[Any, ...]")
+                name = self._selection  # type: ignore[assignment]
+            return self.obj._constructor_sliced(values, index=key_index, name=name)
+        elif not isinstance(first_not_none, Series):
+            # values are not series or array-like but scalars
+            # self._selection not passed through to Series as the
+            # result should not take the name of original selection
+            # of columns
+            if self.as_index:
+                return self.obj._constructor_sliced(values, index=key_index)
+            else:
+                result = self.obj._constructor(values, columns=[self._selection])
+                result = self._insert_inaxis_grouper(result)
+                return result
+        else:
+            # values are Series
+            return self._wrap_applied_output_series(
+                values,
+                not_indexed_same,
+                first_not_none,
+                key_index,
+                is_transform,
+            )
+
+    def _wrap_applied_output_series(
+        self,
+        values: list[Series],
+        not_indexed_same: bool,
+        first_not_none,
+        key_index: Index | None,
+        is_transform: bool,
+    ) -> DataFrame | Series:
+        kwargs = first_not_none._construct_axes_dict()
+        backup = Series(**kwargs)
+        values = [x if (x is not None) else backup for x in values]
+
+        all_indexed_same = all_indexes_same(x.index for x in values)
+
+        if not all_indexed_same:
+            # GH 8467
+            return self._concat_objects(
+                values,
+                not_indexed_same=True,
+                is_transform=is_transform,
+            )
+
+        # Combine values
+        # vstack+constructor is faster than concat and handles MI-columns
+        stacked_values = np.vstack([np.asarray(v) for v in values])
+
+        if self.axis == 0:
+            index = key_index
+            columns = first_not_none.index.copy()
+            if columns.name is None:
+                # GH6124 - propagate name of Series when it's consistent
+                names = {v.name for v in values}
+                if len(names) == 1:
+                    columns.name = next(iter(names))
+        else:
+            index = first_not_none.index
+            columns = key_index
+            stacked_values = stacked_values.T
+
+        if stacked_values.dtype == object:
+            # We'll have the DataFrame constructor do inference
+            stacked_values = stacked_values.tolist()
+        result = self.obj._constructor(stacked_values, index=index, columns=columns)
+
+        if not self.as_index:
+            result = self._insert_inaxis_grouper(result)
+
+        return self._reindex_output(result)
+
+    def _cython_transform(
+        self,
+        how: str,
+        numeric_only: bool = False,
+        axis: AxisInt = 0,
+        **kwargs,
+    ) -> DataFrame:
+        assert axis == 0  # handled by caller
+
+        # With self.axis == 0, we have multi-block tests
+        #  e.g. test_rank_min_int, test_cython_transform_frame
+        #  test_transform_numeric_ret
+        # With self.axis == 1, _get_data_to_aggregate does a transpose
+        #  so we always have a single block.
+        mgr: Manager2D = self._get_data_to_aggregate(
+            numeric_only=numeric_only, name=how
+        )
+
+        def arr_func(bvalues: ArrayLike) -> ArrayLike:
+            return self._grouper._cython_operation(
+                "transform", bvalues, how, 1, **kwargs
+            )
+
+        # We could use `mgr.apply` here and not have to set_axis, but
+        #  we would have to do shape gymnastics for ArrayManager compat
+        res_mgr = mgr.grouped_reduce(arr_func)
+        res_mgr.set_axis(1, mgr.axes[1])
+
+        res_df = self.obj._constructor_from_mgr(res_mgr, axes=res_mgr.axes)
+        res_df = self._maybe_transpose_result(res_df)
+        return res_df
+
+    def _transform_general(self, func, engine, engine_kwargs, *args, **kwargs):
+        if maybe_use_numba(engine):
+            return self._transform_with_numba(
+                func, *args, engine_kwargs=engine_kwargs, **kwargs
+            )
+        from pandas.core.reshape.concat import concat
+
+        applied = []
+        obj = self._obj_with_exclusions
+        gen = self._grouper.get_iterator(obj, axis=self.axis)
+        fast_path, slow_path = self._define_paths(func, *args, **kwargs)
+
+        # Determine whether to use slow or fast path by evaluating on the first group.
+        # Need to handle the case of an empty generator and process the result so that
+        # it does not need to be computed again.
+        try:
+            name, group = next(gen)
+        except StopIteration:
+            pass
+        else:
+            # 2023-02-27 No tests broken by disabling this pinning
+            object.__setattr__(group, "name", name)
+            try:
+                path, res = self._choose_path(fast_path, slow_path, group)
+            except ValueError as err:
+                # e.g. test_transform_with_non_scalar_group
+                msg = "transform must return a scalar value for each group"
+                raise ValueError(msg) from err
+            if group.size > 0:
+                res = _wrap_transform_general_frame(self.obj, group, res)
+                applied.append(res)
+
+        # Compute and process with the remaining groups
+        for name, group in gen:
+            if group.size == 0:
+                continue
+            # 2023-02-27 No tests broken by disabling this pinning
+            object.__setattr__(group, "name", name)
+            res = path(group)
+
+            res = _wrap_transform_general_frame(self.obj, group, res)
+            applied.append(res)
+
+        concat_index = obj.columns if self.axis == 0 else obj.index
+        other_axis = 1 if self.axis == 0 else 0  # switches between 0 & 1
+        concatenated = concat(applied, axis=self.axis, verify_integrity=False)
+        concatenated = concatenated.reindex(concat_index, axis=other_axis, copy=False)
+        return self._set_result_index_ordered(concatenated)
+
+    __examples_dataframe_doc = dedent(
+        """
+    >>> df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
+    ...                           'foo', 'bar'],
+    ...                    'B' : ['one', 'one', 'two', 'three',
+    ...                           'two', 'two'],
+    ...                    'C' : [1, 5, 5, 2, 5, 5],
+    ...                    'D' : [2.0, 5., 8., 1., 2., 9.]})
+    >>> grouped = df.groupby('A')[['C', 'D']]
+    >>> grouped.transform(lambda x: (x - x.mean()) / x.std())
+            C         D
+    0 -1.154701 -0.577350
+    1  0.577350  0.000000
+    2  0.577350  1.154701
+    3 -1.154701 -1.000000
+    4  0.577350 -0.577350
+    5  0.577350  1.000000
+
+    Broadcast result of the transformation
+
+    >>> grouped.transform(lambda x: x.max() - x.min())
+        C    D
+    0  4.0  6.0
+    1  3.0  8.0
+    2  4.0  6.0
+    3  3.0  8.0
+    4  4.0  6.0
+    5  3.0  8.0
+
+    >>> grouped.transform("mean")
+        C    D
+    0  3.666667  4.0
+    1  4.000000  5.0
+    2  3.666667  4.0
+    3  4.000000  5.0
+    4  3.666667  4.0
+    5  4.000000  5.0
+
+    .. versionchanged:: 1.3.0
+
+    The resulting dtype will reflect the return value of the passed ``func``,
+    for example:
+
+    >>> grouped.transform(lambda x: x.astype(int).max())
+    C  D
+    0  5  8
+    1  5  9
+    2  5  8
+    3  5  9
+    4  5  8
+    5  5  9
+    """
+    )
+
+    @Substitution(klass="DataFrame", example=__examples_dataframe_doc)
+    @Appender(_transform_template)
+    def transform(self, func, *args, engine=None, engine_kwargs=None, **kwargs):
+        return self._transform(
+            func, *args, engine=engine, engine_kwargs=engine_kwargs, **kwargs
+        )
+
+    def _define_paths(self, func, *args, **kwargs):
+        if isinstance(func, str):
+            fast_path = lambda group: getattr(group, func)(*args, **kwargs)
+            slow_path = lambda group: group.apply(
+                lambda x: getattr(x, func)(*args, **kwargs), axis=self.axis
+            )
+        else:
+            fast_path = lambda group: func(group, *args, **kwargs)
+            slow_path = lambda group: group.apply(
+                lambda x: func(x, *args, **kwargs), axis=self.axis
+            )
+        return fast_path, slow_path
+
+    def _choose_path(self, fast_path: Callable, slow_path: Callable, group: DataFrame):
+        path = slow_path
+        res = slow_path(group)
+
+        if self.ngroups == 1:
+            # no need to evaluate multiple paths when only
+            # a single group exists
+            return path, res
+
+        # if we make it here, test if we can use the fast path
+        try:
+            res_fast = fast_path(group)
+        except AssertionError:
+            raise  # pragma: no cover
+        except Exception:
+            # GH#29631 For user-defined function, we can't predict what may be
+            #  raised; see test_transform.test_transform_fastpath_raises
+            return path, res
+
+        # verify fast path returns either:
+        # a DataFrame with columns equal to group.columns
+        # OR a Series with index equal to group.columns
+        if isinstance(res_fast, DataFrame):
+            if not res_fast.columns.equals(group.columns):
+                return path, res
+        elif isinstance(res_fast, Series):
+            if not res_fast.index.equals(group.columns):
+                return path, res
+        else:
+            return path, res
+
+        if res_fast.equals(res):
+            path = fast_path
+
+        return path, res
+
+    def filter(self, func, dropna: bool = True, *args, **kwargs):
+        """
+        Filter elements from groups that don't satisfy a criterion.
+
+        Elements from groups are filtered if they do not satisfy the
+        boolean criterion specified by func.
+
+        Parameters
+        ----------
+        func : function
+            Criterion to apply to each group. Should return True or False.
+        dropna : bool
+            Drop groups that do not pass the filter. True by default; if False,
+            groups that evaluate False are filled with NaNs.
+
+        Returns
+        -------
+        DataFrame
+
+        Notes
+        -----
+        Each subframe is endowed the attribute 'name' in case you need to know
+        which group you are working on.
+
+        Functions that mutate the passed object can produce unexpected
+        behavior or errors and are not supported. See :ref:`gotchas.udf-mutation`
+        for more details.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
+        ...                           'foo', 'bar'],
+        ...                    'B' : [1, 2, 3, 4, 5, 6],
+        ...                    'C' : [2.0, 5., 8., 1., 2., 9.]})
+        >>> grouped = df.groupby('A')
+        >>> grouped.filter(lambda x: x['B'].mean() > 3.)
+             A  B    C
+        1  bar  2  5.0
+        3  bar  4  1.0
+        5  bar  6  9.0
+        """
+        indices = []
+
+        obj = self._selected_obj
+        gen = self._grouper.get_iterator(obj, axis=self.axis)
+
+        for name, group in gen:
+            # 2023-02-27 no tests are broken this pinning, but it is documented in the
+            #  docstring above.
+            object.__setattr__(group, "name", name)
+
+            res = func(group, *args, **kwargs)
+
+            try:
+                res = res.squeeze()
+            except AttributeError:  # allow e.g., scalars and frames to pass
+                pass
+
+            # interpret the result of the filter
+            if is_bool(res) or (is_scalar(res) and isna(res)):
+                if notna(res) and res:
+                    indices.append(self._get_index(name))
+            else:
+                # non scalars aren't allowed
+                raise TypeError(
+                    f"filter function returned a {type(res).__name__}, "
+                    "but expected a scalar bool"
+                )
+
+        return self._apply_filter(indices, dropna)
+
+    def __getitem__(self, key) -> DataFrameGroupBy | SeriesGroupBy:
+        if self.axis == 1:
+            # GH 37725
+            raise ValueError("Cannot subset columns when using axis=1")
+        # per GH 23566
+        if isinstance(key, tuple) and len(key) > 1:
+            # if len == 1, then it becomes a SeriesGroupBy and this is actually
+            # valid syntax, so don't raise
+            raise ValueError(
+                "Cannot subset columns with a tuple with more than one element. "
+                "Use a list instead."
+            )
+        return super().__getitem__(key)
+
+    def _gotitem(self, key, ndim: int, subset=None):
+        """
+        sub-classes to define
+        return a sliced object
+
+        Parameters
+        ----------
+        key : string / list of selections
+        ndim : {1, 2}
+            requested ndim of result
+        subset : object, default None
+            subset to act on
+        """
+        if ndim == 2:
+            if subset is None:
+                subset = self.obj
+            return DataFrameGroupBy(
+                subset,
+                self.keys,
+                axis=self.axis,
+                level=self.level,
+                grouper=self._grouper,
+                exclusions=self.exclusions,
+                selection=key,
+                as_index=self.as_index,
+                sort=self.sort,
+                group_keys=self.group_keys,
+                observed=self.observed,
+                dropna=self.dropna,
+            )
+        elif ndim == 1:
+            if subset is None:
+                subset = self.obj[key]
+            return SeriesGroupBy(
+                subset,
+                self.keys,
+                level=self.level,
+                grouper=self._grouper,
+                exclusions=self.exclusions,
+                selection=key,
+                as_index=self.as_index,
+                sort=self.sort,
+                group_keys=self.group_keys,
+                observed=self.observed,
+                dropna=self.dropna,
+            )
+
+        raise AssertionError("invalid ndim for _gotitem")
+
+    def _get_data_to_aggregate(
+        self, *, numeric_only: bool = False, name: str | None = None
+    ) -> Manager2D:
+        obj = self._obj_with_exclusions
+        if self.axis == 1:
+            mgr = obj.T._mgr
+        else:
+            mgr = obj._mgr
+
+        if numeric_only:
+            mgr = mgr.get_numeric_data()
+        return mgr
+
+    def _wrap_agged_manager(self, mgr: Manager2D) -> DataFrame:
+        return self.obj._constructor_from_mgr(mgr, axes=mgr.axes)
+
+    def _apply_to_column_groupbys(self, func) -> DataFrame:
+        from pandas.core.reshape.concat import concat
+
+        obj = self._obj_with_exclusions
+        columns = obj.columns
+        sgbs = [
+            SeriesGroupBy(
+                obj.iloc[:, i],
+                selection=colname,
+                grouper=self._grouper,
+                exclusions=self.exclusions,
+                observed=self.observed,
+            )
+            for i, colname in enumerate(obj.columns)
+        ]
+        results = [func(sgb) for sgb in sgbs]
+
+        if not len(results):
+            # concat would raise
+            res_df = DataFrame([], columns=columns, index=self._grouper.result_index)
+        else:
+            res_df = concat(results, keys=columns, axis=1)
+
+        if not self.as_index:
+            res_df.index = default_index(len(res_df))
+            res_df = self._insert_inaxis_grouper(res_df)
+        return res_df
+
+    def nunique(self, dropna: bool = True) -> DataFrame:
+        """
+        Return DataFrame with counts of unique elements in each position.
+
+        Parameters
+        ----------
+        dropna : bool, default True
+            Don't include NaN in the counts.
+
+        Returns
+        -------
+        nunique: DataFrame
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'id': ['spam', 'egg', 'egg', 'spam',
+        ...                           'ham', 'ham'],
+        ...                    'value1': [1, 5, 5, 2, 5, 5],
+        ...                    'value2': list('abbaxy')})
+        >>> df
+             id  value1 value2
+        0  spam       1      a
+        1   egg       5      b
+        2   egg       5      b
+        3  spam       2      a
+        4   ham       5      x
+        5   ham       5      y
+
+        >>> df.groupby('id').nunique()
+              value1  value2
+        id
+        egg        1       1
+        ham        1       2
+        spam       2       1
+
+        Check for rows with the same id but conflicting values:
+
+        >>> df.groupby('id').filter(lambda g: (g.nunique() > 1).any())
+             id  value1 value2
+        0  spam       1      a
+        3  spam       2      a
+        4   ham       5      x
+        5   ham       5      y
+        """
+
+        if self.axis != 0:
+            # see test_groupby_crash_on_nunique
+            return self._python_apply_general(
+                lambda sgb: sgb.nunique(dropna), self._obj_with_exclusions, is_agg=True
+            )
+
+        return self._apply_to_column_groupbys(lambda sgb: sgb.nunique(dropna))
+
+    def idxmax(
+        self,
+        axis: Axis | None | lib.NoDefault = lib.no_default,
+        skipna: bool = True,
+        numeric_only: bool = False,
+    ) -> DataFrame:
+        """
+        Return index of first occurrence of maximum over requested axis.
+
+        NA/null values are excluded.
+
+        Parameters
+        ----------
+        axis : {{0 or 'index', 1 or 'columns'}}, default None
+            The axis to use. 0 or 'index' for row-wise, 1 or 'columns' for column-wise.
+            If axis is not provided, grouper's axis is used.
+
+            .. versionchanged:: 2.0.0
+
+            .. deprecated:: 2.1.0
+                For axis=1, operate on the underlying object instead. Otherwise
+                the axis keyword is not necessary.
+
+        skipna : bool, default True
+            Exclude NA/null values. If an entire row/column is NA, the result
+            will be NA.
+        numeric_only : bool, default False
+            Include only `float`, `int` or `boolean` data.
+
+            .. versionadded:: 1.5.0
+
+        Returns
+        -------
+        Series
+            Indexes of maxima along the specified axis.
+
+        Raises
+        ------
+        ValueError
+            * If the row/column is empty
+
+        See Also
+        --------
+        Series.idxmax : Return index of the maximum element.
+
+        Notes
+        -----
+        This method is the DataFrame version of ``ndarray.argmax``.
+
+        Examples
+        --------
+        Consider a dataset containing food consumption in Argentina.
+
+        >>> df = pd.DataFrame({'consumption': [10.51, 103.11, 55.48],
+        ...                    'co2_emissions': [37.2, 19.66, 1712]},
+        ...                   index=['Pork', 'Wheat Products', 'Beef'])
+
+        >>> df
+                        consumption  co2_emissions
+        Pork                  10.51         37.20
+        Wheat Products       103.11         19.66
+        Beef                  55.48       1712.00
+
+        By default, it returns the index for the maximum value in each column.
+
+        >>> df.idxmax()
+        consumption     Wheat Products
+        co2_emissions             Beef
+        dtype: object
+
+        To return the index for the maximum value in each row, use ``axis="columns"``.
+
+        >>> df.idxmax(axis="columns")
+        Pork              co2_emissions
+        Wheat Products     consumption
+        Beef              co2_emissions
+        dtype: object
+        """
+        return self._idxmax_idxmin(
+            "idxmax", axis=axis, numeric_only=numeric_only, skipna=skipna
+        )
+
+    def idxmin(
+        self,
+        axis: Axis | None | lib.NoDefault = lib.no_default,
+        skipna: bool = True,
+        numeric_only: bool = False,
+    ) -> DataFrame:
+        """
+        Return index of first occurrence of minimum over requested axis.
+
+        NA/null values are excluded.
+
+        Parameters
+        ----------
+        axis : {{0 or 'index', 1 or 'columns'}}, default None
+            The axis to use. 0 or 'index' for row-wise, 1 or 'columns' for column-wise.
+            If axis is not provided, grouper's axis is used.
+
+            .. versionchanged:: 2.0.0
+
+            .. deprecated:: 2.1.0
+                For axis=1, operate on the underlying object instead. Otherwise
+                the axis keyword is not necessary.
+
+        skipna : bool, default True
+            Exclude NA/null values. If an entire row/column is NA, the result
+            will be NA.
+        numeric_only : bool, default False
+            Include only `float`, `int` or `boolean` data.
+
+            .. versionadded:: 1.5.0
+
+        Returns
+        -------
+        Series
+            Indexes of minima along the specified axis.
+
+        Raises
+        ------
+        ValueError
+            * If the row/column is empty
+
+        See Also
+        --------
+        Series.idxmin : Return index of the minimum element.
+
+        Notes
+        -----
+        This method is the DataFrame version of ``ndarray.argmin``.
+
+        Examples
+        --------
+        Consider a dataset containing food consumption in Argentina.
+
+        >>> df = pd.DataFrame({'consumption': [10.51, 103.11, 55.48],
+        ...                    'co2_emissions': [37.2, 19.66, 1712]},
+        ...                   index=['Pork', 'Wheat Products', 'Beef'])
+
+        >>> df
+                        consumption  co2_emissions
+        Pork                  10.51         37.20
+        Wheat Products       103.11         19.66
+        Beef                  55.48       1712.00
+
+        By default, it returns the index for the minimum value in each column.
+
+        >>> df.idxmin()
+        consumption                Pork
+        co2_emissions    Wheat Products
+        dtype: object
+
+        To return the index for the minimum value in each row, use ``axis="columns"``.
+
+        >>> df.idxmin(axis="columns")
+        Pork                consumption
+        Wheat Products    co2_emissions
+        Beef                consumption
+        dtype: object
+        """
+        return self._idxmax_idxmin(
+            "idxmin", axis=axis, numeric_only=numeric_only, skipna=skipna
+        )
+
+    boxplot = boxplot_frame_groupby
+
+    def value_counts(
+        self,
+        subset: Sequence[Hashable] | None = None,
+        normalize: bool = False,
+        sort: bool = True,
+        ascending: bool = False,
+        dropna: bool = True,
+    ) -> DataFrame | Series:
+        """
+        Return a Series or DataFrame containing counts of unique rows.
+
+        .. versionadded:: 1.4.0
+
+        Parameters
+        ----------
+        subset : list-like, optional
+            Columns to use when counting unique combinations.
+        normalize : bool, default False
+            Return proportions rather than frequencies.
+        sort : bool, default True
+            Sort by frequencies.
+        ascending : bool, default False
+            Sort in ascending order.
+        dropna : bool, default True
+            Don't include counts of rows that contain NA values.
+
+        Returns
+        -------
+        Series or DataFrame
+            Series if the groupby as_index is True, otherwise DataFrame.
+
+        See Also
+        --------
+        Series.value_counts: Equivalent method on Series.
+        DataFrame.value_counts: Equivalent method on DataFrame.
+        SeriesGroupBy.value_counts: Equivalent method on SeriesGroupBy.
+
+        Notes
+        -----
+        - If the groupby as_index is True then the returned Series will have a
+          MultiIndex with one level per input column.
+        - If the groupby as_index is False then the returned DataFrame will have an
+          additional column with the value_counts. The column is labelled 'count' or
+          'proportion', depending on the ``normalize`` parameter.
+
+        By default, rows that contain any NA values are omitted from
+        the result.
+
+        By default, the result will be in descending order so that the
+        first element of each group is the most frequently-occurring row.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({
+        ...     'gender': ['male', 'male', 'female', 'male', 'female', 'male'],
+        ...     'education': ['low', 'medium', 'high', 'low', 'high', 'low'],
+        ...     'country': ['US', 'FR', 'US', 'FR', 'FR', 'FR']
+        ... })
+
+        >>> df
+                gender  education   country
+        0       male    low         US
+        1       male    medium      FR
+        2       female  high        US
+        3       male    low         FR
+        4       female  high        FR
+        5       male    low         FR
+
+        >>> df.groupby('gender').value_counts()
+        gender  education  country
+        female  high       FR         1
+                           US         1
+        male    low        FR         2
+                           US         1
+                medium     FR         1
+        Name: count, dtype: int64
+
+        >>> df.groupby('gender').value_counts(ascending=True)
+        gender  education  country
+        female  high       FR         1
+                           US         1
+        male    low        US         1
+                medium     FR         1
+                low        FR         2
+        Name: count, dtype: int64
+
+        >>> df.groupby('gender').value_counts(normalize=True)
+        gender  education  country
+        female  high       FR         0.50
+                           US         0.50
+        male    low        FR         0.50
+                           US         0.25
+                medium     FR         0.25
+        Name: proportion, dtype: float64
+
+        >>> df.groupby('gender', as_index=False).value_counts()
+           gender education country  count
+        0  female      high      FR      1
+        1  female      high      US      1
+        2    male       low      FR      2
+        3    male       low      US      1
+        4    male    medium      FR      1
+
+        >>> df.groupby('gender', as_index=False).value_counts(normalize=True)
+           gender education country  proportion
+        0  female      high      FR        0.50
+        1  female      high      US        0.50
+        2    male       low      FR        0.50
+        3    male       low      US        0.25
+        4    male    medium      FR        0.25
+        """
+        return self._value_counts(subset, normalize, sort, ascending, dropna)
+
+    def fillna(
+        self,
+        value: Hashable | Mapping | Series | DataFrame | None = None,
+        method: FillnaOptions | None = None,
+        axis: Axis | None | lib.NoDefault = lib.no_default,
+        inplace: bool = False,
+        limit: int | None = None,
+        downcast=lib.no_default,
+    ) -> DataFrame | None:
+        """
+        Fill NA/NaN values using the specified method within groups.
+
+        .. deprecated:: 2.2.0
+            This method is deprecated and will be removed in a future version.
+            Use the :meth:`.DataFrameGroupBy.ffill` or :meth:`.DataFrameGroupBy.bfill`
+            for forward or backward filling instead. If you want to fill with a
+            single value, use :meth:`DataFrame.fillna` instead.
+
+        Parameters
+        ----------
+        value : scalar, dict, Series, or DataFrame
+            Value to use to fill holes (e.g. 0), alternately a
+            dict/Series/DataFrame of values specifying which value to use for
+            each index (for a Series) or column (for a DataFrame).  Values not
+            in the dict/Series/DataFrame will not be filled. This value cannot
+            be a list. Users wanting to use the ``value`` argument and not ``method``
+            should prefer :meth:`.DataFrame.fillna` as this
+            will produce the same result and be more performant.
+        method : {{'bfill', 'ffill', None}}, default None
+            Method to use for filling holes. ``'ffill'`` will propagate
+            the last valid observation forward within a group.
+            ``'bfill'`` will use next valid observation to fill the gap.
+        axis : {0 or 'index', 1 or 'columns'}
+            Axis along which to fill missing values. When the :class:`DataFrameGroupBy`
+            ``axis`` argument is ``0``, using ``axis=1`` here will produce
+            the same results as :meth:`.DataFrame.fillna`. When the
+            :class:`DataFrameGroupBy` ``axis`` argument is ``1``, using ``axis=0``
+            or ``axis=1`` here will produce the same results.
+        inplace : bool, default False
+            Broken. Do not set to True.
+        limit : int, default None
+            If method is specified, this is the maximum number of consecutive
+            NaN values to forward/backward fill within a group. In other words,
+            if there is a gap with more than this number of consecutive NaNs,
+            it will only be partially filled. If method is not specified, this is the
+            maximum number of entries along the entire axis where NaNs will be
+            filled. Must be greater than 0 if not None.
+        downcast : dict, default is None
+            A dict of item->dtype of what to downcast if possible,
+            or the string 'infer' which will try to downcast to an appropriate
+            equal type (e.g. float64 to int64 if possible).
+
+        Returns
+        -------
+        DataFrame
+            Object with missing values filled.
+
+        See Also
+        --------
+        ffill : Forward fill values within a group.
+        bfill : Backward fill values within a group.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame(
+        ...     {
+        ...         "key": [0, 0, 1, 1, 1],
+        ...         "A": [np.nan, 2, np.nan, 3, np.nan],
+        ...         "B": [2, 3, np.nan, np.nan, np.nan],
+        ...         "C": [np.nan, np.nan, 2, np.nan, np.nan],
+        ...     }
+        ... )
+        >>> df
+           key    A    B   C
+        0    0  NaN  2.0 NaN
+        1    0  2.0  3.0 NaN
+        2    1  NaN  NaN 2.0
+        3    1  3.0  NaN NaN
+        4    1  NaN  NaN NaN
+
+        Propagate non-null values forward or backward within each group along columns.
+
+        >>> df.groupby("key").fillna(method="ffill")
+             A    B   C
+        0  NaN  2.0 NaN
+        1  2.0  3.0 NaN
+        2  NaN  NaN 2.0
+        3  3.0  NaN 2.0
+        4  3.0  NaN 2.0
+
+        >>> df.groupby("key").fillna(method="bfill")
+             A    B   C
+        0  2.0  2.0 NaN
+        1  2.0  3.0 NaN
+        2  3.0  NaN 2.0
+        3  3.0  NaN NaN
+        4  NaN  NaN NaN
+
+        Propagate non-null values forward or backward within each group along rows.
+
+        >>> df.T.groupby(np.array([0, 0, 1, 1])).fillna(method="ffill").T
+           key    A    B    C
+        0  0.0  0.0  2.0  2.0
+        1  0.0  2.0  3.0  3.0
+        2  1.0  1.0  NaN  2.0
+        3  1.0  3.0  NaN  NaN
+        4  1.0  1.0  NaN  NaN
+
+        >>> df.T.groupby(np.array([0, 0, 1, 1])).fillna(method="bfill").T
+           key    A    B    C
+        0  0.0  NaN  2.0  NaN
+        1  0.0  2.0  3.0  NaN
+        2  1.0  NaN  2.0  2.0
+        3  1.0  3.0  NaN  NaN
+        4  1.0  NaN  NaN  NaN
+
+        Only replace the first NaN element within a group along rows.
+
+        >>> df.groupby("key").fillna(method="ffill", limit=1)
+             A    B    C
+        0  NaN  2.0  NaN
+        1  2.0  3.0  NaN
+        2  NaN  NaN  2.0
+        3  3.0  NaN  2.0
+        4  3.0  NaN  NaN
+        """
+        warnings.warn(
+            f"{type(self).__name__}.fillna is deprecated and "
+            "will be removed in a future version. Use obj.ffill() or obj.bfill() "
+            "for forward or backward filling instead. If you want to fill with a "
+            f"single value, use {type(self.obj).__name__}.fillna instead",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+
+        result = self._op_via_apply(
+            "fillna",
+            value=value,
+            method=method,
+            axis=axis,
+            inplace=inplace,
+            limit=limit,
+            downcast=downcast,
+        )
+        return result
+
+    def take(
+        self,
+        indices: TakeIndexer,
+        axis: Axis | None | lib.NoDefault = lib.no_default,
+        **kwargs,
+    ) -> DataFrame:
+        """
+        Return the elements in the given *positional* indices in each group.
+
+        This means that we are not indexing according to actual values in
+        the index attribute of the object. We are indexing according to the
+        actual position of the element in the object.
+
+        If a requested index does not exist for some group, this method will raise.
+        To get similar behavior that ignores indices that don't exist, see
+        :meth:`.DataFrameGroupBy.nth`.
+
+        Parameters
+        ----------
+        indices : array-like
+            An array of ints indicating which positions to take.
+        axis : {0 or 'index', 1 or 'columns', None}, default 0
+            The axis on which to select elements. ``0`` means that we are
+            selecting rows, ``1`` means that we are selecting columns.
+
+            .. deprecated:: 2.1.0
+                For axis=1, operate on the underlying object instead. Otherwise
+                the axis keyword is not necessary.
+
+        **kwargs
+            For compatibility with :meth:`numpy.take`. Has no effect on the
+            output.
+
+        Returns
+        -------
+        DataFrame
+            An DataFrame containing the elements taken from each group.
+
+        See Also
+        --------
+        DataFrame.take : Take elements from a Series along an axis.
+        DataFrame.loc : Select a subset of a DataFrame by labels.
+        DataFrame.iloc : Select a subset of a DataFrame by positions.
+        numpy.take : Take elements from an array along an axis.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([('falcon', 'bird', 389.0),
+        ...                    ('parrot', 'bird', 24.0),
+        ...                    ('lion', 'mammal', 80.5),
+        ...                    ('monkey', 'mammal', np.nan),
+        ...                    ('rabbit', 'mammal', 15.0)],
+        ...                   columns=['name', 'class', 'max_speed'],
+        ...                   index=[4, 3, 2, 1, 0])
+        >>> df
+             name   class  max_speed
+        4  falcon    bird      389.0
+        3  parrot    bird       24.0
+        2    lion  mammal       80.5
+        1  monkey  mammal        NaN
+        0  rabbit  mammal       15.0
+        >>> gb = df.groupby([1, 1, 2, 2, 2])
+
+        Take elements at positions 0 and 1 along the axis 0 (default).
+
+        Note how the indices selected in the result do not correspond to
+        our input indices 0 and 1. That's because we are selecting the 0th
+        and 1st rows, not rows whose indices equal 0 and 1.
+
+        >>> gb.take([0, 1])
+               name   class  max_speed
+        1 4  falcon    bird      389.0
+          3  parrot    bird       24.0
+        2 2    lion  mammal       80.5
+          1  monkey  mammal        NaN
+
+        The order of the specified indices influences the order in the result.
+        Here, the order is swapped from the previous example.
+
+        >>> gb.take([1, 0])
+               name   class  max_speed
+        1 3  parrot    bird       24.0
+          4  falcon    bird      389.0
+        2 1  monkey  mammal        NaN
+          2    lion  mammal       80.5
+
+        Take elements at indices 1 and 2 along the axis 1 (column selection).
+
+        We may take elements using negative integers for positive indices,
+        starting from the end of the object, just like with Python lists.
+
+        >>> gb.take([-1, -2])
+               name   class  max_speed
+        1 3  parrot    bird       24.0
+          4  falcon    bird      389.0
+        2 0  rabbit  mammal       15.0
+          1  monkey  mammal        NaN
+        """
+        result = self._op_via_apply("take", indices=indices, axis=axis, **kwargs)
+        return result
+
+    def skew(
+        self,
+        axis: Axis | None | lib.NoDefault = lib.no_default,
+        skipna: bool = True,
+        numeric_only: bool = False,
+        **kwargs,
+    ) -> DataFrame:
+        """
+        Return unbiased skew within groups.
+
+        Normalized by N-1.
+
+        Parameters
+        ----------
+        axis : {0 or 'index', 1 or 'columns', None}, default 0
+            Axis for the function to be applied on.
+
+            Specifying ``axis=None`` will apply the aggregation across both axes.
+
+            .. versionadded:: 2.0.0
+
+            .. deprecated:: 2.1.0
+                For axis=1, operate on the underlying object instead. Otherwise
+                the axis keyword is not necessary.
+
+        skipna : bool, default True
+            Exclude NA/null values when computing the result.
+
+        numeric_only : bool, default False
+            Include only float, int, boolean columns.
+
+        **kwargs
+            Additional keyword arguments to be passed to the function.
+
+        Returns
+        -------
+        DataFrame
+
+        See Also
+        --------
+        DataFrame.skew : Return unbiased skew over requested axis.
+
+        Examples
+        --------
+        >>> arrays = [['falcon', 'parrot', 'cockatoo', 'kiwi',
+        ...            'lion', 'monkey', 'rabbit'],
+        ...           ['bird', 'bird', 'bird', 'bird',
+        ...            'mammal', 'mammal', 'mammal']]
+        >>> index = pd.MultiIndex.from_arrays(arrays, names=('name', 'class'))
+        >>> df = pd.DataFrame({'max_speed': [389.0, 24.0, 70.0, np.nan,
+        ...                                  80.5, 21.5, 15.0]},
+        ...                   index=index)
+        >>> df
+                        max_speed
+        name     class
+        falcon   bird        389.0
+        parrot   bird         24.0
+        cockatoo bird         70.0
+        kiwi     bird          NaN
+        lion     mammal       80.5
+        monkey   mammal       21.5
+        rabbit   mammal       15.0
+        >>> gb = df.groupby(["class"])
+        >>> gb.skew()
+                max_speed
+        class
+        bird     1.628296
+        mammal   1.669046
+        >>> gb.skew(skipna=False)
+                max_speed
+        class
+        bird          NaN
+        mammal   1.669046
+        """
+        if axis is lib.no_default:
+            axis = 0
+
+        if axis != 0:
+            result = self._op_via_apply(
+                "skew",
+                axis=axis,
+                skipna=skipna,
+                numeric_only=numeric_only,
+                **kwargs,
+            )
+            return result
+
+        def alt(obj):
+            # This should not be reached since the cython path should raise
+            #  TypeError and not NotImplementedError.
+            raise TypeError(f"'skew' is not supported for dtype={obj.dtype}")
+
+        return self._cython_agg_general(
+            "skew", alt=alt, skipna=skipna, numeric_only=numeric_only, **kwargs
+        )
+
+    @property
+    @doc(DataFrame.plot.__doc__)
+    def plot(self) -> GroupByPlot:
+        result = GroupByPlot(self)
+        return result
+
+    @doc(DataFrame.corr.__doc__)
+    def corr(
+        self,
+        method: str | Callable[[np.ndarray, np.ndarray], float] = "pearson",
+        min_periods: int = 1,
+        numeric_only: bool = False,
+    ) -> DataFrame:
+        result = self._op_via_apply(
+            "corr", method=method, min_periods=min_periods, numeric_only=numeric_only
+        )
+        return result
+
+    @doc(DataFrame.cov.__doc__)
+    def cov(
+        self,
+        min_periods: int | None = None,
+        ddof: int | None = 1,
+        numeric_only: bool = False,
+    ) -> DataFrame:
+        result = self._op_via_apply(
+            "cov", min_periods=min_periods, ddof=ddof, numeric_only=numeric_only
+        )
+        return result
+
+    @doc(DataFrame.hist.__doc__)
+    def hist(
+        self,
+        column: IndexLabel | None = None,
+        by=None,
+        grid: bool = True,
+        xlabelsize: int | None = None,
+        xrot: float | None = None,
+        ylabelsize: int | None = None,
+        yrot: float | None = None,
+        ax=None,
+        sharex: bool = False,
+        sharey: bool = False,
+        figsize: tuple[int, int] | None = None,
+        layout: tuple[int, int] | None = None,
+        bins: int | Sequence[int] = 10,
+        backend: str | None = None,
+        legend: bool = False,
+        **kwargs,
+    ):
+        result = self._op_via_apply(
+            "hist",
+            column=column,
+            by=by,
+            grid=grid,
+            xlabelsize=xlabelsize,
+            xrot=xrot,
+            ylabelsize=ylabelsize,
+            yrot=yrot,
+            ax=ax,
+            sharex=sharex,
+            sharey=sharey,
+            figsize=figsize,
+            layout=layout,
+            bins=bins,
+            backend=backend,
+            legend=legend,
+            **kwargs,
+        )
+        return result
+
+    @property
+    @doc(DataFrame.dtypes.__doc__)
+    def dtypes(self) -> Series:
+        # GH#51045
+        warnings.warn(
+            f"{type(self).__name__}.dtypes is deprecated and will be removed in "
+            "a future version. Check the dtypes on the base object instead",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+
+        # error: Incompatible return value type (got "DataFrame", expected "Series")
+        return self._python_apply_general(  # type: ignore[return-value]
+            lambda df: df.dtypes, self._selected_obj
+        )
+
+    @doc(DataFrame.corrwith.__doc__)
+    def corrwith(
+        self,
+        other: DataFrame | Series,
+        axis: Axis | lib.NoDefault = lib.no_default,
+        drop: bool = False,
+        method: CorrelationMethod = "pearson",
+        numeric_only: bool = False,
+    ) -> DataFrame:
+        result = self._op_via_apply(
+            "corrwith",
+            other=other,
+            axis=axis,
+            drop=drop,
+            method=method,
+            numeric_only=numeric_only,
+        )
+        return result
+
+
+def _wrap_transform_general_frame(
+    obj: DataFrame, group: DataFrame, res: DataFrame | Series
+) -> DataFrame:
+    from pandas import concat
+
+    if isinstance(res, Series):
+        # we need to broadcast across the
+        # other dimension; this will preserve dtypes
+        # GH14457
+        if res.index.is_(obj.index):
+            res_frame = concat([res] * len(group.columns), axis=1)
+            res_frame.columns = group.columns
+            res_frame.index = group.index
+        else:
+            res_frame = obj._constructor(
+                np.tile(res.values, (len(group.index), 1)),
+                columns=group.columns,
+                index=group.index,
+            )
+        assert isinstance(res_frame, DataFrame)
+        return res_frame
+    elif isinstance(res, DataFrame) and not res.index.is_(group.index):
+        return res._align_frame(group)[0]
+    else:
+        return res
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/groupby/groupby.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/groupby/groupby.py
new file mode 100644
index 0000000000000000000000000000000000000000..5baa2a9b703cc4100aa6a342b3b9f5a3d6775e64
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/groupby/groupby.py
@@ -0,0 +1,6023 @@
+"""
+Provide the groupby split-apply-combine paradigm. Define the GroupBy
+class providing the base-class of operations.
+
+The SeriesGroupBy and DataFrameGroupBy sub-class
+(defined in pandas.core.groupby.generic)
+expose these user-facing objects to provide specific functionality.
+"""
+from __future__ import annotations
+
+from collections.abc import (
+    Hashable,
+    Iterator,
+    Mapping,
+    Sequence,
+)
+import datetime
+from functools import (
+    partial,
+    wraps,
+)
+import inspect
+from textwrap import dedent
+from typing import (
+    TYPE_CHECKING,
+    Callable,
+    Literal,
+    TypeVar,
+    Union,
+    cast,
+    final,
+)
+import warnings
+
+import numpy as np
+
+from pandas._config import using_string_dtype
+from pandas._config.config import option_context
+
+from pandas._libs import (
+    Timestamp,
+    lib,
+)
+from pandas._libs.algos import rank_1d
+import pandas._libs.groupby as libgroupby
+from pandas._libs.missing import NA
+from pandas._typing import (
+    AnyArrayLike,
+    ArrayLike,
+    Axis,
+    AxisInt,
+    DtypeObj,
+    FillnaOptions,
+    IndexLabel,
+    NDFrameT,
+    PositionalIndexer,
+    RandomState,
+    Scalar,
+    T,
+    npt,
+)
+from pandas.compat.numpy import function as nv
+from pandas.errors import (
+    AbstractMethodError,
+    DataError,
+)
+from pandas.util._decorators import (
+    Appender,
+    Substitution,
+    cache_readonly,
+    doc,
+)
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.cast import (
+    coerce_indexer_dtype,
+    ensure_dtype_can_hold_na,
+)
+from pandas.core.dtypes.common import (
+    is_bool_dtype,
+    is_float_dtype,
+    is_hashable,
+    is_integer,
+    is_integer_dtype,
+    is_list_like,
+    is_numeric_dtype,
+    is_object_dtype,
+    is_scalar,
+    is_string_dtype,
+    needs_i8_conversion,
+    pandas_dtype,
+)
+from pandas.core.dtypes.missing import (
+    isna,
+    na_value_for_dtype,
+    notna,
+)
+
+from pandas.core import (
+    algorithms,
+    sample,
+)
+from pandas.core._numba import executor
+from pandas.core.apply import warn_alias_replacement
+from pandas.core.arrays import (
+    ArrowExtensionArray,
+    BaseMaskedArray,
+    Categorical,
+    ExtensionArray,
+    FloatingArray,
+    IntegerArray,
+    SparseArray,
+)
+from pandas.core.arrays.string_ import StringDtype
+from pandas.core.arrays.string_arrow import (
+    ArrowStringArray,
+    ArrowStringArrayNumpySemantics,
+)
+from pandas.core.base import (
+    PandasObject,
+    SelectionMixin,
+)
+import pandas.core.common as com
+from pandas.core.frame import DataFrame
+from pandas.core.generic import NDFrame
+from pandas.core.groupby import (
+    base,
+    numba_,
+    ops,
+)
+from pandas.core.groupby.grouper import get_grouper
+from pandas.core.groupby.indexing import (
+    GroupByIndexingMixin,
+    GroupByNthSelector,
+)
+from pandas.core.indexes.api import (
+    CategoricalIndex,
+    Index,
+    MultiIndex,
+    RangeIndex,
+    default_index,
+)
+from pandas.core.internals.blocks import ensure_block_shape
+from pandas.core.series import Series
+from pandas.core.sorting import get_group_index_sorter
+from pandas.core.util.numba_ import (
+    get_jit_arguments,
+    maybe_use_numba,
+)
+
+if TYPE_CHECKING:
+    from typing import Any
+
+    from pandas.core.resample import Resampler
+    from pandas.core.window import (
+        ExpandingGroupby,
+        ExponentialMovingWindowGroupby,
+        RollingGroupby,
+    )
+
+_common_see_also = """
+        See Also
+        --------
+        Series.%(name)s : Apply a function %(name)s to a Series.
+        DataFrame.%(name)s : Apply a function %(name)s
+            to each row or column of a DataFrame.
+"""
+
+_apply_docs = {
+    "template": """
+    Apply function ``func`` group-wise and combine the results together.
+
+    The function passed to ``apply`` must take a {input} as its first
+    argument and return a DataFrame, Series or scalar. ``apply`` will
+    then take care of combining the results back together into a single
+    dataframe or series. ``apply`` is therefore a highly flexible
+    grouping method.
+
+    While ``apply`` is a very flexible method, its downside is that
+    using it can be quite a bit slower than using more specific methods
+    like ``agg`` or ``transform``. Pandas offers a wide range of method that will
+    be much faster than using ``apply`` for their specific purposes, so try to
+    use them before reaching for ``apply``.
+
+    Parameters
+    ----------
+    func : callable
+        A callable that takes a {input} as its first argument, and
+        returns a dataframe, a series or a scalar. In addition the
+        callable may take positional and keyword arguments.
+    include_groups : bool, default True
+        When True, will attempt to apply ``func`` to the groupings in
+        the case that they are columns of the DataFrame. If this raises a
+        TypeError, the result will be computed with the groupings excluded.
+        When False, the groupings will be excluded when applying ``func``.
+
+        .. versionadded:: 2.2.0
+
+        .. deprecated:: 2.2.0
+
+           Setting include_groups to True is deprecated. Only the value
+           False will be allowed in a future version of pandas.
+
+    args, kwargs : tuple and dict
+        Optional positional and keyword arguments to pass to ``func``.
+
+    Returns
+    -------
+    Series or DataFrame
+
+    See Also
+    --------
+    pipe : Apply function to the full GroupBy object instead of to each
+        group.
+    aggregate : Apply aggregate function to the GroupBy object.
+    transform : Apply function column-by-column to the GroupBy object.
+    Series.apply : Apply a function to a Series.
+    DataFrame.apply : Apply a function to each row or column of a DataFrame.
+
+    Notes
+    -----
+
+    .. versionchanged:: 1.3.0
+
+        The resulting dtype will reflect the return value of the passed ``func``,
+        see the examples below.
+
+    Functions that mutate the passed object can produce unexpected
+    behavior or errors and are not supported. See :ref:`gotchas.udf-mutation`
+    for more details.
+
+    Examples
+    --------
+    {examples}
+    """,
+    "dataframe_examples": """
+    >>> df = pd.DataFrame({'A': 'a a b'.split(),
+    ...                    'B': [1, 2, 3],
+    ...                    'C': [4, 6, 5]})
+    >>> g1 = df.groupby('A', group_keys=False)
+    >>> g2 = df.groupby('A', group_keys=True)
+
+    Notice that ``g1`` and ``g2`` have two groups, ``a`` and ``b``, and only
+    differ in their ``group_keys`` argument. Calling `apply` in various ways,
+    we can get different grouping results:
+
+    Example 1: below the function passed to `apply` takes a DataFrame as
+    its argument and returns a DataFrame. `apply` combines the result for
+    each group together into a new DataFrame:
+
+    >>> g1[['B', 'C']].apply(lambda x: x / x.sum())
+              B    C
+    0  0.333333  0.4
+    1  0.666667  0.6
+    2  1.000000  1.0
+
+    In the above, the groups are not part of the index. We can have them included
+    by using ``g2`` where ``group_keys=True``:
+
+    >>> g2[['B', 'C']].apply(lambda x: x / x.sum())
+                B    C
+    A
+    a 0  0.333333  0.4
+      1  0.666667  0.6
+    b 2  1.000000  1.0
+
+    Example 2: The function passed to `apply` takes a DataFrame as
+    its argument and returns a Series.  `apply` combines the result for
+    each group together into a new DataFrame.
+
+    .. versionchanged:: 1.3.0
+
+        The resulting dtype will reflect the return value of the passed ``func``.
+
+    >>> g1[['B', 'C']].apply(lambda x: x.astype(float).max() - x.min())
+         B    C
+    A
+    a  1.0  2.0
+    b  0.0  0.0
+
+    >>> g2[['B', 'C']].apply(lambda x: x.astype(float).max() - x.min())
+         B    C
+    A
+    a  1.0  2.0
+    b  0.0  0.0
+
+    The ``group_keys`` argument has no effect here because the result is not
+    like-indexed (i.e. :ref:`a transform `) when compared
+    to the input.
+
+    Example 3: The function passed to `apply` takes a DataFrame as
+    its argument and returns a scalar. `apply` combines the result for
+    each group together into a Series, including setting the index as
+    appropriate:
+
+    >>> g1.apply(lambda x: x.C.max() - x.B.min(), include_groups=False)
+    A
+    a    5
+    b    2
+    dtype: int64""",
+    "series_examples": """
+    >>> s = pd.Series([0, 1, 2], index='a a b'.split())
+    >>> g1 = s.groupby(s.index, group_keys=False)
+    >>> g2 = s.groupby(s.index, group_keys=True)
+
+    From ``s`` above we can see that ``g`` has two groups, ``a`` and ``b``.
+    Notice that ``g1`` have ``g2`` have two groups, ``a`` and ``b``, and only
+    differ in their ``group_keys`` argument. Calling `apply` in various ways,
+    we can get different grouping results:
+
+    Example 1: The function passed to `apply` takes a Series as
+    its argument and returns a Series.  `apply` combines the result for
+    each group together into a new Series.
+
+    .. versionchanged:: 1.3.0
+
+        The resulting dtype will reflect the return value of the passed ``func``.
+
+    >>> g1.apply(lambda x: x * 2 if x.name == 'a' else x / 2)
+    a    0.0
+    a    2.0
+    b    1.0
+    dtype: float64
+
+    In the above, the groups are not part of the index. We can have them included
+    by using ``g2`` where ``group_keys=True``:
+
+    >>> g2.apply(lambda x: x * 2 if x.name == 'a' else x / 2)
+    a  a    0.0
+       a    2.0
+    b  b    1.0
+    dtype: float64
+
+    Example 2: The function passed to `apply` takes a Series as
+    its argument and returns a scalar. `apply` combines the result for
+    each group together into a Series, including setting the index as
+    appropriate:
+
+    >>> g1.apply(lambda x: x.max() - x.min())
+    a    1
+    b    0
+    dtype: int64
+
+    The ``group_keys`` argument has no effect here because the result is not
+    like-indexed (i.e. :ref:`a transform `) when compared
+    to the input.
+
+    >>> g2.apply(lambda x: x.max() - x.min())
+    a    1
+    b    0
+    dtype: int64""",
+}
+
+_groupby_agg_method_template = """
+Compute {fname} of group values.
+
+Parameters
+----------
+numeric_only : bool, default {no}
+    Include only float, int, boolean columns.
+
+    .. versionchanged:: 2.0.0
+
+        numeric_only no longer accepts ``None``.
+
+min_count : int, default {mc}
+    The required number of valid values to perform the operation. If fewer
+    than ``min_count`` non-NA values are present the result will be NA.
+
+Returns
+-------
+Series or DataFrame
+    Computed {fname} of values within each group.
+
+Examples
+--------
+{example}
+"""
+
+_groupby_agg_method_engine_template = """
+Compute {fname} of group values.
+
+Parameters
+----------
+numeric_only : bool, default {no}
+    Include only float, int, boolean columns.
+
+    .. versionchanged:: 2.0.0
+
+        numeric_only no longer accepts ``None``.
+
+min_count : int, default {mc}
+    The required number of valid values to perform the operation. If fewer
+    than ``min_count`` non-NA values are present the result will be NA.
+
+engine : str, default None {e}
+    * ``'cython'`` : Runs rolling apply through C-extensions from cython.
+    * ``'numba'`` : Runs rolling apply through JIT compiled code from numba.
+        Only available when ``raw`` is set to ``True``.
+    * ``None`` : Defaults to ``'cython'`` or globally setting ``compute.use_numba``
+
+engine_kwargs : dict, default None {ek}
+    * For ``'cython'`` engine, there are no accepted ``engine_kwargs``
+    * For ``'numba'`` engine, the engine can accept ``nopython``, ``nogil``
+        and ``parallel`` dictionary keys. The values must either be ``True`` or
+        ``False``. The default ``engine_kwargs`` for the ``'numba'`` engine is
+        ``{{'nopython': True, 'nogil': False, 'parallel': False}}`` and will be
+        applied to both the ``func`` and the ``apply`` groupby aggregation.
+
+Returns
+-------
+Series or DataFrame
+    Computed {fname} of values within each group.
+
+Examples
+--------
+{example}
+"""
+
+_pipe_template = """
+Apply a ``func`` with arguments to this %(klass)s object and return its result.
+
+Use `.pipe` when you want to improve readability by chaining together
+functions that expect Series, DataFrames, GroupBy or Resampler objects.
+Instead of writing
+
+>>> h = lambda x, arg2, arg3: x + 1 - arg2 * arg3
+>>> g = lambda x, arg1: x * 5 / arg1
+>>> f = lambda x: x ** 4
+>>> df = pd.DataFrame([["a", 4], ["b", 5]], columns=["group", "value"])
+>>> h(g(f(df.groupby('group')), arg1=1), arg2=2, arg3=3)  # doctest: +SKIP
+
+You can write
+
+>>> (df.groupby('group')
+...    .pipe(f)
+...    .pipe(g, arg1=1)
+...    .pipe(h, arg2=2, arg3=3))  # doctest: +SKIP
+
+which is much more readable.
+
+Parameters
+----------
+func : callable or tuple of (callable, str)
+    Function to apply to this %(klass)s object or, alternatively,
+    a `(callable, data_keyword)` tuple where `data_keyword` is a
+    string indicating the keyword of `callable` that expects the
+    %(klass)s object.
+args : iterable, optional
+       Positional arguments passed into `func`.
+kwargs : dict, optional
+         A dictionary of keyword arguments passed into `func`.
+
+Returns
+-------
+the return type of `func`.
+
+See Also
+--------
+Series.pipe : Apply a function with arguments to a series.
+DataFrame.pipe: Apply a function with arguments to a dataframe.
+apply : Apply function to each group instead of to the
+    full %(klass)s object.
+
+Notes
+-----
+See more `here
+`_
+
+Examples
+--------
+%(examples)s
+"""
+
+_transform_template = """
+Call function producing a same-indexed %(klass)s on each group.
+
+Returns a %(klass)s having the same indexes as the original object
+filled with the transformed values.
+
+Parameters
+----------
+f : function, str
+    Function to apply to each group. See the Notes section below for requirements.
+
+    Accepted inputs are:
+
+    - String
+    - Python function
+    - Numba JIT function with ``engine='numba'`` specified.
+
+    Only passing a single function is supported with this engine.
+    If the ``'numba'`` engine is chosen, the function must be
+    a user defined function with ``values`` and ``index`` as the
+    first and second arguments respectively in the function signature.
+    Each group's index will be passed to the user defined function
+    and optionally available for use.
+
+    If a string is chosen, then it needs to be the name
+    of the groupby method you want to use.
+*args
+    Positional arguments to pass to func.
+engine : str, default None
+    * ``'cython'`` : Runs the function through C-extensions from cython.
+    * ``'numba'`` : Runs the function through JIT compiled code from numba.
+    * ``None`` : Defaults to ``'cython'`` or the global setting ``compute.use_numba``
+
+engine_kwargs : dict, default None
+    * For ``'cython'`` engine, there are no accepted ``engine_kwargs``
+    * For ``'numba'`` engine, the engine can accept ``nopython``, ``nogil``
+      and ``parallel`` dictionary keys. The values must either be ``True`` or
+      ``False``. The default ``engine_kwargs`` for the ``'numba'`` engine is
+      ``{'nopython': True, 'nogil': False, 'parallel': False}`` and will be
+      applied to the function
+
+**kwargs
+    Keyword arguments to be passed into func.
+
+Returns
+-------
+%(klass)s
+
+See Also
+--------
+%(klass)s.groupby.apply : Apply function ``func`` group-wise and combine
+    the results together.
+%(klass)s.groupby.aggregate : Aggregate using one or more
+    operations over the specified axis.
+%(klass)s.transform : Call ``func`` on self producing a %(klass)s with the
+    same axis shape as self.
+
+Notes
+-----
+Each group is endowed the attribute 'name' in case you need to know
+which group you are working on.
+
+The current implementation imposes three requirements on f:
+
+* f must return a value that either has the same shape as the input
+  subframe or can be broadcast to the shape of the input subframe.
+  For example, if `f` returns a scalar it will be broadcast to have the
+  same shape as the input subframe.
+* if this is a DataFrame, f must support application column-by-column
+  in the subframe. If f also supports application to the entire subframe,
+  then a fast path is used starting from the second chunk.
+* f must not mutate groups. Mutation is not supported and may
+  produce unexpected results. See :ref:`gotchas.udf-mutation` for more details.
+
+When using ``engine='numba'``, there will be no "fall back" behavior internally.
+The group data and group index will be passed as numpy arrays to the JITed
+user defined function, and no alternative execution attempts will be tried.
+
+.. versionchanged:: 1.3.0
+
+    The resulting dtype will reflect the return value of the passed ``func``,
+    see the examples below.
+
+.. versionchanged:: 2.0.0
+
+    When using ``.transform`` on a grouped DataFrame and the transformation function
+    returns a DataFrame, pandas now aligns the result's index
+    with the input's index. You can call ``.to_numpy()`` on the
+    result of the transformation function to avoid alignment.
+
+Examples
+--------
+%(example)s"""
+
+_agg_template_series = """
+Aggregate using one or more operations over the specified axis.
+
+Parameters
+----------
+func : function, str, list, dict or None
+    Function to use for aggregating the data. If a function, must either
+    work when passed a {klass} or when passed to {klass}.apply.
+
+    Accepted combinations are:
+
+    - function
+    - string function name
+    - list of functions and/or function names, e.g. ``[np.sum, 'mean']``
+    - None, in which case ``**kwargs`` are used with Named Aggregation. Here the
+      output has one column for each element in ``**kwargs``. The name of the
+      column is keyword, whereas the value determines the aggregation used to compute
+      the values in the column.
+
+      Can also accept a Numba JIT function with
+      ``engine='numba'`` specified. Only passing a single function is supported
+      with this engine.
+
+      If the ``'numba'`` engine is chosen, the function must be
+      a user defined function with ``values`` and ``index`` as the
+      first and second arguments respectively in the function signature.
+      Each group's index will be passed to the user defined function
+      and optionally available for use.
+
+    .. deprecated:: 2.1.0
+
+        Passing a dictionary is deprecated and will raise in a future version
+        of pandas. Pass a list of aggregations instead.
+*args
+    Positional arguments to pass to func.
+engine : str, default None
+    * ``'cython'`` : Runs the function through C-extensions from cython.
+    * ``'numba'`` : Runs the function through JIT compiled code from numba.
+    * ``None`` : Defaults to ``'cython'`` or globally setting ``compute.use_numba``
+
+engine_kwargs : dict, default None
+    * For ``'cython'`` engine, there are no accepted ``engine_kwargs``
+    * For ``'numba'`` engine, the engine can accept ``nopython``, ``nogil``
+      and ``parallel`` dictionary keys. The values must either be ``True`` or
+      ``False``. The default ``engine_kwargs`` for the ``'numba'`` engine is
+      ``{{'nopython': True, 'nogil': False, 'parallel': False}}`` and will be
+      applied to the function
+
+**kwargs
+    * If ``func`` is None, ``**kwargs`` are used to define the output names and
+      aggregations via Named Aggregation. See ``func`` entry.
+    * Otherwise, keyword arguments to be passed into func.
+
+Returns
+-------
+{klass}
+
+See Also
+--------
+{klass}.groupby.apply : Apply function func group-wise
+    and combine the results together.
+{klass}.groupby.transform : Transforms the Series on each group
+    based on the given function.
+{klass}.aggregate : Aggregate using one or more
+    operations over the specified axis.
+
+Notes
+-----
+When using ``engine='numba'``, there will be no "fall back" behavior internally.
+The group data and group index will be passed as numpy arrays to the JITed
+user defined function, and no alternative execution attempts will be tried.
+
+Functions that mutate the passed object can produce unexpected
+behavior or errors and are not supported. See :ref:`gotchas.udf-mutation`
+for more details.
+
+.. versionchanged:: 1.3.0
+
+    The resulting dtype will reflect the return value of the passed ``func``,
+    see the examples below.
+{examples}"""
+
+_agg_template_frame = """
+Aggregate using one or more operations over the specified axis.
+
+Parameters
+----------
+func : function, str, list, dict or None
+    Function to use for aggregating the data. If a function, must either
+    work when passed a {klass} or when passed to {klass}.apply.
+
+    Accepted combinations are:
+
+    - function
+    - string function name
+    - list of functions and/or function names, e.g. ``[np.sum, 'mean']``
+    - dict of axis labels -> functions, function names or list of such.
+    - None, in which case ``**kwargs`` are used with Named Aggregation. Here the
+      output has one column for each element in ``**kwargs``. The name of the
+      column is keyword, whereas the value determines the aggregation used to compute
+      the values in the column.
+
+      Can also accept a Numba JIT function with
+      ``engine='numba'`` specified. Only passing a single function is supported
+      with this engine.
+
+      If the ``'numba'`` engine is chosen, the function must be
+      a user defined function with ``values`` and ``index`` as the
+      first and second arguments respectively in the function signature.
+      Each group's index will be passed to the user defined function
+      and optionally available for use.
+
+*args
+    Positional arguments to pass to func.
+engine : str, default None
+    * ``'cython'`` : Runs the function through C-extensions from cython.
+    * ``'numba'`` : Runs the function through JIT compiled code from numba.
+    * ``None`` : Defaults to ``'cython'`` or globally setting ``compute.use_numba``
+
+engine_kwargs : dict, default None
+    * For ``'cython'`` engine, there are no accepted ``engine_kwargs``
+    * For ``'numba'`` engine, the engine can accept ``nopython``, ``nogil``
+      and ``parallel`` dictionary keys. The values must either be ``True`` or
+      ``False``. The default ``engine_kwargs`` for the ``'numba'`` engine is
+      ``{{'nopython': True, 'nogil': False, 'parallel': False}}`` and will be
+      applied to the function
+
+**kwargs
+    * If ``func`` is None, ``**kwargs`` are used to define the output names and
+      aggregations via Named Aggregation. See ``func`` entry.
+    * Otherwise, keyword arguments to be passed into func.
+
+Returns
+-------
+{klass}
+
+See Also
+--------
+{klass}.groupby.apply : Apply function func group-wise
+    and combine the results together.
+{klass}.groupby.transform : Transforms the Series on each group
+    based on the given function.
+{klass}.aggregate : Aggregate using one or more
+    operations over the specified axis.
+
+Notes
+-----
+When using ``engine='numba'``, there will be no "fall back" behavior internally.
+The group data and group index will be passed as numpy arrays to the JITed
+user defined function, and no alternative execution attempts will be tried.
+
+Functions that mutate the passed object can produce unexpected
+behavior or errors and are not supported. See :ref:`gotchas.udf-mutation`
+for more details.
+
+.. versionchanged:: 1.3.0
+
+    The resulting dtype will reflect the return value of the passed ``func``,
+    see the examples below.
+{examples}"""
+
+
+@final
+class GroupByPlot(PandasObject):
+    """
+    Class implementing the .plot attribute for groupby objects.
+    """
+
+    def __init__(self, groupby: GroupBy) -> None:
+        self._groupby = groupby
+
+    def __call__(self, *args, **kwargs):
+        def f(self):
+            return self.plot(*args, **kwargs)
+
+        f.__name__ = "plot"
+        return self._groupby._python_apply_general(f, self._groupby._selected_obj)
+
+    def __getattr__(self, name: str):
+        def attr(*args, **kwargs):
+            def f(self):
+                return getattr(self.plot, name)(*args, **kwargs)
+
+            return self._groupby._python_apply_general(f, self._groupby._selected_obj)
+
+        return attr
+
+
+_KeysArgType = Union[
+    Hashable,
+    list[Hashable],
+    Callable[[Hashable], Hashable],
+    list[Callable[[Hashable], Hashable]],
+    Mapping[Hashable, Hashable],
+]
+
+
+class BaseGroupBy(PandasObject, SelectionMixin[NDFrameT], GroupByIndexingMixin):
+    _hidden_attrs = PandasObject._hidden_attrs | {
+        "as_index",
+        "axis",
+        "dropna",
+        "exclusions",
+        "grouper",
+        "group_keys",
+        "keys",
+        "level",
+        "obj",
+        "observed",
+        "sort",
+    }
+
+    axis: AxisInt
+    _grouper: ops.BaseGrouper
+    keys: _KeysArgType | None = None
+    level: IndexLabel | None = None
+    group_keys: bool
+
+    @final
+    def __len__(self) -> int:
+        return len(self.groups)
+
+    @final
+    def __repr__(self) -> str:
+        # TODO: Better repr for GroupBy object
+        return object.__repr__(self)
+
+    @final
+    @property
+    def grouper(self) -> ops.BaseGrouper:
+        warnings.warn(
+            f"{type(self).__name__}.grouper is deprecated and will be removed in a "
+            "future version of pandas.",
+            category=FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        return self._grouper
+
+    @final
+    @property
+    def groups(self) -> dict[Hashable, np.ndarray]:
+        """
+        Dict {group name -> group labels}.
+
+        Examples
+        --------
+
+        For SeriesGroupBy:
+
+        >>> lst = ['a', 'a', 'b']
+        >>> ser = pd.Series([1, 2, 3], index=lst)
+        >>> ser
+        a    1
+        a    2
+        b    3
+        dtype: int64
+        >>> ser.groupby(level=0).groups
+        {'a': ['a', 'a'], 'b': ['b']}
+
+        For DataFrameGroupBy:
+
+        >>> data = [[1, 2, 3], [1, 5, 6], [7, 8, 9]]
+        >>> df = pd.DataFrame(data, columns=["a", "b", "c"])
+        >>> df
+           a  b  c
+        0  1  2  3
+        1  1  5  6
+        2  7  8  9
+        >>> df.groupby(by=["a"]).groups
+        {1: [0, 1], 7: [2]}
+
+        For Resampler:
+
+        >>> ser = pd.Series([1, 2, 3, 4], index=pd.DatetimeIndex(
+        ...                 ['2023-01-01', '2023-01-15', '2023-02-01', '2023-02-15']))
+        >>> ser
+        2023-01-01    1
+        2023-01-15    2
+        2023-02-01    3
+        2023-02-15    4
+        dtype: int64
+        >>> ser.resample('MS').groups
+        {Timestamp('2023-01-01 00:00:00'): 2, Timestamp('2023-02-01 00:00:00'): 4}
+        """
+        return self._grouper.groups
+
+    @final
+    @property
+    def ngroups(self) -> int:
+        return self._grouper.ngroups
+
+    @final
+    @property
+    def indices(self) -> dict[Hashable, npt.NDArray[np.intp]]:
+        """
+        Dict {group name -> group indices}.
+
+        Examples
+        --------
+
+        For SeriesGroupBy:
+
+        >>> lst = ['a', 'a', 'b']
+        >>> ser = pd.Series([1, 2, 3], index=lst)
+        >>> ser
+        a    1
+        a    2
+        b    3
+        dtype: int64
+        >>> ser.groupby(level=0).indices
+        {'a': array([0, 1]), 'b': array([2])}
+
+        For DataFrameGroupBy:
+
+        >>> data = [[1, 2, 3], [1, 5, 6], [7, 8, 9]]
+        >>> df = pd.DataFrame(data, columns=["a", "b", "c"],
+        ...                   index=["owl", "toucan", "eagle"])
+        >>> df
+                a  b  c
+        owl     1  2  3
+        toucan  1  5  6
+        eagle   7  8  9
+        >>> df.groupby(by=["a"]).indices
+        {1: array([0, 1]), 7: array([2])}
+
+        For Resampler:
+
+        >>> ser = pd.Series([1, 2, 3, 4], index=pd.DatetimeIndex(
+        ...                 ['2023-01-01', '2023-01-15', '2023-02-01', '2023-02-15']))
+        >>> ser
+        2023-01-01    1
+        2023-01-15    2
+        2023-02-01    3
+        2023-02-15    4
+        dtype: int64
+        >>> ser.resample('MS').indices
+        defaultdict(, {Timestamp('2023-01-01 00:00:00'): [0, 1],
+        Timestamp('2023-02-01 00:00:00'): [2, 3]})
+        """
+        return self._grouper.indices
+
+    @final
+    def _get_indices(self, names):
+        """
+        Safe get multiple indices, translate keys for
+        datelike to underlying repr.
+        """
+
+        def get_converter(s):
+            # possibly convert to the actual key types
+            # in the indices, could be a Timestamp or a np.datetime64
+            if isinstance(s, datetime.datetime):
+                return lambda key: Timestamp(key)
+            elif isinstance(s, np.datetime64):
+                return lambda key: Timestamp(key).asm8
+            else:
+                return lambda key: key
+
+        if len(names) == 0:
+            return []
+
+        if len(self.indices) > 0:
+            index_sample = next(iter(self.indices))
+        else:
+            index_sample = None  # Dummy sample
+
+        name_sample = names[0]
+        if isinstance(index_sample, tuple):
+            if not isinstance(name_sample, tuple):
+                msg = "must supply a tuple to get_group with multiple grouping keys"
+                raise ValueError(msg)
+            if not len(name_sample) == len(index_sample):
+                try:
+                    # If the original grouper was a tuple
+                    return [self.indices[name] for name in names]
+                except KeyError as err:
+                    # turns out it wasn't a tuple
+                    msg = (
+                        "must supply a same-length tuple to get_group "
+                        "with multiple grouping keys"
+                    )
+                    raise ValueError(msg) from err
+
+            converters = [get_converter(s) for s in index_sample]
+            names = (tuple(f(n) for f, n in zip(converters, name)) for name in names)
+
+        else:
+            converter = get_converter(index_sample)
+            names = (converter(name) for name in names)
+
+        return [self.indices.get(name, []) for name in names]
+
+    @final
+    def _get_index(self, name):
+        """
+        Safe get index, translate keys for datelike to underlying repr.
+        """
+        return self._get_indices([name])[0]
+
+    @final
+    @cache_readonly
+    def _selected_obj(self):
+        # Note: _selected_obj is always just `self.obj` for SeriesGroupBy
+        if isinstance(self.obj, Series):
+            return self.obj
+
+        if self._selection is not None:
+            if is_hashable(self._selection):
+                # i.e. a single key, so selecting it will return a Series.
+                #  In this case, _obj_with_exclusions would wrap the key
+                #  in a list and return a single-column DataFrame.
+                return self.obj[self._selection]
+
+            # Otherwise _selection is equivalent to _selection_list, so
+            #  _selected_obj matches _obj_with_exclusions, so we can reuse
+            #  that and avoid making a copy.
+            return self._obj_with_exclusions
+
+        return self.obj
+
+    @final
+    def _dir_additions(self) -> set[str]:
+        return self.obj._dir_additions()
+
+    @Substitution(
+        klass="GroupBy",
+        examples=dedent(
+            """\
+        >>> df = pd.DataFrame({'A': 'a b a b'.split(), 'B': [1, 2, 3, 4]})
+        >>> df
+           A  B
+        0  a  1
+        1  b  2
+        2  a  3
+        3  b  4
+
+        To get the difference between each groups maximum and minimum value in one
+        pass, you can do
+
+        >>> df.groupby('A').pipe(lambda x: x.max() - x.min())
+           B
+        A
+        a  2
+        b  2"""
+        ),
+    )
+    @Appender(_pipe_template)
+    def pipe(
+        self,
+        func: Callable[..., T] | tuple[Callable[..., T], str],
+        *args,
+        **kwargs,
+    ) -> T:
+        return com.pipe(self, func, *args, **kwargs)
+
+    @final
+    def get_group(self, name, obj=None) -> DataFrame | Series:
+        """
+        Construct DataFrame from group with provided name.
+
+        Parameters
+        ----------
+        name : object
+            The name of the group to get as a DataFrame.
+        obj : DataFrame, default None
+            The DataFrame to take the DataFrame out of.  If
+            it is None, the object groupby was called on will
+            be used.
+
+            .. deprecated:: 2.1.0
+                The obj is deprecated and will be removed in a future version.
+                Do ``df.iloc[gb.indices.get(name)]``
+                instead of ``gb.get_group(name, obj=df)``.
+
+        Returns
+        -------
+        same type as obj
+
+        Examples
+        --------
+
+        For SeriesGroupBy:
+
+        >>> lst = ['a', 'a', 'b']
+        >>> ser = pd.Series([1, 2, 3], index=lst)
+        >>> ser
+        a    1
+        a    2
+        b    3
+        dtype: int64
+        >>> ser.groupby(level=0).get_group("a")
+        a    1
+        a    2
+        dtype: int64
+
+        For DataFrameGroupBy:
+
+        >>> data = [[1, 2, 3], [1, 5, 6], [7, 8, 9]]
+        >>> df = pd.DataFrame(data, columns=["a", "b", "c"],
+        ...                   index=["owl", "toucan", "eagle"])
+        >>> df
+                a  b  c
+        owl     1  2  3
+        toucan  1  5  6
+        eagle   7  8  9
+        >>> df.groupby(by=["a"]).get_group((1,))
+                a  b  c
+        owl     1  2  3
+        toucan  1  5  6
+
+        For Resampler:
+
+        >>> ser = pd.Series([1, 2, 3, 4], index=pd.DatetimeIndex(
+        ...                 ['2023-01-01', '2023-01-15', '2023-02-01', '2023-02-15']))
+        >>> ser
+        2023-01-01    1
+        2023-01-15    2
+        2023-02-01    3
+        2023-02-15    4
+        dtype: int64
+        >>> ser.resample('MS').get_group('2023-01-01')
+        2023-01-01    1
+        2023-01-15    2
+        dtype: int64
+        """
+        keys = self.keys
+        level = self.level
+        # mypy doesn't recognize level/keys as being sized when passed to len
+        if (is_list_like(level) and len(level) == 1) or (  # type: ignore[arg-type]
+            is_list_like(keys) and len(keys) == 1  # type: ignore[arg-type]
+        ):
+            # GH#25971
+            if isinstance(name, tuple) and len(name) == 1:
+                # Allow users to pass tuples of length 1 to silence warning
+                name = name[0]
+            elif not isinstance(name, tuple):
+                warnings.warn(
+                    "When grouping with a length-1 list-like, "
+                    "you will need to pass a length-1 tuple to get_group in a future "
+                    "version of pandas. Pass `(name,)` instead of `name` to silence "
+                    "this warning.",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+
+        inds = self._get_index(name)
+        if not len(inds):
+            raise KeyError(name)
+
+        if obj is None:
+            indexer = inds if self.axis == 0 else (slice(None), inds)
+            return self._selected_obj.iloc[indexer]
+        else:
+            warnings.warn(
+                "obj is deprecated and will be removed in a future version. "
+                "Do ``df.iloc[gb.indices.get(name)]`` "
+                "instead of ``gb.get_group(name, obj=df)``.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+            return obj._take_with_is_copy(inds, axis=self.axis)
+
+    @final
+    def __iter__(self) -> Iterator[tuple[Hashable, NDFrameT]]:
+        """
+        Groupby iterator.
+
+        Returns
+        -------
+        Generator yielding sequence of (name, subsetted object)
+        for each group
+
+        Examples
+        --------
+
+        For SeriesGroupBy:
+
+        >>> lst = ['a', 'a', 'b']
+        >>> ser = pd.Series([1, 2, 3], index=lst)
+        >>> ser
+        a    1
+        a    2
+        b    3
+        dtype: int64
+        >>> for x, y in ser.groupby(level=0):
+        ...     print(f'{x}\\n{y}\\n')
+        a
+        a    1
+        a    2
+        dtype: int64
+        b
+        b    3
+        dtype: int64
+
+        For DataFrameGroupBy:
+
+        >>> data = [[1, 2, 3], [1, 5, 6], [7, 8, 9]]
+        >>> df = pd.DataFrame(data, columns=["a", "b", "c"])
+        >>> df
+           a  b  c
+        0  1  2  3
+        1  1  5  6
+        2  7  8  9
+        >>> for x, y in df.groupby(by=["a"]):
+        ...     print(f'{x}\\n{y}\\n')
+        (1,)
+           a  b  c
+        0  1  2  3
+        1  1  5  6
+        (7,)
+           a  b  c
+        2  7  8  9
+
+        For Resampler:
+
+        >>> ser = pd.Series([1, 2, 3, 4], index=pd.DatetimeIndex(
+        ...                 ['2023-01-01', '2023-01-15', '2023-02-01', '2023-02-15']))
+        >>> ser
+        2023-01-01    1
+        2023-01-15    2
+        2023-02-01    3
+        2023-02-15    4
+        dtype: int64
+        >>> for x, y in ser.resample('MS'):
+        ...     print(f'{x}\\n{y}\\n')
+        2023-01-01 00:00:00
+        2023-01-01    1
+        2023-01-15    2
+        dtype: int64
+        2023-02-01 00:00:00
+        2023-02-01    3
+        2023-02-15    4
+        dtype: int64
+        """
+        keys = self.keys
+        level = self.level
+        result = self._grouper.get_iterator(self._selected_obj, axis=self.axis)
+        # error: Argument 1 to "len" has incompatible type "Hashable"; expected "Sized"
+        if is_list_like(level) and len(level) == 1:  # type: ignore[arg-type]
+            # GH 51583
+            warnings.warn(
+                "Creating a Groupby object with a length-1 list-like "
+                "level parameter will yield indexes as tuples in a future version. "
+                "To keep indexes as scalars, create Groupby objects with "
+                "a scalar level parameter instead.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+        if isinstance(keys, list) and len(keys) == 1:
+            # GH#42795 - when keys is a list, return tuples even when length is 1
+            result = (((key,), group) for key, group in result)
+        return result
+
+
+# To track operations that expand dimensions, like ohlc
+OutputFrameOrSeries = TypeVar("OutputFrameOrSeries", bound=NDFrame)
+
+
+class GroupBy(BaseGroupBy[NDFrameT]):
+    """
+    Class for grouping and aggregating relational data.
+
+    See aggregate, transform, and apply functions on this object.
+
+    It's easiest to use obj.groupby(...) to use GroupBy, but you can also do:
+
+    ::
+
+        grouped = groupby(obj, ...)
+
+    Parameters
+    ----------
+    obj : pandas object
+    axis : int, default 0
+    level : int, default None
+        Level of MultiIndex
+    groupings : list of Grouping objects
+        Most users should ignore this
+    exclusions : array-like, optional
+        List of columns to exclude
+    name : str
+        Most users should ignore this
+
+    Returns
+    -------
+    **Attributes**
+    groups : dict
+        {group name -> group labels}
+    len(grouped) : int
+        Number of groups
+
+    Notes
+    -----
+    After grouping, see aggregate, apply, and transform functions. Here are
+    some other brief notes about usage. When grouping by multiple groups, the
+    result index will be a MultiIndex (hierarchical) by default.
+
+    Iteration produces (key, group) tuples, i.e. chunking the data by group. So
+    you can write code like:
+
+    ::
+
+        grouped = obj.groupby(keys, axis=axis)
+        for key, group in grouped:
+            # do something with the data
+
+    Function calls on GroupBy, if not specially implemented, "dispatch" to the
+    grouped data. So if you group a DataFrame and wish to invoke the std()
+    method on each group, you can simply do:
+
+    ::
+
+        df.groupby(mapper).std()
+
+    rather than
+
+    ::
+
+        df.groupby(mapper).aggregate(np.std)
+
+    You can pass arguments to these "wrapped" functions, too.
+
+    See the online documentation for full exposition on these topics and much
+    more
+    """
+
+    _grouper: ops.BaseGrouper
+    as_index: bool
+
+    @final
+    def __init__(
+        self,
+        obj: NDFrameT,
+        keys: _KeysArgType | None = None,
+        axis: Axis = 0,
+        level: IndexLabel | None = None,
+        grouper: ops.BaseGrouper | None = None,
+        exclusions: frozenset[Hashable] | None = None,
+        selection: IndexLabel | None = None,
+        as_index: bool = True,
+        sort: bool = True,
+        group_keys: bool = True,
+        observed: bool | lib.NoDefault = lib.no_default,
+        dropna: bool = True,
+    ) -> None:
+        self._selection = selection
+
+        assert isinstance(obj, NDFrame), type(obj)
+
+        self.level = level
+
+        if not as_index:
+            if axis != 0:
+                raise ValueError("as_index=False only valid for axis=0")
+
+        self.as_index = as_index
+        self.keys = keys
+        self.sort = sort
+        self.group_keys = group_keys
+        self.dropna = dropna
+
+        if grouper is None:
+            grouper, exclusions, obj = get_grouper(
+                obj,
+                keys,
+                axis=axis,
+                level=level,
+                sort=sort,
+                observed=False if observed is lib.no_default else observed,
+                dropna=self.dropna,
+            )
+
+        if observed is lib.no_default:
+            if any(ping._passed_categorical for ping in grouper.groupings):
+                warnings.warn(
+                    "The default of observed=False is deprecated and will be changed "
+                    "to True in a future version of pandas. Pass observed=False to "
+                    "retain current behavior or observed=True to adopt the future "
+                    "default and silence this warning.",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+            observed = False
+        self.observed = observed
+
+        self.obj = obj
+        self.axis = obj._get_axis_number(axis)
+        self._grouper = grouper
+        self.exclusions = frozenset(exclusions) if exclusions else frozenset()
+
+    def __getattr__(self, attr: str):
+        if attr in self._internal_names_set:
+            return object.__getattribute__(self, attr)
+        if attr in self.obj:
+            return self[attr]
+
+        raise AttributeError(
+            f"'{type(self).__name__}' object has no attribute '{attr}'"
+        )
+
+    @final
+    def _deprecate_axis(self, axis: int, name: str) -> None:
+        if axis == 1:
+            warnings.warn(
+                f"{type(self).__name__}.{name} with axis=1 is deprecated and "
+                "will be removed in a future version. Operate on the un-grouped "
+                "DataFrame instead",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+        else:
+            warnings.warn(
+                f"The 'axis' keyword in {type(self).__name__}.{name} is deprecated "
+                "and will be removed in a future version. "
+                "Call without passing 'axis' instead.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+
+    @final
+    def _op_via_apply(self, name: str, *args, **kwargs):
+        """Compute the result of an operation by using GroupBy's apply."""
+        f = getattr(type(self._obj_with_exclusions), name)
+        sig = inspect.signature(f)
+
+        if "axis" in kwargs and kwargs["axis"] is not lib.no_default:
+            axis = self.obj._get_axis_number(kwargs["axis"])
+            self._deprecate_axis(axis, name)
+        elif "axis" in kwargs:
+            # exclude skew here because that was already defaulting to lib.no_default
+            #  before this deprecation was instituted
+            if name == "skew":
+                pass
+            elif name == "fillna":
+                # maintain the behavior from before the deprecation
+                kwargs["axis"] = None
+            else:
+                kwargs["axis"] = 0
+
+        # a little trickery for aggregation functions that need an axis
+        # argument
+        if "axis" in sig.parameters:
+            if kwargs.get("axis", None) is None or kwargs.get("axis") is lib.no_default:
+                kwargs["axis"] = self.axis
+
+        def curried(x):
+            return f(x, *args, **kwargs)
+
+        # preserve the name so we can detect it when calling plot methods,
+        # to avoid duplicates
+        curried.__name__ = name
+
+        # special case otherwise extra plots are created when catching the
+        # exception below
+        if name in base.plotting_methods:
+            return self._python_apply_general(curried, self._selected_obj)
+
+        is_transform = name in base.transformation_kernels
+        result = self._python_apply_general(
+            curried,
+            self._obj_with_exclusions,
+            is_transform=is_transform,
+            not_indexed_same=not is_transform,
+        )
+
+        if self._grouper.has_dropped_na and is_transform:
+            # result will have dropped rows due to nans, fill with null
+            # and ensure index is ordered same as the input
+            result = self._set_result_index_ordered(result)
+        return result
+
+    # -----------------------------------------------------------------
+    # Dispatch/Wrapping
+
+    @final
+    def _concat_objects(
+        self,
+        values,
+        not_indexed_same: bool = False,
+        is_transform: bool = False,
+    ):
+        from pandas.core.reshape.concat import concat
+
+        if self.group_keys and not is_transform:
+            if self.as_index:
+                # possible MI return case
+                group_keys = self._grouper.result_index
+                group_levels = self._grouper.levels
+                group_names = self._grouper.names
+
+                result = concat(
+                    values,
+                    axis=self.axis,
+                    keys=group_keys,
+                    levels=group_levels,
+                    names=group_names,
+                    sort=False,
+                )
+            else:
+                # GH5610, returns a MI, with the first level being a
+                # range index
+                keys = list(range(len(values)))
+                result = concat(values, axis=self.axis, keys=keys)
+
+        elif not not_indexed_same:
+            result = concat(values, axis=self.axis)
+
+            ax = self._selected_obj._get_axis(self.axis)
+            if self.dropna:
+                labels = self._grouper.group_info[0]
+                mask = labels != -1
+                ax = ax[mask]
+
+            # this is a very unfortunate situation
+            # we can't use reindex to restore the original order
+            # when the ax has duplicates
+            # so we resort to this
+            # GH 14776, 30667
+            # TODO: can we reuse e.g. _reindex_non_unique?
+            if ax.has_duplicates and not result.axes[self.axis].equals(ax):
+                # e.g. test_category_order_transformer
+                target = algorithms.unique1d(ax._values)
+                indexer, _ = result.index.get_indexer_non_unique(target)
+                result = result.take(indexer, axis=self.axis)
+            else:
+                result = result.reindex(ax, axis=self.axis, copy=False)
+
+        else:
+            result = concat(values, axis=self.axis)
+
+        if self.obj.ndim == 1:
+            name = self.obj.name
+        elif is_hashable(self._selection):
+            name = self._selection
+        else:
+            name = None
+
+        if isinstance(result, Series) and name is not None:
+            result.name = name
+
+        return result
+
+    @final
+    def _set_result_index_ordered(
+        self, result: OutputFrameOrSeries
+    ) -> OutputFrameOrSeries:
+        # set the result index on the passed values object and
+        # return the new object, xref 8046
+
+        obj_axis = self.obj._get_axis(self.axis)
+
+        if self._grouper.is_monotonic and not self._grouper.has_dropped_na:
+            # shortcut if we have an already ordered grouper
+            result = result.set_axis(obj_axis, axis=self.axis, copy=False)
+            return result
+
+        # row order is scrambled => sort the rows by position in original index
+        original_positions = Index(self._grouper.result_ilocs())
+        result = result.set_axis(original_positions, axis=self.axis, copy=False)
+        result = result.sort_index(axis=self.axis)
+        if self._grouper.has_dropped_na:
+            # Add back in any missing rows due to dropna - index here is integral
+            # with values referring to the row of the input so can use RangeIndex
+            result = result.reindex(RangeIndex(len(obj_axis)), axis=self.axis)
+        result = result.set_axis(obj_axis, axis=self.axis, copy=False)
+
+        return result
+
+    @final
+    def _insert_inaxis_grouper(self, result: Series | DataFrame) -> DataFrame:
+        if isinstance(result, Series):
+            result = result.to_frame()
+
+        # zip in reverse so we can always insert at loc 0
+        columns = result.columns
+        for name, lev, in_axis in zip(
+            reversed(self._grouper.names),
+            reversed(self._grouper.get_group_levels()),
+            reversed([grp.in_axis for grp in self._grouper.groupings]),
+        ):
+            # GH #28549
+            # When using .apply(-), name will be in columns already
+            if name not in columns:
+                if in_axis:
+                    result.insert(0, name, lev)
+                else:
+                    msg = (
+                        "A grouping was used that is not in the columns of the "
+                        "DataFrame and so was excluded from the result. This grouping "
+                        "will be included in a future version of pandas. Add the "
+                        "grouping as a column of the DataFrame to silence this warning."
+                    )
+                    warnings.warn(
+                        message=msg,
+                        category=FutureWarning,
+                        stacklevel=find_stack_level(),
+                    )
+
+        return result
+
+    @final
+    def _maybe_transpose_result(self, result: NDFrameT) -> NDFrameT:
+        if self.axis == 1:
+            # Only relevant for DataFrameGroupBy, no-op for SeriesGroupBy
+            result = result.T
+            if result.index.equals(self.obj.index):
+                # Retain e.g. DatetimeIndex/TimedeltaIndex freq
+                # e.g. test_groupby_crash_on_nunique
+                result.index = self.obj.index.copy()
+        return result
+
+    @final
+    def _wrap_aggregated_output(
+        self,
+        result: Series | DataFrame,
+        qs: npt.NDArray[np.float64] | None = None,
+    ):
+        """
+        Wraps the output of GroupBy aggregations into the expected result.
+
+        Parameters
+        ----------
+        result : Series, DataFrame
+
+        Returns
+        -------
+        Series or DataFrame
+        """
+        # ATM we do not get here for SeriesGroupBy; when we do, we will
+        #  need to require that result.name already match self.obj.name
+
+        if not self.as_index:
+            # `not self.as_index` is only relevant for DataFrameGroupBy,
+            #   enforced in __init__
+            result = self._insert_inaxis_grouper(result)
+            result = result._consolidate()
+            index = Index(range(self._grouper.ngroups))
+
+        else:
+            index = self._grouper.result_index
+
+        if qs is not None:
+            # We get here with len(qs) != 1 and not self.as_index
+            #  in test_pass_args_kwargs
+            index = _insert_quantile_level(index, qs)
+
+        result.index = index
+
+        # error: Argument 1 to "_maybe_transpose_result" of "GroupBy" has
+        # incompatible type "Union[Series, DataFrame]"; expected "NDFrameT"
+        res = self._maybe_transpose_result(result)  # type: ignore[arg-type]
+        return self._reindex_output(res, qs=qs)
+
+    def _wrap_applied_output(
+        self,
+        data,
+        values: list,
+        not_indexed_same: bool = False,
+        is_transform: bool = False,
+    ):
+        raise AbstractMethodError(self)
+
+    # -----------------------------------------------------------------
+    # numba
+
+    @final
+    def _numba_prep(self, data: DataFrame):
+        ids, _, ngroups = self._grouper.group_info
+        sorted_index = self._grouper._sort_idx
+        sorted_ids = self._grouper._sorted_ids
+
+        sorted_data = data.take(sorted_index, axis=self.axis).to_numpy()
+        # GH 46867
+        index_data = data.index
+        if isinstance(index_data, MultiIndex):
+            if len(self._grouper.groupings) > 1:
+                raise NotImplementedError(
+                    "Grouping with more than 1 grouping labels and "
+                    "a MultiIndex is not supported with engine='numba'"
+                )
+            group_key = self._grouper.groupings[0].name
+            index_data = index_data.get_level_values(group_key)
+        sorted_index_data = index_data.take(sorted_index).to_numpy()
+
+        starts, ends = lib.generate_slices(sorted_ids, ngroups)
+        return (
+            starts,
+            ends,
+            sorted_index_data,
+            sorted_data,
+        )
+
+    def _numba_agg_general(
+        self,
+        func: Callable,
+        dtype_mapping: dict[np.dtype, Any],
+        engine_kwargs: dict[str, bool] | None,
+        **aggregator_kwargs,
+    ):
+        """
+        Perform groupby with a standard numerical aggregation function (e.g. mean)
+        with Numba.
+        """
+        if not self.as_index:
+            raise NotImplementedError(
+                "as_index=False is not supported. Use .reset_index() instead."
+            )
+        if self.axis == 1:
+            raise NotImplementedError("axis=1 is not supported.")
+
+        data = self._obj_with_exclusions
+        df = data if data.ndim == 2 else data.to_frame()
+
+        aggregator = executor.generate_shared_aggregator(
+            func,
+            dtype_mapping,
+            True,  # is_grouped_kernel
+            **get_jit_arguments(engine_kwargs),
+        )
+        # Pass group ids to kernel directly if it can handle it
+        # (This is faster since it doesn't require a sort)
+        ids, _, _ = self._grouper.group_info
+        ngroups = self._grouper.ngroups
+
+        res_mgr = df._mgr.apply(
+            aggregator, labels=ids, ngroups=ngroups, **aggregator_kwargs
+        )
+        res_mgr.axes[1] = self._grouper.result_index
+        result = df._constructor_from_mgr(res_mgr, axes=res_mgr.axes)
+
+        if data.ndim == 1:
+            result = result.squeeze("columns")
+            result.name = data.name
+        else:
+            result.columns = data.columns
+        return result
+
+    @final
+    def _transform_with_numba(self, func, *args, engine_kwargs=None, **kwargs):
+        """
+        Perform groupby transform routine with the numba engine.
+
+        This routine mimics the data splitting routine of the DataSplitter class
+        to generate the indices of each group in the sorted data and then passes the
+        data and indices into a Numba jitted function.
+        """
+        data = self._obj_with_exclusions
+        df = data if data.ndim == 2 else data.to_frame()
+
+        starts, ends, sorted_index, sorted_data = self._numba_prep(df)
+        numba_.validate_udf(func)
+        numba_transform_func = numba_.generate_numba_transform_func(
+            func, **get_jit_arguments(engine_kwargs, kwargs)
+        )
+        result = numba_transform_func(
+            sorted_data,
+            sorted_index,
+            starts,
+            ends,
+            len(df.columns),
+            *args,
+        )
+        # result values needs to be resorted to their original positions since we
+        # evaluated the data sorted by group
+        result = result.take(np.argsort(sorted_index), axis=0)
+        index = data.index
+        if data.ndim == 1:
+            result_kwargs = {"name": data.name}
+            result = result.ravel()
+        else:
+            result_kwargs = {"columns": data.columns}
+        return data._constructor(result, index=index, **result_kwargs)
+
+    @final
+    def _aggregate_with_numba(self, func, *args, engine_kwargs=None, **kwargs):
+        """
+        Perform groupby aggregation routine with the numba engine.
+
+        This routine mimics the data splitting routine of the DataSplitter class
+        to generate the indices of each group in the sorted data and then passes the
+        data and indices into a Numba jitted function.
+        """
+        data = self._obj_with_exclusions
+        df = data if data.ndim == 2 else data.to_frame()
+
+        starts, ends, sorted_index, sorted_data = self._numba_prep(df)
+        numba_.validate_udf(func)
+        numba_agg_func = numba_.generate_numba_agg_func(
+            func, **get_jit_arguments(engine_kwargs, kwargs)
+        )
+        result = numba_agg_func(
+            sorted_data,
+            sorted_index,
+            starts,
+            ends,
+            len(df.columns),
+            *args,
+        )
+        index = self._grouper.result_index
+        if data.ndim == 1:
+            result_kwargs = {"name": data.name}
+            result = result.ravel()
+        else:
+            result_kwargs = {"columns": data.columns}
+        res = data._constructor(result, index=index, **result_kwargs)
+        if not self.as_index:
+            res = self._insert_inaxis_grouper(res)
+            res.index = default_index(len(res))
+        return res
+
+    # -----------------------------------------------------------------
+    # apply/agg/transform
+
+    @Appender(
+        _apply_docs["template"].format(
+            input="dataframe", examples=_apply_docs["dataframe_examples"]
+        )
+    )
+    def apply(self, func, *args, include_groups: bool = True, **kwargs) -> NDFrameT:
+        orig_func = func
+        func = com.is_builtin_func(func)
+        if orig_func != func:
+            alias = com._builtin_table_alias[orig_func]
+            warn_alias_replacement(self, orig_func, alias)
+
+        if isinstance(func, str):
+            if hasattr(self, func):
+                res = getattr(self, func)
+                if callable(res):
+                    return res(*args, **kwargs)
+                elif args or kwargs:
+                    raise ValueError(f"Cannot pass arguments to property {func}")
+                return res
+
+            else:
+                raise TypeError(f"apply func should be callable, not '{func}'")
+
+        elif args or kwargs:
+            if callable(func):
+
+                @wraps(func)
+                def f(g):
+                    return func(g, *args, **kwargs)
+
+            else:
+                raise ValueError(
+                    "func must be a callable if args or kwargs are supplied"
+                )
+        else:
+            f = func
+
+        if not include_groups:
+            return self._python_apply_general(f, self._obj_with_exclusions)
+
+        # ignore SettingWithCopy here in case the user mutates
+        with option_context("mode.chained_assignment", None):
+            try:
+                result = self._python_apply_general(f, self._selected_obj)
+                if (
+                    not isinstance(self.obj, Series)
+                    and self._selection is None
+                    and self._selected_obj.shape != self._obj_with_exclusions.shape
+                ):
+                    warnings.warn(
+                        message=_apply_groupings_depr.format(
+                            type(self).__name__, "apply"
+                        ),
+                        category=FutureWarning,
+                        stacklevel=find_stack_level(),
+                    )
+            except TypeError:
+                # gh-20949
+                # try again, with .apply acting as a filtering
+                # operation, by excluding the grouping column
+                # This would normally not be triggered
+                # except if the udf is trying an operation that
+                # fails on *some* columns, e.g. a numeric operation
+                # on a string grouper column
+
+                return self._python_apply_general(f, self._obj_with_exclusions)
+
+        return result
+
+    @final
+    def _python_apply_general(
+        self,
+        f: Callable,
+        data: DataFrame | Series,
+        not_indexed_same: bool | None = None,
+        is_transform: bool = False,
+        is_agg: bool = False,
+    ) -> NDFrameT:
+        """
+        Apply function f in python space
+
+        Parameters
+        ----------
+        f : callable
+            Function to apply
+        data : Series or DataFrame
+            Data to apply f to
+        not_indexed_same: bool, optional
+            When specified, overrides the value of not_indexed_same. Apply behaves
+            differently when the result index is equal to the input index, but
+            this can be coincidental leading to value-dependent behavior.
+        is_transform : bool, default False
+            Indicator for whether the function is actually a transform
+            and should not have group keys prepended.
+        is_agg : bool, default False
+            Indicator for whether the function is an aggregation. When the
+            result is empty, we don't want to warn for this case.
+            See _GroupBy._python_agg_general.
+
+        Returns
+        -------
+        Series or DataFrame
+            data after applying f
+        """
+        values, mutated = self._grouper.apply_groupwise(f, data, self.axis)
+        if not_indexed_same is None:
+            not_indexed_same = mutated
+
+        return self._wrap_applied_output(
+            data,
+            values,
+            not_indexed_same,
+            is_transform,
+        )
+
+    @final
+    def _agg_general(
+        self,
+        numeric_only: bool = False,
+        min_count: int = -1,
+        *,
+        alias: str,
+        npfunc: Callable | None = None,
+        **kwargs,
+    ):
+        result = self._cython_agg_general(
+            how=alias,
+            alt=npfunc,
+            numeric_only=numeric_only,
+            min_count=min_count,
+            **kwargs,
+        )
+        return result.__finalize__(self.obj, method="groupby")
+
+    def _agg_py_fallback(
+        self, how: str, values: ArrayLike, ndim: int, alt: Callable
+    ) -> ArrayLike:
+        """
+        Fallback to pure-python aggregation if _cython_operation raises
+        NotImplementedError.
+        """
+        # We get here with a) EADtypes and b) object dtype
+        assert alt is not None
+
+        if values.ndim == 1:
+            # For DataFrameGroupBy we only get here with ExtensionArray
+            ser = Series(values, copy=False)
+        else:
+            # We only get here with values.dtype == object
+            df = DataFrame(values.T, dtype=values.dtype)
+            # bc we split object blocks in grouped_reduce, we have only 1 col
+            # otherwise we'd have to worry about block-splitting GH#39329
+            assert df.shape[1] == 1
+            # Avoid call to self.values that can occur in DataFrame
+            #  reductions; see GH#28949
+            ser = df.iloc[:, 0]
+
+        # We do not get here with UDFs, so we know that our dtype
+        #  should always be preserved by the implemented aggregations
+        # TODO: Is this exactly right; see WrappedCythonOp get_result_dtype?
+        try:
+            res_values = self._grouper.agg_series(ser, alt, preserve_dtype=True)
+        except Exception as err:
+            msg = f"agg function failed [how->{how},dtype->{ser.dtype}]"
+            # preserve the kind of exception that raised
+            raise type(err)(msg) from err
+
+        dtype = ser.dtype
+        if dtype == object:
+            res_values = res_values.astype(object, copy=False)
+        elif is_string_dtype(dtype):
+            # mypy doesn't infer dtype is an ExtensionDtype
+            string_array_cls = dtype.construct_array_type()  # type: ignore[union-attr]
+            res_values = string_array_cls._from_sequence(res_values, dtype=dtype)
+
+        # If we are DataFrameGroupBy and went through a SeriesGroupByPath
+        # then we need to reshape
+        # GH#32223 includes case with IntegerArray values, ndarray res_values
+        # test_groupby_duplicate_columns with object dtype values
+        return ensure_block_shape(res_values, ndim=ndim)
+
+    @final
+    def _cython_agg_general(
+        self,
+        how: str,
+        alt: Callable | None = None,
+        numeric_only: bool = False,
+        min_count: int = -1,
+        **kwargs,
+    ):
+        # Note: we never get here with how="ohlc" for DataFrameGroupBy;
+        #  that goes through SeriesGroupBy
+
+        data = self._get_data_to_aggregate(numeric_only=numeric_only, name=how)
+
+        def array_func(values: ArrayLike) -> ArrayLike:
+            try:
+                result = self._grouper._cython_operation(
+                    "aggregate",
+                    values,
+                    how,
+                    axis=data.ndim - 1,
+                    min_count=min_count,
+                    **kwargs,
+                )
+            except NotImplementedError:
+                # generally if we have numeric_only=False
+                # and non-applicable functions
+                # try to python agg
+                # TODO: shouldn't min_count matter?
+                # TODO: avoid special casing SparseArray here
+                if how in ["any", "all"] and isinstance(values, SparseArray):
+                    pass
+                elif alt is None or how in ["any", "all", "std", "sem"]:
+                    raise  # TODO: re-raise as TypeError?  should not be reached
+            else:
+                return result
+
+            assert alt is not None
+            result = self._agg_py_fallback(how, values, ndim=data.ndim, alt=alt)
+            return result
+
+        new_mgr = data.grouped_reduce(array_func)
+        res = self._wrap_agged_manager(new_mgr)
+        if how in ["idxmin", "idxmax"]:
+            res = self._wrap_idxmax_idxmin(res)
+        out = self._wrap_aggregated_output(res)
+        if self.axis == 1:
+            out = out.infer_objects(copy=False)
+        return out
+
+    def _cython_transform(
+        self, how: str, numeric_only: bool = False, axis: AxisInt = 0, **kwargs
+    ):
+        raise AbstractMethodError(self)
+
+    @final
+    def _transform(self, func, *args, engine=None, engine_kwargs=None, **kwargs):
+        # optimized transforms
+        orig_func = func
+        func = com.get_cython_func(func) or func
+        if orig_func != func:
+            warn_alias_replacement(self, orig_func, func)
+
+        if not isinstance(func, str):
+            return self._transform_general(func, engine, engine_kwargs, *args, **kwargs)
+
+        elif func not in base.transform_kernel_allowlist:
+            msg = f"'{func}' is not a valid function name for transform(name)"
+            raise ValueError(msg)
+        elif func in base.cythonized_kernels or func in base.transformation_kernels:
+            # cythonized transform or canned "agg+broadcast"
+            if engine is not None:
+                kwargs["engine"] = engine
+                kwargs["engine_kwargs"] = engine_kwargs
+            return getattr(self, func)(*args, **kwargs)
+
+        else:
+            # i.e. func in base.reduction_kernels
+
+            # GH#30918 Use _transform_fast only when we know func is an aggregation
+            # If func is a reduction, we need to broadcast the
+            # result to the whole group. Compute func result
+            # and deal with possible broadcasting below.
+            with com.temp_setattr(self, "as_index", True):
+                # GH#49834 - result needs groups in the index for
+                # _wrap_transform_fast_result
+                if func in ["idxmin", "idxmax"]:
+                    func = cast(Literal["idxmin", "idxmax"], func)
+                    result = self._idxmax_idxmin(func, True, *args, **kwargs)
+                else:
+                    if engine is not None:
+                        kwargs["engine"] = engine
+                        kwargs["engine_kwargs"] = engine_kwargs
+                    result = getattr(self, func)(*args, **kwargs)
+
+            return self._wrap_transform_fast_result(result)
+
+    @final
+    def _wrap_transform_fast_result(self, result: NDFrameT) -> NDFrameT:
+        """
+        Fast transform path for aggregations.
+        """
+        obj = self._obj_with_exclusions
+
+        # for each col, reshape to size of original frame by take operation
+        ids, _, _ = self._grouper.group_info
+        result = result.reindex(self._grouper.result_index, axis=self.axis, copy=False)
+
+        if self.obj.ndim == 1:
+            # i.e. SeriesGroupBy
+            out = algorithms.take_nd(result._values, ids)
+            output = obj._constructor(out, index=obj.index, name=obj.name)
+        else:
+            # `.size()` gives Series output on DataFrame input, need axis 0
+            axis = 0 if result.ndim == 1 else self.axis
+            # GH#46209
+            # Don't convert indices: negative indices need to give rise
+            # to null values in the result
+            new_ax = result.axes[axis].take(ids)
+            output = result._reindex_with_indexers(
+                {axis: (new_ax, ids)}, allow_dups=True, copy=False
+            )
+            output = output.set_axis(obj._get_axis(self.axis), axis=axis)
+        return output
+
+    # -----------------------------------------------------------------
+    # Utilities
+
+    @final
+    def _apply_filter(self, indices, dropna):
+        if len(indices) == 0:
+            indices = np.array([], dtype="int64")
+        else:
+            indices = np.sort(np.concatenate(indices))
+        if dropna:
+            filtered = self._selected_obj.take(indices, axis=self.axis)
+        else:
+            mask = np.empty(len(self._selected_obj.index), dtype=bool)
+            mask.fill(False)
+            mask[indices.astype(int)] = True
+            # mask fails to broadcast when passed to where; broadcast manually.
+            mask = np.tile(mask, list(self._selected_obj.shape[1:]) + [1]).T
+            filtered = self._selected_obj.where(mask)  # Fill with NaNs.
+        return filtered
+
+    @final
+    def _cumcount_array(self, ascending: bool = True) -> np.ndarray:
+        """
+        Parameters
+        ----------
+        ascending : bool, default True
+            If False, number in reverse, from length of group - 1 to 0.
+
+        Notes
+        -----
+        this is currently implementing sort=False
+        (though the default is sort=True) for groupby in general
+        """
+        ids, _, ngroups = self._grouper.group_info
+        sorter = get_group_index_sorter(ids, ngroups)
+        ids, count = ids[sorter], len(ids)
+
+        if count == 0:
+            return np.empty(0, dtype=np.int64)
+
+        run = np.r_[True, ids[:-1] != ids[1:]]
+        rep = np.diff(np.r_[np.nonzero(run)[0], count])
+        out = (~run).cumsum()
+
+        if ascending:
+            out -= np.repeat(out[run], rep)
+        else:
+            out = np.repeat(out[np.r_[run[1:], True]], rep) - out
+
+        if self._grouper.has_dropped_na:
+            out = np.where(ids == -1, np.nan, out.astype(np.float64, copy=False))
+        else:
+            out = out.astype(np.int64, copy=False)
+
+        rev = np.empty(count, dtype=np.intp)
+        rev[sorter] = np.arange(count, dtype=np.intp)
+        return out[rev]
+
+    # -----------------------------------------------------------------
+
+    @final
+    @property
+    def _obj_1d_constructor(self) -> Callable:
+        # GH28330 preserve subclassed Series/DataFrames
+        if isinstance(self.obj, DataFrame):
+            return self.obj._constructor_sliced
+        assert isinstance(self.obj, Series)
+        return self.obj._constructor
+
+    @final
+    @Substitution(name="groupby")
+    @Substitution(see_also=_common_see_also)
+    def any(self, skipna: bool = True) -> NDFrameT:
+        """
+        Return True if any value in the group is truthful, else False.
+
+        Parameters
+        ----------
+        skipna : bool, default True
+            Flag to ignore nan values during truth testing.
+
+        Returns
+        -------
+        Series or DataFrame
+            DataFrame or Series of boolean values, where a value is True if any element
+            is True within its respective group, False otherwise.
+        %(see_also)s
+        Examples
+        --------
+        For SeriesGroupBy:
+
+        >>> lst = ['a', 'a', 'b']
+        >>> ser = pd.Series([1, 2, 0], index=lst)
+        >>> ser
+        a    1
+        a    2
+        b    0
+        dtype: int64
+        >>> ser.groupby(level=0).any()
+        a     True
+        b    False
+        dtype: bool
+
+        For DataFrameGroupBy:
+
+        >>> data = [[1, 0, 3], [1, 0, 6], [7, 1, 9]]
+        >>> df = pd.DataFrame(data, columns=["a", "b", "c"],
+        ...                   index=["ostrich", "penguin", "parrot"])
+        >>> df
+                 a  b  c
+        ostrich  1  0  3
+        penguin  1  0  6
+        parrot   7  1  9
+        >>> df.groupby(by=["a"]).any()
+               b      c
+        a
+        1  False   True
+        7   True   True
+        """
+        return self._cython_agg_general(
+            "any",
+            alt=lambda x: Series(x, copy=False).any(skipna=skipna),
+            skipna=skipna,
+        )
+
+    @final
+    @Substitution(name="groupby")
+    @Substitution(see_also=_common_see_also)
+    def all(self, skipna: bool = True) -> NDFrameT:
+        """
+        Return True if all values in the group are truthful, else False.
+
+        Parameters
+        ----------
+        skipna : bool, default True
+            Flag to ignore nan values during truth testing.
+
+        Returns
+        -------
+        Series or DataFrame
+            DataFrame or Series of boolean values, where a value is True if all elements
+            are True within its respective group, False otherwise.
+        %(see_also)s
+        Examples
+        --------
+
+        For SeriesGroupBy:
+
+        >>> lst = ['a', 'a', 'b']
+        >>> ser = pd.Series([1, 2, 0], index=lst)
+        >>> ser
+        a    1
+        a    2
+        b    0
+        dtype: int64
+        >>> ser.groupby(level=0).all()
+        a     True
+        b    False
+        dtype: bool
+
+        For DataFrameGroupBy:
+
+        >>> data = [[1, 0, 3], [1, 5, 6], [7, 8, 9]]
+        >>> df = pd.DataFrame(data, columns=["a", "b", "c"],
+        ...                   index=["ostrich", "penguin", "parrot"])
+        >>> df
+                 a  b  c
+        ostrich  1  0  3
+        penguin  1  5  6
+        parrot   7  8  9
+        >>> df.groupby(by=["a"]).all()
+               b      c
+        a
+        1  False   True
+        7   True   True
+        """
+        return self._cython_agg_general(
+            "all",
+            alt=lambda x: Series(x, copy=False).all(skipna=skipna),
+            skipna=skipna,
+        )
+
+    @final
+    @Substitution(name="groupby")
+    @Substitution(see_also=_common_see_also)
+    def count(self) -> NDFrameT:
+        """
+        Compute count of group, excluding missing values.
+
+        Returns
+        -------
+        Series or DataFrame
+            Count of values within each group.
+        %(see_also)s
+        Examples
+        --------
+        For SeriesGroupBy:
+
+        >>> lst = ['a', 'a', 'b']
+        >>> ser = pd.Series([1, 2, np.nan], index=lst)
+        >>> ser
+        a    1.0
+        a    2.0
+        b    NaN
+        dtype: float64
+        >>> ser.groupby(level=0).count()
+        a    2
+        b    0
+        dtype: int64
+
+        For DataFrameGroupBy:
+
+        >>> data = [[1, np.nan, 3], [1, np.nan, 6], [7, 8, 9]]
+        >>> df = pd.DataFrame(data, columns=["a", "b", "c"],
+        ...                   index=["cow", "horse", "bull"])
+        >>> df
+                a	  b	c
+        cow     1	NaN	3
+        horse	1	NaN	6
+        bull	7	8.0	9
+        >>> df.groupby("a").count()
+            b   c
+        a
+        1   0   2
+        7   1   1
+
+        For Resampler:
+
+        >>> ser = pd.Series([1, 2, 3, 4], index=pd.DatetimeIndex(
+        ...                 ['2023-01-01', '2023-01-15', '2023-02-01', '2023-02-15']))
+        >>> ser
+        2023-01-01    1
+        2023-01-15    2
+        2023-02-01    3
+        2023-02-15    4
+        dtype: int64
+        >>> ser.resample('MS').count()
+        2023-01-01    2
+        2023-02-01    2
+        Freq: MS, dtype: int64
+        """
+        data = self._get_data_to_aggregate()
+        ids, _, ngroups = self._grouper.group_info
+        mask = ids != -1
+
+        is_series = data.ndim == 1
+
+        def hfunc(bvalues: ArrayLike) -> ArrayLike:
+            # TODO(EA2D): reshape would not be necessary with 2D EAs
+            if bvalues.ndim == 1:
+                # EA
+                masked = mask & ~isna(bvalues).reshape(1, -1)
+            else:
+                masked = mask & ~isna(bvalues)
+
+            counted = lib.count_level_2d(masked, labels=ids, max_bin=ngroups)
+            if isinstance(bvalues, BaseMaskedArray):
+                return IntegerArray(
+                    counted[0], mask=np.zeros(counted.shape[1], dtype=np.bool_)
+                )
+            elif isinstance(bvalues, ArrowExtensionArray) and not isinstance(
+                bvalues.dtype, StringDtype
+            ):
+                dtype = pandas_dtype("int64[pyarrow]")
+                return type(bvalues)._from_sequence(counted[0], dtype=dtype)
+            if is_series:
+                assert counted.ndim == 2
+                assert counted.shape[0] == 1
+                return counted[0]
+            return counted
+
+        new_mgr = data.grouped_reduce(hfunc)
+        new_obj = self._wrap_agged_manager(new_mgr)
+
+        # If we are grouping on categoricals we want unobserved categories to
+        # return zero, rather than the default of NaN which the reindexing in
+        # _wrap_aggregated_output() returns. GH 35028
+        # e.g. test_dataframe_groupby_on_2_categoricals_when_observed_is_false
+        with com.temp_setattr(self, "observed", True):
+            result = self._wrap_aggregated_output(new_obj)
+
+        return self._reindex_output(result, fill_value=0)
+
+    @final
+    @Substitution(name="groupby")
+    @Substitution(see_also=_common_see_also)
+    def mean(
+        self,
+        numeric_only: bool = False,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+    ):
+        """
+        Compute mean of groups, excluding missing values.
+
+        Parameters
+        ----------
+        numeric_only : bool, default False
+            Include only float, int, boolean columns.
+
+            .. versionchanged:: 2.0.0
+
+                numeric_only no longer accepts ``None`` and defaults to ``False``.
+
+        engine : str, default None
+            * ``'cython'`` : Runs the operation through C-extensions from cython.
+            * ``'numba'`` : Runs the operation through JIT compiled code from numba.
+            * ``None`` : Defaults to ``'cython'`` or globally setting
+              ``compute.use_numba``
+
+            .. versionadded:: 1.4.0
+
+        engine_kwargs : dict, default None
+            * For ``'cython'`` engine, there are no accepted ``engine_kwargs``
+            * For ``'numba'`` engine, the engine can accept ``nopython``, ``nogil``
+              and ``parallel`` dictionary keys. The values must either be ``True`` or
+              ``False``. The default ``engine_kwargs`` for the ``'numba'`` engine is
+              ``{{'nopython': True, 'nogil': False, 'parallel': False}}``
+
+            .. versionadded:: 1.4.0
+
+        Returns
+        -------
+        pandas.Series or pandas.DataFrame
+        %(see_also)s
+        Examples
+        --------
+        >>> df = pd.DataFrame({'A': [1, 1, 2, 1, 2],
+        ...                    'B': [np.nan, 2, 3, 4, 5],
+        ...                    'C': [1, 2, 1, 1, 2]}, columns=['A', 'B', 'C'])
+
+        Groupby one column and return the mean of the remaining columns in
+        each group.
+
+        >>> df.groupby('A').mean()
+             B         C
+        A
+        1  3.0  1.333333
+        2  4.0  1.500000
+
+        Groupby two columns and return the mean of the remaining column.
+
+        >>> df.groupby(['A', 'B']).mean()
+                 C
+        A B
+        1 2.0  2.0
+          4.0  1.0
+        2 3.0  1.0
+          5.0  2.0
+
+        Groupby one column and return the mean of only particular column in
+        the group.
+
+        >>> df.groupby('A')['B'].mean()
+        A
+        1    3.0
+        2    4.0
+        Name: B, dtype: float64
+        """
+
+        if maybe_use_numba(engine):
+            from pandas.core._numba.kernels import grouped_mean
+
+            return self._numba_agg_general(
+                grouped_mean,
+                executor.float_dtype_mapping,
+                engine_kwargs,
+                min_periods=0,
+            )
+        else:
+            result = self._cython_agg_general(
+                "mean",
+                alt=lambda x: Series(x, copy=False).mean(numeric_only=numeric_only),
+                numeric_only=numeric_only,
+            )
+            return result.__finalize__(self.obj, method="groupby")
+
+    @final
+    def median(self, numeric_only: bool = False) -> NDFrameT:
+        """
+        Compute median of groups, excluding missing values.
+
+        For multiple groupings, the result index will be a MultiIndex
+
+        Parameters
+        ----------
+        numeric_only : bool, default False
+            Include only float, int, boolean columns.
+
+            .. versionchanged:: 2.0.0
+
+                numeric_only no longer accepts ``None`` and defaults to False.
+
+        Returns
+        -------
+        Series or DataFrame
+            Median of values within each group.
+
+        Examples
+        --------
+        For SeriesGroupBy:
+
+        >>> lst = ['a', 'a', 'a', 'b', 'b', 'b']
+        >>> ser = pd.Series([7, 2, 8, 4, 3, 3], index=lst)
+        >>> ser
+        a     7
+        a     2
+        a     8
+        b     4
+        b     3
+        b     3
+        dtype: int64
+        >>> ser.groupby(level=0).median()
+        a    7.0
+        b    3.0
+        dtype: float64
+
+        For DataFrameGroupBy:
+
+        >>> data = {'a': [1, 3, 5, 7, 7, 8, 3], 'b': [1, 4, 8, 4, 4, 2, 1]}
+        >>> df = pd.DataFrame(data, index=['dog', 'dog', 'dog',
+        ...                   'mouse', 'mouse', 'mouse', 'mouse'])
+        >>> df
+                 a  b
+          dog    1  1
+          dog    3  4
+          dog    5  8
+        mouse    7  4
+        mouse    7  4
+        mouse    8  2
+        mouse    3  1
+        >>> df.groupby(level=0).median()
+                 a    b
+        dog    3.0  4.0
+        mouse  7.0  3.0
+
+        For Resampler:
+
+        >>> ser = pd.Series([1, 2, 3, 3, 4, 5],
+        ...                 index=pd.DatetimeIndex(['2023-01-01',
+        ...                                         '2023-01-10',
+        ...                                         '2023-01-15',
+        ...                                         '2023-02-01',
+        ...                                         '2023-02-10',
+        ...                                         '2023-02-15']))
+        >>> ser.resample('MS').median()
+        2023-01-01    2.0
+        2023-02-01    4.0
+        Freq: MS, dtype: float64
+        """
+        result = self._cython_agg_general(
+            "median",
+            alt=lambda x: Series(x, copy=False).median(numeric_only=numeric_only),
+            numeric_only=numeric_only,
+        )
+        return result.__finalize__(self.obj, method="groupby")
+
+    @final
+    @Substitution(name="groupby")
+    @Substitution(see_also=_common_see_also)
+    def std(
+        self,
+        ddof: int = 1,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+        numeric_only: bool = False,
+    ):
+        """
+        Compute standard deviation of groups, excluding missing values.
+
+        For multiple groupings, the result index will be a MultiIndex.
+
+        Parameters
+        ----------
+        ddof : int, default 1
+            Degrees of freedom.
+
+        engine : str, default None
+            * ``'cython'`` : Runs the operation through C-extensions from cython.
+            * ``'numba'`` : Runs the operation through JIT compiled code from numba.
+            * ``None`` : Defaults to ``'cython'`` or globally setting
+              ``compute.use_numba``
+
+            .. versionadded:: 1.4.0
+
+        engine_kwargs : dict, default None
+            * For ``'cython'`` engine, there are no accepted ``engine_kwargs``
+            * For ``'numba'`` engine, the engine can accept ``nopython``, ``nogil``
+              and ``parallel`` dictionary keys. The values must either be ``True`` or
+              ``False``. The default ``engine_kwargs`` for the ``'numba'`` engine is
+              ``{{'nopython': True, 'nogil': False, 'parallel': False}}``
+
+            .. versionadded:: 1.4.0
+
+        numeric_only : bool, default False
+            Include only `float`, `int` or `boolean` data.
+
+            .. versionadded:: 1.5.0
+
+            .. versionchanged:: 2.0.0
+
+                numeric_only now defaults to ``False``.
+
+        Returns
+        -------
+        Series or DataFrame
+            Standard deviation of values within each group.
+        %(see_also)s
+        Examples
+        --------
+        For SeriesGroupBy:
+
+        >>> lst = ['a', 'a', 'a', 'b', 'b', 'b']
+        >>> ser = pd.Series([7, 2, 8, 4, 3, 3], index=lst)
+        >>> ser
+        a     7
+        a     2
+        a     8
+        b     4
+        b     3
+        b     3
+        dtype: int64
+        >>> ser.groupby(level=0).std()
+        a    3.21455
+        b    0.57735
+        dtype: float64
+
+        For DataFrameGroupBy:
+
+        >>> data = {'a': [1, 3, 5, 7, 7, 8, 3], 'b': [1, 4, 8, 4, 4, 2, 1]}
+        >>> df = pd.DataFrame(data, index=['dog', 'dog', 'dog',
+        ...                   'mouse', 'mouse', 'mouse', 'mouse'])
+        >>> df
+                 a  b
+          dog    1  1
+          dog    3  4
+          dog    5  8
+        mouse    7  4
+        mouse    7  4
+        mouse    8  2
+        mouse    3  1
+        >>> df.groupby(level=0).std()
+                      a         b
+        dog    2.000000  3.511885
+        mouse  2.217356  1.500000
+        """
+        if maybe_use_numba(engine):
+            from pandas.core._numba.kernels import grouped_var
+
+            return np.sqrt(
+                self._numba_agg_general(
+                    grouped_var,
+                    executor.float_dtype_mapping,
+                    engine_kwargs,
+                    min_periods=0,
+                    ddof=ddof,
+                )
+            )
+        else:
+            return self._cython_agg_general(
+                "std",
+                alt=lambda x: Series(x, copy=False).std(ddof=ddof),
+                numeric_only=numeric_only,
+                ddof=ddof,
+            )
+
+    @final
+    @Substitution(name="groupby")
+    @Substitution(see_also=_common_see_also)
+    def var(
+        self,
+        ddof: int = 1,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+        numeric_only: bool = False,
+    ):
+        """
+        Compute variance of groups, excluding missing values.
+
+        For multiple groupings, the result index will be a MultiIndex.
+
+        Parameters
+        ----------
+        ddof : int, default 1
+            Degrees of freedom.
+
+        engine : str, default None
+            * ``'cython'`` : Runs the operation through C-extensions from cython.
+            * ``'numba'`` : Runs the operation through JIT compiled code from numba.
+            * ``None`` : Defaults to ``'cython'`` or globally setting
+              ``compute.use_numba``
+
+            .. versionadded:: 1.4.0
+
+        engine_kwargs : dict, default None
+            * For ``'cython'`` engine, there are no accepted ``engine_kwargs``
+            * For ``'numba'`` engine, the engine can accept ``nopython``, ``nogil``
+              and ``parallel`` dictionary keys. The values must either be ``True`` or
+              ``False``. The default ``engine_kwargs`` for the ``'numba'`` engine is
+              ``{{'nopython': True, 'nogil': False, 'parallel': False}}``
+
+            .. versionadded:: 1.4.0
+
+        numeric_only : bool, default False
+            Include only `float`, `int` or `boolean` data.
+
+            .. versionadded:: 1.5.0
+
+            .. versionchanged:: 2.0.0
+
+                numeric_only now defaults to ``False``.
+
+        Returns
+        -------
+        Series or DataFrame
+            Variance of values within each group.
+        %(see_also)s
+        Examples
+        --------
+        For SeriesGroupBy:
+
+        >>> lst = ['a', 'a', 'a', 'b', 'b', 'b']
+        >>> ser = pd.Series([7, 2, 8, 4, 3, 3], index=lst)
+        >>> ser
+        a     7
+        a     2
+        a     8
+        b     4
+        b     3
+        b     3
+        dtype: int64
+        >>> ser.groupby(level=0).var()
+        a    10.333333
+        b     0.333333
+        dtype: float64
+
+        For DataFrameGroupBy:
+
+        >>> data = {'a': [1, 3, 5, 7, 7, 8, 3], 'b': [1, 4, 8, 4, 4, 2, 1]}
+        >>> df = pd.DataFrame(data, index=['dog', 'dog', 'dog',
+        ...                   'mouse', 'mouse', 'mouse', 'mouse'])
+        >>> df
+                 a  b
+          dog    1  1
+          dog    3  4
+          dog    5  8
+        mouse    7  4
+        mouse    7  4
+        mouse    8  2
+        mouse    3  1
+        >>> df.groupby(level=0).var()
+                      a          b
+        dog    4.000000  12.333333
+        mouse  4.916667   2.250000
+        """
+        if maybe_use_numba(engine):
+            from pandas.core._numba.kernels import grouped_var
+
+            return self._numba_agg_general(
+                grouped_var,
+                executor.float_dtype_mapping,
+                engine_kwargs,
+                min_periods=0,
+                ddof=ddof,
+            )
+        else:
+            return self._cython_agg_general(
+                "var",
+                alt=lambda x: Series(x, copy=False).var(ddof=ddof),
+                numeric_only=numeric_only,
+                ddof=ddof,
+            )
+
+    @final
+    def _value_counts(
+        self,
+        subset: Sequence[Hashable] | None = None,
+        normalize: bool = False,
+        sort: bool = True,
+        ascending: bool = False,
+        dropna: bool = True,
+    ) -> DataFrame | Series:
+        """
+        Shared implementation of value_counts for SeriesGroupBy and DataFrameGroupBy.
+
+        SeriesGroupBy additionally supports a bins argument. See the docstring of
+        DataFrameGroupBy.value_counts for a description of arguments.
+        """
+        if self.axis == 1:
+            raise NotImplementedError(
+                "DataFrameGroupBy.value_counts only handles axis=0"
+            )
+        name = "proportion" if normalize else "count"
+
+        df = self.obj
+        obj = self._obj_with_exclusions
+
+        in_axis_names = {
+            grouping.name for grouping in self._grouper.groupings if grouping.in_axis
+        }
+        if isinstance(obj, Series):
+            _name = obj.name
+            keys = [] if _name in in_axis_names else [obj]
+        else:
+            unique_cols = set(obj.columns)
+            if subset is not None:
+                subsetted = set(subset)
+                clashing = subsetted & set(in_axis_names)
+                if clashing:
+                    raise ValueError(
+                        f"Keys {clashing} in subset cannot be in "
+                        "the groupby column keys."
+                    )
+                doesnt_exist = subsetted - unique_cols
+                if doesnt_exist:
+                    raise ValueError(
+                        f"Keys {doesnt_exist} in subset do not "
+                        f"exist in the DataFrame."
+                    )
+            else:
+                subsetted = unique_cols
+
+            keys = [
+                # Can't use .values because the column label needs to be preserved
+                obj.iloc[:, idx]
+                for idx, _name in enumerate(obj.columns)
+                if _name not in in_axis_names and _name in subsetted
+            ]
+
+        groupings = list(self._grouper.groupings)
+        for key in keys:
+            grouper, _, _ = get_grouper(
+                df,
+                key=key,
+                axis=self.axis,
+                sort=self.sort,
+                observed=False,
+                dropna=dropna,
+            )
+            groupings += list(grouper.groupings)
+
+        # Take the size of the overall columns
+        gb = df.groupby(
+            groupings,
+            sort=self.sort,
+            observed=self.observed,
+            dropna=self.dropna,
+        )
+        result_series = cast(Series, gb.size())
+        result_series.name = name
+
+        # GH-46357 Include non-observed categories
+        # of non-grouping columns regardless of `observed`
+        if any(
+            isinstance(grouping.grouping_vector, (Categorical, CategoricalIndex))
+            and not grouping._observed
+            for grouping in groupings
+        ):
+            levels_list = [ping._result_index for ping in groupings]
+            multi_index = MultiIndex.from_product(
+                levels_list, names=[ping.name for ping in groupings]
+            )
+            result_series = result_series.reindex(multi_index, fill_value=0)
+
+        if sort:
+            # Sort by the values
+            result_series = result_series.sort_values(
+                ascending=ascending, kind="stable"
+            )
+        if self.sort:
+            # Sort by the groupings
+            names = result_series.index.names
+            # GH#55951 - Temporarily replace names in case they are integers
+            result_series.index.names = range(len(names))
+            index_level = list(range(len(self._grouper.groupings)))
+            result_series = result_series.sort_index(
+                level=index_level, sort_remaining=False
+            )
+            result_series.index.names = names
+
+        if normalize:
+            # Normalize the results by dividing by the original group sizes.
+            # We are guaranteed to have the first N levels be the
+            # user-requested grouping.
+            levels = list(
+                range(len(self._grouper.groupings), result_series.index.nlevels)
+            )
+            indexed_group_size = result_series.groupby(
+                result_series.index.droplevel(levels),
+                sort=self.sort,
+                dropna=self.dropna,
+                # GH#43999 - deprecation of observed=False
+                observed=False,
+            ).transform("sum")
+            result_series /= indexed_group_size
+
+            # Handle groups of non-observed categories
+            result_series = result_series.fillna(0.0)
+
+        result: Series | DataFrame
+        if self.as_index:
+            result = result_series
+        else:
+            # Convert to frame
+            index = result_series.index
+            columns = com.fill_missing_names(index.names)
+            if name in columns:
+                raise ValueError(f"Column label '{name}' is duplicate of result column")
+            result_series.name = name
+            result_series.index = index.set_names(range(len(columns)))
+            result_frame = result_series.reset_index()
+            orig_dtype = self._grouper.groupings[0].obj.columns.dtype  # type: ignore[union-attr]
+            cols = Index(columns, dtype=orig_dtype).insert(len(columns), name)
+            result_frame.columns = cols
+            result = result_frame
+        return result.__finalize__(self.obj, method="value_counts")
+
+    @final
+    def sem(self, ddof: int = 1, numeric_only: bool = False) -> NDFrameT:
+        """
+        Compute standard error of the mean of groups, excluding missing values.
+
+        For multiple groupings, the result index will be a MultiIndex.
+
+        Parameters
+        ----------
+        ddof : int, default 1
+            Degrees of freedom.
+
+        numeric_only : bool, default False
+            Include only `float`, `int` or `boolean` data.
+
+            .. versionadded:: 1.5.0
+
+            .. versionchanged:: 2.0.0
+
+                numeric_only now defaults to ``False``.
+
+        Returns
+        -------
+        Series or DataFrame
+            Standard error of the mean of values within each group.
+
+        Examples
+        --------
+        For SeriesGroupBy:
+
+        >>> lst = ['a', 'a', 'b', 'b']
+        >>> ser = pd.Series([5, 10, 8, 14], index=lst)
+        >>> ser
+        a     5
+        a    10
+        b     8
+        b    14
+        dtype: int64
+        >>> ser.groupby(level=0).sem()
+        a    2.5
+        b    3.0
+        dtype: float64
+
+        For DataFrameGroupBy:
+
+        >>> data = [[1, 12, 11], [1, 15, 2], [2, 5, 8], [2, 6, 12]]
+        >>> df = pd.DataFrame(data, columns=["a", "b", "c"],
+        ...                   index=["tuna", "salmon", "catfish", "goldfish"])
+        >>> df
+                   a   b   c
+            tuna   1  12  11
+          salmon   1  15   2
+         catfish   2   5   8
+        goldfish   2   6  12
+        >>> df.groupby("a").sem()
+              b  c
+        a
+        1    1.5  4.5
+        2    0.5  2.0
+
+        For Resampler:
+
+        >>> ser = pd.Series([1, 3, 2, 4, 3, 8],
+        ...                 index=pd.DatetimeIndex(['2023-01-01',
+        ...                                         '2023-01-10',
+        ...                                         '2023-01-15',
+        ...                                         '2023-02-01',
+        ...                                         '2023-02-10',
+        ...                                         '2023-02-15']))
+        >>> ser.resample('MS').sem()
+        2023-01-01    0.577350
+        2023-02-01    1.527525
+        Freq: MS, dtype: float64
+        """
+        if numeric_only and self.obj.ndim == 1 and not is_numeric_dtype(self.obj.dtype):
+            raise TypeError(
+                f"{type(self).__name__}.sem called with "
+                f"numeric_only={numeric_only} and dtype {self.obj.dtype}"
+            )
+        return self._cython_agg_general(
+            "sem",
+            alt=lambda x: Series(x, copy=False).sem(ddof=ddof),
+            numeric_only=numeric_only,
+            ddof=ddof,
+        )
+
+    @final
+    @Substitution(name="groupby")
+    @Substitution(see_also=_common_see_also)
+    def size(self) -> DataFrame | Series:
+        """
+        Compute group sizes.
+
+        Returns
+        -------
+        DataFrame or Series
+            Number of rows in each group as a Series if as_index is True
+            or a DataFrame if as_index is False.
+        %(see_also)s
+        Examples
+        --------
+
+        For SeriesGroupBy:
+
+        >>> lst = ['a', 'a', 'b']
+        >>> ser = pd.Series([1, 2, 3], index=lst)
+        >>> ser
+        a     1
+        a     2
+        b     3
+        dtype: int64
+        >>> ser.groupby(level=0).size()
+        a    2
+        b    1
+        dtype: int64
+
+        >>> data = [[1, 2, 3], [1, 5, 6], [7, 8, 9]]
+        >>> df = pd.DataFrame(data, columns=["a", "b", "c"],
+        ...                   index=["owl", "toucan", "eagle"])
+        >>> df
+                a  b  c
+        owl     1  2  3
+        toucan  1  5  6
+        eagle   7  8  9
+        >>> df.groupby("a").size()
+        a
+        1    2
+        7    1
+        dtype: int64
+
+        For Resampler:
+
+        >>> ser = pd.Series([1, 2, 3], index=pd.DatetimeIndex(
+        ...                 ['2023-01-01', '2023-01-15', '2023-02-01']))
+        >>> ser
+        2023-01-01    1
+        2023-01-15    2
+        2023-02-01    3
+        dtype: int64
+        >>> ser.resample('MS').size()
+        2023-01-01    2
+        2023-02-01    1
+        Freq: MS, dtype: int64
+        """
+        result = self._grouper.size()
+        dtype_backend: None | Literal["pyarrow", "numpy_nullable"] = None
+        if isinstance(self.obj, Series):
+            if isinstance(self.obj.array, ArrowExtensionArray):
+                if isinstance(self.obj.array, ArrowStringArrayNumpySemantics):
+                    dtype_backend = None
+                elif isinstance(self.obj.array, ArrowStringArray):
+                    dtype_backend = "numpy_nullable"
+                else:
+                    dtype_backend = "pyarrow"
+            elif isinstance(self.obj.array, BaseMaskedArray):
+                dtype_backend = "numpy_nullable"
+        # TODO: For DataFrames what if columns are mixed arrow/numpy/masked?
+
+        # GH28330 preserve subclassed Series/DataFrames through calls
+        if isinstance(self.obj, Series):
+            result = self._obj_1d_constructor(result, name=self.obj.name)
+        else:
+            result = self._obj_1d_constructor(result)
+
+        if dtype_backend is not None:
+            result = result.convert_dtypes(
+                infer_objects=False,
+                convert_string=False,
+                convert_boolean=False,
+                convert_floating=False,
+                dtype_backend=dtype_backend,
+            )
+
+        with com.temp_setattr(self, "as_index", True):
+            # size already has the desired behavior in GH#49519, but this makes the
+            # as_index=False path of _reindex_output fail on categorical groupers.
+            result = self._reindex_output(result, fill_value=0)
+        if not self.as_index:
+            # error: Incompatible types in assignment (expression has
+            # type "DataFrame", variable has type "Series")
+            result = result.rename("size").reset_index()  # type: ignore[assignment]
+        return result
+
+    @final
+    @doc(
+        _groupby_agg_method_engine_template,
+        fname="sum",
+        no=False,
+        mc=0,
+        e=None,
+        ek=None,
+        example=dedent(
+            """\
+        For SeriesGroupBy:
+
+        >>> lst = ['a', 'a', 'b', 'b']
+        >>> ser = pd.Series([1, 2, 3, 4], index=lst)
+        >>> ser
+        a    1
+        a    2
+        b    3
+        b    4
+        dtype: int64
+        >>> ser.groupby(level=0).sum()
+        a    3
+        b    7
+        dtype: int64
+
+        For DataFrameGroupBy:
+
+        >>> data = [[1, 8, 2], [1, 2, 5], [2, 5, 8], [2, 6, 9]]
+        >>> df = pd.DataFrame(data, columns=["a", "b", "c"],
+        ...                   index=["tiger", "leopard", "cheetah", "lion"])
+        >>> df
+                  a  b  c
+          tiger   1  8  2
+        leopard   1  2  5
+        cheetah   2  5  8
+           lion   2  6  9
+        >>> df.groupby("a").sum()
+             b   c
+        a
+        1   10   7
+        2   11  17"""
+        ),
+    )
+    def sum(
+        self,
+        numeric_only: bool = False,
+        min_count: int = 0,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+    ):
+        if maybe_use_numba(engine):
+            from pandas.core._numba.kernels import grouped_sum
+
+            return self._numba_agg_general(
+                grouped_sum,
+                executor.default_dtype_mapping,
+                engine_kwargs,
+                min_periods=min_count,
+            )
+        else:
+            # If we are grouping on categoricals we want unobserved categories to
+            # return zero, rather than the default of NaN which the reindexing in
+            # _agg_general() returns. GH #31422
+            with com.temp_setattr(self, "observed", True):
+                result = self._agg_general(
+                    numeric_only=numeric_only,
+                    min_count=min_count,
+                    alias="sum",
+                    npfunc=np.sum,
+                )
+
+            return self._reindex_output(result, fill_value=0, method="sum")
+
+    @final
+    @doc(
+        _groupby_agg_method_template,
+        fname="prod",
+        no=False,
+        mc=0,
+        example=dedent(
+            """\
+        For SeriesGroupBy:
+
+        >>> lst = ['a', 'a', 'b', 'b']
+        >>> ser = pd.Series([1, 2, 3, 4], index=lst)
+        >>> ser
+        a    1
+        a    2
+        b    3
+        b    4
+        dtype: int64
+        >>> ser.groupby(level=0).prod()
+        a    2
+        b   12
+        dtype: int64
+
+        For DataFrameGroupBy:
+
+        >>> data = [[1, 8, 2], [1, 2, 5], [2, 5, 8], [2, 6, 9]]
+        >>> df = pd.DataFrame(data, columns=["a", "b", "c"],
+        ...                   index=["tiger", "leopard", "cheetah", "lion"])
+        >>> df
+                  a  b  c
+          tiger   1  8  2
+        leopard   1  2  5
+        cheetah   2  5  8
+           lion   2  6  9
+        >>> df.groupby("a").prod()
+             b    c
+        a
+        1   16   10
+        2   30   72"""
+        ),
+    )
+    def prod(self, numeric_only: bool = False, min_count: int = 0) -> NDFrameT:
+        return self._agg_general(
+            numeric_only=numeric_only, min_count=min_count, alias="prod", npfunc=np.prod
+        )
+
+    @final
+    @doc(
+        _groupby_agg_method_engine_template,
+        fname="min",
+        no=False,
+        mc=-1,
+        e=None,
+        ek=None,
+        example=dedent(
+            """\
+        For SeriesGroupBy:
+
+        >>> lst = ['a', 'a', 'b', 'b']
+        >>> ser = pd.Series([1, 2, 3, 4], index=lst)
+        >>> ser
+        a    1
+        a    2
+        b    3
+        b    4
+        dtype: int64
+        >>> ser.groupby(level=0).min()
+        a    1
+        b    3
+        dtype: int64
+
+        For DataFrameGroupBy:
+
+        >>> data = [[1, 8, 2], [1, 2, 5], [2, 5, 8], [2, 6, 9]]
+        >>> df = pd.DataFrame(data, columns=["a", "b", "c"],
+        ...                   index=["tiger", "leopard", "cheetah", "lion"])
+        >>> df
+                  a  b  c
+          tiger   1  8  2
+        leopard   1  2  5
+        cheetah   2  5  8
+           lion   2  6  9
+        >>> df.groupby("a").min()
+            b  c
+        a
+        1   2  2
+        2   5  8"""
+        ),
+    )
+    def min(
+        self,
+        numeric_only: bool = False,
+        min_count: int = -1,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+    ):
+        if maybe_use_numba(engine):
+            from pandas.core._numba.kernels import grouped_min_max
+
+            return self._numba_agg_general(
+                grouped_min_max,
+                executor.identity_dtype_mapping,
+                engine_kwargs,
+                min_periods=min_count,
+                is_max=False,
+            )
+        else:
+            return self._agg_general(
+                numeric_only=numeric_only,
+                min_count=min_count,
+                alias="min",
+                npfunc=np.min,
+            )
+
+    @final
+    @doc(
+        _groupby_agg_method_engine_template,
+        fname="max",
+        no=False,
+        mc=-1,
+        e=None,
+        ek=None,
+        example=dedent(
+            """\
+        For SeriesGroupBy:
+
+        >>> lst = ['a', 'a', 'b', 'b']
+        >>> ser = pd.Series([1, 2, 3, 4], index=lst)
+        >>> ser
+        a    1
+        a    2
+        b    3
+        b    4
+        dtype: int64
+        >>> ser.groupby(level=0).max()
+        a    2
+        b    4
+        dtype: int64
+
+        For DataFrameGroupBy:
+
+        >>> data = [[1, 8, 2], [1, 2, 5], [2, 5, 8], [2, 6, 9]]
+        >>> df = pd.DataFrame(data, columns=["a", "b", "c"],
+        ...                   index=["tiger", "leopard", "cheetah", "lion"])
+        >>> df
+                  a  b  c
+          tiger   1  8  2
+        leopard   1  2  5
+        cheetah   2  5  8
+           lion   2  6  9
+        >>> df.groupby("a").max()
+            b  c
+        a
+        1   8  5
+        2   6  9"""
+        ),
+    )
+    def max(
+        self,
+        numeric_only: bool = False,
+        min_count: int = -1,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+    ):
+        if maybe_use_numba(engine):
+            from pandas.core._numba.kernels import grouped_min_max
+
+            return self._numba_agg_general(
+                grouped_min_max,
+                executor.identity_dtype_mapping,
+                engine_kwargs,
+                min_periods=min_count,
+                is_max=True,
+            )
+        else:
+            return self._agg_general(
+                numeric_only=numeric_only,
+                min_count=min_count,
+                alias="max",
+                npfunc=np.max,
+            )
+
+    @final
+    def first(
+        self, numeric_only: bool = False, min_count: int = -1, skipna: bool = True
+    ) -> NDFrameT:
+        """
+        Compute the first entry of each column within each group.
+
+        Defaults to skipping NA elements.
+
+        Parameters
+        ----------
+        numeric_only : bool, default False
+            Include only float, int, boolean columns.
+        min_count : int, default -1
+            The required number of valid values to perform the operation. If fewer
+            than ``min_count`` valid values are present the result will be NA.
+        skipna : bool, default True
+            Exclude NA/null values. If an entire row/column is NA, the result
+            will be NA.
+
+            .. versionadded:: 2.2.1
+
+        Returns
+        -------
+        Series or DataFrame
+            First values within each group.
+
+        See Also
+        --------
+        DataFrame.groupby : Apply a function groupby to each row or column of a
+            DataFrame.
+        pandas.core.groupby.DataFrameGroupBy.last : Compute the last non-null entry
+            of each column.
+        pandas.core.groupby.DataFrameGroupBy.nth : Take the nth row from each group.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame(dict(A=[1, 1, 3], B=[None, 5, 6], C=[1, 2, 3],
+        ...                        D=['3/11/2000', '3/12/2000', '3/13/2000']))
+        >>> df['D'] = pd.to_datetime(df['D'])
+        >>> df.groupby("A").first()
+             B  C          D
+        A
+        1  5.0  1 2000-03-11
+        3  6.0  3 2000-03-13
+        >>> df.groupby("A").first(min_count=2)
+            B    C          D
+        A
+        1 NaN  1.0 2000-03-11
+        3 NaN  NaN        NaT
+        >>> df.groupby("A").first(numeric_only=True)
+             B  C
+        A
+        1  5.0  1
+        3  6.0  3
+        """
+
+        def first_compat(obj: NDFrameT, axis: AxisInt = 0):
+            def first(x: Series):
+                """Helper function for first item that isn't NA."""
+                arr = x.array[notna(x.array)]
+                if not len(arr):
+                    return x.array.dtype.na_value
+                return arr[0]
+
+            if isinstance(obj, DataFrame):
+                return obj.apply(first, axis=axis)
+            elif isinstance(obj, Series):
+                return first(obj)
+            else:  # pragma: no cover
+                raise TypeError(type(obj))
+
+        return self._agg_general(
+            numeric_only=numeric_only,
+            min_count=min_count,
+            alias="first",
+            npfunc=first_compat,
+            skipna=skipna,
+        )
+
+    @final
+    def last(
+        self, numeric_only: bool = False, min_count: int = -1, skipna: bool = True
+    ) -> NDFrameT:
+        """
+        Compute the last entry of each column within each group.
+
+        Defaults to skipping NA elements.
+
+        Parameters
+        ----------
+        numeric_only : bool, default False
+            Include only float, int, boolean columns. If None, will attempt to use
+            everything, then use only numeric data.
+        min_count : int, default -1
+            The required number of valid values to perform the operation. If fewer
+            than ``min_count`` valid values are present the result will be NA.
+        skipna : bool, default True
+            Exclude NA/null values. If an entire row/column is NA, the result
+            will be NA.
+
+            .. versionadded:: 2.2.1
+
+        Returns
+        -------
+        Series or DataFrame
+            Last of values within each group.
+
+        See Also
+        --------
+        DataFrame.groupby : Apply a function groupby to each row or column of a
+            DataFrame.
+        pandas.core.groupby.DataFrameGroupBy.first : Compute the first non-null entry
+            of each column.
+        pandas.core.groupby.DataFrameGroupBy.nth : Take the nth row from each group.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame(dict(A=[1, 1, 3], B=[5, None, 6], C=[1, 2, 3]))
+        >>> df.groupby("A").last()
+             B  C
+        A
+        1  5.0  2
+        3  6.0  3
+        """
+
+        def last_compat(obj: NDFrameT, axis: AxisInt = 0):
+            def last(x: Series):
+                """Helper function for last item that isn't NA."""
+                arr = x.array[notna(x.array)]
+                if not len(arr):
+                    return x.array.dtype.na_value
+                return arr[-1]
+
+            if isinstance(obj, DataFrame):
+                return obj.apply(last, axis=axis)
+            elif isinstance(obj, Series):
+                return last(obj)
+            else:  # pragma: no cover
+                raise TypeError(type(obj))
+
+        return self._agg_general(
+            numeric_only=numeric_only,
+            min_count=min_count,
+            alias="last",
+            npfunc=last_compat,
+            skipna=skipna,
+        )
+
+    @final
+    def ohlc(self) -> DataFrame:
+        """
+        Compute open, high, low and close values of a group, excluding missing values.
+
+        For multiple groupings, the result index will be a MultiIndex
+
+        Returns
+        -------
+        DataFrame
+            Open, high, low and close values within each group.
+
+        Examples
+        --------
+
+        For SeriesGroupBy:
+
+        >>> lst = ['SPX', 'CAC', 'SPX', 'CAC', 'SPX', 'CAC', 'SPX', 'CAC',]
+        >>> ser = pd.Series([3.4, 9.0, 7.2, 5.2, 8.8, 9.4, 0.1, 0.5], index=lst)
+        >>> ser
+        SPX     3.4
+        CAC     9.0
+        SPX     7.2
+        CAC     5.2
+        SPX     8.8
+        CAC     9.4
+        SPX     0.1
+        CAC     0.5
+        dtype: float64
+        >>> ser.groupby(level=0).ohlc()
+             open  high  low  close
+        CAC   9.0   9.4  0.5    0.5
+        SPX   3.4   8.8  0.1    0.1
+
+        For DataFrameGroupBy:
+
+        >>> data = {2022: [1.2, 2.3, 8.9, 4.5, 4.4, 3, 2 , 1],
+        ...         2023: [3.4, 9.0, 7.2, 5.2, 8.8, 9.4, 8.2, 1.0]}
+        >>> df = pd.DataFrame(data, index=['SPX', 'CAC', 'SPX', 'CAC',
+        ...                   'SPX', 'CAC', 'SPX', 'CAC'])
+        >>> df
+             2022  2023
+        SPX   1.2   3.4
+        CAC   2.3   9.0
+        SPX   8.9   7.2
+        CAC   4.5   5.2
+        SPX   4.4   8.8
+        CAC   3.0   9.4
+        SPX   2.0   8.2
+        CAC   1.0   1.0
+        >>> df.groupby(level=0).ohlc()
+            2022                 2023
+            open high  low close open high  low close
+        CAC  2.3  4.5  1.0   1.0  9.0  9.4  1.0   1.0
+        SPX  1.2  8.9  1.2   2.0  3.4  8.8  3.4   8.2
+
+        For Resampler:
+
+        >>> ser = pd.Series([1, 3, 2, 4, 3, 5],
+        ...                 index=pd.DatetimeIndex(['2023-01-01',
+        ...                                         '2023-01-10',
+        ...                                         '2023-01-15',
+        ...                                         '2023-02-01',
+        ...                                         '2023-02-10',
+        ...                                         '2023-02-15']))
+        >>> ser.resample('MS').ohlc()
+                    open  high  low  close
+        2023-01-01     1     3    1      2
+        2023-02-01     4     5    3      5
+        """
+        if self.obj.ndim == 1:
+            obj = self._selected_obj
+
+            is_numeric = is_numeric_dtype(obj.dtype)
+            if not is_numeric:
+                raise DataError("No numeric types to aggregate")
+
+            res_values = self._grouper._cython_operation(
+                "aggregate", obj._values, "ohlc", axis=0, min_count=-1
+            )
+
+            agg_names = ["open", "high", "low", "close"]
+            result = self.obj._constructor_expanddim(
+                res_values, index=self._grouper.result_index, columns=agg_names
+            )
+            return self._reindex_output(result)
+
+        result = self._apply_to_column_groupbys(lambda sgb: sgb.ohlc())
+        return result
+
+    @doc(DataFrame.describe)
+    def describe(
+        self,
+        percentiles=None,
+        include=None,
+        exclude=None,
+    ) -> NDFrameT:
+        obj = self._obj_with_exclusions
+
+        if len(obj) == 0:
+            described = obj.describe(
+                percentiles=percentiles, include=include, exclude=exclude
+            )
+            if obj.ndim == 1:
+                result = described
+            else:
+                result = described.unstack()
+            return result.to_frame().T.iloc[:0]
+
+        with com.temp_setattr(self, "as_index", True):
+            result = self._python_apply_general(
+                lambda x: x.describe(
+                    percentiles=percentiles, include=include, exclude=exclude
+                ),
+                obj,
+                not_indexed_same=True,
+            )
+        if self.axis == 1:
+            return result.T
+
+        # GH#49256 - properly handle the grouping column(s)
+        result = result.unstack()
+        if not self.as_index:
+            result = self._insert_inaxis_grouper(result)
+            result.index = default_index(len(result))
+
+        return result
+
+    @final
+    def resample(self, rule, *args, include_groups: bool = True, **kwargs) -> Resampler:
+        """
+        Provide resampling when using a TimeGrouper.
+
+        Given a grouper, the function resamples it according to a string
+        "string" -> "frequency".
+
+        See the :ref:`frequency aliases `
+        documentation for more details.
+
+        Parameters
+        ----------
+        rule : str or DateOffset
+            The offset string or object representing target grouper conversion.
+        *args
+            Possible arguments are `how`, `fill_method`, `limit`, `kind` and
+            `on`, and other arguments of `TimeGrouper`.
+        include_groups : bool, default True
+            When True, will attempt to include the groupings in the operation in
+            the case that they are columns of the DataFrame. If this raises a
+            TypeError, the result will be computed with the groupings excluded.
+            When False, the groupings will be excluded when applying ``func``.
+
+            .. versionadded:: 2.2.0
+
+            .. deprecated:: 2.2.0
+
+               Setting include_groups to True is deprecated. Only the value
+               False will be allowed in a future version of pandas.
+
+        **kwargs
+            Possible arguments are `how`, `fill_method`, `limit`, `kind` and
+            `on`, and other arguments of `TimeGrouper`.
+
+        Returns
+        -------
+        pandas.api.typing.DatetimeIndexResamplerGroupby,
+        pandas.api.typing.PeriodIndexResamplerGroupby, or
+        pandas.api.typing.TimedeltaIndexResamplerGroupby
+            Return a new groupby object, with type depending on the data
+            being resampled.
+
+        See Also
+        --------
+        Grouper : Specify a frequency to resample with when
+            grouping by a key.
+        DatetimeIndex.resample : Frequency conversion and resampling of
+            time series.
+
+        Examples
+        --------
+        >>> idx = pd.date_range('1/1/2000', periods=4, freq='min')
+        >>> df = pd.DataFrame(data=4 * [range(2)],
+        ...                   index=idx,
+        ...                   columns=['a', 'b'])
+        >>> df.iloc[2, 0] = 5
+        >>> df
+                            a  b
+        2000-01-01 00:00:00  0  1
+        2000-01-01 00:01:00  0  1
+        2000-01-01 00:02:00  5  1
+        2000-01-01 00:03:00  0  1
+
+        Downsample the DataFrame into 3 minute bins and sum the values of
+        the timestamps falling into a bin.
+
+        >>> df.groupby('a').resample('3min', include_groups=False).sum()
+                                 b
+        a
+        0   2000-01-01 00:00:00  2
+            2000-01-01 00:03:00  1
+        5   2000-01-01 00:00:00  1
+
+        Upsample the series into 30 second bins.
+
+        >>> df.groupby('a').resample('30s', include_groups=False).sum()
+                            b
+        a
+        0   2000-01-01 00:00:00  1
+            2000-01-01 00:00:30  0
+            2000-01-01 00:01:00  1
+            2000-01-01 00:01:30  0
+            2000-01-01 00:02:00  0
+            2000-01-01 00:02:30  0
+            2000-01-01 00:03:00  1
+        5   2000-01-01 00:02:00  1
+
+        Resample by month. Values are assigned to the month of the period.
+
+        >>> df.groupby('a').resample('ME', include_groups=False).sum()
+                    b
+        a
+        0   2000-01-31  3
+        5   2000-01-31  1
+
+        Downsample the series into 3 minute bins as above, but close the right
+        side of the bin interval.
+
+        >>> (
+        ...     df.groupby('a')
+        ...     .resample('3min', closed='right', include_groups=False)
+        ...     .sum()
+        ... )
+                                 b
+        a
+        0   1999-12-31 23:57:00  1
+            2000-01-01 00:00:00  2
+        5   2000-01-01 00:00:00  1
+
+        Downsample the series into 3 minute bins and close the right side of
+        the bin interval, but label each bin using the right edge instead of
+        the left.
+
+        >>> (
+        ...     df.groupby('a')
+        ...     .resample('3min', closed='right', label='right', include_groups=False)
+        ...     .sum()
+        ... )
+                                 b
+        a
+        0   2000-01-01 00:00:00  1
+            2000-01-01 00:03:00  2
+        5   2000-01-01 00:03:00  1
+        """
+        from pandas.core.resample import get_resampler_for_grouping
+
+        # mypy flags that include_groups could be specified via `*args` or `**kwargs`
+        # GH#54961 would resolve.
+        return get_resampler_for_grouping(  # type: ignore[misc]
+            self, rule, *args, include_groups=include_groups, **kwargs
+        )
+
+    @final
+    def rolling(self, *args, **kwargs) -> RollingGroupby:
+        """
+        Return a rolling grouper, providing rolling functionality per group.
+
+        Parameters
+        ----------
+        window : int, timedelta, str, offset, or BaseIndexer subclass
+            Size of the moving window.
+
+            If an integer, the fixed number of observations used for
+            each window.
+
+            If a timedelta, str, or offset, the time period of each window. Each
+            window will be a variable sized based on the observations included in
+            the time-period. This is only valid for datetimelike indexes.
+            To learn more about the offsets & frequency strings, please see `this link
+            `__.
+
+            If a BaseIndexer subclass, the window boundaries
+            based on the defined ``get_window_bounds`` method. Additional rolling
+            keyword arguments, namely ``min_periods``, ``center``, ``closed`` and
+            ``step`` will be passed to ``get_window_bounds``.
+
+        min_periods : int, default None
+            Minimum number of observations in window required to have a value;
+            otherwise, result is ``np.nan``.
+
+            For a window that is specified by an offset,
+            ``min_periods`` will default to 1.
+
+            For a window that is specified by an integer, ``min_periods`` will default
+            to the size of the window.
+
+        center : bool, default False
+            If False, set the window labels as the right edge of the window index.
+
+            If True, set the window labels as the center of the window index.
+
+        win_type : str, default None
+            If ``None``, all points are evenly weighted.
+
+            If a string, it must be a valid `scipy.signal window function
+            `__.
+
+            Certain Scipy window types require additional parameters to be passed
+            in the aggregation function. The additional parameters must match
+            the keywords specified in the Scipy window type method signature.
+
+        on : str, optional
+            For a DataFrame, a column label or Index level on which
+            to calculate the rolling window, rather than the DataFrame's index.
+
+            Provided integer column is ignored and excluded from result since
+            an integer index is not used to calculate the rolling window.
+
+        axis : int or str, default 0
+            If ``0`` or ``'index'``, roll across the rows.
+
+            If ``1`` or ``'columns'``, roll across the columns.
+
+            For `Series` this parameter is unused and defaults to 0.
+
+        closed : str, default None
+            If ``'right'``, the first point in the window is excluded from calculations.
+
+            If ``'left'``, the last point in the window is excluded from calculations.
+
+            If ``'both'``, no points in the window are excluded from calculations.
+
+            If ``'neither'``, the first and last points in the window are excluded
+            from calculations.
+
+            Default ``None`` (``'right'``).
+
+        method : str {'single', 'table'}, default 'single'
+            Execute the rolling operation per single column or row (``'single'``)
+            or over the entire object (``'table'``).
+
+            This argument is only implemented when specifying ``engine='numba'``
+            in the method call.
+
+        Returns
+        -------
+        pandas.api.typing.RollingGroupby
+            Return a new grouper with our rolling appended.
+
+        See Also
+        --------
+        Series.rolling : Calling object with Series data.
+        DataFrame.rolling : Calling object with DataFrames.
+        Series.groupby : Apply a function groupby to a Series.
+        DataFrame.groupby : Apply a function groupby.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'A': [1, 1, 2, 2],
+        ...                    'B': [1, 2, 3, 4],
+        ...                    'C': [0.362, 0.227, 1.267, -0.562]})
+        >>> df
+              A  B      C
+        0     1  1  0.362
+        1     1  2  0.227
+        2     2  3  1.267
+        3     2  4 -0.562
+
+        >>> df.groupby('A').rolling(2).sum()
+            B      C
+        A
+        1 0  NaN    NaN
+          1  3.0  0.589
+        2 2  NaN    NaN
+          3  7.0  0.705
+
+        >>> df.groupby('A').rolling(2, min_periods=1).sum()
+            B      C
+        A
+        1 0  1.0  0.362
+          1  3.0  0.589
+        2 2  3.0  1.267
+          3  7.0  0.705
+
+        >>> df.groupby('A').rolling(2, on='B').sum()
+            B      C
+        A
+        1 0  1    NaN
+          1  2  0.589
+        2 2  3    NaN
+          3  4  0.705
+        """
+        from pandas.core.window import RollingGroupby
+
+        return RollingGroupby(
+            self._selected_obj,
+            *args,
+            _grouper=self._grouper,
+            _as_index=self.as_index,
+            **kwargs,
+        )
+
+    @final
+    @Substitution(name="groupby")
+    @Appender(_common_see_also)
+    def expanding(self, *args, **kwargs) -> ExpandingGroupby:
+        """
+        Return an expanding grouper, providing expanding
+        functionality per group.
+
+        Returns
+        -------
+        pandas.api.typing.ExpandingGroupby
+        """
+        from pandas.core.window import ExpandingGroupby
+
+        return ExpandingGroupby(
+            self._selected_obj,
+            *args,
+            _grouper=self._grouper,
+            **kwargs,
+        )
+
+    @final
+    @Substitution(name="groupby")
+    @Appender(_common_see_also)
+    def ewm(self, *args, **kwargs) -> ExponentialMovingWindowGroupby:
+        """
+        Return an ewm grouper, providing ewm functionality per group.
+
+        Returns
+        -------
+        pandas.api.typing.ExponentialMovingWindowGroupby
+        """
+        from pandas.core.window import ExponentialMovingWindowGroupby
+
+        return ExponentialMovingWindowGroupby(
+            self._selected_obj,
+            *args,
+            _grouper=self._grouper,
+            **kwargs,
+        )
+
+    @final
+    def _fill(self, direction: Literal["ffill", "bfill"], limit: int | None = None):
+        """
+        Shared function for `pad` and `backfill` to call Cython method.
+
+        Parameters
+        ----------
+        direction : {'ffill', 'bfill'}
+            Direction passed to underlying Cython function. `bfill` will cause
+            values to be filled backwards. `ffill` and any other values will
+            default to a forward fill
+        limit : int, default None
+            Maximum number of consecutive values to fill. If `None`, this
+            method will convert to -1 prior to passing to Cython
+
+        Returns
+        -------
+        `Series` or `DataFrame` with filled values
+
+        See Also
+        --------
+        pad : Returns Series with minimum number of char in object.
+        backfill : Backward fill the missing values in the dataset.
+        """
+        # Need int value for Cython
+        if limit is None:
+            limit = -1
+
+        ids, _, _ = self._grouper.group_info
+        sorted_labels = np.argsort(ids, kind="mergesort").astype(np.intp, copy=False)
+        if direction == "bfill":
+            sorted_labels = sorted_labels[::-1]
+
+        col_func = partial(
+            libgroupby.group_fillna_indexer,
+            labels=ids,
+            sorted_labels=sorted_labels,
+            limit=limit,
+            dropna=self.dropna,
+        )
+
+        def blk_func(values: ArrayLike) -> ArrayLike:
+            mask = isna(values)
+            if values.ndim == 1:
+                indexer = np.empty(values.shape, dtype=np.intp)
+                col_func(out=indexer, mask=mask)
+                return algorithms.take_nd(values, indexer)
+
+            else:
+                # We broadcast algorithms.take_nd analogous to
+                #  np.take_along_axis
+                if isinstance(values, np.ndarray):
+                    dtype = values.dtype
+                    if self._grouper.has_dropped_na:
+                        # dropped null groups give rise to nan in the result
+                        dtype = ensure_dtype_can_hold_na(values.dtype)
+                    out = np.empty(values.shape, dtype=dtype)
+                else:
+                    # Note: we only get here with backfill/pad,
+                    #  so if we have a dtype that cannot hold NAs,
+                    #  then there will be no -1s in indexer, so we can use
+                    #  the original dtype (no need to ensure_dtype_can_hold_na)
+                    out = type(values)._empty(values.shape, dtype=values.dtype)
+
+                for i, value_element in enumerate(values):
+                    # call group_fillna_indexer column-wise
+                    indexer = np.empty(values.shape[1], dtype=np.intp)
+                    col_func(out=indexer, mask=mask[i])
+                    out[i, :] = algorithms.take_nd(value_element, indexer)
+                return out
+
+        mgr = self._get_data_to_aggregate()
+        res_mgr = mgr.apply(blk_func)
+
+        new_obj = self._wrap_agged_manager(res_mgr)
+
+        if self.axis == 1:
+            # Only relevant for DataFrameGroupBy
+            new_obj = new_obj.T
+            new_obj.columns = self.obj.columns
+
+        new_obj.index = self.obj.index
+        return new_obj
+
+    @final
+    @Substitution(name="groupby")
+    def ffill(self, limit: int | None = None):
+        """
+        Forward fill the values.
+
+        Parameters
+        ----------
+        limit : int, optional
+            Limit of how many values to fill.
+
+        Returns
+        -------
+        Series or DataFrame
+            Object with missing values filled.
+
+        See Also
+        --------
+        Series.ffill: Returns Series with minimum number of char in object.
+        DataFrame.ffill: Object with missing values filled or None if inplace=True.
+        Series.fillna: Fill NaN values of a Series.
+        DataFrame.fillna: Fill NaN values of a DataFrame.
+
+        Examples
+        --------
+
+        For SeriesGroupBy:
+
+        >>> key = [0, 0, 1, 1]
+        >>> ser = pd.Series([np.nan, 2, 3, np.nan], index=key)
+        >>> ser
+        0    NaN
+        0    2.0
+        1    3.0
+        1    NaN
+        dtype: float64
+        >>> ser.groupby(level=0).ffill()
+        0    NaN
+        0    2.0
+        1    3.0
+        1    3.0
+        dtype: float64
+
+        For DataFrameGroupBy:
+
+        >>> df = pd.DataFrame(
+        ...     {
+        ...         "key": [0, 0, 1, 1, 1],
+        ...         "A": [np.nan, 2, np.nan, 3, np.nan],
+        ...         "B": [2, 3, np.nan, np.nan, np.nan],
+        ...         "C": [np.nan, np.nan, 2, np.nan, np.nan],
+        ...     }
+        ... )
+        >>> df
+           key    A    B   C
+        0    0  NaN  2.0 NaN
+        1    0  2.0  3.0 NaN
+        2    1  NaN  NaN 2.0
+        3    1  3.0  NaN NaN
+        4    1  NaN  NaN NaN
+
+        Propagate non-null values forward or backward within each group along columns.
+
+        >>> df.groupby("key").ffill()
+             A    B   C
+        0  NaN  2.0 NaN
+        1  2.0  3.0 NaN
+        2  NaN  NaN 2.0
+        3  3.0  NaN 2.0
+        4  3.0  NaN 2.0
+
+        Propagate non-null values forward or backward within each group along rows.
+
+        >>> df.T.groupby(np.array([0, 0, 1, 1])).ffill().T
+           key    A    B    C
+        0  0.0  0.0  2.0  2.0
+        1  0.0  2.0  3.0  3.0
+        2  1.0  1.0  NaN  2.0
+        3  1.0  3.0  NaN  NaN
+        4  1.0  1.0  NaN  NaN
+
+        Only replace the first NaN element within a group along rows.
+
+        >>> df.groupby("key").ffill(limit=1)
+             A    B    C
+        0  NaN  2.0  NaN
+        1  2.0  3.0  NaN
+        2  NaN  NaN  2.0
+        3  3.0  NaN  2.0
+        4  3.0  NaN  NaN
+        """
+        return self._fill("ffill", limit=limit)
+
+    @final
+    @Substitution(name="groupby")
+    def bfill(self, limit: int | None = None):
+        """
+        Backward fill the values.
+
+        Parameters
+        ----------
+        limit : int, optional
+            Limit of how many values to fill.
+
+        Returns
+        -------
+        Series or DataFrame
+            Object with missing values filled.
+
+        See Also
+        --------
+        Series.bfill :  Backward fill the missing values in the dataset.
+        DataFrame.bfill:  Backward fill the missing values in the dataset.
+        Series.fillna: Fill NaN values of a Series.
+        DataFrame.fillna: Fill NaN values of a DataFrame.
+
+        Examples
+        --------
+
+        With Series:
+
+        >>> index = ['Falcon', 'Falcon', 'Parrot', 'Parrot', 'Parrot']
+        >>> s = pd.Series([None, 1, None, None, 3], index=index)
+        >>> s
+        Falcon    NaN
+        Falcon    1.0
+        Parrot    NaN
+        Parrot    NaN
+        Parrot    3.0
+        dtype: float64
+        >>> s.groupby(level=0).bfill()
+        Falcon    1.0
+        Falcon    1.0
+        Parrot    3.0
+        Parrot    3.0
+        Parrot    3.0
+        dtype: float64
+        >>> s.groupby(level=0).bfill(limit=1)
+        Falcon    1.0
+        Falcon    1.0
+        Parrot    NaN
+        Parrot    3.0
+        Parrot    3.0
+        dtype: float64
+
+        With DataFrame:
+
+        >>> df = pd.DataFrame({'A': [1, None, None, None, 4],
+        ...                    'B': [None, None, 5, None, 7]}, index=index)
+        >>> df
+                  A	    B
+        Falcon	1.0	  NaN
+        Falcon	NaN	  NaN
+        Parrot	NaN	  5.0
+        Parrot	NaN	  NaN
+        Parrot	4.0	  7.0
+        >>> df.groupby(level=0).bfill()
+                  A	    B
+        Falcon	1.0	  NaN
+        Falcon	NaN	  NaN
+        Parrot	4.0	  5.0
+        Parrot	4.0	  7.0
+        Parrot	4.0	  7.0
+        >>> df.groupby(level=0).bfill(limit=1)
+                  A	    B
+        Falcon	1.0	  NaN
+        Falcon	NaN	  NaN
+        Parrot	NaN	  5.0
+        Parrot	4.0	  7.0
+        Parrot	4.0	  7.0
+        """
+        return self._fill("bfill", limit=limit)
+
+    @final
+    @property
+    @Substitution(name="groupby")
+    @Substitution(see_also=_common_see_also)
+    def nth(self) -> GroupByNthSelector:
+        """
+        Take the nth row from each group if n is an int, otherwise a subset of rows.
+
+        Can be either a call or an index. dropna is not available with index notation.
+        Index notation accepts a comma separated list of integers and slices.
+
+        If dropna, will take the nth non-null row, dropna is either
+        'all' or 'any'; this is equivalent to calling dropna(how=dropna)
+        before the groupby.
+
+        Parameters
+        ----------
+        n : int, slice or list of ints and slices
+            A single nth value for the row or a list of nth values or slices.
+
+            .. versionchanged:: 1.4.0
+                Added slice and lists containing slices.
+                Added index notation.
+
+        dropna : {'any', 'all', None}, default None
+            Apply the specified dropna operation before counting which row is
+            the nth row. Only supported if n is an int.
+
+        Returns
+        -------
+        Series or DataFrame
+            N-th value within each group.
+        %(see_also)s
+        Examples
+        --------
+
+        >>> df = pd.DataFrame({'A': [1, 1, 2, 1, 2],
+        ...                    'B': [np.nan, 2, 3, 4, 5]}, columns=['A', 'B'])
+        >>> g = df.groupby('A')
+        >>> g.nth(0)
+           A   B
+        0  1 NaN
+        2  2 3.0
+        >>> g.nth(1)
+           A   B
+        1  1 2.0
+        4  2 5.0
+        >>> g.nth(-1)
+           A   B
+        3  1 4.0
+        4  2 5.0
+        >>> g.nth([0, 1])
+           A   B
+        0  1 NaN
+        1  1 2.0
+        2  2 3.0
+        4  2 5.0
+        >>> g.nth(slice(None, -1))
+           A   B
+        0  1 NaN
+        1  1 2.0
+        2  2 3.0
+
+        Index notation may also be used
+
+        >>> g.nth[0, 1]
+           A   B
+        0  1 NaN
+        1  1 2.0
+        2  2 3.0
+        4  2 5.0
+        >>> g.nth[:-1]
+           A   B
+        0  1 NaN
+        1  1 2.0
+        2  2 3.0
+
+        Specifying `dropna` allows ignoring ``NaN`` values
+
+        >>> g.nth(0, dropna='any')
+           A   B
+        1  1 2.0
+        2  2 3.0
+
+        When the specified ``n`` is larger than any of the groups, an
+        empty DataFrame is returned
+
+        >>> g.nth(3, dropna='any')
+        Empty DataFrame
+        Columns: [A, B]
+        Index: []
+        """
+        return GroupByNthSelector(self)
+
+    def _nth(
+        self,
+        n: PositionalIndexer | tuple,
+        dropna: Literal["any", "all", None] = None,
+    ) -> NDFrameT:
+        if not dropna:
+            mask = self._make_mask_from_positional_indexer(n)
+
+            ids, _, _ = self._grouper.group_info
+
+            # Drop NA values in grouping
+            mask = mask & (ids != -1)
+
+            out = self._mask_selected_obj(mask)
+            return out
+
+        # dropna is truthy
+        if not is_integer(n):
+            raise ValueError("dropna option only supported for an integer argument")
+
+        if dropna not in ["any", "all"]:
+            # Note: when agg-ing picker doesn't raise this, just returns NaN
+            raise ValueError(
+                "For a DataFrame or Series groupby.nth, dropna must be "
+                "either None, 'any' or 'all', "
+                f"(was passed {dropna})."
+            )
+
+        # old behaviour, but with all and any support for DataFrames.
+        # modified in GH 7559 to have better perf
+        n = cast(int, n)
+        dropped = self._selected_obj.dropna(how=dropna, axis=self.axis)
+
+        # get a new grouper for our dropped obj
+        grouper: np.ndarray | Index | ops.BaseGrouper
+        if len(dropped) == len(self._selected_obj):
+            # Nothing was dropped, can use the same grouper
+            grouper = self._grouper
+        else:
+            # we don't have the grouper info available
+            # (e.g. we have selected out
+            # a column that is not in the current object)
+            axis = self._grouper.axis
+            grouper = self._grouper.codes_info[axis.isin(dropped.index)]
+            if self._grouper.has_dropped_na:
+                # Null groups need to still be encoded as -1 when passed to groupby
+                nulls = grouper == -1
+                # error: No overload variant of "where" matches argument types
+                #        "Any", "NAType", "Any"
+                values = np.where(nulls, NA, grouper)  # type: ignore[call-overload]
+                grouper = Index(values, dtype="Int64")
+
+        if self.axis == 1:
+            grb = dropped.T.groupby(grouper, as_index=self.as_index, sort=self.sort)
+        else:
+            grb = dropped.groupby(grouper, as_index=self.as_index, sort=self.sort)
+        return grb.nth(n)
+
+    @final
+    def quantile(
+        self,
+        q: float | AnyArrayLike = 0.5,
+        interpolation: str = "linear",
+        numeric_only: bool = False,
+    ):
+        """
+        Return group values at the given quantile, a la numpy.percentile.
+
+        Parameters
+        ----------
+        q : float or array-like, default 0.5 (50% quantile)
+            Value(s) between 0 and 1 providing the quantile(s) to compute.
+        interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'}
+            Method to use when the desired quantile falls between two points.
+        numeric_only : bool, default False
+            Include only `float`, `int` or `boolean` data.
+
+            .. versionadded:: 1.5.0
+
+            .. versionchanged:: 2.0.0
+
+                numeric_only now defaults to ``False``.
+
+        Returns
+        -------
+        Series or DataFrame
+            Return type determined by caller of GroupBy object.
+
+        See Also
+        --------
+        Series.quantile : Similar method for Series.
+        DataFrame.quantile : Similar method for DataFrame.
+        numpy.percentile : NumPy method to compute qth percentile.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([
+        ...     ['a', 1], ['a', 2], ['a', 3],
+        ...     ['b', 1], ['b', 3], ['b', 5]
+        ... ], columns=['key', 'val'])
+        >>> df.groupby('key').quantile()
+            val
+        key
+        a    2.0
+        b    3.0
+        """
+        mgr = self._get_data_to_aggregate(numeric_only=numeric_only, name="quantile")
+        obj = self._wrap_agged_manager(mgr)
+        if self.axis == 1:
+            splitter = self._grouper._get_splitter(obj.T, axis=self.axis)
+            sdata = splitter._sorted_data.T
+        else:
+            splitter = self._grouper._get_splitter(obj, axis=self.axis)
+            sdata = splitter._sorted_data
+
+        starts, ends = lib.generate_slices(splitter._slabels, splitter.ngroups)
+
+        def pre_processor(vals: ArrayLike) -> tuple[np.ndarray, DtypeObj | None]:
+            if isinstance(vals.dtype, StringDtype) or is_object_dtype(vals.dtype):
+                raise TypeError(
+                    f"dtype '{vals.dtype}' does not support operation 'quantile'"
+                )
+
+            inference: DtypeObj | None = None
+            if isinstance(vals, BaseMaskedArray) and is_numeric_dtype(vals.dtype):
+                out = vals.to_numpy(dtype=float, na_value=np.nan)
+                inference = vals.dtype
+            elif is_integer_dtype(vals.dtype):
+                if isinstance(vals, ExtensionArray):
+                    out = vals.to_numpy(dtype=float, na_value=np.nan)
+                else:
+                    out = vals
+                inference = np.dtype(np.int64)
+            elif is_bool_dtype(vals.dtype) and isinstance(vals, ExtensionArray):
+                out = vals.to_numpy(dtype=float, na_value=np.nan)
+            elif is_bool_dtype(vals.dtype):
+                # GH#51424 deprecate to match Series/DataFrame behavior
+                warnings.warn(
+                    f"Allowing bool dtype in {type(self).__name__}.quantile is "
+                    "deprecated and will raise in a future version, matching "
+                    "the Series/DataFrame behavior. Cast to uint8 dtype before "
+                    "calling quantile instead.",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+                out = np.asarray(vals)
+            elif needs_i8_conversion(vals.dtype):
+                inference = vals.dtype
+                # In this case we need to delay the casting until after the
+                #  np.lexsort below.
+                # error: Incompatible return value type (got
+                # "Tuple[Union[ExtensionArray, ndarray[Any, Any]], Union[Any,
+                # ExtensionDtype]]", expected "Tuple[ndarray[Any, Any],
+                # Optional[Union[dtype[Any], ExtensionDtype]]]")
+                return vals, inference  # type: ignore[return-value]
+            elif isinstance(vals, ExtensionArray) and is_float_dtype(vals.dtype):
+                inference = np.dtype(np.float64)
+                out = vals.to_numpy(dtype=float, na_value=np.nan)
+            else:
+                out = np.asarray(vals)
+
+            return out, inference
+
+        def post_processor(
+            vals: np.ndarray,
+            inference: DtypeObj | None,
+            result_mask: np.ndarray | None,
+            orig_vals: ArrayLike,
+        ) -> ArrayLike:
+            if inference:
+                # Check for edge case
+                if isinstance(orig_vals, BaseMaskedArray):
+                    assert result_mask is not None  # for mypy
+
+                    if interpolation in {"linear", "midpoint"} and not is_float_dtype(
+                        orig_vals
+                    ):
+                        return FloatingArray(vals, result_mask)
+                    else:
+                        # Item "ExtensionDtype" of "Union[ExtensionDtype, str,
+                        # dtype[Any], Type[object]]" has no attribute "numpy_dtype"
+                        # [union-attr]
+                        with warnings.catch_warnings():
+                            # vals.astype with nan can warn with numpy >1.24
+                            warnings.filterwarnings("ignore", category=RuntimeWarning)
+                            return type(orig_vals)(
+                                vals.astype(
+                                    inference.numpy_dtype  # type: ignore[union-attr]
+                                ),
+                                result_mask,
+                            )
+
+                elif not (
+                    is_integer_dtype(inference)
+                    and interpolation in {"linear", "midpoint"}
+                ):
+                    if needs_i8_conversion(inference):
+                        # error: Item "ExtensionArray" of "Union[ExtensionArray,
+                        # ndarray[Any, Any]]" has no attribute "_ndarray"
+                        vals = vals.astype("i8").view(
+                            orig_vals._ndarray.dtype  # type: ignore[union-attr]
+                        )
+                        # error: Item "ExtensionArray" of "Union[ExtensionArray,
+                        # ndarray[Any, Any]]" has no attribute "_from_backing_data"
+                        return orig_vals._from_backing_data(  # type: ignore[union-attr]
+                            vals
+                        )
+
+                    assert isinstance(inference, np.dtype)  # for mypy
+                    return vals.astype(inference)
+
+            return vals
+
+        qs = np.array(q, dtype=np.float64)
+        pass_qs: np.ndarray | None = qs
+        if is_scalar(q):
+            qs = np.array([q], dtype=np.float64)
+            pass_qs = None
+
+        ids, _, ngroups = self._grouper.group_info
+        nqs = len(qs)
+
+        func = partial(
+            libgroupby.group_quantile,
+            labels=ids,
+            qs=qs,
+            interpolation=interpolation,
+            starts=starts,
+            ends=ends,
+        )
+
+        def blk_func(values: ArrayLike) -> ArrayLike:
+            orig_vals = values
+            if isinstance(values, BaseMaskedArray):
+                mask = values._mask
+                result_mask = np.zeros((ngroups, nqs), dtype=np.bool_)
+            else:
+                mask = isna(values)
+                result_mask = None
+
+            is_datetimelike = needs_i8_conversion(values.dtype)
+
+            vals, inference = pre_processor(values)
+
+            ncols = 1
+            if vals.ndim == 2:
+                ncols = vals.shape[0]
+
+            out = np.empty((ncols, ngroups, nqs), dtype=np.float64)
+
+            if is_datetimelike:
+                vals = vals.view("i8")
+
+            if vals.ndim == 1:
+                # EA is always 1d
+                func(
+                    out[0],
+                    values=vals,
+                    mask=mask,
+                    result_mask=result_mask,
+                    is_datetimelike=is_datetimelike,
+                )
+            else:
+                for i in range(ncols):
+                    func(
+                        out[i],
+                        values=vals[i],
+                        mask=mask[i],
+                        result_mask=None,
+                        is_datetimelike=is_datetimelike,
+                    )
+
+            if vals.ndim == 1:
+                out = out.ravel("K")
+                if result_mask is not None:
+                    result_mask = result_mask.ravel("K")
+            else:
+                out = out.reshape(ncols, ngroups * nqs)
+
+            return post_processor(out, inference, result_mask, orig_vals)
+
+        res_mgr = sdata._mgr.grouped_reduce(blk_func)
+
+        res = self._wrap_agged_manager(res_mgr)
+        return self._wrap_aggregated_output(res, qs=pass_qs)
+
+    @final
+    @Substitution(name="groupby")
+    def ngroup(self, ascending: bool = True):
+        """
+        Number each group from 0 to the number of groups - 1.
+
+        This is the enumerative complement of cumcount.  Note that the
+        numbers given to the groups match the order in which the groups
+        would be seen when iterating over the groupby object, not the
+        order they are first observed.
+
+        Groups with missing keys (where `pd.isna()` is True) will be labeled with `NaN`
+        and will be skipped from the count.
+
+        Parameters
+        ----------
+        ascending : bool, default True
+            If False, number in reverse, from number of group - 1 to 0.
+
+        Returns
+        -------
+        Series
+            Unique numbers for each group.
+
+        See Also
+        --------
+        .cumcount : Number the rows in each group.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({"color": ["red", None, "red", "blue", "blue", "red"]})
+        >>> df
+           color
+        0    red
+        1   None
+        2    red
+        3   blue
+        4   blue
+        5    red
+        >>> df.groupby("color").ngroup()
+        0    1.0
+        1    NaN
+        2    1.0
+        3    0.0
+        4    0.0
+        5    1.0
+        dtype: float64
+        >>> df.groupby("color", dropna=False).ngroup()
+        0    1
+        1    2
+        2    1
+        3    0
+        4    0
+        5    1
+        dtype: int64
+        >>> df.groupby("color", dropna=False).ngroup(ascending=False)
+        0    1
+        1    0
+        2    1
+        3    2
+        4    2
+        5    1
+        dtype: int64
+        """
+        obj = self._obj_with_exclusions
+        index = obj._get_axis(self.axis)
+        comp_ids = self._grouper.group_info[0]
+
+        dtype: type
+        if self._grouper.has_dropped_na:
+            comp_ids = np.where(comp_ids == -1, np.nan, comp_ids)
+            dtype = np.float64
+        else:
+            dtype = np.int64
+
+        if any(ping._passed_categorical for ping in self._grouper.groupings):
+            # comp_ids reflect non-observed groups, we need only observed
+            comp_ids = rank_1d(comp_ids, ties_method="dense") - 1
+
+        result = self._obj_1d_constructor(comp_ids, index, dtype=dtype)
+        if not ascending:
+            result = self.ngroups - 1 - result
+        return result
+
+    @final
+    @Substitution(name="groupby")
+    def cumcount(self, ascending: bool = True):
+        """
+        Number each item in each group from 0 to the length of that group - 1.
+
+        Essentially this is equivalent to
+
+        .. code-block:: python
+
+            self.apply(lambda x: pd.Series(np.arange(len(x)), x.index))
+
+        Parameters
+        ----------
+        ascending : bool, default True
+            If False, number in reverse, from length of group - 1 to 0.
+
+        Returns
+        -------
+        Series
+            Sequence number of each element within each group.
+
+        See Also
+        --------
+        .ngroup : Number the groups themselves.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([['a'], ['a'], ['a'], ['b'], ['b'], ['a']],
+        ...                   columns=['A'])
+        >>> df
+           A
+        0  a
+        1  a
+        2  a
+        3  b
+        4  b
+        5  a
+        >>> df.groupby('A').cumcount()
+        0    0
+        1    1
+        2    2
+        3    0
+        4    1
+        5    3
+        dtype: int64
+        >>> df.groupby('A').cumcount(ascending=False)
+        0    3
+        1    2
+        2    1
+        3    1
+        4    0
+        5    0
+        dtype: int64
+        """
+        index = self._obj_with_exclusions._get_axis(self.axis)
+        cumcounts = self._cumcount_array(ascending=ascending)
+        return self._obj_1d_constructor(cumcounts, index)
+
+    @final
+    @Substitution(name="groupby")
+    @Substitution(see_also=_common_see_also)
+    def rank(
+        self,
+        method: str = "average",
+        ascending: bool = True,
+        na_option: str = "keep",
+        pct: bool = False,
+        axis: AxisInt | lib.NoDefault = lib.no_default,
+    ) -> NDFrameT:
+        """
+        Provide the rank of values within each group.
+
+        Parameters
+        ----------
+        method : {'average', 'min', 'max', 'first', 'dense'}, default 'average'
+            * average: average rank of group.
+            * min: lowest rank in group.
+            * max: highest rank in group.
+            * first: ranks assigned in order they appear in the array.
+            * dense: like 'min', but rank always increases by 1 between groups.
+        ascending : bool, default True
+            False for ranks by high (1) to low (N).
+        na_option : {'keep', 'top', 'bottom'}, default 'keep'
+            * keep: leave NA values where they are.
+            * top: smallest rank if ascending.
+            * bottom: smallest rank if descending.
+        pct : bool, default False
+            Compute percentage rank of data within each group.
+        axis : int, default 0
+            The axis of the object over which to compute the rank.
+
+            .. deprecated:: 2.1.0
+                For axis=1, operate on the underlying object instead. Otherwise
+                the axis keyword is not necessary.
+
+        Returns
+        -------
+        DataFrame with ranking of values within each group
+        %(see_also)s
+        Examples
+        --------
+        >>> df = pd.DataFrame(
+        ...     {
+        ...         "group": ["a", "a", "a", "a", "a", "b", "b", "b", "b", "b"],
+        ...         "value": [2, 4, 2, 3, 5, 1, 2, 4, 1, 5],
+        ...     }
+        ... )
+        >>> df
+          group  value
+        0     a      2
+        1     a      4
+        2     a      2
+        3     a      3
+        4     a      5
+        5     b      1
+        6     b      2
+        7     b      4
+        8     b      1
+        9     b      5
+        >>> for method in ['average', 'min', 'max', 'dense', 'first']:
+        ...     df[f'{method}_rank'] = df.groupby('group')['value'].rank(method)
+        >>> df
+          group  value  average_rank  min_rank  max_rank  dense_rank  first_rank
+        0     a      2           1.5       1.0       2.0         1.0         1.0
+        1     a      4           4.0       4.0       4.0         3.0         4.0
+        2     a      2           1.5       1.0       2.0         1.0         2.0
+        3     a      3           3.0       3.0       3.0         2.0         3.0
+        4     a      5           5.0       5.0       5.0         4.0         5.0
+        5     b      1           1.5       1.0       2.0         1.0         1.0
+        6     b      2           3.0       3.0       3.0         2.0         3.0
+        7     b      4           4.0       4.0       4.0         3.0         4.0
+        8     b      1           1.5       1.0       2.0         1.0         2.0
+        9     b      5           5.0       5.0       5.0         4.0         5.0
+        """
+        if na_option not in {"keep", "top", "bottom"}:
+            msg = "na_option must be one of 'keep', 'top', or 'bottom'"
+            raise ValueError(msg)
+
+        if axis is not lib.no_default:
+            axis = self.obj._get_axis_number(axis)
+            self._deprecate_axis(axis, "rank")
+        else:
+            axis = 0
+
+        kwargs = {
+            "ties_method": method,
+            "ascending": ascending,
+            "na_option": na_option,
+            "pct": pct,
+        }
+        if axis != 0:
+            # DataFrame uses different keyword name
+            kwargs["method"] = kwargs.pop("ties_method")
+            f = lambda x: x.rank(axis=axis, numeric_only=False, **kwargs)
+            result = self._python_apply_general(
+                f, self._selected_obj, is_transform=True
+            )
+            return result
+
+        return self._cython_transform(
+            "rank",
+            numeric_only=False,
+            axis=axis,
+            **kwargs,
+        )
+
+    @final
+    @Substitution(name="groupby")
+    @Substitution(see_also=_common_see_also)
+    def cumprod(
+        self, axis: Axis | lib.NoDefault = lib.no_default, *args, **kwargs
+    ) -> NDFrameT:
+        """
+        Cumulative product for each group.
+
+        Returns
+        -------
+        Series or DataFrame
+        %(see_also)s
+        Examples
+        --------
+        For SeriesGroupBy:
+
+        >>> lst = ['a', 'a', 'b']
+        >>> ser = pd.Series([6, 2, 0], index=lst)
+        >>> ser
+        a    6
+        a    2
+        b    0
+        dtype: int64
+        >>> ser.groupby(level=0).cumprod()
+        a    6
+        a   12
+        b    0
+        dtype: int64
+
+        For DataFrameGroupBy:
+
+        >>> data = [[1, 8, 2], [1, 2, 5], [2, 6, 9]]
+        >>> df = pd.DataFrame(data, columns=["a", "b", "c"],
+        ...                   index=["cow", "horse", "bull"])
+        >>> df
+                a   b   c
+        cow     1   8   2
+        horse   1   2   5
+        bull    2   6   9
+        >>> df.groupby("a").groups
+        {1: ['cow', 'horse'], 2: ['bull']}
+        >>> df.groupby("a").cumprod()
+                b   c
+        cow     8   2
+        horse  16  10
+        bull    6   9
+        """
+        nv.validate_groupby_func("cumprod", args, kwargs, ["numeric_only", "skipna"])
+        if axis is not lib.no_default:
+            axis = self.obj._get_axis_number(axis)
+            self._deprecate_axis(axis, "cumprod")
+        else:
+            axis = 0
+
+        if axis != 0:
+            f = lambda x: x.cumprod(axis=axis, **kwargs)
+            return self._python_apply_general(f, self._selected_obj, is_transform=True)
+
+        return self._cython_transform("cumprod", **kwargs)
+
+    @final
+    @Substitution(name="groupby")
+    @Substitution(see_also=_common_see_also)
+    def cumsum(
+        self, axis: Axis | lib.NoDefault = lib.no_default, *args, **kwargs
+    ) -> NDFrameT:
+        """
+        Cumulative sum for each group.
+
+        Returns
+        -------
+        Series or DataFrame
+        %(see_also)s
+        Examples
+        --------
+        For SeriesGroupBy:
+
+        >>> lst = ['a', 'a', 'b']
+        >>> ser = pd.Series([6, 2, 0], index=lst)
+        >>> ser
+        a    6
+        a    2
+        b    0
+        dtype: int64
+        >>> ser.groupby(level=0).cumsum()
+        a    6
+        a    8
+        b    0
+        dtype: int64
+
+        For DataFrameGroupBy:
+
+        >>> data = [[1, 8, 2], [1, 2, 5], [2, 6, 9]]
+        >>> df = pd.DataFrame(data, columns=["a", "b", "c"],
+        ...                   index=["fox", "gorilla", "lion"])
+        >>> df
+                  a   b   c
+        fox       1   8   2
+        gorilla   1   2   5
+        lion      2   6   9
+        >>> df.groupby("a").groups
+        {1: ['fox', 'gorilla'], 2: ['lion']}
+        >>> df.groupby("a").cumsum()
+                  b   c
+        fox       8   2
+        gorilla  10   7
+        lion      6   9
+        """
+        nv.validate_groupby_func("cumsum", args, kwargs, ["numeric_only", "skipna"])
+        if axis is not lib.no_default:
+            axis = self.obj._get_axis_number(axis)
+            self._deprecate_axis(axis, "cumsum")
+        else:
+            axis = 0
+
+        if axis != 0:
+            f = lambda x: x.cumsum(axis=axis, **kwargs)
+            return self._python_apply_general(f, self._selected_obj, is_transform=True)
+
+        return self._cython_transform("cumsum", **kwargs)
+
+    @final
+    @Substitution(name="groupby")
+    @Substitution(see_also=_common_see_also)
+    def cummin(
+        self,
+        axis: AxisInt | lib.NoDefault = lib.no_default,
+        numeric_only: bool = False,
+        **kwargs,
+    ) -> NDFrameT:
+        """
+        Cumulative min for each group.
+
+        Returns
+        -------
+        Series or DataFrame
+        %(see_also)s
+        Examples
+        --------
+        For SeriesGroupBy:
+
+        >>> lst = ['a', 'a', 'a', 'b', 'b', 'b']
+        >>> ser = pd.Series([1, 6, 2, 3, 0, 4], index=lst)
+        >>> ser
+        a    1
+        a    6
+        a    2
+        b    3
+        b    0
+        b    4
+        dtype: int64
+        >>> ser.groupby(level=0).cummin()
+        a    1
+        a    1
+        a    1
+        b    3
+        b    0
+        b    0
+        dtype: int64
+
+        For DataFrameGroupBy:
+
+        >>> data = [[1, 0, 2], [1, 1, 5], [6, 6, 9]]
+        >>> df = pd.DataFrame(data, columns=["a", "b", "c"],
+        ...                   index=["snake", "rabbit", "turtle"])
+        >>> df
+                a   b   c
+        snake   1   0   2
+        rabbit  1   1   5
+        turtle  6   6   9
+        >>> df.groupby("a").groups
+        {1: ['snake', 'rabbit'], 6: ['turtle']}
+        >>> df.groupby("a").cummin()
+                b   c
+        snake   0   2
+        rabbit  0   2
+        turtle  6   9
+        """
+        skipna = kwargs.get("skipna", True)
+        if axis is not lib.no_default:
+            axis = self.obj._get_axis_number(axis)
+            self._deprecate_axis(axis, "cummin")
+        else:
+            axis = 0
+
+        if axis != 0:
+            f = lambda x: np.minimum.accumulate(x, axis)
+            obj = self._selected_obj
+            if numeric_only:
+                obj = obj._get_numeric_data()
+            return self._python_apply_general(f, obj, is_transform=True)
+
+        return self._cython_transform(
+            "cummin", numeric_only=numeric_only, skipna=skipna
+        )
+
+    @final
+    @Substitution(name="groupby")
+    @Substitution(see_also=_common_see_also)
+    def cummax(
+        self,
+        axis: AxisInt | lib.NoDefault = lib.no_default,
+        numeric_only: bool = False,
+        **kwargs,
+    ) -> NDFrameT:
+        """
+        Cumulative max for each group.
+
+        Returns
+        -------
+        Series or DataFrame
+        %(see_also)s
+        Examples
+        --------
+        For SeriesGroupBy:
+
+        >>> lst = ['a', 'a', 'a', 'b', 'b', 'b']
+        >>> ser = pd.Series([1, 6, 2, 3, 1, 4], index=lst)
+        >>> ser
+        a    1
+        a    6
+        a    2
+        b    3
+        b    1
+        b    4
+        dtype: int64
+        >>> ser.groupby(level=0).cummax()
+        a    1
+        a    6
+        a    6
+        b    3
+        b    3
+        b    4
+        dtype: int64
+
+        For DataFrameGroupBy:
+
+        >>> data = [[1, 8, 2], [1, 1, 0], [2, 6, 9]]
+        >>> df = pd.DataFrame(data, columns=["a", "b", "c"],
+        ...                   index=["cow", "horse", "bull"])
+        >>> df
+                a   b   c
+        cow     1   8   2
+        horse   1   1   0
+        bull    2   6   9
+        >>> df.groupby("a").groups
+        {1: ['cow', 'horse'], 2: ['bull']}
+        >>> df.groupby("a").cummax()
+                b   c
+        cow     8   2
+        horse   8   2
+        bull    6   9
+        """
+        skipna = kwargs.get("skipna", True)
+        if axis is not lib.no_default:
+            axis = self.obj._get_axis_number(axis)
+            self._deprecate_axis(axis, "cummax")
+        else:
+            axis = 0
+
+        if axis != 0:
+            f = lambda x: np.maximum.accumulate(x, axis)
+            obj = self._selected_obj
+            if numeric_only:
+                obj = obj._get_numeric_data()
+            return self._python_apply_general(f, obj, is_transform=True)
+
+        return self._cython_transform(
+            "cummax", numeric_only=numeric_only, skipna=skipna
+        )
+
+    @final
+    @Substitution(name="groupby")
+    def shift(
+        self,
+        periods: int | Sequence[int] = 1,
+        freq=None,
+        axis: Axis | lib.NoDefault = lib.no_default,
+        fill_value=lib.no_default,
+        suffix: str | None = None,
+    ):
+        """
+        Shift each group by periods observations.
+
+        If freq is passed, the index will be increased using the periods and the freq.
+
+        Parameters
+        ----------
+        periods : int | Sequence[int], default 1
+            Number of periods to shift. If a list of values, shift each group by
+            each period.
+        freq : str, optional
+            Frequency string.
+        axis : axis to shift, default 0
+            Shift direction.
+
+            .. deprecated:: 2.1.0
+                For axis=1, operate on the underlying object instead. Otherwise
+                the axis keyword is not necessary.
+
+        fill_value : optional
+            The scalar value to use for newly introduced missing values.
+
+            .. versionchanged:: 2.1.0
+                Will raise a ``ValueError`` if ``freq`` is provided too.
+
+        suffix : str, optional
+            A string to add to each shifted column if there are multiple periods.
+            Ignored otherwise.
+
+        Returns
+        -------
+        Series or DataFrame
+            Object shifted within each group.
+
+        See Also
+        --------
+        Index.shift : Shift values of Index.
+
+        Examples
+        --------
+
+        For SeriesGroupBy:
+
+        >>> lst = ['a', 'a', 'b', 'b']
+        >>> ser = pd.Series([1, 2, 3, 4], index=lst)
+        >>> ser
+        a    1
+        a    2
+        b    3
+        b    4
+        dtype: int64
+        >>> ser.groupby(level=0).shift(1)
+        a    NaN
+        a    1.0
+        b    NaN
+        b    3.0
+        dtype: float64
+
+        For DataFrameGroupBy:
+
+        >>> data = [[1, 2, 3], [1, 5, 6], [2, 5, 8], [2, 6, 9]]
+        >>> df = pd.DataFrame(data, columns=["a", "b", "c"],
+        ...                   index=["tuna", "salmon", "catfish", "goldfish"])
+        >>> df
+                   a  b  c
+            tuna   1  2  3
+          salmon   1  5  6
+         catfish   2  5  8
+        goldfish   2  6  9
+        >>> df.groupby("a").shift(1)
+                      b    c
+            tuna    NaN  NaN
+          salmon    2.0  3.0
+         catfish    NaN  NaN
+        goldfish    5.0  8.0
+        """
+        if axis is not lib.no_default:
+            axis = self.obj._get_axis_number(axis)
+            self._deprecate_axis(axis, "shift")
+        else:
+            axis = 0
+
+        if is_list_like(periods):
+            if axis == 1:
+                raise ValueError(
+                    "If `periods` contains multiple shifts, `axis` cannot be 1."
+                )
+            periods = cast(Sequence, periods)
+            if len(periods) == 0:
+                raise ValueError("If `periods` is an iterable, it cannot be empty.")
+            from pandas.core.reshape.concat import concat
+
+            add_suffix = True
+        else:
+            if not is_integer(periods):
+                raise TypeError(
+                    f"Periods must be integer, but {periods} is {type(periods)}."
+                )
+            if suffix:
+                raise ValueError("Cannot specify `suffix` if `periods` is an int.")
+            periods = [cast(int, periods)]
+            add_suffix = False
+
+        shifted_dataframes = []
+        for period in periods:
+            if not is_integer(period):
+                raise TypeError(
+                    f"Periods must be integer, but {period} is {type(period)}."
+                )
+            period = cast(int, period)
+            if freq is not None or axis != 0:
+                f = lambda x: x.shift(
+                    period, freq, axis, fill_value  # pylint: disable=cell-var-from-loop
+                )
+                shifted = self._python_apply_general(
+                    f, self._selected_obj, is_transform=True
+                )
+            else:
+                if fill_value is lib.no_default:
+                    fill_value = None
+                ids, _, ngroups = self._grouper.group_info
+                res_indexer = np.zeros(len(ids), dtype=np.int64)
+
+                libgroupby.group_shift_indexer(res_indexer, ids, ngroups, period)
+
+                obj = self._obj_with_exclusions
+
+                shifted = obj._reindex_with_indexers(
+                    {self.axis: (obj.axes[self.axis], res_indexer)},
+                    fill_value=fill_value,
+                    allow_dups=True,
+                )
+
+            if add_suffix:
+                if isinstance(shifted, Series):
+                    shifted = cast(NDFrameT, shifted.to_frame())
+                shifted = shifted.add_suffix(
+                    f"{suffix}_{period}" if suffix else f"_{period}"
+                )
+            shifted_dataframes.append(cast(Union[Series, DataFrame], shifted))
+
+        return (
+            shifted_dataframes[0]
+            if len(shifted_dataframes) == 1
+            else concat(shifted_dataframes, axis=1)
+        )
+
+    @final
+    @Substitution(name="groupby")
+    @Substitution(see_also=_common_see_also)
+    def diff(
+        self, periods: int = 1, axis: AxisInt | lib.NoDefault = lib.no_default
+    ) -> NDFrameT:
+        """
+        First discrete difference of element.
+
+        Calculates the difference of each element compared with another
+        element in the group (default is element in previous row).
+
+        Parameters
+        ----------
+        periods : int, default 1
+            Periods to shift for calculating difference, accepts negative values.
+        axis : axis to shift, default 0
+            Take difference over rows (0) or columns (1).
+
+            .. deprecated:: 2.1.0
+                For axis=1, operate on the underlying object instead. Otherwise
+                the axis keyword is not necessary.
+
+        Returns
+        -------
+        Series or DataFrame
+            First differences.
+        %(see_also)s
+        Examples
+        --------
+        For SeriesGroupBy:
+
+        >>> lst = ['a', 'a', 'a', 'b', 'b', 'b']
+        >>> ser = pd.Series([7, 2, 8, 4, 3, 3], index=lst)
+        >>> ser
+        a     7
+        a     2
+        a     8
+        b     4
+        b     3
+        b     3
+        dtype: int64
+        >>> ser.groupby(level=0).diff()
+        a    NaN
+        a   -5.0
+        a    6.0
+        b    NaN
+        b   -1.0
+        b    0.0
+        dtype: float64
+
+        For DataFrameGroupBy:
+
+        >>> data = {'a': [1, 3, 5, 7, 7, 8, 3], 'b': [1, 4, 8, 4, 4, 2, 1]}
+        >>> df = pd.DataFrame(data, index=['dog', 'dog', 'dog',
+        ...                   'mouse', 'mouse', 'mouse', 'mouse'])
+        >>> df
+                 a  b
+          dog    1  1
+          dog    3  4
+          dog    5  8
+        mouse    7  4
+        mouse    7  4
+        mouse    8  2
+        mouse    3  1
+        >>> df.groupby(level=0).diff()
+                 a    b
+          dog  NaN  NaN
+          dog  2.0  3.0
+          dog  2.0  4.0
+        mouse  NaN  NaN
+        mouse  0.0  0.0
+        mouse  1.0 -2.0
+        mouse -5.0 -1.0
+        """
+        if axis is not lib.no_default:
+            axis = self.obj._get_axis_number(axis)
+            self._deprecate_axis(axis, "diff")
+        else:
+            axis = 0
+
+        if axis != 0:
+            return self.apply(lambda x: x.diff(periods=periods, axis=axis))
+
+        obj = self._obj_with_exclusions
+        shifted = self.shift(periods=periods)
+
+        # GH45562 - to retain existing behavior and match behavior of Series.diff(),
+        # int8 and int16 are coerced to float32 rather than float64.
+        dtypes_to_f32 = ["int8", "int16"]
+        if obj.ndim == 1:
+            if obj.dtype in dtypes_to_f32:
+                shifted = shifted.astype("float32")
+        else:
+            to_coerce = [c for c, dtype in obj.dtypes.items() if dtype in dtypes_to_f32]
+            if len(to_coerce):
+                shifted = shifted.astype({c: "float32" for c in to_coerce})
+
+        return obj - shifted
+
+    @final
+    @Substitution(name="groupby")
+    @Substitution(see_also=_common_see_also)
+    def pct_change(
+        self,
+        periods: int = 1,
+        fill_method: FillnaOptions | None | lib.NoDefault = lib.no_default,
+        limit: int | None | lib.NoDefault = lib.no_default,
+        freq=None,
+        axis: Axis | lib.NoDefault = lib.no_default,
+    ):
+        """
+        Calculate pct_change of each value to previous entry in group.
+
+        Returns
+        -------
+        Series or DataFrame
+            Percentage changes within each group.
+        %(see_also)s
+        Examples
+        --------
+
+        For SeriesGroupBy:
+
+        >>> lst = ['a', 'a', 'b', 'b']
+        >>> ser = pd.Series([1, 2, 3, 4], index=lst)
+        >>> ser
+        a    1
+        a    2
+        b    3
+        b    4
+        dtype: int64
+        >>> ser.groupby(level=0).pct_change()
+        a         NaN
+        a    1.000000
+        b         NaN
+        b    0.333333
+        dtype: float64
+
+        For DataFrameGroupBy:
+
+        >>> data = [[1, 2, 3], [1, 5, 6], [2, 5, 8], [2, 6, 9]]
+        >>> df = pd.DataFrame(data, columns=["a", "b", "c"],
+        ...                   index=["tuna", "salmon", "catfish", "goldfish"])
+        >>> df
+                   a  b  c
+            tuna   1  2  3
+          salmon   1  5  6
+         catfish   2  5  8
+        goldfish   2  6  9
+        >>> df.groupby("a").pct_change()
+                    b  c
+            tuna    NaN    NaN
+          salmon    1.5  1.000
+         catfish    NaN    NaN
+        goldfish    0.2  0.125
+        """
+        # GH#53491
+        if fill_method not in (lib.no_default, None) or limit is not lib.no_default:
+            warnings.warn(
+                "The 'fill_method' keyword being not None and the 'limit' keyword in "
+                f"{type(self).__name__}.pct_change are deprecated and will be removed "
+                "in a future version. Either fill in any non-leading NA values prior "
+                "to calling pct_change or specify 'fill_method=None' to not fill NA "
+                "values.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+        if fill_method is lib.no_default:
+            if limit is lib.no_default and any(
+                grp.isna().values.any() for _, grp in self
+            ):
+                warnings.warn(
+                    "The default fill_method='ffill' in "
+                    f"{type(self).__name__}.pct_change is deprecated and will "
+                    "be removed in a future version. Either fill in any "
+                    "non-leading NA values prior to calling pct_change or "
+                    "specify 'fill_method=None' to not fill NA values.",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+            fill_method = "ffill"
+        if limit is lib.no_default:
+            limit = None
+
+        if axis is not lib.no_default:
+            axis = self.obj._get_axis_number(axis)
+            self._deprecate_axis(axis, "pct_change")
+        else:
+            axis = 0
+
+        # TODO(GH#23918): Remove this conditional for SeriesGroupBy when
+        #  GH#23918 is fixed
+        if freq is not None or axis != 0:
+            f = lambda x: x.pct_change(
+                periods=periods,
+                fill_method=fill_method,
+                limit=limit,
+                freq=freq,
+                axis=axis,
+            )
+            return self._python_apply_general(f, self._selected_obj, is_transform=True)
+
+        if fill_method is None:  # GH30463
+            fill_method = "ffill"
+            limit = 0
+        filled = getattr(self, fill_method)(limit=limit)
+        if self.axis == 0:
+            fill_grp = filled.groupby(self._grouper.codes, group_keys=self.group_keys)
+        else:
+            fill_grp = filled.T.groupby(self._grouper.codes, group_keys=self.group_keys)
+        shifted = fill_grp.shift(periods=periods, freq=freq)
+        if self.axis == 1:
+            shifted = shifted.T
+        return (filled / shifted) - 1
+
+    @final
+    @Substitution(name="groupby")
+    @Substitution(see_also=_common_see_also)
+    def head(self, n: int = 5) -> NDFrameT:
+        """
+        Return first n rows of each group.
+
+        Similar to ``.apply(lambda x: x.head(n))``, but it returns a subset of rows
+        from the original DataFrame with original index and order preserved
+        (``as_index`` flag is ignored).
+
+        Parameters
+        ----------
+        n : int
+            If positive: number of entries to include from start of each group.
+            If negative: number of entries to exclude from end of each group.
+
+        Returns
+        -------
+        Series or DataFrame
+            Subset of original Series or DataFrame as determined by n.
+        %(see_also)s
+        Examples
+        --------
+
+        >>> df = pd.DataFrame([[1, 2], [1, 4], [5, 6]],
+        ...                   columns=['A', 'B'])
+        >>> df.groupby('A').head(1)
+           A  B
+        0  1  2
+        2  5  6
+        >>> df.groupby('A').head(-1)
+           A  B
+        0  1  2
+        """
+        mask = self._make_mask_from_positional_indexer(slice(None, n))
+        return self._mask_selected_obj(mask)
+
+    @final
+    @Substitution(name="groupby")
+    @Substitution(see_also=_common_see_also)
+    def tail(self, n: int = 5) -> NDFrameT:
+        """
+        Return last n rows of each group.
+
+        Similar to ``.apply(lambda x: x.tail(n))``, but it returns a subset of rows
+        from the original DataFrame with original index and order preserved
+        (``as_index`` flag is ignored).
+
+        Parameters
+        ----------
+        n : int
+            If positive: number of entries to include from end of each group.
+            If negative: number of entries to exclude from start of each group.
+
+        Returns
+        -------
+        Series or DataFrame
+            Subset of original Series or DataFrame as determined by n.
+        %(see_also)s
+        Examples
+        --------
+
+        >>> df = pd.DataFrame([['a', 1], ['a', 2], ['b', 1], ['b', 2]],
+        ...                   columns=['A', 'B'])
+        >>> df.groupby('A').tail(1)
+           A  B
+        1  a  2
+        3  b  2
+        >>> df.groupby('A').tail(-1)
+           A  B
+        1  a  2
+        3  b  2
+        """
+        if n:
+            mask = self._make_mask_from_positional_indexer(slice(-n, None))
+        else:
+            mask = self._make_mask_from_positional_indexer([])
+
+        return self._mask_selected_obj(mask)
+
+    @final
+    def _mask_selected_obj(self, mask: npt.NDArray[np.bool_]) -> NDFrameT:
+        """
+        Return _selected_obj with mask applied to the correct axis.
+
+        Parameters
+        ----------
+        mask : np.ndarray[bool]
+            Boolean mask to apply.
+
+        Returns
+        -------
+        Series or DataFrame
+            Filtered _selected_obj.
+        """
+        ids = self._grouper.group_info[0]
+        mask = mask & (ids != -1)
+
+        if self.axis == 0:
+            return self._selected_obj[mask]
+        else:
+            return self._selected_obj.iloc[:, mask]
+
+    @final
+    def _reindex_output(
+        self,
+        output: OutputFrameOrSeries,
+        fill_value: Scalar = np.nan,
+        qs: npt.NDArray[np.float64] | None = None,
+        method: str | None = None,
+    ) -> OutputFrameOrSeries:
+        """
+        If we have categorical groupers, then we might want to make sure that
+        we have a fully re-indexed output to the levels. This means expanding
+        the output space to accommodate all values in the cartesian product of
+        our groups, regardless of whether they were observed in the data or
+        not. This will expand the output space if there are missing groups.
+
+        The method returns early without modifying the input if the number of
+        groupings is less than 2, self.observed == True or none of the groupers
+        are categorical.
+
+        Parameters
+        ----------
+        output : Series or DataFrame
+            Object resulting from grouping and applying an operation.
+        fill_value : scalar, default np.nan
+            Value to use for unobserved categories if self.observed is False.
+        qs : np.ndarray[float64] or None, default None
+            quantile values, only relevant for quantile.
+
+        Returns
+        -------
+        Series or DataFrame
+            Object (potentially) re-indexed to include all possible groups.
+        """
+        groupings = self._grouper.groupings
+        if len(groupings) == 1:
+            return output
+
+        # if we only care about the observed values
+        # we are done
+        elif self.observed:
+            return output
+
+        # reindexing only applies to a Categorical grouper
+        elif not any(
+            isinstance(ping.grouping_vector, (Categorical, CategoricalIndex))
+            for ping in groupings
+        ):
+            return output
+
+        levels_list = [ping._group_index for ping in groupings]
+        names = self._grouper.names
+        if qs is not None:
+            # error: Argument 1 to "append" of "list" has incompatible type
+            # "ndarray[Any, dtype[floating[_64Bit]]]"; expected "Index"
+            levels_list.append(qs)  # type: ignore[arg-type]
+            names = names + [None]
+        index = MultiIndex.from_product(levels_list, names=names)
+        if self.sort:
+            index = index.sort_values()
+
+        if self.as_index:
+            # Always holds for SeriesGroupBy unless GH#36507 is implemented
+            d = {
+                self.obj._get_axis_name(self.axis): index,
+                "copy": False,
+                "fill_value": fill_value,
+            }
+            if using_string_dtype() and method == "sum":
+                if isinstance(output, Series) and isinstance(output.dtype, StringDtype):
+                    d["fill_value"] = ""
+                    return output.reindex(**d)  # type: ignore[return-value, arg-type]
+                elif isinstance(output, DataFrame) and any(
+                    isinstance(dtype, StringDtype) for dtype in output.dtypes
+                ):
+                    orig_dtypes = output.dtypes
+                    indices = np.nonzero(output.dtypes == "string")[0]
+                    for idx in indices:
+                        output.isetitem(idx, output.iloc[:, idx].astype(object))
+                    output = output.reindex(**d)  # type: ignore[assignment, arg-type]
+                    for idx in indices:
+                        col = output.iloc[:, idx]
+                        output.isetitem(
+                            idx, col.mask(col == 0, "").astype(orig_dtypes.iloc[idx])
+                        )
+                    return output  # type: ignore[return-value]
+            return output.reindex(**d)  # type: ignore[arg-type]
+
+        # GH 13204
+        # Here, the categorical in-axis groupers, which need to be fully
+        # expanded, are columns in `output`. An idea is to do:
+        # output = output.set_index(self._grouper.names)
+        #                .reindex(index).reset_index()
+        # but special care has to be taken because of possible not-in-axis
+        # groupers.
+        # So, we manually select and drop the in-axis grouper columns,
+        # reindex `output`, and then reset the in-axis grouper columns.
+
+        # Select in-axis groupers
+        in_axis_grps = [
+            (i, ping.name) for (i, ping) in enumerate(groupings) if ping.in_axis
+        ]
+        if len(in_axis_grps) > 0:
+            g_nums, g_names = zip(*in_axis_grps)
+            output = output.drop(labels=list(g_names), axis=1)
+
+        # Set a temp index and reindex (possibly expanding)
+        output = output.set_index(self._grouper.result_index).reindex(
+            index, copy=False, fill_value=fill_value
+        )
+
+        # Reset in-axis grouper columns
+        # (using level numbers `g_nums` because level names may not be unique)
+        if len(in_axis_grps) > 0:
+            output = output.reset_index(level=g_nums)
+
+        return output.reset_index(drop=True)
+
+    @final
+    def sample(
+        self,
+        n: int | None = None,
+        frac: float | None = None,
+        replace: bool = False,
+        weights: Sequence | Series | None = None,
+        random_state: RandomState | None = None,
+    ):
+        """
+        Return a random sample of items from each group.
+
+        You can use `random_state` for reproducibility.
+
+        Parameters
+        ----------
+        n : int, optional
+            Number of items to return for each group. Cannot be used with
+            `frac` and must be no larger than the smallest group unless
+            `replace` is True. Default is one if `frac` is None.
+        frac : float, optional
+            Fraction of items to return. Cannot be used with `n`.
+        replace : bool, default False
+            Allow or disallow sampling of the same row more than once.
+        weights : list-like, optional
+            Default None results in equal probability weighting.
+            If passed a list-like then values must have the same length as
+            the underlying DataFrame or Series object and will be used as
+            sampling probabilities after normalization within each group.
+            Values must be non-negative with at least one positive element
+            within each group.
+        random_state : int, array-like, BitGenerator, np.random.RandomState, np.random.Generator, optional
+            If int, array-like, or BitGenerator, seed for random number generator.
+            If np.random.RandomState or np.random.Generator, use as given.
+
+            .. versionchanged:: 1.4.0
+
+                np.random.Generator objects now accepted
+
+        Returns
+        -------
+        Series or DataFrame
+            A new object of same type as caller containing items randomly
+            sampled within each group from the caller object.
+
+        See Also
+        --------
+        DataFrame.sample: Generate random samples from a DataFrame object.
+        numpy.random.choice: Generate a random sample from a given 1-D numpy
+            array.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame(
+        ...     {"a": ["red"] * 2 + ["blue"] * 2 + ["black"] * 2, "b": range(6)}
+        ... )
+        >>> df
+               a  b
+        0    red  0
+        1    red  1
+        2   blue  2
+        3   blue  3
+        4  black  4
+        5  black  5
+
+        Select one row at random for each distinct value in column a. The
+        `random_state` argument can be used to guarantee reproducibility:
+
+        >>> df.groupby("a").sample(n=1, random_state=1)
+               a  b
+        4  black  4
+        2   blue  2
+        1    red  1
+
+        Set `frac` to sample fixed proportions rather than counts:
+
+        >>> df.groupby("a")["b"].sample(frac=0.5, random_state=2)
+        5    5
+        2    2
+        0    0
+        Name: b, dtype: int64
+
+        Control sample probabilities within groups by setting weights:
+
+        >>> df.groupby("a").sample(
+        ...     n=1,
+        ...     weights=[1, 1, 1, 0, 0, 1],
+        ...     random_state=1,
+        ... )
+               a  b
+        5  black  5
+        2   blue  2
+        0    red  0
+        """  # noqa: E501
+        if self._selected_obj.empty:
+            # GH48459 prevent ValueError when object is empty
+            return self._selected_obj
+        size = sample.process_sampling_size(n, frac, replace)
+        if weights is not None:
+            weights_arr = sample.preprocess_weights(
+                self._selected_obj, weights, axis=self.axis
+            )
+
+        random_state = com.random_state(random_state)
+
+        group_iterator = self._grouper.get_iterator(self._selected_obj, self.axis)
+
+        sampled_indices = []
+        for labels, obj in group_iterator:
+            grp_indices = self.indices[labels]
+            group_size = len(grp_indices)
+            if size is not None:
+                sample_size = size
+            else:
+                assert frac is not None
+                sample_size = round(frac * group_size)
+
+            grp_sample = sample.sample(
+                group_size,
+                size=sample_size,
+                replace=replace,
+                weights=None if weights is None else weights_arr[grp_indices],
+                random_state=random_state,
+            )
+            sampled_indices.append(grp_indices[grp_sample])
+
+        sampled_indices = np.concatenate(sampled_indices)
+        return self._selected_obj.take(sampled_indices, axis=self.axis)
+
+    def _idxmax_idxmin(
+        self,
+        how: Literal["idxmax", "idxmin"],
+        ignore_unobserved: bool = False,
+        axis: Axis | None | lib.NoDefault = lib.no_default,
+        skipna: bool = True,
+        numeric_only: bool = False,
+    ) -> NDFrameT:
+        """Compute idxmax/idxmin.
+
+        Parameters
+        ----------
+        how : {'idxmin', 'idxmax'}
+            Whether to compute idxmin or idxmax.
+        axis : {{0 or 'index', 1 or 'columns'}}, default None
+            The axis to use. 0 or 'index' for row-wise, 1 or 'columns' for column-wise.
+            If axis is not provided, grouper's axis is used.
+        numeric_only : bool, default False
+            Include only float, int, boolean columns.
+        skipna : bool, default True
+            Exclude NA/null values. If an entire row/column is NA, the result
+            will be NA.
+        ignore_unobserved : bool, default False
+            When True and an unobserved group is encountered, do not raise. This used
+            for transform where unobserved groups do not play an impact on the result.
+
+        Returns
+        -------
+        Series or DataFrame
+            idxmax or idxmin for the groupby operation.
+        """
+        if axis is not lib.no_default:
+            if axis is None:
+                axis = self.axis
+            axis = self.obj._get_axis_number(axis)
+            self._deprecate_axis(axis, how)
+        else:
+            axis = self.axis
+
+        if not self.observed and any(
+            ping._passed_categorical for ping in self._grouper.groupings
+        ):
+            expected_len = np.prod(
+                [len(ping._group_index) for ping in self._grouper.groupings]
+            )
+            if len(self._grouper.groupings) == 1:
+                result_len = len(self._grouper.groupings[0].grouping_vector.unique())
+            else:
+                # result_index only contains observed groups in this case
+                result_len = len(self._grouper.result_index)
+            assert result_len <= expected_len
+            has_unobserved = result_len < expected_len
+
+            raise_err: bool | np.bool_ = not ignore_unobserved and has_unobserved
+            # Only raise an error if there are columns to compute; otherwise we return
+            # an empty DataFrame with an index (possibly including unobserved) but no
+            # columns
+            data = self._obj_with_exclusions
+            if raise_err and isinstance(data, DataFrame):
+                if numeric_only:
+                    data = data._get_numeric_data()
+                raise_err = len(data.columns) > 0
+
+            if raise_err:
+                raise ValueError(
+                    f"Can't get {how} of an empty group due to unobserved categories. "
+                    "Specify observed=True in groupby instead."
+                )
+        elif not skipna:
+            if self._obj_with_exclusions.isna().any(axis=None):
+                warnings.warn(
+                    f"The behavior of {type(self).__name__}.{how} with all-NA "
+                    "values, or any-NA and skipna=False, is deprecated. In a future "
+                    "version this will raise ValueError",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+
+        if axis == 1:
+            try:
+
+                def func(df):
+                    method = getattr(df, how)
+                    return method(axis=axis, skipna=skipna, numeric_only=numeric_only)
+
+                func.__name__ = how
+                result = self._python_apply_general(
+                    func, self._obj_with_exclusions, not_indexed_same=True
+                )
+            except ValueError as err:
+                name = "argmax" if how == "idxmax" else "argmin"
+                if f"attempt to get {name} of an empty sequence" in str(err):
+                    raise ValueError(
+                        f"Can't get {how} of an empty group due to unobserved "
+                        "categories. Specify observed=True in groupby instead."
+                    ) from None
+                raise
+            return result
+
+        result = self._agg_general(
+            numeric_only=numeric_only,
+            min_count=1,
+            alias=how,
+            skipna=skipna,
+        )
+        return result
+
+    def _wrap_idxmax_idxmin(self, res: NDFrameT) -> NDFrameT:
+        index = self.obj._get_axis(self.axis)
+        if res.size == 0:
+            result = res.astype(index.dtype)
+        else:
+            if isinstance(index, MultiIndex):
+                index = index.to_flat_index()
+            values = res._values
+            assert isinstance(values, np.ndarray)
+            na_value = na_value_for_dtype(index.dtype, compat=False)
+            if isinstance(res, Series):
+                # mypy: expression has type "Series", variable has type "NDFrameT"
+                result = res._constructor(  # type: ignore[assignment]
+                    index.array.take(values, allow_fill=True, fill_value=na_value),
+                    index=res.index,
+                    name=res.name,
+                )
+            else:
+                data = {}
+                for k, column_values in enumerate(values.T):
+                    data[k] = index.array.take(
+                        column_values, allow_fill=True, fill_value=na_value
+                    )
+                result = self.obj._constructor(data, index=res.index)
+                result.columns = res.columns
+        return result
+
+
+@doc(GroupBy)
+def get_groupby(
+    obj: NDFrame,
+    by: _KeysArgType | None = None,
+    axis: AxisInt = 0,
+    grouper: ops.BaseGrouper | None = None,
+    group_keys: bool = True,
+) -> GroupBy:
+    klass: type[GroupBy]
+    if isinstance(obj, Series):
+        from pandas.core.groupby.generic import SeriesGroupBy
+
+        klass = SeriesGroupBy
+    elif isinstance(obj, DataFrame):
+        from pandas.core.groupby.generic import DataFrameGroupBy
+
+        klass = DataFrameGroupBy
+    else:  # pragma: no cover
+        raise TypeError(f"invalid type: {obj}")
+
+    return klass(
+        obj=obj,
+        keys=by,
+        axis=axis,
+        grouper=grouper,
+        group_keys=group_keys,
+    )
+
+
+def _insert_quantile_level(idx: Index, qs: npt.NDArray[np.float64]) -> MultiIndex:
+    """
+    Insert the sequence 'qs' of quantiles as the inner-most level of a MultiIndex.
+
+    The quantile level in the MultiIndex is a repeated copy of 'qs'.
+
+    Parameters
+    ----------
+    idx : Index
+    qs : np.ndarray[float64]
+
+    Returns
+    -------
+    MultiIndex
+    """
+    nqs = len(qs)
+    lev_codes, lev = Index(qs).factorize()
+    lev_codes = coerce_indexer_dtype(lev_codes, lev)
+
+    if idx._is_multi:
+        idx = cast(MultiIndex, idx)
+        levels = list(idx.levels) + [lev]
+        codes = [np.repeat(x, nqs) for x in idx.codes] + [np.tile(lev_codes, len(idx))]
+        mi = MultiIndex(levels=levels, codes=codes, names=idx.names + [None])
+    else:
+        nidx = len(idx)
+        idx_codes = coerce_indexer_dtype(np.arange(nidx), idx)
+        levels = [idx, lev]
+        codes = [np.repeat(idx_codes, nqs), np.tile(lev_codes, nidx)]
+        mi = MultiIndex(levels=levels, codes=codes, names=[idx.name, None])
+
+    return mi
+
+
+# GH#7155
+_apply_groupings_depr = (
+    "{}.{} operated on the grouping columns. This behavior is deprecated, "
+    "and in a future version of pandas the grouping columns will be excluded "
+    "from the operation. Either pass `include_groups=False` to exclude the "
+    "groupings or explicitly select the grouping columns after groupby to silence "
+    "this warning."
+)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/groupby/grouper.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/groupby/grouper.py
new file mode 100644
index 0000000000000000000000000000000000000000..4bf2e8b90a0b09146f41975aa801929c77fd5fce
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/groupby/grouper.py
@@ -0,0 +1,1102 @@
+"""
+Provide user facing operators for doing the split part of the
+split-apply-combine paradigm.
+"""
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    final,
+)
+import warnings
+
+import numpy as np
+
+from pandas._config import (
+    using_copy_on_write,
+    warn_copy_on_write,
+)
+
+from pandas._libs import lib
+from pandas._libs.tslibs import OutOfBoundsDatetime
+from pandas.errors import InvalidIndexError
+from pandas.util._decorators import cache_readonly
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.common import (
+    is_list_like,
+    is_scalar,
+)
+from pandas.core.dtypes.dtypes import CategoricalDtype
+
+from pandas.core import algorithms
+from pandas.core.arrays import (
+    Categorical,
+    ExtensionArray,
+)
+import pandas.core.common as com
+from pandas.core.frame import DataFrame
+from pandas.core.groupby import ops
+from pandas.core.groupby.categorical import recode_for_groupby
+from pandas.core.indexes.api import (
+    CategoricalIndex,
+    Index,
+    MultiIndex,
+)
+from pandas.core.series import Series
+
+from pandas.io.formats.printing import pprint_thing
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Hashable,
+        Iterator,
+    )
+
+    from pandas._typing import (
+        ArrayLike,
+        Axis,
+        NDFrameT,
+        npt,
+    )
+
+    from pandas.core.generic import NDFrame
+
+
+class Grouper:
+    """
+    A Grouper allows the user to specify a groupby instruction for an object.
+
+    This specification will select a column via the key parameter, or if the
+    level and/or axis parameters are given, a level of the index of the target
+    object.
+
+    If `axis` and/or `level` are passed as keywords to both `Grouper` and
+    `groupby`, the values passed to `Grouper` take precedence.
+
+    Parameters
+    ----------
+    key : str, defaults to None
+        Groupby key, which selects the grouping column of the target.
+    level : name/number, defaults to None
+        The level for the target index.
+    freq : str / frequency object, defaults to None
+        This will groupby the specified frequency if the target selection
+        (via key or level) is a datetime-like object. For full specification
+        of available frequencies, please see `here
+        `_.
+    axis : str, int, defaults to 0
+        Number/name of the axis.
+    sort : bool, default to False
+        Whether to sort the resulting labels.
+    closed : {'left' or 'right'}
+        Closed end of interval. Only when `freq` parameter is passed.
+    label : {'left' or 'right'}
+        Interval boundary to use for labeling.
+        Only when `freq` parameter is passed.
+    convention : {'start', 'end', 'e', 's'}
+        If grouper is PeriodIndex and `freq` parameter is passed.
+
+    origin : Timestamp or str, default 'start_day'
+        The timestamp on which to adjust the grouping. The timezone of origin must
+        match the timezone of the index.
+        If string, must be one of the following:
+
+        - 'epoch': `origin` is 1970-01-01
+        - 'start': `origin` is the first value of the timeseries
+        - 'start_day': `origin` is the first day at midnight of the timeseries
+
+        - 'end': `origin` is the last value of the timeseries
+        - 'end_day': `origin` is the ceiling midnight of the last day
+
+        .. versionadded:: 1.3.0
+
+    offset : Timedelta or str, default is None
+        An offset timedelta added to the origin.
+
+    dropna : bool, default True
+        If True, and if group keys contain NA values, NA values together with
+        row/column will be dropped. If False, NA values will also be treated as
+        the key in groups.
+
+    Returns
+    -------
+    Grouper or pandas.api.typing.TimeGrouper
+        A TimeGrouper is returned if ``freq`` is not ``None``. Otherwise, a Grouper
+        is returned.
+
+    Examples
+    --------
+    ``df.groupby(pd.Grouper(key="Animal"))`` is equivalent to ``df.groupby('Animal')``
+
+    >>> df = pd.DataFrame(
+    ...     {
+    ...         "Animal": ["Falcon", "Parrot", "Falcon", "Falcon", "Parrot"],
+    ...         "Speed": [100, 5, 200, 300, 15],
+    ...     }
+    ... )
+    >>> df
+       Animal  Speed
+    0  Falcon    100
+    1  Parrot      5
+    2  Falcon    200
+    3  Falcon    300
+    4  Parrot     15
+    >>> df.groupby(pd.Grouper(key="Animal")).mean()
+            Speed
+    Animal
+    Falcon  200.0
+    Parrot   10.0
+
+    Specify a resample operation on the column 'Publish date'
+
+    >>> df = pd.DataFrame(
+    ...    {
+    ...        "Publish date": [
+    ...             pd.Timestamp("2000-01-02"),
+    ...             pd.Timestamp("2000-01-02"),
+    ...             pd.Timestamp("2000-01-09"),
+    ...             pd.Timestamp("2000-01-16")
+    ...         ],
+    ...         "ID": [0, 1, 2, 3],
+    ...         "Price": [10, 20, 30, 40]
+    ...     }
+    ... )
+    >>> df
+      Publish date  ID  Price
+    0   2000-01-02   0     10
+    1   2000-01-02   1     20
+    2   2000-01-09   2     30
+    3   2000-01-16   3     40
+    >>> df.groupby(pd.Grouper(key="Publish date", freq="1W")).mean()
+                   ID  Price
+    Publish date
+    2000-01-02    0.5   15.0
+    2000-01-09    2.0   30.0
+    2000-01-16    3.0   40.0
+
+    If you want to adjust the start of the bins based on a fixed timestamp:
+
+    >>> start, end = '2000-10-01 23:30:00', '2000-10-02 00:30:00'
+    >>> rng = pd.date_range(start, end, freq='7min')
+    >>> ts = pd.Series(np.arange(len(rng)) * 3, index=rng)
+    >>> ts
+    2000-10-01 23:30:00     0
+    2000-10-01 23:37:00     3
+    2000-10-01 23:44:00     6
+    2000-10-01 23:51:00     9
+    2000-10-01 23:58:00    12
+    2000-10-02 00:05:00    15
+    2000-10-02 00:12:00    18
+    2000-10-02 00:19:00    21
+    2000-10-02 00:26:00    24
+    Freq: 7min, dtype: int64
+
+    >>> ts.groupby(pd.Grouper(freq='17min')).sum()
+    2000-10-01 23:14:00     0
+    2000-10-01 23:31:00     9
+    2000-10-01 23:48:00    21
+    2000-10-02 00:05:00    54
+    2000-10-02 00:22:00    24
+    Freq: 17min, dtype: int64
+
+    >>> ts.groupby(pd.Grouper(freq='17min', origin='epoch')).sum()
+    2000-10-01 23:18:00     0
+    2000-10-01 23:35:00    18
+    2000-10-01 23:52:00    27
+    2000-10-02 00:09:00    39
+    2000-10-02 00:26:00    24
+    Freq: 17min, dtype: int64
+
+    >>> ts.groupby(pd.Grouper(freq='17min', origin='2000-01-01')).sum()
+    2000-10-01 23:24:00     3
+    2000-10-01 23:41:00    15
+    2000-10-01 23:58:00    45
+    2000-10-02 00:15:00    45
+    Freq: 17min, dtype: int64
+
+    If you want to adjust the start of the bins with an `offset` Timedelta, the two
+    following lines are equivalent:
+
+    >>> ts.groupby(pd.Grouper(freq='17min', origin='start')).sum()
+    2000-10-01 23:30:00     9
+    2000-10-01 23:47:00    21
+    2000-10-02 00:04:00    54
+    2000-10-02 00:21:00    24
+    Freq: 17min, dtype: int64
+
+    >>> ts.groupby(pd.Grouper(freq='17min', offset='23h30min')).sum()
+    2000-10-01 23:30:00     9
+    2000-10-01 23:47:00    21
+    2000-10-02 00:04:00    54
+    2000-10-02 00:21:00    24
+    Freq: 17min, dtype: int64
+
+    To replace the use of the deprecated `base` argument, you can now use `offset`,
+    in this example it is equivalent to have `base=2`:
+
+    >>> ts.groupby(pd.Grouper(freq='17min', offset='2min')).sum()
+    2000-10-01 23:16:00     0
+    2000-10-01 23:33:00     9
+    2000-10-01 23:50:00    36
+    2000-10-02 00:07:00    39
+    2000-10-02 00:24:00    24
+    Freq: 17min, dtype: int64
+    """
+
+    sort: bool
+    dropna: bool
+    _gpr_index: Index | None
+    _grouper: Index | None
+
+    _attributes: tuple[str, ...] = ("key", "level", "freq", "axis", "sort", "dropna")
+
+    def __new__(cls, *args, **kwargs):
+        if kwargs.get("freq") is not None:
+            from pandas.core.resample import TimeGrouper
+
+            cls = TimeGrouper
+        return super().__new__(cls)
+
+    def __init__(
+        self,
+        key=None,
+        level=None,
+        freq=None,
+        axis: Axis | lib.NoDefault = lib.no_default,
+        sort: bool = False,
+        dropna: bool = True,
+    ) -> None:
+        if type(self) is Grouper:
+            # i.e. not TimeGrouper
+            if axis is not lib.no_default:
+                warnings.warn(
+                    "Grouper axis keyword is deprecated and will be removed in a "
+                    "future version. To group on axis=1, use obj.T.groupby(...) "
+                    "instead",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+            else:
+                axis = 0
+        if axis is lib.no_default:
+            axis = 0
+
+        self.key = key
+        self.level = level
+        self.freq = freq
+        self.axis = axis
+        self.sort = sort
+        self.dropna = dropna
+
+        self._grouper_deprecated = None
+        self._indexer_deprecated: npt.NDArray[np.intp] | None = None
+        self._obj_deprecated = None
+        self._gpr_index = None
+        self.binner = None
+        self._grouper = None
+        self._indexer: npt.NDArray[np.intp] | None = None
+
+    def _get_grouper(
+        self, obj: NDFrameT, validate: bool = True
+    ) -> tuple[ops.BaseGrouper, NDFrameT]:
+        """
+        Parameters
+        ----------
+        obj : Series or DataFrame
+        validate : bool, default True
+            if True, validate the grouper
+
+        Returns
+        -------
+        a tuple of grouper, obj (possibly sorted)
+        """
+        obj, _, _ = self._set_grouper(obj)
+        grouper, _, obj = get_grouper(
+            obj,
+            [self.key],
+            axis=self.axis,
+            level=self.level,
+            sort=self.sort,
+            validate=validate,
+            dropna=self.dropna,
+        )
+        # Without setting this, subsequent lookups to .groups raise
+        # error: Incompatible types in assignment (expression has type "BaseGrouper",
+        # variable has type "None")
+        self._grouper_deprecated = grouper  # type: ignore[assignment]
+
+        return grouper, obj
+
+    def _set_grouper(
+        self, obj: NDFrameT, sort: bool = False, *, gpr_index: Index | None = None
+    ) -> tuple[NDFrameT, Index, npt.NDArray[np.intp] | None]:
+        """
+        given an object and the specifications, setup the internal grouper
+        for this particular specification
+
+        Parameters
+        ----------
+        obj : Series or DataFrame
+        sort : bool, default False
+            whether the resulting grouper should be sorted
+        gpr_index : Index or None, default None
+
+        Returns
+        -------
+        NDFrame
+        Index
+        np.ndarray[np.intp] | None
+        """
+        assert obj is not None
+
+        if self.key is not None and self.level is not None:
+            raise ValueError("The Grouper cannot specify both a key and a level!")
+
+        # Keep self._grouper value before overriding
+        if self._grouper is None:
+            # TODO: What are we assuming about subsequent calls?
+            self._grouper = gpr_index
+            self._indexer = self._indexer_deprecated
+
+        # the key must be a valid info item
+        if self.key is not None:
+            key = self.key
+            # The 'on' is already defined
+            if getattr(gpr_index, "name", None) == key and isinstance(obj, Series):
+                # Sometimes self._grouper will have been resorted while
+                # obj has not. In this case there is a mismatch when we
+                # call self._grouper.take(obj.index) so we need to undo the sorting
+                # before we call _grouper.take.
+                assert self._grouper is not None
+                if self._indexer is not None:
+                    reverse_indexer = self._indexer.argsort()
+                    unsorted_ax = self._grouper.take(reverse_indexer)
+                    ax = unsorted_ax.take(obj.index)
+                else:
+                    ax = self._grouper.take(obj.index)
+            else:
+                if key not in obj._info_axis:
+                    raise KeyError(f"The grouper name {key} is not found")
+                ax = Index(obj[key], name=key)
+
+        else:
+            ax = obj._get_axis(self.axis)
+            if self.level is not None:
+                level = self.level
+
+                # if a level is given it must be a mi level or
+                # equivalent to the axis name
+                if isinstance(ax, MultiIndex):
+                    level = ax._get_level_number(level)
+                    ax = Index(ax._get_level_values(level), name=ax.names[level])
+
+                else:
+                    if level not in (0, ax.name):
+                        raise ValueError(f"The level {level} is not valid")
+
+        # possibly sort
+        indexer: npt.NDArray[np.intp] | None = None
+        if (self.sort or sort) and not ax.is_monotonic_increasing:
+            # use stable sort to support first, last, nth
+            # TODO: why does putting na_position="first" fix datetimelike cases?
+            indexer = self._indexer_deprecated = ax.array.argsort(
+                kind="mergesort", na_position="first"
+            )
+            ax = ax.take(indexer)
+            obj = obj.take(indexer, axis=self.axis)
+
+        # error: Incompatible types in assignment (expression has type
+        # "NDFrameT", variable has type "None")
+        self._obj_deprecated = obj  # type: ignore[assignment]
+        self._gpr_index = ax
+        return obj, ax, indexer
+
+    @final
+    @property
+    def ax(self) -> Index:
+        warnings.warn(
+            f"{type(self).__name__}.ax is deprecated and will be removed in a "
+            "future version. Use Resampler.ax instead",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        index = self._gpr_index
+        if index is None:
+            raise ValueError("_set_grouper must be called before ax is accessed")
+        return index
+
+    @final
+    @property
+    def indexer(self):
+        warnings.warn(
+            f"{type(self).__name__}.indexer is deprecated and will be removed "
+            "in a future version. Use Resampler.indexer instead.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        return self._indexer_deprecated
+
+    @final
+    @property
+    def obj(self):
+        # TODO(3.0): enforcing these deprecations on Grouper should close
+        #  GH#25564, GH#41930
+        warnings.warn(
+            f"{type(self).__name__}.obj is deprecated and will be removed "
+            "in a future version. Use GroupBy.indexer instead.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        return self._obj_deprecated
+
+    @final
+    @property
+    def grouper(self):
+        warnings.warn(
+            f"{type(self).__name__}.grouper is deprecated and will be removed "
+            "in a future version. Use GroupBy.grouper instead.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        return self._grouper_deprecated
+
+    @final
+    @property
+    def groups(self):
+        warnings.warn(
+            f"{type(self).__name__}.groups is deprecated and will be removed "
+            "in a future version. Use GroupBy.groups instead.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        # error: "None" has no attribute "groups"
+        return self._grouper_deprecated.groups  # type: ignore[attr-defined]
+
+    @final
+    def __repr__(self) -> str:
+        attrs_list = (
+            f"{attr_name}={repr(getattr(self, attr_name))}"
+            for attr_name in self._attributes
+            if getattr(self, attr_name) is not None
+        )
+        attrs = ", ".join(attrs_list)
+        cls_name = type(self).__name__
+        return f"{cls_name}({attrs})"
+
+
+@final
+class Grouping:
+    """
+    Holds the grouping information for a single key
+
+    Parameters
+    ----------
+    index : Index
+    grouper :
+    obj : DataFrame or Series
+    name : Label
+    level :
+    observed : bool, default False
+        If we are a Categorical, use the observed values
+    in_axis : if the Grouping is a column in self.obj and hence among
+        Groupby.exclusions list
+    dropna : bool, default True
+        Whether to drop NA groups.
+    uniques : Array-like, optional
+        When specified, will be used for unique values. Enables including empty groups
+        in the result for a BinGrouper. Must not contain duplicates.
+
+    Attributes
+    -------
+    indices : dict
+        Mapping of {group -> index_list}
+    codes : ndarray
+        Group codes
+    group_index : Index or None
+        unique groups
+    groups : dict
+        Mapping of {group -> label_list}
+    """
+
+    _codes: npt.NDArray[np.signedinteger] | None = None
+    _all_grouper: Categorical | None
+    _orig_cats: Index | None
+    _index: Index
+
+    def __init__(
+        self,
+        index: Index,
+        grouper=None,
+        obj: NDFrame | None = None,
+        level=None,
+        sort: bool = True,
+        observed: bool = False,
+        in_axis: bool = False,
+        dropna: bool = True,
+        uniques: ArrayLike | None = None,
+    ) -> None:
+        self.level = level
+        self._orig_grouper = grouper
+        grouping_vector = _convert_grouper(index, grouper)
+        self._all_grouper = None
+        self._orig_cats = None
+        self._index = index
+        self._sort = sort
+        self.obj = obj
+        self._observed = observed
+        self.in_axis = in_axis
+        self._dropna = dropna
+        self._uniques = uniques
+
+        # we have a single grouper which may be a myriad of things,
+        # some of which are dependent on the passing in level
+
+        ilevel = self._ilevel
+        if ilevel is not None:
+            # In extant tests, the new self.grouping_vector matches
+            #  `index.get_level_values(ilevel)` whenever
+            #  mapper is None and isinstance(index, MultiIndex)
+            if isinstance(index, MultiIndex):
+                index_level = index.get_level_values(ilevel)
+            else:
+                index_level = index
+
+            if grouping_vector is None:
+                grouping_vector = index_level
+            else:
+                mapper = grouping_vector
+                grouping_vector = index_level.map(mapper)
+
+        # a passed Grouper like, directly get the grouper in the same way
+        # as single grouper groupby, use the group_info to get codes
+        elif isinstance(grouping_vector, Grouper):
+            # get the new grouper; we already have disambiguated
+            # what key/level refer to exactly, don't need to
+            # check again as we have by this point converted these
+            # to an actual value (rather than a pd.Grouper)
+            assert self.obj is not None  # for mypy
+            newgrouper, newobj = grouping_vector._get_grouper(self.obj, validate=False)
+            self.obj = newobj
+
+            if isinstance(newgrouper, ops.BinGrouper):
+                # TODO: can we unwrap this and get a tighter typing
+                #  for self.grouping_vector?
+                grouping_vector = newgrouper
+            else:
+                # ops.BaseGrouper
+                # TODO: 2023-02-03 no test cases with len(newgrouper.groupings) > 1.
+                #  If that were to occur, would we be throwing out information?
+                # error: Cannot determine type of "grouping_vector"  [has-type]
+                ng = newgrouper.groupings[0].grouping_vector  # type: ignore[has-type]
+                # use Index instead of ndarray so we can recover the name
+                grouping_vector = Index(ng, name=newgrouper.result_index.name)
+
+        elif not isinstance(
+            grouping_vector, (Series, Index, ExtensionArray, np.ndarray)
+        ):
+            # no level passed
+            if getattr(grouping_vector, "ndim", 1) != 1:
+                t = str(type(grouping_vector))
+                raise ValueError(f"Grouper for '{t}' not 1-dimensional")
+
+            grouping_vector = index.map(grouping_vector)
+
+            if not (
+                hasattr(grouping_vector, "__len__")
+                and len(grouping_vector) == len(index)
+            ):
+                grper = pprint_thing(grouping_vector)
+                errmsg = (
+                    "Grouper result violates len(labels) == "
+                    f"len(data)\nresult: {grper}"
+                )
+                raise AssertionError(errmsg)
+
+        if isinstance(grouping_vector, np.ndarray):
+            if grouping_vector.dtype.kind in "mM":
+                # if we have a date/time-like grouper, make sure that we have
+                # Timestamps like
+                # TODO 2022-10-08 we only have one test that gets here and
+                #  values are already in nanoseconds in that case.
+                grouping_vector = Series(grouping_vector).to_numpy()
+        elif isinstance(getattr(grouping_vector, "dtype", None), CategoricalDtype):
+            # a passed Categorical
+            self._orig_cats = grouping_vector.categories
+            grouping_vector, self._all_grouper = recode_for_groupby(
+                grouping_vector, sort, observed
+            )
+
+        self.grouping_vector = grouping_vector
+
+    def __repr__(self) -> str:
+        return f"Grouping({self.name})"
+
+    def __iter__(self) -> Iterator:
+        return iter(self.indices)
+
+    @cache_readonly
+    def _passed_categorical(self) -> bool:
+        dtype = getattr(self.grouping_vector, "dtype", None)
+        return isinstance(dtype, CategoricalDtype)
+
+    @cache_readonly
+    def name(self) -> Hashable:
+        ilevel = self._ilevel
+        if ilevel is not None:
+            return self._index.names[ilevel]
+
+        if isinstance(self._orig_grouper, (Index, Series)):
+            return self._orig_grouper.name
+
+        elif isinstance(self.grouping_vector, ops.BaseGrouper):
+            return self.grouping_vector.result_index.name
+
+        elif isinstance(self.grouping_vector, Index):
+            return self.grouping_vector.name
+
+        # otherwise we have ndarray or ExtensionArray -> no name
+        return None
+
+    @cache_readonly
+    def _ilevel(self) -> int | None:
+        """
+        If necessary, converted index level name to index level position.
+        """
+        level = self.level
+        if level is None:
+            return None
+        if not isinstance(level, int):
+            index = self._index
+            if level not in index.names:
+                raise AssertionError(f"Level {level} not in index")
+            return index.names.index(level)
+        return level
+
+    @property
+    def ngroups(self) -> int:
+        return len(self._group_index)
+
+    @cache_readonly
+    def indices(self) -> dict[Hashable, npt.NDArray[np.intp]]:
+        # we have a list of groupers
+        if isinstance(self.grouping_vector, ops.BaseGrouper):
+            return self.grouping_vector.indices
+
+        values = Categorical(self.grouping_vector)
+        return values._reverse_indexer()
+
+    @property
+    def codes(self) -> npt.NDArray[np.signedinteger]:
+        return self._codes_and_uniques[0]
+
+    @cache_readonly
+    def _group_arraylike(self) -> ArrayLike:
+        """
+        Analogous to result_index, but holding an ArrayLike to ensure
+        we can retain ExtensionDtypes.
+        """
+        if self._all_grouper is not None:
+            # retain dtype for categories, including unobserved ones
+            return self._result_index._values
+
+        elif self._passed_categorical:
+            return self._group_index._values
+
+        return self._codes_and_uniques[1]
+
+    @property
+    def group_arraylike(self) -> ArrayLike:
+        """
+        Analogous to result_index, but holding an ArrayLike to ensure
+        we can retain ExtensionDtypes.
+        """
+        warnings.warn(
+            "group_arraylike is deprecated and will be removed in a future "
+            "version of pandas",
+            category=FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        return self._group_arraylike
+
+    @cache_readonly
+    def _result_index(self) -> Index:
+        # result_index retains dtype for categories, including unobserved ones,
+        #  which group_index does not
+        if self._all_grouper is not None:
+            group_idx = self._group_index
+            assert isinstance(group_idx, CategoricalIndex)
+            cats = self._orig_cats
+            # set_categories is dynamically added
+            return group_idx.set_categories(cats)  # type: ignore[attr-defined]
+        return self._group_index
+
+    @property
+    def result_index(self) -> Index:
+        warnings.warn(
+            "result_index is deprecated and will be removed in a future "
+            "version of pandas",
+            category=FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        return self._result_index
+
+    @cache_readonly
+    def _group_index(self) -> Index:
+        codes, uniques = self._codes_and_uniques
+        if not self._dropna and self._passed_categorical:
+            assert isinstance(uniques, Categorical)
+            if self._sort and (codes == len(uniques)).any():
+                # Add NA value on the end when sorting
+                uniques = Categorical.from_codes(
+                    np.append(uniques.codes, [-1]), uniques.categories, validate=False
+                )
+            elif len(codes) > 0:
+                # Need to determine proper placement of NA value when not sorting
+                cat = self.grouping_vector
+                na_idx = (cat.codes < 0).argmax()
+                if cat.codes[na_idx] < 0:
+                    # count number of unique codes that comes before the nan value
+                    na_unique_idx = algorithms.nunique_ints(cat.codes[:na_idx])
+                    new_codes = np.insert(uniques.codes, na_unique_idx, -1)
+                    uniques = Categorical.from_codes(
+                        new_codes, uniques.categories, validate=False
+                    )
+        return Index._with_infer(uniques, name=self.name)
+
+    @property
+    def group_index(self) -> Index:
+        warnings.warn(
+            "group_index is deprecated and will be removed in a future "
+            "version of pandas",
+            category=FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        return self._group_index
+
+    @cache_readonly
+    def _codes_and_uniques(self) -> tuple[npt.NDArray[np.signedinteger], ArrayLike]:
+        uniques: ArrayLike
+        if self._passed_categorical:
+            # we make a CategoricalIndex out of the cat grouper
+            # preserving the categories / ordered attributes;
+            # doesn't (yet - GH#46909) handle dropna=False
+            cat = self.grouping_vector
+            categories = cat.categories
+
+            if self._observed:
+                ucodes = algorithms.unique1d(cat.codes)
+                ucodes = ucodes[ucodes != -1]
+                if self._sort:
+                    ucodes = np.sort(ucodes)
+            else:
+                ucodes = np.arange(len(categories))
+
+            uniques = Categorical.from_codes(
+                codes=ucodes, categories=categories, ordered=cat.ordered, validate=False
+            )
+
+            codes = cat.codes
+            if not self._dropna:
+                na_mask = codes < 0
+                if np.any(na_mask):
+                    if self._sort:
+                        # Replace NA codes with `largest code + 1`
+                        na_code = len(categories)
+                        codes = np.where(na_mask, na_code, codes)
+                    else:
+                        # Insert NA code into the codes based on first appearance
+                        # A negative code must exist, no need to check codes[na_idx] < 0
+                        na_idx = na_mask.argmax()
+                        # count number of unique codes that comes before the nan value
+                        na_code = algorithms.nunique_ints(codes[:na_idx])
+                        codes = np.where(codes >= na_code, codes + 1, codes)
+                        codes = np.where(na_mask, na_code, codes)
+
+            if not self._observed:
+                uniques = uniques.reorder_categories(self._orig_cats)
+
+            return codes, uniques
+
+        elif isinstance(self.grouping_vector, ops.BaseGrouper):
+            # we have a list of groupers
+            codes = self.grouping_vector.codes_info
+            uniques = self.grouping_vector.result_index._values
+        elif self._uniques is not None:
+            # GH#50486 Code grouping_vector using _uniques; allows
+            # including uniques that are not present in grouping_vector.
+            cat = Categorical(self.grouping_vector, categories=self._uniques)
+            codes = cat.codes
+            uniques = self._uniques
+        else:
+            # GH35667, replace dropna=False with use_na_sentinel=False
+            # error: Incompatible types in assignment (expression has type "Union[
+            # ndarray[Any, Any], Index]", variable has type "Categorical")
+            codes, uniques = algorithms.factorize(  # type: ignore[assignment]
+                self.grouping_vector, sort=self._sort, use_na_sentinel=self._dropna
+            )
+        return codes, uniques
+
+    @cache_readonly
+    def groups(self) -> dict[Hashable, np.ndarray]:
+        cats = Categorical.from_codes(self.codes, self._group_index, validate=False)
+        return self._index.groupby(cats)
+
+
+def get_grouper(
+    obj: NDFrameT,
+    key=None,
+    axis: Axis = 0,
+    level=None,
+    sort: bool = True,
+    observed: bool = False,
+    validate: bool = True,
+    dropna: bool = True,
+) -> tuple[ops.BaseGrouper, frozenset[Hashable], NDFrameT]:
+    """
+    Create and return a BaseGrouper, which is an internal
+    mapping of how to create the grouper indexers.
+    This may be composed of multiple Grouping objects, indicating
+    multiple groupers
+
+    Groupers are ultimately index mappings. They can originate as:
+    index mappings, keys to columns, functions, or Groupers
+
+    Groupers enable local references to axis,level,sort, while
+    the passed in axis, level, and sort are 'global'.
+
+    This routine tries to figure out what the passing in references
+    are and then creates a Grouping for each one, combined into
+    a BaseGrouper.
+
+    If observed & we have a categorical grouper, only show the observed
+    values.
+
+    If validate, then check for key/level overlaps.
+
+    """
+    group_axis = obj._get_axis(axis)
+
+    # validate that the passed single level is compatible with the passed
+    # axis of the object
+    if level is not None:
+        # TODO: These if-block and else-block are almost same.
+        # MultiIndex instance check is removable, but it seems that there are
+        # some processes only for non-MultiIndex in else-block,
+        # eg. `obj.index.name != level`. We have to consider carefully whether
+        # these are applicable for MultiIndex. Even if these are applicable,
+        # we need to check if it makes no side effect to subsequent processes
+        # on the outside of this condition.
+        # (GH 17621)
+        if isinstance(group_axis, MultiIndex):
+            if is_list_like(level) and len(level) == 1:
+                level = level[0]
+
+            if key is None and is_scalar(level):
+                # Get the level values from group_axis
+                key = group_axis.get_level_values(level)
+                level = None
+
+        else:
+            # allow level to be a length-one list-like object
+            # (e.g., level=[0])
+            # GH 13901
+            if is_list_like(level):
+                nlevels = len(level)
+                if nlevels == 1:
+                    level = level[0]
+                elif nlevels == 0:
+                    raise ValueError("No group keys passed!")
+                else:
+                    raise ValueError("multiple levels only valid with MultiIndex")
+
+            if isinstance(level, str):
+                if obj._get_axis(axis).name != level:
+                    raise ValueError(
+                        f"level name {level} is not the name "
+                        f"of the {obj._get_axis_name(axis)}"
+                    )
+            elif level > 0 or level < -1:
+                raise ValueError("level > 0 or level < -1 only valid with MultiIndex")
+
+            # NOTE: `group_axis` and `group_axis.get_level_values(level)`
+            # are same in this section.
+            level = None
+            key = group_axis
+
+    # a passed-in Grouper, directly convert
+    if isinstance(key, Grouper):
+        grouper, obj = key._get_grouper(obj, validate=False)
+        if key.key is None:
+            return grouper, frozenset(), obj
+        else:
+            return grouper, frozenset({key.key}), obj
+
+    # already have a BaseGrouper, just return it
+    elif isinstance(key, ops.BaseGrouper):
+        return key, frozenset(), obj
+
+    if not isinstance(key, list):
+        keys = [key]
+        match_axis_length = False
+    else:
+        keys = key
+        match_axis_length = len(keys) == len(group_axis)
+
+    # what are we after, exactly?
+    any_callable = any(callable(g) or isinstance(g, dict) for g in keys)
+    any_groupers = any(isinstance(g, (Grouper, Grouping)) for g in keys)
+    any_arraylike = any(
+        isinstance(g, (list, tuple, Series, Index, np.ndarray)) for g in keys
+    )
+
+    # is this an index replacement?
+    if (
+        not any_callable
+        and not any_arraylike
+        and not any_groupers
+        and match_axis_length
+        and level is None
+    ):
+        if isinstance(obj, DataFrame):
+            all_in_columns_index = all(
+                g in obj.columns or g in obj.index.names for g in keys
+            )
+        else:
+            assert isinstance(obj, Series)
+            all_in_columns_index = all(g in obj.index.names for g in keys)
+
+        if not all_in_columns_index:
+            keys = [com.asarray_tuplesafe(keys)]
+
+    if isinstance(level, (tuple, list)):
+        if key is None:
+            keys = [None] * len(level)
+        levels = level
+    else:
+        levels = [level] * len(keys)
+
+    groupings: list[Grouping] = []
+    exclusions: set[Hashable] = set()
+
+    # if the actual grouper should be obj[key]
+    def is_in_axis(key) -> bool:
+        if not _is_label_like(key):
+            if obj.ndim == 1:
+                return False
+
+            # items -> .columns for DataFrame, .index for Series
+            items = obj.axes[-1]
+            try:
+                items.get_loc(key)
+            except (KeyError, TypeError, InvalidIndexError):
+                # TypeError shows up here if we pass e.g. an Index
+                return False
+
+        return True
+
+    # if the grouper is obj[name]
+    def is_in_obj(gpr) -> bool:
+        if not hasattr(gpr, "name"):
+            return False
+        if using_copy_on_write() or warn_copy_on_write():
+            # For the CoW case, we check the references to determine if the
+            # series is part of the object
+            try:
+                obj_gpr_column = obj[gpr.name]
+            except (KeyError, IndexError, InvalidIndexError, OutOfBoundsDatetime):
+                return False
+            if isinstance(gpr, Series) and isinstance(obj_gpr_column, Series):
+                return gpr._mgr.references_same_values(  # type: ignore[union-attr]
+                    obj_gpr_column._mgr, 0  # type: ignore[arg-type]
+                )
+            return False
+        try:
+            return gpr is obj[gpr.name]
+        except (KeyError, IndexError, InvalidIndexError, OutOfBoundsDatetime):
+            # IndexError reached in e.g. test_skip_group_keys when we pass
+            #  lambda here
+            # InvalidIndexError raised on key-types inappropriate for index,
+            #  e.g. DatetimeIndex.get_loc(tuple())
+            # OutOfBoundsDatetime raised when obj is a Series with DatetimeIndex
+            # and gpr.name is month str
+            return False
+
+    for gpr, level in zip(keys, levels):
+        if isinstance(obj, DataFrame) and is_in_obj(gpr):  # df.groupby(df['name'])
+            in_axis = True
+            exclusions.add(gpr.name)
+
+        elif is_in_axis(gpr):  # df.groupby('name')
+            if obj.ndim != 1 and gpr in obj:
+                if validate:
+                    obj._check_label_or_level_ambiguity(gpr, axis=axis)
+                in_axis, name, gpr = True, gpr, obj[gpr]
+                if gpr.ndim != 1:
+                    # non-unique columns; raise here to get the name in the
+                    # exception message
+                    raise ValueError(f"Grouper for '{name}' not 1-dimensional")
+                exclusions.add(name)
+            elif obj._is_level_reference(gpr, axis=axis):
+                in_axis, level, gpr = False, gpr, None
+            else:
+                raise KeyError(gpr)
+        elif isinstance(gpr, Grouper) and gpr.key is not None:
+            # Add key to exclusions
+            exclusions.add(gpr.key)
+            in_axis = True
+        else:
+            in_axis = False
+
+        # create the Grouping
+        # allow us to passing the actual Grouping as the gpr
+        ping = (
+            Grouping(
+                group_axis,
+                gpr,
+                obj=obj,
+                level=level,
+                sort=sort,
+                observed=observed,
+                in_axis=in_axis,
+                dropna=dropna,
+            )
+            if not isinstance(gpr, Grouping)
+            else gpr
+        )
+
+        groupings.append(ping)
+
+    if len(groupings) == 0 and len(obj):
+        raise ValueError("No group keys passed!")
+    if len(groupings) == 0:
+        groupings.append(Grouping(Index([], dtype="int"), np.array([], dtype=np.intp)))
+
+    # create the internals grouper
+    grouper = ops.BaseGrouper(group_axis, groupings, sort=sort, dropna=dropna)
+    return grouper, frozenset(exclusions), obj
+
+
+def _is_label_like(val) -> bool:
+    return isinstance(val, (str, tuple)) or (val is not None and is_scalar(val))
+
+
+def _convert_grouper(axis: Index, grouper):
+    if isinstance(grouper, dict):
+        return grouper.get
+    elif isinstance(grouper, Series):
+        if grouper.index.equals(axis):
+            return grouper._values
+        else:
+            return grouper.reindex(axis)._values
+    elif isinstance(grouper, MultiIndex):
+        return grouper._values
+    elif isinstance(grouper, (list, tuple, Index, Categorical, np.ndarray)):
+        if len(grouper) != len(axis):
+            raise ValueError("Grouper and axis must be same length")
+
+        if isinstance(grouper, (list, tuple)):
+            grouper = com.asarray_tuplesafe(grouper)
+        return grouper
+    else:
+        return grouper
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/groupby/indexing.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/groupby/indexing.py
new file mode 100644
index 0000000000000000000000000000000000000000..a3c5ab8edc94e4f91175891282252d0e8cdfd3ec
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/groupby/indexing.py
@@ -0,0 +1,304 @@
+from __future__ import annotations
+
+from collections.abc import Iterable
+from typing import (
+    TYPE_CHECKING,
+    Literal,
+    cast,
+)
+
+import numpy as np
+
+from pandas.util._decorators import (
+    cache_readonly,
+    doc,
+)
+
+from pandas.core.dtypes.common import (
+    is_integer,
+    is_list_like,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import PositionalIndexer
+
+    from pandas import (
+        DataFrame,
+        Series,
+    )
+    from pandas.core.groupby import groupby
+
+
+class GroupByIndexingMixin:
+    """
+    Mixin for adding ._positional_selector to GroupBy.
+    """
+
+    @cache_readonly
+    def _positional_selector(self) -> GroupByPositionalSelector:
+        """
+        Return positional selection for each group.
+
+        ``groupby._positional_selector[i:j]`` is similar to
+        ``groupby.apply(lambda x: x.iloc[i:j])``
+        but much faster and preserves the original index and order.
+
+        ``_positional_selector[]`` is compatible with and extends :meth:`~GroupBy.head`
+        and :meth:`~GroupBy.tail`. For example:
+
+        - ``head(5)``
+        - ``_positional_selector[5:-5]``
+        - ``tail(5)``
+
+        together return all the rows.
+
+        Allowed inputs for the index are:
+
+        - An integer valued iterable, e.g. ``range(2, 4)``.
+        - A comma separated list of integers and slices, e.g. ``5``, ``2, 4``, ``2:4``.
+
+        The output format is the same as :meth:`~GroupBy.head` and
+        :meth:`~GroupBy.tail`, namely
+        a subset of the ``DataFrame`` or ``Series`` with the index and order preserved.
+
+        Returns
+        -------
+        Series
+            The filtered subset of the original Series.
+        DataFrame
+            The filtered subset of the original DataFrame.
+
+        See Also
+        --------
+        DataFrame.iloc : Purely integer-location based indexing for selection by
+            position.
+        GroupBy.head : Return first n rows of each group.
+        GroupBy.tail : Return last n rows of each group.
+        GroupBy.nth : Take the nth row from each group if n is an int, or a
+            subset of rows, if n is a list of ints.
+
+        Notes
+        -----
+        - The slice step cannot be negative.
+        - If the index specification results in overlaps, the item is not duplicated.
+        - If the index specification changes the order of items, then
+          they are returned in their original order.
+          By contrast, ``DataFrame.iloc`` can change the row order.
+        - ``groupby()`` parameters such as as_index and dropna are ignored.
+
+        The differences between ``_positional_selector[]`` and :meth:`~GroupBy.nth`
+        with ``as_index=False`` are:
+
+        - Input to ``_positional_selector`` can include
+          one or more slices whereas ``nth``
+          just handles an integer or a list of integers.
+        - ``_positional_selector`` can  accept a slice relative to the
+          last row of each group.
+        - ``_positional_selector`` does not have an equivalent to the
+          ``nth()`` ``dropna`` parameter.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([["a", 1], ["a", 2], ["a", 3], ["b", 4], ["b", 5]],
+        ...                   columns=["A", "B"])
+        >>> df.groupby("A")._positional_selector[1:2]
+           A  B
+        1  a  2
+        4  b  5
+
+        >>> df.groupby("A")._positional_selector[1, -1]
+           A  B
+        1  a  2
+        2  a  3
+        4  b  5
+        """
+        if TYPE_CHECKING:
+            # pylint: disable-next=used-before-assignment
+            groupby_self = cast(groupby.GroupBy, self)
+        else:
+            groupby_self = self
+
+        return GroupByPositionalSelector(groupby_self)
+
+    def _make_mask_from_positional_indexer(
+        self,
+        arg: PositionalIndexer | tuple,
+    ) -> np.ndarray:
+        if is_list_like(arg):
+            if all(is_integer(i) for i in cast(Iterable, arg)):
+                mask = self._make_mask_from_list(cast(Iterable[int], arg))
+            else:
+                mask = self._make_mask_from_tuple(cast(tuple, arg))
+
+        elif isinstance(arg, slice):
+            mask = self._make_mask_from_slice(arg)
+        elif is_integer(arg):
+            mask = self._make_mask_from_int(cast(int, arg))
+        else:
+            raise TypeError(
+                f"Invalid index {type(arg)}. "
+                "Must be integer, list-like, slice or a tuple of "
+                "integers and slices"
+            )
+
+        if isinstance(mask, bool):
+            if mask:
+                mask = self._ascending_count >= 0
+            else:
+                mask = self._ascending_count < 0
+
+        return cast(np.ndarray, mask)
+
+    def _make_mask_from_int(self, arg: int) -> np.ndarray:
+        if arg >= 0:
+            return self._ascending_count == arg
+        else:
+            return self._descending_count == (-arg - 1)
+
+    def _make_mask_from_list(self, args: Iterable[int]) -> bool | np.ndarray:
+        positive = [arg for arg in args if arg >= 0]
+        negative = [-arg - 1 for arg in args if arg < 0]
+
+        mask: bool | np.ndarray = False
+
+        if positive:
+            mask |= np.isin(self._ascending_count, positive)
+
+        if negative:
+            mask |= np.isin(self._descending_count, negative)
+
+        return mask
+
+    def _make_mask_from_tuple(self, args: tuple) -> bool | np.ndarray:
+        mask: bool | np.ndarray = False
+
+        for arg in args:
+            if is_integer(arg):
+                mask |= self._make_mask_from_int(cast(int, arg))
+            elif isinstance(arg, slice):
+                mask |= self._make_mask_from_slice(arg)
+            else:
+                raise ValueError(
+                    f"Invalid argument {type(arg)}. Should be int or slice."
+                )
+
+        return mask
+
+    def _make_mask_from_slice(self, arg: slice) -> bool | np.ndarray:
+        start = arg.start
+        stop = arg.stop
+        step = arg.step
+
+        if step is not None and step < 0:
+            raise ValueError(f"Invalid step {step}. Must be non-negative")
+
+        mask: bool | np.ndarray = True
+
+        if step is None:
+            step = 1
+
+        if start is None:
+            if step > 1:
+                mask &= self._ascending_count % step == 0
+
+        elif start >= 0:
+            mask &= self._ascending_count >= start
+
+            if step > 1:
+                mask &= (self._ascending_count - start) % step == 0
+
+        else:
+            mask &= self._descending_count < -start
+
+            offset_array = self._descending_count + start + 1
+            limit_array = (
+                self._ascending_count + self._descending_count + (start + 1)
+            ) < 0
+            offset_array = np.where(limit_array, self._ascending_count, offset_array)
+
+            mask &= offset_array % step == 0
+
+        if stop is not None:
+            if stop >= 0:
+                mask &= self._ascending_count < stop
+            else:
+                mask &= self._descending_count >= -stop
+
+        return mask
+
+    @cache_readonly
+    def _ascending_count(self) -> np.ndarray:
+        if TYPE_CHECKING:
+            groupby_self = cast(groupby.GroupBy, self)
+        else:
+            groupby_self = self
+
+        return groupby_self._cumcount_array()
+
+    @cache_readonly
+    def _descending_count(self) -> np.ndarray:
+        if TYPE_CHECKING:
+            groupby_self = cast(groupby.GroupBy, self)
+        else:
+            groupby_self = self
+
+        return groupby_self._cumcount_array(ascending=False)
+
+
+@doc(GroupByIndexingMixin._positional_selector)
+class GroupByPositionalSelector:
+    def __init__(self, groupby_object: groupby.GroupBy) -> None:
+        self.groupby_object = groupby_object
+
+    def __getitem__(self, arg: PositionalIndexer | tuple) -> DataFrame | Series:
+        """
+        Select by positional index per group.
+
+        Implements GroupBy._positional_selector
+
+        Parameters
+        ----------
+        arg : PositionalIndexer | tuple
+            Allowed values are:
+            - int
+            - int valued iterable such as list or range
+            - slice with step either None or positive
+            - tuple of integers and slices
+
+        Returns
+        -------
+        Series
+            The filtered subset of the original groupby Series.
+        DataFrame
+            The filtered subset of the original groupby DataFrame.
+
+        See Also
+        --------
+        DataFrame.iloc : Integer-location based indexing for selection by position.
+        GroupBy.head : Return first n rows of each group.
+        GroupBy.tail : Return last n rows of each group.
+        GroupBy._positional_selector : Return positional selection for each group.
+        GroupBy.nth : Take the nth row from each group if n is an int, or a
+            subset of rows, if n is a list of ints.
+        """
+        mask = self.groupby_object._make_mask_from_positional_indexer(arg)
+        return self.groupby_object._mask_selected_obj(mask)
+
+
+class GroupByNthSelector:
+    """
+    Dynamically substituted for GroupBy.nth to enable both call and index
+    """
+
+    def __init__(self, groupby_object: groupby.GroupBy) -> None:
+        self.groupby_object = groupby_object
+
+    def __call__(
+        self,
+        n: PositionalIndexer | tuple,
+        dropna: Literal["any", "all", None] = None,
+    ) -> DataFrame | Series:
+        return self.groupby_object._nth(n, dropna)
+
+    def __getitem__(self, n: PositionalIndexer | tuple) -> DataFrame | Series:
+        return self.groupby_object._nth(n)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/groupby/numba_.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/groupby/numba_.py
new file mode 100644
index 0000000000000000000000000000000000000000..3b7a58e87603e578216c4c80e8c88e06828d5dfa
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/groupby/numba_.py
@@ -0,0 +1,181 @@
+"""Common utilities for Numba operations with groupby ops"""
+from __future__ import annotations
+
+import functools
+import inspect
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+)
+
+import numpy as np
+
+from pandas.compat._optional import import_optional_dependency
+
+from pandas.core.util.numba_ import (
+    NumbaUtilError,
+    jit_user_function,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import Scalar
+
+
+def validate_udf(func: Callable) -> None:
+    """
+    Validate user defined function for ops when using Numba with groupby ops.
+
+    The first signature arguments should include:
+
+    def f(values, index, ...):
+        ...
+
+    Parameters
+    ----------
+    func : function, default False
+        user defined function
+
+    Returns
+    -------
+    None
+
+    Raises
+    ------
+    NumbaUtilError
+    """
+    if not callable(func):
+        raise NotImplementedError(
+            "Numba engine can only be used with a single function."
+        )
+    udf_signature = list(inspect.signature(func).parameters.keys())
+    expected_args = ["values", "index"]
+    min_number_args = len(expected_args)
+    if (
+        len(udf_signature) < min_number_args
+        or udf_signature[:min_number_args] != expected_args
+    ):
+        raise NumbaUtilError(
+            f"The first {min_number_args} arguments to {func.__name__} must be "
+            f"{expected_args}"
+        )
+
+
+@functools.cache
+def generate_numba_agg_func(
+    func: Callable[..., Scalar],
+    nopython: bool,
+    nogil: bool,
+    parallel: bool,
+) -> Callable[[np.ndarray, np.ndarray, np.ndarray, np.ndarray, int, Any], np.ndarray]:
+    """
+    Generate a numba jitted agg function specified by values from engine_kwargs.
+
+    1. jit the user's function
+    2. Return a groupby agg function with the jitted function inline
+
+    Configurations specified in engine_kwargs apply to both the user's
+    function _AND_ the groupby evaluation loop.
+
+    Parameters
+    ----------
+    func : function
+        function to be applied to each group and will be JITed
+    nopython : bool
+        nopython to be passed into numba.jit
+    nogil : bool
+        nogil to be passed into numba.jit
+    parallel : bool
+        parallel to be passed into numba.jit
+
+    Returns
+    -------
+    Numba function
+    """
+    numba_func = jit_user_function(func)
+    if TYPE_CHECKING:
+        import numba
+    else:
+        numba = import_optional_dependency("numba")
+
+    @numba.jit(nopython=nopython, nogil=nogil, parallel=parallel)
+    def group_agg(
+        values: np.ndarray,
+        index: np.ndarray,
+        begin: np.ndarray,
+        end: np.ndarray,
+        num_columns: int,
+        *args: Any,
+    ) -> np.ndarray:
+        assert len(begin) == len(end)
+        num_groups = len(begin)
+
+        result = np.empty((num_groups, num_columns))
+        for i in numba.prange(num_groups):
+            group_index = index[begin[i] : end[i]]
+            for j in numba.prange(num_columns):
+                group = values[begin[i] : end[i], j]
+                result[i, j] = numba_func(group, group_index, *args)
+        return result
+
+    return group_agg
+
+
+@functools.cache
+def generate_numba_transform_func(
+    func: Callable[..., np.ndarray],
+    nopython: bool,
+    nogil: bool,
+    parallel: bool,
+) -> Callable[[np.ndarray, np.ndarray, np.ndarray, np.ndarray, int, Any], np.ndarray]:
+    """
+    Generate a numba jitted transform function specified by values from engine_kwargs.
+
+    1. jit the user's function
+    2. Return a groupby transform function with the jitted function inline
+
+    Configurations specified in engine_kwargs apply to both the user's
+    function _AND_ the groupby evaluation loop.
+
+    Parameters
+    ----------
+    func : function
+        function to be applied to each window and will be JITed
+    nopython : bool
+        nopython to be passed into numba.jit
+    nogil : bool
+        nogil to be passed into numba.jit
+    parallel : bool
+        parallel to be passed into numba.jit
+
+    Returns
+    -------
+    Numba function
+    """
+    numba_func = jit_user_function(func)
+    if TYPE_CHECKING:
+        import numba
+    else:
+        numba = import_optional_dependency("numba")
+
+    @numba.jit(nopython=nopython, nogil=nogil, parallel=parallel)
+    def group_transform(
+        values: np.ndarray,
+        index: np.ndarray,
+        begin: np.ndarray,
+        end: np.ndarray,
+        num_columns: int,
+        *args: Any,
+    ) -> np.ndarray:
+        assert len(begin) == len(end)
+        num_groups = len(begin)
+
+        result = np.empty((len(values), num_columns))
+        for i in numba.prange(num_groups):
+            group_index = index[begin[i] : end[i]]
+            for j in numba.prange(num_columns):
+                group = values[begin[i] : end[i], j]
+                result[begin[i] : end[i], j] = numba_func(group, group_index, *args)
+        return result
+
+    return group_transform
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/groupby/ops.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/groupby/ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..e44c28f656eccf5744e0db8aa70dfbc9ac076f15
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/groupby/ops.py
@@ -0,0 +1,1217 @@
+"""
+Provide classes to perform the groupby aggregate operations.
+
+These are not exposed to the user and provide implementations of the grouping
+operations, primarily in cython. These classes (BaseGrouper and BinGrouper)
+are contained *in* the SeriesGroupBy and DataFrameGroupBy objects.
+"""
+from __future__ import annotations
+
+import collections
+import functools
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Generic,
+    final,
+)
+
+import numpy as np
+
+from pandas._libs import (
+    NaT,
+    lib,
+)
+import pandas._libs.groupby as libgroupby
+from pandas._typing import (
+    ArrayLike,
+    AxisInt,
+    NDFrameT,
+    Shape,
+    npt,
+)
+from pandas.errors import AbstractMethodError
+from pandas.util._decorators import cache_readonly
+
+from pandas.core.dtypes.cast import (
+    maybe_cast_pointwise_result,
+    maybe_downcast_to_dtype,
+)
+from pandas.core.dtypes.common import (
+    ensure_float64,
+    ensure_int64,
+    ensure_platform_int,
+    ensure_uint64,
+    is_1d_only_ea_dtype,
+)
+from pandas.core.dtypes.missing import (
+    isna,
+    maybe_fill,
+)
+
+from pandas.core.frame import DataFrame
+from pandas.core.groupby import grouper
+from pandas.core.indexes.api import (
+    CategoricalIndex,
+    Index,
+    MultiIndex,
+    ensure_index,
+)
+from pandas.core.series import Series
+from pandas.core.sorting import (
+    compress_group_index,
+    decons_obs_group_ids,
+    get_flattened_list,
+    get_group_index,
+    get_group_index_sorter,
+    get_indexer_dict,
+)
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Hashable,
+        Iterator,
+        Sequence,
+    )
+
+    from pandas.core.generic import NDFrame
+
+
+def check_result_array(obj, dtype) -> None:
+    # Our operation is supposed to be an aggregation/reduction. If
+    #  it returns an ndarray, this likely means an invalid operation has
+    #  been passed. See test_apply_without_aggregation, test_agg_must_agg
+    if isinstance(obj, np.ndarray):
+        if dtype != object:
+            # If it is object dtype, the function can be a reduction/aggregation
+            #  and still return an ndarray e.g. test_agg_over_numpy_arrays
+            raise ValueError("Must produce aggregated value")
+
+
+def extract_result(res):
+    """
+    Extract the result object, it might be a 0-dim ndarray
+    or a len-1 0-dim, or a scalar
+    """
+    if hasattr(res, "_values"):
+        # Preserve EA
+        res = res._values
+        if res.ndim == 1 and len(res) == 1:
+            # see test_agg_lambda_with_timezone, test_resampler_grouper.py::test_apply
+            res = res[0]
+    return res
+
+
+class WrappedCythonOp:
+    """
+    Dispatch logic for functions defined in _libs.groupby
+
+    Parameters
+    ----------
+    kind: str
+        Whether the operation is an aggregate or transform.
+    how: str
+        Operation name, e.g. "mean".
+    has_dropped_na: bool
+        True precisely when dropna=True and the grouper contains a null value.
+    """
+
+    # Functions for which we do _not_ attempt to cast the cython result
+    #  back to the original dtype.
+    cast_blocklist = frozenset(
+        ["any", "all", "rank", "count", "size", "idxmin", "idxmax"]
+    )
+
+    def __init__(self, kind: str, how: str, has_dropped_na: bool) -> None:
+        self.kind = kind
+        self.how = how
+        self.has_dropped_na = has_dropped_na
+
+    _CYTHON_FUNCTIONS: dict[str, dict] = {
+        "aggregate": {
+            "any": functools.partial(libgroupby.group_any_all, val_test="any"),
+            "all": functools.partial(libgroupby.group_any_all, val_test="all"),
+            "sum": "group_sum",
+            "prod": "group_prod",
+            "idxmin": functools.partial(libgroupby.group_idxmin_idxmax, name="idxmin"),
+            "idxmax": functools.partial(libgroupby.group_idxmin_idxmax, name="idxmax"),
+            "min": "group_min",
+            "max": "group_max",
+            "mean": "group_mean",
+            "median": "group_median_float64",
+            "var": "group_var",
+            "std": functools.partial(libgroupby.group_var, name="std"),
+            "sem": functools.partial(libgroupby.group_var, name="sem"),
+            "skew": "group_skew",
+            "first": "group_nth",
+            "last": "group_last",
+            "ohlc": "group_ohlc",
+        },
+        "transform": {
+            "cumprod": "group_cumprod",
+            "cumsum": "group_cumsum",
+            "cummin": "group_cummin",
+            "cummax": "group_cummax",
+            "rank": "group_rank",
+        },
+    }
+
+    _cython_arity = {"ohlc": 4}  # OHLC
+
+    @classmethod
+    def get_kind_from_how(cls, how: str) -> str:
+        if how in cls._CYTHON_FUNCTIONS["aggregate"]:
+            return "aggregate"
+        return "transform"
+
+    # Note: we make this a classmethod and pass kind+how so that caching
+    #  works at the class level and not the instance level
+    @classmethod
+    @functools.cache
+    def _get_cython_function(
+        cls, kind: str, how: str, dtype: np.dtype, is_numeric: bool
+    ):
+        dtype_str = dtype.name
+        ftype = cls._CYTHON_FUNCTIONS[kind][how]
+
+        # see if there is a fused-type version of function
+        # only valid for numeric
+        if callable(ftype):
+            f = ftype
+        else:
+            f = getattr(libgroupby, ftype)
+        if is_numeric:
+            return f
+        elif dtype == np.dtype(object):
+            if how in ["median", "cumprod"]:
+                # no fused types -> no __signatures__
+                raise NotImplementedError(
+                    f"function is not implemented for this dtype: "
+                    f"[how->{how},dtype->{dtype_str}]"
+                )
+            elif how in ["std", "sem", "idxmin", "idxmax"]:
+                # We have a partial object that does not have __signatures__
+                return f
+            elif how == "skew":
+                # _get_cython_vals will convert to float64
+                pass
+            elif "object" not in f.__signatures__:
+                # raise NotImplementedError here rather than TypeError later
+                raise NotImplementedError(
+                    f"function is not implemented for this dtype: "
+                    f"[how->{how},dtype->{dtype_str}]"
+                )
+            return f
+        else:
+            raise NotImplementedError(
+                "This should not be reached. Please report a bug at "
+                "github.com/pandas-dev/pandas/",
+                dtype,
+            )
+
+    def _get_cython_vals(self, values: np.ndarray) -> np.ndarray:
+        """
+        Cast numeric dtypes to float64 for functions that only support that.
+
+        Parameters
+        ----------
+        values : np.ndarray
+
+        Returns
+        -------
+        values : np.ndarray
+        """
+        how = self.how
+
+        if how in ["median", "std", "sem", "skew"]:
+            # median only has a float64 implementation
+            # We should only get here with is_numeric, as non-numeric cases
+            #  should raise in _get_cython_function
+            values = ensure_float64(values)
+
+        elif values.dtype.kind in "iu":
+            if how in ["var", "mean"] or (
+                self.kind == "transform" and self.has_dropped_na
+            ):
+                # has_dropped_na check need for test_null_group_str_transformer
+                # result may still include NaN, so we have to cast
+                values = ensure_float64(values)
+
+            elif how in ["sum", "ohlc", "prod", "cumsum", "cumprod"]:
+                # Avoid overflow during group op
+                if values.dtype.kind == "i":
+                    values = ensure_int64(values)
+                else:
+                    values = ensure_uint64(values)
+
+        return values
+
+    def _get_output_shape(self, ngroups: int, values: np.ndarray) -> Shape:
+        how = self.how
+        kind = self.kind
+
+        arity = self._cython_arity.get(how, 1)
+
+        out_shape: Shape
+        if how == "ohlc":
+            out_shape = (ngroups, arity)
+        elif arity > 1:
+            raise NotImplementedError(
+                "arity of more than 1 is not supported for the 'how' argument"
+            )
+        elif kind == "transform":
+            out_shape = values.shape
+        else:
+            out_shape = (ngroups,) + values.shape[1:]
+        return out_shape
+
+    def _get_out_dtype(self, dtype: np.dtype) -> np.dtype:
+        how = self.how
+
+        if how == "rank":
+            out_dtype = "float64"
+        elif how in ["idxmin", "idxmax"]:
+            # The Cython implementation only produces the row number; we'll take
+            # from the index using this in post processing
+            out_dtype = "intp"
+        else:
+            if dtype.kind in "iufcb":
+                out_dtype = f"{dtype.kind}{dtype.itemsize}"
+            else:
+                out_dtype = "object"
+        return np.dtype(out_dtype)
+
+    def _get_result_dtype(self, dtype: np.dtype) -> np.dtype:
+        """
+        Get the desired dtype of a result based on the
+        input dtype and how it was computed.
+
+        Parameters
+        ----------
+        dtype : np.dtype
+
+        Returns
+        -------
+        np.dtype
+            The desired dtype of the result.
+        """
+        how = self.how
+
+        if how in ["sum", "cumsum", "sum", "prod", "cumprod"]:
+            if dtype == np.dtype(bool):
+                return np.dtype(np.int64)
+        elif how in ["mean", "median", "var", "std", "sem"]:
+            if dtype.kind in "fc":
+                return dtype
+            elif dtype.kind in "iub":
+                return np.dtype(np.float64)
+        return dtype
+
+    @final
+    def _cython_op_ndim_compat(
+        self,
+        values: np.ndarray,
+        *,
+        min_count: int,
+        ngroups: int,
+        comp_ids: np.ndarray,
+        mask: npt.NDArray[np.bool_] | None = None,
+        result_mask: npt.NDArray[np.bool_] | None = None,
+        initial: Any = 0,
+        **kwargs,
+    ) -> np.ndarray:
+        if values.ndim == 1:
+            # expand to 2d, dispatch, then squeeze if appropriate
+            values2d = values[None, :]
+            if mask is not None:
+                mask = mask[None, :]
+            if result_mask is not None:
+                result_mask = result_mask[None, :]
+            res = self._call_cython_op(
+                values2d,
+                min_count=min_count,
+                ngroups=ngroups,
+                comp_ids=comp_ids,
+                mask=mask,
+                result_mask=result_mask,
+                initial=initial,
+                **kwargs,
+            )
+            if res.shape[0] == 1:
+                return res[0]
+
+            # otherwise we have OHLC
+            return res.T
+
+        return self._call_cython_op(
+            values,
+            min_count=min_count,
+            ngroups=ngroups,
+            comp_ids=comp_ids,
+            mask=mask,
+            result_mask=result_mask,
+            initial=initial,
+            **kwargs,
+        )
+
+    @final
+    def _call_cython_op(
+        self,
+        values: np.ndarray,  # np.ndarray[ndim=2]
+        *,
+        min_count: int,
+        ngroups: int,
+        comp_ids: np.ndarray,
+        mask: npt.NDArray[np.bool_] | None,
+        result_mask: npt.NDArray[np.bool_] | None,
+        initial: Any = 0,
+        **kwargs,
+    ) -> np.ndarray:  # np.ndarray[ndim=2]
+        orig_values = values
+
+        dtype = values.dtype
+        is_numeric = dtype.kind in "iufcb"
+
+        is_datetimelike = dtype.kind in "mM"
+
+        if is_datetimelike:
+            values = values.view("int64")
+            is_numeric = True
+        elif dtype.kind == "b":
+            values = values.view("uint8")
+        if values.dtype == "float16":
+            values = values.astype(np.float32)
+
+        if self.how in ["any", "all"]:
+            if mask is None:
+                mask = isna(values)
+            if dtype == object:
+                if kwargs["skipna"]:
+                    # GH#37501: don't raise on pd.NA when skipna=True
+                    if mask.any():
+                        # mask on original values computed separately
+                        values = values.copy()
+                        values[mask] = True
+            values = values.astype(bool, copy=False).view(np.int8)
+            is_numeric = True
+
+        values = values.T
+        if mask is not None:
+            mask = mask.T
+            if result_mask is not None:
+                result_mask = result_mask.T
+
+        out_shape = self._get_output_shape(ngroups, values)
+        func = self._get_cython_function(self.kind, self.how, values.dtype, is_numeric)
+        values = self._get_cython_vals(values)
+        out_dtype = self._get_out_dtype(values.dtype)
+
+        result = maybe_fill(np.empty(out_shape, dtype=out_dtype))
+        if self.kind == "aggregate":
+            counts = np.zeros(ngroups, dtype=np.int64)
+            if self.how in [
+                "idxmin",
+                "idxmax",
+                "min",
+                "max",
+                "mean",
+                "last",
+                "first",
+                "sum",
+            ]:
+                if self.how == "sum":
+                    # pass in through kwargs only for sum (other functions don't have
+                    # the keyword)
+                    kwargs["initial"] = initial
+                func(
+                    out=result,
+                    counts=counts,
+                    values=values,
+                    labels=comp_ids,
+                    min_count=min_count,
+                    mask=mask,
+                    result_mask=result_mask,
+                    is_datetimelike=is_datetimelike,
+                    **kwargs,
+                )
+            elif self.how in ["sem", "std", "var", "ohlc", "prod", "median"]:
+                if self.how in ["std", "sem"]:
+                    kwargs["is_datetimelike"] = is_datetimelike
+                func(
+                    result,
+                    counts,
+                    values,
+                    comp_ids,
+                    min_count=min_count,
+                    mask=mask,
+                    result_mask=result_mask,
+                    **kwargs,
+                )
+            elif self.how in ["any", "all"]:
+                func(
+                    out=result,
+                    values=values,
+                    labels=comp_ids,
+                    mask=mask,
+                    result_mask=result_mask,
+                    **kwargs,
+                )
+                result = result.astype(bool, copy=False)
+            elif self.how in ["skew"]:
+                func(
+                    out=result,
+                    counts=counts,
+                    values=values,
+                    labels=comp_ids,
+                    mask=mask,
+                    result_mask=result_mask,
+                    **kwargs,
+                )
+                if dtype == object:
+                    result = result.astype(object)
+
+            else:
+                raise NotImplementedError(f"{self.how} is not implemented")
+        else:
+            # TODO: min_count
+            if self.how != "rank":
+                # TODO: should rank take result_mask?
+                kwargs["result_mask"] = result_mask
+            func(
+                out=result,
+                values=values,
+                labels=comp_ids,
+                ngroups=ngroups,
+                is_datetimelike=is_datetimelike,
+                mask=mask,
+                **kwargs,
+            )
+
+        if self.kind == "aggregate" and self.how not in ["idxmin", "idxmax"]:
+            # i.e. counts is defined.  Locations where count None:
+        if values.ndim > 2:
+            raise NotImplementedError("number of dimensions is currently limited to 2")
+        if values.ndim == 2:
+            assert axis == 1, axis
+        elif not is_1d_only_ea_dtype(values.dtype):
+            # Note: it is *not* the case that axis is always 0 for 1-dim values,
+            #  as we can have 1D ExtensionArrays that we need to treat as 2D
+            assert axis == 0
+
+    @final
+    def cython_operation(
+        self,
+        *,
+        values: ArrayLike,
+        axis: AxisInt,
+        min_count: int = -1,
+        comp_ids: np.ndarray,
+        ngroups: int,
+        **kwargs,
+    ) -> ArrayLike:
+        """
+        Call our cython function, with appropriate pre- and post- processing.
+        """
+        self._validate_axis(axis, values)
+
+        if not isinstance(values, np.ndarray):
+            # i.e. ExtensionArray
+            return values._groupby_op(
+                how=self.how,
+                has_dropped_na=self.has_dropped_na,
+                min_count=min_count,
+                ngroups=ngroups,
+                ids=comp_ids,
+                **kwargs,
+            )
+
+        return self._cython_op_ndim_compat(
+            values,
+            min_count=min_count,
+            ngroups=ngroups,
+            comp_ids=comp_ids,
+            mask=None,
+            **kwargs,
+        )
+
+
+class BaseGrouper:
+    """
+    This is an internal Grouper class, which actually holds
+    the generated groups
+
+    Parameters
+    ----------
+    axis : Index
+    groupings : Sequence[Grouping]
+        all the grouping instances to handle in this grouper
+        for example for grouper list to groupby, need to pass the list
+    sort : bool, default True
+        whether this grouper will give sorted result or not
+
+    """
+
+    axis: Index
+
+    def __init__(
+        self,
+        axis: Index,
+        groupings: Sequence[grouper.Grouping],
+        sort: bool = True,
+        dropna: bool = True,
+    ) -> None:
+        assert isinstance(axis, Index), axis
+
+        self.axis = axis
+        self._groupings: list[grouper.Grouping] = list(groupings)
+        self._sort = sort
+        self.dropna = dropna
+
+    @property
+    def groupings(self) -> list[grouper.Grouping]:
+        return self._groupings
+
+    @property
+    def shape(self) -> Shape:
+        return tuple(ping.ngroups for ping in self.groupings)
+
+    def __iter__(self) -> Iterator[Hashable]:
+        return iter(self.indices)
+
+    @property
+    def nkeys(self) -> int:
+        return len(self.groupings)
+
+    def get_iterator(
+        self, data: NDFrameT, axis: AxisInt = 0
+    ) -> Iterator[tuple[Hashable, NDFrameT]]:
+        """
+        Groupby iterator
+
+        Returns
+        -------
+        Generator yielding sequence of (name, subsetted object)
+        for each group
+        """
+        splitter = self._get_splitter(data, axis=axis)
+        keys = self.group_keys_seq
+        yield from zip(keys, splitter)
+
+    @final
+    def _get_splitter(self, data: NDFrame, axis: AxisInt = 0) -> DataSplitter:
+        """
+        Returns
+        -------
+        Generator yielding subsetted objects
+        """
+        ids, _, ngroups = self.group_info
+        return _get_splitter(
+            data,
+            ids,
+            ngroups,
+            sorted_ids=self._sorted_ids,
+            sort_idx=self._sort_idx,
+            axis=axis,
+        )
+
+    @final
+    @cache_readonly
+    def group_keys_seq(self):
+        if len(self.groupings) == 1:
+            return self.levels[0]
+        else:
+            ids, _, ngroups = self.group_info
+
+            # provide "flattened" iterator for multi-group setting
+            return get_flattened_list(ids, ngroups, self.levels, self.codes)
+
+    @cache_readonly
+    def indices(self) -> dict[Hashable, npt.NDArray[np.intp]]:
+        """dict {group name -> group indices}"""
+        if len(self.groupings) == 1 and isinstance(self.result_index, CategoricalIndex):
+            # This shows unused categories in indices GH#38642
+            return self.groupings[0].indices
+        codes_list = [ping.codes for ping in self.groupings]
+        keys = [ping._group_index for ping in self.groupings]
+        return get_indexer_dict(codes_list, keys)
+
+    @final
+    def result_ilocs(self) -> npt.NDArray[np.intp]:
+        """
+        Get the original integer locations of result_index in the input.
+        """
+        # Original indices are where group_index would go via sorting.
+        # But when dropna is true, we need to remove null values while accounting for
+        # any gaps that then occur because of them.
+        group_index = get_group_index(
+            self.codes, self.shape, sort=self._sort, xnull=True
+        )
+        group_index, _ = compress_group_index(group_index, sort=self._sort)
+
+        if self.has_dropped_na:
+            mask = np.where(group_index >= 0)
+            # Count how many gaps are caused by previous null values for each position
+            null_gaps = np.cumsum(group_index == -1)[mask]
+            group_index = group_index[mask]
+
+        result = get_group_index_sorter(group_index, self.ngroups)
+
+        if self.has_dropped_na:
+            # Shift by the number of prior null gaps
+            result += np.take(null_gaps, result)
+
+        return result
+
+    @final
+    @property
+    def codes(self) -> list[npt.NDArray[np.signedinteger]]:
+        return [ping.codes for ping in self.groupings]
+
+    @property
+    def levels(self) -> list[Index]:
+        return [ping._group_index for ping in self.groupings]
+
+    @property
+    def names(self) -> list[Hashable]:
+        return [ping.name for ping in self.groupings]
+
+    @final
+    def size(self) -> Series:
+        """
+        Compute group sizes.
+        """
+        ids, _, ngroups = self.group_info
+        out: np.ndarray | list
+        if ngroups:
+            out = np.bincount(ids[ids != -1], minlength=ngroups)
+        else:
+            out = []
+        return Series(out, index=self.result_index, dtype="int64", copy=False)
+
+    @cache_readonly
+    def groups(self) -> dict[Hashable, np.ndarray]:
+        """dict {group name -> group labels}"""
+        if len(self.groupings) == 1:
+            return self.groupings[0].groups
+        else:
+            to_groupby = []
+            for ping in self.groupings:
+                gv = ping.grouping_vector
+                if not isinstance(gv, BaseGrouper):
+                    to_groupby.append(gv)
+                else:
+                    to_groupby.append(gv.groupings[0].grouping_vector)
+            index = MultiIndex.from_arrays(to_groupby)
+            return self.axis.groupby(index)
+
+    @final
+    @cache_readonly
+    def is_monotonic(self) -> bool:
+        # return if my group orderings are monotonic
+        return Index(self.group_info[0]).is_monotonic_increasing
+
+    @final
+    @cache_readonly
+    def has_dropped_na(self) -> bool:
+        """
+        Whether grouper has null value(s) that are dropped.
+        """
+        return bool((self.group_info[0] < 0).any())
+
+    @cache_readonly
+    def group_info(self) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp], int]:
+        comp_ids, obs_group_ids = self._get_compressed_codes()
+
+        ngroups = len(obs_group_ids)
+        comp_ids = ensure_platform_int(comp_ids)
+
+        return comp_ids, obs_group_ids, ngroups
+
+    @cache_readonly
+    def codes_info(self) -> npt.NDArray[np.intp]:
+        # return the codes of items in original grouped axis
+        ids, _, _ = self.group_info
+        return ids
+
+    @final
+    def _get_compressed_codes(
+        self,
+    ) -> tuple[npt.NDArray[np.signedinteger], npt.NDArray[np.intp]]:
+        # The first returned ndarray may have any signed integer dtype
+        if len(self.groupings) > 1:
+            group_index = get_group_index(self.codes, self.shape, sort=True, xnull=True)
+            return compress_group_index(group_index, sort=self._sort)
+            # FIXME: compress_group_index's second return value is int64, not intp
+
+        ping = self.groupings[0]
+        return ping.codes, np.arange(len(ping._group_index), dtype=np.intp)
+
+    @final
+    @cache_readonly
+    def ngroups(self) -> int:
+        return len(self.result_index)
+
+    @property
+    def reconstructed_codes(self) -> list[npt.NDArray[np.intp]]:
+        codes = self.codes
+        ids, obs_ids, _ = self.group_info
+        return decons_obs_group_ids(ids, obs_ids, self.shape, codes, xnull=True)
+
+    @cache_readonly
+    def result_index(self) -> Index:
+        if len(self.groupings) == 1:
+            return self.groupings[0]._result_index.rename(self.names[0])
+
+        codes = self.reconstructed_codes
+        levels = [ping._result_index for ping in self.groupings]
+        return MultiIndex(
+            levels=levels, codes=codes, verify_integrity=False, names=self.names
+        )
+
+    @final
+    def get_group_levels(self) -> list[ArrayLike]:
+        # Note: only called from _insert_inaxis_grouper, which
+        #  is only called for BaseGrouper, never for BinGrouper
+        if len(self.groupings) == 1:
+            return [self.groupings[0]._group_arraylike]
+
+        name_list = []
+        for ping, codes in zip(self.groupings, self.reconstructed_codes):
+            codes = ensure_platform_int(codes)
+            levels = ping._group_arraylike.take(codes)
+
+            name_list.append(levels)
+
+        return name_list
+
+    # ------------------------------------------------------------
+    # Aggregation functions
+
+    @final
+    def _cython_operation(
+        self,
+        kind: str,
+        values,
+        how: str,
+        axis: AxisInt,
+        min_count: int = -1,
+        **kwargs,
+    ) -> ArrayLike:
+        """
+        Returns the values of a cython operation.
+        """
+        assert kind in ["transform", "aggregate"]
+
+        cy_op = WrappedCythonOp(kind=kind, how=how, has_dropped_na=self.has_dropped_na)
+
+        ids, _, _ = self.group_info
+        ngroups = self.ngroups
+        return cy_op.cython_operation(
+            values=values,
+            axis=axis,
+            min_count=min_count,
+            comp_ids=ids,
+            ngroups=ngroups,
+            **kwargs,
+        )
+
+    @final
+    def agg_series(
+        self, obj: Series, func: Callable, preserve_dtype: bool = False
+    ) -> ArrayLike:
+        """
+        Parameters
+        ----------
+        obj : Series
+        func : function taking a Series and returning a scalar-like
+        preserve_dtype : bool
+            Whether the aggregation is known to be dtype-preserving.
+
+        Returns
+        -------
+        np.ndarray or ExtensionArray
+        """
+
+        if not isinstance(obj._values, np.ndarray):
+            # we can preserve a little bit more aggressively with EA dtype
+            #  because maybe_cast_pointwise_result will do a try/except
+            #  with _from_sequence.  NB we are assuming here that _from_sequence
+            #  is sufficiently strict that it casts appropriately.
+            preserve_dtype = True
+
+        result = self._aggregate_series_pure_python(obj, func)
+
+        npvalues = lib.maybe_convert_objects(result, try_float=False)
+        if preserve_dtype:
+            out = maybe_cast_pointwise_result(npvalues, obj.dtype, numeric_only=True)
+        else:
+            out = npvalues
+        return out
+
+    @final
+    def _aggregate_series_pure_python(
+        self, obj: Series, func: Callable
+    ) -> npt.NDArray[np.object_]:
+        _, _, ngroups = self.group_info
+
+        result = np.empty(ngroups, dtype="O")
+        initialized = False
+
+        splitter = self._get_splitter(obj, axis=0)
+
+        for i, group in enumerate(splitter):
+            res = func(group)
+            res = extract_result(res)
+
+            if not initialized:
+                # We only do this validation on the first iteration
+                check_result_array(res, group.dtype)
+                initialized = True
+
+            result[i] = res
+
+        return result
+
+    @final
+    def apply_groupwise(
+        self, f: Callable, data: DataFrame | Series, axis: AxisInt = 0
+    ) -> tuple[list, bool]:
+        mutated = False
+        splitter = self._get_splitter(data, axis=axis)
+        group_keys = self.group_keys_seq
+        result_values = []
+
+        # This calls DataSplitter.__iter__
+        zipped = zip(group_keys, splitter)
+
+        for key, group in zipped:
+            # Pinning name is needed for
+            #  test_group_apply_once_per_group,
+            #  test_inconsistent_return_type, test_set_group_name,
+            #  test_group_name_available_in_inference_pass,
+            #  test_groupby_multi_timezone
+            object.__setattr__(group, "name", key)
+
+            # group might be modified
+            group_axes = group.axes
+            res = f(group)
+            if not mutated and not _is_indexed_like(res, group_axes, axis):
+                mutated = True
+            result_values.append(res)
+        # getattr pattern for __name__ is needed for functools.partial objects
+        if len(group_keys) == 0 and getattr(f, "__name__", None) in [
+            "skew",
+            "sum",
+            "prod",
+        ]:
+            #  If group_keys is empty, then no function calls have been made,
+            #  so we will not have raised even if this is an invalid dtype.
+            #  So do one dummy call here to raise appropriate TypeError.
+            f(data.iloc[:0])
+
+        return result_values, mutated
+
+    # ------------------------------------------------------------
+    # Methods for sorting subsets of our GroupBy's object
+
+    @final
+    @cache_readonly
+    def _sort_idx(self) -> npt.NDArray[np.intp]:
+        # Counting sort indexer
+        ids, _, ngroups = self.group_info
+        return get_group_index_sorter(ids, ngroups)
+
+    @final
+    @cache_readonly
+    def _sorted_ids(self) -> npt.NDArray[np.intp]:
+        ids, _, _ = self.group_info
+        return ids.take(self._sort_idx)
+
+
+class BinGrouper(BaseGrouper):
+    """
+    This is an internal Grouper class
+
+    Parameters
+    ----------
+    bins : the split index of binlabels to group the item of axis
+    binlabels : the label list
+    indexer : np.ndarray[np.intp], optional
+        the indexer created by Grouper
+        some groupers (TimeGrouper) will sort its axis and its
+        group_info is also sorted, so need the indexer to reorder
+
+    Examples
+    --------
+    bins: [2, 4, 6, 8, 10]
+    binlabels: DatetimeIndex(['2005-01-01', '2005-01-03',
+        '2005-01-05', '2005-01-07', '2005-01-09'],
+        dtype='datetime64[ns]', freq='2D')
+
+    the group_info, which contains the label of each item in grouped
+    axis, the index of label in label list, group number, is
+
+    (array([0, 0, 1, 1, 2, 2, 3, 3, 4, 4]), array([0, 1, 2, 3, 4]), 5)
+
+    means that, the grouped axis has 10 items, can be grouped into 5
+    labels, the first and second items belong to the first label, the
+    third and forth items belong to the second label, and so on
+
+    """
+
+    bins: npt.NDArray[np.int64]
+    binlabels: Index
+
+    def __init__(
+        self,
+        bins,
+        binlabels,
+        indexer=None,
+    ) -> None:
+        self.bins = ensure_int64(bins)
+        self.binlabels = ensure_index(binlabels)
+        self.indexer = indexer
+
+        # These lengths must match, otherwise we could call agg_series
+        #  with empty self.bins, which would raise later.
+        assert len(self.binlabels) == len(self.bins)
+
+    @cache_readonly
+    def groups(self):
+        """dict {group name -> group labels}"""
+        # this is mainly for compat
+        # GH 3881
+        result = {
+            key: value
+            for key, value in zip(self.binlabels, self.bins)
+            if key is not NaT
+        }
+        return result
+
+    @property
+    def nkeys(self) -> int:
+        # still matches len(self.groupings), but we can hard-code
+        return 1
+
+    @cache_readonly
+    def codes_info(self) -> npt.NDArray[np.intp]:
+        # return the codes of items in original grouped axis
+        ids, _, _ = self.group_info
+        if self.indexer is not None:
+            sorter = np.lexsort((ids, self.indexer))
+            ids = ids[sorter]
+        return ids
+
+    def get_iterator(self, data: NDFrame, axis: AxisInt = 0):
+        """
+        Groupby iterator
+
+        Returns
+        -------
+        Generator yielding sequence of (name, subsetted object)
+        for each group
+        """
+        if axis == 0:
+            slicer = lambda start, edge: data.iloc[start:edge]
+        else:
+            slicer = lambda start, edge: data.iloc[:, start:edge]
+
+        length = len(data.axes[axis])
+
+        start = 0
+        for edge, label in zip(self.bins, self.binlabels):
+            if label is not NaT:
+                yield label, slicer(start, edge)
+            start = edge
+
+        if start < length:
+            yield self.binlabels[-1], slicer(start, None)
+
+    @cache_readonly
+    def indices(self):
+        indices = collections.defaultdict(list)
+
+        i = 0
+        for label, bin in zip(self.binlabels, self.bins):
+            if i < bin:
+                if label is not NaT:
+                    indices[label] = list(range(i, bin))
+                i = bin
+        return indices
+
+    @cache_readonly
+    def group_info(self) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp], int]:
+        ngroups = self.ngroups
+        obs_group_ids = np.arange(ngroups, dtype=np.intp)
+        rep = np.diff(np.r_[0, self.bins])
+
+        rep = ensure_platform_int(rep)
+        if ngroups == len(self.bins):
+            comp_ids = np.repeat(np.arange(ngroups), rep)
+        else:
+            comp_ids = np.repeat(np.r_[-1, np.arange(ngroups)], rep)
+
+        return (
+            ensure_platform_int(comp_ids),
+            obs_group_ids,
+            ngroups,
+        )
+
+    @cache_readonly
+    def reconstructed_codes(self) -> list[np.ndarray]:
+        # get unique result indices, and prepend 0 as groupby starts from the first
+        return [np.r_[0, np.flatnonzero(self.bins[1:] != self.bins[:-1]) + 1]]
+
+    @cache_readonly
+    def result_index(self) -> Index:
+        if len(self.binlabels) != 0 and isna(self.binlabels[0]):
+            return self.binlabels[1:]
+
+        return self.binlabels
+
+    @property
+    def levels(self) -> list[Index]:
+        return [self.binlabels]
+
+    @property
+    def names(self) -> list[Hashable]:
+        return [self.binlabels.name]
+
+    @property
+    def groupings(self) -> list[grouper.Grouping]:
+        lev = self.binlabels
+        codes = self.group_info[0]
+        labels = lev.take(codes)
+        ping = grouper.Grouping(
+            labels, labels, in_axis=False, level=None, uniques=lev._values
+        )
+        return [ping]
+
+
+def _is_indexed_like(obj, axes, axis: AxisInt) -> bool:
+    if isinstance(obj, Series):
+        if len(axes) > 1:
+            return False
+        return obj.axes[axis].equals(axes[axis])
+    elif isinstance(obj, DataFrame):
+        return obj.axes[axis].equals(axes[axis])
+
+    return False
+
+
+# ----------------------------------------------------------------------
+# Splitting / application
+
+
+class DataSplitter(Generic[NDFrameT]):
+    def __init__(
+        self,
+        data: NDFrameT,
+        labels: npt.NDArray[np.intp],
+        ngroups: int,
+        *,
+        sort_idx: npt.NDArray[np.intp],
+        sorted_ids: npt.NDArray[np.intp],
+        axis: AxisInt = 0,
+    ) -> None:
+        self.data = data
+        self.labels = ensure_platform_int(labels)  # _should_ already be np.intp
+        self.ngroups = ngroups
+
+        self._slabels = sorted_ids
+        self._sort_idx = sort_idx
+
+        self.axis = axis
+        assert isinstance(axis, int), axis
+
+    def __iter__(self) -> Iterator:
+        sdata = self._sorted_data
+
+        if self.ngroups == 0:
+            # we are inside a generator, rather than raise StopIteration
+            # we merely return signal the end
+            return
+
+        starts, ends = lib.generate_slices(self._slabels, self.ngroups)
+
+        for start, end in zip(starts, ends):
+            yield self._chop(sdata, slice(start, end))
+
+    @cache_readonly
+    def _sorted_data(self) -> NDFrameT:
+        return self.data.take(self._sort_idx, axis=self.axis)
+
+    def _chop(self, sdata, slice_obj: slice) -> NDFrame:
+        raise AbstractMethodError(self)
+
+
+class SeriesSplitter(DataSplitter):
+    def _chop(self, sdata: Series, slice_obj: slice) -> Series:
+        # fastpath equivalent to `sdata.iloc[slice_obj]`
+        mgr = sdata._mgr.get_slice(slice_obj)
+        ser = sdata._constructor_from_mgr(mgr, axes=mgr.axes)
+        ser._name = sdata.name
+        return ser.__finalize__(sdata, method="groupby")
+
+
+class FrameSplitter(DataSplitter):
+    def _chop(self, sdata: DataFrame, slice_obj: slice) -> DataFrame:
+        # Fastpath equivalent to:
+        # if self.axis == 0:
+        #     return sdata.iloc[slice_obj]
+        # else:
+        #     return sdata.iloc[:, slice_obj]
+        mgr = sdata._mgr.get_slice(slice_obj, axis=1 - self.axis)
+        df = sdata._constructor_from_mgr(mgr, axes=mgr.axes)
+        return df.__finalize__(sdata, method="groupby")
+
+
+def _get_splitter(
+    data: NDFrame,
+    labels: npt.NDArray[np.intp],
+    ngroups: int,
+    *,
+    sort_idx: npt.NDArray[np.intp],
+    sorted_ids: npt.NDArray[np.intp],
+    axis: AxisInt = 0,
+) -> DataSplitter:
+    if isinstance(data, Series):
+        klass: type[DataSplitter] = SeriesSplitter
+    else:
+        # i.e. DataFrame
+        klass = FrameSplitter
+
+    return klass(
+        data, labels, ngroups, sort_idx=sort_idx, sorted_ids=sorted_ids, axis=axis
+    )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/indexers/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/indexers/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ba8a4f1d0ee7adb668c6b0ac49b2360d3c0dc356
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/indexers/__init__.py
@@ -0,0 +1,31 @@
+from pandas.core.indexers.utils import (
+    check_array_indexer,
+    check_key_length,
+    check_setitem_lengths,
+    disallow_ndim_indexing,
+    is_empty_indexer,
+    is_list_like_indexer,
+    is_scalar_indexer,
+    is_valid_positional_slice,
+    length_of_indexer,
+    maybe_convert_indices,
+    unpack_1tuple,
+    unpack_tuple_and_ellipses,
+    validate_indices,
+)
+
+__all__ = [
+    "is_valid_positional_slice",
+    "is_list_like_indexer",
+    "is_scalar_indexer",
+    "is_empty_indexer",
+    "check_setitem_lengths",
+    "validate_indices",
+    "maybe_convert_indices",
+    "length_of_indexer",
+    "disallow_ndim_indexing",
+    "unpack_1tuple",
+    "check_key_length",
+    "check_array_indexer",
+    "unpack_tuple_and_ellipses",
+]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/indexers/objects.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/indexers/objects.py
new file mode 100644
index 0000000000000000000000000000000000000000..f2db4886a559017422ed41bb8bd2246d6a3f0fb0
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/indexers/objects.py
@@ -0,0 +1,453 @@
+"""Indexer objects for computing start/end window bounds for rolling operations"""
+from __future__ import annotations
+
+from datetime import timedelta
+
+import numpy as np
+
+from pandas._libs.tslibs import BaseOffset
+from pandas._libs.window.indexers import calculate_variable_window_bounds
+from pandas.util._decorators import Appender
+
+from pandas.core.dtypes.common import ensure_platform_int
+
+from pandas.core.indexes.datetimes import DatetimeIndex
+
+from pandas.tseries.offsets import Nano
+
+get_window_bounds_doc = """
+Computes the bounds of a window.
+
+Parameters
+----------
+num_values : int, default 0
+    number of values that will be aggregated over
+window_size : int, default 0
+    the number of rows in a window
+min_periods : int, default None
+    min_periods passed from the top level rolling API
+center : bool, default None
+    center passed from the top level rolling API
+closed : str, default None
+    closed passed from the top level rolling API
+step : int, default None
+    step passed from the top level rolling API
+    .. versionadded:: 1.5
+win_type : str, default None
+    win_type passed from the top level rolling API
+
+Returns
+-------
+A tuple of ndarray[int64]s, indicating the boundaries of each
+window
+"""
+
+
+class BaseIndexer:
+    """
+    Base class for window bounds calculations.
+
+    Examples
+    --------
+    >>> from pandas.api.indexers import BaseIndexer
+    >>> class CustomIndexer(BaseIndexer):
+    ...     def get_window_bounds(self, num_values, min_periods, center, closed, step):
+    ...         start = np.empty(num_values, dtype=np.int64)
+    ...         end = np.empty(num_values, dtype=np.int64)
+    ...         for i in range(num_values):
+    ...             start[i] = i
+    ...             end[i] = i + self.window_size
+    ...         return start, end
+    >>> df = pd.DataFrame({"values": range(5)})
+    >>> indexer = CustomIndexer(window_size=2)
+    >>> df.rolling(indexer).sum()
+        values
+    0	1.0
+    1	3.0
+    2	5.0
+    3	7.0
+    4	4.0
+    """
+
+    def __init__(
+        self, index_array: np.ndarray | None = None, window_size: int = 0, **kwargs
+    ) -> None:
+        self.index_array = index_array
+        self.window_size = window_size
+        # Set user defined kwargs as attributes that can be used in get_window_bounds
+        for key, value in kwargs.items():
+            setattr(self, key, value)
+
+    @Appender(get_window_bounds_doc)
+    def get_window_bounds(
+        self,
+        num_values: int = 0,
+        min_periods: int | None = None,
+        center: bool | None = None,
+        closed: str | None = None,
+        step: int | None = None,
+    ) -> tuple[np.ndarray, np.ndarray]:
+        raise NotImplementedError
+
+
+class FixedWindowIndexer(BaseIndexer):
+    """Creates window boundaries that are of fixed length."""
+
+    @Appender(get_window_bounds_doc)
+    def get_window_bounds(
+        self,
+        num_values: int = 0,
+        min_periods: int | None = None,
+        center: bool | None = None,
+        closed: str | None = None,
+        step: int | None = None,
+    ) -> tuple[np.ndarray, np.ndarray]:
+        if center or self.window_size == 0:
+            offset = (self.window_size - 1) // 2
+        else:
+            offset = 0
+
+        end = np.arange(1 + offset, num_values + 1 + offset, step, dtype="int64")
+        start = end - self.window_size
+        if closed in ["left", "both"]:
+            start -= 1
+        if closed in ["left", "neither"]:
+            end -= 1
+
+        end = np.clip(end, 0, num_values)
+        start = np.clip(start, 0, num_values)
+
+        return start, end
+
+
+class VariableWindowIndexer(BaseIndexer):
+    """Creates window boundaries that are of variable length, namely for time series."""
+
+    @Appender(get_window_bounds_doc)
+    def get_window_bounds(
+        self,
+        num_values: int = 0,
+        min_periods: int | None = None,
+        center: bool | None = None,
+        closed: str | None = None,
+        step: int | None = None,
+    ) -> tuple[np.ndarray, np.ndarray]:
+        # error: Argument 4 to "calculate_variable_window_bounds" has incompatible
+        # type "Optional[bool]"; expected "bool"
+        # error: Argument 6 to "calculate_variable_window_bounds" has incompatible
+        # type "Optional[ndarray]"; expected "ndarray"
+        return calculate_variable_window_bounds(
+            num_values,
+            self.window_size,
+            min_periods,
+            center,  # type: ignore[arg-type]
+            closed,
+            self.index_array,  # type: ignore[arg-type]
+        )
+
+
+class VariableOffsetWindowIndexer(BaseIndexer):
+    """
+    Calculate window boundaries based on a non-fixed offset such as a BusinessDay.
+
+    Examples
+    --------
+    >>> from pandas.api.indexers import VariableOffsetWindowIndexer
+    >>> df = pd.DataFrame(range(10), index=pd.date_range("2020", periods=10))
+    >>> offset = pd.offsets.BDay(1)
+    >>> indexer = VariableOffsetWindowIndexer(index=df.index, offset=offset)
+    >>> df
+                0
+    2020-01-01  0
+    2020-01-02  1
+    2020-01-03  2
+    2020-01-04  3
+    2020-01-05  4
+    2020-01-06  5
+    2020-01-07  6
+    2020-01-08  7
+    2020-01-09  8
+    2020-01-10  9
+    >>> df.rolling(indexer).sum()
+                   0
+    2020-01-01   0.0
+    2020-01-02   1.0
+    2020-01-03   2.0
+    2020-01-04   3.0
+    2020-01-05   7.0
+    2020-01-06  12.0
+    2020-01-07   6.0
+    2020-01-08   7.0
+    2020-01-09   8.0
+    2020-01-10   9.0
+    """
+
+    def __init__(
+        self,
+        index_array: np.ndarray | None = None,
+        window_size: int = 0,
+        index: DatetimeIndex | None = None,
+        offset: BaseOffset | None = None,
+        **kwargs,
+    ) -> None:
+        super().__init__(index_array, window_size, **kwargs)
+        if not isinstance(index, DatetimeIndex):
+            raise ValueError("index must be a DatetimeIndex.")
+        self.index = index
+        if not isinstance(offset, BaseOffset):
+            raise ValueError("offset must be a DateOffset-like object.")
+        self.offset = offset
+
+    @Appender(get_window_bounds_doc)
+    def get_window_bounds(
+        self,
+        num_values: int = 0,
+        min_periods: int | None = None,
+        center: bool | None = None,
+        closed: str | None = None,
+        step: int | None = None,
+    ) -> tuple[np.ndarray, np.ndarray]:
+        if step is not None:
+            raise NotImplementedError("step not implemented for variable offset window")
+        if num_values <= 0:
+            return np.empty(0, dtype="int64"), np.empty(0, dtype="int64")
+
+        # if windows is variable, default is 'right', otherwise default is 'both'
+        if closed is None:
+            closed = "right" if self.index is not None else "both"
+
+        right_closed = closed in ["right", "both"]
+        left_closed = closed in ["left", "both"]
+
+        if self.index[num_values - 1] < self.index[0]:
+            index_growth_sign = -1
+        else:
+            index_growth_sign = 1
+        offset_diff = index_growth_sign * self.offset
+
+        start = np.empty(num_values, dtype="int64")
+        start.fill(-1)
+        end = np.empty(num_values, dtype="int64")
+        end.fill(-1)
+
+        start[0] = 0
+
+        # right endpoint is closed
+        if right_closed:
+            end[0] = 1
+        # right endpoint is open
+        else:
+            end[0] = 0
+
+        zero = timedelta(0)
+        # start is start of slice interval (including)
+        # end is end of slice interval (not including)
+        for i in range(1, num_values):
+            end_bound = self.index[i]
+            start_bound = end_bound - offset_diff
+
+            # left endpoint is closed
+            if left_closed:
+                start_bound -= Nano(1)
+
+            # advance the start bound until we are
+            # within the constraint
+            start[i] = i
+            for j in range(start[i - 1], i):
+                start_diff = (self.index[j] - start_bound) * index_growth_sign
+                if start_diff > zero:
+                    start[i] = j
+                    break
+
+            # end bound is previous end
+            # or current index
+            end_diff = (self.index[end[i - 1]] - end_bound) * index_growth_sign
+            if end_diff == zero and not right_closed:
+                end[i] = end[i - 1] + 1
+            elif end_diff <= zero:
+                end[i] = i + 1
+            else:
+                end[i] = end[i - 1]
+
+            # right endpoint is open
+            if not right_closed:
+                end[i] -= 1
+
+        return start, end
+
+
+class ExpandingIndexer(BaseIndexer):
+    """Calculate expanding window bounds, mimicking df.expanding()"""
+
+    @Appender(get_window_bounds_doc)
+    def get_window_bounds(
+        self,
+        num_values: int = 0,
+        min_periods: int | None = None,
+        center: bool | None = None,
+        closed: str | None = None,
+        step: int | None = None,
+    ) -> tuple[np.ndarray, np.ndarray]:
+        return (
+            np.zeros(num_values, dtype=np.int64),
+            np.arange(1, num_values + 1, dtype=np.int64),
+        )
+
+
+class FixedForwardWindowIndexer(BaseIndexer):
+    """
+    Creates window boundaries for fixed-length windows that include the current row.
+
+    Examples
+    --------
+    >>> df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]})
+    >>> df
+         B
+    0  0.0
+    1  1.0
+    2  2.0
+    3  NaN
+    4  4.0
+
+    >>> indexer = pd.api.indexers.FixedForwardWindowIndexer(window_size=2)
+    >>> df.rolling(window=indexer, min_periods=1).sum()
+         B
+    0  1.0
+    1  3.0
+    2  2.0
+    3  4.0
+    4  4.0
+    """
+
+    @Appender(get_window_bounds_doc)
+    def get_window_bounds(
+        self,
+        num_values: int = 0,
+        min_periods: int | None = None,
+        center: bool | None = None,
+        closed: str | None = None,
+        step: int | None = None,
+    ) -> tuple[np.ndarray, np.ndarray]:
+        if center:
+            raise ValueError("Forward-looking windows can't have center=True")
+        if closed is not None:
+            raise ValueError(
+                "Forward-looking windows don't support setting the closed argument"
+            )
+        if step is None:
+            step = 1
+
+        start = np.arange(0, num_values, step, dtype="int64")
+        end = start + self.window_size
+        if self.window_size:
+            end = np.clip(end, 0, num_values)
+
+        return start, end
+
+
+class GroupbyIndexer(BaseIndexer):
+    """Calculate bounds to compute groupby rolling, mimicking df.groupby().rolling()"""
+
+    def __init__(
+        self,
+        index_array: np.ndarray | None = None,
+        window_size: int | BaseIndexer = 0,
+        groupby_indices: dict | None = None,
+        window_indexer: type[BaseIndexer] = BaseIndexer,
+        indexer_kwargs: dict | None = None,
+        **kwargs,
+    ) -> None:
+        """
+        Parameters
+        ----------
+        index_array : np.ndarray or None
+            np.ndarray of the index of the original object that we are performing
+            a chained groupby operation over. This index has been pre-sorted relative to
+            the groups
+        window_size : int or BaseIndexer
+            window size during the windowing operation
+        groupby_indices : dict or None
+            dict of {group label: [positional index of rows belonging to the group]}
+        window_indexer : BaseIndexer
+            BaseIndexer class determining the start and end bounds of each group
+        indexer_kwargs : dict or None
+            Custom kwargs to be passed to window_indexer
+        **kwargs :
+            keyword arguments that will be available when get_window_bounds is called
+        """
+        self.groupby_indices = groupby_indices or {}
+        self.window_indexer = window_indexer
+        self.indexer_kwargs = indexer_kwargs.copy() if indexer_kwargs else {}
+        super().__init__(
+            index_array=index_array,
+            window_size=self.indexer_kwargs.pop("window_size", window_size),
+            **kwargs,
+        )
+
+    @Appender(get_window_bounds_doc)
+    def get_window_bounds(
+        self,
+        num_values: int = 0,
+        min_periods: int | None = None,
+        center: bool | None = None,
+        closed: str | None = None,
+        step: int | None = None,
+    ) -> tuple[np.ndarray, np.ndarray]:
+        # 1) For each group, get the indices that belong to the group
+        # 2) Use the indices to calculate the start & end bounds of the window
+        # 3) Append the window bounds in group order
+        start_arrays = []
+        end_arrays = []
+        window_indices_start = 0
+        for key, indices in self.groupby_indices.items():
+            index_array: np.ndarray | None
+
+            if self.index_array is not None:
+                index_array = self.index_array.take(ensure_platform_int(indices))
+            else:
+                index_array = self.index_array
+            indexer = self.window_indexer(
+                index_array=index_array,
+                window_size=self.window_size,
+                **self.indexer_kwargs,
+            )
+            start, end = indexer.get_window_bounds(
+                len(indices), min_periods, center, closed, step
+            )
+            start = start.astype(np.int64)
+            end = end.astype(np.int64)
+            assert len(start) == len(
+                end
+            ), "these should be equal in length from get_window_bounds"
+            # Cannot use groupby_indices as they might not be monotonic with the object
+            # we're rolling over
+            window_indices = np.arange(
+                window_indices_start, window_indices_start + len(indices)
+            )
+            window_indices_start += len(indices)
+            # Extend as we'll be slicing window like [start, end)
+            window_indices = np.append(window_indices, [window_indices[-1] + 1]).astype(
+                np.int64, copy=False
+            )
+            start_arrays.append(window_indices.take(ensure_platform_int(start)))
+            end_arrays.append(window_indices.take(ensure_platform_int(end)))
+        if len(start_arrays) == 0:
+            return np.array([], dtype=np.int64), np.array([], dtype=np.int64)
+        start = np.concatenate(start_arrays)
+        end = np.concatenate(end_arrays)
+        return start, end
+
+
+class ExponentialMovingWindowIndexer(BaseIndexer):
+    """Calculate ewm window bounds (the entire window)"""
+
+    @Appender(get_window_bounds_doc)
+    def get_window_bounds(
+        self,
+        num_values: int = 0,
+        min_periods: int | None = None,
+        center: bool | None = None,
+        closed: str | None = None,
+        step: int | None = None,
+    ) -> tuple[np.ndarray, np.ndarray]:
+        return np.array([0], dtype=np.int64), np.array([num_values], dtype=np.int64)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/indexers/utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/indexers/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..55bb58f3108c3d7004058494284ea6fb4b2fca7f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/indexers/utils.py
@@ -0,0 +1,553 @@
+"""
+Low-dependency indexing utilities.
+"""
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    Any,
+)
+
+import numpy as np
+
+from pandas._libs import lib
+
+from pandas.core.dtypes.common import (
+    is_array_like,
+    is_bool_dtype,
+    is_integer,
+    is_integer_dtype,
+    is_list_like,
+)
+from pandas.core.dtypes.dtypes import ExtensionDtype
+from pandas.core.dtypes.generic import (
+    ABCIndex,
+    ABCSeries,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import AnyArrayLike
+
+    from pandas.core.frame import DataFrame
+    from pandas.core.indexes.base import Index
+
+# -----------------------------------------------------------
+# Indexer Identification
+
+
+def is_valid_positional_slice(slc: slice) -> bool:
+    """
+    Check if a slice object can be interpreted as a positional indexer.
+
+    Parameters
+    ----------
+    slc : slice
+
+    Returns
+    -------
+    bool
+
+    Notes
+    -----
+    A valid positional slice may also be interpreted as a label-based slice
+    depending on the index being sliced.
+    """
+    return (
+        lib.is_int_or_none(slc.start)
+        and lib.is_int_or_none(slc.stop)
+        and lib.is_int_or_none(slc.step)
+    )
+
+
+def is_list_like_indexer(key) -> bool:
+    """
+    Check if we have a list-like indexer that is *not* a NamedTuple.
+
+    Parameters
+    ----------
+    key : object
+
+    Returns
+    -------
+    bool
+    """
+    # allow a list_like, but exclude NamedTuples which can be indexers
+    return is_list_like(key) and not (isinstance(key, tuple) and type(key) is not tuple)
+
+
+def is_scalar_indexer(indexer, ndim: int) -> bool:
+    """
+    Return True if we are all scalar indexers.
+
+    Parameters
+    ----------
+    indexer : object
+    ndim : int
+        Number of dimensions in the object being indexed.
+
+    Returns
+    -------
+    bool
+    """
+    if ndim == 1 and is_integer(indexer):
+        # GH37748: allow indexer to be an integer for Series
+        return True
+    if isinstance(indexer, tuple) and len(indexer) == ndim:
+        return all(is_integer(x) for x in indexer)
+    return False
+
+
+def is_empty_indexer(indexer) -> bool:
+    """
+    Check if we have an empty indexer.
+
+    Parameters
+    ----------
+    indexer : object
+
+    Returns
+    -------
+    bool
+    """
+    if is_list_like(indexer) and not len(indexer):
+        return True
+    if not isinstance(indexer, tuple):
+        indexer = (indexer,)
+    return any(isinstance(idx, np.ndarray) and len(idx) == 0 for idx in indexer)
+
+
+# -----------------------------------------------------------
+# Indexer Validation
+
+
+def check_setitem_lengths(indexer, value, values) -> bool:
+    """
+    Validate that value and indexer are the same length.
+
+    An special-case is allowed for when the indexer is a boolean array
+    and the number of true values equals the length of ``value``. In
+    this case, no exception is raised.
+
+    Parameters
+    ----------
+    indexer : sequence
+        Key for the setitem.
+    value : array-like
+        Value for the setitem.
+    values : array-like
+        Values being set into.
+
+    Returns
+    -------
+    bool
+        Whether this is an empty listlike setting which is a no-op.
+
+    Raises
+    ------
+    ValueError
+        When the indexer is an ndarray or list and the lengths don't match.
+    """
+    no_op = False
+
+    if isinstance(indexer, (np.ndarray, list)):
+        # We can ignore other listlikes because they are either
+        #  a) not necessarily 1-D indexers, e.g. tuple
+        #  b) boolean indexers e.g. BoolArray
+        if is_list_like(value):
+            if len(indexer) != len(value) and values.ndim == 1:
+                # boolean with truth values == len of the value is ok too
+                if isinstance(indexer, list):
+                    indexer = np.array(indexer)
+                if not (
+                    isinstance(indexer, np.ndarray)
+                    and indexer.dtype == np.bool_
+                    and indexer.sum() == len(value)
+                ):
+                    raise ValueError(
+                        "cannot set using a list-like indexer "
+                        "with a different length than the value"
+                    )
+            if not len(indexer):
+                no_op = True
+
+    elif isinstance(indexer, slice):
+        if is_list_like(value):
+            if len(value) != length_of_indexer(indexer, values) and values.ndim == 1:
+                # In case of two dimensional value is used row-wise and broadcasted
+                raise ValueError(
+                    "cannot set using a slice indexer with a "
+                    "different length than the value"
+                )
+            if not len(value):
+                no_op = True
+
+    return no_op
+
+
+def validate_indices(indices: np.ndarray, n: int) -> None:
+    """
+    Perform bounds-checking for an indexer.
+
+    -1 is allowed for indicating missing values.
+
+    Parameters
+    ----------
+    indices : ndarray
+    n : int
+        Length of the array being indexed.
+
+    Raises
+    ------
+    ValueError
+
+    Examples
+    --------
+    >>> validate_indices(np.array([1, 2]), 3) # OK
+
+    >>> validate_indices(np.array([1, -2]), 3)
+    Traceback (most recent call last):
+        ...
+    ValueError: negative dimensions are not allowed
+
+    >>> validate_indices(np.array([1, 2, 3]), 3)
+    Traceback (most recent call last):
+        ...
+    IndexError: indices are out-of-bounds
+
+    >>> validate_indices(np.array([-1, -1]), 0) # OK
+
+    >>> validate_indices(np.array([0, 1]), 0)
+    Traceback (most recent call last):
+        ...
+    IndexError: indices are out-of-bounds
+    """
+    if len(indices):
+        min_idx = indices.min()
+        if min_idx < -1:
+            msg = f"'indices' contains values less than allowed ({min_idx} < -1)"
+            raise ValueError(msg)
+
+        max_idx = indices.max()
+        if max_idx >= n:
+            raise IndexError("indices are out-of-bounds")
+
+
+# -----------------------------------------------------------
+# Indexer Conversion
+
+
+def maybe_convert_indices(indices, n: int, verify: bool = True) -> np.ndarray:
+    """
+    Attempt to convert indices into valid, positive indices.
+
+    If we have negative indices, translate to positive here.
+    If we have indices that are out-of-bounds, raise an IndexError.
+
+    Parameters
+    ----------
+    indices : array-like
+        Array of indices that we are to convert.
+    n : int
+        Number of elements in the array that we are indexing.
+    verify : bool, default True
+        Check that all entries are between 0 and n - 1, inclusive.
+
+    Returns
+    -------
+    array-like
+        An array-like of positive indices that correspond to the ones
+        that were passed in initially to this function.
+
+    Raises
+    ------
+    IndexError
+        One of the converted indices either exceeded the number of,
+        elements (specified by `n`), or was still negative.
+    """
+    if isinstance(indices, list):
+        indices = np.array(indices)
+        if len(indices) == 0:
+            # If `indices` is empty, np.array will return a float,
+            # and will cause indexing errors.
+            return np.empty(0, dtype=np.intp)
+
+    mask = indices < 0
+    if mask.any():
+        indices = indices.copy()
+        indices[mask] += n
+
+    if verify:
+        mask = (indices >= n) | (indices < 0)
+        if mask.any():
+            raise IndexError("indices are out-of-bounds")
+    return indices
+
+
+# -----------------------------------------------------------
+# Unsorted
+
+
+def length_of_indexer(indexer, target=None) -> int:
+    """
+    Return the expected length of target[indexer]
+
+    Returns
+    -------
+    int
+    """
+    if target is not None and isinstance(indexer, slice):
+        target_len = len(target)
+        start = indexer.start
+        stop = indexer.stop
+        step = indexer.step
+        if start is None:
+            start = 0
+        elif start < 0:
+            start += target_len
+        if stop is None or stop > target_len:
+            stop = target_len
+        elif stop < 0:
+            stop += target_len
+        if step is None:
+            step = 1
+        elif step < 0:
+            start, stop = stop + 1, start + 1
+            step = -step
+        return (stop - start + step - 1) // step
+    elif isinstance(indexer, (ABCSeries, ABCIndex, np.ndarray, list)):
+        if isinstance(indexer, list):
+            indexer = np.array(indexer)
+
+        if indexer.dtype == bool:
+            # GH#25774
+            return indexer.sum()
+        return len(indexer)
+    elif isinstance(indexer, range):
+        return (indexer.stop - indexer.start) // indexer.step
+    elif not is_list_like_indexer(indexer):
+        return 1
+    raise AssertionError("cannot find the length of the indexer")
+
+
+def disallow_ndim_indexing(result) -> None:
+    """
+    Helper function to disallow multi-dimensional indexing on 1D Series/Index.
+
+    GH#27125 indexer like idx[:, None] expands dim, but we cannot do that
+    and keep an index, so we used to return ndarray, which was deprecated
+    in GH#30588.
+    """
+    if np.ndim(result) > 1:
+        raise ValueError(
+            "Multi-dimensional indexing (e.g. `obj[:, None]`) is no longer "
+            "supported. Convert to a numpy array before indexing instead."
+        )
+
+
+def unpack_1tuple(tup):
+    """
+    If we have a length-1 tuple/list that contains a slice, unpack to just
+    the slice.
+
+    Notes
+    -----
+    The list case is deprecated.
+    """
+    if len(tup) == 1 and isinstance(tup[0], slice):
+        # if we don't have a MultiIndex, we may still be able to handle
+        #  a 1-tuple.  see test_1tuple_without_multiindex
+
+        if isinstance(tup, list):
+            # GH#31299
+            raise ValueError(
+                "Indexing with a single-item list containing a "
+                "slice is not allowed. Pass a tuple instead.",
+            )
+
+        return tup[0]
+    return tup
+
+
+def check_key_length(columns: Index, key, value: DataFrame) -> None:
+    """
+    Checks if a key used as indexer has the same length as the columns it is
+    associated with.
+
+    Parameters
+    ----------
+    columns : Index The columns of the DataFrame to index.
+    key : A list-like of keys to index with.
+    value : DataFrame The value to set for the keys.
+
+    Raises
+    ------
+    ValueError: If the length of key is not equal to the number of columns in value
+                or if the number of columns referenced by key is not equal to number
+                of columns.
+    """
+    if columns.is_unique:
+        if len(value.columns) != len(key):
+            raise ValueError("Columns must be same length as key")
+    else:
+        # Missing keys in columns are represented as -1
+        if len(columns.get_indexer_non_unique(key)[0]) != len(value.columns):
+            raise ValueError("Columns must be same length as key")
+
+
+def unpack_tuple_and_ellipses(item: tuple):
+    """
+    Possibly unpack arr[..., n] to arr[n]
+    """
+    if len(item) > 1:
+        # Note: we are assuming this indexing is being done on a 1D arraylike
+        if item[0] is Ellipsis:
+            item = item[1:]
+        elif item[-1] is Ellipsis:
+            item = item[:-1]
+
+    if len(item) > 1:
+        raise IndexError("too many indices for array.")
+
+    item = item[0]
+    return item
+
+
+# -----------------------------------------------------------
+# Public indexer validation
+
+
+def check_array_indexer(array: AnyArrayLike, indexer: Any) -> Any:
+    """
+    Check if `indexer` is a valid array indexer for `array`.
+
+    For a boolean mask, `array` and `indexer` are checked to have the same
+    length. The dtype is validated, and if it is an integer or boolean
+    ExtensionArray, it is checked if there are missing values present, and
+    it is converted to the appropriate numpy array. Other dtypes will raise
+    an error.
+
+    Non-array indexers (integer, slice, Ellipsis, tuples, ..) are passed
+    through as is.
+
+    Parameters
+    ----------
+    array : array-like
+        The array that is being indexed (only used for the length).
+    indexer : array-like or list-like
+        The array-like that's used to index. List-like input that is not yet
+        a numpy array or an ExtensionArray is converted to one. Other input
+        types are passed through as is.
+
+    Returns
+    -------
+    numpy.ndarray
+        The validated indexer as a numpy array that can be used to index.
+
+    Raises
+    ------
+    IndexError
+        When the lengths don't match.
+    ValueError
+        When `indexer` cannot be converted to a numpy ndarray to index
+        (e.g. presence of missing values).
+
+    See Also
+    --------
+    api.types.is_bool_dtype : Check if `key` is of boolean dtype.
+
+    Examples
+    --------
+    When checking a boolean mask, a boolean ndarray is returned when the
+    arguments are all valid.
+
+    >>> mask = pd.array([True, False])
+    >>> arr = pd.array([1, 2])
+    >>> pd.api.indexers.check_array_indexer(arr, mask)
+    array([ True, False])
+
+    An IndexError is raised when the lengths don't match.
+
+    >>> mask = pd.array([True, False, True])
+    >>> pd.api.indexers.check_array_indexer(arr, mask)
+    Traceback (most recent call last):
+    ...
+    IndexError: Boolean index has wrong length: 3 instead of 2.
+
+    NA values in a boolean array are treated as False.
+
+    >>> mask = pd.array([True, pd.NA])
+    >>> pd.api.indexers.check_array_indexer(arr, mask)
+    array([ True, False])
+
+    A numpy boolean mask will get passed through (if the length is correct):
+
+    >>> mask = np.array([True, False])
+    >>> pd.api.indexers.check_array_indexer(arr, mask)
+    array([ True, False])
+
+    Similarly for integer indexers, an integer ndarray is returned when it is
+    a valid indexer, otherwise an error is  (for integer indexers, a matching
+    length is not required):
+
+    >>> indexer = pd.array([0, 2], dtype="Int64")
+    >>> arr = pd.array([1, 2, 3])
+    >>> pd.api.indexers.check_array_indexer(arr, indexer)
+    array([0, 2])
+
+    >>> indexer = pd.array([0, pd.NA], dtype="Int64")
+    >>> pd.api.indexers.check_array_indexer(arr, indexer)
+    Traceback (most recent call last):
+    ...
+    ValueError: Cannot index with an integer indexer containing NA values
+
+    For non-integer/boolean dtypes, an appropriate error is raised:
+
+    >>> indexer = np.array([0., 2.], dtype="float64")
+    >>> pd.api.indexers.check_array_indexer(arr, indexer)
+    Traceback (most recent call last):
+    ...
+    IndexError: arrays used as indices must be of integer or boolean type
+    """
+    from pandas.core.construction import array as pd_array
+
+    # whatever is not an array-like is returned as-is (possible valid array
+    # indexers that are not array-like: integer, slice, Ellipsis, None)
+    # In this context, tuples are not considered as array-like, as they have
+    # a specific meaning in indexing (multi-dimensional indexing)
+    if is_list_like(indexer):
+        if isinstance(indexer, tuple):
+            return indexer
+    else:
+        return indexer
+
+    # convert list-likes to array
+    if not is_array_like(indexer):
+        indexer = pd_array(indexer)
+        if len(indexer) == 0:
+            # empty list is converted to float array by pd.array
+            indexer = np.array([], dtype=np.intp)
+
+    dtype = indexer.dtype
+    if is_bool_dtype(dtype):
+        if isinstance(dtype, ExtensionDtype):
+            indexer = indexer.to_numpy(dtype=bool, na_value=False)
+        else:
+            indexer = np.asarray(indexer, dtype=bool)
+
+        # GH26658
+        if len(indexer) != len(array):
+            raise IndexError(
+                f"Boolean index has wrong length: "
+                f"{len(indexer)} instead of {len(array)}"
+            )
+    elif is_integer_dtype(dtype):
+        try:
+            indexer = np.asarray(indexer, dtype=np.intp)
+        except ValueError as err:
+            raise ValueError(
+                "Cannot index with an integer indexer containing NA values"
+            ) from err
+    else:
+        raise IndexError("arrays used as indices must be of integer or boolean type")
+
+    return indexer
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/indexes/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/indexes/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/indexes/accessors.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/indexes/accessors.py
new file mode 100644
index 0000000000000000000000000000000000000000..7e3ba4089ff60e9ab226f7536c6be1843be8ea59
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/indexes/accessors.py
@@ -0,0 +1,643 @@
+"""
+datetimelike delegation
+"""
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    cast,
+)
+import warnings
+
+import numpy as np
+
+from pandas._libs import lib
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.common import (
+    is_integer_dtype,
+    is_list_like,
+)
+from pandas.core.dtypes.dtypes import (
+    ArrowDtype,
+    CategoricalDtype,
+    DatetimeTZDtype,
+    PeriodDtype,
+)
+from pandas.core.dtypes.generic import ABCSeries
+
+from pandas.core.accessor import (
+    PandasDelegate,
+    delegate_names,
+)
+from pandas.core.arrays import (
+    DatetimeArray,
+    PeriodArray,
+    TimedeltaArray,
+)
+from pandas.core.arrays.arrow.array import ArrowExtensionArray
+from pandas.core.base import (
+    NoNewAttributesMixin,
+    PandasObject,
+)
+from pandas.core.indexes.datetimes import DatetimeIndex
+from pandas.core.indexes.timedeltas import TimedeltaIndex
+
+if TYPE_CHECKING:
+    from pandas import (
+        DataFrame,
+        Series,
+    )
+
+
+class Properties(PandasDelegate, PandasObject, NoNewAttributesMixin):
+    _hidden_attrs = PandasObject._hidden_attrs | {
+        "orig",
+        "name",
+    }
+
+    def __init__(self, data: Series, orig) -> None:
+        if not isinstance(data, ABCSeries):
+            raise TypeError(
+                f"cannot convert an object of type {type(data)} to a datetimelike index"
+            )
+
+        self._parent = data
+        self.orig = orig
+        self.name = getattr(data, "name", None)
+        self._freeze()
+
+    def _get_values(self):
+        data = self._parent
+        if lib.is_np_dtype(data.dtype, "M"):
+            return DatetimeIndex(data, copy=False, name=self.name)
+
+        elif isinstance(data.dtype, DatetimeTZDtype):
+            return DatetimeIndex(data, copy=False, name=self.name)
+
+        elif lib.is_np_dtype(data.dtype, "m"):
+            return TimedeltaIndex(data, copy=False, name=self.name)
+
+        elif isinstance(data.dtype, PeriodDtype):
+            return PeriodArray(data, copy=False)
+
+        raise TypeError(
+            f"cannot convert an object of type {type(data)} to a datetimelike index"
+        )
+
+    def _delegate_property_get(self, name: str):
+        from pandas import Series
+
+        values = self._get_values()
+
+        result = getattr(values, name)
+
+        # maybe need to upcast (ints)
+        if isinstance(result, np.ndarray):
+            if is_integer_dtype(result):
+                result = result.astype("int64")
+        elif not is_list_like(result):
+            return result
+
+        result = np.asarray(result)
+
+        if self.orig is not None:
+            index = self.orig.index
+        else:
+            index = self._parent.index
+        # return the result as a Series
+        result = Series(result, index=index, name=self.name).__finalize__(self._parent)
+
+        # setting this object will show a SettingWithCopyWarning/Error
+        result._is_copy = (
+            "modifications to a property of a datetimelike "
+            "object are not supported and are discarded. "
+            "Change values on the original."
+        )
+
+        return result
+
+    def _delegate_property_set(self, name: str, value, *args, **kwargs):
+        raise ValueError(
+            "modifications to a property of a datetimelike object are not supported. "
+            "Change values on the original."
+        )
+
+    def _delegate_method(self, name: str, *args, **kwargs):
+        from pandas import Series
+
+        values = self._get_values()
+
+        method = getattr(values, name)
+        result = method(*args, **kwargs)
+
+        if not is_list_like(result):
+            return result
+
+        result = Series(result, index=self._parent.index, name=self.name).__finalize__(
+            self._parent
+        )
+
+        # setting this object will show a SettingWithCopyWarning/Error
+        result._is_copy = (
+            "modifications to a method of a datetimelike "
+            "object are not supported and are discarded. "
+            "Change values on the original."
+        )
+
+        return result
+
+
+@delegate_names(
+    delegate=ArrowExtensionArray,
+    accessors=TimedeltaArray._datetimelike_ops,
+    typ="property",
+    accessor_mapping=lambda x: f"_dt_{x}",
+    raise_on_missing=False,
+)
+@delegate_names(
+    delegate=ArrowExtensionArray,
+    accessors=TimedeltaArray._datetimelike_methods,
+    typ="method",
+    accessor_mapping=lambda x: f"_dt_{x}",
+    raise_on_missing=False,
+)
+@delegate_names(
+    delegate=ArrowExtensionArray,
+    accessors=DatetimeArray._datetimelike_ops,
+    typ="property",
+    accessor_mapping=lambda x: f"_dt_{x}",
+    raise_on_missing=False,
+)
+@delegate_names(
+    delegate=ArrowExtensionArray,
+    accessors=DatetimeArray._datetimelike_methods,
+    typ="method",
+    accessor_mapping=lambda x: f"_dt_{x}",
+    raise_on_missing=False,
+)
+class ArrowTemporalProperties(PandasDelegate, PandasObject, NoNewAttributesMixin):
+    def __init__(self, data: Series, orig) -> None:
+        if not isinstance(data, ABCSeries):
+            raise TypeError(
+                f"cannot convert an object of type {type(data)} to a datetimelike index"
+            )
+
+        self._parent = data
+        self._orig = orig
+        self._freeze()
+
+    def _delegate_property_get(self, name: str):
+        if not hasattr(self._parent.array, f"_dt_{name}"):
+            raise NotImplementedError(
+                f"dt.{name} is not supported for {self._parent.dtype}"
+            )
+        result = getattr(self._parent.array, f"_dt_{name}")
+
+        if not is_list_like(result):
+            return result
+
+        if self._orig is not None:
+            index = self._orig.index
+        else:
+            index = self._parent.index
+        # return the result as a Series, which is by definition a copy
+        result = type(self._parent)(
+            result, index=index, name=self._parent.name
+        ).__finalize__(self._parent)
+
+        return result
+
+    def _delegate_method(self, name: str, *args, **kwargs):
+        if not hasattr(self._parent.array, f"_dt_{name}"):
+            raise NotImplementedError(
+                f"dt.{name} is not supported for {self._parent.dtype}"
+            )
+
+        result = getattr(self._parent.array, f"_dt_{name}")(*args, **kwargs)
+
+        if self._orig is not None:
+            index = self._orig.index
+        else:
+            index = self._parent.index
+        # return the result as a Series, which is by definition a copy
+        result = type(self._parent)(
+            result, index=index, name=self._parent.name
+        ).__finalize__(self._parent)
+
+        return result
+
+    def to_pytimedelta(self):
+        return cast(ArrowExtensionArray, self._parent.array)._dt_to_pytimedelta()
+
+    def to_pydatetime(self):
+        # GH#20306
+        warnings.warn(
+            f"The behavior of {type(self).__name__}.to_pydatetime is deprecated, "
+            "in a future version this will return a Series containing python "
+            "datetime objects instead of an ndarray. To retain the old behavior, "
+            "call `np.array` on the result",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        return cast(ArrowExtensionArray, self._parent.array)._dt_to_pydatetime()
+
+    def isocalendar(self) -> DataFrame:
+        from pandas import DataFrame
+
+        result = (
+            cast(ArrowExtensionArray, self._parent.array)
+            ._dt_isocalendar()
+            ._pa_array.combine_chunks()
+        )
+        iso_calendar_df = DataFrame(
+            {
+                col: type(self._parent.array)(result.field(i))  # type: ignore[call-arg]
+                for i, col in enumerate(["year", "week", "day"])
+            }
+        )
+        return iso_calendar_df
+
+    @property
+    def components(self) -> DataFrame:
+        from pandas import DataFrame
+
+        components_df = DataFrame(
+            {
+                col: getattr(self._parent.array, f"_dt_{col}")
+                for col in [
+                    "days",
+                    "hours",
+                    "minutes",
+                    "seconds",
+                    "milliseconds",
+                    "microseconds",
+                    "nanoseconds",
+                ]
+            }
+        )
+        return components_df
+
+
+@delegate_names(
+    delegate=DatetimeArray,
+    accessors=DatetimeArray._datetimelike_ops + ["unit"],
+    typ="property",
+)
+@delegate_names(
+    delegate=DatetimeArray,
+    accessors=DatetimeArray._datetimelike_methods + ["as_unit"],
+    typ="method",
+)
+class DatetimeProperties(Properties):
+    """
+    Accessor object for datetimelike properties of the Series values.
+
+    Examples
+    --------
+    >>> seconds_series = pd.Series(pd.date_range("2000-01-01", periods=3, freq="s"))
+    >>> seconds_series
+    0   2000-01-01 00:00:00
+    1   2000-01-01 00:00:01
+    2   2000-01-01 00:00:02
+    dtype: datetime64[ns]
+    >>> seconds_series.dt.second
+    0    0
+    1    1
+    2    2
+    dtype: int32
+
+    >>> hours_series = pd.Series(pd.date_range("2000-01-01", periods=3, freq="h"))
+    >>> hours_series
+    0   2000-01-01 00:00:00
+    1   2000-01-01 01:00:00
+    2   2000-01-01 02:00:00
+    dtype: datetime64[ns]
+    >>> hours_series.dt.hour
+    0    0
+    1    1
+    2    2
+    dtype: int32
+
+    >>> quarters_series = pd.Series(pd.date_range("2000-01-01", periods=3, freq="QE"))
+    >>> quarters_series
+    0   2000-03-31
+    1   2000-06-30
+    2   2000-09-30
+    dtype: datetime64[ns]
+    >>> quarters_series.dt.quarter
+    0    1
+    1    2
+    2    3
+    dtype: int32
+
+    Returns a Series indexed like the original Series.
+    Raises TypeError if the Series does not contain datetimelike values.
+    """
+
+    def to_pydatetime(self) -> np.ndarray:
+        """
+        Return the data as an array of :class:`datetime.datetime` objects.
+
+        .. deprecated:: 2.1.0
+
+            The current behavior of dt.to_pydatetime is deprecated.
+            In a future version this will return a Series containing python
+            datetime objects instead of a ndarray.
+
+        Timezone information is retained if present.
+
+        .. warning::
+
+           Python's datetime uses microsecond resolution, which is lower than
+           pandas (nanosecond). The values are truncated.
+
+        Returns
+        -------
+        numpy.ndarray
+            Object dtype array containing native Python datetime objects.
+
+        See Also
+        --------
+        datetime.datetime : Standard library value for a datetime.
+
+        Examples
+        --------
+        >>> s = pd.Series(pd.date_range('20180310', periods=2))
+        >>> s
+        0   2018-03-10
+        1   2018-03-11
+        dtype: datetime64[ns]
+
+        >>> s.dt.to_pydatetime()
+        array([datetime.datetime(2018, 3, 10, 0, 0),
+               datetime.datetime(2018, 3, 11, 0, 0)], dtype=object)
+
+        pandas' nanosecond precision is truncated to microseconds.
+
+        >>> s = pd.Series(pd.date_range('20180310', periods=2, freq='ns'))
+        >>> s
+        0   2018-03-10 00:00:00.000000000
+        1   2018-03-10 00:00:00.000000001
+        dtype: datetime64[ns]
+
+        >>> s.dt.to_pydatetime()
+        array([datetime.datetime(2018, 3, 10, 0, 0),
+               datetime.datetime(2018, 3, 10, 0, 0)], dtype=object)
+        """
+        # GH#20306
+        warnings.warn(
+            f"The behavior of {type(self).__name__}.to_pydatetime is deprecated, "
+            "in a future version this will return a Series containing python "
+            "datetime objects instead of an ndarray. To retain the old behavior, "
+            "call `np.array` on the result",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        return self._get_values().to_pydatetime()
+
+    @property
+    def freq(self):
+        return self._get_values().inferred_freq
+
+    def isocalendar(self) -> DataFrame:
+        """
+        Calculate year, week, and day according to the ISO 8601 standard.
+
+        Returns
+        -------
+        DataFrame
+            With columns year, week and day.
+
+        See Also
+        --------
+        Timestamp.isocalendar : Function return a 3-tuple containing ISO year,
+            week number, and weekday for the given Timestamp object.
+        datetime.date.isocalendar : Return a named tuple object with
+            three components: year, week and weekday.
+
+        Examples
+        --------
+        >>> ser = pd.to_datetime(pd.Series(["2010-01-01", pd.NaT]))
+        >>> ser.dt.isocalendar()
+           year  week  day
+        0  2009    53     5
+        1      
+        >>> ser.dt.isocalendar().week
+        0      53
+        1    
+        Name: week, dtype: UInt32
+        """
+        return self._get_values().isocalendar().set_index(self._parent.index)
+
+
+@delegate_names(
+    delegate=TimedeltaArray, accessors=TimedeltaArray._datetimelike_ops, typ="property"
+)
+@delegate_names(
+    delegate=TimedeltaArray,
+    accessors=TimedeltaArray._datetimelike_methods,
+    typ="method",
+)
+class TimedeltaProperties(Properties):
+    """
+    Accessor object for datetimelike properties of the Series values.
+
+    Returns a Series indexed like the original Series.
+    Raises TypeError if the Series does not contain datetimelike values.
+
+    Examples
+    --------
+    >>> seconds_series = pd.Series(
+    ...     pd.timedelta_range(start="1 second", periods=3, freq="s")
+    ... )
+    >>> seconds_series
+    0   0 days 00:00:01
+    1   0 days 00:00:02
+    2   0 days 00:00:03
+    dtype: timedelta64[ns]
+    >>> seconds_series.dt.seconds
+    0    1
+    1    2
+    2    3
+    dtype: int32
+    """
+
+    def to_pytimedelta(self) -> np.ndarray:
+        """
+        Return an array of native :class:`datetime.timedelta` objects.
+
+        Python's standard `datetime` library uses a different representation
+        timedelta's. This method converts a Series of pandas Timedeltas
+        to `datetime.timedelta` format with the same length as the original
+        Series.
+
+        Returns
+        -------
+        numpy.ndarray
+            Array of 1D containing data with `datetime.timedelta` type.
+
+        See Also
+        --------
+        datetime.timedelta : A duration expressing the difference
+            between two date, time, or datetime.
+
+        Examples
+        --------
+        >>> s = pd.Series(pd.to_timedelta(np.arange(5), unit="d"))
+        >>> s
+        0   0 days
+        1   1 days
+        2   2 days
+        3   3 days
+        4   4 days
+        dtype: timedelta64[ns]
+
+        >>> s.dt.to_pytimedelta()
+        array([datetime.timedelta(0), datetime.timedelta(days=1),
+        datetime.timedelta(days=2), datetime.timedelta(days=3),
+        datetime.timedelta(days=4)], dtype=object)
+        """
+        return self._get_values().to_pytimedelta()
+
+    @property
+    def components(self):
+        """
+        Return a Dataframe of the components of the Timedeltas.
+
+        Returns
+        -------
+        DataFrame
+
+        Examples
+        --------
+        >>> s = pd.Series(pd.to_timedelta(np.arange(5), unit='s'))
+        >>> s
+        0   0 days 00:00:00
+        1   0 days 00:00:01
+        2   0 days 00:00:02
+        3   0 days 00:00:03
+        4   0 days 00:00:04
+        dtype: timedelta64[ns]
+        >>> s.dt.components
+           days  hours  minutes  seconds  milliseconds  microseconds  nanoseconds
+        0     0      0        0        0             0             0            0
+        1     0      0        0        1             0             0            0
+        2     0      0        0        2             0             0            0
+        3     0      0        0        3             0             0            0
+        4     0      0        0        4             0             0            0
+        """
+        return (
+            self._get_values()
+            .components.set_index(self._parent.index)
+            .__finalize__(self._parent)
+        )
+
+    @property
+    def freq(self):
+        return self._get_values().inferred_freq
+
+
+@delegate_names(
+    delegate=PeriodArray, accessors=PeriodArray._datetimelike_ops, typ="property"
+)
+@delegate_names(
+    delegate=PeriodArray, accessors=PeriodArray._datetimelike_methods, typ="method"
+)
+class PeriodProperties(Properties):
+    """
+    Accessor object for datetimelike properties of the Series values.
+
+    Returns a Series indexed like the original Series.
+    Raises TypeError if the Series does not contain datetimelike values.
+
+    Examples
+    --------
+    >>> seconds_series = pd.Series(
+    ...     pd.period_range(
+    ...         start="2000-01-01 00:00:00", end="2000-01-01 00:00:03", freq="s"
+    ...     )
+    ... )
+    >>> seconds_series
+    0    2000-01-01 00:00:00
+    1    2000-01-01 00:00:01
+    2    2000-01-01 00:00:02
+    3    2000-01-01 00:00:03
+    dtype: period[s]
+    >>> seconds_series.dt.second
+    0    0
+    1    1
+    2    2
+    3    3
+    dtype: int64
+
+    >>> hours_series = pd.Series(
+    ...     pd.period_range(start="2000-01-01 00:00", end="2000-01-01 03:00", freq="h")
+    ... )
+    >>> hours_series
+    0    2000-01-01 00:00
+    1    2000-01-01 01:00
+    2    2000-01-01 02:00
+    3    2000-01-01 03:00
+    dtype: period[h]
+    >>> hours_series.dt.hour
+    0    0
+    1    1
+    2    2
+    3    3
+    dtype: int64
+
+    >>> quarters_series = pd.Series(
+    ...     pd.period_range(start="2000-01-01", end="2000-12-31", freq="Q-DEC")
+    ... )
+    >>> quarters_series
+    0    2000Q1
+    1    2000Q2
+    2    2000Q3
+    3    2000Q4
+    dtype: period[Q-DEC]
+    >>> quarters_series.dt.quarter
+    0    1
+    1    2
+    2    3
+    3    4
+    dtype: int64
+    """
+
+
+class CombinedDatetimelikeProperties(
+    DatetimeProperties, TimedeltaProperties, PeriodProperties
+):
+    def __new__(cls, data: Series):  # pyright: ignore[reportInconsistentConstructor]
+        # CombinedDatetimelikeProperties isn't really instantiated. Instead
+        # we need to choose which parent (datetime or timedelta) is
+        # appropriate. Since we're checking the dtypes anyway, we'll just
+        # do all the validation here.
+
+        if not isinstance(data, ABCSeries):
+            raise TypeError(
+                f"cannot convert an object of type {type(data)} to a datetimelike index"
+            )
+
+        orig = data if isinstance(data.dtype, CategoricalDtype) else None
+        if orig is not None:
+            data = data._constructor(
+                orig.array,
+                name=orig.name,
+                copy=False,
+                dtype=orig._values.categories.dtype,
+                index=orig.index,
+            )
+
+        if isinstance(data.dtype, ArrowDtype) and data.dtype.kind in "Mm":
+            return ArrowTemporalProperties(data, orig)
+        if lib.is_np_dtype(data.dtype, "M"):
+            return DatetimeProperties(data, orig)
+        elif isinstance(data.dtype, DatetimeTZDtype):
+            return DatetimeProperties(data, orig)
+        elif lib.is_np_dtype(data.dtype, "m"):
+            return TimedeltaProperties(data, orig)
+        elif isinstance(data.dtype, PeriodDtype):
+            return PeriodProperties(data, orig)
+
+        raise AttributeError("Can only use .dt accessor with datetimelike values")
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/indexes/api.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/indexes/api.py
new file mode 100644
index 0000000000000000000000000000000000000000..15292953e72d00a8f57c34d2e2cc8a43f6863d39
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/indexes/api.py
@@ -0,0 +1,388 @@
+from __future__ import annotations
+
+import textwrap
+from typing import (
+    TYPE_CHECKING,
+    cast,
+)
+
+import numpy as np
+
+from pandas._libs import (
+    NaT,
+    lib,
+)
+from pandas.errors import InvalidIndexError
+
+from pandas.core.dtypes.cast import find_common_type
+
+from pandas.core.algorithms import safe_sort
+from pandas.core.indexes.base import (
+    Index,
+    _new_Index,
+    ensure_index,
+    ensure_index_from_sequences,
+    get_unanimous_names,
+)
+from pandas.core.indexes.category import CategoricalIndex
+from pandas.core.indexes.datetimes import DatetimeIndex
+from pandas.core.indexes.interval import IntervalIndex
+from pandas.core.indexes.multi import MultiIndex
+from pandas.core.indexes.period import PeriodIndex
+from pandas.core.indexes.range import RangeIndex
+from pandas.core.indexes.timedeltas import TimedeltaIndex
+
+if TYPE_CHECKING:
+    from pandas._typing import Axis
+_sort_msg = textwrap.dedent(
+    """\
+Sorting because non-concatenation axis is not aligned. A future version
+of pandas will change to not sort by default.
+
+To accept the future behavior, pass 'sort=False'.
+
+To retain the current behavior and silence the warning, pass 'sort=True'.
+"""
+)
+
+
+__all__ = [
+    "Index",
+    "MultiIndex",
+    "CategoricalIndex",
+    "IntervalIndex",
+    "RangeIndex",
+    "InvalidIndexError",
+    "TimedeltaIndex",
+    "PeriodIndex",
+    "DatetimeIndex",
+    "_new_Index",
+    "NaT",
+    "ensure_index",
+    "ensure_index_from_sequences",
+    "get_objs_combined_axis",
+    "union_indexes",
+    "get_unanimous_names",
+    "all_indexes_same",
+    "default_index",
+    "safe_sort_index",
+]
+
+
+def get_objs_combined_axis(
+    objs,
+    intersect: bool = False,
+    axis: Axis = 0,
+    sort: bool = True,
+    copy: bool = False,
+) -> Index:
+    """
+    Extract combined index: return intersection or union (depending on the
+    value of "intersect") of indexes on given axis, or None if all objects
+    lack indexes (e.g. they are numpy arrays).
+
+    Parameters
+    ----------
+    objs : list
+        Series or DataFrame objects, may be mix of the two.
+    intersect : bool, default False
+        If True, calculate the intersection between indexes. Otherwise,
+        calculate the union.
+    axis : {0 or 'index', 1 or 'outer'}, default 0
+        The axis to extract indexes from.
+    sort : bool, default True
+        Whether the result index should come out sorted or not.
+    copy : bool, default False
+        If True, return a copy of the combined index.
+
+    Returns
+    -------
+    Index
+    """
+    obs_idxes = [obj._get_axis(axis) for obj in objs]
+    return _get_combined_index(obs_idxes, intersect=intersect, sort=sort, copy=copy)
+
+
+def _get_distinct_objs(objs: list[Index]) -> list[Index]:
+    """
+    Return a list with distinct elements of "objs" (different ids).
+    Preserves order.
+    """
+    ids: set[int] = set()
+    res = []
+    for obj in objs:
+        if id(obj) not in ids:
+            ids.add(id(obj))
+            res.append(obj)
+    return res
+
+
+def _get_combined_index(
+    indexes: list[Index],
+    intersect: bool = False,
+    sort: bool = False,
+    copy: bool = False,
+) -> Index:
+    """
+    Return the union or intersection of indexes.
+
+    Parameters
+    ----------
+    indexes : list of Index or list objects
+        When intersect=True, do not accept list of lists.
+    intersect : bool, default False
+        If True, calculate the intersection between indexes. Otherwise,
+        calculate the union.
+    sort : bool, default False
+        Whether the result index should come out sorted or not.
+    copy : bool, default False
+        If True, return a copy of the combined index.
+
+    Returns
+    -------
+    Index
+    """
+    # TODO: handle index names!
+    indexes = _get_distinct_objs(indexes)
+    if len(indexes) == 0:
+        index = Index([])
+    elif len(indexes) == 1:
+        index = indexes[0]
+    elif intersect:
+        index = indexes[0]
+        for other in indexes[1:]:
+            index = index.intersection(other)
+    else:
+        index = union_indexes(indexes, sort=False)
+        index = ensure_index(index)
+
+    if sort:
+        index = safe_sort_index(index)
+    # GH 29879
+    if copy:
+        index = index.copy()
+
+    return index
+
+
+def safe_sort_index(index: Index) -> Index:
+    """
+    Returns the sorted index
+
+    We keep the dtypes and the name attributes.
+
+    Parameters
+    ----------
+    index : an Index
+
+    Returns
+    -------
+    Index
+    """
+    if index.is_monotonic_increasing:
+        return index
+
+    try:
+        array_sorted = safe_sort(index)
+    except TypeError:
+        pass
+    else:
+        if isinstance(array_sorted, Index):
+            return array_sorted
+
+        array_sorted = cast(np.ndarray, array_sorted)
+        if isinstance(index, MultiIndex):
+            index = MultiIndex.from_tuples(array_sorted, names=index.names)
+        else:
+            index = Index(array_sorted, name=index.name, dtype=index.dtype)
+
+    return index
+
+
+def union_indexes(indexes, sort: bool | None = True) -> Index:
+    """
+    Return the union of indexes.
+
+    The behavior of sort and names is not consistent.
+
+    Parameters
+    ----------
+    indexes : list of Index or list objects
+    sort : bool, default True
+        Whether the result index should come out sorted or not.
+
+    Returns
+    -------
+    Index
+    """
+    if len(indexes) == 0:
+        raise AssertionError("Must have at least 1 Index to union")
+    if len(indexes) == 1:
+        result = indexes[0]
+        if isinstance(result, list):
+            if not sort:
+                result = Index(result)
+            else:
+                result = Index(sorted(result))
+        return result
+
+    indexes, kind = _sanitize_and_check(indexes)
+
+    def _unique_indices(inds, dtype) -> Index:
+        """
+        Concatenate indices and remove duplicates.
+
+        Parameters
+        ----------
+        inds : list of Index or list objects
+        dtype : dtype to set for the resulting Index
+
+        Returns
+        -------
+        Index
+        """
+        if all(isinstance(ind, Index) for ind in inds):
+            inds = [ind.astype(dtype, copy=False) for ind in inds]
+            result = inds[0].unique()
+            other = inds[1].append(inds[2:])
+            diff = other[result.get_indexer_for(other) == -1]
+            if len(diff):
+                result = result.append(diff.unique())
+            if sort:
+                result = result.sort_values()
+            return result
+
+        def conv(i):
+            if isinstance(i, Index):
+                i = i.tolist()
+            return i
+
+        return Index(
+            lib.fast_unique_multiple_list([conv(i) for i in inds], sort=sort),
+            dtype=dtype,
+        )
+
+    def _find_common_index_dtype(inds):
+        """
+        Finds a common type for the indexes to pass through to resulting index.
+
+        Parameters
+        ----------
+        inds: list of Index or list objects
+
+        Returns
+        -------
+        The common type or None if no indexes were given
+        """
+        dtypes = [idx.dtype for idx in indexes if isinstance(idx, Index)]
+        if dtypes:
+            dtype = find_common_type(dtypes)
+        else:
+            dtype = None
+
+        return dtype
+
+    if kind == "special":
+        result = indexes[0]
+
+        dtis = [x for x in indexes if isinstance(x, DatetimeIndex)]
+        dti_tzs = [x for x in dtis if x.tz is not None]
+        if len(dti_tzs) not in [0, len(dtis)]:
+            # TODO: this behavior is not tested (so may not be desired),
+            #  but is kept in order to keep behavior the same when
+            #  deprecating union_many
+            # test_frame_from_dict_with_mixed_indexes
+            raise TypeError("Cannot join tz-naive with tz-aware DatetimeIndex")
+
+        if len(dtis) == len(indexes):
+            sort = True
+            result = indexes[0]
+
+        elif len(dtis) > 1:
+            # If we have mixed timezones, our casting behavior may depend on
+            #  the order of indexes, which we don't want.
+            sort = False
+
+            # TODO: what about Categorical[dt64]?
+            # test_frame_from_dict_with_mixed_indexes
+            indexes = [x.astype(object, copy=False) for x in indexes]
+            result = indexes[0]
+
+        for other in indexes[1:]:
+            result = result.union(other, sort=None if sort else False)
+        return result
+
+    elif kind == "array":
+        dtype = _find_common_index_dtype(indexes)
+        index = indexes[0]
+        if not all(index.equals(other) for other in indexes[1:]):
+            index = _unique_indices(indexes, dtype)
+
+        name = get_unanimous_names(*indexes)[0]
+        if name != index.name:
+            index = index.rename(name)
+        return index
+    else:  # kind='list'
+        dtype = _find_common_index_dtype(indexes)
+        return _unique_indices(indexes, dtype)
+
+
+def _sanitize_and_check(indexes):
+    """
+    Verify the type of indexes and convert lists to Index.
+
+    Cases:
+
+    - [list, list, ...]: Return ([list, list, ...], 'list')
+    - [list, Index, ...]: Return _sanitize_and_check([Index, Index, ...])
+        Lists are sorted and converted to Index.
+    - [Index, Index, ...]: Return ([Index, Index, ...], TYPE)
+        TYPE = 'special' if at least one special type, 'array' otherwise.
+
+    Parameters
+    ----------
+    indexes : list of Index or list objects
+
+    Returns
+    -------
+    sanitized_indexes : list of Index or list objects
+    type : {'list', 'array', 'special'}
+    """
+    kinds = list({type(index) for index in indexes})
+
+    if list in kinds:
+        if len(kinds) > 1:
+            indexes = [
+                Index(list(x)) if not isinstance(x, Index) else x for x in indexes
+            ]
+            kinds.remove(list)
+        else:
+            return indexes, "list"
+
+    if len(kinds) > 1 or Index not in kinds:
+        return indexes, "special"
+    else:
+        return indexes, "array"
+
+
+def all_indexes_same(indexes) -> bool:
+    """
+    Determine if all indexes contain the same elements.
+
+    Parameters
+    ----------
+    indexes : iterable of Index objects
+
+    Returns
+    -------
+    bool
+        True if all indexes contain the same elements, False otherwise.
+    """
+    itr = iter(indexes)
+    first = next(itr)
+    return all(first.equals(index) for index in itr)
+
+
+def default_index(n: int) -> RangeIndex:
+    rng = range(n)
+    return RangeIndex._simple_new(rng, name=None)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/indexes/base.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/indexes/base.py
new file mode 100644
index 0000000000000000000000000000000000000000..ab3eaac852f7a506a35abab154dcc10321f8b5fb
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/indexes/base.py
@@ -0,0 +1,7943 @@
+from __future__ import annotations
+
+from collections import abc
+from datetime import datetime
+import functools
+from itertools import zip_longest
+import operator
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    ClassVar,
+    Literal,
+    NoReturn,
+    cast,
+    final,
+    overload,
+)
+import warnings
+
+import numpy as np
+
+from pandas._config import (
+    get_option,
+    using_copy_on_write,
+    using_string_dtype,
+)
+
+from pandas._libs import (
+    NaT,
+    algos as libalgos,
+    index as libindex,
+    lib,
+    writers,
+)
+from pandas._libs.internals import BlockValuesRefs
+import pandas._libs.join as libjoin
+from pandas._libs.lib import (
+    is_datetime_array,
+    no_default,
+)
+from pandas._libs.tslibs import (
+    IncompatibleFrequency,
+    OutOfBoundsDatetime,
+    Timestamp,
+    tz_compare,
+)
+from pandas._typing import (
+    AnyAll,
+    ArrayLike,
+    Axes,
+    Axis,
+    DropKeep,
+    DtypeObj,
+    F,
+    IgnoreRaise,
+    IndexLabel,
+    JoinHow,
+    Level,
+    NaPosition,
+    ReindexMethod,
+    Self,
+    Shape,
+    npt,
+)
+from pandas.compat.numpy import function as nv
+from pandas.errors import (
+    DuplicateLabelError,
+    InvalidIndexError,
+)
+from pandas.util._decorators import (
+    Appender,
+    cache_readonly,
+    deprecate_nonkeyword_arguments,
+    doc,
+)
+from pandas.util._exceptions import (
+    find_stack_level,
+    rewrite_exception,
+)
+
+from pandas.core.dtypes.astype import (
+    astype_array,
+    astype_is_view,
+)
+from pandas.core.dtypes.cast import (
+    LossySetitemError,
+    can_hold_element,
+    common_dtype_categorical_compat,
+    find_result_type,
+    infer_dtype_from,
+    maybe_cast_pointwise_result,
+    np_can_hold_element,
+)
+from pandas.core.dtypes.common import (
+    ensure_int64,
+    ensure_object,
+    ensure_platform_int,
+    is_any_real_numeric_dtype,
+    is_bool_dtype,
+    is_ea_or_datetimelike_dtype,
+    is_float,
+    is_hashable,
+    is_integer,
+    is_iterator,
+    is_list_like,
+    is_numeric_dtype,
+    is_object_dtype,
+    is_scalar,
+    is_signed_integer_dtype,
+    is_string_dtype,
+    needs_i8_conversion,
+    pandas_dtype,
+    validate_all_hashable,
+)
+from pandas.core.dtypes.concat import concat_compat
+from pandas.core.dtypes.dtypes import (
+    ArrowDtype,
+    CategoricalDtype,
+    DatetimeTZDtype,
+    ExtensionDtype,
+    IntervalDtype,
+    PeriodDtype,
+    SparseDtype,
+)
+from pandas.core.dtypes.generic import (
+    ABCCategoricalIndex,
+    ABCDataFrame,
+    ABCDatetimeIndex,
+    ABCIntervalIndex,
+    ABCMultiIndex,
+    ABCPeriodIndex,
+    ABCRangeIndex,
+    ABCSeries,
+    ABCTimedeltaIndex,
+)
+from pandas.core.dtypes.inference import is_dict_like
+from pandas.core.dtypes.missing import (
+    array_equivalent,
+    is_valid_na_for_dtype,
+    isna,
+)
+
+from pandas.core import (
+    arraylike,
+    nanops,
+    ops,
+)
+from pandas.core.accessor import CachedAccessor
+import pandas.core.algorithms as algos
+from pandas.core.array_algos.putmask import (
+    setitem_datetimelike_compat,
+    validate_putmask,
+)
+from pandas.core.arrays import (
+    ArrowExtensionArray,
+    BaseMaskedArray,
+    Categorical,
+    DatetimeArray,
+    ExtensionArray,
+    TimedeltaArray,
+)
+from pandas.core.arrays.string_ import (
+    StringArray,
+    StringDtype,
+)
+from pandas.core.base import (
+    IndexOpsMixin,
+    PandasObject,
+)
+import pandas.core.common as com
+from pandas.core.construction import (
+    ensure_wrapped_if_datetimelike,
+    extract_array,
+    sanitize_array,
+)
+from pandas.core.indexers import (
+    disallow_ndim_indexing,
+    is_valid_positional_slice,
+)
+from pandas.core.indexes.frozen import FrozenList
+from pandas.core.missing import clean_reindex_fill_method
+from pandas.core.ops import get_op_result_name
+from pandas.core.ops.invalid import make_invalid_op
+from pandas.core.sorting import (
+    ensure_key_mapped,
+    get_group_index_sorter,
+    nargsort,
+)
+from pandas.core.strings.accessor import StringMethods
+
+from pandas.io.formats.printing import (
+    PrettyDict,
+    default_pprint,
+    format_object_summary,
+    pprint_thing,
+)
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Hashable,
+        Iterable,
+        Sequence,
+    )
+
+    from pandas import (
+        CategoricalIndex,
+        DataFrame,
+        MultiIndex,
+        Series,
+    )
+    from pandas.core.arrays import (
+        IntervalArray,
+        PeriodArray,
+    )
+
+__all__ = ["Index"]
+
+_unsortable_types = frozenset(("mixed", "mixed-integer"))
+
+_index_doc_kwargs: dict[str, str] = {
+    "klass": "Index",
+    "inplace": "",
+    "target_klass": "Index",
+    "raises_section": "",
+    "unique": "Index",
+    "duplicated": "np.ndarray",
+}
+_index_shared_docs: dict[str, str] = {}
+str_t = str
+
+_dtype_obj = np.dtype("object")
+
+_masked_engines = {
+    "Complex128": libindex.MaskedComplex128Engine,
+    "Complex64": libindex.MaskedComplex64Engine,
+    "Float64": libindex.MaskedFloat64Engine,
+    "Float32": libindex.MaskedFloat32Engine,
+    "UInt64": libindex.MaskedUInt64Engine,
+    "UInt32": libindex.MaskedUInt32Engine,
+    "UInt16": libindex.MaskedUInt16Engine,
+    "UInt8": libindex.MaskedUInt8Engine,
+    "Int64": libindex.MaskedInt64Engine,
+    "Int32": libindex.MaskedInt32Engine,
+    "Int16": libindex.MaskedInt16Engine,
+    "Int8": libindex.MaskedInt8Engine,
+    "boolean": libindex.MaskedBoolEngine,
+    "double[pyarrow]": libindex.MaskedFloat64Engine,
+    "float64[pyarrow]": libindex.MaskedFloat64Engine,
+    "float32[pyarrow]": libindex.MaskedFloat32Engine,
+    "float[pyarrow]": libindex.MaskedFloat32Engine,
+    "uint64[pyarrow]": libindex.MaskedUInt64Engine,
+    "uint32[pyarrow]": libindex.MaskedUInt32Engine,
+    "uint16[pyarrow]": libindex.MaskedUInt16Engine,
+    "uint8[pyarrow]": libindex.MaskedUInt8Engine,
+    "int64[pyarrow]": libindex.MaskedInt64Engine,
+    "int32[pyarrow]": libindex.MaskedInt32Engine,
+    "int16[pyarrow]": libindex.MaskedInt16Engine,
+    "int8[pyarrow]": libindex.MaskedInt8Engine,
+    "bool[pyarrow]": libindex.MaskedBoolEngine,
+}
+
+
+def _maybe_return_indexers(meth: F) -> F:
+    """
+    Decorator to simplify 'return_indexers' checks in Index.join.
+    """
+
+    @functools.wraps(meth)
+    def join(
+        self,
+        other: Index,
+        *,
+        how: JoinHow = "left",
+        level=None,
+        return_indexers: bool = False,
+        sort: bool = False,
+    ):
+        join_index, lidx, ridx = meth(self, other, how=how, level=level, sort=sort)
+        if not return_indexers:
+            return join_index
+
+        if lidx is not None:
+            lidx = ensure_platform_int(lidx)
+        if ridx is not None:
+            ridx = ensure_platform_int(ridx)
+        return join_index, lidx, ridx
+
+    return cast(F, join)
+
+
+def _new_Index(cls, d):
+    """
+    This is called upon unpickling, rather than the default which doesn't
+    have arguments and breaks __new__.
+    """
+    # required for backward compat, because PI can't be instantiated with
+    # ordinals through __new__ GH #13277
+    if issubclass(cls, ABCPeriodIndex):
+        from pandas.core.indexes.period import _new_PeriodIndex
+
+        return _new_PeriodIndex(cls, **d)
+
+    if issubclass(cls, ABCMultiIndex):
+        if "labels" in d and "codes" not in d:
+            # GH#23752 "labels" kwarg has been replaced with "codes"
+            d["codes"] = d.pop("labels")
+
+        # Since this was a valid MultiIndex at pickle-time, we don't need to
+        #  check validty at un-pickle time.
+        d["verify_integrity"] = False
+
+    elif "dtype" not in d and "data" in d:
+        # Prevent Index.__new__ from conducting inference;
+        #  "data" key not in RangeIndex
+        d["dtype"] = d["data"].dtype
+    return cls.__new__(cls, **d)
+
+
+class Index(IndexOpsMixin, PandasObject):
+    """
+    Immutable sequence used for indexing and alignment.
+
+    The basic object storing axis labels for all pandas objects.
+
+    .. versionchanged:: 2.0.0
+
+       Index can hold all numpy numeric dtypes (except float16). Previously only
+       int64/uint64/float64 dtypes were accepted.
+
+    Parameters
+    ----------
+    data : array-like (1-dimensional)
+    dtype : str, numpy.dtype, or ExtensionDtype, optional
+        Data type for the output Index. If not specified, this will be
+        inferred from `data`.
+        See the :ref:`user guide ` for more usages.
+    copy : bool, default False
+        Copy input data.
+    name : object
+        Name to be stored in the index.
+    tupleize_cols : bool (default: True)
+        When True, attempt to create a MultiIndex if possible.
+
+    See Also
+    --------
+    RangeIndex : Index implementing a monotonic integer range.
+    CategoricalIndex : Index of :class:`Categorical` s.
+    MultiIndex : A multi-level, or hierarchical Index.
+    IntervalIndex : An Index of :class:`Interval` s.
+    DatetimeIndex : Index of datetime64 data.
+    TimedeltaIndex : Index of timedelta64 data.
+    PeriodIndex : Index of Period data.
+
+    Notes
+    -----
+    An Index instance can **only** contain hashable objects.
+    An Index instance *can not* hold numpy float16 dtype.
+
+    Examples
+    --------
+    >>> pd.Index([1, 2, 3])
+    Index([1, 2, 3], dtype='int64')
+
+    >>> pd.Index(list('abc'))
+    Index(['a', 'b', 'c'], dtype='object')
+
+    >>> pd.Index([1, 2, 3], dtype="uint8")
+    Index([1, 2, 3], dtype='uint8')
+    """
+
+    # similar to __array_priority__, positions Index after Series and DataFrame
+    #  but before ExtensionArray.  Should NOT be overridden by subclasses.
+    __pandas_priority__ = 2000
+
+    # Cython methods; see github.com/cython/cython/issues/2647
+    #  for why we need to wrap these instead of making them class attributes
+    # Moreover, cython will choose the appropriate-dtyped sub-function
+    #  given the dtypes of the passed arguments
+
+    @final
+    def _left_indexer_unique(self, other: Self) -> npt.NDArray[np.intp]:
+        # Caller is responsible for ensuring other.dtype == self.dtype
+        sv = self._get_join_target()
+        ov = other._get_join_target()
+        # similar but not identical to ov.searchsorted(sv)
+        return libjoin.left_join_indexer_unique(sv, ov)
+
+    @final
+    def _left_indexer(
+        self, other: Self
+    ) -> tuple[ArrayLike, npt.NDArray[np.intp], npt.NDArray[np.intp]]:
+        # Caller is responsible for ensuring other.dtype == self.dtype
+        sv = self._get_join_target()
+        ov = other._get_join_target()
+        joined_ndarray, lidx, ridx = libjoin.left_join_indexer(sv, ov)
+        joined = self._from_join_target(joined_ndarray)
+        return joined, lidx, ridx
+
+    @final
+    def _inner_indexer(
+        self, other: Self
+    ) -> tuple[ArrayLike, npt.NDArray[np.intp], npt.NDArray[np.intp]]:
+        # Caller is responsible for ensuring other.dtype == self.dtype
+        sv = self._get_join_target()
+        ov = other._get_join_target()
+        joined_ndarray, lidx, ridx = libjoin.inner_join_indexer(sv, ov)
+        joined = self._from_join_target(joined_ndarray)
+        return joined, lidx, ridx
+
+    @final
+    def _outer_indexer(
+        self, other: Self
+    ) -> tuple[ArrayLike, npt.NDArray[np.intp], npt.NDArray[np.intp]]:
+        # Caller is responsible for ensuring other.dtype == self.dtype
+        sv = self._get_join_target()
+        ov = other._get_join_target()
+        joined_ndarray, lidx, ridx = libjoin.outer_join_indexer(sv, ov)
+        joined = self._from_join_target(joined_ndarray)
+        return joined, lidx, ridx
+
+    _typ: str = "index"
+    _data: ExtensionArray | np.ndarray
+    _data_cls: type[ExtensionArray] | tuple[type[np.ndarray], type[ExtensionArray]] = (
+        np.ndarray,
+        ExtensionArray,
+    )
+    _id: object | None = None
+    _name: Hashable = None
+    # MultiIndex.levels previously allowed setting the index name. We
+    # don't allow this anymore, and raise if it happens rather than
+    # failing silently.
+    _no_setting_name: bool = False
+    _comparables: list[str] = ["name"]
+    _attributes: list[str] = ["name"]
+
+    @cache_readonly
+    def _can_hold_strings(self) -> bool:
+        return not is_numeric_dtype(self.dtype)
+
+    _engine_types: dict[np.dtype | ExtensionDtype, type[libindex.IndexEngine]] = {
+        np.dtype(np.int8): libindex.Int8Engine,
+        np.dtype(np.int16): libindex.Int16Engine,
+        np.dtype(np.int32): libindex.Int32Engine,
+        np.dtype(np.int64): libindex.Int64Engine,
+        np.dtype(np.uint8): libindex.UInt8Engine,
+        np.dtype(np.uint16): libindex.UInt16Engine,
+        np.dtype(np.uint32): libindex.UInt32Engine,
+        np.dtype(np.uint64): libindex.UInt64Engine,
+        np.dtype(np.float32): libindex.Float32Engine,
+        np.dtype(np.float64): libindex.Float64Engine,
+        np.dtype(np.complex64): libindex.Complex64Engine,
+        np.dtype(np.complex128): libindex.Complex128Engine,
+    }
+
+    @property
+    def _engine_type(
+        self,
+    ) -> type[libindex.IndexEngine | libindex.ExtensionEngine]:
+        return self._engine_types.get(self.dtype, libindex.ObjectEngine)
+
+    # whether we support partial string indexing. Overridden
+    # in DatetimeIndex and PeriodIndex
+    _supports_partial_string_indexing = False
+
+    _accessors = {"str"}
+
+    str = CachedAccessor("str", StringMethods)
+
+    _references = None
+
+    # --------------------------------------------------------------------
+    # Constructors
+
+    def __new__(
+        cls,
+        data=None,
+        dtype=None,
+        copy: bool = False,
+        name=None,
+        tupleize_cols: bool = True,
+    ) -> Self:
+        from pandas.core.indexes.range import RangeIndex
+
+        name = maybe_extract_name(name, data, cls)
+
+        if dtype is not None:
+            dtype = pandas_dtype(dtype)
+
+        data_dtype = getattr(data, "dtype", None)
+
+        refs = None
+        if not copy and isinstance(data, (ABCSeries, Index)):
+            refs = data._references
+
+        is_pandas_object = isinstance(data, (ABCSeries, Index, ExtensionArray))
+
+        # range
+        if isinstance(data, (range, RangeIndex)):
+            result = RangeIndex(start=data, copy=copy, name=name)
+            if dtype is not None:
+                return result.astype(dtype, copy=False)
+            # error: Incompatible return value type (got "MultiIndex",
+            # expected "Self")
+            return result  # type: ignore[return-value]
+
+        elif is_ea_or_datetimelike_dtype(dtype):
+            # non-EA dtype indexes have special casting logic, so we punt here
+            if isinstance(data, (set, frozenset)):
+                data = list(data)
+
+        elif is_ea_or_datetimelike_dtype(data_dtype):
+            pass
+
+        elif isinstance(data, (np.ndarray, Index, ABCSeries)):
+            if isinstance(data, ABCMultiIndex):
+                data = data._values
+
+            if data.dtype.kind not in "iufcbmM":
+                # GH#11836 we need to avoid having numpy coerce
+                # things that look like ints/floats to ints unless
+                # they are actually ints, e.g. '0' and 0.0
+                # should not be coerced
+                data = com.asarray_tuplesafe(data, dtype=_dtype_obj)
+
+        elif is_scalar(data):
+            raise cls._raise_scalar_data_error(data)
+        elif hasattr(data, "__array__"):
+            return cls(np.asarray(data), dtype=dtype, copy=copy, name=name)
+        elif not is_list_like(data) and not isinstance(data, memoryview):
+            # 2022-11-16 the memoryview check is only necessary on some CI
+            #  builds, not clear why
+            raise cls._raise_scalar_data_error(data)
+
+        else:
+            if tupleize_cols:
+                # GH21470: convert iterable to list before determining if empty
+                if is_iterator(data):
+                    data = list(data)
+
+                if data and all(isinstance(e, tuple) for e in data):
+                    # we must be all tuples, otherwise don't construct
+                    # 10697
+                    from pandas.core.indexes.multi import MultiIndex
+
+                    # error: Incompatible return value type (got "MultiIndex",
+                    # expected "Self")
+                    return MultiIndex.from_tuples(  # type: ignore[return-value]
+                        data, names=name
+                    )
+            # other iterable of some kind
+
+            if not isinstance(data, (list, tuple)):
+                # we allow set/frozenset, which Series/sanitize_array does not, so
+                #  cast to list here
+                data = list(data)
+            if len(data) == 0:
+                # unlike Series, we default to object dtype:
+                data = np.array(data, dtype=object)
+
+            if len(data) and isinstance(data[0], tuple):
+                # Ensure we get 1-D array of tuples instead of 2D array.
+                data = com.asarray_tuplesafe(data, dtype=_dtype_obj)
+
+        try:
+            arr = sanitize_array(data, None, dtype=dtype, copy=copy)
+        except ValueError as err:
+            if "index must be specified when data is not list-like" in str(err):
+                raise cls._raise_scalar_data_error(data) from err
+            if "Data must be 1-dimensional" in str(err):
+                raise ValueError("Index data must be 1-dimensional") from err
+            raise
+        arr = ensure_wrapped_if_datetimelike(arr)
+
+        klass = cls._dtype_to_subclass(arr.dtype)
+
+        arr = klass._ensure_array(arr, arr.dtype, copy=False)
+        result = klass._simple_new(arr, name, refs=refs)
+        if dtype is None and is_pandas_object and data_dtype == np.object_:
+            if result.dtype != data_dtype:
+                warnings.warn(
+                    "Dtype inference on a pandas object "
+                    "(Series, Index, ExtensionArray) is deprecated. The Index "
+                    "constructor will keep the original dtype in the future. "
+                    "Call `infer_objects` on the result to get the old "
+                    "behavior.",
+                    FutureWarning,
+                    stacklevel=2,
+                )
+        return result  # type: ignore[return-value]
+
+    @classmethod
+    def _ensure_array(cls, data, dtype, copy: bool):
+        """
+        Ensure we have a valid array to pass to _simple_new.
+        """
+        if data.ndim > 1:
+            # GH#13601, GH#20285, GH#27125
+            raise ValueError("Index data must be 1-dimensional")
+        elif dtype == np.float16:
+            # float16 not supported (no indexing engine)
+            raise NotImplementedError("float16 indexes are not supported")
+
+        if copy:
+            # asarray_tuplesafe does not always copy underlying data,
+            #  so need to make sure that this happens
+            data = data.copy()
+        return data
+
+    @final
+    @classmethod
+    def _dtype_to_subclass(cls, dtype: DtypeObj):
+        # Delay import for perf. https://github.com/pandas-dev/pandas/pull/31423
+
+        if isinstance(dtype, ExtensionDtype):
+            return dtype.index_class
+
+        if dtype.kind == "M":
+            from pandas import DatetimeIndex
+
+            return DatetimeIndex
+
+        elif dtype.kind == "m":
+            from pandas import TimedeltaIndex
+
+            return TimedeltaIndex
+
+        elif dtype.kind == "O":
+            # NB: assuming away MultiIndex
+            return Index
+
+        elif issubclass(dtype.type, str) or is_numeric_dtype(dtype):
+            return Index
+
+        raise NotImplementedError(dtype)
+
+    # NOTE for new Index creation:
+
+    # - _simple_new: It returns new Index with the same type as the caller.
+    #   All metadata (such as name) must be provided by caller's responsibility.
+    #   Using _shallow_copy is recommended because it fills these metadata
+    #   otherwise specified.
+
+    # - _shallow_copy: It returns new Index with the same type (using
+    #   _simple_new), but fills caller's metadata otherwise specified. Passed
+    #   kwargs will overwrite corresponding metadata.
+
+    # See each method's docstring.
+
+    @classmethod
+    def _simple_new(
+        cls, values: ArrayLike, name: Hashable | None = None, refs=None
+    ) -> Self:
+        """
+        We require that we have a dtype compat for the values. If we are passed
+        a non-dtype compat, then coerce using the constructor.
+
+        Must be careful not to recurse.
+        """
+        assert isinstance(values, cls._data_cls), type(values)
+
+        result = object.__new__(cls)
+        result._data = values
+        result._name = name
+        result._cache = {}
+        result._reset_identity()
+        if refs is not None:
+            result._references = refs
+        else:
+            result._references = BlockValuesRefs()
+        result._references.add_index_reference(result)
+
+        return result
+
+    @classmethod
+    def _with_infer(cls, *args, **kwargs):
+        """
+        Constructor that uses the 1.0.x behavior inferring numeric dtypes
+        for ndarray[object] inputs.
+        """
+        result = cls(*args, **kwargs)
+
+        if result.dtype == _dtype_obj and not result._is_multi:
+            # error: Argument 1 to "maybe_convert_objects" has incompatible type
+            # "Union[ExtensionArray, ndarray[Any, Any]]"; expected
+            # "ndarray[Any, Any]"
+            values = lib.maybe_convert_objects(result._values)  # type: ignore[arg-type]
+            if values.dtype.kind in "iufb":
+                return Index(values, name=result.name)
+
+        return result
+
+    @cache_readonly
+    def _constructor(self) -> type[Self]:
+        return type(self)
+
+    @final
+    def _maybe_check_unique(self) -> None:
+        """
+        Check that an Index has no duplicates.
+
+        This is typically only called via
+        `NDFrame.flags.allows_duplicate_labels.setter` when it's set to
+        True (duplicates aren't allowed).
+
+        Raises
+        ------
+        DuplicateLabelError
+            When the index is not unique.
+        """
+        if not self.is_unique:
+            msg = """Index has duplicates."""
+            duplicates = self._format_duplicate_message()
+            msg += f"\n{duplicates}"
+
+            raise DuplicateLabelError(msg)
+
+    @final
+    def _format_duplicate_message(self) -> DataFrame:
+        """
+        Construct the DataFrame for a DuplicateLabelError.
+
+        This returns a DataFrame indicating the labels and positions
+        of duplicates in an index. This should only be called when it's
+        already known that duplicates are present.
+
+        Examples
+        --------
+        >>> idx = pd.Index(['a', 'b', 'a'])
+        >>> idx._format_duplicate_message()
+            positions
+        label
+        a        [0, 2]
+        """
+        from pandas import Series
+
+        duplicates = self[self.duplicated(keep="first")].unique()
+        assert len(duplicates)
+
+        out = (
+            Series(np.arange(len(self)), copy=False)
+            .groupby(self, observed=False)
+            .agg(list)[duplicates]
+        )
+        if self._is_multi:
+            # test_format_duplicate_labels_message_multi
+            # error: "Type[Index]" has no attribute "from_tuples"  [attr-defined]
+            out.index = type(self).from_tuples(out.index)  # type: ignore[attr-defined]
+
+        if self.nlevels == 1:
+            out = out.rename_axis("label")
+        return out.to_frame(name="positions")
+
+    # --------------------------------------------------------------------
+    # Index Internals Methods
+
+    def _shallow_copy(self, values, name: Hashable = no_default) -> Self:
+        """
+        Create a new Index with the same class as the caller, don't copy the
+        data, use the same object attributes with passed in attributes taking
+        precedence.
+
+        *this is an internal non-public method*
+
+        Parameters
+        ----------
+        values : the values to create the new Index, optional
+        name : Label, defaults to self.name
+        """
+        name = self._name if name is no_default else name
+
+        return self._simple_new(values, name=name, refs=self._references)
+
+    def _view(self) -> Self:
+        """
+        fastpath to make a shallow copy, i.e. new object with same data.
+        """
+        result = self._simple_new(self._values, name=self._name, refs=self._references)
+
+        result._cache = self._cache
+        return result
+
+    @final
+    def _rename(self, name: Hashable) -> Self:
+        """
+        fastpath for rename if new name is already validated.
+        """
+        result = self._view()
+        result._name = name
+        return result
+
+    @final
+    def is_(self, other) -> bool:
+        """
+        More flexible, faster check like ``is`` but that works through views.
+
+        Note: this is *not* the same as ``Index.identical()``, which checks
+        that metadata is also the same.
+
+        Parameters
+        ----------
+        other : object
+            Other object to compare against.
+
+        Returns
+        -------
+        bool
+            True if both have same underlying data, False otherwise.
+
+        See Also
+        --------
+        Index.identical : Works like ``Index.is_`` but also checks metadata.
+
+        Examples
+        --------
+        >>> idx1 = pd.Index(['1', '2', '3'])
+        >>> idx1.is_(idx1.view())
+        True
+
+        >>> idx1.is_(idx1.copy())
+        False
+        """
+        if self is other:
+            return True
+        elif not hasattr(other, "_id"):
+            return False
+        elif self._id is None or other._id is None:
+            return False
+        else:
+            return self._id is other._id
+
+    @final
+    def _reset_identity(self) -> None:
+        """
+        Initializes or resets ``_id`` attribute with new object.
+        """
+        self._id = object()
+
+    @final
+    def _cleanup(self) -> None:
+        self._engine.clear_mapping()
+
+    @cache_readonly
+    def _engine(
+        self,
+    ) -> libindex.IndexEngine | libindex.ExtensionEngine | libindex.MaskedIndexEngine:
+        # For base class (object dtype) we get ObjectEngine
+        target_values = self._get_engine_target()
+
+        if isinstance(self._values, ArrowExtensionArray) and self.dtype.kind in "Mm":
+            import pyarrow as pa
+
+            pa_type = self._values._pa_array.type
+            if pa.types.is_timestamp(pa_type):
+                target_values = self._values._to_datetimearray()
+                return libindex.DatetimeEngine(target_values._ndarray)
+            elif pa.types.is_duration(pa_type):
+                target_values = self._values._to_timedeltaarray()
+                return libindex.TimedeltaEngine(target_values._ndarray)
+
+        if isinstance(target_values, ExtensionArray):
+            if isinstance(target_values, (BaseMaskedArray, ArrowExtensionArray)):
+                try:
+                    return _masked_engines[target_values.dtype.name](target_values)
+                except KeyError:
+                    # Not supported yet e.g. decimal
+                    pass
+            elif self._engine_type is libindex.ObjectEngine:
+                return libindex.ExtensionEngine(target_values)
+
+        target_values = cast(np.ndarray, target_values)
+        # to avoid a reference cycle, bind `target_values` to a local variable, so
+        # `self` is not passed into the lambda.
+        if target_values.dtype == bool:
+            return libindex.BoolEngine(target_values)
+        elif target_values.dtype == np.complex64:
+            return libindex.Complex64Engine(target_values)
+        elif target_values.dtype == np.complex128:
+            return libindex.Complex128Engine(target_values)
+        elif needs_i8_conversion(self.dtype):
+            # We need to keep M8/m8 dtype when initializing the Engine,
+            #  but don't want to change _get_engine_target bc it is used
+            #  elsewhere
+            # error: Item "ExtensionArray" of "Union[ExtensionArray,
+            # ndarray[Any, Any]]" has no attribute "_ndarray"  [union-attr]
+            target_values = self._data._ndarray  # type: ignore[union-attr]
+        elif is_string_dtype(self.dtype) and not is_object_dtype(self.dtype):
+            return libindex.StringObjectEngine(target_values, self.dtype.na_value)  # type: ignore[union-attr]
+
+        # error: Argument 1 to "ExtensionEngine" has incompatible type
+        # "ndarray[Any, Any]"; expected "ExtensionArray"
+        return self._engine_type(target_values)  # type: ignore[arg-type]
+
+    @final
+    @cache_readonly
+    def _dir_additions_for_owner(self) -> set[str_t]:
+        """
+        Add the string-like labels to the owner dataframe/series dir output.
+
+        If this is a MultiIndex, it's first level values are used.
+        """
+        return {
+            c
+            for c in self.unique(level=0)[: get_option("display.max_dir_items")]
+            if isinstance(c, str) and c.isidentifier()
+        }
+
+    # --------------------------------------------------------------------
+    # Array-Like Methods
+
+    # ndarray compat
+    def __len__(self) -> int:
+        """
+        Return the length of the Index.
+        """
+        return len(self._data)
+
+    def __array__(self, dtype=None, copy=None) -> np.ndarray:
+        """
+        The array interface, return my values.
+        """
+        if copy is None:
+            # Note, that the if branch exists for NumPy 1.x support
+            return np.asarray(self._data, dtype=dtype)
+
+        return np.array(self._data, dtype=dtype, copy=copy)
+
+    def __array_ufunc__(self, ufunc: np.ufunc, method: str_t, *inputs, **kwargs):
+        if any(isinstance(other, (ABCSeries, ABCDataFrame)) for other in inputs):
+            return NotImplemented
+
+        result = arraylike.maybe_dispatch_ufunc_to_dunder_op(
+            self, ufunc, method, *inputs, **kwargs
+        )
+        if result is not NotImplemented:
+            return result
+
+        if "out" in kwargs:
+            # e.g. test_dti_isub_tdi
+            return arraylike.dispatch_ufunc_with_out(
+                self, ufunc, method, *inputs, **kwargs
+            )
+
+        if method == "reduce":
+            result = arraylike.dispatch_reduction_ufunc(
+                self, ufunc, method, *inputs, **kwargs
+            )
+            if result is not NotImplemented:
+                return result
+
+        new_inputs = [x if x is not self else x._values for x in inputs]
+        result = getattr(ufunc, method)(*new_inputs, **kwargs)
+        if ufunc.nout == 2:
+            # i.e. np.divmod, np.modf, np.frexp
+            return tuple(self.__array_wrap__(x) for x in result)
+        elif method == "reduce":
+            result = lib.item_from_zerodim(result)
+            return result
+
+        if result.dtype == np.float16:
+            result = result.astype(np.float32)
+
+        return self.__array_wrap__(result)
+
+    @final
+    def __array_wrap__(self, result, context=None, return_scalar=False):
+        """
+        Gets called after a ufunc and other functions e.g. np.split.
+        """
+        result = lib.item_from_zerodim(result)
+        if (not isinstance(result, Index) and is_bool_dtype(result.dtype)) or np.ndim(
+            result
+        ) > 1:
+            # exclude Index to avoid warning from is_bool_dtype deprecation;
+            #  in the Index case it doesn't matter which path we go down.
+            # reached in plotting tests with e.g. np.nonzero(index)
+            return result
+
+        return Index(result, name=self.name)
+
+    @cache_readonly
+    def dtype(self) -> DtypeObj:
+        """
+        Return the dtype object of the underlying data.
+
+        Examples
+        --------
+        >>> idx = pd.Index([1, 2, 3])
+        >>> idx
+        Index([1, 2, 3], dtype='int64')
+        >>> idx.dtype
+        dtype('int64')
+        """
+        return self._data.dtype
+
+    @final
+    def ravel(self, order: str_t = "C") -> Self:
+        """
+        Return a view on self.
+
+        Returns
+        -------
+        Index
+
+        See Also
+        --------
+        numpy.ndarray.ravel : Return a flattened array.
+
+        Examples
+        --------
+        >>> s = pd.Series([1, 2, 3], index=['a', 'b', 'c'])
+        >>> s.index.ravel()
+        Index(['a', 'b', 'c'], dtype='object')
+        """
+        return self[:]
+
+    def view(self, cls=None):
+        # we need to see if we are subclassing an
+        # index type here
+        if cls is not None and not hasattr(cls, "_typ"):
+            dtype = cls
+            if isinstance(cls, str):
+                dtype = pandas_dtype(cls)
+
+            if needs_i8_conversion(dtype):
+                idx_cls = self._dtype_to_subclass(dtype)
+                arr = self.array.view(dtype)
+                if isinstance(arr, ExtensionArray):
+                    # here we exclude non-supported dt64/td64 dtypes
+                    return idx_cls._simple_new(
+                        arr, name=self.name, refs=self._references
+                    )
+                return arr
+
+            result = self._data.view(cls)
+        else:
+            if cls is not None:
+                warnings.warn(
+                    # GH#55709
+                    f"Passing a type in {type(self).__name__}.view is deprecated "
+                    "and will raise in a future version. "
+                    "Call view without any argument to retain the old behavior.",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+
+            result = self._view()
+        if isinstance(result, Index):
+            result._id = self._id
+        return result
+
+    def astype(self, dtype, copy: bool = True):
+        """
+        Create an Index with values cast to dtypes.
+
+        The class of a new Index is determined by dtype. When conversion is
+        impossible, a TypeError exception is raised.
+
+        Parameters
+        ----------
+        dtype : numpy dtype or pandas type
+            Note that any signed integer `dtype` is treated as ``'int64'``,
+            and any unsigned integer `dtype` is treated as ``'uint64'``,
+            regardless of the size.
+        copy : bool, default True
+            By default, astype always returns a newly allocated object.
+            If copy is set to False and internal requirements on dtype are
+            satisfied, the original data is used to create a new Index
+            or the original Index is returned.
+
+        Returns
+        -------
+        Index
+            Index with values cast to specified dtype.
+
+        Examples
+        --------
+        >>> idx = pd.Index([1, 2, 3])
+        >>> idx
+        Index([1, 2, 3], dtype='int64')
+        >>> idx.astype('float')
+        Index([1.0, 2.0, 3.0], dtype='float64')
+        """
+        if dtype is not None:
+            dtype = pandas_dtype(dtype)
+
+        if self.dtype == dtype:
+            # Ensure that self.astype(self.dtype) is self
+            return self.copy() if copy else self
+
+        values = self._data
+        if isinstance(values, ExtensionArray):
+            with rewrite_exception(type(values).__name__, type(self).__name__):
+                new_values = values.astype(dtype, copy=copy)
+
+        elif isinstance(dtype, ExtensionDtype):
+            cls = dtype.construct_array_type()
+            # Note: for RangeIndex and CategoricalDtype self vs self._values
+            #  behaves differently here.
+            new_values = cls._from_sequence(self, dtype=dtype, copy=copy)
+
+        else:
+            # GH#13149 specifically use astype_array instead of astype
+            new_values = astype_array(values, dtype=dtype, copy=copy)
+
+        # pass copy=False because any copying will be done in the astype above
+        result = Index(new_values, name=self.name, dtype=new_values.dtype, copy=False)
+        if (
+            not copy
+            and self._references is not None
+            and astype_is_view(self.dtype, dtype)
+        ):
+            result._references = self._references
+            result._references.add_index_reference(result)
+        return result
+
+    _index_shared_docs[
+        "take"
+    ] = """
+        Return a new %(klass)s of the values selected by the indices.
+
+        For internal compatibility with numpy arrays.
+
+        Parameters
+        ----------
+        indices : array-like
+            Indices to be taken.
+        axis : int, optional
+            The axis over which to select values, always 0.
+        allow_fill : bool, default True
+        fill_value : scalar, default None
+            If allow_fill=True and fill_value is not None, indices specified by
+            -1 are regarded as NA. If Index doesn't hold NA, raise ValueError.
+
+        Returns
+        -------
+        Index
+            An index formed of elements at the given indices. Will be the same
+            type as self, except for RangeIndex.
+
+        See Also
+        --------
+        numpy.ndarray.take: Return an array formed from the
+            elements of a at the given indices.
+
+        Examples
+        --------
+        >>> idx = pd.Index(['a', 'b', 'c'])
+        >>> idx.take([2, 2, 1, 2])
+        Index(['c', 'c', 'b', 'c'], dtype='object')
+        """
+
+    @Appender(_index_shared_docs["take"] % _index_doc_kwargs)
+    def take(
+        self,
+        indices,
+        axis: Axis = 0,
+        allow_fill: bool = True,
+        fill_value=None,
+        **kwargs,
+    ) -> Self:
+        if kwargs:
+            nv.validate_take((), kwargs)
+        if is_scalar(indices):
+            raise TypeError("Expected indices to be array-like")
+        indices = ensure_platform_int(indices)
+        allow_fill = self._maybe_disallow_fill(allow_fill, fill_value, indices)
+
+        # Note: we discard fill_value and use self._na_value, only relevant
+        #  in the case where allow_fill is True and fill_value is not None
+        values = self._values
+        if isinstance(values, np.ndarray):
+            taken = algos.take(
+                values, indices, allow_fill=allow_fill, fill_value=self._na_value
+            )
+        else:
+            # algos.take passes 'axis' keyword which not all EAs accept
+            taken = values.take(
+                indices, allow_fill=allow_fill, fill_value=self._na_value
+            )
+        return self._constructor._simple_new(taken, name=self.name)
+
+    @final
+    def _maybe_disallow_fill(self, allow_fill: bool, fill_value, indices) -> bool:
+        """
+        We only use pandas-style take when allow_fill is True _and_
+        fill_value is not None.
+        """
+        if allow_fill and fill_value is not None:
+            # only fill if we are passing a non-None fill_value
+            if self._can_hold_na:
+                if (indices < -1).any():
+                    raise ValueError(
+                        "When allow_fill=True and fill_value is not None, "
+                        "all indices must be >= -1"
+                    )
+            else:
+                cls_name = type(self).__name__
+                raise ValueError(
+                    f"Unable to fill values because {cls_name} cannot contain NA"
+                )
+        else:
+            allow_fill = False
+        return allow_fill
+
+    _index_shared_docs[
+        "repeat"
+    ] = """
+        Repeat elements of a %(klass)s.
+
+        Returns a new %(klass)s where each element of the current %(klass)s
+        is repeated consecutively a given number of times.
+
+        Parameters
+        ----------
+        repeats : int or array of ints
+            The number of repetitions for each element. This should be a
+            non-negative integer. Repeating 0 times will return an empty
+            %(klass)s.
+        axis : None
+            Must be ``None``. Has no effect but is accepted for compatibility
+            with numpy.
+
+        Returns
+        -------
+        %(klass)s
+            Newly created %(klass)s with repeated elements.
+
+        See Also
+        --------
+        Series.repeat : Equivalent function for Series.
+        numpy.repeat : Similar method for :class:`numpy.ndarray`.
+
+        Examples
+        --------
+        >>> idx = pd.Index(['a', 'b', 'c'])
+        >>> idx
+        Index(['a', 'b', 'c'], dtype='object')
+        >>> idx.repeat(2)
+        Index(['a', 'a', 'b', 'b', 'c', 'c'], dtype='object')
+        >>> idx.repeat([1, 2, 3])
+        Index(['a', 'b', 'b', 'c', 'c', 'c'], dtype='object')
+        """
+
+    @Appender(_index_shared_docs["repeat"] % _index_doc_kwargs)
+    def repeat(self, repeats, axis: None = None) -> Self:
+        repeats = ensure_platform_int(repeats)
+        nv.validate_repeat((), {"axis": axis})
+        res_values = self._values.repeat(repeats)
+
+        # _constructor so RangeIndex-> Index with an int64 dtype
+        return self._constructor._simple_new(res_values, name=self.name)
+
+    # --------------------------------------------------------------------
+    # Copying Methods
+
+    def copy(
+        self,
+        name: Hashable | None = None,
+        deep: bool = False,
+    ) -> Self:
+        """
+        Make a copy of this object.
+
+        Name is set on the new object.
+
+        Parameters
+        ----------
+        name : Label, optional
+            Set name for new object.
+        deep : bool, default False
+
+        Returns
+        -------
+        Index
+            Index refer to new object which is a copy of this object.
+
+        Notes
+        -----
+        In most cases, there should be no functional difference from using
+        ``deep``, but if ``deep`` is passed it will attempt to deepcopy.
+
+        Examples
+        --------
+        >>> idx = pd.Index(['a', 'b', 'c'])
+        >>> new_idx = idx.copy()
+        >>> idx is new_idx
+        False
+        """
+
+        name = self._validate_names(name=name, deep=deep)[0]
+        if deep:
+            new_data = self._data.copy()
+            new_index = type(self)._simple_new(new_data, name=name)
+        else:
+            new_index = self._rename(name=name)
+        return new_index
+
+    @final
+    def __copy__(self, **kwargs) -> Self:
+        return self.copy(**kwargs)
+
+    @final
+    def __deepcopy__(self, memo=None) -> Self:
+        """
+        Parameters
+        ----------
+        memo, default None
+            Standard signature. Unused
+        """
+        return self.copy(deep=True)
+
+    # --------------------------------------------------------------------
+    # Rendering Methods
+
+    @final
+    def __repr__(self) -> str_t:
+        """
+        Return a string representation for this object.
+        """
+        klass_name = type(self).__name__
+        data = self._format_data()
+        attrs = self._format_attrs()
+        attrs_str = [f"{k}={v}" for k, v in attrs]
+        prepr = ", ".join(attrs_str)
+
+        return f"{klass_name}({data}{prepr})"
+
+    @property
+    def _formatter_func(self):
+        """
+        Return the formatter function.
+        """
+        return default_pprint
+
+    @final
+    def _format_data(self, name=None) -> str_t:
+        """
+        Return the formatted data as a unicode string.
+        """
+        # do we want to justify (only do so for non-objects)
+        is_justify = True
+
+        if self.inferred_type == "string":
+            is_justify = False
+        elif isinstance(self.dtype, CategoricalDtype):
+            self = cast("CategoricalIndex", self)
+            if is_object_dtype(self.categories.dtype):
+                is_justify = False
+        elif isinstance(self, ABCRangeIndex):
+            # We will do the relevant formatting via attrs
+            return ""
+
+        return format_object_summary(
+            self,
+            self._formatter_func,
+            is_justify=is_justify,
+            name=name,
+            line_break_each_value=self._is_multi,
+        )
+
+    def _format_attrs(self) -> list[tuple[str_t, str_t | int | bool | None]]:
+        """
+        Return a list of tuples of the (attr,formatted_value).
+        """
+        attrs: list[tuple[str_t, str_t | int | bool | None]] = []
+
+        if not self._is_multi:
+            attrs.append(("dtype", f"'{self.dtype}'"))
+
+        if self.name is not None:
+            attrs.append(("name", default_pprint(self.name)))
+        elif self._is_multi and any(x is not None for x in self.names):
+            attrs.append(("names", default_pprint(self.names)))
+
+        max_seq_items = get_option("display.max_seq_items") or len(self)
+        if len(self) > max_seq_items:
+            attrs.append(("length", len(self)))
+        return attrs
+
+    @final
+    def _get_level_names(self) -> Hashable | Sequence[Hashable]:
+        """
+        Return a name or list of names with None replaced by the level number.
+        """
+        if self._is_multi:
+            return [
+                level if name is None else name for level, name in enumerate(self.names)
+            ]
+        else:
+            return 0 if self.name is None else self.name
+
+    @final
+    def _mpl_repr(self) -> np.ndarray:
+        # how to represent ourselves to matplotlib
+        if isinstance(self.dtype, np.dtype) and self.dtype.kind != "M":
+            return cast(np.ndarray, self.values)
+        return self.astype(object, copy=False)._values
+
+    def format(
+        self,
+        name: bool = False,
+        formatter: Callable | None = None,
+        na_rep: str_t = "NaN",
+    ) -> list[str_t]:
+        """
+        Render a string representation of the Index.
+        """
+        warnings.warn(
+            # GH#55413
+            f"{type(self).__name__}.format is deprecated and will be removed "
+            "in a future version. Convert using index.astype(str) or "
+            "index.map(formatter) instead.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        header = []
+        if name:
+            header.append(
+                pprint_thing(self.name, escape_chars=("\t", "\r", "\n"))
+                if self.name is not None
+                else ""
+            )
+
+        if formatter is not None:
+            return header + list(self.map(formatter))
+
+        return self._format_with_header(header=header, na_rep=na_rep)
+
+    _default_na_rep = "NaN"
+
+    @final
+    def _format_flat(
+        self,
+        *,
+        include_name: bool,
+        formatter: Callable | None = None,
+    ) -> list[str_t]:
+        """
+        Render a string representation of the Index.
+        """
+        header = []
+        if include_name:
+            header.append(
+                pprint_thing(self.name, escape_chars=("\t", "\r", "\n"))
+                if self.name is not None
+                else ""
+            )
+
+        if formatter is not None:
+            return header + list(self.map(formatter))
+
+        return self._format_with_header(header=header, na_rep=self._default_na_rep)
+
+    def _format_with_header(self, *, header: list[str_t], na_rep: str_t) -> list[str_t]:
+        from pandas.io.formats.format import format_array
+
+        values = self._values
+
+        if (
+            is_object_dtype(values.dtype)
+            or is_string_dtype(values.dtype)
+            or isinstance(self.dtype, (IntervalDtype, CategoricalDtype))
+        ):
+            # TODO: why do we need different justify for these cases?
+            justify = "all"
+        else:
+            justify = "left"
+        # passing leading_space=False breaks test_format_missing,
+        #  test_index_repr_in_frame_with_nan, but would otherwise make
+        #  trim_front unnecessary
+        formatted = format_array(values, None, justify=justify)
+        result = trim_front(formatted)
+        return header + result
+
+    def _get_values_for_csv(
+        self,
+        *,
+        na_rep: str_t = "",
+        decimal: str_t = ".",
+        float_format=None,
+        date_format=None,
+        quoting=None,
+    ) -> npt.NDArray[np.object_]:
+        return get_values_for_csv(
+            self._values,
+            na_rep=na_rep,
+            decimal=decimal,
+            float_format=float_format,
+            date_format=date_format,
+            quoting=quoting,
+        )
+
+    def _summary(self, name=None) -> str_t:
+        """
+        Return a summarized representation.
+
+        Parameters
+        ----------
+        name : str
+            name to use in the summary representation
+
+        Returns
+        -------
+        String with a summarized representation of the index
+        """
+        if len(self) > 0:
+            head = self[0]
+            if hasattr(head, "format") and not isinstance(head, str):
+                head = head.format()
+            elif needs_i8_conversion(self.dtype):
+                # e.g. Timedelta, display as values, not quoted
+                head = self._formatter_func(head).replace("'", "")
+            tail = self[-1]
+            if hasattr(tail, "format") and not isinstance(tail, str):
+                tail = tail.format()
+            elif needs_i8_conversion(self.dtype):
+                # e.g. Timedelta, display as values, not quoted
+                tail = self._formatter_func(tail).replace("'", "")
+
+            index_summary = f", {head} to {tail}"
+        else:
+            index_summary = ""
+
+        if name is None:
+            name = type(self).__name__
+        return f"{name}: {len(self)} entries{index_summary}"
+
+    # --------------------------------------------------------------------
+    # Conversion Methods
+
+    def to_flat_index(self) -> Self:
+        """
+        Identity method.
+
+        This is implemented for compatibility with subclass implementations
+        when chaining.
+
+        Returns
+        -------
+        pd.Index
+            Caller.
+
+        See Also
+        --------
+        MultiIndex.to_flat_index : Subclass implementation.
+        """
+        return self
+
+    @final
+    def to_series(self, index=None, name: Hashable | None = None) -> Series:
+        """
+        Create a Series with both index and values equal to the index keys.
+
+        Useful with map for returning an indexer based on an index.
+
+        Parameters
+        ----------
+        index : Index, optional
+            Index of resulting Series. If None, defaults to original index.
+        name : str, optional
+            Name of resulting Series. If None, defaults to name of original
+            index.
+
+        Returns
+        -------
+        Series
+            The dtype will be based on the type of the Index values.
+
+        See Also
+        --------
+        Index.to_frame : Convert an Index to a DataFrame.
+        Series.to_frame : Convert Series to DataFrame.
+
+        Examples
+        --------
+        >>> idx = pd.Index(['Ant', 'Bear', 'Cow'], name='animal')
+
+        By default, the original index and original name is reused.
+
+        >>> idx.to_series()
+        animal
+        Ant      Ant
+        Bear    Bear
+        Cow      Cow
+        Name: animal, dtype: object
+
+        To enforce a new index, specify new labels to ``index``:
+
+        >>> idx.to_series(index=[0, 1, 2])
+        0     Ant
+        1    Bear
+        2     Cow
+        Name: animal, dtype: object
+
+        To override the name of the resulting column, specify ``name``:
+
+        >>> idx.to_series(name='zoo')
+        animal
+        Ant      Ant
+        Bear    Bear
+        Cow      Cow
+        Name: zoo, dtype: object
+        """
+        from pandas import Series
+
+        if index is None:
+            index = self._view()
+        if name is None:
+            name = self.name
+
+        return Series(self._values.copy(), index=index, name=name)
+
+    def to_frame(
+        self, index: bool = True, name: Hashable = lib.no_default
+    ) -> DataFrame:
+        """
+        Create a DataFrame with a column containing the Index.
+
+        Parameters
+        ----------
+        index : bool, default True
+            Set the index of the returned DataFrame as the original Index.
+
+        name : object, defaults to index.name
+            The passed name should substitute for the index name (if it has
+            one).
+
+        Returns
+        -------
+        DataFrame
+            DataFrame containing the original Index data.
+
+        See Also
+        --------
+        Index.to_series : Convert an Index to a Series.
+        Series.to_frame : Convert Series to DataFrame.
+
+        Examples
+        --------
+        >>> idx = pd.Index(['Ant', 'Bear', 'Cow'], name='animal')
+        >>> idx.to_frame()
+               animal
+        animal
+        Ant       Ant
+        Bear     Bear
+        Cow       Cow
+
+        By default, the original Index is reused. To enforce a new Index:
+
+        >>> idx.to_frame(index=False)
+            animal
+        0   Ant
+        1  Bear
+        2   Cow
+
+        To override the name of the resulting column, specify `name`:
+
+        >>> idx.to_frame(index=False, name='zoo')
+            zoo
+        0   Ant
+        1  Bear
+        2   Cow
+        """
+        from pandas import DataFrame
+
+        if name is lib.no_default:
+            name = self._get_level_names()
+        result = DataFrame({name: self}, copy=not using_copy_on_write())
+
+        if index:
+            result.index = self
+        return result
+
+    # --------------------------------------------------------------------
+    # Name-Centric Methods
+
+    @property
+    def name(self) -> Hashable:
+        """
+        Return Index or MultiIndex name.
+
+        Examples
+        --------
+        >>> idx = pd.Index([1, 2, 3], name='x')
+        >>> idx
+        Index([1, 2, 3], dtype='int64',  name='x')
+        >>> idx.name
+        'x'
+        """
+        return self._name
+
+    @name.setter
+    def name(self, value: Hashable) -> None:
+        if self._no_setting_name:
+            # Used in MultiIndex.levels to avoid silently ignoring name updates.
+            raise RuntimeError(
+                "Cannot set name on a level of a MultiIndex. Use "
+                "'MultiIndex.set_names' instead."
+            )
+        maybe_extract_name(value, None, type(self))
+        self._name = value
+
+    @final
+    def _validate_names(
+        self, name=None, names=None, deep: bool = False
+    ) -> list[Hashable]:
+        """
+        Handles the quirks of having a singular 'name' parameter for general
+        Index and plural 'names' parameter for MultiIndex.
+        """
+        from copy import deepcopy
+
+        if names is not None and name is not None:
+            raise TypeError("Can only provide one of `names` and `name`")
+        if names is None and name is None:
+            new_names = deepcopy(self.names) if deep else self.names
+        elif names is not None:
+            if not is_list_like(names):
+                raise TypeError("Must pass list-like as `names`.")
+            new_names = names
+        elif not is_list_like(name):
+            new_names = [name]
+        else:
+            new_names = name
+
+        if len(new_names) != len(self.names):
+            raise ValueError(
+                f"Length of new names must be {len(self.names)}, got {len(new_names)}"
+            )
+
+        # All items in 'new_names' need to be hashable
+        validate_all_hashable(*new_names, error_name=f"{type(self).__name__}.name")
+
+        return new_names
+
+    def _get_default_index_names(
+        self, names: Hashable | Sequence[Hashable] | None = None, default=None
+    ) -> list[Hashable]:
+        """
+        Get names of index.
+
+        Parameters
+        ----------
+        names : int, str or 1-dimensional list, default None
+            Index names to set.
+        default : str
+            Default name of index.
+
+        Raises
+        ------
+        TypeError
+            if names not str or list-like
+        """
+        from pandas.core.indexes.multi import MultiIndex
+
+        if names is not None:
+            if isinstance(names, (int, str)):
+                names = [names]
+
+        if not isinstance(names, list) and names is not None:
+            raise ValueError("Index names must be str or 1-dimensional list")
+
+        if not names:
+            if isinstance(self, MultiIndex):
+                names = com.fill_missing_names(self.names)
+            else:
+                names = [default] if self.name is None else [self.name]
+
+        return names
+
+    def _get_names(self) -> FrozenList:
+        return FrozenList((self.name,))
+
+    def _set_names(self, values, *, level=None) -> None:
+        """
+        Set new names on index. Each name has to be a hashable type.
+
+        Parameters
+        ----------
+        values : str or sequence
+            name(s) to set
+        level : int, level name, or sequence of int/level names (default None)
+            If the index is a MultiIndex (hierarchical), level(s) to set (None
+            for all levels).  Otherwise level must be None
+
+        Raises
+        ------
+        TypeError if each name is not hashable.
+        """
+        if not is_list_like(values):
+            raise ValueError("Names must be a list-like")
+        if len(values) != 1:
+            raise ValueError(f"Length of new names must be 1, got {len(values)}")
+
+        # GH 20527
+        # All items in 'name' need to be hashable:
+        validate_all_hashable(*values, error_name=f"{type(self).__name__}.name")
+
+        self._name = values[0]
+
+    names = property(fset=_set_names, fget=_get_names)
+
+    @overload
+    def set_names(self, names, *, level=..., inplace: Literal[False] = ...) -> Self:
+        ...
+
+    @overload
+    def set_names(self, names, *, level=..., inplace: Literal[True]) -> None:
+        ...
+
+    @overload
+    def set_names(self, names, *, level=..., inplace: bool = ...) -> Self | None:
+        ...
+
+    def set_names(self, names, *, level=None, inplace: bool = False) -> Self | None:
+        """
+        Set Index or MultiIndex name.
+
+        Able to set new names partially and by level.
+
+        Parameters
+        ----------
+
+        names : label or list of label or dict-like for MultiIndex
+            Name(s) to set.
+
+            .. versionchanged:: 1.3.0
+
+        level : int, label or list of int or label, optional
+            If the index is a MultiIndex and names is not dict-like, level(s) to set
+            (None for all levels). Otherwise level must be None.
+
+            .. versionchanged:: 1.3.0
+
+        inplace : bool, default False
+            Modifies the object directly, instead of creating a new Index or
+            MultiIndex.
+
+        Returns
+        -------
+        Index or None
+            The same type as the caller or None if ``inplace=True``.
+
+        See Also
+        --------
+        Index.rename : Able to set new names without level.
+
+        Examples
+        --------
+        >>> idx = pd.Index([1, 2, 3, 4])
+        >>> idx
+        Index([1, 2, 3, 4], dtype='int64')
+        >>> idx.set_names('quarter')
+        Index([1, 2, 3, 4], dtype='int64', name='quarter')
+
+        >>> idx = pd.MultiIndex.from_product([['python', 'cobra'],
+        ...                                   [2018, 2019]])
+        >>> idx
+        MultiIndex([('python', 2018),
+                    ('python', 2019),
+                    ( 'cobra', 2018),
+                    ( 'cobra', 2019)],
+                   )
+        >>> idx = idx.set_names(['kind', 'year'])
+        >>> idx.set_names('species', level=0)
+        MultiIndex([('python', 2018),
+                    ('python', 2019),
+                    ( 'cobra', 2018),
+                    ( 'cobra', 2019)],
+                   names=['species', 'year'])
+
+        When renaming levels with a dict, levels can not be passed.
+
+        >>> idx.set_names({'kind': 'snake'})
+        MultiIndex([('python', 2018),
+                    ('python', 2019),
+                    ( 'cobra', 2018),
+                    ( 'cobra', 2019)],
+                   names=['snake', 'year'])
+        """
+        if level is not None and not isinstance(self, ABCMultiIndex):
+            raise ValueError("Level must be None for non-MultiIndex")
+
+        if level is not None and not is_list_like(level) and is_list_like(names):
+            raise TypeError("Names must be a string when a single level is provided.")
+
+        if not is_list_like(names) and level is None and self.nlevels > 1:
+            raise TypeError("Must pass list-like as `names`.")
+
+        if is_dict_like(names) and not isinstance(self, ABCMultiIndex):
+            raise TypeError("Can only pass dict-like as `names` for MultiIndex.")
+
+        if is_dict_like(names) and level is not None:
+            raise TypeError("Can not pass level for dictlike `names`.")
+
+        if isinstance(self, ABCMultiIndex) and is_dict_like(names) and level is None:
+            # Transform dict to list of new names and corresponding levels
+            level, names_adjusted = [], []
+            for i, name in enumerate(self.names):
+                if name in names.keys():
+                    level.append(i)
+                    names_adjusted.append(names[name])
+            names = names_adjusted
+
+        if not is_list_like(names):
+            names = [names]
+        if level is not None and not is_list_like(level):
+            level = [level]
+
+        if inplace:
+            idx = self
+        else:
+            idx = self._view()
+
+        idx._set_names(names, level=level)
+        if not inplace:
+            return idx
+        return None
+
+    @overload
+    def rename(self, name, *, inplace: Literal[False] = ...) -> Self:
+        ...
+
+    @overload
+    def rename(self, name, *, inplace: Literal[True]) -> None:
+        ...
+
+    @deprecate_nonkeyword_arguments(
+        version="3.0", allowed_args=["self", "name"], name="rename"
+    )
+    def rename(self, name, inplace: bool = False) -> Self | None:
+        """
+        Alter Index or MultiIndex name.
+
+        Able to set new names without level. Defaults to returning new index.
+        Length of names must match number of levels in MultiIndex.
+
+        Parameters
+        ----------
+        name : label or list of labels
+            Name(s) to set.
+        inplace : bool, default False
+            Modifies the object directly, instead of creating a new Index or
+            MultiIndex.
+
+        Returns
+        -------
+        Index or None
+            The same type as the caller or None if ``inplace=True``.
+
+        See Also
+        --------
+        Index.set_names : Able to set new names partially and by level.
+
+        Examples
+        --------
+        >>> idx = pd.Index(['A', 'C', 'A', 'B'], name='score')
+        >>> idx.rename('grade')
+        Index(['A', 'C', 'A', 'B'], dtype='object', name='grade')
+
+        >>> idx = pd.MultiIndex.from_product([['python', 'cobra'],
+        ...                                   [2018, 2019]],
+        ...                                   names=['kind', 'year'])
+        >>> idx
+        MultiIndex([('python', 2018),
+                    ('python', 2019),
+                    ( 'cobra', 2018),
+                    ( 'cobra', 2019)],
+                   names=['kind', 'year'])
+        >>> idx.rename(['species', 'year'])
+        MultiIndex([('python', 2018),
+                    ('python', 2019),
+                    ( 'cobra', 2018),
+                    ( 'cobra', 2019)],
+                   names=['species', 'year'])
+        >>> idx.rename('species')
+        Traceback (most recent call last):
+        TypeError: Must pass list-like as `names`.
+        """
+        return self.set_names([name], inplace=inplace)
+
+    # --------------------------------------------------------------------
+    # Level-Centric Methods
+
+    @property
+    def nlevels(self) -> int:
+        """
+        Number of levels.
+        """
+        return 1
+
+    def _sort_levels_monotonic(self) -> Self:
+        """
+        Compat with MultiIndex.
+        """
+        return self
+
+    @final
+    def _validate_index_level(self, level) -> None:
+        """
+        Validate index level.
+
+        For single-level Index getting level number is a no-op, but some
+        verification must be done like in MultiIndex.
+
+        """
+        if isinstance(level, int):
+            if level < 0 and level != -1:
+                raise IndexError(
+                    "Too many levels: Index has only 1 level, "
+                    f"{level} is not a valid level number"
+                )
+            if level > 0:
+                raise IndexError(
+                    f"Too many levels: Index has only 1 level, not {level + 1}"
+                )
+        elif level != self.name:
+            raise KeyError(
+                f"Requested level ({level}) does not match index name ({self.name})"
+            )
+
+    def _get_level_number(self, level) -> int:
+        self._validate_index_level(level)
+        return 0
+
+    def sortlevel(
+        self,
+        level=None,
+        ascending: bool | list[bool] = True,
+        sort_remaining=None,
+        na_position: NaPosition = "first",
+    ):
+        """
+        For internal compatibility with the Index API.
+
+        Sort the Index. This is for compat with MultiIndex
+
+        Parameters
+        ----------
+        ascending : bool, default True
+            False to sort in descending order
+        na_position : {'first' or 'last'}, default 'first'
+            Argument 'first' puts NaNs at the beginning, 'last' puts NaNs at
+            the end.
+
+            .. versionadded:: 2.1.0
+
+        level, sort_remaining are compat parameters
+
+        Returns
+        -------
+        Index
+        """
+        if not isinstance(ascending, (list, bool)):
+            raise TypeError(
+                "ascending must be a single bool value or"
+                "a list of bool values of length 1"
+            )
+
+        if isinstance(ascending, list):
+            if len(ascending) != 1:
+                raise TypeError("ascending must be a list of bool values of length 1")
+            ascending = ascending[0]
+
+        if not isinstance(ascending, bool):
+            raise TypeError("ascending must be a bool value")
+
+        return self.sort_values(
+            return_indexer=True, ascending=ascending, na_position=na_position
+        )
+
+    def _get_level_values(self, level) -> Index:
+        """
+        Return an Index of values for requested level.
+
+        This is primarily useful to get an individual level of values from a
+        MultiIndex, but is provided on Index as well for compatibility.
+
+        Parameters
+        ----------
+        level : int or str
+            It is either the integer position or the name of the level.
+
+        Returns
+        -------
+        Index
+            Calling object, as there is only one level in the Index.
+
+        See Also
+        --------
+        MultiIndex.get_level_values : Get values for a level of a MultiIndex.
+
+        Notes
+        -----
+        For Index, level should be 0, since there are no multiple levels.
+
+        Examples
+        --------
+        >>> idx = pd.Index(list('abc'))
+        >>> idx
+        Index(['a', 'b', 'c'], dtype='object')
+
+        Get level values by supplying `level` as integer:
+
+        >>> idx.get_level_values(0)
+        Index(['a', 'b', 'c'], dtype='object')
+        """
+        self._validate_index_level(level)
+        return self
+
+    get_level_values = _get_level_values
+
+    @final
+    def droplevel(self, level: IndexLabel = 0):
+        """
+        Return index with requested level(s) removed.
+
+        If resulting index has only 1 level left, the result will be
+        of Index type, not MultiIndex. The original index is not modified inplace.
+
+        Parameters
+        ----------
+        level : int, str, or list-like, default 0
+            If a string is given, must be the name of a level
+            If list-like, elements must be names or indexes of levels.
+
+        Returns
+        -------
+        Index or MultiIndex
+
+        Examples
+        --------
+        >>> mi = pd.MultiIndex.from_arrays(
+        ... [[1, 2], [3, 4], [5, 6]], names=['x', 'y', 'z'])
+        >>> mi
+        MultiIndex([(1, 3, 5),
+                    (2, 4, 6)],
+                   names=['x', 'y', 'z'])
+
+        >>> mi.droplevel()
+        MultiIndex([(3, 5),
+                    (4, 6)],
+                   names=['y', 'z'])
+
+        >>> mi.droplevel(2)
+        MultiIndex([(1, 3),
+                    (2, 4)],
+                   names=['x', 'y'])
+
+        >>> mi.droplevel('z')
+        MultiIndex([(1, 3),
+                    (2, 4)],
+                   names=['x', 'y'])
+
+        >>> mi.droplevel(['x', 'y'])
+        Index([5, 6], dtype='int64', name='z')
+        """
+        if not isinstance(level, (tuple, list)):
+            level = [level]
+
+        levnums = sorted(self._get_level_number(lev) for lev in level)[::-1]
+
+        return self._drop_level_numbers(levnums)
+
+    @final
+    def _drop_level_numbers(self, levnums: list[int]):
+        """
+        Drop MultiIndex levels by level _number_, not name.
+        """
+
+        if not levnums and not isinstance(self, ABCMultiIndex):
+            return self
+        if len(levnums) >= self.nlevels:
+            raise ValueError(
+                f"Cannot remove {len(levnums)} levels from an index with "
+                f"{self.nlevels} levels: at least one level must be left."
+            )
+        # The two checks above guarantee that here self is a MultiIndex
+        self = cast("MultiIndex", self)
+
+        new_levels = list(self.levels)
+        new_codes = list(self.codes)
+        new_names = list(self.names)
+
+        for i in levnums:
+            new_levels.pop(i)
+            new_codes.pop(i)
+            new_names.pop(i)
+
+        if len(new_levels) == 1:
+            lev = new_levels[0]
+
+            if len(lev) == 0:
+                # If lev is empty, lev.take will fail GH#42055
+                if len(new_codes[0]) == 0:
+                    # GH#45230 preserve RangeIndex here
+                    #  see test_reset_index_empty_rangeindex
+                    result = lev[:0]
+                else:
+                    res_values = algos.take(lev._values, new_codes[0], allow_fill=True)
+                    # _constructor instead of type(lev) for RangeIndex compat GH#35230
+                    result = lev._constructor._simple_new(res_values, name=new_names[0])
+            else:
+                # set nan if needed
+                mask = new_codes[0] == -1
+                result = new_levels[0].take(new_codes[0])
+                if mask.any():
+                    result = result.putmask(mask, np.nan)
+
+                result._name = new_names[0]
+
+            return result
+        else:
+            from pandas.core.indexes.multi import MultiIndex
+
+            return MultiIndex(
+                levels=new_levels,
+                codes=new_codes,
+                names=new_names,
+                verify_integrity=False,
+            )
+
+    # --------------------------------------------------------------------
+    # Introspection Methods
+
+    @cache_readonly
+    @final
+    def _can_hold_na(self) -> bool:
+        if isinstance(self.dtype, ExtensionDtype):
+            return self.dtype._can_hold_na
+        if self.dtype.kind in "iub":
+            return False
+        return True
+
+    @property
+    def is_monotonic_increasing(self) -> bool:
+        """
+        Return a boolean if the values are equal or increasing.
+
+        Returns
+        -------
+        bool
+
+        See Also
+        --------
+        Index.is_monotonic_decreasing : Check if the values are equal or decreasing.
+
+        Examples
+        --------
+        >>> pd.Index([1, 2, 3]).is_monotonic_increasing
+        True
+        >>> pd.Index([1, 2, 2]).is_monotonic_increasing
+        True
+        >>> pd.Index([1, 3, 2]).is_monotonic_increasing
+        False
+        """
+        return self._engine.is_monotonic_increasing
+
+    @property
+    def is_monotonic_decreasing(self) -> bool:
+        """
+        Return a boolean if the values are equal or decreasing.
+
+        Returns
+        -------
+        bool
+
+        See Also
+        --------
+        Index.is_monotonic_increasing : Check if the values are equal or increasing.
+
+        Examples
+        --------
+        >>> pd.Index([3, 2, 1]).is_monotonic_decreasing
+        True
+        >>> pd.Index([3, 2, 2]).is_monotonic_decreasing
+        True
+        >>> pd.Index([3, 1, 2]).is_monotonic_decreasing
+        False
+        """
+        return self._engine.is_monotonic_decreasing
+
+    @final
+    @property
+    def _is_strictly_monotonic_increasing(self) -> bool:
+        """
+        Return if the index is strictly monotonic increasing
+        (only increasing) values.
+
+        Examples
+        --------
+        >>> Index([1, 2, 3])._is_strictly_monotonic_increasing
+        True
+        >>> Index([1, 2, 2])._is_strictly_monotonic_increasing
+        False
+        >>> Index([1, 3, 2])._is_strictly_monotonic_increasing
+        False
+        """
+        return self.is_unique and self.is_monotonic_increasing
+
+    @final
+    @property
+    def _is_strictly_monotonic_decreasing(self) -> bool:
+        """
+        Return if the index is strictly monotonic decreasing
+        (only decreasing) values.
+
+        Examples
+        --------
+        >>> Index([3, 2, 1])._is_strictly_monotonic_decreasing
+        True
+        >>> Index([3, 2, 2])._is_strictly_monotonic_decreasing
+        False
+        >>> Index([3, 1, 2])._is_strictly_monotonic_decreasing
+        False
+        """
+        return self.is_unique and self.is_monotonic_decreasing
+
+    @cache_readonly
+    def is_unique(self) -> bool:
+        """
+        Return if the index has unique values.
+
+        Returns
+        -------
+        bool
+
+        See Also
+        --------
+        Index.has_duplicates : Inverse method that checks if it has duplicate values.
+
+        Examples
+        --------
+        >>> idx = pd.Index([1, 5, 7, 7])
+        >>> idx.is_unique
+        False
+
+        >>> idx = pd.Index([1, 5, 7])
+        >>> idx.is_unique
+        True
+
+        >>> idx = pd.Index(["Watermelon", "Orange", "Apple",
+        ...                 "Watermelon"]).astype("category")
+        >>> idx.is_unique
+        False
+
+        >>> idx = pd.Index(["Orange", "Apple",
+        ...                 "Watermelon"]).astype("category")
+        >>> idx.is_unique
+        True
+        """
+        return self._engine.is_unique
+
+    @final
+    @property
+    def has_duplicates(self) -> bool:
+        """
+        Check if the Index has duplicate values.
+
+        Returns
+        -------
+        bool
+            Whether or not the Index has duplicate values.
+
+        See Also
+        --------
+        Index.is_unique : Inverse method that checks if it has unique values.
+
+        Examples
+        --------
+        >>> idx = pd.Index([1, 5, 7, 7])
+        >>> idx.has_duplicates
+        True
+
+        >>> idx = pd.Index([1, 5, 7])
+        >>> idx.has_duplicates
+        False
+
+        >>> idx = pd.Index(["Watermelon", "Orange", "Apple",
+        ...                 "Watermelon"]).astype("category")
+        >>> idx.has_duplicates
+        True
+
+        >>> idx = pd.Index(["Orange", "Apple",
+        ...                 "Watermelon"]).astype("category")
+        >>> idx.has_duplicates
+        False
+        """
+        return not self.is_unique
+
+    @final
+    def is_boolean(self) -> bool:
+        """
+        Check if the Index only consists of booleans.
+
+        .. deprecated:: 2.0.0
+            Use `pandas.api.types.is_bool_dtype` instead.
+
+        Returns
+        -------
+        bool
+            Whether or not the Index only consists of booleans.
+
+        See Also
+        --------
+        is_integer : Check if the Index only consists of integers (deprecated).
+        is_floating : Check if the Index is a floating type (deprecated).
+        is_numeric : Check if the Index only consists of numeric data (deprecated).
+        is_object : Check if the Index is of the object dtype (deprecated).
+        is_categorical : Check if the Index holds categorical data.
+        is_interval : Check if the Index holds Interval objects (deprecated).
+
+        Examples
+        --------
+        >>> idx = pd.Index([True, False, True])
+        >>> idx.is_boolean()  # doctest: +SKIP
+        True
+
+        >>> idx = pd.Index(["True", "False", "True"])
+        >>> idx.is_boolean()  # doctest: +SKIP
+        False
+
+        >>> idx = pd.Index([True, False, "True"])
+        >>> idx.is_boolean()  # doctest: +SKIP
+        False
+        """
+        warnings.warn(
+            f"{type(self).__name__}.is_boolean is deprecated. "
+            "Use pandas.api.types.is_bool_type instead.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        return self.inferred_type in ["boolean"]
+
+    @final
+    def is_integer(self) -> bool:
+        """
+        Check if the Index only consists of integers.
+
+        .. deprecated:: 2.0.0
+            Use `pandas.api.types.is_integer_dtype` instead.
+
+        Returns
+        -------
+        bool
+            Whether or not the Index only consists of integers.
+
+        See Also
+        --------
+        is_boolean : Check if the Index only consists of booleans (deprecated).
+        is_floating : Check if the Index is a floating type (deprecated).
+        is_numeric : Check if the Index only consists of numeric data (deprecated).
+        is_object : Check if the Index is of the object dtype. (deprecated).
+        is_categorical : Check if the Index holds categorical data (deprecated).
+        is_interval : Check if the Index holds Interval objects (deprecated).
+
+        Examples
+        --------
+        >>> idx = pd.Index([1, 2, 3, 4])
+        >>> idx.is_integer()  # doctest: +SKIP
+        True
+
+        >>> idx = pd.Index([1.0, 2.0, 3.0, 4.0])
+        >>> idx.is_integer()  # doctest: +SKIP
+        False
+
+        >>> idx = pd.Index(["Apple", "Mango", "Watermelon"])
+        >>> idx.is_integer()  # doctest: +SKIP
+        False
+        """
+        warnings.warn(
+            f"{type(self).__name__}.is_integer is deprecated. "
+            "Use pandas.api.types.is_integer_dtype instead.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        return self.inferred_type in ["integer"]
+
+    @final
+    def is_floating(self) -> bool:
+        """
+        Check if the Index is a floating type.
+
+        .. deprecated:: 2.0.0
+            Use `pandas.api.types.is_float_dtype` instead
+
+        The Index may consist of only floats, NaNs, or a mix of floats,
+        integers, or NaNs.
+
+        Returns
+        -------
+        bool
+            Whether or not the Index only consists of only consists of floats, NaNs, or
+            a mix of floats, integers, or NaNs.
+
+        See Also
+        --------
+        is_boolean : Check if the Index only consists of booleans (deprecated).
+        is_integer : Check if the Index only consists of integers (deprecated).
+        is_numeric : Check if the Index only consists of numeric data (deprecated).
+        is_object : Check if the Index is of the object dtype. (deprecated).
+        is_categorical : Check if the Index holds categorical data (deprecated).
+        is_interval : Check if the Index holds Interval objects (deprecated).
+
+        Examples
+        --------
+        >>> idx = pd.Index([1.0, 2.0, 3.0, 4.0])
+        >>> idx.is_floating()  # doctest: +SKIP
+        True
+
+        >>> idx = pd.Index([1.0, 2.0, np.nan, 4.0])
+        >>> idx.is_floating()  # doctest: +SKIP
+        True
+
+        >>> idx = pd.Index([1, 2, 3, 4, np.nan])
+        >>> idx.is_floating()  # doctest: +SKIP
+        True
+
+        >>> idx = pd.Index([1, 2, 3, 4])
+        >>> idx.is_floating()  # doctest: +SKIP
+        False
+        """
+        warnings.warn(
+            f"{type(self).__name__}.is_floating is deprecated. "
+            "Use pandas.api.types.is_float_dtype instead.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        return self.inferred_type in ["floating", "mixed-integer-float", "integer-na"]
+
+    @final
+    def is_numeric(self) -> bool:
+        """
+        Check if the Index only consists of numeric data.
+
+        .. deprecated:: 2.0.0
+            Use `pandas.api.types.is_numeric_dtype` instead.
+
+        Returns
+        -------
+        bool
+            Whether or not the Index only consists of numeric data.
+
+        See Also
+        --------
+        is_boolean : Check if the Index only consists of booleans (deprecated).
+        is_integer : Check if the Index only consists of integers (deprecated).
+        is_floating : Check if the Index is a floating type (deprecated).
+        is_object : Check if the Index is of the object dtype. (deprecated).
+        is_categorical : Check if the Index holds categorical data (deprecated).
+        is_interval : Check if the Index holds Interval objects (deprecated).
+
+        Examples
+        --------
+        >>> idx = pd.Index([1.0, 2.0, 3.0, 4.0])
+        >>> idx.is_numeric()  # doctest: +SKIP
+        True
+
+        >>> idx = pd.Index([1, 2, 3, 4.0])
+        >>> idx.is_numeric()  # doctest: +SKIP
+        True
+
+        >>> idx = pd.Index([1, 2, 3, 4])
+        >>> idx.is_numeric()  # doctest: +SKIP
+        True
+
+        >>> idx = pd.Index([1, 2, 3, 4.0, np.nan])
+        >>> idx.is_numeric()  # doctest: +SKIP
+        True
+
+        >>> idx = pd.Index([1, 2, 3, 4.0, np.nan, "Apple"])
+        >>> idx.is_numeric()  # doctest: +SKIP
+        False
+        """
+        warnings.warn(
+            f"{type(self).__name__}.is_numeric is deprecated. "
+            "Use pandas.api.types.is_any_real_numeric_dtype instead",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        return self.inferred_type in ["integer", "floating"]
+
+    @final
+    def is_object(self) -> bool:
+        """
+        Check if the Index is of the object dtype.
+
+        .. deprecated:: 2.0.0
+           Use `pandas.api.types.is_object_dtype` instead.
+
+        Returns
+        -------
+        bool
+            Whether or not the Index is of the object dtype.
+
+        See Also
+        --------
+        is_boolean : Check if the Index only consists of booleans (deprecated).
+        is_integer : Check if the Index only consists of integers (deprecated).
+        is_floating : Check if the Index is a floating type (deprecated).
+        is_numeric : Check if the Index only consists of numeric data (deprecated).
+        is_categorical : Check if the Index holds categorical data (deprecated).
+        is_interval : Check if the Index holds Interval objects (deprecated).
+
+        Examples
+        --------
+        >>> idx = pd.Index(["Apple", "Mango", "Watermelon"])
+        >>> idx.is_object()  # doctest: +SKIP
+        True
+
+        >>> idx = pd.Index(["Apple", "Mango", 2.0])
+        >>> idx.is_object()  # doctest: +SKIP
+        True
+
+        >>> idx = pd.Index(["Watermelon", "Orange", "Apple",
+        ...                 "Watermelon"]).astype("category")
+        >>> idx.is_object()  # doctest: +SKIP
+        False
+
+        >>> idx = pd.Index([1.0, 2.0, 3.0, 4.0])
+        >>> idx.is_object()  # doctest: +SKIP
+        False
+        """
+        warnings.warn(
+            f"{type(self).__name__}.is_object is deprecated."
+            "Use pandas.api.types.is_object_dtype instead",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        return is_object_dtype(self.dtype)
+
+    @final
+    def is_categorical(self) -> bool:
+        """
+        Check if the Index holds categorical data.
+
+        .. deprecated:: 2.0.0
+              Use `isinstance(index.dtype, pd.CategoricalDtype)` instead.
+
+        Returns
+        -------
+        bool
+            True if the Index is categorical.
+
+        See Also
+        --------
+        CategoricalIndex : Index for categorical data.
+        is_boolean : Check if the Index only consists of booleans (deprecated).
+        is_integer : Check if the Index only consists of integers (deprecated).
+        is_floating : Check if the Index is a floating type (deprecated).
+        is_numeric : Check if the Index only consists of numeric data (deprecated).
+        is_object : Check if the Index is of the object dtype. (deprecated).
+        is_interval : Check if the Index holds Interval objects (deprecated).
+
+        Examples
+        --------
+        >>> idx = pd.Index(["Watermelon", "Orange", "Apple",
+        ...                 "Watermelon"]).astype("category")
+        >>> idx.is_categorical()  # doctest: +SKIP
+        True
+
+        >>> idx = pd.Index([1, 3, 5, 7])
+        >>> idx.is_categorical()  # doctest: +SKIP
+        False
+
+        >>> s = pd.Series(["Peter", "Victor", "Elisabeth", "Mar"])
+        >>> s
+        0        Peter
+        1       Victor
+        2    Elisabeth
+        3          Mar
+        dtype: object
+        >>> s.index.is_categorical()  # doctest: +SKIP
+        False
+        """
+        warnings.warn(
+            f"{type(self).__name__}.is_categorical is deprecated."
+            "Use pandas.api.types.is_categorical_dtype instead",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+
+        return self.inferred_type in ["categorical"]
+
+    @final
+    def is_interval(self) -> bool:
+        """
+        Check if the Index holds Interval objects.
+
+        .. deprecated:: 2.0.0
+            Use `isinstance(index.dtype, pd.IntervalDtype)` instead.
+
+        Returns
+        -------
+        bool
+            Whether or not the Index holds Interval objects.
+
+        See Also
+        --------
+        IntervalIndex : Index for Interval objects.
+        is_boolean : Check if the Index only consists of booleans (deprecated).
+        is_integer : Check if the Index only consists of integers (deprecated).
+        is_floating : Check if the Index is a floating type (deprecated).
+        is_numeric : Check if the Index only consists of numeric data (deprecated).
+        is_object : Check if the Index is of the object dtype. (deprecated).
+        is_categorical : Check if the Index holds categorical data (deprecated).
+
+        Examples
+        --------
+        >>> idx = pd.Index([pd.Interval(left=0, right=5),
+        ...                 pd.Interval(left=5, right=10)])
+        >>> idx.is_interval()  # doctest: +SKIP
+        True
+
+        >>> idx = pd.Index([1, 3, 5, 7])
+        >>> idx.is_interval()  # doctest: +SKIP
+        False
+        """
+        warnings.warn(
+            f"{type(self).__name__}.is_interval is deprecated."
+            "Use pandas.api.types.is_interval_dtype instead",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        return self.inferred_type in ["interval"]
+
+    @final
+    def _holds_integer(self) -> bool:
+        """
+        Whether the type is an integer type.
+        """
+        return self.inferred_type in ["integer", "mixed-integer"]
+
+    @final
+    def holds_integer(self) -> bool:
+        """
+        Whether the type is an integer type.
+
+        .. deprecated:: 2.0.0
+            Use `pandas.api.types.infer_dtype` instead
+        """
+        warnings.warn(
+            f"{type(self).__name__}.holds_integer is deprecated. "
+            "Use pandas.api.types.infer_dtype instead.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        return self._holds_integer()
+
+    @cache_readonly
+    def inferred_type(self) -> str_t:
+        """
+        Return a string of the type inferred from the values.
+
+        Examples
+        --------
+        >>> idx = pd.Index([1, 2, 3])
+        >>> idx
+        Index([1, 2, 3], dtype='int64')
+        >>> idx.inferred_type
+        'integer'
+        """
+        return lib.infer_dtype(self._values, skipna=False)
+
+    @cache_readonly
+    @final
+    def _is_all_dates(self) -> bool:
+        """
+        Whether or not the index values only consist of dates.
+        """
+        if needs_i8_conversion(self.dtype):
+            return True
+        elif self.dtype != _dtype_obj:
+            # TODO(ExtensionIndex): 3rd party EA might override?
+            # Note: this includes IntervalIndex, even when the left/right
+            #  contain datetime-like objects.
+            return False
+        elif self._is_multi:
+            return False
+        return is_datetime_array(ensure_object(self._values))
+
+    @final
+    @cache_readonly
+    def _is_multi(self) -> bool:
+        """
+        Cached check equivalent to isinstance(self, MultiIndex)
+        """
+        return isinstance(self, ABCMultiIndex)
+
+    # --------------------------------------------------------------------
+    # Pickle Methods
+
+    def __reduce__(self):
+        d = {"data": self._data, "name": self.name}
+        return _new_Index, (type(self), d), None
+
+    # --------------------------------------------------------------------
+    # Null Handling Methods
+
+    @cache_readonly
+    def _na_value(self):
+        """The expected NA value to use with this index."""
+        dtype = self.dtype
+        if isinstance(dtype, np.dtype):
+            if dtype.kind in "mM":
+                return NaT
+            return np.nan
+        return dtype.na_value
+
+    @cache_readonly
+    def _isnan(self) -> npt.NDArray[np.bool_]:
+        """
+        Return if each value is NaN.
+        """
+        if self._can_hold_na:
+            return isna(self)
+        else:
+            # shouldn't reach to this condition by checking hasnans beforehand
+            values = np.empty(len(self), dtype=np.bool_)
+            values.fill(False)
+            return values
+
+    @cache_readonly
+    def hasnans(self) -> bool:
+        """
+        Return True if there are any NaNs.
+
+        Enables various performance speedups.
+
+        Returns
+        -------
+        bool
+
+        Examples
+        --------
+        >>> s = pd.Series([1, 2, 3], index=['a', 'b', None])
+        >>> s
+        a    1
+        b    2
+        None 3
+        dtype: int64
+        >>> s.index.hasnans
+        True
+        """
+        if self._can_hold_na:
+            return bool(self._isnan.any())
+        else:
+            return False
+
+    @final
+    def isna(self) -> npt.NDArray[np.bool_]:
+        """
+        Detect missing values.
+
+        Return a boolean same-sized object indicating if the values are NA.
+        NA values, such as ``None``, :attr:`numpy.NaN` or :attr:`pd.NaT`, get
+        mapped to ``True`` values.
+        Everything else get mapped to ``False`` values. Characters such as
+        empty strings `''` or :attr:`numpy.inf` are not considered NA values.
+
+        Returns
+        -------
+        numpy.ndarray[bool]
+            A boolean array of whether my values are NA.
+
+        See Also
+        --------
+        Index.notna : Boolean inverse of isna.
+        Index.dropna : Omit entries with missing values.
+        isna : Top-level isna.
+        Series.isna : Detect missing values in Series object.
+
+        Examples
+        --------
+        Show which entries in a pandas.Index are NA. The result is an
+        array.
+
+        >>> idx = pd.Index([5.2, 6.0, np.nan])
+        >>> idx
+        Index([5.2, 6.0, nan], dtype='float64')
+        >>> idx.isna()
+        array([False, False,  True])
+
+        Empty strings are not considered NA values. None is considered an NA
+        value.
+
+        >>> idx = pd.Index(['black', '', 'red', None])
+        >>> idx
+        Index(['black', '', 'red', None], dtype='object')
+        >>> idx.isna()
+        array([False, False, False,  True])
+
+        For datetimes, `NaT` (Not a Time) is considered as an NA value.
+
+        >>> idx = pd.DatetimeIndex([pd.Timestamp('1940-04-25'),
+        ...                         pd.Timestamp(''), None, pd.NaT])
+        >>> idx
+        DatetimeIndex(['1940-04-25', 'NaT', 'NaT', 'NaT'],
+                      dtype='datetime64[ns]', freq=None)
+        >>> idx.isna()
+        array([False,  True,  True,  True])
+        """
+        return self._isnan
+
+    isnull = isna
+
+    @final
+    def notna(self) -> npt.NDArray[np.bool_]:
+        """
+        Detect existing (non-missing) values.
+
+        Return a boolean same-sized object indicating if the values are not NA.
+        Non-missing values get mapped to ``True``. Characters such as empty
+        strings ``''`` or :attr:`numpy.inf` are not considered NA values.
+        NA values, such as None or :attr:`numpy.NaN`, get mapped to ``False``
+        values.
+
+        Returns
+        -------
+        numpy.ndarray[bool]
+            Boolean array to indicate which entries are not NA.
+
+        See Also
+        --------
+        Index.notnull : Alias of notna.
+        Index.isna: Inverse of notna.
+        notna : Top-level notna.
+
+        Examples
+        --------
+        Show which entries in an Index are not NA. The result is an
+        array.
+
+        >>> idx = pd.Index([5.2, 6.0, np.nan])
+        >>> idx
+        Index([5.2, 6.0, nan], dtype='float64')
+        >>> idx.notna()
+        array([ True,  True, False])
+
+        Empty strings are not considered NA values. None is considered a NA
+        value.
+
+        >>> idx = pd.Index(['black', '', 'red', None])
+        >>> idx
+        Index(['black', '', 'red', None], dtype='object')
+        >>> idx.notna()
+        array([ True,  True,  True, False])
+        """
+        return ~self.isna()
+
+    notnull = notna
+
+    def fillna(self, value=None, downcast=lib.no_default):
+        """
+        Fill NA/NaN values with the specified value.
+
+        Parameters
+        ----------
+        value : scalar
+            Scalar value to use to fill holes (e.g. 0).
+            This value cannot be a list-likes.
+        downcast : dict, default is None
+            A dict of item->dtype of what to downcast if possible,
+            or the string 'infer' which will try to downcast to an appropriate
+            equal type (e.g. float64 to int64 if possible).
+
+            .. deprecated:: 2.1.0
+
+        Returns
+        -------
+        Index
+
+        See Also
+        --------
+        DataFrame.fillna : Fill NaN values of a DataFrame.
+        Series.fillna : Fill NaN Values of a Series.
+
+        Examples
+        --------
+        >>> idx = pd.Index([np.nan, np.nan, 3])
+        >>> idx.fillna(0)
+        Index([0.0, 0.0, 3.0], dtype='float64')
+        """
+        if not is_scalar(value):
+            raise TypeError(f"'value' must be a scalar, passed: {type(value).__name__}")
+        if downcast is not lib.no_default:
+            warnings.warn(
+                f"The 'downcast' keyword in {type(self).__name__}.fillna is "
+                "deprecated and will be removed in a future version. "
+                "It was previously silently ignored.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+        else:
+            downcast = None
+
+        if self.hasnans:
+            result = self.putmask(self._isnan, value)
+            if downcast is None:
+                # no need to care metadata other than name
+                # because it can't have freq if it has NaTs
+                # _with_infer needed for test_fillna_categorical
+                return Index._with_infer(result, name=self.name)
+            raise NotImplementedError(
+                f"{type(self).__name__}.fillna does not support 'downcast' "
+                "argument values other than 'None'."
+            )
+        return self._view()
+
+    def dropna(self, how: AnyAll = "any") -> Self:
+        """
+        Return Index without NA/NaN values.
+
+        Parameters
+        ----------
+        how : {'any', 'all'}, default 'any'
+            If the Index is a MultiIndex, drop the value when any or all levels
+            are NaN.
+
+        Returns
+        -------
+        Index
+
+        Examples
+        --------
+        >>> idx = pd.Index([1, np.nan, 3])
+        >>> idx.dropna()
+        Index([1.0, 3.0], dtype='float64')
+        """
+        if how not in ("any", "all"):
+            raise ValueError(f"invalid how option: {how}")
+
+        if self.hasnans:
+            res_values = self._values[~self._isnan]
+            return type(self)._simple_new(res_values, name=self.name)
+        return self._view()
+
+    # --------------------------------------------------------------------
+    # Uniqueness Methods
+
+    def unique(self, level: Hashable | None = None) -> Self:
+        """
+        Return unique values in the index.
+
+        Unique values are returned in order of appearance, this does NOT sort.
+
+        Parameters
+        ----------
+        level : int or hashable, optional
+            Only return values from specified level (for MultiIndex).
+            If int, gets the level by integer position, else by level name.
+
+        Returns
+        -------
+        Index
+
+        See Also
+        --------
+        unique : Numpy array of unique values in that column.
+        Series.unique : Return unique values of Series object.
+
+        Examples
+        --------
+        >>> idx = pd.Index([1, 1, 2, 3, 3])
+        >>> idx.unique()
+        Index([1, 2, 3], dtype='int64')
+        """
+        if level is not None:
+            self._validate_index_level(level)
+
+        if self.is_unique:
+            return self._view()
+
+        result = super().unique()
+        return self._shallow_copy(result)
+
+    def drop_duplicates(self, *, keep: DropKeep = "first") -> Self:
+        """
+        Return Index with duplicate values removed.
+
+        Parameters
+        ----------
+        keep : {'first', 'last', ``False``}, default 'first'
+            - 'first' : Drop duplicates except for the first occurrence.
+            - 'last' : Drop duplicates except for the last occurrence.
+            - ``False`` : Drop all duplicates.
+
+        Returns
+        -------
+        Index
+
+        See Also
+        --------
+        Series.drop_duplicates : Equivalent method on Series.
+        DataFrame.drop_duplicates : Equivalent method on DataFrame.
+        Index.duplicated : Related method on Index, indicating duplicate
+            Index values.
+
+        Examples
+        --------
+        Generate an pandas.Index with duplicate values.
+
+        >>> idx = pd.Index(['lama', 'cow', 'lama', 'beetle', 'lama', 'hippo'])
+
+        The `keep` parameter controls  which duplicate values are removed.
+        The value 'first' keeps the first occurrence for each
+        set of duplicated entries. The default value of keep is 'first'.
+
+        >>> idx.drop_duplicates(keep='first')
+        Index(['lama', 'cow', 'beetle', 'hippo'], dtype='object')
+
+        The value 'last' keeps the last occurrence for each set of duplicated
+        entries.
+
+        >>> idx.drop_duplicates(keep='last')
+        Index(['cow', 'beetle', 'lama', 'hippo'], dtype='object')
+
+        The value ``False`` discards all sets of duplicated entries.
+
+        >>> idx.drop_duplicates(keep=False)
+        Index(['cow', 'beetle', 'hippo'], dtype='object')
+        """
+        if self.is_unique:
+            return self._view()
+
+        return super().drop_duplicates(keep=keep)
+
+    def duplicated(self, keep: DropKeep = "first") -> npt.NDArray[np.bool_]:
+        """
+        Indicate duplicate index values.
+
+        Duplicated values are indicated as ``True`` values in the resulting
+        array. Either all duplicates, all except the first, or all except the
+        last occurrence of duplicates can be indicated.
+
+        Parameters
+        ----------
+        keep : {'first', 'last', False}, default 'first'
+            The value or values in a set of duplicates to mark as missing.
+
+            - 'first' : Mark duplicates as ``True`` except for the first
+              occurrence.
+            - 'last' : Mark duplicates as ``True`` except for the last
+              occurrence.
+            - ``False`` : Mark all duplicates as ``True``.
+
+        Returns
+        -------
+        np.ndarray[bool]
+
+        See Also
+        --------
+        Series.duplicated : Equivalent method on pandas.Series.
+        DataFrame.duplicated : Equivalent method on pandas.DataFrame.
+        Index.drop_duplicates : Remove duplicate values from Index.
+
+        Examples
+        --------
+        By default, for each set of duplicated values, the first occurrence is
+        set to False and all others to True:
+
+        >>> idx = pd.Index(['lama', 'cow', 'lama', 'beetle', 'lama'])
+        >>> idx.duplicated()
+        array([False, False,  True, False,  True])
+
+        which is equivalent to
+
+        >>> idx.duplicated(keep='first')
+        array([False, False,  True, False,  True])
+
+        By using 'last', the last occurrence of each set of duplicated values
+        is set on False and all others on True:
+
+        >>> idx.duplicated(keep='last')
+        array([ True, False,  True, False, False])
+
+        By setting keep on ``False``, all duplicates are True:
+
+        >>> idx.duplicated(keep=False)
+        array([ True, False,  True, False,  True])
+        """
+        if self.is_unique:
+            # fastpath available bc we are immutable
+            return np.zeros(len(self), dtype=bool)
+        return self._duplicated(keep=keep)
+
+    # --------------------------------------------------------------------
+    # Arithmetic & Logical Methods
+
+    def __iadd__(self, other):
+        # alias for __add__
+        return self + other
+
+    @final
+    def __nonzero__(self) -> NoReturn:
+        raise ValueError(
+            f"The truth value of a {type(self).__name__} is ambiguous. "
+            "Use a.empty, a.bool(), a.item(), a.any() or a.all()."
+        )
+
+    __bool__ = __nonzero__
+
+    # --------------------------------------------------------------------
+    # Set Operation Methods
+
+    def _get_reconciled_name_object(self, other):
+        """
+        If the result of a set operation will be self,
+        return self, unless the name changes, in which
+        case make a shallow copy of self.
+        """
+        name = get_op_result_name(self, other)
+        if self.name is not name:
+            return self.rename(name)
+        return self
+
+    @final
+    def _validate_sort_keyword(self, sort):
+        if sort not in [None, False, True]:
+            raise ValueError(
+                "The 'sort' keyword only takes the values of "
+                f"None, True, or False; {sort} was passed."
+            )
+
+    @final
+    def _dti_setop_align_tzs(self, other: Index, setop: str_t) -> tuple[Index, Index]:
+        """
+        With mismatched timezones, cast both to UTC.
+        """
+        # Caller is responsibelf or checking
+        #  `self.dtype != other.dtype`
+        if (
+            isinstance(self, ABCDatetimeIndex)
+            and isinstance(other, ABCDatetimeIndex)
+            and self.tz is not None
+            and other.tz is not None
+        ):
+            # GH#39328, GH#45357
+            left = self.tz_convert("UTC")
+            right = other.tz_convert("UTC")
+            return left, right
+        return self, other
+
+    @final
+    def union(self, other, sort=None):
+        """
+        Form the union of two Index objects.
+
+        If the Index objects are incompatible, both Index objects will be
+        cast to dtype('object') first.
+
+        Parameters
+        ----------
+        other : Index or array-like
+        sort : bool or None, default None
+            Whether to sort the resulting Index.
+
+            * None : Sort the result, except when
+
+              1. `self` and `other` are equal.
+              2. `self` or `other` has length 0.
+              3. Some values in `self` or `other` cannot be compared.
+                 A RuntimeWarning is issued in this case.
+
+            * False : do not sort the result.
+            * True : Sort the result (which may raise TypeError).
+
+        Returns
+        -------
+        Index
+
+        Examples
+        --------
+        Union matching dtypes
+
+        >>> idx1 = pd.Index([1, 2, 3, 4])
+        >>> idx2 = pd.Index([3, 4, 5, 6])
+        >>> idx1.union(idx2)
+        Index([1, 2, 3, 4, 5, 6], dtype='int64')
+
+        Union mismatched dtypes
+
+        >>> idx1 = pd.Index(['a', 'b', 'c', 'd'])
+        >>> idx2 = pd.Index([1, 2, 3, 4])
+        >>> idx1.union(idx2)
+        Index(['a', 'b', 'c', 'd', 1, 2, 3, 4], dtype='object')
+
+        MultiIndex case
+
+        >>> idx1 = pd.MultiIndex.from_arrays(
+        ...     [[1, 1, 2, 2], ["Red", "Blue", "Red", "Blue"]]
+        ... )
+        >>> idx1
+        MultiIndex([(1,  'Red'),
+            (1, 'Blue'),
+            (2,  'Red'),
+            (2, 'Blue')],
+           )
+        >>> idx2 = pd.MultiIndex.from_arrays(
+        ...     [[3, 3, 2, 2], ["Red", "Green", "Red", "Green"]]
+        ... )
+        >>> idx2
+        MultiIndex([(3,   'Red'),
+            (3, 'Green'),
+            (2,   'Red'),
+            (2, 'Green')],
+           )
+        >>> idx1.union(idx2)
+        MultiIndex([(1,  'Blue'),
+            (1,   'Red'),
+            (2,  'Blue'),
+            (2, 'Green'),
+            (2,   'Red'),
+            (3, 'Green'),
+            (3,   'Red')],
+           )
+        >>> idx1.union(idx2, sort=False)
+        MultiIndex([(1,   'Red'),
+            (1,  'Blue'),
+            (2,   'Red'),
+            (2,  'Blue'),
+            (3,   'Red'),
+            (3, 'Green'),
+            (2, 'Green')],
+           )
+        """
+        self._validate_sort_keyword(sort)
+        self._assert_can_do_setop(other)
+        other, result_name = self._convert_can_do_setop(other)
+
+        if self.dtype != other.dtype:
+            if (
+                isinstance(self, ABCMultiIndex)
+                and not is_object_dtype(_unpack_nested_dtype(other))
+                and len(other) > 0
+            ):
+                raise NotImplementedError(
+                    "Can only union MultiIndex with MultiIndex or Index of tuples, "
+                    "try mi.to_flat_index().union(other) instead."
+                )
+            self, other = self._dti_setop_align_tzs(other, "union")
+
+            dtype = self._find_common_type_compat(other)
+            left = self.astype(dtype, copy=False)
+            right = other.astype(dtype, copy=False)
+            return left.union(right, sort=sort)
+
+        elif not len(other) or self.equals(other):
+            # NB: whether this (and the `if not len(self)` check below) come before
+            #  or after the dtype equality check above affects the returned dtype
+            result = self._get_reconciled_name_object(other)
+            if sort is True:
+                return result.sort_values()
+            return result
+
+        elif not len(self):
+            result = other._get_reconciled_name_object(self)
+            if sort is True:
+                return result.sort_values()
+            return result
+
+        result = self._union(other, sort=sort)
+
+        return self._wrap_setop_result(other, result)
+
+    def _union(self, other: Index, sort: bool | None):
+        """
+        Specific union logic should go here. In subclasses, union behavior
+        should be overwritten here rather than in `self.union`.
+
+        Parameters
+        ----------
+        other : Index or array-like
+        sort : False or None, default False
+            Whether to sort the resulting index.
+
+            * True : sort the result
+            * False : do not sort the result.
+            * None : sort the result, except when `self` and `other` are equal
+              or when the values cannot be compared.
+
+        Returns
+        -------
+        Index
+        """
+        lvals = self._values
+        rvals = other._values
+
+        if (
+            sort in (None, True)
+            and self.is_monotonic_increasing
+            and other.is_monotonic_increasing
+            and not (self.has_duplicates and other.has_duplicates)
+            and self._can_use_libjoin
+            and other._can_use_libjoin
+        ):
+            # Both are monotonic and at least one is unique, so can use outer join
+            #  (actually don't need either unique, but without this restriction
+            #  test_union_same_value_duplicated_in_both fails)
+            try:
+                return self._outer_indexer(other)[0]
+            except (TypeError, IncompatibleFrequency):
+                # incomparable objects; should only be for object dtype
+                value_list = list(lvals)
+
+                # worth making this faster? a very unusual case
+                value_set = set(lvals)
+                value_list.extend([x for x in rvals if x not in value_set])
+                # If objects are unorderable, we must have object dtype.
+                return np.array(value_list, dtype=object)
+
+        elif not other.is_unique:
+            # other has duplicates
+            result_dups = algos.union_with_duplicates(self, other)
+            return _maybe_try_sort(result_dups, sort)
+
+        # The rest of this method is analogous to Index._intersection_via_get_indexer
+
+        # Self may have duplicates; other already checked as unique
+        # find indexes of things in "other" that are not in "self"
+        if self._index_as_unique:
+            indexer = self.get_indexer(other)
+            missing = (indexer == -1).nonzero()[0]
+        else:
+            missing = algos.unique1d(self.get_indexer_non_unique(other)[1])
+
+        result: Index | MultiIndex | ArrayLike
+        if self._is_multi:
+            # Preserve MultiIndex to avoid losing dtypes
+            result = self.append(other.take(missing))
+
+        else:
+            if len(missing) > 0:
+                other_diff = rvals.take(missing)
+                result = concat_compat((lvals, other_diff))
+            else:
+                result = lvals
+
+        if not self.is_monotonic_increasing or not other.is_monotonic_increasing:
+            # if both are monotonic then result should already be sorted
+            result = _maybe_try_sort(result, sort)
+
+        return result
+
+    @final
+    def _wrap_setop_result(self, other: Index, result) -> Index:
+        name = get_op_result_name(self, other)
+        if isinstance(result, Index):
+            if result.name != name:
+                result = result.rename(name)
+        else:
+            result = self._shallow_copy(result, name=name)
+        return result
+
+    @final
+    def intersection(self, other, sort: bool = False):
+        # default sort keyword is different here from other setops intentionally
+        #  done in GH#25063
+        """
+        Form the intersection of two Index objects.
+
+        This returns a new Index with elements common to the index and `other`.
+
+        Parameters
+        ----------
+        other : Index or array-like
+        sort : True, False or None, default False
+            Whether to sort the resulting index.
+
+            * None : sort the result, except when `self` and `other` are equal
+              or when the values cannot be compared.
+            * False : do not sort the result.
+            * True : Sort the result (which may raise TypeError).
+
+        Returns
+        -------
+        Index
+
+        Examples
+        --------
+        >>> idx1 = pd.Index([1, 2, 3, 4])
+        >>> idx2 = pd.Index([3, 4, 5, 6])
+        >>> idx1.intersection(idx2)
+        Index([3, 4], dtype='int64')
+        """
+        self._validate_sort_keyword(sort)
+        self._assert_can_do_setop(other)
+        other, result_name = self._convert_can_do_setop(other)
+
+        if self.dtype != other.dtype:
+            self, other = self._dti_setop_align_tzs(other, "intersection")
+
+        if self.equals(other):
+            if not self.is_unique:
+                result = self.unique()._get_reconciled_name_object(other)
+            else:
+                result = self._get_reconciled_name_object(other)
+            if sort is True:
+                result = result.sort_values()
+            return result
+
+        if len(self) == 0 or len(other) == 0:
+            # fastpath; we need to be careful about having commutativity
+
+            if self._is_multi or other._is_multi:
+                # _convert_can_do_setop ensures that we have both or neither
+                # We retain self.levels
+                return self[:0].rename(result_name)
+
+            dtype = self._find_common_type_compat(other)
+            if self.dtype == dtype:
+                # Slicing allows us to retain DTI/TDI.freq, RangeIndex
+
+                # Note: self[:0] vs other[:0] affects
+                #  1) which index's `freq` we get in DTI/TDI cases
+                #     This may be a historical artifact, i.e. no documented
+                #     reason for this choice.
+                #  2) The `step` we get in RangeIndex cases
+                if len(self) == 0:
+                    return self[:0].rename(result_name)
+                else:
+                    return other[:0].rename(result_name)
+
+            return Index([], dtype=dtype, name=result_name)
+
+        elif not self._should_compare(other):
+            # We can infer that the intersection is empty.
+            if isinstance(self, ABCMultiIndex):
+                return self[:0].rename(result_name)
+            return Index([], name=result_name)
+
+        elif self.dtype != other.dtype:
+            dtype = self._find_common_type_compat(other)
+            this = self.astype(dtype, copy=False)
+            other = other.astype(dtype, copy=False)
+            return this.intersection(other, sort=sort)
+
+        result = self._intersection(other, sort=sort)
+        return self._wrap_intersection_result(other, result)
+
+    def _intersection(self, other: Index, sort: bool = False):
+        """
+        intersection specialized to the case with matching dtypes.
+        """
+        if (
+            self.is_monotonic_increasing
+            and other.is_monotonic_increasing
+            and self._can_use_libjoin
+            and other._can_use_libjoin
+        ):
+            try:
+                res_indexer, indexer, _ = self._inner_indexer(other)
+            except TypeError:
+                # non-comparable; should only be for object dtype
+                pass
+            else:
+                # TODO: algos.unique1d should preserve DTA/TDA
+                if is_numeric_dtype(self.dtype):
+                    # This is faster, because Index.unique() checks for uniqueness
+                    # before calculating the unique values.
+                    res = algos.unique1d(res_indexer)
+                else:
+                    result = self.take(indexer)
+                    res = result.drop_duplicates()
+                return ensure_wrapped_if_datetimelike(res)
+
+        res_values = self._intersection_via_get_indexer(other, sort=sort)
+        res_values = _maybe_try_sort(res_values, sort)
+        return res_values
+
+    def _wrap_intersection_result(self, other, result):
+        # We will override for MultiIndex to handle empty results
+        return self._wrap_setop_result(other, result)
+
+    @final
+    def _intersection_via_get_indexer(
+        self, other: Index | MultiIndex, sort
+    ) -> ArrayLike | MultiIndex:
+        """
+        Find the intersection of two Indexes using get_indexer.
+
+        Returns
+        -------
+        np.ndarray or ExtensionArray or MultiIndex
+            The returned array will be unique.
+        """
+        left_unique = self.unique()
+        right_unique = other.unique()
+
+        # even though we are unique, we need get_indexer_for for IntervalIndex
+        indexer = left_unique.get_indexer_for(right_unique)
+
+        mask = indexer != -1
+
+        taker = indexer.take(mask.nonzero()[0])
+        if sort is False:
+            # sort bc we want the elements in the same order they are in self
+            # unnecessary in the case with sort=None bc we will sort later
+            taker = np.sort(taker)
+
+        result: MultiIndex | ExtensionArray | np.ndarray
+        if isinstance(left_unique, ABCMultiIndex):
+            result = left_unique.take(taker)
+        else:
+            result = left_unique.take(taker)._values
+        return result
+
+    @final
+    def difference(self, other, sort=None):
+        """
+        Return a new Index with elements of index not in `other`.
+
+        This is the set difference of two Index objects.
+
+        Parameters
+        ----------
+        other : Index or array-like
+        sort : bool or None, default None
+            Whether to sort the resulting index. By default, the
+            values are attempted to be sorted, but any TypeError from
+            incomparable elements is caught by pandas.
+
+            * None : Attempt to sort the result, but catch any TypeErrors
+              from comparing incomparable elements.
+            * False : Do not sort the result.
+            * True : Sort the result (which may raise TypeError).
+
+        Returns
+        -------
+        Index
+
+        Examples
+        --------
+        >>> idx1 = pd.Index([2, 1, 3, 4])
+        >>> idx2 = pd.Index([3, 4, 5, 6])
+        >>> idx1.difference(idx2)
+        Index([1, 2], dtype='int64')
+        >>> idx1.difference(idx2, sort=False)
+        Index([2, 1], dtype='int64')
+        """
+        self._validate_sort_keyword(sort)
+        self._assert_can_do_setop(other)
+        other, result_name = self._convert_can_do_setop(other)
+
+        # Note: we do NOT call _dti_setop_align_tzs here, as there
+        #  is no requirement that .difference be commutative, so it does
+        #  not cast to object.
+
+        if self.equals(other):
+            # Note: we do not (yet) sort even if sort=None GH#24959
+            return self[:0].rename(result_name)
+
+        if len(other) == 0:
+            # Note: we do not (yet) sort even if sort=None GH#24959
+            result = self.unique().rename(result_name)
+            if sort is True:
+                return result.sort_values()
+            return result
+
+        if not self._should_compare(other):
+            # Nothing matches -> difference is everything
+            result = self.unique().rename(result_name)
+            if sort is True:
+                return result.sort_values()
+            return result
+
+        result = self._difference(other, sort=sort)
+        return self._wrap_difference_result(other, result)
+
+    def _difference(self, other, sort):
+        # overridden by RangeIndex
+        this = self
+        if isinstance(self, ABCCategoricalIndex) and self.hasnans and other.hasnans:
+            this = this.dropna()
+        other = other.unique()
+        the_diff = this[other.get_indexer_for(this) == -1]
+        the_diff = the_diff if this.is_unique else the_diff.unique()
+        the_diff = _maybe_try_sort(the_diff, sort)
+        return the_diff
+
+    def _wrap_difference_result(self, other, result):
+        # We will override for MultiIndex to handle empty results
+        return self._wrap_setop_result(other, result)
+
+    def symmetric_difference(self, other, result_name=None, sort=None):
+        """
+        Compute the symmetric difference of two Index objects.
+
+        Parameters
+        ----------
+        other : Index or array-like
+        result_name : str
+        sort : bool or None, default None
+            Whether to sort the resulting index. By default, the
+            values are attempted to be sorted, but any TypeError from
+            incomparable elements is caught by pandas.
+
+            * None : Attempt to sort the result, but catch any TypeErrors
+              from comparing incomparable elements.
+            * False : Do not sort the result.
+            * True : Sort the result (which may raise TypeError).
+
+        Returns
+        -------
+        Index
+
+        Notes
+        -----
+        ``symmetric_difference`` contains elements that appear in either
+        ``idx1`` or ``idx2`` but not both. Equivalent to the Index created by
+        ``idx1.difference(idx2) | idx2.difference(idx1)`` with duplicates
+        dropped.
+
+        Examples
+        --------
+        >>> idx1 = pd.Index([1, 2, 3, 4])
+        >>> idx2 = pd.Index([2, 3, 4, 5])
+        >>> idx1.symmetric_difference(idx2)
+        Index([1, 5], dtype='int64')
+        """
+        self._validate_sort_keyword(sort)
+        self._assert_can_do_setop(other)
+        other, result_name_update = self._convert_can_do_setop(other)
+        if result_name is None:
+            result_name = result_name_update
+
+        if self.dtype != other.dtype:
+            self, other = self._dti_setop_align_tzs(other, "symmetric_difference")
+
+        if not self._should_compare(other):
+            return self.union(other, sort=sort).rename(result_name)
+
+        elif self.dtype != other.dtype:
+            dtype = self._find_common_type_compat(other)
+            this = self.astype(dtype, copy=False)
+            that = other.astype(dtype, copy=False)
+            return this.symmetric_difference(that, sort=sort).rename(result_name)
+
+        this = self.unique()
+        other = other.unique()
+        indexer = this.get_indexer_for(other)
+
+        # {this} minus {other}
+        common_indexer = indexer.take((indexer != -1).nonzero()[0])
+        left_indexer = np.setdiff1d(
+            np.arange(this.size), common_indexer, assume_unique=True
+        )
+        left_diff = this.take(left_indexer)
+
+        # {other} minus {this}
+        right_indexer = (indexer == -1).nonzero()[0]
+        right_diff = other.take(right_indexer)
+
+        res_values = left_diff.append(right_diff)
+        result = _maybe_try_sort(res_values, sort)
+
+        if not self._is_multi:
+            return Index(result, name=result_name, dtype=res_values.dtype)
+        else:
+            left_diff = cast("MultiIndex", left_diff)
+            if len(result) == 0:
+                # result might be an Index, if other was an Index
+                return left_diff.remove_unused_levels().set_names(result_name)
+            return result.set_names(result_name)
+
+    @final
+    def _assert_can_do_setop(self, other) -> bool:
+        if not is_list_like(other):
+            raise TypeError("Input must be Index or array-like")
+        return True
+
+    def _convert_can_do_setop(self, other) -> tuple[Index, Hashable]:
+        if not isinstance(other, Index):
+            other = Index(other, name=self.name)
+            result_name = self.name
+        else:
+            result_name = get_op_result_name(self, other)
+        return other, result_name
+
+    # --------------------------------------------------------------------
+    # Indexing Methods
+
+    def get_loc(self, key):
+        """
+        Get integer location, slice or boolean mask for requested label.
+
+        Parameters
+        ----------
+        key : label
+
+        Returns
+        -------
+        int if unique index, slice if monotonic index, else mask
+
+        Examples
+        --------
+        >>> unique_index = pd.Index(list('abc'))
+        >>> unique_index.get_loc('b')
+        1
+
+        >>> monotonic_index = pd.Index(list('abbc'))
+        >>> monotonic_index.get_loc('b')
+        slice(1, 3, None)
+
+        >>> non_monotonic_index = pd.Index(list('abcb'))
+        >>> non_monotonic_index.get_loc('b')
+        array([False,  True, False,  True])
+        """
+        casted_key = self._maybe_cast_indexer(key)
+        try:
+            return self._engine.get_loc(casted_key)
+        except KeyError as err:
+            if isinstance(casted_key, slice) or (
+                isinstance(casted_key, abc.Iterable)
+                and any(isinstance(x, slice) for x in casted_key)
+            ):
+                raise InvalidIndexError(key)
+            raise KeyError(key) from err
+        except TypeError:
+            # If we have a listlike key, _check_indexing_error will raise
+            #  InvalidIndexError. Otherwise we fall through and re-raise
+            #  the TypeError.
+            self._check_indexing_error(key)
+            raise
+
+    @final
+    def get_indexer(
+        self,
+        target,
+        method: ReindexMethod | None = None,
+        limit: int | None = None,
+        tolerance=None,
+    ) -> npt.NDArray[np.intp]:
+        """
+        Compute indexer and mask for new index given the current index.
+
+        The indexer should be then used as an input to ndarray.take to align the
+        current data to the new index.
+
+        Parameters
+        ----------
+        target : Index
+        method : {None, 'pad'/'ffill', 'backfill'/'bfill', 'nearest'}, optional
+            * default: exact matches only.
+            * pad / ffill: find the PREVIOUS index value if no exact match.
+            * backfill / bfill: use NEXT index value if no exact match
+            * nearest: use the NEAREST index value if no exact match. Tied
+              distances are broken by preferring the larger index value.
+        limit : int, optional
+            Maximum number of consecutive labels in ``target`` to match for
+            inexact matches.
+        tolerance : optional
+            Maximum distance between original and new labels for inexact
+            matches. The values of the index at the matching locations must
+            satisfy the equation ``abs(index[indexer] - target) <= tolerance``.
+
+            Tolerance may be a scalar value, which applies the same tolerance
+            to all values, or list-like, which applies variable tolerance per
+            element. List-like includes list, tuple, array, Series, and must be
+            the same size as the index and its dtype must exactly match the
+            index's type.
+
+        Returns
+        -------
+        np.ndarray[np.intp]
+            Integers from 0 to n - 1 indicating that the index at these
+            positions matches the corresponding target values. Missing values
+            in the target are marked by -1.
+
+        Notes
+        -----
+        Returns -1 for unmatched values, for further explanation see the
+        example below.
+
+        Examples
+        --------
+        >>> index = pd.Index(['c', 'a', 'b'])
+        >>> index.get_indexer(['a', 'b', 'x'])
+        array([ 1,  2, -1])
+
+        Notice that the return value is an array of locations in ``index``
+        and ``x`` is marked by -1, as it is not in ``index``.
+        """
+        method = clean_reindex_fill_method(method)
+        orig_target = target
+        target = self._maybe_cast_listlike_indexer(target)
+
+        self._check_indexing_method(method, limit, tolerance)
+
+        if not self._index_as_unique:
+            raise InvalidIndexError(self._requires_unique_msg)
+
+        if len(target) == 0:
+            return np.array([], dtype=np.intp)
+
+        if not self._should_compare(target) and not self._should_partial_index(target):
+            # IntervalIndex get special treatment bc numeric scalars can be
+            #  matched to Interval scalars
+            return self._get_indexer_non_comparable(target, method=method, unique=True)
+
+        if isinstance(self.dtype, CategoricalDtype):
+            # _maybe_cast_listlike_indexer ensures target has our dtype
+            #  (could improve perf by doing _should_compare check earlier?)
+            assert self.dtype == target.dtype
+
+            indexer = self._engine.get_indexer(target.codes)
+            if self.hasnans and target.hasnans:
+                # After _maybe_cast_listlike_indexer, target elements which do not
+                # belong to some category are changed to NaNs
+                # Mask to track actual NaN values compared to inserted NaN values
+                # GH#45361
+                target_nans = isna(orig_target)
+                loc = self.get_loc(np.nan)
+                mask = target.isna()
+                indexer[target_nans] = loc
+                indexer[mask & ~target_nans] = -1
+            return indexer
+
+        if isinstance(target.dtype, CategoricalDtype):
+            # potential fastpath
+            # get an indexer for unique categories then propagate to codes via take_nd
+            # get_indexer instead of _get_indexer needed for MultiIndex cases
+            #  e.g. test_append_different_columns_types
+            categories_indexer = self.get_indexer(target.categories)
+
+            indexer = algos.take_nd(categories_indexer, target.codes, fill_value=-1)
+
+            if (not self._is_multi and self.hasnans) and target.hasnans:
+                # Exclude MultiIndex because hasnans raises NotImplementedError
+                # we should only get here if we are unique, so loc is an integer
+                # GH#41934
+                loc = self.get_loc(np.nan)
+                mask = target.isna()
+                indexer[mask] = loc
+
+            return ensure_platform_int(indexer)
+
+        pself, ptarget = self._maybe_downcast_for_indexing(target)
+        if pself is not self or ptarget is not target:
+            return pself.get_indexer(
+                ptarget, method=method, limit=limit, tolerance=tolerance
+            )
+
+        if self.dtype == target.dtype and self.equals(target):
+            # Only call equals if we have same dtype to avoid inference/casting
+            return np.arange(len(target), dtype=np.intp)
+
+        if self.dtype != target.dtype and not self._should_partial_index(target):
+            # _should_partial_index e.g. IntervalIndex with numeric scalars
+            #  that can be matched to Interval scalars.
+            dtype = self._find_common_type_compat(target)
+
+            this = self.astype(dtype, copy=False)
+            target = target.astype(dtype, copy=False)
+            return this._get_indexer(
+                target, method=method, limit=limit, tolerance=tolerance
+            )
+
+        return self._get_indexer(target, method, limit, tolerance)
+
+    def _get_indexer(
+        self,
+        target: Index,
+        method: str_t | None = None,
+        limit: int | None = None,
+        tolerance=None,
+    ) -> npt.NDArray[np.intp]:
+        if tolerance is not None:
+            tolerance = self._convert_tolerance(tolerance, target)
+
+        if method in ["pad", "backfill"]:
+            indexer = self._get_fill_indexer(target, method, limit, tolerance)
+        elif method == "nearest":
+            indexer = self._get_nearest_indexer(target, limit, tolerance)
+        else:
+            if target._is_multi and self._is_multi:
+                engine = self._engine
+                # error: Item "IndexEngine" of "Union[IndexEngine, ExtensionEngine]"
+                # has no attribute "_extract_level_codes"
+                tgt_values = engine._extract_level_codes(  # type: ignore[union-attr]
+                    target
+                )
+            else:
+                tgt_values = target._get_engine_target()
+
+            indexer = self._engine.get_indexer(tgt_values)
+
+        return ensure_platform_int(indexer)
+
+    @final
+    def _should_partial_index(self, target: Index) -> bool:
+        """
+        Should we attempt partial-matching indexing?
+        """
+        if isinstance(self.dtype, IntervalDtype):
+            if isinstance(target.dtype, IntervalDtype):
+                return False
+            # "Index" has no attribute "left"
+            return self.left._should_compare(target)  # type: ignore[attr-defined]
+        return False
+
+    @final
+    def _check_indexing_method(
+        self,
+        method: str_t | None,
+        limit: int | None = None,
+        tolerance=None,
+    ) -> None:
+        """
+        Raise if we have a get_indexer `method` that is not supported or valid.
+        """
+        if method not in [None, "bfill", "backfill", "pad", "ffill", "nearest"]:
+            # in practice the clean_reindex_fill_method call would raise
+            #  before we get here
+            raise ValueError("Invalid fill method")  # pragma: no cover
+
+        if self._is_multi:
+            if method == "nearest":
+                raise NotImplementedError(
+                    "method='nearest' not implemented yet "
+                    "for MultiIndex; see GitHub issue 9365"
+                )
+            if method in ("pad", "backfill"):
+                if tolerance is not None:
+                    raise NotImplementedError(
+                        "tolerance not implemented yet for MultiIndex"
+                    )
+
+        if isinstance(self.dtype, (IntervalDtype, CategoricalDtype)):
+            # GH#37871 for now this is only for IntervalIndex and CategoricalIndex
+            if method is not None:
+                raise NotImplementedError(
+                    f"method {method} not yet implemented for {type(self).__name__}"
+                )
+
+        if method is None:
+            if tolerance is not None:
+                raise ValueError(
+                    "tolerance argument only valid if doing pad, "
+                    "backfill or nearest reindexing"
+                )
+            if limit is not None:
+                raise ValueError(
+                    "limit argument only valid if doing pad, "
+                    "backfill or nearest reindexing"
+                )
+
+    def _convert_tolerance(self, tolerance, target: np.ndarray | Index) -> np.ndarray:
+        # override this method on subclasses
+        tolerance = np.asarray(tolerance)
+        if target.size != tolerance.size and tolerance.size > 1:
+            raise ValueError("list-like tolerance size must match target index size")
+        elif is_numeric_dtype(self) and not np.issubdtype(tolerance.dtype, np.number):
+            if tolerance.ndim > 0:
+                raise ValueError(
+                    f"tolerance argument for {type(self).__name__} with dtype "
+                    f"{self.dtype} must contain numeric elements if it is list type"
+                )
+
+            raise ValueError(
+                f"tolerance argument for {type(self).__name__} with dtype {self.dtype} "
+                f"must be numeric if it is a scalar: {repr(tolerance)}"
+            )
+        return tolerance
+
+    @final
+    def _get_fill_indexer(
+        self, target: Index, method: str_t, limit: int | None = None, tolerance=None
+    ) -> npt.NDArray[np.intp]:
+        if self._is_multi:
+            if not (self.is_monotonic_increasing or self.is_monotonic_decreasing):
+                raise ValueError("index must be monotonic increasing or decreasing")
+            encoded = self.append(target)._engine.values  # type: ignore[union-attr]
+            self_encoded = Index(encoded[: len(self)])
+            target_encoded = Index(encoded[len(self) :])
+            return self_encoded._get_fill_indexer(
+                target_encoded, method, limit, tolerance
+            )
+
+        if self.is_monotonic_increasing and target.is_monotonic_increasing:
+            target_values = target._get_engine_target()
+            own_values = self._get_engine_target()
+            if not isinstance(target_values, np.ndarray) or not isinstance(
+                own_values, np.ndarray
+            ):
+                raise NotImplementedError
+
+            if method == "pad":
+                indexer = libalgos.pad(own_values, target_values, limit=limit)
+            else:
+                # i.e. "backfill"
+                indexer = libalgos.backfill(own_values, target_values, limit=limit)
+        else:
+            indexer = self._get_fill_indexer_searchsorted(target, method, limit)
+        if tolerance is not None and len(self):
+            indexer = self._filter_indexer_tolerance(target, indexer, tolerance)
+        return indexer
+
+    @final
+    def _get_fill_indexer_searchsorted(
+        self, target: Index, method: str_t, limit: int | None = None
+    ) -> npt.NDArray[np.intp]:
+        """
+        Fallback pad/backfill get_indexer that works for monotonic decreasing
+        indexes and non-monotonic targets.
+        """
+        if limit is not None:
+            raise ValueError(
+                f"limit argument for {repr(method)} method only well-defined "
+                "if index and target are monotonic"
+            )
+
+        side: Literal["left", "right"] = "left" if method == "pad" else "right"
+
+        # find exact matches first (this simplifies the algorithm)
+        indexer = self.get_indexer(target)
+        nonexact = indexer == -1
+        indexer[nonexact] = self._searchsorted_monotonic(target[nonexact], side)
+        if side == "left":
+            # searchsorted returns "indices into a sorted array such that,
+            # if the corresponding elements in v were inserted before the
+            # indices, the order of a would be preserved".
+            # Thus, we need to subtract 1 to find values to the left.
+            indexer[nonexact] -= 1
+            # This also mapped not found values (values of 0 from
+            # np.searchsorted) to -1, which conveniently is also our
+            # sentinel for missing values
+        else:
+            # Mark indices to the right of the largest value as not found
+            indexer[indexer == len(self)] = -1
+        return indexer
+
+    @final
+    def _get_nearest_indexer(
+        self, target: Index, limit: int | None, tolerance
+    ) -> npt.NDArray[np.intp]:
+        """
+        Get the indexer for the nearest index labels; requires an index with
+        values that can be subtracted from each other (e.g., not strings or
+        tuples).
+        """
+        if not len(self):
+            return self._get_fill_indexer(target, "pad")
+
+        left_indexer = self.get_indexer(target, "pad", limit=limit)
+        right_indexer = self.get_indexer(target, "backfill", limit=limit)
+
+        left_distances = self._difference_compat(target, left_indexer)
+        right_distances = self._difference_compat(target, right_indexer)
+
+        op = operator.lt if self.is_monotonic_increasing else operator.le
+        indexer = np.where(
+            # error: Argument 1&2 has incompatible type "Union[ExtensionArray,
+            # ndarray[Any, Any]]"; expected "Union[SupportsDunderLE,
+            # SupportsDunderGE, SupportsDunderGT, SupportsDunderLT]"
+            op(left_distances, right_distances)  # type: ignore[arg-type]
+            | (right_indexer == -1),
+            left_indexer,
+            right_indexer,
+        )
+        if tolerance is not None:
+            indexer = self._filter_indexer_tolerance(target, indexer, tolerance)
+        return indexer
+
+    @final
+    def _filter_indexer_tolerance(
+        self,
+        target: Index,
+        indexer: npt.NDArray[np.intp],
+        tolerance,
+    ) -> npt.NDArray[np.intp]:
+        distance = self._difference_compat(target, indexer)
+
+        return np.where(distance <= tolerance, indexer, -1)
+
+    @final
+    def _difference_compat(
+        self, target: Index, indexer: npt.NDArray[np.intp]
+    ) -> ArrayLike:
+        # Compatibility for PeriodArray, for which __sub__ returns an ndarray[object]
+        #  of DateOffset objects, which do not support __abs__ (and would be slow
+        #  if they did)
+
+        if isinstance(self.dtype, PeriodDtype):
+            # Note: we only get here with matching dtypes
+            own_values = cast("PeriodArray", self._data)._ndarray
+            target_values = cast("PeriodArray", target._data)._ndarray
+            diff = own_values[indexer] - target_values
+        else:
+            # error: Unsupported left operand type for - ("ExtensionArray")
+            diff = self._values[indexer] - target._values  # type: ignore[operator]
+        return abs(diff)
+
+    # --------------------------------------------------------------------
+    # Indexer Conversion Methods
+
+    @final
+    def _validate_positional_slice(self, key: slice) -> None:
+        """
+        For positional indexing, a slice must have either int or None
+        for each of start, stop, and step.
+        """
+        self._validate_indexer("positional", key.start, "iloc")
+        self._validate_indexer("positional", key.stop, "iloc")
+        self._validate_indexer("positional", key.step, "iloc")
+
+    def _convert_slice_indexer(self, key: slice, kind: Literal["loc", "getitem"]):
+        """
+        Convert a slice indexer.
+
+        By definition, these are labels unless 'iloc' is passed in.
+        Floats are not allowed as the start, step, or stop of the slice.
+
+        Parameters
+        ----------
+        key : label of the slice bound
+        kind : {'loc', 'getitem'}
+        """
+
+        # potentially cast the bounds to integers
+        start, stop, step = key.start, key.stop, key.step
+
+        # figure out if this is a positional indexer
+        is_index_slice = is_valid_positional_slice(key)
+
+        # TODO(GH#50617): once Series.__[gs]etitem__ is removed we should be able
+        #  to simplify this.
+        if lib.is_np_dtype(self.dtype, "f"):
+            # We always treat __getitem__ slicing as label-based
+            # translate to locations
+            if kind == "getitem" and is_index_slice and not start == stop and step != 0:
+                # exclude step=0 from the warning because it will raise anyway
+                # start/stop both None e.g. [:] or [::-1] won't change.
+                # exclude start==stop since it will be empty either way, or
+                # will be [:] or [::-1] which won't change
+                warnings.warn(
+                    # GH#49612
+                    "The behavior of obj[i:j] with a float-dtype index is "
+                    "deprecated. In a future version, this will be treated as "
+                    "positional instead of label-based. For label-based slicing, "
+                    "use obj.loc[i:j] instead",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+            return self.slice_indexer(start, stop, step)
+
+        if kind == "getitem":
+            # called from the getitem slicers, validate that we are in fact integers
+            if is_index_slice:
+                # In this case the _validate_indexer checks below are redundant
+                return key
+            elif self.dtype.kind in "iu":
+                # Note: these checks are redundant if we know is_index_slice
+                self._validate_indexer("slice", key.start, "getitem")
+                self._validate_indexer("slice", key.stop, "getitem")
+                self._validate_indexer("slice", key.step, "getitem")
+                return key
+
+        # convert the slice to an indexer here; checking that the user didn't
+        #  pass a positional slice to loc
+        is_positional = is_index_slice and self._should_fallback_to_positional
+
+        # if we are mixed and have integers
+        if is_positional:
+            try:
+                # Validate start & stop
+                if start is not None:
+                    self.get_loc(start)
+                if stop is not None:
+                    self.get_loc(stop)
+                is_positional = False
+            except KeyError:
+                pass
+
+        if com.is_null_slice(key):
+            # It doesn't matter if we are positional or label based
+            indexer = key
+        elif is_positional:
+            if kind == "loc":
+                # GH#16121, GH#24612, GH#31810
+                raise TypeError(
+                    "Slicing a positional slice with .loc is not allowed, "
+                    "Use .loc with labels or .iloc with positions instead.",
+                )
+            indexer = key
+        else:
+            indexer = self.slice_indexer(start, stop, step)
+
+        return indexer
+
+    @final
+    def _raise_invalid_indexer(
+        self,
+        form: Literal["slice", "positional"],
+        key,
+        reraise: lib.NoDefault | None | Exception = lib.no_default,
+    ) -> None:
+        """
+        Raise consistent invalid indexer message.
+        """
+        msg = (
+            f"cannot do {form} indexing on {type(self).__name__} with these "
+            f"indexers [{key}] of type {type(key).__name__}"
+        )
+        if reraise is not lib.no_default:
+            raise TypeError(msg) from reraise
+        raise TypeError(msg)
+
+    # --------------------------------------------------------------------
+    # Reindex Methods
+
+    @final
+    def _validate_can_reindex(self, indexer: np.ndarray) -> None:
+        """
+        Check if we are allowing reindexing with this particular indexer.
+
+        Parameters
+        ----------
+        indexer : an integer ndarray
+
+        Raises
+        ------
+        ValueError if its a duplicate axis
+        """
+        # trying to reindex on an axis with duplicates
+        if not self._index_as_unique and len(indexer):
+            raise ValueError("cannot reindex on an axis with duplicate labels")
+
+    def reindex(
+        self,
+        target,
+        method: ReindexMethod | None = None,
+        level=None,
+        limit: int | None = None,
+        tolerance: float | None = None,
+    ) -> tuple[Index, npt.NDArray[np.intp] | None]:
+        """
+        Create index with target's values.
+
+        Parameters
+        ----------
+        target : an iterable
+        method : {None, 'pad'/'ffill', 'backfill'/'bfill', 'nearest'}, optional
+            * default: exact matches only.
+            * pad / ffill: find the PREVIOUS index value if no exact match.
+            * backfill / bfill: use NEXT index value if no exact match
+            * nearest: use the NEAREST index value if no exact match. Tied
+              distances are broken by preferring the larger index value.
+        level : int, optional
+            Level of multiindex.
+        limit : int, optional
+            Maximum number of consecutive labels in ``target`` to match for
+            inexact matches.
+        tolerance : int or float, optional
+            Maximum distance between original and new labels for inexact
+            matches. The values of the index at the matching locations must
+            satisfy the equation ``abs(index[indexer] - target) <= tolerance``.
+
+            Tolerance may be a scalar value, which applies the same tolerance
+            to all values, or list-like, which applies variable tolerance per
+            element. List-like includes list, tuple, array, Series, and must be
+            the same size as the index and its dtype must exactly match the
+            index's type.
+
+        Returns
+        -------
+        new_index : pd.Index
+            Resulting index.
+        indexer : np.ndarray[np.intp] or None
+            Indices of output values in original index.
+
+        Raises
+        ------
+        TypeError
+            If ``method`` passed along with ``level``.
+        ValueError
+            If non-unique multi-index
+        ValueError
+            If non-unique index and ``method`` or ``limit`` passed.
+
+        See Also
+        --------
+        Series.reindex : Conform Series to new index with optional filling logic.
+        DataFrame.reindex : Conform DataFrame to new index with optional filling logic.
+
+        Examples
+        --------
+        >>> idx = pd.Index(['car', 'bike', 'train', 'tractor'])
+        >>> idx
+        Index(['car', 'bike', 'train', 'tractor'], dtype='object')
+        >>> idx.reindex(['car', 'bike'])
+        (Index(['car', 'bike'], dtype='object'), array([0, 1]))
+        """
+        # GH6552: preserve names when reindexing to non-named target
+        # (i.e. neither Index nor Series).
+        preserve_names = not hasattr(target, "name")
+
+        # GH7774: preserve dtype/tz if target is empty and not an Index.
+        target = ensure_has_len(target)  # target may be an iterator
+
+        if not isinstance(target, Index) and len(target) == 0:
+            if level is not None and self._is_multi:
+                # "Index" has no attribute "levels"; maybe "nlevels"?
+                idx = self.levels[level]  # type: ignore[attr-defined]
+            else:
+                idx = self
+            target = idx[:0]
+        else:
+            target = ensure_index(target)
+
+        if level is not None and (
+            isinstance(self, ABCMultiIndex) or isinstance(target, ABCMultiIndex)
+        ):
+            if method is not None:
+                raise TypeError("Fill method not supported if level passed")
+
+            # TODO: tests where passing `keep_order=not self._is_multi`
+            #  makes a difference for non-MultiIndex case
+            target, indexer, _ = self._join_level(
+                target, level, how="right", keep_order=not self._is_multi
+            )
+
+        else:
+            if self.equals(target):
+                indexer = None
+            else:
+                if self._index_as_unique:
+                    indexer = self.get_indexer(
+                        target, method=method, limit=limit, tolerance=tolerance
+                    )
+                elif self._is_multi:
+                    raise ValueError("cannot handle a non-unique multi-index!")
+                elif not self.is_unique:
+                    # GH#42568
+                    raise ValueError("cannot reindex on an axis with duplicate labels")
+                else:
+                    indexer, _ = self.get_indexer_non_unique(target)
+
+        target = self._wrap_reindex_result(target, indexer, preserve_names)
+        return target, indexer
+
+    def _wrap_reindex_result(self, target, indexer, preserve_names: bool):
+        target = self._maybe_preserve_names(target, preserve_names)
+        return target
+
+    def _maybe_preserve_names(self, target: Index, preserve_names: bool):
+        if preserve_names and target.nlevels == 1 and target.name != self.name:
+            target = target.copy(deep=False)
+            target.name = self.name
+        return target
+
+    @final
+    def _reindex_non_unique(
+        self, target: Index
+    ) -> tuple[Index, npt.NDArray[np.intp], npt.NDArray[np.intp] | None]:
+        """
+        Create a new index with target's values (move/add/delete values as
+        necessary) use with non-unique Index and a possibly non-unique target.
+
+        Parameters
+        ----------
+        target : an iterable
+
+        Returns
+        -------
+        new_index : pd.Index
+            Resulting index.
+        indexer : np.ndarray[np.intp]
+            Indices of output values in original index.
+        new_indexer : np.ndarray[np.intp] or None
+
+        """
+        target = ensure_index(target)
+        if len(target) == 0:
+            # GH#13691
+            return self[:0], np.array([], dtype=np.intp), None
+
+        indexer, missing = self.get_indexer_non_unique(target)
+        check = indexer != -1
+        new_labels: Index | np.ndarray = self.take(indexer[check])
+        new_indexer = None
+
+        if len(missing):
+            length = np.arange(len(indexer), dtype=np.intp)
+
+            missing = ensure_platform_int(missing)
+            missing_labels = target.take(missing)
+            missing_indexer = length[~check]
+            cur_labels = self.take(indexer[check]).values
+            cur_indexer = length[check]
+
+            # Index constructor below will do inference
+            new_labels = np.empty((len(indexer),), dtype=object)
+            new_labels[cur_indexer] = cur_labels
+            new_labels[missing_indexer] = missing_labels
+
+            # GH#38906
+            if not len(self):
+                new_indexer = np.arange(0, dtype=np.intp)
+
+            # a unique indexer
+            elif target.is_unique:
+                # see GH5553, make sure we use the right indexer
+                new_indexer = np.arange(len(indexer), dtype=np.intp)
+                new_indexer[cur_indexer] = np.arange(len(cur_labels))
+                new_indexer[missing_indexer] = -1
+
+            # we have a non_unique selector, need to use the original
+            # indexer here
+            else:
+                # need to retake to have the same size as the indexer
+                indexer[~check] = -1
+
+                # reset the new indexer to account for the new size
+                new_indexer = np.arange(len(self.take(indexer)), dtype=np.intp)
+                new_indexer[~check] = -1
+
+        if not isinstance(self, ABCMultiIndex):
+            new_index = Index(new_labels, name=self.name)
+        else:
+            new_index = type(self).from_tuples(new_labels, names=self.names)
+        return new_index, indexer, new_indexer
+
+    # --------------------------------------------------------------------
+    # Join Methods
+
+    @overload
+    def join(
+        self,
+        other: Index,
+        *,
+        how: JoinHow = ...,
+        level: Level = ...,
+        return_indexers: Literal[True],
+        sort: bool = ...,
+    ) -> tuple[Index, npt.NDArray[np.intp] | None, npt.NDArray[np.intp] | None]:
+        ...
+
+    @overload
+    def join(
+        self,
+        other: Index,
+        *,
+        how: JoinHow = ...,
+        level: Level = ...,
+        return_indexers: Literal[False] = ...,
+        sort: bool = ...,
+    ) -> Index:
+        ...
+
+    @overload
+    def join(
+        self,
+        other: Index,
+        *,
+        how: JoinHow = ...,
+        level: Level = ...,
+        return_indexers: bool = ...,
+        sort: bool = ...,
+    ) -> Index | tuple[Index, npt.NDArray[np.intp] | None, npt.NDArray[np.intp] | None]:
+        ...
+
+    @final
+    @_maybe_return_indexers
+    def join(
+        self,
+        other: Index,
+        *,
+        how: JoinHow = "left",
+        level: Level | None = None,
+        return_indexers: bool = False,
+        sort: bool = False,
+    ) -> Index | tuple[Index, npt.NDArray[np.intp] | None, npt.NDArray[np.intp] | None]:
+        """
+        Compute join_index and indexers to conform data structures to the new index.
+
+        Parameters
+        ----------
+        other : Index
+        how : {'left', 'right', 'inner', 'outer'}
+        level : int or level name, default None
+        return_indexers : bool, default False
+        sort : bool, default False
+            Sort the join keys lexicographically in the result Index. If False,
+            the order of the join keys depends on the join type (how keyword).
+
+        Returns
+        -------
+        join_index, (left_indexer, right_indexer)
+
+        Examples
+        --------
+        >>> idx1 = pd.Index([1, 2, 3])
+        >>> idx2 = pd.Index([4, 5, 6])
+        >>> idx1.join(idx2, how='outer')
+        Index([1, 2, 3, 4, 5, 6], dtype='int64')
+        """
+        other = ensure_index(other)
+        sort = sort or how == "outer"
+
+        if isinstance(self, ABCDatetimeIndex) and isinstance(other, ABCDatetimeIndex):
+            if (self.tz is None) ^ (other.tz is None):
+                # Raise instead of casting to object below.
+                raise TypeError("Cannot join tz-naive with tz-aware DatetimeIndex")
+
+        if not self._is_multi and not other._is_multi:
+            # We have specific handling for MultiIndex below
+            pself, pother = self._maybe_downcast_for_indexing(other)
+            if pself is not self or pother is not other:
+                return pself.join(
+                    pother, how=how, level=level, return_indexers=True, sort=sort
+                )
+
+        # try to figure out the join level
+        # GH3662
+        if level is None and (self._is_multi or other._is_multi):
+            # have the same levels/names so a simple join
+            if self.names == other.names:
+                pass
+            else:
+                return self._join_multi(other, how=how)
+
+        # join on the level
+        if level is not None and (self._is_multi or other._is_multi):
+            return self._join_level(other, level, how=how)
+
+        if len(self) == 0 or len(other) == 0:
+            try:
+                return self._join_empty(other, how, sort)
+            except TypeError:
+                # object dtype; non-comparable objects
+                pass
+
+        if self.dtype != other.dtype:
+            dtype = self._find_common_type_compat(other)
+            this = self.astype(dtype, copy=False)
+            other = other.astype(dtype, copy=False)
+            return this.join(other, how=how, return_indexers=True)
+        elif (
+            isinstance(self, ABCCategoricalIndex)
+            and isinstance(other, ABCCategoricalIndex)
+            and not self.ordered
+            and not self.categories.equals(other.categories)
+        ):
+            # dtypes are "equal" but categories are in different order
+            other = Index(other._values.reorder_categories(self.categories))
+
+        _validate_join_method(how)
+
+        if (
+            self.is_monotonic_increasing
+            and other.is_monotonic_increasing
+            and self._can_use_libjoin
+            and other._can_use_libjoin
+            and (self.is_unique or other.is_unique)
+        ):
+            try:
+                return self._join_monotonic(other, how=how)
+            except TypeError:
+                # object dtype; non-comparable objects
+                pass
+        elif not self.is_unique or not other.is_unique:
+            return self._join_non_unique(other, how=how, sort=sort)
+
+        return self._join_via_get_indexer(other, how, sort)
+
+    @final
+    def _join_empty(
+        self, other: Index, how: JoinHow, sort: bool
+    ) -> tuple[Index, npt.NDArray[np.intp] | None, npt.NDArray[np.intp] | None]:
+        assert len(self) == 0 or len(other) == 0
+        _validate_join_method(how)
+
+        lidx: np.ndarray | None
+        ridx: np.ndarray | None
+
+        if len(other):
+            how = cast(JoinHow, {"left": "right", "right": "left"}.get(how, how))
+            join_index, ridx, lidx = other._join_empty(self, how, sort)
+        elif how in ["left", "outer"]:
+            if sort and not self.is_monotonic_increasing:
+                lidx = self.argsort()
+                join_index = self.take(lidx)
+            else:
+                lidx = None
+                join_index = self._view()
+            ridx = np.broadcast_to(np.intp(-1), len(join_index))
+        else:
+            join_index = other._view()
+            lidx = np.array([], dtype=np.intp)
+            ridx = None
+        return join_index, lidx, ridx
+
+    @final
+    def _join_via_get_indexer(
+        self, other: Index, how: JoinHow, sort: bool
+    ) -> tuple[Index, npt.NDArray[np.intp] | None, npt.NDArray[np.intp] | None]:
+        # Fallback if we do not have any fastpaths available based on
+        #  uniqueness/monotonicity
+
+        # Note: at this point we have checked matching dtypes
+
+        if how == "left":
+            join_index = self.sort_values() if sort else self
+        elif how == "right":
+            join_index = other.sort_values() if sort else other
+        elif how == "inner":
+            join_index = self.intersection(other, sort=sort)
+        elif how == "outer":
+            try:
+                join_index = self.union(other, sort=sort)
+            except TypeError:
+                join_index = self.union(other)
+                try:
+                    join_index = _maybe_try_sort(join_index, sort)
+                except TypeError:
+                    pass
+
+        if join_index is self:
+            lindexer = None
+        else:
+            lindexer = self.get_indexer_for(join_index)
+        if join_index is other:
+            rindexer = None
+        else:
+            rindexer = other.get_indexer_for(join_index)
+        return join_index, lindexer, rindexer
+
+    @final
+    def _join_multi(self, other: Index, how: JoinHow):
+        from pandas.core.indexes.multi import MultiIndex
+        from pandas.core.reshape.merge import restore_dropped_levels_multijoin
+
+        # figure out join names
+        self_names_list = list(com.not_none(*self.names))
+        other_names_list = list(com.not_none(*other.names))
+        self_names_order = self_names_list.index
+        other_names_order = other_names_list.index
+        self_names = set(self_names_list)
+        other_names = set(other_names_list)
+        overlap = self_names & other_names
+
+        # need at least 1 in common
+        if not overlap:
+            raise ValueError("cannot join with no overlapping index names")
+
+        if isinstance(self, MultiIndex) and isinstance(other, MultiIndex):
+            # Drop the non-matching levels from left and right respectively
+            ldrop_names = sorted(self_names - overlap, key=self_names_order)
+            rdrop_names = sorted(other_names - overlap, key=other_names_order)
+
+            # if only the order differs
+            if not len(ldrop_names + rdrop_names):
+                self_jnlevels = self
+                other_jnlevels = other.reorder_levels(self.names)
+            else:
+                self_jnlevels = self.droplevel(ldrop_names)
+                other_jnlevels = other.droplevel(rdrop_names)
+
+            # Join left and right
+            # Join on same leveled multi-index frames is supported
+            join_idx, lidx, ridx = self_jnlevels.join(
+                other_jnlevels, how=how, return_indexers=True
+            )
+
+            # Restore the dropped levels
+            # Returned index level order is
+            # common levels, ldrop_names, rdrop_names
+            dropped_names = ldrop_names + rdrop_names
+
+            # error: Argument 5/6 to "restore_dropped_levels_multijoin" has
+            # incompatible type "Optional[ndarray[Any, dtype[signedinteger[Any
+            # ]]]]"; expected "ndarray[Any, dtype[signedinteger[Any]]]"
+            levels, codes, names = restore_dropped_levels_multijoin(
+                self,
+                other,
+                dropped_names,
+                join_idx,
+                lidx,  # type: ignore[arg-type]
+                ridx,  # type: ignore[arg-type]
+            )
+
+            # Re-create the multi-index
+            multi_join_idx = MultiIndex(
+                levels=levels, codes=codes, names=names, verify_integrity=False
+            )
+
+            multi_join_idx = multi_join_idx.remove_unused_levels()
+
+            # maintain the order of the index levels
+            if how == "right":
+                level_order = other_names_list + ldrop_names
+            else:
+                level_order = self_names_list + rdrop_names
+            multi_join_idx = multi_join_idx.reorder_levels(level_order)
+
+            return multi_join_idx, lidx, ridx
+
+        jl = next(iter(overlap))
+
+        # Case where only one index is multi
+        # make the indices into mi's that match
+        flip_order = False
+        if isinstance(self, MultiIndex):
+            self, other = other, self
+            flip_order = True
+            # flip if join method is right or left
+            flip: dict[JoinHow, JoinHow] = {"right": "left", "left": "right"}
+            how = flip.get(how, how)
+
+        level = other.names.index(jl)
+        result = self._join_level(other, level, how=how)
+
+        if flip_order:
+            return result[0], result[2], result[1]
+        return result
+
+    @final
+    def _join_non_unique(
+        self, other: Index, how: JoinHow = "left", sort: bool = False
+    ) -> tuple[Index, npt.NDArray[np.intp], npt.NDArray[np.intp]]:
+        from pandas.core.reshape.merge import get_join_indexers_non_unique
+
+        # We only get here if dtypes match
+        assert self.dtype == other.dtype
+
+        left_idx, right_idx = get_join_indexers_non_unique(
+            self._values, other._values, how=how, sort=sort
+        )
+        mask = left_idx == -1
+
+        join_idx = self.take(left_idx)
+        right = other.take(right_idx)
+        join_index = join_idx.putmask(mask, right)
+        if isinstance(join_index, ABCMultiIndex) and how == "outer":
+            # test_join_index_levels
+            join_index = join_index._sort_levels_monotonic()
+        return join_index, left_idx, right_idx
+
+    @final
+    def _join_level(
+        self, other: Index, level, how: JoinHow = "left", keep_order: bool = True
+    ) -> tuple[MultiIndex, npt.NDArray[np.intp] | None, npt.NDArray[np.intp] | None]:
+        """
+        The join method *only* affects the level of the resulting
+        MultiIndex. Otherwise it just exactly aligns the Index data to the
+        labels of the level in the MultiIndex.
+
+        If ```keep_order == True```, the order of the data indexed by the
+        MultiIndex will not be changed; otherwise, it will tie out
+        with `other`.
+        """
+        from pandas.core.indexes.multi import MultiIndex
+
+        def _get_leaf_sorter(labels: list[np.ndarray]) -> npt.NDArray[np.intp]:
+            """
+            Returns sorter for the inner most level while preserving the
+            order of higher levels.
+
+            Parameters
+            ----------
+            labels : list[np.ndarray]
+                Each ndarray has signed integer dtype, not necessarily identical.
+
+            Returns
+            -------
+            np.ndarray[np.intp]
+            """
+            if labels[0].size == 0:
+                return np.empty(0, dtype=np.intp)
+
+            if len(labels) == 1:
+                return get_group_index_sorter(ensure_platform_int(labels[0]))
+
+            # find indexers of beginning of each set of
+            # same-key labels w.r.t all but last level
+            tic = labels[0][:-1] != labels[0][1:]
+            for lab in labels[1:-1]:
+                tic |= lab[:-1] != lab[1:]
+
+            starts = np.hstack(([True], tic, [True])).nonzero()[0]
+            lab = ensure_int64(labels[-1])
+            return lib.get_level_sorter(lab, ensure_platform_int(starts))
+
+        if isinstance(self, MultiIndex) and isinstance(other, MultiIndex):
+            raise TypeError("Join on level between two MultiIndex objects is ambiguous")
+
+        left, right = self, other
+
+        flip_order = not isinstance(self, MultiIndex)
+        if flip_order:
+            left, right = right, left
+            flip: dict[JoinHow, JoinHow] = {"right": "left", "left": "right"}
+            how = flip.get(how, how)
+
+        assert isinstance(left, MultiIndex)
+
+        level = left._get_level_number(level)
+        old_level = left.levels[level]
+
+        if not right.is_unique:
+            raise NotImplementedError(
+                "Index._join_level on non-unique index is not implemented"
+            )
+
+        new_level, left_lev_indexer, right_lev_indexer = old_level.join(
+            right, how=how, return_indexers=True
+        )
+
+        if left_lev_indexer is None:
+            if keep_order or len(left) == 0:
+                left_indexer = None
+                join_index = left
+            else:  # sort the leaves
+                left_indexer = _get_leaf_sorter(left.codes[: level + 1])
+                join_index = left[left_indexer]
+
+        else:
+            left_lev_indexer = ensure_platform_int(left_lev_indexer)
+            rev_indexer = lib.get_reverse_indexer(left_lev_indexer, len(old_level))
+            old_codes = left.codes[level]
+
+            taker = old_codes[old_codes != -1]
+            new_lev_codes = rev_indexer.take(taker)
+
+            new_codes = list(left.codes)
+            new_codes[level] = new_lev_codes
+
+            new_levels = list(left.levels)
+            new_levels[level] = new_level
+
+            if keep_order:  # just drop missing values. o.w. keep order
+                left_indexer = np.arange(len(left), dtype=np.intp)
+                left_indexer = cast(np.ndarray, left_indexer)
+                mask = new_lev_codes != -1
+                if not mask.all():
+                    new_codes = [lab[mask] for lab in new_codes]
+                    left_indexer = left_indexer[mask]
+
+            else:  # tie out the order with other
+                if level == 0:  # outer most level, take the fast route
+                    max_new_lev = 0 if len(new_lev_codes) == 0 else new_lev_codes.max()
+                    ngroups = 1 + max_new_lev
+                    left_indexer, counts = libalgos.groupsort_indexer(
+                        new_lev_codes, ngroups
+                    )
+
+                    # missing values are placed first; drop them!
+                    left_indexer = left_indexer[counts[0] :]
+                    new_codes = [lab[left_indexer] for lab in new_codes]
+
+                else:  # sort the leaves
+                    mask = new_lev_codes != -1
+                    mask_all = mask.all()
+                    if not mask_all:
+                        new_codes = [lab[mask] for lab in new_codes]
+
+                    left_indexer = _get_leaf_sorter(new_codes[: level + 1])
+                    new_codes = [lab[left_indexer] for lab in new_codes]
+
+                    # left_indexers are w.r.t masked frame.
+                    # reverse to original frame!
+                    if not mask_all:
+                        left_indexer = mask.nonzero()[0][left_indexer]
+
+            join_index = MultiIndex(
+                levels=new_levels,
+                codes=new_codes,
+                names=left.names,
+                verify_integrity=False,
+            )
+
+        if right_lev_indexer is not None:
+            right_indexer = right_lev_indexer.take(join_index.codes[level])
+        else:
+            right_indexer = join_index.codes[level]
+
+        if flip_order:
+            left_indexer, right_indexer = right_indexer, left_indexer
+
+        left_indexer = (
+            None if left_indexer is None else ensure_platform_int(left_indexer)
+        )
+        right_indexer = (
+            None if right_indexer is None else ensure_platform_int(right_indexer)
+        )
+        return join_index, left_indexer, right_indexer
+
+    @final
+    def _join_monotonic(
+        self, other: Index, how: JoinHow = "left"
+    ) -> tuple[Index, npt.NDArray[np.intp] | None, npt.NDArray[np.intp] | None]:
+        # We only get here with matching dtypes and both monotonic increasing
+        assert other.dtype == self.dtype
+        assert self._can_use_libjoin and other._can_use_libjoin
+
+        if self.equals(other):
+            # This is a convenient place for this check, but its correctness
+            #  does not depend on monotonicity, so it could go earlier
+            #  in the calling method.
+            ret_index = other if how == "right" else self
+            return ret_index, None, None
+
+        ridx: npt.NDArray[np.intp] | None
+        lidx: npt.NDArray[np.intp] | None
+
+        if self.is_unique and other.is_unique:
+            # We can perform much better than the general case
+            if how == "left":
+                join_index = self
+                lidx = None
+                ridx = self._left_indexer_unique(other)
+            elif how == "right":
+                join_index = other
+                lidx = other._left_indexer_unique(self)
+                ridx = None
+            elif how == "inner":
+                join_array, lidx, ridx = self._inner_indexer(other)
+                join_index = self._wrap_joined_index(join_array, other, lidx, ridx)
+            elif how == "outer":
+                join_array, lidx, ridx = self._outer_indexer(other)
+                join_index = self._wrap_joined_index(join_array, other, lidx, ridx)
+        else:
+            if how == "left":
+                join_array, lidx, ridx = self._left_indexer(other)
+            elif how == "right":
+                join_array, ridx, lidx = other._left_indexer(self)
+            elif how == "inner":
+                join_array, lidx, ridx = self._inner_indexer(other)
+            elif how == "outer":
+                join_array, lidx, ridx = self._outer_indexer(other)
+
+            assert lidx is not None
+            assert ridx is not None
+
+            join_index = self._wrap_joined_index(join_array, other, lidx, ridx)
+
+        lidx = None if lidx is None else ensure_platform_int(lidx)
+        ridx = None if ridx is None else ensure_platform_int(ridx)
+        return join_index, lidx, ridx
+
+    def _wrap_joined_index(
+        self,
+        joined: ArrayLike,
+        other: Self,
+        lidx: npt.NDArray[np.intp],
+        ridx: npt.NDArray[np.intp],
+    ) -> Self:
+        assert other.dtype == self.dtype
+
+        if isinstance(self, ABCMultiIndex):
+            name = self.names if self.names == other.names else None
+            # error: Incompatible return value type (got "MultiIndex",
+            # expected "Self")
+            mask = lidx == -1
+            join_idx = self.take(lidx)
+            right = cast("MultiIndex", other.take(ridx))
+            join_index = join_idx.putmask(mask, right)._sort_levels_monotonic()
+            return join_index.set_names(name)  # type: ignore[return-value]
+        else:
+            name = get_op_result_name(self, other)
+            return self._constructor._with_infer(joined, name=name, dtype=self.dtype)
+
+    @final
+    @cache_readonly
+    def _can_use_libjoin(self) -> bool:
+        """
+        Whether we can use the fastpaths implemented in _libs.join.
+
+        This is driven by whether (in monotonic increasing cases that are
+        guaranteed not to have NAs) we can convert to a np.ndarray without
+        making a copy. If we cannot, this negates the performance benefit
+        of using libjoin.
+        """
+        if type(self) is Index:
+            # excludes EAs, but include masks, we get here with monotonic
+            # values only, meaning no NA
+            return (
+                isinstance(self.dtype, np.dtype)
+                or isinstance(self._values, (ArrowExtensionArray, BaseMaskedArray))
+                or (
+                    isinstance(self.dtype, StringDtype)
+                    and self.dtype.storage == "python"
+                )
+            )
+        # Exclude index types where the conversion to numpy converts to object dtype,
+        #  which negates the performance benefit of libjoin
+        # Subclasses should override to return False if _get_join_target is
+        #  not zero-copy.
+        # TODO: exclude RangeIndex (which allocates memory)?
+        #  Doing so seems to break test_concat_datetime_timezone
+        return not isinstance(self, (ABCIntervalIndex, ABCMultiIndex))
+
+    # --------------------------------------------------------------------
+    # Uncategorized Methods
+
+    @property
+    def values(self) -> ArrayLike:
+        """
+        Return an array representing the data in the Index.
+
+        .. warning::
+
+           We recommend using :attr:`Index.array` or
+           :meth:`Index.to_numpy`, depending on whether you need
+           a reference to the underlying data or a NumPy array.
+
+        Returns
+        -------
+        array: numpy.ndarray or ExtensionArray
+
+        See Also
+        --------
+        Index.array : Reference to the underlying data.
+        Index.to_numpy : A NumPy array representing the underlying data.
+
+        Examples
+        --------
+        For :class:`pandas.Index`:
+
+        >>> idx = pd.Index([1, 2, 3])
+        >>> idx
+        Index([1, 2, 3], dtype='int64')
+        >>> idx.values
+        array([1, 2, 3])
+
+        For :class:`pandas.IntervalIndex`:
+
+        >>> idx = pd.interval_range(start=0, end=5)
+        >>> idx.values
+        
+        [(0, 1], (1, 2], (2, 3], (3, 4], (4, 5]]
+        Length: 5, dtype: interval[int64, right]
+        """
+        if using_copy_on_write():
+            data = self._data
+            if isinstance(data, np.ndarray):
+                data = data.view()
+                data.flags.writeable = False
+            return data
+        return self._data
+
+    @cache_readonly
+    @doc(IndexOpsMixin.array)
+    def array(self) -> ExtensionArray:
+        array = self._data
+        if isinstance(array, np.ndarray):
+            from pandas.core.arrays.numpy_ import NumpyExtensionArray
+
+            array = NumpyExtensionArray(array)
+        return array
+
+    @property
+    def _values(self) -> ExtensionArray | np.ndarray:
+        """
+        The best array representation.
+
+        This is an ndarray or ExtensionArray.
+
+        ``_values`` are consistent between ``Series`` and ``Index``.
+
+        It may differ from the public '.values' method.
+
+        index             | values          | _values       |
+        ----------------- | --------------- | ------------- |
+        Index             | ndarray         | ndarray       |
+        CategoricalIndex  | Categorical     | Categorical   |
+        DatetimeIndex     | ndarray[M8ns]   | DatetimeArray |
+        DatetimeIndex[tz] | ndarray[M8ns]   | DatetimeArray |
+        PeriodIndex       | ndarray[object] | PeriodArray   |
+        IntervalIndex     | IntervalArray   | IntervalArray |
+
+        See Also
+        --------
+        values : Values
+        """
+        return self._data
+
+    def _get_engine_target(self) -> ArrayLike:
+        """
+        Get the ndarray or ExtensionArray that we can pass to the IndexEngine
+        constructor.
+        """
+        vals = self._values
+        if isinstance(vals, StringArray):
+            # GH#45652 much more performant than ExtensionEngine
+            return vals._ndarray
+        if isinstance(vals, ArrowExtensionArray) and self.dtype.kind in "Mm":
+            import pyarrow as pa
+
+            pa_type = vals._pa_array.type
+            if pa.types.is_timestamp(pa_type):
+                vals = vals._to_datetimearray()
+                return vals._ndarray.view("i8")
+            elif pa.types.is_duration(pa_type):
+                vals = vals._to_timedeltaarray()
+                return vals._ndarray.view("i8")
+        if (
+            type(self) is Index
+            and isinstance(self._values, ExtensionArray)
+            and not isinstance(self._values, BaseMaskedArray)
+            and not (
+                isinstance(self._values, ArrowExtensionArray)
+                and is_numeric_dtype(self.dtype)
+                # Exclude decimal
+                and self.dtype.kind != "O"
+            )
+        ):
+            # TODO(ExtensionIndex): remove special-case, just use self._values
+            return self._values.astype(object)
+        return vals
+
+    @final
+    def _get_join_target(self) -> np.ndarray:
+        """
+        Get the ndarray or ExtensionArray that we can pass to the join
+        functions.
+        """
+        if isinstance(self._values, BaseMaskedArray):
+            # This is only used if our array is monotonic, so no NAs present
+            return self._values._data
+        elif isinstance(self._values, ArrowExtensionArray):
+            # This is only used if our array is monotonic, so no missing values
+            # present
+            return self._values.to_numpy()
+
+        # TODO: exclude ABCRangeIndex case here as it copies
+        target = self._get_engine_target()
+        if not isinstance(target, np.ndarray):
+            raise ValueError("_can_use_libjoin should return False.")
+        return target
+
+    def _from_join_target(self, result: np.ndarray) -> ArrayLike:
+        """
+        Cast the ndarray returned from one of the libjoin.foo_indexer functions
+        back to type(self._data).
+        """
+        if isinstance(self.values, BaseMaskedArray):
+            return type(self.values)(result, np.zeros(result.shape, dtype=np.bool_))
+        elif isinstance(self.values, (ArrowExtensionArray, StringArray)):
+            return type(self.values)._from_sequence(result, dtype=self.dtype)
+        return result
+
+    @doc(IndexOpsMixin._memory_usage)
+    def memory_usage(self, deep: bool = False) -> int:
+        result = self._memory_usage(deep=deep)
+
+        # include our engine hashtable
+        result += self._engine.sizeof(deep=deep)
+        return result
+
+    @final
+    def where(self, cond, other=None) -> Index:
+        """
+        Replace values where the condition is False.
+
+        The replacement is taken from other.
+
+        Parameters
+        ----------
+        cond : bool array-like with the same length as self
+            Condition to select the values on.
+        other : scalar, or array-like, default None
+            Replacement if the condition is False.
+
+        Returns
+        -------
+        pandas.Index
+            A copy of self with values replaced from other
+            where the condition is False.
+
+        See Also
+        --------
+        Series.where : Same method for Series.
+        DataFrame.where : Same method for DataFrame.
+
+        Examples
+        --------
+        >>> idx = pd.Index(['car', 'bike', 'train', 'tractor'])
+        >>> idx
+        Index(['car', 'bike', 'train', 'tractor'], dtype='object')
+        >>> idx.where(idx.isin(['car', 'train']), 'other')
+        Index(['car', 'other', 'train', 'other'], dtype='object')
+        """
+        if isinstance(self, ABCMultiIndex):
+            raise NotImplementedError(
+                ".where is not supported for MultiIndex operations"
+            )
+        cond = np.asarray(cond, dtype=bool)
+        return self.putmask(~cond, other)
+
+    # construction helpers
+    @final
+    @classmethod
+    def _raise_scalar_data_error(cls, data):
+        # We return the TypeError so that we can raise it from the constructor
+        #  in order to keep mypy happy
+        raise TypeError(
+            f"{cls.__name__}(...) must be called with a collection of some "
+            f"kind, {repr(data) if not isinstance(data, np.generic) else str(data)} "
+            "was passed"
+        )
+
+    def _validate_fill_value(self, value):
+        """
+        Check if the value can be inserted into our array without casting,
+        and convert it to an appropriate native type if necessary.
+
+        Raises
+        ------
+        TypeError
+            If the value cannot be inserted into an array of this dtype.
+        """
+        dtype = self.dtype
+        if isinstance(dtype, np.dtype) and dtype.kind not in "mM":
+            # return np_can_hold_element(dtype, value)
+            try:
+                return np_can_hold_element(dtype, value)
+            except LossySetitemError as err:
+                # re-raise as TypeError for consistency
+                raise TypeError from err
+        elif not can_hold_element(self._values, value):
+            raise TypeError
+        return value
+
+    def _is_memory_usage_qualified(self) -> bool:
+        """
+        Return a boolean if we need a qualified .info display.
+        """
+        return is_object_dtype(self.dtype) or (
+            is_string_dtype(self.dtype) and self.dtype.storage == "python"  # type: ignore[union-attr]
+        )
+
+    def __contains__(self, key: Any) -> bool:
+        """
+        Return a boolean indicating whether the provided key is in the index.
+
+        Parameters
+        ----------
+        key : label
+            The key to check if it is present in the index.
+
+        Returns
+        -------
+        bool
+            Whether the key search is in the index.
+
+        Raises
+        ------
+        TypeError
+            If the key is not hashable.
+
+        See Also
+        --------
+        Index.isin : Returns an ndarray of boolean dtype indicating whether the
+            list-like key is in the index.
+
+        Examples
+        --------
+        >>> idx = pd.Index([1, 2, 3, 4])
+        >>> idx
+        Index([1, 2, 3, 4], dtype='int64')
+
+        >>> 2 in idx
+        True
+        >>> 6 in idx
+        False
+        """
+        hash(key)
+        try:
+            return key in self._engine
+        except (OverflowError, TypeError, ValueError):
+            return False
+
+    # https://github.com/python/typeshed/issues/2148#issuecomment-520783318
+    # Incompatible types in assignment (expression has type "None", base class
+    # "object" defined the type as "Callable[[object], int]")
+    __hash__: ClassVar[None]  # type: ignore[assignment]
+
+    @final
+    def __setitem__(self, key, value) -> None:
+        raise TypeError("Index does not support mutable operations")
+
+    def __getitem__(self, key):
+        """
+        Override numpy.ndarray's __getitem__ method to work as desired.
+
+        This function adds lists and Series as valid boolean indexers
+        (ndarrays only supports ndarray with dtype=bool).
+
+        If resulting ndim != 1, plain ndarray is returned instead of
+        corresponding `Index` subclass.
+
+        """
+        getitem = self._data.__getitem__
+
+        if is_integer(key) or is_float(key):
+            # GH#44051 exclude bool, which would return a 2d ndarray
+            key = com.cast_scalar_indexer(key)
+            return getitem(key)
+
+        if isinstance(key, slice):
+            # This case is separated from the conditional above to avoid
+            # pessimization com.is_bool_indexer and ndim checks.
+            return self._getitem_slice(key)
+
+        if com.is_bool_indexer(key):
+            # if we have list[bools, length=1e5] then doing this check+convert
+            #  takes 166 µs + 2.1 ms and cuts the ndarray.__getitem__
+            #  time below from 3.8 ms to 496 µs
+            # if we already have ndarray[bool], the overhead is 1.4 µs or .25%
+            if isinstance(getattr(key, "dtype", None), ExtensionDtype):
+                key = key.to_numpy(dtype=bool, na_value=False)
+            else:
+                key = np.asarray(key, dtype=bool)
+
+            if not isinstance(self.dtype, ExtensionDtype):
+                if len(key) == 0 and len(key) != len(self):
+                    warnings.warn(
+                        "Using a boolean indexer with length 0 on an Index with "
+                        "length greater than 0 is deprecated and will raise in a "
+                        "future version.",
+                        FutureWarning,
+                        stacklevel=find_stack_level(),
+                    )
+
+        result = getitem(key)
+        # Because we ruled out integer above, we always get an arraylike here
+        if result.ndim > 1:
+            disallow_ndim_indexing(result)
+
+        # NB: Using _constructor._simple_new would break if MultiIndex
+        #  didn't override __getitem__
+        return self._constructor._simple_new(result, name=self._name)
+
+    def _getitem_slice(self, slobj: slice) -> Self:
+        """
+        Fastpath for __getitem__ when we know we have a slice.
+        """
+        res = self._data[slobj]
+        result = type(self)._simple_new(res, name=self._name, refs=self._references)
+        if "_engine" in self._cache:
+            reverse = slobj.step is not None and slobj.step < 0
+            result._engine._update_from_sliced(self._engine, reverse=reverse)  # type: ignore[union-attr]
+
+        return result
+
+    @final
+    def _can_hold_identifiers_and_holds_name(self, name) -> bool:
+        """
+        Faster check for ``name in self`` when we know `name` is a Python
+        identifier (e.g. in NDFrame.__getattr__, which hits this to support
+        . key lookup). For indexes that can't hold identifiers (everything
+        but object & categorical) we just return False.
+
+        https://github.com/pandas-dev/pandas/issues/19764
+        """
+        if (
+            is_object_dtype(self.dtype)
+            or is_string_dtype(self.dtype)
+            or isinstance(self.dtype, CategoricalDtype)
+        ):
+            return name in self
+        return False
+
+    def append(self, other: Index | Sequence[Index]) -> Index:
+        """
+        Append a collection of Index options together.
+
+        Parameters
+        ----------
+        other : Index or list/tuple of indices
+
+        Returns
+        -------
+        Index
+
+        Examples
+        --------
+        >>> idx = pd.Index([1, 2, 3])
+        >>> idx.append(pd.Index([4]))
+        Index([1, 2, 3, 4], dtype='int64')
+        """
+        to_concat = [self]
+
+        if isinstance(other, (list, tuple)):
+            to_concat += list(other)
+        else:
+            # error: Argument 1 to "append" of "list" has incompatible type
+            # "Union[Index, Sequence[Index]]"; expected "Index"
+            to_concat.append(other)  # type: ignore[arg-type]
+
+        for obj in to_concat:
+            if not isinstance(obj, Index):
+                raise TypeError("all inputs must be Index")
+
+        names = {obj.name for obj in to_concat}
+        name = None if len(names) > 1 else self.name
+
+        return self._concat(to_concat, name)
+
+    def _concat(self, to_concat: list[Index], name: Hashable) -> Index:
+        """
+        Concatenate multiple Index objects.
+        """
+        to_concat_vals = [x._values for x in to_concat]
+
+        result = concat_compat(to_concat_vals)
+
+        return Index._with_infer(result, name=name)
+
+    def putmask(self, mask, value) -> Index:
+        """
+        Return a new Index of the values set with the mask.
+
+        Returns
+        -------
+        Index
+
+        See Also
+        --------
+        numpy.ndarray.putmask : Changes elements of an array
+            based on conditional and input values.
+
+        Examples
+        --------
+        >>> idx1 = pd.Index([1, 2, 3])
+        >>> idx2 = pd.Index([5, 6, 7])
+        >>> idx1.putmask([True, False, False], idx2)
+        Index([5, 2, 3], dtype='int64')
+        """
+        mask, noop = validate_putmask(self._values, mask)
+        if noop:
+            return self.copy()
+
+        if self.dtype != object and is_valid_na_for_dtype(value, self.dtype):
+            # e.g. None -> np.nan, see also Block._standardize_fill_value
+            value = self._na_value
+
+        try:
+            converted = self._validate_fill_value(value)
+        except (LossySetitemError, ValueError, TypeError) as err:
+            if is_object_dtype(self.dtype):  # pragma: no cover
+                raise err
+
+            # See also: Block.coerce_to_target_dtype
+            dtype = self._find_common_type_compat(value)
+            return self.astype(dtype).putmask(mask, value)
+
+        values = self._values.copy()
+
+        if isinstance(values, np.ndarray):
+            converted = setitem_datetimelike_compat(values, mask.sum(), converted)
+            np.putmask(values, mask, converted)
+
+        else:
+            # Note: we use the original value here, not converted, as
+            #  _validate_fill_value is not idempotent
+            values._putmask(mask, value)
+
+        return self._shallow_copy(values)
+
+    def equals(self, other: Any) -> bool:
+        """
+        Determine if two Index object are equal.
+
+        The things that are being compared are:
+
+        * The elements inside the Index object.
+        * The order of the elements inside the Index object.
+
+        Parameters
+        ----------
+        other : Any
+            The other object to compare against.
+
+        Returns
+        -------
+        bool
+            True if "other" is an Index and it has the same elements and order
+            as the calling index; False otherwise.
+
+        Examples
+        --------
+        >>> idx1 = pd.Index([1, 2, 3])
+        >>> idx1
+        Index([1, 2, 3], dtype='int64')
+        >>> idx1.equals(pd.Index([1, 2, 3]))
+        True
+
+        The elements inside are compared
+
+        >>> idx2 = pd.Index(["1", "2", "3"])
+        >>> idx2
+        Index(['1', '2', '3'], dtype='object')
+
+        >>> idx1.equals(idx2)
+        False
+
+        The order is compared
+
+        >>> ascending_idx = pd.Index([1, 2, 3])
+        >>> ascending_idx
+        Index([1, 2, 3], dtype='int64')
+        >>> descending_idx = pd.Index([3, 2, 1])
+        >>> descending_idx
+        Index([3, 2, 1], dtype='int64')
+        >>> ascending_idx.equals(descending_idx)
+        False
+
+        The dtype is *not* compared
+
+        >>> int64_idx = pd.Index([1, 2, 3], dtype='int64')
+        >>> int64_idx
+        Index([1, 2, 3], dtype='int64')
+        >>> uint64_idx = pd.Index([1, 2, 3], dtype='uint64')
+        >>> uint64_idx
+        Index([1, 2, 3], dtype='uint64')
+        >>> int64_idx.equals(uint64_idx)
+        True
+        """
+        if self.is_(other):
+            return True
+
+        if not isinstance(other, Index):
+            return False
+
+        if len(self) != len(other):
+            # quickly return if the lengths are different
+            return False
+
+        if (
+            isinstance(self.dtype, StringDtype)
+            and self.dtype.na_value is np.nan
+            and other.dtype != self.dtype
+        ):
+            # TODO(infer_string) can we avoid this special case?
+            # special case for object behavior
+            return other.equals(self.astype(object))
+
+        if is_object_dtype(self.dtype) and not is_object_dtype(other.dtype):
+            # if other is not object, use other's logic for coercion
+            return other.equals(self)
+
+        if isinstance(other, ABCMultiIndex):
+            # d-level MultiIndex can equal d-tuple Index
+            return other.equals(self)
+
+        if isinstance(self._values, ExtensionArray):
+            # Dispatch to the ExtensionArray's .equals method.
+            if not isinstance(other, type(self)):
+                return False
+
+            earr = cast(ExtensionArray, self._data)
+            return earr.equals(other._data)
+
+        if isinstance(other.dtype, ExtensionDtype):
+            # All EA-backed Index subclasses override equals
+            return other.equals(self)
+
+        return array_equivalent(self._values, other._values)
+
+    @final
+    def identical(self, other) -> bool:
+        """
+        Similar to equals, but checks that object attributes and types are also equal.
+
+        Returns
+        -------
+        bool
+            If two Index objects have equal elements and same type True,
+            otherwise False.
+
+        Examples
+        --------
+        >>> idx1 = pd.Index(['1', '2', '3'])
+        >>> idx2 = pd.Index(['1', '2', '3'])
+        >>> idx2.identical(idx1)
+        True
+
+        >>> idx1 = pd.Index(['1', '2', '3'], name="A")
+        >>> idx2 = pd.Index(['1', '2', '3'], name="B")
+        >>> idx2.identical(idx1)
+        False
+        """
+        return (
+            self.equals(other)
+            and all(
+                getattr(self, c, None) == getattr(other, c, None)
+                for c in self._comparables
+            )
+            and type(self) == type(other)
+            and self.dtype == other.dtype
+        )
+
+    @final
+    def asof(self, label):
+        """
+        Return the label from the index, or, if not present, the previous one.
+
+        Assuming that the index is sorted, return the passed index label if it
+        is in the index, or return the previous index label if the passed one
+        is not in the index.
+
+        Parameters
+        ----------
+        label : object
+            The label up to which the method returns the latest index label.
+
+        Returns
+        -------
+        object
+            The passed label if it is in the index. The previous label if the
+            passed label is not in the sorted index or `NaN` if there is no
+            such label.
+
+        See Also
+        --------
+        Series.asof : Return the latest value in a Series up to the
+            passed index.
+        merge_asof : Perform an asof merge (similar to left join but it
+            matches on nearest key rather than equal key).
+        Index.get_loc : An `asof` is a thin wrapper around `get_loc`
+            with method='pad'.
+
+        Examples
+        --------
+        `Index.asof` returns the latest index label up to the passed label.
+
+        >>> idx = pd.Index(['2013-12-31', '2014-01-02', '2014-01-03'])
+        >>> idx.asof('2014-01-01')
+        '2013-12-31'
+
+        If the label is in the index, the method returns the passed label.
+
+        >>> idx.asof('2014-01-02')
+        '2014-01-02'
+
+        If all of the labels in the index are later than the passed label,
+        NaN is returned.
+
+        >>> idx.asof('1999-01-02')
+        nan
+
+        If the index is not sorted, an error is raised.
+
+        >>> idx_not_sorted = pd.Index(['2013-12-31', '2015-01-02',
+        ...                            '2014-01-03'])
+        >>> idx_not_sorted.asof('2013-12-31')
+        Traceback (most recent call last):
+        ValueError: index must be monotonic increasing or decreasing
+        """
+        self._searchsorted_monotonic(label)  # validate sortedness
+        try:
+            loc = self.get_loc(label)
+        except (KeyError, TypeError):
+            # KeyError -> No exact match, try for padded
+            # TypeError -> passed e.g. non-hashable, fall through to get
+            #  the tested exception message
+            indexer = self.get_indexer([label], method="pad")
+            if indexer.ndim > 1 or indexer.size > 1:
+                raise TypeError("asof requires scalar valued input")
+            loc = indexer.item()
+            if loc == -1:
+                return self._na_value
+        else:
+            if isinstance(loc, slice):
+                loc = loc.indices(len(self))[-1]
+
+        return self[loc]
+
+    def asof_locs(
+        self, where: Index, mask: npt.NDArray[np.bool_]
+    ) -> npt.NDArray[np.intp]:
+        """
+        Return the locations (indices) of labels in the index.
+
+        As in the :meth:`pandas.Index.asof`, if the label (a particular entry in
+        ``where``) is not in the index, the latest index label up to the
+        passed label is chosen and its index returned.
+
+        If all of the labels in the index are later than a label in ``where``,
+        -1 is returned.
+
+        ``mask`` is used to ignore ``NA`` values in the index during calculation.
+
+        Parameters
+        ----------
+        where : Index
+            An Index consisting of an array of timestamps.
+        mask : np.ndarray[bool]
+            Array of booleans denoting where values in the original
+            data are not ``NA``.
+
+        Returns
+        -------
+        np.ndarray[np.intp]
+            An array of locations (indices) of the labels from the index
+            which correspond to the return values of :meth:`pandas.Index.asof`
+            for every element in ``where``.
+
+        See Also
+        --------
+        Index.asof : Return the label from the index, or, if not present, the
+            previous one.
+
+        Examples
+        --------
+        >>> idx = pd.date_range('2023-06-01', periods=3, freq='D')
+        >>> where = pd.DatetimeIndex(['2023-05-30 00:12:00', '2023-06-01 00:00:00',
+        ...                           '2023-06-02 23:59:59'])
+        >>> mask = np.ones(3, dtype=bool)
+        >>> idx.asof_locs(where, mask)
+        array([-1,  0,  1])
+
+        We can use ``mask`` to ignore certain values in the index during calculation.
+
+        >>> mask[1] = False
+        >>> idx.asof_locs(where, mask)
+        array([-1,  0,  0])
+        """
+        # error: No overload variant of "searchsorted" of "ndarray" matches argument
+        # types "Union[ExtensionArray, ndarray[Any, Any]]", "str"
+        # TODO: will be fixed when ExtensionArray.searchsorted() is fixed
+        locs = self._values[mask].searchsorted(
+            where._values, side="right"  # type: ignore[call-overload]
+        )
+        locs = np.where(locs > 0, locs - 1, 0)
+
+        result = np.arange(len(self), dtype=np.intp)[mask].take(locs)
+
+        first_value = self._values[mask.argmax()]
+        result[(locs == 0) & (where._values < first_value)] = -1
+
+        return result
+
+    @overload
+    def sort_values(
+        self,
+        *,
+        return_indexer: Literal[False] = ...,
+        ascending: bool = ...,
+        na_position: NaPosition = ...,
+        key: Callable | None = ...,
+    ) -> Self:
+        ...
+
+    @overload
+    def sort_values(
+        self,
+        *,
+        return_indexer: Literal[True],
+        ascending: bool = ...,
+        na_position: NaPosition = ...,
+        key: Callable | None = ...,
+    ) -> tuple[Self, np.ndarray]:
+        ...
+
+    @overload
+    def sort_values(
+        self,
+        *,
+        return_indexer: bool = ...,
+        ascending: bool = ...,
+        na_position: NaPosition = ...,
+        key: Callable | None = ...,
+    ) -> Self | tuple[Self, np.ndarray]:
+        ...
+
+    @deprecate_nonkeyword_arguments(
+        version="3.0", allowed_args=["self"], name="sort_values"
+    )
+    def sort_values(
+        self,
+        return_indexer: bool = False,
+        ascending: bool = True,
+        na_position: NaPosition = "last",
+        key: Callable | None = None,
+    ) -> Self | tuple[Self, np.ndarray]:
+        """
+        Return a sorted copy of the index.
+
+        Return a sorted copy of the index, and optionally return the indices
+        that sorted the index itself.
+
+        Parameters
+        ----------
+        return_indexer : bool, default False
+            Should the indices that would sort the index be returned.
+        ascending : bool, default True
+            Should the index values be sorted in an ascending order.
+        na_position : {'first' or 'last'}, default 'last'
+            Argument 'first' puts NaNs at the beginning, 'last' puts NaNs at
+            the end.
+        key : callable, optional
+            If not None, apply the key function to the index values
+            before sorting. This is similar to the `key` argument in the
+            builtin :meth:`sorted` function, with the notable difference that
+            this `key` function should be *vectorized*. It should expect an
+            ``Index`` and return an ``Index`` of the same shape.
+
+        Returns
+        -------
+        sorted_index : pandas.Index
+            Sorted copy of the index.
+        indexer : numpy.ndarray, optional
+            The indices that the index itself was sorted by.
+
+        See Also
+        --------
+        Series.sort_values : Sort values of a Series.
+        DataFrame.sort_values : Sort values in a DataFrame.
+
+        Examples
+        --------
+        >>> idx = pd.Index([10, 100, 1, 1000])
+        >>> idx
+        Index([10, 100, 1, 1000], dtype='int64')
+
+        Sort values in ascending order (default behavior).
+
+        >>> idx.sort_values()
+        Index([1, 10, 100, 1000], dtype='int64')
+
+        Sort values in descending order, and also get the indices `idx` was
+        sorted by.
+
+        >>> idx.sort_values(ascending=False, return_indexer=True)
+        (Index([1000, 100, 10, 1], dtype='int64'), array([3, 1, 0, 2]))
+        """
+        if key is None and (
+            (ascending and self.is_monotonic_increasing)
+            or (not ascending and self.is_monotonic_decreasing)
+        ):
+            if return_indexer:
+                indexer = np.arange(len(self), dtype=np.intp)
+                return self.copy(), indexer
+            else:
+                return self.copy()
+
+        # GH 35584. Sort missing values according to na_position kwarg
+        # ignore na_position for MultiIndex
+        if not isinstance(self, ABCMultiIndex):
+            _as = nargsort(
+                items=self, ascending=ascending, na_position=na_position, key=key
+            )
+        else:
+            idx = cast(Index, ensure_key_mapped(self, key))
+            _as = idx.argsort(na_position=na_position)
+            if not ascending:
+                _as = _as[::-1]
+
+        sorted_index = self.take(_as)
+
+        if return_indexer:
+            return sorted_index, _as
+        else:
+            return sorted_index
+
+    @final
+    def sort(self, *args, **kwargs):
+        """
+        Use sort_values instead.
+        """
+        raise TypeError("cannot sort an Index object in-place, use sort_values instead")
+
+    def shift(self, periods: int = 1, freq=None):
+        """
+        Shift index by desired number of time frequency increments.
+
+        This method is for shifting the values of datetime-like indexes
+        by a specified time increment a given number of times.
+
+        Parameters
+        ----------
+        periods : int, default 1
+            Number of periods (or increments) to shift by,
+            can be positive or negative.
+        freq : pandas.DateOffset, pandas.Timedelta or str, optional
+            Frequency increment to shift by.
+            If None, the index is shifted by its own `freq` attribute.
+            Offset aliases are valid strings, e.g., 'D', 'W', 'M' etc.
+
+        Returns
+        -------
+        pandas.Index
+            Shifted index.
+
+        See Also
+        --------
+        Series.shift : Shift values of Series.
+
+        Notes
+        -----
+        This method is only implemented for datetime-like index classes,
+        i.e., DatetimeIndex, PeriodIndex and TimedeltaIndex.
+
+        Examples
+        --------
+        Put the first 5 month starts of 2011 into an index.
+
+        >>> month_starts = pd.date_range('1/1/2011', periods=5, freq='MS')
+        >>> month_starts
+        DatetimeIndex(['2011-01-01', '2011-02-01', '2011-03-01', '2011-04-01',
+                       '2011-05-01'],
+                      dtype='datetime64[ns]', freq='MS')
+
+        Shift the index by 10 days.
+
+        >>> month_starts.shift(10, freq='D')
+        DatetimeIndex(['2011-01-11', '2011-02-11', '2011-03-11', '2011-04-11',
+                       '2011-05-11'],
+                      dtype='datetime64[ns]', freq=None)
+
+        The default value of `freq` is the `freq` attribute of the index,
+        which is 'MS' (month start) in this example.
+
+        >>> month_starts.shift(10)
+        DatetimeIndex(['2011-11-01', '2011-12-01', '2012-01-01', '2012-02-01',
+                       '2012-03-01'],
+                      dtype='datetime64[ns]', freq='MS')
+        """
+        raise NotImplementedError(
+            f"This method is only implemented for DatetimeIndex, PeriodIndex and "
+            f"TimedeltaIndex; Got type {type(self).__name__}"
+        )
+
+    def argsort(self, *args, **kwargs) -> npt.NDArray[np.intp]:
+        """
+        Return the integer indices that would sort the index.
+
+        Parameters
+        ----------
+        *args
+            Passed to `numpy.ndarray.argsort`.
+        **kwargs
+            Passed to `numpy.ndarray.argsort`.
+
+        Returns
+        -------
+        np.ndarray[np.intp]
+            Integer indices that would sort the index if used as
+            an indexer.
+
+        See Also
+        --------
+        numpy.argsort : Similar method for NumPy arrays.
+        Index.sort_values : Return sorted copy of Index.
+
+        Examples
+        --------
+        >>> idx = pd.Index(['b', 'a', 'd', 'c'])
+        >>> idx
+        Index(['b', 'a', 'd', 'c'], dtype='object')
+
+        >>> order = idx.argsort()
+        >>> order
+        array([1, 0, 3, 2])
+
+        >>> idx[order]
+        Index(['a', 'b', 'c', 'd'], dtype='object')
+        """
+        # This works for either ndarray or EA, is overridden
+        #  by RangeIndex, MultIIndex
+        return self._data.argsort(*args, **kwargs)
+
+    def _check_indexing_error(self, key):
+        if not is_scalar(key):
+            # if key is not a scalar, directly raise an error (the code below
+            # would convert to numpy arrays and raise later any way) - GH29926
+            raise InvalidIndexError(key)
+
+    @cache_readonly
+    def _should_fallback_to_positional(self) -> bool:
+        """
+        Should an integer key be treated as positional?
+        """
+        return self.inferred_type not in {
+            "integer",
+            "mixed-integer",
+            "floating",
+            "complex",
+        }
+
+    _index_shared_docs[
+        "get_indexer_non_unique"
+    ] = """
+        Compute indexer and mask for new index given the current index.
+
+        The indexer should be then used as an input to ndarray.take to align the
+        current data to the new index.
+
+        Parameters
+        ----------
+        target : %(target_klass)s
+
+        Returns
+        -------
+        indexer : np.ndarray[np.intp]
+            Integers from 0 to n - 1 indicating that the index at these
+            positions matches the corresponding target values. Missing values
+            in the target are marked by -1.
+        missing : np.ndarray[np.intp]
+            An indexer into the target of the values not found.
+            These correspond to the -1 in the indexer array.
+
+        Examples
+        --------
+        >>> index = pd.Index(['c', 'b', 'a', 'b', 'b'])
+        >>> index.get_indexer_non_unique(['b', 'b'])
+        (array([1, 3, 4, 1, 3, 4]), array([], dtype=int64))
+
+        In the example below there are no matched values.
+
+        >>> index = pd.Index(['c', 'b', 'a', 'b', 'b'])
+        >>> index.get_indexer_non_unique(['q', 'r', 't'])
+        (array([-1, -1, -1]), array([0, 1, 2]))
+
+        For this reason, the returned ``indexer`` contains only integers equal to -1.
+        It demonstrates that there's no match between the index and the ``target``
+        values at these positions. The mask [0, 1, 2] in the return value shows that
+        the first, second, and third elements are missing.
+
+        Notice that the return value is a tuple contains two items. In the example
+        below the first item is an array of locations in ``index``. The second
+        item is a mask shows that the first and third elements are missing.
+
+        >>> index = pd.Index(['c', 'b', 'a', 'b', 'b'])
+        >>> index.get_indexer_non_unique(['f', 'b', 's'])
+        (array([-1,  1,  3,  4, -1]), array([0, 2]))
+        """
+
+    @Appender(_index_shared_docs["get_indexer_non_unique"] % _index_doc_kwargs)
+    def get_indexer_non_unique(
+        self, target
+    ) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]:
+        target = self._maybe_cast_listlike_indexer(target)
+
+        if not self._should_compare(target) and not self._should_partial_index(target):
+            # _should_partial_index e.g. IntervalIndex with numeric scalars
+            #  that can be matched to Interval scalars.
+            return self._get_indexer_non_comparable(target, method=None, unique=False)
+
+        pself, ptarget = self._maybe_downcast_for_indexing(target)
+        if pself is not self or ptarget is not target:
+            return pself.get_indexer_non_unique(ptarget)
+
+        if self.dtype != target.dtype:
+            # TODO: if object, could use infer_dtype to preempt costly
+            #  conversion if still non-comparable?
+            dtype = self._find_common_type_compat(target)
+
+            this = self.astype(dtype, copy=False)
+            that = target.astype(dtype, copy=False)
+            return this.get_indexer_non_unique(that)
+
+        # TODO: get_indexer has fastpaths for both Categorical-self and
+        #  Categorical-target. Can we do something similar here?
+
+        # Note: _maybe_downcast_for_indexing ensures we never get here
+        #  with MultiIndex self and non-Multi target
+        if self._is_multi and target._is_multi:
+            engine = self._engine
+            # Item "IndexEngine" of "Union[IndexEngine, ExtensionEngine]" has
+            # no attribute "_extract_level_codes"
+            tgt_values = engine._extract_level_codes(target)  # type: ignore[union-attr]
+        else:
+            tgt_values = target._get_engine_target()
+
+        indexer, missing = self._engine.get_indexer_non_unique(tgt_values)
+        return ensure_platform_int(indexer), ensure_platform_int(missing)
+
+    @final
+    def get_indexer_for(self, target) -> npt.NDArray[np.intp]:
+        """
+        Guaranteed return of an indexer even when non-unique.
+
+        This dispatches to get_indexer or get_indexer_non_unique
+        as appropriate.
+
+        Returns
+        -------
+        np.ndarray[np.intp]
+            List of indices.
+
+        Examples
+        --------
+        >>> idx = pd.Index([np.nan, 'var1', np.nan])
+        >>> idx.get_indexer_for([np.nan])
+        array([0, 2])
+        """
+        if self._index_as_unique:
+            return self.get_indexer(target)
+        indexer, _ = self.get_indexer_non_unique(target)
+        return indexer
+
+    def _get_indexer_strict(self, key, axis_name: str_t) -> tuple[Index, np.ndarray]:
+        """
+        Analogue to get_indexer that raises if any elements are missing.
+        """
+        keyarr = key
+        if not isinstance(keyarr, Index):
+            keyarr = com.asarray_tuplesafe(keyarr)
+
+        if self._index_as_unique:
+            indexer = self.get_indexer_for(keyarr)
+            keyarr = self.reindex(keyarr)[0]
+        else:
+            keyarr, indexer, new_indexer = self._reindex_non_unique(keyarr)
+
+        self._raise_if_missing(keyarr, indexer, axis_name)
+
+        keyarr = self.take(indexer)
+        if isinstance(key, Index):
+            # GH 42790 - Preserve name from an Index
+            keyarr.name = key.name
+        if lib.is_np_dtype(keyarr.dtype, "mM") or isinstance(
+            keyarr.dtype, DatetimeTZDtype
+        ):
+            # DTI/TDI.take can infer a freq in some cases when we dont want one
+            if isinstance(key, list) or (
+                isinstance(key, type(self))
+                # "Index" has no attribute "freq"
+                and key.freq is None  # type: ignore[attr-defined]
+            ):
+                keyarr = keyarr._with_freq(None)
+
+        return keyarr, indexer
+
+    def _raise_if_missing(self, key, indexer, axis_name: str_t) -> None:
+        """
+        Check that indexer can be used to return a result.
+
+        e.g. at least one element was found,
+        unless the list of keys was actually empty.
+
+        Parameters
+        ----------
+        key : list-like
+            Targeted labels (only used to show correct error message).
+        indexer: array-like of booleans
+            Indices corresponding to the key,
+            (with -1 indicating not found).
+        axis_name : str
+
+        Raises
+        ------
+        KeyError
+            If at least one key was requested but none was found.
+        """
+        if len(key) == 0:
+            return
+
+        # Count missing values
+        missing_mask = indexer < 0
+        nmissing = missing_mask.sum()
+
+        if nmissing:
+            if nmissing == len(indexer):
+                raise KeyError(f"None of [{key}] are in the [{axis_name}]")
+
+            not_found = list(ensure_index(key)[missing_mask.nonzero()[0]].unique())
+            raise KeyError(f"{not_found} not in index")
+
+    @overload
+    def _get_indexer_non_comparable(
+        self, target: Index, method, unique: Literal[True] = ...
+    ) -> npt.NDArray[np.intp]:
+        ...
+
+    @overload
+    def _get_indexer_non_comparable(
+        self, target: Index, method, unique: Literal[False]
+    ) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]:
+        ...
+
+    @overload
+    def _get_indexer_non_comparable(
+        self, target: Index, method, unique: bool = True
+    ) -> npt.NDArray[np.intp] | tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]:
+        ...
+
+    @final
+    def _get_indexer_non_comparable(
+        self, target: Index, method, unique: bool = True
+    ) -> npt.NDArray[np.intp] | tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]:
+        """
+        Called from get_indexer or get_indexer_non_unique when the target
+        is of a non-comparable dtype.
+
+        For get_indexer lookups with method=None, get_indexer is an _equality_
+        check, so non-comparable dtypes mean we will always have no matches.
+
+        For get_indexer lookups with a method, get_indexer is an _inequality_
+        check, so non-comparable dtypes mean we will always raise TypeError.
+
+        Parameters
+        ----------
+        target : Index
+        method : str or None
+        unique : bool, default True
+            * True if called from get_indexer.
+            * False if called from get_indexer_non_unique.
+
+        Raises
+        ------
+        TypeError
+            If doing an inequality check, i.e. method is not None.
+        """
+        if method is not None:
+            other_dtype = _unpack_nested_dtype(target)
+            raise TypeError(f"Cannot compare dtypes {self.dtype} and {other_dtype}")
+
+        no_matches = -1 * np.ones(target.shape, dtype=np.intp)
+        if unique:
+            # This is for get_indexer
+            return no_matches
+        else:
+            # This is for get_indexer_non_unique
+            missing = np.arange(len(target), dtype=np.intp)
+            return no_matches, missing
+
+    @property
+    def _index_as_unique(self) -> bool:
+        """
+        Whether we should treat this as unique for the sake of
+        get_indexer vs get_indexer_non_unique.
+
+        For IntervalIndex compat.
+        """
+        return self.is_unique
+
+    _requires_unique_msg = "Reindexing only valid with uniquely valued Index objects"
+
+    @final
+    def _maybe_downcast_for_indexing(self, other: Index) -> tuple[Index, Index]:
+        """
+        When dealing with an object-dtype Index and a non-object Index, see
+        if we can upcast the object-dtype one to improve performance.
+        """
+
+        if isinstance(self, ABCDatetimeIndex) and isinstance(other, ABCDatetimeIndex):
+            if (
+                self.tz is not None
+                and other.tz is not None
+                and not tz_compare(self.tz, other.tz)
+            ):
+                # standardize on UTC
+                return self.tz_convert("UTC"), other.tz_convert("UTC")
+
+        elif self.inferred_type == "date" and isinstance(other, ABCDatetimeIndex):
+            try:
+                return type(other)(self), other
+            except OutOfBoundsDatetime:
+                return self, other
+        elif self.inferred_type == "timedelta" and isinstance(other, ABCTimedeltaIndex):
+            # TODO: we dont have tests that get here
+            return type(other)(self), other
+
+        elif self.dtype.kind == "u" and other.dtype.kind == "i":
+            # GH#41873
+            if other.min() >= 0:
+                # lookup min as it may be cached
+                # TODO: may need itemsize check if we have non-64-bit Indexes
+                return self, other.astype(self.dtype)
+
+        elif self._is_multi and not other._is_multi:
+            try:
+                # "Type[Index]" has no attribute "from_tuples"
+                other = type(self).from_tuples(other)  # type: ignore[attr-defined]
+            except (TypeError, ValueError):
+                # let's instead try with a straight Index
+                self = Index(self._values)
+
+        if not is_object_dtype(self.dtype) and is_object_dtype(other.dtype):
+            # Reverse op so we dont need to re-implement on the subclasses
+            other, self = other._maybe_downcast_for_indexing(self)
+
+        return self, other
+
+    @final
+    def _find_common_type_compat(self, target) -> DtypeObj:
+        """
+        Implementation of find_common_type that adjusts for Index-specific
+        special cases.
+        """
+        target_dtype, _ = infer_dtype_from(target)
+
+        if using_string_dtype():
+            # special case: if left or right is a zero-length RangeIndex or
+            # Index[object], those can be created by the default empty constructors
+            # -> for that case ignore this dtype and always return the other
+            # (https://github.com/pandas-dev/pandas/pull/60797)
+            from pandas.core.indexes.range import RangeIndex
+
+            if len(self) == 0 and (
+                isinstance(self, RangeIndex) or self.dtype == np.object_
+            ):
+                if target_dtype.kind == "M":
+                    if hasattr(target_dtype, "tz"):
+                        target_dtype_ns = DatetimeTZDtype("ns", tz=target_dtype.tz)
+                    else:
+                        target_dtype_ns = np.dtype("datetime64[ns]")  # type: ignore[assignment]
+                    try:
+                        Index(target, dtype=target_dtype_ns, copy=False)
+                    except OutOfBoundsDatetime:
+                        return np.dtype(object)
+                    except Exception:
+                        pass
+                    return target_dtype_ns
+                return target_dtype
+            if (
+                isinstance(target, Index)
+                and len(target) == 0
+                and (isinstance(target, RangeIndex) or target_dtype == np.object_)
+            ):
+                return self.dtype
+
+        # special case: if one dtype is uint64 and the other a signed int, return object
+        # See https://github.com/pandas-dev/pandas/issues/26778 for discussion
+        # Now it's:
+        # * float | [u]int -> float
+        # * uint64 | signed int  -> object
+        # We may change union(float | [u]int) to go to object.
+        if self.dtype == "uint64" or target_dtype == "uint64":
+            if is_signed_integer_dtype(self.dtype) or is_signed_integer_dtype(
+                target_dtype
+            ):
+                return _dtype_obj
+
+        dtype = find_result_type(self.dtype, target)
+        dtype = common_dtype_categorical_compat([self, target], dtype)
+        return dtype
+
+    @final
+    def _should_compare(self, other: Index) -> bool:
+        """
+        Check if `self == other` can ever have non-False entries.
+        """
+
+        # NB: we use inferred_type rather than is_bool_dtype to catch
+        #  object_dtype_of_bool and categorical[object_dtype_of_bool] cases
+        if (
+            other.inferred_type == "boolean" and is_any_real_numeric_dtype(self.dtype)
+        ) or (
+            self.inferred_type == "boolean" and is_any_real_numeric_dtype(other.dtype)
+        ):
+            # GH#16877 Treat boolean labels passed to a numeric index as not
+            #  found. Without this fix False and True would be treated as 0 and 1
+            #  respectively.
+            return False
+
+        dtype = _unpack_nested_dtype(other)
+        return (
+            self._is_comparable_dtype(dtype)
+            or is_object_dtype(dtype)
+            or is_string_dtype(dtype)
+        )
+
+    def _is_comparable_dtype(self, dtype: DtypeObj) -> bool:
+        """
+        Can we compare values of the given dtype to our own?
+        """
+        if self.dtype.kind == "b":
+            return dtype.kind == "b"
+        elif is_numeric_dtype(self.dtype):
+            return is_numeric_dtype(dtype)
+        # TODO: this was written assuming we only get here with object-dtype,
+        #  which is no longer correct. Can we specialize for EA?
+        return True
+
+    @final
+    def groupby(self, values) -> PrettyDict[Hashable, np.ndarray]:
+        """
+        Group the index labels by a given array of values.
+
+        Parameters
+        ----------
+        values : array
+            Values used to determine the groups.
+
+        Returns
+        -------
+        dict
+            {group name -> group labels}
+        """
+        # TODO: if we are a MultiIndex, we can do better
+        # that converting to tuples
+        if isinstance(values, ABCMultiIndex):
+            values = values._values
+        values = Categorical(values)
+        result = values._reverse_indexer()
+
+        # map to the label
+        result = {k: self.take(v) for k, v in result.items()}
+
+        return PrettyDict(result)
+
+    def map(self, mapper, na_action: Literal["ignore"] | None = None):
+        """
+        Map values using an input mapping or function.
+
+        Parameters
+        ----------
+        mapper : function, dict, or Series
+            Mapping correspondence.
+        na_action : {None, 'ignore'}
+            If 'ignore', propagate NA values, without passing them to the
+            mapping correspondence.
+
+        Returns
+        -------
+        Union[Index, MultiIndex]
+            The output of the mapping function applied to the index.
+            If the function returns a tuple with more than one element
+            a MultiIndex will be returned.
+
+        Examples
+        --------
+        >>> idx = pd.Index([1, 2, 3])
+        >>> idx.map({1: 'a', 2: 'b', 3: 'c'})
+        Index(['a', 'b', 'c'], dtype='object')
+
+        Using `map` with a function:
+
+        >>> idx = pd.Index([1, 2, 3])
+        >>> idx.map('I am a {}'.format)
+        Index(['I am a 1', 'I am a 2', 'I am a 3'], dtype='object')
+
+        >>> idx = pd.Index(['a', 'b', 'c'])
+        >>> idx.map(lambda x: x.upper())
+        Index(['A', 'B', 'C'], dtype='object')
+        """
+        from pandas.core.indexes.multi import MultiIndex
+
+        new_values = self._map_values(mapper, na_action=na_action)
+
+        # we can return a MultiIndex
+        if new_values.size and isinstance(new_values[0], tuple):
+            if isinstance(self, MultiIndex):
+                names = self.names
+            elif self.name:
+                names = [self.name] * len(new_values[0])
+            else:
+                names = None
+            return MultiIndex.from_tuples(new_values, names=names)
+
+        dtype = None
+        if not new_values.size:
+            # empty
+            dtype = self.dtype
+
+        # e.g. if we are floating and new_values is all ints, then we
+        #  don't want to cast back to floating.  But if we are UInt64
+        #  and new_values is all ints, we want to try.
+        same_dtype = lib.infer_dtype(new_values, skipna=False) == self.inferred_type
+        if same_dtype:
+            new_values = maybe_cast_pointwise_result(
+                new_values, self.dtype, same_dtype=same_dtype
+            )
+
+        return Index._with_infer(new_values, dtype=dtype, copy=False, name=self.name)
+
+    # TODO: De-duplicate with map, xref GH#32349
+    @final
+    def _transform_index(self, func, *, level=None) -> Index:
+        """
+        Apply function to all values found in index.
+
+        This includes transforming multiindex entries separately.
+        Only apply function to one level of the MultiIndex if level is specified.
+        """
+        if isinstance(self, ABCMultiIndex):
+            values = [
+                self.get_level_values(i).map(func)
+                if i == level or level is None
+                else self.get_level_values(i)
+                for i in range(self.nlevels)
+            ]
+            return type(self).from_arrays(values)
+        else:
+            items = [func(x) for x in self]
+            return Index(items, name=self.name, tupleize_cols=False)
+
+    def isin(self, values, level=None) -> npt.NDArray[np.bool_]:
+        """
+        Return a boolean array where the index values are in `values`.
+
+        Compute boolean array of whether each index value is found in the
+        passed set of values. The length of the returned boolean array matches
+        the length of the index.
+
+        Parameters
+        ----------
+        values : set or list-like
+            Sought values.
+        level : str or int, optional
+            Name or position of the index level to use (if the index is a
+            `MultiIndex`).
+
+        Returns
+        -------
+        np.ndarray[bool]
+            NumPy array of boolean values.
+
+        See Also
+        --------
+        Series.isin : Same for Series.
+        DataFrame.isin : Same method for DataFrames.
+
+        Notes
+        -----
+        In the case of `MultiIndex` you must either specify `values` as a
+        list-like object containing tuples that are the same length as the
+        number of levels, or specify `level`. Otherwise it will raise a
+        ``ValueError``.
+
+        If `level` is specified:
+
+        - if it is the name of one *and only one* index level, use that level;
+        - otherwise it should be a number indicating level position.
+
+        Examples
+        --------
+        >>> idx = pd.Index([1,2,3])
+        >>> idx
+        Index([1, 2, 3], dtype='int64')
+
+        Check whether each index value in a list of values.
+
+        >>> idx.isin([1, 4])
+        array([ True, False, False])
+
+        >>> midx = pd.MultiIndex.from_arrays([[1,2,3],
+        ...                                  ['red', 'blue', 'green']],
+        ...                                  names=('number', 'color'))
+        >>> midx
+        MultiIndex([(1,   'red'),
+                    (2,  'blue'),
+                    (3, 'green')],
+                   names=['number', 'color'])
+
+        Check whether the strings in the 'color' level of the MultiIndex
+        are in a list of colors.
+
+        >>> midx.isin(['red', 'orange', 'yellow'], level='color')
+        array([ True, False, False])
+
+        To check across the levels of a MultiIndex, pass a list of tuples:
+
+        >>> midx.isin([(1, 'red'), (3, 'red')])
+        array([ True, False, False])
+        """
+        if level is not None:
+            self._validate_index_level(level)
+        return algos.isin(self._values, values)
+
+    def _get_string_slice(self, key: str_t):
+        # this is for partial string indexing,
+        # overridden in DatetimeIndex, TimedeltaIndex and PeriodIndex
+        raise NotImplementedError
+
+    def slice_indexer(
+        self,
+        start: Hashable | None = None,
+        end: Hashable | None = None,
+        step: int | None = None,
+    ) -> slice:
+        """
+        Compute the slice indexer for input labels and step.
+
+        Index needs to be ordered and unique.
+
+        Parameters
+        ----------
+        start : label, default None
+            If None, defaults to the beginning.
+        end : label, default None
+            If None, defaults to the end.
+        step : int, default None
+
+        Returns
+        -------
+        slice
+
+        Raises
+        ------
+        KeyError : If key does not exist, or key is not unique and index is
+            not ordered.
+
+        Notes
+        -----
+        This function assumes that the data is sorted, so use at your own peril
+
+        Examples
+        --------
+        This is a method on all index types. For example you can do:
+
+        >>> idx = pd.Index(list('abcd'))
+        >>> idx.slice_indexer(start='b', end='c')
+        slice(1, 3, None)
+
+        >>> idx = pd.MultiIndex.from_arrays([list('abcd'), list('efgh')])
+        >>> idx.slice_indexer(start='b', end=('c', 'g'))
+        slice(1, 3, None)
+        """
+        start_slice, end_slice = self.slice_locs(start, end, step=step)
+
+        # return a slice
+        if not is_scalar(start_slice):
+            raise AssertionError("Start slice bound is non-scalar")
+        if not is_scalar(end_slice):
+            raise AssertionError("End slice bound is non-scalar")
+
+        return slice(start_slice, end_slice, step)
+
+    def _maybe_cast_indexer(self, key):
+        """
+        If we have a float key and are not a floating index, then try to cast
+        to an int if equivalent.
+        """
+        return key
+
+    def _maybe_cast_listlike_indexer(self, target) -> Index:
+        """
+        Analogue to maybe_cast_indexer for get_indexer instead of get_loc.
+        """
+        target_index = ensure_index(target)
+        if (
+            not hasattr(target, "dtype")
+            and self.dtype == object
+            and target_index.dtype == "string"
+        ):
+            # If we started with a list-like, avoid inference to string dtype if self
+            # is object dtype (coercing to string dtype will alter the missing values)
+            target_index = Index(target, dtype=self.dtype)
+        return target_index
+
+    @final
+    def _validate_indexer(
+        self,
+        form: Literal["positional", "slice"],
+        key,
+        kind: Literal["getitem", "iloc"],
+    ) -> None:
+        """
+        If we are positional indexer, validate that we have appropriate
+        typed bounds must be an integer.
+        """
+        if not lib.is_int_or_none(key):
+            self._raise_invalid_indexer(form, key)
+
+    def _maybe_cast_slice_bound(self, label, side: str_t):
+        """
+        This function should be overloaded in subclasses that allow non-trivial
+        casting on label-slice bounds, e.g. datetime-like indices allowing
+        strings containing formatted datetimes.
+
+        Parameters
+        ----------
+        label : object
+        side : {'left', 'right'}
+
+        Returns
+        -------
+        label : object
+
+        Notes
+        -----
+        Value of `side` parameter should be validated in caller.
+        """
+
+        # We are a plain index here (sub-class override this method if they
+        # wish to have special treatment for floats/ints, e.g. datetimelike Indexes
+
+        if is_numeric_dtype(self.dtype):
+            return self._maybe_cast_indexer(label)
+
+        # reject them, if index does not contain label
+        if (is_float(label) or is_integer(label)) and label not in self:
+            self._raise_invalid_indexer("slice", label)
+
+        return label
+
+    def _searchsorted_monotonic(self, label, side: Literal["left", "right"] = "left"):
+        if self.is_monotonic_increasing:
+            return self.searchsorted(label, side=side)
+        elif self.is_monotonic_decreasing:
+            # np.searchsorted expects ascending sort order, have to reverse
+            # everything for it to work (element ordering, search side and
+            # resulting value).
+            pos = self[::-1].searchsorted(
+                label, side="right" if side == "left" else "left"
+            )
+            return len(self) - pos
+
+        raise ValueError("index must be monotonic increasing or decreasing")
+
+    def get_slice_bound(self, label, side: Literal["left", "right"]) -> int:
+        """
+        Calculate slice bound that corresponds to given label.
+
+        Returns leftmost (one-past-the-rightmost if ``side=='right'``) position
+        of given label.
+
+        Parameters
+        ----------
+        label : object
+        side : {'left', 'right'}
+
+        Returns
+        -------
+        int
+            Index of label.
+
+        See Also
+        --------
+        Index.get_loc : Get integer location, slice or boolean mask for requested
+            label.
+
+        Examples
+        --------
+        >>> idx = pd.RangeIndex(5)
+        >>> idx.get_slice_bound(3, 'left')
+        3
+
+        >>> idx.get_slice_bound(3, 'right')
+        4
+
+        If ``label`` is non-unique in the index, an error will be raised.
+
+        >>> idx_duplicate = pd.Index(['a', 'b', 'a', 'c', 'd'])
+        >>> idx_duplicate.get_slice_bound('a', 'left')
+        Traceback (most recent call last):
+        KeyError: Cannot get left slice bound for non-unique label: 'a'
+        """
+
+        if side not in ("left", "right"):
+            raise ValueError(
+                "Invalid value for side kwarg, must be either "
+                f"'left' or 'right': {side}"
+            )
+
+        original_label = label
+
+        # For datetime indices label may be a string that has to be converted
+        # to datetime boundary according to its resolution.
+        label = self._maybe_cast_slice_bound(label, side)
+
+        # we need to look up the label
+        try:
+            slc = self.get_loc(label)
+        except KeyError as err:
+            try:
+                return self._searchsorted_monotonic(label, side)
+            except ValueError:
+                # raise the original KeyError
+                raise err
+
+        if isinstance(slc, np.ndarray):
+            # get_loc may return a boolean array, which
+            # is OK as long as they are representable by a slice.
+            assert is_bool_dtype(slc.dtype)
+            slc = lib.maybe_booleans_to_slice(slc.view("u1"))
+            if isinstance(slc, np.ndarray):
+                raise KeyError(
+                    f"Cannot get {side} slice bound for non-unique "
+                    f"label: {repr(original_label)}"
+                )
+
+        if isinstance(slc, slice):
+            if side == "left":
+                return slc.start
+            else:
+                return slc.stop
+        else:
+            if side == "right":
+                return slc + 1
+            else:
+                return slc
+
+    def slice_locs(self, start=None, end=None, step=None) -> tuple[int, int]:
+        """
+        Compute slice locations for input labels.
+
+        Parameters
+        ----------
+        start : label, default None
+            If None, defaults to the beginning.
+        end : label, default None
+            If None, defaults to the end.
+        step : int, defaults None
+            If None, defaults to 1.
+
+        Returns
+        -------
+        tuple[int, int]
+
+        See Also
+        --------
+        Index.get_loc : Get location for a single label.
+
+        Notes
+        -----
+        This method only works if the index is monotonic or unique.
+
+        Examples
+        --------
+        >>> idx = pd.Index(list('abcd'))
+        >>> idx.slice_locs(start='b', end='c')
+        (1, 3)
+        """
+        inc = step is None or step >= 0
+
+        if not inc:
+            # If it's a reverse slice, temporarily swap bounds.
+            start, end = end, start
+
+        # GH 16785: If start and end happen to be date strings with UTC offsets
+        # attempt to parse and check that the offsets are the same
+        if isinstance(start, (str, datetime)) and isinstance(end, (str, datetime)):
+            try:
+                ts_start = Timestamp(start)
+                ts_end = Timestamp(end)
+            except (ValueError, TypeError):
+                pass
+            else:
+                if not tz_compare(ts_start.tzinfo, ts_end.tzinfo):
+                    raise ValueError("Both dates must have the same UTC offset")
+
+        start_slice = None
+        if start is not None:
+            start_slice = self.get_slice_bound(start, "left")
+        if start_slice is None:
+            start_slice = 0
+
+        end_slice = None
+        if end is not None:
+            end_slice = self.get_slice_bound(end, "right")
+        if end_slice is None:
+            end_slice = len(self)
+
+        if not inc:
+            # Bounds at this moment are swapped, swap them back and shift by 1.
+            #
+            # slice_locs('B', 'A', step=-1): s='B', e='A'
+            #
+            #              s='A'                 e='B'
+            # AFTER SWAP:    |                     |
+            #                v ------------------> V
+            #           -----------------------------------
+            #           | | |A|A|A|A| | | | | |B|B| | | | |
+            #           -----------------------------------
+            #              ^ <------------------ ^
+            # SHOULD BE:   |                     |
+            #           end=s-1              start=e-1
+            #
+            end_slice, start_slice = start_slice - 1, end_slice - 1
+
+            # i == -1 triggers ``len(self) + i`` selection that points to the
+            # last element, not before-the-first one, subtracting len(self)
+            # compensates that.
+            if end_slice == -1:
+                end_slice -= len(self)
+            if start_slice == -1:
+                start_slice -= len(self)
+
+        return start_slice, end_slice
+
+    def delete(self, loc) -> Self:
+        """
+        Make new Index with passed location(-s) deleted.
+
+        Parameters
+        ----------
+        loc : int or list of int
+            Location of item(-s) which will be deleted.
+            Use a list of locations to delete more than one value at the same time.
+
+        Returns
+        -------
+        Index
+            Will be same type as self, except for RangeIndex.
+
+        See Also
+        --------
+        numpy.delete : Delete any rows and column from NumPy array (ndarray).
+
+        Examples
+        --------
+        >>> idx = pd.Index(['a', 'b', 'c'])
+        >>> idx.delete(1)
+        Index(['a', 'c'], dtype='object')
+
+        >>> idx = pd.Index(['a', 'b', 'c'])
+        >>> idx.delete([0, 2])
+        Index(['b'], dtype='object')
+        """
+        values = self._values
+        res_values: ArrayLike
+        if isinstance(values, np.ndarray):
+            # TODO(__array_function__): special casing will be unnecessary
+            res_values = np.delete(values, loc)
+        else:
+            res_values = values.delete(loc)
+
+        # _constructor so RangeIndex-> Index with an int64 dtype
+        return self._constructor._simple_new(res_values, name=self.name)
+
+    def insert(self, loc: int, item) -> Index:
+        """
+        Make new Index inserting new item at location.
+
+        Follows Python numpy.insert semantics for negative values.
+
+        Parameters
+        ----------
+        loc : int
+        item : object
+
+        Returns
+        -------
+        Index
+
+        Examples
+        --------
+        >>> idx = pd.Index(['a', 'b', 'c'])
+        >>> idx.insert(1, 'x')
+        Index(['a', 'x', 'b', 'c'], dtype='object')
+        """
+        item = lib.item_from_zerodim(item)
+        if is_valid_na_for_dtype(item, self.dtype) and self.dtype != object:
+            item = self._na_value
+
+        arr = self._values
+
+        if using_string_dtype() and len(self) == 0 and self.dtype == np.object_:
+            # special case: if we are an empty object-dtype Index, also
+            # take into account the inserted item for the resulting dtype
+            # (https://github.com/pandas-dev/pandas/pull/60797)
+            dtype = self._find_common_type_compat(item)
+            if dtype != self.dtype:
+                return self.astype(dtype).insert(loc, item)
+
+        try:
+            if isinstance(arr, ExtensionArray):
+                res_values = arr.insert(loc, item)
+                return type(self)._simple_new(res_values, name=self.name)
+            else:
+                item = self._validate_fill_value(item)
+        except (TypeError, ValueError, LossySetitemError):
+            # e.g. trying to insert an integer into a DatetimeIndex
+            #  We cannot keep the same dtype, so cast to the (often object)
+            #  minimal shared dtype before doing the insert.
+            dtype = self._find_common_type_compat(item)
+            if dtype == self.dtype:
+                # EA's might run into recursion errors if loc is invalid
+                raise
+            return self.astype(dtype).insert(loc, item)
+
+        if arr.dtype != object or not isinstance(
+            item, (tuple, np.datetime64, np.timedelta64)
+        ):
+            # with object-dtype we need to worry about numpy incorrectly casting
+            # dt64/td64 to integer, also about treating tuples as sequences
+            # special-casing dt64/td64 https://github.com/numpy/numpy/issues/12550
+            casted = arr.dtype.type(item)
+            new_values = np.insert(arr, loc, casted)
+
+        else:
+            # error: No overload variant of "insert" matches argument types
+            # "ndarray[Any, Any]", "int", "None"
+            new_values = np.insert(arr, loc, None)  # type: ignore[call-overload]
+            loc = loc if loc >= 0 else loc - 1
+            new_values[loc] = item
+
+        out = Index._with_infer(new_values, name=self.name)
+        if (
+            using_string_dtype()
+            and is_string_dtype(out.dtype)
+            and new_values.dtype == object
+        ):
+            out = out.astype(new_values.dtype)
+        if self.dtype == object and out.dtype != object:
+            # GH#51363
+            warnings.warn(
+                "The behavior of Index.insert with object-dtype is deprecated, "
+                "in a future version this will return an object-dtype Index "
+                "instead of inferring a non-object dtype. To retain the old "
+                "behavior, do `idx.insert(loc, item).infer_objects(copy=False)`",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+        return out
+
+    def drop(
+        self,
+        labels: Index | np.ndarray | Iterable[Hashable],
+        errors: IgnoreRaise = "raise",
+    ) -> Index:
+        """
+        Make new Index with passed list of labels deleted.
+
+        Parameters
+        ----------
+        labels : array-like or scalar
+        errors : {'ignore', 'raise'}, default 'raise'
+            If 'ignore', suppress error and existing labels are dropped.
+
+        Returns
+        -------
+        Index
+            Will be same type as self, except for RangeIndex.
+
+        Raises
+        ------
+        KeyError
+            If not all of the labels are found in the selected axis
+
+        Examples
+        --------
+        >>> idx = pd.Index(['a', 'b', 'c'])
+        >>> idx.drop(['a'])
+        Index(['b', 'c'], dtype='object')
+        """
+        if not isinstance(labels, Index):
+            # avoid materializing e.g. RangeIndex
+            arr_dtype = "object" if self.dtype == "object" else None
+            labels = com.index_labels_to_array(labels, dtype=arr_dtype)
+
+        indexer = self.get_indexer_for(labels)
+        mask = indexer == -1
+        if mask.any():
+            if errors != "ignore":
+                raise KeyError(f"{labels[mask].tolist()} not found in axis")
+            indexer = indexer[~mask]
+        return self.delete(indexer)
+
+    @final
+    def infer_objects(self, copy: bool = True) -> Index:
+        """
+        If we have an object dtype, try to infer a non-object dtype.
+
+        Parameters
+        ----------
+        copy : bool, default True
+            Whether to make a copy in cases where no inference occurs.
+        """
+        if self._is_multi:
+            raise NotImplementedError(
+                "infer_objects is not implemented for MultiIndex. "
+                "Use index.to_frame().infer_objects() instead."
+            )
+        if self.dtype != object:
+            return self.copy() if copy else self
+
+        values = self._values
+        values = cast("npt.NDArray[np.object_]", values)
+        res_values = lib.maybe_convert_objects(
+            values,
+            convert_non_numeric=True,
+        )
+        if copy and res_values is values:
+            return self.copy()
+        result = Index(res_values, name=self.name)
+        if not copy and res_values is values and self._references is not None:
+            result._references = self._references
+            result._references.add_index_reference(result)
+        return result
+
+    @final
+    def diff(self, periods: int = 1) -> Index:
+        """
+        Computes the difference between consecutive values in the Index object.
+
+        If periods is greater than 1, computes the difference between values that
+        are `periods` number of positions apart.
+
+        Parameters
+        ----------
+        periods : int, optional
+            The number of positions between the current and previous
+            value to compute the difference with. Default is 1.
+
+        Returns
+        -------
+        Index
+            A new Index object with the computed differences.
+
+        Examples
+        --------
+        >>> import pandas as pd
+        >>> idx = pd.Index([10, 20, 30, 40, 50])
+        >>> idx.diff()
+        Index([nan, 10.0, 10.0, 10.0, 10.0], dtype='float64')
+
+        """
+        return Index(self.to_series().diff(periods))
+
+    @final
+    def round(self, decimals: int = 0) -> Self:
+        """
+        Round each value in the Index to the given number of decimals.
+
+        Parameters
+        ----------
+        decimals : int, optional
+            Number of decimal places to round to. If decimals is negative,
+            it specifies the number of positions to the left of the decimal point.
+
+        Returns
+        -------
+        Index
+            A new Index with the rounded values.
+
+        Examples
+        --------
+        >>> import pandas as pd
+        >>> idx = pd.Index([10.1234, 20.5678, 30.9123, 40.4567, 50.7890])
+        >>> idx.round(decimals=2)
+        Index([10.12, 20.57, 30.91, 40.46, 50.79], dtype='float64')
+
+        """
+        return self._constructor(self.to_series().round(decimals))
+
+    # --------------------------------------------------------------------
+    # Generated Arithmetic, Comparison, and Unary Methods
+
+    def _cmp_method(self, other, op):
+        """
+        Wrapper used to dispatch comparison operations.
+        """
+        if self.is_(other):
+            # fastpath
+            if op in {operator.eq, operator.le, operator.ge}:
+                arr = np.ones(len(self), dtype=bool)
+                if self._can_hold_na and not isinstance(self, ABCMultiIndex):
+                    # TODO: should set MultiIndex._can_hold_na = False?
+                    arr[self.isna()] = False
+                return arr
+            elif op is operator.ne:
+                arr = np.zeros(len(self), dtype=bool)
+                if self._can_hold_na and not isinstance(self, ABCMultiIndex):
+                    arr[self.isna()] = True
+                return arr
+
+        if isinstance(other, (np.ndarray, Index, ABCSeries, ExtensionArray)) and len(
+            self
+        ) != len(other):
+            raise ValueError("Lengths must match to compare")
+
+        if not isinstance(other, ABCMultiIndex):
+            other = extract_array(other, extract_numpy=True)
+        else:
+            other = np.asarray(other)
+
+        if is_object_dtype(self.dtype) and isinstance(other, ExtensionArray):
+            # e.g. PeriodArray, Categorical
+            result = op(self._values, other)
+
+        elif isinstance(self._values, ExtensionArray):
+            result = op(self._values, other)
+
+        elif is_object_dtype(self.dtype) and not isinstance(self, ABCMultiIndex):
+            # don't pass MultiIndex
+            result = ops.comp_method_OBJECT_ARRAY(op, self._values, other)
+
+        else:
+            result = ops.comparison_op(self._values, other, op)
+
+        return result
+
+    @final
+    def _logical_method(self, other, op):
+        res_name = ops.get_op_result_name(self, other)
+
+        lvalues = self._values
+        rvalues = extract_array(other, extract_numpy=True, extract_range=True)
+
+        res_values = ops.logical_op(lvalues, rvalues, op)
+        return self._construct_result(res_values, name=res_name)
+
+    @final
+    def _construct_result(self, result, name):
+        if isinstance(result, tuple):
+            return (
+                Index(result[0], name=name, dtype=result[0].dtype),
+                Index(result[1], name=name, dtype=result[1].dtype),
+            )
+        return Index(result, name=name, dtype=result.dtype)
+
+    def _arith_method(self, other, op):
+        if (
+            isinstance(other, Index)
+            and is_object_dtype(other.dtype)
+            and type(other) is not Index
+        ):
+            # We return NotImplemented for object-dtype index *subclasses* so they have
+            # a chance to implement ops before we unwrap them.
+            # See https://github.com/pandas-dev/pandas/issues/31109
+            return NotImplemented
+
+        return super()._arith_method(other, op)
+
+    @final
+    def _unary_method(self, op):
+        result = op(self._values)
+        return Index(result, name=self.name)
+
+    def __abs__(self) -> Index:
+        return self._unary_method(operator.abs)
+
+    def __neg__(self) -> Index:
+        return self._unary_method(operator.neg)
+
+    def __pos__(self) -> Index:
+        return self._unary_method(operator.pos)
+
+    def __invert__(self) -> Index:
+        # GH#8875
+        return self._unary_method(operator.inv)
+
+    # --------------------------------------------------------------------
+    # Reductions
+
+    def any(self, *args, **kwargs):
+        """
+        Return whether any element is Truthy.
+
+        Parameters
+        ----------
+        *args
+            Required for compatibility with numpy.
+        **kwargs
+            Required for compatibility with numpy.
+
+        Returns
+        -------
+        bool or array-like (if axis is specified)
+            A single element array-like may be converted to bool.
+
+        See Also
+        --------
+        Index.all : Return whether all elements are True.
+        Series.all : Return whether all elements are True.
+
+        Notes
+        -----
+        Not a Number (NaN), positive infinity and negative infinity
+        evaluate to True because these are not equal to zero.
+
+        Examples
+        --------
+        >>> index = pd.Index([0, 1, 2])
+        >>> index.any()
+        True
+
+        >>> index = pd.Index([0, 0, 0])
+        >>> index.any()
+        False
+        """
+        nv.validate_any(args, kwargs)
+        self._maybe_disable_logical_methods("any")
+        vals = self._values
+        if not isinstance(vals, np.ndarray):
+            # i.e. EA, call _reduce instead of "any" to get TypeError instead
+            #  of AttributeError
+            return vals._reduce("any")
+        return np.any(vals)
+
+    def all(self, *args, **kwargs):
+        """
+        Return whether all elements are Truthy.
+
+        Parameters
+        ----------
+        *args
+            Required for compatibility with numpy.
+        **kwargs
+            Required for compatibility with numpy.
+
+        Returns
+        -------
+        bool or array-like (if axis is specified)
+            A single element array-like may be converted to bool.
+
+        See Also
+        --------
+        Index.any : Return whether any element in an Index is True.
+        Series.any : Return whether any element in a Series is True.
+        Series.all : Return whether all elements in a Series are True.
+
+        Notes
+        -----
+        Not a Number (NaN), positive infinity and negative infinity
+        evaluate to True because these are not equal to zero.
+
+        Examples
+        --------
+        True, because nonzero integers are considered True.
+
+        >>> pd.Index([1, 2, 3]).all()
+        True
+
+        False, because ``0`` is considered False.
+
+        >>> pd.Index([0, 1, 2]).all()
+        False
+        """
+        nv.validate_all(args, kwargs)
+        self._maybe_disable_logical_methods("all")
+        vals = self._values
+        if not isinstance(vals, np.ndarray):
+            # i.e. EA, call _reduce instead of "all" to get TypeError instead
+            #  of AttributeError
+            return vals._reduce("all")
+        return np.all(vals)
+
+    @final
+    def _maybe_disable_logical_methods(self, opname: str_t) -> None:
+        """
+        raise if this Index subclass does not support any or all.
+        """
+        if (
+            isinstance(self, ABCMultiIndex)
+            # TODO(3.0): PeriodArray and DatetimeArray any/all will raise,
+            #  so checking needs_i8_conversion will be unnecessary
+            or (needs_i8_conversion(self.dtype) and self.dtype.kind != "m")
+        ):
+            # This call will raise
+            make_invalid_op(opname)(self)
+
+    @Appender(IndexOpsMixin.argmin.__doc__)
+    def argmin(self, axis=None, skipna: bool = True, *args, **kwargs) -> int:
+        nv.validate_argmin(args, kwargs)
+        nv.validate_minmax_axis(axis)
+
+        if not self._is_multi and self.hasnans:
+            # Take advantage of cache
+            mask = self._isnan
+            if not skipna or mask.all():
+                warnings.warn(
+                    f"The behavior of {type(self).__name__}.argmax/argmin "
+                    "with skipna=False and NAs, or with all-NAs is deprecated. "
+                    "In a future version this will raise ValueError.",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+                return -1
+        return super().argmin(skipna=skipna)
+
+    @Appender(IndexOpsMixin.argmax.__doc__)
+    def argmax(self, axis=None, skipna: bool = True, *args, **kwargs) -> int:
+        nv.validate_argmax(args, kwargs)
+        nv.validate_minmax_axis(axis)
+
+        if not self._is_multi and self.hasnans:
+            # Take advantage of cache
+            mask = self._isnan
+            if not skipna or mask.all():
+                warnings.warn(
+                    f"The behavior of {type(self).__name__}.argmax/argmin "
+                    "with skipna=False and NAs, or with all-NAs is deprecated. "
+                    "In a future version this will raise ValueError.",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+                return -1
+        return super().argmax(skipna=skipna)
+
+    def min(self, axis=None, skipna: bool = True, *args, **kwargs):
+        """
+        Return the minimum value of the Index.
+
+        Parameters
+        ----------
+        axis : {None}
+            Dummy argument for consistency with Series.
+        skipna : bool, default True
+            Exclude NA/null values when showing the result.
+        *args, **kwargs
+            Additional arguments and keywords for compatibility with NumPy.
+
+        Returns
+        -------
+        scalar
+            Minimum value.
+
+        See Also
+        --------
+        Index.max : Return the maximum value of the object.
+        Series.min : Return the minimum value in a Series.
+        DataFrame.min : Return the minimum values in a DataFrame.
+
+        Examples
+        --------
+        >>> idx = pd.Index([3, 2, 1])
+        >>> idx.min()
+        1
+
+        >>> idx = pd.Index(['c', 'b', 'a'])
+        >>> idx.min()
+        'a'
+
+        For a MultiIndex, the minimum is determined lexicographically.
+
+        >>> idx = pd.MultiIndex.from_product([('a', 'b'), (2, 1)])
+        >>> idx.min()
+        ('a', 1)
+        """
+        nv.validate_min(args, kwargs)
+        nv.validate_minmax_axis(axis)
+
+        if not len(self):
+            return self._na_value
+
+        if len(self) and self.is_monotonic_increasing:
+            # quick check
+            first = self[0]
+            if not isna(first):
+                return first
+
+        if not self._is_multi and self.hasnans:
+            # Take advantage of cache
+            mask = self._isnan
+            if not skipna or mask.all():
+                return self._na_value
+
+        if not self._is_multi and not isinstance(self._values, np.ndarray):
+            return self._values._reduce(name="min", skipna=skipna)
+
+        return nanops.nanmin(self._values, skipna=skipna)
+
+    def max(self, axis=None, skipna: bool = True, *args, **kwargs):
+        """
+        Return the maximum value of the Index.
+
+        Parameters
+        ----------
+        axis : int, optional
+            For compatibility with NumPy. Only 0 or None are allowed.
+        skipna : bool, default True
+            Exclude NA/null values when showing the result.
+        *args, **kwargs
+            Additional arguments and keywords for compatibility with NumPy.
+
+        Returns
+        -------
+        scalar
+            Maximum value.
+
+        See Also
+        --------
+        Index.min : Return the minimum value in an Index.
+        Series.max : Return the maximum value in a Series.
+        DataFrame.max : Return the maximum values in a DataFrame.
+
+        Examples
+        --------
+        >>> idx = pd.Index([3, 2, 1])
+        >>> idx.max()
+        3
+
+        >>> idx = pd.Index(['c', 'b', 'a'])
+        >>> idx.max()
+        'c'
+
+        For a MultiIndex, the maximum is determined lexicographically.
+
+        >>> idx = pd.MultiIndex.from_product([('a', 'b'), (2, 1)])
+        >>> idx.max()
+        ('b', 2)
+        """
+
+        nv.validate_max(args, kwargs)
+        nv.validate_minmax_axis(axis)
+
+        if not len(self):
+            return self._na_value
+
+        if len(self) and self.is_monotonic_increasing:
+            # quick check
+            last = self[-1]
+            if not isna(last):
+                return last
+
+        if not self._is_multi and self.hasnans:
+            # Take advantage of cache
+            mask = self._isnan
+            if not skipna or mask.all():
+                return self._na_value
+
+        if not self._is_multi and not isinstance(self._values, np.ndarray):
+            return self._values._reduce(name="max", skipna=skipna)
+
+        return nanops.nanmax(self._values, skipna=skipna)
+
+    # --------------------------------------------------------------------
+
+    @final
+    @property
+    def shape(self) -> Shape:
+        """
+        Return a tuple of the shape of the underlying data.
+
+        Examples
+        --------
+        >>> idx = pd.Index([1, 2, 3])
+        >>> idx
+        Index([1, 2, 3], dtype='int64')
+        >>> idx.shape
+        (3,)
+        """
+        # See GH#27775, GH#27384 for history/reasoning in how this is defined.
+        return (len(self),)
+
+
+def ensure_index_from_sequences(sequences, names=None) -> Index:
+    """
+    Construct an index from sequences of data.
+
+    A single sequence returns an Index. Many sequences returns a
+    MultiIndex.
+
+    Parameters
+    ----------
+    sequences : sequence of sequences
+    names : sequence of str
+
+    Returns
+    -------
+    index : Index or MultiIndex
+
+    Examples
+    --------
+    >>> ensure_index_from_sequences([[1, 2, 3]], names=["name"])
+    Index([1, 2, 3], dtype='int64', name='name')
+
+    >>> ensure_index_from_sequences([["a", "a"], ["a", "b"]], names=["L1", "L2"])
+    MultiIndex([('a', 'a'),
+                ('a', 'b')],
+               names=['L1', 'L2'])
+
+    See Also
+    --------
+    ensure_index
+    """
+    from pandas.core.indexes.multi import MultiIndex
+
+    if len(sequences) == 1:
+        if names is not None:
+            names = names[0]
+        return Index(sequences[0], name=names)
+    else:
+        return MultiIndex.from_arrays(sequences, names=names)
+
+
+def ensure_index(index_like: Axes, copy: bool = False) -> Index:
+    """
+    Ensure that we have an index from some index-like object.
+
+    Parameters
+    ----------
+    index_like : sequence
+        An Index or other sequence
+    copy : bool, default False
+
+    Returns
+    -------
+    index : Index or MultiIndex
+
+    See Also
+    --------
+    ensure_index_from_sequences
+
+    Examples
+    --------
+    >>> ensure_index(['a', 'b'])
+    Index(['a', 'b'], dtype='object')
+
+    >>> ensure_index([('a', 'a'),  ('b', 'c')])
+    Index([('a', 'a'), ('b', 'c')], dtype='object')
+
+    >>> ensure_index([['a', 'a'], ['b', 'c']])
+    MultiIndex([('a', 'b'),
+            ('a', 'c')],
+           )
+    """
+    if isinstance(index_like, Index):
+        if copy:
+            index_like = index_like.copy()
+        return index_like
+
+    if isinstance(index_like, ABCSeries):
+        name = index_like.name
+        return Index(index_like, name=name, copy=copy)
+
+    if is_iterator(index_like):
+        index_like = list(index_like)
+
+    if isinstance(index_like, list):
+        if type(index_like) is not list:  # noqa: E721
+            # must check for exactly list here because of strict type
+            # check in clean_index_list
+            index_like = list(index_like)
+
+        if len(index_like) and lib.is_all_arraylike(index_like):
+            from pandas.core.indexes.multi import MultiIndex
+
+            return MultiIndex.from_arrays(index_like)
+        else:
+            return Index(index_like, copy=copy, tupleize_cols=False)
+    else:
+        return Index(index_like, copy=copy)
+
+
+def ensure_has_len(seq):
+    """
+    If seq is an iterator, put its values into a list.
+    """
+    try:
+        len(seq)
+    except TypeError:
+        return list(seq)
+    else:
+        return seq
+
+
+def trim_front(strings: list[str]) -> list[str]:
+    """
+    Trims zeros and decimal points.
+
+    Examples
+    --------
+    >>> trim_front([" a", " b"])
+    ['a', 'b']
+
+    >>> trim_front([" a", " "])
+    ['a', '']
+    """
+    if not strings:
+        return strings
+    while all(strings) and all(x[0] == " " for x in strings):
+        strings = [x[1:] for x in strings]
+    return strings
+
+
+def _validate_join_method(method: str) -> None:
+    if method not in ["left", "right", "inner", "outer"]:
+        raise ValueError(f"do not recognize join method {method}")
+
+
+def maybe_extract_name(name, obj, cls) -> Hashable:
+    """
+    If no name is passed, then extract it from data, validating hashability.
+    """
+    if name is None and isinstance(obj, (Index, ABCSeries)):
+        # Note we don't just check for "name" attribute since that would
+        #  pick up e.g. dtype.name
+        name = obj.name
+
+    # GH#29069
+    if not is_hashable(name):
+        raise TypeError(f"{cls.__name__}.name must be a hashable type")
+
+    return name
+
+
+def get_unanimous_names(*indexes: Index) -> tuple[Hashable, ...]:
+    """
+    Return common name if all indices agree, otherwise None (level-by-level).
+
+    Parameters
+    ----------
+    indexes : list of Index objects
+
+    Returns
+    -------
+    list
+        A list representing the unanimous 'names' found.
+    """
+    name_tups = [tuple(i.names) for i in indexes]
+    name_sets = [{*ns} for ns in zip_longest(*name_tups)]
+    names = tuple(ns.pop() if len(ns) == 1 else None for ns in name_sets)
+    return names
+
+
+def _unpack_nested_dtype(other: Index) -> DtypeObj:
+    """
+    When checking if our dtype is comparable with another, we need
+    to unpack CategoricalDtype to look at its categories.dtype.
+
+    Parameters
+    ----------
+    other : Index
+
+    Returns
+    -------
+    np.dtype or ExtensionDtype
+    """
+    dtype = other.dtype
+    if isinstance(dtype, CategoricalDtype):
+        # If there is ever a SparseIndex, this could get dispatched
+        #  here too.
+        return dtype.categories.dtype
+    elif isinstance(dtype, ArrowDtype):
+        # GH 53617
+        import pyarrow as pa
+
+        if pa.types.is_dictionary(dtype.pyarrow_dtype):
+            other = other[:0].astype(ArrowDtype(dtype.pyarrow_dtype.value_type))
+    return other.dtype
+
+
+def _maybe_try_sort(result: Index | ArrayLike, sort: bool | None):
+    if sort is not False:
+        try:
+            # error: Incompatible types in assignment (expression has type
+            # "Union[ExtensionArray, ndarray[Any, Any], Index, Series,
+            # Tuple[Union[Union[ExtensionArray, ndarray[Any, Any]], Index, Series],
+            # ndarray[Any, Any]]]", variable has type "Union[Index,
+            # Union[ExtensionArray, ndarray[Any, Any]]]")
+            result = algos.safe_sort(result)  # type: ignore[assignment]
+        except TypeError as err:
+            if sort is True:
+                raise
+            warnings.warn(
+                f"{err}, sort order is undefined for incomparable objects.",
+                RuntimeWarning,
+                stacklevel=find_stack_level(),
+            )
+    return result
+
+
+def get_values_for_csv(
+    values: ArrayLike,
+    *,
+    date_format,
+    na_rep: str = "nan",
+    quoting=None,
+    float_format=None,
+    decimal: str = ".",
+) -> npt.NDArray[np.object_]:
+    """
+    Convert to types which can be consumed by the standard library's
+    csv.writer.writerows.
+    """
+    if isinstance(values, Categorical) and values.categories.dtype.kind in "Mm":
+        # GH#40754 Convert categorical datetimes to datetime array
+        values = algos.take_nd(
+            values.categories._values,
+            ensure_platform_int(values._codes),
+            fill_value=na_rep,
+        )
+
+    values = ensure_wrapped_if_datetimelike(values)
+
+    if isinstance(values, (DatetimeArray, TimedeltaArray)):
+        if values.ndim == 1:
+            result = values._format_native_types(na_rep=na_rep, date_format=date_format)
+            result = result.astype(object, copy=False)
+            return result
+
+        # GH#21734 Process every column separately, they might have different formats
+        results_converted = []
+        for i in range(len(values)):
+            result = values[i, :]._format_native_types(
+                na_rep=na_rep, date_format=date_format
+            )
+            results_converted.append(result.astype(object, copy=False))
+        return np.vstack(results_converted)
+
+    elif isinstance(values.dtype, PeriodDtype):
+        # TODO: tests that get here in column path
+        values = cast("PeriodArray", values)
+        res = values._format_native_types(na_rep=na_rep, date_format=date_format)
+        return res
+
+    elif isinstance(values.dtype, IntervalDtype):
+        # TODO: tests that get here in column path
+        values = cast("IntervalArray", values)
+        mask = values.isna()
+        if not quoting:
+            result = np.asarray(values).astype(str)
+        else:
+            result = np.array(values, dtype=object, copy=True)
+
+        result[mask] = na_rep
+        return result
+
+    elif values.dtype.kind == "f" and not isinstance(values.dtype, SparseDtype):
+        # see GH#13418: no special formatting is desired at the
+        # output (important for appropriate 'quoting' behaviour),
+        # so do not pass it through the FloatArrayFormatter
+        if float_format is None and decimal == ".":
+            mask = isna(values)
+
+            if not quoting:
+                values = values.astype(str)
+            else:
+                values = np.array(values, dtype="object")
+
+            values[mask] = na_rep
+            values = values.astype(object, copy=False)
+            return values
+
+        from pandas.io.formats.format import FloatArrayFormatter
+
+        formatter = FloatArrayFormatter(
+            values,
+            na_rep=na_rep,
+            float_format=float_format,
+            decimal=decimal,
+            quoting=quoting,
+            fixed_width=False,
+        )
+        res = formatter.get_result_as_array()
+        res = res.astype(object, copy=False)
+        return res
+
+    elif isinstance(values, ExtensionArray):
+        mask = isna(values)
+
+        new_values = np.asarray(values.astype(object))
+        new_values[mask] = na_rep
+        return new_values
+
+    else:
+        mask = isna(values)
+        itemsize = writers.word_len(na_rep)
+
+        if values.dtype != _dtype_obj and not quoting and itemsize:
+            values = values.astype(str)
+            if values.dtype.itemsize / np.dtype("U1").itemsize < itemsize:
+                # enlarge for the na_rep
+                values = values.astype(f"`__
+    for more.
+
+    Examples
+    --------
+    >>> pd.CategoricalIndex(["a", "b", "c", "a", "b", "c"])
+    CategoricalIndex(['a', 'b', 'c', 'a', 'b', 'c'],
+                     categories=['a', 'b', 'c'], ordered=False, dtype='category')
+
+    ``CategoricalIndex`` can also be instantiated from a ``Categorical``:
+
+    >>> c = pd.Categorical(["a", "b", "c", "a", "b", "c"])
+    >>> pd.CategoricalIndex(c)
+    CategoricalIndex(['a', 'b', 'c', 'a', 'b', 'c'],
+                     categories=['a', 'b', 'c'], ordered=False, dtype='category')
+
+    Ordered ``CategoricalIndex`` can have a min and max value.
+
+    >>> ci = pd.CategoricalIndex(
+    ...     ["a", "b", "c", "a", "b", "c"], ordered=True, categories=["c", "b", "a"]
+    ... )
+    >>> ci
+    CategoricalIndex(['a', 'b', 'c', 'a', 'b', 'c'],
+                     categories=['c', 'b', 'a'], ordered=True, dtype='category')
+    >>> ci.min()
+    'c'
+    """
+
+    _typ = "categoricalindex"
+    _data_cls = Categorical
+
+    @property
+    def _can_hold_strings(self):
+        return self.categories._can_hold_strings
+
+    @cache_readonly
+    def _should_fallback_to_positional(self) -> bool:
+        return self.categories._should_fallback_to_positional
+
+    codes: np.ndarray
+    categories: Index
+    ordered: bool | None
+    _data: Categorical
+    _values: Categorical
+
+    @property
+    def _engine_type(self) -> type[libindex.IndexEngine]:
+        # self.codes can have dtype int8, int16, int32 or int64, so we need
+        # to return the corresponding engine type (libindex.Int8Engine, etc.).
+        return {
+            np.int8: libindex.Int8Engine,
+            np.int16: libindex.Int16Engine,
+            np.int32: libindex.Int32Engine,
+            np.int64: libindex.Int64Engine,
+        }[self.codes.dtype.type]
+
+    # --------------------------------------------------------------------
+    # Constructors
+
+    def __new__(
+        cls,
+        data=None,
+        categories=None,
+        ordered=None,
+        dtype: Dtype | None = None,
+        copy: bool = False,
+        name: Hashable | None = None,
+    ) -> Self:
+        name = maybe_extract_name(name, data, cls)
+
+        if is_scalar(data):
+            # GH#38944 include None here, which pre-2.0 subbed in []
+            cls._raise_scalar_data_error(data)
+
+        data = Categorical(
+            data, categories=categories, ordered=ordered, dtype=dtype, copy=copy
+        )
+
+        return cls._simple_new(data, name=name)
+
+    # --------------------------------------------------------------------
+
+    def _is_dtype_compat(self, other: Index) -> Categorical:
+        """
+        *this is an internal non-public method*
+
+        provide a comparison between the dtype of self and other (coercing if
+        needed)
+
+        Parameters
+        ----------
+        other : Index
+
+        Returns
+        -------
+        Categorical
+
+        Raises
+        ------
+        TypeError if the dtypes are not compatible
+        """
+        if isinstance(other.dtype, CategoricalDtype):
+            cat = extract_array(other)
+            cat = cast(Categorical, cat)
+            if not cat._categories_match_up_to_permutation(self._values):
+                raise TypeError(
+                    "categories must match existing categories when appending"
+                )
+
+        elif other._is_multi:
+            # preempt raising NotImplementedError in isna call
+            raise TypeError("MultiIndex is not dtype-compatible with CategoricalIndex")
+        else:
+            values = other
+
+            cat = Categorical(other, dtype=self.dtype)
+            other = CategoricalIndex(cat)
+            if not other.isin(values).all():
+                raise TypeError(
+                    "cannot append a non-category item to a CategoricalIndex"
+                )
+            cat = other._values
+
+            if not ((cat == values) | (isna(cat) & isna(values))).all():
+                # GH#37667 see test_equals_non_category
+                raise TypeError(
+                    "categories must match existing categories when appending"
+                )
+
+        return cat
+
+    def equals(self, other: object) -> bool:
+        """
+        Determine if two CategoricalIndex objects contain the same elements.
+
+        Returns
+        -------
+        bool
+            ``True`` if two :class:`pandas.CategoricalIndex` objects have equal
+            elements, ``False`` otherwise.
+
+        Examples
+        --------
+        >>> ci = pd.CategoricalIndex(['a', 'b', 'c', 'a', 'b', 'c'])
+        >>> ci2 = pd.CategoricalIndex(pd.Categorical(['a', 'b', 'c', 'a', 'b', 'c']))
+        >>> ci.equals(ci2)
+        True
+
+        The order of elements matters.
+
+        >>> ci3 = pd.CategoricalIndex(['c', 'b', 'a', 'a', 'b', 'c'])
+        >>> ci.equals(ci3)
+        False
+
+        The orderedness also matters.
+
+        >>> ci4 = ci.as_ordered()
+        >>> ci.equals(ci4)
+        False
+
+        The categories matter, but the order of the categories matters only when
+        ``ordered=True``.
+
+        >>> ci5 = ci.set_categories(['a', 'b', 'c', 'd'])
+        >>> ci.equals(ci5)
+        False
+
+        >>> ci6 = ci.set_categories(['b', 'c', 'a'])
+        >>> ci.equals(ci6)
+        True
+        >>> ci_ordered = pd.CategoricalIndex(['a', 'b', 'c', 'a', 'b', 'c'],
+        ...                                  ordered=True)
+        >>> ci2_ordered = ci_ordered.set_categories(['b', 'c', 'a'])
+        >>> ci_ordered.equals(ci2_ordered)
+        False
+        """
+        if self.is_(other):
+            return True
+
+        if not isinstance(other, Index):
+            return False
+
+        try:
+            other = self._is_dtype_compat(other)
+        except (TypeError, ValueError):
+            return False
+
+        return self._data.equals(other)
+
+    # --------------------------------------------------------------------
+    # Rendering Methods
+
+    @property
+    def _formatter_func(self):
+        return self.categories._formatter_func
+
+    def _format_attrs(self):
+        """
+        Return a list of tuples of the (attr,formatted_value)
+        """
+        attrs: list[tuple[str, str | int | bool | None]]
+
+        attrs = [
+            (
+                "categories",
+                f"[{', '.join(self._data._repr_categories())}]",
+            ),
+            ("ordered", self.ordered),
+        ]
+        extra = super()._format_attrs()
+        return attrs + extra
+
+    # --------------------------------------------------------------------
+
+    @property
+    def inferred_type(self) -> str:
+        return "categorical"
+
+    @doc(Index.__contains__)
+    def __contains__(self, key: Any) -> bool:
+        # if key is a NaN, check if any NaN is in self.
+        if is_valid_na_for_dtype(key, self.categories.dtype):
+            return self.hasnans
+
+        return contains(self, key, container=self._engine)
+
+    def reindex(
+        self, target, method=None, level=None, limit: int | None = None, tolerance=None
+    ) -> tuple[Index, npt.NDArray[np.intp] | None]:
+        """
+        Create index with target's values (move/add/delete values as necessary)
+
+        Returns
+        -------
+        new_index : pd.Index
+            Resulting index
+        indexer : np.ndarray[np.intp] or None
+            Indices of output values in original index
+
+        """
+        if method is not None:
+            raise NotImplementedError(
+                "argument method is not implemented for CategoricalIndex.reindex"
+            )
+        if level is not None:
+            raise NotImplementedError(
+                "argument level is not implemented for CategoricalIndex.reindex"
+            )
+        if limit is not None:
+            raise NotImplementedError(
+                "argument limit is not implemented for CategoricalIndex.reindex"
+            )
+        return super().reindex(target)
+
+    # --------------------------------------------------------------------
+    # Indexing Methods
+
+    def _maybe_cast_indexer(self, key) -> int:
+        # GH#41933: we have to do this instead of self._data._validate_scalar
+        #  because this will correctly get partial-indexing on Interval categories
+        try:
+            return self._data._unbox_scalar(key)
+        except KeyError:
+            if is_valid_na_for_dtype(key, self.categories.dtype):
+                return -1
+            raise
+
+    def _maybe_cast_listlike_indexer(self, values) -> CategoricalIndex:
+        if isinstance(values, CategoricalIndex):
+            values = values._data
+        if isinstance(values, Categorical):
+            # Indexing on codes is more efficient if categories are the same,
+            #  so we can apply some optimizations based on the degree of
+            #  dtype-matching.
+            cat = self._data._encode_with_my_categories(values)
+            codes = cat._codes
+        else:
+            codes = self.categories.get_indexer(values)
+            codes = codes.astype(self.codes.dtype, copy=False)
+            cat = self._data._from_backing_data(codes)
+        return type(self)._simple_new(cat)
+
+    # --------------------------------------------------------------------
+
+    def _is_comparable_dtype(self, dtype: DtypeObj) -> bool:
+        return self.categories._is_comparable_dtype(dtype)
+
+    def map(self, mapper, na_action: Literal["ignore"] | None = None):
+        """
+        Map values using input an input mapping or function.
+
+        Maps the values (their categories, not the codes) of the index to new
+        categories. If the mapping correspondence is one-to-one the result is a
+        :class:`~pandas.CategoricalIndex` which has the same order property as
+        the original, otherwise an :class:`~pandas.Index` is returned.
+
+        If a `dict` or :class:`~pandas.Series` is used any unmapped category is
+        mapped to `NaN`. Note that if this happens an :class:`~pandas.Index`
+        will be returned.
+
+        Parameters
+        ----------
+        mapper : function, dict, or Series
+            Mapping correspondence.
+
+        Returns
+        -------
+        pandas.CategoricalIndex or pandas.Index
+            Mapped index.
+
+        See Also
+        --------
+        Index.map : Apply a mapping correspondence on an
+            :class:`~pandas.Index`.
+        Series.map : Apply a mapping correspondence on a
+            :class:`~pandas.Series`.
+        Series.apply : Apply more complex functions on a
+            :class:`~pandas.Series`.
+
+        Examples
+        --------
+        >>> idx = pd.CategoricalIndex(['a', 'b', 'c'])
+        >>> idx
+        CategoricalIndex(['a', 'b', 'c'], categories=['a', 'b', 'c'],
+                          ordered=False, dtype='category')
+        >>> idx.map(lambda x: x.upper())
+        CategoricalIndex(['A', 'B', 'C'], categories=['A', 'B', 'C'],
+                         ordered=False, dtype='category')
+        >>> idx.map({'a': 'first', 'b': 'second', 'c': 'third'})
+        CategoricalIndex(['first', 'second', 'third'], categories=['first',
+                         'second', 'third'], ordered=False, dtype='category')
+
+        If the mapping is one-to-one the ordering of the categories is
+        preserved:
+
+        >>> idx = pd.CategoricalIndex(['a', 'b', 'c'], ordered=True)
+        >>> idx
+        CategoricalIndex(['a', 'b', 'c'], categories=['a', 'b', 'c'],
+                         ordered=True, dtype='category')
+        >>> idx.map({'a': 3, 'b': 2, 'c': 1})
+        CategoricalIndex([3, 2, 1], categories=[3, 2, 1], ordered=True,
+                         dtype='category')
+
+        If the mapping is not one-to-one an :class:`~pandas.Index` is returned:
+
+        >>> idx.map({'a': 'first', 'b': 'second', 'c': 'first'})
+        Index(['first', 'second', 'first'], dtype='object')
+
+        If a `dict` is used, all unmapped categories are mapped to `NaN` and
+        the result is an :class:`~pandas.Index`:
+
+        >>> idx.map({'a': 'first', 'b': 'second'})
+        Index(['first', 'second', nan], dtype='object')
+        """
+        mapped = self._values.map(mapper, na_action=na_action)
+        return Index(mapped, name=self.name)
+
+    def _concat(self, to_concat: list[Index], name: Hashable) -> Index:
+        # if calling index is category, don't check dtype of others
+        try:
+            cat = Categorical._concat_same_type(
+                [self._is_dtype_compat(c) for c in to_concat]
+            )
+        except TypeError:
+            # not all to_concat elements are among our categories (or NA)
+
+            res = concat_compat([x._values for x in to_concat])
+            return Index(res, name=name)
+        else:
+            return type(self)._simple_new(cat, name=name)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/indexes/datetimelike.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/indexes/datetimelike.py
new file mode 100644
index 0000000000000000000000000000000000000000..cad8737a987d44f23518a8b6fa88e9a686755c65
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/indexes/datetimelike.py
@@ -0,0 +1,843 @@
+"""
+Base and utility classes for tseries type pandas objects.
+"""
+from __future__ import annotations
+
+from abc import (
+    ABC,
+    abstractmethod,
+)
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    cast,
+    final,
+)
+import warnings
+
+import numpy as np
+
+from pandas._config import using_copy_on_write
+
+from pandas._libs import (
+    NaT,
+    Timedelta,
+    lib,
+)
+from pandas._libs.tslibs import (
+    BaseOffset,
+    Resolution,
+    Tick,
+    parsing,
+    to_offset,
+)
+from pandas._libs.tslibs.dtypes import freq_to_period_freqstr
+from pandas.compat.numpy import function as nv
+from pandas.errors import (
+    InvalidIndexError,
+    NullFrequencyError,
+)
+from pandas.util._decorators import (
+    Appender,
+    cache_readonly,
+    doc,
+)
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.common import (
+    is_integer,
+    is_list_like,
+)
+from pandas.core.dtypes.concat import concat_compat
+from pandas.core.dtypes.dtypes import CategoricalDtype
+
+from pandas.core.arrays import (
+    DatetimeArray,
+    ExtensionArray,
+    PeriodArray,
+    TimedeltaArray,
+)
+from pandas.core.arrays.datetimelike import DatetimeLikeArrayMixin
+import pandas.core.common as com
+import pandas.core.indexes.base as ibase
+from pandas.core.indexes.base import (
+    Index,
+    _index_shared_docs,
+)
+from pandas.core.indexes.extension import NDArrayBackedExtensionIndex
+from pandas.core.indexes.range import RangeIndex
+from pandas.core.tools.timedeltas import to_timedelta
+
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+    from datetime import datetime
+
+    from pandas._typing import (
+        Axis,
+        Self,
+        npt,
+    )
+
+    from pandas import CategoricalIndex
+
+_index_doc_kwargs = dict(ibase._index_doc_kwargs)
+
+
+class DatetimeIndexOpsMixin(NDArrayBackedExtensionIndex, ABC):
+    """
+    Common ops mixin to support a unified interface datetimelike Index.
+    """
+
+    _can_hold_strings = False
+    _data: DatetimeArray | TimedeltaArray | PeriodArray
+
+    @doc(DatetimeLikeArrayMixin.mean)
+    def mean(self, *, skipna: bool = True, axis: int | None = 0):
+        return self._data.mean(skipna=skipna, axis=axis)
+
+    @property
+    def freq(self) -> BaseOffset | None:
+        return self._data.freq
+
+    @freq.setter
+    def freq(self, value) -> None:
+        # error: Property "freq" defined in "PeriodArray" is read-only  [misc]
+        self._data.freq = value  # type: ignore[misc]
+
+    @property
+    def asi8(self) -> npt.NDArray[np.int64]:
+        return self._data.asi8
+
+    @property
+    @doc(DatetimeLikeArrayMixin.freqstr)
+    def freqstr(self) -> str:
+        from pandas import PeriodIndex
+
+        if self._data.freqstr is not None and isinstance(
+            self._data, (PeriodArray, PeriodIndex)
+        ):
+            freq = freq_to_period_freqstr(self._data.freq.n, self._data.freq.name)
+            return freq
+        else:
+            return self._data.freqstr  # type: ignore[return-value]
+
+    @cache_readonly
+    @abstractmethod
+    def _resolution_obj(self) -> Resolution:
+        ...
+
+    @cache_readonly
+    @doc(DatetimeLikeArrayMixin.resolution)
+    def resolution(self) -> str:
+        return self._data.resolution
+
+    # ------------------------------------------------------------------------
+
+    @cache_readonly
+    def hasnans(self) -> bool:
+        return self._data._hasna
+
+    def equals(self, other: Any) -> bool:
+        """
+        Determines if two Index objects contain the same elements.
+        """
+        if self.is_(other):
+            return True
+
+        if not isinstance(other, Index):
+            return False
+        elif other.dtype.kind in "iufc":
+            return False
+        elif not isinstance(other, type(self)):
+            should_try = False
+            inferable = self._data._infer_matches
+            if other.dtype == object:
+                should_try = other.inferred_type in inferable
+            elif isinstance(other.dtype, CategoricalDtype):
+                other = cast("CategoricalIndex", other)
+                should_try = other.categories.inferred_type in inferable
+
+            if should_try:
+                try:
+                    other = type(self)(other)
+                except (ValueError, TypeError, OverflowError):
+                    # e.g.
+                    #  ValueError -> cannot parse str entry, or OutOfBoundsDatetime
+                    #  TypeError  -> trying to convert IntervalIndex to DatetimeIndex
+                    #  OverflowError -> Index([very_large_timedeltas])
+                    return False
+
+        if self.dtype != other.dtype:
+            # have different timezone
+            return False
+
+        return np.array_equal(self.asi8, other.asi8)
+
+    @Appender(Index.__contains__.__doc__)
+    def __contains__(self, key: Any) -> bool:
+        hash(key)
+        try:
+            self.get_loc(key)
+        except (KeyError, TypeError, ValueError, InvalidIndexError):
+            return False
+        return True
+
+    def _convert_tolerance(self, tolerance, target):
+        tolerance = np.asarray(to_timedelta(tolerance).to_numpy())
+        return super()._convert_tolerance(tolerance, target)
+
+    # --------------------------------------------------------------------
+    # Rendering Methods
+    _default_na_rep = "NaT"
+
+    def format(
+        self,
+        name: bool = False,
+        formatter: Callable | None = None,
+        na_rep: str = "NaT",
+        date_format: str | None = None,
+    ) -> list[str]:
+        """
+        Render a string representation of the Index.
+        """
+        warnings.warn(
+            # GH#55413
+            f"{type(self).__name__}.format is deprecated and will be removed "
+            "in a future version. Convert using index.astype(str) or "
+            "index.map(formatter) instead.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        header = []
+        if name:
+            header.append(
+                ibase.pprint_thing(self.name, escape_chars=("\t", "\r", "\n"))
+                if self.name is not None
+                else ""
+            )
+
+        if formatter is not None:
+            return header + list(self.map(formatter))
+
+        return self._format_with_header(
+            header=header, na_rep=na_rep, date_format=date_format
+        )
+
+    def _format_with_header(
+        self, *, header: list[str], na_rep: str, date_format: str | None = None
+    ) -> list[str]:
+        # TODO: not reached in tests 2023-10-11
+        # matches base class except for whitespace padding and date_format
+        return header + list(
+            self._get_values_for_csv(na_rep=na_rep, date_format=date_format)
+        )
+
+    @property
+    def _formatter_func(self):
+        return self._data._formatter()
+
+    def _format_attrs(self):
+        """
+        Return a list of tuples of the (attr,formatted_value).
+        """
+        attrs = super()._format_attrs()
+        for attrib in self._attributes:
+            # iterating over _attributes prevents us from doing this for PeriodIndex
+            if attrib == "freq":
+                freq = self.freqstr
+                if freq is not None:
+                    freq = repr(freq)  # e.g. D -> 'D'
+                attrs.append(("freq", freq))
+        return attrs
+
+    @Appender(Index._summary.__doc__)
+    def _summary(self, name=None) -> str:
+        result = super()._summary(name=name)
+        if self.freq:
+            result += f"\nFreq: {self.freqstr}"
+
+        return result
+
+    # --------------------------------------------------------------------
+    # Indexing Methods
+
+    @final
+    def _can_partial_date_slice(self, reso: Resolution) -> bool:
+        # e.g. test_getitem_setitem_periodindex
+        # History of conversation GH#3452, GH#3931, GH#2369, GH#14826
+        return reso > self._resolution_obj
+        # NB: for DTI/PI, not TDI
+
+    def _parsed_string_to_bounds(self, reso: Resolution, parsed):
+        raise NotImplementedError
+
+    def _parse_with_reso(self, label: str):
+        # overridden by TimedeltaIndex
+        try:
+            if self.freq is None or hasattr(self.freq, "rule_code"):
+                freq = self.freq
+        except NotImplementedError:
+            freq = getattr(self, "freqstr", getattr(self, "inferred_freq", None))
+
+        freqstr: str | None
+        if freq is not None and not isinstance(freq, str):
+            freqstr = freq.rule_code
+        else:
+            freqstr = freq
+
+        if isinstance(label, np.str_):
+            # GH#45580
+            label = str(label)
+
+        parsed, reso_str = parsing.parse_datetime_string_with_reso(label, freqstr)
+        reso = Resolution.from_attrname(reso_str)
+        return parsed, reso
+
+    def _get_string_slice(self, key: str):
+        # overridden by TimedeltaIndex
+        parsed, reso = self._parse_with_reso(key)
+        try:
+            return self._partial_date_slice(reso, parsed)
+        except KeyError as err:
+            raise KeyError(key) from err
+
+    @final
+    def _partial_date_slice(
+        self,
+        reso: Resolution,
+        parsed: datetime,
+    ) -> slice | npt.NDArray[np.intp]:
+        """
+        Parameters
+        ----------
+        reso : Resolution
+        parsed : datetime
+
+        Returns
+        -------
+        slice or ndarray[intp]
+        """
+        if not self._can_partial_date_slice(reso):
+            raise ValueError
+
+        t1, t2 = self._parsed_string_to_bounds(reso, parsed)
+        vals = self._data._ndarray
+        unbox = self._data._unbox
+
+        if self.is_monotonic_increasing:
+            if len(self) and (
+                (t1 < self[0] and t2 < self[0]) or (t1 > self[-1] and t2 > self[-1])
+            ):
+                # we are out of range
+                raise KeyError
+
+            # TODO: does this depend on being monotonic _increasing_?
+
+            # a monotonic (sorted) series can be sliced
+            left = vals.searchsorted(unbox(t1), side="left")
+            right = vals.searchsorted(unbox(t2), side="right")
+            return slice(left, right)
+
+        else:
+            lhs_mask = vals >= unbox(t1)
+            rhs_mask = vals <= unbox(t2)
+
+            # try to find the dates
+            return (lhs_mask & rhs_mask).nonzero()[0]
+
+    def _maybe_cast_slice_bound(self, label, side: str):
+        """
+        If label is a string, cast it to scalar type according to resolution.
+
+        Parameters
+        ----------
+        label : object
+        side : {'left', 'right'}
+
+        Returns
+        -------
+        label : object
+
+        Notes
+        -----
+        Value of `side` parameter should be validated in caller.
+        """
+        if isinstance(label, str):
+            try:
+                parsed, reso = self._parse_with_reso(label)
+            except ValueError as err:
+                # DTI -> parsing.DateParseError
+                # TDI -> 'unit abbreviation w/o a number'
+                # PI -> string cannot be parsed as datetime-like
+                self._raise_invalid_indexer("slice", label, err)
+
+            lower, upper = self._parsed_string_to_bounds(reso, parsed)
+            return lower if side == "left" else upper
+        elif not isinstance(label, self._data._recognized_scalars):
+            self._raise_invalid_indexer("slice", label)
+
+        return label
+
+    # --------------------------------------------------------------------
+    # Arithmetic Methods
+
+    def shift(self, periods: int = 1, freq=None) -> Self:
+        """
+        Shift index by desired number of time frequency increments.
+
+        This method is for shifting the values of datetime-like indexes
+        by a specified time increment a given number of times.
+
+        Parameters
+        ----------
+        periods : int, default 1
+            Number of periods (or increments) to shift by,
+            can be positive or negative.
+        freq : pandas.DateOffset, pandas.Timedelta or string, optional
+            Frequency increment to shift by.
+            If None, the index is shifted by its own `freq` attribute.
+            Offset aliases are valid strings, e.g., 'D', 'W', 'M' etc.
+
+        Returns
+        -------
+        pandas.DatetimeIndex
+            Shifted index.
+
+        See Also
+        --------
+        Index.shift : Shift values of Index.
+        PeriodIndex.shift : Shift values of PeriodIndex.
+        """
+        raise NotImplementedError
+
+    # --------------------------------------------------------------------
+
+    @doc(Index._maybe_cast_listlike_indexer)
+    def _maybe_cast_listlike_indexer(self, keyarr):
+        try:
+            res = self._data._validate_listlike(keyarr, allow_object=True)
+        except (ValueError, TypeError):
+            if not isinstance(keyarr, ExtensionArray):
+                # e.g. we don't want to cast DTA to ndarray[object]
+                res = com.asarray_tuplesafe(keyarr)
+                # TODO: com.asarray_tuplesafe shouldn't cast e.g. DatetimeArray
+            else:
+                res = keyarr
+        return Index(res, dtype=res.dtype)
+
+
+class DatetimeTimedeltaMixin(DatetimeIndexOpsMixin, ABC):
+    """
+    Mixin class for methods shared by DatetimeIndex and TimedeltaIndex,
+    but not PeriodIndex
+    """
+
+    _data: DatetimeArray | TimedeltaArray
+    _comparables = ["name", "freq"]
+    _attributes = ["name", "freq"]
+
+    # Compat for frequency inference, see GH#23789
+    _is_monotonic_increasing = Index.is_monotonic_increasing
+    _is_monotonic_decreasing = Index.is_monotonic_decreasing
+    _is_unique = Index.is_unique
+
+    @property
+    def unit(self) -> str:
+        return self._data.unit
+
+    def as_unit(self, unit: str) -> Self:
+        """
+        Convert to a dtype with the given unit resolution.
+
+        Parameters
+        ----------
+        unit : {'s', 'ms', 'us', 'ns'}
+
+        Returns
+        -------
+        same type as self
+
+        Examples
+        --------
+        For :class:`pandas.DatetimeIndex`:
+
+        >>> idx = pd.DatetimeIndex(['2020-01-02 01:02:03.004005006'])
+        >>> idx
+        DatetimeIndex(['2020-01-02 01:02:03.004005006'],
+                      dtype='datetime64[ns]', freq=None)
+        >>> idx.as_unit('s')
+        DatetimeIndex(['2020-01-02 01:02:03'], dtype='datetime64[s]', freq=None)
+
+        For :class:`pandas.TimedeltaIndex`:
+
+        >>> tdelta_idx = pd.to_timedelta(['1 day 3 min 2 us 42 ns'])
+        >>> tdelta_idx
+        TimedeltaIndex(['1 days 00:03:00.000002042'],
+                        dtype='timedelta64[ns]', freq=None)
+        >>> tdelta_idx.as_unit('s')
+        TimedeltaIndex(['1 days 00:03:00'], dtype='timedelta64[s]', freq=None)
+        """
+        arr = self._data.as_unit(unit)
+        return type(self)._simple_new(arr, name=self.name)
+
+    def _with_freq(self, freq):
+        arr = self._data._with_freq(freq)
+        return type(self)._simple_new(arr, name=self._name)
+
+    @property
+    def values(self) -> np.ndarray:
+        # NB: For Datetime64TZ this is lossy
+        data = self._data._ndarray
+        if using_copy_on_write():
+            data = data.view()
+            data.flags.writeable = False
+        return data
+
+    @doc(DatetimeIndexOpsMixin.shift)
+    def shift(self, periods: int = 1, freq=None) -> Self:
+        if freq is not None and freq != self.freq:
+            if isinstance(freq, str):
+                freq = to_offset(freq)
+            offset = periods * freq
+            return self + offset
+
+        if periods == 0 or len(self) == 0:
+            # GH#14811 empty case
+            return self.copy()
+
+        if self.freq is None:
+            raise NullFrequencyError("Cannot shift with no freq")
+
+        start = self[0] + periods * self.freq
+        end = self[-1] + periods * self.freq
+
+        # Note: in the DatetimeTZ case, _generate_range will infer the
+        #  appropriate timezone from `start` and `end`, so tz does not need
+        #  to be passed explicitly.
+        result = self._data._generate_range(
+            start=start, end=end, periods=None, freq=self.freq, unit=self.unit
+        )
+        return type(self)._simple_new(result, name=self.name)
+
+    @cache_readonly
+    @doc(DatetimeLikeArrayMixin.inferred_freq)
+    def inferred_freq(self) -> str | None:
+        return self._data.inferred_freq
+
+    # --------------------------------------------------------------------
+    # Set Operation Methods
+
+    @cache_readonly
+    def _as_range_index(self) -> RangeIndex:
+        # Convert our i8 representations to RangeIndex
+        # Caller is responsible for checking isinstance(self.freq, Tick)
+        freq = cast(Tick, self.freq)
+        tick = Timedelta(freq).as_unit("ns")._value
+        rng = range(self[0]._value, self[-1]._value + tick, tick)
+        return RangeIndex(rng)
+
+    def _can_range_setop(self, other) -> bool:
+        return isinstance(self.freq, Tick) and isinstance(other.freq, Tick)
+
+    def _wrap_range_setop(self, other, res_i8) -> Self:
+        new_freq = None
+        if not len(res_i8):
+            # RangeIndex defaults to step=1, which we don't want.
+            new_freq = self.freq
+        elif isinstance(res_i8, RangeIndex):
+            new_freq = to_offset(Timedelta(res_i8.step))
+
+        # TODO(GH#41493): we cannot just do
+        #  type(self._data)(res_i8.values, dtype=self.dtype, freq=new_freq)
+        # because test_setops_preserve_freq fails with _validate_frequency raising.
+        # This raising is incorrect, as 'on_freq' is incorrect. This will
+        # be fixed by GH#41493
+        res_values = res_i8.values.view(self._data._ndarray.dtype)
+        result = type(self._data)._simple_new(
+            # error: Argument "dtype" to "_simple_new" of "DatetimeArray" has
+            # incompatible type "Union[dtype[Any], ExtensionDtype]"; expected
+            # "Union[dtype[datetime64], DatetimeTZDtype]"
+            res_values,
+            dtype=self.dtype,  # type: ignore[arg-type]
+            freq=new_freq,  # type: ignore[arg-type]
+        )
+        return cast("Self", self._wrap_setop_result(other, result))
+
+    def _range_intersect(self, other, sort) -> Self:
+        # Dispatch to RangeIndex intersection logic.
+        left = self._as_range_index
+        right = other._as_range_index
+        res_i8 = left.intersection(right, sort=sort)
+        return self._wrap_range_setop(other, res_i8)
+
+    def _range_union(self, other, sort) -> Self:
+        # Dispatch to RangeIndex union logic.
+        left = self._as_range_index
+        right = other._as_range_index
+        res_i8 = left.union(right, sort=sort)
+        return self._wrap_range_setop(other, res_i8)
+
+    def _intersection(self, other: Index, sort: bool = False) -> Index:
+        """
+        intersection specialized to the case with matching dtypes and both non-empty.
+        """
+        other = cast("DatetimeTimedeltaMixin", other)
+
+        if self._can_range_setop(other):
+            return self._range_intersect(other, sort=sort)
+
+        if not self._can_fast_intersect(other):
+            result = Index._intersection(self, other, sort=sort)
+            # We need to invalidate the freq because Index._intersection
+            #  uses _shallow_copy on a view of self._data, which will preserve
+            #  self.freq if we're not careful.
+            # At this point we should have result.dtype == self.dtype
+            #  and type(result) is type(self._data)
+            result = self._wrap_setop_result(other, result)
+            return result._with_freq(None)._with_freq("infer")
+
+        else:
+            return self._fast_intersect(other, sort)
+
+    def _fast_intersect(self, other, sort):
+        # to make our life easier, "sort" the two ranges
+        if self[0] <= other[0]:
+            left, right = self, other
+        else:
+            left, right = other, self
+
+        # after sorting, the intersection always starts with the right index
+        # and ends with the index of which the last elements is smallest
+        end = min(left[-1], right[-1])
+        start = right[0]
+
+        if end < start:
+            result = self[:0]
+        else:
+            lslice = slice(*left.slice_locs(start, end))
+            result = left._values[lslice]
+
+        return result
+
+    def _can_fast_intersect(self, other: Self) -> bool:
+        # Note: we only get here with len(self) > 0 and len(other) > 0
+        if self.freq is None:
+            return False
+
+        elif other.freq != self.freq:
+            return False
+
+        elif not self.is_monotonic_increasing:
+            # Because freq is not None, we must then be monotonic decreasing
+            return False
+
+        # this along with matching freqs ensure that we "line up",
+        #  so intersection will preserve freq
+        # Note we are assuming away Ticks, as those go through _range_intersect
+        # GH#42104
+        return self.freq.n == 1
+
+    def _can_fast_union(self, other: Self) -> bool:
+        # Assumes that type(self) == type(other), as per the annotation
+        # The ability to fast_union also implies that `freq` should be
+        #  retained on union.
+        freq = self.freq
+
+        if freq is None or freq != other.freq:
+            return False
+
+        if not self.is_monotonic_increasing:
+            # Because freq is not None, we must then be monotonic decreasing
+            # TODO: do union on the reversed indexes?
+            return False
+
+        if len(self) == 0 or len(other) == 0:
+            # only reached via union_many
+            return True
+
+        # to make our life easier, "sort" the two ranges
+        if self[0] <= other[0]:
+            left, right = self, other
+        else:
+            left, right = other, self
+
+        right_start = right[0]
+        left_end = left[-1]
+
+        # Only need to "adjoin", not overlap
+        return (right_start == left_end + freq) or right_start in left
+
+    def _fast_union(self, other: Self, sort=None) -> Self:
+        # Caller is responsible for ensuring self and other are non-empty
+
+        # to make our life easier, "sort" the two ranges
+        if self[0] <= other[0]:
+            left, right = self, other
+        elif sort is False:
+            # TDIs are not in the "correct" order and we don't want
+            #  to sort but want to remove overlaps
+            left, right = self, other
+            left_start = left[0]
+            loc = right.searchsorted(left_start, side="left")
+            right_chunk = right._values[:loc]
+            dates = concat_compat((left._values, right_chunk))
+            result = type(self)._simple_new(dates, name=self.name)
+            return result
+        else:
+            left, right = other, self
+
+        left_end = left[-1]
+        right_end = right[-1]
+
+        # concatenate
+        if left_end < right_end:
+            loc = right.searchsorted(left_end, side="right")
+            right_chunk = right._values[loc:]
+            dates = concat_compat([left._values, right_chunk])
+            # The can_fast_union check ensures that the result.freq
+            #  should match self.freq
+            assert isinstance(dates, type(self._data))
+            # error: Item "ExtensionArray" of "ExtensionArray |
+            # ndarray[Any, Any]" has no attribute "_freq"
+            assert dates._freq == self.freq  # type: ignore[union-attr]
+            result = type(self)._simple_new(dates)
+            return result
+        else:
+            return left
+
+    def _union(self, other, sort):
+        # We are called by `union`, which is responsible for this validation
+        assert isinstance(other, type(self))
+        assert self.dtype == other.dtype
+
+        if self._can_range_setop(other):
+            return self._range_union(other, sort=sort)
+
+        if self._can_fast_union(other):
+            result = self._fast_union(other, sort=sort)
+            # in the case with sort=None, the _can_fast_union check ensures
+            #  that result.freq == self.freq
+            return result
+        else:
+            return super()._union(other, sort)._with_freq("infer")
+
+    # --------------------------------------------------------------------
+    # Join Methods
+
+    def _get_join_freq(self, other):
+        """
+        Get the freq to attach to the result of a join operation.
+        """
+        freq = None
+        if self._can_fast_union(other):
+            freq = self.freq
+        return freq
+
+    def _wrap_joined_index(
+        self, joined, other, lidx: npt.NDArray[np.intp], ridx: npt.NDArray[np.intp]
+    ):
+        assert other.dtype == self.dtype, (other.dtype, self.dtype)
+        result = super()._wrap_joined_index(joined, other, lidx, ridx)
+        result._data._freq = self._get_join_freq(other)
+        return result
+
+    def _get_engine_target(self) -> np.ndarray:
+        # engine methods and libjoin methods need dt64/td64 values cast to i8
+        return self._data._ndarray.view("i8")
+
+    def _from_join_target(self, result: np.ndarray):
+        # view e.g. i8 back to M8[ns]
+        result = result.view(self._data._ndarray.dtype)
+        return self._data._from_backing_data(result)
+
+    # --------------------------------------------------------------------
+    # List-like Methods
+
+    def _get_delete_freq(self, loc: int | slice | Sequence[int]):
+        """
+        Find the `freq` for self.delete(loc).
+        """
+        freq = None
+        if self.freq is not None:
+            if is_integer(loc):
+                if loc in (0, -len(self), -1, len(self) - 1):
+                    freq = self.freq
+            else:
+                if is_list_like(loc):
+                    # error: Incompatible types in assignment (expression has
+                    # type "Union[slice, ndarray]", variable has type
+                    # "Union[int, slice, Sequence[int]]")
+                    loc = lib.maybe_indices_to_slice(  # type: ignore[assignment]
+                        np.asarray(loc, dtype=np.intp), len(self)
+                    )
+                if isinstance(loc, slice) and loc.step in (1, None):
+                    if loc.start in (0, None) or loc.stop in (len(self), None):
+                        freq = self.freq
+        return freq
+
+    def _get_insert_freq(self, loc: int, item):
+        """
+        Find the `freq` for self.insert(loc, item).
+        """
+        value = self._data._validate_scalar(item)
+        item = self._data._box_func(value)
+
+        freq = None
+        if self.freq is not None:
+            # freq can be preserved on edge cases
+            if self.size:
+                if item is NaT:
+                    pass
+                elif loc in (0, -len(self)) and item + self.freq == self[0]:
+                    freq = self.freq
+                elif (loc == len(self)) and item - self.freq == self[-1]:
+                    freq = self.freq
+            else:
+                # Adding a single item to an empty index may preserve freq
+                if isinstance(self.freq, Tick):
+                    # all TimedeltaIndex cases go through here; is_on_offset
+                    #  would raise TypeError
+                    freq = self.freq
+                elif self.freq.is_on_offset(item):
+                    freq = self.freq
+        return freq
+
+    @doc(NDArrayBackedExtensionIndex.delete)
+    def delete(self, loc) -> Self:
+        result = super().delete(loc)
+        result._data._freq = self._get_delete_freq(loc)
+        return result
+
+    @doc(NDArrayBackedExtensionIndex.insert)
+    def insert(self, loc: int, item):
+        result = super().insert(loc, item)
+        if isinstance(result, type(self)):
+            # i.e. parent class method did not cast
+            result._data._freq = self._get_insert_freq(loc, item)
+        return result
+
+    # --------------------------------------------------------------------
+    # NDArray-Like Methods
+
+    @Appender(_index_shared_docs["take"] % _index_doc_kwargs)
+    def take(
+        self,
+        indices,
+        axis: Axis = 0,
+        allow_fill: bool = True,
+        fill_value=None,
+        **kwargs,
+    ) -> Self:
+        nv.validate_take((), kwargs)
+        indices = np.asarray(indices, dtype=np.intp)
+
+        result = NDArrayBackedExtensionIndex.take(
+            self, indices, axis, allow_fill, fill_value, **kwargs
+        )
+
+        maybe_slice = lib.maybe_indices_to_slice(indices, len(self))
+        if isinstance(maybe_slice, slice):
+            freq = self._data._get_getitem_freq(maybe_slice)
+            result._data._freq = freq
+        return result
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/indexes/datetimes.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/indexes/datetimes.py
new file mode 100644
index 0000000000000000000000000000000000000000..3204a9c97ee73fa873a4cbe461e2f3c4690c781f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/indexes/datetimes.py
@@ -0,0 +1,1127 @@
+from __future__ import annotations
+
+import datetime as dt
+import operator
+from typing import TYPE_CHECKING
+import warnings
+
+import numpy as np
+import pytz
+
+from pandas._libs import (
+    NaT,
+    Period,
+    Timestamp,
+    index as libindex,
+    lib,
+)
+from pandas._libs.tslibs import (
+    Resolution,
+    Tick,
+    Timedelta,
+    periods_per_day,
+    timezones,
+    to_offset,
+)
+from pandas._libs.tslibs.offsets import prefix_mapping
+from pandas.util._decorators import (
+    cache_readonly,
+    doc,
+)
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.common import is_scalar
+from pandas.core.dtypes.dtypes import DatetimeTZDtype
+from pandas.core.dtypes.generic import ABCSeries
+from pandas.core.dtypes.missing import is_valid_na_for_dtype
+
+from pandas.core.arrays.datetimes import (
+    DatetimeArray,
+    tz_to_dtype,
+)
+import pandas.core.common as com
+from pandas.core.indexes.base import (
+    Index,
+    maybe_extract_name,
+)
+from pandas.core.indexes.datetimelike import DatetimeTimedeltaMixin
+from pandas.core.indexes.extension import inherit_names
+from pandas.core.tools.times import to_time
+
+if TYPE_CHECKING:
+    from collections.abc import Hashable
+
+    from pandas._typing import (
+        Dtype,
+        DtypeObj,
+        Frequency,
+        IntervalClosedType,
+        Self,
+        TimeAmbiguous,
+        TimeNonexistent,
+        npt,
+    )
+
+    from pandas.core.api import (
+        DataFrame,
+        PeriodIndex,
+    )
+
+from pandas._libs.tslibs.dtypes import OFFSET_TO_PERIOD_FREQSTR
+
+
+def _new_DatetimeIndex(cls, d):
+    """
+    This is called upon unpickling, rather than the default which doesn't
+    have arguments and breaks __new__
+    """
+    if "data" in d and not isinstance(d["data"], DatetimeIndex):
+        # Avoid need to verify integrity by calling simple_new directly
+        data = d.pop("data")
+        if not isinstance(data, DatetimeArray):
+            # For backward compat with older pickles, we may need to construct
+            #  a DatetimeArray to adapt to the newer _simple_new signature
+            tz = d.pop("tz")
+            freq = d.pop("freq")
+            dta = DatetimeArray._simple_new(data, dtype=tz_to_dtype(tz), freq=freq)
+        else:
+            dta = data
+            for key in ["tz", "freq"]:
+                # These are already stored in our DatetimeArray; if they are
+                #  also in the pickle and don't match, we have a problem.
+                if key in d:
+                    assert d[key] == getattr(dta, key)
+                    d.pop(key)
+        result = cls._simple_new(dta, **d)
+    else:
+        with warnings.catch_warnings():
+            # TODO: If we knew what was going in to **d, we might be able to
+            #  go through _simple_new instead
+            warnings.simplefilter("ignore")
+            result = cls.__new__(cls, **d)
+
+    return result
+
+
+@inherit_names(
+    DatetimeArray._field_ops
+    + [
+        method
+        for method in DatetimeArray._datetimelike_methods
+        if method not in ("tz_localize", "tz_convert", "strftime")
+    ],
+    DatetimeArray,
+    wrap=True,
+)
+@inherit_names(["is_normalized"], DatetimeArray, cache=True)
+@inherit_names(
+    [
+        "tz",
+        "tzinfo",
+        "dtype",
+        "to_pydatetime",
+        "date",
+        "time",
+        "timetz",
+        "std",
+    ]
+    + DatetimeArray._bool_ops,
+    DatetimeArray,
+)
+class DatetimeIndex(DatetimeTimedeltaMixin):
+    """
+    Immutable ndarray-like of datetime64 data.
+
+    Represented internally as int64, and which can be boxed to Timestamp objects
+    that are subclasses of datetime and carry metadata.
+
+    .. versionchanged:: 2.0.0
+        The various numeric date/time attributes (:attr:`~DatetimeIndex.day`,
+        :attr:`~DatetimeIndex.month`, :attr:`~DatetimeIndex.year` etc.) now have dtype
+        ``int32``. Previously they had dtype ``int64``.
+
+    Parameters
+    ----------
+    data : array-like (1-dimensional)
+        Datetime-like data to construct index with.
+    freq : str or pandas offset object, optional
+        One of pandas date offset strings or corresponding objects. The string
+        'infer' can be passed in order to set the frequency of the index as the
+        inferred frequency upon creation.
+    tz : pytz.timezone or dateutil.tz.tzfile or datetime.tzinfo or str
+        Set the Timezone of the data.
+    normalize : bool, default False
+        Normalize start/end dates to midnight before generating date range.
+
+        .. deprecated:: 2.1.0
+
+    closed : {'left', 'right'}, optional
+        Set whether to include `start` and `end` that are on the
+        boundary. The default includes boundary points on either end.
+
+        .. deprecated:: 2.1.0
+
+    ambiguous : 'infer', bool-ndarray, 'NaT', default 'raise'
+        When clocks moved backward due to DST, ambiguous times may arise.
+        For example in Central European Time (UTC+01), when going from 03:00
+        DST to 02:00 non-DST, 02:30:00 local time occurs both at 00:30:00 UTC
+        and at 01:30:00 UTC. In such a situation, the `ambiguous` parameter
+        dictates how ambiguous times should be handled.
+
+        - 'infer' will attempt to infer fall dst-transition hours based on
+          order
+        - bool-ndarray where True signifies a DST time, False signifies a
+          non-DST time (note that this flag is only applicable for ambiguous
+          times)
+        - 'NaT' will return NaT where there are ambiguous times
+        - 'raise' will raise an AmbiguousTimeError if there are ambiguous times.
+    dayfirst : bool, default False
+        If True, parse dates in `data` with the day first order.
+    yearfirst : bool, default False
+        If True parse dates in `data` with the year first order.
+    dtype : numpy.dtype or DatetimeTZDtype or str, default None
+        Note that the only NumPy dtype allowed is `datetime64[ns]`.
+    copy : bool, default False
+        Make a copy of input ndarray.
+    name : label, default None
+        Name to be stored in the index.
+
+    Attributes
+    ----------
+    year
+    month
+    day
+    hour
+    minute
+    second
+    microsecond
+    nanosecond
+    date
+    time
+    timetz
+    dayofyear
+    day_of_year
+    dayofweek
+    day_of_week
+    weekday
+    quarter
+    tz
+    freq
+    freqstr
+    is_month_start
+    is_month_end
+    is_quarter_start
+    is_quarter_end
+    is_year_start
+    is_year_end
+    is_leap_year
+    inferred_freq
+
+    Methods
+    -------
+    normalize
+    strftime
+    snap
+    tz_convert
+    tz_localize
+    round
+    floor
+    ceil
+    to_period
+    to_pydatetime
+    to_series
+    to_frame
+    month_name
+    day_name
+    mean
+    std
+
+    See Also
+    --------
+    Index : The base pandas Index type.
+    TimedeltaIndex : Index of timedelta64 data.
+    PeriodIndex : Index of Period data.
+    to_datetime : Convert argument to datetime.
+    date_range : Create a fixed-frequency DatetimeIndex.
+
+    Notes
+    -----
+    To learn more about the frequency strings, please see `this link
+    `__.
+
+    Examples
+    --------
+    >>> idx = pd.DatetimeIndex(["1/1/2020 10:00:00+00:00", "2/1/2020 11:00:00+00:00"])
+    >>> idx
+    DatetimeIndex(['2020-01-01 10:00:00+00:00', '2020-02-01 11:00:00+00:00'],
+    dtype='datetime64[ns, UTC]', freq=None)
+    """
+
+    _typ = "datetimeindex"
+
+    _data_cls = DatetimeArray
+    _supports_partial_string_indexing = True
+
+    @property
+    def _engine_type(self) -> type[libindex.DatetimeEngine]:
+        return libindex.DatetimeEngine
+
+    _data: DatetimeArray
+    _values: DatetimeArray
+    tz: dt.tzinfo | None
+
+    # --------------------------------------------------------------------
+    # methods that dispatch to DatetimeArray and wrap result
+
+    @doc(DatetimeArray.strftime)
+    def strftime(self, date_format) -> Index:
+        arr = self._data.strftime(date_format)
+        return Index(arr, name=self.name, dtype=arr.dtype)
+
+    @doc(DatetimeArray.tz_convert)
+    def tz_convert(self, tz) -> Self:
+        arr = self._data.tz_convert(tz)
+        return type(self)._simple_new(arr, name=self.name, refs=self._references)
+
+    @doc(DatetimeArray.tz_localize)
+    def tz_localize(
+        self,
+        tz,
+        ambiguous: TimeAmbiguous = "raise",
+        nonexistent: TimeNonexistent = "raise",
+    ) -> Self:
+        arr = self._data.tz_localize(tz, ambiguous, nonexistent)
+        return type(self)._simple_new(arr, name=self.name)
+
+    @doc(DatetimeArray.to_period)
+    def to_period(self, freq=None) -> PeriodIndex:
+        from pandas.core.indexes.api import PeriodIndex
+
+        arr = self._data.to_period(freq)
+        return PeriodIndex._simple_new(arr, name=self.name)
+
+    @doc(DatetimeArray.to_julian_date)
+    def to_julian_date(self) -> Index:
+        arr = self._data.to_julian_date()
+        return Index._simple_new(arr, name=self.name)
+
+    @doc(DatetimeArray.isocalendar)
+    def isocalendar(self) -> DataFrame:
+        df = self._data.isocalendar()
+        return df.set_index(self)
+
+    @cache_readonly
+    def _resolution_obj(self) -> Resolution:
+        return self._data._resolution_obj
+
+    # --------------------------------------------------------------------
+    # Constructors
+
+    def __new__(
+        cls,
+        data=None,
+        freq: Frequency | lib.NoDefault = lib.no_default,
+        tz=lib.no_default,
+        normalize: bool | lib.NoDefault = lib.no_default,
+        closed=lib.no_default,
+        ambiguous: TimeAmbiguous = "raise",
+        dayfirst: bool = False,
+        yearfirst: bool = False,
+        dtype: Dtype | None = None,
+        copy: bool = False,
+        name: Hashable | None = None,
+    ) -> Self:
+        if closed is not lib.no_default:
+            # GH#52628
+            warnings.warn(
+                f"The 'closed' keyword in {cls.__name__} construction is "
+                "deprecated and will be removed in a future version.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+        if normalize is not lib.no_default:
+            # GH#52628
+            warnings.warn(
+                f"The 'normalize' keyword in {cls.__name__} construction is "
+                "deprecated and will be removed in a future version.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+
+        if is_scalar(data):
+            cls._raise_scalar_data_error(data)
+
+        # - Cases checked above all return/raise before reaching here - #
+
+        name = maybe_extract_name(name, data, cls)
+
+        if (
+            isinstance(data, DatetimeArray)
+            and freq is lib.no_default
+            and tz is lib.no_default
+            and dtype is None
+        ):
+            # fastpath, similar logic in TimedeltaIndex.__new__;
+            # Note in this particular case we retain non-nano.
+            if copy:
+                data = data.copy()
+            return cls._simple_new(data, name=name)
+
+        dtarr = DatetimeArray._from_sequence_not_strict(
+            data,
+            dtype=dtype,
+            copy=copy,
+            tz=tz,
+            freq=freq,
+            dayfirst=dayfirst,
+            yearfirst=yearfirst,
+            ambiguous=ambiguous,
+        )
+        refs = None
+        if not copy and isinstance(data, (Index, ABCSeries)):
+            refs = data._references
+
+        subarr = cls._simple_new(dtarr, name=name, refs=refs)
+        return subarr
+
+    # --------------------------------------------------------------------
+
+    @cache_readonly
+    def _is_dates_only(self) -> bool:
+        """
+        Return a boolean if we are only dates (and don't have a timezone)
+
+        Returns
+        -------
+        bool
+        """
+        if isinstance(self.freq, Tick):
+            delta = Timedelta(self.freq)
+
+            if delta % dt.timedelta(days=1) != dt.timedelta(days=0):
+                return False
+
+        return self._values._is_dates_only
+
+    def __reduce__(self):
+        d = {"data": self._data, "name": self.name}
+        return _new_DatetimeIndex, (type(self), d), None
+
+    def _is_comparable_dtype(self, dtype: DtypeObj) -> bool:
+        """
+        Can we compare values of the given dtype to our own?
+        """
+        if self.tz is not None:
+            # If we have tz, we can compare to tzaware
+            return isinstance(dtype, DatetimeTZDtype)
+        # if we dont have tz, we can only compare to tznaive
+        return lib.is_np_dtype(dtype, "M")
+
+    # --------------------------------------------------------------------
+    # Rendering Methods
+
+    @cache_readonly
+    def _formatter_func(self):
+        # Note this is equivalent to the DatetimeIndexOpsMixin method but
+        #  uses the maybe-cached self._is_dates_only instead of re-computing it.
+        from pandas.io.formats.format import get_format_datetime64
+
+        formatter = get_format_datetime64(is_dates_only=self._is_dates_only)
+        return lambda x: f"'{formatter(x)}'"
+
+    # --------------------------------------------------------------------
+    # Set Operation Methods
+
+    def _can_range_setop(self, other) -> bool:
+        # GH 46702: If self or other have non-UTC tzs, DST transitions prevent
+        # range representation due to no singular step
+        if (
+            self.tz is not None
+            and not timezones.is_utc(self.tz)
+            and not timezones.is_fixed_offset(self.tz)
+        ):
+            return False
+        if (
+            other.tz is not None
+            and not timezones.is_utc(other.tz)
+            and not timezones.is_fixed_offset(other.tz)
+        ):
+            return False
+        return super()._can_range_setop(other)
+
+    # --------------------------------------------------------------------
+
+    def _get_time_micros(self) -> npt.NDArray[np.int64]:
+        """
+        Return the number of microseconds since midnight.
+
+        Returns
+        -------
+        ndarray[int64_t]
+        """
+        values = self._data._local_timestamps()
+
+        ppd = periods_per_day(self._data._creso)
+
+        frac = values % ppd
+        if self.unit == "ns":
+            micros = frac // 1000
+        elif self.unit == "us":
+            micros = frac
+        elif self.unit == "ms":
+            micros = frac * 1000
+        elif self.unit == "s":
+            micros = frac * 1_000_000
+        else:  # pragma: no cover
+            raise NotImplementedError(self.unit)
+
+        micros[self._isnan] = -1
+        return micros
+
+    def snap(self, freq: Frequency = "S") -> DatetimeIndex:
+        """
+        Snap time stamps to nearest occurring frequency.
+
+        Returns
+        -------
+        DatetimeIndex
+
+        Examples
+        --------
+        >>> idx = pd.DatetimeIndex(['2023-01-01', '2023-01-02',
+        ...                        '2023-02-01', '2023-02-02'])
+        >>> idx
+        DatetimeIndex(['2023-01-01', '2023-01-02', '2023-02-01', '2023-02-02'],
+        dtype='datetime64[ns]', freq=None)
+        >>> idx.snap('MS')
+        DatetimeIndex(['2023-01-01', '2023-01-01', '2023-02-01', '2023-02-01'],
+        dtype='datetime64[ns]', freq=None)
+        """
+        # Superdumb, punting on any optimizing
+        freq = to_offset(freq)
+
+        dta = self._data.copy()
+
+        for i, v in enumerate(self):
+            s = v
+            if not freq.is_on_offset(s):
+                t0 = freq.rollback(s)
+                t1 = freq.rollforward(s)
+                if abs(s - t0) < abs(t1 - s):
+                    s = t0
+                else:
+                    s = t1
+            dta[i] = s
+
+        return DatetimeIndex._simple_new(dta, name=self.name)
+
+    # --------------------------------------------------------------------
+    # Indexing Methods
+
+    def _parsed_string_to_bounds(self, reso: Resolution, parsed: dt.datetime):
+        """
+        Calculate datetime bounds for parsed time string and its resolution.
+
+        Parameters
+        ----------
+        reso : Resolution
+            Resolution provided by parsed string.
+        parsed : datetime
+            Datetime from parsed string.
+
+        Returns
+        -------
+        lower, upper: pd.Timestamp
+        """
+        freq = OFFSET_TO_PERIOD_FREQSTR.get(reso.attr_abbrev, reso.attr_abbrev)
+        per = Period(parsed, freq=freq)
+        start, end = per.start_time, per.end_time
+
+        # GH 24076
+        # If an incoming date string contained a UTC offset, need to localize
+        # the parsed date to this offset first before aligning with the index's
+        # timezone
+        start = start.tz_localize(parsed.tzinfo)
+        end = end.tz_localize(parsed.tzinfo)
+
+        if parsed.tzinfo is not None:
+            if self.tz is None:
+                raise ValueError(
+                    "The index must be timezone aware when indexing "
+                    "with a date string with a UTC offset"
+                )
+        # The flipped case with parsed.tz is None and self.tz is not None
+        #  is ruled out bc parsed and reso are produced by _parse_with_reso,
+        #  which localizes parsed.
+        return start, end
+
+    def _parse_with_reso(self, label: str):
+        parsed, reso = super()._parse_with_reso(label)
+
+        parsed = Timestamp(parsed)
+
+        if self.tz is not None and parsed.tzinfo is None:
+            # we special-case timezone-naive strings and timezone-aware
+            #  DatetimeIndex
+            # https://github.com/pandas-dev/pandas/pull/36148#issuecomment-687883081
+            parsed = parsed.tz_localize(self.tz)
+
+        return parsed, reso
+
+    def _disallow_mismatched_indexing(self, key) -> None:
+        """
+        Check for mismatched-tzawareness indexing and re-raise as KeyError.
+        """
+        # we get here with isinstance(key, self._data._recognized_scalars)
+        try:
+            # GH#36148
+            self._data._assert_tzawareness_compat(key)
+        except TypeError as err:
+            raise KeyError(key) from err
+
+    def get_loc(self, key):
+        """
+        Get integer location for requested label
+
+        Returns
+        -------
+        loc : int
+        """
+        self._check_indexing_error(key)
+
+        orig_key = key
+        if is_valid_na_for_dtype(key, self.dtype):
+            key = NaT
+
+        if isinstance(key, self._data._recognized_scalars):
+            # needed to localize naive datetimes
+            self._disallow_mismatched_indexing(key)
+            key = Timestamp(key)
+
+        elif isinstance(key, str):
+            try:
+                parsed, reso = self._parse_with_reso(key)
+            except (ValueError, pytz.NonExistentTimeError) as err:
+                raise KeyError(key) from err
+            self._disallow_mismatched_indexing(parsed)
+
+            if self._can_partial_date_slice(reso):
+                try:
+                    return self._partial_date_slice(reso, parsed)
+                except KeyError as err:
+                    raise KeyError(key) from err
+
+            key = parsed
+
+        elif isinstance(key, dt.timedelta):
+            # GH#20464
+            raise TypeError(
+                f"Cannot index {type(self).__name__} with {type(key).__name__}"
+            )
+
+        elif isinstance(key, dt.time):
+            return self.indexer_at_time(key)
+
+        else:
+            # unrecognized type
+            raise KeyError(key)
+
+        try:
+            return Index.get_loc(self, key)
+        except KeyError as err:
+            raise KeyError(orig_key) from err
+
+    @doc(DatetimeTimedeltaMixin._maybe_cast_slice_bound)
+    def _maybe_cast_slice_bound(self, label, side: str):
+        # GH#42855 handle date here instead of get_slice_bound
+        if isinstance(label, dt.date) and not isinstance(label, dt.datetime):
+            # Pandas supports slicing with dates, treated as datetimes at midnight.
+            # https://github.com/pandas-dev/pandas/issues/31501
+            label = Timestamp(label).to_pydatetime()
+
+        label = super()._maybe_cast_slice_bound(label, side)
+        self._data._assert_tzawareness_compat(label)
+        return Timestamp(label)
+
+    def slice_indexer(self, start=None, end=None, step=None):
+        """
+        Return indexer for specified label slice.
+        Index.slice_indexer, customized to handle time slicing.
+
+        In addition to functionality provided by Index.slice_indexer, does the
+        following:
+
+        - if both `start` and `end` are instances of `datetime.time`, it
+          invokes `indexer_between_time`
+        - if `start` and `end` are both either string or None perform
+          value-based selection in non-monotonic cases.
+
+        """
+        # For historical reasons DatetimeIndex supports slices between two
+        # instances of datetime.time as if it were applying a slice mask to
+        # an array of (self.hour, self.minute, self.seconds, self.microsecond).
+        if isinstance(start, dt.time) and isinstance(end, dt.time):
+            if step is not None and step != 1:
+                raise ValueError("Must have step size of 1 with time slices")
+            return self.indexer_between_time(start, end)
+
+        if isinstance(start, dt.time) or isinstance(end, dt.time):
+            raise KeyError("Cannot mix time and non-time slice keys")
+
+        def check_str_or_none(point) -> bool:
+            return point is not None and not isinstance(point, str)
+
+        # GH#33146 if start and end are combinations of str and None and Index is not
+        # monotonic, we can not use Index.slice_indexer because it does not honor the
+        # actual elements, is only searching for start and end
+        if (
+            check_str_or_none(start)
+            or check_str_or_none(end)
+            or self.is_monotonic_increasing
+        ):
+            return Index.slice_indexer(self, start, end, step)
+
+        mask = np.array(True)
+        in_index = True
+        if start is not None:
+            start_casted = self._maybe_cast_slice_bound(start, "left")
+            mask = start_casted <= self
+            in_index &= (start_casted == self).any()
+
+        if end is not None:
+            end_casted = self._maybe_cast_slice_bound(end, "right")
+            mask = (self <= end_casted) & mask
+            in_index &= (end_casted == self).any()
+
+        if not in_index:
+            raise KeyError(
+                "Value based partial slicing on non-monotonic DatetimeIndexes "
+                "with non-existing keys is not allowed.",
+            )
+        indexer = mask.nonzero()[0][::step]
+        if len(indexer) == len(self):
+            return slice(None)
+        else:
+            return indexer
+
+    # --------------------------------------------------------------------
+
+    @property
+    def inferred_type(self) -> str:
+        # b/c datetime is represented as microseconds since the epoch, make
+        # sure we can't have ambiguous indexing
+        return "datetime64"
+
+    def indexer_at_time(self, time, asof: bool = False) -> npt.NDArray[np.intp]:
+        """
+        Return index locations of values at particular time of day.
+
+        Parameters
+        ----------
+        time : datetime.time or str
+            Time passed in either as object (datetime.time) or as string in
+            appropriate format ("%H:%M", "%H%M", "%I:%M%p", "%I%M%p",
+            "%H:%M:%S", "%H%M%S", "%I:%M:%S%p", "%I%M%S%p").
+
+        Returns
+        -------
+        np.ndarray[np.intp]
+
+        See Also
+        --------
+        indexer_between_time : Get index locations of values between particular
+            times of day.
+        DataFrame.at_time : Select values at particular time of day.
+
+        Examples
+        --------
+        >>> idx = pd.DatetimeIndex(["1/1/2020 10:00", "2/1/2020 11:00",
+        ...                         "3/1/2020 10:00"])
+        >>> idx.indexer_at_time("10:00")
+        array([0, 2])
+        """
+        if asof:
+            raise NotImplementedError("'asof' argument is not supported")
+
+        if isinstance(time, str):
+            from dateutil.parser import parse
+
+            time = parse(time).time()
+
+        if time.tzinfo:
+            if self.tz is None:
+                raise ValueError("Index must be timezone aware.")
+            time_micros = self.tz_convert(time.tzinfo)._get_time_micros()
+        else:
+            time_micros = self._get_time_micros()
+        micros = _time_to_micros(time)
+        return (time_micros == micros).nonzero()[0]
+
+    def indexer_between_time(
+        self, start_time, end_time, include_start: bool = True, include_end: bool = True
+    ) -> npt.NDArray[np.intp]:
+        """
+        Return index locations of values between particular times of day.
+
+        Parameters
+        ----------
+        start_time, end_time : datetime.time, str
+            Time passed either as object (datetime.time) or as string in
+            appropriate format ("%H:%M", "%H%M", "%I:%M%p", "%I%M%p",
+            "%H:%M:%S", "%H%M%S", "%I:%M:%S%p","%I%M%S%p").
+        include_start : bool, default True
+        include_end : bool, default True
+
+        Returns
+        -------
+        np.ndarray[np.intp]
+
+        See Also
+        --------
+        indexer_at_time : Get index locations of values at particular time of day.
+        DataFrame.between_time : Select values between particular times of day.
+
+        Examples
+        --------
+        >>> idx = pd.date_range("2023-01-01", periods=4, freq="h")
+        >>> idx
+        DatetimeIndex(['2023-01-01 00:00:00', '2023-01-01 01:00:00',
+                           '2023-01-01 02:00:00', '2023-01-01 03:00:00'],
+                          dtype='datetime64[ns]', freq='h')
+        >>> idx.indexer_between_time("00:00", "2:00", include_end=False)
+        array([0, 1])
+        """
+        start_time = to_time(start_time)
+        end_time = to_time(end_time)
+        time_micros = self._get_time_micros()
+        start_micros = _time_to_micros(start_time)
+        end_micros = _time_to_micros(end_time)
+
+        if include_start and include_end:
+            lop = rop = operator.le
+        elif include_start:
+            lop = operator.le
+            rop = operator.lt
+        elif include_end:
+            lop = operator.lt
+            rop = operator.le
+        else:
+            lop = rop = operator.lt
+
+        if start_time <= end_time:
+            join_op = operator.and_
+        else:
+            join_op = operator.or_
+
+        mask = join_op(lop(start_micros, time_micros), rop(time_micros, end_micros))
+
+        return mask.nonzero()[0]
+
+
+def date_range(
+    start=None,
+    end=None,
+    periods=None,
+    freq=None,
+    tz=None,
+    normalize: bool = False,
+    name: Hashable | None = None,
+    inclusive: IntervalClosedType = "both",
+    *,
+    unit: str | None = None,
+    **kwargs,
+) -> DatetimeIndex:
+    """
+    Return a fixed frequency DatetimeIndex.
+
+    Returns the range of equally spaced time points (where the difference between any
+    two adjacent points is specified by the given frequency) such that they all
+    satisfy `start <[=] x <[=] end`, where the first one and the last one are, resp.,
+    the first and last time points in that range that fall on the boundary of ``freq``
+    (if given as a frequency string) or that are valid for ``freq`` (if given as a
+    :class:`pandas.tseries.offsets.DateOffset`). (If exactly one of ``start``,
+    ``end``, or ``freq`` is *not* specified, this missing parameter can be computed
+    given ``periods``, the number of timesteps in the range. See the note below.)
+
+    Parameters
+    ----------
+    start : str or datetime-like, optional
+        Left bound for generating dates.
+    end : str or datetime-like, optional
+        Right bound for generating dates.
+    periods : int, optional
+        Number of periods to generate.
+    freq : str, Timedelta, datetime.timedelta, or DateOffset, default 'D'
+        Frequency strings can have multiples, e.g. '5h'. See
+        :ref:`here ` for a list of
+        frequency aliases.
+    tz : str or tzinfo, optional
+        Time zone name for returning localized DatetimeIndex, for example
+        'Asia/Hong_Kong'. By default, the resulting DatetimeIndex is
+        timezone-naive unless timezone-aware datetime-likes are passed.
+    normalize : bool, default False
+        Normalize start/end dates to midnight before generating date range.
+    name : str, default None
+        Name of the resulting DatetimeIndex.
+    inclusive : {"both", "neither", "left", "right"}, default "both"
+        Include boundaries; Whether to set each bound as closed or open.
+
+        .. versionadded:: 1.4.0
+    unit : str, default None
+        Specify the desired resolution of the result.
+
+        .. versionadded:: 2.0.0
+    **kwargs
+        For compatibility. Has no effect on the result.
+
+    Returns
+    -------
+    DatetimeIndex
+
+    See Also
+    --------
+    DatetimeIndex : An immutable container for datetimes.
+    timedelta_range : Return a fixed frequency TimedeltaIndex.
+    period_range : Return a fixed frequency PeriodIndex.
+    interval_range : Return a fixed frequency IntervalIndex.
+
+    Notes
+    -----
+    Of the four parameters ``start``, ``end``, ``periods``, and ``freq``,
+    exactly three must be specified. If ``freq`` is omitted, the resulting
+    ``DatetimeIndex`` will have ``periods`` linearly spaced elements between
+    ``start`` and ``end`` (closed on both sides).
+
+    To learn more about the frequency strings, please see `this link
+    `__.
+
+    Examples
+    --------
+    **Specifying the values**
+
+    The next four examples generate the same `DatetimeIndex`, but vary
+    the combination of `start`, `end` and `periods`.
+
+    Specify `start` and `end`, with the default daily frequency.
+
+    >>> pd.date_range(start='1/1/2018', end='1/08/2018')
+    DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04',
+                   '2018-01-05', '2018-01-06', '2018-01-07', '2018-01-08'],
+                  dtype='datetime64[ns]', freq='D')
+
+    Specify timezone-aware `start` and `end`, with the default daily frequency.
+
+    >>> pd.date_range(
+    ...     start=pd.to_datetime("1/1/2018").tz_localize("Europe/Berlin"),
+    ...     end=pd.to_datetime("1/08/2018").tz_localize("Europe/Berlin"),
+    ... )
+    DatetimeIndex(['2018-01-01 00:00:00+01:00', '2018-01-02 00:00:00+01:00',
+                   '2018-01-03 00:00:00+01:00', '2018-01-04 00:00:00+01:00',
+                   '2018-01-05 00:00:00+01:00', '2018-01-06 00:00:00+01:00',
+                   '2018-01-07 00:00:00+01:00', '2018-01-08 00:00:00+01:00'],
+                  dtype='datetime64[ns, Europe/Berlin]', freq='D')
+
+    Specify `start` and `periods`, the number of periods (days).
+
+    >>> pd.date_range(start='1/1/2018', periods=8)
+    DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04',
+                   '2018-01-05', '2018-01-06', '2018-01-07', '2018-01-08'],
+                  dtype='datetime64[ns]', freq='D')
+
+    Specify `end` and `periods`, the number of periods (days).
+
+    >>> pd.date_range(end='1/1/2018', periods=8)
+    DatetimeIndex(['2017-12-25', '2017-12-26', '2017-12-27', '2017-12-28',
+                   '2017-12-29', '2017-12-30', '2017-12-31', '2018-01-01'],
+                  dtype='datetime64[ns]', freq='D')
+
+    Specify `start`, `end`, and `periods`; the frequency is generated
+    automatically (linearly spaced).
+
+    >>> pd.date_range(start='2018-04-24', end='2018-04-27', periods=3)
+    DatetimeIndex(['2018-04-24 00:00:00', '2018-04-25 12:00:00',
+                   '2018-04-27 00:00:00'],
+                  dtype='datetime64[ns]', freq=None)
+
+    **Other Parameters**
+
+    Changed the `freq` (frequency) to ``'ME'`` (month end frequency).
+
+    >>> pd.date_range(start='1/1/2018', periods=5, freq='ME')
+    DatetimeIndex(['2018-01-31', '2018-02-28', '2018-03-31', '2018-04-30',
+                   '2018-05-31'],
+                  dtype='datetime64[ns]', freq='ME')
+
+    Multiples are allowed
+
+    >>> pd.date_range(start='1/1/2018', periods=5, freq='3ME')
+    DatetimeIndex(['2018-01-31', '2018-04-30', '2018-07-31', '2018-10-31',
+                   '2019-01-31'],
+                  dtype='datetime64[ns]', freq='3ME')
+
+    `freq` can also be specified as an Offset object.
+
+    >>> pd.date_range(start='1/1/2018', periods=5, freq=pd.offsets.MonthEnd(3))
+    DatetimeIndex(['2018-01-31', '2018-04-30', '2018-07-31', '2018-10-31',
+                   '2019-01-31'],
+                  dtype='datetime64[ns]', freq='3ME')
+
+    Specify `tz` to set the timezone.
+
+    >>> pd.date_range(start='1/1/2018', periods=5, tz='Asia/Tokyo')
+    DatetimeIndex(['2018-01-01 00:00:00+09:00', '2018-01-02 00:00:00+09:00',
+                   '2018-01-03 00:00:00+09:00', '2018-01-04 00:00:00+09:00',
+                   '2018-01-05 00:00:00+09:00'],
+                  dtype='datetime64[ns, Asia/Tokyo]', freq='D')
+
+    `inclusive` controls whether to include `start` and `end` that are on the
+    boundary. The default, "both", includes boundary points on either end.
+
+    >>> pd.date_range(start='2017-01-01', end='2017-01-04', inclusive="both")
+    DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03', '2017-01-04'],
+                  dtype='datetime64[ns]', freq='D')
+
+    Use ``inclusive='left'`` to exclude `end` if it falls on the boundary.
+
+    >>> pd.date_range(start='2017-01-01', end='2017-01-04', inclusive='left')
+    DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03'],
+                  dtype='datetime64[ns]', freq='D')
+
+    Use ``inclusive='right'`` to exclude `start` if it falls on the boundary, and
+    similarly ``inclusive='neither'`` will exclude both `start` and `end`.
+
+    >>> pd.date_range(start='2017-01-01', end='2017-01-04', inclusive='right')
+    DatetimeIndex(['2017-01-02', '2017-01-03', '2017-01-04'],
+                  dtype='datetime64[ns]', freq='D')
+
+    **Specify a unit**
+
+    >>> pd.date_range(start="2017-01-01", periods=10, freq="100YS", unit="s")
+    DatetimeIndex(['2017-01-01', '2117-01-01', '2217-01-01', '2317-01-01',
+                   '2417-01-01', '2517-01-01', '2617-01-01', '2717-01-01',
+                   '2817-01-01', '2917-01-01'],
+                  dtype='datetime64[s]', freq='100YS-JAN')
+    """
+    if freq is None and com.any_none(periods, start, end):
+        freq = "D"
+
+    dtarr = DatetimeArray._generate_range(
+        start=start,
+        end=end,
+        periods=periods,
+        freq=freq,
+        tz=tz,
+        normalize=normalize,
+        inclusive=inclusive,
+        unit=unit,
+        **kwargs,
+    )
+    return DatetimeIndex._simple_new(dtarr, name=name)
+
+
+def bdate_range(
+    start=None,
+    end=None,
+    periods: int | None = None,
+    freq: Frequency | dt.timedelta = "B",
+    tz=None,
+    normalize: bool = True,
+    name: Hashable | None = None,
+    weekmask=None,
+    holidays=None,
+    inclusive: IntervalClosedType = "both",
+    **kwargs,
+) -> DatetimeIndex:
+    """
+    Return a fixed frequency DatetimeIndex with business day as the default.
+
+    Parameters
+    ----------
+    start : str or datetime-like, default None
+        Left bound for generating dates.
+    end : str or datetime-like, default None
+        Right bound for generating dates.
+    periods : int, default None
+        Number of periods to generate.
+    freq : str, Timedelta, datetime.timedelta, or DateOffset, default 'B'
+        Frequency strings can have multiples, e.g. '5h'. The default is
+        business daily ('B').
+    tz : str or None
+        Time zone name for returning localized DatetimeIndex, for example
+        Asia/Beijing.
+    normalize : bool, default False
+        Normalize start/end dates to midnight before generating date range.
+    name : str, default None
+        Name of the resulting DatetimeIndex.
+    weekmask : str or None, default None
+        Weekmask of valid business days, passed to ``numpy.busdaycalendar``,
+        only used when custom frequency strings are passed.  The default
+        value None is equivalent to 'Mon Tue Wed Thu Fri'.
+    holidays : list-like or None, default None
+        Dates to exclude from the set of valid business days, passed to
+        ``numpy.busdaycalendar``, only used when custom frequency strings
+        are passed.
+    inclusive : {"both", "neither", "left", "right"}, default "both"
+        Include boundaries; Whether to set each bound as closed or open.
+
+        .. versionadded:: 1.4.0
+    **kwargs
+        For compatibility. Has no effect on the result.
+
+    Returns
+    -------
+    DatetimeIndex
+
+    Notes
+    -----
+    Of the four parameters: ``start``, ``end``, ``periods``, and ``freq``,
+    exactly three must be specified.  Specifying ``freq`` is a requirement
+    for ``bdate_range``.  Use ``date_range`` if specifying ``freq`` is not
+    desired.
+
+    To learn more about the frequency strings, please see `this link
+    `__.
+
+    Examples
+    --------
+    Note how the two weekend days are skipped in the result.
+
+    >>> pd.bdate_range(start='1/1/2018', end='1/08/2018')
+    DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04',
+               '2018-01-05', '2018-01-08'],
+              dtype='datetime64[ns]', freq='B')
+    """
+    if freq is None:
+        msg = "freq must be specified for bdate_range; use date_range instead"
+        raise TypeError(msg)
+
+    if isinstance(freq, str) and freq.startswith("C"):
+        try:
+            weekmask = weekmask or "Mon Tue Wed Thu Fri"
+            freq = prefix_mapping[freq](holidays=holidays, weekmask=weekmask)
+        except (KeyError, TypeError) as err:
+            msg = f"invalid custom frequency string: {freq}"
+            raise ValueError(msg) from err
+    elif holidays or weekmask:
+        msg = (
+            "a custom frequency string is required when holidays or "
+            f"weekmask are passed, got frequency {freq}"
+        )
+        raise ValueError(msg)
+
+    return date_range(
+        start=start,
+        end=end,
+        periods=periods,
+        freq=freq,
+        tz=tz,
+        normalize=normalize,
+        name=name,
+        inclusive=inclusive,
+        **kwargs,
+    )
+
+
+def _time_to_micros(time_obj: dt.time) -> int:
+    seconds = time_obj.hour * 60 * 60 + 60 * time_obj.minute + time_obj.second
+    return 1_000_000 * seconds + time_obj.microsecond
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/indexes/extension.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/indexes/extension.py
new file mode 100644
index 0000000000000000000000000000000000000000..371d3c811e772ba10af5071e6cc9cb97ba9f3f58
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/indexes/extension.py
@@ -0,0 +1,172 @@
+"""
+Shared methods for Index subclasses backed by ExtensionArray.
+"""
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    Callable,
+    TypeVar,
+)
+
+from pandas.util._decorators import cache_readonly
+
+from pandas.core.dtypes.generic import ABCDataFrame
+
+from pandas.core.indexes.base import Index
+
+if TYPE_CHECKING:
+    import numpy as np
+
+    from pandas._typing import (
+        ArrayLike,
+        npt,
+    )
+
+    from pandas.core.arrays import IntervalArray
+    from pandas.core.arrays._mixins import NDArrayBackedExtensionArray
+
+_ExtensionIndexT = TypeVar("_ExtensionIndexT", bound="ExtensionIndex")
+
+
+def _inherit_from_data(
+    name: str, delegate: type, cache: bool = False, wrap: bool = False
+):
+    """
+    Make an alias for a method of the underlying ExtensionArray.
+
+    Parameters
+    ----------
+    name : str
+        Name of an attribute the class should inherit from its EA parent.
+    delegate : class
+    cache : bool, default False
+        Whether to convert wrapped properties into cache_readonly
+    wrap : bool, default False
+        Whether to wrap the inherited result in an Index.
+
+    Returns
+    -------
+    attribute, method, property, or cache_readonly
+    """
+    attr = getattr(delegate, name)
+
+    if isinstance(attr, property) or type(attr).__name__ == "getset_descriptor":
+        # getset_descriptor i.e. property defined in cython class
+        if cache:
+
+            def cached(self):
+                return getattr(self._data, name)
+
+            cached.__name__ = name
+            cached.__doc__ = attr.__doc__
+            method = cache_readonly(cached)
+
+        else:
+
+            def fget(self):
+                result = getattr(self._data, name)
+                if wrap:
+                    if isinstance(result, type(self._data)):
+                        return type(self)._simple_new(result, name=self.name)
+                    elif isinstance(result, ABCDataFrame):
+                        return result.set_index(self)
+                    return Index(result, name=self.name, dtype=result.dtype)
+                return result
+
+            def fset(self, value) -> None:
+                setattr(self._data, name, value)
+
+            fget.__name__ = name
+            fget.__doc__ = attr.__doc__
+
+            method = property(fget, fset)
+
+    elif not callable(attr):
+        # just a normal attribute, no wrapping
+        method = attr
+
+    else:
+        # error: Incompatible redefinition (redefinition with type "Callable[[Any,
+        # VarArg(Any), KwArg(Any)], Any]", original type "property")
+        def method(self, *args, **kwargs):  # type: ignore[misc]
+            if "inplace" in kwargs:
+                raise ValueError(f"cannot use inplace with {type(self).__name__}")
+            result = attr(self._data, *args, **kwargs)
+            if wrap:
+                if isinstance(result, type(self._data)):
+                    return type(self)._simple_new(result, name=self.name)
+                elif isinstance(result, ABCDataFrame):
+                    return result.set_index(self)
+                return Index(result, name=self.name, dtype=result.dtype)
+            return result
+
+        # error: "property" has no attribute "__name__"
+        method.__name__ = name  # type: ignore[attr-defined]
+        method.__doc__ = attr.__doc__
+    return method
+
+
+def inherit_names(
+    names: list[str], delegate: type, cache: bool = False, wrap: bool = False
+) -> Callable[[type[_ExtensionIndexT]], type[_ExtensionIndexT]]:
+    """
+    Class decorator to pin attributes from an ExtensionArray to a Index subclass.
+
+    Parameters
+    ----------
+    names : List[str]
+    delegate : class
+    cache : bool, default False
+    wrap : bool, default False
+        Whether to wrap the inherited result in an Index.
+    """
+
+    def wrapper(cls: type[_ExtensionIndexT]) -> type[_ExtensionIndexT]:
+        for name in names:
+            meth = _inherit_from_data(name, delegate, cache=cache, wrap=wrap)
+            setattr(cls, name, meth)
+
+        return cls
+
+    return wrapper
+
+
+class ExtensionIndex(Index):
+    """
+    Index subclass for indexes backed by ExtensionArray.
+    """
+
+    # The base class already passes through to _data:
+    #  size, __len__, dtype
+
+    _data: IntervalArray | NDArrayBackedExtensionArray
+
+    # ---------------------------------------------------------------------
+
+    def _validate_fill_value(self, value):
+        """
+        Convert value to be insertable to underlying array.
+        """
+        return self._data._validate_setitem_value(value)
+
+    @cache_readonly
+    def _isnan(self) -> npt.NDArray[np.bool_]:
+        # error: Incompatible return value type (got "ExtensionArray", expected
+        # "ndarray")
+        return self._data.isna()  # type: ignore[return-value]
+
+
+class NDArrayBackedExtensionIndex(ExtensionIndex):
+    """
+    Index subclass for indexes backed by NDArrayBackedExtensionArray.
+    """
+
+    _data: NDArrayBackedExtensionArray
+
+    def _get_engine_target(self) -> np.ndarray:
+        return self._data._ndarray
+
+    def _from_join_target(self, result: np.ndarray) -> ArrayLike:
+        assert result.dtype == self._data._ndarray.dtype
+        return self._data._from_backing_data(result)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/indexes/frozen.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/indexes/frozen.py
new file mode 100644
index 0000000000000000000000000000000000000000..9d528d34e36845efd44126c087cd15cd81e1e02e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/indexes/frozen.py
@@ -0,0 +1,120 @@
+"""
+frozen (immutable) data structures to support MultiIndexing
+
+These are used for:
+
+- .names (FrozenList)
+
+"""
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    NoReturn,
+)
+
+from pandas.core.base import PandasObject
+
+from pandas.io.formats.printing import pprint_thing
+
+if TYPE_CHECKING:
+    from pandas._typing import Self
+
+
+class FrozenList(PandasObject, list):
+    """
+    Container that doesn't allow setting item *but*
+    because it's technically hashable, will be used
+    for lookups, appropriately, etc.
+    """
+
+    # Side note: This has to be of type list. Otherwise,
+    #            it messes up PyTables type checks.
+
+    def union(self, other) -> FrozenList:
+        """
+        Returns a FrozenList with other concatenated to the end of self.
+
+        Parameters
+        ----------
+        other : array-like
+            The array-like whose elements we are concatenating.
+
+        Returns
+        -------
+        FrozenList
+            The collection difference between self and other.
+        """
+        if isinstance(other, tuple):
+            other = list(other)
+        return type(self)(super().__add__(other))
+
+    def difference(self, other) -> FrozenList:
+        """
+        Returns a FrozenList with elements from other removed from self.
+
+        Parameters
+        ----------
+        other : array-like
+            The array-like whose elements we are removing self.
+
+        Returns
+        -------
+        FrozenList
+            The collection difference between self and other.
+        """
+        other = set(other)
+        temp = [x for x in self if x not in other]
+        return type(self)(temp)
+
+    # TODO: Consider deprecating these in favor of `union` (xref gh-15506)
+    # error: Incompatible types in assignment (expression has type
+    # "Callable[[FrozenList, Any], FrozenList]", base class "list" defined the
+    # type as overloaded function)
+    __add__ = __iadd__ = union  # type: ignore[assignment]
+
+    def __getitem__(self, n):
+        if isinstance(n, slice):
+            return type(self)(super().__getitem__(n))
+        return super().__getitem__(n)
+
+    def __radd__(self, other) -> Self:
+        if isinstance(other, tuple):
+            other = list(other)
+        return type(self)(other + list(self))
+
+    def __eq__(self, other: object) -> bool:
+        if isinstance(other, (tuple, FrozenList)):
+            other = list(other)
+        return super().__eq__(other)
+
+    __req__ = __eq__
+
+    def __mul__(self, other) -> Self:
+        return type(self)(super().__mul__(other))
+
+    __imul__ = __mul__
+
+    def __reduce__(self):
+        return type(self), (list(self),)
+
+    # error: Signature of "__hash__" incompatible with supertype "list"
+    def __hash__(self) -> int:  # type: ignore[override]
+        return hash(tuple(self))
+
+    def _disabled(self, *args, **kwargs) -> NoReturn:
+        """
+        This method will not function because object is immutable.
+        """
+        raise TypeError(f"'{type(self).__name__}' does not support mutable operations.")
+
+    def __str__(self) -> str:
+        return pprint_thing(self, quote_strings=True, escape_chars=("\t", "\r", "\n"))
+
+    def __repr__(self) -> str:
+        return f"{type(self).__name__}({str(self)})"
+
+    __setitem__ = __setslice__ = _disabled  # type: ignore[assignment]
+    __delitem__ = __delslice__ = _disabled
+    pop = append = extend = _disabled
+    remove = sort = insert = _disabled  # type: ignore[assignment]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/indexes/interval.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/indexes/interval.py
new file mode 100644
index 0000000000000000000000000000000000000000..635924674d9f4aec5537f77e5fbcf5e81180e2d4
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/indexes/interval.py
@@ -0,0 +1,1137 @@
+""" define the IntervalIndex """
+from __future__ import annotations
+
+from operator import (
+    le,
+    lt,
+)
+import textwrap
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Literal,
+)
+
+import numpy as np
+
+from pandas._libs import lib
+from pandas._libs.interval import (
+    Interval,
+    IntervalMixin,
+    IntervalTree,
+)
+from pandas._libs.tslibs import (
+    BaseOffset,
+    Period,
+    Timedelta,
+    Timestamp,
+    to_offset,
+)
+from pandas.errors import InvalidIndexError
+from pandas.util._decorators import (
+    Appender,
+    cache_readonly,
+)
+from pandas.util._exceptions import rewrite_exception
+
+from pandas.core.dtypes.cast import (
+    find_common_type,
+    infer_dtype_from_scalar,
+    maybe_box_datetimelike,
+    maybe_downcast_numeric,
+    maybe_upcast_numeric_to_64bit,
+)
+from pandas.core.dtypes.common import (
+    ensure_platform_int,
+    is_float_dtype,
+    is_integer,
+    is_integer_dtype,
+    is_list_like,
+    is_number,
+    is_object_dtype,
+    is_scalar,
+    is_string_dtype,
+    pandas_dtype,
+)
+from pandas.core.dtypes.dtypes import (
+    DatetimeTZDtype,
+    IntervalDtype,
+)
+from pandas.core.dtypes.missing import is_valid_na_for_dtype
+
+from pandas.core.algorithms import unique
+from pandas.core.arrays.datetimelike import validate_periods
+from pandas.core.arrays.interval import (
+    IntervalArray,
+    _interval_shared_docs,
+)
+import pandas.core.common as com
+from pandas.core.indexers import is_valid_positional_slice
+import pandas.core.indexes.base as ibase
+from pandas.core.indexes.base import (
+    Index,
+    _index_shared_docs,
+    ensure_index,
+    maybe_extract_name,
+)
+from pandas.core.indexes.datetimes import (
+    DatetimeIndex,
+    date_range,
+)
+from pandas.core.indexes.extension import (
+    ExtensionIndex,
+    inherit_names,
+)
+from pandas.core.indexes.multi import MultiIndex
+from pandas.core.indexes.timedeltas import (
+    TimedeltaIndex,
+    timedelta_range,
+)
+
+if TYPE_CHECKING:
+    from collections.abc import Hashable
+
+    from pandas._typing import (
+        Dtype,
+        DtypeObj,
+        IntervalClosedType,
+        Self,
+        npt,
+    )
+_index_doc_kwargs = dict(ibase._index_doc_kwargs)
+
+_index_doc_kwargs.update(
+    {
+        "klass": "IntervalIndex",
+        "qualname": "IntervalIndex",
+        "target_klass": "IntervalIndex or list of Intervals",
+        "name": textwrap.dedent(
+            """\
+         name : object, optional
+              Name to be stored in the index.
+         """
+        ),
+    }
+)
+
+
+def _get_next_label(label):
+    # see test_slice_locs_with_ints_and_floats_succeeds
+    dtype = getattr(label, "dtype", type(label))
+    if isinstance(label, (Timestamp, Timedelta)):
+        dtype = "datetime64[ns]"
+    dtype = pandas_dtype(dtype)
+
+    if lib.is_np_dtype(dtype, "mM") or isinstance(dtype, DatetimeTZDtype):
+        return label + np.timedelta64(1, "ns")
+    elif is_integer_dtype(dtype):
+        return label + 1
+    elif is_float_dtype(dtype):
+        return np.nextafter(label, np.inf)
+    else:
+        raise TypeError(f"cannot determine next label for type {repr(type(label))}")
+
+
+def _get_prev_label(label):
+    # see test_slice_locs_with_ints_and_floats_succeeds
+    dtype = getattr(label, "dtype", type(label))
+    if isinstance(label, (Timestamp, Timedelta)):
+        dtype = "datetime64[ns]"
+    dtype = pandas_dtype(dtype)
+
+    if lib.is_np_dtype(dtype, "mM") or isinstance(dtype, DatetimeTZDtype):
+        return label - np.timedelta64(1, "ns")
+    elif is_integer_dtype(dtype):
+        return label - 1
+    elif is_float_dtype(dtype):
+        return np.nextafter(label, -np.inf)
+    else:
+        raise TypeError(f"cannot determine next label for type {repr(type(label))}")
+
+
+def _new_IntervalIndex(cls, d):
+    """
+    This is called upon unpickling, rather than the default which doesn't have
+    arguments and breaks __new__.
+    """
+    return cls.from_arrays(**d)
+
+
+@Appender(
+    _interval_shared_docs["class"]
+    % {
+        "klass": "IntervalIndex",
+        "summary": "Immutable index of intervals that are closed on the same side.",
+        "name": _index_doc_kwargs["name"],
+        "extra_attributes": "is_overlapping\nvalues\n",
+        "extra_methods": "",
+        "examples": textwrap.dedent(
+            """\
+    Examples
+    --------
+    A new ``IntervalIndex`` is typically constructed using
+    :func:`interval_range`:
+
+    >>> pd.interval_range(start=0, end=5)
+    IntervalIndex([(0, 1], (1, 2], (2, 3], (3, 4], (4, 5]],
+                  dtype='interval[int64, right]')
+
+    It may also be constructed using one of the constructor
+    methods: :meth:`IntervalIndex.from_arrays`,
+    :meth:`IntervalIndex.from_breaks`, and :meth:`IntervalIndex.from_tuples`.
+
+    See further examples in the doc strings of ``interval_range`` and the
+    mentioned constructor methods.
+    """
+        ),
+    }
+)
+@inherit_names(["set_closed", "to_tuples"], IntervalArray, wrap=True)
+@inherit_names(
+    [
+        "__array__",
+        "overlaps",
+        "contains",
+        "closed_left",
+        "closed_right",
+        "open_left",
+        "open_right",
+        "is_empty",
+    ],
+    IntervalArray,
+)
+@inherit_names(["is_non_overlapping_monotonic", "closed"], IntervalArray, cache=True)
+class IntervalIndex(ExtensionIndex):
+    _typ = "intervalindex"
+
+    # annotate properties pinned via inherit_names
+    closed: IntervalClosedType
+    is_non_overlapping_monotonic: bool
+    closed_left: bool
+    closed_right: bool
+    open_left: bool
+    open_right: bool
+
+    _data: IntervalArray
+    _values: IntervalArray
+    _can_hold_strings = False
+    _data_cls = IntervalArray
+
+    # --------------------------------------------------------------------
+    # Constructors
+
+    def __new__(
+        cls,
+        data,
+        closed: IntervalClosedType | None = None,
+        dtype: Dtype | None = None,
+        copy: bool = False,
+        name: Hashable | None = None,
+        verify_integrity: bool = True,
+    ) -> Self:
+        name = maybe_extract_name(name, data, cls)
+
+        with rewrite_exception("IntervalArray", cls.__name__):
+            array = IntervalArray(
+                data,
+                closed=closed,
+                copy=copy,
+                dtype=dtype,
+                verify_integrity=verify_integrity,
+            )
+
+        return cls._simple_new(array, name)
+
+    @classmethod
+    @Appender(
+        _interval_shared_docs["from_breaks"]
+        % {
+            "klass": "IntervalIndex",
+            "name": textwrap.dedent(
+                """
+             name : str, optional
+                  Name of the resulting IntervalIndex."""
+            ),
+            "examples": textwrap.dedent(
+                """\
+        Examples
+        --------
+        >>> pd.IntervalIndex.from_breaks([0, 1, 2, 3])
+        IntervalIndex([(0, 1], (1, 2], (2, 3]],
+                      dtype='interval[int64, right]')
+        """
+            ),
+        }
+    )
+    def from_breaks(
+        cls,
+        breaks,
+        closed: IntervalClosedType | None = "right",
+        name: Hashable | None = None,
+        copy: bool = False,
+        dtype: Dtype | None = None,
+    ) -> IntervalIndex:
+        with rewrite_exception("IntervalArray", cls.__name__):
+            array = IntervalArray.from_breaks(
+                breaks, closed=closed, copy=copy, dtype=dtype
+            )
+        return cls._simple_new(array, name=name)
+
+    @classmethod
+    @Appender(
+        _interval_shared_docs["from_arrays"]
+        % {
+            "klass": "IntervalIndex",
+            "name": textwrap.dedent(
+                """
+             name : str, optional
+                  Name of the resulting IntervalIndex."""
+            ),
+            "examples": textwrap.dedent(
+                """\
+        Examples
+        --------
+        >>> pd.IntervalIndex.from_arrays([0, 1, 2], [1, 2, 3])
+        IntervalIndex([(0, 1], (1, 2], (2, 3]],
+                      dtype='interval[int64, right]')
+        """
+            ),
+        }
+    )
+    def from_arrays(
+        cls,
+        left,
+        right,
+        closed: IntervalClosedType = "right",
+        name: Hashable | None = None,
+        copy: bool = False,
+        dtype: Dtype | None = None,
+    ) -> IntervalIndex:
+        with rewrite_exception("IntervalArray", cls.__name__):
+            array = IntervalArray.from_arrays(
+                left, right, closed, copy=copy, dtype=dtype
+            )
+        return cls._simple_new(array, name=name)
+
+    @classmethod
+    @Appender(
+        _interval_shared_docs["from_tuples"]
+        % {
+            "klass": "IntervalIndex",
+            "name": textwrap.dedent(
+                """
+             name : str, optional
+                  Name of the resulting IntervalIndex."""
+            ),
+            "examples": textwrap.dedent(
+                """\
+        Examples
+        --------
+        >>> pd.IntervalIndex.from_tuples([(0, 1), (1, 2)])
+        IntervalIndex([(0, 1], (1, 2]],
+                       dtype='interval[int64, right]')
+        """
+            ),
+        }
+    )
+    def from_tuples(
+        cls,
+        data,
+        closed: IntervalClosedType = "right",
+        name: Hashable | None = None,
+        copy: bool = False,
+        dtype: Dtype | None = None,
+    ) -> IntervalIndex:
+        with rewrite_exception("IntervalArray", cls.__name__):
+            arr = IntervalArray.from_tuples(data, closed=closed, copy=copy, dtype=dtype)
+        return cls._simple_new(arr, name=name)
+
+    # --------------------------------------------------------------------
+    # error: Return type "IntervalTree" of "_engine" incompatible with return type
+    # "Union[IndexEngine, ExtensionEngine]" in supertype "Index"
+    @cache_readonly
+    def _engine(self) -> IntervalTree:  # type: ignore[override]
+        # IntervalTree does not supports numpy array unless they are 64 bit
+        left = self._maybe_convert_i8(self.left)
+        left = maybe_upcast_numeric_to_64bit(left)
+        right = self._maybe_convert_i8(self.right)
+        right = maybe_upcast_numeric_to_64bit(right)
+        return IntervalTree(left, right, closed=self.closed)
+
+    def __contains__(self, key: Any) -> bool:
+        """
+        return a boolean if this key is IN the index
+        We *only* accept an Interval
+
+        Parameters
+        ----------
+        key : Interval
+
+        Returns
+        -------
+        bool
+        """
+        hash(key)
+        if not isinstance(key, Interval):
+            if is_valid_na_for_dtype(key, self.dtype):
+                return self.hasnans
+            return False
+
+        try:
+            self.get_loc(key)
+            return True
+        except KeyError:
+            return False
+
+    def _getitem_slice(self, slobj: slice) -> IntervalIndex:
+        """
+        Fastpath for __getitem__ when we know we have a slice.
+        """
+        res = self._data[slobj]
+        return type(self)._simple_new(res, name=self._name)
+
+    @cache_readonly
+    def _multiindex(self) -> MultiIndex:
+        return MultiIndex.from_arrays([self.left, self.right], names=["left", "right"])
+
+    def __reduce__(self):
+        d = {
+            "left": self.left,
+            "right": self.right,
+            "closed": self.closed,
+            "name": self.name,
+        }
+        return _new_IntervalIndex, (type(self), d), None
+
+    @property
+    def inferred_type(self) -> str:
+        """Return a string of the type inferred from the values"""
+        return "interval"
+
+    # Cannot determine type of "memory_usage"
+    @Appender(Index.memory_usage.__doc__)  # type: ignore[has-type]
+    def memory_usage(self, deep: bool = False) -> int:
+        # we don't use an explicit engine
+        # so return the bytes here
+        return self.left.memory_usage(deep=deep) + self.right.memory_usage(deep=deep)
+
+    # IntervalTree doesn't have a is_monotonic_decreasing, so have to override
+    #  the Index implementation
+    @cache_readonly
+    def is_monotonic_decreasing(self) -> bool:
+        """
+        Return True if the IntervalIndex is monotonic decreasing (only equal or
+        decreasing values), else False
+        """
+        return self[::-1].is_monotonic_increasing
+
+    @cache_readonly
+    def is_unique(self) -> bool:
+        """
+        Return True if the IntervalIndex contains unique elements, else False.
+        """
+        left = self.left
+        right = self.right
+
+        if self.isna().sum() > 1:
+            return False
+
+        if left.is_unique or right.is_unique:
+            return True
+
+        seen_pairs = set()
+        check_idx = np.where(left.duplicated(keep=False))[0]
+        for idx in check_idx:
+            pair = (left[idx], right[idx])
+            if pair in seen_pairs:
+                return False
+            seen_pairs.add(pair)
+
+        return True
+
+    @property
+    def is_overlapping(self) -> bool:
+        """
+        Return True if the IntervalIndex has overlapping intervals, else False.
+
+        Two intervals overlap if they share a common point, including closed
+        endpoints. Intervals that only have an open endpoint in common do not
+        overlap.
+
+        Returns
+        -------
+        bool
+            Boolean indicating if the IntervalIndex has overlapping intervals.
+
+        See Also
+        --------
+        Interval.overlaps : Check whether two Interval objects overlap.
+        IntervalIndex.overlaps : Check an IntervalIndex elementwise for
+            overlaps.
+
+        Examples
+        --------
+        >>> index = pd.IntervalIndex.from_tuples([(0, 2), (1, 3), (4, 5)])
+        >>> index
+        IntervalIndex([(0, 2], (1, 3], (4, 5]],
+              dtype='interval[int64, right]')
+        >>> index.is_overlapping
+        True
+
+        Intervals that share closed endpoints overlap:
+
+        >>> index = pd.interval_range(0, 3, closed='both')
+        >>> index
+        IntervalIndex([[0, 1], [1, 2], [2, 3]],
+              dtype='interval[int64, both]')
+        >>> index.is_overlapping
+        True
+
+        Intervals that only have an open endpoint in common do not overlap:
+
+        >>> index = pd.interval_range(0, 3, closed='left')
+        >>> index
+        IntervalIndex([[0, 1), [1, 2), [2, 3)],
+              dtype='interval[int64, left]')
+        >>> index.is_overlapping
+        False
+        """
+        # GH 23309
+        return self._engine.is_overlapping
+
+    def _needs_i8_conversion(self, key) -> bool:
+        """
+        Check if a given key needs i8 conversion. Conversion is necessary for
+        Timestamp, Timedelta, DatetimeIndex, and TimedeltaIndex keys. An
+        Interval-like requires conversion if its endpoints are one of the
+        aforementioned types.
+
+        Assumes that any list-like data has already been cast to an Index.
+
+        Parameters
+        ----------
+        key : scalar or Index-like
+            The key that should be checked for i8 conversion
+
+        Returns
+        -------
+        bool
+        """
+        key_dtype = getattr(key, "dtype", None)
+        if isinstance(key_dtype, IntervalDtype) or isinstance(key, Interval):
+            return self._needs_i8_conversion(key.left)
+
+        i8_types = (Timestamp, Timedelta, DatetimeIndex, TimedeltaIndex)
+        return isinstance(key, i8_types)
+
+    def _maybe_convert_i8(self, key):
+        """
+        Maybe convert a given key to its equivalent i8 value(s). Used as a
+        preprocessing step prior to IntervalTree queries (self._engine), which
+        expects numeric data.
+
+        Parameters
+        ----------
+        key : scalar or list-like
+            The key that should maybe be converted to i8.
+
+        Returns
+        -------
+        scalar or list-like
+            The original key if no conversion occurred, int if converted scalar,
+            Index with an int64 dtype if converted list-like.
+        """
+        if is_list_like(key):
+            key = ensure_index(key)
+            key = maybe_upcast_numeric_to_64bit(key)
+
+        if not self._needs_i8_conversion(key):
+            return key
+
+        scalar = is_scalar(key)
+        key_dtype = getattr(key, "dtype", None)
+        if isinstance(key_dtype, IntervalDtype) or isinstance(key, Interval):
+            # convert left/right and reconstruct
+            left = self._maybe_convert_i8(key.left)
+            right = self._maybe_convert_i8(key.right)
+            constructor = Interval if scalar else IntervalIndex.from_arrays
+            # error: "object" not callable
+            return constructor(
+                left, right, closed=self.closed
+            )  # type: ignore[operator]
+
+        if scalar:
+            # Timestamp/Timedelta
+            key_dtype, key_i8 = infer_dtype_from_scalar(key)
+            if isinstance(key, Period):
+                key_i8 = key.ordinal
+            elif isinstance(key_i8, Timestamp):
+                key_i8 = key_i8._value
+            elif isinstance(key_i8, (np.datetime64, np.timedelta64)):
+                key_i8 = key_i8.view("i8")
+        else:
+            # DatetimeIndex/TimedeltaIndex
+            key_dtype, key_i8 = key.dtype, Index(key.asi8)
+            if key.hasnans:
+                # convert NaT from its i8 value to np.nan so it's not viewed
+                # as a valid value, maybe causing errors (e.g. is_overlapping)
+                key_i8 = key_i8.where(~key._isnan)
+
+        # ensure consistency with IntervalIndex subtype
+        # error: Item "ExtensionDtype"/"dtype[Any]" of "Union[dtype[Any],
+        # ExtensionDtype]" has no attribute "subtype"
+        subtype = self.dtype.subtype  # type: ignore[union-attr]
+
+        if subtype != key_dtype:
+            raise ValueError(
+                f"Cannot index an IntervalIndex of subtype {subtype} with "
+                f"values of dtype {key_dtype}"
+            )
+
+        return key_i8
+
+    def _searchsorted_monotonic(self, label, side: Literal["left", "right"] = "left"):
+        if not self.is_non_overlapping_monotonic:
+            raise KeyError(
+                "can only get slices from an IntervalIndex if bounds are "
+                "non-overlapping and all monotonic increasing or decreasing"
+            )
+
+        if isinstance(label, (IntervalMixin, IntervalIndex)):
+            raise NotImplementedError("Interval objects are not currently supported")
+
+        # GH 20921: "not is_monotonic_increasing" for the second condition
+        # instead of "is_monotonic_decreasing" to account for single element
+        # indexes being both increasing and decreasing
+        if (side == "left" and self.left.is_monotonic_increasing) or (
+            side == "right" and not self.left.is_monotonic_increasing
+        ):
+            sub_idx = self.right
+            if self.open_right:
+                label = _get_next_label(label)
+        else:
+            sub_idx = self.left
+            if self.open_left:
+                label = _get_prev_label(label)
+
+        return sub_idx._searchsorted_monotonic(label, side)
+
+    # --------------------------------------------------------------------
+    # Indexing Methods
+
+    def get_loc(self, key) -> int | slice | np.ndarray:
+        """
+        Get integer location, slice or boolean mask for requested label.
+
+        Parameters
+        ----------
+        key : label
+
+        Returns
+        -------
+        int if unique index, slice if monotonic index, else mask
+
+        Examples
+        --------
+        >>> i1, i2 = pd.Interval(0, 1), pd.Interval(1, 2)
+        >>> index = pd.IntervalIndex([i1, i2])
+        >>> index.get_loc(1)
+        0
+
+        You can also supply a point inside an interval.
+
+        >>> index.get_loc(1.5)
+        1
+
+        If a label is in several intervals, you get the locations of all the
+        relevant intervals.
+
+        >>> i3 = pd.Interval(0, 2)
+        >>> overlapping_index = pd.IntervalIndex([i1, i2, i3])
+        >>> overlapping_index.get_loc(0.5)
+        array([ True, False,  True])
+
+        Only exact matches will be returned if an interval is provided.
+
+        >>> index.get_loc(pd.Interval(0, 1))
+        0
+        """
+        self._check_indexing_error(key)
+
+        if isinstance(key, Interval):
+            if self.closed != key.closed:
+                raise KeyError(key)
+            mask = (self.left == key.left) & (self.right == key.right)
+        elif is_valid_na_for_dtype(key, self.dtype):
+            mask = self.isna()
+        else:
+            # assume scalar
+            op_left = le if self.closed_left else lt
+            op_right = le if self.closed_right else lt
+            try:
+                mask = op_left(self.left, key) & op_right(key, self.right)
+            except TypeError as err:
+                # scalar is not comparable to II subtype --> invalid label
+                raise KeyError(key) from err
+
+        matches = mask.sum()
+        if matches == 0:
+            raise KeyError(key)
+        if matches == 1:
+            return mask.argmax()
+
+        res = lib.maybe_booleans_to_slice(mask.view("u1"))
+        if isinstance(res, slice) and res.stop is None:
+            # TODO: DO this in maybe_booleans_to_slice?
+            res = slice(res.start, len(self), res.step)
+        return res
+
+    def _get_indexer(
+        self,
+        target: Index,
+        method: str | None = None,
+        limit: int | None = None,
+        tolerance: Any | None = None,
+    ) -> npt.NDArray[np.intp]:
+        if isinstance(target, IntervalIndex):
+            # We only get here with not self.is_overlapping
+            # -> at most one match per interval in target
+            # want exact matches -> need both left/right to match, so defer to
+            # left/right get_indexer, compare elementwise, equality -> match
+            indexer = self._get_indexer_unique_sides(target)
+
+        elif not (is_object_dtype(target.dtype) or is_string_dtype(target.dtype)):
+            # homogeneous scalar index: use IntervalTree
+            # we should always have self._should_partial_index(target) here
+            target = self._maybe_convert_i8(target)
+            indexer = self._engine.get_indexer(target.values)
+        else:
+            # heterogeneous scalar index: defer elementwise to get_loc
+            # we should always have self._should_partial_index(target) here
+            return self._get_indexer_pointwise(target)[0]
+
+        return ensure_platform_int(indexer)
+
+    @Appender(_index_shared_docs["get_indexer_non_unique"] % _index_doc_kwargs)
+    def get_indexer_non_unique(
+        self, target: Index
+    ) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]:
+        target = ensure_index(target)
+
+        if not self._should_compare(target) and not self._should_partial_index(target):
+            # e.g. IntervalIndex with different closed or incompatible subtype
+            #  -> no matches
+            return self._get_indexer_non_comparable(target, None, unique=False)
+
+        elif isinstance(target, IntervalIndex):
+            if self.left.is_unique and self.right.is_unique:
+                # fastpath available even if we don't have self._index_as_unique
+                indexer = self._get_indexer_unique_sides(target)
+                missing = (indexer == -1).nonzero()[0]
+            else:
+                return self._get_indexer_pointwise(target)
+
+        elif is_object_dtype(target.dtype) or not self._should_partial_index(target):
+            # target might contain intervals: defer elementwise to get_loc
+            return self._get_indexer_pointwise(target)
+
+        else:
+            # Note: this case behaves differently from other Index subclasses
+            #  because IntervalIndex does partial-int indexing
+            target = self._maybe_convert_i8(target)
+            indexer, missing = self._engine.get_indexer_non_unique(target.values)
+
+        return ensure_platform_int(indexer), ensure_platform_int(missing)
+
+    def _get_indexer_unique_sides(self, target: IntervalIndex) -> npt.NDArray[np.intp]:
+        """
+        _get_indexer specialized to the case where both of our sides are unique.
+        """
+        # Caller is responsible for checking
+        #  `self.left.is_unique and self.right.is_unique`
+
+        left_indexer = self.left.get_indexer(target.left)
+        right_indexer = self.right.get_indexer(target.right)
+        indexer = np.where(left_indexer == right_indexer, left_indexer, -1)
+        return indexer
+
+    def _get_indexer_pointwise(
+        self, target: Index
+    ) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]:
+        """
+        pointwise implementation for get_indexer and get_indexer_non_unique.
+        """
+        indexer, missing = [], []
+        for i, key in enumerate(target):
+            try:
+                locs = self.get_loc(key)
+                if isinstance(locs, slice):
+                    # Only needed for get_indexer_non_unique
+                    locs = np.arange(locs.start, locs.stop, locs.step, dtype="intp")
+                elif lib.is_integer(locs):
+                    locs = np.array(locs, ndmin=1)
+                else:
+                    # otherwise we have ndarray[bool]
+                    locs = np.where(locs)[0]
+            except KeyError:
+                missing.append(i)
+                locs = np.array([-1])
+            except InvalidIndexError:
+                # i.e. non-scalar key e.g. a tuple.
+                # see test_append_different_columns_types_raises
+                missing.append(i)
+                locs = np.array([-1])
+
+            indexer.append(locs)
+
+        indexer = np.concatenate(indexer)
+        return ensure_platform_int(indexer), ensure_platform_int(missing)
+
+    @cache_readonly
+    def _index_as_unique(self) -> bool:
+        return not self.is_overlapping and self._engine._na_count < 2
+
+    _requires_unique_msg = (
+        "cannot handle overlapping indices; use IntervalIndex.get_indexer_non_unique"
+    )
+
+    def _convert_slice_indexer(self, key: slice, kind: Literal["loc", "getitem"]):
+        if not (key.step is None or key.step == 1):
+            # GH#31658 if label-based, we require step == 1,
+            #  if positional, we disallow float start/stop
+            msg = "label-based slicing with step!=1 is not supported for IntervalIndex"
+            if kind == "loc":
+                raise ValueError(msg)
+            if kind == "getitem":
+                if not is_valid_positional_slice(key):
+                    # i.e. this cannot be interpreted as a positional slice
+                    raise ValueError(msg)
+
+        return super()._convert_slice_indexer(key, kind)
+
+    @cache_readonly
+    def _should_fallback_to_positional(self) -> bool:
+        # integer lookups in Series.__getitem__ are unambiguously
+        #  positional in this case
+        # error: Item "ExtensionDtype"/"dtype[Any]" of "Union[dtype[Any],
+        # ExtensionDtype]" has no attribute "subtype"
+        return self.dtype.subtype.kind in "mM"  # type: ignore[union-attr]
+
+    def _maybe_cast_slice_bound(self, label, side: str):
+        return getattr(self, side)._maybe_cast_slice_bound(label, side)
+
+    def _is_comparable_dtype(self, dtype: DtypeObj) -> bool:
+        if not isinstance(dtype, IntervalDtype):
+            return False
+        common_subtype = find_common_type([self.dtype, dtype])
+        return not is_object_dtype(common_subtype)
+
+    # --------------------------------------------------------------------
+
+    @cache_readonly
+    def left(self) -> Index:
+        return Index(self._data.left, copy=False)
+
+    @cache_readonly
+    def right(self) -> Index:
+        return Index(self._data.right, copy=False)
+
+    @cache_readonly
+    def mid(self) -> Index:
+        return Index(self._data.mid, copy=False)
+
+    @property
+    def length(self) -> Index:
+        return Index(self._data.length, copy=False)
+
+    # --------------------------------------------------------------------
+    # Set Operations
+
+    def _intersection(self, other, sort):
+        """
+        intersection specialized to the case with matching dtypes.
+        """
+        # For IntervalIndex we also know other.closed == self.closed
+        if self.left.is_unique and self.right.is_unique:
+            taken = self._intersection_unique(other)
+        elif other.left.is_unique and other.right.is_unique and self.isna().sum() <= 1:
+            # Swap other/self if other is unique and self does not have
+            # multiple NaNs
+            taken = other._intersection_unique(self)
+        else:
+            # duplicates
+            taken = self._intersection_non_unique(other)
+
+        if sort is None:
+            taken = taken.sort_values()
+
+        return taken
+
+    def _intersection_unique(self, other: IntervalIndex) -> IntervalIndex:
+        """
+        Used when the IntervalIndex does not have any common endpoint,
+        no matter left or right.
+        Return the intersection with another IntervalIndex.
+        Parameters
+        ----------
+        other : IntervalIndex
+        Returns
+        -------
+        IntervalIndex
+        """
+        # Note: this is much more performant than super()._intersection(other)
+        lindexer = self.left.get_indexer(other.left)
+        rindexer = self.right.get_indexer(other.right)
+
+        match = (lindexer == rindexer) & (lindexer != -1)
+        indexer = lindexer.take(match.nonzero()[0])
+        indexer = unique(indexer)
+
+        return self.take(indexer)
+
+    def _intersection_non_unique(self, other: IntervalIndex) -> IntervalIndex:
+        """
+        Used when the IntervalIndex does have some common endpoints,
+        on either sides.
+        Return the intersection with another IntervalIndex.
+
+        Parameters
+        ----------
+        other : IntervalIndex
+
+        Returns
+        -------
+        IntervalIndex
+        """
+        # Note: this is about 3.25x faster than super()._intersection(other)
+        #  in IntervalIndexMethod.time_intersection_both_duplicate(1000)
+        mask = np.zeros(len(self), dtype=bool)
+
+        if self.hasnans and other.hasnans:
+            first_nan_loc = np.arange(len(self))[self.isna()][0]
+            mask[first_nan_loc] = True
+
+        other_tups = set(zip(other.left, other.right))
+        for i, tup in enumerate(zip(self.left, self.right)):
+            if tup in other_tups:
+                mask[i] = True
+
+        return self[mask]
+
+    # --------------------------------------------------------------------
+
+    def _get_engine_target(self) -> np.ndarray:
+        # Note: we _could_ use libjoin functions by either casting to object
+        #  dtype or constructing tuples (faster than constructing Intervals)
+        #  but the libjoin fastpaths are no longer fast in these cases.
+        raise NotImplementedError(
+            "IntervalIndex does not use libjoin fastpaths or pass values to "
+            "IndexEngine objects"
+        )
+
+    def _from_join_target(self, result):
+        raise NotImplementedError("IntervalIndex does not use libjoin fastpaths")
+
+    # TODO: arithmetic operations
+
+
+def _is_valid_endpoint(endpoint) -> bool:
+    """
+    Helper for interval_range to check if start/end are valid types.
+    """
+    return any(
+        [
+            is_number(endpoint),
+            isinstance(endpoint, Timestamp),
+            isinstance(endpoint, Timedelta),
+            endpoint is None,
+        ]
+    )
+
+
+def _is_type_compatible(a, b) -> bool:
+    """
+    Helper for interval_range to check type compat of start/end/freq.
+    """
+    is_ts_compat = lambda x: isinstance(x, (Timestamp, BaseOffset))
+    is_td_compat = lambda x: isinstance(x, (Timedelta, BaseOffset))
+    return (
+        (is_number(a) and is_number(b))
+        or (is_ts_compat(a) and is_ts_compat(b))
+        or (is_td_compat(a) and is_td_compat(b))
+        or com.any_none(a, b)
+    )
+
+
+def interval_range(
+    start=None,
+    end=None,
+    periods=None,
+    freq=None,
+    name: Hashable | None = None,
+    closed: IntervalClosedType = "right",
+) -> IntervalIndex:
+    """
+    Return a fixed frequency IntervalIndex.
+
+    Parameters
+    ----------
+    start : numeric or datetime-like, default None
+        Left bound for generating intervals.
+    end : numeric or datetime-like, default None
+        Right bound for generating intervals.
+    periods : int, default None
+        Number of periods to generate.
+    freq : numeric, str, Timedelta, datetime.timedelta, or DateOffset, default None
+        The length of each interval. Must be consistent with the type of start
+        and end, e.g. 2 for numeric, or '5H' for datetime-like.  Default is 1
+        for numeric and 'D' for datetime-like.
+    name : str, default None
+        Name of the resulting IntervalIndex.
+    closed : {'left', 'right', 'both', 'neither'}, default 'right'
+        Whether the intervals are closed on the left-side, right-side, both
+        or neither.
+
+    Returns
+    -------
+    IntervalIndex
+
+    See Also
+    --------
+    IntervalIndex : An Index of intervals that are all closed on the same side.
+
+    Notes
+    -----
+    Of the four parameters ``start``, ``end``, ``periods``, and ``freq``,
+    exactly three must be specified. If ``freq`` is omitted, the resulting
+    ``IntervalIndex`` will have ``periods`` linearly spaced elements between
+    ``start`` and ``end``, inclusively.
+
+    To learn more about datetime-like frequency strings, please see `this link
+    `__.
+
+    Examples
+    --------
+    Numeric ``start`` and  ``end`` is supported.
+
+    >>> pd.interval_range(start=0, end=5)
+    IntervalIndex([(0, 1], (1, 2], (2, 3], (3, 4], (4, 5]],
+                  dtype='interval[int64, right]')
+
+    Additionally, datetime-like input is also supported.
+
+    >>> pd.interval_range(start=pd.Timestamp('2017-01-01'),
+    ...                   end=pd.Timestamp('2017-01-04'))
+    IntervalIndex([(2017-01-01 00:00:00, 2017-01-02 00:00:00],
+                   (2017-01-02 00:00:00, 2017-01-03 00:00:00],
+                   (2017-01-03 00:00:00, 2017-01-04 00:00:00]],
+                  dtype='interval[datetime64[ns], right]')
+
+    The ``freq`` parameter specifies the frequency between the left and right.
+    endpoints of the individual intervals within the ``IntervalIndex``.  For
+    numeric ``start`` and ``end``, the frequency must also be numeric.
+
+    >>> pd.interval_range(start=0, periods=4, freq=1.5)
+    IntervalIndex([(0.0, 1.5], (1.5, 3.0], (3.0, 4.5], (4.5, 6.0]],
+                  dtype='interval[float64, right]')
+
+    Similarly, for datetime-like ``start`` and ``end``, the frequency must be
+    convertible to a DateOffset.
+
+    >>> pd.interval_range(start=pd.Timestamp('2017-01-01'),
+    ...                   periods=3, freq='MS')
+    IntervalIndex([(2017-01-01 00:00:00, 2017-02-01 00:00:00],
+                   (2017-02-01 00:00:00, 2017-03-01 00:00:00],
+                   (2017-03-01 00:00:00, 2017-04-01 00:00:00]],
+                  dtype='interval[datetime64[ns], right]')
+
+    Specify ``start``, ``end``, and ``periods``; the frequency is generated
+    automatically (linearly spaced).
+
+    >>> pd.interval_range(start=0, end=6, periods=4)
+    IntervalIndex([(0.0, 1.5], (1.5, 3.0], (3.0, 4.5], (4.5, 6.0]],
+              dtype='interval[float64, right]')
+
+    The ``closed`` parameter specifies which endpoints of the individual
+    intervals within the ``IntervalIndex`` are closed.
+
+    >>> pd.interval_range(end=5, periods=4, closed='both')
+    IntervalIndex([[1, 2], [2, 3], [3, 4], [4, 5]],
+                  dtype='interval[int64, both]')
+    """
+    start = maybe_box_datetimelike(start)
+    end = maybe_box_datetimelike(end)
+    endpoint = start if start is not None else end
+
+    if freq is None and com.any_none(periods, start, end):
+        freq = 1 if is_number(endpoint) else "D"
+
+    if com.count_not_none(start, end, periods, freq) != 3:
+        raise ValueError(
+            "Of the four parameters: start, end, periods, and "
+            "freq, exactly three must be specified"
+        )
+
+    if not _is_valid_endpoint(start):
+        raise ValueError(f"start must be numeric or datetime-like, got {start}")
+    if not _is_valid_endpoint(end):
+        raise ValueError(f"end must be numeric or datetime-like, got {end}")
+
+    periods = validate_periods(periods)
+
+    if freq is not None and not is_number(freq):
+        try:
+            freq = to_offset(freq)
+        except ValueError as err:
+            raise ValueError(
+                f"freq must be numeric or convertible to DateOffset, got {freq}"
+            ) from err
+
+    # verify type compatibility
+    if not all(
+        [
+            _is_type_compatible(start, end),
+            _is_type_compatible(start, freq),
+            _is_type_compatible(end, freq),
+        ]
+    ):
+        raise TypeError("start, end, freq need to be type compatible")
+
+    # +1 to convert interval count to breaks count (n breaks = n-1 intervals)
+    if periods is not None:
+        periods += 1
+
+    breaks: np.ndarray | TimedeltaIndex | DatetimeIndex
+
+    if is_number(endpoint):
+        if com.all_not_none(start, end, freq):
+            # 0.1 ensures we capture end
+            breaks = np.arange(start, end + (freq * 0.1), freq)
+        else:
+            # compute the period/start/end if unspecified (at most one)
+            if periods is None:
+                periods = int((end - start) // freq) + 1
+            elif start is None:
+                start = end - (periods - 1) * freq
+            elif end is None:
+                end = start + (periods - 1) * freq
+
+            breaks = np.linspace(start, end, periods)
+        if all(is_integer(x) for x in com.not_none(start, end, freq)):
+            # np.linspace always produces float output
+
+            # error: Argument 1 to "maybe_downcast_numeric" has incompatible type
+            # "Union[ndarray[Any, Any], TimedeltaIndex, DatetimeIndex]";
+            # expected "ndarray[Any, Any]"  [
+            breaks = maybe_downcast_numeric(
+                breaks,  # type: ignore[arg-type]
+                np.dtype("int64"),
+            )
+    else:
+        # delegate to the appropriate range function
+        if isinstance(endpoint, Timestamp):
+            breaks = date_range(start=start, end=end, periods=periods, freq=freq)
+        else:
+            breaks = timedelta_range(start=start, end=end, periods=periods, freq=freq)
+
+    return IntervalIndex.from_breaks(breaks, name=name, closed=closed)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/indexes/multi.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/indexes/multi.py
new file mode 100644
index 0000000000000000000000000000000000000000..8954d49649a2b132927980f5874a9006a65301f4
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/indexes/multi.py
@@ -0,0 +1,4176 @@
+from __future__ import annotations
+
+from collections.abc import (
+    Collection,
+    Generator,
+    Hashable,
+    Iterable,
+    Sequence,
+)
+from functools import wraps
+from sys import getsizeof
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Literal,
+    cast,
+)
+import warnings
+
+import numpy as np
+
+from pandas._config import get_option
+
+from pandas._libs import (
+    algos as libalgos,
+    index as libindex,
+    lib,
+)
+from pandas._libs.hashtable import duplicated
+from pandas._typing import (
+    AnyAll,
+    AnyArrayLike,
+    Axis,
+    DropKeep,
+    DtypeObj,
+    F,
+    IgnoreRaise,
+    IndexLabel,
+    Scalar,
+    Self,
+    Shape,
+    npt,
+)
+from pandas.compat.numpy import function as nv
+from pandas.errors import (
+    InvalidIndexError,
+    PerformanceWarning,
+    UnsortedIndexError,
+)
+from pandas.util._decorators import (
+    Appender,
+    cache_readonly,
+    doc,
+)
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.cast import coerce_indexer_dtype
+from pandas.core.dtypes.common import (
+    ensure_int64,
+    ensure_platform_int,
+    is_hashable,
+    is_integer,
+    is_iterator,
+    is_list_like,
+    is_object_dtype,
+    is_scalar,
+    is_string_dtype,
+    pandas_dtype,
+)
+from pandas.core.dtypes.dtypes import (
+    CategoricalDtype,
+    ExtensionDtype,
+)
+from pandas.core.dtypes.generic import (
+    ABCDataFrame,
+    ABCSeries,
+)
+from pandas.core.dtypes.inference import is_array_like
+from pandas.core.dtypes.missing import (
+    array_equivalent,
+    isna,
+)
+
+import pandas.core.algorithms as algos
+from pandas.core.array_algos.putmask import validate_putmask
+from pandas.core.arrays import (
+    Categorical,
+    ExtensionArray,
+)
+from pandas.core.arrays.categorical import (
+    factorize_from_iterables,
+    recode_for_categories,
+)
+import pandas.core.common as com
+from pandas.core.construction import sanitize_array
+import pandas.core.indexes.base as ibase
+from pandas.core.indexes.base import (
+    Index,
+    _index_shared_docs,
+    ensure_index,
+    get_unanimous_names,
+)
+from pandas.core.indexes.frozen import FrozenList
+from pandas.core.ops.invalid import make_invalid_op
+from pandas.core.sorting import (
+    get_group_index,
+    lexsort_indexer,
+)
+
+from pandas.io.formats.printing import (
+    get_adjustment,
+    pprint_thing,
+)
+
+if TYPE_CHECKING:
+    from pandas import (
+        CategoricalIndex,
+        DataFrame,
+        Series,
+    )
+
+_index_doc_kwargs = dict(ibase._index_doc_kwargs)
+_index_doc_kwargs.update(
+    {"klass": "MultiIndex", "target_klass": "MultiIndex or list of tuples"}
+)
+
+
+class MultiIndexUIntEngine(libindex.BaseMultiIndexCodesEngine, libindex.UInt64Engine):
+    """
+    This class manages a MultiIndex by mapping label combinations to positive
+    integers.
+    """
+
+    _base = libindex.UInt64Engine
+
+    def _codes_to_ints(self, codes):
+        """
+        Transform combination(s) of uint64 in one uint64 (each), in a strictly
+        monotonic way (i.e. respecting the lexicographic order of integer
+        combinations): see BaseMultiIndexCodesEngine documentation.
+
+        Parameters
+        ----------
+        codes : 1- or 2-dimensional array of dtype uint64
+            Combinations of integers (one per row)
+
+        Returns
+        -------
+        scalar or 1-dimensional array, of dtype uint64
+            Integer(s) representing one combination (each).
+        """
+        # Shift the representation of each level by the pre-calculated number
+        # of bits:
+        codes <<= self.offsets
+
+        # Now sum and OR are in fact interchangeable. This is a simple
+        # composition of the (disjunct) significant bits of each level (i.e.
+        # each column in "codes") in a single positive integer:
+        if codes.ndim == 1:
+            # Single key
+            return np.bitwise_or.reduce(codes)
+
+        # Multiple keys
+        return np.bitwise_or.reduce(codes, axis=1)
+
+
+class MultiIndexPyIntEngine(libindex.BaseMultiIndexCodesEngine, libindex.ObjectEngine):
+    """
+    This class manages those (extreme) cases in which the number of possible
+    label combinations overflows the 64 bits integers, and uses an ObjectEngine
+    containing Python integers.
+    """
+
+    _base = libindex.ObjectEngine
+
+    def _codes_to_ints(self, codes):
+        """
+        Transform combination(s) of uint64 in one Python integer (each), in a
+        strictly monotonic way (i.e. respecting the lexicographic order of
+        integer combinations): see BaseMultiIndexCodesEngine documentation.
+
+        Parameters
+        ----------
+        codes : 1- or 2-dimensional array of dtype uint64
+            Combinations of integers (one per row)
+
+        Returns
+        -------
+        int, or 1-dimensional array of dtype object
+            Integer(s) representing one combination (each).
+        """
+        # Shift the representation of each level by the pre-calculated number
+        # of bits. Since this can overflow uint64, first make sure we are
+        # working with Python integers:
+        codes = codes.astype("object") << self.offsets
+
+        # Now sum and OR are in fact interchangeable. This is a simple
+        # composition of the (disjunct) significant bits of each level (i.e.
+        # each column in "codes") in a single positive integer (per row):
+        if codes.ndim == 1:
+            # Single key
+            return np.bitwise_or.reduce(codes)
+
+        # Multiple keys
+        return np.bitwise_or.reduce(codes, axis=1)
+
+
+def names_compat(meth: F) -> F:
+    """
+    A decorator to allow either `name` or `names` keyword but not both.
+
+    This makes it easier to share code with base class.
+    """
+
+    @wraps(meth)
+    def new_meth(self_or_cls, *args, **kwargs):
+        if "name" in kwargs and "names" in kwargs:
+            raise TypeError("Can only provide one of `names` and `name`")
+        if "name" in kwargs:
+            kwargs["names"] = kwargs.pop("name")
+
+        return meth(self_or_cls, *args, **kwargs)
+
+    return cast(F, new_meth)
+
+
+class MultiIndex(Index):
+    """
+    A multi-level, or hierarchical, index object for pandas objects.
+
+    Parameters
+    ----------
+    levels : sequence of arrays
+        The unique labels for each level.
+    codes : sequence of arrays
+        Integers for each level designating which label at each location.
+    sortorder : optional int
+        Level of sortedness (must be lexicographically sorted by that
+        level).
+    names : optional sequence of objects
+        Names for each of the index levels. (name is accepted for compat).
+    copy : bool, default False
+        Copy the meta-data.
+    verify_integrity : bool, default True
+        Check that the levels/codes are consistent and valid.
+
+    Attributes
+    ----------
+    names
+    levels
+    codes
+    nlevels
+    levshape
+    dtypes
+
+    Methods
+    -------
+    from_arrays
+    from_tuples
+    from_product
+    from_frame
+    set_levels
+    set_codes
+    to_frame
+    to_flat_index
+    sortlevel
+    droplevel
+    swaplevel
+    reorder_levels
+    remove_unused_levels
+    get_level_values
+    get_indexer
+    get_loc
+    get_locs
+    get_loc_level
+    drop
+
+    See Also
+    --------
+    MultiIndex.from_arrays  : Convert list of arrays to MultiIndex.
+    MultiIndex.from_product : Create a MultiIndex from the cartesian product
+                              of iterables.
+    MultiIndex.from_tuples  : Convert list of tuples to a MultiIndex.
+    MultiIndex.from_frame   : Make a MultiIndex from a DataFrame.
+    Index : The base pandas Index type.
+
+    Notes
+    -----
+    See the `user guide
+    `__
+    for more.
+
+    Examples
+    --------
+    A new ``MultiIndex`` is typically constructed using one of the helper
+    methods :meth:`MultiIndex.from_arrays`, :meth:`MultiIndex.from_product`
+    and :meth:`MultiIndex.from_tuples`. For example (using ``.from_arrays``):
+
+    >>> arrays = [[1, 1, 2, 2], ['red', 'blue', 'red', 'blue']]
+    >>> pd.MultiIndex.from_arrays(arrays, names=('number', 'color'))
+    MultiIndex([(1,  'red'),
+                (1, 'blue'),
+                (2,  'red'),
+                (2, 'blue')],
+               names=['number', 'color'])
+
+    See further examples for how to construct a MultiIndex in the doc strings
+    of the mentioned helper methods.
+    """
+
+    _hidden_attrs = Index._hidden_attrs | frozenset()
+
+    # initialize to zero-length tuples to make everything work
+    _typ = "multiindex"
+    _names: list[Hashable | None] = []
+    _levels = FrozenList()
+    _codes = FrozenList()
+    _comparables = ["names"]
+
+    sortorder: int | None
+
+    # --------------------------------------------------------------------
+    # Constructors
+
+    def __new__(
+        cls,
+        levels=None,
+        codes=None,
+        sortorder=None,
+        names=None,
+        dtype=None,
+        copy: bool = False,
+        name=None,
+        verify_integrity: bool = True,
+    ) -> Self:
+        # compat with Index
+        if name is not None:
+            names = name
+        if levels is None or codes is None:
+            raise TypeError("Must pass both levels and codes")
+        if len(levels) != len(codes):
+            raise ValueError("Length of levels and codes must be the same.")
+        if len(levels) == 0:
+            raise ValueError("Must pass non-zero number of levels/codes")
+
+        result = object.__new__(cls)
+        result._cache = {}
+
+        # we've already validated levels and codes, so shortcut here
+        result._set_levels(levels, copy=copy, validate=False)
+        result._set_codes(codes, copy=copy, validate=False)
+
+        result._names = [None] * len(levels)
+        if names is not None:
+            # handles name validation
+            result._set_names(names)
+
+        if sortorder is not None:
+            result.sortorder = int(sortorder)
+        else:
+            result.sortorder = sortorder
+
+        if verify_integrity:
+            new_codes = result._verify_integrity()
+            result._codes = new_codes
+
+        result._reset_identity()
+        result._references = None
+
+        return result
+
+    def _validate_codes(self, level: list, code: list):
+        """
+        Reassign code values as -1 if their corresponding levels are NaN.
+
+        Parameters
+        ----------
+        code : list
+            Code to reassign.
+        level : list
+            Level to check for missing values (NaN, NaT, None).
+
+        Returns
+        -------
+        new code where code value = -1 if it corresponds
+        to a level with missing values (NaN, NaT, None).
+        """
+        null_mask = isna(level)
+        if np.any(null_mask):
+            # error: Incompatible types in assignment
+            # (expression has type "ndarray[Any, dtype[Any]]",
+            # variable has type "List[Any]")
+            code = np.where(null_mask[code], -1, code)  # type: ignore[assignment]
+        return code
+
+    def _verify_integrity(
+        self,
+        codes: list | None = None,
+        levels: list | None = None,
+        levels_to_verify: list[int] | range | None = None,
+    ):
+        """
+        Parameters
+        ----------
+        codes : optional list
+            Codes to check for validity. Defaults to current codes.
+        levels : optional list
+            Levels to check for validity. Defaults to current levels.
+        levels_to_validate: optional list
+            Specifies the levels to verify.
+
+        Raises
+        ------
+        ValueError
+            If length of levels and codes don't match, if the codes for any
+            level would exceed level bounds, or there are any duplicate levels.
+
+        Returns
+        -------
+        new codes where code value = -1 if it corresponds to a
+        NaN level.
+        """
+        # NOTE: Currently does not check, among other things, that cached
+        # nlevels matches nor that sortorder matches actually sortorder.
+        codes = codes or self.codes
+        levels = levels or self.levels
+        if levels_to_verify is None:
+            levels_to_verify = range(len(levels))
+
+        if len(levels) != len(codes):
+            raise ValueError(
+                "Length of levels and codes must match. NOTE: "
+                "this index is in an inconsistent state."
+            )
+        codes_length = len(codes[0])
+        for i in levels_to_verify:
+            level = levels[i]
+            level_codes = codes[i]
+
+            if len(level_codes) != codes_length:
+                raise ValueError(
+                    f"Unequal code lengths: {[len(code_) for code_ in codes]}"
+                )
+            if len(level_codes) and level_codes.max() >= len(level):
+                raise ValueError(
+                    f"On level {i}, code max ({level_codes.max()}) >= length of "
+                    f"level ({len(level)}). NOTE: this index is in an "
+                    "inconsistent state"
+                )
+            if len(level_codes) and level_codes.min() < -1:
+                raise ValueError(f"On level {i}, code value ({level_codes.min()}) < -1")
+            if not level.is_unique:
+                raise ValueError(
+                    f"Level values must be unique: {list(level)} on level {i}"
+                )
+        if self.sortorder is not None:
+            if self.sortorder > _lexsort_depth(self.codes, self.nlevels):
+                raise ValueError(
+                    "Value for sortorder must be inferior or equal to actual "
+                    f"lexsort_depth: sortorder {self.sortorder} "
+                    f"with lexsort_depth {_lexsort_depth(self.codes, self.nlevels)}"
+                )
+
+        result_codes = []
+        for i in range(len(levels)):
+            if i in levels_to_verify:
+                result_codes.append(self._validate_codes(levels[i], codes[i]))
+            else:
+                result_codes.append(codes[i])
+
+        new_codes = FrozenList(result_codes)
+        return new_codes
+
+    @classmethod
+    def from_arrays(
+        cls,
+        arrays,
+        sortorder: int | None = None,
+        names: Sequence[Hashable] | Hashable | lib.NoDefault = lib.no_default,
+    ) -> MultiIndex:
+        """
+        Convert arrays to MultiIndex.
+
+        Parameters
+        ----------
+        arrays : list / sequence of array-likes
+            Each array-like gives one level's value for each data point.
+            len(arrays) is the number of levels.
+        sortorder : int or None
+            Level of sortedness (must be lexicographically sorted by that
+            level).
+        names : list / sequence of str, optional
+            Names for the levels in the index.
+
+        Returns
+        -------
+        MultiIndex
+
+        See Also
+        --------
+        MultiIndex.from_tuples : Convert list of tuples to MultiIndex.
+        MultiIndex.from_product : Make a MultiIndex from cartesian product
+                                  of iterables.
+        MultiIndex.from_frame : Make a MultiIndex from a DataFrame.
+
+        Examples
+        --------
+        >>> arrays = [[1, 1, 2, 2], ['red', 'blue', 'red', 'blue']]
+        >>> pd.MultiIndex.from_arrays(arrays, names=('number', 'color'))
+        MultiIndex([(1,  'red'),
+                    (1, 'blue'),
+                    (2,  'red'),
+                    (2, 'blue')],
+                   names=['number', 'color'])
+        """
+        error_msg = "Input must be a list / sequence of array-likes."
+        if not is_list_like(arrays):
+            raise TypeError(error_msg)
+        if is_iterator(arrays):
+            arrays = list(arrays)
+
+        # Check if elements of array are list-like
+        for array in arrays:
+            if not is_list_like(array):
+                raise TypeError(error_msg)
+
+        # Check if lengths of all arrays are equal or not,
+        # raise ValueError, if not
+        for i in range(1, len(arrays)):
+            if len(arrays[i]) != len(arrays[i - 1]):
+                raise ValueError("all arrays must be same length")
+
+        codes, levels = factorize_from_iterables(arrays)
+        if names is lib.no_default:
+            names = [getattr(arr, "name", None) for arr in arrays]
+
+        return cls(
+            levels=levels,
+            codes=codes,
+            sortorder=sortorder,
+            names=names,
+            verify_integrity=False,
+        )
+
+    @classmethod
+    @names_compat
+    def from_tuples(
+        cls,
+        tuples: Iterable[tuple[Hashable, ...]],
+        sortorder: int | None = None,
+        names: Sequence[Hashable] | Hashable | None = None,
+    ) -> MultiIndex:
+        """
+        Convert list of tuples to MultiIndex.
+
+        Parameters
+        ----------
+        tuples : list / sequence of tuple-likes
+            Each tuple is the index of one row/column.
+        sortorder : int or None
+            Level of sortedness (must be lexicographically sorted by that
+            level).
+        names : list / sequence of str, optional
+            Names for the levels in the index.
+
+        Returns
+        -------
+        MultiIndex
+
+        See Also
+        --------
+        MultiIndex.from_arrays : Convert list of arrays to MultiIndex.
+        MultiIndex.from_product : Make a MultiIndex from cartesian product
+                                  of iterables.
+        MultiIndex.from_frame : Make a MultiIndex from a DataFrame.
+
+        Examples
+        --------
+        >>> tuples = [(1, 'red'), (1, 'blue'),
+        ...           (2, 'red'), (2, 'blue')]
+        >>> pd.MultiIndex.from_tuples(tuples, names=('number', 'color'))
+        MultiIndex([(1,  'red'),
+                    (1, 'blue'),
+                    (2,  'red'),
+                    (2, 'blue')],
+                   names=['number', 'color'])
+        """
+        if not is_list_like(tuples):
+            raise TypeError("Input must be a list / sequence of tuple-likes.")
+        if is_iterator(tuples):
+            tuples = list(tuples)
+        tuples = cast(Collection[tuple[Hashable, ...]], tuples)
+
+        # handling the empty tuple cases
+        if len(tuples) and all(isinstance(e, tuple) and not e for e in tuples):
+            codes = [np.zeros(len(tuples))]
+            levels = [Index(com.asarray_tuplesafe(tuples, dtype=np.dtype("object")))]
+            return cls(
+                levels=levels,
+                codes=codes,
+                sortorder=sortorder,
+                names=names,
+                verify_integrity=False,
+            )
+
+        arrays: list[Sequence[Hashable]]
+        if len(tuples) == 0:
+            if names is None:
+                raise TypeError("Cannot infer number of levels from empty list")
+            # error: Argument 1 to "len" has incompatible type "Hashable";
+            # expected "Sized"
+            arrays = [[]] * len(names)  # type: ignore[arg-type]
+        elif isinstance(tuples, (np.ndarray, Index)):
+            if isinstance(tuples, Index):
+                tuples = np.asarray(tuples._values)
+
+            arrays = list(lib.tuples_to_object_array(tuples).T)
+        elif isinstance(tuples, list):
+            arrays = list(lib.to_object_array_tuples(tuples).T)
+        else:
+            arrs = zip(*tuples)
+            arrays = cast(list[Sequence[Hashable]], arrs)
+
+        return cls.from_arrays(arrays, sortorder=sortorder, names=names)
+
+    @classmethod
+    def from_product(
+        cls,
+        iterables: Sequence[Iterable[Hashable]],
+        sortorder: int | None = None,
+        names: Sequence[Hashable] | Hashable | lib.NoDefault = lib.no_default,
+    ) -> MultiIndex:
+        """
+        Make a MultiIndex from the cartesian product of multiple iterables.
+
+        Parameters
+        ----------
+        iterables : list / sequence of iterables
+            Each iterable has unique labels for each level of the index.
+        sortorder : int or None
+            Level of sortedness (must be lexicographically sorted by that
+            level).
+        names : list / sequence of str, optional
+            Names for the levels in the index.
+            If not explicitly provided, names will be inferred from the
+            elements of iterables if an element has a name attribute.
+
+        Returns
+        -------
+        MultiIndex
+
+        See Also
+        --------
+        MultiIndex.from_arrays : Convert list of arrays to MultiIndex.
+        MultiIndex.from_tuples : Convert list of tuples to MultiIndex.
+        MultiIndex.from_frame : Make a MultiIndex from a DataFrame.
+
+        Examples
+        --------
+        >>> numbers = [0, 1, 2]
+        >>> colors = ['green', 'purple']
+        >>> pd.MultiIndex.from_product([numbers, colors],
+        ...                            names=['number', 'color'])
+        MultiIndex([(0,  'green'),
+                    (0, 'purple'),
+                    (1,  'green'),
+                    (1, 'purple'),
+                    (2,  'green'),
+                    (2, 'purple')],
+                   names=['number', 'color'])
+        """
+        from pandas.core.reshape.util import cartesian_product
+
+        if not is_list_like(iterables):
+            raise TypeError("Input must be a list / sequence of iterables.")
+        if is_iterator(iterables):
+            iterables = list(iterables)
+
+        codes, levels = factorize_from_iterables(iterables)
+        if names is lib.no_default:
+            names = [getattr(it, "name", None) for it in iterables]
+
+        # codes are all ndarrays, so cartesian_product is lossless
+        codes = cartesian_product(codes)
+        return cls(levels, codes, sortorder=sortorder, names=names)
+
+    @classmethod
+    def from_frame(
+        cls,
+        df: DataFrame,
+        sortorder: int | None = None,
+        names: Sequence[Hashable] | Hashable | None = None,
+    ) -> MultiIndex:
+        """
+        Make a MultiIndex from a DataFrame.
+
+        Parameters
+        ----------
+        df : DataFrame
+            DataFrame to be converted to MultiIndex.
+        sortorder : int, optional
+            Level of sortedness (must be lexicographically sorted by that
+            level).
+        names : list-like, optional
+            If no names are provided, use the column names, or tuple of column
+            names if the columns is a MultiIndex. If a sequence, overwrite
+            names with the given sequence.
+
+        Returns
+        -------
+        MultiIndex
+            The MultiIndex representation of the given DataFrame.
+
+        See Also
+        --------
+        MultiIndex.from_arrays : Convert list of arrays to MultiIndex.
+        MultiIndex.from_tuples : Convert list of tuples to MultiIndex.
+        MultiIndex.from_product : Make a MultiIndex from cartesian product
+                                  of iterables.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([['HI', 'Temp'], ['HI', 'Precip'],
+        ...                    ['NJ', 'Temp'], ['NJ', 'Precip']],
+        ...                   columns=['a', 'b'])
+        >>> df
+              a       b
+        0    HI    Temp
+        1    HI  Precip
+        2    NJ    Temp
+        3    NJ  Precip
+
+        >>> pd.MultiIndex.from_frame(df)
+        MultiIndex([('HI',   'Temp'),
+                    ('HI', 'Precip'),
+                    ('NJ',   'Temp'),
+                    ('NJ', 'Precip')],
+                   names=['a', 'b'])
+
+        Using explicit names, instead of the column names
+
+        >>> pd.MultiIndex.from_frame(df, names=['state', 'observation'])
+        MultiIndex([('HI',   'Temp'),
+                    ('HI', 'Precip'),
+                    ('NJ',   'Temp'),
+                    ('NJ', 'Precip')],
+                   names=['state', 'observation'])
+        """
+        if not isinstance(df, ABCDataFrame):
+            raise TypeError("Input must be a DataFrame")
+
+        column_names, columns = zip(*df.items())
+        names = column_names if names is None else names
+        return cls.from_arrays(columns, sortorder=sortorder, names=names)
+
+    # --------------------------------------------------------------------
+
+    @cache_readonly
+    def _values(self) -> np.ndarray:
+        # We override here, since our parent uses _data, which we don't use.
+        values = []
+
+        for i in range(self.nlevels):
+            index = self.levels[i]
+            codes = self.codes[i]
+
+            vals = index
+            if isinstance(vals.dtype, CategoricalDtype):
+                vals = cast("CategoricalIndex", vals)
+                vals = vals._data._internal_get_values()
+
+            if isinstance(vals.dtype, ExtensionDtype) or lib.is_np_dtype(
+                vals.dtype, "mM"
+            ):
+                vals = vals.astype(object)
+
+            vals = np.asarray(vals)
+            vals = algos.take_nd(vals, codes, fill_value=index._na_value)
+            values.append(vals)
+
+        arr = lib.fast_zip(values)
+        return arr
+
+    @property
+    def values(self) -> np.ndarray:
+        return self._values
+
+    @property
+    def array(self):
+        """
+        Raises a ValueError for `MultiIndex` because there's no single
+        array backing a MultiIndex.
+
+        Raises
+        ------
+        ValueError
+        """
+        raise ValueError(
+            "MultiIndex has no single backing array. Use "
+            "'MultiIndex.to_numpy()' to get a NumPy array of tuples."
+        )
+
+    @cache_readonly
+    def dtypes(self) -> Series:
+        """
+        Return the dtypes as a Series for the underlying MultiIndex.
+
+        Examples
+        --------
+        >>> idx = pd.MultiIndex.from_product([(0, 1, 2), ('green', 'purple')],
+        ...                                  names=['number', 'color'])
+        >>> idx
+        MultiIndex([(0,  'green'),
+                    (0, 'purple'),
+                    (1,  'green'),
+                    (1, 'purple'),
+                    (2,  'green'),
+                    (2, 'purple')],
+                   names=['number', 'color'])
+        >>> idx.dtypes
+        number     int64
+        color     object
+        dtype: object
+        """
+        from pandas import Series
+
+        names = com.fill_missing_names([level.name for level in self.levels])
+        return Series([level.dtype for level in self.levels], index=Index(names))
+
+    def __len__(self) -> int:
+        return len(self.codes[0])
+
+    @property
+    def size(self) -> int:
+        """
+        Return the number of elements in the underlying data.
+        """
+        # override Index.size to avoid materializing _values
+        return len(self)
+
+    # --------------------------------------------------------------------
+    # Levels Methods
+
+    @cache_readonly
+    def levels(self) -> FrozenList:
+        """
+        Levels of the MultiIndex.
+
+        Levels refer to the different hierarchical levels or layers in a MultiIndex.
+        In a MultiIndex, each level represents a distinct dimension or category of
+        the index.
+
+        To access the levels, you can use the levels attribute of the MultiIndex,
+        which returns a tuple of Index objects. Each Index object represents a
+        level in the MultiIndex and contains the unique values found in that
+        specific level.
+
+        If a MultiIndex is created with levels A, B, C, and the DataFrame using
+        it filters out all rows of the level C, MultiIndex.levels will still
+        return A, B, C.
+
+        Examples
+        --------
+        >>> index = pd.MultiIndex.from_product([['mammal'],
+        ...                                     ('goat', 'human', 'cat', 'dog')],
+        ...                                    names=['Category', 'Animals'])
+        >>> leg_num = pd.DataFrame(data=(4, 2, 4, 4), index=index, columns=['Legs'])
+        >>> leg_num
+                          Legs
+        Category Animals
+        mammal   goat        4
+                 human       2
+                 cat         4
+                 dog         4
+
+        >>> leg_num.index.levels
+        FrozenList([['mammal'], ['cat', 'dog', 'goat', 'human']])
+
+        MultiIndex levels will not change even if the DataFrame using the MultiIndex
+        does not contain all them anymore.
+        See how "human" is not in the DataFrame, but it is still in levels:
+
+        >>> large_leg_num = leg_num[leg_num.Legs > 2]
+        >>> large_leg_num
+                          Legs
+        Category Animals
+        mammal   goat        4
+                 cat         4
+                 dog         4
+
+        >>> large_leg_num.index.levels
+        FrozenList([['mammal'], ['cat', 'dog', 'goat', 'human']])
+        """
+        # Use cache_readonly to ensure that self.get_locs doesn't repeatedly
+        # create new IndexEngine
+        # https://github.com/pandas-dev/pandas/issues/31648
+        result = [x._rename(name=name) for x, name in zip(self._levels, self._names)]
+        for level in result:
+            # disallow midx.levels[0].name = "foo"
+            level._no_setting_name = True
+        return FrozenList(result)
+
+    def _set_levels(
+        self,
+        levels,
+        *,
+        level=None,
+        copy: bool = False,
+        validate: bool = True,
+        verify_integrity: bool = False,
+    ) -> None:
+        # This is NOT part of the levels property because it should be
+        # externally not allowed to set levels. User beware if you change
+        # _levels directly
+        if validate:
+            if len(levels) == 0:
+                raise ValueError("Must set non-zero number of levels.")
+            if level is None and len(levels) != self.nlevels:
+                raise ValueError("Length of levels must match number of levels.")
+            if level is not None and len(levels) != len(level):
+                raise ValueError("Length of levels must match length of level.")
+
+        if level is None:
+            new_levels = FrozenList(
+                ensure_index(lev, copy=copy)._view() for lev in levels
+            )
+            level_numbers = list(range(len(new_levels)))
+        else:
+            level_numbers = [self._get_level_number(lev) for lev in level]
+            new_levels_list = list(self._levels)
+            for lev_num, lev in zip(level_numbers, levels):
+                new_levels_list[lev_num] = ensure_index(lev, copy=copy)._view()
+            new_levels = FrozenList(new_levels_list)
+
+        if verify_integrity:
+            new_codes = self._verify_integrity(
+                levels=new_levels, levels_to_verify=level_numbers
+            )
+            self._codes = new_codes
+
+        names = self.names
+        self._levels = new_levels
+        if any(names):
+            self._set_names(names)
+
+        self._reset_cache()
+
+    def set_levels(
+        self, levels, *, level=None, verify_integrity: bool = True
+    ) -> MultiIndex:
+        """
+        Set new levels on MultiIndex. Defaults to returning new index.
+
+        Parameters
+        ----------
+        levels : sequence or list of sequence
+            New level(s) to apply.
+        level : int, level name, or sequence of int/level names (default None)
+            Level(s) to set (None for all levels).
+        verify_integrity : bool, default True
+            If True, checks that levels and codes are compatible.
+
+        Returns
+        -------
+        MultiIndex
+
+        Examples
+        --------
+        >>> idx = pd.MultiIndex.from_tuples(
+        ...     [
+        ...         (1, "one"),
+        ...         (1, "two"),
+        ...         (2, "one"),
+        ...         (2, "two"),
+        ...         (3, "one"),
+        ...         (3, "two")
+        ...     ],
+        ...     names=["foo", "bar"]
+        ... )
+        >>> idx
+        MultiIndex([(1, 'one'),
+            (1, 'two'),
+            (2, 'one'),
+            (2, 'two'),
+            (3, 'one'),
+            (3, 'two')],
+           names=['foo', 'bar'])
+
+        >>> idx.set_levels([['a', 'b', 'c'], [1, 2]])
+        MultiIndex([('a', 1),
+                    ('a', 2),
+                    ('b', 1),
+                    ('b', 2),
+                    ('c', 1),
+                    ('c', 2)],
+                   names=['foo', 'bar'])
+        >>> idx.set_levels(['a', 'b', 'c'], level=0)
+        MultiIndex([('a', 'one'),
+                    ('a', 'two'),
+                    ('b', 'one'),
+                    ('b', 'two'),
+                    ('c', 'one'),
+                    ('c', 'two')],
+                   names=['foo', 'bar'])
+        >>> idx.set_levels(['a', 'b'], level='bar')
+        MultiIndex([(1, 'a'),
+                    (1, 'b'),
+                    (2, 'a'),
+                    (2, 'b'),
+                    (3, 'a'),
+                    (3, 'b')],
+                   names=['foo', 'bar'])
+
+        If any of the levels passed to ``set_levels()`` exceeds the
+        existing length, all of the values from that argument will
+        be stored in the MultiIndex levels, though the values will
+        be truncated in the MultiIndex output.
+
+        >>> idx.set_levels([['a', 'b', 'c'], [1, 2, 3, 4]], level=[0, 1])
+        MultiIndex([('a', 1),
+            ('a', 2),
+            ('b', 1),
+            ('b', 2),
+            ('c', 1),
+            ('c', 2)],
+           names=['foo', 'bar'])
+        >>> idx.set_levels([['a', 'b', 'c'], [1, 2, 3, 4]], level=[0, 1]).levels
+        FrozenList([['a', 'b', 'c'], [1, 2, 3, 4]])
+        """
+
+        if isinstance(levels, Index):
+            pass
+        elif is_array_like(levels):
+            levels = Index(levels)
+        elif is_list_like(levels):
+            levels = list(levels)
+
+        level, levels = _require_listlike(level, levels, "Levels")
+        idx = self._view()
+        idx._reset_identity()
+        idx._set_levels(
+            levels, level=level, validate=True, verify_integrity=verify_integrity
+        )
+        return idx
+
+    @property
+    def nlevels(self) -> int:
+        """
+        Integer number of levels in this MultiIndex.
+
+        Examples
+        --------
+        >>> mi = pd.MultiIndex.from_arrays([['a'], ['b'], ['c']])
+        >>> mi
+        MultiIndex([('a', 'b', 'c')],
+                   )
+        >>> mi.nlevels
+        3
+        """
+        return len(self._levels)
+
+    @property
+    def levshape(self) -> Shape:
+        """
+        A tuple with the length of each level.
+
+        Examples
+        --------
+        >>> mi = pd.MultiIndex.from_arrays([['a'], ['b'], ['c']])
+        >>> mi
+        MultiIndex([('a', 'b', 'c')],
+                   )
+        >>> mi.levshape
+        (1, 1, 1)
+        """
+        return tuple(len(x) for x in self.levels)
+
+    # --------------------------------------------------------------------
+    # Codes Methods
+
+    @property
+    def codes(self) -> FrozenList:
+        return self._codes
+
+    def _set_codes(
+        self,
+        codes,
+        *,
+        level=None,
+        copy: bool = False,
+        validate: bool = True,
+        verify_integrity: bool = False,
+    ) -> None:
+        if validate:
+            if level is None and len(codes) != self.nlevels:
+                raise ValueError("Length of codes must match number of levels")
+            if level is not None and len(codes) != len(level):
+                raise ValueError("Length of codes must match length of levels.")
+
+        level_numbers: list[int] | range
+        if level is None:
+            new_codes = FrozenList(
+                _coerce_indexer_frozen(level_codes, lev, copy=copy).view()
+                for lev, level_codes in zip(self._levels, codes)
+            )
+            level_numbers = range(len(new_codes))
+        else:
+            level_numbers = [self._get_level_number(lev) for lev in level]
+            new_codes_list = list(self._codes)
+            for lev_num, level_codes in zip(level_numbers, codes):
+                lev = self.levels[lev_num]
+                new_codes_list[lev_num] = _coerce_indexer_frozen(
+                    level_codes, lev, copy=copy
+                )
+            new_codes = FrozenList(new_codes_list)
+
+        if verify_integrity:
+            new_codes = self._verify_integrity(
+                codes=new_codes, levels_to_verify=level_numbers
+            )
+
+        self._codes = new_codes
+
+        self._reset_cache()
+
+    def set_codes(
+        self, codes, *, level=None, verify_integrity: bool = True
+    ) -> MultiIndex:
+        """
+        Set new codes on MultiIndex. Defaults to returning new index.
+
+        Parameters
+        ----------
+        codes : sequence or list of sequence
+            New codes to apply.
+        level : int, level name, or sequence of int/level names (default None)
+            Level(s) to set (None for all levels).
+        verify_integrity : bool, default True
+            If True, checks that levels and codes are compatible.
+
+        Returns
+        -------
+        new index (of same type and class...etc) or None
+            The same type as the caller or None if ``inplace=True``.
+
+        Examples
+        --------
+        >>> idx = pd.MultiIndex.from_tuples(
+        ...     [(1, "one"), (1, "two"), (2, "one"), (2, "two")], names=["foo", "bar"]
+        ... )
+        >>> idx
+        MultiIndex([(1, 'one'),
+            (1, 'two'),
+            (2, 'one'),
+            (2, 'two')],
+           names=['foo', 'bar'])
+
+        >>> idx.set_codes([[1, 0, 1, 0], [0, 0, 1, 1]])
+        MultiIndex([(2, 'one'),
+                    (1, 'one'),
+                    (2, 'two'),
+                    (1, 'two')],
+                   names=['foo', 'bar'])
+        >>> idx.set_codes([1, 0, 1, 0], level=0)
+        MultiIndex([(2, 'one'),
+                    (1, 'two'),
+                    (2, 'one'),
+                    (1, 'two')],
+                   names=['foo', 'bar'])
+        >>> idx.set_codes([0, 0, 1, 1], level='bar')
+        MultiIndex([(1, 'one'),
+                    (1, 'one'),
+                    (2, 'two'),
+                    (2, 'two')],
+                   names=['foo', 'bar'])
+        >>> idx.set_codes([[1, 0, 1, 0], [0, 0, 1, 1]], level=[0, 1])
+        MultiIndex([(2, 'one'),
+                    (1, 'one'),
+                    (2, 'two'),
+                    (1, 'two')],
+                   names=['foo', 'bar'])
+        """
+
+        level, codes = _require_listlike(level, codes, "Codes")
+        idx = self._view()
+        idx._reset_identity()
+        idx._set_codes(codes, level=level, verify_integrity=verify_integrity)
+        return idx
+
+    # --------------------------------------------------------------------
+    # Index Internals
+
+    @cache_readonly
+    def _engine(self):
+        # Calculate the number of bits needed to represent labels in each
+        # level, as log2 of their sizes:
+        # NaN values are shifted to 1 and missing values in other while
+        # calculating the indexer are shifted to 0
+        sizes = np.ceil(
+            np.log2(
+                [len(level) + libindex.multiindex_nulls_shift for level in self.levels]
+            )
+        )
+
+        # Sum bit counts, starting from the _right_....
+        lev_bits = np.cumsum(sizes[::-1])[::-1]
+
+        # ... in order to obtain offsets such that sorting the combination of
+        # shifted codes (one for each level, resulting in a unique integer) is
+        # equivalent to sorting lexicographically the codes themselves. Notice
+        # that each level needs to be shifted by the number of bits needed to
+        # represent the _previous_ ones:
+        offsets = np.concatenate([lev_bits[1:], [0]]).astype("uint64")
+
+        # Check the total number of bits needed for our representation:
+        if lev_bits[0] > 64:
+            # The levels would overflow a 64 bit uint - use Python integers:
+            return MultiIndexPyIntEngine(self.levels, self.codes, offsets)
+        return MultiIndexUIntEngine(self.levels, self.codes, offsets)
+
+    # Return type "Callable[..., MultiIndex]" of "_constructor" incompatible with return
+    # type "Type[MultiIndex]" in supertype "Index"
+    @property
+    def _constructor(self) -> Callable[..., MultiIndex]:  # type: ignore[override]
+        return type(self).from_tuples
+
+    @doc(Index._shallow_copy)
+    def _shallow_copy(self, values: np.ndarray, name=lib.no_default) -> MultiIndex:
+        names = name if name is not lib.no_default else self.names
+
+        return type(self).from_tuples(values, sortorder=None, names=names)
+
+    def _view(self) -> MultiIndex:
+        result = type(self)(
+            levels=self.levels,
+            codes=self.codes,
+            sortorder=self.sortorder,
+            names=self.names,
+            verify_integrity=False,
+        )
+        result._cache = self._cache.copy()
+        result._cache.pop("levels", None)  # GH32669
+        return result
+
+    # --------------------------------------------------------------------
+
+    # error: Signature of "copy" incompatible with supertype "Index"
+    def copy(  # type: ignore[override]
+        self,
+        names=None,
+        deep: bool = False,
+        name=None,
+    ) -> Self:
+        """
+        Make a copy of this object.
+
+        Names, dtype, levels and codes can be passed and will be set on new copy.
+
+        Parameters
+        ----------
+        names : sequence, optional
+        deep : bool, default False
+        name : Label
+            Kept for compatibility with 1-dimensional Index. Should not be used.
+
+        Returns
+        -------
+        MultiIndex
+
+        Notes
+        -----
+        In most cases, there should be no functional difference from using
+        ``deep``, but if ``deep`` is passed it will attempt to deepcopy.
+        This could be potentially expensive on large MultiIndex objects.
+
+        Examples
+        --------
+        >>> mi = pd.MultiIndex.from_arrays([['a'], ['b'], ['c']])
+        >>> mi
+        MultiIndex([('a', 'b', 'c')],
+                   )
+        >>> mi.copy()
+        MultiIndex([('a', 'b', 'c')],
+                   )
+        """
+        names = self._validate_names(name=name, names=names, deep=deep)
+        keep_id = not deep
+        levels, codes = None, None
+
+        if deep:
+            from copy import deepcopy
+
+            levels = deepcopy(self.levels)
+            codes = deepcopy(self.codes)
+
+        levels = levels if levels is not None else self.levels
+        codes = codes if codes is not None else self.codes
+
+        new_index = type(self)(
+            levels=levels,
+            codes=codes,
+            sortorder=self.sortorder,
+            names=names,
+            verify_integrity=False,
+        )
+        new_index._cache = self._cache.copy()
+        new_index._cache.pop("levels", None)  # GH32669
+        if keep_id:
+            new_index._id = self._id
+        return new_index
+
+    def __array__(self, dtype=None, copy=None) -> np.ndarray:
+        """the array interface, return my values"""
+        if copy is False:
+            # self.values is always a newly construct array, so raise.
+            warnings.warn(
+                "Starting with NumPy 2.0, the behavior of the 'copy' keyword has "
+                "changed and passing 'copy=False' raises an error when returning "
+                "a zero-copy NumPy array is not possible. pandas will follow "
+                "this behavior starting with pandas 3.0.\nThis conversion to "
+                "NumPy requires a copy, but 'copy=False' was passed. Consider "
+                "using 'np.asarray(..)' instead.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+        if copy is True:
+            # explicit np.array call to ensure a copy is made and unique objects
+            # are returned, because self.values is cached
+            return np.array(self.values, dtype=dtype)
+        return self.values
+
+    def view(self, cls=None) -> Self:
+        """this is defined as a copy with the same identity"""
+        result = self.copy()
+        result._id = self._id
+        return result
+
+    @doc(Index.__contains__)
+    def __contains__(self, key: Any) -> bool:
+        hash(key)
+        try:
+            self.get_loc(key)
+            return True
+        except (LookupError, TypeError, ValueError):
+            return False
+
+    @cache_readonly
+    def dtype(self) -> np.dtype:
+        return np.dtype("O")
+
+    def _is_memory_usage_qualified(self) -> bool:
+        """return a boolean if we need a qualified .info display"""
+
+        def f(dtype) -> bool:
+            return is_object_dtype(dtype) or (
+                is_string_dtype(dtype) and dtype.storage == "python"
+            )
+
+        return any(f(level.dtype) for level in self.levels)
+
+    # Cannot determine type of "memory_usage"
+    @doc(Index.memory_usage)  # type: ignore[has-type]
+    def memory_usage(self, deep: bool = False) -> int:
+        # we are overwriting our base class to avoid
+        # computing .values here which could materialize
+        # a tuple representation unnecessarily
+        return self._nbytes(deep)
+
+    @cache_readonly
+    def nbytes(self) -> int:
+        """return the number of bytes in the underlying data"""
+        return self._nbytes(False)
+
+    def _nbytes(self, deep: bool = False) -> int:
+        """
+        return the number of bytes in the underlying data
+        deeply introspect the level data if deep=True
+
+        include the engine hashtable
+
+        *this is in internal routine*
+
+        """
+        # for implementations with no useful getsizeof (PyPy)
+        objsize = 24
+
+        level_nbytes = sum(i.memory_usage(deep=deep) for i in self.levels)
+        label_nbytes = sum(i.nbytes for i in self.codes)
+        names_nbytes = sum(getsizeof(i, objsize) for i in self.names)
+        result = level_nbytes + label_nbytes + names_nbytes
+
+        # include our engine hashtable
+        result += self._engine.sizeof(deep=deep)
+        return result
+
+    # --------------------------------------------------------------------
+    # Rendering Methods
+
+    def _formatter_func(self, tup):
+        """
+        Formats each item in tup according to its level's formatter function.
+        """
+        formatter_funcs = [level._formatter_func for level in self.levels]
+        return tuple(func(val) for func, val in zip(formatter_funcs, tup))
+
+    def _get_values_for_csv(
+        self, *, na_rep: str = "nan", **kwargs
+    ) -> npt.NDArray[np.object_]:
+        new_levels = []
+        new_codes = []
+
+        # go through the levels and format them
+        for level, level_codes in zip(self.levels, self.codes):
+            level_strs = level._get_values_for_csv(na_rep=na_rep, **kwargs)
+            # add nan values, if there are any
+            mask = level_codes == -1
+            if mask.any():
+                nan_index = len(level_strs)
+                # numpy 1.21 deprecated implicit string casting
+                level_strs = level_strs.astype(str)
+                level_strs = np.append(level_strs, na_rep)
+                assert not level_codes.flags.writeable  # i.e. copy is needed
+                level_codes = level_codes.copy()  # make writeable
+                level_codes[mask] = nan_index
+            new_levels.append(level_strs)
+            new_codes.append(level_codes)
+
+        if len(new_levels) == 1:
+            # a single-level multi-index
+            return Index(new_levels[0].take(new_codes[0]))._get_values_for_csv()
+        else:
+            # reconstruct the multi-index
+            mi = MultiIndex(
+                levels=new_levels,
+                codes=new_codes,
+                names=self.names,
+                sortorder=self.sortorder,
+                verify_integrity=False,
+            )
+            return mi._values
+
+    def format(
+        self,
+        name: bool | None = None,
+        formatter: Callable | None = None,
+        na_rep: str | None = None,
+        names: bool = False,
+        space: int = 2,
+        sparsify=None,
+        adjoin: bool = True,
+    ) -> list:
+        warnings.warn(
+            # GH#55413
+            f"{type(self).__name__}.format is deprecated and will be removed "
+            "in a future version. Convert using index.astype(str) or "
+            "index.map(formatter) instead.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+
+        if name is not None:
+            names = name
+
+        if len(self) == 0:
+            return []
+
+        stringified_levels = []
+        for lev, level_codes in zip(self.levels, self.codes):
+            na = na_rep if na_rep is not None else _get_na_rep(lev.dtype)
+
+            if len(lev) > 0:
+                formatted = lev.take(level_codes).format(formatter=formatter)
+
+                # we have some NA
+                mask = level_codes == -1
+                if mask.any():
+                    formatted = np.array(formatted, dtype=object)
+                    formatted[mask] = na
+                    formatted = formatted.tolist()
+
+            else:
+                # weird all NA case
+                formatted = [
+                    pprint_thing(na if isna(x) else x, escape_chars=("\t", "\r", "\n"))
+                    for x in algos.take_nd(lev._values, level_codes)
+                ]
+            stringified_levels.append(formatted)
+
+        result_levels = []
+        for lev, lev_name in zip(stringified_levels, self.names):
+            level = []
+
+            if names:
+                level.append(
+                    pprint_thing(lev_name, escape_chars=("\t", "\r", "\n"))
+                    if lev_name is not None
+                    else ""
+                )
+
+            level.extend(np.array(lev, dtype=object))
+            result_levels.append(level)
+
+        if sparsify is None:
+            sparsify = get_option("display.multi_sparse")
+
+        if sparsify:
+            sentinel: Literal[""] | bool | lib.NoDefault = ""
+            # GH3547 use value of sparsify as sentinel if it's "Falsey"
+            assert isinstance(sparsify, bool) or sparsify is lib.no_default
+            if sparsify in [False, lib.no_default]:
+                sentinel = sparsify
+            # little bit of a kludge job for #1217
+            result_levels = sparsify_labels(
+                result_levels, start=int(names), sentinel=sentinel
+            )
+
+        if adjoin:
+            adj = get_adjustment()
+            return adj.adjoin(space, *result_levels).split("\n")
+        else:
+            return result_levels
+
+    def _format_multi(
+        self,
+        *,
+        include_names: bool,
+        sparsify: bool | None | lib.NoDefault,
+        formatter: Callable | None = None,
+    ) -> list:
+        if len(self) == 0:
+            return []
+
+        stringified_levels = []
+        for lev, level_codes in zip(self.levels, self.codes):
+            na = _get_na_rep(lev.dtype)
+
+            if len(lev) > 0:
+                taken = formatted = lev.take(level_codes)
+                formatted = taken._format_flat(include_name=False, formatter=formatter)
+
+                # we have some NA
+                mask = level_codes == -1
+                if mask.any():
+                    formatted = np.array(formatted, dtype=object)
+                    formatted[mask] = na
+                    formatted = formatted.tolist()
+
+            else:
+                # weird all NA case
+                formatted = [
+                    pprint_thing(na if isna(x) else x, escape_chars=("\t", "\r", "\n"))
+                    for x in algos.take_nd(lev._values, level_codes)
+                ]
+            stringified_levels.append(formatted)
+
+        result_levels = []
+        for lev, lev_name in zip(stringified_levels, self.names):
+            level = []
+
+            if include_names:
+                level.append(
+                    pprint_thing(lev_name, escape_chars=("\t", "\r", "\n"))
+                    if lev_name is not None
+                    else ""
+                )
+
+            level.extend(np.array(lev, dtype=object))
+            result_levels.append(level)
+
+        if sparsify is None:
+            sparsify = get_option("display.multi_sparse")
+
+        if sparsify:
+            sentinel: Literal[""] | bool | lib.NoDefault = ""
+            # GH3547 use value of sparsify as sentinel if it's "Falsey"
+            assert isinstance(sparsify, bool) or sparsify is lib.no_default
+            if sparsify is lib.no_default:
+                sentinel = sparsify
+            # little bit of a kludge job for #1217
+            result_levels = sparsify_labels(
+                result_levels, start=int(include_names), sentinel=sentinel
+            )
+
+        return result_levels
+
+    # --------------------------------------------------------------------
+    # Names Methods
+
+    def _get_names(self) -> FrozenList:
+        return FrozenList(self._names)
+
+    def _set_names(self, names, *, level=None, validate: bool = True):
+        """
+        Set new names on index. Each name has to be a hashable type.
+
+        Parameters
+        ----------
+        values : str or sequence
+            name(s) to set
+        level : int, level name, or sequence of int/level names (default None)
+            If the index is a MultiIndex (hierarchical), level(s) to set (None
+            for all levels).  Otherwise level must be None
+        validate : bool, default True
+            validate that the names match level lengths
+
+        Raises
+        ------
+        TypeError if each name is not hashable.
+
+        Notes
+        -----
+        sets names on levels. WARNING: mutates!
+
+        Note that you generally want to set this *after* changing levels, so
+        that it only acts on copies
+        """
+        # GH 15110
+        # Don't allow a single string for names in a MultiIndex
+        if names is not None and not is_list_like(names):
+            raise ValueError("Names should be list-like for a MultiIndex")
+        names = list(names)
+
+        if validate:
+            if level is not None and len(names) != len(level):
+                raise ValueError("Length of names must match length of level.")
+            if level is None and len(names) != self.nlevels:
+                raise ValueError(
+                    "Length of names must match number of levels in MultiIndex."
+                )
+
+        if level is None:
+            level = range(self.nlevels)
+        else:
+            level = [self._get_level_number(lev) for lev in level]
+
+        # set the name
+        for lev, name in zip(level, names):
+            if name is not None:
+                # GH 20527
+                # All items in 'names' need to be hashable:
+                if not is_hashable(name):
+                    raise TypeError(
+                        f"{type(self).__name__}.name must be a hashable type"
+                    )
+            self._names[lev] = name
+
+        # If .levels has been accessed, the names in our cache will be stale.
+        self._reset_cache()
+
+    names = property(
+        fset=_set_names,
+        fget=_get_names,
+        doc="""
+        Names of levels in MultiIndex.
+
+        Examples
+        --------
+        >>> mi = pd.MultiIndex.from_arrays(
+        ... [[1, 2], [3, 4], [5, 6]], names=['x', 'y', 'z'])
+        >>> mi
+        MultiIndex([(1, 3, 5),
+                    (2, 4, 6)],
+                   names=['x', 'y', 'z'])
+        >>> mi.names
+        FrozenList(['x', 'y', 'z'])
+        """,
+    )
+
+    # --------------------------------------------------------------------
+
+    @cache_readonly
+    def inferred_type(self) -> str:
+        return "mixed"
+
+    def _get_level_number(self, level) -> int:
+        count = self.names.count(level)
+        if (count > 1) and not is_integer(level):
+            raise ValueError(
+                f"The name {level} occurs multiple times, use a level number"
+            )
+        try:
+            level = self.names.index(level)
+        except ValueError as err:
+            if not is_integer(level):
+                raise KeyError(f"Level {level} not found") from err
+            if level < 0:
+                level += self.nlevels
+                if level < 0:
+                    orig_level = level - self.nlevels
+                    raise IndexError(
+                        f"Too many levels: Index has only {self.nlevels} levels, "
+                        f"{orig_level} is not a valid level number"
+                    ) from err
+            # Note: levels are zero-based
+            elif level >= self.nlevels:
+                raise IndexError(
+                    f"Too many levels: Index has only {self.nlevels} levels, "
+                    f"not {level + 1}"
+                ) from err
+        return level
+
+    @cache_readonly
+    def is_monotonic_increasing(self) -> bool:
+        """
+        Return a boolean if the values are equal or increasing.
+        """
+        if any(-1 in code for code in self.codes):
+            return False
+
+        if all(level.is_monotonic_increasing for level in self.levels):
+            # If each level is sorted, we can operate on the codes directly. GH27495
+            return libalgos.is_lexsorted(
+                [x.astype("int64", copy=False) for x in self.codes]
+            )
+
+        # reversed() because lexsort() wants the most significant key last.
+        values = [
+            self._get_level_values(i)._values for i in reversed(range(len(self.levels)))
+        ]
+        try:
+            # error: Argument 1 to "lexsort" has incompatible type
+            # "List[Union[ExtensionArray, ndarray[Any, Any]]]";
+            # expected "Union[_SupportsArray[dtype[Any]],
+            # _NestedSequence[_SupportsArray[dtype[Any]]], bool,
+            # int, float, complex, str, bytes, _NestedSequence[Union
+            # [bool, int, float, complex, str, bytes]]]"
+            sort_order = np.lexsort(values)  # type: ignore[arg-type]
+            return Index(sort_order).is_monotonic_increasing
+        except TypeError:
+            # we have mixed types and np.lexsort is not happy
+            return Index(self._values).is_monotonic_increasing
+
+    @cache_readonly
+    def is_monotonic_decreasing(self) -> bool:
+        """
+        Return a boolean if the values are equal or decreasing.
+        """
+        # monotonic decreasing if and only if reverse is monotonic increasing
+        return self[::-1].is_monotonic_increasing
+
+    @cache_readonly
+    def _inferred_type_levels(self) -> list[str]:
+        """return a list of the inferred types, one for each level"""
+        return [i.inferred_type for i in self.levels]
+
+    @doc(Index.duplicated)
+    def duplicated(self, keep: DropKeep = "first") -> npt.NDArray[np.bool_]:
+        shape = tuple(len(lev) for lev in self.levels)
+        ids = get_group_index(self.codes, shape, sort=False, xnull=False)
+
+        return duplicated(ids, keep)
+
+    # error: Cannot override final attribute "_duplicated"
+    # (previously declared in base class "IndexOpsMixin")
+    _duplicated = duplicated  # type: ignore[misc]
+
+    def fillna(self, value=None, downcast=None):
+        """
+        fillna is not implemented for MultiIndex
+        """
+        raise NotImplementedError("isna is not defined for MultiIndex")
+
+    @doc(Index.dropna)
+    def dropna(self, how: AnyAll = "any") -> MultiIndex:
+        nans = [level_codes == -1 for level_codes in self.codes]
+        if how == "any":
+            indexer = np.any(nans, axis=0)
+        elif how == "all":
+            indexer = np.all(nans, axis=0)
+        else:
+            raise ValueError(f"invalid how option: {how}")
+
+        new_codes = [level_codes[~indexer] for level_codes in self.codes]
+        return self.set_codes(codes=new_codes)
+
+    def _get_level_values(self, level: int, unique: bool = False) -> Index:
+        """
+        Return vector of label values for requested level,
+        equal to the length of the index
+
+        **this is an internal method**
+
+        Parameters
+        ----------
+        level : int
+        unique : bool, default False
+            if True, drop duplicated values
+
+        Returns
+        -------
+        Index
+        """
+        lev = self.levels[level]
+        level_codes = self.codes[level]
+        name = self._names[level]
+        if unique:
+            level_codes = algos.unique(level_codes)
+        filled = algos.take_nd(lev._values, level_codes, fill_value=lev._na_value)
+        return lev._shallow_copy(filled, name=name)
+
+    # error: Signature of "get_level_values" incompatible with supertype "Index"
+    def get_level_values(self, level) -> Index:  # type: ignore[override]
+        """
+        Return vector of label values for requested level.
+
+        Length of returned vector is equal to the length of the index.
+
+        Parameters
+        ----------
+        level : int or str
+            ``level`` is either the integer position of the level in the
+            MultiIndex, or the name of the level.
+
+        Returns
+        -------
+        Index
+            Values is a level of this MultiIndex converted to
+            a single :class:`Index` (or subclass thereof).
+
+        Notes
+        -----
+        If the level contains missing values, the result may be casted to
+        ``float`` with missing values specified as ``NaN``. This is because
+        the level is converted to a regular ``Index``.
+
+        Examples
+        --------
+        Create a MultiIndex:
+
+        >>> mi = pd.MultiIndex.from_arrays((list('abc'), list('def')))
+        >>> mi.names = ['level_1', 'level_2']
+
+        Get level values by supplying level as either integer or name:
+
+        >>> mi.get_level_values(0)
+        Index(['a', 'b', 'c'], dtype='object', name='level_1')
+        >>> mi.get_level_values('level_2')
+        Index(['d', 'e', 'f'], dtype='object', name='level_2')
+
+        If a level contains missing values, the return type of the level
+        may be cast to ``float``.
+
+        >>> pd.MultiIndex.from_arrays([[1, None, 2], [3, 4, 5]]).dtypes
+        level_0    int64
+        level_1    int64
+        dtype: object
+        >>> pd.MultiIndex.from_arrays([[1, None, 2], [3, 4, 5]]).get_level_values(0)
+        Index([1.0, nan, 2.0], dtype='float64')
+        """
+        level = self._get_level_number(level)
+        values = self._get_level_values(level)
+        return values
+
+    @doc(Index.unique)
+    def unique(self, level=None):
+        if level is None:
+            return self.drop_duplicates()
+        else:
+            level = self._get_level_number(level)
+            return self._get_level_values(level=level, unique=True)
+
+    def to_frame(
+        self,
+        index: bool = True,
+        name=lib.no_default,
+        allow_duplicates: bool = False,
+    ) -> DataFrame:
+        """
+        Create a DataFrame with the levels of the MultiIndex as columns.
+
+        Column ordering is determined by the DataFrame constructor with data as
+        a dict.
+
+        Parameters
+        ----------
+        index : bool, default True
+            Set the index of the returned DataFrame as the original MultiIndex.
+
+        name : list / sequence of str, optional
+            The passed names should substitute index level names.
+
+        allow_duplicates : bool, optional default False
+            Allow duplicate column labels to be created.
+
+            .. versionadded:: 1.5.0
+
+        Returns
+        -------
+        DataFrame
+
+        See Also
+        --------
+        DataFrame : Two-dimensional, size-mutable, potentially heterogeneous
+            tabular data.
+
+        Examples
+        --------
+        >>> mi = pd.MultiIndex.from_arrays([['a', 'b'], ['c', 'd']])
+        >>> mi
+        MultiIndex([('a', 'c'),
+                    ('b', 'd')],
+                   )
+
+        >>> df = mi.to_frame()
+        >>> df
+             0  1
+        a c  a  c
+        b d  b  d
+
+        >>> df = mi.to_frame(index=False)
+        >>> df
+           0  1
+        0  a  c
+        1  b  d
+
+        >>> df = mi.to_frame(name=['x', 'y'])
+        >>> df
+             x  y
+        a c  a  c
+        b d  b  d
+        """
+        from pandas import DataFrame
+
+        if name is not lib.no_default:
+            if not is_list_like(name):
+                raise TypeError("'name' must be a list / sequence of column names.")
+
+            if len(name) != len(self.levels):
+                raise ValueError(
+                    "'name' should have same length as number of levels on index."
+                )
+            idx_names = name
+        else:
+            idx_names = self._get_level_names()
+
+        if not allow_duplicates and len(set(idx_names)) != len(idx_names):
+            raise ValueError(
+                "Cannot create duplicate column labels if allow_duplicates is False"
+            )
+
+        # Guarantee resulting column order - PY36+ dict maintains insertion order
+        result = DataFrame(
+            {level: self._get_level_values(level) for level in range(len(self.levels))},
+            copy=False,
+        )
+        result.columns = idx_names
+
+        if index:
+            result.index = self
+        return result
+
+    # error: Return type "Index" of "to_flat_index" incompatible with return type
+    # "MultiIndex" in supertype "Index"
+    def to_flat_index(self) -> Index:  # type: ignore[override]
+        """
+        Convert a MultiIndex to an Index of Tuples containing the level values.
+
+        Returns
+        -------
+        pd.Index
+            Index with the MultiIndex data represented in Tuples.
+
+        See Also
+        --------
+        MultiIndex.from_tuples : Convert flat index back to MultiIndex.
+
+        Notes
+        -----
+        This method will simply return the caller if called by anything other
+        than a MultiIndex.
+
+        Examples
+        --------
+        >>> index = pd.MultiIndex.from_product(
+        ...     [['foo', 'bar'], ['baz', 'qux']],
+        ...     names=['a', 'b'])
+        >>> index.to_flat_index()
+        Index([('foo', 'baz'), ('foo', 'qux'),
+               ('bar', 'baz'), ('bar', 'qux')],
+              dtype='object')
+        """
+        return Index(self._values, tupleize_cols=False)
+
+    def _is_lexsorted(self) -> bool:
+        """
+        Return True if the codes are lexicographically sorted.
+
+        Returns
+        -------
+        bool
+
+        Examples
+        --------
+        In the below examples, the first level of the MultiIndex is sorted because
+        a>> pd.MultiIndex.from_arrays([['a', 'b', 'c'],
+        ...                            ['d', 'e', 'f']])._is_lexsorted()
+        True
+        >>> pd.MultiIndex.from_arrays([['a', 'b', 'c'],
+        ...                            ['d', 'f', 'e']])._is_lexsorted()
+        True
+
+        In case there is a tie, the lexicographical sorting looks
+        at the next level of the MultiIndex.
+
+        >>> pd.MultiIndex.from_arrays([[0, 1, 1], ['a', 'b', 'c']])._is_lexsorted()
+        True
+        >>> pd.MultiIndex.from_arrays([[0, 1, 1], ['a', 'c', 'b']])._is_lexsorted()
+        False
+        >>> pd.MultiIndex.from_arrays([['a', 'a', 'b', 'b'],
+        ...                            ['aa', 'bb', 'aa', 'bb']])._is_lexsorted()
+        True
+        >>> pd.MultiIndex.from_arrays([['a', 'a', 'b', 'b'],
+        ...                            ['bb', 'aa', 'aa', 'bb']])._is_lexsorted()
+        False
+        """
+        return self._lexsort_depth == self.nlevels
+
+    @cache_readonly
+    def _lexsort_depth(self) -> int:
+        """
+        Compute and return the lexsort_depth, the number of levels of the
+        MultiIndex that are sorted lexically
+
+        Returns
+        -------
+        int
+        """
+        if self.sortorder is not None:
+            return self.sortorder
+        return _lexsort_depth(self.codes, self.nlevels)
+
+    def _sort_levels_monotonic(self, raise_if_incomparable: bool = False) -> MultiIndex:
+        """
+        This is an *internal* function.
+
+        Create a new MultiIndex from the current to monotonically sorted
+        items IN the levels. This does not actually make the entire MultiIndex
+        monotonic, JUST the levels.
+
+        The resulting MultiIndex will have the same outward
+        appearance, meaning the same .values and ordering. It will also
+        be .equals() to the original.
+
+        Returns
+        -------
+        MultiIndex
+
+        Examples
+        --------
+        >>> mi = pd.MultiIndex(levels=[['a', 'b'], ['bb', 'aa']],
+        ...                    codes=[[0, 0, 1, 1], [0, 1, 0, 1]])
+        >>> mi
+        MultiIndex([('a', 'bb'),
+                    ('a', 'aa'),
+                    ('b', 'bb'),
+                    ('b', 'aa')],
+                   )
+
+        >>> mi.sort_values()
+        MultiIndex([('a', 'aa'),
+                    ('a', 'bb'),
+                    ('b', 'aa'),
+                    ('b', 'bb')],
+                   )
+        """
+        if self._is_lexsorted() and self.is_monotonic_increasing:
+            return self
+
+        new_levels = []
+        new_codes = []
+
+        for lev, level_codes in zip(self.levels, self.codes):
+            if not lev.is_monotonic_increasing:
+                try:
+                    # indexer to reorder the levels
+                    indexer = lev.argsort()
+                except TypeError:
+                    if raise_if_incomparable:
+                        raise
+                else:
+                    lev = lev.take(indexer)
+
+                    # indexer to reorder the level codes
+                    indexer = ensure_platform_int(indexer)
+                    ri = lib.get_reverse_indexer(indexer, len(indexer))
+                    level_codes = algos.take_nd(ri, level_codes, fill_value=-1)
+
+            new_levels.append(lev)
+            new_codes.append(level_codes)
+
+        return MultiIndex(
+            new_levels,
+            new_codes,
+            names=self.names,
+            sortorder=self.sortorder,
+            verify_integrity=False,
+        )
+
+    def remove_unused_levels(self) -> MultiIndex:
+        """
+        Create new MultiIndex from current that removes unused levels.
+
+        Unused level(s) means levels that are not expressed in the
+        labels. The resulting MultiIndex will have the same outward
+        appearance, meaning the same .values and ordering. It will
+        also be .equals() to the original.
+
+        Returns
+        -------
+        MultiIndex
+
+        Examples
+        --------
+        >>> mi = pd.MultiIndex.from_product([range(2), list('ab')])
+        >>> mi
+        MultiIndex([(0, 'a'),
+                    (0, 'b'),
+                    (1, 'a'),
+                    (1, 'b')],
+                   )
+
+        >>> mi[2:]
+        MultiIndex([(1, 'a'),
+                    (1, 'b')],
+                   )
+
+        The 0 from the first level is not represented
+        and can be removed
+
+        >>> mi2 = mi[2:].remove_unused_levels()
+        >>> mi2.levels
+        FrozenList([[1], ['a', 'b']])
+        """
+        new_levels = []
+        new_codes = []
+
+        changed = False
+        for lev, level_codes in zip(self.levels, self.codes):
+            # Since few levels are typically unused, bincount() is more
+            # efficient than unique() - however it only accepts positive values
+            # (and drops order):
+            uniques = np.where(np.bincount(level_codes + 1) > 0)[0] - 1
+            has_na = int(len(uniques) and (uniques[0] == -1))
+
+            if len(uniques) != len(lev) + has_na:
+                if lev.isna().any() and len(uniques) == len(lev):
+                    break
+                # We have unused levels
+                changed = True
+
+                # Recalculate uniques, now preserving order.
+                # Can easily be cythonized by exploiting the already existing
+                # "uniques" and stop parsing "level_codes" when all items
+                # are found:
+                uniques = algos.unique(level_codes)
+                if has_na:
+                    na_idx = np.where(uniques == -1)[0]
+                    # Just ensure that -1 is in first position:
+                    uniques[[0, na_idx[0]]] = uniques[[na_idx[0], 0]]
+
+                # codes get mapped from uniques to 0:len(uniques)
+                # -1 (if present) is mapped to last position
+                code_mapping = np.zeros(len(lev) + has_na)
+                # ... and reassigned value -1:
+                code_mapping[uniques] = np.arange(len(uniques)) - has_na
+
+                level_codes = code_mapping[level_codes]
+
+                # new levels are simple
+                lev = lev.take(uniques[has_na:])
+
+            new_levels.append(lev)
+            new_codes.append(level_codes)
+
+        result = self.view()
+
+        if changed:
+            result._reset_identity()
+            result._set_levels(new_levels, validate=False)
+            result._set_codes(new_codes, validate=False)
+
+        return result
+
+    # --------------------------------------------------------------------
+    # Pickling Methods
+
+    def __reduce__(self):
+        """Necessary for making this object picklable"""
+        d = {
+            "levels": list(self.levels),
+            "codes": list(self.codes),
+            "sortorder": self.sortorder,
+            "names": list(self.names),
+        }
+        return ibase._new_Index, (type(self), d), None
+
+    # --------------------------------------------------------------------
+
+    def __getitem__(self, key):
+        if is_scalar(key):
+            key = com.cast_scalar_indexer(key)
+
+            retval = []
+            for lev, level_codes in zip(self.levels, self.codes):
+                if level_codes[key] == -1:
+                    retval.append(np.nan)
+                else:
+                    retval.append(lev[level_codes[key]])
+
+            return tuple(retval)
+        else:
+            # in general cannot be sure whether the result will be sorted
+            sortorder = None
+            if com.is_bool_indexer(key):
+                key = np.asarray(key, dtype=bool)
+                sortorder = self.sortorder
+            elif isinstance(key, slice):
+                if key.step is None or key.step > 0:
+                    sortorder = self.sortorder
+            elif isinstance(key, Index):
+                key = np.asarray(key)
+
+            new_codes = [level_codes[key] for level_codes in self.codes]
+
+            return MultiIndex(
+                levels=self.levels,
+                codes=new_codes,
+                names=self.names,
+                sortorder=sortorder,
+                verify_integrity=False,
+            )
+
+    def _getitem_slice(self: MultiIndex, slobj: slice) -> MultiIndex:
+        """
+        Fastpath for __getitem__ when we know we have a slice.
+        """
+        sortorder = None
+        if slobj.step is None or slobj.step > 0:
+            sortorder = self.sortorder
+
+        new_codes = [level_codes[slobj] for level_codes in self.codes]
+
+        return type(self)(
+            levels=self.levels,
+            codes=new_codes,
+            names=self._names,
+            sortorder=sortorder,
+            verify_integrity=False,
+        )
+
+    @Appender(_index_shared_docs["take"] % _index_doc_kwargs)
+    def take(
+        self: MultiIndex,
+        indices,
+        axis: Axis = 0,
+        allow_fill: bool = True,
+        fill_value=None,
+        **kwargs,
+    ) -> MultiIndex:
+        nv.validate_take((), kwargs)
+        indices = ensure_platform_int(indices)
+
+        # only fill if we are passing a non-None fill_value
+        allow_fill = self._maybe_disallow_fill(allow_fill, fill_value, indices)
+
+        na_value = -1
+
+        taken = [lab.take(indices) for lab in self.codes]
+        if allow_fill:
+            mask = indices == -1
+            if mask.any():
+                masked = []
+                for new_label in taken:
+                    label_values = new_label
+                    label_values[mask] = na_value
+                    masked.append(np.asarray(label_values))
+                taken = masked
+
+        return MultiIndex(
+            levels=self.levels, codes=taken, names=self.names, verify_integrity=False
+        )
+
+    def append(self, other):
+        """
+        Append a collection of Index options together.
+
+        Parameters
+        ----------
+        other : Index or list/tuple of indices
+
+        Returns
+        -------
+        Index
+            The combined index.
+
+        Examples
+        --------
+        >>> mi = pd.MultiIndex.from_arrays([['a'], ['b']])
+        >>> mi
+        MultiIndex([('a', 'b')],
+                   )
+        >>> mi.append(mi)
+        MultiIndex([('a', 'b'), ('a', 'b')],
+                   )
+        """
+        if not isinstance(other, (list, tuple)):
+            other = [other]
+
+        if all(
+            (isinstance(o, MultiIndex) and o.nlevels >= self.nlevels) for o in other
+        ):
+            codes = []
+            levels = []
+            names = []
+            for i in range(self.nlevels):
+                level_values = self.levels[i]
+                for mi in other:
+                    level_values = level_values.union(mi.levels[i])
+                level_codes = [
+                    recode_for_categories(
+                        mi.codes[i], mi.levels[i], level_values, copy=False
+                    )
+                    for mi in ([self, *other])
+                ]
+                level_name = self.names[i]
+                if any(mi.names[i] != level_name for mi in other):
+                    level_name = None
+                codes.append(np.concatenate(level_codes))
+                levels.append(level_values)
+                names.append(level_name)
+            return MultiIndex(
+                codes=codes, levels=levels, names=names, verify_integrity=False
+            )
+
+        to_concat = (self._values,) + tuple(k._values for k in other)
+        new_tuples = np.concatenate(to_concat)
+
+        # if all(isinstance(x, MultiIndex) for x in other):
+        try:
+            # We only get here if other contains at least one index with tuples,
+            # setting names to None automatically
+            return MultiIndex.from_tuples(new_tuples)
+        except (TypeError, IndexError):
+            return Index(new_tuples)
+
+    def argsort(
+        self, *args, na_position: str = "last", **kwargs
+    ) -> npt.NDArray[np.intp]:
+        target = self._sort_levels_monotonic(raise_if_incomparable=True)
+        keys = [lev.codes for lev in target._get_codes_for_sorting()]
+        return lexsort_indexer(keys, na_position=na_position, codes_given=True)
+
+    @Appender(_index_shared_docs["repeat"] % _index_doc_kwargs)
+    def repeat(self, repeats: int, axis=None) -> MultiIndex:
+        nv.validate_repeat((), {"axis": axis})
+        # error: Incompatible types in assignment (expression has type "ndarray",
+        # variable has type "int")
+        repeats = ensure_platform_int(repeats)  # type: ignore[assignment]
+        return MultiIndex(
+            levels=self.levels,
+            codes=[
+                level_codes.view(np.ndarray).astype(np.intp, copy=False).repeat(repeats)
+                for level_codes in self.codes
+            ],
+            names=self.names,
+            sortorder=self.sortorder,
+            verify_integrity=False,
+        )
+
+    # error: Signature of "drop" incompatible with supertype "Index"
+    def drop(  # type: ignore[override]
+        self,
+        codes,
+        level: Index | np.ndarray | Iterable[Hashable] | None = None,
+        errors: IgnoreRaise = "raise",
+    ) -> MultiIndex:
+        """
+        Make a new :class:`pandas.MultiIndex` with the passed list of codes deleted.
+
+        Parameters
+        ----------
+        codes : array-like
+            Must be a list of tuples when ``level`` is not specified.
+        level : int or level name, default None
+        errors : str, default 'raise'
+
+        Returns
+        -------
+        MultiIndex
+
+        Examples
+        --------
+        >>> idx = pd.MultiIndex.from_product([(0, 1, 2), ('green', 'purple')],
+        ...                                  names=["number", "color"])
+        >>> idx
+        MultiIndex([(0,  'green'),
+                    (0, 'purple'),
+                    (1,  'green'),
+                    (1, 'purple'),
+                    (2,  'green'),
+                    (2, 'purple')],
+                   names=['number', 'color'])
+        >>> idx.drop([(1, 'green'), (2, 'purple')])
+        MultiIndex([(0,  'green'),
+                    (0, 'purple'),
+                    (1, 'purple'),
+                    (2,  'green')],
+                   names=['number', 'color'])
+
+        We can also drop from a specific level.
+
+        >>> idx.drop('green', level='color')
+        MultiIndex([(0, 'purple'),
+                    (1, 'purple'),
+                    (2, 'purple')],
+                   names=['number', 'color'])
+
+        >>> idx.drop([1, 2], level=0)
+        MultiIndex([(0,  'green'),
+                    (0, 'purple')],
+                   names=['number', 'color'])
+        """
+        if level is not None:
+            return self._drop_from_level(codes, level, errors)
+
+        if not isinstance(codes, (np.ndarray, Index)):
+            try:
+                codes = com.index_labels_to_array(codes, dtype=np.dtype("object"))
+            except ValueError:
+                pass
+
+        inds = []
+        for level_codes in codes:
+            try:
+                loc = self.get_loc(level_codes)
+                # get_loc returns either an integer, a slice, or a boolean
+                # mask
+                if isinstance(loc, int):
+                    inds.append(loc)
+                elif isinstance(loc, slice):
+                    step = loc.step if loc.step is not None else 1
+                    inds.extend(range(loc.start, loc.stop, step))
+                elif com.is_bool_indexer(loc):
+                    if self._lexsort_depth == 0:
+                        warnings.warn(
+                            "dropping on a non-lexsorted multi-index "
+                            "without a level parameter may impact performance.",
+                            PerformanceWarning,
+                            stacklevel=find_stack_level(),
+                        )
+                    loc = loc.nonzero()[0]
+                    inds.extend(loc)
+                else:
+                    msg = f"unsupported indexer of type {type(loc)}"
+                    raise AssertionError(msg)
+            except KeyError:
+                if errors != "ignore":
+                    raise
+
+        return self.delete(inds)
+
+    def _drop_from_level(
+        self, codes, level, errors: IgnoreRaise = "raise"
+    ) -> MultiIndex:
+        codes = com.index_labels_to_array(codes)
+        i = self._get_level_number(level)
+        index = self.levels[i]
+        values = index.get_indexer(codes)
+        # If nan should be dropped it will equal -1 here. We have to check which values
+        # are not nan and equal -1, this means they are missing in the index
+        nan_codes = isna(codes)
+        values[(np.equal(nan_codes, False)) & (values == -1)] = -2
+        if index.shape[0] == self.shape[0]:
+            values[np.equal(nan_codes, True)] = -2
+
+        not_found = codes[values == -2]
+        if len(not_found) != 0 and errors != "ignore":
+            raise KeyError(f"labels {not_found} not found in level")
+        mask = ~algos.isin(self.codes[i], values)
+
+        return self[mask]
+
+    def swaplevel(self, i=-2, j=-1) -> MultiIndex:
+        """
+        Swap level i with level j.
+
+        Calling this method does not change the ordering of the values.
+
+        Parameters
+        ----------
+        i : int, str, default -2
+            First level of index to be swapped. Can pass level name as string.
+            Type of parameters can be mixed.
+        j : int, str, default -1
+            Second level of index to be swapped. Can pass level name as string.
+            Type of parameters can be mixed.
+
+        Returns
+        -------
+        MultiIndex
+            A new MultiIndex.
+
+        See Also
+        --------
+        Series.swaplevel : Swap levels i and j in a MultiIndex.
+        DataFrame.swaplevel : Swap levels i and j in a MultiIndex on a
+            particular axis.
+
+        Examples
+        --------
+        >>> mi = pd.MultiIndex(levels=[['a', 'b'], ['bb', 'aa']],
+        ...                    codes=[[0, 0, 1, 1], [0, 1, 0, 1]])
+        >>> mi
+        MultiIndex([('a', 'bb'),
+                    ('a', 'aa'),
+                    ('b', 'bb'),
+                    ('b', 'aa')],
+                   )
+        >>> mi.swaplevel(0, 1)
+        MultiIndex([('bb', 'a'),
+                    ('aa', 'a'),
+                    ('bb', 'b'),
+                    ('aa', 'b')],
+                   )
+        """
+        new_levels = list(self.levels)
+        new_codes = list(self.codes)
+        new_names = list(self.names)
+
+        i = self._get_level_number(i)
+        j = self._get_level_number(j)
+
+        new_levels[i], new_levels[j] = new_levels[j], new_levels[i]
+        new_codes[i], new_codes[j] = new_codes[j], new_codes[i]
+        new_names[i], new_names[j] = new_names[j], new_names[i]
+
+        return MultiIndex(
+            levels=new_levels, codes=new_codes, names=new_names, verify_integrity=False
+        )
+
+    def reorder_levels(self, order) -> MultiIndex:
+        """
+        Rearrange levels using input order. May not drop or duplicate levels.
+
+        Parameters
+        ----------
+        order : list of int or list of str
+            List representing new level order. Reference level by number
+            (position) or by key (label).
+
+        Returns
+        -------
+        MultiIndex
+
+        Examples
+        --------
+        >>> mi = pd.MultiIndex.from_arrays([[1, 2], [3, 4]], names=['x', 'y'])
+        >>> mi
+        MultiIndex([(1, 3),
+                    (2, 4)],
+                   names=['x', 'y'])
+
+        >>> mi.reorder_levels(order=[1, 0])
+        MultiIndex([(3, 1),
+                    (4, 2)],
+                   names=['y', 'x'])
+
+        >>> mi.reorder_levels(order=['y', 'x'])
+        MultiIndex([(3, 1),
+                    (4, 2)],
+                   names=['y', 'x'])
+        """
+        order = [self._get_level_number(i) for i in order]
+        result = self._reorder_ilevels(order)
+        return result
+
+    def _reorder_ilevels(self, order) -> MultiIndex:
+        if len(order) != self.nlevels:
+            raise AssertionError(
+                f"Length of order must be same as number of levels ({self.nlevels}), "
+                f"got {len(order)}"
+            )
+        new_levels = [self.levels[i] for i in order]
+        new_codes = [self.codes[i] for i in order]
+        new_names = [self.names[i] for i in order]
+
+        return MultiIndex(
+            levels=new_levels, codes=new_codes, names=new_names, verify_integrity=False
+        )
+
+    def _recode_for_new_levels(
+        self, new_levels, copy: bool = True
+    ) -> Generator[np.ndarray, None, None]:
+        if len(new_levels) > self.nlevels:
+            raise AssertionError(
+                f"Length of new_levels ({len(new_levels)}) "
+                f"must be <= self.nlevels ({self.nlevels})"
+            )
+        for i in range(len(new_levels)):
+            yield recode_for_categories(
+                self.codes[i], self.levels[i], new_levels[i], copy=copy
+            )
+
+    def _get_codes_for_sorting(self) -> list[Categorical]:
+        """
+        we are categorizing our codes by using the
+        available categories (all, not just observed)
+        excluding any missing ones (-1); this is in preparation
+        for sorting, where we need to disambiguate that -1 is not
+        a valid valid
+        """
+
+        def cats(level_codes):
+            return np.arange(
+                np.array(level_codes).max() + 1 if len(level_codes) else 0,
+                dtype=level_codes.dtype,
+            )
+
+        return [
+            Categorical.from_codes(level_codes, cats(level_codes), True, validate=False)
+            for level_codes in self.codes
+        ]
+
+    def sortlevel(
+        self,
+        level: IndexLabel = 0,
+        ascending: bool | list[bool] = True,
+        sort_remaining: bool = True,
+        na_position: str = "first",
+    ) -> tuple[MultiIndex, npt.NDArray[np.intp]]:
+        """
+        Sort MultiIndex at the requested level.
+
+        The result will respect the original ordering of the associated
+        factor at that level.
+
+        Parameters
+        ----------
+        level : list-like, int or str, default 0
+            If a string is given, must be a name of the level.
+            If list-like must be names or ints of levels.
+        ascending : bool, default True
+            False to sort in descending order.
+            Can also be a list to specify a directed ordering.
+        sort_remaining : sort by the remaining levels after level
+        na_position : {'first' or 'last'}, default 'first'
+            Argument 'first' puts NaNs at the beginning, 'last' puts NaNs at
+            the end.
+
+            .. versionadded:: 2.1.0
+
+        Returns
+        -------
+        sorted_index : pd.MultiIndex
+            Resulting index.
+        indexer : np.ndarray[np.intp]
+            Indices of output values in original index.
+
+        Examples
+        --------
+        >>> mi = pd.MultiIndex.from_arrays([[0, 0], [2, 1]])
+        >>> mi
+        MultiIndex([(0, 2),
+                    (0, 1)],
+                   )
+
+        >>> mi.sortlevel()
+        (MultiIndex([(0, 1),
+                    (0, 2)],
+                   ), array([1, 0]))
+
+        >>> mi.sortlevel(sort_remaining=False)
+        (MultiIndex([(0, 2),
+                    (0, 1)],
+                   ), array([0, 1]))
+
+        >>> mi.sortlevel(1)
+        (MultiIndex([(0, 1),
+                    (0, 2)],
+                   ), array([1, 0]))
+
+        >>> mi.sortlevel(1, ascending=False)
+        (MultiIndex([(0, 2),
+                    (0, 1)],
+                   ), array([0, 1]))
+        """
+        if not is_list_like(level):
+            level = [level]
+        # error: Item "Hashable" of "Union[Hashable, Sequence[Hashable]]" has
+        # no attribute "__iter__" (not iterable)
+        level = [
+            self._get_level_number(lev) for lev in level  # type: ignore[union-attr]
+        ]
+        sortorder = None
+
+        codes = [self.codes[lev] for lev in level]
+        # we have a directed ordering via ascending
+        if isinstance(ascending, list):
+            if not len(level) == len(ascending):
+                raise ValueError("level must have same length as ascending")
+        elif sort_remaining:
+            codes.extend(
+                [self.codes[lev] for lev in range(len(self.levels)) if lev not in level]
+            )
+        else:
+            sortorder = level[0]
+
+        indexer = lexsort_indexer(
+            codes, orders=ascending, na_position=na_position, codes_given=True
+        )
+
+        indexer = ensure_platform_int(indexer)
+        new_codes = [level_codes.take(indexer) for level_codes in self.codes]
+
+        new_index = MultiIndex(
+            codes=new_codes,
+            levels=self.levels,
+            names=self.names,
+            sortorder=sortorder,
+            verify_integrity=False,
+        )
+
+        return new_index, indexer
+
+    def _wrap_reindex_result(self, target, indexer, preserve_names: bool):
+        if not isinstance(target, MultiIndex):
+            if indexer is None:
+                target = self
+            elif (indexer >= 0).all():
+                target = self.take(indexer)
+            else:
+                try:
+                    target = MultiIndex.from_tuples(target)
+                except TypeError:
+                    # not all tuples, see test_constructor_dict_multiindex_reindex_flat
+                    return target
+
+        target = self._maybe_preserve_names(target, preserve_names)
+        return target
+
+    def _maybe_preserve_names(self, target: Index, preserve_names: bool) -> Index:
+        if (
+            preserve_names
+            and target.nlevels == self.nlevels
+            and target.names != self.names
+        ):
+            target = target.copy(deep=False)
+            target.names = self.names
+        return target
+
+    # --------------------------------------------------------------------
+    # Indexing Methods
+
+    def _check_indexing_error(self, key) -> None:
+        if not is_hashable(key) or is_iterator(key):
+            # We allow tuples if they are hashable, whereas other Index
+            #  subclasses require scalar.
+            # We have to explicitly exclude generators, as these are hashable.
+            raise InvalidIndexError(key)
+
+    @cache_readonly
+    def _should_fallback_to_positional(self) -> bool:
+        """
+        Should integer key(s) be treated as positional?
+        """
+        # GH#33355
+        return self.levels[0]._should_fallback_to_positional
+
+    def _get_indexer_strict(
+        self, key, axis_name: str
+    ) -> tuple[Index, npt.NDArray[np.intp]]:
+        keyarr = key
+        if not isinstance(keyarr, Index):
+            keyarr = com.asarray_tuplesafe(keyarr)
+
+        if len(keyarr) and not isinstance(keyarr[0], tuple):
+            indexer = self._get_indexer_level_0(keyarr)
+
+            self._raise_if_missing(key, indexer, axis_name)
+            return self[indexer], indexer
+
+        return super()._get_indexer_strict(key, axis_name)
+
+    def _raise_if_missing(self, key, indexer, axis_name: str) -> None:
+        keyarr = key
+        if not isinstance(key, Index):
+            keyarr = com.asarray_tuplesafe(key)
+
+        if len(keyarr) and not isinstance(keyarr[0], tuple):
+            # i.e. same condition for special case in MultiIndex._get_indexer_strict
+
+            mask = indexer == -1
+            if mask.any():
+                check = self.levels[0].get_indexer(keyarr)
+                cmask = check == -1
+                if cmask.any():
+                    raise KeyError(f"{keyarr[cmask]} not in index")
+                # We get here when levels still contain values which are not
+                # actually in Index anymore
+                raise KeyError(f"{keyarr} not in index")
+        else:
+            return super()._raise_if_missing(key, indexer, axis_name)
+
+    def _get_indexer_level_0(self, target) -> npt.NDArray[np.intp]:
+        """
+        Optimized equivalent to `self.get_level_values(0).get_indexer_for(target)`.
+        """
+        lev = self.levels[0]
+        codes = self._codes[0]
+        cat = Categorical.from_codes(codes=codes, categories=lev, validate=False)
+        ci = Index(cat)
+        return ci.get_indexer_for(target)
+
+    def get_slice_bound(
+        self,
+        label: Hashable | Sequence[Hashable],
+        side: Literal["left", "right"],
+    ) -> int:
+        """
+        For an ordered MultiIndex, compute slice bound
+        that corresponds to given label.
+
+        Returns leftmost (one-past-the-rightmost if `side=='right') position
+        of given label.
+
+        Parameters
+        ----------
+        label : object or tuple of objects
+        side : {'left', 'right'}
+
+        Returns
+        -------
+        int
+            Index of label.
+
+        Notes
+        -----
+        This method only works if level 0 index of the MultiIndex is lexsorted.
+
+        Examples
+        --------
+        >>> mi = pd.MultiIndex.from_arrays([list('abbc'), list('gefd')])
+
+        Get the locations from the leftmost 'b' in the first level
+        until the end of the multiindex:
+
+        >>> mi.get_slice_bound('b', side="left")
+        1
+
+        Like above, but if you get the locations from the rightmost
+        'b' in the first level and 'f' in the second level:
+
+        >>> mi.get_slice_bound(('b','f'), side="right")
+        3
+
+        See Also
+        --------
+        MultiIndex.get_loc : Get location for a label or a tuple of labels.
+        MultiIndex.get_locs : Get location for a label/slice/list/mask or a
+                              sequence of such.
+        """
+        if not isinstance(label, tuple):
+            label = (label,)
+        return self._partial_tup_index(label, side=side)
+
+    # pylint: disable-next=useless-parent-delegation
+    def slice_locs(self, start=None, end=None, step=None) -> tuple[int, int]:
+        """
+        For an ordered MultiIndex, compute the slice locations for input
+        labels.
+
+        The input labels can be tuples representing partial levels, e.g. for a
+        MultiIndex with 3 levels, you can pass a single value (corresponding to
+        the first level), or a 1-, 2-, or 3-tuple.
+
+        Parameters
+        ----------
+        start : label or tuple, default None
+            If None, defaults to the beginning
+        end : label or tuple
+            If None, defaults to the end
+        step : int or None
+            Slice step
+
+        Returns
+        -------
+        (start, end) : (int, int)
+
+        Notes
+        -----
+        This method only works if the MultiIndex is properly lexsorted. So,
+        if only the first 2 levels of a 3-level MultiIndex are lexsorted,
+        you can only pass two levels to ``.slice_locs``.
+
+        Examples
+        --------
+        >>> mi = pd.MultiIndex.from_arrays([list('abbd'), list('deff')],
+        ...                                names=['A', 'B'])
+
+        Get the slice locations from the beginning of 'b' in the first level
+        until the end of the multiindex:
+
+        >>> mi.slice_locs(start='b')
+        (1, 4)
+
+        Like above, but stop at the end of 'b' in the first level and 'f' in
+        the second level:
+
+        >>> mi.slice_locs(start='b', end=('b', 'f'))
+        (1, 3)
+
+        See Also
+        --------
+        MultiIndex.get_loc : Get location for a label or a tuple of labels.
+        MultiIndex.get_locs : Get location for a label/slice/list/mask or a
+                              sequence of such.
+        """
+        # This function adds nothing to its parent implementation (the magic
+        # happens in get_slice_bound method), but it adds meaningful doc.
+        return super().slice_locs(start, end, step)
+
+    def _partial_tup_index(self, tup: tuple, side: Literal["left", "right"] = "left"):
+        if len(tup) > self._lexsort_depth:
+            raise UnsortedIndexError(
+                f"Key length ({len(tup)}) was greater than MultiIndex lexsort depth "
+                f"({self._lexsort_depth})"
+            )
+
+        n = len(tup)
+        start, end = 0, len(self)
+        zipped = zip(tup, self.levels, self.codes)
+        for k, (lab, lev, level_codes) in enumerate(zipped):
+            section = level_codes[start:end]
+
+            loc: npt.NDArray[np.intp] | np.intp | int
+            if lab not in lev and not isna(lab):
+                # short circuit
+                try:
+                    loc = algos.searchsorted(lev, lab, side=side)
+                except TypeError as err:
+                    # non-comparable e.g. test_slice_locs_with_type_mismatch
+                    raise TypeError(f"Level type mismatch: {lab}") from err
+                if not is_integer(loc):
+                    # non-comparable level, e.g. test_groupby_example
+                    raise TypeError(f"Level type mismatch: {lab}")
+                if side == "right" and loc >= 0:
+                    loc -= 1
+                return start + algos.searchsorted(section, loc, side=side)
+
+            idx = self._get_loc_single_level_index(lev, lab)
+            if isinstance(idx, slice) and k < n - 1:
+                # Get start and end value from slice, necessary when a non-integer
+                # interval is given as input GH#37707
+                start = idx.start
+                end = idx.stop
+            elif k < n - 1:
+                # error: Incompatible types in assignment (expression has type
+                # "Union[ndarray[Any, dtype[signedinteger[Any]]]
+                end = start + algos.searchsorted(  # type: ignore[assignment]
+                    section, idx, side="right"
+                )
+                # error: Incompatible types in assignment (expression has type
+                # "Union[ndarray[Any, dtype[signedinteger[Any]]]
+                start = start + algos.searchsorted(  # type: ignore[assignment]
+                    section, idx, side="left"
+                )
+            elif isinstance(idx, slice):
+                idx = idx.start
+                return start + algos.searchsorted(section, idx, side=side)
+            else:
+                return start + algos.searchsorted(section, idx, side=side)
+
+    def _get_loc_single_level_index(self, level_index: Index, key: Hashable) -> int:
+        """
+        If key is NA value, location of index unify as -1.
+
+        Parameters
+        ----------
+        level_index: Index
+        key : label
+
+        Returns
+        -------
+        loc : int
+            If key is NA value, loc is -1
+            Else, location of key in index.
+
+        See Also
+        --------
+        Index.get_loc : The get_loc method for (single-level) index.
+        """
+        if is_scalar(key) and isna(key):
+            # TODO: need is_valid_na_for_dtype(key, level_index.dtype)
+            return -1
+        else:
+            return level_index.get_loc(key)
+
+    def get_loc(self, key):
+        """
+        Get location for a label or a tuple of labels.
+
+        The location is returned as an integer/slice or boolean
+        mask.
+
+        Parameters
+        ----------
+        key : label or tuple of labels (one for each level)
+
+        Returns
+        -------
+        int, slice object or boolean mask
+            If the key is past the lexsort depth, the return may be a
+            boolean mask array, otherwise it is always a slice or int.
+
+        See Also
+        --------
+        Index.get_loc : The get_loc method for (single-level) index.
+        MultiIndex.slice_locs : Get slice location given start label(s) and
+                                end label(s).
+        MultiIndex.get_locs : Get location for a label/slice/list/mask or a
+                              sequence of such.
+
+        Notes
+        -----
+        The key cannot be a slice, list of same-level labels, a boolean mask,
+        or a sequence of such. If you want to use those, use
+        :meth:`MultiIndex.get_locs` instead.
+
+        Examples
+        --------
+        >>> mi = pd.MultiIndex.from_arrays([list('abb'), list('def')])
+
+        >>> mi.get_loc('b')
+        slice(1, 3, None)
+
+        >>> mi.get_loc(('b', 'e'))
+        1
+        """
+        self._check_indexing_error(key)
+
+        def _maybe_to_slice(loc):
+            """convert integer indexer to boolean mask or slice if possible"""
+            if not isinstance(loc, np.ndarray) or loc.dtype != np.intp:
+                return loc
+
+            loc = lib.maybe_indices_to_slice(loc, len(self))
+            if isinstance(loc, slice):
+                return loc
+
+            mask = np.empty(len(self), dtype="bool")
+            mask.fill(False)
+            mask[loc] = True
+            return mask
+
+        if not isinstance(key, tuple):
+            loc = self._get_level_indexer(key, level=0)
+            return _maybe_to_slice(loc)
+
+        keylen = len(key)
+        if self.nlevels < keylen:
+            raise KeyError(
+                f"Key length ({keylen}) exceeds index depth ({self.nlevels})"
+            )
+
+        if keylen == self.nlevels and self.is_unique:
+            # TODO: what if we have an IntervalIndex level?
+            #  i.e. do we need _index_as_unique on that level?
+            try:
+                return self._engine.get_loc(key)
+            except KeyError as err:
+                raise KeyError(key) from err
+            except TypeError:
+                # e.g. test_partial_slicing_with_multiindex partial string slicing
+                loc, _ = self.get_loc_level(key, list(range(self.nlevels)))
+                return loc
+
+        # -- partial selection or non-unique index
+        # break the key into 2 parts based on the lexsort_depth of the index;
+        # the first part returns a continuous slice of the index; the 2nd part
+        # needs linear search within the slice
+        i = self._lexsort_depth
+        lead_key, follow_key = key[:i], key[i:]
+
+        if not lead_key:
+            start = 0
+            stop = len(self)
+        else:
+            try:
+                start, stop = self.slice_locs(lead_key, lead_key)
+            except TypeError as err:
+                # e.g. test_groupby_example key = ((0, 0, 1, 2), "new_col")
+                #  when self has 5 integer levels
+                raise KeyError(key) from err
+
+        if start == stop:
+            raise KeyError(key)
+
+        if not follow_key:
+            return slice(start, stop)
+
+        warnings.warn(
+            "indexing past lexsort depth may impact performance.",
+            PerformanceWarning,
+            stacklevel=find_stack_level(),
+        )
+
+        loc = np.arange(start, stop, dtype=np.intp)
+
+        for i, k in enumerate(follow_key, len(lead_key)):
+            mask = self.codes[i][loc] == self._get_loc_single_level_index(
+                self.levels[i], k
+            )
+            if not mask.all():
+                loc = loc[mask]
+            if not len(loc):
+                raise KeyError(key)
+
+        return _maybe_to_slice(loc) if len(loc) != stop - start else slice(start, stop)
+
+    def get_loc_level(self, key, level: IndexLabel = 0, drop_level: bool = True):
+        """
+        Get location and sliced index for requested label(s)/level(s).
+
+        Parameters
+        ----------
+        key : label or sequence of labels
+        level : int/level name or list thereof, optional
+        drop_level : bool, default True
+            If ``False``, the resulting index will not drop any level.
+
+        Returns
+        -------
+        tuple
+            A 2-tuple where the elements :
+
+            Element 0: int, slice object or boolean array.
+
+            Element 1: The resulting sliced multiindex/index. If the key
+            contains all levels, this will be ``None``.
+
+        See Also
+        --------
+        MultiIndex.get_loc  : Get location for a label or a tuple of labels.
+        MultiIndex.get_locs : Get location for a label/slice/list/mask or a
+                              sequence of such.
+
+        Examples
+        --------
+        >>> mi = pd.MultiIndex.from_arrays([list('abb'), list('def')],
+        ...                                names=['A', 'B'])
+
+        >>> mi.get_loc_level('b')
+        (slice(1, 3, None), Index(['e', 'f'], dtype='object', name='B'))
+
+        >>> mi.get_loc_level('e', level='B')
+        (array([False,  True, False]), Index(['b'], dtype='object', name='A'))
+
+        >>> mi.get_loc_level(['b', 'e'])
+        (1, None)
+        """
+        if not isinstance(level, (list, tuple)):
+            level = self._get_level_number(level)
+        else:
+            level = [self._get_level_number(lev) for lev in level]
+
+        loc, mi = self._get_loc_level(key, level=level)
+        if not drop_level:
+            if lib.is_integer(loc):
+                # Slice index must be an integer or None
+                mi = self[loc : loc + 1]
+            else:
+                mi = self[loc]
+        return loc, mi
+
+    def _get_loc_level(self, key, level: int | list[int] = 0):
+        """
+        get_loc_level but with `level` known to be positional, not name-based.
+        """
+
+        # different name to distinguish from maybe_droplevels
+        def maybe_mi_droplevels(indexer, levels):
+            """
+            If level does not exist or all levels were dropped, the exception
+            has to be handled outside.
+            """
+            new_index = self[indexer]
+
+            for i in sorted(levels, reverse=True):
+                new_index = new_index._drop_level_numbers([i])
+
+            return new_index
+
+        if isinstance(level, (tuple, list)):
+            if len(key) != len(level):
+                raise AssertionError(
+                    "Key for location must have same length as number of levels"
+                )
+            result = None
+            for lev, k in zip(level, key):
+                loc, new_index = self._get_loc_level(k, level=lev)
+                if isinstance(loc, slice):
+                    mask = np.zeros(len(self), dtype=bool)
+                    mask[loc] = True
+                    loc = mask
+                result = loc if result is None else result & loc
+
+            try:
+                # FIXME: we should be only dropping levels on which we are
+                #  scalar-indexing
+                mi = maybe_mi_droplevels(result, level)
+            except ValueError:
+                # droplevel failed because we tried to drop all levels,
+                #  i.e. len(level) == self.nlevels
+                mi = self[result]
+
+            return result, mi
+
+        # kludge for #1796
+        if isinstance(key, list):
+            key = tuple(key)
+
+        if isinstance(key, tuple) and level == 0:
+            try:
+                # Check if this tuple is a single key in our first level
+                if key in self.levels[0]:
+                    indexer = self._get_level_indexer(key, level=level)
+                    new_index = maybe_mi_droplevels(indexer, [0])
+                    return indexer, new_index
+            except (TypeError, InvalidIndexError):
+                pass
+
+            if not any(isinstance(k, slice) for k in key):
+                if len(key) == self.nlevels and self.is_unique:
+                    # Complete key in unique index -> standard get_loc
+                    try:
+                        return (self._engine.get_loc(key), None)
+                    except KeyError as err:
+                        raise KeyError(key) from err
+                    except TypeError:
+                        # e.g. partial string indexing
+                        #  test_partial_string_timestamp_multiindex
+                        pass
+
+                # partial selection
+                indexer = self.get_loc(key)
+                ilevels = [i for i in range(len(key)) if key[i] != slice(None, None)]
+                if len(ilevels) == self.nlevels:
+                    if is_integer(indexer):
+                        # we are dropping all levels
+                        return indexer, None
+
+                    # TODO: in some cases we still need to drop some levels,
+                    #  e.g. test_multiindex_perf_warn
+                    # test_partial_string_timestamp_multiindex
+                    ilevels = [
+                        i
+                        for i in range(len(key))
+                        if (
+                            not isinstance(key[i], str)
+                            or not self.levels[i]._supports_partial_string_indexing
+                        )
+                        and key[i] != slice(None, None)
+                    ]
+                    if len(ilevels) == self.nlevels:
+                        # TODO: why?
+                        ilevels = []
+                return indexer, maybe_mi_droplevels(indexer, ilevels)
+
+            else:
+                indexer = None
+                for i, k in enumerate(key):
+                    if not isinstance(k, slice):
+                        loc_level = self._get_level_indexer(k, level=i)
+                        if isinstance(loc_level, slice):
+                            if com.is_null_slice(loc_level) or com.is_full_slice(
+                                loc_level, len(self)
+                            ):
+                                # everything
+                                continue
+
+                            # e.g. test_xs_IndexSlice_argument_not_implemented
+                            k_index = np.zeros(len(self), dtype=bool)
+                            k_index[loc_level] = True
+
+                        else:
+                            k_index = loc_level
+
+                    elif com.is_null_slice(k):
+                        # taking everything, does not affect `indexer` below
+                        continue
+
+                    else:
+                        # FIXME: this message can be inaccurate, e.g.
+                        #  test_series_varied_multiindex_alignment
+                        raise TypeError(f"Expected label or tuple of labels, got {key}")
+
+                    if indexer is None:
+                        indexer = k_index
+                    else:
+                        indexer &= k_index
+                if indexer is None:
+                    indexer = slice(None, None)
+                ilevels = [i for i in range(len(key)) if key[i] != slice(None, None)]
+                return indexer, maybe_mi_droplevels(indexer, ilevels)
+        else:
+            indexer = self._get_level_indexer(key, level=level)
+            if (
+                isinstance(key, str)
+                and self.levels[level]._supports_partial_string_indexing
+            ):
+                # check to see if we did an exact lookup vs sliced
+                check = self.levels[level].get_loc(key)
+                if not is_integer(check):
+                    # e.g. test_partial_string_timestamp_multiindex
+                    return indexer, self[indexer]
+
+            try:
+                result_index = maybe_mi_droplevels(indexer, [level])
+            except ValueError:
+                result_index = self[indexer]
+
+            return indexer, result_index
+
+    def _get_level_indexer(
+        self, key, level: int = 0, indexer: npt.NDArray[np.bool_] | None = None
+    ):
+        # `level` kwarg is _always_ positional, never name
+        # return a boolean array or slice showing where the key is
+        # in the totality of values
+        # if the indexer is provided, then use this
+
+        level_index = self.levels[level]
+        level_codes = self.codes[level]
+
+        def convert_indexer(start, stop, step, indexer=indexer, codes=level_codes):
+            # Compute a bool indexer to identify the positions to take.
+            # If we have an existing indexer, we only need to examine the
+            # subset of positions where the existing indexer is True.
+            if indexer is not None:
+                # we only need to look at the subset of codes where the
+                # existing indexer equals True
+                codes = codes[indexer]
+
+            if step is None or step == 1:
+                new_indexer = (codes >= start) & (codes < stop)
+            else:
+                r = np.arange(start, stop, step, dtype=codes.dtype)
+                new_indexer = algos.isin(codes, r)
+
+            if indexer is None:
+                return new_indexer
+
+            indexer = indexer.copy()
+            indexer[indexer] = new_indexer
+            return indexer
+
+        if isinstance(key, slice):
+            # handle a slice, returning a slice if we can
+            # otherwise a boolean indexer
+            step = key.step
+            is_negative_step = step is not None and step < 0
+
+            try:
+                if key.start is not None:
+                    start = level_index.get_loc(key.start)
+                elif is_negative_step:
+                    start = len(level_index) - 1
+                else:
+                    start = 0
+
+                if key.stop is not None:
+                    stop = level_index.get_loc(key.stop)
+                elif is_negative_step:
+                    stop = 0
+                elif isinstance(start, slice):
+                    stop = len(level_index)
+                else:
+                    stop = len(level_index) - 1
+            except KeyError:
+                # we have a partial slice (like looking up a partial date
+                # string)
+                start = stop = level_index.slice_indexer(key.start, key.stop, key.step)
+                step = start.step
+
+            if isinstance(start, slice) or isinstance(stop, slice):
+                # we have a slice for start and/or stop
+                # a partial date slicer on a DatetimeIndex generates a slice
+                # note that the stop ALREADY includes the stopped point (if
+                # it was a string sliced)
+                start = getattr(start, "start", start)
+                stop = getattr(stop, "stop", stop)
+                return convert_indexer(start, stop, step)
+
+            elif level > 0 or self._lexsort_depth == 0 or step is not None:
+                # need to have like semantics here to right
+                # searching as when we are using a slice
+                # so adjust the stop by 1 (so we include stop)
+                stop = (stop - 1) if is_negative_step else (stop + 1)
+                return convert_indexer(start, stop, step)
+            else:
+                # sorted, so can return slice object -> view
+                i = algos.searchsorted(level_codes, start, side="left")
+                j = algos.searchsorted(level_codes, stop, side="right")
+                return slice(i, j, step)
+
+        else:
+            idx = self._get_loc_single_level_index(level_index, key)
+
+            if level > 0 or self._lexsort_depth == 0:
+                # Desired level is not sorted
+                if isinstance(idx, slice):
+                    # test_get_loc_partial_timestamp_multiindex
+                    locs = (level_codes >= idx.start) & (level_codes < idx.stop)
+                    return locs
+
+                locs = np.asarray(level_codes == idx, dtype=bool)
+
+                if not locs.any():
+                    # The label is present in self.levels[level] but unused:
+                    raise KeyError(key)
+                return locs
+
+            if isinstance(idx, slice):
+                # e.g. test_partial_string_timestamp_multiindex
+                start = algos.searchsorted(level_codes, idx.start, side="left")
+                # NB: "left" here bc of slice semantics
+                end = algos.searchsorted(level_codes, idx.stop, side="left")
+            else:
+                start = algos.searchsorted(level_codes, idx, side="left")
+                end = algos.searchsorted(level_codes, idx, side="right")
+
+            if start == end:
+                # The label is present in self.levels[level] but unused:
+                raise KeyError(key)
+            return slice(start, end)
+
+    def get_locs(self, seq) -> npt.NDArray[np.intp]:
+        """
+        Get location for a sequence of labels.
+
+        Parameters
+        ----------
+        seq : label, slice, list, mask or a sequence of such
+           You should use one of the above for each level.
+           If a level should not be used, set it to ``slice(None)``.
+
+        Returns
+        -------
+        numpy.ndarray
+            NumPy array of integers suitable for passing to iloc.
+
+        See Also
+        --------
+        MultiIndex.get_loc : Get location for a label or a tuple of labels.
+        MultiIndex.slice_locs : Get slice location given start label(s) and
+                                end label(s).
+
+        Examples
+        --------
+        >>> mi = pd.MultiIndex.from_arrays([list('abb'), list('def')])
+
+        >>> mi.get_locs('b')  # doctest: +SKIP
+        array([1, 2], dtype=int64)
+
+        >>> mi.get_locs([slice(None), ['e', 'f']])  # doctest: +SKIP
+        array([1, 2], dtype=int64)
+
+        >>> mi.get_locs([[True, False, True], slice('e', 'f')])  # doctest: +SKIP
+        array([2], dtype=int64)
+        """
+
+        # must be lexsorted to at least as many levels
+        true_slices = [i for (i, s) in enumerate(com.is_true_slices(seq)) if s]
+        if true_slices and true_slices[-1] >= self._lexsort_depth:
+            raise UnsortedIndexError(
+                "MultiIndex slicing requires the index to be lexsorted: slicing "
+                f"on levels {true_slices}, lexsort depth {self._lexsort_depth}"
+            )
+
+        if any(x is Ellipsis for x in seq):
+            raise NotImplementedError(
+                "MultiIndex does not support indexing with Ellipsis"
+            )
+
+        n = len(self)
+
+        def _to_bool_indexer(indexer) -> npt.NDArray[np.bool_]:
+            if isinstance(indexer, slice):
+                new_indexer = np.zeros(n, dtype=np.bool_)
+                new_indexer[indexer] = True
+                return new_indexer
+            return indexer
+
+        # a bool indexer for the positions we want to take
+        indexer: npt.NDArray[np.bool_] | None = None
+
+        for i, k in enumerate(seq):
+            lvl_indexer: npt.NDArray[np.bool_] | slice | None = None
+
+            if com.is_bool_indexer(k):
+                if len(k) != n:
+                    raise ValueError(
+                        "cannot index with a boolean indexer that "
+                        "is not the same length as the index"
+                    )
+                lvl_indexer = np.asarray(k)
+                if indexer is None:
+                    lvl_indexer = lvl_indexer.copy()
+
+            elif is_list_like(k):
+                # a collection of labels to include from this level (these are or'd)
+
+                # GH#27591 check if this is a single tuple key in the level
+                try:
+                    lvl_indexer = self._get_level_indexer(k, level=i, indexer=indexer)
+                except (InvalidIndexError, TypeError, KeyError) as err:
+                    # InvalidIndexError e.g. non-hashable, fall back to treating
+                    #  this as a sequence of labels
+                    # KeyError it can be ambiguous if this is a label or sequence
+                    #  of labels
+                    #  github.com/pandas-dev/pandas/issues/39424#issuecomment-871626708
+                    for x in k:
+                        if not is_hashable(x):
+                            # e.g. slice
+                            raise err
+                        # GH 39424: Ignore not founds
+                        # GH 42351: No longer ignore not founds & enforced in 2.0
+                        # TODO: how to handle IntervalIndex level? (no test cases)
+                        item_indexer = self._get_level_indexer(
+                            x, level=i, indexer=indexer
+                        )
+                        if lvl_indexer is None:
+                            lvl_indexer = _to_bool_indexer(item_indexer)
+                        elif isinstance(item_indexer, slice):
+                            lvl_indexer[item_indexer] = True  # type: ignore[index]
+                        else:
+                            lvl_indexer |= item_indexer
+
+                if lvl_indexer is None:
+                    # no matches we are done
+                    # test_loc_getitem_duplicates_multiindex_empty_indexer
+                    return np.array([], dtype=np.intp)
+
+            elif com.is_null_slice(k):
+                # empty slice
+                if indexer is None and i == len(seq) - 1:
+                    return np.arange(n, dtype=np.intp)
+                continue
+
+            else:
+                # a slice or a single label
+                lvl_indexer = self._get_level_indexer(k, level=i, indexer=indexer)
+
+            # update indexer
+            lvl_indexer = _to_bool_indexer(lvl_indexer)
+            if indexer is None:
+                indexer = lvl_indexer
+            else:
+                indexer &= lvl_indexer
+                if not np.any(indexer) and np.any(lvl_indexer):
+                    raise KeyError(seq)
+
+        # empty indexer
+        if indexer is None:
+            return np.array([], dtype=np.intp)
+
+        pos_indexer = indexer.nonzero()[0]
+        return self._reorder_indexer(seq, pos_indexer)
+
+    # --------------------------------------------------------------------
+
+    def _reorder_indexer(
+        self,
+        seq: tuple[Scalar | Iterable | AnyArrayLike, ...],
+        indexer: npt.NDArray[np.intp],
+    ) -> npt.NDArray[np.intp]:
+        """
+        Reorder an indexer of a MultiIndex (self) so that the labels are in the
+        same order as given in seq
+
+        Parameters
+        ----------
+        seq : label/slice/list/mask or a sequence of such
+        indexer: a position indexer of self
+
+        Returns
+        -------
+        indexer : a sorted position indexer of self ordered as seq
+        """
+
+        # check if sorting is necessary
+        need_sort = False
+        for i, k in enumerate(seq):
+            if com.is_null_slice(k) or com.is_bool_indexer(k) or is_scalar(k):
+                pass
+            elif is_list_like(k):
+                if len(k) <= 1:  # type: ignore[arg-type]
+                    pass
+                elif self._is_lexsorted():
+                    # If the index is lexsorted and the list_like label
+                    # in seq are sorted then we do not need to sort
+                    k_codes = self.levels[i].get_indexer(k)
+                    k_codes = k_codes[k_codes >= 0]  # Filter absent keys
+                    # True if the given codes are not ordered
+                    need_sort = (k_codes[:-1] > k_codes[1:]).any()
+                else:
+                    need_sort = True
+            elif isinstance(k, slice):
+                if self._is_lexsorted():
+                    need_sort = k.step is not None and k.step < 0
+                else:
+                    need_sort = True
+            else:
+                need_sort = True
+            if need_sort:
+                break
+        if not need_sort:
+            return indexer
+
+        n = len(self)
+        keys: tuple[np.ndarray, ...] = ()
+        # For each level of the sequence in seq, map the level codes with the
+        # order they appears in a list-like sequence
+        # This mapping is then use to reorder the indexer
+        for i, k in enumerate(seq):
+            if is_scalar(k):
+                # GH#34603 we want to treat a scalar the same as an all equal list
+                k = [k]
+            if com.is_bool_indexer(k):
+                new_order = np.arange(n)[indexer]
+            elif is_list_like(k):
+                # Generate a map with all level codes as sorted initially
+                if not isinstance(k, (np.ndarray, ExtensionArray, Index, ABCSeries)):
+                    k = sanitize_array(k, None)
+                k = algos.unique(k)
+                key_order_map = np.ones(len(self.levels[i]), dtype=np.uint64) * len(
+                    self.levels[i]
+                )
+                # Set order as given in the indexer list
+                level_indexer = self.levels[i].get_indexer(k)
+                level_indexer = level_indexer[level_indexer >= 0]  # Filter absent keys
+                key_order_map[level_indexer] = np.arange(len(level_indexer))
+
+                new_order = key_order_map[self.codes[i][indexer]]
+            elif isinstance(k, slice) and k.step is not None and k.step < 0:
+                # flip order for negative step
+                new_order = np.arange(n)[::-1][indexer]
+            elif isinstance(k, slice) and k.start is None and k.stop is None:
+                # slice(None) should not determine order GH#31330
+                new_order = np.ones((n,), dtype=np.intp)[indexer]
+            else:
+                # For all other case, use the same order as the level
+                new_order = np.arange(n)[indexer]
+            keys = (new_order,) + keys
+
+        # Find the reordering using lexsort on the keys mapping
+        ind = np.lexsort(keys)
+        return indexer[ind]
+
+    def truncate(self, before=None, after=None) -> MultiIndex:
+        """
+        Slice index between two labels / tuples, return new MultiIndex.
+
+        Parameters
+        ----------
+        before : label or tuple, can be partial. Default None
+            None defaults to start.
+        after : label or tuple, can be partial. Default None
+            None defaults to end.
+
+        Returns
+        -------
+        MultiIndex
+            The truncated MultiIndex.
+
+        Examples
+        --------
+        >>> mi = pd.MultiIndex.from_arrays([['a', 'b', 'c'], ['x', 'y', 'z']])
+        >>> mi
+        MultiIndex([('a', 'x'), ('b', 'y'), ('c', 'z')],
+                   )
+        >>> mi.truncate(before='a', after='b')
+        MultiIndex([('a', 'x'), ('b', 'y')],
+                   )
+        """
+        if after and before and after < before:
+            raise ValueError("after < before")
+
+        i, j = self.levels[0].slice_locs(before, after)
+        left, right = self.slice_locs(before, after)
+
+        new_levels = list(self.levels)
+        new_levels[0] = new_levels[0][i:j]
+
+        new_codes = [level_codes[left:right] for level_codes in self.codes]
+        new_codes[0] = new_codes[0] - i
+
+        return MultiIndex(
+            levels=new_levels,
+            codes=new_codes,
+            names=self._names,
+            verify_integrity=False,
+        )
+
+    def equals(self, other: object) -> bool:
+        """
+        Determines if two MultiIndex objects have the same labeling information
+        (the levels themselves do not necessarily have to be the same)
+
+        See Also
+        --------
+        equal_levels
+        """
+        if self.is_(other):
+            return True
+
+        if not isinstance(other, Index):
+            return False
+
+        if len(self) != len(other):
+            return False
+
+        if not isinstance(other, MultiIndex):
+            # d-level MultiIndex can equal d-tuple Index
+            if not self._should_compare(other):
+                # object Index or Categorical[object] may contain tuples
+                return False
+            return array_equivalent(self._values, other._values)
+
+        if self.nlevels != other.nlevels:
+            return False
+
+        for i in range(self.nlevels):
+            self_codes = self.codes[i]
+            other_codes = other.codes[i]
+            self_mask = self_codes == -1
+            other_mask = other_codes == -1
+            if not np.array_equal(self_mask, other_mask):
+                return False
+            self_codes = self_codes[~self_mask]
+            self_values = self.levels[i]._values.take(self_codes)
+
+            other_codes = other_codes[~other_mask]
+            other_values = other.levels[i]._values.take(other_codes)
+
+            # since we use NaT both datetime64 and timedelta64 we can have a
+            # situation where a level is typed say timedelta64 in self (IOW it
+            # has other values than NaT) but types datetime64 in other (where
+            # its all NaT) but these are equivalent
+            if len(self_values) == 0 and len(other_values) == 0:
+                continue
+
+            if not isinstance(self_values, np.ndarray):
+                # i.e. ExtensionArray
+                if not self_values.equals(other_values):
+                    return False
+            elif not isinstance(other_values, np.ndarray):
+                # i.e. other is ExtensionArray
+                if not other_values.equals(self_values):
+                    return False
+            else:
+                if not array_equivalent(self_values, other_values):
+                    return False
+
+        return True
+
+    def equal_levels(self, other: MultiIndex) -> bool:
+        """
+        Return True if the levels of both MultiIndex objects are the same
+
+        """
+        if self.nlevels != other.nlevels:
+            return False
+
+        for i in range(self.nlevels):
+            if not self.levels[i].equals(other.levels[i]):
+                return False
+        return True
+
+    # --------------------------------------------------------------------
+    # Set Methods
+
+    def _union(self, other, sort) -> MultiIndex:
+        other, result_names = self._convert_can_do_setop(other)
+        if other.has_duplicates:
+            # This is only necessary if other has dupes,
+            # otherwise difference is faster
+            result = super()._union(other, sort)
+
+            if isinstance(result, MultiIndex):
+                return result
+            return MultiIndex.from_arrays(
+                zip(*result), sortorder=None, names=result_names
+            )
+
+        else:
+            right_missing = other.difference(self, sort=False)
+            if len(right_missing):
+                result = self.append(right_missing)
+            else:
+                result = self._get_reconciled_name_object(other)
+
+            if sort is not False:
+                try:
+                    result = result.sort_values()
+                except TypeError:
+                    if sort is True:
+                        raise
+                    warnings.warn(
+                        "The values in the array are unorderable. "
+                        "Pass `sort=False` to suppress this warning.",
+                        RuntimeWarning,
+                        stacklevel=find_stack_level(),
+                    )
+            return result
+
+    def _is_comparable_dtype(self, dtype: DtypeObj) -> bool:
+        return is_object_dtype(dtype)
+
+    def _get_reconciled_name_object(self, other) -> MultiIndex:
+        """
+        If the result of a set operation will be self,
+        return self, unless the names change, in which
+        case make a shallow copy of self.
+        """
+        names = self._maybe_match_names(other)
+        if self.names != names:
+            # error: Cannot determine type of "rename"
+            return self.rename(names)  # type: ignore[has-type]
+        return self
+
+    def _maybe_match_names(self, other):
+        """
+        Try to find common names to attach to the result of an operation between
+        a and b. Return a consensus list of names if they match at least partly
+        or list of None if they have completely different names.
+        """
+        if len(self.names) != len(other.names):
+            return [None] * len(self.names)
+        names = []
+        for a_name, b_name in zip(self.names, other.names):
+            if a_name == b_name:
+                names.append(a_name)
+            else:
+                # TODO: what if they both have np.nan for their names?
+                names.append(None)
+        return names
+
+    def _wrap_intersection_result(self, other, result) -> MultiIndex:
+        _, result_names = self._convert_can_do_setop(other)
+        return result.set_names(result_names)
+
+    def _wrap_difference_result(self, other, result: MultiIndex) -> MultiIndex:
+        _, result_names = self._convert_can_do_setop(other)
+
+        if len(result) == 0:
+            return result.remove_unused_levels().set_names(result_names)
+        else:
+            return result.set_names(result_names)
+
+    def _convert_can_do_setop(self, other):
+        result_names = self.names
+
+        if not isinstance(other, Index):
+            if len(other) == 0:
+                return self[:0], self.names
+            else:
+                msg = "other must be a MultiIndex or a list of tuples"
+                try:
+                    other = MultiIndex.from_tuples(other, names=self.names)
+                except (ValueError, TypeError) as err:
+                    # ValueError raised by tuples_to_object_array if we
+                    #  have non-object dtype
+                    raise TypeError(msg) from err
+        else:
+            result_names = get_unanimous_names(self, other)
+
+        return other, result_names
+
+    # --------------------------------------------------------------------
+
+    @doc(Index.astype)
+    def astype(self, dtype, copy: bool = True):
+        dtype = pandas_dtype(dtype)
+        if isinstance(dtype, CategoricalDtype):
+            msg = "> 1 ndim Categorical are not supported at this time"
+            raise NotImplementedError(msg)
+        if not is_object_dtype(dtype):
+            raise TypeError(
+                "Setting a MultiIndex dtype to anything other than object "
+                "is not supported"
+            )
+        if copy is True:
+            return self._view()
+        return self
+
+    def _validate_fill_value(self, item):
+        if isinstance(item, MultiIndex):
+            # GH#43212
+            if item.nlevels != self.nlevels:
+                raise ValueError("Item must have length equal to number of levels.")
+            return item._values
+        elif not isinstance(item, tuple):
+            # Pad the key with empty strings if lower levels of the key
+            # aren't specified:
+            item = (item,) + ("",) * (self.nlevels - 1)
+        elif len(item) != self.nlevels:
+            raise ValueError("Item must have length equal to number of levels.")
+        return item
+
+    def putmask(self, mask, value: MultiIndex) -> MultiIndex:
+        """
+        Return a new MultiIndex of the values set with the mask.
+
+        Parameters
+        ----------
+        mask : array like
+        value : MultiIndex
+            Must either be the same length as self or length one
+
+        Returns
+        -------
+        MultiIndex
+        """
+        mask, noop = validate_putmask(self, mask)
+        if noop:
+            return self.copy()
+
+        if len(mask) == len(value):
+            subset = value[mask].remove_unused_levels()
+        else:
+            subset = value.remove_unused_levels()
+
+        new_levels = []
+        new_codes = []
+
+        for i, (value_level, level, level_codes) in enumerate(
+            zip(subset.levels, self.levels, self.codes)
+        ):
+            new_level = level.union(value_level, sort=False)
+            value_codes = new_level.get_indexer_for(subset.get_level_values(i))
+            new_code = ensure_int64(level_codes)
+            new_code[mask] = value_codes
+            new_levels.append(new_level)
+            new_codes.append(new_code)
+
+        return MultiIndex(
+            levels=new_levels, codes=new_codes, names=self.names, verify_integrity=False
+        )
+
+    def insert(self, loc: int, item) -> MultiIndex:
+        """
+        Make new MultiIndex inserting new item at location
+
+        Parameters
+        ----------
+        loc : int
+        item : tuple
+            Must be same length as number of levels in the MultiIndex
+
+        Returns
+        -------
+        new_index : Index
+        """
+        item = self._validate_fill_value(item)
+
+        new_levels = []
+        new_codes = []
+        for k, level, level_codes in zip(item, self.levels, self.codes):
+            if k not in level:
+                # have to insert into level
+                # must insert at end otherwise you have to recompute all the
+                # other codes
+                lev_loc = len(level)
+                level = level.insert(lev_loc, k)
+            else:
+                lev_loc = level.get_loc(k)
+
+            new_levels.append(level)
+            new_codes.append(np.insert(ensure_int64(level_codes), loc, lev_loc))
+
+        return MultiIndex(
+            levels=new_levels, codes=new_codes, names=self.names, verify_integrity=False
+        )
+
+    def delete(self, loc) -> MultiIndex:
+        """
+        Make new index with passed location deleted
+
+        Returns
+        -------
+        new_index : MultiIndex
+        """
+        new_codes = [np.delete(level_codes, loc) for level_codes in self.codes]
+        return MultiIndex(
+            levels=self.levels,
+            codes=new_codes,
+            names=self.names,
+            verify_integrity=False,
+        )
+
+    @doc(Index.isin)
+    def isin(self, values, level=None) -> npt.NDArray[np.bool_]:
+        if isinstance(values, Generator):
+            values = list(values)
+
+        if level is None:
+            if len(values) == 0:
+                return np.zeros((len(self),), dtype=np.bool_)
+            if not isinstance(values, MultiIndex):
+                values = MultiIndex.from_tuples(values)
+            return values.unique().get_indexer_for(self) != -1
+        else:
+            num = self._get_level_number(level)
+            levs = self.get_level_values(num)
+
+            if levs.size == 0:
+                return np.zeros(len(levs), dtype=np.bool_)
+            return levs.isin(values)
+
+    # error: Incompatible types in assignment (expression has type overloaded function,
+    # base class "Index" defined the type as "Callable[[Index, Any, bool], Any]")
+    rename = Index.set_names  # type: ignore[assignment]
+
+    # ---------------------------------------------------------------
+    # Arithmetic/Numeric Methods - Disabled
+
+    __add__ = make_invalid_op("__add__")
+    __radd__ = make_invalid_op("__radd__")
+    __iadd__ = make_invalid_op("__iadd__")
+    __sub__ = make_invalid_op("__sub__")
+    __rsub__ = make_invalid_op("__rsub__")
+    __isub__ = make_invalid_op("__isub__")
+    __pow__ = make_invalid_op("__pow__")
+    __rpow__ = make_invalid_op("__rpow__")
+    __mul__ = make_invalid_op("__mul__")
+    __rmul__ = make_invalid_op("__rmul__")
+    __floordiv__ = make_invalid_op("__floordiv__")
+    __rfloordiv__ = make_invalid_op("__rfloordiv__")
+    __truediv__ = make_invalid_op("__truediv__")
+    __rtruediv__ = make_invalid_op("__rtruediv__")
+    __mod__ = make_invalid_op("__mod__")
+    __rmod__ = make_invalid_op("__rmod__")
+    __divmod__ = make_invalid_op("__divmod__")
+    __rdivmod__ = make_invalid_op("__rdivmod__")
+    # Unary methods disabled
+    __neg__ = make_invalid_op("__neg__")
+    __pos__ = make_invalid_op("__pos__")
+    __abs__ = make_invalid_op("__abs__")
+    __invert__ = make_invalid_op("__invert__")
+
+
+def _lexsort_depth(codes: list[np.ndarray], nlevels: int) -> int:
+    """Count depth (up to a maximum of `nlevels`) with which codes are lexsorted."""
+    int64_codes = [ensure_int64(level_codes) for level_codes in codes]
+    for k in range(nlevels, 0, -1):
+        if libalgos.is_lexsorted(int64_codes[:k]):
+            return k
+    return 0
+
+
+def sparsify_labels(label_list, start: int = 0, sentinel: object = ""):
+    pivoted = list(zip(*label_list))
+    k = len(label_list)
+
+    result = pivoted[: start + 1]
+    prev = pivoted[start]
+
+    for cur in pivoted[start + 1 :]:
+        sparse_cur = []
+
+        for i, (p, t) in enumerate(zip(prev, cur)):
+            if i == k - 1:
+                sparse_cur.append(t)
+                # error: Argument 1 to "append" of "list" has incompatible
+                # type "list[Any]"; expected "tuple[Any, ...]"
+                result.append(sparse_cur)  # type: ignore[arg-type]
+                break
+
+            if p == t:
+                sparse_cur.append(sentinel)
+            else:
+                sparse_cur.extend(cur[i:])
+                # error: Argument 1 to "append" of "list" has incompatible
+                # type "list[Any]"; expected "tuple[Any, ...]"
+                result.append(sparse_cur)  # type: ignore[arg-type]
+                break
+
+        prev = cur
+
+    return list(zip(*result))
+
+
+def _get_na_rep(dtype: DtypeObj) -> str:
+    if isinstance(dtype, ExtensionDtype):
+        return f"{dtype.na_value}"
+    else:
+        dtype_type = dtype.type
+
+    return {np.datetime64: "NaT", np.timedelta64: "NaT"}.get(dtype_type, "NaN")
+
+
+def maybe_droplevels(index: Index, key) -> Index:
+    """
+    Attempt to drop level or levels from the given index.
+
+    Parameters
+    ----------
+    index: Index
+    key : scalar or tuple
+
+    Returns
+    -------
+    Index
+    """
+    # drop levels
+    original_index = index
+    if isinstance(key, tuple):
+        # Caller is responsible for ensuring the key is not an entry in the first
+        #  level of the MultiIndex.
+        for _ in key:
+            try:
+                index = index._drop_level_numbers([0])
+            except ValueError:
+                # we have dropped too much, so back out
+                return original_index
+    else:
+        try:
+            index = index._drop_level_numbers([0])
+        except ValueError:
+            pass
+
+    return index
+
+
+def _coerce_indexer_frozen(array_like, categories, copy: bool = False) -> np.ndarray:
+    """
+    Coerce the array-like indexer to the smallest integer dtype that can encode all
+    of the given categories.
+
+    Parameters
+    ----------
+    array_like : array-like
+    categories : array-like
+    copy : bool
+
+    Returns
+    -------
+    np.ndarray
+        Non-writeable.
+    """
+    array_like = coerce_indexer_dtype(array_like, categories)
+    if copy:
+        array_like = array_like.copy()
+    array_like.flags.writeable = False
+    return array_like
+
+
+def _require_listlike(level, arr, arrname: str):
+    """
+    Ensure that level is either None or listlike, and arr is list-of-listlike.
+    """
+    if level is not None and not is_list_like(level):
+        if not is_list_like(arr):
+            raise TypeError(f"{arrname} must be list-like")
+        if len(arr) > 0 and is_list_like(arr[0]):
+            raise TypeError(f"{arrname} must be list-like")
+        level = [level]
+        arr = [arr]
+    elif level is None or is_list_like(level):
+        if not is_list_like(arr) or not is_list_like(arr[0]):
+            raise TypeError(f"{arrname} must be list of lists-like")
+    return level, arr
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/indexes/period.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/indexes/period.py
new file mode 100644
index 0000000000000000000000000000000000000000..b2f1933800fd383df9dc52a211b54190985fc32e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/indexes/period.py
@@ -0,0 +1,614 @@
+from __future__ import annotations
+
+from datetime import (
+    datetime,
+    timedelta,
+)
+from typing import TYPE_CHECKING
+import warnings
+
+import numpy as np
+
+from pandas._libs import index as libindex
+from pandas._libs.tslibs import (
+    BaseOffset,
+    NaT,
+    Period,
+    Resolution,
+    Tick,
+)
+from pandas._libs.tslibs.dtypes import OFFSET_TO_PERIOD_FREQSTR
+from pandas.util._decorators import (
+    cache_readonly,
+    doc,
+)
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.common import is_integer
+from pandas.core.dtypes.dtypes import PeriodDtype
+from pandas.core.dtypes.generic import ABCSeries
+from pandas.core.dtypes.missing import is_valid_na_for_dtype
+
+from pandas.core.arrays.period import (
+    PeriodArray,
+    period_array,
+    raise_on_incompatible,
+    validate_dtype_freq,
+)
+import pandas.core.common as com
+import pandas.core.indexes.base as ibase
+from pandas.core.indexes.base import maybe_extract_name
+from pandas.core.indexes.datetimelike import DatetimeIndexOpsMixin
+from pandas.core.indexes.datetimes import (
+    DatetimeIndex,
+    Index,
+)
+from pandas.core.indexes.extension import inherit_names
+
+if TYPE_CHECKING:
+    from collections.abc import Hashable
+
+    from pandas._typing import (
+        Dtype,
+        DtypeObj,
+        Self,
+        npt,
+    )
+
+
+_index_doc_kwargs = dict(ibase._index_doc_kwargs)
+_index_doc_kwargs.update({"target_klass": "PeriodIndex or list of Periods"})
+_shared_doc_kwargs = {
+    "klass": "PeriodArray",
+}
+
+# --- Period index sketch
+
+
+def _new_PeriodIndex(cls, **d):
+    # GH13277 for unpickling
+    values = d.pop("data")
+    if values.dtype == "int64":
+        freq = d.pop("freq", None)
+        dtype = PeriodDtype(freq)
+        values = PeriodArray(values, dtype=dtype)
+        return cls._simple_new(values, **d)
+    else:
+        return cls(values, **d)
+
+
+@inherit_names(
+    ["strftime", "start_time", "end_time"] + PeriodArray._field_ops,
+    PeriodArray,
+    wrap=True,
+)
+@inherit_names(["is_leap_year"], PeriodArray)
+class PeriodIndex(DatetimeIndexOpsMixin):
+    """
+    Immutable ndarray holding ordinal values indicating regular periods in time.
+
+    Index keys are boxed to Period objects which carries the metadata (eg,
+    frequency information).
+
+    Parameters
+    ----------
+    data : array-like (1d int np.ndarray or PeriodArray), optional
+        Optional period-like data to construct index with.
+    copy : bool
+        Make a copy of input ndarray.
+    freq : str or period object, optional
+        One of pandas period strings or corresponding objects.
+    year : int, array, or Series, default None
+
+        .. deprecated:: 2.2.0
+           Use PeriodIndex.from_fields instead.
+    month : int, array, or Series, default None
+
+        .. deprecated:: 2.2.0
+           Use PeriodIndex.from_fields instead.
+    quarter : int, array, or Series, default None
+
+        .. deprecated:: 2.2.0
+           Use PeriodIndex.from_fields instead.
+    day : int, array, or Series, default None
+
+        .. deprecated:: 2.2.0
+           Use PeriodIndex.from_fields instead.
+    hour : int, array, or Series, default None
+
+        .. deprecated:: 2.2.0
+           Use PeriodIndex.from_fields instead.
+    minute : int, array, or Series, default None
+
+        .. deprecated:: 2.2.0
+           Use PeriodIndex.from_fields instead.
+    second : int, array, or Series, default None
+
+        .. deprecated:: 2.2.0
+           Use PeriodIndex.from_fields instead.
+    dtype : str or PeriodDtype, default None
+
+    Attributes
+    ----------
+    day
+    dayofweek
+    day_of_week
+    dayofyear
+    day_of_year
+    days_in_month
+    daysinmonth
+    end_time
+    freq
+    freqstr
+    hour
+    is_leap_year
+    minute
+    month
+    quarter
+    qyear
+    second
+    start_time
+    week
+    weekday
+    weekofyear
+    year
+
+    Methods
+    -------
+    asfreq
+    strftime
+    to_timestamp
+    from_fields
+    from_ordinals
+
+    See Also
+    --------
+    Index : The base pandas Index type.
+    Period : Represents a period of time.
+    DatetimeIndex : Index with datetime64 data.
+    TimedeltaIndex : Index of timedelta64 data.
+    period_range : Create a fixed-frequency PeriodIndex.
+
+    Examples
+    --------
+    >>> idx = pd.PeriodIndex.from_fields(year=[2000, 2002], quarter=[1, 3])
+    >>> idx
+    PeriodIndex(['2000Q1', '2002Q3'], dtype='period[Q-DEC]')
+    """
+
+    _typ = "periodindex"
+
+    _data: PeriodArray
+    freq: BaseOffset
+    dtype: PeriodDtype
+
+    _data_cls = PeriodArray
+    _supports_partial_string_indexing = True
+
+    @property
+    def _engine_type(self) -> type[libindex.PeriodEngine]:
+        return libindex.PeriodEngine
+
+    @cache_readonly
+    def _resolution_obj(self) -> Resolution:
+        # for compat with DatetimeIndex
+        return self.dtype._resolution_obj
+
+    # --------------------------------------------------------------------
+    # methods that dispatch to array and wrap result in Index
+    # These are defined here instead of via inherit_names for mypy
+
+    @doc(
+        PeriodArray.asfreq,
+        other="pandas.arrays.PeriodArray",
+        other_name="PeriodArray",
+        **_shared_doc_kwargs,
+    )
+    def asfreq(self, freq=None, how: str = "E") -> Self:
+        arr = self._data.asfreq(freq, how)
+        return type(self)._simple_new(arr, name=self.name)
+
+    @doc(PeriodArray.to_timestamp)
+    def to_timestamp(self, freq=None, how: str = "start") -> DatetimeIndex:
+        arr = self._data.to_timestamp(freq, how)
+        return DatetimeIndex._simple_new(arr, name=self.name)
+
+    @property
+    @doc(PeriodArray.hour.fget)
+    def hour(self) -> Index:
+        return Index(self._data.hour, name=self.name)
+
+    @property
+    @doc(PeriodArray.minute.fget)
+    def minute(self) -> Index:
+        return Index(self._data.minute, name=self.name)
+
+    @property
+    @doc(PeriodArray.second.fget)
+    def second(self) -> Index:
+        return Index(self._data.second, name=self.name)
+
+    # ------------------------------------------------------------------------
+    # Index Constructors
+
+    def __new__(
+        cls,
+        data=None,
+        ordinal=None,
+        freq=None,
+        dtype: Dtype | None = None,
+        copy: bool = False,
+        name: Hashable | None = None,
+        **fields,
+    ) -> Self:
+        valid_field_set = {
+            "year",
+            "month",
+            "day",
+            "quarter",
+            "hour",
+            "minute",
+            "second",
+        }
+
+        refs = None
+        if not copy and isinstance(data, (Index, ABCSeries)):
+            refs = data._references
+
+        if not set(fields).issubset(valid_field_set):
+            argument = next(iter(set(fields) - valid_field_set))
+            raise TypeError(f"__new__() got an unexpected keyword argument {argument}")
+        elif len(fields):
+            # GH#55960
+            warnings.warn(
+                "Constructing PeriodIndex from fields is deprecated. Use "
+                "PeriodIndex.from_fields instead.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+
+        if ordinal is not None:
+            # GH#55960
+            warnings.warn(
+                "The 'ordinal' keyword in PeriodIndex is deprecated and will "
+                "be removed in a future version. Use PeriodIndex.from_ordinals "
+                "instead.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+
+        name = maybe_extract_name(name, data, cls)
+
+        if data is None and ordinal is None:
+            # range-based.
+            if not fields:
+                # test_pickle_compat_construction
+                cls._raise_scalar_data_error(None)
+            data = cls.from_fields(**fields, freq=freq)._data
+            copy = False
+
+        elif fields:
+            if data is not None:
+                raise ValueError("Cannot pass both data and fields")
+            raise ValueError("Cannot pass both ordinal and fields")
+
+        else:
+            freq = validate_dtype_freq(dtype, freq)
+
+            # PeriodIndex allow PeriodIndex(period_index, freq=different)
+            # Let's not encourage that kind of behavior in PeriodArray.
+
+            if freq and isinstance(data, cls) and data.freq != freq:
+                # TODO: We can do some of these with no-copy / coercion?
+                # e.g. D -> 2D seems to be OK
+                data = data.asfreq(freq)
+
+            if data is None and ordinal is not None:
+                ordinal = np.asarray(ordinal, dtype=np.int64)
+                dtype = PeriodDtype(freq)
+                data = PeriodArray(ordinal, dtype=dtype)
+            elif data is not None and ordinal is not None:
+                raise ValueError("Cannot pass both data and ordinal")
+            else:
+                # don't pass copy here, since we copy later.
+                data = period_array(data=data, freq=freq)
+
+        if copy:
+            data = data.copy()
+
+        return cls._simple_new(data, name=name, refs=refs)
+
+    @classmethod
+    def from_fields(
+        cls,
+        *,
+        year=None,
+        quarter=None,
+        month=None,
+        day=None,
+        hour=None,
+        minute=None,
+        second=None,
+        freq=None,
+    ) -> Self:
+        fields = {
+            "year": year,
+            "quarter": quarter,
+            "month": month,
+            "day": day,
+            "hour": hour,
+            "minute": minute,
+            "second": second,
+        }
+        fields = {key: value for key, value in fields.items() if value is not None}
+        arr = PeriodArray._from_fields(fields=fields, freq=freq)
+        return cls._simple_new(arr)
+
+    @classmethod
+    def from_ordinals(cls, ordinals, *, freq, name=None) -> Self:
+        ordinals = np.asarray(ordinals, dtype=np.int64)
+        dtype = PeriodDtype(freq)
+        data = PeriodArray._simple_new(ordinals, dtype=dtype)
+        return cls._simple_new(data, name=name)
+
+    # ------------------------------------------------------------------------
+    # Data
+
+    @property
+    def values(self) -> npt.NDArray[np.object_]:
+        return np.asarray(self, dtype=object)
+
+    def _maybe_convert_timedelta(self, other) -> int | npt.NDArray[np.int64]:
+        """
+        Convert timedelta-like input to an integer multiple of self.freq
+
+        Parameters
+        ----------
+        other : timedelta, np.timedelta64, DateOffset, int, np.ndarray
+
+        Returns
+        -------
+        converted : int, np.ndarray[int64]
+
+        Raises
+        ------
+        IncompatibleFrequency : if the input cannot be written as a multiple
+            of self.freq.  Note IncompatibleFrequency subclasses ValueError.
+        """
+        if isinstance(other, (timedelta, np.timedelta64, Tick, np.ndarray)):
+            if isinstance(self.freq, Tick):
+                # _check_timedeltalike_freq_compat will raise if incompatible
+                delta = self._data._check_timedeltalike_freq_compat(other)
+                return delta
+        elif isinstance(other, BaseOffset):
+            if other.base == self.freq.base:
+                return other.n
+
+            raise raise_on_incompatible(self, other)
+        elif is_integer(other):
+            assert isinstance(other, int)
+            return other
+
+        # raise when input doesn't have freq
+        raise raise_on_incompatible(self, None)
+
+    def _is_comparable_dtype(self, dtype: DtypeObj) -> bool:
+        """
+        Can we compare values of the given dtype to our own?
+        """
+        return self.dtype == dtype
+
+    # ------------------------------------------------------------------------
+    # Index Methods
+
+    def asof_locs(self, where: Index, mask: npt.NDArray[np.bool_]) -> np.ndarray:
+        """
+        where : array of timestamps
+        mask : np.ndarray[bool]
+            Array of booleans where data is not NA.
+        """
+        if isinstance(where, DatetimeIndex):
+            where = PeriodIndex(where._values, freq=self.freq)
+        elif not isinstance(where, PeriodIndex):
+            raise TypeError("asof_locs `where` must be DatetimeIndex or PeriodIndex")
+
+        return super().asof_locs(where, mask)
+
+    @property
+    def is_full(self) -> bool:
+        """
+        Returns True if this PeriodIndex is range-like in that all Periods
+        between start and end are present, in order.
+        """
+        if len(self) == 0:
+            return True
+        if not self.is_monotonic_increasing:
+            raise ValueError("Index is not monotonic")
+        values = self.asi8
+        return bool(((values[1:] - values[:-1]) < 2).all())
+
+    @property
+    def inferred_type(self) -> str:
+        # b/c data is represented as ints make sure we can't have ambiguous
+        # indexing
+        return "period"
+
+    # ------------------------------------------------------------------------
+    # Indexing Methods
+
+    def _convert_tolerance(self, tolerance, target):
+        # Returned tolerance must be in dtype/units so that
+        #  `|self._get_engine_target() - target._engine_target()| <= tolerance`
+        #  is meaningful.  Since PeriodIndex returns int64 for engine_target,
+        #  we may need to convert timedelta64 tolerance to int64.
+        tolerance = super()._convert_tolerance(tolerance, target)
+
+        if self.dtype == target.dtype:
+            # convert tolerance to i8
+            tolerance = self._maybe_convert_timedelta(tolerance)
+
+        return tolerance
+
+    def get_loc(self, key):
+        """
+        Get integer location for requested label.
+
+        Parameters
+        ----------
+        key : Period, NaT, str, or datetime
+            String or datetime key must be parsable as Period.
+
+        Returns
+        -------
+        loc : int or ndarray[int64]
+
+        Raises
+        ------
+        KeyError
+            Key is not present in the index.
+        TypeError
+            If key is listlike or otherwise not hashable.
+        """
+        orig_key = key
+
+        self._check_indexing_error(key)
+
+        if is_valid_na_for_dtype(key, self.dtype):
+            key = NaT
+
+        elif isinstance(key, str):
+            try:
+                parsed, reso = self._parse_with_reso(key)
+            except ValueError as err:
+                # A string with invalid format
+                raise KeyError(f"Cannot interpret '{key}' as period") from err
+
+            if self._can_partial_date_slice(reso):
+                try:
+                    return self._partial_date_slice(reso, parsed)
+                except KeyError as err:
+                    raise KeyError(key) from err
+
+            if reso == self._resolution_obj:
+                # the reso < self._resolution_obj case goes
+                #  through _get_string_slice
+                key = self._cast_partial_indexing_scalar(parsed)
+            else:
+                raise KeyError(key)
+
+        elif isinstance(key, Period):
+            self._disallow_mismatched_indexing(key)
+
+        elif isinstance(key, datetime):
+            key = self._cast_partial_indexing_scalar(key)
+
+        else:
+            # in particular integer, which Period constructor would cast to string
+            raise KeyError(key)
+
+        try:
+            return Index.get_loc(self, key)
+        except KeyError as err:
+            raise KeyError(orig_key) from err
+
+    def _disallow_mismatched_indexing(self, key: Period) -> None:
+        if key._dtype != self.dtype:
+            raise KeyError(key)
+
+    def _cast_partial_indexing_scalar(self, label: datetime) -> Period:
+        try:
+            period = Period(label, freq=self.freq)
+        except ValueError as err:
+            # we cannot construct the Period
+            raise KeyError(label) from err
+        return period
+
+    @doc(DatetimeIndexOpsMixin._maybe_cast_slice_bound)
+    def _maybe_cast_slice_bound(self, label, side: str):
+        if isinstance(label, datetime):
+            label = self._cast_partial_indexing_scalar(label)
+
+        return super()._maybe_cast_slice_bound(label, side)
+
+    def _parsed_string_to_bounds(self, reso: Resolution, parsed: datetime):
+        freq = OFFSET_TO_PERIOD_FREQSTR.get(reso.attr_abbrev, reso.attr_abbrev)
+        iv = Period(parsed, freq=freq)
+        return (iv.asfreq(self.freq, how="start"), iv.asfreq(self.freq, how="end"))
+
+    @doc(DatetimeIndexOpsMixin.shift)
+    def shift(self, periods: int = 1, freq=None) -> Self:
+        if freq is not None:
+            raise TypeError(
+                f"`freq` argument is not supported for {type(self).__name__}.shift"
+            )
+        return self + periods
+
+
+def period_range(
+    start=None,
+    end=None,
+    periods: int | None = None,
+    freq=None,
+    name: Hashable | None = None,
+) -> PeriodIndex:
+    """
+    Return a fixed frequency PeriodIndex.
+
+    The day (calendar) is the default frequency.
+
+    Parameters
+    ----------
+    start : str, datetime, date, pandas.Timestamp, or period-like, default None
+        Left bound for generating periods.
+    end : str, datetime, date, pandas.Timestamp, or period-like, default None
+        Right bound for generating periods.
+    periods : int, default None
+        Number of periods to generate.
+    freq : str or DateOffset, optional
+        Frequency alias. By default the freq is taken from `start` or `end`
+        if those are Period objects. Otherwise, the default is ``"D"`` for
+        daily frequency.
+    name : str, default None
+        Name of the resulting PeriodIndex.
+
+    Returns
+    -------
+    PeriodIndex
+
+    Notes
+    -----
+    Of the three parameters: ``start``, ``end``, and ``periods``, exactly two
+    must be specified.
+
+    To learn more about the frequency strings, please see `this link
+    `__.
+
+    Examples
+    --------
+    >>> pd.period_range(start='2017-01-01', end='2018-01-01', freq='M')
+    PeriodIndex(['2017-01', '2017-02', '2017-03', '2017-04', '2017-05', '2017-06',
+             '2017-07', '2017-08', '2017-09', '2017-10', '2017-11', '2017-12',
+             '2018-01'],
+            dtype='period[M]')
+
+    If ``start`` or ``end`` are ``Period`` objects, they will be used as anchor
+    endpoints for a ``PeriodIndex`` with frequency matching that of the
+    ``period_range`` constructor.
+
+    >>> pd.period_range(start=pd.Period('2017Q1', freq='Q'),
+    ...                 end=pd.Period('2017Q2', freq='Q'), freq='M')
+    PeriodIndex(['2017-03', '2017-04', '2017-05', '2017-06'],
+                dtype='period[M]')
+    """
+    if com.count_not_none(start, end, periods) != 2:
+        raise ValueError(
+            "Of the three parameters: start, end, and periods, "
+            "exactly two must be specified"
+        )
+    if freq is None and (not isinstance(start, Period) and not isinstance(end, Period)):
+        freq = "D"
+
+    data, freq = PeriodArray._generate_range(start, end, periods, freq)
+    dtype = PeriodDtype(freq)
+    data = PeriodArray(data, dtype=dtype)
+    return PeriodIndex(data, name=name)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/indexes/range.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/indexes/range.py
new file mode 100644
index 0000000000000000000000000000000000000000..62afcf8badb50d95f2cc006bf8d89e79f6fe8615
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/indexes/range.py
@@ -0,0 +1,1187 @@
+from __future__ import annotations
+
+from collections.abc import (
+    Hashable,
+    Iterator,
+)
+from datetime import timedelta
+import operator
+from sys import getsizeof
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Literal,
+    cast,
+    overload,
+)
+
+import numpy as np
+
+from pandas._libs import (
+    index as libindex,
+    lib,
+)
+from pandas._libs.algos import unique_deltas
+from pandas._libs.lib import no_default
+from pandas.compat.numpy import function as nv
+from pandas.util._decorators import (
+    cache_readonly,
+    deprecate_nonkeyword_arguments,
+    doc,
+)
+
+from pandas.core.dtypes.common import (
+    ensure_platform_int,
+    ensure_python_int,
+    is_float,
+    is_integer,
+    is_scalar,
+    is_signed_integer_dtype,
+)
+from pandas.core.dtypes.generic import ABCTimedeltaIndex
+
+from pandas.core import ops
+import pandas.core.common as com
+from pandas.core.construction import extract_array
+import pandas.core.indexes.base as ibase
+from pandas.core.indexes.base import (
+    Index,
+    maybe_extract_name,
+)
+from pandas.core.ops.common import unpack_zerodim_and_defer
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        Axis,
+        Dtype,
+        NaPosition,
+        Self,
+        npt,
+    )
+_empty_range = range(0)
+_dtype_int64 = np.dtype(np.int64)
+
+
+class RangeIndex(Index):
+    """
+    Immutable Index implementing a monotonic integer range.
+
+    RangeIndex is a memory-saving special case of an Index limited to representing
+    monotonic ranges with a 64-bit dtype. Using RangeIndex may in some instances
+    improve computing speed.
+
+    This is the default index type used
+    by DataFrame and Series when no explicit index is provided by the user.
+
+    Parameters
+    ----------
+    start : int (default: 0), range, or other RangeIndex instance
+        If int and "stop" is not given, interpreted as "stop" instead.
+    stop : int (default: 0)
+    step : int (default: 1)
+    dtype : np.int64
+        Unused, accepted for homogeneity with other index types.
+    copy : bool, default False
+        Unused, accepted for homogeneity with other index types.
+    name : object, optional
+        Name to be stored in the index.
+
+    Attributes
+    ----------
+    start
+    stop
+    step
+
+    Methods
+    -------
+    from_range
+
+    See Also
+    --------
+    Index : The base pandas Index type.
+
+    Examples
+    --------
+    >>> list(pd.RangeIndex(5))
+    [0, 1, 2, 3, 4]
+
+    >>> list(pd.RangeIndex(-2, 4))
+    [-2, -1, 0, 1, 2, 3]
+
+    >>> list(pd.RangeIndex(0, 10, 2))
+    [0, 2, 4, 6, 8]
+
+    >>> list(pd.RangeIndex(2, -10, -3))
+    [2, -1, -4, -7]
+
+    >>> list(pd.RangeIndex(0))
+    []
+
+    >>> list(pd.RangeIndex(1, 0))
+    []
+    """
+
+    _typ = "rangeindex"
+    _dtype_validation_metadata = (is_signed_integer_dtype, "signed integer")
+    _range: range
+    _values: np.ndarray
+
+    @property
+    def _engine_type(self) -> type[libindex.Int64Engine]:
+        return libindex.Int64Engine
+
+    # --------------------------------------------------------------------
+    # Constructors
+
+    def __new__(
+        cls,
+        start=None,
+        stop=None,
+        step=None,
+        dtype: Dtype | None = None,
+        copy: bool = False,
+        name: Hashable | None = None,
+    ) -> Self:
+        cls._validate_dtype(dtype)
+        name = maybe_extract_name(name, start, cls)
+
+        # RangeIndex
+        if isinstance(start, cls):
+            return start.copy(name=name)
+        elif isinstance(start, range):
+            return cls._simple_new(start, name=name)
+
+        # validate the arguments
+        if com.all_none(start, stop, step):
+            raise TypeError("RangeIndex(...) must be called with integers")
+
+        start = ensure_python_int(start) if start is not None else 0
+
+        if stop is None:
+            start, stop = 0, start
+        else:
+            stop = ensure_python_int(stop)
+
+        step = ensure_python_int(step) if step is not None else 1
+        if step == 0:
+            raise ValueError("Step must not be zero")
+
+        rng = range(start, stop, step)
+        return cls._simple_new(rng, name=name)
+
+    @classmethod
+    def from_range(cls, data: range, name=None, dtype: Dtype | None = None) -> Self:
+        """
+        Create :class:`pandas.RangeIndex` from a ``range`` object.
+
+        Returns
+        -------
+        RangeIndex
+
+        Examples
+        --------
+        >>> pd.RangeIndex.from_range(range(5))
+        RangeIndex(start=0, stop=5, step=1)
+
+        >>> pd.RangeIndex.from_range(range(2, -10, -3))
+        RangeIndex(start=2, stop=-10, step=-3)
+        """
+        if not isinstance(data, range):
+            raise TypeError(
+                f"{cls.__name__}(...) must be called with object coercible to a "
+                f"range, {repr(data)} was passed"
+            )
+        cls._validate_dtype(dtype)
+        return cls._simple_new(data, name=name)
+
+    #  error: Argument 1 of "_simple_new" is incompatible with supertype "Index";
+    #  supertype defines the argument type as
+    #  "Union[ExtensionArray, ndarray[Any, Any]]"  [override]
+    @classmethod
+    def _simple_new(  # type: ignore[override]
+        cls, values: range, name: Hashable | None = None
+    ) -> Self:
+        result = object.__new__(cls)
+
+        assert isinstance(values, range)
+
+        result._range = values
+        result._name = name
+        result._cache = {}
+        result._reset_identity()
+        result._references = None
+        return result
+
+    @classmethod
+    def _validate_dtype(cls, dtype: Dtype | None) -> None:
+        if dtype is None:
+            return
+
+        validation_func, expected = cls._dtype_validation_metadata
+        if not validation_func(dtype):
+            raise ValueError(
+                f"Incorrect `dtype` passed: expected {expected}, received {dtype}"
+            )
+
+    # --------------------------------------------------------------------
+
+    # error: Return type "Type[Index]" of "_constructor" incompatible with return
+    # type "Type[RangeIndex]" in supertype "Index"
+    @cache_readonly
+    def _constructor(self) -> type[Index]:  # type: ignore[override]
+        """return the class to use for construction"""
+        return Index
+
+    # error: Signature of "_data" incompatible with supertype "Index"
+    @cache_readonly
+    def _data(self) -> np.ndarray:  # type: ignore[override]
+        """
+        An int array that for performance reasons is created only when needed.
+
+        The constructed array is saved in ``_cache``.
+        """
+        return np.arange(self.start, self.stop, self.step, dtype=np.int64)
+
+    def _get_data_as_items(self) -> list[tuple[str, int]]:
+        """return a list of tuples of start, stop, step"""
+        rng = self._range
+        return [("start", rng.start), ("stop", rng.stop), ("step", rng.step)]
+
+    def __reduce__(self):
+        d = {"name": self._name}
+        d.update(dict(self._get_data_as_items()))
+        return ibase._new_Index, (type(self), d), None
+
+    # --------------------------------------------------------------------
+    # Rendering Methods
+
+    def _format_attrs(self):
+        """
+        Return a list of tuples of the (attr, formatted_value)
+        """
+        attrs = cast("list[tuple[str, str | int]]", self._get_data_as_items())
+        if self._name is not None:
+            attrs.append(("name", ibase.default_pprint(self._name)))
+        return attrs
+
+    def _format_with_header(self, *, header: list[str], na_rep: str) -> list[str]:
+        # Equivalent to Index implementation, but faster
+        if not len(self._range):
+            return header
+        first_val_str = str(self._range[0])
+        last_val_str = str(self._range[-1])
+        max_length = max(len(first_val_str), len(last_val_str))
+
+        return header + [f"{x:<{max_length}}" for x in self._range]
+
+    # --------------------------------------------------------------------
+
+    @property
+    def start(self) -> int:
+        """
+        The value of the `start` parameter (``0`` if this was not supplied).
+
+        Examples
+        --------
+        >>> idx = pd.RangeIndex(5)
+        >>> idx.start
+        0
+
+        >>> idx = pd.RangeIndex(2, -10, -3)
+        >>> idx.start
+        2
+        """
+        # GH 25710
+        return self._range.start
+
+    @property
+    def stop(self) -> int:
+        """
+        The value of the `stop` parameter.
+
+        Examples
+        --------
+        >>> idx = pd.RangeIndex(5)
+        >>> idx.stop
+        5
+
+        >>> idx = pd.RangeIndex(2, -10, -3)
+        >>> idx.stop
+        -10
+        """
+        return self._range.stop
+
+    @property
+    def step(self) -> int:
+        """
+        The value of the `step` parameter (``1`` if this was not supplied).
+
+        Examples
+        --------
+        >>> idx = pd.RangeIndex(5)
+        >>> idx.step
+        1
+
+        >>> idx = pd.RangeIndex(2, -10, -3)
+        >>> idx.step
+        -3
+
+        Even if :class:`pandas.RangeIndex` is empty, ``step`` is still ``1`` if
+        not supplied.
+
+        >>> idx = pd.RangeIndex(1, 0)
+        >>> idx.step
+        1
+        """
+        # GH 25710
+        return self._range.step
+
+    @cache_readonly
+    def nbytes(self) -> int:
+        """
+        Return the number of bytes in the underlying data.
+        """
+        rng = self._range
+        return getsizeof(rng) + sum(
+            getsizeof(getattr(rng, attr_name))
+            for attr_name in ["start", "stop", "step"]
+        )
+
+    def memory_usage(self, deep: bool = False) -> int:
+        """
+        Memory usage of my values
+
+        Parameters
+        ----------
+        deep : bool
+            Introspect the data deeply, interrogate
+            `object` dtypes for system-level memory consumption
+
+        Returns
+        -------
+        bytes used
+
+        Notes
+        -----
+        Memory usage does not include memory consumed by elements that
+        are not components of the array if deep=False
+
+        See Also
+        --------
+        numpy.ndarray.nbytes
+        """
+        return self.nbytes
+
+    @property
+    def dtype(self) -> np.dtype:
+        return _dtype_int64
+
+    @property
+    def is_unique(self) -> bool:
+        """return if the index has unique values"""
+        return True
+
+    @cache_readonly
+    def is_monotonic_increasing(self) -> bool:
+        return self._range.step > 0 or len(self) <= 1
+
+    @cache_readonly
+    def is_monotonic_decreasing(self) -> bool:
+        return self._range.step < 0 or len(self) <= 1
+
+    def __contains__(self, key: Any) -> bool:
+        hash(key)
+        try:
+            key = ensure_python_int(key)
+        except TypeError:
+            return False
+        return key in self._range
+
+    @property
+    def inferred_type(self) -> str:
+        return "integer"
+
+    # --------------------------------------------------------------------
+    # Indexing Methods
+
+    @doc(Index.get_loc)
+    def get_loc(self, key) -> int:
+        if is_integer(key) or (is_float(key) and key.is_integer()):
+            new_key = int(key)
+            try:
+                return self._range.index(new_key)
+            except ValueError as err:
+                raise KeyError(key) from err
+        if isinstance(key, Hashable):
+            raise KeyError(key)
+        self._check_indexing_error(key)
+        raise KeyError(key)
+
+    def _get_indexer(
+        self,
+        target: Index,
+        method: str | None = None,
+        limit: int | None = None,
+        tolerance=None,
+    ) -> npt.NDArray[np.intp]:
+        if com.any_not_none(method, tolerance, limit):
+            return super()._get_indexer(
+                target, method=method, tolerance=tolerance, limit=limit
+            )
+
+        if self.step > 0:
+            start, stop, step = self.start, self.stop, self.step
+        else:
+            # GH 28678: work on reversed range for simplicity
+            reverse = self._range[::-1]
+            start, stop, step = reverse.start, reverse.stop, reverse.step
+
+        target_array = np.asarray(target)
+        locs = target_array - start
+        valid = (locs % step == 0) & (locs >= 0) & (target_array < stop)
+        locs[~valid] = -1
+        locs[valid] = locs[valid] / step
+
+        if step != self.step:
+            # We reversed this range: transform to original locs
+            locs[valid] = len(self) - 1 - locs[valid]
+        return ensure_platform_int(locs)
+
+    @cache_readonly
+    def _should_fallback_to_positional(self) -> bool:
+        """
+        Should an integer key be treated as positional?
+        """
+        return False
+
+    # --------------------------------------------------------------------
+
+    def tolist(self) -> list[int]:
+        return list(self._range)
+
+    @doc(Index.__iter__)
+    def __iter__(self) -> Iterator[int]:
+        yield from self._range
+
+    @doc(Index._shallow_copy)
+    def _shallow_copy(self, values, name: Hashable = no_default):
+        name = self._name if name is no_default else name
+
+        if values.dtype.kind == "f":
+            return Index(values, name=name, dtype=np.float64)
+        # GH 46675 & 43885: If values is equally spaced, return a
+        # more memory-compact RangeIndex instead of Index with 64-bit dtype
+        unique_diffs = unique_deltas(values)
+        if len(unique_diffs) == 1 and unique_diffs[0] != 0:
+            diff = unique_diffs[0]
+            new_range = range(values[0], values[-1] + diff, diff)
+            return type(self)._simple_new(new_range, name=name)
+        else:
+            return self._constructor._simple_new(values, name=name)
+
+    def _view(self) -> Self:
+        result = type(self)._simple_new(self._range, name=self._name)
+        result._cache = self._cache
+        return result
+
+    @doc(Index.copy)
+    def copy(self, name: Hashable | None = None, deep: bool = False) -> Self:
+        name = self._validate_names(name=name, deep=deep)[0]
+        new_index = self._rename(name=name)
+        return new_index
+
+    def _minmax(self, meth: str):
+        no_steps = len(self) - 1
+        if no_steps == -1:
+            return np.nan
+        elif (meth == "min" and self.step > 0) or (meth == "max" and self.step < 0):
+            return self.start
+
+        return self.start + self.step * no_steps
+
+    def min(self, axis=None, skipna: bool = True, *args, **kwargs) -> int:
+        """The minimum value of the RangeIndex"""
+        nv.validate_minmax_axis(axis)
+        nv.validate_min(args, kwargs)
+        return self._minmax("min")
+
+    def max(self, axis=None, skipna: bool = True, *args, **kwargs) -> int:
+        """The maximum value of the RangeIndex"""
+        nv.validate_minmax_axis(axis)
+        nv.validate_max(args, kwargs)
+        return self._minmax("max")
+
+    def argsort(self, *args, **kwargs) -> npt.NDArray[np.intp]:
+        """
+        Returns the indices that would sort the index and its
+        underlying data.
+
+        Returns
+        -------
+        np.ndarray[np.intp]
+
+        See Also
+        --------
+        numpy.ndarray.argsort
+        """
+        ascending = kwargs.pop("ascending", True)  # EA compat
+        kwargs.pop("kind", None)  # e.g. "mergesort" is irrelevant
+        nv.validate_argsort(args, kwargs)
+
+        if self._range.step > 0:
+            result = np.arange(len(self), dtype=np.intp)
+        else:
+            result = np.arange(len(self) - 1, -1, -1, dtype=np.intp)
+
+        if not ascending:
+            result = result[::-1]
+        return result
+
+    def factorize(
+        self,
+        sort: bool = False,
+        use_na_sentinel: bool = True,
+    ) -> tuple[npt.NDArray[np.intp], RangeIndex]:
+        codes = np.arange(len(self), dtype=np.intp)
+        uniques = self
+        if sort and self.step < 0:
+            codes = codes[::-1]
+            uniques = uniques[::-1]
+        return codes, uniques
+
+    def equals(self, other: object) -> bool:
+        """
+        Determines if two Index objects contain the same elements.
+        """
+        if isinstance(other, RangeIndex):
+            return self._range == other._range
+        return super().equals(other)
+
+    # error: Signature of "sort_values" incompatible with supertype "Index"
+    @overload  # type: ignore[override]
+    def sort_values(
+        self,
+        *,
+        return_indexer: Literal[False] = ...,
+        ascending: bool = ...,
+        na_position: NaPosition = ...,
+        key: Callable | None = ...,
+    ) -> Self:
+        ...
+
+    @overload
+    def sort_values(
+        self,
+        *,
+        return_indexer: Literal[True],
+        ascending: bool = ...,
+        na_position: NaPosition = ...,
+        key: Callable | None = ...,
+    ) -> tuple[Self, np.ndarray | RangeIndex]:
+        ...
+
+    @overload
+    def sort_values(
+        self,
+        *,
+        return_indexer: bool = ...,
+        ascending: bool = ...,
+        na_position: NaPosition = ...,
+        key: Callable | None = ...,
+    ) -> Self | tuple[Self, np.ndarray | RangeIndex]:
+        ...
+
+    @deprecate_nonkeyword_arguments(
+        version="3.0", allowed_args=["self"], name="sort_values"
+    )
+    def sort_values(
+        self,
+        return_indexer: bool = False,
+        ascending: bool = True,
+        na_position: NaPosition = "last",
+        key: Callable | None = None,
+    ) -> Self | tuple[Self, np.ndarray | RangeIndex]:
+        if key is not None:
+            return super().sort_values(
+                return_indexer=return_indexer,
+                ascending=ascending,
+                na_position=na_position,
+                key=key,
+            )
+        else:
+            sorted_index = self
+            inverse_indexer = False
+            if ascending:
+                if self.step < 0:
+                    sorted_index = self[::-1]
+                    inverse_indexer = True
+            else:
+                if self.step > 0:
+                    sorted_index = self[::-1]
+                    inverse_indexer = True
+
+        if return_indexer:
+            if inverse_indexer:
+                rng = range(len(self) - 1, -1, -1)
+            else:
+                rng = range(len(self))
+            return sorted_index, RangeIndex(rng)
+        else:
+            return sorted_index
+
+    # --------------------------------------------------------------------
+    # Set Operations
+
+    def _intersection(self, other: Index, sort: bool = False):
+        # caller is responsible for checking self and other are both non-empty
+
+        if not isinstance(other, RangeIndex):
+            return super()._intersection(other, sort=sort)
+
+        first = self._range[::-1] if self.step < 0 else self._range
+        second = other._range[::-1] if other.step < 0 else other._range
+
+        # check whether intervals intersect
+        # deals with in- and decreasing ranges
+        int_low = max(first.start, second.start)
+        int_high = min(first.stop, second.stop)
+        if int_high <= int_low:
+            return self._simple_new(_empty_range)
+
+        # Method hint: linear Diophantine equation
+        # solve intersection problem
+        # performance hint: for identical step sizes, could use
+        # cheaper alternative
+        gcd, s, _ = self._extended_gcd(first.step, second.step)
+
+        # check whether element sets intersect
+        if (first.start - second.start) % gcd:
+            return self._simple_new(_empty_range)
+
+        # calculate parameters for the RangeIndex describing the
+        # intersection disregarding the lower bounds
+        tmp_start = first.start + (second.start - first.start) * first.step // gcd * s
+        new_step = first.step * second.step // gcd
+        new_range = range(tmp_start, int_high, new_step)
+        new_index = self._simple_new(new_range)
+
+        # adjust index to limiting interval
+        new_start = new_index._min_fitting_element(int_low)
+        new_range = range(new_start, new_index.stop, new_index.step)
+        new_index = self._simple_new(new_range)
+
+        if (self.step < 0 and other.step < 0) is not (new_index.step < 0):
+            new_index = new_index[::-1]
+
+        if sort is None:
+            new_index = new_index.sort_values()
+
+        return new_index
+
+    def _min_fitting_element(self, lower_limit: int) -> int:
+        """Returns the smallest element greater than or equal to the limit"""
+        no_steps = -(-(lower_limit - self.start) // abs(self.step))
+        return self.start + abs(self.step) * no_steps
+
+    def _extended_gcd(self, a: int, b: int) -> tuple[int, int, int]:
+        """
+        Extended Euclidean algorithms to solve Bezout's identity:
+           a*x + b*y = gcd(x, y)
+        Finds one particular solution for x, y: s, t
+        Returns: gcd, s, t
+        """
+        s, old_s = 0, 1
+        t, old_t = 1, 0
+        r, old_r = b, a
+        while r:
+            quotient = old_r // r
+            old_r, r = r, old_r - quotient * r
+            old_s, s = s, old_s - quotient * s
+            old_t, t = t, old_t - quotient * t
+        return old_r, old_s, old_t
+
+    def _range_in_self(self, other: range) -> bool:
+        """Check if other range is contained in self"""
+        # https://stackoverflow.com/a/32481015
+        if not other:
+            return True
+        if not self._range:
+            return False
+        if len(other) > 1 and other.step % self._range.step:
+            return False
+        return other.start in self._range and other[-1] in self._range
+
+    def _union(self, other: Index, sort: bool | None):
+        """
+        Form the union of two Index objects and sorts if possible
+
+        Parameters
+        ----------
+        other : Index or array-like
+
+        sort : bool or None, default None
+            Whether to sort (monotonically increasing) the resulting index.
+            ``sort=None|True`` returns a ``RangeIndex`` if possible or a sorted
+            ``Index`` with a int64 dtype if not.
+            ``sort=False`` can return a ``RangeIndex`` if self is monotonically
+            increasing and other is fully contained in self. Otherwise, returns
+            an unsorted ``Index`` with an int64 dtype.
+
+        Returns
+        -------
+        union : Index
+        """
+        if isinstance(other, RangeIndex):
+            if sort in (None, True) or (
+                sort is False and self.step > 0 and self._range_in_self(other._range)
+            ):
+                # GH 47557: Can still return a RangeIndex
+                # if other range in self and sort=False
+                start_s, step_s = self.start, self.step
+                end_s = self.start + self.step * (len(self) - 1)
+                start_o, step_o = other.start, other.step
+                end_o = other.start + other.step * (len(other) - 1)
+                if self.step < 0:
+                    start_s, step_s, end_s = end_s, -step_s, start_s
+                if other.step < 0:
+                    start_o, step_o, end_o = end_o, -step_o, start_o
+                if len(self) == 1 and len(other) == 1:
+                    step_s = step_o = abs(self.start - other.start)
+                elif len(self) == 1:
+                    step_s = step_o
+                elif len(other) == 1:
+                    step_o = step_s
+                start_r = min(start_s, start_o)
+                end_r = max(end_s, end_o)
+                if step_o == step_s:
+                    if (
+                        (start_s - start_o) % step_s == 0
+                        and (start_s - end_o) <= step_s
+                        and (start_o - end_s) <= step_s
+                    ):
+                        return type(self)(start_r, end_r + step_s, step_s)
+                    if (
+                        (step_s % 2 == 0)
+                        and (abs(start_s - start_o) == step_s / 2)
+                        and (abs(end_s - end_o) == step_s / 2)
+                    ):
+                        # e.g. range(0, 10, 2) and range(1, 11, 2)
+                        #  but not range(0, 20, 4) and range(1, 21, 4) GH#44019
+                        return type(self)(start_r, end_r + step_s / 2, step_s / 2)
+
+                elif step_o % step_s == 0:
+                    if (
+                        (start_o - start_s) % step_s == 0
+                        and (start_o + step_s >= start_s)
+                        and (end_o - step_s <= end_s)
+                    ):
+                        return type(self)(start_r, end_r + step_s, step_s)
+                elif step_s % step_o == 0:
+                    if (
+                        (start_s - start_o) % step_o == 0
+                        and (start_s + step_o >= start_o)
+                        and (end_s - step_o <= end_o)
+                    ):
+                        return type(self)(start_r, end_r + step_o, step_o)
+
+        return super()._union(other, sort=sort)
+
+    def _difference(self, other, sort=None):
+        # optimized set operation if we have another RangeIndex
+        self._validate_sort_keyword(sort)
+        self._assert_can_do_setop(other)
+        other, result_name = self._convert_can_do_setop(other)
+
+        if not isinstance(other, RangeIndex):
+            return super()._difference(other, sort=sort)
+
+        if sort is not False and self.step < 0:
+            return self[::-1]._difference(other)
+
+        res_name = ops.get_op_result_name(self, other)
+
+        first = self._range[::-1] if self.step < 0 else self._range
+        overlap = self.intersection(other)
+        if overlap.step < 0:
+            overlap = overlap[::-1]
+
+        if len(overlap) == 0:
+            return self.rename(name=res_name)
+        if len(overlap) == len(self):
+            return self[:0].rename(res_name)
+
+        # overlap.step will always be a multiple of self.step (see _intersection)
+
+        if len(overlap) == 1:
+            if overlap[0] == self[0]:
+                return self[1:]
+
+            elif overlap[0] == self[-1]:
+                return self[:-1]
+
+            elif len(self) == 3 and overlap[0] == self[1]:
+                return self[::2]
+
+            else:
+                return super()._difference(other, sort=sort)
+
+        elif len(overlap) == 2 and overlap[0] == first[0] and overlap[-1] == first[-1]:
+            # e.g. range(-8, 20, 7) and range(13, -9, -3)
+            return self[1:-1]
+
+        if overlap.step == first.step:
+            if overlap[0] == first.start:
+                # The difference is everything after the intersection
+                new_rng = range(overlap[-1] + first.step, first.stop, first.step)
+            elif overlap[-1] == first[-1]:
+                # The difference is everything before the intersection
+                new_rng = range(first.start, overlap[0], first.step)
+            elif overlap._range == first[1:-1]:
+                # e.g. range(4) and range(1, 3)
+                step = len(first) - 1
+                new_rng = first[::step]
+            else:
+                # The difference is not range-like
+                # e.g. range(1, 10, 1) and range(3, 7, 1)
+                return super()._difference(other, sort=sort)
+
+        else:
+            # We must have len(self) > 1, bc we ruled out above
+            #  len(overlap) == 0 and len(overlap) == len(self)
+            assert len(self) > 1
+
+            if overlap.step == first.step * 2:
+                if overlap[0] == first[0] and overlap[-1] in (first[-1], first[-2]):
+                    # e.g. range(1, 10, 1) and range(1, 10, 2)
+                    new_rng = first[1::2]
+
+                elif overlap[0] == first[1] and overlap[-1] in (first[-1], first[-2]):
+                    # e.g. range(1, 10, 1) and range(2, 10, 2)
+                    new_rng = first[::2]
+
+                else:
+                    # We can get here with  e.g. range(20) and range(0, 10, 2)
+                    return super()._difference(other, sort=sort)
+
+            else:
+                # e.g. range(10) and range(0, 10, 3)
+                return super()._difference(other, sort=sort)
+
+        new_index = type(self)._simple_new(new_rng, name=res_name)
+        if first is not self._range:
+            new_index = new_index[::-1]
+
+        return new_index
+
+    def symmetric_difference(
+        self, other, result_name: Hashable | None = None, sort=None
+    ):
+        if not isinstance(other, RangeIndex) or sort is not None:
+            return super().symmetric_difference(other, result_name, sort)
+
+        left = self.difference(other)
+        right = other.difference(self)
+        result = left.union(right)
+
+        if result_name is not None:
+            result = result.rename(result_name)
+        return result
+
+    # --------------------------------------------------------------------
+
+    # error: Return type "Index" of "delete" incompatible with return type
+    #  "RangeIndex" in supertype "Index"
+    def delete(self, loc) -> Index:  # type: ignore[override]
+        # In some cases we can retain RangeIndex, see also
+        #  DatetimeTimedeltaMixin._get_delete_Freq
+        if is_integer(loc):
+            if loc in (0, -len(self)):
+                return self[1:]
+            if loc in (-1, len(self) - 1):
+                return self[:-1]
+            if len(self) == 3 and loc in (1, -2):
+                return self[::2]
+
+        elif lib.is_list_like(loc):
+            slc = lib.maybe_indices_to_slice(np.asarray(loc, dtype=np.intp), len(self))
+
+            if isinstance(slc, slice):
+                # defer to RangeIndex._difference, which is optimized to return
+                #  a RangeIndex whenever possible
+                other = self[slc]
+                return self.difference(other, sort=False)
+
+        return super().delete(loc)
+
+    def insert(self, loc: int, item) -> Index:
+        if len(self) and (is_integer(item) or is_float(item)):
+            # We can retain RangeIndex is inserting at the beginning or end,
+            #  or right in the middle.
+            rng = self._range
+            if loc == 0 and item == self[0] - self.step:
+                new_rng = range(rng.start - rng.step, rng.stop, rng.step)
+                return type(self)._simple_new(new_rng, name=self._name)
+
+            elif loc == len(self) and item == self[-1] + self.step:
+                new_rng = range(rng.start, rng.stop + rng.step, rng.step)
+                return type(self)._simple_new(new_rng, name=self._name)
+
+            elif len(self) == 2 and item == self[0] + self.step / 2:
+                # e.g. inserting 1 into [0, 2]
+                step = int(self.step / 2)
+                new_rng = range(self.start, self.stop, step)
+                return type(self)._simple_new(new_rng, name=self._name)
+
+        return super().insert(loc, item)
+
+    def _concat(self, indexes: list[Index], name: Hashable) -> Index:
+        """
+        Overriding parent method for the case of all RangeIndex instances.
+
+        When all members of "indexes" are of type RangeIndex: result will be
+        RangeIndex if possible, Index with a int64 dtype otherwise. E.g.:
+        indexes = [RangeIndex(3), RangeIndex(3, 6)] -> RangeIndex(6)
+        indexes = [RangeIndex(3), RangeIndex(4, 6)] -> Index([0,1,2,4,5], dtype='int64')
+        """
+        if not all(isinstance(x, RangeIndex) for x in indexes):
+            return super()._concat(indexes, name)
+
+        elif len(indexes) == 1:
+            return indexes[0]
+
+        rng_indexes = cast(list[RangeIndex], indexes)
+
+        start = step = next_ = None
+
+        # Filter the empty indexes
+        non_empty_indexes = [obj for obj in rng_indexes if len(obj)]
+
+        for obj in non_empty_indexes:
+            rng = obj._range
+
+            if start is None:
+                # This is set by the first non-empty index
+                start = rng.start
+                if step is None and len(rng) > 1:
+                    step = rng.step
+            elif step is None:
+                # First non-empty index had only one element
+                if rng.start == start:
+                    values = np.concatenate([x._values for x in rng_indexes])
+                    result = self._constructor(values)
+                    return result.rename(name)
+
+                step = rng.start - start
+
+            non_consecutive = (step != rng.step and len(rng) > 1) or (
+                next_ is not None and rng.start != next_
+            )
+            if non_consecutive:
+                result = self._constructor(
+                    np.concatenate([x._values for x in rng_indexes])
+                )
+                return result.rename(name)
+
+            if step is not None:
+                next_ = rng[-1] + step
+
+        if non_empty_indexes:
+            # Get the stop value from "next" or alternatively
+            # from the last non-empty index
+            stop = non_empty_indexes[-1].stop if next_ is None else next_
+            return RangeIndex(start, stop, step).rename(name)
+
+        # Here all "indexes" had 0 length, i.e. were empty.
+        # In this case return an empty range index.
+        return RangeIndex(0, 0).rename(name)
+
+    def __len__(self) -> int:
+        """
+        return the length of the RangeIndex
+        """
+        return len(self._range)
+
+    @property
+    def size(self) -> int:
+        return len(self)
+
+    def __getitem__(self, key):
+        """
+        Conserve RangeIndex type for scalar and slice keys.
+        """
+        if isinstance(key, slice):
+            return self._getitem_slice(key)
+        elif is_integer(key):
+            new_key = int(key)
+            try:
+                return self._range[new_key]
+            except IndexError as err:
+                raise IndexError(
+                    f"index {key} is out of bounds for axis 0 with size {len(self)}"
+                ) from err
+        elif is_scalar(key):
+            raise IndexError(
+                "only integers, slices (`:`), "
+                "ellipsis (`...`), numpy.newaxis (`None`) "
+                "and integer or boolean "
+                "arrays are valid indices"
+            )
+        return super().__getitem__(key)
+
+    def _getitem_slice(self, slobj: slice) -> Self:
+        """
+        Fastpath for __getitem__ when we know we have a slice.
+        """
+        res = self._range[slobj]
+        return type(self)._simple_new(res, name=self._name)
+
+    @unpack_zerodim_and_defer("__floordiv__")
+    def __floordiv__(self, other):
+        if is_integer(other) and other != 0:
+            if len(self) == 0 or self.start % other == 0 and self.step % other == 0:
+                start = self.start // other
+                step = self.step // other
+                stop = start + len(self) * step
+                new_range = range(start, stop, step or 1)
+                return self._simple_new(new_range, name=self._name)
+            if len(self) == 1:
+                start = self.start // other
+                new_range = range(start, start + 1, 1)
+                return self._simple_new(new_range, name=self._name)
+
+        return super().__floordiv__(other)
+
+    # --------------------------------------------------------------------
+    # Reductions
+
+    def all(self, *args, **kwargs) -> bool:
+        return 0 not in self._range
+
+    def any(self, *args, **kwargs) -> bool:
+        return any(self._range)
+
+    # --------------------------------------------------------------------
+
+    def _cmp_method(self, other, op):
+        if isinstance(other, RangeIndex) and self._range == other._range:
+            # Both are immutable so if ._range attr. are equal, shortcut is possible
+            return super()._cmp_method(self, op)
+        return super()._cmp_method(other, op)
+
+    def _arith_method(self, other, op):
+        """
+        Parameters
+        ----------
+        other : Any
+        op : callable that accepts 2 params
+            perform the binary op
+        """
+
+        if isinstance(other, ABCTimedeltaIndex):
+            # Defer to TimedeltaIndex implementation
+            return NotImplemented
+        elif isinstance(other, (timedelta, np.timedelta64)):
+            # GH#19333 is_integer evaluated True on timedelta64,
+            # so we need to catch these explicitly
+            return super()._arith_method(other, op)
+        elif lib.is_np_dtype(getattr(other, "dtype", None), "m"):
+            # Must be an np.ndarray; GH#22390
+            return super()._arith_method(other, op)
+
+        if op in [
+            operator.pow,
+            ops.rpow,
+            operator.mod,
+            ops.rmod,
+            operator.floordiv,
+            ops.rfloordiv,
+            divmod,
+            ops.rdivmod,
+        ]:
+            return super()._arith_method(other, op)
+
+        step: Callable | None = None
+        if op in [operator.mul, ops.rmul, operator.truediv, ops.rtruediv]:
+            step = op
+
+        # TODO: if other is a RangeIndex we may have more efficient options
+        right = extract_array(other, extract_numpy=True, extract_range=True)
+        left = self
+
+        try:
+            # apply if we have an override
+            if step:
+                with np.errstate(all="ignore"):
+                    rstep = step(left.step, right)
+
+                # we don't have a representable op
+                # so return a base index
+                if not is_integer(rstep) or not rstep:
+                    raise ValueError
+
+            # GH#53255
+            else:
+                rstep = -left.step if op == ops.rsub else left.step
+
+            with np.errstate(all="ignore"):
+                rstart = op(left.start, right)
+                rstop = op(left.stop, right)
+
+            res_name = ops.get_op_result_name(self, other)
+            result = type(self)(rstart, rstop, rstep, name=res_name)
+
+            # for compat with numpy / Index with int64 dtype
+            # even if we can represent as a RangeIndex, return
+            # as a float64 Index if we have float-like descriptors
+            if not all(is_integer(x) for x in [rstart, rstop, rstep]):
+                result = result.astype("float64")
+
+            return result
+
+        except (ValueError, TypeError, ZeroDivisionError):
+            # test_arithmetic_explicit_conversions
+            return super()._arith_method(other, op)
+
+    # error: Return type "Index" of "take" incompatible with return type
+    # "RangeIndex" in supertype "Index"
+    def take(  # type: ignore[override]
+        self,
+        indices,
+        axis: Axis = 0,
+        allow_fill: bool = True,
+        fill_value=None,
+        **kwargs,
+    ) -> Index:
+        if kwargs:
+            nv.validate_take((), kwargs)
+        if is_scalar(indices):
+            raise TypeError("Expected indices to be array-like")
+        indices = ensure_platform_int(indices)
+
+        # raise an exception if allow_fill is True and fill_value is not None
+        self._maybe_disallow_fill(allow_fill, fill_value, indices)
+
+        if len(indices) == 0:
+            taken = np.array([], dtype=self.dtype)
+        else:
+            ind_max = indices.max()
+            if ind_max >= len(self):
+                raise IndexError(
+                    f"index {ind_max} is out of bounds for axis 0 with size {len(self)}"
+                )
+            ind_min = indices.min()
+            if ind_min < -len(self):
+                raise IndexError(
+                    f"index {ind_min} is out of bounds for axis 0 with size {len(self)}"
+                )
+            taken = indices.astype(self.dtype, casting="safe")
+            if ind_min < 0:
+                taken %= len(self)
+            if self.step != 1:
+                taken *= self.step
+            if self.start != 0:
+                taken += self.start
+
+        # _constructor so RangeIndex-> Index with an int64 dtype
+        return self._constructor._simple_new(taken, name=self.name)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/indexes/timedeltas.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/indexes/timedeltas.py
new file mode 100644
index 0000000000000000000000000000000000000000..08a265ba4764892fde0bc50670b6706ff788c8bc
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/indexes/timedeltas.py
@@ -0,0 +1,356 @@
+""" implement the TimedeltaIndex """
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+import warnings
+
+from pandas._libs import (
+    index as libindex,
+    lib,
+)
+from pandas._libs.tslibs import (
+    Resolution,
+    Timedelta,
+    to_offset,
+)
+from pandas._libs.tslibs.timedeltas import disallow_ambiguous_unit
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.common import (
+    is_scalar,
+    pandas_dtype,
+)
+from pandas.core.dtypes.generic import ABCSeries
+
+from pandas.core.arrays.timedeltas import TimedeltaArray
+import pandas.core.common as com
+from pandas.core.indexes.base import (
+    Index,
+    maybe_extract_name,
+)
+from pandas.core.indexes.datetimelike import DatetimeTimedeltaMixin
+from pandas.core.indexes.extension import inherit_names
+
+if TYPE_CHECKING:
+    from pandas._typing import DtypeObj
+
+
+@inherit_names(
+    ["__neg__", "__pos__", "__abs__", "total_seconds", "round", "floor", "ceil"]
+    + TimedeltaArray._field_ops,
+    TimedeltaArray,
+    wrap=True,
+)
+@inherit_names(
+    [
+        "components",
+        "to_pytimedelta",
+        "sum",
+        "std",
+        "median",
+    ],
+    TimedeltaArray,
+)
+class TimedeltaIndex(DatetimeTimedeltaMixin):
+    """
+    Immutable Index of timedelta64 data.
+
+    Represented internally as int64, and scalars returned Timedelta objects.
+
+    Parameters
+    ----------
+    data : array-like (1-dimensional), optional
+        Optional timedelta-like data to construct index with.
+    unit : {'D', 'h', 'm', 's', 'ms', 'us', 'ns'}, optional
+        The unit of ``data``.
+
+        .. deprecated:: 2.2.0
+         Use ``pd.to_timedelta`` instead.
+
+    freq : str or pandas offset object, optional
+        One of pandas date offset strings or corresponding objects. The string
+        ``'infer'`` can be passed in order to set the frequency of the index as
+        the inferred frequency upon creation.
+    dtype : numpy.dtype or str, default None
+        Valid ``numpy`` dtypes are ``timedelta64[ns]``, ``timedelta64[us]``,
+        ``timedelta64[ms]``, and ``timedelta64[s]``.
+    copy : bool
+        Make a copy of input array.
+    name : object
+        Name to be stored in the index.
+
+    Attributes
+    ----------
+    days
+    seconds
+    microseconds
+    nanoseconds
+    components
+    inferred_freq
+
+    Methods
+    -------
+    to_pytimedelta
+    to_series
+    round
+    floor
+    ceil
+    to_frame
+    mean
+
+    See Also
+    --------
+    Index : The base pandas Index type.
+    Timedelta : Represents a duration between two dates or times.
+    DatetimeIndex : Index of datetime64 data.
+    PeriodIndex : Index of Period data.
+    timedelta_range : Create a fixed-frequency TimedeltaIndex.
+
+    Notes
+    -----
+    To learn more about the frequency strings, please see `this link
+    `__.
+
+    Examples
+    --------
+    >>> pd.TimedeltaIndex(['0 days', '1 days', '2 days', '3 days', '4 days'])
+    TimedeltaIndex(['0 days', '1 days', '2 days', '3 days', '4 days'],
+                   dtype='timedelta64[ns]', freq=None)
+
+    We can also let pandas infer the frequency when possible.
+
+    >>> pd.TimedeltaIndex(np.arange(5) * 24 * 3600 * 1e9, freq='infer')
+    TimedeltaIndex(['0 days', '1 days', '2 days', '3 days', '4 days'],
+                   dtype='timedelta64[ns]', freq='D')
+    """
+
+    _typ = "timedeltaindex"
+
+    _data_cls = TimedeltaArray
+
+    @property
+    def _engine_type(self) -> type[libindex.TimedeltaEngine]:
+        return libindex.TimedeltaEngine
+
+    _data: TimedeltaArray
+
+    # Use base class method instead of DatetimeTimedeltaMixin._get_string_slice
+    _get_string_slice = Index._get_string_slice
+
+    # error: Signature of "_resolution_obj" incompatible with supertype
+    # "DatetimeIndexOpsMixin"
+    @property
+    def _resolution_obj(self) -> Resolution | None:  # type: ignore[override]
+        return self._data._resolution_obj
+
+    # -------------------------------------------------------------------
+    # Constructors
+
+    def __new__(
+        cls,
+        data=None,
+        unit=lib.no_default,
+        freq=lib.no_default,
+        closed=lib.no_default,
+        dtype=None,
+        copy: bool = False,
+        name=None,
+    ):
+        if closed is not lib.no_default:
+            # GH#52628
+            warnings.warn(
+                f"The 'closed' keyword in {cls.__name__} construction is "
+                "deprecated and will be removed in a future version.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+
+        if unit is not lib.no_default:
+            # GH#55499
+            warnings.warn(
+                f"The 'unit' keyword in {cls.__name__} construction is "
+                "deprecated and will be removed in a future version. "
+                "Use pd.to_timedelta instead.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+        else:
+            unit = None
+
+        name = maybe_extract_name(name, data, cls)
+
+        if is_scalar(data):
+            cls._raise_scalar_data_error(data)
+
+        disallow_ambiguous_unit(unit)
+        if dtype is not None:
+            dtype = pandas_dtype(dtype)
+
+        if (
+            isinstance(data, TimedeltaArray)
+            and freq is lib.no_default
+            and (dtype is None or dtype == data.dtype)
+        ):
+            if copy:
+                data = data.copy()
+            return cls._simple_new(data, name=name)
+
+        if (
+            isinstance(data, TimedeltaIndex)
+            and freq is lib.no_default
+            and name is None
+            and (dtype is None or dtype == data.dtype)
+        ):
+            if copy:
+                return data.copy()
+            else:
+                return data._view()
+
+        # - Cases checked above all return/raise before reaching here - #
+
+        tdarr = TimedeltaArray._from_sequence_not_strict(
+            data, freq=freq, unit=unit, dtype=dtype, copy=copy
+        )
+        refs = None
+        if not copy and isinstance(data, (ABCSeries, Index)):
+            refs = data._references
+
+        return cls._simple_new(tdarr, name=name, refs=refs)
+
+    # -------------------------------------------------------------------
+
+    def _is_comparable_dtype(self, dtype: DtypeObj) -> bool:
+        """
+        Can we compare values of the given dtype to our own?
+        """
+        return lib.is_np_dtype(dtype, "m")  # aka self._data._is_recognized_dtype
+
+    # -------------------------------------------------------------------
+    # Indexing Methods
+
+    def get_loc(self, key):
+        """
+        Get integer location for requested label
+
+        Returns
+        -------
+        loc : int, slice, or ndarray[int]
+        """
+        self._check_indexing_error(key)
+
+        try:
+            key = self._data._validate_scalar(key, unbox=False)
+        except TypeError as err:
+            raise KeyError(key) from err
+
+        return Index.get_loc(self, key)
+
+    def _parse_with_reso(self, label: str):
+        # the "with_reso" is a no-op for TimedeltaIndex
+        parsed = Timedelta(label)
+        return parsed, None
+
+    def _parsed_string_to_bounds(self, reso, parsed: Timedelta):
+        # reso is unused, included to match signature of DTI/PI
+        lbound = parsed.round(parsed.resolution_string)
+        rbound = lbound + to_offset(parsed.resolution_string) - Timedelta(1, "ns")
+        return lbound, rbound
+
+    # -------------------------------------------------------------------
+
+    @property
+    def inferred_type(self) -> str:
+        return "timedelta64"
+
+
+def timedelta_range(
+    start=None,
+    end=None,
+    periods: int | None = None,
+    freq=None,
+    name=None,
+    closed=None,
+    *,
+    unit: str | None = None,
+) -> TimedeltaIndex:
+    """
+    Return a fixed frequency TimedeltaIndex with day as the default.
+
+    Parameters
+    ----------
+    start : str or timedelta-like, default None
+        Left bound for generating timedeltas.
+    end : str or timedelta-like, default None
+        Right bound for generating timedeltas.
+    periods : int, default None
+        Number of periods to generate.
+    freq : str, Timedelta, datetime.timedelta, or DateOffset, default 'D'
+        Frequency strings can have multiples, e.g. '5h'.
+    name : str, default None
+        Name of the resulting TimedeltaIndex.
+    closed : str, default None
+        Make the interval closed with respect to the given frequency to
+        the 'left', 'right', or both sides (None).
+    unit : str, default None
+        Specify the desired resolution of the result.
+
+        .. versionadded:: 2.0.0
+
+    Returns
+    -------
+    TimedeltaIndex
+
+    Notes
+    -----
+    Of the four parameters ``start``, ``end``, ``periods``, and ``freq``,
+    exactly three must be specified. If ``freq`` is omitted, the resulting
+    ``TimedeltaIndex`` will have ``periods`` linearly spaced elements between
+    ``start`` and ``end`` (closed on both sides).
+
+    To learn more about the frequency strings, please see `this link
+    `__.
+
+    Examples
+    --------
+    >>> pd.timedelta_range(start='1 day', periods=4)
+    TimedeltaIndex(['1 days', '2 days', '3 days', '4 days'],
+                   dtype='timedelta64[ns]', freq='D')
+
+    The ``closed`` parameter specifies which endpoint is included.  The default
+    behavior is to include both endpoints.
+
+    >>> pd.timedelta_range(start='1 day', periods=4, closed='right')
+    TimedeltaIndex(['2 days', '3 days', '4 days'],
+                   dtype='timedelta64[ns]', freq='D')
+
+    The ``freq`` parameter specifies the frequency of the TimedeltaIndex.
+    Only fixed frequencies can be passed, non-fixed frequencies such as
+    'M' (month end) will raise.
+
+    >>> pd.timedelta_range(start='1 day', end='2 days', freq='6h')
+    TimedeltaIndex(['1 days 00:00:00', '1 days 06:00:00', '1 days 12:00:00',
+                    '1 days 18:00:00', '2 days 00:00:00'],
+                   dtype='timedelta64[ns]', freq='6h')
+
+    Specify ``start``, ``end``, and ``periods``; the frequency is generated
+    automatically (linearly spaced).
+
+    >>> pd.timedelta_range(start='1 day', end='5 days', periods=4)
+    TimedeltaIndex(['1 days 00:00:00', '2 days 08:00:00', '3 days 16:00:00',
+                    '5 days 00:00:00'],
+                   dtype='timedelta64[ns]', freq=None)
+
+    **Specify a unit**
+
+    >>> pd.timedelta_range("1 Day", periods=3, freq="100000D", unit="s")
+    TimedeltaIndex(['1 days', '100001 days', '200001 days'],
+                   dtype='timedelta64[s]', freq='100000D')
+    """
+    if freq is None and com.any_none(periods, start, end):
+        freq = "D"
+
+    freq = to_offset(freq)
+    tdarr = TimedeltaArray._generate_range(
+        start, end, periods, freq, closed=closed, unit=unit
+    )
+    return TimedeltaIndex._simple_new(tdarr, name=name)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/indexing.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/indexing.py
new file mode 100644
index 0000000000000000000000000000000000000000..c7bab4860411f37c177366177fb5d53d16837d17
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/indexing.py
@@ -0,0 +1,2821 @@
+from __future__ import annotations
+
+from contextlib import suppress
+import sys
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    TypeVar,
+    cast,
+    final,
+)
+import warnings
+
+import numpy as np
+
+from pandas._config import (
+    using_copy_on_write,
+    warn_copy_on_write,
+)
+
+from pandas._libs.indexing import NDFrameIndexerBase
+from pandas._libs.lib import item_from_zerodim
+from pandas.compat import PYPY
+from pandas.compat._constants import WARNING_CHECK_DISABLED
+from pandas.errors import (
+    AbstractMethodError,
+    ChainedAssignmentError,
+    IndexingError,
+    InvalidIndexError,
+    LossySetitemError,
+    _chained_assignment_msg,
+    _chained_assignment_warning_msg,
+    _check_cacher,
+)
+from pandas.util._decorators import doc
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.cast import (
+    can_hold_element,
+    maybe_promote,
+)
+from pandas.core.dtypes.common import (
+    is_array_like,
+    is_bool_dtype,
+    is_hashable,
+    is_integer,
+    is_iterator,
+    is_list_like,
+    is_numeric_dtype,
+    is_object_dtype,
+    is_scalar,
+    is_sequence,
+)
+from pandas.core.dtypes.concat import concat_compat
+from pandas.core.dtypes.dtypes import ExtensionDtype
+from pandas.core.dtypes.generic import (
+    ABCDataFrame,
+    ABCSeries,
+)
+from pandas.core.dtypes.missing import (
+    construct_1d_array_from_inferred_fill_value,
+    infer_fill_value,
+    is_valid_na_for_dtype,
+    isna,
+    na_value_for_dtype,
+)
+
+from pandas.core import algorithms as algos
+import pandas.core.common as com
+from pandas.core.construction import (
+    array as pd_array,
+    extract_array,
+)
+from pandas.core.indexers import (
+    check_array_indexer,
+    is_list_like_indexer,
+    is_scalar_indexer,
+    length_of_indexer,
+)
+from pandas.core.indexes.api import (
+    Index,
+    MultiIndex,
+)
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Hashable,
+        Sequence,
+    )
+
+    from pandas._typing import (
+        Axis,
+        AxisInt,
+        Self,
+        npt,
+    )
+
+    from pandas import (
+        DataFrame,
+        Series,
+    )
+
+T = TypeVar("T")
+# "null slice"
+_NS = slice(None, None)
+_one_ellipsis_message = "indexer may only contain one '...' entry"
+
+
+# the public IndexSlicerMaker
+class _IndexSlice:
+    """
+    Create an object to more easily perform multi-index slicing.
+
+    See Also
+    --------
+    MultiIndex.remove_unused_levels : New MultiIndex with no unused levels.
+
+    Notes
+    -----
+    See :ref:`Defined Levels `
+    for further info on slicing a MultiIndex.
+
+    Examples
+    --------
+    >>> midx = pd.MultiIndex.from_product([['A0','A1'], ['B0','B1','B2','B3']])
+    >>> columns = ['foo', 'bar']
+    >>> dfmi = pd.DataFrame(np.arange(16).reshape((len(midx), len(columns))),
+    ...                     index=midx, columns=columns)
+
+    Using the default slice command:
+
+    >>> dfmi.loc[(slice(None), slice('B0', 'B1')), :]
+               foo  bar
+        A0 B0    0    1
+           B1    2    3
+        A1 B0    8    9
+           B1   10   11
+
+    Using the IndexSlice class for a more intuitive command:
+
+    >>> idx = pd.IndexSlice
+    >>> dfmi.loc[idx[:, 'B0':'B1'], :]
+               foo  bar
+        A0 B0    0    1
+           B1    2    3
+        A1 B0    8    9
+           B1   10   11
+    """
+
+    def __getitem__(self, arg):
+        return arg
+
+
+IndexSlice = _IndexSlice()
+
+
+class IndexingMixin:
+    """
+    Mixin for adding .loc/.iloc/.at/.iat to Dataframes and Series.
+    """
+
+    @property
+    def iloc(self) -> _iLocIndexer:
+        """
+        Purely integer-location based indexing for selection by position.
+
+        .. deprecated:: 2.2.0
+
+           Returning a tuple from a callable is deprecated.
+
+        ``.iloc[]`` is primarily integer position based (from ``0`` to
+        ``length-1`` of the axis), but may also be used with a boolean
+        array.
+
+        Allowed inputs are:
+
+        - An integer, e.g. ``5``.
+        - A list or array of integers, e.g. ``[4, 3, 0]``.
+        - A slice object with ints, e.g. ``1:7``.
+        - A boolean array.
+        - A ``callable`` function with one argument (the calling Series or
+          DataFrame) and that returns valid output for indexing (one of the above).
+          This is useful in method chains, when you don't have a reference to the
+          calling object, but would like to base your selection on
+          some value.
+        - A tuple of row and column indexes. The tuple elements consist of one of the
+          above inputs, e.g. ``(0, 1)``.
+
+        ``.iloc`` will raise ``IndexError`` if a requested indexer is
+        out-of-bounds, except *slice* indexers which allow out-of-bounds
+        indexing (this conforms with python/numpy *slice* semantics).
+
+        See more at :ref:`Selection by Position `.
+
+        See Also
+        --------
+        DataFrame.iat : Fast integer location scalar accessor.
+        DataFrame.loc : Purely label-location based indexer for selection by label.
+        Series.iloc : Purely integer-location based indexing for
+                       selection by position.
+
+        Examples
+        --------
+        >>> mydict = [{'a': 1, 'b': 2, 'c': 3, 'd': 4},
+        ...           {'a': 100, 'b': 200, 'c': 300, 'd': 400},
+        ...           {'a': 1000, 'b': 2000, 'c': 3000, 'd': 4000}]
+        >>> df = pd.DataFrame(mydict)
+        >>> df
+              a     b     c     d
+        0     1     2     3     4
+        1   100   200   300   400
+        2  1000  2000  3000  4000
+
+        **Indexing just the rows**
+
+        With a scalar integer.
+
+        >>> type(df.iloc[0])
+        
+        >>> df.iloc[0]
+        a    1
+        b    2
+        c    3
+        d    4
+        Name: 0, dtype: int64
+
+        With a list of integers.
+
+        >>> df.iloc[[0]]
+           a  b  c  d
+        0  1  2  3  4
+        >>> type(df.iloc[[0]])
+        
+
+        >>> df.iloc[[0, 1]]
+             a    b    c    d
+        0    1    2    3    4
+        1  100  200  300  400
+
+        With a `slice` object.
+
+        >>> df.iloc[:3]
+              a     b     c     d
+        0     1     2     3     4
+        1   100   200   300   400
+        2  1000  2000  3000  4000
+
+        With a boolean mask the same length as the index.
+
+        >>> df.iloc[[True, False, True]]
+              a     b     c     d
+        0     1     2     3     4
+        2  1000  2000  3000  4000
+
+        With a callable, useful in method chains. The `x` passed
+        to the ``lambda`` is the DataFrame being sliced. This selects
+        the rows whose index label even.
+
+        >>> df.iloc[lambda x: x.index % 2 == 0]
+              a     b     c     d
+        0     1     2     3     4
+        2  1000  2000  3000  4000
+
+        **Indexing both axes**
+
+        You can mix the indexer types for the index and columns. Use ``:`` to
+        select the entire axis.
+
+        With scalar integers.
+
+        >>> df.iloc[0, 1]
+        2
+
+        With lists of integers.
+
+        >>> df.iloc[[0, 2], [1, 3]]
+              b     d
+        0     2     4
+        2  2000  4000
+
+        With `slice` objects.
+
+        >>> df.iloc[1:3, 0:3]
+              a     b     c
+        1   100   200   300
+        2  1000  2000  3000
+
+        With a boolean array whose length matches the columns.
+
+        >>> df.iloc[:, [True, False, True, False]]
+              a     c
+        0     1     3
+        1   100   300
+        2  1000  3000
+
+        With a callable function that expects the Series or DataFrame.
+
+        >>> df.iloc[:, lambda df: [0, 2]]
+              a     c
+        0     1     3
+        1   100   300
+        2  1000  3000
+        """
+        return _iLocIndexer("iloc", self)
+
+    @property
+    def loc(self) -> _LocIndexer:
+        """
+        Access a group of rows and columns by label(s) or a boolean array.
+
+        ``.loc[]`` is primarily label based, but may also be used with a
+        boolean array.
+
+        Allowed inputs are:
+
+        - A single label, e.g. ``5`` or ``'a'``, (note that ``5`` is
+          interpreted as a *label* of the index, and **never** as an
+          integer position along the index).
+        - A list or array of labels, e.g. ``['a', 'b', 'c']``.
+        - A slice object with labels, e.g. ``'a':'f'``.
+
+          .. warning:: Note that contrary to usual python slices, **both** the
+              start and the stop are included
+
+        - A boolean array of the same length as the axis being sliced,
+          e.g. ``[True, False, True]``.
+        - An alignable boolean Series. The index of the key will be aligned before
+          masking.
+        - An alignable Index. The Index of the returned selection will be the input.
+        - A ``callable`` function with one argument (the calling Series or
+          DataFrame) and that returns valid output for indexing (one of the above)
+
+        See more at :ref:`Selection by Label `.
+
+        Raises
+        ------
+        KeyError
+            If any items are not found.
+        IndexingError
+            If an indexed key is passed and its index is unalignable to the frame index.
+
+        See Also
+        --------
+        DataFrame.at : Access a single value for a row/column label pair.
+        DataFrame.iloc : Access group of rows and columns by integer position(s).
+        DataFrame.xs : Returns a cross-section (row(s) or column(s)) from the
+                       Series/DataFrame.
+        Series.loc : Access group of values using labels.
+
+        Examples
+        --------
+        **Getting values**
+
+        >>> df = pd.DataFrame([[1, 2], [4, 5], [7, 8]],
+        ...                   index=['cobra', 'viper', 'sidewinder'],
+        ...                   columns=['max_speed', 'shield'])
+        >>> df
+                    max_speed  shield
+        cobra               1       2
+        viper               4       5
+        sidewinder          7       8
+
+        Single label. Note this returns the row as a Series.
+
+        >>> df.loc['viper']
+        max_speed    4
+        shield       5
+        Name: viper, dtype: int64
+
+        List of labels. Note using ``[[]]`` returns a DataFrame.
+
+        >>> df.loc[['viper', 'sidewinder']]
+                    max_speed  shield
+        viper               4       5
+        sidewinder          7       8
+
+        Single label for row and column
+
+        >>> df.loc['cobra', 'shield']
+        2
+
+        Slice with labels for row and single label for column. As mentioned
+        above, note that both the start and stop of the slice are included.
+
+        >>> df.loc['cobra':'viper', 'max_speed']
+        cobra    1
+        viper    4
+        Name: max_speed, dtype: int64
+
+        Boolean list with the same length as the row axis
+
+        >>> df.loc[[False, False, True]]
+                    max_speed  shield
+        sidewinder          7       8
+
+        Alignable boolean Series:
+
+        >>> df.loc[pd.Series([False, True, False],
+        ...                  index=['viper', 'sidewinder', 'cobra'])]
+                             max_speed  shield
+        sidewinder          7       8
+
+        Index (same behavior as ``df.reindex``)
+
+        >>> df.loc[pd.Index(["cobra", "viper"], name="foo")]
+               max_speed  shield
+        foo
+        cobra          1       2
+        viper          4       5
+
+        Conditional that returns a boolean Series
+
+        >>> df.loc[df['shield'] > 6]
+                    max_speed  shield
+        sidewinder          7       8
+
+        Conditional that returns a boolean Series with column labels specified
+
+        >>> df.loc[df['shield'] > 6, ['max_speed']]
+                    max_speed
+        sidewinder          7
+
+        Multiple conditional using ``&`` that returns a boolean Series
+
+        >>> df.loc[(df['max_speed'] > 1) & (df['shield'] < 8)]
+                    max_speed  shield
+        viper          4       5
+
+        Multiple conditional using ``|`` that returns a boolean Series
+
+        >>> df.loc[(df['max_speed'] > 4) | (df['shield'] < 5)]
+                    max_speed  shield
+        cobra               1       2
+        sidewinder          7       8
+
+        Please ensure that each condition is wrapped in parentheses ``()``.
+        See the :ref:`user guide`
+        for more details and explanations of Boolean indexing.
+
+        .. note::
+            If you find yourself using 3 or more conditionals in ``.loc[]``,
+            consider using :ref:`advanced indexing`.
+
+            See below for using ``.loc[]`` on MultiIndex DataFrames.
+
+        Callable that returns a boolean Series
+
+        >>> df.loc[lambda df: df['shield'] == 8]
+                    max_speed  shield
+        sidewinder          7       8
+
+        **Setting values**
+
+        Set value for all items matching the list of labels
+
+        >>> df.loc[['viper', 'sidewinder'], ['shield']] = 50
+        >>> df
+                    max_speed  shield
+        cobra               1       2
+        viper               4      50
+        sidewinder          7      50
+
+        Set value for an entire row
+
+        >>> df.loc['cobra'] = 10
+        >>> df
+                    max_speed  shield
+        cobra              10      10
+        viper               4      50
+        sidewinder          7      50
+
+        Set value for an entire column
+
+        >>> df.loc[:, 'max_speed'] = 30
+        >>> df
+                    max_speed  shield
+        cobra              30      10
+        viper              30      50
+        sidewinder         30      50
+
+        Set value for rows matching callable condition
+
+        >>> df.loc[df['shield'] > 35] = 0
+        >>> df
+                    max_speed  shield
+        cobra              30      10
+        viper               0       0
+        sidewinder          0       0
+
+        Add value matching location
+
+        >>> df.loc["viper", "shield"] += 5
+        >>> df
+                    max_speed  shield
+        cobra              30      10
+        viper               0       5
+        sidewinder          0       0
+
+        Setting using a ``Series`` or a ``DataFrame`` sets the values matching the
+        index labels, not the index positions.
+
+        >>> shuffled_df = df.loc[["viper", "cobra", "sidewinder"]]
+        >>> df.loc[:] += shuffled_df
+        >>> df
+                    max_speed  shield
+        cobra              60      20
+        viper               0      10
+        sidewinder          0       0
+
+        **Getting values on a DataFrame with an index that has integer labels**
+
+        Another example using integers for the index
+
+        >>> df = pd.DataFrame([[1, 2], [4, 5], [7, 8]],
+        ...                   index=[7, 8, 9], columns=['max_speed', 'shield'])
+        >>> df
+           max_speed  shield
+        7          1       2
+        8          4       5
+        9          7       8
+
+        Slice with integer labels for rows. As mentioned above, note that both
+        the start and stop of the slice are included.
+
+        >>> df.loc[7:9]
+           max_speed  shield
+        7          1       2
+        8          4       5
+        9          7       8
+
+        **Getting values with a MultiIndex**
+
+        A number of examples using a DataFrame with a MultiIndex
+
+        >>> tuples = [
+        ...     ('cobra', 'mark i'), ('cobra', 'mark ii'),
+        ...     ('sidewinder', 'mark i'), ('sidewinder', 'mark ii'),
+        ...     ('viper', 'mark ii'), ('viper', 'mark iii')
+        ... ]
+        >>> index = pd.MultiIndex.from_tuples(tuples)
+        >>> values = [[12, 2], [0, 4], [10, 20],
+        ...           [1, 4], [7, 1], [16, 36]]
+        >>> df = pd.DataFrame(values, columns=['max_speed', 'shield'], index=index)
+        >>> df
+                             max_speed  shield
+        cobra      mark i           12       2
+                   mark ii           0       4
+        sidewinder mark i           10      20
+                   mark ii           1       4
+        viper      mark ii           7       1
+                   mark iii         16      36
+
+        Single label. Note this returns a DataFrame with a single index.
+
+        >>> df.loc['cobra']
+                 max_speed  shield
+        mark i          12       2
+        mark ii          0       4
+
+        Single index tuple. Note this returns a Series.
+
+        >>> df.loc[('cobra', 'mark ii')]
+        max_speed    0
+        shield       4
+        Name: (cobra, mark ii), dtype: int64
+
+        Single label for row and column. Similar to passing in a tuple, this
+        returns a Series.
+
+        >>> df.loc['cobra', 'mark i']
+        max_speed    12
+        shield        2
+        Name: (cobra, mark i), dtype: int64
+
+        Single tuple. Note using ``[[]]`` returns a DataFrame.
+
+        >>> df.loc[[('cobra', 'mark ii')]]
+                       max_speed  shield
+        cobra mark ii          0       4
+
+        Single tuple for the index with a single label for the column
+
+        >>> df.loc[('cobra', 'mark i'), 'shield']
+        2
+
+        Slice from index tuple to single label
+
+        >>> df.loc[('cobra', 'mark i'):'viper']
+                             max_speed  shield
+        cobra      mark i           12       2
+                   mark ii           0       4
+        sidewinder mark i           10      20
+                   mark ii           1       4
+        viper      mark ii           7       1
+                   mark iii         16      36
+
+        Slice from index tuple to index tuple
+
+        >>> df.loc[('cobra', 'mark i'):('viper', 'mark ii')]
+                            max_speed  shield
+        cobra      mark i          12       2
+                   mark ii          0       4
+        sidewinder mark i          10      20
+                   mark ii          1       4
+        viper      mark ii          7       1
+
+        Please see the :ref:`user guide`
+        for more details and explanations of advanced indexing.
+        """
+        return _LocIndexer("loc", self)
+
+    @property
+    def at(self) -> _AtIndexer:
+        """
+        Access a single value for a row/column label pair.
+
+        Similar to ``loc``, in that both provide label-based lookups. Use
+        ``at`` if you only need to get or set a single value in a DataFrame
+        or Series.
+
+        Raises
+        ------
+        KeyError
+            If getting a value and 'label' does not exist in a DataFrame or Series.
+
+        ValueError
+            If row/column label pair is not a tuple or if any label
+            from the pair is not a scalar for DataFrame.
+            If label is list-like (*excluding* NamedTuple) for Series.
+
+        See Also
+        --------
+        DataFrame.at : Access a single value for a row/column pair by label.
+        DataFrame.iat : Access a single value for a row/column pair by integer
+            position.
+        DataFrame.loc : Access a group of rows and columns by label(s).
+        DataFrame.iloc : Access a group of rows and columns by integer
+            position(s).
+        Series.at : Access a single value by label.
+        Series.iat : Access a single value by integer position.
+        Series.loc : Access a group of rows by label(s).
+        Series.iloc : Access a group of rows by integer position(s).
+
+        Notes
+        -----
+        See :ref:`Fast scalar value getting and setting `
+        for more details.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([[0, 2, 3], [0, 4, 1], [10, 20, 30]],
+        ...                   index=[4, 5, 6], columns=['A', 'B', 'C'])
+        >>> df
+            A   B   C
+        4   0   2   3
+        5   0   4   1
+        6  10  20  30
+
+        Get value at specified row/column pair
+
+        >>> df.at[4, 'B']
+        2
+
+        Set value at specified row/column pair
+
+        >>> df.at[4, 'B'] = 10
+        >>> df.at[4, 'B']
+        10
+
+        Get value within a Series
+
+        >>> df.loc[5].at['B']
+        4
+        """
+        return _AtIndexer("at", self)
+
+    @property
+    def iat(self) -> _iAtIndexer:
+        """
+        Access a single value for a row/column pair by integer position.
+
+        Similar to ``iloc``, in that both provide integer-based lookups. Use
+        ``iat`` if you only need to get or set a single value in a DataFrame
+        or Series.
+
+        Raises
+        ------
+        IndexError
+            When integer position is out of bounds.
+
+        See Also
+        --------
+        DataFrame.at : Access a single value for a row/column label pair.
+        DataFrame.loc : Access a group of rows and columns by label(s).
+        DataFrame.iloc : Access a group of rows and columns by integer position(s).
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([[0, 2, 3], [0, 4, 1], [10, 20, 30]],
+        ...                   columns=['A', 'B', 'C'])
+        >>> df
+            A   B   C
+        0   0   2   3
+        1   0   4   1
+        2  10  20  30
+
+        Get value at specified row/column pair
+
+        >>> df.iat[1, 2]
+        1
+
+        Set value at specified row/column pair
+
+        >>> df.iat[1, 2] = 10
+        >>> df.iat[1, 2]
+        10
+
+        Get value within a series
+
+        >>> df.loc[0].iat[1]
+        2
+        """
+        return _iAtIndexer("iat", self)
+
+
+class _LocationIndexer(NDFrameIndexerBase):
+    _valid_types: str
+    axis: AxisInt | None = None
+
+    # sub-classes need to set _takeable
+    _takeable: bool
+
+    @final
+    def __call__(self, axis: Axis | None = None) -> Self:
+        # we need to return a copy of ourselves
+        new_self = type(self)(self.name, self.obj)
+
+        if axis is not None:
+            axis_int_none = self.obj._get_axis_number(axis)
+        else:
+            axis_int_none = axis
+        new_self.axis = axis_int_none
+        return new_self
+
+    def _get_setitem_indexer(self, key):
+        """
+        Convert a potentially-label-based key into a positional indexer.
+        """
+        if self.name == "loc":
+            # always holds here bc iloc overrides _get_setitem_indexer
+            self._ensure_listlike_indexer(key)
+
+        if isinstance(key, tuple):
+            for x in key:
+                check_dict_or_set_indexers(x)
+
+        if self.axis is not None:
+            key = _tupleize_axis_indexer(self.ndim, self.axis, key)
+
+        ax = self.obj._get_axis(0)
+
+        if (
+            isinstance(ax, MultiIndex)
+            and self.name != "iloc"
+            and is_hashable(key)
+            and not isinstance(key, slice)
+        ):
+            with suppress(KeyError, InvalidIndexError):
+                # TypeError e.g. passed a bool
+                return ax.get_loc(key)
+
+        if isinstance(key, tuple):
+            with suppress(IndexingError):
+                # suppress "Too many indexers"
+                return self._convert_tuple(key)
+
+        if isinstance(key, range):
+            # GH#45479 test_loc_setitem_range_key
+            key = list(key)
+
+        return self._convert_to_indexer(key, axis=0)
+
+    @final
+    def _maybe_mask_setitem_value(self, indexer, value):
+        """
+        If we have obj.iloc[mask] = series_or_frame and series_or_frame has the
+        same length as obj, we treat this as obj.iloc[mask] = series_or_frame[mask],
+        similar to Series.__setitem__.
+
+        Note this is only for loc, not iloc.
+        """
+
+        if (
+            isinstance(indexer, tuple)
+            and len(indexer) == 2
+            and isinstance(value, (ABCSeries, ABCDataFrame))
+        ):
+            pi, icols = indexer
+            ndim = value.ndim
+            if com.is_bool_indexer(pi) and len(value) == len(pi):
+                newkey = pi.nonzero()[0]
+
+                if is_scalar_indexer(icols, self.ndim - 1) and ndim == 1:
+                    # e.g. test_loc_setitem_boolean_mask_allfalse
+                    # test_loc_setitem_ndframe_values_alignment
+                    value = self.obj.iloc._align_series(indexer, value)
+                    indexer = (newkey, icols)
+
+                elif (
+                    isinstance(icols, np.ndarray)
+                    and icols.dtype.kind == "i"
+                    and len(icols) == 1
+                ):
+                    if ndim == 1:
+                        # We implicitly broadcast, though numpy does not, see
+                        # github.com/pandas-dev/pandas/pull/45501#discussion_r789071825
+                        # test_loc_setitem_ndframe_values_alignment
+                        value = self.obj.iloc._align_series(indexer, value)
+                        indexer = (newkey, icols)
+
+                    elif ndim == 2 and value.shape[1] == 1:
+                        # test_loc_setitem_ndframe_values_alignment
+                        value = self.obj.iloc._align_frame(indexer, value)
+                        indexer = (newkey, icols)
+        elif com.is_bool_indexer(indexer):
+            indexer = indexer.nonzero()[0]
+
+        return indexer, value
+
+    @final
+    def _ensure_listlike_indexer(self, key, axis=None, value=None) -> None:
+        """
+        Ensure that a list-like of column labels are all present by adding them if
+        they do not already exist.
+
+        Parameters
+        ----------
+        key : list-like of column labels
+            Target labels.
+        axis : key axis if known
+        """
+        column_axis = 1
+
+        # column only exists in 2-dimensional DataFrame
+        if self.ndim != 2:
+            return
+
+        if isinstance(key, tuple) and len(key) > 1:
+            # key may be a tuple if we are .loc
+            # if length of key is > 1 set key to column part
+            key = key[column_axis]
+            axis = column_axis
+
+        if (
+            axis == column_axis
+            and not isinstance(self.obj.columns, MultiIndex)
+            and is_list_like_indexer(key)
+            and not com.is_bool_indexer(key)
+            and all(is_hashable(k) for k in key)
+        ):
+            # GH#38148
+            keys = self.obj.columns.union(key, sort=False)
+            diff = Index(key).difference(self.obj.columns, sort=False)
+
+            if len(diff):
+                # e.g. if we are doing df.loc[:, ["A", "B"]] = 7 and "B"
+                #  is a new column, add the new columns with dtype=np.void
+                #  so that later when we go through setitem_single_column
+                #  we will use isetitem. Without this, the reindex_axis
+                #  below would create float64 columns in this example, which
+                #  would successfully hold 7, so we would end up with the wrong
+                #  dtype.
+                indexer = np.arange(len(keys), dtype=np.intp)
+                indexer[len(self.obj.columns) :] = -1
+                new_mgr = self.obj._mgr.reindex_indexer(
+                    keys, indexer=indexer, axis=0, only_slice=True, use_na_proxy=True
+                )
+                self.obj._mgr = new_mgr
+                return
+
+            self.obj._mgr = self.obj._mgr.reindex_axis(keys, axis=0, only_slice=True)
+
+    @final
+    def __setitem__(self, key, value) -> None:
+        if not PYPY and not WARNING_CHECK_DISABLED and using_copy_on_write():
+            if sys.getrefcount(self.obj) <= 2:
+                warnings.warn(
+                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
+                )
+        elif not PYPY and not WARNING_CHECK_DISABLED and not using_copy_on_write():
+            ctr = sys.getrefcount(self.obj)
+            ref_count = 2
+            if not warn_copy_on_write() and _check_cacher(self.obj):
+                # see https://github.com/pandas-dev/pandas/pull/56060#discussion_r1399245221
+                ref_count += 1
+            if ctr <= ref_count:
+                warnings.warn(
+                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
+                )
+
+        check_dict_or_set_indexers(key)
+        if isinstance(key, tuple):
+            key = tuple(list(x) if is_iterator(x) else x for x in key)
+            key = tuple(com.apply_if_callable(x, self.obj) for x in key)
+        else:
+            maybe_callable = com.apply_if_callable(key, self.obj)
+            key = self._check_deprecated_callable_usage(key, maybe_callable)
+        indexer = self._get_setitem_indexer(key)
+        self._has_valid_setitem_indexer(key)
+
+        iloc = self if self.name == "iloc" else self.obj.iloc
+        iloc._setitem_with_indexer(indexer, value, self.name)
+
+    def _validate_key(self, key, axis: AxisInt):
+        """
+        Ensure that key is valid for current indexer.
+
+        Parameters
+        ----------
+        key : scalar, slice or list-like
+            Key requested.
+        axis : int
+            Dimension on which the indexing is being made.
+
+        Raises
+        ------
+        TypeError
+            If the key (or some element of it) has wrong type.
+        IndexError
+            If the key (or some element of it) is out of bounds.
+        KeyError
+            If the key was not found.
+        """
+        raise AbstractMethodError(self)
+
+    @final
+    def _expand_ellipsis(self, tup: tuple) -> tuple:
+        """
+        If a tuple key includes an Ellipsis, replace it with an appropriate
+        number of null slices.
+        """
+        if any(x is Ellipsis for x in tup):
+            if tup.count(Ellipsis) > 1:
+                raise IndexingError(_one_ellipsis_message)
+
+            if len(tup) == self.ndim:
+                # It is unambiguous what axis this Ellipsis is indexing,
+                #  treat as a single null slice.
+                i = tup.index(Ellipsis)
+                # FIXME: this assumes only one Ellipsis
+                new_key = tup[:i] + (_NS,) + tup[i + 1 :]
+                return new_key
+
+            # TODO: other cases?  only one test gets here, and that is covered
+            #  by _validate_key_length
+        return tup
+
+    @final
+    def _validate_tuple_indexer(self, key: tuple) -> tuple:
+        """
+        Check the key for valid keys across my indexer.
+        """
+        key = self._validate_key_length(key)
+        key = self._expand_ellipsis(key)
+        for i, k in enumerate(key):
+            try:
+                self._validate_key(k, i)
+            except ValueError as err:
+                raise ValueError(
+                    "Location based indexing can only have "
+                    f"[{self._valid_types}] types"
+                ) from err
+        return key
+
+    @final
+    def _is_nested_tuple_indexer(self, tup: tuple) -> bool:
+        """
+        Returns
+        -------
+        bool
+        """
+        if any(isinstance(ax, MultiIndex) for ax in self.obj.axes):
+            return any(is_nested_tuple(tup, ax) for ax in self.obj.axes)
+        return False
+
+    @final
+    def _convert_tuple(self, key: tuple) -> tuple:
+        # Note: we assume _tupleize_axis_indexer has been called, if necessary.
+        self._validate_key_length(key)
+        keyidx = [self._convert_to_indexer(k, axis=i) for i, k in enumerate(key)]
+        return tuple(keyidx)
+
+    @final
+    def _validate_key_length(self, key: tuple) -> tuple:
+        if len(key) > self.ndim:
+            if key[0] is Ellipsis:
+                # e.g. Series.iloc[..., 3] reduces to just Series.iloc[3]
+                key = key[1:]
+                if Ellipsis in key:
+                    raise IndexingError(_one_ellipsis_message)
+                return self._validate_key_length(key)
+            raise IndexingError("Too many indexers")
+        return key
+
+    @final
+    def _getitem_tuple_same_dim(self, tup: tuple):
+        """
+        Index with indexers that should return an object of the same dimension
+        as self.obj.
+
+        This is only called after a failed call to _getitem_lowerdim.
+        """
+        retval = self.obj
+        # Selecting columns before rows is significantly faster
+        start_val = (self.ndim - len(tup)) + 1
+        for i, key in enumerate(reversed(tup)):
+            i = self.ndim - i - start_val
+            if com.is_null_slice(key):
+                continue
+
+            retval = getattr(retval, self.name)._getitem_axis(key, axis=i)
+            # We should never have retval.ndim < self.ndim, as that should
+            #  be handled by the _getitem_lowerdim call above.
+            assert retval.ndim == self.ndim
+
+        if retval is self.obj:
+            # if all axes were a null slice (`df.loc[:, :]`), ensure we still
+            # return a new object (https://github.com/pandas-dev/pandas/pull/49469)
+            retval = retval.copy(deep=False)
+
+        return retval
+
+    @final
+    def _getitem_lowerdim(self, tup: tuple):
+        # we can directly get the axis result since the axis is specified
+        if self.axis is not None:
+            axis = self.obj._get_axis_number(self.axis)
+            return self._getitem_axis(tup, axis=axis)
+
+        # we may have a nested tuples indexer here
+        if self._is_nested_tuple_indexer(tup):
+            return self._getitem_nested_tuple(tup)
+
+        # we maybe be using a tuple to represent multiple dimensions here
+        ax0 = self.obj._get_axis(0)
+        # ...but iloc should handle the tuple as simple integer-location
+        # instead of checking it as multiindex representation (GH 13797)
+        if (
+            isinstance(ax0, MultiIndex)
+            and self.name != "iloc"
+            and not any(isinstance(x, slice) for x in tup)
+        ):
+            # Note: in all extant test cases, replacing the slice condition with
+            #  `all(is_hashable(x) or com.is_null_slice(x) for x in tup)`
+            #  is equivalent.
+            #  (see the other place where we call _handle_lowerdim_multi_index_axis0)
+            with suppress(IndexingError):
+                return cast(_LocIndexer, self)._handle_lowerdim_multi_index_axis0(tup)
+
+        tup = self._validate_key_length(tup)
+
+        for i, key in enumerate(tup):
+            if is_label_like(key):
+                # We don't need to check for tuples here because those are
+                #  caught by the _is_nested_tuple_indexer check above.
+                section = self._getitem_axis(key, axis=i)
+
+                # We should never have a scalar section here, because
+                #  _getitem_lowerdim is only called after a check for
+                #  is_scalar_access, which that would be.
+                if section.ndim == self.ndim:
+                    # we're in the middle of slicing through a MultiIndex
+                    # revise the key wrt to `section` by inserting an _NS
+                    new_key = tup[:i] + (_NS,) + tup[i + 1 :]
+
+                else:
+                    # Note: the section.ndim == self.ndim check above
+                    #  rules out having DataFrame here, so we dont need to worry
+                    #  about transposing.
+                    new_key = tup[:i] + tup[i + 1 :]
+
+                    if len(new_key) == 1:
+                        new_key = new_key[0]
+
+                # Slices should return views, but calling iloc/loc with a null
+                # slice returns a new object.
+                if com.is_null_slice(new_key):
+                    return section
+                # This is an elided recursive call to iloc/loc
+                return getattr(section, self.name)[new_key]
+
+        raise IndexingError("not applicable")
+
+    @final
+    def _getitem_nested_tuple(self, tup: tuple):
+        # we have a nested tuple so have at least 1 multi-index level
+        # we should be able to match up the dimensionality here
+
+        def _contains_slice(x: object) -> bool:
+            # Check if object is a slice or a tuple containing a slice
+            if isinstance(x, tuple):
+                return any(isinstance(v, slice) for v in x)
+            elif isinstance(x, slice):
+                return True
+            return False
+
+        for key in tup:
+            check_dict_or_set_indexers(key)
+
+        # we have too many indexers for our dim, but have at least 1
+        # multi-index dimension, try to see if we have something like
+        # a tuple passed to a series with a multi-index
+        if len(tup) > self.ndim:
+            if self.name != "loc":
+                # This should never be reached, but let's be explicit about it
+                raise ValueError("Too many indices")  # pragma: no cover
+            if all(
+                (is_hashable(x) and not _contains_slice(x)) or com.is_null_slice(x)
+                for x in tup
+            ):
+                # GH#10521 Series should reduce MultiIndex dimensions instead of
+                #  DataFrame, IndexingError is not raised when slice(None,None,None)
+                #  with one row.
+                with suppress(IndexingError):
+                    return cast(_LocIndexer, self)._handle_lowerdim_multi_index_axis0(
+                        tup
+                    )
+            elif isinstance(self.obj, ABCSeries) and any(
+                isinstance(k, tuple) for k in tup
+            ):
+                # GH#35349 Raise if tuple in tuple for series
+                # Do this after the all-hashable-or-null-slice check so that
+                #  we are only getting non-hashable tuples, in particular ones
+                #  that themselves contain a slice entry
+                # See test_loc_series_getitem_too_many_dimensions
+                raise IndexingError("Too many indexers")
+
+            # this is a series with a multi-index specified a tuple of
+            # selectors
+            axis = self.axis or 0
+            return self._getitem_axis(tup, axis=axis)
+
+        # handle the multi-axis by taking sections and reducing
+        # this is iterative
+        obj = self.obj
+        # GH#41369 Loop in reverse order ensures indexing along columns before rows
+        # which selects only necessary blocks which avoids dtype conversion if possible
+        axis = len(tup) - 1
+        for key in tup[::-1]:
+            if com.is_null_slice(key):
+                axis -= 1
+                continue
+
+            obj = getattr(obj, self.name)._getitem_axis(key, axis=axis)
+            axis -= 1
+
+            # if we have a scalar, we are done
+            if is_scalar(obj) or not hasattr(obj, "ndim"):
+                break
+
+        return obj
+
+    def _convert_to_indexer(self, key, axis: AxisInt):
+        raise AbstractMethodError(self)
+
+    def _check_deprecated_callable_usage(self, key: Any, maybe_callable: T) -> T:
+        # GH53533
+        if self.name == "iloc" and callable(key) and isinstance(maybe_callable, tuple):
+            warnings.warn(
+                "Returning a tuple from a callable with iloc "
+                "is deprecated and will be removed in a future version",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+        return maybe_callable
+
+    @final
+    def __getitem__(self, key):
+        check_dict_or_set_indexers(key)
+        if type(key) is tuple:
+            key = tuple(list(x) if is_iterator(x) else x for x in key)
+            key = tuple(com.apply_if_callable(x, self.obj) for x in key)
+            if self._is_scalar_access(key):
+                return self.obj._get_value(*key, takeable=self._takeable)
+            return self._getitem_tuple(key)
+        else:
+            # we by definition only have the 0th axis
+            axis = self.axis or 0
+
+            maybe_callable = com.apply_if_callable(key, self.obj)
+            maybe_callable = self._check_deprecated_callable_usage(key, maybe_callable)
+            return self._getitem_axis(maybe_callable, axis=axis)
+
+    def _is_scalar_access(self, key: tuple):
+        raise NotImplementedError()
+
+    def _getitem_tuple(self, tup: tuple):
+        raise AbstractMethodError(self)
+
+    def _getitem_axis(self, key, axis: AxisInt):
+        raise NotImplementedError()
+
+    def _has_valid_setitem_indexer(self, indexer) -> bool:
+        raise AbstractMethodError(self)
+
+    @final
+    def _getbool_axis(self, key, axis: AxisInt):
+        # caller is responsible for ensuring non-None axis
+        labels = self.obj._get_axis(axis)
+        key = check_bool_indexer(labels, key)
+        inds = key.nonzero()[0]
+        return self.obj._take_with_is_copy(inds, axis=axis)
+
+
+@doc(IndexingMixin.loc)
+class _LocIndexer(_LocationIndexer):
+    _takeable: bool = False
+    _valid_types = (
+        "labels (MUST BE IN THE INDEX), slices of labels (BOTH "
+        "endpoints included! Can be slices of integers if the "
+        "index is integers), listlike of labels, boolean"
+    )
+
+    # -------------------------------------------------------------------
+    # Key Checks
+
+    @doc(_LocationIndexer._validate_key)
+    def _validate_key(self, key, axis: Axis):
+        # valid for a collection of labels (we check their presence later)
+        # slice of labels (where start-end in labels)
+        # slice of integers (only if in the labels)
+        # boolean not in slice and with boolean index
+        ax = self.obj._get_axis(axis)
+        if isinstance(key, bool) and not (
+            is_bool_dtype(ax.dtype)
+            or ax.dtype.name == "boolean"
+            or isinstance(ax, MultiIndex)
+            and is_bool_dtype(ax.get_level_values(0).dtype)
+        ):
+            raise KeyError(
+                f"{key}: boolean label can not be used without a boolean index"
+            )
+
+        if isinstance(key, slice) and (
+            isinstance(key.start, bool) or isinstance(key.stop, bool)
+        ):
+            raise TypeError(f"{key}: boolean values can not be used in a slice")
+
+    def _has_valid_setitem_indexer(self, indexer) -> bool:
+        return True
+
+    def _is_scalar_access(self, key: tuple) -> bool:
+        """
+        Returns
+        -------
+        bool
+        """
+        # this is a shortcut accessor to both .loc and .iloc
+        # that provide the equivalent access of .at and .iat
+        # a) avoid getting things via sections and (to minimize dtype changes)
+        # b) provide a performant path
+        if len(key) != self.ndim:
+            return False
+
+        for i, k in enumerate(key):
+            if not is_scalar(k):
+                return False
+
+            ax = self.obj.axes[i]
+            if isinstance(ax, MultiIndex):
+                return False
+
+            if isinstance(k, str) and ax._supports_partial_string_indexing:
+                # partial string indexing, df.loc['2000', 'A']
+                # should not be considered scalar
+                return False
+
+            if not ax._index_as_unique:
+                return False
+
+        return True
+
+    # -------------------------------------------------------------------
+    # MultiIndex Handling
+
+    def _multi_take_opportunity(self, tup: tuple) -> bool:
+        """
+        Check whether there is the possibility to use ``_multi_take``.
+
+        Currently the limit is that all axes being indexed, must be indexed with
+        list-likes.
+
+        Parameters
+        ----------
+        tup : tuple
+            Tuple of indexers, one per axis.
+
+        Returns
+        -------
+        bool
+            Whether the current indexing,
+            can be passed through `_multi_take`.
+        """
+        if not all(is_list_like_indexer(x) for x in tup):
+            return False
+
+        # just too complicated
+        return not any(com.is_bool_indexer(x) for x in tup)
+
+    def _multi_take(self, tup: tuple):
+        """
+        Create the indexers for the passed tuple of keys, and
+        executes the take operation. This allows the take operation to be
+        executed all at once, rather than once for each dimension.
+        Improving efficiency.
+
+        Parameters
+        ----------
+        tup : tuple
+            Tuple of indexers, one per axis.
+
+        Returns
+        -------
+        values: same type as the object being indexed
+        """
+        # GH 836
+        d = {
+            axis: self._get_listlike_indexer(key, axis)
+            for (key, axis) in zip(tup, self.obj._AXIS_ORDERS)
+        }
+        return self.obj._reindex_with_indexers(d, copy=True, allow_dups=True)
+
+    # -------------------------------------------------------------------
+
+    def _getitem_iterable(self, key, axis: AxisInt):
+        """
+        Index current object with an iterable collection of keys.
+
+        Parameters
+        ----------
+        key : iterable
+            Targeted labels.
+        axis : int
+            Dimension on which the indexing is being made.
+
+        Raises
+        ------
+        KeyError
+            If no key was found. Will change in the future to raise if not all
+            keys were found.
+
+        Returns
+        -------
+        scalar, DataFrame, or Series: indexed value(s).
+        """
+        # we assume that not com.is_bool_indexer(key), as that is
+        #  handled before we get here.
+        self._validate_key(key, axis)
+
+        # A collection of keys
+        keyarr, indexer = self._get_listlike_indexer(key, axis)
+        return self.obj._reindex_with_indexers(
+            {axis: [keyarr, indexer]}, copy=True, allow_dups=True
+        )
+
+    def _getitem_tuple(self, tup: tuple):
+        with suppress(IndexingError):
+            tup = self._expand_ellipsis(tup)
+            return self._getitem_lowerdim(tup)
+
+        # no multi-index, so validate all of the indexers
+        tup = self._validate_tuple_indexer(tup)
+
+        # ugly hack for GH #836
+        if self._multi_take_opportunity(tup):
+            return self._multi_take(tup)
+
+        return self._getitem_tuple_same_dim(tup)
+
+    def _get_label(self, label, axis: AxisInt):
+        # GH#5567 this will fail if the label is not present in the axis.
+        return self.obj.xs(label, axis=axis)
+
+    def _handle_lowerdim_multi_index_axis0(self, tup: tuple):
+        # we have an axis0 multi-index, handle or raise
+        axis = self.axis or 0
+        try:
+            # fast path for series or for tup devoid of slices
+            return self._get_label(tup, axis=axis)
+
+        except KeyError as ek:
+            # raise KeyError if number of indexers match
+            # else IndexingError will be raised
+            if self.ndim < len(tup) <= self.obj.index.nlevels:
+                raise ek
+            raise IndexingError("No label returned") from ek
+
+    def _getitem_axis(self, key, axis: AxisInt):
+        key = item_from_zerodim(key)
+        if is_iterator(key):
+            key = list(key)
+        if key is Ellipsis:
+            key = slice(None)
+
+        labels = self.obj._get_axis(axis)
+
+        if isinstance(key, tuple) and isinstance(labels, MultiIndex):
+            key = tuple(key)
+
+        if isinstance(key, slice):
+            self._validate_key(key, axis)
+            return self._get_slice_axis(key, axis=axis)
+        elif com.is_bool_indexer(key):
+            return self._getbool_axis(key, axis=axis)
+        elif is_list_like_indexer(key):
+            # an iterable multi-selection
+            if not (isinstance(key, tuple) and isinstance(labels, MultiIndex)):
+                if hasattr(key, "ndim") and key.ndim > 1:
+                    raise ValueError("Cannot index with multidimensional key")
+
+                return self._getitem_iterable(key, axis=axis)
+
+            # nested tuple slicing
+            if is_nested_tuple(key, labels):
+                locs = labels.get_locs(key)
+                indexer: list[slice | npt.NDArray[np.intp]] = [slice(None)] * self.ndim
+                indexer[axis] = locs
+                return self.obj.iloc[tuple(indexer)]
+
+        # fall thru to straight lookup
+        self._validate_key(key, axis)
+        return self._get_label(key, axis=axis)
+
+    def _get_slice_axis(self, slice_obj: slice, axis: AxisInt):
+        """
+        This is pretty simple as we just have to deal with labels.
+        """
+        # caller is responsible for ensuring non-None axis
+        obj = self.obj
+        if not need_slice(slice_obj):
+            return obj.copy(deep=False)
+
+        labels = obj._get_axis(axis)
+        indexer = labels.slice_indexer(slice_obj.start, slice_obj.stop, slice_obj.step)
+
+        if isinstance(indexer, slice):
+            return self.obj._slice(indexer, axis=axis)
+        else:
+            # DatetimeIndex overrides Index.slice_indexer and may
+            #  return a DatetimeIndex instead of a slice object.
+            return self.obj.take(indexer, axis=axis)
+
+    def _convert_to_indexer(self, key, axis: AxisInt):
+        """
+        Convert indexing key into something we can use to do actual fancy
+        indexing on a ndarray.
+
+        Examples
+        ix[:5] -> slice(0, 5)
+        ix[[1,2,3]] -> [1,2,3]
+        ix[['foo', 'bar', 'baz']] -> [i, j, k] (indices of foo, bar, baz)
+
+        Going by Zen of Python?
+        'In the face of ambiguity, refuse the temptation to guess.'
+        raise AmbiguousIndexError with integer labels?
+        - No, prefer label-based indexing
+        """
+        labels = self.obj._get_axis(axis)
+
+        if isinstance(key, slice):
+            return labels._convert_slice_indexer(key, kind="loc")
+
+        if (
+            isinstance(key, tuple)
+            and not isinstance(labels, MultiIndex)
+            and self.ndim < 2
+            and len(key) > 1
+        ):
+            raise IndexingError("Too many indexers")
+
+        # Slices are not valid keys passed in by the user,
+        # even though they are hashable in Python 3.12
+        contains_slice = False
+        if isinstance(key, tuple):
+            contains_slice = any(isinstance(v, slice) for v in key)
+
+        if is_scalar(key) or (
+            isinstance(labels, MultiIndex) and is_hashable(key) and not contains_slice
+        ):
+            # Otherwise get_loc will raise InvalidIndexError
+
+            # if we are a label return me
+            try:
+                return labels.get_loc(key)
+            except LookupError:
+                if isinstance(key, tuple) and isinstance(labels, MultiIndex):
+                    if len(key) == labels.nlevels:
+                        return {"key": key}
+                    raise
+            except InvalidIndexError:
+                # GH35015, using datetime as column indices raises exception
+                if not isinstance(labels, MultiIndex):
+                    raise
+            except ValueError:
+                if not is_integer(key):
+                    raise
+                return {"key": key}
+
+        if is_nested_tuple(key, labels):
+            if self.ndim == 1 and any(isinstance(k, tuple) for k in key):
+                # GH#35349 Raise if tuple in tuple for series
+                raise IndexingError("Too many indexers")
+            return labels.get_locs(key)
+
+        elif is_list_like_indexer(key):
+            if is_iterator(key):
+                key = list(key)
+
+            if com.is_bool_indexer(key):
+                key = check_bool_indexer(labels, key)
+                return key
+            else:
+                return self._get_listlike_indexer(key, axis)[1]
+        else:
+            try:
+                return labels.get_loc(key)
+            except LookupError:
+                # allow a not found key only if we are a setter
+                if not is_list_like_indexer(key):
+                    return {"key": key}
+                raise
+
+    def _get_listlike_indexer(self, key, axis: AxisInt):
+        """
+        Transform a list-like of keys into a new index and an indexer.
+
+        Parameters
+        ----------
+        key : list-like
+            Targeted labels.
+        axis:  int
+            Dimension on which the indexing is being made.
+
+        Raises
+        ------
+        KeyError
+            If at least one key was requested but none was found.
+
+        Returns
+        -------
+        keyarr: Index
+            New index (coinciding with 'key' if the axis is unique).
+        values : array-like
+            Indexer for the return object, -1 denotes keys not found.
+        """
+        ax = self.obj._get_axis(axis)
+        axis_name = self.obj._get_axis_name(axis)
+
+        keyarr, indexer = ax._get_indexer_strict(key, axis_name)
+
+        return keyarr, indexer
+
+
+@doc(IndexingMixin.iloc)
+class _iLocIndexer(_LocationIndexer):
+    _valid_types = (
+        "integer, integer slice (START point is INCLUDED, END "
+        "point is EXCLUDED), listlike of integers, boolean array"
+    )
+    _takeable = True
+
+    # -------------------------------------------------------------------
+    # Key Checks
+
+    def _validate_key(self, key, axis: AxisInt):
+        if com.is_bool_indexer(key):
+            if hasattr(key, "index") and isinstance(key.index, Index):
+                if key.index.inferred_type == "integer":
+                    raise NotImplementedError(
+                        "iLocation based boolean "
+                        "indexing on an integer type "
+                        "is not available"
+                    )
+                raise ValueError(
+                    "iLocation based boolean indexing cannot use "
+                    "an indexable as a mask"
+                )
+            return
+
+        if isinstance(key, slice):
+            return
+        elif is_integer(key):
+            self._validate_integer(key, axis)
+        elif isinstance(key, tuple):
+            # a tuple should already have been caught by this point
+            # so don't treat a tuple as a valid indexer
+            raise IndexingError("Too many indexers")
+        elif is_list_like_indexer(key):
+            if isinstance(key, ABCSeries):
+                arr = key._values
+            elif is_array_like(key):
+                arr = key
+            else:
+                arr = np.array(key)
+            len_axis = len(self.obj._get_axis(axis))
+
+            # check that the key has a numeric dtype
+            if not is_numeric_dtype(arr.dtype):
+                raise IndexError(f".iloc requires numeric indexers, got {arr}")
+
+            # check that the key does not exceed the maximum size of the index
+            if len(arr) and (arr.max() >= len_axis or arr.min() < -len_axis):
+                raise IndexError("positional indexers are out-of-bounds")
+        else:
+            raise ValueError(f"Can only index by location with a [{self._valid_types}]")
+
+    def _has_valid_setitem_indexer(self, indexer) -> bool:
+        """
+        Validate that a positional indexer cannot enlarge its target
+        will raise if needed, does not modify the indexer externally.
+
+        Returns
+        -------
+        bool
+        """
+        if isinstance(indexer, dict):
+            raise IndexError("iloc cannot enlarge its target object")
+
+        if isinstance(indexer, ABCDataFrame):
+            raise TypeError(
+                "DataFrame indexer for .iloc is not supported. "
+                "Consider using .loc with a DataFrame indexer for automatic alignment.",
+            )
+
+        if not isinstance(indexer, tuple):
+            indexer = _tuplify(self.ndim, indexer)
+
+        for ax, i in zip(self.obj.axes, indexer):
+            if isinstance(i, slice):
+                # should check the stop slice?
+                pass
+            elif is_list_like_indexer(i):
+                # should check the elements?
+                pass
+            elif is_integer(i):
+                if i >= len(ax):
+                    raise IndexError("iloc cannot enlarge its target object")
+            elif isinstance(i, dict):
+                raise IndexError("iloc cannot enlarge its target object")
+
+        return True
+
+    def _is_scalar_access(self, key: tuple) -> bool:
+        """
+        Returns
+        -------
+        bool
+        """
+        # this is a shortcut accessor to both .loc and .iloc
+        # that provide the equivalent access of .at and .iat
+        # a) avoid getting things via sections and (to minimize dtype changes)
+        # b) provide a performant path
+        if len(key) != self.ndim:
+            return False
+
+        return all(is_integer(k) for k in key)
+
+    def _validate_integer(self, key: int | np.integer, axis: AxisInt) -> None:
+        """
+        Check that 'key' is a valid position in the desired axis.
+
+        Parameters
+        ----------
+        key : int
+            Requested position.
+        axis : int
+            Desired axis.
+
+        Raises
+        ------
+        IndexError
+            If 'key' is not a valid position in axis 'axis'.
+        """
+        len_axis = len(self.obj._get_axis(axis))
+        if key >= len_axis or key < -len_axis:
+            raise IndexError("single positional indexer is out-of-bounds")
+
+    # -------------------------------------------------------------------
+
+    def _getitem_tuple(self, tup: tuple):
+        tup = self._validate_tuple_indexer(tup)
+        with suppress(IndexingError):
+            return self._getitem_lowerdim(tup)
+
+        return self._getitem_tuple_same_dim(tup)
+
+    def _get_list_axis(self, key, axis: AxisInt):
+        """
+        Return Series values by list or array of integers.
+
+        Parameters
+        ----------
+        key : list-like positional indexer
+        axis : int
+
+        Returns
+        -------
+        Series object
+
+        Notes
+        -----
+        `axis` can only be zero.
+        """
+        try:
+            return self.obj._take_with_is_copy(key, axis=axis)
+        except IndexError as err:
+            # re-raise with different error message, e.g. test_getitem_ndarray_3d
+            raise IndexError("positional indexers are out-of-bounds") from err
+
+    def _getitem_axis(self, key, axis: AxisInt):
+        if key is Ellipsis:
+            key = slice(None)
+        elif isinstance(key, ABCDataFrame):
+            raise IndexError(
+                "DataFrame indexer is not allowed for .iloc\n"
+                "Consider using .loc for automatic alignment."
+            )
+
+        if isinstance(key, slice):
+            return self._get_slice_axis(key, axis=axis)
+
+        if is_iterator(key):
+            key = list(key)
+
+        if isinstance(key, list):
+            key = np.asarray(key)
+
+        if com.is_bool_indexer(key):
+            self._validate_key(key, axis)
+            return self._getbool_axis(key, axis=axis)
+
+        # a list of integers
+        elif is_list_like_indexer(key):
+            return self._get_list_axis(key, axis=axis)
+
+        # a single integer
+        else:
+            key = item_from_zerodim(key)
+            if not is_integer(key):
+                raise TypeError("Cannot index by location index with a non-integer key")
+
+            # validate the location
+            self._validate_integer(key, axis)
+
+            return self.obj._ixs(key, axis=axis)
+
+    def _get_slice_axis(self, slice_obj: slice, axis: AxisInt):
+        # caller is responsible for ensuring non-None axis
+        obj = self.obj
+
+        if not need_slice(slice_obj):
+            return obj.copy(deep=False)
+
+        labels = obj._get_axis(axis)
+        labels._validate_positional_slice(slice_obj)
+        return self.obj._slice(slice_obj, axis=axis)
+
+    def _convert_to_indexer(self, key, axis: AxisInt):
+        """
+        Much simpler as we only have to deal with our valid types.
+        """
+        return key
+
+    def _get_setitem_indexer(self, key):
+        # GH#32257 Fall through to let numpy do validation
+        if is_iterator(key):
+            key = list(key)
+
+        if self.axis is not None:
+            key = _tupleize_axis_indexer(self.ndim, self.axis, key)
+
+        return key
+
+    # -------------------------------------------------------------------
+
+    def _setitem_with_indexer(self, indexer, value, name: str = "iloc"):
+        """
+        _setitem_with_indexer is for setting values on a Series/DataFrame
+        using positional indexers.
+
+        If the relevant keys are not present, the Series/DataFrame may be
+        expanded.
+
+        This method is currently broken when dealing with non-unique Indexes,
+        since it goes from positional indexers back to labels when calling
+        BlockManager methods, see GH#12991, GH#22046, GH#15686.
+        """
+        info_axis = self.obj._info_axis_number
+
+        # maybe partial set
+        take_split_path = not self.obj._mgr.is_single_block
+
+        if not take_split_path and isinstance(value, ABCDataFrame):
+            # Avoid cast of values
+            take_split_path = not value._mgr.is_single_block
+
+        # if there is only one block/type, still have to take split path
+        # unless the block is one-dimensional or it can hold the value
+        if not take_split_path and len(self.obj._mgr.arrays) and self.ndim > 1:
+            # in case of dict, keys are indices
+            val = list(value.values()) if isinstance(value, dict) else value
+            arr = self.obj._mgr.arrays[0]
+            take_split_path = not can_hold_element(
+                arr, extract_array(val, extract_numpy=True)
+            )
+
+        # if we have any multi-indexes that have non-trivial slices
+        # (not null slices) then we must take the split path, xref
+        # GH 10360, GH 27841
+        if isinstance(indexer, tuple) and len(indexer) == len(self.obj.axes):
+            for i, ax in zip(indexer, self.obj.axes):
+                if isinstance(ax, MultiIndex) and not (
+                    is_integer(i) or com.is_null_slice(i)
+                ):
+                    take_split_path = True
+                    break
+
+        if isinstance(indexer, tuple):
+            nindexer = []
+            for i, idx in enumerate(indexer):
+                if isinstance(idx, dict):
+                    # reindex the axis to the new value
+                    # and set inplace
+                    key, _ = convert_missing_indexer(idx)
+
+                    # if this is the items axes, then take the main missing
+                    # path first
+                    # this correctly sets the dtype and avoids cache issues
+                    # essentially this separates out the block that is needed
+                    # to possibly be modified
+                    if self.ndim > 1 and i == info_axis:
+                        # add the new item, and set the value
+                        # must have all defined axes if we have a scalar
+                        # or a list-like on the non-info axes if we have a
+                        # list-like
+                        if not len(self.obj):
+                            if not is_list_like_indexer(value):
+                                raise ValueError(
+                                    "cannot set a frame with no "
+                                    "defined index and a scalar"
+                                )
+                            self.obj[key] = value
+                            return
+
+                        # add a new item with the dtype setup
+                        if com.is_null_slice(indexer[0]):
+                            # We are setting an entire column
+                            self.obj[key] = value
+                            return
+                        elif is_array_like(value):
+                            # GH#42099
+                            arr = extract_array(value, extract_numpy=True)
+                            taker = -1 * np.ones(len(self.obj), dtype=np.intp)
+                            empty_value = algos.take_nd(arr, taker)
+                            if not isinstance(value, ABCSeries):
+                                # if not Series (in which case we need to align),
+                                #  we can short-circuit
+                                if (
+                                    isinstance(arr, np.ndarray)
+                                    and arr.ndim == 1
+                                    and len(arr) == 1
+                                ):
+                                    # NumPy 1.25 deprecation: https://github.com/numpy/numpy/pull/10615
+                                    arr = arr[0, ...]
+                                empty_value[indexer[0]] = arr
+                                self.obj[key] = empty_value
+                                return
+
+                            self.obj[key] = empty_value
+                        elif not is_list_like(value):
+                            self.obj[key] = construct_1d_array_from_inferred_fill_value(
+                                value, len(self.obj)
+                            )
+                        else:
+                            # FIXME: GH#42099#issuecomment-864326014
+                            self.obj[key] = infer_fill_value(value)
+
+                        new_indexer = convert_from_missing_indexer_tuple(
+                            indexer, self.obj.axes
+                        )
+                        self._setitem_with_indexer(new_indexer, value, name)
+
+                        return
+
+                    # reindex the axis
+                    # make sure to clear the cache because we are
+                    # just replacing the block manager here
+                    # so the object is the same
+                    index = self.obj._get_axis(i)
+                    with warnings.catch_warnings():
+                        # TODO: re-issue this with setitem-specific message?
+                        warnings.filterwarnings(
+                            "ignore",
+                            "The behavior of Index.insert with object-dtype "
+                            "is deprecated",
+                            category=FutureWarning,
+                        )
+                        labels = index.insert(len(index), key)
+
+                    # We are expanding the Series/DataFrame values to match
+                    #  the length of thenew index `labels`.  GH#40096 ensure
+                    #  this is valid even if the index has duplicates.
+                    taker = np.arange(len(index) + 1, dtype=np.intp)
+                    taker[-1] = -1
+                    reindexers = {i: (labels, taker)}
+                    new_obj = self.obj._reindex_with_indexers(
+                        reindexers, allow_dups=True
+                    )
+                    self.obj._mgr = new_obj._mgr
+                    self.obj._maybe_update_cacher(clear=True)
+                    self.obj._is_copy = None
+
+                    nindexer.append(labels.get_loc(key))
+
+                else:
+                    nindexer.append(idx)
+
+            indexer = tuple(nindexer)
+        else:
+            indexer, missing = convert_missing_indexer(indexer)
+
+            if missing:
+                self._setitem_with_indexer_missing(indexer, value)
+                return
+
+        if name == "loc":
+            # must come after setting of missing
+            indexer, value = self._maybe_mask_setitem_value(indexer, value)
+
+        # align and set the values
+        if take_split_path:
+            # We have to operate column-wise
+            self._setitem_with_indexer_split_path(indexer, value, name)
+        else:
+            self._setitem_single_block(indexer, value, name)
+
+    def _setitem_with_indexer_split_path(self, indexer, value, name: str):
+        """
+        Setitem column-wise.
+        """
+        # Above we only set take_split_path to True for 2D cases
+        assert self.ndim == 2
+
+        if not isinstance(indexer, tuple):
+            indexer = _tuplify(self.ndim, indexer)
+        if len(indexer) > self.ndim:
+            raise IndexError("too many indices for array")
+        if isinstance(indexer[0], np.ndarray) and indexer[0].ndim > 2:
+            raise ValueError(r"Cannot set values with ndim > 2")
+
+        if (isinstance(value, ABCSeries) and name != "iloc") or isinstance(value, dict):
+            from pandas import Series
+
+            value = self._align_series(indexer, Series(value))
+
+        # Ensure we have something we can iterate over
+        info_axis = indexer[1]
+        ilocs = self._ensure_iterable_column_indexer(info_axis)
+
+        pi = indexer[0]
+        lplane_indexer = length_of_indexer(pi, self.obj.index)
+        # lplane_indexer gives the expected length of obj[indexer[0]]
+
+        # we need an iterable, with a ndim of at least 1
+        # eg. don't pass through np.array(0)
+        if is_list_like_indexer(value) and getattr(value, "ndim", 1) > 0:
+            if isinstance(value, ABCDataFrame):
+                self._setitem_with_indexer_frame_value(indexer, value, name)
+
+            elif np.ndim(value) == 2:
+                # TODO: avoid np.ndim call in case it isn't an ndarray, since
+                #  that will construct an ndarray, which will be wasteful
+                self._setitem_with_indexer_2d_value(indexer, value)
+
+            elif len(ilocs) == 1 and lplane_indexer == len(value) and not is_scalar(pi):
+                # We are setting multiple rows in a single column.
+                self._setitem_single_column(ilocs[0], value, pi)
+
+            elif len(ilocs) == 1 and 0 != lplane_indexer != len(value):
+                # We are trying to set N values into M entries of a single
+                #  column, which is invalid for N != M
+                # Exclude zero-len for e.g. boolean masking that is all-false
+
+                if len(value) == 1 and not is_integer(info_axis):
+                    # This is a case like df.iloc[:3, [1]] = [0]
+                    #  where we treat as df.iloc[:3, 1] = 0
+                    return self._setitem_with_indexer((pi, info_axis[0]), value[0])
+
+                raise ValueError(
+                    "Must have equal len keys and value "
+                    "when setting with an iterable"
+                )
+
+            elif lplane_indexer == 0 and len(value) == len(self.obj.index):
+                # We get here in one case via .loc with a all-False mask
+                pass
+
+            elif self._is_scalar_access(indexer) and is_object_dtype(
+                self.obj.dtypes._values[ilocs[0]]
+            ):
+                # We are setting nested data, only possible for object dtype data
+                self._setitem_single_column(indexer[1], value, pi)
+
+            elif len(ilocs) == len(value):
+                # We are setting multiple columns in a single row.
+                for loc, v in zip(ilocs, value):
+                    self._setitem_single_column(loc, v, pi)
+
+            elif len(ilocs) == 1 and com.is_null_slice(pi) and len(self.obj) == 0:
+                # This is a setitem-with-expansion, see
+                #  test_loc_setitem_empty_append_expands_rows_mixed_dtype
+                # e.g. df = DataFrame(columns=["x", "y"])
+                #  df["x"] = df["x"].astype(np.int64)
+                #  df.loc[:, "x"] = [1, 2, 3]
+                self._setitem_single_column(ilocs[0], value, pi)
+
+            else:
+                raise ValueError(
+                    "Must have equal len keys and value "
+                    "when setting with an iterable"
+                )
+
+        else:
+            # scalar value
+            for loc in ilocs:
+                self._setitem_single_column(loc, value, pi)
+
+    def _setitem_with_indexer_2d_value(self, indexer, value):
+        # We get here with np.ndim(value) == 2, excluding DataFrame,
+        #  which goes through _setitem_with_indexer_frame_value
+        pi = indexer[0]
+
+        ilocs = self._ensure_iterable_column_indexer(indexer[1])
+
+        if not is_array_like(value):
+            # cast lists to array
+            value = np.array(value, dtype=object)
+        if len(ilocs) != value.shape[1]:
+            raise ValueError(
+                "Must have equal len keys and value when setting with an ndarray"
+            )
+
+        for i, loc in enumerate(ilocs):
+            value_col = value[:, i]
+            if is_object_dtype(value_col.dtype):
+                # casting to list so that we do type inference in setitem_single_column
+                value_col = value_col.tolist()
+            self._setitem_single_column(loc, value_col, pi)
+
+    def _setitem_with_indexer_frame_value(self, indexer, value: DataFrame, name: str):
+        ilocs = self._ensure_iterable_column_indexer(indexer[1])
+
+        sub_indexer = list(indexer)
+        pi = indexer[0]
+
+        multiindex_indexer = isinstance(self.obj.columns, MultiIndex)
+
+        unique_cols = value.columns.is_unique
+
+        # We do not want to align the value in case of iloc GH#37728
+        if name == "iloc":
+            for i, loc in enumerate(ilocs):
+                val = value.iloc[:, i]
+                self._setitem_single_column(loc, val, pi)
+
+        elif not unique_cols and value.columns.equals(self.obj.columns):
+            # We assume we are already aligned, see
+            # test_iloc_setitem_frame_duplicate_columns_multiple_blocks
+            for loc in ilocs:
+                item = self.obj.columns[loc]
+                if item in value:
+                    sub_indexer[1] = item
+                    val = self._align_series(
+                        tuple(sub_indexer),
+                        value.iloc[:, loc],
+                        multiindex_indexer,
+                    )
+                else:
+                    val = np.nan
+
+                self._setitem_single_column(loc, val, pi)
+
+        elif not unique_cols:
+            raise ValueError("Setting with non-unique columns is not allowed.")
+
+        else:
+            for loc in ilocs:
+                item = self.obj.columns[loc]
+                if item in value:
+                    sub_indexer[1] = item
+                    val = self._align_series(
+                        tuple(sub_indexer),
+                        value[item],
+                        multiindex_indexer,
+                        using_cow=using_copy_on_write(),
+                    )
+                else:
+                    val = np.nan
+
+                self._setitem_single_column(loc, val, pi)
+
+    def _setitem_single_column(self, loc: int, value, plane_indexer) -> None:
+        """
+
+        Parameters
+        ----------
+        loc : int
+            Indexer for column position
+        plane_indexer : int, slice, listlike[int]
+            The indexer we use for setitem along axis=0.
+        """
+        pi = plane_indexer
+
+        is_full_setter = com.is_null_slice(pi) or com.is_full_slice(pi, len(self.obj))
+
+        is_null_setter = com.is_empty_slice(pi) or is_array_like(pi) and len(pi) == 0
+
+        if is_null_setter:
+            # no-op, don't cast dtype later
+            return
+
+        elif is_full_setter:
+            try:
+                self.obj._mgr.column_setitem(
+                    loc, plane_indexer, value, inplace_only=True
+                )
+            except (ValueError, TypeError, LossySetitemError):
+                # If we're setting an entire column and we can't do it inplace,
+                #  then we can use value's dtype (or inferred dtype)
+                #  instead of object
+                dtype = self.obj.dtypes.iloc[loc]
+                if dtype not in (np.void, object) and not self.obj.empty:
+                    # - Exclude np.void, as that is a special case for expansion.
+                    #   We want to warn for
+                    #       df = pd.DataFrame({'a': [1, 2]})
+                    #       df.loc[:, 'a'] = .3
+                    #   but not for
+                    #       df = pd.DataFrame({'a': [1, 2]})
+                    #       df.loc[:, 'b'] = .3
+                    # - Exclude `object`, as then no upcasting happens.
+                    # - Exclude empty initial object with enlargement,
+                    #   as then there's nothing to be inconsistent with.
+                    warnings.warn(
+                        f"Setting an item of incompatible dtype is deprecated "
+                        "and will raise in a future error of pandas. "
+                        f"Value '{value}' has dtype incompatible with {dtype}, "
+                        "please explicitly cast to a compatible dtype first.",
+                        FutureWarning,
+                        stacklevel=find_stack_level(),
+                    )
+                self.obj.isetitem(loc, value)
+        else:
+            # set value into the column (first attempting to operate inplace, then
+            #  falling back to casting if necessary)
+            dtype = self.obj.dtypes.iloc[loc]
+            if dtype == np.void:
+                # This means we're expanding, with multiple columns, e.g.
+                #     df = pd.DataFrame({'A': [1,2,3], 'B': [4,5,6]})
+                #     df.loc[df.index <= 2, ['F', 'G']] = (1, 'abc')
+                # Columns F and G will initially be set to np.void.
+                # Here, we replace those temporary `np.void` columns with
+                # columns of the appropriate dtype, based on `value`.
+                self.obj.iloc[:, loc] = construct_1d_array_from_inferred_fill_value(
+                    value, len(self.obj)
+                )
+            self.obj._mgr.column_setitem(loc, plane_indexer, value)
+
+        self.obj._clear_item_cache()
+
+    def _setitem_single_block(self, indexer, value, name: str) -> None:
+        """
+        _setitem_with_indexer for the case when we have a single Block.
+        """
+        from pandas import Series
+
+        if (isinstance(value, ABCSeries) and name != "iloc") or isinstance(value, dict):
+            # TODO(EA): ExtensionBlock.setitem this causes issues with
+            # setting for extensionarrays that store dicts. Need to decide
+            # if it's worth supporting that.
+            value = self._align_series(indexer, Series(value))
+
+        info_axis = self.obj._info_axis_number
+        item_labels = self.obj._get_axis(info_axis)
+        if isinstance(indexer, tuple):
+            # if we are setting on the info axis ONLY
+            # set using those methods to avoid block-splitting
+            # logic here
+            if (
+                self.ndim == len(indexer) == 2
+                and is_integer(indexer[1])
+                and com.is_null_slice(indexer[0])
+            ):
+                col = item_labels[indexer[info_axis]]
+                if len(item_labels.get_indexer_for([col])) == 1:
+                    # e.g. test_loc_setitem_empty_append_expands_rows
+                    loc = item_labels.get_loc(col)
+                    self._setitem_single_column(loc, value, indexer[0])
+                    return
+
+            indexer = maybe_convert_ix(*indexer)  # e.g. test_setitem_frame_align
+
+        if isinstance(value, ABCDataFrame) and name != "iloc":
+            value = self._align_frame(indexer, value)._values
+
+        # check for chained assignment
+        self.obj._check_is_chained_assignment_possible()
+
+        # actually do the set
+        self.obj._mgr = self.obj._mgr.setitem(indexer=indexer, value=value)
+        self.obj._maybe_update_cacher(clear=True, inplace=True)
+
+    def _setitem_with_indexer_missing(self, indexer, value):
+        """
+        Insert new row(s) or column(s) into the Series or DataFrame.
+        """
+        from pandas import Series
+
+        # reindex the axis to the new value
+        # and set inplace
+        if self.ndim == 1:
+            index = self.obj.index
+            with warnings.catch_warnings():
+                # TODO: re-issue this with setitem-specific message?
+                warnings.filterwarnings(
+                    "ignore",
+                    "The behavior of Index.insert with object-dtype is deprecated",
+                    category=FutureWarning,
+                )
+                new_index = index.insert(len(index), indexer)
+
+            # we have a coerced indexer, e.g. a float
+            # that matches in an int64 Index, so
+            # we will not create a duplicate index, rather
+            # index to that element
+            # e.g. 0.0 -> 0
+            # GH#12246
+            if index.is_unique:
+                # pass new_index[-1:] instead if [new_index[-1]]
+                #  so that we retain dtype
+                new_indexer = index.get_indexer(new_index[-1:])
+                if (new_indexer != -1).any():
+                    # We get only here with loc, so can hard code
+                    return self._setitem_with_indexer(new_indexer, value, "loc")
+
+            # this preserves dtype of the value and of the object
+            if not is_scalar(value):
+                new_dtype = None
+
+            elif is_valid_na_for_dtype(value, self.obj.dtype):
+                if not is_object_dtype(self.obj.dtype):
+                    # Every NA value is suitable for object, no conversion needed
+                    value = na_value_for_dtype(self.obj.dtype, compat=False)
+
+                new_dtype = maybe_promote(self.obj.dtype, value)[0]
+
+            elif isna(value):
+                new_dtype = None
+            elif not self.obj.empty and not is_object_dtype(self.obj.dtype):
+                # We should not cast, if we have object dtype because we can
+                # set timedeltas into object series
+                curr_dtype = self.obj.dtype
+                curr_dtype = getattr(curr_dtype, "numpy_dtype", curr_dtype)
+                new_dtype = maybe_promote(curr_dtype, value)[0]
+            else:
+                new_dtype = None
+
+            new_values = Series([value], dtype=new_dtype)._values
+
+            if len(self.obj._values):
+                # GH#22717 handle casting compatibility that np.concatenate
+                #  does incorrectly
+                new_values = concat_compat([self.obj._values, new_values])
+            self.obj._mgr = self.obj._constructor(
+                new_values, index=new_index, name=self.obj.name
+            )._mgr
+            self.obj._maybe_update_cacher(clear=True)
+
+        elif self.ndim == 2:
+            if not len(self.obj.columns):
+                # no columns and scalar
+                raise ValueError("cannot set a frame with no defined columns")
+
+            has_dtype = hasattr(value, "dtype")
+            if isinstance(value, ABCSeries):
+                # append a Series
+                value = value.reindex(index=self.obj.columns, copy=True)
+                value.name = indexer
+            elif isinstance(value, dict):
+                value = Series(
+                    value, index=self.obj.columns, name=indexer, dtype=object
+                )
+            else:
+                # a list-list
+                if is_list_like_indexer(value):
+                    # must have conforming columns
+                    if len(value) != len(self.obj.columns):
+                        raise ValueError("cannot set a row with mismatched columns")
+
+                value = Series(value, index=self.obj.columns, name=indexer)
+
+            if not len(self.obj):
+                # We will ignore the existing dtypes instead of using
+                #  internals.concat logic
+                df = value.to_frame().T
+
+                idx = self.obj.index
+                if isinstance(idx, MultiIndex):
+                    name = idx.names
+                else:
+                    name = idx.name
+
+                df.index = Index([indexer], name=name)
+                if not has_dtype:
+                    # i.e. if we already had a Series or ndarray, keep that
+                    #  dtype.  But if we had a list or dict, then do inference
+                    df = df.infer_objects(copy=False)
+                self.obj._mgr = df._mgr
+            else:
+                self.obj._mgr = self.obj._append(value)._mgr
+            self.obj._maybe_update_cacher(clear=True)
+
+    def _ensure_iterable_column_indexer(self, column_indexer):
+        """
+        Ensure that our column indexer is something that can be iterated over.
+        """
+        ilocs: Sequence[int | np.integer] | np.ndarray
+        if is_integer(column_indexer):
+            ilocs = [column_indexer]
+        elif isinstance(column_indexer, slice):
+            ilocs = np.arange(len(self.obj.columns))[column_indexer]
+        elif (
+            isinstance(column_indexer, np.ndarray) and column_indexer.dtype.kind == "b"
+        ):
+            ilocs = np.arange(len(column_indexer))[column_indexer]
+        else:
+            ilocs = column_indexer
+        return ilocs
+
+    def _align_series(
+        self,
+        indexer,
+        ser: Series,
+        multiindex_indexer: bool = False,
+        using_cow: bool = False,
+    ):
+        """
+        Parameters
+        ----------
+        indexer : tuple, slice, scalar
+            Indexer used to get the locations that will be set to `ser`.
+        ser : pd.Series
+            Values to assign to the locations specified by `indexer`.
+        multiindex_indexer : bool, optional
+            Defaults to False. Should be set to True if `indexer` was from
+            a `pd.MultiIndex`, to avoid unnecessary broadcasting.
+
+        Returns
+        -------
+        `np.array` of `ser` broadcast to the appropriate shape for assignment
+        to the locations selected by `indexer`
+        """
+        if isinstance(indexer, (slice, np.ndarray, list, Index)):
+            indexer = (indexer,)
+
+        if isinstance(indexer, tuple):
+            # flatten np.ndarray indexers
+            def ravel(i):
+                return i.ravel() if isinstance(i, np.ndarray) else i
+
+            indexer = tuple(map(ravel, indexer))
+
+            aligners = [not com.is_null_slice(idx) for idx in indexer]
+            sum_aligners = sum(aligners)
+            single_aligner = sum_aligners == 1
+            is_frame = self.ndim == 2
+            obj = self.obj
+
+            # are we a single alignable value on a non-primary
+            # dim (e.g. panel: 1,2, or frame: 0) ?
+            # hence need to align to a single axis dimension
+            # rather that find all valid dims
+
+            # frame
+            if is_frame:
+                single_aligner = single_aligner and aligners[0]
+
+            # we have a frame, with multiple indexers on both axes; and a
+            # series, so need to broadcast (see GH5206)
+            if sum_aligners == self.ndim and all(is_sequence(_) for _ in indexer):
+                ser_values = ser.reindex(obj.axes[0][indexer[0]], copy=True)._values
+
+                # single indexer
+                if len(indexer) > 1 and not multiindex_indexer:
+                    len_indexer = len(indexer[1])
+                    ser_values = (
+                        np.tile(ser_values, len_indexer).reshape(len_indexer, -1).T
+                    )
+
+                return ser_values
+
+            for i, idx in enumerate(indexer):
+                ax = obj.axes[i]
+
+                # multiple aligners (or null slices)
+                if is_sequence(idx) or isinstance(idx, slice):
+                    if single_aligner and com.is_null_slice(idx):
+                        continue
+                    new_ix = ax[idx]
+                    if not is_list_like_indexer(new_ix):
+                        new_ix = Index([new_ix])
+                    else:
+                        new_ix = Index(new_ix)
+                    if ser.index.equals(new_ix):
+                        if using_cow:
+                            return ser
+                        return ser._values.copy()
+
+                    return ser.reindex(new_ix)._values
+
+                # 2 dims
+                elif single_aligner:
+                    # reindex along index
+                    ax = self.obj.axes[1]
+                    if ser.index.equals(ax) or not len(ax):
+                        return ser._values.copy()
+                    return ser.reindex(ax)._values
+
+        elif is_integer(indexer) and self.ndim == 1:
+            if is_object_dtype(self.obj.dtype):
+                return ser
+            ax = self.obj._get_axis(0)
+
+            if ser.index.equals(ax):
+                return ser._values.copy()
+
+            return ser.reindex(ax)._values[indexer]
+
+        elif is_integer(indexer):
+            ax = self.obj._get_axis(1)
+
+            if ser.index.equals(ax):
+                return ser._values.copy()
+
+            return ser.reindex(ax)._values
+
+        raise ValueError("Incompatible indexer with Series")
+
+    def _align_frame(self, indexer, df: DataFrame) -> DataFrame:
+        is_frame = self.ndim == 2
+
+        if isinstance(indexer, tuple):
+            idx, cols = None, None
+            sindexers = []
+            for i, ix in enumerate(indexer):
+                ax = self.obj.axes[i]
+                if is_sequence(ix) or isinstance(ix, slice):
+                    if isinstance(ix, np.ndarray):
+                        ix = ix.ravel()
+                    if idx is None:
+                        idx = ax[ix]
+                    elif cols is None:
+                        cols = ax[ix]
+                    else:
+                        break
+                else:
+                    sindexers.append(i)
+
+            if idx is not None and cols is not None:
+                if df.index.equals(idx) and df.columns.equals(cols):
+                    val = df.copy()
+                else:
+                    val = df.reindex(idx, columns=cols)
+                return val
+
+        elif (isinstance(indexer, slice) or is_list_like_indexer(indexer)) and is_frame:
+            ax = self.obj.index[indexer]
+            if df.index.equals(ax):
+                val = df.copy()
+            else:
+                # we have a multi-index and are trying to align
+                # with a particular, level GH3738
+                if (
+                    isinstance(ax, MultiIndex)
+                    and isinstance(df.index, MultiIndex)
+                    and ax.nlevels != df.index.nlevels
+                ):
+                    raise TypeError(
+                        "cannot align on a multi-index with out "
+                        "specifying the join levels"
+                    )
+
+                val = df.reindex(index=ax)
+            return val
+
+        raise ValueError("Incompatible indexer with DataFrame")
+
+
+class _ScalarAccessIndexer(NDFrameIndexerBase):
+    """
+    Access scalars quickly.
+    """
+
+    # sub-classes need to set _takeable
+    _takeable: bool
+
+    def _convert_key(self, key):
+        raise AbstractMethodError(self)
+
+    def __getitem__(self, key):
+        if not isinstance(key, tuple):
+            # we could have a convertible item here (e.g. Timestamp)
+            if not is_list_like_indexer(key):
+                key = (key,)
+            else:
+                raise ValueError("Invalid call for scalar access (getting)!")
+
+        key = self._convert_key(key)
+        return self.obj._get_value(*key, takeable=self._takeable)
+
+    def __setitem__(self, key, value) -> None:
+        if isinstance(key, tuple):
+            key = tuple(com.apply_if_callable(x, self.obj) for x in key)
+        else:
+            # scalar callable may return tuple
+            key = com.apply_if_callable(key, self.obj)
+
+        if not isinstance(key, tuple):
+            key = _tuplify(self.ndim, key)
+        key = list(self._convert_key(key))
+        if len(key) != self.ndim:
+            raise ValueError("Not enough indexers for scalar access (setting)!")
+
+        self.obj._set_value(*key, value=value, takeable=self._takeable)
+
+
+@doc(IndexingMixin.at)
+class _AtIndexer(_ScalarAccessIndexer):
+    _takeable = False
+
+    def _convert_key(self, key):
+        """
+        Require they keys to be the same type as the index. (so we don't
+        fallback)
+        """
+        # GH 26989
+        # For series, unpacking key needs to result in the label.
+        # This is already the case for len(key) == 1; e.g. (1,)
+        if self.ndim == 1 and len(key) > 1:
+            key = (key,)
+
+        return key
+
+    @property
+    def _axes_are_unique(self) -> bool:
+        # Only relevant for self.ndim == 2
+        assert self.ndim == 2
+        return self.obj.index.is_unique and self.obj.columns.is_unique
+
+    def __getitem__(self, key):
+        if self.ndim == 2 and not self._axes_are_unique:
+            # GH#33041 fall back to .loc
+            if not isinstance(key, tuple) or not all(is_scalar(x) for x in key):
+                raise ValueError("Invalid call for scalar access (getting)!")
+            return self.obj.loc[key]
+
+        return super().__getitem__(key)
+
+    def __setitem__(self, key, value) -> None:
+        if not PYPY and not WARNING_CHECK_DISABLED and using_copy_on_write():
+            if sys.getrefcount(self.obj) <= 2:
+                warnings.warn(
+                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
+                )
+        elif not PYPY and not WARNING_CHECK_DISABLED and not using_copy_on_write():
+            ctr = sys.getrefcount(self.obj)
+            ref_count = 2
+            if not warn_copy_on_write() and _check_cacher(self.obj):
+                # see https://github.com/pandas-dev/pandas/pull/56060#discussion_r1399245221
+                ref_count += 1
+            if ctr <= ref_count:
+                warnings.warn(
+                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
+                )
+
+        if self.ndim == 2 and not self._axes_are_unique:
+            # GH#33041 fall back to .loc
+            if not isinstance(key, tuple) or not all(is_scalar(x) for x in key):
+                raise ValueError("Invalid call for scalar access (setting)!")
+
+            self.obj.loc[key] = value
+            return
+
+        return super().__setitem__(key, value)
+
+
+@doc(IndexingMixin.iat)
+class _iAtIndexer(_ScalarAccessIndexer):
+    _takeable = True
+
+    def _convert_key(self, key):
+        """
+        Require integer args. (and convert to label arguments)
+        """
+        for i in key:
+            if not is_integer(i):
+                raise ValueError("iAt based indexing can only have integer indexers")
+        return key
+
+    def __setitem__(self, key, value) -> None:
+        if not PYPY and not WARNING_CHECK_DISABLED and using_copy_on_write():
+            if sys.getrefcount(self.obj) <= 2:
+                warnings.warn(
+                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
+                )
+        elif not PYPY and not WARNING_CHECK_DISABLED and not using_copy_on_write():
+            ctr = sys.getrefcount(self.obj)
+            ref_count = 2
+            if not warn_copy_on_write() and _check_cacher(self.obj):
+                # see https://github.com/pandas-dev/pandas/pull/56060#discussion_r1399245221
+                ref_count += 1
+            if ctr <= ref_count:
+                warnings.warn(
+                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
+                )
+
+        return super().__setitem__(key, value)
+
+
+def _tuplify(ndim: int, loc: Hashable) -> tuple[Hashable | slice, ...]:
+    """
+    Given an indexer for the first dimension, create an equivalent tuple
+    for indexing over all dimensions.
+
+    Parameters
+    ----------
+    ndim : int
+    loc : object
+
+    Returns
+    -------
+    tuple
+    """
+    _tup: list[Hashable | slice]
+    _tup = [slice(None, None) for _ in range(ndim)]
+    _tup[0] = loc
+    return tuple(_tup)
+
+
+def _tupleize_axis_indexer(ndim: int, axis: AxisInt, key) -> tuple:
+    """
+    If we have an axis, adapt the given key to be axis-independent.
+    """
+    new_key = [slice(None)] * ndim
+    new_key[axis] = key
+    return tuple(new_key)
+
+
+def check_bool_indexer(index: Index, key) -> np.ndarray:
+    """
+    Check if key is a valid boolean indexer for an object with such index and
+    perform reindexing or conversion if needed.
+
+    This function assumes that is_bool_indexer(key) == True.
+
+    Parameters
+    ----------
+    index : Index
+        Index of the object on which the indexing is done.
+    key : list-like
+        Boolean indexer to check.
+
+    Returns
+    -------
+    np.array
+        Resulting key.
+
+    Raises
+    ------
+    IndexError
+        If the key does not have the same length as index.
+    IndexingError
+        If the index of the key is unalignable to index.
+    """
+    result = key
+    if isinstance(key, ABCSeries) and not key.index.equals(index):
+        indexer = result.index.get_indexer_for(index)
+        if -1 in indexer:
+            raise IndexingError(
+                "Unalignable boolean Series provided as "
+                "indexer (index of the boolean Series and of "
+                "the indexed object do not match)."
+            )
+
+        result = result.take(indexer)
+
+        # fall through for boolean
+        if not isinstance(result.dtype, ExtensionDtype):
+            return result.astype(bool)._values
+
+    if is_object_dtype(key):
+        # key might be object-dtype bool, check_array_indexer needs bool array
+        result = np.asarray(result, dtype=bool)
+    elif not is_array_like(result):
+        # GH 33924
+        # key may contain nan elements, check_array_indexer needs bool array
+        result = pd_array(result, dtype=bool)
+    return check_array_indexer(index, result)
+
+
+def convert_missing_indexer(indexer):
+    """
+    Reverse convert a missing indexer, which is a dict
+    return the scalar indexer and a boolean indicating if we converted
+    """
+    if isinstance(indexer, dict):
+        # a missing key (but not a tuple indexer)
+        indexer = indexer["key"]
+
+        if isinstance(indexer, bool):
+            raise KeyError("cannot use a single bool to index into setitem")
+        return indexer, True
+
+    return indexer, False
+
+
+def convert_from_missing_indexer_tuple(indexer, axes):
+    """
+    Create a filtered indexer that doesn't have any missing indexers.
+    """
+
+    def get_indexer(_i, _idx):
+        return axes[_i].get_loc(_idx["key"]) if isinstance(_idx, dict) else _idx
+
+    return tuple(get_indexer(_i, _idx) for _i, _idx in enumerate(indexer))
+
+
+def maybe_convert_ix(*args):
+    """
+    We likely want to take the cross-product.
+    """
+    for arg in args:
+        if not isinstance(arg, (np.ndarray, list, ABCSeries, Index)):
+            return args
+    return np.ix_(*args)
+
+
+def is_nested_tuple(tup, labels) -> bool:
+    """
+    Returns
+    -------
+    bool
+    """
+    # check for a compatible nested tuple and multiindexes among the axes
+    if not isinstance(tup, tuple):
+        return False
+
+    for k in tup:
+        if is_list_like(k) or isinstance(k, slice):
+            return isinstance(labels, MultiIndex)
+
+    return False
+
+
+def is_label_like(key) -> bool:
+    """
+    Returns
+    -------
+    bool
+    """
+    # select a label or row
+    return (
+        not isinstance(key, slice)
+        and not is_list_like_indexer(key)
+        and key is not Ellipsis
+    )
+
+
+def need_slice(obj: slice) -> bool:
+    """
+    Returns
+    -------
+    bool
+    """
+    return (
+        obj.start is not None
+        or obj.stop is not None
+        or (obj.step is not None and obj.step != 1)
+    )
+
+
+def check_dict_or_set_indexers(key) -> None:
+    """
+    Check if the indexer is or contains a dict or set, which is no longer allowed.
+    """
+    if (
+        isinstance(key, set)
+        or isinstance(key, tuple)
+        and any(isinstance(x, set) for x in key)
+    ):
+        raise TypeError(
+            "Passing a set as an indexer is not supported. Use a list instead."
+        )
+
+    if (
+        isinstance(key, dict)
+        or isinstance(key, tuple)
+        and any(isinstance(x, dict) for x in key)
+    ):
+        raise TypeError(
+            "Passing a dict as an indexer is not supported. Use a list instead."
+        )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/interchange/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/interchange/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/interchange/buffer.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/interchange/buffer.py
new file mode 100644
index 0000000000000000000000000000000000000000..5d24325e67f62a5da0fa3863d81576dd61f86869
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/interchange/buffer.py
@@ -0,0 +1,136 @@
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    Any,
+)
+
+from pandas.core.interchange.dataframe_protocol import (
+    Buffer,
+    DlpackDeviceType,
+)
+
+if TYPE_CHECKING:
+    import numpy as np
+    import pyarrow as pa
+
+
+class PandasBuffer(Buffer):
+    """
+    Data in the buffer is guaranteed to be contiguous in memory.
+    """
+
+    def __init__(self, x: np.ndarray, allow_copy: bool = True) -> None:
+        """
+        Handle only regular columns (= numpy arrays) for now.
+        """
+        if x.strides[0] and not x.strides == (x.dtype.itemsize,):
+            # The protocol does not support strided buffers, so a copy is
+            # necessary. If that's not allowed, we need to raise an exception.
+            if allow_copy:
+                x = x.copy()
+            else:
+                raise RuntimeError(
+                    "Exports cannot be zero-copy in the case "
+                    "of a non-contiguous buffer"
+                )
+
+        # Store the numpy array in which the data resides as a private
+        # attribute, so we can use it to retrieve the public attributes
+        self._x = x
+
+    @property
+    def bufsize(self) -> int:
+        """
+        Buffer size in bytes.
+        """
+        return self._x.size * self._x.dtype.itemsize
+
+    @property
+    def ptr(self) -> int:
+        """
+        Pointer to start of the buffer as an integer.
+        """
+        return self._x.__array_interface__["data"][0]
+
+    def __dlpack__(self) -> Any:
+        """
+        Represent this structure as DLPack interface.
+        """
+        return self._x.__dlpack__()
+
+    def __dlpack_device__(self) -> tuple[DlpackDeviceType, int | None]:
+        """
+        Device type and device ID for where the data in the buffer resides.
+        """
+        return (DlpackDeviceType.CPU, None)
+
+    def __repr__(self) -> str:
+        return (
+            "PandasBuffer("
+            + str(
+                {
+                    "bufsize": self.bufsize,
+                    "ptr": self.ptr,
+                    "device": self.__dlpack_device__()[0].name,
+                }
+            )
+            + ")"
+        )
+
+
+class PandasBufferPyarrow(Buffer):
+    """
+    Data in the buffer is guaranteed to be contiguous in memory.
+    """
+
+    def __init__(
+        self,
+        buffer: pa.Buffer,
+        *,
+        length: int,
+    ) -> None:
+        """
+        Handle pyarrow chunked arrays.
+        """
+        self._buffer = buffer
+        self._length = length
+
+    @property
+    def bufsize(self) -> int:
+        """
+        Buffer size in bytes.
+        """
+        return self._buffer.size
+
+    @property
+    def ptr(self) -> int:
+        """
+        Pointer to start of the buffer as an integer.
+        """
+        return self._buffer.address
+
+    def __dlpack__(self) -> Any:
+        """
+        Represent this structure as DLPack interface.
+        """
+        raise NotImplementedError()
+
+    def __dlpack_device__(self) -> tuple[DlpackDeviceType, int | None]:
+        """
+        Device type and device ID for where the data in the buffer resides.
+        """
+        return (DlpackDeviceType.CPU, None)
+
+    def __repr__(self) -> str:
+        return (
+            "PandasBuffer[pyarrow]("
+            + str(
+                {
+                    "bufsize": self.bufsize,
+                    "ptr": self.ptr,
+                    "device": "CPU",
+                }
+            )
+            + ")"
+        )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/interchange/column.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/interchange/column.py
new file mode 100644
index 0000000000000000000000000000000000000000..d59a3df694bb3f6ab5716c25592704d49737a215
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/interchange/column.py
@@ -0,0 +1,461 @@
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    Any,
+)
+
+import numpy as np
+
+from pandas._libs.lib import infer_dtype
+from pandas._libs.tslibs import iNaT
+from pandas.errors import NoBufferPresent
+from pandas.util._decorators import cache_readonly
+
+from pandas.core.dtypes.dtypes import BaseMaskedDtype
+
+import pandas as pd
+from pandas import (
+    ArrowDtype,
+    DatetimeTZDtype,
+)
+from pandas.api.types import is_string_dtype
+from pandas.core.interchange.buffer import (
+    PandasBuffer,
+    PandasBufferPyarrow,
+)
+from pandas.core.interchange.dataframe_protocol import (
+    Column,
+    ColumnBuffers,
+    ColumnNullType,
+    DtypeKind,
+)
+from pandas.core.interchange.utils import (
+    ArrowCTypes,
+    Endianness,
+    dtype_to_arrow_c_fmt,
+)
+
+if TYPE_CHECKING:
+    from pandas.core.interchange.dataframe_protocol import Buffer
+
+_NP_KINDS = {
+    "i": DtypeKind.INT,
+    "u": DtypeKind.UINT,
+    "f": DtypeKind.FLOAT,
+    "b": DtypeKind.BOOL,
+    "U": DtypeKind.STRING,
+    "M": DtypeKind.DATETIME,
+    "m": DtypeKind.DATETIME,
+}
+
+_NULL_DESCRIPTION = {
+    DtypeKind.FLOAT: (ColumnNullType.USE_NAN, None),
+    DtypeKind.DATETIME: (ColumnNullType.USE_SENTINEL, iNaT),
+    DtypeKind.INT: (ColumnNullType.NON_NULLABLE, None),
+    DtypeKind.UINT: (ColumnNullType.NON_NULLABLE, None),
+    DtypeKind.BOOL: (ColumnNullType.NON_NULLABLE, None),
+    # Null values for categoricals are stored as `-1` sentinel values
+    # in the category date (e.g., `col.values.codes` is int8 np.ndarray)
+    DtypeKind.CATEGORICAL: (ColumnNullType.USE_SENTINEL, -1),
+    # follow Arrow in using 1 as valid value and 0 for missing/null value
+    DtypeKind.STRING: (ColumnNullType.USE_BYTEMASK, 0),
+}
+
+_NO_VALIDITY_BUFFER = {
+    ColumnNullType.NON_NULLABLE: "This column is non-nullable",
+    ColumnNullType.USE_NAN: "This column uses NaN as null",
+    ColumnNullType.USE_SENTINEL: "This column uses a sentinel value",
+}
+
+
+class PandasColumn(Column):
+    """
+    A column object, with only the methods and properties required by the
+    interchange protocol defined.
+    A column can contain one or more chunks. Each chunk can contain up to three
+    buffers - a data buffer, a mask buffer (depending on null representation),
+    and an offsets buffer (if variable-size binary; e.g., variable-length
+    strings).
+    Note: this Column object can only be produced by ``__dataframe__``, so
+          doesn't need its own version or ``__column__`` protocol.
+    """
+
+    def __init__(self, column: pd.Series, allow_copy: bool = True) -> None:
+        """
+        Note: doesn't deal with extension arrays yet, just assume a regular
+        Series/ndarray for now.
+        """
+        if isinstance(column, pd.DataFrame):
+            raise TypeError(
+                "Expected a Series, got a DataFrame. This likely happened "
+                "because you called __dataframe__ on a DataFrame which, "
+                "after converting column names to string, resulted in duplicated "
+                f"names: {column.columns}. Please rename these columns before "
+                "using the interchange protocol."
+            )
+        if not isinstance(column, pd.Series):
+            raise NotImplementedError(f"Columns of type {type(column)} not handled yet")
+
+        # Store the column as a private attribute
+        self._col = column
+        self._allow_copy = allow_copy
+
+    def size(self) -> int:
+        """
+        Size of the column, in elements.
+        """
+        return self._col.size
+
+    @property
+    def offset(self) -> int:
+        """
+        Offset of first element. Always zero.
+        """
+        # TODO: chunks are implemented now, probably this should return something
+        return 0
+
+    @cache_readonly
+    def dtype(self) -> tuple[DtypeKind, int, str, str]:
+        dtype = self._col.dtype
+
+        if isinstance(dtype, pd.CategoricalDtype):
+            codes = self._col.values.codes
+            (
+                _,
+                bitwidth,
+                c_arrow_dtype_f_str,
+                _,
+            ) = self._dtype_from_pandasdtype(codes.dtype)
+            return (
+                DtypeKind.CATEGORICAL,
+                bitwidth,
+                c_arrow_dtype_f_str,
+                Endianness.NATIVE,
+            )
+        elif is_string_dtype(dtype):
+            if infer_dtype(self._col) in ("string", "empty"):
+                return (
+                    DtypeKind.STRING,
+                    8,
+                    dtype_to_arrow_c_fmt(dtype),
+                    Endianness.NATIVE,
+                )
+            raise NotImplementedError("Non-string object dtypes are not supported yet")
+        else:
+            return self._dtype_from_pandasdtype(dtype)
+
+    def _dtype_from_pandasdtype(self, dtype) -> tuple[DtypeKind, int, str, str]:
+        """
+        See `self.dtype` for details.
+        """
+        # Note: 'c' (complex) not handled yet (not in array spec v1).
+        #       'b', 'B' (bytes), 'S', 'a', (old-style string) 'V' (void) not handled
+        #       datetime and timedelta both map to datetime (is timedelta handled?)
+
+        kind = _NP_KINDS.get(dtype.kind, None)
+        if kind is None:
+            # Not a NumPy dtype. Check if it's a categorical maybe
+            raise ValueError(f"Data type {dtype} not supported by interchange protocol")
+        if isinstance(dtype, ArrowDtype):
+            byteorder = dtype.numpy_dtype.byteorder
+        elif isinstance(dtype, DatetimeTZDtype):
+            byteorder = dtype.base.byteorder  # type: ignore[union-attr]
+        elif isinstance(dtype, BaseMaskedDtype):
+            byteorder = dtype.numpy_dtype.byteorder
+        else:
+            byteorder = dtype.byteorder
+
+        if dtype == "bool[pyarrow]":
+            # return early to avoid the `* 8` below, as this is a bitmask
+            # rather than a bytemask
+            return (
+                kind,
+                dtype.itemsize,  # pyright: ignore[reportGeneralTypeIssues]
+                ArrowCTypes.BOOL,
+                byteorder,
+            )
+
+        return kind, dtype.itemsize * 8, dtype_to_arrow_c_fmt(dtype), byteorder
+
+    @property
+    def describe_categorical(self):
+        """
+        If the dtype is categorical, there are two options:
+        - There are only values in the data buffer.
+        - There is a separate non-categorical Column encoding for categorical values.
+
+        Raises TypeError if the dtype is not categorical
+
+        Content of returned dict:
+            - "is_ordered" : bool, whether the ordering of dictionary indices is
+                             semantically meaningful.
+            - "is_dictionary" : bool, whether a dictionary-style mapping of
+                                categorical values to other objects exists
+            - "categories" : Column representing the (implicit) mapping of indices to
+                             category values (e.g. an array of cat1, cat2, ...).
+                             None if not a dictionary-style categorical.
+        """
+        if not self.dtype[0] == DtypeKind.CATEGORICAL:
+            raise TypeError(
+                "describe_categorical only works on a column with categorical dtype!"
+            )
+
+        return {
+            "is_ordered": self._col.cat.ordered,
+            "is_dictionary": True,
+            "categories": PandasColumn(pd.Series(self._col.cat.categories)),
+        }
+
+    @property
+    def describe_null(self):
+        if isinstance(self._col.dtype, BaseMaskedDtype):
+            column_null_dtype = ColumnNullType.USE_BYTEMASK
+            null_value = 1
+            return column_null_dtype, null_value
+        if isinstance(self._col.dtype, ArrowDtype):
+            # We already rechunk (if necessary / allowed) upon initialization, so this
+            # is already single-chunk by the time we get here.
+            if self._col.array._pa_array.chunks[0].buffers()[0] is None:  # type: ignore[attr-defined]
+                return ColumnNullType.NON_NULLABLE, None
+            return ColumnNullType.USE_BITMASK, 0
+        kind = self.dtype[0]
+        try:
+            null, value = _NULL_DESCRIPTION[kind]
+        except KeyError:
+            raise NotImplementedError(f"Data type {kind} not yet supported")
+
+        return null, value
+
+    @cache_readonly
+    def null_count(self) -> int:
+        """
+        Number of null elements. Should always be known.
+        """
+        return self._col.isna().sum().item()
+
+    @property
+    def metadata(self) -> dict[str, pd.Index]:
+        """
+        Store specific metadata of the column.
+        """
+        return {"pandas.index": self._col.index}
+
+    def num_chunks(self) -> int:
+        """
+        Return the number of chunks the column consists of.
+        """
+        return 1
+
+    def get_chunks(self, n_chunks: int | None = None):
+        """
+        Return an iterator yielding the chunks.
+        See `DataFrame.get_chunks` for details on ``n_chunks``.
+        """
+        if n_chunks and n_chunks > 1:
+            size = len(self._col)
+            step = size // n_chunks
+            if size % n_chunks != 0:
+                step += 1
+            for start in range(0, step * n_chunks, step):
+                yield PandasColumn(
+                    self._col.iloc[start : start + step], self._allow_copy
+                )
+        else:
+            yield self
+
+    def get_buffers(self) -> ColumnBuffers:
+        """
+        Return a dictionary containing the underlying buffers.
+        The returned dictionary has the following contents:
+            - "data": a two-element tuple whose first element is a buffer
+                      containing the data and whose second element is the data
+                      buffer's associated dtype.
+            - "validity": a two-element tuple whose first element is a buffer
+                          containing mask values indicating missing data and
+                          whose second element is the mask value buffer's
+                          associated dtype. None if the null representation is
+                          not a bit or byte mask.
+            - "offsets": a two-element tuple whose first element is a buffer
+                         containing the offset values for variable-size binary
+                         data (e.g., variable-length strings) and whose second
+                         element is the offsets buffer's associated dtype. None
+                         if the data buffer does not have an associated offsets
+                         buffer.
+        """
+        buffers: ColumnBuffers = {
+            "data": self._get_data_buffer(),
+            "validity": None,
+            "offsets": None,
+        }
+
+        try:
+            buffers["validity"] = self._get_validity_buffer()
+        except NoBufferPresent:
+            pass
+
+        try:
+            buffers["offsets"] = self._get_offsets_buffer()
+        except NoBufferPresent:
+            pass
+
+        return buffers
+
+    def _get_data_buffer(
+        self,
+    ) -> tuple[Buffer, tuple[DtypeKind, int, str, str]]:
+        """
+        Return the buffer containing the data and the buffer's associated dtype.
+        """
+        buffer: Buffer
+        if self.dtype[0] in (
+            DtypeKind.INT,
+            DtypeKind.UINT,
+            DtypeKind.FLOAT,
+            DtypeKind.BOOL,
+            DtypeKind.DATETIME,
+        ):
+            # self.dtype[2] is an ArrowCTypes.TIMESTAMP where the tz will make
+            # it longer than 4 characters
+            dtype = self.dtype
+            if self.dtype[0] == DtypeKind.DATETIME and len(self.dtype[2]) > 4:
+                np_arr = self._col.dt.tz_convert(None).to_numpy()
+            else:
+                arr = self._col.array
+                if isinstance(self._col.dtype, BaseMaskedDtype):
+                    np_arr = arr._data  # type: ignore[attr-defined]
+                elif isinstance(self._col.dtype, ArrowDtype):
+                    # We already rechunk (if necessary / allowed) upon initialization,
+                    # so this is already single-chunk by the time we get here.
+                    arr = arr._pa_array.chunks[0]  # type: ignore[attr-defined]
+                    buffer = PandasBufferPyarrow(
+                        arr.buffers()[1],  # type: ignore[attr-defined]
+                        length=len(arr),
+                    )
+                    return buffer, dtype
+                else:
+                    np_arr = arr._ndarray  # type: ignore[attr-defined]
+            buffer = PandasBuffer(np_arr, allow_copy=self._allow_copy)
+        elif self.dtype[0] == DtypeKind.CATEGORICAL:
+            codes = self._col.values._codes
+            buffer = PandasBuffer(codes, allow_copy=self._allow_copy)
+            dtype = self._dtype_from_pandasdtype(codes.dtype)
+        elif self.dtype[0] == DtypeKind.STRING:
+            # Marshal the strings from a NumPy object array into a byte array
+            buf = self._col.to_numpy()
+            b = bytearray()
+
+            # TODO: this for-loop is slow; can be implemented in Cython/C/C++ later
+            for obj in buf:
+                if isinstance(obj, str):
+                    b.extend(obj.encode(encoding="utf-8"))
+
+            # Convert the byte array to a Pandas "buffer" using
+            # a NumPy array as the backing store
+            buffer = PandasBuffer(np.frombuffer(b, dtype="uint8"))
+
+            # Define the dtype for the returned buffer
+            # TODO: this will need correcting
+            # https://github.com/pandas-dev/pandas/issues/54781
+            dtype = self.dtype
+        else:
+            raise NotImplementedError(f"Data type {self._col.dtype} not handled yet")
+
+        return buffer, dtype
+
+    def _get_validity_buffer(self) -> tuple[Buffer, Any] | None:
+        """
+        Return the buffer containing the mask values indicating missing data and
+        the buffer's associated dtype.
+        Raises NoBufferPresent if null representation is not a bit or byte mask.
+        """
+        null, invalid = self.describe_null
+        buffer: Buffer
+        if isinstance(self._col.dtype, ArrowDtype):
+            # We already rechunk (if necessary / allowed) upon initialization, so this
+            # is already single-chunk by the time we get here.
+            arr = self._col.array._pa_array.chunks[0]  # type: ignore[attr-defined]
+            dtype = (DtypeKind.BOOL, 1, ArrowCTypes.BOOL, Endianness.NATIVE)
+            if arr.buffers()[0] is None:
+                return None
+            buffer = PandasBufferPyarrow(
+                arr.buffers()[0],
+                length=len(arr),
+            )
+            return buffer, dtype
+
+        if isinstance(self._col.dtype, BaseMaskedDtype):
+            mask = self._col.array._mask  # type: ignore[attr-defined]
+            buffer = PandasBuffer(mask)
+            dtype = (DtypeKind.BOOL, 8, ArrowCTypes.BOOL, Endianness.NATIVE)
+            return buffer, dtype
+
+        if self.dtype[0] == DtypeKind.STRING:
+            # For now, use byte array as the mask.
+            # TODO: maybe store as bit array to save space?..
+            buf = self._col.to_numpy()
+
+            # Determine the encoding for valid values
+            valid = invalid == 0
+            invalid = not valid
+
+            mask = np.zeros(shape=(len(buf),), dtype=np.bool_)
+            for i, obj in enumerate(buf):
+                mask[i] = valid if isinstance(obj, str) else invalid
+
+            # Convert the mask array to a Pandas "buffer" using
+            # a NumPy array as the backing store
+            buffer = PandasBuffer(mask)
+
+            # Define the dtype of the returned buffer
+            dtype = (DtypeKind.BOOL, 8, ArrowCTypes.BOOL, Endianness.NATIVE)
+
+            return buffer, dtype
+
+        try:
+            msg = f"{_NO_VALIDITY_BUFFER[null]} so does not have a separate mask"
+        except KeyError:
+            # TODO: implement for other bit/byte masks?
+            raise NotImplementedError("See self.describe_null")
+
+        raise NoBufferPresent(msg)
+
+    def _get_offsets_buffer(self) -> tuple[PandasBuffer, Any]:
+        """
+        Return the buffer containing the offset values for variable-size binary
+        data (e.g., variable-length strings) and the buffer's associated dtype.
+        Raises NoBufferPresent if the data buffer does not have an associated
+        offsets buffer.
+        """
+        if self.dtype[0] == DtypeKind.STRING:
+            # For each string, we need to manually determine the next offset
+            values = self._col.to_numpy()
+            ptr = 0
+            offsets = np.zeros(shape=(len(values) + 1,), dtype=np.int64)
+            for i, v in enumerate(values):
+                # For missing values (in this case, `np.nan` values)
+                # we don't increment the pointer
+                if isinstance(v, str):
+                    b = v.encode(encoding="utf-8")
+                    ptr += len(b)
+
+                offsets[i + 1] = ptr
+
+            # Convert the offsets to a Pandas "buffer" using
+            # the NumPy array as the backing store
+            buffer = PandasBuffer(offsets)
+
+            # Assemble the buffer dtype info
+            dtype = (
+                DtypeKind.INT,
+                64,
+                ArrowCTypes.INT64,
+                Endianness.NATIVE,
+            )  # note: currently only support native endianness
+        else:
+            raise NoBufferPresent(
+                "This column has a fixed-length dtype so "
+                "it does not have an offsets buffer"
+            )
+
+        return buffer, dtype
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/interchange/dataframe.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/interchange/dataframe.py
new file mode 100644
index 0000000000000000000000000000000000000000..1abacddfc7e3b7035a9446cd4118097c6accd385
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/interchange/dataframe.py
@@ -0,0 +1,113 @@
+from __future__ import annotations
+
+from collections import abc
+from typing import TYPE_CHECKING
+
+from pandas.core.interchange.column import PandasColumn
+from pandas.core.interchange.dataframe_protocol import DataFrame as DataFrameXchg
+from pandas.core.interchange.utils import maybe_rechunk
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Iterable,
+        Sequence,
+    )
+
+    from pandas import (
+        DataFrame,
+        Index,
+    )
+
+
+class PandasDataFrameXchg(DataFrameXchg):
+    """
+    A data frame class, with only the methods required by the interchange
+    protocol defined.
+    Instances of this (private) class are returned from
+    ``pd.DataFrame.__dataframe__`` as objects with the methods and
+    attributes defined on this class.
+    """
+
+    def __init__(self, df: DataFrame, allow_copy: bool = True) -> None:
+        """
+        Constructor - an instance of this (private) class is returned from
+        `pd.DataFrame.__dataframe__`.
+        """
+        self._df = df.rename(columns=str, copy=False)
+        self._allow_copy = allow_copy
+        for i, _col in enumerate(self._df.columns):
+            rechunked = maybe_rechunk(self._df.iloc[:, i], allow_copy=allow_copy)
+            if rechunked is not None:
+                self._df.isetitem(i, rechunked)
+
+    def __dataframe__(
+        self, nan_as_null: bool = False, allow_copy: bool = True
+    ) -> PandasDataFrameXchg:
+        # `nan_as_null` can be removed here once it's removed from
+        # Dataframe.__dataframe__
+        return PandasDataFrameXchg(self._df, allow_copy)
+
+    @property
+    def metadata(self) -> dict[str, Index]:
+        # `index` isn't a regular column, and the protocol doesn't support row
+        # labels - so we export it as Pandas-specific metadata here.
+        return {"pandas.index": self._df.index}
+
+    def num_columns(self) -> int:
+        return len(self._df.columns)
+
+    def num_rows(self) -> int:
+        return len(self._df)
+
+    def num_chunks(self) -> int:
+        return 1
+
+    def column_names(self) -> Index:
+        return self._df.columns
+
+    def get_column(self, i: int) -> PandasColumn:
+        return PandasColumn(self._df.iloc[:, i], allow_copy=self._allow_copy)
+
+    def get_column_by_name(self, name: str) -> PandasColumn:
+        return PandasColumn(self._df[name], allow_copy=self._allow_copy)
+
+    def get_columns(self) -> list[PandasColumn]:
+        return [
+            PandasColumn(self._df[name], allow_copy=self._allow_copy)
+            for name in self._df.columns
+        ]
+
+    def select_columns(self, indices: Sequence[int]) -> PandasDataFrameXchg:
+        if not isinstance(indices, abc.Sequence):
+            raise ValueError("`indices` is not a sequence")
+        if not isinstance(indices, list):
+            indices = list(indices)
+
+        return PandasDataFrameXchg(
+            self._df.iloc[:, indices], allow_copy=self._allow_copy
+        )
+
+    def select_columns_by_name(self, names: list[str]) -> PandasDataFrameXchg:  # type: ignore[override]
+        if not isinstance(names, abc.Sequence):
+            raise ValueError("`names` is not a sequence")
+        if not isinstance(names, list):
+            names = list(names)
+
+        return PandasDataFrameXchg(self._df.loc[:, names], allow_copy=self._allow_copy)
+
+    def get_chunks(self, n_chunks: int | None = None) -> Iterable[PandasDataFrameXchg]:
+        """
+        Return an iterator yielding the chunks.
+        """
+        if n_chunks and n_chunks > 1:
+            size = len(self._df)
+            step = size // n_chunks
+            if size % n_chunks != 0:
+                step += 1
+            for start in range(0, step * n_chunks, step):
+                yield PandasDataFrameXchg(
+                    self._df.iloc[start : start + step, :],
+                    allow_copy=self._allow_copy,
+                )
+        else:
+            yield self
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/interchange/dataframe_protocol.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/interchange/dataframe_protocol.py
new file mode 100644
index 0000000000000000000000000000000000000000..95e7b6a26f93a8cd10048076bd6906190e04d2ba
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/interchange/dataframe_protocol.py
@@ -0,0 +1,465 @@
+"""
+A verbatim copy (vendored) of the spec from https://github.com/data-apis/dataframe-api
+"""
+
+from __future__ import annotations
+
+from abc import (
+    ABC,
+    abstractmethod,
+)
+import enum
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    TypedDict,
+)
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Iterable,
+        Sequence,
+    )
+
+
+class DlpackDeviceType(enum.IntEnum):
+    """Integer enum for device type codes matching DLPack."""
+
+    CPU = 1
+    CUDA = 2
+    CPU_PINNED = 3
+    OPENCL = 4
+    VULKAN = 7
+    METAL = 8
+    VPI = 9
+    ROCM = 10
+
+
+class DtypeKind(enum.IntEnum):
+    """
+    Integer enum for data types.
+
+    Attributes
+    ----------
+    INT : int
+        Matches to signed integer data type.
+    UINT : int
+        Matches to unsigned integer data type.
+    FLOAT : int
+        Matches to floating point data type.
+    BOOL : int
+        Matches to boolean data type.
+    STRING : int
+        Matches to string data type (UTF-8 encoded).
+    DATETIME : int
+        Matches to datetime data type.
+    CATEGORICAL : int
+        Matches to categorical data type.
+    """
+
+    INT = 0
+    UINT = 1
+    FLOAT = 2
+    BOOL = 20
+    STRING = 21  # UTF-8
+    DATETIME = 22
+    CATEGORICAL = 23
+
+
+class ColumnNullType(enum.IntEnum):
+    """
+    Integer enum for null type representation.
+
+    Attributes
+    ----------
+    NON_NULLABLE : int
+        Non-nullable column.
+    USE_NAN : int
+        Use explicit float NaN value.
+    USE_SENTINEL : int
+        Sentinel value besides NaN/NaT.
+    USE_BITMASK : int
+        The bit is set/unset representing a null on a certain position.
+    USE_BYTEMASK : int
+        The byte is set/unset representing a null on a certain position.
+    """
+
+    NON_NULLABLE = 0
+    USE_NAN = 1
+    USE_SENTINEL = 2
+    USE_BITMASK = 3
+    USE_BYTEMASK = 4
+
+
+class ColumnBuffers(TypedDict):
+    # first element is a buffer containing the column data;
+    # second element is the data buffer's associated dtype
+    data: tuple[Buffer, Any]
+
+    # first element is a buffer containing mask values indicating missing data;
+    # second element is the mask value buffer's associated dtype.
+    # None if the null representation is not a bit or byte mask
+    validity: tuple[Buffer, Any] | None
+
+    # first element is a buffer containing the offset values for
+    # variable-size binary data (e.g., variable-length strings);
+    # second element is the offsets buffer's associated dtype.
+    # None if the data buffer does not have an associated offsets buffer
+    offsets: tuple[Buffer, Any] | None
+
+
+class CategoricalDescription(TypedDict):
+    # whether the ordering of dictionary indices is semantically meaningful
+    is_ordered: bool
+    # whether a dictionary-style mapping of categorical values to other objects exists
+    is_dictionary: bool
+    # Python-level only (e.g. ``{int: str}``).
+    # None if not a dictionary-style categorical.
+    categories: Column | None
+
+
+class Buffer(ABC):
+    """
+    Data in the buffer is guaranteed to be contiguous in memory.
+
+    Note that there is no dtype attribute present, a buffer can be thought of
+    as simply a block of memory. However, if the column that the buffer is
+    attached to has a dtype that's supported by DLPack and ``__dlpack__`` is
+    implemented, then that dtype information will be contained in the return
+    value from ``__dlpack__``.
+
+    This distinction is useful to support both data exchange via DLPack on a
+    buffer and (b) dtypes like variable-length strings which do not have a
+    fixed number of bytes per element.
+    """
+
+    @property
+    @abstractmethod
+    def bufsize(self) -> int:
+        """
+        Buffer size in bytes.
+        """
+
+    @property
+    @abstractmethod
+    def ptr(self) -> int:
+        """
+        Pointer to start of the buffer as an integer.
+        """
+
+    @abstractmethod
+    def __dlpack__(self):
+        """
+        Produce DLPack capsule (see array API standard).
+
+        Raises:
+
+            - TypeError : if the buffer contains unsupported dtypes.
+            - NotImplementedError : if DLPack support is not implemented
+
+        Useful to have to connect to array libraries. Support optional because
+        it's not completely trivial to implement for a Python-only library.
+        """
+        raise NotImplementedError("__dlpack__")
+
+    @abstractmethod
+    def __dlpack_device__(self) -> tuple[DlpackDeviceType, int | None]:
+        """
+        Device type and device ID for where the data in the buffer resides.
+        Uses device type codes matching DLPack.
+        Note: must be implemented even if ``__dlpack__`` is not.
+        """
+
+
+class Column(ABC):
+    """
+    A column object, with only the methods and properties required by the
+    interchange protocol defined.
+
+    A column can contain one or more chunks. Each chunk can contain up to three
+    buffers - a data buffer, a mask buffer (depending on null representation),
+    and an offsets buffer (if variable-size binary; e.g., variable-length
+    strings).
+
+    TBD: Arrow has a separate "null" dtype, and has no separate mask concept.
+         Instead, it seems to use "children" for both columns with a bit mask,
+         and for nested dtypes. Unclear whether this is elegant or confusing.
+         This design requires checking the null representation explicitly.
+
+         The Arrow design requires checking:
+         1. the ARROW_FLAG_NULLABLE (for sentinel values)
+         2. if a column has two children, combined with one of those children
+            having a null dtype.
+
+         Making the mask concept explicit seems useful. One null dtype would
+         not be enough to cover both bit and byte masks, so that would mean
+         even more checking if we did it the Arrow way.
+
+    TBD: there's also the "chunk" concept here, which is implicit in Arrow as
+         multiple buffers per array (= column here). Semantically it may make
+         sense to have both: chunks were meant for example for lazy evaluation
+         of data which doesn't fit in memory, while multiple buffers per column
+         could also come from doing a selection operation on a single
+         contiguous buffer.
+
+         Given these concepts, one would expect chunks to be all of the same
+         size (say a 10,000 row dataframe could have 10 chunks of 1,000 rows),
+         while multiple buffers could have data-dependent lengths. Not an issue
+         in pandas if one column is backed by a single NumPy array, but in
+         Arrow it seems possible.
+         Are multiple chunks *and* multiple buffers per column necessary for
+         the purposes of this interchange protocol, or must producers either
+         reuse the chunk concept for this or copy the data?
+
+    Note: this Column object can only be produced by ``__dataframe__``, so
+          doesn't need its own version or ``__column__`` protocol.
+    """
+
+    @abstractmethod
+    def size(self) -> int:
+        """
+        Size of the column, in elements.
+
+        Corresponds to DataFrame.num_rows() if column is a single chunk;
+        equal to size of this current chunk otherwise.
+        """
+
+    @property
+    @abstractmethod
+    def offset(self) -> int:
+        """
+        Offset of first element.
+
+        May be > 0 if using chunks; for example for a column with N chunks of
+        equal size M (only the last chunk may be shorter),
+        ``offset = n * M``, ``n = 0 .. N-1``.
+        """
+
+    @property
+    @abstractmethod
+    def dtype(self) -> tuple[DtypeKind, int, str, str]:
+        """
+        Dtype description as a tuple ``(kind, bit-width, format string, endianness)``.
+
+        Bit-width : the number of bits as an integer
+        Format string : data type description format string in Apache Arrow C
+                        Data Interface format.
+        Endianness : current only native endianness (``=``) is supported
+
+        Notes:
+            - Kind specifiers are aligned with DLPack where possible (hence the
+              jump to 20, leave enough room for future extension)
+            - Masks must be specified as boolean with either bit width 1 (for bit
+              masks) or 8 (for byte masks).
+            - Dtype width in bits was preferred over bytes
+            - Endianness isn't too useful, but included now in case in the future
+              we need to support non-native endianness
+            - Went with Apache Arrow format strings over NumPy format strings
+              because they're more complete from a dataframe perspective
+            - Format strings are mostly useful for datetime specification, and
+              for categoricals.
+            - For categoricals, the format string describes the type of the
+              categorical in the data buffer. In case of a separate encoding of
+              the categorical (e.g. an integer to string mapping), this can
+              be derived from ``self.describe_categorical``.
+            - Data types not included: complex, Arrow-style null, binary, decimal,
+              and nested (list, struct, map, union) dtypes.
+        """
+
+    @property
+    @abstractmethod
+    def describe_categorical(self) -> CategoricalDescription:
+        """
+        If the dtype is categorical, there are two options:
+        - There are only values in the data buffer.
+        - There is a separate non-categorical Column encoding for categorical values.
+
+        Raises TypeError if the dtype is not categorical
+
+        Returns the dictionary with description on how to interpret the data buffer:
+            - "is_ordered" : bool, whether the ordering of dictionary indices is
+                             semantically meaningful.
+            - "is_dictionary" : bool, whether a mapping of
+                                categorical values to other objects exists
+            - "categories" : Column representing the (implicit) mapping of indices to
+                             category values (e.g. an array of cat1, cat2, ...).
+                             None if not a dictionary-style categorical.
+
+        TBD: are there any other in-memory representations that are needed?
+        """
+
+    @property
+    @abstractmethod
+    def describe_null(self) -> tuple[ColumnNullType, Any]:
+        """
+        Return the missing value (or "null") representation the column dtype
+        uses, as a tuple ``(kind, value)``.
+
+        Value : if kind is "sentinel value", the actual value. If kind is a bit
+        mask or a byte mask, the value (0 or 1) indicating a missing value. None
+        otherwise.
+        """
+
+    @property
+    @abstractmethod
+    def null_count(self) -> int | None:
+        """
+        Number of null elements, if known.
+
+        Note: Arrow uses -1 to indicate "unknown", but None seems cleaner.
+        """
+
+    @property
+    @abstractmethod
+    def metadata(self) -> dict[str, Any]:
+        """
+        The metadata for the column. See `DataFrame.metadata` for more details.
+        """
+
+    @abstractmethod
+    def num_chunks(self) -> int:
+        """
+        Return the number of chunks the column consists of.
+        """
+
+    @abstractmethod
+    def get_chunks(self, n_chunks: int | None = None) -> Iterable[Column]:
+        """
+        Return an iterator yielding the chunks.
+
+        See `DataFrame.get_chunks` for details on ``n_chunks``.
+        """
+
+    @abstractmethod
+    def get_buffers(self) -> ColumnBuffers:
+        """
+        Return a dictionary containing the underlying buffers.
+
+        The returned dictionary has the following contents:
+
+            - "data": a two-element tuple whose first element is a buffer
+                      containing the data and whose second element is the data
+                      buffer's associated dtype.
+            - "validity": a two-element tuple whose first element is a buffer
+                          containing mask values indicating missing data and
+                          whose second element is the mask value buffer's
+                          associated dtype. None if the null representation is
+                          not a bit or byte mask.
+            - "offsets": a two-element tuple whose first element is a buffer
+                         containing the offset values for variable-size binary
+                         data (e.g., variable-length strings) and whose second
+                         element is the offsets buffer's associated dtype. None
+                         if the data buffer does not have an associated offsets
+                         buffer.
+        """
+
+
+#    def get_children(self) -> Iterable[Column]:
+#        """
+#        Children columns underneath the column, each object in this iterator
+#        must adhere to the column specification.
+#        """
+#        pass
+
+
+class DataFrame(ABC):
+    """
+    A data frame class, with only the methods required by the interchange
+    protocol defined.
+
+    A "data frame" represents an ordered collection of named columns.
+    A column's "name" must be a unique string.
+    Columns may be accessed by name or by position.
+
+    This could be a public data frame class, or an object with the methods and
+    attributes defined on this DataFrame class could be returned from the
+    ``__dataframe__`` method of a public data frame class in a library adhering
+    to the dataframe interchange protocol specification.
+    """
+
+    version = 0  # version of the protocol
+
+    @abstractmethod
+    def __dataframe__(self, nan_as_null: bool = False, allow_copy: bool = True):
+        """Construct a new interchange object, potentially changing the parameters."""
+
+    @property
+    @abstractmethod
+    def metadata(self) -> dict[str, Any]:
+        """
+        The metadata for the data frame, as a dictionary with string keys. The
+        contents of `metadata` may be anything, they are meant for a library
+        to store information that it needs to, e.g., roundtrip losslessly or
+        for two implementations to share data that is not (yet) part of the
+        interchange protocol specification. For avoiding collisions with other
+        entries, please add name the keys with the name of the library
+        followed by a period and the desired name, e.g, ``pandas.indexcol``.
+        """
+
+    @abstractmethod
+    def num_columns(self) -> int:
+        """
+        Return the number of columns in the DataFrame.
+        """
+
+    @abstractmethod
+    def num_rows(self) -> int | None:
+        # TODO: not happy with Optional, but need to flag it may be expensive
+        #       why include it if it may be None - what do we expect consumers
+        #       to do here?
+        """
+        Return the number of rows in the DataFrame, if available.
+        """
+
+    @abstractmethod
+    def num_chunks(self) -> int:
+        """
+        Return the number of chunks the DataFrame consists of.
+        """
+
+    @abstractmethod
+    def column_names(self) -> Iterable[str]:
+        """
+        Return an iterator yielding the column names.
+        """
+
+    @abstractmethod
+    def get_column(self, i: int) -> Column:
+        """
+        Return the column at the indicated position.
+        """
+
+    @abstractmethod
+    def get_column_by_name(self, name: str) -> Column:
+        """
+        Return the column whose name is the indicated name.
+        """
+
+    @abstractmethod
+    def get_columns(self) -> Iterable[Column]:
+        """
+        Return an iterator yielding the columns.
+        """
+
+    @abstractmethod
+    def select_columns(self, indices: Sequence[int]) -> DataFrame:
+        """
+        Create a new DataFrame by selecting a subset of columns by index.
+        """
+
+    @abstractmethod
+    def select_columns_by_name(self, names: Sequence[str]) -> DataFrame:
+        """
+        Create a new DataFrame by selecting a subset of columns by name.
+        """
+
+    @abstractmethod
+    def get_chunks(self, n_chunks: int | None = None) -> Iterable[DataFrame]:
+        """
+        Return an iterator yielding the chunks.
+
+        By default (None), yields the chunks that the data is stored as by the
+        producer. If given, ``n_chunks`` must be a multiple of
+        ``self.num_chunks()``, meaning the producer must subdivide each chunk
+        before yielding it.
+        """
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/interchange/from_dataframe.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/interchange/from_dataframe.py
new file mode 100644
index 0000000000000000000000000000000000000000..c0df1c17e3a7c5276ff055e4fe23b5fca90988ca
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/interchange/from_dataframe.py
@@ -0,0 +1,557 @@
+from __future__ import annotations
+
+import ctypes
+import re
+from typing import Any
+
+import numpy as np
+
+from pandas._config import using_string_dtype
+
+from pandas.compat._optional import import_optional_dependency
+from pandas.errors import SettingWithCopyError
+
+import pandas as pd
+from pandas.core.interchange.dataframe_protocol import (
+    Buffer,
+    Column,
+    ColumnNullType,
+    DataFrame as DataFrameXchg,
+    DtypeKind,
+)
+from pandas.core.interchange.utils import (
+    ArrowCTypes,
+    Endianness,
+)
+
+_NP_DTYPES: dict[DtypeKind, dict[int, Any]] = {
+    DtypeKind.INT: {8: np.int8, 16: np.int16, 32: np.int32, 64: np.int64},
+    DtypeKind.UINT: {8: np.uint8, 16: np.uint16, 32: np.uint32, 64: np.uint64},
+    DtypeKind.FLOAT: {32: np.float32, 64: np.float64},
+    DtypeKind.BOOL: {1: bool, 8: bool},
+}
+
+
+def from_dataframe(df, allow_copy: bool = True) -> pd.DataFrame:
+    """
+    Build a ``pd.DataFrame`` from any DataFrame supporting the interchange protocol.
+
+    .. note::
+
+       For new development, we highly recommend using the Arrow C Data Interface
+       alongside the Arrow PyCapsule Interface instead of the interchange protocol.
+       From pandas 2.3 onwards, `from_dataframe` uses the PyCapsule Interface,
+       only falling back to the interchange protocol if that fails.
+
+    .. warning::
+
+        Due to severe implementation issues, we recommend only considering using the
+        interchange protocol in the following cases:
+
+        - converting to pandas: for pandas >= 2.0.3
+        - converting from pandas: for pandas >= 3.0.0
+
+    Parameters
+    ----------
+    df : DataFrameXchg
+        Object supporting the interchange protocol, i.e. `__dataframe__` method.
+    allow_copy : bool, default: True
+        Whether to allow copying the memory to perform the conversion
+        (if false then zero-copy approach is requested).
+
+    Returns
+    -------
+    pd.DataFrame
+
+    Examples
+    --------
+    >>> df_not_necessarily_pandas = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
+    >>> interchange_object = df_not_necessarily_pandas.__dataframe__()
+    >>> interchange_object.column_names()
+    Index(['A', 'B'], dtype='object')
+    >>> df_pandas = (pd.api.interchange.from_dataframe
+    ...              (interchange_object.select_columns_by_name(['A'])))
+    >>> df_pandas
+         A
+    0    1
+    1    2
+
+    These methods (``column_names``, ``select_columns_by_name``) should work
+    for any dataframe library which implements the interchange protocol.
+    """
+    if isinstance(df, pd.DataFrame):
+        return df
+
+    if hasattr(df, "__arrow_c_stream__"):
+        try:
+            pa = import_optional_dependency("pyarrow", min_version="14.0.0")
+        except ImportError:
+            # fallback to _from_dataframe
+            pass
+        else:
+            try:
+                return pa.table(df).to_pandas(zero_copy_only=not allow_copy)
+            except pa.ArrowInvalid as e:
+                raise RuntimeError(e) from e
+
+    if not hasattr(df, "__dataframe__"):
+        raise ValueError("`df` does not support __dataframe__")
+
+    return _from_dataframe(
+        df.__dataframe__(allow_copy=allow_copy), allow_copy=allow_copy
+    )
+
+
+def _from_dataframe(df: DataFrameXchg, allow_copy: bool = True):
+    """
+    Build a ``pd.DataFrame`` from the DataFrame interchange object.
+
+    Parameters
+    ----------
+    df : DataFrameXchg
+        Object supporting the interchange protocol, i.e. `__dataframe__` method.
+    allow_copy : bool, default: True
+        Whether to allow copying the memory to perform the conversion
+        (if false then zero-copy approach is requested).
+
+    Returns
+    -------
+    pd.DataFrame
+    """
+    pandas_dfs = []
+    for chunk in df.get_chunks():
+        pandas_df = protocol_df_chunk_to_pandas(chunk)
+        pandas_dfs.append(pandas_df)
+
+    if not allow_copy and len(pandas_dfs) > 1:
+        raise RuntimeError(
+            "To join chunks a copy is required which is forbidden by allow_copy=False"
+        )
+    if not pandas_dfs:
+        pandas_df = protocol_df_chunk_to_pandas(df)
+    elif len(pandas_dfs) == 1:
+        pandas_df = pandas_dfs[0]
+    else:
+        pandas_df = pd.concat(pandas_dfs, axis=0, ignore_index=True, copy=False)
+
+    index_obj = df.metadata.get("pandas.index", None)
+    if index_obj is not None:
+        pandas_df.index = index_obj
+
+    return pandas_df
+
+
+def protocol_df_chunk_to_pandas(df: DataFrameXchg) -> pd.DataFrame:
+    """
+    Convert interchange protocol chunk to ``pd.DataFrame``.
+
+    Parameters
+    ----------
+    df : DataFrameXchg
+
+    Returns
+    -------
+    pd.DataFrame
+    """
+    columns: dict[str, Any] = {}
+    buffers = []  # hold on to buffers, keeps memory alive
+    for name in df.column_names():
+        if not isinstance(name, str):
+            raise ValueError(f"Column {name} is not a string")
+        if name in columns:
+            raise ValueError(f"Column {name} is not unique")
+        col = df.get_column_by_name(name)
+        dtype = col.dtype[0]
+        if dtype in (
+            DtypeKind.INT,
+            DtypeKind.UINT,
+            DtypeKind.FLOAT,
+            DtypeKind.BOOL,
+        ):
+            columns[name], buf = primitive_column_to_ndarray(col)
+        elif dtype == DtypeKind.CATEGORICAL:
+            columns[name], buf = categorical_column_to_series(col)
+        elif dtype == DtypeKind.STRING:
+            columns[name], buf = string_column_to_ndarray(col)
+        elif dtype == DtypeKind.DATETIME:
+            columns[name], buf = datetime_column_to_ndarray(col)
+        else:
+            raise NotImplementedError(f"Data type {dtype} not handled yet")
+
+        buffers.append(buf)
+
+    pandas_df = pd.DataFrame(columns)
+    pandas_df.attrs["_INTERCHANGE_PROTOCOL_BUFFERS"] = buffers
+    return pandas_df
+
+
+def primitive_column_to_ndarray(col: Column) -> tuple[np.ndarray, Any]:
+    """
+    Convert a column holding one of the primitive dtypes to a NumPy array.
+
+    A primitive type is one of: int, uint, float, bool.
+
+    Parameters
+    ----------
+    col : Column
+
+    Returns
+    -------
+    tuple
+        Tuple of np.ndarray holding the data and the memory owner object
+        that keeps the memory alive.
+    """
+    buffers = col.get_buffers()
+
+    data_buff, data_dtype = buffers["data"]
+    data = buffer_to_ndarray(
+        data_buff, data_dtype, offset=col.offset, length=col.size()
+    )
+
+    data = set_nulls(data, col, buffers["validity"])
+    return data, buffers
+
+
+def categorical_column_to_series(col: Column) -> tuple[pd.Series, Any]:
+    """
+    Convert a column holding categorical data to a pandas Series.
+
+    Parameters
+    ----------
+    col : Column
+
+    Returns
+    -------
+    tuple
+        Tuple of pd.Series holding the data and the memory owner object
+        that keeps the memory alive.
+    """
+    categorical = col.describe_categorical
+
+    if not categorical["is_dictionary"]:
+        raise NotImplementedError("Non-dictionary categoricals not supported yet")
+
+    cat_column = categorical["categories"]
+    if hasattr(cat_column, "_col"):
+        # Item "Column" of "Optional[Column]" has no attribute "_col"
+        # Item "None" of "Optional[Column]" has no attribute "_col"
+        categories = np.array(cat_column._col)  # type: ignore[union-attr]
+    else:
+        raise NotImplementedError(
+            "Interchanging categorical columns isn't supported yet, and our "
+            "fallback of using the `col._col` attribute (a ndarray) failed."
+        )
+    buffers = col.get_buffers()
+
+    codes_buff, codes_dtype = buffers["data"]
+    codes = buffer_to_ndarray(
+        codes_buff, codes_dtype, offset=col.offset, length=col.size()
+    )
+
+    # Doing module in order to not get ``IndexError`` for
+    # out-of-bounds sentinel values in `codes`
+    if len(categories) > 0:
+        values = categories[codes % len(categories)]
+    else:
+        values = codes
+
+    cat = pd.Categorical(
+        values, categories=categories, ordered=categorical["is_ordered"]
+    )
+    data = pd.Series(cat)
+
+    data = set_nulls(data, col, buffers["validity"])
+    return data, buffers
+
+
+def string_column_to_ndarray(col: Column) -> tuple[np.ndarray, Any]:
+    """
+    Convert a column holding string data to a NumPy array.
+
+    Parameters
+    ----------
+    col : Column
+
+    Returns
+    -------
+    tuple
+        Tuple of np.ndarray holding the data and the memory owner object
+        that keeps the memory alive.
+    """
+    null_kind, sentinel_val = col.describe_null
+
+    if null_kind not in (
+        ColumnNullType.NON_NULLABLE,
+        ColumnNullType.USE_BITMASK,
+        ColumnNullType.USE_BYTEMASK,
+    ):
+        raise NotImplementedError(
+            f"{null_kind} null kind is not yet supported for string columns."
+        )
+
+    buffers = col.get_buffers()
+
+    assert buffers["offsets"], "String buffers must contain offsets"
+    # Retrieve the data buffer containing the UTF-8 code units
+    data_buff, _ = buffers["data"]
+    # We're going to reinterpret the buffer as uint8, so make sure we can do it safely
+    assert col.dtype[2] in (
+        ArrowCTypes.STRING,
+        ArrowCTypes.LARGE_STRING,
+    )  # format_str == utf-8
+    # Convert the buffers to NumPy arrays. In order to go from STRING to
+    # an equivalent ndarray, we claim that the buffer is uint8 (i.e., a byte array)
+    data_dtype = (
+        DtypeKind.UINT,
+        8,
+        ArrowCTypes.UINT8,
+        Endianness.NATIVE,
+    )
+    # Specify zero offset as we don't want to chunk the string data
+    data = buffer_to_ndarray(data_buff, data_dtype, offset=0, length=data_buff.bufsize)
+
+    # Retrieve the offsets buffer containing the index offsets demarcating
+    # the beginning and the ending of each string
+    offset_buff, offset_dtype = buffers["offsets"]
+    # Offsets buffer contains start-stop positions of strings in the data buffer,
+    # meaning that it has more elements than in the data buffer, do `col.size() + 1`
+    # here to pass a proper offsets buffer size
+    offsets = buffer_to_ndarray(
+        offset_buff, offset_dtype, offset=col.offset, length=col.size() + 1
+    )
+
+    null_pos = None
+    if null_kind in (ColumnNullType.USE_BITMASK, ColumnNullType.USE_BYTEMASK):
+        validity = buffers["validity"]
+        if validity is not None:
+            valid_buff, valid_dtype = validity
+            null_pos = buffer_to_ndarray(
+                valid_buff, valid_dtype, offset=col.offset, length=col.size()
+            )
+            if sentinel_val == 0:
+                null_pos = ~null_pos
+
+    # Assemble the strings from the code units
+    str_list: list[None | float | str] = [None] * col.size()
+    for i in range(col.size()):
+        # Check for missing values
+        if null_pos is not None and null_pos[i]:
+            str_list[i] = np.nan
+            continue
+
+        # Extract a range of code units
+        units = data[offsets[i] : offsets[i + 1]]
+
+        # Convert the list of code units to bytes
+        str_bytes = bytes(units)
+
+        # Create the string
+        string = str_bytes.decode(encoding="utf-8")
+
+        # Add to our list of strings
+        str_list[i] = string
+
+    if using_string_dtype():
+        res = pd.Series(str_list, dtype="str")
+    else:
+        res = np.asarray(str_list, dtype="object")  # type: ignore[assignment]
+
+    return res, buffers  # type: ignore[return-value]
+
+
+def parse_datetime_format_str(format_str, data) -> pd.Series | np.ndarray:
+    """Parse datetime `format_str` to interpret the `data`."""
+    # timestamp 'ts{unit}:tz'
+    timestamp_meta = re.match(r"ts([smun]):(.*)", format_str)
+    if timestamp_meta:
+        unit, tz = timestamp_meta.group(1), timestamp_meta.group(2)
+        if unit != "s":
+            # the format string describes only a first letter of the unit, so
+            # add one extra letter to convert the unit to numpy-style:
+            # 'm' -> 'ms', 'u' -> 'us', 'n' -> 'ns'
+            unit += "s"
+        data = data.astype(f"datetime64[{unit}]")
+        if tz != "":
+            data = pd.Series(data).dt.tz_localize("UTC").dt.tz_convert(tz)
+        return data
+
+    # date 'td{Days/Ms}'
+    date_meta = re.match(r"td([Dm])", format_str)
+    if date_meta:
+        unit = date_meta.group(1)
+        if unit == "D":
+            # NumPy doesn't support DAY unit, so converting days to seconds
+            # (converting to uint64 to avoid overflow)
+            data = (data.astype(np.uint64) * (24 * 60 * 60)).astype("datetime64[s]")
+        elif unit == "m":
+            data = data.astype("datetime64[ms]")
+        else:
+            raise NotImplementedError(f"Date unit is not supported: {unit}")
+        return data
+
+    raise NotImplementedError(f"DateTime kind is not supported: {format_str}")
+
+
+def datetime_column_to_ndarray(col: Column) -> tuple[np.ndarray | pd.Series, Any]:
+    """
+    Convert a column holding DateTime data to a NumPy array.
+
+    Parameters
+    ----------
+    col : Column
+
+    Returns
+    -------
+    tuple
+        Tuple of np.ndarray holding the data and the memory owner object
+        that keeps the memory alive.
+    """
+    buffers = col.get_buffers()
+
+    _, col_bit_width, format_str, _ = col.dtype
+    dbuf, _ = buffers["data"]
+    # Consider dtype being `uint` to get number of units passed since the 01.01.1970
+
+    data = buffer_to_ndarray(
+        dbuf,
+        (
+            DtypeKind.INT,
+            col_bit_width,
+            getattr(ArrowCTypes, f"INT{col_bit_width}"),
+            Endianness.NATIVE,
+        ),
+        offset=col.offset,
+        length=col.size(),
+    )
+
+    data = parse_datetime_format_str(format_str, data)  # type: ignore[assignment]
+    data = set_nulls(data, col, buffers["validity"])
+    return data, buffers
+
+
+def buffer_to_ndarray(
+    buffer: Buffer,
+    dtype: tuple[DtypeKind, int, str, str],
+    *,
+    length: int,
+    offset: int = 0,
+) -> np.ndarray:
+    """
+    Build a NumPy array from the passed buffer.
+
+    Parameters
+    ----------
+    buffer : Buffer
+        Buffer to build a NumPy array from.
+    dtype : tuple
+        Data type of the buffer conforming protocol dtypes format.
+    offset : int, default: 0
+        Number of elements to offset from the start of the buffer.
+    length : int, optional
+        If the buffer is a bit-mask, specifies a number of bits to read
+        from the buffer. Has no effect otherwise.
+
+    Returns
+    -------
+    np.ndarray
+
+    Notes
+    -----
+    The returned array doesn't own the memory. The caller of this function is
+    responsible for keeping the memory owner object alive as long as
+    the returned NumPy array is being used.
+    """
+    kind, bit_width, _, _ = dtype
+
+    column_dtype = _NP_DTYPES.get(kind, {}).get(bit_width, None)
+    if column_dtype is None:
+        raise NotImplementedError(f"Conversion for {dtype} is not yet supported.")
+
+    # TODO: No DLPack yet, so need to construct a new ndarray from the data pointer
+    # and size in the buffer plus the dtype on the column. Use DLPack as NumPy supports
+    # it since https://github.com/numpy/numpy/pull/19083
+    ctypes_type = np.ctypeslib.as_ctypes_type(column_dtype)
+
+    if bit_width == 1:
+        assert length is not None, "`length` must be specified for a bit-mask buffer."
+        pa = import_optional_dependency("pyarrow")
+        arr = pa.BooleanArray.from_buffers(
+            pa.bool_(),
+            length,
+            [None, pa.foreign_buffer(buffer.ptr, length)],
+            offset=offset,
+        )
+        return np.asarray(arr)
+    else:
+        data_pointer = ctypes.cast(
+            buffer.ptr + (offset * bit_width // 8), ctypes.POINTER(ctypes_type)
+        )
+        if length > 0:
+            return np.ctypeslib.as_array(data_pointer, shape=(length,))
+        return np.array([], dtype=ctypes_type)
+
+
+def set_nulls(
+    data: np.ndarray | pd.Series,
+    col: Column,
+    validity: tuple[Buffer, tuple[DtypeKind, int, str, str]] | None,
+    allow_modify_inplace: bool = True,
+):
+    """
+    Set null values for the data according to the column null kind.
+
+    Parameters
+    ----------
+    data : np.ndarray or pd.Series
+        Data to set nulls in.
+    col : Column
+        Column object that describes the `data`.
+    validity : tuple(Buffer, dtype) or None
+        The return value of ``col.buffers()``. We do not access the ``col.buffers()``
+        here to not take the ownership of the memory of buffer objects.
+    allow_modify_inplace : bool, default: True
+        Whether to modify the `data` inplace when zero-copy is possible (True) or always
+        modify a copy of the `data` (False).
+
+    Returns
+    -------
+    np.ndarray or pd.Series
+        Data with the nulls being set.
+    """
+    if validity is None:
+        return data
+    null_kind, sentinel_val = col.describe_null
+    null_pos = None
+
+    if null_kind == ColumnNullType.USE_SENTINEL:
+        null_pos = pd.Series(data) == sentinel_val
+    elif null_kind in (ColumnNullType.USE_BITMASK, ColumnNullType.USE_BYTEMASK):
+        assert validity, "Expected to have a validity buffer for the mask"
+        valid_buff, valid_dtype = validity
+        null_pos = buffer_to_ndarray(
+            valid_buff, valid_dtype, offset=col.offset, length=col.size()
+        )
+        if sentinel_val == 0:
+            null_pos = ~null_pos
+    elif null_kind in (ColumnNullType.NON_NULLABLE, ColumnNullType.USE_NAN):
+        pass
+    else:
+        raise NotImplementedError(f"Null kind {null_kind} is not yet supported.")
+
+    if null_pos is not None and np.any(null_pos):
+        if not allow_modify_inplace:
+            data = data.copy()
+        try:
+            data[null_pos] = None
+        except TypeError:
+            # TypeError happens if the `data` dtype appears to be non-nullable
+            # in numpy notation (bool, int, uint). If this happens,
+            # cast the `data` to nullable float dtype.
+            data = data.astype(float)
+            data[null_pos] = None
+        except SettingWithCopyError:
+            # `SettingWithCopyError` may happen for datetime-like with missing values.
+            data = data.copy()
+            data[null_pos] = None
+
+    return data
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/interchange/utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/interchange/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..035a1f8abdbc5d07ccec000d38c3fc159ccf94b6
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/interchange/utils.py
@@ -0,0 +1,183 @@
+"""
+Utility functions and objects for implementing the interchange API.
+"""
+
+from __future__ import annotations
+
+import typing
+
+import numpy as np
+
+from pandas._libs import lib
+
+from pandas.core.dtypes.dtypes import (
+    ArrowDtype,
+    CategoricalDtype,
+    DatetimeTZDtype,
+)
+
+import pandas as pd
+
+if typing.TYPE_CHECKING:
+    from pandas._typing import DtypeObj
+
+
+# Maps str(pyarrow.DataType) = C type format string
+# Currently, no pyarrow API for this
+PYARROW_CTYPES = {
+    "null": "n",
+    "bool": "b",
+    "uint8": "C",
+    "uint16": "S",
+    "uint32": "I",
+    "uint64": "L",
+    "int8": "c",
+    "int16": "S",
+    "int32": "i",
+    "int64": "l",
+    "halffloat": "e",  # float16
+    "float": "f",  # float32
+    "double": "g",  # float64
+    "string": "u",
+    "large_string": "U",
+    "binary": "z",
+    "time32[s]": "tts",
+    "time32[ms]": "ttm",
+    "time64[us]": "ttu",
+    "time64[ns]": "ttn",
+    "date32[day]": "tdD",
+    "date64[ms]": "tdm",
+    "timestamp[s]": "tss:",
+    "timestamp[ms]": "tsm:",
+    "timestamp[us]": "tsu:",
+    "timestamp[ns]": "tsn:",
+    "duration[s]": "tDs",
+    "duration[ms]": "tDm",
+    "duration[us]": "tDu",
+    "duration[ns]": "tDn",
+}
+
+
+class ArrowCTypes:
+    """
+    Enum for Apache Arrow C type format strings.
+
+    The Arrow C data interface:
+    https://arrow.apache.org/docs/format/CDataInterface.html#data-type-description-format-strings
+    """
+
+    NULL = "n"
+    BOOL = "b"
+    INT8 = "c"
+    UINT8 = "C"
+    INT16 = "s"
+    UINT16 = "S"
+    INT32 = "i"
+    UINT32 = "I"
+    INT64 = "l"
+    UINT64 = "L"
+    FLOAT16 = "e"
+    FLOAT32 = "f"
+    FLOAT64 = "g"
+    STRING = "u"  # utf-8
+    LARGE_STRING = "U"  # utf-8
+    DATE32 = "tdD"
+    DATE64 = "tdm"
+    # Resoulution:
+    #   - seconds -> 's'
+    #   - milliseconds -> 'm'
+    #   - microseconds -> 'u'
+    #   - nanoseconds -> 'n'
+    TIMESTAMP = "ts{resolution}:{tz}"
+    TIME = "tt{resolution}"
+
+
+class Endianness:
+    """Enum indicating the byte-order of a data-type."""
+
+    LITTLE = "<"
+    BIG = ">"
+    NATIVE = "="
+    NA = "|"
+
+
+def dtype_to_arrow_c_fmt(dtype: DtypeObj) -> str:
+    """
+    Represent pandas `dtype` as a format string in Apache Arrow C notation.
+
+    Parameters
+    ----------
+    dtype : np.dtype
+        Datatype of pandas DataFrame to represent.
+
+    Returns
+    -------
+    str
+        Format string in Apache Arrow C notation of the given `dtype`.
+    """
+    if isinstance(dtype, CategoricalDtype):
+        return ArrowCTypes.INT64
+    elif dtype == np.dtype("O"):
+        return ArrowCTypes.STRING
+    elif isinstance(dtype, ArrowDtype):
+        import pyarrow as pa
+
+        pa_type = dtype.pyarrow_dtype
+        if pa.types.is_decimal(pa_type):
+            return f"d:{pa_type.precision},{pa_type.scale}"
+        elif pa.types.is_timestamp(pa_type) and pa_type.tz is not None:
+            return f"ts{pa_type.unit[0]}:{pa_type.tz}"
+        format_str = PYARROW_CTYPES.get(str(pa_type), None)
+        if format_str is not None:
+            return format_str
+
+    format_str = getattr(ArrowCTypes, dtype.name.upper(), None)
+    if format_str is not None:
+        return format_str
+
+    if isinstance(dtype, pd.StringDtype):
+        # TODO(infer_string) this should be LARGE_STRING for pyarrow storage,
+        # but current tests don't cover this distinction
+        return ArrowCTypes.STRING
+
+    elif lib.is_np_dtype(dtype, "M"):
+        # Selecting the first char of resolution string:
+        # dtype.str -> ' 'n'
+        resolution = np.datetime_data(dtype)[0][0]
+        return ArrowCTypes.TIMESTAMP.format(resolution=resolution, tz="")
+
+    elif isinstance(dtype, DatetimeTZDtype):
+        return ArrowCTypes.TIMESTAMP.format(resolution=dtype.unit[0], tz=dtype.tz)
+
+    elif isinstance(dtype, pd.BooleanDtype):
+        return ArrowCTypes.BOOL
+
+    raise NotImplementedError(
+        f"Conversion of {dtype} to Arrow C format string is not implemented."
+    )
+
+
+def maybe_rechunk(series: pd.Series, *, allow_copy: bool) -> pd.Series | None:
+    """
+    Rechunk a multi-chunk pyarrow array into a single-chunk array, if necessary.
+
+    - Returns `None` if the input series is not backed by a multi-chunk pyarrow array
+      (and so doesn't need rechunking)
+    - Returns a single-chunk-backed-Series if the input is backed by a multi-chunk
+      pyarrow array and `allow_copy` is `True`.
+    - Raises a `RuntimeError` if `allow_copy` is `False` and input is a
+      based by a multi-chunk pyarrow array.
+    """
+    if not isinstance(series.dtype, pd.ArrowDtype):
+        return None
+    chunked_array = series.array._pa_array  # type: ignore[attr-defined]
+    if len(chunked_array.chunks) == 1:
+        return None
+    if not allow_copy:
+        raise RuntimeError(
+            "Found multi-chunk pyarrow array, but `allow_copy` is False. "
+            "Please rechunk the array before calling this function, or set "
+            "`allow_copy=True`."
+        )
+    arr = chunked_array.combine_chunks()
+    return pd.Series(arr, dtype=series.dtype, name=series.name, index=series.index)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/internals/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/internals/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..2eb413440ba9c1ef4c016cd874d19c2aba6d791e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/internals/__init__.py
@@ -0,0 +1,85 @@
+from pandas.core.internals.api import make_block  # 2023-09-18 pyarrow uses this
+from pandas.core.internals.array_manager import (
+    ArrayManager,
+    SingleArrayManager,
+)
+from pandas.core.internals.base import (
+    DataManager,
+    SingleDataManager,
+)
+from pandas.core.internals.concat import concatenate_managers
+from pandas.core.internals.managers import (
+    BlockManager,
+    SingleBlockManager,
+)
+
+__all__ = [
+    "Block",  # pylint: disable=undefined-all-variable
+    "DatetimeTZBlock",  # pylint: disable=undefined-all-variable
+    "ExtensionBlock",  # pylint: disable=undefined-all-variable
+    "make_block",
+    "DataManager",
+    "ArrayManager",
+    "BlockManager",
+    "SingleDataManager",
+    "SingleBlockManager",
+    "SingleArrayManager",
+    "concatenate_managers",
+]
+
+
+def __getattr__(name: str):
+    # GH#55139
+    import warnings
+
+    if name == "create_block_manager_from_blocks":
+        # GH#33892
+        warnings.warn(
+            f"{name} is deprecated and will be removed in a future version. "
+            "Use public APIs instead.",
+            DeprecationWarning,
+            # https://github.com/pandas-dev/pandas/pull/55139#pullrequestreview-1720690758
+            # on hard-coding stacklevel
+            stacklevel=2,
+        )
+        from pandas.core.internals.managers import create_block_manager_from_blocks
+
+        return create_block_manager_from_blocks
+
+    if name in [
+        "NumericBlock",
+        "ObjectBlock",
+        "Block",
+        "ExtensionBlock",
+        "DatetimeTZBlock",
+    ]:
+        warnings.warn(
+            f"{name} is deprecated and will be removed in a future version. "
+            "Use public APIs instead.",
+            DeprecationWarning,
+            # https://github.com/pandas-dev/pandas/pull/55139#pullrequestreview-1720690758
+            # on hard-coding stacklevel
+            stacklevel=2,
+        )
+        if name == "NumericBlock":
+            from pandas.core.internals.blocks import NumericBlock
+
+            return NumericBlock
+        elif name == "DatetimeTZBlock":
+            from pandas.core.internals.blocks import DatetimeTZBlock
+
+            return DatetimeTZBlock
+        elif name == "ExtensionBlock":
+            from pandas.core.internals.blocks import ExtensionBlock
+
+            return ExtensionBlock
+        elif name == "Block":
+            from pandas.core.internals.blocks import Block
+
+            return Block
+        else:
+            from pandas.core.internals.blocks import ObjectBlock
+
+            return ObjectBlock
+
+    raise AttributeError(f"module 'pandas.core.internals' has no attribute '{name}'")
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/internals/api.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/internals/api.py
new file mode 100644
index 0000000000000000000000000000000000000000..b0b3937ca47ea06c42b4b51964f6a74830a5d9ee
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/internals/api.py
@@ -0,0 +1,156 @@
+"""
+This is a pseudo-public API for downstream libraries.  We ask that downstream
+authors
+
+1) Try to avoid using internals directly altogether, and failing that,
+2) Use only functions exposed here (or in core.internals)
+
+"""
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import numpy as np
+
+from pandas._libs.internals import BlockPlacement
+
+from pandas.core.dtypes.common import pandas_dtype
+from pandas.core.dtypes.dtypes import (
+    DatetimeTZDtype,
+    PeriodDtype,
+)
+
+from pandas.core.arrays import DatetimeArray
+from pandas.core.construction import extract_array
+from pandas.core.internals.blocks import (
+    check_ndim,
+    ensure_block_shape,
+    extract_pandas_array,
+    get_block_type,
+    maybe_coerce_values,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import Dtype
+
+    from pandas.core.internals.blocks import Block
+
+
+def make_block(
+    values, placement, klass=None, ndim=None, dtype: Dtype | None = None
+) -> Block:
+    """
+    This is a pseudo-public analogue to blocks.new_block.
+
+    We ask that downstream libraries use this rather than any fully-internal
+    APIs, including but not limited to:
+
+    - core.internals.blocks.make_block
+    - Block.make_block
+    - Block.make_block_same_class
+    - Block.__init__
+    """
+    if dtype is not None:
+        dtype = pandas_dtype(dtype)
+
+    values, dtype = extract_pandas_array(values, dtype, ndim)
+
+    from pandas.core.internals.blocks import (
+        DatetimeTZBlock,
+        ExtensionBlock,
+    )
+
+    if klass is ExtensionBlock and isinstance(values.dtype, PeriodDtype):
+        # GH-44681 changed PeriodArray to be stored in the 2D
+        # NDArrayBackedExtensionBlock instead of ExtensionBlock
+        # -> still allow ExtensionBlock to be passed in this case for back compat
+        klass = None
+
+    if klass is None:
+        dtype = dtype or values.dtype
+        klass = get_block_type(dtype)
+
+    elif klass is DatetimeTZBlock and not isinstance(values.dtype, DatetimeTZDtype):
+        # pyarrow calls get here
+        values = DatetimeArray._simple_new(
+            # error: Argument "dtype" to "_simple_new" of "DatetimeArray" has
+            # incompatible type "Union[ExtensionDtype, dtype[Any], None]";
+            # expected "Union[dtype[datetime64], DatetimeTZDtype]"
+            values,
+            dtype=dtype,  # type: ignore[arg-type]
+        )
+
+    if not isinstance(placement, BlockPlacement):
+        placement = BlockPlacement(placement)
+
+    ndim = maybe_infer_ndim(values, placement, ndim)
+    if isinstance(values.dtype, (PeriodDtype, DatetimeTZDtype)):
+        # GH#41168 ensure we can pass 1D dt64tz values
+        # More generally, any EA dtype that isn't is_1d_only_ea_dtype
+        values = extract_array(values, extract_numpy=True)
+        values = ensure_block_shape(values, ndim)
+
+    check_ndim(values, placement, ndim)
+    values = maybe_coerce_values(values)
+    return klass(values, ndim=ndim, placement=placement)
+
+
+def maybe_infer_ndim(values, placement: BlockPlacement, ndim: int | None) -> int:
+    """
+    If `ndim` is not provided, infer it from placement and values.
+    """
+    if ndim is None:
+        # GH#38134 Block constructor now assumes ndim is not None
+        if not isinstance(values.dtype, np.dtype):
+            if len(placement) != 1:
+                ndim = 1
+            else:
+                ndim = 2
+        else:
+            ndim = values.ndim
+    return ndim
+
+
+def __getattr__(name: str):
+    # GH#55139
+    import warnings
+
+    if name in [
+        "Block",
+        "ExtensionBlock",
+        "DatetimeTZBlock",
+        "create_block_manager_from_blocks",
+    ]:
+        # GH#33892
+        warnings.warn(
+            f"{name} is deprecated and will be removed in a future version. "
+            "Use public APIs instead.",
+            DeprecationWarning,
+            # https://github.com/pandas-dev/pandas/pull/55139#pullrequestreview-1720690758
+            # on hard-coding stacklevel
+            stacklevel=2,
+        )
+
+        if name == "create_block_manager_from_blocks":
+            from pandas.core.internals.managers import create_block_manager_from_blocks
+
+            return create_block_manager_from_blocks
+
+        elif name == "Block":
+            from pandas.core.internals.blocks import Block
+
+            return Block
+
+        elif name == "DatetimeTZBlock":
+            from pandas.core.internals.blocks import DatetimeTZBlock
+
+            return DatetimeTZBlock
+
+        elif name == "ExtensionBlock":
+            from pandas.core.internals.blocks import ExtensionBlock
+
+            return ExtensionBlock
+
+    raise AttributeError(
+        f"module 'pandas.core.internals.api' has no attribute '{name}'"
+    )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/internals/array_manager.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/internals/array_manager.py
new file mode 100644
index 0000000000000000000000000000000000000000..e253f82256a5f6dd8b277b576a33597355d69dcc
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/internals/array_manager.py
@@ -0,0 +1,1340 @@
+"""
+Experimental manager based on storing a collection of 1D arrays
+"""
+from __future__ import annotations
+
+import itertools
+from typing import (
+    TYPE_CHECKING,
+    Callable,
+    Literal,
+)
+
+import numpy as np
+
+from pandas._libs import (
+    NaT,
+    lib,
+)
+
+from pandas.core.dtypes.astype import (
+    astype_array,
+    astype_array_safe,
+)
+from pandas.core.dtypes.cast import (
+    ensure_dtype_can_hold_na,
+    find_common_type,
+    infer_dtype_from_scalar,
+    np_find_common_type,
+)
+from pandas.core.dtypes.common import (
+    ensure_platform_int,
+    is_datetime64_ns_dtype,
+    is_integer,
+    is_numeric_dtype,
+    is_object_dtype,
+    is_timedelta64_ns_dtype,
+)
+from pandas.core.dtypes.dtypes import ExtensionDtype
+from pandas.core.dtypes.generic import (
+    ABCDataFrame,
+    ABCSeries,
+)
+from pandas.core.dtypes.missing import (
+    array_equals,
+    isna,
+    na_value_for_dtype,
+)
+
+import pandas.core.algorithms as algos
+from pandas.core.array_algos.quantile import quantile_compat
+from pandas.core.array_algos.take import take_1d
+from pandas.core.arrays import (
+    DatetimeArray,
+    ExtensionArray,
+    NumpyExtensionArray,
+    TimedeltaArray,
+)
+from pandas.core.construction import (
+    ensure_wrapped_if_datetimelike,
+    extract_array,
+    sanitize_array,
+)
+from pandas.core.indexers import (
+    maybe_convert_indices,
+    validate_indices,
+)
+from pandas.core.indexes.api import (
+    Index,
+    ensure_index,
+)
+from pandas.core.indexes.base import get_values_for_csv
+from pandas.core.internals.base import (
+    DataManager,
+    SingleDataManager,
+    ensure_np_dtype,
+    interleaved_dtype,
+)
+from pandas.core.internals.blocks import (
+    BlockPlacement,
+    ensure_block_shape,
+    external_values,
+    extract_pandas_array,
+    maybe_coerce_values,
+    new_block,
+)
+from pandas.core.internals.managers import make_na_array
+
+if TYPE_CHECKING:
+    from collections.abc import Hashable
+
+    from pandas._typing import (
+        ArrayLike,
+        AxisInt,
+        DtypeObj,
+        QuantileInterpolation,
+        Self,
+        npt,
+    )
+
+
+class BaseArrayManager(DataManager):
+    """
+    Core internal data structure to implement DataFrame and Series.
+
+    Alternative to the BlockManager, storing a list of 1D arrays instead of
+    Blocks.
+
+    This is *not* a public API class
+
+    Parameters
+    ----------
+    arrays : Sequence of arrays
+    axes : Sequence of Index
+    verify_integrity : bool, default True
+
+    """
+
+    __slots__ = [
+        "_axes",  # private attribute, because 'axes' has different order, see below
+        "arrays",
+    ]
+
+    arrays: list[np.ndarray | ExtensionArray]
+    _axes: list[Index]
+
+    def __init__(
+        self,
+        arrays: list[np.ndarray | ExtensionArray],
+        axes: list[Index],
+        verify_integrity: bool = True,
+    ) -> None:
+        raise NotImplementedError
+
+    def make_empty(self, axes=None) -> Self:
+        """Return an empty ArrayManager with the items axis of len 0 (no columns)"""
+        if axes is None:
+            axes = [self.axes[1:], Index([])]
+
+        arrays: list[np.ndarray | ExtensionArray] = []
+        return type(self)(arrays, axes)
+
+    @property
+    def items(self) -> Index:
+        return self._axes[-1]
+
+    @property
+    # error: Signature of "axes" incompatible with supertype "DataManager"
+    def axes(self) -> list[Index]:  # type: ignore[override]
+        # mypy doesn't work to override attribute with property
+        # see https://github.com/python/mypy/issues/4125
+        """Axes is BlockManager-compatible order (columns, rows)"""
+        return [self._axes[1], self._axes[0]]
+
+    @property
+    def shape_proper(self) -> tuple[int, ...]:
+        # this returns (n_rows, n_columns)
+        return tuple(len(ax) for ax in self._axes)
+
+    @staticmethod
+    def _normalize_axis(axis: AxisInt) -> int:
+        # switch axis
+        axis = 1 if axis == 0 else 0
+        return axis
+
+    def set_axis(self, axis: AxisInt, new_labels: Index) -> None:
+        # Caller is responsible for ensuring we have an Index object.
+        self._validate_set_axis(axis, new_labels)
+        axis = self._normalize_axis(axis)
+        self._axes[axis] = new_labels
+
+    def get_dtypes(self) -> npt.NDArray[np.object_]:
+        return np.array([arr.dtype for arr in self.arrays], dtype="object")
+
+    def add_references(self, mgr: BaseArrayManager) -> None:
+        """
+        Only implemented on the BlockManager level
+        """
+        return
+
+    def __getstate__(self):
+        return self.arrays, self._axes
+
+    def __setstate__(self, state) -> None:
+        self.arrays = state[0]
+        self._axes = state[1]
+
+    def __repr__(self) -> str:
+        output = type(self).__name__
+        output += f"\nIndex: {self._axes[0]}"
+        if self.ndim == 2:
+            output += f"\nColumns: {self._axes[1]}"
+        output += f"\n{len(self.arrays)} arrays:"
+        for arr in self.arrays:
+            output += f"\n{arr.dtype}"
+        return output
+
+    def apply(
+        self,
+        f,
+        align_keys: list[str] | None = None,
+        **kwargs,
+    ) -> Self:
+        """
+        Iterate over the arrays, collect and create a new ArrayManager.
+
+        Parameters
+        ----------
+        f : str or callable
+            Name of the Array method to apply.
+        align_keys: List[str] or None, default None
+        **kwargs
+            Keywords to pass to `f`
+
+        Returns
+        -------
+        ArrayManager
+        """
+        assert "filter" not in kwargs
+
+        align_keys = align_keys or []
+        result_arrays: list[ArrayLike] = []
+        # fillna: Series/DataFrame is responsible for making sure value is aligned
+
+        aligned_args = {k: kwargs[k] for k in align_keys}
+
+        if f == "apply":
+            f = kwargs.pop("func")
+
+        for i, arr in enumerate(self.arrays):
+            if aligned_args:
+                for k, obj in aligned_args.items():
+                    if isinstance(obj, (ABCSeries, ABCDataFrame)):
+                        # The caller is responsible for ensuring that
+                        #  obj.axes[-1].equals(self.items)
+                        if obj.ndim == 1:
+                            kwargs[k] = obj.iloc[i]
+                        else:
+                            kwargs[k] = obj.iloc[:, i]._values
+                    else:
+                        # otherwise we have an array-like
+                        kwargs[k] = obj[i]
+
+            if callable(f):
+                applied = f(arr, **kwargs)
+            else:
+                applied = getattr(arr, f)(**kwargs)
+
+            result_arrays.append(applied)
+
+        new_axes = self._axes
+        return type(self)(result_arrays, new_axes)
+
+    def apply_with_block(self, f, align_keys=None, **kwargs) -> Self:
+        # switch axis to follow BlockManager logic
+        swap_axis = True
+        if f == "interpolate":
+            swap_axis = False
+        if swap_axis and "axis" in kwargs and self.ndim == 2:
+            kwargs["axis"] = 1 if kwargs["axis"] == 0 else 0
+
+        align_keys = align_keys or []
+        aligned_args = {k: kwargs[k] for k in align_keys}
+
+        result_arrays = []
+
+        for i, arr in enumerate(self.arrays):
+            if aligned_args:
+                for k, obj in aligned_args.items():
+                    if isinstance(obj, (ABCSeries, ABCDataFrame)):
+                        # The caller is responsible for ensuring that
+                        #  obj.axes[-1].equals(self.items)
+                        if obj.ndim == 1:
+                            if self.ndim == 2:
+                                kwargs[k] = obj.iloc[slice(i, i + 1)]._values
+                            else:
+                                kwargs[k] = obj.iloc[:]._values
+                        else:
+                            kwargs[k] = obj.iloc[:, [i]]._values
+                    else:
+                        # otherwise we have an ndarray
+                        if obj.ndim == 2:
+                            kwargs[k] = obj[[i]]
+
+            if isinstance(arr.dtype, np.dtype) and not isinstance(arr, np.ndarray):
+                # i.e. TimedeltaArray, DatetimeArray with tz=None. Need to
+                #  convert for the Block constructors.
+                arr = np.asarray(arr)
+
+            arr = maybe_coerce_values(arr)
+            if self.ndim == 2:
+                arr = ensure_block_shape(arr, 2)
+                bp = BlockPlacement(slice(0, 1, 1))
+                block = new_block(arr, placement=bp, ndim=2)
+            else:
+                bp = BlockPlacement(slice(0, len(self), 1))
+                block = new_block(arr, placement=bp, ndim=1)
+
+            applied = getattr(block, f)(**kwargs)
+            if isinstance(applied, list):
+                applied = applied[0]
+            arr = applied.values
+            if self.ndim == 2 and arr.ndim == 2:
+                # 2D for np.ndarray or DatetimeArray/TimedeltaArray
+                assert len(arr) == 1
+                # error: No overload variant of "__getitem__" of "ExtensionArray"
+                # matches argument type "Tuple[int, slice]"
+                arr = arr[0, :]  # type: ignore[call-overload]
+            result_arrays.append(arr)
+
+        return type(self)(result_arrays, self._axes)
+
+    def setitem(self, indexer, value, warn: bool = True) -> Self:
+        return self.apply_with_block("setitem", indexer=indexer, value=value)
+
+    def diff(self, n: int) -> Self:
+        assert self.ndim == 2  # caller ensures
+        return self.apply(algos.diff, n=n)
+
+    def astype(self, dtype, copy: bool | None = False, errors: str = "raise") -> Self:
+        if copy is None:
+            copy = True
+
+        return self.apply(astype_array_safe, dtype=dtype, copy=copy, errors=errors)
+
+    def convert(self, copy: bool | None) -> Self:
+        if copy is None:
+            copy = True
+
+        def _convert(arr):
+            if is_object_dtype(arr.dtype):
+                # extract NumpyExtensionArray for tests that patch
+                #  NumpyExtensionArray._typ
+                arr = np.asarray(arr)
+                result = lib.maybe_convert_objects(
+                    arr,
+                    convert_non_numeric=True,
+                )
+                if result is arr and copy:
+                    return arr.copy()
+                return result
+            else:
+                return arr.copy() if copy else arr
+
+        return self.apply(_convert)
+
+    def get_values_for_csv(
+        self, *, float_format, date_format, decimal, na_rep: str = "nan", quoting=None
+    ) -> Self:
+        return self.apply(
+            get_values_for_csv,
+            na_rep=na_rep,
+            quoting=quoting,
+            float_format=float_format,
+            date_format=date_format,
+            decimal=decimal,
+        )
+
+    @property
+    def any_extension_types(self) -> bool:
+        """Whether any of the blocks in this manager are extension blocks"""
+        return False  # any(block.is_extension for block in self.blocks)
+
+    @property
+    def is_view(self) -> bool:
+        """return a boolean if we are a single block and are a view"""
+        # TODO what is this used for?
+        return False
+
+    @property
+    def is_single_block(self) -> bool:
+        return len(self.arrays) == 1
+
+    def _get_data_subset(self, predicate: Callable) -> Self:
+        indices = [i for i, arr in enumerate(self.arrays) if predicate(arr)]
+        arrays = [self.arrays[i] for i in indices]
+        # TODO copy?
+        # Note: using Index.take ensures we can retain e.g. DatetimeIndex.freq,
+        #  see test_describe_datetime_columns
+        taker = np.array(indices, dtype="intp")
+        new_cols = self._axes[1].take(taker)
+        new_axes = [self._axes[0], new_cols]
+        return type(self)(arrays, new_axes, verify_integrity=False)
+
+    def get_bool_data(self, copy: bool = False) -> Self:
+        """
+        Select columns that are bool-dtype and object-dtype columns that are all-bool.
+
+        Parameters
+        ----------
+        copy : bool, default False
+            Whether to copy the blocks
+        """
+        return self._get_data_subset(lambda x: x.dtype == np.dtype(bool))
+
+    def get_numeric_data(self, copy: bool = False) -> Self:
+        """
+        Select columns that have a numeric dtype.
+
+        Parameters
+        ----------
+        copy : bool, default False
+            Whether to copy the blocks
+        """
+        return self._get_data_subset(
+            lambda arr: is_numeric_dtype(arr.dtype)
+            or getattr(arr.dtype, "_is_numeric", False)
+        )
+
+    def copy(self, deep: bool | Literal["all"] | None = True) -> Self:
+        """
+        Make deep or shallow copy of ArrayManager
+
+        Parameters
+        ----------
+        deep : bool or string, default True
+            If False, return shallow copy (do not copy data)
+            If 'all', copy data and a deep copy of the index
+
+        Returns
+        -------
+        BlockManager
+        """
+        if deep is None:
+            # ArrayManager does not yet support CoW, so deep=None always means
+            # deep=True for now
+            deep = True
+
+        # this preserves the notion of view copying of axes
+        if deep:
+            # hit in e.g. tests.io.json.test_pandas
+
+            def copy_func(ax):
+                return ax.copy(deep=True) if deep == "all" else ax.view()
+
+            new_axes = [copy_func(ax) for ax in self._axes]
+        else:
+            new_axes = list(self._axes)
+
+        if deep:
+            new_arrays = [arr.copy() for arr in self.arrays]
+        else:
+            new_arrays = list(self.arrays)
+        return type(self)(new_arrays, new_axes, verify_integrity=False)
+
+    def reindex_indexer(
+        self,
+        new_axis,
+        indexer,
+        axis: AxisInt,
+        fill_value=None,
+        allow_dups: bool = False,
+        copy: bool | None = True,
+        # ignored keywords
+        only_slice: bool = False,
+        # ArrayManager specific keywords
+        use_na_proxy: bool = False,
+    ) -> Self:
+        axis = self._normalize_axis(axis)
+        return self._reindex_indexer(
+            new_axis,
+            indexer,
+            axis,
+            fill_value,
+            allow_dups,
+            copy,
+            use_na_proxy,
+        )
+
+    def _reindex_indexer(
+        self,
+        new_axis,
+        indexer: npt.NDArray[np.intp] | None,
+        axis: AxisInt,
+        fill_value=None,
+        allow_dups: bool = False,
+        copy: bool | None = True,
+        use_na_proxy: bool = False,
+    ) -> Self:
+        """
+        Parameters
+        ----------
+        new_axis : Index
+        indexer : ndarray[intp] or None
+        axis : int
+        fill_value : object, default None
+        allow_dups : bool, default False
+        copy : bool, default True
+
+
+        pandas-indexer with -1's only.
+        """
+        if copy is None:
+            # ArrayManager does not yet support CoW, so deep=None always means
+            # deep=True for now
+            copy = True
+
+        if indexer is None:
+            if new_axis is self._axes[axis] and not copy:
+                return self
+
+            result = self.copy(deep=copy)
+            result._axes = list(self._axes)
+            result._axes[axis] = new_axis
+            return result
+
+        # some axes don't allow reindexing with dups
+        if not allow_dups:
+            self._axes[axis]._validate_can_reindex(indexer)
+
+        if axis >= self.ndim:
+            raise IndexError("Requested axis not found in manager")
+
+        if axis == 1:
+            new_arrays = []
+            for i in indexer:
+                if i == -1:
+                    arr = self._make_na_array(
+                        fill_value=fill_value, use_na_proxy=use_na_proxy
+                    )
+                else:
+                    arr = self.arrays[i]
+                    if copy:
+                        arr = arr.copy()
+                new_arrays.append(arr)
+
+        else:
+            validate_indices(indexer, len(self._axes[0]))
+            indexer = ensure_platform_int(indexer)
+            mask = indexer == -1
+            needs_masking = mask.any()
+            new_arrays = [
+                take_1d(
+                    arr,
+                    indexer,
+                    allow_fill=needs_masking,
+                    fill_value=fill_value,
+                    mask=mask,
+                    # if fill_value is not None else blk.fill_value
+                )
+                for arr in self.arrays
+            ]
+
+        new_axes = list(self._axes)
+        new_axes[axis] = new_axis
+
+        return type(self)(new_arrays, new_axes, verify_integrity=False)
+
+    def take(
+        self,
+        indexer: npt.NDArray[np.intp],
+        axis: AxisInt = 1,
+        verify: bool = True,
+    ) -> Self:
+        """
+        Take items along any axis.
+        """
+        assert isinstance(indexer, np.ndarray), type(indexer)
+        assert indexer.dtype == np.intp, indexer.dtype
+
+        axis = self._normalize_axis(axis)
+
+        if not indexer.ndim == 1:
+            raise ValueError("indexer should be 1-dimensional")
+
+        n = self.shape_proper[axis]
+        indexer = maybe_convert_indices(indexer, n, verify=verify)
+
+        new_labels = self._axes[axis].take(indexer)
+        return self._reindex_indexer(
+            new_axis=new_labels, indexer=indexer, axis=axis, allow_dups=True
+        )
+
+    def _make_na_array(self, fill_value=None, use_na_proxy: bool = False):
+        if use_na_proxy:
+            assert fill_value is None
+            return NullArrayProxy(self.shape_proper[0])
+
+        if fill_value is None:
+            fill_value = np.nan
+
+        dtype, fill_value = infer_dtype_from_scalar(fill_value)
+        array_values = make_na_array(dtype, self.shape_proper[:1], fill_value)
+        return array_values
+
+    def _equal_values(self, other) -> bool:
+        """
+        Used in .equals defined in base class. Only check the column values
+        assuming shape and indexes have already been checked.
+        """
+        for left, right in zip(self.arrays, other.arrays):
+            if not array_equals(left, right):
+                return False
+        return True
+
+    # TODO
+    # to_dict
+
+
+class ArrayManager(BaseArrayManager):
+    @property
+    def ndim(self) -> Literal[2]:
+        return 2
+
+    def __init__(
+        self,
+        arrays: list[np.ndarray | ExtensionArray],
+        axes: list[Index],
+        verify_integrity: bool = True,
+    ) -> None:
+        # Note: we are storing the axes in "_axes" in the (row, columns) order
+        # which contrasts the order how it is stored in BlockManager
+        self._axes = axes
+        self.arrays = arrays
+
+        if verify_integrity:
+            self._axes = [ensure_index(ax) for ax in axes]
+            arrays = [extract_pandas_array(x, None, 1)[0] for x in arrays]
+            self.arrays = [maybe_coerce_values(arr) for arr in arrays]
+            self._verify_integrity()
+
+    def _verify_integrity(self) -> None:
+        n_rows, n_columns = self.shape_proper
+        if not len(self.arrays) == n_columns:
+            raise ValueError(
+                "Number of passed arrays must equal the size of the column Index: "
+                f"{len(self.arrays)} arrays vs {n_columns} columns."
+            )
+        for arr in self.arrays:
+            if not len(arr) == n_rows:
+                raise ValueError(
+                    "Passed arrays should have the same length as the rows Index: "
+                    f"{len(arr)} vs {n_rows} rows"
+                )
+            if not isinstance(arr, (np.ndarray, ExtensionArray)):
+                raise ValueError(
+                    "Passed arrays should be np.ndarray or ExtensionArray instances, "
+                    f"got {type(arr)} instead"
+                )
+            if not arr.ndim == 1:
+                raise ValueError(
+                    "Passed arrays should be 1-dimensional, got array with "
+                    f"{arr.ndim} dimensions instead."
+                )
+
+    # --------------------------------------------------------------------
+    # Indexing
+
+    def fast_xs(self, loc: int) -> SingleArrayManager:
+        """
+        Return the array corresponding to `frame.iloc[loc]`.
+
+        Parameters
+        ----------
+        loc : int
+
+        Returns
+        -------
+        np.ndarray or ExtensionArray
+        """
+        dtype = interleaved_dtype([arr.dtype for arr in self.arrays])
+
+        values = [arr[loc] for arr in self.arrays]
+        if isinstance(dtype, ExtensionDtype):
+            result = dtype.construct_array_type()._from_sequence(values, dtype=dtype)
+        # for datetime64/timedelta64, the np.ndarray constructor cannot handle pd.NaT
+        elif is_datetime64_ns_dtype(dtype):
+            result = DatetimeArray._from_sequence(values, dtype=dtype)._ndarray
+        elif is_timedelta64_ns_dtype(dtype):
+            result = TimedeltaArray._from_sequence(values, dtype=dtype)._ndarray
+        else:
+            result = np.array(values, dtype=dtype)
+        return SingleArrayManager([result], [self._axes[1]])
+
+    def get_slice(self, slobj: slice, axis: AxisInt = 0) -> ArrayManager:
+        axis = self._normalize_axis(axis)
+
+        if axis == 0:
+            arrays = [arr[slobj] for arr in self.arrays]
+        elif axis == 1:
+            arrays = self.arrays[slobj]
+
+        new_axes = list(self._axes)
+        new_axes[axis] = new_axes[axis]._getitem_slice(slobj)
+
+        return type(self)(arrays, new_axes, verify_integrity=False)
+
+    def iget(self, i: int) -> SingleArrayManager:
+        """
+        Return the data as a SingleArrayManager.
+        """
+        values = self.arrays[i]
+        return SingleArrayManager([values], [self._axes[0]])
+
+    def iget_values(self, i: int) -> ArrayLike:
+        """
+        Return the data for column i as the values (ndarray or ExtensionArray).
+        """
+        return self.arrays[i]
+
+    @property
+    def column_arrays(self) -> list[ArrayLike]:
+        """
+        Used in the JSON C code to access column arrays.
+        """
+
+        return [np.asarray(arr) for arr in self.arrays]
+
+    def iset(
+        self,
+        loc: int | slice | np.ndarray,
+        value: ArrayLike,
+        inplace: bool = False,
+        refs=None,
+    ) -> None:
+        """
+        Set new column(s).
+
+        This changes the ArrayManager in-place, but replaces (an) existing
+        column(s), not changing column values in-place).
+
+        Parameters
+        ----------
+        loc : integer, slice or boolean mask
+            Positional location (already bounds checked)
+        value : np.ndarray or ExtensionArray
+        inplace : bool, default False
+            Whether overwrite existing array as opposed to replacing it.
+        """
+        # single column -> single integer index
+        if lib.is_integer(loc):
+            # TODO can we avoid needing to unpack this here? That means converting
+            # DataFrame into 1D array when loc is an integer
+            if isinstance(value, np.ndarray) and value.ndim == 2:
+                assert value.shape[1] == 1
+                value = value[:, 0]
+
+            # TODO we receive a datetime/timedelta64 ndarray from DataFrame._iset_item
+            # but we should avoid that and pass directly the proper array
+            value = maybe_coerce_values(value)
+
+            assert isinstance(value, (np.ndarray, ExtensionArray))
+            assert value.ndim == 1
+            assert len(value) == len(self._axes[0])
+            self.arrays[loc] = value
+            return
+
+        # multiple columns -> convert slice or array to integer indices
+        elif isinstance(loc, slice):
+            indices: range | np.ndarray = range(
+                loc.start if loc.start is not None else 0,
+                loc.stop if loc.stop is not None else self.shape_proper[1],
+                loc.step if loc.step is not None else 1,
+            )
+        else:
+            assert isinstance(loc, np.ndarray)
+            assert loc.dtype == "bool"
+            indices = np.nonzero(loc)[0]
+
+        assert value.ndim == 2
+        assert value.shape[0] == len(self._axes[0])
+
+        for value_idx, mgr_idx in enumerate(indices):
+            # error: No overload variant of "__getitem__" of "ExtensionArray" matches
+            # argument type "Tuple[slice, int]"
+            value_arr = value[:, value_idx]  # type: ignore[call-overload]
+            self.arrays[mgr_idx] = value_arr
+        return
+
+    def column_setitem(
+        self, loc: int, idx: int | slice | np.ndarray, value, inplace_only: bool = False
+    ) -> None:
+        """
+        Set values ("setitem") into a single column (not setting the full column).
+
+        This is a method on the ArrayManager level, to avoid creating an
+        intermediate Series at the DataFrame level (`s = df[loc]; s[idx] = value`)
+        """
+        if not is_integer(loc):
+            raise TypeError("The column index should be an integer")
+        arr = self.arrays[loc]
+        mgr = SingleArrayManager([arr], [self._axes[0]])
+        if inplace_only:
+            mgr.setitem_inplace(idx, value)
+        else:
+            new_mgr = mgr.setitem((idx,), value)
+            # update existing ArrayManager in-place
+            self.arrays[loc] = new_mgr.arrays[0]
+
+    def insert(self, loc: int, item: Hashable, value: ArrayLike, refs=None) -> None:
+        """
+        Insert item at selected position.
+
+        Parameters
+        ----------
+        loc : int
+        item : hashable
+        value : np.ndarray or ExtensionArray
+        """
+        # insert to the axis; this could possibly raise a TypeError
+        new_axis = self.items.insert(loc, item)
+
+        value = extract_array(value, extract_numpy=True)
+        if value.ndim == 2:
+            if value.shape[0] == 1:
+                # error: No overload variant of "__getitem__" of "ExtensionArray"
+                # matches argument type "Tuple[int, slice]"
+                value = value[0, :]  # type: ignore[call-overload]
+            else:
+                raise ValueError(
+                    f"Expected a 1D array, got an array with shape {value.shape}"
+                )
+        value = maybe_coerce_values(value)
+
+        # TODO self.arrays can be empty
+        # assert len(value) == len(self.arrays[0])
+
+        # TODO is this copy needed?
+        arrays = self.arrays.copy()
+        arrays.insert(loc, value)
+
+        self.arrays = arrays
+        self._axes[1] = new_axis
+
+    def idelete(self, indexer) -> ArrayManager:
+        """
+        Delete selected locations in-place (new block and array, same BlockManager)
+        """
+        to_keep = np.ones(self.shape[0], dtype=np.bool_)
+        to_keep[indexer] = False
+
+        self.arrays = [self.arrays[i] for i in np.nonzero(to_keep)[0]]
+        self._axes = [self._axes[0], self._axes[1][to_keep]]
+        return self
+
+    # --------------------------------------------------------------------
+    # Array-wise Operation
+
+    def grouped_reduce(self, func: Callable) -> Self:
+        """
+        Apply grouped reduction function columnwise, returning a new ArrayManager.
+
+        Parameters
+        ----------
+        func : grouped reduction function
+
+        Returns
+        -------
+        ArrayManager
+        """
+        result_arrays: list[np.ndarray] = []
+        result_indices: list[int] = []
+
+        for i, arr in enumerate(self.arrays):
+            # grouped_reduce functions all expect 2D arrays
+            arr = ensure_block_shape(arr, ndim=2)
+            res = func(arr)
+            if res.ndim == 2:
+                # reverse of ensure_block_shape
+                assert res.shape[0] == 1
+                res = res[0]
+
+            result_arrays.append(res)
+            result_indices.append(i)
+
+        if len(result_arrays) == 0:
+            nrows = 0
+        else:
+            nrows = result_arrays[0].shape[0]
+        index = Index(range(nrows))
+
+        columns = self.items
+
+        # error: Argument 1 to "ArrayManager" has incompatible type "List[ndarray]";
+        # expected "List[Union[ndarray, ExtensionArray]]"
+        return type(self)(result_arrays, [index, columns])  # type: ignore[arg-type]
+
+    def reduce(self, func: Callable) -> Self:
+        """
+        Apply reduction function column-wise, returning a single-row ArrayManager.
+
+        Parameters
+        ----------
+        func : reduction function
+
+        Returns
+        -------
+        ArrayManager
+        """
+        result_arrays: list[np.ndarray] = []
+        for i, arr in enumerate(self.arrays):
+            res = func(arr, axis=0)
+
+            # TODO NaT doesn't preserve dtype, so we need to ensure to create
+            # a timedelta result array if original was timedelta
+            # what if datetime results in timedelta? (eg std)
+            dtype = arr.dtype if res is NaT else None
+            result_arrays.append(
+                sanitize_array([res], None, dtype=dtype)  # type: ignore[arg-type]
+            )
+
+        index = Index._simple_new(np.array([None], dtype=object))  # placeholder
+        columns = self.items
+
+        # error: Argument 1 to "ArrayManager" has incompatible type "List[ndarray]";
+        # expected "List[Union[ndarray, ExtensionArray]]"
+        new_mgr = type(self)(result_arrays, [index, columns])  # type: ignore[arg-type]
+        return new_mgr
+
+    def operate_blockwise(self, other: ArrayManager, array_op) -> ArrayManager:
+        """
+        Apply array_op blockwise with another (aligned) BlockManager.
+        """
+        # TODO what if `other` is BlockManager ?
+        left_arrays = self.arrays
+        right_arrays = other.arrays
+        result_arrays = [
+            array_op(left, right) for left, right in zip(left_arrays, right_arrays)
+        ]
+        return type(self)(result_arrays, self._axes)
+
+    def quantile(
+        self,
+        *,
+        qs: Index,  # with dtype float64
+        transposed: bool = False,
+        interpolation: QuantileInterpolation = "linear",
+    ) -> ArrayManager:
+        arrs = [ensure_block_shape(x, 2) for x in self.arrays]
+        new_arrs = [
+            quantile_compat(x, np.asarray(qs._values), interpolation) for x in arrs
+        ]
+        for i, arr in enumerate(new_arrs):
+            if arr.ndim == 2:
+                assert arr.shape[0] == 1, arr.shape
+                new_arrs[i] = arr[0]
+
+        axes = [qs, self._axes[1]]
+        return type(self)(new_arrs, axes)
+
+    # ----------------------------------------------------------------
+
+    def unstack(self, unstacker, fill_value) -> ArrayManager:
+        """
+        Return a BlockManager with all blocks unstacked.
+
+        Parameters
+        ----------
+        unstacker : reshape._Unstacker
+        fill_value : Any
+            fill_value for newly introduced missing values.
+
+        Returns
+        -------
+        unstacked : BlockManager
+        """
+        indexer, _ = unstacker._indexer_and_to_sort
+        if unstacker.mask.all():
+            new_indexer = indexer
+            allow_fill = False
+            new_mask2D = None
+            needs_masking = None
+        else:
+            new_indexer = np.full(unstacker.mask.shape, -1)
+            new_indexer[unstacker.mask] = indexer
+            allow_fill = True
+            # calculating the full mask once and passing it to take_1d is faster
+            # than letting take_1d calculate it in each repeated call
+            new_mask2D = (~unstacker.mask).reshape(*unstacker.full_shape)
+            needs_masking = new_mask2D.any(axis=0)
+        new_indexer2D = new_indexer.reshape(*unstacker.full_shape)
+        new_indexer2D = ensure_platform_int(new_indexer2D)
+
+        new_arrays = []
+        for arr in self.arrays:
+            for i in range(unstacker.full_shape[1]):
+                if allow_fill:
+                    # error: Value of type "Optional[Any]" is not indexable  [index]
+                    new_arr = take_1d(
+                        arr,
+                        new_indexer2D[:, i],
+                        allow_fill=needs_masking[i],  # type: ignore[index]
+                        fill_value=fill_value,
+                        mask=new_mask2D[:, i],  # type: ignore[index]
+                    )
+                else:
+                    new_arr = take_1d(arr, new_indexer2D[:, i], allow_fill=False)
+                new_arrays.append(new_arr)
+
+        new_index = unstacker.new_index
+        new_columns = unstacker.get_new_columns(self._axes[1])
+        new_axes = [new_index, new_columns]
+
+        return type(self)(new_arrays, new_axes, verify_integrity=False)
+
+    def as_array(
+        self,
+        dtype=None,
+        copy: bool = False,
+        na_value: object = lib.no_default,
+    ) -> np.ndarray:
+        """
+        Convert the blockmanager data into an numpy array.
+
+        Parameters
+        ----------
+        dtype : object, default None
+            Data type of the return array.
+        copy : bool, default False
+            If True then guarantee that a copy is returned. A value of
+            False does not guarantee that the underlying data is not
+            copied.
+        na_value : object, default lib.no_default
+            Value to be used as the missing value sentinel.
+
+        Returns
+        -------
+        arr : ndarray
+        """
+        if len(self.arrays) == 0:
+            empty_arr = np.empty(self.shape, dtype=float)
+            return empty_arr.transpose()
+
+        # We want to copy when na_value is provided to avoid
+        # mutating the original object
+        copy = copy or na_value is not lib.no_default
+
+        if not dtype:
+            dtype = interleaved_dtype([arr.dtype for arr in self.arrays])
+
+        dtype = ensure_np_dtype(dtype)
+
+        result = np.empty(self.shape_proper, dtype=dtype)
+
+        for i, arr in enumerate(self.arrays):
+            arr = arr.astype(dtype, copy=copy)
+            result[:, i] = arr
+
+        if na_value is not lib.no_default:
+            result[isna(result)] = na_value
+
+        return result
+
+    @classmethod
+    def concat_horizontal(cls, mgrs: list[Self], axes: list[Index]) -> Self:
+        """
+        Concatenate uniformly-indexed ArrayManagers horizontally.
+        """
+        # concatting along the columns -> combine reindexed arrays in a single manager
+        arrays = list(itertools.chain.from_iterable([mgr.arrays for mgr in mgrs]))
+        new_mgr = cls(arrays, [axes[1], axes[0]], verify_integrity=False)
+        return new_mgr
+
+    @classmethod
+    def concat_vertical(cls, mgrs: list[Self], axes: list[Index]) -> Self:
+        """
+        Concatenate uniformly-indexed ArrayManagers vertically.
+        """
+        # concatting along the rows -> concat the reindexed arrays
+        # TODO(ArrayManager) doesn't yet preserve the correct dtype
+        arrays = [
+            concat_arrays([mgrs[i].arrays[j] for i in range(len(mgrs))])
+            for j in range(len(mgrs[0].arrays))
+        ]
+        new_mgr = cls(arrays, [axes[1], axes[0]], verify_integrity=False)
+        return new_mgr
+
+
+class SingleArrayManager(BaseArrayManager, SingleDataManager):
+    __slots__ = [
+        "_axes",  # private attribute, because 'axes' has different order, see below
+        "arrays",
+    ]
+
+    arrays: list[np.ndarray | ExtensionArray]
+    _axes: list[Index]
+
+    @property
+    def ndim(self) -> Literal[1]:
+        return 1
+
+    def __init__(
+        self,
+        arrays: list[np.ndarray | ExtensionArray],
+        axes: list[Index],
+        verify_integrity: bool = True,
+    ) -> None:
+        self._axes = axes
+        self.arrays = arrays
+
+        if verify_integrity:
+            assert len(axes) == 1
+            assert len(arrays) == 1
+            self._axes = [ensure_index(ax) for ax in self._axes]
+            arr = arrays[0]
+            arr = maybe_coerce_values(arr)
+            arr = extract_pandas_array(arr, None, 1)[0]
+            self.arrays = [arr]
+            self._verify_integrity()
+
+    def _verify_integrity(self) -> None:
+        (n_rows,) = self.shape
+        assert len(self.arrays) == 1
+        arr = self.arrays[0]
+        assert len(arr) == n_rows
+        if not arr.ndim == 1:
+            raise ValueError(
+                "Passed array should be 1-dimensional, got array with "
+                f"{arr.ndim} dimensions instead."
+            )
+
+    @staticmethod
+    def _normalize_axis(axis):
+        return axis
+
+    def make_empty(self, axes=None) -> Self:
+        """Return an empty ArrayManager with index/array of length 0"""
+        if axes is None:
+            axes = [Index([], dtype=object)]
+        array: np.ndarray = np.array([], dtype=self.dtype)
+        return type(self)([array], axes)
+
+    @classmethod
+    def from_array(cls, array, index) -> SingleArrayManager:
+        return cls([array], [index])
+
+    # error: Cannot override writeable attribute with read-only property
+    @property
+    def axes(self) -> list[Index]:  # type: ignore[override]
+        return self._axes
+
+    @property
+    def index(self) -> Index:
+        return self._axes[0]
+
+    @property
+    def dtype(self):
+        return self.array.dtype
+
+    def external_values(self):
+        """The array that Series.values returns"""
+        return external_values(self.array)
+
+    def internal_values(self):
+        """The array that Series._values returns"""
+        return self.array
+
+    def array_values(self):
+        """The array that Series.array returns"""
+        arr = self.array
+        if isinstance(arr, np.ndarray):
+            arr = NumpyExtensionArray(arr)
+        return arr
+
+    @property
+    def _can_hold_na(self) -> bool:
+        if isinstance(self.array, np.ndarray):
+            return self.array.dtype.kind not in "iub"
+        else:
+            # ExtensionArray
+            return self.array._can_hold_na
+
+    @property
+    def is_single_block(self) -> bool:
+        return True
+
+    def fast_xs(self, loc: int) -> SingleArrayManager:
+        raise NotImplementedError("Use series._values[loc] instead")
+
+    def get_slice(self, slobj: slice, axis: AxisInt = 0) -> SingleArrayManager:
+        if axis >= self.ndim:
+            raise IndexError("Requested axis not found in manager")
+
+        new_array = self.array[slobj]
+        new_index = self.index._getitem_slice(slobj)
+        return type(self)([new_array], [new_index], verify_integrity=False)
+
+    def get_rows_with_mask(self, indexer: npt.NDArray[np.bool_]) -> SingleArrayManager:
+        new_array = self.array[indexer]
+        new_index = self.index[indexer]
+        return type(self)([new_array], [new_index])
+
+    # error: Signature of "apply" incompatible with supertype "BaseArrayManager"
+    def apply(self, func, **kwargs) -> Self:  # type: ignore[override]
+        if callable(func):
+            new_array = func(self.array, **kwargs)
+        else:
+            new_array = getattr(self.array, func)(**kwargs)
+        return type(self)([new_array], self._axes)
+
+    def setitem(self, indexer, value, warn: bool = True) -> SingleArrayManager:
+        """
+        Set values with indexer.
+
+        For SingleArrayManager, this backs s[indexer] = value
+
+        See `setitem_inplace` for a version that works inplace and doesn't
+        return a new Manager.
+        """
+        if isinstance(indexer, np.ndarray) and indexer.ndim > self.ndim:
+            raise ValueError(f"Cannot set values with ndim > {self.ndim}")
+        return self.apply_with_block("setitem", indexer=indexer, value=value)
+
+    def idelete(self, indexer) -> SingleArrayManager:
+        """
+        Delete selected locations in-place (new array, same ArrayManager)
+        """
+        to_keep = np.ones(self.shape[0], dtype=np.bool_)
+        to_keep[indexer] = False
+
+        self.arrays = [self.arrays[0][to_keep]]
+        self._axes = [self._axes[0][to_keep]]
+        return self
+
+    def _get_data_subset(self, predicate: Callable) -> SingleArrayManager:
+        # used in get_numeric_data / get_bool_data
+        if predicate(self.array):
+            return type(self)(self.arrays, self._axes, verify_integrity=False)
+        else:
+            return self.make_empty()
+
+    def set_values(self, values: ArrayLike) -> None:
+        """
+        Set (replace) the values of the SingleArrayManager in place.
+
+        Use at your own risk! This does not check if the passed values are
+        valid for the current SingleArrayManager (length, dtype, etc).
+        """
+        self.arrays[0] = values
+
+    def to_2d_mgr(self, columns: Index) -> ArrayManager:
+        """
+        Manager analogue of Series.to_frame
+        """
+        arrays = [self.arrays[0]]
+        axes = [self.axes[0], columns]
+
+        return ArrayManager(arrays, axes, verify_integrity=False)
+
+
+class NullArrayProxy:
+    """
+    Proxy object for an all-NA array.
+
+    Only stores the length of the array, and not the dtype. The dtype
+    will only be known when actually concatenating (after determining the
+    common dtype, for which this proxy is ignored).
+    Using this object avoids that the internals/concat.py needs to determine
+    the proper dtype and array type.
+    """
+
+    ndim = 1
+
+    def __init__(self, n: int) -> None:
+        self.n = n
+
+    @property
+    def shape(self) -> tuple[int]:
+        return (self.n,)
+
+    def to_array(self, dtype: DtypeObj) -> ArrayLike:
+        """
+        Helper function to create the actual all-NA array from the NullArrayProxy
+        object.
+
+        Parameters
+        ----------
+        arr : NullArrayProxy
+        dtype : the dtype for the resulting array
+
+        Returns
+        -------
+        np.ndarray or ExtensionArray
+        """
+        if isinstance(dtype, ExtensionDtype):
+            empty = dtype.construct_array_type()._from_sequence([], dtype=dtype)
+            indexer = -np.ones(self.n, dtype=np.intp)
+            return empty.take(indexer, allow_fill=True)
+        else:
+            # when introducing missing values, int becomes float, bool becomes object
+            dtype = ensure_dtype_can_hold_na(dtype)
+            fill_value = na_value_for_dtype(dtype)
+            arr = np.empty(self.n, dtype=dtype)
+            arr.fill(fill_value)
+            return ensure_wrapped_if_datetimelike(arr)
+
+
+def concat_arrays(to_concat: list) -> ArrayLike:
+    """
+    Alternative for concat_compat but specialized for use in the ArrayManager.
+
+    Differences: only deals with 1D arrays (no axis keyword), assumes
+    ensure_wrapped_if_datetimelike and does not skip empty arrays to determine
+    the dtype.
+    In addition ensures that all NullArrayProxies get replaced with actual
+    arrays.
+
+    Parameters
+    ----------
+    to_concat : list of arrays
+
+    Returns
+    -------
+    np.ndarray or ExtensionArray
+    """
+    # ignore the all-NA proxies to determine the resulting dtype
+    to_concat_no_proxy = [x for x in to_concat if not isinstance(x, NullArrayProxy)]
+
+    dtypes = {x.dtype for x in to_concat_no_proxy}
+    single_dtype = len(dtypes) == 1
+
+    if single_dtype:
+        target_dtype = to_concat_no_proxy[0].dtype
+    elif all(lib.is_np_dtype(x, "iub") for x in dtypes):
+        # GH#42092
+        target_dtype = np_find_common_type(*dtypes)
+    else:
+        target_dtype = find_common_type([arr.dtype for arr in to_concat_no_proxy])
+
+    to_concat = [
+        arr.to_array(target_dtype)
+        if isinstance(arr, NullArrayProxy)
+        else astype_array(arr, target_dtype, copy=False)
+        for arr in to_concat
+    ]
+
+    if isinstance(to_concat[0], ExtensionArray):
+        cls = type(to_concat[0])
+        return cls._concat_same_type(to_concat)
+
+    result = np.concatenate(to_concat)
+
+    # TODO decide on exact behaviour (we shouldn't do this only for empty result)
+    # see https://github.com/pandas-dev/pandas/issues/39817
+    if len(result) == 0:
+        # all empties -> check for bool to not coerce to float
+        kinds = {obj.dtype.kind for obj in to_concat_no_proxy}
+        if len(kinds) != 1:
+            if "b" in kinds:
+                result = result.astype(object)
+    return result
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/internals/base.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/internals/base.py
new file mode 100644
index 0000000000000000000000000000000000000000..ae91f167205a0628c4bcf9b61ce58e888fe6ec8e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/internals/base.py
@@ -0,0 +1,407 @@
+"""
+Base class for the internal managers. Both BlockManager and ArrayManager
+inherit from this class.
+"""
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Literal,
+    cast,
+    final,
+)
+
+import numpy as np
+
+from pandas._config import (
+    using_copy_on_write,
+    warn_copy_on_write,
+)
+
+from pandas._libs import (
+    algos as libalgos,
+    lib,
+)
+from pandas.errors import AbstractMethodError
+from pandas.util._validators import validate_bool_kwarg
+
+from pandas.core.dtypes.cast import (
+    find_common_type,
+    np_can_hold_element,
+)
+from pandas.core.dtypes.dtypes import (
+    ExtensionDtype,
+    SparseDtype,
+)
+
+from pandas.core.base import PandasObject
+from pandas.core.construction import extract_array
+from pandas.core.indexes.api import (
+    Index,
+    default_index,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        ArrayLike,
+        AxisInt,
+        DtypeObj,
+        Self,
+        Shape,
+    )
+
+
+class _AlreadyWarned:
+    def __init__(self):
+        # This class is used on the manager level to the block level to
+        # ensure that we warn only once. The block method can update the
+        # warned_already option without returning a value to keep the
+        # interface consistent. This is only a temporary solution for
+        # CoW warnings.
+        self.warned_already = False
+
+
+class DataManager(PandasObject):
+    # TODO share more methods/attributes
+
+    axes: list[Index]
+
+    @property
+    def items(self) -> Index:
+        raise AbstractMethodError(self)
+
+    @final
+    def __len__(self) -> int:
+        return len(self.items)
+
+    @property
+    def ndim(self) -> int:
+        return len(self.axes)
+
+    @property
+    def shape(self) -> Shape:
+        return tuple(len(ax) for ax in self.axes)
+
+    @final
+    def _validate_set_axis(self, axis: AxisInt, new_labels: Index) -> None:
+        # Caller is responsible for ensuring we have an Index object.
+        old_len = len(self.axes[axis])
+        new_len = len(new_labels)
+
+        if axis == 1 and len(self.items) == 0:
+            # If we are setting the index on a DataFrame with no columns,
+            #  it is OK to change the length.
+            pass
+
+        elif new_len != old_len:
+            raise ValueError(
+                f"Length mismatch: Expected axis has {old_len} elements, new "
+                f"values have {new_len} elements"
+            )
+
+    def reindex_indexer(
+        self,
+        new_axis,
+        indexer,
+        axis: AxisInt,
+        fill_value=None,
+        allow_dups: bool = False,
+        copy: bool = True,
+        only_slice: bool = False,
+    ) -> Self:
+        raise AbstractMethodError(self)
+
+    @final
+    def reindex_axis(
+        self,
+        new_index: Index,
+        axis: AxisInt,
+        fill_value=None,
+        only_slice: bool = False,
+    ) -> Self:
+        """
+        Conform data manager to new index.
+        """
+        new_index, indexer = self.axes[axis].reindex(new_index)
+
+        return self.reindex_indexer(
+            new_index,
+            indexer,
+            axis=axis,
+            fill_value=fill_value,
+            copy=False,
+            only_slice=only_slice,
+        )
+
+    def _equal_values(self, other: Self) -> bool:
+        """
+        To be implemented by the subclasses. Only check the column values
+        assuming shape and indexes have already been checked.
+        """
+        raise AbstractMethodError(self)
+
+    @final
+    def equals(self, other: object) -> bool:
+        """
+        Implementation for DataFrame.equals
+        """
+        if not isinstance(other, type(self)):
+            return False
+
+        self_axes, other_axes = self.axes, other.axes
+        if len(self_axes) != len(other_axes):
+            return False
+        if not all(ax1.equals(ax2) for ax1, ax2 in zip(self_axes, other_axes)):
+            return False
+
+        return self._equal_values(other)
+
+    def apply(
+        self,
+        f,
+        align_keys: list[str] | None = None,
+        **kwargs,
+    ) -> Self:
+        raise AbstractMethodError(self)
+
+    def apply_with_block(
+        self,
+        f,
+        align_keys: list[str] | None = None,
+        **kwargs,
+    ) -> Self:
+        raise AbstractMethodError(self)
+
+    @final
+    def isna(self, func) -> Self:
+        return self.apply("apply", func=func)
+
+    @final
+    def fillna(self, value, limit: int | None, inplace: bool, downcast) -> Self:
+        if limit is not None:
+            # Do this validation even if we go through one of the no-op paths
+            limit = libalgos.validate_limit(None, limit=limit)
+
+        return self.apply_with_block(
+            "fillna",
+            value=value,
+            limit=limit,
+            inplace=inplace,
+            downcast=downcast,
+            using_cow=using_copy_on_write(),
+            already_warned=_AlreadyWarned(),
+        )
+
+    @final
+    def where(self, other, cond, align: bool) -> Self:
+        if align:
+            align_keys = ["other", "cond"]
+        else:
+            align_keys = ["cond"]
+            other = extract_array(other, extract_numpy=True)
+
+        return self.apply_with_block(
+            "where",
+            align_keys=align_keys,
+            other=other,
+            cond=cond,
+            using_cow=using_copy_on_write(),
+        )
+
+    @final
+    def putmask(self, mask, new, align: bool = True, warn: bool = True) -> Self:
+        if align:
+            align_keys = ["new", "mask"]
+        else:
+            align_keys = ["mask"]
+            new = extract_array(new, extract_numpy=True)
+
+        already_warned = None
+        if warn_copy_on_write():
+            already_warned = _AlreadyWarned()
+            if not warn:
+                already_warned.warned_already = True
+
+        return self.apply_with_block(
+            "putmask",
+            align_keys=align_keys,
+            mask=mask,
+            new=new,
+            using_cow=using_copy_on_write(),
+            already_warned=already_warned,
+        )
+
+    @final
+    def round(self, decimals: int, using_cow: bool = False) -> Self:
+        return self.apply_with_block(
+            "round",
+            decimals=decimals,
+            using_cow=using_cow,
+        )
+
+    @final
+    def replace(self, to_replace, value, inplace: bool) -> Self:
+        inplace = validate_bool_kwarg(inplace, "inplace")
+        # NDFrame.replace ensures the not-is_list_likes here
+        assert not lib.is_list_like(to_replace)
+        assert not lib.is_list_like(value)
+        return self.apply_with_block(
+            "replace",
+            to_replace=to_replace,
+            value=value,
+            inplace=inplace,
+            using_cow=using_copy_on_write(),
+            already_warned=_AlreadyWarned(),
+        )
+
+    @final
+    def replace_regex(self, **kwargs) -> Self:
+        return self.apply_with_block(
+            "_replace_regex",
+            **kwargs,
+            using_cow=using_copy_on_write(),
+            already_warned=_AlreadyWarned(),
+        )
+
+    @final
+    def replace_list(
+        self,
+        src_list: list[Any],
+        dest_list: list[Any],
+        inplace: bool = False,
+        regex: bool = False,
+    ) -> Self:
+        """do a list replace"""
+        inplace = validate_bool_kwarg(inplace, "inplace")
+
+        bm = self.apply_with_block(
+            "replace_list",
+            src_list=src_list,
+            dest_list=dest_list,
+            inplace=inplace,
+            regex=regex,
+            using_cow=using_copy_on_write(),
+            already_warned=_AlreadyWarned(),
+        )
+        bm._consolidate_inplace()
+        return bm
+
+    def interpolate(self, inplace: bool, **kwargs) -> Self:
+        return self.apply_with_block(
+            "interpolate",
+            inplace=inplace,
+            **kwargs,
+            using_cow=using_copy_on_write(),
+            already_warned=_AlreadyWarned(),
+        )
+
+    def pad_or_backfill(self, inplace: bool, **kwargs) -> Self:
+        return self.apply_with_block(
+            "pad_or_backfill",
+            inplace=inplace,
+            **kwargs,
+            using_cow=using_copy_on_write(),
+            already_warned=_AlreadyWarned(),
+        )
+
+    def shift(self, periods: int, fill_value) -> Self:
+        if fill_value is lib.no_default:
+            fill_value = None
+
+        return self.apply_with_block("shift", periods=periods, fill_value=fill_value)
+
+    # --------------------------------------------------------------------
+    # Consolidation: No-ops for all but BlockManager
+
+    def is_consolidated(self) -> bool:
+        return True
+
+    def consolidate(self) -> Self:
+        return self
+
+    def _consolidate_inplace(self) -> None:
+        return
+
+
+class SingleDataManager(DataManager):
+    @property
+    def ndim(self) -> Literal[1]:
+        return 1
+
+    @final
+    @property
+    def array(self) -> ArrayLike:
+        """
+        Quick access to the backing array of the Block or SingleArrayManager.
+        """
+        # error: "SingleDataManager" has no attribute "arrays"; maybe "array"
+        return self.arrays[0]  # type: ignore[attr-defined]
+
+    def setitem_inplace(self, indexer, value, warn: bool = True) -> None:
+        """
+        Set values with indexer.
+
+        For Single[Block/Array]Manager, this backs s[indexer] = value
+
+        This is an inplace version of `setitem()`, mutating the manager/values
+        in place, not returning a new Manager (and Block), and thus never changing
+        the dtype.
+        """
+        arr = self.array
+
+        # EAs will do this validation in their own __setitem__ methods.
+        if isinstance(arr, np.ndarray):
+            # Note: checking for ndarray instead of np.dtype means we exclude
+            #  dt64/td64, which do their own validation.
+            value = np_can_hold_element(arr.dtype, value)
+
+        if isinstance(value, np.ndarray) and value.ndim == 1 and len(value) == 1:
+            # NumPy 1.25 deprecation: https://github.com/numpy/numpy/pull/10615
+            value = value[0, ...]
+
+        arr[indexer] = value
+
+    def grouped_reduce(self, func):
+        arr = self.array
+        res = func(arr)
+        index = default_index(len(res))
+
+        mgr = type(self).from_array(res, index)
+        return mgr
+
+    @classmethod
+    def from_array(cls, arr: ArrayLike, index: Index):
+        raise AbstractMethodError(cls)
+
+
+def interleaved_dtype(dtypes: list[DtypeObj]) -> DtypeObj | None:
+    """
+    Find the common dtype for `blocks`.
+
+    Parameters
+    ----------
+    blocks : List[DtypeObj]
+
+    Returns
+    -------
+    dtype : np.dtype, ExtensionDtype, or None
+        None is returned when `blocks` is empty.
+    """
+    if not len(dtypes):
+        return None
+
+    return find_common_type(dtypes)
+
+
+def ensure_np_dtype(dtype: DtypeObj) -> np.dtype:
+    # TODO: https://github.com/pandas-dev/pandas/issues/22791
+    # Give EAs some input on what happens here. Sparse needs this.
+    if isinstance(dtype, SparseDtype):
+        dtype = dtype.subtype
+        dtype = cast(np.dtype, dtype)
+    elif isinstance(dtype, ExtensionDtype):
+        dtype = np.dtype("object")
+    elif dtype == np.dtype(str):
+        dtype = np.dtype("object")
+    return dtype
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/internals/blocks.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/internals/blocks.py
new file mode 100644
index 0000000000000000000000000000000000000000..452c919449ec40c042bdb68b249801f2267b2b19
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/internals/blocks.py
@@ -0,0 +1,2923 @@
+from __future__ import annotations
+
+from functools import wraps
+import inspect
+import re
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Literal,
+    cast,
+    final,
+)
+import warnings
+import weakref
+
+import numpy as np
+
+from pandas._config import (
+    get_option,
+    using_copy_on_write,
+    warn_copy_on_write,
+)
+
+from pandas._libs import (
+    NaT,
+    internals as libinternals,
+    lib,
+)
+from pandas._libs.internals import (
+    BlockPlacement,
+    BlockValuesRefs,
+)
+from pandas._libs.missing import NA
+from pandas._typing import (
+    ArrayLike,
+    AxisInt,
+    DtypeBackend,
+    DtypeObj,
+    F,
+    FillnaOptions,
+    IgnoreRaise,
+    InterpolateOptions,
+    QuantileInterpolation,
+    Self,
+    Shape,
+    npt,
+)
+from pandas.errors import AbstractMethodError
+from pandas.util._decorators import cache_readonly
+from pandas.util._exceptions import find_stack_level
+from pandas.util._validators import validate_bool_kwarg
+
+from pandas.core.dtypes.astype import (
+    astype_array_safe,
+    astype_is_view,
+)
+from pandas.core.dtypes.cast import (
+    LossySetitemError,
+    can_hold_element,
+    convert_dtypes,
+    find_result_type,
+    maybe_downcast_to_dtype,
+    np_can_hold_element,
+)
+from pandas.core.dtypes.common import (
+    is_1d_only_ea_dtype,
+    is_float_dtype,
+    is_integer_dtype,
+    is_list_like,
+    is_scalar,
+    is_string_dtype,
+)
+from pandas.core.dtypes.dtypes import (
+    DatetimeTZDtype,
+    ExtensionDtype,
+    IntervalDtype,
+    NumpyEADtype,
+    PeriodDtype,
+)
+from pandas.core.dtypes.generic import (
+    ABCDataFrame,
+    ABCIndex,
+    ABCNumpyExtensionArray,
+    ABCSeries,
+)
+from pandas.core.dtypes.inference import is_re
+from pandas.core.dtypes.missing import (
+    is_valid_na_for_dtype,
+    isna,
+    na_value_for_dtype,
+)
+
+from pandas.core import missing
+import pandas.core.algorithms as algos
+from pandas.core.array_algos.putmask import (
+    extract_bool_array,
+    putmask_inplace,
+    putmask_without_repeat,
+    setitem_datetimelike_compat,
+    validate_putmask,
+)
+from pandas.core.array_algos.quantile import quantile_compat
+from pandas.core.array_algos.replace import (
+    compare_or_regex_search,
+    replace_regex,
+    should_use_regex,
+)
+from pandas.core.array_algos.transforms import shift
+from pandas.core.arrays import (
+    Categorical,
+    DatetimeArray,
+    ExtensionArray,
+    IntervalArray,
+    NumpyExtensionArray,
+    PeriodArray,
+    TimedeltaArray,
+)
+from pandas.core.arrays.string_ import StringDtype
+from pandas.core.base import PandasObject
+import pandas.core.common as com
+from pandas.core.computation import expressions
+from pandas.core.construction import (
+    ensure_wrapped_if_datetimelike,
+    extract_array,
+)
+from pandas.core.indexers import check_setitem_lengths
+from pandas.core.indexes.base import get_values_for_csv
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Iterable,
+        Sequence,
+    )
+
+    from pandas.core.api import Index
+    from pandas.core.arrays._mixins import NDArrayBackedExtensionArray
+
+# comparison is faster than is_object_dtype
+_dtype_obj = np.dtype("object")
+
+
+COW_WARNING_GENERAL_MSG = """\
+Setting a value on a view: behaviour will change in pandas 3.0.
+You are mutating a Series or DataFrame object, and currently this mutation will
+also have effect on other Series or DataFrame objects that share data with this
+object. In pandas 3.0 (with Copy-on-Write), updating one Series or DataFrame object
+will never modify another.
+"""
+
+
+COW_WARNING_SETITEM_MSG = """\
+Setting a value on a view: behaviour will change in pandas 3.0.
+Currently, the mutation will also have effect on the object that shares data
+with this object. For example, when setting a value in a Series that was
+extracted from a column of a DataFrame, that DataFrame will also be updated:
+
+    ser = df["col"]
+    ser[0] = 0     <--- in pandas 2, this also updates `df`
+
+In pandas 3.0 (with Copy-on-Write), updating one Series/DataFrame will never
+modify another, and thus in the example above, `df` will not be changed.
+"""
+
+
+def maybe_split(meth: F) -> F:
+    """
+    If we have a multi-column block, split and operate block-wise.  Otherwise
+    use the original method.
+    """
+
+    @wraps(meth)
+    def newfunc(self, *args, **kwargs) -> list[Block]:
+        if self.ndim == 1 or self.shape[0] == 1:
+            return meth(self, *args, **kwargs)
+        else:
+            # Split and operate column-by-column
+            return self.split_and_operate(meth, *args, **kwargs)
+
+    return cast(F, newfunc)
+
+
+class Block(PandasObject, libinternals.Block):
+    """
+    Canonical n-dimensional unit of homogeneous dtype contained in a pandas
+    data structure
+
+    Index-ignorant; let the container take care of that
+    """
+
+    values: np.ndarray | ExtensionArray
+    ndim: int
+    refs: BlockValuesRefs
+    __init__: Callable
+
+    __slots__ = ()
+    is_numeric = False
+
+    @final
+    @cache_readonly
+    def _validate_ndim(self) -> bool:
+        """
+        We validate dimension for blocks that can hold 2D values, which for now
+        means numpy dtypes or DatetimeTZDtype.
+        """
+        dtype = self.dtype
+        return not isinstance(dtype, ExtensionDtype) or isinstance(
+            dtype, DatetimeTZDtype
+        )
+
+    @final
+    @cache_readonly
+    def is_object(self) -> bool:
+        return self.values.dtype == _dtype_obj
+
+    @final
+    @cache_readonly
+    def is_extension(self) -> bool:
+        return not lib.is_np_dtype(self.values.dtype)
+
+    @final
+    @cache_readonly
+    def _can_consolidate(self) -> bool:
+        # We _could_ consolidate for DatetimeTZDtype but don't for now.
+        return not self.is_extension
+
+    @final
+    @cache_readonly
+    def _consolidate_key(self):
+        return self._can_consolidate, self.dtype.name
+
+    @final
+    @cache_readonly
+    def _can_hold_na(self) -> bool:
+        """
+        Can we store NA values in this Block?
+        """
+        dtype = self.dtype
+        if isinstance(dtype, np.dtype):
+            return dtype.kind not in "iub"
+        return dtype._can_hold_na
+
+    @final
+    @property
+    def is_bool(self) -> bool:
+        """
+        We can be bool if a) we are bool dtype or b) object dtype with bool objects.
+        """
+        return self.values.dtype == np.dtype(bool)
+
+    @final
+    def external_values(self):
+        return external_values(self.values)
+
+    @final
+    @cache_readonly
+    def fill_value(self):
+        # Used in reindex_indexer
+        return na_value_for_dtype(self.dtype, compat=False)
+
+    @final
+    def _standardize_fill_value(self, value):
+        # if we are passed a scalar None, convert it here
+        if self.dtype != _dtype_obj and is_valid_na_for_dtype(value, self.dtype):
+            value = self.fill_value
+        return value
+
+    @property
+    def mgr_locs(self) -> BlockPlacement:
+        return self._mgr_locs
+
+    @mgr_locs.setter
+    def mgr_locs(self, new_mgr_locs: BlockPlacement) -> None:
+        self._mgr_locs = new_mgr_locs
+
+    @final
+    def make_block(
+        self,
+        values,
+        placement: BlockPlacement | None = None,
+        refs: BlockValuesRefs | None = None,
+    ) -> Block:
+        """
+        Create a new block, with type inference propagate any values that are
+        not specified
+        """
+        if placement is None:
+            placement = self._mgr_locs
+        if self.is_extension:
+            values = ensure_block_shape(values, ndim=self.ndim)
+
+        return new_block(values, placement=placement, ndim=self.ndim, refs=refs)
+
+    @final
+    def make_block_same_class(
+        self,
+        values,
+        placement: BlockPlacement | None = None,
+        refs: BlockValuesRefs | None = None,
+    ) -> Self:
+        """Wrap given values in a block of same type as self."""
+        # Pre-2.0 we called ensure_wrapped_if_datetimelike because fastparquet
+        #  relied on it, as of 2.0 the caller is responsible for this.
+        if placement is None:
+            placement = self._mgr_locs
+
+        # We assume maybe_coerce_values has already been called
+        return type(self)(values, placement=placement, ndim=self.ndim, refs=refs)
+
+    @final
+    def __repr__(self) -> str:
+        # don't want to print out all of the items here
+        name = type(self).__name__
+        if self.ndim == 1:
+            result = f"{name}: {len(self)} dtype: {self.dtype}"
+        else:
+            shape = " x ".join([str(s) for s in self.shape])
+            result = f"{name}: {self.mgr_locs.indexer}, {shape}, dtype: {self.dtype}"
+
+        return result
+
+    @final
+    def __len__(self) -> int:
+        return len(self.values)
+
+    @final
+    def slice_block_columns(self, slc: slice) -> Self:
+        """
+        Perform __getitem__-like, return result as block.
+        """
+        new_mgr_locs = self._mgr_locs[slc]
+
+        new_values = self._slice(slc)
+        refs = self.refs
+        return type(self)(new_values, new_mgr_locs, self.ndim, refs=refs)
+
+    @final
+    def take_block_columns(self, indices: npt.NDArray[np.intp]) -> Self:
+        """
+        Perform __getitem__-like, return result as block.
+
+        Only supports slices that preserve dimensionality.
+        """
+        # Note: only called from is from internals.concat, and we can verify
+        #  that never happens with 1-column blocks, i.e. never for ExtensionBlock.
+
+        new_mgr_locs = self._mgr_locs[indices]
+
+        new_values = self._slice(indices)
+        return type(self)(new_values, new_mgr_locs, self.ndim, refs=None)
+
+    @final
+    def getitem_block_columns(
+        self, slicer: slice, new_mgr_locs: BlockPlacement, ref_inplace_op: bool = False
+    ) -> Self:
+        """
+        Perform __getitem__-like, return result as block.
+
+        Only supports slices that preserve dimensionality.
+        """
+        new_values = self._slice(slicer)
+        refs = self.refs if not ref_inplace_op or self.refs.has_reference() else None
+        return type(self)(new_values, new_mgr_locs, self.ndim, refs=refs)
+
+    @final
+    def _can_hold_element(self, element: Any) -> bool:
+        """require the same dtype as ourselves"""
+        element = extract_array(element, extract_numpy=True)
+        return can_hold_element(self.values, element)
+
+    @final
+    def should_store(self, value: ArrayLike) -> bool:
+        """
+        Should we set self.values[indexer] = value inplace or do we need to cast?
+
+        Parameters
+        ----------
+        value : np.ndarray or ExtensionArray
+
+        Returns
+        -------
+        bool
+        """
+        return value.dtype == self.dtype
+
+    # ---------------------------------------------------------------------
+    # Apply/Reduce and Helpers
+
+    @final
+    def apply(self, func, **kwargs) -> list[Block]:
+        """
+        apply the function to my values; return a block if we are not
+        one
+        """
+        result = func(self.values, **kwargs)
+
+        result = maybe_coerce_values(result)
+        return self._split_op_result(result)
+
+    @final
+    def reduce(self, func) -> list[Block]:
+        # We will apply the function and reshape the result into a single-row
+        #  Block with the same mgr_locs; squeezing will be done at a higher level
+        assert self.ndim == 2
+
+        result = func(self.values)
+
+        if self.values.ndim == 1:
+            res_values = result
+        else:
+            res_values = result.reshape(-1, 1)
+
+        nb = self.make_block(res_values)
+        return [nb]
+
+    @final
+    def _split_op_result(self, result: ArrayLike) -> list[Block]:
+        # See also: split_and_operate
+        if result.ndim > 1 and isinstance(result.dtype, ExtensionDtype):
+            # TODO(EA2D): unnecessary with 2D EAs
+            # if we get a 2D ExtensionArray, we need to split it into 1D pieces
+            nbs = []
+            for i, loc in enumerate(self._mgr_locs):
+                if not is_1d_only_ea_dtype(result.dtype):
+                    vals = result[i : i + 1]
+                else:
+                    vals = result[i]
+
+                bp = BlockPlacement(loc)
+                block = self.make_block(values=vals, placement=bp)
+                nbs.append(block)
+            return nbs
+
+        nb = self.make_block(result)
+
+        return [nb]
+
+    @final
+    def _split(self) -> list[Block]:
+        """
+        Split a block into a list of single-column blocks.
+        """
+        assert self.ndim == 2
+
+        new_blocks = []
+        for i, ref_loc in enumerate(self._mgr_locs):
+            vals = self.values[slice(i, i + 1)]
+
+            bp = BlockPlacement(ref_loc)
+            nb = type(self)(vals, placement=bp, ndim=2, refs=self.refs)
+            new_blocks.append(nb)
+        return new_blocks
+
+    @final
+    def split_and_operate(self, func, *args, **kwargs) -> list[Block]:
+        """
+        Split the block and apply func column-by-column.
+
+        Parameters
+        ----------
+        func : Block method
+        *args
+        **kwargs
+
+        Returns
+        -------
+        List[Block]
+        """
+        assert self.ndim == 2 and self.shape[0] != 1
+
+        res_blocks = []
+        for nb in self._split():
+            rbs = func(nb, *args, **kwargs)
+            res_blocks.extend(rbs)
+        return res_blocks
+
+    # ---------------------------------------------------------------------
+    # Up/Down-casting
+
+    @final
+    def coerce_to_target_dtype(
+        self, other, warn_on_upcast: bool = False, using_cow: bool = False
+    ) -> Block:
+        """
+        coerce the current block to a dtype compat for other
+        we will return a block, possibly object, and not raise
+
+        we can also safely try to coerce to the same dtype
+        and will receive the same block
+        """
+        new_dtype = find_result_type(self.values.dtype, other)
+        if new_dtype == self.dtype:
+            # GH#52927 avoid RecursionError
+            raise AssertionError(
+                "Something has gone wrong, please report a bug at "
+                "https://github.com/pandas-dev/pandas/issues"
+            )
+
+        # In a future version of pandas, the default will be that
+        # setting `nan` into an integer series won't raise.
+        if (
+            is_scalar(other)
+            and is_integer_dtype(self.values.dtype)
+            and isna(other)
+            and other is not NaT
+            and not (
+                isinstance(other, (np.datetime64, np.timedelta64)) and np.isnat(other)
+            )
+        ):
+            warn_on_upcast = False
+        elif (
+            isinstance(other, np.ndarray)
+            and other.ndim == 1
+            and is_integer_dtype(self.values.dtype)
+            and is_float_dtype(other.dtype)
+            and lib.has_only_ints_or_nan(other)
+        ):
+            warn_on_upcast = False
+
+        if warn_on_upcast:
+            warnings.warn(
+                f"Setting an item of incompatible dtype is deprecated "
+                "and will raise an error in a future version of pandas. "
+                f"Value '{other}' has dtype incompatible with {self.values.dtype}, "
+                "please explicitly cast to a compatible dtype first.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+        if self.values.dtype == new_dtype:
+            raise AssertionError(
+                f"Did not expect new dtype {new_dtype} to equal self.dtype "
+                f"{self.values.dtype}. Please report a bug at "
+                "https://github.com/pandas-dev/pandas/issues."
+            )
+        copy = False
+        if (
+            not using_cow
+            and isinstance(self.dtype, StringDtype)
+            and self.dtype.storage == "python"
+        ):
+            copy = True
+        return self.astype(new_dtype, copy=copy, using_cow=using_cow)
+
+    @final
+    def _maybe_downcast(
+        self,
+        blocks: list[Block],
+        downcast,
+        using_cow: bool,
+        caller: str,
+    ) -> list[Block]:
+        if downcast is False:
+            return blocks
+
+        if self.dtype == _dtype_obj:
+            # TODO: does it matter that self.dtype might not match blocks[i].dtype?
+            # GH#44241 We downcast regardless of the argument;
+            #  respecting 'downcast=None' may be worthwhile at some point,
+            #  but ATM it breaks too much existing code.
+            # split and convert the blocks
+
+            if caller == "fillna" and get_option("future.no_silent_downcasting"):
+                return blocks
+
+            nbs = extend_blocks(
+                [
+                    blk.convert(
+                        using_cow=using_cow, copy=not using_cow, convert_string=False
+                    )
+                    for blk in blocks
+                ]
+            )
+            if caller == "fillna":
+                if len(nbs) != len(blocks) or not all(
+                    x.dtype == y.dtype for x, y in zip(nbs, blocks)
+                ):
+                    # GH#54261
+                    warnings.warn(
+                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill "
+                        "is deprecated and will change in a future version. "
+                        "Call result.infer_objects(copy=False) instead. "
+                        "To opt-in to the future "
+                        "behavior, set "
+                        "`pd.set_option('future.no_silent_downcasting', True)`",
+                        FutureWarning,
+                        stacklevel=find_stack_level(),
+                    )
+
+            return nbs
+
+        elif downcast is None:
+            return blocks
+        elif caller == "where" and get_option("future.no_silent_downcasting") is True:
+            return blocks
+        else:
+            nbs = extend_blocks([b._downcast_2d(downcast, using_cow) for b in blocks])
+
+        # When _maybe_downcast is called with caller="where", it is either
+        #  a) with downcast=False, which is a no-op (the desired future behavior)
+        #  b) with downcast="infer", which is _not_ passed by the user.
+        # In the latter case the future behavior is to stop doing inference,
+        #  so we issue a warning if and only if some inference occurred.
+        if caller == "where":
+            # GH#53656
+            if len(blocks) != len(nbs) or any(
+                left.dtype != right.dtype for left, right in zip(blocks, nbs)
+            ):
+                # In this case _maybe_downcast was _not_ a no-op, so the behavior
+                #  will change, so we issue a warning.
+                warnings.warn(
+                    "Downcasting behavior in Series and DataFrame methods 'where', "
+                    "'mask', and 'clip' is deprecated. In a future "
+                    "version this will not infer object dtypes or cast all-round "
+                    "floats to integers. Instead call "
+                    "result.infer_objects(copy=False) for object inference, "
+                    "or cast round floats explicitly. To opt-in to the future "
+                    "behavior, set "
+                    "`pd.set_option('future.no_silent_downcasting', True)`",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+
+        return nbs
+
+    @final
+    @maybe_split
+    def _downcast_2d(self, dtype, using_cow: bool = False) -> list[Block]:
+        """
+        downcast specialized to 2D case post-validation.
+
+        Refactored to allow use of maybe_split.
+        """
+        new_values = maybe_downcast_to_dtype(self.values, dtype=dtype)
+        new_values = maybe_coerce_values(new_values)
+        refs = self.refs if new_values is self.values else None
+        return [self.make_block(new_values, refs=refs)]
+
+    @final
+    def convert(
+        self,
+        *,
+        copy: bool = True,
+        using_cow: bool = False,
+        convert_string: bool = True,
+    ) -> list[Block]:
+        """
+        Attempt to coerce any object types to better types. Return a copy
+        of the block (if copy = True).
+        """
+        if not self.is_object:
+            if not copy and using_cow:
+                return [self.copy(deep=False)]
+            return [self.copy()] if copy else [self]
+
+        if self.ndim != 1 and self.shape[0] != 1:
+            blocks = self.split_and_operate(
+                Block.convert,
+                copy=copy,
+                using_cow=using_cow,
+                convert_string=convert_string,
+            )
+            if all(blk.dtype.kind == "O" for blk in blocks):
+                # Avoid fragmenting the block if convert is a no-op
+                if using_cow:
+                    return [self.copy(deep=False)]
+                return [self.copy()] if copy else [self]
+            return blocks
+
+        values = self.values
+        if values.ndim == 2:
+            # the check above ensures we only get here with values.shape[0] == 1,
+            # avoid doing .ravel as that might make a copy
+            values = values[0]
+
+        res_values = lib.maybe_convert_objects(
+            values,  # type: ignore[arg-type]
+            convert_non_numeric=True,
+            convert_string=convert_string,
+        )
+        refs = None
+        if (
+            copy
+            and res_values is values
+            or isinstance(res_values, NumpyExtensionArray)
+            and res_values._ndarray is values
+        ):
+            res_values = res_values.copy()
+        elif res_values is values:
+            refs = self.refs
+
+        res_values = ensure_block_shape(res_values, self.ndim)
+        res_values = maybe_coerce_values(res_values)
+        return [self.make_block(res_values, refs=refs)]
+
+    def convert_dtypes(
+        self,
+        copy: bool,
+        using_cow: bool,
+        infer_objects: bool = True,
+        convert_string: bool = True,
+        convert_integer: bool = True,
+        convert_boolean: bool = True,
+        convert_floating: bool = True,
+        dtype_backend: DtypeBackend = "numpy_nullable",
+    ) -> list[Block]:
+        if infer_objects and self.is_object:
+            blks = self.convert(copy=False, using_cow=using_cow)
+        else:
+            blks = [self]
+
+        if not any(
+            [convert_floating, convert_integer, convert_boolean, convert_string]
+        ):
+            return [b.copy(deep=copy) for b in blks]
+
+        rbs = []
+        for blk in blks:
+            # Determine dtype column by column
+            sub_blks = [blk] if blk.ndim == 1 or self.shape[0] == 1 else blk._split()
+            dtypes = [
+                convert_dtypes(
+                    b.values,
+                    convert_string,
+                    convert_integer,
+                    convert_boolean,
+                    convert_floating,
+                    infer_objects,
+                    dtype_backend,
+                )
+                for b in sub_blks
+            ]
+            if all(dtype == self.dtype for dtype in dtypes):
+                # Avoid block splitting if no dtype changes
+                rbs.append(blk.copy(deep=copy))
+                continue
+
+            for dtype, b in zip(dtypes, sub_blks):
+                rbs.append(b.astype(dtype=dtype, copy=copy, squeeze=b.ndim != 1))
+        return rbs
+
+    # ---------------------------------------------------------------------
+    # Array-Like Methods
+
+    @final
+    @cache_readonly
+    def dtype(self) -> DtypeObj:
+        return self.values.dtype
+
+    @final
+    def astype(
+        self,
+        dtype: DtypeObj,
+        copy: bool = False,
+        errors: IgnoreRaise = "raise",
+        using_cow: bool = False,
+        squeeze: bool = False,
+    ) -> Block:
+        """
+        Coerce to the new dtype.
+
+        Parameters
+        ----------
+        dtype : np.dtype or ExtensionDtype
+        copy : bool, default False
+            copy if indicated
+        errors : str, {'raise', 'ignore'}, default 'raise'
+            - ``raise`` : allow exceptions to be raised
+            - ``ignore`` : suppress exceptions. On error return original object
+        using_cow: bool, default False
+            Signaling if copy on write copy logic is used.
+        squeeze : bool, default False
+            squeeze values to ndim=1 if only one column is given
+
+        Returns
+        -------
+        Block
+        """
+        values = self.values
+        if squeeze and values.ndim == 2 and is_1d_only_ea_dtype(dtype):
+            if values.shape[0] != 1:
+                raise ValueError("Can not squeeze with more than one column.")
+            values = values[0, :]  # type: ignore[call-overload]
+
+        new_values = astype_array_safe(values, dtype, copy=copy, errors=errors)
+
+        new_values = maybe_coerce_values(new_values)
+
+        refs = None
+        if (using_cow or not copy) and astype_is_view(values.dtype, new_values.dtype):
+            refs = self.refs
+
+        newb = self.make_block(new_values, refs=refs)
+        if newb.shape != self.shape:
+            raise TypeError(
+                f"cannot set astype for copy = [{copy}] for dtype "
+                f"({self.dtype.name} [{self.shape}]) to different shape "
+                f"({newb.dtype.name} [{newb.shape}])"
+            )
+        return newb
+
+    @final
+    def get_values_for_csv(
+        self, *, float_format, date_format, decimal, na_rep: str = "nan", quoting=None
+    ) -> Block:
+        """convert to our native types format"""
+        result = get_values_for_csv(
+            self.values,
+            na_rep=na_rep,
+            quoting=quoting,
+            float_format=float_format,
+            date_format=date_format,
+            decimal=decimal,
+        )
+        return self.make_block(result)
+
+    @final
+    def copy(self, deep: bool = True) -> Self:
+        """copy constructor"""
+        values = self.values
+        refs: BlockValuesRefs | None
+        if deep:
+            values = values.copy()
+            refs = None
+        else:
+            refs = self.refs
+        return type(self)(values, placement=self._mgr_locs, ndim=self.ndim, refs=refs)
+
+    # ---------------------------------------------------------------------
+    # Copy-on-Write Helpers
+
+    @final
+    def _maybe_copy(self, using_cow: bool, inplace: bool) -> Self:
+        if using_cow and inplace:
+            deep = self.refs.has_reference()
+            blk = self.copy(deep=deep)
+        else:
+            blk = self if inplace else self.copy()
+        return blk
+
+    @final
+    def _get_refs_and_copy(self, using_cow: bool, inplace: bool):
+        refs = None
+        copy = not inplace
+        if inplace:
+            if using_cow and self.refs.has_reference():
+                copy = True
+            else:
+                refs = self.refs
+        return copy, refs
+
+    # ---------------------------------------------------------------------
+    # Replace
+
+    @final
+    def replace(
+        self,
+        to_replace,
+        value,
+        inplace: bool = False,
+        # mask may be pre-computed if we're called from replace_list
+        mask: npt.NDArray[np.bool_] | None = None,
+        using_cow: bool = False,
+        already_warned=None,
+        convert_string=None,
+    ) -> list[Block]:
+        """
+        replace the to_replace value with value, possible to create new
+        blocks here this is just a call to putmask.
+        """
+
+        # Note: the checks we do in NDFrame.replace ensure we never get
+        #  here with listlike to_replace or value, as those cases
+        #  go through replace_list
+        values = self.values
+
+        if isinstance(values, Categorical):
+            # TODO: avoid special-casing
+            # GH49404
+            blk = self._maybe_copy(using_cow, inplace)
+            values = cast(Categorical, blk.values)
+            values._replace(to_replace=to_replace, value=value, inplace=True)
+            return [blk]
+
+        if not self._can_hold_element(to_replace):
+            # We cannot hold `to_replace`, so we know immediately that
+            #  replacing it is a no-op.
+            # Note: If to_replace were a list, NDFrame.replace would call
+            #  replace_list instead of replace.
+            if using_cow:
+                return [self.copy(deep=False)]
+            else:
+                return [self] if inplace else [self.copy()]
+
+        if mask is None:
+            mask = missing.mask_missing(values, to_replace)
+        if not mask.any():
+            # Note: we get here with test_replace_extension_other incorrectly
+            #  bc _can_hold_element is incorrect.
+            if using_cow:
+                return [self.copy(deep=False)]
+            else:
+                return [self] if inplace else [self.copy()]
+
+        elif self._can_hold_element(value) or (self.dtype == "string" and is_re(value)):
+            # TODO(CoW): Maybe split here as well into columns where mask has True
+            # and rest?
+            blk = self._maybe_copy(using_cow, inplace)
+            putmask_inplace(blk.values, mask, value)
+            if (
+                inplace
+                and warn_copy_on_write()
+                and already_warned is not None
+                and not already_warned.warned_already
+            ):
+                if self.refs.has_reference():
+                    warnings.warn(
+                        COW_WARNING_GENERAL_MSG,
+                        FutureWarning,
+                        stacklevel=find_stack_level(),
+                    )
+                    already_warned.warned_already = True
+
+            if not (self.is_object and value is None):
+                # if the user *explicitly* gave None, we keep None, otherwise
+                #  may downcast to NaN
+                if get_option("future.no_silent_downcasting") is True:
+                    blocks = [blk]
+                else:
+                    blocks = blk.convert(
+                        copy=False,
+                        using_cow=using_cow,
+                        convert_string=convert_string or self.dtype == "string",
+                    )
+                    if len(blocks) > 1 or blocks[0].dtype != blk.dtype:
+                        warnings.warn(
+                            # GH#54710
+                            "Downcasting behavior in `replace` is deprecated and "
+                            "will be removed in a future version. To retain the old "
+                            "behavior, explicitly call "
+                            "`result.infer_objects(copy=False)`. "
+                            "To opt-in to the future "
+                            "behavior, set "
+                            "`pd.set_option('future.no_silent_downcasting', True)`",
+                            FutureWarning,
+                            stacklevel=find_stack_level(),
+                        )
+            else:
+                blocks = [blk]
+            return blocks
+
+        elif self.ndim == 1 or self.shape[0] == 1:
+            if value is None or value is NA:
+                blk = self.astype(np.dtype(object))
+            else:
+                blk = self.coerce_to_target_dtype(value, using_cow=using_cow)
+            return blk.replace(
+                to_replace=to_replace,
+                value=value,
+                inplace=True,
+                mask=mask,
+                using_cow=using_cow,
+                convert_string=convert_string,
+            )
+
+        else:
+            # split so that we only upcast where necessary
+            blocks = []
+            for i, nb in enumerate(self._split()):
+                blocks.extend(
+                    type(self).replace(
+                        nb,
+                        to_replace=to_replace,
+                        value=value,
+                        inplace=True,
+                        mask=mask[i : i + 1],
+                        using_cow=using_cow,
+                        convert_string=convert_string,
+                    )
+                )
+            return blocks
+
+    @final
+    def _replace_regex(
+        self,
+        to_replace,
+        value,
+        inplace: bool = False,
+        mask=None,
+        using_cow: bool = False,
+        convert_string=None,
+        already_warned=None,
+    ) -> list[Block]:
+        """
+        Replace elements by the given value.
+
+        Parameters
+        ----------
+        to_replace : object or pattern
+            Scalar to replace or regular expression to match.
+        value : object
+            Replacement object.
+        inplace : bool, default False
+            Perform inplace modification.
+        mask : array-like of bool, optional
+            True indicate corresponding element is ignored.
+        using_cow: bool, default False
+            Specifying if copy on write is enabled.
+
+        Returns
+        -------
+        List[Block]
+        """
+        if not is_re(to_replace) and not self._can_hold_element(to_replace):
+            # i.e. only if self.is_object is True, but could in principle include a
+            #  String ExtensionBlock
+            if using_cow:
+                return [self.copy(deep=False)]
+            return [self] if inplace else [self.copy()]
+
+        if is_re(to_replace) and self.dtype not in [object, "string"]:
+            # only object or string dtype can hold strings, and a regex object
+            # will only match strings
+            return [self.copy(deep=False)]
+
+        if not (
+            self._can_hold_element(value) or (self.dtype == "string" and is_re(value))
+        ):
+            block = self.astype(np.dtype(object))
+        else:
+            block = self._maybe_copy(using_cow, inplace)
+
+        rx = re.compile(to_replace)
+
+        replace_regex(block.values, rx, value, mask)
+
+        if (
+            inplace
+            and warn_copy_on_write()
+            and already_warned is not None
+            and not already_warned.warned_already
+        ):
+            if self.refs.has_reference():
+                warnings.warn(
+                    COW_WARNING_GENERAL_MSG,
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+                already_warned.warned_already = True
+
+        nbs = block.convert(
+            copy=False,
+            using_cow=using_cow,
+            convert_string=convert_string or self.dtype == "string",
+        )
+        opt = get_option("future.no_silent_downcasting")
+        if (
+            len(nbs) > 1
+            or (
+                nbs[0].dtype != block.dtype
+                and not (self.dtype == "string" and nbs[0].dtype == "string")
+            )
+        ) and not opt:
+            warnings.warn(
+                # GH#54710
+                "Downcasting behavior in `replace` is deprecated and "
+                "will be removed in a future version. To retain the old "
+                "behavior, explicitly call `result.infer_objects(copy=False)`. "
+                "To opt-in to the future "
+                "behavior, set "
+                "`pd.set_option('future.no_silent_downcasting', True)`",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+        return nbs
+
+    @final
+    def replace_list(
+        self,
+        src_list: Iterable[Any],
+        dest_list: Sequence[Any],
+        inplace: bool = False,
+        regex: bool = False,
+        using_cow: bool = False,
+        already_warned=None,
+    ) -> list[Block]:
+        """
+        See BlockManager.replace_list docstring.
+        """
+        values = self.values
+
+        if isinstance(values, Categorical):
+            # TODO: avoid special-casing
+            # GH49404
+            blk = self._maybe_copy(using_cow, inplace)
+            values = cast(Categorical, blk.values)
+            values._replace(to_replace=src_list, value=dest_list, inplace=True)
+            return [blk]
+
+        convert_string = self.dtype == "string"
+
+        # Exclude anything that we know we won't contain
+        pairs = [
+            (x, y)
+            for x, y in zip(src_list, dest_list)
+            if (self._can_hold_element(x) or (self.dtype == "string" and is_re(x)))
+        ]
+        if not len(pairs):
+            if using_cow:
+                return [self.copy(deep=False)]
+            # shortcut, nothing to replace
+            return [self] if inplace else [self.copy()]
+
+        src_len = len(pairs) - 1
+
+        if is_string_dtype(values.dtype):
+            # Calculate the mask once, prior to the call of comp
+            # in order to avoid repeating the same computations
+            na_mask = ~isna(values)
+            masks: Iterable[npt.NDArray[np.bool_]] = (
+                extract_bool_array(
+                    cast(
+                        ArrayLike,
+                        compare_or_regex_search(
+                            values, s[0], regex=regex, mask=na_mask
+                        ),
+                    )
+                )
+                for s in pairs
+            )
+        else:
+            # GH#38086 faster if we know we dont need to check for regex
+            masks = (missing.mask_missing(values, s[0]) for s in pairs)
+        # Materialize if inplace = True, since the masks can change
+        # as we replace
+        if inplace:
+            masks = list(masks)
+
+        if using_cow:
+            # Don't set up refs here, otherwise we will think that we have
+            # references when we check again later
+            rb = [self]
+        else:
+            rb = [self if inplace else self.copy()]
+
+        if (
+            inplace
+            and warn_copy_on_write()
+            and already_warned is not None
+            and not already_warned.warned_already
+        ):
+            if self.refs.has_reference():
+                warnings.warn(
+                    COW_WARNING_GENERAL_MSG,
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+                already_warned.warned_already = True
+
+        opt = get_option("future.no_silent_downcasting")
+        for i, ((src, dest), mask) in enumerate(zip(pairs, masks)):
+            convert = i == src_len  # only convert once at the end
+            new_rb: list[Block] = []
+
+            # GH-39338: _replace_coerce can split a block into
+            # single-column blocks, so track the index so we know
+            # where to index into the mask
+            for blk_num, blk in enumerate(rb):
+                if len(rb) == 1:
+                    m = mask
+                else:
+                    mib = mask
+                    assert not isinstance(mib, bool)
+                    m = mib[blk_num : blk_num + 1]
+
+                # error: Argument "mask" to "_replace_coerce" of "Block" has
+                # incompatible type "Union[ExtensionArray, ndarray[Any, Any], bool]";
+                # expected "ndarray[Any, dtype[bool_]]"
+                result = blk._replace_coerce(
+                    to_replace=src,
+                    value=dest,
+                    mask=m,
+                    inplace=inplace,
+                    regex=regex,
+                    using_cow=using_cow,
+                    convert_string=convert_string,
+                )
+
+                if using_cow and i != src_len:
+                    # This is ugly, but we have to get rid of intermediate refs
+                    # that did not go out of scope yet, otherwise we will trigger
+                    # many unnecessary copies
+                    for b in result:
+                        ref = weakref.ref(b)
+                        b.refs.referenced_blocks.pop(
+                            b.refs.referenced_blocks.index(ref)
+                        )
+
+                if (
+                    not opt
+                    and convert
+                    and blk.is_object
+                    and not all(x is None for x in dest_list)
+                ):
+                    # GH#44498 avoid unwanted cast-back
+                    nbs = []
+                    for res_blk in result:
+                        converted = res_blk.convert(
+                            copy=True and not using_cow,
+                            using_cow=using_cow,
+                            convert_string=convert_string,
+                        )
+                        if len(converted) > 1 or converted[0].dtype != res_blk.dtype:
+                            warnings.warn(
+                                # GH#54710
+                                "Downcasting behavior in `replace` is deprecated "
+                                "and will be removed in a future version. To "
+                                "retain the old behavior, explicitly call "
+                                "`result.infer_objects(copy=False)`. "
+                                "To opt-in to the future "
+                                "behavior, set "
+                                "`pd.set_option('future.no_silent_downcasting', True)`",
+                                FutureWarning,
+                                stacklevel=find_stack_level(),
+                            )
+                        nbs.extend(converted)
+                    result = nbs
+                new_rb.extend(result)
+            rb = new_rb
+        return rb
+
+    @final
+    def _replace_coerce(
+        self,
+        to_replace,
+        value,
+        mask: npt.NDArray[np.bool_],
+        inplace: bool = True,
+        regex: bool = False,
+        using_cow: bool = False,
+        convert_string: bool = True,
+    ) -> list[Block]:
+        """
+        Replace value corresponding to the given boolean array with another
+        value.
+
+        Parameters
+        ----------
+        to_replace : object or pattern
+            Scalar to replace or regular expression to match.
+        value : object
+            Replacement object.
+        mask : np.ndarray[bool]
+            True indicate corresponding element is ignored.
+        inplace : bool, default True
+            Perform inplace modification.
+        regex : bool, default False
+            If true, perform regular expression substitution.
+
+        Returns
+        -------
+        List[Block]
+        """
+        if should_use_regex(regex, to_replace):
+            return self._replace_regex(
+                to_replace,
+                value,
+                inplace=inplace,
+                mask=mask,
+                using_cow=using_cow,
+                convert_string=convert_string,
+            )
+        else:
+            if value is None:
+                # gh-45601, gh-45836, gh-46634
+                if mask.any():
+                    has_ref = self.refs.has_reference()
+                    nb = self.astype(np.dtype(object), copy=False, using_cow=using_cow)
+                    if (nb is self or using_cow) and not inplace:
+                        nb = nb.copy()
+                    elif inplace and has_ref and nb.refs.has_reference() and using_cow:
+                        # no copy in astype and we had refs before
+                        nb = nb.copy()
+                    putmask_inplace(nb.values, mask, value)
+                    return [nb]
+                if using_cow:
+                    return [self.copy(deep=False)]
+                return [self] if inplace else [self.copy()]
+            return self.replace(
+                to_replace=to_replace,
+                value=value,
+                inplace=inplace,
+                mask=mask,
+                using_cow=using_cow,
+                convert_string=convert_string,
+            )
+
+    # ---------------------------------------------------------------------
+    # 2D Methods - Shared by NumpyBlock and NDArrayBackedExtensionBlock
+    #  but not ExtensionBlock
+
+    def _maybe_squeeze_arg(self, arg: np.ndarray) -> np.ndarray:
+        """
+        For compatibility with 1D-only ExtensionArrays.
+        """
+        return arg
+
+    def _unwrap_setitem_indexer(self, indexer):
+        """
+        For compatibility with 1D-only ExtensionArrays.
+        """
+        return indexer
+
+    # NB: this cannot be made cache_readonly because in mgr.set_values we pin
+    #  new .values that can have different shape GH#42631
+    @property
+    def shape(self) -> Shape:
+        return self.values.shape
+
+    def iget(self, i: int | tuple[int, int] | tuple[slice, int]) -> np.ndarray:
+        # In the case where we have a tuple[slice, int], the slice will always
+        #  be slice(None)
+        # Note: only reached with self.ndim == 2
+        # Invalid index type "Union[int, Tuple[int, int], Tuple[slice, int]]"
+        # for "Union[ndarray[Any, Any], ExtensionArray]"; expected type
+        # "Union[int, integer[Any]]"
+        return self.values[i]  # type: ignore[index]
+
+    def _slice(
+        self, slicer: slice | npt.NDArray[np.bool_] | npt.NDArray[np.intp]
+    ) -> ArrayLike:
+        """return a slice of my values"""
+
+        return self.values[slicer]
+
+    def set_inplace(self, locs, values: ArrayLike, copy: bool = False) -> None:
+        """
+        Modify block values in-place with new item value.
+
+        If copy=True, first copy the underlying values in place before modifying
+        (for Copy-on-Write).
+
+        Notes
+        -----
+        `set_inplace` never creates a new array or new Block, whereas `setitem`
+        _may_ create a new array and always creates a new Block.
+
+        Caller is responsible for checking values.dtype == self.dtype.
+        """
+        if copy:
+            self.values = self.values.copy()
+        self.values[locs] = values
+
+    @final
+    def take_nd(
+        self,
+        indexer: npt.NDArray[np.intp],
+        axis: AxisInt,
+        new_mgr_locs: BlockPlacement | None = None,
+        fill_value=lib.no_default,
+    ) -> Block:
+        """
+        Take values according to indexer and return them as a block.
+        """
+        values = self.values
+
+        if fill_value is lib.no_default:
+            fill_value = self.fill_value
+            allow_fill = False
+        else:
+            allow_fill = True
+
+        # Note: algos.take_nd has upcast logic similar to coerce_to_target_dtype
+        new_values = algos.take_nd(
+            values, indexer, axis=axis, allow_fill=allow_fill, fill_value=fill_value
+        )
+
+        # Called from three places in managers, all of which satisfy
+        #  these assertions
+        if isinstance(self, ExtensionBlock):
+            # NB: in this case, the 'axis' kwarg will be ignored in the
+            #  algos.take_nd call above.
+            assert not (self.ndim == 1 and new_mgr_locs is None)
+        assert not (axis == 0 and new_mgr_locs is None)
+
+        if new_mgr_locs is None:
+            new_mgr_locs = self._mgr_locs
+
+        if new_values.dtype != self.dtype:
+            return self.make_block(new_values, new_mgr_locs)
+        else:
+            return self.make_block_same_class(new_values, new_mgr_locs)
+
+    def _unstack(
+        self,
+        unstacker,
+        fill_value,
+        new_placement: npt.NDArray[np.intp],
+        needs_masking: npt.NDArray[np.bool_],
+    ):
+        """
+        Return a list of unstacked blocks of self
+
+        Parameters
+        ----------
+        unstacker : reshape._Unstacker
+        fill_value : int
+            Only used in ExtensionBlock._unstack
+        new_placement : np.ndarray[np.intp]
+        allow_fill : bool
+        needs_masking : np.ndarray[bool]
+
+        Returns
+        -------
+        blocks : list of Block
+            New blocks of unstacked values.
+        mask : array-like of bool
+            The mask of columns of `blocks` we should keep.
+        """
+        new_values, mask = unstacker.get_new_values(
+            self.values.T, fill_value=fill_value
+        )
+
+        mask = mask.any(0)
+        # TODO: in all tests we have mask.all(); can we rely on that?
+
+        # Note: these next two lines ensure that
+        #  mask.sum() == sum(len(nb.mgr_locs) for nb in blocks)
+        #  which the calling function needs in order to pass verify_integrity=False
+        #  to the BlockManager constructor
+        new_values = new_values.T[mask]
+        new_placement = new_placement[mask]
+
+        bp = BlockPlacement(new_placement)
+        blocks = [new_block_2d(new_values, placement=bp)]
+        return blocks, mask
+
+    # ---------------------------------------------------------------------
+
+    def setitem(self, indexer, value, using_cow: bool = False) -> Block:
+        """
+        Attempt self.values[indexer] = value, possibly creating a new array.
+
+        Parameters
+        ----------
+        indexer : tuple, list-like, array-like, slice, int
+            The subset of self.values to set
+        value : object
+            The value being set
+        using_cow: bool, default False
+            Signaling if CoW is used.
+
+        Returns
+        -------
+        Block
+
+        Notes
+        -----
+        `indexer` is a direct slice/positional indexer. `value` must
+        be a compatible shape.
+        """
+
+        value = self._standardize_fill_value(value)
+
+        values = cast(np.ndarray, self.values)
+        if self.ndim == 2:
+            values = values.T
+
+        # length checking
+        check_setitem_lengths(indexer, value, values)
+
+        if self.dtype != _dtype_obj:
+            # GH48933: extract_array would convert a pd.Series value to np.ndarray
+            value = extract_array(value, extract_numpy=True)
+        try:
+            casted = np_can_hold_element(values.dtype, value)
+        except LossySetitemError:
+            # current dtype cannot store value, coerce to common dtype
+            nb = self.coerce_to_target_dtype(value, warn_on_upcast=True)
+            return nb.setitem(indexer, value)
+        else:
+            if self.dtype == _dtype_obj:
+                # TODO: avoid having to construct values[indexer]
+                vi = values[indexer]
+                if lib.is_list_like(vi):
+                    # checking lib.is_scalar here fails on
+                    #  test_iloc_setitem_custom_object
+                    casted = setitem_datetimelike_compat(values, len(vi), casted)
+
+            self = self._maybe_copy(using_cow, inplace=True)
+            values = cast(np.ndarray, self.values.T)
+            if isinstance(casted, np.ndarray) and casted.ndim == 1 and len(casted) == 1:
+                # NumPy 1.25 deprecation: https://github.com/numpy/numpy/pull/10615
+                casted = casted[0, ...]
+            try:
+                values[indexer] = casted
+            except (TypeError, ValueError) as err:
+                if is_list_like(casted):
+                    raise ValueError(
+                        "setting an array element with a sequence."
+                    ) from err
+                raise
+        return self
+
+    def putmask(
+        self, mask, new, using_cow: bool = False, already_warned=None
+    ) -> list[Block]:
+        """
+        putmask the data to the block; it is possible that we may create a
+        new dtype of block
+
+        Return the resulting block(s).
+
+        Parameters
+        ----------
+        mask : np.ndarray[bool], SparseArray[bool], or BooleanArray
+        new : a ndarray/object
+        using_cow: bool, default False
+
+        Returns
+        -------
+        List[Block]
+        """
+        orig_mask = mask
+        values = cast(np.ndarray, self.values)
+        mask, noop = validate_putmask(values.T, mask)
+        assert not isinstance(new, (ABCIndex, ABCSeries, ABCDataFrame))
+
+        if new is lib.no_default:
+            new = self.fill_value
+
+        new = self._standardize_fill_value(new)
+        new = extract_array(new, extract_numpy=True)
+
+        if noop:
+            if using_cow:
+                return [self.copy(deep=False)]
+            return [self]
+
+        if (
+            warn_copy_on_write()
+            and already_warned is not None
+            and not already_warned.warned_already
+        ):
+            if self.refs.has_reference():
+                warnings.warn(
+                    COW_WARNING_GENERAL_MSG,
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+                already_warned.warned_already = True
+
+        try:
+            casted = np_can_hold_element(values.dtype, new)
+
+            self = self._maybe_copy(using_cow, inplace=True)
+            values = cast(np.ndarray, self.values)
+
+            putmask_without_repeat(values.T, mask, casted)
+            return [self]
+        except LossySetitemError:
+            if self.ndim == 1 or self.shape[0] == 1:
+                # no need to split columns
+
+                if not is_list_like(new):
+                    # using just new[indexer] can't save us the need to cast
+                    return self.coerce_to_target_dtype(
+                        new, warn_on_upcast=True
+                    ).putmask(mask, new)
+                else:
+                    indexer = mask.nonzero()[0]
+                    nb = self.setitem(indexer, new[indexer], using_cow=using_cow)
+                    return [nb]
+
+            else:
+                is_array = isinstance(new, np.ndarray)
+
+                res_blocks = []
+                nbs = self._split()
+                for i, nb in enumerate(nbs):
+                    n = new
+                    if is_array:
+                        # we have a different value per-column
+                        n = new[:, i : i + 1]
+
+                    submask = orig_mask[:, i : i + 1]
+                    rbs = nb.putmask(submask, n, using_cow=using_cow)
+                    res_blocks.extend(rbs)
+                return res_blocks
+
+    def where(
+        self, other, cond, _downcast: str | bool = "infer", using_cow: bool = False
+    ) -> list[Block]:
+        """
+        evaluate the block; return result block(s) from the result
+
+        Parameters
+        ----------
+        other : a ndarray/object
+        cond : np.ndarray[bool], SparseArray[bool], or BooleanArray
+        _downcast : str or None, default "infer"
+            Private because we only specify it when calling from fillna.
+
+        Returns
+        -------
+        List[Block]
+        """
+        assert cond.ndim == self.ndim
+        assert not isinstance(other, (ABCIndex, ABCSeries, ABCDataFrame))
+
+        transpose = self.ndim == 2
+
+        cond = extract_bool_array(cond)
+
+        # EABlocks override where
+        values = cast(np.ndarray, self.values)
+        orig_other = other
+        if transpose:
+            values = values.T
+
+        icond, noop = validate_putmask(values, ~cond)
+        if noop:
+            # GH-39595: Always return a copy; short-circuit up/downcasting
+            if using_cow:
+                return [self.copy(deep=False)]
+            return [self.copy()]
+
+        if other is lib.no_default:
+            other = self.fill_value
+
+        other = self._standardize_fill_value(other)
+
+        try:
+            # try/except here is equivalent to a self._can_hold_element check,
+            #  but this gets us back 'casted' which we will reuse below;
+            #  without using 'casted', expressions.where may do unwanted upcasts.
+            casted = np_can_hold_element(values.dtype, other)
+        except (ValueError, TypeError, LossySetitemError):
+            # we cannot coerce, return a compat dtype
+
+            if self.ndim == 1 or self.shape[0] == 1:
+                # no need to split columns
+
+                block = self.coerce_to_target_dtype(other)
+                blocks = block.where(orig_other, cond, using_cow=using_cow)
+                return self._maybe_downcast(
+                    blocks, downcast=_downcast, using_cow=using_cow, caller="where"
+                )
+
+            else:
+                # since _maybe_downcast would split blocks anyway, we
+                #  can avoid some potential upcast/downcast by splitting
+                #  on the front end.
+                is_array = isinstance(other, (np.ndarray, ExtensionArray))
+
+                res_blocks = []
+                nbs = self._split()
+                for i, nb in enumerate(nbs):
+                    oth = other
+                    if is_array:
+                        # we have a different value per-column
+                        oth = other[:, i : i + 1]
+
+                    submask = cond[:, i : i + 1]
+                    rbs = nb.where(
+                        oth, submask, _downcast=_downcast, using_cow=using_cow
+                    )
+                    res_blocks.extend(rbs)
+                return res_blocks
+
+        else:
+            other = casted
+            alt = setitem_datetimelike_compat(values, icond.sum(), other)
+            if alt is not other:
+                if is_list_like(other) and len(other) < len(values):
+                    # call np.where with other to get the appropriate ValueError
+                    np.where(~icond, values, other)
+                    raise NotImplementedError(
+                        "This should not be reached; call to np.where above is "
+                        "expected to raise ValueError. Please report a bug at "
+                        "github.com/pandas-dev/pandas"
+                    )
+                result = values.copy()
+                np.putmask(result, icond, alt)
+            else:
+                # By the time we get here, we should have all Series/Index
+                #  args extracted to ndarray
+                if (
+                    is_list_like(other)
+                    and not isinstance(other, np.ndarray)
+                    and len(other) == self.shape[-1]
+                ):
+                    # If we don't do this broadcasting here, then expressions.where
+                    #  will broadcast a 1D other to be row-like instead of
+                    #  column-like.
+                    other = np.array(other).reshape(values.shape)
+                    # If lengths don't match (or len(other)==1), we will raise
+                    #  inside expressions.where, see test_series_where
+
+                # Note: expressions.where may upcast.
+                result = expressions.where(~icond, values, other)
+                # The np_can_hold_element check _should_ ensure that we always
+                #  have result.dtype == self.dtype here.
+
+        if transpose:
+            result = result.T
+
+        return [self.make_block(result)]
+
+    def fillna(
+        self,
+        value,
+        limit: int | None = None,
+        inplace: bool = False,
+        downcast=None,
+        using_cow: bool = False,
+        already_warned=None,
+    ) -> list[Block]:
+        """
+        fillna on the block with the value. If we fail, then convert to
+        block to hold objects instead and try again
+        """
+        # Caller is responsible for validating limit; if int it is strictly positive
+        inplace = validate_bool_kwarg(inplace, "inplace")
+
+        if not self._can_hold_na:
+            # can short-circuit the isna call
+            noop = True
+        else:
+            mask = isna(self.values)
+            mask, noop = validate_putmask(self.values, mask)
+
+        if noop:
+            # we can't process the value, but nothing to do
+            if inplace:
+                if using_cow:
+                    return [self.copy(deep=False)]
+                # Arbitrarily imposing the convention that we ignore downcast
+                #  on no-op when inplace=True
+                return [self]
+            else:
+                # GH#45423 consistent downcasting on no-ops.
+                nb = self.copy(deep=not using_cow)
+                nbs = nb._maybe_downcast(
+                    [nb], downcast=downcast, using_cow=using_cow, caller="fillna"
+                )
+                return nbs
+
+        if limit is not None:
+            mask[mask.cumsum(self.values.ndim - 1) > limit] = False
+
+        if inplace:
+            nbs = self.putmask(
+                mask.T, value, using_cow=using_cow, already_warned=already_warned
+            )
+        else:
+            # without _downcast, we would break
+            #  test_fillna_dtype_conversion_equiv_replace
+            nbs = self.where(value, ~mask.T, _downcast=False)
+
+        # Note: blk._maybe_downcast vs self._maybe_downcast(nbs)
+        #  makes a difference bc blk may have object dtype, which has
+        #  different behavior in _maybe_downcast.
+        return extend_blocks(
+            [
+                blk._maybe_downcast(
+                    [blk], downcast=downcast, using_cow=using_cow, caller="fillna"
+                )
+                for blk in nbs
+            ]
+        )
+
+    def pad_or_backfill(
+        self,
+        *,
+        method: FillnaOptions,
+        axis: AxisInt = 0,
+        inplace: bool = False,
+        limit: int | None = None,
+        limit_area: Literal["inside", "outside"] | None = None,
+        downcast: Literal["infer"] | None = None,
+        using_cow: bool = False,
+        already_warned=None,
+    ) -> list[Block]:
+        if not self._can_hold_na:
+            # If there are no NAs, then interpolate is a no-op
+            if using_cow:
+                return [self.copy(deep=False)]
+            return [self] if inplace else [self.copy()]
+
+        copy, refs = self._get_refs_and_copy(using_cow, inplace)
+
+        # Dispatch to the NumpyExtensionArray method.
+        # We know self.array_values is a NumpyExtensionArray bc EABlock overrides
+        vals = cast(NumpyExtensionArray, self.array_values)
+        if axis == 1:
+            vals = vals.T
+        new_values = vals._pad_or_backfill(
+            method=method,
+            limit=limit,
+            limit_area=limit_area,
+            copy=copy,
+        )
+        if (
+            not copy
+            and warn_copy_on_write()
+            and already_warned is not None
+            and not already_warned.warned_already
+        ):
+            if self.refs.has_reference():
+                warnings.warn(
+                    COW_WARNING_GENERAL_MSG,
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+                already_warned.warned_already = True
+        if axis == 1:
+            new_values = new_values.T
+
+        data = extract_array(new_values, extract_numpy=True)
+
+        nb = self.make_block_same_class(data, refs=refs)
+        return nb._maybe_downcast([nb], downcast, using_cow, caller="fillna")
+
+    @final
+    def interpolate(
+        self,
+        *,
+        method: InterpolateOptions,
+        index: Index,
+        inplace: bool = False,
+        limit: int | None = None,
+        limit_direction: Literal["forward", "backward", "both"] = "forward",
+        limit_area: Literal["inside", "outside"] | None = None,
+        downcast: Literal["infer"] | None = None,
+        using_cow: bool = False,
+        already_warned=None,
+        **kwargs,
+    ) -> list[Block]:
+        inplace = validate_bool_kwarg(inplace, "inplace")
+        # error: Non-overlapping equality check [...]
+        if method == "asfreq":  # type: ignore[comparison-overlap]
+            # clean_fill_method used to allow this
+            missing.clean_fill_method(method)
+
+        if not self._can_hold_na:
+            # If there are no NAs, then interpolate is a no-op
+            if using_cow:
+                return [self.copy(deep=False)]
+            return [self] if inplace else [self.copy()]
+
+        # TODO(3.0): this case will not be reachable once GH#53638 is enforced
+        if self.dtype == _dtype_obj:
+            # only deal with floats
+            # bc we already checked that can_hold_na, we don't have int dtype here
+            # test_interp_basic checks that we make a copy here
+            if using_cow:
+                return [self.copy(deep=False)]
+            return [self] if inplace else [self.copy()]
+
+        copy, refs = self._get_refs_and_copy(using_cow, inplace)
+
+        # Dispatch to the EA method.
+        new_values = self.array_values.interpolate(
+            method=method,
+            axis=self.ndim - 1,
+            index=index,
+            limit=limit,
+            limit_direction=limit_direction,
+            limit_area=limit_area,
+            copy=copy,
+            **kwargs,
+        )
+        data = extract_array(new_values, extract_numpy=True)
+
+        if (
+            not copy
+            and warn_copy_on_write()
+            and already_warned is not None
+            and not already_warned.warned_already
+        ):
+            if self.refs.has_reference():
+                warnings.warn(
+                    COW_WARNING_GENERAL_MSG,
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+                already_warned.warned_already = True
+
+        nb = self.make_block_same_class(data, refs=refs)
+        return nb._maybe_downcast([nb], downcast, using_cow, caller="interpolate")
+
+    @final
+    def diff(self, n: int) -> list[Block]:
+        """return block for the diff of the values"""
+        # only reached with ndim == 2
+        # TODO(EA2D): transpose will be unnecessary with 2D EAs
+        new_values = algos.diff(self.values.T, n, axis=0).T
+        return [self.make_block(values=new_values)]
+
+    def shift(self, periods: int, fill_value: Any = None) -> list[Block]:
+        """shift the block by periods, possibly upcast"""
+        # convert integer to float if necessary. need to do a lot more than
+        # that, handle boolean etc also
+        axis = self.ndim - 1
+
+        # Note: periods is never 0 here, as that is handled at the top of
+        #  NDFrame.shift.  If that ever changes, we can do a check for periods=0
+        #  and possibly avoid coercing.
+
+        if not lib.is_scalar(fill_value) and self.dtype != _dtype_obj:
+            # with object dtype there is nothing to promote, and the user can
+            #  pass pretty much any weird fill_value they like
+            # see test_shift_object_non_scalar_fill
+            raise ValueError("fill_value must be a scalar")
+
+        fill_value = self._standardize_fill_value(fill_value)
+
+        try:
+            # error: Argument 1 to "np_can_hold_element" has incompatible type
+            # "Union[dtype[Any], ExtensionDtype]"; expected "dtype[Any]"
+            casted = np_can_hold_element(
+                self.dtype, fill_value  # type: ignore[arg-type]
+            )
+        except LossySetitemError:
+            nb = self.coerce_to_target_dtype(fill_value)
+            return nb.shift(periods, fill_value=fill_value)
+
+        else:
+            values = cast(np.ndarray, self.values)
+            new_values = shift(values, periods, axis, casted)
+            return [self.make_block_same_class(new_values)]
+
+    @final
+    def quantile(
+        self,
+        qs: Index,  # with dtype float64
+        interpolation: QuantileInterpolation = "linear",
+    ) -> Block:
+        """
+        compute the quantiles of the
+
+        Parameters
+        ----------
+        qs : Index
+            The quantiles to be computed in float64.
+        interpolation : str, default 'linear'
+            Type of interpolation.
+
+        Returns
+        -------
+        Block
+        """
+        # We should always have ndim == 2 because Series dispatches to DataFrame
+        assert self.ndim == 2
+        assert is_list_like(qs)  # caller is responsible for this
+
+        result = quantile_compat(self.values, np.asarray(qs._values), interpolation)
+        # ensure_block_shape needed for cases where we start with EA and result
+        #  is ndarray, e.g. IntegerArray, SparseArray
+        result = ensure_block_shape(result, ndim=2)
+        return new_block_2d(result, placement=self._mgr_locs)
+
+    @final
+    def round(self, decimals: int, using_cow: bool = False) -> Self:
+        """
+        Rounds the values.
+        If the block is not of an integer or float dtype, nothing happens.
+        This is consistent with DataFrame.round behavivor.
+        (Note: Series.round would raise)
+
+        Parameters
+        ----------
+        decimals: int,
+            Number of decimal places to round to.
+            Caller is responsible for validating this
+        using_cow: bool,
+            Whether Copy on Write is enabled right now
+        """
+        if not self.is_numeric or self.is_bool:
+            return self.copy(deep=not using_cow)
+        refs = None
+        # TODO: round only defined on BaseMaskedArray
+        # Series also does this, so would need to fix both places
+        # error: Item "ExtensionArray" of "Union[ndarray[Any, Any], ExtensionArray]"
+        # has no attribute "round"
+        values = self.values.round(decimals)  # type: ignore[union-attr]
+        if values is self.values:
+            if not using_cow:
+                # Normally would need to do this before, but
+                # numpy only returns same array when round operation
+                # is no-op
+                # https://github.com/numpy/numpy/blob/486878b37fc7439a3b2b87747f50db9b62fea8eb/numpy/core/src/multiarray/calculation.c#L625-L636
+                values = values.copy()
+            else:
+                refs = self.refs
+        return self.make_block_same_class(values, refs=refs)
+
+    # ---------------------------------------------------------------------
+    # Abstract Methods Overridden By EABackedBlock and NumpyBlock
+
+    def delete(self, loc) -> list[Block]:
+        """Deletes the locs from the block.
+
+        We split the block to avoid copying the underlying data. We create new
+        blocks for every connected segment of the initial block that is not deleted.
+        The new blocks point to the initial array.
+        """
+        if not is_list_like(loc):
+            loc = [loc]
+
+        if self.ndim == 1:
+            values = cast(np.ndarray, self.values)
+            values = np.delete(values, loc)
+            mgr_locs = self._mgr_locs.delete(loc)
+            return [type(self)(values, placement=mgr_locs, ndim=self.ndim)]
+
+        if np.max(loc) >= self.values.shape[0]:
+            raise IndexError
+
+        # Add one out-of-bounds indexer as maximum to collect
+        # all columns after our last indexer if any
+        loc = np.concatenate([loc, [self.values.shape[0]]])
+        mgr_locs_arr = self._mgr_locs.as_array
+        new_blocks: list[Block] = []
+
+        previous_loc = -1
+        # TODO(CoW): This is tricky, if parent block goes out of scope
+        # all split blocks are referencing each other even though they
+        # don't share data
+        refs = self.refs if self.refs.has_reference() else None
+        for idx in loc:
+            if idx == previous_loc + 1:
+                # There is no column between current and last idx
+                pass
+            else:
+                # No overload variant of "__getitem__" of "ExtensionArray" matches
+                # argument type "Tuple[slice, slice]"
+                values = self.values[previous_loc + 1 : idx, :]  # type: ignore[call-overload]
+                locs = mgr_locs_arr[previous_loc + 1 : idx]
+                nb = type(self)(
+                    values, placement=BlockPlacement(locs), ndim=self.ndim, refs=refs
+                )
+                new_blocks.append(nb)
+
+            previous_loc = idx
+
+        return new_blocks
+
+    @property
+    def is_view(self) -> bool:
+        """return a boolean if I am possibly a view"""
+        raise AbstractMethodError(self)
+
+    @property
+    def array_values(self) -> ExtensionArray:
+        """
+        The array that Series.array returns. Always an ExtensionArray.
+        """
+        raise AbstractMethodError(self)
+
+    def get_values(self, dtype: DtypeObj | None = None) -> np.ndarray:
+        """
+        return an internal format, currently just the ndarray
+        this is often overridden to handle to_dense like operations
+        """
+        raise AbstractMethodError(self)
+
+
+class EABackedBlock(Block):
+    """
+    Mixin for Block subclasses backed by ExtensionArray.
+    """
+
+    values: ExtensionArray
+
+    @final
+    def shift(self, periods: int, fill_value: Any = None) -> list[Block]:
+        """
+        Shift the block by `periods`.
+
+        Dispatches to underlying ExtensionArray and re-boxes in an
+        ExtensionBlock.
+        """
+        # Transpose since EA.shift is always along axis=0, while we want to shift
+        #  along rows.
+        new_values = self.values.T.shift(periods=periods, fill_value=fill_value).T
+        return [self.make_block_same_class(new_values)]
+
+    @final
+    def setitem(self, indexer, value, using_cow: bool = False):
+        """
+        Attempt self.values[indexer] = value, possibly creating a new array.
+
+        This differs from Block.setitem by not allowing setitem to change
+        the dtype of the Block.
+
+        Parameters
+        ----------
+        indexer : tuple, list-like, array-like, slice, int
+            The subset of self.values to set
+        value : object
+            The value being set
+        using_cow: bool, default False
+            Signaling if CoW is used.
+
+        Returns
+        -------
+        Block
+
+        Notes
+        -----
+        `indexer` is a direct slice/positional indexer. `value` must
+        be a compatible shape.
+        """
+        orig_indexer = indexer
+        orig_value = value
+
+        indexer = self._unwrap_setitem_indexer(indexer)
+        value = self._maybe_squeeze_arg(value)
+
+        values = self.values
+        if values.ndim == 2:
+            # TODO(GH#45419): string[pyarrow] tests break if we transpose
+            #  unconditionally
+            values = values.T
+        check_setitem_lengths(indexer, value, values)
+
+        try:
+            values[indexer] = value
+        except (ValueError, TypeError):
+            if isinstance(self.dtype, IntervalDtype):
+                # see TestSetitemFloatIntervalWithIntIntervalValues
+                nb = self.coerce_to_target_dtype(orig_value, warn_on_upcast=True)
+                return nb.setitem(orig_indexer, orig_value)
+
+            elif isinstance(self, NDArrayBackedExtensionBlock):
+                nb = self.coerce_to_target_dtype(orig_value, warn_on_upcast=True)
+                return nb.setitem(orig_indexer, orig_value)
+
+            else:
+                raise
+
+        else:
+            return self
+
+    @final
+    def where(
+        self, other, cond, _downcast: str | bool = "infer", using_cow: bool = False
+    ) -> list[Block]:
+        # _downcast private bc we only specify it when calling from fillna
+        arr = self.values.T
+
+        cond = extract_bool_array(cond)
+
+        orig_other = other
+        orig_cond = cond
+        other = self._maybe_squeeze_arg(other)
+        cond = self._maybe_squeeze_arg(cond)
+
+        if other is lib.no_default:
+            other = self.fill_value
+
+        icond, noop = validate_putmask(arr, ~cond)
+        if noop:
+            # GH#44181, GH#45135
+            # Avoid a) raising for Interval/PeriodDtype and b) unnecessary object upcast
+            if using_cow:
+                return [self.copy(deep=False)]
+            return [self.copy()]
+
+        try:
+            res_values = arr._where(cond, other).T
+        except (ValueError, TypeError):
+            if self.ndim == 1 or self.shape[0] == 1:
+                if isinstance(self.dtype, (IntervalDtype, StringDtype)):
+                    # TestSetitemFloatIntervalWithIntIntervalValues
+                    blk = self.coerce_to_target_dtype(orig_other)
+                    if (
+                        self.ndim == 2
+                        and isinstance(orig_cond, np.ndarray)
+                        and orig_cond.ndim == 1
+                        and not is_1d_only_ea_dtype(blk.dtype)
+                    ):
+                        orig_cond = orig_cond[:, None]
+                    nbs = blk.where(orig_other, orig_cond, using_cow=using_cow)
+                    return self._maybe_downcast(
+                        nbs, downcast=_downcast, using_cow=using_cow, caller="where"
+                    )
+
+                elif isinstance(self, NDArrayBackedExtensionBlock):
+                    # NB: not (yet) the same as
+                    #  isinstance(values, NDArrayBackedExtensionArray)
+                    blk = self.coerce_to_target_dtype(orig_other)
+                    nbs = blk.where(orig_other, orig_cond, using_cow=using_cow)
+                    return self._maybe_downcast(
+                        nbs, downcast=_downcast, using_cow=using_cow, caller="where"
+                    )
+
+                else:
+                    raise
+
+            else:
+                # Same pattern we use in Block.putmask
+                is_array = isinstance(orig_other, (np.ndarray, ExtensionArray))
+
+                res_blocks = []
+                nbs = self._split()
+                for i, nb in enumerate(nbs):
+                    n = orig_other
+                    if is_array:
+                        # we have a different value per-column
+                        n = orig_other[:, i : i + 1]
+
+                    submask = orig_cond[:, i : i + 1]
+                    rbs = nb.where(n, submask, using_cow=using_cow)
+                    res_blocks.extend(rbs)
+                return res_blocks
+
+        nb = self.make_block_same_class(res_values)
+        return [nb]
+
+    @final
+    def putmask(
+        self, mask, new, using_cow: bool = False, already_warned=None
+    ) -> list[Block]:
+        """
+        See Block.putmask.__doc__
+        """
+        mask = extract_bool_array(mask)
+        if new is lib.no_default:
+            new = self.fill_value
+
+        orig_new = new
+        orig_mask = mask
+        new = self._maybe_squeeze_arg(new)
+        mask = self._maybe_squeeze_arg(mask)
+
+        if not mask.any():
+            if using_cow:
+                return [self.copy(deep=False)]
+            return [self]
+
+        if (
+            warn_copy_on_write()
+            and already_warned is not None
+            and not already_warned.warned_already
+        ):
+            if self.refs.has_reference():
+                warnings.warn(
+                    COW_WARNING_GENERAL_MSG,
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+                already_warned.warned_already = True
+
+        self = self._maybe_copy(using_cow, inplace=True)
+        values = self.values
+        if values.ndim == 2:
+            values = values.T
+
+        try:
+            # Caller is responsible for ensuring matching lengths
+            values._putmask(mask, new)
+        except (TypeError, ValueError):
+            if self.ndim == 1 or self.shape[0] == 1:
+                if isinstance(self.dtype, IntervalDtype):
+                    # Discussion about what we want to support in the general
+                    #  case GH#39584
+                    blk = self.coerce_to_target_dtype(orig_new, warn_on_upcast=True)
+                    return blk.putmask(orig_mask, orig_new)
+
+                elif isinstance(self, NDArrayBackedExtensionBlock):
+                    # NB: not (yet) the same as
+                    #  isinstance(values, NDArrayBackedExtensionArray)
+                    blk = self.coerce_to_target_dtype(orig_new, warn_on_upcast=True)
+                    return blk.putmask(orig_mask, orig_new)
+
+                else:
+                    raise
+
+            else:
+                # Same pattern we use in Block.putmask
+                is_array = isinstance(orig_new, (np.ndarray, ExtensionArray))
+
+                res_blocks = []
+                nbs = self._split()
+                for i, nb in enumerate(nbs):
+                    n = orig_new
+                    if is_array:
+                        # we have a different value per-column
+                        n = orig_new[:, i : i + 1]
+
+                    submask = orig_mask[:, i : i + 1]
+                    rbs = nb.putmask(submask, n)
+                    res_blocks.extend(rbs)
+                return res_blocks
+
+        return [self]
+
+    @final
+    def delete(self, loc) -> list[Block]:
+        # This will be unnecessary if/when __array_function__ is implemented
+        if self.ndim == 1:
+            values = self.values.delete(loc)
+            mgr_locs = self._mgr_locs.delete(loc)
+            return [type(self)(values, placement=mgr_locs, ndim=self.ndim)]
+        elif self.values.ndim == 1:
+            # We get here through to_stata
+            return []
+        return super().delete(loc)
+
+    @final
+    @cache_readonly
+    def array_values(self) -> ExtensionArray:
+        return self.values
+
+    @final
+    def get_values(self, dtype: DtypeObj | None = None) -> np.ndarray:
+        """
+        return object dtype as boxed values, such as Timestamps/Timedelta
+        """
+        values: ArrayLike = self.values
+        if dtype == _dtype_obj:
+            values = values.astype(object)
+        # TODO(EA2D): reshape not needed with 2D EAs
+        return np.asarray(values).reshape(self.shape)
+
+    @final
+    def pad_or_backfill(
+        self,
+        *,
+        method: FillnaOptions,
+        axis: AxisInt = 0,
+        inplace: bool = False,
+        limit: int | None = None,
+        limit_area: Literal["inside", "outside"] | None = None,
+        downcast: Literal["infer"] | None = None,
+        using_cow: bool = False,
+        already_warned=None,
+    ) -> list[Block]:
+        values = self.values
+
+        kwargs: dict[str, Any] = {"method": method, "limit": limit}
+        if "limit_area" in inspect.signature(values._pad_or_backfill).parameters:
+            kwargs["limit_area"] = limit_area
+        elif limit_area is not None:
+            raise NotImplementedError(
+                f"{type(values).__name__} does not implement limit_area "
+                "(added in pandas 2.2). 3rd-party ExtnsionArray authors "
+                "need to add this argument to _pad_or_backfill."
+            )
+
+        if values.ndim == 2 and axis == 1:
+            # NDArrayBackedExtensionArray.fillna assumes axis=0
+            new_values = values.T._pad_or_backfill(**kwargs).T
+        else:
+            new_values = values._pad_or_backfill(**kwargs)
+        return [self.make_block_same_class(new_values)]
+
+
+class ExtensionBlock(EABackedBlock):
+    """
+    Block for holding extension types.
+
+    Notes
+    -----
+    This holds all 3rd-party extension array types. It's also the immediate
+    parent class for our internal extension types' blocks.
+
+    ExtensionArrays are limited to 1-D.
+    """
+
+    values: ExtensionArray
+
+    def fillna(
+        self,
+        value,
+        limit: int | None = None,
+        inplace: bool = False,
+        downcast=None,
+        using_cow: bool = False,
+        already_warned=None,
+    ) -> list[Block]:
+        if isinstance(self.dtype, (IntervalDtype, StringDtype)):
+            # Block.fillna handles coercion (test_fillna_interval)
+            return super().fillna(
+                value=value,
+                limit=limit,
+                inplace=inplace,
+                downcast=downcast,
+                using_cow=using_cow,
+                already_warned=already_warned,
+            )
+        if using_cow and self._can_hold_na and not self.values._hasna:
+            refs = self.refs
+            new_values = self.values
+        else:
+            copy, refs = self._get_refs_and_copy(using_cow, inplace)
+
+            try:
+                new_values = self.values.fillna(
+                    value=value, method=None, limit=limit, copy=copy
+                )
+            except TypeError:
+                # 3rd party EA that has not implemented copy keyword yet
+                refs = None
+                new_values = self.values.fillna(value=value, method=None, limit=limit)
+                # issue the warning *after* retrying, in case the TypeError
+                #  was caused by an invalid fill_value
+                warnings.warn(
+                    # GH#53278
+                    "ExtensionArray.fillna added a 'copy' keyword in pandas "
+                    "2.1.0. In a future version, ExtensionArray subclasses will "
+                    "need to implement this keyword or an exception will be "
+                    "raised. In the interim, the keyword is ignored by "
+                    f"{type(self.values).__name__}.",
+                    DeprecationWarning,
+                    stacklevel=find_stack_level(),
+                )
+            else:
+                if (
+                    not copy
+                    and warn_copy_on_write()
+                    and already_warned is not None
+                    and not already_warned.warned_already
+                ):
+                    if self.refs.has_reference():
+                        warnings.warn(
+                            COW_WARNING_GENERAL_MSG,
+                            FutureWarning,
+                            stacklevel=find_stack_level(),
+                        )
+                        already_warned.warned_already = True
+
+        nb = self.make_block_same_class(new_values, refs=refs)
+        return nb._maybe_downcast([nb], downcast, using_cow=using_cow, caller="fillna")
+
+    @cache_readonly
+    def shape(self) -> Shape:
+        # TODO(EA2D): override unnecessary with 2D EAs
+        if self.ndim == 1:
+            return (len(self.values),)
+        return len(self._mgr_locs), len(self.values)
+
+    def iget(self, i: int | tuple[int, int] | tuple[slice, int]):
+        # In the case where we have a tuple[slice, int], the slice will always
+        #  be slice(None)
+        # We _could_ make the annotation more specific, but mypy would
+        #  complain about override mismatch:
+        #  Literal[0] | tuple[Literal[0], int] | tuple[slice, int]
+
+        # Note: only reached with self.ndim == 2
+
+        if isinstance(i, tuple):
+            # TODO(EA2D): unnecessary with 2D EAs
+            col, loc = i
+            if not com.is_null_slice(col) and col != 0:
+                raise IndexError(f"{self} only contains one item")
+            if isinstance(col, slice):
+                # the is_null_slice check above assures that col is slice(None)
+                #  so what we want is a view on all our columns and row loc
+                if loc < 0:
+                    loc += len(self.values)
+                # Note: loc:loc+1 vs [[loc]] makes a difference when called
+                #  from fast_xs because we want to get a view back.
+                return self.values[loc : loc + 1]
+            return self.values[loc]
+        else:
+            if i != 0:
+                raise IndexError(f"{self} only contains one item")
+            return self.values
+
+    def set_inplace(self, locs, values: ArrayLike, copy: bool = False) -> None:
+        # When an ndarray, we should have locs.tolist() == [0]
+        # When a BlockPlacement we should have list(locs) == [0]
+        if copy:
+            self.values = self.values.copy()
+        self.values[:] = values
+
+    def _maybe_squeeze_arg(self, arg):
+        """
+        If necessary, squeeze a (N, 1) ndarray to (N,)
+        """
+        # e.g. if we are passed a 2D mask for putmask
+        if (
+            isinstance(arg, (np.ndarray, ExtensionArray))
+            and arg.ndim == self.values.ndim + 1
+        ):
+            # TODO(EA2D): unnecessary with 2D EAs
+            assert arg.shape[1] == 1
+            # error: No overload variant of "__getitem__" of "ExtensionArray"
+            # matches argument type "Tuple[slice, int]"
+            arg = arg[:, 0]  # type: ignore[call-overload]
+        elif isinstance(arg, ABCDataFrame):
+            # 2022-01-06 only reached for setitem
+            # TODO: should we avoid getting here with DataFrame?
+            assert arg.shape[1] == 1
+            arg = arg._ixs(0, axis=1)._values
+
+        return arg
+
+    def _unwrap_setitem_indexer(self, indexer):
+        """
+        Adapt a 2D-indexer to our 1D values.
+
+        This is intended for 'setitem', not 'iget' or '_slice'.
+        """
+        # TODO: ATM this doesn't work for iget/_slice, can we change that?
+
+        if isinstance(indexer, tuple) and len(indexer) == 2:
+            # TODO(EA2D): not needed with 2D EAs
+            #  Should never have length > 2.  Caller is responsible for checking.
+            #  Length 1 is reached vis setitem_single_block and setitem_single_column
+            #  each of which pass indexer=(pi,)
+            if all(isinstance(x, np.ndarray) and x.ndim == 2 for x in indexer):
+                # GH#44703 went through indexing.maybe_convert_ix
+                first, second = indexer
+                if not (
+                    second.size == 1 and (second == 0).all() and first.shape[1] == 1
+                ):
+                    raise NotImplementedError(
+                        "This should not be reached. Please report a bug at "
+                        "github.com/pandas-dev/pandas/"
+                    )
+                indexer = first[:, 0]
+
+            elif lib.is_integer(indexer[1]) and indexer[1] == 0:
+                # reached via setitem_single_block passing the whole indexer
+                indexer = indexer[0]
+
+            elif com.is_null_slice(indexer[1]):
+                indexer = indexer[0]
+
+            elif is_list_like(indexer[1]) and indexer[1][0] == 0:
+                indexer = indexer[0]
+
+            else:
+                raise NotImplementedError(
+                    "This should not be reached. Please report a bug at "
+                    "github.com/pandas-dev/pandas/"
+                )
+        return indexer
+
+    @property
+    def is_view(self) -> bool:
+        """Extension arrays are never treated as views."""
+        return False
+
+    # error: Cannot override writeable attribute with read-only property
+    @cache_readonly
+    def is_numeric(self) -> bool:  # type: ignore[override]
+        return self.values.dtype._is_numeric
+
+    def _slice(
+        self, slicer: slice | npt.NDArray[np.bool_] | npt.NDArray[np.intp]
+    ) -> ExtensionArray:
+        """
+        Return a slice of my values.
+
+        Parameters
+        ----------
+        slicer : slice, ndarray[int], or ndarray[bool]
+            Valid (non-reducing) indexer for self.values.
+
+        Returns
+        -------
+        ExtensionArray
+        """
+        # Notes: ndarray[bool] is only reachable when via get_rows_with_mask, which
+        #  is only for Series, i.e. self.ndim == 1.
+
+        # return same dims as we currently have
+        if self.ndim == 2:
+            # reached via getitem_block via _slice_take_blocks_ax0
+            # TODO(EA2D): won't be necessary with 2D EAs
+
+            if not isinstance(slicer, slice):
+                raise AssertionError(
+                    "invalid slicing for a 1-ndim ExtensionArray", slicer
+                )
+            # GH#32959 only full-slicers along fake-dim0 are valid
+            # TODO(EA2D): won't be necessary with 2D EAs
+            # range(1) instead of self._mgr_locs to avoid exception on [::-1]
+            #  see test_iloc_getitem_slice_negative_step_ea_block
+            new_locs = range(1)[slicer]
+            if not len(new_locs):
+                raise AssertionError(
+                    "invalid slicing for a 1-ndim ExtensionArray", slicer
+                )
+            slicer = slice(None)
+
+        return self.values[slicer]
+
+    @final
+    def slice_block_rows(self, slicer: slice) -> Self:
+        """
+        Perform __getitem__-like specialized to slicing along index.
+        """
+        # GH#42787 in principle this is equivalent to values[..., slicer], but we don't
+        # require subclasses of ExtensionArray to support that form (for now).
+        new_values = self.values[slicer]
+        return type(self)(new_values, self._mgr_locs, ndim=self.ndim, refs=self.refs)
+
+    def _unstack(
+        self,
+        unstacker,
+        fill_value,
+        new_placement: npt.NDArray[np.intp],
+        needs_masking: npt.NDArray[np.bool_],
+    ):
+        # ExtensionArray-safe unstack.
+        # We override Block._unstack, which unstacks directly on the
+        # values of the array. For EA-backed blocks, this would require
+        # converting to a 2-D ndarray of objects.
+        # Instead, we unstack an ndarray of integer positions, followed by
+        # a `take` on the actual values.
+
+        # Caller is responsible for ensuring self.shape[-1] == len(unstacker.index)
+        new_values, mask = unstacker.arange_result
+
+        # Note: these next two lines ensure that
+        #  mask.sum() == sum(len(nb.mgr_locs) for nb in blocks)
+        #  which the calling function needs in order to pass verify_integrity=False
+        #  to the BlockManager constructor
+        new_values = new_values.T[mask]
+        new_placement = new_placement[mask]
+
+        # needs_masking[i] calculated once in BlockManager.unstack tells
+        #  us if there are any -1s in the relevant indices.  When False,
+        #  that allows us to go through a faster path in 'take', among
+        #  other things avoiding e.g. Categorical._validate_scalar.
+        blocks = [
+            # TODO: could cast to object depending on fill_value?
+            type(self)(
+                self.values.take(
+                    indices, allow_fill=needs_masking[i], fill_value=fill_value
+                ),
+                BlockPlacement(place),
+                ndim=2,
+            )
+            for i, (indices, place) in enumerate(zip(new_values, new_placement))
+        ]
+        return blocks, mask
+
+
+class NumpyBlock(Block):
+    values: np.ndarray
+    __slots__ = ()
+
+    @property
+    def is_view(self) -> bool:
+        """return a boolean if I am possibly a view"""
+        return self.values.base is not None
+
+    @property
+    def array_values(self) -> ExtensionArray:
+        return NumpyExtensionArray(self.values)
+
+    def get_values(self, dtype: DtypeObj | None = None) -> np.ndarray:
+        if dtype == _dtype_obj:
+            return self.values.astype(_dtype_obj)
+        return self.values
+
+    @cache_readonly
+    def is_numeric(self) -> bool:  # type: ignore[override]
+        dtype = self.values.dtype
+        kind = dtype.kind
+
+        return kind in "fciub"
+
+
+class NumericBlock(NumpyBlock):
+    # this Block type is kept for backwards-compatibility
+    # TODO(3.0): delete and remove deprecation in __init__.py.
+    __slots__ = ()
+
+
+class ObjectBlock(NumpyBlock):
+    # this Block type is kept for backwards-compatibility
+    # TODO(3.0): delete and remove deprecation in __init__.py.
+    __slots__ = ()
+
+
+class NDArrayBackedExtensionBlock(EABackedBlock):
+    """
+    Block backed by an NDArrayBackedExtensionArray
+    """
+
+    values: NDArrayBackedExtensionArray
+
+    @property
+    def is_view(self) -> bool:
+        """return a boolean if I am possibly a view"""
+        # check the ndarray values of the DatetimeIndex values
+        return self.values._ndarray.base is not None
+
+
+class DatetimeLikeBlock(NDArrayBackedExtensionBlock):
+    """Block for datetime64[ns], timedelta64[ns]."""
+
+    __slots__ = ()
+    is_numeric = False
+    values: DatetimeArray | TimedeltaArray
+
+
+class DatetimeTZBlock(DatetimeLikeBlock):
+    """implement a datetime64 block with a tz attribute"""
+
+    values: DatetimeArray
+
+    __slots__ = ()
+
+
+# -----------------------------------------------------------------
+# Constructor Helpers
+
+
+def maybe_coerce_values(values: ArrayLike) -> ArrayLike:
+    """
+    Input validation for values passed to __init__. Ensure that
+    any datetime64/timedelta64 dtypes are in nanoseconds.  Ensure
+    that we do not have string dtypes.
+
+    Parameters
+    ----------
+    values : np.ndarray or ExtensionArray
+
+    Returns
+    -------
+    values : np.ndarray or ExtensionArray
+    """
+    # Caller is responsible for ensuring NumpyExtensionArray is already extracted.
+
+    if isinstance(values, np.ndarray):
+        values = ensure_wrapped_if_datetimelike(values)
+
+        if issubclass(values.dtype.type, str):
+            values = np.array(values, dtype=object)
+
+    if isinstance(values, (DatetimeArray, TimedeltaArray)) and values.freq is not None:
+        # freq is only stored in DatetimeIndex/TimedeltaIndex, not in Series/DataFrame
+        values = values._with_freq(None)
+
+    return values
+
+
+def get_block_type(dtype: DtypeObj) -> type[Block]:
+    """
+    Find the appropriate Block subclass to use for the given values and dtype.
+
+    Parameters
+    ----------
+    dtype : numpy or pandas dtype
+
+    Returns
+    -------
+    cls : class, subclass of Block
+    """
+    if isinstance(dtype, DatetimeTZDtype):
+        return DatetimeTZBlock
+    elif isinstance(dtype, PeriodDtype):
+        return NDArrayBackedExtensionBlock
+    elif isinstance(dtype, ExtensionDtype):
+        # Note: need to be sure NumpyExtensionArray is unwrapped before we get here
+        return ExtensionBlock
+
+    # We use kind checks because it is much more performant
+    #  than is_foo_dtype
+    kind = dtype.kind
+    if kind in "Mm":
+        return DatetimeLikeBlock
+
+    return NumpyBlock
+
+
+def new_block_2d(
+    values: ArrayLike, placement: BlockPlacement, refs: BlockValuesRefs | None = None
+):
+    # new_block specialized to case with
+    #  ndim=2
+    #  isinstance(placement, BlockPlacement)
+    #  check_ndim/ensure_block_shape already checked
+    klass = get_block_type(values.dtype)
+
+    values = maybe_coerce_values(values)
+    return klass(values, ndim=2, placement=placement, refs=refs)
+
+
+def new_block(
+    values,
+    placement: BlockPlacement,
+    *,
+    ndim: int,
+    refs: BlockValuesRefs | None = None,
+) -> Block:
+    # caller is responsible for ensuring:
+    # - values is NOT a NumpyExtensionArray
+    # - check_ndim/ensure_block_shape already checked
+    # - maybe_coerce_values already called/unnecessary
+    klass = get_block_type(values.dtype)
+    return klass(values, ndim=ndim, placement=placement, refs=refs)
+
+
+def check_ndim(values, placement: BlockPlacement, ndim: int) -> None:
+    """
+    ndim inference and validation.
+
+    Validates that values.ndim and ndim are consistent.
+    Validates that len(values) and len(placement) are consistent.
+
+    Parameters
+    ----------
+    values : array-like
+    placement : BlockPlacement
+    ndim : int
+
+    Raises
+    ------
+    ValueError : the number of dimensions do not match
+    """
+
+    if values.ndim > ndim:
+        # Check for both np.ndarray and ExtensionArray
+        raise ValueError(
+            "Wrong number of dimensions. "
+            f"values.ndim > ndim [{values.ndim} > {ndim}]"
+        )
+
+    if not is_1d_only_ea_dtype(values.dtype):
+        # TODO(EA2D): special case not needed with 2D EAs
+        if values.ndim != ndim:
+            raise ValueError(
+                "Wrong number of dimensions. "
+                f"values.ndim != ndim [{values.ndim} != {ndim}]"
+            )
+        if len(placement) != len(values):
+            raise ValueError(
+                f"Wrong number of items passed {len(values)}, "
+                f"placement implies {len(placement)}"
+            )
+    elif ndim == 2 and len(placement) != 1:
+        # TODO(EA2D): special case unnecessary with 2D EAs
+        raise ValueError("need to split")
+
+
+def extract_pandas_array(
+    values: ArrayLike, dtype: DtypeObj | None, ndim: int
+) -> tuple[ArrayLike, DtypeObj | None]:
+    """
+    Ensure that we don't allow NumpyExtensionArray / NumpyEADtype in internals.
+    """
+    # For now, blocks should be backed by ndarrays when possible.
+    if isinstance(values, ABCNumpyExtensionArray):
+        values = values.to_numpy()
+        if ndim and ndim > 1:
+            # TODO(EA2D): special case not needed with 2D EAs
+            values = np.atleast_2d(values)
+
+    if isinstance(dtype, NumpyEADtype):
+        dtype = dtype.numpy_dtype
+
+    return values, dtype
+
+
+# -----------------------------------------------------------------
+
+
+def extend_blocks(result, blocks=None) -> list[Block]:
+    """return a new extended blocks, given the result"""
+    if blocks is None:
+        blocks = []
+    if isinstance(result, list):
+        for r in result:
+            if isinstance(r, list):
+                blocks.extend(r)
+            else:
+                blocks.append(r)
+    else:
+        assert isinstance(result, Block), type(result)
+        blocks.append(result)
+    return blocks
+
+
+def ensure_block_shape(values: ArrayLike, ndim: int = 1) -> ArrayLike:
+    """
+    Reshape if possible to have values.ndim == ndim.
+    """
+
+    if values.ndim < ndim:
+        if not is_1d_only_ea_dtype(values.dtype):
+            # TODO(EA2D): https://github.com/pandas-dev/pandas/issues/23023
+            # block.shape is incorrect for "2D" ExtensionArrays
+            # We can't, and don't need to, reshape.
+            values = cast("np.ndarray | DatetimeArray | TimedeltaArray", values)
+            values = values.reshape(1, -1)
+
+    return values
+
+
+def external_values(values: ArrayLike) -> ArrayLike:
+    """
+    The array that Series.values returns (public attribute).
+
+    This has some historical constraints, and is overridden in block
+    subclasses to return the correct array (e.g. period returns
+    object ndarray and datetimetz a datetime64[ns] ndarray instead of
+    proper extension array).
+    """
+    if isinstance(values, (PeriodArray, IntervalArray)):
+        return values.astype(object)
+    elif isinstance(values, (DatetimeArray, TimedeltaArray)):
+        # NB: for datetime64tz this is different from np.asarray(values), since
+        #  that returns an object-dtype ndarray of Timestamps.
+        # Avoid raising in .astype in casting from dt64tz to dt64
+        values = values._ndarray
+
+    if isinstance(values, np.ndarray) and using_copy_on_write():
+        values = values.view()
+        values.flags.writeable = False
+
+    # TODO(CoW) we should also mark our ExtensionArrays as read-only
+
+    return values
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/internals/concat.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/internals/concat.py
new file mode 100644
index 0000000000000000000000000000000000000000..b2d463a8c6c26f62ded5a06283f29275612c9b40
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/internals/concat.py
@@ -0,0 +1,598 @@
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    cast,
+)
+import warnings
+
+import numpy as np
+
+from pandas._libs import (
+    NaT,
+    algos as libalgos,
+    internals as libinternals,
+    lib,
+)
+from pandas._libs.missing import NA
+from pandas.util._decorators import cache_readonly
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.cast import (
+    ensure_dtype_can_hold_na,
+    find_common_type,
+)
+from pandas.core.dtypes.common import (
+    is_1d_only_ea_dtype,
+    is_scalar,
+    needs_i8_conversion,
+)
+from pandas.core.dtypes.concat import concat_compat
+from pandas.core.dtypes.dtypes import (
+    ExtensionDtype,
+    SparseDtype,
+)
+from pandas.core.dtypes.missing import (
+    is_valid_na_for_dtype,
+    isna,
+    isna_all,
+)
+
+from pandas.core.construction import ensure_wrapped_if_datetimelike
+from pandas.core.internals.array_manager import ArrayManager
+from pandas.core.internals.blocks import (
+    ensure_block_shape,
+    new_block_2d,
+)
+from pandas.core.internals.managers import (
+    BlockManager,
+    make_na_array,
+)
+
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+
+    from pandas._typing import (
+        ArrayLike,
+        AxisInt,
+        DtypeObj,
+        Manager2D,
+        Shape,
+    )
+
+    from pandas import Index
+    from pandas.core.internals.blocks import (
+        Block,
+        BlockPlacement,
+    )
+
+
+def _concatenate_array_managers(
+    mgrs: list[ArrayManager], axes: list[Index], concat_axis: AxisInt
+) -> Manager2D:
+    """
+    Concatenate array managers into one.
+
+    Parameters
+    ----------
+    mgrs_indexers : list of (ArrayManager, {axis: indexer,...}) tuples
+    axes : list of Index
+    concat_axis : int
+
+    Returns
+    -------
+    ArrayManager
+    """
+    if concat_axis == 1:
+        return mgrs[0].concat_vertical(mgrs, axes)
+    else:
+        # concatting along the columns -> combine reindexed arrays in a single manager
+        assert concat_axis == 0
+        return mgrs[0].concat_horizontal(mgrs, axes)
+
+
+def concatenate_managers(
+    mgrs_indexers, axes: list[Index], concat_axis: AxisInt, copy: bool
+) -> Manager2D:
+    """
+    Concatenate block managers into one.
+
+    Parameters
+    ----------
+    mgrs_indexers : list of (BlockManager, {axis: indexer,...}) tuples
+    axes : list of Index
+    concat_axis : int
+    copy : bool
+
+    Returns
+    -------
+    BlockManager
+    """
+
+    needs_copy = copy and concat_axis == 0
+
+    # TODO(ArrayManager) this assumes that all managers are of the same type
+    if isinstance(mgrs_indexers[0][0], ArrayManager):
+        mgrs = _maybe_reindex_columns_na_proxy(axes, mgrs_indexers, needs_copy)
+        # error: Argument 1 to "_concatenate_array_managers" has incompatible
+        # type "List[BlockManager]"; expected "List[Union[ArrayManager,
+        # SingleArrayManager, BlockManager, SingleBlockManager]]"
+        return _concatenate_array_managers(
+            mgrs, axes, concat_axis  # type: ignore[arg-type]
+        )
+
+    # Assertions disabled for performance
+    # for tup in mgrs_indexers:
+    #    # caller is responsible for ensuring this
+    #    indexers = tup[1]
+    #    assert concat_axis not in indexers
+
+    if concat_axis == 0:
+        mgrs = _maybe_reindex_columns_na_proxy(axes, mgrs_indexers, needs_copy)
+        return mgrs[0].concat_horizontal(mgrs, axes)
+
+    if len(mgrs_indexers) > 0 and mgrs_indexers[0][0].nblocks > 0:
+        first_dtype = mgrs_indexers[0][0].blocks[0].dtype
+        if first_dtype in [np.float64, np.float32]:
+            # TODO: support more dtypes here.  This will be simpler once
+            #  JoinUnit.is_na behavior is deprecated.
+            if (
+                all(_is_homogeneous_mgr(mgr, first_dtype) for mgr, _ in mgrs_indexers)
+                and len(mgrs_indexers) > 1
+            ):
+                # Fastpath!
+                # Length restriction is just to avoid having to worry about 'copy'
+                shape = tuple(len(x) for x in axes)
+                nb = _concat_homogeneous_fastpath(mgrs_indexers, shape, first_dtype)
+                return BlockManager((nb,), axes)
+
+    mgrs = _maybe_reindex_columns_na_proxy(axes, mgrs_indexers, needs_copy)
+
+    if len(mgrs) == 1:
+        mgr = mgrs[0]
+        out = mgr.copy(deep=False)
+        out.axes = axes
+        return out
+
+    concat_plan = _get_combined_plan(mgrs)
+
+    blocks = []
+    values: ArrayLike
+
+    for placement, join_units in concat_plan:
+        unit = join_units[0]
+        blk = unit.block
+
+        if _is_uniform_join_units(join_units):
+            vals = [ju.block.values for ju in join_units]
+
+            if not blk.is_extension:
+                # _is_uniform_join_units ensures a single dtype, so
+                #  we can use np.concatenate, which is more performant
+                #  than concat_compat
+                # error: Argument 1 to "concatenate" has incompatible type
+                # "List[Union[ndarray[Any, Any], ExtensionArray]]";
+                # expected "Union[_SupportsArray[dtype[Any]],
+                # _NestedSequence[_SupportsArray[dtype[Any]]]]"
+                values = np.concatenate(vals, axis=1)  # type: ignore[arg-type]
+            elif is_1d_only_ea_dtype(blk.dtype):
+                # TODO(EA2D): special-casing not needed with 2D EAs
+                values = concat_compat(vals, axis=0, ea_compat_axis=True)
+                values = ensure_block_shape(values, ndim=2)
+            else:
+                values = concat_compat(vals, axis=1)
+
+            values = ensure_wrapped_if_datetimelike(values)
+
+            fastpath = blk.values.dtype == values.dtype
+        else:
+            values = _concatenate_join_units(join_units, copy=copy)
+            fastpath = False
+
+        if fastpath:
+            b = blk.make_block_same_class(values, placement=placement)
+        else:
+            b = new_block_2d(values, placement=placement)
+
+        blocks.append(b)
+
+    return BlockManager(tuple(blocks), axes)
+
+
+def _maybe_reindex_columns_na_proxy(
+    axes: list[Index],
+    mgrs_indexers: list[tuple[BlockManager, dict[int, np.ndarray]]],
+    needs_copy: bool,
+) -> list[BlockManager]:
+    """
+    Reindex along columns so that all of the BlockManagers being concatenated
+    have matching columns.
+
+    Columns added in this reindexing have dtype=np.void, indicating they
+    should be ignored when choosing a column's final dtype.
+    """
+    new_mgrs = []
+
+    for mgr, indexers in mgrs_indexers:
+        # For axis=0 (i.e. columns) we use_na_proxy and only_slice, so this
+        #  is a cheap reindexing.
+        for i, indexer in indexers.items():
+            mgr = mgr.reindex_indexer(
+                axes[i],
+                indexers[i],
+                axis=i,
+                copy=False,
+                only_slice=True,  # only relevant for i==0
+                allow_dups=True,
+                use_na_proxy=True,  # only relevant for i==0
+            )
+        if needs_copy and not indexers:
+            mgr = mgr.copy()
+
+        new_mgrs.append(mgr)
+    return new_mgrs
+
+
+def _is_homogeneous_mgr(mgr: BlockManager, first_dtype: DtypeObj) -> bool:
+    """
+    Check if this Manager can be treated as a single ndarray.
+    """
+    if mgr.nblocks != 1:
+        return False
+    blk = mgr.blocks[0]
+    if not (blk.mgr_locs.is_slice_like and blk.mgr_locs.as_slice.step == 1):
+        return False
+
+    return blk.dtype == first_dtype
+
+
+def _concat_homogeneous_fastpath(
+    mgrs_indexers, shape: Shape, first_dtype: np.dtype
+) -> Block:
+    """
+    With single-Block managers with homogeneous dtypes (that can already hold nan),
+    we avoid [...]
+    """
+    # assumes
+    #  all(_is_homogeneous_mgr(mgr, first_dtype) for mgr, _ in in mgrs_indexers)
+
+    if all(not indexers for _, indexers in mgrs_indexers):
+        # https://github.com/pandas-dev/pandas/pull/52685#issuecomment-1523287739
+        arrs = [mgr.blocks[0].values.T for mgr, _ in mgrs_indexers]
+        arr = np.concatenate(arrs).T
+        bp = libinternals.BlockPlacement(slice(shape[0]))
+        nb = new_block_2d(arr, bp)
+        return nb
+
+    arr = np.empty(shape, dtype=first_dtype)
+
+    if first_dtype == np.float64:
+        take_func = libalgos.take_2d_axis0_float64_float64
+    else:
+        take_func = libalgos.take_2d_axis0_float32_float32
+
+    start = 0
+    for mgr, indexers in mgrs_indexers:
+        mgr_len = mgr.shape[1]
+        end = start + mgr_len
+
+        if 0 in indexers:
+            take_func(
+                mgr.blocks[0].values,
+                indexers[0],
+                arr[:, start:end],
+            )
+        else:
+            # No reindexing necessary, we can copy values directly
+            arr[:, start:end] = mgr.blocks[0].values
+
+        start += mgr_len
+
+    bp = libinternals.BlockPlacement(slice(shape[0]))
+    nb = new_block_2d(arr, bp)
+    return nb
+
+
+def _get_combined_plan(
+    mgrs: list[BlockManager],
+) -> list[tuple[BlockPlacement, list[JoinUnit]]]:
+    plan = []
+
+    max_len = mgrs[0].shape[0]
+
+    blknos_list = [mgr.blknos for mgr in mgrs]
+    pairs = libinternals.get_concat_blkno_indexers(blknos_list)
+    for ind, (blknos, bp) in enumerate(pairs):
+        # assert bp.is_slice_like
+        # assert len(bp) > 0
+
+        units_for_bp = []
+        for k, mgr in enumerate(mgrs):
+            blkno = blknos[k]
+
+            nb = _get_block_for_concat_plan(mgr, bp, blkno, max_len=max_len)
+            unit = JoinUnit(nb)
+            units_for_bp.append(unit)
+
+        plan.append((bp, units_for_bp))
+
+    return plan
+
+
+def _get_block_for_concat_plan(
+    mgr: BlockManager, bp: BlockPlacement, blkno: int, *, max_len: int
+) -> Block:
+    blk = mgr.blocks[blkno]
+    # Assertions disabled for performance:
+    #  assert bp.is_slice_like
+    #  assert blkno != -1
+    #  assert (mgr.blknos[bp] == blkno).all()
+
+    if len(bp) == len(blk.mgr_locs) and (
+        blk.mgr_locs.is_slice_like and blk.mgr_locs.as_slice.step == 1
+    ):
+        nb = blk
+    else:
+        ax0_blk_indexer = mgr.blklocs[bp.indexer]
+
+        slc = lib.maybe_indices_to_slice(ax0_blk_indexer, max_len)
+        # TODO: in all extant test cases 2023-04-08 we have a slice here.
+        #  Will this always be the case?
+        if isinstance(slc, slice):
+            nb = blk.slice_block_columns(slc)
+        else:
+            nb = blk.take_block_columns(slc)
+
+    # assert nb.shape == (len(bp), mgr.shape[1])
+    return nb
+
+
+class JoinUnit:
+    def __init__(self, block: Block) -> None:
+        self.block = block
+
+    def __repr__(self) -> str:
+        return f"{type(self).__name__}({repr(self.block)})"
+
+    def _is_valid_na_for(self, dtype: DtypeObj) -> bool:
+        """
+        Check that we are all-NA of a type/dtype that is compatible with this dtype.
+        Augments `self.is_na` with an additional check of the type of NA values.
+        """
+        if not self.is_na:
+            return False
+
+        blk = self.block
+        if blk.dtype.kind == "V":
+            return True
+
+        if blk.dtype == object:
+            values = blk.values
+            return all(is_valid_na_for_dtype(x, dtype) for x in values.ravel(order="K"))
+
+        na_value = blk.fill_value
+        if na_value is NaT and blk.dtype != dtype:
+            # e.g. we are dt64 and other is td64
+            # fill_values match but we should not cast blk.values to dtype
+            # TODO: this will need updating if we ever have non-nano dt64/td64
+            return False
+
+        if na_value is NA and needs_i8_conversion(dtype):
+            # FIXME: kludge; test_append_empty_frame_with_timedelta64ns_nat
+            #  e.g. blk.dtype == "Int64" and dtype is td64, we dont want
+            #  to consider these as matching
+            return False
+
+        # TODO: better to use can_hold_element?
+        return is_valid_na_for_dtype(na_value, dtype)
+
+    @cache_readonly
+    def is_na(self) -> bool:
+        blk = self.block
+        if blk.dtype.kind == "V":
+            return True
+
+        if not blk._can_hold_na:
+            return False
+
+        values = blk.values
+        if values.size == 0:
+            # GH#39122 this case will return False once deprecation is enforced
+            return True
+
+        if isinstance(values.dtype, SparseDtype):
+            return False
+
+        if values.ndim == 1:
+            # TODO(EA2D): no need for special case with 2D EAs
+            val = values[0]
+            if not is_scalar(val) or not isna(val):
+                # ideally isna_all would do this short-circuiting
+                return False
+            return isna_all(values)
+        else:
+            val = values[0][0]
+            if not is_scalar(val) or not isna(val):
+                # ideally isna_all would do this short-circuiting
+                return False
+            return all(isna_all(row) for row in values)
+
+    @cache_readonly
+    def is_na_after_size_and_isna_all_deprecation(self) -> bool:
+        """
+        Will self.is_na be True after values.size == 0 deprecation and isna_all
+        deprecation are enforced?
+        """
+        blk = self.block
+        if blk.dtype.kind == "V":
+            return True
+        return False
+
+    def get_reindexed_values(self, empty_dtype: DtypeObj, upcasted_na) -> ArrayLike:
+        values: ArrayLike
+
+        if upcasted_na is None and self.block.dtype.kind != "V":
+            # No upcasting is necessary
+            return self.block.values
+        else:
+            fill_value = upcasted_na
+
+            if self._is_valid_na_for(empty_dtype):
+                # note: always holds when self.block.dtype.kind == "V"
+                blk_dtype = self.block.dtype
+
+                if blk_dtype == np.dtype("object"):
+                    # we want to avoid filling with np.nan if we are
+                    # using None; we already know that we are all
+                    # nulls
+                    values = cast(np.ndarray, self.block.values)
+                    if values.size and values[0, 0] is None:
+                        fill_value = None
+
+                return make_na_array(empty_dtype, self.block.shape, fill_value)
+
+            return self.block.values
+
+
+def _concatenate_join_units(join_units: list[JoinUnit], copy: bool) -> ArrayLike:
+    """
+    Concatenate values from several join units along axis=1.
+    """
+    empty_dtype, empty_dtype_future = _get_empty_dtype(join_units)
+
+    has_none_blocks = any(unit.block.dtype.kind == "V" for unit in join_units)
+    upcasted_na = _dtype_to_na_value(empty_dtype, has_none_blocks)
+
+    to_concat = [
+        ju.get_reindexed_values(empty_dtype=empty_dtype, upcasted_na=upcasted_na)
+        for ju in join_units
+    ]
+
+    if any(is_1d_only_ea_dtype(t.dtype) for t in to_concat):
+        # TODO(EA2D): special case not needed if all EAs used HybridBlocks
+
+        # error: No overload variant of "__getitem__" of "ExtensionArray" matches
+        # argument type "Tuple[int, slice]"
+        to_concat = [
+            t
+            if is_1d_only_ea_dtype(t.dtype)
+            else t[0, :]  # type: ignore[call-overload]
+            for t in to_concat
+        ]
+        concat_values = concat_compat(to_concat, axis=0, ea_compat_axis=True)
+        concat_values = ensure_block_shape(concat_values, 2)
+
+    else:
+        concat_values = concat_compat(to_concat, axis=1)
+
+    if empty_dtype != empty_dtype_future:
+        if empty_dtype == concat_values.dtype:
+            # GH#39122, GH#40893
+            warnings.warn(
+                "The behavior of DataFrame concatenation with empty or all-NA "
+                "entries is deprecated. In a future version, this will no longer "
+                "exclude empty or all-NA columns when determining the result dtypes. "
+                "To retain the old behavior, exclude the relevant entries before "
+                "the concat operation.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+    return concat_values
+
+
+def _dtype_to_na_value(dtype: DtypeObj, has_none_blocks: bool):
+    """
+    Find the NA value to go with this dtype.
+    """
+    if isinstance(dtype, ExtensionDtype):
+        return dtype.na_value
+    elif dtype.kind in "mM":
+        return dtype.type("NaT")
+    elif dtype.kind in "fc":
+        return dtype.type("NaN")
+    elif dtype.kind == "b":
+        # different from missing.na_value_for_dtype
+        return None
+    elif dtype.kind in "iu":
+        if not has_none_blocks:
+            # different from missing.na_value_for_dtype
+            return None
+        return np.nan
+    elif dtype.kind == "O":
+        return np.nan
+    raise NotImplementedError
+
+
+def _get_empty_dtype(join_units: Sequence[JoinUnit]) -> tuple[DtypeObj, DtypeObj]:
+    """
+    Return dtype and N/A values to use when concatenating specified units.
+
+    Returned N/A value may be None which means there was no casting involved.
+
+    Returns
+    -------
+    dtype
+    """
+    if lib.dtypes_all_equal([ju.block.dtype for ju in join_units]):
+        empty_dtype = join_units[0].block.dtype
+        return empty_dtype, empty_dtype
+
+    has_none_blocks = any(unit.block.dtype.kind == "V" for unit in join_units)
+
+    dtypes = [unit.block.dtype for unit in join_units if not unit.is_na]
+    if not len(dtypes):
+        dtypes = [
+            unit.block.dtype for unit in join_units if unit.block.dtype.kind != "V"
+        ]
+
+    dtype = find_common_type(dtypes)
+    if has_none_blocks:
+        dtype = ensure_dtype_can_hold_na(dtype)
+
+    dtype_future = dtype
+    if len(dtypes) != len(join_units):
+        dtypes_future = [
+            unit.block.dtype
+            for unit in join_units
+            if not unit.is_na_after_size_and_isna_all_deprecation
+        ]
+        if not len(dtypes_future):
+            dtypes_future = [
+                unit.block.dtype for unit in join_units if unit.block.dtype.kind != "V"
+            ]
+
+        if len(dtypes) != len(dtypes_future):
+            dtype_future = find_common_type(dtypes_future)
+            if has_none_blocks:
+                dtype_future = ensure_dtype_can_hold_na(dtype_future)
+
+    return dtype, dtype_future
+
+
+def _is_uniform_join_units(join_units: list[JoinUnit]) -> bool:
+    """
+    Check if the join units consist of blocks of uniform type that can
+    be concatenated using Block.concat_same_type instead of the generic
+    _concatenate_join_units (which uses `concat_compat`).
+
+    """
+    first = join_units[0].block
+    if first.dtype.kind == "V":
+        return False
+    return (
+        # exclude cases where a) ju.block is None or b) we have e.g. Int64+int64
+        all(type(ju.block) is type(first) for ju in join_units)
+        and
+        # e.g. DatetimeLikeBlock can be dt64 or td64, but these are not uniform
+        all(
+            ju.block.dtype == first.dtype
+            # GH#42092 we only want the dtype_equal check for non-numeric blocks
+            #  (for now, may change but that would need a deprecation)
+            or ju.block.dtype.kind in "iub"
+            for ju in join_units
+        )
+        and
+        # no blocks that would get missing values (can lead to type upcasts)
+        # unless we're an extension dtype.
+        all(not ju.is_na or ju.block.is_extension for ju in join_units)
+    )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/internals/construction.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/internals/construction.py
new file mode 100644
index 0000000000000000000000000000000000000000..64fac5fcfcdc29aab451cce31b3591e56da744a0
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/internals/construction.py
@@ -0,0 +1,1073 @@
+"""
+Functions for preparing various inputs passed to the DataFrame or Series
+constructors before passing them to a BlockManager.
+"""
+from __future__ import annotations
+
+from collections import abc
+from typing import (
+    TYPE_CHECKING,
+    Any,
+)
+
+import numpy as np
+from numpy import ma
+
+from pandas._config import using_string_dtype
+
+from pandas._libs import lib
+
+from pandas.core.dtypes.astype import astype_is_view
+from pandas.core.dtypes.cast import (
+    construct_1d_arraylike_from_scalar,
+    dict_compat,
+    maybe_cast_to_datetime,
+    maybe_convert_platform,
+    maybe_infer_to_datetimelike,
+)
+from pandas.core.dtypes.common import (
+    is_1d_only_ea_dtype,
+    is_integer_dtype,
+    is_list_like,
+    is_named_tuple,
+    is_object_dtype,
+)
+from pandas.core.dtypes.dtypes import ExtensionDtype
+from pandas.core.dtypes.generic import (
+    ABCDataFrame,
+    ABCSeries,
+)
+
+from pandas.core import (
+    algorithms,
+    common as com,
+)
+from pandas.core.arrays import ExtensionArray
+from pandas.core.arrays.string_ import StringDtype
+from pandas.core.construction import (
+    array as pd_array,
+    ensure_wrapped_if_datetimelike,
+    extract_array,
+    range_to_ndarray,
+    sanitize_array,
+)
+from pandas.core.indexes.api import (
+    DatetimeIndex,
+    Index,
+    TimedeltaIndex,
+    default_index,
+    ensure_index,
+    get_objs_combined_axis,
+    union_indexes,
+)
+from pandas.core.internals.array_manager import (
+    ArrayManager,
+    SingleArrayManager,
+)
+from pandas.core.internals.blocks import (
+    BlockPlacement,
+    ensure_block_shape,
+    new_block,
+    new_block_2d,
+)
+from pandas.core.internals.managers import (
+    BlockManager,
+    SingleBlockManager,
+    create_block_manager_from_blocks,
+    create_block_manager_from_column_arrays,
+)
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Hashable,
+        Sequence,
+    )
+
+    from pandas._typing import (
+        ArrayLike,
+        DtypeObj,
+        Manager,
+        npt,
+    )
+# ---------------------------------------------------------------------
+# BlockManager Interface
+
+
+def arrays_to_mgr(
+    arrays,
+    columns: Index,
+    index,
+    *,
+    dtype: DtypeObj | None = None,
+    verify_integrity: bool = True,
+    typ: str | None = None,
+    consolidate: bool = True,
+) -> Manager:
+    """
+    Segregate Series based on type and coerce into matrices.
+
+    Needs to handle a lot of exceptional cases.
+    """
+    if verify_integrity:
+        # figure out the index, if necessary
+        if index is None:
+            index = _extract_index(arrays)
+        else:
+            index = ensure_index(index)
+
+        # don't force copy because getting jammed in an ndarray anyway
+        arrays, refs = _homogenize(arrays, index, dtype)
+        # _homogenize ensures
+        #  - all(len(x) == len(index) for x in arrays)
+        #  - all(x.ndim == 1 for x in arrays)
+        #  - all(isinstance(x, (np.ndarray, ExtensionArray)) for x in arrays)
+        #  - all(type(x) is not NumpyExtensionArray for x in arrays)
+
+    else:
+        index = ensure_index(index)
+        arrays = [extract_array(x, extract_numpy=True) for x in arrays]
+        # with _from_arrays, the passed arrays should never be Series objects
+        refs = [None] * len(arrays)
+
+        # Reached via DataFrame._from_arrays; we do minimal validation here
+        for arr in arrays:
+            if (
+                not isinstance(arr, (np.ndarray, ExtensionArray))
+                or arr.ndim != 1
+                or len(arr) != len(index)
+            ):
+                raise ValueError(
+                    "Arrays must be 1-dimensional np.ndarray or ExtensionArray "
+                    "with length matching len(index)"
+                )
+
+    columns = ensure_index(columns)
+    if len(columns) != len(arrays):
+        raise ValueError("len(arrays) must match len(columns)")
+
+    # from BlockManager perspective
+    axes = [columns, index]
+
+    if typ == "block":
+        return create_block_manager_from_column_arrays(
+            arrays, axes, consolidate=consolidate, refs=refs
+        )
+    elif typ == "array":
+        return ArrayManager(arrays, [index, columns])
+    else:
+        raise ValueError(f"'typ' needs to be one of {{'block', 'array'}}, got '{typ}'")
+
+
+def rec_array_to_mgr(
+    data: np.rec.recarray | np.ndarray,
+    index,
+    columns,
+    dtype: DtypeObj | None,
+    copy: bool,
+    typ: str,
+) -> Manager:
+    """
+    Extract from a masked rec array and create the manager.
+    """
+    # essentially process a record array then fill it
+    fdata = ma.getdata(data)
+    if index is None:
+        index = default_index(len(fdata))
+    else:
+        index = ensure_index(index)
+
+    if columns is not None:
+        columns = ensure_index(columns)
+    arrays, arr_columns = to_arrays(fdata, columns)
+
+    # create the manager
+
+    arrays, arr_columns = reorder_arrays(arrays, arr_columns, columns, len(index))
+    if columns is None:
+        columns = arr_columns
+
+    mgr = arrays_to_mgr(arrays, columns, index, dtype=dtype, typ=typ)
+
+    if copy:
+        mgr = mgr.copy()
+    return mgr
+
+
+def mgr_to_mgr(mgr, typ: str, copy: bool = True) -> Manager:
+    """
+    Convert to specific type of Manager. Does not copy if the type is already
+    correct. Does not guarantee a copy otherwise. `copy` keyword only controls
+    whether conversion from Block->ArrayManager copies the 1D arrays.
+    """
+    new_mgr: Manager
+
+    if typ == "block":
+        if isinstance(mgr, BlockManager):
+            new_mgr = mgr
+        else:
+            if mgr.ndim == 2:
+                new_mgr = arrays_to_mgr(
+                    mgr.arrays, mgr.axes[0], mgr.axes[1], typ="block"
+                )
+            else:
+                new_mgr = SingleBlockManager.from_array(mgr.arrays[0], mgr.index)
+    elif typ == "array":
+        if isinstance(mgr, ArrayManager):
+            new_mgr = mgr
+        else:
+            if mgr.ndim == 2:
+                arrays = [mgr.iget_values(i) for i in range(len(mgr.axes[0]))]
+                if copy:
+                    arrays = [arr.copy() for arr in arrays]
+                new_mgr = ArrayManager(arrays, [mgr.axes[1], mgr.axes[0]])
+            else:
+                array = mgr.internal_values()
+                if copy:
+                    array = array.copy()
+                new_mgr = SingleArrayManager([array], [mgr.index])
+    else:
+        raise ValueError(f"'typ' needs to be one of {{'block', 'array'}}, got '{typ}'")
+    return new_mgr
+
+
+# ---------------------------------------------------------------------
+# DataFrame Constructor Interface
+
+
+def ndarray_to_mgr(
+    values, index, columns, dtype: DtypeObj | None, copy: bool, typ: str
+) -> Manager:
+    # used in DataFrame.__init__
+    # input must be a ndarray, list, Series, Index, ExtensionArray
+
+    if isinstance(values, ABCSeries):
+        if columns is None:
+            if values.name is not None:
+                columns = Index([values.name])
+        if index is None:
+            index = values.index
+        else:
+            values = values.reindex(index)
+
+        # zero len case (GH #2234)
+        if not len(values) and columns is not None and len(columns):
+            values = np.empty((0, 1), dtype=object)
+
+    # if the array preparation does a copy -> avoid this for ArrayManager,
+    # since the copy is done on conversion to 1D arrays
+    copy_on_sanitize = False if typ == "array" else copy
+
+    vdtype = getattr(values, "dtype", None)
+    refs = None
+    if is_1d_only_ea_dtype(vdtype) or is_1d_only_ea_dtype(dtype):
+        # GH#19157
+
+        if isinstance(values, (np.ndarray, ExtensionArray)) and values.ndim > 1:
+            # GH#12513 a EA dtype passed with a 2D array, split into
+            #  multiple EAs that view the values
+            # error: No overload variant of "__getitem__" of "ExtensionArray"
+            # matches argument type "Tuple[slice, int]"
+            values = [
+                values[:, n]  # type: ignore[call-overload]
+                for n in range(values.shape[1])
+            ]
+        else:
+            values = [values]
+
+        if columns is None:
+            columns = Index(range(len(values)))
+        else:
+            columns = ensure_index(columns)
+
+        return arrays_to_mgr(values, columns, index, dtype=dtype, typ=typ)
+
+    elif isinstance(vdtype, ExtensionDtype):
+        # i.e. Datetime64TZ, PeriodDtype; cases with is_1d_only_ea_dtype(vdtype)
+        #  are already caught above
+        values = extract_array(values, extract_numpy=True)
+        if copy:
+            values = values.copy()
+        if values.ndim == 1:
+            values = values.reshape(-1, 1)
+
+    elif isinstance(values, (ABCSeries, Index)):
+        if not copy_on_sanitize and (
+            dtype is None or astype_is_view(values.dtype, dtype)
+        ):
+            refs = values._references
+
+        if copy_on_sanitize:
+            values = values._values.copy()
+        else:
+            values = values._values
+
+        values = _ensure_2d(values)
+
+    elif isinstance(values, (np.ndarray, ExtensionArray)):
+        # drop subclass info
+        if copy_on_sanitize and (dtype is None or astype_is_view(values.dtype, dtype)):
+            # only force a copy now if copy=True was requested
+            # and a subsequent `astype` will not already result in a copy
+            values = np.array(values, copy=True, order="F")
+        else:
+            values = np.asarray(values)
+        values = _ensure_2d(values)
+
+    else:
+        # by definition an array here
+        # the dtypes will be coerced to a single dtype
+        values = _prep_ndarraylike(values, copy=copy_on_sanitize)
+
+    if dtype is not None and values.dtype != dtype:
+        # GH#40110 see similar check inside sanitize_array
+        values = sanitize_array(
+            values,
+            None,
+            dtype=dtype,
+            copy=copy_on_sanitize,
+            allow_2d=True,
+        )
+
+    # _prep_ndarraylike ensures that values.ndim == 2 at this point
+    index, columns = _get_axes(
+        values.shape[0], values.shape[1], index=index, columns=columns
+    )
+
+    _check_values_indices_shape_match(values, index, columns)
+
+    if typ == "array":
+        if issubclass(values.dtype.type, str):
+            values = np.array(values, dtype=object)
+
+        if dtype is None and is_object_dtype(values.dtype):
+            arrays = [
+                ensure_wrapped_if_datetimelike(
+                    maybe_infer_to_datetimelike(values[:, i])
+                )
+                for i in range(values.shape[1])
+            ]
+        else:
+            if lib.is_np_dtype(values.dtype, "mM"):
+                values = ensure_wrapped_if_datetimelike(values)
+            arrays = [values[:, i] for i in range(values.shape[1])]
+
+        if copy:
+            arrays = [arr.copy() for arr in arrays]
+
+        return ArrayManager(arrays, [index, columns], verify_integrity=False)
+
+    values = values.T
+
+    # if we don't have a dtype specified, then try to convert objects
+    # on the entire block; this is to convert if we have datetimelike's
+    # embedded in an object type
+    if dtype is None and is_object_dtype(values.dtype):
+        obj_columns = list(values)
+        maybe_datetime = [maybe_infer_to_datetimelike(x) for x in obj_columns]
+        # don't convert (and copy) the objects if no type inference occurs
+        if any(x is not y for x, y in zip(obj_columns, maybe_datetime)):
+            dvals_list = [ensure_block_shape(dval, 2) for dval in maybe_datetime]
+            block_values = [
+                new_block_2d(dvals_list[n], placement=BlockPlacement(n))
+                for n in range(len(dvals_list))
+            ]
+        else:
+            bp = BlockPlacement(slice(len(columns)))
+            nb = new_block_2d(values, placement=bp, refs=refs)
+            block_values = [nb]
+    elif dtype is None and values.dtype.kind == "U" and using_string_dtype():
+        dtype = StringDtype(na_value=np.nan)
+
+        obj_columns = list(values)
+        block_values = [
+            new_block(
+                dtype.construct_array_type()._from_sequence(data, dtype=dtype),
+                BlockPlacement(slice(i, i + 1)),
+                ndim=2,
+            )
+            for i, data in enumerate(obj_columns)
+        ]
+
+    else:
+        bp = BlockPlacement(slice(len(columns)))
+        nb = new_block_2d(values, placement=bp, refs=refs)
+        block_values = [nb]
+
+    if len(columns) == 0:
+        # TODO: check len(values) == 0?
+        block_values = []
+
+    return create_block_manager_from_blocks(
+        block_values, [columns, index], verify_integrity=False
+    )
+
+
+def _check_values_indices_shape_match(
+    values: np.ndarray, index: Index, columns: Index
+) -> None:
+    """
+    Check that the shape implied by our axes matches the actual shape of the
+    data.
+    """
+    if values.shape[1] != len(columns) or values.shape[0] != len(index):
+        # Could let this raise in Block constructor, but we get a more
+        #  helpful exception message this way.
+        if values.shape[0] == 0 < len(index):
+            raise ValueError("Empty data passed with indices specified.")
+
+        passed = values.shape
+        implied = (len(index), len(columns))
+        raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}")
+
+
+def dict_to_mgr(
+    data: dict,
+    index,
+    columns,
+    *,
+    dtype: DtypeObj | None = None,
+    typ: str = "block",
+    copy: bool = True,
+) -> Manager:
+    """
+    Segregate Series based on type and coerce into matrices.
+    Needs to handle a lot of exceptional cases.
+
+    Used in DataFrame.__init__
+    """
+    arrays: Sequence[Any] | Series
+
+    if columns is not None:
+        from pandas.core.series import Series
+
+        arrays = Series(data, index=columns, dtype=object)
+        missing = arrays.isna()
+        if index is None:
+            # GH10856
+            # raise ValueError if only scalars in dict
+            index = _extract_index(arrays[~missing])
+        else:
+            index = ensure_index(index)
+
+        # no obvious "empty" int column
+        if missing.any() and not is_integer_dtype(dtype):
+            nan_dtype: DtypeObj
+
+            if dtype is not None:
+                # calling sanitize_array ensures we don't mix-and-match
+                #  NA dtypes
+                midxs = missing.values.nonzero()[0]
+                for i in midxs:
+                    arr = sanitize_array(arrays.iat[i], index, dtype=dtype)
+                    arrays.iat[i] = arr
+            else:
+                # GH#1783
+                nan_dtype = np.dtype("object")
+                val = construct_1d_arraylike_from_scalar(np.nan, len(index), nan_dtype)
+                nmissing = missing.sum()
+                if copy:
+                    rhs = [val] * nmissing
+                else:
+                    # GH#45369
+                    rhs = [val.copy() for _ in range(nmissing)]
+                arrays.loc[missing] = rhs
+
+        arrays = list(arrays)
+        columns = ensure_index(columns)
+
+    else:
+        keys = list(data.keys())
+        columns = Index(keys) if keys else default_index(0)
+        arrays = [com.maybe_iterable_to_list(data[k]) for k in keys]
+
+    if copy:
+        if typ == "block":
+            # We only need to copy arrays that will not get consolidated, i.e.
+            #  only EA arrays
+            arrays = [
+                x.copy()
+                if isinstance(x, ExtensionArray)
+                else x.copy(deep=True)
+                if (
+                    isinstance(x, Index)
+                    or isinstance(x, ABCSeries)
+                    and is_1d_only_ea_dtype(x.dtype)
+                )
+                else x
+                for x in arrays
+            ]
+        else:
+            # dtype check to exclude e.g. range objects, scalars
+            arrays = [x.copy() if hasattr(x, "dtype") else x for x in arrays]
+
+    return arrays_to_mgr(arrays, columns, index, dtype=dtype, typ=typ, consolidate=copy)
+
+
+def nested_data_to_arrays(
+    data: Sequence,
+    columns: Index | None,
+    index: Index | None,
+    dtype: DtypeObj | None,
+) -> tuple[list[ArrayLike], Index, Index]:
+    """
+    Convert a single sequence of arrays to multiple arrays.
+    """
+    # By the time we get here we have already checked treat_as_nested(data)
+
+    if is_named_tuple(data[0]) and columns is None:
+        columns = ensure_index(data[0]._fields)
+
+    arrays, columns = to_arrays(data, columns, dtype=dtype)
+    columns = ensure_index(columns)
+
+    if index is None:
+        if isinstance(data[0], ABCSeries):
+            index = _get_names_from_index(data)
+        else:
+            index = default_index(len(data))
+
+    return arrays, columns, index
+
+
+def treat_as_nested(data) -> bool:
+    """
+    Check if we should use nested_data_to_arrays.
+    """
+    return (
+        len(data) > 0
+        and is_list_like(data[0])
+        and getattr(data[0], "ndim", 1) == 1
+        and not (isinstance(data, ExtensionArray) and data.ndim == 2)
+    )
+
+
+# ---------------------------------------------------------------------
+
+
+def _prep_ndarraylike(values, copy: bool = True) -> np.ndarray:
+    # values is specifically _not_ ndarray, EA, Index, or Series
+    # We only get here with `not treat_as_nested(values)`
+
+    if len(values) == 0:
+        # TODO: check for length-zero range, in which case return int64 dtype?
+        # TODO: reuse anything in try_cast?
+        return np.empty((0, 0), dtype=object)
+    elif isinstance(values, range):
+        arr = range_to_ndarray(values)
+        return arr[..., np.newaxis]
+
+    def convert(v):
+        if not is_list_like(v) or isinstance(v, ABCDataFrame):
+            return v
+
+        v = extract_array(v, extract_numpy=True)
+        res = maybe_convert_platform(v)
+        # We don't do maybe_infer_to_datetimelike here bc we will end up doing
+        #  it column-by-column in ndarray_to_mgr
+        return res
+
+    # we could have a 1-dim or 2-dim list here
+    # this is equiv of np.asarray, but does object conversion
+    # and platform dtype preservation
+    # does not convert e.g. [1, "a", True] to ["1", "a", "True"] like
+    #  np.asarray would
+    if is_list_like(values[0]):
+        values = np.array([convert(v) for v in values])
+    elif isinstance(values[0], np.ndarray) and values[0].ndim == 0:
+        # GH#21861 see test_constructor_list_of_lists
+        values = np.array([convert(v) for v in values])
+    else:
+        values = convert(values)
+
+    return _ensure_2d(values)
+
+
+def _ensure_2d(values: np.ndarray) -> np.ndarray:
+    """
+    Reshape 1D values, raise on anything else other than 2D.
+    """
+    if values.ndim == 1:
+        values = values.reshape((values.shape[0], 1))
+    elif values.ndim != 2:
+        raise ValueError(f"Must pass 2-d input. shape={values.shape}")
+    return values
+
+
+def _homogenize(
+    data, index: Index, dtype: DtypeObj | None
+) -> tuple[list[ArrayLike], list[Any]]:
+    oindex = None
+    homogenized = []
+    # if the original array-like in `data` is a Series, keep track of this Series' refs
+    refs: list[Any] = []
+
+    for val in data:
+        if isinstance(val, (ABCSeries, Index)):
+            if dtype is not None:
+                val = val.astype(dtype, copy=False)
+            if isinstance(val, ABCSeries) and val.index is not index:
+                # Forces alignment. No need to copy data since we
+                # are putting it into an ndarray later
+                val = val.reindex(index, copy=False)
+            refs.append(val._references)
+            val = val._values
+        else:
+            if isinstance(val, dict):
+                # GH#41785 this _should_ be equivalent to (but faster than)
+                #  val = Series(val, index=index)._values
+                if oindex is None:
+                    oindex = index.astype("O")
+
+                if isinstance(index, (DatetimeIndex, TimedeltaIndex)):
+                    # see test_constructor_dict_datetime64_index
+                    val = dict_compat(val)
+                else:
+                    # see test_constructor_subclass_dict
+                    val = dict(val)
+                val = lib.fast_multiget(val, oindex._values, default=np.nan)
+
+            val = sanitize_array(val, index, dtype=dtype, copy=False)
+            com.require_length_match(val, index)
+            refs.append(None)
+
+        homogenized.append(val)
+
+    return homogenized, refs
+
+
+def _extract_index(data) -> Index:
+    """
+    Try to infer an Index from the passed data, raise ValueError on failure.
+    """
+    index: Index
+    if len(data) == 0:
+        return default_index(0)
+
+    raw_lengths = []
+    indexes: list[list[Hashable] | Index] = []
+
+    have_raw_arrays = False
+    have_series = False
+    have_dicts = False
+
+    for val in data:
+        if isinstance(val, ABCSeries):
+            have_series = True
+            indexes.append(val.index)
+        elif isinstance(val, dict):
+            have_dicts = True
+            indexes.append(list(val.keys()))
+        elif is_list_like(val) and getattr(val, "ndim", 1) == 1:
+            have_raw_arrays = True
+            raw_lengths.append(len(val))
+        elif isinstance(val, np.ndarray) and val.ndim > 1:
+            raise ValueError("Per-column arrays must each be 1-dimensional")
+
+    if not indexes and not raw_lengths:
+        raise ValueError("If using all scalar values, you must pass an index")
+
+    if have_series:
+        index = union_indexes(indexes)
+    elif have_dicts:
+        index = union_indexes(indexes, sort=False)
+
+    if have_raw_arrays:
+        lengths = list(set(raw_lengths))
+        if len(lengths) > 1:
+            raise ValueError("All arrays must be of the same length")
+
+        if have_dicts:
+            raise ValueError(
+                "Mixing dicts with non-Series may lead to ambiguous ordering."
+            )
+
+        if have_series:
+            if lengths[0] != len(index):
+                msg = (
+                    f"array length {lengths[0]} does not match index "
+                    f"length {len(index)}"
+                )
+                raise ValueError(msg)
+        else:
+            index = default_index(lengths[0])
+
+    return ensure_index(index)
+
+
+def reorder_arrays(
+    arrays: list[ArrayLike], arr_columns: Index, columns: Index | None, length: int
+) -> tuple[list[ArrayLike], Index]:
+    """
+    Pre-emptively (cheaply) reindex arrays with new columns.
+    """
+    # reorder according to the columns
+    if columns is not None:
+        if not columns.equals(arr_columns):
+            # if they are equal, there is nothing to do
+            new_arrays: list[ArrayLike] = []
+            indexer = arr_columns.get_indexer(columns)
+            for i, k in enumerate(indexer):
+                if k == -1:
+                    # by convention default is all-NaN object dtype
+                    arr = np.empty(length, dtype=object)
+                    arr.fill(np.nan)
+                else:
+                    arr = arrays[k]
+                new_arrays.append(arr)
+
+            arrays = new_arrays
+            arr_columns = columns
+
+    return arrays, arr_columns
+
+
+def _get_names_from_index(data) -> Index:
+    has_some_name = any(getattr(s, "name", None) is not None for s in data)
+    if not has_some_name:
+        return default_index(len(data))
+
+    index: list[Hashable] = list(range(len(data)))
+    count = 0
+    for i, s in enumerate(data):
+        n = getattr(s, "name", None)
+        if n is not None:
+            index[i] = n
+        else:
+            index[i] = f"Unnamed {count}"
+            count += 1
+
+    return Index(index)
+
+
+def _get_axes(
+    N: int, K: int, index: Index | None, columns: Index | None
+) -> tuple[Index, Index]:
+    # helper to create the axes as indexes
+    # return axes or defaults
+
+    if index is None:
+        index = default_index(N)
+    else:
+        index = ensure_index(index)
+
+    if columns is None:
+        columns = default_index(K)
+    else:
+        columns = ensure_index(columns)
+    return index, columns
+
+
+def dataclasses_to_dicts(data):
+    """
+    Converts a list of dataclass instances to a list of dictionaries.
+
+    Parameters
+    ----------
+    data : List[Type[dataclass]]
+
+    Returns
+    --------
+    list_dict : List[dict]
+
+    Examples
+    --------
+    >>> from dataclasses import dataclass
+    >>> @dataclass
+    ... class Point:
+    ...     x: int
+    ...     y: int
+
+    >>> dataclasses_to_dicts([Point(1, 2), Point(2, 3)])
+    [{'x': 1, 'y': 2}, {'x': 2, 'y': 3}]
+
+    """
+    from dataclasses import asdict
+
+    return list(map(asdict, data))
+
+
+# ---------------------------------------------------------------------
+# Conversion of Inputs to Arrays
+
+
+def to_arrays(
+    data, columns: Index | None, dtype: DtypeObj | None = None
+) -> tuple[list[ArrayLike], Index]:
+    """
+    Return list of arrays, columns.
+
+    Returns
+    -------
+    list[ArrayLike]
+        These will become columns in a DataFrame.
+    Index
+        This will become frame.columns.
+
+    Notes
+    -----
+    Ensures that len(result_arrays) == len(result_index).
+    """
+
+    if not len(data):
+        if isinstance(data, np.ndarray):
+            if data.dtype.names is not None:
+                # i.e. numpy structured array
+                columns = ensure_index(data.dtype.names)
+                arrays = [data[name] for name in columns]
+
+                if len(data) == 0:
+                    # GH#42456 the indexing above results in list of 2D ndarrays
+                    # TODO: is that an issue with numpy?
+                    for i, arr in enumerate(arrays):
+                        if arr.ndim == 2:
+                            arrays[i] = arr[:, 0]
+
+                return arrays, columns
+        return [], ensure_index([])
+
+    elif isinstance(data, np.ndarray) and data.dtype.names is not None:
+        # e.g. recarray
+        columns = Index(list(data.dtype.names))
+        arrays = [data[k] for k in columns]
+        return arrays, columns
+
+    if isinstance(data[0], (list, tuple)):
+        arr = _list_to_arrays(data)
+    elif isinstance(data[0], abc.Mapping):
+        arr, columns = _list_of_dict_to_arrays(data, columns)
+    elif isinstance(data[0], ABCSeries):
+        arr, columns = _list_of_series_to_arrays(data, columns)
+    else:
+        # last ditch effort
+        data = [tuple(x) for x in data]
+        arr = _list_to_arrays(data)
+
+    content, columns = _finalize_columns_and_data(arr, columns, dtype)
+    return content, columns
+
+
+def _list_to_arrays(data: list[tuple | list]) -> np.ndarray:
+    # Returned np.ndarray has ndim = 2
+    # Note: we already check len(data) > 0 before getting hre
+    if isinstance(data[0], tuple):
+        content = lib.to_object_array_tuples(data)
+    else:
+        # list of lists
+        content = lib.to_object_array(data)
+    return content
+
+
+def _list_of_series_to_arrays(
+    data: list,
+    columns: Index | None,
+) -> tuple[np.ndarray, Index]:
+    # returned np.ndarray has ndim == 2
+
+    if columns is None:
+        # We know pass_data is non-empty because data[0] is a Series
+        pass_data = [x for x in data if isinstance(x, (ABCSeries, ABCDataFrame))]
+        columns = get_objs_combined_axis(pass_data, sort=False)
+
+    indexer_cache: dict[int, np.ndarray] = {}
+
+    aligned_values = []
+    for s in data:
+        index = getattr(s, "index", None)
+        if index is None:
+            index = default_index(len(s))
+
+        if id(index) in indexer_cache:
+            indexer = indexer_cache[id(index)]
+        else:
+            indexer = indexer_cache[id(index)] = index.get_indexer(columns)
+
+        values = extract_array(s, extract_numpy=True)
+        aligned_values.append(algorithms.take_nd(values, indexer))
+
+    content = np.vstack(aligned_values)
+    return content, columns
+
+
+def _list_of_dict_to_arrays(
+    data: list[dict],
+    columns: Index | None,
+) -> tuple[np.ndarray, Index]:
+    """
+    Convert list of dicts to numpy arrays
+
+    if `columns` is not passed, column names are inferred from the records
+    - for OrderedDict and dicts, the column names match
+      the key insertion-order from the first record to the last.
+    - For other kinds of dict-likes, the keys are lexically sorted.
+
+    Parameters
+    ----------
+    data : iterable
+        collection of records (OrderedDict, dict)
+    columns: iterables or None
+
+    Returns
+    -------
+    content : np.ndarray[object, ndim=2]
+    columns : Index
+    """
+    if columns is None:
+        gen = (list(x.keys()) for x in data)
+        sort = not any(isinstance(d, dict) for d in data)
+        pre_cols = lib.fast_unique_multiple_list_gen(gen, sort=sort)
+        columns = ensure_index(pre_cols)
+
+    # assure that they are of the base dict class and not of derived
+    # classes
+    data = [d if type(d) is dict else dict(d) for d in data]  # noqa: E721
+
+    content = lib.dicts_to_array(data, list(columns))
+    return content, columns
+
+
+def _finalize_columns_and_data(
+    content: np.ndarray,  # ndim == 2
+    columns: Index | None,
+    dtype: DtypeObj | None,
+) -> tuple[list[ArrayLike], Index]:
+    """
+    Ensure we have valid columns, cast object dtypes if possible.
+    """
+    contents = list(content.T)
+
+    try:
+        columns = _validate_or_indexify_columns(contents, columns)
+    except AssertionError as err:
+        # GH#26429 do not raise user-facing AssertionError
+        raise ValueError(err) from err
+
+    if len(contents) and contents[0].dtype == np.object_:
+        contents = convert_object_array(contents, dtype=dtype)
+
+    return contents, columns
+
+
+def _validate_or_indexify_columns(
+    content: list[np.ndarray], columns: Index | None
+) -> Index:
+    """
+    If columns is None, make numbers as column names; Otherwise, validate that
+    columns have valid length.
+
+    Parameters
+    ----------
+    content : list of np.ndarrays
+    columns : Index or None
+
+    Returns
+    -------
+    Index
+        If columns is None, assign positional column index value as columns.
+
+    Raises
+    ------
+    1. AssertionError when content is not composed of list of lists, and if
+        length of columns is not equal to length of content.
+    2. ValueError when content is list of lists, but length of each sub-list
+        is not equal
+    3. ValueError when content is list of lists, but length of sub-list is
+        not equal to length of content
+    """
+    if columns is None:
+        columns = default_index(len(content))
+    else:
+        # Add mask for data which is composed of list of lists
+        is_mi_list = isinstance(columns, list) and all(
+            isinstance(col, list) for col in columns
+        )
+
+        if not is_mi_list and len(columns) != len(content):  # pragma: no cover
+            # caller's responsibility to check for this...
+            raise AssertionError(
+                f"{len(columns)} columns passed, passed data had "
+                f"{len(content)} columns"
+            )
+        if is_mi_list:
+            # check if nested list column, length of each sub-list should be equal
+            if len({len(col) for col in columns}) > 1:
+                raise ValueError(
+                    "Length of columns passed for MultiIndex columns is different"
+                )
+
+            # if columns is not empty and length of sublist is not equal to content
+            if columns and len(columns[0]) != len(content):
+                raise ValueError(
+                    f"{len(columns[0])} columns passed, passed data had "
+                    f"{len(content)} columns"
+                )
+    return columns
+
+
+def convert_object_array(
+    content: list[npt.NDArray[np.object_]],
+    dtype: DtypeObj | None,
+    dtype_backend: str = "numpy",
+    coerce_float: bool = False,
+) -> list[ArrayLike]:
+    """
+    Internal function to convert object array.
+
+    Parameters
+    ----------
+    content: List[np.ndarray]
+    dtype: np.dtype or ExtensionDtype
+    dtype_backend: Controls if nullable/pyarrow dtypes are returned.
+    coerce_float: Cast floats that are integers to int.
+
+    Returns
+    -------
+    List[ArrayLike]
+    """
+    # provide soft conversion of object dtypes
+
+    def convert(arr):
+        if dtype != np.dtype("O"):
+            arr = lib.maybe_convert_objects(
+                arr,
+                try_float=coerce_float,
+                convert_to_nullable_dtype=dtype_backend != "numpy",
+            )
+            # Notes on cases that get here 2023-02-15
+            # 1) we DO get here when arr is all Timestamps and dtype=None
+            # 2) disabling this doesn't break the world, so this must be
+            #    getting caught at a higher level
+            # 3) passing convert_non_numeric to maybe_convert_objects get this right
+            # 4) convert_non_numeric?
+
+            if dtype is None:
+                if arr.dtype == np.dtype("O"):
+                    # i.e. maybe_convert_objects didn't convert
+                    convert_to_nullable_dtype = dtype_backend != "numpy"
+                    arr = maybe_infer_to_datetimelike(arr, convert_to_nullable_dtype)
+                    if convert_to_nullable_dtype and arr.dtype == np.dtype("O"):
+                        new_dtype = StringDtype()
+                        arr_cls = new_dtype.construct_array_type()
+                        arr = arr_cls._from_sequence(arr, dtype=new_dtype)
+                elif dtype_backend != "numpy" and isinstance(arr, np.ndarray):
+                    if arr.dtype.kind in "iufb":
+                        arr = pd_array(arr, copy=False)
+
+            elif isinstance(dtype, ExtensionDtype):
+                # TODO: test(s) that get here
+                # TODO: try to de-duplicate this convert function with
+                #  core.construction functions
+                cls = dtype.construct_array_type()
+                arr = cls._from_sequence(arr, dtype=dtype, copy=False)
+            elif dtype.kind in "mM":
+                # This restriction is harmless bc these are the only cases
+                #  where maybe_cast_to_datetime is not a no-op.
+                # Here we know:
+                #  1) dtype.kind in "mM" and
+                #  2) arr is either object or numeric dtype
+                arr = maybe_cast_to_datetime(arr, dtype)
+
+        return arr
+
+    arrays = [convert(arr) for arr in content]
+
+    return arrays
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/internals/managers.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/internals/managers.py
new file mode 100644
index 0000000000000000000000000000000000000000..5e3a67d5363fdd7333217df1256c29df58df8665
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/internals/managers.py
@@ -0,0 +1,2394 @@
+from __future__ import annotations
+
+from collections.abc import (
+    Hashable,
+    Sequence,
+)
+import itertools
+from typing import (
+    TYPE_CHECKING,
+    Callable,
+    Literal,
+    cast,
+)
+import warnings
+
+import numpy as np
+
+from pandas._config import (
+    using_copy_on_write,
+    warn_copy_on_write,
+)
+
+from pandas._libs import (
+    internals as libinternals,
+    lib,
+)
+from pandas._libs.internals import (
+    BlockPlacement,
+    BlockValuesRefs,
+)
+from pandas._libs.tslibs import Timestamp
+from pandas.errors import PerformanceWarning
+from pandas.util._decorators import cache_readonly
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.cast import infer_dtype_from_scalar
+from pandas.core.dtypes.common import (
+    ensure_platform_int,
+    is_1d_only_ea_dtype,
+    is_list_like,
+)
+from pandas.core.dtypes.dtypes import (
+    DatetimeTZDtype,
+    ExtensionDtype,
+)
+from pandas.core.dtypes.generic import (
+    ABCDataFrame,
+    ABCSeries,
+)
+from pandas.core.dtypes.missing import (
+    array_equals,
+    isna,
+)
+
+import pandas.core.algorithms as algos
+from pandas.core.arrays import (
+    ArrowExtensionArray,
+    ArrowStringArray,
+    DatetimeArray,
+)
+from pandas.core.arrays._mixins import NDArrayBackedExtensionArray
+from pandas.core.construction import (
+    ensure_wrapped_if_datetimelike,
+    extract_array,
+)
+from pandas.core.indexers import maybe_convert_indices
+from pandas.core.indexes.api import (
+    Index,
+    ensure_index,
+)
+from pandas.core.internals.base import (
+    DataManager,
+    SingleDataManager,
+    ensure_np_dtype,
+    interleaved_dtype,
+)
+from pandas.core.internals.blocks import (
+    COW_WARNING_GENERAL_MSG,
+    COW_WARNING_SETITEM_MSG,
+    Block,
+    NumpyBlock,
+    ensure_block_shape,
+    extend_blocks,
+    get_block_type,
+    maybe_coerce_values,
+    new_block,
+    new_block_2d,
+)
+from pandas.core.internals.ops import (
+    blockwise_all,
+    operate_blockwise,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        ArrayLike,
+        AxisInt,
+        DtypeObj,
+        QuantileInterpolation,
+        Self,
+        Shape,
+        npt,
+    )
+
+    from pandas.api.extensions import ExtensionArray
+
+
+class BaseBlockManager(DataManager):
+    """
+    Core internal data structure to implement DataFrame, Series, etc.
+
+    Manage a bunch of labeled 2D mixed-type ndarrays. Essentially it's a
+    lightweight blocked set of labeled data to be manipulated by the DataFrame
+    public API class
+
+    Attributes
+    ----------
+    shape
+    ndim
+    axes
+    values
+    items
+
+    Methods
+    -------
+    set_axis(axis, new_labels)
+    copy(deep=True)
+
+    get_dtypes
+
+    apply(func, axes, block_filter_fn)
+
+    get_bool_data
+    get_numeric_data
+
+    get_slice(slice_like, axis)
+    get(label)
+    iget(loc)
+
+    take(indexer, axis)
+    reindex_axis(new_labels, axis)
+    reindex_indexer(new_labels, indexer, axis)
+
+    delete(label)
+    insert(loc, label, value)
+    set(label, value)
+
+    Parameters
+    ----------
+    blocks: Sequence of Block
+    axes: Sequence of Index
+    verify_integrity: bool, default True
+
+    Notes
+    -----
+    This is *not* a public API class
+    """
+
+    __slots__ = ()
+
+    _blknos: npt.NDArray[np.intp]
+    _blklocs: npt.NDArray[np.intp]
+    blocks: tuple[Block, ...]
+    axes: list[Index]
+
+    @property
+    def ndim(self) -> int:
+        raise NotImplementedError
+
+    _known_consolidated: bool
+    _is_consolidated: bool
+
+    def __init__(self, blocks, axes, verify_integrity: bool = True) -> None:
+        raise NotImplementedError
+
+    @classmethod
+    def from_blocks(cls, blocks: list[Block], axes: list[Index]) -> Self:
+        raise NotImplementedError
+
+    @property
+    def blknos(self) -> npt.NDArray[np.intp]:
+        """
+        Suppose we want to find the array corresponding to our i'th column.
+
+        blknos[i] identifies the block from self.blocks that contains this column.
+
+        blklocs[i] identifies the column of interest within
+        self.blocks[self.blknos[i]]
+        """
+        if self._blknos is None:
+            # Note: these can be altered by other BlockManager methods.
+            self._rebuild_blknos_and_blklocs()
+
+        return self._blknos
+
+    @property
+    def blklocs(self) -> npt.NDArray[np.intp]:
+        """
+        See blknos.__doc__
+        """
+        if self._blklocs is None:
+            # Note: these can be altered by other BlockManager methods.
+            self._rebuild_blknos_and_blklocs()
+
+        return self._blklocs
+
+    def make_empty(self, axes=None) -> Self:
+        """return an empty BlockManager with the items axis of len 0"""
+        if axes is None:
+            axes = [Index([])] + self.axes[1:]
+
+        # preserve dtype if possible
+        if self.ndim == 1:
+            assert isinstance(self, SingleBlockManager)  # for mypy
+            blk = self.blocks[0]
+            arr = blk.values[:0]
+            bp = BlockPlacement(slice(0, 0))
+            nb = blk.make_block_same_class(arr, placement=bp)
+            blocks = [nb]
+        else:
+            blocks = []
+        return type(self).from_blocks(blocks, axes)
+
+    def __nonzero__(self) -> bool:
+        return True
+
+    # Python3 compat
+    __bool__ = __nonzero__
+
+    def _normalize_axis(self, axis: AxisInt) -> int:
+        # switch axis to follow BlockManager logic
+        if self.ndim == 2:
+            axis = 1 if axis == 0 else 0
+        return axis
+
+    def set_axis(self, axis: AxisInt, new_labels: Index) -> None:
+        # Caller is responsible for ensuring we have an Index object.
+        self._validate_set_axis(axis, new_labels)
+        self.axes[axis] = new_labels
+
+    @property
+    def is_single_block(self) -> bool:
+        # Assumes we are 2D; overridden by SingleBlockManager
+        return len(self.blocks) == 1
+
+    @property
+    def items(self) -> Index:
+        return self.axes[0]
+
+    def _has_no_reference(self, i: int) -> bool:
+        """
+        Check for column `i` if it has references.
+        (whether it references another array or is itself being referenced)
+        Returns True if the column has no references.
+        """
+        blkno = self.blknos[i]
+        return self._has_no_reference_block(blkno)
+
+    def _has_no_reference_block(self, blkno: int) -> bool:
+        """
+        Check for block `i` if it has references.
+        (whether it references another array or is itself being referenced)
+        Returns True if the block has no references.
+        """
+        return not self.blocks[blkno].refs.has_reference()
+
+    def add_references(self, mgr: BaseBlockManager) -> None:
+        """
+        Adds the references from one manager to another. We assume that both
+        managers have the same block structure.
+        """
+        if len(self.blocks) != len(mgr.blocks):
+            # If block structure changes, then we made a copy
+            return
+        for i, blk in enumerate(self.blocks):
+            blk.refs = mgr.blocks[i].refs
+            blk.refs.add_reference(blk)
+
+    def references_same_values(self, mgr: BaseBlockManager, blkno: int) -> bool:
+        """
+        Checks if two blocks from two different block managers reference the
+        same underlying values.
+        """
+        blk = self.blocks[blkno]
+        return any(blk is ref() for ref in mgr.blocks[blkno].refs.referenced_blocks)
+
+    def get_dtypes(self) -> npt.NDArray[np.object_]:
+        dtypes = np.array([blk.dtype for blk in self.blocks], dtype=object)
+        return dtypes.take(self.blknos)
+
+    @property
+    def arrays(self) -> list[ArrayLike]:
+        """
+        Quick access to the backing arrays of the Blocks.
+
+        Only for compatibility with ArrayManager for testing convenience.
+        Not to be used in actual code, and return value is not the same as the
+        ArrayManager method (list of 1D arrays vs iterator of 2D ndarrays / 1D EAs).
+
+        Warning! The returned arrays don't handle Copy-on-Write, so this should
+        be used with caution (only in read-mode).
+        """
+        return [blk.values for blk in self.blocks]
+
+    def __repr__(self) -> str:
+        output = type(self).__name__
+        for i, ax in enumerate(self.axes):
+            if i == 0:
+                output += f"\nItems: {ax}"
+            else:
+                output += f"\nAxis {i}: {ax}"
+
+        for block in self.blocks:
+            output += f"\n{block}"
+        return output
+
+    def apply(
+        self,
+        f,
+        align_keys: list[str] | None = None,
+        **kwargs,
+    ) -> Self:
+        """
+        Iterate over the blocks, collect and create a new BlockManager.
+
+        Parameters
+        ----------
+        f : str or callable
+            Name of the Block method to apply.
+        align_keys: List[str] or None, default None
+        **kwargs
+            Keywords to pass to `f`
+
+        Returns
+        -------
+        BlockManager
+        """
+        assert "filter" not in kwargs
+
+        align_keys = align_keys or []
+        result_blocks: list[Block] = []
+        # fillna: Series/DataFrame is responsible for making sure value is aligned
+
+        aligned_args = {k: kwargs[k] for k in align_keys}
+
+        for b in self.blocks:
+            if aligned_args:
+                for k, obj in aligned_args.items():
+                    if isinstance(obj, (ABCSeries, ABCDataFrame)):
+                        # The caller is responsible for ensuring that
+                        #  obj.axes[-1].equals(self.items)
+                        if obj.ndim == 1:
+                            kwargs[k] = obj.iloc[b.mgr_locs.indexer]._values
+                        else:
+                            kwargs[k] = obj.iloc[:, b.mgr_locs.indexer]._values
+                    else:
+                        # otherwise we have an ndarray
+                        kwargs[k] = obj[b.mgr_locs.indexer]
+
+            if callable(f):
+                applied = b.apply(f, **kwargs)
+            else:
+                applied = getattr(b, f)(**kwargs)
+            result_blocks = extend_blocks(applied, result_blocks)
+
+        out = type(self).from_blocks(result_blocks, self.axes)
+        return out
+
+    # Alias so we can share code with ArrayManager
+    apply_with_block = apply
+
+    def setitem(self, indexer, value, warn: bool = True) -> Self:
+        """
+        Set values with indexer.
+
+        For SingleBlockManager, this backs s[indexer] = value
+        """
+        if isinstance(indexer, np.ndarray) and indexer.ndim > self.ndim:
+            raise ValueError(f"Cannot set values with ndim > {self.ndim}")
+
+        if warn and warn_copy_on_write() and not self._has_no_reference(0):
+            warnings.warn(
+                COW_WARNING_GENERAL_MSG,
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+
+        elif using_copy_on_write() and not self._has_no_reference(0):
+            # this method is only called if there is a single block -> hardcoded 0
+            # Split blocks to only copy the columns we want to modify
+            if self.ndim == 2 and isinstance(indexer, tuple):
+                blk_loc = self.blklocs[indexer[1]]
+                if is_list_like(blk_loc) and blk_loc.ndim == 2:
+                    blk_loc = np.squeeze(blk_loc, axis=0)
+                elif not is_list_like(blk_loc):
+                    # Keep dimension and copy data later
+                    blk_loc = [blk_loc]  # type: ignore[assignment]
+                if len(blk_loc) == 0:
+                    return self.copy(deep=False)
+
+                values = self.blocks[0].values
+                if values.ndim == 2:
+                    values = values[blk_loc]
+                    # "T" has no attribute "_iset_split_block"
+                    self._iset_split_block(  # type: ignore[attr-defined]
+                        0, blk_loc, values
+                    )
+
+                    indexer = list(indexer)
+                    # first block equals values we are setting to -> set to all columns
+                    if lib.is_integer(indexer[1]):
+                        col_indexer = 0
+                    elif len(blk_loc) > 1:
+                        col_indexer = slice(None)  # type: ignore[assignment]
+                    else:
+                        col_indexer = np.arange(len(blk_loc))  # type: ignore[assignment]
+                    indexer[1] = col_indexer
+
+                    row_indexer = indexer[0]
+                    if isinstance(row_indexer, np.ndarray) and row_indexer.ndim == 2:
+                        # numpy cannot handle a 2d indexer in combo with a slice
+                        row_indexer = np.squeeze(row_indexer, axis=1)
+                    if isinstance(row_indexer, np.ndarray) and len(row_indexer) == 0:
+                        # numpy does not like empty indexer combined with slice
+                        # and we are setting nothing anyway
+                        return self
+                    indexer[0] = row_indexer
+                    self.blocks[0].setitem(tuple(indexer), value)
+                    return self
+            # No need to split if we either set all columns or on a single block
+            # manager
+            self = self.copy()
+
+        return self.apply("setitem", indexer=indexer, value=value)
+
+    def diff(self, n: int) -> Self:
+        # only reached with self.ndim == 2
+        return self.apply("diff", n=n)
+
+    def astype(self, dtype, copy: bool | None = False, errors: str = "raise") -> Self:
+        if copy is None:
+            if using_copy_on_write():
+                copy = False
+            else:
+                copy = True
+        elif using_copy_on_write():
+            copy = False
+
+        return self.apply(
+            "astype",
+            dtype=dtype,
+            copy=copy,
+            errors=errors,
+            using_cow=using_copy_on_write(),
+        )
+
+    def convert(self, copy: bool | None) -> Self:
+        if copy is None:
+            if using_copy_on_write():
+                copy = False
+            else:
+                copy = True
+        elif using_copy_on_write():
+            copy = False
+
+        return self.apply("convert", copy=copy, using_cow=using_copy_on_write())
+
+    def convert_dtypes(self, **kwargs):
+        if using_copy_on_write():
+            copy = False
+        else:
+            copy = True
+
+        return self.apply(
+            "convert_dtypes", copy=copy, using_cow=using_copy_on_write(), **kwargs
+        )
+
+    def get_values_for_csv(
+        self, *, float_format, date_format, decimal, na_rep: str = "nan", quoting=None
+    ) -> Self:
+        """
+        Convert values to native types (strings / python objects) that are used
+        in formatting (repr / csv).
+        """
+        return self.apply(
+            "get_values_for_csv",
+            na_rep=na_rep,
+            quoting=quoting,
+            float_format=float_format,
+            date_format=date_format,
+            decimal=decimal,
+        )
+
+    @property
+    def any_extension_types(self) -> bool:
+        """Whether any of the blocks in this manager are extension blocks"""
+        return any(block.is_extension for block in self.blocks)
+
+    @property
+    def is_view(self) -> bool:
+        """return a boolean if we are a single block and are a view"""
+        if len(self.blocks) == 1:
+            return self.blocks[0].is_view
+
+        # It is technically possible to figure out which blocks are views
+        # e.g. [ b.values.base is not None for b in self.blocks ]
+        # but then we have the case of possibly some blocks being a view
+        # and some blocks not. setting in theory is possible on the non-view
+        # blocks w/o causing a SettingWithCopy raise/warn. But this is a bit
+        # complicated
+
+        return False
+
+    def _get_data_subset(self, predicate: Callable) -> Self:
+        blocks = [blk for blk in self.blocks if predicate(blk.values)]
+        return self._combine(blocks)
+
+    def get_bool_data(self) -> Self:
+        """
+        Select blocks that are bool-dtype and columns from object-dtype blocks
+        that are all-bool.
+        """
+
+        new_blocks = []
+
+        for blk in self.blocks:
+            if blk.dtype == bool:
+                new_blocks.append(blk)
+
+            elif blk.is_object:
+                nbs = blk._split()
+                new_blocks.extend(nb for nb in nbs if nb.is_bool)
+
+        return self._combine(new_blocks)
+
+    def get_numeric_data(self) -> Self:
+        numeric_blocks = [blk for blk in self.blocks if blk.is_numeric]
+        if len(numeric_blocks) == len(self.blocks):
+            # Avoid somewhat expensive _combine
+            return self
+        return self._combine(numeric_blocks)
+
+    def _combine(self, blocks: list[Block], index: Index | None = None) -> Self:
+        """return a new manager with the blocks"""
+        if len(blocks) == 0:
+            if self.ndim == 2:
+                # retain our own Index dtype
+                if index is not None:
+                    axes = [self.items[:0], index]
+                else:
+                    axes = [self.items[:0]] + self.axes[1:]
+                return self.make_empty(axes)
+            return self.make_empty()
+
+        # FIXME: optimization potential
+        indexer = np.sort(np.concatenate([b.mgr_locs.as_array for b in blocks]))
+        inv_indexer = lib.get_reverse_indexer(indexer, self.shape[0])
+
+        new_blocks: list[Block] = []
+        for b in blocks:
+            nb = b.copy(deep=False)
+            nb.mgr_locs = BlockPlacement(inv_indexer[nb.mgr_locs.indexer])
+            new_blocks.append(nb)
+
+        axes = list(self.axes)
+        if index is not None:
+            axes[-1] = index
+        axes[0] = self.items.take(indexer)
+
+        return type(self).from_blocks(new_blocks, axes)
+
+    @property
+    def nblocks(self) -> int:
+        return len(self.blocks)
+
+    def copy(self, deep: bool | None | Literal["all"] = True) -> Self:
+        """
+        Make deep or shallow copy of BlockManager
+
+        Parameters
+        ----------
+        deep : bool, string or None, default True
+            If False or None, return a shallow copy (do not copy data)
+            If 'all', copy data and a deep copy of the index
+
+        Returns
+        -------
+        BlockManager
+        """
+        if deep is None:
+            if using_copy_on_write():
+                # use shallow copy
+                deep = False
+            else:
+                # preserve deep copy for BlockManager with copy=None
+                deep = True
+
+        # this preserves the notion of view copying of axes
+        if deep:
+            # hit in e.g. tests.io.json.test_pandas
+
+            def copy_func(ax):
+                return ax.copy(deep=True) if deep == "all" else ax.view()
+
+            new_axes = [copy_func(ax) for ax in self.axes]
+        else:
+            if using_copy_on_write():
+                new_axes = [ax.view() for ax in self.axes]
+            else:
+                new_axes = list(self.axes)
+
+        res = self.apply("copy", deep=deep)
+        res.axes = new_axes
+
+        if self.ndim > 1:
+            # Avoid needing to re-compute these
+            blknos = self._blknos
+            if blknos is not None:
+                res._blknos = blknos.copy()
+                res._blklocs = self._blklocs.copy()
+
+        if deep:
+            res._consolidate_inplace()
+        return res
+
+    def consolidate(self) -> Self:
+        """
+        Join together blocks having same dtype
+
+        Returns
+        -------
+        y : BlockManager
+        """
+        if self.is_consolidated():
+            return self
+
+        bm = type(self)(self.blocks, self.axes, verify_integrity=False)
+        bm._is_consolidated = False
+        bm._consolidate_inplace()
+        return bm
+
+    def reindex_indexer(
+        self,
+        new_axis: Index,
+        indexer: npt.NDArray[np.intp] | None,
+        axis: AxisInt,
+        fill_value=None,
+        allow_dups: bool = False,
+        copy: bool | None = True,
+        only_slice: bool = False,
+        *,
+        use_na_proxy: bool = False,
+    ) -> Self:
+        """
+        Parameters
+        ----------
+        new_axis : Index
+        indexer : ndarray[intp] or None
+        axis : int
+        fill_value : object, default None
+        allow_dups : bool, default False
+        copy : bool or None, default True
+            If None, regard as False to get shallow copy.
+        only_slice : bool, default False
+            Whether to take views, not copies, along columns.
+        use_na_proxy : bool, default False
+            Whether to use a np.void ndarray for newly introduced columns.
+
+        pandas-indexer with -1's only.
+        """
+        if copy is None:
+            if using_copy_on_write():
+                # use shallow copy
+                copy = False
+            else:
+                # preserve deep copy for BlockManager with copy=None
+                copy = True
+
+        if indexer is None:
+            if new_axis is self.axes[axis] and not copy:
+                return self
+
+            result = self.copy(deep=copy)
+            result.axes = list(self.axes)
+            result.axes[axis] = new_axis
+            return result
+
+        # Should be intp, but in some cases we get int64 on 32bit builds
+        assert isinstance(indexer, np.ndarray)
+
+        # some axes don't allow reindexing with dups
+        if not allow_dups:
+            self.axes[axis]._validate_can_reindex(indexer)
+
+        if axis >= self.ndim:
+            raise IndexError("Requested axis not found in manager")
+
+        if axis == 0:
+            new_blocks = self._slice_take_blocks_ax0(
+                indexer,
+                fill_value=fill_value,
+                only_slice=only_slice,
+                use_na_proxy=use_na_proxy,
+            )
+        else:
+            new_blocks = [
+                blk.take_nd(
+                    indexer,
+                    axis=1,
+                    fill_value=(
+                        fill_value if fill_value is not None else blk.fill_value
+                    ),
+                )
+                for blk in self.blocks
+            ]
+
+        new_axes = list(self.axes)
+        new_axes[axis] = new_axis
+
+        new_mgr = type(self).from_blocks(new_blocks, new_axes)
+        if axis == 1:
+            # We can avoid the need to rebuild these
+            new_mgr._blknos = self.blknos.copy()
+            new_mgr._blklocs = self.blklocs.copy()
+        return new_mgr
+
+    def _slice_take_blocks_ax0(
+        self,
+        slice_or_indexer: slice | np.ndarray,
+        fill_value=lib.no_default,
+        only_slice: bool = False,
+        *,
+        use_na_proxy: bool = False,
+        ref_inplace_op: bool = False,
+    ) -> list[Block]:
+        """
+        Slice/take blocks along axis=0.
+
+        Overloaded for SingleBlock
+
+        Parameters
+        ----------
+        slice_or_indexer : slice or np.ndarray[int64]
+        fill_value : scalar, default lib.no_default
+        only_slice : bool, default False
+            If True, we always return views on existing arrays, never copies.
+            This is used when called from ops.blockwise.operate_blockwise.
+        use_na_proxy : bool, default False
+            Whether to use a np.void ndarray for newly introduced columns.
+        ref_inplace_op: bool, default False
+            Don't track refs if True because we operate inplace
+
+        Returns
+        -------
+        new_blocks : list of Block
+        """
+        allow_fill = fill_value is not lib.no_default
+
+        sl_type, slobj, sllen = _preprocess_slice_or_indexer(
+            slice_or_indexer, self.shape[0], allow_fill=allow_fill
+        )
+
+        if self.is_single_block:
+            blk = self.blocks[0]
+
+            if sl_type == "slice":
+                # GH#32959 EABlock would fail since we can't make 0-width
+                # TODO(EA2D): special casing unnecessary with 2D EAs
+                if sllen == 0:
+                    return []
+                bp = BlockPlacement(slice(0, sllen))
+                return [blk.getitem_block_columns(slobj, new_mgr_locs=bp)]
+            elif not allow_fill or self.ndim == 1:
+                if allow_fill and fill_value is None:
+                    fill_value = blk.fill_value
+
+                if not allow_fill and only_slice:
+                    # GH#33597 slice instead of take, so we get
+                    #  views instead of copies
+                    blocks = [
+                        blk.getitem_block_columns(
+                            slice(ml, ml + 1),
+                            new_mgr_locs=BlockPlacement(i),
+                            ref_inplace_op=ref_inplace_op,
+                        )
+                        for i, ml in enumerate(slobj)
+                    ]
+                    return blocks
+                else:
+                    bp = BlockPlacement(slice(0, sllen))
+                    return [
+                        blk.take_nd(
+                            slobj,
+                            axis=0,
+                            new_mgr_locs=bp,
+                            fill_value=fill_value,
+                        )
+                    ]
+
+        if sl_type == "slice":
+            blknos = self.blknos[slobj]
+            blklocs = self.blklocs[slobj]
+        else:
+            blknos = algos.take_nd(
+                self.blknos, slobj, fill_value=-1, allow_fill=allow_fill
+            )
+            blklocs = algos.take_nd(
+                self.blklocs, slobj, fill_value=-1, allow_fill=allow_fill
+            )
+
+        # When filling blknos, make sure blknos is updated before appending to
+        # blocks list, that way new blkno is exactly len(blocks).
+        blocks = []
+        group = not only_slice
+        for blkno, mgr_locs in libinternals.get_blkno_placements(blknos, group=group):
+            if blkno == -1:
+                # If we've got here, fill_value was not lib.no_default
+
+                blocks.append(
+                    self._make_na_block(
+                        placement=mgr_locs,
+                        fill_value=fill_value,
+                        use_na_proxy=use_na_proxy,
+                    )
+                )
+            else:
+                blk = self.blocks[blkno]
+
+                # Otherwise, slicing along items axis is necessary.
+                if not blk._can_consolidate and not blk._validate_ndim:
+                    # i.e. we dont go through here for DatetimeTZBlock
+                    # A non-consolidatable block, it's easy, because there's
+                    # only one item and each mgr loc is a copy of that single
+                    # item.
+                    deep = not (only_slice or using_copy_on_write())
+                    for mgr_loc in mgr_locs:
+                        newblk = blk.copy(deep=deep)
+                        newblk.mgr_locs = BlockPlacement(slice(mgr_loc, mgr_loc + 1))
+                        blocks.append(newblk)
+
+                else:
+                    # GH#32779 to avoid the performance penalty of copying,
+                    #  we may try to only slice
+                    taker = blklocs[mgr_locs.indexer]
+                    max_len = max(len(mgr_locs), taker.max() + 1)
+                    if only_slice or using_copy_on_write():
+                        taker = lib.maybe_indices_to_slice(taker, max_len)
+
+                    if isinstance(taker, slice):
+                        nb = blk.getitem_block_columns(taker, new_mgr_locs=mgr_locs)
+                        blocks.append(nb)
+                    elif only_slice:
+                        # GH#33597 slice instead of take, so we get
+                        #  views instead of copies
+                        for i, ml in zip(taker, mgr_locs):
+                            slc = slice(i, i + 1)
+                            bp = BlockPlacement(ml)
+                            nb = blk.getitem_block_columns(slc, new_mgr_locs=bp)
+                            # We have np.shares_memory(nb.values, blk.values)
+                            blocks.append(nb)
+                    else:
+                        nb = blk.take_nd(taker, axis=0, new_mgr_locs=mgr_locs)
+                        blocks.append(nb)
+
+        return blocks
+
+    def _make_na_block(
+        self, placement: BlockPlacement, fill_value=None, use_na_proxy: bool = False
+    ) -> Block:
+        # Note: we only get here with self.ndim == 2
+
+        if use_na_proxy:
+            assert fill_value is None
+            shape = (len(placement), self.shape[1])
+            vals = np.empty(shape, dtype=np.void)
+            nb = NumpyBlock(vals, placement, ndim=2)
+            return nb
+
+        if fill_value is None:
+            fill_value = np.nan
+
+        shape = (len(placement), self.shape[1])
+
+        dtype, fill_value = infer_dtype_from_scalar(fill_value)
+        block_values = make_na_array(dtype, shape, fill_value)
+        return new_block_2d(block_values, placement=placement)
+
+    def take(
+        self,
+        indexer: npt.NDArray[np.intp],
+        axis: AxisInt = 1,
+        verify: bool = True,
+    ) -> Self:
+        """
+        Take items along any axis.
+
+        indexer : np.ndarray[np.intp]
+        axis : int, default 1
+        verify : bool, default True
+            Check that all entries are between 0 and len(self) - 1, inclusive.
+            Pass verify=False if this check has been done by the caller.
+
+        Returns
+        -------
+        BlockManager
+        """
+        # Caller is responsible for ensuring indexer annotation is accurate
+
+        n = self.shape[axis]
+        indexer = maybe_convert_indices(indexer, n, verify=verify)
+
+        new_labels = self.axes[axis].take(indexer)
+        return self.reindex_indexer(
+            new_axis=new_labels,
+            indexer=indexer,
+            axis=axis,
+            allow_dups=True,
+            copy=None,
+        )
+
+
+class BlockManager(libinternals.BlockManager, BaseBlockManager):
+    """
+    BaseBlockManager that holds 2D blocks.
+    """
+
+    ndim = 2
+
+    # ----------------------------------------------------------------
+    # Constructors
+
+    def __init__(
+        self,
+        blocks: Sequence[Block],
+        axes: Sequence[Index],
+        verify_integrity: bool = True,
+    ) -> None:
+        if verify_integrity:
+            # Assertion disabled for performance
+            # assert all(isinstance(x, Index) for x in axes)
+
+            for block in blocks:
+                if self.ndim != block.ndim:
+                    raise AssertionError(
+                        f"Number of Block dimensions ({block.ndim}) must equal "
+                        f"number of axes ({self.ndim})"
+                    )
+                # As of 2.0, the caller is responsible for ensuring that
+                #  DatetimeTZBlock with block.ndim == 2 has block.values.ndim ==2;
+                #  previously there was a special check for fastparquet compat.
+
+            self._verify_integrity()
+
+    def _verify_integrity(self) -> None:
+        mgr_shape = self.shape
+        tot_items = sum(len(x.mgr_locs) for x in self.blocks)
+        for block in self.blocks:
+            if block.shape[1:] != mgr_shape[1:]:
+                raise_construction_error(tot_items, block.shape[1:], self.axes)
+        if len(self.items) != tot_items:
+            raise AssertionError(
+                "Number of manager items must equal union of "
+                f"block items\n# manager items: {len(self.items)}, # "
+                f"tot_items: {tot_items}"
+            )
+
+    @classmethod
+    def from_blocks(cls, blocks: list[Block], axes: list[Index]) -> Self:
+        """
+        Constructor for BlockManager and SingleBlockManager with same signature.
+        """
+        return cls(blocks, axes, verify_integrity=False)
+
+    # ----------------------------------------------------------------
+    # Indexing
+
+    def fast_xs(self, loc: int) -> SingleBlockManager:
+        """
+        Return the array corresponding to `frame.iloc[loc]`.
+
+        Parameters
+        ----------
+        loc : int
+
+        Returns
+        -------
+        np.ndarray or ExtensionArray
+        """
+        if len(self.blocks) == 1:
+            # TODO: this could be wrong if blk.mgr_locs is not slice(None)-like;
+            #  is this ruled out in the general case?
+            result = self.blocks[0].iget((slice(None), loc))
+            # in the case of a single block, the new block is a view
+            bp = BlockPlacement(slice(0, len(result)))
+            block = new_block(
+                result,
+                placement=bp,
+                ndim=1,
+                refs=self.blocks[0].refs,
+            )
+            return SingleBlockManager(block, self.axes[0])
+
+        dtype = interleaved_dtype([blk.dtype for blk in self.blocks])
+
+        n = len(self)
+
+        if isinstance(dtype, ExtensionDtype):
+            # TODO: use object dtype as workaround for non-performant
+            #  EA.__setitem__ methods. (primarily ArrowExtensionArray.__setitem__
+            #  when iteratively setting individual values)
+            #  https://github.com/pandas-dev/pandas/pull/54508#issuecomment-1675827918
+            result = np.empty(n, dtype=object)
+        else:
+            result = np.empty(n, dtype=dtype)
+            result = ensure_wrapped_if_datetimelike(result)
+
+        for blk in self.blocks:
+            # Such assignment may incorrectly coerce NaT to None
+            # result[blk.mgr_locs] = blk._slice((slice(None), loc))
+            for i, rl in enumerate(blk.mgr_locs):
+                result[rl] = blk.iget((i, loc))
+
+        if isinstance(dtype, ExtensionDtype):
+            cls = dtype.construct_array_type()
+            result = cls._from_sequence(result, dtype=dtype)
+
+        bp = BlockPlacement(slice(0, len(result)))
+        block = new_block(result, placement=bp, ndim=1)
+        return SingleBlockManager(block, self.axes[0])
+
+    def iget(self, i: int, track_ref: bool = True) -> SingleBlockManager:
+        """
+        Return the data as a SingleBlockManager.
+        """
+        block = self.blocks[self.blknos[i]]
+        values = block.iget(self.blklocs[i])
+
+        # shortcut for select a single-dim from a 2-dim BM
+        bp = BlockPlacement(slice(0, len(values)))
+        nb = type(block)(
+            values, placement=bp, ndim=1, refs=block.refs if track_ref else None
+        )
+        return SingleBlockManager(nb, self.axes[1])
+
+    def iget_values(self, i: int) -> ArrayLike:
+        """
+        Return the data for column i as the values (ndarray or ExtensionArray).
+
+        Warning! The returned array is a view but doesn't handle Copy-on-Write,
+        so this should be used with caution.
+        """
+        # TODO(CoW) making the arrays read-only might make this safer to use?
+        block = self.blocks[self.blknos[i]]
+        values = block.iget(self.blklocs[i])
+        return values
+
+    @property
+    def column_arrays(self) -> list[np.ndarray]:
+        """
+        Used in the JSON C code to access column arrays.
+        This optimizes compared to using `iget_values` by converting each
+
+        Warning! This doesn't handle Copy-on-Write, so should be used with
+        caution (current use case of consuming this in the JSON code is fine).
+        """
+        # This is an optimized equivalent to
+        #  result = [self.iget_values(i) for i in range(len(self.items))]
+        result: list[np.ndarray | None] = [None] * len(self.items)
+
+        for blk in self.blocks:
+            mgr_locs = blk._mgr_locs
+            values = blk.array_values._values_for_json()
+            if values.ndim == 1:
+                # TODO(EA2D): special casing not needed with 2D EAs
+                result[mgr_locs[0]] = values
+
+            else:
+                for i, loc in enumerate(mgr_locs):
+                    result[loc] = values[i]
+
+        # error: Incompatible return value type (got "List[None]",
+        # expected "List[ndarray[Any, Any]]")
+        return result  # type: ignore[return-value]
+
+    def iset(
+        self,
+        loc: int | slice | np.ndarray,
+        value: ArrayLike,
+        inplace: bool = False,
+        refs: BlockValuesRefs | None = None,
+    ) -> None:
+        """
+        Set new item in-place. Does not consolidate. Adds new Block if not
+        contained in the current set of items
+        """
+
+        # FIXME: refactor, clearly separate broadcasting & zip-like assignment
+        #        can prob also fix the various if tests for sparse/categorical
+        if self._blklocs is None and self.ndim > 1:
+            self._rebuild_blknos_and_blklocs()
+
+        # Note: we exclude DTA/TDA here
+        value_is_extension_type = is_1d_only_ea_dtype(value.dtype)
+        if not value_is_extension_type:
+            if value.ndim == 2:
+                value = value.T
+            else:
+                value = ensure_block_shape(value, ndim=2)
+
+            if value.shape[1:] != self.shape[1:]:
+                raise AssertionError(
+                    "Shape of new values must be compatible with manager shape"
+                )
+
+        if lib.is_integer(loc):
+            # We have 6 tests where loc is _not_ an int.
+            # In this case, get_blkno_placements will yield only one tuple,
+            #  containing (self._blknos[loc], BlockPlacement(slice(0, 1, 1)))
+
+            # Check if we can use _iset_single fastpath
+            loc = cast(int, loc)
+            blkno = self.blknos[loc]
+            blk = self.blocks[blkno]
+            if len(blk._mgr_locs) == 1:  # TODO: fastest way to check this?
+                return self._iset_single(
+                    loc,
+                    value,
+                    inplace=inplace,
+                    blkno=blkno,
+                    blk=blk,
+                    refs=refs,
+                )
+
+            # error: Incompatible types in assignment (expression has type
+            # "List[Union[int, slice, ndarray]]", variable has type "Union[int,
+            # slice, ndarray]")
+            loc = [loc]  # type: ignore[assignment]
+
+        # categorical/sparse/datetimetz
+        if value_is_extension_type:
+
+            def value_getitem(placement):
+                return value
+
+        else:
+
+            def value_getitem(placement):
+                return value[placement.indexer]
+
+        # Accessing public blknos ensures the public versions are initialized
+        blknos = self.blknos[loc]
+        blklocs = self.blklocs[loc].copy()
+
+        unfit_mgr_locs = []
+        unfit_val_locs = []
+        removed_blknos = []
+        for blkno_l, val_locs in libinternals.get_blkno_placements(blknos, group=True):
+            blk = self.blocks[blkno_l]
+            blk_locs = blklocs[val_locs.indexer]
+            if inplace and blk.should_store(value):
+                # Updating inplace -> check if we need to do Copy-on-Write
+                if using_copy_on_write() and not self._has_no_reference_block(blkno_l):
+                    self._iset_split_block(
+                        blkno_l, blk_locs, value_getitem(val_locs), refs=refs
+                    )
+                else:
+                    blk.set_inplace(blk_locs, value_getitem(val_locs))
+                    continue
+            else:
+                unfit_mgr_locs.append(blk.mgr_locs.as_array[blk_locs])
+                unfit_val_locs.append(val_locs)
+
+                # If all block items are unfit, schedule the block for removal.
+                if len(val_locs) == len(blk.mgr_locs):
+                    removed_blknos.append(blkno_l)
+                    continue
+                else:
+                    # Defer setting the new values to enable consolidation
+                    self._iset_split_block(blkno_l, blk_locs, refs=refs)
+
+        if len(removed_blknos):
+            # Remove blocks & update blknos accordingly
+            is_deleted = np.zeros(self.nblocks, dtype=np.bool_)
+            is_deleted[removed_blknos] = True
+
+            new_blknos = np.empty(self.nblocks, dtype=np.intp)
+            new_blknos.fill(-1)
+            new_blknos[~is_deleted] = np.arange(self.nblocks - len(removed_blknos))
+            self._blknos = new_blknos[self._blknos]
+            self.blocks = tuple(
+                blk for i, blk in enumerate(self.blocks) if i not in set(removed_blknos)
+            )
+
+        if unfit_val_locs:
+            unfit_idxr = np.concatenate(unfit_mgr_locs)
+            unfit_count = len(unfit_idxr)
+
+            new_blocks: list[Block] = []
+            if value_is_extension_type:
+                # This code (ab-)uses the fact that EA blocks contain only
+                # one item.
+                # TODO(EA2D): special casing unnecessary with 2D EAs
+                new_blocks.extend(
+                    new_block_2d(
+                        values=value,
+                        placement=BlockPlacement(slice(mgr_loc, mgr_loc + 1)),
+                        refs=refs,
+                    )
+                    for mgr_loc in unfit_idxr
+                )
+
+                self._blknos[unfit_idxr] = np.arange(unfit_count) + len(self.blocks)
+                self._blklocs[unfit_idxr] = 0
+
+            else:
+                # unfit_val_locs contains BlockPlacement objects
+                unfit_val_items = unfit_val_locs[0].append(unfit_val_locs[1:])
+
+                new_blocks.append(
+                    new_block_2d(
+                        values=value_getitem(unfit_val_items),
+                        placement=BlockPlacement(unfit_idxr),
+                        refs=refs,
+                    )
+                )
+
+                self._blknos[unfit_idxr] = len(self.blocks)
+                self._blklocs[unfit_idxr] = np.arange(unfit_count)
+
+            self.blocks += tuple(new_blocks)
+
+            # Newly created block's dtype may already be present.
+            self._known_consolidated = False
+
+    def _iset_split_block(
+        self,
+        blkno_l: int,
+        blk_locs: np.ndarray | list[int],
+        value: ArrayLike | None = None,
+        refs: BlockValuesRefs | None = None,
+    ) -> None:
+        """Removes columns from a block by splitting the block.
+
+        Avoids copying the whole block through slicing and updates the manager
+        after determinint the new block structure. Optionally adds a new block,
+        otherwise has to be done by the caller.
+
+        Parameters
+        ----------
+        blkno_l: The block number to operate on, relevant for updating the manager
+        blk_locs: The locations of our block that should be deleted.
+        value: The value to set as a replacement.
+        refs: The reference tracking object of the value to set.
+        """
+        blk = self.blocks[blkno_l]
+
+        if self._blklocs is None:
+            self._rebuild_blknos_and_blklocs()
+
+        nbs_tup = tuple(blk.delete(blk_locs))
+        if value is not None:
+            locs = blk.mgr_locs.as_array[blk_locs]
+            first_nb = new_block_2d(value, BlockPlacement(locs), refs=refs)
+        else:
+            first_nb = nbs_tup[0]
+            nbs_tup = tuple(nbs_tup[1:])
+
+        nr_blocks = len(self.blocks)
+        blocks_tup = (
+            self.blocks[:blkno_l] + (first_nb,) + self.blocks[blkno_l + 1 :] + nbs_tup
+        )
+        self.blocks = blocks_tup
+
+        if not nbs_tup and value is not None:
+            # No need to update anything if split did not happen
+            return
+
+        self._blklocs[first_nb.mgr_locs.indexer] = np.arange(len(first_nb))
+
+        for i, nb in enumerate(nbs_tup):
+            self._blklocs[nb.mgr_locs.indexer] = np.arange(len(nb))
+            self._blknos[nb.mgr_locs.indexer] = i + nr_blocks
+
+    def _iset_single(
+        self,
+        loc: int,
+        value: ArrayLike,
+        inplace: bool,
+        blkno: int,
+        blk: Block,
+        refs: BlockValuesRefs | None = None,
+    ) -> None:
+        """
+        Fastpath for iset when we are only setting a single position and
+        the Block currently in that position is itself single-column.
+
+        In this case we can swap out the entire Block and blklocs and blknos
+        are unaffected.
+        """
+        # Caller is responsible for verifying value.shape
+
+        if inplace and blk.should_store(value):
+            copy = False
+            if using_copy_on_write() and not self._has_no_reference_block(blkno):
+                # perform Copy-on-Write and clear the reference
+                copy = True
+            iloc = self.blklocs[loc]
+            blk.set_inplace(slice(iloc, iloc + 1), value, copy=copy)
+            return
+
+        nb = new_block_2d(value, placement=blk._mgr_locs, refs=refs)
+        old_blocks = self.blocks
+        new_blocks = old_blocks[:blkno] + (nb,) + old_blocks[blkno + 1 :]
+        self.blocks = new_blocks
+        return
+
+    def column_setitem(
+        self, loc: int, idx: int | slice | np.ndarray, value, inplace_only: bool = False
+    ) -> None:
+        """
+        Set values ("setitem") into a single column (not setting the full column).
+
+        This is a method on the BlockManager level, to avoid creating an
+        intermediate Series at the DataFrame level (`s = df[loc]; s[idx] = value`)
+        """
+        needs_to_warn = False
+        if warn_copy_on_write() and not self._has_no_reference(loc):
+            if not isinstance(
+                self.blocks[self.blknos[loc]].values,
+                (ArrowExtensionArray, ArrowStringArray),
+            ):
+                # We might raise if we are in an expansion case, so defer
+                # warning till we actually updated
+                needs_to_warn = True
+
+        elif using_copy_on_write() and not self._has_no_reference(loc):
+            blkno = self.blknos[loc]
+            # Split blocks to only copy the column we want to modify
+            blk_loc = self.blklocs[loc]
+            # Copy our values
+            values = self.blocks[blkno].values
+            if values.ndim == 1:
+                values = values.copy()
+            else:
+                # Use [blk_loc] as indexer to keep ndim=2, this already results in a
+                # copy
+                values = values[[blk_loc]]
+            self._iset_split_block(blkno, [blk_loc], values)
+
+        # this manager is only created temporarily to mutate the values in place
+        # so don't track references, otherwise the `setitem` would perform CoW again
+        col_mgr = self.iget(loc, track_ref=False)
+        if inplace_only:
+            col_mgr.setitem_inplace(idx, value)
+        else:
+            new_mgr = col_mgr.setitem((idx,), value)
+            self.iset(loc, new_mgr._block.values, inplace=True)
+
+        if needs_to_warn:
+            warnings.warn(
+                COW_WARNING_GENERAL_MSG,
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+
+    def insert(self, loc: int, item: Hashable, value: ArrayLike, refs=None) -> None:
+        """
+        Insert item at selected position.
+
+        Parameters
+        ----------
+        loc : int
+        item : hashable
+        value : np.ndarray or ExtensionArray
+        refs : The reference tracking object of the value to set.
+        """
+        with warnings.catch_warnings():
+            # TODO: re-issue this with setitem-specific message?
+            warnings.filterwarnings(
+                "ignore",
+                "The behavior of Index.insert with object-dtype is deprecated",
+                category=FutureWarning,
+            )
+            new_axis = self.items.insert(loc, item)
+
+        if value.ndim == 2:
+            value = value.T
+            if len(value) > 1:
+                raise ValueError(
+                    f"Expected a 1D array, got an array with shape {value.T.shape}"
+                )
+        else:
+            value = ensure_block_shape(value, ndim=self.ndim)
+
+        bp = BlockPlacement(slice(loc, loc + 1))
+        block = new_block_2d(values=value, placement=bp, refs=refs)
+
+        if not len(self.blocks):
+            # Fastpath
+            self._blklocs = np.array([0], dtype=np.intp)
+            self._blknos = np.array([0], dtype=np.intp)
+        else:
+            self._insert_update_mgr_locs(loc)
+            self._insert_update_blklocs_and_blknos(loc)
+
+        self.axes[0] = new_axis
+        self.blocks += (block,)
+
+        self._known_consolidated = False
+
+        if sum(not block.is_extension for block in self.blocks) > 100:
+            warnings.warn(
+                "DataFrame is highly fragmented.  This is usually the result "
+                "of calling `frame.insert` many times, which has poor performance.  "
+                "Consider joining all columns at once using pd.concat(axis=1) "
+                "instead. To get a de-fragmented frame, use `newframe = frame.copy()`",
+                PerformanceWarning,
+                stacklevel=find_stack_level(),
+            )
+
+    def _insert_update_mgr_locs(self, loc) -> None:
+        """
+        When inserting a new Block at location 'loc', we increment
+        all of the mgr_locs of blocks above that by one.
+        """
+        for blkno, count in _fast_count_smallints(self.blknos[loc:]):
+            # .620 this way, .326 of which is in increment_above
+            blk = self.blocks[blkno]
+            blk._mgr_locs = blk._mgr_locs.increment_above(loc)
+
+    def _insert_update_blklocs_and_blknos(self, loc) -> None:
+        """
+        When inserting a new Block at location 'loc', we update our
+        _blklocs and _blknos.
+        """
+
+        # Accessing public blklocs ensures the public versions are initialized
+        if loc == self.blklocs.shape[0]:
+            # np.append is a lot faster, let's use it if we can.
+            self._blklocs = np.append(self._blklocs, 0)
+            self._blknos = np.append(self._blknos, len(self.blocks))
+        elif loc == 0:
+            # np.append is a lot faster, let's use it if we can.
+            self._blklocs = np.append(self._blklocs[::-1], 0)[::-1]
+            self._blknos = np.append(self._blknos[::-1], len(self.blocks))[::-1]
+        else:
+            new_blklocs, new_blknos = libinternals.update_blklocs_and_blknos(
+                self.blklocs, self.blknos, loc, len(self.blocks)
+            )
+            self._blklocs = new_blklocs
+            self._blknos = new_blknos
+
+    def idelete(self, indexer) -> BlockManager:
+        """
+        Delete selected locations, returning a new BlockManager.
+        """
+        is_deleted = np.zeros(self.shape[0], dtype=np.bool_)
+        is_deleted[indexer] = True
+        taker = (~is_deleted).nonzero()[0]
+
+        nbs = self._slice_take_blocks_ax0(taker, only_slice=True, ref_inplace_op=True)
+        new_columns = self.items[~is_deleted]
+        axes = [new_columns, self.axes[1]]
+        return type(self)(tuple(nbs), axes, verify_integrity=False)
+
+    # ----------------------------------------------------------------
+    # Block-wise Operation
+
+    def grouped_reduce(self, func: Callable) -> Self:
+        """
+        Apply grouped reduction function blockwise, returning a new BlockManager.
+
+        Parameters
+        ----------
+        func : grouped reduction function
+
+        Returns
+        -------
+        BlockManager
+        """
+        result_blocks: list[Block] = []
+
+        for blk in self.blocks:
+            if blk.is_object:
+                # split on object-dtype blocks bc some columns may raise
+                #  while others do not.
+                for sb in blk._split():
+                    applied = sb.apply(func)
+                    result_blocks = extend_blocks(applied, result_blocks)
+            else:
+                applied = blk.apply(func)
+                result_blocks = extend_blocks(applied, result_blocks)
+
+        if len(result_blocks) == 0:
+            nrows = 0
+        else:
+            nrows = result_blocks[0].values.shape[-1]
+        index = Index(range(nrows))
+
+        return type(self).from_blocks(result_blocks, [self.axes[0], index])
+
+    def reduce(self, func: Callable) -> Self:
+        """
+        Apply reduction function blockwise, returning a single-row BlockManager.
+
+        Parameters
+        ----------
+        func : reduction function
+
+        Returns
+        -------
+        BlockManager
+        """
+        # If 2D, we assume that we're operating column-wise
+        assert self.ndim == 2
+
+        res_blocks: list[Block] = []
+        for blk in self.blocks:
+            nbs = blk.reduce(func)
+            res_blocks.extend(nbs)
+
+        index = Index([None])  # placeholder
+        new_mgr = type(self).from_blocks(res_blocks, [self.items, index])
+        return new_mgr
+
+    def operate_blockwise(self, other: BlockManager, array_op) -> BlockManager:
+        """
+        Apply array_op blockwise with another (aligned) BlockManager.
+        """
+        return operate_blockwise(self, other, array_op)
+
+    def _equal_values(self: BlockManager, other: BlockManager) -> bool:
+        """
+        Used in .equals defined in base class. Only check the column values
+        assuming shape and indexes have already been checked.
+        """
+        return blockwise_all(self, other, array_equals)
+
+    def quantile(
+        self,
+        *,
+        qs: Index,  # with dtype float 64
+        interpolation: QuantileInterpolation = "linear",
+    ) -> Self:
+        """
+        Iterate over blocks applying quantile reduction.
+        This routine is intended for reduction type operations and
+        will do inference on the generated blocks.
+
+        Parameters
+        ----------
+        interpolation : type of interpolation, default 'linear'
+        qs : list of the quantiles to be computed
+
+        Returns
+        -------
+        BlockManager
+        """
+        # Series dispatches to DataFrame for quantile, which allows us to
+        #  simplify some of the code here and in the blocks
+        assert self.ndim >= 2
+        assert is_list_like(qs)  # caller is responsible for this
+
+        new_axes = list(self.axes)
+        new_axes[1] = Index(qs, dtype=np.float64)
+
+        blocks = [
+            blk.quantile(qs=qs, interpolation=interpolation) for blk in self.blocks
+        ]
+
+        return type(self)(blocks, new_axes)
+
+    # ----------------------------------------------------------------
+
+    def unstack(self, unstacker, fill_value) -> BlockManager:
+        """
+        Return a BlockManager with all blocks unstacked.
+
+        Parameters
+        ----------
+        unstacker : reshape._Unstacker
+        fill_value : Any
+            fill_value for newly introduced missing values.
+
+        Returns
+        -------
+        unstacked : BlockManager
+        """
+        new_columns = unstacker.get_new_columns(self.items)
+        new_index = unstacker.new_index
+
+        allow_fill = not unstacker.mask_all
+        if allow_fill:
+            # calculating the full mask once and passing it to Block._unstack is
+            #  faster than letting calculating it in each repeated call
+            new_mask2D = (~unstacker.mask).reshape(*unstacker.full_shape)
+            needs_masking = new_mask2D.any(axis=0)
+        else:
+            needs_masking = np.zeros(unstacker.full_shape[1], dtype=bool)
+
+        new_blocks: list[Block] = []
+        columns_mask: list[np.ndarray] = []
+
+        if len(self.items) == 0:
+            factor = 1
+        else:
+            fac = len(new_columns) / len(self.items)
+            assert fac == int(fac)
+            factor = int(fac)
+
+        for blk in self.blocks:
+            mgr_locs = blk.mgr_locs
+            new_placement = mgr_locs.tile_for_unstack(factor)
+
+            blocks, mask = blk._unstack(
+                unstacker,
+                fill_value,
+                new_placement=new_placement,
+                needs_masking=needs_masking,
+            )
+
+            new_blocks.extend(blocks)
+            columns_mask.extend(mask)
+
+            # Block._unstack should ensure this holds,
+            assert mask.sum() == sum(len(nb._mgr_locs) for nb in blocks)
+            # In turn this ensures that in the BlockManager call below
+            #  we have len(new_columns) == sum(x.shape[0] for x in new_blocks)
+            #  which suffices to allow us to pass verify_inegrity=False
+
+        new_columns = new_columns[columns_mask]
+
+        bm = BlockManager(new_blocks, [new_columns, new_index], verify_integrity=False)
+        return bm
+
+    def to_dict(self) -> dict[str, Self]:
+        """
+        Return a dict of str(dtype) -> BlockManager
+
+        Returns
+        -------
+        values : a dict of dtype -> BlockManager
+        """
+
+        bd: dict[str, list[Block]] = {}
+        for b in self.blocks:
+            bd.setdefault(str(b.dtype), []).append(b)
+
+        # TODO(EA2D): the combine will be unnecessary with 2D EAs
+        return {dtype: self._combine(blocks) for dtype, blocks in bd.items()}
+
+    def as_array(
+        self,
+        dtype: np.dtype | None = None,
+        copy: bool = False,
+        na_value: object = lib.no_default,
+    ) -> np.ndarray:
+        """
+        Convert the blockmanager data into an numpy array.
+
+        Parameters
+        ----------
+        dtype : np.dtype or None, default None
+            Data type of the return array.
+        copy : bool, default False
+            If True then guarantee that a copy is returned. A value of
+            False does not guarantee that the underlying data is not
+            copied.
+        na_value : object, default lib.no_default
+            Value to be used as the missing value sentinel.
+
+        Returns
+        -------
+        arr : ndarray
+        """
+        passed_nan = lib.is_float(na_value) and isna(na_value)
+
+        if len(self.blocks) == 0:
+            arr = np.empty(self.shape, dtype=float)
+            return arr.transpose()
+
+        if self.is_single_block:
+            blk = self.blocks[0]
+
+            if na_value is not lib.no_default:
+                # We want to copy when na_value is provided to avoid
+                # mutating the original object
+                if lib.is_np_dtype(blk.dtype, "f") and passed_nan:
+                    # We are already numpy-float and na_value=np.nan
+                    pass
+                else:
+                    copy = True
+
+            if blk.is_extension:
+                # Avoid implicit conversion of extension blocks to object
+
+                # error: Item "ndarray" of "Union[ndarray, ExtensionArray]" has no
+                # attribute "to_numpy"
+                arr = blk.values.to_numpy(  # type: ignore[union-attr]
+                    dtype=dtype,
+                    na_value=na_value,
+                    copy=copy,
+                ).reshape(blk.shape)
+            elif not copy:
+                arr = np.asarray(blk.values, dtype=dtype)
+            else:
+                arr = np.array(blk.values, dtype=dtype, copy=copy)
+
+            if using_copy_on_write() and not copy:
+                arr = arr.view()
+                arr.flags.writeable = False
+        else:
+            arr = self._interleave(dtype=dtype, na_value=na_value)
+            # The underlying data was copied within _interleave, so no need
+            # to further copy if copy=True or setting na_value
+
+        if na_value is lib.no_default:
+            pass
+        elif arr.dtype.kind == "f" and passed_nan:
+            pass
+        else:
+            arr[isna(arr)] = na_value
+
+        return arr.transpose()
+
+    def _interleave(
+        self,
+        dtype: np.dtype | None = None,
+        na_value: object = lib.no_default,
+    ) -> np.ndarray:
+        """
+        Return ndarray from blocks with specified item order
+        Items must be contained in the blocks
+        """
+        if not dtype:
+            # Incompatible types in assignment (expression has type
+            # "Optional[Union[dtype[Any], ExtensionDtype]]", variable has
+            # type "Optional[dtype[Any]]")
+            dtype = interleaved_dtype(  # type: ignore[assignment]
+                [blk.dtype for blk in self.blocks]
+            )
+
+        # error: Argument 1 to "ensure_np_dtype" has incompatible type
+        # "Optional[dtype[Any]]"; expected "Union[dtype[Any], ExtensionDtype]"
+        dtype = ensure_np_dtype(dtype)  # type: ignore[arg-type]
+        result = np.empty(self.shape, dtype=dtype)
+
+        itemmask = np.zeros(self.shape[0])
+
+        if dtype == np.dtype("object") and na_value is lib.no_default:
+            # much more performant than using to_numpy below
+            for blk in self.blocks:
+                rl = blk.mgr_locs
+                arr = blk.get_values(dtype)
+                result[rl.indexer] = arr
+                itemmask[rl.indexer] = 1
+            return result
+
+        for blk in self.blocks:
+            rl = blk.mgr_locs
+            if blk.is_extension:
+                # Avoid implicit conversion of extension blocks to object
+
+                # error: Item "ndarray" of "Union[ndarray, ExtensionArray]" has no
+                # attribute "to_numpy"
+                arr = blk.values.to_numpy(  # type: ignore[union-attr]
+                    dtype=dtype,
+                    na_value=na_value,
+                )
+            else:
+                arr = blk.get_values(dtype)
+            result[rl.indexer] = arr
+            itemmask[rl.indexer] = 1
+
+        if not itemmask.all():
+            raise AssertionError("Some items were not contained in blocks")
+
+        return result
+
+    # ----------------------------------------------------------------
+    # Consolidation
+
+    def is_consolidated(self) -> bool:
+        """
+        Return True if more than one block with the same dtype
+        """
+        if not self._known_consolidated:
+            self._consolidate_check()
+        return self._is_consolidated
+
+    def _consolidate_check(self) -> None:
+        if len(self.blocks) == 1:
+            # fastpath
+            self._is_consolidated = True
+            self._known_consolidated = True
+            return
+        dtypes = [blk.dtype for blk in self.blocks if blk._can_consolidate]
+        self._is_consolidated = len(dtypes) == len(set(dtypes))
+        self._known_consolidated = True
+
+    def _consolidate_inplace(self) -> None:
+        # In general, _consolidate_inplace should only be called via
+        #  DataFrame._consolidate_inplace, otherwise we will fail to invalidate
+        #  the DataFrame's _item_cache. The exception is for newly-created
+        #  BlockManager objects not yet attached to a DataFrame.
+        if not self.is_consolidated():
+            self.blocks = _consolidate(self.blocks)
+            self._is_consolidated = True
+            self._known_consolidated = True
+            self._rebuild_blknos_and_blklocs()
+
+    # ----------------------------------------------------------------
+    # Concatenation
+
+    @classmethod
+    def concat_horizontal(cls, mgrs: list[Self], axes: list[Index]) -> Self:
+        """
+        Concatenate uniformly-indexed BlockManagers horizontally.
+        """
+        offset = 0
+        blocks: list[Block] = []
+        for mgr in mgrs:
+            for blk in mgr.blocks:
+                # We need to do getitem_block here otherwise we would be altering
+                #  blk.mgr_locs in place, which would render it invalid. This is only
+                #  relevant in the copy=False case.
+                nb = blk.slice_block_columns(slice(None))
+                nb._mgr_locs = nb._mgr_locs.add(offset)
+                blocks.append(nb)
+
+            offset += len(mgr.items)
+
+        new_mgr = cls(tuple(blocks), axes)
+        return new_mgr
+
+    @classmethod
+    def concat_vertical(cls, mgrs: list[Self], axes: list[Index]) -> Self:
+        """
+        Concatenate uniformly-indexed BlockManagers vertically.
+        """
+        raise NotImplementedError("This logic lives (for now) in internals.concat")
+
+
+class SingleBlockManager(BaseBlockManager, SingleDataManager):
+    """manage a single block with"""
+
+    @property
+    def ndim(self) -> Literal[1]:
+        return 1
+
+    _is_consolidated = True
+    _known_consolidated = True
+    __slots__ = ()
+    is_single_block = True
+
+    def __init__(
+        self,
+        block: Block,
+        axis: Index,
+        verify_integrity: bool = False,
+    ) -> None:
+        # Assertions disabled for performance
+        # assert isinstance(block, Block), type(block)
+        # assert isinstance(axis, Index), type(axis)
+
+        self.axes = [axis]
+        self.blocks = (block,)
+
+    @classmethod
+    def from_blocks(
+        cls,
+        blocks: list[Block],
+        axes: list[Index],
+    ) -> Self:
+        """
+        Constructor for BlockManager and SingleBlockManager with same signature.
+        """
+        assert len(blocks) == 1
+        assert len(axes) == 1
+        return cls(blocks[0], axes[0], verify_integrity=False)
+
+    @classmethod
+    def from_array(
+        cls, array: ArrayLike, index: Index, refs: BlockValuesRefs | None = None
+    ) -> SingleBlockManager:
+        """
+        Constructor for if we have an array that is not yet a Block.
+        """
+        array = maybe_coerce_values(array)
+        bp = BlockPlacement(slice(0, len(index)))
+        block = new_block(array, placement=bp, ndim=1, refs=refs)
+        return cls(block, index)
+
+    def to_2d_mgr(self, columns: Index) -> BlockManager:
+        """
+        Manager analogue of Series.to_frame
+        """
+        blk = self.blocks[0]
+        arr = ensure_block_shape(blk.values, ndim=2)
+        bp = BlockPlacement(0)
+        new_blk = type(blk)(arr, placement=bp, ndim=2, refs=blk.refs)
+        axes = [columns, self.axes[0]]
+        return BlockManager([new_blk], axes=axes, verify_integrity=False)
+
+    def _has_no_reference(self, i: int = 0) -> bool:
+        """
+        Check for column `i` if it has references.
+        (whether it references another array or is itself being referenced)
+        Returns True if the column has no references.
+        """
+        return not self.blocks[0].refs.has_reference()
+
+    def __getstate__(self):
+        block_values = [b.values for b in self.blocks]
+        block_items = [self.items[b.mgr_locs.indexer] for b in self.blocks]
+        axes_array = list(self.axes)
+
+        extra_state = {
+            "0.14.1": {
+                "axes": axes_array,
+                "blocks": [
+                    {"values": b.values, "mgr_locs": b.mgr_locs.indexer}
+                    for b in self.blocks
+                ],
+            }
+        }
+
+        # First three elements of the state are to maintain forward
+        # compatibility with 0.13.1.
+        return axes_array, block_values, block_items, extra_state
+
+    def __setstate__(self, state) -> None:
+        def unpickle_block(values, mgr_locs, ndim: int) -> Block:
+            # TODO(EA2D): ndim would be unnecessary with 2D EAs
+            # older pickles may store e.g. DatetimeIndex instead of DatetimeArray
+            values = extract_array(values, extract_numpy=True)
+            if not isinstance(mgr_locs, BlockPlacement):
+                mgr_locs = BlockPlacement(mgr_locs)
+
+            values = maybe_coerce_values(values)
+            return new_block(values, placement=mgr_locs, ndim=ndim)
+
+        if isinstance(state, tuple) and len(state) >= 4 and "0.14.1" in state[3]:
+            state = state[3]["0.14.1"]
+            self.axes = [ensure_index(ax) for ax in state["axes"]]
+            ndim = len(self.axes)
+            self.blocks = tuple(
+                unpickle_block(b["values"], b["mgr_locs"], ndim=ndim)
+                for b in state["blocks"]
+            )
+        else:
+            raise NotImplementedError("pre-0.14.1 pickles are no longer supported")
+
+        self._post_setstate()
+
+    def _post_setstate(self) -> None:
+        pass
+
+    @cache_readonly
+    def _block(self) -> Block:
+        return self.blocks[0]
+
+    @property
+    def _blknos(self):
+        """compat with BlockManager"""
+        return None
+
+    @property
+    def _blklocs(self):
+        """compat with BlockManager"""
+        return None
+
+    def get_rows_with_mask(self, indexer: npt.NDArray[np.bool_]) -> Self:
+        # similar to get_slice, but not restricted to slice indexer
+        blk = self._block
+        if using_copy_on_write() and len(indexer) > 0 and indexer.all():
+            return type(self)(blk.copy(deep=False), self.index)
+        array = blk.values[indexer]
+
+        if isinstance(indexer, np.ndarray) and indexer.dtype.kind == "b":
+            # boolean indexing always gives a copy with numpy
+            refs = None
+        else:
+            # TODO(CoW) in theory only need to track reference if new_array is a view
+            refs = blk.refs
+
+        bp = BlockPlacement(slice(0, len(array)))
+        block = type(blk)(array, placement=bp, ndim=1, refs=refs)
+
+        new_idx = self.index[indexer]
+        return type(self)(block, new_idx)
+
+    def get_slice(self, slobj: slice, axis: AxisInt = 0) -> SingleBlockManager:
+        # Assertion disabled for performance
+        # assert isinstance(slobj, slice), type(slobj)
+        if axis >= self.ndim:
+            raise IndexError("Requested axis not found in manager")
+
+        blk = self._block
+        array = blk.values[slobj]
+        bp = BlockPlacement(slice(0, len(array)))
+        # TODO this method is only used in groupby SeriesSplitter at the moment,
+        # so passing refs is not yet covered by the tests
+        block = type(blk)(array, placement=bp, ndim=1, refs=blk.refs)
+        new_index = self.index._getitem_slice(slobj)
+        return type(self)(block, new_index)
+
+    @property
+    def index(self) -> Index:
+        return self.axes[0]
+
+    @property
+    def dtype(self) -> DtypeObj:
+        return self._block.dtype
+
+    def get_dtypes(self) -> npt.NDArray[np.object_]:
+        return np.array([self._block.dtype], dtype=object)
+
+    def external_values(self):
+        """The array that Series.values returns"""
+        return self._block.external_values()
+
+    def internal_values(self):
+        """The array that Series._values returns"""
+        return self._block.values
+
+    def array_values(self) -> ExtensionArray:
+        """The array that Series.array returns"""
+        return self._block.array_values
+
+    def get_numeric_data(self) -> Self:
+        if self._block.is_numeric:
+            return self.copy(deep=False)
+        return self.make_empty()
+
+    @property
+    def _can_hold_na(self) -> bool:
+        return self._block._can_hold_na
+
+    def setitem_inplace(self, indexer, value, warn: bool = True) -> None:
+        """
+        Set values with indexer.
+
+        For Single[Block/Array]Manager, this backs s[indexer] = value
+
+        This is an inplace version of `setitem()`, mutating the manager/values
+        in place, not returning a new Manager (and Block), and thus never changing
+        the dtype.
+        """
+        using_cow = using_copy_on_write()
+        warn_cow = warn_copy_on_write()
+        if (using_cow or warn_cow) and not self._has_no_reference(0):
+            if using_cow:
+                self.blocks = (self._block.copy(),)
+                self._cache.clear()
+            elif warn_cow and warn:
+                warnings.warn(
+                    COW_WARNING_SETITEM_MSG,
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+
+        super().setitem_inplace(indexer, value)
+
+    def idelete(self, indexer) -> SingleBlockManager:
+        """
+        Delete single location from SingleBlockManager.
+
+        Ensures that self.blocks doesn't become empty.
+        """
+        nb = self._block.delete(indexer)[0]
+        self.blocks = (nb,)
+        self.axes[0] = self.axes[0].delete(indexer)
+        self._cache.clear()
+        return self
+
+    def fast_xs(self, loc):
+        """
+        fast path for getting a cross-section
+        return a view of the data
+        """
+        raise NotImplementedError("Use series._values[loc] instead")
+
+    def set_values(self, values: ArrayLike) -> None:
+        """
+        Set the values of the single block in place.
+
+        Use at your own risk! This does not check if the passed values are
+        valid for the current Block/SingleBlockManager (length, dtype, etc),
+        and this does not properly keep track of references.
+        """
+        # NOTE(CoW) Currently this is only used for FrameColumnApply.series_generator
+        # which handles CoW by setting the refs manually if necessary
+        self.blocks[0].values = values
+        self.blocks[0]._mgr_locs = BlockPlacement(slice(len(values)))
+
+    def _equal_values(self, other: Self) -> bool:
+        """
+        Used in .equals defined in base class. Only check the column values
+        assuming shape and indexes have already been checked.
+        """
+        # For SingleBlockManager (i.e.Series)
+        if other.ndim != 1:
+            return False
+        left = self.blocks[0].values
+        right = other.blocks[0].values
+        return array_equals(left, right)
+
+
+# --------------------------------------------------------------------
+# Constructor Helpers
+
+
+def create_block_manager_from_blocks(
+    blocks: list[Block],
+    axes: list[Index],
+    consolidate: bool = True,
+    verify_integrity: bool = True,
+) -> BlockManager:
+    # If verify_integrity=False, then caller is responsible for checking
+    #  all(x.shape[-1] == len(axes[1]) for x in blocks)
+    #  sum(x.shape[0] for x in blocks) == len(axes[0])
+    #  set(x for blk in blocks for x in blk.mgr_locs) == set(range(len(axes[0])))
+    #  all(blk.ndim == 2 for blk in blocks)
+    # This allows us to safely pass verify_integrity=False
+
+    try:
+        mgr = BlockManager(blocks, axes, verify_integrity=verify_integrity)
+
+    except ValueError as err:
+        arrays = [blk.values for blk in blocks]
+        tot_items = sum(arr.shape[0] for arr in arrays)
+        raise_construction_error(tot_items, arrays[0].shape[1:], axes, err)
+
+    if consolidate:
+        mgr._consolidate_inplace()
+    return mgr
+
+
+def create_block_manager_from_column_arrays(
+    arrays: list[ArrayLike],
+    axes: list[Index],
+    consolidate: bool,
+    refs: list,
+) -> BlockManager:
+    # Assertions disabled for performance (caller is responsible for verifying)
+    # assert isinstance(axes, list)
+    # assert all(isinstance(x, Index) for x in axes)
+    # assert all(isinstance(x, (np.ndarray, ExtensionArray)) for x in arrays)
+    # assert all(type(x) is not NumpyExtensionArray for x in arrays)
+    # assert all(x.ndim == 1 for x in arrays)
+    # assert all(len(x) == len(axes[1]) for x in arrays)
+    # assert len(arrays) == len(axes[0])
+    # These last three are sufficient to allow us to safely pass
+    #  verify_integrity=False below.
+
+    try:
+        blocks = _form_blocks(arrays, consolidate, refs)
+        mgr = BlockManager(blocks, axes, verify_integrity=False)
+    except ValueError as e:
+        raise_construction_error(len(arrays), arrays[0].shape, axes, e)
+    if consolidate:
+        mgr._consolidate_inplace()
+    return mgr
+
+
+def raise_construction_error(
+    tot_items: int,
+    block_shape: Shape,
+    axes: list[Index],
+    e: ValueError | None = None,
+):
+    """raise a helpful message about our construction"""
+    passed = tuple(map(int, [tot_items] + list(block_shape)))
+    # Correcting the user facing error message during dataframe construction
+    if len(passed) <= 2:
+        passed = passed[::-1]
+
+    implied = tuple(len(ax) for ax in axes)
+    # Correcting the user facing error message during dataframe construction
+    if len(implied) <= 2:
+        implied = implied[::-1]
+
+    # We return the exception object instead of raising it so that we
+    #  can raise it in the caller; mypy plays better with that
+    if passed == implied and e is not None:
+        raise e
+    if block_shape[0] == 0:
+        raise ValueError("Empty data passed with indices specified.")
+    raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}")
+
+
+# -----------------------------------------------------------------------
+
+
+def _grouping_func(tup: tuple[int, ArrayLike]) -> tuple[int, DtypeObj]:
+    dtype = tup[1].dtype
+
+    if is_1d_only_ea_dtype(dtype):
+        # We know these won't be consolidated, so don't need to group these.
+        # This avoids expensive comparisons of CategoricalDtype objects
+        sep = id(dtype)
+    else:
+        sep = 0
+
+    return sep, dtype
+
+
+def _form_blocks(arrays: list[ArrayLike], consolidate: bool, refs: list) -> list[Block]:
+    tuples = list(enumerate(arrays))
+
+    if not consolidate:
+        return _tuples_to_blocks_no_consolidate(tuples, refs)
+
+    # when consolidating, we can ignore refs (either stacking always copies,
+    # or the EA is already copied in the calling dict_to_mgr)
+
+    # group by dtype
+    grouper = itertools.groupby(tuples, _grouping_func)
+
+    nbs: list[Block] = []
+    for (_, dtype), tup_block in grouper:
+        block_type = get_block_type(dtype)
+
+        if isinstance(dtype, np.dtype):
+            is_dtlike = dtype.kind in "mM"
+
+            if issubclass(dtype.type, (str, bytes)):
+                dtype = np.dtype(object)
+
+            values, placement = _stack_arrays(list(tup_block), dtype)
+            if is_dtlike:
+                values = ensure_wrapped_if_datetimelike(values)
+            blk = block_type(values, placement=BlockPlacement(placement), ndim=2)
+            nbs.append(blk)
+
+        elif is_1d_only_ea_dtype(dtype):
+            dtype_blocks = [
+                block_type(x[1], placement=BlockPlacement(x[0]), ndim=2)
+                for x in tup_block
+            ]
+            nbs.extend(dtype_blocks)
+
+        else:
+            dtype_blocks = [
+                block_type(
+                    ensure_block_shape(x[1], 2), placement=BlockPlacement(x[0]), ndim=2
+                )
+                for x in tup_block
+            ]
+            nbs.extend(dtype_blocks)
+    return nbs
+
+
+def _tuples_to_blocks_no_consolidate(tuples, refs) -> list[Block]:
+    # tuples produced within _form_blocks are of the form (placement, array)
+    return [
+        new_block_2d(
+            ensure_block_shape(arr, ndim=2), placement=BlockPlacement(i), refs=ref
+        )
+        for ((i, arr), ref) in zip(tuples, refs)
+    ]
+
+
+def _stack_arrays(tuples, dtype: np.dtype):
+    placement, arrays = zip(*tuples)
+
+    first = arrays[0]
+    shape = (len(arrays),) + first.shape
+
+    stacked = np.empty(shape, dtype=dtype)
+    for i, arr in enumerate(arrays):
+        stacked[i] = arr
+
+    return stacked, placement
+
+
+def _consolidate(blocks: tuple[Block, ...]) -> tuple[Block, ...]:
+    """
+    Merge blocks having same dtype, exclude non-consolidating blocks
+    """
+    # sort by _can_consolidate, dtype
+    gkey = lambda x: x._consolidate_key
+    grouper = itertools.groupby(sorted(blocks, key=gkey), gkey)
+
+    new_blocks: list[Block] = []
+    for (_can_consolidate, dtype), group_blocks in grouper:
+        merged_blocks, _ = _merge_blocks(
+            list(group_blocks), dtype=dtype, can_consolidate=_can_consolidate
+        )
+        new_blocks = extend_blocks(merged_blocks, new_blocks)
+    return tuple(new_blocks)
+
+
+def _merge_blocks(
+    blocks: list[Block], dtype: DtypeObj, can_consolidate: bool
+) -> tuple[list[Block], bool]:
+    if len(blocks) == 1:
+        return blocks, False
+
+    if can_consolidate:
+        # TODO: optimization potential in case all mgrs contain slices and
+        # combination of those slices is a slice, too.
+        new_mgr_locs = np.concatenate([b.mgr_locs.as_array for b in blocks])
+
+        new_values: ArrayLike
+
+        if isinstance(blocks[0].dtype, np.dtype):
+            # error: List comprehension has incompatible type List[Union[ndarray,
+            # ExtensionArray]]; expected List[Union[complex, generic,
+            # Sequence[Union[int, float, complex, str, bytes, generic]],
+            # Sequence[Sequence[Any]], SupportsArray]]
+            new_values = np.vstack([b.values for b in blocks])  # type: ignore[misc]
+        else:
+            bvals = [blk.values for blk in blocks]
+            bvals2 = cast(Sequence[NDArrayBackedExtensionArray], bvals)
+            new_values = bvals2[0]._concat_same_type(bvals2, axis=0)
+
+        argsort = np.argsort(new_mgr_locs)
+        new_values = new_values[argsort]
+        new_mgr_locs = new_mgr_locs[argsort]
+
+        bp = BlockPlacement(new_mgr_locs)
+        return [new_block_2d(new_values, placement=bp)], True
+
+    # can't consolidate --> no merge
+    return blocks, False
+
+
+def _fast_count_smallints(arr: npt.NDArray[np.intp]):
+    """Faster version of set(arr) for sequences of small numbers."""
+    counts = np.bincount(arr)
+    nz = counts.nonzero()[0]
+    # Note: list(zip(...) outperforms list(np.c_[nz, counts[nz]]) here,
+    #  in one benchmark by a factor of 11
+    return zip(nz, counts[nz])
+
+
+def _preprocess_slice_or_indexer(
+    slice_or_indexer: slice | np.ndarray, length: int, allow_fill: bool
+):
+    if isinstance(slice_or_indexer, slice):
+        return (
+            "slice",
+            slice_or_indexer,
+            libinternals.slice_len(slice_or_indexer, length),
+        )
+    else:
+        if (
+            not isinstance(slice_or_indexer, np.ndarray)
+            or slice_or_indexer.dtype.kind != "i"
+        ):
+            dtype = getattr(slice_or_indexer, "dtype", None)
+            raise TypeError(type(slice_or_indexer), dtype)
+
+        indexer = ensure_platform_int(slice_or_indexer)
+        if not allow_fill:
+            indexer = maybe_convert_indices(indexer, length)
+        return "fancy", indexer, len(indexer)
+
+
+def make_na_array(dtype: DtypeObj, shape: Shape, fill_value) -> ArrayLike:
+    if isinstance(dtype, DatetimeTZDtype):
+        # NB: exclude e.g. pyarrow[dt64tz] dtypes
+        ts = Timestamp(fill_value).as_unit(dtype.unit)
+        i8values = np.full(shape, ts._value)
+        dt64values = i8values.view(f"M8[{dtype.unit}]")
+        return DatetimeArray._simple_new(dt64values, dtype=dtype)
+
+    elif is_1d_only_ea_dtype(dtype):
+        dtype = cast(ExtensionDtype, dtype)
+        cls = dtype.construct_array_type()
+
+        missing_arr = cls._from_sequence([], dtype=dtype)
+        ncols, nrows = shape
+        assert ncols == 1, ncols
+        empty_arr = -1 * np.ones((nrows,), dtype=np.intp)
+        return missing_arr.take(empty_arr, allow_fill=True, fill_value=fill_value)
+    elif isinstance(dtype, ExtensionDtype):
+        # TODO: no tests get here, a handful would if we disabled
+        #  the dt64tz special-case above (which is faster)
+        cls = dtype.construct_array_type()
+        missing_arr = cls._empty(shape=shape, dtype=dtype)
+        missing_arr[:] = fill_value
+        return missing_arr
+    else:
+        # NB: we should never get here with dtype integer or bool;
+        #  if we did, the missing_arr.fill would cast to gibberish
+        missing_arr = np.empty(shape, dtype=dtype)
+        missing_arr.fill(fill_value)
+
+        if dtype.kind in "mM":
+            missing_arr = ensure_wrapped_if_datetimelike(missing_arr)
+        return missing_arr
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/internals/ops.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/internals/ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..cf9466c0bdf0bf4df623e2d819faf3ea7b36c878
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/internals/ops.py
@@ -0,0 +1,154 @@
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    NamedTuple,
+)
+
+from pandas.core.dtypes.common import is_1d_only_ea_dtype
+
+if TYPE_CHECKING:
+    from collections.abc import Iterator
+
+    from pandas._libs.internals import BlockPlacement
+    from pandas._typing import ArrayLike
+
+    from pandas.core.internals.blocks import Block
+    from pandas.core.internals.managers import BlockManager
+
+
+class BlockPairInfo(NamedTuple):
+    lvals: ArrayLike
+    rvals: ArrayLike
+    locs: BlockPlacement
+    left_ea: bool
+    right_ea: bool
+    rblk: Block
+
+
+def _iter_block_pairs(
+    left: BlockManager, right: BlockManager
+) -> Iterator[BlockPairInfo]:
+    # At this point we have already checked the parent DataFrames for
+    #  assert rframe._indexed_same(lframe)
+
+    for blk in left.blocks:
+        locs = blk.mgr_locs
+        blk_vals = blk.values
+
+        left_ea = blk_vals.ndim == 1
+
+        rblks = right._slice_take_blocks_ax0(locs.indexer, only_slice=True)
+
+        # Assertions are disabled for performance, but should hold:
+        # if left_ea:
+        #    assert len(locs) == 1, locs
+        #    assert len(rblks) == 1, rblks
+        #    assert rblks[0].shape[0] == 1, rblks[0].shape
+
+        for rblk in rblks:
+            right_ea = rblk.values.ndim == 1
+
+            lvals, rvals = _get_same_shape_values(blk, rblk, left_ea, right_ea)
+            info = BlockPairInfo(lvals, rvals, locs, left_ea, right_ea, rblk)
+            yield info
+
+
+def operate_blockwise(
+    left: BlockManager, right: BlockManager, array_op
+) -> BlockManager:
+    # At this point we have already checked the parent DataFrames for
+    #  assert rframe._indexed_same(lframe)
+
+    res_blks: list[Block] = []
+    for lvals, rvals, locs, left_ea, right_ea, rblk in _iter_block_pairs(left, right):
+        res_values = array_op(lvals, rvals)
+        if (
+            left_ea
+            and not right_ea
+            and hasattr(res_values, "reshape")
+            and not is_1d_only_ea_dtype(res_values.dtype)
+        ):
+            res_values = res_values.reshape(1, -1)
+        nbs = rblk._split_op_result(res_values)
+
+        # Assertions are disabled for performance, but should hold:
+        # if right_ea or left_ea:
+        #    assert len(nbs) == 1
+        # else:
+        #    assert res_values.shape == lvals.shape, (res_values.shape, lvals.shape)
+
+        _reset_block_mgr_locs(nbs, locs)
+
+        res_blks.extend(nbs)
+
+    # Assertions are disabled for performance, but should hold:
+    #  slocs = {y for nb in res_blks for y in nb.mgr_locs.as_array}
+    #  nlocs = sum(len(nb.mgr_locs.as_array) for nb in res_blks)
+    #  assert nlocs == len(left.items), (nlocs, len(left.items))
+    #  assert len(slocs) == nlocs, (len(slocs), nlocs)
+    #  assert slocs == set(range(nlocs)), slocs
+
+    new_mgr = type(right)(tuple(res_blks), axes=right.axes, verify_integrity=False)
+    return new_mgr
+
+
+def _reset_block_mgr_locs(nbs: list[Block], locs) -> None:
+    """
+    Reset mgr_locs to correspond to our original DataFrame.
+    """
+    for nb in nbs:
+        nblocs = locs[nb.mgr_locs.indexer]
+        nb.mgr_locs = nblocs
+        # Assertions are disabled for performance, but should hold:
+        #  assert len(nblocs) == nb.shape[0], (len(nblocs), nb.shape)
+        #  assert all(x in locs.as_array for x in nb.mgr_locs.as_array)
+
+
+def _get_same_shape_values(
+    lblk: Block, rblk: Block, left_ea: bool, right_ea: bool
+) -> tuple[ArrayLike, ArrayLike]:
+    """
+    Slice lblk.values to align with rblk.  Squeeze if we have EAs.
+    """
+    lvals = lblk.values
+    rvals = rblk.values
+
+    # Require that the indexing into lvals be slice-like
+    assert rblk.mgr_locs.is_slice_like, rblk.mgr_locs
+
+    # TODO(EA2D): with 2D EAs only this first clause would be needed
+    if not (left_ea or right_ea):
+        # error: No overload variant of "__getitem__" of "ExtensionArray" matches
+        # argument type "Tuple[Union[ndarray, slice], slice]"
+        lvals = lvals[rblk.mgr_locs.indexer, :]  # type: ignore[call-overload]
+        assert lvals.shape == rvals.shape, (lvals.shape, rvals.shape)
+    elif left_ea and right_ea:
+        assert lvals.shape == rvals.shape, (lvals.shape, rvals.shape)
+    elif right_ea:
+        # lvals are 2D, rvals are 1D
+
+        # error: No overload variant of "__getitem__" of "ExtensionArray" matches
+        # argument type "Tuple[Union[ndarray, slice], slice]"
+        lvals = lvals[rblk.mgr_locs.indexer, :]  # type: ignore[call-overload]
+        assert lvals.shape[0] == 1, lvals.shape
+        lvals = lvals[0, :]
+    else:
+        # lvals are 1D, rvals are 2D
+        assert rvals.shape[0] == 1, rvals.shape
+        # error: No overload variant of "__getitem__" of "ExtensionArray" matches
+        # argument type "Tuple[int, slice]"
+        rvals = rvals[0, :]  # type: ignore[call-overload]
+
+    return lvals, rvals
+
+
+def blockwise_all(left: BlockManager, right: BlockManager, op) -> bool:
+    """
+    Blockwise `all` reduction.
+    """
+    for info in _iter_block_pairs(left, right):
+        res = op(info.lvals, info.rvals)
+        if not res:
+            return False
+    return True
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/methods/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/methods/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/methods/describe.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/methods/describe.py
new file mode 100644
index 0000000000000000000000000000000000000000..c620bb9d17976a9deeb682ff323476c484f283ff
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/methods/describe.py
@@ -0,0 +1,416 @@
+"""
+Module responsible for execution of NDFrame.describe() method.
+
+Method NDFrame.describe() delegates actual execution to function describe_ndframe().
+"""
+from __future__ import annotations
+
+from abc import (
+    ABC,
+    abstractmethod,
+)
+from typing import (
+    TYPE_CHECKING,
+    Callable,
+    cast,
+)
+
+import numpy as np
+
+from pandas._libs.tslibs import Timestamp
+from pandas._typing import (
+    DtypeObj,
+    NDFrameT,
+    npt,
+)
+from pandas.util._validators import validate_percentile
+
+from pandas.core.dtypes.common import (
+    is_bool_dtype,
+    is_numeric_dtype,
+)
+from pandas.core.dtypes.dtypes import (
+    ArrowDtype,
+    DatetimeTZDtype,
+    ExtensionDtype,
+)
+
+from pandas.core.arrays.floating import Float64Dtype
+from pandas.core.reshape.concat import concat
+
+from pandas.io.formats.format import format_percentiles
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Hashable,
+        Sequence,
+    )
+
+    from pandas import (
+        DataFrame,
+        Series,
+    )
+
+
+def describe_ndframe(
+    *,
+    obj: NDFrameT,
+    include: str | Sequence[str] | None,
+    exclude: str | Sequence[str] | None,
+    percentiles: Sequence[float] | np.ndarray | None,
+) -> NDFrameT:
+    """Describe series or dataframe.
+
+    Called from pandas.core.generic.NDFrame.describe()
+
+    Parameters
+    ----------
+    obj: DataFrame or Series
+        Either dataframe or series to be described.
+    include : 'all', list-like of dtypes or None (default), optional
+        A white list of data types to include in the result. Ignored for ``Series``.
+    exclude : list-like of dtypes or None (default), optional,
+        A black list of data types to omit from the result. Ignored for ``Series``.
+    percentiles : list-like of numbers, optional
+        The percentiles to include in the output. All should fall between 0 and 1.
+        The default is ``[.25, .5, .75]``, which returns the 25th, 50th, and
+        75th percentiles.
+
+    Returns
+    -------
+    Dataframe or series description.
+    """
+    percentiles = _refine_percentiles(percentiles)
+
+    describer: NDFrameDescriberAbstract
+    if obj.ndim == 1:
+        describer = SeriesDescriber(
+            obj=cast("Series", obj),
+        )
+    else:
+        describer = DataFrameDescriber(
+            obj=cast("DataFrame", obj),
+            include=include,
+            exclude=exclude,
+        )
+
+    result = describer.describe(percentiles=percentiles)
+    return cast(NDFrameT, result)
+
+
+class NDFrameDescriberAbstract(ABC):
+    """Abstract class for describing dataframe or series.
+
+    Parameters
+    ----------
+    obj : Series or DataFrame
+        Object to be described.
+    """
+
+    def __init__(self, obj: DataFrame | Series) -> None:
+        self.obj = obj
+
+    @abstractmethod
+    def describe(self, percentiles: Sequence[float] | np.ndarray) -> DataFrame | Series:
+        """Do describe either series or dataframe.
+
+        Parameters
+        ----------
+        percentiles : list-like of numbers
+            The percentiles to include in the output.
+        """
+
+
+class SeriesDescriber(NDFrameDescriberAbstract):
+    """Class responsible for creating series description."""
+
+    obj: Series
+
+    def describe(self, percentiles: Sequence[float] | np.ndarray) -> Series:
+        describe_func = select_describe_func(
+            self.obj,
+        )
+        return describe_func(self.obj, percentiles)
+
+
+class DataFrameDescriber(NDFrameDescriberAbstract):
+    """Class responsible for creating dataobj description.
+
+    Parameters
+    ----------
+    obj : DataFrame
+        DataFrame to be described.
+    include : 'all', list-like of dtypes or None
+        A white list of data types to include in the result.
+    exclude : list-like of dtypes or None
+        A black list of data types to omit from the result.
+    """
+
+    obj: DataFrame
+
+    def __init__(
+        self,
+        obj: DataFrame,
+        *,
+        include: str | Sequence[str] | None,
+        exclude: str | Sequence[str] | None,
+    ) -> None:
+        self.include = include
+        self.exclude = exclude
+
+        if obj.ndim == 2 and obj.columns.size == 0:
+            raise ValueError("Cannot describe a DataFrame without columns")
+
+        super().__init__(obj)
+
+    def describe(self, percentiles: Sequence[float] | np.ndarray) -> DataFrame:
+        data = self._select_data()
+
+        ldesc: list[Series] = []
+        for _, series in data.items():
+            describe_func = select_describe_func(series)
+            ldesc.append(describe_func(series, percentiles))
+
+        col_names = reorder_columns(ldesc)
+        d = concat(
+            [x.reindex(col_names, copy=False) for x in ldesc],
+            axis=1,
+            sort=False,
+        )
+        d.columns = data.columns.copy()
+        return d
+
+    def _select_data(self) -> DataFrame:
+        """Select columns to be described."""
+        if (self.include is None) and (self.exclude is None):
+            # when some numerics are found, keep only numerics
+            default_include: list[npt.DTypeLike] = [np.number, "datetime"]
+            data = self.obj.select_dtypes(include=default_include)
+            if len(data.columns) == 0:
+                data = self.obj
+        elif self.include == "all":
+            if self.exclude is not None:
+                msg = "exclude must be None when include is 'all'"
+                raise ValueError(msg)
+            data = self.obj
+        else:
+            data = self.obj.select_dtypes(
+                include=self.include,
+                exclude=self.exclude,
+            )
+        return data
+
+
+def reorder_columns(ldesc: Sequence[Series]) -> list[Hashable]:
+    """Set a convenient order for rows for display."""
+    names: list[Hashable] = []
+    seen_names: set[Hashable] = set()
+    ldesc_indexes = sorted((x.index for x in ldesc), key=len)
+    for idxnames in ldesc_indexes:
+        for name in idxnames:
+            if name not in seen_names:
+                seen_names.add(name)
+                names.append(name)
+    return names
+
+
+def describe_numeric_1d(series: Series, percentiles: Sequence[float]) -> Series:
+    """Describe series containing numerical data.
+
+    Parameters
+    ----------
+    series : Series
+        Series to be described.
+    percentiles : list-like of numbers
+        The percentiles to include in the output.
+    """
+    from pandas import Series
+
+    formatted_percentiles = format_percentiles(percentiles)
+
+    stat_index = ["count", "mean", "std", "min"] + formatted_percentiles + ["max"]
+    d = (
+        [series.count(), series.mean(), series.std(), series.min()]
+        + series.quantile(percentiles).tolist()
+        + [series.max()]
+    )
+    # GH#48340 - always return float on non-complex numeric data
+    dtype: DtypeObj | None
+    if isinstance(series.dtype, ExtensionDtype):
+        if isinstance(series.dtype, ArrowDtype):
+            if series.dtype.kind == "m":
+                # GH53001: describe timedeltas with object dtype
+                dtype = None
+            else:
+                import pyarrow as pa
+
+                dtype = ArrowDtype(pa.float64())
+        else:
+            dtype = Float64Dtype()
+    elif series.dtype.kind in "iufb":
+        # i.e. numeric but exclude complex dtype
+        dtype = np.dtype("float")
+    else:
+        dtype = None
+    return Series(d, index=stat_index, name=series.name, dtype=dtype)
+
+
+def describe_categorical_1d(
+    data: Series,
+    percentiles_ignored: Sequence[float],
+) -> Series:
+    """Describe series containing categorical data.
+
+    Parameters
+    ----------
+    data : Series
+        Series to be described.
+    percentiles_ignored : list-like of numbers
+        Ignored, but in place to unify interface.
+    """
+    names = ["count", "unique", "top", "freq"]
+    objcounts = data.value_counts()
+    count_unique = len(objcounts[objcounts != 0])
+    if count_unique > 0:
+        top, freq = objcounts.index[0], objcounts.iloc[0]
+        dtype = None
+    else:
+        # If the DataFrame is empty, set 'top' and 'freq' to None
+        # to maintain output shape consistency
+        top, freq = np.nan, np.nan
+        dtype = "object"
+
+    result = [data.count(), count_unique, top, freq]
+
+    from pandas import Series
+
+    return Series(result, index=names, name=data.name, dtype=dtype)
+
+
+def describe_timestamp_as_categorical_1d(
+    data: Series,
+    percentiles_ignored: Sequence[float],
+) -> Series:
+    """Describe series containing timestamp data treated as categorical.
+
+    Parameters
+    ----------
+    data : Series
+        Series to be described.
+    percentiles_ignored : list-like of numbers
+        Ignored, but in place to unify interface.
+    """
+    names = ["count", "unique"]
+    objcounts = data.value_counts()
+    count_unique = len(objcounts[objcounts != 0])
+    result: list[float | Timestamp] = [data.count(), count_unique]
+    dtype = None
+    if count_unique > 0:
+        top, freq = objcounts.index[0], objcounts.iloc[0]
+        tz = data.dt.tz
+        asint = data.dropna().values.view("i8")
+        top = Timestamp(top)
+        if top.tzinfo is not None and tz is not None:
+            # Don't tz_localize(None) if key is already tz-aware
+            top = top.tz_convert(tz)
+        else:
+            top = top.tz_localize(tz)
+        names += ["top", "freq", "first", "last"]
+        result += [
+            top,
+            freq,
+            Timestamp(asint.min(), tz=tz),
+            Timestamp(asint.max(), tz=tz),
+        ]
+
+    # If the DataFrame is empty, set 'top' and 'freq' to None
+    # to maintain output shape consistency
+    else:
+        names += ["top", "freq"]
+        result += [np.nan, np.nan]
+        dtype = "object"
+
+    from pandas import Series
+
+    return Series(result, index=names, name=data.name, dtype=dtype)
+
+
+def describe_timestamp_1d(data: Series, percentiles: Sequence[float]) -> Series:
+    """Describe series containing datetime64 dtype.
+
+    Parameters
+    ----------
+    data : Series
+        Series to be described.
+    percentiles : list-like of numbers
+        The percentiles to include in the output.
+    """
+    # GH-30164
+    from pandas import Series
+
+    formatted_percentiles = format_percentiles(percentiles)
+
+    stat_index = ["count", "mean", "min"] + formatted_percentiles + ["max"]
+    d = (
+        [data.count(), data.mean(), data.min()]
+        + data.quantile(percentiles).tolist()
+        + [data.max()]
+    )
+    return Series(d, index=stat_index, name=data.name)
+
+
+def select_describe_func(
+    data: Series,
+) -> Callable:
+    """Select proper function for describing series based on data type.
+
+    Parameters
+    ----------
+    data : Series
+        Series to be described.
+    """
+    if is_bool_dtype(data.dtype):
+        return describe_categorical_1d
+    elif is_numeric_dtype(data):
+        return describe_numeric_1d
+    elif data.dtype.kind == "M" or isinstance(data.dtype, DatetimeTZDtype):
+        return describe_timestamp_1d
+    elif data.dtype.kind == "m":
+        return describe_numeric_1d
+    else:
+        return describe_categorical_1d
+
+
+def _refine_percentiles(
+    percentiles: Sequence[float] | np.ndarray | None,
+) -> npt.NDArray[np.float64]:
+    """
+    Ensure that percentiles are unique and sorted.
+
+    Parameters
+    ----------
+    percentiles : list-like of numbers, optional
+        The percentiles to include in the output.
+    """
+    if percentiles is None:
+        return np.array([0.25, 0.5, 0.75])
+
+    # explicit conversion of `percentiles` to list
+    percentiles = list(percentiles)
+
+    # get them all to be in [0, 1]
+    validate_percentile(percentiles)
+
+    # median should always be included
+    if 0.5 not in percentiles:
+        percentiles.append(0.5)
+
+    percentiles = np.asarray(percentiles)
+
+    # sort and check for duplicates
+    unique_pcts = np.unique(percentiles)
+    assert percentiles is not None
+    if len(unique_pcts) < len(percentiles):
+        raise ValueError("percentiles cannot contain duplicates")
+
+    return unique_pcts
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/methods/selectn.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/methods/selectn.py
new file mode 100644
index 0000000000000000000000000000000000000000..a2f8ca94134b897bd3f7824a6cb45ca13516e5ff
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/methods/selectn.py
@@ -0,0 +1,269 @@
+"""
+Implementation of nlargest and nsmallest.
+"""
+
+from __future__ import annotations
+
+from collections.abc import (
+    Hashable,
+    Sequence,
+)
+from typing import (
+    TYPE_CHECKING,
+    cast,
+    final,
+)
+
+import numpy as np
+
+from pandas._libs import algos as libalgos
+
+from pandas.core.dtypes.common import (
+    is_bool_dtype,
+    is_complex_dtype,
+    is_integer_dtype,
+    is_list_like,
+    is_numeric_dtype,
+    needs_i8_conversion,
+)
+from pandas.core.dtypes.dtypes import BaseMaskedDtype
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        DtypeObj,
+        IndexLabel,
+    )
+
+    from pandas import (
+        DataFrame,
+        Series,
+    )
+
+
+class SelectN:
+    def __init__(self, obj, n: int, keep: str) -> None:
+        self.obj = obj
+        self.n = n
+        self.keep = keep
+
+        if self.keep not in ("first", "last", "all"):
+            raise ValueError('keep must be either "first", "last" or "all"')
+
+    def compute(self, method: str) -> DataFrame | Series:
+        raise NotImplementedError
+
+    @final
+    def nlargest(self):
+        return self.compute("nlargest")
+
+    @final
+    def nsmallest(self):
+        return self.compute("nsmallest")
+
+    @final
+    @staticmethod
+    def is_valid_dtype_n_method(dtype: DtypeObj) -> bool:
+        """
+        Helper function to determine if dtype is valid for
+        nsmallest/nlargest methods
+        """
+        if is_numeric_dtype(dtype):
+            return not is_complex_dtype(dtype)
+        return needs_i8_conversion(dtype)
+
+
+class SelectNSeries(SelectN):
+    """
+    Implement n largest/smallest for Series
+
+    Parameters
+    ----------
+    obj : Series
+    n : int
+    keep : {'first', 'last'}, default 'first'
+
+    Returns
+    -------
+    nordered : Series
+    """
+
+    def compute(self, method: str) -> Series:
+        from pandas.core.reshape.concat import concat
+
+        n = self.n
+        dtype = self.obj.dtype
+        if not self.is_valid_dtype_n_method(dtype):
+            raise TypeError(f"Cannot use method '{method}' with dtype {dtype}")
+
+        if n <= 0:
+            return self.obj[[]]
+
+        dropped = self.obj.dropna()
+        nan_index = self.obj.drop(dropped.index)
+
+        # slow method
+        if n >= len(self.obj):
+            ascending = method == "nsmallest"
+            return self.obj.sort_values(ascending=ascending).head(n)
+
+        # fast method
+        new_dtype = dropped.dtype
+
+        # Similar to algorithms._ensure_data
+        arr = dropped._values
+        if needs_i8_conversion(arr.dtype):
+            arr = arr.view("i8")
+        elif isinstance(arr.dtype, BaseMaskedDtype):
+            arr = arr._data
+        else:
+            arr = np.asarray(arr)
+        if arr.dtype.kind == "b":
+            arr = arr.view(np.uint8)
+
+        if method == "nlargest":
+            arr = -arr
+            if is_integer_dtype(new_dtype):
+                # GH 21426: ensure reverse ordering at boundaries
+                arr -= 1
+
+            elif is_bool_dtype(new_dtype):
+                # GH 26154: ensure False is smaller than True
+                arr = 1 - (-arr)
+
+        if self.keep == "last":
+            arr = arr[::-1]
+
+        nbase = n
+        narr = len(arr)
+        n = min(n, narr)
+
+        # arr passed into kth_smallest must be contiguous. We copy
+        # here because kth_smallest will modify its input
+        # avoid OOB access with kth_smallest_c when n <= 0
+        if len(arr) > 0:
+            kth_val = libalgos.kth_smallest(arr.copy(order="C"), n - 1)
+        else:
+            kth_val = np.nan
+        (ns,) = np.nonzero(arr <= kth_val)
+        inds = ns[arr[ns].argsort(kind="mergesort")]
+
+        if self.keep != "all":
+            inds = inds[:n]
+            findex = nbase
+        else:
+            if len(inds) < nbase <= len(nan_index) + len(inds):
+                findex = len(nan_index) + len(inds)
+            else:
+                findex = len(inds)
+
+        if self.keep == "last":
+            # reverse indices
+            inds = narr - 1 - inds
+
+        return concat([dropped.iloc[inds], nan_index]).iloc[:findex]
+
+
+class SelectNFrame(SelectN):
+    """
+    Implement n largest/smallest for DataFrame
+
+    Parameters
+    ----------
+    obj : DataFrame
+    n : int
+    keep : {'first', 'last'}, default 'first'
+    columns : list or str
+
+    Returns
+    -------
+    nordered : DataFrame
+    """
+
+    def __init__(self, obj: DataFrame, n: int, keep: str, columns: IndexLabel) -> None:
+        super().__init__(obj, n, keep)
+        if not is_list_like(columns) or isinstance(columns, tuple):
+            columns = [columns]
+
+        columns = cast(Sequence[Hashable], columns)
+        columns = list(columns)
+        self.columns = columns
+
+    def compute(self, method: str) -> DataFrame:
+        from pandas.core.api import Index
+
+        n = self.n
+        frame = self.obj
+        columns = self.columns
+
+        for column in columns:
+            dtype = frame[column].dtype
+            if not self.is_valid_dtype_n_method(dtype):
+                raise TypeError(
+                    f"Column {repr(column)} has dtype {dtype}, "
+                    f"cannot use method {repr(method)} with this dtype"
+                )
+
+        def get_indexer(current_indexer, other_indexer):
+            """
+            Helper function to concat `current_indexer` and `other_indexer`
+            depending on `method`
+            """
+            if method == "nsmallest":
+                return current_indexer.append(other_indexer)
+            else:
+                return other_indexer.append(current_indexer)
+
+        # Below we save and reset the index in case index contains duplicates
+        original_index = frame.index
+        cur_frame = frame = frame.reset_index(drop=True)
+        cur_n = n
+        indexer = Index([], dtype=np.int64)
+
+        for i, column in enumerate(columns):
+            # For each column we apply method to cur_frame[column].
+            # If it's the last column or if we have the number of
+            # results desired we are done.
+            # Otherwise there are duplicates of the largest/smallest
+            # value and we need to look at the rest of the columns
+            # to determine which of the rows with the largest/smallest
+            # value in the column to keep.
+            series = cur_frame[column]
+            is_last_column = len(columns) - 1 == i
+            values = getattr(series, method)(
+                cur_n, keep=self.keep if is_last_column else "all"
+            )
+
+            if is_last_column or len(values) <= cur_n:
+                indexer = get_indexer(indexer, values.index)
+                break
+
+            # Now find all values which are equal to
+            # the (nsmallest: largest)/(nlargest: smallest)
+            # from our series.
+            border_value = values == values[values.index[-1]]
+
+            # Some of these values are among the top-n
+            # some aren't.
+            unsafe_values = values[border_value]
+
+            # These values are definitely among the top-n
+            safe_values = values[~border_value]
+            indexer = get_indexer(indexer, safe_values.index)
+
+            # Go on and separate the unsafe_values on the remaining
+            # columns.
+            cur_frame = cur_frame.loc[unsafe_values.index]
+            cur_n = n - len(indexer)
+
+        frame = frame.take(indexer)
+
+        # Restore the index on frame
+        frame.index = original_index.take(indexer)
+
+        # If there is only one column, the frame is already sorted.
+        if len(columns) == 1:
+            return frame
+
+        ascending = method == "nsmallest"
+
+        return frame.sort_values(columns, ascending=ascending, kind="mergesort")
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/methods/to_dict.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/methods/to_dict.py
new file mode 100644
index 0000000000000000000000000000000000000000..accbd92a91ed66fe4dfd1e3939fe8c498184285d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/methods/to_dict.py
@@ -0,0 +1,272 @@
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    Literal,
+    overload,
+)
+import warnings
+
+import numpy as np
+
+from pandas._libs import (
+    lib,
+    missing as libmissing,
+)
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.cast import maybe_box_native
+from pandas.core.dtypes.dtypes import (
+    BaseMaskedDtype,
+    ExtensionDtype,
+)
+
+from pandas.core import common as com
+
+if TYPE_CHECKING:
+    from pandas._typing import MutableMappingT
+
+    from pandas import DataFrame
+
+
+@overload
+def to_dict(
+    df: DataFrame,
+    orient: Literal["dict", "list", "series", "split", "tight", "index"] = ...,
+    *,
+    into: type[MutableMappingT] | MutableMappingT,
+    index: bool = ...,
+) -> MutableMappingT:
+    ...
+
+
+@overload
+def to_dict(
+    df: DataFrame,
+    orient: Literal["records"],
+    *,
+    into: type[MutableMappingT] | MutableMappingT,
+    index: bool = ...,
+) -> list[MutableMappingT]:
+    ...
+
+
+@overload
+def to_dict(
+    df: DataFrame,
+    orient: Literal["dict", "list", "series", "split", "tight", "index"] = ...,
+    *,
+    into: type[dict] = ...,
+    index: bool = ...,
+) -> dict:
+    ...
+
+
+@overload
+def to_dict(
+    df: DataFrame,
+    orient: Literal["records"],
+    *,
+    into: type[dict] = ...,
+    index: bool = ...,
+) -> list[dict]:
+    ...
+
+
+# error: Incompatible default for argument "into" (default has type "type[dict
+# [Any, Any]]", argument has type "type[MutableMappingT] | MutableMappingT")
+def to_dict(
+    df: DataFrame,
+    orient: Literal[
+        "dict", "list", "series", "split", "tight", "records", "index"
+    ] = "dict",
+    *,
+    into: type[MutableMappingT] | MutableMappingT = dict,  # type: ignore[assignment]
+    index: bool = True,
+) -> MutableMappingT | list[MutableMappingT]:
+    """
+    Convert the DataFrame to a dictionary.
+
+    The type of the key-value pairs can be customized with the parameters
+    (see below).
+
+    Parameters
+    ----------
+    orient : str {'dict', 'list', 'series', 'split', 'tight', 'records', 'index'}
+        Determines the type of the values of the dictionary.
+
+        - 'dict' (default) : dict like {column -> {index -> value}}
+        - 'list' : dict like {column -> [values]}
+        - 'series' : dict like {column -> Series(values)}
+        - 'split' : dict like
+          {'index' -> [index], 'columns' -> [columns], 'data' -> [values]}
+        - 'tight' : dict like
+          {'index' -> [index], 'columns' -> [columns], 'data' -> [values],
+          'index_names' -> [index.names], 'column_names' -> [column.names]}
+        - 'records' : list like
+          [{column -> value}, ... , {column -> value}]
+        - 'index' : dict like {index -> {column -> value}}
+
+        .. versionadded:: 1.4.0
+            'tight' as an allowed value for the ``orient`` argument
+
+    into : class, default dict
+        The collections.abc.MutableMapping subclass used for all Mappings
+        in the return value.  Can be the actual class or an empty
+        instance of the mapping type you want.  If you want a
+        collections.defaultdict, you must pass it initialized.
+
+    index : bool, default True
+        Whether to include the index item (and index_names item if `orient`
+        is 'tight') in the returned dictionary. Can only be ``False``
+        when `orient` is 'split' or 'tight'.
+
+        .. versionadded:: 2.0.0
+
+    Returns
+    -------
+    dict, list or collections.abc.Mapping
+        Return a collections.abc.MutableMapping object representing the
+        DataFrame. The resulting transformation depends on the `orient` parameter.
+    """
+    if not df.columns.is_unique:
+        warnings.warn(
+            "DataFrame columns are not unique, some columns will be omitted.",
+            UserWarning,
+            stacklevel=find_stack_level(),
+        )
+    # GH16122
+    into_c = com.standardize_mapping(into)
+
+    #  error: Incompatible types in assignment (expression has type "str",
+    # variable has type "Literal['dict', 'list', 'series', 'split', 'tight',
+    # 'records', 'index']")
+    orient = orient.lower()  # type: ignore[assignment]
+
+    if not index and orient not in ["split", "tight"]:
+        raise ValueError(
+            "'index=False' is only valid when 'orient' is 'split' or 'tight'"
+        )
+
+    if orient == "series":
+        # GH46470 Return quickly if orient series to avoid creating dtype objects
+        return into_c((k, v) for k, v in df.items())
+
+    box_native_indices = [
+        i
+        for i, col_dtype in enumerate(df.dtypes.values)
+        if col_dtype == np.dtype(object) or isinstance(col_dtype, ExtensionDtype)
+    ]
+    box_na_values = [
+        lib.no_default if not isinstance(col_dtype, BaseMaskedDtype) else libmissing.NA
+        for i, col_dtype in enumerate(df.dtypes.values)
+    ]
+    are_all_object_dtype_cols = len(box_native_indices) == len(df.dtypes)
+
+    if orient == "dict":
+        return into_c((k, v.to_dict(into=into)) for k, v in df.items())
+
+    elif orient == "list":
+        object_dtype_indices_as_set: set[int] = set(box_native_indices)
+        return into_c(
+            (
+                k,
+                list(map(maybe_box_native, v.to_numpy(na_value=box_na_values[i])))
+                if i in object_dtype_indices_as_set
+                else list(map(maybe_box_native, v.to_numpy())),
+            )
+            for i, (k, v) in enumerate(df.items())
+        )
+
+    elif orient == "split":
+        data = df._create_data_for_split_and_tight_to_dict(
+            are_all_object_dtype_cols, box_native_indices
+        )
+
+        return into_c(
+            ((("index", df.index.tolist()),) if index else ())
+            + (
+                ("columns", df.columns.tolist()),
+                ("data", data),
+            )
+        )
+
+    elif orient == "tight":
+        data = df._create_data_for_split_and_tight_to_dict(
+            are_all_object_dtype_cols, box_native_indices
+        )
+
+        return into_c(
+            ((("index", df.index.tolist()),) if index else ())
+            + (
+                ("columns", df.columns.tolist()),
+                (
+                    "data",
+                    [
+                        list(map(maybe_box_native, t))
+                        for t in df.itertuples(index=False, name=None)
+                    ],
+                ),
+            )
+            + ((("index_names", list(df.index.names)),) if index else ())
+            + (("column_names", list(df.columns.names)),)
+        )
+
+    elif orient == "records":
+        columns = df.columns.tolist()
+        if are_all_object_dtype_cols:
+            rows = (
+                dict(zip(columns, row)) for row in df.itertuples(index=False, name=None)
+            )
+            return [
+                into_c((k, maybe_box_native(v)) for k, v in row.items()) for row in rows
+            ]
+        else:
+            data = [
+                into_c(zip(columns, t)) for t in df.itertuples(index=False, name=None)
+            ]
+            if box_native_indices:
+                object_dtype_indices_as_set = set(box_native_indices)
+                object_dtype_cols = {
+                    col
+                    for i, col in enumerate(df.columns)
+                    if i in object_dtype_indices_as_set
+                }
+                for row in data:
+                    for col in object_dtype_cols:
+                        row[col] = maybe_box_native(row[col])
+            return data
+
+    elif orient == "index":
+        if not df.index.is_unique:
+            raise ValueError("DataFrame index must be unique for orient='index'.")
+        columns = df.columns.tolist()
+        if are_all_object_dtype_cols:
+            return into_c(
+                (t[0], dict(zip(df.columns, map(maybe_box_native, t[1:]))))
+                for t in df.itertuples(name=None)
+            )
+        elif box_native_indices:
+            object_dtype_indices_as_set = set(box_native_indices)
+            is_object_dtype_by_index = [
+                i in object_dtype_indices_as_set for i in range(len(df.columns))
+            ]
+            return into_c(
+                (
+                    t[0],
+                    {
+                        columns[i]: maybe_box_native(v)
+                        if is_object_dtype_by_index[i]
+                        else v
+                        for i, v in enumerate(t[1:])
+                    },
+                )
+                for t in df.itertuples(name=None)
+            )
+        else:
+            return into_c(
+                (t[0], dict(zip(df.columns, t[1:]))) for t in df.itertuples(name=None)
+            )
+
+    else:
+        raise ValueError(f"orient '{orient}' not understood")
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/missing.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/missing.py
new file mode 100644
index 0000000000000000000000000000000000000000..c016aab8ad0748220d04d047f84d3aa14564ab80
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/missing.py
@@ -0,0 +1,1158 @@
+"""
+Routines for filling missing data.
+"""
+from __future__ import annotations
+
+from functools import wraps
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Literal,
+    cast,
+    overload,
+)
+
+import numpy as np
+
+from pandas._libs import (
+    NaT,
+    algos,
+    lib,
+)
+from pandas._typing import (
+    ArrayLike,
+    AxisInt,
+    F,
+    ReindexMethod,
+    npt,
+)
+from pandas.compat._optional import import_optional_dependency
+
+from pandas.core.dtypes.cast import infer_dtype_from
+from pandas.core.dtypes.common import (
+    is_array_like,
+    is_bool_dtype,
+    is_numeric_dtype,
+    is_numeric_v_string_like,
+    is_object_dtype,
+    needs_i8_conversion,
+)
+from pandas.core.dtypes.dtypes import DatetimeTZDtype
+from pandas.core.dtypes.missing import (
+    is_valid_na_for_dtype,
+    isna,
+    na_value_for_dtype,
+)
+
+if TYPE_CHECKING:
+    from pandas import Index
+
+
+def check_value_size(value, mask: npt.NDArray[np.bool_], length: int):
+    """
+    Validate the size of the values passed to ExtensionArray.fillna.
+    """
+    if is_array_like(value):
+        if len(value) != length:
+            raise ValueError(
+                f"Length of 'value' does not match. Got ({len(value)}) "
+                f" expected {length}"
+            )
+        value = value[mask]
+
+    return value
+
+
+def mask_missing(arr: ArrayLike, values_to_mask) -> npt.NDArray[np.bool_]:
+    """
+    Return a masking array of same size/shape as arr
+    with entries equaling any member of values_to_mask set to True
+
+    Parameters
+    ----------
+    arr : ArrayLike
+    values_to_mask: list, tuple, or scalar
+
+    Returns
+    -------
+    np.ndarray[bool]
+    """
+    # When called from Block.replace/replace_list, values_to_mask is a scalar
+    #  known to be holdable by arr.
+    # When called from Series._single_replace, values_to_mask is tuple or list
+    dtype, values_to_mask = infer_dtype_from(values_to_mask)
+
+    if isinstance(dtype, np.dtype):
+        values_to_mask = np.array(values_to_mask, dtype=dtype)
+    else:
+        cls = dtype.construct_array_type()
+        if not lib.is_list_like(values_to_mask):
+            values_to_mask = [values_to_mask]
+        values_to_mask = cls._from_sequence(values_to_mask, dtype=dtype, copy=False)
+
+    potential_na = False
+    if is_object_dtype(arr.dtype):
+        # pre-compute mask to avoid comparison to NA
+        potential_na = True
+        arr_mask = ~isna(arr)
+
+    na_mask = isna(values_to_mask)
+    nonna = values_to_mask[~na_mask]
+
+    # GH 21977
+    mask = np.zeros(arr.shape, dtype=bool)
+    if (
+        is_numeric_dtype(arr.dtype)
+        and not is_bool_dtype(arr.dtype)
+        and is_bool_dtype(nonna.dtype)
+    ):
+        pass
+    elif (
+        is_bool_dtype(arr.dtype)
+        and is_numeric_dtype(nonna.dtype)
+        and not is_bool_dtype(nonna.dtype)
+    ):
+        pass
+    else:
+        for x in nonna:
+            if is_numeric_v_string_like(arr, x):
+                # GH#29553 prevent numpy deprecation warnings
+                pass
+            else:
+                if potential_na:
+                    new_mask = np.zeros(arr.shape, dtype=np.bool_)
+                    new_mask[arr_mask] = arr[arr_mask] == x
+                else:
+                    new_mask = arr == x
+
+                    if not isinstance(new_mask, np.ndarray):
+                        # usually BooleanArray
+                        new_mask = new_mask.to_numpy(dtype=bool, na_value=False)
+                mask |= new_mask
+
+    if na_mask.any():
+        mask |= isna(arr)
+
+    return mask
+
+
+@overload
+def clean_fill_method(
+    method: Literal["ffill", "pad", "bfill", "backfill"],
+    *,
+    allow_nearest: Literal[False] = ...,
+) -> Literal["pad", "backfill"]:
+    ...
+
+
+@overload
+def clean_fill_method(
+    method: Literal["ffill", "pad", "bfill", "backfill", "nearest"],
+    *,
+    allow_nearest: Literal[True],
+) -> Literal["pad", "backfill", "nearest"]:
+    ...
+
+
+def clean_fill_method(
+    method: Literal["ffill", "pad", "bfill", "backfill", "nearest"],
+    *,
+    allow_nearest: bool = False,
+) -> Literal["pad", "backfill", "nearest"]:
+    if isinstance(method, str):
+        # error: Incompatible types in assignment (expression has type "str", variable
+        # has type "Literal['ffill', 'pad', 'bfill', 'backfill', 'nearest']")
+        method = method.lower()  # type: ignore[assignment]
+        if method == "ffill":
+            method = "pad"
+        elif method == "bfill":
+            method = "backfill"
+
+    valid_methods = ["pad", "backfill"]
+    expecting = "pad (ffill) or backfill (bfill)"
+    if allow_nearest:
+        valid_methods.append("nearest")
+        expecting = "pad (ffill), backfill (bfill) or nearest"
+    if method not in valid_methods:
+        raise ValueError(f"Invalid fill method. Expecting {expecting}. Got {method}")
+    return method
+
+
+# interpolation methods that dispatch to np.interp
+
+NP_METHODS = ["linear", "time", "index", "values"]
+
+# interpolation methods that dispatch to _interpolate_scipy_wrapper
+
+SP_METHODS = [
+    "nearest",
+    "zero",
+    "slinear",
+    "quadratic",
+    "cubic",
+    "barycentric",
+    "krogh",
+    "spline",
+    "polynomial",
+    "from_derivatives",
+    "piecewise_polynomial",
+    "pchip",
+    "akima",
+    "cubicspline",
+]
+
+
+def clean_interp_method(method: str, index: Index, **kwargs) -> str:
+    order = kwargs.get("order")
+
+    if method in ("spline", "polynomial") and order is None:
+        raise ValueError("You must specify the order of the spline or polynomial.")
+
+    valid = NP_METHODS + SP_METHODS
+    if method not in valid:
+        raise ValueError(f"method must be one of {valid}. Got '{method}' instead.")
+
+    if method in ("krogh", "piecewise_polynomial", "pchip"):
+        if not index.is_monotonic_increasing:
+            raise ValueError(
+                f"{method} interpolation requires that the index be monotonic."
+            )
+
+    return method
+
+
+def find_valid_index(how: str, is_valid: npt.NDArray[np.bool_]) -> int | None:
+    """
+    Retrieves the positional index of the first valid value.
+
+    Parameters
+    ----------
+    how : {'first', 'last'}
+        Use this parameter to change between the first or last valid index.
+    is_valid: np.ndarray
+        Mask to find na_values.
+
+    Returns
+    -------
+    int or None
+    """
+    assert how in ["first", "last"]
+
+    if len(is_valid) == 0:  # early stop
+        return None
+
+    if is_valid.ndim == 2:
+        is_valid = is_valid.any(axis=1)  # reduce axis 1
+
+    if how == "first":
+        idxpos = is_valid[::].argmax()
+
+    elif how == "last":
+        idxpos = len(is_valid) - 1 - is_valid[::-1].argmax()
+
+    chk_notna = is_valid[idxpos]
+
+    if not chk_notna:
+        return None
+    # Incompatible return value type (got "signedinteger[Any]",
+    # expected "Optional[int]")
+    return idxpos  # type: ignore[return-value]
+
+
+def validate_limit_direction(
+    limit_direction: str,
+) -> Literal["forward", "backward", "both"]:
+    valid_limit_directions = ["forward", "backward", "both"]
+    limit_direction = limit_direction.lower()
+    if limit_direction not in valid_limit_directions:
+        raise ValueError(
+            "Invalid limit_direction: expecting one of "
+            f"{valid_limit_directions}, got '{limit_direction}'."
+        )
+    # error: Incompatible return value type (got "str", expected
+    # "Literal['forward', 'backward', 'both']")
+    return limit_direction  # type: ignore[return-value]
+
+
+def validate_limit_area(limit_area: str | None) -> Literal["inside", "outside"] | None:
+    if limit_area is not None:
+        valid_limit_areas = ["inside", "outside"]
+        limit_area = limit_area.lower()
+        if limit_area not in valid_limit_areas:
+            raise ValueError(
+                f"Invalid limit_area: expecting one of {valid_limit_areas}, got "
+                f"{limit_area}."
+            )
+    # error: Incompatible return value type (got "Optional[str]", expected
+    # "Optional[Literal['inside', 'outside']]")
+    return limit_area  # type: ignore[return-value]
+
+
+def infer_limit_direction(
+    limit_direction: Literal["backward", "forward", "both"] | None, method: str
+) -> Literal["backward", "forward", "both"]:
+    # Set `limit_direction` depending on `method`
+    if limit_direction is None:
+        if method in ("backfill", "bfill"):
+            limit_direction = "backward"
+        else:
+            limit_direction = "forward"
+    else:
+        if method in ("pad", "ffill") and limit_direction != "forward":
+            raise ValueError(
+                f"`limit_direction` must be 'forward' for method `{method}`"
+            )
+        if method in ("backfill", "bfill") and limit_direction != "backward":
+            raise ValueError(
+                f"`limit_direction` must be 'backward' for method `{method}`"
+            )
+    return limit_direction
+
+
+def get_interp_index(method, index: Index) -> Index:
+    # create/use the index
+    if method == "linear":
+        # prior default
+        from pandas import Index
+
+        index = Index(np.arange(len(index)))
+    else:
+        methods = {"index", "values", "nearest", "time"}
+        is_numeric_or_datetime = (
+            is_numeric_dtype(index.dtype)
+            or isinstance(index.dtype, DatetimeTZDtype)
+            or lib.is_np_dtype(index.dtype, "mM")
+        )
+        if method not in methods and not is_numeric_or_datetime:
+            raise ValueError(
+                "Index column must be numeric or datetime type when "
+                f"using {method} method other than linear. "
+                "Try setting a numeric or datetime index column before "
+                "interpolating."
+            )
+
+    if isna(index).any():
+        raise NotImplementedError(
+            "Interpolation with NaNs in the index "
+            "has not been implemented. Try filling "
+            "those NaNs before interpolating."
+        )
+    return index
+
+
+def interpolate_2d_inplace(
+    data: np.ndarray,  # floating dtype
+    index: Index,
+    axis: AxisInt,
+    method: str = "linear",
+    limit: int | None = None,
+    limit_direction: str = "forward",
+    limit_area: str | None = None,
+    fill_value: Any | None = None,
+    mask=None,
+    **kwargs,
+) -> None:
+    """
+    Column-wise application of _interpolate_1d.
+
+    Notes
+    -----
+    Alters 'data' in-place.
+
+    The signature does differ from _interpolate_1d because it only
+    includes what is needed for Block.interpolate.
+    """
+    # validate the interp method
+    clean_interp_method(method, index, **kwargs)
+
+    if is_valid_na_for_dtype(fill_value, data.dtype):
+        fill_value = na_value_for_dtype(data.dtype, compat=False)
+
+    if method == "time":
+        if not needs_i8_conversion(index.dtype):
+            raise ValueError(
+                "time-weighted interpolation only works "
+                "on Series or DataFrames with a "
+                "DatetimeIndex"
+            )
+        method = "values"
+
+    limit_direction = validate_limit_direction(limit_direction)
+    limit_area_validated = validate_limit_area(limit_area)
+
+    # default limit is unlimited GH #16282
+    limit = algos.validate_limit(nobs=None, limit=limit)
+
+    indices = _index_to_interp_indices(index, method)
+
+    def func(yvalues: np.ndarray) -> None:
+        # process 1-d slices in the axis direction
+
+        _interpolate_1d(
+            indices=indices,
+            yvalues=yvalues,
+            method=method,
+            limit=limit,
+            limit_direction=limit_direction,
+            limit_area=limit_area_validated,
+            fill_value=fill_value,
+            bounds_error=False,
+            mask=mask,
+            **kwargs,
+        )
+
+    # error: Argument 1 to "apply_along_axis" has incompatible type
+    # "Callable[[ndarray[Any, Any]], None]"; expected "Callable[...,
+    # Union[_SupportsArray[dtype[]], Sequence[_SupportsArray
+    # [dtype[]]], Sequence[Sequence[_SupportsArray[dtype[]]]],
+    # Sequence[Sequence[Sequence[_SupportsArray[dtype[]]]]],
+    # Sequence[Sequence[Sequence[Sequence[_SupportsArray[dtype[]]]]]]]]"
+    np.apply_along_axis(func, axis, data)  # type: ignore[arg-type]
+
+
+def _index_to_interp_indices(index: Index, method: str) -> np.ndarray:
+    """
+    Convert Index to ndarray of indices to pass to NumPy/SciPy.
+    """
+    xarr = index._values
+    if needs_i8_conversion(xarr.dtype):
+        # GH#1646 for dt64tz
+        xarr = xarr.view("i8")
+
+    if method == "linear":
+        inds = xarr
+        inds = cast(np.ndarray, inds)
+    else:
+        inds = np.asarray(xarr)
+
+        if method in ("values", "index"):
+            if inds.dtype == np.object_:
+                inds = lib.maybe_convert_objects(inds)
+
+    return inds
+
+
+def _interpolate_1d(
+    indices: np.ndarray,
+    yvalues: np.ndarray,
+    method: str = "linear",
+    limit: int | None = None,
+    limit_direction: str = "forward",
+    limit_area: Literal["inside", "outside"] | None = None,
+    fill_value: Any | None = None,
+    bounds_error: bool = False,
+    order: int | None = None,
+    mask=None,
+    **kwargs,
+) -> None:
+    """
+    Logic for the 1-d interpolation.  The input
+    indices and yvalues will each be 1-d arrays of the same length.
+
+    Bounds_error is currently hardcoded to False since non-scipy ones don't
+    take it as an argument.
+
+    Notes
+    -----
+    Fills 'yvalues' in-place.
+    """
+    if mask is not None:
+        invalid = mask
+    else:
+        invalid = isna(yvalues)
+    valid = ~invalid
+
+    if not valid.any():
+        return
+
+    if valid.all():
+        return
+
+    # These are sets of index pointers to invalid values... i.e. {0, 1, etc...
+    all_nans = set(np.flatnonzero(invalid))
+
+    first_valid_index = find_valid_index(how="first", is_valid=valid)
+    if first_valid_index is None:  # no nan found in start
+        first_valid_index = 0
+    start_nans = set(range(first_valid_index))
+
+    last_valid_index = find_valid_index(how="last", is_valid=valid)
+    if last_valid_index is None:  # no nan found in end
+        last_valid_index = len(yvalues)
+    end_nans = set(range(1 + last_valid_index, len(valid)))
+
+    # Like the sets above, preserve_nans contains indices of invalid values,
+    # but in this case, it is the final set of indices that need to be
+    # preserved as NaN after the interpolation.
+
+    # For example if limit_direction='forward' then preserve_nans will
+    # contain indices of NaNs at the beginning of the series, and NaNs that
+    # are more than 'limit' away from the prior non-NaN.
+
+    # set preserve_nans based on direction using _interp_limit
+    preserve_nans: list | set
+    if limit_direction == "forward":
+        preserve_nans = start_nans | set(_interp_limit(invalid, limit, 0))
+    elif limit_direction == "backward":
+        preserve_nans = end_nans | set(_interp_limit(invalid, 0, limit))
+    else:
+        # both directions... just use _interp_limit
+        preserve_nans = set(_interp_limit(invalid, limit, limit))
+
+    # if limit_area is set, add either mid or outside indices
+    # to preserve_nans GH #16284
+    if limit_area == "inside":
+        # preserve NaNs on the outside
+        preserve_nans |= start_nans | end_nans
+    elif limit_area == "outside":
+        # preserve NaNs on the inside
+        mid_nans = all_nans - start_nans - end_nans
+        preserve_nans |= mid_nans
+
+    # sort preserve_nans and convert to list
+    preserve_nans = sorted(preserve_nans)
+
+    is_datetimelike = yvalues.dtype.kind in "mM"
+
+    if is_datetimelike:
+        yvalues = yvalues.view("i8")
+
+    if method in NP_METHODS:
+        # np.interp requires sorted X values, #21037
+
+        indexer = np.argsort(indices[valid])
+        yvalues[invalid] = np.interp(
+            indices[invalid], indices[valid][indexer], yvalues[valid][indexer]
+        )
+    else:
+        yvalues[invalid] = _interpolate_scipy_wrapper(
+            indices[valid],
+            yvalues[valid],
+            indices[invalid],
+            method=method,
+            fill_value=fill_value,
+            bounds_error=bounds_error,
+            order=order,
+            **kwargs,
+        )
+
+    if mask is not None:
+        mask[:] = False
+        mask[preserve_nans] = True
+    elif is_datetimelike:
+        yvalues[preserve_nans] = NaT.value
+    else:
+        yvalues[preserve_nans] = np.nan
+    return
+
+
+def _interpolate_scipy_wrapper(
+    x: np.ndarray,
+    y: np.ndarray,
+    new_x: np.ndarray,
+    method: str,
+    fill_value=None,
+    bounds_error: bool = False,
+    order=None,
+    **kwargs,
+):
+    """
+    Passed off to scipy.interpolate.interp1d. method is scipy's kind.
+    Returns an array interpolated at new_x.  Add any new methods to
+    the list in _clean_interp_method.
+    """
+    extra = f"{method} interpolation requires SciPy."
+    import_optional_dependency("scipy", extra=extra)
+    from scipy import interpolate
+
+    new_x = np.asarray(new_x)
+
+    # ignores some kwargs that could be passed along.
+    alt_methods = {
+        "barycentric": interpolate.barycentric_interpolate,
+        "krogh": interpolate.krogh_interpolate,
+        "from_derivatives": _from_derivatives,
+        "piecewise_polynomial": _from_derivatives,
+        "cubicspline": _cubicspline_interpolate,
+        "akima": _akima_interpolate,
+        "pchip": interpolate.pchip_interpolate,
+    }
+
+    interp1d_methods = [
+        "nearest",
+        "zero",
+        "slinear",
+        "quadratic",
+        "cubic",
+        "polynomial",
+    ]
+    if method in interp1d_methods:
+        if method == "polynomial":
+            kind = order
+        else:
+            kind = method
+        terp = interpolate.interp1d(
+            x, y, kind=kind, fill_value=fill_value, bounds_error=bounds_error
+        )
+        new_y = terp(new_x)
+    elif method == "spline":
+        # GH #10633, #24014
+        if isna(order) or (order <= 0):
+            raise ValueError(
+                f"order needs to be specified and greater than 0; got order: {order}"
+            )
+        terp = interpolate.UnivariateSpline(x, y, k=order, **kwargs)
+        new_y = terp(new_x)
+    else:
+        # GH 7295: need to be able to write for some reason
+        # in some circumstances: check all three
+        if not x.flags.writeable:
+            x = x.copy()
+        if not y.flags.writeable:
+            y = y.copy()
+        if not new_x.flags.writeable:
+            new_x = new_x.copy()
+        terp = alt_methods[method]
+        new_y = terp(x, y, new_x, **kwargs)
+    return new_y
+
+
+def _from_derivatives(
+    xi: np.ndarray,
+    yi: np.ndarray,
+    x: np.ndarray,
+    order=None,
+    der: int | list[int] | None = 0,
+    extrapolate: bool = False,
+):
+    """
+    Convenience function for interpolate.BPoly.from_derivatives.
+
+    Construct a piecewise polynomial in the Bernstein basis, compatible
+    with the specified values and derivatives at breakpoints.
+
+    Parameters
+    ----------
+    xi : array-like
+        sorted 1D array of x-coordinates
+    yi : array-like or list of array-likes
+        yi[i][j] is the j-th derivative known at xi[i]
+    order: None or int or array-like of ints. Default: None.
+        Specifies the degree of local polynomials. If not None, some
+        derivatives are ignored.
+    der : int or list
+        How many derivatives to extract; None for all potentially nonzero
+        derivatives (that is a number equal to the number of points), or a
+        list of derivatives to extract. This number includes the function
+        value as 0th derivative.
+     extrapolate : bool, optional
+        Whether to extrapolate to ouf-of-bounds points based on first and last
+        intervals, or to return NaNs. Default: True.
+
+    See Also
+    --------
+    scipy.interpolate.BPoly.from_derivatives
+
+    Returns
+    -------
+    y : scalar or array-like
+        The result, of length R or length M or M by R.
+    """
+    from scipy import interpolate
+
+    # return the method for compat with scipy version & backwards compat
+    method = interpolate.BPoly.from_derivatives
+    m = method(xi, yi.reshape(-1, 1), orders=order, extrapolate=extrapolate)
+
+    return m(x)
+
+
+def _akima_interpolate(
+    xi: np.ndarray,
+    yi: np.ndarray,
+    x: np.ndarray,
+    der: int | list[int] | None = 0,
+    axis: AxisInt = 0,
+):
+    """
+    Convenience function for akima interpolation.
+    xi and yi are arrays of values used to approximate some function f,
+    with ``yi = f(xi)``.
+
+    See `Akima1DInterpolator` for details.
+
+    Parameters
+    ----------
+    xi : np.ndarray
+        A sorted list of x-coordinates, of length N.
+    yi : np.ndarray
+        A 1-D array of real values.  `yi`'s length along the interpolation
+        axis must be equal to the length of `xi`. If N-D array, use axis
+        parameter to select correct axis.
+    x : np.ndarray
+        Of length M.
+    der : int, optional
+        How many derivatives to extract; None for all potentially
+        nonzero derivatives (that is a number equal to the number
+        of points), or a list of derivatives to extract. This number
+        includes the function value as 0th derivative.
+    axis : int, optional
+        Axis in the yi array corresponding to the x-coordinate values.
+
+    See Also
+    --------
+    scipy.interpolate.Akima1DInterpolator
+
+    Returns
+    -------
+    y : scalar or array-like
+        The result, of length R or length M or M by R,
+
+    """
+    from scipy import interpolate
+
+    P = interpolate.Akima1DInterpolator(xi, yi, axis=axis)
+
+    return P(x, nu=der)
+
+
+def _cubicspline_interpolate(
+    xi: np.ndarray,
+    yi: np.ndarray,
+    x: np.ndarray,
+    axis: AxisInt = 0,
+    bc_type: str | tuple[Any, Any] = "not-a-knot",
+    extrapolate=None,
+):
+    """
+    Convenience function for cubic spline data interpolator.
+
+    See `scipy.interpolate.CubicSpline` for details.
+
+    Parameters
+    ----------
+    xi : np.ndarray, shape (n,)
+        1-d array containing values of the independent variable.
+        Values must be real, finite and in strictly increasing order.
+    yi : np.ndarray
+        Array containing values of the dependent variable. It can have
+        arbitrary number of dimensions, but the length along ``axis``
+        (see below) must match the length of ``x``. Values must be finite.
+    x : np.ndarray, shape (m,)
+    axis : int, optional
+        Axis along which `y` is assumed to be varying. Meaning that for
+        ``x[i]`` the corresponding values are ``np.take(y, i, axis=axis)``.
+        Default is 0.
+    bc_type : string or 2-tuple, optional
+        Boundary condition type. Two additional equations, given by the
+        boundary conditions, are required to determine all coefficients of
+        polynomials on each segment [2]_.
+        If `bc_type` is a string, then the specified condition will be applied
+        at both ends of a spline. Available conditions are:
+        * 'not-a-knot' (default): The first and second segment at a curve end
+          are the same polynomial. It is a good default when there is no
+          information on boundary conditions.
+        * 'periodic': The interpolated functions is assumed to be periodic
+          of period ``x[-1] - x[0]``. The first and last value of `y` must be
+          identical: ``y[0] == y[-1]``. This boundary condition will result in
+          ``y'[0] == y'[-1]`` and ``y''[0] == y''[-1]``.
+        * 'clamped': The first derivative at curves ends are zero. Assuming
+          a 1D `y`, ``bc_type=((1, 0.0), (1, 0.0))`` is the same condition.
+        * 'natural': The second derivative at curve ends are zero. Assuming
+          a 1D `y`, ``bc_type=((2, 0.0), (2, 0.0))`` is the same condition.
+        If `bc_type` is a 2-tuple, the first and the second value will be
+        applied at the curve start and end respectively. The tuple values can
+        be one of the previously mentioned strings (except 'periodic') or a
+        tuple `(order, deriv_values)` allowing to specify arbitrary
+        derivatives at curve ends:
+        * `order`: the derivative order, 1 or 2.
+        * `deriv_value`: array-like containing derivative values, shape must
+          be the same as `y`, excluding ``axis`` dimension. For example, if
+          `y` is 1D, then `deriv_value` must be a scalar. If `y` is 3D with
+          the shape (n0, n1, n2) and axis=2, then `deriv_value` must be 2D
+          and have the shape (n0, n1).
+    extrapolate : {bool, 'periodic', None}, optional
+        If bool, determines whether to extrapolate to out-of-bounds points
+        based on first and last intervals, or to return NaNs. If 'periodic',
+        periodic extrapolation is used. If None (default), ``extrapolate`` is
+        set to 'periodic' for ``bc_type='periodic'`` and to True otherwise.
+
+    See Also
+    --------
+    scipy.interpolate.CubicHermiteSpline
+
+    Returns
+    -------
+    y : scalar or array-like
+        The result, of shape (m,)
+
+    References
+    ----------
+    .. [1] `Cubic Spline Interpolation
+            `_
+            on Wikiversity.
+    .. [2] Carl de Boor, "A Practical Guide to Splines", Springer-Verlag, 1978.
+    """
+    from scipy import interpolate
+
+    P = interpolate.CubicSpline(
+        xi, yi, axis=axis, bc_type=bc_type, extrapolate=extrapolate
+    )
+
+    return P(x)
+
+
+def _interpolate_with_limit_area(
+    values: np.ndarray,
+    method: Literal["pad", "backfill"],
+    limit: int | None,
+    limit_area: Literal["inside", "outside"],
+) -> None:
+    """
+    Apply interpolation and limit_area logic to values along a to-be-specified axis.
+
+    Parameters
+    ----------
+    values: np.ndarray
+        Input array.
+    method: str
+        Interpolation method. Could be "bfill" or "pad"
+    limit: int, optional
+        Index limit on interpolation.
+    limit_area: {'inside', 'outside'}
+        Limit area for interpolation.
+
+    Notes
+    -----
+    Modifies values in-place.
+    """
+
+    invalid = isna(values)
+    is_valid = ~invalid
+
+    if not invalid.all():
+        first = find_valid_index(how="first", is_valid=is_valid)
+        if first is None:
+            first = 0
+        last = find_valid_index(how="last", is_valid=is_valid)
+        if last is None:
+            last = len(values)
+
+        pad_or_backfill_inplace(
+            values,
+            method=method,
+            limit=limit,
+            limit_area=limit_area,
+        )
+
+        if limit_area == "inside":
+            invalid[first : last + 1] = False
+        elif limit_area == "outside":
+            invalid[:first] = invalid[last + 1 :] = False
+        else:
+            raise ValueError("limit_area should be 'inside' or 'outside'")
+
+        values[invalid] = np.nan
+
+
+def pad_or_backfill_inplace(
+    values: np.ndarray,
+    method: Literal["pad", "backfill"] = "pad",
+    axis: AxisInt = 0,
+    limit: int | None = None,
+    limit_area: Literal["inside", "outside"] | None = None,
+) -> None:
+    """
+    Perform an actual interpolation of values, values will be make 2-d if
+    needed fills inplace, returns the result.
+
+    Parameters
+    ----------
+    values: np.ndarray
+        Input array.
+    method: str, default "pad"
+        Interpolation method. Could be "bfill" or "pad"
+    axis: 0 or 1
+        Interpolation axis
+    limit: int, optional
+        Index limit on interpolation.
+    limit_area: str, optional
+        Limit area for interpolation. Can be "inside" or "outside"
+
+    Notes
+    -----
+    Modifies values in-place.
+    """
+    transf = (lambda x: x) if axis == 0 else (lambda x: x.T)
+
+    # reshape a 1 dim if needed
+    if values.ndim == 1:
+        if axis != 0:  # pragma: no cover
+            raise AssertionError("cannot interpolate on a ndim == 1 with axis != 0")
+        values = values.reshape(tuple((1,) + values.shape))
+
+    method = clean_fill_method(method)
+    tvalues = transf(values)
+
+    func = get_fill_func(method, ndim=2)
+    # _pad_2d and _backfill_2d both modify tvalues inplace
+    func(tvalues, limit=limit, limit_area=limit_area)
+
+
+def _fillna_prep(
+    values, mask: npt.NDArray[np.bool_] | None = None
+) -> npt.NDArray[np.bool_]:
+    # boilerplate for _pad_1d, _backfill_1d, _pad_2d, _backfill_2d
+
+    if mask is None:
+        mask = isna(values)
+
+    return mask
+
+
+def _datetimelike_compat(func: F) -> F:
+    """
+    Wrapper to handle datetime64 and timedelta64 dtypes.
+    """
+
+    @wraps(func)
+    def new_func(
+        values,
+        limit: int | None = None,
+        limit_area: Literal["inside", "outside"] | None = None,
+        mask=None,
+    ):
+        if needs_i8_conversion(values.dtype):
+            if mask is None:
+                # This needs to occur before casting to int64
+                mask = isna(values)
+
+            result, mask = func(
+                values.view("i8"), limit=limit, limit_area=limit_area, mask=mask
+            )
+            return result.view(values.dtype), mask
+
+        return func(values, limit=limit, limit_area=limit_area, mask=mask)
+
+    return cast(F, new_func)
+
+
+@_datetimelike_compat
+def _pad_1d(
+    values: np.ndarray,
+    limit: int | None = None,
+    limit_area: Literal["inside", "outside"] | None = None,
+    mask: npt.NDArray[np.bool_] | None = None,
+) -> tuple[np.ndarray, npt.NDArray[np.bool_]]:
+    mask = _fillna_prep(values, mask)
+    if limit_area is not None and not mask.all():
+        _fill_limit_area_1d(mask, limit_area)
+    algos.pad_inplace(values, mask, limit=limit)
+    return values, mask
+
+
+@_datetimelike_compat
+def _backfill_1d(
+    values: np.ndarray,
+    limit: int | None = None,
+    limit_area: Literal["inside", "outside"] | None = None,
+    mask: npt.NDArray[np.bool_] | None = None,
+) -> tuple[np.ndarray, npt.NDArray[np.bool_]]:
+    mask = _fillna_prep(values, mask)
+    if limit_area is not None and not mask.all():
+        _fill_limit_area_1d(mask, limit_area)
+    algos.backfill_inplace(values, mask, limit=limit)
+    return values, mask
+
+
+@_datetimelike_compat
+def _pad_2d(
+    values: np.ndarray,
+    limit: int | None = None,
+    limit_area: Literal["inside", "outside"] | None = None,
+    mask: npt.NDArray[np.bool_] | None = None,
+):
+    mask = _fillna_prep(values, mask)
+    if limit_area is not None:
+        _fill_limit_area_2d(mask, limit_area)
+
+    if values.size:
+        algos.pad_2d_inplace(values, mask, limit=limit)
+    else:
+        # for test coverage
+        pass
+    return values, mask
+
+
+@_datetimelike_compat
+def _backfill_2d(
+    values,
+    limit: int | None = None,
+    limit_area: Literal["inside", "outside"] | None = None,
+    mask: npt.NDArray[np.bool_] | None = None,
+):
+    mask = _fillna_prep(values, mask)
+    if limit_area is not None:
+        _fill_limit_area_2d(mask, limit_area)
+
+    if values.size:
+        algos.backfill_2d_inplace(values, mask, limit=limit)
+    else:
+        # for test coverage
+        pass
+    return values, mask
+
+
+def _fill_limit_area_1d(
+    mask: npt.NDArray[np.bool_], limit_area: Literal["outside", "inside"]
+) -> None:
+    """Prepare 1d mask for ffill/bfill with limit_area.
+
+    Caller is responsible for checking at least one value of mask is False.
+    When called, mask will no longer faithfully represent when
+    the corresponding are NA or not.
+
+    Parameters
+    ----------
+    mask : np.ndarray[bool, ndim=1]
+        Mask representing NA values when filling.
+    limit_area : { "outside", "inside" }
+        Whether to limit filling to outside or inside the outer most non-NA value.
+    """
+    neg_mask = ~mask
+    first = neg_mask.argmax()
+    last = len(neg_mask) - neg_mask[::-1].argmax() - 1
+    if limit_area == "inside":
+        mask[:first] = False
+        mask[last + 1 :] = False
+    elif limit_area == "outside":
+        mask[first + 1 : last] = False
+
+
+def _fill_limit_area_2d(
+    mask: npt.NDArray[np.bool_], limit_area: Literal["outside", "inside"]
+) -> None:
+    """Prepare 2d mask for ffill/bfill with limit_area.
+
+    When called, mask will no longer faithfully represent when
+    the corresponding are NA or not.
+
+    Parameters
+    ----------
+    mask : np.ndarray[bool, ndim=1]
+        Mask representing NA values when filling.
+    limit_area : { "outside", "inside" }
+        Whether to limit filling to outside or inside the outer most non-NA value.
+    """
+    neg_mask = ~mask.T
+    if limit_area == "outside":
+        # Identify inside
+        la_mask = (
+            np.maximum.accumulate(neg_mask, axis=0)
+            & np.maximum.accumulate(neg_mask[::-1], axis=0)[::-1]
+        )
+    else:
+        # Identify outside
+        la_mask = (
+            ~np.maximum.accumulate(neg_mask, axis=0)
+            | ~np.maximum.accumulate(neg_mask[::-1], axis=0)[::-1]
+        )
+    mask[la_mask.T] = False
+
+
+_fill_methods = {"pad": _pad_1d, "backfill": _backfill_1d}
+
+
+def get_fill_func(method, ndim: int = 1):
+    method = clean_fill_method(method)
+    if ndim == 1:
+        return _fill_methods[method]
+    return {"pad": _pad_2d, "backfill": _backfill_2d}[method]
+
+
+def clean_reindex_fill_method(method) -> ReindexMethod | None:
+    if method is None:
+        return None
+    return clean_fill_method(method, allow_nearest=True)
+
+
+def _interp_limit(
+    invalid: npt.NDArray[np.bool_], fw_limit: int | None, bw_limit: int | None
+):
+    """
+    Get indexers of values that won't be filled
+    because they exceed the limits.
+
+    Parameters
+    ----------
+    invalid : np.ndarray[bool]
+    fw_limit : int or None
+        forward limit to index
+    bw_limit : int or None
+        backward limit to index
+
+    Returns
+    -------
+    set of indexers
+
+    Notes
+    -----
+    This is equivalent to the more readable, but slower
+
+    .. code-block:: python
+
+        def _interp_limit(invalid, fw_limit, bw_limit):
+            for x in np.where(invalid)[0]:
+                if invalid[max(0, x - fw_limit):x + bw_limit + 1].all():
+                    yield x
+    """
+    # handle forward first; the backward direction is the same except
+    # 1. operate on the reversed array
+    # 2. subtract the returned indices from N - 1
+    N = len(invalid)
+    f_idx = set()
+    b_idx = set()
+
+    def inner(invalid, limit: int):
+        limit = min(limit, N)
+        windowed = _rolling_window(invalid, limit + 1).all(1)
+        idx = set(np.where(windowed)[0] + limit) | set(
+            np.where((~invalid[: limit + 1]).cumsum() == 0)[0]
+        )
+        return idx
+
+    if fw_limit is not None:
+        if fw_limit == 0:
+            f_idx = set(np.where(invalid)[0])
+        else:
+            f_idx = inner(invalid, fw_limit)
+
+    if bw_limit is not None:
+        if bw_limit == 0:
+            # then we don't even need to care about backwards
+            # just use forwards
+            return f_idx
+        else:
+            b_idx_inv = list(inner(invalid[::-1], bw_limit))
+            b_idx = set(N - 1 - np.asarray(b_idx_inv))
+            if fw_limit == 0:
+                return b_idx
+
+    return f_idx & b_idx
+
+
+def _rolling_window(a: npt.NDArray[np.bool_], window: int) -> npt.NDArray[np.bool_]:
+    """
+    [True, True, False, True, False], 2 ->
+
+    [
+        [True,  True],
+        [True, False],
+        [False, True],
+        [True, False],
+    ]
+    """
+    # https://stackoverflow.com/a/6811241
+    shape = a.shape[:-1] + (a.shape[-1] - window + 1, window)
+    strides = a.strides + (a.strides[-1],)
+    return np.lib.stride_tricks.as_strided(a, shape=shape, strides=strides)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/nanops.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/nanops.py
new file mode 100644
index 0000000000000000000000000000000000000000..6cb825e9b79a25742d6f9f622b7226dbd68d88fe
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/nanops.py
@@ -0,0 +1,1748 @@
+from __future__ import annotations
+
+import functools
+import itertools
+from typing import (
+    Any,
+    Callable,
+    cast,
+)
+import warnings
+
+import numpy as np
+
+from pandas._config import get_option
+
+from pandas._libs import (
+    NaT,
+    NaTType,
+    iNaT,
+    lib,
+)
+from pandas._typing import (
+    ArrayLike,
+    AxisInt,
+    CorrelationMethod,
+    Dtype,
+    DtypeObj,
+    F,
+    Scalar,
+    Shape,
+    npt,
+)
+from pandas.compat._optional import import_optional_dependency
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.common import (
+    is_complex,
+    is_float,
+    is_float_dtype,
+    is_integer,
+    is_numeric_dtype,
+    is_object_dtype,
+    needs_i8_conversion,
+    pandas_dtype,
+)
+from pandas.core.dtypes.missing import (
+    isna,
+    na_value_for_dtype,
+    notna,
+)
+
+bn = import_optional_dependency("bottleneck", errors="warn")
+_BOTTLENECK_INSTALLED = bn is not None
+_USE_BOTTLENECK = False
+
+
+def set_use_bottleneck(v: bool = True) -> None:
+    # set/unset to use bottleneck
+    global _USE_BOTTLENECK
+    if _BOTTLENECK_INSTALLED:
+        _USE_BOTTLENECK = v
+
+
+set_use_bottleneck(get_option("compute.use_bottleneck"))
+
+
+class disallow:
+    def __init__(self, *dtypes: Dtype) -> None:
+        super().__init__()
+        self.dtypes = tuple(pandas_dtype(dtype).type for dtype in dtypes)
+
+    def check(self, obj) -> bool:
+        return hasattr(obj, "dtype") and issubclass(obj.dtype.type, self.dtypes)
+
+    def __call__(self, f: F) -> F:
+        @functools.wraps(f)
+        def _f(*args, **kwargs):
+            obj_iter = itertools.chain(args, kwargs.values())
+            if any(self.check(obj) for obj in obj_iter):
+                f_name = f.__name__.replace("nan", "")
+                raise TypeError(
+                    f"reduction operation '{f_name}' not allowed for this dtype"
+                )
+            try:
+                return f(*args, **kwargs)
+            except ValueError as e:
+                # we want to transform an object array
+                # ValueError message to the more typical TypeError
+                # e.g. this is normally a disallowed function on
+                # object arrays that contain strings
+                if is_object_dtype(args[0]):
+                    raise TypeError(e) from e
+                raise
+
+        return cast(F, _f)
+
+
+class bottleneck_switch:
+    def __init__(self, name=None, **kwargs) -> None:
+        self.name = name
+        self.kwargs = kwargs
+
+    def __call__(self, alt: F) -> F:
+        bn_name = self.name or alt.__name__
+
+        try:
+            bn_func = getattr(bn, bn_name)
+        except (AttributeError, NameError):  # pragma: no cover
+            bn_func = None
+
+        @functools.wraps(alt)
+        def f(
+            values: np.ndarray,
+            *,
+            axis: AxisInt | None = None,
+            skipna: bool = True,
+            **kwds,
+        ):
+            if len(self.kwargs) > 0:
+                for k, v in self.kwargs.items():
+                    if k not in kwds:
+                        kwds[k] = v
+
+            if values.size == 0 and kwds.get("min_count") is None:
+                # We are empty, returning NA for our type
+                # Only applies for the default `min_count` of None
+                # since that affects how empty arrays are handled.
+                # TODO(GH-18976) update all the nanops methods to
+                # correctly handle empty inputs and remove this check.
+                # It *may* just be `var`
+                return _na_for_min_count(values, axis)
+
+            if _USE_BOTTLENECK and skipna and _bn_ok_dtype(values.dtype, bn_name):
+                if kwds.get("mask", None) is None:
+                    # `mask` is not recognised by bottleneck, would raise
+                    #  TypeError if called
+                    kwds.pop("mask", None)
+                    result = bn_func(values, axis=axis, **kwds)
+
+                    # prefer to treat inf/-inf as NA, but must compute the func
+                    # twice :(
+                    if _has_infs(result):
+                        result = alt(values, axis=axis, skipna=skipna, **kwds)
+                else:
+                    result = alt(values, axis=axis, skipna=skipna, **kwds)
+            else:
+                result = alt(values, axis=axis, skipna=skipna, **kwds)
+
+            return result
+
+        return cast(F, f)
+
+
+def _bn_ok_dtype(dtype: DtypeObj, name: str) -> bool:
+    # Bottleneck chokes on datetime64, PeriodDtype (or and EA)
+    if dtype != object and not needs_i8_conversion(dtype):
+        # GH 42878
+        # Bottleneck uses naive summation leading to O(n) loss of precision
+        # unlike numpy which implements pairwise summation, which has O(log(n)) loss
+        # crossref: https://github.com/pydata/bottleneck/issues/379
+
+        # GH 15507
+        # bottleneck does not properly upcast during the sum
+        # so can overflow
+
+        # GH 9422
+        # further we also want to preserve NaN when all elements
+        # are NaN, unlike bottleneck/numpy which consider this
+        # to be 0
+        return name not in ["nansum", "nanprod", "nanmean"]
+    return False
+
+
+def _has_infs(result) -> bool:
+    if isinstance(result, np.ndarray):
+        if result.dtype in ("f8", "f4"):
+            # Note: outside of an nanops-specific test, we always have
+            #  result.ndim == 1, so there is no risk of this ravel making a copy.
+            return lib.has_infs(result.ravel("K"))
+    try:
+        return np.isinf(result).any()
+    except (TypeError, NotImplementedError):
+        # if it doesn't support infs, then it can't have infs
+        return False
+
+
+def _get_fill_value(
+    dtype: DtypeObj, fill_value: Scalar | None = None, fill_value_typ=None
+):
+    """return the correct fill value for the dtype of the values"""
+    if fill_value is not None:
+        return fill_value
+    if _na_ok_dtype(dtype):
+        if fill_value_typ is None:
+            return np.nan
+        else:
+            if fill_value_typ == "+inf":
+                return np.inf
+            else:
+                return -np.inf
+    else:
+        if fill_value_typ == "+inf":
+            # need the max int here
+            return lib.i8max
+        else:
+            return iNaT
+
+
+def _maybe_get_mask(
+    values: np.ndarray, skipna: bool, mask: npt.NDArray[np.bool_] | None
+) -> npt.NDArray[np.bool_] | None:
+    """
+    Compute a mask if and only if necessary.
+
+    This function will compute a mask iff it is necessary. Otherwise,
+    return the provided mask (potentially None) when a mask does not need to be
+    computed.
+
+    A mask is never necessary if the values array is of boolean or integer
+    dtypes, as these are incapable of storing NaNs. If passing a NaN-capable
+    dtype that is interpretable as either boolean or integer data (eg,
+    timedelta64), a mask must be provided.
+
+    If the skipna parameter is False, a new mask will not be computed.
+
+    The mask is computed using isna() by default. Setting invert=True selects
+    notna() as the masking function.
+
+    Parameters
+    ----------
+    values : ndarray
+        input array to potentially compute mask for
+    skipna : bool
+        boolean for whether NaNs should be skipped
+    mask : Optional[ndarray]
+        nan-mask if known
+
+    Returns
+    -------
+    Optional[np.ndarray[bool]]
+    """
+    if mask is None:
+        if values.dtype.kind in "biu":
+            # Boolean data cannot contain nulls, so signal via mask being None
+            return None
+
+        if skipna or values.dtype.kind in "mM":
+            mask = isna(values)
+
+    return mask
+
+
+def _get_values(
+    values: np.ndarray,
+    skipna: bool,
+    fill_value: Any = None,
+    fill_value_typ: str | None = None,
+    mask: npt.NDArray[np.bool_] | None = None,
+) -> tuple[np.ndarray, npt.NDArray[np.bool_] | None]:
+    """
+    Utility to get the values view, mask, dtype, dtype_max, and fill_value.
+
+    If both mask and fill_value/fill_value_typ are not None and skipna is True,
+    the values array will be copied.
+
+    For input arrays of boolean or integer dtypes, copies will only occur if a
+    precomputed mask, a fill_value/fill_value_typ, and skipna=True are
+    provided.
+
+    Parameters
+    ----------
+    values : ndarray
+        input array to potentially compute mask for
+    skipna : bool
+        boolean for whether NaNs should be skipped
+    fill_value : Any
+        value to fill NaNs with
+    fill_value_typ : str
+        Set to '+inf' or '-inf' to handle dtype-specific infinities
+    mask : Optional[np.ndarray[bool]]
+        nan-mask if known
+
+    Returns
+    -------
+    values : ndarray
+        Potential copy of input value array
+    mask : Optional[ndarray[bool]]
+        Mask for values, if deemed necessary to compute
+    """
+    # In _get_values is only called from within nanops, and in all cases
+    #  with scalar fill_value.  This guarantee is important for the
+    #  np.where call below
+
+    mask = _maybe_get_mask(values, skipna, mask)
+
+    dtype = values.dtype
+
+    datetimelike = False
+    if values.dtype.kind in "mM":
+        # changing timedelta64/datetime64 to int64 needs to happen after
+        #  finding `mask` above
+        values = np.asarray(values.view("i8"))
+        datetimelike = True
+
+    if skipna and (mask is not None):
+        # get our fill value (in case we need to provide an alternative
+        # dtype for it)
+        fill_value = _get_fill_value(
+            dtype, fill_value=fill_value, fill_value_typ=fill_value_typ
+        )
+
+        if fill_value is not None:
+            if mask.any():
+                if datetimelike or _na_ok_dtype(dtype):
+                    values = values.copy()
+                    np.putmask(values, mask, fill_value)
+                else:
+                    # np.where will promote if needed
+                    values = np.where(~mask, values, fill_value)
+
+    return values, mask
+
+
+def _get_dtype_max(dtype: np.dtype) -> np.dtype:
+    # return a platform independent precision dtype
+    dtype_max = dtype
+    if dtype.kind in "bi":
+        dtype_max = np.dtype(np.int64)
+    elif dtype.kind == "u":
+        dtype_max = np.dtype(np.uint64)
+    elif dtype.kind == "f":
+        dtype_max = np.dtype(np.float64)
+    return dtype_max
+
+
+def _na_ok_dtype(dtype: DtypeObj) -> bool:
+    if needs_i8_conversion(dtype):
+        return False
+    return not issubclass(dtype.type, np.integer)
+
+
+def _wrap_results(result, dtype: np.dtype, fill_value=None):
+    """wrap our results if needed"""
+    if result is NaT:
+        pass
+
+    elif dtype.kind == "M":
+        if fill_value is None:
+            # GH#24293
+            fill_value = iNaT
+        if not isinstance(result, np.ndarray):
+            assert not isna(fill_value), "Expected non-null fill_value"
+            if result == fill_value:
+                result = np.nan
+
+            if isna(result):
+                result = np.datetime64("NaT", "ns").astype(dtype)
+            else:
+                result = np.int64(result).view(dtype)
+            # retain original unit
+            result = result.astype(dtype, copy=False)
+        else:
+            # If we have float dtype, taking a view will give the wrong result
+            result = result.astype(dtype)
+    elif dtype.kind == "m":
+        if not isinstance(result, np.ndarray):
+            if result == fill_value or np.isnan(result):
+                result = np.timedelta64("NaT").astype(dtype)
+
+            elif np.fabs(result) > lib.i8max:
+                # raise if we have a timedelta64[ns] which is too large
+                raise ValueError("overflow in timedelta operation")
+            else:
+                # return a timedelta64 with the original unit
+                result = np.int64(result).astype(dtype, copy=False)
+
+        else:
+            result = result.astype("m8[ns]").view(dtype)
+
+    return result
+
+
+def _datetimelike_compat(func: F) -> F:
+    """
+    If we have datetime64 or timedelta64 values, ensure we have a correct
+    mask before calling the wrapped function, then cast back afterwards.
+    """
+
+    @functools.wraps(func)
+    def new_func(
+        values: np.ndarray,
+        *,
+        axis: AxisInt | None = None,
+        skipna: bool = True,
+        mask: npt.NDArray[np.bool_] | None = None,
+        **kwargs,
+    ):
+        orig_values = values
+
+        datetimelike = values.dtype.kind in "mM"
+        if datetimelike and mask is None:
+            mask = isna(values)
+
+        result = func(values, axis=axis, skipna=skipna, mask=mask, **kwargs)
+
+        if datetimelike:
+            result = _wrap_results(result, orig_values.dtype, fill_value=iNaT)
+            if not skipna:
+                assert mask is not None  # checked above
+                result = _mask_datetimelike_result(result, axis, mask, orig_values)
+
+        return result
+
+    return cast(F, new_func)
+
+
+def _na_for_min_count(values: np.ndarray, axis: AxisInt | None) -> Scalar | np.ndarray:
+    """
+    Return the missing value for `values`.
+
+    Parameters
+    ----------
+    values : ndarray
+    axis : int or None
+        axis for the reduction, required if values.ndim > 1.
+
+    Returns
+    -------
+    result : scalar or ndarray
+        For 1-D values, returns a scalar of the correct missing type.
+        For 2-D values, returns a 1-D array where each element is missing.
+    """
+    # we either return np.nan or pd.NaT
+    if values.dtype.kind in "iufcb":
+        values = values.astype("float64")
+    fill_value = na_value_for_dtype(values.dtype)
+
+    if values.ndim == 1:
+        return fill_value
+    elif axis is None:
+        return fill_value
+    else:
+        result_shape = values.shape[:axis] + values.shape[axis + 1 :]
+
+        return np.full(result_shape, fill_value, dtype=values.dtype)
+
+
+def maybe_operate_rowwise(func: F) -> F:
+    """
+    NumPy operations on C-contiguous ndarrays with axis=1 can be
+    very slow if axis 1 >> axis 0.
+    Operate row-by-row and concatenate the results.
+    """
+
+    @functools.wraps(func)
+    def newfunc(values: np.ndarray, *, axis: AxisInt | None = None, **kwargs):
+        if (
+            axis == 1
+            and values.ndim == 2
+            and values.flags["C_CONTIGUOUS"]
+            # only takes this path for wide arrays (long dataframes), for threshold see
+            # https://github.com/pandas-dev/pandas/pull/43311#issuecomment-974891737
+            and (values.shape[1] / 1000) > values.shape[0]
+            and values.dtype != object
+            and values.dtype != bool
+        ):
+            arrs = list(values)
+            if kwargs.get("mask") is not None:
+                mask = kwargs.pop("mask")
+                results = [
+                    func(arrs[i], mask=mask[i], **kwargs) for i in range(len(arrs))
+                ]
+            else:
+                results = [func(x, **kwargs) for x in arrs]
+            return np.array(results)
+
+        return func(values, axis=axis, **kwargs)
+
+    return cast(F, newfunc)
+
+
+def nanany(
+    values: np.ndarray,
+    *,
+    axis: AxisInt | None = None,
+    skipna: bool = True,
+    mask: npt.NDArray[np.bool_] | None = None,
+) -> bool:
+    """
+    Check if any elements along an axis evaluate to True.
+
+    Parameters
+    ----------
+    values : ndarray
+    axis : int, optional
+    skipna : bool, default True
+    mask : ndarray[bool], optional
+        nan-mask if known
+
+    Returns
+    -------
+    result : bool
+
+    Examples
+    --------
+    >>> from pandas.core import nanops
+    >>> s = pd.Series([1, 2])
+    >>> nanops.nanany(s.values)
+    True
+
+    >>> from pandas.core import nanops
+    >>> s = pd.Series([np.nan])
+    >>> nanops.nanany(s.values)
+    False
+    """
+    if values.dtype.kind in "iub" and mask is None:
+        # GH#26032 fastpath
+        # error: Incompatible return value type (got "Union[bool_, ndarray]",
+        # expected "bool")
+        return values.any(axis)  # type: ignore[return-value]
+
+    if values.dtype.kind == "M":
+        # GH#34479
+        warnings.warn(
+            "'any' with datetime64 dtypes is deprecated and will raise in a "
+            "future version. Use (obj != pd.Timestamp(0)).any() instead.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+
+    values, _ = _get_values(values, skipna, fill_value=False, mask=mask)
+
+    # For object type, any won't necessarily return
+    # boolean values (numpy/numpy#4352)
+    if values.dtype == object:
+        values = values.astype(bool)
+
+    # error: Incompatible return value type (got "Union[bool_, ndarray]", expected
+    # "bool")
+    return values.any(axis)  # type: ignore[return-value]
+
+
+def nanall(
+    values: np.ndarray,
+    *,
+    axis: AxisInt | None = None,
+    skipna: bool = True,
+    mask: npt.NDArray[np.bool_] | None = None,
+) -> bool:
+    """
+    Check if all elements along an axis evaluate to True.
+
+    Parameters
+    ----------
+    values : ndarray
+    axis : int, optional
+    skipna : bool, default True
+    mask : ndarray[bool], optional
+        nan-mask if known
+
+    Returns
+    -------
+    result : bool
+
+    Examples
+    --------
+    >>> from pandas.core import nanops
+    >>> s = pd.Series([1, 2, np.nan])
+    >>> nanops.nanall(s.values)
+    True
+
+    >>> from pandas.core import nanops
+    >>> s = pd.Series([1, 0])
+    >>> nanops.nanall(s.values)
+    False
+    """
+    if values.dtype.kind in "iub" and mask is None:
+        # GH#26032 fastpath
+        # error: Incompatible return value type (got "Union[bool_, ndarray]",
+        # expected "bool")
+        return values.all(axis)  # type: ignore[return-value]
+
+    if values.dtype.kind == "M":
+        # GH#34479
+        warnings.warn(
+            "'all' with datetime64 dtypes is deprecated and will raise in a "
+            "future version. Use (obj != pd.Timestamp(0)).all() instead.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+
+    values, _ = _get_values(values, skipna, fill_value=True, mask=mask)
+
+    # For object type, all won't necessarily return
+    # boolean values (numpy/numpy#4352)
+    if values.dtype == object:
+        values = values.astype(bool)
+
+    # error: Incompatible return value type (got "Union[bool_, ndarray]", expected
+    # "bool")
+    return values.all(axis)  # type: ignore[return-value]
+
+
+@disallow("M8")
+@_datetimelike_compat
+@maybe_operate_rowwise
+def nansum(
+    values: np.ndarray,
+    *,
+    axis: AxisInt | None = None,
+    skipna: bool = True,
+    min_count: int = 0,
+    mask: npt.NDArray[np.bool_] | None = None,
+) -> float:
+    """
+    Sum the elements along an axis ignoring NaNs
+
+    Parameters
+    ----------
+    values : ndarray[dtype]
+    axis : int, optional
+    skipna : bool, default True
+    min_count: int, default 0
+    mask : ndarray[bool], optional
+        nan-mask if known
+
+    Returns
+    -------
+    result : dtype
+
+    Examples
+    --------
+    >>> from pandas.core import nanops
+    >>> s = pd.Series([1, 2, np.nan])
+    >>> nanops.nansum(s.values)
+    3.0
+    """
+    dtype = values.dtype
+    values, mask = _get_values(values, skipna, fill_value=0, mask=mask)
+    dtype_sum = _get_dtype_max(dtype)
+    if dtype.kind == "f":
+        dtype_sum = dtype
+    elif dtype.kind == "m":
+        dtype_sum = np.dtype(np.float64)
+
+    the_sum = values.sum(axis, dtype=dtype_sum)
+    the_sum = _maybe_null_out(the_sum, axis, mask, values.shape, min_count=min_count)
+
+    return the_sum
+
+
+def _mask_datetimelike_result(
+    result: np.ndarray | np.datetime64 | np.timedelta64,
+    axis: AxisInt | None,
+    mask: npt.NDArray[np.bool_],
+    orig_values: np.ndarray,
+) -> np.ndarray | np.datetime64 | np.timedelta64 | NaTType:
+    if isinstance(result, np.ndarray):
+        # we need to apply the mask
+        result = result.astype("i8").view(orig_values.dtype)
+        axis_mask = mask.any(axis=axis)
+        # error: Unsupported target for indexed assignment ("Union[ndarray[Any, Any],
+        # datetime64, timedelta64]")
+        result[axis_mask] = iNaT  # type: ignore[index]
+    else:
+        if mask.any():
+            return np.int64(iNaT).view(orig_values.dtype)
+    return result
+
+
+@bottleneck_switch()
+@_datetimelike_compat
+def nanmean(
+    values: np.ndarray,
+    *,
+    axis: AxisInt | None = None,
+    skipna: bool = True,
+    mask: npt.NDArray[np.bool_] | None = None,
+) -> float:
+    """
+    Compute the mean of the element along an axis ignoring NaNs
+
+    Parameters
+    ----------
+    values : ndarray
+    axis : int, optional
+    skipna : bool, default True
+    mask : ndarray[bool], optional
+        nan-mask if known
+
+    Returns
+    -------
+    float
+        Unless input is a float array, in which case use the same
+        precision as the input array.
+
+    Examples
+    --------
+    >>> from pandas.core import nanops
+    >>> s = pd.Series([1, 2, np.nan])
+    >>> nanops.nanmean(s.values)
+    1.5
+    """
+    dtype = values.dtype
+    values, mask = _get_values(values, skipna, fill_value=0, mask=mask)
+    dtype_sum = _get_dtype_max(dtype)
+    dtype_count = np.dtype(np.float64)
+
+    # not using needs_i8_conversion because that includes period
+    if dtype.kind in "mM":
+        dtype_sum = np.dtype(np.float64)
+    elif dtype.kind in "iu":
+        dtype_sum = np.dtype(np.float64)
+    elif dtype.kind == "f":
+        dtype_sum = dtype
+        dtype_count = dtype
+
+    count = _get_counts(values.shape, mask, axis, dtype=dtype_count)
+    the_sum = values.sum(axis, dtype=dtype_sum)
+    the_sum = _ensure_numeric(the_sum)
+
+    if axis is not None and getattr(the_sum, "ndim", False):
+        count = cast(np.ndarray, count)
+        with np.errstate(all="ignore"):
+            # suppress division by zero warnings
+            the_mean = the_sum / count
+        ct_mask = count == 0
+        if ct_mask.any():
+            the_mean[ct_mask] = np.nan
+    else:
+        the_mean = the_sum / count if count > 0 else np.nan
+
+    return the_mean
+
+
+@bottleneck_switch()
+def nanmedian(values, *, axis: AxisInt | None = None, skipna: bool = True, mask=None):
+    """
+    Parameters
+    ----------
+    values : ndarray
+    axis : int, optional
+    skipna : bool, default True
+    mask : ndarray[bool], optional
+        nan-mask if known
+
+    Returns
+    -------
+    result : float
+        Unless input is a float array, in which case use the same
+        precision as the input array.
+
+    Examples
+    --------
+    >>> from pandas.core import nanops
+    >>> s = pd.Series([1, np.nan, 2, 2])
+    >>> nanops.nanmedian(s.values)
+    2.0
+    """
+    # for floats without mask, the data already uses NaN as missing value
+    # indicator, and `mask` will be calculated from that below -> in those
+    # cases we never need to set NaN to the masked values
+    using_nan_sentinel = values.dtype.kind == "f" and mask is None
+
+    def get_median(x, _mask=None):
+        if _mask is None:
+            _mask = notna(x)
+        else:
+            _mask = ~_mask
+        if not skipna and not _mask.all():
+            return np.nan
+        with warnings.catch_warnings():
+            # Suppress RuntimeWarning about All-NaN slice
+            warnings.filterwarnings(
+                "ignore", "All-NaN slice encountered", RuntimeWarning
+            )
+            res = np.nanmedian(x[_mask])
+        return res
+
+    dtype = values.dtype
+    values, mask = _get_values(values, skipna, mask=mask, fill_value=None)
+    if values.dtype.kind != "f":
+        if values.dtype == object:
+            # GH#34671 avoid casting strings to numeric
+            inferred = lib.infer_dtype(values)
+            if inferred in ["string", "mixed"]:
+                raise TypeError(f"Cannot convert {values} to numeric")
+        try:
+            values = values.astype("f8")
+        except ValueError as err:
+            # e.g. "could not convert string to float: 'a'"
+            raise TypeError(str(err)) from err
+    if not using_nan_sentinel and mask is not None:
+        if not values.flags.writeable:
+            values = values.copy()
+        values[mask] = np.nan
+
+    notempty = values.size
+
+    # an array from a frame
+    if values.ndim > 1 and axis is not None:
+        # there's a non-empty array to apply over otherwise numpy raises
+        if notempty:
+            if not skipna:
+                res = np.apply_along_axis(get_median, axis, values)
+
+            else:
+                # fastpath for the skipna case
+                with warnings.catch_warnings():
+                    # Suppress RuntimeWarning about All-NaN slice
+                    warnings.filterwarnings(
+                        "ignore", "All-NaN slice encountered", RuntimeWarning
+                    )
+                    if (values.shape[1] == 1 and axis == 0) or (
+                        values.shape[0] == 1 and axis == 1
+                    ):
+                        # GH52788: fastpath when squeezable, nanmedian for 2D array slow
+                        res = np.nanmedian(np.squeeze(values), keepdims=True)
+                    else:
+                        res = np.nanmedian(values, axis=axis)
+
+        else:
+            # must return the correct shape, but median is not defined for the
+            # empty set so return nans of shape "everything but the passed axis"
+            # since "axis" is where the reduction would occur if we had a nonempty
+            # array
+            res = _get_empty_reduction_result(values.shape, axis)
+
+    else:
+        # otherwise return a scalar value
+        res = get_median(values, mask) if notempty else np.nan
+    return _wrap_results(res, dtype)
+
+
+def _get_empty_reduction_result(
+    shape: Shape,
+    axis: AxisInt,
+) -> np.ndarray:
+    """
+    The result from a reduction on an empty ndarray.
+
+    Parameters
+    ----------
+    shape : Tuple[int, ...]
+    axis : int
+
+    Returns
+    -------
+    np.ndarray
+    """
+    shp = np.array(shape)
+    dims = np.arange(len(shape))
+    ret = np.empty(shp[dims != axis], dtype=np.float64)
+    ret.fill(np.nan)
+    return ret
+
+
+def _get_counts_nanvar(
+    values_shape: Shape,
+    mask: npt.NDArray[np.bool_] | None,
+    axis: AxisInt | None,
+    ddof: int,
+    dtype: np.dtype = np.dtype(np.float64),
+) -> tuple[float | np.ndarray, float | np.ndarray]:
+    """
+    Get the count of non-null values along an axis, accounting
+    for degrees of freedom.
+
+    Parameters
+    ----------
+    values_shape : Tuple[int, ...]
+        shape tuple from values ndarray, used if mask is None
+    mask : Optional[ndarray[bool]]
+        locations in values that should be considered missing
+    axis : Optional[int]
+        axis to count along
+    ddof : int
+        degrees of freedom
+    dtype : type, optional
+        type to use for count
+
+    Returns
+    -------
+    count : int, np.nan or np.ndarray
+    d : int, np.nan or np.ndarray
+    """
+    count = _get_counts(values_shape, mask, axis, dtype=dtype)
+    d = count - dtype.type(ddof)
+
+    # always return NaN, never inf
+    if is_float(count):
+        if count <= ddof:
+            # error: Incompatible types in assignment (expression has type
+            # "float", variable has type "Union[floating[Any], ndarray[Any,
+            # dtype[floating[Any]]]]")
+            count = np.nan  # type: ignore[assignment]
+            d = np.nan
+    else:
+        # count is not narrowed by is_float check
+        count = cast(np.ndarray, count)
+        mask = count <= ddof
+        if mask.any():
+            np.putmask(d, mask, np.nan)
+            np.putmask(count, mask, np.nan)
+    return count, d
+
+
+@bottleneck_switch(ddof=1)
+def nanstd(
+    values,
+    *,
+    axis: AxisInt | None = None,
+    skipna: bool = True,
+    ddof: int = 1,
+    mask=None,
+):
+    """
+    Compute the standard deviation along given axis while ignoring NaNs
+
+    Parameters
+    ----------
+    values : ndarray
+    axis : int, optional
+    skipna : bool, default True
+    ddof : int, default 1
+        Delta Degrees of Freedom. The divisor used in calculations is N - ddof,
+        where N represents the number of elements.
+    mask : ndarray[bool], optional
+        nan-mask if known
+
+    Returns
+    -------
+    result : float
+        Unless input is a float array, in which case use the same
+        precision as the input array.
+
+    Examples
+    --------
+    >>> from pandas.core import nanops
+    >>> s = pd.Series([1, np.nan, 2, 3])
+    >>> nanops.nanstd(s.values)
+    1.0
+    """
+    if values.dtype == "M8[ns]":
+        values = values.view("m8[ns]")
+
+    orig_dtype = values.dtype
+    values, mask = _get_values(values, skipna, mask=mask)
+
+    result = np.sqrt(nanvar(values, axis=axis, skipna=skipna, ddof=ddof, mask=mask))
+    return _wrap_results(result, orig_dtype)
+
+
+@disallow("M8", "m8")
+@bottleneck_switch(ddof=1)
+def nanvar(
+    values: np.ndarray,
+    *,
+    axis: AxisInt | None = None,
+    skipna: bool = True,
+    ddof: int = 1,
+    mask=None,
+):
+    """
+    Compute the variance along given axis while ignoring NaNs
+
+    Parameters
+    ----------
+    values : ndarray
+    axis : int, optional
+    skipna : bool, default True
+    ddof : int, default 1
+        Delta Degrees of Freedom. The divisor used in calculations is N - ddof,
+        where N represents the number of elements.
+    mask : ndarray[bool], optional
+        nan-mask if known
+
+    Returns
+    -------
+    result : float
+        Unless input is a float array, in which case use the same
+        precision as the input array.
+
+    Examples
+    --------
+    >>> from pandas.core import nanops
+    >>> s = pd.Series([1, np.nan, 2, 3])
+    >>> nanops.nanvar(s.values)
+    1.0
+    """
+    dtype = values.dtype
+    mask = _maybe_get_mask(values, skipna, mask)
+    if dtype.kind in "iu":
+        values = values.astype("f8")
+        if mask is not None:
+            values[mask] = np.nan
+
+    if values.dtype.kind == "f":
+        count, d = _get_counts_nanvar(values.shape, mask, axis, ddof, values.dtype)
+    else:
+        count, d = _get_counts_nanvar(values.shape, mask, axis, ddof)
+
+    if skipna and mask is not None:
+        values = values.copy()
+        np.putmask(values, mask, 0)
+
+    # xref GH10242
+    # Compute variance via two-pass algorithm, which is stable against
+    # cancellation errors and relatively accurate for small numbers of
+    # observations.
+    #
+    # See https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance
+    avg = _ensure_numeric(values.sum(axis=axis, dtype=np.float64)) / count
+    if axis is not None:
+        avg = np.expand_dims(avg, axis)
+    sqr = _ensure_numeric((avg - values) ** 2)
+    if mask is not None:
+        np.putmask(sqr, mask, 0)
+    result = sqr.sum(axis=axis, dtype=np.float64) / d
+
+    # Return variance as np.float64 (the datatype used in the accumulator),
+    # unless we were dealing with a float array, in which case use the same
+    # precision as the original values array.
+    if dtype.kind == "f":
+        result = result.astype(dtype, copy=False)
+    return result
+
+
+@disallow("M8", "m8")
+def nansem(
+    values: np.ndarray,
+    *,
+    axis: AxisInt | None = None,
+    skipna: bool = True,
+    ddof: int = 1,
+    mask: npt.NDArray[np.bool_] | None = None,
+) -> float:
+    """
+    Compute the standard error in the mean along given axis while ignoring NaNs
+
+    Parameters
+    ----------
+    values : ndarray
+    axis : int, optional
+    skipna : bool, default True
+    ddof : int, default 1
+        Delta Degrees of Freedom. The divisor used in calculations is N - ddof,
+        where N represents the number of elements.
+    mask : ndarray[bool], optional
+        nan-mask if known
+
+    Returns
+    -------
+    result : float64
+        Unless input is a float array, in which case use the same
+        precision as the input array.
+
+    Examples
+    --------
+    >>> from pandas.core import nanops
+    >>> s = pd.Series([1, np.nan, 2, 3])
+    >>> nanops.nansem(s.values)
+     0.5773502691896258
+    """
+    # This checks if non-numeric-like data is passed with numeric_only=False
+    # and raises a TypeError otherwise
+    nanvar(values, axis=axis, skipna=skipna, ddof=ddof, mask=mask)
+
+    mask = _maybe_get_mask(values, skipna, mask)
+    if values.dtype.kind != "f":
+        values = values.astype("f8")
+
+    if not skipna and mask is not None and mask.any():
+        return np.nan
+
+    count, _ = _get_counts_nanvar(values.shape, mask, axis, ddof, values.dtype)
+    var = nanvar(values, axis=axis, skipna=skipna, ddof=ddof, mask=mask)
+
+    return np.sqrt(var) / np.sqrt(count)
+
+
+def _nanminmax(meth, fill_value_typ):
+    @bottleneck_switch(name=f"nan{meth}")
+    @_datetimelike_compat
+    def reduction(
+        values: np.ndarray,
+        *,
+        axis: AxisInt | None = None,
+        skipna: bool = True,
+        mask: npt.NDArray[np.bool_] | None = None,
+    ):
+        if values.size == 0:
+            return _na_for_min_count(values, axis)
+
+        values, mask = _get_values(
+            values, skipna, fill_value_typ=fill_value_typ, mask=mask
+        )
+        result = getattr(values, meth)(axis)
+        result = _maybe_null_out(result, axis, mask, values.shape)
+        return result
+
+    return reduction
+
+
+nanmin = _nanminmax("min", fill_value_typ="+inf")
+nanmax = _nanminmax("max", fill_value_typ="-inf")
+
+
+def nanargmax(
+    values: np.ndarray,
+    *,
+    axis: AxisInt | None = None,
+    skipna: bool = True,
+    mask: npt.NDArray[np.bool_] | None = None,
+) -> int | np.ndarray:
+    """
+    Parameters
+    ----------
+    values : ndarray
+    axis : int, optional
+    skipna : bool, default True
+    mask : ndarray[bool], optional
+        nan-mask if known
+
+    Returns
+    -------
+    result : int or ndarray[int]
+        The index/indices  of max value in specified axis or -1 in the NA case
+
+    Examples
+    --------
+    >>> from pandas.core import nanops
+    >>> arr = np.array([1, 2, 3, np.nan, 4])
+    >>> nanops.nanargmax(arr)
+    4
+
+    >>> arr = np.array(range(12), dtype=np.float64).reshape(4, 3)
+    >>> arr[2:, 2] = np.nan
+    >>> arr
+    array([[ 0.,  1.,  2.],
+           [ 3.,  4.,  5.],
+           [ 6.,  7., nan],
+           [ 9., 10., nan]])
+    >>> nanops.nanargmax(arr, axis=1)
+    array([2, 2, 1, 1])
+    """
+    values, mask = _get_values(values, True, fill_value_typ="-inf", mask=mask)
+    result = values.argmax(axis)
+    # error: Argument 1 to "_maybe_arg_null_out" has incompatible type "Any |
+    # signedinteger[Any]"; expected "ndarray[Any, Any]"
+    result = _maybe_arg_null_out(result, axis, mask, skipna)  # type: ignore[arg-type]
+    return result
+
+
+def nanargmin(
+    values: np.ndarray,
+    *,
+    axis: AxisInt | None = None,
+    skipna: bool = True,
+    mask: npt.NDArray[np.bool_] | None = None,
+) -> int | np.ndarray:
+    """
+    Parameters
+    ----------
+    values : ndarray
+    axis : int, optional
+    skipna : bool, default True
+    mask : ndarray[bool], optional
+        nan-mask if known
+
+    Returns
+    -------
+    result : int or ndarray[int]
+        The index/indices of min value in specified axis or -1 in the NA case
+
+    Examples
+    --------
+    >>> from pandas.core import nanops
+    >>> arr = np.array([1, 2, 3, np.nan, 4])
+    >>> nanops.nanargmin(arr)
+    0
+
+    >>> arr = np.array(range(12), dtype=np.float64).reshape(4, 3)
+    >>> arr[2:, 0] = np.nan
+    >>> arr
+    array([[ 0.,  1.,  2.],
+           [ 3.,  4.,  5.],
+           [nan,  7.,  8.],
+           [nan, 10., 11.]])
+    >>> nanops.nanargmin(arr, axis=1)
+    array([0, 0, 1, 1])
+    """
+    values, mask = _get_values(values, True, fill_value_typ="+inf", mask=mask)
+    result = values.argmin(axis)
+    # error: Argument 1 to "_maybe_arg_null_out" has incompatible type "Any |
+    # signedinteger[Any]"; expected "ndarray[Any, Any]"
+    result = _maybe_arg_null_out(result, axis, mask, skipna)  # type: ignore[arg-type]
+    return result
+
+
+@disallow("M8", "m8")
+@maybe_operate_rowwise
+def nanskew(
+    values: np.ndarray,
+    *,
+    axis: AxisInt | None = None,
+    skipna: bool = True,
+    mask: npt.NDArray[np.bool_] | None = None,
+) -> float:
+    """
+    Compute the sample skewness.
+
+    The statistic computed here is the adjusted Fisher-Pearson standardized
+    moment coefficient G1. The algorithm computes this coefficient directly
+    from the second and third central moment.
+
+    Parameters
+    ----------
+    values : ndarray
+    axis : int, optional
+    skipna : bool, default True
+    mask : ndarray[bool], optional
+        nan-mask if known
+
+    Returns
+    -------
+    result : float64
+        Unless input is a float array, in which case use the same
+        precision as the input array.
+
+    Examples
+    --------
+    >>> from pandas.core import nanops
+    >>> s = pd.Series([1, np.nan, 1, 2])
+    >>> nanops.nanskew(s.values)
+    1.7320508075688787
+    """
+    mask = _maybe_get_mask(values, skipna, mask)
+    if values.dtype.kind != "f":
+        values = values.astype("f8")
+        count = _get_counts(values.shape, mask, axis)
+    else:
+        count = _get_counts(values.shape, mask, axis, dtype=values.dtype)
+
+    if skipna and mask is not None:
+        values = values.copy()
+        np.putmask(values, mask, 0)
+    elif not skipna and mask is not None and mask.any():
+        return np.nan
+
+    with np.errstate(invalid="ignore", divide="ignore"):
+        mean = values.sum(axis, dtype=np.float64) / count
+    if axis is not None:
+        mean = np.expand_dims(mean, axis)
+
+    adjusted = values - mean
+    if skipna and mask is not None:
+        np.putmask(adjusted, mask, 0)
+    adjusted2 = adjusted**2
+    adjusted3 = adjusted2 * adjusted
+    m2 = adjusted2.sum(axis, dtype=np.float64)
+    m3 = adjusted3.sum(axis, dtype=np.float64)
+
+    # floating point error
+    #
+    # #18044 in _libs/windows.pyx calc_skew follow this behavior
+    # to fix the fperr to treat m2 <1e-14 as zero
+    m2 = _zero_out_fperr(m2)
+    m3 = _zero_out_fperr(m3)
+
+    with np.errstate(invalid="ignore", divide="ignore"):
+        result = (count * (count - 1) ** 0.5 / (count - 2)) * (m3 / m2**1.5)
+
+    dtype = values.dtype
+    if dtype.kind == "f":
+        result = result.astype(dtype, copy=False)
+
+    if isinstance(result, np.ndarray):
+        result = np.where(m2 == 0, 0, result)
+        result[count < 3] = np.nan
+    else:
+        result = dtype.type(0) if m2 == 0 else result
+        if count < 3:
+            return np.nan
+
+    return result
+
+
+@disallow("M8", "m8")
+@maybe_operate_rowwise
+def nankurt(
+    values: np.ndarray,
+    *,
+    axis: AxisInt | None = None,
+    skipna: bool = True,
+    mask: npt.NDArray[np.bool_] | None = None,
+) -> float:
+    """
+    Compute the sample excess kurtosis
+
+    The statistic computed here is the adjusted Fisher-Pearson standardized
+    moment coefficient G2, computed directly from the second and fourth
+    central moment.
+
+    Parameters
+    ----------
+    values : ndarray
+    axis : int, optional
+    skipna : bool, default True
+    mask : ndarray[bool], optional
+        nan-mask if known
+
+    Returns
+    -------
+    result : float64
+        Unless input is a float array, in which case use the same
+        precision as the input array.
+
+    Examples
+    --------
+    >>> from pandas.core import nanops
+    >>> s = pd.Series([1, np.nan, 1, 3, 2])
+    >>> nanops.nankurt(s.values)
+    -1.2892561983471076
+    """
+    mask = _maybe_get_mask(values, skipna, mask)
+    if values.dtype.kind != "f":
+        values = values.astype("f8")
+        count = _get_counts(values.shape, mask, axis)
+    else:
+        count = _get_counts(values.shape, mask, axis, dtype=values.dtype)
+
+    if skipna and mask is not None:
+        values = values.copy()
+        np.putmask(values, mask, 0)
+    elif not skipna and mask is not None and mask.any():
+        return np.nan
+
+    with np.errstate(invalid="ignore", divide="ignore"):
+        mean = values.sum(axis, dtype=np.float64) / count
+    if axis is not None:
+        mean = np.expand_dims(mean, axis)
+
+    adjusted = values - mean
+    if skipna and mask is not None:
+        np.putmask(adjusted, mask, 0)
+    adjusted2 = adjusted**2
+    adjusted4 = adjusted2**2
+    m2 = adjusted2.sum(axis, dtype=np.float64)
+    m4 = adjusted4.sum(axis, dtype=np.float64)
+
+    with np.errstate(invalid="ignore", divide="ignore"):
+        adj = 3 * (count - 1) ** 2 / ((count - 2) * (count - 3))
+        numerator = count * (count + 1) * (count - 1) * m4
+        denominator = (count - 2) * (count - 3) * m2**2
+
+    # floating point error
+    #
+    # #18044 in _libs/windows.pyx calc_kurt follow this behavior
+    # to fix the fperr to treat denom <1e-14 as zero
+    numerator = _zero_out_fperr(numerator)
+    denominator = _zero_out_fperr(denominator)
+
+    if not isinstance(denominator, np.ndarray):
+        # if ``denom`` is a scalar, check these corner cases first before
+        # doing division
+        if count < 4:
+            return np.nan
+        if denominator == 0:
+            return values.dtype.type(0)
+
+    with np.errstate(invalid="ignore", divide="ignore"):
+        result = numerator / denominator - adj
+
+    dtype = values.dtype
+    if dtype.kind == "f":
+        result = result.astype(dtype, copy=False)
+
+    if isinstance(result, np.ndarray):
+        result = np.where(denominator == 0, 0, result)
+        result[count < 4] = np.nan
+
+    return result
+
+
+@disallow("M8", "m8")
+@maybe_operate_rowwise
+def nanprod(
+    values: np.ndarray,
+    *,
+    axis: AxisInt | None = None,
+    skipna: bool = True,
+    min_count: int = 0,
+    mask: npt.NDArray[np.bool_] | None = None,
+) -> float:
+    """
+    Parameters
+    ----------
+    values : ndarray[dtype]
+    axis : int, optional
+    skipna : bool, default True
+    min_count: int, default 0
+    mask : ndarray[bool], optional
+        nan-mask if known
+
+    Returns
+    -------
+    Dtype
+        The product of all elements on a given axis. ( NaNs are treated as 1)
+
+    Examples
+    --------
+    >>> from pandas.core import nanops
+    >>> s = pd.Series([1, 2, 3, np.nan])
+    >>> nanops.nanprod(s.values)
+    6.0
+    """
+    mask = _maybe_get_mask(values, skipna, mask)
+
+    if skipna and mask is not None:
+        values = values.copy()
+        values[mask] = 1
+    result = values.prod(axis)
+    # error: Incompatible return value type (got "Union[ndarray, float]", expected
+    # "float")
+    return _maybe_null_out(  # type: ignore[return-value]
+        result, axis, mask, values.shape, min_count=min_count
+    )
+
+
+def _maybe_arg_null_out(
+    result: np.ndarray,
+    axis: AxisInt | None,
+    mask: npt.NDArray[np.bool_] | None,
+    skipna: bool,
+) -> np.ndarray | int:
+    # helper function for nanargmin/nanargmax
+    if mask is None:
+        return result
+
+    if axis is None or not getattr(result, "ndim", False):
+        if skipna:
+            if mask.all():
+                return -1
+        else:
+            if mask.any():
+                return -1
+    else:
+        if skipna:
+            na_mask = mask.all(axis)
+        else:
+            na_mask = mask.any(axis)
+        if na_mask.any():
+            result[na_mask] = -1
+    return result
+
+
+def _get_counts(
+    values_shape: Shape,
+    mask: npt.NDArray[np.bool_] | None,
+    axis: AxisInt | None,
+    dtype: np.dtype[np.floating] = np.dtype(np.float64),
+) -> np.floating | npt.NDArray[np.floating]:
+    """
+    Get the count of non-null values along an axis
+
+    Parameters
+    ----------
+    values_shape : tuple of int
+        shape tuple from values ndarray, used if mask is None
+    mask : Optional[ndarray[bool]]
+        locations in values that should be considered missing
+    axis : Optional[int]
+        axis to count along
+    dtype : type, optional
+        type to use for count
+
+    Returns
+    -------
+    count : scalar or array
+    """
+    if axis is None:
+        if mask is not None:
+            n = mask.size - mask.sum()
+        else:
+            n = np.prod(values_shape)
+        return dtype.type(n)
+
+    if mask is not None:
+        count = mask.shape[axis] - mask.sum(axis)
+    else:
+        count = values_shape[axis]
+
+    if is_integer(count):
+        return dtype.type(count)
+    return count.astype(dtype, copy=False)
+
+
+def _maybe_null_out(
+    result: np.ndarray | float | NaTType,
+    axis: AxisInt | None,
+    mask: npt.NDArray[np.bool_] | None,
+    shape: tuple[int, ...],
+    min_count: int = 1,
+) -> np.ndarray | float | NaTType:
+    """
+    Returns
+    -------
+    Dtype
+        The product of all elements on a given axis. ( NaNs are treated as 1)
+    """
+    if mask is None and min_count == 0:
+        # nothing to check; short-circuit
+        return result
+
+    if axis is not None and isinstance(result, np.ndarray):
+        if mask is not None:
+            null_mask = (mask.shape[axis] - mask.sum(axis) - min_count) < 0
+        else:
+            # we have no nulls, kept mask=None in _maybe_get_mask
+            below_count = shape[axis] - min_count < 0
+            new_shape = shape[:axis] + shape[axis + 1 :]
+            null_mask = np.broadcast_to(below_count, new_shape)
+
+        if np.any(null_mask):
+            if is_numeric_dtype(result):
+                if np.iscomplexobj(result):
+                    result = result.astype("c16")
+                elif not is_float_dtype(result):
+                    result = result.astype("f8", copy=False)
+                result[null_mask] = np.nan
+            else:
+                # GH12941, use None to auto cast null
+                result[null_mask] = None
+    elif result is not NaT:
+        if check_below_min_count(shape, mask, min_count):
+            result_dtype = getattr(result, "dtype", None)
+            if is_float_dtype(result_dtype):
+                # error: Item "None" of "Optional[Any]" has no attribute "type"
+                result = result_dtype.type("nan")  # type: ignore[union-attr]
+            else:
+                result = np.nan
+
+    return result
+
+
+def check_below_min_count(
+    shape: tuple[int, ...], mask: npt.NDArray[np.bool_] | None, min_count: int
+) -> bool:
+    """
+    Check for the `min_count` keyword. Returns True if below `min_count` (when
+    missing value should be returned from the reduction).
+
+    Parameters
+    ----------
+    shape : tuple
+        The shape of the values (`values.shape`).
+    mask : ndarray[bool] or None
+        Boolean numpy array (typically of same shape as `shape`) or None.
+    min_count : int
+        Keyword passed through from sum/prod call.
+
+    Returns
+    -------
+    bool
+    """
+    if min_count > 0:
+        if mask is None:
+            # no missing values, only check size
+            non_nulls = np.prod(shape)
+        else:
+            non_nulls = mask.size - mask.sum()
+        if non_nulls < min_count:
+            return True
+    return False
+
+
+def _zero_out_fperr(arg):
+    # #18044 reference this behavior to fix rolling skew/kurt issue
+    if isinstance(arg, np.ndarray):
+        return np.where(np.abs(arg) < 1e-14, 0, arg)
+    else:
+        return arg.dtype.type(0) if np.abs(arg) < 1e-14 else arg
+
+
+@disallow("M8", "m8")
+def nancorr(
+    a: np.ndarray,
+    b: np.ndarray,
+    *,
+    method: CorrelationMethod = "pearson",
+    min_periods: int | None = None,
+) -> float:
+    """
+    a, b: ndarrays
+    """
+    if len(a) != len(b):
+        raise AssertionError("Operands to nancorr must have same size")
+
+    if min_periods is None:
+        min_periods = 1
+
+    valid = notna(a) & notna(b)
+    if not valid.all():
+        a = a[valid]
+        b = b[valid]
+
+    if len(a) < min_periods:
+        return np.nan
+
+    a = _ensure_numeric(a)
+    b = _ensure_numeric(b)
+
+    f = get_corr_func(method)
+    return f(a, b)
+
+
+def get_corr_func(
+    method: CorrelationMethod,
+) -> Callable[[np.ndarray, np.ndarray], float]:
+    if method == "kendall":
+        from scipy.stats import kendalltau
+
+        def func(a, b):
+            return kendalltau(a, b)[0]
+
+        return func
+    elif method == "spearman":
+        from scipy.stats import spearmanr
+
+        def func(a, b):
+            return spearmanr(a, b)[0]
+
+        return func
+    elif method == "pearson":
+
+        def func(a, b):
+            return np.corrcoef(a, b)[0, 1]
+
+        return func
+    elif callable(method):
+        return method
+
+    raise ValueError(
+        f"Unknown method '{method}', expected one of "
+        "'kendall', 'spearman', 'pearson', or callable"
+    )
+
+
+@disallow("M8", "m8")
+def nancov(
+    a: np.ndarray,
+    b: np.ndarray,
+    *,
+    min_periods: int | None = None,
+    ddof: int | None = 1,
+) -> float:
+    if len(a) != len(b):
+        raise AssertionError("Operands to nancov must have same size")
+
+    if min_periods is None:
+        min_periods = 1
+
+    valid = notna(a) & notna(b)
+    if not valid.all():
+        a = a[valid]
+        b = b[valid]
+
+    if len(a) < min_periods:
+        return np.nan
+
+    a = _ensure_numeric(a)
+    b = _ensure_numeric(b)
+
+    return np.cov(a, b, ddof=ddof)[0, 1]
+
+
+def _ensure_numeric(x):
+    if isinstance(x, np.ndarray):
+        if x.dtype.kind in "biu":
+            x = x.astype(np.float64)
+        elif x.dtype == object:
+            inferred = lib.infer_dtype(x)
+            if inferred in ["string", "mixed"]:
+                # GH#44008, GH#36703 avoid casting e.g. strings to numeric
+                raise TypeError(f"Could not convert {x} to numeric")
+            try:
+                x = x.astype(np.complex128)
+            except (TypeError, ValueError):
+                try:
+                    x = x.astype(np.float64)
+                except ValueError as err:
+                    # GH#29941 we get here with object arrays containing strs
+                    raise TypeError(f"Could not convert {x} to numeric") from err
+            else:
+                if not np.any(np.imag(x)):
+                    x = x.real
+    elif not (is_float(x) or is_integer(x) or is_complex(x)):
+        if isinstance(x, str):
+            # GH#44008, GH#36703 avoid casting e.g. strings to numeric
+            raise TypeError(f"Could not convert string '{x}' to numeric")
+        try:
+            x = float(x)
+        except (TypeError, ValueError):
+            # e.g. "1+1j" or "foo"
+            try:
+                x = complex(x)
+            except ValueError as err:
+                # e.g. "foo"
+                raise TypeError(f"Could not convert {x} to numeric") from err
+    return x
+
+
+def na_accum_func(values: ArrayLike, accum_func, *, skipna: bool) -> ArrayLike:
+    """
+    Cumulative function with skipna support.
+
+    Parameters
+    ----------
+    values : np.ndarray or ExtensionArray
+    accum_func : {np.cumprod, np.maximum.accumulate, np.cumsum, np.minimum.accumulate}
+    skipna : bool
+
+    Returns
+    -------
+    np.ndarray or ExtensionArray
+    """
+    mask_a, mask_b = {
+        np.cumprod: (1.0, np.nan),
+        np.maximum.accumulate: (-np.inf, np.nan),
+        np.cumsum: (0.0, np.nan),
+        np.minimum.accumulate: (np.inf, np.nan),
+    }[accum_func]
+
+    # This should go through ea interface
+    assert values.dtype.kind not in "mM"
+
+    # We will be applying this function to block values
+    if skipna and not issubclass(values.dtype.type, (np.integer, np.bool_)):
+        vals = values.copy()
+        mask = isna(vals)
+        vals[mask] = mask_a
+        result = accum_func(vals, axis=0)
+        result[mask] = mask_b
+    else:
+        result = accum_func(values, axis=0)
+
+    return result
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/ops/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/ops/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ae889a7fdbc24935c0884e8bbcdf56bde8946460
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/ops/__init__.py
@@ -0,0 +1,93 @@
+"""
+Arithmetic operations for PandasObjects
+
+This is not a public API.
+"""
+from __future__ import annotations
+
+from pandas.core.ops.array_ops import (
+    arithmetic_op,
+    comp_method_OBJECT_ARRAY,
+    comparison_op,
+    fill_binop,
+    get_array_op,
+    logical_op,
+    maybe_prepare_scalar_for_op,
+)
+from pandas.core.ops.common import (
+    get_op_result_name,
+    unpack_zerodim_and_defer,
+)
+from pandas.core.ops.docstrings import make_flex_doc
+from pandas.core.ops.invalid import invalid_comparison
+from pandas.core.ops.mask_ops import (
+    kleene_and,
+    kleene_or,
+    kleene_xor,
+)
+from pandas.core.roperator import (
+    radd,
+    rand_,
+    rdiv,
+    rdivmod,
+    rfloordiv,
+    rmod,
+    rmul,
+    ror_,
+    rpow,
+    rsub,
+    rtruediv,
+    rxor,
+)
+
+# -----------------------------------------------------------------------------
+# constants
+ARITHMETIC_BINOPS: set[str] = {
+    "add",
+    "sub",
+    "mul",
+    "pow",
+    "mod",
+    "floordiv",
+    "truediv",
+    "divmod",
+    "radd",
+    "rsub",
+    "rmul",
+    "rpow",
+    "rmod",
+    "rfloordiv",
+    "rtruediv",
+    "rdivmod",
+}
+
+
+__all__ = [
+    "ARITHMETIC_BINOPS",
+    "arithmetic_op",
+    "comparison_op",
+    "comp_method_OBJECT_ARRAY",
+    "invalid_comparison",
+    "fill_binop",
+    "kleene_and",
+    "kleene_or",
+    "kleene_xor",
+    "logical_op",
+    "make_flex_doc",
+    "radd",
+    "rand_",
+    "rdiv",
+    "rdivmod",
+    "rfloordiv",
+    "rmod",
+    "rmul",
+    "ror_",
+    "rpow",
+    "rsub",
+    "rtruediv",
+    "rxor",
+    "unpack_zerodim_and_defer",
+    "get_op_result_name",
+    "maybe_prepare_scalar_for_op",
+    "get_array_op",
+]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/ops/array_ops.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/ops/array_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..4b762a359d321ea61660e78ec63d392f8436939d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/ops/array_ops.py
@@ -0,0 +1,604 @@
+"""
+Functions for arithmetic and comparison operations on NumPy arrays and
+ExtensionArrays.
+"""
+from __future__ import annotations
+
+import datetime
+from functools import partial
+import operator
+from typing import (
+    TYPE_CHECKING,
+    Any,
+)
+import warnings
+
+import numpy as np
+
+from pandas._libs import (
+    NaT,
+    Timedelta,
+    Timestamp,
+    lib,
+    ops as libops,
+)
+from pandas._libs.tslibs import (
+    BaseOffset,
+    get_supported_dtype,
+    is_supported_dtype,
+    is_unitless,
+)
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.cast import (
+    construct_1d_object_array_from_listlike,
+    find_common_type,
+)
+from pandas.core.dtypes.common import (
+    ensure_object,
+    is_bool_dtype,
+    is_list_like,
+    is_numeric_v_string_like,
+    is_object_dtype,
+    is_scalar,
+)
+from pandas.core.dtypes.generic import (
+    ABCExtensionArray,
+    ABCIndex,
+    ABCSeries,
+)
+from pandas.core.dtypes.missing import (
+    isna,
+    notna,
+)
+
+from pandas.core import roperator
+from pandas.core.computation import expressions
+from pandas.core.construction import ensure_wrapped_if_datetimelike
+from pandas.core.ops import missing
+from pandas.core.ops.dispatch import should_extension_dispatch
+from pandas.core.ops.invalid import invalid_comparison
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        ArrayLike,
+        Shape,
+    )
+
+# -----------------------------------------------------------------------------
+# Masking NA values and fallbacks for operations numpy does not support
+
+
+def fill_binop(left, right, fill_value):
+    """
+    If a non-None fill_value is given, replace null entries in left and right
+    with this value, but only in positions where _one_ of left/right is null,
+    not both.
+
+    Parameters
+    ----------
+    left : array-like
+    right : array-like
+    fill_value : object
+
+    Returns
+    -------
+    left : array-like
+    right : array-like
+
+    Notes
+    -----
+    Makes copies if fill_value is not None and NAs are present.
+    """
+    if fill_value is not None:
+        left_mask = isna(left)
+        right_mask = isna(right)
+
+        # one but not both
+        mask = left_mask ^ right_mask
+
+        if left_mask.any():
+            # Avoid making a copy if we can
+            left = left.copy()
+            left[left_mask & mask] = fill_value
+
+        if right_mask.any():
+            # Avoid making a copy if we can
+            right = right.copy()
+            right[right_mask & mask] = fill_value
+
+    return left, right
+
+
+def comp_method_OBJECT_ARRAY(op, x, y):
+    if isinstance(y, list):
+        # e.g. test_tuple_categories
+        y = construct_1d_object_array_from_listlike(y)
+
+    if isinstance(y, (np.ndarray, ABCSeries, ABCIndex)):
+        if not is_object_dtype(y.dtype):
+            y = y.astype(np.object_)
+
+        if isinstance(y, (ABCSeries, ABCIndex)):
+            y = y._values
+
+        if x.shape != y.shape:
+            raise ValueError("Shapes must match", x.shape, y.shape)
+        result = libops.vec_compare(x.ravel(), y.ravel(), op)
+    else:
+        result = libops.scalar_compare(x.ravel(), y, op)
+    return result.reshape(x.shape)
+
+
+def _masked_arith_op(x: np.ndarray, y, op):
+    """
+    If the given arithmetic operation fails, attempt it again on
+    only the non-null elements of the input array(s).
+
+    Parameters
+    ----------
+    x : np.ndarray
+    y : np.ndarray, Series, Index
+    op : binary operator
+    """
+    # For Series `x` is 1D so ravel() is a no-op; calling it anyway makes
+    # the logic valid for both Series and DataFrame ops.
+    xrav = x.ravel()
+
+    if isinstance(y, np.ndarray):
+        dtype = find_common_type([x.dtype, y.dtype])
+        result = np.empty(x.size, dtype=dtype)
+
+        if len(x) != len(y):
+            raise ValueError(x.shape, y.shape)
+        ymask = notna(y)
+
+        # NB: ravel() is only safe since y is ndarray; for e.g. PeriodIndex
+        #  we would get int64 dtype, see GH#19956
+        yrav = y.ravel()
+        mask = notna(xrav) & ymask.ravel()
+
+        # See GH#5284, GH#5035, GH#19448 for historical reference
+        if mask.any():
+            result[mask] = op(xrav[mask], yrav[mask])
+
+    else:
+        if not is_scalar(y):
+            raise TypeError(
+                f"Cannot broadcast np.ndarray with operand of type { type(y) }"
+            )
+
+        # mask is only meaningful for x
+        result = np.empty(x.size, dtype=x.dtype)
+        mask = notna(xrav)
+
+        # 1 ** np.nan is 1. So we have to unmask those.
+        if op is pow:
+            mask = np.where(x == 1, False, mask)
+        elif op is roperator.rpow:
+            mask = np.where(y == 1, False, mask)
+
+        if mask.any():
+            result[mask] = op(xrav[mask], y)
+
+    np.putmask(result, ~mask, np.nan)
+    result = result.reshape(x.shape)  # 2D compat
+    return result
+
+
+def _na_arithmetic_op(left: np.ndarray, right, op, is_cmp: bool = False):
+    """
+    Return the result of evaluating op on the passed in values.
+
+    If native types are not compatible, try coercion to object dtype.
+
+    Parameters
+    ----------
+    left : np.ndarray
+    right : np.ndarray or scalar
+        Excludes DataFrame, Series, Index, ExtensionArray.
+    is_cmp : bool, default False
+        If this a comparison operation.
+
+    Returns
+    -------
+    array-like
+
+    Raises
+    ------
+    TypeError : invalid operation
+    """
+    if isinstance(right, str):
+        # can never use numexpr
+        func = op
+    else:
+        func = partial(expressions.evaluate, op)
+
+    try:
+        result = func(left, right)
+    except TypeError:
+        if not is_cmp and (
+            left.dtype == object or getattr(right, "dtype", None) == object
+        ):
+            # For object dtype, fallback to a masked operation (only operating
+            #  on the non-missing values)
+            # Don't do this for comparisons, as that will handle complex numbers
+            #  incorrectly, see GH#32047
+            result = _masked_arith_op(left, right, op)
+        else:
+            raise
+
+    if is_cmp and (is_scalar(result) or result is NotImplemented):
+        # numpy returned a scalar instead of operating element-wise
+        # e.g. numeric array vs str
+        # TODO: can remove this after dropping some future numpy version?
+        return invalid_comparison(left, right, op)
+
+    return missing.dispatch_fill_zeros(op, left, right, result)
+
+
+def arithmetic_op(left: ArrayLike, right: Any, op):
+    """
+    Evaluate an arithmetic operation `+`, `-`, `*`, `/`, `//`, `%`, `**`, ...
+
+    Note: the caller is responsible for ensuring that numpy warnings are
+    suppressed (with np.errstate(all="ignore")) if needed.
+
+    Parameters
+    ----------
+    left : np.ndarray or ExtensionArray
+    right : object
+        Cannot be a DataFrame or Index.  Series is *not* excluded.
+    op : {operator.add, operator.sub, ...}
+        Or one of the reversed variants from roperator.
+
+    Returns
+    -------
+    ndarray or ExtensionArray
+        Or a 2-tuple of these in the case of divmod or rdivmod.
+    """
+    # NB: We assume that extract_array and ensure_wrapped_if_datetimelike
+    #  have already been called on `left` and `right`,
+    #  and `maybe_prepare_scalar_for_op` has already been called on `right`
+    # We need to special-case datetime64/timedelta64 dtypes (e.g. because numpy
+    # casts integer dtypes to timedelta64 when operating with timedelta64 - GH#22390)
+
+    if (
+        should_extension_dispatch(left, right)
+        or isinstance(right, (Timedelta, BaseOffset, Timestamp))
+        or right is NaT
+    ):
+        # Timedelta/Timestamp and other custom scalars are included in the check
+        # because numexpr will fail on it, see GH#31457
+        res_values = op(left, right)
+    else:
+        # TODO we should handle EAs consistently and move this check before the if/else
+        # (https://github.com/pandas-dev/pandas/issues/41165)
+        # error: Argument 2 to "_bool_arith_check" has incompatible type
+        # "Union[ExtensionArray, ndarray[Any, Any]]"; expected "ndarray[Any, Any]"
+        _bool_arith_check(op, left, right)  # type: ignore[arg-type]
+
+        # error: Argument 1 to "_na_arithmetic_op" has incompatible type
+        # "Union[ExtensionArray, ndarray[Any, Any]]"; expected "ndarray[Any, Any]"
+        res_values = _na_arithmetic_op(left, right, op)  # type: ignore[arg-type]
+
+    return res_values
+
+
+def comparison_op(left: ArrayLike, right: Any, op) -> ArrayLike:
+    """
+    Evaluate a comparison operation `=`, `!=`, `>=`, `>`, `<=`, or `<`.
+
+    Note: the caller is responsible for ensuring that numpy warnings are
+    suppressed (with np.errstate(all="ignore")) if needed.
+
+    Parameters
+    ----------
+    left : np.ndarray or ExtensionArray
+    right : object
+        Cannot be a DataFrame, Series, or Index.
+    op : {operator.eq, operator.ne, operator.gt, operator.ge, operator.lt, operator.le}
+
+    Returns
+    -------
+    ndarray or ExtensionArray
+    """
+    # NB: We assume extract_array has already been called on left and right
+    lvalues = ensure_wrapped_if_datetimelike(left)
+    rvalues = ensure_wrapped_if_datetimelike(right)
+
+    rvalues = lib.item_from_zerodim(rvalues)
+    if isinstance(rvalues, list):
+        # We don't catch tuple here bc we may be comparing e.g. MultiIndex
+        #  to a tuple that represents a single entry, see test_compare_tuple_strs
+        rvalues = np.asarray(rvalues)
+
+    if isinstance(rvalues, (np.ndarray, ABCExtensionArray)):
+        # TODO: make this treatment consistent across ops and classes.
+        #  We are not catching all listlikes here (e.g. frozenset, tuple)
+        #  The ambiguous case is object-dtype.  See GH#27803
+        if len(lvalues) != len(rvalues):
+            raise ValueError(
+                "Lengths must match to compare", lvalues.shape, rvalues.shape
+            )
+
+    if should_extension_dispatch(lvalues, rvalues) or (
+        (isinstance(rvalues, (Timedelta, BaseOffset, Timestamp)) or right is NaT)
+        and lvalues.dtype != object
+    ):
+        # Call the method on lvalues
+        res_values = op(lvalues, rvalues)
+
+    elif is_scalar(rvalues) and isna(rvalues):  # TODO: but not pd.NA?
+        # numpy does not like comparisons vs None
+        if op is operator.ne:
+            res_values = np.ones(lvalues.shape, dtype=bool)
+        else:
+            res_values = np.zeros(lvalues.shape, dtype=bool)
+
+    elif is_numeric_v_string_like(lvalues, rvalues):
+        # GH#36377 going through the numexpr path would incorrectly raise
+        return invalid_comparison(lvalues, rvalues, op)
+
+    elif lvalues.dtype == object or isinstance(rvalues, str):
+        res_values = comp_method_OBJECT_ARRAY(op, lvalues, rvalues)
+
+    else:
+        res_values = _na_arithmetic_op(lvalues, rvalues, op, is_cmp=True)
+
+    return res_values
+
+
+def na_logical_op(x: np.ndarray, y, op):
+    try:
+        # For exposition, write:
+        #  yarr = isinstance(y, np.ndarray)
+        #  yint = is_integer(y) or (yarr and y.dtype.kind == "i")
+        #  ybool = is_bool(y) or (yarr and y.dtype.kind == "b")
+        #  xint = x.dtype.kind == "i"
+        #  xbool = x.dtype.kind == "b"
+        # Then Cases where this goes through without raising include:
+        #  (xint or xbool) and (yint or bool)
+        result = op(x, y)
+    except TypeError:
+        if isinstance(y, np.ndarray):
+            # bool-bool dtype operations should be OK, should not get here
+            assert not (x.dtype.kind == "b" and y.dtype.kind == "b")
+            x = ensure_object(x)
+            y = ensure_object(y)
+            result = libops.vec_binop(x.ravel(), y.ravel(), op)
+        else:
+            # let null fall thru
+            assert lib.is_scalar(y)
+            if not isna(y):
+                y = bool(y)
+            try:
+                result = libops.scalar_binop(x, y, op)
+            except (
+                TypeError,
+                ValueError,
+                AttributeError,
+                OverflowError,
+                NotImplementedError,
+            ) as err:
+                typ = type(y).__name__
+                raise TypeError(
+                    f"Cannot perform '{op.__name__}' with a dtyped [{x.dtype}] array "
+                    f"and scalar of type [{typ}]"
+                ) from err
+
+    return result.reshape(x.shape)
+
+
+def logical_op(left: ArrayLike, right: Any, op) -> ArrayLike:
+    """
+    Evaluate a logical operation `|`, `&`, or `^`.
+
+    Parameters
+    ----------
+    left : np.ndarray or ExtensionArray
+    right : object
+        Cannot be a DataFrame, Series, or Index.
+    op : {operator.and_, operator.or_, operator.xor}
+        Or one of the reversed variants from roperator.
+
+    Returns
+    -------
+    ndarray or ExtensionArray
+    """
+
+    def fill_bool(x, left=None):
+        # if `left` is specifically not-boolean, we do not cast to bool
+        if x.dtype.kind in "cfO":
+            # dtypes that can hold NA
+            mask = isna(x)
+            if mask.any():
+                x = x.astype(object)
+                x[mask] = False
+
+        if left is None or left.dtype.kind == "b":
+            x = x.astype(bool)
+        return x
+
+    right = lib.item_from_zerodim(right)
+    if is_list_like(right) and not hasattr(right, "dtype"):
+        # e.g. list, tuple
+        warnings.warn(
+            "Logical ops (and, or, xor) between Pandas objects and dtype-less "
+            "sequences (e.g. list, tuple) are deprecated and will raise in a "
+            "future version. Wrap the object in a Series, Index, or np.array "
+            "before operating instead.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        right = construct_1d_object_array_from_listlike(right)
+
+    # NB: We assume extract_array has already been called on left and right
+    lvalues = ensure_wrapped_if_datetimelike(left)
+    rvalues = right
+
+    if should_extension_dispatch(lvalues, rvalues):
+        # Call the method on lvalues
+        res_values = op(lvalues, rvalues)
+
+    else:
+        if isinstance(rvalues, np.ndarray):
+            is_other_int_dtype = rvalues.dtype.kind in "iu"
+            if not is_other_int_dtype:
+                rvalues = fill_bool(rvalues, lvalues)
+
+        else:
+            # i.e. scalar
+            is_other_int_dtype = lib.is_integer(rvalues)
+
+        res_values = na_logical_op(lvalues, rvalues, op)
+
+        # For int vs int `^`, `|`, `&` are bitwise operators and return
+        #   integer dtypes.  Otherwise these are boolean ops
+        if not (left.dtype.kind in "iu" and is_other_int_dtype):
+            res_values = fill_bool(res_values)
+
+    return res_values
+
+
+def get_array_op(op):
+    """
+    Return a binary array operation corresponding to the given operator op.
+
+    Parameters
+    ----------
+    op : function
+        Binary operator from operator or roperator module.
+
+    Returns
+    -------
+    functools.partial
+    """
+    if isinstance(op, partial):
+        # We get here via dispatch_to_series in DataFrame case
+        # e.g. test_rolling_consistency_var_debiasing_factors
+        return op
+
+    op_name = op.__name__.strip("_").lstrip("r")
+    if op_name == "arith_op":
+        # Reached via DataFrame._combine_frame i.e. flex methods
+        # e.g. test_df_add_flex_filled_mixed_dtypes
+        return op
+
+    if op_name in {"eq", "ne", "lt", "le", "gt", "ge"}:
+        return partial(comparison_op, op=op)
+    elif op_name in {"and", "or", "xor", "rand", "ror", "rxor"}:
+        return partial(logical_op, op=op)
+    elif op_name in {
+        "add",
+        "sub",
+        "mul",
+        "truediv",
+        "floordiv",
+        "mod",
+        "divmod",
+        "pow",
+    }:
+        return partial(arithmetic_op, op=op)
+    else:
+        raise NotImplementedError(op_name)
+
+
+def maybe_prepare_scalar_for_op(obj, shape: Shape):
+    """
+    Cast non-pandas objects to pandas types to unify behavior of arithmetic
+    and comparison operations.
+
+    Parameters
+    ----------
+    obj: object
+    shape : tuple[int]
+
+    Returns
+    -------
+    out : object
+
+    Notes
+    -----
+    Be careful to call this *after* determining the `name` attribute to be
+    attached to the result of the arithmetic operation.
+    """
+    if type(obj) is datetime.timedelta:
+        # GH#22390  cast up to Timedelta to rely on Timedelta
+        # implementation; otherwise operation against numeric-dtype
+        # raises TypeError
+        return Timedelta(obj)
+    elif type(obj) is datetime.datetime:
+        # cast up to Timestamp to rely on Timestamp implementation, see Timedelta above
+        return Timestamp(obj)
+    elif isinstance(obj, np.datetime64):
+        # GH#28080 numpy casts integer-dtype to datetime64 when doing
+        #  array[int] + datetime64, which we do not allow
+        if isna(obj):
+            from pandas.core.arrays import DatetimeArray
+
+            # Avoid possible ambiguities with pd.NaT
+            # GH 52295
+            if is_unitless(obj.dtype):
+                obj = obj.astype("datetime64[ns]")
+            elif not is_supported_dtype(obj.dtype):
+                new_dtype = get_supported_dtype(obj.dtype)
+                obj = obj.astype(new_dtype)
+            right = np.broadcast_to(obj, shape)
+            return DatetimeArray._simple_new(right, dtype=right.dtype)
+
+        return Timestamp(obj)
+
+    elif isinstance(obj, np.timedelta64):
+        if isna(obj):
+            from pandas.core.arrays import TimedeltaArray
+
+            # wrapping timedelta64("NaT") in Timedelta returns NaT,
+            #  which would incorrectly be treated as a datetime-NaT, so
+            #  we broadcast and wrap in a TimedeltaArray
+            # GH 52295
+            if is_unitless(obj.dtype):
+                obj = obj.astype("timedelta64[ns]")
+            elif not is_supported_dtype(obj.dtype):
+                new_dtype = get_supported_dtype(obj.dtype)
+                obj = obj.astype(new_dtype)
+            right = np.broadcast_to(obj, shape)
+            return TimedeltaArray._simple_new(right, dtype=right.dtype)
+
+        # In particular non-nanosecond timedelta64 needs to be cast to
+        #  nanoseconds, or else we get undesired behavior like
+        #  np.timedelta64(3, 'D') / 2 == np.timedelta64(1, 'D')
+        return Timedelta(obj)
+
+    # We want NumPy numeric scalars to behave like Python scalars
+    # post NEP 50
+    elif isinstance(obj, np.integer):
+        return int(obj)
+
+    elif isinstance(obj, np.floating):
+        return float(obj)
+
+    return obj
+
+
+_BOOL_OP_NOT_ALLOWED = {
+    operator.truediv,
+    roperator.rtruediv,
+    operator.floordiv,
+    roperator.rfloordiv,
+    operator.pow,
+    roperator.rpow,
+}
+
+
+def _bool_arith_check(op, a: np.ndarray, b):
+    """
+    In contrast to numpy, pandas raises an error for certain operations
+    with booleans.
+    """
+    if op in _BOOL_OP_NOT_ALLOWED:
+        if a.dtype.kind == "b" and (is_bool_dtype(b) or lib.is_bool(b)):
+            op_name = op.__name__.strip("_").lstrip("r")
+            raise NotImplementedError(
+                f"operator '{op_name}' not implemented for bool dtypes"
+            )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/ops/common.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/ops/common.py
new file mode 100644
index 0000000000000000000000000000000000000000..559977bacf881552d546e7704d4cf4b12b4a32fe
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/ops/common.py
@@ -0,0 +1,146 @@
+"""
+Boilerplate functions used in defining binary operations.
+"""
+from __future__ import annotations
+
+from functools import wraps
+from typing import (
+    TYPE_CHECKING,
+    Callable,
+)
+
+from pandas._libs.lib import item_from_zerodim
+from pandas._libs.missing import is_matching_na
+
+from pandas.core.dtypes.generic import (
+    ABCIndex,
+    ABCSeries,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import F
+
+
+def unpack_zerodim_and_defer(name: str) -> Callable[[F], F]:
+    """
+    Boilerplate for pandas conventions in arithmetic and comparison methods.
+
+    Parameters
+    ----------
+    name : str
+
+    Returns
+    -------
+    decorator
+    """
+
+    def wrapper(method: F) -> F:
+        return _unpack_zerodim_and_defer(method, name)
+
+    return wrapper
+
+
+def _unpack_zerodim_and_defer(method, name: str):
+    """
+    Boilerplate for pandas conventions in arithmetic and comparison methods.
+
+    Ensure method returns NotImplemented when operating against "senior"
+    classes.  Ensure zero-dimensional ndarrays are always unpacked.
+
+    Parameters
+    ----------
+    method : binary method
+    name : str
+
+    Returns
+    -------
+    method
+    """
+    stripped_name = name.removeprefix("__").removesuffix("__")
+    is_cmp = stripped_name in {"eq", "ne", "lt", "le", "gt", "ge"}
+
+    @wraps(method)
+    def new_method(self, other):
+        if is_cmp and isinstance(self, ABCIndex) and isinstance(other, ABCSeries):
+            # For comparison ops, Index does *not* defer to Series
+            pass
+        else:
+            prio = getattr(other, "__pandas_priority__", None)
+            if prio is not None:
+                if prio > self.__pandas_priority__:
+                    # e.g. other is DataFrame while self is Index/Series/EA
+                    return NotImplemented
+
+        other = item_from_zerodim(other)
+
+        return method(self, other)
+
+    return new_method
+
+
+def get_op_result_name(left, right):
+    """
+    Find the appropriate name to pin to an operation result.  This result
+    should always be either an Index or a Series.
+
+    Parameters
+    ----------
+    left : {Series, Index}
+    right : object
+
+    Returns
+    -------
+    name : object
+        Usually a string
+    """
+    if isinstance(right, (ABCSeries, ABCIndex)):
+        name = _maybe_match_name(left, right)
+    else:
+        name = left.name
+    return name
+
+
+def _maybe_match_name(a, b):
+    """
+    Try to find a name to attach to the result of an operation between
+    a and b.  If only one of these has a `name` attribute, return that
+    name.  Otherwise return a consensus name if they match or None if
+    they have different names.
+
+    Parameters
+    ----------
+    a : object
+    b : object
+
+    Returns
+    -------
+    name : str or None
+
+    See Also
+    --------
+    pandas.core.common.consensus_name_attr
+    """
+    a_has = hasattr(a, "name")
+    b_has = hasattr(b, "name")
+    if a_has and b_has:
+        try:
+            if a.name == b.name:
+                return a.name
+            elif is_matching_na(a.name, b.name):
+                # e.g. both are np.nan
+                return a.name
+            else:
+                return None
+        except TypeError:
+            # pd.NA
+            if is_matching_na(a.name, b.name):
+                return a.name
+            return None
+        except ValueError:
+            # e.g. np.int64(1) vs (np.int64(1), np.int64(2))
+            return None
+    elif a_has:
+        return a.name
+    elif b_has:
+        return b.name
+    return None
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/ops/dispatch.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/ops/dispatch.py
new file mode 100644
index 0000000000000000000000000000000000000000..a939fdd3d041e9f99dde7ea40fd7aa0572d0d9b7
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/ops/dispatch.py
@@ -0,0 +1,30 @@
+"""
+Functions for defining unary operations.
+"""
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    Any,
+)
+
+from pandas.core.dtypes.generic import ABCExtensionArray
+
+if TYPE_CHECKING:
+    from pandas._typing import ArrayLike
+
+
+def should_extension_dispatch(left: ArrayLike, right: Any) -> bool:
+    """
+    Identify cases where Series operation should dispatch to ExtensionArray method.
+
+    Parameters
+    ----------
+    left : np.ndarray or ExtensionArray
+    right : object
+
+    Returns
+    -------
+    bool
+    """
+    return isinstance(left, ABCExtensionArray) or isinstance(right, ABCExtensionArray)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/ops/docstrings.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/ops/docstrings.py
new file mode 100644
index 0000000000000000000000000000000000000000..bd2e532536d8491af44631e52982217a04ef5b17
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/ops/docstrings.py
@@ -0,0 +1,772 @@
+"""
+Templating for ops docstrings
+"""
+from __future__ import annotations
+
+
+def make_flex_doc(op_name: str, typ: str) -> str:
+    """
+    Make the appropriate substitutions for the given operation and class-typ
+    into either _flex_doc_SERIES or _flex_doc_FRAME to return the docstring
+    to attach to a generated method.
+
+    Parameters
+    ----------
+    op_name : str {'__add__', '__sub__', ... '__eq__', '__ne__', ...}
+    typ : str {series, 'dataframe']}
+
+    Returns
+    -------
+    doc : str
+    """
+    op_name = op_name.replace("__", "")
+    op_desc = _op_descriptions[op_name]
+
+    op_desc_op = op_desc["op"]
+    assert op_desc_op is not None  # for mypy
+    if op_name.startswith("r"):
+        equiv = f"other {op_desc_op} {typ}"
+    elif op_name == "divmod":
+        equiv = f"{op_name}({typ}, other)"
+    else:
+        equiv = f"{typ} {op_desc_op} other"
+
+    if typ == "series":
+        base_doc = _flex_doc_SERIES
+        if op_desc["reverse"]:
+            base_doc += _see_also_reverse_SERIES.format(
+                reverse=op_desc["reverse"], see_also_desc=op_desc["see_also_desc"]
+            )
+        doc_no_examples = base_doc.format(
+            desc=op_desc["desc"],
+            op_name=op_name,
+            equiv=equiv,
+            series_returns=op_desc["series_returns"],
+        )
+        ser_example = op_desc["series_examples"]
+        if ser_example:
+            doc = doc_no_examples + ser_example
+        else:
+            doc = doc_no_examples
+    elif typ == "dataframe":
+        if op_name in ["eq", "ne", "le", "lt", "ge", "gt"]:
+            base_doc = _flex_comp_doc_FRAME
+            doc = _flex_comp_doc_FRAME.format(
+                op_name=op_name,
+                desc=op_desc["desc"],
+            )
+        else:
+            base_doc = _flex_doc_FRAME
+            doc = base_doc.format(
+                desc=op_desc["desc"],
+                op_name=op_name,
+                equiv=equiv,
+                reverse=op_desc["reverse"],
+            )
+    else:
+        raise AssertionError("Invalid typ argument.")
+    return doc
+
+
+_common_examples_algebra_SERIES = """
+Examples
+--------
+>>> a = pd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd'])
+>>> a
+a    1.0
+b    1.0
+c    1.0
+d    NaN
+dtype: float64
+>>> b = pd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e'])
+>>> b
+a    1.0
+b    NaN
+d    1.0
+e    NaN
+dtype: float64"""
+
+_common_examples_comparison_SERIES = """
+Examples
+--------
+>>> a = pd.Series([1, 1, 1, np.nan, 1], index=['a', 'b', 'c', 'd', 'e'])
+>>> a
+a    1.0
+b    1.0
+c    1.0
+d    NaN
+e    1.0
+dtype: float64
+>>> b = pd.Series([0, 1, 2, np.nan, 1], index=['a', 'b', 'c', 'd', 'f'])
+>>> b
+a    0.0
+b    1.0
+c    2.0
+d    NaN
+f    1.0
+dtype: float64"""
+
+_add_example_SERIES = (
+    _common_examples_algebra_SERIES
+    + """
+>>> a.add(b, fill_value=0)
+a    2.0
+b    1.0
+c    1.0
+d    1.0
+e    NaN
+dtype: float64
+"""
+)
+
+_sub_example_SERIES = (
+    _common_examples_algebra_SERIES
+    + """
+>>> a.subtract(b, fill_value=0)
+a    0.0
+b    1.0
+c    1.0
+d   -1.0
+e    NaN
+dtype: float64
+"""
+)
+
+_mul_example_SERIES = (
+    _common_examples_algebra_SERIES
+    + """
+>>> a.multiply(b, fill_value=0)
+a    1.0
+b    0.0
+c    0.0
+d    0.0
+e    NaN
+dtype: float64
+"""
+)
+
+_div_example_SERIES = (
+    _common_examples_algebra_SERIES
+    + """
+>>> a.divide(b, fill_value=0)
+a    1.0
+b    inf
+c    inf
+d    0.0
+e    NaN
+dtype: float64
+"""
+)
+
+_floordiv_example_SERIES = (
+    _common_examples_algebra_SERIES
+    + """
+>>> a.floordiv(b, fill_value=0)
+a    1.0
+b    inf
+c    inf
+d    0.0
+e    NaN
+dtype: float64
+"""
+)
+
+_divmod_example_SERIES = (
+    _common_examples_algebra_SERIES
+    + """
+>>> a.divmod(b, fill_value=0)
+(a    1.0
+ b    inf
+ c    inf
+ d    0.0
+ e    NaN
+ dtype: float64,
+ a    0.0
+ b    NaN
+ c    NaN
+ d    0.0
+ e    NaN
+ dtype: float64)
+"""
+)
+
+_mod_example_SERIES = (
+    _common_examples_algebra_SERIES
+    + """
+>>> a.mod(b, fill_value=0)
+a    0.0
+b    NaN
+c    NaN
+d    0.0
+e    NaN
+dtype: float64
+"""
+)
+_pow_example_SERIES = (
+    _common_examples_algebra_SERIES
+    + """
+>>> a.pow(b, fill_value=0)
+a    1.0
+b    1.0
+c    1.0
+d    0.0
+e    NaN
+dtype: float64
+"""
+)
+
+_ne_example_SERIES = (
+    _common_examples_algebra_SERIES
+    + """
+>>> a.ne(b, fill_value=0)
+a    False
+b     True
+c     True
+d     True
+e     True
+dtype: bool
+"""
+)
+
+_eq_example_SERIES = (
+    _common_examples_algebra_SERIES
+    + """
+>>> a.eq(b, fill_value=0)
+a     True
+b    False
+c    False
+d    False
+e    False
+dtype: bool
+"""
+)
+
+_lt_example_SERIES = (
+    _common_examples_comparison_SERIES
+    + """
+>>> a.lt(b, fill_value=0)
+a    False
+b    False
+c     True
+d    False
+e    False
+f     True
+dtype: bool
+"""
+)
+
+_le_example_SERIES = (
+    _common_examples_comparison_SERIES
+    + """
+>>> a.le(b, fill_value=0)
+a    False
+b     True
+c     True
+d    False
+e    False
+f     True
+dtype: bool
+"""
+)
+
+_gt_example_SERIES = (
+    _common_examples_comparison_SERIES
+    + """
+>>> a.gt(b, fill_value=0)
+a     True
+b    False
+c    False
+d    False
+e     True
+f    False
+dtype: bool
+"""
+)
+
+_ge_example_SERIES = (
+    _common_examples_comparison_SERIES
+    + """
+>>> a.ge(b, fill_value=0)
+a     True
+b     True
+c    False
+d    False
+e     True
+f    False
+dtype: bool
+"""
+)
+
+_returns_series = """Series\n    The result of the operation."""
+
+_returns_tuple = """2-Tuple of Series\n    The result of the operation."""
+
+_op_descriptions: dict[str, dict[str, str | None]] = {
+    # Arithmetic Operators
+    "add": {
+        "op": "+",
+        "desc": "Addition",
+        "reverse": "radd",
+        "series_examples": _add_example_SERIES,
+        "series_returns": _returns_series,
+    },
+    "sub": {
+        "op": "-",
+        "desc": "Subtraction",
+        "reverse": "rsub",
+        "series_examples": _sub_example_SERIES,
+        "series_returns": _returns_series,
+    },
+    "mul": {
+        "op": "*",
+        "desc": "Multiplication",
+        "reverse": "rmul",
+        "series_examples": _mul_example_SERIES,
+        "series_returns": _returns_series,
+        "df_examples": None,
+    },
+    "mod": {
+        "op": "%",
+        "desc": "Modulo",
+        "reverse": "rmod",
+        "series_examples": _mod_example_SERIES,
+        "series_returns": _returns_series,
+    },
+    "pow": {
+        "op": "**",
+        "desc": "Exponential power",
+        "reverse": "rpow",
+        "series_examples": _pow_example_SERIES,
+        "series_returns": _returns_series,
+        "df_examples": None,
+    },
+    "truediv": {
+        "op": "/",
+        "desc": "Floating division",
+        "reverse": "rtruediv",
+        "series_examples": _div_example_SERIES,
+        "series_returns": _returns_series,
+        "df_examples": None,
+    },
+    "floordiv": {
+        "op": "//",
+        "desc": "Integer division",
+        "reverse": "rfloordiv",
+        "series_examples": _floordiv_example_SERIES,
+        "series_returns": _returns_series,
+        "df_examples": None,
+    },
+    "divmod": {
+        "op": "divmod",
+        "desc": "Integer division and modulo",
+        "reverse": "rdivmod",
+        "series_examples": _divmod_example_SERIES,
+        "series_returns": _returns_tuple,
+        "df_examples": None,
+    },
+    # Comparison Operators
+    "eq": {
+        "op": "==",
+        "desc": "Equal to",
+        "reverse": None,
+        "series_examples": _eq_example_SERIES,
+        "series_returns": _returns_series,
+    },
+    "ne": {
+        "op": "!=",
+        "desc": "Not equal to",
+        "reverse": None,
+        "series_examples": _ne_example_SERIES,
+        "series_returns": _returns_series,
+    },
+    "lt": {
+        "op": "<",
+        "desc": "Less than",
+        "reverse": None,
+        "series_examples": _lt_example_SERIES,
+        "series_returns": _returns_series,
+    },
+    "le": {
+        "op": "<=",
+        "desc": "Less than or equal to",
+        "reverse": None,
+        "series_examples": _le_example_SERIES,
+        "series_returns": _returns_series,
+    },
+    "gt": {
+        "op": ">",
+        "desc": "Greater than",
+        "reverse": None,
+        "series_examples": _gt_example_SERIES,
+        "series_returns": _returns_series,
+    },
+    "ge": {
+        "op": ">=",
+        "desc": "Greater than or equal to",
+        "reverse": None,
+        "series_examples": _ge_example_SERIES,
+        "series_returns": _returns_series,
+    },
+}
+
+_py_num_ref = """see
+    `Python documentation
+    `_
+    for more details"""
+_op_names = list(_op_descriptions.keys())
+for key in _op_names:
+    reverse_op = _op_descriptions[key]["reverse"]
+    if reverse_op is not None:
+        _op_descriptions[reverse_op] = _op_descriptions[key].copy()
+        _op_descriptions[reverse_op]["reverse"] = key
+        _op_descriptions[key][
+            "see_also_desc"
+        ] = f"Reverse of the {_op_descriptions[key]['desc']} operator, {_py_num_ref}"
+        _op_descriptions[reverse_op][
+            "see_also_desc"
+        ] = f"Element-wise {_op_descriptions[key]['desc']}, {_py_num_ref}"
+
+_flex_doc_SERIES = """
+Return {desc} of series and other, element-wise (binary operator `{op_name}`).
+
+Equivalent to ``{equiv}``, but with support to substitute a fill_value for
+missing data in either one of the inputs.
+
+Parameters
+----------
+other : Series or scalar value
+level : int or name
+    Broadcast across a level, matching Index values on the
+    passed MultiIndex level.
+fill_value : None or float value, default None (NaN)
+    Fill existing missing (NaN) values, and any new element needed for
+    successful Series alignment, with this value before computation.
+    If data in both corresponding Series locations is missing
+    the result of filling (at that location) will be missing.
+axis : {{0 or 'index'}}
+    Unused. Parameter needed for compatibility with DataFrame.
+
+Returns
+-------
+{series_returns}
+"""
+
+_see_also_reverse_SERIES = """
+See Also
+--------
+Series.{reverse} : {see_also_desc}.
+"""
+
+_flex_doc_FRAME = """
+Get {desc} of dataframe and other, element-wise (binary operator `{op_name}`).
+
+Equivalent to ``{equiv}``, but with support to substitute a fill_value
+for missing data in one of the inputs. With reverse version, `{reverse}`.
+
+Among flexible wrappers (`add`, `sub`, `mul`, `div`, `floordiv`, `mod`, `pow`) to
+arithmetic operators: `+`, `-`, `*`, `/`, `//`, `%`, `**`.
+
+Parameters
+----------
+other : scalar, sequence, Series, dict or DataFrame
+    Any single or multiple element data structure, or list-like object.
+axis : {{0 or 'index', 1 or 'columns'}}
+    Whether to compare by the index (0 or 'index') or columns.
+    (1 or 'columns'). For Series input, axis to match Series index on.
+level : int or label
+    Broadcast across a level, matching Index values on the
+    passed MultiIndex level.
+fill_value : float or None, default None
+    Fill existing missing (NaN) values, and any new element needed for
+    successful DataFrame alignment, with this value before computation.
+    If data in both corresponding DataFrame locations is missing
+    the result will be missing.
+
+Returns
+-------
+DataFrame
+    Result of the arithmetic operation.
+
+See Also
+--------
+DataFrame.add : Add DataFrames.
+DataFrame.sub : Subtract DataFrames.
+DataFrame.mul : Multiply DataFrames.
+DataFrame.div : Divide DataFrames (float division).
+DataFrame.truediv : Divide DataFrames (float division).
+DataFrame.floordiv : Divide DataFrames (integer division).
+DataFrame.mod : Calculate modulo (remainder after division).
+DataFrame.pow : Calculate exponential power.
+
+Notes
+-----
+Mismatched indices will be unioned together.
+
+Examples
+--------
+>>> df = pd.DataFrame({{'angles': [0, 3, 4],
+...                    'degrees': [360, 180, 360]}},
+...                   index=['circle', 'triangle', 'rectangle'])
+>>> df
+           angles  degrees
+circle          0      360
+triangle        3      180
+rectangle       4      360
+
+Add a scalar with operator version which return the same
+results.
+
+>>> df + 1
+           angles  degrees
+circle          1      361
+triangle        4      181
+rectangle       5      361
+
+>>> df.add(1)
+           angles  degrees
+circle          1      361
+triangle        4      181
+rectangle       5      361
+
+Divide by constant with reverse version.
+
+>>> df.div(10)
+           angles  degrees
+circle        0.0     36.0
+triangle      0.3     18.0
+rectangle     0.4     36.0
+
+>>> df.rdiv(10)
+             angles   degrees
+circle          inf  0.027778
+triangle   3.333333  0.055556
+rectangle  2.500000  0.027778
+
+Subtract a list and Series by axis with operator version.
+
+>>> df - [1, 2]
+           angles  degrees
+circle         -1      358
+triangle        2      178
+rectangle       3      358
+
+>>> df.sub([1, 2], axis='columns')
+           angles  degrees
+circle         -1      358
+triangle        2      178
+rectangle       3      358
+
+>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
+...        axis='index')
+           angles  degrees
+circle         -1      359
+triangle        2      179
+rectangle       3      359
+
+Multiply a dictionary by axis.
+
+>>> df.mul({{'angles': 0, 'degrees': 2}})
+            angles  degrees
+circle           0      720
+triangle         0      360
+rectangle        0      720
+
+>>> df.mul({{'circle': 0, 'triangle': 2, 'rectangle': 3}}, axis='index')
+            angles  degrees
+circle           0        0
+triangle         6      360
+rectangle       12     1080
+
+Multiply a DataFrame of different shape with operator version.
+
+>>> other = pd.DataFrame({{'angles': [0, 3, 4]}},
+...                      index=['circle', 'triangle', 'rectangle'])
+>>> other
+           angles
+circle          0
+triangle        3
+rectangle       4
+
+>>> df * other
+           angles  degrees
+circle          0      NaN
+triangle        9      NaN
+rectangle      16      NaN
+
+>>> df.mul(other, fill_value=0)
+           angles  degrees
+circle          0      0.0
+triangle        9      0.0
+rectangle      16      0.0
+
+Divide by a MultiIndex by level.
+
+>>> df_multindex = pd.DataFrame({{'angles': [0, 3, 4, 4, 5, 6],
+...                              'degrees': [360, 180, 360, 360, 540, 720]}},
+...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
+...                                    ['circle', 'triangle', 'rectangle',
+...                                     'square', 'pentagon', 'hexagon']])
+>>> df_multindex
+             angles  degrees
+A circle          0      360
+  triangle        3      180
+  rectangle       4      360
+B square          4      360
+  pentagon        5      540
+  hexagon         6      720
+
+>>> df.div(df_multindex, level=1, fill_value=0)
+             angles  degrees
+A circle        NaN      1.0
+  triangle      1.0      1.0
+  rectangle     1.0      1.0
+B square        0.0      0.0
+  pentagon      0.0      0.0
+  hexagon       0.0      0.0
+"""
+
+_flex_comp_doc_FRAME = """
+Get {desc} of dataframe and other, element-wise (binary operator `{op_name}`).
+
+Among flexible wrappers (`eq`, `ne`, `le`, `lt`, `ge`, `gt`) to comparison
+operators.
+
+Equivalent to `==`, `!=`, `<=`, `<`, `>=`, `>` with support to choose axis
+(rows or columns) and level for comparison.
+
+Parameters
+----------
+other : scalar, sequence, Series, or DataFrame
+    Any single or multiple element data structure, or list-like object.
+axis : {{0 or 'index', 1 or 'columns'}}, default 'columns'
+    Whether to compare by the index (0 or 'index') or columns
+    (1 or 'columns').
+level : int or label
+    Broadcast across a level, matching Index values on the passed
+    MultiIndex level.
+
+Returns
+-------
+DataFrame of bool
+    Result of the comparison.
+
+See Also
+--------
+DataFrame.eq : Compare DataFrames for equality elementwise.
+DataFrame.ne : Compare DataFrames for inequality elementwise.
+DataFrame.le : Compare DataFrames for less than inequality
+    or equality elementwise.
+DataFrame.lt : Compare DataFrames for strictly less than
+    inequality elementwise.
+DataFrame.ge : Compare DataFrames for greater than inequality
+    or equality elementwise.
+DataFrame.gt : Compare DataFrames for strictly greater than
+    inequality elementwise.
+
+Notes
+-----
+Mismatched indices will be unioned together.
+`NaN` values are considered different (i.e. `NaN` != `NaN`).
+
+Examples
+--------
+>>> df = pd.DataFrame({{'cost': [250, 150, 100],
+...                    'revenue': [100, 250, 300]}},
+...                   index=['A', 'B', 'C'])
+>>> df
+   cost  revenue
+A   250      100
+B   150      250
+C   100      300
+
+Comparison with a scalar, using either the operator or method:
+
+>>> df == 100
+    cost  revenue
+A  False     True
+B  False    False
+C   True    False
+
+>>> df.eq(100)
+    cost  revenue
+A  False     True
+B  False    False
+C   True    False
+
+When `other` is a :class:`Series`, the columns of a DataFrame are aligned
+with the index of `other` and broadcast:
+
+>>> df != pd.Series([100, 250], index=["cost", "revenue"])
+    cost  revenue
+A   True     True
+B   True    False
+C  False     True
+
+Use the method to control the broadcast axis:
+
+>>> df.ne(pd.Series([100, 300], index=["A", "D"]), axis='index')
+   cost  revenue
+A  True    False
+B  True     True
+C  True     True
+D  True     True
+
+When comparing to an arbitrary sequence, the number of columns must
+match the number elements in `other`:
+
+>>> df == [250, 100]
+    cost  revenue
+A   True     True
+B  False    False
+C  False    False
+
+Use the method to control the axis:
+
+>>> df.eq([250, 250, 100], axis='index')
+    cost  revenue
+A   True    False
+B  False     True
+C   True    False
+
+Compare to a DataFrame of different shape.
+
+>>> other = pd.DataFrame({{'revenue': [300, 250, 100, 150]}},
+...                      index=['A', 'B', 'C', 'D'])
+>>> other
+   revenue
+A      300
+B      250
+C      100
+D      150
+
+>>> df.gt(other)
+    cost  revenue
+A  False    False
+B  False    False
+C  False     True
+D  False    False
+
+Compare to a MultiIndex by level.
+
+>>> df_multindex = pd.DataFrame({{'cost': [250, 150, 100, 150, 300, 220],
+...                              'revenue': [100, 250, 300, 200, 175, 225]}},
+...                             index=[['Q1', 'Q1', 'Q1', 'Q2', 'Q2', 'Q2'],
+...                                    ['A', 'B', 'C', 'A', 'B', 'C']])
+>>> df_multindex
+      cost  revenue
+Q1 A   250      100
+   B   150      250
+   C   100      300
+Q2 A   150      200
+   B   300      175
+   C   220      225
+
+>>> df.le(df_multindex, level=1)
+       cost  revenue
+Q1 A   True     True
+   B   True     True
+   C   True     True
+Q2 A  False     True
+   B   True    False
+   C   True    False
+"""
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/ops/invalid.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/ops/invalid.py
new file mode 100644
index 0000000000000000000000000000000000000000..e5ae6d359ac2205b01706211382d116b29176c7a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/ops/invalid.py
@@ -0,0 +1,62 @@
+"""
+Templates for invalid operations.
+"""
+from __future__ import annotations
+
+import operator
+from typing import TYPE_CHECKING
+
+import numpy as np
+
+if TYPE_CHECKING:
+    from pandas._typing import npt
+
+
+def invalid_comparison(left, right, op) -> npt.NDArray[np.bool_]:
+    """
+    If a comparison has mismatched types and is not necessarily meaningful,
+    follow python3 conventions by:
+
+        - returning all-False for equality
+        - returning all-True for inequality
+        - raising TypeError otherwise
+
+    Parameters
+    ----------
+    left : array-like
+    right : scalar, array-like
+    op : operator.{eq, ne, lt, le, gt}
+
+    Raises
+    ------
+    TypeError : on inequality comparisons
+    """
+    if op is operator.eq:
+        res_values = np.zeros(left.shape, dtype=bool)
+    elif op is operator.ne:
+        res_values = np.ones(left.shape, dtype=bool)
+    else:
+        typ = type(right).__name__
+        raise TypeError(f"Invalid comparison between dtype={left.dtype} and {typ}")
+    return res_values
+
+
+def make_invalid_op(name: str):
+    """
+    Return a binary method that always raises a TypeError.
+
+    Parameters
+    ----------
+    name : str
+
+    Returns
+    -------
+    invalid_op : function
+    """
+
+    def invalid_op(self, other=None):
+        typ = type(self).__name__
+        raise TypeError(f"cannot perform {name} with this index type: {typ}")
+
+    invalid_op.__name__ = name
+    return invalid_op
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/ops/mask_ops.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/ops/mask_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..adc1f63c568bf579f31b13446f4614435d443df1
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/ops/mask_ops.py
@@ -0,0 +1,189 @@
+"""
+Ops for masked arrays.
+"""
+from __future__ import annotations
+
+import numpy as np
+
+from pandas._libs import (
+    lib,
+    missing as libmissing,
+)
+
+
+def kleene_or(
+    left: bool | np.ndarray | libmissing.NAType,
+    right: bool | np.ndarray | libmissing.NAType,
+    left_mask: np.ndarray | None,
+    right_mask: np.ndarray | None,
+):
+    """
+    Boolean ``or`` using Kleene logic.
+
+    Values are NA where we have ``NA | NA`` or ``NA | False``.
+    ``NA | True`` is considered True.
+
+    Parameters
+    ----------
+    left, right : ndarray, NA, or bool
+        The values of the array.
+    left_mask, right_mask : ndarray, optional
+        The masks. Only one of these may be None, which implies that
+        the associated `left` or `right` value is a scalar.
+
+    Returns
+    -------
+    result, mask: ndarray[bool]
+        The result of the logical or, and the new mask.
+    """
+    # To reduce the number of cases, we ensure that `left` & `left_mask`
+    # always come from an array, not a scalar. This is safe, since
+    # A | B == B | A
+    if left_mask is None:
+        return kleene_or(right, left, right_mask, left_mask)
+
+    if not isinstance(left, np.ndarray):
+        raise TypeError("Either `left` or `right` need to be a np.ndarray.")
+
+    raise_for_nan(right, method="or")
+
+    if right is libmissing.NA:
+        result = left.copy()
+    else:
+        result = left | right
+
+    if right_mask is not None:
+        # output is unknown where (False & NA), (NA & False), (NA & NA)
+        left_false = ~(left | left_mask)
+        right_false = ~(right | right_mask)
+        mask = (
+            (left_false & right_mask)
+            | (right_false & left_mask)
+            | (left_mask & right_mask)
+        )
+    else:
+        if right is True:
+            mask = np.zeros_like(left_mask)
+        elif right is libmissing.NA:
+            mask = (~left & ~left_mask) | left_mask
+        else:
+            # False
+            mask = left_mask.copy()
+
+    return result, mask
+
+
+def kleene_xor(
+    left: bool | np.ndarray | libmissing.NAType,
+    right: bool | np.ndarray | libmissing.NAType,
+    left_mask: np.ndarray | None,
+    right_mask: np.ndarray | None,
+):
+    """
+    Boolean ``xor`` using Kleene logic.
+
+    This is the same as ``or``, with the following adjustments
+
+    * True, True -> False
+    * True, NA   -> NA
+
+    Parameters
+    ----------
+    left, right : ndarray, NA, or bool
+        The values of the array.
+    left_mask, right_mask : ndarray, optional
+        The masks. Only one of these may be None, which implies that
+        the associated `left` or `right` value is a scalar.
+
+    Returns
+    -------
+    result, mask: ndarray[bool]
+        The result of the logical xor, and the new mask.
+    """
+    # To reduce the number of cases, we ensure that `left` & `left_mask`
+    # always come from an array, not a scalar. This is safe, since
+    # A ^ B == B ^ A
+    if left_mask is None:
+        return kleene_xor(right, left, right_mask, left_mask)
+
+    if not isinstance(left, np.ndarray):
+        raise TypeError("Either `left` or `right` need to be a np.ndarray.")
+
+    raise_for_nan(right, method="xor")
+    if right is libmissing.NA:
+        result = np.zeros_like(left)
+    else:
+        result = left ^ right
+
+    if right_mask is None:
+        if right is libmissing.NA:
+            mask = np.ones_like(left_mask)
+        else:
+            mask = left_mask.copy()
+    else:
+        mask = left_mask | right_mask
+
+    return result, mask
+
+
+def kleene_and(
+    left: bool | libmissing.NAType | np.ndarray,
+    right: bool | libmissing.NAType | np.ndarray,
+    left_mask: np.ndarray | None,
+    right_mask: np.ndarray | None,
+):
+    """
+    Boolean ``and`` using Kleene logic.
+
+    Values are ``NA`` for ``NA & NA`` or ``True & NA``.
+
+    Parameters
+    ----------
+    left, right : ndarray, NA, or bool
+        The values of the array.
+    left_mask, right_mask : ndarray, optional
+        The masks. Only one of these may be None, which implies that
+        the associated `left` or `right` value is a scalar.
+
+    Returns
+    -------
+    result, mask: ndarray[bool]
+        The result of the logical xor, and the new mask.
+    """
+    # To reduce the number of cases, we ensure that `left` & `left_mask`
+    # always come from an array, not a scalar. This is safe, since
+    # A & B == B & A
+    if left_mask is None:
+        return kleene_and(right, left, right_mask, left_mask)
+
+    if not isinstance(left, np.ndarray):
+        raise TypeError("Either `left` or `right` need to be a np.ndarray.")
+    raise_for_nan(right, method="and")
+
+    if right is libmissing.NA:
+        result = np.zeros_like(left)
+    else:
+        result = left & right
+
+    if right_mask is None:
+        # Scalar `right`
+        if right is libmissing.NA:
+            mask = (left & ~left_mask) | left_mask
+
+        else:
+            mask = left_mask.copy()
+            if right is False:
+                # unmask everything
+                mask[:] = False
+    else:
+        # unmask where either left or right is False
+        left_false = ~(left | left_mask)
+        right_false = ~(right | right_mask)
+        mask = (left_mask & ~right_false) | (right_mask & ~left_false)
+
+    return result, mask
+
+
+def raise_for_nan(value, method: str) -> None:
+    if lib.is_float(value) and np.isnan(value):
+        raise ValueError(f"Cannot perform logical '{method}' with floating NaN")
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/ops/missing.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/ops/missing.py
new file mode 100644
index 0000000000000000000000000000000000000000..fc685935a35fceab74012912d3c3cae65b9c1818
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/ops/missing.py
@@ -0,0 +1,176 @@
+"""
+Missing data handling for arithmetic operations.
+
+In particular, pandas conventions regarding division by zero differ
+from numpy in the following ways:
+    1) np.array([-1, 0, 1], dtype=dtype1) // np.array([0, 0, 0], dtype=dtype2)
+       gives [nan, nan, nan] for most dtype combinations, and [0, 0, 0] for
+       the remaining pairs
+       (the remaining being dtype1==dtype2==intN and dtype==dtype2==uintN).
+
+       pandas convention is to return [-inf, nan, inf] for all dtype
+       combinations.
+
+       Note: the numpy behavior described here is py3-specific.
+
+    2) np.array([-1, 0, 1], dtype=dtype1) % np.array([0, 0, 0], dtype=dtype2)
+       gives precisely the same results as the // operation.
+
+       pandas convention is to return [nan, nan, nan] for all dtype
+       combinations.
+
+    3) divmod behavior consistent with 1) and 2).
+"""
+from __future__ import annotations
+
+import operator
+
+import numpy as np
+
+from pandas.core import roperator
+
+
+def _fill_zeros(result: np.ndarray, x, y):
+    """
+    If this is a reversed op, then flip x,y
+
+    If we have an integer value (or array in y)
+    and we have 0's, fill them with np.nan,
+    return the result.
+
+    Mask the nan's from x.
+    """
+    if result.dtype.kind == "f":
+        return result
+
+    is_variable_type = hasattr(y, "dtype")
+    is_scalar_type = not isinstance(y, np.ndarray)
+
+    if not is_variable_type and not is_scalar_type:
+        # e.g. test_series_ops_name_retention with mod we get here with list/tuple
+        return result
+
+    if is_scalar_type:
+        y = np.array(y)
+
+    if y.dtype.kind in "iu":
+        ymask = y == 0
+        if ymask.any():
+            # GH#7325, mask and nans must be broadcastable
+            mask = ymask & ~np.isnan(result)
+
+            # GH#9308 doing ravel on result and mask can improve putmask perf,
+            #  but can also make unwanted copies.
+            result = result.astype("float64", copy=False)
+
+            np.putmask(result, mask, np.nan)
+
+    return result
+
+
+def mask_zero_div_zero(x, y, result: np.ndarray) -> np.ndarray:
+    """
+    Set results of  0 // 0 to np.nan, regardless of the dtypes
+    of the numerator or the denominator.
+
+    Parameters
+    ----------
+    x : ndarray
+    y : ndarray
+    result : ndarray
+
+    Returns
+    -------
+    ndarray
+        The filled result.
+
+    Examples
+    --------
+    >>> x = np.array([1, 0, -1], dtype=np.int64)
+    >>> x
+    array([ 1,  0, -1])
+    >>> y = 0       # int 0; numpy behavior is different with float
+    >>> result = x // y
+    >>> result      # raw numpy result does not fill division by zero
+    array([0, 0, 0])
+    >>> mask_zero_div_zero(x, y, result)
+    array([ inf,  nan, -inf])
+    """
+
+    if not hasattr(y, "dtype"):
+        # e.g. scalar, tuple
+        y = np.array(y)
+    if not hasattr(x, "dtype"):
+        # e.g scalar, tuple
+        x = np.array(x)
+
+    zmask = y == 0
+
+    if zmask.any():
+        # Flip sign if necessary for -0.0
+        zneg_mask = zmask & np.signbit(y)
+        zpos_mask = zmask & ~zneg_mask
+
+        x_lt0 = x < 0
+        x_gt0 = x > 0
+        nan_mask = zmask & (x == 0)
+        neginf_mask = (zpos_mask & x_lt0) | (zneg_mask & x_gt0)
+        posinf_mask = (zpos_mask & x_gt0) | (zneg_mask & x_lt0)
+
+        if nan_mask.any() or neginf_mask.any() or posinf_mask.any():
+            # Fill negative/0 with -inf, positive/0 with +inf, 0/0 with NaN
+            result = result.astype("float64", copy=False)
+
+            result[nan_mask] = np.nan
+            result[posinf_mask] = np.inf
+            result[neginf_mask] = -np.inf
+
+    return result
+
+
+def dispatch_fill_zeros(op, left, right, result):
+    """
+    Call _fill_zeros with the appropriate fill value depending on the operation,
+    with special logic for divmod and rdivmod.
+
+    Parameters
+    ----------
+    op : function (operator.add, operator.div, ...)
+    left : object (np.ndarray for non-reversed ops)
+        We have excluded ExtensionArrays here
+    right : object (np.ndarray for reversed ops)
+        We have excluded ExtensionArrays here
+    result : ndarray
+
+    Returns
+    -------
+    result : np.ndarray
+
+    Notes
+    -----
+    For divmod and rdivmod, the `result` parameter and returned `result`
+    is a 2-tuple of ndarray objects.
+    """
+    if op is divmod:
+        result = (
+            mask_zero_div_zero(left, right, result[0]),
+            _fill_zeros(result[1], left, right),
+        )
+    elif op is roperator.rdivmod:
+        result = (
+            mask_zero_div_zero(right, left, result[0]),
+            _fill_zeros(result[1], right, left),
+        )
+    elif op is operator.floordiv:
+        # Note: no need to do this for truediv; in py3 numpy behaves the way
+        #  we want.
+        result = mask_zero_div_zero(left, right, result)
+    elif op is roperator.rfloordiv:
+        # Note: no need to do this for rtruediv; in py3 numpy behaves the way
+        #  we want.
+        result = mask_zero_div_zero(right, left, result)
+    elif op is operator.mod:
+        result = _fill_zeros(result, left, right)
+    elif op is roperator.rmod:
+        result = _fill_zeros(result, right, left)
+    return result
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/resample.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/resample.py
new file mode 100644
index 0000000000000000000000000000000000000000..6c5da08a86fdc883ed9f2b66bf07d0e743b1d63b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/resample.py
@@ -0,0 +1,2906 @@
+from __future__ import annotations
+
+import copy
+from textwrap import dedent
+from typing import (
+    TYPE_CHECKING,
+    Callable,
+    Literal,
+    cast,
+    final,
+    no_type_check,
+)
+import warnings
+
+import numpy as np
+
+from pandas._libs import lib
+from pandas._libs.tslibs import (
+    BaseOffset,
+    IncompatibleFrequency,
+    NaT,
+    Period,
+    Timedelta,
+    Timestamp,
+    to_offset,
+)
+from pandas._libs.tslibs.dtypes import freq_to_period_freqstr
+from pandas._typing import NDFrameT
+from pandas.compat.numpy import function as nv
+from pandas.errors import AbstractMethodError
+from pandas.util._decorators import (
+    Appender,
+    Substitution,
+    doc,
+)
+from pandas.util._exceptions import (
+    find_stack_level,
+    rewrite_warning,
+)
+
+from pandas.core.dtypes.dtypes import ArrowDtype
+from pandas.core.dtypes.generic import (
+    ABCDataFrame,
+    ABCSeries,
+)
+
+import pandas.core.algorithms as algos
+from pandas.core.apply import (
+    ResamplerWindowApply,
+    warn_alias_replacement,
+)
+from pandas.core.arrays import ArrowExtensionArray
+from pandas.core.base import (
+    PandasObject,
+    SelectionMixin,
+)
+import pandas.core.common as com
+from pandas.core.generic import (
+    NDFrame,
+    _shared_docs,
+)
+from pandas.core.groupby.generic import SeriesGroupBy
+from pandas.core.groupby.groupby import (
+    BaseGroupBy,
+    GroupBy,
+    _apply_groupings_depr,
+    _pipe_template,
+    get_groupby,
+)
+from pandas.core.groupby.grouper import Grouper
+from pandas.core.groupby.ops import BinGrouper
+from pandas.core.indexes.api import MultiIndex
+from pandas.core.indexes.base import Index
+from pandas.core.indexes.datetimes import (
+    DatetimeIndex,
+    date_range,
+)
+from pandas.core.indexes.period import (
+    PeriodIndex,
+    period_range,
+)
+from pandas.core.indexes.timedeltas import (
+    TimedeltaIndex,
+    timedelta_range,
+)
+
+from pandas.tseries.frequencies import (
+    is_subperiod,
+    is_superperiod,
+)
+from pandas.tseries.offsets import (
+    Day,
+    Tick,
+)
+
+if TYPE_CHECKING:
+    from collections.abc import Hashable
+
+    from pandas._typing import (
+        AnyArrayLike,
+        Axis,
+        AxisInt,
+        Frequency,
+        IndexLabel,
+        InterpolateOptions,
+        T,
+        TimedeltaConvertibleTypes,
+        TimeGrouperOrigin,
+        TimestampConvertibleTypes,
+        npt,
+    )
+
+    from pandas import (
+        DataFrame,
+        Series,
+    )
+
+_shared_docs_kwargs: dict[str, str] = {}
+
+
+class Resampler(BaseGroupBy, PandasObject):
+    """
+    Class for resampling datetimelike data, a groupby-like operation.
+    See aggregate, transform, and apply functions on this object.
+
+    It's easiest to use obj.resample(...) to use Resampler.
+
+    Parameters
+    ----------
+    obj : Series or DataFrame
+    groupby : TimeGrouper
+    axis : int, default 0
+    kind : str or None
+        'period', 'timestamp' to override default index treatment
+
+    Returns
+    -------
+    a Resampler of the appropriate type
+
+    Notes
+    -----
+    After resampling, see aggregate, apply, and transform functions.
+    """
+
+    _grouper: BinGrouper
+    _timegrouper: TimeGrouper
+    binner: DatetimeIndex | TimedeltaIndex | PeriodIndex  # depends on subclass
+    exclusions: frozenset[Hashable] = frozenset()  # for SelectionMixin compat
+    _internal_names_set = set({"obj", "ax", "_indexer"})
+
+    # to the groupby descriptor
+    _attributes = [
+        "freq",
+        "axis",
+        "closed",
+        "label",
+        "convention",
+        "kind",
+        "origin",
+        "offset",
+    ]
+
+    def __init__(
+        self,
+        obj: NDFrame,
+        timegrouper: TimeGrouper,
+        axis: Axis = 0,
+        kind=None,
+        *,
+        gpr_index: Index,
+        group_keys: bool = False,
+        selection=None,
+        include_groups: bool = True,
+    ) -> None:
+        self._timegrouper = timegrouper
+        self.keys = None
+        self.sort = True
+        self.axis = obj._get_axis_number(axis)
+        self.kind = kind
+        self.group_keys = group_keys
+        self.as_index = True
+        self.include_groups = include_groups
+
+        self.obj, self.ax, self._indexer = self._timegrouper._set_grouper(
+            self._convert_obj(obj), sort=True, gpr_index=gpr_index
+        )
+        self.binner, self._grouper = self._get_binner()
+        self._selection = selection
+        if self._timegrouper.key is not None:
+            self.exclusions = frozenset([self._timegrouper.key])
+        else:
+            self.exclusions = frozenset()
+
+    @final
+    def __str__(self) -> str:
+        """
+        Provide a nice str repr of our rolling object.
+        """
+        attrs = (
+            f"{k}={getattr(self._timegrouper, k)}"
+            for k in self._attributes
+            if getattr(self._timegrouper, k, None) is not None
+        )
+        return f"{type(self).__name__} [{', '.join(attrs)}]"
+
+    @final
+    def __getattr__(self, attr: str):
+        if attr in self._internal_names_set:
+            return object.__getattribute__(self, attr)
+        if attr in self._attributes:
+            return getattr(self._timegrouper, attr)
+        if attr in self.obj:
+            return self[attr]
+
+        return object.__getattribute__(self, attr)
+
+    @final
+    @property
+    def _from_selection(self) -> bool:
+        """
+        Is the resampling from a DataFrame column or MultiIndex level.
+        """
+        # upsampling and PeriodIndex resampling do not work
+        # with selection, this state used to catch and raise an error
+        return self._timegrouper is not None and (
+            self._timegrouper.key is not None or self._timegrouper.level is not None
+        )
+
+    def _convert_obj(self, obj: NDFrameT) -> NDFrameT:
+        """
+        Provide any conversions for the object in order to correctly handle.
+
+        Parameters
+        ----------
+        obj : Series or DataFrame
+
+        Returns
+        -------
+        Series or DataFrame
+        """
+        return obj._consolidate()
+
+    def _get_binner_for_time(self):
+        raise AbstractMethodError(self)
+
+    @final
+    def _get_binner(self):
+        """
+        Create the BinGrouper, assume that self.set_grouper(obj)
+        has already been called.
+        """
+        binner, bins, binlabels = self._get_binner_for_time()
+        assert len(bins) == len(binlabels)
+        bin_grouper = BinGrouper(bins, binlabels, indexer=self._indexer)
+        return binner, bin_grouper
+
+    @final
+    @Substitution(
+        klass="Resampler",
+        examples="""
+    >>> df = pd.DataFrame({'A': [1, 2, 3, 4]},
+    ...                   index=pd.date_range('2012-08-02', periods=4))
+    >>> df
+                A
+    2012-08-02  1
+    2012-08-03  2
+    2012-08-04  3
+    2012-08-05  4
+
+    To get the difference between each 2-day period's maximum and minimum
+    value in one pass, you can do
+
+    >>> df.resample('2D').pipe(lambda x: x.max() - x.min())
+                A
+    2012-08-02  1
+    2012-08-04  1""",
+    )
+    @Appender(_pipe_template)
+    def pipe(
+        self,
+        func: Callable[..., T] | tuple[Callable[..., T], str],
+        *args,
+        **kwargs,
+    ) -> T:
+        return super().pipe(func, *args, **kwargs)
+
+    _agg_see_also_doc = dedent(
+        """
+    See Also
+    --------
+    DataFrame.groupby.aggregate : Aggregate using callable, string, dict,
+        or list of string/callables.
+    DataFrame.resample.transform : Transforms the Series on each group
+        based on the given function.
+    DataFrame.aggregate: Aggregate using one or more
+        operations over the specified axis.
+    """
+    )
+
+    _agg_examples_doc = dedent(
+        """
+    Examples
+    --------
+    >>> s = pd.Series([1, 2, 3, 4, 5],
+    ...               index=pd.date_range('20130101', periods=5, freq='s'))
+    >>> s
+    2013-01-01 00:00:00    1
+    2013-01-01 00:00:01    2
+    2013-01-01 00:00:02    3
+    2013-01-01 00:00:03    4
+    2013-01-01 00:00:04    5
+    Freq: s, dtype: int64
+
+    >>> r = s.resample('2s')
+
+    >>> r.agg("sum")
+    2013-01-01 00:00:00    3
+    2013-01-01 00:00:02    7
+    2013-01-01 00:00:04    5
+    Freq: 2s, dtype: int64
+
+    >>> r.agg(['sum', 'mean', 'max'])
+                         sum  mean  max
+    2013-01-01 00:00:00    3   1.5    2
+    2013-01-01 00:00:02    7   3.5    4
+    2013-01-01 00:00:04    5   5.0    5
+
+    >>> r.agg({'result': lambda x: x.mean() / x.std(),
+    ...        'total': "sum"})
+                           result  total
+    2013-01-01 00:00:00  2.121320      3
+    2013-01-01 00:00:02  4.949747      7
+    2013-01-01 00:00:04       NaN      5
+
+    >>> r.agg(average="mean", total="sum")
+                             average  total
+    2013-01-01 00:00:00      1.5      3
+    2013-01-01 00:00:02      3.5      7
+    2013-01-01 00:00:04      5.0      5
+    """
+    )
+
+    @final
+    @doc(
+        _shared_docs["aggregate"],
+        see_also=_agg_see_also_doc,
+        examples=_agg_examples_doc,
+        klass="DataFrame",
+        axis="",
+    )
+    def aggregate(self, func=None, *args, **kwargs):
+        result = ResamplerWindowApply(self, func, args=args, kwargs=kwargs).agg()
+        if result is None:
+            how = func
+            result = self._groupby_and_aggregate(how, *args, **kwargs)
+
+        return result
+
+    agg = aggregate
+    apply = aggregate
+
+    @final
+    def transform(self, arg, *args, **kwargs):
+        """
+        Call function producing a like-indexed Series on each group.
+
+        Return a Series with the transformed values.
+
+        Parameters
+        ----------
+        arg : function
+            To apply to each group. Should return a Series with the same index.
+
+        Returns
+        -------
+        Series
+
+        Examples
+        --------
+        >>> s = pd.Series([1, 2],
+        ...               index=pd.date_range('20180101',
+        ...                                   periods=2,
+        ...                                   freq='1h'))
+        >>> s
+        2018-01-01 00:00:00    1
+        2018-01-01 01:00:00    2
+        Freq: h, dtype: int64
+
+        >>> resampled = s.resample('15min')
+        >>> resampled.transform(lambda x: (x - x.mean()) / x.std())
+        2018-01-01 00:00:00   NaN
+        2018-01-01 01:00:00   NaN
+        Freq: h, dtype: float64
+        """
+        return self._selected_obj.groupby(self._timegrouper).transform(
+            arg, *args, **kwargs
+        )
+
+    def _downsample(self, f, **kwargs):
+        raise AbstractMethodError(self)
+
+    def _upsample(self, f, limit: int | None = None, fill_value=None):
+        raise AbstractMethodError(self)
+
+    def _gotitem(self, key, ndim: int, subset=None):
+        """
+        Sub-classes to define. Return a sliced object.
+
+        Parameters
+        ----------
+        key : string / list of selections
+        ndim : {1, 2}
+            requested ndim of result
+        subset : object, default None
+            subset to act on
+        """
+        grouper = self._grouper
+        if subset is None:
+            subset = self.obj
+            if key is not None:
+                subset = subset[key]
+            else:
+                # reached via Apply.agg_dict_like with selection=None and ndim=1
+                assert subset.ndim == 1
+        if ndim == 1:
+            assert subset.ndim == 1
+
+        grouped = get_groupby(
+            subset, by=None, grouper=grouper, axis=self.axis, group_keys=self.group_keys
+        )
+        return grouped
+
+    def _groupby_and_aggregate(self, how, *args, **kwargs):
+        """
+        Re-evaluate the obj with a groupby aggregation.
+        """
+        grouper = self._grouper
+
+        # Excludes `on` column when provided
+        obj = self._obj_with_exclusions
+
+        grouped = get_groupby(
+            obj, by=None, grouper=grouper, axis=self.axis, group_keys=self.group_keys
+        )
+
+        try:
+            if callable(how):
+                # TODO: test_resample_apply_with_additional_args fails if we go
+                #  through the non-lambda path, not clear that it should.
+                func = lambda x: how(x, *args, **kwargs)
+                result = grouped.aggregate(func)
+            else:
+                result = grouped.aggregate(how, *args, **kwargs)
+        except (AttributeError, KeyError):
+            # we have a non-reducing function; try to evaluate
+            # alternatively we want to evaluate only a column of the input
+
+            # test_apply_to_one_column_of_df the function being applied references
+            #  a DataFrame column, but aggregate_item_by_item operates column-wise
+            #  on Series, raising AttributeError or KeyError
+            #  (depending on whether the column lookup uses getattr/__getitem__)
+            result = _apply(
+                grouped, how, *args, include_groups=self.include_groups, **kwargs
+            )
+
+        except ValueError as err:
+            if "Must produce aggregated value" in str(err):
+                # raised in _aggregate_named
+                # see test_apply_without_aggregation, test_apply_with_mutated_index
+                pass
+            else:
+                raise
+
+            # we have a non-reducing function
+            # try to evaluate
+            result = _apply(
+                grouped, how, *args, include_groups=self.include_groups, **kwargs
+            )
+
+        return self._wrap_result(result)
+
+    @final
+    def _get_resampler_for_grouping(
+        self, groupby: GroupBy, key, include_groups: bool = True
+    ):
+        """
+        Return the correct class for resampling with groupby.
+        """
+        return self._resampler_for_grouping(
+            groupby=groupby, key=key, parent=self, include_groups=include_groups
+        )
+
+    def _wrap_result(self, result):
+        """
+        Potentially wrap any results.
+        """
+        # GH 47705
+        obj = self.obj
+        if (
+            isinstance(result, ABCDataFrame)
+            and len(result) == 0
+            and not isinstance(result.index, PeriodIndex)
+        ):
+            result = result.set_index(
+                _asfreq_compat(obj.index[:0], freq=self.freq), append=True
+            )
+
+        if isinstance(result, ABCSeries) and self._selection is not None:
+            result.name = self._selection
+
+        if isinstance(result, ABCSeries) and result.empty:
+            # When index is all NaT, result is empty but index is not
+            result.index = _asfreq_compat(obj.index[:0], freq=self.freq)
+            result.name = getattr(obj, "name", None)
+
+        if self._timegrouper._arrow_dtype is not None:
+            result.index = result.index.astype(self._timegrouper._arrow_dtype)
+
+        return result
+
+    @final
+    def ffill(self, limit: int | None = None):
+        """
+        Forward fill the values.
+
+        Parameters
+        ----------
+        limit : int, optional
+            Limit of how many values to fill.
+
+        Returns
+        -------
+        An upsampled Series.
+
+        See Also
+        --------
+        Series.fillna: Fill NA/NaN values using the specified method.
+        DataFrame.fillna: Fill NA/NaN values using the specified method.
+
+        Examples
+        --------
+        Here we only create a ``Series``.
+
+        >>> ser = pd.Series([1, 2, 3, 4], index=pd.DatetimeIndex(
+        ...                 ['2023-01-01', '2023-01-15', '2023-02-01', '2023-02-15']))
+        >>> ser
+        2023-01-01    1
+        2023-01-15    2
+        2023-02-01    3
+        2023-02-15    4
+        dtype: int64
+
+        Example for ``ffill`` with downsampling (we have fewer dates after resampling):
+
+        >>> ser.resample('MS').ffill()
+        2023-01-01    1
+        2023-02-01    3
+        Freq: MS, dtype: int64
+
+        Example for ``ffill`` with upsampling (fill the new dates with
+        the previous value):
+
+        >>> ser.resample('W').ffill()
+        2023-01-01    1
+        2023-01-08    1
+        2023-01-15    2
+        2023-01-22    2
+        2023-01-29    2
+        2023-02-05    3
+        2023-02-12    3
+        2023-02-19    4
+        Freq: W-SUN, dtype: int64
+
+        With upsampling and limiting (only fill the first new date with the
+        previous value):
+
+        >>> ser.resample('W').ffill(limit=1)
+        2023-01-01    1.0
+        2023-01-08    1.0
+        2023-01-15    2.0
+        2023-01-22    2.0
+        2023-01-29    NaN
+        2023-02-05    3.0
+        2023-02-12    NaN
+        2023-02-19    4.0
+        Freq: W-SUN, dtype: float64
+        """
+        return self._upsample("ffill", limit=limit)
+
+    @final
+    def nearest(self, limit: int | None = None):
+        """
+        Resample by using the nearest value.
+
+        When resampling data, missing values may appear (e.g., when the
+        resampling frequency is higher than the original frequency).
+        The `nearest` method will replace ``NaN`` values that appeared in
+        the resampled data with the value from the nearest member of the
+        sequence, based on the index value.
+        Missing values that existed in the original data will not be modified.
+        If `limit` is given, fill only this many values in each direction for
+        each of the original values.
+
+        Parameters
+        ----------
+        limit : int, optional
+            Limit of how many values to fill.
+
+        Returns
+        -------
+        Series or DataFrame
+            An upsampled Series or DataFrame with ``NaN`` values filled with
+            their nearest value.
+
+        See Also
+        --------
+        backfill : Backward fill the new missing values in the resampled data.
+        pad : Forward fill ``NaN`` values.
+
+        Examples
+        --------
+        >>> s = pd.Series([1, 2],
+        ...               index=pd.date_range('20180101',
+        ...                                   periods=2,
+        ...                                   freq='1h'))
+        >>> s
+        2018-01-01 00:00:00    1
+        2018-01-01 01:00:00    2
+        Freq: h, dtype: int64
+
+        >>> s.resample('15min').nearest()
+        2018-01-01 00:00:00    1
+        2018-01-01 00:15:00    1
+        2018-01-01 00:30:00    2
+        2018-01-01 00:45:00    2
+        2018-01-01 01:00:00    2
+        Freq: 15min, dtype: int64
+
+        Limit the number of upsampled values imputed by the nearest:
+
+        >>> s.resample('15min').nearest(limit=1)
+        2018-01-01 00:00:00    1.0
+        2018-01-01 00:15:00    1.0
+        2018-01-01 00:30:00    NaN
+        2018-01-01 00:45:00    2.0
+        2018-01-01 01:00:00    2.0
+        Freq: 15min, dtype: float64
+        """
+        return self._upsample("nearest", limit=limit)
+
+    @final
+    def bfill(self, limit: int | None = None):
+        """
+        Backward fill the new missing values in the resampled data.
+
+        In statistics, imputation is the process of replacing missing data with
+        substituted values [1]_. When resampling data, missing values may
+        appear (e.g., when the resampling frequency is higher than the original
+        frequency). The backward fill will replace NaN values that appeared in
+        the resampled data with the next value in the original sequence.
+        Missing values that existed in the original data will not be modified.
+
+        Parameters
+        ----------
+        limit : int, optional
+            Limit of how many values to fill.
+
+        Returns
+        -------
+        Series, DataFrame
+            An upsampled Series or DataFrame with backward filled NaN values.
+
+        See Also
+        --------
+        bfill : Alias of backfill.
+        fillna : Fill NaN values using the specified method, which can be
+            'backfill'.
+        nearest : Fill NaN values with nearest neighbor starting from center.
+        ffill : Forward fill NaN values.
+        Series.fillna : Fill NaN values in the Series using the
+            specified method, which can be 'backfill'.
+        DataFrame.fillna : Fill NaN values in the DataFrame using the
+            specified method, which can be 'backfill'.
+
+        References
+        ----------
+        .. [1] https://en.wikipedia.org/wiki/Imputation_(statistics)
+
+        Examples
+        --------
+        Resampling a Series:
+
+        >>> s = pd.Series([1, 2, 3],
+        ...               index=pd.date_range('20180101', periods=3, freq='h'))
+        >>> s
+        2018-01-01 00:00:00    1
+        2018-01-01 01:00:00    2
+        2018-01-01 02:00:00    3
+        Freq: h, dtype: int64
+
+        >>> s.resample('30min').bfill()
+        2018-01-01 00:00:00    1
+        2018-01-01 00:30:00    2
+        2018-01-01 01:00:00    2
+        2018-01-01 01:30:00    3
+        2018-01-01 02:00:00    3
+        Freq: 30min, dtype: int64
+
+        >>> s.resample('15min').bfill(limit=2)
+        2018-01-01 00:00:00    1.0
+        2018-01-01 00:15:00    NaN
+        2018-01-01 00:30:00    2.0
+        2018-01-01 00:45:00    2.0
+        2018-01-01 01:00:00    2.0
+        2018-01-01 01:15:00    NaN
+        2018-01-01 01:30:00    3.0
+        2018-01-01 01:45:00    3.0
+        2018-01-01 02:00:00    3.0
+        Freq: 15min, dtype: float64
+
+        Resampling a DataFrame that has missing values:
+
+        >>> df = pd.DataFrame({'a': [2, np.nan, 6], 'b': [1, 3, 5]},
+        ...                   index=pd.date_range('20180101', periods=3,
+        ...                                       freq='h'))
+        >>> df
+                               a  b
+        2018-01-01 00:00:00  2.0  1
+        2018-01-01 01:00:00  NaN  3
+        2018-01-01 02:00:00  6.0  5
+
+        >>> df.resample('30min').bfill()
+                               a  b
+        2018-01-01 00:00:00  2.0  1
+        2018-01-01 00:30:00  NaN  3
+        2018-01-01 01:00:00  NaN  3
+        2018-01-01 01:30:00  6.0  5
+        2018-01-01 02:00:00  6.0  5
+
+        >>> df.resample('15min').bfill(limit=2)
+                               a    b
+        2018-01-01 00:00:00  2.0  1.0
+        2018-01-01 00:15:00  NaN  NaN
+        2018-01-01 00:30:00  NaN  3.0
+        2018-01-01 00:45:00  NaN  3.0
+        2018-01-01 01:00:00  NaN  3.0
+        2018-01-01 01:15:00  NaN  NaN
+        2018-01-01 01:30:00  6.0  5.0
+        2018-01-01 01:45:00  6.0  5.0
+        2018-01-01 02:00:00  6.0  5.0
+        """
+        return self._upsample("bfill", limit=limit)
+
+    @final
+    def fillna(self, method, limit: int | None = None):
+        """
+        Fill missing values introduced by upsampling.
+
+        In statistics, imputation is the process of replacing missing data with
+        substituted values [1]_. When resampling data, missing values may
+        appear (e.g., when the resampling frequency is higher than the original
+        frequency).
+
+        Missing values that existed in the original data will
+        not be modified.
+
+        Parameters
+        ----------
+        method : {'pad', 'backfill', 'ffill', 'bfill', 'nearest'}
+            Method to use for filling holes in resampled data
+
+            * 'pad' or 'ffill': use previous valid observation to fill gap
+              (forward fill).
+            * 'backfill' or 'bfill': use next valid observation to fill gap.
+            * 'nearest': use nearest valid observation to fill gap.
+
+        limit : int, optional
+            Limit of how many consecutive missing values to fill.
+
+        Returns
+        -------
+        Series or DataFrame
+            An upsampled Series or DataFrame with missing values filled.
+
+        See Also
+        --------
+        bfill : Backward fill NaN values in the resampled data.
+        ffill : Forward fill NaN values in the resampled data.
+        nearest : Fill NaN values in the resampled data
+            with nearest neighbor starting from center.
+        interpolate : Fill NaN values using interpolation.
+        Series.fillna : Fill NaN values in the Series using the
+            specified method, which can be 'bfill' and 'ffill'.
+        DataFrame.fillna : Fill NaN values in the DataFrame using the
+            specified method, which can be 'bfill' and 'ffill'.
+
+        References
+        ----------
+        .. [1] https://en.wikipedia.org/wiki/Imputation_(statistics)
+
+        Examples
+        --------
+        Resampling a Series:
+
+        >>> s = pd.Series([1, 2, 3],
+        ...               index=pd.date_range('20180101', periods=3, freq='h'))
+        >>> s
+        2018-01-01 00:00:00    1
+        2018-01-01 01:00:00    2
+        2018-01-01 02:00:00    3
+        Freq: h, dtype: int64
+
+        Without filling the missing values you get:
+
+        >>> s.resample("30min").asfreq()
+        2018-01-01 00:00:00    1.0
+        2018-01-01 00:30:00    NaN
+        2018-01-01 01:00:00    2.0
+        2018-01-01 01:30:00    NaN
+        2018-01-01 02:00:00    3.0
+        Freq: 30min, dtype: float64
+
+        >>> s.resample('30min').fillna("backfill")
+        2018-01-01 00:00:00    1
+        2018-01-01 00:30:00    2
+        2018-01-01 01:00:00    2
+        2018-01-01 01:30:00    3
+        2018-01-01 02:00:00    3
+        Freq: 30min, dtype: int64
+
+        >>> s.resample('15min').fillna("backfill", limit=2)
+        2018-01-01 00:00:00    1.0
+        2018-01-01 00:15:00    NaN
+        2018-01-01 00:30:00    2.0
+        2018-01-01 00:45:00    2.0
+        2018-01-01 01:00:00    2.0
+        2018-01-01 01:15:00    NaN
+        2018-01-01 01:30:00    3.0
+        2018-01-01 01:45:00    3.0
+        2018-01-01 02:00:00    3.0
+        Freq: 15min, dtype: float64
+
+        >>> s.resample('30min').fillna("pad")
+        2018-01-01 00:00:00    1
+        2018-01-01 00:30:00    1
+        2018-01-01 01:00:00    2
+        2018-01-01 01:30:00    2
+        2018-01-01 02:00:00    3
+        Freq: 30min, dtype: int64
+
+        >>> s.resample('30min').fillna("nearest")
+        2018-01-01 00:00:00    1
+        2018-01-01 00:30:00    2
+        2018-01-01 01:00:00    2
+        2018-01-01 01:30:00    3
+        2018-01-01 02:00:00    3
+        Freq: 30min, dtype: int64
+
+        Missing values present before the upsampling are not affected.
+
+        >>> sm = pd.Series([1, None, 3],
+        ...                index=pd.date_range('20180101', periods=3, freq='h'))
+        >>> sm
+        2018-01-01 00:00:00    1.0
+        2018-01-01 01:00:00    NaN
+        2018-01-01 02:00:00    3.0
+        Freq: h, dtype: float64
+
+        >>> sm.resample('30min').fillna('backfill')
+        2018-01-01 00:00:00    1.0
+        2018-01-01 00:30:00    NaN
+        2018-01-01 01:00:00    NaN
+        2018-01-01 01:30:00    3.0
+        2018-01-01 02:00:00    3.0
+        Freq: 30min, dtype: float64
+
+        >>> sm.resample('30min').fillna('pad')
+        2018-01-01 00:00:00    1.0
+        2018-01-01 00:30:00    1.0
+        2018-01-01 01:00:00    NaN
+        2018-01-01 01:30:00    NaN
+        2018-01-01 02:00:00    3.0
+        Freq: 30min, dtype: float64
+
+        >>> sm.resample('30min').fillna('nearest')
+        2018-01-01 00:00:00    1.0
+        2018-01-01 00:30:00    NaN
+        2018-01-01 01:00:00    NaN
+        2018-01-01 01:30:00    3.0
+        2018-01-01 02:00:00    3.0
+        Freq: 30min, dtype: float64
+
+        DataFrame resampling is done column-wise. All the same options are
+        available.
+
+        >>> df = pd.DataFrame({'a': [2, np.nan, 6], 'b': [1, 3, 5]},
+        ...                   index=pd.date_range('20180101', periods=3,
+        ...                                       freq='h'))
+        >>> df
+                               a  b
+        2018-01-01 00:00:00  2.0  1
+        2018-01-01 01:00:00  NaN  3
+        2018-01-01 02:00:00  6.0  5
+
+        >>> df.resample('30min').fillna("bfill")
+                               a  b
+        2018-01-01 00:00:00  2.0  1
+        2018-01-01 00:30:00  NaN  3
+        2018-01-01 01:00:00  NaN  3
+        2018-01-01 01:30:00  6.0  5
+        2018-01-01 02:00:00  6.0  5
+        """
+        warnings.warn(
+            f"{type(self).__name__}.fillna is deprecated and will be removed "
+            "in a future version. Use obj.ffill(), obj.bfill(), "
+            "or obj.nearest() instead.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        return self._upsample(method, limit=limit)
+
+    @final
+    def interpolate(
+        self,
+        method: InterpolateOptions = "linear",
+        *,
+        axis: Axis = 0,
+        limit: int | None = None,
+        inplace: bool = False,
+        limit_direction: Literal["forward", "backward", "both"] = "forward",
+        limit_area=None,
+        downcast=lib.no_default,
+        **kwargs,
+    ):
+        """
+        Interpolate values between target timestamps according to different methods.
+
+        The original index is first reindexed to target timestamps
+        (see :meth:`core.resample.Resampler.asfreq`),
+        then the interpolation of ``NaN`` values via :meth:`DataFrame.interpolate`
+        happens.
+
+        Parameters
+        ----------
+        method : str, default 'linear'
+            Interpolation technique to use. One of:
+
+            * 'linear': Ignore the index and treat the values as equally
+              spaced. This is the only method supported on MultiIndexes.
+            * 'time': Works on daily and higher resolution data to interpolate
+              given length of interval.
+            * 'index', 'values': use the actual numerical values of the index.
+            * 'pad': Fill in NaNs using existing values.
+            * 'nearest', 'zero', 'slinear', 'quadratic', 'cubic',
+              'barycentric', 'polynomial': Passed to
+              `scipy.interpolate.interp1d`, whereas 'spline' is passed to
+              `scipy.interpolate.UnivariateSpline`. These methods use the numerical
+              values of the index.  Both 'polynomial' and 'spline' require that
+              you also specify an `order` (int), e.g.
+              ``df.interpolate(method='polynomial', order=5)``. Note that,
+              `slinear` method in Pandas refers to the Scipy first order `spline`
+              instead of Pandas first order `spline`.
+            * 'krogh', 'piecewise_polynomial', 'spline', 'pchip', 'akima',
+              'cubicspline': Wrappers around the SciPy interpolation methods of
+              similar names. See `Notes`.
+            * 'from_derivatives': Refers to
+              `scipy.interpolate.BPoly.from_derivatives`.
+
+        axis : {{0 or 'index', 1 or 'columns', None}}, default None
+            Axis to interpolate along. For `Series` this parameter is unused
+            and defaults to 0.
+        limit : int, optional
+            Maximum number of consecutive NaNs to fill. Must be greater than
+            0.
+        inplace : bool, default False
+            Update the data in place if possible.
+        limit_direction : {{'forward', 'backward', 'both'}}, Optional
+            Consecutive NaNs will be filled in this direction.
+
+            If limit is specified:
+                * If 'method' is 'pad' or 'ffill', 'limit_direction' must be 'forward'.
+                * If 'method' is 'backfill' or 'bfill', 'limit_direction' must be
+                  'backwards'.
+
+            If 'limit' is not specified:
+                * If 'method' is 'backfill' or 'bfill', the default is 'backward'
+                * else the default is 'forward'
+
+                raises ValueError if `limit_direction` is 'forward' or 'both' and
+                    method is 'backfill' or 'bfill'.
+                raises ValueError if `limit_direction` is 'backward' or 'both' and
+                    method is 'pad' or 'ffill'.
+
+        limit_area : {{`None`, 'inside', 'outside'}}, default None
+            If limit is specified, consecutive NaNs will be filled with this
+            restriction.
+
+            * ``None``: No fill restriction.
+            * 'inside': Only fill NaNs surrounded by valid values
+              (interpolate).
+            * 'outside': Only fill NaNs outside valid values (extrapolate).
+
+        downcast : optional, 'infer' or None, defaults to None
+            Downcast dtypes if possible.
+
+            .. deprecated:: 2.1.0
+
+        ``**kwargs`` : optional
+            Keyword arguments to pass on to the interpolating function.
+
+        Returns
+        -------
+        DataFrame or Series
+            Interpolated values at the specified freq.
+
+        See Also
+        --------
+        core.resample.Resampler.asfreq: Return the values at the new freq,
+            essentially a reindex.
+        DataFrame.interpolate: Fill NaN values using an interpolation method.
+
+        Notes
+        -----
+        For high-frequent or non-equidistant time-series with timestamps
+        the reindexing followed by interpolation may lead to information loss
+        as shown in the last example.
+
+        Examples
+        --------
+
+        >>> start = "2023-03-01T07:00:00"
+        >>> timesteps = pd.date_range(start, periods=5, freq="s")
+        >>> series = pd.Series(data=[1, -1, 2, 1, 3], index=timesteps)
+        >>> series
+        2023-03-01 07:00:00    1
+        2023-03-01 07:00:01   -1
+        2023-03-01 07:00:02    2
+        2023-03-01 07:00:03    1
+        2023-03-01 07:00:04    3
+        Freq: s, dtype: int64
+
+        Upsample the dataframe to 0.5Hz by providing the period time of 2s.
+
+        >>> series.resample("2s").interpolate("linear")
+        2023-03-01 07:00:00    1
+        2023-03-01 07:00:02    2
+        2023-03-01 07:00:04    3
+        Freq: 2s, dtype: int64
+
+        Downsample the dataframe to 2Hz by providing the period time of 500ms.
+
+        >>> series.resample("500ms").interpolate("linear")
+        2023-03-01 07:00:00.000    1.0
+        2023-03-01 07:00:00.500    0.0
+        2023-03-01 07:00:01.000   -1.0
+        2023-03-01 07:00:01.500    0.5
+        2023-03-01 07:00:02.000    2.0
+        2023-03-01 07:00:02.500    1.5
+        2023-03-01 07:00:03.000    1.0
+        2023-03-01 07:00:03.500    2.0
+        2023-03-01 07:00:04.000    3.0
+        Freq: 500ms, dtype: float64
+
+        Internal reindexing with ``asfreq()`` prior to interpolation leads to
+        an interpolated timeseries on the basis the reindexed timestamps (anchors).
+        Since not all datapoints from original series become anchors,
+        it can lead to misleading interpolation results as in the following example:
+
+        >>> series.resample("400ms").interpolate("linear")
+        2023-03-01 07:00:00.000    1.0
+        2023-03-01 07:00:00.400    1.2
+        2023-03-01 07:00:00.800    1.4
+        2023-03-01 07:00:01.200    1.6
+        2023-03-01 07:00:01.600    1.8
+        2023-03-01 07:00:02.000    2.0
+        2023-03-01 07:00:02.400    2.2
+        2023-03-01 07:00:02.800    2.4
+        2023-03-01 07:00:03.200    2.6
+        2023-03-01 07:00:03.600    2.8
+        2023-03-01 07:00:04.000    3.0
+        Freq: 400ms, dtype: float64
+
+        Note that the series erroneously increases between two anchors
+        ``07:00:00`` and ``07:00:02``.
+        """
+        assert downcast is lib.no_default  # just checking coverage
+        result = self._upsample("asfreq")
+        return result.interpolate(
+            method=method,
+            axis=axis,
+            limit=limit,
+            inplace=inplace,
+            limit_direction=limit_direction,
+            limit_area=limit_area,
+            downcast=downcast,
+            **kwargs,
+        )
+
+    @final
+    def asfreq(self, fill_value=None):
+        """
+        Return the values at the new freq, essentially a reindex.
+
+        Parameters
+        ----------
+        fill_value : scalar, optional
+            Value to use for missing values, applied during upsampling (note
+            this does not fill NaNs that already were present).
+
+        Returns
+        -------
+        DataFrame or Series
+            Values at the specified freq.
+
+        See Also
+        --------
+        Series.asfreq: Convert TimeSeries to specified frequency.
+        DataFrame.asfreq: Convert TimeSeries to specified frequency.
+
+        Examples
+        --------
+
+        >>> ser = pd.Series([1, 2, 3, 4], index=pd.DatetimeIndex(
+        ...                 ['2023-01-01', '2023-01-31', '2023-02-01', '2023-02-28']))
+        >>> ser
+        2023-01-01    1
+        2023-01-31    2
+        2023-02-01    3
+        2023-02-28    4
+        dtype: int64
+        >>> ser.resample('MS').asfreq()
+        2023-01-01    1
+        2023-02-01    3
+        Freq: MS, dtype: int64
+        """
+        return self._upsample("asfreq", fill_value=fill_value)
+
+    @final
+    def sum(
+        self,
+        numeric_only: bool = False,
+        min_count: int = 0,
+        *args,
+        **kwargs,
+    ):
+        """
+        Compute sum of group values.
+
+        Parameters
+        ----------
+        numeric_only : bool, default False
+            Include only float, int, boolean columns.
+
+            .. versionchanged:: 2.0.0
+
+                numeric_only no longer accepts ``None``.
+
+        min_count : int, default 0
+            The required number of valid values to perform the operation. If fewer
+            than ``min_count`` non-NA values are present the result will be NA.
+
+        Returns
+        -------
+        Series or DataFrame
+            Computed sum of values within each group.
+
+        Examples
+        --------
+        >>> ser = pd.Series([1, 2, 3, 4], index=pd.DatetimeIndex(
+        ...                 ['2023-01-01', '2023-01-15', '2023-02-01', '2023-02-15']))
+        >>> ser
+        2023-01-01    1
+        2023-01-15    2
+        2023-02-01    3
+        2023-02-15    4
+        dtype: int64
+        >>> ser.resample('MS').sum()
+        2023-01-01    3
+        2023-02-01    7
+        Freq: MS, dtype: int64
+        """
+        maybe_warn_args_and_kwargs(type(self), "sum", args, kwargs)
+        nv.validate_resampler_func("sum", args, kwargs)
+        return self._downsample("sum", numeric_only=numeric_only, min_count=min_count)
+
+    @final
+    def prod(
+        self,
+        numeric_only: bool = False,
+        min_count: int = 0,
+        *args,
+        **kwargs,
+    ):
+        """
+        Compute prod of group values.
+
+        Parameters
+        ----------
+        numeric_only : bool, default False
+            Include only float, int, boolean columns.
+
+            .. versionchanged:: 2.0.0
+
+                numeric_only no longer accepts ``None``.
+
+        min_count : int, default 0
+            The required number of valid values to perform the operation. If fewer
+            than ``min_count`` non-NA values are present the result will be NA.
+
+        Returns
+        -------
+        Series or DataFrame
+            Computed prod of values within each group.
+
+        Examples
+        --------
+        >>> ser = pd.Series([1, 2, 3, 4], index=pd.DatetimeIndex(
+        ...                 ['2023-01-01', '2023-01-15', '2023-02-01', '2023-02-15']))
+        >>> ser
+        2023-01-01    1
+        2023-01-15    2
+        2023-02-01    3
+        2023-02-15    4
+        dtype: int64
+        >>> ser.resample('MS').prod()
+        2023-01-01    2
+        2023-02-01   12
+        Freq: MS, dtype: int64
+        """
+        maybe_warn_args_and_kwargs(type(self), "prod", args, kwargs)
+        nv.validate_resampler_func("prod", args, kwargs)
+        return self._downsample("prod", numeric_only=numeric_only, min_count=min_count)
+
+    @final
+    def min(
+        self,
+        numeric_only: bool = False,
+        min_count: int = 0,
+        *args,
+        **kwargs,
+    ):
+        """
+        Compute min value of group.
+
+        Returns
+        -------
+        Series or DataFrame
+
+        Examples
+        --------
+        >>> ser = pd.Series([1, 2, 3, 4], index=pd.DatetimeIndex(
+        ...                 ['2023-01-01', '2023-01-15', '2023-02-01', '2023-02-15']))
+        >>> ser
+        2023-01-01    1
+        2023-01-15    2
+        2023-02-01    3
+        2023-02-15    4
+        dtype: int64
+        >>> ser.resample('MS').min()
+        2023-01-01    1
+        2023-02-01    3
+        Freq: MS, dtype: int64
+        """
+
+        maybe_warn_args_and_kwargs(type(self), "min", args, kwargs)
+        nv.validate_resampler_func("min", args, kwargs)
+        return self._downsample("min", numeric_only=numeric_only, min_count=min_count)
+
+    @final
+    def max(
+        self,
+        numeric_only: bool = False,
+        min_count: int = 0,
+        *args,
+        **kwargs,
+    ):
+        """
+        Compute max value of group.
+
+        Returns
+        -------
+        Series or DataFrame
+
+        Examples
+        --------
+        >>> ser = pd.Series([1, 2, 3, 4], index=pd.DatetimeIndex(
+        ...                 ['2023-01-01', '2023-01-15', '2023-02-01', '2023-02-15']))
+        >>> ser
+        2023-01-01    1
+        2023-01-15    2
+        2023-02-01    3
+        2023-02-15    4
+        dtype: int64
+        >>> ser.resample('MS').max()
+        2023-01-01    2
+        2023-02-01    4
+        Freq: MS, dtype: int64
+        """
+        maybe_warn_args_and_kwargs(type(self), "max", args, kwargs)
+        nv.validate_resampler_func("max", args, kwargs)
+        return self._downsample("max", numeric_only=numeric_only, min_count=min_count)
+
+    @final
+    @doc(GroupBy.first)
+    def first(
+        self,
+        numeric_only: bool = False,
+        min_count: int = 0,
+        skipna: bool = True,
+        *args,
+        **kwargs,
+    ):
+        maybe_warn_args_and_kwargs(type(self), "first", args, kwargs)
+        nv.validate_resampler_func("first", args, kwargs)
+        return self._downsample(
+            "first", numeric_only=numeric_only, min_count=min_count, skipna=skipna
+        )
+
+    @final
+    @doc(GroupBy.last)
+    def last(
+        self,
+        numeric_only: bool = False,
+        min_count: int = 0,
+        skipna: bool = True,
+        *args,
+        **kwargs,
+    ):
+        maybe_warn_args_and_kwargs(type(self), "last", args, kwargs)
+        nv.validate_resampler_func("last", args, kwargs)
+        return self._downsample(
+            "last", numeric_only=numeric_only, min_count=min_count, skipna=skipna
+        )
+
+    @final
+    @doc(GroupBy.median)
+    def median(self, numeric_only: bool = False, *args, **kwargs):
+        maybe_warn_args_and_kwargs(type(self), "median", args, kwargs)
+        nv.validate_resampler_func("median", args, kwargs)
+        return self._downsample("median", numeric_only=numeric_only)
+
+    @final
+    def mean(
+        self,
+        numeric_only: bool = False,
+        *args,
+        **kwargs,
+    ):
+        """
+        Compute mean of groups, excluding missing values.
+
+        Parameters
+        ----------
+        numeric_only : bool, default False
+            Include only `float`, `int` or `boolean` data.
+
+            .. versionchanged:: 2.0.0
+
+                numeric_only now defaults to ``False``.
+
+        Returns
+        -------
+        DataFrame or Series
+            Mean of values within each group.
+
+        Examples
+        --------
+
+        >>> ser = pd.Series([1, 2, 3, 4], index=pd.DatetimeIndex(
+        ...                 ['2023-01-01', '2023-01-15', '2023-02-01', '2023-02-15']))
+        >>> ser
+        2023-01-01    1
+        2023-01-15    2
+        2023-02-01    3
+        2023-02-15    4
+        dtype: int64
+        >>> ser.resample('MS').mean()
+        2023-01-01    1.5
+        2023-02-01    3.5
+        Freq: MS, dtype: float64
+        """
+        maybe_warn_args_and_kwargs(type(self), "mean", args, kwargs)
+        nv.validate_resampler_func("mean", args, kwargs)
+        return self._downsample("mean", numeric_only=numeric_only)
+
+    @final
+    def std(
+        self,
+        ddof: int = 1,
+        numeric_only: bool = False,
+        *args,
+        **kwargs,
+    ):
+        """
+        Compute standard deviation of groups, excluding missing values.
+
+        Parameters
+        ----------
+        ddof : int, default 1
+            Degrees of freedom.
+        numeric_only : bool, default False
+            Include only `float`, `int` or `boolean` data.
+
+            .. versionadded:: 1.5.0
+
+            .. versionchanged:: 2.0.0
+
+                numeric_only now defaults to ``False``.
+
+        Returns
+        -------
+        DataFrame or Series
+            Standard deviation of values within each group.
+
+        Examples
+        --------
+
+        >>> ser = pd.Series([1, 3, 2, 4, 3, 8],
+        ...                 index=pd.DatetimeIndex(['2023-01-01',
+        ...                                         '2023-01-10',
+        ...                                         '2023-01-15',
+        ...                                         '2023-02-01',
+        ...                                         '2023-02-10',
+        ...                                         '2023-02-15']))
+        >>> ser.resample('MS').std()
+        2023-01-01    1.000000
+        2023-02-01    2.645751
+        Freq: MS, dtype: float64
+        """
+        maybe_warn_args_and_kwargs(type(self), "std", args, kwargs)
+        nv.validate_resampler_func("std", args, kwargs)
+        return self._downsample("std", ddof=ddof, numeric_only=numeric_only)
+
+    @final
+    def var(
+        self,
+        ddof: int = 1,
+        numeric_only: bool = False,
+        *args,
+        **kwargs,
+    ):
+        """
+        Compute variance of groups, excluding missing values.
+
+        Parameters
+        ----------
+        ddof : int, default 1
+            Degrees of freedom.
+
+        numeric_only : bool, default False
+            Include only `float`, `int` or `boolean` data.
+
+            .. versionadded:: 1.5.0
+
+            .. versionchanged:: 2.0.0
+
+                numeric_only now defaults to ``False``.
+
+        Returns
+        -------
+        DataFrame or Series
+            Variance of values within each group.
+
+        Examples
+        --------
+
+        >>> ser = pd.Series([1, 3, 2, 4, 3, 8],
+        ...                 index=pd.DatetimeIndex(['2023-01-01',
+        ...                                         '2023-01-10',
+        ...                                         '2023-01-15',
+        ...                                         '2023-02-01',
+        ...                                         '2023-02-10',
+        ...                                         '2023-02-15']))
+        >>> ser.resample('MS').var()
+        2023-01-01    1.0
+        2023-02-01    7.0
+        Freq: MS, dtype: float64
+
+        >>> ser.resample('MS').var(ddof=0)
+        2023-01-01    0.666667
+        2023-02-01    4.666667
+        Freq: MS, dtype: float64
+        """
+        maybe_warn_args_and_kwargs(type(self), "var", args, kwargs)
+        nv.validate_resampler_func("var", args, kwargs)
+        return self._downsample("var", ddof=ddof, numeric_only=numeric_only)
+
+    @final
+    @doc(GroupBy.sem)
+    def sem(
+        self,
+        ddof: int = 1,
+        numeric_only: bool = False,
+        *args,
+        **kwargs,
+    ):
+        maybe_warn_args_and_kwargs(type(self), "sem", args, kwargs)
+        nv.validate_resampler_func("sem", args, kwargs)
+        return self._downsample("sem", ddof=ddof, numeric_only=numeric_only)
+
+    @final
+    @doc(GroupBy.ohlc)
+    def ohlc(
+        self,
+        *args,
+        **kwargs,
+    ):
+        maybe_warn_args_and_kwargs(type(self), "ohlc", args, kwargs)
+        nv.validate_resampler_func("ohlc", args, kwargs)
+
+        ax = self.ax
+        obj = self._obj_with_exclusions
+        if len(ax) == 0:
+            # GH#42902
+            obj = obj.copy()
+            obj.index = _asfreq_compat(obj.index, self.freq)
+            if obj.ndim == 1:
+                obj = obj.to_frame()
+                obj = obj.reindex(["open", "high", "low", "close"], axis=1)
+            else:
+                mi = MultiIndex.from_product(
+                    [obj.columns, ["open", "high", "low", "close"]]
+                )
+                obj = obj.reindex(mi, axis=1)
+            return obj
+
+        return self._downsample("ohlc")
+
+    @final
+    @doc(SeriesGroupBy.nunique)
+    def nunique(
+        self,
+        *args,
+        **kwargs,
+    ):
+        maybe_warn_args_and_kwargs(type(self), "nunique", args, kwargs)
+        nv.validate_resampler_func("nunique", args, kwargs)
+        return self._downsample("nunique")
+
+    @final
+    @doc(GroupBy.size)
+    def size(self):
+        result = self._downsample("size")
+
+        # If the result is a non-empty DataFrame we stack to get a Series
+        # GH 46826
+        if isinstance(result, ABCDataFrame) and not result.empty:
+            result = result.stack(future_stack=True)
+
+        if not len(self.ax):
+            from pandas import Series
+
+            if self._selected_obj.ndim == 1:
+                name = self._selected_obj.name
+            else:
+                name = None
+            result = Series([], index=result.index, dtype="int64", name=name)
+        return result
+
+    @final
+    @doc(GroupBy.count)
+    def count(self):
+        result = self._downsample("count")
+        if not len(self.ax):
+            if self._selected_obj.ndim == 1:
+                result = type(self._selected_obj)(
+                    [], index=result.index, dtype="int64", name=self._selected_obj.name
+                )
+            else:
+                from pandas import DataFrame
+
+                result = DataFrame(
+                    [], index=result.index, columns=result.columns, dtype="int64"
+                )
+
+        return result
+
+    @final
+    def quantile(self, q: float | list[float] | AnyArrayLike = 0.5, **kwargs):
+        """
+        Return value at the given quantile.
+
+        Parameters
+        ----------
+        q : float or array-like, default 0.5 (50% quantile)
+
+        Returns
+        -------
+        DataFrame or Series
+            Quantile of values within each group.
+
+        See Also
+        --------
+        Series.quantile
+            Return a series, where the index is q and the values are the quantiles.
+        DataFrame.quantile
+            Return a DataFrame, where the columns are the columns of self,
+            and the values are the quantiles.
+        DataFrameGroupBy.quantile
+            Return a DataFrame, where the columns are groupby columns,
+            and the values are its quantiles.
+
+        Examples
+        --------
+
+        >>> ser = pd.Series([1, 3, 2, 4, 3, 8],
+        ...                 index=pd.DatetimeIndex(['2023-01-01',
+        ...                                         '2023-01-10',
+        ...                                         '2023-01-15',
+        ...                                         '2023-02-01',
+        ...                                         '2023-02-10',
+        ...                                         '2023-02-15']))
+        >>> ser.resample('MS').quantile()
+        2023-01-01    2.0
+        2023-02-01    4.0
+        Freq: MS, dtype: float64
+
+        >>> ser.resample('MS').quantile(.25)
+        2023-01-01    1.5
+        2023-02-01    3.5
+        Freq: MS, dtype: float64
+        """
+        return self._downsample("quantile", q=q, **kwargs)
+
+
+class _GroupByMixin(PandasObject, SelectionMixin):
+    """
+    Provide the groupby facilities.
+    """
+
+    _attributes: list[str]  # in practice the same as Resampler._attributes
+    _selection: IndexLabel | None = None
+    _groupby: GroupBy
+    _timegrouper: TimeGrouper
+
+    def __init__(
+        self,
+        *,
+        parent: Resampler,
+        groupby: GroupBy,
+        key=None,
+        selection: IndexLabel | None = None,
+        include_groups: bool = False,
+    ) -> None:
+        # reached via ._gotitem and _get_resampler_for_grouping
+
+        assert isinstance(groupby, GroupBy), type(groupby)
+
+        # parent is always a Resampler, sometimes a _GroupByMixin
+        assert isinstance(parent, Resampler), type(parent)
+
+        # initialize our GroupByMixin object with
+        # the resampler attributes
+        for attr in self._attributes:
+            setattr(self, attr, getattr(parent, attr))
+        self._selection = selection
+
+        self.binner = parent.binner
+        self.key = key
+
+        self._groupby = groupby
+        self._timegrouper = copy.copy(parent._timegrouper)
+
+        self.ax = parent.ax
+        self.obj = parent.obj
+        self.include_groups = include_groups
+
+    @no_type_check
+    def _apply(self, f, *args, **kwargs):
+        """
+        Dispatch to _upsample; we are stripping all of the _upsample kwargs and
+        performing the original function call on the grouped object.
+        """
+
+        def func(x):
+            x = self._resampler_cls(x, timegrouper=self._timegrouper, gpr_index=self.ax)
+
+            if isinstance(f, str):
+                return getattr(x, f)(**kwargs)
+
+            return x.apply(f, *args, **kwargs)
+
+        result = _apply(self._groupby, func, include_groups=self.include_groups)
+        return self._wrap_result(result)
+
+    _upsample = _apply
+    _downsample = _apply
+    _groupby_and_aggregate = _apply
+
+    @final
+    def _gotitem(self, key, ndim, subset=None):
+        """
+        Sub-classes to define. Return a sliced object.
+
+        Parameters
+        ----------
+        key : string / list of selections
+        ndim : {1, 2}
+            requested ndim of result
+        subset : object, default None
+            subset to act on
+        """
+        # create a new object to prevent aliasing
+        if subset is None:
+            subset = self.obj
+            if key is not None:
+                subset = subset[key]
+            else:
+                # reached via Apply.agg_dict_like with selection=None, ndim=1
+                assert subset.ndim == 1
+
+        # Try to select from a DataFrame, falling back to a Series
+        try:
+            if isinstance(key, list) and self.key not in key and self.key is not None:
+                key.append(self.key)
+            groupby = self._groupby[key]
+        except IndexError:
+            groupby = self._groupby
+
+        selection = self._infer_selection(key, subset)
+
+        new_rs = type(self)(
+            groupby=groupby,
+            parent=cast(Resampler, self),
+            selection=selection,
+        )
+        return new_rs
+
+
+class DatetimeIndexResampler(Resampler):
+    ax: DatetimeIndex
+
+    @property
+    def _resampler_for_grouping(self):
+        return DatetimeIndexResamplerGroupby
+
+    def _get_binner_for_time(self):
+        # this is how we are actually creating the bins
+        if self.kind == "period":
+            return self._timegrouper._get_time_period_bins(self.ax)
+        return self._timegrouper._get_time_bins(self.ax)
+
+    def _downsample(self, how, **kwargs):
+        """
+        Downsample the cython defined function.
+
+        Parameters
+        ----------
+        how : string / cython mapped function
+        **kwargs : kw args passed to how function
+        """
+        orig_how = how
+        how = com.get_cython_func(how) or how
+        if orig_how != how:
+            warn_alias_replacement(self, orig_how, how)
+        ax = self.ax
+
+        # Excludes `on` column when provided
+        obj = self._obj_with_exclusions
+
+        if not len(ax):
+            # reset to the new freq
+            obj = obj.copy()
+            obj.index = obj.index._with_freq(self.freq)
+            assert obj.index.freq == self.freq, (obj.index.freq, self.freq)
+            return obj
+
+        # do we have a regular frequency
+
+        # error: Item "None" of "Optional[Any]" has no attribute "binlabels"
+        if (
+            (ax.freq is not None or ax.inferred_freq is not None)
+            and len(self._grouper.binlabels) > len(ax)
+            and how is None
+        ):
+            # let's do an asfreq
+            return self.asfreq()
+
+        # we are downsampling
+        # we want to call the actual grouper method here
+        if self.axis == 0:
+            result = obj.groupby(self._grouper).aggregate(how, **kwargs)
+        else:
+            # test_resample_axis1
+            result = obj.T.groupby(self._grouper).aggregate(how, **kwargs).T
+
+        return self._wrap_result(result)
+
+    def _adjust_binner_for_upsample(self, binner):
+        """
+        Adjust our binner when upsampling.
+
+        The range of a new index should not be outside specified range
+        """
+        if self.closed == "right":
+            binner = binner[1:]
+        else:
+            binner = binner[:-1]
+        return binner
+
+    def _upsample(self, method, limit: int | None = None, fill_value=None):
+        """
+        Parameters
+        ----------
+        method : string {'backfill', 'bfill', 'pad',
+            'ffill', 'asfreq'} method for upsampling
+        limit : int, default None
+            Maximum size gap to fill when reindexing
+        fill_value : scalar, default None
+            Value to use for missing values
+
+        See Also
+        --------
+        .fillna: Fill NA/NaN values using the specified method.
+
+        """
+        if self.axis:
+            raise AssertionError("axis must be 0")
+        if self._from_selection:
+            raise ValueError(
+                "Upsampling from level= or on= selection "
+                "is not supported, use .set_index(...) "
+                "to explicitly set index to datetime-like"
+            )
+
+        ax = self.ax
+        obj = self._selected_obj
+        binner = self.binner
+        res_index = self._adjust_binner_for_upsample(binner)
+
+        # if we have the same frequency as our axis, then we are equal sampling
+        if (
+            limit is None
+            and to_offset(ax.inferred_freq) == self.freq
+            and len(obj) == len(res_index)
+        ):
+            result = obj.copy()
+            result.index = res_index
+        else:
+            if method == "asfreq":
+                method = None
+            result = obj.reindex(
+                res_index, method=method, limit=limit, fill_value=fill_value
+            )
+
+        return self._wrap_result(result)
+
+    def _wrap_result(self, result):
+        result = super()._wrap_result(result)
+
+        # we may have a different kind that we were asked originally
+        # convert if needed
+        if self.kind == "period" and not isinstance(result.index, PeriodIndex):
+            if isinstance(result.index, MultiIndex):
+                # GH 24103 - e.g. groupby resample
+                if not isinstance(result.index.levels[-1], PeriodIndex):
+                    new_level = result.index.levels[-1].to_period(self.freq)
+                    result.index = result.index.set_levels(new_level, level=-1)
+            else:
+                result.index = result.index.to_period(self.freq)
+        return result
+
+
+# error: Definition of "ax" in base class "_GroupByMixin" is incompatible
+# with definition in base class "DatetimeIndexResampler"
+class DatetimeIndexResamplerGroupby(  # type: ignore[misc]
+    _GroupByMixin, DatetimeIndexResampler
+):
+    """
+    Provides a resample of a groupby implementation
+    """
+
+    @property
+    def _resampler_cls(self):
+        return DatetimeIndexResampler
+
+
+class PeriodIndexResampler(DatetimeIndexResampler):
+    # error: Incompatible types in assignment (expression has type "PeriodIndex", base
+    # class "DatetimeIndexResampler" defined the type as "DatetimeIndex")
+    ax: PeriodIndex  # type: ignore[assignment]
+
+    @property
+    def _resampler_for_grouping(self):
+        return PeriodIndexResamplerGroupby
+
+    def _get_binner_for_time(self):
+        if self.kind == "timestamp":
+            return super()._get_binner_for_time()
+        return self._timegrouper._get_period_bins(self.ax)
+
+    def _convert_obj(self, obj: NDFrameT) -> NDFrameT:
+        obj = super()._convert_obj(obj)
+
+        if self._from_selection:
+            # see GH 14008, GH 12871
+            msg = (
+                "Resampling from level= or on= selection "
+                "with a PeriodIndex is not currently supported, "
+                "use .set_index(...) to explicitly set index"
+            )
+            raise NotImplementedError(msg)
+
+        # convert to timestamp
+        if self.kind == "timestamp":
+            obj = obj.to_timestamp(how=self.convention)
+
+        return obj
+
+    def _downsample(self, how, **kwargs):
+        """
+        Downsample the cython defined function.
+
+        Parameters
+        ----------
+        how : string / cython mapped function
+        **kwargs : kw args passed to how function
+        """
+        # we may need to actually resample as if we are timestamps
+        if self.kind == "timestamp":
+            return super()._downsample(how, **kwargs)
+
+        orig_how = how
+        how = com.get_cython_func(how) or how
+        if orig_how != how:
+            warn_alias_replacement(self, orig_how, how)
+        ax = self.ax
+
+        if is_subperiod(ax.freq, self.freq):
+            # Downsampling
+            return self._groupby_and_aggregate(how, **kwargs)
+        elif is_superperiod(ax.freq, self.freq):
+            if how == "ohlc":
+                # GH #13083
+                # upsampling to subperiods is handled as an asfreq, which works
+                # for pure aggregating/reducing methods
+                # OHLC reduces along the time dimension, but creates multiple
+                # values for each period -> handle by _groupby_and_aggregate()
+                return self._groupby_and_aggregate(how)
+            return self.asfreq()
+        elif ax.freq == self.freq:
+            return self.asfreq()
+
+        raise IncompatibleFrequency(
+            f"Frequency {ax.freq} cannot be resampled to {self.freq}, "
+            "as they are not sub or super periods"
+        )
+
+    def _upsample(self, method, limit: int | None = None, fill_value=None):
+        """
+        Parameters
+        ----------
+        method : {'backfill', 'bfill', 'pad', 'ffill'}
+            Method for upsampling.
+        limit : int, default None
+            Maximum size gap to fill when reindexing.
+        fill_value : scalar, default None
+            Value to use for missing values.
+
+        See Also
+        --------
+        .fillna: Fill NA/NaN values using the specified method.
+
+        """
+        # we may need to actually resample as if we are timestamps
+        if self.kind == "timestamp":
+            return super()._upsample(method, limit=limit, fill_value=fill_value)
+
+        ax = self.ax
+        obj = self.obj
+        new_index = self.binner
+
+        # Start vs. end of period
+        memb = ax.asfreq(self.freq, how=self.convention)
+
+        # Get the fill indexer
+        if method == "asfreq":
+            method = None
+        indexer = memb.get_indexer(new_index, method=method, limit=limit)
+        new_obj = _take_new_index(
+            obj,
+            indexer,
+            new_index,
+            axis=self.axis,
+        )
+        return self._wrap_result(new_obj)
+
+
+# error: Definition of "ax" in base class "_GroupByMixin" is incompatible with
+# definition in base class "PeriodIndexResampler"
+class PeriodIndexResamplerGroupby(  # type: ignore[misc]
+    _GroupByMixin, PeriodIndexResampler
+):
+    """
+    Provides a resample of a groupby implementation.
+    """
+
+    @property
+    def _resampler_cls(self):
+        return PeriodIndexResampler
+
+
+class TimedeltaIndexResampler(DatetimeIndexResampler):
+    # error: Incompatible types in assignment (expression has type "TimedeltaIndex",
+    # base class "DatetimeIndexResampler" defined the type as "DatetimeIndex")
+    ax: TimedeltaIndex  # type: ignore[assignment]
+
+    @property
+    def _resampler_for_grouping(self):
+        return TimedeltaIndexResamplerGroupby
+
+    def _get_binner_for_time(self):
+        return self._timegrouper._get_time_delta_bins(self.ax)
+
+    def _adjust_binner_for_upsample(self, binner):
+        """
+        Adjust our binner when upsampling.
+
+        The range of a new index is allowed to be greater than original range
+        so we don't need to change the length of a binner, GH 13022
+        """
+        return binner
+
+
+# error: Definition of "ax" in base class "_GroupByMixin" is incompatible with
+# definition in base class "DatetimeIndexResampler"
+class TimedeltaIndexResamplerGroupby(  # type: ignore[misc]
+    _GroupByMixin, TimedeltaIndexResampler
+):
+    """
+    Provides a resample of a groupby implementation.
+    """
+
+    @property
+    def _resampler_cls(self):
+        return TimedeltaIndexResampler
+
+
+def get_resampler(obj: Series | DataFrame, kind=None, **kwds) -> Resampler:
+    """
+    Create a TimeGrouper and return our resampler.
+    """
+    tg = TimeGrouper(obj, **kwds)  # type: ignore[arg-type]
+    return tg._get_resampler(obj, kind=kind)
+
+
+get_resampler.__doc__ = Resampler.__doc__
+
+
+def get_resampler_for_grouping(
+    groupby: GroupBy,
+    rule,
+    how=None,
+    fill_method=None,
+    limit: int | None = None,
+    kind=None,
+    on=None,
+    include_groups: bool = True,
+    **kwargs,
+) -> Resampler:
+    """
+    Return our appropriate resampler when grouping as well.
+    """
+    # .resample uses 'on' similar to how .groupby uses 'key'
+    tg = TimeGrouper(freq=rule, key=on, **kwargs)
+    resampler = tg._get_resampler(groupby.obj, kind=kind)
+    return resampler._get_resampler_for_grouping(
+        groupby=groupby, include_groups=include_groups, key=tg.key
+    )
+
+
+class TimeGrouper(Grouper):
+    """
+    Custom groupby class for time-interval grouping.
+
+    Parameters
+    ----------
+    freq : pandas date offset or offset alias for identifying bin edges
+    closed : closed end of interval; 'left' or 'right'
+    label : interval boundary to use for labeling; 'left' or 'right'
+    convention : {'start', 'end', 'e', 's'}
+        If axis is PeriodIndex
+    """
+
+    _attributes = Grouper._attributes + (
+        "closed",
+        "label",
+        "how",
+        "kind",
+        "convention",
+        "origin",
+        "offset",
+    )
+
+    origin: TimeGrouperOrigin
+
+    def __init__(
+        self,
+        obj: Grouper | None = None,
+        freq: Frequency = "Min",
+        key: str | None = None,
+        closed: Literal["left", "right"] | None = None,
+        label: Literal["left", "right"] | None = None,
+        how: str = "mean",
+        axis: Axis = 0,
+        fill_method=None,
+        limit: int | None = None,
+        kind: str | None = None,
+        convention: Literal["start", "end", "e", "s"] | None = None,
+        origin: Literal["epoch", "start", "start_day", "end", "end_day"]
+        | TimestampConvertibleTypes = "start_day",
+        offset: TimedeltaConvertibleTypes | None = None,
+        group_keys: bool = False,
+        **kwargs,
+    ) -> None:
+        # Check for correctness of the keyword arguments which would
+        # otherwise silently use the default if misspelled
+        if label not in {None, "left", "right"}:
+            raise ValueError(f"Unsupported value {label} for `label`")
+        if closed not in {None, "left", "right"}:
+            raise ValueError(f"Unsupported value {closed} for `closed`")
+        if convention not in {None, "start", "end", "e", "s"}:
+            raise ValueError(f"Unsupported value {convention} for `convention`")
+
+        if (
+            key is None
+            and obj is not None
+            and isinstance(obj.index, PeriodIndex)  # type: ignore[attr-defined]
+            or (
+                key is not None
+                and obj is not None
+                and getattr(obj[key], "dtype", None) == "period"  # type: ignore[index]
+            )
+        ):
+            freq = to_offset(freq, is_period=True)
+        else:
+            freq = to_offset(freq)
+
+        end_types = {"ME", "YE", "QE", "BME", "BYE", "BQE", "W"}
+        rule = freq.rule_code
+        if rule in end_types or ("-" in rule and rule[: rule.find("-")] in end_types):
+            if closed is None:
+                closed = "right"
+            if label is None:
+                label = "right"
+        else:
+            # The backward resample sets ``closed`` to ``'right'`` by default
+            # since the last value should be considered as the edge point for
+            # the last bin. When origin in "end" or "end_day", the value for a
+            # specific ``Timestamp`` index stands for the resample result from
+            # the current ``Timestamp`` minus ``freq`` to the current
+            # ``Timestamp`` with a right close.
+            if origin in ["end", "end_day"]:
+                if closed is None:
+                    closed = "right"
+                if label is None:
+                    label = "right"
+            else:
+                if closed is None:
+                    closed = "left"
+                if label is None:
+                    label = "left"
+
+        self.closed = closed
+        self.label = label
+        self.kind = kind
+        self.convention = convention if convention is not None else "e"
+        self.how = how
+        self.fill_method = fill_method
+        self.limit = limit
+        self.group_keys = group_keys
+        self._arrow_dtype: ArrowDtype | None = None
+
+        if origin in ("epoch", "start", "start_day", "end", "end_day"):
+            # error: Incompatible types in assignment (expression has type "Union[Union[
+            # Timestamp, datetime, datetime64, signedinteger[_64Bit], float, str],
+            # Literal['epoch', 'start', 'start_day', 'end', 'end_day']]", variable has
+            # type "Union[Timestamp, Literal['epoch', 'start', 'start_day', 'end',
+            # 'end_day']]")
+            self.origin = origin  # type: ignore[assignment]
+        else:
+            try:
+                self.origin = Timestamp(origin)
+            except (ValueError, TypeError) as err:
+                raise ValueError(
+                    "'origin' should be equal to 'epoch', 'start', 'start_day', "
+                    "'end', 'end_day' or "
+                    f"should be a Timestamp convertible type. Got '{origin}' instead."
+                ) from err
+
+        try:
+            self.offset = Timedelta(offset) if offset is not None else None
+        except (ValueError, TypeError) as err:
+            raise ValueError(
+                "'offset' should be a Timedelta convertible type. "
+                f"Got '{offset}' instead."
+            ) from err
+
+        # always sort time groupers
+        kwargs["sort"] = True
+
+        super().__init__(freq=freq, key=key, axis=axis, **kwargs)
+
+    def _get_resampler(self, obj: NDFrame, kind=None) -> Resampler:
+        """
+        Return my resampler or raise if we have an invalid axis.
+
+        Parameters
+        ----------
+        obj : Series or DataFrame
+        kind : string, optional
+            'period','timestamp','timedelta' are valid
+
+        Returns
+        -------
+        Resampler
+
+        Raises
+        ------
+        TypeError if incompatible axis
+
+        """
+        _, ax, _ = self._set_grouper(obj, gpr_index=None)
+        if isinstance(ax, DatetimeIndex):
+            return DatetimeIndexResampler(
+                obj,
+                timegrouper=self,
+                kind=kind,
+                axis=self.axis,
+                group_keys=self.group_keys,
+                gpr_index=ax,
+            )
+        elif isinstance(ax, PeriodIndex) or kind == "period":
+            if not isinstance(ax, PeriodIndex):
+                warnings.warn(
+                    "Resampling with kind='period' is deprecated.  "
+                    "Use datetime paths instead.",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+            return PeriodIndexResampler(
+                obj,
+                timegrouper=self,
+                kind=kind,
+                axis=self.axis,
+                group_keys=self.group_keys,
+                gpr_index=ax,
+            )
+        elif isinstance(ax, TimedeltaIndex):
+            return TimedeltaIndexResampler(
+                obj,
+                timegrouper=self,
+                axis=self.axis,
+                group_keys=self.group_keys,
+                gpr_index=ax,
+            )
+
+        raise TypeError(
+            "Only valid with DatetimeIndex, "
+            "TimedeltaIndex or PeriodIndex, "
+            f"but got an instance of '{type(ax).__name__}'"
+        )
+
+    def _get_grouper(
+        self, obj: NDFrameT, validate: bool = True
+    ) -> tuple[BinGrouper, NDFrameT]:
+        # create the resampler and return our binner
+        r = self._get_resampler(obj)
+        return r._grouper, cast(NDFrameT, r.obj)
+
+    def _get_time_bins(self, ax: DatetimeIndex):
+        if not isinstance(ax, DatetimeIndex):
+            raise TypeError(
+                "axis must be a DatetimeIndex, but got "
+                f"an instance of {type(ax).__name__}"
+            )
+
+        if len(ax) == 0:
+            binner = labels = DatetimeIndex(
+                data=[], freq=self.freq, name=ax.name, dtype=ax.dtype
+            )
+            return binner, [], labels
+
+        first, last = _get_timestamp_range_edges(
+            ax.min(),
+            ax.max(),
+            self.freq,
+            unit=ax.unit,
+            closed=self.closed,
+            origin=self.origin,
+            offset=self.offset,
+        )
+        # GH #12037
+        # use first/last directly instead of call replace() on them
+        # because replace() will swallow the nanosecond part
+        # thus last bin maybe slightly before the end if the end contains
+        # nanosecond part and lead to `Values falls after last bin` error
+        # GH 25758: If DST lands at midnight (e.g. 'America/Havana'), user feedback
+        # has noted that ambiguous=True provides the most sensible result
+        binner = labels = date_range(
+            freq=self.freq,
+            start=first,
+            end=last,
+            tz=ax.tz,
+            name=ax.name,
+            ambiguous=True,
+            nonexistent="shift_forward",
+            unit=ax.unit,
+        )
+
+        ax_values = ax.asi8
+        binner, bin_edges = self._adjust_bin_edges(binner, ax_values)
+
+        # general version, knowing nothing about relative frequencies
+        bins = lib.generate_bins_dt64(
+            ax_values, bin_edges, self.closed, hasnans=ax.hasnans
+        )
+
+        if self.closed == "right":
+            labels = binner
+            if self.label == "right":
+                labels = labels[1:]
+        elif self.label == "right":
+            labels = labels[1:]
+
+        if ax.hasnans:
+            binner = binner.insert(0, NaT)
+            labels = labels.insert(0, NaT)
+
+        # if we end up with more labels than bins
+        # adjust the labels
+        # GH4076
+        if len(bins) < len(labels):
+            labels = labels[: len(bins)]
+
+        return binner, bins, labels
+
+    def _adjust_bin_edges(
+        self, binner: DatetimeIndex, ax_values: npt.NDArray[np.int64]
+    ) -> tuple[DatetimeIndex, npt.NDArray[np.int64]]:
+        # Some hacks for > daily data, see #1471, #1458, #1483
+
+        if self.freq.name in ("BME", "ME", "W") or self.freq.name.split("-")[0] in (
+            "BQE",
+            "BYE",
+            "QE",
+            "YE",
+            "W",
+        ):
+            # If the right end-point is on the last day of the month, roll forwards
+            # until the last moment of that day. Note that we only do this for offsets
+            # which correspond to the end of a super-daily period - "month start", for
+            # example, is excluded.
+            if self.closed == "right":
+                # GH 21459, GH 9119: Adjust the bins relative to the wall time
+                edges_dti = binner.tz_localize(None)
+                edges_dti = (
+                    edges_dti
+                    + Timedelta(days=1, unit=edges_dti.unit).as_unit(edges_dti.unit)
+                    - Timedelta(1, unit=edges_dti.unit).as_unit(edges_dti.unit)
+                )
+                bin_edges = edges_dti.tz_localize(binner.tz).asi8
+            else:
+                bin_edges = binner.asi8
+
+            # intraday values on last day
+            if bin_edges[-2] > ax_values.max():
+                bin_edges = bin_edges[:-1]
+                binner = binner[:-1]
+        else:
+            bin_edges = binner.asi8
+        return binner, bin_edges
+
+    def _get_time_delta_bins(self, ax: TimedeltaIndex):
+        if not isinstance(ax, TimedeltaIndex):
+            raise TypeError(
+                "axis must be a TimedeltaIndex, but got "
+                f"an instance of {type(ax).__name__}"
+            )
+
+        if not isinstance(self.freq, Tick):
+            # GH#51896
+            raise ValueError(
+                "Resampling on a TimedeltaIndex requires fixed-duration `freq`, "
+                f"e.g. '24h' or '3D', not {self.freq}"
+            )
+
+        if not len(ax):
+            binner = labels = TimedeltaIndex(data=[], freq=self.freq, name=ax.name)
+            return binner, [], labels
+
+        start, end = ax.min(), ax.max()
+
+        if self.closed == "right":
+            end += self.freq
+
+        labels = binner = timedelta_range(
+            start=start, end=end, freq=self.freq, name=ax.name
+        )
+
+        end_stamps = labels
+        if self.closed == "left":
+            end_stamps += self.freq
+
+        bins = ax.searchsorted(end_stamps, side=self.closed)
+
+        if self.offset:
+            # GH 10530 & 31809
+            labels += self.offset
+
+        return binner, bins, labels
+
+    def _get_time_period_bins(self, ax: DatetimeIndex):
+        if not isinstance(ax, DatetimeIndex):
+            raise TypeError(
+                "axis must be a DatetimeIndex, but got "
+                f"an instance of {type(ax).__name__}"
+            )
+
+        freq = self.freq
+
+        if len(ax) == 0:
+            binner = labels = PeriodIndex(
+                data=[], freq=freq, name=ax.name, dtype=ax.dtype
+            )
+            return binner, [], labels
+
+        labels = binner = period_range(start=ax[0], end=ax[-1], freq=freq, name=ax.name)
+
+        end_stamps = (labels + freq).asfreq(freq, "s").to_timestamp()
+        if ax.tz:
+            end_stamps = end_stamps.tz_localize(ax.tz)
+        bins = ax.searchsorted(end_stamps, side="left")
+
+        return binner, bins, labels
+
+    def _get_period_bins(self, ax: PeriodIndex):
+        if not isinstance(ax, PeriodIndex):
+            raise TypeError(
+                "axis must be a PeriodIndex, but got "
+                f"an instance of {type(ax).__name__}"
+            )
+
+        memb = ax.asfreq(self.freq, how=self.convention)
+
+        # NaT handling as in pandas._lib.lib.generate_bins_dt64()
+        nat_count = 0
+        if memb.hasnans:
+            # error: Incompatible types in assignment (expression has type
+            # "bool_", variable has type "int")  [assignment]
+            nat_count = np.sum(memb._isnan)  # type: ignore[assignment]
+            memb = memb[~memb._isnan]
+
+        if not len(memb):
+            # index contains no valid (non-NaT) values
+            bins = np.array([], dtype=np.int64)
+            binner = labels = PeriodIndex(data=[], freq=self.freq, name=ax.name)
+            if len(ax) > 0:
+                # index is all NaT
+                binner, bins, labels = _insert_nat_bin(binner, bins, labels, len(ax))
+            return binner, bins, labels
+
+        freq_mult = self.freq.n
+
+        start = ax.min().asfreq(self.freq, how=self.convention)
+        end = ax.max().asfreq(self.freq, how="end")
+        bin_shift = 0
+
+        if isinstance(self.freq, Tick):
+            # GH 23882 & 31809: get adjusted bin edge labels with 'origin'
+            # and 'origin' support. This call only makes sense if the freq is a
+            # Tick since offset and origin are only used in those cases.
+            # Not doing this check could create an extra empty bin.
+            p_start, end = _get_period_range_edges(
+                start,
+                end,
+                self.freq,
+                closed=self.closed,
+                origin=self.origin,
+                offset=self.offset,
+            )
+
+            # Get offset for bin edge (not label edge) adjustment
+            start_offset = Period(start, self.freq) - Period(p_start, self.freq)
+            # error: Item "Period" of "Union[Period, Any]" has no attribute "n"
+            bin_shift = start_offset.n % freq_mult  # type: ignore[union-attr]
+            start = p_start
+
+        labels = binner = period_range(
+            start=start, end=end, freq=self.freq, name=ax.name
+        )
+
+        i8 = memb.asi8
+
+        # when upsampling to subperiods, we need to generate enough bins
+        expected_bins_count = len(binner) * freq_mult
+        i8_extend = expected_bins_count - (i8[-1] - i8[0])
+        rng = np.arange(i8[0], i8[-1] + i8_extend, freq_mult)
+        rng += freq_mult
+        # adjust bin edge indexes to account for base
+        rng -= bin_shift
+
+        # Wrap in PeriodArray for PeriodArray.searchsorted
+        prng = type(memb._data)(rng, dtype=memb.dtype)
+        bins = memb.searchsorted(prng, side="left")
+
+        if nat_count > 0:
+            binner, bins, labels = _insert_nat_bin(binner, bins, labels, nat_count)
+
+        return binner, bins, labels
+
+    def _set_grouper(
+        self, obj: NDFrameT, sort: bool = False, *, gpr_index: Index | None = None
+    ) -> tuple[NDFrameT, Index, npt.NDArray[np.intp] | None]:
+        obj, ax, indexer = super()._set_grouper(obj, sort, gpr_index=gpr_index)
+        if isinstance(ax.dtype, ArrowDtype) and ax.dtype.kind in "Mm":
+            self._arrow_dtype = ax.dtype
+            ax = Index(
+                cast(ArrowExtensionArray, ax.array)._maybe_convert_datelike_array()
+            )
+        return obj, ax, indexer
+
+
+def _take_new_index(
+    obj: NDFrameT, indexer: npt.NDArray[np.intp], new_index: Index, axis: AxisInt = 0
+) -> NDFrameT:
+    if isinstance(obj, ABCSeries):
+        new_values = algos.take_nd(obj._values, indexer)
+        # error: Incompatible return value type (got "Series", expected "NDFrameT")
+        return obj._constructor(  # type: ignore[return-value]
+            new_values, index=new_index, name=obj.name
+        )
+    elif isinstance(obj, ABCDataFrame):
+        if axis == 1:
+            raise NotImplementedError("axis 1 is not supported")
+        new_mgr = obj._mgr.reindex_indexer(new_axis=new_index, indexer=indexer, axis=1)
+        # error: Incompatible return value type (got "DataFrame", expected "NDFrameT")
+        return obj._constructor_from_mgr(new_mgr, axes=new_mgr.axes)  # type: ignore[return-value]
+    else:
+        raise ValueError("'obj' should be either a Series or a DataFrame")
+
+
+def _get_timestamp_range_edges(
+    first: Timestamp,
+    last: Timestamp,
+    freq: BaseOffset,
+    unit: str,
+    closed: Literal["right", "left"] = "left",
+    origin: TimeGrouperOrigin = "start_day",
+    offset: Timedelta | None = None,
+) -> tuple[Timestamp, Timestamp]:
+    """
+    Adjust the `first` Timestamp to the preceding Timestamp that resides on
+    the provided offset. Adjust the `last` Timestamp to the following
+    Timestamp that resides on the provided offset. Input Timestamps that
+    already reside on the offset will be adjusted depending on the type of
+    offset and the `closed` parameter.
+
+    Parameters
+    ----------
+    first : pd.Timestamp
+        The beginning Timestamp of the range to be adjusted.
+    last : pd.Timestamp
+        The ending Timestamp of the range to be adjusted.
+    freq : pd.DateOffset
+        The dateoffset to which the Timestamps will be adjusted.
+    closed : {'right', 'left'}, default "left"
+        Which side of bin interval is closed.
+    origin : {'epoch', 'start', 'start_day'} or Timestamp, default 'start_day'
+        The timestamp on which to adjust the grouping. The timezone of origin must
+        match the timezone of the index.
+        If a timestamp is not used, these values are also supported:
+
+        - 'epoch': `origin` is 1970-01-01
+        - 'start': `origin` is the first value of the timeseries
+        - 'start_day': `origin` is the first day at midnight of the timeseries
+    offset : pd.Timedelta, default is None
+        An offset timedelta added to the origin.
+
+    Returns
+    -------
+    A tuple of length 2, containing the adjusted pd.Timestamp objects.
+    """
+    if isinstance(freq, Tick):
+        index_tz = first.tz
+        if isinstance(origin, Timestamp) and (origin.tz is None) != (index_tz is None):
+            raise ValueError("The origin must have the same timezone as the index.")
+        if origin == "epoch":
+            # set the epoch based on the timezone to have similar bins results when
+            # resampling on the same kind of indexes on different timezones
+            origin = Timestamp("1970-01-01", tz=index_tz)
+
+        if isinstance(freq, Day):
+            # _adjust_dates_anchored assumes 'D' means 24h, but first/last
+            # might contain a DST transition (23h, 24h, or 25h).
+            # So "pretend" the dates are naive when adjusting the endpoints
+            first = first.tz_localize(None)
+            last = last.tz_localize(None)
+            if isinstance(origin, Timestamp):
+                origin = origin.tz_localize(None)
+
+        first, last = _adjust_dates_anchored(
+            first, last, freq, closed=closed, origin=origin, offset=offset, unit=unit
+        )
+        if isinstance(freq, Day):
+            first = first.tz_localize(index_tz)
+            last = last.tz_localize(index_tz)
+    else:
+        first = first.normalize()
+        last = last.normalize()
+
+        if closed == "left":
+            first = Timestamp(freq.rollback(first))
+        else:
+            first = Timestamp(first - freq)
+
+        last = Timestamp(last + freq)
+
+    return first, last
+
+
+def _get_period_range_edges(
+    first: Period,
+    last: Period,
+    freq: BaseOffset,
+    closed: Literal["right", "left"] = "left",
+    origin: TimeGrouperOrigin = "start_day",
+    offset: Timedelta | None = None,
+) -> tuple[Period, Period]:
+    """
+    Adjust the provided `first` and `last` Periods to the respective Period of
+    the given offset that encompasses them.
+
+    Parameters
+    ----------
+    first : pd.Period
+        The beginning Period of the range to be adjusted.
+    last : pd.Period
+        The ending Period of the range to be adjusted.
+    freq : pd.DateOffset
+        The freq to which the Periods will be adjusted.
+    closed : {'right', 'left'}, default "left"
+        Which side of bin interval is closed.
+    origin : {'epoch', 'start', 'start_day'}, Timestamp, default 'start_day'
+        The timestamp on which to adjust the grouping. The timezone of origin must
+        match the timezone of the index.
+
+        If a timestamp is not used, these values are also supported:
+
+        - 'epoch': `origin` is 1970-01-01
+        - 'start': `origin` is the first value of the timeseries
+        - 'start_day': `origin` is the first day at midnight of the timeseries
+    offset : pd.Timedelta, default is None
+        An offset timedelta added to the origin.
+
+    Returns
+    -------
+    A tuple of length 2, containing the adjusted pd.Period objects.
+    """
+    if not all(isinstance(obj, Period) for obj in [first, last]):
+        raise TypeError("'first' and 'last' must be instances of type Period")
+
+    # GH 23882
+    first_ts = first.to_timestamp()
+    last_ts = last.to_timestamp()
+    adjust_first = not freq.is_on_offset(first_ts)
+    adjust_last = freq.is_on_offset(last_ts)
+
+    first_ts, last_ts = _get_timestamp_range_edges(
+        first_ts, last_ts, freq, unit="ns", closed=closed, origin=origin, offset=offset
+    )
+
+    first = (first_ts + int(adjust_first) * freq).to_period(freq)
+    last = (last_ts - int(adjust_last) * freq).to_period(freq)
+    return first, last
+
+
+def _insert_nat_bin(
+    binner: PeriodIndex, bins: np.ndarray, labels: PeriodIndex, nat_count: int
+) -> tuple[PeriodIndex, np.ndarray, PeriodIndex]:
+    # NaT handling as in pandas._lib.lib.generate_bins_dt64()
+    # shift bins by the number of NaT
+    assert nat_count > 0
+    bins += nat_count
+    bins = np.insert(bins, 0, nat_count)
+
+    # Incompatible types in assignment (expression has type "Index", variable
+    # has type "PeriodIndex")
+    binner = binner.insert(0, NaT)  # type: ignore[assignment]
+    # Incompatible types in assignment (expression has type "Index", variable
+    # has type "PeriodIndex")
+    labels = labels.insert(0, NaT)  # type: ignore[assignment]
+    return binner, bins, labels
+
+
+def _adjust_dates_anchored(
+    first: Timestamp,
+    last: Timestamp,
+    freq: Tick,
+    closed: Literal["right", "left"] = "right",
+    origin: TimeGrouperOrigin = "start_day",
+    offset: Timedelta | None = None,
+    unit: str = "ns",
+) -> tuple[Timestamp, Timestamp]:
+    # First and last offsets should be calculated from the start day to fix an
+    # error cause by resampling across multiple days when a one day period is
+    # not a multiple of the frequency. See GH 8683
+    # To handle frequencies that are not multiple or divisible by a day we let
+    # the possibility to define a fixed origin timestamp. See GH 31809
+    first = first.as_unit(unit)
+    last = last.as_unit(unit)
+    if offset is not None:
+        offset = offset.as_unit(unit)
+
+    freq_value = Timedelta(freq).as_unit(unit)._value
+
+    origin_timestamp = 0  # origin == "epoch"
+    if origin == "start_day":
+        origin_timestamp = first.normalize()._value
+    elif origin == "start":
+        origin_timestamp = first._value
+    elif isinstance(origin, Timestamp):
+        origin_timestamp = origin.as_unit(unit)._value
+    elif origin in ["end", "end_day"]:
+        origin_last = last if origin == "end" else last.ceil("D")
+        sub_freq_times = (origin_last._value - first._value) // freq_value
+        if closed == "left":
+            sub_freq_times += 1
+        first = origin_last - sub_freq_times * freq
+        origin_timestamp = first._value
+    origin_timestamp += offset._value if offset else 0
+
+    # GH 10117 & GH 19375. If first and last contain timezone information,
+    # Perform the calculation in UTC in order to avoid localizing on an
+    # Ambiguous or Nonexistent time.
+    first_tzinfo = first.tzinfo
+    last_tzinfo = last.tzinfo
+    if first_tzinfo is not None:
+        first = first.tz_convert("UTC")
+    if last_tzinfo is not None:
+        last = last.tz_convert("UTC")
+
+    foffset = (first._value - origin_timestamp) % freq_value
+    loffset = (last._value - origin_timestamp) % freq_value
+
+    if closed == "right":
+        if foffset > 0:
+            # roll back
+            fresult_int = first._value - foffset
+        else:
+            fresult_int = first._value - freq_value
+
+        if loffset > 0:
+            # roll forward
+            lresult_int = last._value + (freq_value - loffset)
+        else:
+            # already the end of the road
+            lresult_int = last._value
+    else:  # closed == 'left'
+        if foffset > 0:
+            fresult_int = first._value - foffset
+        else:
+            # start of the road
+            fresult_int = first._value
+
+        if loffset > 0:
+            # roll forward
+            lresult_int = last._value + (freq_value - loffset)
+        else:
+            lresult_int = last._value + freq_value
+    fresult = Timestamp(fresult_int, unit=unit)
+    lresult = Timestamp(lresult_int, unit=unit)
+    if first_tzinfo is not None:
+        fresult = fresult.tz_localize("UTC").tz_convert(first_tzinfo)
+    if last_tzinfo is not None:
+        lresult = lresult.tz_localize("UTC").tz_convert(last_tzinfo)
+    return fresult, lresult
+
+
+def asfreq(
+    obj: NDFrameT,
+    freq,
+    method=None,
+    how=None,
+    normalize: bool = False,
+    fill_value=None,
+) -> NDFrameT:
+    """
+    Utility frequency conversion method for Series/DataFrame.
+
+    See :meth:`pandas.NDFrame.asfreq` for full documentation.
+    """
+    if isinstance(obj.index, PeriodIndex):
+        if method is not None:
+            raise NotImplementedError("'method' argument is not supported")
+
+        if how is None:
+            how = "E"
+
+        if isinstance(freq, BaseOffset):
+            if hasattr(freq, "_period_dtype_code"):
+                freq = freq_to_period_freqstr(freq.n, freq.name)
+            else:
+                raise ValueError(
+                    f"Invalid offset: '{freq.base}' for converting time series "
+                    f"with PeriodIndex."
+                )
+
+        new_obj = obj.copy()
+        new_obj.index = obj.index.asfreq(freq, how=how)
+
+    elif len(obj.index) == 0:
+        new_obj = obj.copy()
+
+        new_obj.index = _asfreq_compat(obj.index, freq)
+    else:
+        unit = None
+        if isinstance(obj.index, DatetimeIndex):
+            # TODO: should we disallow non-DatetimeIndex?
+            unit = obj.index.unit
+        dti = date_range(obj.index.min(), obj.index.max(), freq=freq, unit=unit)
+        dti.name = obj.index.name
+        new_obj = obj.reindex(dti, method=method, fill_value=fill_value)
+        if normalize:
+            new_obj.index = new_obj.index.normalize()
+
+    return new_obj
+
+
+def _asfreq_compat(index: DatetimeIndex | PeriodIndex | TimedeltaIndex, freq):
+    """
+    Helper to mimic asfreq on (empty) DatetimeIndex and TimedeltaIndex.
+
+    Parameters
+    ----------
+    index : PeriodIndex, DatetimeIndex, or TimedeltaIndex
+    freq : DateOffset
+
+    Returns
+    -------
+    same type as index
+    """
+    if len(index) != 0:
+        # This should never be reached, always checked by the caller
+        raise ValueError(
+            "Can only set arbitrary freq for empty DatetimeIndex or TimedeltaIndex"
+        )
+    new_index: Index
+    if isinstance(index, PeriodIndex):
+        new_index = index.asfreq(freq=freq)
+    elif isinstance(index, DatetimeIndex):
+        new_index = DatetimeIndex([], dtype=index.dtype, freq=freq, name=index.name)
+    elif isinstance(index, TimedeltaIndex):
+        new_index = TimedeltaIndex([], dtype=index.dtype, freq=freq, name=index.name)
+    else:  # pragma: no cover
+        raise TypeError(type(index))
+    return new_index
+
+
+def maybe_warn_args_and_kwargs(cls, kernel: str, args, kwargs) -> None:
+    """
+    Warn for deprecation of args and kwargs in resample functions.
+
+    Parameters
+    ----------
+    cls : type
+        Class to warn about.
+    kernel : str
+        Operation name.
+    args : tuple or None
+        args passed by user. Will be None if and only if kernel does not have args.
+    kwargs : dict or None
+        kwargs passed by user. Will be None if and only if kernel does not have kwargs.
+    """
+    warn_args = args is not None and len(args) > 0
+    warn_kwargs = kwargs is not None and len(kwargs) > 0
+    if warn_args and warn_kwargs:
+        msg = "args and kwargs"
+    elif warn_args:
+        msg = "args"
+    elif warn_kwargs:
+        msg = "kwargs"
+    else:
+        return
+    warnings.warn(
+        f"Passing additional {msg} to {cls.__name__}.{kernel} has "
+        "no impact on the result and is deprecated. This will "
+        "raise a TypeError in a future version of pandas.",
+        category=FutureWarning,
+        stacklevel=find_stack_level(),
+    )
+
+
+def _apply(
+    grouped: GroupBy, how: Callable, *args, include_groups: bool, **kwargs
+) -> DataFrame:
+    # GH#7155 - rewrite warning to appear as if it came from `.resample`
+    target_message = "DataFrameGroupBy.apply operated on the grouping columns"
+    new_message = _apply_groupings_depr.format("DataFrameGroupBy", "resample")
+    with rewrite_warning(
+        target_message=target_message,
+        target_category=FutureWarning,
+        new_message=new_message,
+    ):
+        result = grouped.apply(how, *args, include_groups=include_groups, **kwargs)
+    return result
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/reshape/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/reshape/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/reshape/api.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/reshape/api.py
new file mode 100644
index 0000000000000000000000000000000000000000..b1884c497f0ad7000351e131ef11dadab4a7c700
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/reshape/api.py
@@ -0,0 +1,41 @@
+from pandas.core.reshape.concat import concat
+from pandas.core.reshape.encoding import (
+    from_dummies,
+    get_dummies,
+)
+from pandas.core.reshape.melt import (
+    lreshape,
+    melt,
+    wide_to_long,
+)
+from pandas.core.reshape.merge import (
+    merge,
+    merge_asof,
+    merge_ordered,
+)
+from pandas.core.reshape.pivot import (
+    crosstab,
+    pivot,
+    pivot_table,
+)
+from pandas.core.reshape.tile import (
+    cut,
+    qcut,
+)
+
+__all__ = [
+    "concat",
+    "crosstab",
+    "cut",
+    "from_dummies",
+    "get_dummies",
+    "lreshape",
+    "melt",
+    "merge",
+    "merge_asof",
+    "merge_ordered",
+    "pivot",
+    "pivot_table",
+    "qcut",
+    "wide_to_long",
+]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/reshape/concat.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/reshape/concat.py
new file mode 100644
index 0000000000000000000000000000000000000000..dc18bb65b35bcfa5c3789b35e7d41690923b50a7
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/reshape/concat.py
@@ -0,0 +1,894 @@
+"""
+Concat routines.
+"""
+from __future__ import annotations
+
+from collections import abc
+from typing import (
+    TYPE_CHECKING,
+    Callable,
+    Literal,
+    cast,
+    overload,
+)
+import warnings
+
+import numpy as np
+
+from pandas._config import using_copy_on_write
+
+from pandas.util._decorators import cache_readonly
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.common import (
+    is_bool,
+    is_iterator,
+)
+from pandas.core.dtypes.concat import concat_compat
+from pandas.core.dtypes.generic import (
+    ABCDataFrame,
+    ABCSeries,
+)
+from pandas.core.dtypes.missing import isna
+
+from pandas.core.arrays.categorical import (
+    factorize_from_iterable,
+    factorize_from_iterables,
+)
+import pandas.core.common as com
+from pandas.core.indexes.api import (
+    Index,
+    MultiIndex,
+    all_indexes_same,
+    default_index,
+    ensure_index,
+    get_objs_combined_axis,
+    get_unanimous_names,
+)
+from pandas.core.internals import concatenate_managers
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Hashable,
+        Iterable,
+        Mapping,
+    )
+
+    from pandas._typing import (
+        Axis,
+        AxisInt,
+        HashableT,
+    )
+
+    from pandas import (
+        DataFrame,
+        Series,
+    )
+
+# ---------------------------------------------------------------------
+# Concatenate DataFrame objects
+
+
+@overload
+def concat(
+    objs: Iterable[DataFrame] | Mapping[HashableT, DataFrame],
+    *,
+    axis: Literal[0, "index"] = ...,
+    join: str = ...,
+    ignore_index: bool = ...,
+    keys: Iterable[Hashable] | None = ...,
+    levels=...,
+    names: list[HashableT] | None = ...,
+    verify_integrity: bool = ...,
+    sort: bool = ...,
+    copy: bool | None = ...,
+) -> DataFrame:
+    ...
+
+
+@overload
+def concat(
+    objs: Iterable[Series] | Mapping[HashableT, Series],
+    *,
+    axis: Literal[0, "index"] = ...,
+    join: str = ...,
+    ignore_index: bool = ...,
+    keys: Iterable[Hashable] | None = ...,
+    levels=...,
+    names: list[HashableT] | None = ...,
+    verify_integrity: bool = ...,
+    sort: bool = ...,
+    copy: bool | None = ...,
+) -> Series:
+    ...
+
+
+@overload
+def concat(
+    objs: Iterable[Series | DataFrame] | Mapping[HashableT, Series | DataFrame],
+    *,
+    axis: Literal[0, "index"] = ...,
+    join: str = ...,
+    ignore_index: bool = ...,
+    keys: Iterable[Hashable] | None = ...,
+    levels=...,
+    names: list[HashableT] | None = ...,
+    verify_integrity: bool = ...,
+    sort: bool = ...,
+    copy: bool | None = ...,
+) -> DataFrame | Series:
+    ...
+
+
+@overload
+def concat(
+    objs: Iterable[Series | DataFrame] | Mapping[HashableT, Series | DataFrame],
+    *,
+    axis: Literal[1, "columns"],
+    join: str = ...,
+    ignore_index: bool = ...,
+    keys: Iterable[Hashable] | None = ...,
+    levels=...,
+    names: list[HashableT] | None = ...,
+    verify_integrity: bool = ...,
+    sort: bool = ...,
+    copy: bool | None = ...,
+) -> DataFrame:
+    ...
+
+
+@overload
+def concat(
+    objs: Iterable[Series | DataFrame] | Mapping[HashableT, Series | DataFrame],
+    *,
+    axis: Axis = ...,
+    join: str = ...,
+    ignore_index: bool = ...,
+    keys: Iterable[Hashable] | None = ...,
+    levels=...,
+    names: list[HashableT] | None = ...,
+    verify_integrity: bool = ...,
+    sort: bool = ...,
+    copy: bool | None = ...,
+) -> DataFrame | Series:
+    ...
+
+
+def concat(
+    objs: Iterable[Series | DataFrame] | Mapping[HashableT, Series | DataFrame],
+    *,
+    axis: Axis = 0,
+    join: str = "outer",
+    ignore_index: bool = False,
+    keys: Iterable[Hashable] | None = None,
+    levels=None,
+    names: list[HashableT] | None = None,
+    verify_integrity: bool = False,
+    sort: bool = False,
+    copy: bool | None = None,
+) -> DataFrame | Series:
+    """
+    Concatenate pandas objects along a particular axis.
+
+    Allows optional set logic along the other axes.
+
+    Can also add a layer of hierarchical indexing on the concatenation axis,
+    which may be useful if the labels are the same (or overlapping) on
+    the passed axis number.
+
+    Parameters
+    ----------
+    objs : a sequence or mapping of Series or DataFrame objects
+        If a mapping is passed, the sorted keys will be used as the `keys`
+        argument, unless it is passed, in which case the values will be
+        selected (see below). Any None objects will be dropped silently unless
+        they are all None in which case a ValueError will be raised.
+    axis : {0/'index', 1/'columns'}, default 0
+        The axis to concatenate along.
+    join : {'inner', 'outer'}, default 'outer'
+        How to handle indexes on other axis (or axes).
+    ignore_index : bool, default False
+        If True, do not use the index values along the concatenation axis. The
+        resulting axis will be labeled 0, ..., n - 1. This is useful if you are
+        concatenating objects where the concatenation axis does not have
+        meaningful indexing information. Note the index values on the other
+        axes are still respected in the join.
+    keys : sequence, default None
+        If multiple levels passed, should contain tuples. Construct
+        hierarchical index using the passed keys as the outermost level.
+    levels : list of sequences, default None
+        Specific levels (unique values) to use for constructing a
+        MultiIndex. Otherwise they will be inferred from the keys.
+    names : list, default None
+        Names for the levels in the resulting hierarchical index.
+    verify_integrity : bool, default False
+        Check whether the new concatenated axis contains duplicates. This can
+        be very expensive relative to the actual data concatenation.
+    sort : bool, default False
+        Sort non-concatenation axis if it is not already aligned. One exception to
+        this is when the non-concatentation axis is a DatetimeIndex and join='outer'
+        and the axis is not already aligned. In that case, the non-concatenation
+        axis is always sorted lexicographically.
+    copy : bool, default True
+        If False, do not copy data unnecessarily.
+
+    Returns
+    -------
+    object, type of objs
+        When concatenating all ``Series`` along the index (axis=0), a
+        ``Series`` is returned. When ``objs`` contains at least one
+        ``DataFrame``, a ``DataFrame`` is returned. When concatenating along
+        the columns (axis=1), a ``DataFrame`` is returned.
+
+    See Also
+    --------
+    DataFrame.join : Join DataFrames using indexes.
+    DataFrame.merge : Merge DataFrames by indexes or columns.
+
+    Notes
+    -----
+    The keys, levels, and names arguments are all optional.
+
+    A walkthrough of how this method fits in with other tools for combining
+    pandas objects can be found `here
+    `__.
+
+    It is not recommended to build DataFrames by adding single rows in a
+    for loop. Build a list of rows and make a DataFrame in a single concat.
+
+    Examples
+    --------
+    Combine two ``Series``.
+
+    >>> s1 = pd.Series(['a', 'b'])
+    >>> s2 = pd.Series(['c', 'd'])
+    >>> pd.concat([s1, s2])
+    0    a
+    1    b
+    0    c
+    1    d
+    dtype: object
+
+    Clear the existing index and reset it in the result
+    by setting the ``ignore_index`` option to ``True``.
+
+    >>> pd.concat([s1, s2], ignore_index=True)
+    0    a
+    1    b
+    2    c
+    3    d
+    dtype: object
+
+    Add a hierarchical index at the outermost level of
+    the data with the ``keys`` option.
+
+    >>> pd.concat([s1, s2], keys=['s1', 's2'])
+    s1  0    a
+        1    b
+    s2  0    c
+        1    d
+    dtype: object
+
+    Label the index keys you create with the ``names`` option.
+
+    >>> pd.concat([s1, s2], keys=['s1', 's2'],
+    ...           names=['Series name', 'Row ID'])
+    Series name  Row ID
+    s1           0         a
+                 1         b
+    s2           0         c
+                 1         d
+    dtype: object
+
+    Combine two ``DataFrame`` objects with identical columns.
+
+    >>> df1 = pd.DataFrame([['a', 1], ['b', 2]],
+    ...                    columns=['letter', 'number'])
+    >>> df1
+      letter  number
+    0      a       1
+    1      b       2
+    >>> df2 = pd.DataFrame([['c', 3], ['d', 4]],
+    ...                    columns=['letter', 'number'])
+    >>> df2
+      letter  number
+    0      c       3
+    1      d       4
+    >>> pd.concat([df1, df2])
+      letter  number
+    0      a       1
+    1      b       2
+    0      c       3
+    1      d       4
+
+    Combine ``DataFrame`` objects with overlapping columns
+    and return everything. Columns outside the intersection will
+    be filled with ``NaN`` values.
+
+    >>> df3 = pd.DataFrame([['c', 3, 'cat'], ['d', 4, 'dog']],
+    ...                    columns=['letter', 'number', 'animal'])
+    >>> df3
+      letter  number animal
+    0      c       3    cat
+    1      d       4    dog
+    >>> pd.concat([df1, df3], sort=False)
+      letter  number animal
+    0      a       1    NaN
+    1      b       2    NaN
+    0      c       3    cat
+    1      d       4    dog
+
+    Combine ``DataFrame`` objects with overlapping columns
+    and return only those that are shared by passing ``inner`` to
+    the ``join`` keyword argument.
+
+    >>> pd.concat([df1, df3], join="inner")
+      letter  number
+    0      a       1
+    1      b       2
+    0      c       3
+    1      d       4
+
+    Combine ``DataFrame`` objects horizontally along the x axis by
+    passing in ``axis=1``.
+
+    >>> df4 = pd.DataFrame([['bird', 'polly'], ['monkey', 'george']],
+    ...                    columns=['animal', 'name'])
+    >>> pd.concat([df1, df4], axis=1)
+      letter  number  animal    name
+    0      a       1    bird   polly
+    1      b       2  monkey  george
+
+    Prevent the result from including duplicate index values with the
+    ``verify_integrity`` option.
+
+    >>> df5 = pd.DataFrame([1], index=['a'])
+    >>> df5
+       0
+    a  1
+    >>> df6 = pd.DataFrame([2], index=['a'])
+    >>> df6
+       0
+    a  2
+    >>> pd.concat([df5, df6], verify_integrity=True)
+    Traceback (most recent call last):
+        ...
+    ValueError: Indexes have overlapping values: ['a']
+
+    Append a single row to the end of a ``DataFrame`` object.
+
+    >>> df7 = pd.DataFrame({'a': 1, 'b': 2}, index=[0])
+    >>> df7
+        a   b
+    0   1   2
+    >>> new_row = pd.Series({'a': 3, 'b': 4})
+    >>> new_row
+    a    3
+    b    4
+    dtype: int64
+    >>> pd.concat([df7, new_row.to_frame().T], ignore_index=True)
+        a   b
+    0   1   2
+    1   3   4
+    """
+    if copy is None:
+        if using_copy_on_write():
+            copy = False
+        else:
+            copy = True
+    elif copy and using_copy_on_write():
+        copy = False
+
+    op = _Concatenator(
+        objs,
+        axis=axis,
+        ignore_index=ignore_index,
+        join=join,
+        keys=keys,
+        levels=levels,
+        names=names,
+        verify_integrity=verify_integrity,
+        copy=copy,
+        sort=sort,
+    )
+
+    return op.get_result()
+
+
+class _Concatenator:
+    """
+    Orchestrates a concatenation operation for BlockManagers
+    """
+
+    sort: bool
+
+    def __init__(
+        self,
+        objs: Iterable[Series | DataFrame] | Mapping[HashableT, Series | DataFrame],
+        axis: Axis = 0,
+        join: str = "outer",
+        keys: Iterable[Hashable] | None = None,
+        levels=None,
+        names: list[HashableT] | None = None,
+        ignore_index: bool = False,
+        verify_integrity: bool = False,
+        copy: bool = True,
+        sort: bool = False,
+    ) -> None:
+        if isinstance(objs, (ABCSeries, ABCDataFrame, str)):
+            raise TypeError(
+                "first argument must be an iterable of pandas "
+                f'objects, you passed an object of type "{type(objs).__name__}"'
+            )
+
+        if join == "outer":
+            self.intersect = False
+        elif join == "inner":
+            self.intersect = True
+        else:  # pragma: no cover
+            raise ValueError(
+                "Only can inner (intersect) or outer (union) join the other axis"
+            )
+
+        if not is_bool(sort):
+            raise ValueError(
+                f"The 'sort' keyword only accepts boolean values; {sort} was passed."
+            )
+        # Incompatible types in assignment (expression has type "Union[bool, bool_]",
+        # variable has type "bool")
+        self.sort = sort  # type: ignore[assignment]
+
+        self.ignore_index = ignore_index
+        self.verify_integrity = verify_integrity
+        self.copy = copy
+
+        objs, keys = self._clean_keys_and_objs(objs, keys)
+
+        # figure out what our result ndim is going to be
+        ndims = self._get_ndims(objs)
+        sample, objs = self._get_sample_object(objs, ndims, keys, names, levels)
+
+        # Standardize axis parameter to int
+        if sample.ndim == 1:
+            from pandas import DataFrame
+
+            axis = DataFrame._get_axis_number(axis)
+            self._is_frame = False
+            self._is_series = True
+        else:
+            axis = sample._get_axis_number(axis)
+            self._is_frame = True
+            self._is_series = False
+
+            # Need to flip BlockManager axis in the DataFrame special case
+            axis = sample._get_block_manager_axis(axis)
+
+        # if we have mixed ndims, then convert to highest ndim
+        # creating column numbers as needed
+        if len(ndims) > 1:
+            objs = self._sanitize_mixed_ndim(objs, sample, ignore_index, axis)
+
+        self.objs = objs
+
+        # note: this is the BlockManager axis (since DataFrame is transposed)
+        self.bm_axis = axis
+        self.axis = 1 - self.bm_axis if self._is_frame else 0
+        self.keys = keys
+        self.names = names or getattr(keys, "names", None)
+        self.levels = levels
+
+    def _get_ndims(self, objs: list[Series | DataFrame]) -> set[int]:
+        # figure out what our result ndim is going to be
+        ndims = set()
+        for obj in objs:
+            if not isinstance(obj, (ABCSeries, ABCDataFrame)):
+                msg = (
+                    f"cannot concatenate object of type '{type(obj)}'; "
+                    "only Series and DataFrame objs are valid"
+                )
+                raise TypeError(msg)
+
+            ndims.add(obj.ndim)
+        return ndims
+
+    def _clean_keys_and_objs(
+        self,
+        objs: Iterable[Series | DataFrame] | Mapping[HashableT, Series | DataFrame],
+        keys,
+    ) -> tuple[list[Series | DataFrame], Index | None]:
+        if isinstance(objs, abc.Mapping):
+            if keys is None:
+                keys = list(objs.keys())
+            objs_list = [objs[k] for k in keys]
+        else:
+            objs_list = list(objs)
+
+        if len(objs_list) == 0:
+            raise ValueError("No objects to concatenate")
+
+        if keys is None:
+            objs_list = list(com.not_none(*objs_list))
+        else:
+            # GH#1649
+            clean_keys = []
+            clean_objs = []
+            if is_iterator(keys):
+                keys = list(keys)
+            if len(keys) != len(objs_list):
+                # GH#43485
+                warnings.warn(
+                    "The behavior of pd.concat with len(keys) != len(objs) is "
+                    "deprecated. In a future version this will raise instead of "
+                    "truncating to the smaller of the two sequences",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+            for k, v in zip(keys, objs_list):
+                if v is None:
+                    continue
+                clean_keys.append(k)
+                clean_objs.append(v)
+            objs_list = clean_objs
+
+            if isinstance(keys, MultiIndex):
+                # TODO: retain levels?
+                keys = type(keys).from_tuples(clean_keys, names=keys.names)
+            else:
+                name = getattr(keys, "name", None)
+                keys = Index(clean_keys, name=name, dtype=getattr(keys, "dtype", None))
+
+        if len(objs_list) == 0:
+            raise ValueError("All objects passed were None")
+
+        return objs_list, keys
+
+    def _get_sample_object(
+        self,
+        objs: list[Series | DataFrame],
+        ndims: set[int],
+        keys,
+        names,
+        levels,
+    ) -> tuple[Series | DataFrame, list[Series | DataFrame]]:
+        # get the sample
+        # want the highest ndim that we have, and must be non-empty
+        # unless all objs are empty
+        sample: Series | DataFrame | None = None
+        if len(ndims) > 1:
+            max_ndim = max(ndims)
+            for obj in objs:
+                if obj.ndim == max_ndim and np.sum(obj.shape):
+                    sample = obj
+                    break
+
+        else:
+            # filter out the empties if we have not multi-index possibilities
+            # note to keep empty Series as it affect to result columns / name
+            non_empties = [obj for obj in objs if sum(obj.shape) > 0 or obj.ndim == 1]
+
+            if len(non_empties) and (
+                keys is None and names is None and levels is None and not self.intersect
+            ):
+                objs = non_empties
+                sample = objs[0]
+
+        if sample is None:
+            sample = objs[0]
+        return sample, objs
+
+    def _sanitize_mixed_ndim(
+        self,
+        objs: list[Series | DataFrame],
+        sample: Series | DataFrame,
+        ignore_index: bool,
+        axis: AxisInt,
+    ) -> list[Series | DataFrame]:
+        # if we have mixed ndims, then convert to highest ndim
+        # creating column numbers as needed
+
+        new_objs = []
+
+        current_column = 0
+        max_ndim = sample.ndim
+        for obj in objs:
+            ndim = obj.ndim
+            if ndim == max_ndim:
+                pass
+
+            elif ndim != max_ndim - 1:
+                raise ValueError(
+                    "cannot concatenate unaligned mixed dimensional NDFrame objects"
+                )
+
+            else:
+                name = getattr(obj, "name", None)
+                if ignore_index or name is None:
+                    if axis == 1:
+                        # doing a row-wise concatenation so need everything
+                        # to line up
+                        name = 0
+                    else:
+                        # doing a column-wise concatenation so need series
+                        # to have unique names
+                        name = current_column
+                        current_column += 1
+
+                obj = sample._constructor({name: obj}, copy=False)
+
+            new_objs.append(obj)
+
+        return new_objs
+
+    def get_result(self):
+        cons: Callable[..., DataFrame | Series]
+        sample: DataFrame | Series
+
+        # series only
+        if self._is_series:
+            sample = cast("Series", self.objs[0])
+
+            # stack blocks
+            if self.bm_axis == 0:
+                name = com.consensus_name_attr(self.objs)
+                cons = sample._constructor
+
+                arrs = [ser._values for ser in self.objs]
+
+                res = concat_compat(arrs, axis=0)
+
+                new_index: Index
+                if self.ignore_index:
+                    # We can avoid surprisingly-expensive _get_concat_axis
+                    new_index = default_index(len(res))
+                else:
+                    new_index = self.new_axes[0]
+
+                mgr = type(sample._mgr).from_array(res, index=new_index)
+
+                result = sample._constructor_from_mgr(mgr, axes=mgr.axes)
+                result._name = name
+                return result.__finalize__(self, method="concat")
+
+            # combine as columns in a frame
+            else:
+                data = dict(zip(range(len(self.objs)), self.objs))
+
+                # GH28330 Preserves subclassed objects through concat
+                cons = sample._constructor_expanddim
+
+                index, columns = self.new_axes
+                df = cons(data, index=index, copy=self.copy)
+                df.columns = columns
+                return df.__finalize__(self, method="concat")
+
+        # combine block managers
+        else:
+            sample = cast("DataFrame", self.objs[0])
+
+            mgrs_indexers = []
+            for obj in self.objs:
+                indexers = {}
+                for ax, new_labels in enumerate(self.new_axes):
+                    # ::-1 to convert BlockManager ax to DataFrame ax
+                    if ax == self.bm_axis:
+                        # Suppress reindexing on concat axis
+                        continue
+
+                    # 1-ax to convert BlockManager axis to DataFrame axis
+                    obj_labels = obj.axes[1 - ax]
+                    if not new_labels.equals(obj_labels):
+                        indexers[ax] = obj_labels.get_indexer(new_labels)
+
+                mgrs_indexers.append((obj._mgr, indexers))
+
+            new_data = concatenate_managers(
+                mgrs_indexers, self.new_axes, concat_axis=self.bm_axis, copy=self.copy
+            )
+            if not self.copy and not using_copy_on_write():
+                new_data._consolidate_inplace()
+
+            out = sample._constructor_from_mgr(new_data, axes=new_data.axes)
+            return out.__finalize__(self, method="concat")
+
+    def _get_result_dim(self) -> int:
+        if self._is_series and self.bm_axis == 1:
+            return 2
+        else:
+            return self.objs[0].ndim
+
+    @cache_readonly
+    def new_axes(self) -> list[Index]:
+        ndim = self._get_result_dim()
+        return [
+            self._get_concat_axis if i == self.bm_axis else self._get_comb_axis(i)
+            for i in range(ndim)
+        ]
+
+    def _get_comb_axis(self, i: AxisInt) -> Index:
+        data_axis = self.objs[0]._get_block_manager_axis(i)
+        return get_objs_combined_axis(
+            self.objs,
+            axis=data_axis,
+            intersect=self.intersect,
+            sort=self.sort,
+            copy=self.copy,
+        )
+
+    @cache_readonly
+    def _get_concat_axis(self) -> Index:
+        """
+        Return index to be used along concatenation axis.
+        """
+        if self._is_series:
+            if self.bm_axis == 0:
+                indexes = [x.index for x in self.objs]
+            elif self.ignore_index:
+                idx = default_index(len(self.objs))
+                return idx
+            elif self.keys is None:
+                names: list[Hashable] = [None] * len(self.objs)
+                num = 0
+                has_names = False
+                for i, x in enumerate(self.objs):
+                    if x.ndim != 1:
+                        raise TypeError(
+                            f"Cannot concatenate type 'Series' with "
+                            f"object of type '{type(x).__name__}'"
+                        )
+                    if x.name is not None:
+                        names[i] = x.name
+                        has_names = True
+                    else:
+                        names[i] = num
+                        num += 1
+                if has_names:
+                    return Index(names)
+                else:
+                    return default_index(len(self.objs))
+            else:
+                return ensure_index(self.keys).set_names(self.names)
+        else:
+            indexes = [x.axes[self.axis] for x in self.objs]
+
+        if self.ignore_index:
+            idx = default_index(sum(len(i) for i in indexes))
+            return idx
+
+        if self.keys is None:
+            if self.levels is not None:
+                raise ValueError("levels supported only when keys is not None")
+            concat_axis = _concat_indexes(indexes)
+        else:
+            concat_axis = _make_concat_multiindex(
+                indexes, self.keys, self.levels, self.names
+            )
+
+        self._maybe_check_integrity(concat_axis)
+
+        return concat_axis
+
+    def _maybe_check_integrity(self, concat_index: Index):
+        if self.verify_integrity:
+            if not concat_index.is_unique:
+                overlap = concat_index[concat_index.duplicated()].unique()
+                raise ValueError(f"Indexes have overlapping values: {overlap}")
+
+
+def _concat_indexes(indexes) -> Index:
+    return indexes[0].append(indexes[1:])
+
+
+def _make_concat_multiindex(indexes, keys, levels=None, names=None) -> MultiIndex:
+    if (levels is None and isinstance(keys[0], tuple)) or (
+        levels is not None and len(levels) > 1
+    ):
+        zipped = list(zip(*keys))
+        if names is None:
+            names = [None] * len(zipped)
+
+        if levels is None:
+            _, levels = factorize_from_iterables(zipped)
+        else:
+            levels = [ensure_index(x) for x in levels]
+    else:
+        zipped = [keys]
+        if names is None:
+            names = [None]
+
+        if levels is None:
+            levels = [ensure_index(keys).unique()]
+        else:
+            levels = [ensure_index(x) for x in levels]
+
+    for level in levels:
+        if not level.is_unique:
+            raise ValueError(f"Level values not unique: {level.tolist()}")
+
+    if not all_indexes_same(indexes) or not all(level.is_unique for level in levels):
+        codes_list = []
+
+        # things are potentially different sizes, so compute the exact codes
+        # for each level and pass those to MultiIndex.from_arrays
+
+        for hlevel, level in zip(zipped, levels):
+            to_concat = []
+            if isinstance(hlevel, Index) and hlevel.equals(level):
+                lens = [len(idx) for idx in indexes]
+                codes_list.append(np.repeat(np.arange(len(hlevel)), lens))
+            else:
+                for key, index in zip(hlevel, indexes):
+                    # Find matching codes, include matching nan values as equal.
+                    mask = (isna(level) & isna(key)) | (level == key)
+                    if not mask.any():
+                        raise ValueError(f"Key {key} not in level {level}")
+                    i = np.nonzero(mask)[0][0]
+
+                    to_concat.append(np.repeat(i, len(index)))
+                codes_list.append(np.concatenate(to_concat))
+
+        concat_index = _concat_indexes(indexes)
+
+        # these go at the end
+        if isinstance(concat_index, MultiIndex):
+            levels.extend(concat_index.levels)
+            codes_list.extend(concat_index.codes)
+        else:
+            codes, categories = factorize_from_iterable(concat_index)
+            levels.append(categories)
+            codes_list.append(codes)
+
+        if len(names) == len(levels):
+            names = list(names)
+        else:
+            # make sure that all of the passed indices have the same nlevels
+            if not len({idx.nlevels for idx in indexes}) == 1:
+                raise AssertionError(
+                    "Cannot concat indices that do not have the same number of levels"
+                )
+
+            # also copies
+            names = list(names) + list(get_unanimous_names(*indexes))
+
+        return MultiIndex(
+            levels=levels, codes=codes_list, names=names, verify_integrity=False
+        )
+
+    new_index = indexes[0]
+    n = len(new_index)
+    kpieces = len(indexes)
+
+    # also copies
+    new_names = list(names)
+    new_levels = list(levels)
+
+    # construct codes
+    new_codes = []
+
+    # do something a bit more speedy
+
+    for hlevel, level in zip(zipped, levels):
+        hlevel_index = ensure_index(hlevel)
+        mapped = level.get_indexer(hlevel_index)
+
+        mask = mapped == -1
+        if mask.any():
+            raise ValueError(
+                f"Values not found in passed level: {hlevel_index[mask]!s}"
+            )
+
+        new_codes.append(np.repeat(mapped, n))
+
+    if isinstance(new_index, MultiIndex):
+        new_levels.extend(new_index.levels)
+        new_codes.extend([np.tile(lab, kpieces) for lab in new_index.codes])
+    else:
+        new_levels.append(new_index.unique())
+        single_codes = new_index.unique().get_indexer(new_index)
+        new_codes.append(np.tile(single_codes, kpieces))
+
+    if len(new_names) < len(new_levels):
+        new_names.extend(new_index.names)
+
+    return MultiIndex(
+        levels=new_levels, codes=new_codes, names=new_names, verify_integrity=False
+    )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/reshape/encoding.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/reshape/encoding.py
new file mode 100644
index 0000000000000000000000000000000000000000..85c10f11665776680b3249fcec304f9c029a5599
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/reshape/encoding.py
@@ -0,0 +1,571 @@
+from __future__ import annotations
+
+from collections import defaultdict
+from collections.abc import (
+    Hashable,
+    Iterable,
+)
+import itertools
+from typing import (
+    TYPE_CHECKING,
+    cast,
+)
+
+import numpy as np
+
+from pandas._libs import missing as libmissing
+from pandas._libs.sparse import IntIndex
+
+from pandas.core.dtypes.common import (
+    is_integer_dtype,
+    is_list_like,
+    is_object_dtype,
+    pandas_dtype,
+)
+from pandas.core.dtypes.dtypes import (
+    ArrowDtype,
+    CategoricalDtype,
+)
+
+from pandas.core.arrays import SparseArray
+from pandas.core.arrays.categorical import factorize_from_iterable
+from pandas.core.arrays.string_ import StringDtype
+from pandas.core.frame import DataFrame
+from pandas.core.indexes.api import (
+    Index,
+    default_index,
+)
+from pandas.core.series import Series
+
+if TYPE_CHECKING:
+    from pandas._typing import NpDtype
+
+
+def get_dummies(
+    data,
+    prefix=None,
+    prefix_sep: str | Iterable[str] | dict[str, str] = "_",
+    dummy_na: bool = False,
+    columns=None,
+    sparse: bool = False,
+    drop_first: bool = False,
+    dtype: NpDtype | None = None,
+) -> DataFrame:
+    """
+    Convert categorical variable into dummy/indicator variables.
+
+    Each variable is converted in as many 0/1 variables as there are different
+    values. Columns in the output are each named after a value; if the input is
+    a DataFrame, the name of the original variable is prepended to the value.
+
+    Parameters
+    ----------
+    data : array-like, Series, or DataFrame
+        Data of which to get dummy indicators.
+    prefix : str, list of str, or dict of str, default None
+        String to append DataFrame column names.
+        Pass a list with length equal to the number of columns
+        when calling get_dummies on a DataFrame. Alternatively, `prefix`
+        can be a dictionary mapping column names to prefixes.
+    prefix_sep : str, default '_'
+        If appending prefix, separator/delimiter to use. Or pass a
+        list or dictionary as with `prefix`.
+    dummy_na : bool, default False
+        Add a column to indicate NaNs, if False NaNs are ignored.
+    columns : list-like, default None
+        Column names in the DataFrame to be encoded.
+        If `columns` is None then all the columns with
+        `object`, `string`, or `category` dtype will be converted.
+    sparse : bool, default False
+        Whether the dummy-encoded columns should be backed by
+        a :class:`SparseArray` (True) or a regular NumPy array (False).
+    drop_first : bool, default False
+        Whether to get k-1 dummies out of k categorical levels by removing the
+        first level.
+    dtype : dtype, default bool
+        Data type for new columns. Only a single dtype is allowed.
+
+    Returns
+    -------
+    DataFrame
+        Dummy-coded data. If `data` contains other columns than the
+        dummy-coded one(s), these will be prepended, unaltered, to the result.
+
+    See Also
+    --------
+    Series.str.get_dummies : Convert Series of strings to dummy codes.
+    :func:`~pandas.from_dummies` : Convert dummy codes to categorical ``DataFrame``.
+
+    Notes
+    -----
+    Reference :ref:`the user guide ` for more examples.
+
+    Examples
+    --------
+    >>> s = pd.Series(list('abca'))
+
+    >>> pd.get_dummies(s)
+           a      b      c
+    0   True  False  False
+    1  False   True  False
+    2  False  False   True
+    3   True  False  False
+
+    >>> s1 = ['a', 'b', np.nan]
+
+    >>> pd.get_dummies(s1)
+           a      b
+    0   True  False
+    1  False   True
+    2  False  False
+
+    >>> pd.get_dummies(s1, dummy_na=True)
+           a      b    NaN
+    0   True  False  False
+    1  False   True  False
+    2  False  False   True
+
+    >>> df = pd.DataFrame({'A': ['a', 'b', 'a'], 'B': ['b', 'a', 'c'],
+    ...                    'C': [1, 2, 3]})
+
+    >>> pd.get_dummies(df, prefix=['col1', 'col2'])
+       C  col1_a  col1_b  col2_a  col2_b  col2_c
+    0  1    True   False   False    True   False
+    1  2   False    True    True   False   False
+    2  3    True   False   False   False    True
+
+    >>> pd.get_dummies(pd.Series(list('abcaa')))
+           a      b      c
+    0   True  False  False
+    1  False   True  False
+    2  False  False   True
+    3   True  False  False
+    4   True  False  False
+
+    >>> pd.get_dummies(pd.Series(list('abcaa')), drop_first=True)
+           b      c
+    0  False  False
+    1   True  False
+    2  False   True
+    3  False  False
+    4  False  False
+
+    >>> pd.get_dummies(pd.Series(list('abc')), dtype=float)
+         a    b    c
+    0  1.0  0.0  0.0
+    1  0.0  1.0  0.0
+    2  0.0  0.0  1.0
+    """
+    from pandas.core.reshape.concat import concat
+
+    dtypes_to_encode = ["object", "string", "category"]
+
+    if isinstance(data, DataFrame):
+        # determine columns being encoded
+        if columns is None:
+            data_to_encode = data.select_dtypes(include=dtypes_to_encode)
+        elif not is_list_like(columns):
+            raise TypeError("Input must be a list-like for parameter `columns`")
+        else:
+            data_to_encode = data[columns]
+
+        # validate prefixes and separator to avoid silently dropping cols
+        def check_len(item, name: str):
+            if is_list_like(item):
+                if not len(item) == data_to_encode.shape[1]:
+                    len_msg = (
+                        f"Length of '{name}' ({len(item)}) did not match the "
+                        "length of the columns being encoded "
+                        f"({data_to_encode.shape[1]})."
+                    )
+                    raise ValueError(len_msg)
+
+        check_len(prefix, "prefix")
+        check_len(prefix_sep, "prefix_sep")
+
+        if isinstance(prefix, str):
+            prefix = itertools.cycle([prefix])
+        if isinstance(prefix, dict):
+            prefix = [prefix[col] for col in data_to_encode.columns]
+
+        if prefix is None:
+            prefix = data_to_encode.columns
+
+        # validate separators
+        if isinstance(prefix_sep, str):
+            prefix_sep = itertools.cycle([prefix_sep])
+        elif isinstance(prefix_sep, dict):
+            prefix_sep = [prefix_sep[col] for col in data_to_encode.columns]
+
+        with_dummies: list[DataFrame]
+        if data_to_encode.shape == data.shape:
+            # Encoding the entire df, do not prepend any dropped columns
+            with_dummies = []
+        elif columns is not None:
+            # Encoding only cols specified in columns. Get all cols not in
+            # columns to prepend to result.
+            with_dummies = [data.drop(columns, axis=1)]
+        else:
+            # Encoding only object and category dtype columns. Get remaining
+            # columns to prepend to result.
+            with_dummies = [data.select_dtypes(exclude=dtypes_to_encode)]
+
+        for col, pre, sep in zip(data_to_encode.items(), prefix, prefix_sep):
+            # col is (column_name, column), use just column data here
+            dummy = _get_dummies_1d(
+                col[1],
+                prefix=pre,
+                prefix_sep=sep,
+                dummy_na=dummy_na,
+                sparse=sparse,
+                drop_first=drop_first,
+                dtype=dtype,
+            )
+            with_dummies.append(dummy)
+        result = concat(with_dummies, axis=1)
+    else:
+        result = _get_dummies_1d(
+            data,
+            prefix,
+            prefix_sep,
+            dummy_na,
+            sparse=sparse,
+            drop_first=drop_first,
+            dtype=dtype,
+        )
+    return result
+
+
+def _get_dummies_1d(
+    data,
+    prefix,
+    prefix_sep: str | Iterable[str] | dict[str, str] = "_",
+    dummy_na: bool = False,
+    sparse: bool = False,
+    drop_first: bool = False,
+    dtype: NpDtype | None = None,
+) -> DataFrame:
+    from pandas.core.reshape.concat import concat
+
+    # Series avoids inconsistent NaN handling
+    codes, levels = factorize_from_iterable(Series(data, copy=False))
+
+    if dtype is None and hasattr(data, "dtype"):
+        input_dtype = data.dtype
+        if isinstance(input_dtype, CategoricalDtype):
+            input_dtype = input_dtype.categories.dtype
+
+        if isinstance(input_dtype, ArrowDtype):
+            import pyarrow as pa
+
+            dtype = ArrowDtype(pa.bool_())  # type: ignore[assignment]
+        elif (
+            isinstance(input_dtype, StringDtype)
+            and input_dtype.na_value is libmissing.NA
+        ):
+            dtype = pandas_dtype("boolean")  # type: ignore[assignment]
+        else:
+            dtype = np.dtype(bool)
+    elif dtype is None:
+        dtype = np.dtype(bool)
+
+    _dtype = pandas_dtype(dtype)
+
+    if is_object_dtype(_dtype):
+        raise ValueError("dtype=object is not a valid dtype for get_dummies")
+
+    def get_empty_frame(data) -> DataFrame:
+        index: Index | np.ndarray
+        if isinstance(data, Series):
+            index = data.index
+        else:
+            index = default_index(len(data))
+        return DataFrame(index=index)
+
+    # if all NaN
+    if not dummy_na and len(levels) == 0:
+        return get_empty_frame(data)
+
+    codes = codes.copy()
+    if dummy_na:
+        codes[codes == -1] = len(levels)
+        levels = levels.insert(len(levels), np.nan)
+
+    # if dummy_na, we just fake a nan level. drop_first will drop it again
+    if drop_first and len(levels) == 1:
+        return get_empty_frame(data)
+
+    number_of_cols = len(levels)
+
+    if prefix is None:
+        dummy_cols = levels
+    else:
+        dummy_cols = Index([f"{prefix}{prefix_sep}{level}" for level in levels])
+
+    index: Index | None
+    if isinstance(data, Series):
+        index = data.index
+    else:
+        index = None
+
+    if sparse:
+        fill_value: bool | float
+        if is_integer_dtype(dtype):
+            fill_value = 0
+        elif dtype == np.dtype(bool):
+            fill_value = False
+        else:
+            fill_value = 0.0
+
+        sparse_series = []
+        N = len(data)
+        sp_indices: list[list] = [[] for _ in range(len(dummy_cols))]
+        mask = codes != -1
+        codes = codes[mask]
+        n_idx = np.arange(N)[mask]
+
+        for ndx, code in zip(n_idx, codes):
+            sp_indices[code].append(ndx)
+
+        if drop_first:
+            # remove first categorical level to avoid perfect collinearity
+            # GH12042
+            sp_indices = sp_indices[1:]
+            dummy_cols = dummy_cols[1:]
+        for col, ixs in zip(dummy_cols, sp_indices):
+            sarr = SparseArray(
+                np.ones(len(ixs), dtype=dtype),
+                sparse_index=IntIndex(N, ixs),
+                fill_value=fill_value,
+                dtype=dtype,
+            )
+            sparse_series.append(Series(data=sarr, index=index, name=col, copy=False))
+
+        return concat(sparse_series, axis=1, copy=False)
+
+    else:
+        # ensure ndarray layout is column-major
+        shape = len(codes), number_of_cols
+        dummy_dtype: NpDtype
+        if isinstance(_dtype, np.dtype):
+            dummy_dtype = _dtype
+        else:
+            dummy_dtype = np.bool_
+        dummy_mat = np.zeros(shape=shape, dtype=dummy_dtype, order="F")
+        dummy_mat[np.arange(len(codes)), codes] = 1
+
+        if not dummy_na:
+            # reset NaN GH4446
+            dummy_mat[codes == -1] = 0
+
+        if drop_first:
+            # remove first GH12042
+            dummy_mat = dummy_mat[:, 1:]
+            dummy_cols = dummy_cols[1:]
+        return DataFrame(dummy_mat, index=index, columns=dummy_cols, dtype=_dtype)
+
+
+def from_dummies(
+    data: DataFrame,
+    sep: None | str = None,
+    default_category: None | Hashable | dict[str, Hashable] = None,
+) -> DataFrame:
+    """
+    Create a categorical ``DataFrame`` from a ``DataFrame`` of dummy variables.
+
+    Inverts the operation performed by :func:`~pandas.get_dummies`.
+
+    .. versionadded:: 1.5.0
+
+    Parameters
+    ----------
+    data : DataFrame
+        Data which contains dummy-coded variables in form of integer columns of
+        1's and 0's.
+    sep : str, default None
+        Separator used in the column names of the dummy categories they are
+        character indicating the separation of the categorical names from the prefixes.
+        For example, if your column names are 'prefix_A' and 'prefix_B',
+        you can strip the underscore by specifying sep='_'.
+    default_category : None, Hashable or dict of Hashables, default None
+        The default category is the implied category when a value has none of the
+        listed categories specified with a one, i.e. if all dummies in a row are
+        zero. Can be a single value for all variables or a dict directly mapping
+        the default categories to a prefix of a variable.
+
+    Returns
+    -------
+    DataFrame
+        Categorical data decoded from the dummy input-data.
+
+    Raises
+    ------
+    ValueError
+        * When the input ``DataFrame`` ``data`` contains NA values.
+        * When the input ``DataFrame`` ``data`` contains column names with separators
+          that do not match the separator specified with ``sep``.
+        * When a ``dict`` passed to ``default_category`` does not include an implied
+          category for each prefix.
+        * When a value in ``data`` has more than one category assigned to it.
+        * When ``default_category=None`` and a value in ``data`` has no category
+          assigned to it.
+    TypeError
+        * When the input ``data`` is not of type ``DataFrame``.
+        * When the input ``DataFrame`` ``data`` contains non-dummy data.
+        * When the passed ``sep`` is of a wrong data type.
+        * When the passed ``default_category`` is of a wrong data type.
+
+    See Also
+    --------
+    :func:`~pandas.get_dummies` : Convert ``Series`` or ``DataFrame`` to dummy codes.
+    :class:`~pandas.Categorical` : Represent a categorical variable in classic.
+
+    Notes
+    -----
+    The columns of the passed dummy data should only include 1's and 0's,
+    or boolean values.
+
+    Examples
+    --------
+    >>> df = pd.DataFrame({"a": [1, 0, 0, 1], "b": [0, 1, 0, 0],
+    ...                    "c": [0, 0, 1, 0]})
+
+    >>> df
+       a  b  c
+    0  1  0  0
+    1  0  1  0
+    2  0  0  1
+    3  1  0  0
+
+    >>> pd.from_dummies(df)
+    0     a
+    1     b
+    2     c
+    3     a
+
+    >>> df = pd.DataFrame({"col1_a": [1, 0, 1], "col1_b": [0, 1, 0],
+    ...                    "col2_a": [0, 1, 0], "col2_b": [1, 0, 0],
+    ...                    "col2_c": [0, 0, 1]})
+
+    >>> df
+          col1_a  col1_b  col2_a  col2_b  col2_c
+    0       1       0       0       1       0
+    1       0       1       1       0       0
+    2       1       0       0       0       1
+
+    >>> pd.from_dummies(df, sep="_")
+        col1    col2
+    0    a       b
+    1    b       a
+    2    a       c
+
+    >>> df = pd.DataFrame({"col1_a": [1, 0, 0], "col1_b": [0, 1, 0],
+    ...                    "col2_a": [0, 1, 0], "col2_b": [1, 0, 0],
+    ...                    "col2_c": [0, 0, 0]})
+
+    >>> df
+          col1_a  col1_b  col2_a  col2_b  col2_c
+    0       1       0       0       1       0
+    1       0       1       1       0       0
+    2       0       0       0       0       0
+
+    >>> pd.from_dummies(df, sep="_", default_category={"col1": "d", "col2": "e"})
+        col1    col2
+    0    a       b
+    1    b       a
+    2    d       e
+    """
+    from pandas.core.reshape.concat import concat
+
+    if not isinstance(data, DataFrame):
+        raise TypeError(
+            "Expected 'data' to be a 'DataFrame'; "
+            f"Received 'data' of type: {type(data).__name__}"
+        )
+
+    col_isna_mask = cast(Series, data.isna().any())
+
+    if col_isna_mask.any():
+        raise ValueError(
+            "Dummy DataFrame contains NA value in column: "
+            f"'{col_isna_mask.idxmax()}'"
+        )
+
+    # index data with a list of all columns that are dummies
+    try:
+        data_to_decode = data.astype("boolean", copy=False)
+    except TypeError:
+        raise TypeError("Passed DataFrame contains non-dummy data")
+
+    # collect prefixes and get lists to slice data for each prefix
+    variables_slice = defaultdict(list)
+    if sep is None:
+        variables_slice[""] = list(data.columns)
+    elif isinstance(sep, str):
+        for col in data_to_decode.columns:
+            prefix = col.split(sep)[0]
+            if len(prefix) == len(col):
+                raise ValueError(f"Separator not specified for column: {col}")
+            variables_slice[prefix].append(col)
+    else:
+        raise TypeError(
+            "Expected 'sep' to be of type 'str' or 'None'; "
+            f"Received 'sep' of type: {type(sep).__name__}"
+        )
+
+    if default_category is not None:
+        if isinstance(default_category, dict):
+            if not len(default_category) == len(variables_slice):
+                len_msg = (
+                    f"Length of 'default_category' ({len(default_category)}) "
+                    f"did not match the length of the columns being encoded "
+                    f"({len(variables_slice)})"
+                )
+                raise ValueError(len_msg)
+        elif isinstance(default_category, Hashable):
+            default_category = dict(
+                zip(variables_slice, [default_category] * len(variables_slice))
+            )
+        else:
+            raise TypeError(
+                "Expected 'default_category' to be of type "
+                "'None', 'Hashable', or 'dict'; "
+                "Received 'default_category' of type: "
+                f"{type(default_category).__name__}"
+            )
+
+    cat_data = {}
+    for prefix, prefix_slice in variables_slice.items():
+        if sep is None:
+            cats = prefix_slice.copy()
+        else:
+            cats = [col[len(prefix + sep) :] for col in prefix_slice]
+        assigned = data_to_decode.loc[:, prefix_slice].sum(axis=1)
+        if any(assigned > 1):
+            raise ValueError(
+                "Dummy DataFrame contains multi-assignment(s); "
+                f"First instance in row: {assigned.idxmax()}"
+            )
+        if any(assigned == 0):
+            if isinstance(default_category, dict):
+                cats.append(default_category[prefix])
+            else:
+                raise ValueError(
+                    "Dummy DataFrame contains unassigned value(s); "
+                    f"First instance in row: {assigned.idxmin()}"
+                )
+            data_slice = concat(
+                (data_to_decode.loc[:, prefix_slice], assigned == 0), axis=1
+            )
+        else:
+            data_slice = data_to_decode.loc[:, prefix_slice]
+        cats_array = data._constructor_sliced(cats, dtype=data.columns.dtype)
+        # get indices of True entries along axis=1
+        true_values = data_slice.idxmax(axis=1)
+        indexer = data_slice.columns.get_indexer_for(true_values)
+        cat_data[prefix] = cats_array.take(indexer).set_axis(data.index)
+
+    result = DataFrame(cat_data)
+    if sep is not None:
+        result.columns = result.columns.astype(data.columns.dtype)
+    return result
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/reshape/melt.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/reshape/melt.py
new file mode 100644
index 0000000000000000000000000000000000000000..e54f847895f1a42cf1782392da684f2bcfa7e81c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/reshape/melt.py
@@ -0,0 +1,512 @@
+from __future__ import annotations
+
+import re
+from typing import TYPE_CHECKING
+
+import numpy as np
+
+from pandas.util._decorators import Appender
+
+from pandas.core.dtypes.common import is_list_like
+from pandas.core.dtypes.concat import concat_compat
+from pandas.core.dtypes.missing import notna
+
+import pandas.core.algorithms as algos
+from pandas.core.indexes.api import MultiIndex
+from pandas.core.reshape.concat import concat
+from pandas.core.reshape.util import tile_compat
+from pandas.core.shared_docs import _shared_docs
+from pandas.core.tools.numeric import to_numeric
+
+if TYPE_CHECKING:
+    from collections.abc import Hashable
+
+    from pandas._typing import AnyArrayLike
+
+    from pandas import DataFrame
+
+
+def ensure_list_vars(arg_vars, variable: str, columns) -> list:
+    if arg_vars is not None:
+        if not is_list_like(arg_vars):
+            return [arg_vars]
+        elif isinstance(columns, MultiIndex) and not isinstance(arg_vars, list):
+            raise ValueError(
+                f"{variable} must be a list of tuples when columns are a MultiIndex"
+            )
+        else:
+            return list(arg_vars)
+    else:
+        return []
+
+
+@Appender(_shared_docs["melt"] % {"caller": "pd.melt(df, ", "other": "DataFrame.melt"})
+def melt(
+    frame: DataFrame,
+    id_vars=None,
+    value_vars=None,
+    var_name=None,
+    value_name: Hashable = "value",
+    col_level=None,
+    ignore_index: bool = True,
+) -> DataFrame:
+    if value_name in frame.columns:
+        raise ValueError(
+            f"value_name ({value_name}) cannot match an element in "
+            "the DataFrame columns."
+        )
+    id_vars = ensure_list_vars(id_vars, "id_vars", frame.columns)
+    value_vars_was_not_none = value_vars is not None
+    value_vars = ensure_list_vars(value_vars, "value_vars", frame.columns)
+
+    if id_vars or value_vars:
+        if col_level is not None:
+            level = frame.columns.get_level_values(col_level)
+        else:
+            level = frame.columns
+        labels = id_vars + value_vars
+        idx = level.get_indexer_for(labels)
+        missing = idx == -1
+        if missing.any():
+            missing_labels = [
+                lab for lab, not_found in zip(labels, missing) if not_found
+            ]
+            raise KeyError(
+                "The following id_vars or value_vars are not present in "
+                f"the DataFrame: {missing_labels}"
+            )
+        if value_vars_was_not_none:
+            frame = frame.iloc[:, algos.unique(idx)]
+        else:
+            frame = frame.copy()
+    else:
+        frame = frame.copy()
+
+    if col_level is not None:  # allow list or other?
+        # frame is a copy
+        frame.columns = frame.columns.get_level_values(col_level)
+
+    if var_name is None:
+        if isinstance(frame.columns, MultiIndex):
+            if len(frame.columns.names) == len(set(frame.columns.names)):
+                var_name = frame.columns.names
+            else:
+                var_name = [f"variable_{i}" for i in range(len(frame.columns.names))]
+        else:
+            var_name = [
+                frame.columns.name if frame.columns.name is not None else "variable"
+            ]
+    elif is_list_like(var_name):
+        raise ValueError(f"{var_name=} must be a scalar.")
+    else:
+        var_name = [var_name]
+
+    num_rows, K = frame.shape
+    num_cols_adjusted = K - len(id_vars)
+
+    mdata: dict[Hashable, AnyArrayLike] = {}
+    for col in id_vars:
+        id_data = frame.pop(col)
+        if not isinstance(id_data.dtype, np.dtype):
+            # i.e. ExtensionDtype
+            if num_cols_adjusted > 0:
+                mdata[col] = concat([id_data] * num_cols_adjusted, ignore_index=True)
+            else:
+                # We can't concat empty list. (GH 46044)
+                mdata[col] = type(id_data)([], name=id_data.name, dtype=id_data.dtype)
+        else:
+            mdata[col] = np.tile(id_data._values, num_cols_adjusted)
+
+    mcolumns = id_vars + var_name + [value_name]
+
+    if frame.shape[1] > 0 and not any(
+        not isinstance(dt, np.dtype) and dt._supports_2d for dt in frame.dtypes
+    ):
+        mdata[value_name] = concat(
+            [frame.iloc[:, i] for i in range(frame.shape[1])]
+        ).values
+    else:
+        mdata[value_name] = frame._values.ravel("F")
+    for i, col in enumerate(var_name):
+        mdata[col] = frame.columns._get_level_values(i).repeat(num_rows)
+
+    result = frame._constructor(mdata, columns=mcolumns)
+
+    if not ignore_index:
+        result.index = tile_compat(frame.index, num_cols_adjusted)
+
+    return result
+
+
+def lreshape(data: DataFrame, groups: dict, dropna: bool = True) -> DataFrame:
+    """
+    Reshape wide-format data to long. Generalized inverse of DataFrame.pivot.
+
+    Accepts a dictionary, ``groups``, in which each key is a new column name
+    and each value is a list of old column names that will be "melted" under
+    the new column name as part of the reshape.
+
+    Parameters
+    ----------
+    data : DataFrame
+        The wide-format DataFrame.
+    groups : dict
+        {new_name : list_of_columns}.
+    dropna : bool, default True
+        Do not include columns whose entries are all NaN.
+
+    Returns
+    -------
+    DataFrame
+        Reshaped DataFrame.
+
+    See Also
+    --------
+    melt : Unpivot a DataFrame from wide to long format, optionally leaving
+        identifiers set.
+    pivot : Create a spreadsheet-style pivot table as a DataFrame.
+    DataFrame.pivot : Pivot without aggregation that can handle
+        non-numeric data.
+    DataFrame.pivot_table : Generalization of pivot that can handle
+        duplicate values for one index/column pair.
+    DataFrame.unstack : Pivot based on the index values instead of a
+        column.
+    wide_to_long : Wide panel to long format. Less flexible but more
+        user-friendly than melt.
+
+    Examples
+    --------
+    >>> data = pd.DataFrame({'hr1': [514, 573], 'hr2': [545, 526],
+    ...                      'team': ['Red Sox', 'Yankees'],
+    ...                      'year1': [2007, 2007], 'year2': [2008, 2008]})
+    >>> data
+       hr1  hr2     team  year1  year2
+    0  514  545  Red Sox   2007   2008
+    1  573  526  Yankees   2007   2008
+
+    >>> pd.lreshape(data, {'year': ['year1', 'year2'], 'hr': ['hr1', 'hr2']})
+          team  year   hr
+    0  Red Sox  2007  514
+    1  Yankees  2007  573
+    2  Red Sox  2008  545
+    3  Yankees  2008  526
+    """
+    mdata = {}
+    pivot_cols = []
+    all_cols: set[Hashable] = set()
+    K = len(next(iter(groups.values())))
+    for target, names in groups.items():
+        if len(names) != K:
+            raise ValueError("All column lists must be same length")
+        to_concat = [data[col]._values for col in names]
+
+        mdata[target] = concat_compat(to_concat)
+        pivot_cols.append(target)
+        all_cols = all_cols.union(names)
+
+    id_cols = list(data.columns.difference(all_cols))
+    for col in id_cols:
+        mdata[col] = np.tile(data[col]._values, K)
+
+    if dropna:
+        mask = np.ones(len(mdata[pivot_cols[0]]), dtype=bool)
+        for c in pivot_cols:
+            mask &= notna(mdata[c])
+        if not mask.all():
+            mdata = {k: v[mask] for k, v in mdata.items()}
+
+    return data._constructor(mdata, columns=id_cols + pivot_cols)
+
+
+def wide_to_long(
+    df: DataFrame, stubnames, i, j, sep: str = "", suffix: str = r"\d+"
+) -> DataFrame:
+    r"""
+    Unpivot a DataFrame from wide to long format.
+
+    Less flexible but more user-friendly than melt.
+
+    With stubnames ['A', 'B'], this function expects to find one or more
+    group of columns with format
+    A-suffix1, A-suffix2,..., B-suffix1, B-suffix2,...
+    You specify what you want to call this suffix in the resulting long format
+    with `j` (for example `j='year'`)
+
+    Each row of these wide variables are assumed to be uniquely identified by
+    `i` (can be a single column name or a list of column names)
+
+    All remaining variables in the data frame are left intact.
+
+    Parameters
+    ----------
+    df : DataFrame
+        The wide-format DataFrame.
+    stubnames : str or list-like
+        The stub name(s). The wide format variables are assumed to
+        start with the stub names.
+    i : str or list-like
+        Column(s) to use as id variable(s).
+    j : str
+        The name of the sub-observation variable. What you wish to name your
+        suffix in the long format.
+    sep : str, default ""
+        A character indicating the separation of the variable names
+        in the wide format, to be stripped from the names in the long format.
+        For example, if your column names are A-suffix1, A-suffix2, you
+        can strip the hyphen by specifying `sep='-'`.
+    suffix : str, default '\\d+'
+        A regular expression capturing the wanted suffixes. '\\d+' captures
+        numeric suffixes. Suffixes with no numbers could be specified with the
+        negated character class '\\D+'. You can also further disambiguate
+        suffixes, for example, if your wide variables are of the form A-one,
+        B-two,.., and you have an unrelated column A-rating, you can ignore the
+        last one by specifying `suffix='(!?one|two)'`. When all suffixes are
+        numeric, they are cast to int64/float64.
+
+    Returns
+    -------
+    DataFrame
+        A DataFrame that contains each stub name as a variable, with new index
+        (i, j).
+
+    See Also
+    --------
+    melt : Unpivot a DataFrame from wide to long format, optionally leaving
+        identifiers set.
+    pivot : Create a spreadsheet-style pivot table as a DataFrame.
+    DataFrame.pivot : Pivot without aggregation that can handle
+        non-numeric data.
+    DataFrame.pivot_table : Generalization of pivot that can handle
+        duplicate values for one index/column pair.
+    DataFrame.unstack : Pivot based on the index values instead of a
+        column.
+
+    Notes
+    -----
+    All extra variables are left untouched. This simply uses
+    `pandas.melt` under the hood, but is hard-coded to "do the right thing"
+    in a typical case.
+
+    Examples
+    --------
+    >>> np.random.seed(123)
+    >>> df = pd.DataFrame({"A1970" : {0 : "a", 1 : "b", 2 : "c"},
+    ...                    "A1980" : {0 : "d", 1 : "e", 2 : "f"},
+    ...                    "B1970" : {0 : 2.5, 1 : 1.2, 2 : .7},
+    ...                    "B1980" : {0 : 3.2, 1 : 1.3, 2 : .1},
+    ...                    "X"     : dict(zip(range(3), np.random.randn(3)))
+    ...                   })
+    >>> df["id"] = df.index
+    >>> df
+      A1970 A1980  B1970  B1980         X  id
+    0     a     d    2.5    3.2 -1.085631   0
+    1     b     e    1.2    1.3  0.997345   1
+    2     c     f    0.7    0.1  0.282978   2
+    >>> pd.wide_to_long(df, ["A", "B"], i="id", j="year")
+    ... # doctest: +NORMALIZE_WHITESPACE
+                    X  A    B
+    id year
+    0  1970 -1.085631  a  2.5
+    1  1970  0.997345  b  1.2
+    2  1970  0.282978  c  0.7
+    0  1980 -1.085631  d  3.2
+    1  1980  0.997345  e  1.3
+    2  1980  0.282978  f  0.1
+
+    With multiple id columns
+
+    >>> df = pd.DataFrame({
+    ...     'famid': [1, 1, 1, 2, 2, 2, 3, 3, 3],
+    ...     'birth': [1, 2, 3, 1, 2, 3, 1, 2, 3],
+    ...     'ht1': [2.8, 2.9, 2.2, 2, 1.8, 1.9, 2.2, 2.3, 2.1],
+    ...     'ht2': [3.4, 3.8, 2.9, 3.2, 2.8, 2.4, 3.3, 3.4, 2.9]
+    ... })
+    >>> df
+       famid  birth  ht1  ht2
+    0      1      1  2.8  3.4
+    1      1      2  2.9  3.8
+    2      1      3  2.2  2.9
+    3      2      1  2.0  3.2
+    4      2      2  1.8  2.8
+    5      2      3  1.9  2.4
+    6      3      1  2.2  3.3
+    7      3      2  2.3  3.4
+    8      3      3  2.1  2.9
+    >>> l = pd.wide_to_long(df, stubnames='ht', i=['famid', 'birth'], j='age')
+    >>> l
+    ... # doctest: +NORMALIZE_WHITESPACE
+                      ht
+    famid birth age
+    1     1     1    2.8
+                2    3.4
+          2     1    2.9
+                2    3.8
+          3     1    2.2
+                2    2.9
+    2     1     1    2.0
+                2    3.2
+          2     1    1.8
+                2    2.8
+          3     1    1.9
+                2    2.4
+    3     1     1    2.2
+                2    3.3
+          2     1    2.3
+                2    3.4
+          3     1    2.1
+                2    2.9
+
+    Going from long back to wide just takes some creative use of `unstack`
+
+    >>> w = l.unstack()
+    >>> w.columns = w.columns.map('{0[0]}{0[1]}'.format)
+    >>> w.reset_index()
+       famid  birth  ht1  ht2
+    0      1      1  2.8  3.4
+    1      1      2  2.9  3.8
+    2      1      3  2.2  2.9
+    3      2      1  2.0  3.2
+    4      2      2  1.8  2.8
+    5      2      3  1.9  2.4
+    6      3      1  2.2  3.3
+    7      3      2  2.3  3.4
+    8      3      3  2.1  2.9
+
+    Less wieldy column names are also handled
+
+    >>> np.random.seed(0)
+    >>> df = pd.DataFrame({'A(weekly)-2010': np.random.rand(3),
+    ...                    'A(weekly)-2011': np.random.rand(3),
+    ...                    'B(weekly)-2010': np.random.rand(3),
+    ...                    'B(weekly)-2011': np.random.rand(3),
+    ...                    'X' : np.random.randint(3, size=3)})
+    >>> df['id'] = df.index
+    >>> df # doctest: +NORMALIZE_WHITESPACE, +ELLIPSIS
+       A(weekly)-2010  A(weekly)-2011  B(weekly)-2010  B(weekly)-2011  X  id
+    0        0.548814        0.544883        0.437587        0.383442  0   0
+    1        0.715189        0.423655        0.891773        0.791725  1   1
+    2        0.602763        0.645894        0.963663        0.528895  1   2
+
+    >>> pd.wide_to_long(df, ['A(weekly)', 'B(weekly)'], i='id',
+    ...                 j='year', sep='-')
+    ... # doctest: +NORMALIZE_WHITESPACE
+             X  A(weekly)  B(weekly)
+    id year
+    0  2010  0   0.548814   0.437587
+    1  2010  1   0.715189   0.891773
+    2  2010  1   0.602763   0.963663
+    0  2011  0   0.544883   0.383442
+    1  2011  1   0.423655   0.791725
+    2  2011  1   0.645894   0.528895
+
+    If we have many columns, we could also use a regex to find our
+    stubnames and pass that list on to wide_to_long
+
+    >>> stubnames = sorted(
+    ...     set([match[0] for match in df.columns.str.findall(
+    ...         r'[A-B]\(.*\)').values if match != []])
+    ... )
+    >>> list(stubnames)
+    ['A(weekly)', 'B(weekly)']
+
+    All of the above examples have integers as suffixes. It is possible to
+    have non-integers as suffixes.
+
+    >>> df = pd.DataFrame({
+    ...     'famid': [1, 1, 1, 2, 2, 2, 3, 3, 3],
+    ...     'birth': [1, 2, 3, 1, 2, 3, 1, 2, 3],
+    ...     'ht_one': [2.8, 2.9, 2.2, 2, 1.8, 1.9, 2.2, 2.3, 2.1],
+    ...     'ht_two': [3.4, 3.8, 2.9, 3.2, 2.8, 2.4, 3.3, 3.4, 2.9]
+    ... })
+    >>> df
+       famid  birth  ht_one  ht_two
+    0      1      1     2.8     3.4
+    1      1      2     2.9     3.8
+    2      1      3     2.2     2.9
+    3      2      1     2.0     3.2
+    4      2      2     1.8     2.8
+    5      2      3     1.9     2.4
+    6      3      1     2.2     3.3
+    7      3      2     2.3     3.4
+    8      3      3     2.1     2.9
+
+    >>> l = pd.wide_to_long(df, stubnames='ht', i=['famid', 'birth'], j='age',
+    ...                     sep='_', suffix=r'\w+')
+    >>> l
+    ... # doctest: +NORMALIZE_WHITESPACE
+                      ht
+    famid birth age
+    1     1     one  2.8
+                two  3.4
+          2     one  2.9
+                two  3.8
+          3     one  2.2
+                two  2.9
+    2     1     one  2.0
+                two  3.2
+          2     one  1.8
+                two  2.8
+          3     one  1.9
+                two  2.4
+    3     1     one  2.2
+                two  3.3
+          2     one  2.3
+                two  3.4
+          3     one  2.1
+                two  2.9
+    """
+
+    def get_var_names(df, stub: str, sep: str, suffix: str):
+        regex = rf"^{re.escape(stub)}{re.escape(sep)}{suffix}$"
+        return df.columns[df.columns.str.match(regex)]
+
+    def melt_stub(df, stub: str, i, j, value_vars, sep: str):
+        newdf = melt(
+            df,
+            id_vars=i,
+            value_vars=value_vars,
+            value_name=stub.rstrip(sep),
+            var_name=j,
+        )
+        newdf[j] = newdf[j].str.replace(re.escape(stub + sep), "", regex=True)
+
+        # GH17627 Cast numerics suffixes to int/float
+        try:
+            newdf[j] = to_numeric(newdf[j])
+        except (TypeError, ValueError, OverflowError):
+            # TODO: anything else to catch?
+            pass
+
+        return newdf.set_index(i + [j])
+
+    if not is_list_like(stubnames):
+        stubnames = [stubnames]
+    else:
+        stubnames = list(stubnames)
+
+    if df.columns.isin(stubnames).any():
+        raise ValueError("stubname can't be identical to a column name")
+
+    if not is_list_like(i):
+        i = [i]
+    else:
+        i = list(i)
+
+    if df[i].duplicated().any():
+        raise ValueError("the id variables need to uniquely identify each row")
+
+    _melted = []
+    value_vars_flattened = []
+    for stub in stubnames:
+        value_var = get_var_names(df, stub, sep, suffix)
+        value_vars_flattened.extend(value_var)
+        _melted.append(melt_stub(df, stub, i, j, value_var, sep))
+
+    melted = concat(_melted, axis=1)
+    id_vars = df.columns.difference(value_vars_flattened)
+    new = df[id_vars]
+
+    if len(i) == 1:
+        return new.set_index(i).join(melted)
+    else:
+        return new.merge(melted.reset_index(), on=i).set_index(i + [j])
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/reshape/merge.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/reshape/merge.py
new file mode 100644
index 0000000000000000000000000000000000000000..a1e003c2001d24de56a066f30f17b7b60a8ed8f8
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/reshape/merge.py
@@ -0,0 +1,2762 @@
+"""
+SQL-style merge routines
+"""
+from __future__ import annotations
+
+from collections.abc import (
+    Hashable,
+    Sequence,
+)
+import datetime
+from functools import partial
+from typing import (
+    TYPE_CHECKING,
+    Literal,
+    cast,
+    final,
+)
+import uuid
+import warnings
+
+import numpy as np
+
+from pandas._libs import (
+    Timedelta,
+    hashtable as libhashtable,
+    join as libjoin,
+    lib,
+)
+from pandas._libs.lib import is_range_indexer
+from pandas._typing import (
+    AnyArrayLike,
+    ArrayLike,
+    IndexLabel,
+    JoinHow,
+    MergeHow,
+    Shape,
+    Suffixes,
+    npt,
+)
+from pandas.errors import MergeError
+from pandas.util._decorators import (
+    Appender,
+    Substitution,
+    cache_readonly,
+)
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.base import ExtensionDtype
+from pandas.core.dtypes.cast import find_common_type
+from pandas.core.dtypes.common import (
+    ensure_int64,
+    ensure_object,
+    is_bool,
+    is_bool_dtype,
+    is_float_dtype,
+    is_integer,
+    is_integer_dtype,
+    is_list_like,
+    is_number,
+    is_numeric_dtype,
+    is_object_dtype,
+    is_string_dtype,
+    needs_i8_conversion,
+)
+from pandas.core.dtypes.dtypes import (
+    CategoricalDtype,
+    DatetimeTZDtype,
+)
+from pandas.core.dtypes.generic import (
+    ABCDataFrame,
+    ABCSeries,
+)
+from pandas.core.dtypes.missing import (
+    isna,
+    na_value_for_dtype,
+)
+
+from pandas import (
+    ArrowDtype,
+    Categorical,
+    Index,
+    MultiIndex,
+    Series,
+)
+import pandas.core.algorithms as algos
+from pandas.core.arrays import (
+    ArrowExtensionArray,
+    BaseMaskedArray,
+    ExtensionArray,
+)
+from pandas.core.arrays.string_ import StringDtype
+import pandas.core.common as com
+from pandas.core.construction import (
+    ensure_wrapped_if_datetimelike,
+    extract_array,
+)
+from pandas.core.frame import _merge_doc
+from pandas.core.indexes.api import default_index
+from pandas.core.sorting import (
+    get_group_index,
+    is_int64_overflow_possible,
+)
+
+if TYPE_CHECKING:
+    from pandas import DataFrame
+    from pandas.core import groupby
+    from pandas.core.arrays import DatetimeArray
+    from pandas.core.indexes.frozen import FrozenList
+
+_factorizers = {
+    np.int64: libhashtable.Int64Factorizer,
+    np.longlong: libhashtable.Int64Factorizer,
+    np.int32: libhashtable.Int32Factorizer,
+    np.int16: libhashtable.Int16Factorizer,
+    np.int8: libhashtable.Int8Factorizer,
+    np.uint64: libhashtable.UInt64Factorizer,
+    np.uint32: libhashtable.UInt32Factorizer,
+    np.uint16: libhashtable.UInt16Factorizer,
+    np.uint8: libhashtable.UInt8Factorizer,
+    np.bool_: libhashtable.UInt8Factorizer,
+    np.float64: libhashtable.Float64Factorizer,
+    np.float32: libhashtable.Float32Factorizer,
+    np.complex64: libhashtable.Complex64Factorizer,
+    np.complex128: libhashtable.Complex128Factorizer,
+    np.object_: libhashtable.ObjectFactorizer,
+}
+
+# See https://github.com/pandas-dev/pandas/issues/52451
+if np.intc is not np.int32:
+    _factorizers[np.intc] = libhashtable.Int64Factorizer
+
+_known = (np.ndarray, ExtensionArray, Index, ABCSeries)
+
+
+@Substitution("\nleft : DataFrame or named Series")
+@Appender(_merge_doc, indents=0)
+def merge(
+    left: DataFrame | Series,
+    right: DataFrame | Series,
+    how: MergeHow = "inner",
+    on: IndexLabel | AnyArrayLike | None = None,
+    left_on: IndexLabel | AnyArrayLike | None = None,
+    right_on: IndexLabel | AnyArrayLike | None = None,
+    left_index: bool = False,
+    right_index: bool = False,
+    sort: bool = False,
+    suffixes: Suffixes = ("_x", "_y"),
+    copy: bool | None = None,
+    indicator: str | bool = False,
+    validate: str | None = None,
+) -> DataFrame:
+    left_df = _validate_operand(left)
+    right_df = _validate_operand(right)
+    if how == "cross":
+        return _cross_merge(
+            left_df,
+            right_df,
+            on=on,
+            left_on=left_on,
+            right_on=right_on,
+            left_index=left_index,
+            right_index=right_index,
+            sort=sort,
+            suffixes=suffixes,
+            indicator=indicator,
+            validate=validate,
+            copy=copy,
+        )
+    else:
+        op = _MergeOperation(
+            left_df,
+            right_df,
+            how=how,
+            on=on,
+            left_on=left_on,
+            right_on=right_on,
+            left_index=left_index,
+            right_index=right_index,
+            sort=sort,
+            suffixes=suffixes,
+            indicator=indicator,
+            validate=validate,
+        )
+        return op.get_result(copy=copy)
+
+
+def _cross_merge(
+    left: DataFrame,
+    right: DataFrame,
+    on: IndexLabel | AnyArrayLike | None = None,
+    left_on: IndexLabel | AnyArrayLike | None = None,
+    right_on: IndexLabel | AnyArrayLike | None = None,
+    left_index: bool = False,
+    right_index: bool = False,
+    sort: bool = False,
+    suffixes: Suffixes = ("_x", "_y"),
+    copy: bool | None = None,
+    indicator: str | bool = False,
+    validate: str | None = None,
+) -> DataFrame:
+    """
+    See merge.__doc__ with how='cross'
+    """
+
+    if (
+        left_index
+        or right_index
+        or right_on is not None
+        or left_on is not None
+        or on is not None
+    ):
+        raise MergeError(
+            "Can not pass on, right_on, left_on or set right_index=True or "
+            "left_index=True"
+        )
+
+    cross_col = f"_cross_{uuid.uuid4()}"
+    left = left.assign(**{cross_col: 1})
+    right = right.assign(**{cross_col: 1})
+
+    left_on = right_on = [cross_col]
+
+    res = merge(
+        left,
+        right,
+        how="inner",
+        on=on,
+        left_on=left_on,
+        right_on=right_on,
+        left_index=left_index,
+        right_index=right_index,
+        sort=sort,
+        suffixes=suffixes,
+        indicator=indicator,
+        validate=validate,
+        copy=copy,
+    )
+    del res[cross_col]
+    return res
+
+
+def _groupby_and_merge(
+    by, left: DataFrame | Series, right: DataFrame | Series, merge_pieces
+):
+    """
+    groupby & merge; we are always performing a left-by type operation
+
+    Parameters
+    ----------
+    by: field to group
+    left: DataFrame
+    right: DataFrame
+    merge_pieces: function for merging
+    """
+    pieces = []
+    if not isinstance(by, (list, tuple)):
+        by = [by]
+
+    lby = left.groupby(by, sort=False)
+    rby: groupby.DataFrameGroupBy | groupby.SeriesGroupBy | None = None
+
+    # if we can groupby the rhs
+    # then we can get vastly better perf
+    if all(item in right.columns for item in by):
+        rby = right.groupby(by, sort=False)
+
+    for key, lhs in lby._grouper.get_iterator(lby._selected_obj, axis=lby.axis):
+        if rby is None:
+            rhs = right
+        else:
+            try:
+                rhs = right.take(rby.indices[key])
+            except KeyError:
+                # key doesn't exist in left
+                lcols = lhs.columns.tolist()
+                cols = lcols + [r for r in right.columns if r not in set(lcols)]
+                merged = lhs.reindex(columns=cols)
+                merged.index = range(len(merged))
+                pieces.append(merged)
+                continue
+
+        merged = merge_pieces(lhs, rhs)
+
+        # make sure join keys are in the merged
+        # TODO, should merge_pieces do this?
+        merged[by] = key
+
+        pieces.append(merged)
+
+    # preserve the original order
+    # if we have a missing piece this can be reset
+    from pandas.core.reshape.concat import concat
+
+    result = concat(pieces, ignore_index=True)
+    result = result.reindex(columns=pieces[0].columns, copy=False)
+    return result, lby
+
+
+def merge_ordered(
+    left: DataFrame | Series,
+    right: DataFrame | Series,
+    on: IndexLabel | None = None,
+    left_on: IndexLabel | None = None,
+    right_on: IndexLabel | None = None,
+    left_by=None,
+    right_by=None,
+    fill_method: str | None = None,
+    suffixes: Suffixes = ("_x", "_y"),
+    how: JoinHow = "outer",
+) -> DataFrame:
+    """
+    Perform a merge for ordered data with optional filling/interpolation.
+
+    Designed for ordered data like time series data. Optionally
+    perform group-wise merge (see examples).
+
+    Parameters
+    ----------
+    left : DataFrame or named Series
+    right : DataFrame or named Series
+    on : label or list
+        Field names to join on. Must be found in both DataFrames.
+    left_on : label or list, or array-like
+        Field names to join on in left DataFrame. Can be a vector or list of
+        vectors of the length of the DataFrame to use a particular vector as
+        the join key instead of columns.
+    right_on : label or list, or array-like
+        Field names to join on in right DataFrame or vector/list of vectors per
+        left_on docs.
+    left_by : column name or list of column names
+        Group left DataFrame by group columns and merge piece by piece with
+        right DataFrame. Must be None if either left or right are a Series.
+    right_by : column name or list of column names
+        Group right DataFrame by group columns and merge piece by piece with
+        left DataFrame. Must be None if either left or right are a Series.
+    fill_method : {'ffill', None}, default None
+        Interpolation method for data.
+    suffixes : list-like, default is ("_x", "_y")
+        A length-2 sequence where each element is optionally a string
+        indicating the suffix to add to overlapping column names in
+        `left` and `right` respectively. Pass a value of `None` instead
+        of a string to indicate that the column name from `left` or
+        `right` should be left as-is, with no suffix. At least one of the
+        values must not be None.
+
+    how : {'left', 'right', 'outer', 'inner'}, default 'outer'
+        * left: use only keys from left frame (SQL: left outer join)
+        * right: use only keys from right frame (SQL: right outer join)
+        * outer: use union of keys from both frames (SQL: full outer join)
+        * inner: use intersection of keys from both frames (SQL: inner join).
+
+    Returns
+    -------
+    DataFrame
+        The merged DataFrame output type will be the same as
+        'left', if it is a subclass of DataFrame.
+
+    See Also
+    --------
+    merge : Merge with a database-style join.
+    merge_asof : Merge on nearest keys.
+
+    Examples
+    --------
+    >>> from pandas import merge_ordered
+    >>> df1 = pd.DataFrame(
+    ...     {
+    ...         "key": ["a", "c", "e", "a", "c", "e"],
+    ...         "lvalue": [1, 2, 3, 1, 2, 3],
+    ...         "group": ["a", "a", "a", "b", "b", "b"]
+    ...     }
+    ... )
+    >>> df1
+      key  lvalue group
+    0   a       1     a
+    1   c       2     a
+    2   e       3     a
+    3   a       1     b
+    4   c       2     b
+    5   e       3     b
+
+    >>> df2 = pd.DataFrame({"key": ["b", "c", "d"], "rvalue": [1, 2, 3]})
+    >>> df2
+      key  rvalue
+    0   b       1
+    1   c       2
+    2   d       3
+
+    >>> merge_ordered(df1, df2, fill_method="ffill", left_by="group")
+      key  lvalue group  rvalue
+    0   a       1     a     NaN
+    1   b       1     a     1.0
+    2   c       2     a     2.0
+    3   d       2     a     3.0
+    4   e       3     a     3.0
+    5   a       1     b     NaN
+    6   b       1     b     1.0
+    7   c       2     b     2.0
+    8   d       2     b     3.0
+    9   e       3     b     3.0
+    """
+
+    def _merger(x, y) -> DataFrame:
+        # perform the ordered merge operation
+        op = _OrderedMerge(
+            x,
+            y,
+            on=on,
+            left_on=left_on,
+            right_on=right_on,
+            suffixes=suffixes,
+            fill_method=fill_method,
+            how=how,
+        )
+        return op.get_result()
+
+    if left_by is not None and right_by is not None:
+        raise ValueError("Can only group either left or right frames")
+    if left_by is not None:
+        if isinstance(left_by, str):
+            left_by = [left_by]
+        check = set(left_by).difference(left.columns)
+        if len(check) != 0:
+            raise KeyError(f"{check} not found in left columns")
+        result, _ = _groupby_and_merge(left_by, left, right, lambda x, y: _merger(x, y))
+    elif right_by is not None:
+        if isinstance(right_by, str):
+            right_by = [right_by]
+        check = set(right_by).difference(right.columns)
+        if len(check) != 0:
+            raise KeyError(f"{check} not found in right columns")
+        result, _ = _groupby_and_merge(
+            right_by, right, left, lambda x, y: _merger(y, x)
+        )
+    else:
+        result = _merger(left, right)
+    return result
+
+
+def merge_asof(
+    left: DataFrame | Series,
+    right: DataFrame | Series,
+    on: IndexLabel | None = None,
+    left_on: IndexLabel | None = None,
+    right_on: IndexLabel | None = None,
+    left_index: bool = False,
+    right_index: bool = False,
+    by=None,
+    left_by=None,
+    right_by=None,
+    suffixes: Suffixes = ("_x", "_y"),
+    tolerance: int | Timedelta | None = None,
+    allow_exact_matches: bool = True,
+    direction: str = "backward",
+) -> DataFrame:
+    """
+    Perform a merge by key distance.
+
+    This is similar to a left-join except that we match on nearest
+    key rather than equal keys. Both DataFrames must be sorted by the key.
+
+    For each row in the left DataFrame:
+
+      - A "backward" search selects the last row in the right DataFrame whose
+        'on' key is less than or equal to the left's key.
+
+      - A "forward" search selects the first row in the right DataFrame whose
+        'on' key is greater than or equal to the left's key.
+
+      - A "nearest" search selects the row in the right DataFrame whose 'on'
+        key is closest in absolute distance to the left's key.
+
+    Optionally match on equivalent keys with 'by' before searching with 'on'.
+
+    Parameters
+    ----------
+    left : DataFrame or named Series
+    right : DataFrame or named Series
+    on : label
+        Field name to join on. Must be found in both DataFrames.
+        The data MUST be ordered. Furthermore this must be a numeric column,
+        such as datetimelike, integer, or float. On or left_on/right_on
+        must be given.
+    left_on : label
+        Field name to join on in left DataFrame.
+    right_on : label
+        Field name to join on in right DataFrame.
+    left_index : bool
+        Use the index of the left DataFrame as the join key.
+    right_index : bool
+        Use the index of the right DataFrame as the join key.
+    by : column name or list of column names
+        Match on these columns before performing merge operation.
+    left_by : column name
+        Field names to match on in the left DataFrame.
+    right_by : column name
+        Field names to match on in the right DataFrame.
+    suffixes : 2-length sequence (tuple, list, ...)
+        Suffix to apply to overlapping column names in the left and right
+        side, respectively.
+    tolerance : int or Timedelta, optional, default None
+        Select asof tolerance within this range; must be compatible
+        with the merge index.
+    allow_exact_matches : bool, default True
+
+        - If True, allow matching with the same 'on' value
+          (i.e. less-than-or-equal-to / greater-than-or-equal-to)
+        - If False, don't match the same 'on' value
+          (i.e., strictly less-than / strictly greater-than).
+
+    direction : 'backward' (default), 'forward', or 'nearest'
+        Whether to search for prior, subsequent, or closest matches.
+
+    Returns
+    -------
+    DataFrame
+
+    See Also
+    --------
+    merge : Merge with a database-style join.
+    merge_ordered : Merge with optional filling/interpolation.
+
+    Examples
+    --------
+    >>> left = pd.DataFrame({"a": [1, 5, 10], "left_val": ["a", "b", "c"]})
+    >>> left
+        a left_val
+    0   1        a
+    1   5        b
+    2  10        c
+
+    >>> right = pd.DataFrame({"a": [1, 2, 3, 6, 7], "right_val": [1, 2, 3, 6, 7]})
+    >>> right
+       a  right_val
+    0  1          1
+    1  2          2
+    2  3          3
+    3  6          6
+    4  7          7
+
+    >>> pd.merge_asof(left, right, on="a")
+        a left_val  right_val
+    0   1        a          1
+    1   5        b          3
+    2  10        c          7
+
+    >>> pd.merge_asof(left, right, on="a", allow_exact_matches=False)
+        a left_val  right_val
+    0   1        a        NaN
+    1   5        b        3.0
+    2  10        c        7.0
+
+    >>> pd.merge_asof(left, right, on="a", direction="forward")
+        a left_val  right_val
+    0   1        a        1.0
+    1   5        b        6.0
+    2  10        c        NaN
+
+    >>> pd.merge_asof(left, right, on="a", direction="nearest")
+        a left_val  right_val
+    0   1        a          1
+    1   5        b          6
+    2  10        c          7
+
+    We can use indexed DataFrames as well.
+
+    >>> left = pd.DataFrame({"left_val": ["a", "b", "c"]}, index=[1, 5, 10])
+    >>> left
+       left_val
+    1         a
+    5         b
+    10        c
+
+    >>> right = pd.DataFrame({"right_val": [1, 2, 3, 6, 7]}, index=[1, 2, 3, 6, 7])
+    >>> right
+       right_val
+    1          1
+    2          2
+    3          3
+    6          6
+    7          7
+
+    >>> pd.merge_asof(left, right, left_index=True, right_index=True)
+       left_val  right_val
+    1         a          1
+    5         b          3
+    10        c          7
+
+    Here is a real-world times-series example
+
+    >>> quotes = pd.DataFrame(
+    ...     {
+    ...         "time": [
+    ...             pd.Timestamp("2016-05-25 13:30:00.023"),
+    ...             pd.Timestamp("2016-05-25 13:30:00.023"),
+    ...             pd.Timestamp("2016-05-25 13:30:00.030"),
+    ...             pd.Timestamp("2016-05-25 13:30:00.041"),
+    ...             pd.Timestamp("2016-05-25 13:30:00.048"),
+    ...             pd.Timestamp("2016-05-25 13:30:00.049"),
+    ...             pd.Timestamp("2016-05-25 13:30:00.072"),
+    ...             pd.Timestamp("2016-05-25 13:30:00.075")
+    ...         ],
+    ...         "ticker": [
+    ...                "GOOG",
+    ...                "MSFT",
+    ...                "MSFT",
+    ...                "MSFT",
+    ...                "GOOG",
+    ...                "AAPL",
+    ...                "GOOG",
+    ...                "MSFT"
+    ...            ],
+    ...            "bid": [720.50, 51.95, 51.97, 51.99, 720.50, 97.99, 720.50, 52.01],
+    ...            "ask": [720.93, 51.96, 51.98, 52.00, 720.93, 98.01, 720.88, 52.03]
+    ...     }
+    ... )
+    >>> quotes
+                         time ticker     bid     ask
+    0 2016-05-25 13:30:00.023   GOOG  720.50  720.93
+    1 2016-05-25 13:30:00.023   MSFT   51.95   51.96
+    2 2016-05-25 13:30:00.030   MSFT   51.97   51.98
+    3 2016-05-25 13:30:00.041   MSFT   51.99   52.00
+    4 2016-05-25 13:30:00.048   GOOG  720.50  720.93
+    5 2016-05-25 13:30:00.049   AAPL   97.99   98.01
+    6 2016-05-25 13:30:00.072   GOOG  720.50  720.88
+    7 2016-05-25 13:30:00.075   MSFT   52.01   52.03
+
+    >>> trades = pd.DataFrame(
+    ...        {
+    ...            "time": [
+    ...                pd.Timestamp("2016-05-25 13:30:00.023"),
+    ...                pd.Timestamp("2016-05-25 13:30:00.038"),
+    ...                pd.Timestamp("2016-05-25 13:30:00.048"),
+    ...                pd.Timestamp("2016-05-25 13:30:00.048"),
+    ...                pd.Timestamp("2016-05-25 13:30:00.048")
+    ...            ],
+    ...            "ticker": ["MSFT", "MSFT", "GOOG", "GOOG", "AAPL"],
+    ...            "price": [51.95, 51.95, 720.77, 720.92, 98.0],
+    ...            "quantity": [75, 155, 100, 100, 100]
+    ...        }
+    ...    )
+    >>> trades
+                         time ticker   price  quantity
+    0 2016-05-25 13:30:00.023   MSFT   51.95        75
+    1 2016-05-25 13:30:00.038   MSFT   51.95       155
+    2 2016-05-25 13:30:00.048   GOOG  720.77       100
+    3 2016-05-25 13:30:00.048   GOOG  720.92       100
+    4 2016-05-25 13:30:00.048   AAPL   98.00       100
+
+    By default we are taking the asof of the quotes
+
+    >>> pd.merge_asof(trades, quotes, on="time", by="ticker")
+                         time ticker   price  quantity     bid     ask
+    0 2016-05-25 13:30:00.023   MSFT   51.95        75   51.95   51.96
+    1 2016-05-25 13:30:00.038   MSFT   51.95       155   51.97   51.98
+    2 2016-05-25 13:30:00.048   GOOG  720.77       100  720.50  720.93
+    3 2016-05-25 13:30:00.048   GOOG  720.92       100  720.50  720.93
+    4 2016-05-25 13:30:00.048   AAPL   98.00       100     NaN     NaN
+
+    We only asof within 2ms between the quote time and the trade time
+
+    >>> pd.merge_asof(
+    ...     trades, quotes, on="time", by="ticker", tolerance=pd.Timedelta("2ms")
+    ... )
+                         time ticker   price  quantity     bid     ask
+    0 2016-05-25 13:30:00.023   MSFT   51.95        75   51.95   51.96
+    1 2016-05-25 13:30:00.038   MSFT   51.95       155     NaN     NaN
+    2 2016-05-25 13:30:00.048   GOOG  720.77       100  720.50  720.93
+    3 2016-05-25 13:30:00.048   GOOG  720.92       100  720.50  720.93
+    4 2016-05-25 13:30:00.048   AAPL   98.00       100     NaN     NaN
+
+    We only asof within 10ms between the quote time and the trade time
+    and we exclude exact matches on time. However *prior* data will
+    propagate forward
+
+    >>> pd.merge_asof(
+    ...     trades,
+    ...     quotes,
+    ...     on="time",
+    ...     by="ticker",
+    ...     tolerance=pd.Timedelta("10ms"),
+    ...     allow_exact_matches=False
+    ... )
+                         time ticker   price  quantity     bid     ask
+    0 2016-05-25 13:30:00.023   MSFT   51.95        75     NaN     NaN
+    1 2016-05-25 13:30:00.038   MSFT   51.95       155   51.97   51.98
+    2 2016-05-25 13:30:00.048   GOOG  720.77       100     NaN     NaN
+    3 2016-05-25 13:30:00.048   GOOG  720.92       100     NaN     NaN
+    4 2016-05-25 13:30:00.048   AAPL   98.00       100     NaN     NaN
+    """
+    op = _AsOfMerge(
+        left,
+        right,
+        on=on,
+        left_on=left_on,
+        right_on=right_on,
+        left_index=left_index,
+        right_index=right_index,
+        by=by,
+        left_by=left_by,
+        right_by=right_by,
+        suffixes=suffixes,
+        how="asof",
+        tolerance=tolerance,
+        allow_exact_matches=allow_exact_matches,
+        direction=direction,
+    )
+    return op.get_result()
+
+
+# TODO: transformations??
+# TODO: only copy DataFrames when modification necessary
+class _MergeOperation:
+    """
+    Perform a database (SQL) merge operation between two DataFrame or Series
+    objects using either columns as keys or their row indexes
+    """
+
+    _merge_type = "merge"
+    how: JoinHow | Literal["asof"]
+    on: IndexLabel | None
+    # left_on/right_on may be None when passed, but in validate_specification
+    #  get replaced with non-None.
+    left_on: Sequence[Hashable | AnyArrayLike]
+    right_on: Sequence[Hashable | AnyArrayLike]
+    left_index: bool
+    right_index: bool
+    sort: bool
+    suffixes: Suffixes
+    copy: bool
+    indicator: str | bool
+    validate: str | None
+    join_names: list[Hashable]
+    right_join_keys: list[ArrayLike]
+    left_join_keys: list[ArrayLike]
+
+    def __init__(
+        self,
+        left: DataFrame | Series,
+        right: DataFrame | Series,
+        how: JoinHow | Literal["asof"] = "inner",
+        on: IndexLabel | AnyArrayLike | None = None,
+        left_on: IndexLabel | AnyArrayLike | None = None,
+        right_on: IndexLabel | AnyArrayLike | None = None,
+        left_index: bool = False,
+        right_index: bool = False,
+        sort: bool = True,
+        suffixes: Suffixes = ("_x", "_y"),
+        indicator: str | bool = False,
+        validate: str | None = None,
+    ) -> None:
+        _left = _validate_operand(left)
+        _right = _validate_operand(right)
+        self.left = self.orig_left = _left
+        self.right = self.orig_right = _right
+        self.how = how
+
+        self.on = com.maybe_make_list(on)
+
+        self.suffixes = suffixes
+        self.sort = sort or how == "outer"
+
+        self.left_index = left_index
+        self.right_index = right_index
+
+        self.indicator = indicator
+
+        if not is_bool(left_index):
+            raise ValueError(
+                f"left_index parameter must be of type bool, not {type(left_index)}"
+            )
+        if not is_bool(right_index):
+            raise ValueError(
+                f"right_index parameter must be of type bool, not {type(right_index)}"
+            )
+
+        # GH 40993: raise when merging between different levels; enforced in 2.0
+        if _left.columns.nlevels != _right.columns.nlevels:
+            msg = (
+                "Not allowed to merge between different levels. "
+                f"({_left.columns.nlevels} levels on the left, "
+                f"{_right.columns.nlevels} on the right)"
+            )
+            raise MergeError(msg)
+
+        self.left_on, self.right_on = self._validate_left_right_on(left_on, right_on)
+
+        (
+            self.left_join_keys,
+            self.right_join_keys,
+            self.join_names,
+            left_drop,
+            right_drop,
+        ) = self._get_merge_keys()
+
+        if left_drop:
+            self.left = self.left._drop_labels_or_levels(left_drop)
+
+        if right_drop:
+            self.right = self.right._drop_labels_or_levels(right_drop)
+
+        self._maybe_require_matching_dtypes(self.left_join_keys, self.right_join_keys)
+        self._validate_tolerance(self.left_join_keys)
+
+        # validate the merge keys dtypes. We may need to coerce
+        # to avoid incompatible dtypes
+        self._maybe_coerce_merge_keys()
+
+        # If argument passed to validate,
+        # check if columns specified as unique
+        # are in fact unique.
+        if validate is not None:
+            self._validate_validate_kwd(validate)
+
+    def _maybe_require_matching_dtypes(
+        self, left_join_keys: list[ArrayLike], right_join_keys: list[ArrayLike]
+    ) -> None:
+        # Overridden by AsOfMerge
+        pass
+
+    def _validate_tolerance(self, left_join_keys: list[ArrayLike]) -> None:
+        # Overridden by AsOfMerge
+        pass
+
+    @final
+    def _reindex_and_concat(
+        self,
+        join_index: Index,
+        left_indexer: npt.NDArray[np.intp] | None,
+        right_indexer: npt.NDArray[np.intp] | None,
+        copy: bool | None,
+    ) -> DataFrame:
+        """
+        reindex along index and concat along columns.
+        """
+        # Take views so we do not alter the originals
+        left = self.left[:]
+        right = self.right[:]
+
+        llabels, rlabels = _items_overlap_with_suffix(
+            self.left._info_axis, self.right._info_axis, self.suffixes
+        )
+
+        if left_indexer is not None and not is_range_indexer(left_indexer, len(left)):
+            # Pinning the index here (and in the right code just below) is not
+            #  necessary, but makes the `.take` more performant if we have e.g.
+            #  a MultiIndex for left.index.
+            lmgr = left._mgr.reindex_indexer(
+                join_index,
+                left_indexer,
+                axis=1,
+                copy=False,
+                only_slice=True,
+                allow_dups=True,
+                use_na_proxy=True,
+            )
+            left = left._constructor_from_mgr(lmgr, axes=lmgr.axes)
+        left.index = join_index
+
+        if right_indexer is not None and not is_range_indexer(
+            right_indexer, len(right)
+        ):
+            rmgr = right._mgr.reindex_indexer(
+                join_index,
+                right_indexer,
+                axis=1,
+                copy=False,
+                only_slice=True,
+                allow_dups=True,
+                use_na_proxy=True,
+            )
+            right = right._constructor_from_mgr(rmgr, axes=rmgr.axes)
+        right.index = join_index
+
+        from pandas import concat
+
+        left.columns = llabels
+        right.columns = rlabels
+        result = concat([left, right], axis=1, copy=copy)
+        return result
+
+    def get_result(self, copy: bool | None = True) -> DataFrame:
+        if self.indicator:
+            self.left, self.right = self._indicator_pre_merge(self.left, self.right)
+
+        join_index, left_indexer, right_indexer = self._get_join_info()
+
+        result = self._reindex_and_concat(
+            join_index, left_indexer, right_indexer, copy=copy
+        )
+        result = result.__finalize__(self, method=self._merge_type)
+
+        if self.indicator:
+            result = self._indicator_post_merge(result)
+
+        self._maybe_add_join_keys(result, left_indexer, right_indexer)
+
+        self._maybe_restore_index_levels(result)
+
+        return result.__finalize__(self, method="merge")
+
+    @final
+    @cache_readonly
+    def _indicator_name(self) -> str | None:
+        if isinstance(self.indicator, str):
+            return self.indicator
+        elif isinstance(self.indicator, bool):
+            return "_merge" if self.indicator else None
+        else:
+            raise ValueError(
+                "indicator option can only accept boolean or string arguments"
+            )
+
+    @final
+    def _indicator_pre_merge(
+        self, left: DataFrame, right: DataFrame
+    ) -> tuple[DataFrame, DataFrame]:
+        columns = left.columns.union(right.columns)
+
+        for i in ["_left_indicator", "_right_indicator"]:
+            if i in columns:
+                raise ValueError(
+                    "Cannot use `indicator=True` option when "
+                    f"data contains a column named {i}"
+                )
+        if self._indicator_name in columns:
+            raise ValueError(
+                "Cannot use name of an existing column for indicator column"
+            )
+
+        left = left.copy()
+        right = right.copy()
+
+        left["_left_indicator"] = 1
+        left["_left_indicator"] = left["_left_indicator"].astype("int8")
+
+        right["_right_indicator"] = 2
+        right["_right_indicator"] = right["_right_indicator"].astype("int8")
+
+        return left, right
+
+    @final
+    def _indicator_post_merge(self, result: DataFrame) -> DataFrame:
+        result["_left_indicator"] = result["_left_indicator"].fillna(0)
+        result["_right_indicator"] = result["_right_indicator"].fillna(0)
+
+        result[self._indicator_name] = Categorical(
+            (result["_left_indicator"] + result["_right_indicator"]),
+            categories=[1, 2, 3],
+        )
+        result[self._indicator_name] = result[
+            self._indicator_name
+        ].cat.rename_categories(["left_only", "right_only", "both"])
+
+        result = result.drop(labels=["_left_indicator", "_right_indicator"], axis=1)
+        return result
+
+    @final
+    def _maybe_restore_index_levels(self, result: DataFrame) -> None:
+        """
+        Restore index levels specified as `on` parameters
+
+        Here we check for cases where `self.left_on` and `self.right_on` pairs
+        each reference an index level in their respective DataFrames. The
+        joined columns corresponding to these pairs are then restored to the
+        index of `result`.
+
+        **Note:** This method has side effects. It modifies `result` in-place
+
+        Parameters
+        ----------
+        result: DataFrame
+            merge result
+
+        Returns
+        -------
+        None
+        """
+        names_to_restore = []
+        for name, left_key, right_key in zip(
+            self.join_names, self.left_on, self.right_on
+        ):
+            if (
+                # Argument 1 to "_is_level_reference" of "NDFrame" has incompatible
+                # type "Union[Hashable, ExtensionArray, Index, Series]"; expected
+                # "Hashable"
+                self.orig_left._is_level_reference(left_key)  # type: ignore[arg-type]
+                # Argument 1 to "_is_level_reference" of "NDFrame" has incompatible
+                # type "Union[Hashable, ExtensionArray, Index, Series]"; expected
+                # "Hashable"
+                and self.orig_right._is_level_reference(
+                    right_key  # type: ignore[arg-type]
+                )
+                and left_key == right_key
+                and name not in result.index.names
+            ):
+                names_to_restore.append(name)
+
+        if names_to_restore:
+            result.set_index(names_to_restore, inplace=True)
+
+    @final
+    def _maybe_add_join_keys(
+        self,
+        result: DataFrame,
+        left_indexer: npt.NDArray[np.intp] | None,
+        right_indexer: npt.NDArray[np.intp] | None,
+    ) -> None:
+        left_has_missing = None
+        right_has_missing = None
+
+        assert all(isinstance(x, _known) for x in self.left_join_keys)
+
+        keys = zip(self.join_names, self.left_on, self.right_on)
+        for i, (name, lname, rname) in enumerate(keys):
+            if not _should_fill(lname, rname):
+                continue
+
+            take_left, take_right = None, None
+
+            if name in result:
+                if left_indexer is not None or right_indexer is not None:
+                    if name in self.left:
+                        if left_has_missing is None:
+                            left_has_missing = (
+                                False
+                                if left_indexer is None
+                                else (left_indexer == -1).any()
+                            )
+
+                        if left_has_missing:
+                            take_right = self.right_join_keys[i]
+
+                            if result[name].dtype != self.left[name].dtype:
+                                take_left = self.left[name]._values
+
+                    elif name in self.right:
+                        if right_has_missing is None:
+                            right_has_missing = (
+                                False
+                                if right_indexer is None
+                                else (right_indexer == -1).any()
+                            )
+
+                        if right_has_missing:
+                            take_left = self.left_join_keys[i]
+
+                            if result[name].dtype != self.right[name].dtype:
+                                take_right = self.right[name]._values
+
+            else:
+                take_left = self.left_join_keys[i]
+                take_right = self.right_join_keys[i]
+
+            if take_left is not None or take_right is not None:
+                if take_left is None:
+                    lvals = result[name]._values
+                elif left_indexer is None:
+                    lvals = take_left
+                else:
+                    # TODO: can we pin down take_left's type earlier?
+                    take_left = extract_array(take_left, extract_numpy=True)
+                    lfill = na_value_for_dtype(take_left.dtype)
+                    lvals = algos.take_nd(take_left, left_indexer, fill_value=lfill)
+
+                if take_right is None:
+                    rvals = result[name]._values
+                elif right_indexer is None:
+                    rvals = take_right
+                else:
+                    # TODO: can we pin down take_right's type earlier?
+                    taker = extract_array(take_right, extract_numpy=True)
+                    rfill = na_value_for_dtype(taker.dtype)
+                    rvals = algos.take_nd(taker, right_indexer, fill_value=rfill)
+
+                # if we have an all missing left_indexer
+                # make sure to just use the right values or vice-versa
+                if left_indexer is not None and (left_indexer == -1).all():
+                    key_col = Index(rvals, dtype=rvals.dtype, copy=False)
+                    result_dtype = rvals.dtype
+                elif right_indexer is not None and (right_indexer == -1).all():
+                    key_col = Index(lvals, dtype=lvals.dtype, copy=False)
+                    result_dtype = lvals.dtype
+                else:
+                    key_col = Index(lvals, dtype=lvals.dtype, copy=False)
+                    if left_indexer is not None:
+                        mask_left = left_indexer == -1
+                        key_col = key_col.where(~mask_left, rvals)
+                    result_dtype = find_common_type([lvals.dtype, rvals.dtype])
+                    if (
+                        lvals.dtype.kind == "M"
+                        and rvals.dtype.kind == "M"
+                        and result_dtype.kind == "O"
+                    ):
+                        # TODO(non-nano) Workaround for common_type not dealing
+                        # with different resolutions
+                        result_dtype = key_col.dtype
+
+                if result._is_label_reference(name):
+                    result[name] = result._constructor_sliced(
+                        key_col, dtype=result_dtype, index=result.index
+                    )
+                elif result._is_level_reference(name):
+                    if isinstance(result.index, MultiIndex):
+                        key_col.name = name
+                        idx_list = [
+                            result.index.get_level_values(level_name)
+                            if level_name != name
+                            else key_col
+                            for level_name in result.index.names
+                        ]
+
+                        result.set_index(idx_list, inplace=True)
+                    else:
+                        key_col.name = name
+                        result.index = key_col
+                else:
+                    result.insert(i, name or f"key_{i}", key_col)
+
+    def _get_join_indexers(
+        self,
+    ) -> tuple[npt.NDArray[np.intp] | None, npt.NDArray[np.intp] | None]:
+        """return the join indexers"""
+        # make mypy happy
+        assert self.how != "asof"
+        return get_join_indexers(
+            self.left_join_keys, self.right_join_keys, sort=self.sort, how=self.how
+        )
+
+    @final
+    def _get_join_info(
+        self,
+    ) -> tuple[Index, npt.NDArray[np.intp] | None, npt.NDArray[np.intp] | None]:
+        left_ax = self.left.index
+        right_ax = self.right.index
+
+        if self.left_index and self.right_index and self.how != "asof":
+            join_index, left_indexer, right_indexer = left_ax.join(
+                right_ax, how=self.how, return_indexers=True, sort=self.sort
+            )
+
+        elif self.right_index and self.how == "left":
+            join_index, left_indexer, right_indexer = _left_join_on_index(
+                left_ax, right_ax, self.left_join_keys, sort=self.sort
+            )
+
+        elif self.left_index and self.how == "right":
+            join_index, right_indexer, left_indexer = _left_join_on_index(
+                right_ax, left_ax, self.right_join_keys, sort=self.sort
+            )
+        else:
+            (left_indexer, right_indexer) = self._get_join_indexers()
+
+            if self.right_index:
+                if len(self.left) > 0:
+                    join_index = self._create_join_index(
+                        left_ax,
+                        right_ax,
+                        left_indexer,
+                        how="right",
+                    )
+                elif right_indexer is None:
+                    join_index = right_ax.copy()
+                else:
+                    join_index = right_ax.take(right_indexer)
+            elif self.left_index:
+                if self.how == "asof":
+                    # GH#33463 asof should always behave like a left merge
+                    join_index = self._create_join_index(
+                        left_ax,
+                        right_ax,
+                        left_indexer,
+                        how="left",
+                    )
+
+                elif len(self.right) > 0:
+                    join_index = self._create_join_index(
+                        right_ax,
+                        left_ax,
+                        right_indexer,
+                        how="left",
+                    )
+                elif left_indexer is None:
+                    join_index = left_ax.copy()
+                else:
+                    join_index = left_ax.take(left_indexer)
+            else:
+                n = len(left_ax) if left_indexer is None else len(left_indexer)
+                join_index = default_index(n)
+
+        return join_index, left_indexer, right_indexer
+
+    @final
+    def _create_join_index(
+        self,
+        index: Index,
+        other_index: Index,
+        indexer: npt.NDArray[np.intp] | None,
+        how: JoinHow = "left",
+    ) -> Index:
+        """
+        Create a join index by rearranging one index to match another
+
+        Parameters
+        ----------
+        index : Index
+            index being rearranged
+        other_index : Index
+            used to supply values not found in index
+        indexer : np.ndarray[np.intp] or None
+            how to rearrange index
+        how : str
+            Replacement is only necessary if indexer based on other_index.
+
+        Returns
+        -------
+        Index
+        """
+        if self.how in (how, "outer") and not isinstance(other_index, MultiIndex):
+            # if final index requires values in other_index but not target
+            # index, indexer may hold missing (-1) values, causing Index.take
+            # to take the final value in target index. So, we set the last
+            # element to be the desired fill value. We do not use allow_fill
+            # and fill_value because it throws a ValueError on integer indices
+            mask = indexer == -1
+            if np.any(mask):
+                fill_value = na_value_for_dtype(index.dtype, compat=False)
+                index = index.append(Index([fill_value]))
+        if indexer is None:
+            return index.copy()
+        return index.take(indexer)
+
+    @final
+    def _get_merge_keys(
+        self,
+    ) -> tuple[
+        list[ArrayLike],
+        list[ArrayLike],
+        list[Hashable],
+        list[Hashable],
+        list[Hashable],
+    ]:
+        """
+        Returns
+        -------
+        left_keys, right_keys, join_names, left_drop, right_drop
+        """
+        left_keys: list[ArrayLike] = []
+        right_keys: list[ArrayLike] = []
+        join_names: list[Hashable] = []
+        right_drop: list[Hashable] = []
+        left_drop: list[Hashable] = []
+
+        left, right = self.left, self.right
+
+        is_lkey = lambda x: isinstance(x, _known) and len(x) == len(left)
+        is_rkey = lambda x: isinstance(x, _known) and len(x) == len(right)
+
+        # Note that pd.merge_asof() has separate 'on' and 'by' parameters. A
+        # user could, for example, request 'left_index' and 'left_by'. In a
+        # regular pd.merge(), users cannot specify both 'left_index' and
+        # 'left_on'. (Instead, users have a MultiIndex). That means the
+        # self.left_on in this function is always empty in a pd.merge(), but
+        # a pd.merge_asof(left_index=True, left_by=...) will result in a
+        # self.left_on array with a None in the middle of it. This requires
+        # a work-around as designated in the code below.
+        # See _validate_left_right_on() for where this happens.
+
+        # ugh, spaghetti re #733
+        if _any(self.left_on) and _any(self.right_on):
+            for lk, rk in zip(self.left_on, self.right_on):
+                lk = extract_array(lk, extract_numpy=True)
+                rk = extract_array(rk, extract_numpy=True)
+                if is_lkey(lk):
+                    lk = cast(ArrayLike, lk)
+                    left_keys.append(lk)
+                    if is_rkey(rk):
+                        rk = cast(ArrayLike, rk)
+                        right_keys.append(rk)
+                        join_names.append(None)  # what to do?
+                    else:
+                        # Then we're either Hashable or a wrong-length arraylike,
+                        #  the latter of which will raise
+                        rk = cast(Hashable, rk)
+                        if rk is not None:
+                            right_keys.append(right._get_label_or_level_values(rk))
+                            join_names.append(rk)
+                        else:
+                            # work-around for merge_asof(right_index=True)
+                            right_keys.append(right.index._values)
+                            join_names.append(right.index.name)
+                else:
+                    if not is_rkey(rk):
+                        # Then we're either Hashable or a wrong-length arraylike,
+                        #  the latter of which will raise
+                        rk = cast(Hashable, rk)
+                        if rk is not None:
+                            right_keys.append(right._get_label_or_level_values(rk))
+                        else:
+                            # work-around for merge_asof(right_index=True)
+                            right_keys.append(right.index._values)
+                        if lk is not None and lk == rk:  # FIXME: what about other NAs?
+                            right_drop.append(rk)
+                    else:
+                        rk = cast(ArrayLike, rk)
+                        right_keys.append(rk)
+                    if lk is not None:
+                        # Then we're either Hashable or a wrong-length arraylike,
+                        #  the latter of which will raise
+                        lk = cast(Hashable, lk)
+                        left_keys.append(left._get_label_or_level_values(lk))
+                        join_names.append(lk)
+                    else:
+                        # work-around for merge_asof(left_index=True)
+                        left_keys.append(left.index._values)
+                        join_names.append(left.index.name)
+        elif _any(self.left_on):
+            for k in self.left_on:
+                if is_lkey(k):
+                    k = extract_array(k, extract_numpy=True)
+                    k = cast(ArrayLike, k)
+                    left_keys.append(k)
+                    join_names.append(None)
+                else:
+                    # Then we're either Hashable or a wrong-length arraylike,
+                    #  the latter of which will raise
+                    k = cast(Hashable, k)
+                    left_keys.append(left._get_label_or_level_values(k))
+                    join_names.append(k)
+            if isinstance(self.right.index, MultiIndex):
+                right_keys = [
+                    lev._values.take(lev_codes)
+                    for lev, lev_codes in zip(
+                        self.right.index.levels, self.right.index.codes
+                    )
+                ]
+            else:
+                right_keys = [self.right.index._values]
+        elif _any(self.right_on):
+            for k in self.right_on:
+                k = extract_array(k, extract_numpy=True)
+                if is_rkey(k):
+                    k = cast(ArrayLike, k)
+                    right_keys.append(k)
+                    join_names.append(None)
+                else:
+                    # Then we're either Hashable or a wrong-length arraylike,
+                    #  the latter of which will raise
+                    k = cast(Hashable, k)
+                    right_keys.append(right._get_label_or_level_values(k))
+                    join_names.append(k)
+            if isinstance(self.left.index, MultiIndex):
+                left_keys = [
+                    lev._values.take(lev_codes)
+                    for lev, lev_codes in zip(
+                        self.left.index.levels, self.left.index.codes
+                    )
+                ]
+            else:
+                left_keys = [self.left.index._values]
+
+        return left_keys, right_keys, join_names, left_drop, right_drop
+
+    @final
+    def _maybe_coerce_merge_keys(self) -> None:
+        # we have valid merges but we may have to further
+        # coerce these if they are originally incompatible types
+        #
+        # for example if these are categorical, but are not dtype_equal
+        # or if we have object and integer dtypes
+
+        for lk, rk, name in zip(
+            self.left_join_keys, self.right_join_keys, self.join_names
+        ):
+            if (len(lk) and not len(rk)) or (not len(lk) and len(rk)):
+                continue
+
+            lk = extract_array(lk, extract_numpy=True)
+            rk = extract_array(rk, extract_numpy=True)
+
+            lk_is_cat = isinstance(lk.dtype, CategoricalDtype)
+            rk_is_cat = isinstance(rk.dtype, CategoricalDtype)
+            lk_is_object_or_string = is_object_dtype(lk.dtype) or is_string_dtype(
+                lk.dtype
+            )
+            rk_is_object_or_string = is_object_dtype(rk.dtype) or is_string_dtype(
+                rk.dtype
+            )
+
+            # if either left or right is a categorical
+            # then the must match exactly in categories & ordered
+            if lk_is_cat and rk_is_cat:
+                lk = cast(Categorical, lk)
+                rk = cast(Categorical, rk)
+                if lk._categories_match_up_to_permutation(rk):
+                    continue
+
+            elif lk_is_cat or rk_is_cat:
+                pass
+
+            elif lk.dtype == rk.dtype:
+                continue
+
+            msg = (
+                f"You are trying to merge on {lk.dtype} and {rk.dtype} columns "
+                f"for key '{name}'. If you wish to proceed you should use pd.concat"
+            )
+
+            # if we are numeric, then allow differing
+            # kinds to proceed, eg. int64 and int8, int and float
+            # further if we are object, but we infer to
+            # the same, then proceed
+            if is_numeric_dtype(lk.dtype) and is_numeric_dtype(rk.dtype):
+                if lk.dtype.kind == rk.dtype.kind:
+                    continue
+
+                if isinstance(lk.dtype, ExtensionDtype) and not isinstance(
+                    rk.dtype, ExtensionDtype
+                ):
+                    ct = find_common_type([lk.dtype, rk.dtype])
+                    if isinstance(ct, ExtensionDtype):
+                        com_cls = ct.construct_array_type()
+                        rk = com_cls._from_sequence(rk, dtype=ct, copy=False)
+                    else:
+                        rk = rk.astype(ct)
+                elif isinstance(rk.dtype, ExtensionDtype):
+                    ct = find_common_type([lk.dtype, rk.dtype])
+                    if isinstance(ct, ExtensionDtype):
+                        com_cls = ct.construct_array_type()
+                        lk = com_cls._from_sequence(lk, dtype=ct, copy=False)
+                    else:
+                        lk = lk.astype(ct)
+
+                # check whether ints and floats
+                if is_integer_dtype(rk.dtype) and is_float_dtype(lk.dtype):
+                    # GH 47391 numpy > 1.24 will raise a RuntimeError for nan -> int
+                    with np.errstate(invalid="ignore"):
+                        # error: Argument 1 to "astype" of "ndarray" has incompatible
+                        # type "Union[ExtensionDtype, Any, dtype[Any]]"; expected
+                        # "Union[dtype[Any], Type[Any], _SupportsDType[dtype[Any]]]"
+                        casted = lk.astype(rk.dtype)  # type: ignore[arg-type]
+
+                    mask = ~np.isnan(lk)
+                    match = lk == casted
+                    if not match[mask].all():
+                        warnings.warn(
+                            "You are merging on int and float "
+                            "columns where the float values "
+                            "are not equal to their int representation.",
+                            UserWarning,
+                            stacklevel=find_stack_level(),
+                        )
+                    continue
+
+                if is_float_dtype(rk.dtype) and is_integer_dtype(lk.dtype):
+                    # GH 47391 numpy > 1.24 will raise a RuntimeError for nan -> int
+                    with np.errstate(invalid="ignore"):
+                        # error: Argument 1 to "astype" of "ndarray" has incompatible
+                        # type "Union[ExtensionDtype, Any, dtype[Any]]"; expected
+                        # "Union[dtype[Any], Type[Any], _SupportsDType[dtype[Any]]]"
+                        casted = rk.astype(lk.dtype)  # type: ignore[arg-type]
+
+                    mask = ~np.isnan(rk)
+                    match = rk == casted
+                    if not match[mask].all():
+                        warnings.warn(
+                            "You are merging on int and float "
+                            "columns where the float values "
+                            "are not equal to their int representation.",
+                            UserWarning,
+                            stacklevel=find_stack_level(),
+                        )
+                    continue
+
+                # let's infer and see if we are ok
+                if lib.infer_dtype(lk, skipna=False) == lib.infer_dtype(
+                    rk, skipna=False
+                ):
+                    continue
+
+            # Check if we are trying to merge on obviously
+            # incompatible dtypes GH 9780, GH 15800
+
+            # bool values are coerced to object
+            elif (lk_is_object_or_string and is_bool_dtype(rk.dtype)) or (
+                is_bool_dtype(lk.dtype) and rk_is_object_or_string
+            ):
+                pass
+
+            # object values are allowed to be merged
+            elif (lk_is_object_or_string and is_numeric_dtype(rk.dtype)) or (
+                is_numeric_dtype(lk.dtype) and rk_is_object_or_string
+            ):
+                inferred_left = lib.infer_dtype(lk, skipna=False)
+                inferred_right = lib.infer_dtype(rk, skipna=False)
+                bool_types = ["integer", "mixed-integer", "boolean", "empty"]
+                string_types = ["string", "unicode", "mixed", "bytes", "empty"]
+
+                # inferred bool
+                if inferred_left in bool_types and inferred_right in bool_types:
+                    pass
+
+                # unless we are merging non-string-like with string-like
+                elif (
+                    inferred_left in string_types and inferred_right not in string_types
+                ) or (
+                    inferred_right in string_types and inferred_left not in string_types
+                ):
+                    raise ValueError(msg)
+
+            # datetimelikes must match exactly
+            elif needs_i8_conversion(lk.dtype) and not needs_i8_conversion(rk.dtype):
+                raise ValueError(msg)
+            elif not needs_i8_conversion(lk.dtype) and needs_i8_conversion(rk.dtype):
+                raise ValueError(msg)
+            elif isinstance(lk.dtype, DatetimeTZDtype) and not isinstance(
+                rk.dtype, DatetimeTZDtype
+            ):
+                raise ValueError(msg)
+            elif not isinstance(lk.dtype, DatetimeTZDtype) and isinstance(
+                rk.dtype, DatetimeTZDtype
+            ):
+                raise ValueError(msg)
+            elif (
+                isinstance(lk.dtype, DatetimeTZDtype)
+                and isinstance(rk.dtype, DatetimeTZDtype)
+            ) or (lk.dtype.kind == "M" and rk.dtype.kind == "M"):
+                # allows datetime with different resolutions
+                continue
+            # datetime and timedelta not allowed
+            elif lk.dtype.kind == "M" and rk.dtype.kind == "m":
+                raise ValueError(msg)
+            elif lk.dtype.kind == "m" and rk.dtype.kind == "M":
+                raise ValueError(msg)
+
+            elif is_object_dtype(lk.dtype) and is_object_dtype(rk.dtype):
+                continue
+
+            # Houston, we have a problem!
+            # let's coerce to object if the dtypes aren't
+            # categorical, otherwise coerce to the category
+            # dtype. If we coerced categories to object,
+            # then we would lose type information on some
+            # columns, and end up trying to merge
+            # incompatible dtypes. See GH 16900.
+            if name in self.left.columns:
+                typ = cast(Categorical, lk).categories.dtype if lk_is_cat else object
+                self.left = self.left.copy()
+                self.left[name] = self.left[name].astype(typ)
+            if name in self.right.columns:
+                typ = cast(Categorical, rk).categories.dtype if rk_is_cat else object
+                self.right = self.right.copy()
+                self.right[name] = self.right[name].astype(typ)
+
+    def _validate_left_right_on(self, left_on, right_on):
+        left_on = com.maybe_make_list(left_on)
+        right_on = com.maybe_make_list(right_on)
+
+        # Hm, any way to make this logic less complicated??
+        if self.on is None and left_on is None and right_on is None:
+            if self.left_index and self.right_index:
+                left_on, right_on = (), ()
+            elif self.left_index:
+                raise MergeError("Must pass right_on or right_index=True")
+            elif self.right_index:
+                raise MergeError("Must pass left_on or left_index=True")
+            else:
+                # use the common columns
+                left_cols = self.left.columns
+                right_cols = self.right.columns
+                common_cols = left_cols.intersection(right_cols)
+                if len(common_cols) == 0:
+                    raise MergeError(
+                        "No common columns to perform merge on. "
+                        f"Merge options: left_on={left_on}, "
+                        f"right_on={right_on}, "
+                        f"left_index={self.left_index}, "
+                        f"right_index={self.right_index}"
+                    )
+                if (
+                    not left_cols.join(common_cols, how="inner").is_unique
+                    or not right_cols.join(common_cols, how="inner").is_unique
+                ):
+                    raise MergeError(f"Data columns not unique: {repr(common_cols)}")
+                left_on = right_on = common_cols
+        elif self.on is not None:
+            if left_on is not None or right_on is not None:
+                raise MergeError(
+                    'Can only pass argument "on" OR "left_on" '
+                    'and "right_on", not a combination of both.'
+                )
+            if self.left_index or self.right_index:
+                raise MergeError(
+                    'Can only pass argument "on" OR "left_index" '
+                    'and "right_index", not a combination of both.'
+                )
+            left_on = right_on = self.on
+        elif left_on is not None:
+            if self.left_index:
+                raise MergeError(
+                    'Can only pass argument "left_on" OR "left_index" not both.'
+                )
+            if not self.right_index and right_on is None:
+                raise MergeError('Must pass "right_on" OR "right_index".')
+            n = len(left_on)
+            if self.right_index:
+                if len(left_on) != self.right.index.nlevels:
+                    raise ValueError(
+                        "len(left_on) must equal the number "
+                        'of levels in the index of "right"'
+                    )
+                right_on = [None] * n
+        elif right_on is not None:
+            if self.right_index:
+                raise MergeError(
+                    'Can only pass argument "right_on" OR "right_index" not both.'
+                )
+            if not self.left_index and left_on is None:
+                raise MergeError('Must pass "left_on" OR "left_index".')
+            n = len(right_on)
+            if self.left_index:
+                if len(right_on) != self.left.index.nlevels:
+                    raise ValueError(
+                        "len(right_on) must equal the number "
+                        'of levels in the index of "left"'
+                    )
+                left_on = [None] * n
+        if len(right_on) != len(left_on):
+            raise ValueError("len(right_on) must equal len(left_on)")
+
+        return left_on, right_on
+
+    @final
+    def _validate_validate_kwd(self, validate: str) -> None:
+        # Check uniqueness of each
+        if self.left_index:
+            left_unique = self.orig_left.index.is_unique
+        else:
+            left_unique = MultiIndex.from_arrays(self.left_join_keys).is_unique
+
+        if self.right_index:
+            right_unique = self.orig_right.index.is_unique
+        else:
+            right_unique = MultiIndex.from_arrays(self.right_join_keys).is_unique
+
+        # Check data integrity
+        if validate in ["one_to_one", "1:1"]:
+            if not left_unique and not right_unique:
+                raise MergeError(
+                    "Merge keys are not unique in either left "
+                    "or right dataset; not a one-to-one merge"
+                )
+            if not left_unique:
+                raise MergeError(
+                    "Merge keys are not unique in left dataset; not a one-to-one merge"
+                )
+            if not right_unique:
+                raise MergeError(
+                    "Merge keys are not unique in right dataset; not a one-to-one merge"
+                )
+
+        elif validate in ["one_to_many", "1:m"]:
+            if not left_unique:
+                raise MergeError(
+                    "Merge keys are not unique in left dataset; not a one-to-many merge"
+                )
+
+        elif validate in ["many_to_one", "m:1"]:
+            if not right_unique:
+                raise MergeError(
+                    "Merge keys are not unique in right dataset; "
+                    "not a many-to-one merge"
+                )
+
+        elif validate in ["many_to_many", "m:m"]:
+            pass
+
+        else:
+            raise ValueError(
+                f'"{validate}" is not a valid argument. '
+                "Valid arguments are:\n"
+                '- "1:1"\n'
+                '- "1:m"\n'
+                '- "m:1"\n'
+                '- "m:m"\n'
+                '- "one_to_one"\n'
+                '- "one_to_many"\n'
+                '- "many_to_one"\n'
+                '- "many_to_many"'
+            )
+
+
+def get_join_indexers(
+    left_keys: list[ArrayLike],
+    right_keys: list[ArrayLike],
+    sort: bool = False,
+    how: JoinHow = "inner",
+) -> tuple[npt.NDArray[np.intp] | None, npt.NDArray[np.intp] | None]:
+    """
+
+    Parameters
+    ----------
+    left_keys : list[ndarray, ExtensionArray, Index, Series]
+    right_keys : list[ndarray, ExtensionArray, Index, Series]
+    sort : bool, default False
+    how : {'inner', 'outer', 'left', 'right'}, default 'inner'
+
+    Returns
+    -------
+    np.ndarray[np.intp] or None
+        Indexer into the left_keys.
+    np.ndarray[np.intp] or None
+        Indexer into the right_keys.
+    """
+    assert len(left_keys) == len(
+        right_keys
+    ), "left_keys and right_keys must be the same length"
+
+    # fast-path for empty left/right
+    left_n = len(left_keys[0])
+    right_n = len(right_keys[0])
+    if left_n == 0:
+        if how in ["left", "inner"]:
+            return _get_empty_indexer()
+        elif not sort and how in ["right", "outer"]:
+            return _get_no_sort_one_missing_indexer(right_n, True)
+    elif right_n == 0:
+        if how in ["right", "inner"]:
+            return _get_empty_indexer()
+        elif not sort and how in ["left", "outer"]:
+            return _get_no_sort_one_missing_indexer(left_n, False)
+
+    lkey: ArrayLike
+    rkey: ArrayLike
+    if len(left_keys) > 1:
+        # get left & right join labels and num. of levels at each location
+        mapped = (
+            _factorize_keys(left_keys[n], right_keys[n], sort=sort)
+            for n in range(len(left_keys))
+        )
+        zipped = zip(*mapped)
+        llab, rlab, shape = (list(x) for x in zipped)
+
+        # get flat i8 keys from label lists
+        lkey, rkey = _get_join_keys(llab, rlab, tuple(shape), sort)
+    else:
+        lkey = left_keys[0]
+        rkey = right_keys[0]
+
+    left = Index(lkey)
+    right = Index(rkey)
+
+    if (
+        left.is_monotonic_increasing
+        and right.is_monotonic_increasing
+        and (left.is_unique or right.is_unique)
+    ):
+        _, lidx, ridx = left.join(right, how=how, return_indexers=True, sort=sort)
+    else:
+        lidx, ridx = get_join_indexers_non_unique(
+            left._values, right._values, sort, how
+        )
+
+    if lidx is not None and is_range_indexer(lidx, len(left)):
+        lidx = None
+    if ridx is not None and is_range_indexer(ridx, len(right)):
+        ridx = None
+    return lidx, ridx
+
+
+def get_join_indexers_non_unique(
+    left: ArrayLike,
+    right: ArrayLike,
+    sort: bool = False,
+    how: JoinHow = "inner",
+) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]:
+    """
+    Get join indexers for left and right.
+
+    Parameters
+    ----------
+    left : ArrayLike
+    right : ArrayLike
+    sort : bool, default False
+    how : {'inner', 'outer', 'left', 'right'}, default 'inner'
+
+    Returns
+    -------
+    np.ndarray[np.intp]
+        Indexer into left.
+    np.ndarray[np.intp]
+        Indexer into right.
+    """
+    lkey, rkey, count = _factorize_keys(left, right, sort=sort)
+    if how == "left":
+        lidx, ridx = libjoin.left_outer_join(lkey, rkey, count, sort=sort)
+    elif how == "right":
+        ridx, lidx = libjoin.left_outer_join(rkey, lkey, count, sort=sort)
+    elif how == "inner":
+        lidx, ridx = libjoin.inner_join(lkey, rkey, count, sort=sort)
+    elif how == "outer":
+        lidx, ridx = libjoin.full_outer_join(lkey, rkey, count)
+    return lidx, ridx
+
+
+def restore_dropped_levels_multijoin(
+    left: MultiIndex,
+    right: MultiIndex,
+    dropped_level_names,
+    join_index: Index,
+    lindexer: npt.NDArray[np.intp],
+    rindexer: npt.NDArray[np.intp],
+) -> tuple[FrozenList, FrozenList, FrozenList]:
+    """
+    *this is an internal non-public method*
+
+    Returns the levels, labels and names of a multi-index to multi-index join.
+    Depending on the type of join, this method restores the appropriate
+    dropped levels of the joined multi-index.
+    The method relies on lindexer, rindexer which hold the index positions of
+    left and right, where a join was feasible
+
+    Parameters
+    ----------
+    left : MultiIndex
+        left index
+    right : MultiIndex
+        right index
+    dropped_level_names : str array
+        list of non-common level names
+    join_index : Index
+        the index of the join between the
+        common levels of left and right
+    lindexer : np.ndarray[np.intp]
+        left indexer
+    rindexer : np.ndarray[np.intp]
+        right indexer
+
+    Returns
+    -------
+    levels : list of Index
+        levels of combined multiindexes
+    labels : np.ndarray[np.intp]
+        labels of combined multiindexes
+    names : List[Hashable]
+        names of combined multiindex levels
+
+    """
+
+    def _convert_to_multiindex(index: Index) -> MultiIndex:
+        if isinstance(index, MultiIndex):
+            return index
+        else:
+            return MultiIndex.from_arrays([index._values], names=[index.name])
+
+    # For multi-multi joins with one overlapping level,
+    # the returned index if of type Index
+    # Assure that join_index is of type MultiIndex
+    # so that dropped levels can be appended
+    join_index = _convert_to_multiindex(join_index)
+
+    join_levels = join_index.levels
+    join_codes = join_index.codes
+    join_names = join_index.names
+
+    # Iterate through the levels that must be restored
+    for dropped_level_name in dropped_level_names:
+        if dropped_level_name in left.names:
+            idx = left
+            indexer = lindexer
+        else:
+            idx = right
+            indexer = rindexer
+
+        # The index of the level name to be restored
+        name_idx = idx.names.index(dropped_level_name)
+
+        restore_levels = idx.levels[name_idx]
+        # Inject -1 in the codes list where a join was not possible
+        # IOW indexer[i]=-1
+        codes = idx.codes[name_idx]
+        if indexer is None:
+            restore_codes = codes
+        else:
+            restore_codes = algos.take_nd(codes, indexer, fill_value=-1)
+
+        # error: Cannot determine type of "__add__"
+        join_levels = join_levels + [restore_levels]  # type: ignore[has-type]
+        join_codes = join_codes + [restore_codes]  # type: ignore[has-type]
+        join_names = join_names + [dropped_level_name]
+
+    return join_levels, join_codes, join_names
+
+
+class _OrderedMerge(_MergeOperation):
+    _merge_type = "ordered_merge"
+
+    def __init__(
+        self,
+        left: DataFrame | Series,
+        right: DataFrame | Series,
+        on: IndexLabel | None = None,
+        left_on: IndexLabel | None = None,
+        right_on: IndexLabel | None = None,
+        left_index: bool = False,
+        right_index: bool = False,
+        suffixes: Suffixes = ("_x", "_y"),
+        fill_method: str | None = None,
+        how: JoinHow | Literal["asof"] = "outer",
+    ) -> None:
+        self.fill_method = fill_method
+        _MergeOperation.__init__(
+            self,
+            left,
+            right,
+            on=on,
+            left_on=left_on,
+            left_index=left_index,
+            right_index=right_index,
+            right_on=right_on,
+            how=how,
+            suffixes=suffixes,
+            sort=True,  # factorize sorts
+        )
+
+    def get_result(self, copy: bool | None = True) -> DataFrame:
+        join_index, left_indexer, right_indexer = self._get_join_info()
+
+        left_join_indexer: npt.NDArray[np.intp] | None
+        right_join_indexer: npt.NDArray[np.intp] | None
+
+        if self.fill_method == "ffill":
+            if left_indexer is None:
+                left_join_indexer = None
+            else:
+                left_join_indexer = libjoin.ffill_indexer(left_indexer)
+            if right_indexer is None:
+                right_join_indexer = None
+            else:
+                right_join_indexer = libjoin.ffill_indexer(right_indexer)
+        elif self.fill_method is None:
+            left_join_indexer = left_indexer
+            right_join_indexer = right_indexer
+        else:
+            raise ValueError("fill_method must be 'ffill' or None")
+
+        result = self._reindex_and_concat(
+            join_index, left_join_indexer, right_join_indexer, copy=copy
+        )
+        self._maybe_add_join_keys(result, left_indexer, right_indexer)
+
+        return result
+
+
+def _asof_by_function(direction: str):
+    name = f"asof_join_{direction}_on_X_by_Y"
+    return getattr(libjoin, name, None)
+
+
+class _AsOfMerge(_OrderedMerge):
+    _merge_type = "asof_merge"
+
+    def __init__(
+        self,
+        left: DataFrame | Series,
+        right: DataFrame | Series,
+        on: IndexLabel | None = None,
+        left_on: IndexLabel | None = None,
+        right_on: IndexLabel | None = None,
+        left_index: bool = False,
+        right_index: bool = False,
+        by=None,
+        left_by=None,
+        right_by=None,
+        suffixes: Suffixes = ("_x", "_y"),
+        how: Literal["asof"] = "asof",
+        tolerance=None,
+        allow_exact_matches: bool = True,
+        direction: str = "backward",
+    ) -> None:
+        self.by = by
+        self.left_by = left_by
+        self.right_by = right_by
+        self.tolerance = tolerance
+        self.allow_exact_matches = allow_exact_matches
+        self.direction = direction
+
+        # check 'direction' is valid
+        if self.direction not in ["backward", "forward", "nearest"]:
+            raise MergeError(f"direction invalid: {self.direction}")
+
+        # validate allow_exact_matches
+        if not is_bool(self.allow_exact_matches):
+            msg = (
+                "allow_exact_matches must be boolean, "
+                f"passed {self.allow_exact_matches}"
+            )
+            raise MergeError(msg)
+
+        _OrderedMerge.__init__(
+            self,
+            left,
+            right,
+            on=on,
+            left_on=left_on,
+            right_on=right_on,
+            left_index=left_index,
+            right_index=right_index,
+            how=how,
+            suffixes=suffixes,
+            fill_method=None,
+        )
+
+    def _validate_left_right_on(self, left_on, right_on):
+        left_on, right_on = super()._validate_left_right_on(left_on, right_on)
+
+        # we only allow on to be a single item for on
+        if len(left_on) != 1 and not self.left_index:
+            raise MergeError("can only asof on a key for left")
+
+        if len(right_on) != 1 and not self.right_index:
+            raise MergeError("can only asof on a key for right")
+
+        if self.left_index and isinstance(self.left.index, MultiIndex):
+            raise MergeError("left can only have one index")
+
+        if self.right_index and isinstance(self.right.index, MultiIndex):
+            raise MergeError("right can only have one index")
+
+        # set 'by' columns
+        if self.by is not None:
+            if self.left_by is not None or self.right_by is not None:
+                raise MergeError("Can only pass by OR left_by and right_by")
+            self.left_by = self.right_by = self.by
+        if self.left_by is None and self.right_by is not None:
+            raise MergeError("missing left_by")
+        if self.left_by is not None and self.right_by is None:
+            raise MergeError("missing right_by")
+
+        # GH#29130 Check that merge keys do not have dtype object
+        if not self.left_index:
+            left_on_0 = left_on[0]
+            if isinstance(left_on_0, _known):
+                lo_dtype = left_on_0.dtype
+            else:
+                lo_dtype = (
+                    self.left._get_label_or_level_values(left_on_0).dtype
+                    if left_on_0 in self.left.columns
+                    else self.left.index.get_level_values(left_on_0)
+                )
+        else:
+            lo_dtype = self.left.index.dtype
+
+        if not self.right_index:
+            right_on_0 = right_on[0]
+            if isinstance(right_on_0, _known):
+                ro_dtype = right_on_0.dtype
+            else:
+                ro_dtype = (
+                    self.right._get_label_or_level_values(right_on_0).dtype
+                    if right_on_0 in self.right.columns
+                    else self.right.index.get_level_values(right_on_0)
+                )
+        else:
+            ro_dtype = self.right.index.dtype
+
+        if (
+            is_object_dtype(lo_dtype)
+            or is_object_dtype(ro_dtype)
+            or is_string_dtype(lo_dtype)
+            or is_string_dtype(ro_dtype)
+        ):
+            raise MergeError(
+                f"Incompatible merge dtype, {repr(ro_dtype)} and "
+                f"{repr(lo_dtype)}, both sides must have numeric dtype"
+            )
+
+        # add 'by' to our key-list so we can have it in the
+        # output as a key
+        if self.left_by is not None:
+            if not is_list_like(self.left_by):
+                self.left_by = [self.left_by]
+            if not is_list_like(self.right_by):
+                self.right_by = [self.right_by]
+
+            if len(self.left_by) != len(self.right_by):
+                raise MergeError("left_by and right_by must be the same length")
+
+            left_on = self.left_by + list(left_on)
+            right_on = self.right_by + list(right_on)
+
+        return left_on, right_on
+
+    def _maybe_require_matching_dtypes(
+        self, left_join_keys: list[ArrayLike], right_join_keys: list[ArrayLike]
+    ) -> None:
+        # TODO: why do we do this for AsOfMerge but not the others?
+
+        def _check_dtype_match(left: ArrayLike, right: ArrayLike, i: int):
+            if left.dtype != right.dtype:
+                if isinstance(left.dtype, CategoricalDtype) and isinstance(
+                    right.dtype, CategoricalDtype
+                ):
+                    # The generic error message is confusing for categoricals.
+                    #
+                    # In this function, the join keys include both the original
+                    # ones of the merge_asof() call, and also the keys passed
+                    # to its by= argument. Unordered but equal categories
+                    # are not supported for the former, but will fail
+                    # later with a ValueError, so we don't *need* to check
+                    # for them here.
+                    msg = (
+                        f"incompatible merge keys [{i}] {repr(left.dtype)} and "
+                        f"{repr(right.dtype)}, both sides category, but not equal ones"
+                    )
+                else:
+                    msg = (
+                        f"incompatible merge keys [{i}] {repr(left.dtype)} and "
+                        f"{repr(right.dtype)}, must be the same type"
+                    )
+                raise MergeError(msg)
+
+        # validate index types are the same
+        for i, (lk, rk) in enumerate(zip(left_join_keys, right_join_keys)):
+            _check_dtype_match(lk, rk, i)
+
+        if self.left_index:
+            lt = self.left.index._values
+        else:
+            lt = left_join_keys[-1]
+
+        if self.right_index:
+            rt = self.right.index._values
+        else:
+            rt = right_join_keys[-1]
+
+        _check_dtype_match(lt, rt, 0)
+
+    def _validate_tolerance(self, left_join_keys: list[ArrayLike]) -> None:
+        # validate tolerance; datetime.timedelta or Timedelta if we have a DTI
+        if self.tolerance is not None:
+            if self.left_index:
+                lt = self.left.index._values
+            else:
+                lt = left_join_keys[-1]
+
+            msg = (
+                f"incompatible tolerance {self.tolerance}, must be compat "
+                f"with type {repr(lt.dtype)}"
+            )
+
+            if needs_i8_conversion(lt.dtype) or (
+                isinstance(lt, ArrowExtensionArray) and lt.dtype.kind in "mM"
+            ):
+                if not isinstance(self.tolerance, datetime.timedelta):
+                    raise MergeError(msg)
+                if self.tolerance < Timedelta(0):
+                    raise MergeError("tolerance must be positive")
+
+            elif is_integer_dtype(lt.dtype):
+                if not is_integer(self.tolerance):
+                    raise MergeError(msg)
+                if self.tolerance < 0:
+                    raise MergeError("tolerance must be positive")
+
+            elif is_float_dtype(lt.dtype):
+                if not is_number(self.tolerance):
+                    raise MergeError(msg)
+                # error: Unsupported operand types for > ("int" and "Number")
+                if self.tolerance < 0:  # type: ignore[operator]
+                    raise MergeError("tolerance must be positive")
+
+            else:
+                raise MergeError("key must be integer, timestamp or float")
+
+    def _convert_values_for_libjoin(
+        self, values: AnyArrayLike, side: str
+    ) -> np.ndarray:
+        # we require sortedness and non-null values in the join keys
+        if not Index(values).is_monotonic_increasing:
+            if isna(values).any():
+                raise ValueError(f"Merge keys contain null values on {side} side")
+            raise ValueError(f"{side} keys must be sorted")
+
+        if isinstance(values, ArrowExtensionArray):
+            values = values._maybe_convert_datelike_array()
+
+        if needs_i8_conversion(values.dtype):
+            values = values.view("i8")
+
+        elif isinstance(values, BaseMaskedArray):
+            # we've verified above that no nulls exist
+            values = values._data
+        elif isinstance(values, ExtensionArray):
+            values = values.to_numpy()
+
+        # error: Incompatible return value type (got "Union[ExtensionArray,
+        # Any, ndarray[Any, Any], ndarray[Any, dtype[Any]], Index, Series]",
+        # expected "ndarray[Any, Any]")
+        return values  # type: ignore[return-value]
+
+    def _get_join_indexers(self) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]:
+        """return the join indexers"""
+
+        # values to compare
+        left_values = (
+            self.left.index._values if self.left_index else self.left_join_keys[-1]
+        )
+        right_values = (
+            self.right.index._values if self.right_index else self.right_join_keys[-1]
+        )
+
+        # _maybe_require_matching_dtypes already checked for dtype matching
+        assert left_values.dtype == right_values.dtype
+
+        tolerance = self.tolerance
+        if tolerance is not None:
+            # TODO: can we reuse a tolerance-conversion function from
+            #  e.g. TimedeltaIndex?
+            if needs_i8_conversion(left_values.dtype) or (
+                isinstance(left_values, ArrowExtensionArray)
+                and left_values.dtype.kind in "mM"
+            ):
+                tolerance = Timedelta(tolerance)
+                # TODO: we have no test cases with PeriodDtype here; probably
+                #  need to adjust tolerance for that case.
+                if left_values.dtype.kind in "mM":
+                    # Make sure the i8 representation for tolerance
+                    #  matches that for left_values/right_values.
+                    if isinstance(left_values, ArrowExtensionArray):
+                        unit = left_values.dtype.pyarrow_dtype.unit
+                    else:
+                        unit = ensure_wrapped_if_datetimelike(left_values).unit
+                    tolerance = tolerance.as_unit(unit)
+
+                tolerance = tolerance._value
+
+        # initial type conversion as needed
+        left_values = self._convert_values_for_libjoin(left_values, "left")
+        right_values = self._convert_values_for_libjoin(right_values, "right")
+
+        # a "by" parameter requires special handling
+        if self.left_by is not None:
+            # remove 'on' parameter from values if one existed
+            if self.left_index and self.right_index:
+                left_join_keys = self.left_join_keys
+                right_join_keys = self.right_join_keys
+            else:
+                left_join_keys = self.left_join_keys[0:-1]
+                right_join_keys = self.right_join_keys[0:-1]
+
+            mapped = [
+                _factorize_keys(
+                    left_join_keys[n],
+                    right_join_keys[n],
+                    sort=False,
+                )
+                for n in range(len(left_join_keys))
+            ]
+
+            if len(left_join_keys) == 1:
+                left_by_values = mapped[0][0]
+                right_by_values = mapped[0][1]
+            else:
+                arrs = [np.concatenate(m[:2]) for m in mapped]
+                shape = tuple(m[2] for m in mapped)
+                group_index = get_group_index(
+                    arrs, shape=shape, sort=False, xnull=False
+                )
+                left_len = len(left_join_keys[0])
+                left_by_values = group_index[:left_len]
+                right_by_values = group_index[left_len:]
+
+            left_by_values = ensure_int64(left_by_values)
+            right_by_values = ensure_int64(right_by_values)
+
+            # choose appropriate function by type
+            func = _asof_by_function(self.direction)
+            return func(
+                left_values,
+                right_values,
+                left_by_values,
+                right_by_values,
+                self.allow_exact_matches,
+                tolerance,
+            )
+        else:
+            # choose appropriate function by type
+            func = _asof_by_function(self.direction)
+            return func(
+                left_values,
+                right_values,
+                None,
+                None,
+                self.allow_exact_matches,
+                tolerance,
+                False,
+            )
+
+
+def _get_multiindex_indexer(
+    join_keys: list[ArrayLike], index: MultiIndex, sort: bool
+) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]:
+    # left & right join labels and num. of levels at each location
+    mapped = (
+        _factorize_keys(index.levels[n]._values, join_keys[n], sort=sort)
+        for n in range(index.nlevels)
+    )
+    zipped = zip(*mapped)
+    rcodes, lcodes, shape = (list(x) for x in zipped)
+    if sort:
+        rcodes = list(map(np.take, rcodes, index.codes))
+    else:
+        i8copy = lambda a: a.astype("i8", subok=False, copy=True)
+        rcodes = list(map(i8copy, index.codes))
+
+    # fix right labels if there were any nulls
+    for i, join_key in enumerate(join_keys):
+        mask = index.codes[i] == -1
+        if mask.any():
+            # check if there already was any nulls at this location
+            # if there was, it is factorized to `shape[i] - 1`
+            a = join_key[lcodes[i] == shape[i] - 1]
+            if a.size == 0 or not a[0] != a[0]:
+                shape[i] += 1
+
+            rcodes[i][mask] = shape[i] - 1
+
+    # get flat i8 join keys
+    lkey, rkey = _get_join_keys(lcodes, rcodes, tuple(shape), sort)
+    return lkey, rkey
+
+
+def _get_empty_indexer() -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]:
+    """Return empty join indexers."""
+    return (
+        np.array([], dtype=np.intp),
+        np.array([], dtype=np.intp),
+    )
+
+
+def _get_no_sort_one_missing_indexer(
+    n: int, left_missing: bool
+) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]:
+    """
+    Return join indexers where all of one side is selected without sorting
+    and none of the other side is selected.
+
+    Parameters
+    ----------
+    n : int
+        Length of indexers to create.
+    left_missing : bool
+        If True, the left indexer will contain only -1's.
+        If False, the right indexer will contain only -1's.
+
+    Returns
+    -------
+    np.ndarray[np.intp]
+        Left indexer
+    np.ndarray[np.intp]
+        Right indexer
+    """
+    idx = np.arange(n, dtype=np.intp)
+    idx_missing = np.full(shape=n, fill_value=-1, dtype=np.intp)
+    if left_missing:
+        return idx_missing, idx
+    return idx, idx_missing
+
+
+def _left_join_on_index(
+    left_ax: Index, right_ax: Index, join_keys: list[ArrayLike], sort: bool = False
+) -> tuple[Index, npt.NDArray[np.intp] | None, npt.NDArray[np.intp]]:
+    if isinstance(right_ax, MultiIndex):
+        lkey, rkey = _get_multiindex_indexer(join_keys, right_ax, sort=sort)
+    else:
+        # error: Incompatible types in assignment (expression has type
+        # "Union[Union[ExtensionArray, ndarray[Any, Any]], Index, Series]",
+        # variable has type "ndarray[Any, dtype[signedinteger[Any]]]")
+        lkey = join_keys[0]  # type: ignore[assignment]
+        # error: Incompatible types in assignment (expression has type "Index",
+        # variable has type "ndarray[Any, dtype[signedinteger[Any]]]")
+        rkey = right_ax._values  # type: ignore[assignment]
+
+    left_key, right_key, count = _factorize_keys(lkey, rkey, sort=sort)
+    left_indexer, right_indexer = libjoin.left_outer_join(
+        left_key, right_key, count, sort=sort
+    )
+
+    if sort or len(left_ax) != len(left_indexer):
+        # if asked to sort or there are 1-to-many matches
+        join_index = left_ax.take(left_indexer)
+        return join_index, left_indexer, right_indexer
+
+    # left frame preserves order & length of its index
+    return left_ax, None, right_indexer
+
+
+def _factorize_keys(
+    lk: ArrayLike, rk: ArrayLike, sort: bool = True
+) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp], int]:
+    """
+    Encode left and right keys as enumerated types.
+
+    This is used to get the join indexers to be used when merging DataFrames.
+
+    Parameters
+    ----------
+    lk : ndarray, ExtensionArray
+        Left key.
+    rk : ndarray, ExtensionArray
+        Right key.
+    sort : bool, defaults to True
+        If True, the encoding is done such that the unique elements in the
+        keys are sorted.
+
+    Returns
+    -------
+    np.ndarray[np.intp]
+        Left (resp. right if called with `key='right'`) labels, as enumerated type.
+    np.ndarray[np.intp]
+        Right (resp. left if called with `key='right'`) labels, as enumerated type.
+    int
+        Number of unique elements in union of left and right labels.
+
+    See Also
+    --------
+    merge : Merge DataFrame or named Series objects
+        with a database-style join.
+    algorithms.factorize : Encode the object as an enumerated type
+        or categorical variable.
+
+    Examples
+    --------
+    >>> lk = np.array(["a", "c", "b"])
+    >>> rk = np.array(["a", "c"])
+
+    Here, the unique values are `'a', 'b', 'c'`. With the default
+    `sort=True`, the encoding will be `{0: 'a', 1: 'b', 2: 'c'}`:
+
+    >>> pd.core.reshape.merge._factorize_keys(lk, rk)
+    (array([0, 2, 1]), array([0, 2]), 3)
+
+    With the `sort=False`, the encoding will correspond to the order
+    in which the unique elements first appear: `{0: 'a', 1: 'c', 2: 'b'}`:
+
+    >>> pd.core.reshape.merge._factorize_keys(lk, rk, sort=False)
+    (array([0, 1, 2]), array([0, 1]), 3)
+    """
+    # TODO: if either is a RangeIndex, we can likely factorize more efficiently?
+
+    if (
+        isinstance(lk.dtype, DatetimeTZDtype) and isinstance(rk.dtype, DatetimeTZDtype)
+    ) or (lib.is_np_dtype(lk.dtype, "M") and lib.is_np_dtype(rk.dtype, "M")):
+        # Extract the ndarray (UTC-localized) values
+        # Note: we dont need the dtypes to match, as these can still be compared
+        lk, rk = cast("DatetimeArray", lk)._ensure_matching_resos(rk)
+        lk = cast("DatetimeArray", lk)._ndarray
+        rk = cast("DatetimeArray", rk)._ndarray
+
+    elif (
+        isinstance(lk.dtype, CategoricalDtype)
+        and isinstance(rk.dtype, CategoricalDtype)
+        and lk.dtype == rk.dtype
+    ):
+        assert isinstance(lk, Categorical)
+        assert isinstance(rk, Categorical)
+        # Cast rk to encoding so we can compare codes with lk
+
+        rk = lk._encode_with_my_categories(rk)
+
+        lk = ensure_int64(lk.codes)
+        rk = ensure_int64(rk.codes)
+
+    elif isinstance(lk, ExtensionArray) and lk.dtype == rk.dtype:
+        if (isinstance(lk.dtype, ArrowDtype) and is_string_dtype(lk.dtype)) or (
+            isinstance(lk.dtype, StringDtype) and lk.dtype.storage == "pyarrow"
+        ):
+            import pyarrow as pa
+            import pyarrow.compute as pc
+
+            len_lk = len(lk)
+            lk = lk._pa_array  # type: ignore[attr-defined]
+            rk = rk._pa_array  # type: ignore[union-attr]
+            dc = (
+                pa.chunked_array(lk.chunks + rk.chunks)  # type: ignore[union-attr]
+                .combine_chunks()
+                .dictionary_encode()
+            )
+
+            llab, rlab, count = (
+                pc.fill_null(dc.indices[slice(len_lk)], -1)
+                .to_numpy()
+                .astype(np.intp, copy=False),
+                pc.fill_null(dc.indices[slice(len_lk, None)], -1)
+                .to_numpy()
+                .astype(np.intp, copy=False),
+                len(dc.dictionary),
+            )
+
+            if sort:
+                uniques = dc.dictionary.to_numpy(zero_copy_only=False)
+                llab, rlab = _sort_labels(uniques, llab, rlab)
+
+            if dc.null_count > 0:
+                lmask = llab == -1
+                lany = lmask.any()
+                rmask = rlab == -1
+                rany = rmask.any()
+                if lany:
+                    np.putmask(llab, lmask, count)
+                if rany:
+                    np.putmask(rlab, rmask, count)
+                count += 1
+            return llab, rlab, count
+
+        if not isinstance(lk, BaseMaskedArray) and not (
+            # exclude arrow dtypes that would get cast to object
+            isinstance(lk.dtype, ArrowDtype)
+            and (
+                is_numeric_dtype(lk.dtype.numpy_dtype)
+                or is_string_dtype(lk.dtype)
+                and not sort
+            )
+        ):
+            lk, _ = lk._values_for_factorize()
+
+            # error: Item "ndarray" of "Union[Any, ndarray]" has no attribute
+            # "_values_for_factorize"
+            rk, _ = rk._values_for_factorize()  # type: ignore[union-attr]
+
+    if needs_i8_conversion(lk.dtype) and lk.dtype == rk.dtype:
+        # GH#23917 TODO: Needs tests for non-matching dtypes
+        # GH#23917 TODO: needs tests for case where lk is integer-dtype
+        #  and rk is datetime-dtype
+        lk = np.asarray(lk, dtype=np.int64)
+        rk = np.asarray(rk, dtype=np.int64)
+
+    klass, lk, rk = _convert_arrays_and_get_rizer_klass(lk, rk)
+
+    rizer = klass(max(len(lk), len(rk)))
+
+    if isinstance(lk, BaseMaskedArray):
+        assert isinstance(rk, BaseMaskedArray)
+        llab = rizer.factorize(lk._data, mask=lk._mask)
+        rlab = rizer.factorize(rk._data, mask=rk._mask)
+    elif isinstance(lk, ArrowExtensionArray):
+        assert isinstance(rk, ArrowExtensionArray)
+        # we can only get here with numeric dtypes
+        # TODO: Remove when we have a Factorizer for Arrow
+        llab = rizer.factorize(
+            lk.to_numpy(na_value=1, dtype=lk.dtype.numpy_dtype), mask=lk.isna()
+        )
+        rlab = rizer.factorize(
+            rk.to_numpy(na_value=1, dtype=lk.dtype.numpy_dtype), mask=rk.isna()
+        )
+    else:
+        # Argument 1 to "factorize" of "ObjectFactorizer" has incompatible type
+        # "Union[ndarray[Any, dtype[signedinteger[_64Bit]]],
+        # ndarray[Any, dtype[object_]]]"; expected "ndarray[Any, dtype[object_]]"
+        llab = rizer.factorize(lk)  # type: ignore[arg-type]
+        rlab = rizer.factorize(rk)  # type: ignore[arg-type]
+    assert llab.dtype == np.dtype(np.intp), llab.dtype
+    assert rlab.dtype == np.dtype(np.intp), rlab.dtype
+
+    count = rizer.get_count()
+
+    if sort:
+        uniques = rizer.uniques.to_array()
+        llab, rlab = _sort_labels(uniques, llab, rlab)
+
+    # NA group
+    lmask = llab == -1
+    lany = lmask.any()
+    rmask = rlab == -1
+    rany = rmask.any()
+
+    if lany or rany:
+        if lany:
+            np.putmask(llab, lmask, count)
+        if rany:
+            np.putmask(rlab, rmask, count)
+        count += 1
+
+    return llab, rlab, count
+
+
+def _convert_arrays_and_get_rizer_klass(
+    lk: ArrayLike, rk: ArrayLike
+) -> tuple[type[libhashtable.Factorizer], ArrayLike, ArrayLike]:
+    klass: type[libhashtable.Factorizer]
+    if is_numeric_dtype(lk.dtype):
+        if lk.dtype != rk.dtype:
+            dtype = find_common_type([lk.dtype, rk.dtype])
+            if isinstance(dtype, ExtensionDtype):
+                cls = dtype.construct_array_type()
+                if not isinstance(lk, ExtensionArray):
+                    lk = cls._from_sequence(lk, dtype=dtype, copy=False)
+                else:
+                    lk = lk.astype(dtype, copy=False)
+
+                if not isinstance(rk, ExtensionArray):
+                    rk = cls._from_sequence(rk, dtype=dtype, copy=False)
+                else:
+                    rk = rk.astype(dtype, copy=False)
+            else:
+                lk = lk.astype(dtype, copy=False)
+                rk = rk.astype(dtype, copy=False)
+        if isinstance(lk, BaseMaskedArray):
+            #  Invalid index type "type" for "Dict[Type[object], Type[Factorizer]]";
+            #  expected type "Type[object]"
+            klass = _factorizers[lk.dtype.type]  # type: ignore[index]
+        elif isinstance(lk.dtype, ArrowDtype):
+            klass = _factorizers[lk.dtype.numpy_dtype.type]
+        else:
+            klass = _factorizers[lk.dtype.type]
+
+    else:
+        klass = libhashtable.ObjectFactorizer
+        lk = ensure_object(lk)
+        rk = ensure_object(rk)
+    return klass, lk, rk
+
+
+def _sort_labels(
+    uniques: np.ndarray, left: npt.NDArray[np.intp], right: npt.NDArray[np.intp]
+) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]:
+    llength = len(left)
+    labels = np.concatenate([left, right])
+
+    _, new_labels = algos.safe_sort(uniques, labels, use_na_sentinel=True)
+    new_left, new_right = new_labels[:llength], new_labels[llength:]
+
+    return new_left, new_right
+
+
+def _get_join_keys(
+    llab: list[npt.NDArray[np.int64 | np.intp]],
+    rlab: list[npt.NDArray[np.int64 | np.intp]],
+    shape: Shape,
+    sort: bool,
+) -> tuple[npt.NDArray[np.int64], npt.NDArray[np.int64]]:
+    # how many levels can be done without overflow
+    nlev = next(
+        lev
+        for lev in range(len(shape), 0, -1)
+        if not is_int64_overflow_possible(shape[:lev])
+    )
+
+    # get keys for the first `nlev` levels
+    stride = np.prod(shape[1:nlev], dtype="i8")
+    lkey = stride * llab[0].astype("i8", subok=False, copy=False)
+    rkey = stride * rlab[0].astype("i8", subok=False, copy=False)
+
+    for i in range(1, nlev):
+        with np.errstate(divide="ignore"):
+            stride //= shape[i]
+        lkey += llab[i] * stride
+        rkey += rlab[i] * stride
+
+    if nlev == len(shape):  # all done!
+        return lkey, rkey
+
+    # densify current keys to avoid overflow
+    lkey, rkey, count = _factorize_keys(lkey, rkey, sort=sort)
+
+    llab = [lkey] + llab[nlev:]
+    rlab = [rkey] + rlab[nlev:]
+    shape = (count,) + shape[nlev:]
+
+    return _get_join_keys(llab, rlab, shape, sort)
+
+
+def _should_fill(lname, rname) -> bool:
+    if not isinstance(lname, str) or not isinstance(rname, str):
+        return True
+    return lname == rname
+
+
+def _any(x) -> bool:
+    return x is not None and com.any_not_none(*x)
+
+
+def _validate_operand(obj: DataFrame | Series) -> DataFrame:
+    if isinstance(obj, ABCDataFrame):
+        return obj
+    elif isinstance(obj, ABCSeries):
+        if obj.name is None:
+            raise ValueError("Cannot merge a Series without a name")
+        return obj.to_frame()
+    else:
+        raise TypeError(
+            f"Can only merge Series or DataFrame objects, a {type(obj)} was passed"
+        )
+
+
+def _items_overlap_with_suffix(
+    left: Index, right: Index, suffixes: Suffixes
+) -> tuple[Index, Index]:
+    """
+    Suffixes type validation.
+
+    If two indices overlap, add suffixes to overlapping entries.
+
+    If corresponding suffix is empty, the entry is simply converted to string.
+
+    """
+    if not is_list_like(suffixes, allow_sets=False) or isinstance(suffixes, dict):
+        raise TypeError(
+            f"Passing 'suffixes' as a {type(suffixes)}, is not supported. "
+            "Provide 'suffixes' as a tuple instead."
+        )
+
+    to_rename = left.intersection(right)
+    if len(to_rename) == 0:
+        return left, right
+
+    lsuffix, rsuffix = suffixes
+
+    if not lsuffix and not rsuffix:
+        raise ValueError(f"columns overlap but no suffix specified: {to_rename}")
+
+    def renamer(x, suffix: str | None):
+        """
+        Rename the left and right indices.
+
+        If there is overlap, and suffix is not None, add
+        suffix, otherwise, leave it as-is.
+
+        Parameters
+        ----------
+        x : original column name
+        suffix : str or None
+
+        Returns
+        -------
+        x : renamed column name
+        """
+        if x in to_rename and suffix is not None:
+            return f"{x}{suffix}"
+        return x
+
+    lrenamer = partial(renamer, suffix=lsuffix)
+    rrenamer = partial(renamer, suffix=rsuffix)
+
+    llabels = left._transform_index(lrenamer)
+    rlabels = right._transform_index(rrenamer)
+
+    dups = []
+    if not llabels.is_unique:
+        # Only warn when duplicates are caused because of suffixes, already duplicated
+        # columns in origin should not warn
+        dups = llabels[(llabels.duplicated()) & (~left.duplicated())].tolist()
+    if not rlabels.is_unique:
+        dups.extend(rlabels[(rlabels.duplicated()) & (~right.duplicated())].tolist())
+    if dups:
+        raise MergeError(
+            f"Passing 'suffixes' which cause duplicate columns {set(dups)} is "
+            f"not allowed.",
+        )
+
+    return llabels, rlabels
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/reshape/pivot.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/reshape/pivot.py
new file mode 100644
index 0000000000000000000000000000000000000000..b2a915589cba756c16ecaa95c3d056bd7c902c68
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/reshape/pivot.py
@@ -0,0 +1,899 @@
+from __future__ import annotations
+
+from collections.abc import (
+    Hashable,
+    Sequence,
+)
+from typing import (
+    TYPE_CHECKING,
+    Callable,
+    Literal,
+    cast,
+)
+import warnings
+
+import numpy as np
+
+from pandas._libs import lib
+from pandas.util._decorators import (
+    Appender,
+    Substitution,
+)
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.cast import maybe_downcast_to_dtype
+from pandas.core.dtypes.common import (
+    is_list_like,
+    is_nested_list_like,
+    is_scalar,
+)
+from pandas.core.dtypes.dtypes import ExtensionDtype
+from pandas.core.dtypes.generic import (
+    ABCDataFrame,
+    ABCSeries,
+)
+
+import pandas.core.common as com
+from pandas.core.frame import _shared_docs
+from pandas.core.groupby import Grouper
+from pandas.core.indexes.api import (
+    Index,
+    MultiIndex,
+    get_objs_combined_axis,
+)
+from pandas.core.reshape.concat import concat
+from pandas.core.reshape.util import cartesian_product
+from pandas.core.series import Series
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        AggFuncType,
+        AggFuncTypeBase,
+        AggFuncTypeDict,
+        IndexLabel,
+    )
+
+    from pandas import DataFrame
+
+
+# Note: We need to make sure `frame` is imported before `pivot`, otherwise
+# _shared_docs['pivot_table'] will not yet exist.  TODO: Fix this dependency
+@Substitution("\ndata : DataFrame")
+@Appender(_shared_docs["pivot_table"], indents=1)
+def pivot_table(
+    data: DataFrame,
+    values=None,
+    index=None,
+    columns=None,
+    aggfunc: AggFuncType = "mean",
+    fill_value=None,
+    margins: bool = False,
+    dropna: bool = True,
+    margins_name: Hashable = "All",
+    observed: bool | lib.NoDefault = lib.no_default,
+    sort: bool = True,
+) -> DataFrame:
+    index = _convert_by(index)
+    columns = _convert_by(columns)
+
+    if isinstance(aggfunc, list):
+        pieces: list[DataFrame] = []
+        keys = []
+        for func in aggfunc:
+            _table = __internal_pivot_table(
+                data,
+                values=values,
+                index=index,
+                columns=columns,
+                fill_value=fill_value,
+                aggfunc=func,
+                margins=margins,
+                dropna=dropna,
+                margins_name=margins_name,
+                observed=observed,
+                sort=sort,
+            )
+            pieces.append(_table)
+            keys.append(getattr(func, "__name__", func))
+
+        table = concat(pieces, keys=keys, axis=1)
+        return table.__finalize__(data, method="pivot_table")
+
+    table = __internal_pivot_table(
+        data,
+        values,
+        index,
+        columns,
+        aggfunc,
+        fill_value,
+        margins,
+        dropna,
+        margins_name,
+        observed,
+        sort,
+    )
+    return table.__finalize__(data, method="pivot_table")
+
+
+def __internal_pivot_table(
+    data: DataFrame,
+    values,
+    index,
+    columns,
+    aggfunc: AggFuncTypeBase | AggFuncTypeDict,
+    fill_value,
+    margins: bool,
+    dropna: bool,
+    margins_name: Hashable,
+    observed: bool | lib.NoDefault,
+    sort: bool,
+) -> DataFrame:
+    """
+    Helper of :func:`pandas.pivot_table` for any non-list ``aggfunc``.
+    """
+    keys = index + columns
+
+    values_passed = values is not None
+    if values_passed:
+        if is_list_like(values):
+            values_multi = True
+            values = list(values)
+        else:
+            values_multi = False
+            values = [values]
+
+        # GH14938 Make sure value labels are in data
+        for i in values:
+            if i not in data:
+                raise KeyError(i)
+
+        to_filter = []
+        for x in keys + values:
+            if isinstance(x, Grouper):
+                x = x.key
+            try:
+                if x in data:
+                    to_filter.append(x)
+            except TypeError:
+                pass
+        if len(to_filter) < len(data.columns):
+            data = data[to_filter]
+
+    else:
+        values = data.columns
+        for key in keys:
+            try:
+                values = values.drop(key)
+            except (TypeError, ValueError, KeyError):
+                pass
+        values = list(values)
+
+    observed_bool = False if observed is lib.no_default else observed
+    grouped = data.groupby(keys, observed=observed_bool, sort=sort, dropna=dropna)
+    if observed is lib.no_default and any(
+        ping._passed_categorical for ping in grouped._grouper.groupings
+    ):
+        warnings.warn(
+            "The default value of observed=False is deprecated and will change "
+            "to observed=True in a future version of pandas. Specify "
+            "observed=False to silence this warning and retain the current behavior",
+            category=FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+    agged = grouped.agg(aggfunc)
+
+    if dropna and isinstance(agged, ABCDataFrame) and len(agged.columns):
+        agged = agged.dropna(how="all")
+
+    table = agged
+
+    # GH17038, this check should only happen if index is defined (not None)
+    if table.index.nlevels > 1 and index:
+        # Related GH #17123
+        # If index_names are integers, determine whether the integers refer
+        # to the level position or name.
+        index_names = agged.index.names[: len(index)]
+        to_unstack = []
+        for i in range(len(index), len(keys)):
+            name = agged.index.names[i]
+            if name is None or name in index_names:
+                to_unstack.append(i)
+            else:
+                to_unstack.append(name)
+        table = agged.unstack(to_unstack, fill_value=fill_value)
+
+    if not dropna:
+        if isinstance(table.index, MultiIndex):
+            m = MultiIndex.from_arrays(
+                cartesian_product(table.index.levels), names=table.index.names
+            )
+            table = table.reindex(m, axis=0, fill_value=fill_value)
+
+        if isinstance(table.columns, MultiIndex):
+            m = MultiIndex.from_arrays(
+                cartesian_product(table.columns.levels), names=table.columns.names
+            )
+            table = table.reindex(m, axis=1, fill_value=fill_value)
+
+    if sort is True and isinstance(table, ABCDataFrame):
+        table = table.sort_index(axis=1)
+
+    if fill_value is not None:
+        table = table.fillna(fill_value)
+        if aggfunc is len and not observed and lib.is_integer(fill_value):
+            # TODO: can we avoid this?  this used to be handled by
+            #  downcast="infer" in fillna
+            table = table.astype(np.int64)
+
+    if margins:
+        if dropna:
+            data = data[data.notna().all(axis=1)]
+        table = _add_margins(
+            table,
+            data,
+            values,
+            rows=index,
+            cols=columns,
+            aggfunc=aggfunc,
+            observed=dropna,
+            margins_name=margins_name,
+            fill_value=fill_value,
+        )
+
+    # discard the top level
+    if values_passed and not values_multi and table.columns.nlevels > 1:
+        table.columns = table.columns.droplevel(0)
+    if len(index) == 0 and len(columns) > 0:
+        table = table.T
+
+    # GH 15193 Make sure empty columns are removed if dropna=True
+    if isinstance(table, ABCDataFrame) and dropna:
+        table = table.dropna(how="all", axis=1)
+
+    return table
+
+
+def _add_margins(
+    table: DataFrame | Series,
+    data: DataFrame,
+    values,
+    rows,
+    cols,
+    aggfunc,
+    observed: bool,
+    margins_name: Hashable = "All",
+    fill_value=None,
+):
+    if not isinstance(margins_name, str):
+        raise ValueError("margins_name argument must be a string")
+
+    msg = f'Conflicting name "{margins_name}" in margins'
+    for level in table.index.names:
+        if margins_name in table.index.get_level_values(level):
+            raise ValueError(msg)
+
+    grand_margin = _compute_grand_margin(data, values, aggfunc, margins_name)
+
+    if table.ndim == 2:
+        # i.e. DataFrame
+        for level in table.columns.names[1:]:
+            if margins_name in table.columns.get_level_values(level):
+                raise ValueError(msg)
+
+    key: str | tuple[str, ...]
+    if len(rows) > 1:
+        key = (margins_name,) + ("",) * (len(rows) - 1)
+    else:
+        key = margins_name
+
+    if not values and isinstance(table, ABCSeries):
+        # If there are no values and the table is a series, then there is only
+        # one column in the data. Compute grand margin and return it.
+        return table._append(table._constructor({key: grand_margin[margins_name]}))
+
+    elif values:
+        marginal_result_set = _generate_marginal_results(
+            table, data, values, rows, cols, aggfunc, observed, margins_name
+        )
+        if not isinstance(marginal_result_set, tuple):
+            return marginal_result_set
+        result, margin_keys, row_margin = marginal_result_set
+    else:
+        # no values, and table is a DataFrame
+        assert isinstance(table, ABCDataFrame)
+        marginal_result_set = _generate_marginal_results_without_values(
+            table, data, rows, cols, aggfunc, observed, margins_name
+        )
+        if not isinstance(marginal_result_set, tuple):
+            return marginal_result_set
+        result, margin_keys, row_margin = marginal_result_set
+
+    row_margin = row_margin.reindex(result.columns, fill_value=fill_value)
+    # populate grand margin
+    for k in margin_keys:
+        if isinstance(k, str):
+            row_margin[k] = grand_margin[k]
+        else:
+            row_margin[k] = grand_margin[k[0]]
+
+    from pandas import DataFrame
+
+    margin_dummy = DataFrame(row_margin, columns=Index([key])).T
+
+    row_names = result.index.names
+    # check the result column and leave floats
+
+    for dtype in set(result.dtypes):
+        if isinstance(dtype, ExtensionDtype):
+            # Can hold NA already
+            continue
+
+        cols = result.select_dtypes([dtype]).columns
+        margin_dummy[cols] = margin_dummy[cols].apply(
+            maybe_downcast_to_dtype, args=(dtype,)
+        )
+    result = result._append(margin_dummy)
+    result.index.names = row_names
+
+    return result
+
+
+def _compute_grand_margin(
+    data: DataFrame, values, aggfunc, margins_name: Hashable = "All"
+):
+    if values:
+        grand_margin = {}
+        for k, v in data[values].items():
+            try:
+                if isinstance(aggfunc, str):
+                    grand_margin[k] = getattr(v, aggfunc)()
+                elif isinstance(aggfunc, dict):
+                    if isinstance(aggfunc[k], str):
+                        grand_margin[k] = getattr(v, aggfunc[k])()
+                    else:
+                        grand_margin[k] = aggfunc[k](v)
+                else:
+                    grand_margin[k] = aggfunc(v)
+            except TypeError:
+                pass
+        return grand_margin
+    else:
+        return {margins_name: aggfunc(data.index)}
+
+
+def _generate_marginal_results(
+    table,
+    data: DataFrame,
+    values,
+    rows,
+    cols,
+    aggfunc,
+    observed: bool,
+    margins_name: Hashable = "All",
+):
+    margin_keys: list | Index
+    if len(cols) > 0:
+        # need to "interleave" the margins
+        table_pieces = []
+        margin_keys = []
+
+        def _all_key(key):
+            return (key, margins_name) + ("",) * (len(cols) - 1)
+
+        if len(rows) > 0:
+            margin = data[rows + values].groupby(rows, observed=observed).agg(aggfunc)
+            cat_axis = 1
+
+            for key, piece in table.T.groupby(level=0, observed=observed):
+                piece = piece.T
+                all_key = _all_key(key)
+
+                # we are going to mutate this, so need to copy!
+                piece = piece.copy()
+                piece[all_key] = margin[key]
+
+                table_pieces.append(piece)
+                margin_keys.append(all_key)
+        else:
+            from pandas import DataFrame
+
+            cat_axis = 0
+            for key, piece in table.groupby(level=0, observed=observed):
+                if len(cols) > 1:
+                    all_key = _all_key(key)
+                else:
+                    all_key = margins_name
+                table_pieces.append(piece)
+                # GH31016 this is to calculate margin for each group, and assign
+                # corresponded key as index
+                transformed_piece = DataFrame(piece.apply(aggfunc)).T
+                if isinstance(piece.index, MultiIndex):
+                    # We are adding an empty level
+                    transformed_piece.index = MultiIndex.from_tuples(
+                        [all_key], names=piece.index.names + [None]
+                    )
+                else:
+                    transformed_piece.index = Index([all_key], name=piece.index.name)
+
+                # append piece for margin into table_piece
+                table_pieces.append(transformed_piece)
+                margin_keys.append(all_key)
+
+        if not table_pieces:
+            # GH 49240
+            return table
+        else:
+            result = concat(table_pieces, axis=cat_axis)
+
+        if len(rows) == 0:
+            return result
+    else:
+        result = table
+        margin_keys = table.columns
+
+    if len(cols) > 0:
+        row_margin = data[cols + values].groupby(cols, observed=observed).agg(aggfunc)
+        row_margin = row_margin.stack(future_stack=True)
+
+        # GH#26568. Use names instead of indices in case of numeric names
+        new_order_indices = [len(cols)] + list(range(len(cols)))
+        new_order_names = [row_margin.index.names[i] for i in new_order_indices]
+        row_margin.index = row_margin.index.reorder_levels(new_order_names)
+    else:
+        row_margin = data._constructor_sliced(np.nan, index=result.columns)
+
+    return result, margin_keys, row_margin
+
+
+def _generate_marginal_results_without_values(
+    table: DataFrame,
+    data: DataFrame,
+    rows,
+    cols,
+    aggfunc,
+    observed: bool,
+    margins_name: Hashable = "All",
+):
+    margin_keys: list | Index
+    if len(cols) > 0:
+        # need to "interleave" the margins
+        margin_keys = []
+
+        def _all_key():
+            if len(cols) == 1:
+                return margins_name
+            return (margins_name,) + ("",) * (len(cols) - 1)
+
+        if len(rows) > 0:
+            margin = data.groupby(rows, observed=observed)[rows].apply(aggfunc)
+            all_key = _all_key()
+            table[all_key] = margin
+            result = table
+            margin_keys.append(all_key)
+
+        else:
+            margin = data.groupby(level=0, axis=0, observed=observed).apply(aggfunc)
+            all_key = _all_key()
+            table[all_key] = margin
+            result = table
+            margin_keys.append(all_key)
+            return result
+    else:
+        result = table
+        margin_keys = table.columns
+
+    if len(cols):
+        row_margin = data.groupby(cols, observed=observed)[cols].apply(aggfunc)
+    else:
+        row_margin = Series(np.nan, index=result.columns)
+
+    return result, margin_keys, row_margin
+
+
+def _convert_by(by):
+    if by is None:
+        by = []
+    elif (
+        is_scalar(by)
+        or isinstance(by, (np.ndarray, Index, ABCSeries, Grouper))
+        or callable(by)
+    ):
+        by = [by]
+    else:
+        by = list(by)
+    return by
+
+
+@Substitution("\ndata : DataFrame")
+@Appender(_shared_docs["pivot"], indents=1)
+def pivot(
+    data: DataFrame,
+    *,
+    columns: IndexLabel,
+    index: IndexLabel | lib.NoDefault = lib.no_default,
+    values: IndexLabel | lib.NoDefault = lib.no_default,
+) -> DataFrame:
+    columns_listlike = com.convert_to_list_like(columns)
+
+    # If columns is None we will create a MultiIndex level with None as name
+    # which might cause duplicated names because None is the default for
+    # level names
+    data = data.copy(deep=False)
+    data.index = data.index.copy()
+    data.index.names = [
+        name if name is not None else lib.no_default for name in data.index.names
+    ]
+
+    indexed: DataFrame | Series
+    if values is lib.no_default:
+        if index is not lib.no_default:
+            cols = com.convert_to_list_like(index)
+        else:
+            cols = []
+
+        append = index is lib.no_default
+        # error: Unsupported operand types for + ("List[Any]" and "ExtensionArray")
+        # error: Unsupported left operand type for + ("ExtensionArray")
+        indexed = data.set_index(
+            cols + columns_listlike, append=append  # type: ignore[operator]
+        )
+    else:
+        index_list: list[Index] | list[Series]
+        if index is lib.no_default:
+            if isinstance(data.index, MultiIndex):
+                # GH 23955
+                index_list = [
+                    data.index.get_level_values(i) for i in range(data.index.nlevels)
+                ]
+            else:
+                index_list = [
+                    data._constructor_sliced(data.index, name=data.index.name)
+                ]
+        else:
+            index_list = [data[idx] for idx in com.convert_to_list_like(index)]
+
+        data_columns = [data[col] for col in columns_listlike]
+        index_list.extend(data_columns)
+        multiindex = MultiIndex.from_arrays(index_list)
+
+        if is_list_like(values) and not isinstance(values, tuple):
+            # Exclude tuple because it is seen as a single column name
+            values = cast(Sequence[Hashable], values)
+            indexed = data._constructor(
+                data[values]._values, index=multiindex, columns=values
+            )
+        else:
+            indexed = data._constructor_sliced(data[values]._values, index=multiindex)
+    # error: Argument 1 to "unstack" of "DataFrame" has incompatible type "Union
+    # [List[Any], ExtensionArray, ndarray[Any, Any], Index, Series]"; expected
+    # "Hashable"
+    result = indexed.unstack(columns_listlike)  # type: ignore[arg-type]
+    result.index.names = [
+        name if name is not lib.no_default else None for name in result.index.names
+    ]
+
+    return result
+
+
+def crosstab(
+    index,
+    columns,
+    values=None,
+    rownames=None,
+    colnames=None,
+    aggfunc=None,
+    margins: bool = False,
+    margins_name: Hashable = "All",
+    dropna: bool = True,
+    normalize: bool | Literal[0, 1, "all", "index", "columns"] = False,
+) -> DataFrame:
+    """
+    Compute a simple cross tabulation of two (or more) factors.
+
+    By default, computes a frequency table of the factors unless an
+    array of values and an aggregation function are passed.
+
+    Parameters
+    ----------
+    index : array-like, Series, or list of arrays/Series
+        Values to group by in the rows.
+    columns : array-like, Series, or list of arrays/Series
+        Values to group by in the columns.
+    values : array-like, optional
+        Array of values to aggregate according to the factors.
+        Requires `aggfunc` be specified.
+    rownames : sequence, default None
+        If passed, must match number of row arrays passed.
+    colnames : sequence, default None
+        If passed, must match number of column arrays passed.
+    aggfunc : function, optional
+        If specified, requires `values` be specified as well.
+    margins : bool, default False
+        Add row/column margins (subtotals).
+    margins_name : str, default 'All'
+        Name of the row/column that will contain the totals
+        when margins is True.
+    dropna : bool, default True
+        Do not include columns whose entries are all NaN.
+    normalize : bool, {'all', 'index', 'columns'}, or {0,1}, default False
+        Normalize by dividing all values by the sum of values.
+
+        - If passed 'all' or `True`, will normalize over all values.
+        - If passed 'index' will normalize over each row.
+        - If passed 'columns' will normalize over each column.
+        - If margins is `True`, will also normalize margin values.
+
+    Returns
+    -------
+    DataFrame
+        Cross tabulation of the data.
+
+    See Also
+    --------
+    DataFrame.pivot : Reshape data based on column values.
+    pivot_table : Create a pivot table as a DataFrame.
+
+    Notes
+    -----
+    Any Series passed will have their name attributes used unless row or column
+    names for the cross-tabulation are specified.
+
+    Any input passed containing Categorical data will have **all** of its
+    categories included in the cross-tabulation, even if the actual data does
+    not contain any instances of a particular category.
+
+    In the event that there aren't overlapping indexes an empty DataFrame will
+    be returned.
+
+    Reference :ref:`the user guide ` for more examples.
+
+    Examples
+    --------
+    >>> a = np.array(["foo", "foo", "foo", "foo", "bar", "bar",
+    ...               "bar", "bar", "foo", "foo", "foo"], dtype=object)
+    >>> b = np.array(["one", "one", "one", "two", "one", "one",
+    ...               "one", "two", "two", "two", "one"], dtype=object)
+    >>> c = np.array(["dull", "dull", "shiny", "dull", "dull", "shiny",
+    ...               "shiny", "dull", "shiny", "shiny", "shiny"],
+    ...              dtype=object)
+    >>> pd.crosstab(a, [b, c], rownames=['a'], colnames=['b', 'c'])
+    b   one        two
+    c   dull shiny dull shiny
+    a
+    bar    1     2    1     0
+    foo    2     2    1     2
+
+    Here 'c' and 'f' are not represented in the data and will not be
+    shown in the output because dropna is True by default. Set
+    dropna=False to preserve categories with no data.
+
+    >>> foo = pd.Categorical(['a', 'b'], categories=['a', 'b', 'c'])
+    >>> bar = pd.Categorical(['d', 'e'], categories=['d', 'e', 'f'])
+    >>> pd.crosstab(foo, bar)
+    col_0  d  e
+    row_0
+    a      1  0
+    b      0  1
+    >>> pd.crosstab(foo, bar, dropna=False)
+    col_0  d  e  f
+    row_0
+    a      1  0  0
+    b      0  1  0
+    c      0  0  0
+    """
+    if values is None and aggfunc is not None:
+        raise ValueError("aggfunc cannot be used without values.")
+
+    if values is not None and aggfunc is None:
+        raise ValueError("values cannot be used without an aggfunc.")
+
+    if not is_nested_list_like(index):
+        index = [index]
+    if not is_nested_list_like(columns):
+        columns = [columns]
+
+    common_idx = None
+    pass_objs = [x for x in index + columns if isinstance(x, (ABCSeries, ABCDataFrame))]
+    if pass_objs:
+        common_idx = get_objs_combined_axis(pass_objs, intersect=True, sort=False)
+
+    rownames = _get_names(index, rownames, prefix="row")
+    colnames = _get_names(columns, colnames, prefix="col")
+
+    # duplicate names mapped to unique names for pivot op
+    (
+        rownames_mapper,
+        unique_rownames,
+        colnames_mapper,
+        unique_colnames,
+    ) = _build_names_mapper(rownames, colnames)
+
+    from pandas import DataFrame
+
+    data = {
+        **dict(zip(unique_rownames, index)),
+        **dict(zip(unique_colnames, columns)),
+    }
+    df = DataFrame(data, index=common_idx)
+
+    if values is None:
+        df["__dummy__"] = 0
+        kwargs = {"aggfunc": len, "fill_value": 0}
+    else:
+        df["__dummy__"] = values
+        kwargs = {"aggfunc": aggfunc}
+
+    # error: Argument 7 to "pivot_table" of "DataFrame" has incompatible type
+    # "**Dict[str, object]"; expected "Union[...]"
+    table = df.pivot_table(
+        "__dummy__",
+        index=unique_rownames,
+        columns=unique_colnames,
+        margins=margins,
+        margins_name=margins_name,
+        dropna=dropna,
+        observed=False,
+        **kwargs,  # type: ignore[arg-type]
+    )
+
+    # Post-process
+    if normalize is not False:
+        table = _normalize(
+            table, normalize=normalize, margins=margins, margins_name=margins_name
+        )
+
+    table = table.rename_axis(index=rownames_mapper, axis=0)
+    table = table.rename_axis(columns=colnames_mapper, axis=1)
+
+    return table
+
+
+def _normalize(
+    table: DataFrame, normalize, margins: bool, margins_name: Hashable = "All"
+) -> DataFrame:
+    if not isinstance(normalize, (bool, str)):
+        axis_subs = {0: "index", 1: "columns"}
+        try:
+            normalize = axis_subs[normalize]
+        except KeyError as err:
+            raise ValueError("Not a valid normalize argument") from err
+
+    if margins is False:
+        # Actual Normalizations
+        normalizers: dict[bool | str, Callable] = {
+            "all": lambda x: x / x.sum(axis=1).sum(axis=0),
+            "columns": lambda x: x / x.sum(),
+            "index": lambda x: x.div(x.sum(axis=1), axis=0),
+        }
+
+        normalizers[True] = normalizers["all"]
+
+        try:
+            f = normalizers[normalize]
+        except KeyError as err:
+            raise ValueError("Not a valid normalize argument") from err
+
+        table = f(table)
+        table = table.fillna(0)
+
+    elif margins is True:
+        # keep index and column of pivoted table
+        table_index = table.index
+        table_columns = table.columns
+        last_ind_or_col = table.iloc[-1, :].name
+
+        # check if margin name is not in (for MI cases) and not equal to last
+        # index/column and save the column and index margin
+        if (margins_name not in last_ind_or_col) & (margins_name != last_ind_or_col):
+            raise ValueError(f"{margins_name} not in pivoted DataFrame")
+        column_margin = table.iloc[:-1, -1]
+        index_margin = table.iloc[-1, :-1]
+
+        # keep the core table
+        table = table.iloc[:-1, :-1]
+
+        # Normalize core
+        table = _normalize(table, normalize=normalize, margins=False)
+
+        # Fix Margins
+        if normalize == "columns":
+            column_margin = column_margin / column_margin.sum()
+            table = concat([table, column_margin], axis=1)
+            table = table.fillna(0)
+            table.columns = table_columns
+
+        elif normalize == "index":
+            index_margin = index_margin / index_margin.sum()
+            table = table._append(index_margin)
+            table = table.fillna(0)
+            table.index = table_index
+
+        elif normalize == "all" or normalize is True:
+            column_margin = column_margin / column_margin.sum()
+            index_margin = index_margin / index_margin.sum()
+            index_margin.loc[margins_name] = 1
+            table = concat([table, column_margin], axis=1)
+            table = table._append(index_margin)
+
+            table = table.fillna(0)
+            table.index = table_index
+            table.columns = table_columns
+
+        else:
+            raise ValueError("Not a valid normalize argument")
+
+    else:
+        raise ValueError("Not a valid margins argument")
+
+    return table
+
+
+def _get_names(arrs, names, prefix: str = "row"):
+    if names is None:
+        names = []
+        for i, arr in enumerate(arrs):
+            if isinstance(arr, ABCSeries) and arr.name is not None:
+                names.append(arr.name)
+            else:
+                names.append(f"{prefix}_{i}")
+    else:
+        if len(names) != len(arrs):
+            raise AssertionError("arrays and names must have the same length")
+        if not isinstance(names, list):
+            names = list(names)
+
+    return names
+
+
+def _build_names_mapper(
+    rownames: list[str], colnames: list[str]
+) -> tuple[dict[str, str], list[str], dict[str, str], list[str]]:
+    """
+    Given the names of a DataFrame's rows and columns, returns a set of unique row
+    and column names and mappers that convert to original names.
+
+    A row or column name is replaced if it is duplicate among the rows of the inputs,
+    among the columns of the inputs or between the rows and the columns.
+
+    Parameters
+    ----------
+    rownames: list[str]
+    colnames: list[str]
+
+    Returns
+    -------
+    Tuple(Dict[str, str], List[str], Dict[str, str], List[str])
+
+    rownames_mapper: dict[str, str]
+        a dictionary with new row names as keys and original rownames as values
+    unique_rownames: list[str]
+        a list of rownames with duplicate names replaced by dummy names
+    colnames_mapper: dict[str, str]
+        a dictionary with new column names as keys and original column names as values
+    unique_colnames: list[str]
+        a list of column names with duplicate names replaced by dummy names
+
+    """
+
+    def get_duplicates(names):
+        seen: set = set()
+        return {name for name in names if name not in seen}
+
+    shared_names = set(rownames).intersection(set(colnames))
+    dup_names = get_duplicates(rownames) | get_duplicates(colnames) | shared_names
+
+    rownames_mapper = {
+        f"row_{i}": name for i, name in enumerate(rownames) if name in dup_names
+    }
+    unique_rownames = [
+        f"row_{i}" if name in dup_names else name for i, name in enumerate(rownames)
+    ]
+
+    colnames_mapper = {
+        f"col_{i}": name for i, name in enumerate(colnames) if name in dup_names
+    }
+    unique_colnames = [
+        f"col_{i}" if name in dup_names else name for i, name in enumerate(colnames)
+    ]
+
+    return rownames_mapper, unique_rownames, colnames_mapper, unique_colnames
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/reshape/reshape.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/reshape/reshape.py
new file mode 100644
index 0000000000000000000000000000000000000000..7a49682d7c57c90ed26e890777758ad806bd961b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/reshape/reshape.py
@@ -0,0 +1,989 @@
+from __future__ import annotations
+
+import itertools
+from typing import (
+    TYPE_CHECKING,
+    cast,
+)
+import warnings
+
+import numpy as np
+
+import pandas._libs.reshape as libreshape
+from pandas.errors import PerformanceWarning
+from pandas.util._decorators import cache_readonly
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.cast import (
+    find_common_type,
+    maybe_promote,
+)
+from pandas.core.dtypes.common import (
+    ensure_platform_int,
+    is_1d_only_ea_dtype,
+    is_integer,
+    needs_i8_conversion,
+)
+from pandas.core.dtypes.dtypes import ExtensionDtype
+from pandas.core.dtypes.missing import notna
+
+import pandas.core.algorithms as algos
+from pandas.core.algorithms import (
+    factorize,
+    unique,
+)
+from pandas.core.arrays.categorical import factorize_from_iterable
+from pandas.core.construction import ensure_wrapped_if_datetimelike
+from pandas.core.frame import DataFrame
+from pandas.core.indexes.api import (
+    Index,
+    MultiIndex,
+    RangeIndex,
+)
+from pandas.core.reshape.concat import concat
+from pandas.core.series import Series
+from pandas.core.sorting import (
+    compress_group_index,
+    decons_obs_group_ids,
+    get_compressed_ids,
+    get_group_index,
+    get_group_index_sorter,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        ArrayLike,
+        Level,
+        npt,
+    )
+
+    from pandas.core.arrays import ExtensionArray
+    from pandas.core.indexes.frozen import FrozenList
+
+
+class _Unstacker:
+    """
+    Helper class to unstack data / pivot with multi-level index
+
+    Parameters
+    ----------
+    index : MultiIndex
+    level : int or str, default last level
+        Level to "unstack". Accepts a name for the level.
+    fill_value : scalar, optional
+        Default value to fill in missing values if subgroups do not have the
+        same set of labels. By default, missing values will be replaced with
+        the default fill value for that data type, NaN for float, NaT for
+        datetimelike, etc. For integer types, by default data will converted to
+        float and missing values will be set to NaN.
+    constructor : object
+        Pandas ``DataFrame`` or subclass used to create unstacked
+        response.  If None, DataFrame will be used.
+
+    Examples
+    --------
+    >>> index = pd.MultiIndex.from_tuples([('one', 'a'), ('one', 'b'),
+    ...                                    ('two', 'a'), ('two', 'b')])
+    >>> s = pd.Series(np.arange(1, 5, dtype=np.int64), index=index)
+    >>> s
+    one  a    1
+         b    2
+    two  a    3
+         b    4
+    dtype: int64
+
+    >>> s.unstack(level=-1)
+         a  b
+    one  1  2
+    two  3  4
+
+    >>> s.unstack(level=0)
+       one  two
+    a    1    3
+    b    2    4
+
+    Returns
+    -------
+    unstacked : DataFrame
+    """
+
+    def __init__(
+        self, index: MultiIndex, level: Level, constructor, sort: bool = True
+    ) -> None:
+        self.constructor = constructor
+        self.sort = sort
+
+        self.index = index.remove_unused_levels()
+
+        self.level = self.index._get_level_number(level)
+
+        # when index includes `nan`, need to lift levels/strides by 1
+        self.lift = 1 if -1 in self.index.codes[self.level] else 0
+
+        # Note: the "pop" below alters these in-place.
+        self.new_index_levels = list(self.index.levels)
+        self.new_index_names = list(self.index.names)
+
+        self.removed_name = self.new_index_names.pop(self.level)
+        self.removed_level = self.new_index_levels.pop(self.level)
+        self.removed_level_full = index.levels[self.level]
+        if not self.sort:
+            unique_codes = unique(self.index.codes[self.level])
+            self.removed_level = self.removed_level.take(unique_codes)
+            self.removed_level_full = self.removed_level_full.take(unique_codes)
+
+        # Bug fix GH 20601
+        # If the data frame is too big, the number of unique index combination
+        # will cause int32 overflow on windows environments.
+        # We want to check and raise an warning before this happens
+        num_rows = np.max([index_level.size for index_level in self.new_index_levels])
+        num_columns = self.removed_level.size
+
+        # GH20601: This forces an overflow if the number of cells is too high.
+        num_cells = num_rows * num_columns
+
+        # GH 26314: Previous ValueError raised was too restrictive for many users.
+        if num_cells > np.iinfo(np.int32).max:
+            warnings.warn(
+                f"The following operation may generate {num_cells} cells "
+                f"in the resulting pandas object.",
+                PerformanceWarning,
+                stacklevel=find_stack_level(),
+            )
+
+        self._make_selectors()
+
+    @cache_readonly
+    def _indexer_and_to_sort(
+        self,
+    ) -> tuple[
+        npt.NDArray[np.intp],
+        list[np.ndarray],  # each has _some_ signed integer dtype
+    ]:
+        v = self.level
+
+        codes = list(self.index.codes)
+        levs = list(self.index.levels)
+        to_sort = codes[:v] + codes[v + 1 :] + [codes[v]]
+        sizes = tuple(len(x) for x in levs[:v] + levs[v + 1 :] + [levs[v]])
+
+        comp_index, obs_ids = get_compressed_ids(to_sort, sizes)
+        ngroups = len(obs_ids)
+
+        indexer = get_group_index_sorter(comp_index, ngroups)
+        return indexer, to_sort
+
+    @cache_readonly
+    def sorted_labels(self) -> list[np.ndarray]:
+        indexer, to_sort = self._indexer_and_to_sort
+        if self.sort:
+            return [line.take(indexer) for line in to_sort]
+        return to_sort
+
+    def _make_sorted_values(self, values: np.ndarray) -> np.ndarray:
+        if self.sort:
+            indexer, _ = self._indexer_and_to_sort
+
+            sorted_values = algos.take_nd(values, indexer, axis=0)
+            return sorted_values
+        return values
+
+    def _make_selectors(self):
+        new_levels = self.new_index_levels
+
+        # make the mask
+        remaining_labels = self.sorted_labels[:-1]
+        level_sizes = tuple(len(x) for x in new_levels)
+
+        comp_index, obs_ids = get_compressed_ids(remaining_labels, level_sizes)
+        ngroups = len(obs_ids)
+
+        comp_index = ensure_platform_int(comp_index)
+        stride = self.index.levshape[self.level] + self.lift
+        self.full_shape = ngroups, stride
+
+        selector = self.sorted_labels[-1] + stride * comp_index + self.lift
+        mask = np.zeros(np.prod(self.full_shape), dtype=bool)
+        mask.put(selector, True)
+
+        if mask.sum() < len(self.index):
+            raise ValueError("Index contains duplicate entries, cannot reshape")
+
+        self.group_index = comp_index
+        self.mask = mask
+        if self.sort:
+            self.compressor = comp_index.searchsorted(np.arange(ngroups))
+        else:
+            self.compressor = np.sort(np.unique(comp_index, return_index=True)[1])
+
+    @cache_readonly
+    def mask_all(self) -> bool:
+        return bool(self.mask.all())
+
+    @cache_readonly
+    def arange_result(self) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.bool_]]:
+        # We cache this for reuse in ExtensionBlock._unstack
+        dummy_arr = np.arange(len(self.index), dtype=np.intp)
+        new_values, mask = self.get_new_values(dummy_arr, fill_value=-1)
+        return new_values, mask.any(0)
+        # TODO: in all tests we have mask.any(0).all(); can we rely on that?
+
+    def get_result(self, values, value_columns, fill_value) -> DataFrame:
+        if values.ndim == 1:
+            values = values[:, np.newaxis]
+
+        if value_columns is None and values.shape[1] != 1:  # pragma: no cover
+            raise ValueError("must pass column labels for multi-column data")
+
+        values, _ = self.get_new_values(values, fill_value)
+        columns = self.get_new_columns(value_columns)
+        index = self.new_index
+
+        return self.constructor(
+            values, index=index, columns=columns, dtype=values.dtype
+        )
+
+    def get_new_values(self, values, fill_value=None):
+        if values.ndim == 1:
+            values = values[:, np.newaxis]
+
+        sorted_values = self._make_sorted_values(values)
+
+        # place the values
+        length, width = self.full_shape
+        stride = values.shape[1]
+        result_width = width * stride
+        result_shape = (length, result_width)
+        mask = self.mask
+        mask_all = self.mask_all
+
+        # we can simply reshape if we don't have a mask
+        if mask_all and len(values):
+            # TODO: Under what circumstances can we rely on sorted_values
+            #  matching values?  When that holds, we can slice instead
+            #  of take (in particular for EAs)
+            new_values = (
+                sorted_values.reshape(length, width, stride)
+                .swapaxes(1, 2)
+                .reshape(result_shape)
+            )
+            new_mask = np.ones(result_shape, dtype=bool)
+            return new_values, new_mask
+
+        dtype = values.dtype
+
+        # if our mask is all True, then we can use our existing dtype
+        if mask_all:
+            dtype = values.dtype
+            new_values = np.empty(result_shape, dtype=dtype)
+        else:
+            if isinstance(dtype, ExtensionDtype):
+                # GH#41875
+                # We are assuming that fill_value can be held by this dtype,
+                #  unlike the non-EA case that promotes.
+                cls = dtype.construct_array_type()
+                new_values = cls._empty(result_shape, dtype=dtype)
+                new_values[:] = fill_value
+            else:
+                dtype, fill_value = maybe_promote(dtype, fill_value)
+                new_values = np.empty(result_shape, dtype=dtype)
+                new_values.fill(fill_value)
+
+        name = dtype.name
+        new_mask = np.zeros(result_shape, dtype=bool)
+
+        # we need to convert to a basic dtype
+        # and possibly coerce an input to our output dtype
+        # e.g. ints -> floats
+        if needs_i8_conversion(values.dtype):
+            sorted_values = sorted_values.view("i8")
+            new_values = new_values.view("i8")
+        else:
+            sorted_values = sorted_values.astype(name, copy=False)
+
+        # fill in our values & mask
+        libreshape.unstack(
+            sorted_values,
+            mask.view("u1"),
+            stride,
+            length,
+            width,
+            new_values,
+            new_mask.view("u1"),
+        )
+
+        # reconstruct dtype if needed
+        if needs_i8_conversion(values.dtype):
+            # view as datetime64 so we can wrap in DatetimeArray and use
+            #  DTA's view method
+            new_values = new_values.view("M8[ns]")
+            new_values = ensure_wrapped_if_datetimelike(new_values)
+            new_values = new_values.view(values.dtype)
+
+        return new_values, new_mask
+
+    def get_new_columns(self, value_columns: Index | None):
+        if value_columns is None:
+            if self.lift == 0:
+                return self.removed_level._rename(name=self.removed_name)
+
+            lev = self.removed_level.insert(0, item=self.removed_level._na_value)
+            return lev.rename(self.removed_name)
+
+        stride = len(self.removed_level) + self.lift
+        width = len(value_columns)
+        propagator = np.repeat(np.arange(width), stride)
+
+        new_levels: FrozenList | list[Index]
+
+        if isinstance(value_columns, MultiIndex):
+            # error: Cannot determine type of "__add__"  [has-type]
+            new_levels = value_columns.levels + (  # type: ignore[has-type]
+                self.removed_level_full,
+            )
+            new_names = value_columns.names + (self.removed_name,)
+
+            new_codes = [lab.take(propagator) for lab in value_columns.codes]
+        else:
+            new_levels = [
+                value_columns,
+                self.removed_level_full,
+            ]
+            new_names = [value_columns.name, self.removed_name]
+            new_codes = [propagator]
+
+        repeater = self._repeater
+
+        # The entire level is then just a repetition of the single chunk:
+        new_codes.append(np.tile(repeater, width))
+        return MultiIndex(
+            levels=new_levels, codes=new_codes, names=new_names, verify_integrity=False
+        )
+
+    @cache_readonly
+    def _repeater(self) -> np.ndarray:
+        # The two indices differ only if the unstacked level had unused items:
+        if len(self.removed_level_full) != len(self.removed_level):
+            # In this case, we remap the new codes to the original level:
+            repeater = self.removed_level_full.get_indexer(self.removed_level)
+            if self.lift:
+                repeater = np.insert(repeater, 0, -1)
+        else:
+            # Otherwise, we just use each level item exactly once:
+            stride = len(self.removed_level) + self.lift
+            repeater = np.arange(stride) - self.lift
+
+        return repeater
+
+    @cache_readonly
+    def new_index(self) -> MultiIndex:
+        # Does not depend on values or value_columns
+        result_codes = [lab.take(self.compressor) for lab in self.sorted_labels[:-1]]
+
+        # construct the new index
+        if len(self.new_index_levels) == 1:
+            level, level_codes = self.new_index_levels[0], result_codes[0]
+            if (level_codes == -1).any():
+                level = level.insert(len(level), level._na_value)
+            return level.take(level_codes).rename(self.new_index_names[0])
+
+        return MultiIndex(
+            levels=self.new_index_levels,
+            codes=result_codes,
+            names=self.new_index_names,
+            verify_integrity=False,
+        )
+
+
+def _unstack_multiple(
+    data: Series | DataFrame, clocs, fill_value=None, sort: bool = True
+):
+    if len(clocs) == 0:
+        return data
+
+    # NOTE: This doesn't deal with hierarchical columns yet
+
+    index = data.index
+    index = cast(MultiIndex, index)  # caller is responsible for checking
+
+    # GH 19966 Make sure if MultiIndexed index has tuple name, they will be
+    # recognised as a whole
+    if clocs in index.names:
+        clocs = [clocs]
+    clocs = [index._get_level_number(i) for i in clocs]
+
+    rlocs = [i for i in range(index.nlevels) if i not in clocs]
+
+    clevels = [index.levels[i] for i in clocs]
+    ccodes = [index.codes[i] for i in clocs]
+    cnames = [index.names[i] for i in clocs]
+    rlevels = [index.levels[i] for i in rlocs]
+    rcodes = [index.codes[i] for i in rlocs]
+    rnames = [index.names[i] for i in rlocs]
+
+    shape = tuple(len(x) for x in clevels)
+    group_index = get_group_index(ccodes, shape, sort=False, xnull=False)
+
+    comp_ids, obs_ids = compress_group_index(group_index, sort=False)
+    recons_codes = decons_obs_group_ids(comp_ids, obs_ids, shape, ccodes, xnull=False)
+
+    if not rlocs:
+        # Everything is in clocs, so the dummy df has a regular index
+        dummy_index = Index(obs_ids, name="__placeholder__")
+    else:
+        dummy_index = MultiIndex(
+            levels=rlevels + [obs_ids],
+            codes=rcodes + [comp_ids],
+            names=rnames + ["__placeholder__"],
+            verify_integrity=False,
+        )
+
+    if isinstance(data, Series):
+        dummy = data.copy()
+        dummy.index = dummy_index
+
+        unstacked = dummy.unstack("__placeholder__", fill_value=fill_value, sort=sort)
+        new_levels = clevels
+        new_names = cnames
+        new_codes = recons_codes
+    else:
+        if isinstance(data.columns, MultiIndex):
+            result = data
+            while clocs:
+                val = clocs.pop(0)
+                result = result.unstack(val, fill_value=fill_value, sort=sort)
+                clocs = [v if v < val else v - 1 for v in clocs]
+
+            return result
+
+        # GH#42579 deep=False to avoid consolidating
+        dummy_df = data.copy(deep=False)
+        dummy_df.index = dummy_index
+
+        unstacked = dummy_df.unstack(
+            "__placeholder__", fill_value=fill_value, sort=sort
+        )
+        if isinstance(unstacked, Series):
+            unstcols = unstacked.index
+        else:
+            unstcols = unstacked.columns
+        assert isinstance(unstcols, MultiIndex)  # for mypy
+        new_levels = [unstcols.levels[0]] + clevels
+        new_names = [data.columns.name] + cnames
+
+        new_codes = [unstcols.codes[0]]
+        new_codes.extend(rec.take(unstcols.codes[-1]) for rec in recons_codes)
+
+    new_columns = MultiIndex(
+        levels=new_levels, codes=new_codes, names=new_names, verify_integrity=False
+    )
+
+    if isinstance(unstacked, Series):
+        unstacked.index = new_columns
+    else:
+        unstacked.columns = new_columns
+
+    return unstacked
+
+
+def unstack(obj: Series | DataFrame, level, fill_value=None, sort: bool = True):
+    if isinstance(level, (tuple, list)):
+        if len(level) != 1:
+            # _unstack_multiple only handles MultiIndexes,
+            # and isn't needed for a single level
+            return _unstack_multiple(obj, level, fill_value=fill_value, sort=sort)
+        else:
+            level = level[0]
+
+    if not is_integer(level) and not level == "__placeholder__":
+        # check if level is valid in case of regular index
+        obj.index._get_level_number(level)
+
+    if isinstance(obj, DataFrame):
+        if isinstance(obj.index, MultiIndex):
+            return _unstack_frame(obj, level, fill_value=fill_value, sort=sort)
+        else:
+            return obj.T.stack(future_stack=True)
+    elif not isinstance(obj.index, MultiIndex):
+        # GH 36113
+        # Give nicer error messages when unstack a Series whose
+        # Index is not a MultiIndex.
+        raise ValueError(
+            f"index must be a MultiIndex to unstack, {type(obj.index)} was passed"
+        )
+    else:
+        if is_1d_only_ea_dtype(obj.dtype):
+            return _unstack_extension_series(obj, level, fill_value, sort=sort)
+        unstacker = _Unstacker(
+            obj.index, level=level, constructor=obj._constructor_expanddim, sort=sort
+        )
+        return unstacker.get_result(
+            obj._values, value_columns=None, fill_value=fill_value
+        )
+
+
+def _unstack_frame(
+    obj: DataFrame, level, fill_value=None, sort: bool = True
+) -> DataFrame:
+    assert isinstance(obj.index, MultiIndex)  # checked by caller
+    unstacker = _Unstacker(
+        obj.index, level=level, constructor=obj._constructor, sort=sort
+    )
+
+    if not obj._can_fast_transpose:
+        mgr = obj._mgr.unstack(unstacker, fill_value=fill_value)
+        return obj._constructor_from_mgr(mgr, axes=mgr.axes)
+    else:
+        return unstacker.get_result(
+            obj._values, value_columns=obj.columns, fill_value=fill_value
+        )
+
+
+def _unstack_extension_series(
+    series: Series, level, fill_value, sort: bool
+) -> DataFrame:
+    """
+    Unstack an ExtensionArray-backed Series.
+
+    The ExtensionDtype is preserved.
+
+    Parameters
+    ----------
+    series : Series
+        A Series with an ExtensionArray for values
+    level : Any
+        The level name or number.
+    fill_value : Any
+        The user-level (not physical storage) fill value to use for
+        missing values introduced by the reshape. Passed to
+        ``series.values.take``.
+    sort : bool
+        Whether to sort the resulting MuliIndex levels
+
+    Returns
+    -------
+    DataFrame
+        Each column of the DataFrame will have the same dtype as
+        the input Series.
+    """
+    # Defer to the logic in ExtensionBlock._unstack
+    df = series.to_frame()
+    result = df.unstack(level=level, fill_value=fill_value, sort=sort)
+
+    # equiv: result.droplevel(level=0, axis=1)
+    #  but this avoids an extra copy
+    result.columns = result.columns._drop_level_numbers([0])
+    return result
+
+
+def stack(frame: DataFrame, level=-1, dropna: bool = True, sort: bool = True):
+    """
+    Convert DataFrame to Series with multi-level Index. Columns become the
+    second level of the resulting hierarchical index
+
+    Returns
+    -------
+    stacked : Series or DataFrame
+    """
+
+    def stack_factorize(index):
+        if index.is_unique:
+            return index, np.arange(len(index))
+        codes, categories = factorize_from_iterable(index)
+        return categories, codes
+
+    N, K = frame.shape
+
+    # Will also convert negative level numbers and check if out of bounds.
+    level_num = frame.columns._get_level_number(level)
+
+    if isinstance(frame.columns, MultiIndex):
+        return _stack_multi_columns(
+            frame, level_num=level_num, dropna=dropna, sort=sort
+        )
+    elif isinstance(frame.index, MultiIndex):
+        new_levels = list(frame.index.levels)
+        new_codes = [lab.repeat(K) for lab in frame.index.codes]
+
+        clev, clab = stack_factorize(frame.columns)
+        new_levels.append(clev)
+        new_codes.append(np.tile(clab, N).ravel())
+
+        new_names = list(frame.index.names)
+        new_names.append(frame.columns.name)
+        new_index = MultiIndex(
+            levels=new_levels, codes=new_codes, names=new_names, verify_integrity=False
+        )
+    else:
+        levels, (ilab, clab) = zip(*map(stack_factorize, (frame.index, frame.columns)))
+        codes = ilab.repeat(K), np.tile(clab, N).ravel()
+        new_index = MultiIndex(
+            levels=levels,
+            codes=codes,
+            names=[frame.index.name, frame.columns.name],
+            verify_integrity=False,
+        )
+
+    new_values: ArrayLike
+    if not frame.empty and frame._is_homogeneous_type:
+        # For homogeneous EAs, frame._values will coerce to object. So
+        # we concatenate instead.
+        dtypes = list(frame.dtypes._values)
+        dtype = dtypes[0]
+
+        if isinstance(dtype, ExtensionDtype):
+            arr = dtype.construct_array_type()
+            new_values = arr._concat_same_type(
+                [col._values for _, col in frame.items()]
+            )
+            new_values = _reorder_for_extension_array_stack(new_values, N, K)
+        else:
+            # homogeneous, non-EA
+            new_values = frame._values.ravel()
+
+    else:
+        # non-homogeneous
+        new_values = frame._values.ravel()
+
+    if dropna:
+        mask = notna(new_values)
+        new_values = new_values[mask]
+        new_index = new_index[mask]
+
+    return frame._constructor_sliced(new_values, index=new_index)
+
+
+def stack_multiple(frame: DataFrame, level, dropna: bool = True, sort: bool = True):
+    # If all passed levels match up to column names, no
+    # ambiguity about what to do
+    if all(lev in frame.columns.names for lev in level):
+        result = frame
+        for lev in level:
+            result = stack(result, lev, dropna=dropna, sort=sort)
+
+    # Otherwise, level numbers may change as each successive level is stacked
+    elif all(isinstance(lev, int) for lev in level):
+        # As each stack is done, the level numbers decrease, so we need
+        #  to account for that when level is a sequence of ints
+        result = frame
+        # _get_level_number() checks level numbers are in range and converts
+        # negative numbers to positive
+        level = [frame.columns._get_level_number(lev) for lev in level]
+
+        while level:
+            lev = level.pop(0)
+            result = stack(result, lev, dropna=dropna, sort=sort)
+            # Decrement all level numbers greater than current, as these
+            # have now shifted down by one
+            level = [v if v <= lev else v - 1 for v in level]
+
+    else:
+        raise ValueError(
+            "level should contain all level names or all level "
+            "numbers, not a mixture of the two."
+        )
+
+    return result
+
+
+def _stack_multi_column_index(columns: MultiIndex) -> MultiIndex:
+    """Creates a MultiIndex from the first N-1 levels of this MultiIndex."""
+    if len(columns.levels) <= 2:
+        return columns.levels[0]._rename(name=columns.names[0])
+
+    levs = [
+        [lev[c] if c >= 0 else None for c in codes]
+        for lev, codes in zip(columns.levels[:-1], columns.codes[:-1])
+    ]
+
+    # Remove duplicate tuples in the MultiIndex.
+    tuples = zip(*levs)
+    unique_tuples = (key for key, _ in itertools.groupby(tuples))
+    new_levs = zip(*unique_tuples)
+
+    # The dtype of each level must be explicitly set to avoid inferring the wrong type.
+    # See GH-36991.
+    return MultiIndex.from_arrays(
+        [
+            # Not all indices can accept None values.
+            Index(new_lev, dtype=lev.dtype) if None not in new_lev else new_lev
+            for new_lev, lev in zip(new_levs, columns.levels)
+        ],
+        names=columns.names[:-1],
+    )
+
+
+def _stack_multi_columns(
+    frame: DataFrame, level_num: int = -1, dropna: bool = True, sort: bool = True
+) -> DataFrame:
+    def _convert_level_number(level_num: int, columns: Index):
+        """
+        Logic for converting the level number to something we can safely pass
+        to swaplevel.
+
+        If `level_num` matches a column name return the name from
+        position `level_num`, otherwise return `level_num`.
+        """
+        if level_num in columns.names:
+            return columns.names[level_num]
+
+        return level_num
+
+    this = frame.copy(deep=False)
+    mi_cols = this.columns  # cast(MultiIndex, this.columns)
+    assert isinstance(mi_cols, MultiIndex)  # caller is responsible
+
+    # this makes life much simpler
+    if level_num != mi_cols.nlevels - 1:
+        # roll levels to put selected level at end
+        roll_columns = mi_cols
+        for i in range(level_num, mi_cols.nlevels - 1):
+            # Need to check if the ints conflict with level names
+            lev1 = _convert_level_number(i, roll_columns)
+            lev2 = _convert_level_number(i + 1, roll_columns)
+            roll_columns = roll_columns.swaplevel(lev1, lev2)
+        this.columns = mi_cols = roll_columns
+
+    if not mi_cols._is_lexsorted() and sort:
+        # Workaround the edge case where 0 is one of the column names,
+        # which interferes with trying to sort based on the first
+        # level
+        level_to_sort = _convert_level_number(0, mi_cols)
+        this = this.sort_index(level=level_to_sort, axis=1)
+        mi_cols = this.columns
+
+    mi_cols = cast(MultiIndex, mi_cols)
+    new_columns = _stack_multi_column_index(mi_cols)
+
+    # time to ravel the values
+    new_data = {}
+    level_vals = mi_cols.levels[-1]
+    level_codes = unique(mi_cols.codes[-1])
+    if sort:
+        level_codes = np.sort(level_codes)
+    level_vals_nan = level_vals.insert(len(level_vals), None)
+
+    level_vals_used = np.take(level_vals_nan, level_codes)
+    levsize = len(level_codes)
+    drop_cols = []
+    for key in new_columns:
+        try:
+            loc = this.columns.get_loc(key)
+        except KeyError:
+            drop_cols.append(key)
+            continue
+
+        # can make more efficient?
+        # we almost always return a slice
+        # but if unsorted can get a boolean
+        # indexer
+        if not isinstance(loc, slice):
+            slice_len = len(loc)
+        else:
+            slice_len = loc.stop - loc.start
+
+        if slice_len != levsize:
+            chunk = this.loc[:, this.columns[loc]]
+            chunk.columns = level_vals_nan.take(chunk.columns.codes[-1])
+            value_slice = chunk.reindex(columns=level_vals_used).values
+        else:
+            subset = this.iloc[:, loc]
+            dtype = find_common_type(subset.dtypes.tolist())
+            if isinstance(dtype, ExtensionDtype):
+                # TODO(EA2D): won't need special case, can go through .values
+                #  paths below (might change to ._values)
+                value_slice = dtype.construct_array_type()._concat_same_type(
+                    [x._values.astype(dtype, copy=False) for _, x in subset.items()]
+                )
+                N, K = subset.shape
+                idx = np.arange(N * K).reshape(K, N).T.ravel()
+                value_slice = value_slice.take(idx)
+            else:
+                value_slice = subset.values
+
+        if value_slice.ndim > 1:
+            # i.e. not extension
+            value_slice = value_slice.ravel()
+
+        new_data[key] = value_slice
+
+    if len(drop_cols) > 0:
+        new_columns = new_columns.difference(drop_cols)
+
+    N = len(this)
+
+    if isinstance(this.index, MultiIndex):
+        new_levels = list(this.index.levels)
+        new_names = list(this.index.names)
+        new_codes = [lab.repeat(levsize) for lab in this.index.codes]
+    else:
+        old_codes, old_levels = factorize_from_iterable(this.index)
+        new_levels = [old_levels]
+        new_codes = [old_codes.repeat(levsize)]
+        new_names = [this.index.name]  # something better?
+
+    new_levels.append(level_vals)
+    new_codes.append(np.tile(level_codes, N))
+    new_names.append(frame.columns.names[level_num])
+
+    new_index = MultiIndex(
+        levels=new_levels, codes=new_codes, names=new_names, verify_integrity=False
+    )
+
+    result = frame._constructor(new_data, index=new_index, columns=new_columns)
+
+    if frame.columns.nlevels > 1:
+        desired_columns = frame.columns._drop_level_numbers([level_num]).unique()
+        if not result.columns.equals(desired_columns):
+            result = result[desired_columns]
+
+    # more efficient way to go about this? can do the whole masking biz but
+    # will only save a small amount of time...
+    if dropna:
+        result = result.dropna(axis=0, how="all")
+
+    return result
+
+
+def _reorder_for_extension_array_stack(
+    arr: ExtensionArray, n_rows: int, n_columns: int
+) -> ExtensionArray:
+    """
+    Re-orders the values when stacking multiple extension-arrays.
+
+    The indirect stacking method used for EAs requires a followup
+    take to get the order correct.
+
+    Parameters
+    ----------
+    arr : ExtensionArray
+    n_rows, n_columns : int
+        The number of rows and columns in the original DataFrame.
+
+    Returns
+    -------
+    taken : ExtensionArray
+        The original `arr` with elements re-ordered appropriately
+
+    Examples
+    --------
+    >>> arr = np.array(['a', 'b', 'c', 'd', 'e', 'f'])
+    >>> _reorder_for_extension_array_stack(arr, 2, 3)
+    array(['a', 'c', 'e', 'b', 'd', 'f'], dtype='>> _reorder_for_extension_array_stack(arr, 3, 2)
+    array(['a', 'd', 'b', 'e', 'c', 'f'], dtype=' Series | DataFrame:
+    if frame.columns.nunique() != len(frame.columns):
+        raise ValueError("Columns with duplicate values are not supported in stack")
+
+    # If we need to drop `level` from columns, it needs to be in descending order
+    drop_levnums = sorted(level, reverse=True)
+    stack_cols = frame.columns._drop_level_numbers(
+        [k for k in range(frame.columns.nlevels) if k not in level][::-1]
+    )
+    if len(level) > 1:
+        # Arrange columns in the order we want to take them, e.g. level=[2, 0, 1]
+        sorter = np.argsort(level)
+        ordered_stack_cols = stack_cols._reorder_ilevels(sorter)
+    else:
+        ordered_stack_cols = stack_cols
+
+    stack_cols_unique = stack_cols.unique()
+    ordered_stack_cols_unique = ordered_stack_cols.unique()
+
+    # Grab data for each unique index to be stacked
+    buf = []
+    for idx in stack_cols_unique:
+        if len(frame.columns) == 1:
+            data = frame.copy()
+        else:
+            # Take the data from frame corresponding to this idx value
+            if len(level) == 1:
+                idx = (idx,)
+            gen = iter(idx)
+            column_indexer = tuple(
+                next(gen) if k in level else slice(None)
+                for k in range(frame.columns.nlevels)
+            )
+            data = frame.loc[:, column_indexer]
+
+        if len(level) < frame.columns.nlevels:
+            data.columns = data.columns._drop_level_numbers(drop_levnums)
+        elif stack_cols.nlevels == 1:
+            if data.ndim == 1:
+                data.name = 0
+            else:
+                data.columns = RangeIndex(len(data.columns))
+        buf.append(data)
+
+    result: Series | DataFrame
+    if len(buf) > 0 and not frame.empty:
+        result = concat(buf)
+        ratio = len(result) // len(frame)
+    else:
+        # input is empty
+        if len(level) < frame.columns.nlevels:
+            # concat column order may be different from dropping the levels
+            new_columns = frame.columns._drop_level_numbers(drop_levnums).unique()
+        else:
+            new_columns = [0]
+        result = DataFrame(columns=new_columns, dtype=frame._values.dtype)
+        ratio = 0
+
+    if len(level) < frame.columns.nlevels:
+        # concat column order may be different from dropping the levels
+        desired_columns = frame.columns._drop_level_numbers(drop_levnums).unique()
+        if not result.columns.equals(desired_columns):
+            result = result[desired_columns]
+
+    # Construct the correct MultiIndex by combining the frame's index and
+    # stacked columns.
+    index_levels: list | FrozenList
+    if isinstance(frame.index, MultiIndex):
+        index_levels = frame.index.levels
+        index_codes = list(np.tile(frame.index.codes, (1, ratio)))
+    else:
+        codes, uniques = factorize(frame.index, use_na_sentinel=False)
+        index_levels = [uniques]
+        index_codes = list(np.tile(codes, (1, ratio)))
+    if isinstance(stack_cols, MultiIndex):
+        column_levels = ordered_stack_cols.levels
+        column_codes = ordered_stack_cols.drop_duplicates().codes
+    else:
+        column_levels = [ordered_stack_cols.unique()]
+        column_codes = [factorize(ordered_stack_cols_unique, use_na_sentinel=False)[0]]
+    column_codes = [np.repeat(codes, len(frame)) for codes in column_codes]
+    result.index = MultiIndex(
+        levels=index_levels + column_levels,
+        codes=index_codes + column_codes,
+        names=frame.index.names + list(ordered_stack_cols.names),
+        verify_integrity=False,
+    )
+
+    # sort result, but faster than calling sort_index since we know the order we need
+    len_df = len(frame)
+    n_uniques = len(ordered_stack_cols_unique)
+    indexer = np.arange(n_uniques)
+    idxs = np.tile(len_df * indexer, len_df) + np.repeat(np.arange(len_df), n_uniques)
+    result = result.take(idxs)
+
+    # Reshape/rename if needed and dropna
+    if result.ndim == 2 and frame.columns.nlevels == len(level):
+        if len(result.columns) == 0:
+            result = Series(index=result.index)
+        else:
+            result = result.iloc[:, 0]
+    if result.ndim == 1:
+        result.name = None
+
+    return result
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/reshape/tile.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/reshape/tile.py
new file mode 100644
index 0000000000000000000000000000000000000000..2b0c6fbb8e3bf69fcb60ce3257450260af2a9f6b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/reshape/tile.py
@@ -0,0 +1,638 @@
+"""
+Quantilization functions and related stuff
+"""
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Literal,
+)
+
+import numpy as np
+
+from pandas._libs import (
+    Timedelta,
+    Timestamp,
+    lib,
+)
+
+from pandas.core.dtypes.common import (
+    ensure_platform_int,
+    is_bool_dtype,
+    is_integer,
+    is_list_like,
+    is_numeric_dtype,
+    is_scalar,
+)
+from pandas.core.dtypes.dtypes import (
+    CategoricalDtype,
+    DatetimeTZDtype,
+    ExtensionDtype,
+)
+from pandas.core.dtypes.generic import ABCSeries
+from pandas.core.dtypes.missing import isna
+
+from pandas import (
+    Categorical,
+    Index,
+    IntervalIndex,
+)
+import pandas.core.algorithms as algos
+from pandas.core.arrays.datetimelike import dtype_to_unit
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        DtypeObj,
+        IntervalLeftRight,
+    )
+
+
+def cut(
+    x,
+    bins,
+    right: bool = True,
+    labels=None,
+    retbins: bool = False,
+    precision: int = 3,
+    include_lowest: bool = False,
+    duplicates: str = "raise",
+    ordered: bool = True,
+):
+    """
+    Bin values into discrete intervals.
+
+    Use `cut` when you need to segment and sort data values into bins. This
+    function is also useful for going from a continuous variable to a
+    categorical variable. For example, `cut` could convert ages to groups of
+    age ranges. Supports binning into an equal number of bins, or a
+    pre-specified array of bins.
+
+    Parameters
+    ----------
+    x : array-like
+        The input array to be binned. Must be 1-dimensional.
+    bins : int, sequence of scalars, or IntervalIndex
+        The criteria to bin by.
+
+        * int : Defines the number of equal-width bins in the range of `x`. The
+          range of `x` is extended by .1% on each side to include the minimum
+          and maximum values of `x`.
+        * sequence of scalars : Defines the bin edges allowing for non-uniform
+          width. No extension of the range of `x` is done.
+        * IntervalIndex : Defines the exact bins to be used. Note that
+          IntervalIndex for `bins` must be non-overlapping.
+
+    right : bool, default True
+        Indicates whether `bins` includes the rightmost edge or not. If
+        ``right == True`` (the default), then the `bins` ``[1, 2, 3, 4]``
+        indicate (1,2], (2,3], (3,4]. This argument is ignored when
+        `bins` is an IntervalIndex.
+    labels : array or False, default None
+        Specifies the labels for the returned bins. Must be the same length as
+        the resulting bins. If False, returns only integer indicators of the
+        bins. This affects the type of the output container (see below).
+        This argument is ignored when `bins` is an IntervalIndex. If True,
+        raises an error. When `ordered=False`, labels must be provided.
+    retbins : bool, default False
+        Whether to return the bins or not. Useful when bins is provided
+        as a scalar.
+    precision : int, default 3
+        The precision at which to store and display the bins labels.
+    include_lowest : bool, default False
+        Whether the first interval should be left-inclusive or not.
+    duplicates : {default 'raise', 'drop'}, optional
+        If bin edges are not unique, raise ValueError or drop non-uniques.
+    ordered : bool, default True
+        Whether the labels are ordered or not. Applies to returned types
+        Categorical and Series (with Categorical dtype). If True,
+        the resulting categorical will be ordered. If False, the resulting
+        categorical will be unordered (labels must be provided).
+
+    Returns
+    -------
+    out : Categorical, Series, or ndarray
+        An array-like object representing the respective bin for each value
+        of `x`. The type depends on the value of `labels`.
+
+        * None (default) : returns a Series for Series `x` or a
+          Categorical for all other inputs. The values stored within
+          are Interval dtype.
+
+        * sequence of scalars : returns a Series for Series `x` or a
+          Categorical for all other inputs. The values stored within
+          are whatever the type in the sequence is.
+
+        * False : returns an ndarray of integers.
+
+    bins : numpy.ndarray or IntervalIndex.
+        The computed or specified bins. Only returned when `retbins=True`.
+        For scalar or sequence `bins`, this is an ndarray with the computed
+        bins. If set `duplicates=drop`, `bins` will drop non-unique bin. For
+        an IntervalIndex `bins`, this is equal to `bins`.
+
+    See Also
+    --------
+    qcut : Discretize variable into equal-sized buckets based on rank
+        or based on sample quantiles.
+    Categorical : Array type for storing data that come from a
+        fixed set of values.
+    Series : One-dimensional array with axis labels (including time series).
+    IntervalIndex : Immutable Index implementing an ordered, sliceable set.
+
+    Notes
+    -----
+    Any NA values will be NA in the result. Out of bounds values will be NA in
+    the resulting Series or Categorical object.
+
+    Reference :ref:`the user guide ` for more examples.
+
+    Examples
+    --------
+    Discretize into three equal-sized bins.
+
+    >>> pd.cut(np.array([1, 7, 5, 4, 6, 3]), 3)
+    ... # doctest: +ELLIPSIS
+    [(0.994, 3.0], (5.0, 7.0], (3.0, 5.0], (3.0, 5.0], (5.0, 7.0], ...
+    Categories (3, interval[float64, right]): [(0.994, 3.0] < (3.0, 5.0] ...
+
+    >>> pd.cut(np.array([1, 7, 5, 4, 6, 3]), 3, retbins=True)
+    ... # doctest: +ELLIPSIS
+    ([(0.994, 3.0], (5.0, 7.0], (3.0, 5.0], (3.0, 5.0], (5.0, 7.0], ...
+    Categories (3, interval[float64, right]): [(0.994, 3.0] < (3.0, 5.0] ...
+    array([0.994, 3.   , 5.   , 7.   ]))
+
+    Discovers the same bins, but assign them specific labels. Notice that
+    the returned Categorical's categories are `labels` and is ordered.
+
+    >>> pd.cut(np.array([1, 7, 5, 4, 6, 3]),
+    ...        3, labels=["bad", "medium", "good"])
+    ['bad', 'good', 'medium', 'medium', 'good', 'bad']
+    Categories (3, object): ['bad' < 'medium' < 'good']
+
+    ``ordered=False`` will result in unordered categories when labels are passed.
+    This parameter can be used to allow non-unique labels:
+
+    >>> pd.cut(np.array([1, 7, 5, 4, 6, 3]), 3,
+    ...        labels=["B", "A", "B"], ordered=False)
+    ['B', 'B', 'A', 'A', 'B', 'B']
+    Categories (2, object): ['A', 'B']
+
+    ``labels=False`` implies you just want the bins back.
+
+    >>> pd.cut([0, 1, 1, 2], bins=4, labels=False)
+    array([0, 1, 1, 3])
+
+    Passing a Series as an input returns a Series with categorical dtype:
+
+    >>> s = pd.Series(np.array([2, 4, 6, 8, 10]),
+    ...               index=['a', 'b', 'c', 'd', 'e'])
+    >>> pd.cut(s, 3)
+    ... # doctest: +ELLIPSIS
+    a    (1.992, 4.667]
+    b    (1.992, 4.667]
+    c    (4.667, 7.333]
+    d     (7.333, 10.0]
+    e     (7.333, 10.0]
+    dtype: category
+    Categories (3, interval[float64, right]): [(1.992, 4.667] < (4.667, ...
+
+    Passing a Series as an input returns a Series with mapping value.
+    It is used to map numerically to intervals based on bins.
+
+    >>> s = pd.Series(np.array([2, 4, 6, 8, 10]),
+    ...               index=['a', 'b', 'c', 'd', 'e'])
+    >>> pd.cut(s, [0, 2, 4, 6, 8, 10], labels=False, retbins=True, right=False)
+    ... # doctest: +ELLIPSIS
+    (a    1.0
+     b    2.0
+     c    3.0
+     d    4.0
+     e    NaN
+     dtype: float64,
+     array([ 0,  2,  4,  6,  8, 10]))
+
+    Use `drop` optional when bins is not unique
+
+    >>> pd.cut(s, [0, 2, 4, 6, 10, 10], labels=False, retbins=True,
+    ...        right=False, duplicates='drop')
+    ... # doctest: +ELLIPSIS
+    (a    1.0
+     b    2.0
+     c    3.0
+     d    3.0
+     e    NaN
+     dtype: float64,
+     array([ 0,  2,  4,  6, 10]))
+
+    Passing an IntervalIndex for `bins` results in those categories exactly.
+    Notice that values not covered by the IntervalIndex are set to NaN. 0
+    is to the left of the first bin (which is closed on the right), and 1.5
+    falls between two bins.
+
+    >>> bins = pd.IntervalIndex.from_tuples([(0, 1), (2, 3), (4, 5)])
+    >>> pd.cut([0, 0.5, 1.5, 2.5, 4.5], bins)
+    [NaN, (0.0, 1.0], NaN, (2.0, 3.0], (4.0, 5.0]]
+    Categories (3, interval[int64, right]): [(0, 1] < (2, 3] < (4, 5]]
+    """
+    # NOTE: this binning code is changed a bit from histogram for var(x) == 0
+
+    original = x
+    x_idx = _preprocess_for_cut(x)
+    x_idx, _ = _coerce_to_type(x_idx)
+
+    if not np.iterable(bins):
+        bins = _nbins_to_bins(x_idx, bins, right)
+
+    elif isinstance(bins, IntervalIndex):
+        if bins.is_overlapping:
+            raise ValueError("Overlapping IntervalIndex is not accepted.")
+
+    else:
+        bins = Index(bins)
+        if not bins.is_monotonic_increasing:
+            raise ValueError("bins must increase monotonically.")
+
+    fac, bins = _bins_to_cuts(
+        x_idx,
+        bins,
+        right=right,
+        labels=labels,
+        precision=precision,
+        include_lowest=include_lowest,
+        duplicates=duplicates,
+        ordered=ordered,
+    )
+
+    return _postprocess_for_cut(fac, bins, retbins, original)
+
+
+def qcut(
+    x,
+    q,
+    labels=None,
+    retbins: bool = False,
+    precision: int = 3,
+    duplicates: str = "raise",
+):
+    """
+    Quantile-based discretization function.
+
+    Discretize variable into equal-sized buckets based on rank or based
+    on sample quantiles. For example 1000 values for 10 quantiles would
+    produce a Categorical object indicating quantile membership for each data point.
+
+    Parameters
+    ----------
+    x : 1d ndarray or Series
+    q : int or list-like of float
+        Number of quantiles. 10 for deciles, 4 for quartiles, etc. Alternately
+        array of quantiles, e.g. [0, .25, .5, .75, 1.] for quartiles.
+    labels : array or False, default None
+        Used as labels for the resulting bins. Must be of the same length as
+        the resulting bins. If False, return only integer indicators of the
+        bins. If True, raises an error.
+    retbins : bool, optional
+        Whether to return the (bins, labels) or not. Can be useful if bins
+        is given as a scalar.
+    precision : int, optional
+        The precision at which to store and display the bins labels.
+    duplicates : {default 'raise', 'drop'}, optional
+        If bin edges are not unique, raise ValueError or drop non-uniques.
+
+    Returns
+    -------
+    out : Categorical or Series or array of integers if labels is False
+        The return type (Categorical or Series) depends on the input: a Series
+        of type category if input is a Series else Categorical. Bins are
+        represented as categories when categorical data is returned.
+    bins : ndarray of floats
+        Returned only if `retbins` is True.
+
+    Notes
+    -----
+    Out of bounds values will be NA in the resulting Categorical object
+
+    Examples
+    --------
+    >>> pd.qcut(range(5), 4)
+    ... # doctest: +ELLIPSIS
+    [(-0.001, 1.0], (-0.001, 1.0], (1.0, 2.0], (2.0, 3.0], (3.0, 4.0]]
+    Categories (4, interval[float64, right]): [(-0.001, 1.0] < (1.0, 2.0] ...
+
+    >>> pd.qcut(range(5), 3, labels=["good", "medium", "bad"])
+    ... # doctest: +SKIP
+    [good, good, medium, bad, bad]
+    Categories (3, object): [good < medium < bad]
+
+    >>> pd.qcut(range(5), 4, labels=False)
+    array([0, 0, 1, 2, 3])
+    """
+    original = x
+    x_idx = _preprocess_for_cut(x)
+    x_idx, _ = _coerce_to_type(x_idx)
+
+    quantiles = np.linspace(0, 1, q + 1) if is_integer(q) else q
+
+    bins = x_idx.to_series().dropna().quantile(quantiles)
+
+    fac, bins = _bins_to_cuts(
+        x_idx,
+        Index(bins),
+        labels=labels,
+        precision=precision,
+        include_lowest=True,
+        duplicates=duplicates,
+    )
+
+    return _postprocess_for_cut(fac, bins, retbins, original)
+
+
+def _nbins_to_bins(x_idx: Index, nbins: int, right: bool) -> Index:
+    """
+    If a user passed an integer N for bins, convert this to a sequence of N
+    equal(ish)-sized bins.
+    """
+    if is_scalar(nbins) and nbins < 1:
+        raise ValueError("`bins` should be a positive integer.")
+
+    if x_idx.size == 0:
+        raise ValueError("Cannot cut empty array")
+
+    rng = (x_idx.min(), x_idx.max())
+    mn, mx = rng
+
+    if is_numeric_dtype(x_idx.dtype) and (np.isinf(mn) or np.isinf(mx)):
+        # GH#24314
+        raise ValueError(
+            "cannot specify integer `bins` when input data contains infinity"
+        )
+
+    if mn == mx:  # adjust end points before binning
+        if _is_dt_or_td(x_idx.dtype):
+            # using seconds=1 is pretty arbitrary here
+            # error: Argument 1 to "dtype_to_unit" has incompatible type
+            # "dtype[Any] | ExtensionDtype"; expected "DatetimeTZDtype | dtype[Any]"
+            unit = dtype_to_unit(x_idx.dtype)  # type: ignore[arg-type]
+            td = Timedelta(seconds=1).as_unit(unit)
+            # Use DatetimeArray/TimedeltaArray method instead of linspace
+            # error: Item "ExtensionArray" of "ExtensionArray | ndarray[Any, Any]"
+            # has no attribute "_generate_range"
+            bins = x_idx._values._generate_range(  # type: ignore[union-attr]
+                start=mn - td, end=mx + td, periods=nbins + 1, freq=None, unit=unit
+            )
+        else:
+            mn -= 0.001 * abs(mn) if mn != 0 else 0.001
+            mx += 0.001 * abs(mx) if mx != 0 else 0.001
+
+            bins = np.linspace(mn, mx, nbins + 1, endpoint=True)
+    else:  # adjust end points after binning
+        if _is_dt_or_td(x_idx.dtype):
+            # Use DatetimeArray/TimedeltaArray method instead of linspace
+
+            # error: Argument 1 to "dtype_to_unit" has incompatible type
+            # "dtype[Any] | ExtensionDtype"; expected "DatetimeTZDtype | dtype[Any]"
+            unit = dtype_to_unit(x_idx.dtype)  # type: ignore[arg-type]
+            # error: Item "ExtensionArray" of "ExtensionArray | ndarray[Any, Any]"
+            # has no attribute "_generate_range"
+            bins = x_idx._values._generate_range(  # type: ignore[union-attr]
+                start=mn, end=mx, periods=nbins + 1, freq=None, unit=unit
+            )
+        else:
+            bins = np.linspace(mn, mx, nbins + 1, endpoint=True)
+        adj = (mx - mn) * 0.001  # 0.1% of the range
+        if right:
+            bins[0] -= adj
+        else:
+            bins[-1] += adj
+
+    return Index(bins)
+
+
+def _bins_to_cuts(
+    x_idx: Index,
+    bins: Index,
+    right: bool = True,
+    labels=None,
+    precision: int = 3,
+    include_lowest: bool = False,
+    duplicates: str = "raise",
+    ordered: bool = True,
+):
+    if not ordered and labels is None:
+        raise ValueError("'labels' must be provided if 'ordered = False'")
+
+    if duplicates not in ["raise", "drop"]:
+        raise ValueError(
+            "invalid value for 'duplicates' parameter, valid options are: raise, drop"
+        )
+
+    result: Categorical | np.ndarray
+
+    if isinstance(bins, IntervalIndex):
+        # we have a fast-path here
+        ids = bins.get_indexer(x_idx)
+        cat_dtype = CategoricalDtype(bins, ordered=True)
+        result = Categorical.from_codes(ids, dtype=cat_dtype, validate=False)
+        return result, bins
+
+    unique_bins = algos.unique(bins)
+    if len(unique_bins) < len(bins) and len(bins) != 2:
+        if duplicates == "raise":
+            raise ValueError(
+                f"Bin edges must be unique: {repr(bins)}.\n"
+                f"You can drop duplicate edges by setting the 'duplicates' kwarg"
+            )
+        bins = unique_bins
+
+    side: Literal["left", "right"] = "left" if right else "right"
+
+    try:
+        ids = bins.searchsorted(x_idx, side=side)
+    except TypeError as err:
+        # e.g. test_datetime_nan_error if bins are DatetimeArray and x_idx
+        #  is integers
+        if x_idx.dtype.kind == "m":
+            raise ValueError("bins must be of timedelta64 dtype") from err
+        elif x_idx.dtype.kind == bins.dtype.kind == "M":
+            raise ValueError(
+                "Cannot use timezone-naive bins with timezone-aware values, "
+                "or vice-versa"
+            ) from err
+        elif x_idx.dtype.kind == "M":
+            raise ValueError("bins must be of datetime64 dtype") from err
+        else:
+            raise
+    ids = ensure_platform_int(ids)
+
+    if include_lowest:
+        ids[x_idx == bins[0]] = 1
+
+    na_mask = isna(x_idx) | (ids == len(bins)) | (ids == 0)
+    has_nas = na_mask.any()
+
+    if labels is not False:
+        if not (labels is None or is_list_like(labels)):
+            raise ValueError(
+                "Bin labels must either be False, None or passed in as a "
+                "list-like argument"
+            )
+
+        if labels is None:
+            labels = _format_labels(
+                bins, precision, right=right, include_lowest=include_lowest
+            )
+        elif ordered and len(set(labels)) != len(labels):
+            raise ValueError(
+                "labels must be unique if ordered=True; pass ordered=False "
+                "for duplicate labels"
+            )
+        else:
+            if len(labels) != len(bins) - 1:
+                raise ValueError(
+                    "Bin labels must be one fewer than the number of bin edges"
+                )
+
+        if not isinstance(getattr(labels, "dtype", None), CategoricalDtype):
+            labels = Categorical(
+                labels,
+                categories=labels if len(set(labels)) == len(labels) else None,
+                ordered=ordered,
+            )
+        # TODO: handle mismatch between categorical label order and pandas.cut order.
+        np.putmask(ids, na_mask, 0)
+        result = algos.take_nd(labels, ids - 1)
+
+    else:
+        result = ids - 1
+        if has_nas:
+            result = result.astype(np.float64)
+            np.putmask(result, na_mask, np.nan)
+
+    return result, bins
+
+
+def _coerce_to_type(x: Index) -> tuple[Index, DtypeObj | None]:
+    """
+    if the passed data is of datetime/timedelta, bool or nullable int type,
+    this method converts it to numeric so that cut or qcut method can
+    handle it
+    """
+    dtype: DtypeObj | None = None
+
+    if _is_dt_or_td(x.dtype):
+        dtype = x.dtype
+    elif is_bool_dtype(x.dtype):
+        # GH 20303
+        x = x.astype(np.int64)
+    # To support cut and qcut for IntegerArray we convert to float dtype.
+    # Will properly support in the future.
+    # https://github.com/pandas-dev/pandas/pull/31290
+    # https://github.com/pandas-dev/pandas/issues/31389
+    elif isinstance(x.dtype, ExtensionDtype) and is_numeric_dtype(x.dtype):
+        x_arr = x.to_numpy(dtype=np.float64, na_value=np.nan)
+        x = Index(x_arr)
+
+    return Index(x), dtype
+
+
+def _is_dt_or_td(dtype: DtypeObj) -> bool:
+    # Note: the dtype here comes from an Index.dtype, so we know that that any
+    #  dt64/td64 dtype is of a supported unit.
+    return isinstance(dtype, DatetimeTZDtype) or lib.is_np_dtype(dtype, "mM")
+
+
+def _format_labels(
+    bins: Index,
+    precision: int,
+    right: bool = True,
+    include_lowest: bool = False,
+):
+    """based on the dtype, return our labels"""
+    closed: IntervalLeftRight = "right" if right else "left"
+
+    formatter: Callable[[Any], Timestamp] | Callable[[Any], Timedelta]
+
+    if _is_dt_or_td(bins.dtype):
+        # error: Argument 1 to "dtype_to_unit" has incompatible type
+        # "dtype[Any] | ExtensionDtype"; expected "DatetimeTZDtype | dtype[Any]"
+        unit = dtype_to_unit(bins.dtype)  # type: ignore[arg-type]
+        formatter = lambda x: x
+        adjust = lambda x: x - Timedelta(1, unit=unit).as_unit(unit)
+    else:
+        precision = _infer_precision(precision, bins)
+        formatter = lambda x: _round_frac(x, precision)
+        adjust = lambda x: x - 10 ** (-precision)
+
+    breaks = [formatter(b) for b in bins]
+    if right and include_lowest:
+        # adjust lhs of first interval by precision to account for being right closed
+        breaks[0] = adjust(breaks[0])
+
+    if _is_dt_or_td(bins.dtype):
+        # error: "Index" has no attribute "as_unit"
+        breaks = type(bins)(breaks).as_unit(unit)  # type: ignore[attr-defined]
+
+    return IntervalIndex.from_breaks(breaks, closed=closed)
+
+
+def _preprocess_for_cut(x) -> Index:
+    """
+    handles preprocessing for cut where we convert passed
+    input to array, strip the index information and store it
+    separately
+    """
+    # Check that the passed array is a Pandas or Numpy object
+    # We don't want to strip away a Pandas data-type here (e.g. datetimetz)
+    ndim = getattr(x, "ndim", None)
+    if ndim is None:
+        x = np.asarray(x)
+    if x.ndim != 1:
+        raise ValueError("Input array must be 1 dimensional")
+
+    return Index(x)
+
+
+def _postprocess_for_cut(fac, bins, retbins: bool, original):
+    """
+    handles post processing for the cut method where
+    we combine the index information if the originally passed
+    datatype was a series
+    """
+    if isinstance(original, ABCSeries):
+        fac = original._constructor(fac, index=original.index, name=original.name)
+
+    if not retbins:
+        return fac
+
+    if isinstance(bins, Index) and is_numeric_dtype(bins.dtype):
+        bins = bins._values
+
+    return fac, bins
+
+
+def _round_frac(x, precision: int):
+    """
+    Round the fractional part of the given number
+    """
+    if not np.isfinite(x) or x == 0:
+        return x
+    else:
+        frac, whole = np.modf(x)
+        if whole == 0:
+            digits = -int(np.floor(np.log10(abs(frac)))) - 1 + precision
+        else:
+            digits = precision
+        return np.around(x, digits)
+
+
+def _infer_precision(base_precision: int, bins: Index) -> int:
+    """
+    Infer an appropriate precision for _round_frac
+    """
+    for precision in range(base_precision, 20):
+        levels = np.asarray([_round_frac(b, precision) for b in bins])
+        if algos.unique(levels).size == bins.size:
+            return precision
+    return base_precision  # default
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/reshape/util.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/reshape/util.py
new file mode 100644
index 0000000000000000000000000000000000000000..476e3922b6989e4267aeeafd5943a80c1599b1d5
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/reshape/util.py
@@ -0,0 +1,85 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import numpy as np
+
+from pandas.core.dtypes.common import is_list_like
+
+if TYPE_CHECKING:
+    from pandas._typing import NumpyIndexT
+
+
+def cartesian_product(X) -> list[np.ndarray]:
+    """
+    Numpy version of itertools.product.
+    Sometimes faster (for large inputs)...
+
+    Parameters
+    ----------
+    X : list-like of list-likes
+
+    Returns
+    -------
+    product : list of ndarrays
+
+    Examples
+    --------
+    >>> cartesian_product([list('ABC'), [1, 2]])
+    [array(['A', 'A', 'B', 'B', 'C', 'C'], dtype=' NumpyIndexT:
+    """
+    Index compat for np.tile.
+
+    Notes
+    -----
+    Does not support multi-dimensional `num`.
+    """
+    if isinstance(arr, np.ndarray):
+        return np.tile(arr, num)
+
+    # Otherwise we have an Index
+    taker = np.tile(np.arange(len(arr)), num)
+    return arr.take(taker)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/roperator.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/roperator.py
new file mode 100644
index 0000000000000000000000000000000000000000..2f320f4e9c6b984b64e0fc1268e50a8ad1a7e1fe
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/roperator.py
@@ -0,0 +1,62 @@
+"""
+Reversed Operations not available in the stdlib operator module.
+Defining these instead of using lambdas allows us to reference them by name.
+"""
+from __future__ import annotations
+
+import operator
+
+
+def radd(left, right):
+    return right + left
+
+
+def rsub(left, right):
+    return right - left
+
+
+def rmul(left, right):
+    return right * left
+
+
+def rdiv(left, right):
+    return right / left
+
+
+def rtruediv(left, right):
+    return right / left
+
+
+def rfloordiv(left, right):
+    return right // left
+
+
+def rmod(left, right):
+    # check if right is a string as % is the string
+    # formatting operation; this is a TypeError
+    # otherwise perform the op
+    if isinstance(right, str):
+        typ = type(left).__name__
+        raise TypeError(f"{typ} cannot perform the operation mod")
+
+    return right % left
+
+
+def rdivmod(left, right):
+    return divmod(right, left)
+
+
+def rpow(left, right):
+    return right**left
+
+
+def rand_(left, right):
+    return operator.and_(right, left)
+
+
+def ror_(left, right):
+    return operator.or_(right, left)
+
+
+def rxor(left, right):
+    return operator.xor(right, left)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/sample.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/sample.py
new file mode 100644
index 0000000000000000000000000000000000000000..eebbed3512c4eca42e401c40605838c6f69011df
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/sample.py
@@ -0,0 +1,154 @@
+"""
+Module containing utilities for NDFrame.sample() and .GroupBy.sample()
+"""
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import numpy as np
+
+from pandas._libs import lib
+
+from pandas.core.dtypes.generic import (
+    ABCDataFrame,
+    ABCSeries,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import AxisInt
+
+    from pandas.core.generic import NDFrame
+
+
+def preprocess_weights(obj: NDFrame, weights, axis: AxisInt) -> np.ndarray:
+    """
+    Process and validate the `weights` argument to `NDFrame.sample` and
+    `.GroupBy.sample`.
+
+    Returns `weights` as an ndarray[np.float64], validated except for normalizing
+    weights (because that must be done groupwise in groupby sampling).
+    """
+    # If a series, align with frame
+    if isinstance(weights, ABCSeries):
+        weights = weights.reindex(obj.axes[axis])
+
+    # Strings acceptable if a dataframe and axis = 0
+    if isinstance(weights, str):
+        if isinstance(obj, ABCDataFrame):
+            if axis == 0:
+                try:
+                    weights = obj[weights]
+                except KeyError as err:
+                    raise KeyError(
+                        "String passed to weights not a valid column"
+                    ) from err
+            else:
+                raise ValueError(
+                    "Strings can only be passed to "
+                    "weights when sampling from rows on "
+                    "a DataFrame"
+                )
+        else:
+            raise ValueError(
+                "Strings cannot be passed as weights when sampling from a Series."
+            )
+
+    if isinstance(obj, ABCSeries):
+        func = obj._constructor
+    else:
+        func = obj._constructor_sliced
+
+    weights = func(weights, dtype="float64")._values
+
+    if len(weights) != obj.shape[axis]:
+        raise ValueError("Weights and axis to be sampled must be of same length")
+
+    if lib.has_infs(weights):
+        raise ValueError("weight vector may not include `inf` values")
+
+    if (weights < 0).any():
+        raise ValueError("weight vector many not include negative values")
+
+    missing = np.isnan(weights)
+    if missing.any():
+        # Don't modify weights in place
+        weights = weights.copy()
+        weights[missing] = 0
+    return weights
+
+
+def process_sampling_size(
+    n: int | None, frac: float | None, replace: bool
+) -> int | None:
+    """
+    Process and validate the `n` and `frac` arguments to `NDFrame.sample` and
+    `.GroupBy.sample`.
+
+    Returns None if `frac` should be used (variable sampling sizes), otherwise returns
+    the constant sampling size.
+    """
+    # If no frac or n, default to n=1.
+    if n is None and frac is None:
+        n = 1
+    elif n is not None and frac is not None:
+        raise ValueError("Please enter a value for `frac` OR `n`, not both")
+    elif n is not None:
+        if n < 0:
+            raise ValueError(
+                "A negative number of rows requested. Please provide `n` >= 0."
+            )
+        if n % 1 != 0:
+            raise ValueError("Only integers accepted as `n` values")
+    else:
+        assert frac is not None  # for mypy
+        if frac > 1 and not replace:
+            raise ValueError(
+                "Replace has to be set to `True` when "
+                "upsampling the population `frac` > 1."
+            )
+        if frac < 0:
+            raise ValueError(
+                "A negative number of rows requested. Please provide `frac` >= 0."
+            )
+
+    return n
+
+
+def sample(
+    obj_len: int,
+    size: int,
+    replace: bool,
+    weights: np.ndarray | None,
+    random_state: np.random.RandomState | np.random.Generator,
+) -> np.ndarray:
+    """
+    Randomly sample `size` indices in `np.arange(obj_len)`
+
+    Parameters
+    ----------
+    obj_len : int
+        The length of the indices being considered
+    size : int
+        The number of values to choose
+    replace : bool
+        Allow or disallow sampling of the same row more than once.
+    weights : np.ndarray[np.float64] or None
+        If None, equal probability weighting, otherwise weights according
+        to the vector normalized
+    random_state: np.random.RandomState or np.random.Generator
+        State used for the random sampling
+
+    Returns
+    -------
+    np.ndarray[np.intp]
+    """
+    if weights is not None:
+        weight_sum = weights.sum()
+        if weight_sum != 0:
+            weights = weights / weight_sum
+        else:
+            raise ValueError("Invalid weights: weights sum to zero")
+
+    return random_state.choice(obj_len, size=size, replace=replace, p=weights).astype(
+        np.intp, copy=False
+    )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/series.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/series.py
new file mode 100644
index 0000000000000000000000000000000000000000..ef0b41f3ae552221a9e3b186945f1f0fbee85b61
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/series.py
@@ -0,0 +1,6652 @@
+"""
+Data structure for 1-dimensional cross-sectional and time series data
+"""
+from __future__ import annotations
+
+from collections.abc import (
+    Hashable,
+    Iterable,
+    Mapping,
+    Sequence,
+)
+import operator
+import sys
+from textwrap import dedent
+from typing import (
+    IO,
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Literal,
+    cast,
+    overload,
+)
+import warnings
+import weakref
+
+import numpy as np
+
+from pandas._config import (
+    using_copy_on_write,
+    warn_copy_on_write,
+)
+from pandas._config.config import _get_option
+
+from pandas._libs import (
+    lib,
+    properties,
+    reshape,
+)
+from pandas._libs.lib import is_range_indexer
+from pandas.compat import PYPY
+from pandas.compat._constants import (
+    REF_COUNT,
+    WARNING_CHECK_DISABLED,
+)
+from pandas.compat._optional import import_optional_dependency
+from pandas.compat.numpy import function as nv
+from pandas.errors import (
+    ChainedAssignmentError,
+    InvalidIndexError,
+    _chained_assignment_method_msg,
+    _chained_assignment_msg,
+    _chained_assignment_warning_method_msg,
+    _chained_assignment_warning_msg,
+    _check_cacher,
+)
+from pandas.util._decorators import (
+    Appender,
+    Substitution,
+    deprecate_nonkeyword_arguments,
+    doc,
+)
+from pandas.util._exceptions import find_stack_level
+from pandas.util._validators import (
+    validate_ascending,
+    validate_bool_kwarg,
+    validate_percentile,
+)
+
+from pandas.core.dtypes.astype import astype_is_view
+from pandas.core.dtypes.cast import (
+    LossySetitemError,
+    construct_1d_arraylike_from_scalar,
+    find_common_type,
+    infer_dtype_from,
+    maybe_box_native,
+    maybe_cast_pointwise_result,
+)
+from pandas.core.dtypes.common import (
+    is_dict_like,
+    is_integer,
+    is_iterator,
+    is_list_like,
+    is_object_dtype,
+    is_scalar,
+    pandas_dtype,
+    validate_all_hashable,
+)
+from pandas.core.dtypes.dtypes import (
+    CategoricalDtype,
+    ExtensionDtype,
+    SparseDtype,
+)
+from pandas.core.dtypes.generic import (
+    ABCDataFrame,
+    ABCSeries,
+)
+from pandas.core.dtypes.inference import is_hashable
+from pandas.core.dtypes.missing import (
+    isna,
+    na_value_for_dtype,
+    notna,
+    remove_na_arraylike,
+)
+
+from pandas.core import (
+    algorithms,
+    base,
+    common as com,
+    missing,
+    nanops,
+    ops,
+    roperator,
+)
+from pandas.core.accessor import CachedAccessor
+from pandas.core.apply import SeriesApply
+from pandas.core.arrays import ExtensionArray
+from pandas.core.arrays.arrow import (
+    ListAccessor,
+    StructAccessor,
+)
+from pandas.core.arrays.categorical import CategoricalAccessor
+from pandas.core.arrays.sparse import SparseAccessor
+from pandas.core.arrays.string_ import StringDtype
+from pandas.core.construction import (
+    array as pd_array,
+    extract_array,
+    sanitize_array,
+)
+from pandas.core.generic import (
+    NDFrame,
+    make_doc,
+)
+from pandas.core.indexers import (
+    disallow_ndim_indexing,
+    unpack_1tuple,
+)
+from pandas.core.indexes.accessors import CombinedDatetimelikeProperties
+from pandas.core.indexes.api import (
+    DatetimeIndex,
+    Index,
+    MultiIndex,
+    PeriodIndex,
+    default_index,
+    ensure_index,
+)
+import pandas.core.indexes.base as ibase
+from pandas.core.indexes.multi import maybe_droplevels
+from pandas.core.indexing import (
+    check_bool_indexer,
+    check_dict_or_set_indexers,
+)
+from pandas.core.internals import (
+    SingleArrayManager,
+    SingleBlockManager,
+)
+from pandas.core.methods import selectn
+from pandas.core.shared_docs import _shared_docs
+from pandas.core.sorting import (
+    ensure_key_mapped,
+    nargsort,
+)
+from pandas.core.strings.accessor import StringMethods
+from pandas.core.tools.datetimes import to_datetime
+
+import pandas.io.formats.format as fmt
+from pandas.io.formats.info import (
+    INFO_DOCSTRING,
+    SeriesInfo,
+    series_sub_kwargs,
+)
+import pandas.plotting
+
+if TYPE_CHECKING:
+    from pandas._libs.internals import BlockValuesRefs
+    from pandas._typing import (
+        AggFuncType,
+        AnyAll,
+        AnyArrayLike,
+        ArrayLike,
+        Axis,
+        AxisInt,
+        CorrelationMethod,
+        DropKeep,
+        Dtype,
+        DtypeObj,
+        FilePath,
+        Frequency,
+        IgnoreRaise,
+        IndexKeyFunc,
+        IndexLabel,
+        Level,
+        MutableMappingT,
+        NaPosition,
+        NumpySorter,
+        NumpyValueArrayLike,
+        QuantileInterpolation,
+        ReindexMethod,
+        Renamer,
+        Scalar,
+        Self,
+        SingleManager,
+        SortKind,
+        StorageOptions,
+        Suffixes,
+        ValueKeyFunc,
+        WriteBuffer,
+        npt,
+    )
+
+    from pandas.core.frame import DataFrame
+    from pandas.core.groupby.generic import SeriesGroupBy
+
+__all__ = ["Series"]
+
+_shared_doc_kwargs = {
+    "axes": "index",
+    "klass": "Series",
+    "axes_single_arg": "{0 or 'index'}",
+    "axis": """axis : {0 or 'index'}
+        Unused. Parameter needed for compatibility with DataFrame.""",
+    "inplace": """inplace : bool, default False
+        If True, performs operation inplace and returns None.""",
+    "unique": "np.ndarray",
+    "duplicated": "Series",
+    "optional_by": "",
+    "optional_reindex": """
+index : array-like, optional
+    New labels for the index. Preferably an Index object to avoid
+    duplicating data.
+axis : int or str, optional
+    Unused.""",
+}
+
+
+def _coerce_method(converter):
+    """
+    Install the scalar coercion methods.
+    """
+
+    def wrapper(self):
+        if len(self) == 1:
+            warnings.warn(
+                f"Calling {converter.__name__} on a single element Series is "
+                "deprecated and will raise a TypeError in the future. "
+                f"Use {converter.__name__}(ser.iloc[0]) instead",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+            return converter(self.iloc[0])
+        raise TypeError(f"cannot convert the series to {converter}")
+
+    wrapper.__name__ = f"__{converter.__name__}__"
+    return wrapper
+
+
+# ----------------------------------------------------------------------
+# Series class
+
+
+# error: Cannot override final attribute "ndim" (previously declared in base
+# class "NDFrame")
+# error: Cannot override final attribute "size" (previously declared in base
+# class "NDFrame")
+# definition in base class "NDFrame"
+class Series(base.IndexOpsMixin, NDFrame):  # type: ignore[misc]
+    """
+    One-dimensional ndarray with axis labels (including time series).
+
+    Labels need not be unique but must be a hashable type. The object
+    supports both integer- and label-based indexing and provides a host of
+    methods for performing operations involving the index. Statistical
+    methods from ndarray have been overridden to automatically exclude
+    missing data (currently represented as NaN).
+
+    Operations between Series (+, -, /, \\*, \\*\\*) align values based on their
+    associated index values-- they need not be the same length. The result
+    index will be the sorted union of the two indexes.
+
+    Parameters
+    ----------
+    data : array-like, Iterable, dict, or scalar value
+        Contains data stored in Series. If data is a dict, argument order is
+        maintained.
+    index : array-like or Index (1d)
+        Values must be hashable and have the same length as `data`.
+        Non-unique index values are allowed. Will default to
+        RangeIndex (0, 1, 2, ..., n) if not provided. If data is dict-like
+        and index is None, then the keys in the data are used as the index. If the
+        index is not None, the resulting Series is reindexed with the index values.
+    dtype : str, numpy.dtype, or ExtensionDtype, optional
+        Data type for the output Series. If not specified, this will be
+        inferred from `data`.
+        See the :ref:`user guide ` for more usages.
+    name : Hashable, default None
+        The name to give to the Series.
+    copy : bool, default False
+        Copy input data. Only affects Series or 1d ndarray input. See examples.
+
+    Notes
+    -----
+    Please reference the :ref:`User Guide ` for more information.
+
+    Examples
+    --------
+    Constructing Series from a dictionary with an Index specified
+
+    >>> d = {'a': 1, 'b': 2, 'c': 3}
+    >>> ser = pd.Series(data=d, index=['a', 'b', 'c'])
+    >>> ser
+    a   1
+    b   2
+    c   3
+    dtype: int64
+
+    The keys of the dictionary match with the Index values, hence the Index
+    values have no effect.
+
+    >>> d = {'a': 1, 'b': 2, 'c': 3}
+    >>> ser = pd.Series(data=d, index=['x', 'y', 'z'])
+    >>> ser
+    x   NaN
+    y   NaN
+    z   NaN
+    dtype: float64
+
+    Note that the Index is first build with the keys from the dictionary.
+    After this the Series is reindexed with the given Index values, hence we
+    get all NaN as a result.
+
+    Constructing Series from a list with `copy=False`.
+
+    >>> r = [1, 2]
+    >>> ser = pd.Series(r, copy=False)
+    >>> ser.iloc[0] = 999
+    >>> r
+    [1, 2]
+    >>> ser
+    0    999
+    1      2
+    dtype: int64
+
+    Due to input data type the Series has a `copy` of
+    the original data even though `copy=False`, so
+    the data is unchanged.
+
+    Constructing Series from a 1d ndarray with `copy=False`.
+
+    >>> r = np.array([1, 2])
+    >>> ser = pd.Series(r, copy=False)
+    >>> ser.iloc[0] = 999
+    >>> r
+    array([999,   2])
+    >>> ser
+    0    999
+    1      2
+    dtype: int64
+
+    Due to input data type the Series has a `view` on
+    the original data, so
+    the data is changed as well.
+    """
+
+    _typ = "series"
+    _HANDLED_TYPES = (Index, ExtensionArray, np.ndarray)
+
+    _name: Hashable
+    _metadata: list[str] = ["_name"]
+    _internal_names_set = {"index", "name"} | NDFrame._internal_names_set
+    _accessors = {"dt", "cat", "str", "sparse"}
+    _hidden_attrs = (
+        base.IndexOpsMixin._hidden_attrs | NDFrame._hidden_attrs | frozenset([])
+    )
+
+    # similar to __array_priority__, positions Series after DataFrame
+    #  but before Index and ExtensionArray.  Should NOT be overridden by subclasses.
+    __pandas_priority__ = 3000
+
+    # Override cache_readonly bc Series is mutable
+    # error: Incompatible types in assignment (expression has type "property",
+    # base class "IndexOpsMixin" defined the type as "Callable[[IndexOpsMixin], bool]")
+    hasnans = property(  # type: ignore[assignment]
+        # error: "Callable[[IndexOpsMixin], bool]" has no attribute "fget"
+        base.IndexOpsMixin.hasnans.fget,  # type: ignore[attr-defined]
+        doc=base.IndexOpsMixin.hasnans.__doc__,
+    )
+    _mgr: SingleManager
+
+    # ----------------------------------------------------------------------
+    # Constructors
+
+    def __init__(
+        self,
+        data=None,
+        index=None,
+        dtype: Dtype | None = None,
+        name=None,
+        copy: bool | None = None,
+        fastpath: bool | lib.NoDefault = lib.no_default,
+    ) -> None:
+        if fastpath is not lib.no_default:
+            warnings.warn(
+                "The 'fastpath' keyword in pd.Series is deprecated and will "
+                "be removed in a future version.",
+                DeprecationWarning,
+                stacklevel=find_stack_level(),
+            )
+        else:
+            fastpath = False
+
+        allow_mgr = False
+        if (
+            isinstance(data, (SingleBlockManager, SingleArrayManager))
+            and index is None
+            and dtype is None
+            and (copy is False or copy is None)
+        ):
+            if not allow_mgr:
+                # GH#52419
+                warnings.warn(
+                    f"Passing a {type(data).__name__} to {type(self).__name__} "
+                    "is deprecated and will raise in a future version. "
+                    "Use public APIs instead.",
+                    DeprecationWarning,
+                    stacklevel=2,
+                )
+            if using_copy_on_write():
+                data = data.copy(deep=False)
+            # GH#33357 called with just the SingleBlockManager
+            NDFrame.__init__(self, data)
+            if fastpath:
+                # e.g. from _box_col_values, skip validation of name
+                object.__setattr__(self, "_name", name)
+            else:
+                self.name = name
+            return
+
+        is_pandas_object = isinstance(data, (Series, Index, ExtensionArray))
+        data_dtype = getattr(data, "dtype", None)
+        original_dtype = dtype
+
+        if isinstance(data, (ExtensionArray, np.ndarray)):
+            if copy is not False and using_copy_on_write():
+                if dtype is None or astype_is_view(data.dtype, pandas_dtype(dtype)):
+                    data = data.copy()
+        if copy is None:
+            copy = False
+
+        # we are called internally, so short-circuit
+        if fastpath:
+            # data is a ndarray, index is defined
+            if not isinstance(data, (SingleBlockManager, SingleArrayManager)):
+                manager = _get_option("mode.data_manager", silent=True)
+                if manager == "block":
+                    data = SingleBlockManager.from_array(data, index)
+                elif manager == "array":
+                    data = SingleArrayManager.from_array(data, index)
+                allow_mgr = True
+            elif using_copy_on_write() and not copy:
+                data = data.copy(deep=False)
+
+            if not allow_mgr:
+                warnings.warn(
+                    f"Passing a {type(data).__name__} to {type(self).__name__} "
+                    "is deprecated and will raise in a future version. "
+                    "Use public APIs instead.",
+                    DeprecationWarning,
+                    stacklevel=2,
+                )
+
+            if copy:
+                data = data.copy()
+            # skips validation of the name
+            object.__setattr__(self, "_name", name)
+            NDFrame.__init__(self, data)
+            return
+
+        if isinstance(data, SingleBlockManager) and using_copy_on_write() and not copy:
+            data = data.copy(deep=False)
+
+            if not allow_mgr:
+                warnings.warn(
+                    f"Passing a {type(data).__name__} to {type(self).__name__} "
+                    "is deprecated and will raise in a future version. "
+                    "Use public APIs instead.",
+                    DeprecationWarning,
+                    stacklevel=2,
+                )
+
+        name = ibase.maybe_extract_name(name, data, type(self))
+
+        if index is not None:
+            index = ensure_index(index)
+
+        if dtype is not None:
+            dtype = self._validate_dtype(dtype)
+
+        if data is None:
+            index = index if index is not None else default_index(0)
+            if len(index) or dtype is not None:
+                data = na_value_for_dtype(pandas_dtype(dtype), compat=False)
+            else:
+                data = []
+
+        if isinstance(data, MultiIndex):
+            raise NotImplementedError(
+                "initializing a Series from a MultiIndex is not supported"
+            )
+
+        refs = None
+        if isinstance(data, Index):
+            if dtype is not None:
+                data = data.astype(dtype, copy=False)
+
+            if using_copy_on_write():
+                refs = data._references
+                data = data._values
+            else:
+                # GH#24096 we need to ensure the index remains immutable
+                data = data._values.copy()
+            copy = False
+
+        elif isinstance(data, np.ndarray):
+            if len(data.dtype):
+                # GH#13296 we are dealing with a compound dtype, which
+                #  should be treated as 2D
+                raise ValueError(
+                    "Cannot construct a Series from an ndarray with "
+                    "compound dtype.  Use DataFrame instead."
+                )
+        elif isinstance(data, Series):
+            if index is None:
+                index = data.index
+                data = data._mgr.copy(deep=False)
+            else:
+                data = data.reindex(index, copy=copy)
+                copy = False
+                data = data._mgr
+        elif isinstance(data, Mapping):
+            data, index = self._init_dict(data, index, dtype)
+            dtype = None
+            copy = False
+        elif isinstance(data, (SingleBlockManager, SingleArrayManager)):
+            if index is None:
+                index = data.index
+            elif not data.index.equals(index) or copy:
+                # GH#19275 SingleBlockManager input should only be called
+                # internally
+                raise AssertionError(
+                    "Cannot pass both SingleBlockManager "
+                    "`data` argument and a different "
+                    "`index` argument. `copy` must be False."
+                )
+
+            if not allow_mgr:
+                warnings.warn(
+                    f"Passing a {type(data).__name__} to {type(self).__name__} "
+                    "is deprecated and will raise in a future version. "
+                    "Use public APIs instead.",
+                    DeprecationWarning,
+                    stacklevel=2,
+                )
+                allow_mgr = True
+
+        elif isinstance(data, ExtensionArray):
+            pass
+        else:
+            data = com.maybe_iterable_to_list(data)
+            if is_list_like(data) and not len(data) and dtype is None:
+                # GH 29405: Pre-2.0, this defaulted to float.
+                dtype = np.dtype(object)
+
+        if index is None:
+            if not is_list_like(data):
+                data = [data]
+            index = default_index(len(data))
+        elif is_list_like(data):
+            com.require_length_match(data, index)
+
+        # create/copy the manager
+        if isinstance(data, (SingleBlockManager, SingleArrayManager)):
+            if dtype is not None:
+                data = data.astype(dtype=dtype, errors="ignore", copy=copy)
+            elif copy:
+                data = data.copy()
+        else:
+            data = sanitize_array(data, index, dtype, copy)
+
+            manager = _get_option("mode.data_manager", silent=True)
+            if manager == "block":
+                data = SingleBlockManager.from_array(data, index, refs=refs)
+            elif manager == "array":
+                data = SingleArrayManager.from_array(data, index)
+
+        NDFrame.__init__(self, data)
+        self.name = name
+        self._set_axis(0, index)
+
+        if original_dtype is None and is_pandas_object and data_dtype == np.object_:
+            if self.dtype != data_dtype:
+                warnings.warn(
+                    "Dtype inference on a pandas object "
+                    "(Series, Index, ExtensionArray) is deprecated. The Series "
+                    "constructor will keep the original dtype in the future. "
+                    "Call `infer_objects` on the result to get the old behavior.",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+
+    def _init_dict(
+        self, data: Mapping, index: Index | None = None, dtype: DtypeObj | None = None
+    ):
+        """
+        Derive the "_mgr" and "index" attributes of a new Series from a
+        dictionary input.
+
+        Parameters
+        ----------
+        data : dict or dict-like
+            Data used to populate the new Series.
+        index : Index or None, default None
+            Index for the new Series: if None, use dict keys.
+        dtype : np.dtype, ExtensionDtype, or None, default None
+            The dtype for the new Series: if None, infer from data.
+
+        Returns
+        -------
+        _data : BlockManager for the new Series
+        index : index for the new Series
+        """
+        keys: Index | tuple
+
+        # Looking for NaN in dict doesn't work ({np.nan : 1}[float('nan')]
+        # raises KeyError), so we iterate the entire dict, and align
+        if data:
+            # GH:34717, issue was using zip to extract key and values from data.
+            # using generators in effects the performance.
+            # Below is the new way of extracting the keys and values
+
+            keys = tuple(data.keys())
+            values = list(data.values())  # Generating list of values- faster way
+        elif index is not None:
+            # fastpath for Series(data=None). Just use broadcasting a scalar
+            # instead of reindexing.
+            if len(index) or dtype is not None:
+                values = na_value_for_dtype(pandas_dtype(dtype), compat=False)
+            else:
+                values = []
+            keys = index
+        else:
+            keys, values = default_index(0), []
+
+        # Input is now list-like, so rely on "standard" construction:
+        s = Series(values, index=keys, dtype=dtype)
+
+        # Now we just make sure the order is respected, if any
+        if data and index is not None:
+            s = s.reindex(index, copy=False)
+        return s._mgr, s.index
+
+    # ----------------------------------------------------------------------
+
+    @property
+    def _constructor(self) -> Callable[..., Series]:
+        return Series
+
+    def _constructor_from_mgr(self, mgr, axes):
+        ser = Series._from_mgr(mgr, axes=axes)
+        ser._name = None  # caller is responsible for setting real name
+
+        if type(self) is Series:
+            # This would also work `if self._constructor is Series`, but
+            #  this check is slightly faster, benefiting the most-common case.
+            return ser
+
+        # We assume that the subclass __init__ knows how to handle a
+        #  pd.Series object.
+        return self._constructor(ser)
+
+    @property
+    def _constructor_expanddim(self) -> Callable[..., DataFrame]:
+        """
+        Used when a manipulation result has one higher dimension as the
+        original, such as Series.to_frame()
+        """
+        from pandas.core.frame import DataFrame
+
+        return DataFrame
+
+    def _constructor_expanddim_from_mgr(self, mgr, axes):
+        from pandas.core.frame import DataFrame
+
+        df = DataFrame._from_mgr(mgr, axes=mgr.axes)
+
+        if type(self) is Series:
+            # This would also work `if self._constructor_expanddim is DataFrame`,
+            #  but this check is slightly faster, benefiting the most-common case.
+            return df
+
+        # We assume that the subclass __init__ knows how to handle a
+        #  pd.DataFrame object.
+        return self._constructor_expanddim(df)
+
+    # types
+    @property
+    def _can_hold_na(self) -> bool:
+        return self._mgr._can_hold_na
+
+    # ndarray compatibility
+    @property
+    def dtype(self) -> DtypeObj:
+        """
+        Return the dtype object of the underlying data.
+
+        Examples
+        --------
+        >>> s = pd.Series([1, 2, 3])
+        >>> s.dtype
+        dtype('int64')
+        """
+        return self._mgr.dtype
+
+    @property
+    def dtypes(self) -> DtypeObj:
+        """
+        Return the dtype object of the underlying data.
+
+        Examples
+        --------
+        >>> s = pd.Series([1, 2, 3])
+        >>> s.dtypes
+        dtype('int64')
+        """
+        # DataFrame compatibility
+        return self.dtype
+
+    @property
+    def name(self) -> Hashable:
+        """
+        Return the name of the Series.
+
+        The name of a Series becomes its index or column name if it is used
+        to form a DataFrame. It is also used whenever displaying the Series
+        using the interpreter.
+
+        Returns
+        -------
+        label (hashable object)
+            The name of the Series, also the column name if part of a DataFrame.
+
+        See Also
+        --------
+        Series.rename : Sets the Series name when given a scalar input.
+        Index.name : Corresponding Index property.
+
+        Examples
+        --------
+        The Series name can be set initially when calling the constructor.
+
+        >>> s = pd.Series([1, 2, 3], dtype=np.int64, name='Numbers')
+        >>> s
+        0    1
+        1    2
+        2    3
+        Name: Numbers, dtype: int64
+        >>> s.name = "Integers"
+        >>> s
+        0    1
+        1    2
+        2    3
+        Name: Integers, dtype: int64
+
+        The name of a Series within a DataFrame is its column name.
+
+        >>> df = pd.DataFrame([[1, 2], [3, 4], [5, 6]],
+        ...                   columns=["Odd Numbers", "Even Numbers"])
+        >>> df
+           Odd Numbers  Even Numbers
+        0            1             2
+        1            3             4
+        2            5             6
+        >>> df["Even Numbers"].name
+        'Even Numbers'
+        """
+        return self._name
+
+    @name.setter
+    def name(self, value: Hashable) -> None:
+        validate_all_hashable(value, error_name=f"{type(self).__name__}.name")
+        object.__setattr__(self, "_name", value)
+
+    @property
+    def values(self):
+        """
+        Return Series as ndarray or ndarray-like depending on the dtype.
+
+        .. warning::
+
+           We recommend using :attr:`Series.array` or
+           :meth:`Series.to_numpy`, depending on whether you need
+           a reference to the underlying data or a NumPy array.
+
+        Returns
+        -------
+        numpy.ndarray or ndarray-like
+
+        See Also
+        --------
+        Series.array : Reference to the underlying data.
+        Series.to_numpy : A NumPy array representing the underlying data.
+
+        Examples
+        --------
+        >>> pd.Series([1, 2, 3]).values
+        array([1, 2, 3])
+
+        >>> pd.Series(list('aabc')).values
+        array(['a', 'a', 'b', 'c'], dtype=object)
+
+        >>> pd.Series(list('aabc')).astype('category').values
+        ['a', 'a', 'b', 'c']
+        Categories (3, object): ['a', 'b', 'c']
+
+        Timezone aware datetime data is converted to UTC:
+
+        >>> pd.Series(pd.date_range('20130101', periods=3,
+        ...                         tz='US/Eastern')).values
+        array(['2013-01-01T05:00:00.000000000',
+               '2013-01-02T05:00:00.000000000',
+               '2013-01-03T05:00:00.000000000'], dtype='datetime64[ns]')
+        """
+        return self._mgr.external_values()
+
+    @property
+    def _values(self):
+        """
+        Return the internal repr of this data (defined by Block.interval_values).
+        This are the values as stored in the Block (ndarray or ExtensionArray
+        depending on the Block class), with datetime64[ns] and timedelta64[ns]
+        wrapped in ExtensionArrays to match Index._values behavior.
+
+        Differs from the public ``.values`` for certain data types, because of
+        historical backwards compatibility of the public attribute (e.g. period
+        returns object ndarray and datetimetz a datetime64[ns] ndarray for
+        ``.values`` while it returns an ExtensionArray for ``._values`` in those
+        cases).
+
+        Differs from ``.array`` in that this still returns the numpy array if
+        the Block is backed by a numpy array (except for datetime64 and
+        timedelta64 dtypes), while ``.array`` ensures to always return an
+        ExtensionArray.
+
+        Overview:
+
+        dtype       | values        | _values       | array                 |
+        ----------- | ------------- | ------------- | --------------------- |
+        Numeric     | ndarray       | ndarray       | NumpyExtensionArray   |
+        Category    | Categorical   | Categorical   | Categorical           |
+        dt64[ns]    | ndarray[M8ns] | DatetimeArray | DatetimeArray         |
+        dt64[ns tz] | ndarray[M8ns] | DatetimeArray | DatetimeArray         |
+        td64[ns]    | ndarray[m8ns] | TimedeltaArray| TimedeltaArray        |
+        Period      | ndarray[obj]  | PeriodArray   | PeriodArray           |
+        Nullable    | EA            | EA            | EA                    |
+
+        """
+        return self._mgr.internal_values()
+
+    @property
+    def _references(self) -> BlockValuesRefs | None:
+        if isinstance(self._mgr, SingleArrayManager):
+            return None
+        return self._mgr._block.refs
+
+    # error: Decorated property not supported
+    @Appender(base.IndexOpsMixin.array.__doc__)  # type: ignore[misc]
+    @property
+    def array(self) -> ExtensionArray:
+        return self._mgr.array_values()
+
+    # ops
+    def ravel(self, order: str = "C") -> ArrayLike:
+        """
+        Return the flattened underlying data as an ndarray or ExtensionArray.
+
+        .. deprecated:: 2.2.0
+            Series.ravel is deprecated. The underlying array is already 1D, so
+            ravel is not necessary.  Use :meth:`to_numpy` for conversion to a numpy
+            array instead.
+
+        Returns
+        -------
+        numpy.ndarray or ExtensionArray
+            Flattened data of the Series.
+
+        See Also
+        --------
+        numpy.ndarray.ravel : Return a flattened array.
+
+        Examples
+        --------
+        >>> s = pd.Series([1, 2, 3])
+        >>> s.ravel()  # doctest: +SKIP
+        array([1, 2, 3])
+        """
+        warnings.warn(
+            "Series.ravel is deprecated. The underlying array is already 1D, so "
+            "ravel is not necessary.  Use `to_numpy()` for conversion to a numpy "
+            "array instead.",
+            FutureWarning,
+            stacklevel=2,
+        )
+        arr = self._values.ravel(order=order)
+        if isinstance(arr, np.ndarray) and using_copy_on_write():
+            arr.flags.writeable = False
+        return arr
+
+    def __len__(self) -> int:
+        """
+        Return the length of the Series.
+        """
+        return len(self._mgr)
+
+    def view(self, dtype: Dtype | None = None) -> Series:
+        """
+        Create a new view of the Series.
+
+        .. deprecated:: 2.2.0
+            ``Series.view`` is deprecated and will be removed in a future version.
+            Use :meth:`Series.astype` as an alternative to change the dtype.
+
+        This function will return a new Series with a view of the same
+        underlying values in memory, optionally reinterpreted with a new data
+        type. The new data type must preserve the same size in bytes as to not
+        cause index misalignment.
+
+        Parameters
+        ----------
+        dtype : data type
+            Data type object or one of their string representations.
+
+        Returns
+        -------
+        Series
+            A new Series object as a view of the same data in memory.
+
+        See Also
+        --------
+        numpy.ndarray.view : Equivalent numpy function to create a new view of
+            the same data in memory.
+
+        Notes
+        -----
+        Series are instantiated with ``dtype=float64`` by default. While
+        ``numpy.ndarray.view()`` will return a view with the same data type as
+        the original array, ``Series.view()`` (without specified dtype)
+        will try using ``float64`` and may fail if the original data type size
+        in bytes is not the same.
+
+        Examples
+        --------
+        Use ``astype`` to change the dtype instead.
+        """
+        warnings.warn(
+            "Series.view is deprecated and will be removed in a future version. "
+            "Use ``astype`` as an alternative to change the dtype.",
+            FutureWarning,
+            stacklevel=2,
+        )
+        # self.array instead of self._values so we piggyback on NumpyExtensionArray
+        #  implementation
+        res_values = self.array.view(dtype)
+        res_ser = self._constructor(res_values, index=self.index, copy=False)
+        if isinstance(res_ser._mgr, SingleBlockManager):
+            blk = res_ser._mgr._block
+            blk.refs = cast("BlockValuesRefs", self._references)
+            blk.refs.add_reference(blk)
+        return res_ser.__finalize__(self, method="view")
+
+    # ----------------------------------------------------------------------
+    # NDArray Compat
+    def __array__(
+        self, dtype: npt.DTypeLike | None = None, copy: bool | None = None
+    ) -> np.ndarray:
+        """
+        Return the values as a NumPy array.
+
+        Users should not call this directly. Rather, it is invoked by
+        :func:`numpy.array` and :func:`numpy.asarray`.
+
+        Parameters
+        ----------
+        dtype : str or numpy.dtype, optional
+            The dtype to use for the resulting NumPy array. By default,
+            the dtype is inferred from the data.
+
+        copy : bool or None, optional
+            See :func:`numpy.asarray`.
+
+        Returns
+        -------
+        numpy.ndarray
+            The values in the series converted to a :class:`numpy.ndarray`
+            with the specified `dtype`.
+
+        See Also
+        --------
+        array : Create a new array from data.
+        Series.array : Zero-copy view to the array backing the Series.
+        Series.to_numpy : Series method for similar behavior.
+
+        Examples
+        --------
+        >>> ser = pd.Series([1, 2, 3])
+        >>> np.asarray(ser)
+        array([1, 2, 3])
+
+        For timezone-aware data, the timezones may be retained with
+        ``dtype='object'``
+
+        >>> tzser = pd.Series(pd.date_range('2000', periods=2, tz="CET"))
+        >>> np.asarray(tzser, dtype="object")
+        array([Timestamp('2000-01-01 00:00:00+0100', tz='CET'),
+               Timestamp('2000-01-02 00:00:00+0100', tz='CET')],
+              dtype=object)
+
+        Or the values may be localized to UTC and the tzinfo discarded with
+        ``dtype='datetime64[ns]'``
+
+        >>> np.asarray(tzser, dtype="datetime64[ns]")  # doctest: +ELLIPSIS
+        array(['1999-12-31T23:00:00.000000000', ...],
+              dtype='datetime64[ns]')
+        """
+        values = self._values
+        if copy is None:
+            # Note: branch avoids `copy=None` for NumPy 1.x support
+            arr = np.asarray(values, dtype=dtype)
+        else:
+            arr = np.array(values, dtype=dtype, copy=copy)
+
+        if copy is True:
+            return arr
+        if using_copy_on_write() and (
+            copy is False or astype_is_view(values.dtype, arr.dtype)
+        ):
+            arr = arr.view()
+            arr.flags.writeable = False
+        return arr
+
+    # ----------------------------------------------------------------------
+
+    def __column_consortium_standard__(self, *, api_version: str | None = None) -> Any:
+        """
+        Provide entry point to the Consortium DataFrame Standard API.
+
+        This is developed and maintained outside of pandas.
+        Please report any issues to https://github.com/data-apis/dataframe-api-compat.
+        """
+        dataframe_api_compat = import_optional_dependency("dataframe_api_compat")
+        return (
+            dataframe_api_compat.pandas_standard.convert_to_standard_compliant_column(
+                self, api_version=api_version
+            )
+        )
+
+    # ----------------------------------------------------------------------
+    # Unary Methods
+
+    # coercion
+    __float__ = _coerce_method(float)
+    __int__ = _coerce_method(int)
+
+    # ----------------------------------------------------------------------
+
+    # indexers
+    @property
+    def axes(self) -> list[Index]:
+        """
+        Return a list of the row axis labels.
+        """
+        return [self.index]
+
+    # ----------------------------------------------------------------------
+    # Indexing Methods
+
+    def _ixs(self, i: int, axis: AxisInt = 0) -> Any:
+        """
+        Return the i-th value or values in the Series by location.
+
+        Parameters
+        ----------
+        i : int
+
+        Returns
+        -------
+        scalar
+        """
+        return self._values[i]
+
+    def _slice(self, slobj: slice, axis: AxisInt = 0) -> Series:
+        # axis kwarg is retained for compat with NDFrame method
+        #  _slice is *always* positional
+        mgr = self._mgr.get_slice(slobj, axis=axis)
+        out = self._constructor_from_mgr(mgr, axes=mgr.axes)
+        out._name = self._name
+        return out.__finalize__(self)
+
+    def __getitem__(self, key):
+        check_dict_or_set_indexers(key)
+        key = com.apply_if_callable(key, self)
+
+        if key is Ellipsis:
+            if using_copy_on_write() or warn_copy_on_write():
+                return self.copy(deep=False)
+            return self
+
+        key_is_scalar = is_scalar(key)
+        if isinstance(key, (list, tuple)):
+            key = unpack_1tuple(key)
+
+        if is_integer(key) and self.index._should_fallback_to_positional:
+            warnings.warn(
+                # GH#50617
+                "Series.__getitem__ treating keys as positions is deprecated. "
+                "In a future version, integer keys will always be treated "
+                "as labels (consistent with DataFrame behavior). To access "
+                "a value by position, use `ser.iloc[pos]`",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+            return self._values[key]
+
+        elif key_is_scalar:
+            return self._get_value(key)
+
+        # Convert generator to list before going through hashable part
+        # (We will iterate through the generator there to check for slices)
+        if is_iterator(key):
+            key = list(key)
+
+        if is_hashable(key) and not isinstance(key, slice):
+            # Otherwise index.get_value will raise InvalidIndexError
+            try:
+                # For labels that don't resolve as scalars like tuples and frozensets
+                result = self._get_value(key)
+
+                return result
+
+            except (KeyError, TypeError, InvalidIndexError):
+                # InvalidIndexError for e.g. generator
+                #  see test_series_getitem_corner_generator
+                if isinstance(key, tuple) and isinstance(self.index, MultiIndex):
+                    # We still have the corner case where a tuple is a key
+                    # in the first level of our MultiIndex
+                    return self._get_values_tuple(key)
+
+        if isinstance(key, slice):
+            # Do slice check before somewhat-costly is_bool_indexer
+            return self._getitem_slice(key)
+
+        if com.is_bool_indexer(key):
+            key = check_bool_indexer(self.index, key)
+            key = np.asarray(key, dtype=bool)
+            return self._get_rows_with_mask(key)
+
+        return self._get_with(key)
+
+    def _get_with(self, key):
+        # other: fancy integer or otherwise
+        if isinstance(key, ABCDataFrame):
+            raise TypeError(
+                "Indexing a Series with DataFrame is not "
+                "supported, use the appropriate DataFrame column"
+            )
+        elif isinstance(key, tuple):
+            return self._get_values_tuple(key)
+
+        elif not is_list_like(key):
+            # e.g. scalars that aren't recognized by lib.is_scalar, GH#32684
+            return self.loc[key]
+
+        if not isinstance(key, (list, np.ndarray, ExtensionArray, Series, Index)):
+            key = list(key)
+
+        key_type = lib.infer_dtype(key, skipna=False)
+
+        # Note: The key_type == "boolean" case should be caught by the
+        #  com.is_bool_indexer check in __getitem__
+        if key_type == "integer":
+            # We need to decide whether to treat this as a positional indexer
+            #  (i.e. self.iloc) or label-based (i.e. self.loc)
+            if not self.index._should_fallback_to_positional:
+                return self.loc[key]
+            else:
+                warnings.warn(
+                    # GH#50617
+                    "Series.__getitem__ treating keys as positions is deprecated. "
+                    "In a future version, integer keys will always be treated "
+                    "as labels (consistent with DataFrame behavior). To access "
+                    "a value by position, use `ser.iloc[pos]`",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+                return self.iloc[key]
+
+        # handle the dup indexing case GH#4246
+        return self.loc[key]
+
+    def _get_values_tuple(self, key: tuple):
+        # mpl hackaround
+        if com.any_none(*key):
+            # mpl compat if we look up e.g. ser[:, np.newaxis];
+            #  see tests.series.timeseries.test_mpl_compat_hack
+            # the asarray is needed to avoid returning a 2D DatetimeArray
+            result = np.asarray(self._values[key])
+            disallow_ndim_indexing(result)
+            return result
+
+        if not isinstance(self.index, MultiIndex):
+            raise KeyError("key of type tuple not found and not a MultiIndex")
+
+        # If key is contained, would have returned by now
+        indexer, new_index = self.index.get_loc_level(key)
+        new_ser = self._constructor(self._values[indexer], index=new_index, copy=False)
+        if isinstance(indexer, slice):
+            new_ser._mgr.add_references(self._mgr)  # type: ignore[arg-type]
+        return new_ser.__finalize__(self)
+
+    def _get_rows_with_mask(self, indexer: npt.NDArray[np.bool_]) -> Series:
+        new_mgr = self._mgr.get_rows_with_mask(indexer)
+        return self._constructor_from_mgr(new_mgr, axes=new_mgr.axes).__finalize__(self)
+
+    def _get_value(self, label, takeable: bool = False):
+        """
+        Quickly retrieve single value at passed index label.
+
+        Parameters
+        ----------
+        label : object
+        takeable : interpret the index as indexers, default False
+
+        Returns
+        -------
+        scalar value
+        """
+        if takeable:
+            return self._values[label]
+
+        # Similar to Index.get_value, but we do not fall back to positional
+        loc = self.index.get_loc(label)
+
+        if is_integer(loc):
+            return self._values[loc]
+
+        if isinstance(self.index, MultiIndex):
+            mi = self.index
+            new_values = self._values[loc]
+            if len(new_values) == 1 and mi.nlevels == 1:
+                # If more than one level left, we can not return a scalar
+                return new_values[0]
+
+            new_index = mi[loc]
+            new_index = maybe_droplevels(new_index, label)
+            new_ser = self._constructor(
+                new_values, index=new_index, name=self.name, copy=False
+            )
+            if isinstance(loc, slice):
+                new_ser._mgr.add_references(self._mgr)  # type: ignore[arg-type]
+            return new_ser.__finalize__(self)
+
+        else:
+            return self.iloc[loc]
+
+    def __setitem__(self, key, value) -> None:
+        warn = True
+        if not PYPY and not WARNING_CHECK_DISABLED and using_copy_on_write():
+            if sys.getrefcount(self) <= 3:
+                warnings.warn(
+                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
+                )
+        elif not PYPY and not WARNING_CHECK_DISABLED and not using_copy_on_write():
+            ctr = sys.getrefcount(self)
+            ref_count = 3
+            if not warn_copy_on_write() and _check_cacher(self):
+                # see https://github.com/pandas-dev/pandas/pull/56060#discussion_r1399245221
+                ref_count += 1
+            if ctr <= ref_count and (
+                warn_copy_on_write()
+                or (
+                    not warn_copy_on_write()
+                    and self._mgr.blocks[0].refs.has_reference()  # type: ignore[union-attr]
+                )
+            ):
+                warn = False
+                warnings.warn(
+                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
+                )
+
+        check_dict_or_set_indexers(key)
+        key = com.apply_if_callable(key, self)
+        cacher_needs_updating = self._check_is_chained_assignment_possible()
+
+        if key is Ellipsis:
+            key = slice(None)
+
+        if isinstance(key, slice):
+            indexer = self.index._convert_slice_indexer(key, kind="getitem")
+            return self._set_values(indexer, value, warn=warn)
+
+        try:
+            self._set_with_engine(key, value, warn=warn)
+        except KeyError:
+            # We have a scalar (or for MultiIndex or object-dtype, scalar-like)
+            #  key that is not present in self.index.
+            if is_integer(key):
+                if not self.index._should_fallback_to_positional:
+                    # GH#33469
+                    self.loc[key] = value
+                else:
+                    # positional setter
+                    # can't use _mgr.setitem_inplace yet bc could have *both*
+                    #  KeyError and then ValueError, xref GH#45070
+                    warnings.warn(
+                        # GH#50617
+                        "Series.__setitem__ treating keys as positions is deprecated. "
+                        "In a future version, integer keys will always be treated "
+                        "as labels (consistent with DataFrame behavior). To set "
+                        "a value by position, use `ser.iloc[pos] = value`",
+                        FutureWarning,
+                        stacklevel=find_stack_level(),
+                    )
+                    self._set_values(key, value)
+            else:
+                # GH#12862 adding a new key to the Series
+                self.loc[key] = value
+
+        except (TypeError, ValueError, LossySetitemError):
+            # The key was OK, but we cannot set the value losslessly
+            indexer = self.index.get_loc(key)
+            self._set_values(indexer, value)
+
+        except InvalidIndexError as err:
+            if isinstance(key, tuple) and not isinstance(self.index, MultiIndex):
+                # cases with MultiIndex don't get here bc they raise KeyError
+                # e.g. test_basic_getitem_setitem_corner
+                raise KeyError(
+                    "key of type tuple not found and not a MultiIndex"
+                ) from err
+
+            if com.is_bool_indexer(key):
+                key = check_bool_indexer(self.index, key)
+                key = np.asarray(key, dtype=bool)
+
+                if (
+                    is_list_like(value)
+                    and len(value) != len(self)
+                    and not isinstance(value, Series)
+                    and not is_object_dtype(self.dtype)
+                ):
+                    # Series will be reindexed to have matching length inside
+                    #  _where call below
+                    # GH#44265
+                    indexer = key.nonzero()[0]
+                    self._set_values(indexer, value)
+                    return
+
+                # otherwise with listlike other we interpret series[mask] = other
+                #  as series[mask] = other[mask]
+                try:
+                    self._where(~key, value, inplace=True, warn=warn)
+                except InvalidIndexError:
+                    # test_where_dups
+                    self.iloc[key] = value
+                return
+
+            else:
+                self._set_with(key, value, warn=warn)
+
+        if cacher_needs_updating:
+            self._maybe_update_cacher(inplace=True)
+
+    def _set_with_engine(self, key, value, warn: bool = True) -> None:
+        loc = self.index.get_loc(key)
+
+        # this is equivalent to self._values[key] = value
+        self._mgr.setitem_inplace(loc, value, warn=warn)
+
+    def _set_with(self, key, value, warn: bool = True) -> None:
+        # We got here via exception-handling off of InvalidIndexError, so
+        #  key should always be listlike at this point.
+        assert not isinstance(key, tuple)
+
+        if is_iterator(key):
+            # Without this, the call to infer_dtype will consume the generator
+            key = list(key)
+
+        if not self.index._should_fallback_to_positional:
+            # Regardless of the key type, we're treating it as labels
+            self._set_labels(key, value, warn=warn)
+
+        else:
+            # Note: key_type == "boolean" should not occur because that
+            #  should be caught by the is_bool_indexer check in __setitem__
+            key_type = lib.infer_dtype(key, skipna=False)
+
+            if key_type == "integer":
+                warnings.warn(
+                    # GH#50617
+                    "Series.__setitem__ treating keys as positions is deprecated. "
+                    "In a future version, integer keys will always be treated "
+                    "as labels (consistent with DataFrame behavior). To set "
+                    "a value by position, use `ser.iloc[pos] = value`",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+                self._set_values(key, value, warn=warn)
+            else:
+                self._set_labels(key, value, warn=warn)
+
+    def _set_labels(self, key, value, warn: bool = True) -> None:
+        key = com.asarray_tuplesafe(key)
+        indexer: np.ndarray = self.index.get_indexer(key)
+        mask = indexer == -1
+        if mask.any():
+            raise KeyError(f"{key[mask]} not in index")
+        self._set_values(indexer, value, warn=warn)
+
+    def _set_values(self, key, value, warn: bool = True) -> None:
+        if isinstance(key, (Index, Series)):
+            key = key._values
+
+        self._mgr = self._mgr.setitem(indexer=key, value=value, warn=warn)
+        self._maybe_update_cacher()
+
+    def _set_value(self, label, value, takeable: bool = False) -> None:
+        """
+        Quickly set single value at passed label.
+
+        If label is not contained, a new object is created with the label
+        placed at the end of the result index.
+
+        Parameters
+        ----------
+        label : object
+            Partial indexing with MultiIndex not allowed.
+        value : object
+            Scalar value.
+        takeable : interpret the index as indexers, default False
+        """
+        if not takeable:
+            try:
+                loc = self.index.get_loc(label)
+            except KeyError:
+                # set using a non-recursive method
+                self.loc[label] = value
+                return
+        else:
+            loc = label
+
+        self._set_values(loc, value)
+
+    # ----------------------------------------------------------------------
+    # Lookup Caching
+
+    @property
+    def _is_cached(self) -> bool:
+        """Return boolean indicating if self is cached or not."""
+        return getattr(self, "_cacher", None) is not None
+
+    def _get_cacher(self):
+        """return my cacher or None"""
+        cacher = getattr(self, "_cacher", None)
+        if cacher is not None:
+            cacher = cacher[1]()
+        return cacher
+
+    def _reset_cacher(self) -> None:
+        """
+        Reset the cacher.
+        """
+        if hasattr(self, "_cacher"):
+            del self._cacher
+
+    def _set_as_cached(self, item, cacher) -> None:
+        """
+        Set the _cacher attribute on the calling object with a weakref to
+        cacher.
+        """
+        if using_copy_on_write():
+            return
+        self._cacher = (item, weakref.ref(cacher))
+
+    def _clear_item_cache(self) -> None:
+        # no-op for Series
+        pass
+
+    def _check_is_chained_assignment_possible(self) -> bool:
+        """
+        See NDFrame._check_is_chained_assignment_possible.__doc__
+        """
+        if self._is_view and self._is_cached:
+            ref = self._get_cacher()
+            if ref is not None and ref._is_mixed_type:
+                self._check_setitem_copy(t="referent", force=True)
+            return True
+        return super()._check_is_chained_assignment_possible()
+
+    def _maybe_update_cacher(
+        self, clear: bool = False, verify_is_copy: bool = True, inplace: bool = False
+    ) -> None:
+        """
+        See NDFrame._maybe_update_cacher.__doc__
+        """
+        # for CoW, we never want to update the parent DataFrame cache
+        # if the Series changed, but don't keep track of any cacher
+        if using_copy_on_write():
+            return
+        cacher = getattr(self, "_cacher", None)
+        if cacher is not None:
+            ref: DataFrame = cacher[1]()
+
+            # we are trying to reference a dead referent, hence
+            # a copy
+            if ref is None:
+                del self._cacher
+            elif len(self) == len(ref) and self.name in ref.columns:
+                # GH#42530 self.name must be in ref.columns
+                # to ensure column still in dataframe
+                # otherwise, either self or ref has swapped in new arrays
+                ref._maybe_cache_changed(cacher[0], self, inplace=inplace)
+            else:
+                # GH#33675 we have swapped in a new array, so parent
+                #  reference to self is now invalid
+                ref._item_cache.pop(cacher[0], None)
+
+        super()._maybe_update_cacher(
+            clear=clear, verify_is_copy=verify_is_copy, inplace=inplace
+        )
+
+    # ----------------------------------------------------------------------
+    # Unsorted
+
+    def repeat(self, repeats: int | Sequence[int], axis: None = None) -> Series:
+        """
+        Repeat elements of a Series.
+
+        Returns a new Series where each element of the current Series
+        is repeated consecutively a given number of times.
+
+        Parameters
+        ----------
+        repeats : int or array of ints
+            The number of repetitions for each element. This should be a
+            non-negative integer. Repeating 0 times will return an empty
+            Series.
+        axis : None
+            Unused. Parameter needed for compatibility with DataFrame.
+
+        Returns
+        -------
+        Series
+            Newly created Series with repeated elements.
+
+        See Also
+        --------
+        Index.repeat : Equivalent function for Index.
+        numpy.repeat : Similar method for :class:`numpy.ndarray`.
+
+        Examples
+        --------
+        >>> s = pd.Series(['a', 'b', 'c'])
+        >>> s
+        0    a
+        1    b
+        2    c
+        dtype: object
+        >>> s.repeat(2)
+        0    a
+        0    a
+        1    b
+        1    b
+        2    c
+        2    c
+        dtype: object
+        >>> s.repeat([1, 2, 3])
+        0    a
+        1    b
+        1    b
+        2    c
+        2    c
+        2    c
+        dtype: object
+        """
+        nv.validate_repeat((), {"axis": axis})
+        new_index = self.index.repeat(repeats)
+        new_values = self._values.repeat(repeats)
+        return self._constructor(new_values, index=new_index, copy=False).__finalize__(
+            self, method="repeat"
+        )
+
+    @overload
+    def reset_index(
+        self,
+        level: IndexLabel = ...,
+        *,
+        drop: Literal[False] = ...,
+        name: Level = ...,
+        inplace: Literal[False] = ...,
+        allow_duplicates: bool = ...,
+    ) -> DataFrame:
+        ...
+
+    @overload
+    def reset_index(
+        self,
+        level: IndexLabel = ...,
+        *,
+        drop: Literal[True],
+        name: Level = ...,
+        inplace: Literal[False] = ...,
+        allow_duplicates: bool = ...,
+    ) -> Series:
+        ...
+
+    @overload
+    def reset_index(
+        self,
+        level: IndexLabel = ...,
+        *,
+        drop: bool = ...,
+        name: Level = ...,
+        inplace: Literal[True],
+        allow_duplicates: bool = ...,
+    ) -> None:
+        ...
+
+    def reset_index(
+        self,
+        level: IndexLabel | None = None,
+        *,
+        drop: bool = False,
+        name: Level = lib.no_default,
+        inplace: bool = False,
+        allow_duplicates: bool = False,
+    ) -> DataFrame | Series | None:
+        """
+        Generate a new DataFrame or Series with the index reset.
+
+        This is useful when the index needs to be treated as a column, or
+        when the index is meaningless and needs to be reset to the default
+        before another operation.
+
+        Parameters
+        ----------
+        level : int, str, tuple, or list, default optional
+            For a Series with a MultiIndex, only remove the specified levels
+            from the index. Removes all levels by default.
+        drop : bool, default False
+            Just reset the index, without inserting it as a column in
+            the new DataFrame.
+        name : object, optional
+            The name to use for the column containing the original Series
+            values. Uses ``self.name`` by default. This argument is ignored
+            when `drop` is True.
+        inplace : bool, default False
+            Modify the Series in place (do not create a new object).
+        allow_duplicates : bool, default False
+            Allow duplicate column labels to be created.
+
+            .. versionadded:: 1.5.0
+
+        Returns
+        -------
+        Series or DataFrame or None
+            When `drop` is False (the default), a DataFrame is returned.
+            The newly created columns will come first in the DataFrame,
+            followed by the original Series values.
+            When `drop` is True, a `Series` is returned.
+            In either case, if ``inplace=True``, no value is returned.
+
+        See Also
+        --------
+        DataFrame.reset_index: Analogous function for DataFrame.
+
+        Examples
+        --------
+        >>> s = pd.Series([1, 2, 3, 4], name='foo',
+        ...               index=pd.Index(['a', 'b', 'c', 'd'], name='idx'))
+
+        Generate a DataFrame with default index.
+
+        >>> s.reset_index()
+          idx  foo
+        0   a    1
+        1   b    2
+        2   c    3
+        3   d    4
+
+        To specify the name of the new column use `name`.
+
+        >>> s.reset_index(name='values')
+          idx  values
+        0   a       1
+        1   b       2
+        2   c       3
+        3   d       4
+
+        To generate a new Series with the default set `drop` to True.
+
+        >>> s.reset_index(drop=True)
+        0    1
+        1    2
+        2    3
+        3    4
+        Name: foo, dtype: int64
+
+        The `level` parameter is interesting for Series with a multi-level
+        index.
+
+        >>> arrays = [np.array(['bar', 'bar', 'baz', 'baz']),
+        ...           np.array(['one', 'two', 'one', 'two'])]
+        >>> s2 = pd.Series(
+        ...     range(4), name='foo',
+        ...     index=pd.MultiIndex.from_arrays(arrays,
+        ...                                     names=['a', 'b']))
+
+        To remove a specific level from the Index, use `level`.
+
+        >>> s2.reset_index(level='a')
+               a  foo
+        b
+        one  bar    0
+        two  bar    1
+        one  baz    2
+        two  baz    3
+
+        If `level` is not set, all levels are removed from the Index.
+
+        >>> s2.reset_index()
+             a    b  foo
+        0  bar  one    0
+        1  bar  two    1
+        2  baz  one    2
+        3  baz  two    3
+        """
+        inplace = validate_bool_kwarg(inplace, "inplace")
+        if drop:
+            new_index = default_index(len(self))
+            if level is not None:
+                level_list: Sequence[Hashable]
+                if not isinstance(level, (tuple, list)):
+                    level_list = [level]
+                else:
+                    level_list = level
+                level_list = [self.index._get_level_number(lev) for lev in level_list]
+                if len(level_list) < self.index.nlevels:
+                    new_index = self.index.droplevel(level_list)
+
+            if inplace:
+                self.index = new_index
+            elif using_copy_on_write():
+                new_ser = self.copy(deep=False)
+                new_ser.index = new_index
+                return new_ser.__finalize__(self, method="reset_index")
+            else:
+                return self._constructor(
+                    self._values.copy(), index=new_index, copy=False, dtype=self.dtype
+                ).__finalize__(self, method="reset_index")
+        elif inplace:
+            raise TypeError(
+                "Cannot reset_index inplace on a Series to create a DataFrame"
+            )
+        else:
+            if name is lib.no_default:
+                # For backwards compatibility, keep columns as [0] instead of
+                #  [None] when self.name is None
+                if self.name is None:
+                    name = 0
+                else:
+                    name = self.name
+
+            df = self.to_frame(name)
+            return df.reset_index(
+                level=level, drop=drop, allow_duplicates=allow_duplicates
+            )
+        return None
+
+    # ----------------------------------------------------------------------
+    # Rendering Methods
+
+    def __repr__(self) -> str:
+        """
+        Return a string representation for a particular Series.
+        """
+        # pylint: disable=invalid-repr-returned
+        repr_params = fmt.get_series_repr_params()
+        return self.to_string(**repr_params)
+
+    @overload
+    def to_string(
+        self,
+        buf: None = ...,
+        na_rep: str = ...,
+        float_format: str | None = ...,
+        header: bool = ...,
+        index: bool = ...,
+        length: bool = ...,
+        dtype=...,
+        name=...,
+        max_rows: int | None = ...,
+        min_rows: int | None = ...,
+    ) -> str:
+        ...
+
+    @overload
+    def to_string(
+        self,
+        buf: FilePath | WriteBuffer[str],
+        na_rep: str = ...,
+        float_format: str | None = ...,
+        header: bool = ...,
+        index: bool = ...,
+        length: bool = ...,
+        dtype=...,
+        name=...,
+        max_rows: int | None = ...,
+        min_rows: int | None = ...,
+    ) -> None:
+        ...
+
+    def to_string(
+        self,
+        buf: FilePath | WriteBuffer[str] | None = None,
+        na_rep: str = "NaN",
+        float_format: str | None = None,
+        header: bool = True,
+        index: bool = True,
+        length: bool = False,
+        dtype: bool = False,
+        name: bool = False,
+        max_rows: int | None = None,
+        min_rows: int | None = None,
+    ) -> str | None:
+        """
+        Render a string representation of the Series.
+
+        Parameters
+        ----------
+        buf : StringIO-like, optional
+            Buffer to write to.
+        na_rep : str, optional
+            String representation of NaN to use, default 'NaN'.
+        float_format : one-parameter function, optional
+            Formatter function to apply to columns' elements if they are
+            floats, default None.
+        header : bool, default True
+            Add the Series header (index name).
+        index : bool, optional
+            Add index (row) labels, default True.
+        length : bool, default False
+            Add the Series length.
+        dtype : bool, default False
+            Add the Series dtype.
+        name : bool, default False
+            Add the Series name if not None.
+        max_rows : int, optional
+            Maximum number of rows to show before truncating. If None, show
+            all.
+        min_rows : int, optional
+            The number of rows to display in a truncated repr (when number
+            of rows is above `max_rows`).
+
+        Returns
+        -------
+        str or None
+            String representation of Series if ``buf=None``, otherwise None.
+
+        Examples
+        --------
+        >>> ser = pd.Series([1, 2, 3]).to_string()
+        >>> ser
+        '0    1\\n1    2\\n2    3'
+        """
+        formatter = fmt.SeriesFormatter(
+            self,
+            name=name,
+            length=length,
+            header=header,
+            index=index,
+            dtype=dtype,
+            na_rep=na_rep,
+            float_format=float_format,
+            min_rows=min_rows,
+            max_rows=max_rows,
+        )
+        result = formatter.to_string()
+
+        # catch contract violations
+        if not isinstance(result, str):
+            raise AssertionError(
+                "result must be of type str, type "
+                f"of result is {repr(type(result).__name__)}"
+            )
+
+        if buf is None:
+            return result
+        else:
+            if hasattr(buf, "write"):
+                buf.write(result)
+            else:
+                with open(buf, "w", encoding="utf-8") as f:
+                    f.write(result)
+        return None
+
+    @doc(
+        klass=_shared_doc_kwargs["klass"],
+        storage_options=_shared_docs["storage_options"],
+        examples=dedent(
+            """Examples
+            --------
+            >>> s = pd.Series(["elk", "pig", "dog", "quetzal"], name="animal")
+            >>> print(s.to_markdown())
+            |    | animal   |
+            |---:|:---------|
+            |  0 | elk      |
+            |  1 | pig      |
+            |  2 | dog      |
+            |  3 | quetzal  |
+
+            Output markdown with a tabulate option.
+
+            >>> print(s.to_markdown(tablefmt="grid"))
+            +----+----------+
+            |    | animal   |
+            +====+==========+
+            |  0 | elk      |
+            +----+----------+
+            |  1 | pig      |
+            +----+----------+
+            |  2 | dog      |
+            +----+----------+
+            |  3 | quetzal  |
+            +----+----------+"""
+        ),
+    )
+    def to_markdown(
+        self,
+        buf: IO[str] | None = None,
+        mode: str = "wt",
+        index: bool = True,
+        storage_options: StorageOptions | None = None,
+        **kwargs,
+    ) -> str | None:
+        """
+        Print {klass} in Markdown-friendly format.
+
+        Parameters
+        ----------
+        buf : str, Path or StringIO-like, optional, default None
+            Buffer to write to. If None, the output is returned as a string.
+        mode : str, optional
+            Mode in which file is opened, "wt" by default.
+        index : bool, optional, default True
+            Add index (row) labels.
+
+        {storage_options}
+
+        **kwargs
+            These parameters will be passed to `tabulate \
+                `_.
+
+        Returns
+        -------
+        str
+            {klass} in Markdown-friendly format.
+
+        Notes
+        -----
+        Requires the `tabulate `_ package.
+
+        {examples}
+        """
+        return self.to_frame().to_markdown(
+            buf, mode=mode, index=index, storage_options=storage_options, **kwargs
+        )
+
+    # ----------------------------------------------------------------------
+
+    def items(self) -> Iterable[tuple[Hashable, Any]]:
+        """
+        Lazily iterate over (index, value) tuples.
+
+        This method returns an iterable tuple (index, value). This is
+        convenient if you want to create a lazy iterator.
+
+        Returns
+        -------
+        iterable
+            Iterable of tuples containing the (index, value) pairs from a
+            Series.
+
+        See Also
+        --------
+        DataFrame.items : Iterate over (column name, Series) pairs.
+        DataFrame.iterrows : Iterate over DataFrame rows as (index, Series) pairs.
+
+        Examples
+        --------
+        >>> s = pd.Series(['A', 'B', 'C'])
+        >>> for index, value in s.items():
+        ...     print(f"Index : {index}, Value : {value}")
+        Index : 0, Value : A
+        Index : 1, Value : B
+        Index : 2, Value : C
+        """
+        return zip(iter(self.index), iter(self))
+
+    # ----------------------------------------------------------------------
+    # Misc public methods
+
+    def keys(self) -> Index:
+        """
+        Return alias for index.
+
+        Returns
+        -------
+        Index
+            Index of the Series.
+
+        Examples
+        --------
+        >>> s = pd.Series([1, 2, 3], index=[0, 1, 2])
+        >>> s.keys()
+        Index([0, 1, 2], dtype='int64')
+        """
+        return self.index
+
+    @overload
+    def to_dict(
+        self, *, into: type[MutableMappingT] | MutableMappingT
+    ) -> MutableMappingT:
+        ...
+
+    @overload
+    def to_dict(self, *, into: type[dict] = ...) -> dict:
+        ...
+
+    # error: Incompatible default for argument "into" (default has type "type[
+    # dict[Any, Any]]", argument has type "type[MutableMappingT] | MutableMappingT")
+    @deprecate_nonkeyword_arguments(
+        version="3.0", allowed_args=["self"], name="to_dict"
+    )
+    def to_dict(
+        self,
+        into: type[MutableMappingT]
+        | MutableMappingT = dict,  # type: ignore[assignment]
+    ) -> MutableMappingT:
+        """
+        Convert Series to {label -> value} dict or dict-like object.
+
+        Parameters
+        ----------
+        into : class, default dict
+            The collections.abc.MutableMapping subclass to use as the return
+            object. Can be the actual class or an empty instance of the mapping
+            type you want.  If you want a collections.defaultdict, you must
+            pass it initialized.
+
+        Returns
+        -------
+        collections.abc.MutableMapping
+            Key-value representation of Series.
+
+        Examples
+        --------
+        >>> s = pd.Series([1, 2, 3, 4])
+        >>> s.to_dict()
+        {0: 1, 1: 2, 2: 3, 3: 4}
+        >>> from collections import OrderedDict, defaultdict
+        >>> s.to_dict(into=OrderedDict)
+        OrderedDict([(0, 1), (1, 2), (2, 3), (3, 4)])
+        >>> dd = defaultdict(list)
+        >>> s.to_dict(into=dd)
+        defaultdict(, {0: 1, 1: 2, 2: 3, 3: 4})
+        """
+        # GH16122
+        into_c = com.standardize_mapping(into)
+
+        if is_object_dtype(self.dtype) or isinstance(self.dtype, ExtensionDtype):
+            return into_c((k, maybe_box_native(v)) for k, v in self.items())
+        else:
+            # Not an object dtype => all types will be the same so let the default
+            # indexer return native python type
+            return into_c(self.items())
+
+    def to_frame(self, name: Hashable = lib.no_default) -> DataFrame:
+        """
+        Convert Series to DataFrame.
+
+        Parameters
+        ----------
+        name : object, optional
+            The passed name should substitute for the series name (if it has
+            one).
+
+        Returns
+        -------
+        DataFrame
+            DataFrame representation of Series.
+
+        Examples
+        --------
+        >>> s = pd.Series(["a", "b", "c"],
+        ...               name="vals")
+        >>> s.to_frame()
+          vals
+        0    a
+        1    b
+        2    c
+        """
+        columns: Index
+        if name is lib.no_default:
+            name = self.name
+            if name is None:
+                # default to [0], same as we would get with DataFrame(self)
+                columns = default_index(1)
+            else:
+                columns = Index([name])
+        else:
+            columns = Index([name])
+
+        mgr = self._mgr.to_2d_mgr(columns)
+        df = self._constructor_expanddim_from_mgr(mgr, axes=mgr.axes)
+        return df.__finalize__(self, method="to_frame")
+
+    def _set_name(
+        self, name, inplace: bool = False, deep: bool | None = None
+    ) -> Series:
+        """
+        Set the Series name.
+
+        Parameters
+        ----------
+        name : str
+        inplace : bool
+            Whether to modify `self` directly or return a copy.
+        deep : bool|None, default None
+            Whether to do a deep copy, a shallow copy, or Copy on Write(None)
+        """
+        inplace = validate_bool_kwarg(inplace, "inplace")
+        ser = self if inplace else self.copy(deep and not using_copy_on_write())
+        ser.name = name
+        return ser
+
+    @Appender(
+        dedent(
+            """
+        Examples
+        --------
+        >>> ser = pd.Series([390., 350., 30., 20.],
+        ...                 index=['Falcon', 'Falcon', 'Parrot', 'Parrot'],
+        ...                 name="Max Speed")
+        >>> ser
+        Falcon    390.0
+        Falcon    350.0
+        Parrot     30.0
+        Parrot     20.0
+        Name: Max Speed, dtype: float64
+        >>> ser.groupby(["a", "b", "a", "b"]).mean()
+        a    210.0
+        b    185.0
+        Name: Max Speed, dtype: float64
+        >>> ser.groupby(level=0).mean()
+        Falcon    370.0
+        Parrot     25.0
+        Name: Max Speed, dtype: float64
+        >>> ser.groupby(ser > 100).mean()
+        Max Speed
+        False     25.0
+        True     370.0
+        Name: Max Speed, dtype: float64
+
+        **Grouping by Indexes**
+
+        We can groupby different levels of a hierarchical index
+        using the `level` parameter:
+
+        >>> arrays = [['Falcon', 'Falcon', 'Parrot', 'Parrot'],
+        ...           ['Captive', 'Wild', 'Captive', 'Wild']]
+        >>> index = pd.MultiIndex.from_arrays(arrays, names=('Animal', 'Type'))
+        >>> ser = pd.Series([390., 350., 30., 20.], index=index, name="Max Speed")
+        >>> ser
+        Animal  Type
+        Falcon  Captive    390.0
+                Wild       350.0
+        Parrot  Captive     30.0
+                Wild        20.0
+        Name: Max Speed, dtype: float64
+        >>> ser.groupby(level=0).mean()
+        Animal
+        Falcon    370.0
+        Parrot     25.0
+        Name: Max Speed, dtype: float64
+        >>> ser.groupby(level="Type").mean()
+        Type
+        Captive    210.0
+        Wild       185.0
+        Name: Max Speed, dtype: float64
+
+        We can also choose to include `NA` in group keys or not by defining
+        `dropna` parameter, the default setting is `True`.
+
+        >>> ser = pd.Series([1, 2, 3, 3], index=["a", 'a', 'b', np.nan])
+        >>> ser.groupby(level=0).sum()
+        a    3
+        b    3
+        dtype: int64
+
+        >>> ser.groupby(level=0, dropna=False).sum()
+        a    3
+        b    3
+        NaN  3
+        dtype: int64
+
+        >>> arrays = ['Falcon', 'Falcon', 'Parrot', 'Parrot']
+        >>> ser = pd.Series([390., 350., 30., 20.], index=arrays, name="Max Speed")
+        >>> ser.groupby(["a", "b", "a", np.nan]).mean()
+        a    210.0
+        b    350.0
+        Name: Max Speed, dtype: float64
+
+        >>> ser.groupby(["a", "b", "a", np.nan], dropna=False).mean()
+        a    210.0
+        b    350.0
+        NaN   20.0
+        Name: Max Speed, dtype: float64
+        """
+        )
+    )
+    @Appender(_shared_docs["groupby"] % _shared_doc_kwargs)
+    def groupby(
+        self,
+        by=None,
+        axis: Axis = 0,
+        level: IndexLabel | None = None,
+        as_index: bool = True,
+        sort: bool = True,
+        group_keys: bool = True,
+        observed: bool | lib.NoDefault = lib.no_default,
+        dropna: bool = True,
+    ) -> SeriesGroupBy:
+        from pandas.core.groupby.generic import SeriesGroupBy
+
+        if level is None and by is None:
+            raise TypeError("You have to supply one of 'by' and 'level'")
+        if not as_index:
+            raise TypeError("as_index=False only valid with DataFrame")
+        axis = self._get_axis_number(axis)
+
+        return SeriesGroupBy(
+            obj=self,
+            keys=by,
+            axis=axis,
+            level=level,
+            as_index=as_index,
+            sort=sort,
+            group_keys=group_keys,
+            observed=observed,
+            dropna=dropna,
+        )
+
+    # ----------------------------------------------------------------------
+    # Statistics, overridden ndarray methods
+
+    # TODO: integrate bottleneck
+    def count(self) -> int:
+        """
+        Return number of non-NA/null observations in the Series.
+
+        Returns
+        -------
+        int
+            Number of non-null values in the Series.
+
+        See Also
+        --------
+        DataFrame.count : Count non-NA cells for each column or row.
+
+        Examples
+        --------
+        >>> s = pd.Series([0.0, 1.0, np.nan])
+        >>> s.count()
+        2
+        """
+        return notna(self._values).sum().astype("int64")
+
+    def mode(self, dropna: bool = True) -> Series:
+        """
+        Return the mode(s) of the Series.
+
+        The mode is the value that appears most often. There can be multiple modes.
+
+        Always returns Series even if only one value is returned.
+
+        Parameters
+        ----------
+        dropna : bool, default True
+            Don't consider counts of NaN/NaT.
+
+        Returns
+        -------
+        Series
+            Modes of the Series in sorted order.
+
+        Examples
+        --------
+        >>> s = pd.Series([2, 4, 2, 2, 4, None])
+        >>> s.mode()
+        0    2.0
+        dtype: float64
+
+        More than one mode:
+
+        >>> s = pd.Series([2, 4, 8, 2, 4, None])
+        >>> s.mode()
+        0    2.0
+        1    4.0
+        dtype: float64
+
+        With and without considering null value:
+
+        >>> s = pd.Series([2, 4, None, None, 4, None])
+        >>> s.mode(dropna=False)
+        0   NaN
+        dtype: float64
+        >>> s = pd.Series([2, 4, None, None, 4, None])
+        >>> s.mode()
+        0    4.0
+        dtype: float64
+        """
+        # TODO: Add option for bins like value_counts()
+        values = self._values
+        if isinstance(values, np.ndarray):
+            res_values = algorithms.mode(values, dropna=dropna)
+        else:
+            res_values = values._mode(dropna=dropna)
+
+        # Ensure index is type stable (should always use int index)
+        return self._constructor(
+            res_values,
+            index=range(len(res_values)),
+            name=self.name,
+            copy=False,
+            dtype=self.dtype,
+        ).__finalize__(self, method="mode")
+
+    def unique(self) -> ArrayLike:  # pylint: disable=useless-parent-delegation
+        """
+        Return unique values of Series object.
+
+        Uniques are returned in order of appearance. Hash table-based unique,
+        therefore does NOT sort.
+
+        Returns
+        -------
+        ndarray or ExtensionArray
+            The unique values returned as a NumPy array. See Notes.
+
+        See Also
+        --------
+        Series.drop_duplicates : Return Series with duplicate values removed.
+        unique : Top-level unique method for any 1-d array-like object.
+        Index.unique : Return Index with unique values from an Index object.
+
+        Notes
+        -----
+        Returns the unique values as a NumPy array. In case of an
+        extension-array backed Series, a new
+        :class:`~api.extensions.ExtensionArray` of that type with just
+        the unique values is returned. This includes
+
+            * Categorical
+            * Period
+            * Datetime with Timezone
+            * Datetime without Timezone
+            * Timedelta
+            * Interval
+            * Sparse
+            * IntegerNA
+
+        See Examples section.
+
+        Examples
+        --------
+        >>> pd.Series([2, 1, 3, 3], name='A').unique()
+        array([2, 1, 3])
+
+        >>> pd.Series([pd.Timestamp('2016-01-01') for _ in range(3)]).unique()
+        
+        ['2016-01-01 00:00:00']
+        Length: 1, dtype: datetime64[ns]
+
+        >>> pd.Series([pd.Timestamp('2016-01-01', tz='US/Eastern')
+        ...            for _ in range(3)]).unique()
+        
+        ['2016-01-01 00:00:00-05:00']
+        Length: 1, dtype: datetime64[ns, US/Eastern]
+
+        An Categorical will return categories in the order of
+        appearance and with the same dtype.
+
+        >>> pd.Series(pd.Categorical(list('baabc'))).unique()
+        ['b', 'a', 'c']
+        Categories (3, object): ['a', 'b', 'c']
+        >>> pd.Series(pd.Categorical(list('baabc'), categories=list('abc'),
+        ...                          ordered=True)).unique()
+        ['b', 'a', 'c']
+        Categories (3, object): ['a' < 'b' < 'c']
+        """
+        return super().unique()
+
+    @overload
+    def drop_duplicates(
+        self,
+        *,
+        keep: DropKeep = ...,
+        inplace: Literal[False] = ...,
+        ignore_index: bool = ...,
+    ) -> Series:
+        ...
+
+    @overload
+    def drop_duplicates(
+        self, *, keep: DropKeep = ..., inplace: Literal[True], ignore_index: bool = ...
+    ) -> None:
+        ...
+
+    @overload
+    def drop_duplicates(
+        self, *, keep: DropKeep = ..., inplace: bool = ..., ignore_index: bool = ...
+    ) -> Series | None:
+        ...
+
+    def drop_duplicates(
+        self,
+        *,
+        keep: DropKeep = "first",
+        inplace: bool = False,
+        ignore_index: bool = False,
+    ) -> Series | None:
+        """
+        Return Series with duplicate values removed.
+
+        Parameters
+        ----------
+        keep : {'first', 'last', ``False``}, default 'first'
+            Method to handle dropping duplicates:
+
+            - 'first' : Drop duplicates except for the first occurrence.
+            - 'last' : Drop duplicates except for the last occurrence.
+            - ``False`` : Drop all duplicates.
+
+        inplace : bool, default ``False``
+            If ``True``, performs operation inplace and returns None.
+
+        ignore_index : bool, default ``False``
+            If ``True``, the resulting axis will be labeled 0, 1, …, n - 1.
+
+            .. versionadded:: 2.0.0
+
+        Returns
+        -------
+        Series or None
+            Series with duplicates dropped or None if ``inplace=True``.
+
+        See Also
+        --------
+        Index.drop_duplicates : Equivalent method on Index.
+        DataFrame.drop_duplicates : Equivalent method on DataFrame.
+        Series.duplicated : Related method on Series, indicating duplicate
+            Series values.
+        Series.unique : Return unique values as an array.
+
+        Examples
+        --------
+        Generate a Series with duplicated entries.
+
+        >>> s = pd.Series(['llama', 'cow', 'llama', 'beetle', 'llama', 'hippo'],
+        ...               name='animal')
+        >>> s
+        0     llama
+        1       cow
+        2     llama
+        3    beetle
+        4     llama
+        5     hippo
+        Name: animal, dtype: object
+
+        With the 'keep' parameter, the selection behaviour of duplicated values
+        can be changed. The value 'first' keeps the first occurrence for each
+        set of duplicated entries. The default value of keep is 'first'.
+
+        >>> s.drop_duplicates()
+        0     llama
+        1       cow
+        3    beetle
+        5     hippo
+        Name: animal, dtype: object
+
+        The value 'last' for parameter 'keep' keeps the last occurrence for
+        each set of duplicated entries.
+
+        >>> s.drop_duplicates(keep='last')
+        1       cow
+        3    beetle
+        4     llama
+        5     hippo
+        Name: animal, dtype: object
+
+        The value ``False`` for parameter 'keep' discards all sets of
+        duplicated entries.
+
+        >>> s.drop_duplicates(keep=False)
+        1       cow
+        3    beetle
+        5     hippo
+        Name: animal, dtype: object
+        """
+        inplace = validate_bool_kwarg(inplace, "inplace")
+        result = super().drop_duplicates(keep=keep)
+
+        if ignore_index:
+            result.index = default_index(len(result))
+
+        if inplace:
+            self._update_inplace(result)
+            return None
+        else:
+            return result
+
+    def duplicated(self, keep: DropKeep = "first") -> Series:
+        """
+        Indicate duplicate Series values.
+
+        Duplicated values are indicated as ``True`` values in the resulting
+        Series. Either all duplicates, all except the first or all except the
+        last occurrence of duplicates can be indicated.
+
+        Parameters
+        ----------
+        keep : {'first', 'last', False}, default 'first'
+            Method to handle dropping duplicates:
+
+            - 'first' : Mark duplicates as ``True`` except for the first
+              occurrence.
+            - 'last' : Mark duplicates as ``True`` except for the last
+              occurrence.
+            - ``False`` : Mark all duplicates as ``True``.
+
+        Returns
+        -------
+        Series[bool]
+            Series indicating whether each value has occurred in the
+            preceding values.
+
+        See Also
+        --------
+        Index.duplicated : Equivalent method on pandas.Index.
+        DataFrame.duplicated : Equivalent method on pandas.DataFrame.
+        Series.drop_duplicates : Remove duplicate values from Series.
+
+        Examples
+        --------
+        By default, for each set of duplicated values, the first occurrence is
+        set on False and all others on True:
+
+        >>> animals = pd.Series(['llama', 'cow', 'llama', 'beetle', 'llama'])
+        >>> animals.duplicated()
+        0    False
+        1    False
+        2     True
+        3    False
+        4     True
+        dtype: bool
+
+        which is equivalent to
+
+        >>> animals.duplicated(keep='first')
+        0    False
+        1    False
+        2     True
+        3    False
+        4     True
+        dtype: bool
+
+        By using 'last', the last occurrence of each set of duplicated values
+        is set on False and all others on True:
+
+        >>> animals.duplicated(keep='last')
+        0     True
+        1    False
+        2     True
+        3    False
+        4    False
+        dtype: bool
+
+        By setting keep on ``False``, all duplicates are True:
+
+        >>> animals.duplicated(keep=False)
+        0     True
+        1    False
+        2     True
+        3    False
+        4     True
+        dtype: bool
+        """
+        res = self._duplicated(keep=keep)
+        result = self._constructor(res, index=self.index, copy=False)
+        return result.__finalize__(self, method="duplicated")
+
+    def idxmin(self, axis: Axis = 0, skipna: bool = True, *args, **kwargs) -> Hashable:
+        """
+        Return the row label of the minimum value.
+
+        If multiple values equal the minimum, the first row label with that
+        value is returned.
+
+        Parameters
+        ----------
+        axis : {0 or 'index'}
+            Unused. Parameter needed for compatibility with DataFrame.
+        skipna : bool, default True
+            Exclude NA/null values. If the entire Series is NA, the result
+            will be NA.
+        *args, **kwargs
+            Additional arguments and keywords have no effect but might be
+            accepted for compatibility with NumPy.
+
+        Returns
+        -------
+        Index
+            Label of the minimum value.
+
+        Raises
+        ------
+        ValueError
+            If the Series is empty.
+
+        See Also
+        --------
+        numpy.argmin : Return indices of the minimum values
+            along the given axis.
+        DataFrame.idxmin : Return index of first occurrence of minimum
+            over requested axis.
+        Series.idxmax : Return index *label* of the first occurrence
+            of maximum of values.
+
+        Notes
+        -----
+        This method is the Series version of ``ndarray.argmin``. This method
+        returns the label of the minimum, while ``ndarray.argmin`` returns
+        the position. To get the position, use ``series.values.argmin()``.
+
+        Examples
+        --------
+        >>> s = pd.Series(data=[1, None, 4, 1],
+        ...               index=['A', 'B', 'C', 'D'])
+        >>> s
+        A    1.0
+        B    NaN
+        C    4.0
+        D    1.0
+        dtype: float64
+
+        >>> s.idxmin()
+        'A'
+
+        If `skipna` is False and there is an NA value in the data,
+        the function returns ``nan``.
+
+        >>> s.idxmin(skipna=False)
+        nan
+        """
+        axis = self._get_axis_number(axis)
+        with warnings.catch_warnings():
+            # TODO(3.0): this catching/filtering can be removed
+            # ignore warning produced by argmin since we will issue a different
+            #  warning for idxmin
+            warnings.simplefilter("ignore")
+            i = self.argmin(axis, skipna, *args, **kwargs)
+
+        if i == -1:
+            # GH#43587 give correct NA value for Index.
+            warnings.warn(
+                f"The behavior of {type(self).__name__}.idxmin with all-NA "
+                "values, or any-NA and skipna=False, is deprecated. In a future "
+                "version this will raise ValueError",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+            return self.index._na_value
+        return self.index[i]
+
+    def idxmax(self, axis: Axis = 0, skipna: bool = True, *args, **kwargs) -> Hashable:
+        """
+        Return the row label of the maximum value.
+
+        If multiple values equal the maximum, the first row label with that
+        value is returned.
+
+        Parameters
+        ----------
+        axis : {0 or 'index'}
+            Unused. Parameter needed for compatibility with DataFrame.
+        skipna : bool, default True
+            Exclude NA/null values. If the entire Series is NA, the result
+            will be NA.
+        *args, **kwargs
+            Additional arguments and keywords have no effect but might be
+            accepted for compatibility with NumPy.
+
+        Returns
+        -------
+        Index
+            Label of the maximum value.
+
+        Raises
+        ------
+        ValueError
+            If the Series is empty.
+
+        See Also
+        --------
+        numpy.argmax : Return indices of the maximum values
+            along the given axis.
+        DataFrame.idxmax : Return index of first occurrence of maximum
+            over requested axis.
+        Series.idxmin : Return index *label* of the first occurrence
+            of minimum of values.
+
+        Notes
+        -----
+        This method is the Series version of ``ndarray.argmax``. This method
+        returns the label of the maximum, while ``ndarray.argmax`` returns
+        the position. To get the position, use ``series.values.argmax()``.
+
+        Examples
+        --------
+        >>> s = pd.Series(data=[1, None, 4, 3, 4],
+        ...               index=['A', 'B', 'C', 'D', 'E'])
+        >>> s
+        A    1.0
+        B    NaN
+        C    4.0
+        D    3.0
+        E    4.0
+        dtype: float64
+
+        >>> s.idxmax()
+        'C'
+
+        If `skipna` is False and there is an NA value in the data,
+        the function returns ``nan``.
+
+        >>> s.idxmax(skipna=False)
+        nan
+        """
+        axis = self._get_axis_number(axis)
+        with warnings.catch_warnings():
+            # TODO(3.0): this catching/filtering can be removed
+            # ignore warning produced by argmax since we will issue a different
+            #  warning for argmax
+            warnings.simplefilter("ignore")
+            i = self.argmax(axis, skipna, *args, **kwargs)
+
+        if i == -1:
+            # GH#43587 give correct NA value for Index.
+            warnings.warn(
+                f"The behavior of {type(self).__name__}.idxmax with all-NA "
+                "values, or any-NA and skipna=False, is deprecated. In a future "
+                "version this will raise ValueError",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+            return self.index._na_value
+        return self.index[i]
+
+    def round(self, decimals: int = 0, *args, **kwargs) -> Series:
+        """
+        Round each value in a Series to the given number of decimals.
+
+        Parameters
+        ----------
+        decimals : int, default 0
+            Number of decimal places to round to. If decimals is negative,
+            it specifies the number of positions to the left of the decimal point.
+        *args, **kwargs
+            Additional arguments and keywords have no effect but might be
+            accepted for compatibility with NumPy.
+
+        Returns
+        -------
+        Series
+            Rounded values of the Series.
+
+        See Also
+        --------
+        numpy.around : Round values of an np.array.
+        DataFrame.round : Round values of a DataFrame.
+
+        Examples
+        --------
+        >>> s = pd.Series([0.1, 1.3, 2.7])
+        >>> s.round()
+        0    0.0
+        1    1.0
+        2    3.0
+        dtype: float64
+        """
+        nv.validate_round(args, kwargs)
+        if self.dtype == "object":
+            raise TypeError("Expected numeric dtype, got object instead.")
+        new_mgr = self._mgr.round(decimals=decimals, using_cow=using_copy_on_write())
+        return self._constructor_from_mgr(new_mgr, axes=new_mgr.axes).__finalize__(
+            self, method="round"
+        )
+
+    @overload
+    def quantile(
+        self, q: float = ..., interpolation: QuantileInterpolation = ...
+    ) -> float:
+        ...
+
+    @overload
+    def quantile(
+        self,
+        q: Sequence[float] | AnyArrayLike,
+        interpolation: QuantileInterpolation = ...,
+    ) -> Series:
+        ...
+
+    @overload
+    def quantile(
+        self,
+        q: float | Sequence[float] | AnyArrayLike = ...,
+        interpolation: QuantileInterpolation = ...,
+    ) -> float | Series:
+        ...
+
+    def quantile(
+        self,
+        q: float | Sequence[float] | AnyArrayLike = 0.5,
+        interpolation: QuantileInterpolation = "linear",
+    ) -> float | Series:
+        """
+        Return value at the given quantile.
+
+        Parameters
+        ----------
+        q : float or array-like, default 0.5 (50% quantile)
+            The quantile(s) to compute, which can lie in range: 0 <= q <= 1.
+        interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'}
+            This optional parameter specifies the interpolation method to use,
+            when the desired quantile lies between two data points `i` and `j`:
+
+                * linear: `i + (j - i) * (x-i)/(j-i)`, where `(x-i)/(j-i)` is
+                  the fractional part of the index surrounded by `i > j`.
+                * lower: `i`.
+                * higher: `j`.
+                * nearest: `i` or `j` whichever is nearest.
+                * midpoint: (`i` + `j`) / 2.
+
+        Returns
+        -------
+        float or Series
+            If ``q`` is an array, a Series will be returned where the
+            index is ``q`` and the values are the quantiles, otherwise
+            a float will be returned.
+
+        See Also
+        --------
+        core.window.Rolling.quantile : Calculate the rolling quantile.
+        numpy.percentile : Returns the q-th percentile(s) of the array elements.
+
+        Examples
+        --------
+        >>> s = pd.Series([1, 2, 3, 4])
+        >>> s.quantile(.5)
+        2.5
+        >>> s.quantile([.25, .5, .75])
+        0.25    1.75
+        0.50    2.50
+        0.75    3.25
+        dtype: float64
+        """
+        validate_percentile(q)
+
+        # We dispatch to DataFrame so that core.internals only has to worry
+        #  about 2D cases.
+        df = self.to_frame()
+
+        result = df.quantile(q=q, interpolation=interpolation, numeric_only=False)
+        if result.ndim == 2:
+            result = result.iloc[:, 0]
+
+        if is_list_like(q):
+            result.name = self.name
+            idx = Index(q, dtype=np.float64)
+            return self._constructor(result, index=idx, name=self.name)
+        else:
+            # scalar
+            return result.iloc[0]
+
+    def corr(
+        self,
+        other: Series,
+        method: CorrelationMethod = "pearson",
+        min_periods: int | None = None,
+    ) -> float:
+        """
+        Compute correlation with `other` Series, excluding missing values.
+
+        The two `Series` objects are not required to be the same length and will be
+        aligned internally before the correlation function is applied.
+
+        Parameters
+        ----------
+        other : Series
+            Series with which to compute the correlation.
+        method : {'pearson', 'kendall', 'spearman'} or callable
+            Method used to compute correlation:
+
+            - pearson : Standard correlation coefficient
+            - kendall : Kendall Tau correlation coefficient
+            - spearman : Spearman rank correlation
+            - callable: Callable with input two 1d ndarrays and returning a float.
+
+            .. warning::
+                Note that the returned matrix from corr will have 1 along the
+                diagonals and will be symmetric regardless of the callable's
+                behavior.
+        min_periods : int, optional
+            Minimum number of observations needed to have a valid result.
+
+        Returns
+        -------
+        float
+            Correlation with other.
+
+        See Also
+        --------
+        DataFrame.corr : Compute pairwise correlation between columns.
+        DataFrame.corrwith : Compute pairwise correlation with another
+            DataFrame or Series.
+
+        Notes
+        -----
+        Pearson, Kendall and Spearman correlation are currently computed using pairwise complete observations.
+
+        * `Pearson correlation coefficient `_
+        * `Kendall rank correlation coefficient `_
+        * `Spearman's rank correlation coefficient `_
+
+        Automatic data alignment: as with all pandas operations, automatic data alignment is performed for this method.
+        ``corr()`` automatically considers values with matching indices.
+
+        Examples
+        --------
+        >>> def histogram_intersection(a, b):
+        ...     v = np.minimum(a, b).sum().round(decimals=1)
+        ...     return v
+        >>> s1 = pd.Series([.2, .0, .6, .2])
+        >>> s2 = pd.Series([.3, .6, .0, .1])
+        >>> s1.corr(s2, method=histogram_intersection)
+        0.3
+
+        Pandas auto-aligns the values with matching indices
+
+        >>> s1 = pd.Series([1, 2, 3], index=[0, 1, 2])
+        >>> s2 = pd.Series([1, 2, 3], index=[2, 1, 0])
+        >>> s1.corr(s2)
+        -1.0
+        """  # noqa: E501
+        this, other = self.align(other, join="inner", copy=False)
+        if len(this) == 0:
+            return np.nan
+
+        this_values = this.to_numpy(dtype=float, na_value=np.nan, copy=False)
+        other_values = other.to_numpy(dtype=float, na_value=np.nan, copy=False)
+
+        if method in ["pearson", "spearman", "kendall"] or callable(method):
+            return nanops.nancorr(
+                this_values, other_values, method=method, min_periods=min_periods
+            )
+
+        raise ValueError(
+            "method must be either 'pearson', "
+            "'spearman', 'kendall', or a callable, "
+            f"'{method}' was supplied"
+        )
+
+    def cov(
+        self,
+        other: Series,
+        min_periods: int | None = None,
+        ddof: int | None = 1,
+    ) -> float:
+        """
+        Compute covariance with Series, excluding missing values.
+
+        The two `Series` objects are not required to be the same length and
+        will be aligned internally before the covariance is calculated.
+
+        Parameters
+        ----------
+        other : Series
+            Series with which to compute the covariance.
+        min_periods : int, optional
+            Minimum number of observations needed to have a valid result.
+        ddof : int, default 1
+            Delta degrees of freedom.  The divisor used in calculations
+            is ``N - ddof``, where ``N`` represents the number of elements.
+
+        Returns
+        -------
+        float
+            Covariance between Series and other normalized by N-1
+            (unbiased estimator).
+
+        See Also
+        --------
+        DataFrame.cov : Compute pairwise covariance of columns.
+
+        Examples
+        --------
+        >>> s1 = pd.Series([0.90010907, 0.13484424, 0.62036035])
+        >>> s2 = pd.Series([0.12528585, 0.26962463, 0.51111198])
+        >>> s1.cov(s2)
+        -0.01685762652715874
+        """
+        this, other = self.align(other, join="inner", copy=False)
+        if len(this) == 0:
+            return np.nan
+        this_values = this.to_numpy(dtype=float, na_value=np.nan, copy=False)
+        other_values = other.to_numpy(dtype=float, na_value=np.nan, copy=False)
+        return nanops.nancov(
+            this_values, other_values, min_periods=min_periods, ddof=ddof
+        )
+
+    @doc(
+        klass="Series",
+        extra_params="",
+        other_klass="DataFrame",
+        examples=dedent(
+            """
+        Difference with previous row
+
+        >>> s = pd.Series([1, 1, 2, 3, 5, 8])
+        >>> s.diff()
+        0    NaN
+        1    0.0
+        2    1.0
+        3    1.0
+        4    2.0
+        5    3.0
+        dtype: float64
+
+        Difference with 3rd previous row
+
+        >>> s.diff(periods=3)
+        0    NaN
+        1    NaN
+        2    NaN
+        3    2.0
+        4    4.0
+        5    6.0
+        dtype: float64
+
+        Difference with following row
+
+        >>> s.diff(periods=-1)
+        0    0.0
+        1   -1.0
+        2   -1.0
+        3   -2.0
+        4   -3.0
+        5    NaN
+        dtype: float64
+
+        Overflow in input dtype
+
+        >>> s = pd.Series([1, 0], dtype=np.uint8)
+        >>> s.diff()
+        0      NaN
+        1    255.0
+        dtype: float64"""
+        ),
+    )
+    def diff(self, periods: int = 1) -> Series:
+        """
+        First discrete difference of element.
+
+        Calculates the difference of a {klass} element compared with another
+        element in the {klass} (default is element in previous row).
+
+        Parameters
+        ----------
+        periods : int, default 1
+            Periods to shift for calculating difference, accepts negative
+            values.
+        {extra_params}
+        Returns
+        -------
+        {klass}
+            First differences of the Series.
+
+        See Also
+        --------
+        {klass}.pct_change: Percent change over given number of periods.
+        {klass}.shift: Shift index by desired number of periods with an
+            optional time freq.
+        {other_klass}.diff: First discrete difference of object.
+
+        Notes
+        -----
+        For boolean dtypes, this uses :meth:`operator.xor` rather than
+        :meth:`operator.sub`.
+        The result is calculated according to current dtype in {klass},
+        however dtype of the result is always float64.
+
+        Examples
+        --------
+        {examples}
+        """
+        result = algorithms.diff(self._values, periods)
+        return self._constructor(result, index=self.index, copy=False).__finalize__(
+            self, method="diff"
+        )
+
+    def autocorr(self, lag: int = 1) -> float:
+        """
+        Compute the lag-N autocorrelation.
+
+        This method computes the Pearson correlation between
+        the Series and its shifted self.
+
+        Parameters
+        ----------
+        lag : int, default 1
+            Number of lags to apply before performing autocorrelation.
+
+        Returns
+        -------
+        float
+            The Pearson correlation between self and self.shift(lag).
+
+        See Also
+        --------
+        Series.corr : Compute the correlation between two Series.
+        Series.shift : Shift index by desired number of periods.
+        DataFrame.corr : Compute pairwise correlation of columns.
+        DataFrame.corrwith : Compute pairwise correlation between rows or
+            columns of two DataFrame objects.
+
+        Notes
+        -----
+        If the Pearson correlation is not well defined return 'NaN'.
+
+        Examples
+        --------
+        >>> s = pd.Series([0.25, 0.5, 0.2, -0.05])
+        >>> s.autocorr()  # doctest: +ELLIPSIS
+        0.10355...
+        >>> s.autocorr(lag=2)  # doctest: +ELLIPSIS
+        -0.99999...
+
+        If the Pearson correlation is not well defined, then 'NaN' is returned.
+
+        >>> s = pd.Series([1, 0, 0, 0])
+        >>> s.autocorr()
+        nan
+        """
+        return self.corr(cast(Series, self.shift(lag)))
+
+    def dot(self, other: AnyArrayLike) -> Series | np.ndarray:
+        """
+        Compute the dot product between the Series and the columns of other.
+
+        This method computes the dot product between the Series and another
+        one, or the Series and each columns of a DataFrame, or the Series and
+        each columns of an array.
+
+        It can also be called using `self @ other`.
+
+        Parameters
+        ----------
+        other : Series, DataFrame or array-like
+            The other object to compute the dot product with its columns.
+
+        Returns
+        -------
+        scalar, Series or numpy.ndarray
+            Return the dot product of the Series and other if other is a
+            Series, the Series of the dot product of Series and each rows of
+            other if other is a DataFrame or a numpy.ndarray between the Series
+            and each columns of the numpy array.
+
+        See Also
+        --------
+        DataFrame.dot: Compute the matrix product with the DataFrame.
+        Series.mul: Multiplication of series and other, element-wise.
+
+        Notes
+        -----
+        The Series and other has to share the same index if other is a Series
+        or a DataFrame.
+
+        Examples
+        --------
+        >>> s = pd.Series([0, 1, 2, 3])
+        >>> other = pd.Series([-1, 2, -3, 4])
+        >>> s.dot(other)
+        8
+        >>> s @ other
+        8
+        >>> df = pd.DataFrame([[0, 1], [-2, 3], [4, -5], [6, 7]])
+        >>> s.dot(df)
+        0    24
+        1    14
+        dtype: int64
+        >>> arr = np.array([[0, 1], [-2, 3], [4, -5], [6, 7]])
+        >>> s.dot(arr)
+        array([24, 14])
+        """
+        if isinstance(other, (Series, ABCDataFrame)):
+            common = self.index.union(other.index)
+            if len(common) > len(self.index) or len(common) > len(other.index):
+                raise ValueError("matrices are not aligned")
+
+            left = self.reindex(index=common, copy=False)
+            right = other.reindex(index=common, copy=False)
+            lvals = left.values
+            rvals = right.values
+        else:
+            lvals = self.values
+            rvals = np.asarray(other)
+            if lvals.shape[0] != rvals.shape[0]:
+                raise Exception(
+                    f"Dot product shape mismatch, {lvals.shape} vs {rvals.shape}"
+                )
+
+        if isinstance(other, ABCDataFrame):
+            return self._constructor(
+                np.dot(lvals, rvals), index=other.columns, copy=False
+            ).__finalize__(self, method="dot")
+        elif isinstance(other, Series):
+            return np.dot(lvals, rvals)
+        elif isinstance(rvals, np.ndarray):
+            return np.dot(lvals, rvals)
+        else:  # pragma: no cover
+            raise TypeError(f"unsupported type: {type(other)}")
+
+    def __matmul__(self, other):
+        """
+        Matrix multiplication using binary `@` operator.
+        """
+        return self.dot(other)
+
+    def __rmatmul__(self, other):
+        """
+        Matrix multiplication using binary `@` operator.
+        """
+        return self.dot(np.transpose(other))
+
+    @doc(base.IndexOpsMixin.searchsorted, klass="Series")
+    # Signature of "searchsorted" incompatible with supertype "IndexOpsMixin"
+    def searchsorted(  # type: ignore[override]
+        self,
+        value: NumpyValueArrayLike | ExtensionArray,
+        side: Literal["left", "right"] = "left",
+        sorter: NumpySorter | None = None,
+    ) -> npt.NDArray[np.intp] | np.intp:
+        return base.IndexOpsMixin.searchsorted(self, value, side=side, sorter=sorter)
+
+    # -------------------------------------------------------------------
+    # Combination
+
+    def _append(
+        self, to_append, ignore_index: bool = False, verify_integrity: bool = False
+    ):
+        from pandas.core.reshape.concat import concat
+
+        if isinstance(to_append, (list, tuple)):
+            to_concat = [self]
+            to_concat.extend(to_append)
+        else:
+            to_concat = [self, to_append]
+        if any(isinstance(x, (ABCDataFrame,)) for x in to_concat[1:]):
+            msg = "to_append should be a Series or list/tuple of Series, got DataFrame"
+            raise TypeError(msg)
+        return concat(
+            to_concat, ignore_index=ignore_index, verify_integrity=verify_integrity
+        )
+
+    @doc(
+        _shared_docs["compare"],
+        dedent(
+            """
+        Returns
+        -------
+        Series or DataFrame
+            If axis is 0 or 'index' the result will be a Series.
+            The resulting index will be a MultiIndex with 'self' and 'other'
+            stacked alternately at the inner level.
+
+            If axis is 1 or 'columns' the result will be a DataFrame.
+            It will have two columns namely 'self' and 'other'.
+
+        See Also
+        --------
+        DataFrame.compare : Compare with another DataFrame and show differences.
+
+        Notes
+        -----
+        Matching NaNs will not appear as a difference.
+
+        Examples
+        --------
+        >>> s1 = pd.Series(["a", "b", "c", "d", "e"])
+        >>> s2 = pd.Series(["a", "a", "c", "b", "e"])
+
+        Align the differences on columns
+
+        >>> s1.compare(s2)
+          self other
+        1    b     a
+        3    d     b
+
+        Stack the differences on indices
+
+        >>> s1.compare(s2, align_axis=0)
+        1  self     b
+           other    a
+        3  self     d
+           other    b
+        dtype: object
+
+        Keep all original rows
+
+        >>> s1.compare(s2, keep_shape=True)
+          self other
+        0  NaN   NaN
+        1    b     a
+        2  NaN   NaN
+        3    d     b
+        4  NaN   NaN
+
+        Keep all original rows and also all original values
+
+        >>> s1.compare(s2, keep_shape=True, keep_equal=True)
+          self other
+        0    a     a
+        1    b     a
+        2    c     c
+        3    d     b
+        4    e     e
+        """
+        ),
+        klass=_shared_doc_kwargs["klass"],
+    )
+    def compare(
+        self,
+        other: Series,
+        align_axis: Axis = 1,
+        keep_shape: bool = False,
+        keep_equal: bool = False,
+        result_names: Suffixes = ("self", "other"),
+    ) -> DataFrame | Series:
+        return super().compare(
+            other=other,
+            align_axis=align_axis,
+            keep_shape=keep_shape,
+            keep_equal=keep_equal,
+            result_names=result_names,
+        )
+
+    def combine(
+        self,
+        other: Series | Hashable,
+        func: Callable[[Hashable, Hashable], Hashable],
+        fill_value: Hashable | None = None,
+    ) -> Series:
+        """
+        Combine the Series with a Series or scalar according to `func`.
+
+        Combine the Series and `other` using `func` to perform elementwise
+        selection for combined Series.
+        `fill_value` is assumed when value is missing at some index
+        from one of the two objects being combined.
+
+        Parameters
+        ----------
+        other : Series or scalar
+            The value(s) to be combined with the `Series`.
+        func : function
+            Function that takes two scalars as inputs and returns an element.
+        fill_value : scalar, optional
+            The value to assume when an index is missing from
+            one Series or the other. The default specifies to use the
+            appropriate NaN value for the underlying dtype of the Series.
+
+        Returns
+        -------
+        Series
+            The result of combining the Series with the other object.
+
+        See Also
+        --------
+        Series.combine_first : Combine Series values, choosing the calling
+            Series' values first.
+
+        Examples
+        --------
+        Consider 2 Datasets ``s1`` and ``s2`` containing
+        highest clocked speeds of different birds.
+
+        >>> s1 = pd.Series({'falcon': 330.0, 'eagle': 160.0})
+        >>> s1
+        falcon    330.0
+        eagle     160.0
+        dtype: float64
+        >>> s2 = pd.Series({'falcon': 345.0, 'eagle': 200.0, 'duck': 30.0})
+        >>> s2
+        falcon    345.0
+        eagle     200.0
+        duck       30.0
+        dtype: float64
+
+        Now, to combine the two datasets and view the highest speeds
+        of the birds across the two datasets
+
+        >>> s1.combine(s2, max)
+        duck        NaN
+        eagle     200.0
+        falcon    345.0
+        dtype: float64
+
+        In the previous example, the resulting value for duck is missing,
+        because the maximum of a NaN and a float is a NaN.
+        So, in the example, we set ``fill_value=0``,
+        so the maximum value returned will be the value from some dataset.
+
+        >>> s1.combine(s2, max, fill_value=0)
+        duck       30.0
+        eagle     200.0
+        falcon    345.0
+        dtype: float64
+        """
+        if fill_value is None:
+            fill_value = na_value_for_dtype(self.dtype, compat=False)
+
+        if isinstance(other, Series):
+            # If other is a Series, result is based on union of Series,
+            # so do this element by element
+            new_index = self.index.union(other.index)
+            new_name = ops.get_op_result_name(self, other)
+            new_values = np.empty(len(new_index), dtype=object)
+            with np.errstate(all="ignore"):
+                for i, idx in enumerate(new_index):
+                    lv = self.get(idx, fill_value)
+                    rv = other.get(idx, fill_value)
+                    new_values[i] = func(lv, rv)
+        else:
+            # Assume that other is a scalar, so apply the function for
+            # each element in the Series
+            new_index = self.index
+            new_values = np.empty(len(new_index), dtype=object)
+            with np.errstate(all="ignore"):
+                new_values[:] = [func(lv, other) for lv in self._values]
+            new_name = self.name
+
+        # try_float=False is to match agg_series
+        npvalues = lib.maybe_convert_objects(new_values, try_float=False)
+        # same_dtype here is a kludge to avoid casting e.g. [True, False] to
+        #  ["True", "False"]
+        same_dtype = isinstance(self.dtype, (StringDtype, CategoricalDtype))
+        res_values = maybe_cast_pointwise_result(
+            npvalues, self.dtype, same_dtype=same_dtype
+        )
+        return self._constructor(res_values, index=new_index, name=new_name, copy=False)
+
+    def combine_first(self, other) -> Series:
+        """
+        Update null elements with value in the same location in 'other'.
+
+        Combine two Series objects by filling null values in one Series with
+        non-null values from the other Series. Result index will be the union
+        of the two indexes.
+
+        Parameters
+        ----------
+        other : Series
+            The value(s) to be used for filling null values.
+
+        Returns
+        -------
+        Series
+            The result of combining the provided Series with the other object.
+
+        See Also
+        --------
+        Series.combine : Perform element-wise operation on two Series
+            using a given function.
+
+        Examples
+        --------
+        >>> s1 = pd.Series([1, np.nan])
+        >>> s2 = pd.Series([3, 4, 5])
+        >>> s1.combine_first(s2)
+        0    1.0
+        1    4.0
+        2    5.0
+        dtype: float64
+
+        Null values still persist if the location of that null value
+        does not exist in `other`
+
+        >>> s1 = pd.Series({'falcon': np.nan, 'eagle': 160.0})
+        >>> s2 = pd.Series({'eagle': 200.0, 'duck': 30.0})
+        >>> s1.combine_first(s2)
+        duck       30.0
+        eagle     160.0
+        falcon      NaN
+        dtype: float64
+        """
+        from pandas.core.reshape.concat import concat
+
+        if self.dtype == other.dtype:
+            if self.index.equals(other.index):
+                return self.mask(self.isna(), other)
+            elif self._can_hold_na and not isinstance(self.dtype, SparseDtype):
+                this, other = self.align(other, join="outer")
+                return this.mask(this.isna(), other)
+
+        new_index = self.index.union(other.index)
+
+        this = self
+        # identify the index subset to keep for each series
+        keep_other = other.index.difference(this.index[notna(this)])
+        keep_this = this.index.difference(keep_other)
+
+        this = this.reindex(keep_this, copy=False)
+        other = other.reindex(keep_other, copy=False)
+
+        if this.dtype.kind == "M" and other.dtype.kind != "M":
+            other = to_datetime(other)
+        combined = concat([this, other])
+        combined = combined.reindex(new_index, copy=False)
+        return combined.__finalize__(self, method="combine_first")
+
+    def update(self, other: Series | Sequence | Mapping) -> None:
+        """
+        Modify Series in place using values from passed Series.
+
+        Uses non-NA values from passed Series to make updates. Aligns
+        on index.
+
+        Parameters
+        ----------
+        other : Series, or object coercible into Series
+
+        Examples
+        --------
+        >>> s = pd.Series([1, 2, 3])
+        >>> s.update(pd.Series([4, 5, 6]))
+        >>> s
+        0    4
+        1    5
+        2    6
+        dtype: int64
+
+        >>> s = pd.Series(['a', 'b', 'c'])
+        >>> s.update(pd.Series(['d', 'e'], index=[0, 2]))
+        >>> s
+        0    d
+        1    b
+        2    e
+        dtype: object
+
+        >>> s = pd.Series([1, 2, 3])
+        >>> s.update(pd.Series([4, 5, 6, 7, 8]))
+        >>> s
+        0    4
+        1    5
+        2    6
+        dtype: int64
+
+        If ``other`` contains NaNs the corresponding values are not updated
+        in the original Series.
+
+        >>> s = pd.Series([1, 2, 3])
+        >>> s.update(pd.Series([4, np.nan, 6]))
+        >>> s
+        0    4
+        1    2
+        2    6
+        dtype: int64
+
+        ``other`` can also be a non-Series object type
+        that is coercible into a Series
+
+        >>> s = pd.Series([1, 2, 3])
+        >>> s.update([4, np.nan, 6])
+        >>> s
+        0    4
+        1    2
+        2    6
+        dtype: int64
+
+        >>> s = pd.Series([1, 2, 3])
+        >>> s.update({1: 9})
+        >>> s
+        0    1
+        1    9
+        2    3
+        dtype: int64
+        """
+        if not PYPY and not WARNING_CHECK_DISABLED and using_copy_on_write():
+            if sys.getrefcount(self) <= REF_COUNT:
+                warnings.warn(
+                    _chained_assignment_method_msg,
+                    ChainedAssignmentError,
+                    stacklevel=2,
+                )
+        elif (
+            not PYPY
+            and not WARNING_CHECK_DISABLED
+            and not using_copy_on_write()
+            and self._is_view_after_cow_rules()
+        ):
+            ctr = sys.getrefcount(self)
+            ref_count = REF_COUNT
+            if _check_cacher(self):
+                # see https://github.com/pandas-dev/pandas/pull/56060#discussion_r1399245221
+                ref_count += 1
+            if ctr <= ref_count:
+                warnings.warn(
+                    _chained_assignment_warning_method_msg,
+                    FutureWarning,
+                    stacklevel=2,
+                )
+
+        if not isinstance(other, Series):
+            other = Series(other)
+
+        other = other.reindex_like(self)
+        mask = notna(other)
+
+        self._mgr = self._mgr.putmask(mask=mask, new=other)
+        self._maybe_update_cacher()
+
+    # ----------------------------------------------------------------------
+    # Reindexing, sorting
+
+    @overload
+    def sort_values(
+        self,
+        *,
+        axis: Axis = ...,
+        ascending: bool | Sequence[bool] = ...,
+        inplace: Literal[False] = ...,
+        kind: SortKind = ...,
+        na_position: NaPosition = ...,
+        ignore_index: bool = ...,
+        key: ValueKeyFunc = ...,
+    ) -> Series:
+        ...
+
+    @overload
+    def sort_values(
+        self,
+        *,
+        axis: Axis = ...,
+        ascending: bool | Sequence[bool] = ...,
+        inplace: Literal[True],
+        kind: SortKind = ...,
+        na_position: NaPosition = ...,
+        ignore_index: bool = ...,
+        key: ValueKeyFunc = ...,
+    ) -> None:
+        ...
+
+    @overload
+    def sort_values(
+        self,
+        *,
+        axis: Axis = ...,
+        ascending: bool | Sequence[bool] = ...,
+        inplace: bool = ...,
+        kind: SortKind = ...,
+        na_position: NaPosition = ...,
+        ignore_index: bool = ...,
+        key: ValueKeyFunc = ...,
+    ) -> Series | None:
+        ...
+
+    def sort_values(
+        self,
+        *,
+        axis: Axis = 0,
+        ascending: bool | Sequence[bool] = True,
+        inplace: bool = False,
+        kind: SortKind = "quicksort",
+        na_position: NaPosition = "last",
+        ignore_index: bool = False,
+        key: ValueKeyFunc | None = None,
+    ) -> Series | None:
+        """
+        Sort by the values.
+
+        Sort a Series in ascending or descending order by some
+        criterion.
+
+        Parameters
+        ----------
+        axis : {0 or 'index'}
+            Unused. Parameter needed for compatibility with DataFrame.
+        ascending : bool or list of bools, default True
+            If True, sort values in ascending order, otherwise descending.
+        inplace : bool, default False
+            If True, perform operation in-place.
+        kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, default 'quicksort'
+            Choice of sorting algorithm. See also :func:`numpy.sort` for more
+            information. 'mergesort' and 'stable' are the only stable  algorithms.
+        na_position : {'first' or 'last'}, default 'last'
+            Argument 'first' puts NaNs at the beginning, 'last' puts NaNs at
+            the end.
+        ignore_index : bool, default False
+            If True, the resulting axis will be labeled 0, 1, …, n - 1.
+        key : callable, optional
+            If not None, apply the key function to the series values
+            before sorting. This is similar to the `key` argument in the
+            builtin :meth:`sorted` function, with the notable difference that
+            this `key` function should be *vectorized*. It should expect a
+            ``Series`` and return an array-like.
+
+        Returns
+        -------
+        Series or None
+            Series ordered by values or None if ``inplace=True``.
+
+        See Also
+        --------
+        Series.sort_index : Sort by the Series indices.
+        DataFrame.sort_values : Sort DataFrame by the values along either axis.
+        DataFrame.sort_index : Sort DataFrame by indices.
+
+        Examples
+        --------
+        >>> s = pd.Series([np.nan, 1, 3, 10, 5])
+        >>> s
+        0     NaN
+        1     1.0
+        2     3.0
+        3     10.0
+        4     5.0
+        dtype: float64
+
+        Sort values ascending order (default behaviour)
+
+        >>> s.sort_values(ascending=True)
+        1     1.0
+        2     3.0
+        4     5.0
+        3    10.0
+        0     NaN
+        dtype: float64
+
+        Sort values descending order
+
+        >>> s.sort_values(ascending=False)
+        3    10.0
+        4     5.0
+        2     3.0
+        1     1.0
+        0     NaN
+        dtype: float64
+
+        Sort values putting NAs first
+
+        >>> s.sort_values(na_position='first')
+        0     NaN
+        1     1.0
+        2     3.0
+        4     5.0
+        3    10.0
+        dtype: float64
+
+        Sort a series of strings
+
+        >>> s = pd.Series(['z', 'b', 'd', 'a', 'c'])
+        >>> s
+        0    z
+        1    b
+        2    d
+        3    a
+        4    c
+        dtype: object
+
+        >>> s.sort_values()
+        3    a
+        1    b
+        4    c
+        2    d
+        0    z
+        dtype: object
+
+        Sort using a key function. Your `key` function will be
+        given the ``Series`` of values and should return an array-like.
+
+        >>> s = pd.Series(['a', 'B', 'c', 'D', 'e'])
+        >>> s.sort_values()
+        1    B
+        3    D
+        0    a
+        2    c
+        4    e
+        dtype: object
+        >>> s.sort_values(key=lambda x: x.str.lower())
+        0    a
+        1    B
+        2    c
+        3    D
+        4    e
+        dtype: object
+
+        NumPy ufuncs work well here. For example, we can
+        sort by the ``sin`` of the value
+
+        >>> s = pd.Series([-4, -2, 0, 2, 4])
+        >>> s.sort_values(key=np.sin)
+        1   -2
+        4    4
+        2    0
+        0   -4
+        3    2
+        dtype: int64
+
+        More complicated user-defined functions can be used,
+        as long as they expect a Series and return an array-like
+
+        >>> s.sort_values(key=lambda x: (np.tan(x.cumsum())))
+        0   -4
+        3    2
+        4    4
+        1   -2
+        2    0
+        dtype: int64
+        """
+        inplace = validate_bool_kwarg(inplace, "inplace")
+        # Validate the axis parameter
+        self._get_axis_number(axis)
+
+        # GH 5856/5853
+        if inplace and self._is_cached:
+            raise ValueError(
+                "This Series is a view of some other array, to "
+                "sort in-place you must create a copy"
+            )
+
+        if is_list_like(ascending):
+            ascending = cast(Sequence[bool], ascending)
+            if len(ascending) != 1:
+                raise ValueError(
+                    f"Length of ascending ({len(ascending)}) must be 1 for Series"
+                )
+            ascending = ascending[0]
+
+        ascending = validate_ascending(ascending)
+
+        if na_position not in ["first", "last"]:
+            raise ValueError(f"invalid na_position: {na_position}")
+
+        # GH 35922. Make sorting stable by leveraging nargsort
+        if key:
+            values_to_sort = cast(Series, ensure_key_mapped(self, key))._values
+        else:
+            values_to_sort = self._values
+        sorted_index = nargsort(values_to_sort, kind, bool(ascending), na_position)
+
+        if is_range_indexer(sorted_index, len(sorted_index)):
+            if inplace:
+                return self._update_inplace(self)
+            return self.copy(deep=None)
+
+        result = self._constructor(
+            self._values[sorted_index], index=self.index[sorted_index], copy=False
+        )
+
+        if ignore_index:
+            result.index = default_index(len(sorted_index))
+
+        if not inplace:
+            return result.__finalize__(self, method="sort_values")
+        self._update_inplace(result)
+        return None
+
+    @overload
+    def sort_index(
+        self,
+        *,
+        axis: Axis = ...,
+        level: IndexLabel = ...,
+        ascending: bool | Sequence[bool] = ...,
+        inplace: Literal[True],
+        kind: SortKind = ...,
+        na_position: NaPosition = ...,
+        sort_remaining: bool = ...,
+        ignore_index: bool = ...,
+        key: IndexKeyFunc = ...,
+    ) -> None:
+        ...
+
+    @overload
+    def sort_index(
+        self,
+        *,
+        axis: Axis = ...,
+        level: IndexLabel = ...,
+        ascending: bool | Sequence[bool] = ...,
+        inplace: Literal[False] = ...,
+        kind: SortKind = ...,
+        na_position: NaPosition = ...,
+        sort_remaining: bool = ...,
+        ignore_index: bool = ...,
+        key: IndexKeyFunc = ...,
+    ) -> Series:
+        ...
+
+    @overload
+    def sort_index(
+        self,
+        *,
+        axis: Axis = ...,
+        level: IndexLabel = ...,
+        ascending: bool | Sequence[bool] = ...,
+        inplace: bool = ...,
+        kind: SortKind = ...,
+        na_position: NaPosition = ...,
+        sort_remaining: bool = ...,
+        ignore_index: bool = ...,
+        key: IndexKeyFunc = ...,
+    ) -> Series | None:
+        ...
+
+    def sort_index(
+        self,
+        *,
+        axis: Axis = 0,
+        level: IndexLabel | None = None,
+        ascending: bool | Sequence[bool] = True,
+        inplace: bool = False,
+        kind: SortKind = "quicksort",
+        na_position: NaPosition = "last",
+        sort_remaining: bool = True,
+        ignore_index: bool = False,
+        key: IndexKeyFunc | None = None,
+    ) -> Series | None:
+        """
+        Sort Series by index labels.
+
+        Returns a new Series sorted by label if `inplace` argument is
+        ``False``, otherwise updates the original series and returns None.
+
+        Parameters
+        ----------
+        axis : {0 or 'index'}
+            Unused. Parameter needed for compatibility with DataFrame.
+        level : int, optional
+            If not None, sort on values in specified index level(s).
+        ascending : bool or list-like of bools, default True
+            Sort ascending vs. descending. When the index is a MultiIndex the
+            sort direction can be controlled for each level individually.
+        inplace : bool, default False
+            If True, perform operation in-place.
+        kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, default 'quicksort'
+            Choice of sorting algorithm. See also :func:`numpy.sort` for more
+            information. 'mergesort' and 'stable' are the only stable algorithms. For
+            DataFrames, this option is only applied when sorting on a single
+            column or label.
+        na_position : {'first', 'last'}, default 'last'
+            If 'first' puts NaNs at the beginning, 'last' puts NaNs at the end.
+            Not implemented for MultiIndex.
+        sort_remaining : bool, default True
+            If True and sorting by level and index is multilevel, sort by other
+            levels too (in order) after sorting by specified level.
+        ignore_index : bool, default False
+            If True, the resulting axis will be labeled 0, 1, …, n - 1.
+        key : callable, optional
+            If not None, apply the key function to the index values
+            before sorting. This is similar to the `key` argument in the
+            builtin :meth:`sorted` function, with the notable difference that
+            this `key` function should be *vectorized*. It should expect an
+            ``Index`` and return an ``Index`` of the same shape.
+
+        Returns
+        -------
+        Series or None
+            The original Series sorted by the labels or None if ``inplace=True``.
+
+        See Also
+        --------
+        DataFrame.sort_index: Sort DataFrame by the index.
+        DataFrame.sort_values: Sort DataFrame by the value.
+        Series.sort_values : Sort Series by the value.
+
+        Examples
+        --------
+        >>> s = pd.Series(['a', 'b', 'c', 'd'], index=[3, 2, 1, 4])
+        >>> s.sort_index()
+        1    c
+        2    b
+        3    a
+        4    d
+        dtype: object
+
+        Sort Descending
+
+        >>> s.sort_index(ascending=False)
+        4    d
+        3    a
+        2    b
+        1    c
+        dtype: object
+
+        By default NaNs are put at the end, but use `na_position` to place
+        them at the beginning
+
+        >>> s = pd.Series(['a', 'b', 'c', 'd'], index=[3, 2, 1, np.nan])
+        >>> s.sort_index(na_position='first')
+        NaN     d
+         1.0    c
+         2.0    b
+         3.0    a
+        dtype: object
+
+        Specify index level to sort
+
+        >>> arrays = [np.array(['qux', 'qux', 'foo', 'foo',
+        ...                     'baz', 'baz', 'bar', 'bar']),
+        ...           np.array(['two', 'one', 'two', 'one',
+        ...                     'two', 'one', 'two', 'one'])]
+        >>> s = pd.Series([1, 2, 3, 4, 5, 6, 7, 8], index=arrays)
+        >>> s.sort_index(level=1)
+        bar  one    8
+        baz  one    6
+        foo  one    4
+        qux  one    2
+        bar  two    7
+        baz  two    5
+        foo  two    3
+        qux  two    1
+        dtype: int64
+
+        Does not sort by remaining levels when sorting by levels
+
+        >>> s.sort_index(level=1, sort_remaining=False)
+        qux  one    2
+        foo  one    4
+        baz  one    6
+        bar  one    8
+        qux  two    1
+        foo  two    3
+        baz  two    5
+        bar  two    7
+        dtype: int64
+
+        Apply a key function before sorting
+
+        >>> s = pd.Series([1, 2, 3, 4], index=['A', 'b', 'C', 'd'])
+        >>> s.sort_index(key=lambda x : x.str.lower())
+        A    1
+        b    2
+        C    3
+        d    4
+        dtype: int64
+        """
+
+        return super().sort_index(
+            axis=axis,
+            level=level,
+            ascending=ascending,
+            inplace=inplace,
+            kind=kind,
+            na_position=na_position,
+            sort_remaining=sort_remaining,
+            ignore_index=ignore_index,
+            key=key,
+        )
+
+    def argsort(
+        self,
+        axis: Axis = 0,
+        kind: SortKind = "quicksort",
+        order: None = None,
+        stable: None = None,
+    ) -> Series:
+        """
+        Return the integer indices that would sort the Series values.
+
+        Override ndarray.argsort. Argsorts the value, omitting NA/null values,
+        and places the result in the same locations as the non-NA values.
+
+        Parameters
+        ----------
+        axis : {0 or 'index'}
+            Unused. Parameter needed for compatibility with DataFrame.
+        kind : {'mergesort', 'quicksort', 'heapsort', 'stable'}, default 'quicksort'
+            Choice of sorting algorithm. See :func:`numpy.sort` for more
+            information. 'mergesort' and 'stable' are the only stable algorithms.
+        order : None
+            Has no effect but is accepted for compatibility with numpy.
+        stable : None
+            Has no effect but is accepted for compatibility with numpy.
+
+        Returns
+        -------
+        Series[np.intp]
+            Positions of values within the sort order with -1 indicating
+            nan values.
+
+        See Also
+        --------
+        numpy.ndarray.argsort : Returns the indices that would sort this array.
+
+        Examples
+        --------
+        >>> s = pd.Series([3, 2, 1])
+        >>> s.argsort()
+        0    2
+        1    1
+        2    0
+        dtype: int64
+        """
+        if axis != -1:
+            # GH#54257 We allow -1 here so that np.argsort(series) works
+            self._get_axis_number(axis)
+
+        values = self._values
+        mask = isna(values)
+
+        if mask.any():
+            # TODO(3.0): once this deprecation is enforced we can call
+            #  self.array.argsort directly, which will close GH#43840 and
+            #  GH#12694
+            warnings.warn(
+                "The behavior of Series.argsort in the presence of NA values is "
+                "deprecated. In a future version, NA values will be ordered "
+                "last instead of set to -1.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+            result = np.full(len(self), -1, dtype=np.intp)
+            notmask = ~mask
+            result[notmask] = np.argsort(values[notmask], kind=kind)
+        else:
+            result = np.argsort(values, kind=kind)
+
+        res = self._constructor(
+            result, index=self.index, name=self.name, dtype=np.intp, copy=False
+        )
+        return res.__finalize__(self, method="argsort")
+
+    def nlargest(
+        self, n: int = 5, keep: Literal["first", "last", "all"] = "first"
+    ) -> Series:
+        """
+        Return the largest `n` elements.
+
+        Parameters
+        ----------
+        n : int, default 5
+            Return this many descending sorted values.
+        keep : {'first', 'last', 'all'}, default 'first'
+            When there are duplicate values that cannot all fit in a
+            Series of `n` elements:
+
+            - ``first`` : return the first `n` occurrences in order
+              of appearance.
+            - ``last`` : return the last `n` occurrences in reverse
+              order of appearance.
+            - ``all`` : keep all occurrences. This can result in a Series of
+              size larger than `n`.
+
+        Returns
+        -------
+        Series
+            The `n` largest values in the Series, sorted in decreasing order.
+
+        See Also
+        --------
+        Series.nsmallest: Get the `n` smallest elements.
+        Series.sort_values: Sort Series by values.
+        Series.head: Return the first `n` rows.
+
+        Notes
+        -----
+        Faster than ``.sort_values(ascending=False).head(n)`` for small `n`
+        relative to the size of the ``Series`` object.
+
+        Examples
+        --------
+        >>> countries_population = {"Italy": 59000000, "France": 65000000,
+        ...                         "Malta": 434000, "Maldives": 434000,
+        ...                         "Brunei": 434000, "Iceland": 337000,
+        ...                         "Nauru": 11300, "Tuvalu": 11300,
+        ...                         "Anguilla": 11300, "Montserrat": 5200}
+        >>> s = pd.Series(countries_population)
+        >>> s
+        Italy       59000000
+        France      65000000
+        Malta         434000
+        Maldives      434000
+        Brunei        434000
+        Iceland       337000
+        Nauru          11300
+        Tuvalu         11300
+        Anguilla       11300
+        Montserrat      5200
+        dtype: int64
+
+        The `n` largest elements where ``n=5`` by default.
+
+        >>> s.nlargest()
+        France      65000000
+        Italy       59000000
+        Malta         434000
+        Maldives      434000
+        Brunei        434000
+        dtype: int64
+
+        The `n` largest elements where ``n=3``. Default `keep` value is 'first'
+        so Malta will be kept.
+
+        >>> s.nlargest(3)
+        France    65000000
+        Italy     59000000
+        Malta       434000
+        dtype: int64
+
+        The `n` largest elements where ``n=3`` and keeping the last duplicates.
+        Brunei will be kept since it is the last with value 434000 based on
+        the index order.
+
+        >>> s.nlargest(3, keep='last')
+        France      65000000
+        Italy       59000000
+        Brunei        434000
+        dtype: int64
+
+        The `n` largest elements where ``n=3`` with all duplicates kept. Note
+        that the returned Series has five elements due to the three duplicates.
+
+        >>> s.nlargest(3, keep='all')
+        France      65000000
+        Italy       59000000
+        Malta         434000
+        Maldives      434000
+        Brunei        434000
+        dtype: int64
+        """
+        return selectn.SelectNSeries(self, n=n, keep=keep).nlargest()
+
+    def nsmallest(
+        self, n: int = 5, keep: Literal["first", "last", "all"] = "first"
+    ) -> Series:
+        """
+        Return the smallest `n` elements.
+
+        Parameters
+        ----------
+        n : int, default 5
+            Return this many ascending sorted values.
+        keep : {'first', 'last', 'all'}, default 'first'
+            When there are duplicate values that cannot all fit in a
+            Series of `n` elements:
+
+            - ``first`` : return the first `n` occurrences in order
+              of appearance.
+            - ``last`` : return the last `n` occurrences in reverse
+              order of appearance.
+            - ``all`` : keep all occurrences. This can result in a Series of
+              size larger than `n`.
+
+        Returns
+        -------
+        Series
+            The `n` smallest values in the Series, sorted in increasing order.
+
+        See Also
+        --------
+        Series.nlargest: Get the `n` largest elements.
+        Series.sort_values: Sort Series by values.
+        Series.head: Return the first `n` rows.
+
+        Notes
+        -----
+        Faster than ``.sort_values().head(n)`` for small `n` relative to
+        the size of the ``Series`` object.
+
+        Examples
+        --------
+        >>> countries_population = {"Italy": 59000000, "France": 65000000,
+        ...                         "Brunei": 434000, "Malta": 434000,
+        ...                         "Maldives": 434000, "Iceland": 337000,
+        ...                         "Nauru": 11300, "Tuvalu": 11300,
+        ...                         "Anguilla": 11300, "Montserrat": 5200}
+        >>> s = pd.Series(countries_population)
+        >>> s
+        Italy       59000000
+        France      65000000
+        Brunei        434000
+        Malta         434000
+        Maldives      434000
+        Iceland       337000
+        Nauru          11300
+        Tuvalu         11300
+        Anguilla       11300
+        Montserrat      5200
+        dtype: int64
+
+        The `n` smallest elements where ``n=5`` by default.
+
+        >>> s.nsmallest()
+        Montserrat    5200
+        Nauru        11300
+        Tuvalu       11300
+        Anguilla     11300
+        Iceland     337000
+        dtype: int64
+
+        The `n` smallest elements where ``n=3``. Default `keep` value is
+        'first' so Nauru and Tuvalu will be kept.
+
+        >>> s.nsmallest(3)
+        Montserrat   5200
+        Nauru       11300
+        Tuvalu      11300
+        dtype: int64
+
+        The `n` smallest elements where ``n=3`` and keeping the last
+        duplicates. Anguilla and Tuvalu will be kept since they are the last
+        with value 11300 based on the index order.
+
+        >>> s.nsmallest(3, keep='last')
+        Montserrat   5200
+        Anguilla    11300
+        Tuvalu      11300
+        dtype: int64
+
+        The `n` smallest elements where ``n=3`` with all duplicates kept. Note
+        that the returned Series has four elements due to the three duplicates.
+
+        >>> s.nsmallest(3, keep='all')
+        Montserrat   5200
+        Nauru       11300
+        Tuvalu      11300
+        Anguilla    11300
+        dtype: int64
+        """
+        return selectn.SelectNSeries(self, n=n, keep=keep).nsmallest()
+
+    @doc(
+        klass=_shared_doc_kwargs["klass"],
+        extra_params=dedent(
+            """copy : bool, default True
+            Whether to copy underlying data.
+
+            .. note::
+                The `copy` keyword will change behavior in pandas 3.0.
+                `Copy-on-Write
+                `__
+                will be enabled by default, which means that all methods with a
+                `copy` keyword will use a lazy copy mechanism to defer the copy and
+                ignore the `copy` keyword. The `copy` keyword will be removed in a
+                future version of pandas.
+
+                You can already get the future behavior and improvements through
+                enabling copy on write ``pd.options.mode.copy_on_write = True``"""
+        ),
+        examples=dedent(
+            """\
+        Examples
+        --------
+        >>> s = pd.Series(
+        ...     ["A", "B", "A", "C"],
+        ...     index=[
+        ...         ["Final exam", "Final exam", "Coursework", "Coursework"],
+        ...         ["History", "Geography", "History", "Geography"],
+        ...         ["January", "February", "March", "April"],
+        ...     ],
+        ... )
+        >>> s
+        Final exam  History     January      A
+                    Geography   February     B
+        Coursework  History     March        A
+                    Geography   April        C
+        dtype: object
+
+        In the following example, we will swap the levels of the indices.
+        Here, we will swap the levels column-wise, but levels can be swapped row-wise
+        in a similar manner. Note that column-wise is the default behaviour.
+        By not supplying any arguments for i and j, we swap the last and second to
+        last indices.
+
+        >>> s.swaplevel()
+        Final exam  January     History         A
+                    February    Geography       B
+        Coursework  March       History         A
+                    April       Geography       C
+        dtype: object
+
+        By supplying one argument, we can choose which index to swap the last
+        index with. We can for example swap the first index with the last one as
+        follows.
+
+        >>> s.swaplevel(0)
+        January     History     Final exam      A
+        February    Geography   Final exam      B
+        March       History     Coursework      A
+        April       Geography   Coursework      C
+        dtype: object
+
+        We can also define explicitly which indices we want to swap by supplying values
+        for both i and j. Here, we for example swap the first and second indices.
+
+        >>> s.swaplevel(0, 1)
+        History     Final exam  January         A
+        Geography   Final exam  February        B
+        History     Coursework  March           A
+        Geography   Coursework  April           C
+        dtype: object"""
+        ),
+    )
+    def swaplevel(
+        self, i: Level = -2, j: Level = -1, copy: bool | None = None
+    ) -> Series:
+        """
+        Swap levels i and j in a :class:`MultiIndex`.
+
+        Default is to swap the two innermost levels of the index.
+
+        Parameters
+        ----------
+        i, j : int or str
+            Levels of the indices to be swapped. Can pass level name as string.
+        {extra_params}
+
+        Returns
+        -------
+        {klass}
+            {klass} with levels swapped in MultiIndex.
+
+        {examples}
+        """
+        assert isinstance(self.index, MultiIndex)
+        result = self.copy(deep=copy and not using_copy_on_write())
+        result.index = self.index.swaplevel(i, j)
+        return result
+
+    def reorder_levels(self, order: Sequence[Level]) -> Series:
+        """
+        Rearrange index levels using input order.
+
+        May not drop or duplicate levels.
+
+        Parameters
+        ----------
+        order : list of int representing new level order
+            Reference level by number or key.
+
+        Returns
+        -------
+        type of caller (new object)
+
+        Examples
+        --------
+        >>> arrays = [np.array(["dog", "dog", "cat", "cat", "bird", "bird"]),
+        ...           np.array(["white", "black", "white", "black", "white", "black"])]
+        >>> s = pd.Series([1, 2, 3, 3, 5, 2], index=arrays)
+        >>> s
+        dog   white    1
+              black    2
+        cat   white    3
+              black    3
+        bird  white    5
+              black    2
+        dtype: int64
+        >>> s.reorder_levels([1, 0])
+        white  dog     1
+        black  dog     2
+        white  cat     3
+        black  cat     3
+        white  bird    5
+        black  bird    2
+        dtype: int64
+        """
+        if not isinstance(self.index, MultiIndex):  # pragma: no cover
+            raise Exception("Can only reorder levels on a hierarchical axis.")
+
+        result = self.copy(deep=None)
+        assert isinstance(result.index, MultiIndex)
+        result.index = result.index.reorder_levels(order)
+        return result
+
+    def explode(self, ignore_index: bool = False) -> Series:
+        """
+        Transform each element of a list-like to a row.
+
+        Parameters
+        ----------
+        ignore_index : bool, default False
+            If True, the resulting index will be labeled 0, 1, …, n - 1.
+
+        Returns
+        -------
+        Series
+            Exploded lists to rows; index will be duplicated for these rows.
+
+        See Also
+        --------
+        Series.str.split : Split string values on specified separator.
+        Series.unstack : Unstack, a.k.a. pivot, Series with MultiIndex
+            to produce DataFrame.
+        DataFrame.melt : Unpivot a DataFrame from wide format to long format.
+        DataFrame.explode : Explode a DataFrame from list-like
+            columns to long format.
+
+        Notes
+        -----
+        This routine will explode list-likes including lists, tuples, sets,
+        Series, and np.ndarray. The result dtype of the subset rows will
+        be object. Scalars will be returned unchanged, and empty list-likes will
+        result in a np.nan for that row. In addition, the ordering of elements in
+        the output will be non-deterministic when exploding sets.
+
+        Reference :ref:`the user guide ` for more examples.
+
+        Examples
+        --------
+        >>> s = pd.Series([[1, 2, 3], 'foo', [], [3, 4]])
+        >>> s
+        0    [1, 2, 3]
+        1          foo
+        2           []
+        3       [3, 4]
+        dtype: object
+
+        >>> s.explode()
+        0      1
+        0      2
+        0      3
+        1    foo
+        2    NaN
+        3      3
+        3      4
+        dtype: object
+        """
+        if isinstance(self.dtype, ExtensionDtype):
+            values, counts = self._values._explode()
+        elif len(self) and is_object_dtype(self.dtype):
+            values, counts = reshape.explode(np.asarray(self._values))
+        else:
+            result = self.copy()
+            return result.reset_index(drop=True) if ignore_index else result
+
+        if ignore_index:
+            index: Index = default_index(len(values))
+        else:
+            index = self.index.repeat(counts)
+
+        return self._constructor(values, index=index, name=self.name, copy=False)
+
+    def unstack(
+        self,
+        level: IndexLabel = -1,
+        fill_value: Hashable | None = None,
+        sort: bool = True,
+    ) -> DataFrame:
+        """
+        Unstack, also known as pivot, Series with MultiIndex to produce DataFrame.
+
+        Parameters
+        ----------
+        level : int, str, or list of these, default last level
+            Level(s) to unstack, can pass level name.
+        fill_value : scalar value, default None
+            Value to use when replacing NaN values.
+        sort : bool, default True
+            Sort the level(s) in the resulting MultiIndex columns.
+
+        Returns
+        -------
+        DataFrame
+            Unstacked Series.
+
+        Notes
+        -----
+        Reference :ref:`the user guide ` for more examples.
+
+        Examples
+        --------
+        >>> s = pd.Series([1, 2, 3, 4],
+        ...               index=pd.MultiIndex.from_product([['one', 'two'],
+        ...                                                 ['a', 'b']]))
+        >>> s
+        one  a    1
+             b    2
+        two  a    3
+             b    4
+        dtype: int64
+
+        >>> s.unstack(level=-1)
+             a  b
+        one  1  2
+        two  3  4
+
+        >>> s.unstack(level=0)
+           one  two
+        a    1    3
+        b    2    4
+        """
+        from pandas.core.reshape.reshape import unstack
+
+        return unstack(self, level, fill_value, sort)
+
+    # ----------------------------------------------------------------------
+    # function application
+
+    def map(
+        self,
+        arg: Callable | Mapping | Series,
+        na_action: Literal["ignore"] | None = None,
+    ) -> Series:
+        """
+        Map values of Series according to an input mapping or function.
+
+        Used for substituting each value in a Series with another value,
+        that may be derived from a function, a ``dict`` or
+        a :class:`Series`.
+
+        Parameters
+        ----------
+        arg : function, collections.abc.Mapping subclass or Series
+            Mapping correspondence.
+        na_action : {None, 'ignore'}, default None
+            If 'ignore', propagate NaN values, without passing them to the
+            mapping correspondence.
+
+        Returns
+        -------
+        Series
+            Same index as caller.
+
+        See Also
+        --------
+        Series.apply : For applying more complex functions on a Series.
+        Series.replace: Replace values given in `to_replace` with `value`.
+        DataFrame.apply : Apply a function row-/column-wise.
+        DataFrame.map : Apply a function elementwise on a whole DataFrame.
+
+        Notes
+        -----
+        When ``arg`` is a dictionary, values in Series that are not in the
+        dictionary (as keys) are converted to ``NaN``. However, if the
+        dictionary is a ``dict`` subclass that defines ``__missing__`` (i.e.
+        provides a method for default values), then this default is used
+        rather than ``NaN``.
+
+        Examples
+        --------
+        >>> s = pd.Series(['cat', 'dog', np.nan, 'rabbit'])
+        >>> s
+        0      cat
+        1      dog
+        2      NaN
+        3   rabbit
+        dtype: object
+
+        ``map`` accepts a ``dict`` or a ``Series``. Values that are not found
+        in the ``dict`` are converted to ``NaN``, unless the dict has a default
+        value (e.g. ``defaultdict``):
+
+        >>> s.map({'cat': 'kitten', 'dog': 'puppy'})
+        0   kitten
+        1    puppy
+        2      NaN
+        3      NaN
+        dtype: object
+
+        It also accepts a function:
+
+        >>> s.map('I am a {}'.format)
+        0       I am a cat
+        1       I am a dog
+        2       I am a nan
+        3    I am a rabbit
+        dtype: object
+
+        To avoid applying the function to missing values (and keep them as
+        ``NaN``) ``na_action='ignore'`` can be used:
+
+        >>> s.map('I am a {}'.format, na_action='ignore')
+        0     I am a cat
+        1     I am a dog
+        2            NaN
+        3  I am a rabbit
+        dtype: object
+        """
+        new_values = self._map_values(arg, na_action=na_action)
+        return self._constructor(new_values, index=self.index, copy=False).__finalize__(
+            self, method="map"
+        )
+
+    def _gotitem(self, key, ndim, subset=None) -> Self:
+        """
+        Sub-classes to define. Return a sliced object.
+
+        Parameters
+        ----------
+        key : string / list of selections
+        ndim : {1, 2}
+            Requested ndim of result.
+        subset : object, default None
+            Subset to act on.
+        """
+        return self
+
+    _agg_see_also_doc = dedent(
+        """
+    See Also
+    --------
+    Series.apply : Invoke function on a Series.
+    Series.transform : Transform function producing a Series with like indexes.
+    """
+    )
+
+    _agg_examples_doc = dedent(
+        """
+    Examples
+    --------
+    >>> s = pd.Series([1, 2, 3, 4])
+    >>> s
+    0    1
+    1    2
+    2    3
+    3    4
+    dtype: int64
+
+    >>> s.agg('min')
+    1
+
+    >>> s.agg(['min', 'max'])
+    min   1
+    max   4
+    dtype: int64
+    """
+    )
+
+    @doc(
+        _shared_docs["aggregate"],
+        klass=_shared_doc_kwargs["klass"],
+        axis=_shared_doc_kwargs["axis"],
+        see_also=_agg_see_also_doc,
+        examples=_agg_examples_doc,
+    )
+    def aggregate(self, func=None, axis: Axis = 0, *args, **kwargs):
+        # Validate the axis parameter
+        self._get_axis_number(axis)
+
+        # if func is None, will switch to user-provided "named aggregation" kwargs
+        if func is None:
+            func = dict(kwargs.items())
+
+        op = SeriesApply(self, func, args=args, kwargs=kwargs)
+        result = op.agg()
+        return result
+
+    agg = aggregate
+
+    @doc(
+        _shared_docs["transform"],
+        klass=_shared_doc_kwargs["klass"],
+        axis=_shared_doc_kwargs["axis"],
+    )
+    def transform(
+        self, func: AggFuncType, axis: Axis = 0, *args, **kwargs
+    ) -> DataFrame | Series:
+        # Validate axis argument
+        self._get_axis_number(axis)
+        ser = (
+            self.copy(deep=False)
+            if using_copy_on_write() or warn_copy_on_write()
+            else self
+        )
+        result = SeriesApply(ser, func=func, args=args, kwargs=kwargs).transform()
+        return result
+
+    def apply(
+        self,
+        func: AggFuncType,
+        convert_dtype: bool | lib.NoDefault = lib.no_default,
+        args: tuple[Any, ...] = (),
+        *,
+        by_row: Literal[False, "compat"] = "compat",
+        **kwargs,
+    ) -> DataFrame | Series:
+        """
+        Invoke function on values of Series.
+
+        Can be ufunc (a NumPy function that applies to the entire Series)
+        or a Python function that only works on single values.
+
+        Parameters
+        ----------
+        func : function
+            Python function or NumPy ufunc to apply.
+        convert_dtype : bool, default True
+            Try to find better dtype for elementwise function results. If
+            False, leave as dtype=object. Note that the dtype is always
+            preserved for some extension array dtypes, such as Categorical.
+
+            .. deprecated:: 2.1.0
+                ``convert_dtype`` has been deprecated. Do ``ser.astype(object).apply()``
+                instead if you want ``convert_dtype=False``.
+        args : tuple
+            Positional arguments passed to func after the series value.
+        by_row : False or "compat", default "compat"
+            If ``"compat"`` and func is a callable, func will be passed each element of
+            the Series, like ``Series.map``. If func is a list or dict of
+            callables, will first try to translate each func into pandas methods. If
+            that doesn't work, will try call to apply again with ``by_row="compat"``
+            and if that fails, will call apply again with ``by_row=False``
+            (backward compatible).
+            If False, the func will be passed the whole Series at once.
+
+            ``by_row`` has no effect when ``func`` is a string.
+
+            .. versionadded:: 2.1.0
+        **kwargs
+            Additional keyword arguments passed to func.
+
+        Returns
+        -------
+        Series or DataFrame
+            If func returns a Series object the result will be a DataFrame.
+
+        See Also
+        --------
+        Series.map: For element-wise operations.
+        Series.agg: Only perform aggregating type operations.
+        Series.transform: Only perform transforming type operations.
+
+        Notes
+        -----
+        Functions that mutate the passed object can produce unexpected
+        behavior or errors and are not supported. See :ref:`gotchas.udf-mutation`
+        for more details.
+
+        Examples
+        --------
+        Create a series with typical summer temperatures for each city.
+
+        >>> s = pd.Series([20, 21, 12],
+        ...               index=['London', 'New York', 'Helsinki'])
+        >>> s
+        London      20
+        New York    21
+        Helsinki    12
+        dtype: int64
+
+        Square the values by defining a function and passing it as an
+        argument to ``apply()``.
+
+        >>> def square(x):
+        ...     return x ** 2
+        >>> s.apply(square)
+        London      400
+        New York    441
+        Helsinki    144
+        dtype: int64
+
+        Square the values by passing an anonymous function as an
+        argument to ``apply()``.
+
+        >>> s.apply(lambda x: x ** 2)
+        London      400
+        New York    441
+        Helsinki    144
+        dtype: int64
+
+        Define a custom function that needs additional positional
+        arguments and pass these additional arguments using the
+        ``args`` keyword.
+
+        >>> def subtract_custom_value(x, custom_value):
+        ...     return x - custom_value
+
+        >>> s.apply(subtract_custom_value, args=(5,))
+        London      15
+        New York    16
+        Helsinki     7
+        dtype: int64
+
+        Define a custom function that takes keyword arguments
+        and pass these arguments to ``apply``.
+
+        >>> def add_custom_values(x, **kwargs):
+        ...     for month in kwargs:
+        ...         x += kwargs[month]
+        ...     return x
+
+        >>> s.apply(add_custom_values, june=30, july=20, august=25)
+        London      95
+        New York    96
+        Helsinki    87
+        dtype: int64
+
+        Use a function from the Numpy library.
+
+        >>> s.apply(np.log)
+        London      2.995732
+        New York    3.044522
+        Helsinki    2.484907
+        dtype: float64
+        """
+        return SeriesApply(
+            self,
+            func,
+            convert_dtype=convert_dtype,
+            by_row=by_row,
+            args=args,
+            kwargs=kwargs,
+        ).apply()
+
+    def _reindex_indexer(
+        self,
+        new_index: Index | None,
+        indexer: npt.NDArray[np.intp] | None,
+        copy: bool | None,
+    ) -> Series:
+        # Note: new_index is None iff indexer is None
+        # if not None, indexer is np.intp
+        if indexer is None and (
+            new_index is None or new_index.names == self.index.names
+        ):
+            if using_copy_on_write():
+                return self.copy(deep=copy)
+            if copy or copy is None:
+                return self.copy(deep=copy)
+            return self
+
+        new_values = algorithms.take_nd(
+            self._values, indexer, allow_fill=True, fill_value=None
+        )
+        return self._constructor(new_values, index=new_index, copy=False)
+
+    def _needs_reindex_multi(self, axes, method, level) -> bool:
+        """
+        Check if we do need a multi reindex; this is for compat with
+        higher dims.
+        """
+        return False
+
+    @overload
+    def rename(
+        self,
+        index: Renamer | Hashable | None = ...,
+        *,
+        axis: Axis | None = ...,
+        copy: bool = ...,
+        inplace: Literal[True],
+        level: Level | None = ...,
+        errors: IgnoreRaise = ...,
+    ) -> None:
+        ...
+
+    @overload
+    def rename(
+        self,
+        index: Renamer | Hashable | None = ...,
+        *,
+        axis: Axis | None = ...,
+        copy: bool = ...,
+        inplace: Literal[False] = ...,
+        level: Level | None = ...,
+        errors: IgnoreRaise = ...,
+    ) -> Series:
+        ...
+
+    @overload
+    def rename(
+        self,
+        index: Renamer | Hashable | None = ...,
+        *,
+        axis: Axis | None = ...,
+        copy: bool = ...,
+        inplace: bool = ...,
+        level: Level | None = ...,
+        errors: IgnoreRaise = ...,
+    ) -> Series | None:
+        ...
+
+    def rename(
+        self,
+        index: Renamer | Hashable | None = None,
+        *,
+        axis: Axis | None = None,
+        copy: bool | None = None,
+        inplace: bool = False,
+        level: Level | None = None,
+        errors: IgnoreRaise = "ignore",
+    ) -> Series | None:
+        """
+        Alter Series index labels or name.
+
+        Function / dict values must be unique (1-to-1). Labels not contained in
+        a dict / Series will be left as-is. Extra labels listed don't throw an
+        error.
+
+        Alternatively, change ``Series.name`` with a scalar value.
+
+        See the :ref:`user guide ` for more.
+
+        Parameters
+        ----------
+        index : scalar, hashable sequence, dict-like or function optional
+            Functions or dict-like are transformations to apply to
+            the index.
+            Scalar or hashable sequence-like will alter the ``Series.name``
+            attribute.
+        axis : {0 or 'index'}
+            Unused. Parameter needed for compatibility with DataFrame.
+        copy : bool, default True
+            Also copy underlying data.
+
+            .. note::
+                The `copy` keyword will change behavior in pandas 3.0.
+                `Copy-on-Write
+                `__
+                will be enabled by default, which means that all methods with a
+                `copy` keyword will use a lazy copy mechanism to defer the copy and
+                ignore the `copy` keyword. The `copy` keyword will be removed in a
+                future version of pandas.
+
+                You can already get the future behavior and improvements through
+                enabling copy on write ``pd.options.mode.copy_on_write = True``
+        inplace : bool, default False
+            Whether to return a new Series. If True the value of copy is ignored.
+        level : int or level name, default None
+            In case of MultiIndex, only rename labels in the specified level.
+        errors : {'ignore', 'raise'}, default 'ignore'
+            If 'raise', raise `KeyError` when a `dict-like mapper` or
+            `index` contains labels that are not present in the index being transformed.
+            If 'ignore', existing keys will be renamed and extra keys will be ignored.
+
+        Returns
+        -------
+        Series or None
+            Series with index labels or name altered or None if ``inplace=True``.
+
+        See Also
+        --------
+        DataFrame.rename : Corresponding DataFrame method.
+        Series.rename_axis : Set the name of the axis.
+
+        Examples
+        --------
+        >>> s = pd.Series([1, 2, 3])
+        >>> s
+        0    1
+        1    2
+        2    3
+        dtype: int64
+        >>> s.rename("my_name")  # scalar, changes Series.name
+        0    1
+        1    2
+        2    3
+        Name: my_name, dtype: int64
+        >>> s.rename(lambda x: x ** 2)  # function, changes labels
+        0    1
+        1    2
+        4    3
+        dtype: int64
+        >>> s.rename({1: 3, 2: 5})  # mapping, changes labels
+        0    1
+        3    2
+        5    3
+        dtype: int64
+        """
+        if axis is not None:
+            # Make sure we raise if an invalid 'axis' is passed.
+            axis = self._get_axis_number(axis)
+
+        if callable(index) or is_dict_like(index):
+            # error: Argument 1 to "_rename" of "NDFrame" has incompatible
+            # type "Union[Union[Mapping[Any, Hashable], Callable[[Any],
+            # Hashable]], Hashable, None]"; expected "Union[Mapping[Any,
+            # Hashable], Callable[[Any], Hashable], None]"
+            return super()._rename(
+                index,  # type: ignore[arg-type]
+                copy=copy,
+                inplace=inplace,
+                level=level,
+                errors=errors,
+            )
+        else:
+            return self._set_name(index, inplace=inplace, deep=copy)
+
+    @Appender(
+        """
+        Examples
+        --------
+        >>> s = pd.Series([1, 2, 3])
+        >>> s
+        0    1
+        1    2
+        2    3
+        dtype: int64
+
+        >>> s.set_axis(['a', 'b', 'c'], axis=0)
+        a    1
+        b    2
+        c    3
+        dtype: int64
+    """
+    )
+    @Substitution(
+        klass=_shared_doc_kwargs["klass"],
+        axes_single_arg=_shared_doc_kwargs["axes_single_arg"],
+        extended_summary_sub="",
+        axis_description_sub="",
+        see_also_sub="",
+    )
+    @Appender(NDFrame.set_axis.__doc__)
+    def set_axis(
+        self,
+        labels,
+        *,
+        axis: Axis = 0,
+        copy: bool | None = None,
+    ) -> Series:
+        return super().set_axis(labels, axis=axis, copy=copy)
+
+    # error: Cannot determine type of 'reindex'
+    @doc(
+        NDFrame.reindex,  # type: ignore[has-type]
+        klass=_shared_doc_kwargs["klass"],
+        optional_reindex=_shared_doc_kwargs["optional_reindex"],
+    )
+    def reindex(  # type: ignore[override]
+        self,
+        index=None,
+        *,
+        axis: Axis | None = None,
+        method: ReindexMethod | None = None,
+        copy: bool | None = None,
+        level: Level | None = None,
+        fill_value: Scalar | None = None,
+        limit: int | None = None,
+        tolerance=None,
+    ) -> Series:
+        return super().reindex(
+            index=index,
+            method=method,
+            copy=copy,
+            level=level,
+            fill_value=fill_value,
+            limit=limit,
+            tolerance=tolerance,
+        )
+
+    @overload  # type: ignore[override]
+    def rename_axis(
+        self,
+        mapper: IndexLabel | lib.NoDefault = ...,
+        *,
+        index=...,
+        axis: Axis = ...,
+        copy: bool = ...,
+        inplace: Literal[True],
+    ) -> None:
+        ...
+
+    @overload
+    def rename_axis(
+        self,
+        mapper: IndexLabel | lib.NoDefault = ...,
+        *,
+        index=...,
+        axis: Axis = ...,
+        copy: bool = ...,
+        inplace: Literal[False] = ...,
+    ) -> Self:
+        ...
+
+    @overload
+    def rename_axis(
+        self,
+        mapper: IndexLabel | lib.NoDefault = ...,
+        *,
+        index=...,
+        axis: Axis = ...,
+        copy: bool = ...,
+        inplace: bool = ...,
+    ) -> Self | None:
+        ...
+
+    @doc(NDFrame.rename_axis)
+    def rename_axis(
+        self,
+        mapper: IndexLabel | lib.NoDefault = lib.no_default,
+        *,
+        index=lib.no_default,
+        axis: Axis = 0,
+        copy: bool = True,
+        inplace: bool = False,
+    ) -> Self | None:
+        return super().rename_axis(
+            mapper=mapper,
+            index=index,
+            axis=axis,
+            copy=copy,
+            inplace=inplace,
+        )
+
+    @overload
+    def drop(
+        self,
+        labels: IndexLabel = ...,
+        *,
+        axis: Axis = ...,
+        index: IndexLabel = ...,
+        columns: IndexLabel = ...,
+        level: Level | None = ...,
+        inplace: Literal[True],
+        errors: IgnoreRaise = ...,
+    ) -> None:
+        ...
+
+    @overload
+    def drop(
+        self,
+        labels: IndexLabel = ...,
+        *,
+        axis: Axis = ...,
+        index: IndexLabel = ...,
+        columns: IndexLabel = ...,
+        level: Level | None = ...,
+        inplace: Literal[False] = ...,
+        errors: IgnoreRaise = ...,
+    ) -> Series:
+        ...
+
+    @overload
+    def drop(
+        self,
+        labels: IndexLabel = ...,
+        *,
+        axis: Axis = ...,
+        index: IndexLabel = ...,
+        columns: IndexLabel = ...,
+        level: Level | None = ...,
+        inplace: bool = ...,
+        errors: IgnoreRaise = ...,
+    ) -> Series | None:
+        ...
+
+    def drop(
+        self,
+        labels: IndexLabel | None = None,
+        *,
+        axis: Axis = 0,
+        index: IndexLabel | None = None,
+        columns: IndexLabel | None = None,
+        level: Level | None = None,
+        inplace: bool = False,
+        errors: IgnoreRaise = "raise",
+    ) -> Series | None:
+        """
+        Return Series with specified index labels removed.
+
+        Remove elements of a Series based on specifying the index labels.
+        When using a multi-index, labels on different levels can be removed
+        by specifying the level.
+
+        Parameters
+        ----------
+        labels : single label or list-like
+            Index labels to drop.
+        axis : {0 or 'index'}
+            Unused. Parameter needed for compatibility with DataFrame.
+        index : single label or list-like
+            Redundant for application on Series, but 'index' can be used instead
+            of 'labels'.
+        columns : single label or list-like
+            No change is made to the Series; use 'index' or 'labels' instead.
+        level : int or level name, optional
+            For MultiIndex, level for which the labels will be removed.
+        inplace : bool, default False
+            If True, do operation inplace and return None.
+        errors : {'ignore', 'raise'}, default 'raise'
+            If 'ignore', suppress error and only existing labels are dropped.
+
+        Returns
+        -------
+        Series or None
+            Series with specified index labels removed or None if ``inplace=True``.
+
+        Raises
+        ------
+        KeyError
+            If none of the labels are found in the index.
+
+        See Also
+        --------
+        Series.reindex : Return only specified index labels of Series.
+        Series.dropna : Return series without null values.
+        Series.drop_duplicates : Return Series with duplicate values removed.
+        DataFrame.drop : Drop specified labels from rows or columns.
+
+        Examples
+        --------
+        >>> s = pd.Series(data=np.arange(3), index=['A', 'B', 'C'])
+        >>> s
+        A  0
+        B  1
+        C  2
+        dtype: int64
+
+        Drop labels B en C
+
+        >>> s.drop(labels=['B', 'C'])
+        A  0
+        dtype: int64
+
+        Drop 2nd level label in MultiIndex Series
+
+        >>> midx = pd.MultiIndex(levels=[['llama', 'cow', 'falcon'],
+        ...                              ['speed', 'weight', 'length']],
+        ...                      codes=[[0, 0, 0, 1, 1, 1, 2, 2, 2],
+        ...                             [0, 1, 2, 0, 1, 2, 0, 1, 2]])
+        >>> s = pd.Series([45, 200, 1.2, 30, 250, 1.5, 320, 1, 0.3],
+        ...               index=midx)
+        >>> s
+        llama   speed      45.0
+                weight    200.0
+                length      1.2
+        cow     speed      30.0
+                weight    250.0
+                length      1.5
+        falcon  speed     320.0
+                weight      1.0
+                length      0.3
+        dtype: float64
+
+        >>> s.drop(labels='weight', level=1)
+        llama   speed      45.0
+                length      1.2
+        cow     speed      30.0
+                length      1.5
+        falcon  speed     320.0
+                length      0.3
+        dtype: float64
+        """
+        return super().drop(
+            labels=labels,
+            axis=axis,
+            index=index,
+            columns=columns,
+            level=level,
+            inplace=inplace,
+            errors=errors,
+        )
+
+    def pop(self, item: Hashable) -> Any:
+        """
+        Return item and drops from series. Raise KeyError if not found.
+
+        Parameters
+        ----------
+        item : label
+            Index of the element that needs to be removed.
+
+        Returns
+        -------
+        Value that is popped from series.
+
+        Examples
+        --------
+        >>> ser = pd.Series([1, 2, 3])
+
+        >>> ser.pop(0)
+        1
+
+        >>> ser
+        1    2
+        2    3
+        dtype: int64
+        """
+        return super().pop(item=item)
+
+    @doc(INFO_DOCSTRING, **series_sub_kwargs)
+    def info(
+        self,
+        verbose: bool | None = None,
+        buf: IO[str] | None = None,
+        max_cols: int | None = None,
+        memory_usage: bool | str | None = None,
+        show_counts: bool = True,
+    ) -> None:
+        return SeriesInfo(self, memory_usage).render(
+            buf=buf,
+            max_cols=max_cols,
+            verbose=verbose,
+            show_counts=show_counts,
+        )
+
+    # TODO(3.0): this can be removed once GH#33302 deprecation is enforced
+    def _replace_single(self, to_replace, method: str, inplace: bool, limit):
+        """
+        Replaces values in a Series using the fill method specified when no
+        replacement value is given in the replace method
+        """
+
+        result = self if inplace else self.copy()
+
+        values = result._values
+        mask = missing.mask_missing(values, to_replace)
+
+        if isinstance(values, ExtensionArray):
+            # dispatch to the EA's _pad_mask_inplace method
+            values._fill_mask_inplace(method, limit, mask)
+        else:
+            fill_f = missing.get_fill_func(method)
+            fill_f(values, limit=limit, mask=mask)
+
+        if inplace:
+            return
+        return result
+
+    def memory_usage(self, index: bool = True, deep: bool = False) -> int:
+        """
+        Return the memory usage of the Series.
+
+        The memory usage can optionally include the contribution of
+        the index and of elements of `object` dtype.
+
+        Parameters
+        ----------
+        index : bool, default True
+            Specifies whether to include the memory usage of the Series index.
+        deep : bool, default False
+            If True, introspect the data deeply by interrogating
+            `object` dtypes for system-level memory consumption, and include
+            it in the returned value.
+
+        Returns
+        -------
+        int
+            Bytes of memory consumed.
+
+        See Also
+        --------
+        numpy.ndarray.nbytes : Total bytes consumed by the elements of the
+            array.
+        DataFrame.memory_usage : Bytes consumed by a DataFrame.
+
+        Examples
+        --------
+        >>> s = pd.Series(range(3))
+        >>> s.memory_usage()
+        152
+
+        Not including the index gives the size of the rest of the data, which
+        is necessarily smaller:
+
+        >>> s.memory_usage(index=False)
+        24
+
+        The memory footprint of `object` values is ignored by default:
+
+        >>> s = pd.Series(["a", "b"])
+        >>> s.values
+        array(['a', 'b'], dtype=object)
+        >>> s.memory_usage()
+        144
+        >>> s.memory_usage(deep=True)
+        244
+        """
+        v = self._memory_usage(deep=deep)
+        if index:
+            v += self.index.memory_usage(deep=deep)
+        return v
+
+    def isin(self, values) -> Series:
+        """
+        Whether elements in Series are contained in `values`.
+
+        Return a boolean Series showing whether each element in the Series
+        matches an element in the passed sequence of `values` exactly.
+
+        Parameters
+        ----------
+        values : set or list-like
+            The sequence of values to test. Passing in a single string will
+            raise a ``TypeError``. Instead, turn a single string into a
+            list of one element.
+
+        Returns
+        -------
+        Series
+            Series of booleans indicating if each element is in values.
+
+        Raises
+        ------
+        TypeError
+          * If `values` is a string
+
+        See Also
+        --------
+        DataFrame.isin : Equivalent method on DataFrame.
+
+        Examples
+        --------
+        >>> s = pd.Series(['llama', 'cow', 'llama', 'beetle', 'llama',
+        ...                'hippo'], name='animal')
+        >>> s.isin(['cow', 'llama'])
+        0     True
+        1     True
+        2     True
+        3    False
+        4     True
+        5    False
+        Name: animal, dtype: bool
+
+        To invert the boolean values, use the ``~`` operator:
+
+        >>> ~s.isin(['cow', 'llama'])
+        0    False
+        1    False
+        2    False
+        3     True
+        4    False
+        5     True
+        Name: animal, dtype: bool
+
+        Passing a single string as ``s.isin('llama')`` will raise an error. Use
+        a list of one element instead:
+
+        >>> s.isin(['llama'])
+        0     True
+        1    False
+        2     True
+        3    False
+        4     True
+        5    False
+        Name: animal, dtype: bool
+
+        Strings and integers are distinct and are therefore not comparable:
+
+        >>> pd.Series([1]).isin(['1'])
+        0    False
+        dtype: bool
+        >>> pd.Series([1.1]).isin(['1.1'])
+        0    False
+        dtype: bool
+        """
+        result = algorithms.isin(self._values, values)
+        return self._constructor(result, index=self.index, copy=False).__finalize__(
+            self, method="isin"
+        )
+
+    def between(
+        self,
+        left,
+        right,
+        inclusive: Literal["both", "neither", "left", "right"] = "both",
+    ) -> Series:
+        """
+        Return boolean Series equivalent to left <= series <= right.
+
+        This function returns a boolean vector containing `True` wherever the
+        corresponding Series element is between the boundary values `left` and
+        `right`. NA values are treated as `False`.
+
+        Parameters
+        ----------
+        left : scalar or list-like
+            Left boundary.
+        right : scalar or list-like
+            Right boundary.
+        inclusive : {"both", "neither", "left", "right"}
+            Include boundaries. Whether to set each bound as closed or open.
+
+            .. versionchanged:: 1.3.0
+
+        Returns
+        -------
+        Series
+            Series representing whether each element is between left and
+            right (inclusive).
+
+        See Also
+        --------
+        Series.gt : Greater than of series and other.
+        Series.lt : Less than of series and other.
+
+        Notes
+        -----
+        This function is equivalent to ``(left <= ser) & (ser <= right)``
+
+        Examples
+        --------
+        >>> s = pd.Series([2, 0, 4, 8, np.nan])
+
+        Boundary values are included by default:
+
+        >>> s.between(1, 4)
+        0     True
+        1    False
+        2     True
+        3    False
+        4    False
+        dtype: bool
+
+        With `inclusive` set to ``"neither"`` boundary values are excluded:
+
+        >>> s.between(1, 4, inclusive="neither")
+        0     True
+        1    False
+        2    False
+        3    False
+        4    False
+        dtype: bool
+
+        `left` and `right` can be any scalar value:
+
+        >>> s = pd.Series(['Alice', 'Bob', 'Carol', 'Eve'])
+        >>> s.between('Anna', 'Daniel')
+        0    False
+        1     True
+        2     True
+        3    False
+        dtype: bool
+        """
+        if inclusive == "both":
+            lmask = self >= left
+            rmask = self <= right
+        elif inclusive == "left":
+            lmask = self >= left
+            rmask = self < right
+        elif inclusive == "right":
+            lmask = self > left
+            rmask = self <= right
+        elif inclusive == "neither":
+            lmask = self > left
+            rmask = self < right
+        else:
+            raise ValueError(
+                "Inclusive has to be either string of 'both',"
+                "'left', 'right', or 'neither'."
+            )
+
+        return lmask & rmask
+
+    def case_when(
+        self,
+        caselist: list[
+            tuple[
+                ArrayLike | Callable[[Series], Series | np.ndarray | Sequence[bool]],
+                ArrayLike | Scalar | Callable[[Series], Series | np.ndarray],
+            ],
+        ],
+    ) -> Series:
+        """
+        Replace values where the conditions are True.
+
+        Parameters
+        ----------
+        caselist : A list of tuples of conditions and expected replacements
+            Takes the form:  ``(condition0, replacement0)``,
+            ``(condition1, replacement1)``, ... .
+            ``condition`` should be a 1-D boolean array-like object
+            or a callable. If ``condition`` is a callable,
+            it is computed on the Series
+            and should return a boolean Series or array.
+            The callable must not change the input Series
+            (though pandas doesn`t check it). ``replacement`` should be a
+            1-D array-like object, a scalar or a callable.
+            If ``replacement`` is a callable, it is computed on the Series
+            and should return a scalar or Series. The callable
+            must not change the input Series
+            (though pandas doesn`t check it).
+
+            .. versionadded:: 2.2.0
+
+        Returns
+        -------
+        Series
+
+        See Also
+        --------
+        Series.mask : Replace values where the condition is True.
+
+        Examples
+        --------
+        >>> c = pd.Series([6, 7, 8, 9], name='c')
+        >>> a = pd.Series([0, 0, 1, 2])
+        >>> b = pd.Series([0, 3, 4, 5])
+
+        >>> c.case_when(caselist=[(a.gt(0), a),  # condition, replacement
+        ...                       (b.gt(0), b)])
+        0    6
+        1    3
+        2    1
+        3    2
+        Name: c, dtype: int64
+        """
+        if not isinstance(caselist, list):
+            raise TypeError(
+                f"The caselist argument should be a list; instead got {type(caselist)}"
+            )
+
+        if not caselist:
+            raise ValueError(
+                "provide at least one boolean condition, "
+                "with a corresponding replacement."
+            )
+
+        for num, entry in enumerate(caselist):
+            if not isinstance(entry, tuple):
+                raise TypeError(
+                    f"Argument {num} must be a tuple; instead got {type(entry)}."
+                )
+            if len(entry) != 2:
+                raise ValueError(
+                    f"Argument {num} must have length 2; "
+                    "a condition and replacement; "
+                    f"instead got length {len(entry)}."
+                )
+        caselist = [
+            (
+                com.apply_if_callable(condition, self),
+                com.apply_if_callable(replacement, self),
+            )
+            for condition, replacement in caselist
+        ]
+        default = self.copy()
+        conditions, replacements = zip(*caselist)
+        common_dtypes = [infer_dtype_from(arg)[0] for arg in [*replacements, default]]
+        if len(set(common_dtypes)) > 1:
+            common_dtype = find_common_type(common_dtypes)
+            updated_replacements = []
+            for condition, replacement in zip(conditions, replacements):
+                if is_scalar(replacement):
+                    replacement = construct_1d_arraylike_from_scalar(
+                        value=replacement, length=len(condition), dtype=common_dtype
+                    )
+                elif isinstance(replacement, ABCSeries):
+                    replacement = replacement.astype(common_dtype)
+                else:
+                    replacement = pd_array(replacement, dtype=common_dtype)
+                updated_replacements.append(replacement)
+            replacements = updated_replacements
+            default = default.astype(common_dtype)
+
+        counter = reversed(range(len(conditions)))
+        for position, condition, replacement in zip(
+            counter, conditions[::-1], replacements[::-1]
+        ):
+            try:
+                default = default.mask(
+                    condition, other=replacement, axis=0, inplace=False, level=None
+                )
+            except Exception as error:
+                raise ValueError(
+                    f"Failed to apply condition{position} and replacement{position}."
+                ) from error
+        return default
+
+    # error: Cannot determine type of 'isna'
+    @doc(NDFrame.isna, klass=_shared_doc_kwargs["klass"])  # type: ignore[has-type]
+    def isna(self) -> Series:
+        return NDFrame.isna(self)
+
+    # error: Cannot determine type of 'isna'
+    @doc(NDFrame.isna, klass=_shared_doc_kwargs["klass"])  # type: ignore[has-type]
+    def isnull(self) -> Series:
+        """
+        Series.isnull is an alias for Series.isna.
+        """
+        return super().isnull()
+
+    # error: Cannot determine type of 'notna'
+    @doc(NDFrame.notna, klass=_shared_doc_kwargs["klass"])  # type: ignore[has-type]
+    def notna(self) -> Series:
+        return super().notna()
+
+    # error: Cannot determine type of 'notna'
+    @doc(NDFrame.notna, klass=_shared_doc_kwargs["klass"])  # type: ignore[has-type]
+    def notnull(self) -> Series:
+        """
+        Series.notnull is an alias for Series.notna.
+        """
+        return super().notnull()
+
+    @overload
+    def dropna(
+        self,
+        *,
+        axis: Axis = ...,
+        inplace: Literal[False] = ...,
+        how: AnyAll | None = ...,
+        ignore_index: bool = ...,
+    ) -> Series:
+        ...
+
+    @overload
+    def dropna(
+        self,
+        *,
+        axis: Axis = ...,
+        inplace: Literal[True],
+        how: AnyAll | None = ...,
+        ignore_index: bool = ...,
+    ) -> None:
+        ...
+
+    def dropna(
+        self,
+        *,
+        axis: Axis = 0,
+        inplace: bool = False,
+        how: AnyAll | None = None,
+        ignore_index: bool = False,
+    ) -> Series | None:
+        """
+        Return a new Series with missing values removed.
+
+        See the :ref:`User Guide ` for more on which values are
+        considered missing, and how to work with missing data.
+
+        Parameters
+        ----------
+        axis : {0 or 'index'}
+            Unused. Parameter needed for compatibility with DataFrame.
+        inplace : bool, default False
+            If True, do operation inplace and return None.
+        how : str, optional
+            Not in use. Kept for compatibility.
+        ignore_index : bool, default ``False``
+            If ``True``, the resulting axis will be labeled 0, 1, …, n - 1.
+
+            .. versionadded:: 2.0.0
+
+        Returns
+        -------
+        Series or None
+            Series with NA entries dropped from it or None if ``inplace=True``.
+
+        See Also
+        --------
+        Series.isna: Indicate missing values.
+        Series.notna : Indicate existing (non-missing) values.
+        Series.fillna : Replace missing values.
+        DataFrame.dropna : Drop rows or columns which contain NA values.
+        Index.dropna : Drop missing indices.
+
+        Examples
+        --------
+        >>> ser = pd.Series([1., 2., np.nan])
+        >>> ser
+        0    1.0
+        1    2.0
+        2    NaN
+        dtype: float64
+
+        Drop NA values from a Series.
+
+        >>> ser.dropna()
+        0    1.0
+        1    2.0
+        dtype: float64
+
+        Empty strings are not considered NA values. ``None`` is considered an
+        NA value.
+
+        >>> ser = pd.Series([np.nan, 2, pd.NaT, '', None, 'I stay'])
+        >>> ser
+        0       NaN
+        1         2
+        2       NaT
+        3
+        4      None
+        5    I stay
+        dtype: object
+        >>> ser.dropna()
+        1         2
+        3
+        5    I stay
+        dtype: object
+        """
+        inplace = validate_bool_kwarg(inplace, "inplace")
+        ignore_index = validate_bool_kwarg(ignore_index, "ignore_index")
+        # Validate the axis parameter
+        self._get_axis_number(axis or 0)
+
+        if self._can_hold_na:
+            result = remove_na_arraylike(self)
+        else:
+            if not inplace:
+                result = self.copy(deep=None)
+            else:
+                result = self
+
+        if ignore_index:
+            result.index = default_index(len(result))
+
+        if inplace:
+            return self._update_inplace(result)
+        else:
+            return result
+
+    # ----------------------------------------------------------------------
+    # Time series-oriented methods
+
+    def to_timestamp(
+        self,
+        freq: Frequency | None = None,
+        how: Literal["s", "e", "start", "end"] = "start",
+        copy: bool | None = None,
+    ) -> Series:
+        """
+        Cast to DatetimeIndex of Timestamps, at *beginning* of period.
+
+        Parameters
+        ----------
+        freq : str, default frequency of PeriodIndex
+            Desired frequency.
+        how : {'s', 'e', 'start', 'end'}
+            Convention for converting period to timestamp; start of period
+            vs. end.
+        copy : bool, default True
+            Whether or not to return a copy.
+
+            .. note::
+                The `copy` keyword will change behavior in pandas 3.0.
+                `Copy-on-Write
+                `__
+                will be enabled by default, which means that all methods with a
+                `copy` keyword will use a lazy copy mechanism to defer the copy and
+                ignore the `copy` keyword. The `copy` keyword will be removed in a
+                future version of pandas.
+
+                You can already get the future behavior and improvements through
+                enabling copy on write ``pd.options.mode.copy_on_write = True``
+
+        Returns
+        -------
+        Series with DatetimeIndex
+
+        Examples
+        --------
+        >>> idx = pd.PeriodIndex(['2023', '2024', '2025'], freq='Y')
+        >>> s1 = pd.Series([1, 2, 3], index=idx)
+        >>> s1
+        2023    1
+        2024    2
+        2025    3
+        Freq: Y-DEC, dtype: int64
+
+        The resulting frequency of the Timestamps is `YearBegin`
+
+        >>> s1 = s1.to_timestamp()
+        >>> s1
+        2023-01-01    1
+        2024-01-01    2
+        2025-01-01    3
+        Freq: YS-JAN, dtype: int64
+
+        Using `freq` which is the offset that the Timestamps will have
+
+        >>> s2 = pd.Series([1, 2, 3], index=idx)
+        >>> s2 = s2.to_timestamp(freq='M')
+        >>> s2
+        2023-01-31    1
+        2024-01-31    2
+        2025-01-31    3
+        Freq: YE-JAN, dtype: int64
+        """
+        if not isinstance(self.index, PeriodIndex):
+            raise TypeError(f"unsupported Type {type(self.index).__name__}")
+
+        new_obj = self.copy(deep=copy and not using_copy_on_write())
+        new_index = self.index.to_timestamp(freq=freq, how=how)
+        setattr(new_obj, "index", new_index)
+        return new_obj
+
+    def to_period(self, freq: str | None = None, copy: bool | None = None) -> Series:
+        """
+        Convert Series from DatetimeIndex to PeriodIndex.
+
+        Parameters
+        ----------
+        freq : str, default None
+            Frequency associated with the PeriodIndex.
+        copy : bool, default True
+            Whether or not to return a copy.
+
+            .. note::
+                The `copy` keyword will change behavior in pandas 3.0.
+                `Copy-on-Write
+                `__
+                will be enabled by default, which means that all methods with a
+                `copy` keyword will use a lazy copy mechanism to defer the copy and
+                ignore the `copy` keyword. The `copy` keyword will be removed in a
+                future version of pandas.
+
+                You can already get the future behavior and improvements through
+                enabling copy on write ``pd.options.mode.copy_on_write = True``
+
+        Returns
+        -------
+        Series
+            Series with index converted to PeriodIndex.
+
+        Examples
+        --------
+        >>> idx = pd.DatetimeIndex(['2023', '2024', '2025'])
+        >>> s = pd.Series([1, 2, 3], index=idx)
+        >>> s = s.to_period()
+        >>> s
+        2023    1
+        2024    2
+        2025    3
+        Freq: Y-DEC, dtype: int64
+
+        Viewing the index
+
+        >>> s.index
+        PeriodIndex(['2023', '2024', '2025'], dtype='period[Y-DEC]')
+        """
+        if not isinstance(self.index, DatetimeIndex):
+            raise TypeError(f"unsupported Type {type(self.index).__name__}")
+
+        new_obj = self.copy(deep=copy and not using_copy_on_write())
+        new_index = self.index.to_period(freq=freq)
+        setattr(new_obj, "index", new_index)
+        return new_obj
+
+    # ----------------------------------------------------------------------
+    # Add index
+    _AXIS_ORDERS: list[Literal["index", "columns"]] = ["index"]
+    _AXIS_LEN = len(_AXIS_ORDERS)
+    _info_axis_number: Literal[0] = 0
+    _info_axis_name: Literal["index"] = "index"
+
+    index = properties.AxisProperty(
+        axis=0,
+        doc="""
+        The index (axis labels) of the Series.
+
+        The index of a Series is used to label and identify each element of the
+        underlying data. The index can be thought of as an immutable ordered set
+        (technically a multi-set, as it may contain duplicate labels), and is
+        used to index and align data in pandas.
+
+        Returns
+        -------
+        Index
+            The index labels of the Series.
+
+        See Also
+        --------
+        Series.reindex : Conform Series to new index.
+        Index : The base pandas index type.
+
+        Notes
+        -----
+        For more information on pandas indexing, see the `indexing user guide
+        `__.
+
+        Examples
+        --------
+        To create a Series with a custom index and view the index labels:
+
+        >>> cities = ['Kolkata', 'Chicago', 'Toronto', 'Lisbon']
+        >>> populations = [14.85, 2.71, 2.93, 0.51]
+        >>> city_series = pd.Series(populations, index=cities)
+        >>> city_series.index
+        Index(['Kolkata', 'Chicago', 'Toronto', 'Lisbon'], dtype='object')
+
+        To change the index labels of an existing Series:
+
+        >>> city_series.index = ['KOL', 'CHI', 'TOR', 'LIS']
+        >>> city_series.index
+        Index(['KOL', 'CHI', 'TOR', 'LIS'], dtype='object')
+        """,
+    )
+
+    # ----------------------------------------------------------------------
+    # Accessor Methods
+    # ----------------------------------------------------------------------
+    str = CachedAccessor("str", StringMethods)
+    dt = CachedAccessor("dt", CombinedDatetimelikeProperties)
+    cat = CachedAccessor("cat", CategoricalAccessor)
+    plot = CachedAccessor("plot", pandas.plotting.PlotAccessor)
+    sparse = CachedAccessor("sparse", SparseAccessor)
+    struct = CachedAccessor("struct", StructAccessor)
+    list = CachedAccessor("list", ListAccessor)
+
+    # ----------------------------------------------------------------------
+    # Add plotting methods to Series
+    hist = pandas.plotting.hist_series
+
+    # ----------------------------------------------------------------------
+    # Template-Based Arithmetic/Comparison Methods
+
+    def _cmp_method(self, other, op):
+        res_name = ops.get_op_result_name(self, other)
+
+        if isinstance(other, Series) and not self._indexed_same(other):
+            raise ValueError("Can only compare identically-labeled Series objects")
+
+        lvalues = self._values
+        rvalues = extract_array(other, extract_numpy=True, extract_range=True)
+
+        res_values = ops.comparison_op(lvalues, rvalues, op)
+
+        return self._construct_result(res_values, name=res_name)
+
+    def _logical_method(self, other, op):
+        res_name = ops.get_op_result_name(self, other)
+        self, other = self._align_for_op(other, align_asobject=True)
+
+        lvalues = self._values
+        rvalues = extract_array(other, extract_numpy=True, extract_range=True)
+
+        res_values = ops.logical_op(lvalues, rvalues, op)
+        return self._construct_result(res_values, name=res_name)
+
+    def _arith_method(self, other, op):
+        self, other = self._align_for_op(other)
+        return base.IndexOpsMixin._arith_method(self, other, op)
+
+    def _align_for_op(self, right, align_asobject: bool = False):
+        """align lhs and rhs Series"""
+        # TODO: Different from DataFrame._align_for_op, list, tuple and ndarray
+        # are not coerced here
+        # because Series has inconsistencies described in GH#13637
+        left = self
+
+        if isinstance(right, Series):
+            # avoid repeated alignment
+            if not left.index.equals(right.index):
+                if align_asobject:
+                    if left.dtype not in (object, np.bool_) or right.dtype not in (
+                        object,
+                        np.bool_,
+                    ):
+                        warnings.warn(
+                            "Operation between Series with different indexes "
+                            "that are not of numpy boolean or object dtype "
+                            "will no longer return a numpy boolean result "
+                            "in a future version. "
+                            "Cast both Series to object type "
+                            "to maintain the prior behavior.",
+                            FutureWarning,
+                            stacklevel=find_stack_level(),
+                        )
+                    # to keep original value's dtype for bool ops
+                    left = left.astype(object)
+                    right = right.astype(object)
+
+                left, right = left.align(right, copy=False)
+
+        return left, right
+
+    def _binop(self, other: Series, func, level=None, fill_value=None) -> Series:
+        """
+        Perform generic binary operation with optional fill value.
+
+        Parameters
+        ----------
+        other : Series
+        func : binary operator
+        fill_value : float or object
+            Value to substitute for NA/null values. If both Series are NA in a
+            location, the result will be NA regardless of the passed fill value.
+        level : int or level name, default None
+            Broadcast across a level, matching Index values on the
+            passed MultiIndex level.
+
+        Returns
+        -------
+        Series
+        """
+        this = self
+
+        if not self.index.equals(other.index):
+            this, other = self.align(other, level=level, join="outer", copy=False)
+
+        this_vals, other_vals = ops.fill_binop(this._values, other._values, fill_value)
+
+        with np.errstate(all="ignore"):
+            result = func(this_vals, other_vals)
+
+        name = ops.get_op_result_name(self, other)
+        out = this._construct_result(result, name)
+        return cast(Series, out)
+
+    def _construct_result(
+        self, result: ArrayLike | tuple[ArrayLike, ArrayLike], name: Hashable
+    ) -> Series | tuple[Series, Series]:
+        """
+        Construct an appropriately-labelled Series from the result of an op.
+
+        Parameters
+        ----------
+        result : ndarray or ExtensionArray
+        name : Label
+
+        Returns
+        -------
+        Series
+            In the case of __divmod__ or __rdivmod__, a 2-tuple of Series.
+        """
+        if isinstance(result, tuple):
+            # produced by divmod or rdivmod
+
+            res1 = self._construct_result(result[0], name=name)
+            res2 = self._construct_result(result[1], name=name)
+
+            # GH#33427 assertions to keep mypy happy
+            assert isinstance(res1, Series)
+            assert isinstance(res2, Series)
+            return (res1, res2)
+
+        # TODO: result should always be ArrayLike, but this fails for some
+        #  JSONArray tests
+        dtype = getattr(result, "dtype", None)
+        out = self._constructor(result, index=self.index, dtype=dtype, copy=False)
+        out = out.__finalize__(self)
+
+        # Set the result's name after __finalize__ is called because __finalize__
+        #  would set it back to self.name
+        out.name = name
+        return out
+
+    def _flex_method(self, other, op, *, level=None, fill_value=None, axis: Axis = 0):
+        if axis is not None:
+            self._get_axis_number(axis)
+
+        res_name = ops.get_op_result_name(self, other)
+
+        if isinstance(other, Series):
+            return self._binop(other, op, level=level, fill_value=fill_value)
+        elif isinstance(other, (np.ndarray, list, tuple)):
+            if len(other) != len(self):
+                raise ValueError("Lengths must be equal")
+            other = self._constructor(other, self.index, copy=False)
+            result = self._binop(other, op, level=level, fill_value=fill_value)
+            result._name = res_name
+            return result
+        else:
+            if fill_value is not None:
+                if isna(other):
+                    return op(self, fill_value)
+                self = self.fillna(fill_value)
+
+            return op(self, other)
+
+    @Appender(ops.make_flex_doc("eq", "series"))
+    def eq(
+        self,
+        other,
+        level: Level | None = None,
+        fill_value: float | None = None,
+        axis: Axis = 0,
+    ) -> Series:
+        return self._flex_method(
+            other, operator.eq, level=level, fill_value=fill_value, axis=axis
+        )
+
+    @Appender(ops.make_flex_doc("ne", "series"))
+    def ne(self, other, level=None, fill_value=None, axis: Axis = 0) -> Series:
+        return self._flex_method(
+            other, operator.ne, level=level, fill_value=fill_value, axis=axis
+        )
+
+    @Appender(ops.make_flex_doc("le", "series"))
+    def le(self, other, level=None, fill_value=None, axis: Axis = 0) -> Series:
+        return self._flex_method(
+            other, operator.le, level=level, fill_value=fill_value, axis=axis
+        )
+
+    @Appender(ops.make_flex_doc("lt", "series"))
+    def lt(self, other, level=None, fill_value=None, axis: Axis = 0) -> Series:
+        return self._flex_method(
+            other, operator.lt, level=level, fill_value=fill_value, axis=axis
+        )
+
+    @Appender(ops.make_flex_doc("ge", "series"))
+    def ge(self, other, level=None, fill_value=None, axis: Axis = 0) -> Series:
+        return self._flex_method(
+            other, operator.ge, level=level, fill_value=fill_value, axis=axis
+        )
+
+    @Appender(ops.make_flex_doc("gt", "series"))
+    def gt(self, other, level=None, fill_value=None, axis: Axis = 0) -> Series:
+        return self._flex_method(
+            other, operator.gt, level=level, fill_value=fill_value, axis=axis
+        )
+
+    @Appender(ops.make_flex_doc("add", "series"))
+    def add(self, other, level=None, fill_value=None, axis: Axis = 0) -> Series:
+        return self._flex_method(
+            other, operator.add, level=level, fill_value=fill_value, axis=axis
+        )
+
+    @Appender(ops.make_flex_doc("radd", "series"))
+    def radd(self, other, level=None, fill_value=None, axis: Axis = 0) -> Series:
+        return self._flex_method(
+            other, roperator.radd, level=level, fill_value=fill_value, axis=axis
+        )
+
+    @Appender(ops.make_flex_doc("sub", "series"))
+    def sub(self, other, level=None, fill_value=None, axis: Axis = 0) -> Series:
+        return self._flex_method(
+            other, operator.sub, level=level, fill_value=fill_value, axis=axis
+        )
+
+    subtract = sub
+
+    @Appender(ops.make_flex_doc("rsub", "series"))
+    def rsub(self, other, level=None, fill_value=None, axis: Axis = 0) -> Series:
+        return self._flex_method(
+            other, roperator.rsub, level=level, fill_value=fill_value, axis=axis
+        )
+
+    @Appender(ops.make_flex_doc("mul", "series"))
+    def mul(
+        self,
+        other,
+        level: Level | None = None,
+        fill_value: float | None = None,
+        axis: Axis = 0,
+    ) -> Series:
+        return self._flex_method(
+            other, operator.mul, level=level, fill_value=fill_value, axis=axis
+        )
+
+    multiply = mul
+
+    @Appender(ops.make_flex_doc("rmul", "series"))
+    def rmul(self, other, level=None, fill_value=None, axis: Axis = 0) -> Series:
+        return self._flex_method(
+            other, roperator.rmul, level=level, fill_value=fill_value, axis=axis
+        )
+
+    @Appender(ops.make_flex_doc("truediv", "series"))
+    def truediv(self, other, level=None, fill_value=None, axis: Axis = 0) -> Series:
+        return self._flex_method(
+            other, operator.truediv, level=level, fill_value=fill_value, axis=axis
+        )
+
+    div = truediv
+    divide = truediv
+
+    @Appender(ops.make_flex_doc("rtruediv", "series"))
+    def rtruediv(self, other, level=None, fill_value=None, axis: Axis = 0) -> Series:
+        return self._flex_method(
+            other, roperator.rtruediv, level=level, fill_value=fill_value, axis=axis
+        )
+
+    rdiv = rtruediv
+
+    @Appender(ops.make_flex_doc("floordiv", "series"))
+    def floordiv(self, other, level=None, fill_value=None, axis: Axis = 0) -> Series:
+        return self._flex_method(
+            other, operator.floordiv, level=level, fill_value=fill_value, axis=axis
+        )
+
+    @Appender(ops.make_flex_doc("rfloordiv", "series"))
+    def rfloordiv(self, other, level=None, fill_value=None, axis: Axis = 0) -> Series:
+        return self._flex_method(
+            other, roperator.rfloordiv, level=level, fill_value=fill_value, axis=axis
+        )
+
+    @Appender(ops.make_flex_doc("mod", "series"))
+    def mod(self, other, level=None, fill_value=None, axis: Axis = 0) -> Series:
+        return self._flex_method(
+            other, operator.mod, level=level, fill_value=fill_value, axis=axis
+        )
+
+    @Appender(ops.make_flex_doc("rmod", "series"))
+    def rmod(self, other, level=None, fill_value=None, axis: Axis = 0) -> Series:
+        return self._flex_method(
+            other, roperator.rmod, level=level, fill_value=fill_value, axis=axis
+        )
+
+    @Appender(ops.make_flex_doc("pow", "series"))
+    def pow(self, other, level=None, fill_value=None, axis: Axis = 0) -> Series:
+        return self._flex_method(
+            other, operator.pow, level=level, fill_value=fill_value, axis=axis
+        )
+
+    @Appender(ops.make_flex_doc("rpow", "series"))
+    def rpow(self, other, level=None, fill_value=None, axis: Axis = 0) -> Series:
+        return self._flex_method(
+            other, roperator.rpow, level=level, fill_value=fill_value, axis=axis
+        )
+
+    @Appender(ops.make_flex_doc("divmod", "series"))
+    def divmod(self, other, level=None, fill_value=None, axis: Axis = 0) -> Series:
+        return self._flex_method(
+            other, divmod, level=level, fill_value=fill_value, axis=axis
+        )
+
+    @Appender(ops.make_flex_doc("rdivmod", "series"))
+    def rdivmod(self, other, level=None, fill_value=None, axis: Axis = 0) -> Series:
+        return self._flex_method(
+            other, roperator.rdivmod, level=level, fill_value=fill_value, axis=axis
+        )
+
+    # ----------------------------------------------------------------------
+    # Reductions
+
+    def _reduce(
+        self,
+        op,
+        # error: Variable "pandas.core.series.Series.str" is not valid as a type
+        name: str,  # type: ignore[valid-type]
+        *,
+        axis: Axis = 0,
+        skipna: bool = True,
+        numeric_only: bool = False,
+        filter_type=None,
+        **kwds,
+    ):
+        """
+        Perform a reduction operation.
+
+        If we have an ndarray as a value, then simply perform the operation,
+        otherwise delegate to the object.
+        """
+        delegate = self._values
+
+        if axis is not None:
+            self._get_axis_number(axis)
+
+        if isinstance(delegate, ExtensionArray):
+            # dispatch to ExtensionArray interface
+            return delegate._reduce(name, skipna=skipna, **kwds)
+
+        else:
+            # dispatch to numpy arrays
+            if numeric_only and self.dtype.kind not in "iufcb":
+                # i.e. not is_numeric_dtype(self.dtype)
+                kwd_name = "numeric_only"
+                if name in ["any", "all"]:
+                    kwd_name = "bool_only"
+                # GH#47500 - change to TypeError to match other methods
+                raise TypeError(
+                    f"Series.{name} does not allow {kwd_name}={numeric_only} "
+                    "with non-numeric dtypes."
+                )
+            return op(delegate, skipna=skipna, **kwds)
+
+    @Appender(make_doc("any", ndim=1))
+    # error: Signature of "any" incompatible with supertype "NDFrame"
+    def any(  # type: ignore[override]
+        self,
+        *,
+        axis: Axis = 0,
+        bool_only: bool = False,
+        skipna: bool = True,
+        **kwargs,
+    ) -> bool:
+        nv.validate_logical_func((), kwargs, fname="any")
+        validate_bool_kwarg(skipna, "skipna", none_allowed=False)
+        return self._reduce(
+            nanops.nanany,
+            name="any",
+            axis=axis,
+            numeric_only=bool_only,
+            skipna=skipna,
+            filter_type="bool",
+        )
+
+    @Appender(make_doc("all", ndim=1))
+    def all(
+        self,
+        axis: Axis = 0,
+        bool_only: bool = False,
+        skipna: bool = True,
+        **kwargs,
+    ) -> bool:
+        nv.validate_logical_func((), kwargs, fname="all")
+        validate_bool_kwarg(skipna, "skipna", none_allowed=False)
+        return self._reduce(
+            nanops.nanall,
+            name="all",
+            axis=axis,
+            numeric_only=bool_only,
+            skipna=skipna,
+            filter_type="bool",
+        )
+
+    @doc(make_doc("min", ndim=1))
+    def min(
+        self,
+        axis: Axis | None = 0,
+        skipna: bool = True,
+        numeric_only: bool = False,
+        **kwargs,
+    ):
+        return NDFrame.min(self, axis, skipna, numeric_only, **kwargs)
+
+    @doc(make_doc("max", ndim=1))
+    def max(
+        self,
+        axis: Axis | None = 0,
+        skipna: bool = True,
+        numeric_only: bool = False,
+        **kwargs,
+    ):
+        return NDFrame.max(self, axis, skipna, numeric_only, **kwargs)
+
+    @doc(make_doc("sum", ndim=1))
+    def sum(
+        self,
+        axis: Axis | None = None,
+        skipna: bool = True,
+        numeric_only: bool = False,
+        min_count: int = 0,
+        **kwargs,
+    ):
+        return NDFrame.sum(self, axis, skipna, numeric_only, min_count, **kwargs)
+
+    @doc(make_doc("prod", ndim=1))
+    def prod(
+        self,
+        axis: Axis | None = None,
+        skipna: bool = True,
+        numeric_only: bool = False,
+        min_count: int = 0,
+        **kwargs,
+    ):
+        return NDFrame.prod(self, axis, skipna, numeric_only, min_count, **kwargs)
+
+    @doc(make_doc("mean", ndim=1))
+    def mean(
+        self,
+        axis: Axis | None = 0,
+        skipna: bool = True,
+        numeric_only: bool = False,
+        **kwargs,
+    ):
+        return NDFrame.mean(self, axis, skipna, numeric_only, **kwargs)
+
+    @doc(make_doc("median", ndim=1))
+    def median(
+        self,
+        axis: Axis | None = 0,
+        skipna: bool = True,
+        numeric_only: bool = False,
+        **kwargs,
+    ):
+        return NDFrame.median(self, axis, skipna, numeric_only, **kwargs)
+
+    @doc(make_doc("sem", ndim=1))
+    def sem(
+        self,
+        axis: Axis | None = None,
+        skipna: bool = True,
+        ddof: int = 1,
+        numeric_only: bool = False,
+        **kwargs,
+    ):
+        return NDFrame.sem(self, axis, skipna, ddof, numeric_only, **kwargs)
+
+    @doc(make_doc("var", ndim=1))
+    def var(
+        self,
+        axis: Axis | None = None,
+        skipna: bool = True,
+        ddof: int = 1,
+        numeric_only: bool = False,
+        **kwargs,
+    ):
+        return NDFrame.var(self, axis, skipna, ddof, numeric_only, **kwargs)
+
+    @doc(make_doc("std", ndim=1))
+    def std(
+        self,
+        axis: Axis | None = None,
+        skipna: bool = True,
+        ddof: int = 1,
+        numeric_only: bool = False,
+        **kwargs,
+    ):
+        return NDFrame.std(self, axis, skipna, ddof, numeric_only, **kwargs)
+
+    @doc(make_doc("skew", ndim=1))
+    def skew(
+        self,
+        axis: Axis | None = 0,
+        skipna: bool = True,
+        numeric_only: bool = False,
+        **kwargs,
+    ):
+        return NDFrame.skew(self, axis, skipna, numeric_only, **kwargs)
+
+    @doc(make_doc("kurt", ndim=1))
+    def kurt(
+        self,
+        axis: Axis | None = 0,
+        skipna: bool = True,
+        numeric_only: bool = False,
+        **kwargs,
+    ):
+        return NDFrame.kurt(self, axis, skipna, numeric_only, **kwargs)
+
+    kurtosis = kurt
+    product = prod
+
+    @doc(make_doc("cummin", ndim=1))
+    def cummin(self, axis: Axis | None = None, skipna: bool = True, *args, **kwargs):
+        return NDFrame.cummin(self, axis, skipna, *args, **kwargs)
+
+    @doc(make_doc("cummax", ndim=1))
+    def cummax(self, axis: Axis | None = None, skipna: bool = True, *args, **kwargs):
+        return NDFrame.cummax(self, axis, skipna, *args, **kwargs)
+
+    @doc(make_doc("cumsum", ndim=1))
+    def cumsum(self, axis: Axis | None = None, skipna: bool = True, *args, **kwargs):
+        return NDFrame.cumsum(self, axis, skipna, *args, **kwargs)
+
+    @doc(make_doc("cumprod", 1))
+    def cumprod(self, axis: Axis | None = None, skipna: bool = True, *args, **kwargs):
+        return NDFrame.cumprod(self, axis, skipna, *args, **kwargs)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/shared_docs.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/shared_docs.py
new file mode 100644
index 0000000000000000000000000000000000000000..25f7e7e9f832b0142af32acbb9690d0ae3f0b5e3
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/shared_docs.py
@@ -0,0 +1,952 @@
+from __future__ import annotations
+
+_shared_docs: dict[str, str] = {}
+
+_shared_docs[
+    "aggregate"
+] = """
+Aggregate using one or more operations over the specified axis.
+
+Parameters
+----------
+func : function, str, list or dict
+    Function to use for aggregating the data. If a function, must either
+    work when passed a {klass} or when passed to {klass}.apply.
+
+    Accepted combinations are:
+
+    - function
+    - string function name
+    - list of functions and/or function names, e.g. ``[np.sum, 'mean']``
+    - dict of axis labels -> functions, function names or list of such.
+{axis}
+*args
+    Positional arguments to pass to `func`.
+**kwargs
+    Keyword arguments to pass to `func`.
+
+Returns
+-------
+scalar, Series or DataFrame
+
+    The return can be:
+
+    * scalar : when Series.agg is called with single function
+    * Series : when DataFrame.agg is called with a single function
+    * DataFrame : when DataFrame.agg is called with several functions
+{see_also}
+Notes
+-----
+The aggregation operations are always performed over an axis, either the
+index (default) or the column axis. This behavior is different from
+`numpy` aggregation functions (`mean`, `median`, `prod`, `sum`, `std`,
+`var`), where the default is to compute the aggregation of the flattened
+array, e.g., ``numpy.mean(arr_2d)`` as opposed to
+``numpy.mean(arr_2d, axis=0)``.
+
+`agg` is an alias for `aggregate`. Use the alias.
+
+Functions that mutate the passed object can produce unexpected
+behavior or errors and are not supported. See :ref:`gotchas.udf-mutation`
+for more details.
+
+A passed user-defined-function will be passed a Series for evaluation.
+{examples}"""
+
+_shared_docs[
+    "compare"
+] = """
+Compare to another {klass} and show the differences.
+
+Parameters
+----------
+other : {klass}
+    Object to compare with.
+
+align_axis : {{0 or 'index', 1 or 'columns'}}, default 1
+    Determine which axis to align the comparison on.
+
+    * 0, or 'index' : Resulting differences are stacked vertically
+        with rows drawn alternately from self and other.
+    * 1, or 'columns' : Resulting differences are aligned horizontally
+        with columns drawn alternately from self and other.
+
+keep_shape : bool, default False
+    If true, all rows and columns are kept.
+    Otherwise, only the ones with different values are kept.
+
+keep_equal : bool, default False
+    If true, the result keeps values that are equal.
+    Otherwise, equal values are shown as NaNs.
+
+result_names : tuple, default ('self', 'other')
+    Set the dataframes names in the comparison.
+
+    .. versionadded:: 1.5.0
+"""
+
+_shared_docs[
+    "groupby"
+] = """
+Group %(klass)s using a mapper or by a Series of columns.
+
+A groupby operation involves some combination of splitting the
+object, applying a function, and combining the results. This can be
+used to group large amounts of data and compute operations on these
+groups.
+
+Parameters
+----------
+by : mapping, function, label, pd.Grouper or list of such
+    Used to determine the groups for the groupby.
+    If ``by`` is a function, it's called on each value of the object's
+    index. If a dict or Series is passed, the Series or dict VALUES
+    will be used to determine the groups (the Series' values are first
+    aligned; see ``.align()`` method). If a list or ndarray of length
+    equal to the selected axis is passed (see the `groupby user guide
+    `_),
+    the values are used as-is to determine the groups. A label or list
+    of labels may be passed to group by the columns in ``self``.
+    Notice that a tuple is interpreted as a (single) key.
+axis : {0 or 'index', 1 or 'columns'}, default 0
+    Split along rows (0) or columns (1). For `Series` this parameter
+    is unused and defaults to 0.
+
+    .. deprecated:: 2.1.0
+
+        Will be removed and behave like axis=0 in a future version.
+        For ``axis=1``, do ``frame.T.groupby(...)`` instead.
+
+level : int, level name, or sequence of such, default None
+    If the axis is a MultiIndex (hierarchical), group by a particular
+    level or levels. Do not specify both ``by`` and ``level``.
+as_index : bool, default True
+    Return object with group labels as the
+    index. Only relevant for DataFrame input. as_index=False is
+    effectively "SQL-style" grouped output. This argument has no effect
+    on filtrations (see the `filtrations in the user guide
+    `_),
+    such as ``head()``, ``tail()``, ``nth()`` and in transformations
+    (see the `transformations in the user guide
+    `_).
+sort : bool, default True
+    Sort group keys. Get better performance by turning this off.
+    Note this does not influence the order of observations within each
+    group. Groupby preserves the order of rows within each group. If False,
+    the groups will appear in the same order as they did in the original DataFrame.
+    This argument has no effect on filtrations (see the `filtrations in the user guide
+    `_),
+    such as ``head()``, ``tail()``, ``nth()`` and in transformations
+    (see the `transformations in the user guide
+    `_).
+
+    .. versionchanged:: 2.0.0
+
+        Specifying ``sort=False`` with an ordered categorical grouper will no
+        longer sort the values.
+
+group_keys : bool, default True
+    When calling apply and the ``by`` argument produces a like-indexed
+    (i.e. :ref:`a transform `) result, add group keys to
+    index to identify pieces. By default group keys are not included
+    when the result's index (and column) labels match the inputs, and
+    are included otherwise.
+
+    .. versionchanged:: 1.5.0
+
+       Warns that ``group_keys`` will no longer be ignored when the
+       result from ``apply`` is a like-indexed Series or DataFrame.
+       Specify ``group_keys`` explicitly to include the group keys or
+       not.
+
+    .. versionchanged:: 2.0.0
+
+       ``group_keys`` now defaults to ``True``.
+
+observed : bool, default False
+    This only applies if any of the groupers are Categoricals.
+    If True: only show observed values for categorical groupers.
+    If False: show all values for categorical groupers.
+
+    .. deprecated:: 2.1.0
+
+        The default value will change to True in a future version of pandas.
+
+dropna : bool, default True
+    If True, and if group keys contain NA values, NA values together
+    with row/column will be dropped.
+    If False, NA values will also be treated as the key in groups.
+
+Returns
+-------
+pandas.api.typing.%(klass)sGroupBy
+    Returns a groupby object that contains information about the groups.
+
+See Also
+--------
+resample : Convenience method for frequency conversion and resampling
+    of time series.
+
+Notes
+-----
+See the `user guide
+`__ for more
+detailed usage and examples, including splitting an object into groups,
+iterating through groups, selecting a group, aggregation, and more.
+"""
+
+_shared_docs[
+    "melt"
+] = """
+Unpivot a DataFrame from wide to long format, optionally leaving identifiers set.
+
+This function is useful to massage a DataFrame into a format where one
+or more columns are identifier variables (`id_vars`), while all other
+columns, considered measured variables (`value_vars`), are "unpivoted" to
+the row axis, leaving just two non-identifier columns, 'variable' and
+'value'.
+
+Parameters
+----------
+id_vars : scalar, tuple, list, or ndarray, optional
+    Column(s) to use as identifier variables.
+value_vars : scalar, tuple, list, or ndarray, optional
+    Column(s) to unpivot. If not specified, uses all columns that
+    are not set as `id_vars`.
+var_name : scalar, default None
+    Name to use for the 'variable' column. If None it uses
+    ``frame.columns.name`` or 'variable'.
+value_name : scalar, default 'value'
+    Name to use for the 'value' column, can't be an existing column label.
+col_level : scalar, optional
+    If columns are a MultiIndex then use this level to melt.
+ignore_index : bool, default True
+    If True, original index is ignored. If False, the original index is retained.
+    Index labels will be repeated as necessary.
+
+Returns
+-------
+DataFrame
+    Unpivoted DataFrame.
+
+See Also
+--------
+%(other)s : Identical method.
+pivot_table : Create a spreadsheet-style pivot table as a DataFrame.
+DataFrame.pivot : Return reshaped DataFrame organized
+    by given index / column values.
+DataFrame.explode : Explode a DataFrame from list-like
+        columns to long format.
+
+Notes
+-----
+Reference :ref:`the user guide ` for more examples.
+
+Examples
+--------
+>>> df = pd.DataFrame({'A': {0: 'a', 1: 'b', 2: 'c'},
+...                    'B': {0: 1, 1: 3, 2: 5},
+...                    'C': {0: 2, 1: 4, 2: 6}})
+>>> df
+   A  B  C
+0  a  1  2
+1  b  3  4
+2  c  5  6
+
+>>> %(caller)sid_vars=['A'], value_vars=['B'])
+   A variable  value
+0  a        B      1
+1  b        B      3
+2  c        B      5
+
+>>> %(caller)sid_vars=['A'], value_vars=['B', 'C'])
+   A variable  value
+0  a        B      1
+1  b        B      3
+2  c        B      5
+3  a        C      2
+4  b        C      4
+5  c        C      6
+
+The names of 'variable' and 'value' columns can be customized:
+
+>>> %(caller)sid_vars=['A'], value_vars=['B'],
+...         var_name='myVarname', value_name='myValname')
+   A myVarname  myValname
+0  a         B          1
+1  b         B          3
+2  c         B          5
+
+Original index values can be kept around:
+
+>>> %(caller)sid_vars=['A'], value_vars=['B', 'C'], ignore_index=False)
+   A variable  value
+0  a        B      1
+1  b        B      3
+2  c        B      5
+0  a        C      2
+1  b        C      4
+2  c        C      6
+
+If you have multi-index columns:
+
+>>> df.columns = [list('ABC'), list('DEF')]
+>>> df
+   A  B  C
+   D  E  F
+0  a  1  2
+1  b  3  4
+2  c  5  6
+
+>>> %(caller)scol_level=0, id_vars=['A'], value_vars=['B'])
+   A variable  value
+0  a        B      1
+1  b        B      3
+2  c        B      5
+
+>>> %(caller)sid_vars=[('A', 'D')], value_vars=[('B', 'E')])
+  (A, D) variable_0 variable_1  value
+0      a          B          E      1
+1      b          B          E      3
+2      c          B          E      5
+"""
+
+_shared_docs[
+    "transform"
+] = """
+Call ``func`` on self producing a {klass} with the same axis shape as self.
+
+Parameters
+----------
+func : function, str, list-like or dict-like
+    Function to use for transforming the data. If a function, must either
+    work when passed a {klass} or when passed to {klass}.apply. If func
+    is both list-like and dict-like, dict-like behavior takes precedence.
+
+    Accepted combinations are:
+
+    - function
+    - string function name
+    - list-like of functions and/or function names, e.g. ``[np.exp, 'sqrt']``
+    - dict-like of axis labels -> functions, function names or list-like of such.
+{axis}
+*args
+    Positional arguments to pass to `func`.
+**kwargs
+    Keyword arguments to pass to `func`.
+
+Returns
+-------
+{klass}
+    A {klass} that must have the same length as self.
+
+Raises
+------
+ValueError : If the returned {klass} has a different length than self.
+
+See Also
+--------
+{klass}.agg : Only perform aggregating type operations.
+{klass}.apply : Invoke function on a {klass}.
+
+Notes
+-----
+Functions that mutate the passed object can produce unexpected
+behavior or errors and are not supported. See :ref:`gotchas.udf-mutation`
+for more details.
+
+Examples
+--------
+>>> df = pd.DataFrame({{'A': range(3), 'B': range(1, 4)}})
+>>> df
+   A  B
+0  0  1
+1  1  2
+2  2  3
+>>> df.transform(lambda x: x + 1)
+   A  B
+0  1  2
+1  2  3
+2  3  4
+
+Even though the resulting {klass} must have the same length as the
+input {klass}, it is possible to provide several input functions:
+
+>>> s = pd.Series(range(3))
+>>> s
+0    0
+1    1
+2    2
+dtype: int64
+>>> s.transform([np.sqrt, np.exp])
+       sqrt        exp
+0  0.000000   1.000000
+1  1.000000   2.718282
+2  1.414214   7.389056
+
+You can call transform on a GroupBy object:
+
+>>> df = pd.DataFrame({{
+...     "Date": [
+...         "2015-05-08", "2015-05-07", "2015-05-06", "2015-05-05",
+...         "2015-05-08", "2015-05-07", "2015-05-06", "2015-05-05"],
+...     "Data": [5, 8, 6, 1, 50, 100, 60, 120],
+... }})
+>>> df
+         Date  Data
+0  2015-05-08     5
+1  2015-05-07     8
+2  2015-05-06     6
+3  2015-05-05     1
+4  2015-05-08    50
+5  2015-05-07   100
+6  2015-05-06    60
+7  2015-05-05   120
+>>> df.groupby('Date')['Data'].transform('sum')
+0     55
+1    108
+2     66
+3    121
+4     55
+5    108
+6     66
+7    121
+Name: Data, dtype: int64
+
+>>> df = pd.DataFrame({{
+...     "c": [1, 1, 1, 2, 2, 2, 2],
+...     "type": ["m", "n", "o", "m", "m", "n", "n"]
+... }})
+>>> df
+   c type
+0  1    m
+1  1    n
+2  1    o
+3  2    m
+4  2    m
+5  2    n
+6  2    n
+>>> df['size'] = df.groupby('c')['type'].transform(len)
+>>> df
+   c type size
+0  1    m    3
+1  1    n    3
+2  1    o    3
+3  2    m    4
+4  2    m    4
+5  2    n    4
+6  2    n    4
+"""
+
+_shared_docs[
+    "storage_options"
+] = """storage_options : dict, optional
+    Extra options that make sense for a particular storage connection, e.g.
+    host, port, username, password, etc. For HTTP(S) URLs the key-value pairs
+    are forwarded to ``urllib.request.Request`` as header options. For other
+    URLs (e.g. starting with "s3://", and "gcs://") the key-value pairs are
+    forwarded to ``fsspec.open``. Please see ``fsspec`` and ``urllib`` for more
+    details, and for more examples on storage options refer `here
+    `_."""
+
+_shared_docs[
+    "compression_options"
+] = """compression : str or dict, default 'infer'
+    For on-the-fly compression of the output data. If 'infer' and '%s' is
+    path-like, then detect compression from the following extensions: '.gz',
+    '.bz2', '.zip', '.xz', '.zst', '.tar', '.tar.gz', '.tar.xz' or '.tar.bz2'
+    (otherwise no compression).
+    Set to ``None`` for no compression.
+    Can also be a dict with key ``'method'`` set
+    to one of {``'zip'``, ``'gzip'``, ``'bz2'``, ``'zstd'``, ``'xz'``, ``'tar'``} and
+    other key-value pairs are forwarded to
+    ``zipfile.ZipFile``, ``gzip.GzipFile``,
+    ``bz2.BZ2File``, ``zstandard.ZstdCompressor``, ``lzma.LZMAFile`` or
+    ``tarfile.TarFile``, respectively.
+    As an example, the following could be passed for faster compression and to create
+    a reproducible gzip archive:
+    ``compression={'method': 'gzip', 'compresslevel': 1, 'mtime': 1}``.
+
+    .. versionadded:: 1.5.0
+        Added support for `.tar` files."""
+
+_shared_docs[
+    "decompression_options"
+] = """compression : str or dict, default 'infer'
+    For on-the-fly decompression of on-disk data. If 'infer' and '%s' is
+    path-like, then detect compression from the following extensions: '.gz',
+    '.bz2', '.zip', '.xz', '.zst', '.tar', '.tar.gz', '.tar.xz' or '.tar.bz2'
+    (otherwise no compression).
+    If using 'zip' or 'tar', the ZIP file must contain only one data file to be read in.
+    Set to ``None`` for no decompression.
+    Can also be a dict with key ``'method'`` set
+    to one of {``'zip'``, ``'gzip'``, ``'bz2'``, ``'zstd'``, ``'xz'``, ``'tar'``} and
+    other key-value pairs are forwarded to
+    ``zipfile.ZipFile``, ``gzip.GzipFile``,
+    ``bz2.BZ2File``, ``zstandard.ZstdDecompressor``, ``lzma.LZMAFile`` or
+    ``tarfile.TarFile``, respectively.
+    As an example, the following could be passed for Zstandard decompression using a
+    custom compression dictionary:
+    ``compression={'method': 'zstd', 'dict_data': my_compression_dict}``.
+
+    .. versionadded:: 1.5.0
+        Added support for `.tar` files."""
+
+_shared_docs[
+    "replace"
+] = """
+    Replace values given in `to_replace` with `value`.
+
+    Values of the {klass} are replaced with other values dynamically.
+    This differs from updating with ``.loc`` or ``.iloc``, which require
+    you to specify a location to update with some value.
+
+    Parameters
+    ----------
+    to_replace : str, regex, list, dict, Series, int, float, or None
+        How to find the values that will be replaced.
+
+        * numeric, str or regex:
+
+            - numeric: numeric values equal to `to_replace` will be
+              replaced with `value`
+            - str: string exactly matching `to_replace` will be replaced
+              with `value`
+            - regex: regexs matching `to_replace` will be replaced with
+              `value`
+
+        * list of str, regex, or numeric:
+
+            - First, if `to_replace` and `value` are both lists, they
+              **must** be the same length.
+            - Second, if ``regex=True`` then all of the strings in **both**
+              lists will be interpreted as regexs otherwise they will match
+              directly. This doesn't matter much for `value` since there
+              are only a few possible substitution regexes you can use.
+            - str, regex and numeric rules apply as above.
+
+        * dict:
+
+            - Dicts can be used to specify different replacement values
+              for different existing values. For example,
+              ``{{'a': 'b', 'y': 'z'}}`` replaces the value 'a' with 'b' and
+              'y' with 'z'. To use a dict in this way, the optional `value`
+              parameter should not be given.
+            - For a DataFrame a dict can specify that different values
+              should be replaced in different columns. For example,
+              ``{{'a': 1, 'b': 'z'}}`` looks for the value 1 in column 'a'
+              and the value 'z' in column 'b' and replaces these values
+              with whatever is specified in `value`. The `value` parameter
+              should not be ``None`` in this case. You can treat this as a
+              special case of passing two lists except that you are
+              specifying the column to search in.
+            - For a DataFrame nested dictionaries, e.g.,
+              ``{{'a': {{'b': np.nan}}}}``, are read as follows: look in column
+              'a' for the value 'b' and replace it with NaN. The optional `value`
+              parameter should not be specified to use a nested dict in this
+              way. You can nest regular expressions as well. Note that
+              column names (the top-level dictionary keys in a nested
+              dictionary) **cannot** be regular expressions.
+
+        * None:
+
+            - This means that the `regex` argument must be a string,
+              compiled regular expression, or list, dict, ndarray or
+              Series of such elements. If `value` is also ``None`` then
+              this **must** be a nested dictionary or Series.
+
+        See the examples section for examples of each of these.
+    value : scalar, dict, list, str, regex, default None
+        Value to replace any values matching `to_replace` with.
+        For a DataFrame a dict of values can be used to specify which
+        value to use for each column (columns not in the dict will not be
+        filled). Regular expressions, strings and lists or dicts of such
+        objects are also allowed.
+    {inplace}
+    limit : int, default None
+        Maximum size gap to forward or backward fill.
+
+        .. deprecated:: 2.1.0
+    regex : bool or same types as `to_replace`, default False
+        Whether to interpret `to_replace` and/or `value` as regular
+        expressions. Alternatively, this could be a regular expression or a
+        list, dict, or array of regular expressions in which case
+        `to_replace` must be ``None``.
+    method : {{'pad', 'ffill', 'bfill'}}
+        The method to use when for replacement, when `to_replace` is a
+        scalar, list or tuple and `value` is ``None``.
+
+        .. deprecated:: 2.1.0
+
+    Returns
+    -------
+    {klass}
+        Object after replacement.
+
+    Raises
+    ------
+    AssertionError
+        * If `regex` is not a ``bool`` and `to_replace` is not
+          ``None``.
+
+    TypeError
+        * If `to_replace` is not a scalar, array-like, ``dict``, or ``None``
+        * If `to_replace` is a ``dict`` and `value` is not a ``list``,
+          ``dict``, ``ndarray``, or ``Series``
+        * If `to_replace` is ``None`` and `regex` is not compilable
+          into a regular expression or is a list, dict, ndarray, or
+          Series.
+        * When replacing multiple ``bool`` or ``datetime64`` objects and
+          the arguments to `to_replace` does not match the type of the
+          value being replaced
+
+    ValueError
+        * If a ``list`` or an ``ndarray`` is passed to `to_replace` and
+          `value` but they are not the same length.
+
+    See Also
+    --------
+    Series.fillna : Fill NA values.
+    DataFrame.fillna : Fill NA values.
+    Series.where : Replace values based on boolean condition.
+    DataFrame.where : Replace values based on boolean condition.
+    DataFrame.map: Apply a function to a Dataframe elementwise.
+    Series.map: Map values of Series according to an input mapping or function.
+    Series.str.replace : Simple string replacement.
+
+    Notes
+    -----
+    * Regex substitution is performed under the hood with ``re.sub``. The
+      rules for substitution for ``re.sub`` are the same.
+    * Regular expressions will only substitute on strings, meaning you
+      cannot provide, for example, a regular expression matching floating
+      point numbers and expect the columns in your frame that have a
+      numeric dtype to be matched. However, if those floating point
+      numbers *are* strings, then you can do this.
+    * This method has *a lot* of options. You are encouraged to experiment
+      and play with this method to gain intuition about how it works.
+    * When dict is used as the `to_replace` value, it is like
+      key(s) in the dict are the to_replace part and
+      value(s) in the dict are the value parameter.
+
+    Examples
+    --------
+
+    **Scalar `to_replace` and `value`**
+
+    >>> s = pd.Series([1, 2, 3, 4, 5])
+    >>> s.replace(1, 5)
+    0    5
+    1    2
+    2    3
+    3    4
+    4    5
+    dtype: int64
+
+    >>> df = pd.DataFrame({{'A': [0, 1, 2, 3, 4],
+    ...                    'B': [5, 6, 7, 8, 9],
+    ...                    'C': ['a', 'b', 'c', 'd', 'e']}})
+    >>> df.replace(0, 5)
+        A  B  C
+    0  5  5  a
+    1  1  6  b
+    2  2  7  c
+    3  3  8  d
+    4  4  9  e
+
+    **List-like `to_replace`**
+
+    >>> df.replace([0, 1, 2, 3], 4)
+        A  B  C
+    0  4  5  a
+    1  4  6  b
+    2  4  7  c
+    3  4  8  d
+    4  4  9  e
+
+    >>> df.replace([0, 1, 2, 3], [4, 3, 2, 1])
+        A  B  C
+    0  4  5  a
+    1  3  6  b
+    2  2  7  c
+    3  1  8  d
+    4  4  9  e
+
+    >>> s.replace([1, 2], method='bfill')
+    0    3
+    1    3
+    2    3
+    3    4
+    4    5
+    dtype: int64
+
+    **dict-like `to_replace`**
+
+    >>> df.replace({{0: 10, 1: 100}})
+            A  B  C
+    0   10  5  a
+    1  100  6  b
+    2    2  7  c
+    3    3  8  d
+    4    4  9  e
+
+    >>> df.replace({{'A': 0, 'B': 5}}, 100)
+            A    B  C
+    0  100  100  a
+    1    1    6  b
+    2    2    7  c
+    3    3    8  d
+    4    4    9  e
+
+    >>> df.replace({{'A': {{0: 100, 4: 400}}}})
+            A  B  C
+    0  100  5  a
+    1    1  6  b
+    2    2  7  c
+    3    3  8  d
+    4  400  9  e
+
+    **Regular expression `to_replace`**
+
+    >>> df = pd.DataFrame({{'A': ['bat', 'foo', 'bait'],
+    ...                    'B': ['abc', 'bar', 'xyz']}})
+    >>> df.replace(to_replace=r'^ba.$', value='new', regex=True)
+            A    B
+    0   new  abc
+    1   foo  new
+    2  bait  xyz
+
+    >>> df.replace({{'A': r'^ba.$'}}, {{'A': 'new'}}, regex=True)
+            A    B
+    0   new  abc
+    1   foo  bar
+    2  bait  xyz
+
+    >>> df.replace(regex=r'^ba.$', value='new')
+            A    B
+    0   new  abc
+    1   foo  new
+    2  bait  xyz
+
+    >>> df.replace(regex={{r'^ba.$': 'new', 'foo': 'xyz'}})
+            A    B
+    0   new  abc
+    1   xyz  new
+    2  bait  xyz
+
+    >>> df.replace(regex=[r'^ba.$', 'foo'], value='new')
+            A    B
+    0   new  abc
+    1   new  new
+    2  bait  xyz
+
+    Compare the behavior of ``s.replace({{'a': None}})`` and
+    ``s.replace('a', None)`` to understand the peculiarities
+    of the `to_replace` parameter:
+
+    >>> s = pd.Series([10, 'a', 'a', 'b', 'a'])
+
+    When one uses a dict as the `to_replace` value, it is like the
+    value(s) in the dict are equal to the `value` parameter.
+    ``s.replace({{'a': None}})`` is equivalent to
+    ``s.replace(to_replace={{'a': None}}, value=None, method=None)``:
+
+    >>> s.replace({{'a': None}})
+    0      10
+    1    None
+    2    None
+    3       b
+    4    None
+    dtype: object
+
+    When ``value`` is not explicitly passed and `to_replace` is a scalar, list
+    or tuple, `replace` uses the method parameter (default 'pad') to do the
+    replacement. So this is why the 'a' values are being replaced by 10
+    in rows 1 and 2 and 'b' in row 4 in this case.
+
+    >>> s.replace('a')
+    0    10
+    1    10
+    2    10
+    3     b
+    4     b
+    dtype: object
+
+        .. deprecated:: 2.1.0
+            The 'method' parameter and padding behavior are deprecated.
+
+    On the other hand, if ``None`` is explicitly passed for ``value``, it will
+    be respected:
+
+    >>> s.replace('a', None)
+    0      10
+    1    None
+    2    None
+    3       b
+    4    None
+    dtype: object
+
+        .. versionchanged:: 1.4.0
+            Previously the explicit ``None`` was silently ignored.
+
+    When ``regex=True``, ``value`` is not ``None`` and `to_replace` is a string,
+    the replacement will be applied in all columns of the DataFrame.
+
+    >>> df = pd.DataFrame({{'A': [0, 1, 2, 3, 4],
+    ...                    'B': ['a', 'b', 'c', 'd', 'e'],
+    ...                    'C': ['f', 'g', 'h', 'i', 'j']}})
+
+    >>> df.replace(to_replace='^[a-g]', value='e', regex=True)
+        A  B  C
+    0  0  e  e
+    1  1  e  e
+    2  2  e  h
+    3  3  e  i
+    4  4  e  j
+
+    If ``value`` is not ``None`` and `to_replace` is a dictionary, the dictionary
+    keys will be the DataFrame columns that the replacement will be applied.
+
+    >>> df.replace(to_replace={{'B': '^[a-c]', 'C': '^[h-j]'}}, value='e', regex=True)
+        A  B  C
+    0  0  e  f
+    1  1  e  g
+    2  2  e  e
+    3  3  d  e
+    4  4  e  e
+"""
+
+_shared_docs[
+    "idxmin"
+] = """
+    Return index of first occurrence of minimum over requested axis.
+
+    NA/null values are excluded.
+
+    Parameters
+    ----------
+    axis : {{0 or 'index', 1 or 'columns'}}, default 0
+        The axis to use. 0 or 'index' for row-wise, 1 or 'columns' for column-wise.
+    skipna : bool, default True
+        Exclude NA/null values. If an entire row/column is NA, the result
+        will be NA.
+    numeric_only : bool, default {numeric_only_default}
+        Include only `float`, `int` or `boolean` data.
+
+        .. versionadded:: 1.5.0
+
+    Returns
+    -------
+    Series
+        Indexes of minima along the specified axis.
+
+    Raises
+    ------
+    ValueError
+        * If the row/column is empty
+
+    See Also
+    --------
+    Series.idxmin : Return index of the minimum element.
+
+    Notes
+    -----
+    This method is the DataFrame version of ``ndarray.argmin``.
+
+    Examples
+    --------
+    Consider a dataset containing food consumption in Argentina.
+
+    >>> df = pd.DataFrame({{'consumption': [10.51, 103.11, 55.48],
+    ...                     'co2_emissions': [37.2, 19.66, 1712]}},
+    ...                   index=['Pork', 'Wheat Products', 'Beef'])
+
+    >>> df
+                    consumption  co2_emissions
+    Pork                  10.51         37.20
+    Wheat Products       103.11         19.66
+    Beef                  55.48       1712.00
+
+    By default, it returns the index for the minimum value in each column.
+
+    >>> df.idxmin()
+    consumption                Pork
+    co2_emissions    Wheat Products
+    dtype: object
+
+    To return the index for the minimum value in each row, use ``axis="columns"``.
+
+    >>> df.idxmin(axis="columns")
+    Pork                consumption
+    Wheat Products    co2_emissions
+    Beef                consumption
+    dtype: object
+"""
+
+_shared_docs[
+    "idxmax"
+] = """
+    Return index of first occurrence of maximum over requested axis.
+
+    NA/null values are excluded.
+
+    Parameters
+    ----------
+    axis : {{0 or 'index', 1 or 'columns'}}, default 0
+        The axis to use. 0 or 'index' for row-wise, 1 or 'columns' for column-wise.
+    skipna : bool, default True
+        Exclude NA/null values. If an entire row/column is NA, the result
+        will be NA.
+    numeric_only : bool, default {numeric_only_default}
+        Include only `float`, `int` or `boolean` data.
+
+        .. versionadded:: 1.5.0
+
+    Returns
+    -------
+    Series
+        Indexes of maxima along the specified axis.
+
+    Raises
+    ------
+    ValueError
+        * If the row/column is empty
+
+    See Also
+    --------
+    Series.idxmax : Return index of the maximum element.
+
+    Notes
+    -----
+    This method is the DataFrame version of ``ndarray.argmax``.
+
+    Examples
+    --------
+    Consider a dataset containing food consumption in Argentina.
+
+    >>> df = pd.DataFrame({{'consumption': [10.51, 103.11, 55.48],
+    ...                     'co2_emissions': [37.2, 19.66, 1712]}},
+    ...                   index=['Pork', 'Wheat Products', 'Beef'])
+
+    >>> df
+                    consumption  co2_emissions
+    Pork                  10.51         37.20
+    Wheat Products       103.11         19.66
+    Beef                  55.48       1712.00
+
+    By default, it returns the index for the maximum value in each column.
+
+    >>> df.idxmax()
+    consumption     Wheat Products
+    co2_emissions             Beef
+    dtype: object
+
+    To return the index for the maximum value in each row, use ``axis="columns"``.
+
+    >>> df.idxmax(axis="columns")
+    Pork              co2_emissions
+    Wheat Products     consumption
+    Beef              co2_emissions
+    dtype: object
+"""
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/sorting.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/sorting.py
new file mode 100644
index 0000000000000000000000000000000000000000..a431842218b3bbb97cb5373e03d2a31a4f69ca10
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/sorting.py
@@ -0,0 +1,748 @@
+""" miscellaneous sorting / groupby utilities """
+from __future__ import annotations
+
+from collections import defaultdict
+from typing import (
+    TYPE_CHECKING,
+    Callable,
+    DefaultDict,
+    cast,
+)
+
+import numpy as np
+
+from pandas._libs import (
+    algos,
+    hashtable,
+    lib,
+)
+from pandas._libs.hashtable import unique_label_indices
+
+from pandas.core.dtypes.common import (
+    ensure_int64,
+    ensure_platform_int,
+)
+from pandas.core.dtypes.generic import (
+    ABCMultiIndex,
+    ABCRangeIndex,
+)
+from pandas.core.dtypes.missing import isna
+
+from pandas.core.construction import extract_array
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Hashable,
+        Iterable,
+        Sequence,
+    )
+
+    from pandas._typing import (
+        ArrayLike,
+        AxisInt,
+        IndexKeyFunc,
+        Level,
+        NaPosition,
+        Shape,
+        SortKind,
+        npt,
+    )
+
+    from pandas import (
+        MultiIndex,
+        Series,
+    )
+    from pandas.core.arrays import ExtensionArray
+    from pandas.core.indexes.base import Index
+
+
+def get_indexer_indexer(
+    target: Index,
+    level: Level | list[Level] | None,
+    ascending: list[bool] | bool,
+    kind: SortKind,
+    na_position: NaPosition,
+    sort_remaining: bool,
+    key: IndexKeyFunc,
+) -> npt.NDArray[np.intp] | None:
+    """
+    Helper method that return the indexer according to input parameters for
+    the sort_index method of DataFrame and Series.
+
+    Parameters
+    ----------
+    target : Index
+    level : int or level name or list of ints or list of level names
+    ascending : bool or list of bools, default True
+    kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}
+    na_position : {'first', 'last'}
+    sort_remaining : bool
+    key : callable, optional
+
+    Returns
+    -------
+    Optional[ndarray[intp]]
+        The indexer for the new index.
+    """
+
+    # error: Incompatible types in assignment (expression has type
+    # "Union[ExtensionArray, ndarray[Any, Any], Index, Series]", variable has
+    # type "Index")
+    target = ensure_key_mapped(target, key, levels=level)  # type: ignore[assignment]
+    target = target._sort_levels_monotonic()
+
+    if level is not None:
+        _, indexer = target.sortlevel(
+            level,
+            ascending=ascending,
+            sort_remaining=sort_remaining,
+            na_position=na_position,
+        )
+    elif (np.all(ascending) and target.is_monotonic_increasing) or (
+        not np.any(ascending) and target.is_monotonic_decreasing
+    ):
+        # Check monotonic-ness before sort an index (GH 11080)
+        return None
+    elif isinstance(target, ABCMultiIndex):
+        codes = [lev.codes for lev in target._get_codes_for_sorting()]
+        indexer = lexsort_indexer(
+            codes, orders=ascending, na_position=na_position, codes_given=True
+        )
+    else:
+        # ascending can only be a Sequence for MultiIndex
+        indexer = nargsort(
+            target,
+            kind=kind,
+            ascending=cast(bool, ascending),
+            na_position=na_position,
+        )
+    return indexer
+
+
+def get_group_index(
+    labels, shape: Shape, sort: bool, xnull: bool
+) -> npt.NDArray[np.int64]:
+    """
+    For the particular label_list, gets the offsets into the hypothetical list
+    representing the totally ordered cartesian product of all possible label
+    combinations, *as long as* this space fits within int64 bounds;
+    otherwise, though group indices identify unique combinations of
+    labels, they cannot be deconstructed.
+    - If `sort`, rank of returned ids preserve lexical ranks of labels.
+      i.e. returned id's can be used to do lexical sort on labels;
+    - If `xnull` nulls (-1 labels) are passed through.
+
+    Parameters
+    ----------
+    labels : sequence of arrays
+        Integers identifying levels at each location
+    shape : tuple[int, ...]
+        Number of unique levels at each location
+    sort : bool
+        If the ranks of returned ids should match lexical ranks of labels
+    xnull : bool
+        If true nulls are excluded. i.e. -1 values in the labels are
+        passed through.
+
+    Returns
+    -------
+    An array of type int64 where two elements are equal if their corresponding
+    labels are equal at all location.
+
+    Notes
+    -----
+    The length of `labels` and `shape` must be identical.
+    """
+
+    def _int64_cut_off(shape) -> int:
+        acc = 1
+        for i, mul in enumerate(shape):
+            acc *= int(mul)
+            if not acc < lib.i8max:
+                return i
+        return len(shape)
+
+    def maybe_lift(lab, size: int) -> tuple[np.ndarray, int]:
+        # promote nan values (assigned -1 label in lab array)
+        # so that all output values are non-negative
+        return (lab + 1, size + 1) if (lab == -1).any() else (lab, size)
+
+    labels = [ensure_int64(x) for x in labels]
+    lshape = list(shape)
+    if not xnull:
+        for i, (lab, size) in enumerate(zip(labels, shape)):
+            labels[i], lshape[i] = maybe_lift(lab, size)
+
+    labels = list(labels)
+
+    # Iteratively process all the labels in chunks sized so less
+    # than lib.i8max unique int ids will be required for each chunk
+    while True:
+        # how many levels can be done without overflow:
+        nlev = _int64_cut_off(lshape)
+
+        # compute flat ids for the first `nlev` levels
+        stride = np.prod(lshape[1:nlev], dtype="i8")
+        out = stride * labels[0].astype("i8", subok=False, copy=False)
+
+        for i in range(1, nlev):
+            if lshape[i] == 0:
+                stride = np.int64(0)
+            else:
+                stride //= lshape[i]
+            out += labels[i] * stride
+
+        if xnull:  # exclude nulls
+            mask = labels[0] == -1
+            for lab in labels[1:nlev]:
+                mask |= lab == -1
+            out[mask] = -1
+
+        if nlev == len(lshape):  # all levels done!
+            break
+
+        # compress what has been done so far in order to avoid overflow
+        # to retain lexical ranks, obs_ids should be sorted
+        comp_ids, obs_ids = compress_group_index(out, sort=sort)
+
+        labels = [comp_ids] + labels[nlev:]
+        lshape = [len(obs_ids)] + lshape[nlev:]
+
+    return out
+
+
+def get_compressed_ids(
+    labels, sizes: Shape
+) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.int64]]:
+    """
+    Group_index is offsets into cartesian product of all possible labels. This
+    space can be huge, so this function compresses it, by computing offsets
+    (comp_ids) into the list of unique labels (obs_group_ids).
+
+    Parameters
+    ----------
+    labels : list of label arrays
+    sizes : tuple[int] of size of the levels
+
+    Returns
+    -------
+    np.ndarray[np.intp]
+        comp_ids
+    np.ndarray[np.int64]
+        obs_group_ids
+    """
+    ids = get_group_index(labels, sizes, sort=True, xnull=False)
+    return compress_group_index(ids, sort=True)
+
+
+def is_int64_overflow_possible(shape: Shape) -> bool:
+    the_prod = 1
+    for x in shape:
+        the_prod *= int(x)
+
+    return the_prod >= lib.i8max
+
+
+def _decons_group_index(
+    comp_labels: npt.NDArray[np.intp], shape: Shape
+) -> list[npt.NDArray[np.intp]]:
+    # reconstruct labels
+    if is_int64_overflow_possible(shape):
+        # at some point group indices are factorized,
+        # and may not be deconstructed here! wrong path!
+        raise ValueError("cannot deconstruct factorized group indices!")
+
+    label_list = []
+    factor = 1
+    y = np.array(0)
+    x = comp_labels
+    for i in reversed(range(len(shape))):
+        labels = (x - y) % (factor * shape[i]) // factor
+        np.putmask(labels, comp_labels < 0, -1)
+        label_list.append(labels)
+        y = labels * factor
+        factor *= shape[i]
+    return label_list[::-1]
+
+
+def decons_obs_group_ids(
+    comp_ids: npt.NDArray[np.intp],
+    obs_ids: npt.NDArray[np.intp],
+    shape: Shape,
+    labels: Sequence[npt.NDArray[np.signedinteger]],
+    xnull: bool,
+) -> list[npt.NDArray[np.intp]]:
+    """
+    Reconstruct labels from observed group ids.
+
+    Parameters
+    ----------
+    comp_ids : np.ndarray[np.intp]
+    obs_ids: np.ndarray[np.intp]
+    shape : tuple[int]
+    labels : Sequence[np.ndarray[np.signedinteger]]
+    xnull : bool
+        If nulls are excluded; i.e. -1 labels are passed through.
+    """
+    if not xnull:
+        lift = np.fromiter(((a == -1).any() for a in labels), dtype=np.intp)
+        arr_shape = np.asarray(shape, dtype=np.intp) + lift
+        shape = tuple(arr_shape)
+
+    if not is_int64_overflow_possible(shape):
+        # obs ids are deconstructable! take the fast route!
+        out = _decons_group_index(obs_ids, shape)
+        return out if xnull or not lift.any() else [x - y for x, y in zip(out, lift)]
+
+    indexer = unique_label_indices(comp_ids)
+    return [lab[indexer].astype(np.intp, subok=False, copy=True) for lab in labels]
+
+
+def lexsort_indexer(
+    keys: Sequence[ArrayLike | Index | Series],
+    orders=None,
+    na_position: str = "last",
+    key: Callable | None = None,
+    codes_given: bool = False,
+) -> npt.NDArray[np.intp]:
+    """
+    Performs lexical sorting on a set of keys
+
+    Parameters
+    ----------
+    keys : Sequence[ArrayLike | Index | Series]
+        Sequence of arrays to be sorted by the indexer
+        Sequence[Series] is only if key is not None.
+    orders : bool or list of booleans, optional
+        Determines the sorting order for each element in keys. If a list,
+        it must be the same length as keys. This determines whether the
+        corresponding element in keys should be sorted in ascending
+        (True) or descending (False) order. if bool, applied to all
+        elements as above. if None, defaults to True.
+    na_position : {'first', 'last'}, default 'last'
+        Determines placement of NA elements in the sorted list ("last" or "first")
+    key : Callable, optional
+        Callable key function applied to every element in keys before sorting
+    codes_given: bool, False
+        Avoid categorical materialization if codes are already provided.
+
+    Returns
+    -------
+    np.ndarray[np.intp]
+    """
+    from pandas.core.arrays import Categorical
+
+    if na_position not in ["last", "first"]:
+        raise ValueError(f"invalid na_position: {na_position}")
+
+    if isinstance(orders, bool):
+        orders = [orders] * len(keys)
+    elif orders is None:
+        orders = [True] * len(keys)
+
+    labels = []
+
+    for k, order in zip(keys, orders):
+        k = ensure_key_mapped(k, key)
+        if codes_given:
+            codes = cast(np.ndarray, k)
+            n = codes.max() + 1 if len(codes) else 0
+        else:
+            cat = Categorical(k, ordered=True)
+            codes = cat.codes
+            n = len(cat.categories)
+
+        mask = codes == -1
+
+        if na_position == "last" and mask.any():
+            codes = np.where(mask, n, codes)
+
+        # not order means descending
+        if not order:
+            codes = np.where(mask, codes, n - codes - 1)
+
+        labels.append(codes)
+
+    return np.lexsort(labels[::-1])
+
+
+def nargsort(
+    items: ArrayLike | Index | Series,
+    kind: SortKind = "quicksort",
+    ascending: bool = True,
+    na_position: str = "last",
+    key: Callable | None = None,
+    mask: npt.NDArray[np.bool_] | None = None,
+) -> npt.NDArray[np.intp]:
+    """
+    Intended to be a drop-in replacement for np.argsort which handles NaNs.
+
+    Adds ascending, na_position, and key parameters.
+
+    (GH #6399, #5231, #27237)
+
+    Parameters
+    ----------
+    items : np.ndarray, ExtensionArray, Index, or Series
+    kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, default 'quicksort'
+    ascending : bool, default True
+    na_position : {'first', 'last'}, default 'last'
+    key : Optional[Callable], default None
+    mask : Optional[np.ndarray[bool]], default None
+        Passed when called by ExtensionArray.argsort.
+
+    Returns
+    -------
+    np.ndarray[np.intp]
+    """
+
+    if key is not None:
+        # see TestDataFrameSortKey, TestRangeIndex::test_sort_values_key
+        items = ensure_key_mapped(items, key)
+        return nargsort(
+            items,
+            kind=kind,
+            ascending=ascending,
+            na_position=na_position,
+            key=None,
+            mask=mask,
+        )
+
+    if isinstance(items, ABCRangeIndex):
+        return items.argsort(ascending=ascending)
+    elif not isinstance(items, ABCMultiIndex):
+        items = extract_array(items)
+    else:
+        raise TypeError(
+            "nargsort does not support MultiIndex. Use index.sort_values instead."
+        )
+
+    if mask is None:
+        mask = np.asarray(isna(items))
+
+    if not isinstance(items, np.ndarray):
+        # i.e. ExtensionArray
+        return items.argsort(
+            ascending=ascending,
+            kind=kind,
+            na_position=na_position,
+        )
+
+    idx = np.arange(len(items))
+    non_nans = items[~mask]
+    non_nan_idx = idx[~mask]
+
+    nan_idx = np.nonzero(mask)[0]
+    if not ascending:
+        non_nans = non_nans[::-1]
+        non_nan_idx = non_nan_idx[::-1]
+    indexer = non_nan_idx[non_nans.argsort(kind=kind)]
+    if not ascending:
+        indexer = indexer[::-1]
+    # Finally, place the NaNs at the end or the beginning according to
+    # na_position
+    if na_position == "last":
+        indexer = np.concatenate([indexer, nan_idx])
+    elif na_position == "first":
+        indexer = np.concatenate([nan_idx, indexer])
+    else:
+        raise ValueError(f"invalid na_position: {na_position}")
+    return ensure_platform_int(indexer)
+
+
+def nargminmax(values: ExtensionArray, method: str, axis: AxisInt = 0):
+    """
+    Implementation of np.argmin/argmax but for ExtensionArray and which
+    handles missing values.
+
+    Parameters
+    ----------
+    values : ExtensionArray
+    method : {"argmax", "argmin"}
+    axis : int, default 0
+
+    Returns
+    -------
+    int
+    """
+    assert method in {"argmax", "argmin"}
+    func = np.argmax if method == "argmax" else np.argmin
+
+    mask = np.asarray(isna(values))
+    arr_values = values._values_for_argsort()
+
+    if arr_values.ndim > 1:
+        if mask.any():
+            if axis == 1:
+                zipped = zip(arr_values, mask)
+            else:
+                zipped = zip(arr_values.T, mask.T)
+            return np.array([_nanargminmax(v, m, func) for v, m in zipped])
+        return func(arr_values, axis=axis)
+
+    return _nanargminmax(arr_values, mask, func)
+
+
+def _nanargminmax(values: np.ndarray, mask: npt.NDArray[np.bool_], func) -> int:
+    """
+    See nanargminmax.__doc__.
+    """
+    idx = np.arange(values.shape[0])
+    non_nans = values[~mask]
+    non_nan_idx = idx[~mask]
+
+    return non_nan_idx[func(non_nans)]
+
+
+def _ensure_key_mapped_multiindex(
+    index: MultiIndex, key: Callable, level=None
+) -> MultiIndex:
+    """
+    Returns a new MultiIndex in which key has been applied
+    to all levels specified in level (or all levels if level
+    is None). Used for key sorting for MultiIndex.
+
+    Parameters
+    ----------
+    index : MultiIndex
+        Index to which to apply the key function on the
+        specified levels.
+    key : Callable
+        Function that takes an Index and returns an Index of
+        the same shape. This key is applied to each level
+        separately. The name of the level can be used to
+        distinguish different levels for application.
+    level : list-like, int or str, default None
+        Level or list of levels to apply the key function to.
+        If None, key function is applied to all levels. Other
+        levels are left unchanged.
+
+    Returns
+    -------
+    labels : MultiIndex
+        Resulting MultiIndex with modified levels.
+    """
+
+    if level is not None:
+        if isinstance(level, (str, int)):
+            sort_levels = [level]
+        else:
+            sort_levels = level
+
+        sort_levels = [index._get_level_number(lev) for lev in sort_levels]
+    else:
+        sort_levels = list(range(index.nlevels))  # satisfies mypy
+
+    mapped = [
+        ensure_key_mapped(index._get_level_values(level), key)
+        if level in sort_levels
+        else index._get_level_values(level)
+        for level in range(index.nlevels)
+    ]
+
+    return type(index).from_arrays(mapped)
+
+
+def ensure_key_mapped(
+    values: ArrayLike | Index | Series, key: Callable | None, levels=None
+) -> ArrayLike | Index | Series:
+    """
+    Applies a callable key function to the values function and checks
+    that the resulting value has the same shape. Can be called on Index
+    subclasses, Series, DataFrames, or ndarrays.
+
+    Parameters
+    ----------
+    values : Series, DataFrame, Index subclass, or ndarray
+    key : Optional[Callable], key to be called on the values array
+    levels : Optional[List], if values is a MultiIndex, list of levels to
+    apply the key to.
+    """
+    from pandas.core.indexes.api import Index
+
+    if not key:
+        return values
+
+    if isinstance(values, ABCMultiIndex):
+        return _ensure_key_mapped_multiindex(values, key, level=levels)
+
+    result = key(values.copy())
+    if len(result) != len(values):
+        raise ValueError(
+            "User-provided `key` function must not change the shape of the array."
+        )
+
+    try:
+        if isinstance(
+            values, Index
+        ):  # convert to a new Index subclass, not necessarily the same
+            result = Index(result)
+        else:
+            # try to revert to original type otherwise
+            type_of_values = type(values)
+            #  error: Too many arguments for "ExtensionArray"
+            result = type_of_values(result)  # type: ignore[call-arg]
+    except TypeError:
+        raise TypeError(
+            f"User-provided `key` function returned an invalid type {type(result)} \
+            which could not be converted to {type(values)}."
+        )
+
+    return result
+
+
+def get_flattened_list(
+    comp_ids: npt.NDArray[np.intp],
+    ngroups: int,
+    levels: Iterable[Index],
+    labels: Iterable[np.ndarray],
+) -> list[tuple]:
+    """Map compressed group id -> key tuple."""
+    comp_ids = comp_ids.astype(np.int64, copy=False)
+    arrays: DefaultDict[int, list[int]] = defaultdict(list)
+    for labs, level in zip(labels, levels):
+        table = hashtable.Int64HashTable(ngroups)
+        table.map_keys_to_values(comp_ids, labs.astype(np.int64, copy=False))
+        for i in range(ngroups):
+            arrays[i].append(level[table.get_item(i)])
+    return [tuple(array) for array in arrays.values()]
+
+
+def get_indexer_dict(
+    label_list: list[np.ndarray], keys: list[Index]
+) -> dict[Hashable, npt.NDArray[np.intp]]:
+    """
+    Returns
+    -------
+    dict:
+        Labels mapped to indexers.
+    """
+    shape = tuple(len(x) for x in keys)
+
+    group_index = get_group_index(label_list, shape, sort=True, xnull=True)
+    if np.all(group_index == -1):
+        # Short-circuit, lib.indices_fast will return the same
+        return {}
+    ngroups = (
+        ((group_index.size and group_index.max()) + 1)
+        if is_int64_overflow_possible(shape)
+        else np.prod(shape, dtype="i8")
+    )
+
+    sorter = get_group_index_sorter(group_index, ngroups)
+
+    sorted_labels = [lab.take(sorter) for lab in label_list]
+    group_index = group_index.take(sorter)
+
+    return lib.indices_fast(sorter, group_index, keys, sorted_labels)
+
+
+# ----------------------------------------------------------------------
+# sorting levels...cleverly?
+
+
+def get_group_index_sorter(
+    group_index: npt.NDArray[np.intp], ngroups: int | None = None
+) -> npt.NDArray[np.intp]:
+    """
+    algos.groupsort_indexer implements `counting sort` and it is at least
+    O(ngroups), where
+        ngroups = prod(shape)
+        shape = map(len, keys)
+    that is, linear in the number of combinations (cartesian product) of unique
+    values of groupby keys. This can be huge when doing multi-key groupby.
+    np.argsort(kind='mergesort') is O(count x log(count)) where count is the
+    length of the data-frame;
+    Both algorithms are `stable` sort and that is necessary for correctness of
+    groupby operations. e.g. consider:
+        df.groupby(key)[col].transform('first')
+
+    Parameters
+    ----------
+    group_index : np.ndarray[np.intp]
+        signed integer dtype
+    ngroups : int or None, default None
+
+    Returns
+    -------
+    np.ndarray[np.intp]
+    """
+    if ngroups is None:
+        ngroups = 1 + group_index.max()
+    count = len(group_index)
+    alpha = 0.0  # taking complexities literally; there may be
+    beta = 1.0  # some room for fine-tuning these parameters
+    do_groupsort = count > 0 and ((alpha + beta * ngroups) < (count * np.log(count)))
+    if do_groupsort:
+        sorter, _ = algos.groupsort_indexer(
+            ensure_platform_int(group_index),
+            ngroups,
+        )
+        # sorter _should_ already be intp, but mypy is not yet able to verify
+    else:
+        sorter = group_index.argsort(kind="mergesort")
+    return ensure_platform_int(sorter)
+
+
+def compress_group_index(
+    group_index: npt.NDArray[np.int64], sort: bool = True
+) -> tuple[npt.NDArray[np.int64], npt.NDArray[np.int64]]:
+    """
+    Group_index is offsets into cartesian product of all possible labels. This
+    space can be huge, so this function compresses it, by computing offsets
+    (comp_ids) into the list of unique labels (obs_group_ids).
+    """
+    if len(group_index) and np.all(group_index[1:] >= group_index[:-1]):
+        # GH 53806: fast path for sorted group_index
+        unique_mask = np.concatenate(
+            [group_index[:1] > -1, group_index[1:] != group_index[:-1]]
+        )
+        comp_ids = unique_mask.cumsum()
+        comp_ids -= 1
+        obs_group_ids = group_index[unique_mask]
+    else:
+        size_hint = len(group_index)
+        table = hashtable.Int64HashTable(size_hint)
+
+        group_index = ensure_int64(group_index)
+
+        # note, group labels come out ascending (ie, 1,2,3 etc)
+        comp_ids, obs_group_ids = table.get_labels_groupby(group_index)
+
+        if sort and len(obs_group_ids) > 0:
+            obs_group_ids, comp_ids = _reorder_by_uniques(obs_group_ids, comp_ids)
+
+    return ensure_int64(comp_ids), ensure_int64(obs_group_ids)
+
+
+def _reorder_by_uniques(
+    uniques: npt.NDArray[np.int64], labels: npt.NDArray[np.intp]
+) -> tuple[npt.NDArray[np.int64], npt.NDArray[np.intp]]:
+    """
+    Parameters
+    ----------
+    uniques : np.ndarray[np.int64]
+    labels : np.ndarray[np.intp]
+
+    Returns
+    -------
+    np.ndarray[np.int64]
+    np.ndarray[np.intp]
+    """
+    # sorter is index where elements ought to go
+    sorter = uniques.argsort()
+
+    # reverse_indexer is where elements came from
+    reverse_indexer = np.empty(len(sorter), dtype=np.intp)
+    reverse_indexer.put(sorter, np.arange(len(sorter)))
+
+    mask = labels < 0
+
+    # move labels to right locations (ie, unsort ascending labels)
+    labels = reverse_indexer.take(labels)
+    np.putmask(labels, mask, -1)
+
+    # sort observed ids
+    uniques = uniques.take(sorter)
+
+    return uniques, labels
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/sparse/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/sparse/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/sparse/api.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/sparse/api.py
new file mode 100644
index 0000000000000000000000000000000000000000..6650a5c4e90a0f73a43e6e35cdd26c1189daf256
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/sparse/api.py
@@ -0,0 +1,5 @@
+from pandas.core.dtypes.dtypes import SparseDtype
+
+from pandas.core.arrays.sparse import SparseArray
+
+__all__ = ["SparseArray", "SparseDtype"]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/strings/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/strings/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d4ce75f768c5d1dcd8586264fe1faf756d5d5e94
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/strings/__init__.py
@@ -0,0 +1,28 @@
+"""
+Implementation of pandas.Series.str and its interface.
+
+* strings.accessor.StringMethods : Accessor for Series.str
+* strings.base.BaseStringArrayMethods: Mixin ABC for EAs to implement str methods
+
+Most methods on the StringMethods accessor follow the pattern:
+
+    1. extract the array from the series (or index)
+    2. Call that array's implementation of the string method
+    3. Wrap the result (in a Series, index, or DataFrame)
+
+Pandas extension arrays implementing string methods should inherit from
+pandas.core.strings.base.BaseStringArrayMethods. This is an ABC defining
+the various string methods. To avoid namespace clashes and pollution,
+these are prefixed with `_str_`. So ``Series.str.upper()`` calls
+``Series.array._str_upper()``. The interface isn't currently public
+to other string extension arrays.
+"""
+# Pandas current implementation is in ObjectStringArrayMixin. This is designed
+# to work on object-dtype ndarrays.
+#
+# BaseStringArrayMethods
+#  - ObjectStringArrayMixin
+#     - StringArray
+#     - NumpyExtensionArray
+#     - Categorical
+#     - ArrowStringArray
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/strings/accessor.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/strings/accessor.py
new file mode 100644
index 0000000000000000000000000000000000000000..da17543f8470d2394ad7d9aac66c6db99b100df0
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/strings/accessor.py
@@ -0,0 +1,3571 @@
+from __future__ import annotations
+
+import codecs
+from functools import wraps
+import re
+from typing import (
+    TYPE_CHECKING,
+    Callable,
+    Literal,
+    cast,
+)
+import warnings
+
+import numpy as np
+
+from pandas._config import get_option
+
+from pandas._libs import lib
+from pandas._typing import (
+    AlignJoin,
+    DtypeObj,
+    F,
+    Scalar,
+    npt,
+)
+from pandas.util._decorators import Appender
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.common import (
+    ensure_object,
+    is_bool_dtype,
+    is_integer,
+    is_list_like,
+    is_object_dtype,
+    is_re,
+    is_string_dtype,
+)
+from pandas.core.dtypes.dtypes import (
+    ArrowDtype,
+    CategoricalDtype,
+)
+from pandas.core.dtypes.generic import (
+    ABCDataFrame,
+    ABCIndex,
+    ABCMultiIndex,
+    ABCSeries,
+)
+from pandas.core.dtypes.missing import isna
+
+from pandas.core.arrays import ExtensionArray
+from pandas.core.base import NoNewAttributesMixin
+from pandas.core.construction import extract_array
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Hashable,
+        Iterator,
+    )
+
+    from pandas import (
+        DataFrame,
+        Index,
+        Series,
+    )
+
+_shared_docs: dict[str, str] = {}
+_cpython_optimized_encoders = (
+    "utf-8",
+    "utf8",
+    "latin-1",
+    "latin1",
+    "iso-8859-1",
+    "mbcs",
+    "ascii",
+)
+_cpython_optimized_decoders = _cpython_optimized_encoders + ("utf-16", "utf-32")
+
+
+def forbid_nonstring_types(
+    forbidden: list[str] | None, name: str | None = None
+) -> Callable[[F], F]:
+    """
+    Decorator to forbid specific types for a method of StringMethods.
+
+    For calling `.str.{method}` on a Series or Index, it is necessary to first
+    initialize the :class:`StringMethods` object, and then call the method.
+    However, different methods allow different input types, and so this can not
+    be checked during :meth:`StringMethods.__init__`, but must be done on a
+    per-method basis. This decorator exists to facilitate this process, and
+    make it explicit which (inferred) types are disallowed by the method.
+
+    :meth:`StringMethods.__init__` allows the *union* of types its different
+    methods allow (after skipping NaNs; see :meth:`StringMethods._validate`),
+    namely: ['string', 'empty', 'bytes', 'mixed', 'mixed-integer'].
+
+    The default string types ['string', 'empty'] are allowed for all methods.
+    For the additional types ['bytes', 'mixed', 'mixed-integer'], each method
+    then needs to forbid the types it is not intended for.
+
+    Parameters
+    ----------
+    forbidden : list-of-str or None
+        List of forbidden non-string types, may be one or more of
+        `['bytes', 'mixed', 'mixed-integer']`.
+    name : str, default None
+        Name of the method to use in the error message. By default, this is
+        None, in which case the name from the method being wrapped will be
+        copied. However, for working with further wrappers (like _pat_wrapper
+        and _noarg_wrapper), it is necessary to specify the name.
+
+    Returns
+    -------
+    func : wrapper
+        The method to which the decorator is applied, with an added check that
+        enforces the inferred type to not be in the list of forbidden types.
+
+    Raises
+    ------
+    TypeError
+        If the inferred type of the underlying data is in `forbidden`.
+    """
+    # deal with None
+    forbidden = [] if forbidden is None else forbidden
+
+    allowed_types = {"string", "empty", "bytes", "mixed", "mixed-integer"} - set(
+        forbidden
+    )
+
+    def _forbid_nonstring_types(func: F) -> F:
+        func_name = func.__name__ if name is None else name
+
+        @wraps(func)
+        def wrapper(self, *args, **kwargs):
+            if self._inferred_dtype not in allowed_types:
+                msg = (
+                    f"Cannot use .str.{func_name} with values of "
+                    f"inferred dtype '{self._inferred_dtype}'."
+                )
+                raise TypeError(msg)
+            return func(self, *args, **kwargs)
+
+        wrapper.__name__ = func_name
+        return cast(F, wrapper)
+
+    return _forbid_nonstring_types
+
+
+def _map_and_wrap(name: str | None, docstring: str | None):
+    @forbid_nonstring_types(["bytes"], name=name)
+    def wrapper(self):
+        result = getattr(self._data.array, f"_str_{name}")()
+        return self._wrap_result(
+            result, returns_string=name not in ("isnumeric", "isdecimal")
+        )
+
+    wrapper.__doc__ = docstring
+    return wrapper
+
+
+class StringMethods(NoNewAttributesMixin):
+    """
+    Vectorized string functions for Series and Index.
+
+    NAs stay NA unless handled otherwise by a particular method.
+    Patterned after Python's string methods, with some inspiration from
+    R's stringr package.
+
+    Examples
+    --------
+    >>> s = pd.Series(["A_Str_Series"])
+    >>> s
+    0    A_Str_Series
+    dtype: object
+
+    >>> s.str.split("_")
+    0    [A, Str, Series]
+    dtype: object
+
+    >>> s.str.replace("_", "")
+    0    AStrSeries
+    dtype: object
+    """
+
+    # Note: see the docstring in pandas.core.strings.__init__
+    # for an explanation of the implementation.
+    # TODO: Dispatch all the methods
+    # Currently the following are not dispatched to the array
+    # * cat
+    # * extractall
+
+    def __init__(self, data) -> None:
+        from pandas.core.arrays.string_ import StringDtype
+
+        self._inferred_dtype = self._validate(data)
+        self._is_categorical = isinstance(data.dtype, CategoricalDtype)
+        self._is_string = isinstance(data.dtype, StringDtype)
+        self._data = data
+
+        self._index = self._name = None
+        if isinstance(data, ABCSeries):
+            self._index = data.index
+            self._name = data.name
+
+        # ._values.categories works for both Series/Index
+        self._parent = data._values.categories if self._is_categorical else data
+        # save orig to blow up categoricals to the right type
+        self._orig = data
+        self._freeze()
+
+    @staticmethod
+    def _validate(data):
+        """
+        Auxiliary function for StringMethods, infers and checks dtype of data.
+
+        This is a "first line of defence" at the creation of the StringMethods-
+        object, and just checks that the dtype is in the
+        *union* of the allowed types over all string methods below; this
+        restriction is then refined on a per-method basis using the decorator
+        @forbid_nonstring_types (more info in the corresponding docstring).
+
+        This really should exclude all series/index with any non-string values,
+        but that isn't practical for performance reasons until we have a str
+        dtype (GH 9343 / 13877)
+
+        Parameters
+        ----------
+        data : The content of the Series
+
+        Returns
+        -------
+        dtype : inferred dtype of data
+        """
+        if isinstance(data, ABCMultiIndex):
+            raise AttributeError(
+                "Can only use .str accessor with Index, not MultiIndex"
+            )
+
+        # see _libs/lib.pyx for list of inferred types
+        allowed_types = ["string", "empty", "bytes", "mixed", "mixed-integer"]
+
+        data = extract_array(data)
+
+        values = getattr(data, "categories", data)  # categorical / normal
+
+        inferred_dtype = lib.infer_dtype(values, skipna=True)
+
+        if inferred_dtype not in allowed_types:
+            raise AttributeError("Can only use .str accessor with string values!")
+        return inferred_dtype
+
+    def __getitem__(self, key):
+        result = self._data.array._str_getitem(key)
+        return self._wrap_result(result)
+
+    def __iter__(self) -> Iterator:
+        raise TypeError(f"'{type(self).__name__}' object is not iterable")
+
+    def _wrap_result(
+        self,
+        result,
+        name=None,
+        expand: bool | None = None,
+        fill_value=np.nan,
+        returns_string: bool = True,
+        returns_bool: bool = False,
+        dtype=None,
+    ):
+        from pandas import (
+            Index,
+            MultiIndex,
+        )
+
+        if not hasattr(result, "ndim") or not hasattr(result, "dtype"):
+            if isinstance(result, ABCDataFrame):
+                result = result.__finalize__(self._orig, name="str")
+            return result
+        assert result.ndim < 3
+
+        # We can be wrapping a string / object / categorical result, in which
+        # case we'll want to return the same dtype as the input.
+        # Or we can be wrapping a numeric output, in which case we don't want
+        # to return a StringArray.
+        # Ideally the array method returns the right array type.
+        if expand is None:
+            # infer from ndim if expand is not specified
+            expand = result.ndim != 1
+        elif expand is True and not isinstance(self._orig, ABCIndex):
+            # required when expand=True is explicitly specified
+            # not needed when inferred
+            if isinstance(result.dtype, ArrowDtype):
+                import pyarrow as pa
+
+                from pandas.compat import pa_version_under11p0
+
+                from pandas.core.arrays.arrow.array import ArrowExtensionArray
+
+                value_lengths = pa.compute.list_value_length(result._pa_array)
+                max_len = pa.compute.max(value_lengths).as_py()
+                min_len = pa.compute.min(value_lengths).as_py()
+                if result._hasna:
+                    # ArrowExtensionArray.fillna doesn't work for list scalars
+                    result = ArrowExtensionArray(
+                        result._pa_array.fill_null([None] * max_len)
+                    )
+                if min_len < max_len:
+                    # append nulls to each scalar list element up to max_len
+                    if not pa_version_under11p0:
+                        result = ArrowExtensionArray(
+                            pa.compute.list_slice(
+                                result._pa_array,
+                                start=0,
+                                stop=max_len,
+                                return_fixed_size_list=True,
+                            )
+                        )
+                    else:
+                        all_null = np.full(max_len, fill_value=None, dtype=object)
+                        values = result.to_numpy()
+                        new_values = []
+                        for row in values:
+                            if len(row) < max_len:
+                                nulls = all_null[: max_len - len(row)]
+                                row = np.append(row, nulls)
+                            new_values.append(row)
+                        pa_type = result._pa_array.type
+                        result = ArrowExtensionArray(pa.array(new_values, type=pa_type))
+                if name is not None:
+                    labels = name
+                else:
+                    labels = range(max_len)
+                result = (
+                    pa.compute.list_flatten(result._pa_array)
+                    .to_numpy()
+                    .reshape(len(result), max_len)
+                )
+                result = {
+                    label: ArrowExtensionArray(pa.array(res))
+                    for label, res in zip(labels, result.T)
+                }
+            elif is_object_dtype(result):
+
+                def cons_row(x):
+                    if is_list_like(x):
+                        return x
+                    else:
+                        return [x]
+
+                result = [cons_row(x) for x in result]
+                if result and not self._is_string:
+                    # propagate nan values to match longest sequence (GH 18450)
+                    max_len = max(len(x) for x in result)
+                    result = [
+                        x * max_len if len(x) == 0 or x[0] is np.nan else x
+                        for x in result
+                    ]
+
+        if not isinstance(expand, bool):
+            raise ValueError("expand must be True or False")
+
+        if expand is False:
+            # if expand is False, result should have the same name
+            # as the original otherwise specified
+            if name is None:
+                name = getattr(result, "name", None)
+            if name is None:
+                # do not use logical or, _orig may be a DataFrame
+                # which has "name" column
+                name = self._orig.name
+
+        # Wait until we are sure result is a Series or Index before
+        # checking attributes (GH 12180)
+        if isinstance(self._orig, ABCIndex):
+            # if result is a boolean np.array, return the np.array
+            # instead of wrapping it into a boolean Index (GH 8875)
+            if is_bool_dtype(result):
+                return result
+
+            if expand:
+                result = list(result)
+                out: Index = MultiIndex.from_tuples(result, names=name)
+                if out.nlevels == 1:
+                    # We had all tuples of length-one, which are
+                    # better represented as a regular Index.
+                    out = out.get_level_values(0)
+                return out
+            else:
+                return Index(result, name=name, dtype=dtype)
+        else:
+            index = self._orig.index
+            # This is a mess.
+            _dtype: DtypeObj | str | None = dtype
+            vdtype = getattr(result, "dtype", None)
+            if _dtype is not None:
+                pass
+            elif self._is_string:
+                if is_bool_dtype(vdtype):
+                    _dtype = result.dtype
+                elif returns_string:
+                    _dtype = self._orig.dtype
+                else:
+                    _dtype = vdtype
+            elif vdtype is not None:
+                _dtype = vdtype
+
+            if expand:
+                cons = self._orig._constructor_expanddim
+                result = cons(result, columns=name, index=index, dtype=_dtype)
+            else:
+                # Must be a Series
+                cons = self._orig._constructor
+                result = cons(result, name=name, index=index, dtype=_dtype)
+            result = result.__finalize__(self._orig, method="str")
+            if name is not None and result.ndim == 1:
+                # __finalize__ might copy over the original name, but we may
+                # want the new name (e.g. str.extract).
+                result.name = name
+            return result
+
+    def _get_series_list(self, others):
+        """
+        Auxiliary function for :meth:`str.cat`. Turn potentially mixed input
+        into a list of Series (elements without an index must match the length
+        of the calling Series/Index).
+
+        Parameters
+        ----------
+        others : Series, DataFrame, np.ndarray, list-like or list-like of
+            Objects that are either Series, Index or np.ndarray (1-dim).
+
+        Returns
+        -------
+        list of Series
+            Others transformed into list of Series.
+        """
+        from pandas import (
+            DataFrame,
+            Series,
+        )
+
+        # self._orig is either Series or Index
+        idx = self._orig if isinstance(self._orig, ABCIndex) else self._orig.index
+
+        # Generally speaking, all objects without an index inherit the index
+        # `idx` of the calling Series/Index - i.e. must have matching length.
+        # Objects with an index (i.e. Series/Index/DataFrame) keep their own.
+        if isinstance(others, ABCSeries):
+            return [others]
+        elif isinstance(others, ABCIndex):
+            return [Series(others, index=idx, dtype=others.dtype)]
+        elif isinstance(others, ABCDataFrame):
+            return [others[x] for x in others]
+        elif isinstance(others, np.ndarray) and others.ndim == 2:
+            others = DataFrame(others, index=idx)
+            return [others[x] for x in others]
+        elif is_list_like(others, allow_sets=False):
+            try:
+                others = list(others)  # ensure iterators do not get read twice etc
+            except TypeError:
+                # e.g. ser.str, raise below
+                pass
+            else:
+                # in case of list-like `others`, all elements must be
+                # either Series/Index/np.ndarray (1-dim)...
+                if all(
+                    isinstance(x, (ABCSeries, ABCIndex, ExtensionArray))
+                    or (isinstance(x, np.ndarray) and x.ndim == 1)
+                    for x in others
+                ):
+                    los: list[Series] = []
+                    while others:  # iterate through list and append each element
+                        los = los + self._get_series_list(others.pop(0))
+                    return los
+                # ... or just strings
+                elif all(not is_list_like(x) for x in others):
+                    return [Series(others, index=idx)]
+        raise TypeError(
+            "others must be Series, Index, DataFrame, np.ndarray "
+            "or list-like (either containing only strings or "
+            "containing only objects of type Series/Index/"
+            "np.ndarray[1-dim])"
+        )
+
+    @forbid_nonstring_types(["bytes", "mixed", "mixed-integer"])
+    def cat(
+        self,
+        others=None,
+        sep: str | None = None,
+        na_rep=None,
+        join: AlignJoin = "left",
+    ) -> str | Series | Index:
+        """
+        Concatenate strings in the Series/Index with given separator.
+
+        If `others` is specified, this function concatenates the Series/Index
+        and elements of `others` element-wise.
+        If `others` is not passed, then all values in the Series/Index are
+        concatenated into a single string with a given `sep`.
+
+        Parameters
+        ----------
+        others : Series, Index, DataFrame, np.ndarray or list-like
+            Series, Index, DataFrame, np.ndarray (one- or two-dimensional) and
+            other list-likes of strings must have the same length as the
+            calling Series/Index, with the exception of indexed objects (i.e.
+            Series/Index/DataFrame) if `join` is not None.
+
+            If others is a list-like that contains a combination of Series,
+            Index or np.ndarray (1-dim), then all elements will be unpacked and
+            must satisfy the above criteria individually.
+
+            If others is None, the method returns the concatenation of all
+            strings in the calling Series/Index.
+        sep : str, default ''
+            The separator between the different elements/columns. By default
+            the empty string `''` is used.
+        na_rep : str or None, default None
+            Representation that is inserted for all missing values:
+
+            - If `na_rep` is None, and `others` is None, missing values in the
+              Series/Index are omitted from the result.
+            - If `na_rep` is None, and `others` is not None, a row containing a
+              missing value in any of the columns (before concatenation) will
+              have a missing value in the result.
+        join : {'left', 'right', 'outer', 'inner'}, default 'left'
+            Determines the join-style between the calling Series/Index and any
+            Series/Index/DataFrame in `others` (objects without an index need
+            to match the length of the calling Series/Index). To disable
+            alignment, use `.values` on any Series/Index/DataFrame in `others`.
+
+        Returns
+        -------
+        str, Series or Index
+            If `others` is None, `str` is returned, otherwise a `Series/Index`
+            (same type as caller) of objects is returned.
+
+        See Also
+        --------
+        split : Split each string in the Series/Index.
+        join : Join lists contained as elements in the Series/Index.
+
+        Examples
+        --------
+        When not passing `others`, all values are concatenated into a single
+        string:
+
+        >>> s = pd.Series(['a', 'b', np.nan, 'd'])
+        >>> s.str.cat(sep=' ')
+        'a b d'
+
+        By default, NA values in the Series are ignored. Using `na_rep`, they
+        can be given a representation:
+
+        >>> s.str.cat(sep=' ', na_rep='?')
+        'a b ? d'
+
+        If `others` is specified, corresponding values are concatenated with
+        the separator. Result will be a Series of strings.
+
+        >>> s.str.cat(['A', 'B', 'C', 'D'], sep=',')
+        0    a,A
+        1    b,B
+        2    NaN
+        3    d,D
+        dtype: object
+
+        Missing values will remain missing in the result, but can again be
+        represented using `na_rep`
+
+        >>> s.str.cat(['A', 'B', 'C', 'D'], sep=',', na_rep='-')
+        0    a,A
+        1    b,B
+        2    -,C
+        3    d,D
+        dtype: object
+
+        If `sep` is not specified, the values are concatenated without
+        separation.
+
+        >>> s.str.cat(['A', 'B', 'C', 'D'], na_rep='-')
+        0    aA
+        1    bB
+        2    -C
+        3    dD
+        dtype: object
+
+        Series with different indexes can be aligned before concatenation. The
+        `join`-keyword works as in other methods.
+
+        >>> t = pd.Series(['d', 'a', 'e', 'c'], index=[3, 0, 4, 2])
+        >>> s.str.cat(t, join='left', na_rep='-')
+        0    aa
+        1    b-
+        2    -c
+        3    dd
+        dtype: object
+        >>>
+        >>> s.str.cat(t, join='outer', na_rep='-')
+        0    aa
+        1    b-
+        2    -c
+        3    dd
+        4    -e
+        dtype: object
+        >>>
+        >>> s.str.cat(t, join='inner', na_rep='-')
+        0    aa
+        2    -c
+        3    dd
+        dtype: object
+        >>>
+        >>> s.str.cat(t, join='right', na_rep='-')
+        3    dd
+        0    aa
+        4    -e
+        2    -c
+        dtype: object
+
+        For more examples, see :ref:`here `.
+        """
+        # TODO: dispatch
+        from pandas import (
+            Index,
+            Series,
+            concat,
+        )
+
+        if isinstance(others, str):
+            raise ValueError("Did you mean to supply a `sep` keyword?")
+        if sep is None:
+            sep = ""
+
+        if isinstance(self._orig, ABCIndex):
+            data = Series(self._orig, index=self._orig, dtype=self._orig.dtype)
+        else:  # Series
+            data = self._orig
+
+        # concatenate Series/Index with itself if no "others"
+        if others is None:
+            # error: Incompatible types in assignment (expression has type
+            # "ndarray", variable has type "Series")
+            data = ensure_object(data)  # type: ignore[assignment]
+            na_mask = isna(data)
+            if na_rep is None and na_mask.any():
+                return sep.join(data[~na_mask])
+            elif na_rep is not None and na_mask.any():
+                return sep.join(np.where(na_mask, na_rep, data))
+            else:
+                return sep.join(data)
+
+        try:
+            # turn anything in "others" into lists of Series
+            others = self._get_series_list(others)
+        except ValueError as err:  # do not catch TypeError raised by _get_series_list
+            raise ValueError(
+                "If `others` contains arrays or lists (or other "
+                "list-likes without an index), these must all be "
+                "of the same length as the calling Series/Index."
+            ) from err
+
+        # align if required
+        if any(not data.index.equals(x.index) for x in others):
+            # Need to add keys for uniqueness in case of duplicate columns
+            others = concat(
+                others,
+                axis=1,
+                join=(join if join == "inner" else "outer"),
+                keys=range(len(others)),
+                sort=False,
+                copy=False,
+            )
+            data, others = data.align(others, join=join)
+            others = [others[x] for x in others]  # again list of Series
+
+        all_cols = [ensure_object(x) for x in [data] + others]
+        na_masks = np.array([isna(x) for x in all_cols])
+        union_mask = np.logical_or.reduce(na_masks, axis=0)
+
+        if na_rep is None and union_mask.any():
+            # no na_rep means NaNs for all rows where any column has a NaN
+            # only necessary if there are actually any NaNs
+            result = np.empty(len(data), dtype=object)
+            np.putmask(result, union_mask, np.nan)
+
+            not_masked = ~union_mask
+            result[not_masked] = cat_safe([x[not_masked] for x in all_cols], sep)
+        elif na_rep is not None and union_mask.any():
+            # fill NaNs with na_rep in case there are actually any NaNs
+            all_cols = [
+                np.where(nm, na_rep, col) for nm, col in zip(na_masks, all_cols)
+            ]
+            result = cat_safe(all_cols, sep)
+        else:
+            # no NaNs - can just concatenate
+            result = cat_safe(all_cols, sep)
+
+        out: Index | Series
+        if isinstance(self._orig.dtype, CategoricalDtype):
+            # We need to infer the new categories.
+            dtype = self._orig.dtype.categories.dtype
+        else:
+            dtype = self._orig.dtype
+        if isinstance(self._orig, ABCIndex):
+            # add dtype for case that result is all-NA
+            if isna(result).all():
+                dtype = object  # type: ignore[assignment]
+
+            out = Index(result, dtype=dtype, name=self._orig.name)
+        else:  # Series
+            res_ser = Series(
+                result, dtype=dtype, index=data.index, name=self._orig.name, copy=False
+            )
+            out = res_ser.__finalize__(self._orig, method="str_cat")
+        return out
+
+    _shared_docs[
+        "str_split"
+    ] = r"""
+    Split strings around given separator/delimiter.
+
+    Splits the string in the Series/Index from the %(side)s,
+    at the specified delimiter string.
+
+    Parameters
+    ----------
+    pat : str%(pat_regex)s, optional
+        %(pat_description)s.
+        If not specified, split on whitespace.
+    n : int, default -1 (all)
+        Limit number of splits in output.
+        ``None``, 0 and -1 will be interpreted as return all splits.
+    expand : bool, default False
+        Expand the split strings into separate columns.
+
+        - If ``True``, return DataFrame/MultiIndex expanding dimensionality.
+        - If ``False``, return Series/Index, containing lists of strings.
+    %(regex_argument)s
+    Returns
+    -------
+    Series, Index, DataFrame or MultiIndex
+        Type matches caller unless ``expand=True`` (see Notes).
+    %(raises_split)s
+    See Also
+    --------
+    Series.str.split : Split strings around given separator/delimiter.
+    Series.str.rsplit : Splits string around given separator/delimiter,
+        starting from the right.
+    Series.str.join : Join lists contained as elements in the Series/Index
+        with passed delimiter.
+    str.split : Standard library version for split.
+    str.rsplit : Standard library version for rsplit.
+
+    Notes
+    -----
+    The handling of the `n` keyword depends on the number of found splits:
+
+    - If found splits > `n`,  make first `n` splits only
+    - If found splits <= `n`, make all splits
+    - If for a certain row the number of found splits < `n`,
+      append `None` for padding up to `n` if ``expand=True``
+
+    If using ``expand=True``, Series and Index callers return DataFrame and
+    MultiIndex objects, respectively.
+    %(regex_pat_note)s
+    Examples
+    --------
+    >>> s = pd.Series(
+    ...     [
+    ...         "this is a regular sentence",
+    ...         "https://docs.python.org/3/tutorial/index.html",
+    ...         np.nan
+    ...     ]
+    ... )
+    >>> s
+    0                       this is a regular sentence
+    1    https://docs.python.org/3/tutorial/index.html
+    2                                              NaN
+    dtype: object
+
+    In the default setting, the string is split by whitespace.
+
+    >>> s.str.split()
+    0                   [this, is, a, regular, sentence]
+    1    [https://docs.python.org/3/tutorial/index.html]
+    2                                                NaN
+    dtype: object
+
+    Without the `n` parameter, the outputs of `rsplit` and `split`
+    are identical.
+
+    >>> s.str.rsplit()
+    0                   [this, is, a, regular, sentence]
+    1    [https://docs.python.org/3/tutorial/index.html]
+    2                                                NaN
+    dtype: object
+
+    The `n` parameter can be used to limit the number of splits on the
+    delimiter. The outputs of `split` and `rsplit` are different.
+
+    >>> s.str.split(n=2)
+    0                     [this, is, a regular sentence]
+    1    [https://docs.python.org/3/tutorial/index.html]
+    2                                                NaN
+    dtype: object
+
+    >>> s.str.rsplit(n=2)
+    0                     [this is a, regular, sentence]
+    1    [https://docs.python.org/3/tutorial/index.html]
+    2                                                NaN
+    dtype: object
+
+    The `pat` parameter can be used to split by other characters.
+
+    >>> s.str.split(pat="/")
+    0                         [this is a regular sentence]
+    1    [https:, , docs.python.org, 3, tutorial, index...
+    2                                                  NaN
+    dtype: object
+
+    When using ``expand=True``, the split elements will expand out into
+    separate columns. If NaN is present, it is propagated throughout
+    the columns during the split.
+
+    >>> s.str.split(expand=True)
+                                                   0     1     2        3         4
+    0                                           this    is     a  regular  sentence
+    1  https://docs.python.org/3/tutorial/index.html  None  None     None      None
+    2                                            NaN   NaN   NaN      NaN       NaN
+
+    For slightly more complex use cases like splitting the html document name
+    from a url, a combination of parameter settings can be used.
+
+    >>> s.str.rsplit("/", n=1, expand=True)
+                                        0           1
+    0          this is a regular sentence        None
+    1  https://docs.python.org/3/tutorial  index.html
+    2                                 NaN         NaN
+    %(regex_examples)s"""
+
+    @Appender(
+        _shared_docs["str_split"]
+        % {
+            "side": "beginning",
+            "pat_regex": " or compiled regex",
+            "pat_description": "String or regular expression to split on",
+            "regex_argument": """
+    regex : bool, default None
+        Determines if the passed-in pattern is a regular expression:
+
+        - If ``True``, assumes the passed-in pattern is a regular expression
+        - If ``False``, treats the pattern as a literal string.
+        - If ``None`` and `pat` length is 1, treats `pat` as a literal string.
+        - If ``None`` and `pat` length is not 1, treats `pat` as a regular expression.
+        - Cannot be set to False if `pat` is a compiled regex
+
+        .. versionadded:: 1.4.0
+         """,
+            "raises_split": """
+                      Raises
+                      ------
+                      ValueError
+                          * if `regex` is False and `pat` is a compiled regex
+                      """,
+            "regex_pat_note": """
+    Use of `regex =False` with a `pat` as a compiled regex will raise an error.
+            """,
+            "method": "split",
+            "regex_examples": r"""
+    Remember to escape special characters when explicitly using regular expressions.
+
+    >>> s = pd.Series(["foo and bar plus baz"])
+    >>> s.str.split(r"and|plus", expand=True)
+        0   1   2
+    0 foo bar baz
+
+    Regular expressions can be used to handle urls or file names.
+    When `pat` is a string and ``regex=None`` (the default), the given `pat` is compiled
+    as a regex only if ``len(pat) != 1``.
+
+    >>> s = pd.Series(['foojpgbar.jpg'])
+    >>> s.str.split(r".", expand=True)
+               0    1
+    0  foojpgbar  jpg
+
+    >>> s.str.split(r"\.jpg", expand=True)
+               0 1
+    0  foojpgbar
+
+    When ``regex=True``, `pat` is interpreted as a regex
+
+    >>> s.str.split(r"\.jpg", regex=True, expand=True)
+               0 1
+    0  foojpgbar
+
+    A compiled regex can be passed as `pat`
+
+    >>> import re
+    >>> s.str.split(re.compile(r"\.jpg"), expand=True)
+               0 1
+    0  foojpgbar
+
+    When ``regex=False``, `pat` is interpreted as the string itself
+
+    >>> s.str.split(r"\.jpg", regex=False, expand=True)
+                   0
+    0  foojpgbar.jpg
+    """,
+        }
+    )
+    @forbid_nonstring_types(["bytes"])
+    def split(
+        self,
+        pat: str | re.Pattern | None = None,
+        *,
+        n=-1,
+        expand: bool = False,
+        regex: bool | None = None,
+    ):
+        if regex is False and is_re(pat):
+            raise ValueError(
+                "Cannot use a compiled regex as replacement pattern with regex=False"
+            )
+        if is_re(pat):
+            regex = True
+        result = self._data.array._str_split(pat, n, expand, regex)
+        if self._data.dtype == "category":
+            dtype = self._data.dtype.categories.dtype
+        else:
+            dtype = object if self._data.dtype == object else None
+        return self._wrap_result(
+            result, expand=expand, returns_string=expand, dtype=dtype
+        )
+
+    @Appender(
+        _shared_docs["str_split"]
+        % {
+            "side": "end",
+            "pat_regex": "",
+            "pat_description": "String to split on",
+            "regex_argument": "",
+            "raises_split": "",
+            "regex_pat_note": "",
+            "method": "rsplit",
+            "regex_examples": "",
+        }
+    )
+    @forbid_nonstring_types(["bytes"])
+    def rsplit(self, pat=None, *, n=-1, expand: bool = False):
+        result = self._data.array._str_rsplit(pat, n=n)
+        dtype = object if self._data.dtype == object else None
+        return self._wrap_result(
+            result, expand=expand, returns_string=expand, dtype=dtype
+        )
+
+    _shared_docs[
+        "str_partition"
+    ] = """
+    Split the string at the %(side)s occurrence of `sep`.
+
+    This method splits the string at the %(side)s occurrence of `sep`,
+    and returns 3 elements containing the part before the separator,
+    the separator itself, and the part after the separator.
+    If the separator is not found, return %(return)s.
+
+    Parameters
+    ----------
+    sep : str, default whitespace
+        String to split on.
+    expand : bool, default True
+        If True, return DataFrame/MultiIndex expanding dimensionality.
+        If False, return Series/Index.
+
+    Returns
+    -------
+    DataFrame/MultiIndex or Series/Index of objects
+
+    See Also
+    --------
+    %(also)s
+    Series.str.split : Split strings around given separators.
+    str.partition : Standard library version.
+
+    Examples
+    --------
+
+    >>> s = pd.Series(['Linda van der Berg', 'George Pitt-Rivers'])
+    >>> s
+    0    Linda van der Berg
+    1    George Pitt-Rivers
+    dtype: object
+
+    >>> s.str.partition()
+            0  1             2
+    0   Linda     van der Berg
+    1  George      Pitt-Rivers
+
+    To partition by the last space instead of the first one:
+
+    >>> s.str.rpartition()
+                   0  1            2
+    0  Linda van der            Berg
+    1         George     Pitt-Rivers
+
+    To partition by something different than a space:
+
+    >>> s.str.partition('-')
+                        0  1       2
+    0  Linda van der Berg
+    1         George Pitt  -  Rivers
+
+    To return a Series containing tuples instead of a DataFrame:
+
+    >>> s.str.partition('-', expand=False)
+    0    (Linda van der Berg, , )
+    1    (George Pitt, -, Rivers)
+    dtype: object
+
+    Also available on indices:
+
+    >>> idx = pd.Index(['X 123', 'Y 999'])
+    >>> idx
+    Index(['X 123', 'Y 999'], dtype='object')
+
+    Which will create a MultiIndex:
+
+    >>> idx.str.partition()
+    MultiIndex([('X', ' ', '123'),
+                ('Y', ' ', '999')],
+               )
+
+    Or an index with tuples with ``expand=False``:
+
+    >>> idx.str.partition(expand=False)
+    Index([('X', ' ', '123'), ('Y', ' ', '999')], dtype='object')
+    """
+
+    @Appender(
+        _shared_docs["str_partition"]
+        % {
+            "side": "first",
+            "return": "3 elements containing the string itself, followed by two "
+            "empty strings",
+            "also": "rpartition : Split the string at the last occurrence of `sep`.",
+        }
+    )
+    @forbid_nonstring_types(["bytes"])
+    def partition(self, sep: str = " ", expand: bool = True):
+        result = self._data.array._str_partition(sep, expand)
+        if self._data.dtype == "category":
+            dtype = self._data.dtype.categories.dtype
+        else:
+            dtype = object if self._data.dtype == object else None
+        return self._wrap_result(
+            result, expand=expand, returns_string=expand, dtype=dtype
+        )
+
+    @Appender(
+        _shared_docs["str_partition"]
+        % {
+            "side": "last",
+            "return": "3 elements containing two empty strings, followed by the "
+            "string itself",
+            "also": "partition : Split the string at the first occurrence of `sep`.",
+        }
+    )
+    @forbid_nonstring_types(["bytes"])
+    def rpartition(self, sep: str = " ", expand: bool = True):
+        result = self._data.array._str_rpartition(sep, expand)
+        if self._data.dtype == "category":
+            dtype = self._data.dtype.categories.dtype
+        else:
+            dtype = object if self._data.dtype == object else None
+        return self._wrap_result(
+            result, expand=expand, returns_string=expand, dtype=dtype
+        )
+
+    def get(self, i):
+        """
+        Extract element from each component at specified position or with specified key.
+
+        Extract element from lists, tuples, dict, or strings in each element in the
+        Series/Index.
+
+        Parameters
+        ----------
+        i : int or hashable dict label
+            Position or key of element to extract.
+
+        Returns
+        -------
+        Series or Index
+
+        Examples
+        --------
+        >>> s = pd.Series(["String",
+        ...               (1, 2, 3),
+        ...               ["a", "b", "c"],
+        ...               123,
+        ...               -456,
+        ...               {1: "Hello", "2": "World"}])
+        >>> s
+        0                        String
+        1                     (1, 2, 3)
+        2                     [a, b, c]
+        3                           123
+        4                          -456
+        5    {1: 'Hello', '2': 'World'}
+        dtype: object
+
+        >>> s.str.get(1)
+        0        t
+        1        2
+        2        b
+        3      NaN
+        4      NaN
+        5    Hello
+        dtype: object
+
+        >>> s.str.get(-1)
+        0      g
+        1      3
+        2      c
+        3    NaN
+        4    NaN
+        5    None
+        dtype: object
+
+        Return element with given key
+
+        >>> s = pd.Series([{"name": "Hello", "value": "World"},
+        ...               {"name": "Goodbye", "value": "Planet"}])
+        >>> s.str.get('name')
+        0      Hello
+        1    Goodbye
+        dtype: object
+        """
+        result = self._data.array._str_get(i)
+        return self._wrap_result(result)
+
+    @forbid_nonstring_types(["bytes"])
+    def join(self, sep: str):
+        """
+        Join lists contained as elements in the Series/Index with passed delimiter.
+
+        If the elements of a Series are lists themselves, join the content of these
+        lists using the delimiter passed to the function.
+        This function is an equivalent to :meth:`str.join`.
+
+        Parameters
+        ----------
+        sep : str
+            Delimiter to use between list entries.
+
+        Returns
+        -------
+        Series/Index: object
+            The list entries concatenated by intervening occurrences of the
+            delimiter.
+
+        Raises
+        ------
+        AttributeError
+            If the supplied Series contains neither strings nor lists.
+
+        See Also
+        --------
+        str.join : Standard library version of this method.
+        Series.str.split : Split strings around given separator/delimiter.
+
+        Notes
+        -----
+        If any of the list items is not a string object, the result of the join
+        will be `NaN`.
+
+        Examples
+        --------
+        Example with a list that contains non-string elements.
+
+        >>> s = pd.Series([['lion', 'elephant', 'zebra'],
+        ...                [1.1, 2.2, 3.3],
+        ...                ['cat', np.nan, 'dog'],
+        ...                ['cow', 4.5, 'goat'],
+        ...                ['duck', ['swan', 'fish'], 'guppy']])
+        >>> s
+        0        [lion, elephant, zebra]
+        1                [1.1, 2.2, 3.3]
+        2                [cat, nan, dog]
+        3               [cow, 4.5, goat]
+        4    [duck, [swan, fish], guppy]
+        dtype: object
+
+        Join all lists using a '-'. The lists containing object(s) of types other
+        than str will produce a NaN.
+
+        >>> s.str.join('-')
+        0    lion-elephant-zebra
+        1                    NaN
+        2                    NaN
+        3                    NaN
+        4                    NaN
+        dtype: object
+        """
+        result = self._data.array._str_join(sep)
+        return self._wrap_result(result)
+
+    @forbid_nonstring_types(["bytes"])
+    def contains(
+        self,
+        pat,
+        case: bool = True,
+        flags: int = 0,
+        na=lib.no_default,
+        regex: bool = True,
+    ):
+        r"""
+        Test if pattern or regex is contained within a string of a Series or Index.
+
+        Return boolean Series or Index based on whether a given pattern or regex is
+        contained within a string of a Series or Index.
+
+        Parameters
+        ----------
+        pat : str
+            Character sequence or regular expression.
+        case : bool, default True
+            If True, case sensitive.
+        flags : int, default 0 (no flags)
+            Flags to pass through to the re module, e.g. re.IGNORECASE.
+        na : scalar, optional
+            Fill value for missing values. The default depends on dtype of the
+            array. For object-dtype, ``numpy.nan`` is used. For the nullable
+            ``StringDtype``, ``pandas.NA`` is used. For the ``"str"`` dtype,
+            ``False`` is used.
+        regex : bool, default True
+            If True, assumes the pat is a regular expression.
+
+            If False, treats the pat as a literal string.
+
+        Returns
+        -------
+        Series or Index of boolean values
+            A Series or Index of boolean values indicating whether the
+            given pattern is contained within the string of each element
+            of the Series or Index.
+
+        See Also
+        --------
+        match : Analogous, but stricter, relying on re.match instead of re.search.
+        Series.str.startswith : Test if the start of each string element matches a
+            pattern.
+        Series.str.endswith : Same as startswith, but tests the end of string.
+
+        Examples
+        --------
+        Returning a Series of booleans using only a literal pattern.
+
+        >>> s1 = pd.Series(['Mouse', 'dog', 'house and parrot', '23', np.nan])
+        >>> s1.str.contains('og', regex=False)
+        0    False
+        1     True
+        2    False
+        3    False
+        4      NaN
+        dtype: object
+
+        Returning an Index of booleans using only a literal pattern.
+
+        >>> ind = pd.Index(['Mouse', 'dog', 'house and parrot', '23.0', np.nan])
+        >>> ind.str.contains('23', regex=False)
+        Index([False, False, False, True, nan], dtype='object')
+
+        Specifying case sensitivity using `case`.
+
+        >>> s1.str.contains('oG', case=True, regex=True)
+        0    False
+        1    False
+        2    False
+        3    False
+        4      NaN
+        dtype: object
+
+        Specifying `na` to be `False` instead of `NaN` replaces NaN values
+        with `False`. If Series or Index does not contain NaN values
+        the resultant dtype will be `bool`, otherwise, an `object` dtype.
+
+        >>> s1.str.contains('og', na=False, regex=True)
+        0    False
+        1     True
+        2    False
+        3    False
+        4    False
+        dtype: bool
+
+        Returning 'house' or 'dog' when either expression occurs in a string.
+
+        >>> s1.str.contains('house|dog', regex=True)
+        0    False
+        1     True
+        2     True
+        3    False
+        4      NaN
+        dtype: object
+
+        Ignoring case sensitivity using `flags` with regex.
+
+        >>> import re
+        >>> s1.str.contains('PARROT', flags=re.IGNORECASE, regex=True)
+        0    False
+        1    False
+        2     True
+        3    False
+        4      NaN
+        dtype: object
+
+        Returning any digit using regular expression.
+
+        >>> s1.str.contains('\\d', regex=True)
+        0    False
+        1    False
+        2    False
+        3     True
+        4      NaN
+        dtype: object
+
+        Ensure `pat` is a not a literal pattern when `regex` is set to True.
+        Note in the following example one might expect only `s2[1]` and `s2[3]` to
+        return `True`. However, '.0' as a regex matches any character
+        followed by a 0.
+
+        >>> s2 = pd.Series(['40', '40.0', '41', '41.0', '35'])
+        >>> s2.str.contains('.0', regex=True)
+        0     True
+        1     True
+        2    False
+        3     True
+        4    False
+        dtype: bool
+        """
+        if regex and re.compile(pat).groups:
+            warnings.warn(
+                "This pattern is interpreted as a regular expression, and has "
+                "match groups. To actually get the groups, use str.extract.",
+                UserWarning,
+                stacklevel=find_stack_level(),
+            )
+
+        result = self._data.array._str_contains(pat, case, flags, na, regex)
+        return self._wrap_result(result, fill_value=na, returns_string=False)
+
+    @forbid_nonstring_types(["bytes"])
+    def match(self, pat: str, case: bool = True, flags: int = 0, na=lib.no_default):
+        """
+        Determine if each string starts with a match of a regular expression.
+
+        Parameters
+        ----------
+        pat : str or compiled regex
+            Character sequence or regular expression.
+        case : bool, default True
+            If True, case sensitive.
+        flags : int, default 0 (no flags)
+            Regex module flags, e.g. re.IGNORECASE.
+        na : scalar, optional
+            Fill value for missing values. The default depends on dtype of the
+            array. For object-dtype, ``numpy.nan`` is used. For the nullable
+            ``StringDtype``, ``pandas.NA`` is used. For the ``"str"`` dtype,
+            ``False`` is used.
+
+        Returns
+        -------
+        Series/Index/array of boolean values
+
+        See Also
+        --------
+        fullmatch : Stricter matching that requires the entire string to match.
+        contains : Analogous, but less strict, relying on re.search instead of
+            re.match.
+        extract : Extract matched groups.
+
+        Examples
+        --------
+        >>> ser = pd.Series(["horse", "eagle", "donkey"])
+        >>> ser.str.match("e")
+        0   False
+        1   True
+        2   False
+        dtype: bool
+        """
+        result = self._data.array._str_match(pat, case=case, flags=flags, na=na)
+        return self._wrap_result(result, fill_value=na, returns_string=False)
+
+    @forbid_nonstring_types(["bytes"])
+    def fullmatch(self, pat, case: bool = True, flags: int = 0, na=lib.no_default):
+        """
+        Determine if each string entirely matches a regular expression.
+
+        Parameters
+        ----------
+        pat : str
+            Character sequence or regular expression.
+        case : bool, default True
+            If True, case sensitive.
+        flags : int, default 0 (no flags)
+            Regex module flags, e.g. re.IGNORECASE.
+        na : scalar, optional
+            Fill value for missing values. The default depends on dtype of the
+            array. For object-dtype, ``numpy.nan`` is used. For the nullable
+            ``StringDtype``, ``pandas.NA`` is used. For the ``"str"`` dtype,
+            ``False`` is used.
+
+        Returns
+        -------
+        Series/Index/array of boolean values
+
+        See Also
+        --------
+        match : Similar, but also returns `True` when only a *prefix* of the string
+            matches the regular expression.
+        extract : Extract matched groups.
+
+        Examples
+        --------
+        >>> ser = pd.Series(["cat", "duck", "dove"])
+        >>> ser.str.fullmatch(r'd.+')
+        0   False
+        1    True
+        2    True
+        dtype: bool
+        """
+        result = self._data.array._str_fullmatch(pat, case=case, flags=flags, na=na)
+        return self._wrap_result(result, fill_value=na, returns_string=False)
+
+    @forbid_nonstring_types(["bytes"])
+    def replace(
+        self,
+        pat: str | re.Pattern,
+        repl: str | Callable,
+        n: int = -1,
+        case: bool | None = None,
+        flags: int = 0,
+        regex: bool = False,
+    ):
+        r"""
+        Replace each occurrence of pattern/regex in the Series/Index.
+
+        Equivalent to :meth:`str.replace` or :func:`re.sub`, depending on
+        the regex value.
+
+        Parameters
+        ----------
+        pat : str or compiled regex
+            String can be a character sequence or regular expression.
+        repl : str or callable
+            Replacement string or a callable. The callable is passed the regex
+            match object and must return a replacement string to be used.
+            See :func:`re.sub`.
+        n : int, default -1 (all)
+            Number of replacements to make from start.
+        case : bool, default None
+            Determines if replace is case sensitive:
+
+            - If True, case sensitive (the default if `pat` is a string)
+            - Set to False for case insensitive
+            - Cannot be set if `pat` is a compiled regex.
+
+        flags : int, default 0 (no flags)
+            Regex module flags, e.g. re.IGNORECASE. Cannot be set if `pat` is a compiled
+            regex.
+        regex : bool, default False
+            Determines if the passed-in pattern is a regular expression:
+
+            - If True, assumes the passed-in pattern is a regular expression.
+            - If False, treats the pattern as a literal string
+            - Cannot be set to False if `pat` is a compiled regex or `repl` is
+              a callable.
+
+        Returns
+        -------
+        Series or Index of object
+            A copy of the object with all matching occurrences of `pat` replaced by
+            `repl`.
+
+        Raises
+        ------
+        ValueError
+            * if `regex` is False and `repl` is a callable or `pat` is a compiled
+              regex
+            * if `pat` is a compiled regex and `case` or `flags` is set
+
+        Notes
+        -----
+        When `pat` is a compiled regex, all flags should be included in the
+        compiled regex. Use of `case`, `flags`, or `regex=False` with a compiled
+        regex will raise an error.
+
+        Examples
+        --------
+        When `pat` is a string and `regex` is True, the given `pat`
+        is compiled as a regex. When `repl` is a string, it replaces matching
+        regex patterns as with :meth:`re.sub`. NaN value(s) in the Series are
+        left as is:
+
+        >>> pd.Series(['foo', 'fuz', np.nan]).str.replace('f.', 'ba', regex=True)
+        0    bao
+        1    baz
+        2    NaN
+        dtype: object
+
+        When `pat` is a string and `regex` is False, every `pat` is replaced with
+        `repl` as with :meth:`str.replace`:
+
+        >>> pd.Series(['f.o', 'fuz', np.nan]).str.replace('f.', 'ba', regex=False)
+        0    bao
+        1    fuz
+        2    NaN
+        dtype: object
+
+        When `repl` is a callable, it is called on every `pat` using
+        :func:`re.sub`. The callable should expect one positional argument
+        (a regex object) and return a string.
+
+        To get the idea:
+
+        >>> pd.Series(['foo', 'fuz', np.nan]).str.replace('f', repr, regex=True)
+        0    oo
+        1    uz
+        2                                            NaN
+        dtype: object
+
+        Reverse every lowercase alphabetic word:
+
+        >>> repl = lambda m: m.group(0)[::-1]
+        >>> ser = pd.Series(['foo 123', 'bar baz', np.nan])
+        >>> ser.str.replace(r'[a-z]+', repl, regex=True)
+        0    oof 123
+        1    rab zab
+        2        NaN
+        dtype: object
+
+        Using regex groups (extract second group and swap case):
+
+        >>> pat = r"(?P\w+) (?P\w+) (?P\w+)"
+        >>> repl = lambda m: m.group('two').swapcase()
+        >>> ser = pd.Series(['One Two Three', 'Foo Bar Baz'])
+        >>> ser.str.replace(pat, repl, regex=True)
+        0    tWO
+        1    bAR
+        dtype: object
+
+        Using a compiled regex with flags
+
+        >>> import re
+        >>> regex_pat = re.compile(r'FUZ', flags=re.IGNORECASE)
+        >>> pd.Series(['foo', 'fuz', np.nan]).str.replace(regex_pat, 'bar', regex=True)
+        0    foo
+        1    bar
+        2    NaN
+        dtype: object
+        """
+        # Check whether repl is valid (GH 13438, GH 15055)
+        if not (isinstance(repl, str) or callable(repl)):
+            raise TypeError("repl must be a string or callable")
+
+        is_compiled_re = is_re(pat)
+        if regex or regex is None:
+            if is_compiled_re and (case is not None or flags != 0):
+                raise ValueError(
+                    "case and flags cannot be set when pat is a compiled regex"
+                )
+
+        elif is_compiled_re:
+            raise ValueError(
+                "Cannot use a compiled regex as replacement pattern with regex=False"
+            )
+        elif callable(repl):
+            raise ValueError("Cannot use a callable replacement when regex=False")
+
+        if case is None:
+            case = True
+
+        result = self._data.array._str_replace(
+            pat, repl, n=n, case=case, flags=flags, regex=regex
+        )
+        return self._wrap_result(result)
+
+    @forbid_nonstring_types(["bytes"])
+    def repeat(self, repeats):
+        """
+        Duplicate each string in the Series or Index.
+
+        Parameters
+        ----------
+        repeats : int or sequence of int
+            Same value for all (int) or different value per (sequence).
+
+        Returns
+        -------
+        Series or pandas.Index
+            Series or Index of repeated string objects specified by
+            input parameter repeats.
+
+        Examples
+        --------
+        >>> s = pd.Series(['a', 'b', 'c'])
+        >>> s
+        0    a
+        1    b
+        2    c
+        dtype: object
+
+        Single int repeats string in Series
+
+        >>> s.str.repeat(repeats=2)
+        0    aa
+        1    bb
+        2    cc
+        dtype: object
+
+        Sequence of int repeats corresponding string in Series
+
+        >>> s.str.repeat(repeats=[1, 2, 3])
+        0      a
+        1     bb
+        2    ccc
+        dtype: object
+        """
+        result = self._data.array._str_repeat(repeats)
+        return self._wrap_result(result)
+
+    @forbid_nonstring_types(["bytes"])
+    def pad(
+        self,
+        width: int,
+        side: Literal["left", "right", "both"] = "left",
+        fillchar: str = " ",
+    ):
+        """
+        Pad strings in the Series/Index up to width.
+
+        Parameters
+        ----------
+        width : int
+            Minimum width of resulting string; additional characters will be filled
+            with character defined in `fillchar`.
+        side : {'left', 'right', 'both'}, default 'left'
+            Side from which to fill resulting string.
+        fillchar : str, default ' '
+            Additional character for filling, default is whitespace.
+
+        Returns
+        -------
+        Series or Index of object
+            Returns Series or Index with minimum number of char in object.
+
+        See Also
+        --------
+        Series.str.rjust : Fills the left side of strings with an arbitrary
+            character. Equivalent to ``Series.str.pad(side='left')``.
+        Series.str.ljust : Fills the right side of strings with an arbitrary
+            character. Equivalent to ``Series.str.pad(side='right')``.
+        Series.str.center : Fills both sides of strings with an arbitrary
+            character. Equivalent to ``Series.str.pad(side='both')``.
+        Series.str.zfill : Pad strings in the Series/Index by prepending '0'
+            character. Equivalent to ``Series.str.pad(side='left', fillchar='0')``.
+
+        Examples
+        --------
+        >>> s = pd.Series(["caribou", "tiger"])
+        >>> s
+        0    caribou
+        1      tiger
+        dtype: object
+
+        >>> s.str.pad(width=10)
+        0       caribou
+        1         tiger
+        dtype: object
+
+        >>> s.str.pad(width=10, side='right', fillchar='-')
+        0    caribou---
+        1    tiger-----
+        dtype: object
+
+        >>> s.str.pad(width=10, side='both', fillchar='-')
+        0    -caribou--
+        1    --tiger---
+        dtype: object
+        """
+        if not isinstance(fillchar, str):
+            msg = f"fillchar must be a character, not {type(fillchar).__name__}"
+            raise TypeError(msg)
+
+        if len(fillchar) != 1:
+            raise TypeError("fillchar must be a character, not str")
+
+        if not is_integer(width):
+            msg = f"width must be of integer type, not {type(width).__name__}"
+            raise TypeError(msg)
+
+        result = self._data.array._str_pad(width, side=side, fillchar=fillchar)
+        return self._wrap_result(result)
+
+    _shared_docs[
+        "str_pad"
+    ] = """
+    Pad %(side)s side of strings in the Series/Index.
+
+    Equivalent to :meth:`str.%(method)s`.
+
+    Parameters
+    ----------
+    width : int
+        Minimum width of resulting string; additional characters will be filled
+        with ``fillchar``.
+    fillchar : str
+        Additional character for filling, default is whitespace.
+
+    Returns
+    -------
+    Series/Index of objects.
+
+    Examples
+    --------
+    For Series.str.center:
+
+    >>> ser = pd.Series(['dog', 'bird', 'mouse'])
+    >>> ser.str.center(8, fillchar='.')
+    0   ..dog...
+    1   ..bird..
+    2   .mouse..
+    dtype: object
+
+    For Series.str.ljust:
+
+    >>> ser = pd.Series(['dog', 'bird', 'mouse'])
+    >>> ser.str.ljust(8, fillchar='.')
+    0   dog.....
+    1   bird....
+    2   mouse...
+    dtype: object
+
+    For Series.str.rjust:
+
+    >>> ser = pd.Series(['dog', 'bird', 'mouse'])
+    >>> ser.str.rjust(8, fillchar='.')
+    0   .....dog
+    1   ....bird
+    2   ...mouse
+    dtype: object
+    """
+
+    @Appender(_shared_docs["str_pad"] % {"side": "left and right", "method": "center"})
+    @forbid_nonstring_types(["bytes"])
+    def center(self, width: int, fillchar: str = " "):
+        return self.pad(width, side="both", fillchar=fillchar)
+
+    @Appender(_shared_docs["str_pad"] % {"side": "right", "method": "ljust"})
+    @forbid_nonstring_types(["bytes"])
+    def ljust(self, width: int, fillchar: str = " "):
+        return self.pad(width, side="right", fillchar=fillchar)
+
+    @Appender(_shared_docs["str_pad"] % {"side": "left", "method": "rjust"})
+    @forbid_nonstring_types(["bytes"])
+    def rjust(self, width: int, fillchar: str = " "):
+        return self.pad(width, side="left", fillchar=fillchar)
+
+    @forbid_nonstring_types(["bytes"])
+    def zfill(self, width: int):
+        """
+        Pad strings in the Series/Index by prepending '0' characters.
+
+        Strings in the Series/Index are padded with '0' characters on the
+        left of the string to reach a total string length  `width`. Strings
+        in the Series/Index with length greater or equal to `width` are
+        unchanged.
+
+        Parameters
+        ----------
+        width : int
+            Minimum length of resulting string; strings with length less
+            than `width` be prepended with '0' characters.
+
+        Returns
+        -------
+        Series/Index of objects.
+
+        See Also
+        --------
+        Series.str.rjust : Fills the left side of strings with an arbitrary
+            character.
+        Series.str.ljust : Fills the right side of strings with an arbitrary
+            character.
+        Series.str.pad : Fills the specified sides of strings with an arbitrary
+            character.
+        Series.str.center : Fills both sides of strings with an arbitrary
+            character.
+
+        Notes
+        -----
+        Differs from :meth:`str.zfill` which has special handling
+        for '+'/'-' in the string.
+
+        Examples
+        --------
+        >>> s = pd.Series(['-1', '1', '1000', 10, np.nan])
+        >>> s
+        0      -1
+        1       1
+        2    1000
+        3      10
+        4     NaN
+        dtype: object
+
+        Note that ``10`` and ``NaN`` are not strings, therefore they are
+        converted to ``NaN``. The minus sign in ``'-1'`` is treated as a
+        special character and the zero is added to the right of it
+        (:meth:`str.zfill` would have moved it to the left). ``1000``
+        remains unchanged as it is longer than `width`.
+
+        >>> s.str.zfill(3)
+        0     -01
+        1     001
+        2    1000
+        3     NaN
+        4     NaN
+        dtype: object
+        """
+        if not is_integer(width):
+            msg = f"width must be of integer type, not {type(width).__name__}"
+            raise TypeError(msg)
+        f = lambda x: x.zfill(width)
+        result = self._data.array._str_map(f)
+        return self._wrap_result(result)
+
+    def slice(self, start=None, stop=None, step=None):
+        """
+        Slice substrings from each element in the Series or Index.
+
+        Parameters
+        ----------
+        start : int, optional
+            Start position for slice operation.
+        stop : int, optional
+            Stop position for slice operation.
+        step : int, optional
+            Step size for slice operation.
+
+        Returns
+        -------
+        Series or Index of object
+            Series or Index from sliced substring from original string object.
+
+        See Also
+        --------
+        Series.str.slice_replace : Replace a slice with a string.
+        Series.str.get : Return element at position.
+            Equivalent to `Series.str.slice(start=i, stop=i+1)` with `i`
+            being the position.
+
+        Examples
+        --------
+        >>> s = pd.Series(["koala", "dog", "chameleon"])
+        >>> s
+        0        koala
+        1          dog
+        2    chameleon
+        dtype: object
+
+        >>> s.str.slice(start=1)
+        0        oala
+        1          og
+        2    hameleon
+        dtype: object
+
+        >>> s.str.slice(start=-1)
+        0           a
+        1           g
+        2           n
+        dtype: object
+
+        >>> s.str.slice(stop=2)
+        0    ko
+        1    do
+        2    ch
+        dtype: object
+
+        >>> s.str.slice(step=2)
+        0      kaa
+        1       dg
+        2    caeen
+        dtype: object
+
+        >>> s.str.slice(start=0, stop=5, step=3)
+        0    kl
+        1     d
+        2    cm
+        dtype: object
+
+        Equivalent behaviour to:
+
+        >>> s.str[0:5:3]
+        0    kl
+        1     d
+        2    cm
+        dtype: object
+        """
+        result = self._data.array._str_slice(start, stop, step)
+        return self._wrap_result(result)
+
+    @forbid_nonstring_types(["bytes"])
+    def slice_replace(self, start=None, stop=None, repl=None):
+        """
+        Replace a positional slice of a string with another value.
+
+        Parameters
+        ----------
+        start : int, optional
+            Left index position to use for the slice. If not specified (None),
+            the slice is unbounded on the left, i.e. slice from the start
+            of the string.
+        stop : int, optional
+            Right index position to use for the slice. If not specified (None),
+            the slice is unbounded on the right, i.e. slice until the
+            end of the string.
+        repl : str, optional
+            String for replacement. If not specified (None), the sliced region
+            is replaced with an empty string.
+
+        Returns
+        -------
+        Series or Index
+            Same type as the original object.
+
+        See Also
+        --------
+        Series.str.slice : Just slicing without replacement.
+
+        Examples
+        --------
+        >>> s = pd.Series(['a', 'ab', 'abc', 'abdc', 'abcde'])
+        >>> s
+        0        a
+        1       ab
+        2      abc
+        3     abdc
+        4    abcde
+        dtype: object
+
+        Specify just `start`, meaning replace `start` until the end of the
+        string with `repl`.
+
+        >>> s.str.slice_replace(1, repl='X')
+        0    aX
+        1    aX
+        2    aX
+        3    aX
+        4    aX
+        dtype: object
+
+        Specify just `stop`, meaning the start of the string to `stop` is replaced
+        with `repl`, and the rest of the string is included.
+
+        >>> s.str.slice_replace(stop=2, repl='X')
+        0       X
+        1       X
+        2      Xc
+        3     Xdc
+        4    Xcde
+        dtype: object
+
+        Specify `start` and `stop`, meaning the slice from `start` to `stop` is
+        replaced with `repl`. Everything before or after `start` and `stop` is
+        included as is.
+
+        >>> s.str.slice_replace(start=1, stop=3, repl='X')
+        0      aX
+        1      aX
+        2      aX
+        3     aXc
+        4    aXde
+        dtype: object
+        """
+        result = self._data.array._str_slice_replace(start, stop, repl)
+        return self._wrap_result(result)
+
+    def decode(
+        self, encoding, errors: str = "strict", dtype: str | DtypeObj | None = None
+    ):
+        """
+        Decode character string in the Series/Index using indicated encoding.
+
+        Equivalent to :meth:`str.decode` in python2 and :meth:`bytes.decode` in
+        python3.
+
+        Parameters
+        ----------
+        encoding : str
+        errors : str, optional
+            Specifies the error handling scheme.
+            Possible values are those supported by :meth:`bytes.decode`.
+        dtype : str or dtype, optional
+            The dtype of the result. When not ``None``, must be either a string or
+            object dtype. When ``None``, the dtype of the result is determined by
+            ``pd.options.future.infer_string``.
+
+            .. versionadded:: 2.3.0
+
+        Returns
+        -------
+        Series or Index
+
+        Examples
+        --------
+        For Series:
+
+        >>> ser = pd.Series([b'cow', b'123', b'()'])
+        >>> ser.str.decode('ascii')
+        0   cow
+        1   123
+        2   ()
+        dtype: object
+        """
+        if dtype is not None and not is_string_dtype(dtype):
+            raise ValueError(f"dtype must be string or object, got {dtype=}")
+        if dtype is None and get_option("future.infer_string"):
+            dtype = "str"
+        # TODO: Add a similar _bytes interface.
+        if encoding in _cpython_optimized_decoders:
+            # CPython optimized implementation
+            f = lambda x: x.decode(encoding, errors)
+        else:
+            decoder = codecs.getdecoder(encoding)
+            f = lambda x: decoder(x, errors)[0]
+        arr = self._data.array
+        result = arr._str_map(f)
+        return self._wrap_result(result, dtype=dtype)
+
+    @forbid_nonstring_types(["bytes"])
+    def encode(self, encoding, errors: str = "strict"):
+        """
+        Encode character string in the Series/Index using indicated encoding.
+
+        Equivalent to :meth:`str.encode`.
+
+        Parameters
+        ----------
+        encoding : str
+        errors : str, optional
+
+        Returns
+        -------
+        Series/Index of objects
+
+        Examples
+        --------
+        >>> ser = pd.Series(['cow', '123', '()'])
+        >>> ser.str.encode(encoding='ascii')
+        0     b'cow'
+        1     b'123'
+        2      b'()'
+        dtype: object
+        """
+        result = self._data.array._str_encode(encoding, errors)
+        return self._wrap_result(result, returns_string=False)
+
+    _shared_docs[
+        "str_strip"
+    ] = r"""
+    Remove %(position)s characters.
+
+    Strip whitespaces (including newlines) or a set of specified characters
+    from each string in the Series/Index from %(side)s.
+    Replaces any non-strings in Series with NaNs.
+    Equivalent to :meth:`str.%(method)s`.
+
+    Parameters
+    ----------
+    to_strip : str or None, default None
+        Specifying the set of characters to be removed.
+        All combinations of this set of characters will be stripped.
+        If None then whitespaces are removed.
+
+    Returns
+    -------
+    Series or Index of object
+
+    See Also
+    --------
+    Series.str.strip : Remove leading and trailing characters in Series/Index.
+    Series.str.lstrip : Remove leading characters in Series/Index.
+    Series.str.rstrip : Remove trailing characters in Series/Index.
+
+    Examples
+    --------
+    >>> s = pd.Series(['1. Ant.  ', '2. Bee!\n', '3. Cat?\t', np.nan, 10, True])
+    >>> s
+    0    1. Ant.
+    1    2. Bee!\n
+    2    3. Cat?\t
+    3          NaN
+    4           10
+    5         True
+    dtype: object
+
+    >>> s.str.strip()
+    0    1. Ant.
+    1    2. Bee!
+    2    3. Cat?
+    3        NaN
+    4        NaN
+    5        NaN
+    dtype: object
+
+    >>> s.str.lstrip('123.')
+    0    Ant.
+    1    Bee!\n
+    2    Cat?\t
+    3       NaN
+    4       NaN
+    5       NaN
+    dtype: object
+
+    >>> s.str.rstrip('.!? \n\t')
+    0    1. Ant
+    1    2. Bee
+    2    3. Cat
+    3       NaN
+    4       NaN
+    5       NaN
+    dtype: object
+
+    >>> s.str.strip('123.!? \n\t')
+    0    Ant
+    1    Bee
+    2    Cat
+    3    NaN
+    4    NaN
+    5    NaN
+    dtype: object
+    """
+
+    @Appender(
+        _shared_docs["str_strip"]
+        % {
+            "side": "left and right sides",
+            "method": "strip",
+            "position": "leading and trailing",
+        }
+    )
+    @forbid_nonstring_types(["bytes"])
+    def strip(self, to_strip=None):
+        result = self._data.array._str_strip(to_strip)
+        return self._wrap_result(result)
+
+    @Appender(
+        _shared_docs["str_strip"]
+        % {"side": "left side", "method": "lstrip", "position": "leading"}
+    )
+    @forbid_nonstring_types(["bytes"])
+    def lstrip(self, to_strip=None):
+        result = self._data.array._str_lstrip(to_strip)
+        return self._wrap_result(result)
+
+    @Appender(
+        _shared_docs["str_strip"]
+        % {"side": "right side", "method": "rstrip", "position": "trailing"}
+    )
+    @forbid_nonstring_types(["bytes"])
+    def rstrip(self, to_strip=None):
+        result = self._data.array._str_rstrip(to_strip)
+        return self._wrap_result(result)
+
+    _shared_docs[
+        "str_removefix"
+    ] = r"""
+    Remove a %(side)s from an object series.
+
+    If the %(side)s is not present, the original string will be returned.
+
+    Parameters
+    ----------
+    %(side)s : str
+        Remove the %(side)s of the string.
+
+    Returns
+    -------
+    Series/Index: object
+        The Series or Index with given %(side)s removed.
+
+    See Also
+    --------
+    Series.str.remove%(other_side)s : Remove a %(other_side)s from an object series.
+
+    Examples
+    --------
+    >>> s = pd.Series(["str_foo", "str_bar", "no_prefix"])
+    >>> s
+    0    str_foo
+    1    str_bar
+    2    no_prefix
+    dtype: object
+    >>> s.str.removeprefix("str_")
+    0    foo
+    1    bar
+    2    no_prefix
+    dtype: object
+
+    >>> s = pd.Series(["foo_str", "bar_str", "no_suffix"])
+    >>> s
+    0    foo_str
+    1    bar_str
+    2    no_suffix
+    dtype: object
+    >>> s.str.removesuffix("_str")
+    0    foo
+    1    bar
+    2    no_suffix
+    dtype: object
+    """
+
+    @Appender(
+        _shared_docs["str_removefix"] % {"side": "prefix", "other_side": "suffix"}
+    )
+    @forbid_nonstring_types(["bytes"])
+    def removeprefix(self, prefix: str):
+        result = self._data.array._str_removeprefix(prefix)
+        return self._wrap_result(result)
+
+    @Appender(
+        _shared_docs["str_removefix"] % {"side": "suffix", "other_side": "prefix"}
+    )
+    @forbid_nonstring_types(["bytes"])
+    def removesuffix(self, suffix: str):
+        result = self._data.array._str_removesuffix(suffix)
+        return self._wrap_result(result)
+
+    @forbid_nonstring_types(["bytes"])
+    def wrap(self, width: int, **kwargs):
+        r"""
+        Wrap strings in Series/Index at specified line width.
+
+        This method has the same keyword parameters and defaults as
+        :class:`textwrap.TextWrapper`.
+
+        Parameters
+        ----------
+        width : int
+            Maximum line width.
+        expand_tabs : bool, optional
+            If True, tab characters will be expanded to spaces (default: True).
+        replace_whitespace : bool, optional
+            If True, each whitespace character (as defined by string.whitespace)
+            remaining after tab expansion will be replaced by a single space
+            (default: True).
+        drop_whitespace : bool, optional
+            If True, whitespace that, after wrapping, happens to end up at the
+            beginning or end of a line is dropped (default: True).
+        break_long_words : bool, optional
+            If True, then words longer than width will be broken in order to ensure
+            that no lines are longer than width. If it is false, long words will
+            not be broken, and some lines may be longer than width (default: True).
+        break_on_hyphens : bool, optional
+            If True, wrapping will occur preferably on whitespace and right after
+            hyphens in compound words, as it is customary in English. If false,
+            only whitespaces will be considered as potentially good places for line
+            breaks, but you need to set break_long_words to false if you want truly
+            insecable words (default: True).
+
+        Returns
+        -------
+        Series or Index
+
+        Notes
+        -----
+        Internally, this method uses a :class:`textwrap.TextWrapper` instance with
+        default settings. To achieve behavior matching R's stringr library str_wrap
+        function, use the arguments:
+
+        - expand_tabs = False
+        - replace_whitespace = True
+        - drop_whitespace = True
+        - break_long_words = False
+        - break_on_hyphens = False
+
+        Examples
+        --------
+        >>> s = pd.Series(['line to be wrapped', 'another line to be wrapped'])
+        >>> s.str.wrap(12)
+        0             line to be\nwrapped
+        1    another line\nto be\nwrapped
+        dtype: object
+        """
+        result = self._data.array._str_wrap(width, **kwargs)
+        return self._wrap_result(result)
+
+    @forbid_nonstring_types(["bytes"])
+    def get_dummies(self, sep: str = "|"):
+        """
+        Return DataFrame of dummy/indicator variables for Series.
+
+        Each string in Series is split by sep and returned as a DataFrame
+        of dummy/indicator variables.
+
+        Parameters
+        ----------
+        sep : str, default "|"
+            String to split on.
+
+        Returns
+        -------
+        DataFrame
+            Dummy variables corresponding to values of the Series.
+
+        See Also
+        --------
+        get_dummies : Convert categorical variable into dummy/indicator
+            variables.
+
+        Examples
+        --------
+        >>> pd.Series(['a|b', 'a', 'a|c']).str.get_dummies()
+           a  b  c
+        0  1  1  0
+        1  1  0  0
+        2  1  0  1
+
+        >>> pd.Series(['a|b', np.nan, 'a|c']).str.get_dummies()
+           a  b  c
+        0  1  1  0
+        1  0  0  0
+        2  1  0  1
+        """
+        # we need to cast to Series of strings as only that has all
+        # methods available for making the dummies...
+        result, name = self._data.array._str_get_dummies(sep)
+        return self._wrap_result(
+            result,
+            name=name,
+            expand=True,
+            returns_string=False,
+        )
+
+    @forbid_nonstring_types(["bytes"])
+    def translate(self, table):
+        """
+        Map all characters in the string through the given mapping table.
+
+        Equivalent to standard :meth:`str.translate`.
+
+        Parameters
+        ----------
+        table : dict
+            Table is a mapping of Unicode ordinals to Unicode ordinals, strings, or
+            None. Unmapped characters are left untouched.
+            Characters mapped to None are deleted. :meth:`str.maketrans` is a
+            helper function for making translation tables.
+
+        Returns
+        -------
+        Series or Index
+
+        Examples
+        --------
+        >>> ser = pd.Series(["El niño", "Françoise"])
+        >>> mytable = str.maketrans({'ñ': 'n', 'ç': 'c'})
+        >>> ser.str.translate(mytable)
+        0   El nino
+        1   Francoise
+        dtype: object
+        """
+        result = self._data.array._str_translate(table)
+        dtype = object if self._data.dtype == "object" else None
+        return self._wrap_result(result, dtype=dtype)
+
+    @forbid_nonstring_types(["bytes"])
+    def count(self, pat, flags: int = 0):
+        r"""
+        Count occurrences of pattern in each string of the Series/Index.
+
+        This function is used to count the number of times a particular regex
+        pattern is repeated in each of the string elements of the
+        :class:`~pandas.Series`.
+
+        Parameters
+        ----------
+        pat : str
+            Valid regular expression.
+        flags : int, default 0, meaning no flags
+            Flags for the `re` module. For a complete list, `see here
+            `_.
+        **kwargs
+            For compatibility with other string methods. Not used.
+
+        Returns
+        -------
+        Series or Index
+            Same type as the calling object containing the integer counts.
+
+        See Also
+        --------
+        re : Standard library module for regular expressions.
+        str.count : Standard library version, without regular expression support.
+
+        Notes
+        -----
+        Some characters need to be escaped when passing in `pat`.
+        eg. ``'$'`` has a special meaning in regex and must be escaped when
+        finding this literal character.
+
+        Examples
+        --------
+        >>> s = pd.Series(['A', 'B', 'Aaba', 'Baca', np.nan, 'CABA', 'cat'])
+        >>> s.str.count('a')
+        0    0.0
+        1    0.0
+        2    2.0
+        3    2.0
+        4    NaN
+        5    0.0
+        6    1.0
+        dtype: float64
+
+        Escape ``'$'`` to find the literal dollar sign.
+
+        >>> s = pd.Series(['$', 'B', 'Aab$', '$$ca', 'C$B$', 'cat'])
+        >>> s.str.count('\\$')
+        0    1
+        1    0
+        2    1
+        3    2
+        4    2
+        5    0
+        dtype: int64
+
+        This is also available on Index
+
+        >>> pd.Index(['A', 'A', 'Aaba', 'cat']).str.count('a')
+        Index([0, 0, 2, 1], dtype='int64')
+        """
+        result = self._data.array._str_count(pat, flags)
+        return self._wrap_result(result, returns_string=False)
+
+    @forbid_nonstring_types(["bytes"])
+    def startswith(
+        self, pat: str | tuple[str, ...], na: Scalar | lib.NoDefault = lib.no_default
+    ) -> Series | Index:
+        """
+        Test if the start of each string element matches a pattern.
+
+        Equivalent to :meth:`str.startswith`.
+
+        Parameters
+        ----------
+        pat : str or tuple[str, ...]
+            Character sequence or tuple of strings. Regular expressions are not
+            accepted.
+        na : scalar, optional
+            Object shown if element tested is not a string. The default depends
+            on dtype of the array. For object-dtype, ``numpy.nan`` is used.
+            For the nullable ``StringDtype``, ``pandas.NA`` is used.
+            For the ``"str"`` dtype, ``False`` is used.
+
+        Returns
+        -------
+        Series or Index of bool
+            A Series of booleans indicating whether the given pattern matches
+            the start of each string element.
+
+        See Also
+        --------
+        str.startswith : Python standard library string method.
+        Series.str.endswith : Same as startswith, but tests the end of string.
+        Series.str.contains : Tests if string element contains a pattern.
+
+        Examples
+        --------
+        >>> s = pd.Series(['bat', 'Bear', 'cat', np.nan])
+        >>> s
+        0     bat
+        1    Bear
+        2     cat
+        3     NaN
+        dtype: object
+
+        >>> s.str.startswith('b')
+        0     True
+        1    False
+        2    False
+        3      NaN
+        dtype: object
+
+        >>> s.str.startswith(('b', 'B'))
+        0     True
+        1     True
+        2    False
+        3      NaN
+        dtype: object
+
+        Specifying `na` to be `False` instead of `NaN`.
+
+        >>> s.str.startswith('b', na=False)
+        0     True
+        1    False
+        2    False
+        3    False
+        dtype: bool
+        """
+        if not isinstance(pat, (str, tuple)):
+            msg = f"expected a string or tuple, not {type(pat).__name__}"
+            raise TypeError(msg)
+        result = self._data.array._str_startswith(pat, na=na)
+        return self._wrap_result(result, returns_string=False)
+
+    @forbid_nonstring_types(["bytes"])
+    def endswith(
+        self, pat: str | tuple[str, ...], na: Scalar | lib.NoDefault = lib.no_default
+    ) -> Series | Index:
+        """
+        Test if the end of each string element matches a pattern.
+
+        Equivalent to :meth:`str.endswith`.
+
+        Parameters
+        ----------
+        pat : str or tuple[str, ...]
+            Character sequence or tuple of strings. Regular expressions are not
+            accepted.
+        na : scalar, optional
+            Object shown if element tested is not a string. The default depends
+            on dtype of the array. For object-dtype, ``numpy.nan`` is used.
+            For the nullable ``StringDtype``, ``pandas.NA`` is used.
+            For the ``"str"`` dtype, ``False`` is used.
+
+        Returns
+        -------
+        Series or Index of bool
+            A Series of booleans indicating whether the given pattern matches
+            the end of each string element.
+
+        See Also
+        --------
+        str.endswith : Python standard library string method.
+        Series.str.startswith : Same as endswith, but tests the start of string.
+        Series.str.contains : Tests if string element contains a pattern.
+
+        Examples
+        --------
+        >>> s = pd.Series(['bat', 'bear', 'caT', np.nan])
+        >>> s
+        0     bat
+        1    bear
+        2     caT
+        3     NaN
+        dtype: object
+
+        >>> s.str.endswith('t')
+        0     True
+        1    False
+        2    False
+        3      NaN
+        dtype: object
+
+        >>> s.str.endswith(('t', 'T'))
+        0     True
+        1    False
+        2     True
+        3      NaN
+        dtype: object
+
+        Specifying `na` to be `False` instead of `NaN`.
+
+        >>> s.str.endswith('t', na=False)
+        0     True
+        1    False
+        2    False
+        3    False
+        dtype: bool
+        """
+        if not isinstance(pat, (str, tuple)):
+            msg = f"expected a string or tuple, not {type(pat).__name__}"
+            raise TypeError(msg)
+        result = self._data.array._str_endswith(pat, na=na)
+        return self._wrap_result(result, returns_string=False)
+
+    @forbid_nonstring_types(["bytes"])
+    def findall(self, pat, flags: int = 0):
+        """
+        Find all occurrences of pattern or regular expression in the Series/Index.
+
+        Equivalent to applying :func:`re.findall` to all the elements in the
+        Series/Index.
+
+        Parameters
+        ----------
+        pat : str
+            Pattern or regular expression.
+        flags : int, default 0
+            Flags from ``re`` module, e.g. `re.IGNORECASE` (default is 0, which
+            means no flags).
+
+        Returns
+        -------
+        Series/Index of lists of strings
+            All non-overlapping matches of pattern or regular expression in each
+            string of this Series/Index.
+
+        See Also
+        --------
+        count : Count occurrences of pattern or regular expression in each string
+            of the Series/Index.
+        extractall : For each string in the Series, extract groups from all matches
+            of regular expression and return a DataFrame with one row for each
+            match and one column for each group.
+        re.findall : The equivalent ``re`` function to all non-overlapping matches
+            of pattern or regular expression in string, as a list of strings.
+
+        Examples
+        --------
+        >>> s = pd.Series(['Lion', 'Monkey', 'Rabbit'])
+
+        The search for the pattern 'Monkey' returns one match:
+
+        >>> s.str.findall('Monkey')
+        0          []
+        1    [Monkey]
+        2          []
+        dtype: object
+
+        On the other hand, the search for the pattern 'MONKEY' doesn't return any
+        match:
+
+        >>> s.str.findall('MONKEY')
+        0    []
+        1    []
+        2    []
+        dtype: object
+
+        Flags can be added to the pattern or regular expression. For instance,
+        to find the pattern 'MONKEY' ignoring the case:
+
+        >>> import re
+        >>> s.str.findall('MONKEY', flags=re.IGNORECASE)
+        0          []
+        1    [Monkey]
+        2          []
+        dtype: object
+
+        When the pattern matches more than one string in the Series, all matches
+        are returned:
+
+        >>> s.str.findall('on')
+        0    [on]
+        1    [on]
+        2      []
+        dtype: object
+
+        Regular expressions are supported too. For instance, the search for all the
+        strings ending with the word 'on' is shown next:
+
+        >>> s.str.findall('on$')
+        0    [on]
+        1      []
+        2      []
+        dtype: object
+
+        If the pattern is found more than once in the same string, then a list of
+        multiple strings is returned:
+
+        >>> s.str.findall('b')
+        0        []
+        1        []
+        2    [b, b]
+        dtype: object
+        """
+        result = self._data.array._str_findall(pat, flags)
+        return self._wrap_result(result, returns_string=False)
+
+    @forbid_nonstring_types(["bytes"])
+    def extract(
+        self, pat: str, flags: int = 0, expand: bool = True
+    ) -> DataFrame | Series | Index:
+        r"""
+        Extract capture groups in the regex `pat` as columns in a DataFrame.
+
+        For each subject string in the Series, extract groups from the
+        first match of regular expression `pat`.
+
+        Parameters
+        ----------
+        pat : str
+            Regular expression pattern with capturing groups.
+        flags : int, default 0 (no flags)
+            Flags from the ``re`` module, e.g. ``re.IGNORECASE``, that
+            modify regular expression matching for things like case,
+            spaces, etc. For more details, see :mod:`re`.
+        expand : bool, default True
+            If True, return DataFrame with one column per capture group.
+            If False, return a Series/Index if there is one capture group
+            or DataFrame if there are multiple capture groups.
+
+        Returns
+        -------
+        DataFrame or Series or Index
+            A DataFrame with one row for each subject string, and one
+            column for each group. Any capture group names in regular
+            expression pat will be used for column names; otherwise
+            capture group numbers will be used. The dtype of each result
+            column is always object, even when no match is found. If
+            ``expand=False`` and pat has only one capture group, then
+            return a Series (if subject is a Series) or Index (if subject
+            is an Index).
+
+        See Also
+        --------
+        extractall : Returns all matches (not just the first match).
+
+        Examples
+        --------
+        A pattern with two groups will return a DataFrame with two columns.
+        Non-matches will be NaN.
+
+        >>> s = pd.Series(['a1', 'b2', 'c3'])
+        >>> s.str.extract(r'([ab])(\d)')
+            0    1
+        0    a    1
+        1    b    2
+        2  NaN  NaN
+
+        A pattern may contain optional groups.
+
+        >>> s.str.extract(r'([ab])?(\d)')
+            0  1
+        0    a  1
+        1    b  2
+        2  NaN  3
+
+        Named groups will become column names in the result.
+
+        >>> s.str.extract(r'(?P[ab])(?P\d)')
+        letter digit
+        0      a     1
+        1      b     2
+        2    NaN   NaN
+
+        A pattern with one group will return a DataFrame with one column
+        if expand=True.
+
+        >>> s.str.extract(r'[ab](\d)', expand=True)
+            0
+        0    1
+        1    2
+        2  NaN
+
+        A pattern with one group will return a Series if expand=False.
+
+        >>> s.str.extract(r'[ab](\d)', expand=False)
+        0      1
+        1      2
+        2    NaN
+        dtype: object
+        """
+        from pandas import DataFrame
+
+        if not isinstance(expand, bool):
+            raise ValueError("expand must be True or False")
+
+        regex = re.compile(pat, flags=flags)
+        if regex.groups == 0:
+            raise ValueError("pattern contains no capture groups")
+
+        if not expand and regex.groups > 1 and isinstance(self._data, ABCIndex):
+            raise ValueError("only one regex group is supported with Index")
+
+        obj = self._data
+        result_dtype = _result_dtype(obj)
+
+        returns_df = regex.groups > 1 or expand
+
+        if returns_df:
+            name = None
+            columns = _get_group_names(regex)
+
+            if obj.array.size == 0:
+                result = DataFrame(columns=columns, dtype=result_dtype)
+
+            else:
+                result_list = self._data.array._str_extract(
+                    pat, flags=flags, expand=returns_df
+                )
+
+                result_index: Index | None
+                if isinstance(obj, ABCSeries):
+                    result_index = obj.index
+                else:
+                    result_index = None
+
+                result = DataFrame(
+                    result_list, columns=columns, index=result_index, dtype=result_dtype
+                )
+
+        else:
+            name = _get_single_group_name(regex)
+            result = self._data.array._str_extract(pat, flags=flags, expand=returns_df)
+        return self._wrap_result(result, name=name, dtype=result_dtype)
+
+    @forbid_nonstring_types(["bytes"])
+    def extractall(self, pat, flags: int = 0) -> DataFrame:
+        r"""
+        Extract capture groups in the regex `pat` as columns in DataFrame.
+
+        For each subject string in the Series, extract groups from all
+        matches of regular expression pat. When each subject string in the
+        Series has exactly one match, extractall(pat).xs(0, level='match')
+        is the same as extract(pat).
+
+        Parameters
+        ----------
+        pat : str
+            Regular expression pattern with capturing groups.
+        flags : int, default 0 (no flags)
+            A ``re`` module flag, for example ``re.IGNORECASE``. These allow
+            to modify regular expression matching for things like case, spaces,
+            etc. Multiple flags can be combined with the bitwise OR operator,
+            for example ``re.IGNORECASE | re.MULTILINE``.
+
+        Returns
+        -------
+        DataFrame
+            A ``DataFrame`` with one row for each match, and one column for each
+            group. Its rows have a ``MultiIndex`` with first levels that come from
+            the subject ``Series``. The last level is named 'match' and indexes the
+            matches in each item of the ``Series``. Any capture group names in
+            regular expression pat will be used for column names; otherwise capture
+            group numbers will be used.
+
+        See Also
+        --------
+        extract : Returns first match only (not all matches).
+
+        Examples
+        --------
+        A pattern with one group will return a DataFrame with one column.
+        Indices with no matches will not appear in the result.
+
+        >>> s = pd.Series(["a1a2", "b1", "c1"], index=["A", "B", "C"])
+        >>> s.str.extractall(r"[ab](\d)")
+                0
+        match
+        A 0      1
+          1      2
+        B 0      1
+
+        Capture group names are used for column names of the result.
+
+        >>> s.str.extractall(r"[ab](?P\d)")
+                digit
+        match
+        A 0         1
+          1         2
+        B 0         1
+
+        A pattern with two groups will return a DataFrame with two columns.
+
+        >>> s.str.extractall(r"(?P[ab])(?P\d)")
+                letter digit
+        match
+        A 0          a     1
+          1          a     2
+        B 0          b     1
+
+        Optional groups that do not match are NaN in the result.
+
+        >>> s.str.extractall(r"(?P[ab])?(?P\d)")
+                letter digit
+        match
+        A 0          a     1
+          1          a     2
+        B 0          b     1
+        C 0        NaN     1
+        """
+        # TODO: dispatch
+        return str_extractall(self._orig, pat, flags)
+
+    _shared_docs[
+        "find"
+    ] = """
+    Return %(side)s indexes in each strings in the Series/Index.
+
+    Each of returned indexes corresponds to the position where the
+    substring is fully contained between [start:end]. Return -1 on
+    failure. Equivalent to standard :meth:`str.%(method)s`.
+
+    Parameters
+    ----------
+    sub : str
+        Substring being searched.
+    start : int
+        Left edge index.
+    end : int
+        Right edge index.
+
+    Returns
+    -------
+    Series or Index of int.
+
+    See Also
+    --------
+    %(also)s
+
+    Examples
+    --------
+    For Series.str.find:
+
+    >>> ser = pd.Series(["cow_", "duck_", "do_ve"])
+    >>> ser.str.find("_")
+    0   3
+    1   4
+    2   2
+    dtype: int64
+
+    For Series.str.rfind:
+
+    >>> ser = pd.Series(["_cow_", "duck_", "do_v_e"])
+    >>> ser.str.rfind("_")
+    0   4
+    1   4
+    2   4
+    dtype: int64
+    """
+
+    @Appender(
+        _shared_docs["find"]
+        % {
+            "side": "lowest",
+            "method": "find",
+            "also": "rfind : Return highest indexes in each strings.",
+        }
+    )
+    @forbid_nonstring_types(["bytes"])
+    def find(self, sub, start: int = 0, end=None):
+        if not isinstance(sub, str):
+            msg = f"expected a string object, not {type(sub).__name__}"
+            raise TypeError(msg)
+
+        result = self._data.array._str_find(sub, start, end)
+        return self._wrap_result(result, returns_string=False)
+
+    @Appender(
+        _shared_docs["find"]
+        % {
+            "side": "highest",
+            "method": "rfind",
+            "also": "find : Return lowest indexes in each strings.",
+        }
+    )
+    @forbid_nonstring_types(["bytes"])
+    def rfind(self, sub, start: int = 0, end=None):
+        if not isinstance(sub, str):
+            msg = f"expected a string object, not {type(sub).__name__}"
+            raise TypeError(msg)
+
+        result = self._data.array._str_rfind(sub, start=start, end=end)
+        return self._wrap_result(result, returns_string=False)
+
+    @forbid_nonstring_types(["bytes"])
+    def normalize(self, form):
+        """
+        Return the Unicode normal form for the strings in the Series/Index.
+
+        For more information on the forms, see the
+        :func:`unicodedata.normalize`.
+
+        Parameters
+        ----------
+        form : {'NFC', 'NFKC', 'NFD', 'NFKD'}
+            Unicode form.
+
+        Returns
+        -------
+        Series/Index of objects
+
+        Examples
+        --------
+        >>> ser = pd.Series(['ñ'])
+        >>> ser.str.normalize('NFC') == ser.str.normalize('NFD')
+        0   False
+        dtype: bool
+        """
+        result = self._data.array._str_normalize(form)
+        return self._wrap_result(result)
+
+    _shared_docs[
+        "index"
+    ] = """
+    Return %(side)s indexes in each string in Series/Index.
+
+    Each of the returned indexes corresponds to the position where the
+    substring is fully contained between [start:end]. This is the same
+    as ``str.%(similar)s`` except instead of returning -1, it raises a
+    ValueError when the substring is not found. Equivalent to standard
+    ``str.%(method)s``.
+
+    Parameters
+    ----------
+    sub : str
+        Substring being searched.
+    start : int
+        Left edge index.
+    end : int
+        Right edge index.
+
+    Returns
+    -------
+    Series or Index of object
+
+    See Also
+    --------
+    %(also)s
+
+    Examples
+    --------
+    For Series.str.index:
+
+    >>> ser = pd.Series(["horse", "eagle", "donkey"])
+    >>> ser.str.index("e")
+    0   4
+    1   0
+    2   4
+    dtype: int64
+
+    For Series.str.rindex:
+
+    >>> ser = pd.Series(["Deer", "eagle", "Sheep"])
+    >>> ser.str.rindex("e")
+    0   2
+    1   4
+    2   3
+    dtype: int64
+    """
+
+    @Appender(
+        _shared_docs["index"]
+        % {
+            "side": "lowest",
+            "similar": "find",
+            "method": "index",
+            "also": "rindex : Return highest indexes in each strings.",
+        }
+    )
+    @forbid_nonstring_types(["bytes"])
+    def index(self, sub, start: int = 0, end=None):
+        if not isinstance(sub, str):
+            msg = f"expected a string object, not {type(sub).__name__}"
+            raise TypeError(msg)
+
+        result = self._data.array._str_index(sub, start=start, end=end)
+        return self._wrap_result(result, returns_string=False)
+
+    @Appender(
+        _shared_docs["index"]
+        % {
+            "side": "highest",
+            "similar": "rfind",
+            "method": "rindex",
+            "also": "index : Return lowest indexes in each strings.",
+        }
+    )
+    @forbid_nonstring_types(["bytes"])
+    def rindex(self, sub, start: int = 0, end=None):
+        if not isinstance(sub, str):
+            msg = f"expected a string object, not {type(sub).__name__}"
+            raise TypeError(msg)
+
+        result = self._data.array._str_rindex(sub, start=start, end=end)
+        return self._wrap_result(result, returns_string=False)
+
+    def len(self):
+        """
+        Compute the length of each element in the Series/Index.
+
+        The element may be a sequence (such as a string, tuple or list) or a collection
+        (such as a dictionary).
+
+        Returns
+        -------
+        Series or Index of int
+            A Series or Index of integer values indicating the length of each
+            element in the Series or Index.
+
+        See Also
+        --------
+        str.len : Python built-in function returning the length of an object.
+        Series.size : Returns the length of the Series.
+
+        Examples
+        --------
+        Returns the length (number of characters) in a string. Returns the
+        number of entries for dictionaries, lists or tuples.
+
+        >>> s = pd.Series(['dog',
+        ...                 '',
+        ...                 5,
+        ...                 {'foo' : 'bar'},
+        ...                 [2, 3, 5, 7],
+        ...                 ('one', 'two', 'three')])
+        >>> s
+        0                  dog
+        1
+        2                    5
+        3       {'foo': 'bar'}
+        4         [2, 3, 5, 7]
+        5    (one, two, three)
+        dtype: object
+        >>> s.str.len()
+        0    3.0
+        1    0.0
+        2    NaN
+        3    1.0
+        4    4.0
+        5    3.0
+        dtype: float64
+        """
+        result = self._data.array._str_len()
+        return self._wrap_result(result, returns_string=False)
+
+    _shared_docs[
+        "casemethods"
+    ] = """
+    Convert strings in the Series/Index to %(type)s.
+    %(version)s
+    Equivalent to :meth:`str.%(method)s`.
+
+    Returns
+    -------
+    Series or Index of object
+
+    See Also
+    --------
+    Series.str.lower : Converts all characters to lowercase.
+    Series.str.upper : Converts all characters to uppercase.
+    Series.str.title : Converts first character of each word to uppercase and
+        remaining to lowercase.
+    Series.str.capitalize : Converts first character to uppercase and
+        remaining to lowercase.
+    Series.str.swapcase : Converts uppercase to lowercase and lowercase to
+        uppercase.
+    Series.str.casefold: Removes all case distinctions in the string.
+
+    Examples
+    --------
+    >>> s = pd.Series(['lower', 'CAPITALS', 'this is a sentence', 'SwApCaSe'])
+    >>> s
+    0                 lower
+    1              CAPITALS
+    2    this is a sentence
+    3              SwApCaSe
+    dtype: object
+
+    >>> s.str.lower()
+    0                 lower
+    1              capitals
+    2    this is a sentence
+    3              swapcase
+    dtype: object
+
+    >>> s.str.upper()
+    0                 LOWER
+    1              CAPITALS
+    2    THIS IS A SENTENCE
+    3              SWAPCASE
+    dtype: object
+
+    >>> s.str.title()
+    0                 Lower
+    1              Capitals
+    2    This Is A Sentence
+    3              Swapcase
+    dtype: object
+
+    >>> s.str.capitalize()
+    0                 Lower
+    1              Capitals
+    2    This is a sentence
+    3              Swapcase
+    dtype: object
+
+    >>> s.str.swapcase()
+    0                 LOWER
+    1              capitals
+    2    THIS IS A SENTENCE
+    3              sWaPcAsE
+    dtype: object
+    """
+    # Types:
+    #   cases:
+    #       upper, lower, title, capitalize, swapcase, casefold
+    #   boolean:
+    #     isalpha, isnumeric isalnum isdigit isdecimal isspace islower isupper istitle
+    # _doc_args holds dict of strings to use in substituting casemethod docs
+    _doc_args: dict[str, dict[str, str]] = {}
+    _doc_args["lower"] = {"type": "lowercase", "method": "lower", "version": ""}
+    _doc_args["upper"] = {"type": "uppercase", "method": "upper", "version": ""}
+    _doc_args["title"] = {"type": "titlecase", "method": "title", "version": ""}
+    _doc_args["capitalize"] = {
+        "type": "be capitalized",
+        "method": "capitalize",
+        "version": "",
+    }
+    _doc_args["swapcase"] = {
+        "type": "be swapcased",
+        "method": "swapcase",
+        "version": "",
+    }
+    _doc_args["casefold"] = {
+        "type": "be casefolded",
+        "method": "casefold",
+        "version": "",
+    }
+
+    @Appender(_shared_docs["casemethods"] % _doc_args["lower"])
+    @forbid_nonstring_types(["bytes"])
+    def lower(self):
+        result = self._data.array._str_lower()
+        return self._wrap_result(result)
+
+    @Appender(_shared_docs["casemethods"] % _doc_args["upper"])
+    @forbid_nonstring_types(["bytes"])
+    def upper(self):
+        result = self._data.array._str_upper()
+        return self._wrap_result(result)
+
+    @Appender(_shared_docs["casemethods"] % _doc_args["title"])
+    @forbid_nonstring_types(["bytes"])
+    def title(self):
+        result = self._data.array._str_title()
+        return self._wrap_result(result)
+
+    @Appender(_shared_docs["casemethods"] % _doc_args["capitalize"])
+    @forbid_nonstring_types(["bytes"])
+    def capitalize(self):
+        result = self._data.array._str_capitalize()
+        return self._wrap_result(result)
+
+    @Appender(_shared_docs["casemethods"] % _doc_args["swapcase"])
+    @forbid_nonstring_types(["bytes"])
+    def swapcase(self):
+        result = self._data.array._str_swapcase()
+        return self._wrap_result(result)
+
+    @Appender(_shared_docs["casemethods"] % _doc_args["casefold"])
+    @forbid_nonstring_types(["bytes"])
+    def casefold(self):
+        result = self._data.array._str_casefold()
+        return self._wrap_result(result)
+
+    _shared_docs[
+        "ismethods"
+    ] = """
+    Check whether all characters in each string are %(type)s.
+
+    This is equivalent to running the Python string method
+    :meth:`str.%(method)s` for each element of the Series/Index. If a string
+    has zero characters, ``False`` is returned for that check.
+
+    Returns
+    -------
+    Series or Index of bool
+        Series or Index of boolean values with the same length as the original
+        Series/Index.
+
+    See Also
+    --------
+    Series.str.isalpha : Check whether all characters are alphabetic.
+    Series.str.isnumeric : Check whether all characters are numeric.
+    Series.str.isalnum : Check whether all characters are alphanumeric.
+    Series.str.isdigit : Check whether all characters are digits.
+    Series.str.isdecimal : Check whether all characters are decimal.
+    Series.str.isspace : Check whether all characters are whitespace.
+    Series.str.islower : Check whether all characters are lowercase.
+    Series.str.isupper : Check whether all characters are uppercase.
+    Series.str.istitle : Check whether all characters are titlecase.
+
+    Examples
+    --------
+    **Checks for Alphabetic and Numeric Characters**
+
+    >>> s1 = pd.Series(['one', 'one1', '1', ''])
+
+    >>> s1.str.isalpha()
+    0     True
+    1    False
+    2    False
+    3    False
+    dtype: bool
+
+    >>> s1.str.isnumeric()
+    0    False
+    1    False
+    2     True
+    3    False
+    dtype: bool
+
+    >>> s1.str.isalnum()
+    0     True
+    1     True
+    2     True
+    3    False
+    dtype: bool
+
+    Note that checks against characters mixed with any additional punctuation
+    or whitespace will evaluate to false for an alphanumeric check.
+
+    >>> s2 = pd.Series(['A B', '1.5', '3,000'])
+    >>> s2.str.isalnum()
+    0    False
+    1    False
+    2    False
+    dtype: bool
+
+    **More Detailed Checks for Numeric Characters**
+
+    There are several different but overlapping sets of numeric characters that
+    can be checked for.
+
+    >>> s3 = pd.Series(['23', '³', '⅕', ''])
+
+    The ``s3.str.isdecimal`` method checks for characters used to form numbers
+    in base 10.
+
+    >>> s3.str.isdecimal()
+    0     True
+    1    False
+    2    False
+    3    False
+    dtype: bool
+
+    The ``s.str.isdigit`` method is the same as ``s3.str.isdecimal`` but also
+    includes special digits, like superscripted and subscripted digits in
+    unicode.
+
+    >>> s3.str.isdigit()
+    0     True
+    1     True
+    2    False
+    3    False
+    dtype: bool
+
+    The ``s.str.isnumeric`` method is the same as ``s3.str.isdigit`` but also
+    includes other characters that can represent quantities such as unicode
+    fractions.
+
+    >>> s3.str.isnumeric()
+    0     True
+    1     True
+    2     True
+    3    False
+    dtype: bool
+
+    **Checks for Whitespace**
+
+    >>> s4 = pd.Series([' ', '\\t\\r\\n ', ''])
+    >>> s4.str.isspace()
+    0     True
+    1     True
+    2    False
+    dtype: bool
+
+    **Checks for Character Case**
+
+    >>> s5 = pd.Series(['leopard', 'Golden Eagle', 'SNAKE', ''])
+
+    >>> s5.str.islower()
+    0     True
+    1    False
+    2    False
+    3    False
+    dtype: bool
+
+    >>> s5.str.isupper()
+    0    False
+    1    False
+    2     True
+    3    False
+    dtype: bool
+
+    The ``s5.str.istitle`` method checks for whether all words are in title
+    case (whether only the first letter of each word is capitalized). Words are
+    assumed to be as any sequence of non-numeric characters separated by
+    whitespace characters.
+
+    >>> s5.str.istitle()
+    0    False
+    1     True
+    2    False
+    3    False
+    dtype: bool
+    """
+    _doc_args["isalnum"] = {"type": "alphanumeric", "method": "isalnum"}
+    _doc_args["isalpha"] = {"type": "alphabetic", "method": "isalpha"}
+    _doc_args["isdigit"] = {"type": "digits", "method": "isdigit"}
+    _doc_args["isspace"] = {"type": "whitespace", "method": "isspace"}
+    _doc_args["islower"] = {"type": "lowercase", "method": "islower"}
+    _doc_args["isupper"] = {"type": "uppercase", "method": "isupper"}
+    _doc_args["istitle"] = {"type": "titlecase", "method": "istitle"}
+    _doc_args["isnumeric"] = {"type": "numeric", "method": "isnumeric"}
+    _doc_args["isdecimal"] = {"type": "decimal", "method": "isdecimal"}
+    # force _noarg_wrapper return type with dtype=np.dtype(bool) (GH 29624)
+
+    isalnum = _map_and_wrap(
+        "isalnum", docstring=_shared_docs["ismethods"] % _doc_args["isalnum"]
+    )
+    isalpha = _map_and_wrap(
+        "isalpha", docstring=_shared_docs["ismethods"] % _doc_args["isalpha"]
+    )
+    isdigit = _map_and_wrap(
+        "isdigit", docstring=_shared_docs["ismethods"] % _doc_args["isdigit"]
+    )
+    isspace = _map_and_wrap(
+        "isspace", docstring=_shared_docs["ismethods"] % _doc_args["isspace"]
+    )
+    islower = _map_and_wrap(
+        "islower", docstring=_shared_docs["ismethods"] % _doc_args["islower"]
+    )
+    isupper = _map_and_wrap(
+        "isupper", docstring=_shared_docs["ismethods"] % _doc_args["isupper"]
+    )
+    istitle = _map_and_wrap(
+        "istitle", docstring=_shared_docs["ismethods"] % _doc_args["istitle"]
+    )
+    isnumeric = _map_and_wrap(
+        "isnumeric", docstring=_shared_docs["ismethods"] % _doc_args["isnumeric"]
+    )
+    isdecimal = _map_and_wrap(
+        "isdecimal", docstring=_shared_docs["ismethods"] % _doc_args["isdecimal"]
+    )
+
+
+def cat_safe(list_of_columns: list[npt.NDArray[np.object_]], sep: str):
+    """
+    Auxiliary function for :meth:`str.cat`.
+
+    Same signature as cat_core, but handles TypeErrors in concatenation, which
+    happen if the arrays in list_of columns have the wrong dtypes or content.
+
+    Parameters
+    ----------
+    list_of_columns : list of numpy arrays
+        List of arrays to be concatenated with sep;
+        these arrays may not contain NaNs!
+    sep : string
+        The separator string for concatenating the columns.
+
+    Returns
+    -------
+    nd.array
+        The concatenation of list_of_columns with sep.
+    """
+    try:
+        result = cat_core(list_of_columns, sep)
+    except TypeError:
+        # if there are any non-string values (wrong dtype or hidden behind
+        # object dtype), np.sum will fail; catch and return with better message
+        for column in list_of_columns:
+            dtype = lib.infer_dtype(column, skipna=True)
+            if dtype not in ["string", "empty"]:
+                raise TypeError(
+                    "Concatenation requires list-likes containing only "
+                    "strings (or missing values). Offending values found in "
+                    f"column {dtype}"
+                ) from None
+    return result
+
+
+def cat_core(list_of_columns: list, sep: str):
+    """
+    Auxiliary function for :meth:`str.cat`
+
+    Parameters
+    ----------
+    list_of_columns : list of numpy arrays
+        List of arrays to be concatenated with sep;
+        these arrays may not contain NaNs!
+    sep : string
+        The separator string for concatenating the columns.
+
+    Returns
+    -------
+    nd.array
+        The concatenation of list_of_columns with sep.
+    """
+    if sep == "":
+        # no need to interleave sep if it is empty
+        arr_of_cols = np.asarray(list_of_columns, dtype=object)
+        return np.sum(arr_of_cols, axis=0)
+    list_with_sep = [sep] * (2 * len(list_of_columns) - 1)
+    list_with_sep[::2] = list_of_columns
+    arr_with_sep = np.asarray(list_with_sep, dtype=object)
+    return np.sum(arr_with_sep, axis=0)
+
+
+def _result_dtype(arr):
+    # workaround #27953
+    # ideally we just pass `dtype=arr.dtype` unconditionally, but this fails
+    # when the list of values is empty.
+    from pandas.core.arrays.string_ import StringDtype
+
+    if isinstance(arr.dtype, (ArrowDtype, StringDtype)):
+        return arr.dtype
+    return object
+
+
+def _get_single_group_name(regex: re.Pattern) -> Hashable:
+    if regex.groupindex:
+        return next(iter(regex.groupindex))
+    else:
+        return None
+
+
+def _get_group_names(regex: re.Pattern) -> list[Hashable]:
+    """
+    Get named groups from compiled regex.
+
+    Unnamed groups are numbered.
+
+    Parameters
+    ----------
+    regex : compiled regex
+
+    Returns
+    -------
+    list of column labels
+    """
+    names = {v: k for k, v in regex.groupindex.items()}
+    return [names.get(1 + i, i) for i in range(regex.groups)]
+
+
+def str_extractall(arr, pat, flags: int = 0) -> DataFrame:
+    regex = re.compile(pat, flags=flags)
+    # the regex must contain capture groups.
+    if regex.groups == 0:
+        raise ValueError("pattern contains no capture groups")
+
+    if isinstance(arr, ABCIndex):
+        arr = arr.to_series().reset_index(drop=True).astype(arr.dtype)
+
+    columns = _get_group_names(regex)
+    match_list = []
+    index_list = []
+    is_mi = arr.index.nlevels > 1
+
+    for subject_key, subject in arr.items():
+        if isinstance(subject, str):
+            if not is_mi:
+                subject_key = (subject_key,)
+
+            for match_i, match_tuple in enumerate(regex.findall(subject)):
+                if isinstance(match_tuple, str):
+                    match_tuple = (match_tuple,)
+                na_tuple = [np.nan if group == "" else group for group in match_tuple]
+                match_list.append(na_tuple)
+                result_key = tuple(subject_key + (match_i,))
+                index_list.append(result_key)
+
+    from pandas import MultiIndex
+
+    index = MultiIndex.from_tuples(index_list, names=arr.index.names + ["match"])
+    dtype = _result_dtype(arr)
+
+    result = arr._constructor_expanddim(
+        match_list, index=index, columns=columns, dtype=dtype
+    )
+    return result
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/strings/base.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/strings/base.py
new file mode 100644
index 0000000000000000000000000000000000000000..316c86d152db32048f4de154ead0ec8fc76ec027
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/strings/base.py
@@ -0,0 +1,266 @@
+from __future__ import annotations
+
+import abc
+from typing import (
+    TYPE_CHECKING,
+    Callable,
+    Literal,
+)
+
+from pandas._libs import lib
+
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+    import re
+
+    from pandas._typing import Scalar
+
+    from pandas import Series
+
+
+class BaseStringArrayMethods(abc.ABC):
+    """
+    Base class for extension arrays implementing string methods.
+
+    This is where our ExtensionArrays can override the implementation of
+    Series.str.. We don't expect this to work with
+    3rd-party extension arrays.
+
+    * User calls Series.str.
+    * pandas extracts the extension array from the Series
+    * pandas calls ``extension_array._str_(*args, **kwargs)``
+    * pandas wraps the result, to return to the user.
+
+    See :ref:`Series.str` for the docstring of each method.
+    """
+
+    def _str_getitem(self, key):
+        if isinstance(key, slice):
+            return self._str_slice(start=key.start, stop=key.stop, step=key.step)
+        else:
+            return self._str_get(key)
+
+    @abc.abstractmethod
+    def _str_count(self, pat, flags: int = 0):
+        pass
+
+    @abc.abstractmethod
+    def _str_pad(
+        self,
+        width: int,
+        side: Literal["left", "right", "both"] = "left",
+        fillchar: str = " ",
+    ):
+        pass
+
+    @abc.abstractmethod
+    def _str_contains(
+        self, pat, case: bool = True, flags: int = 0, na=None, regex: bool = True
+    ):
+        pass
+
+    @abc.abstractmethod
+    def _str_startswith(self, pat, na=None):
+        pass
+
+    @abc.abstractmethod
+    def _str_endswith(self, pat, na=None):
+        pass
+
+    @abc.abstractmethod
+    def _str_replace(
+        self,
+        pat: str | re.Pattern,
+        repl: str | Callable,
+        n: int = -1,
+        case: bool = True,
+        flags: int = 0,
+        regex: bool = True,
+    ):
+        pass
+
+    @abc.abstractmethod
+    def _str_repeat(self, repeats: int | Sequence[int]):
+        pass
+
+    @abc.abstractmethod
+    def _str_match(
+        self,
+        pat: str,
+        case: bool = True,
+        flags: int = 0,
+        na: Scalar | lib.NoDefault = lib.no_default,
+    ):
+        pass
+
+    @abc.abstractmethod
+    def _str_fullmatch(
+        self,
+        pat: str | re.Pattern,
+        case: bool = True,
+        flags: int = 0,
+        na: Scalar | lib.NoDefault = lib.no_default,
+    ):
+        pass
+
+    @abc.abstractmethod
+    def _str_encode(self, encoding, errors: str = "strict"):
+        pass
+
+    @abc.abstractmethod
+    def _str_find(self, sub, start: int = 0, end=None):
+        pass
+
+    @abc.abstractmethod
+    def _str_rfind(self, sub, start: int = 0, end=None):
+        pass
+
+    @abc.abstractmethod
+    def _str_findall(self, pat, flags: int = 0):
+        pass
+
+    @abc.abstractmethod
+    def _str_get(self, i):
+        pass
+
+    @abc.abstractmethod
+    def _str_index(self, sub, start: int = 0, end=None):
+        pass
+
+    @abc.abstractmethod
+    def _str_rindex(self, sub, start: int = 0, end=None):
+        pass
+
+    @abc.abstractmethod
+    def _str_join(self, sep: str):
+        pass
+
+    @abc.abstractmethod
+    def _str_partition(self, sep: str, expand):
+        pass
+
+    @abc.abstractmethod
+    def _str_rpartition(self, sep: str, expand):
+        pass
+
+    @abc.abstractmethod
+    def _str_len(self):
+        pass
+
+    @abc.abstractmethod
+    def _str_slice(self, start=None, stop=None, step=None):
+        pass
+
+    @abc.abstractmethod
+    def _str_slice_replace(self, start=None, stop=None, repl=None):
+        pass
+
+    @abc.abstractmethod
+    def _str_translate(self, table):
+        pass
+
+    @abc.abstractmethod
+    def _str_wrap(self, width: int, **kwargs):
+        pass
+
+    @abc.abstractmethod
+    def _str_get_dummies(self, sep: str = "|"):
+        pass
+
+    @abc.abstractmethod
+    def _str_isalnum(self):
+        pass
+
+    @abc.abstractmethod
+    def _str_isalpha(self):
+        pass
+
+    @abc.abstractmethod
+    def _str_isdecimal(self):
+        pass
+
+    @abc.abstractmethod
+    def _str_isdigit(self):
+        pass
+
+    @abc.abstractmethod
+    def _str_islower(self):
+        pass
+
+    @abc.abstractmethod
+    def _str_isnumeric(self):
+        pass
+
+    @abc.abstractmethod
+    def _str_isspace(self):
+        pass
+
+    @abc.abstractmethod
+    def _str_istitle(self):
+        pass
+
+    @abc.abstractmethod
+    def _str_isupper(self):
+        pass
+
+    @abc.abstractmethod
+    def _str_capitalize(self):
+        pass
+
+    @abc.abstractmethod
+    def _str_casefold(self):
+        pass
+
+    @abc.abstractmethod
+    def _str_title(self):
+        pass
+
+    @abc.abstractmethod
+    def _str_swapcase(self):
+        pass
+
+    @abc.abstractmethod
+    def _str_lower(self):
+        pass
+
+    @abc.abstractmethod
+    def _str_upper(self):
+        pass
+
+    @abc.abstractmethod
+    def _str_normalize(self, form):
+        pass
+
+    @abc.abstractmethod
+    def _str_strip(self, to_strip=None):
+        pass
+
+    @abc.abstractmethod
+    def _str_lstrip(self, to_strip=None):
+        pass
+
+    @abc.abstractmethod
+    def _str_rstrip(self, to_strip=None):
+        pass
+
+    @abc.abstractmethod
+    def _str_removeprefix(self, prefix: str) -> Series:
+        pass
+
+    @abc.abstractmethod
+    def _str_removesuffix(self, suffix: str) -> Series:
+        pass
+
+    @abc.abstractmethod
+    def _str_split(
+        self, pat=None, n=-1, expand: bool = False, regex: bool | None = None
+    ):
+        pass
+
+    @abc.abstractmethod
+    def _str_rsplit(self, pat=None, n=-1):
+        pass
+
+    @abc.abstractmethod
+    def _str_extract(self, pat: str, flags: int = 0, expand: bool = True):
+        pass
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/strings/object_array.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/strings/object_array.py
new file mode 100644
index 0000000000000000000000000000000000000000..05ffee662dbac69e2e942e37f0701d4b65adb6af
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/strings/object_array.py
@@ -0,0 +1,534 @@
+from __future__ import annotations
+
+import functools
+import re
+import textwrap
+from typing import (
+    TYPE_CHECKING,
+    Callable,
+    Literal,
+    cast,
+)
+import unicodedata
+import warnings
+
+import numpy as np
+
+from pandas._libs import lib
+import pandas._libs.missing as libmissing
+import pandas._libs.ops as libops
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.missing import isna
+
+from pandas.core.strings.base import BaseStringArrayMethods
+
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+
+    from pandas._typing import (
+        NpDtype,
+        Scalar,
+    )
+
+    from pandas import Series
+
+
+class ObjectStringArrayMixin(BaseStringArrayMethods):
+    """
+    String Methods operating on object-dtype ndarrays.
+    """
+
+    def __len__(self) -> int:
+        # For typing, _str_map relies on the object being sized.
+        raise NotImplementedError
+
+    def _str_map(
+        self,
+        f,
+        na_value=lib.no_default,
+        dtype: NpDtype | None = None,
+        convert: bool = True,
+    ):
+        """
+        Map a callable over valid elements of the array.
+
+        Parameters
+        ----------
+        f : Callable
+            A function to call on each non-NA element.
+        na_value : Scalar, optional
+            The value to set for NA values. Might also be used for the
+            fill value if the callable `f` raises an exception.
+            This defaults to ``self.dtype.na_value`` which is ``np.nan``
+            for object-dtype and Categorical and ``pd.NA`` for StringArray.
+        dtype : Dtype, optional
+            The dtype of the result array.
+        convert : bool, default True
+            Whether to call `maybe_convert_objects` on the resulting ndarray
+        """
+        if dtype is None:
+            dtype = np.dtype("object")
+        if na_value is lib.no_default:
+            na_value = self.dtype.na_value  # type: ignore[attr-defined]
+
+        if not len(self):
+            return np.array([], dtype=dtype)
+
+        arr = np.asarray(self, dtype=object)
+        mask = isna(arr)
+        map_convert = convert and not np.all(mask)
+        try:
+            result = lib.map_infer_mask(arr, f, mask.view(np.uint8), map_convert)
+        except (TypeError, AttributeError) as err:
+            # Reraise the exception if callable `f` got wrong number of args.
+            # The user may want to be warned by this, instead of getting NaN
+            p_err = (
+                r"((takes)|(missing)) (?(2)from \d+ to )?\d+ "
+                r"(?(3)required )positional arguments?"
+            )
+
+            if len(err.args) >= 1 and re.search(p_err, err.args[0]):
+                # FIXME: this should be totally avoidable
+                raise err
+
+            def g(x):
+                # This type of fallback behavior can be removed once
+                # we remove object-dtype .str accessor.
+                try:
+                    return f(x)
+                except (TypeError, AttributeError):
+                    return na_value
+
+            return self._str_map(g, na_value=na_value, dtype=dtype)
+        if not isinstance(result, np.ndarray):
+            return result
+        if na_value is not np.nan:
+            np.putmask(result, mask, na_value)
+            if convert and result.dtype == object:
+                result = lib.maybe_convert_objects(result)
+        return result
+
+    def _str_count(self, pat, flags: int = 0):
+        regex = re.compile(pat, flags=flags)
+        f = lambda x: len(regex.findall(x))
+        return self._str_map(f, dtype="int64")
+
+    def _str_pad(
+        self,
+        width: int,
+        side: Literal["left", "right", "both"] = "left",
+        fillchar: str = " ",
+    ):
+        if side == "left":
+            f = lambda x: x.rjust(width, fillchar)
+        elif side == "right":
+            f = lambda x: x.ljust(width, fillchar)
+        elif side == "both":
+            f = lambda x: x.center(width, fillchar)
+        else:  # pragma: no cover
+            raise ValueError("Invalid side")
+        return self._str_map(f)
+
+    def _str_contains(
+        self,
+        pat,
+        case: bool = True,
+        flags: int = 0,
+        na=lib.no_default,
+        regex: bool = True,
+    ):
+        if regex:
+            if not case:
+                flags |= re.IGNORECASE
+
+            pat = re.compile(pat, flags=flags)
+
+            f = lambda x: pat.search(x) is not None
+        else:
+            if case:
+                f = lambda x: pat in x
+            else:
+                upper_pat = pat.upper()
+                f = lambda x: upper_pat in x.upper()
+        if na is not lib.no_default and not isna(na) and not isinstance(na, bool):
+            # GH#59561
+            warnings.warn(
+                "Allowing a non-bool 'na' in obj.str.contains is deprecated "
+                "and will raise in a future version.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+        return self._str_map(f, na, dtype=np.dtype("bool"))
+
+    def _str_startswith(self, pat, na=lib.no_default):
+        f = lambda x: x.startswith(pat)
+        if na is not lib.no_default and not isna(na) and not isinstance(na, bool):
+            # GH#59561
+            warnings.warn(
+                "Allowing a non-bool 'na' in obj.str.startswith is deprecated "
+                "and will raise in a future version.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+        return self._str_map(f, na_value=na, dtype=np.dtype(bool))
+
+    def _str_endswith(self, pat, na=lib.no_default):
+        f = lambda x: x.endswith(pat)
+        if na is not lib.no_default and not isna(na) and not isinstance(na, bool):
+            # GH#59561
+            warnings.warn(
+                "Allowing a non-bool 'na' in obj.str.endswith is deprecated "
+                "and will raise in a future version.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+        return self._str_map(f, na_value=na, dtype=np.dtype(bool))
+
+    def _str_replace(
+        self,
+        pat: str | re.Pattern,
+        repl: str | Callable,
+        n: int = -1,
+        case: bool = True,
+        flags: int = 0,
+        regex: bool = True,
+    ):
+        if case is False:
+            # add case flag, if provided
+            flags |= re.IGNORECASE
+
+        if regex or flags or callable(repl):
+            if not isinstance(pat, re.Pattern):
+                if regex is False:
+                    pat = re.escape(pat)
+                pat = re.compile(pat, flags=flags)
+
+            n = n if n >= 0 else 0
+            f = lambda x: pat.sub(repl=repl, string=x, count=n)
+        else:
+            f = lambda x: x.replace(pat, repl, n)
+
+        return self._str_map(f, dtype=str)
+
+    def _str_repeat(self, repeats: int | Sequence[int]):
+        if lib.is_integer(repeats):
+            rint = cast(int, repeats)
+
+            def scalar_rep(x):
+                try:
+                    return bytes.__mul__(x, rint)
+                except TypeError:
+                    return str.__mul__(x, rint)
+
+            return self._str_map(scalar_rep, dtype=str)
+        else:
+            from pandas.core.arrays.string_ import BaseStringArray
+
+            def rep(x, r):
+                if x is libmissing.NA:
+                    return x
+                try:
+                    return bytes.__mul__(x, r)
+                except TypeError:
+                    return str.__mul__(x, r)
+
+            result = libops.vec_binop(
+                np.asarray(self),
+                np.asarray(repeats, dtype=object),
+                rep,
+            )
+            if isinstance(self, BaseStringArray):
+                # Not going through map, so we have to do this here.
+                result = type(self)._from_sequence(result, dtype=self.dtype)
+            return result
+
+    def _str_match(
+        self,
+        pat: str | re.Pattern,
+        case: bool = True,
+        flags: int = 0,
+        na: Scalar | lib.NoDefault = lib.no_default,
+    ):
+        if not case:
+            flags |= re.IGNORECASE
+
+        regex = re.compile(pat, flags=flags)
+
+        f = lambda x: regex.match(x) is not None
+        return self._str_map(f, na_value=na, dtype=np.dtype(bool))
+
+    def _str_fullmatch(
+        self,
+        pat: str | re.Pattern,
+        case: bool = True,
+        flags: int = 0,
+        na: Scalar | lib.NoDefault = lib.no_default,
+    ):
+        if not case:
+            flags |= re.IGNORECASE
+
+        regex = re.compile(pat, flags=flags)
+
+        f = lambda x: regex.fullmatch(x) is not None
+        return self._str_map(f, na_value=na, dtype=np.dtype(bool))
+
+    def _str_encode(self, encoding, errors: str = "strict"):
+        f = lambda x: x.encode(encoding, errors=errors)
+        return self._str_map(f, dtype=object)
+
+    def _str_find(self, sub, start: int = 0, end=None):
+        return self._str_find_(sub, start, end, side="left")
+
+    def _str_rfind(self, sub, start: int = 0, end=None):
+        return self._str_find_(sub, start, end, side="right")
+
+    def _str_find_(self, sub, start, end, side):
+        if side == "left":
+            method = "find"
+        elif side == "right":
+            method = "rfind"
+        else:  # pragma: no cover
+            raise ValueError("Invalid side")
+
+        if end is None:
+            f = lambda x: getattr(x, method)(sub, start)
+        else:
+            f = lambda x: getattr(x, method)(sub, start, end)
+        return self._str_map(f, dtype="int64")
+
+    def _str_findall(self, pat, flags: int = 0):
+        regex = re.compile(pat, flags=flags)
+        return self._str_map(regex.findall, dtype="object")
+
+    def _str_get(self, i):
+        def f(x):
+            if isinstance(x, dict):
+                return x.get(i)
+            elif len(x) > i >= -len(x):
+                return x[i]
+            return self.dtype.na_value  # type: ignore[attr-defined]
+
+        return self._str_map(f)
+
+    def _str_index(self, sub, start: int = 0, end=None):
+        if end:
+            f = lambda x: x.index(sub, start, end)
+        else:
+            f = lambda x: x.index(sub, start, end)
+        return self._str_map(f, dtype="int64")
+
+    def _str_rindex(self, sub, start: int = 0, end=None):
+        if end:
+            f = lambda x: x.rindex(sub, start, end)
+        else:
+            f = lambda x: x.rindex(sub, start, end)
+        return self._str_map(f, dtype="int64")
+
+    def _str_join(self, sep: str):
+        return self._str_map(sep.join)
+
+    def _str_partition(self, sep: str, expand):
+        result = self._str_map(lambda x: x.partition(sep), dtype="object")
+        return result
+
+    def _str_rpartition(self, sep: str, expand):
+        return self._str_map(lambda x: x.rpartition(sep), dtype="object")
+
+    def _str_len(self):
+        return self._str_map(len, dtype="int64")
+
+    def _str_slice(self, start=None, stop=None, step=None):
+        obj = slice(start, stop, step)
+        return self._str_map(lambda x: x[obj])
+
+    def _str_slice_replace(self, start=None, stop=None, repl=None):
+        if repl is None:
+            repl = ""
+
+        def f(x):
+            if x[start:stop] == "":
+                local_stop = start
+            else:
+                local_stop = stop
+            y = ""
+            if start is not None:
+                y += x[:start]
+            y += repl
+            if stop is not None:
+                y += x[local_stop:]
+            return y
+
+        return self._str_map(f)
+
+    def _str_split(
+        self,
+        pat: str | re.Pattern | None = None,
+        n=-1,
+        expand: bool = False,
+        regex: bool | None = None,
+    ):
+        if pat is None:
+            if n is None or n == 0:
+                n = -1
+            f = lambda x: x.split(pat, n)
+        else:
+            new_pat: str | re.Pattern
+            if regex is True or isinstance(pat, re.Pattern):
+                new_pat = re.compile(pat)
+            elif regex is False:
+                new_pat = pat
+            # regex is None so link to old behavior #43563
+            else:
+                if len(pat) == 1:
+                    new_pat = pat
+                else:
+                    new_pat = re.compile(pat)
+
+            if isinstance(new_pat, re.Pattern):
+                if n is None or n == -1:
+                    n = 0
+                f = lambda x: new_pat.split(x, maxsplit=n)
+            else:
+                if n is None or n == 0:
+                    n = -1
+                f = lambda x: x.split(pat, n)
+        return self._str_map(f, dtype=object)
+
+    def _str_rsplit(self, pat=None, n=-1):
+        if n is None or n == 0:
+            n = -1
+        f = lambda x: x.rsplit(pat, n)
+        return self._str_map(f, dtype="object")
+
+    def _str_translate(self, table):
+        return self._str_map(lambda x: x.translate(table))
+
+    def _str_wrap(self, width: int, **kwargs):
+        kwargs["width"] = width
+        tw = textwrap.TextWrapper(**kwargs)
+        return self._str_map(lambda s: "\n".join(tw.wrap(s)))
+
+    def _str_get_dummies(self, sep: str = "|"):
+        from pandas import Series
+
+        arr = Series(self).fillna("")
+        try:
+            arr = sep + arr + sep
+        except (TypeError, NotImplementedError):
+            arr = sep + arr.astype(str) + sep
+
+        tags: set[str] = set()
+        for ts in Series(arr, copy=False).str.split(sep):
+            tags.update(ts)
+        tags2 = sorted(tags - {""})
+
+        dummies = np.empty((len(arr), len(tags2)), dtype=np.int64)
+
+        def _isin(test_elements: str, element: str) -> bool:
+            return element in test_elements
+
+        for i, t in enumerate(tags2):
+            pat = sep + t + sep
+            dummies[:, i] = lib.map_infer(
+                arr.to_numpy(), functools.partial(_isin, element=pat)
+            )
+        return dummies, tags2
+
+    def _str_upper(self):
+        return self._str_map(lambda x: x.upper())
+
+    def _str_isalnum(self):
+        return self._str_map(str.isalnum, dtype="bool")
+
+    def _str_isalpha(self):
+        return self._str_map(str.isalpha, dtype="bool")
+
+    def _str_isdecimal(self):
+        return self._str_map(str.isdecimal, dtype="bool")
+
+    def _str_isdigit(self):
+        return self._str_map(str.isdigit, dtype="bool")
+
+    def _str_islower(self):
+        return self._str_map(str.islower, dtype="bool")
+
+    def _str_isnumeric(self):
+        return self._str_map(str.isnumeric, dtype="bool")
+
+    def _str_isspace(self):
+        return self._str_map(str.isspace, dtype="bool")
+
+    def _str_istitle(self):
+        return self._str_map(str.istitle, dtype="bool")
+
+    def _str_isupper(self):
+        return self._str_map(str.isupper, dtype="bool")
+
+    def _str_capitalize(self):
+        return self._str_map(str.capitalize)
+
+    def _str_casefold(self):
+        return self._str_map(str.casefold)
+
+    def _str_title(self):
+        return self._str_map(str.title)
+
+    def _str_swapcase(self):
+        return self._str_map(str.swapcase)
+
+    def _str_lower(self):
+        return self._str_map(str.lower)
+
+    def _str_normalize(self, form):
+        f = lambda x: unicodedata.normalize(form, x)
+        return self._str_map(f)
+
+    def _str_strip(self, to_strip=None):
+        return self._str_map(lambda x: x.strip(to_strip))
+
+    def _str_lstrip(self, to_strip=None):
+        return self._str_map(lambda x: x.lstrip(to_strip))
+
+    def _str_rstrip(self, to_strip=None):
+        return self._str_map(lambda x: x.rstrip(to_strip))
+
+    def _str_removeprefix(self, prefix: str) -> Series:
+        # outstanding question on whether to use native methods for users on Python 3.9+
+        # https://github.com/pandas-dev/pandas/pull/39226#issuecomment-836719770,
+        # in which case we could do return self._str_map(str.removeprefix)
+
+        def removeprefix(text: str) -> str:
+            if text.startswith(prefix):
+                return text[len(prefix) :]
+            return text
+
+        return self._str_map(removeprefix)
+
+    def _str_removesuffix(self, suffix: str) -> Series:
+        return self._str_map(lambda x: x.removesuffix(suffix))
+
+    def _str_extract(self, pat: str, flags: int = 0, expand: bool = True):
+        regex = re.compile(pat, flags=flags)
+        na_value = self.dtype.na_value  # type: ignore[attr-defined]
+
+        if not expand:
+
+            def g(x):
+                m = regex.search(x)
+                return m.groups()[0] if m else na_value
+
+            return self._str_map(g, convert=False)
+
+        empty_row = [na_value] * regex.groups
+
+        def f(x):
+            if not isinstance(x, str):
+                return empty_row
+            m = regex.search(x)
+            if m:
+                return [na_value if item is None else item for item in m.groups()]
+            else:
+                return empty_row
+
+        return [f(val) for val in np.asarray(self)]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/tools/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/tools/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/tools/datetimes.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/tools/datetimes.py
new file mode 100644
index 0000000000000000000000000000000000000000..8f700cfa631327fb09b1d6d50f86eefb212f3f87
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/tools/datetimes.py
@@ -0,0 +1,1240 @@
+from __future__ import annotations
+
+from collections import abc
+from datetime import date
+from functools import partial
+from itertools import islice
+from typing import (
+    TYPE_CHECKING,
+    Callable,
+    TypedDict,
+    Union,
+    cast,
+    overload,
+)
+import warnings
+
+import numpy as np
+
+from pandas._config import using_string_dtype
+
+from pandas._libs import (
+    lib,
+    tslib,
+)
+from pandas._libs.tslibs import (
+    OutOfBoundsDatetime,
+    Timedelta,
+    Timestamp,
+    astype_overflowsafe,
+    is_supported_dtype,
+    timezones as libtimezones,
+)
+from pandas._libs.tslibs.conversion import cast_from_unit_vectorized
+from pandas._libs.tslibs.parsing import (
+    DateParseError,
+    guess_datetime_format,
+)
+from pandas._libs.tslibs.strptime import array_strptime
+from pandas._typing import (
+    AnyArrayLike,
+    ArrayLike,
+    DateTimeErrorChoices,
+)
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.common import (
+    ensure_object,
+    is_float,
+    is_integer,
+    is_integer_dtype,
+    is_list_like,
+    is_numeric_dtype,
+)
+from pandas.core.dtypes.dtypes import (
+    ArrowDtype,
+    DatetimeTZDtype,
+)
+from pandas.core.dtypes.generic import (
+    ABCDataFrame,
+    ABCSeries,
+)
+
+from pandas.arrays import (
+    DatetimeArray,
+    IntegerArray,
+    NumpyExtensionArray,
+)
+from pandas.core.algorithms import unique
+from pandas.core.arrays import ArrowExtensionArray
+from pandas.core.arrays.base import ExtensionArray
+from pandas.core.arrays.datetimes import (
+    maybe_convert_dtype,
+    objects_to_datetime64,
+    tz_to_dtype,
+)
+from pandas.core.construction import extract_array
+from pandas.core.indexes.base import Index
+from pandas.core.indexes.datetimes import DatetimeIndex
+
+if TYPE_CHECKING:
+    from collections.abc import Hashable
+
+    from pandas._libs.tslibs.nattype import NaTType
+    from pandas._libs.tslibs.timedeltas import UnitChoices
+
+    from pandas import (
+        DataFrame,
+        Series,
+    )
+
+# ---------------------------------------------------------------------
+# types used in annotations
+
+ArrayConvertible = Union[list, tuple, AnyArrayLike]
+Scalar = Union[float, str]
+DatetimeScalar = Union[Scalar, date, np.datetime64]
+
+DatetimeScalarOrArrayConvertible = Union[DatetimeScalar, ArrayConvertible]
+
+DatetimeDictArg = Union[list[Scalar], tuple[Scalar, ...], AnyArrayLike]
+
+
+class YearMonthDayDict(TypedDict, total=True):
+    year: DatetimeDictArg
+    month: DatetimeDictArg
+    day: DatetimeDictArg
+
+
+class FulldatetimeDict(YearMonthDayDict, total=False):
+    hour: DatetimeDictArg
+    hours: DatetimeDictArg
+    minute: DatetimeDictArg
+    minutes: DatetimeDictArg
+    second: DatetimeDictArg
+    seconds: DatetimeDictArg
+    ms: DatetimeDictArg
+    us: DatetimeDictArg
+    ns: DatetimeDictArg
+
+
+DictConvertible = Union[FulldatetimeDict, "DataFrame"]
+start_caching_at = 50
+
+
+# ---------------------------------------------------------------------
+
+
+def _guess_datetime_format_for_array(arr, dayfirst: bool | None = False) -> str | None:
+    # Try to guess the format based on the first non-NaN element, return None if can't
+    if (first_non_null := tslib.first_non_null(arr)) != -1:
+        if type(first_non_nan_element := arr[first_non_null]) is str:  # noqa: E721
+            # GH#32264 np.str_ object
+            guessed_format = guess_datetime_format(
+                first_non_nan_element, dayfirst=dayfirst
+            )
+            if guessed_format is not None:
+                return guessed_format
+            # If there are multiple non-null elements, warn about
+            # how parsing might not be consistent
+            if tslib.first_non_null(arr[first_non_null + 1 :]) != -1:
+                warnings.warn(
+                    "Could not infer format, so each element will be parsed "
+                    "individually, falling back to `dateutil`. To ensure parsing is "
+                    "consistent and as-expected, please specify a format.",
+                    UserWarning,
+                    stacklevel=find_stack_level(),
+                )
+    return None
+
+
+def should_cache(
+    arg: ArrayConvertible, unique_share: float = 0.7, check_count: int | None = None
+) -> bool:
+    """
+    Decides whether to do caching.
+
+    If the percent of unique elements among `check_count` elements less
+    than `unique_share * 100` then we can do caching.
+
+    Parameters
+    ----------
+    arg: listlike, tuple, 1-d array, Series
+    unique_share: float, default=0.7, optional
+        0 < unique_share < 1
+    check_count: int, optional
+        0 <= check_count <= len(arg)
+
+    Returns
+    -------
+    do_caching: bool
+
+    Notes
+    -----
+    By default for a sequence of less than 50 items in size, we don't do
+    caching; for the number of elements less than 5000, we take ten percent of
+    all elements to check for a uniqueness share; if the sequence size is more
+    than 5000, then we check only the first 500 elements.
+    All constants were chosen empirically by.
+    """
+    do_caching = True
+
+    # default realization
+    if check_count is None:
+        # in this case, the gain from caching is negligible
+        if len(arg) <= start_caching_at:
+            return False
+
+        if len(arg) <= 5000:
+            check_count = len(arg) // 10
+        else:
+            check_count = 500
+    else:
+        assert (
+            0 <= check_count <= len(arg)
+        ), "check_count must be in next bounds: [0; len(arg)]"
+        if check_count == 0:
+            return False
+
+    assert 0 < unique_share < 1, "unique_share must be in next bounds: (0; 1)"
+
+    try:
+        # We can't cache if the items are not hashable.
+        unique_elements = set(islice(arg, check_count))
+    except TypeError:
+        return False
+    if len(unique_elements) > check_count * unique_share:
+        do_caching = False
+    return do_caching
+
+
+def _maybe_cache(
+    arg: ArrayConvertible,
+    format: str | None,
+    cache: bool,
+    convert_listlike: Callable,
+) -> Series:
+    """
+    Create a cache of unique dates from an array of dates
+
+    Parameters
+    ----------
+    arg : listlike, tuple, 1-d array, Series
+    format : string
+        Strftime format to parse time
+    cache : bool
+        True attempts to create a cache of converted values
+    convert_listlike : function
+        Conversion function to apply on dates
+
+    Returns
+    -------
+    cache_array : Series
+        Cache of converted, unique dates. Can be empty
+    """
+    from pandas import Series
+
+    cache_array = Series(dtype=object)
+
+    if cache:
+        # Perform a quicker unique check
+        if not should_cache(arg):
+            return cache_array
+
+        if not isinstance(arg, (np.ndarray, ExtensionArray, Index, ABCSeries)):
+            arg = np.array(arg)
+
+        unique_dates = unique(arg)
+        if len(unique_dates) < len(arg):
+            cache_dates = convert_listlike(unique_dates, format)
+            # GH#45319
+            try:
+                cache_array = Series(cache_dates, index=unique_dates, copy=False)
+            except OutOfBoundsDatetime:
+                return cache_array
+            # GH#39882 and GH#35888 in case of None and NaT we get duplicates
+            if not cache_array.index.is_unique:
+                cache_array = cache_array[~cache_array.index.duplicated()]
+    return cache_array
+
+
+def _box_as_indexlike(
+    dt_array: ArrayLike, utc: bool = False, name: Hashable | None = None
+) -> Index:
+    """
+    Properly boxes the ndarray of datetimes to DatetimeIndex
+    if it is possible or to generic Index instead
+
+    Parameters
+    ----------
+    dt_array: 1-d array
+        Array of datetimes to be wrapped in an Index.
+    utc : bool
+        Whether to convert/localize timestamps to UTC.
+    name : string, default None
+        Name for a resulting index
+
+    Returns
+    -------
+    result : datetime of converted dates
+        - DatetimeIndex if convertible to sole datetime64 type
+        - general Index otherwise
+    """
+
+    if lib.is_np_dtype(dt_array.dtype, "M"):
+        tz = "utc" if utc else None
+        return DatetimeIndex(dt_array, tz=tz, name=name)
+    return Index(dt_array, name=name, dtype=dt_array.dtype)
+
+
+def _convert_and_box_cache(
+    arg: DatetimeScalarOrArrayConvertible,
+    cache_array: Series,
+    name: Hashable | None = None,
+) -> Index:
+    """
+    Convert array of dates with a cache and wrap the result in an Index.
+
+    Parameters
+    ----------
+    arg : integer, float, string, datetime, list, tuple, 1-d array, Series
+    cache_array : Series
+        Cache of converted, unique dates
+    name : string, default None
+        Name for a DatetimeIndex
+
+    Returns
+    -------
+    result : Index-like of converted dates
+    """
+    from pandas import Series
+
+    result = Series(arg, dtype=cache_array.index.dtype).map(cache_array)
+    return _box_as_indexlike(result._values, utc=False, name=name)
+
+
+def _convert_listlike_datetimes(
+    arg,
+    format: str | None,
+    name: Hashable | None = None,
+    utc: bool = False,
+    unit: str | None = None,
+    errors: DateTimeErrorChoices = "raise",
+    dayfirst: bool | None = None,
+    yearfirst: bool | None = None,
+    exact: bool = True,
+):
+    """
+    Helper function for to_datetime. Performs the conversions of 1D listlike
+    of dates
+
+    Parameters
+    ----------
+    arg : list, tuple, ndarray, Series, Index
+        date to be parsed
+    name : object
+        None or string for the Index name
+    utc : bool
+        Whether to convert/localize timestamps to UTC.
+    unit : str
+        None or string of the frequency of the passed data
+    errors : str
+        error handing behaviors from to_datetime, 'raise', 'coerce', 'ignore'
+    dayfirst : bool
+        dayfirst parsing behavior from to_datetime
+    yearfirst : bool
+        yearfirst parsing behavior from to_datetime
+    exact : bool, default True
+        exact format matching behavior from to_datetime
+
+    Returns
+    -------
+    Index-like of parsed dates
+    """
+    if isinstance(arg, (list, tuple)):
+        arg = np.array(arg, dtype="O")
+    elif isinstance(arg, NumpyExtensionArray):
+        arg = np.array(arg)
+
+    arg_dtype = getattr(arg, "dtype", None)
+    # these are shortcutable
+    tz = "utc" if utc else None
+    if isinstance(arg_dtype, DatetimeTZDtype):
+        if not isinstance(arg, (DatetimeArray, DatetimeIndex)):
+            return DatetimeIndex(arg, tz=tz, name=name)
+        if utc:
+            arg = arg.tz_convert(None).tz_localize("utc")
+        return arg
+
+    elif isinstance(arg_dtype, ArrowDtype) and arg_dtype.type is Timestamp:
+        # TODO: Combine with above if DTI/DTA supports Arrow timestamps
+        if utc:
+            # pyarrow uses UTC, not lowercase utc
+            if isinstance(arg, Index):
+                arg_array = cast(ArrowExtensionArray, arg.array)
+                if arg_dtype.pyarrow_dtype.tz is not None:
+                    arg_array = arg_array._dt_tz_convert("UTC")
+                else:
+                    arg_array = arg_array._dt_tz_localize("UTC")
+                arg = Index(arg_array)
+            else:
+                # ArrowExtensionArray
+                if arg_dtype.pyarrow_dtype.tz is not None:
+                    arg = arg._dt_tz_convert("UTC")
+                else:
+                    arg = arg._dt_tz_localize("UTC")
+        return arg
+
+    elif lib.is_np_dtype(arg_dtype, "M"):
+        if not is_supported_dtype(arg_dtype):
+            # We go to closest supported reso, i.e. "s"
+            arg = astype_overflowsafe(
+                # TODO: looks like we incorrectly raise with errors=="ignore"
+                np.asarray(arg),
+                np.dtype("M8[s]"),
+                is_coerce=errors == "coerce",
+            )
+
+        if not isinstance(arg, (DatetimeArray, DatetimeIndex)):
+            return DatetimeIndex(arg, tz=tz, name=name)
+        elif utc:
+            # DatetimeArray, DatetimeIndex
+            return arg.tz_localize("utc")
+
+        return arg
+
+    elif unit is not None:
+        if format is not None:
+            raise ValueError("cannot specify both format and unit")
+        return _to_datetime_with_unit(arg, unit, name, utc, errors)
+    elif getattr(arg, "ndim", 1) > 1:
+        raise TypeError(
+            "arg must be a string, datetime, list, tuple, 1-d array, or Series"
+        )
+
+    # warn if passing timedelta64, raise for PeriodDtype
+    # NB: this must come after unit transformation
+    try:
+        arg, _ = maybe_convert_dtype(arg, copy=False, tz=libtimezones.maybe_get_tz(tz))
+    except TypeError:
+        if errors == "coerce":
+            npvalues = np.array(["NaT"], dtype="datetime64[ns]").repeat(len(arg))
+            return DatetimeIndex(npvalues, name=name)
+        elif errors == "ignore":
+            idx = Index(arg, name=name)
+            return idx
+        raise
+
+    arg = ensure_object(arg)
+
+    if format is None:
+        format = _guess_datetime_format_for_array(arg, dayfirst=dayfirst)
+
+    # `format` could be inferred, or user didn't ask for mixed-format parsing.
+    if format is not None and format != "mixed":
+        return _array_strptime_with_fallback(arg, name, utc, format, exact, errors)
+
+    result, tz_parsed = objects_to_datetime64(
+        arg,
+        dayfirst=dayfirst,
+        yearfirst=yearfirst,
+        utc=utc,
+        errors=errors,
+        allow_object=True,
+    )
+
+    if tz_parsed is not None:
+        # We can take a shortcut since the datetime64 numpy array
+        # is in UTC
+        out_unit = np.datetime_data(result.dtype)[0]
+        dtype = cast(DatetimeTZDtype, tz_to_dtype(tz_parsed, out_unit))
+        dt64_values = result.view(f"M8[{dtype.unit}]")
+        dta = DatetimeArray._simple_new(dt64_values, dtype=dtype)
+        return DatetimeIndex._simple_new(dta, name=name)
+
+    return _box_as_indexlike(result, utc=utc, name=name)
+
+
+def _array_strptime_with_fallback(
+    arg,
+    name,
+    utc: bool,
+    fmt: str,
+    exact: bool,
+    errors: str,
+) -> Index:
+    """
+    Call array_strptime, with fallback behavior depending on 'errors'.
+    """
+    result, tz_out = array_strptime(arg, fmt, exact=exact, errors=errors, utc=utc)
+    if tz_out is not None:
+        unit = np.datetime_data(result.dtype)[0]
+        dtype = DatetimeTZDtype(tz=tz_out, unit=unit)
+        dta = DatetimeArray._simple_new(result, dtype=dtype)
+        if utc:
+            dta = dta.tz_convert("UTC")
+        return Index(dta, name=name)
+    elif result.dtype != object and utc:
+        unit = np.datetime_data(result.dtype)[0]
+        res = Index(result, dtype=f"M8[{unit}, UTC]", name=name)
+        return res
+    elif using_string_dtype() and result.dtype == object:
+        if lib.is_string_array(result):
+            return Index(result, dtype="str", name=name)
+    return Index(result, dtype=result.dtype, name=name)
+
+
+def _to_datetime_with_unit(arg, unit, name, utc: bool, errors: str) -> Index:
+    """
+    to_datetime specalized to the case where a 'unit' is passed.
+    """
+    arg = extract_array(arg, extract_numpy=True)
+
+    # GH#30050 pass an ndarray to tslib.array_with_unit_to_datetime
+    # because it expects an ndarray argument
+    if isinstance(arg, IntegerArray):
+        arr = arg.astype(f"datetime64[{unit}]")
+        tz_parsed = None
+    else:
+        arg = np.asarray(arg)
+
+        if arg.dtype.kind in "iu":
+            # Note we can't do "f" here because that could induce unwanted
+            #  rounding GH#14156, GH#20445
+            arr = arg.astype(f"datetime64[{unit}]", copy=False)
+            try:
+                arr = astype_overflowsafe(arr, np.dtype("M8[ns]"), copy=False)
+            except OutOfBoundsDatetime:
+                if errors == "raise":
+                    raise
+                arg = arg.astype(object)
+                return _to_datetime_with_unit(arg, unit, name, utc, errors)
+            tz_parsed = None
+
+        elif arg.dtype.kind == "f":
+            with np.errstate(over="raise"):
+                try:
+                    arr = cast_from_unit_vectorized(arg, unit=unit)
+                except OutOfBoundsDatetime:
+                    if errors != "raise":
+                        return _to_datetime_with_unit(
+                            arg.astype(object), unit, name, utc, errors
+                        )
+                    raise OutOfBoundsDatetime(
+                        f"cannot convert input with unit '{unit}'"
+                    )
+
+            arr = arr.view("M8[ns]")
+            tz_parsed = None
+        else:
+            arg = arg.astype(object, copy=False)
+            arr, tz_parsed = tslib.array_with_unit_to_datetime(arg, unit, errors=errors)
+
+    if errors == "ignore":
+        # Index constructor _may_ infer to DatetimeIndex
+        result = Index._with_infer(arr, name=name)
+    else:
+        result = DatetimeIndex(arr, name=name)
+
+    if not isinstance(result, DatetimeIndex):
+        return result
+
+    # GH#23758: We may still need to localize the result with tz
+    # GH#25546: Apply tz_parsed first (from arg), then tz (from caller)
+    # result will be naive but in UTC
+    result = result.tz_localize("UTC").tz_convert(tz_parsed)
+
+    if utc:
+        if result.tz is None:
+            result = result.tz_localize("utc")
+        else:
+            result = result.tz_convert("utc")
+    return result
+
+
+def _adjust_to_origin(arg, origin, unit):
+    """
+    Helper function for to_datetime.
+    Adjust input argument to the specified origin
+
+    Parameters
+    ----------
+    arg : list, tuple, ndarray, Series, Index
+        date to be adjusted
+    origin : 'julian' or Timestamp
+        origin offset for the arg
+    unit : str
+        passed unit from to_datetime, must be 'D'
+
+    Returns
+    -------
+    ndarray or scalar of adjusted date(s)
+    """
+    if origin == "julian":
+        original = arg
+        j0 = Timestamp(0).to_julian_date()
+        if unit != "D":
+            raise ValueError("unit must be 'D' for origin='julian'")
+        try:
+            arg = arg - j0
+        except TypeError as err:
+            raise ValueError(
+                "incompatible 'arg' type for given 'origin'='julian'"
+            ) from err
+
+        # preemptively check this for a nice range
+        j_max = Timestamp.max.to_julian_date() - j0
+        j_min = Timestamp.min.to_julian_date() - j0
+        if np.any(arg > j_max) or np.any(arg < j_min):
+            raise OutOfBoundsDatetime(
+                f"{original} is Out of Bounds for origin='julian'"
+            )
+    else:
+        # arg must be numeric
+        if not (
+            (is_integer(arg) or is_float(arg)) or is_numeric_dtype(np.asarray(arg))
+        ):
+            raise ValueError(
+                f"'{arg}' is not compatible with origin='{origin}'; "
+                "it must be numeric with a unit specified"
+            )
+
+        # we are going to offset back to unix / epoch time
+        try:
+            offset = Timestamp(origin, unit=unit)
+        except OutOfBoundsDatetime as err:
+            raise OutOfBoundsDatetime(f"origin {origin} is Out of Bounds") from err
+        except ValueError as err:
+            raise ValueError(
+                f"origin {origin} cannot be converted to a Timestamp"
+            ) from err
+
+        if offset.tz is not None:
+            raise ValueError(f"origin offset {offset} must be tz-naive")
+        td_offset = offset - Timestamp(0)
+
+        # convert the offset to the unit of the arg
+        # this should be lossless in terms of precision
+        ioffset = td_offset // Timedelta(1, unit=unit)
+
+        # scalars & ndarray-like can handle the addition
+        if is_list_like(arg) and not isinstance(arg, (ABCSeries, Index, np.ndarray)):
+            arg = np.asarray(arg)
+        arg = arg + ioffset
+    return arg
+
+
+@overload
+def to_datetime(
+    arg: DatetimeScalar,
+    errors: DateTimeErrorChoices = ...,
+    dayfirst: bool = ...,
+    yearfirst: bool = ...,
+    utc: bool = ...,
+    format: str | None = ...,
+    exact: bool = ...,
+    unit: str | None = ...,
+    infer_datetime_format: bool = ...,
+    origin=...,
+    cache: bool = ...,
+) -> Timestamp:
+    ...
+
+
+@overload
+def to_datetime(
+    arg: Series | DictConvertible,
+    errors: DateTimeErrorChoices = ...,
+    dayfirst: bool = ...,
+    yearfirst: bool = ...,
+    utc: bool = ...,
+    format: str | None = ...,
+    exact: bool = ...,
+    unit: str | None = ...,
+    infer_datetime_format: bool = ...,
+    origin=...,
+    cache: bool = ...,
+) -> Series:
+    ...
+
+
+@overload
+def to_datetime(
+    arg: list | tuple | Index | ArrayLike,
+    errors: DateTimeErrorChoices = ...,
+    dayfirst: bool = ...,
+    yearfirst: bool = ...,
+    utc: bool = ...,
+    format: str | None = ...,
+    exact: bool = ...,
+    unit: str | None = ...,
+    infer_datetime_format: bool = ...,
+    origin=...,
+    cache: bool = ...,
+) -> DatetimeIndex:
+    ...
+
+
+def to_datetime(
+    arg: DatetimeScalarOrArrayConvertible | DictConvertible,
+    errors: DateTimeErrorChoices = "raise",
+    dayfirst: bool = False,
+    yearfirst: bool = False,
+    utc: bool = False,
+    format: str | None = None,
+    exact: bool | lib.NoDefault = lib.no_default,
+    unit: str | None = None,
+    infer_datetime_format: lib.NoDefault | bool = lib.no_default,
+    origin: str = "unix",
+    cache: bool = True,
+) -> DatetimeIndex | Series | DatetimeScalar | NaTType | None:
+    """
+    Convert argument to datetime.
+
+    This function converts a scalar, array-like, :class:`Series` or
+    :class:`DataFrame`/dict-like to a pandas datetime object.
+
+    Parameters
+    ----------
+    arg : int, float, str, datetime, list, tuple, 1-d array, Series, DataFrame/dict-like
+        The object to convert to a datetime. If a :class:`DataFrame` is provided, the
+        method expects minimally the following columns: :const:`"year"`,
+        :const:`"month"`, :const:`"day"`. The column "year"
+        must be specified in 4-digit format.
+    errors : {'ignore', 'raise', 'coerce'}, default 'raise'
+        - If :const:`'raise'`, then invalid parsing will raise an exception.
+        - If :const:`'coerce'`, then invalid parsing will be set as :const:`NaT`.
+        - If :const:`'ignore'`, then invalid parsing will return the input.
+    dayfirst : bool, default False
+        Specify a date parse order if `arg` is str or is list-like.
+        If :const:`True`, parses dates with the day first, e.g. :const:`"10/11/12"`
+        is parsed as :const:`2012-11-10`.
+
+        .. warning::
+
+            ``dayfirst=True`` is not strict, but will prefer to parse
+            with day first.
+
+    yearfirst : bool, default False
+        Specify a date parse order if `arg` is str or is list-like.
+
+        - If :const:`True` parses dates with the year first, e.g.
+          :const:`"10/11/12"` is parsed as :const:`2010-11-12`.
+        - If both `dayfirst` and `yearfirst` are :const:`True`, `yearfirst` is
+          preceded (same as :mod:`dateutil`).
+
+        .. warning::
+
+            ``yearfirst=True`` is not strict, but will prefer to parse
+            with year first.
+
+    utc : bool, default False
+        Control timezone-related parsing, localization and conversion.
+
+        - If :const:`True`, the function *always* returns a timezone-aware
+          UTC-localized :class:`Timestamp`, :class:`Series` or
+          :class:`DatetimeIndex`. To do this, timezone-naive inputs are
+          *localized* as UTC, while timezone-aware inputs are *converted* to UTC.
+
+        - If :const:`False` (default), inputs will not be coerced to UTC.
+          Timezone-naive inputs will remain naive, while timezone-aware ones
+          will keep their time offsets. Limitations exist for mixed
+          offsets (typically, daylight savings), see :ref:`Examples
+          ` section for details.
+
+        .. warning::
+
+            In a future version of pandas, parsing datetimes with mixed time
+            zones will raise an error unless `utc=True`.
+            Please specify `utc=True` to opt in to the new behaviour
+            and silence this warning. To create a `Series` with mixed offsets and
+            `object` dtype, please use `apply` and `datetime.datetime.strptime`.
+
+        See also: pandas general documentation about `timezone conversion and
+        localization
+        `_.
+
+    format : str, default None
+        The strftime to parse time, e.g. :const:`"%d/%m/%Y"`. See
+        `strftime documentation
+        `_ for more information on choices, though
+        note that :const:`"%f"` will parse all the way up to nanoseconds.
+        You can also pass:
+
+        - "ISO8601", to parse any `ISO8601 `_
+          time string (not necessarily in exactly the same format);
+        - "mixed", to infer the format for each element individually. This is risky,
+          and you should probably use it along with `dayfirst`.
+
+        .. note::
+
+            If a :class:`DataFrame` is passed, then `format` has no effect.
+
+    exact : bool, default True
+        Control how `format` is used:
+
+        - If :const:`True`, require an exact `format` match.
+        - If :const:`False`, allow the `format` to match anywhere in the target
+          string.
+
+        Cannot be used alongside ``format='ISO8601'`` or ``format='mixed'``.
+    unit : str, default 'ns'
+        The unit of the arg (D,s,ms,us,ns) denote the unit, which is an
+        integer or float number. This will be based off the origin.
+        Example, with ``unit='ms'`` and ``origin='unix'``, this would calculate
+        the number of milliseconds to the unix epoch start.
+    infer_datetime_format : bool, default False
+        If :const:`True` and no `format` is given, attempt to infer the format
+        of the datetime strings based on the first non-NaN element,
+        and if it can be inferred, switch to a faster method of parsing them.
+        In some cases this can increase the parsing speed by ~5-10x.
+
+        .. deprecated:: 2.0.0
+            A strict version of this argument is now the default, passing it has
+            no effect.
+
+    origin : scalar, default 'unix'
+        Define the reference date. The numeric values would be parsed as number
+        of units (defined by `unit`) since this reference date.
+
+        - If :const:`'unix'` (or POSIX) time; origin is set to 1970-01-01.
+        - If :const:`'julian'`, unit must be :const:`'D'`, and origin is set to
+          beginning of Julian Calendar. Julian day number :const:`0` is assigned
+          to the day starting at noon on January 1, 4713 BC.
+        - If Timestamp convertible (Timestamp, dt.datetime, np.datetimt64 or date
+          string), origin is set to Timestamp identified by origin.
+        - If a float or integer, origin is the difference
+          (in units determined by the ``unit`` argument) relative to 1970-01-01.
+    cache : bool, default True
+        If :const:`True`, use a cache of unique, converted dates to apply the
+        datetime conversion. May produce significant speed-up when parsing
+        duplicate date strings, especially ones with timezone offsets. The cache
+        is only used when there are at least 50 values. The presence of
+        out-of-bounds values will render the cache unusable and may slow down
+        parsing.
+
+    Returns
+    -------
+    datetime
+        If parsing succeeded.
+        Return type depends on input (types in parenthesis correspond to
+        fallback in case of unsuccessful timezone or out-of-range timestamp
+        parsing):
+
+        - scalar: :class:`Timestamp` (or :class:`datetime.datetime`)
+        - array-like: :class:`DatetimeIndex` (or :class:`Series` with
+          :class:`object` dtype containing :class:`datetime.datetime`)
+        - Series: :class:`Series` of :class:`datetime64` dtype (or
+          :class:`Series` of :class:`object` dtype containing
+          :class:`datetime.datetime`)
+        - DataFrame: :class:`Series` of :class:`datetime64` dtype (or
+          :class:`Series` of :class:`object` dtype containing
+          :class:`datetime.datetime`)
+
+    Raises
+    ------
+    ParserError
+        When parsing a date from string fails.
+    ValueError
+        When another datetime conversion error happens. For example when one
+        of 'year', 'month', day' columns is missing in a :class:`DataFrame`, or
+        when a Timezone-aware :class:`datetime.datetime` is found in an array-like
+        of mixed time offsets, and ``utc=False``.
+
+    See Also
+    --------
+    DataFrame.astype : Cast argument to a specified dtype.
+    to_timedelta : Convert argument to timedelta.
+    convert_dtypes : Convert dtypes.
+
+    Notes
+    -----
+
+    Many input types are supported, and lead to different output types:
+
+    - **scalars** can be int, float, str, datetime object (from stdlib :mod:`datetime`
+      module or :mod:`numpy`). They are converted to :class:`Timestamp` when
+      possible, otherwise they are converted to :class:`datetime.datetime`.
+      None/NaN/null scalars are converted to :const:`NaT`.
+
+    - **array-like** can contain int, float, str, datetime objects. They are
+      converted to :class:`DatetimeIndex` when possible, otherwise they are
+      converted to :class:`Index` with :class:`object` dtype, containing
+      :class:`datetime.datetime`. None/NaN/null entries are converted to
+      :const:`NaT` in both cases.
+
+    - **Series** are converted to :class:`Series` with :class:`datetime64`
+      dtype when possible, otherwise they are converted to :class:`Series` with
+      :class:`object` dtype, containing :class:`datetime.datetime`. None/NaN/null
+      entries are converted to :const:`NaT` in both cases.
+
+    - **DataFrame/dict-like** are converted to :class:`Series` with
+      :class:`datetime64` dtype. For each row a datetime is created from assembling
+      the various dataframe columns. Column keys can be common abbreviations
+      like ['year', 'month', 'day', 'minute', 'second', 'ms', 'us', 'ns']) or
+      plurals of the same.
+
+    The following causes are responsible for :class:`datetime.datetime` objects
+    being returned (possibly inside an :class:`Index` or a :class:`Series` with
+    :class:`object` dtype) instead of a proper pandas designated type
+    (:class:`Timestamp`, :class:`DatetimeIndex` or :class:`Series`
+    with :class:`datetime64` dtype):
+
+    - when any input element is before :const:`Timestamp.min` or after
+      :const:`Timestamp.max`, see `timestamp limitations
+      `_.
+
+    - when ``utc=False`` (default) and the input is an array-like or
+      :class:`Series` containing mixed naive/aware datetime, or aware with mixed
+      time offsets. Note that this happens in the (quite frequent) situation when
+      the timezone has a daylight savings policy. In that case you may wish to
+      use ``utc=True``.
+
+    Examples
+    --------
+
+    **Handling various input formats**
+
+    Assembling a datetime from multiple columns of a :class:`DataFrame`. The keys
+    can be common abbreviations like ['year', 'month', 'day', 'minute', 'second',
+    'ms', 'us', 'ns']) or plurals of the same
+
+    >>> df = pd.DataFrame({'year': [2015, 2016],
+    ...                    'month': [2, 3],
+    ...                    'day': [4, 5]})
+    >>> pd.to_datetime(df)
+    0   2015-02-04
+    1   2016-03-05
+    dtype: datetime64[ns]
+
+    Using a unix epoch time
+
+    >>> pd.to_datetime(1490195805, unit='s')
+    Timestamp('2017-03-22 15:16:45')
+    >>> pd.to_datetime(1490195805433502912, unit='ns')
+    Timestamp('2017-03-22 15:16:45.433502912')
+
+    .. warning:: For float arg, precision rounding might happen. To prevent
+        unexpected behavior use a fixed-width exact type.
+
+    Using a non-unix epoch origin
+
+    >>> pd.to_datetime([1, 2, 3], unit='D',
+    ...                origin=pd.Timestamp('1960-01-01'))
+    DatetimeIndex(['1960-01-02', '1960-01-03', '1960-01-04'],
+                  dtype='datetime64[ns]', freq=None)
+
+    **Differences with strptime behavior**
+
+    :const:`"%f"` will parse all the way up to nanoseconds.
+
+    >>> pd.to_datetime('2018-10-26 12:00:00.0000000011',
+    ...                format='%Y-%m-%d %H:%M:%S.%f')
+    Timestamp('2018-10-26 12:00:00.000000001')
+
+    **Non-convertible date/times**
+
+    Passing ``errors='coerce'`` will force an out-of-bounds date to :const:`NaT`,
+    in addition to forcing non-dates (or non-parseable dates) to :const:`NaT`.
+
+    >>> pd.to_datetime('13000101', format='%Y%m%d', errors='coerce')
+    NaT
+
+    .. _to_datetime_tz_examples:
+
+    **Timezones and time offsets**
+
+    The default behaviour (``utc=False``) is as follows:
+
+    - Timezone-naive inputs are converted to timezone-naive :class:`DatetimeIndex`:
+
+    >>> pd.to_datetime(['2018-10-26 12:00:00', '2018-10-26 13:00:15'])
+    DatetimeIndex(['2018-10-26 12:00:00', '2018-10-26 13:00:15'],
+                  dtype='datetime64[ns]', freq=None)
+
+    - Timezone-aware inputs *with constant time offset* are converted to
+      timezone-aware :class:`DatetimeIndex`:
+
+    >>> pd.to_datetime(['2018-10-26 12:00 -0500', '2018-10-26 13:00 -0500'])
+    DatetimeIndex(['2018-10-26 12:00:00-05:00', '2018-10-26 13:00:00-05:00'],
+                  dtype='datetime64[ns, UTC-05:00]', freq=None)
+
+    - However, timezone-aware inputs *with mixed time offsets* (for example
+      issued from a timezone with daylight savings, such as Europe/Paris)
+      are **not successfully converted** to a :class:`DatetimeIndex`.
+      Parsing datetimes with mixed time zones will show a warning unless
+      `utc=True`. If you specify `utc=False` the warning below will be shown
+      and a simple :class:`Index` containing :class:`datetime.datetime`
+      objects will be returned:
+
+    >>> pd.to_datetime(['2020-10-25 02:00 +0200',
+    ...                 '2020-10-25 04:00 +0100'])  # doctest: +SKIP
+    FutureWarning: In a future version of pandas, parsing datetimes with mixed
+    time zones will raise an error unless `utc=True`. Please specify `utc=True`
+    to opt in to the new behaviour and silence this warning. To create a `Series`
+    with mixed offsets and `object` dtype, please use `apply` and
+    `datetime.datetime.strptime`.
+    Index([2020-10-25 02:00:00+02:00, 2020-10-25 04:00:00+01:00],
+          dtype='object')
+
+    - A mix of timezone-aware and timezone-naive inputs is also converted to
+      a simple :class:`Index` containing :class:`datetime.datetime` objects:
+
+    >>> from datetime import datetime
+    >>> pd.to_datetime(["2020-01-01 01:00:00-01:00",
+    ...                 datetime(2020, 1, 1, 3, 0)])  # doctest: +SKIP
+    FutureWarning: In a future version of pandas, parsing datetimes with mixed
+    time zones will raise an error unless `utc=True`. Please specify `utc=True`
+    to opt in to the new behaviour and silence this warning. To create a `Series`
+    with mixed offsets and `object` dtype, please use `apply` and
+    `datetime.datetime.strptime`.
+    Index([2020-01-01 01:00:00-01:00, 2020-01-01 03:00:00], dtype='object')
+
+    |
+
+    Setting ``utc=True`` solves most of the above issues:
+
+    - Timezone-naive inputs are *localized* as UTC
+
+    >>> pd.to_datetime(['2018-10-26 12:00', '2018-10-26 13:00'], utc=True)
+    DatetimeIndex(['2018-10-26 12:00:00+00:00', '2018-10-26 13:00:00+00:00'],
+                  dtype='datetime64[ns, UTC]', freq=None)
+
+    - Timezone-aware inputs are *converted* to UTC (the output represents the
+      exact same datetime, but viewed from the UTC time offset `+00:00`).
+
+    >>> pd.to_datetime(['2018-10-26 12:00 -0530', '2018-10-26 12:00 -0500'],
+    ...                utc=True)
+    DatetimeIndex(['2018-10-26 17:30:00+00:00', '2018-10-26 17:00:00+00:00'],
+                  dtype='datetime64[ns, UTC]', freq=None)
+
+    - Inputs can contain both string or datetime, the above
+      rules still apply
+
+    >>> pd.to_datetime(['2018-10-26 12:00', datetime(2020, 1, 1, 18)], utc=True)
+    DatetimeIndex(['2018-10-26 12:00:00+00:00', '2020-01-01 18:00:00+00:00'],
+                  dtype='datetime64[ns, UTC]', freq=None)
+    """
+    if exact is not lib.no_default and format in {"mixed", "ISO8601"}:
+        raise ValueError("Cannot use 'exact' when 'format' is 'mixed' or 'ISO8601'")
+    if infer_datetime_format is not lib.no_default:
+        warnings.warn(
+            "The argument 'infer_datetime_format' is deprecated and will "
+            "be removed in a future version. "
+            "A strict version of it is now the default, see "
+            "https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. "
+            "You can safely remove this argument.",
+            stacklevel=find_stack_level(),
+        )
+    if errors == "ignore":
+        # GH#54467
+        warnings.warn(
+            "errors='ignore' is deprecated and will raise in a future version. "
+            "Use to_datetime without passing `errors` and catch exceptions "
+            "explicitly instead",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+
+    if arg is None:
+        return None
+
+    if origin != "unix":
+        arg = _adjust_to_origin(arg, origin, unit)
+
+    convert_listlike = partial(
+        _convert_listlike_datetimes,
+        utc=utc,
+        unit=unit,
+        dayfirst=dayfirst,
+        yearfirst=yearfirst,
+        errors=errors,
+        exact=exact,
+    )
+    # pylint: disable-next=used-before-assignment
+    result: Timestamp | NaTType | Series | Index
+
+    if isinstance(arg, Timestamp):
+        result = arg
+        if utc:
+            if arg.tz is not None:
+                result = arg.tz_convert("utc")
+            else:
+                result = arg.tz_localize("utc")
+    elif isinstance(arg, ABCSeries):
+        cache_array = _maybe_cache(arg, format, cache, convert_listlike)
+        if not cache_array.empty:
+            result = arg.map(cache_array)
+        else:
+            values = convert_listlike(arg._values, format)
+            result = arg._constructor(values, index=arg.index, name=arg.name)
+    elif isinstance(arg, (ABCDataFrame, abc.MutableMapping)):
+        result = _assemble_from_unit_mappings(arg, errors, utc)
+    elif isinstance(arg, Index):
+        cache_array = _maybe_cache(arg, format, cache, convert_listlike)
+        if not cache_array.empty:
+            result = _convert_and_box_cache(arg, cache_array, name=arg.name)
+        else:
+            result = convert_listlike(arg, format, name=arg.name)
+    elif is_list_like(arg):
+        try:
+            # error: Argument 1 to "_maybe_cache" has incompatible type
+            # "Union[float, str, datetime, List[Any], Tuple[Any, ...], ExtensionArray,
+            # ndarray[Any, Any], Series]"; expected "Union[List[Any], Tuple[Any, ...],
+            # Union[Union[ExtensionArray, ndarray[Any, Any]], Index, Series], Series]"
+            argc = cast(
+                Union[list, tuple, ExtensionArray, np.ndarray, "Series", Index], arg
+            )
+            cache_array = _maybe_cache(argc, format, cache, convert_listlike)
+        except OutOfBoundsDatetime:
+            # caching attempts to create a DatetimeIndex, which may raise
+            # an OOB. If that's the desired behavior, then just reraise...
+            if errors == "raise":
+                raise
+            # ... otherwise, continue without the cache.
+            from pandas import Series
+
+            cache_array = Series([], dtype=object)  # just an empty array
+        if not cache_array.empty:
+            result = _convert_and_box_cache(argc, cache_array)
+        else:
+            result = convert_listlike(argc, format)
+    else:
+        result = convert_listlike(np.array([arg]), format)[0]
+        if isinstance(arg, bool) and isinstance(result, np.bool_):
+            result = bool(result)  # TODO: avoid this kludge.
+
+    #  error: Incompatible return value type (got "Union[Timestamp, NaTType,
+    # Series, Index]", expected "Union[DatetimeIndex, Series, float, str,
+    # NaTType, None]")
+    return result  # type: ignore[return-value]
+
+
+# mappings for assembling units
+_unit_map = {
+    "year": "year",
+    "years": "year",
+    "month": "month",
+    "months": "month",
+    "day": "day",
+    "days": "day",
+    "hour": "h",
+    "hours": "h",
+    "minute": "m",
+    "minutes": "m",
+    "second": "s",
+    "seconds": "s",
+    "ms": "ms",
+    "millisecond": "ms",
+    "milliseconds": "ms",
+    "us": "us",
+    "microsecond": "us",
+    "microseconds": "us",
+    "ns": "ns",
+    "nanosecond": "ns",
+    "nanoseconds": "ns",
+}
+
+
+def _assemble_from_unit_mappings(arg, errors: DateTimeErrorChoices, utc: bool):
+    """
+    assemble the unit specified fields from the arg (DataFrame)
+    Return a Series for actual parsing
+
+    Parameters
+    ----------
+    arg : DataFrame
+    errors : {'ignore', 'raise', 'coerce'}, default 'raise'
+
+        - If :const:`'raise'`, then invalid parsing will raise an exception
+        - If :const:`'coerce'`, then invalid parsing will be set as :const:`NaT`
+        - If :const:`'ignore'`, then invalid parsing will return the input
+    utc : bool
+        Whether to convert/localize timestamps to UTC.
+
+    Returns
+    -------
+    Series
+    """
+    from pandas import (
+        DataFrame,
+        to_numeric,
+        to_timedelta,
+    )
+
+    arg = DataFrame(arg)
+    if not arg.columns.is_unique:
+        raise ValueError("cannot assemble with duplicate keys")
+
+    # replace passed unit with _unit_map
+    def f(value):
+        if value in _unit_map:
+            return _unit_map[value]
+
+        # m is case significant
+        if value.lower() in _unit_map:
+            return _unit_map[value.lower()]
+
+        return value
+
+    unit = {k: f(k) for k in arg.keys()}
+    unit_rev = {v: k for k, v in unit.items()}
+
+    # we require at least Ymd
+    required = ["year", "month", "day"]
+    req = sorted(set(required) - set(unit_rev.keys()))
+    if len(req):
+        _required = ",".join(req)
+        raise ValueError(
+            "to assemble mappings requires at least that "
+            f"[year, month, day] be specified: [{_required}] is missing"
+        )
+
+    # keys we don't recognize
+    excess = sorted(set(unit_rev.keys()) - set(_unit_map.values()))
+    if len(excess):
+        _excess = ",".join(excess)
+        raise ValueError(
+            f"extra keys have been passed to the datetime assemblage: [{_excess}]"
+        )
+
+    def coerce(values):
+        # we allow coercion to if errors allows
+        values = to_numeric(values, errors=errors)
+
+        # prevent overflow in case of int8 or int16
+        if is_integer_dtype(values.dtype):
+            values = values.astype("int64", copy=False)
+        return values
+
+    values = (
+        coerce(arg[unit_rev["year"]]) * 10000
+        + coerce(arg[unit_rev["month"]]) * 100
+        + coerce(arg[unit_rev["day"]])
+    )
+    try:
+        values = to_datetime(values, format="%Y%m%d", errors=errors, utc=utc)
+    except (TypeError, ValueError) as err:
+        raise ValueError(f"cannot assemble the datetimes: {err}") from err
+
+    units: list[UnitChoices] = ["h", "m", "s", "ms", "us", "ns"]
+    for u in units:
+        value = unit_rev.get(u)
+        if value is not None and value in arg:
+            try:
+                values += to_timedelta(coerce(arg[value]), unit=u, errors=errors)
+            except (TypeError, ValueError) as err:
+                raise ValueError(
+                    f"cannot assemble the datetimes [{value}]: {err}"
+                ) from err
+    return values
+
+
+__all__ = [
+    "DateParseError",
+    "should_cache",
+    "to_datetime",
+]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/tools/numeric.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/tools/numeric.py
new file mode 100644
index 0000000000000000000000000000000000000000..ca703e0362611b9b2ccecdbf282104ad450a8d83
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/tools/numeric.py
@@ -0,0 +1,332 @@
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    Literal,
+)
+import warnings
+
+import numpy as np
+
+from pandas._libs import (
+    lib,
+    missing as libmissing,
+)
+from pandas.util._exceptions import find_stack_level
+from pandas.util._validators import check_dtype_backend
+
+from pandas.core.dtypes.cast import maybe_downcast_numeric
+from pandas.core.dtypes.common import (
+    ensure_object,
+    is_bool_dtype,
+    is_decimal,
+    is_integer_dtype,
+    is_number,
+    is_numeric_dtype,
+    is_scalar,
+    is_string_dtype,
+    needs_i8_conversion,
+)
+from pandas.core.dtypes.dtypes import ArrowDtype
+from pandas.core.dtypes.generic import (
+    ABCIndex,
+    ABCSeries,
+)
+
+from pandas.core.arrays import BaseMaskedArray
+from pandas.core.arrays.string_ import StringDtype
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        DateTimeErrorChoices,
+        DtypeBackend,
+        npt,
+    )
+
+
+def to_numeric(
+    arg,
+    errors: DateTimeErrorChoices = "raise",
+    downcast: Literal["integer", "signed", "unsigned", "float"] | None = None,
+    dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
+):
+    """
+    Convert argument to a numeric type.
+
+    The default return dtype is `float64` or `int64`
+    depending on the data supplied. Use the `downcast` parameter
+    to obtain other dtypes.
+
+    Please note that precision loss may occur if really large numbers
+    are passed in. Due to the internal limitations of `ndarray`, if
+    numbers smaller than `-9223372036854775808` (np.iinfo(np.int64).min)
+    or larger than `18446744073709551615` (np.iinfo(np.uint64).max) are
+    passed in, it is very likely they will be converted to float so that
+    they can be stored in an `ndarray`. These warnings apply similarly to
+    `Series` since it internally leverages `ndarray`.
+
+    Parameters
+    ----------
+    arg : scalar, list, tuple, 1-d array, or Series
+        Argument to be converted.
+    errors : {'ignore', 'raise', 'coerce'}, default 'raise'
+        - If 'raise', then invalid parsing will raise an exception.
+        - If 'coerce', then invalid parsing will be set as NaN.
+        - If 'ignore', then invalid parsing will return the input.
+
+        .. versionchanged:: 2.2
+
+        "ignore" is deprecated. Catch exceptions explicitly instead.
+
+    downcast : str, default None
+        Can be 'integer', 'signed', 'unsigned', or 'float'.
+        If not None, and if the data has been successfully cast to a
+        numerical dtype (or if the data was numeric to begin with),
+        downcast that resulting data to the smallest numerical dtype
+        possible according to the following rules:
+
+        - 'integer' or 'signed': smallest signed int dtype (min.: np.int8)
+        - 'unsigned': smallest unsigned int dtype (min.: np.uint8)
+        - 'float': smallest float dtype (min.: np.float32)
+
+        As this behaviour is separate from the core conversion to
+        numeric values, any errors raised during the downcasting
+        will be surfaced regardless of the value of the 'errors' input.
+
+        In addition, downcasting will only occur if the size
+        of the resulting data's dtype is strictly larger than
+        the dtype it is to be cast to, so if none of the dtypes
+        checked satisfy that specification, no downcasting will be
+        performed on the data.
+    dtype_backend : {'numpy_nullable', 'pyarrow'}, default 'numpy_nullable'
+        Back-end data type applied to the resultant :class:`DataFrame`
+        (still experimental). Behaviour is as follows:
+
+        * ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame`
+          (default).
+        * ``"pyarrow"``: returns pyarrow-backed nullable :class:`ArrowDtype`
+          DataFrame.
+
+        .. versionadded:: 2.0
+
+    Returns
+    -------
+    ret
+        Numeric if parsing succeeded.
+        Return type depends on input.  Series if Series, otherwise ndarray.
+
+    See Also
+    --------
+    DataFrame.astype : Cast argument to a specified dtype.
+    to_datetime : Convert argument to datetime.
+    to_timedelta : Convert argument to timedelta.
+    numpy.ndarray.astype : Cast a numpy array to a specified type.
+    DataFrame.convert_dtypes : Convert dtypes.
+
+    Examples
+    --------
+    Take separate series and convert to numeric, coercing when told to
+
+    >>> s = pd.Series(['1.0', '2', -3])
+    >>> pd.to_numeric(s)
+    0    1.0
+    1    2.0
+    2   -3.0
+    dtype: float64
+    >>> pd.to_numeric(s, downcast='float')
+    0    1.0
+    1    2.0
+    2   -3.0
+    dtype: float32
+    >>> pd.to_numeric(s, downcast='signed')
+    0    1
+    1    2
+    2   -3
+    dtype: int8
+    >>> s = pd.Series(['apple', '1.0', '2', -3])
+    >>> pd.to_numeric(s, errors='coerce')
+    0    NaN
+    1    1.0
+    2    2.0
+    3   -3.0
+    dtype: float64
+
+    Downcasting of nullable integer and floating dtypes is supported:
+
+    >>> s = pd.Series([1, 2, 3], dtype="Int64")
+    >>> pd.to_numeric(s, downcast="integer")
+    0    1
+    1    2
+    2    3
+    dtype: Int8
+    >>> s = pd.Series([1.0, 2.1, 3.0], dtype="Float64")
+    >>> pd.to_numeric(s, downcast="float")
+    0    1.0
+    1    2.1
+    2    3.0
+    dtype: Float32
+    """
+    if downcast not in (None, "integer", "signed", "unsigned", "float"):
+        raise ValueError("invalid downcasting method provided")
+
+    if errors not in ("ignore", "raise", "coerce"):
+        raise ValueError("invalid error value specified")
+    if errors == "ignore":
+        # GH#54467
+        warnings.warn(
+            "errors='ignore' is deprecated and will raise in a future version. "
+            "Use to_numeric without passing `errors` and catch exceptions "
+            "explicitly instead",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+
+    check_dtype_backend(dtype_backend)
+
+    is_series = False
+    is_index = False
+    is_scalars = False
+
+    if isinstance(arg, ABCSeries):
+        is_series = True
+        values = arg.values
+    elif isinstance(arg, ABCIndex):
+        is_index = True
+        if needs_i8_conversion(arg.dtype):
+            values = arg.view("i8")
+        else:
+            values = arg.values
+    elif isinstance(arg, (list, tuple)):
+        values = np.array(arg, dtype="O")
+    elif is_scalar(arg):
+        if is_decimal(arg):
+            return float(arg)
+        if is_number(arg):
+            return arg
+        is_scalars = True
+        values = np.array([arg], dtype="O")
+    elif getattr(arg, "ndim", 1) > 1:
+        raise TypeError("arg must be a list, tuple, 1-d array, or Series")
+    else:
+        values = arg
+
+    orig_values = values
+
+    # GH33013: for IntegerArray & FloatingArray extract non-null values for casting
+    # save mask to reconstruct the full array after casting
+    mask: npt.NDArray[np.bool_] | None = None
+    if isinstance(values, BaseMaskedArray):
+        mask = values._mask
+        values = values._data[~mask]
+
+    values_dtype = getattr(values, "dtype", None)
+    if isinstance(values_dtype, ArrowDtype):
+        mask = values.isna()
+        values = values.dropna().to_numpy()
+    new_mask: np.ndarray | None = None
+    if is_numeric_dtype(values_dtype):
+        pass
+    elif lib.is_np_dtype(values_dtype, "mM"):
+        values = values.view(np.int64)
+    else:
+        values = ensure_object(values)
+        coerce_numeric = errors not in ("ignore", "raise")
+        try:
+            values, new_mask = lib.maybe_convert_numeric(  # type: ignore[call-overload]
+                values,
+                set(),
+                coerce_numeric=coerce_numeric,
+                convert_to_masked_nullable=dtype_backend is not lib.no_default
+                or isinstance(values_dtype, StringDtype)
+                and values_dtype.na_value is libmissing.NA,
+            )
+        except (ValueError, TypeError):
+            if errors == "raise":
+                raise
+            values = orig_values
+
+    if new_mask is not None:
+        # Remove unnecessary values, is expected later anyway and enables
+        # downcasting
+        values = values[~new_mask]
+    elif (
+        dtype_backend is not lib.no_default
+        and new_mask is None
+        or isinstance(values_dtype, StringDtype)
+        and values_dtype.na_value is libmissing.NA
+    ):
+        new_mask = np.zeros(values.shape, dtype=np.bool_)
+
+    # attempt downcast only if the data has been successfully converted
+    # to a numerical dtype and if a downcast method has been specified
+    if downcast is not None and is_numeric_dtype(values.dtype):
+        typecodes: str | None = None
+
+        if downcast in ("integer", "signed"):
+            typecodes = np.typecodes["Integer"]
+        elif downcast == "unsigned" and (not len(values) or np.min(values) >= 0):
+            typecodes = np.typecodes["UnsignedInteger"]
+        elif downcast == "float":
+            typecodes = np.typecodes["Float"]
+
+            # pandas support goes only to np.float32,
+            # as float dtypes smaller than that are
+            # extremely rare and not well supported
+            float_32_char = np.dtype(np.float32).char
+            float_32_ind = typecodes.index(float_32_char)
+            typecodes = typecodes[float_32_ind:]
+
+        if typecodes is not None:
+            # from smallest to largest
+            for typecode in typecodes:
+                dtype = np.dtype(typecode)
+                if dtype.itemsize <= values.dtype.itemsize:
+                    values = maybe_downcast_numeric(values, dtype)
+
+                    # successful conversion
+                    if values.dtype == dtype:
+                        break
+
+    # GH33013: for IntegerArray, BooleanArray & FloatingArray need to reconstruct
+    # masked array
+    if (mask is not None or new_mask is not None) and not is_string_dtype(values.dtype):
+        if mask is None or (new_mask is not None and new_mask.shape == mask.shape):
+            # GH 52588
+            mask = new_mask
+        else:
+            mask = mask.copy()
+        assert isinstance(mask, np.ndarray)
+        data = np.zeros(mask.shape, dtype=values.dtype)
+        data[~mask] = values
+
+        from pandas.core.arrays import (
+            ArrowExtensionArray,
+            BooleanArray,
+            FloatingArray,
+            IntegerArray,
+        )
+
+        klass: type[IntegerArray | BooleanArray | FloatingArray]
+        if is_integer_dtype(data.dtype):
+            klass = IntegerArray
+        elif is_bool_dtype(data.dtype):
+            klass = BooleanArray
+        else:
+            klass = FloatingArray
+        values = klass(data, mask)
+
+        if dtype_backend == "pyarrow" or isinstance(values_dtype, ArrowDtype):
+            values = ArrowExtensionArray(values.__arrow_array__())
+
+    if is_series:
+        return arg._constructor(values, index=arg.index, name=arg.name)
+    elif is_index:
+        # because we want to coerce to numeric if possible,
+        # do not use _shallow_copy
+        from pandas import Index
+
+        return Index(values, name=arg.name)
+    elif is_scalars:
+        return values[0]
+    else:
+        return values
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/tools/timedeltas.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/tools/timedeltas.py
new file mode 100644
index 0000000000000000000000000000000000000000..d772c908c473109fcf7e37e06014b43226328e31
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/tools/timedeltas.py
@@ -0,0 +1,283 @@
+"""
+timedelta support tools
+"""
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    overload,
+)
+import warnings
+
+import numpy as np
+
+from pandas._libs import lib
+from pandas._libs.tslibs import (
+    NaT,
+    NaTType,
+)
+from pandas._libs.tslibs.timedeltas import (
+    Timedelta,
+    disallow_ambiguous_unit,
+    parse_timedelta_unit,
+)
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.common import is_list_like
+from pandas.core.dtypes.dtypes import ArrowDtype
+from pandas.core.dtypes.generic import (
+    ABCIndex,
+    ABCSeries,
+)
+
+from pandas.core.arrays.timedeltas import sequence_to_td64ns
+
+if TYPE_CHECKING:
+    from collections.abc import Hashable
+    from datetime import timedelta
+
+    from pandas._libs.tslibs.timedeltas import UnitChoices
+    from pandas._typing import (
+        ArrayLike,
+        DateTimeErrorChoices,
+    )
+
+    from pandas import (
+        Index,
+        Series,
+        TimedeltaIndex,
+    )
+
+
+@overload
+def to_timedelta(
+    arg: str | float | timedelta,
+    unit: UnitChoices | None = ...,
+    errors: DateTimeErrorChoices = ...,
+) -> Timedelta:
+    ...
+
+
+@overload
+def to_timedelta(
+    arg: Series,
+    unit: UnitChoices | None = ...,
+    errors: DateTimeErrorChoices = ...,
+) -> Series:
+    ...
+
+
+@overload
+def to_timedelta(
+    arg: list | tuple | range | ArrayLike | Index,
+    unit: UnitChoices | None = ...,
+    errors: DateTimeErrorChoices = ...,
+) -> TimedeltaIndex:
+    ...
+
+
+def to_timedelta(
+    arg: str
+    | int
+    | float
+    | timedelta
+    | list
+    | tuple
+    | range
+    | ArrayLike
+    | Index
+    | Series,
+    unit: UnitChoices | None = None,
+    errors: DateTimeErrorChoices = "raise",
+) -> Timedelta | TimedeltaIndex | Series:
+    """
+    Convert argument to timedelta.
+
+    Timedeltas are absolute differences in times, expressed in difference
+    units (e.g. days, hours, minutes, seconds). This method converts
+    an argument from a recognized timedelta format / value into
+    a Timedelta type.
+
+    Parameters
+    ----------
+    arg : str, timedelta, list-like or Series
+        The data to be converted to timedelta.
+
+        .. versionchanged:: 2.0
+            Strings with units 'M', 'Y' and 'y' do not represent
+            unambiguous timedelta values and will raise an exception.
+
+    unit : str, optional
+        Denotes the unit of the arg for numeric `arg`. Defaults to ``"ns"``.
+
+        Possible values:
+
+        * 'W'
+        * 'D' / 'days' / 'day'
+        * 'hours' / 'hour' / 'hr' / 'h' / 'H'
+        * 'm' / 'minute' / 'min' / 'minutes' / 'T'
+        * 's' / 'seconds' / 'sec' / 'second' / 'S'
+        * 'ms' / 'milliseconds' / 'millisecond' / 'milli' / 'millis' / 'L'
+        * 'us' / 'microseconds' / 'microsecond' / 'micro' / 'micros' / 'U'
+        * 'ns' / 'nanoseconds' / 'nano' / 'nanos' / 'nanosecond' / 'N'
+
+        Must not be specified when `arg` contains strings and ``errors="raise"``.
+
+        .. deprecated:: 2.2.0
+            Units 'H', 'T', 'S', 'L', 'U' and 'N' are deprecated and will be removed
+            in a future version. Please use 'h', 'min', 's', 'ms', 'us', and 'ns'
+            instead of 'H', 'T', 'S', 'L', 'U' and 'N'.
+
+    errors : {'ignore', 'raise', 'coerce'}, default 'raise'
+        - If 'raise', then invalid parsing will raise an exception.
+        - If 'coerce', then invalid parsing will be set as NaT.
+        - If 'ignore', then invalid parsing will return the input.
+
+    Returns
+    -------
+    timedelta
+        If parsing succeeded.
+        Return type depends on input:
+
+        - list-like: TimedeltaIndex of timedelta64 dtype
+        - Series: Series of timedelta64 dtype
+        - scalar: Timedelta
+
+    See Also
+    --------
+    DataFrame.astype : Cast argument to a specified dtype.
+    to_datetime : Convert argument to datetime.
+    convert_dtypes : Convert dtypes.
+
+    Notes
+    -----
+    If the precision is higher than nanoseconds, the precision of the duration is
+    truncated to nanoseconds for string inputs.
+
+    Examples
+    --------
+    Parsing a single string to a Timedelta:
+
+    >>> pd.to_timedelta('1 days 06:05:01.00003')
+    Timedelta('1 days 06:05:01.000030')
+    >>> pd.to_timedelta('15.5us')
+    Timedelta('0 days 00:00:00.000015500')
+
+    Parsing a list or array of strings:
+
+    >>> pd.to_timedelta(['1 days 06:05:01.00003', '15.5us', 'nan'])
+    TimedeltaIndex(['1 days 06:05:01.000030', '0 days 00:00:00.000015500', NaT],
+                   dtype='timedelta64[ns]', freq=None)
+
+    Converting numbers by specifying the `unit` keyword argument:
+
+    >>> pd.to_timedelta(np.arange(5), unit='s')
+    TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:01', '0 days 00:00:02',
+                    '0 days 00:00:03', '0 days 00:00:04'],
+                   dtype='timedelta64[ns]', freq=None)
+    >>> pd.to_timedelta(np.arange(5), unit='d')
+    TimedeltaIndex(['0 days', '1 days', '2 days', '3 days', '4 days'],
+                   dtype='timedelta64[ns]', freq=None)
+    """
+    if unit is not None:
+        unit = parse_timedelta_unit(unit)
+        disallow_ambiguous_unit(unit)
+
+    if errors not in ("ignore", "raise", "coerce"):
+        raise ValueError("errors must be one of 'ignore', 'raise', or 'coerce'.")
+    if errors == "ignore":
+        # GH#54467
+        warnings.warn(
+            "errors='ignore' is deprecated and will raise in a future version. "
+            "Use to_timedelta without passing `errors` and catch exceptions "
+            "explicitly instead",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+
+    if arg is None:
+        return arg
+    elif isinstance(arg, ABCSeries):
+        values = _convert_listlike(arg._values, unit=unit, errors=errors)
+        return arg._constructor(values, index=arg.index, name=arg.name)
+    elif isinstance(arg, ABCIndex):
+        return _convert_listlike(arg, unit=unit, errors=errors, name=arg.name)
+    elif isinstance(arg, np.ndarray) and arg.ndim == 0:
+        # extract array scalar and process below
+        # error: Incompatible types in assignment (expression has type "object",
+        # variable has type "Union[str, int, float, timedelta, List[Any],
+        # Tuple[Any, ...], Union[Union[ExtensionArray, ndarray[Any, Any]], Index,
+        # Series]]")  [assignment]
+        arg = lib.item_from_zerodim(arg)  # type: ignore[assignment]
+    elif is_list_like(arg) and getattr(arg, "ndim", 1) == 1:
+        return _convert_listlike(arg, unit=unit, errors=errors)
+    elif getattr(arg, "ndim", 1) > 1:
+        raise TypeError(
+            "arg must be a string, timedelta, list, tuple, 1-d array, or Series"
+        )
+
+    if isinstance(arg, str) and unit is not None:
+        raise ValueError("unit must not be specified if the input is/contains a str")
+
+    # ...so it must be a scalar value. Return scalar.
+    return _coerce_scalar_to_timedelta_type(arg, unit=unit, errors=errors)
+
+
+def _coerce_scalar_to_timedelta_type(
+    r, unit: UnitChoices | None = "ns", errors: DateTimeErrorChoices = "raise"
+):
+    """Convert string 'r' to a timedelta object."""
+    result: Timedelta | NaTType
+
+    try:
+        result = Timedelta(r, unit)
+    except ValueError:
+        if errors == "raise":
+            raise
+        if errors == "ignore":
+            return r
+
+        # coerce
+        result = NaT
+
+    return result
+
+
+def _convert_listlike(
+    arg,
+    unit: UnitChoices | None = None,
+    errors: DateTimeErrorChoices = "raise",
+    name: Hashable | None = None,
+):
+    """Convert a list of objects to a timedelta index object."""
+    arg_dtype = getattr(arg, "dtype", None)
+    if isinstance(arg, (list, tuple)) or arg_dtype is None:
+        # This is needed only to ensure that in the case where we end up
+        #  returning arg (errors == "ignore"), and where the input is a
+        #  generator, we return a useful list-like instead of a
+        #  used-up generator
+        if not hasattr(arg, "__array__"):
+            arg = list(arg)
+        arg = np.array(arg, dtype=object)
+    elif isinstance(arg_dtype, ArrowDtype) and arg_dtype.kind == "m":
+        return arg
+
+    try:
+        td64arr = sequence_to_td64ns(arg, unit=unit, errors=errors, copy=False)[0]
+    except ValueError:
+        if errors == "ignore":
+            return arg
+        else:
+            # This else-block accounts for the cases when errors='raise'
+            # and errors='coerce'. If errors == 'raise', these errors
+            # should be raised. If errors == 'coerce', we shouldn't
+            # expect any errors to be raised, since all parsing errors
+            # cause coercion to pd.NaT. However, if an error / bug is
+            # introduced that causes an Exception to be raised, we would
+            # like to surface it.
+            raise
+
+    from pandas import TimedeltaIndex
+
+    value = TimedeltaIndex(td64arr, name=name)
+    return value
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/tools/times.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/tools/times.py
new file mode 100644
index 0000000000000000000000000000000000000000..d77bcc91df7096bfad4b3eddf6355435a4b9e7f6
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/tools/times.py
@@ -0,0 +1,168 @@
+from __future__ import annotations
+
+from datetime import (
+    datetime,
+    time,
+)
+from typing import TYPE_CHECKING
+import warnings
+
+import numpy as np
+
+from pandas._libs.lib import is_list_like
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.generic import (
+    ABCIndex,
+    ABCSeries,
+)
+from pandas.core.dtypes.missing import notna
+
+if TYPE_CHECKING:
+    from pandas._typing import DateTimeErrorChoices
+
+
+def to_time(
+    arg,
+    format: str | None = None,
+    infer_time_format: bool = False,
+    errors: DateTimeErrorChoices = "raise",
+):
+    """
+    Parse time strings to time objects using fixed strptime formats ("%H:%M",
+    "%H%M", "%I:%M%p", "%I%M%p", "%H:%M:%S", "%H%M%S", "%I:%M:%S%p",
+    "%I%M%S%p")
+
+    Use infer_time_format if all the strings are in the same format to speed
+    up conversion.
+
+    Parameters
+    ----------
+    arg : string in time format, datetime.time, list, tuple, 1-d array,  Series
+    format : str, default None
+        Format used to convert arg into a time object.  If None, fixed formats
+        are used.
+    infer_time_format: bool, default False
+        Infer the time format based on the first non-NaN element.  If all
+        strings are in the same format, this will speed up conversion.
+    errors : {'ignore', 'raise', 'coerce'}, default 'raise'
+        - If 'raise', then invalid parsing will raise an exception
+        - If 'coerce', then invalid parsing will be set as None
+        - If 'ignore', then invalid parsing will return the input
+
+    Returns
+    -------
+    datetime.time
+    """
+    if errors == "ignore":
+        # GH#54467
+        warnings.warn(
+            "errors='ignore' is deprecated and will raise in a future version. "
+            "Use to_time without passing `errors` and catch exceptions "
+            "explicitly instead",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+
+    def _convert_listlike(arg, format):
+        if isinstance(arg, (list, tuple)):
+            arg = np.array(arg, dtype="O")
+
+        elif getattr(arg, "ndim", 1) > 1:
+            raise TypeError(
+                "arg must be a string, datetime, list, tuple, 1-d array, or Series"
+            )
+
+        arg = np.asarray(arg, dtype="O")
+
+        if infer_time_format and format is None:
+            format = _guess_time_format_for_array(arg)
+
+        times: list[time | None] = []
+        if format is not None:
+            for element in arg:
+                try:
+                    times.append(datetime.strptime(element, format).time())
+                except (ValueError, TypeError) as err:
+                    if errors == "raise":
+                        msg = (
+                            f"Cannot convert {element} to a time with given "
+                            f"format {format}"
+                        )
+                        raise ValueError(msg) from err
+                    if errors == "ignore":
+                        return arg
+                    else:
+                        times.append(None)
+        else:
+            formats = _time_formats[:]
+            format_found = False
+            for element in arg:
+                time_object = None
+                try:
+                    time_object = time.fromisoformat(element)
+                except (ValueError, TypeError):
+                    for time_format in formats:
+                        try:
+                            time_object = datetime.strptime(element, time_format).time()
+                            if not format_found:
+                                # Put the found format in front
+                                fmt = formats.pop(formats.index(time_format))
+                                formats.insert(0, fmt)
+                                format_found = True
+                            break
+                        except (ValueError, TypeError):
+                            continue
+
+                if time_object is not None:
+                    times.append(time_object)
+                elif errors == "raise":
+                    raise ValueError(f"Cannot convert arg {arg} to a time")
+                elif errors == "ignore":
+                    return arg
+                else:
+                    times.append(None)
+
+        return times
+
+    if arg is None:
+        return arg
+    elif isinstance(arg, time):
+        return arg
+    elif isinstance(arg, ABCSeries):
+        values = _convert_listlike(arg._values, format)
+        return arg._constructor(values, index=arg.index, name=arg.name)
+    elif isinstance(arg, ABCIndex):
+        return _convert_listlike(arg, format)
+    elif is_list_like(arg):
+        return _convert_listlike(arg, format)
+
+    return _convert_listlike(np.array([arg]), format)[0]
+
+
+# Fixed time formats for time parsing
+_time_formats = [
+    "%H:%M",
+    "%H%M",
+    "%I:%M%p",
+    "%I%M%p",
+    "%H:%M:%S",
+    "%H%M%S",
+    "%I:%M:%S%p",
+    "%I%M%S%p",
+]
+
+
+def _guess_time_format_for_array(arr):
+    # Try to guess the format based on the first non-NaN element
+    non_nan_elements = notna(arr).nonzero()[0]
+    if len(non_nan_elements):
+        element = arr[non_nan_elements[0]]
+        for time_format in _time_formats:
+            try:
+                datetime.strptime(element, time_format)
+                return time_format
+            except ValueError:
+                pass
+
+    return None
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/util/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/util/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/util/hashing.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/util/hashing.py
new file mode 100644
index 0000000000000000000000000000000000000000..4933de32125814baa6cc96926721c0c839540b2a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/util/hashing.py
@@ -0,0 +1,339 @@
+"""
+data hash pandas / numpy objects
+"""
+from __future__ import annotations
+
+import itertools
+from typing import TYPE_CHECKING
+
+import numpy as np
+
+from pandas._libs.hashing import hash_object_array
+
+from pandas.core.dtypes.common import is_list_like
+from pandas.core.dtypes.dtypes import CategoricalDtype
+from pandas.core.dtypes.generic import (
+    ABCDataFrame,
+    ABCExtensionArray,
+    ABCIndex,
+    ABCMultiIndex,
+    ABCSeries,
+)
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Hashable,
+        Iterable,
+        Iterator,
+    )
+
+    from pandas._typing import (
+        ArrayLike,
+        npt,
+    )
+
+    from pandas import (
+        DataFrame,
+        Index,
+        MultiIndex,
+        Series,
+    )
+
+
+# 16 byte long hashing key
+_default_hash_key = "0123456789123456"
+
+
+def combine_hash_arrays(
+    arrays: Iterator[np.ndarray], num_items: int
+) -> npt.NDArray[np.uint64]:
+    """
+    Parameters
+    ----------
+    arrays : Iterator[np.ndarray]
+    num_items : int
+
+    Returns
+    -------
+    np.ndarray[uint64]
+
+    Should be the same as CPython's tupleobject.c
+    """
+    try:
+        first = next(arrays)
+    except StopIteration:
+        return np.array([], dtype=np.uint64)
+
+    arrays = itertools.chain([first], arrays)
+
+    mult = np.uint64(1000003)
+    out = np.zeros_like(first) + np.uint64(0x345678)
+    last_i = 0
+    for i, a in enumerate(arrays):
+        inverse_i = num_items - i
+        out ^= a
+        out *= mult
+        mult += np.uint64(82520 + inverse_i + inverse_i)
+        last_i = i
+    assert last_i + 1 == num_items, "Fed in wrong num_items"
+    out += np.uint64(97531)
+    return out
+
+
+def hash_pandas_object(
+    obj: Index | DataFrame | Series,
+    index: bool = True,
+    encoding: str = "utf8",
+    hash_key: str | None = _default_hash_key,
+    categorize: bool = True,
+) -> Series:
+    """
+    Return a data hash of the Index/Series/DataFrame.
+
+    Parameters
+    ----------
+    obj : Index, Series, or DataFrame
+    index : bool, default True
+        Include the index in the hash (if Series/DataFrame).
+    encoding : str, default 'utf8'
+        Encoding for data & key when strings.
+    hash_key : str, default _default_hash_key
+        Hash_key for string key to encode.
+    categorize : bool, default True
+        Whether to first categorize object arrays before hashing. This is more
+        efficient when the array contains duplicate values.
+
+    Returns
+    -------
+    Series of uint64, same length as the object
+
+    Examples
+    --------
+    >>> pd.util.hash_pandas_object(pd.Series([1, 2, 3]))
+    0    14639053686158035780
+    1     3869563279212530728
+    2      393322362522515241
+    dtype: uint64
+    """
+    from pandas import Series
+
+    if hash_key is None:
+        hash_key = _default_hash_key
+
+    if isinstance(obj, ABCMultiIndex):
+        return Series(hash_tuples(obj, encoding, hash_key), dtype="uint64", copy=False)
+
+    elif isinstance(obj, ABCIndex):
+        h = hash_array(obj._values, encoding, hash_key, categorize).astype(
+            "uint64", copy=False
+        )
+        ser = Series(h, index=obj, dtype="uint64", copy=False)
+
+    elif isinstance(obj, ABCSeries):
+        h = hash_array(obj._values, encoding, hash_key, categorize).astype(
+            "uint64", copy=False
+        )
+        if index:
+            index_iter = (
+                hash_pandas_object(
+                    obj.index,
+                    index=False,
+                    encoding=encoding,
+                    hash_key=hash_key,
+                    categorize=categorize,
+                )._values
+                for _ in [None]
+            )
+            arrays = itertools.chain([h], index_iter)
+            h = combine_hash_arrays(arrays, 2)
+
+        ser = Series(h, index=obj.index, dtype="uint64", copy=False)
+
+    elif isinstance(obj, ABCDataFrame):
+        hashes = (
+            hash_array(series._values, encoding, hash_key, categorize)
+            for _, series in obj.items()
+        )
+        num_items = len(obj.columns)
+        if index:
+            index_hash_generator = (
+                hash_pandas_object(
+                    obj.index,
+                    index=False,
+                    encoding=encoding,
+                    hash_key=hash_key,
+                    categorize=categorize,
+                )._values
+                for _ in [None]
+            )
+            num_items += 1
+
+            # keep `hashes` specifically a generator to keep mypy happy
+            _hashes = itertools.chain(hashes, index_hash_generator)
+            hashes = (x for x in _hashes)
+        h = combine_hash_arrays(hashes, num_items)
+
+        ser = Series(h, index=obj.index, dtype="uint64", copy=False)
+    else:
+        raise TypeError(f"Unexpected type for hashing {type(obj)}")
+
+    return ser
+
+
+def hash_tuples(
+    vals: MultiIndex | Iterable[tuple[Hashable, ...]],
+    encoding: str = "utf8",
+    hash_key: str = _default_hash_key,
+) -> npt.NDArray[np.uint64]:
+    """
+    Hash an MultiIndex / listlike-of-tuples efficiently.
+
+    Parameters
+    ----------
+    vals : MultiIndex or listlike-of-tuples
+    encoding : str, default 'utf8'
+    hash_key : str, default _default_hash_key
+
+    Returns
+    -------
+    ndarray[np.uint64] of hashed values
+    """
+    if not is_list_like(vals):
+        raise TypeError("must be convertible to a list-of-tuples")
+
+    from pandas import (
+        Categorical,
+        MultiIndex,
+    )
+
+    if not isinstance(vals, ABCMultiIndex):
+        mi = MultiIndex.from_tuples(vals)
+    else:
+        mi = vals
+
+    # create a list-of-Categoricals
+    cat_vals = [
+        Categorical._simple_new(
+            mi.codes[level],
+            CategoricalDtype(categories=mi.levels[level], ordered=False),
+        )
+        for level in range(mi.nlevels)
+    ]
+
+    # hash the list-of-ndarrays
+    hashes = (
+        cat._hash_pandas_object(encoding=encoding, hash_key=hash_key, categorize=False)
+        for cat in cat_vals
+    )
+    h = combine_hash_arrays(hashes, len(cat_vals))
+
+    return h
+
+
+def hash_array(
+    vals: ArrayLike,
+    encoding: str = "utf8",
+    hash_key: str = _default_hash_key,
+    categorize: bool = True,
+) -> npt.NDArray[np.uint64]:
+    """
+    Given a 1d array, return an array of deterministic integers.
+
+    Parameters
+    ----------
+    vals : ndarray or ExtensionArray
+    encoding : str, default 'utf8'
+        Encoding for data & key when strings.
+    hash_key : str, default _default_hash_key
+        Hash_key for string key to encode.
+    categorize : bool, default True
+        Whether to first categorize object arrays before hashing. This is more
+        efficient when the array contains duplicate values.
+
+    Returns
+    -------
+    ndarray[np.uint64, ndim=1]
+        Hashed values, same length as the vals.
+
+    Examples
+    --------
+    >>> pd.util.hash_array(np.array([1, 2, 3]))
+    array([ 6238072747940578789, 15839785061582574730,  2185194620014831856],
+      dtype=uint64)
+    """
+    if not hasattr(vals, "dtype"):
+        raise TypeError("must pass a ndarray-like")
+
+    if isinstance(vals, ABCExtensionArray):
+        return vals._hash_pandas_object(
+            encoding=encoding, hash_key=hash_key, categorize=categorize
+        )
+
+    if not isinstance(vals, np.ndarray):
+        # GH#42003
+        raise TypeError(
+            "hash_array requires np.ndarray or ExtensionArray, not "
+            f"{type(vals).__name__}. Use hash_pandas_object instead."
+        )
+
+    return _hash_ndarray(vals, encoding, hash_key, categorize)
+
+
+def _hash_ndarray(
+    vals: np.ndarray,
+    encoding: str = "utf8",
+    hash_key: str = _default_hash_key,
+    categorize: bool = True,
+) -> npt.NDArray[np.uint64]:
+    """
+    See hash_array.__doc__.
+    """
+    dtype = vals.dtype
+
+    # _hash_ndarray only takes 64-bit values, so handle 128-bit by parts
+    if np.issubdtype(dtype, np.complex128):
+        hash_real = _hash_ndarray(vals.real, encoding, hash_key, categorize)
+        hash_imag = _hash_ndarray(vals.imag, encoding, hash_key, categorize)
+        return hash_real + 23 * hash_imag
+
+    # First, turn whatever array this is into unsigned 64-bit ints, if we can
+    # manage it.
+    if dtype == bool:
+        vals = vals.astype("u8")
+    elif issubclass(dtype.type, (np.datetime64, np.timedelta64)):
+        vals = vals.view("i8").astype("u8", copy=False)
+    elif issubclass(dtype.type, np.number) and dtype.itemsize <= 8:
+        vals = vals.view(f"u{vals.dtype.itemsize}").astype("u8")
+    else:
+        # With repeated values, its MUCH faster to categorize object dtypes,
+        # then hash and rename categories. We allow skipping the categorization
+        # when the values are known/likely to be unique.
+        if categorize:
+            from pandas import (
+                Categorical,
+                Index,
+                factorize,
+            )
+
+            codes, categories = factorize(vals, sort=False)
+            dtype = CategoricalDtype(categories=Index(categories), ordered=False)
+            cat = Categorical._simple_new(codes, dtype)
+            return cat._hash_pandas_object(
+                encoding=encoding, hash_key=hash_key, categorize=False
+            )
+
+        try:
+            vals = hash_object_array(vals, hash_key, encoding)
+        except TypeError:
+            # we have mixed types
+            vals = hash_object_array(
+                vals.astype(str).astype(object), hash_key, encoding
+            )
+
+    # Then, redistribute these 64-bit ints within the space of 64-bit ints
+    vals ^= vals >> 30
+    vals *= np.uint64(0xBF58476D1CE4E5B9)
+    vals ^= vals >> 27
+    vals *= np.uint64(0x94D049BB133111EB)
+    vals ^= vals >> 31
+    return vals
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/util/numba_.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/util/numba_.py
new file mode 100644
index 0000000000000000000000000000000000000000..4825c9fee24b1b1272c5f245a9c7ae49f53003fc
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/util/numba_.py
@@ -0,0 +1,98 @@
+"""Common utilities for Numba operations"""
+from __future__ import annotations
+
+import types
+from typing import (
+    TYPE_CHECKING,
+    Callable,
+)
+
+import numpy as np
+
+from pandas.compat._optional import import_optional_dependency
+from pandas.errors import NumbaUtilError
+
+GLOBAL_USE_NUMBA: bool = False
+
+
+def maybe_use_numba(engine: str | None) -> bool:
+    """Signal whether to use numba routines."""
+    return engine == "numba" or (engine is None and GLOBAL_USE_NUMBA)
+
+
+def set_use_numba(enable: bool = False) -> None:
+    global GLOBAL_USE_NUMBA
+    if enable:
+        import_optional_dependency("numba")
+    GLOBAL_USE_NUMBA = enable
+
+
+def get_jit_arguments(
+    engine_kwargs: dict[str, bool] | None = None, kwargs: dict | None = None
+) -> dict[str, bool]:
+    """
+    Return arguments to pass to numba.JIT, falling back on pandas default JIT settings.
+
+    Parameters
+    ----------
+    engine_kwargs : dict, default None
+        user passed keyword arguments for numba.JIT
+    kwargs : dict, default None
+        user passed keyword arguments to pass into the JITed function
+
+    Returns
+    -------
+    dict[str, bool]
+        nopython, nogil, parallel
+
+    Raises
+    ------
+    NumbaUtilError
+    """
+    if engine_kwargs is None:
+        engine_kwargs = {}
+
+    nopython = engine_kwargs.get("nopython", True)
+    if kwargs and nopython:
+        raise NumbaUtilError(
+            "numba does not support kwargs with nopython=True: "
+            "https://github.com/numba/numba/issues/2916"
+        )
+    nogil = engine_kwargs.get("nogil", False)
+    parallel = engine_kwargs.get("parallel", False)
+    return {"nopython": nopython, "nogil": nogil, "parallel": parallel}
+
+
+def jit_user_function(func: Callable) -> Callable:
+    """
+    If user function is not jitted already, mark the user's function
+    as jitable.
+
+    Parameters
+    ----------
+    func : function
+        user defined function
+
+    Returns
+    -------
+    function
+        Numba JITed function, or function marked as JITable by numba
+    """
+    if TYPE_CHECKING:
+        import numba
+    else:
+        numba = import_optional_dependency("numba")
+
+    if numba.extending.is_jitted(func):
+        # Don't jit a user passed jitted function
+        numba_func = func
+    elif getattr(np, func.__name__, False) is func or isinstance(
+        func, types.BuiltinFunctionType
+    ):
+        # Not necessary to jit builtins or np functions
+        # This will mess up register_jitable
+        numba_func = func
+    else:
+        numba_func = numba.extending.register_jitable(func)
+
+    return numba_func
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/window/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/window/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..857e12e5467a6a7d2263d9add33e65b9499778fa
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/window/__init__.py
@@ -0,0 +1,23 @@
+from pandas.core.window.ewm import (
+    ExponentialMovingWindow,
+    ExponentialMovingWindowGroupby,
+)
+from pandas.core.window.expanding import (
+    Expanding,
+    ExpandingGroupby,
+)
+from pandas.core.window.rolling import (
+    Rolling,
+    RollingGroupby,
+    Window,
+)
+
+__all__ = [
+    "Expanding",
+    "ExpandingGroupby",
+    "ExponentialMovingWindow",
+    "ExponentialMovingWindowGroupby",
+    "Rolling",
+    "RollingGroupby",
+    "Window",
+]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/window/common.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/window/common.py
new file mode 100644
index 0000000000000000000000000000000000000000..fc8eddca09c84029b9ac92b658a47b45e4764414
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/window/common.py
@@ -0,0 +1,169 @@
+"""Common utility functions for rolling operations"""
+from __future__ import annotations
+
+from collections import defaultdict
+from typing import cast
+
+import numpy as np
+
+from pandas.core.dtypes.generic import (
+    ABCDataFrame,
+    ABCSeries,
+)
+
+from pandas.core.indexes.api import MultiIndex
+
+
+def flex_binary_moment(arg1, arg2, f, pairwise: bool = False):
+    if isinstance(arg1, ABCSeries) and isinstance(arg2, ABCSeries):
+        X, Y = prep_binary(arg1, arg2)
+        return f(X, Y)
+
+    elif isinstance(arg1, ABCDataFrame):
+        from pandas import DataFrame
+
+        def dataframe_from_int_dict(data, frame_template) -> DataFrame:
+            result = DataFrame(data, index=frame_template.index)
+            if len(result.columns) > 0:
+                result.columns = frame_template.columns[result.columns]
+            else:
+                result.columns = frame_template.columns.copy()
+            return result
+
+        results = {}
+        if isinstance(arg2, ABCDataFrame):
+            if pairwise is False:
+                if arg1 is arg2:
+                    # special case in order to handle duplicate column names
+                    for i in range(len(arg1.columns)):
+                        results[i] = f(arg1.iloc[:, i], arg2.iloc[:, i])
+                    return dataframe_from_int_dict(results, arg1)
+                else:
+                    if not arg1.columns.is_unique:
+                        raise ValueError("'arg1' columns are not unique")
+                    if not arg2.columns.is_unique:
+                        raise ValueError("'arg2' columns are not unique")
+                    X, Y = arg1.align(arg2, join="outer")
+                    X, Y = prep_binary(X, Y)
+                    res_columns = arg1.columns.union(arg2.columns)
+                    for col in res_columns:
+                        if col in X and col in Y:
+                            results[col] = f(X[col], Y[col])
+                    return DataFrame(results, index=X.index, columns=res_columns)
+            elif pairwise is True:
+                results = defaultdict(dict)
+                for i in range(len(arg1.columns)):
+                    for j in range(len(arg2.columns)):
+                        if j < i and arg2 is arg1:
+                            # Symmetric case
+                            results[i][j] = results[j][i]
+                        else:
+                            results[i][j] = f(
+                                *prep_binary(arg1.iloc[:, i], arg2.iloc[:, j])
+                            )
+
+                from pandas import concat
+
+                result_index = arg1.index.union(arg2.index)
+                if len(result_index):
+                    # construct result frame
+                    result = concat(
+                        [
+                            concat(
+                                [results[i][j] for j in range(len(arg2.columns))],
+                                ignore_index=True,
+                            )
+                            for i in range(len(arg1.columns))
+                        ],
+                        ignore_index=True,
+                        axis=1,
+                    )
+                    result.columns = arg1.columns
+
+                    # set the index and reorder
+                    if arg2.columns.nlevels > 1:
+                        # mypy needs to know columns is a MultiIndex, Index doesn't
+                        # have levels attribute
+                        arg2.columns = cast(MultiIndex, arg2.columns)
+                        # GH 21157: Equivalent to MultiIndex.from_product(
+                        #  [result_index], ,
+                        # )
+                        # A normal MultiIndex.from_product will produce too many
+                        # combinations.
+                        result_level = np.tile(
+                            result_index, len(result) // len(result_index)
+                        )
+                        arg2_levels = (
+                            np.repeat(
+                                arg2.columns.get_level_values(i),
+                                len(result) // len(arg2.columns),
+                            )
+                            for i in range(arg2.columns.nlevels)
+                        )
+                        result_names = list(arg2.columns.names) + [result_index.name]
+                        result.index = MultiIndex.from_arrays(
+                            [*arg2_levels, result_level], names=result_names
+                        )
+                        # GH 34440
+                        num_levels = len(result.index.levels)
+                        new_order = [num_levels - 1] + list(range(num_levels - 1))
+                        result = result.reorder_levels(new_order).sort_index()
+                    else:
+                        result.index = MultiIndex.from_product(
+                            [range(len(arg2.columns)), range(len(result_index))]
+                        )
+                        result = result.swaplevel(1, 0).sort_index()
+                        result.index = MultiIndex.from_product(
+                            [result_index] + [arg2.columns]
+                        )
+                else:
+                    # empty result
+                    result = DataFrame(
+                        index=MultiIndex(
+                            levels=[arg1.index, arg2.columns], codes=[[], []]
+                        ),
+                        columns=arg2.columns,
+                        dtype="float64",
+                    )
+
+                # reset our index names to arg1 names
+                # reset our column names to arg2 names
+                # careful not to mutate the original names
+                result.columns = result.columns.set_names(arg1.columns.names)
+                result.index = result.index.set_names(
+                    result_index.names + arg2.columns.names
+                )
+
+                return result
+        else:
+            results = {
+                i: f(*prep_binary(arg1.iloc[:, i], arg2))
+                for i in range(len(arg1.columns))
+            }
+            return dataframe_from_int_dict(results, arg1)
+
+    else:
+        return flex_binary_moment(arg2, arg1, f)
+
+
+def zsqrt(x):
+    with np.errstate(all="ignore"):
+        result = np.sqrt(x)
+        mask = x < 0
+
+    if isinstance(x, ABCDataFrame):
+        if mask._values.any():
+            result[mask] = 0
+    else:
+        if mask.any():
+            result[mask] = 0
+
+    return result
+
+
+def prep_binary(arg1, arg2):
+    # mask out values, this also makes a common index...
+    X = arg1 + 0 * arg2
+    Y = arg2 + 0 * arg1
+
+    return X, Y
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/window/doc.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/window/doc.py
new file mode 100644
index 0000000000000000000000000000000000000000..2a5cbc04921fadacf18a89608f2c0665bd8177e2
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/window/doc.py
@@ -0,0 +1,116 @@
+"""Any shareable docstring components for rolling/expanding/ewm"""
+from __future__ import annotations
+
+from textwrap import dedent
+
+from pandas.core.shared_docs import _shared_docs
+
+_shared_docs = dict(**_shared_docs)
+
+
+def create_section_header(header: str) -> str:
+    """Create numpydoc section header"""
+    return f"{header}\n{'-' * len(header)}\n"
+
+
+template_header = "\nCalculate the {window_method} {aggregation_description}.\n\n"
+
+template_returns = dedent(
+    """
+    Series or DataFrame
+        Return type is the same as the original object with ``np.float64`` dtype.\n
+    """
+).replace("\n", "", 1)
+
+template_see_also = dedent(
+    """
+    pandas.Series.{window_method} : Calling {window_method} with Series data.
+    pandas.DataFrame.{window_method} : Calling {window_method} with DataFrames.
+    pandas.Series.{agg_method} : Aggregating {agg_method} for Series.
+    pandas.DataFrame.{agg_method} : Aggregating {agg_method} for DataFrame.\n
+    """
+).replace("\n", "", 1)
+
+kwargs_numeric_only = dedent(
+    """
+    numeric_only : bool, default False
+        Include only float, int, boolean columns.
+
+        .. versionadded:: 1.5.0\n
+    """
+).replace("\n", "", 1)
+
+kwargs_scipy = dedent(
+    """
+    **kwargs
+        Keyword arguments to configure the ``SciPy`` weighted window type.\n
+    """
+).replace("\n", "", 1)
+
+window_apply_parameters = dedent(
+    """
+    func : function
+        Must produce a single value from an ndarray input if ``raw=True``
+        or a single value from a Series if ``raw=False``. Can also accept a
+        Numba JIT function with ``engine='numba'`` specified.
+
+    raw : bool, default False
+        * ``False`` : passes each row or column as a Series to the
+          function.
+        * ``True`` : the passed function will receive ndarray
+          objects instead.
+          If you are just applying a NumPy reduction function this will
+          achieve much better performance.
+
+    engine : str, default None
+        * ``'cython'`` : Runs rolling apply through C-extensions from cython.
+        * ``'numba'`` : Runs rolling apply through JIT compiled code from numba.
+          Only available when ``raw`` is set to ``True``.
+        * ``None`` : Defaults to ``'cython'`` or globally setting ``compute.use_numba``
+
+    engine_kwargs : dict, default None
+        * For ``'cython'`` engine, there are no accepted ``engine_kwargs``
+        * For ``'numba'`` engine, the engine can accept ``nopython``, ``nogil``
+          and ``parallel`` dictionary keys. The values must either be ``True`` or
+          ``False``. The default ``engine_kwargs`` for the ``'numba'`` engine is
+          ``{{'nopython': True, 'nogil': False, 'parallel': False}}`` and will be
+          applied to both the ``func`` and the ``apply`` rolling aggregation.
+
+    args : tuple, default None
+        Positional arguments to be passed into func.
+
+    kwargs : dict, default None
+        Keyword arguments to be passed into func.\n
+    """
+).replace("\n", "", 1)
+
+numba_notes = (
+    "See :ref:`window.numba_engine` and :ref:`enhancingperf.numba` for "
+    "extended documentation and performance considerations for the Numba engine.\n\n"
+)
+
+
+def window_agg_numba_parameters(version: str = "1.3") -> str:
+    return (
+        dedent(
+            """
+    engine : str, default None
+        * ``'cython'`` : Runs the operation through C-extensions from cython.
+        * ``'numba'`` : Runs the operation through JIT compiled code from numba.
+        * ``None`` : Defaults to ``'cython'`` or globally setting ``compute.use_numba``
+
+          .. versionadded:: {version}.0
+
+    engine_kwargs : dict, default None
+        * For ``'cython'`` engine, there are no accepted ``engine_kwargs``
+        * For ``'numba'`` engine, the engine can accept ``nopython``, ``nogil``
+          and ``parallel`` dictionary keys. The values must either be ``True`` or
+          ``False``. The default ``engine_kwargs`` for the ``'numba'`` engine is
+          ``{{'nopython': True, 'nogil': False, 'parallel': False}}``
+
+          .. versionadded:: {version}.0\n
+    """
+        )
+        .replace("\n", "", 1)
+        .replace("{version}", version)
+    )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/window/ewm.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/window/ewm.py
new file mode 100644
index 0000000000000000000000000000000000000000..9ebf32d3e536eee7233b911acb880f3d40521793
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/window/ewm.py
@@ -0,0 +1,1095 @@
+from __future__ import annotations
+
+import datetime
+from functools import partial
+from textwrap import dedent
+from typing import TYPE_CHECKING
+
+import numpy as np
+
+from pandas._libs.tslibs import Timedelta
+import pandas._libs.window.aggregations as window_aggregations
+from pandas.util._decorators import doc
+
+from pandas.core.dtypes.common import (
+    is_datetime64_dtype,
+    is_numeric_dtype,
+)
+from pandas.core.dtypes.dtypes import DatetimeTZDtype
+from pandas.core.dtypes.generic import ABCSeries
+from pandas.core.dtypes.missing import isna
+
+from pandas.core import common
+from pandas.core.arrays.datetimelike import dtype_to_unit
+from pandas.core.indexers.objects import (
+    BaseIndexer,
+    ExponentialMovingWindowIndexer,
+    GroupbyIndexer,
+)
+from pandas.core.util.numba_ import (
+    get_jit_arguments,
+    maybe_use_numba,
+)
+from pandas.core.window.common import zsqrt
+from pandas.core.window.doc import (
+    _shared_docs,
+    create_section_header,
+    kwargs_numeric_only,
+    numba_notes,
+    template_header,
+    template_returns,
+    template_see_also,
+    window_agg_numba_parameters,
+)
+from pandas.core.window.numba_ import (
+    generate_numba_ewm_func,
+    generate_numba_ewm_table_func,
+)
+from pandas.core.window.online import (
+    EWMMeanState,
+    generate_online_numba_ewma_func,
+)
+from pandas.core.window.rolling import (
+    BaseWindow,
+    BaseWindowGroupby,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        Axis,
+        TimedeltaConvertibleTypes,
+        npt,
+    )
+
+    from pandas import (
+        DataFrame,
+        Series,
+    )
+    from pandas.core.generic import NDFrame
+
+
+def get_center_of_mass(
+    comass: float | None,
+    span: float | None,
+    halflife: float | None,
+    alpha: float | None,
+) -> float:
+    valid_count = common.count_not_none(comass, span, halflife, alpha)
+    if valid_count > 1:
+        raise ValueError("comass, span, halflife, and alpha are mutually exclusive")
+
+    # Convert to center of mass; domain checks ensure 0 < alpha <= 1
+    if comass is not None:
+        if comass < 0:
+            raise ValueError("comass must satisfy: comass >= 0")
+    elif span is not None:
+        if span < 1:
+            raise ValueError("span must satisfy: span >= 1")
+        comass = (span - 1) / 2
+    elif halflife is not None:
+        if halflife <= 0:
+            raise ValueError("halflife must satisfy: halflife > 0")
+        decay = 1 - np.exp(np.log(0.5) / halflife)
+        comass = 1 / decay - 1
+    elif alpha is not None:
+        if alpha <= 0 or alpha > 1:
+            raise ValueError("alpha must satisfy: 0 < alpha <= 1")
+        comass = (1 - alpha) / alpha
+    else:
+        raise ValueError("Must pass one of comass, span, halflife, or alpha")
+
+    return float(comass)
+
+
+def _calculate_deltas(
+    times: np.ndarray | NDFrame,
+    halflife: float | TimedeltaConvertibleTypes | None,
+) -> npt.NDArray[np.float64]:
+    """
+    Return the diff of the times divided by the half-life. These values are used in
+    the calculation of the ewm mean.
+
+    Parameters
+    ----------
+    times : np.ndarray, Series
+        Times corresponding to the observations. Must be monotonically increasing
+        and ``datetime64[ns]`` dtype.
+    halflife : float, str, timedelta, optional
+        Half-life specifying the decay
+
+    Returns
+    -------
+    np.ndarray
+        Diff of the times divided by the half-life
+    """
+    unit = dtype_to_unit(times.dtype)
+    if isinstance(times, ABCSeries):
+        times = times._values
+    _times = np.asarray(times.view(np.int64), dtype=np.float64)
+    _halflife = float(Timedelta(halflife).as_unit(unit)._value)
+    return np.diff(_times) / _halflife
+
+
+class ExponentialMovingWindow(BaseWindow):
+    r"""
+    Provide exponentially weighted (EW) calculations.
+
+    Exactly one of ``com``, ``span``, ``halflife``, or ``alpha`` must be
+    provided if ``times`` is not provided. If ``times`` is provided,
+    ``halflife`` and one of ``com``, ``span`` or ``alpha`` may be provided.
+
+    Parameters
+    ----------
+    com : float, optional
+        Specify decay in terms of center of mass
+
+        :math:`\alpha = 1 / (1 + com)`, for :math:`com \geq 0`.
+
+    span : float, optional
+        Specify decay in terms of span
+
+        :math:`\alpha = 2 / (span + 1)`, for :math:`span \geq 1`.
+
+    halflife : float, str, timedelta, optional
+        Specify decay in terms of half-life
+
+        :math:`\alpha = 1 - \exp\left(-\ln(2) / halflife\right)`, for
+        :math:`halflife > 0`.
+
+        If ``times`` is specified, a timedelta convertible unit over which an
+        observation decays to half its value. Only applicable to ``mean()``,
+        and halflife value will not apply to the other functions.
+
+    alpha : float, optional
+        Specify smoothing factor :math:`\alpha` directly
+
+        :math:`0 < \alpha \leq 1`.
+
+    min_periods : int, default 0
+        Minimum number of observations in window required to have a value;
+        otherwise, result is ``np.nan``.
+
+    adjust : bool, default True
+        Divide by decaying adjustment factor in beginning periods to account
+        for imbalance in relative weightings (viewing EWMA as a moving average).
+
+        - When ``adjust=True`` (default), the EW function is calculated using weights
+          :math:`w_i = (1 - \alpha)^i`. For example, the EW moving average of the series
+          [:math:`x_0, x_1, ..., x_t`] would be:
+
+        .. math::
+            y_t = \frac{x_t + (1 - \alpha)x_{t-1} + (1 - \alpha)^2 x_{t-2} + ... + (1 -
+            \alpha)^t x_0}{1 + (1 - \alpha) + (1 - \alpha)^2 + ... + (1 - \alpha)^t}
+
+        - When ``adjust=False``, the exponentially weighted function is calculated
+          recursively:
+
+        .. math::
+            \begin{split}
+                y_0 &= x_0\\
+                y_t &= (1 - \alpha) y_{t-1} + \alpha x_t,
+            \end{split}
+    ignore_na : bool, default False
+        Ignore missing values when calculating weights.
+
+        - When ``ignore_na=False`` (default), weights are based on absolute positions.
+          For example, the weights of :math:`x_0` and :math:`x_2` used in calculating
+          the final weighted average of [:math:`x_0`, None, :math:`x_2`] are
+          :math:`(1-\alpha)^2` and :math:`1` if ``adjust=True``, and
+          :math:`(1-\alpha)^2` and :math:`\alpha` if ``adjust=False``.
+
+        - When ``ignore_na=True``, weights are based
+          on relative positions. For example, the weights of :math:`x_0` and :math:`x_2`
+          used in calculating the final weighted average of
+          [:math:`x_0`, None, :math:`x_2`] are :math:`1-\alpha` and :math:`1` if
+          ``adjust=True``, and :math:`1-\alpha` and :math:`\alpha` if ``adjust=False``.
+
+    axis : {0, 1}, default 0
+        If ``0`` or ``'index'``, calculate across the rows.
+
+        If ``1`` or ``'columns'``, calculate across the columns.
+
+        For `Series` this parameter is unused and defaults to 0.
+
+    times : np.ndarray, Series, default None
+
+        Only applicable to ``mean()``.
+
+        Times corresponding to the observations. Must be monotonically increasing and
+        ``datetime64[ns]`` dtype.
+
+        If 1-D array like, a sequence with the same shape as the observations.
+
+    method : str {'single', 'table'}, default 'single'
+        .. versionadded:: 1.4.0
+
+        Execute the rolling operation per single column or row (``'single'``)
+        or over the entire object (``'table'``).
+
+        This argument is only implemented when specifying ``engine='numba'``
+        in the method call.
+
+        Only applicable to ``mean()``
+
+    Returns
+    -------
+    pandas.api.typing.ExponentialMovingWindow
+
+    See Also
+    --------
+    rolling : Provides rolling window calculations.
+    expanding : Provides expanding transformations.
+
+    Notes
+    -----
+    See :ref:`Windowing Operations `
+    for further usage details and examples.
+
+    Examples
+    --------
+    >>> df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]})
+    >>> df
+         B
+    0  0.0
+    1  1.0
+    2  2.0
+    3  NaN
+    4  4.0
+
+    >>> df.ewm(com=0.5).mean()
+              B
+    0  0.000000
+    1  0.750000
+    2  1.615385
+    3  1.615385
+    4  3.670213
+    >>> df.ewm(alpha=2 / 3).mean()
+              B
+    0  0.000000
+    1  0.750000
+    2  1.615385
+    3  1.615385
+    4  3.670213
+
+    **adjust**
+
+    >>> df.ewm(com=0.5, adjust=True).mean()
+              B
+    0  0.000000
+    1  0.750000
+    2  1.615385
+    3  1.615385
+    4  3.670213
+    >>> df.ewm(com=0.5, adjust=False).mean()
+              B
+    0  0.000000
+    1  0.666667
+    2  1.555556
+    3  1.555556
+    4  3.650794
+
+    **ignore_na**
+
+    >>> df.ewm(com=0.5, ignore_na=True).mean()
+              B
+    0  0.000000
+    1  0.750000
+    2  1.615385
+    3  1.615385
+    4  3.225000
+    >>> df.ewm(com=0.5, ignore_na=False).mean()
+              B
+    0  0.000000
+    1  0.750000
+    2  1.615385
+    3  1.615385
+    4  3.670213
+
+    **times**
+
+    Exponentially weighted mean with weights calculated with a timedelta ``halflife``
+    relative to ``times``.
+
+    >>> times = ['2020-01-01', '2020-01-03', '2020-01-10', '2020-01-15', '2020-01-17']
+    >>> df.ewm(halflife='4 days', times=pd.DatetimeIndex(times)).mean()
+              B
+    0  0.000000
+    1  0.585786
+    2  1.523889
+    3  1.523889
+    4  3.233686
+    """
+
+    _attributes = [
+        "com",
+        "span",
+        "halflife",
+        "alpha",
+        "min_periods",
+        "adjust",
+        "ignore_na",
+        "axis",
+        "times",
+        "method",
+    ]
+
+    def __init__(
+        self,
+        obj: NDFrame,
+        com: float | None = None,
+        span: float | None = None,
+        halflife: float | TimedeltaConvertibleTypes | None = None,
+        alpha: float | None = None,
+        min_periods: int | None = 0,
+        adjust: bool = True,
+        ignore_na: bool = False,
+        axis: Axis = 0,
+        times: np.ndarray | NDFrame | None = None,
+        method: str = "single",
+        *,
+        selection=None,
+    ) -> None:
+        super().__init__(
+            obj=obj,
+            min_periods=1 if min_periods is None else max(int(min_periods), 1),
+            on=None,
+            center=False,
+            closed=None,
+            method=method,
+            axis=axis,
+            selection=selection,
+        )
+        self.com = com
+        self.span = span
+        self.halflife = halflife
+        self.alpha = alpha
+        self.adjust = adjust
+        self.ignore_na = ignore_na
+        self.times = times
+        if self.times is not None:
+            if not self.adjust:
+                raise NotImplementedError("times is not supported with adjust=False.")
+            times_dtype = getattr(self.times, "dtype", None)
+            if not (
+                is_datetime64_dtype(times_dtype)
+                or isinstance(times_dtype, DatetimeTZDtype)
+            ):
+                raise ValueError("times must be datetime64 dtype.")
+            if len(self.times) != len(obj):
+                raise ValueError("times must be the same length as the object.")
+            if not isinstance(self.halflife, (str, datetime.timedelta, np.timedelta64)):
+                raise ValueError("halflife must be a timedelta convertible object")
+            if isna(self.times).any():
+                raise ValueError("Cannot convert NaT values to integer")
+            self._deltas = _calculate_deltas(self.times, self.halflife)
+            # Halflife is no longer applicable when calculating COM
+            # But allow COM to still be calculated if the user passes other decay args
+            if common.count_not_none(self.com, self.span, self.alpha) > 0:
+                self._com = get_center_of_mass(self.com, self.span, None, self.alpha)
+            else:
+                self._com = 1.0
+        else:
+            if self.halflife is not None and isinstance(
+                self.halflife, (str, datetime.timedelta, np.timedelta64)
+            ):
+                raise ValueError(
+                    "halflife can only be a timedelta convertible argument if "
+                    "times is not None."
+                )
+            # Without times, points are equally spaced
+            self._deltas = np.ones(
+                max(self.obj.shape[self.axis] - 1, 0), dtype=np.float64
+            )
+            self._com = get_center_of_mass(
+                # error: Argument 3 to "get_center_of_mass" has incompatible type
+                # "Union[float, Any, None, timedelta64, signedinteger[_64Bit]]";
+                # expected "Optional[float]"
+                self.com,
+                self.span,
+                self.halflife,  # type: ignore[arg-type]
+                self.alpha,
+            )
+
+    def _check_window_bounds(
+        self, start: np.ndarray, end: np.ndarray, num_vals: int
+    ) -> None:
+        # emw algorithms are iterative with each point
+        # ExponentialMovingWindowIndexer "bounds" are the entire window
+        pass
+
+    def _get_window_indexer(self) -> BaseIndexer:
+        """
+        Return an indexer class that will compute the window start and end bounds
+        """
+        return ExponentialMovingWindowIndexer()
+
+    def online(
+        self, engine: str = "numba", engine_kwargs=None
+    ) -> OnlineExponentialMovingWindow:
+        """
+        Return an ``OnlineExponentialMovingWindow`` object to calculate
+        exponentially moving window aggregations in an online method.
+
+        .. versionadded:: 1.3.0
+
+        Parameters
+        ----------
+        engine: str, default ``'numba'``
+            Execution engine to calculate online aggregations.
+            Applies to all supported aggregation methods.
+
+        engine_kwargs : dict, default None
+            Applies to all supported aggregation methods.
+
+            * For ``'numba'`` engine, the engine can accept ``nopython``, ``nogil``
+              and ``parallel`` dictionary keys. The values must either be ``True`` or
+              ``False``. The default ``engine_kwargs`` for the ``'numba'`` engine is
+              ``{{'nopython': True, 'nogil': False, 'parallel': False}}`` and will be
+              applied to the function
+
+        Returns
+        -------
+        OnlineExponentialMovingWindow
+        """
+        return OnlineExponentialMovingWindow(
+            obj=self.obj,
+            com=self.com,
+            span=self.span,
+            halflife=self.halflife,
+            alpha=self.alpha,
+            min_periods=self.min_periods,
+            adjust=self.adjust,
+            ignore_na=self.ignore_na,
+            axis=self.axis,
+            times=self.times,
+            engine=engine,
+            engine_kwargs=engine_kwargs,
+            selection=self._selection,
+        )
+
+    @doc(
+        _shared_docs["aggregate"],
+        see_also=dedent(
+            """
+        See Also
+        --------
+        pandas.DataFrame.rolling.aggregate
+        """
+        ),
+        examples=dedent(
+            """
+        Examples
+        --------
+        >>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6], "C": [7, 8, 9]})
+        >>> df
+           A  B  C
+        0  1  4  7
+        1  2  5  8
+        2  3  6  9
+
+        >>> df.ewm(alpha=0.5).mean()
+                  A         B         C
+        0  1.000000  4.000000  7.000000
+        1  1.666667  4.666667  7.666667
+        2  2.428571  5.428571  8.428571
+        """
+        ),
+        klass="Series/Dataframe",
+        axis="",
+    )
+    def aggregate(self, func, *args, **kwargs):
+        return super().aggregate(func, *args, **kwargs)
+
+    agg = aggregate
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        kwargs_numeric_only,
+        window_agg_numba_parameters(),
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Notes"),
+        numba_notes,
+        create_section_header("Examples"),
+        dedent(
+            """\
+        >>> ser = pd.Series([1, 2, 3, 4])
+        >>> ser.ewm(alpha=.2).mean()
+        0    1.000000
+        1    1.555556
+        2    2.147541
+        3    2.775068
+        dtype: float64
+        """
+        ),
+        window_method="ewm",
+        aggregation_description="(exponential weighted moment) mean",
+        agg_method="mean",
+    )
+    def mean(
+        self,
+        numeric_only: bool = False,
+        engine=None,
+        engine_kwargs=None,
+    ):
+        if maybe_use_numba(engine):
+            if self.method == "single":
+                func = generate_numba_ewm_func
+            else:
+                func = generate_numba_ewm_table_func
+            ewm_func = func(
+                **get_jit_arguments(engine_kwargs),
+                com=self._com,
+                adjust=self.adjust,
+                ignore_na=self.ignore_na,
+                deltas=tuple(self._deltas),
+                normalize=True,
+            )
+            return self._apply(ewm_func, name="mean")
+        elif engine in ("cython", None):
+            if engine_kwargs is not None:
+                raise ValueError("cython engine does not accept engine_kwargs")
+
+            deltas = None if self.times is None else self._deltas
+            window_func = partial(
+                window_aggregations.ewm,
+                com=self._com,
+                adjust=self.adjust,
+                ignore_na=self.ignore_na,
+                deltas=deltas,
+                normalize=True,
+            )
+            return self._apply(window_func, name="mean", numeric_only=numeric_only)
+        else:
+            raise ValueError("engine must be either 'numba' or 'cython'")
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        kwargs_numeric_only,
+        window_agg_numba_parameters(),
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Notes"),
+        numba_notes,
+        create_section_header("Examples"),
+        dedent(
+            """\
+        >>> ser = pd.Series([1, 2, 3, 4])
+        >>> ser.ewm(alpha=.2).sum()
+        0    1.000
+        1    2.800
+        2    5.240
+        3    8.192
+        dtype: float64
+        """
+        ),
+        window_method="ewm",
+        aggregation_description="(exponential weighted moment) sum",
+        agg_method="sum",
+    )
+    def sum(
+        self,
+        numeric_only: bool = False,
+        engine=None,
+        engine_kwargs=None,
+    ):
+        if not self.adjust:
+            raise NotImplementedError("sum is not implemented with adjust=False")
+        if maybe_use_numba(engine):
+            if self.method == "single":
+                func = generate_numba_ewm_func
+            else:
+                func = generate_numba_ewm_table_func
+            ewm_func = func(
+                **get_jit_arguments(engine_kwargs),
+                com=self._com,
+                adjust=self.adjust,
+                ignore_na=self.ignore_na,
+                deltas=tuple(self._deltas),
+                normalize=False,
+            )
+            return self._apply(ewm_func, name="sum")
+        elif engine in ("cython", None):
+            if engine_kwargs is not None:
+                raise ValueError("cython engine does not accept engine_kwargs")
+
+            deltas = None if self.times is None else self._deltas
+            window_func = partial(
+                window_aggregations.ewm,
+                com=self._com,
+                adjust=self.adjust,
+                ignore_na=self.ignore_na,
+                deltas=deltas,
+                normalize=False,
+            )
+            return self._apply(window_func, name="sum", numeric_only=numeric_only)
+        else:
+            raise ValueError("engine must be either 'numba' or 'cython'")
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        dedent(
+            """\
+        bias : bool, default False
+            Use a standard estimation bias correction.
+        """
+        ),
+        kwargs_numeric_only,
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Examples"),
+        dedent(
+            """\
+        >>> ser = pd.Series([1, 2, 3, 4])
+        >>> ser.ewm(alpha=.2).std()
+        0         NaN
+        1    0.707107
+        2    0.995893
+        3    1.277320
+        dtype: float64
+        """
+        ),
+        window_method="ewm",
+        aggregation_description="(exponential weighted moment) standard deviation",
+        agg_method="std",
+    )
+    def std(self, bias: bool = False, numeric_only: bool = False):
+        if (
+            numeric_only
+            and self._selected_obj.ndim == 1
+            and not is_numeric_dtype(self._selected_obj.dtype)
+        ):
+            # Raise directly so error message says std instead of var
+            raise NotImplementedError(
+                f"{type(self).__name__}.std does not implement numeric_only"
+            )
+        return zsqrt(self.var(bias=bias, numeric_only=numeric_only))
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        dedent(
+            """\
+        bias : bool, default False
+            Use a standard estimation bias correction.
+        """
+        ),
+        kwargs_numeric_only,
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Examples"),
+        dedent(
+            """\
+        >>> ser = pd.Series([1, 2, 3, 4])
+        >>> ser.ewm(alpha=.2).var()
+        0         NaN
+        1    0.500000
+        2    0.991803
+        3    1.631547
+        dtype: float64
+        """
+        ),
+        window_method="ewm",
+        aggregation_description="(exponential weighted moment) variance",
+        agg_method="var",
+    )
+    def var(self, bias: bool = False, numeric_only: bool = False):
+        window_func = window_aggregations.ewmcov
+        wfunc = partial(
+            window_func,
+            com=self._com,
+            adjust=self.adjust,
+            ignore_na=self.ignore_na,
+            bias=bias,
+        )
+
+        def var_func(values, begin, end, min_periods):
+            return wfunc(values, begin, end, min_periods, values)
+
+        return self._apply(var_func, name="var", numeric_only=numeric_only)
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        dedent(
+            """\
+        other : Series or DataFrame , optional
+            If not supplied then will default to self and produce pairwise
+            output.
+        pairwise : bool, default None
+            If False then only matching columns between self and other will be
+            used and the output will be a DataFrame.
+            If True then all pairwise combinations will be calculated and the
+            output will be a MultiIndex DataFrame in the case of DataFrame
+            inputs. In the case of missing elements, only complete pairwise
+            observations will be used.
+        bias : bool, default False
+            Use a standard estimation bias correction.
+        """
+        ),
+        kwargs_numeric_only,
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Examples"),
+        dedent(
+            """\
+        >>> ser1 = pd.Series([1, 2, 3, 4])
+        >>> ser2 = pd.Series([10, 11, 13, 16])
+        >>> ser1.ewm(alpha=.2).cov(ser2)
+        0         NaN
+        1    0.500000
+        2    1.524590
+        3    3.408836
+        dtype: float64
+        """
+        ),
+        window_method="ewm",
+        aggregation_description="(exponential weighted moment) sample covariance",
+        agg_method="cov",
+    )
+    def cov(
+        self,
+        other: DataFrame | Series | None = None,
+        pairwise: bool | None = None,
+        bias: bool = False,
+        numeric_only: bool = False,
+    ):
+        from pandas import Series
+
+        self._validate_numeric_only("cov", numeric_only)
+
+        def cov_func(x, y):
+            x_array = self._prep_values(x)
+            y_array = self._prep_values(y)
+            window_indexer = self._get_window_indexer()
+            min_periods = (
+                self.min_periods
+                if self.min_periods is not None
+                else window_indexer.window_size
+            )
+            start, end = window_indexer.get_window_bounds(
+                num_values=len(x_array),
+                min_periods=min_periods,
+                center=self.center,
+                closed=self.closed,
+                step=self.step,
+            )
+            result = window_aggregations.ewmcov(
+                x_array,
+                start,
+                end,
+                # error: Argument 4 to "ewmcov" has incompatible type
+                # "Optional[int]"; expected "int"
+                self.min_periods,  # type: ignore[arg-type]
+                y_array,
+                self._com,
+                self.adjust,
+                self.ignore_na,
+                bias,
+            )
+            return Series(result, index=x.index, name=x.name, copy=False)
+
+        return self._apply_pairwise(
+            self._selected_obj, other, pairwise, cov_func, numeric_only
+        )
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        dedent(
+            """\
+        other : Series or DataFrame, optional
+            If not supplied then will default to self and produce pairwise
+            output.
+        pairwise : bool, default None
+            If False then only matching columns between self and other will be
+            used and the output will be a DataFrame.
+            If True then all pairwise combinations will be calculated and the
+            output will be a MultiIndex DataFrame in the case of DataFrame
+            inputs. In the case of missing elements, only complete pairwise
+            observations will be used.
+        """
+        ),
+        kwargs_numeric_only,
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Examples"),
+        dedent(
+            """\
+        >>> ser1 = pd.Series([1, 2, 3, 4])
+        >>> ser2 = pd.Series([10, 11, 13, 16])
+        >>> ser1.ewm(alpha=.2).corr(ser2)
+        0         NaN
+        1    1.000000
+        2    0.982821
+        3    0.977802
+        dtype: float64
+        """
+        ),
+        window_method="ewm",
+        aggregation_description="(exponential weighted moment) sample correlation",
+        agg_method="corr",
+    )
+    def corr(
+        self,
+        other: DataFrame | Series | None = None,
+        pairwise: bool | None = None,
+        numeric_only: bool = False,
+    ):
+        from pandas import Series
+
+        self._validate_numeric_only("corr", numeric_only)
+
+        def cov_func(x, y):
+            x_array = self._prep_values(x)
+            y_array = self._prep_values(y)
+            window_indexer = self._get_window_indexer()
+            min_periods = (
+                self.min_periods
+                if self.min_periods is not None
+                else window_indexer.window_size
+            )
+            start, end = window_indexer.get_window_bounds(
+                num_values=len(x_array),
+                min_periods=min_periods,
+                center=self.center,
+                closed=self.closed,
+                step=self.step,
+            )
+
+            def _cov(X, Y):
+                return window_aggregations.ewmcov(
+                    X,
+                    start,
+                    end,
+                    min_periods,
+                    Y,
+                    self._com,
+                    self.adjust,
+                    self.ignore_na,
+                    True,
+                )
+
+            with np.errstate(all="ignore"):
+                cov = _cov(x_array, y_array)
+                x_var = _cov(x_array, x_array)
+                y_var = _cov(y_array, y_array)
+                result = cov / zsqrt(x_var * y_var)
+            return Series(result, index=x.index, name=x.name, copy=False)
+
+        return self._apply_pairwise(
+            self._selected_obj, other, pairwise, cov_func, numeric_only
+        )
+
+
+class ExponentialMovingWindowGroupby(BaseWindowGroupby, ExponentialMovingWindow):
+    """
+    Provide an exponential moving window groupby implementation.
+    """
+
+    _attributes = ExponentialMovingWindow._attributes + BaseWindowGroupby._attributes
+
+    def __init__(self, obj, *args, _grouper=None, **kwargs) -> None:
+        super().__init__(obj, *args, _grouper=_grouper, **kwargs)
+
+        if not obj.empty and self.times is not None:
+            # sort the times and recalculate the deltas according to the groups
+            groupby_order = np.concatenate(list(self._grouper.indices.values()))
+            self._deltas = _calculate_deltas(
+                self.times.take(groupby_order),
+                self.halflife,
+            )
+
+    def _get_window_indexer(self) -> GroupbyIndexer:
+        """
+        Return an indexer class that will compute the window start and end bounds
+
+        Returns
+        -------
+        GroupbyIndexer
+        """
+        window_indexer = GroupbyIndexer(
+            groupby_indices=self._grouper.indices,
+            window_indexer=ExponentialMovingWindowIndexer,
+        )
+        return window_indexer
+
+
+class OnlineExponentialMovingWindow(ExponentialMovingWindow):
+    def __init__(
+        self,
+        obj: NDFrame,
+        com: float | None = None,
+        span: float | None = None,
+        halflife: float | TimedeltaConvertibleTypes | None = None,
+        alpha: float | None = None,
+        min_periods: int | None = 0,
+        adjust: bool = True,
+        ignore_na: bool = False,
+        axis: Axis = 0,
+        times: np.ndarray | NDFrame | None = None,
+        engine: str = "numba",
+        engine_kwargs: dict[str, bool] | None = None,
+        *,
+        selection=None,
+    ) -> None:
+        if times is not None:
+            raise NotImplementedError(
+                "times is not implemented with online operations."
+            )
+        super().__init__(
+            obj=obj,
+            com=com,
+            span=span,
+            halflife=halflife,
+            alpha=alpha,
+            min_periods=min_periods,
+            adjust=adjust,
+            ignore_na=ignore_na,
+            axis=axis,
+            times=times,
+            selection=selection,
+        )
+        self._mean = EWMMeanState(
+            self._com, self.adjust, self.ignore_na, self.axis, obj.shape
+        )
+        if maybe_use_numba(engine):
+            self.engine = engine
+            self.engine_kwargs = engine_kwargs
+        else:
+            raise ValueError("'numba' is the only supported engine")
+
+    def reset(self) -> None:
+        """
+        Reset the state captured by `update` calls.
+        """
+        self._mean.reset()
+
+    def aggregate(self, func, *args, **kwargs):
+        raise NotImplementedError("aggregate is not implemented.")
+
+    def std(self, bias: bool = False, *args, **kwargs):
+        raise NotImplementedError("std is not implemented.")
+
+    def corr(
+        self,
+        other: DataFrame | Series | None = None,
+        pairwise: bool | None = None,
+        numeric_only: bool = False,
+    ):
+        raise NotImplementedError("corr is not implemented.")
+
+    def cov(
+        self,
+        other: DataFrame | Series | None = None,
+        pairwise: bool | None = None,
+        bias: bool = False,
+        numeric_only: bool = False,
+    ):
+        raise NotImplementedError("cov is not implemented.")
+
+    def var(self, bias: bool = False, numeric_only: bool = False):
+        raise NotImplementedError("var is not implemented.")
+
+    def mean(self, *args, update=None, update_times=None, **kwargs):
+        """
+        Calculate an online exponentially weighted mean.
+
+        Parameters
+        ----------
+        update: DataFrame or Series, default None
+            New values to continue calculating the
+            exponentially weighted mean from the last values and weights.
+            Values should be float64 dtype.
+
+            ``update`` needs to be ``None`` the first time the
+            exponentially weighted mean is calculated.
+
+        update_times: Series or 1-D np.ndarray, default None
+            New times to continue calculating the
+            exponentially weighted mean from the last values and weights.
+            If ``None``, values are assumed to be evenly spaced
+            in time.
+            This feature is currently unsupported.
+
+        Returns
+        -------
+        DataFrame or Series
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({"a": range(5), "b": range(5, 10)})
+        >>> online_ewm = df.head(2).ewm(0.5).online()
+        >>> online_ewm.mean()
+              a     b
+        0  0.00  5.00
+        1  0.75  5.75
+        >>> online_ewm.mean(update=df.tail(3))
+                  a         b
+        2  1.615385  6.615385
+        3  2.550000  7.550000
+        4  3.520661  8.520661
+        >>> online_ewm.reset()
+        >>> online_ewm.mean()
+              a     b
+        0  0.00  5.00
+        1  0.75  5.75
+        """
+        result_kwargs = {}
+        is_frame = self._selected_obj.ndim == 2
+        if update_times is not None:
+            raise NotImplementedError("update_times is not implemented.")
+        update_deltas = np.ones(
+            max(self._selected_obj.shape[self.axis - 1] - 1, 0), dtype=np.float64
+        )
+        if update is not None:
+            if self._mean.last_ewm is None:
+                raise ValueError(
+                    "Must call mean with update=None first before passing update"
+                )
+            result_from = 1
+            result_kwargs["index"] = update.index
+            if is_frame:
+                last_value = self._mean.last_ewm[np.newaxis, :]
+                result_kwargs["columns"] = update.columns
+            else:
+                last_value = self._mean.last_ewm
+                result_kwargs["name"] = update.name
+            np_array = np.concatenate((last_value, update.to_numpy()))
+        else:
+            result_from = 0
+            result_kwargs["index"] = self._selected_obj.index
+            if is_frame:
+                result_kwargs["columns"] = self._selected_obj.columns
+            else:
+                result_kwargs["name"] = self._selected_obj.name
+            np_array = self._selected_obj.astype(np.float64, copy=False).to_numpy()
+        ewma_func = generate_online_numba_ewma_func(
+            **get_jit_arguments(self.engine_kwargs)
+        )
+        result = self._mean.run_ewm(
+            np_array if is_frame else np_array[:, np.newaxis],
+            update_deltas,
+            self.min_periods,
+            ewma_func,
+        )
+        if not is_frame:
+            result = result.squeeze()
+        result = result[result_from:]
+        result = self._selected_obj._constructor(result, **result_kwargs)
+        return result
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/window/expanding.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/window/expanding.py
new file mode 100644
index 0000000000000000000000000000000000000000..aac10596ffc699c2b229f959b9c1b26393384b03
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/window/expanding.py
@@ -0,0 +1,964 @@
+from __future__ import annotations
+
+from textwrap import dedent
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Literal,
+)
+
+from pandas.util._decorators import (
+    deprecate_kwarg,
+    doc,
+)
+
+from pandas.core.indexers.objects import (
+    BaseIndexer,
+    ExpandingIndexer,
+    GroupbyIndexer,
+)
+from pandas.core.window.doc import (
+    _shared_docs,
+    create_section_header,
+    kwargs_numeric_only,
+    numba_notes,
+    template_header,
+    template_returns,
+    template_see_also,
+    window_agg_numba_parameters,
+    window_apply_parameters,
+)
+from pandas.core.window.rolling import (
+    BaseWindowGroupby,
+    RollingAndExpandingMixin,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        Axis,
+        QuantileInterpolation,
+        WindowingRankType,
+    )
+
+    from pandas import (
+        DataFrame,
+        Series,
+    )
+    from pandas.core.generic import NDFrame
+
+
+class Expanding(RollingAndExpandingMixin):
+    """
+    Provide expanding window calculations.
+
+    Parameters
+    ----------
+    min_periods : int, default 1
+        Minimum number of observations in window required to have a value;
+        otherwise, result is ``np.nan``.
+
+    axis : int or str, default 0
+        If ``0`` or ``'index'``, roll across the rows.
+
+        If ``1`` or ``'columns'``, roll across the columns.
+
+        For `Series` this parameter is unused and defaults to 0.
+
+    method : str {'single', 'table'}, default 'single'
+        Execute the rolling operation per single column or row (``'single'``)
+        or over the entire object (``'table'``).
+
+        This argument is only implemented when specifying ``engine='numba'``
+        in the method call.
+
+        .. versionadded:: 1.3.0
+
+    Returns
+    -------
+    pandas.api.typing.Expanding
+
+    See Also
+    --------
+    rolling : Provides rolling window calculations.
+    ewm : Provides exponential weighted functions.
+
+    Notes
+    -----
+    See :ref:`Windowing Operations ` for further usage details
+    and examples.
+
+    Examples
+    --------
+    >>> df = pd.DataFrame({"B": [0, 1, 2, np.nan, 4]})
+    >>> df
+         B
+    0  0.0
+    1  1.0
+    2  2.0
+    3  NaN
+    4  4.0
+
+    **min_periods**
+
+    Expanding sum with 1 vs 3 observations needed to calculate a value.
+
+    >>> df.expanding(1).sum()
+         B
+    0  0.0
+    1  1.0
+    2  3.0
+    3  3.0
+    4  7.0
+    >>> df.expanding(3).sum()
+         B
+    0  NaN
+    1  NaN
+    2  3.0
+    3  3.0
+    4  7.0
+    """
+
+    _attributes: list[str] = ["min_periods", "axis", "method"]
+
+    def __init__(
+        self,
+        obj: NDFrame,
+        min_periods: int = 1,
+        axis: Axis = 0,
+        method: str = "single",
+        selection=None,
+    ) -> None:
+        super().__init__(
+            obj=obj,
+            min_periods=min_periods,
+            axis=axis,
+            method=method,
+            selection=selection,
+        )
+
+    def _get_window_indexer(self) -> BaseIndexer:
+        """
+        Return an indexer class that will compute the window start and end bounds
+        """
+        return ExpandingIndexer()
+
+    @doc(
+        _shared_docs["aggregate"],
+        see_also=dedent(
+            """
+        See Also
+        --------
+        pandas.DataFrame.aggregate : Similar DataFrame method.
+        pandas.Series.aggregate : Similar Series method.
+        """
+        ),
+        examples=dedent(
+            """
+        Examples
+        --------
+        >>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6], "C": [7, 8, 9]})
+        >>> df
+           A  B  C
+        0  1  4  7
+        1  2  5  8
+        2  3  6  9
+
+        >>> df.ewm(alpha=0.5).mean()
+                  A         B         C
+        0  1.000000  4.000000  7.000000
+        1  1.666667  4.666667  7.666667
+        2  2.428571  5.428571  8.428571
+        """
+        ),
+        klass="Series/Dataframe",
+        axis="",
+    )
+    def aggregate(self, func, *args, **kwargs):
+        return super().aggregate(func, *args, **kwargs)
+
+    agg = aggregate
+
+    @doc(
+        template_header,
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Examples"),
+        dedent(
+            """\
+        >>> ser = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])
+        >>> ser.expanding().count()
+        a    1.0
+        b    2.0
+        c    3.0
+        d    4.0
+        dtype: float64
+        """
+        ),
+        window_method="expanding",
+        aggregation_description="count of non NaN observations",
+        agg_method="count",
+    )
+    def count(self, numeric_only: bool = False):
+        return super().count(numeric_only=numeric_only)
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        window_apply_parameters,
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Examples"),
+        dedent(
+            """\
+        >>> ser = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])
+        >>> ser.expanding().apply(lambda s: s.max() - 2 * s.min())
+        a   -1.0
+        b    0.0
+        c    1.0
+        d    2.0
+        dtype: float64
+        """
+        ),
+        window_method="expanding",
+        aggregation_description="custom aggregation function",
+        agg_method="apply",
+    )
+    def apply(
+        self,
+        func: Callable[..., Any],
+        raw: bool = False,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+        args: tuple[Any, ...] | None = None,
+        kwargs: dict[str, Any] | None = None,
+    ):
+        return super().apply(
+            func,
+            raw=raw,
+            engine=engine,
+            engine_kwargs=engine_kwargs,
+            args=args,
+            kwargs=kwargs,
+        )
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        kwargs_numeric_only,
+        window_agg_numba_parameters(),
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Notes"),
+        numba_notes,
+        create_section_header("Examples"),
+        dedent(
+            """\
+        >>> ser = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])
+        >>> ser.expanding().sum()
+        a     1.0
+        b     3.0
+        c     6.0
+        d    10.0
+        dtype: float64
+        """
+        ),
+        window_method="expanding",
+        aggregation_description="sum",
+        agg_method="sum",
+    )
+    def sum(
+        self,
+        numeric_only: bool = False,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+    ):
+        return super().sum(
+            numeric_only=numeric_only,
+            engine=engine,
+            engine_kwargs=engine_kwargs,
+        )
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        kwargs_numeric_only,
+        window_agg_numba_parameters(),
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Notes"),
+        numba_notes,
+        create_section_header("Examples"),
+        dedent(
+            """\
+        >>> ser = pd.Series([3, 2, 1, 4], index=['a', 'b', 'c', 'd'])
+        >>> ser.expanding().max()
+        a    3.0
+        b    3.0
+        c    3.0
+        d    4.0
+        dtype: float64
+        """
+        ),
+        window_method="expanding",
+        aggregation_description="maximum",
+        agg_method="max",
+    )
+    def max(
+        self,
+        numeric_only: bool = False,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+    ):
+        return super().max(
+            numeric_only=numeric_only,
+            engine=engine,
+            engine_kwargs=engine_kwargs,
+        )
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        kwargs_numeric_only,
+        window_agg_numba_parameters(),
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Notes"),
+        numba_notes,
+        create_section_header("Examples"),
+        dedent(
+            """\
+        >>> ser = pd.Series([2, 3, 4, 1], index=['a', 'b', 'c', 'd'])
+        >>> ser.expanding().min()
+        a    2.0
+        b    2.0
+        c    2.0
+        d    1.0
+        dtype: float64
+        """
+        ),
+        window_method="expanding",
+        aggregation_description="minimum",
+        agg_method="min",
+    )
+    def min(
+        self,
+        numeric_only: bool = False,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+    ):
+        return super().min(
+            numeric_only=numeric_only,
+            engine=engine,
+            engine_kwargs=engine_kwargs,
+        )
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        kwargs_numeric_only,
+        window_agg_numba_parameters(),
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Notes"),
+        numba_notes,
+        create_section_header("Examples"),
+        dedent(
+            """\
+        >>> ser = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])
+        >>> ser.expanding().mean()
+        a    1.0
+        b    1.5
+        c    2.0
+        d    2.5
+        dtype: float64
+        """
+        ),
+        window_method="expanding",
+        aggregation_description="mean",
+        agg_method="mean",
+    )
+    def mean(
+        self,
+        numeric_only: bool = False,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+    ):
+        return super().mean(
+            numeric_only=numeric_only,
+            engine=engine,
+            engine_kwargs=engine_kwargs,
+        )
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        kwargs_numeric_only,
+        window_agg_numba_parameters(),
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Notes"),
+        numba_notes,
+        create_section_header("Examples"),
+        dedent(
+            """\
+        >>> ser = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])
+        >>> ser.expanding().median()
+        a    1.0
+        b    1.5
+        c    2.0
+        d    2.5
+        dtype: float64
+        """
+        ),
+        window_method="expanding",
+        aggregation_description="median",
+        agg_method="median",
+    )
+    def median(
+        self,
+        numeric_only: bool = False,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+    ):
+        return super().median(
+            numeric_only=numeric_only,
+            engine=engine,
+            engine_kwargs=engine_kwargs,
+        )
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        dedent(
+            """
+        ddof : int, default 1
+            Delta Degrees of Freedom.  The divisor used in calculations
+            is ``N - ddof``, where ``N`` represents the number of elements.\n
+        """
+        ).replace("\n", "", 1),
+        kwargs_numeric_only,
+        window_agg_numba_parameters("1.4"),
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        "numpy.std : Equivalent method for NumPy array.\n",
+        template_see_also,
+        create_section_header("Notes"),
+        dedent(
+            """
+        The default ``ddof`` of 1 used in :meth:`Series.std` is different
+        than the default ``ddof`` of 0 in :func:`numpy.std`.
+
+        A minimum of one period is required for the rolling calculation.\n
+        """
+        ).replace("\n", "", 1),
+        create_section_header("Examples"),
+        dedent(
+            """
+        >>> s = pd.Series([5, 5, 6, 7, 5, 5, 5])
+
+        >>> s.expanding(3).std()
+        0         NaN
+        1         NaN
+        2    0.577350
+        3    0.957427
+        4    0.894427
+        5    0.836660
+        6    0.786796
+        dtype: float64
+        """
+        ).replace("\n", "", 1),
+        window_method="expanding",
+        aggregation_description="standard deviation",
+        agg_method="std",
+    )
+    def std(
+        self,
+        ddof: int = 1,
+        numeric_only: bool = False,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+    ):
+        return super().std(
+            ddof=ddof,
+            numeric_only=numeric_only,
+            engine=engine,
+            engine_kwargs=engine_kwargs,
+        )
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        dedent(
+            """
+        ddof : int, default 1
+            Delta Degrees of Freedom.  The divisor used in calculations
+            is ``N - ddof``, where ``N`` represents the number of elements.\n
+        """
+        ).replace("\n", "", 1),
+        kwargs_numeric_only,
+        window_agg_numba_parameters("1.4"),
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        "numpy.var : Equivalent method for NumPy array.\n",
+        template_see_also,
+        create_section_header("Notes"),
+        dedent(
+            """
+        The default ``ddof`` of 1 used in :meth:`Series.var` is different
+        than the default ``ddof`` of 0 in :func:`numpy.var`.
+
+        A minimum of one period is required for the rolling calculation.\n
+        """
+        ).replace("\n", "", 1),
+        create_section_header("Examples"),
+        dedent(
+            """
+        >>> s = pd.Series([5, 5, 6, 7, 5, 5, 5])
+
+        >>> s.expanding(3).var()
+        0         NaN
+        1         NaN
+        2    0.333333
+        3    0.916667
+        4    0.800000
+        5    0.700000
+        6    0.619048
+        dtype: float64
+        """
+        ).replace("\n", "", 1),
+        window_method="expanding",
+        aggregation_description="variance",
+        agg_method="var",
+    )
+    def var(
+        self,
+        ddof: int = 1,
+        numeric_only: bool = False,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+    ):
+        return super().var(
+            ddof=ddof,
+            numeric_only=numeric_only,
+            engine=engine,
+            engine_kwargs=engine_kwargs,
+        )
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        dedent(
+            """
+        ddof : int, default 1
+            Delta Degrees of Freedom.  The divisor used in calculations
+            is ``N - ddof``, where ``N`` represents the number of elements.\n
+        """
+        ).replace("\n", "", 1),
+        kwargs_numeric_only,
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Notes"),
+        "A minimum of one period is required for the calculation.\n\n",
+        create_section_header("Examples"),
+        dedent(
+            """
+        >>> s = pd.Series([0, 1, 2, 3])
+
+        >>> s.expanding().sem()
+        0         NaN
+        1    0.707107
+        2    0.707107
+        3    0.745356
+        dtype: float64
+        """
+        ).replace("\n", "", 1),
+        window_method="expanding",
+        aggregation_description="standard error of mean",
+        agg_method="sem",
+    )
+    def sem(self, ddof: int = 1, numeric_only: bool = False):
+        return super().sem(ddof=ddof, numeric_only=numeric_only)
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        kwargs_numeric_only,
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        "scipy.stats.skew : Third moment of a probability density.\n",
+        template_see_also,
+        create_section_header("Notes"),
+        "A minimum of three periods is required for the rolling calculation.\n\n",
+        create_section_header("Examples"),
+        dedent(
+            """\
+        >>> ser = pd.Series([-1, 0, 2, -1, 2], index=['a', 'b', 'c', 'd', 'e'])
+        >>> ser.expanding().skew()
+        a         NaN
+        b         NaN
+        c    0.935220
+        d    1.414214
+        e    0.315356
+        dtype: float64
+        """
+        ),
+        window_method="expanding",
+        aggregation_description="unbiased skewness",
+        agg_method="skew",
+    )
+    def skew(self, numeric_only: bool = False):
+        return super().skew(numeric_only=numeric_only)
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        kwargs_numeric_only,
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        "scipy.stats.kurtosis : Reference SciPy method.\n",
+        template_see_also,
+        create_section_header("Notes"),
+        "A minimum of four periods is required for the calculation.\n\n",
+        create_section_header("Examples"),
+        dedent(
+            """
+        The example below will show a rolling calculation with a window size of
+        four matching the equivalent function call using `scipy.stats`.
+
+        >>> arr = [1, 2, 3, 4, 999]
+        >>> import scipy.stats
+        >>> print(f"{{scipy.stats.kurtosis(arr[:-1], bias=False):.6f}}")
+        -1.200000
+        >>> print(f"{{scipy.stats.kurtosis(arr, bias=False):.6f}}")
+        4.999874
+        >>> s = pd.Series(arr)
+        >>> s.expanding(4).kurt()
+        0         NaN
+        1         NaN
+        2         NaN
+        3   -1.200000
+        4    4.999874
+        dtype: float64
+        """
+        ).replace("\n", "", 1),
+        window_method="expanding",
+        aggregation_description="Fisher's definition of kurtosis without bias",
+        agg_method="kurt",
+    )
+    def kurt(self, numeric_only: bool = False):
+        return super().kurt(numeric_only=numeric_only)
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        dedent(
+            """
+        quantile : float
+            Quantile to compute. 0 <= quantile <= 1.
+
+            .. deprecated:: 2.1.0
+                This will be renamed to 'q' in a future version.
+        interpolation : {{'linear', 'lower', 'higher', 'midpoint', 'nearest'}}
+            This optional parameter specifies the interpolation method to use,
+            when the desired quantile lies between two data points `i` and `j`:
+
+                * linear: `i + (j - i) * fraction`, where `fraction` is the
+                  fractional part of the index surrounded by `i` and `j`.
+                * lower: `i`.
+                * higher: `j`.
+                * nearest: `i` or `j` whichever is nearest.
+                * midpoint: (`i` + `j`) / 2.
+        """
+        ).replace("\n", "", 1),
+        kwargs_numeric_only,
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Examples"),
+        dedent(
+            """\
+        >>> ser = pd.Series([1, 2, 3, 4, 5, 6], index=['a', 'b', 'c', 'd', 'e', 'f'])
+        >>> ser.expanding(min_periods=4).quantile(.25)
+        a     NaN
+        b     NaN
+        c     NaN
+        d    1.75
+        e    2.00
+        f    2.25
+        dtype: float64
+        """
+        ),
+        window_method="expanding",
+        aggregation_description="quantile",
+        agg_method="quantile",
+    )
+    @deprecate_kwarg(old_arg_name="quantile", new_arg_name="q")
+    def quantile(
+        self,
+        q: float,
+        interpolation: QuantileInterpolation = "linear",
+        numeric_only: bool = False,
+    ):
+        return super().quantile(
+            q=q,
+            interpolation=interpolation,
+            numeric_only=numeric_only,
+        )
+
+    @doc(
+        template_header,
+        ".. versionadded:: 1.4.0 \n\n",
+        create_section_header("Parameters"),
+        dedent(
+            """
+        method : {{'average', 'min', 'max'}}, default 'average'
+            How to rank the group of records that have the same value (i.e. ties):
+
+            * average: average rank of the group
+            * min: lowest rank in the group
+            * max: highest rank in the group
+
+        ascending : bool, default True
+            Whether or not the elements should be ranked in ascending order.
+        pct : bool, default False
+            Whether or not to display the returned rankings in percentile
+            form.
+        """
+        ).replace("\n", "", 1),
+        kwargs_numeric_only,
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Examples"),
+        dedent(
+            """
+        >>> s = pd.Series([1, 4, 2, 3, 5, 3])
+        >>> s.expanding().rank()
+        0    1.0
+        1    2.0
+        2    2.0
+        3    3.0
+        4    5.0
+        5    3.5
+        dtype: float64
+
+        >>> s.expanding().rank(method="max")
+        0    1.0
+        1    2.0
+        2    2.0
+        3    3.0
+        4    5.0
+        5    4.0
+        dtype: float64
+
+        >>> s.expanding().rank(method="min")
+        0    1.0
+        1    2.0
+        2    2.0
+        3    3.0
+        4    5.0
+        5    3.0
+        dtype: float64
+        """
+        ).replace("\n", "", 1),
+        window_method="expanding",
+        aggregation_description="rank",
+        agg_method="rank",
+    )
+    def rank(
+        self,
+        method: WindowingRankType = "average",
+        ascending: bool = True,
+        pct: bool = False,
+        numeric_only: bool = False,
+    ):
+        return super().rank(
+            method=method,
+            ascending=ascending,
+            pct=pct,
+            numeric_only=numeric_only,
+        )
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        dedent(
+            """
+        other : Series or DataFrame, optional
+            If not supplied then will default to self and produce pairwise
+            output.
+        pairwise : bool, default None
+            If False then only matching columns between self and other will be
+            used and the output will be a DataFrame.
+            If True then all pairwise combinations will be calculated and the
+            output will be a MultiIndexed DataFrame in the case of DataFrame
+            inputs. In the case of missing elements, only complete pairwise
+            observations will be used.
+        ddof : int, default 1
+            Delta Degrees of Freedom.  The divisor used in calculations
+            is ``N - ddof``, where ``N`` represents the number of elements.
+        """
+        ).replace("\n", "", 1),
+        kwargs_numeric_only,
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Examples"),
+        dedent(
+            """\
+        >>> ser1 = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])
+        >>> ser2 = pd.Series([10, 11, 13, 16], index=['a', 'b', 'c', 'd'])
+        >>> ser1.expanding().cov(ser2)
+        a         NaN
+        b    0.500000
+        c    1.500000
+        d    3.333333
+        dtype: float64
+        """
+        ),
+        window_method="expanding",
+        aggregation_description="sample covariance",
+        agg_method="cov",
+    )
+    def cov(
+        self,
+        other: DataFrame | Series | None = None,
+        pairwise: bool | None = None,
+        ddof: int = 1,
+        numeric_only: bool = False,
+    ):
+        return super().cov(
+            other=other,
+            pairwise=pairwise,
+            ddof=ddof,
+            numeric_only=numeric_only,
+        )
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        dedent(
+            """
+        other : Series or DataFrame, optional
+            If not supplied then will default to self and produce pairwise
+            output.
+        pairwise : bool, default None
+            If False then only matching columns between self and other will be
+            used and the output will be a DataFrame.
+            If True then all pairwise combinations will be calculated and the
+            output will be a MultiIndexed DataFrame in the case of DataFrame
+            inputs. In the case of missing elements, only complete pairwise
+            observations will be used.
+        """
+        ).replace("\n", "", 1),
+        kwargs_numeric_only,
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        dedent(
+            """
+        cov : Similar method to calculate covariance.
+        numpy.corrcoef : NumPy Pearson's correlation calculation.
+        """
+        ).replace("\n", "", 1),
+        template_see_also,
+        create_section_header("Notes"),
+        dedent(
+            """
+        This function uses Pearson's definition of correlation
+        (https://en.wikipedia.org/wiki/Pearson_correlation_coefficient).
+
+        When `other` is not specified, the output will be self correlation (e.g.
+        all 1's), except for :class:`~pandas.DataFrame` inputs with `pairwise`
+        set to `True`.
+
+        Function will return ``NaN`` for correlations of equal valued sequences;
+        this is the result of a 0/0 division error.
+
+        When `pairwise` is set to `False`, only matching columns between `self` and
+        `other` will be used.
+
+        When `pairwise` is set to `True`, the output will be a MultiIndex DataFrame
+        with the original index on the first level, and the `other` DataFrame
+        columns on the second level.
+
+        In the case of missing elements, only complete pairwise observations
+        will be used.\n
+        """
+        ),
+        create_section_header("Examples"),
+        dedent(
+            """\
+        >>> ser1 = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])
+        >>> ser2 = pd.Series([10, 11, 13, 16], index=['a', 'b', 'c', 'd'])
+        >>> ser1.expanding().corr(ser2)
+        a         NaN
+        b    1.000000
+        c    0.981981
+        d    0.975900
+        dtype: float64
+        """
+        ),
+        window_method="expanding",
+        aggregation_description="correlation",
+        agg_method="corr",
+    )
+    def corr(
+        self,
+        other: DataFrame | Series | None = None,
+        pairwise: bool | None = None,
+        ddof: int = 1,
+        numeric_only: bool = False,
+    ):
+        return super().corr(
+            other=other,
+            pairwise=pairwise,
+            ddof=ddof,
+            numeric_only=numeric_only,
+        )
+
+
+class ExpandingGroupby(BaseWindowGroupby, Expanding):
+    """
+    Provide a expanding groupby implementation.
+    """
+
+    _attributes = Expanding._attributes + BaseWindowGroupby._attributes
+
+    def _get_window_indexer(self) -> GroupbyIndexer:
+        """
+        Return an indexer class that will compute the window start and end bounds
+
+        Returns
+        -------
+        GroupbyIndexer
+        """
+        window_indexer = GroupbyIndexer(
+            groupby_indices=self._grouper.indices,
+            window_indexer=ExpandingIndexer,
+        )
+        return window_indexer
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/window/numba_.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/window/numba_.py
new file mode 100644
index 0000000000000000000000000000000000000000..9357945e78c631a3fada24ec3015ca0cf183b99c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/window/numba_.py
@@ -0,0 +1,351 @@
+from __future__ import annotations
+
+import functools
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+)
+
+import numpy as np
+
+from pandas.compat._optional import import_optional_dependency
+
+from pandas.core.util.numba_ import jit_user_function
+
+if TYPE_CHECKING:
+    from pandas._typing import Scalar
+
+
+@functools.cache
+def generate_numba_apply_func(
+    func: Callable[..., Scalar],
+    nopython: bool,
+    nogil: bool,
+    parallel: bool,
+):
+    """
+    Generate a numba jitted apply function specified by values from engine_kwargs.
+
+    1. jit the user's function
+    2. Return a rolling apply function with the jitted function inline
+
+    Configurations specified in engine_kwargs apply to both the user's
+    function _AND_ the rolling apply function.
+
+    Parameters
+    ----------
+    func : function
+        function to be applied to each window and will be JITed
+    nopython : bool
+        nopython to be passed into numba.jit
+    nogil : bool
+        nogil to be passed into numba.jit
+    parallel : bool
+        parallel to be passed into numba.jit
+
+    Returns
+    -------
+    Numba function
+    """
+    numba_func = jit_user_function(func)
+    if TYPE_CHECKING:
+        import numba
+    else:
+        numba = import_optional_dependency("numba")
+
+    @numba.jit(nopython=nopython, nogil=nogil, parallel=parallel)
+    def roll_apply(
+        values: np.ndarray,
+        begin: np.ndarray,
+        end: np.ndarray,
+        minimum_periods: int,
+        *args: Any,
+    ) -> np.ndarray:
+        result = np.empty(len(begin))
+        for i in numba.prange(len(result)):
+            start = begin[i]
+            stop = end[i]
+            window = values[start:stop]
+            count_nan = np.sum(np.isnan(window))
+            if len(window) - count_nan >= minimum_periods:
+                result[i] = numba_func(window, *args)
+            else:
+                result[i] = np.nan
+        return result
+
+    return roll_apply
+
+
+@functools.cache
+def generate_numba_ewm_func(
+    nopython: bool,
+    nogil: bool,
+    parallel: bool,
+    com: float,
+    adjust: bool,
+    ignore_na: bool,
+    deltas: tuple,
+    normalize: bool,
+):
+    """
+    Generate a numba jitted ewm mean or sum function specified by values
+    from engine_kwargs.
+
+    Parameters
+    ----------
+    nopython : bool
+        nopython to be passed into numba.jit
+    nogil : bool
+        nogil to be passed into numba.jit
+    parallel : bool
+        parallel to be passed into numba.jit
+    com : float
+    adjust : bool
+    ignore_na : bool
+    deltas : tuple
+    normalize : bool
+
+    Returns
+    -------
+    Numba function
+    """
+    if TYPE_CHECKING:
+        import numba
+    else:
+        numba = import_optional_dependency("numba")
+
+    @numba.jit(nopython=nopython, nogil=nogil, parallel=parallel)
+    def ewm(
+        values: np.ndarray,
+        begin: np.ndarray,
+        end: np.ndarray,
+        minimum_periods: int,
+    ) -> np.ndarray:
+        result = np.empty(len(values))
+        alpha = 1.0 / (1.0 + com)
+        old_wt_factor = 1.0 - alpha
+        new_wt = 1.0 if adjust else alpha
+
+        for i in numba.prange(len(begin)):
+            start = begin[i]
+            stop = end[i]
+            window = values[start:stop]
+            sub_result = np.empty(len(window))
+
+            weighted = window[0]
+            nobs = int(not np.isnan(weighted))
+            sub_result[0] = weighted if nobs >= minimum_periods else np.nan
+            old_wt = 1.0
+
+            for j in range(1, len(window)):
+                cur = window[j]
+                is_observation = not np.isnan(cur)
+                nobs += is_observation
+                if not np.isnan(weighted):
+                    if is_observation or not ignore_na:
+                        if normalize:
+                            # note that len(deltas) = len(vals) - 1 and deltas[i]
+                            # is to be used in conjunction with vals[i+1]
+                            old_wt *= old_wt_factor ** deltas[start + j - 1]
+                        else:
+                            weighted = old_wt_factor * weighted
+                        if is_observation:
+                            if normalize:
+                                # avoid numerical errors on constant series
+                                if weighted != cur:
+                                    weighted = old_wt * weighted + new_wt * cur
+                                    if normalize:
+                                        weighted = weighted / (old_wt + new_wt)
+                                if adjust:
+                                    old_wt += new_wt
+                                else:
+                                    old_wt = 1.0
+                            else:
+                                weighted += cur
+                elif is_observation:
+                    weighted = cur
+
+                sub_result[j] = weighted if nobs >= minimum_periods else np.nan
+
+            result[start:stop] = sub_result
+
+        return result
+
+    return ewm
+
+
+@functools.cache
+def generate_numba_table_func(
+    func: Callable[..., np.ndarray],
+    nopython: bool,
+    nogil: bool,
+    parallel: bool,
+):
+    """
+    Generate a numba jitted function to apply window calculations table-wise.
+
+    Func will be passed a M window size x N number of columns array, and
+    must return a 1 x N number of columns array. Func is intended to operate
+    row-wise, but the result will be transposed for axis=1.
+
+    1. jit the user's function
+    2. Return a rolling apply function with the jitted function inline
+
+    Parameters
+    ----------
+    func : function
+        function to be applied to each window and will be JITed
+    nopython : bool
+        nopython to be passed into numba.jit
+    nogil : bool
+        nogil to be passed into numba.jit
+    parallel : bool
+        parallel to be passed into numba.jit
+
+    Returns
+    -------
+    Numba function
+    """
+    numba_func = jit_user_function(func)
+    if TYPE_CHECKING:
+        import numba
+    else:
+        numba = import_optional_dependency("numba")
+
+    @numba.jit(nopython=nopython, nogil=nogil, parallel=parallel)
+    def roll_table(
+        values: np.ndarray,
+        begin: np.ndarray,
+        end: np.ndarray,
+        minimum_periods: int,
+        *args: Any,
+    ):
+        result = np.empty((len(begin), values.shape[1]))
+        min_periods_mask = np.empty(result.shape)
+        for i in numba.prange(len(result)):
+            start = begin[i]
+            stop = end[i]
+            window = values[start:stop]
+            count_nan = np.sum(np.isnan(window), axis=0)
+            sub_result = numba_func(window, *args)
+            nan_mask = len(window) - count_nan >= minimum_periods
+            min_periods_mask[i, :] = nan_mask
+            result[i, :] = sub_result
+        result = np.where(min_periods_mask, result, np.nan)
+        return result
+
+    return roll_table
+
+
+# This function will no longer be needed once numba supports
+# axis for all np.nan* agg functions
+# https://github.com/numba/numba/issues/1269
+@functools.cache
+def generate_manual_numpy_nan_agg_with_axis(nan_func):
+    if TYPE_CHECKING:
+        import numba
+    else:
+        numba = import_optional_dependency("numba")
+
+    @numba.jit(nopython=True, nogil=True, parallel=True)
+    def nan_agg_with_axis(table):
+        result = np.empty(table.shape[1])
+        for i in numba.prange(table.shape[1]):
+            partition = table[:, i]
+            result[i] = nan_func(partition)
+        return result
+
+    return nan_agg_with_axis
+
+
+@functools.cache
+def generate_numba_ewm_table_func(
+    nopython: bool,
+    nogil: bool,
+    parallel: bool,
+    com: float,
+    adjust: bool,
+    ignore_na: bool,
+    deltas: tuple,
+    normalize: bool,
+):
+    """
+    Generate a numba jitted ewm mean or sum function applied table wise specified
+    by values from engine_kwargs.
+
+    Parameters
+    ----------
+    nopython : bool
+        nopython to be passed into numba.jit
+    nogil : bool
+        nogil to be passed into numba.jit
+    parallel : bool
+        parallel to be passed into numba.jit
+    com : float
+    adjust : bool
+    ignore_na : bool
+    deltas : tuple
+    normalize: bool
+
+    Returns
+    -------
+    Numba function
+    """
+    if TYPE_CHECKING:
+        import numba
+    else:
+        numba = import_optional_dependency("numba")
+
+    @numba.jit(nopython=nopython, nogil=nogil, parallel=parallel)
+    def ewm_table(
+        values: np.ndarray,
+        begin: np.ndarray,
+        end: np.ndarray,
+        minimum_periods: int,
+    ) -> np.ndarray:
+        alpha = 1.0 / (1.0 + com)
+        old_wt_factor = 1.0 - alpha
+        new_wt = 1.0 if adjust else alpha
+        old_wt = np.ones(values.shape[1])
+
+        result = np.empty(values.shape)
+        weighted = values[0].copy()
+        nobs = (~np.isnan(weighted)).astype(np.int64)
+        result[0] = np.where(nobs >= minimum_periods, weighted, np.nan)
+        for i in range(1, len(values)):
+            cur = values[i]
+            is_observations = ~np.isnan(cur)
+            nobs += is_observations.astype(np.int64)
+            for j in numba.prange(len(cur)):
+                if not np.isnan(weighted[j]):
+                    if is_observations[j] or not ignore_na:
+                        if normalize:
+                            # note that len(deltas) = len(vals) - 1 and deltas[i]
+                            # is to be used in conjunction with vals[i+1]
+                            old_wt[j] *= old_wt_factor ** deltas[i - 1]
+                        else:
+                            weighted[j] = old_wt_factor * weighted[j]
+                        if is_observations[j]:
+                            if normalize:
+                                # avoid numerical errors on constant series
+                                if weighted[j] != cur[j]:
+                                    weighted[j] = (
+                                        old_wt[j] * weighted[j] + new_wt * cur[j]
+                                    )
+                                    if normalize:
+                                        weighted[j] = weighted[j] / (old_wt[j] + new_wt)
+                                if adjust:
+                                    old_wt[j] += new_wt
+                                else:
+                                    old_wt[j] = 1.0
+                            else:
+                                weighted[j] += cur[j]
+                elif is_observations[j]:
+                    weighted[j] = cur[j]
+
+            result[i] = np.where(nobs >= minimum_periods, weighted, np.nan)
+
+        return result
+
+    return ewm_table
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/window/online.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/window/online.py
new file mode 100644
index 0000000000000000000000000000000000000000..29d1f740e021fd30d3bfc7d72a5e6baefe62ac4c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/window/online.py
@@ -0,0 +1,118 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import numpy as np
+
+from pandas.compat._optional import import_optional_dependency
+
+
+def generate_online_numba_ewma_func(
+    nopython: bool,
+    nogil: bool,
+    parallel: bool,
+):
+    """
+    Generate a numba jitted groupby ewma function specified by values
+    from engine_kwargs.
+
+    Parameters
+    ----------
+    nopython : bool
+        nopython to be passed into numba.jit
+    nogil : bool
+        nogil to be passed into numba.jit
+    parallel : bool
+        parallel to be passed into numba.jit
+
+    Returns
+    -------
+    Numba function
+    """
+    if TYPE_CHECKING:
+        import numba
+    else:
+        numba = import_optional_dependency("numba")
+
+    @numba.jit(nopython=nopython, nogil=nogil, parallel=parallel)
+    def online_ewma(
+        values: np.ndarray,
+        deltas: np.ndarray,
+        minimum_periods: int,
+        old_wt_factor: float,
+        new_wt: float,
+        old_wt: np.ndarray,
+        adjust: bool,
+        ignore_na: bool,
+    ):
+        """
+        Compute online exponentially weighted mean per column over 2D values.
+
+        Takes the first observation as is, then computes the subsequent
+        exponentially weighted mean accounting minimum periods.
+        """
+        result = np.empty(values.shape)
+        weighted_avg = values[0].copy()
+        nobs = (~np.isnan(weighted_avg)).astype(np.int64)
+        result[0] = np.where(nobs >= minimum_periods, weighted_avg, np.nan)
+
+        for i in range(1, len(values)):
+            cur = values[i]
+            is_observations = ~np.isnan(cur)
+            nobs += is_observations.astype(np.int64)
+            for j in numba.prange(len(cur)):
+                if not np.isnan(weighted_avg[j]):
+                    if is_observations[j] or not ignore_na:
+                        # note that len(deltas) = len(vals) - 1 and deltas[i] is to be
+                        # used in conjunction with vals[i+1]
+                        old_wt[j] *= old_wt_factor ** deltas[j - 1]
+                        if is_observations[j]:
+                            # avoid numerical errors on constant series
+                            if weighted_avg[j] != cur[j]:
+                                weighted_avg[j] = (
+                                    (old_wt[j] * weighted_avg[j]) + (new_wt * cur[j])
+                                ) / (old_wt[j] + new_wt)
+                            if adjust:
+                                old_wt[j] += new_wt
+                            else:
+                                old_wt[j] = 1.0
+                elif is_observations[j]:
+                    weighted_avg[j] = cur[j]
+
+            result[i] = np.where(nobs >= minimum_periods, weighted_avg, np.nan)
+
+        return result, old_wt
+
+    return online_ewma
+
+
+class EWMMeanState:
+    def __init__(self, com, adjust, ignore_na, axis, shape) -> None:
+        alpha = 1.0 / (1.0 + com)
+        self.axis = axis
+        self.shape = shape
+        self.adjust = adjust
+        self.ignore_na = ignore_na
+        self.new_wt = 1.0 if adjust else alpha
+        self.old_wt_factor = 1.0 - alpha
+        self.old_wt = np.ones(self.shape[self.axis - 1])
+        self.last_ewm = None
+
+    def run_ewm(self, weighted_avg, deltas, min_periods, ewm_func):
+        result, old_wt = ewm_func(
+            weighted_avg,
+            deltas,
+            min_periods,
+            self.old_wt_factor,
+            self.new_wt,
+            self.old_wt,
+            self.adjust,
+            self.ignore_na,
+        )
+        self.old_wt = old_wt
+        self.last_ewm = result[-1]
+        return result
+
+    def reset(self) -> None:
+        self.old_wt = np.ones(self.shape[self.axis - 1])
+        self.last_ewm = None
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/window/rolling.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/window/rolling.py
new file mode 100644
index 0000000000000000000000000000000000000000..68cec16ec9eca8b34fadd9e67221219ebd7fe736
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/core/window/rolling.py
@@ -0,0 +1,2930 @@
+"""
+Provide a generic structure to support window functions,
+similar to how we have a Groupby object.
+"""
+from __future__ import annotations
+
+import copy
+from datetime import timedelta
+from functools import partial
+import inspect
+from textwrap import dedent
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Literal,
+)
+
+import numpy as np
+
+from pandas._libs.tslibs import (
+    BaseOffset,
+    Timedelta,
+    to_offset,
+)
+import pandas._libs.window.aggregations as window_aggregations
+from pandas.compat._optional import import_optional_dependency
+from pandas.errors import DataError
+from pandas.util._decorators import (
+    deprecate_kwarg,
+    doc,
+)
+
+from pandas.core.dtypes.common import (
+    ensure_float64,
+    is_bool,
+    is_integer,
+    is_numeric_dtype,
+    needs_i8_conversion,
+)
+from pandas.core.dtypes.dtypes import ArrowDtype
+from pandas.core.dtypes.generic import (
+    ABCDataFrame,
+    ABCSeries,
+)
+from pandas.core.dtypes.missing import notna
+
+from pandas.core._numba import executor
+from pandas.core.algorithms import factorize
+from pandas.core.apply import ResamplerWindowApply
+from pandas.core.arrays import ExtensionArray
+from pandas.core.base import SelectionMixin
+import pandas.core.common as com
+from pandas.core.indexers.objects import (
+    BaseIndexer,
+    FixedWindowIndexer,
+    GroupbyIndexer,
+    VariableWindowIndexer,
+)
+from pandas.core.indexes.api import (
+    DatetimeIndex,
+    Index,
+    MultiIndex,
+    PeriodIndex,
+    TimedeltaIndex,
+)
+from pandas.core.reshape.concat import concat
+from pandas.core.util.numba_ import (
+    get_jit_arguments,
+    maybe_use_numba,
+)
+from pandas.core.window.common import (
+    flex_binary_moment,
+    zsqrt,
+)
+from pandas.core.window.doc import (
+    _shared_docs,
+    create_section_header,
+    kwargs_numeric_only,
+    kwargs_scipy,
+    numba_notes,
+    template_header,
+    template_returns,
+    template_see_also,
+    window_agg_numba_parameters,
+    window_apply_parameters,
+)
+from pandas.core.window.numba_ import (
+    generate_manual_numpy_nan_agg_with_axis,
+    generate_numba_apply_func,
+    generate_numba_table_func,
+)
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Hashable,
+        Iterator,
+        Sized,
+    )
+
+    from pandas._typing import (
+        ArrayLike,
+        Axis,
+        NDFrameT,
+        QuantileInterpolation,
+        WindowingRankType,
+        npt,
+    )
+
+    from pandas import (
+        DataFrame,
+        Series,
+    )
+    from pandas.core.generic import NDFrame
+    from pandas.core.groupby.ops import BaseGrouper
+
+from pandas.core.arrays.datetimelike import dtype_to_unit
+
+
+class BaseWindow(SelectionMixin):
+    """Provides utilities for performing windowing operations."""
+
+    _attributes: list[str] = []
+    exclusions: frozenset[Hashable] = frozenset()
+    _on: Index
+
+    def __init__(
+        self,
+        obj: NDFrame,
+        window=None,
+        min_periods: int | None = None,
+        center: bool | None = False,
+        win_type: str | None = None,
+        axis: Axis = 0,
+        on: str | Index | None = None,
+        closed: str | None = None,
+        step: int | None = None,
+        method: str = "single",
+        *,
+        selection=None,
+    ) -> None:
+        self.obj = obj
+        self.on = on
+        self.closed = closed
+        self.step = step
+        self.window = window
+        self.min_periods = min_periods
+        self.center = center
+        self.win_type = win_type
+        self.axis = obj._get_axis_number(axis) if axis is not None else None
+        self.method = method
+        self._win_freq_i8: int | None = None
+        if self.on is None:
+            if self.axis == 0:
+                self._on = self.obj.index
+            else:
+                # i.e. self.axis == 1
+                self._on = self.obj.columns
+        elif isinstance(self.on, Index):
+            self._on = self.on
+        elif isinstance(self.obj, ABCDataFrame) and self.on in self.obj.columns:
+            self._on = Index(self.obj[self.on])
+        else:
+            raise ValueError(
+                f"invalid on specified as {self.on}, "
+                "must be a column (of DataFrame), an Index or None"
+            )
+
+        self._selection = selection
+        self._validate()
+
+    def _validate(self) -> None:
+        if self.center is not None and not is_bool(self.center):
+            raise ValueError("center must be a boolean")
+        if self.min_periods is not None:
+            if not is_integer(self.min_periods):
+                raise ValueError("min_periods must be an integer")
+            if self.min_periods < 0:
+                raise ValueError("min_periods must be >= 0")
+            if is_integer(self.window) and self.min_periods > self.window:
+                raise ValueError(
+                    f"min_periods {self.min_periods} must be <= window {self.window}"
+                )
+        if self.closed is not None and self.closed not in [
+            "right",
+            "both",
+            "left",
+            "neither",
+        ]:
+            raise ValueError("closed must be 'right', 'left', 'both' or 'neither'")
+        if not isinstance(self.obj, (ABCSeries, ABCDataFrame)):
+            raise TypeError(f"invalid type: {type(self)}")
+        if isinstance(self.window, BaseIndexer):
+            # Validate that the passed BaseIndexer subclass has
+            # a get_window_bounds with the correct signature.
+            get_window_bounds_signature = inspect.signature(
+                self.window.get_window_bounds
+            ).parameters.keys()
+            expected_signature = inspect.signature(
+                BaseIndexer().get_window_bounds
+            ).parameters.keys()
+            if get_window_bounds_signature != expected_signature:
+                raise ValueError(
+                    f"{type(self.window).__name__} does not implement "
+                    f"the correct signature for get_window_bounds"
+                )
+        if self.method not in ["table", "single"]:
+            raise ValueError("method must be 'table' or 'single")
+        if self.step is not None:
+            if not is_integer(self.step):
+                raise ValueError("step must be an integer")
+            if self.step < 0:
+                raise ValueError("step must be >= 0")
+
+    def _check_window_bounds(
+        self, start: np.ndarray, end: np.ndarray, num_vals: int
+    ) -> None:
+        if len(start) != len(end):
+            raise ValueError(
+                f"start ({len(start)}) and end ({len(end)}) bounds must be the "
+                f"same length"
+            )
+        if len(start) != (num_vals + (self.step or 1) - 1) // (self.step or 1):
+            raise ValueError(
+                f"start and end bounds ({len(start)}) must be the same length "
+                f"as the object ({num_vals}) divided by the step ({self.step}) "
+                f"if given and rounded up"
+            )
+
+    def _slice_axis_for_step(self, index: Index, result: Sized | None = None) -> Index:
+        """
+        Slices the index for a given result and the preset step.
+        """
+        return (
+            index
+            if result is None or len(result) == len(index)
+            else index[:: self.step]
+        )
+
+    def _validate_numeric_only(self, name: str, numeric_only: bool) -> None:
+        """
+        Validate numeric_only argument, raising if invalid for the input.
+
+        Parameters
+        ----------
+        name : str
+            Name of the operator (kernel).
+        numeric_only : bool
+            Value passed by user.
+        """
+        if (
+            self._selected_obj.ndim == 1
+            and numeric_only
+            and not is_numeric_dtype(self._selected_obj.dtype)
+        ):
+            raise NotImplementedError(
+                f"{type(self).__name__}.{name} does not implement numeric_only"
+            )
+
+    def _make_numeric_only(self, obj: NDFrameT) -> NDFrameT:
+        """Subset DataFrame to numeric columns.
+
+        Parameters
+        ----------
+        obj : DataFrame
+
+        Returns
+        -------
+        obj subset to numeric-only columns.
+        """
+        result = obj.select_dtypes(include=["number"], exclude=["timedelta"])
+        return result
+
+    def _create_data(self, obj: NDFrameT, numeric_only: bool = False) -> NDFrameT:
+        """
+        Split data into blocks & return conformed data.
+        """
+        # filter out the on from the object
+        if self.on is not None and not isinstance(self.on, Index) and obj.ndim == 2:
+            obj = obj.reindex(columns=obj.columns.difference([self.on]), copy=False)
+        if obj.ndim > 1 and (numeric_only or self.axis == 1):
+            # GH: 20649 in case of mixed dtype and axis=1 we have to convert everything
+            # to float to calculate the complete row at once. We exclude all non-numeric
+            # dtypes.
+            obj = self._make_numeric_only(obj)
+        if self.axis == 1:
+            obj = obj.astype("float64", copy=False)
+            obj._mgr = obj._mgr.consolidate()
+        return obj
+
+    def _gotitem(self, key, ndim, subset=None):
+        """
+        Sub-classes to define. Return a sliced object.
+
+        Parameters
+        ----------
+        key : str / list of selections
+        ndim : {1, 2}
+            requested ndim of result
+        subset : object, default None
+            subset to act on
+        """
+        # create a new object to prevent aliasing
+        if subset is None:
+            subset = self.obj
+
+        # we need to make a shallow copy of ourselves
+        # with the same groupby
+        kwargs = {attr: getattr(self, attr) for attr in self._attributes}
+
+        selection = self._infer_selection(key, subset)
+        new_win = type(self)(subset, selection=selection, **kwargs)
+        return new_win
+
+    def __getattr__(self, attr: str):
+        if attr in self._internal_names_set:
+            return object.__getattribute__(self, attr)
+        if attr in self.obj:
+            return self[attr]
+
+        raise AttributeError(
+            f"'{type(self).__name__}' object has no attribute '{attr}'"
+        )
+
+    def _dir_additions(self):
+        return self.obj._dir_additions()
+
+    def __repr__(self) -> str:
+        """
+        Provide a nice str repr of our rolling object.
+        """
+        attrs_list = (
+            f"{attr_name}={getattr(self, attr_name)}"
+            for attr_name in self._attributes
+            if getattr(self, attr_name, None) is not None and attr_name[0] != "_"
+        )
+        attrs = ",".join(attrs_list)
+        return f"{type(self).__name__} [{attrs}]"
+
+    def __iter__(self) -> Iterator:
+        obj = self._selected_obj.set_axis(self._on)
+        obj = self._create_data(obj)
+        indexer = self._get_window_indexer()
+
+        start, end = indexer.get_window_bounds(
+            num_values=len(obj),
+            min_periods=self.min_periods,
+            center=self.center,
+            closed=self.closed,
+            step=self.step,
+        )
+        self._check_window_bounds(start, end, len(obj))
+
+        for s, e in zip(start, end):
+            result = obj.iloc[slice(s, e)]
+            yield result
+
+    def _prep_values(self, values: ArrayLike) -> np.ndarray:
+        """Convert input to numpy arrays for Cython routines"""
+        if needs_i8_conversion(values.dtype):
+            raise NotImplementedError(
+                f"ops for {type(self).__name__} for this "
+                f"dtype {values.dtype} are not implemented"
+            )
+        # GH #12373 : rolling functions error on float32 data
+        # make sure the data is coerced to float64
+        try:
+            if isinstance(values, ExtensionArray):
+                values = values.to_numpy(np.float64, na_value=np.nan)
+            else:
+                values = ensure_float64(values)
+        except (ValueError, TypeError) as err:
+            raise TypeError(f"cannot handle this type -> {values.dtype}") from err
+
+        # Convert inf to nan for C funcs
+        inf = np.isinf(values)
+        if inf.any():
+            values = np.where(inf, np.nan, values)
+
+        return values
+
+    def _insert_on_column(self, result: DataFrame, obj: DataFrame) -> None:
+        # if we have an 'on' column we want to put it back into
+        # the results in the same location
+        from pandas import Series
+
+        if self.on is not None and not self._on.equals(obj.index):
+            name = self._on.name
+            extra_col = Series(self._on, index=self.obj.index, name=name, copy=False)
+            if name in result.columns:
+                # TODO: sure we want to overwrite results?
+                result[name] = extra_col
+            elif name in result.index.names:
+                pass
+            elif name in self._selected_obj.columns:
+                # insert in the same location as we had in _selected_obj
+                old_cols = self._selected_obj.columns
+                new_cols = result.columns
+                old_loc = old_cols.get_loc(name)
+                overlap = new_cols.intersection(old_cols[:old_loc])
+                new_loc = len(overlap)
+                result.insert(new_loc, name, extra_col)
+            else:
+                # insert at the end
+                result[name] = extra_col
+
+    @property
+    def _index_array(self) -> npt.NDArray[np.int64] | None:
+        # TODO: why do we get here with e.g. MultiIndex?
+        if isinstance(self._on, (PeriodIndex, DatetimeIndex, TimedeltaIndex)):
+            return self._on.asi8
+        elif isinstance(self._on.dtype, ArrowDtype) and self._on.dtype.kind in "mM":
+            return self._on.to_numpy(dtype=np.int64)
+        return None
+
+    def _resolve_output(self, out: DataFrame, obj: DataFrame) -> DataFrame:
+        """Validate and finalize result."""
+        if out.shape[1] == 0 and obj.shape[1] > 0:
+            raise DataError("No numeric types to aggregate")
+        if out.shape[1] == 0:
+            return obj.astype("float64")
+
+        self._insert_on_column(out, obj)
+        return out
+
+    def _get_window_indexer(self) -> BaseIndexer:
+        """
+        Return an indexer class that will compute the window start and end bounds
+        """
+        if isinstance(self.window, BaseIndexer):
+            return self.window
+        if self._win_freq_i8 is not None:
+            return VariableWindowIndexer(
+                index_array=self._index_array,
+                window_size=self._win_freq_i8,
+                center=self.center,
+            )
+        return FixedWindowIndexer(window_size=self.window)
+
+    def _apply_series(
+        self, homogeneous_func: Callable[..., ArrayLike], name: str | None = None
+    ) -> Series:
+        """
+        Series version of _apply_columnwise
+        """
+        obj = self._create_data(self._selected_obj)
+
+        if name == "count":
+            # GH 12541: Special case for count where we support date-like types
+            obj = notna(obj).astype(int)
+        try:
+            values = self._prep_values(obj._values)
+        except (TypeError, NotImplementedError) as err:
+            raise DataError("No numeric types to aggregate") from err
+
+        result = homogeneous_func(values)
+        index = self._slice_axis_for_step(obj.index, result)
+        return obj._constructor(result, index=index, name=obj.name)
+
+    def _apply_columnwise(
+        self,
+        homogeneous_func: Callable[..., ArrayLike],
+        name: str,
+        numeric_only: bool = False,
+    ) -> DataFrame | Series:
+        """
+        Apply the given function to the DataFrame broken down into homogeneous
+        sub-frames.
+        """
+        self._validate_numeric_only(name, numeric_only)
+        if self._selected_obj.ndim == 1:
+            return self._apply_series(homogeneous_func, name)
+
+        obj = self._create_data(self._selected_obj, numeric_only)
+        if name == "count":
+            # GH 12541: Special case for count where we support date-like types
+            obj = notna(obj).astype(int)
+            obj._mgr = obj._mgr.consolidate()
+
+        if self.axis == 1:
+            obj = obj.T
+
+        taker = []
+        res_values = []
+        for i, arr in enumerate(obj._iter_column_arrays()):
+            # GH#42736 operate column-wise instead of block-wise
+            # As of 2.0, hfunc will raise for nuisance columns
+            try:
+                arr = self._prep_values(arr)
+            except (TypeError, NotImplementedError) as err:
+                raise DataError(
+                    f"Cannot aggregate non-numeric type: {arr.dtype}"
+                ) from err
+            res = homogeneous_func(arr)
+            res_values.append(res)
+            taker.append(i)
+
+        index = self._slice_axis_for_step(
+            obj.index, res_values[0] if len(res_values) > 0 else None
+        )
+        df = type(obj)._from_arrays(
+            res_values,
+            index=index,
+            columns=obj.columns.take(taker),
+            verify_integrity=False,
+        )
+
+        if self.axis == 1:
+            df = df.T
+
+        return self._resolve_output(df, obj)
+
+    def _apply_tablewise(
+        self,
+        homogeneous_func: Callable[..., ArrayLike],
+        name: str | None = None,
+        numeric_only: bool = False,
+    ) -> DataFrame | Series:
+        """
+        Apply the given function to the DataFrame across the entire object
+        """
+        if self._selected_obj.ndim == 1:
+            raise ValueError("method='table' not applicable for Series objects.")
+        obj = self._create_data(self._selected_obj, numeric_only)
+        values = self._prep_values(obj.to_numpy())
+        values = values.T if self.axis == 1 else values
+        result = homogeneous_func(values)
+        result = result.T if self.axis == 1 else result
+        index = self._slice_axis_for_step(obj.index, result)
+        columns = (
+            obj.columns
+            if result.shape[1] == len(obj.columns)
+            else obj.columns[:: self.step]
+        )
+        out = obj._constructor(result, index=index, columns=columns)
+
+        return self._resolve_output(out, obj)
+
+    def _apply_pairwise(
+        self,
+        target: DataFrame | Series,
+        other: DataFrame | Series | None,
+        pairwise: bool | None,
+        func: Callable[[DataFrame | Series, DataFrame | Series], DataFrame | Series],
+        numeric_only: bool,
+    ) -> DataFrame | Series:
+        """
+        Apply the given pairwise function given 2 pandas objects (DataFrame/Series)
+        """
+        target = self._create_data(target, numeric_only)
+        if other is None:
+            other = target
+            # only default unset
+            pairwise = True if pairwise is None else pairwise
+        elif not isinstance(other, (ABCDataFrame, ABCSeries)):
+            raise ValueError("other must be a DataFrame or Series")
+        elif other.ndim == 2 and numeric_only:
+            other = self._make_numeric_only(other)
+
+        return flex_binary_moment(target, other, func, pairwise=bool(pairwise))
+
+    def _apply(
+        self,
+        func: Callable[..., Any],
+        name: str,
+        numeric_only: bool = False,
+        numba_args: tuple[Any, ...] = (),
+        **kwargs,
+    ):
+        """
+        Rolling statistical measure using supplied function.
+
+        Designed to be used with passed-in Cython array-based functions.
+
+        Parameters
+        ----------
+        func : callable function to apply
+        name : str,
+        numba_args : tuple
+            args to be passed when func is a numba func
+        **kwargs
+            additional arguments for rolling function and window function
+
+        Returns
+        -------
+        y : type of input
+        """
+        window_indexer = self._get_window_indexer()
+        min_periods = (
+            self.min_periods
+            if self.min_periods is not None
+            else window_indexer.window_size
+        )
+
+        def homogeneous_func(values: np.ndarray):
+            # calculation function
+
+            if values.size == 0:
+                return values.copy()
+
+            def calc(x):
+                start, end = window_indexer.get_window_bounds(
+                    num_values=len(x),
+                    min_periods=min_periods,
+                    center=self.center,
+                    closed=self.closed,
+                    step=self.step,
+                )
+                self._check_window_bounds(start, end, len(x))
+
+                return func(x, start, end, min_periods, *numba_args)
+
+            with np.errstate(all="ignore"):
+                result = calc(values)
+
+            return result
+
+        if self.method == "single":
+            return self._apply_columnwise(homogeneous_func, name, numeric_only)
+        else:
+            return self._apply_tablewise(homogeneous_func, name, numeric_only)
+
+    def _numba_apply(
+        self,
+        func: Callable[..., Any],
+        engine_kwargs: dict[str, bool] | None = None,
+        **func_kwargs,
+    ):
+        window_indexer = self._get_window_indexer()
+        min_periods = (
+            self.min_periods
+            if self.min_periods is not None
+            else window_indexer.window_size
+        )
+        obj = self._create_data(self._selected_obj)
+        if self.axis == 1:
+            obj = obj.T
+        values = self._prep_values(obj.to_numpy())
+        if values.ndim == 1:
+            values = values.reshape(-1, 1)
+        start, end = window_indexer.get_window_bounds(
+            num_values=len(values),
+            min_periods=min_periods,
+            center=self.center,
+            closed=self.closed,
+            step=self.step,
+        )
+        self._check_window_bounds(start, end, len(values))
+        # For now, map everything to float to match the Cython impl
+        # even though it is wrong
+        # TODO: Could preserve correct dtypes in future
+        # xref #53214
+        dtype_mapping = executor.float_dtype_mapping
+        aggregator = executor.generate_shared_aggregator(
+            func,
+            dtype_mapping,
+            is_grouped_kernel=False,
+            **get_jit_arguments(engine_kwargs),
+        )
+        result = aggregator(
+            values.T, start=start, end=end, min_periods=min_periods, **func_kwargs
+        ).T
+        result = result.T if self.axis == 1 else result
+        index = self._slice_axis_for_step(obj.index, result)
+        if obj.ndim == 1:
+            result = result.squeeze()
+            out = obj._constructor(result, index=index, name=obj.name)
+            return out
+        else:
+            columns = self._slice_axis_for_step(obj.columns, result.T)
+            out = obj._constructor(result, index=index, columns=columns)
+            return self._resolve_output(out, obj)
+
+    def aggregate(self, func, *args, **kwargs):
+        result = ResamplerWindowApply(self, func, args=args, kwargs=kwargs).agg()
+        if result is None:
+            return self.apply(func, raw=False, args=args, kwargs=kwargs)
+        return result
+
+    agg = aggregate
+
+
+class BaseWindowGroupby(BaseWindow):
+    """
+    Provide the groupby windowing facilities.
+    """
+
+    _grouper: BaseGrouper
+    _as_index: bool
+    _attributes: list[str] = ["_grouper"]
+
+    def __init__(
+        self,
+        obj: DataFrame | Series,
+        *args,
+        _grouper: BaseGrouper,
+        _as_index: bool = True,
+        **kwargs,
+    ) -> None:
+        from pandas.core.groupby.ops import BaseGrouper
+
+        if not isinstance(_grouper, BaseGrouper):
+            raise ValueError("Must pass a BaseGrouper object.")
+        self._grouper = _grouper
+        self._as_index = _as_index
+        # GH 32262: It's convention to keep the grouping column in
+        # groupby., but unexpected to users in
+        # groupby.rolling.
+        obj = obj.drop(columns=self._grouper.names, errors="ignore")
+        # GH 15354
+        if kwargs.get("step") is not None:
+            raise NotImplementedError("step not implemented for groupby")
+        super().__init__(obj, *args, **kwargs)
+
+    def _apply(
+        self,
+        func: Callable[..., Any],
+        name: str,
+        numeric_only: bool = False,
+        numba_args: tuple[Any, ...] = (),
+        **kwargs,
+    ) -> DataFrame | Series:
+        result = super()._apply(
+            func,
+            name,
+            numeric_only,
+            numba_args,
+            **kwargs,
+        )
+        # Reconstruct the resulting MultiIndex
+        # 1st set of levels = group by labels
+        # 2nd set of levels = original DataFrame/Series index
+        grouped_object_index = self.obj.index
+        grouped_index_name = [*grouped_object_index.names]
+        groupby_keys = copy.copy(self._grouper.names)
+        result_index_names = groupby_keys + grouped_index_name
+
+        drop_columns = [
+            key
+            for key in self._grouper.names
+            if key not in self.obj.index.names or key is None
+        ]
+
+        if len(drop_columns) != len(groupby_keys):
+            # Our result will have still kept the column in the result
+            result = result.drop(columns=drop_columns, errors="ignore")
+
+        codes = self._grouper.codes
+        levels = copy.copy(self._grouper.levels)
+
+        group_indices = self._grouper.indices.values()
+        if group_indices:
+            indexer = np.concatenate(list(group_indices))
+        else:
+            indexer = np.array([], dtype=np.intp)
+        codes = [c.take(indexer) for c in codes]
+
+        # if the index of the original dataframe needs to be preserved, append
+        # this index (but reordered) to the codes/levels from the groupby
+        if grouped_object_index is not None:
+            idx = grouped_object_index.take(indexer)
+            if not isinstance(idx, MultiIndex):
+                idx = MultiIndex.from_arrays([idx])
+            codes.extend(list(idx.codes))
+            levels.extend(list(idx.levels))
+
+        result_index = MultiIndex(
+            levels, codes, names=result_index_names, verify_integrity=False
+        )
+
+        result.index = result_index
+        if not self._as_index:
+            result = result.reset_index(level=list(range(len(groupby_keys))))
+        return result
+
+    def _apply_pairwise(
+        self,
+        target: DataFrame | Series,
+        other: DataFrame | Series | None,
+        pairwise: bool | None,
+        func: Callable[[DataFrame | Series, DataFrame | Series], DataFrame | Series],
+        numeric_only: bool,
+    ) -> DataFrame | Series:
+        """
+        Apply the given pairwise function given 2 pandas objects (DataFrame/Series)
+        """
+        # Manually drop the grouping column first
+        target = target.drop(columns=self._grouper.names, errors="ignore")
+        result = super()._apply_pairwise(target, other, pairwise, func, numeric_only)
+        # 1) Determine the levels + codes of the groupby levels
+        if other is not None and not all(
+            len(group) == len(other) for group in self._grouper.indices.values()
+        ):
+            # GH 42915
+            # len(other) != len(any group), so must reindex (expand) the result
+            # from flex_binary_moment to a "transform"-like result
+            # per groupby combination
+            old_result_len = len(result)
+            result = concat(
+                [
+                    result.take(gb_indices).reindex(result.index)
+                    for gb_indices in self._grouper.indices.values()
+                ]
+            )
+
+            gb_pairs = (
+                com.maybe_make_list(pair) for pair in self._grouper.indices.keys()
+            )
+            groupby_codes = []
+            groupby_levels = []
+            # e.g. [[1, 2], [4, 5]] as [[1, 4], [2, 5]]
+            for gb_level_pair in map(list, zip(*gb_pairs)):
+                labels = np.repeat(np.array(gb_level_pair), old_result_len)
+                codes, levels = factorize(labels)
+                groupby_codes.append(codes)
+                groupby_levels.append(levels)
+        else:
+            # pairwise=True or len(other) == len(each group), so repeat
+            # the groupby labels by the number of columns in the original object
+            groupby_codes = self._grouper.codes
+            # error: Incompatible types in assignment (expression has type
+            # "List[Index]", variable has type "List[Union[ndarray, Index]]")
+            groupby_levels = self._grouper.levels  # type: ignore[assignment]
+
+            group_indices = self._grouper.indices.values()
+            if group_indices:
+                indexer = np.concatenate(list(group_indices))
+            else:
+                indexer = np.array([], dtype=np.intp)
+
+            if target.ndim == 1:
+                repeat_by = 1
+            else:
+                repeat_by = len(target.columns)
+            groupby_codes = [
+                np.repeat(c.take(indexer), repeat_by) for c in groupby_codes
+            ]
+        # 2) Determine the levels + codes of the result from super()._apply_pairwise
+        if isinstance(result.index, MultiIndex):
+            result_codes = list(result.index.codes)
+            result_levels = list(result.index.levels)
+            result_names = list(result.index.names)
+        else:
+            idx_codes, idx_levels = factorize(result.index)
+            result_codes = [idx_codes]
+            result_levels = [idx_levels]
+            result_names = [result.index.name]
+
+        # 3) Create the resulting index by combining 1) + 2)
+        result_codes = groupby_codes + result_codes
+        result_levels = groupby_levels + result_levels
+        result_names = self._grouper.names + result_names
+
+        result_index = MultiIndex(
+            result_levels, result_codes, names=result_names, verify_integrity=False
+        )
+        result.index = result_index
+        return result
+
+    def _create_data(self, obj: NDFrameT, numeric_only: bool = False) -> NDFrameT:
+        """
+        Split data into blocks & return conformed data.
+        """
+        # Ensure the object we're rolling over is monotonically sorted relative
+        # to the groups
+        # GH 36197
+        if not obj.empty:
+            groupby_order = np.concatenate(list(self._grouper.indices.values())).astype(
+                np.int64
+            )
+            obj = obj.take(groupby_order)
+        return super()._create_data(obj, numeric_only)
+
+    def _gotitem(self, key, ndim, subset=None):
+        # we are setting the index on the actual object
+        # here so our index is carried through to the selected obj
+        # when we do the splitting for the groupby
+        if self.on is not None:
+            # GH 43355
+            subset = self.obj.set_index(self._on)
+        return super()._gotitem(key, ndim, subset=subset)
+
+
+class Window(BaseWindow):
+    """
+    Provide rolling window calculations.
+
+    Parameters
+    ----------
+    window : int, timedelta, str, offset, or BaseIndexer subclass
+        Size of the moving window.
+
+        If an integer, the fixed number of observations used for
+        each window.
+
+        If a timedelta, str, or offset, the time period of each window. Each
+        window will be a variable sized based on the observations included in
+        the time-period. This is only valid for datetimelike indexes.
+        To learn more about the offsets & frequency strings, please see `this link
+        `__.
+
+        If a BaseIndexer subclass, the window boundaries
+        based on the defined ``get_window_bounds`` method. Additional rolling
+        keyword arguments, namely ``min_periods``, ``center``, ``closed`` and
+        ``step`` will be passed to ``get_window_bounds``.
+
+    min_periods : int, default None
+        Minimum number of observations in window required to have a value;
+        otherwise, result is ``np.nan``.
+
+        For a window that is specified by an offset, ``min_periods`` will default to 1.
+
+        For a window that is specified by an integer, ``min_periods`` will default
+        to the size of the window.
+
+    center : bool, default False
+        If False, set the window labels as the right edge of the window index.
+
+        If True, set the window labels as the center of the window index.
+
+    win_type : str, default None
+        If ``None``, all points are evenly weighted.
+
+        If a string, it must be a valid `scipy.signal window function
+        `__.
+
+        Certain Scipy window types require additional parameters to be passed
+        in the aggregation function. The additional parameters must match
+        the keywords specified in the Scipy window type method signature.
+
+    on : str, optional
+        For a DataFrame, a column label or Index level on which
+        to calculate the rolling window, rather than the DataFrame's index.
+
+        Provided integer column is ignored and excluded from result since
+        an integer index is not used to calculate the rolling window.
+
+    axis : int or str, default 0
+        If ``0`` or ``'index'``, roll across the rows.
+
+        If ``1`` or ``'columns'``, roll across the columns.
+
+        For `Series` this parameter is unused and defaults to 0.
+
+        .. deprecated:: 2.1.0
+
+            The axis keyword is deprecated. For ``axis=1``,
+            transpose the DataFrame first instead.
+
+    closed : str, default None
+        If ``'right'``, the first point in the window is excluded from calculations.
+
+        If ``'left'``, the last point in the window is excluded from calculations.
+
+        If ``'both'``, the no points in the window are excluded from calculations.
+
+        If ``'neither'``, the first and last points in the window are excluded
+        from calculations.
+
+        Default ``None`` (``'right'``).
+
+    step : int, default None
+
+        .. versionadded:: 1.5.0
+
+        Evaluate the window at every ``step`` result, equivalent to slicing as
+        ``[::step]``. ``window`` must be an integer. Using a step argument other
+        than None or 1 will produce a result with a different shape than the input.
+
+    method : str {'single', 'table'}, default 'single'
+
+        .. versionadded:: 1.3.0
+
+        Execute the rolling operation per single column or row (``'single'``)
+        or over the entire object (``'table'``).
+
+        This argument is only implemented when specifying ``engine='numba'``
+        in the method call.
+
+    Returns
+    -------
+    pandas.api.typing.Window or pandas.api.typing.Rolling
+        An instance of Window is returned if ``win_type`` is passed. Otherwise,
+        an instance of Rolling is returned.
+
+    See Also
+    --------
+    expanding : Provides expanding transformations.
+    ewm : Provides exponential weighted functions.
+
+    Notes
+    -----
+    See :ref:`Windowing Operations ` for further usage details
+    and examples.
+
+    Examples
+    --------
+    >>> df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]})
+    >>> df
+         B
+    0  0.0
+    1  1.0
+    2  2.0
+    3  NaN
+    4  4.0
+
+    **window**
+
+    Rolling sum with a window length of 2 observations.
+
+    >>> df.rolling(2).sum()
+         B
+    0  NaN
+    1  1.0
+    2  3.0
+    3  NaN
+    4  NaN
+
+    Rolling sum with a window span of 2 seconds.
+
+    >>> df_time = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]},
+    ...                        index=[pd.Timestamp('20130101 09:00:00'),
+    ...                               pd.Timestamp('20130101 09:00:02'),
+    ...                               pd.Timestamp('20130101 09:00:03'),
+    ...                               pd.Timestamp('20130101 09:00:05'),
+    ...                               pd.Timestamp('20130101 09:00:06')])
+
+    >>> df_time
+                           B
+    2013-01-01 09:00:00  0.0
+    2013-01-01 09:00:02  1.0
+    2013-01-01 09:00:03  2.0
+    2013-01-01 09:00:05  NaN
+    2013-01-01 09:00:06  4.0
+
+    >>> df_time.rolling('2s').sum()
+                           B
+    2013-01-01 09:00:00  0.0
+    2013-01-01 09:00:02  1.0
+    2013-01-01 09:00:03  3.0
+    2013-01-01 09:00:05  NaN
+    2013-01-01 09:00:06  4.0
+
+    Rolling sum with forward looking windows with 2 observations.
+
+    >>> indexer = pd.api.indexers.FixedForwardWindowIndexer(window_size=2)
+    >>> df.rolling(window=indexer, min_periods=1).sum()
+         B
+    0  1.0
+    1  3.0
+    2  2.0
+    3  4.0
+    4  4.0
+
+    **min_periods**
+
+    Rolling sum with a window length of 2 observations, but only needs a minimum of 1
+    observation to calculate a value.
+
+    >>> df.rolling(2, min_periods=1).sum()
+         B
+    0  0.0
+    1  1.0
+    2  3.0
+    3  2.0
+    4  4.0
+
+    **center**
+
+    Rolling sum with the result assigned to the center of the window index.
+
+    >>> df.rolling(3, min_periods=1, center=True).sum()
+         B
+    0  1.0
+    1  3.0
+    2  3.0
+    3  6.0
+    4  4.0
+
+    >>> df.rolling(3, min_periods=1, center=False).sum()
+         B
+    0  0.0
+    1  1.0
+    2  3.0
+    3  3.0
+    4  6.0
+
+    **step**
+
+    Rolling sum with a window length of 2 observations, minimum of 1 observation to
+    calculate a value, and a step of 2.
+
+    >>> df.rolling(2, min_periods=1, step=2).sum()
+         B
+    0  0.0
+    2  3.0
+    4  4.0
+
+    **win_type**
+
+    Rolling sum with a window length of 2, using the Scipy ``'gaussian'``
+    window type. ``std`` is required in the aggregation function.
+
+    >>> df.rolling(2, win_type='gaussian').sum(std=3)
+              B
+    0       NaN
+    1  0.986207
+    2  2.958621
+    3       NaN
+    4       NaN
+
+    **on**
+
+    Rolling sum with a window length of 2 days.
+
+    >>> df = pd.DataFrame({
+    ...     'A': [pd.to_datetime('2020-01-01'),
+    ...           pd.to_datetime('2020-01-01'),
+    ...           pd.to_datetime('2020-01-02'),],
+    ...     'B': [1, 2, 3], },
+    ...     index=pd.date_range('2020', periods=3))
+
+    >>> df
+                        A  B
+    2020-01-01 2020-01-01  1
+    2020-01-02 2020-01-01  2
+    2020-01-03 2020-01-02  3
+
+    >>> df.rolling('2D', on='A').sum()
+                        A    B
+    2020-01-01 2020-01-01  1.0
+    2020-01-02 2020-01-01  3.0
+    2020-01-03 2020-01-02  6.0
+    """
+
+    _attributes = [
+        "window",
+        "min_periods",
+        "center",
+        "win_type",
+        "axis",
+        "on",
+        "closed",
+        "step",
+        "method",
+    ]
+
+    def _validate(self):
+        super()._validate()
+
+        if not isinstance(self.win_type, str):
+            raise ValueError(f"Invalid win_type {self.win_type}")
+        signal = import_optional_dependency(
+            "scipy.signal.windows", extra="Scipy is required to generate window weight."
+        )
+        self._scipy_weight_generator = getattr(signal, self.win_type, None)
+        if self._scipy_weight_generator is None:
+            raise ValueError(f"Invalid win_type {self.win_type}")
+
+        if isinstance(self.window, BaseIndexer):
+            raise NotImplementedError(
+                "BaseIndexer subclasses not implemented with win_types."
+            )
+        if not is_integer(self.window) or self.window < 0:
+            raise ValueError("window must be an integer 0 or greater")
+
+        if self.method != "single":
+            raise NotImplementedError("'single' is the only supported method type.")
+
+    def _center_window(self, result: np.ndarray, offset: int) -> np.ndarray:
+        """
+        Center the result in the window for weighted rolling aggregations.
+        """
+        if offset > 0:
+            lead_indexer = [slice(offset, None)]
+            result = np.copy(result[tuple(lead_indexer)])
+        return result
+
+    def _apply(
+        self,
+        func: Callable[[np.ndarray, int, int], np.ndarray],
+        name: str,
+        numeric_only: bool = False,
+        numba_args: tuple[Any, ...] = (),
+        **kwargs,
+    ):
+        """
+        Rolling with weights statistical measure using supplied function.
+
+        Designed to be used with passed-in Cython array-based functions.
+
+        Parameters
+        ----------
+        func : callable function to apply
+        name : str,
+        numeric_only : bool, default False
+            Whether to only operate on bool, int, and float columns
+        numba_args : tuple
+            unused
+        **kwargs
+            additional arguments for scipy windows if necessary
+
+        Returns
+        -------
+        y : type of input
+        """
+        # "None" not callable  [misc]
+        window = self._scipy_weight_generator(  # type: ignore[misc]
+            self.window, **kwargs
+        )
+        offset = (len(window) - 1) // 2 if self.center else 0
+
+        def homogeneous_func(values: np.ndarray):
+            # calculation function
+
+            if values.size == 0:
+                return values.copy()
+
+            def calc(x):
+                additional_nans = np.array([np.nan] * offset)
+                x = np.concatenate((x, additional_nans))
+                return func(
+                    x,
+                    window,
+                    self.min_periods if self.min_periods is not None else len(window),
+                )
+
+            with np.errstate(all="ignore"):
+                # Our weighted aggregations return memoryviews
+                result = np.asarray(calc(values))
+
+            if self.center:
+                result = self._center_window(result, offset)
+
+            return result
+
+        return self._apply_columnwise(homogeneous_func, name, numeric_only)[
+            :: self.step
+        ]
+
+    @doc(
+        _shared_docs["aggregate"],
+        see_also=dedent(
+            """
+        See Also
+        --------
+        pandas.DataFrame.aggregate : Similar DataFrame method.
+        pandas.Series.aggregate : Similar Series method.
+        """
+        ),
+        examples=dedent(
+            """
+        Examples
+        --------
+        >>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6], "C": [7, 8, 9]})
+        >>> df
+           A  B  C
+        0  1  4  7
+        1  2  5  8
+        2  3  6  9
+
+        >>> df.rolling(2, win_type="boxcar").agg("mean")
+             A    B    C
+        0  NaN  NaN  NaN
+        1  1.5  4.5  7.5
+        2  2.5  5.5  8.5
+        """
+        ),
+        klass="Series/DataFrame",
+        axis="",
+    )
+    def aggregate(self, func, *args, **kwargs):
+        result = ResamplerWindowApply(self, func, args=args, kwargs=kwargs).agg()
+        if result is None:
+            # these must apply directly
+            result = func(self)
+
+        return result
+
+    agg = aggregate
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        kwargs_numeric_only,
+        kwargs_scipy,
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Examples"),
+        dedent(
+            """\
+        >>> ser = pd.Series([0, 1, 5, 2, 8])
+
+        To get an instance of :class:`~pandas.core.window.rolling.Window` we need
+        to pass the parameter `win_type`.
+
+        >>> type(ser.rolling(2, win_type='gaussian'))
+        
+
+        In order to use the `SciPy` Gaussian window we need to provide the parameters
+        `M` and `std`. The parameter `M` corresponds to 2 in our example.
+        We pass the second parameter `std` as a parameter of the following method
+        (`sum` in this case):
+
+        >>> ser.rolling(2, win_type='gaussian').sum(std=3)
+        0         NaN
+        1    0.986207
+        2    5.917243
+        3    6.903450
+        4    9.862071
+        dtype: float64
+        """
+        ),
+        window_method="rolling",
+        aggregation_description="weighted window sum",
+        agg_method="sum",
+    )
+    def sum(self, numeric_only: bool = False, **kwargs):
+        window_func = window_aggregations.roll_weighted_sum
+        # error: Argument 1 to "_apply" of "Window" has incompatible type
+        # "Callable[[ndarray, ndarray, int], ndarray]"; expected
+        # "Callable[[ndarray, int, int], ndarray]"
+        return self._apply(
+            window_func,  # type: ignore[arg-type]
+            name="sum",
+            numeric_only=numeric_only,
+            **kwargs,
+        )
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        kwargs_numeric_only,
+        kwargs_scipy,
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Examples"),
+        dedent(
+            """\
+        >>> ser = pd.Series([0, 1, 5, 2, 8])
+
+        To get an instance of :class:`~pandas.core.window.rolling.Window` we need
+        to pass the parameter `win_type`.
+
+        >>> type(ser.rolling(2, win_type='gaussian'))
+        
+
+        In order to use the `SciPy` Gaussian window we need to provide the parameters
+        `M` and `std`. The parameter `M` corresponds to 2 in our example.
+        We pass the second parameter `std` as a parameter of the following method:
+
+        >>> ser.rolling(2, win_type='gaussian').mean(std=3)
+        0    NaN
+        1    0.5
+        2    3.0
+        3    3.5
+        4    5.0
+        dtype: float64
+        """
+        ),
+        window_method="rolling",
+        aggregation_description="weighted window mean",
+        agg_method="mean",
+    )
+    def mean(self, numeric_only: bool = False, **kwargs):
+        window_func = window_aggregations.roll_weighted_mean
+        # error: Argument 1 to "_apply" of "Window" has incompatible type
+        # "Callable[[ndarray, ndarray, int], ndarray]"; expected
+        # "Callable[[ndarray, int, int], ndarray]"
+        return self._apply(
+            window_func,  # type: ignore[arg-type]
+            name="mean",
+            numeric_only=numeric_only,
+            **kwargs,
+        )
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        kwargs_numeric_only,
+        kwargs_scipy,
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Examples"),
+        dedent(
+            """\
+        >>> ser = pd.Series([0, 1, 5, 2, 8])
+
+        To get an instance of :class:`~pandas.core.window.rolling.Window` we need
+        to pass the parameter `win_type`.
+
+        >>> type(ser.rolling(2, win_type='gaussian'))
+        
+
+        In order to use the `SciPy` Gaussian window we need to provide the parameters
+        `M` and `std`. The parameter `M` corresponds to 2 in our example.
+        We pass the second parameter `std` as a parameter of the following method:
+
+        >>> ser.rolling(2, win_type='gaussian').var(std=3)
+        0     NaN
+        1     0.5
+        2     8.0
+        3     4.5
+        4    18.0
+        dtype: float64
+        """
+        ),
+        window_method="rolling",
+        aggregation_description="weighted window variance",
+        agg_method="var",
+    )
+    def var(self, ddof: int = 1, numeric_only: bool = False, **kwargs):
+        window_func = partial(window_aggregations.roll_weighted_var, ddof=ddof)
+        kwargs.pop("name", None)
+        return self._apply(window_func, name="var", numeric_only=numeric_only, **kwargs)
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        kwargs_numeric_only,
+        kwargs_scipy,
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Examples"),
+        dedent(
+            """\
+        >>> ser = pd.Series([0, 1, 5, 2, 8])
+
+        To get an instance of :class:`~pandas.core.window.rolling.Window` we need
+        to pass the parameter `win_type`.
+
+        >>> type(ser.rolling(2, win_type='gaussian'))
+        
+
+        In order to use the `SciPy` Gaussian window we need to provide the parameters
+        `M` and `std`. The parameter `M` corresponds to 2 in our example.
+        We pass the second parameter `std` as a parameter of the following method:
+
+        >>> ser.rolling(2, win_type='gaussian').std(std=3)
+        0         NaN
+        1    0.707107
+        2    2.828427
+        3    2.121320
+        4    4.242641
+        dtype: float64
+        """
+        ),
+        window_method="rolling",
+        aggregation_description="weighted window standard deviation",
+        agg_method="std",
+    )
+    def std(self, ddof: int = 1, numeric_only: bool = False, **kwargs):
+        return zsqrt(
+            self.var(ddof=ddof, name="std", numeric_only=numeric_only, **kwargs)
+        )
+
+
+class RollingAndExpandingMixin(BaseWindow):
+    def count(self, numeric_only: bool = False):
+        window_func = window_aggregations.roll_sum
+        return self._apply(window_func, name="count", numeric_only=numeric_only)
+
+    def apply(
+        self,
+        func: Callable[..., Any],
+        raw: bool = False,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+        args: tuple[Any, ...] | None = None,
+        kwargs: dict[str, Any] | None = None,
+    ):
+        if args is None:
+            args = ()
+        if kwargs is None:
+            kwargs = {}
+
+        if not is_bool(raw):
+            raise ValueError("raw parameter must be `True` or `False`")
+
+        numba_args: tuple[Any, ...] = ()
+        if maybe_use_numba(engine):
+            if raw is False:
+                raise ValueError("raw must be `True` when using the numba engine")
+            numba_args = args
+            if self.method == "single":
+                apply_func = generate_numba_apply_func(
+                    func, **get_jit_arguments(engine_kwargs, kwargs)
+                )
+            else:
+                apply_func = generate_numba_table_func(
+                    func, **get_jit_arguments(engine_kwargs, kwargs)
+                )
+        elif engine in ("cython", None):
+            if engine_kwargs is not None:
+                raise ValueError("cython engine does not accept engine_kwargs")
+            apply_func = self._generate_cython_apply_func(args, kwargs, raw, func)
+        else:
+            raise ValueError("engine must be either 'numba' or 'cython'")
+
+        return self._apply(
+            apply_func,
+            name="apply",
+            numba_args=numba_args,
+        )
+
+    def _generate_cython_apply_func(
+        self,
+        args: tuple[Any, ...],
+        kwargs: dict[str, Any],
+        raw: bool | np.bool_,
+        function: Callable[..., Any],
+    ) -> Callable[[np.ndarray, np.ndarray, np.ndarray, int], np.ndarray]:
+        from pandas import Series
+
+        window_func = partial(
+            window_aggregations.roll_apply,
+            args=args,
+            kwargs=kwargs,
+            raw=raw,
+            function=function,
+        )
+
+        def apply_func(values, begin, end, min_periods, raw=raw):
+            if not raw:
+                # GH 45912
+                values = Series(values, index=self._on, copy=False)
+            return window_func(values, begin, end, min_periods)
+
+        return apply_func
+
+    def sum(
+        self,
+        numeric_only: bool = False,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+    ):
+        if maybe_use_numba(engine):
+            if self.method == "table":
+                func = generate_manual_numpy_nan_agg_with_axis(np.nansum)
+                return self.apply(
+                    func,
+                    raw=True,
+                    engine=engine,
+                    engine_kwargs=engine_kwargs,
+                )
+            else:
+                from pandas.core._numba.kernels import sliding_sum
+
+                return self._numba_apply(sliding_sum, engine_kwargs)
+        window_func = window_aggregations.roll_sum
+        return self._apply(window_func, name="sum", numeric_only=numeric_only)
+
+    def max(
+        self,
+        numeric_only: bool = False,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+    ):
+        if maybe_use_numba(engine):
+            if self.method == "table":
+                func = generate_manual_numpy_nan_agg_with_axis(np.nanmax)
+                return self.apply(
+                    func,
+                    raw=True,
+                    engine=engine,
+                    engine_kwargs=engine_kwargs,
+                )
+            else:
+                from pandas.core._numba.kernels import sliding_min_max
+
+                return self._numba_apply(sliding_min_max, engine_kwargs, is_max=True)
+        window_func = window_aggregations.roll_max
+        return self._apply(window_func, name="max", numeric_only=numeric_only)
+
+    def min(
+        self,
+        numeric_only: bool = False,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+    ):
+        if maybe_use_numba(engine):
+            if self.method == "table":
+                func = generate_manual_numpy_nan_agg_with_axis(np.nanmin)
+                return self.apply(
+                    func,
+                    raw=True,
+                    engine=engine,
+                    engine_kwargs=engine_kwargs,
+                )
+            else:
+                from pandas.core._numba.kernels import sliding_min_max
+
+                return self._numba_apply(sliding_min_max, engine_kwargs, is_max=False)
+        window_func = window_aggregations.roll_min
+        return self._apply(window_func, name="min", numeric_only=numeric_only)
+
+    def mean(
+        self,
+        numeric_only: bool = False,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+    ):
+        if maybe_use_numba(engine):
+            if self.method == "table":
+                func = generate_manual_numpy_nan_agg_with_axis(np.nanmean)
+                return self.apply(
+                    func,
+                    raw=True,
+                    engine=engine,
+                    engine_kwargs=engine_kwargs,
+                )
+            else:
+                from pandas.core._numba.kernels import sliding_mean
+
+                return self._numba_apply(sliding_mean, engine_kwargs)
+        window_func = window_aggregations.roll_mean
+        return self._apply(window_func, name="mean", numeric_only=numeric_only)
+
+    def median(
+        self,
+        numeric_only: bool = False,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+    ):
+        if maybe_use_numba(engine):
+            if self.method == "table":
+                func = generate_manual_numpy_nan_agg_with_axis(np.nanmedian)
+            else:
+                func = np.nanmedian
+
+            return self.apply(
+                func,
+                raw=True,
+                engine=engine,
+                engine_kwargs=engine_kwargs,
+            )
+        window_func = window_aggregations.roll_median_c
+        return self._apply(window_func, name="median", numeric_only=numeric_only)
+
+    def std(
+        self,
+        ddof: int = 1,
+        numeric_only: bool = False,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+    ):
+        if maybe_use_numba(engine):
+            if self.method == "table":
+                raise NotImplementedError("std not supported with method='table'")
+            from pandas.core._numba.kernels import sliding_var
+
+            return zsqrt(self._numba_apply(sliding_var, engine_kwargs, ddof=ddof))
+        window_func = window_aggregations.roll_var
+
+        def zsqrt_func(values, begin, end, min_periods):
+            return zsqrt(window_func(values, begin, end, min_periods, ddof=ddof))
+
+        return self._apply(
+            zsqrt_func,
+            name="std",
+            numeric_only=numeric_only,
+        )
+
+    def var(
+        self,
+        ddof: int = 1,
+        numeric_only: bool = False,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+    ):
+        if maybe_use_numba(engine):
+            if self.method == "table":
+                raise NotImplementedError("var not supported with method='table'")
+            from pandas.core._numba.kernels import sliding_var
+
+            return self._numba_apply(sliding_var, engine_kwargs, ddof=ddof)
+        window_func = partial(window_aggregations.roll_var, ddof=ddof)
+        return self._apply(
+            window_func,
+            name="var",
+            numeric_only=numeric_only,
+        )
+
+    def skew(self, numeric_only: bool = False):
+        window_func = window_aggregations.roll_skew
+        return self._apply(
+            window_func,
+            name="skew",
+            numeric_only=numeric_only,
+        )
+
+    def sem(self, ddof: int = 1, numeric_only: bool = False):
+        # Raise here so error message says sem instead of std
+        self._validate_numeric_only("sem", numeric_only)
+        return self.std(numeric_only=numeric_only) / (
+            self.count(numeric_only=numeric_only) - ddof
+        ).pow(0.5)
+
+    def kurt(self, numeric_only: bool = False):
+        window_func = window_aggregations.roll_kurt
+        return self._apply(
+            window_func,
+            name="kurt",
+            numeric_only=numeric_only,
+        )
+
+    def quantile(
+        self,
+        q: float,
+        interpolation: QuantileInterpolation = "linear",
+        numeric_only: bool = False,
+    ):
+        if q == 1.0:
+            window_func = window_aggregations.roll_max
+        elif q == 0.0:
+            window_func = window_aggregations.roll_min
+        else:
+            window_func = partial(
+                window_aggregations.roll_quantile,
+                quantile=q,
+                interpolation=interpolation,
+            )
+
+        return self._apply(window_func, name="quantile", numeric_only=numeric_only)
+
+    def rank(
+        self,
+        method: WindowingRankType = "average",
+        ascending: bool = True,
+        pct: bool = False,
+        numeric_only: bool = False,
+    ):
+        window_func = partial(
+            window_aggregations.roll_rank,
+            method=method,
+            ascending=ascending,
+            percentile=pct,
+        )
+
+        return self._apply(window_func, name="rank", numeric_only=numeric_only)
+
+    def cov(
+        self,
+        other: DataFrame | Series | None = None,
+        pairwise: bool | None = None,
+        ddof: int = 1,
+        numeric_only: bool = False,
+    ):
+        if self.step is not None:
+            raise NotImplementedError("step not implemented for cov")
+        self._validate_numeric_only("cov", numeric_only)
+
+        from pandas import Series
+
+        def cov_func(x, y):
+            x_array = self._prep_values(x)
+            y_array = self._prep_values(y)
+            window_indexer = self._get_window_indexer()
+            min_periods = (
+                self.min_periods
+                if self.min_periods is not None
+                else window_indexer.window_size
+            )
+            start, end = window_indexer.get_window_bounds(
+                num_values=len(x_array),
+                min_periods=min_periods,
+                center=self.center,
+                closed=self.closed,
+                step=self.step,
+            )
+            self._check_window_bounds(start, end, len(x_array))
+
+            with np.errstate(all="ignore"):
+                mean_x_y = window_aggregations.roll_mean(
+                    x_array * y_array, start, end, min_periods
+                )
+                mean_x = window_aggregations.roll_mean(x_array, start, end, min_periods)
+                mean_y = window_aggregations.roll_mean(y_array, start, end, min_periods)
+                count_x_y = window_aggregations.roll_sum(
+                    notna(x_array + y_array).astype(np.float64), start, end, 0
+                )
+                result = (mean_x_y - mean_x * mean_y) * (count_x_y / (count_x_y - ddof))
+            return Series(result, index=x.index, name=x.name, copy=False)
+
+        return self._apply_pairwise(
+            self._selected_obj, other, pairwise, cov_func, numeric_only
+        )
+
+    def corr(
+        self,
+        other: DataFrame | Series | None = None,
+        pairwise: bool | None = None,
+        ddof: int = 1,
+        numeric_only: bool = False,
+    ):
+        if self.step is not None:
+            raise NotImplementedError("step not implemented for corr")
+        self._validate_numeric_only("corr", numeric_only)
+
+        from pandas import Series
+
+        def corr_func(x, y):
+            x_array = self._prep_values(x)
+            y_array = self._prep_values(y)
+            window_indexer = self._get_window_indexer()
+            min_periods = (
+                self.min_periods
+                if self.min_periods is not None
+                else window_indexer.window_size
+            )
+            start, end = window_indexer.get_window_bounds(
+                num_values=len(x_array),
+                min_periods=min_periods,
+                center=self.center,
+                closed=self.closed,
+                step=self.step,
+            )
+            self._check_window_bounds(start, end, len(x_array))
+
+            with np.errstate(all="ignore"):
+                mean_x_y = window_aggregations.roll_mean(
+                    x_array * y_array, start, end, min_periods
+                )
+                mean_x = window_aggregations.roll_mean(x_array, start, end, min_periods)
+                mean_y = window_aggregations.roll_mean(y_array, start, end, min_periods)
+                count_x_y = window_aggregations.roll_sum(
+                    notna(x_array + y_array).astype(np.float64), start, end, 0
+                )
+                x_var = window_aggregations.roll_var(
+                    x_array, start, end, min_periods, ddof
+                )
+                y_var = window_aggregations.roll_var(
+                    y_array, start, end, min_periods, ddof
+                )
+                numerator = (mean_x_y - mean_x * mean_y) * (
+                    count_x_y / (count_x_y - ddof)
+                )
+                denominator = (x_var * y_var) ** 0.5
+                result = numerator / denominator
+            return Series(result, index=x.index, name=x.name, copy=False)
+
+        return self._apply_pairwise(
+            self._selected_obj, other, pairwise, corr_func, numeric_only
+        )
+
+
+class Rolling(RollingAndExpandingMixin):
+    _attributes: list[str] = [
+        "window",
+        "min_periods",
+        "center",
+        "win_type",
+        "axis",
+        "on",
+        "closed",
+        "step",
+        "method",
+    ]
+
+    def _validate(self):
+        super()._validate()
+
+        # we allow rolling on a datetimelike index
+        if (
+            self.obj.empty
+            or isinstance(self._on, (DatetimeIndex, TimedeltaIndex, PeriodIndex))
+            or (isinstance(self._on.dtype, ArrowDtype) and self._on.dtype.kind in "mM")
+        ) and isinstance(self.window, (str, BaseOffset, timedelta)):
+            self._validate_datetimelike_monotonic()
+
+            # this will raise ValueError on non-fixed freqs
+            try:
+                freq = to_offset(self.window)
+            except (TypeError, ValueError) as err:
+                raise ValueError(
+                    f"passed window {self.window} is not "
+                    "compatible with a datetimelike index"
+                ) from err
+            if isinstance(self._on, PeriodIndex):
+                # error: Incompatible types in assignment (expression has type
+                # "float", variable has type "Optional[int]")
+                self._win_freq_i8 = freq.nanos / (  # type: ignore[assignment]
+                    self._on.freq.nanos / self._on.freq.n
+                )
+            else:
+                try:
+                    unit = dtype_to_unit(self._on.dtype)  # type: ignore[arg-type]
+                except TypeError:
+                    # if not a datetime dtype, eg for empty dataframes
+                    unit = "ns"
+                self._win_freq_i8 = Timedelta(freq.nanos).as_unit(unit)._value
+
+            # min_periods must be an integer
+            if self.min_periods is None:
+                self.min_periods = 1
+
+            if self.step is not None:
+                raise NotImplementedError(
+                    "step is not supported with frequency windows"
+                )
+
+        elif isinstance(self.window, BaseIndexer):
+            # Passed BaseIndexer subclass should handle all other rolling kwargs
+            pass
+        elif not is_integer(self.window) or self.window < 0:
+            raise ValueError("window must be an integer 0 or greater")
+
+    def _validate_datetimelike_monotonic(self) -> None:
+        """
+        Validate self._on is monotonic (increasing or decreasing) and has
+        no NaT values for frequency windows.
+        """
+        if self._on.hasnans:
+            self._raise_monotonic_error("values must not have NaT")
+        if not (self._on.is_monotonic_increasing or self._on.is_monotonic_decreasing):
+            self._raise_monotonic_error("values must be monotonic")
+
+    def _raise_monotonic_error(self, msg: str):
+        on = self.on
+        if on is None:
+            if self.axis == 0:
+                on = "index"
+            else:
+                on = "column"
+        raise ValueError(f"{on} {msg}")
+
+    @doc(
+        _shared_docs["aggregate"],
+        see_also=dedent(
+            """
+        See Also
+        --------
+        pandas.Series.rolling : Calling object with Series data.
+        pandas.DataFrame.rolling : Calling object with DataFrame data.
+        """
+        ),
+        examples=dedent(
+            """
+        Examples
+        --------
+        >>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6], "C": [7, 8, 9]})
+        >>> df
+           A  B  C
+        0  1  4  7
+        1  2  5  8
+        2  3  6  9
+
+        >>> df.rolling(2).sum()
+             A     B     C
+        0  NaN   NaN   NaN
+        1  3.0   9.0  15.0
+        2  5.0  11.0  17.0
+
+        >>> df.rolling(2).agg({"A": "sum", "B": "min"})
+             A    B
+        0  NaN  NaN
+        1  3.0  4.0
+        2  5.0  5.0
+        """
+        ),
+        klass="Series/Dataframe",
+        axis="",
+    )
+    def aggregate(self, func, *args, **kwargs):
+        return super().aggregate(func, *args, **kwargs)
+
+    agg = aggregate
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        kwargs_numeric_only,
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Examples"),
+        dedent(
+            """
+        >>> s = pd.Series([2, 3, np.nan, 10])
+        >>> s.rolling(2).count()
+        0    NaN
+        1    2.0
+        2    1.0
+        3    1.0
+        dtype: float64
+        >>> s.rolling(3).count()
+        0    NaN
+        1    NaN
+        2    2.0
+        3    2.0
+        dtype: float64
+        >>> s.rolling(4).count()
+        0    NaN
+        1    NaN
+        2    NaN
+        3    3.0
+        dtype: float64
+        """
+        ).replace("\n", "", 1),
+        window_method="rolling",
+        aggregation_description="count of non NaN observations",
+        agg_method="count",
+    )
+    def count(self, numeric_only: bool = False):
+        return super().count(numeric_only)
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        window_apply_parameters,
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Examples"),
+        dedent(
+            """\
+        >>> ser = pd.Series([1, 6, 5, 4])
+        >>> ser.rolling(2).apply(lambda s: s.sum() - s.min())
+        0    NaN
+        1    6.0
+        2    6.0
+        3    5.0
+        dtype: float64
+        """
+        ),
+        window_method="rolling",
+        aggregation_description="custom aggregation function",
+        agg_method="apply",
+    )
+    def apply(
+        self,
+        func: Callable[..., Any],
+        raw: bool = False,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+        args: tuple[Any, ...] | None = None,
+        kwargs: dict[str, Any] | None = None,
+    ):
+        return super().apply(
+            func,
+            raw=raw,
+            engine=engine,
+            engine_kwargs=engine_kwargs,
+            args=args,
+            kwargs=kwargs,
+        )
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        kwargs_numeric_only,
+        window_agg_numba_parameters(),
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Notes"),
+        numba_notes,
+        create_section_header("Examples"),
+        dedent(
+            """
+        >>> s = pd.Series([1, 2, 3, 4, 5])
+        >>> s
+        0    1
+        1    2
+        2    3
+        3    4
+        4    5
+        dtype: int64
+
+        >>> s.rolling(3).sum()
+        0     NaN
+        1     NaN
+        2     6.0
+        3     9.0
+        4    12.0
+        dtype: float64
+
+        >>> s.rolling(3, center=True).sum()
+        0     NaN
+        1     6.0
+        2     9.0
+        3    12.0
+        4     NaN
+        dtype: float64
+
+        For DataFrame, each sum is computed column-wise.
+
+        >>> df = pd.DataFrame({{"A": s, "B": s ** 2}})
+        >>> df
+           A   B
+        0  1   1
+        1  2   4
+        2  3   9
+        3  4  16
+        4  5  25
+
+        >>> df.rolling(3).sum()
+              A     B
+        0   NaN   NaN
+        1   NaN   NaN
+        2   6.0  14.0
+        3   9.0  29.0
+        4  12.0  50.0
+        """
+        ).replace("\n", "", 1),
+        window_method="rolling",
+        aggregation_description="sum",
+        agg_method="sum",
+    )
+    def sum(
+        self,
+        numeric_only: bool = False,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+    ):
+        return super().sum(
+            numeric_only=numeric_only,
+            engine=engine,
+            engine_kwargs=engine_kwargs,
+        )
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        kwargs_numeric_only,
+        window_agg_numba_parameters(),
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Notes"),
+        numba_notes,
+        create_section_header("Examples"),
+        dedent(
+            """\
+        >>> ser = pd.Series([1, 2, 3, 4])
+        >>> ser.rolling(2).max()
+        0    NaN
+        1    2.0
+        2    3.0
+        3    4.0
+        dtype: float64
+        """
+        ),
+        window_method="rolling",
+        aggregation_description="maximum",
+        agg_method="max",
+    )
+    def max(
+        self,
+        numeric_only: bool = False,
+        *args,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+        **kwargs,
+    ):
+        return super().max(
+            numeric_only=numeric_only,
+            engine=engine,
+            engine_kwargs=engine_kwargs,
+        )
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        kwargs_numeric_only,
+        window_agg_numba_parameters(),
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Notes"),
+        numba_notes,
+        create_section_header("Examples"),
+        dedent(
+            """
+        Performing a rolling minimum with a window size of 3.
+
+        >>> s = pd.Series([4, 3, 5, 2, 6])
+        >>> s.rolling(3).min()
+        0    NaN
+        1    NaN
+        2    3.0
+        3    2.0
+        4    2.0
+        dtype: float64
+        """
+        ).replace("\n", "", 1),
+        window_method="rolling",
+        aggregation_description="minimum",
+        agg_method="min",
+    )
+    def min(
+        self,
+        numeric_only: bool = False,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+    ):
+        return super().min(
+            numeric_only=numeric_only,
+            engine=engine,
+            engine_kwargs=engine_kwargs,
+        )
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        kwargs_numeric_only,
+        window_agg_numba_parameters(),
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Notes"),
+        numba_notes,
+        create_section_header("Examples"),
+        dedent(
+            """
+        The below examples will show rolling mean calculations with window sizes of
+        two and three, respectively.
+
+        >>> s = pd.Series([1, 2, 3, 4])
+        >>> s.rolling(2).mean()
+        0    NaN
+        1    1.5
+        2    2.5
+        3    3.5
+        dtype: float64
+
+        >>> s.rolling(3).mean()
+        0    NaN
+        1    NaN
+        2    2.0
+        3    3.0
+        dtype: float64
+        """
+        ).replace("\n", "", 1),
+        window_method="rolling",
+        aggregation_description="mean",
+        agg_method="mean",
+    )
+    def mean(
+        self,
+        numeric_only: bool = False,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+    ):
+        return super().mean(
+            numeric_only=numeric_only,
+            engine=engine,
+            engine_kwargs=engine_kwargs,
+        )
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        kwargs_numeric_only,
+        window_agg_numba_parameters(),
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Notes"),
+        numba_notes,
+        create_section_header("Examples"),
+        dedent(
+            """
+        Compute the rolling median of a series with a window size of 3.
+
+        >>> s = pd.Series([0, 1, 2, 3, 4])
+        >>> s.rolling(3).median()
+        0    NaN
+        1    NaN
+        2    1.0
+        3    2.0
+        4    3.0
+        dtype: float64
+        """
+        ).replace("\n", "", 1),
+        window_method="rolling",
+        aggregation_description="median",
+        agg_method="median",
+    )
+    def median(
+        self,
+        numeric_only: bool = False,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+    ):
+        return super().median(
+            numeric_only=numeric_only,
+            engine=engine,
+            engine_kwargs=engine_kwargs,
+        )
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        dedent(
+            """
+        ddof : int, default 1
+            Delta Degrees of Freedom.  The divisor used in calculations
+            is ``N - ddof``, where ``N`` represents the number of elements.
+        """
+        ).replace("\n", "", 1),
+        kwargs_numeric_only,
+        window_agg_numba_parameters("1.4"),
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        "numpy.std : Equivalent method for NumPy array.\n",
+        template_see_also,
+        create_section_header("Notes"),
+        dedent(
+            """
+        The default ``ddof`` of 1 used in :meth:`Series.std` is different
+        than the default ``ddof`` of 0 in :func:`numpy.std`.
+
+        A minimum of one period is required for the rolling calculation.\n
+        """
+        ).replace("\n", "", 1),
+        create_section_header("Examples"),
+        dedent(
+            """
+        >>> s = pd.Series([5, 5, 6, 7, 5, 5, 5])
+        >>> s.rolling(3).std()
+        0         NaN
+        1         NaN
+        2    0.577350
+        3    1.000000
+        4    1.000000
+        5    1.154701
+        6    0.000000
+        dtype: float64
+        """
+        ).replace("\n", "", 1),
+        window_method="rolling",
+        aggregation_description="standard deviation",
+        agg_method="std",
+    )
+    def std(
+        self,
+        ddof: int = 1,
+        numeric_only: bool = False,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+    ):
+        return super().std(
+            ddof=ddof,
+            numeric_only=numeric_only,
+            engine=engine,
+            engine_kwargs=engine_kwargs,
+        )
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        dedent(
+            """
+        ddof : int, default 1
+            Delta Degrees of Freedom.  The divisor used in calculations
+            is ``N - ddof``, where ``N`` represents the number of elements.
+        """
+        ).replace("\n", "", 1),
+        kwargs_numeric_only,
+        window_agg_numba_parameters("1.4"),
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        "numpy.var : Equivalent method for NumPy array.\n",
+        template_see_also,
+        create_section_header("Notes"),
+        dedent(
+            """
+        The default ``ddof`` of 1 used in :meth:`Series.var` is different
+        than the default ``ddof`` of 0 in :func:`numpy.var`.
+
+        A minimum of one period is required for the rolling calculation.\n
+        """
+        ).replace("\n", "", 1),
+        create_section_header("Examples"),
+        dedent(
+            """
+        >>> s = pd.Series([5, 5, 6, 7, 5, 5, 5])
+        >>> s.rolling(3).var()
+        0         NaN
+        1         NaN
+        2    0.333333
+        3    1.000000
+        4    1.000000
+        5    1.333333
+        6    0.000000
+        dtype: float64
+        """
+        ).replace("\n", "", 1),
+        window_method="rolling",
+        aggregation_description="variance",
+        agg_method="var",
+    )
+    def var(
+        self,
+        ddof: int = 1,
+        numeric_only: bool = False,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+    ):
+        return super().var(
+            ddof=ddof,
+            numeric_only=numeric_only,
+            engine=engine,
+            engine_kwargs=engine_kwargs,
+        )
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        kwargs_numeric_only,
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        "scipy.stats.skew : Third moment of a probability density.\n",
+        template_see_also,
+        create_section_header("Notes"),
+        dedent(
+            """
+        A minimum of three periods is required for the rolling calculation.\n
+        """
+        ),
+        create_section_header("Examples"),
+        dedent(
+            """\
+        >>> ser = pd.Series([1, 5, 2, 7, 15, 6])
+        >>> ser.rolling(3).skew().round(6)
+        0         NaN
+        1         NaN
+        2    1.293343
+        3   -0.585583
+        4    0.670284
+        5    1.652317
+        dtype: float64
+        """
+        ),
+        window_method="rolling",
+        aggregation_description="unbiased skewness",
+        agg_method="skew",
+    )
+    def skew(self, numeric_only: bool = False):
+        return super().skew(numeric_only=numeric_only)
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        dedent(
+            """
+        ddof : int, default 1
+            Delta Degrees of Freedom.  The divisor used in calculations
+            is ``N - ddof``, where ``N`` represents the number of elements.
+        """
+        ).replace("\n", "", 1),
+        kwargs_numeric_only,
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Notes"),
+        "A minimum of one period is required for the calculation.\n\n",
+        create_section_header("Examples"),
+        dedent(
+            """
+        >>> s = pd.Series([0, 1, 2, 3])
+        >>> s.rolling(2, min_periods=1).sem()
+        0         NaN
+        1    0.707107
+        2    0.707107
+        3    0.707107
+        dtype: float64
+        """
+        ).replace("\n", "", 1),
+        window_method="rolling",
+        aggregation_description="standard error of mean",
+        agg_method="sem",
+    )
+    def sem(self, ddof: int = 1, numeric_only: bool = False):
+        # Raise here so error message says sem instead of std
+        self._validate_numeric_only("sem", numeric_only)
+        return self.std(numeric_only=numeric_only) / (
+            self.count(numeric_only) - ddof
+        ).pow(0.5)
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        kwargs_numeric_only,
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        "scipy.stats.kurtosis : Reference SciPy method.\n",
+        template_see_also,
+        create_section_header("Notes"),
+        "A minimum of four periods is required for the calculation.\n\n",
+        create_section_header("Examples"),
+        dedent(
+            """
+        The example below will show a rolling calculation with a window size of
+        four matching the equivalent function call using `scipy.stats`.
+
+        >>> arr = [1, 2, 3, 4, 999]
+        >>> import scipy.stats
+        >>> print(f"{{scipy.stats.kurtosis(arr[:-1], bias=False):.6f}}")
+        -1.200000
+        >>> print(f"{{scipy.stats.kurtosis(arr[1:], bias=False):.6f}}")
+        3.999946
+        >>> s = pd.Series(arr)
+        >>> s.rolling(4).kurt()
+        0         NaN
+        1         NaN
+        2         NaN
+        3   -1.200000
+        4    3.999946
+        dtype: float64
+        """
+        ).replace("\n", "", 1),
+        window_method="rolling",
+        aggregation_description="Fisher's definition of kurtosis without bias",
+        agg_method="kurt",
+    )
+    def kurt(self, numeric_only: bool = False):
+        return super().kurt(numeric_only=numeric_only)
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        dedent(
+            """
+        quantile : float
+            Quantile to compute. 0 <= quantile <= 1.
+
+            .. deprecated:: 2.1.0
+                This will be renamed to 'q' in a future version.
+        interpolation : {{'linear', 'lower', 'higher', 'midpoint', 'nearest'}}
+            This optional parameter specifies the interpolation method to use,
+            when the desired quantile lies between two data points `i` and `j`:
+
+                * linear: `i + (j - i) * fraction`, where `fraction` is the
+                  fractional part of the index surrounded by `i` and `j`.
+                * lower: `i`.
+                * higher: `j`.
+                * nearest: `i` or `j` whichever is nearest.
+                * midpoint: (`i` + `j`) / 2.
+        """
+        ).replace("\n", "", 1),
+        kwargs_numeric_only,
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Examples"),
+        dedent(
+            """
+        >>> s = pd.Series([1, 2, 3, 4])
+        >>> s.rolling(2).quantile(.4, interpolation='lower')
+        0    NaN
+        1    1.0
+        2    2.0
+        3    3.0
+        dtype: float64
+
+        >>> s.rolling(2).quantile(.4, interpolation='midpoint')
+        0    NaN
+        1    1.5
+        2    2.5
+        3    3.5
+        dtype: float64
+        """
+        ).replace("\n", "", 1),
+        window_method="rolling",
+        aggregation_description="quantile",
+        agg_method="quantile",
+    )
+    @deprecate_kwarg(old_arg_name="quantile", new_arg_name="q")
+    def quantile(
+        self,
+        q: float,
+        interpolation: QuantileInterpolation = "linear",
+        numeric_only: bool = False,
+    ):
+        return super().quantile(
+            q=q,
+            interpolation=interpolation,
+            numeric_only=numeric_only,
+        )
+
+    @doc(
+        template_header,
+        ".. versionadded:: 1.4.0 \n\n",
+        create_section_header("Parameters"),
+        dedent(
+            """
+        method : {{'average', 'min', 'max'}}, default 'average'
+            How to rank the group of records that have the same value (i.e. ties):
+
+            * average: average rank of the group
+            * min: lowest rank in the group
+            * max: highest rank in the group
+
+        ascending : bool, default True
+            Whether or not the elements should be ranked in ascending order.
+        pct : bool, default False
+            Whether or not to display the returned rankings in percentile
+            form.
+        """
+        ).replace("\n", "", 1),
+        kwargs_numeric_only,
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Examples"),
+        dedent(
+            """
+        >>> s = pd.Series([1, 4, 2, 3, 5, 3])
+        >>> s.rolling(3).rank()
+        0    NaN
+        1    NaN
+        2    2.0
+        3    2.0
+        4    3.0
+        5    1.5
+        dtype: float64
+
+        >>> s.rolling(3).rank(method="max")
+        0    NaN
+        1    NaN
+        2    2.0
+        3    2.0
+        4    3.0
+        5    2.0
+        dtype: float64
+
+        >>> s.rolling(3).rank(method="min")
+        0    NaN
+        1    NaN
+        2    2.0
+        3    2.0
+        4    3.0
+        5    1.0
+        dtype: float64
+        """
+        ).replace("\n", "", 1),
+        window_method="rolling",
+        aggregation_description="rank",
+        agg_method="rank",
+    )
+    def rank(
+        self,
+        method: WindowingRankType = "average",
+        ascending: bool = True,
+        pct: bool = False,
+        numeric_only: bool = False,
+    ):
+        return super().rank(
+            method=method,
+            ascending=ascending,
+            pct=pct,
+            numeric_only=numeric_only,
+        )
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        dedent(
+            """
+        other : Series or DataFrame, optional
+            If not supplied then will default to self and produce pairwise
+            output.
+        pairwise : bool, default None
+            If False then only matching columns between self and other will be
+            used and the output will be a DataFrame.
+            If True then all pairwise combinations will be calculated and the
+            output will be a MultiIndexed DataFrame in the case of DataFrame
+            inputs. In the case of missing elements, only complete pairwise
+            observations will be used.
+        ddof : int, default 1
+            Delta Degrees of Freedom.  The divisor used in calculations
+            is ``N - ddof``, where ``N`` represents the number of elements.
+        """
+        ).replace("\n", "", 1),
+        kwargs_numeric_only,
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Examples"),
+        dedent(
+            """\
+        >>> ser1 = pd.Series([1, 2, 3, 4])
+        >>> ser2 = pd.Series([1, 4, 5, 8])
+        >>> ser1.rolling(2).cov(ser2)
+        0    NaN
+        1    1.5
+        2    0.5
+        3    1.5
+        dtype: float64
+        """
+        ),
+        window_method="rolling",
+        aggregation_description="sample covariance",
+        agg_method="cov",
+    )
+    def cov(
+        self,
+        other: DataFrame | Series | None = None,
+        pairwise: bool | None = None,
+        ddof: int = 1,
+        numeric_only: bool = False,
+    ):
+        return super().cov(
+            other=other,
+            pairwise=pairwise,
+            ddof=ddof,
+            numeric_only=numeric_only,
+        )
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        dedent(
+            """
+        other : Series or DataFrame, optional
+            If not supplied then will default to self and produce pairwise
+            output.
+        pairwise : bool, default None
+            If False then only matching columns between self and other will be
+            used and the output will be a DataFrame.
+            If True then all pairwise combinations will be calculated and the
+            output will be a MultiIndexed DataFrame in the case of DataFrame
+            inputs. In the case of missing elements, only complete pairwise
+            observations will be used.
+        ddof : int, default 1
+            Delta Degrees of Freedom.  The divisor used in calculations
+            is ``N - ddof``, where ``N`` represents the number of elements.
+        """
+        ).replace("\n", "", 1),
+        kwargs_numeric_only,
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        dedent(
+            """
+        cov : Similar method to calculate covariance.
+        numpy.corrcoef : NumPy Pearson's correlation calculation.
+        """
+        ).replace("\n", "", 1),
+        template_see_also,
+        create_section_header("Notes"),
+        dedent(
+            """
+        This function uses Pearson's definition of correlation
+        (https://en.wikipedia.org/wiki/Pearson_correlation_coefficient).
+
+        When `other` is not specified, the output will be self correlation (e.g.
+        all 1's), except for :class:`~pandas.DataFrame` inputs with `pairwise`
+        set to `True`.
+
+        Function will return ``NaN`` for correlations of equal valued sequences;
+        this is the result of a 0/0 division error.
+
+        When `pairwise` is set to `False`, only matching columns between `self` and
+        `other` will be used.
+
+        When `pairwise` is set to `True`, the output will be a MultiIndex DataFrame
+        with the original index on the first level, and the `other` DataFrame
+        columns on the second level.
+
+        In the case of missing elements, only complete pairwise observations
+        will be used.\n
+        """
+        ).replace("\n", "", 1),
+        create_section_header("Examples"),
+        dedent(
+            """
+        The below example shows a rolling calculation with a window size of
+        four matching the equivalent function call using :meth:`numpy.corrcoef`.
+
+        >>> v1 = [3, 3, 3, 5, 8]
+        >>> v2 = [3, 4, 4, 4, 8]
+        >>> np.corrcoef(v1[:-1], v2[:-1])
+        array([[1.        , 0.33333333],
+               [0.33333333, 1.        ]])
+        >>> np.corrcoef(v1[1:], v2[1:])
+        array([[1.       , 0.9169493],
+               [0.9169493, 1.       ]])
+        >>> s1 = pd.Series(v1)
+        >>> s2 = pd.Series(v2)
+        >>> s1.rolling(4).corr(s2)
+        0         NaN
+        1         NaN
+        2         NaN
+        3    0.333333
+        4    0.916949
+        dtype: float64
+
+        The below example shows a similar rolling calculation on a
+        DataFrame using the pairwise option.
+
+        >>> matrix = np.array([[51., 35.],
+        ...                    [49., 30.],
+        ...                    [47., 32.],
+        ...                    [46., 31.],
+        ...                    [50., 36.]])
+        >>> np.corrcoef(matrix[:-1, 0], matrix[:-1, 1])
+        array([[1.       , 0.6263001],
+               [0.6263001, 1.       ]])
+        >>> np.corrcoef(matrix[1:, 0], matrix[1:, 1])
+        array([[1.        , 0.55536811],
+               [0.55536811, 1.        ]])
+        >>> df = pd.DataFrame(matrix, columns=['X', 'Y'])
+        >>> df
+              X     Y
+        0  51.0  35.0
+        1  49.0  30.0
+        2  47.0  32.0
+        3  46.0  31.0
+        4  50.0  36.0
+        >>> df.rolling(4).corr(pairwise=True)
+                    X         Y
+        0 X       NaN       NaN
+          Y       NaN       NaN
+        1 X       NaN       NaN
+          Y       NaN       NaN
+        2 X       NaN       NaN
+          Y       NaN       NaN
+        3 X  1.000000  0.626300
+          Y  0.626300  1.000000
+        4 X  1.000000  0.555368
+          Y  0.555368  1.000000
+        """
+        ).replace("\n", "", 1),
+        window_method="rolling",
+        aggregation_description="correlation",
+        agg_method="corr",
+    )
+    def corr(
+        self,
+        other: DataFrame | Series | None = None,
+        pairwise: bool | None = None,
+        ddof: int = 1,
+        numeric_only: bool = False,
+    ):
+        return super().corr(
+            other=other,
+            pairwise=pairwise,
+            ddof=ddof,
+            numeric_only=numeric_only,
+        )
+
+
+Rolling.__doc__ = Window.__doc__
+
+
+class RollingGroupby(BaseWindowGroupby, Rolling):
+    """
+    Provide a rolling groupby implementation.
+    """
+
+    _attributes = Rolling._attributes + BaseWindowGroupby._attributes
+
+    def _get_window_indexer(self) -> GroupbyIndexer:
+        """
+        Return an indexer class that will compute the window start and end bounds
+
+        Returns
+        -------
+        GroupbyIndexer
+        """
+        rolling_indexer: type[BaseIndexer]
+        indexer_kwargs: dict[str, Any] | None = None
+        index_array = self._index_array
+        if isinstance(self.window, BaseIndexer):
+            rolling_indexer = type(self.window)
+            indexer_kwargs = self.window.__dict__.copy()
+            assert isinstance(indexer_kwargs, dict)  # for mypy
+            # We'll be using the index of each group later
+            indexer_kwargs.pop("index_array", None)
+            window = self.window
+        elif self._win_freq_i8 is not None:
+            rolling_indexer = VariableWindowIndexer
+            # error: Incompatible types in assignment (expression has type
+            # "int", variable has type "BaseIndexer")
+            window = self._win_freq_i8  # type: ignore[assignment]
+        else:
+            rolling_indexer = FixedWindowIndexer
+            window = self.window
+        window_indexer = GroupbyIndexer(
+            index_array=index_array,
+            window_size=window,
+            groupby_indices=self._grouper.indices,
+            window_indexer=rolling_indexer,
+            indexer_kwargs=indexer_kwargs,
+        )
+        return window_indexer
+
+    def _validate_datetimelike_monotonic(self):
+        """
+        Validate that each group in self._on is monotonic
+        """
+        # GH 46061
+        if self._on.hasnans:
+            self._raise_monotonic_error("values must not have NaT")
+        for group_indices in self._grouper.indices.values():
+            group_on = self._on.take(group_indices)
+            if not (
+                group_on.is_monotonic_increasing or group_on.is_monotonic_decreasing
+            ):
+                on = "index" if self.on is None else self.on
+                raise ValueError(
+                    f"Each group within {on} must be monotonic. "
+                    f"Sort the values in {on} first."
+                )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/errors/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/errors/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..01094ba36b9dd5f3414c32a9a4f832b85902e021
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/errors/__init__.py
@@ -0,0 +1,850 @@
+"""
+Expose public exceptions & warnings
+"""
+from __future__ import annotations
+
+import ctypes
+
+from pandas._config.config import OptionError
+
+from pandas._libs.tslibs import (
+    OutOfBoundsDatetime,
+    OutOfBoundsTimedelta,
+)
+
+from pandas.util.version import InvalidVersion
+
+
+class IntCastingNaNError(ValueError):
+    """
+    Exception raised when converting (``astype``) an array with NaN to an integer type.
+
+    Examples
+    --------
+    >>> pd.DataFrame(np.array([[1, np.nan], [2, 3]]), dtype="i8")
+    Traceback (most recent call last):
+    IntCastingNaNError: Cannot convert non-finite values (NA or inf) to integer
+    """
+
+
+class NullFrequencyError(ValueError):
+    """
+    Exception raised when a ``freq`` cannot be null.
+
+    Particularly ``DatetimeIndex.shift``, ``TimedeltaIndex.shift``,
+    ``PeriodIndex.shift``.
+
+    Examples
+    --------
+    >>> df = pd.DatetimeIndex(["2011-01-01 10:00", "2011-01-01"], freq=None)
+    >>> df.shift(2)
+    Traceback (most recent call last):
+    NullFrequencyError: Cannot shift with no freq
+    """
+
+
+class PerformanceWarning(Warning):
+    """
+    Warning raised when there is a possible performance impact.
+
+    Examples
+    --------
+    >>> df = pd.DataFrame({"jim": [0, 0, 1, 1],
+    ...                    "joe": ["x", "x", "z", "y"],
+    ...                    "jolie": [1, 2, 3, 4]})
+    >>> df = df.set_index(["jim", "joe"])
+    >>> df
+              jolie
+    jim  joe
+    0    x    1
+         x    2
+    1    z    3
+         y    4
+    >>> df.loc[(1, 'z')]  # doctest: +SKIP
+    # PerformanceWarning: indexing past lexsort depth may impact performance.
+    df.loc[(1, 'z')]
+              jolie
+    jim  joe
+    1    z        3
+    """
+
+
+class UnsupportedFunctionCall(ValueError):
+    """
+    Exception raised when attempting to call a unsupported numpy function.
+
+    For example, ``np.cumsum(groupby_object)``.
+
+    Examples
+    --------
+    >>> df = pd.DataFrame({"A": [0, 0, 1, 1],
+    ...                    "B": ["x", "x", "z", "y"],
+    ...                    "C": [1, 2, 3, 4]}
+    ...                   )
+    >>> np.cumsum(df.groupby(["A"]))
+    Traceback (most recent call last):
+    UnsupportedFunctionCall: numpy operations are not valid with groupby.
+    Use .groupby(...).cumsum() instead
+    """
+
+
+class UnsortedIndexError(KeyError):
+    """
+    Error raised when slicing a MultiIndex which has not been lexsorted.
+
+    Subclass of `KeyError`.
+
+    Examples
+    --------
+    >>> df = pd.DataFrame({"cat": [0, 0, 1, 1],
+    ...                    "color": ["white", "white", "brown", "black"],
+    ...                    "lives": [4, 4, 3, 7]},
+    ...                   )
+    >>> df = df.set_index(["cat", "color"])
+    >>> df
+                lives
+    cat  color
+    0    white    4
+         white    4
+    1    brown    3
+         black    7
+    >>> df.loc[(0, "black"):(1, "white")]
+    Traceback (most recent call last):
+    UnsortedIndexError: 'Key length (2) was greater
+    than MultiIndex lexsort depth (1)'
+    """
+
+
+class ParserError(ValueError):
+    """
+    Exception that is raised by an error encountered in parsing file contents.
+
+    This is a generic error raised for errors encountered when functions like
+    `read_csv` or `read_html` are parsing contents of a file.
+
+    See Also
+    --------
+    read_csv : Read CSV (comma-separated) file into a DataFrame.
+    read_html : Read HTML table into a DataFrame.
+
+    Examples
+    --------
+    >>> data = '''a,b,c
+    ... cat,foo,bar
+    ... dog,foo,"baz'''
+    >>> from io import StringIO
+    >>> pd.read_csv(StringIO(data), skipfooter=1, engine='python')
+    Traceback (most recent call last):
+    ParserError: ',' expected after '"'. Error could possibly be due
+    to parsing errors in the skipped footer rows
+    """
+
+
+class DtypeWarning(Warning):
+    """
+    Warning raised when reading different dtypes in a column from a file.
+
+    Raised for a dtype incompatibility. This can happen whenever `read_csv`
+    or `read_table` encounter non-uniform dtypes in a column(s) of a given
+    CSV file.
+
+    See Also
+    --------
+    read_csv : Read CSV (comma-separated) file into a DataFrame.
+    read_table : Read general delimited file into a DataFrame.
+
+    Notes
+    -----
+    This warning is issued when dealing with larger files because the dtype
+    checking happens per chunk read.
+
+    Despite the warning, the CSV file is read with mixed types in a single
+    column which will be an object type. See the examples below to better
+    understand this issue.
+
+    Examples
+    --------
+    This example creates and reads a large CSV file with a column that contains
+    `int` and `str`.
+
+    >>> df = pd.DataFrame({'a': (['1'] * 100000 + ['X'] * 100000 +
+    ...                          ['1'] * 100000),
+    ...                    'b': ['b'] * 300000})  # doctest: +SKIP
+    >>> df.to_csv('test.csv', index=False)  # doctest: +SKIP
+    >>> df2 = pd.read_csv('test.csv')  # doctest: +SKIP
+    ... # DtypeWarning: Columns (0) have mixed types
+
+    Important to notice that ``df2`` will contain both `str` and `int` for the
+    same input, '1'.
+
+    >>> df2.iloc[262140, 0]  # doctest: +SKIP
+    '1'
+    >>> type(df2.iloc[262140, 0])  # doctest: +SKIP
+    
+    >>> df2.iloc[262150, 0]  # doctest: +SKIP
+    1
+    >>> type(df2.iloc[262150, 0])  # doctest: +SKIP
+    
+
+    One way to solve this issue is using the `dtype` parameter in the
+    `read_csv` and `read_table` functions to explicit the conversion:
+
+    >>> df2 = pd.read_csv('test.csv', sep=',', dtype={'a': str})  # doctest: +SKIP
+
+    No warning was issued.
+    """
+
+
+class EmptyDataError(ValueError):
+    """
+    Exception raised in ``pd.read_csv`` when empty data or header is encountered.
+
+    Examples
+    --------
+    >>> from io import StringIO
+    >>> empty = StringIO()
+    >>> pd.read_csv(empty)
+    Traceback (most recent call last):
+    EmptyDataError: No columns to parse from file
+    """
+
+
+class ParserWarning(Warning):
+    """
+    Warning raised when reading a file that doesn't use the default 'c' parser.
+
+    Raised by `pd.read_csv` and `pd.read_table` when it is necessary to change
+    parsers, generally from the default 'c' parser to 'python'.
+
+    It happens due to a lack of support or functionality for parsing a
+    particular attribute of a CSV file with the requested engine.
+
+    Currently, 'c' unsupported options include the following parameters:
+
+    1. `sep` other than a single character (e.g. regex separators)
+    2. `skipfooter` higher than 0
+    3. `sep=None` with `delim_whitespace=False`
+
+    The warning can be avoided by adding `engine='python'` as a parameter in
+    `pd.read_csv` and `pd.read_table` methods.
+
+    See Also
+    --------
+    pd.read_csv : Read CSV (comma-separated) file into DataFrame.
+    pd.read_table : Read general delimited file into DataFrame.
+
+    Examples
+    --------
+    Using a `sep` in `pd.read_csv` other than a single character:
+
+    >>> import io
+    >>> csv = '''a;b;c
+    ...           1;1,8
+    ...           1;2,1'''
+    >>> df = pd.read_csv(io.StringIO(csv), sep='[;,]')  # doctest: +SKIP
+    ... # ParserWarning: Falling back to the 'python' engine...
+
+    Adding `engine='python'` to `pd.read_csv` removes the Warning:
+
+    >>> df = pd.read_csv(io.StringIO(csv), sep='[;,]', engine='python')
+    """
+
+
+class MergeError(ValueError):
+    """
+    Exception raised when merging data.
+
+    Subclass of ``ValueError``.
+
+    Examples
+    --------
+    >>> left = pd.DataFrame({"a": ["a", "b", "b", "d"],
+    ...                     "b": ["cat", "dog", "weasel", "horse"]},
+    ...                     index=range(4))
+    >>> right = pd.DataFrame({"a": ["a", "b", "c", "d"],
+    ...                      "c": ["meow", "bark", "chirp", "nay"]},
+    ...                      index=range(4)).set_index("a")
+    >>> left.join(right, on="a", validate="one_to_one",)
+    Traceback (most recent call last):
+    MergeError: Merge keys are not unique in left dataset; not a one-to-one merge
+    """
+
+
+class AbstractMethodError(NotImplementedError):
+    """
+    Raise this error instead of NotImplementedError for abstract methods.
+
+    Examples
+    --------
+    >>> class Foo:
+    ...     @classmethod
+    ...     def classmethod(cls):
+    ...         raise pd.errors.AbstractMethodError(cls, methodtype="classmethod")
+    ...     def method(self):
+    ...         raise pd.errors.AbstractMethodError(self)
+    >>> test = Foo.classmethod()
+    Traceback (most recent call last):
+    AbstractMethodError: This classmethod must be defined in the concrete class Foo
+
+    >>> test2 = Foo().method()
+    Traceback (most recent call last):
+    AbstractMethodError: This classmethod must be defined in the concrete class Foo
+    """
+
+    def __init__(self, class_instance, methodtype: str = "method") -> None:
+        types = {"method", "classmethod", "staticmethod", "property"}
+        if methodtype not in types:
+            raise ValueError(
+                f"methodtype must be one of {methodtype}, got {types} instead."
+            )
+        self.methodtype = methodtype
+        self.class_instance = class_instance
+
+    def __str__(self) -> str:
+        if self.methodtype == "classmethod":
+            name = self.class_instance.__name__
+        else:
+            name = type(self.class_instance).__name__
+        return f"This {self.methodtype} must be defined in the concrete class {name}"
+
+
+class NumbaUtilError(Exception):
+    """
+    Error raised for unsupported Numba engine routines.
+
+    Examples
+    --------
+    >>> df = pd.DataFrame({"key": ["a", "a", "b", "b"], "data": [1, 2, 3, 4]},
+    ...                   columns=["key", "data"])
+    >>> def incorrect_function(x):
+    ...     return sum(x) * 2.7
+    >>> df.groupby("key").agg(incorrect_function, engine="numba")
+    Traceback (most recent call last):
+    NumbaUtilError: The first 2 arguments to incorrect_function
+    must be ['values', 'index']
+    """
+
+
+class DuplicateLabelError(ValueError):
+    """
+    Error raised when an operation would introduce duplicate labels.
+
+    Examples
+    --------
+    >>> s = pd.Series([0, 1, 2], index=['a', 'b', 'c']).set_flags(
+    ...     allows_duplicate_labels=False
+    ... )
+    >>> s.reindex(['a', 'a', 'b'])
+    Traceback (most recent call last):
+       ...
+    DuplicateLabelError: Index has duplicates.
+          positions
+    label
+    a        [0, 1]
+    """
+
+
+class InvalidIndexError(Exception):
+    """
+    Exception raised when attempting to use an invalid index key.
+
+    Examples
+    --------
+    >>> idx = pd.MultiIndex.from_product([["x", "y"], [0, 1]])
+    >>> df = pd.DataFrame([[1, 1, 2, 2],
+    ...                   [3, 3, 4, 4]], columns=idx)
+    >>> df
+        x       y
+        0   1   0   1
+    0   1   1   2   2
+    1   3   3   4   4
+    >>> df[:, 0]
+    Traceback (most recent call last):
+    InvalidIndexError: (slice(None, None, None), 0)
+    """
+
+
+class DataError(Exception):
+    """
+    Exceptionn raised when performing an operation on non-numerical data.
+
+    For example, calling ``ohlc`` on a non-numerical column or a function
+    on a rolling window.
+
+    Examples
+    --------
+    >>> ser = pd.Series(['a', 'b', 'c'])
+    >>> ser.rolling(2).sum()
+    Traceback (most recent call last):
+    DataError: No numeric types to aggregate
+    """
+
+
+class SpecificationError(Exception):
+    """
+    Exception raised by ``agg`` when the functions are ill-specified.
+
+    The exception raised in two scenarios.
+
+    The first way is calling ``agg`` on a
+    Dataframe or Series using a nested renamer (dict-of-dict).
+
+    The second way is calling ``agg`` on a Dataframe with duplicated functions
+    names without assigning column name.
+
+    Examples
+    --------
+    >>> df = pd.DataFrame({'A': [1, 1, 1, 2, 2],
+    ...                    'B': range(5),
+    ...                    'C': range(5)})
+    >>> df.groupby('A').B.agg({'foo': 'count'}) # doctest: +SKIP
+    ... # SpecificationError: nested renamer is not supported
+
+    >>> df.groupby('A').agg({'B': {'foo': ['sum', 'max']}}) # doctest: +SKIP
+    ... # SpecificationError: nested renamer is not supported
+
+    >>> df.groupby('A').agg(['min', 'min']) # doctest: +SKIP
+    ... # SpecificationError: nested renamer is not supported
+    """
+
+
+class SettingWithCopyError(ValueError):
+    """
+    Exception raised when trying to set on a copied slice from a ``DataFrame``.
+
+    The ``mode.chained_assignment`` needs to be set to set to 'raise.' This can
+    happen unintentionally when chained indexing.
+
+    For more information on evaluation order,
+    see :ref:`the user guide`.
+
+    For more information on view vs. copy,
+    see :ref:`the user guide`.
+
+    Examples
+    --------
+    >>> pd.options.mode.chained_assignment = 'raise'
+    >>> df = pd.DataFrame({'A': [1, 1, 1, 2, 2]}, columns=['A'])
+    >>> df.loc[0:3]['A'] = 'a' # doctest: +SKIP
+    ... # SettingWithCopyError: A value is trying to be set on a copy of a...
+    """
+
+
+class SettingWithCopyWarning(Warning):
+    """
+    Warning raised when trying to set on a copied slice from a ``DataFrame``.
+
+    The ``mode.chained_assignment`` needs to be set to set to 'warn.'
+    'Warn' is the default option. This can happen unintentionally when
+    chained indexing.
+
+    For more information on evaluation order,
+    see :ref:`the user guide`.
+
+    For more information on view vs. copy,
+    see :ref:`the user guide`.
+
+    Examples
+    --------
+    >>> df = pd.DataFrame({'A': [1, 1, 1, 2, 2]}, columns=['A'])
+    >>> df.loc[0:3]['A'] = 'a' # doctest: +SKIP
+    ... # SettingWithCopyWarning: A value is trying to be set on a copy of a...
+    """
+
+
+class ChainedAssignmentError(Warning):
+    """
+    Warning raised when trying to set using chained assignment.
+
+    When the ``mode.copy_on_write`` option is enabled, chained assignment can
+    never work. In such a situation, we are always setting into a temporary
+    object that is the result of an indexing operation (getitem), which under
+    Copy-on-Write always behaves as a copy. Thus, assigning through a chain
+    can never update the original Series or DataFrame.
+
+    For more information on view vs. copy,
+    see :ref:`the user guide`.
+
+    Examples
+    --------
+    >>> pd.options.mode.copy_on_write = True
+    >>> df = pd.DataFrame({'A': [1, 1, 1, 2, 2]}, columns=['A'])
+    >>> df["A"][0:3] = 10 # doctest: +SKIP
+    ... # ChainedAssignmentError: ...
+    >>> pd.options.mode.copy_on_write = False
+    """
+
+
+_chained_assignment_msg = (
+    "A value is trying to be set on a copy of a DataFrame or Series "
+    "through chained assignment.\n"
+    "When using the Copy-on-Write mode, such chained assignment never works "
+    "to update the original DataFrame or Series, because the intermediate "
+    "object on which we are setting values always behaves as a copy.\n\n"
+    "Try using '.loc[row_indexer, col_indexer] = value' instead, to perform "
+    "the assignment in a single step.\n\n"
+    "See the caveats in the documentation: "
+    "https://pandas.pydata.org/pandas-docs/stable/user_guide/"
+    "indexing.html#returning-a-view-versus-a-copy"
+)
+
+
+_chained_assignment_method_msg = (
+    "A value is trying to be set on a copy of a DataFrame or Series "
+    "through chained assignment using an inplace method.\n"
+    "When using the Copy-on-Write mode, such inplace method never works "
+    "to update the original DataFrame or Series, because the intermediate "
+    "object on which we are setting values always behaves as a copy.\n\n"
+    "For example, when doing 'df[col].method(value, inplace=True)', try "
+    "using 'df.method({col: value}, inplace=True)' instead, to perform "
+    "the operation inplace on the original object.\n\n"
+)
+
+
+_chained_assignment_warning_msg = (
+    "ChainedAssignmentError: behaviour will change in pandas 3.0!\n"
+    "You are setting values through chained assignment. Currently this works "
+    "in certain cases, but when using Copy-on-Write (which will become the "
+    "default behaviour in pandas 3.0) this will never work to update the "
+    "original DataFrame or Series, because the intermediate object on which "
+    "we are setting values will behave as a copy.\n"
+    "A typical example is when you are setting values in a column of a "
+    "DataFrame, like:\n\n"
+    'df["col"][row_indexer] = value\n\n'
+    'Use `df.loc[row_indexer, "col"] = values` instead, to perform the '
+    "assignment in a single step and ensure this keeps updating the original `df`.\n\n"
+    "See the caveats in the documentation: "
+    "https://pandas.pydata.org/pandas-docs/stable/user_guide/"
+    "indexing.html#returning-a-view-versus-a-copy\n"
+)
+
+
+_chained_assignment_warning_method_msg = (
+    "A value is trying to be set on a copy of a DataFrame or Series "
+    "through chained assignment using an inplace method.\n"
+    "The behavior will change in pandas 3.0. This inplace method will "
+    "never work because the intermediate object on which we are setting "
+    "values always behaves as a copy.\n\n"
+    "For example, when doing 'df[col].method(value, inplace=True)', try "
+    "using 'df.method({col: value}, inplace=True)' or "
+    "df[col] = df[col].method(value) instead, to perform "
+    "the operation inplace on the original object.\n\n"
+)
+
+
+def _check_cacher(obj):
+    # This is a mess, selection paths that return a view set the _cacher attribute
+    # on the Series; most of them also set _item_cache which adds 1 to our relevant
+    # reference count, but iloc does not, so we have to check if we are actually
+    # in the item cache
+    if hasattr(obj, "_cacher"):
+        parent = obj._cacher[1]()
+        # parent could be dead
+        if parent is None:
+            return False
+        if hasattr(parent, "_item_cache"):
+            if obj._cacher[0] in parent._item_cache:
+                # Check if we are actually the item from item_cache, iloc creates a
+                # new object
+                return obj is parent._item_cache[obj._cacher[0]]
+    return False
+
+
+class NumExprClobberingError(NameError):
+    """
+    Exception raised when trying to use a built-in numexpr name as a variable name.
+
+    ``eval`` or ``query`` will throw the error if the engine is set
+    to 'numexpr'. 'numexpr' is the default engine value for these methods if the
+    numexpr package is installed.
+
+    Examples
+    --------
+    >>> df = pd.DataFrame({'abs': [1, 1, 1]})
+    >>> df.query("abs > 2") # doctest: +SKIP
+    ... # NumExprClobberingError: Variables in expression "(abs) > (2)" overlap...
+    >>> sin, a = 1, 2
+    >>> pd.eval("sin + a", engine='numexpr') # doctest: +SKIP
+    ... # NumExprClobberingError: Variables in expression "(sin) + (a)" overlap...
+    """
+
+
+class UndefinedVariableError(NameError):
+    """
+    Exception raised by ``query`` or ``eval`` when using an undefined variable name.
+
+    It will also specify whether the undefined variable is local or not.
+
+    Examples
+    --------
+    >>> df = pd.DataFrame({'A': [1, 1, 1]})
+    >>> df.query("A > x") # doctest: +SKIP
+    ... # UndefinedVariableError: name 'x' is not defined
+    >>> df.query("A > @y") # doctest: +SKIP
+    ... # UndefinedVariableError: local variable 'y' is not defined
+    >>> pd.eval('x + 1') # doctest: +SKIP
+    ... # UndefinedVariableError: name 'x' is not defined
+    """
+
+    def __init__(self, name: str, is_local: bool | None = None) -> None:
+        base_msg = f"{repr(name)} is not defined"
+        if is_local:
+            msg = f"local variable {base_msg}"
+        else:
+            msg = f"name {base_msg}"
+        super().__init__(msg)
+
+
+class IndexingError(Exception):
+    """
+    Exception is raised when trying to index and there is a mismatch in dimensions.
+
+    Examples
+    --------
+    >>> df = pd.DataFrame({'A': [1, 1, 1]})
+    >>> df.loc[..., ..., 'A'] # doctest: +SKIP
+    ... # IndexingError: indexer may only contain one '...' entry
+    >>> df = pd.DataFrame({'A': [1, 1, 1]})
+    >>> df.loc[1, ..., ...] # doctest: +SKIP
+    ... # IndexingError: Too many indexers
+    >>> df[pd.Series([True], dtype=bool)] # doctest: +SKIP
+    ... # IndexingError: Unalignable boolean Series provided as indexer...
+    >>> s = pd.Series(range(2),
+    ...               index = pd.MultiIndex.from_product([["a", "b"], ["c"]]))
+    >>> s.loc["a", "c", "d"] # doctest: +SKIP
+    ... # IndexingError: Too many indexers
+    """
+
+
+class PyperclipException(RuntimeError):
+    """
+    Exception raised when clipboard functionality is unsupported.
+
+    Raised by ``to_clipboard()`` and ``read_clipboard()``.
+    """
+
+
+class PyperclipWindowsException(PyperclipException):
+    """
+    Exception raised when clipboard functionality is unsupported by Windows.
+
+    Access to the clipboard handle would be denied due to some other
+    window process is accessing it.
+    """
+
+    def __init__(self, message: str) -> None:
+        # attr only exists on Windows, so typing fails on other platforms
+        message += f" ({ctypes.WinError()})"  # type: ignore[attr-defined]
+        super().__init__(message)
+
+
+class CSSWarning(UserWarning):
+    """
+    Warning is raised when converting css styling fails.
+
+    This can be due to the styling not having an equivalent value or because the
+    styling isn't properly formatted.
+
+    Examples
+    --------
+    >>> df = pd.DataFrame({'A': [1, 1, 1]})
+    >>> df.style.applymap(
+    ...     lambda x: 'background-color: blueGreenRed;'
+    ... ).to_excel('styled.xlsx')  # doctest: +SKIP
+    CSSWarning: Unhandled color format: 'blueGreenRed'
+    >>> df.style.applymap(
+    ...     lambda x: 'border: 1px solid red red;'
+    ... ).to_excel('styled.xlsx')  # doctest: +SKIP
+    CSSWarning: Unhandled color format: 'blueGreenRed'
+    """
+
+
+class PossibleDataLossError(Exception):
+    """
+    Exception raised when trying to open a HDFStore file when already opened.
+
+    Examples
+    --------
+    >>> store = pd.HDFStore('my-store', 'a') # doctest: +SKIP
+    >>> store.open("w") # doctest: +SKIP
+    ... # PossibleDataLossError: Re-opening the file [my-store] with mode [a]...
+    """
+
+
+class ClosedFileError(Exception):
+    """
+    Exception is raised when trying to perform an operation on a closed HDFStore file.
+
+    Examples
+    --------
+    >>> store = pd.HDFStore('my-store', 'a') # doctest: +SKIP
+    >>> store.close() # doctest: +SKIP
+    >>> store.keys() # doctest: +SKIP
+    ... # ClosedFileError: my-store file is not open!
+    """
+
+
+class IncompatibilityWarning(Warning):
+    """
+    Warning raised when trying to use where criteria on an incompatible HDF5 file.
+    """
+
+
+class AttributeConflictWarning(Warning):
+    """
+    Warning raised when index attributes conflict when using HDFStore.
+
+    Occurs when attempting to append an index with a different
+    name than the existing index on an HDFStore or attempting to append an index with a
+    different frequency than the existing index on an HDFStore.
+
+    Examples
+    --------
+    >>> idx1 = pd.Index(['a', 'b'], name='name1')
+    >>> df1 = pd.DataFrame([[1, 2], [3, 4]], index=idx1)
+    >>> df1.to_hdf('file', 'data', 'w', append=True)  # doctest: +SKIP
+    >>> idx2 = pd.Index(['c', 'd'], name='name2')
+    >>> df2 = pd.DataFrame([[5, 6], [7, 8]], index=idx2)
+    >>> df2.to_hdf('file', 'data', 'a', append=True)  # doctest: +SKIP
+    AttributeConflictWarning: the [index_name] attribute of the existing index is
+    [name1] which conflicts with the new [name2]...
+    """
+
+
+class DatabaseError(OSError):
+    """
+    Error is raised when executing sql with bad syntax or sql that throws an error.
+
+    Examples
+    --------
+    >>> from sqlite3 import connect
+    >>> conn = connect(':memory:')
+    >>> pd.read_sql('select * test', conn) # doctest: +SKIP
+    ... # DatabaseError: Execution failed on sql 'test': near "test": syntax error
+    """
+
+
+class PossiblePrecisionLoss(Warning):
+    """
+    Warning raised by to_stata on a column with a value outside or equal to int64.
+
+    When the column value is outside or equal to the int64 value the column is
+    converted to a float64 dtype.
+
+    Examples
+    --------
+    >>> df = pd.DataFrame({"s": pd.Series([1, 2**53], dtype=np.int64)})
+    >>> df.to_stata('test') # doctest: +SKIP
+    ... # PossiblePrecisionLoss: Column converted from int64 to float64...
+    """
+
+
+class ValueLabelTypeMismatch(Warning):
+    """
+    Warning raised by to_stata on a category column that contains non-string values.
+
+    Examples
+    --------
+    >>> df = pd.DataFrame({"categories": pd.Series(["a", 2], dtype="category")})
+    >>> df.to_stata('test') # doctest: +SKIP
+    ... # ValueLabelTypeMismatch: Stata value labels (pandas categories) must be str...
+    """
+
+
+class InvalidColumnName(Warning):
+    """
+    Warning raised by to_stata the column contains a non-valid stata name.
+
+    Because the column name is an invalid Stata variable, the name needs to be
+    converted.
+
+    Examples
+    --------
+    >>> df = pd.DataFrame({"0categories": pd.Series([2, 2])})
+    >>> df.to_stata('test') # doctest: +SKIP
+    ... # InvalidColumnName: Not all pandas column names were valid Stata variable...
+    """
+
+
+class CategoricalConversionWarning(Warning):
+    """
+    Warning is raised when reading a partial labeled Stata file using a iterator.
+
+    Examples
+    --------
+    >>> from pandas.io.stata import StataReader
+    >>> with StataReader('dta_file', chunksize=2) as reader: # doctest: +SKIP
+    ...   for i, block in enumerate(reader):
+    ...      print(i, block)
+    ... # CategoricalConversionWarning: One or more series with value labels...
+    """
+
+
+class LossySetitemError(Exception):
+    """
+    Raised when trying to do a __setitem__ on an np.ndarray that is not lossless.
+
+    Notes
+    -----
+    This is an internal error.
+    """
+
+
+class NoBufferPresent(Exception):
+    """
+    Exception is raised in _get_data_buffer to signal that there is no requested buffer.
+    """
+
+
+class InvalidComparison(Exception):
+    """
+    Exception is raised by _validate_comparison_value to indicate an invalid comparison.
+
+    Notes
+    -----
+    This is an internal error.
+    """
+
+
+__all__ = [
+    "AbstractMethodError",
+    "AttributeConflictWarning",
+    "CategoricalConversionWarning",
+    "ClosedFileError",
+    "CSSWarning",
+    "DatabaseError",
+    "DataError",
+    "DtypeWarning",
+    "DuplicateLabelError",
+    "EmptyDataError",
+    "IncompatibilityWarning",
+    "IntCastingNaNError",
+    "InvalidColumnName",
+    "InvalidComparison",
+    "InvalidIndexError",
+    "InvalidVersion",
+    "IndexingError",
+    "LossySetitemError",
+    "MergeError",
+    "NoBufferPresent",
+    "NullFrequencyError",
+    "NumbaUtilError",
+    "NumExprClobberingError",
+    "OptionError",
+    "OutOfBoundsDatetime",
+    "OutOfBoundsTimedelta",
+    "ParserError",
+    "ParserWarning",
+    "PerformanceWarning",
+    "PossibleDataLossError",
+    "PossiblePrecisionLoss",
+    "PyperclipException",
+    "PyperclipWindowsException",
+    "SettingWithCopyError",
+    "SettingWithCopyWarning",
+    "SpecificationError",
+    "UndefinedVariableError",
+    "UnsortedIndexError",
+    "UnsupportedFunctionCall",
+    "ValueLabelTypeMismatch",
+]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..c804b81c49e7c8abb406f2132909df6036df1c09
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/__init__.py
@@ -0,0 +1,13 @@
+# ruff: noqa: TCH004
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    # import modules that have public classes/functions
+    from pandas.io import (
+        formats,
+        json,
+        stata,
+    )
+
+    # mark only those modules as public
+    __all__ = ["formats", "json", "stata"]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/_util.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..35fdfb1a9ee82b4e09afc2da35bb89cdc5f10739
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/_util.py
@@ -0,0 +1,94 @@
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    Literal,
+)
+
+import numpy as np
+
+from pandas._config import using_string_dtype
+
+from pandas._libs import lib
+from pandas.compat import (
+    pa_version_under18p0,
+    pa_version_under19p0,
+)
+from pandas.compat._optional import import_optional_dependency
+
+import pandas as pd
+
+if TYPE_CHECKING:
+    from collections.abc import Callable
+
+    import pyarrow
+
+    from pandas._typing import DtypeBackend
+
+
+def _arrow_dtype_mapping() -> dict:
+    pa = import_optional_dependency("pyarrow")
+    return {
+        pa.int8(): pd.Int8Dtype(),
+        pa.int16(): pd.Int16Dtype(),
+        pa.int32(): pd.Int32Dtype(),
+        pa.int64(): pd.Int64Dtype(),
+        pa.uint8(): pd.UInt8Dtype(),
+        pa.uint16(): pd.UInt16Dtype(),
+        pa.uint32(): pd.UInt32Dtype(),
+        pa.uint64(): pd.UInt64Dtype(),
+        pa.bool_(): pd.BooleanDtype(),
+        pa.string(): pd.StringDtype(),
+        pa.float32(): pd.Float32Dtype(),
+        pa.float64(): pd.Float64Dtype(),
+        pa.string(): pd.StringDtype(),
+        pa.large_string(): pd.StringDtype(),
+    }
+
+
+def _arrow_string_types_mapper() -> Callable:
+    pa = import_optional_dependency("pyarrow")
+
+    mapping = {
+        pa.string(): pd.StringDtype(na_value=np.nan),
+        pa.large_string(): pd.StringDtype(na_value=np.nan),
+    }
+    if not pa_version_under18p0:
+        mapping[pa.string_view()] = pd.StringDtype(na_value=np.nan)
+
+    return mapping.get
+
+
+def arrow_table_to_pandas(
+    table: pyarrow.Table,
+    dtype_backend: DtypeBackend | Literal["numpy"] | lib.NoDefault = lib.no_default,
+    null_to_int64: bool = False,
+    to_pandas_kwargs: dict | None = None,
+) -> pd.DataFrame:
+    if to_pandas_kwargs is None:
+        to_pandas_kwargs = {}
+
+    pa = import_optional_dependency("pyarrow")
+
+    types_mapper: type[pd.ArrowDtype] | None | Callable
+    if dtype_backend == "numpy_nullable":
+        mapping = _arrow_dtype_mapping()
+        if null_to_int64:
+            # Modify the default mapping to also map null to Int64
+            # (to match other engines - only for CSV parser)
+            mapping[pa.null()] = pd.Int64Dtype()
+        types_mapper = mapping.get
+    elif dtype_backend == "pyarrow":
+        types_mapper = pd.ArrowDtype
+    elif using_string_dtype():
+        if pa_version_under19p0:
+            types_mapper = _arrow_string_types_mapper()
+        else:
+            types_mapper = None
+    elif dtype_backend is lib.no_default or dtype_backend == "numpy":
+        types_mapper = None
+    else:
+        raise NotImplementedError
+
+    df = table.to_pandas(types_mapper=types_mapper, **to_pandas_kwargs)
+    return df
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/api.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/api.py
new file mode 100644
index 0000000000000000000000000000000000000000..4e8b34a61dfc62992a37d9fab3263ee00a28d1fc
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/api.py
@@ -0,0 +1,65 @@
+"""
+Data IO api
+"""
+
+from pandas.io.clipboards import read_clipboard
+from pandas.io.excel import (
+    ExcelFile,
+    ExcelWriter,
+    read_excel,
+)
+from pandas.io.feather_format import read_feather
+from pandas.io.gbq import read_gbq
+from pandas.io.html import read_html
+from pandas.io.json import read_json
+from pandas.io.orc import read_orc
+from pandas.io.parquet import read_parquet
+from pandas.io.parsers import (
+    read_csv,
+    read_fwf,
+    read_table,
+)
+from pandas.io.pickle import (
+    read_pickle,
+    to_pickle,
+)
+from pandas.io.pytables import (
+    HDFStore,
+    read_hdf,
+)
+from pandas.io.sas import read_sas
+from pandas.io.spss import read_spss
+from pandas.io.sql import (
+    read_sql,
+    read_sql_query,
+    read_sql_table,
+)
+from pandas.io.stata import read_stata
+from pandas.io.xml import read_xml
+
+__all__ = [
+    "ExcelFile",
+    "ExcelWriter",
+    "HDFStore",
+    "read_clipboard",
+    "read_csv",
+    "read_excel",
+    "read_feather",
+    "read_fwf",
+    "read_gbq",
+    "read_hdf",
+    "read_html",
+    "read_json",
+    "read_orc",
+    "read_parquet",
+    "read_pickle",
+    "read_sas",
+    "read_spss",
+    "read_sql",
+    "read_sql_query",
+    "read_sql_table",
+    "read_stata",
+    "read_table",
+    "read_xml",
+    "to_pickle",
+]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/clipboard/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/clipboard/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..6491849925e863c35a98390a31729cb13e28ca19
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/clipboard/__init__.py
@@ -0,0 +1,747 @@
+"""
+Pyperclip
+
+A cross-platform clipboard module for Python,
+with copy & paste functions for plain text.
+By Al Sweigart al@inventwithpython.com
+Licence at LICENSES/PYPERCLIP_LICENSE
+
+Usage:
+  import pyperclip
+  pyperclip.copy('The text to be copied to the clipboard.')
+  spam = pyperclip.paste()
+
+  if not pyperclip.is_available():
+    print("Copy functionality unavailable!")
+
+On Windows, no additional modules are needed.
+On Mac, the pyobjc module is used, falling back to the pbcopy and pbpaste cli
+    commands. (These commands should come with OS X.).
+On Linux, install xclip, xsel, or wl-clipboard (for "wayland" sessions) via
+package manager.
+For example, in Debian:
+    sudo apt-get install xclip
+    sudo apt-get install xsel
+    sudo apt-get install wl-clipboard
+
+Otherwise on Linux, you will need the PyQt5 modules installed.
+
+This module does not work with PyGObject yet.
+
+Cygwin is currently not supported.
+
+Security Note: This module runs programs with these names:
+    - pbcopy
+    - pbpaste
+    - xclip
+    - xsel
+    - wl-copy/wl-paste
+    - klipper
+    - qdbus
+A malicious user could rename or add programs with these names, tricking
+Pyperclip into running them with whatever permissions the Python process has.
+
+"""
+
+__version__ = "1.8.2"
+
+
+import contextlib
+import ctypes
+from ctypes import (
+    c_size_t,
+    c_wchar,
+    c_wchar_p,
+    get_errno,
+    sizeof,
+)
+import os
+import platform
+from shutil import which as _executable_exists
+import subprocess
+import time
+import warnings
+
+from pandas.errors import (
+    PyperclipException,
+    PyperclipWindowsException,
+)
+from pandas.util._exceptions import find_stack_level
+
+# `import PyQt4` sys.exit()s if DISPLAY is not in the environment.
+# Thus, we need to detect the presence of $DISPLAY manually
+# and not load PyQt4 if it is absent.
+HAS_DISPLAY = os.getenv("DISPLAY")
+
+EXCEPT_MSG = """
+    Pyperclip could not find a copy/paste mechanism for your system.
+    For more information, please visit
+    https://pyperclip.readthedocs.io/en/latest/index.html#not-implemented-error
+    """
+
+ENCODING = "utf-8"
+
+
+class PyperclipTimeoutException(PyperclipException):
+    pass
+
+
+def _stringifyText(text) -> str:
+    acceptedTypes = (str, int, float, bool)
+    if not isinstance(text, acceptedTypes):
+        raise PyperclipException(
+            f"only str, int, float, and bool values "
+            f"can be copied to the clipboard, not {type(text).__name__}"
+        )
+    return str(text)
+
+
+def init_osx_pbcopy_clipboard():
+    def copy_osx_pbcopy(text):
+        text = _stringifyText(text)  # Converts non-str values to str.
+        with subprocess.Popen(
+            ["pbcopy", "w"], stdin=subprocess.PIPE, close_fds=True
+        ) as p:
+            p.communicate(input=text.encode(ENCODING))
+
+    def paste_osx_pbcopy():
+        with subprocess.Popen(
+            ["pbpaste", "r"], stdout=subprocess.PIPE, close_fds=True
+        ) as p:
+            stdout = p.communicate()[0]
+        return stdout.decode(ENCODING)
+
+    return copy_osx_pbcopy, paste_osx_pbcopy
+
+
+def init_osx_pyobjc_clipboard():
+    def copy_osx_pyobjc(text):
+        """Copy string argument to clipboard"""
+        text = _stringifyText(text)  # Converts non-str values to str.
+        newStr = Foundation.NSString.stringWithString_(text).nsstring()
+        newData = newStr.dataUsingEncoding_(Foundation.NSUTF8StringEncoding)
+        board = AppKit.NSPasteboard.generalPasteboard()
+        board.declareTypes_owner_([AppKit.NSStringPboardType], None)
+        board.setData_forType_(newData, AppKit.NSStringPboardType)
+
+    def paste_osx_pyobjc():
+        """Returns contents of clipboard"""
+        board = AppKit.NSPasteboard.generalPasteboard()
+        content = board.stringForType_(AppKit.NSStringPboardType)
+        return content
+
+    return copy_osx_pyobjc, paste_osx_pyobjc
+
+
+def init_qt_clipboard():
+    global QApplication
+    # $DISPLAY should exist
+
+    # Try to import from qtpy, but if that fails try PyQt5 then PyQt4
+    try:
+        from qtpy.QtWidgets import QApplication
+    except ImportError:
+        try:
+            from PyQt5.QtWidgets import QApplication
+        except ImportError:
+            from PyQt4.QtGui import QApplication
+
+    app = QApplication.instance()
+    if app is None:
+        app = QApplication([])
+
+    def copy_qt(text):
+        text = _stringifyText(text)  # Converts non-str values to str.
+        cb = app.clipboard()
+        cb.setText(text)
+
+    def paste_qt() -> str:
+        cb = app.clipboard()
+        return str(cb.text())
+
+    return copy_qt, paste_qt
+
+
+def init_xclip_clipboard():
+    DEFAULT_SELECTION = "c"
+    PRIMARY_SELECTION = "p"
+
+    def copy_xclip(text, primary=False):
+        text = _stringifyText(text)  # Converts non-str values to str.
+        selection = DEFAULT_SELECTION
+        if primary:
+            selection = PRIMARY_SELECTION
+        with subprocess.Popen(
+            ["xclip", "-selection", selection], stdin=subprocess.PIPE, close_fds=True
+        ) as p:
+            p.communicate(input=text.encode(ENCODING))
+
+    def paste_xclip(primary=False):
+        selection = DEFAULT_SELECTION
+        if primary:
+            selection = PRIMARY_SELECTION
+        with subprocess.Popen(
+            ["xclip", "-selection", selection, "-o"],
+            stdout=subprocess.PIPE,
+            stderr=subprocess.PIPE,
+            close_fds=True,
+        ) as p:
+            stdout = p.communicate()[0]
+        # Intentionally ignore extraneous output on stderr when clipboard is empty
+        return stdout.decode(ENCODING)
+
+    return copy_xclip, paste_xclip
+
+
+def init_xsel_clipboard():
+    DEFAULT_SELECTION = "-b"
+    PRIMARY_SELECTION = "-p"
+
+    def copy_xsel(text, primary=False):
+        text = _stringifyText(text)  # Converts non-str values to str.
+        selection_flag = DEFAULT_SELECTION
+        if primary:
+            selection_flag = PRIMARY_SELECTION
+        with subprocess.Popen(
+            ["xsel", selection_flag, "-i"], stdin=subprocess.PIPE, close_fds=True
+        ) as p:
+            p.communicate(input=text.encode(ENCODING))
+
+    def paste_xsel(primary=False):
+        selection_flag = DEFAULT_SELECTION
+        if primary:
+            selection_flag = PRIMARY_SELECTION
+        with subprocess.Popen(
+            ["xsel", selection_flag, "-o"], stdout=subprocess.PIPE, close_fds=True
+        ) as p:
+            stdout = p.communicate()[0]
+        return stdout.decode(ENCODING)
+
+    return copy_xsel, paste_xsel
+
+
+def init_wl_clipboard():
+    PRIMARY_SELECTION = "-p"
+
+    def copy_wl(text, primary=False):
+        text = _stringifyText(text)  # Converts non-str values to str.
+        args = ["wl-copy"]
+        if primary:
+            args.append(PRIMARY_SELECTION)
+        if not text:
+            args.append("--clear")
+            subprocess.check_call(args, close_fds=True)
+        else:
+            p = subprocess.Popen(args, stdin=subprocess.PIPE, close_fds=True)
+            p.communicate(input=text.encode(ENCODING))
+
+    def paste_wl(primary=False):
+        args = ["wl-paste", "-n"]
+        if primary:
+            args.append(PRIMARY_SELECTION)
+        p = subprocess.Popen(args, stdout=subprocess.PIPE, close_fds=True)
+        stdout, _stderr = p.communicate()
+        return stdout.decode(ENCODING)
+
+    return copy_wl, paste_wl
+
+
+def init_klipper_clipboard():
+    def copy_klipper(text):
+        text = _stringifyText(text)  # Converts non-str values to str.
+        with subprocess.Popen(
+            [
+                "qdbus",
+                "org.kde.klipper",
+                "/klipper",
+                "setClipboardContents",
+                text.encode(ENCODING),
+            ],
+            stdin=subprocess.PIPE,
+            close_fds=True,
+        ) as p:
+            p.communicate(input=None)
+
+    def paste_klipper():
+        with subprocess.Popen(
+            ["qdbus", "org.kde.klipper", "/klipper", "getClipboardContents"],
+            stdout=subprocess.PIPE,
+            close_fds=True,
+        ) as p:
+            stdout = p.communicate()[0]
+
+        # Workaround for https://bugs.kde.org/show_bug.cgi?id=342874
+        # TODO: https://github.com/asweigart/pyperclip/issues/43
+        clipboardContents = stdout.decode(ENCODING)
+        # even if blank, Klipper will append a newline at the end
+        assert len(clipboardContents) > 0
+        # make sure that newline is there
+        assert clipboardContents.endswith("\n")
+        if clipboardContents.endswith("\n"):
+            clipboardContents = clipboardContents[:-1]
+        return clipboardContents
+
+    return copy_klipper, paste_klipper
+
+
+def init_dev_clipboard_clipboard():
+    def copy_dev_clipboard(text):
+        text = _stringifyText(text)  # Converts non-str values to str.
+        if text == "":
+            warnings.warn(
+                "Pyperclip cannot copy a blank string to the clipboard on Cygwin. "
+                "This is effectively a no-op.",
+                stacklevel=find_stack_level(),
+            )
+        if "\r" in text:
+            warnings.warn(
+                "Pyperclip cannot handle \\r characters on Cygwin.",
+                stacklevel=find_stack_level(),
+            )
+
+        with open("/dev/clipboard", "w", encoding="utf-8") as fd:
+            fd.write(text)
+
+    def paste_dev_clipboard() -> str:
+        with open("/dev/clipboard", encoding="utf-8") as fd:
+            content = fd.read()
+        return content
+
+    return copy_dev_clipboard, paste_dev_clipboard
+
+
+def init_no_clipboard():
+    class ClipboardUnavailable:
+        def __call__(self, *args, **kwargs):
+            raise PyperclipException(EXCEPT_MSG)
+
+        def __bool__(self) -> bool:
+            return False
+
+    return ClipboardUnavailable(), ClipboardUnavailable()
+
+
+# Windows-related clipboard functions:
+class CheckedCall:
+    def __init__(self, f) -> None:
+        super().__setattr__("f", f)
+
+    def __call__(self, *args):
+        ret = self.f(*args)
+        if not ret and get_errno():
+            raise PyperclipWindowsException("Error calling " + self.f.__name__)
+        return ret
+
+    def __setattr__(self, key, value):
+        setattr(self.f, key, value)
+
+
+def init_windows_clipboard():
+    global HGLOBAL, LPVOID, DWORD, LPCSTR, INT
+    global HWND, HINSTANCE, HMENU, BOOL, UINT, HANDLE
+    from ctypes.wintypes import (
+        BOOL,
+        DWORD,
+        HANDLE,
+        HGLOBAL,
+        HINSTANCE,
+        HMENU,
+        HWND,
+        INT,
+        LPCSTR,
+        LPVOID,
+        UINT,
+    )
+
+    windll = ctypes.windll
+    msvcrt = ctypes.CDLL("msvcrt")
+
+    safeCreateWindowExA = CheckedCall(windll.user32.CreateWindowExA)
+    safeCreateWindowExA.argtypes = [
+        DWORD,
+        LPCSTR,
+        LPCSTR,
+        DWORD,
+        INT,
+        INT,
+        INT,
+        INT,
+        HWND,
+        HMENU,
+        HINSTANCE,
+        LPVOID,
+    ]
+    safeCreateWindowExA.restype = HWND
+
+    safeDestroyWindow = CheckedCall(windll.user32.DestroyWindow)
+    safeDestroyWindow.argtypes = [HWND]
+    safeDestroyWindow.restype = BOOL
+
+    OpenClipboard = windll.user32.OpenClipboard
+    OpenClipboard.argtypes = [HWND]
+    OpenClipboard.restype = BOOL
+
+    safeCloseClipboard = CheckedCall(windll.user32.CloseClipboard)
+    safeCloseClipboard.argtypes = []
+    safeCloseClipboard.restype = BOOL
+
+    safeEmptyClipboard = CheckedCall(windll.user32.EmptyClipboard)
+    safeEmptyClipboard.argtypes = []
+    safeEmptyClipboard.restype = BOOL
+
+    safeGetClipboardData = CheckedCall(windll.user32.GetClipboardData)
+    safeGetClipboardData.argtypes = [UINT]
+    safeGetClipboardData.restype = HANDLE
+
+    safeSetClipboardData = CheckedCall(windll.user32.SetClipboardData)
+    safeSetClipboardData.argtypes = [UINT, HANDLE]
+    safeSetClipboardData.restype = HANDLE
+
+    safeGlobalAlloc = CheckedCall(windll.kernel32.GlobalAlloc)
+    safeGlobalAlloc.argtypes = [UINT, c_size_t]
+    safeGlobalAlloc.restype = HGLOBAL
+
+    safeGlobalLock = CheckedCall(windll.kernel32.GlobalLock)
+    safeGlobalLock.argtypes = [HGLOBAL]
+    safeGlobalLock.restype = LPVOID
+
+    safeGlobalUnlock = CheckedCall(windll.kernel32.GlobalUnlock)
+    safeGlobalUnlock.argtypes = [HGLOBAL]
+    safeGlobalUnlock.restype = BOOL
+
+    wcslen = CheckedCall(msvcrt.wcslen)
+    wcslen.argtypes = [c_wchar_p]
+    wcslen.restype = UINT
+
+    GMEM_MOVEABLE = 0x0002
+    CF_UNICODETEXT = 13
+
+    @contextlib.contextmanager
+    def window():
+        """
+        Context that provides a valid Windows hwnd.
+        """
+        # we really just need the hwnd, so setting "STATIC"
+        # as predefined lpClass is just fine.
+        hwnd = safeCreateWindowExA(
+            0, b"STATIC", None, 0, 0, 0, 0, 0, None, None, None, None
+        )
+        try:
+            yield hwnd
+        finally:
+            safeDestroyWindow(hwnd)
+
+    @contextlib.contextmanager
+    def clipboard(hwnd):
+        """
+        Context manager that opens the clipboard and prevents
+        other applications from modifying the clipboard content.
+        """
+        # We may not get the clipboard handle immediately because
+        # some other application is accessing it (?)
+        # We try for at least 500ms to get the clipboard.
+        t = time.time() + 0.5
+        success = False
+        while time.time() < t:
+            success = OpenClipboard(hwnd)
+            if success:
+                break
+            time.sleep(0.01)
+        if not success:
+            raise PyperclipWindowsException("Error calling OpenClipboard")
+
+        try:
+            yield
+        finally:
+            safeCloseClipboard()
+
+    def copy_windows(text):
+        # This function is heavily based on
+        # http://msdn.com/ms649016#_win32_Copying_Information_to_the_Clipboard
+
+        text = _stringifyText(text)  # Converts non-str values to str.
+
+        with window() as hwnd:
+            # http://msdn.com/ms649048
+            # If an application calls OpenClipboard with hwnd set to NULL,
+            # EmptyClipboard sets the clipboard owner to NULL;
+            # this causes SetClipboardData to fail.
+            # => We need a valid hwnd to copy something.
+            with clipboard(hwnd):
+                safeEmptyClipboard()
+
+                if text:
+                    # http://msdn.com/ms649051
+                    # If the hMem parameter identifies a memory object,
+                    # the object must have been allocated using the
+                    # function with the GMEM_MOVEABLE flag.
+                    count = wcslen(text) + 1
+                    handle = safeGlobalAlloc(GMEM_MOVEABLE, count * sizeof(c_wchar))
+                    locked_handle = safeGlobalLock(handle)
+
+                    ctypes.memmove(
+                        c_wchar_p(locked_handle),
+                        c_wchar_p(text),
+                        count * sizeof(c_wchar),
+                    )
+
+                    safeGlobalUnlock(handle)
+                    safeSetClipboardData(CF_UNICODETEXT, handle)
+
+    def paste_windows():
+        with clipboard(None):
+            handle = safeGetClipboardData(CF_UNICODETEXT)
+            if not handle:
+                # GetClipboardData may return NULL with errno == NO_ERROR
+                # if the clipboard is empty.
+                # (Also, it may return a handle to an empty buffer,
+                # but technically that's not empty)
+                return ""
+            return c_wchar_p(handle).value
+
+    return copy_windows, paste_windows
+
+
+def init_wsl_clipboard():
+    def copy_wsl(text):
+        text = _stringifyText(text)  # Converts non-str values to str.
+        with subprocess.Popen(["clip.exe"], stdin=subprocess.PIPE, close_fds=True) as p:
+            p.communicate(input=text.encode(ENCODING))
+
+    def paste_wsl():
+        with subprocess.Popen(
+            ["powershell.exe", "-command", "Get-Clipboard"],
+            stdout=subprocess.PIPE,
+            stderr=subprocess.PIPE,
+            close_fds=True,
+        ) as p:
+            stdout = p.communicate()[0]
+        # WSL appends "\r\n" to the contents.
+        return stdout[:-2].decode(ENCODING)
+
+    return copy_wsl, paste_wsl
+
+
+# Automatic detection of clipboard mechanisms
+# and importing is done in determine_clipboard():
+def determine_clipboard():
+    """
+    Determine the OS/platform and set the copy() and paste() functions
+    accordingly.
+    """
+    global Foundation, AppKit, qtpy, PyQt4, PyQt5
+
+    # Setup for the CYGWIN platform:
+    if (
+        "cygwin" in platform.system().lower()
+    ):  # Cygwin has a variety of values returned by platform.system(),
+        # such as 'CYGWIN_NT-6.1'
+        # FIXME(pyperclip#55): pyperclip currently does not support Cygwin,
+        # see https://github.com/asweigart/pyperclip/issues/55
+        if os.path.exists("/dev/clipboard"):
+            warnings.warn(
+                "Pyperclip's support for Cygwin is not perfect, "
+                "see https://github.com/asweigart/pyperclip/issues/55",
+                stacklevel=find_stack_level(),
+            )
+            return init_dev_clipboard_clipboard()
+
+    # Setup for the WINDOWS platform:
+    elif os.name == "nt" or platform.system() == "Windows":
+        return init_windows_clipboard()
+
+    if platform.system() == "Linux":
+        if _executable_exists("wslconfig.exe"):
+            return init_wsl_clipboard()
+
+    # Setup for the macOS platform:
+    if os.name == "mac" or platform.system() == "Darwin":
+        try:
+            import AppKit
+            import Foundation  # check if pyobjc is installed
+        except ImportError:
+            return init_osx_pbcopy_clipboard()
+        else:
+            return init_osx_pyobjc_clipboard()
+
+    # Setup for the LINUX platform:
+    if HAS_DISPLAY:
+        if os.environ.get("WAYLAND_DISPLAY") and _executable_exists("wl-copy"):
+            return init_wl_clipboard()
+        if _executable_exists("xsel"):
+            return init_xsel_clipboard()
+        if _executable_exists("xclip"):
+            return init_xclip_clipboard()
+        if _executable_exists("klipper") and _executable_exists("qdbus"):
+            return init_klipper_clipboard()
+
+        try:
+            # qtpy is a small abstraction layer that lets you write applications
+            # using a single api call to either PyQt or PySide.
+            # https://pypi.python.org/project/QtPy
+            import qtpy  # check if qtpy is installed
+        except ImportError:
+            # If qtpy isn't installed, fall back on importing PyQt4.
+            try:
+                import PyQt5  # check if PyQt5 is installed
+            except ImportError:
+                try:
+                    import PyQt4  # check if PyQt4 is installed
+                except ImportError:
+                    pass  # We want to fail fast for all non-ImportError exceptions.
+                else:
+                    return init_qt_clipboard()
+            else:
+                return init_qt_clipboard()
+        else:
+            return init_qt_clipboard()
+
+    return init_no_clipboard()
+
+
+def set_clipboard(clipboard):
+    """
+    Explicitly sets the clipboard mechanism. The "clipboard mechanism" is how
+    the copy() and paste() functions interact with the operating system to
+    implement the copy/paste feature. The clipboard parameter must be one of:
+        - pbcopy
+        - pyobjc (default on macOS)
+        - qt
+        - xclip
+        - xsel
+        - klipper
+        - windows (default on Windows)
+        - no (this is what is set when no clipboard mechanism can be found)
+    """
+    global copy, paste
+
+    clipboard_types = {
+        "pbcopy": init_osx_pbcopy_clipboard,
+        "pyobjc": init_osx_pyobjc_clipboard,
+        "qt": init_qt_clipboard,  # TODO - split this into 'qtpy', 'pyqt4', and 'pyqt5'
+        "xclip": init_xclip_clipboard,
+        "xsel": init_xsel_clipboard,
+        "wl-clipboard": init_wl_clipboard,
+        "klipper": init_klipper_clipboard,
+        "windows": init_windows_clipboard,
+        "no": init_no_clipboard,
+    }
+
+    if clipboard not in clipboard_types:
+        allowed_clipboard_types = [repr(_) for _ in clipboard_types]
+        raise ValueError(
+            f"Argument must be one of {', '.join(allowed_clipboard_types)}"
+        )
+
+    # Sets pyperclip's copy() and paste() functions:
+    copy, paste = clipboard_types[clipboard]()
+
+
+def lazy_load_stub_copy(text):
+    """
+    A stub function for copy(), which will load the real copy() function when
+    called so that the real copy() function is used for later calls.
+
+    This allows users to import pyperclip without having determine_clipboard()
+    automatically run, which will automatically select a clipboard mechanism.
+    This could be a problem if it selects, say, the memory-heavy PyQt4 module
+    but the user was just going to immediately call set_clipboard() to use a
+    different clipboard mechanism.
+
+    The lazy loading this stub function implements gives the user a chance to
+    call set_clipboard() to pick another clipboard mechanism. Or, if the user
+    simply calls copy() or paste() without calling set_clipboard() first,
+    will fall back on whatever clipboard mechanism that determine_clipboard()
+    automatically chooses.
+    """
+    global copy, paste
+    copy, paste = determine_clipboard()
+    return copy(text)
+
+
+def lazy_load_stub_paste():
+    """
+    A stub function for paste(), which will load the real paste() function when
+    called so that the real paste() function is used for later calls.
+
+    This allows users to import pyperclip without having determine_clipboard()
+    automatically run, which will automatically select a clipboard mechanism.
+    This could be a problem if it selects, say, the memory-heavy PyQt4 module
+    but the user was just going to immediately call set_clipboard() to use a
+    different clipboard mechanism.
+
+    The lazy loading this stub function implements gives the user a chance to
+    call set_clipboard() to pick another clipboard mechanism. Or, if the user
+    simply calls copy() or paste() without calling set_clipboard() first,
+    will fall back on whatever clipboard mechanism that determine_clipboard()
+    automatically chooses.
+    """
+    global copy, paste
+    copy, paste = determine_clipboard()
+    return paste()
+
+
+def is_available() -> bool:
+    return copy != lazy_load_stub_copy and paste != lazy_load_stub_paste
+
+
+# Initially, copy() and paste() are set to lazy loading wrappers which will
+# set `copy` and `paste` to real functions the first time they're used, unless
+# set_clipboard() or determine_clipboard() is called first.
+copy, paste = lazy_load_stub_copy, lazy_load_stub_paste
+
+
+def waitForPaste(timeout=None):
+    """This function call blocks until a non-empty text string exists on the
+    clipboard. It returns this text.
+
+    This function raises PyperclipTimeoutException if timeout was set to
+    a number of seconds that has elapsed without non-empty text being put on
+    the clipboard."""
+    startTime = time.time()
+    while True:
+        clipboardText = paste()
+        if clipboardText != "":
+            return clipboardText
+        time.sleep(0.01)
+
+        if timeout is not None and time.time() > startTime + timeout:
+            raise PyperclipTimeoutException(
+                "waitForPaste() timed out after " + str(timeout) + " seconds."
+            )
+
+
+def waitForNewPaste(timeout=None):
+    """This function call blocks until a new text string exists on the
+    clipboard that is different from the text that was there when the function
+    was first called. It returns this text.
+
+    This function raises PyperclipTimeoutException if timeout was set to
+    a number of seconds that has elapsed without non-empty text being put on
+    the clipboard."""
+    startTime = time.time()
+    originalText = paste()
+    while True:
+        currentText = paste()
+        if currentText != originalText:
+            return currentText
+        time.sleep(0.01)
+
+        if timeout is not None and time.time() > startTime + timeout:
+            raise PyperclipTimeoutException(
+                "waitForNewPaste() timed out after " + str(timeout) + " seconds."
+            )
+
+
+__all__ = [
+    "copy",
+    "paste",
+    "waitForPaste",
+    "waitForNewPaste",
+    "set_clipboard",
+    "determine_clipboard",
+]
+
+# pandas aliases
+clipboard_get = paste
+clipboard_set = copy
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/clipboards.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/clipboards.py
new file mode 100644
index 0000000000000000000000000000000000000000..a15e37328e9fa95587d53b58b1af10e1e57fd60c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/clipboards.py
@@ -0,0 +1,197 @@
+""" io on the clipboard """
+from __future__ import annotations
+
+from io import StringIO
+from typing import TYPE_CHECKING
+import warnings
+
+from pandas._libs import lib
+from pandas.util._exceptions import find_stack_level
+from pandas.util._validators import check_dtype_backend
+
+from pandas.core.dtypes.generic import ABCDataFrame
+
+from pandas import (
+    get_option,
+    option_context,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import DtypeBackend
+
+
+def read_clipboard(
+    sep: str = r"\s+",
+    dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
+    **kwargs,
+):  # pragma: no cover
+    r"""
+    Read text from clipboard and pass to :func:`~pandas.read_csv`.
+
+    Parses clipboard contents similar to how CSV files are parsed
+    using :func:`~pandas.read_csv`.
+
+    Parameters
+    ----------
+    sep : str, default '\\s+'
+        A string or regex delimiter. The default of ``'\\s+'`` denotes
+        one or more whitespace characters.
+
+    dtype_backend : {'numpy_nullable', 'pyarrow'}, default 'numpy_nullable'
+        Back-end data type applied to the resultant :class:`DataFrame`
+        (still experimental). Behaviour is as follows:
+
+        * ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame`
+          (default).
+        * ``"pyarrow"``: returns pyarrow-backed nullable :class:`ArrowDtype`
+          DataFrame.
+
+        .. versionadded:: 2.0
+
+    **kwargs
+        See :func:`~pandas.read_csv` for the full argument list.
+
+    Returns
+    -------
+    DataFrame
+        A parsed :class:`~pandas.DataFrame` object.
+
+    See Also
+    --------
+    DataFrame.to_clipboard : Copy object to the system clipboard.
+    read_csv : Read a comma-separated values (csv) file into DataFrame.
+    read_fwf : Read a table of fixed-width formatted lines into DataFrame.
+
+    Examples
+    --------
+    >>> df = pd.DataFrame([[1, 2, 3], [4, 5, 6]], columns=['A', 'B', 'C'])
+    >>> df.to_clipboard()  # doctest: +SKIP
+    >>> pd.read_clipboard()  # doctest: +SKIP
+         A  B  C
+    0    1  2  3
+    1    4  5  6
+    """
+    encoding = kwargs.pop("encoding", "utf-8")
+
+    # only utf-8 is valid for passed value because that's what clipboard
+    # supports
+    if encoding is not None and encoding.lower().replace("-", "") != "utf8":
+        raise NotImplementedError("reading from clipboard only supports utf-8 encoding")
+
+    check_dtype_backend(dtype_backend)
+
+    from pandas.io.clipboard import clipboard_get
+    from pandas.io.parsers import read_csv
+
+    text = clipboard_get()
+
+    # Try to decode (if needed, as "text" might already be a string here).
+    try:
+        text = text.decode(kwargs.get("encoding") or get_option("display.encoding"))
+    except AttributeError:
+        pass
+
+    # Excel copies into clipboard with \t separation
+    # inspect no more then the 10 first lines, if they
+    # all contain an equal number (>0) of tabs, infer
+    # that this came from excel and set 'sep' accordingly
+    lines = text[:10000].split("\n")[:-1][:10]
+
+    # Need to remove leading white space, since read_csv
+    # accepts:
+    #    a  b
+    # 0  1  2
+    # 1  3  4
+
+    counts = {x.lstrip(" ").count("\t") for x in lines}
+    if len(lines) > 1 and len(counts) == 1 and counts.pop() != 0:
+        sep = "\t"
+        # check the number of leading tabs in the first line
+        # to account for index columns
+        index_length = len(lines[0]) - len(lines[0].lstrip(" \t"))
+        if index_length != 0:
+            kwargs.setdefault("index_col", list(range(index_length)))
+
+    # Edge case where sep is specified to be None, return to default
+    if sep is None and kwargs.get("delim_whitespace") is None:
+        sep = r"\s+"
+
+    # Regex separator currently only works with python engine.
+    # Default to python if separator is multi-character (regex)
+    if len(sep) > 1 and kwargs.get("engine") is None:
+        kwargs["engine"] = "python"
+    elif len(sep) > 1 and kwargs.get("engine") == "c":
+        warnings.warn(
+            "read_clipboard with regex separator does not work properly with c engine.",
+            stacklevel=find_stack_level(),
+        )
+
+    return read_csv(StringIO(text), sep=sep, dtype_backend=dtype_backend, **kwargs)
+
+
+def to_clipboard(
+    obj, excel: bool | None = True, sep: str | None = None, **kwargs
+) -> None:  # pragma: no cover
+    """
+    Attempt to write text representation of object to the system clipboard
+    The clipboard can be then pasted into Excel for example.
+
+    Parameters
+    ----------
+    obj : the object to write to the clipboard
+    excel : bool, defaults to True
+            if True, use the provided separator, writing in a csv
+            format for allowing easy pasting into excel.
+            if False, write a string representation of the object
+            to the clipboard
+    sep : optional, defaults to tab
+    other keywords are passed to to_csv
+
+    Notes
+    -----
+    Requirements for your platform
+      - Linux: xclip, or xsel (with PyQt4 modules)
+      - Windows:
+      - OS X:
+    """
+    encoding = kwargs.pop("encoding", "utf-8")
+
+    # testing if an invalid encoding is passed to clipboard
+    if encoding is not None and encoding.lower().replace("-", "") != "utf8":
+        raise ValueError("clipboard only supports utf-8 encoding")
+
+    from pandas.io.clipboard import clipboard_set
+
+    if excel is None:
+        excel = True
+
+    if excel:
+        try:
+            if sep is None:
+                sep = "\t"
+            buf = StringIO()
+
+            # clipboard_set (pyperclip) expects unicode
+            obj.to_csv(buf, sep=sep, encoding="utf-8", **kwargs)
+            text = buf.getvalue()
+
+            clipboard_set(text)
+            return
+        except TypeError:
+            warnings.warn(
+                "to_clipboard in excel mode requires a single character separator.",
+                stacklevel=find_stack_level(),
+            )
+    elif sep is not None:
+        warnings.warn(
+            "to_clipboard with excel=False ignores the sep argument.",
+            stacklevel=find_stack_level(),
+        )
+
+    if isinstance(obj, ABCDataFrame):
+        # str(df) has various unhelpful defaults, like truncation
+        with option_context("display.max_colwidth", None):
+            objstr = obj.to_string(**kwargs)
+    else:
+        objstr = str(obj)
+    clipboard_set(objstr)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/common.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/common.py
new file mode 100644
index 0000000000000000000000000000000000000000..72c9deeb54fc7aaab781b2870171cf983a47da1f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/common.py
@@ -0,0 +1,1267 @@
+"""Common IO api utilities"""
+from __future__ import annotations
+
+from abc import (
+    ABC,
+    abstractmethod,
+)
+import codecs
+from collections import defaultdict
+from collections.abc import (
+    Hashable,
+    Mapping,
+    Sequence,
+)
+import dataclasses
+import functools
+import gzip
+from io import (
+    BufferedIOBase,
+    BytesIO,
+    RawIOBase,
+    StringIO,
+    TextIOBase,
+    TextIOWrapper,
+)
+import mmap
+import os
+from pathlib import Path
+import re
+import tarfile
+from typing import (
+    IO,
+    TYPE_CHECKING,
+    Any,
+    AnyStr,
+    DefaultDict,
+    Generic,
+    Literal,
+    TypeVar,
+    cast,
+    overload,
+)
+from urllib.parse import (
+    urljoin,
+    urlparse as parse_url,
+    uses_netloc,
+    uses_params,
+    uses_relative,
+)
+import warnings
+import zipfile
+
+from pandas._typing import (
+    BaseBuffer,
+    ReadCsvBuffer,
+)
+from pandas.compat import (
+    get_bz2_file,
+    get_lzma_file,
+)
+from pandas.compat._optional import import_optional_dependency
+from pandas.util._decorators import doc
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.common import (
+    is_bool,
+    is_file_like,
+    is_integer,
+    is_list_like,
+)
+from pandas.core.dtypes.generic import ABCMultiIndex
+
+from pandas.core.shared_docs import _shared_docs
+
+_VALID_URLS = set(uses_relative + uses_netloc + uses_params)
+_VALID_URLS.discard("")
+_RFC_3986_PATTERN = re.compile(r"^[A-Za-z][A-Za-z0-9+\-+.]*://")
+
+BaseBufferT = TypeVar("BaseBufferT", bound=BaseBuffer)
+
+
+if TYPE_CHECKING:
+    from types import TracebackType
+
+    from pandas._typing import (
+        CompressionDict,
+        CompressionOptions,
+        FilePath,
+        ReadBuffer,
+        StorageOptions,
+        WriteBuffer,
+    )
+
+    from pandas import MultiIndex
+
+
+@dataclasses.dataclass
+class IOArgs:
+    """
+    Return value of io/common.py:_get_filepath_or_buffer.
+    """
+
+    filepath_or_buffer: str | BaseBuffer
+    encoding: str
+    mode: str
+    compression: CompressionDict
+    should_close: bool = False
+
+
+@dataclasses.dataclass
+class IOHandles(Generic[AnyStr]):
+    """
+    Return value of io/common.py:get_handle
+
+    Can be used as a context manager.
+
+    This is used to easily close created buffers and to handle corner cases when
+    TextIOWrapper is inserted.
+
+    handle: The file handle to be used.
+    created_handles: All file handles that are created by get_handle
+    is_wrapped: Whether a TextIOWrapper needs to be detached.
+    """
+
+    # handle might not implement the IO-interface
+    handle: IO[AnyStr]
+    compression: CompressionDict
+    created_handles: list[IO[bytes] | IO[str]] = dataclasses.field(default_factory=list)
+    is_wrapped: bool = False
+
+    def close(self) -> None:
+        """
+        Close all created buffers.
+
+        Note: If a TextIOWrapper was inserted, it is flushed and detached to
+        avoid closing the potentially user-created buffer.
+        """
+        if self.is_wrapped:
+            assert isinstance(self.handle, TextIOWrapper)
+            self.handle.flush()
+            self.handle.detach()
+            self.created_handles.remove(self.handle)
+        for handle in self.created_handles:
+            handle.close()
+        self.created_handles = []
+        self.is_wrapped = False
+
+    def __enter__(self) -> IOHandles[AnyStr]:
+        return self
+
+    def __exit__(
+        self,
+        exc_type: type[BaseException] | None,
+        exc_value: BaseException | None,
+        traceback: TracebackType | None,
+    ) -> None:
+        self.close()
+
+
+def is_url(url: object) -> bool:
+    """
+    Check to see if a URL has a valid protocol.
+
+    Parameters
+    ----------
+    url : str or unicode
+
+    Returns
+    -------
+    isurl : bool
+        If `url` has a valid protocol return True otherwise False.
+    """
+    if not isinstance(url, str):
+        return False
+    return parse_url(url).scheme in _VALID_URLS
+
+
+@overload
+def _expand_user(filepath_or_buffer: str) -> str:
+    ...
+
+
+@overload
+def _expand_user(filepath_or_buffer: BaseBufferT) -> BaseBufferT:
+    ...
+
+
+def _expand_user(filepath_or_buffer: str | BaseBufferT) -> str | BaseBufferT:
+    """
+    Return the argument with an initial component of ~ or ~user
+    replaced by that user's home directory.
+
+    Parameters
+    ----------
+    filepath_or_buffer : object to be converted if possible
+
+    Returns
+    -------
+    expanded_filepath_or_buffer : an expanded filepath or the
+                                  input if not expandable
+    """
+    if isinstance(filepath_or_buffer, str):
+        return os.path.expanduser(filepath_or_buffer)
+    return filepath_or_buffer
+
+
+def validate_header_arg(header: object) -> None:
+    if header is None:
+        return
+    if is_integer(header):
+        header = cast(int, header)
+        if header < 0:
+            # GH 27779
+            raise ValueError(
+                "Passing negative integer to header is invalid. "
+                "For no header, use header=None instead"
+            )
+        return
+    if is_list_like(header, allow_sets=False):
+        header = cast(Sequence, header)
+        if not all(map(is_integer, header)):
+            raise ValueError("header must be integer or list of integers")
+        if any(i < 0 for i in header):
+            raise ValueError("cannot specify multi-index header with negative integers")
+        return
+    if is_bool(header):
+        raise TypeError(
+            "Passing a bool to header is invalid. Use header=None for no header or "
+            "header=int or list-like of ints to specify "
+            "the row(s) making up the column names"
+        )
+    # GH 16338
+    raise ValueError("header must be integer or list of integers")
+
+
+@overload
+def stringify_path(filepath_or_buffer: FilePath, convert_file_like: bool = ...) -> str:
+    ...
+
+
+@overload
+def stringify_path(
+    filepath_or_buffer: BaseBufferT, convert_file_like: bool = ...
+) -> BaseBufferT:
+    ...
+
+
+def stringify_path(
+    filepath_or_buffer: FilePath | BaseBufferT,
+    convert_file_like: bool = False,
+) -> str | BaseBufferT:
+    """
+    Attempt to convert a path-like object to a string.
+
+    Parameters
+    ----------
+    filepath_or_buffer : object to be converted
+
+    Returns
+    -------
+    str_filepath_or_buffer : maybe a string version of the object
+
+    Notes
+    -----
+    Objects supporting the fspath protocol are coerced
+    according to its __fspath__ method.
+
+    Any other object is passed through unchanged, which includes bytes,
+    strings, buffers, or anything else that's not even path-like.
+    """
+    if not convert_file_like and is_file_like(filepath_or_buffer):
+        # GH 38125: some fsspec objects implement os.PathLike but have already opened a
+        # file. This prevents opening the file a second time. infer_compression calls
+        # this function with convert_file_like=True to infer the compression.
+        return cast(BaseBufferT, filepath_or_buffer)
+
+    if isinstance(filepath_or_buffer, os.PathLike):
+        filepath_or_buffer = filepath_or_buffer.__fspath__()
+    return _expand_user(filepath_or_buffer)
+
+
+def urlopen(*args, **kwargs):
+    """
+    Lazy-import wrapper for stdlib urlopen, as that imports a big chunk of
+    the stdlib.
+    """
+    import urllib.request
+
+    return urllib.request.urlopen(*args, **kwargs)
+
+
+def is_fsspec_url(url: FilePath | BaseBuffer) -> bool:
+    """
+    Returns true if the given URL looks like
+    something fsspec can handle
+    """
+    return (
+        isinstance(url, str)
+        and bool(_RFC_3986_PATTERN.match(url))
+        and not url.startswith(("http://", "https://"))
+    )
+
+
+@doc(
+    storage_options=_shared_docs["storage_options"],
+    compression_options=_shared_docs["compression_options"] % "filepath_or_buffer",
+)
+def _get_filepath_or_buffer(
+    filepath_or_buffer: FilePath | BaseBuffer,
+    encoding: str = "utf-8",
+    compression: CompressionOptions | None = None,
+    mode: str = "r",
+    storage_options: StorageOptions | None = None,
+) -> IOArgs:
+    """
+    If the filepath_or_buffer is a url, translate and return the buffer.
+    Otherwise passthrough.
+
+    Parameters
+    ----------
+    filepath_or_buffer : a url, filepath (str, py.path.local or pathlib.Path),
+                         or buffer
+    {compression_options}
+
+        .. versionchanged:: 1.4.0 Zstandard support.
+
+    encoding : the encoding to use to decode bytes, default is 'utf-8'
+    mode : str, optional
+
+    {storage_options}
+
+
+    Returns the dataclass IOArgs.
+    """
+    filepath_or_buffer = stringify_path(filepath_or_buffer)
+
+    # handle compression dict
+    compression_method, compression = get_compression_method(compression)
+    compression_method = infer_compression(filepath_or_buffer, compression_method)
+
+    # GH21227 internal compression is not used for non-binary handles.
+    if compression_method and hasattr(filepath_or_buffer, "write") and "b" not in mode:
+        warnings.warn(
+            "compression has no effect when passing a non-binary object as input.",
+            RuntimeWarning,
+            stacklevel=find_stack_level(),
+        )
+        compression_method = None
+
+    compression = dict(compression, method=compression_method)
+
+    # bz2 and xz do not write the byte order mark for utf-16 and utf-32
+    # print a warning when writing such files
+    if (
+        "w" in mode
+        and compression_method in ["bz2", "xz"]
+        and encoding in ["utf-16", "utf-32"]
+    ):
+        warnings.warn(
+            f"{compression} will not write the byte order mark for {encoding}",
+            UnicodeWarning,
+            stacklevel=find_stack_level(),
+        )
+
+    # Use binary mode when converting path-like objects to file-like objects (fsspec)
+    # except when text mode is explicitly requested. The original mode is returned if
+    # fsspec is not used.
+    fsspec_mode = mode
+    if "t" not in fsspec_mode and "b" not in fsspec_mode:
+        fsspec_mode += "b"
+
+    if isinstance(filepath_or_buffer, str) and is_url(filepath_or_buffer):
+        # TODO: fsspec can also handle HTTP via requests, but leaving this
+        # unchanged. using fsspec appears to break the ability to infer if the
+        # server responded with gzipped data
+        storage_options = storage_options or {}
+
+        # waiting until now for importing to match intended lazy logic of
+        # urlopen function defined elsewhere in this module
+        import urllib.request
+
+        # assuming storage_options is to be interpreted as headers
+        req_info = urllib.request.Request(filepath_or_buffer, headers=storage_options)
+        with urlopen(req_info) as req:
+            content_encoding = req.headers.get("Content-Encoding", None)
+            if content_encoding == "gzip":
+                # Override compression based on Content-Encoding header
+                compression = {"method": "gzip"}
+            reader = BytesIO(req.read())
+        return IOArgs(
+            filepath_or_buffer=reader,
+            encoding=encoding,
+            compression=compression,
+            should_close=True,
+            mode=fsspec_mode,
+        )
+
+    if is_fsspec_url(filepath_or_buffer):
+        assert isinstance(
+            filepath_or_buffer, str
+        )  # just to appease mypy for this branch
+        # two special-case s3-like protocols; these have special meaning in Hadoop,
+        # but are equivalent to just "s3" from fsspec's point of view
+        # cc #11071
+        if filepath_or_buffer.startswith("s3a://"):
+            filepath_or_buffer = filepath_or_buffer.replace("s3a://", "s3://")
+        if filepath_or_buffer.startswith("s3n://"):
+            filepath_or_buffer = filepath_or_buffer.replace("s3n://", "s3://")
+        fsspec = import_optional_dependency("fsspec")
+
+        # If botocore is installed we fallback to reading with anon=True
+        # to allow reads from public buckets
+        err_types_to_retry_with_anon: list[Any] = []
+        try:
+            import_optional_dependency("botocore")
+            from botocore.exceptions import (
+                ClientError,
+                NoCredentialsError,
+            )
+
+            err_types_to_retry_with_anon = [
+                ClientError,
+                NoCredentialsError,
+                PermissionError,
+            ]
+        except ImportError:
+            pass
+
+        try:
+            file_obj = fsspec.open(
+                filepath_or_buffer, mode=fsspec_mode, **(storage_options or {})
+            ).open()
+        # GH 34626 Reads from Public Buckets without Credentials needs anon=True
+        except tuple(err_types_to_retry_with_anon):
+            if storage_options is None:
+                storage_options = {"anon": True}
+            else:
+                # don't mutate user input.
+                storage_options = dict(storage_options)
+                storage_options["anon"] = True
+            file_obj = fsspec.open(
+                filepath_or_buffer, mode=fsspec_mode, **(storage_options or {})
+            ).open()
+
+        return IOArgs(
+            filepath_or_buffer=file_obj,
+            encoding=encoding,
+            compression=compression,
+            should_close=True,
+            mode=fsspec_mode,
+        )
+    elif storage_options:
+        raise ValueError(
+            "storage_options passed with file object or non-fsspec file path"
+        )
+
+    if isinstance(filepath_or_buffer, (str, bytes, mmap.mmap)):
+        return IOArgs(
+            filepath_or_buffer=_expand_user(filepath_or_buffer),
+            encoding=encoding,
+            compression=compression,
+            should_close=False,
+            mode=mode,
+        )
+
+    # is_file_like requires (read | write) & __iter__ but __iter__ is only
+    # needed for read_csv(engine=python)
+    if not (
+        hasattr(filepath_or_buffer, "read") or hasattr(filepath_or_buffer, "write")
+    ):
+        msg = f"Invalid file path or buffer object type: {type(filepath_or_buffer)}"
+        raise ValueError(msg)
+
+    return IOArgs(
+        filepath_or_buffer=filepath_or_buffer,
+        encoding=encoding,
+        compression=compression,
+        should_close=False,
+        mode=mode,
+    )
+
+
+def file_path_to_url(path: str) -> str:
+    """
+    converts an absolute native path to a FILE URL.
+
+    Parameters
+    ----------
+    path : a path in native format
+
+    Returns
+    -------
+    a valid FILE URL
+    """
+    # lazify expensive import (~30ms)
+    from urllib.request import pathname2url
+
+    return urljoin("file:", pathname2url(path))
+
+
+extension_to_compression = {
+    ".tar": "tar",
+    ".tar.gz": "tar",
+    ".tar.bz2": "tar",
+    ".tar.xz": "tar",
+    ".gz": "gzip",
+    ".bz2": "bz2",
+    ".zip": "zip",
+    ".xz": "xz",
+    ".zst": "zstd",
+}
+_supported_compressions = set(extension_to_compression.values())
+
+
+def get_compression_method(
+    compression: CompressionOptions,
+) -> tuple[str | None, CompressionDict]:
+    """
+    Simplifies a compression argument to a compression method string and
+    a mapping containing additional arguments.
+
+    Parameters
+    ----------
+    compression : str or mapping
+        If string, specifies the compression method. If mapping, value at key
+        'method' specifies compression method.
+
+    Returns
+    -------
+    tuple of ({compression method}, Optional[str]
+              {compression arguments}, Dict[str, Any])
+
+    Raises
+    ------
+    ValueError on mapping missing 'method' key
+    """
+    compression_method: str | None
+    if isinstance(compression, Mapping):
+        compression_args = dict(compression)
+        try:
+            compression_method = compression_args.pop("method")
+        except KeyError as err:
+            raise ValueError("If mapping, compression must have key 'method'") from err
+    else:
+        compression_args = {}
+        compression_method = compression
+    return compression_method, compression_args
+
+
+@doc(compression_options=_shared_docs["compression_options"] % "filepath_or_buffer")
+def infer_compression(
+    filepath_or_buffer: FilePath | BaseBuffer, compression: str | None
+) -> str | None:
+    """
+    Get the compression method for filepath_or_buffer. If compression='infer',
+    the inferred compression method is returned. Otherwise, the input
+    compression method is returned unchanged, unless it's invalid, in which
+    case an error is raised.
+
+    Parameters
+    ----------
+    filepath_or_buffer : str or file handle
+        File path or object.
+    {compression_options}
+
+        .. versionchanged:: 1.4.0 Zstandard support.
+
+    Returns
+    -------
+    string or None
+
+    Raises
+    ------
+    ValueError on invalid compression specified.
+    """
+    if compression is None:
+        return None
+
+    # Infer compression
+    if compression == "infer":
+        # Convert all path types (e.g. pathlib.Path) to strings
+        filepath_or_buffer = stringify_path(filepath_or_buffer, convert_file_like=True)
+        if not isinstance(filepath_or_buffer, str):
+            # Cannot infer compression of a buffer, assume no compression
+            return None
+
+        # Infer compression from the filename/URL extension
+        for extension, compression in extension_to_compression.items():
+            if filepath_or_buffer.lower().endswith(extension):
+                return compression
+        return None
+
+    # Compression has been specified. Check that it's valid
+    if compression in _supported_compressions:
+        return compression
+
+    valid = ["infer", None] + sorted(_supported_compressions)
+    msg = (
+        f"Unrecognized compression type: {compression}\n"
+        f"Valid compression types are {valid}"
+    )
+    raise ValueError(msg)
+
+
+def check_parent_directory(path: Path | str) -> None:
+    """
+    Check if parent directory of a file exists, raise OSError if it does not
+
+    Parameters
+    ----------
+    path: Path or str
+        Path to check parent directory of
+    """
+    parent = Path(path).parent
+    if not parent.is_dir():
+        raise OSError(rf"Cannot save file into a non-existent directory: '{parent}'")
+
+
+@overload
+def get_handle(
+    path_or_buf: FilePath | BaseBuffer,
+    mode: str,
+    *,
+    encoding: str | None = ...,
+    compression: CompressionOptions = ...,
+    memory_map: bool = ...,
+    is_text: Literal[False],
+    errors: str | None = ...,
+    storage_options: StorageOptions = ...,
+) -> IOHandles[bytes]:
+    ...
+
+
+@overload
+def get_handle(
+    path_or_buf: FilePath | BaseBuffer,
+    mode: str,
+    *,
+    encoding: str | None = ...,
+    compression: CompressionOptions = ...,
+    memory_map: bool = ...,
+    is_text: Literal[True] = ...,
+    errors: str | None = ...,
+    storage_options: StorageOptions = ...,
+) -> IOHandles[str]:
+    ...
+
+
+@overload
+def get_handle(
+    path_or_buf: FilePath | BaseBuffer,
+    mode: str,
+    *,
+    encoding: str | None = ...,
+    compression: CompressionOptions = ...,
+    memory_map: bool = ...,
+    is_text: bool = ...,
+    errors: str | None = ...,
+    storage_options: StorageOptions = ...,
+) -> IOHandles[str] | IOHandles[bytes]:
+    ...
+
+
+@doc(compression_options=_shared_docs["compression_options"] % "path_or_buf")
+def get_handle(
+    path_or_buf: FilePath | BaseBuffer,
+    mode: str,
+    *,
+    encoding: str | None = None,
+    compression: CompressionOptions | None = None,
+    memory_map: bool = False,
+    is_text: bool = True,
+    errors: str | None = None,
+    storage_options: StorageOptions | None = None,
+) -> IOHandles[str] | IOHandles[bytes]:
+    """
+    Get file handle for given path/buffer and mode.
+
+    Parameters
+    ----------
+    path_or_buf : str or file handle
+        File path or object.
+    mode : str
+        Mode to open path_or_buf with.
+    encoding : str or None
+        Encoding to use.
+    {compression_options}
+
+           May be a dict with key 'method' as compression mode
+           and other keys as compression options if compression
+           mode is 'zip'.
+
+           Passing compression options as keys in dict is
+           supported for compression modes 'gzip', 'bz2', 'zstd' and 'zip'.
+
+        .. versionchanged:: 1.4.0 Zstandard support.
+
+    memory_map : bool, default False
+        See parsers._parser_params for more information. Only used by read_csv.
+    is_text : bool, default True
+        Whether the type of the content passed to the file/buffer is string or
+        bytes. This is not the same as `"b" not in mode`. If a string content is
+        passed to a binary file/buffer, a wrapper is inserted.
+    errors : str, default 'strict'
+        Specifies how encoding and decoding errors are to be handled.
+        See the errors argument for :func:`open` for a full list
+        of options.
+    storage_options: StorageOptions = None
+        Passed to _get_filepath_or_buffer
+
+    Returns the dataclass IOHandles
+    """
+    # Windows does not default to utf-8. Set to utf-8 for a consistent behavior
+    encoding = encoding or "utf-8"
+
+    errors = errors or "strict"
+
+    # read_csv does not know whether the buffer is opened in binary/text mode
+    if _is_binary_mode(path_or_buf, mode) and "b" not in mode:
+        mode += "b"
+
+    # validate encoding and errors
+    codecs.lookup(encoding)
+    if isinstance(errors, str):
+        codecs.lookup_error(errors)
+
+    # open URLs
+    ioargs = _get_filepath_or_buffer(
+        path_or_buf,
+        encoding=encoding,
+        compression=compression,
+        mode=mode,
+        storage_options=storage_options,
+    )
+
+    handle = ioargs.filepath_or_buffer
+    handles: list[BaseBuffer]
+
+    # memory mapping needs to be the first step
+    # only used for read_csv
+    handle, memory_map, handles = _maybe_memory_map(handle, memory_map)
+
+    is_path = isinstance(handle, str)
+    compression_args = dict(ioargs.compression)
+    compression = compression_args.pop("method")
+
+    # Only for write methods
+    if "r" not in mode and is_path:
+        check_parent_directory(str(handle))
+
+    if compression:
+        if compression != "zstd":
+            # compression libraries do not like an explicit text-mode
+            ioargs.mode = ioargs.mode.replace("t", "")
+        elif compression == "zstd" and "b" not in ioargs.mode:
+            # python-zstandard defaults to text mode, but we always expect
+            # compression libraries to use binary mode.
+            ioargs.mode += "b"
+
+        # GZ Compression
+        if compression == "gzip":
+            if isinstance(handle, str):
+                # error: Incompatible types in assignment (expression has type
+                # "GzipFile", variable has type "Union[str, BaseBuffer]")
+                handle = gzip.GzipFile(  # type: ignore[assignment]
+                    filename=handle,
+                    mode=ioargs.mode,
+                    **compression_args,
+                )
+            else:
+                handle = gzip.GzipFile(
+                    # No overload variant of "GzipFile" matches argument types
+                    # "Union[str, BaseBuffer]", "str", "Dict[str, Any]"
+                    fileobj=handle,  # type: ignore[call-overload]
+                    mode=ioargs.mode,
+                    **compression_args,
+                )
+
+        # BZ Compression
+        elif compression == "bz2":
+            # Overload of "BZ2File" to handle pickle protocol 5
+            # "Union[str, BaseBuffer]", "str", "Dict[str, Any]"
+            handle = get_bz2_file()(  # type: ignore[call-overload]
+                handle,
+                mode=ioargs.mode,
+                **compression_args,
+            )
+
+        # ZIP Compression
+        elif compression == "zip":
+            # error: Argument 1 to "_BytesZipFile" has incompatible type
+            # "Union[str, BaseBuffer]"; expected "Union[Union[str, PathLike[str]],
+            # ReadBuffer[bytes], WriteBuffer[bytes]]"
+            handle = _BytesZipFile(
+                handle, ioargs.mode, **compression_args  # type: ignore[arg-type]
+            )
+            if handle.buffer.mode == "r":
+                handles.append(handle)
+                zip_names = handle.buffer.namelist()
+                if len(zip_names) == 1:
+                    handle = handle.buffer.open(zip_names.pop())
+                elif not zip_names:
+                    raise ValueError(f"Zero files found in ZIP file {path_or_buf}")
+                else:
+                    raise ValueError(
+                        "Multiple files found in ZIP file. "
+                        f"Only one file per ZIP: {zip_names}"
+                    )
+
+        # TAR Encoding
+        elif compression == "tar":
+            compression_args.setdefault("mode", ioargs.mode)
+            if isinstance(handle, str):
+                handle = _BytesTarFile(name=handle, **compression_args)
+            else:
+                # error: Argument "fileobj" to "_BytesTarFile" has incompatible
+                # type "BaseBuffer"; expected "Union[ReadBuffer[bytes],
+                # WriteBuffer[bytes], None]"
+                handle = _BytesTarFile(
+                    fileobj=handle, **compression_args  # type: ignore[arg-type]
+                )
+            assert isinstance(handle, _BytesTarFile)
+            if "r" in handle.buffer.mode:
+                handles.append(handle)
+                files = handle.buffer.getnames()
+                if len(files) == 1:
+                    file = handle.buffer.extractfile(files[0])
+                    assert file is not None
+                    handle = file
+                elif not files:
+                    raise ValueError(f"Zero files found in TAR archive {path_or_buf}")
+                else:
+                    raise ValueError(
+                        "Multiple files found in TAR archive. "
+                        f"Only one file per TAR archive: {files}"
+                    )
+
+        # XZ Compression
+        elif compression == "xz":
+            # error: Argument 1 to "LZMAFile" has incompatible type "Union[str,
+            # BaseBuffer]"; expected "Optional[Union[Union[str, bytes, PathLike[str],
+            # PathLike[bytes]], IO[bytes]], None]"
+            handle = get_lzma_file()(
+                handle, ioargs.mode, **compression_args  # type: ignore[arg-type]
+            )
+
+        # Zstd Compression
+        elif compression == "zstd":
+            zstd = import_optional_dependency("zstandard")
+            if "r" in ioargs.mode:
+                open_args = {"dctx": zstd.ZstdDecompressor(**compression_args)}
+            else:
+                open_args = {"cctx": zstd.ZstdCompressor(**compression_args)}
+            handle = zstd.open(
+                handle,
+                mode=ioargs.mode,
+                **open_args,
+            )
+
+        # Unrecognized Compression
+        else:
+            msg = f"Unrecognized compression type: {compression}"
+            raise ValueError(msg)
+
+        assert not isinstance(handle, str)
+        handles.append(handle)
+
+    elif isinstance(handle, str):
+        # Check whether the filename is to be opened in binary mode.
+        # Binary mode does not support 'encoding' and 'newline'.
+        if ioargs.encoding and "b" not in ioargs.mode:
+            # Encoding
+            handle = open(
+                handle,
+                ioargs.mode,
+                encoding=ioargs.encoding,
+                errors=errors,
+                newline="",
+            )
+        else:
+            # Binary mode
+            handle = open(handle, ioargs.mode)
+        handles.append(handle)
+
+    # Convert BytesIO or file objects passed with an encoding
+    is_wrapped = False
+    if not is_text and ioargs.mode == "rb" and isinstance(handle, TextIOBase):
+        # not added to handles as it does not open/buffer resources
+        handle = _BytesIOWrapper(
+            handle,
+            encoding=ioargs.encoding,
+        )
+    elif is_text and (
+        compression or memory_map or _is_binary_mode(handle, ioargs.mode)
+    ):
+        if (
+            not hasattr(handle, "readable")
+            or not hasattr(handle, "writable")
+            or not hasattr(handle, "seekable")
+        ):
+            handle = _IOWrapper(handle)
+        # error: Argument 1 to "TextIOWrapper" has incompatible type
+        # "_IOWrapper"; expected "IO[bytes]"
+        handle = TextIOWrapper(
+            handle,  # type: ignore[arg-type]
+            encoding=ioargs.encoding,
+            errors=errors,
+            newline="",
+        )
+        handles.append(handle)
+        # only marked as wrapped when the caller provided a handle
+        is_wrapped = not (
+            isinstance(ioargs.filepath_or_buffer, str) or ioargs.should_close
+        )
+
+    if "r" in ioargs.mode and not hasattr(handle, "read"):
+        raise TypeError(
+            "Expected file path name or file-like object, "
+            f"got {type(ioargs.filepath_or_buffer)} type"
+        )
+
+    handles.reverse()  # close the most recently added buffer first
+    if ioargs.should_close:
+        assert not isinstance(ioargs.filepath_or_buffer, str)
+        handles.append(ioargs.filepath_or_buffer)
+
+    return IOHandles(
+        # error: Argument "handle" to "IOHandles" has incompatible type
+        # "Union[TextIOWrapper, GzipFile, BaseBuffer, typing.IO[bytes],
+        # typing.IO[Any]]"; expected "pandas._typing.IO[Any]"
+        handle=handle,  # type: ignore[arg-type]
+        # error: Argument "created_handles" to "IOHandles" has incompatible type
+        # "List[BaseBuffer]"; expected "List[Union[IO[bytes], IO[str]]]"
+        created_handles=handles,  # type: ignore[arg-type]
+        is_wrapped=is_wrapped,
+        compression=ioargs.compression,
+    )
+
+
+# error: Definition of "__enter__" in base class "IOBase" is incompatible
+# with definition in base class "BinaryIO"
+class _BufferedWriter(BytesIO, ABC):  # type: ignore[misc]
+    """
+    Some objects do not support multiple .write() calls (TarFile and ZipFile).
+    This wrapper writes to the underlying buffer on close.
+    """
+
+    buffer = BytesIO()
+
+    @abstractmethod
+    def write_to_buffer(self) -> None:
+        ...
+
+    def close(self) -> None:
+        if self.closed:
+            # already closed
+            return
+        if self.getbuffer().nbytes:
+            # write to buffer
+            self.seek(0)
+            with self.buffer:
+                self.write_to_buffer()
+        else:
+            self.buffer.close()
+        super().close()
+
+
+class _BytesTarFile(_BufferedWriter):
+    def __init__(
+        self,
+        name: str | None = None,
+        mode: Literal["r", "a", "w", "x"] = "r",
+        fileobj: ReadBuffer[bytes] | WriteBuffer[bytes] | None = None,
+        archive_name: str | None = None,
+        **kwargs,
+    ) -> None:
+        super().__init__()
+        self.archive_name = archive_name
+        self.name = name
+        # error: Incompatible types in assignment (expression has type "TarFile",
+        # base class "_BufferedWriter" defined the type as "BytesIO")
+        self.buffer: tarfile.TarFile = tarfile.TarFile.open(  # type: ignore[assignment]
+            name=name,
+            mode=self.extend_mode(mode),
+            fileobj=fileobj,
+            **kwargs,
+        )
+
+    def extend_mode(self, mode: str) -> str:
+        mode = mode.replace("b", "")
+        if mode != "w":
+            return mode
+        if self.name is not None:
+            suffix = Path(self.name).suffix
+            if suffix in (".gz", ".xz", ".bz2"):
+                mode = f"{mode}:{suffix[1:]}"
+        return mode
+
+    def infer_filename(self) -> str | None:
+        """
+        If an explicit archive_name is not given, we still want the file inside the zip
+        file not to be named something.tar, because that causes confusion (GH39465).
+        """
+        if self.name is None:
+            return None
+
+        filename = Path(self.name)
+        if filename.suffix == ".tar":
+            return filename.with_suffix("").name
+        elif filename.suffix in (".tar.gz", ".tar.bz2", ".tar.xz"):
+            return filename.with_suffix("").with_suffix("").name
+        return filename.name
+
+    def write_to_buffer(self) -> None:
+        # TarFile needs a non-empty string
+        archive_name = self.archive_name or self.infer_filename() or "tar"
+        tarinfo = tarfile.TarInfo(name=archive_name)
+        tarinfo.size = len(self.getvalue())
+        self.buffer.addfile(tarinfo, self)
+
+
+class _BytesZipFile(_BufferedWriter):
+    def __init__(
+        self,
+        file: FilePath | ReadBuffer[bytes] | WriteBuffer[bytes],
+        mode: str,
+        archive_name: str | None = None,
+        **kwargs,
+    ) -> None:
+        super().__init__()
+        mode = mode.replace("b", "")
+        self.archive_name = archive_name
+
+        kwargs.setdefault("compression", zipfile.ZIP_DEFLATED)
+        # error: Incompatible types in assignment (expression has type "ZipFile",
+        # base class "_BufferedWriter" defined the type as "BytesIO")
+        self.buffer: zipfile.ZipFile = zipfile.ZipFile(  # type: ignore[assignment]
+            file, mode, **kwargs
+        )
+
+    def infer_filename(self) -> str | None:
+        """
+        If an explicit archive_name is not given, we still want the file inside the zip
+        file not to be named something.zip, because that causes confusion (GH39465).
+        """
+        if isinstance(self.buffer.filename, (os.PathLike, str)):
+            filename = Path(self.buffer.filename)
+            if filename.suffix == ".zip":
+                return filename.with_suffix("").name
+            return filename.name
+        return None
+
+    def write_to_buffer(self) -> None:
+        # ZipFile needs a non-empty string
+        archive_name = self.archive_name or self.infer_filename() or "zip"
+        self.buffer.writestr(archive_name, self.getvalue())
+
+
+class _IOWrapper:
+    # TextIOWrapper is overly strict: it request that the buffer has seekable, readable,
+    # and writable. If we have a read-only buffer, we shouldn't need writable and vice
+    # versa. Some buffers, are seek/read/writ-able but they do not have the "-able"
+    # methods, e.g., tempfile.SpooledTemporaryFile.
+    # If a buffer does not have the above "-able" methods, we simple assume they are
+    # seek/read/writ-able.
+    def __init__(self, buffer: BaseBuffer) -> None:
+        self.buffer = buffer
+
+    def __getattr__(self, name: str):
+        return getattr(self.buffer, name)
+
+    def readable(self) -> bool:
+        if hasattr(self.buffer, "readable"):
+            return self.buffer.readable()
+        return True
+
+    def seekable(self) -> bool:
+        if hasattr(self.buffer, "seekable"):
+            return self.buffer.seekable()
+        return True
+
+    def writable(self) -> bool:
+        if hasattr(self.buffer, "writable"):
+            return self.buffer.writable()
+        return True
+
+
+class _BytesIOWrapper:
+    # Wrapper that wraps a StringIO buffer and reads bytes from it
+    # Created for compat with pyarrow read_csv
+    def __init__(self, buffer: StringIO | TextIOBase, encoding: str = "utf-8") -> None:
+        self.buffer = buffer
+        self.encoding = encoding
+        # Because a character can be represented by more than 1 byte,
+        # it is possible that reading will produce more bytes than n
+        # We store the extra bytes in this overflow variable, and append the
+        # overflow to the front of the bytestring the next time reading is performed
+        self.overflow = b""
+
+    def __getattr__(self, attr: str):
+        return getattr(self.buffer, attr)
+
+    def read(self, n: int | None = -1) -> bytes:
+        assert self.buffer is not None
+        bytestring = self.buffer.read(n).encode(self.encoding)
+        # When n=-1/n greater than remaining bytes: Read entire file/rest of file
+        combined_bytestring = self.overflow + bytestring
+        if n is None or n < 0 or n >= len(combined_bytestring):
+            self.overflow = b""
+            return combined_bytestring
+        else:
+            to_return = combined_bytestring[:n]
+            self.overflow = combined_bytestring[n:]
+            return to_return
+
+
+def _maybe_memory_map(
+    handle: str | BaseBuffer, memory_map: bool
+) -> tuple[str | BaseBuffer, bool, list[BaseBuffer]]:
+    """Try to memory map file/buffer."""
+    handles: list[BaseBuffer] = []
+    memory_map &= hasattr(handle, "fileno") or isinstance(handle, str)
+    if not memory_map:
+        return handle, memory_map, handles
+
+    # mmap used by only read_csv
+    handle = cast(ReadCsvBuffer, handle)
+
+    # need to open the file first
+    if isinstance(handle, str):
+        handle = open(handle, "rb")
+        handles.append(handle)
+
+    try:
+        # open mmap and adds *-able
+        # error: Argument 1 to "_IOWrapper" has incompatible type "mmap";
+        # expected "BaseBuffer"
+        wrapped = _IOWrapper(
+            mmap.mmap(
+                handle.fileno(), 0, access=mmap.ACCESS_READ  # type: ignore[arg-type]
+            )
+        )
+    finally:
+        for handle in reversed(handles):
+            # error: "BaseBuffer" has no attribute "close"
+            handle.close()  # type: ignore[attr-defined]
+
+    return wrapped, memory_map, [wrapped]
+
+
+def file_exists(filepath_or_buffer: FilePath | BaseBuffer) -> bool:
+    """Test whether file exists."""
+    exists = False
+    filepath_or_buffer = stringify_path(filepath_or_buffer)
+    if not isinstance(filepath_or_buffer, str):
+        return exists
+    try:
+        exists = os.path.exists(filepath_or_buffer)
+        # gh-5874: if the filepath is too long will raise here
+    except (TypeError, ValueError):
+        pass
+    return exists
+
+
+def _is_binary_mode(handle: FilePath | BaseBuffer, mode: str) -> bool:
+    """Whether the handle is opened in binary mode"""
+    # specified by user
+    if "t" in mode or "b" in mode:
+        return "b" in mode
+
+    # exceptions
+    text_classes = (
+        # classes that expect string but have 'b' in mode
+        codecs.StreamWriter,
+        codecs.StreamReader,
+        codecs.StreamReaderWriter,
+    )
+    if issubclass(type(handle), text_classes):
+        return False
+
+    return isinstance(handle, _get_binary_io_classes()) or "b" in getattr(
+        handle, "mode", mode
+    )
+
+
+@functools.lru_cache
+def _get_binary_io_classes() -> tuple[type, ...]:
+    """IO classes that that expect bytes"""
+    binary_classes: tuple[type, ...] = (BufferedIOBase, RawIOBase)
+
+    # python-zstandard doesn't use any of the builtin base classes; instead we
+    # have to use the `zstd.ZstdDecompressionReader` class for isinstance checks.
+    # Unfortunately `zstd.ZstdDecompressionReader` isn't exposed by python-zstandard
+    # so we have to get it from a `zstd.ZstdDecompressor` instance.
+    # See also https://github.com/indygreg/python-zstandard/pull/165.
+    zstd = import_optional_dependency("zstandard", errors="ignore")
+    if zstd is not None:
+        with zstd.ZstdDecompressor().stream_reader(b"") as reader:
+            binary_classes += (type(reader),)
+
+    return binary_classes
+
+
+def is_potential_multi_index(
+    columns: Sequence[Hashable] | MultiIndex,
+    index_col: bool | Sequence[int] | None = None,
+) -> bool:
+    """
+    Check whether or not the `columns` parameter
+    could be converted into a MultiIndex.
+
+    Parameters
+    ----------
+    columns : array-like
+        Object which may or may not be convertible into a MultiIndex
+    index_col : None, bool or list, optional
+        Column or columns to use as the (possibly hierarchical) index
+
+    Returns
+    -------
+    bool : Whether or not columns could become a MultiIndex
+    """
+    if index_col is None or isinstance(index_col, bool):
+        index_col = []
+
+    return bool(
+        len(columns)
+        and not isinstance(columns, ABCMultiIndex)
+        and all(isinstance(c, tuple) for c in columns if c not in list(index_col))
+    )
+
+
+def dedup_names(
+    names: Sequence[Hashable], is_potential_multiindex: bool
+) -> Sequence[Hashable]:
+    """
+    Rename column names if duplicates exist.
+
+    Currently the renaming is done by appending a period and an autonumeric,
+    but a custom pattern may be supported in the future.
+
+    Examples
+    --------
+    >>> dedup_names(["x", "y", "x", "x"], is_potential_multiindex=False)
+    ['x', 'y', 'x.1', 'x.2']
+    """
+    names = list(names)  # so we can index
+    counts: DefaultDict[Hashable, int] = defaultdict(int)
+
+    for i, col in enumerate(names):
+        cur_count = counts[col]
+
+        while cur_count > 0:
+            counts[col] = cur_count + 1
+
+            if is_potential_multiindex:
+                # for mypy
+                assert isinstance(col, tuple)
+                col = col[:-1] + (f"{col[-1]}.{cur_count}",)
+            else:
+                col = f"{col}.{cur_count}"
+            cur_count = counts[col]
+
+        names[i] = col
+        counts[col] = cur_count + 1
+
+    return names
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/excel/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/excel/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..275cbf0148f944eb04ca6c40c624cc5df77aa626
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/excel/__init__.py
@@ -0,0 +1,19 @@
+from pandas.io.excel._base import (
+    ExcelFile,
+    ExcelWriter,
+    read_excel,
+)
+from pandas.io.excel._odswriter import ODSWriter as _ODSWriter
+from pandas.io.excel._openpyxl import OpenpyxlWriter as _OpenpyxlWriter
+from pandas.io.excel._util import register_writer
+from pandas.io.excel._xlsxwriter import XlsxWriter as _XlsxWriter
+
+__all__ = ["read_excel", "ExcelWriter", "ExcelFile"]
+
+
+register_writer(_OpenpyxlWriter)
+
+register_writer(_XlsxWriter)
+
+
+register_writer(_ODSWriter)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/excel/_base.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/excel/_base.py
new file mode 100644
index 0000000000000000000000000000000000000000..786f719337b84a29e5b6ea7577edd412b596920f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/excel/_base.py
@@ -0,0 +1,1659 @@
+from __future__ import annotations
+
+from collections.abc import (
+    Hashable,
+    Iterable,
+    Mapping,
+    Sequence,
+)
+import datetime
+from functools import partial
+from io import BytesIO
+import os
+from textwrap import fill
+from typing import (
+    IO,
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Generic,
+    Literal,
+    TypeVar,
+    Union,
+    cast,
+    overload,
+)
+import warnings
+import zipfile
+
+from pandas._config import config
+
+from pandas._libs import lib
+from pandas._libs.parsers import STR_NA_VALUES
+from pandas.compat._optional import (
+    get_version,
+    import_optional_dependency,
+)
+from pandas.errors import EmptyDataError
+from pandas.util._decorators import (
+    Appender,
+    doc,
+)
+from pandas.util._exceptions import find_stack_level
+from pandas.util._validators import check_dtype_backend
+
+from pandas.core.dtypes.common import (
+    is_bool,
+    is_float,
+    is_integer,
+    is_list_like,
+)
+
+from pandas.core.frame import DataFrame
+from pandas.core.shared_docs import _shared_docs
+from pandas.util.version import Version
+
+from pandas.io.common import (
+    IOHandles,
+    get_handle,
+    stringify_path,
+    validate_header_arg,
+)
+from pandas.io.excel._util import (
+    fill_mi_header,
+    get_default_engine,
+    get_writer,
+    maybe_convert_usecols,
+    pop_header_name,
+)
+from pandas.io.parsers import TextParser
+from pandas.io.parsers.readers import validate_integer
+
+if TYPE_CHECKING:
+    from types import TracebackType
+
+    from pandas._typing import (
+        DtypeArg,
+        DtypeBackend,
+        ExcelWriterIfSheetExists,
+        FilePath,
+        IntStrT,
+        ReadBuffer,
+        Self,
+        SequenceNotStr,
+        StorageOptions,
+        WriteExcelBuffer,
+    )
+_read_excel_doc = (
+    """
+Read an Excel file into a ``pandas`` ``DataFrame``.
+
+Supports `xls`, `xlsx`, `xlsm`, `xlsb`, `odf`, `ods` and `odt` file extensions
+read from a local filesystem or URL. Supports an option to read
+a single sheet or a list of sheets.
+
+Parameters
+----------
+io : str, bytes, ExcelFile, xlrd.Book, path object, or file-like object
+    Any valid string path is acceptable. The string could be a URL. Valid
+    URL schemes include http, ftp, s3, and file. For file URLs, a host is
+    expected. A local file could be: ``file://localhost/path/to/table.xlsx``.
+
+    If you want to pass in a path object, pandas accepts any ``os.PathLike``.
+
+    By file-like object, we refer to objects with a ``read()`` method,
+    such as a file handle (e.g. via builtin ``open`` function)
+    or ``StringIO``.
+
+    .. deprecated:: 2.1.0
+        Passing byte strings is deprecated. To read from a
+        byte string, wrap it in a ``BytesIO`` object.
+sheet_name : str, int, list, or None, default 0
+    Strings are used for sheet names. Integers are used in zero-indexed
+    sheet positions (chart sheets do not count as a sheet position).
+    Lists of strings/integers are used to request multiple sheets.
+    Specify ``None`` to get all worksheets.
+
+    Available cases:
+
+    * Defaults to ``0``: 1st sheet as a `DataFrame`
+    * ``1``: 2nd sheet as a `DataFrame`
+    * ``"Sheet1"``: Load sheet with name "Sheet1"
+    * ``[0, 1, "Sheet5"]``: Load first, second and sheet named "Sheet5"
+      as a dict of `DataFrame`
+    * ``None``: All worksheets.
+
+header : int, list of int, default 0
+    Row (0-indexed) to use for the column labels of the parsed
+    DataFrame. If a list of integers is passed those row positions will
+    be combined into a ``MultiIndex``. Use None if there is no header.
+names : array-like, default None
+    List of column names to use. If file contains no header row,
+    then you should explicitly pass header=None.
+index_col : int, str, list of int, default None
+    Column (0-indexed) to use as the row labels of the DataFrame.
+    Pass None if there is no such column.  If a list is passed,
+    those columns will be combined into a ``MultiIndex``.  If a
+    subset of data is selected with ``usecols``, index_col
+    is based on the subset.
+
+    Missing values will be forward filled to allow roundtripping with
+    ``to_excel`` for ``merged_cells=True``. To avoid forward filling the
+    missing values use ``set_index`` after reading the data instead of
+    ``index_col``.
+usecols : str, list-like, or callable, default None
+    * If None, then parse all columns.
+    * If str, then indicates comma separated list of Excel column letters
+      and column ranges (e.g. "A:E" or "A,C,E:F"). Ranges are inclusive of
+      both sides.
+    * If list of int, then indicates list of column numbers to be parsed
+      (0-indexed).
+    * If list of string, then indicates list of column names to be parsed.
+    * If callable, then evaluate each column name against it and parse the
+      column if the callable returns ``True``.
+
+    Returns a subset of the columns according to behavior above.
+dtype : Type name or dict of column -> type, default None
+    Data type for data or columns. E.g. {{'a': np.float64, 'b': np.int32}}
+    Use ``object`` to preserve data as stored in Excel and not interpret dtype,
+    which will necessarily result in ``object`` dtype.
+    If converters are specified, they will be applied INSTEAD
+    of dtype conversion.
+    If you use ``None``, it will infer the dtype of each column based on the data.
+engine : {{'openpyxl', 'calamine', 'odf', 'pyxlsb', 'xlrd'}}, default None
+    If io is not a buffer or path, this must be set to identify io.
+    Engine compatibility :
+
+    - ``openpyxl`` supports newer Excel file formats.
+    - ``calamine`` supports Excel (.xls, .xlsx, .xlsm, .xlsb)
+      and OpenDocument (.ods) file formats.
+    - ``odf`` supports OpenDocument file formats (.odf, .ods, .odt).
+    - ``pyxlsb`` supports Binary Excel files.
+    - ``xlrd`` supports old-style Excel files (.xls).
+
+    When ``engine=None``, the following logic will be used to determine the engine:
+
+    - If ``path_or_buffer`` is an OpenDocument format (.odf, .ods, .odt),
+      then `odf `_ will be used.
+    - Otherwise if ``path_or_buffer`` is an xls format, ``xlrd`` will be used.
+    - Otherwise if ``path_or_buffer`` is in xlsb format, ``pyxlsb`` will be used.
+    - Otherwise ``openpyxl`` will be used.
+converters : dict, default None
+    Dict of functions for converting values in certain columns. Keys can
+    either be integers or column labels, values are functions that take one
+    input argument, the Excel cell content, and return the transformed
+    content.
+true_values : list, default None
+    Values to consider as True.
+false_values : list, default None
+    Values to consider as False.
+skiprows : list-like, int, or callable, optional
+    Line numbers to skip (0-indexed) or number of lines to skip (int) at the
+    start of the file. If callable, the callable function will be evaluated
+    against the row indices, returning True if the row should be skipped and
+    False otherwise. An example of a valid callable argument would be ``lambda
+    x: x in [0, 2]``.
+nrows : int, default None
+    Number of rows to parse.
+na_values : scalar, str, list-like, or dict, default None
+    Additional strings to recognize as NA/NaN. If dict passed, specific
+    per-column NA values. By default the following values are interpreted
+    as NaN: '"""
+    + fill("', '".join(sorted(STR_NA_VALUES)), 70, subsequent_indent="    ")
+    + """'.
+keep_default_na : bool, default True
+    Whether or not to include the default NaN values when parsing the data.
+    Depending on whether ``na_values`` is passed in, the behavior is as follows:
+
+    * If ``keep_default_na`` is True, and ``na_values`` are specified,
+      ``na_values`` is appended to the default NaN values used for parsing.
+    * If ``keep_default_na`` is True, and ``na_values`` are not specified, only
+      the default NaN values are used for parsing.
+    * If ``keep_default_na`` is False, and ``na_values`` are specified, only
+      the NaN values specified ``na_values`` are used for parsing.
+    * If ``keep_default_na`` is False, and ``na_values`` are not specified, no
+      strings will be parsed as NaN.
+
+    Note that if `na_filter` is passed in as False, the ``keep_default_na`` and
+    ``na_values`` parameters will be ignored.
+na_filter : bool, default True
+    Detect missing value markers (empty strings and the value of na_values). In
+    data without any NAs, passing ``na_filter=False`` can improve the
+    performance of reading a large file.
+verbose : bool, default False
+    Indicate number of NA values placed in non-numeric columns.
+parse_dates : bool, list-like, or dict, default False
+    The behavior is as follows:
+
+    * ``bool``. If True -> try parsing the index.
+    * ``list`` of int or names. e.g. If [1, 2, 3] -> try parsing columns 1, 2, 3
+      each as a separate date column.
+    * ``list`` of lists. e.g.  If [[1, 3]] -> combine columns 1 and 3 and parse as
+      a single date column.
+    * ``dict``, e.g. {{'foo' : [1, 3]}} -> parse columns 1, 3 as date and call
+      result 'foo'
+
+    If a column or index contains an unparsable date, the entire column or
+    index will be returned unaltered as an object data type. If you don`t want to
+    parse some cells as date just change their type in Excel to "Text".
+    For non-standard datetime parsing, use ``pd.to_datetime`` after ``pd.read_excel``.
+
+    Note: A fast-path exists for iso8601-formatted dates.
+date_parser : function, optional
+    Function to use for converting a sequence of string columns to an array of
+    datetime instances. The default uses ``dateutil.parser.parser`` to do the
+    conversion. Pandas will try to call `date_parser` in three different ways,
+    advancing to the next if an exception occurs: 1) Pass one or more arrays
+    (as defined by `parse_dates`) as arguments; 2) concatenate (row-wise) the
+    string values from the columns defined by `parse_dates` into a single array
+    and pass that; and 3) call `date_parser` once for each row using one or
+    more strings (corresponding to the columns defined by `parse_dates`) as
+    arguments.
+
+    .. deprecated:: 2.0.0
+       Use ``date_format`` instead, or read in as ``object`` and then apply
+       :func:`to_datetime` as-needed.
+date_format : str or dict of column -> format, default ``None``
+   If used in conjunction with ``parse_dates``, will parse dates according to this
+   format. For anything more complex,
+   please read in as ``object`` and then apply :func:`to_datetime` as-needed.
+
+   .. versionadded:: 2.0.0
+thousands : str, default None
+    Thousands separator for parsing string columns to numeric.  Note that
+    this parameter is only necessary for columns stored as TEXT in Excel,
+    any numeric columns will automatically be parsed, regardless of display
+    format.
+decimal : str, default '.'
+    Character to recognize as decimal point for parsing string columns to numeric.
+    Note that this parameter is only necessary for columns stored as TEXT in Excel,
+    any numeric columns will automatically be parsed, regardless of display
+    format.(e.g. use ',' for European data).
+
+    .. versionadded:: 1.4.0
+
+comment : str, default None
+    Comments out remainder of line. Pass a character or characters to this
+    argument to indicate comments in the input file. Any data between the
+    comment string and the end of the current line is ignored.
+skipfooter : int, default 0
+    Rows at the end to skip (0-indexed).
+{storage_options}
+
+dtype_backend : {{'numpy_nullable', 'pyarrow'}}, default 'numpy_nullable'
+    Back-end data type applied to the resultant :class:`DataFrame`
+    (still experimental). Behaviour is as follows:
+
+    * ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame`
+      (default).
+    * ``"pyarrow"``: returns pyarrow-backed nullable :class:`ArrowDtype`
+      DataFrame.
+
+    .. versionadded:: 2.0
+
+engine_kwargs : dict, optional
+    Arbitrary keyword arguments passed to excel engine.
+
+Returns
+-------
+DataFrame or dict of DataFrames
+    DataFrame from the passed in Excel file. See notes in sheet_name
+    argument for more information on when a dict of DataFrames is returned.
+
+See Also
+--------
+DataFrame.to_excel : Write DataFrame to an Excel file.
+DataFrame.to_csv : Write DataFrame to a comma-separated values (csv) file.
+read_csv : Read a comma-separated values (csv) file into DataFrame.
+read_fwf : Read a table of fixed-width formatted lines into DataFrame.
+
+Notes
+-----
+For specific information on the methods used for each Excel engine, refer to the pandas
+:ref:`user guide `
+
+Examples
+--------
+The file can be read using the file name as string or an open file object:
+
+>>> pd.read_excel('tmp.xlsx', index_col=0)  # doctest: +SKIP
+       Name  Value
+0   string1      1
+1   string2      2
+2  #Comment      3
+
+>>> pd.read_excel(open('tmp.xlsx', 'rb'),
+...               sheet_name='Sheet3')  # doctest: +SKIP
+   Unnamed: 0      Name  Value
+0           0   string1      1
+1           1   string2      2
+2           2  #Comment      3
+
+Index and header can be specified via the `index_col` and `header` arguments
+
+>>> pd.read_excel('tmp.xlsx', index_col=None, header=None)  # doctest: +SKIP
+     0         1      2
+0  NaN      Name  Value
+1  0.0   string1      1
+2  1.0   string2      2
+3  2.0  #Comment      3
+
+Column types are inferred but can be explicitly specified
+
+>>> pd.read_excel('tmp.xlsx', index_col=0,
+...               dtype={{'Name': str, 'Value': float}})  # doctest: +SKIP
+       Name  Value
+0   string1    1.0
+1   string2    2.0
+2  #Comment    3.0
+
+True, False, and NA values, and thousands separators have defaults,
+but can be explicitly specified, too. Supply the values you would like
+as strings or lists of strings!
+
+>>> pd.read_excel('tmp.xlsx', index_col=0,
+...               na_values=['string1', 'string2'])  # doctest: +SKIP
+       Name  Value
+0       NaN      1
+1       NaN      2
+2  #Comment      3
+
+Comment lines in the excel input file can be skipped using the
+``comment`` kwarg.
+
+>>> pd.read_excel('tmp.xlsx', index_col=0, comment='#')  # doctest: +SKIP
+      Name  Value
+0  string1    1.0
+1  string2    2.0
+2     None    NaN
+"""
+)
+
+
+@overload
+def read_excel(
+    io,
+    # sheet name is str or int -> DataFrame
+    sheet_name: str | int = ...,
+    *,
+    header: int | Sequence[int] | None = ...,
+    names: SequenceNotStr[Hashable] | range | None = ...,
+    index_col: int | str | Sequence[int] | None = ...,
+    usecols: int
+    | str
+    | Sequence[int]
+    | Sequence[str]
+    | Callable[[str], bool]
+    | None = ...,
+    dtype: DtypeArg | None = ...,
+    engine: Literal["xlrd", "openpyxl", "odf", "pyxlsb", "calamine"] | None = ...,
+    converters: dict[str, Callable] | dict[int, Callable] | None = ...,
+    true_values: Iterable[Hashable] | None = ...,
+    false_values: Iterable[Hashable] | None = ...,
+    skiprows: Sequence[int] | int | Callable[[int], object] | None = ...,
+    nrows: int | None = ...,
+    na_values=...,
+    keep_default_na: bool = ...,
+    na_filter: bool = ...,
+    verbose: bool = ...,
+    parse_dates: list | dict | bool = ...,
+    date_parser: Callable | lib.NoDefault = ...,
+    date_format: dict[Hashable, str] | str | None = ...,
+    thousands: str | None = ...,
+    decimal: str = ...,
+    comment: str | None = ...,
+    skipfooter: int = ...,
+    storage_options: StorageOptions = ...,
+    dtype_backend: DtypeBackend | lib.NoDefault = ...,
+) -> DataFrame:
+    ...
+
+
+@overload
+def read_excel(
+    io,
+    # sheet name is list or None -> dict[IntStrT, DataFrame]
+    sheet_name: list[IntStrT] | None,
+    *,
+    header: int | Sequence[int] | None = ...,
+    names: SequenceNotStr[Hashable] | range | None = ...,
+    index_col: int | str | Sequence[int] | None = ...,
+    usecols: int
+    | str
+    | Sequence[int]
+    | Sequence[str]
+    | Callable[[str], bool]
+    | None = ...,
+    dtype: DtypeArg | None = ...,
+    engine: Literal["xlrd", "openpyxl", "odf", "pyxlsb", "calamine"] | None = ...,
+    converters: dict[str, Callable] | dict[int, Callable] | None = ...,
+    true_values: Iterable[Hashable] | None = ...,
+    false_values: Iterable[Hashable] | None = ...,
+    skiprows: Sequence[int] | int | Callable[[int], object] | None = ...,
+    nrows: int | None = ...,
+    na_values=...,
+    keep_default_na: bool = ...,
+    na_filter: bool = ...,
+    verbose: bool = ...,
+    parse_dates: list | dict | bool = ...,
+    date_parser: Callable | lib.NoDefault = ...,
+    date_format: dict[Hashable, str] | str | None = ...,
+    thousands: str | None = ...,
+    decimal: str = ...,
+    comment: str | None = ...,
+    skipfooter: int = ...,
+    storage_options: StorageOptions = ...,
+    dtype_backend: DtypeBackend | lib.NoDefault = ...,
+) -> dict[IntStrT, DataFrame]:
+    ...
+
+
+@doc(storage_options=_shared_docs["storage_options"])
+@Appender(_read_excel_doc)
+def read_excel(
+    io,
+    sheet_name: str | int | list[IntStrT] | None = 0,
+    *,
+    header: int | Sequence[int] | None = 0,
+    names: SequenceNotStr[Hashable] | range | None = None,
+    index_col: int | str | Sequence[int] | None = None,
+    usecols: int
+    | str
+    | Sequence[int]
+    | Sequence[str]
+    | Callable[[str], bool]
+    | None = None,
+    dtype: DtypeArg | None = None,
+    engine: Literal["xlrd", "openpyxl", "odf", "pyxlsb", "calamine"] | None = None,
+    converters: dict[str, Callable] | dict[int, Callable] | None = None,
+    true_values: Iterable[Hashable] | None = None,
+    false_values: Iterable[Hashable] | None = None,
+    skiprows: Sequence[int] | int | Callable[[int], object] | None = None,
+    nrows: int | None = None,
+    na_values=None,
+    keep_default_na: bool = True,
+    na_filter: bool = True,
+    verbose: bool = False,
+    parse_dates: list | dict | bool = False,
+    date_parser: Callable | lib.NoDefault = lib.no_default,
+    date_format: dict[Hashable, str] | str | None = None,
+    thousands: str | None = None,
+    decimal: str = ".",
+    comment: str | None = None,
+    skipfooter: int = 0,
+    storage_options: StorageOptions | None = None,
+    dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
+    engine_kwargs: dict | None = None,
+) -> DataFrame | dict[IntStrT, DataFrame]:
+    check_dtype_backend(dtype_backend)
+    should_close = False
+    if engine_kwargs is None:
+        engine_kwargs = {}
+
+    if not isinstance(io, ExcelFile):
+        should_close = True
+        io = ExcelFile(
+            io,
+            storage_options=storage_options,
+            engine=engine,
+            engine_kwargs=engine_kwargs,
+        )
+    elif engine and engine != io.engine:
+        raise ValueError(
+            "Engine should not be specified when passing "
+            "an ExcelFile - ExcelFile already has the engine set"
+        )
+
+    try:
+        data = io.parse(
+            sheet_name=sheet_name,
+            header=header,
+            names=names,
+            index_col=index_col,
+            usecols=usecols,
+            dtype=dtype,
+            converters=converters,
+            true_values=true_values,
+            false_values=false_values,
+            skiprows=skiprows,
+            nrows=nrows,
+            na_values=na_values,
+            keep_default_na=keep_default_na,
+            na_filter=na_filter,
+            verbose=verbose,
+            parse_dates=parse_dates,
+            date_parser=date_parser,
+            date_format=date_format,
+            thousands=thousands,
+            decimal=decimal,
+            comment=comment,
+            skipfooter=skipfooter,
+            dtype_backend=dtype_backend,
+        )
+    finally:
+        # make sure to close opened file handles
+        if should_close:
+            io.close()
+    return data
+
+
+_WorkbookT = TypeVar("_WorkbookT")
+
+
+class BaseExcelReader(Generic[_WorkbookT]):
+    book: _WorkbookT
+
+    def __init__(
+        self,
+        filepath_or_buffer,
+        storage_options: StorageOptions | None = None,
+        engine_kwargs: dict | None = None,
+    ) -> None:
+        if engine_kwargs is None:
+            engine_kwargs = {}
+
+        # First argument can also be bytes, so create a buffer
+        if isinstance(filepath_or_buffer, bytes):
+            filepath_or_buffer = BytesIO(filepath_or_buffer)
+
+        self.handles = IOHandles(
+            handle=filepath_or_buffer, compression={"method": None}
+        )
+        if not isinstance(filepath_or_buffer, (ExcelFile, self._workbook_class)):
+            self.handles = get_handle(
+                filepath_or_buffer, "rb", storage_options=storage_options, is_text=False
+            )
+
+        if isinstance(self.handles.handle, self._workbook_class):
+            self.book = self.handles.handle
+        elif hasattr(self.handles.handle, "read"):
+            # N.B. xlrd.Book has a read attribute too
+            self.handles.handle.seek(0)
+            try:
+                self.book = self.load_workbook(self.handles.handle, engine_kwargs)
+            except Exception:
+                self.close()
+                raise
+        else:
+            raise ValueError(
+                "Must explicitly set engine if not passing in buffer or path for io."
+            )
+
+    @property
+    def _workbook_class(self) -> type[_WorkbookT]:
+        raise NotImplementedError
+
+    def load_workbook(self, filepath_or_buffer, engine_kwargs) -> _WorkbookT:
+        raise NotImplementedError
+
+    def close(self) -> None:
+        if hasattr(self, "book"):
+            if hasattr(self.book, "close"):
+                # pyxlsb: opens a TemporaryFile
+                # openpyxl: https://stackoverflow.com/questions/31416842/
+                #     openpyxl-does-not-close-excel-workbook-in-read-only-mode
+                self.book.close()
+            elif hasattr(self.book, "release_resources"):
+                # xlrd
+                # https://github.com/python-excel/xlrd/blob/2.0.1/xlrd/book.py#L548
+                self.book.release_resources()
+        self.handles.close()
+
+    @property
+    def sheet_names(self) -> list[str]:
+        raise NotImplementedError
+
+    def get_sheet_by_name(self, name: str):
+        raise NotImplementedError
+
+    def get_sheet_by_index(self, index: int):
+        raise NotImplementedError
+
+    def get_sheet_data(self, sheet, rows: int | None = None):
+        raise NotImplementedError
+
+    def raise_if_bad_sheet_by_index(self, index: int) -> None:
+        n_sheets = len(self.sheet_names)
+        if index >= n_sheets:
+            raise ValueError(
+                f"Worksheet index {index} is invalid, {n_sheets} worksheets found"
+            )
+
+    def raise_if_bad_sheet_by_name(self, name: str) -> None:
+        if name not in self.sheet_names:
+            raise ValueError(f"Worksheet named '{name}' not found")
+
+    def _check_skiprows_func(
+        self,
+        skiprows: Callable,
+        rows_to_use: int,
+    ) -> int:
+        """
+        Determine how many file rows are required to obtain `nrows` data
+        rows when `skiprows` is a function.
+
+        Parameters
+        ----------
+        skiprows : function
+            The function passed to read_excel by the user.
+        rows_to_use : int
+            The number of rows that will be needed for the header and
+            the data.
+
+        Returns
+        -------
+        int
+        """
+        i = 0
+        rows_used_so_far = 0
+        while rows_used_so_far < rows_to_use:
+            if not skiprows(i):
+                rows_used_so_far += 1
+            i += 1
+        return i
+
+    def _calc_rows(
+        self,
+        header: int | Sequence[int] | None,
+        index_col: int | Sequence[int] | None,
+        skiprows: Sequence[int] | int | Callable[[int], object] | None,
+        nrows: int | None,
+    ) -> int | None:
+        """
+        If nrows specified, find the number of rows needed from the
+        file, otherwise return None.
+
+
+        Parameters
+        ----------
+        header : int, list of int, or None
+            See read_excel docstring.
+        index_col : int, str, list of int, or None
+            See read_excel docstring.
+        skiprows : list-like, int, callable, or None
+            See read_excel docstring.
+        nrows : int or None
+            See read_excel docstring.
+
+        Returns
+        -------
+        int or None
+        """
+        if nrows is None:
+            return None
+        if header is None:
+            header_rows = 1
+        elif is_integer(header):
+            header = cast(int, header)
+            header_rows = 1 + header
+        else:
+            header = cast(Sequence, header)
+            header_rows = 1 + header[-1]
+        # If there is a MultiIndex header and an index then there is also
+        # a row containing just the index name(s)
+        if is_list_like(header) and index_col is not None:
+            header = cast(Sequence, header)
+            if len(header) > 1:
+                header_rows += 1
+        if skiprows is None:
+            return header_rows + nrows
+        if is_integer(skiprows):
+            skiprows = cast(int, skiprows)
+            return header_rows + nrows + skiprows
+        if is_list_like(skiprows):
+
+            def f(skiprows: Sequence, x: int) -> bool:
+                return x in skiprows
+
+            skiprows = cast(Sequence, skiprows)
+            return self._check_skiprows_func(partial(f, skiprows), header_rows + nrows)
+        if callable(skiprows):
+            return self._check_skiprows_func(
+                skiprows,
+                header_rows + nrows,
+            )
+        # else unexpected skiprows type: read_excel will not optimize
+        # the number of rows read from file
+        return None
+
+    def parse(
+        self,
+        sheet_name: str | int | list[int] | list[str] | None = 0,
+        header: int | Sequence[int] | None = 0,
+        names: SequenceNotStr[Hashable] | range | None = None,
+        index_col: int | Sequence[int] | None = None,
+        usecols=None,
+        dtype: DtypeArg | None = None,
+        true_values: Iterable[Hashable] | None = None,
+        false_values: Iterable[Hashable] | None = None,
+        skiprows: Sequence[int] | int | Callable[[int], object] | None = None,
+        nrows: int | None = None,
+        na_values=None,
+        verbose: bool = False,
+        parse_dates: list | dict | bool = False,
+        date_parser: Callable | lib.NoDefault = lib.no_default,
+        date_format: dict[Hashable, str] | str | None = None,
+        thousands: str | None = None,
+        decimal: str = ".",
+        comment: str | None = None,
+        skipfooter: int = 0,
+        dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
+        **kwds,
+    ):
+        validate_header_arg(header)
+        validate_integer("nrows", nrows)
+
+        ret_dict = False
+
+        # Keep sheetname to maintain backwards compatibility.
+        sheets: list[int] | list[str]
+        if isinstance(sheet_name, list):
+            sheets = sheet_name
+            ret_dict = True
+        elif sheet_name is None:
+            sheets = self.sheet_names
+            ret_dict = True
+        elif isinstance(sheet_name, str):
+            sheets = [sheet_name]
+        else:
+            sheets = [sheet_name]
+
+        # handle same-type duplicates.
+        sheets = cast(Union[list[int], list[str]], list(dict.fromkeys(sheets).keys()))
+
+        output = {}
+
+        last_sheetname = None
+        for asheetname in sheets:
+            last_sheetname = asheetname
+            if verbose:
+                print(f"Reading sheet {asheetname}")
+
+            if isinstance(asheetname, str):
+                sheet = self.get_sheet_by_name(asheetname)
+            else:  # assume an integer if not a string
+                sheet = self.get_sheet_by_index(asheetname)
+
+            file_rows_needed = self._calc_rows(header, index_col, skiprows, nrows)
+            data = self.get_sheet_data(sheet, file_rows_needed)
+            if hasattr(sheet, "close"):
+                # pyxlsb opens two TemporaryFiles
+                sheet.close()
+            usecols = maybe_convert_usecols(usecols)
+
+            if not data:
+                output[asheetname] = DataFrame()
+                continue
+
+            is_list_header = False
+            is_len_one_list_header = False
+            if is_list_like(header):
+                assert isinstance(header, Sequence)
+                is_list_header = True
+                if len(header) == 1:
+                    is_len_one_list_header = True
+
+            if is_len_one_list_header:
+                header = cast(Sequence[int], header)[0]
+
+            # forward fill and pull out names for MultiIndex column
+            header_names = None
+            if header is not None and is_list_like(header):
+                assert isinstance(header, Sequence)
+
+                header_names = []
+                control_row = [True] * len(data[0])
+
+                for row in header:
+                    if is_integer(skiprows):
+                        assert isinstance(skiprows, int)
+                        row += skiprows
+
+                    if row > len(data) - 1:
+                        raise ValueError(
+                            f"header index {row} exceeds maximum index "
+                            f"{len(data) - 1} of data.",
+                        )
+
+                    data[row], control_row = fill_mi_header(data[row], control_row)
+
+                    if index_col is not None:
+                        header_name, _ = pop_header_name(data[row], index_col)
+                        header_names.append(header_name)
+
+            # If there is a MultiIndex header and an index then there is also
+            # a row containing just the index name(s)
+            has_index_names = False
+            if is_list_header and not is_len_one_list_header and index_col is not None:
+                index_col_list: Sequence[int]
+                if isinstance(index_col, int):
+                    index_col_list = [index_col]
+                else:
+                    assert isinstance(index_col, Sequence)
+                    index_col_list = index_col
+
+                # We have to handle mi without names. If any of the entries in the data
+                # columns are not empty, this is a regular row
+                assert isinstance(header, Sequence)
+                if len(header) < len(data):
+                    potential_index_names = data[len(header)]
+                    potential_data = [
+                        x
+                        for i, x in enumerate(potential_index_names)
+                        if not control_row[i] and i not in index_col_list
+                    ]
+                    has_index_names = all(x == "" or x is None for x in potential_data)
+
+            if is_list_like(index_col):
+                # Forward fill values for MultiIndex index.
+                if header is None:
+                    offset = 0
+                elif isinstance(header, int):
+                    offset = 1 + header
+                else:
+                    offset = 1 + max(header)
+
+                # GH34673: if MultiIndex names present and not defined in the header,
+                # offset needs to be incremented so that forward filling starts
+                # from the first MI value instead of the name
+                if has_index_names:
+                    offset += 1
+
+                # Check if we have an empty dataset
+                # before trying to collect data.
+                if offset < len(data):
+                    assert isinstance(index_col, Sequence)
+
+                    for col in index_col:
+                        last = data[offset][col]
+
+                        for row in range(offset + 1, len(data)):
+                            if data[row][col] == "" or data[row][col] is None:
+                                data[row][col] = last
+                            else:
+                                last = data[row][col]
+
+            # GH 12292 : error when read one empty column from excel file
+            try:
+                parser = TextParser(
+                    data,
+                    names=names,
+                    header=header,
+                    index_col=index_col,
+                    has_index_names=has_index_names,
+                    dtype=dtype,
+                    true_values=true_values,
+                    false_values=false_values,
+                    skiprows=skiprows,
+                    nrows=nrows,
+                    na_values=na_values,
+                    skip_blank_lines=False,  # GH 39808
+                    parse_dates=parse_dates,
+                    date_parser=date_parser,
+                    date_format=date_format,
+                    thousands=thousands,
+                    decimal=decimal,
+                    comment=comment,
+                    skipfooter=skipfooter,
+                    usecols=usecols,
+                    dtype_backend=dtype_backend,
+                    **kwds,
+                )
+
+                output[asheetname] = parser.read(nrows=nrows)
+
+                if header_names:
+                    output[asheetname].columns = output[asheetname].columns.set_names(
+                        header_names
+                    )
+
+            except EmptyDataError:
+                # No Data, return an empty DataFrame
+                output[asheetname] = DataFrame()
+
+            except Exception as err:
+                err.args = (f"{err.args[0]} (sheet: {asheetname})", *err.args[1:])
+                raise err
+
+        if last_sheetname is None:
+            raise ValueError("Sheet name is an empty list")
+
+        if ret_dict:
+            return output
+        else:
+            return output[last_sheetname]
+
+
+@doc(storage_options=_shared_docs["storage_options"])
+class ExcelWriter(Generic[_WorkbookT]):
+    """
+    Class for writing DataFrame objects into excel sheets.
+
+    Default is to use:
+
+    * `xlsxwriter `__ for xlsx files if xlsxwriter
+      is installed otherwise `openpyxl `__
+    * `odswriter `__ for ods files
+
+    See ``DataFrame.to_excel`` for typical usage.
+
+    The writer should be used as a context manager. Otherwise, call `close()` to save
+    and close any opened file handles.
+
+    Parameters
+    ----------
+    path : str or typing.BinaryIO
+        Path to xls or xlsx or ods file.
+    engine : str (optional)
+        Engine to use for writing. If None, defaults to
+        ``io.excel..writer``.  NOTE: can only be passed as a keyword
+        argument.
+    date_format : str, default None
+        Format string for dates written into Excel files (e.g. 'YYYY-MM-DD').
+    datetime_format : str, default None
+        Format string for datetime objects written into Excel files.
+        (e.g. 'YYYY-MM-DD HH:MM:SS').
+    mode : {{'w', 'a'}}, default 'w'
+        File mode to use (write or append). Append does not work with fsspec URLs.
+    {storage_options}
+
+    if_sheet_exists : {{'error', 'new', 'replace', 'overlay'}}, default 'error'
+        How to behave when trying to write to a sheet that already
+        exists (append mode only).
+
+        * error: raise a ValueError.
+        * new: Create a new sheet, with a name determined by the engine.
+        * replace: Delete the contents of the sheet before writing to it.
+        * overlay: Write contents to the existing sheet without first removing,
+          but possibly over top of, the existing contents.
+
+        .. versionadded:: 1.3.0
+
+        .. versionchanged:: 1.4.0
+
+           Added ``overlay`` option
+
+    engine_kwargs : dict, optional
+        Keyword arguments to be passed into the engine. These will be passed to
+        the following functions of the respective engines:
+
+        * xlsxwriter: ``xlsxwriter.Workbook(file, **engine_kwargs)``
+        * openpyxl (write mode): ``openpyxl.Workbook(**engine_kwargs)``
+        * openpyxl (append mode): ``openpyxl.load_workbook(file, **engine_kwargs)``
+        * odswriter: ``odf.opendocument.OpenDocumentSpreadsheet(**engine_kwargs)``
+
+        .. versionadded:: 1.3.0
+
+    Notes
+    -----
+    For compatibility with CSV writers, ExcelWriter serializes lists
+    and dicts to strings before writing.
+
+    Examples
+    --------
+    Default usage:
+
+    >>> df = pd.DataFrame([["ABC", "XYZ"]], columns=["Foo", "Bar"])  # doctest: +SKIP
+    >>> with pd.ExcelWriter("path_to_file.xlsx") as writer:
+    ...     df.to_excel(writer)  # doctest: +SKIP
+
+    To write to separate sheets in a single file:
+
+    >>> df1 = pd.DataFrame([["AAA", "BBB"]], columns=["Spam", "Egg"])  # doctest: +SKIP
+    >>> df2 = pd.DataFrame([["ABC", "XYZ"]], columns=["Foo", "Bar"])  # doctest: +SKIP
+    >>> with pd.ExcelWriter("path_to_file.xlsx") as writer:
+    ...     df1.to_excel(writer, sheet_name="Sheet1")  # doctest: +SKIP
+    ...     df2.to_excel(writer, sheet_name="Sheet2")  # doctest: +SKIP
+
+    You can set the date format or datetime format:
+
+    >>> from datetime import date, datetime  # doctest: +SKIP
+    >>> df = pd.DataFrame(
+    ...     [
+    ...         [date(2014, 1, 31), date(1999, 9, 24)],
+    ...         [datetime(1998, 5, 26, 23, 33, 4), datetime(2014, 2, 28, 13, 5, 13)],
+    ...     ],
+    ...     index=["Date", "Datetime"],
+    ...     columns=["X", "Y"],
+    ... )  # doctest: +SKIP
+    >>> with pd.ExcelWriter(
+    ...     "path_to_file.xlsx",
+    ...     date_format="YYYY-MM-DD",
+    ...     datetime_format="YYYY-MM-DD HH:MM:SS"
+    ... ) as writer:
+    ...     df.to_excel(writer)  # doctest: +SKIP
+
+    You can also append to an existing Excel file:
+
+    >>> with pd.ExcelWriter("path_to_file.xlsx", mode="a", engine="openpyxl") as writer:
+    ...     df.to_excel(writer, sheet_name="Sheet3")  # doctest: +SKIP
+
+    Here, the `if_sheet_exists` parameter can be set to replace a sheet if it
+    already exists:
+
+    >>> with ExcelWriter(
+    ...     "path_to_file.xlsx",
+    ...     mode="a",
+    ...     engine="openpyxl",
+    ...     if_sheet_exists="replace",
+    ... ) as writer:
+    ...     df.to_excel(writer, sheet_name="Sheet1")  # doctest: +SKIP
+
+    You can also write multiple DataFrames to a single sheet. Note that the
+    ``if_sheet_exists`` parameter needs to be set to ``overlay``:
+
+    >>> with ExcelWriter("path_to_file.xlsx",
+    ...     mode="a",
+    ...     engine="openpyxl",
+    ...     if_sheet_exists="overlay",
+    ... ) as writer:
+    ...     df1.to_excel(writer, sheet_name="Sheet1")
+    ...     df2.to_excel(writer, sheet_name="Sheet1", startcol=3)  # doctest: +SKIP
+
+    You can store Excel file in RAM:
+
+    >>> import io
+    >>> df = pd.DataFrame([["ABC", "XYZ"]], columns=["Foo", "Bar"])
+    >>> buffer = io.BytesIO()
+    >>> with pd.ExcelWriter(buffer) as writer:
+    ...     df.to_excel(writer)
+
+    You can pack Excel file into zip archive:
+
+    >>> import zipfile  # doctest: +SKIP
+    >>> df = pd.DataFrame([["ABC", "XYZ"]], columns=["Foo", "Bar"])  # doctest: +SKIP
+    >>> with zipfile.ZipFile("path_to_file.zip", "w") as zf:
+    ...     with zf.open("filename.xlsx", "w") as buffer:
+    ...         with pd.ExcelWriter(buffer) as writer:
+    ...             df.to_excel(writer)  # doctest: +SKIP
+
+    You can specify additional arguments to the underlying engine:
+
+    >>> with pd.ExcelWriter(
+    ...     "path_to_file.xlsx",
+    ...     engine="xlsxwriter",
+    ...     engine_kwargs={{"options": {{"nan_inf_to_errors": True}}}}
+    ... ) as writer:
+    ...     df.to_excel(writer)  # doctest: +SKIP
+
+    In append mode, ``engine_kwargs`` are passed through to
+    openpyxl's ``load_workbook``:
+
+    >>> with pd.ExcelWriter(
+    ...     "path_to_file.xlsx",
+    ...     engine="openpyxl",
+    ...     mode="a",
+    ...     engine_kwargs={{"keep_vba": True}}
+    ... ) as writer:
+    ...     df.to_excel(writer, sheet_name="Sheet2")  # doctest: +SKIP
+    """
+
+    # Defining an ExcelWriter implementation (see abstract methods for more...)
+
+    # - Mandatory
+    #   - ``write_cells(self, cells, sheet_name=None, startrow=0, startcol=0)``
+    #     --> called to write additional DataFrames to disk
+    #   - ``_supported_extensions`` (tuple of supported extensions), used to
+    #      check that engine supports the given extension.
+    #   - ``_engine`` - string that gives the engine name. Necessary to
+    #     instantiate class directly and bypass ``ExcelWriterMeta`` engine
+    #     lookup.
+    #   - ``save(self)`` --> called to save file to disk
+    # - Mostly mandatory (i.e. should at least exist)
+    #   - book, cur_sheet, path
+
+    # - Optional:
+    #   - ``__init__(self, path, engine=None, **kwargs)`` --> always called
+    #     with path as first argument.
+
+    # You also need to register the class with ``register_writer()``.
+    # Technically, ExcelWriter implementations don't need to subclass
+    # ExcelWriter.
+
+    _engine: str
+    _supported_extensions: tuple[str, ...]
+
+    def __new__(
+        cls,
+        path: FilePath | WriteExcelBuffer | ExcelWriter,
+        engine: str | None = None,
+        date_format: str | None = None,
+        datetime_format: str | None = None,
+        mode: str = "w",
+        storage_options: StorageOptions | None = None,
+        if_sheet_exists: ExcelWriterIfSheetExists | None = None,
+        engine_kwargs: dict | None = None,
+    ) -> Self:
+        # only switch class if generic(ExcelWriter)
+        if cls is ExcelWriter:
+            if engine is None or (isinstance(engine, str) and engine == "auto"):
+                if isinstance(path, str):
+                    ext = os.path.splitext(path)[-1][1:]
+                else:
+                    ext = "xlsx"
+
+                try:
+                    engine = config.get_option(f"io.excel.{ext}.writer", silent=True)
+                    if engine == "auto":
+                        engine = get_default_engine(ext, mode="writer")
+                except KeyError as err:
+                    raise ValueError(f"No engine for filetype: '{ext}'") from err
+
+            # for mypy
+            assert engine is not None
+            #  error: Incompatible types in assignment (expression has type
+            #  "type[ExcelWriter[Any]]", variable has type "type[Self]")
+            cls = get_writer(engine)  # type: ignore[assignment]
+
+        return object.__new__(cls)
+
+    # declare external properties you can count on
+    _path = None
+
+    @property
+    def supported_extensions(self) -> tuple[str, ...]:
+        """Extensions that writer engine supports."""
+        return self._supported_extensions
+
+    @property
+    def engine(self) -> str:
+        """Name of engine."""
+        return self._engine
+
+    @property
+    def sheets(self) -> dict[str, Any]:
+        """Mapping of sheet names to sheet objects."""
+        raise NotImplementedError
+
+    @property
+    def book(self) -> _WorkbookT:
+        """
+        Book instance. Class type will depend on the engine used.
+
+        This attribute can be used to access engine-specific features.
+        """
+        raise NotImplementedError
+
+    def _write_cells(
+        self,
+        cells,
+        sheet_name: str | None = None,
+        startrow: int = 0,
+        startcol: int = 0,
+        freeze_panes: tuple[int, int] | None = None,
+    ) -> None:
+        """
+        Write given formatted cells into Excel an excel sheet
+
+        Parameters
+        ----------
+        cells : generator
+            cell of formatted data to save to Excel sheet
+        sheet_name : str, default None
+            Name of Excel sheet, if None, then use self.cur_sheet
+        startrow : upper left cell row to dump data frame
+        startcol : upper left cell column to dump data frame
+        freeze_panes: int tuple of length 2
+            contains the bottom-most row and right-most column to freeze
+        """
+        raise NotImplementedError
+
+    def _save(self) -> None:
+        """
+        Save workbook to disk.
+        """
+        raise NotImplementedError
+
+    def __init__(
+        self,
+        path: FilePath | WriteExcelBuffer | ExcelWriter,
+        engine: str | None = None,
+        date_format: str | None = None,
+        datetime_format: str | None = None,
+        mode: str = "w",
+        storage_options: StorageOptions | None = None,
+        if_sheet_exists: ExcelWriterIfSheetExists | None = None,
+        engine_kwargs: dict[str, Any] | None = None,
+    ) -> None:
+        # validate that this engine can handle the extension
+        if isinstance(path, str):
+            ext = os.path.splitext(path)[-1]
+            self.check_extension(ext)
+
+        # use mode to open the file
+        if "b" not in mode:
+            mode += "b"
+        # use "a" for the user to append data to excel but internally use "r+" to let
+        # the excel backend first read the existing file and then write any data to it
+        mode = mode.replace("a", "r+")
+
+        if if_sheet_exists not in (None, "error", "new", "replace", "overlay"):
+            raise ValueError(
+                f"'{if_sheet_exists}' is not valid for if_sheet_exists. "
+                "Valid options are 'error', 'new', 'replace' and 'overlay'."
+            )
+        if if_sheet_exists and "r+" not in mode:
+            raise ValueError("if_sheet_exists is only valid in append mode (mode='a')")
+        if if_sheet_exists is None:
+            if_sheet_exists = "error"
+        self._if_sheet_exists = if_sheet_exists
+
+        # cast ExcelWriter to avoid adding 'if self._handles is not None'
+        self._handles = IOHandles(
+            cast(IO[bytes], path), compression={"compression": None}
+        )
+        if not isinstance(path, ExcelWriter):
+            self._handles = get_handle(
+                path, mode, storage_options=storage_options, is_text=False
+            )
+        self._cur_sheet = None
+
+        if date_format is None:
+            self._date_format = "YYYY-MM-DD"
+        else:
+            self._date_format = date_format
+        if datetime_format is None:
+            self._datetime_format = "YYYY-MM-DD HH:MM:SS"
+        else:
+            self._datetime_format = datetime_format
+
+        self._mode = mode
+
+    @property
+    def date_format(self) -> str:
+        """
+        Format string for dates written into Excel files (e.g. 'YYYY-MM-DD').
+        """
+        return self._date_format
+
+    @property
+    def datetime_format(self) -> str:
+        """
+        Format string for dates written into Excel files (e.g. 'YYYY-MM-DD').
+        """
+        return self._datetime_format
+
+    @property
+    def if_sheet_exists(self) -> str:
+        """
+        How to behave when writing to a sheet that already exists in append mode.
+        """
+        return self._if_sheet_exists
+
+    def __fspath__(self) -> str:
+        return getattr(self._handles.handle, "name", "")
+
+    def _get_sheet_name(self, sheet_name: str | None) -> str:
+        if sheet_name is None:
+            sheet_name = self._cur_sheet
+        if sheet_name is None:  # pragma: no cover
+            raise ValueError("Must pass explicit sheet_name or set _cur_sheet property")
+        return sheet_name
+
+    def _value_with_fmt(
+        self, val
+    ) -> tuple[
+        int | float | bool | str | datetime.datetime | datetime.date, str | None
+    ]:
+        """
+        Convert numpy types to Python types for the Excel writers.
+
+        Parameters
+        ----------
+        val : object
+            Value to be written into cells
+
+        Returns
+        -------
+        Tuple with the first element being the converted value and the second
+            being an optional format
+        """
+        fmt = None
+
+        if is_integer(val):
+            val = int(val)
+        elif is_float(val):
+            val = float(val)
+        elif is_bool(val):
+            val = bool(val)
+        elif isinstance(val, datetime.datetime):
+            fmt = self._datetime_format
+        elif isinstance(val, datetime.date):
+            fmt = self._date_format
+        elif isinstance(val, datetime.timedelta):
+            val = val.total_seconds() / 86400
+            fmt = "0"
+        else:
+            val = str(val)
+
+        return val, fmt
+
+    @classmethod
+    def check_extension(cls, ext: str) -> Literal[True]:
+        """
+        checks that path's extension against the Writer's supported
+        extensions.  If it isn't supported, raises UnsupportedFiletypeError.
+        """
+        if ext.startswith("."):
+            ext = ext[1:]
+        if not any(ext in extension for extension in cls._supported_extensions):
+            raise ValueError(f"Invalid extension for engine '{cls.engine}': '{ext}'")
+        return True
+
+    # Allow use as a contextmanager
+    def __enter__(self) -> Self:
+        return self
+
+    def __exit__(
+        self,
+        exc_type: type[BaseException] | None,
+        exc_value: BaseException | None,
+        traceback: TracebackType | None,
+    ) -> None:
+        self.close()
+
+    def close(self) -> None:
+        """synonym for save, to make it more file-like"""
+        self._save()
+        self._handles.close()
+
+
+XLS_SIGNATURES = (
+    b"\x09\x00\x04\x00\x07\x00\x10\x00",  # BIFF2
+    b"\x09\x02\x06\x00\x00\x00\x10\x00",  # BIFF3
+    b"\x09\x04\x06\x00\x00\x00\x10\x00",  # BIFF4
+    b"\xD0\xCF\x11\xE0\xA1\xB1\x1A\xE1",  # Compound File Binary
+)
+ZIP_SIGNATURE = b"PK\x03\x04"
+PEEK_SIZE = max(map(len, XLS_SIGNATURES + (ZIP_SIGNATURE,)))
+
+
+@doc(storage_options=_shared_docs["storage_options"])
+def inspect_excel_format(
+    content_or_path: FilePath | ReadBuffer[bytes],
+    storage_options: StorageOptions | None = None,
+) -> str | None:
+    """
+    Inspect the path or content of an excel file and get its format.
+
+    Adopted from xlrd: https://github.com/python-excel/xlrd.
+
+    Parameters
+    ----------
+    content_or_path : str or file-like object
+        Path to file or content of file to inspect. May be a URL.
+    {storage_options}
+
+    Returns
+    -------
+    str or None
+        Format of file if it can be determined.
+
+    Raises
+    ------
+    ValueError
+        If resulting stream is empty.
+    BadZipFile
+        If resulting stream does not have an XLS signature and is not a valid zipfile.
+    """
+    if isinstance(content_or_path, bytes):
+        content_or_path = BytesIO(content_or_path)
+
+    with get_handle(
+        content_or_path, "rb", storage_options=storage_options, is_text=False
+    ) as handle:
+        stream = handle.handle
+        stream.seek(0)
+        buf = stream.read(PEEK_SIZE)
+        if buf is None:
+            raise ValueError("stream is empty")
+        assert isinstance(buf, bytes)
+        peek = buf
+        stream.seek(0)
+
+        if any(peek.startswith(sig) for sig in XLS_SIGNATURES):
+            return "xls"
+        elif not peek.startswith(ZIP_SIGNATURE):
+            return None
+
+        with zipfile.ZipFile(stream) as zf:
+            # Workaround for some third party files that use forward slashes and
+            # lower case names.
+            component_names = [
+                name.replace("\\", "/").lower() for name in zf.namelist()
+            ]
+
+        if "xl/workbook.xml" in component_names:
+            return "xlsx"
+        if "xl/workbook.bin" in component_names:
+            return "xlsb"
+        if "content.xml" in component_names:
+            return "ods"
+        return "zip"
+
+
+class ExcelFile:
+    """
+    Class for parsing tabular Excel sheets into DataFrame objects.
+
+    See read_excel for more documentation.
+
+    Parameters
+    ----------
+    path_or_buffer : str, bytes, path object (pathlib.Path or py._path.local.LocalPath),
+        A file-like object, xlrd workbook or openpyxl workbook.
+        If a string or path object, expected to be a path to a
+        .xls, .xlsx, .xlsb, .xlsm, .odf, .ods, or .odt file.
+    engine : str, default None
+        If io is not a buffer or path, this must be set to identify io.
+        Supported engines: ``xlrd``, ``openpyxl``, ``odf``, ``pyxlsb``, ``calamine``
+        Engine compatibility :
+
+        - ``xlrd`` supports old-style Excel files (.xls).
+        - ``openpyxl`` supports newer Excel file formats.
+        - ``odf`` supports OpenDocument file formats (.odf, .ods, .odt).
+        - ``pyxlsb`` supports Binary Excel files.
+        - ``calamine`` supports Excel (.xls, .xlsx, .xlsm, .xlsb)
+          and OpenDocument (.ods) file formats.
+
+        .. versionchanged:: 1.2.0
+
+           The engine `xlrd `_
+           now only supports old-style ``.xls`` files.
+           When ``engine=None``, the following logic will be
+           used to determine the engine:
+
+           - If ``path_or_buffer`` is an OpenDocument format (.odf, .ods, .odt),
+             then `odf `_ will be used.
+           - Otherwise if ``path_or_buffer`` is an xls format,
+             ``xlrd`` will be used.
+           - Otherwise if ``path_or_buffer`` is in xlsb format,
+             `pyxlsb `_ will be used.
+
+           .. versionadded:: 1.3.0
+
+           - Otherwise if `openpyxl `_ is installed,
+             then ``openpyxl`` will be used.
+           - Otherwise if ``xlrd >= 2.0`` is installed, a ``ValueError`` will be raised.
+
+           .. warning::
+
+            Please do not report issues when using ``xlrd`` to read ``.xlsx`` files.
+            This is not supported, switch to using ``openpyxl`` instead.
+    engine_kwargs : dict, optional
+        Arbitrary keyword arguments passed to excel engine.
+
+    Examples
+    --------
+    >>> file = pd.ExcelFile('myfile.xlsx')  # doctest: +SKIP
+    >>> with pd.ExcelFile("myfile.xls") as xls:  # doctest: +SKIP
+    ...     df1 = pd.read_excel(xls, "Sheet1")  # doctest: +SKIP
+    """
+
+    from pandas.io.excel._calamine import CalamineReader
+    from pandas.io.excel._odfreader import ODFReader
+    from pandas.io.excel._openpyxl import OpenpyxlReader
+    from pandas.io.excel._pyxlsb import PyxlsbReader
+    from pandas.io.excel._xlrd import XlrdReader
+
+    _engines: Mapping[str, Any] = {
+        "xlrd": XlrdReader,
+        "openpyxl": OpenpyxlReader,
+        "odf": ODFReader,
+        "pyxlsb": PyxlsbReader,
+        "calamine": CalamineReader,
+    }
+
+    def __init__(
+        self,
+        path_or_buffer,
+        engine: str | None = None,
+        storage_options: StorageOptions | None = None,
+        engine_kwargs: dict | None = None,
+    ) -> None:
+        if engine_kwargs is None:
+            engine_kwargs = {}
+
+        if engine is not None and engine not in self._engines:
+            raise ValueError(f"Unknown engine: {engine}")
+
+        # First argument can also be bytes, so create a buffer
+        if isinstance(path_or_buffer, bytes):
+            path_or_buffer = BytesIO(path_or_buffer)
+            warnings.warn(
+                "Passing bytes to 'read_excel' is deprecated and "
+                "will be removed in a future version. To read from a "
+                "byte string, wrap it in a `BytesIO` object.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+
+        # Could be a str, ExcelFile, Book, etc.
+        self.io = path_or_buffer
+        # Always a string
+        self._io = stringify_path(path_or_buffer)
+
+        # Determine xlrd version if installed
+        if import_optional_dependency("xlrd", errors="ignore") is None:
+            xlrd_version = None
+        else:
+            import xlrd
+
+            xlrd_version = Version(get_version(xlrd))
+
+        if engine is None:
+            # Only determine ext if it is needed
+            ext: str | None
+            if xlrd_version is not None and isinstance(path_or_buffer, xlrd.Book):
+                ext = "xls"
+            else:
+                ext = inspect_excel_format(
+                    content_or_path=path_or_buffer, storage_options=storage_options
+                )
+                if ext is None:
+                    raise ValueError(
+                        "Excel file format cannot be determined, you must specify "
+                        "an engine manually."
+                    )
+
+            engine = config.get_option(f"io.excel.{ext}.reader", silent=True)
+            if engine == "auto":
+                engine = get_default_engine(ext, mode="reader")
+
+        assert engine is not None
+        self.engine = engine
+        self.storage_options = storage_options
+
+        self._reader = self._engines[engine](
+            self._io,
+            storage_options=storage_options,
+            engine_kwargs=engine_kwargs,
+        )
+
+    def __fspath__(self):
+        return self._io
+
+    def parse(
+        self,
+        sheet_name: str | int | list[int] | list[str] | None = 0,
+        header: int | Sequence[int] | None = 0,
+        names: SequenceNotStr[Hashable] | range | None = None,
+        index_col: int | Sequence[int] | None = None,
+        usecols=None,
+        converters=None,
+        true_values: Iterable[Hashable] | None = None,
+        false_values: Iterable[Hashable] | None = None,
+        skiprows: Sequence[int] | int | Callable[[int], object] | None = None,
+        nrows: int | None = None,
+        na_values=None,
+        parse_dates: list | dict | bool = False,
+        date_parser: Callable | lib.NoDefault = lib.no_default,
+        date_format: str | dict[Hashable, str] | None = None,
+        thousands: str | None = None,
+        comment: str | None = None,
+        skipfooter: int = 0,
+        dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
+        **kwds,
+    ) -> DataFrame | dict[str, DataFrame] | dict[int, DataFrame]:
+        """
+        Parse specified sheet(s) into a DataFrame.
+
+        Equivalent to read_excel(ExcelFile, ...)  See the read_excel
+        docstring for more info on accepted parameters.
+
+        Returns
+        -------
+        DataFrame or dict of DataFrames
+            DataFrame from the passed in Excel file.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([[1, 2, 3], [4, 5, 6]], columns=['A', 'B', 'C'])
+        >>> df.to_excel('myfile.xlsx')  # doctest: +SKIP
+        >>> file = pd.ExcelFile('myfile.xlsx')  # doctest: +SKIP
+        >>> file.parse()  # doctest: +SKIP
+        """
+        return self._reader.parse(
+            sheet_name=sheet_name,
+            header=header,
+            names=names,
+            index_col=index_col,
+            usecols=usecols,
+            converters=converters,
+            true_values=true_values,
+            false_values=false_values,
+            skiprows=skiprows,
+            nrows=nrows,
+            na_values=na_values,
+            parse_dates=parse_dates,
+            date_parser=date_parser,
+            date_format=date_format,
+            thousands=thousands,
+            comment=comment,
+            skipfooter=skipfooter,
+            dtype_backend=dtype_backend,
+            **kwds,
+        )
+
+    @property
+    def book(self):
+        return self._reader.book
+
+    @property
+    def sheet_names(self):
+        return self._reader.sheet_names
+
+    def close(self) -> None:
+        """close io if necessary"""
+        self._reader.close()
+
+    def __enter__(self) -> Self:
+        return self
+
+    def __exit__(
+        self,
+        exc_type: type[BaseException] | None,
+        exc_value: BaseException | None,
+        traceback: TracebackType | None,
+    ) -> None:
+        self.close()
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/excel/_calamine.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/excel/_calamine.py
new file mode 100644
index 0000000000000000000000000000000000000000..5259469f7a569a1913aa49635b3c14e89a18d157
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/excel/_calamine.py
@@ -0,0 +1,121 @@
+from __future__ import annotations
+
+from datetime import (
+    date,
+    datetime,
+    time,
+    timedelta,
+)
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Union,
+)
+
+from pandas.compat._optional import import_optional_dependency
+from pandas.util._decorators import doc
+
+import pandas as pd
+from pandas.core.shared_docs import _shared_docs
+
+from pandas.io.excel._base import BaseExcelReader
+
+if TYPE_CHECKING:
+    from python_calamine import (
+        CalamineSheet,
+        CalamineWorkbook,
+    )
+
+    from pandas._typing import (
+        FilePath,
+        NaTType,
+        ReadBuffer,
+        Scalar,
+        StorageOptions,
+    )
+
+_CellValue = Union[int, float, str, bool, time, date, datetime, timedelta]
+
+
+class CalamineReader(BaseExcelReader["CalamineWorkbook"]):
+    @doc(storage_options=_shared_docs["storage_options"])
+    def __init__(
+        self,
+        filepath_or_buffer: FilePath | ReadBuffer[bytes],
+        storage_options: StorageOptions | None = None,
+        engine_kwargs: dict | None = None,
+    ) -> None:
+        """
+        Reader using calamine engine (xlsx/xls/xlsb/ods).
+
+        Parameters
+        ----------
+        filepath_or_buffer : str, path to be parsed or
+            an open readable stream.
+        {storage_options}
+        engine_kwargs : dict, optional
+            Arbitrary keyword arguments passed to excel engine.
+        """
+        import_optional_dependency("python_calamine")
+        super().__init__(
+            filepath_or_buffer,
+            storage_options=storage_options,
+            engine_kwargs=engine_kwargs,
+        )
+
+    @property
+    def _workbook_class(self) -> type[CalamineWorkbook]:
+        from python_calamine import CalamineWorkbook
+
+        return CalamineWorkbook
+
+    def load_workbook(
+        self, filepath_or_buffer: FilePath | ReadBuffer[bytes], engine_kwargs: Any
+    ) -> CalamineWorkbook:
+        from python_calamine import load_workbook
+
+        return load_workbook(filepath_or_buffer, **engine_kwargs)
+
+    @property
+    def sheet_names(self) -> list[str]:
+        from python_calamine import SheetTypeEnum
+
+        return [
+            sheet.name
+            for sheet in self.book.sheets_metadata
+            if sheet.typ == SheetTypeEnum.WorkSheet
+        ]
+
+    def get_sheet_by_name(self, name: str) -> CalamineSheet:
+        self.raise_if_bad_sheet_by_name(name)
+        return self.book.get_sheet_by_name(name)
+
+    def get_sheet_by_index(self, index: int) -> CalamineSheet:
+        self.raise_if_bad_sheet_by_index(index)
+        return self.book.get_sheet_by_index(index)
+
+    def get_sheet_data(
+        self, sheet: CalamineSheet, file_rows_needed: int | None = None
+    ) -> list[list[Scalar | NaTType | time]]:
+        def _convert_cell(value: _CellValue) -> Scalar | NaTType | time:
+            if isinstance(value, float):
+                val = int(value)
+                if val == value:
+                    return val
+                else:
+                    return value
+            elif isinstance(value, date):
+                return pd.Timestamp(value)
+            elif isinstance(value, timedelta):
+                return pd.Timedelta(value)
+            elif isinstance(value, time):
+                return value
+
+            return value
+
+        rows: list[list[_CellValue]] = sheet.to_python(
+            skip_empty_area=False, nrows=file_rows_needed
+        )
+        data = [[_convert_cell(cell) for cell in row] for row in rows]
+
+        return data
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/excel/_odfreader.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/excel/_odfreader.py
new file mode 100644
index 0000000000000000000000000000000000000000..69b514da32857119f048a25f647d1002315a9889
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/excel/_odfreader.py
@@ -0,0 +1,253 @@
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    cast,
+)
+
+import numpy as np
+
+from pandas._typing import (
+    FilePath,
+    ReadBuffer,
+    Scalar,
+    StorageOptions,
+)
+from pandas.compat._optional import import_optional_dependency
+from pandas.util._decorators import doc
+
+import pandas as pd
+from pandas.core.shared_docs import _shared_docs
+
+from pandas.io.excel._base import BaseExcelReader
+
+if TYPE_CHECKING:
+    from odf.opendocument import OpenDocument
+
+    from pandas._libs.tslibs.nattype import NaTType
+
+
+@doc(storage_options=_shared_docs["storage_options"])
+class ODFReader(BaseExcelReader["OpenDocument"]):
+    def __init__(
+        self,
+        filepath_or_buffer: FilePath | ReadBuffer[bytes],
+        storage_options: StorageOptions | None = None,
+        engine_kwargs: dict | None = None,
+    ) -> None:
+        """
+        Read tables out of OpenDocument formatted files.
+
+        Parameters
+        ----------
+        filepath_or_buffer : str, path to be parsed or
+            an open readable stream.
+        {storage_options}
+        engine_kwargs : dict, optional
+            Arbitrary keyword arguments passed to excel engine.
+        """
+        import_optional_dependency("odf")
+        super().__init__(
+            filepath_or_buffer,
+            storage_options=storage_options,
+            engine_kwargs=engine_kwargs,
+        )
+
+    @property
+    def _workbook_class(self) -> type[OpenDocument]:
+        from odf.opendocument import OpenDocument
+
+        return OpenDocument
+
+    def load_workbook(
+        self, filepath_or_buffer: FilePath | ReadBuffer[bytes], engine_kwargs
+    ) -> OpenDocument:
+        from odf.opendocument import load
+
+        return load(filepath_or_buffer, **engine_kwargs)
+
+    @property
+    def empty_value(self) -> str:
+        """Property for compat with other readers."""
+        return ""
+
+    @property
+    def sheet_names(self) -> list[str]:
+        """Return a list of sheet names present in the document"""
+        from odf.table import Table
+
+        tables = self.book.getElementsByType(Table)
+        return [t.getAttribute("name") for t in tables]
+
+    def get_sheet_by_index(self, index: int):
+        from odf.table import Table
+
+        self.raise_if_bad_sheet_by_index(index)
+        tables = self.book.getElementsByType(Table)
+        return tables[index]
+
+    def get_sheet_by_name(self, name: str):
+        from odf.table import Table
+
+        self.raise_if_bad_sheet_by_name(name)
+        tables = self.book.getElementsByType(Table)
+
+        for table in tables:
+            if table.getAttribute("name") == name:
+                return table
+
+        self.close()
+        raise ValueError(f"sheet {name} not found")
+
+    def get_sheet_data(
+        self, sheet, file_rows_needed: int | None = None
+    ) -> list[list[Scalar | NaTType]]:
+        """
+        Parse an ODF Table into a list of lists
+        """
+        from odf.table import (
+            CoveredTableCell,
+            TableCell,
+            TableRow,
+        )
+
+        covered_cell_name = CoveredTableCell().qname
+        table_cell_name = TableCell().qname
+        cell_names = {covered_cell_name, table_cell_name}
+
+        sheet_rows = sheet.getElementsByType(TableRow)
+        empty_rows = 0
+        max_row_len = 0
+
+        table: list[list[Scalar | NaTType]] = []
+
+        for sheet_row in sheet_rows:
+            sheet_cells = [
+                x
+                for x in sheet_row.childNodes
+                if hasattr(x, "qname") and x.qname in cell_names
+            ]
+            empty_cells = 0
+            table_row: list[Scalar | NaTType] = []
+
+            for sheet_cell in sheet_cells:
+                if sheet_cell.qname == table_cell_name:
+                    value = self._get_cell_value(sheet_cell)
+                else:
+                    value = self.empty_value
+
+                column_repeat = self._get_column_repeat(sheet_cell)
+
+                # Queue up empty values, writing only if content succeeds them
+                if value == self.empty_value:
+                    empty_cells += column_repeat
+                else:
+                    table_row.extend([self.empty_value] * empty_cells)
+                    empty_cells = 0
+                    table_row.extend([value] * column_repeat)
+
+            if max_row_len < len(table_row):
+                max_row_len = len(table_row)
+
+            row_repeat = self._get_row_repeat(sheet_row)
+            if len(table_row) == 0:
+                empty_rows += row_repeat
+            else:
+                # add blank rows to our table
+                table.extend([[self.empty_value]] * empty_rows)
+                empty_rows = 0
+                table.extend(table_row for _ in range(row_repeat))
+            if file_rows_needed is not None and len(table) >= file_rows_needed:
+                break
+
+        # Make our table square
+        for row in table:
+            if len(row) < max_row_len:
+                row.extend([self.empty_value] * (max_row_len - len(row)))
+
+        return table
+
+    def _get_row_repeat(self, row) -> int:
+        """
+        Return number of times this row was repeated
+        Repeating an empty row appeared to be a common way
+        of representing sparse rows in the table.
+        """
+        from odf.namespaces import TABLENS
+
+        return int(row.attributes.get((TABLENS, "number-rows-repeated"), 1))
+
+    def _get_column_repeat(self, cell) -> int:
+        from odf.namespaces import TABLENS
+
+        return int(cell.attributes.get((TABLENS, "number-columns-repeated"), 1))
+
+    def _get_cell_value(self, cell) -> Scalar | NaTType:
+        from odf.namespaces import OFFICENS
+
+        if str(cell) == "#N/A":
+            return np.nan
+
+        cell_type = cell.attributes.get((OFFICENS, "value-type"))
+        if cell_type == "boolean":
+            if str(cell) == "TRUE":
+                return True
+            return False
+        if cell_type is None:
+            return self.empty_value
+        elif cell_type == "float":
+            # GH5394
+            cell_value = float(cell.attributes.get((OFFICENS, "value")))
+            val = int(cell_value)
+            if val == cell_value:
+                return val
+            return cell_value
+        elif cell_type == "percentage":
+            cell_value = cell.attributes.get((OFFICENS, "value"))
+            return float(cell_value)
+        elif cell_type == "string":
+            return self._get_cell_string_value(cell)
+        elif cell_type == "currency":
+            cell_value = cell.attributes.get((OFFICENS, "value"))
+            return float(cell_value)
+        elif cell_type == "date":
+            cell_value = cell.attributes.get((OFFICENS, "date-value"))
+            return pd.Timestamp(cell_value)
+        elif cell_type == "time":
+            stamp = pd.Timestamp(str(cell))
+            # cast needed here because Scalar doesn't include datetime.time
+            return cast(Scalar, stamp.time())
+        else:
+            self.close()
+            raise ValueError(f"Unrecognized type {cell_type}")
+
+    def _get_cell_string_value(self, cell) -> str:
+        """
+        Find and decode OpenDocument text:s tags that represent
+        a run length encoded sequence of space characters.
+        """
+        from odf.element import Element
+        from odf.namespaces import TEXTNS
+        from odf.office import Annotation
+        from odf.text import S
+
+        office_annotation = Annotation().qname
+        text_s = S().qname
+
+        value = []
+
+        for fragment in cell.childNodes:
+            if isinstance(fragment, Element):
+                if fragment.qname == text_s:
+                    spaces = int(fragment.attributes.get((TEXTNS, "c"), 1))
+                    value.append(" " * spaces)
+                elif fragment.qname == office_annotation:
+                    continue
+                else:
+                    # recursive impl needed in case of nested fragments
+                    # with multiple spaces
+                    # https://github.com/pandas-dev/pandas/pull/36175#discussion_r484639704
+                    value.append(self._get_cell_string_value(fragment))
+            else:
+                value.append(str(fragment).strip("\n"))
+        return "".join(value)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/excel/_odswriter.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/excel/_odswriter.py
new file mode 100644
index 0000000000000000000000000000000000000000..bc7dca2d95b6b434279f8290fdf929e737f75459
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/excel/_odswriter.py
@@ -0,0 +1,357 @@
+from __future__ import annotations
+
+from collections import defaultdict
+import datetime
+import json
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    DefaultDict,
+    cast,
+    overload,
+)
+
+from pandas.io.excel._base import ExcelWriter
+from pandas.io.excel._util import (
+    combine_kwargs,
+    validate_freeze_panes,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        ExcelWriterIfSheetExists,
+        FilePath,
+        StorageOptions,
+        WriteExcelBuffer,
+    )
+
+    from pandas.io.formats.excel import ExcelCell
+
+
+class ODSWriter(ExcelWriter):
+    _engine = "odf"
+    _supported_extensions = (".ods",)
+
+    def __init__(
+        self,
+        path: FilePath | WriteExcelBuffer | ExcelWriter,
+        engine: str | None = None,
+        date_format: str | None = None,
+        datetime_format=None,
+        mode: str = "w",
+        storage_options: StorageOptions | None = None,
+        if_sheet_exists: ExcelWriterIfSheetExists | None = None,
+        engine_kwargs: dict[str, Any] | None = None,
+        **kwargs,
+    ) -> None:
+        from odf.opendocument import OpenDocumentSpreadsheet
+
+        if mode == "a":
+            raise ValueError("Append mode is not supported with odf!")
+
+        engine_kwargs = combine_kwargs(engine_kwargs, kwargs)
+        self._book = OpenDocumentSpreadsheet(**engine_kwargs)
+
+        super().__init__(
+            path,
+            mode=mode,
+            storage_options=storage_options,
+            if_sheet_exists=if_sheet_exists,
+            engine_kwargs=engine_kwargs,
+        )
+
+        self._style_dict: dict[str, str] = {}
+
+    @property
+    def book(self):
+        """
+        Book instance of class odf.opendocument.OpenDocumentSpreadsheet.
+
+        This attribute can be used to access engine-specific features.
+        """
+        return self._book
+
+    @property
+    def sheets(self) -> dict[str, Any]:
+        """Mapping of sheet names to sheet objects."""
+        from odf.table import Table
+
+        result = {
+            sheet.getAttribute("name"): sheet
+            for sheet in self.book.getElementsByType(Table)
+        }
+        return result
+
+    def _save(self) -> None:
+        """
+        Save workbook to disk.
+        """
+        for sheet in self.sheets.values():
+            self.book.spreadsheet.addElement(sheet)
+        self.book.save(self._handles.handle)
+
+    def _write_cells(
+        self,
+        cells: list[ExcelCell],
+        sheet_name: str | None = None,
+        startrow: int = 0,
+        startcol: int = 0,
+        freeze_panes: tuple[int, int] | None = None,
+    ) -> None:
+        """
+        Write the frame cells using odf
+        """
+        from odf.table import (
+            Table,
+            TableCell,
+            TableRow,
+        )
+        from odf.text import P
+
+        sheet_name = self._get_sheet_name(sheet_name)
+        assert sheet_name is not None
+
+        if sheet_name in self.sheets:
+            wks = self.sheets[sheet_name]
+        else:
+            wks = Table(name=sheet_name)
+            self.book.spreadsheet.addElement(wks)
+
+        if validate_freeze_panes(freeze_panes):
+            freeze_panes = cast(tuple[int, int], freeze_panes)
+            self._create_freeze_panes(sheet_name, freeze_panes)
+
+        for _ in range(startrow):
+            wks.addElement(TableRow())
+
+        rows: DefaultDict = defaultdict(TableRow)
+        col_count: DefaultDict = defaultdict(int)
+
+        for cell in sorted(cells, key=lambda cell: (cell.row, cell.col)):
+            # only add empty cells if the row is still empty
+            if not col_count[cell.row]:
+                for _ in range(startcol):
+                    rows[cell.row].addElement(TableCell())
+
+            # fill with empty cells if needed
+            for _ in range(cell.col - col_count[cell.row]):
+                rows[cell.row].addElement(TableCell())
+                col_count[cell.row] += 1
+
+            pvalue, tc = self._make_table_cell(cell)
+            rows[cell.row].addElement(tc)
+            col_count[cell.row] += 1
+            p = P(text=pvalue)
+            tc.addElement(p)
+
+        # add all rows to the sheet
+        if len(rows) > 0:
+            for row_nr in range(max(rows.keys()) + 1):
+                wks.addElement(rows[row_nr])
+
+    def _make_table_cell_attributes(self, cell) -> dict[str, int | str]:
+        """Convert cell attributes to OpenDocument attributes
+
+        Parameters
+        ----------
+        cell : ExcelCell
+            Spreadsheet cell data
+
+        Returns
+        -------
+        attributes : Dict[str, Union[int, str]]
+            Dictionary with attributes and attribute values
+        """
+        attributes: dict[str, int | str] = {}
+        style_name = self._process_style(cell.style)
+        if style_name is not None:
+            attributes["stylename"] = style_name
+        if cell.mergestart is not None and cell.mergeend is not None:
+            attributes["numberrowsspanned"] = max(1, cell.mergestart)
+            attributes["numbercolumnsspanned"] = cell.mergeend
+        return attributes
+
+    def _make_table_cell(self, cell) -> tuple[object, Any]:
+        """Convert cell data to an OpenDocument spreadsheet cell
+
+        Parameters
+        ----------
+        cell : ExcelCell
+            Spreadsheet cell data
+
+        Returns
+        -------
+        pvalue, cell : Tuple[str, TableCell]
+            Display value, Cell value
+        """
+        from odf.table import TableCell
+
+        attributes = self._make_table_cell_attributes(cell)
+        val, fmt = self._value_with_fmt(cell.val)
+        pvalue = value = val
+        if isinstance(val, bool):
+            value = str(val).lower()
+            pvalue = str(val).upper()
+            return (
+                pvalue,
+                TableCell(
+                    valuetype="boolean",
+                    booleanvalue=value,
+                    attributes=attributes,
+                ),
+            )
+        elif isinstance(val, datetime.datetime):
+            # Fast formatting
+            value = val.isoformat()
+            # Slow but locale-dependent
+            pvalue = val.strftime("%c")
+            return (
+                pvalue,
+                TableCell(valuetype="date", datevalue=value, attributes=attributes),
+            )
+        elif isinstance(val, datetime.date):
+            # Fast formatting
+            value = f"{val.year}-{val.month:02d}-{val.day:02d}"
+            # Slow but locale-dependent
+            pvalue = val.strftime("%x")
+            return (
+                pvalue,
+                TableCell(valuetype="date", datevalue=value, attributes=attributes),
+            )
+        elif isinstance(val, str):
+            return (
+                pvalue,
+                TableCell(
+                    valuetype="string",
+                    stringvalue=value,
+                    attributes=attributes,
+                ),
+            )
+        else:
+            return (
+                pvalue,
+                TableCell(
+                    valuetype="float",
+                    value=value,
+                    attributes=attributes,
+                ),
+            )
+
+    @overload
+    def _process_style(self, style: dict[str, Any]) -> str:
+        ...
+
+    @overload
+    def _process_style(self, style: None) -> None:
+        ...
+
+    def _process_style(self, style: dict[str, Any] | None) -> str | None:
+        """Convert a style dictionary to a OpenDocument style sheet
+
+        Parameters
+        ----------
+        style : Dict
+            Style dictionary
+
+        Returns
+        -------
+        style_key : str
+            Unique style key for later reference in sheet
+        """
+        from odf.style import (
+            ParagraphProperties,
+            Style,
+            TableCellProperties,
+            TextProperties,
+        )
+
+        if style is None:
+            return None
+        style_key = json.dumps(style)
+        if style_key in self._style_dict:
+            return self._style_dict[style_key]
+        name = f"pd{len(self._style_dict)+1}"
+        self._style_dict[style_key] = name
+        odf_style = Style(name=name, family="table-cell")
+        if "font" in style:
+            font = style["font"]
+            if font.get("bold", False):
+                odf_style.addElement(TextProperties(fontweight="bold"))
+        if "borders" in style:
+            borders = style["borders"]
+            for side, thickness in borders.items():
+                thickness_translation = {"thin": "0.75pt solid #000000"}
+                odf_style.addElement(
+                    TableCellProperties(
+                        attributes={f"border{side}": thickness_translation[thickness]}
+                    )
+                )
+        if "alignment" in style:
+            alignment = style["alignment"]
+            horizontal = alignment.get("horizontal")
+            if horizontal:
+                odf_style.addElement(ParagraphProperties(textalign=horizontal))
+            vertical = alignment.get("vertical")
+            if vertical:
+                odf_style.addElement(TableCellProperties(verticalalign=vertical))
+        self.book.styles.addElement(odf_style)
+        return name
+
+    def _create_freeze_panes(
+        self, sheet_name: str, freeze_panes: tuple[int, int]
+    ) -> None:
+        """
+        Create freeze panes in the sheet.
+
+        Parameters
+        ----------
+        sheet_name : str
+            Name of the spreadsheet
+        freeze_panes : tuple of (int, int)
+            Freeze pane location x and y
+        """
+        from odf.config import (
+            ConfigItem,
+            ConfigItemMapEntry,
+            ConfigItemMapIndexed,
+            ConfigItemMapNamed,
+            ConfigItemSet,
+        )
+
+        config_item_set = ConfigItemSet(name="ooo:view-settings")
+        self.book.settings.addElement(config_item_set)
+
+        config_item_map_indexed = ConfigItemMapIndexed(name="Views")
+        config_item_set.addElement(config_item_map_indexed)
+
+        config_item_map_entry = ConfigItemMapEntry()
+        config_item_map_indexed.addElement(config_item_map_entry)
+
+        config_item_map_named = ConfigItemMapNamed(name="Tables")
+        config_item_map_entry.addElement(config_item_map_named)
+
+        config_item_map_entry = ConfigItemMapEntry(name=sheet_name)
+        config_item_map_named.addElement(config_item_map_entry)
+
+        config_item_map_entry.addElement(
+            ConfigItem(name="HorizontalSplitMode", type="short", text="2")
+        )
+        config_item_map_entry.addElement(
+            ConfigItem(name="VerticalSplitMode", type="short", text="2")
+        )
+        config_item_map_entry.addElement(
+            ConfigItem(
+                name="HorizontalSplitPosition", type="int", text=str(freeze_panes[0])
+            )
+        )
+        config_item_map_entry.addElement(
+            ConfigItem(
+                name="VerticalSplitPosition", type="int", text=str(freeze_panes[1])
+            )
+        )
+        config_item_map_entry.addElement(
+            ConfigItem(name="PositionRight", type="int", text=str(freeze_panes[0]))
+        )
+        config_item_map_entry.addElement(
+            ConfigItem(name="PositionBottom", type="int", text=str(freeze_panes[1]))
+        )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/excel/_openpyxl.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/excel/_openpyxl.py
new file mode 100644
index 0000000000000000000000000000000000000000..c546443868a62aed062bf3fd41d80933e4fbc59e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/excel/_openpyxl.py
@@ -0,0 +1,639 @@
+from __future__ import annotations
+
+import mmap
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    cast,
+)
+
+import numpy as np
+
+from pandas.compat._optional import import_optional_dependency
+from pandas.util._decorators import doc
+
+from pandas.core.shared_docs import _shared_docs
+
+from pandas.io.excel._base import (
+    BaseExcelReader,
+    ExcelWriter,
+)
+from pandas.io.excel._util import (
+    combine_kwargs,
+    validate_freeze_panes,
+)
+
+if TYPE_CHECKING:
+    from openpyxl import Workbook
+    from openpyxl.descriptors.serialisable import Serialisable
+
+    from pandas._typing import (
+        ExcelWriterIfSheetExists,
+        FilePath,
+        ReadBuffer,
+        Scalar,
+        StorageOptions,
+        WriteExcelBuffer,
+    )
+
+
+class OpenpyxlWriter(ExcelWriter):
+    _engine = "openpyxl"
+    _supported_extensions = (".xlsx", ".xlsm")
+
+    def __init__(
+        self,
+        path: FilePath | WriteExcelBuffer | ExcelWriter,
+        engine: str | None = None,
+        date_format: str | None = None,
+        datetime_format: str | None = None,
+        mode: str = "w",
+        storage_options: StorageOptions | None = None,
+        if_sheet_exists: ExcelWriterIfSheetExists | None = None,
+        engine_kwargs: dict[str, Any] | None = None,
+        **kwargs,
+    ) -> None:
+        # Use the openpyxl module as the Excel writer.
+        from openpyxl.workbook import Workbook
+
+        engine_kwargs = combine_kwargs(engine_kwargs, kwargs)
+
+        super().__init__(
+            path,
+            mode=mode,
+            storage_options=storage_options,
+            if_sheet_exists=if_sheet_exists,
+            engine_kwargs=engine_kwargs,
+        )
+
+        # ExcelWriter replaced "a" by "r+" to allow us to first read the excel file from
+        # the file and later write to it
+        if "r+" in self._mode:  # Load from existing workbook
+            from openpyxl import load_workbook
+
+            try:
+                self._book = load_workbook(self._handles.handle, **engine_kwargs)
+            except TypeError:
+                self._handles.handle.close()
+                raise
+            self._handles.handle.seek(0)
+        else:
+            # Create workbook object with default optimized_write=True.
+            try:
+                self._book = Workbook(**engine_kwargs)
+            except TypeError:
+                self._handles.handle.close()
+                raise
+
+            if self.book.worksheets:
+                self.book.remove(self.book.worksheets[0])
+
+    @property
+    def book(self) -> Workbook:
+        """
+        Book instance of class openpyxl.workbook.Workbook.
+
+        This attribute can be used to access engine-specific features.
+        """
+        return self._book
+
+    @property
+    def sheets(self) -> dict[str, Any]:
+        """Mapping of sheet names to sheet objects."""
+        result = {name: self.book[name] for name in self.book.sheetnames}
+        return result
+
+    def _save(self) -> None:
+        """
+        Save workbook to disk.
+        """
+        self.book.save(self._handles.handle)
+        if "r+" in self._mode and not isinstance(self._handles.handle, mmap.mmap):
+            # truncate file to the written content
+            self._handles.handle.truncate()
+
+    @classmethod
+    def _convert_to_style_kwargs(cls, style_dict: dict) -> dict[str, Serialisable]:
+        """
+        Convert a style_dict to a set of kwargs suitable for initializing
+        or updating-on-copy an openpyxl v2 style object.
+
+        Parameters
+        ----------
+        style_dict : dict
+            A dict with zero or more of the following keys (or their synonyms).
+                'font'
+                'fill'
+                'border' ('borders')
+                'alignment'
+                'number_format'
+                'protection'
+
+        Returns
+        -------
+        style_kwargs : dict
+            A dict with the same, normalized keys as ``style_dict`` but each
+            value has been replaced with a native openpyxl style object of the
+            appropriate class.
+        """
+        _style_key_map = {"borders": "border"}
+
+        style_kwargs: dict[str, Serialisable] = {}
+        for k, v in style_dict.items():
+            k = _style_key_map.get(k, k)
+            _conv_to_x = getattr(cls, f"_convert_to_{k}", lambda x: None)
+            new_v = _conv_to_x(v)
+            if new_v:
+                style_kwargs[k] = new_v
+
+        return style_kwargs
+
+    @classmethod
+    def _convert_to_color(cls, color_spec):
+        """
+        Convert ``color_spec`` to an openpyxl v2 Color object.
+
+        Parameters
+        ----------
+        color_spec : str, dict
+            A 32-bit ARGB hex string, or a dict with zero or more of the
+            following keys.
+                'rgb'
+                'indexed'
+                'auto'
+                'theme'
+                'tint'
+                'index'
+                'type'
+
+        Returns
+        -------
+        color : openpyxl.styles.Color
+        """
+        from openpyxl.styles import Color
+
+        if isinstance(color_spec, str):
+            return Color(color_spec)
+        else:
+            return Color(**color_spec)
+
+    @classmethod
+    def _convert_to_font(cls, font_dict):
+        """
+        Convert ``font_dict`` to an openpyxl v2 Font object.
+
+        Parameters
+        ----------
+        font_dict : dict
+            A dict with zero or more of the following keys (or their synonyms).
+                'name'
+                'size' ('sz')
+                'bold' ('b')
+                'italic' ('i')
+                'underline' ('u')
+                'strikethrough' ('strike')
+                'color'
+                'vertAlign' ('vertalign')
+                'charset'
+                'scheme'
+                'family'
+                'outline'
+                'shadow'
+                'condense'
+
+        Returns
+        -------
+        font : openpyxl.styles.Font
+        """
+        from openpyxl.styles import Font
+
+        _font_key_map = {
+            "sz": "size",
+            "b": "bold",
+            "i": "italic",
+            "u": "underline",
+            "strike": "strikethrough",
+            "vertalign": "vertAlign",
+        }
+
+        font_kwargs = {}
+        for k, v in font_dict.items():
+            k = _font_key_map.get(k, k)
+            if k == "color":
+                v = cls._convert_to_color(v)
+            font_kwargs[k] = v
+
+        return Font(**font_kwargs)
+
+    @classmethod
+    def _convert_to_stop(cls, stop_seq):
+        """
+        Convert ``stop_seq`` to a list of openpyxl v2 Color objects,
+        suitable for initializing the ``GradientFill`` ``stop`` parameter.
+
+        Parameters
+        ----------
+        stop_seq : iterable
+            An iterable that yields objects suitable for consumption by
+            ``_convert_to_color``.
+
+        Returns
+        -------
+        stop : list of openpyxl.styles.Color
+        """
+        return map(cls._convert_to_color, stop_seq)
+
+    @classmethod
+    def _convert_to_fill(cls, fill_dict: dict[str, Any]):
+        """
+        Convert ``fill_dict`` to an openpyxl v2 Fill object.
+
+        Parameters
+        ----------
+        fill_dict : dict
+            A dict with one or more of the following keys (or their synonyms),
+                'fill_type' ('patternType', 'patterntype')
+                'start_color' ('fgColor', 'fgcolor')
+                'end_color' ('bgColor', 'bgcolor')
+            or one or more of the following keys (or their synonyms).
+                'type' ('fill_type')
+                'degree'
+                'left'
+                'right'
+                'top'
+                'bottom'
+                'stop'
+
+        Returns
+        -------
+        fill : openpyxl.styles.Fill
+        """
+        from openpyxl.styles import (
+            GradientFill,
+            PatternFill,
+        )
+
+        _pattern_fill_key_map = {
+            "patternType": "fill_type",
+            "patterntype": "fill_type",
+            "fgColor": "start_color",
+            "fgcolor": "start_color",
+            "bgColor": "end_color",
+            "bgcolor": "end_color",
+        }
+
+        _gradient_fill_key_map = {"fill_type": "type"}
+
+        pfill_kwargs = {}
+        gfill_kwargs = {}
+        for k, v in fill_dict.items():
+            pk = _pattern_fill_key_map.get(k)
+            gk = _gradient_fill_key_map.get(k)
+            if pk in ["start_color", "end_color"]:
+                v = cls._convert_to_color(v)
+            if gk == "stop":
+                v = cls._convert_to_stop(v)
+            if pk:
+                pfill_kwargs[pk] = v
+            elif gk:
+                gfill_kwargs[gk] = v
+            else:
+                pfill_kwargs[k] = v
+                gfill_kwargs[k] = v
+
+        try:
+            return PatternFill(**pfill_kwargs)
+        except TypeError:
+            return GradientFill(**gfill_kwargs)
+
+    @classmethod
+    def _convert_to_side(cls, side_spec):
+        """
+        Convert ``side_spec`` to an openpyxl v2 Side object.
+
+        Parameters
+        ----------
+        side_spec : str, dict
+            A string specifying the border style, or a dict with zero or more
+            of the following keys (or their synonyms).
+                'style' ('border_style')
+                'color'
+
+        Returns
+        -------
+        side : openpyxl.styles.Side
+        """
+        from openpyxl.styles import Side
+
+        _side_key_map = {"border_style": "style"}
+
+        if isinstance(side_spec, str):
+            return Side(style=side_spec)
+
+        side_kwargs = {}
+        for k, v in side_spec.items():
+            k = _side_key_map.get(k, k)
+            if k == "color":
+                v = cls._convert_to_color(v)
+            side_kwargs[k] = v
+
+        return Side(**side_kwargs)
+
+    @classmethod
+    def _convert_to_border(cls, border_dict):
+        """
+        Convert ``border_dict`` to an openpyxl v2 Border object.
+
+        Parameters
+        ----------
+        border_dict : dict
+            A dict with zero or more of the following keys (or their synonyms).
+                'left'
+                'right'
+                'top'
+                'bottom'
+                'diagonal'
+                'diagonal_direction'
+                'vertical'
+                'horizontal'
+                'diagonalUp' ('diagonalup')
+                'diagonalDown' ('diagonaldown')
+                'outline'
+
+        Returns
+        -------
+        border : openpyxl.styles.Border
+        """
+        from openpyxl.styles import Border
+
+        _border_key_map = {"diagonalup": "diagonalUp", "diagonaldown": "diagonalDown"}
+
+        border_kwargs = {}
+        for k, v in border_dict.items():
+            k = _border_key_map.get(k, k)
+            if k == "color":
+                v = cls._convert_to_color(v)
+            if k in ["left", "right", "top", "bottom", "diagonal"]:
+                v = cls._convert_to_side(v)
+            border_kwargs[k] = v
+
+        return Border(**border_kwargs)
+
+    @classmethod
+    def _convert_to_alignment(cls, alignment_dict):
+        """
+        Convert ``alignment_dict`` to an openpyxl v2 Alignment object.
+
+        Parameters
+        ----------
+        alignment_dict : dict
+            A dict with zero or more of the following keys (or their synonyms).
+                'horizontal'
+                'vertical'
+                'text_rotation'
+                'wrap_text'
+                'shrink_to_fit'
+                'indent'
+        Returns
+        -------
+        alignment : openpyxl.styles.Alignment
+        """
+        from openpyxl.styles import Alignment
+
+        return Alignment(**alignment_dict)
+
+    @classmethod
+    def _convert_to_number_format(cls, number_format_dict):
+        """
+        Convert ``number_format_dict`` to an openpyxl v2.1.0 number format
+        initializer.
+
+        Parameters
+        ----------
+        number_format_dict : dict
+            A dict with zero or more of the following keys.
+                'format_code' : str
+
+        Returns
+        -------
+        number_format : str
+        """
+        return number_format_dict["format_code"]
+
+    @classmethod
+    def _convert_to_protection(cls, protection_dict):
+        """
+        Convert ``protection_dict`` to an openpyxl v2 Protection object.
+
+        Parameters
+        ----------
+        protection_dict : dict
+            A dict with zero or more of the following keys.
+                'locked'
+                'hidden'
+
+        Returns
+        -------
+        """
+        from openpyxl.styles import Protection
+
+        return Protection(**protection_dict)
+
+    def _write_cells(
+        self,
+        cells,
+        sheet_name: str | None = None,
+        startrow: int = 0,
+        startcol: int = 0,
+        freeze_panes: tuple[int, int] | None = None,
+    ) -> None:
+        # Write the frame cells using openpyxl.
+        sheet_name = self._get_sheet_name(sheet_name)
+
+        _style_cache: dict[str, dict[str, Serialisable]] = {}
+
+        if sheet_name in self.sheets and self._if_sheet_exists != "new":
+            if "r+" in self._mode:
+                if self._if_sheet_exists == "replace":
+                    old_wks = self.sheets[sheet_name]
+                    target_index = self.book.index(old_wks)
+                    del self.book[sheet_name]
+                    wks = self.book.create_sheet(sheet_name, target_index)
+                elif self._if_sheet_exists == "error":
+                    raise ValueError(
+                        f"Sheet '{sheet_name}' already exists and "
+                        f"if_sheet_exists is set to 'error'."
+                    )
+                elif self._if_sheet_exists == "overlay":
+                    wks = self.sheets[sheet_name]
+                else:
+                    raise ValueError(
+                        f"'{self._if_sheet_exists}' is not valid for if_sheet_exists. "
+                        "Valid options are 'error', 'new', 'replace' and 'overlay'."
+                    )
+            else:
+                wks = self.sheets[sheet_name]
+        else:
+            wks = self.book.create_sheet()
+            wks.title = sheet_name
+
+        if validate_freeze_panes(freeze_panes):
+            freeze_panes = cast(tuple[int, int], freeze_panes)
+            wks.freeze_panes = wks.cell(
+                row=freeze_panes[0] + 1, column=freeze_panes[1] + 1
+            )
+
+        for cell in cells:
+            xcell = wks.cell(
+                row=startrow + cell.row + 1, column=startcol + cell.col + 1
+            )
+            xcell.value, fmt = self._value_with_fmt(cell.val)
+            if fmt:
+                xcell.number_format = fmt
+
+            style_kwargs: dict[str, Serialisable] | None = {}
+            if cell.style:
+                key = str(cell.style)
+                style_kwargs = _style_cache.get(key)
+                if style_kwargs is None:
+                    style_kwargs = self._convert_to_style_kwargs(cell.style)
+                    _style_cache[key] = style_kwargs
+
+            if style_kwargs:
+                for k, v in style_kwargs.items():
+                    setattr(xcell, k, v)
+
+            if cell.mergestart is not None and cell.mergeend is not None:
+                wks.merge_cells(
+                    start_row=startrow + cell.row + 1,
+                    start_column=startcol + cell.col + 1,
+                    end_column=startcol + cell.mergeend + 1,
+                    end_row=startrow + cell.mergestart + 1,
+                )
+
+                # When cells are merged only the top-left cell is preserved
+                # The behaviour of the other cells in a merged range is
+                # undefined
+                if style_kwargs:
+                    first_row = startrow + cell.row + 1
+                    last_row = startrow + cell.mergestart + 1
+                    first_col = startcol + cell.col + 1
+                    last_col = startcol + cell.mergeend + 1
+
+                    for row in range(first_row, last_row + 1):
+                        for col in range(first_col, last_col + 1):
+                            if row == first_row and col == first_col:
+                                # Ignore first cell. It is already handled.
+                                continue
+                            xcell = wks.cell(column=col, row=row)
+                            for k, v in style_kwargs.items():
+                                setattr(xcell, k, v)
+
+
+class OpenpyxlReader(BaseExcelReader["Workbook"]):
+    @doc(storage_options=_shared_docs["storage_options"])
+    def __init__(
+        self,
+        filepath_or_buffer: FilePath | ReadBuffer[bytes],
+        storage_options: StorageOptions | None = None,
+        engine_kwargs: dict | None = None,
+    ) -> None:
+        """
+        Reader using openpyxl engine.
+
+        Parameters
+        ----------
+        filepath_or_buffer : str, path object or Workbook
+            Object to be parsed.
+        {storage_options}
+        engine_kwargs : dict, optional
+            Arbitrary keyword arguments passed to excel engine.
+        """
+        import_optional_dependency("openpyxl")
+        super().__init__(
+            filepath_or_buffer,
+            storage_options=storage_options,
+            engine_kwargs=engine_kwargs,
+        )
+
+    @property
+    def _workbook_class(self) -> type[Workbook]:
+        from openpyxl import Workbook
+
+        return Workbook
+
+    def load_workbook(
+        self, filepath_or_buffer: FilePath | ReadBuffer[bytes], engine_kwargs
+    ) -> Workbook:
+        from openpyxl import load_workbook
+
+        default_kwargs = {"read_only": True, "data_only": True, "keep_links": False}
+
+        return load_workbook(
+            filepath_or_buffer,
+            **(default_kwargs | engine_kwargs),
+        )
+
+    @property
+    def sheet_names(self) -> list[str]:
+        return [sheet.title for sheet in self.book.worksheets]
+
+    def get_sheet_by_name(self, name: str):
+        self.raise_if_bad_sheet_by_name(name)
+        return self.book[name]
+
+    def get_sheet_by_index(self, index: int):
+        self.raise_if_bad_sheet_by_index(index)
+        return self.book.worksheets[index]
+
+    def _convert_cell(self, cell) -> Scalar:
+        from openpyxl.cell.cell import (
+            TYPE_ERROR,
+            TYPE_NUMERIC,
+        )
+
+        if cell.value is None:
+            return ""  # compat with xlrd
+        elif cell.data_type == TYPE_ERROR:
+            return np.nan
+        elif cell.data_type == TYPE_NUMERIC:
+            val = int(cell.value)
+            if val == cell.value:
+                return val
+            return float(cell.value)
+
+        return cell.value
+
+    def get_sheet_data(
+        self, sheet, file_rows_needed: int | None = None
+    ) -> list[list[Scalar]]:
+        if self.book.read_only:
+            sheet.reset_dimensions()
+
+        data: list[list[Scalar]] = []
+        last_row_with_data = -1
+        for row_number, row in enumerate(sheet.rows):
+            converted_row = [self._convert_cell(cell) for cell in row]
+            while converted_row and converted_row[-1] == "":
+                # trim trailing empty elements
+                converted_row.pop()
+            if converted_row:
+                last_row_with_data = row_number
+            data.append(converted_row)
+            if file_rows_needed is not None and len(data) >= file_rows_needed:
+                break
+
+        # Trim trailing empty rows
+        data = data[: last_row_with_data + 1]
+
+        if len(data) > 0:
+            # extend rows to max width
+            max_width = max(len(data_row) for data_row in data)
+            if min(len(data_row) for data_row in data) < max_width:
+                empty_cell: list[Scalar] = [""]
+                data = [
+                    data_row + (max_width - len(data_row)) * empty_cell
+                    for data_row in data
+                ]
+
+        return data
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/excel/_pyxlsb.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/excel/_pyxlsb.py
new file mode 100644
index 0000000000000000000000000000000000000000..a6e42616c20438fa4cab16e94b5d16a01c9c61df
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/excel/_pyxlsb.py
@@ -0,0 +1,127 @@
+# pyright: reportMissingImports=false
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+from pandas.compat._optional import import_optional_dependency
+from pandas.util._decorators import doc
+
+from pandas.core.shared_docs import _shared_docs
+
+from pandas.io.excel._base import BaseExcelReader
+
+if TYPE_CHECKING:
+    from pyxlsb import Workbook
+
+    from pandas._typing import (
+        FilePath,
+        ReadBuffer,
+        Scalar,
+        StorageOptions,
+    )
+
+
+class PyxlsbReader(BaseExcelReader["Workbook"]):
+    @doc(storage_options=_shared_docs["storage_options"])
+    def __init__(
+        self,
+        filepath_or_buffer: FilePath | ReadBuffer[bytes],
+        storage_options: StorageOptions | None = None,
+        engine_kwargs: dict | None = None,
+    ) -> None:
+        """
+        Reader using pyxlsb engine.
+
+        Parameters
+        ----------
+        filepath_or_buffer : str, path object, or Workbook
+            Object to be parsed.
+        {storage_options}
+        engine_kwargs : dict, optional
+            Arbitrary keyword arguments passed to excel engine.
+        """
+        import_optional_dependency("pyxlsb")
+        # This will call load_workbook on the filepath or buffer
+        # And set the result to the book-attribute
+        super().__init__(
+            filepath_or_buffer,
+            storage_options=storage_options,
+            engine_kwargs=engine_kwargs,
+        )
+
+    @property
+    def _workbook_class(self) -> type[Workbook]:
+        from pyxlsb import Workbook
+
+        return Workbook
+
+    def load_workbook(
+        self, filepath_or_buffer: FilePath | ReadBuffer[bytes], engine_kwargs
+    ) -> Workbook:
+        from pyxlsb import open_workbook
+
+        # TODO: hack in buffer capability
+        # This might need some modifications to the Pyxlsb library
+        # Actual work for opening it is in xlsbpackage.py, line 20-ish
+
+        return open_workbook(filepath_or_buffer, **engine_kwargs)
+
+    @property
+    def sheet_names(self) -> list[str]:
+        return self.book.sheets
+
+    def get_sheet_by_name(self, name: str):
+        self.raise_if_bad_sheet_by_name(name)
+        return self.book.get_sheet(name)
+
+    def get_sheet_by_index(self, index: int):
+        self.raise_if_bad_sheet_by_index(index)
+        # pyxlsb sheets are indexed from 1 onwards
+        # There's a fix for this in the source, but the pypi package doesn't have it
+        return self.book.get_sheet(index + 1)
+
+    def _convert_cell(self, cell) -> Scalar:
+        # TODO: there is no way to distinguish between floats and datetimes in pyxlsb
+        # This means that there is no way to read datetime types from an xlsb file yet
+        if cell.v is None:
+            return ""  # Prevents non-named columns from not showing up as Unnamed: i
+        if isinstance(cell.v, float):
+            val = int(cell.v)
+            if val == cell.v:
+                return val
+            else:
+                return float(cell.v)
+
+        return cell.v
+
+    def get_sheet_data(
+        self,
+        sheet,
+        file_rows_needed: int | None = None,
+    ) -> list[list[Scalar]]:
+        data: list[list[Scalar]] = []
+        previous_row_number = -1
+        # When sparse=True the rows can have different lengths and empty rows are
+        # not returned. The cells are namedtuples of row, col, value (r, c, v).
+        for row in sheet.rows(sparse=True):
+            row_number = row[0].r
+            converted_row = [self._convert_cell(cell) for cell in row]
+            while converted_row and converted_row[-1] == "":
+                # trim trailing empty elements
+                converted_row.pop()
+            if converted_row:
+                data.extend([[]] * (row_number - previous_row_number - 1))
+                data.append(converted_row)
+                previous_row_number = row_number
+            if file_rows_needed is not None and len(data) >= file_rows_needed:
+                break
+        if data:
+            # extend rows to max_width
+            max_width = max(len(data_row) for data_row in data)
+            if min(len(data_row) for data_row in data) < max_width:
+                empty_cell: list[Scalar] = [""]
+                data = [
+                    data_row + (max_width - len(data_row)) * empty_cell
+                    for data_row in data
+                ]
+        return data
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/excel/_util.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/excel/_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..f7a1fcb8052e391d0853be64866663f4e6de9d08
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/excel/_util.py
@@ -0,0 +1,334 @@
+from __future__ import annotations
+
+from collections.abc import (
+    Hashable,
+    Iterable,
+    MutableMapping,
+    Sequence,
+)
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Literal,
+    TypeVar,
+    overload,
+)
+
+from pandas.compat._optional import import_optional_dependency
+
+from pandas.core.dtypes.common import (
+    is_integer,
+    is_list_like,
+)
+
+if TYPE_CHECKING:
+    from pandas.io.excel._base import ExcelWriter
+
+    ExcelWriter_t = type[ExcelWriter]
+    usecols_func = TypeVar("usecols_func", bound=Callable[[Hashable], object])
+
+_writers: MutableMapping[str, ExcelWriter_t] = {}
+
+
+def register_writer(klass: ExcelWriter_t) -> None:
+    """
+    Add engine to the excel writer registry.io.excel.
+
+    You must use this method to integrate with ``to_excel``.
+
+    Parameters
+    ----------
+    klass : ExcelWriter
+    """
+    if not callable(klass):
+        raise ValueError("Can only register callables as engines")
+    engine_name = klass._engine
+    _writers[engine_name] = klass
+
+
+def get_default_engine(ext: str, mode: Literal["reader", "writer"] = "reader") -> str:
+    """
+    Return the default reader/writer for the given extension.
+
+    Parameters
+    ----------
+    ext : str
+        The excel file extension for which to get the default engine.
+    mode : str {'reader', 'writer'}
+        Whether to get the default engine for reading or writing.
+        Either 'reader' or 'writer'
+
+    Returns
+    -------
+    str
+        The default engine for the extension.
+    """
+    _default_readers = {
+        "xlsx": "openpyxl",
+        "xlsm": "openpyxl",
+        "xlsb": "pyxlsb",
+        "xls": "xlrd",
+        "ods": "odf",
+    }
+    _default_writers = {
+        "xlsx": "openpyxl",
+        "xlsm": "openpyxl",
+        "xlsb": "pyxlsb",
+        "ods": "odf",
+    }
+    assert mode in ["reader", "writer"]
+    if mode == "writer":
+        # Prefer xlsxwriter over openpyxl if installed
+        xlsxwriter = import_optional_dependency("xlsxwriter", errors="warn")
+        if xlsxwriter:
+            _default_writers["xlsx"] = "xlsxwriter"
+        return _default_writers[ext]
+    else:
+        return _default_readers[ext]
+
+
+def get_writer(engine_name: str) -> ExcelWriter_t:
+    try:
+        return _writers[engine_name]
+    except KeyError as err:
+        raise ValueError(f"No Excel writer '{engine_name}'") from err
+
+
+def _excel2num(x: str) -> int:
+    """
+    Convert Excel column name like 'AB' to 0-based column index.
+
+    Parameters
+    ----------
+    x : str
+        The Excel column name to convert to a 0-based column index.
+
+    Returns
+    -------
+    num : int
+        The column index corresponding to the name.
+
+    Raises
+    ------
+    ValueError
+        Part of the Excel column name was invalid.
+    """
+    index = 0
+
+    for c in x.upper().strip():
+        cp = ord(c)
+
+        if cp < ord("A") or cp > ord("Z"):
+            raise ValueError(f"Invalid column name: {x}")
+
+        index = index * 26 + cp - ord("A") + 1
+
+    return index - 1
+
+
+def _range2cols(areas: str) -> list[int]:
+    """
+    Convert comma separated list of column names and ranges to indices.
+
+    Parameters
+    ----------
+    areas : str
+        A string containing a sequence of column ranges (or areas).
+
+    Returns
+    -------
+    cols : list
+        A list of 0-based column indices.
+
+    Examples
+    --------
+    >>> _range2cols('A:E')
+    [0, 1, 2, 3, 4]
+    >>> _range2cols('A,C,Z:AB')
+    [0, 2, 25, 26, 27]
+    """
+    cols: list[int] = []
+
+    for rng in areas.split(","):
+        if ":" in rng:
+            rngs = rng.split(":")
+            cols.extend(range(_excel2num(rngs[0]), _excel2num(rngs[1]) + 1))
+        else:
+            cols.append(_excel2num(rng))
+
+    return cols
+
+
+@overload
+def maybe_convert_usecols(usecols: str | list[int]) -> list[int]:
+    ...
+
+
+@overload
+def maybe_convert_usecols(usecols: list[str]) -> list[str]:
+    ...
+
+
+@overload
+def maybe_convert_usecols(usecols: usecols_func) -> usecols_func:
+    ...
+
+
+@overload
+def maybe_convert_usecols(usecols: None) -> None:
+    ...
+
+
+def maybe_convert_usecols(
+    usecols: str | list[int] | list[str] | usecols_func | None,
+) -> None | list[int] | list[str] | usecols_func:
+    """
+    Convert `usecols` into a compatible format for parsing in `parsers.py`.
+
+    Parameters
+    ----------
+    usecols : object
+        The use-columns object to potentially convert.
+
+    Returns
+    -------
+    converted : object
+        The compatible format of `usecols`.
+    """
+    if usecols is None:
+        return usecols
+
+    if is_integer(usecols):
+        raise ValueError(
+            "Passing an integer for `usecols` is no longer supported.  "
+            "Please pass in a list of int from 0 to `usecols` inclusive instead."
+        )
+
+    if isinstance(usecols, str):
+        return _range2cols(usecols)
+
+    return usecols
+
+
+@overload
+def validate_freeze_panes(freeze_panes: tuple[int, int]) -> Literal[True]:
+    ...
+
+
+@overload
+def validate_freeze_panes(freeze_panes: None) -> Literal[False]:
+    ...
+
+
+def validate_freeze_panes(freeze_panes: tuple[int, int] | None) -> bool:
+    if freeze_panes is not None:
+        if len(freeze_panes) == 2 and all(
+            isinstance(item, int) for item in freeze_panes
+        ):
+            return True
+
+        raise ValueError(
+            "freeze_panes must be of form (row, column) "
+            "where row and column are integers"
+        )
+
+    # freeze_panes wasn't specified, return False so it won't be applied
+    # to output sheet
+    return False
+
+
+def fill_mi_header(
+    row: list[Hashable], control_row: list[bool]
+) -> tuple[list[Hashable], list[bool]]:
+    """
+    Forward fill blank entries in row but only inside the same parent index.
+
+    Used for creating headers in Multiindex.
+
+    Parameters
+    ----------
+    row : list
+        List of items in a single row.
+    control_row : list of bool
+        Helps to determine if particular column is in same parent index as the
+        previous value. Used to stop propagation of empty cells between
+        different indexes.
+
+    Returns
+    -------
+    Returns changed row and control_row
+    """
+    last = row[0]
+    for i in range(1, len(row)):
+        if not control_row[i]:
+            last = row[i]
+
+        if row[i] == "" or row[i] is None:
+            row[i] = last
+        else:
+            control_row[i] = False
+            last = row[i]
+
+    return row, control_row
+
+
+def pop_header_name(
+    row: list[Hashable], index_col: int | Sequence[int]
+) -> tuple[Hashable | None, list[Hashable]]:
+    """
+    Pop the header name for MultiIndex parsing.
+
+    Parameters
+    ----------
+    row : list
+        The data row to parse for the header name.
+    index_col : int, list
+        The index columns for our data. Assumed to be non-null.
+
+    Returns
+    -------
+    header_name : str
+        The extracted header name.
+    trimmed_row : list
+        The original data row with the header name removed.
+    """
+    # Pop out header name and fill w/blank.
+    if is_list_like(index_col):
+        assert isinstance(index_col, Iterable)
+        i = max(index_col)
+    else:
+        assert not isinstance(index_col, Iterable)
+        i = index_col
+
+    header_name = row[i]
+    header_name = None if header_name == "" else header_name
+
+    return header_name, row[:i] + [""] + row[i + 1 :]
+
+
+def combine_kwargs(engine_kwargs: dict[str, Any] | None, kwargs: dict) -> dict:
+    """
+    Used to combine two sources of kwargs for the backend engine.
+
+    Use of kwargs is deprecated, this function is solely for use in 1.3 and should
+    be removed in 1.4/2.0. Also _base.ExcelWriter.__new__ ensures either engine_kwargs
+    or kwargs must be None or empty respectively.
+
+    Parameters
+    ----------
+    engine_kwargs: dict
+        kwargs to be passed through to the engine.
+    kwargs: dict
+        kwargs to be psased through to the engine (deprecated)
+
+    Returns
+    -------
+    engine_kwargs combined with kwargs
+    """
+    if engine_kwargs is None:
+        result = {}
+    else:
+        result = engine_kwargs.copy()
+    result.update(kwargs)
+    return result
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/excel/_xlrd.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/excel/_xlrd.py
new file mode 100644
index 0000000000000000000000000000000000000000..a444970792e6e65faf3d8947b721fff59487d994
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/excel/_xlrd.py
@@ -0,0 +1,143 @@
+from __future__ import annotations
+
+from datetime import time
+import math
+from typing import TYPE_CHECKING
+
+import numpy as np
+
+from pandas.compat._optional import import_optional_dependency
+from pandas.util._decorators import doc
+
+from pandas.core.shared_docs import _shared_docs
+
+from pandas.io.excel._base import BaseExcelReader
+
+if TYPE_CHECKING:
+    from xlrd import Book
+
+    from pandas._typing import (
+        Scalar,
+        StorageOptions,
+    )
+
+
+class XlrdReader(BaseExcelReader["Book"]):
+    @doc(storage_options=_shared_docs["storage_options"])
+    def __init__(
+        self,
+        filepath_or_buffer,
+        storage_options: StorageOptions | None = None,
+        engine_kwargs: dict | None = None,
+    ) -> None:
+        """
+        Reader using xlrd engine.
+
+        Parameters
+        ----------
+        filepath_or_buffer : str, path object or Workbook
+            Object to be parsed.
+        {storage_options}
+        engine_kwargs : dict, optional
+            Arbitrary keyword arguments passed to excel engine.
+        """
+        err_msg = "Install xlrd >= 2.0.1 for xls Excel support"
+        import_optional_dependency("xlrd", extra=err_msg)
+        super().__init__(
+            filepath_or_buffer,
+            storage_options=storage_options,
+            engine_kwargs=engine_kwargs,
+        )
+
+    @property
+    def _workbook_class(self) -> type[Book]:
+        from xlrd import Book
+
+        return Book
+
+    def load_workbook(self, filepath_or_buffer, engine_kwargs) -> Book:
+        from xlrd import open_workbook
+
+        if hasattr(filepath_or_buffer, "read"):
+            data = filepath_or_buffer.read()
+            return open_workbook(file_contents=data, **engine_kwargs)
+        else:
+            return open_workbook(filepath_or_buffer, **engine_kwargs)
+
+    @property
+    def sheet_names(self):
+        return self.book.sheet_names()
+
+    def get_sheet_by_name(self, name):
+        self.raise_if_bad_sheet_by_name(name)
+        return self.book.sheet_by_name(name)
+
+    def get_sheet_by_index(self, index):
+        self.raise_if_bad_sheet_by_index(index)
+        return self.book.sheet_by_index(index)
+
+    def get_sheet_data(
+        self, sheet, file_rows_needed: int | None = None
+    ) -> list[list[Scalar]]:
+        from xlrd import (
+            XL_CELL_BOOLEAN,
+            XL_CELL_DATE,
+            XL_CELL_ERROR,
+            XL_CELL_NUMBER,
+            xldate,
+        )
+
+        epoch1904 = self.book.datemode
+
+        def _parse_cell(cell_contents, cell_typ):
+            """
+            converts the contents of the cell into a pandas appropriate object
+            """
+            if cell_typ == XL_CELL_DATE:
+                # Use the newer xlrd datetime handling.
+                try:
+                    cell_contents = xldate.xldate_as_datetime(cell_contents, epoch1904)
+                except OverflowError:
+                    return cell_contents
+
+                # Excel doesn't distinguish between dates and time,
+                # so we treat dates on the epoch as times only.
+                # Also, Excel supports 1900 and 1904 epochs.
+                year = (cell_contents.timetuple())[0:3]
+                if (not epoch1904 and year == (1899, 12, 31)) or (
+                    epoch1904 and year == (1904, 1, 1)
+                ):
+                    cell_contents = time(
+                        cell_contents.hour,
+                        cell_contents.minute,
+                        cell_contents.second,
+                        cell_contents.microsecond,
+                    )
+
+            elif cell_typ == XL_CELL_ERROR:
+                cell_contents = np.nan
+            elif cell_typ == XL_CELL_BOOLEAN:
+                cell_contents = bool(cell_contents)
+            elif cell_typ == XL_CELL_NUMBER:
+                # GH5394 - Excel 'numbers' are always floats
+                # it's a minimal perf hit and less surprising
+                if math.isfinite(cell_contents):
+                    # GH54564 - don't attempt to convert NaN/Inf
+                    val = int(cell_contents)
+                    if val == cell_contents:
+                        cell_contents = val
+            return cell_contents
+
+        data = []
+
+        nrows = sheet.nrows
+        if file_rows_needed is not None:
+            nrows = min(nrows, file_rows_needed)
+        for i in range(nrows):
+            row = [
+                _parse_cell(value, typ)
+                for value, typ in zip(sheet.row_values(i), sheet.row_types(i))
+            ]
+            data.append(row)
+
+        return data
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/excel/_xlsxwriter.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/excel/_xlsxwriter.py
new file mode 100644
index 0000000000000000000000000000000000000000..e7a9789ccd0f3317c83b1542b823586d3419c485
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/excel/_xlsxwriter.py
@@ -0,0 +1,284 @@
+from __future__ import annotations
+
+import json
+from typing import (
+    TYPE_CHECKING,
+    Any,
+)
+
+from pandas.io.excel._base import ExcelWriter
+from pandas.io.excel._util import (
+    combine_kwargs,
+    validate_freeze_panes,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        ExcelWriterIfSheetExists,
+        FilePath,
+        StorageOptions,
+        WriteExcelBuffer,
+    )
+
+
+class _XlsxStyler:
+    # Map from openpyxl-oriented styles to flatter xlsxwriter representation
+    # Ordering necessary for both determinism and because some are keyed by
+    # prefixes of others.
+    STYLE_MAPPING: dict[str, list[tuple[tuple[str, ...], str]]] = {
+        "font": [
+            (("name",), "font_name"),
+            (("sz",), "font_size"),
+            (("size",), "font_size"),
+            (("color", "rgb"), "font_color"),
+            (("color",), "font_color"),
+            (("b",), "bold"),
+            (("bold",), "bold"),
+            (("i",), "italic"),
+            (("italic",), "italic"),
+            (("u",), "underline"),
+            (("underline",), "underline"),
+            (("strike",), "font_strikeout"),
+            (("vertAlign",), "font_script"),
+            (("vertalign",), "font_script"),
+        ],
+        "number_format": [(("format_code",), "num_format"), ((), "num_format")],
+        "protection": [(("locked",), "locked"), (("hidden",), "hidden")],
+        "alignment": [
+            (("horizontal",), "align"),
+            (("vertical",), "valign"),
+            (("text_rotation",), "rotation"),
+            (("wrap_text",), "text_wrap"),
+            (("indent",), "indent"),
+            (("shrink_to_fit",), "shrink"),
+        ],
+        "fill": [
+            (("patternType",), "pattern"),
+            (("patterntype",), "pattern"),
+            (("fill_type",), "pattern"),
+            (("start_color", "rgb"), "fg_color"),
+            (("fgColor", "rgb"), "fg_color"),
+            (("fgcolor", "rgb"), "fg_color"),
+            (("start_color",), "fg_color"),
+            (("fgColor",), "fg_color"),
+            (("fgcolor",), "fg_color"),
+            (("end_color", "rgb"), "bg_color"),
+            (("bgColor", "rgb"), "bg_color"),
+            (("bgcolor", "rgb"), "bg_color"),
+            (("end_color",), "bg_color"),
+            (("bgColor",), "bg_color"),
+            (("bgcolor",), "bg_color"),
+        ],
+        "border": [
+            (("color", "rgb"), "border_color"),
+            (("color",), "border_color"),
+            (("style",), "border"),
+            (("top", "color", "rgb"), "top_color"),
+            (("top", "color"), "top_color"),
+            (("top", "style"), "top"),
+            (("top",), "top"),
+            (("right", "color", "rgb"), "right_color"),
+            (("right", "color"), "right_color"),
+            (("right", "style"), "right"),
+            (("right",), "right"),
+            (("bottom", "color", "rgb"), "bottom_color"),
+            (("bottom", "color"), "bottom_color"),
+            (("bottom", "style"), "bottom"),
+            (("bottom",), "bottom"),
+            (("left", "color", "rgb"), "left_color"),
+            (("left", "color"), "left_color"),
+            (("left", "style"), "left"),
+            (("left",), "left"),
+        ],
+    }
+
+    @classmethod
+    def convert(cls, style_dict, num_format_str=None):
+        """
+        converts a style_dict to an xlsxwriter format dict
+
+        Parameters
+        ----------
+        style_dict : style dictionary to convert
+        num_format_str : optional number format string
+        """
+        # Create a XlsxWriter format object.
+        props = {}
+
+        if num_format_str is not None:
+            props["num_format"] = num_format_str
+
+        if style_dict is None:
+            return props
+
+        if "borders" in style_dict:
+            style_dict = style_dict.copy()
+            style_dict["border"] = style_dict.pop("borders")
+
+        for style_group_key, style_group in style_dict.items():
+            for src, dst in cls.STYLE_MAPPING.get(style_group_key, []):
+                # src is a sequence of keys into a nested dict
+                # dst is a flat key
+                if dst in props:
+                    continue
+                v = style_group
+                for k in src:
+                    try:
+                        v = v[k]
+                    except (KeyError, TypeError):
+                        break
+                else:
+                    props[dst] = v
+
+        if isinstance(props.get("pattern"), str):
+            # TODO: support other fill patterns
+            props["pattern"] = 0 if props["pattern"] == "none" else 1
+
+        for k in ["border", "top", "right", "bottom", "left"]:
+            if isinstance(props.get(k), str):
+                try:
+                    props[k] = [
+                        "none",
+                        "thin",
+                        "medium",
+                        "dashed",
+                        "dotted",
+                        "thick",
+                        "double",
+                        "hair",
+                        "mediumDashed",
+                        "dashDot",
+                        "mediumDashDot",
+                        "dashDotDot",
+                        "mediumDashDotDot",
+                        "slantDashDot",
+                    ].index(props[k])
+                except ValueError:
+                    props[k] = 2
+
+        if isinstance(props.get("font_script"), str):
+            props["font_script"] = ["baseline", "superscript", "subscript"].index(
+                props["font_script"]
+            )
+
+        if isinstance(props.get("underline"), str):
+            props["underline"] = {
+                "none": 0,
+                "single": 1,
+                "double": 2,
+                "singleAccounting": 33,
+                "doubleAccounting": 34,
+            }[props["underline"]]
+
+        # GH 30107 - xlsxwriter uses different name
+        if props.get("valign") == "center":
+            props["valign"] = "vcenter"
+
+        return props
+
+
+class XlsxWriter(ExcelWriter):
+    _engine = "xlsxwriter"
+    _supported_extensions = (".xlsx",)
+
+    def __init__(
+        self,
+        path: FilePath | WriteExcelBuffer | ExcelWriter,
+        engine: str | None = None,
+        date_format: str | None = None,
+        datetime_format: str | None = None,
+        mode: str = "w",
+        storage_options: StorageOptions | None = None,
+        if_sheet_exists: ExcelWriterIfSheetExists | None = None,
+        engine_kwargs: dict[str, Any] | None = None,
+        **kwargs,
+    ) -> None:
+        # Use the xlsxwriter module as the Excel writer.
+        from xlsxwriter import Workbook
+
+        engine_kwargs = combine_kwargs(engine_kwargs, kwargs)
+
+        if mode == "a":
+            raise ValueError("Append mode is not supported with xlsxwriter!")
+
+        super().__init__(
+            path,
+            engine=engine,
+            date_format=date_format,
+            datetime_format=datetime_format,
+            mode=mode,
+            storage_options=storage_options,
+            if_sheet_exists=if_sheet_exists,
+            engine_kwargs=engine_kwargs,
+        )
+
+        try:
+            self._book = Workbook(self._handles.handle, **engine_kwargs)  # type: ignore[arg-type]
+        except TypeError:
+            self._handles.handle.close()
+            raise
+
+    @property
+    def book(self):
+        """
+        Book instance of class xlsxwriter.Workbook.
+
+        This attribute can be used to access engine-specific features.
+        """
+        return self._book
+
+    @property
+    def sheets(self) -> dict[str, Any]:
+        result = self.book.sheetnames
+        return result
+
+    def _save(self) -> None:
+        """
+        Save workbook to disk.
+        """
+        self.book.close()
+
+    def _write_cells(
+        self,
+        cells,
+        sheet_name: str | None = None,
+        startrow: int = 0,
+        startcol: int = 0,
+        freeze_panes: tuple[int, int] | None = None,
+    ) -> None:
+        # Write the frame cells using xlsxwriter.
+        sheet_name = self._get_sheet_name(sheet_name)
+
+        wks = self.book.get_worksheet_by_name(sheet_name)
+        if wks is None:
+            wks = self.book.add_worksheet(sheet_name)
+
+        style_dict = {"null": None}
+
+        if validate_freeze_panes(freeze_panes):
+            wks.freeze_panes(*(freeze_panes))
+
+        for cell in cells:
+            val, fmt = self._value_with_fmt(cell.val)
+
+            stylekey = json.dumps(cell.style)
+            if fmt:
+                stylekey += fmt
+
+            if stylekey in style_dict:
+                style = style_dict[stylekey]
+            else:
+                style = self.book.add_format(_XlsxStyler.convert(cell.style, fmt))
+                style_dict[stylekey] = style
+
+            if cell.mergestart is not None and cell.mergeend is not None:
+                wks.merge_range(
+                    startrow + cell.row,
+                    startcol + cell.col,
+                    startrow + cell.mergestart,
+                    startcol + cell.mergeend,
+                    val,
+                    style,
+                )
+            else:
+                wks.write(startrow + cell.row, startcol + cell.col, val, style)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/feather_format.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/feather_format.py
new file mode 100644
index 0000000000000000000000000000000000000000..1bdb732cb10de7c9622a10f6b353b766d049376c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/feather_format.py
@@ -0,0 +1,130 @@
+""" feather-format compat """
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    Any,
+)
+
+from pandas._config import using_string_dtype
+
+from pandas._libs import lib
+from pandas.compat._optional import import_optional_dependency
+from pandas.util._decorators import doc
+from pandas.util._validators import check_dtype_backend
+
+from pandas.core.api import DataFrame
+from pandas.core.shared_docs import _shared_docs
+
+from pandas.io._util import arrow_table_to_pandas
+from pandas.io.common import get_handle
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Hashable,
+        Sequence,
+    )
+
+    from pandas._typing import (
+        DtypeBackend,
+        FilePath,
+        ReadBuffer,
+        StorageOptions,
+        WriteBuffer,
+    )
+
+
+@doc(storage_options=_shared_docs["storage_options"])
+def to_feather(
+    df: DataFrame,
+    path: FilePath | WriteBuffer[bytes],
+    storage_options: StorageOptions | None = None,
+    **kwargs: Any,
+) -> None:
+    """
+    Write a DataFrame to the binary Feather format.
+
+    Parameters
+    ----------
+    df : DataFrame
+    path : str, path object, or file-like object
+    {storage_options}
+    **kwargs :
+        Additional keywords passed to `pyarrow.feather.write_feather`.
+
+    """
+    import_optional_dependency("pyarrow")
+    from pyarrow import feather
+
+    if not isinstance(df, DataFrame):
+        raise ValueError("feather only support IO with DataFrames")
+
+    with get_handle(
+        path, "wb", storage_options=storage_options, is_text=False
+    ) as handles:
+        feather.write_feather(df, handles.handle, **kwargs)
+
+
+@doc(storage_options=_shared_docs["storage_options"])
+def read_feather(
+    path: FilePath | ReadBuffer[bytes],
+    columns: Sequence[Hashable] | None = None,
+    use_threads: bool = True,
+    storage_options: StorageOptions | None = None,
+    dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
+) -> DataFrame:
+    """
+    Load a feather-format object from the file path.
+
+    Parameters
+    ----------
+    path : str, path object, or file-like object
+        String, path object (implementing ``os.PathLike[str]``), or file-like
+        object implementing a binary ``read()`` function. The string could be a URL.
+        Valid URL schemes include http, ftp, s3, and file. For file URLs, a host is
+        expected. A local file could be: ``file://localhost/path/to/table.feather``.
+    columns : sequence, default None
+        If not provided, all columns are read.
+    use_threads : bool, default True
+        Whether to parallelize reading using multiple threads.
+    {storage_options}
+
+    dtype_backend : {{'numpy_nullable', 'pyarrow'}}, default 'numpy_nullable'
+        Back-end data type applied to the resultant :class:`DataFrame`
+        (still experimental). Behaviour is as follows:
+
+        * ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame`
+          (default).
+        * ``"pyarrow"``: returns pyarrow-backed nullable :class:`ArrowDtype`
+          DataFrame.
+
+        .. versionadded:: 2.0
+
+    Returns
+    -------
+    type of object stored in file
+
+    Examples
+    --------
+    >>> df = pd.read_feather("path/to/file.feather")  # doctest: +SKIP
+    """
+    import_optional_dependency("pyarrow")
+    from pyarrow import feather
+
+    # import utils to register the pyarrow extension types
+    import pandas.core.arrays.arrow.extension_types  # pyright: ignore[reportUnusedImport] # noqa: F401
+
+    check_dtype_backend(dtype_backend)
+
+    with get_handle(
+        path, "rb", storage_options=storage_options, is_text=False
+    ) as handles:
+        if dtype_backend is lib.no_default and not using_string_dtype():
+            return feather.read_feather(
+                handles.handle, columns=columns, use_threads=bool(use_threads)
+            )
+
+        pa_table = feather.read_table(
+            handles.handle, columns=columns, use_threads=bool(use_threads)
+        )
+        return arrow_table_to_pandas(pa_table, dtype_backend=dtype_backend)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/formats/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/formats/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5e56b1bc7ba4377cc5de9d68a1424524aef21cb5
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/formats/__init__.py
@@ -0,0 +1,9 @@
+# ruff: noqa: TCH004
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    # import modules that have public classes/functions
+    from pandas.io.formats import style
+
+    # and mark only those modules as public
+    __all__ = ["style"]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/formats/_color_data.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/formats/_color_data.py
new file mode 100644
index 0000000000000000000000000000000000000000..2e7cb7f29646eb11c0ec83d8a909a8cfd7953182
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/formats/_color_data.py
@@ -0,0 +1,157 @@
+# GH37967: Enable the use of CSS named colors, as defined in
+# matplotlib.colors.CSS4_COLORS, when exporting to Excel.
+# This data has been copied here, instead of being imported from matplotlib,
+# not to have ``to_excel`` methods require matplotlib.
+# source: matplotlib._color_data (3.3.3)
+from __future__ import annotations
+
+CSS4_COLORS = {
+    "aliceblue": "F0F8FF",
+    "antiquewhite": "FAEBD7",
+    "aqua": "00FFFF",
+    "aquamarine": "7FFFD4",
+    "azure": "F0FFFF",
+    "beige": "F5F5DC",
+    "bisque": "FFE4C4",
+    "black": "000000",
+    "blanchedalmond": "FFEBCD",
+    "blue": "0000FF",
+    "blueviolet": "8A2BE2",
+    "brown": "A52A2A",
+    "burlywood": "DEB887",
+    "cadetblue": "5F9EA0",
+    "chartreuse": "7FFF00",
+    "chocolate": "D2691E",
+    "coral": "FF7F50",
+    "cornflowerblue": "6495ED",
+    "cornsilk": "FFF8DC",
+    "crimson": "DC143C",
+    "cyan": "00FFFF",
+    "darkblue": "00008B",
+    "darkcyan": "008B8B",
+    "darkgoldenrod": "B8860B",
+    "darkgray": "A9A9A9",
+    "darkgreen": "006400",
+    "darkgrey": "A9A9A9",
+    "darkkhaki": "BDB76B",
+    "darkmagenta": "8B008B",
+    "darkolivegreen": "556B2F",
+    "darkorange": "FF8C00",
+    "darkorchid": "9932CC",
+    "darkred": "8B0000",
+    "darksalmon": "E9967A",
+    "darkseagreen": "8FBC8F",
+    "darkslateblue": "483D8B",
+    "darkslategray": "2F4F4F",
+    "darkslategrey": "2F4F4F",
+    "darkturquoise": "00CED1",
+    "darkviolet": "9400D3",
+    "deeppink": "FF1493",
+    "deepskyblue": "00BFFF",
+    "dimgray": "696969",
+    "dimgrey": "696969",
+    "dodgerblue": "1E90FF",
+    "firebrick": "B22222",
+    "floralwhite": "FFFAF0",
+    "forestgreen": "228B22",
+    "fuchsia": "FF00FF",
+    "gainsboro": "DCDCDC",
+    "ghostwhite": "F8F8FF",
+    "gold": "FFD700",
+    "goldenrod": "DAA520",
+    "gray": "808080",
+    "green": "008000",
+    "greenyellow": "ADFF2F",
+    "grey": "808080",
+    "honeydew": "F0FFF0",
+    "hotpink": "FF69B4",
+    "indianred": "CD5C5C",
+    "indigo": "4B0082",
+    "ivory": "FFFFF0",
+    "khaki": "F0E68C",
+    "lavender": "E6E6FA",
+    "lavenderblush": "FFF0F5",
+    "lawngreen": "7CFC00",
+    "lemonchiffon": "FFFACD",
+    "lightblue": "ADD8E6",
+    "lightcoral": "F08080",
+    "lightcyan": "E0FFFF",
+    "lightgoldenrodyellow": "FAFAD2",
+    "lightgray": "D3D3D3",
+    "lightgreen": "90EE90",
+    "lightgrey": "D3D3D3",
+    "lightpink": "FFB6C1",
+    "lightsalmon": "FFA07A",
+    "lightseagreen": "20B2AA",
+    "lightskyblue": "87CEFA",
+    "lightslategray": "778899",
+    "lightslategrey": "778899",
+    "lightsteelblue": "B0C4DE",
+    "lightyellow": "FFFFE0",
+    "lime": "00FF00",
+    "limegreen": "32CD32",
+    "linen": "FAF0E6",
+    "magenta": "FF00FF",
+    "maroon": "800000",
+    "mediumaquamarine": "66CDAA",
+    "mediumblue": "0000CD",
+    "mediumorchid": "BA55D3",
+    "mediumpurple": "9370DB",
+    "mediumseagreen": "3CB371",
+    "mediumslateblue": "7B68EE",
+    "mediumspringgreen": "00FA9A",
+    "mediumturquoise": "48D1CC",
+    "mediumvioletred": "C71585",
+    "midnightblue": "191970",
+    "mintcream": "F5FFFA",
+    "mistyrose": "FFE4E1",
+    "moccasin": "FFE4B5",
+    "navajowhite": "FFDEAD",
+    "navy": "000080",
+    "oldlace": "FDF5E6",
+    "olive": "808000",
+    "olivedrab": "6B8E23",
+    "orange": "FFA500",
+    "orangered": "FF4500",
+    "orchid": "DA70D6",
+    "palegoldenrod": "EEE8AA",
+    "palegreen": "98FB98",
+    "paleturquoise": "AFEEEE",
+    "palevioletred": "DB7093",
+    "papayawhip": "FFEFD5",
+    "peachpuff": "FFDAB9",
+    "peru": "CD853F",
+    "pink": "FFC0CB",
+    "plum": "DDA0DD",
+    "powderblue": "B0E0E6",
+    "purple": "800080",
+    "rebeccapurple": "663399",
+    "red": "FF0000",
+    "rosybrown": "BC8F8F",
+    "royalblue": "4169E1",
+    "saddlebrown": "8B4513",
+    "salmon": "FA8072",
+    "sandybrown": "F4A460",
+    "seagreen": "2E8B57",
+    "seashell": "FFF5EE",
+    "sienna": "A0522D",
+    "silver": "C0C0C0",
+    "skyblue": "87CEEB",
+    "slateblue": "6A5ACD",
+    "slategray": "708090",
+    "slategrey": "708090",
+    "snow": "FFFAFA",
+    "springgreen": "00FF7F",
+    "steelblue": "4682B4",
+    "tan": "D2B48C",
+    "teal": "008080",
+    "thistle": "D8BFD8",
+    "tomato": "FF6347",
+    "turquoise": "40E0D0",
+    "violet": "EE82EE",
+    "wheat": "F5DEB3",
+    "white": "FFFFFF",
+    "whitesmoke": "F5F5F5",
+    "yellow": "FFFF00",
+    "yellowgreen": "9ACD32",
+}
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/formats/console.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/formats/console.py
new file mode 100644
index 0000000000000000000000000000000000000000..2a6cbe07629031687c249f70b51bdfbe2dd84041
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/formats/console.py
@@ -0,0 +1,94 @@
+"""
+Internal module for console introspection
+"""
+from __future__ import annotations
+
+from shutil import get_terminal_size
+
+
+def get_console_size() -> tuple[int | None, int | None]:
+    """
+    Return console size as tuple = (width, height).
+
+    Returns (None,None) in non-interactive session.
+    """
+    from pandas import get_option
+
+    display_width = get_option("display.width")
+    display_height = get_option("display.max_rows")
+
+    # Consider
+    # interactive shell terminal, can detect term size
+    # interactive non-shell terminal (ipnb/ipqtconsole), cannot detect term
+    # size non-interactive script, should disregard term size
+
+    # in addition
+    # width,height have default values, but setting to 'None' signals
+    # should use Auto-Detection, But only in interactive shell-terminal.
+    # Simple. yeah.
+
+    if in_interactive_session():
+        if in_ipython_frontend():
+            # sane defaults for interactive non-shell terminal
+            # match default for width,height in config_init
+            from pandas._config.config import get_default_val
+
+            terminal_width = get_default_val("display.width")
+            terminal_height = get_default_val("display.max_rows")
+        else:
+            # pure terminal
+            terminal_width, terminal_height = get_terminal_size()
+    else:
+        terminal_width, terminal_height = None, None
+
+    # Note if the User sets width/Height to None (auto-detection)
+    # and we're in a script (non-inter), this will return (None,None)
+    # caller needs to deal.
+    return display_width or terminal_width, display_height or terminal_height
+
+
+# ----------------------------------------------------------------------
+# Detect our environment
+
+
+def in_interactive_session() -> bool:
+    """
+    Check if we're running in an interactive shell.
+
+    Returns
+    -------
+    bool
+        True if running under python/ipython interactive shell.
+    """
+    from pandas import get_option
+
+    def check_main():
+        try:
+            import __main__ as main
+        except ModuleNotFoundError:
+            return get_option("mode.sim_interactive")
+        return not hasattr(main, "__file__") or get_option("mode.sim_interactive")
+
+    try:
+        # error: Name '__IPYTHON__' is not defined
+        return __IPYTHON__ or check_main()  # type: ignore[name-defined]
+    except NameError:
+        return check_main()
+
+
+def in_ipython_frontend() -> bool:
+    """
+    Check if we're inside an IPython zmq frontend.
+
+    Returns
+    -------
+    bool
+    """
+    try:
+        # error: Name 'get_ipython' is not defined
+        ip = get_ipython()  # type: ignore[name-defined]
+        return "zmq" in str(type(ip)).lower()
+    except NameError:
+        pass
+
+    return False
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/formats/css.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/formats/css.py
new file mode 100644
index 0000000000000000000000000000000000000000..ccce60c00a9e02bf3bb7f21c5ec799b7123e8eed
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/formats/css.py
@@ -0,0 +1,421 @@
+"""
+Utilities for interpreting CSS from Stylers for formatting non-HTML outputs.
+"""
+from __future__ import annotations
+
+import re
+from typing import (
+    TYPE_CHECKING,
+    Callable,
+)
+import warnings
+
+from pandas.errors import CSSWarning
+from pandas.util._exceptions import find_stack_level
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Generator,
+        Iterable,
+        Iterator,
+    )
+
+
+def _side_expander(prop_fmt: str) -> Callable:
+    """
+    Wrapper to expand shorthand property into top, right, bottom, left properties
+
+    Parameters
+    ----------
+    side : str
+        The border side to expand into properties
+
+    Returns
+    -------
+        function: Return to call when a 'border(-{side}): {value}' string is encountered
+    """
+
+    def expand(self, prop, value: str) -> Generator[tuple[str, str], None, None]:
+        """
+        Expand shorthand property into side-specific property (top, right, bottom, left)
+
+        Parameters
+        ----------
+            prop (str): CSS property name
+            value (str): String token for property
+
+        Yields
+        ------
+            Tuple (str, str): Expanded property, value
+        """
+        tokens = value.split()
+        try:
+            mapping = self.SIDE_SHORTHANDS[len(tokens)]
+        except KeyError:
+            warnings.warn(
+                f'Could not expand "{prop}: {value}"',
+                CSSWarning,
+                stacklevel=find_stack_level(),
+            )
+            return
+        for key, idx in zip(self.SIDES, mapping):
+            yield prop_fmt.format(key), tokens[idx]
+
+    return expand
+
+
+def _border_expander(side: str = "") -> Callable:
+    """
+    Wrapper to expand 'border' property into border color, style, and width properties
+
+    Parameters
+    ----------
+    side : str
+        The border side to expand into properties
+
+    Returns
+    -------
+        function: Return to call when a 'border(-{side}): {value}' string is encountered
+    """
+    if side != "":
+        side = f"-{side}"
+
+    def expand(self, prop, value: str) -> Generator[tuple[str, str], None, None]:
+        """
+        Expand border into color, style, and width tuples
+
+        Parameters
+        ----------
+            prop : str
+                CSS property name passed to styler
+            value : str
+                Value passed to styler for property
+
+        Yields
+        ------
+            Tuple (str, str): Expanded property, value
+        """
+        tokens = value.split()
+        if len(tokens) == 0 or len(tokens) > 3:
+            warnings.warn(
+                f'Too many tokens provided to "{prop}" (expected 1-3)',
+                CSSWarning,
+                stacklevel=find_stack_level(),
+            )
+
+        # TODO: Can we use current color as initial value to comply with CSS standards?
+        border_declarations = {
+            f"border{side}-color": "black",
+            f"border{side}-style": "none",
+            f"border{side}-width": "medium",
+        }
+        for token in tokens:
+            if token.lower() in self.BORDER_STYLES:
+                border_declarations[f"border{side}-style"] = token
+            elif any(ratio in token.lower() for ratio in self.BORDER_WIDTH_RATIOS):
+                border_declarations[f"border{side}-width"] = token
+            else:
+                border_declarations[f"border{side}-color"] = token
+            # TODO: Warn user if item entered more than once (e.g. "border: red green")
+
+        # Per CSS, "border" will reset previous "border-*" definitions
+        yield from self.atomize(border_declarations.items())
+
+    return expand
+
+
+class CSSResolver:
+    """
+    A callable for parsing and resolving CSS to atomic properties.
+    """
+
+    UNIT_RATIOS = {
+        "pt": ("pt", 1),
+        "em": ("em", 1),
+        "rem": ("pt", 12),
+        "ex": ("em", 0.5),
+        # 'ch':
+        "px": ("pt", 0.75),
+        "pc": ("pt", 12),
+        "in": ("pt", 72),
+        "cm": ("in", 1 / 2.54),
+        "mm": ("in", 1 / 25.4),
+        "q": ("mm", 0.25),
+        "!!default": ("em", 0),
+    }
+
+    FONT_SIZE_RATIOS = UNIT_RATIOS.copy()
+    FONT_SIZE_RATIOS.update(
+        {
+            "%": ("em", 0.01),
+            "xx-small": ("rem", 0.5),
+            "x-small": ("rem", 0.625),
+            "small": ("rem", 0.8),
+            "medium": ("rem", 1),
+            "large": ("rem", 1.125),
+            "x-large": ("rem", 1.5),
+            "xx-large": ("rem", 2),
+            "smaller": ("em", 1 / 1.2),
+            "larger": ("em", 1.2),
+            "!!default": ("em", 1),
+        }
+    )
+
+    MARGIN_RATIOS = UNIT_RATIOS.copy()
+    MARGIN_RATIOS.update({"none": ("pt", 0)})
+
+    BORDER_WIDTH_RATIOS = UNIT_RATIOS.copy()
+    BORDER_WIDTH_RATIOS.update(
+        {
+            "none": ("pt", 0),
+            "thick": ("px", 4),
+            "medium": ("px", 2),
+            "thin": ("px", 1),
+            # Default: medium only if solid
+        }
+    )
+
+    BORDER_STYLES = [
+        "none",
+        "hidden",
+        "dotted",
+        "dashed",
+        "solid",
+        "double",
+        "groove",
+        "ridge",
+        "inset",
+        "outset",
+        "mediumdashdot",
+        "dashdotdot",
+        "hair",
+        "mediumdashdotdot",
+        "dashdot",
+        "slantdashdot",
+        "mediumdashed",
+    ]
+
+    SIDE_SHORTHANDS = {
+        1: [0, 0, 0, 0],
+        2: [0, 1, 0, 1],
+        3: [0, 1, 2, 1],
+        4: [0, 1, 2, 3],
+    }
+
+    SIDES = ("top", "right", "bottom", "left")
+
+    CSS_EXPANSIONS = {
+        **{
+            (f"border-{prop}" if prop else "border"): _border_expander(prop)
+            for prop in ["", "top", "right", "bottom", "left"]
+        },
+        **{
+            f"border-{prop}": _side_expander(f"border-{{:s}}-{prop}")
+            for prop in ["color", "style", "width"]
+        },
+        "margin": _side_expander("margin-{:s}"),
+        "padding": _side_expander("padding-{:s}"),
+    }
+
+    def __call__(
+        self,
+        declarations: str | Iterable[tuple[str, str]],
+        inherited: dict[str, str] | None = None,
+    ) -> dict[str, str]:
+        """
+        The given declarations to atomic properties.
+
+        Parameters
+        ----------
+        declarations_str : str | Iterable[tuple[str, str]]
+            A CSS string or set of CSS declaration tuples
+            e.g. "font-weight: bold; background: blue" or
+            {("font-weight", "bold"), ("background", "blue")}
+        inherited : dict, optional
+            Atomic properties indicating the inherited style context in which
+            declarations_str is to be resolved. ``inherited`` should already
+            be resolved, i.e. valid output of this method.
+
+        Returns
+        -------
+        dict
+            Atomic CSS 2.2 properties.
+
+        Examples
+        --------
+        >>> resolve = CSSResolver()
+        >>> inherited = {'font-family': 'serif', 'font-weight': 'bold'}
+        >>> out = resolve('''
+        ...               border-color: BLUE RED;
+        ...               font-size: 1em;
+        ...               font-size: 2em;
+        ...               font-weight: normal;
+        ...               font-weight: inherit;
+        ...               ''', inherited)
+        >>> sorted(out.items())  # doctest: +NORMALIZE_WHITESPACE
+        [('border-bottom-color', 'blue'),
+         ('border-left-color', 'red'),
+         ('border-right-color', 'red'),
+         ('border-top-color', 'blue'),
+         ('font-family', 'serif'),
+         ('font-size', '24pt'),
+         ('font-weight', 'bold')]
+        """
+        if isinstance(declarations, str):
+            declarations = self.parse(declarations)
+        props = dict(self.atomize(declarations))
+        if inherited is None:
+            inherited = {}
+
+        props = self._update_initial(props, inherited)
+        props = self._update_font_size(props, inherited)
+        return self._update_other_units(props)
+
+    def _update_initial(
+        self,
+        props: dict[str, str],
+        inherited: dict[str, str],
+    ) -> dict[str, str]:
+        # 1. resolve inherited, initial
+        for prop, val in inherited.items():
+            if prop not in props:
+                props[prop] = val
+
+        new_props = props.copy()
+        for prop, val in props.items():
+            if val == "inherit":
+                val = inherited.get(prop, "initial")
+
+            if val in ("initial", None):
+                # we do not define a complete initial stylesheet
+                del new_props[prop]
+            else:
+                new_props[prop] = val
+        return new_props
+
+    def _update_font_size(
+        self,
+        props: dict[str, str],
+        inherited: dict[str, str],
+    ) -> dict[str, str]:
+        # 2. resolve relative font size
+        if props.get("font-size"):
+            props["font-size"] = self.size_to_pt(
+                props["font-size"],
+                self._get_font_size(inherited),
+                conversions=self.FONT_SIZE_RATIOS,
+            )
+        return props
+
+    def _get_font_size(self, props: dict[str, str]) -> float | None:
+        if props.get("font-size"):
+            font_size_string = props["font-size"]
+            return self._get_float_font_size_from_pt(font_size_string)
+        return None
+
+    def _get_float_font_size_from_pt(self, font_size_string: str) -> float:
+        assert font_size_string.endswith("pt")
+        return float(font_size_string.rstrip("pt"))
+
+    def _update_other_units(self, props: dict[str, str]) -> dict[str, str]:
+        font_size = self._get_font_size(props)
+        # 3. TODO: resolve other font-relative units
+        for side in self.SIDES:
+            prop = f"border-{side}-width"
+            if prop in props:
+                props[prop] = self.size_to_pt(
+                    props[prop],
+                    em_pt=font_size,
+                    conversions=self.BORDER_WIDTH_RATIOS,
+                )
+
+            for prop in [f"margin-{side}", f"padding-{side}"]:
+                if prop in props:
+                    # TODO: support %
+                    props[prop] = self.size_to_pt(
+                        props[prop],
+                        em_pt=font_size,
+                        conversions=self.MARGIN_RATIOS,
+                    )
+        return props
+
+    def size_to_pt(self, in_val, em_pt=None, conversions=UNIT_RATIOS) -> str:
+        def _error():
+            warnings.warn(
+                f"Unhandled size: {repr(in_val)}",
+                CSSWarning,
+                stacklevel=find_stack_level(),
+            )
+            return self.size_to_pt("1!!default", conversions=conversions)
+
+        match = re.match(r"^(\S*?)([a-zA-Z%!].*)", in_val)
+        if match is None:
+            return _error()
+
+        val, unit = match.groups()
+        if val == "":
+            # hack for 'large' etc.
+            val = 1
+        else:
+            try:
+                val = float(val)
+            except ValueError:
+                return _error()
+
+        while unit != "pt":
+            if unit == "em":
+                if em_pt is None:
+                    unit = "rem"
+                else:
+                    val *= em_pt
+                    unit = "pt"
+                continue
+
+            try:
+                unit, mul = conversions[unit]
+            except KeyError:
+                return _error()
+            val *= mul
+
+        val = round(val, 5)
+        if int(val) == val:
+            size_fmt = f"{int(val):d}pt"
+        else:
+            size_fmt = f"{val:f}pt"
+        return size_fmt
+
+    def atomize(self, declarations: Iterable) -> Generator[tuple[str, str], None, None]:
+        for prop, value in declarations:
+            prop = prop.lower()
+            value = value.lower()
+            if prop in self.CSS_EXPANSIONS:
+                expand = self.CSS_EXPANSIONS[prop]
+                yield from expand(self, prop, value)
+            else:
+                yield prop, value
+
+    def parse(self, declarations_str: str) -> Iterator[tuple[str, str]]:
+        """
+        Generates (prop, value) pairs from declarations.
+
+        In a future version may generate parsed tokens from tinycss/tinycss2
+
+        Parameters
+        ----------
+        declarations_str : str
+        """
+        for decl in declarations_str.split(";"):
+            if not decl.strip():
+                continue
+            prop, sep, val = decl.partition(":")
+            prop = prop.strip().lower()
+            # TODO: don't lowercase case sensitive parts of values (strings)
+            val = val.strip().lower()
+            if sep:
+                yield prop, val
+            else:
+                warnings.warn(
+                    f"Ill-formatted attribute: expected a colon in {repr(decl)}",
+                    CSSWarning,
+                    stacklevel=find_stack_level(),
+                )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/formats/csvs.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/formats/csvs.py
new file mode 100644
index 0000000000000000000000000000000000000000..50503e862ef433901f40715987c2105f6f16263a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/formats/csvs.py
@@ -0,0 +1,330 @@
+"""
+Module for formatting output data into CSV files.
+"""
+
+from __future__ import annotations
+
+from collections.abc import (
+    Hashable,
+    Iterable,
+    Iterator,
+    Sequence,
+)
+import csv as csvlib
+import os
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    cast,
+)
+
+import numpy as np
+
+from pandas._libs import writers as libwriters
+from pandas._typing import SequenceNotStr
+from pandas.util._decorators import cache_readonly
+
+from pandas.core.dtypes.generic import (
+    ABCDatetimeIndex,
+    ABCIndex,
+    ABCMultiIndex,
+    ABCPeriodIndex,
+)
+from pandas.core.dtypes.missing import notna
+
+from pandas.core.indexes.api import Index
+
+from pandas.io.common import get_handle
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        CompressionOptions,
+        FilePath,
+        FloatFormatType,
+        IndexLabel,
+        StorageOptions,
+        WriteBuffer,
+        npt,
+    )
+
+    from pandas.io.formats.format import DataFrameFormatter
+
+
+_DEFAULT_CHUNKSIZE_CELLS = 100_000
+
+
+class CSVFormatter:
+    cols: npt.NDArray[np.object_]
+
+    def __init__(
+        self,
+        formatter: DataFrameFormatter,
+        path_or_buf: FilePath | WriteBuffer[str] | WriteBuffer[bytes] = "",
+        sep: str = ",",
+        cols: Sequence[Hashable] | None = None,
+        index_label: IndexLabel | None = None,
+        mode: str = "w",
+        encoding: str | None = None,
+        errors: str = "strict",
+        compression: CompressionOptions = "infer",
+        quoting: int | None = None,
+        lineterminator: str | None = "\n",
+        chunksize: int | None = None,
+        quotechar: str | None = '"',
+        date_format: str | None = None,
+        doublequote: bool = True,
+        escapechar: str | None = None,
+        storage_options: StorageOptions | None = None,
+    ) -> None:
+        self.fmt = formatter
+
+        self.obj = self.fmt.frame
+
+        self.filepath_or_buffer = path_or_buf
+        self.encoding = encoding
+        self.compression: CompressionOptions = compression
+        self.mode = mode
+        self.storage_options = storage_options
+
+        self.sep = sep
+        self.index_label = self._initialize_index_label(index_label)
+        self.errors = errors
+        self.quoting = quoting or csvlib.QUOTE_MINIMAL
+        self.quotechar = self._initialize_quotechar(quotechar)
+        self.doublequote = doublequote
+        self.escapechar = escapechar
+        self.lineterminator = lineterminator or os.linesep
+        self.date_format = date_format
+        self.cols = self._initialize_columns(cols)
+        self.chunksize = self._initialize_chunksize(chunksize)
+
+    @property
+    def na_rep(self) -> str:
+        return self.fmt.na_rep
+
+    @property
+    def float_format(self) -> FloatFormatType | None:
+        return self.fmt.float_format
+
+    @property
+    def decimal(self) -> str:
+        return self.fmt.decimal
+
+    @property
+    def header(self) -> bool | SequenceNotStr[str]:
+        return self.fmt.header
+
+    @property
+    def index(self) -> bool:
+        return self.fmt.index
+
+    def _initialize_index_label(self, index_label: IndexLabel | None) -> IndexLabel:
+        if index_label is not False:
+            if index_label is None:
+                return self._get_index_label_from_obj()
+            elif not isinstance(index_label, (list, tuple, np.ndarray, ABCIndex)):
+                # given a string for a DF with Index
+                return [index_label]
+        return index_label
+
+    def _get_index_label_from_obj(self) -> Sequence[Hashable]:
+        if isinstance(self.obj.index, ABCMultiIndex):
+            return self._get_index_label_multiindex()
+        else:
+            return self._get_index_label_flat()
+
+    def _get_index_label_multiindex(self) -> Sequence[Hashable]:
+        return [name or "" for name in self.obj.index.names]
+
+    def _get_index_label_flat(self) -> Sequence[Hashable]:
+        index_label = self.obj.index.name
+        return [""] if index_label is None else [index_label]
+
+    def _initialize_quotechar(self, quotechar: str | None) -> str | None:
+        if self.quoting != csvlib.QUOTE_NONE:
+            # prevents crash in _csv
+            return quotechar
+        return None
+
+    @property
+    def has_mi_columns(self) -> bool:
+        return bool(isinstance(self.obj.columns, ABCMultiIndex))
+
+    def _initialize_columns(
+        self, cols: Iterable[Hashable] | None
+    ) -> npt.NDArray[np.object_]:
+        # validate mi options
+        if self.has_mi_columns:
+            if cols is not None:
+                msg = "cannot specify cols with a MultiIndex on the columns"
+                raise TypeError(msg)
+
+        if cols is not None:
+            if isinstance(cols, ABCIndex):
+                cols = cols._get_values_for_csv(**self._number_format)
+            else:
+                cols = list(cols)
+            self.obj = self.obj.loc[:, cols]
+
+        # update columns to include possible multiplicity of dupes
+        # and make sure cols is just a list of labels
+        new_cols = self.obj.columns
+        return new_cols._get_values_for_csv(**self._number_format)
+
+    def _initialize_chunksize(self, chunksize: int | None) -> int:
+        if chunksize is None:
+            return (_DEFAULT_CHUNKSIZE_CELLS // (len(self.cols) or 1)) or 1
+        return int(chunksize)
+
+    @property
+    def _number_format(self) -> dict[str, Any]:
+        """Dictionary used for storing number formatting settings."""
+        return {
+            "na_rep": self.na_rep,
+            "float_format": self.float_format,
+            "date_format": self.date_format,
+            "quoting": self.quoting,
+            "decimal": self.decimal,
+        }
+
+    @cache_readonly
+    def data_index(self) -> Index:
+        data_index = self.obj.index
+        if (
+            isinstance(data_index, (ABCDatetimeIndex, ABCPeriodIndex))
+            and self.date_format is not None
+        ):
+            data_index = Index(
+                [x.strftime(self.date_format) if notna(x) else "" for x in data_index]
+            )
+        elif isinstance(data_index, ABCMultiIndex):
+            data_index = data_index.remove_unused_levels()
+        return data_index
+
+    @property
+    def nlevels(self) -> int:
+        if self.index:
+            return getattr(self.data_index, "nlevels", 1)
+        else:
+            return 0
+
+    @property
+    def _has_aliases(self) -> bool:
+        return isinstance(self.header, (tuple, list, np.ndarray, ABCIndex))
+
+    @property
+    def _need_to_save_header(self) -> bool:
+        return bool(self._has_aliases or self.header)
+
+    @property
+    def write_cols(self) -> SequenceNotStr[Hashable]:
+        if self._has_aliases:
+            assert not isinstance(self.header, bool)
+            if len(self.header) != len(self.cols):
+                raise ValueError(
+                    f"Writing {len(self.cols)} cols but got {len(self.header)} aliases"
+                )
+            return self.header
+        else:
+            # self.cols is an ndarray derived from Index._get_values_for_csv,
+            #  so its entries are strings, i.e. hashable
+            return cast(SequenceNotStr[Hashable], self.cols)
+
+    @property
+    def encoded_labels(self) -> list[Hashable]:
+        encoded_labels: list[Hashable] = []
+
+        if self.index and self.index_label:
+            assert isinstance(self.index_label, Sequence)
+            encoded_labels = list(self.index_label)
+
+        if not self.has_mi_columns or self._has_aliases:
+            encoded_labels += list(self.write_cols)
+
+        return encoded_labels
+
+    def save(self) -> None:
+        """
+        Create the writer & save.
+        """
+        # apply compression and byte/text conversion
+        with get_handle(
+            self.filepath_or_buffer,
+            self.mode,
+            encoding=self.encoding,
+            errors=self.errors,
+            compression=self.compression,
+            storage_options=self.storage_options,
+        ) as handles:
+            # Note: self.encoding is irrelevant here
+            self.writer = csvlib.writer(
+                handles.handle,
+                lineterminator=self.lineterminator,
+                delimiter=self.sep,
+                quoting=self.quoting,
+                doublequote=self.doublequote,
+                escapechar=self.escapechar,
+                quotechar=self.quotechar,
+            )
+
+            self._save()
+
+    def _save(self) -> None:
+        if self._need_to_save_header:
+            self._save_header()
+        self._save_body()
+
+    def _save_header(self) -> None:
+        if not self.has_mi_columns or self._has_aliases:
+            self.writer.writerow(self.encoded_labels)
+        else:
+            for row in self._generate_multiindex_header_rows():
+                self.writer.writerow(row)
+
+    def _generate_multiindex_header_rows(self) -> Iterator[list[Hashable]]:
+        columns = self.obj.columns
+        for i in range(columns.nlevels):
+            # we need at least 1 index column to write our col names
+            col_line = []
+            if self.index:
+                # name is the first column
+                col_line.append(columns.names[i])
+
+                if isinstance(self.index_label, list) and len(self.index_label) > 1:
+                    col_line.extend([""] * (len(self.index_label) - 1))
+
+            col_line.extend(columns._get_level_values(i))
+            yield col_line
+
+        # Write out the index line if it's not empty.
+        # Otherwise, we will print out an extraneous
+        # blank line between the mi and the data rows.
+        if self.encoded_labels and set(self.encoded_labels) != {""}:
+            yield self.encoded_labels + [""] * len(columns)
+
+    def _save_body(self) -> None:
+        nrows = len(self.data_index)
+        chunks = (nrows // self.chunksize) + 1
+        for i in range(chunks):
+            start_i = i * self.chunksize
+            end_i = min(start_i + self.chunksize, nrows)
+            if start_i >= end_i:
+                break
+            self._save_chunk(start_i, end_i)
+
+    def _save_chunk(self, start_i: int, end_i: int) -> None:
+        # create the data for a chunk
+        slicer = slice(start_i, end_i)
+        df = self.obj.iloc[slicer]
+
+        res = df._get_values_for_csv(**self._number_format)
+        data = list(res._iter_column_arrays())
+
+        ix = self.data_index[slicer]._get_values_for_csv(**self._number_format)
+        libwriters.write_csv_rows(
+            data,
+            ix,
+            self.nlevels,
+            self.cols,
+            self.writer,
+        )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/formats/excel.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/formats/excel.py
new file mode 100644
index 0000000000000000000000000000000000000000..5fd23cd7d918ad0efddb1088d79fd78f6079cca7
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/formats/excel.py
@@ -0,0 +1,962 @@
+"""
+Utilities for conversion to writer-agnostic Excel representation.
+"""
+from __future__ import annotations
+
+from collections.abc import (
+    Hashable,
+    Iterable,
+    Mapping,
+    Sequence,
+)
+import functools
+import itertools
+import re
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    cast,
+)
+import warnings
+
+import numpy as np
+
+from pandas._libs.lib import is_list_like
+from pandas.util._decorators import doc
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes import missing
+from pandas.core.dtypes.common import (
+    is_float,
+    is_scalar,
+)
+
+from pandas import (
+    DataFrame,
+    Index,
+    MultiIndex,
+    PeriodIndex,
+)
+import pandas.core.common as com
+from pandas.core.shared_docs import _shared_docs
+
+from pandas.io.formats._color_data import CSS4_COLORS
+from pandas.io.formats.css import (
+    CSSResolver,
+    CSSWarning,
+)
+from pandas.io.formats.format import get_level_lengths
+from pandas.io.formats.printing import pprint_thing
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        FilePath,
+        IndexLabel,
+        StorageOptions,
+        WriteExcelBuffer,
+    )
+
+    from pandas import ExcelWriter
+
+
+class ExcelCell:
+    __fields__ = ("row", "col", "val", "style", "mergestart", "mergeend")
+    __slots__ = __fields__
+
+    def __init__(
+        self,
+        row: int,
+        col: int,
+        val,
+        style=None,
+        mergestart: int | None = None,
+        mergeend: int | None = None,
+    ) -> None:
+        self.row = row
+        self.col = col
+        self.val = val
+        self.style = style
+        self.mergestart = mergestart
+        self.mergeend = mergeend
+
+
+class CssExcelCell(ExcelCell):
+    def __init__(
+        self,
+        row: int,
+        col: int,
+        val,
+        style: dict | None,
+        css_styles: dict[tuple[int, int], list[tuple[str, Any]]] | None,
+        css_row: int,
+        css_col: int,
+        css_converter: Callable | None,
+        **kwargs,
+    ) -> None:
+        if css_styles and css_converter:
+            # Use dict to get only one (case-insensitive) declaration per property
+            declaration_dict = {
+                prop.lower(): val for prop, val in css_styles[css_row, css_col]
+            }
+            # Convert to frozenset for order-invariant caching
+            unique_declarations = frozenset(declaration_dict.items())
+            style = css_converter(unique_declarations)
+
+        super().__init__(row=row, col=col, val=val, style=style, **kwargs)
+
+
+class CSSToExcelConverter:
+    """
+    A callable for converting CSS declarations to ExcelWriter styles
+
+    Supports parts of CSS 2.2, with minimal CSS 3.0 support (e.g. text-shadow),
+    focusing on font styling, backgrounds, borders and alignment.
+
+    Operates by first computing CSS styles in a fairly generic
+    way (see :meth:`compute_css`) then determining Excel style
+    properties from CSS properties (see :meth:`build_xlstyle`).
+
+    Parameters
+    ----------
+    inherited : str, optional
+        CSS declarations understood to be the containing scope for the
+        CSS processed by :meth:`__call__`.
+    """
+
+    NAMED_COLORS = CSS4_COLORS
+
+    VERTICAL_MAP = {
+        "top": "top",
+        "text-top": "top",
+        "middle": "center",
+        "baseline": "bottom",
+        "bottom": "bottom",
+        "text-bottom": "bottom",
+        # OpenXML also has 'justify', 'distributed'
+    }
+
+    BOLD_MAP = {
+        "bold": True,
+        "bolder": True,
+        "600": True,
+        "700": True,
+        "800": True,
+        "900": True,
+        "normal": False,
+        "lighter": False,
+        "100": False,
+        "200": False,
+        "300": False,
+        "400": False,
+        "500": False,
+    }
+
+    ITALIC_MAP = {
+        "normal": False,
+        "italic": True,
+        "oblique": True,
+    }
+
+    FAMILY_MAP = {
+        "serif": 1,  # roman
+        "sans-serif": 2,  # swiss
+        "cursive": 4,  # script
+        "fantasy": 5,  # decorative
+    }
+
+    BORDER_STYLE_MAP = {
+        style.lower(): style
+        for style in [
+            "dashed",
+            "mediumDashDot",
+            "dashDotDot",
+            "hair",
+            "dotted",
+            "mediumDashDotDot",
+            "double",
+            "dashDot",
+            "slantDashDot",
+            "mediumDashed",
+        ]
+    }
+
+    # NB: Most of the methods here could be classmethods, as only __init__
+    #     and __call__ make use of instance attributes.  We leave them as
+    #     instancemethods so that users can easily experiment with extensions
+    #     without monkey-patching.
+    inherited: dict[str, str] | None
+
+    def __init__(self, inherited: str | None = None) -> None:
+        if inherited is not None:
+            self.inherited = self.compute_css(inherited)
+        else:
+            self.inherited = None
+        # We should avoid cache on the __call__ method.
+        # Otherwise once the method __call__ has been called
+        # garbage collection no longer deletes the instance.
+        self._call_cached = functools.cache(self._call_uncached)
+
+    compute_css = CSSResolver()
+
+    def __call__(
+        self, declarations: str | frozenset[tuple[str, str]]
+    ) -> dict[str, dict[str, str]]:
+        """
+        Convert CSS declarations to ExcelWriter style.
+
+        Parameters
+        ----------
+        declarations : str | frozenset[tuple[str, str]]
+            CSS string or set of CSS declaration tuples.
+            e.g. "font-weight: bold; background: blue" or
+            {("font-weight", "bold"), ("background", "blue")}
+
+        Returns
+        -------
+        xlstyle : dict
+            A style as interpreted by ExcelWriter when found in
+            ExcelCell.style.
+        """
+        return self._call_cached(declarations)
+
+    def _call_uncached(
+        self, declarations: str | frozenset[tuple[str, str]]
+    ) -> dict[str, dict[str, str]]:
+        properties = self.compute_css(declarations, self.inherited)
+        return self.build_xlstyle(properties)
+
+    def build_xlstyle(self, props: Mapping[str, str]) -> dict[str, dict[str, str]]:
+        out = {
+            "alignment": self.build_alignment(props),
+            "border": self.build_border(props),
+            "fill": self.build_fill(props),
+            "font": self.build_font(props),
+            "number_format": self.build_number_format(props),
+        }
+
+        # TODO: handle cell width and height: needs support in pandas.io.excel
+
+        def remove_none(d: dict[str, str | None]) -> None:
+            """Remove key where value is None, through nested dicts"""
+            for k, v in list(d.items()):
+                if v is None:
+                    del d[k]
+                elif isinstance(v, dict):
+                    remove_none(v)
+                    if not v:
+                        del d[k]
+
+        remove_none(out)
+        return out
+
+    def build_alignment(self, props: Mapping[str, str]) -> dict[str, bool | str | None]:
+        # TODO: text-indent, padding-left -> alignment.indent
+        return {
+            "horizontal": props.get("text-align"),
+            "vertical": self._get_vertical_alignment(props),
+            "wrap_text": self._get_is_wrap_text(props),
+        }
+
+    def _get_vertical_alignment(self, props: Mapping[str, str]) -> str | None:
+        vertical_align = props.get("vertical-align")
+        if vertical_align:
+            return self.VERTICAL_MAP.get(vertical_align)
+        return None
+
+    def _get_is_wrap_text(self, props: Mapping[str, str]) -> bool | None:
+        if props.get("white-space") is None:
+            return None
+        return bool(props["white-space"] not in ("nowrap", "pre", "pre-line"))
+
+    def build_border(
+        self, props: Mapping[str, str]
+    ) -> dict[str, dict[str, str | None]]:
+        return {
+            side: {
+                "style": self._border_style(
+                    props.get(f"border-{side}-style"),
+                    props.get(f"border-{side}-width"),
+                    self.color_to_excel(props.get(f"border-{side}-color")),
+                ),
+                "color": self.color_to_excel(props.get(f"border-{side}-color")),
+            }
+            for side in ["top", "right", "bottom", "left"]
+        }
+
+    def _border_style(self, style: str | None, width: str | None, color: str | None):
+        # convert styles and widths to openxml, one of:
+        #       'dashDot'
+        #       'dashDotDot'
+        #       'dashed'
+        #       'dotted'
+        #       'double'
+        #       'hair'
+        #       'medium'
+        #       'mediumDashDot'
+        #       'mediumDashDotDot'
+        #       'mediumDashed'
+        #       'slantDashDot'
+        #       'thick'
+        #       'thin'
+        if width is None and style is None and color is None:
+            # Return None will remove "border" from style dictionary
+            return None
+
+        if width is None and style is None:
+            # Return "none" will keep "border" in style dictionary
+            return "none"
+
+        if style in ("none", "hidden"):
+            return "none"
+
+        width_name = self._get_width_name(width)
+        if width_name is None:
+            return "none"
+
+        if style in (None, "groove", "ridge", "inset", "outset", "solid"):
+            # not handled
+            return width_name
+
+        if style == "double":
+            return "double"
+        if style == "dotted":
+            if width_name in ("hair", "thin"):
+                return "dotted"
+            return "mediumDashDotDot"
+        if style == "dashed":
+            if width_name in ("hair", "thin"):
+                return "dashed"
+            return "mediumDashed"
+        elif style in self.BORDER_STYLE_MAP:
+            # Excel-specific styles
+            return self.BORDER_STYLE_MAP[style]
+        else:
+            warnings.warn(
+                f"Unhandled border style format: {repr(style)}",
+                CSSWarning,
+                stacklevel=find_stack_level(),
+            )
+            return "none"
+
+    def _get_width_name(self, width_input: str | None) -> str | None:
+        width = self._width_to_float(width_input)
+        if width < 1e-5:
+            return None
+        elif width < 1.3:
+            return "thin"
+        elif width < 2.8:
+            return "medium"
+        return "thick"
+
+    def _width_to_float(self, width: str | None) -> float:
+        if width is None:
+            width = "2pt"
+        return self._pt_to_float(width)
+
+    def _pt_to_float(self, pt_string: str) -> float:
+        assert pt_string.endswith("pt")
+        return float(pt_string.rstrip("pt"))
+
+    def build_fill(self, props: Mapping[str, str]):
+        # TODO: perhaps allow for special properties
+        #       -excel-pattern-bgcolor and -excel-pattern-type
+        fill_color = props.get("background-color")
+        if fill_color not in (None, "transparent", "none"):
+            return {"fgColor": self.color_to_excel(fill_color), "patternType": "solid"}
+
+    def build_number_format(self, props: Mapping[str, str]) -> dict[str, str | None]:
+        fc = props.get("number-format")
+        fc = fc.replace("§", ";") if isinstance(fc, str) else fc
+        return {"format_code": fc}
+
+    def build_font(
+        self, props: Mapping[str, str]
+    ) -> dict[str, bool | float | str | None]:
+        font_names = self._get_font_names(props)
+        decoration = self._get_decoration(props)
+        return {
+            "name": font_names[0] if font_names else None,
+            "family": self._select_font_family(font_names),
+            "size": self._get_font_size(props),
+            "bold": self._get_is_bold(props),
+            "italic": self._get_is_italic(props),
+            "underline": ("single" if "underline" in decoration else None),
+            "strike": ("line-through" in decoration) or None,
+            "color": self.color_to_excel(props.get("color")),
+            # shadow if nonzero digit before shadow color
+            "shadow": self._get_shadow(props),
+        }
+
+    def _get_is_bold(self, props: Mapping[str, str]) -> bool | None:
+        weight = props.get("font-weight")
+        if weight:
+            return self.BOLD_MAP.get(weight)
+        return None
+
+    def _get_is_italic(self, props: Mapping[str, str]) -> bool | None:
+        font_style = props.get("font-style")
+        if font_style:
+            return self.ITALIC_MAP.get(font_style)
+        return None
+
+    def _get_decoration(self, props: Mapping[str, str]) -> Sequence[str]:
+        decoration = props.get("text-decoration")
+        if decoration is not None:
+            return decoration.split()
+        else:
+            return ()
+
+    def _get_underline(self, decoration: Sequence[str]) -> str | None:
+        if "underline" in decoration:
+            return "single"
+        return None
+
+    def _get_shadow(self, props: Mapping[str, str]) -> bool | None:
+        if "text-shadow" in props:
+            return bool(re.search("^[^#(]*[1-9]", props["text-shadow"]))
+        return None
+
+    def _get_font_names(self, props: Mapping[str, str]) -> Sequence[str]:
+        font_names_tmp = re.findall(
+            r"""(?x)
+            (
+            "(?:[^"]|\\")+"
+            |
+            '(?:[^']|\\')+'
+            |
+            [^'",]+
+            )(?=,|\s*$)
+        """,
+            props.get("font-family", ""),
+        )
+
+        font_names = []
+        for name in font_names_tmp:
+            if name[:1] == '"':
+                name = name[1:-1].replace('\\"', '"')
+            elif name[:1] == "'":
+                name = name[1:-1].replace("\\'", "'")
+            else:
+                name = name.strip()
+            if name:
+                font_names.append(name)
+        return font_names
+
+    def _get_font_size(self, props: Mapping[str, str]) -> float | None:
+        size = props.get("font-size")
+        if size is None:
+            return size
+        return self._pt_to_float(size)
+
+    def _select_font_family(self, font_names: Sequence[str]) -> int | None:
+        family = None
+        for name in font_names:
+            family = self.FAMILY_MAP.get(name)
+            if family:
+                break
+
+        return family
+
+    def color_to_excel(self, val: str | None) -> str | None:
+        if val is None:
+            return None
+
+        if self._is_hex_color(val):
+            return self._convert_hex_to_excel(val)
+
+        try:
+            return self.NAMED_COLORS[val]
+        except KeyError:
+            warnings.warn(
+                f"Unhandled color format: {repr(val)}",
+                CSSWarning,
+                stacklevel=find_stack_level(),
+            )
+        return None
+
+    def _is_hex_color(self, color_string: str) -> bool:
+        return bool(color_string.startswith("#"))
+
+    def _convert_hex_to_excel(self, color_string: str) -> str:
+        code = color_string.lstrip("#")
+        if self._is_shorthand_color(color_string):
+            return (code[0] * 2 + code[1] * 2 + code[2] * 2).upper()
+        else:
+            return code.upper()
+
+    def _is_shorthand_color(self, color_string: str) -> bool:
+        """Check if color code is shorthand.
+
+        #FFF is a shorthand as opposed to full #FFFFFF.
+        """
+        code = color_string.lstrip("#")
+        if len(code) == 3:
+            return True
+        elif len(code) == 6:
+            return False
+        else:
+            raise ValueError(f"Unexpected color {color_string}")
+
+
+class ExcelFormatter:
+    """
+    Class for formatting a DataFrame to a list of ExcelCells,
+
+    Parameters
+    ----------
+    df : DataFrame or Styler
+    na_rep: na representation
+    float_format : str, default None
+        Format string for floating point numbers
+    cols : sequence, optional
+        Columns to write
+    header : bool or sequence of str, default True
+        Write out column names. If a list of string is given it is
+        assumed to be aliases for the column names
+    index : bool, default True
+        output row names (index)
+    index_label : str or sequence, default None
+        Column label for index column(s) if desired. If None is given, and
+        `header` and `index` are True, then the index names are used. A
+        sequence should be given if the DataFrame uses MultiIndex.
+    merge_cells : bool, default False
+        Format MultiIndex and Hierarchical Rows as merged cells.
+    inf_rep : str, default `'inf'`
+        representation for np.inf values (which aren't representable in Excel)
+        A `'-'` sign will be added in front of -inf.
+    style_converter : callable, optional
+        This translates Styler styles (CSS) into ExcelWriter styles.
+        Defaults to ``CSSToExcelConverter()``.
+        It should have signature css_declarations string -> excel style.
+        This is only called for body cells.
+    """
+
+    max_rows = 2**20
+    max_cols = 2**14
+
+    def __init__(
+        self,
+        df,
+        na_rep: str = "",
+        float_format: str | None = None,
+        cols: Sequence[Hashable] | None = None,
+        header: Sequence[Hashable] | bool = True,
+        index: bool = True,
+        index_label: IndexLabel | None = None,
+        merge_cells: bool = False,
+        inf_rep: str = "inf",
+        style_converter: Callable | None = None,
+    ) -> None:
+        self.rowcounter = 0
+        self.na_rep = na_rep
+        if not isinstance(df, DataFrame):
+            self.styler = df
+            self.styler._compute()  # calculate applied styles
+            df = df.data
+            if style_converter is None:
+                style_converter = CSSToExcelConverter()
+            self.style_converter: Callable | None = style_converter
+        else:
+            self.styler = None
+            self.style_converter = None
+        self.df = df
+        if cols is not None:
+            # all missing, raise
+            if not len(Index(cols).intersection(df.columns)):
+                raise KeyError("passes columns are not ALL present dataframe")
+
+            if len(Index(cols).intersection(df.columns)) != len(set(cols)):
+                # Deprecated in GH#17295, enforced in 1.0.0
+                raise KeyError("Not all names specified in 'columns' are found")
+
+            self.df = df.reindex(columns=cols)
+
+        self.columns = self.df.columns
+        self.float_format = float_format
+        self.index = index
+        self.index_label = index_label
+        self.header = header
+        self.merge_cells = merge_cells
+        self.inf_rep = inf_rep
+
+    @property
+    def header_style(self) -> dict[str, dict[str, str | bool]]:
+        return {
+            "font": {"bold": True},
+            "borders": {
+                "top": "thin",
+                "right": "thin",
+                "bottom": "thin",
+                "left": "thin",
+            },
+            "alignment": {"horizontal": "center", "vertical": "top"},
+        }
+
+    def _format_value(self, val):
+        if is_scalar(val) and missing.isna(val):
+            val = self.na_rep
+        elif is_float(val):
+            if missing.isposinf_scalar(val):
+                val = self.inf_rep
+            elif missing.isneginf_scalar(val):
+                val = f"-{self.inf_rep}"
+            elif self.float_format is not None:
+                val = float(self.float_format % val)
+        if getattr(val, "tzinfo", None) is not None:
+            raise ValueError(
+                "Excel does not support datetimes with "
+                "timezones. Please ensure that datetimes "
+                "are timezone unaware before writing to Excel."
+            )
+        return val
+
+    def _format_header_mi(self) -> Iterable[ExcelCell]:
+        if self.columns.nlevels > 1:
+            if not self.index:
+                raise NotImplementedError(
+                    "Writing to Excel with MultiIndex columns and no "
+                    "index ('index'=False) is not yet implemented."
+                )
+
+        if not (self._has_aliases or self.header):
+            return
+
+        columns = self.columns
+        level_strs = columns._format_multi(
+            sparsify=self.merge_cells, include_names=False
+        )
+        level_lengths = get_level_lengths(level_strs)
+        coloffset = 0
+        lnum = 0
+
+        if self.index and isinstance(self.df.index, MultiIndex):
+            coloffset = len(self.df.index[0]) - 1
+
+        if self.merge_cells:
+            # Format multi-index as a merged cells.
+            for lnum, name in enumerate(columns.names):
+                yield ExcelCell(
+                    row=lnum,
+                    col=coloffset,
+                    val=name,
+                    style=self.header_style,
+                )
+
+            for lnum, (spans, levels, level_codes) in enumerate(
+                zip(level_lengths, columns.levels, columns.codes)
+            ):
+                values = levels.take(level_codes)
+                for i, span_val in spans.items():
+                    mergestart, mergeend = None, None
+                    if span_val > 1:
+                        mergestart, mergeend = lnum, coloffset + i + span_val
+                    yield CssExcelCell(
+                        row=lnum,
+                        col=coloffset + i + 1,
+                        val=values[i],
+                        style=self.header_style,
+                        css_styles=getattr(self.styler, "ctx_columns", None),
+                        css_row=lnum,
+                        css_col=i,
+                        css_converter=self.style_converter,
+                        mergestart=mergestart,
+                        mergeend=mergeend,
+                    )
+        else:
+            # Format in legacy format with dots to indicate levels.
+            for i, values in enumerate(zip(*level_strs)):
+                v = ".".join(map(pprint_thing, values))
+                yield CssExcelCell(
+                    row=lnum,
+                    col=coloffset + i + 1,
+                    val=v,
+                    style=self.header_style,
+                    css_styles=getattr(self.styler, "ctx_columns", None),
+                    css_row=lnum,
+                    css_col=i,
+                    css_converter=self.style_converter,
+                )
+
+        self.rowcounter = lnum
+
+    def _format_header_regular(self) -> Iterable[ExcelCell]:
+        if self._has_aliases or self.header:
+            coloffset = 0
+
+            if self.index:
+                coloffset = 1
+                if isinstance(self.df.index, MultiIndex):
+                    coloffset = len(self.df.index.names)
+
+            colnames = self.columns
+            if self._has_aliases:
+                self.header = cast(Sequence, self.header)
+                if len(self.header) != len(self.columns):
+                    raise ValueError(
+                        f"Writing {len(self.columns)} cols "
+                        f"but got {len(self.header)} aliases"
+                    )
+                colnames = self.header
+
+            for colindex, colname in enumerate(colnames):
+                yield CssExcelCell(
+                    row=self.rowcounter,
+                    col=colindex + coloffset,
+                    val=colname,
+                    style=self.header_style,
+                    css_styles=getattr(self.styler, "ctx_columns", None),
+                    css_row=0,
+                    css_col=colindex,
+                    css_converter=self.style_converter,
+                )
+
+    def _format_header(self) -> Iterable[ExcelCell]:
+        gen: Iterable[ExcelCell]
+
+        if isinstance(self.columns, MultiIndex):
+            gen = self._format_header_mi()
+        else:
+            gen = self._format_header_regular()
+
+        gen2: Iterable[ExcelCell] = ()
+
+        if self.df.index.names:
+            row = [x if x is not None else "" for x in self.df.index.names] + [
+                ""
+            ] * len(self.columns)
+            if functools.reduce(lambda x, y: x and y, (x != "" for x in row)):
+                gen2 = (
+                    ExcelCell(self.rowcounter, colindex, val, self.header_style)
+                    for colindex, val in enumerate(row)
+                )
+                self.rowcounter += 1
+        return itertools.chain(gen, gen2)
+
+    def _format_body(self) -> Iterable[ExcelCell]:
+        if isinstance(self.df.index, MultiIndex):
+            return self._format_hierarchical_rows()
+        else:
+            return self._format_regular_rows()
+
+    def _format_regular_rows(self) -> Iterable[ExcelCell]:
+        if self._has_aliases or self.header:
+            self.rowcounter += 1
+
+        # output index and index_label?
+        if self.index:
+            # check aliases
+            # if list only take first as this is not a MultiIndex
+            if self.index_label and isinstance(
+                self.index_label, (list, tuple, np.ndarray, Index)
+            ):
+                index_label = self.index_label[0]
+            # if string good to go
+            elif self.index_label and isinstance(self.index_label, str):
+                index_label = self.index_label
+            else:
+                index_label = self.df.index.names[0]
+
+            if isinstance(self.columns, MultiIndex):
+                self.rowcounter += 1
+
+            if index_label and self.header is not False:
+                yield ExcelCell(self.rowcounter - 1, 0, index_label, self.header_style)
+
+            # write index_values
+            index_values = self.df.index
+            if isinstance(self.df.index, PeriodIndex):
+                index_values = self.df.index.to_timestamp()
+
+            for idx, idxval in enumerate(index_values):
+                yield CssExcelCell(
+                    row=self.rowcounter + idx,
+                    col=0,
+                    val=idxval,
+                    style=self.header_style,
+                    css_styles=getattr(self.styler, "ctx_index", None),
+                    css_row=idx,
+                    css_col=0,
+                    css_converter=self.style_converter,
+                )
+            coloffset = 1
+        else:
+            coloffset = 0
+
+        yield from self._generate_body(coloffset)
+
+    def _format_hierarchical_rows(self) -> Iterable[ExcelCell]:
+        if self._has_aliases or self.header:
+            self.rowcounter += 1
+
+        gcolidx = 0
+
+        if self.index:
+            index_labels = self.df.index.names
+            # check for aliases
+            if self.index_label and isinstance(
+                self.index_label, (list, tuple, np.ndarray, Index)
+            ):
+                index_labels = self.index_label
+
+            # MultiIndex columns require an extra row
+            # with index names (blank if None) for
+            # unambiguous round-trip, unless not merging,
+            # in which case the names all go on one row Issue #11328
+            if isinstance(self.columns, MultiIndex) and self.merge_cells:
+                self.rowcounter += 1
+
+            # if index labels are not empty go ahead and dump
+            if com.any_not_none(*index_labels) and self.header is not False:
+                for cidx, name in enumerate(index_labels):
+                    yield ExcelCell(self.rowcounter - 1, cidx, name, self.header_style)
+
+            if self.merge_cells:
+                # Format hierarchical rows as merged cells.
+                level_strs = self.df.index._format_multi(
+                    sparsify=True, include_names=False
+                )
+                level_lengths = get_level_lengths(level_strs)
+
+                for spans, levels, level_codes in zip(
+                    level_lengths, self.df.index.levels, self.df.index.codes
+                ):
+                    values = levels.take(
+                        level_codes,
+                        allow_fill=levels._can_hold_na,
+                        fill_value=levels._na_value,
+                    )
+
+                    for i, span_val in spans.items():
+                        mergestart, mergeend = None, None
+                        if span_val > 1:
+                            mergestart = self.rowcounter + i + span_val - 1
+                            mergeend = gcolidx
+                        yield CssExcelCell(
+                            row=self.rowcounter + i,
+                            col=gcolidx,
+                            val=values[i],
+                            style=self.header_style,
+                            css_styles=getattr(self.styler, "ctx_index", None),
+                            css_row=i,
+                            css_col=gcolidx,
+                            css_converter=self.style_converter,
+                            mergestart=mergestart,
+                            mergeend=mergeend,
+                        )
+                    gcolidx += 1
+
+            else:
+                # Format hierarchical rows with non-merged values.
+                for indexcolvals in zip(*self.df.index):
+                    for idx, indexcolval in enumerate(indexcolvals):
+                        yield CssExcelCell(
+                            row=self.rowcounter + idx,
+                            col=gcolidx,
+                            val=indexcolval,
+                            style=self.header_style,
+                            css_styles=getattr(self.styler, "ctx_index", None),
+                            css_row=idx,
+                            css_col=gcolidx,
+                            css_converter=self.style_converter,
+                        )
+                    gcolidx += 1
+
+        yield from self._generate_body(gcolidx)
+
+    @property
+    def _has_aliases(self) -> bool:
+        """Whether the aliases for column names are present."""
+        return is_list_like(self.header)
+
+    def _generate_body(self, coloffset: int) -> Iterable[ExcelCell]:
+        # Write the body of the frame data series by series.
+        for colidx in range(len(self.columns)):
+            series = self.df.iloc[:, colidx]
+            for i, val in enumerate(series):
+                yield CssExcelCell(
+                    row=self.rowcounter + i,
+                    col=colidx + coloffset,
+                    val=val,
+                    style=None,
+                    css_styles=getattr(self.styler, "ctx", None),
+                    css_row=i,
+                    css_col=colidx,
+                    css_converter=self.style_converter,
+                )
+
+    def get_formatted_cells(self) -> Iterable[ExcelCell]:
+        for cell in itertools.chain(self._format_header(), self._format_body()):
+            cell.val = self._format_value(cell.val)
+            yield cell
+
+    @doc(storage_options=_shared_docs["storage_options"])
+    def write(
+        self,
+        writer: FilePath | WriteExcelBuffer | ExcelWriter,
+        sheet_name: str = "Sheet1",
+        startrow: int = 0,
+        startcol: int = 0,
+        freeze_panes: tuple[int, int] | None = None,
+        engine: str | None = None,
+        storage_options: StorageOptions | None = None,
+        engine_kwargs: dict | None = None,
+    ) -> None:
+        """
+        writer : path-like, file-like, or ExcelWriter object
+            File path or existing ExcelWriter
+        sheet_name : str, default 'Sheet1'
+            Name of sheet which will contain DataFrame
+        startrow :
+            upper left cell row to dump data frame
+        startcol :
+            upper left cell column to dump data frame
+        freeze_panes : tuple of integer (length 2), default None
+            Specifies the one-based bottommost row and rightmost column that
+            is to be frozen
+        engine : string, default None
+            write engine to use if writer is a path - you can also set this
+            via the options ``io.excel.xlsx.writer``,
+            or ``io.excel.xlsm.writer``.
+
+        {storage_options}
+
+        engine_kwargs: dict, optional
+            Arbitrary keyword arguments passed to excel engine.
+        """
+        from pandas.io.excel import ExcelWriter
+
+        num_rows, num_cols = self.df.shape
+        if num_rows > self.max_rows or num_cols > self.max_cols:
+            raise ValueError(
+                f"This sheet is too large! Your sheet size is: {num_rows}, {num_cols} "
+                f"Max sheet size is: {self.max_rows}, {self.max_cols}"
+            )
+
+        if engine_kwargs is None:
+            engine_kwargs = {}
+
+        formatted_cells = self.get_formatted_cells()
+        if isinstance(writer, ExcelWriter):
+            need_save = False
+        else:
+            writer = ExcelWriter(
+                writer,
+                engine=engine,
+                storage_options=storage_options,
+                engine_kwargs=engine_kwargs,
+            )
+            need_save = True
+
+        try:
+            writer._write_cells(
+                formatted_cells,
+                sheet_name,
+                startrow=startrow,
+                startcol=startcol,
+                freeze_panes=freeze_panes,
+            )
+        finally:
+            # make sure to close opened file handles
+            if need_save:
+                writer.close()
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/formats/format.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/formats/format.py
new file mode 100644
index 0000000000000000000000000000000000000000..bcbee6ce2662f00646b20eba39d0509fb228db1a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/formats/format.py
@@ -0,0 +1,2058 @@
+"""
+Internal module for formatting output data in csv, html, xml,
+and latex files. This module also applies to display formatting.
+"""
+from __future__ import annotations
+
+from collections.abc import (
+    Generator,
+    Hashable,
+    Mapping,
+    Sequence,
+)
+from contextlib import contextmanager
+from csv import QUOTE_NONE
+from decimal import Decimal
+from functools import partial
+from io import StringIO
+import math
+import re
+from shutil import get_terminal_size
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Final,
+    cast,
+)
+
+import numpy as np
+
+from pandas._config.config import (
+    get_option,
+    set_option,
+)
+
+from pandas._libs import lib
+from pandas._libs.missing import NA
+from pandas._libs.tslibs import (
+    NaT,
+    Timedelta,
+    Timestamp,
+)
+from pandas._libs.tslibs.nattype import NaTType
+
+from pandas.core.dtypes.common import (
+    is_complex_dtype,
+    is_float,
+    is_integer,
+    is_list_like,
+    is_numeric_dtype,
+    is_scalar,
+)
+from pandas.core.dtypes.dtypes import (
+    CategoricalDtype,
+    DatetimeTZDtype,
+    ExtensionDtype,
+)
+from pandas.core.dtypes.missing import (
+    isna,
+    notna,
+)
+
+from pandas.core.arrays import (
+    Categorical,
+    DatetimeArray,
+    ExtensionArray,
+    TimedeltaArray,
+)
+from pandas.core.arrays.string_ import StringDtype
+from pandas.core.base import PandasObject
+import pandas.core.common as com
+from pandas.core.indexes.api import (
+    Index,
+    MultiIndex,
+    PeriodIndex,
+    ensure_index,
+)
+from pandas.core.indexes.datetimes import DatetimeIndex
+from pandas.core.indexes.timedeltas import TimedeltaIndex
+from pandas.core.reshape.concat import concat
+
+from pandas.io.common import (
+    check_parent_directory,
+    stringify_path,
+)
+from pandas.io.formats import printing
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        ArrayLike,
+        Axes,
+        ColspaceArgType,
+        ColspaceType,
+        CompressionOptions,
+        FilePath,
+        FloatFormatType,
+        FormattersType,
+        IndexLabel,
+        SequenceNotStr,
+        StorageOptions,
+        WriteBuffer,
+    )
+
+    from pandas import (
+        DataFrame,
+        Series,
+    )
+
+
+common_docstring: Final = """
+        Parameters
+        ----------
+        buf : str, Path or StringIO-like, optional, default None
+            Buffer to write to. If None, the output is returned as a string.
+        columns : array-like, optional, default None
+            The subset of columns to write. Writes all columns by default.
+        col_space : %(col_space_type)s, optional
+            %(col_space)s.
+        header : %(header_type)s, optional
+            %(header)s.
+        index : bool, optional, default True
+            Whether to print index (row) labels.
+        na_rep : str, optional, default 'NaN'
+            String representation of ``NaN`` to use.
+        formatters : list, tuple or dict of one-param. functions, optional
+            Formatter functions to apply to columns' elements by position or
+            name.
+            The result of each function must be a unicode string.
+            List/tuple must be of length equal to the number of columns.
+        float_format : one-parameter function, optional, default None
+            Formatter function to apply to columns' elements if they are
+            floats. This function must return a unicode string and will be
+            applied only to the non-``NaN`` elements, with ``NaN`` being
+            handled by ``na_rep``.
+        sparsify : bool, optional, default True
+            Set to False for a DataFrame with a hierarchical index to print
+            every multiindex key at each row.
+        index_names : bool, optional, default True
+            Prints the names of the indexes.
+        justify : str, default None
+            How to justify the column labels. If None uses the option from
+            the print configuration (controlled by set_option), 'right' out
+            of the box. Valid values are
+
+            * left
+            * right
+            * center
+            * justify
+            * justify-all
+            * start
+            * end
+            * inherit
+            * match-parent
+            * initial
+            * unset.
+        max_rows : int, optional
+            Maximum number of rows to display in the console.
+        max_cols : int, optional
+            Maximum number of columns to display in the console.
+        show_dimensions : bool, default False
+            Display DataFrame dimensions (number of rows by number of columns).
+        decimal : str, default '.'
+            Character recognized as decimal separator, e.g. ',' in Europe.
+    """
+
+VALID_JUSTIFY_PARAMETERS = (
+    "left",
+    "right",
+    "center",
+    "justify",
+    "justify-all",
+    "start",
+    "end",
+    "inherit",
+    "match-parent",
+    "initial",
+    "unset",
+)
+
+return_docstring: Final = """
+        Returns
+        -------
+        str or None
+            If buf is None, returns the result as a string. Otherwise returns
+            None.
+    """
+
+
+class SeriesFormatter:
+    """
+    Implement the main logic of Series.to_string, which underlies
+    Series.__repr__.
+    """
+
+    def __init__(
+        self,
+        series: Series,
+        *,
+        length: bool | str = True,
+        header: bool = True,
+        index: bool = True,
+        na_rep: str = "NaN",
+        name: bool = False,
+        float_format: str | None = None,
+        dtype: bool = True,
+        max_rows: int | None = None,
+        min_rows: int | None = None,
+    ) -> None:
+        self.series = series
+        self.buf = StringIO()
+        self.name = name
+        self.na_rep = na_rep
+        self.header = header
+        self.length = length
+        self.index = index
+        self.max_rows = max_rows
+        self.min_rows = min_rows
+
+        if float_format is None:
+            float_format = get_option("display.float_format")
+        self.float_format = float_format
+        self.dtype = dtype
+        self.adj = printing.get_adjustment()
+
+        self._chk_truncate()
+
+    def _chk_truncate(self) -> None:
+        self.tr_row_num: int | None
+
+        min_rows = self.min_rows
+        max_rows = self.max_rows
+        # truncation determined by max_rows, actual truncated number of rows
+        # used below by min_rows
+        is_truncated_vertically = max_rows and (len(self.series) > max_rows)
+        series = self.series
+        if is_truncated_vertically:
+            max_rows = cast(int, max_rows)
+            if min_rows:
+                # if min_rows is set (not None or 0), set max_rows to minimum
+                # of both
+                max_rows = min(min_rows, max_rows)
+            if max_rows == 1:
+                row_num = max_rows
+                series = series.iloc[:max_rows]
+            else:
+                row_num = max_rows // 2
+                series = concat((series.iloc[:row_num], series.iloc[-row_num:]))
+            self.tr_row_num = row_num
+        else:
+            self.tr_row_num = None
+        self.tr_series = series
+        self.is_truncated_vertically = is_truncated_vertically
+
+    def _get_footer(self) -> str:
+        name = self.series.name
+        footer = ""
+
+        index = self.series.index
+        if (
+            isinstance(index, (DatetimeIndex, PeriodIndex, TimedeltaIndex))
+            and index.freq is not None
+        ):
+            footer += f"Freq: {index.freqstr}"
+
+        if self.name is not False and name is not None:
+            if footer:
+                footer += ", "
+
+            series_name = printing.pprint_thing(name, escape_chars=("\t", "\r", "\n"))
+            footer += f"Name: {series_name}"
+
+        if self.length is True or (
+            self.length == "truncate" and self.is_truncated_vertically
+        ):
+            if footer:
+                footer += ", "
+            footer += f"Length: {len(self.series)}"
+
+        if self.dtype is not False and self.dtype is not None:
+            dtype_name = getattr(self.tr_series.dtype, "name", None)
+            if dtype_name:
+                if footer:
+                    footer += ", "
+                footer += f"dtype: {printing.pprint_thing(dtype_name)}"
+
+        # level infos are added to the end and in a new line, like it is done
+        # for Categoricals
+        if isinstance(self.tr_series.dtype, CategoricalDtype):
+            level_info = self.tr_series._values._get_repr_footer()
+            if footer:
+                footer += "\n"
+            footer += level_info
+
+        return str(footer)
+
+    def _get_formatted_values(self) -> list[str]:
+        return format_array(
+            self.tr_series._values,
+            None,
+            float_format=self.float_format,
+            na_rep=self.na_rep,
+            leading_space=self.index,
+        )
+
+    def to_string(self) -> str:
+        series = self.tr_series
+        footer = self._get_footer()
+
+        if len(series) == 0:
+            return f"{type(self.series).__name__}([], {footer})"
+
+        index = series.index
+        have_header = _has_names(index)
+        if isinstance(index, MultiIndex):
+            fmt_index = index._format_multi(include_names=True, sparsify=None)
+            adj = printing.get_adjustment()
+            fmt_index = adj.adjoin(2, *fmt_index).split("\n")
+        else:
+            fmt_index = index._format_flat(include_name=True)
+        fmt_values = self._get_formatted_values()
+
+        if self.is_truncated_vertically:
+            n_header_rows = 0
+            row_num = self.tr_row_num
+            row_num = cast(int, row_num)
+            width = self.adj.len(fmt_values[row_num - 1])
+            if width > 3:
+                dot_str = "..."
+            else:
+                dot_str = ".."
+            # Series uses mode=center because it has single value columns
+            # DataFrame uses mode=left
+            dot_str = self.adj.justify([dot_str], width, mode="center")[0]
+            fmt_values.insert(row_num + n_header_rows, dot_str)
+            fmt_index.insert(row_num + 1, "")
+
+        if self.index:
+            result = self.adj.adjoin(3, *[fmt_index[1:], fmt_values])
+        else:
+            result = self.adj.adjoin(3, fmt_values)
+
+        if self.header and have_header:
+            result = fmt_index[0] + "\n" + result
+
+        if footer:
+            result += "\n" + footer
+
+        return str("".join(result))
+
+
+def get_dataframe_repr_params() -> dict[str, Any]:
+    """Get the parameters used to repr(dataFrame) calls using DataFrame.to_string.
+
+    Supplying these parameters to DataFrame.to_string is equivalent to calling
+    ``repr(DataFrame)``. This is useful if you want to adjust the repr output.
+
+    .. versionadded:: 1.4.0
+
+    Example
+    -------
+    >>> import pandas as pd
+    >>>
+    >>> df = pd.DataFrame([[1, 2], [3, 4]])
+    >>> repr_params = pd.io.formats.format.get_dataframe_repr_params()
+    >>> repr(df) == df.to_string(**repr_params)
+    True
+    """
+    from pandas.io.formats import console
+
+    if get_option("display.expand_frame_repr"):
+        line_width, _ = console.get_console_size()
+    else:
+        line_width = None
+    return {
+        "max_rows": get_option("display.max_rows"),
+        "min_rows": get_option("display.min_rows"),
+        "max_cols": get_option("display.max_columns"),
+        "max_colwidth": get_option("display.max_colwidth"),
+        "show_dimensions": get_option("display.show_dimensions"),
+        "line_width": line_width,
+    }
+
+
+def get_series_repr_params() -> dict[str, Any]:
+    """Get the parameters used to repr(Series) calls using Series.to_string.
+
+    Supplying these parameters to Series.to_string is equivalent to calling
+    ``repr(series)``. This is useful if you want to adjust the series repr output.
+
+    .. versionadded:: 1.4.0
+
+    Example
+    -------
+    >>> import pandas as pd
+    >>>
+    >>> ser = pd.Series([1, 2, 3, 4])
+    >>> repr_params = pd.io.formats.format.get_series_repr_params()
+    >>> repr(ser) == ser.to_string(**repr_params)
+    True
+    """
+    width, height = get_terminal_size()
+    max_rows_opt = get_option("display.max_rows")
+    max_rows = height if max_rows_opt == 0 else max_rows_opt
+    min_rows = height if max_rows_opt == 0 else get_option("display.min_rows")
+
+    return {
+        "name": True,
+        "dtype": True,
+        "min_rows": min_rows,
+        "max_rows": max_rows,
+        "length": get_option("display.show_dimensions"),
+    }
+
+
+class DataFrameFormatter:
+    """
+    Class for processing dataframe formatting options and data.
+
+    Used by DataFrame.to_string, which backs DataFrame.__repr__.
+    """
+
+    __doc__ = __doc__ if __doc__ else ""
+    __doc__ += common_docstring + return_docstring
+
+    def __init__(
+        self,
+        frame: DataFrame,
+        columns: Axes | None = None,
+        col_space: ColspaceArgType | None = None,
+        header: bool | SequenceNotStr[str] = True,
+        index: bool = True,
+        na_rep: str = "NaN",
+        formatters: FormattersType | None = None,
+        justify: str | None = None,
+        float_format: FloatFormatType | None = None,
+        sparsify: bool | None = None,
+        index_names: bool = True,
+        max_rows: int | None = None,
+        min_rows: int | None = None,
+        max_cols: int | None = None,
+        show_dimensions: bool | str = False,
+        decimal: str = ".",
+        bold_rows: bool = False,
+        escape: bool = True,
+    ) -> None:
+        self.frame = frame
+        self.columns = self._initialize_columns(columns)
+        self.col_space = self._initialize_colspace(col_space)
+        self.header = header
+        self.index = index
+        self.na_rep = na_rep
+        self.formatters = self._initialize_formatters(formatters)
+        self.justify = self._initialize_justify(justify)
+        self.float_format = float_format
+        self.sparsify = self._initialize_sparsify(sparsify)
+        self.show_index_names = index_names
+        self.decimal = decimal
+        self.bold_rows = bold_rows
+        self.escape = escape
+        self.max_rows = max_rows
+        self.min_rows = min_rows
+        self.max_cols = max_cols
+        self.show_dimensions = show_dimensions
+
+        self.max_cols_fitted = self._calc_max_cols_fitted()
+        self.max_rows_fitted = self._calc_max_rows_fitted()
+
+        self.tr_frame = self.frame
+        self.truncate()
+        self.adj = printing.get_adjustment()
+
+    def get_strcols(self) -> list[list[str]]:
+        """
+        Render a DataFrame to a list of columns (as lists of strings).
+        """
+        strcols = self._get_strcols_without_index()
+
+        if self.index:
+            str_index = self._get_formatted_index(self.tr_frame)
+            strcols.insert(0, str_index)
+
+        return strcols
+
+    @property
+    def should_show_dimensions(self) -> bool:
+        return self.show_dimensions is True or (
+            self.show_dimensions == "truncate" and self.is_truncated
+        )
+
+    @property
+    def is_truncated(self) -> bool:
+        return bool(self.is_truncated_horizontally or self.is_truncated_vertically)
+
+    @property
+    def is_truncated_horizontally(self) -> bool:
+        return bool(self.max_cols_fitted and (len(self.columns) > self.max_cols_fitted))
+
+    @property
+    def is_truncated_vertically(self) -> bool:
+        return bool(self.max_rows_fitted and (len(self.frame) > self.max_rows_fitted))
+
+    @property
+    def dimensions_info(self) -> str:
+        return f"\n\n[{len(self.frame)} rows x {len(self.frame.columns)} columns]"
+
+    @property
+    def has_index_names(self) -> bool:
+        return _has_names(self.frame.index)
+
+    @property
+    def has_column_names(self) -> bool:
+        return _has_names(self.frame.columns)
+
+    @property
+    def show_row_idx_names(self) -> bool:
+        return all((self.has_index_names, self.index, self.show_index_names))
+
+    @property
+    def show_col_idx_names(self) -> bool:
+        return all((self.has_column_names, self.show_index_names, self.header))
+
+    @property
+    def max_rows_displayed(self) -> int:
+        return min(self.max_rows or len(self.frame), len(self.frame))
+
+    def _initialize_sparsify(self, sparsify: bool | None) -> bool:
+        if sparsify is None:
+            return get_option("display.multi_sparse")
+        return sparsify
+
+    def _initialize_formatters(
+        self, formatters: FormattersType | None
+    ) -> FormattersType:
+        if formatters is None:
+            return {}
+        elif len(self.frame.columns) == len(formatters) or isinstance(formatters, dict):
+            return formatters
+        else:
+            raise ValueError(
+                f"Formatters length({len(formatters)}) should match "
+                f"DataFrame number of columns({len(self.frame.columns)})"
+            )
+
+    def _initialize_justify(self, justify: str | None) -> str:
+        if justify is None:
+            return get_option("display.colheader_justify")
+        else:
+            return justify
+
+    def _initialize_columns(self, columns: Axes | None) -> Index:
+        if columns is not None:
+            cols = ensure_index(columns)
+            self.frame = self.frame[cols]
+            return cols
+        else:
+            return self.frame.columns
+
+    def _initialize_colspace(self, col_space: ColspaceArgType | None) -> ColspaceType:
+        result: ColspaceType
+
+        if col_space is None:
+            result = {}
+        elif isinstance(col_space, (int, str)):
+            result = {"": col_space}
+            result.update({column: col_space for column in self.frame.columns})
+        elif isinstance(col_space, Mapping):
+            for column in col_space.keys():
+                if column not in self.frame.columns and column != "":
+                    raise ValueError(
+                        f"Col_space is defined for an unknown column: {column}"
+                    )
+            result = col_space
+        else:
+            if len(self.frame.columns) != len(col_space):
+                raise ValueError(
+                    f"Col_space length({len(col_space)}) should match "
+                    f"DataFrame number of columns({len(self.frame.columns)})"
+                )
+            result = dict(zip(self.frame.columns, col_space))
+        return result
+
+    def _calc_max_cols_fitted(self) -> int | None:
+        """Number of columns fitting the screen."""
+        if not self._is_in_terminal():
+            return self.max_cols
+
+        width, _ = get_terminal_size()
+        if self._is_screen_narrow(width):
+            return width
+        else:
+            return self.max_cols
+
+    def _calc_max_rows_fitted(self) -> int | None:
+        """Number of rows with data fitting the screen."""
+        max_rows: int | None
+
+        if self._is_in_terminal():
+            _, height = get_terminal_size()
+            if self.max_rows == 0:
+                # rows available to fill with actual data
+                return height - self._get_number_of_auxiliary_rows()
+
+            if self._is_screen_short(height):
+                max_rows = height
+            else:
+                max_rows = self.max_rows
+        else:
+            max_rows = self.max_rows
+
+        return self._adjust_max_rows(max_rows)
+
+    def _adjust_max_rows(self, max_rows: int | None) -> int | None:
+        """Adjust max_rows using display logic.
+
+        See description here:
+        https://pandas.pydata.org/docs/dev/user_guide/options.html#frequently-used-options
+
+        GH #37359
+        """
+        if max_rows:
+            if (len(self.frame) > max_rows) and self.min_rows:
+                # if truncated, set max_rows showed to min_rows
+                max_rows = min(self.min_rows, max_rows)
+        return max_rows
+
+    def _is_in_terminal(self) -> bool:
+        """Check if the output is to be shown in terminal."""
+        return bool(self.max_cols == 0 or self.max_rows == 0)
+
+    def _is_screen_narrow(self, max_width) -> bool:
+        return bool(self.max_cols == 0 and len(self.frame.columns) > max_width)
+
+    def _is_screen_short(self, max_height) -> bool:
+        return bool(self.max_rows == 0 and len(self.frame) > max_height)
+
+    def _get_number_of_auxiliary_rows(self) -> int:
+        """Get number of rows occupied by prompt, dots and dimension info."""
+        dot_row = 1
+        prompt_row = 1
+        num_rows = dot_row + prompt_row
+
+        if self.show_dimensions:
+            num_rows += len(self.dimensions_info.splitlines())
+
+        if self.header:
+            num_rows += 1
+
+        return num_rows
+
+    def truncate(self) -> None:
+        """
+        Check whether the frame should be truncated. If so, slice the frame up.
+        """
+        if self.is_truncated_horizontally:
+            self._truncate_horizontally()
+
+        if self.is_truncated_vertically:
+            self._truncate_vertically()
+
+    def _truncate_horizontally(self) -> None:
+        """Remove columns, which are not to be displayed and adjust formatters.
+
+        Attributes affected:
+            - tr_frame
+            - formatters
+            - tr_col_num
+        """
+        assert self.max_cols_fitted is not None
+        col_num = self.max_cols_fitted // 2
+        if col_num >= 1:
+            left = self.tr_frame.iloc[:, :col_num]
+            right = self.tr_frame.iloc[:, -col_num:]
+            self.tr_frame = concat((left, right), axis=1)
+
+            # truncate formatter
+            if isinstance(self.formatters, (list, tuple)):
+                self.formatters = [
+                    *self.formatters[:col_num],
+                    *self.formatters[-col_num:],
+                ]
+        else:
+            col_num = cast(int, self.max_cols)
+            self.tr_frame = self.tr_frame.iloc[:, :col_num]
+        self.tr_col_num = col_num
+
+    def _truncate_vertically(self) -> None:
+        """Remove rows, which are not to be displayed.
+
+        Attributes affected:
+            - tr_frame
+            - tr_row_num
+        """
+        assert self.max_rows_fitted is not None
+        row_num = self.max_rows_fitted // 2
+        if row_num >= 1:
+            _len = len(self.tr_frame)
+            _slice = np.hstack([np.arange(row_num), np.arange(_len - row_num, _len)])
+            self.tr_frame = self.tr_frame.iloc[_slice]
+        else:
+            row_num = cast(int, self.max_rows)
+            self.tr_frame = self.tr_frame.iloc[:row_num, :]
+        self.tr_row_num = row_num
+
+    def _get_strcols_without_index(self) -> list[list[str]]:
+        strcols: list[list[str]] = []
+
+        if not is_list_like(self.header) and not self.header:
+            for i, c in enumerate(self.tr_frame):
+                fmt_values = self.format_col(i)
+                fmt_values = _make_fixed_width(
+                    strings=fmt_values,
+                    justify=self.justify,
+                    minimum=int(self.col_space.get(c, 0)),
+                    adj=self.adj,
+                )
+                strcols.append(fmt_values)
+            return strcols
+
+        if is_list_like(self.header):
+            # cast here since can't be bool if is_list_like
+            self.header = cast(list[str], self.header)
+            if len(self.header) != len(self.columns):
+                raise ValueError(
+                    f"Writing {len(self.columns)} cols "
+                    f"but got {len(self.header)} aliases"
+                )
+            str_columns = [[label] for label in self.header]
+        else:
+            str_columns = self._get_formatted_column_labels(self.tr_frame)
+
+        if self.show_row_idx_names:
+            for x in str_columns:
+                x.append("")
+
+        for i, c in enumerate(self.tr_frame):
+            cheader = str_columns[i]
+            header_colwidth = max(
+                int(self.col_space.get(c, 0)), *(self.adj.len(x) for x in cheader)
+            )
+            fmt_values = self.format_col(i)
+            fmt_values = _make_fixed_width(
+                fmt_values, self.justify, minimum=header_colwidth, adj=self.adj
+            )
+
+            max_len = max(*(self.adj.len(x) for x in fmt_values), header_colwidth)
+            cheader = self.adj.justify(cheader, max_len, mode=self.justify)
+            strcols.append(cheader + fmt_values)
+
+        return strcols
+
+    def format_col(self, i: int) -> list[str]:
+        frame = self.tr_frame
+        formatter = self._get_formatter(i)
+        return format_array(
+            frame.iloc[:, i]._values,
+            formatter,
+            float_format=self.float_format,
+            na_rep=self.na_rep,
+            space=self.col_space.get(frame.columns[i]),
+            decimal=self.decimal,
+            leading_space=self.index,
+        )
+
+    def _get_formatter(self, i: str | int) -> Callable | None:
+        if isinstance(self.formatters, (list, tuple)):
+            if is_integer(i):
+                i = cast(int, i)
+                return self.formatters[i]
+            else:
+                return None
+        else:
+            if is_integer(i) and i not in self.columns:
+                i = self.columns[i]
+            return self.formatters.get(i, None)
+
+    def _get_formatted_column_labels(self, frame: DataFrame) -> list[list[str]]:
+        from pandas.core.indexes.multi import sparsify_labels
+
+        columns = frame.columns
+
+        if isinstance(columns, MultiIndex):
+            fmt_columns = columns._format_multi(sparsify=False, include_names=False)
+            fmt_columns = list(zip(*fmt_columns))
+            dtypes = self.frame.dtypes._values
+
+            # if we have a Float level, they don't use leading space at all
+            restrict_formatting = any(level.is_floating for level in columns.levels)
+            need_leadsp = dict(zip(fmt_columns, map(is_numeric_dtype, dtypes)))
+
+            def space_format(x, y):
+                if (
+                    y not in self.formatters
+                    and need_leadsp[x]
+                    and not restrict_formatting
+                ):
+                    return " " + y
+                return y
+
+            str_columns_tuple = list(
+                zip(*([space_format(x, y) for y in x] for x in fmt_columns))
+            )
+            if self.sparsify and len(str_columns_tuple):
+                str_columns_tuple = sparsify_labels(str_columns_tuple)
+
+            str_columns = [list(x) for x in zip(*str_columns_tuple)]
+        else:
+            fmt_columns = columns._format_flat(include_name=False)
+            dtypes = self.frame.dtypes
+            need_leadsp = dict(zip(fmt_columns, map(is_numeric_dtype, dtypes)))
+            str_columns = [
+                [" " + x if not self._get_formatter(i) and need_leadsp[x] else x]
+                for i, x in enumerate(fmt_columns)
+            ]
+        # self.str_columns = str_columns
+        return str_columns
+
+    def _get_formatted_index(self, frame: DataFrame) -> list[str]:
+        # Note: this is only used by to_string() and to_latex(), not by
+        # to_html(). so safe to cast col_space here.
+        col_space = {k: cast(int, v) for k, v in self.col_space.items()}
+        index = frame.index
+        columns = frame.columns
+        fmt = self._get_formatter("__index__")
+
+        if isinstance(index, MultiIndex):
+            fmt_index = index._format_multi(
+                sparsify=self.sparsify,
+                include_names=self.show_row_idx_names,
+                formatter=fmt,
+            )
+        else:
+            fmt_index = [
+                index._format_flat(include_name=self.show_row_idx_names, formatter=fmt)
+            ]
+
+        fmt_index = [
+            tuple(
+                _make_fixed_width(
+                    list(x), justify="left", minimum=col_space.get("", 0), adj=self.adj
+                )
+            )
+            for x in fmt_index
+        ]
+
+        adjoined = self.adj.adjoin(1, *fmt_index).split("\n")
+
+        # empty space for columns
+        if self.show_col_idx_names:
+            col_header = [str(x) for x in self._get_column_name_list()]
+        else:
+            col_header = [""] * columns.nlevels
+
+        if self.header:
+            return col_header + adjoined
+        else:
+            return adjoined
+
+    def _get_column_name_list(self) -> list[Hashable]:
+        names: list[Hashable] = []
+        columns = self.frame.columns
+        if isinstance(columns, MultiIndex):
+            names.extend("" if name is None else name for name in columns.names)
+        else:
+            names.append("" if columns.name is None else columns.name)
+        return names
+
+
+class DataFrameRenderer:
+    """Class for creating dataframe output in multiple formats.
+
+    Called in pandas.core.generic.NDFrame:
+        - to_csv
+        - to_latex
+
+    Called in pandas.core.frame.DataFrame:
+        - to_html
+        - to_string
+
+    Parameters
+    ----------
+    fmt : DataFrameFormatter
+        Formatter with the formatting options.
+    """
+
+    def __init__(self, fmt: DataFrameFormatter) -> None:
+        self.fmt = fmt
+
+    def to_html(
+        self,
+        buf: FilePath | WriteBuffer[str] | None = None,
+        encoding: str | None = None,
+        classes: str | list | tuple | None = None,
+        notebook: bool = False,
+        border: int | bool | None = None,
+        table_id: str | None = None,
+        render_links: bool = False,
+    ) -> str | None:
+        """
+        Render a DataFrame to a html table.
+
+        Parameters
+        ----------
+        buf : str, path object, file-like object, or None, default None
+            String, path object (implementing ``os.PathLike[str]``), or file-like
+            object implementing a string ``write()`` function. If None, the result is
+            returned as a string.
+        encoding : str, default “utf-8”
+            Set character encoding.
+        classes : str or list-like
+            classes to include in the `class` attribute of the opening
+            ```` tag, in addition to the default "dataframe".
+        notebook : {True, False}, optional, default False
+            Whether the generated HTML is for IPython Notebook.
+        border : int
+            A ``border=border`` attribute is included in the opening
+            ``
`` tag. Default ``pd.options.display.html.border``.
+        table_id : str, optional
+            A css id is included in the opening `
` tag if specified.
+        render_links : bool, default False
+            Convert URLs to HTML links.
+        """
+        from pandas.io.formats.html import (
+            HTMLFormatter,
+            NotebookFormatter,
+        )
+
+        Klass = NotebookFormatter if notebook else HTMLFormatter
+
+        html_formatter = Klass(
+            self.fmt,
+            classes=classes,
+            border=border,
+            table_id=table_id,
+            render_links=render_links,
+        )
+        string = html_formatter.to_string()
+        return save_to_buffer(string, buf=buf, encoding=encoding)
+
+    def to_string(
+        self,
+        buf: FilePath | WriteBuffer[str] | None = None,
+        encoding: str | None = None,
+        line_width: int | None = None,
+    ) -> str | None:
+        """
+        Render a DataFrame to a console-friendly tabular output.
+
+        Parameters
+        ----------
+        buf : str, path object, file-like object, or None, default None
+            String, path object (implementing ``os.PathLike[str]``), or file-like
+            object implementing a string ``write()`` function. If None, the result is
+            returned as a string.
+        encoding: str, default “utf-8”
+            Set character encoding.
+        line_width : int, optional
+            Width to wrap a line in characters.
+        """
+        from pandas.io.formats.string import StringFormatter
+
+        string_formatter = StringFormatter(self.fmt, line_width=line_width)
+        string = string_formatter.to_string()
+        return save_to_buffer(string, buf=buf, encoding=encoding)
+
+    def to_csv(
+        self,
+        path_or_buf: FilePath | WriteBuffer[bytes] | WriteBuffer[str] | None = None,
+        encoding: str | None = None,
+        sep: str = ",",
+        columns: Sequence[Hashable] | None = None,
+        index_label: IndexLabel | None = None,
+        mode: str = "w",
+        compression: CompressionOptions = "infer",
+        quoting: int | None = None,
+        quotechar: str = '"',
+        lineterminator: str | None = None,
+        chunksize: int | None = None,
+        date_format: str | None = None,
+        doublequote: bool = True,
+        escapechar: str | None = None,
+        errors: str = "strict",
+        storage_options: StorageOptions | None = None,
+    ) -> str | None:
+        """
+        Render dataframe as comma-separated file.
+        """
+        from pandas.io.formats.csvs import CSVFormatter
+
+        if path_or_buf is None:
+            created_buffer = True
+            path_or_buf = StringIO()
+        else:
+            created_buffer = False
+
+        csv_formatter = CSVFormatter(
+            path_or_buf=path_or_buf,
+            lineterminator=lineterminator,
+            sep=sep,
+            encoding=encoding,
+            errors=errors,
+            compression=compression,
+            quoting=quoting,
+            cols=columns,
+            index_label=index_label,
+            mode=mode,
+            chunksize=chunksize,
+            quotechar=quotechar,
+            date_format=date_format,
+            doublequote=doublequote,
+            escapechar=escapechar,
+            storage_options=storage_options,
+            formatter=self.fmt,
+        )
+        csv_formatter.save()
+
+        if created_buffer:
+            assert isinstance(path_or_buf, StringIO)
+            content = path_or_buf.getvalue()
+            path_or_buf.close()
+            return content
+
+        return None
+
+
+def save_to_buffer(
+    string: str,
+    buf: FilePath | WriteBuffer[str] | None = None,
+    encoding: str | None = None,
+) -> str | None:
+    """
+    Perform serialization. Write to buf or return as string if buf is None.
+    """
+    with _get_buffer(buf, encoding=encoding) as fd:
+        fd.write(string)
+        if buf is None:
+            # error: "WriteBuffer[str]" has no attribute "getvalue"
+            return fd.getvalue()  # type: ignore[attr-defined]
+        return None
+
+
+@contextmanager
+def _get_buffer(
+    buf: FilePath | WriteBuffer[str] | None, encoding: str | None = None
+) -> Generator[WriteBuffer[str], None, None] | Generator[StringIO, None, None]:
+    """
+    Context manager to open, yield and close buffer for filenames or Path-like
+    objects, otherwise yield buf unchanged.
+    """
+    if buf is not None:
+        buf = stringify_path(buf)
+    else:
+        buf = StringIO()
+
+    if encoding is None:
+        encoding = "utf-8"
+    elif not isinstance(buf, str):
+        raise ValueError("buf is not a file name and encoding is specified.")
+
+    if hasattr(buf, "write"):
+        # Incompatible types in "yield" (actual type "Union[str, WriteBuffer[str],
+        # StringIO]", expected type "Union[WriteBuffer[str], StringIO]")
+        yield buf  # type: ignore[misc]
+    elif isinstance(buf, str):
+        check_parent_directory(str(buf))
+        with open(buf, "w", encoding=encoding, newline="") as f:
+            # GH#30034 open instead of codecs.open prevents a file leak
+            #  if we have an invalid encoding argument.
+            # newline="" is needed to roundtrip correctly on
+            #  windows test_to_latex_filename
+            yield f
+    else:
+        raise TypeError("buf is not a file name and it has no write method")
+
+
+# ----------------------------------------------------------------------
+# Array formatters
+
+
+def format_array(
+    values: ArrayLike,
+    formatter: Callable | None,
+    float_format: FloatFormatType | None = None,
+    na_rep: str = "NaN",
+    digits: int | None = None,
+    space: str | int | None = None,
+    justify: str = "right",
+    decimal: str = ".",
+    leading_space: bool | None = True,
+    quoting: int | None = None,
+    fallback_formatter: Callable | None = None,
+) -> list[str]:
+    """
+    Format an array for printing.
+
+    Parameters
+    ----------
+    values : np.ndarray or ExtensionArray
+    formatter
+    float_format
+    na_rep
+    digits
+    space
+    justify
+    decimal
+    leading_space : bool, optional, default True
+        Whether the array should be formatted with a leading space.
+        When an array as a column of a Series or DataFrame, we do want
+        the leading space to pad between columns.
+
+        When formatting an Index subclass
+        (e.g. IntervalIndex._get_values_for_csv), we don't want the
+        leading space since it should be left-aligned.
+    fallback_formatter
+
+    Returns
+    -------
+    List[str]
+    """
+    fmt_klass: type[_GenericArrayFormatter]
+    if lib.is_np_dtype(values.dtype, "M"):
+        fmt_klass = _Datetime64Formatter
+        values = cast(DatetimeArray, values)
+    elif isinstance(values.dtype, DatetimeTZDtype):
+        fmt_klass = _Datetime64TZFormatter
+        values = cast(DatetimeArray, values)
+    elif lib.is_np_dtype(values.dtype, "m"):
+        fmt_klass = _Timedelta64Formatter
+        values = cast(TimedeltaArray, values)
+    elif isinstance(values.dtype, ExtensionDtype):
+        fmt_klass = _ExtensionArrayFormatter
+    elif lib.is_np_dtype(values.dtype, "fc"):
+        fmt_klass = FloatArrayFormatter
+    elif lib.is_np_dtype(values.dtype, "iu"):
+        fmt_klass = _IntArrayFormatter
+    else:
+        fmt_klass = _GenericArrayFormatter
+
+    if space is None:
+        space = 12
+
+    if float_format is None:
+        float_format = get_option("display.float_format")
+
+    if digits is None:
+        digits = get_option("display.precision")
+
+    fmt_obj = fmt_klass(
+        values,
+        digits=digits,
+        na_rep=na_rep,
+        float_format=float_format,
+        formatter=formatter,
+        space=space,
+        justify=justify,
+        decimal=decimal,
+        leading_space=leading_space,
+        quoting=quoting,
+        fallback_formatter=fallback_formatter,
+    )
+
+    return fmt_obj.get_result()
+
+
+class _GenericArrayFormatter:
+    def __init__(
+        self,
+        values: ArrayLike,
+        digits: int = 7,
+        formatter: Callable | None = None,
+        na_rep: str = "NaN",
+        space: str | int = 12,
+        float_format: FloatFormatType | None = None,
+        justify: str = "right",
+        decimal: str = ".",
+        quoting: int | None = None,
+        fixed_width: bool = True,
+        leading_space: bool | None = True,
+        fallback_formatter: Callable | None = None,
+    ) -> None:
+        self.values = values
+        self.digits = digits
+        self.na_rep = na_rep
+        self.space = space
+        self.formatter = formatter
+        self.float_format = float_format
+        self.justify = justify
+        self.decimal = decimal
+        self.quoting = quoting
+        self.fixed_width = fixed_width
+        self.leading_space = leading_space
+        self.fallback_formatter = fallback_formatter
+
+    def get_result(self) -> list[str]:
+        fmt_values = self._format_strings()
+        return _make_fixed_width(fmt_values, self.justify)
+
+    def _format_strings(self) -> list[str]:
+        if self.float_format is None:
+            float_format = get_option("display.float_format")
+            if float_format is None:
+                precision = get_option("display.precision")
+                float_format = lambda x: _trim_zeros_single_float(
+                    f"{x: .{precision:d}f}"
+                )
+        else:
+            float_format = self.float_format
+
+        if self.formatter is not None:
+            formatter = self.formatter
+        elif self.fallback_formatter is not None:
+            formatter = self.fallback_formatter
+        else:
+            quote_strings = self.quoting is not None and self.quoting != QUOTE_NONE
+            formatter = partial(
+                printing.pprint_thing,
+                escape_chars=("\t", "\r", "\n"),
+                quote_strings=quote_strings,
+            )
+
+        def _format(x):
+            if self.na_rep is not None and is_scalar(x) and isna(x):
+                if x is None:
+                    return "None"
+                elif x is NA:
+                    return str(NA)
+                elif lib.is_float(x) and np.isinf(x):
+                    # TODO(3.0): this will be unreachable when use_inf_as_na
+                    #  deprecation is enforced
+                    return str(x)
+                elif x is NaT or isinstance(x, (np.datetime64, np.timedelta64)):
+                    return "NaT"
+                return self.na_rep
+            elif isinstance(x, PandasObject):
+                return str(x)
+            elif isinstance(x, StringDtype) and x.na_value is NA:
+                return repr(x)
+            else:
+                # object dtype
+                return str(formatter(x))
+
+        vals = self.values
+        if not isinstance(vals, np.ndarray):
+            raise TypeError(
+                "ExtensionArray formatting should use _ExtensionArrayFormatter"
+            )
+        inferred = lib.map_infer(vals, is_float)
+        is_float_type = (
+            inferred
+            # vals may have 2 or more dimensions
+            & np.all(notna(vals), axis=tuple(range(1, len(vals.shape))))
+        )
+        leading_space = self.leading_space
+        if leading_space is None:
+            leading_space = is_float_type.any()
+
+        fmt_values = []
+        for i, v in enumerate(vals):
+            if (not is_float_type[i] or self.formatter is not None) and leading_space:
+                fmt_values.append(f" {_format(v)}")
+            elif is_float_type[i]:
+                fmt_values.append(float_format(v))
+            else:
+                if leading_space is False:
+                    # False specifically, so that the default is
+                    # to include a space if we get here.
+                    tpl = "{v}"
+                else:
+                    tpl = " {v}"
+                fmt_values.append(tpl.format(v=_format(v)))
+
+        return fmt_values
+
+
+class FloatArrayFormatter(_GenericArrayFormatter):
+    def __init__(self, *args, **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+
+        # float_format is expected to be a string
+        # formatter should be used to pass a function
+        if self.float_format is not None and self.formatter is None:
+            # GH21625, GH22270
+            self.fixed_width = False
+            if callable(self.float_format):
+                self.formatter = self.float_format
+                self.float_format = None
+
+    def _value_formatter(
+        self,
+        float_format: FloatFormatType | None = None,
+        threshold: float | None = None,
+    ) -> Callable:
+        """Returns a function to be applied on each value to format it"""
+        # the float_format parameter supersedes self.float_format
+        if float_format is None:
+            float_format = self.float_format
+
+        # we are going to compose different functions, to first convert to
+        # a string, then replace the decimal symbol, and finally chop according
+        # to the threshold
+
+        # when there is no float_format, we use str instead of '%g'
+        # because str(0.0) = '0.0' while '%g' % 0.0 = '0'
+        if float_format:
+
+            def base_formatter(v):
+                assert float_format is not None  # for mypy
+                # error: "str" not callable
+                # error: Unexpected keyword argument "value" for "__call__" of
+                # "EngFormatter"
+                return (
+                    float_format(value=v)  # type: ignore[operator,call-arg]
+                    if notna(v)
+                    else self.na_rep
+                )
+
+        else:
+
+            def base_formatter(v):
+                return str(v) if notna(v) else self.na_rep
+
+        if self.decimal != ".":
+
+            def decimal_formatter(v):
+                return base_formatter(v).replace(".", self.decimal, 1)
+
+        else:
+            decimal_formatter = base_formatter
+
+        if threshold is None:
+            return decimal_formatter
+
+        def formatter(value):
+            if notna(value):
+                if abs(value) > threshold:
+                    return decimal_formatter(value)
+                else:
+                    return decimal_formatter(0.0)
+            else:
+                return self.na_rep
+
+        return formatter
+
+    def get_result_as_array(self) -> np.ndarray:
+        """
+        Returns the float values converted into strings using
+        the parameters given at initialisation, as a numpy array
+        """
+
+        def format_with_na_rep(values: ArrayLike, formatter: Callable, na_rep: str):
+            mask = isna(values)
+            formatted = np.array(
+                [
+                    formatter(val) if not m else na_rep
+                    for val, m in zip(values.ravel(), mask.ravel())
+                ]
+            ).reshape(values.shape)
+            return formatted
+
+        def format_complex_with_na_rep(
+            values: ArrayLike, formatter: Callable, na_rep: str
+        ):
+            real_values = np.real(values).ravel()  # type: ignore[arg-type]
+            imag_values = np.imag(values).ravel()  # type: ignore[arg-type]
+            real_mask, imag_mask = isna(real_values), isna(imag_values)
+            formatted_lst = []
+            for val, real_val, imag_val, re_isna, im_isna in zip(
+                values.ravel(),
+                real_values,
+                imag_values,
+                real_mask,
+                imag_mask,
+            ):
+                if not re_isna and not im_isna:
+                    formatted_lst.append(formatter(val))
+                elif not re_isna:  # xxx+nanj
+                    formatted_lst.append(f"{formatter(real_val)}+{na_rep}j")
+                elif not im_isna:  # nan[+/-]xxxj
+                    # The imaginary part may either start with a "-" or a space
+                    imag_formatted = formatter(imag_val).strip()
+                    if imag_formatted.startswith("-"):
+                        formatted_lst.append(f"{na_rep}{imag_formatted}j")
+                    else:
+                        formatted_lst.append(f"{na_rep}+{imag_formatted}j")
+                else:  # nan+nanj
+                    formatted_lst.append(f"{na_rep}+{na_rep}j")
+            return np.array(formatted_lst).reshape(values.shape)
+
+        if self.formatter is not None:
+            return format_with_na_rep(self.values, self.formatter, self.na_rep)
+
+        if self.fixed_width:
+            threshold = get_option("display.chop_threshold")
+        else:
+            threshold = None
+
+        # if we have a fixed_width, we'll need to try different float_format
+        def format_values_with(float_format):
+            formatter = self._value_formatter(float_format, threshold)
+
+            # default formatter leaves a space to the left when formatting
+            # floats, must be consistent for left-justifying NaNs (GH #25061)
+            na_rep = " " + self.na_rep if self.justify == "left" else self.na_rep
+
+            # different formatting strategies for complex and non-complex data
+            # need to distinguish complex and float NaNs (GH #53762)
+            values = self.values
+            is_complex = is_complex_dtype(values)
+
+            # separate the wheat from the chaff
+            if is_complex:
+                values = format_complex_with_na_rep(values, formatter, na_rep)
+            else:
+                values = format_with_na_rep(values, formatter, na_rep)
+
+            if self.fixed_width:
+                if is_complex:
+                    result = _trim_zeros_complex(values, self.decimal)
+                else:
+                    result = _trim_zeros_float(values, self.decimal)
+                return np.asarray(result, dtype="object")
+
+            return values
+
+        # There is a special default string when we are fixed-width
+        # The default is otherwise to use str instead of a formatting string
+        float_format: FloatFormatType | None
+        if self.float_format is None:
+            if self.fixed_width:
+                if self.leading_space is True:
+                    fmt_str = "{value: .{digits:d}f}"
+                else:
+                    fmt_str = "{value:.{digits:d}f}"
+                float_format = partial(fmt_str.format, digits=self.digits)
+            else:
+                float_format = self.float_format
+        else:
+            float_format = lambda value: self.float_format % value
+
+        formatted_values = format_values_with(float_format)
+
+        if not self.fixed_width:
+            return formatted_values
+
+        # we need do convert to engineering format if some values are too small
+        # and would appear as 0, or if some values are too big and take too
+        # much space
+
+        if len(formatted_values) > 0:
+            maxlen = max(len(x) for x in formatted_values)
+            too_long = maxlen > self.digits + 6
+        else:
+            too_long = False
+
+        abs_vals = np.abs(self.values)
+        # this is pretty arbitrary for now
+        # large values: more that 8 characters including decimal symbol
+        # and first digit, hence > 1e6
+        has_large_values = (abs_vals > 1e6).any()
+        has_small_values = ((abs_vals < 10 ** (-self.digits)) & (abs_vals > 0)).any()
+
+        if has_small_values or (too_long and has_large_values):
+            if self.leading_space is True:
+                fmt_str = "{value: .{digits:d}e}"
+            else:
+                fmt_str = "{value:.{digits:d}e}"
+            float_format = partial(fmt_str.format, digits=self.digits)
+            formatted_values = format_values_with(float_format)
+
+        return formatted_values
+
+    def _format_strings(self) -> list[str]:
+        return list(self.get_result_as_array())
+
+
+class _IntArrayFormatter(_GenericArrayFormatter):
+    def _format_strings(self) -> list[str]:
+        if self.leading_space is False:
+            formatter_str = lambda x: f"{x:d}".format(x=x)
+        else:
+            formatter_str = lambda x: f"{x: d}".format(x=x)
+        formatter = self.formatter or formatter_str
+        fmt_values = [formatter(x) for x in self.values]
+        return fmt_values
+
+
+class _Datetime64Formatter(_GenericArrayFormatter):
+    values: DatetimeArray
+
+    def __init__(
+        self,
+        values: DatetimeArray,
+        nat_rep: str = "NaT",
+        date_format: None = None,
+        **kwargs,
+    ) -> None:
+        super().__init__(values, **kwargs)
+        self.nat_rep = nat_rep
+        self.date_format = date_format
+
+    def _format_strings(self) -> list[str]:
+        """we by definition have DO NOT have a TZ"""
+        values = self.values
+
+        if self.formatter is not None:
+            return [self.formatter(x) for x in values]
+
+        fmt_values = values._format_native_types(
+            na_rep=self.nat_rep, date_format=self.date_format
+        )
+        return fmt_values.tolist()
+
+
+class _ExtensionArrayFormatter(_GenericArrayFormatter):
+    values: ExtensionArray
+
+    def _format_strings(self) -> list[str]:
+        values = self.values
+
+        formatter = self.formatter
+        fallback_formatter = None
+        if formatter is None:
+            fallback_formatter = values._formatter(boxed=True)
+
+        if isinstance(values, Categorical):
+            # Categorical is special for now, so that we can preserve tzinfo
+            array = values._internal_get_values()
+        else:
+            array = np.asarray(values, dtype=object)
+
+        fmt_values = format_array(
+            array,
+            formatter,
+            float_format=self.float_format,
+            na_rep=self.na_rep,
+            digits=self.digits,
+            space=self.space,
+            justify=self.justify,
+            decimal=self.decimal,
+            leading_space=self.leading_space,
+            quoting=self.quoting,
+            fallback_formatter=fallback_formatter,
+        )
+        return fmt_values
+
+
+def format_percentiles(
+    percentiles: (np.ndarray | Sequence[float]),
+) -> list[str]:
+    """
+    Outputs rounded and formatted percentiles.
+
+    Parameters
+    ----------
+    percentiles : list-like, containing floats from interval [0,1]
+
+    Returns
+    -------
+    formatted : list of strings
+
+    Notes
+    -----
+    Rounding precision is chosen so that: (1) if any two elements of
+    ``percentiles`` differ, they remain different after rounding
+    (2) no entry is *rounded* to 0% or 100%.
+    Any non-integer is always rounded to at least 1 decimal place.
+
+    Examples
+    --------
+    Keeps all entries different after rounding:
+
+    >>> format_percentiles([0.01999, 0.02001, 0.5, 0.666666, 0.9999])
+    ['1.999%', '2.001%', '50%', '66.667%', '99.99%']
+
+    No element is rounded to 0% or 100% (unless already equal to it).
+    Duplicates are allowed:
+
+    >>> format_percentiles([0, 0.5, 0.02001, 0.5, 0.666666, 0.9999])
+    ['0%', '50%', '2.0%', '50%', '66.67%', '99.99%']
+    """
+    percentiles = np.asarray(percentiles)
+
+    # It checks for np.nan as well
+    if (
+        not is_numeric_dtype(percentiles)
+        or not np.all(percentiles >= 0)
+        or not np.all(percentiles <= 1)
+    ):
+        raise ValueError("percentiles should all be in the interval [0,1]")
+
+    percentiles = 100 * percentiles
+    prec = get_precision(percentiles)
+    percentiles_round_type = percentiles.round(prec).astype(int)
+
+    int_idx = np.isclose(percentiles_round_type, percentiles)
+
+    if np.all(int_idx):
+        out = percentiles_round_type.astype(str)
+        return [i + "%" for i in out]
+
+    unique_pcts = np.unique(percentiles)
+    prec = get_precision(unique_pcts)
+    out = np.empty_like(percentiles, dtype=object)
+    out[int_idx] = percentiles[int_idx].round().astype(int).astype(str)
+
+    out[~int_idx] = percentiles[~int_idx].round(prec).astype(str)
+    return [i + "%" for i in out]
+
+
+def get_precision(array: np.ndarray | Sequence[float]) -> int:
+    to_begin = array[0] if array[0] > 0 else None
+    to_end = 100 - array[-1] if array[-1] < 100 else None
+    diff = np.ediff1d(array, to_begin=to_begin, to_end=to_end)
+    diff = abs(diff)
+    prec = -np.floor(np.log10(np.min(diff))).astype(int)
+    prec = max(1, prec)
+    return prec
+
+
+def _format_datetime64(x: NaTType | Timestamp, nat_rep: str = "NaT") -> str:
+    if x is NaT:
+        return nat_rep
+
+    # Timestamp.__str__ falls back to datetime.datetime.__str__ = isoformat(sep=' ')
+    # so it already uses string formatting rather than strftime (faster).
+    return str(x)
+
+
+def _format_datetime64_dateonly(
+    x: NaTType | Timestamp,
+    nat_rep: str = "NaT",
+    date_format: str | None = None,
+) -> str:
+    if isinstance(x, NaTType):
+        return nat_rep
+
+    if date_format:
+        return x.strftime(date_format)
+    else:
+        # Timestamp._date_repr relies on string formatting (faster than strftime)
+        return x._date_repr
+
+
+def get_format_datetime64(
+    is_dates_only: bool, nat_rep: str = "NaT", date_format: str | None = None
+) -> Callable:
+    """Return a formatter callable taking a datetime64 as input and providing
+    a string as output"""
+
+    if is_dates_only:
+        return lambda x: _format_datetime64_dateonly(
+            x, nat_rep=nat_rep, date_format=date_format
+        )
+    else:
+        return lambda x: _format_datetime64(x, nat_rep=nat_rep)
+
+
+class _Datetime64TZFormatter(_Datetime64Formatter):
+    values: DatetimeArray
+
+    def _format_strings(self) -> list[str]:
+        """we by definition have a TZ"""
+        ido = self.values._is_dates_only
+        values = self.values.astype(object)
+        formatter = self.formatter or get_format_datetime64(
+            ido, date_format=self.date_format
+        )
+        fmt_values = [formatter(x) for x in values]
+
+        return fmt_values
+
+
+class _Timedelta64Formatter(_GenericArrayFormatter):
+    values: TimedeltaArray
+
+    def __init__(
+        self,
+        values: TimedeltaArray,
+        nat_rep: str = "NaT",
+        **kwargs,
+    ) -> None:
+        # TODO: nat_rep is never passed, na_rep is.
+        super().__init__(values, **kwargs)
+        self.nat_rep = nat_rep
+
+    def _format_strings(self) -> list[str]:
+        formatter = self.formatter or get_format_timedelta64(
+            self.values, nat_rep=self.nat_rep, box=False
+        )
+        return [formatter(x) for x in self.values]
+
+
+def get_format_timedelta64(
+    values: TimedeltaArray,
+    nat_rep: str | float = "NaT",
+    box: bool = False,
+) -> Callable:
+    """
+    Return a formatter function for a range of timedeltas.
+    These will all have the same format argument
+
+    If box, then show the return in quotes
+    """
+    even_days = values._is_dates_only
+
+    if even_days:
+        format = None
+    else:
+        format = "long"
+
+    def _formatter(x):
+        if x is None or (is_scalar(x) and isna(x)):
+            return nat_rep
+
+        if not isinstance(x, Timedelta):
+            x = Timedelta(x)
+
+        # Timedelta._repr_base uses string formatting (faster than strftime)
+        result = x._repr_base(format=format)
+        if box:
+            result = f"'{result}'"
+        return result
+
+    return _formatter
+
+
+def _make_fixed_width(
+    strings: list[str],
+    justify: str = "right",
+    minimum: int | None = None,
+    adj: printing._TextAdjustment | None = None,
+) -> list[str]:
+    if len(strings) == 0 or justify == "all":
+        return strings
+
+    if adj is None:
+        adjustment = printing.get_adjustment()
+    else:
+        adjustment = adj
+
+    max_len = max(adjustment.len(x) for x in strings)
+
+    if minimum is not None:
+        max_len = max(minimum, max_len)
+
+    conf_max = get_option("display.max_colwidth")
+    if conf_max is not None and max_len > conf_max:
+        max_len = conf_max
+
+    def just(x: str) -> str:
+        if conf_max is not None:
+            if (conf_max > 3) & (adjustment.len(x) > max_len):
+                x = x[: max_len - 3] + "..."
+        return x
+
+    strings = [just(x) for x in strings]
+    result = adjustment.justify(strings, max_len, mode=justify)
+    return result
+
+
+def _trim_zeros_complex(str_complexes: ArrayLike, decimal: str = ".") -> list[str]:
+    """
+    Separates the real and imaginary parts from the complex number, and
+    executes the _trim_zeros_float method on each of those.
+    """
+    real_part, imag_part = [], []
+    for x in str_complexes:
+        # Complex numbers are represented as "(-)xxx(+/-)xxxj"
+        # The split will give [{"", "-"}, "xxx", "+/-", "xxx", "j", ""]
+        # Therefore, the imaginary part is the 4th and 3rd last elements,
+        # and the real part is everything before the imaginary part
+        trimmed = re.split(r"([j+-])", x)
+        real_part.append("".join(trimmed[:-4]))
+        imag_part.append("".join(trimmed[-4:-2]))
+
+    # We want to align the lengths of the real and imaginary parts of each complex
+    # number, as well as the lengths the real (resp. complex) parts of all numbers
+    # in the array
+    n = len(str_complexes)
+    padded_parts = _trim_zeros_float(real_part + imag_part, decimal)
+    if len(padded_parts) == 0:
+        return []
+    padded_length = max(len(part) for part in padded_parts) - 1
+    padded = [
+        real_pt  # real part, possibly NaN
+        + imag_pt[0]  # +/-
+        + f"{imag_pt[1:]:>{padded_length}}"  # complex part (no sign), possibly nan
+        + "j"
+        for real_pt, imag_pt in zip(padded_parts[:n], padded_parts[n:])
+    ]
+    return padded
+
+
+def _trim_zeros_single_float(str_float: str) -> str:
+    """
+    Trims trailing zeros after a decimal point,
+    leaving just one if necessary.
+    """
+    str_float = str_float.rstrip("0")
+    if str_float.endswith("."):
+        str_float += "0"
+
+    return str_float
+
+
+def _trim_zeros_float(
+    str_floats: ArrayLike | list[str], decimal: str = "."
+) -> list[str]:
+    """
+    Trims the maximum number of trailing zeros equally from
+    all numbers containing decimals, leaving just one if
+    necessary.
+    """
+    trimmed = str_floats
+    number_regex = re.compile(rf"^\s*[\+-]?[0-9]+\{decimal}[0-9]*$")
+
+    def is_number_with_decimal(x) -> bool:
+        return re.match(number_regex, x) is not None
+
+    def should_trim(values: ArrayLike | list[str]) -> bool:
+        """
+        Determine if an array of strings should be trimmed.
+
+        Returns True if all numbers containing decimals (defined by the
+        above regular expression) within the array end in a zero, otherwise
+        returns False.
+        """
+        numbers = [x for x in values if is_number_with_decimal(x)]
+        return len(numbers) > 0 and all(x.endswith("0") for x in numbers)
+
+    while should_trim(trimmed):
+        trimmed = [x[:-1] if is_number_with_decimal(x) else x for x in trimmed]
+
+    # leave one 0 after the decimal points if need be.
+    result = [
+        x + "0" if is_number_with_decimal(x) and x.endswith(decimal) else x
+        for x in trimmed
+    ]
+    return result
+
+
+def _has_names(index: Index) -> bool:
+    if isinstance(index, MultiIndex):
+        return com.any_not_none(*index.names)
+    else:
+        return index.name is not None
+
+
+class EngFormatter:
+    """
+    Formats float values according to engineering format.
+
+    Based on matplotlib.ticker.EngFormatter
+    """
+
+    # The SI engineering prefixes
+    ENG_PREFIXES = {
+        -24: "y",
+        -21: "z",
+        -18: "a",
+        -15: "f",
+        -12: "p",
+        -9: "n",
+        -6: "u",
+        -3: "m",
+        0: "",
+        3: "k",
+        6: "M",
+        9: "G",
+        12: "T",
+        15: "P",
+        18: "E",
+        21: "Z",
+        24: "Y",
+    }
+
+    def __init__(
+        self, accuracy: int | None = None, use_eng_prefix: bool = False
+    ) -> None:
+        self.accuracy = accuracy
+        self.use_eng_prefix = use_eng_prefix
+
+    def __call__(self, num: float) -> str:
+        """
+        Formats a number in engineering notation, appending a letter
+        representing the power of 1000 of the original number. Some examples:
+        >>> format_eng = EngFormatter(accuracy=0, use_eng_prefix=True)
+        >>> format_eng(0)
+        ' 0'
+        >>> format_eng = EngFormatter(accuracy=1, use_eng_prefix=True)
+        >>> format_eng(1_000_000)
+        ' 1.0M'
+        >>> format_eng = EngFormatter(accuracy=2, use_eng_prefix=False)
+        >>> format_eng("-1e-6")
+        '-1.00E-06'
+
+        @param num: the value to represent
+        @type num: either a numeric value or a string that can be converted to
+                   a numeric value (as per decimal.Decimal constructor)
+
+        @return: engineering formatted string
+        """
+        dnum = Decimal(str(num))
+
+        if Decimal.is_nan(dnum):
+            return "NaN"
+
+        if Decimal.is_infinite(dnum):
+            return "inf"
+
+        sign = 1
+
+        if dnum < 0:  # pragma: no cover
+            sign = -1
+            dnum = -dnum
+
+        if dnum != 0:
+            pow10 = Decimal(int(math.floor(dnum.log10() / 3) * 3))
+        else:
+            pow10 = Decimal(0)
+
+        pow10 = pow10.min(max(self.ENG_PREFIXES.keys()))
+        pow10 = pow10.max(min(self.ENG_PREFIXES.keys()))
+        int_pow10 = int(pow10)
+
+        if self.use_eng_prefix:
+            prefix = self.ENG_PREFIXES[int_pow10]
+        elif int_pow10 < 0:
+            prefix = f"E-{-int_pow10:02d}"
+        else:
+            prefix = f"E+{int_pow10:02d}"
+
+        mant = sign * dnum / (10**pow10)
+
+        if self.accuracy is None:  # pragma: no cover
+            format_str = "{mant: g}{prefix}"
+        else:
+            format_str = f"{{mant: .{self.accuracy:d}f}}{{prefix}}"
+
+        formatted = format_str.format(mant=mant, prefix=prefix)
+
+        return formatted
+
+
+def set_eng_float_format(accuracy: int = 3, use_eng_prefix: bool = False) -> None:
+    """
+    Format float representation in DataFrame with SI notation.
+
+    Parameters
+    ----------
+    accuracy : int, default 3
+        Number of decimal digits after the floating point.
+    use_eng_prefix : bool, default False
+        Whether to represent a value with SI prefixes.
+
+    Returns
+    -------
+    None
+
+    Examples
+    --------
+    >>> df = pd.DataFrame([1e-9, 1e-3, 1, 1e3, 1e6])
+    >>> df
+                  0
+    0  1.000000e-09
+    1  1.000000e-03
+    2  1.000000e+00
+    3  1.000000e+03
+    4  1.000000e+06
+
+    >>> pd.set_eng_float_format(accuracy=1)
+    >>> df
+             0
+    0  1.0E-09
+    1  1.0E-03
+    2  1.0E+00
+    3  1.0E+03
+    4  1.0E+06
+
+    >>> pd.set_eng_float_format(use_eng_prefix=True)
+    >>> df
+            0
+    0  1.000n
+    1  1.000m
+    2   1.000
+    3  1.000k
+    4  1.000M
+
+    >>> pd.set_eng_float_format(accuracy=1, use_eng_prefix=True)
+    >>> df
+          0
+    0  1.0n
+    1  1.0m
+    2   1.0
+    3  1.0k
+    4  1.0M
+
+    >>> pd.set_option("display.float_format", None)  # unset option
+    """
+    set_option("display.float_format", EngFormatter(accuracy, use_eng_prefix))
+
+
+def get_level_lengths(
+    levels: Any, sentinel: bool | object | str = ""
+) -> list[dict[int, int]]:
+    """
+    For each index in each level the function returns lengths of indexes.
+
+    Parameters
+    ----------
+    levels : list of lists
+        List of values on for level.
+    sentinel : string, optional
+        Value which states that no new index starts on there.
+
+    Returns
+    -------
+    Returns list of maps. For each level returns map of indexes (key is index
+    in row and value is length of index).
+    """
+    if len(levels) == 0:
+        return []
+
+    control = [True] * len(levels[0])
+
+    result = []
+    for level in levels:
+        last_index = 0
+
+        lengths = {}
+        for i, key in enumerate(level):
+            if control[i] and key == sentinel:
+                pass
+            else:
+                control[i] = False
+                lengths[last_index] = i - last_index
+                last_index = i
+
+        lengths[last_index] = len(level) - last_index
+
+        result.append(lengths)
+
+    return result
+
+
+def buffer_put_lines(buf: WriteBuffer[str], lines: list[str]) -> None:
+    """
+    Appends lines to a buffer.
+
+    Parameters
+    ----------
+    buf
+        The buffer to write to
+    lines
+        The lines to append.
+    """
+    if any(isinstance(x, str) for x in lines):
+        lines = [str(x) for x in lines]
+    buf.write("\n".join(lines))
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/formats/html.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/formats/html.py
new file mode 100644
index 0000000000000000000000000000000000000000..794ce77b3b45ec38d9fa58a708939e53bb8ae629
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/formats/html.py
@@ -0,0 +1,646 @@
+"""
+Module for formatting output data in HTML.
+"""
+from __future__ import annotations
+
+from textwrap import dedent
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Final,
+    cast,
+)
+
+from pandas._config import get_option
+
+from pandas._libs import lib
+
+from pandas import (
+    MultiIndex,
+    option_context,
+)
+
+from pandas.io.common import is_url
+from pandas.io.formats.format import (
+    DataFrameFormatter,
+    get_level_lengths,
+)
+from pandas.io.formats.printing import pprint_thing
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Hashable,
+        Iterable,
+        Mapping,
+    )
+
+
+class HTMLFormatter:
+    """
+    Internal class for formatting output data in html.
+    This class is intended for shared functionality between
+    DataFrame.to_html() and DataFrame._repr_html_().
+    Any logic in common with other output formatting methods
+    should ideally be inherited from classes in format.py
+    and this class responsible for only producing html markup.
+    """
+
+    indent_delta: Final = 2
+
+    def __init__(
+        self,
+        formatter: DataFrameFormatter,
+        classes: str | list[str] | tuple[str, ...] | None = None,
+        border: int | bool | None = None,
+        table_id: str | None = None,
+        render_links: bool = False,
+    ) -> None:
+        self.fmt = formatter
+        self.classes = classes
+
+        self.frame = self.fmt.frame
+        self.columns = self.fmt.tr_frame.columns
+        self.elements: list[str] = []
+        self.bold_rows = self.fmt.bold_rows
+        self.escape = self.fmt.escape
+        self.show_dimensions = self.fmt.show_dimensions
+        if border is None or border is True:
+            border = cast(int, get_option("display.html.border"))
+        elif not border:
+            border = None
+
+        self.border = border
+        self.table_id = table_id
+        self.render_links = render_links
+
+        self.col_space = {}
+        is_multi_index = isinstance(self.columns, MultiIndex)
+        for column, value in self.fmt.col_space.items():
+            col_space_value = f"{value}px" if isinstance(value, int) else value
+            self.col_space[column] = col_space_value
+            # GH 53885: Handling case where column is index
+            # Flatten the data in the multi index and add in the map
+            if is_multi_index and isinstance(column, tuple):
+                for column_index in column:
+                    self.col_space[str(column_index)] = col_space_value
+
+    def to_string(self) -> str:
+        lines = self.render()
+        if any(isinstance(x, str) for x in lines):
+            lines = [str(x) for x in lines]
+        return "\n".join(lines)
+
+    def render(self) -> list[str]:
+        self._write_table()
+
+        if self.should_show_dimensions:
+            by = chr(215)  # ×  # noqa: RUF003
+            self.write(
+                f"
{len(self.frame)} rows {by} {len(self.frame.columns)} columns
"
+            )
+
+        return self.elements
+
+    @property
+    def should_show_dimensions(self) -> bool:
+        return self.fmt.should_show_dimensions
+
+    @property
+    def show_row_idx_names(self) -> bool:
+        return self.fmt.show_row_idx_names
+
+    @property
+    def show_col_idx_names(self) -> bool:
+        return self.fmt.show_col_idx_names
+
+    @property
+    def row_levels(self) -> int:
+        if self.fmt.index:
+            # showing (row) index
+            return self.frame.index.nlevels
+        elif self.show_col_idx_names:
+            # see gh-22579
+            # Column misalignment also occurs for
+            # a standard index when the columns index is named.
+            # If the row index is not displayed a column of
+            # blank cells need to be included before the DataFrame values.
+            return 1
+        # not showing (row) index
+        return 0
+
+    def _get_columns_formatted_values(self) -> Iterable:
+        return self.columns
+
+    @property
+    def is_truncated(self) -> bool:
+        return self.fmt.is_truncated
+
+    @property
+    def ncols(self) -> int:
+        return len(self.fmt.tr_frame.columns)
+
+    def write(self, s: Any, indent: int = 0) -> None:
+        rs = pprint_thing(s)
+        self.elements.append(" " * indent + rs)
+
+    def write_th(
+        self, s: Any, header: bool = False, indent: int = 0, tags: str | None = None
+    ) -> None:
+        """
+        Method for writing a formatted .  This will
+            cause min-width to be set if there is one.
+        indent : int, default 0
+            The indentation level of the cell.
+        tags : str, default None
+            Tags to include in the cell.
+
+        Returns
+        -------
+        A written ", indent)
+        else:
+            self.write(f'', indent)
+        indent += indent_delta
+
+        for i, s in enumerate(line):
+            val_tag = tags.get(i, None)
+            if header or (self.bold_rows and i < nindex_levels):
+                self.write_th(s, indent=indent, header=header, tags=val_tag)
+            else:
+                self.write_td(s, indent, tags=val_tag)
+
+        indent -= indent_delta
+        self.write("", indent)
+
+    def _write_table(self, indent: int = 0) -> None:
+        _classes = ["dataframe"]  # Default class.
+        use_mathjax = get_option("display.html.use_mathjax")
+        if not use_mathjax:
+            _classes.append("tex2jax_ignore")
+        if self.classes is not None:
+            if isinstance(self.classes, str):
+                self.classes = self.classes.split()
+            if not isinstance(self.classes, (list, tuple)):
+                raise TypeError(
+                    "classes must be a string, list, "
+                    f"or tuple, not {type(self.classes)}"
+                )
+            _classes.extend(self.classes)
+
+        if self.table_id is None:
+            id_section = ""
+        else:
+            id_section = f' id="{self.table_id}"'
+
+        if self.border is None:
+            border_attr = ""
+        else:
+            border_attr = f' border="{self.border}"'
+
+        self.write(
+            f'',
+            indent,
+        )
+
+        if self.fmt.header or self.show_row_idx_names:
+            self._write_header(indent + self.indent_delta)
+
+        self._write_body(indent + self.indent_delta)
+
+        self.write("
 cell.
+
+        If col_space is set on the formatter then that is used for
+        the value of min-width.
+
+        Parameters
+        ----------
+        s : object
+            The data to be written inside the cell.
+        header : bool, default False
+            Set to True if the  is for use inside 
 cell.
+        """
+        col_space = self.col_space.get(s, None)
+
+        if header and col_space is not None:
+            tags = tags or ""
+            tags += f'style="min-width: {col_space};"'
+
+        self._write_cell(s, kind="th", indent=indent, tags=tags)
+
+    def write_td(self, s: Any, indent: int = 0, tags: str | None = None) -> None:
+        self._write_cell(s, kind="td", indent=indent, tags=tags)
+
+    def _write_cell(
+        self, s: Any, kind: str = "td", indent: int = 0, tags: str | None = None
+    ) -> None:
+        if tags is not None:
+            start_tag = f"<{kind} {tags}>"
+        else:
+            start_tag = f"<{kind}>"
+
+        if self.escape:
+            # escape & first to prevent double escaping of &
+            esc = {"&": r"&", "<": r"<", ">": r">"}
+        else:
+            esc = {}
+
+        rs = pprint_thing(s, escape_chars=esc).strip()
+
+        if self.render_links and is_url(rs):
+            rs_unescaped = pprint_thing(s, escape_chars={}).strip()
+            start_tag += f''
+            end_a = ""
+        else:
+            end_a = ""
+
+        self.write(f"{start_tag}{rs}{end_a}", indent)
+
+    def write_tr(
+        self,
+        line: Iterable,
+        indent: int = 0,
+        indent_delta: int = 0,
+        header: bool = False,
+        align: str | None = None,
+        tags: dict[int, str] | None = None,
+        nindex_levels: int = 0,
+    ) -> None:
+        if tags is None:
+            tags = {}
+
+        if align is None:
+            self.write("
", indent)
+
+    def _write_col_header(self, indent: int) -> None:
+        row: list[Hashable]
+        is_truncated_horizontally = self.fmt.is_truncated_horizontally
+        if isinstance(self.columns, MultiIndex):
+            template = 'colspan="{span:d}" halign="left"'
+
+            sentinel: lib.NoDefault | bool
+            if self.fmt.sparsify:
+                # GH3547
+                sentinel = lib.no_default
+            else:
+                sentinel = False
+            levels = self.columns._format_multi(sparsify=sentinel, include_names=False)
+            level_lengths = get_level_lengths(levels, sentinel)
+            inner_lvl = len(level_lengths) - 1
+            for lnum, (records, values) in enumerate(zip(level_lengths, levels)):
+                if is_truncated_horizontally:
+                    # modify the header lines
+                    ins_col = self.fmt.tr_col_num
+                    if self.fmt.sparsify:
+                        recs_new = {}
+                        # Increment tags after ... col.
+                        for tag, span in list(records.items()):
+                            if tag >= ins_col:
+                                recs_new[tag + 1] = span
+                            elif tag + span > ins_col:
+                                recs_new[tag] = span + 1
+                                if lnum == inner_lvl:
+                                    values = (
+                                        values[:ins_col] + ("...",) + values[ins_col:]
+                                    )
+                                else:
+                                    # sparse col headers do not receive a ...
+                                    values = (
+                                        values[:ins_col]
+                                        + (values[ins_col - 1],)
+                                        + values[ins_col:]
+                                    )
+                            else:
+                                recs_new[tag] = span
+                            # if ins_col lies between tags, all col headers
+                            # get ...
+                            if tag + span == ins_col:
+                                recs_new[ins_col] = 1
+                                values = values[:ins_col] + ("...",) + values[ins_col:]
+                        records = recs_new
+                        inner_lvl = len(level_lengths) - 1
+                        if lnum == inner_lvl:
+                            records[ins_col] = 1
+                    else:
+                        recs_new = {}
+                        for tag, span in list(records.items()):
+                            if tag >= ins_col:
+                                recs_new[tag + 1] = span
+                            else:
+                                recs_new[tag] = span
+                        recs_new[ins_col] = 1
+                        records = recs_new
+                        values = values[:ins_col] + ["..."] + values[ins_col:]
+
+                # see gh-22579
+                # Column Offset Bug with to_html(index=False) with
+                # MultiIndex Columns and Index.
+                # Initially fill row with blank cells before column names.
+                # TODO: Refactor to remove code duplication with code
+                # block below for standard columns index.
+                row = [""] * (self.row_levels - 1)
+                if self.fmt.index or self.show_col_idx_names:
+                    # see gh-22747
+                    # If to_html(index_names=False) do not show columns
+                    # index names.
+                    # TODO: Refactor to use _get_column_name_list from
+                    # DataFrameFormatter class and create a
+                    # _get_formatted_column_labels function for code
+                    # parity with DataFrameFormatter class.
+                    if self.fmt.show_index_names:
+                        name = self.columns.names[lnum]
+                        row.append(pprint_thing(name or ""))
+                    else:
+                        row.append("")
+
+                tags = {}
+                j = len(row)
+                for i, v in enumerate(values):
+                    if i in records:
+                        if records[i] > 1:
+                            tags[j] = template.format(span=records[i])
+                    else:
+                        continue
+                    j += 1
+                    row.append(v)
+                self.write_tr(row, indent, self.indent_delta, tags=tags, header=True)
+        else:
+            # see gh-22579
+            # Column misalignment also occurs for
+            # a standard index when the columns index is named.
+            # Initially fill row with blank cells before column names.
+            # TODO: Refactor to remove code duplication with code block
+            # above for columns MultiIndex.
+            row = [""] * (self.row_levels - 1)
+            if self.fmt.index or self.show_col_idx_names:
+                # see gh-22747
+                # If to_html(index_names=False) do not show columns
+                # index names.
+                # TODO: Refactor to use _get_column_name_list from
+                # DataFrameFormatter class.
+                if self.fmt.show_index_names:
+                    row.append(self.columns.name or "")
+                else:
+                    row.append("")
+            row.extend(self._get_columns_formatted_values())
+            align = self.fmt.justify
+
+            if is_truncated_horizontally:
+                ins_col = self.row_levels + self.fmt.tr_col_num
+                row.insert(ins_col, "...")
+
+            self.write_tr(row, indent, self.indent_delta, header=True, align=align)
+
+    def _write_row_header(self, indent: int) -> None:
+        is_truncated_horizontally = self.fmt.is_truncated_horizontally
+        row = [x if x is not None else "" for x in self.frame.index.names] + [""] * (
+            self.ncols + (1 if is_truncated_horizontally else 0)
+        )
+        self.write_tr(row, indent, self.indent_delta, header=True)
+
+    def _write_header(self, indent: int) -> None:
+        self.write("
`` tag
+        in addition to automatic (by default) id.
+    cell_ids : bool, default True
+        If True, each cell will have an ``id`` attribute in their HTML tag.
+        The ``id`` takes the form ``T__row_col``
+        where ```` is the unique identifier, ```` is the row
+        number and ```` is the column number.
+    na_rep : str, optional
+        Representation for missing values.
+        If ``na_rep`` is None, no special formatting is applied, and falls back to
+        ``pandas.options.styler.format.na_rep``.
+
+    uuid_len : int, default 5
+        If ``uuid`` is not specified, the length of the ``uuid`` to randomly generate
+        expressed in hex characters, in range [0, 32].
+    decimal : str, optional
+        Character used as decimal separator for floats, complex and integers. If not
+        given uses ``pandas.options.styler.format.decimal``.
+
+        .. versionadded:: 1.3.0
+
+    thousands : str, optional, default None
+        Character used as thousands separator for floats, complex and integers. If not
+        given uses ``pandas.options.styler.format.thousands``.
+
+        .. versionadded:: 1.3.0
+
+    escape : str, optional
+        Use 'html' to replace the characters ``&``, ``<``, ``>``, ``'``, and ``"``
+        in cell display string with HTML-safe sequences.
+        Use 'latex' to replace the characters ``&``, ``%``, ``$``, ``#``, ``_``,
+        ``{``, ``}``, ``~``, ``^``, and ``\`` in the cell display string with
+        LaTeX-safe sequences. Use 'latex-math' to replace the characters
+        the same way as in 'latex' mode, except for math substrings,
+        which either are surrounded by two characters ``$`` or start with
+        the character ``\(`` and end with ``\)``.
+        If not given uses ``pandas.options.styler.format.escape``.
+
+        .. versionadded:: 1.3.0
+    formatter : str, callable, dict, optional
+        Object to define how values are displayed. See ``Styler.format``. If not given
+        uses ``pandas.options.styler.format.formatter``.
+
+        .. versionadded:: 1.4.0
+
+    Attributes
+    ----------
+    env : Jinja2 jinja2.Environment
+    template_html : Jinja2 Template
+    template_html_table : Jinja2 Template
+    template_html_style : Jinja2 Template
+    template_latex : Jinja2 Template
+    loader : Jinja2 Loader
+
+    See Also
+    --------
+    DataFrame.style : Return a Styler object containing methods for building
+        a styled HTML representation for the DataFrame.
+
+    Notes
+    -----
+    Most styling will be done by passing style functions into
+    ``Styler.apply`` or ``Styler.map``. Style functions should
+    return values with strings containing CSS ``'attr: value'`` that will
+    be applied to the indicated cells.
+
+    If using in the Jupyter notebook, Styler has defined a ``_repr_html_``
+    to automatically render itself. Otherwise call Styler.to_html to get
+    the generated HTML.
+
+    CSS classes are attached to the generated HTML
+
+    * Index and Column names include ``index_name`` and ``level``
+      where `k` is its level in a MultiIndex
+    * Index label cells include
+
+      * ``row_heading``
+      * ``row`` where `n` is the numeric position of the row
+      * ``level`` where `k` is the level in a MultiIndex
+
+    * Column label cells include
+      * ``col_heading``
+      * ``col`` where `n` is the numeric position of the column
+      * ``level`` where `k` is the level in a MultiIndex
+
+    * Blank cells include ``blank``
+    * Data cells include ``data``
+    * Trimmed cells include ``col_trim`` or ``row_trim``.
+
+    Any, or all, or these classes can be renamed by using the ``css_class_names``
+    argument in ``Styler.set_table_classes``, giving a value such as
+    *{"row": "MY_ROW_CLASS", "col_trim": "", "row_trim": ""}*.
+
+    Examples
+    --------
+    >>> df = pd.DataFrame([[1.0, 2.0, 3.0], [4, 5, 6]], index=['a', 'b'],
+    ...                   columns=['A', 'B', 'C'])
+    >>> pd.io.formats.style.Styler(df, precision=2,
+    ...                            caption="My table")  # doctest: +SKIP
+
+    Please see:
+    `Table Visualization <../../user_guide/style.ipynb>`_ for more examples.
+    """
+
+    def __init__(
+        self,
+        data: DataFrame | Series,
+        precision: int | None = None,
+        table_styles: CSSStyles | None = None,
+        uuid: str | None = None,
+        caption: str | tuple | list | None = None,
+        table_attributes: str | None = None,
+        cell_ids: bool = True,
+        na_rep: str | None = None,
+        uuid_len: int = 5,
+        decimal: str | None = None,
+        thousands: str | None = None,
+        escape: str | None = None,
+        formatter: ExtFormatter | None = None,
+    ) -> None:
+        super().__init__(
+            data=data,
+            uuid=uuid,
+            uuid_len=uuid_len,
+            table_styles=table_styles,
+            table_attributes=table_attributes,
+            caption=caption,
+            cell_ids=cell_ids,
+            precision=precision,
+        )
+
+        # validate ordered args
+        thousands = thousands or get_option("styler.format.thousands")
+        decimal = decimal or get_option("styler.format.decimal")
+        na_rep = na_rep or get_option("styler.format.na_rep")
+        escape = escape or get_option("styler.format.escape")
+        formatter = formatter or get_option("styler.format.formatter")
+        # precision is handled by superclass as default for performance
+
+        self.format(
+            formatter=formatter,
+            precision=precision,
+            na_rep=na_rep,
+            escape=escape,
+            decimal=decimal,
+            thousands=thousands,
+        )
+
+    def concat(self, other: Styler) -> Styler:
+        """
+        Append another Styler to combine the output into a single table.
+
+        .. versionadded:: 1.5.0
+
+        Parameters
+        ----------
+        other : Styler
+            The other Styler object which has already been styled and formatted. The
+            data for this Styler must have the same columns as the original, and the
+            number of index levels must also be the same to render correctly.
+
+        Returns
+        -------
+        Styler
+
+        Notes
+        -----
+        The purpose of this method is to extend existing styled dataframes with other
+        metrics that may be useful but may not conform to the original's structure.
+        For example adding a sub total row, or displaying metrics such as means,
+        variance or counts.
+
+        Styles that are applied using the ``apply``, ``map``, ``apply_index``
+        and ``map_index``, and formatting applied with ``format`` and
+        ``format_index`` will be preserved.
+
+        .. warning::
+            Only the output methods ``to_html``, ``to_string`` and ``to_latex``
+            currently work with concatenated Stylers.
+
+            Other output methods, including ``to_excel``, **do not** work with
+            concatenated Stylers.
+
+        The following should be noted:
+
+          - ``table_styles``, ``table_attributes``, ``caption`` and ``uuid`` are all
+            inherited from the original Styler and not ``other``.
+          - hidden columns and hidden index levels will be inherited from the
+            original Styler
+          - ``css`` will be inherited from the original Styler, and the value of
+            keys ``data``, ``row_heading`` and ``row`` will be prepended with
+            ``foot0_``. If more concats are chained, their styles will be prepended
+            with ``foot1_``, ''foot_2'', etc., and if a concatenated style have
+            another concatanated style, the second style will be prepended with
+            ``foot{parent}_foot{child}_``.
+
+        A common use case is to concatenate user defined functions with
+        ``DataFrame.agg`` or with described statistics via ``DataFrame.describe``.
+        See examples.
+
+        Examples
+        --------
+        A common use case is adding totals rows, or otherwise, via methods calculated
+        in ``DataFrame.agg``.
+
+        >>> df = pd.DataFrame([[4, 6], [1, 9], [3, 4], [5, 5], [9, 6]],
+        ...                   columns=["Mike", "Jim"],
+        ...                   index=["Mon", "Tue", "Wed", "Thurs", "Fri"])
+        >>> styler = df.style.concat(df.agg(["sum"]).style)  # doctest: +SKIP
+
+        .. figure:: ../../_static/style/footer_simple.png
+
+        Since the concatenated object is a Styler the existing functionality can be
+        used to conditionally format it as well as the original.
+
+        >>> descriptors = df.agg(["sum", "mean", lambda s: s.dtype])
+        >>> descriptors.index = ["Total", "Average", "dtype"]
+        >>> other = (descriptors.style
+        ...          .highlight_max(axis=1, subset=(["Total", "Average"], slice(None)))
+        ...          .format(subset=("Average", slice(None)), precision=2, decimal=",")
+        ...          .map(lambda v: "font-weight: bold;"))
+        >>> styler = (df.style
+        ...             .highlight_max(color="salmon")
+        ...             .set_table_styles([{"selector": ".foot_row0",
+        ...                                 "props": "border-top: 1px solid black;"}]))
+        >>> styler.concat(other)  # doctest: +SKIP
+
+        .. figure:: ../../_static/style/footer_extended.png
+
+        When ``other`` has fewer index levels than the original Styler it is possible
+        to extend the index in ``other``, with placeholder levels.
+
+        >>> df = pd.DataFrame([[1], [2]],
+        ...                   index=pd.MultiIndex.from_product([[0], [1, 2]]))
+        >>> descriptors = df.agg(["sum"])
+        >>> descriptors.index = pd.MultiIndex.from_product([[""], descriptors.index])
+        >>> df.style.concat(descriptors.style)  # doctest: +SKIP
+        """
+        if not isinstance(other, Styler):
+            raise TypeError("`other` must be of type `Styler`")
+        if not self.data.columns.equals(other.data.columns):
+            raise ValueError("`other.data` must have same columns as `Styler.data`")
+        if not self.data.index.nlevels == other.data.index.nlevels:
+            raise ValueError(
+                "number of index levels must be same in `other` "
+                "as in `Styler`. See documentation for suggestions."
+            )
+        self.concatenated.append(other)
+        return self
+
+    def _repr_html_(self) -> str | None:
+        """
+        Hooks into Jupyter notebook rich display system, which calls _repr_html_ by
+        default if an object is returned at the end of a cell.
+        """
+        if get_option("styler.render.repr") == "html":
+            return self.to_html()
+        return None
+
+    def _repr_latex_(self) -> str | None:
+        if get_option("styler.render.repr") == "latex":
+            return self.to_latex()
+        return None
+
+    def set_tooltips(
+        self,
+        ttips: DataFrame,
+        props: CSSProperties | None = None,
+        css_class: str | None = None,
+    ) -> Styler:
+        """
+        Set the DataFrame of strings on ``Styler`` generating ``:hover`` tooltips.
+
+        These string based tooltips are only applicable to ``
`` HTML elements,
+        and cannot be used for column or index headers.
+
+        .. versionadded:: 1.3.0
+
+        Parameters
+        ----------
+        ttips : DataFrame
+            DataFrame containing strings that will be translated to tooltips, mapped
+            by identical column and index values that must exist on the underlying
+            Styler data. None, NaN values, and empty strings will be ignored and
+            not affect the rendered HTML.
+        props : list-like or str, optional
+            List of (attr, value) tuples or a valid CSS string. If ``None`` adopts
+            the internal default values described in notes.
+        css_class : str, optional
+            Name of the tooltip class used in CSS, should conform to HTML standards.
+            Only useful if integrating tooltips with external CSS. If ``None`` uses the
+            internal default value 'pd-t'.
+
+        Returns
+        -------
+        Styler
+
+        Notes
+        -----
+        Tooltips are created by adding `` to each data cell
+        and then manipulating the table level CSS to attach pseudo hover and pseudo
+        after selectors to produce the required the results.
+
+        The default properties for the tooltip CSS class are:
+
+        - visibility: hidden
+        - position: absolute
+        - z-index: 1
+        - background-color: black
+        - color: white
+        - transform: translate(-20px, -20px)
+
+        The property 'visibility: hidden;' is a key prerequisite to the hover
+        functionality, and should always be included in any manual properties
+        specification, using the ``props`` argument.
+
+        Tooltips are not designed to be efficient, and can add large amounts of
+        additional HTML for larger tables, since they also require that ``cell_ids``
+        is forced to `True`.
+
+        Examples
+        --------
+        Basic application
+
+        >>> df = pd.DataFrame(data=[[0, 1], [2, 3]])
+        >>> ttips = pd.DataFrame(
+        ...    data=[["Min", ""], [np.nan, "Max"]], columns=df.columns, index=df.index
+        ... )
+        >>> s = df.style.set_tooltips(ttips).to_html()
+
+        Optionally controlling the tooltip visual display
+
+        >>> df.style.set_tooltips(ttips, css_class='tt-add', props=[
+        ...     ('visibility', 'hidden'),
+        ...     ('position', 'absolute'),
+        ...     ('z-index', 1)])  # doctest: +SKIP
+        >>> df.style.set_tooltips(ttips, css_class='tt-add',
+        ...     props='visibility:hidden; position:absolute; z-index:1;')
+        ... # doctest: +SKIP
+        """
+        if not self.cell_ids:
+            # tooltips not optimised for individual cell check. requires reasonable
+            # redesign and more extensive code for a feature that might be rarely used.
+            raise NotImplementedError(
+                "Tooltips can only render with 'cell_ids' is True."
+            )
+        if not ttips.index.is_unique or not ttips.columns.is_unique:
+            raise KeyError(
+                "Tooltips render only if `ttips` has unique index and columns."
+            )
+        if self.tooltips is None:  # create a default instance if necessary
+            self.tooltips = Tooltips()
+        self.tooltips.tt_data = ttips
+        if props:
+            self.tooltips.class_properties = props
+        if css_class:
+            self.tooltips.class_name = css_class
+
+        return self
+
+    @doc(
+        NDFrame.to_excel,
+        klass="Styler",
+        storage_options=_shared_docs["storage_options"],
+        storage_options_versionadded="1.5.0",
+    )
+    def to_excel(
+        self,
+        excel_writer: FilePath | WriteExcelBuffer | ExcelWriter,
+        sheet_name: str = "Sheet1",
+        na_rep: str = "",
+        float_format: str | None = None,
+        columns: Sequence[Hashable] | None = None,
+        header: Sequence[Hashable] | bool = True,
+        index: bool = True,
+        index_label: IndexLabel | None = None,
+        startrow: int = 0,
+        startcol: int = 0,
+        engine: str | None = None,
+        merge_cells: bool = True,
+        encoding: str | None = None,
+        inf_rep: str = "inf",
+        verbose: bool = True,
+        freeze_panes: tuple[int, int] | None = None,
+        storage_options: StorageOptions | None = None,
+    ) -> None:
+        from pandas.io.formats.excel import ExcelFormatter
+
+        formatter = ExcelFormatter(
+            self,
+            na_rep=na_rep,
+            cols=columns,
+            header=header,
+            float_format=float_format,
+            index=index,
+            index_label=index_label,
+            merge_cells=merge_cells,
+            inf_rep=inf_rep,
+        )
+        formatter.write(
+            excel_writer,
+            sheet_name=sheet_name,
+            startrow=startrow,
+            startcol=startcol,
+            freeze_panes=freeze_panes,
+            engine=engine,
+            storage_options=storage_options,
+        )
+
+    @overload
+    def to_latex(
+        self,
+        buf: FilePath | WriteBuffer[str],
+        *,
+        column_format: str | None = ...,
+        position: str | None = ...,
+        position_float: str | None = ...,
+        hrules: bool | None = ...,
+        clines: str | None = ...,
+        label: str | None = ...,
+        caption: str | tuple | None = ...,
+        sparse_index: bool | None = ...,
+        sparse_columns: bool | None = ...,
+        multirow_align: str | None = ...,
+        multicol_align: str | None = ...,
+        siunitx: bool = ...,
+        environment: str | None = ...,
+        encoding: str | None = ...,
+        convert_css: bool = ...,
+    ) -> None:
+        ...
+
+    @overload
+    def to_latex(
+        self,
+        buf: None = ...,
+        *,
+        column_format: str | None = ...,
+        position: str | None = ...,
+        position_float: str | None = ...,
+        hrules: bool | None = ...,
+        clines: str | None = ...,
+        label: str | None = ...,
+        caption: str | tuple | None = ...,
+        sparse_index: bool | None = ...,
+        sparse_columns: bool | None = ...,
+        multirow_align: str | None = ...,
+        multicol_align: str | None = ...,
+        siunitx: bool = ...,
+        environment: str | None = ...,
+        encoding: str | None = ...,
+        convert_css: bool = ...,
+    ) -> str:
+        ...
+
+    def to_latex(
+        self,
+        buf: FilePath | WriteBuffer[str] | None = None,
+        *,
+        column_format: str | None = None,
+        position: str | None = None,
+        position_float: str | None = None,
+        hrules: bool | None = None,
+        clines: str | None = None,
+        label: str | None = None,
+        caption: str | tuple | None = None,
+        sparse_index: bool | None = None,
+        sparse_columns: bool | None = None,
+        multirow_align: str | None = None,
+        multicol_align: str | None = None,
+        siunitx: bool = False,
+        environment: str | None = None,
+        encoding: str | None = None,
+        convert_css: bool = False,
+    ) -> str | None:
+        r"""
+        Write Styler to a file, buffer or string in LaTeX format.
+
+        .. versionadded:: 1.3.0
+
+        Parameters
+        ----------
+        buf : str, path object, file-like object, or None, default None
+            String, path object (implementing ``os.PathLike[str]``), or file-like
+            object implementing a string ``write()`` function. If None, the result is
+            returned as a string.
+        column_format : str, optional
+            The LaTeX column specification placed in location:
+
+            \\begin{tabular}{}
+
+            Defaults to 'l' for index and
+            non-numeric data columns, and, for numeric data columns,
+            to 'r' by default, or 'S' if ``siunitx`` is ``True``.
+        position : str, optional
+            The LaTeX positional argument (e.g. 'h!') for tables, placed in location:
+
+            ``\\begin{table}[]``.
+        position_float : {"centering", "raggedleft", "raggedright"}, optional
+            The LaTeX float command placed in location:
+
+            \\begin{table}[]
+
+            \\
+
+            Cannot be used if ``environment`` is "longtable".
+        hrules : bool
+            Set to `True` to add \\toprule, \\midrule and \\bottomrule from the
+            {booktabs} LaTeX package.
+            Defaults to ``pandas.options.styler.latex.hrules``, which is `False`.
+
+            .. versionchanged:: 1.4.0
+        clines : str, optional
+            Use to control adding \\cline commands for the index labels separation.
+            Possible values are:
+
+              - `None`: no cline commands are added (default).
+              - `"all;data"`: a cline is added for every index value extending the
+                width of the table, including data entries.
+              - `"all;index"`: as above with lines extending only the width of the
+                index entries.
+              - `"skip-last;data"`: a cline is added for each index value except the
+                last level (which is never sparsified), extending the widtn of the
+                table.
+              - `"skip-last;index"`: as above with lines extending only the width of the
+                index entries.
+
+            .. versionadded:: 1.4.0
+        label : str, optional
+            The LaTeX label included as: \\label{
}.
+            If tuple, i.e ("full caption", "short caption"), the caption included
+            as: \\caption[]{}.
+        sparse_index : bool, optional
+            Whether to sparsify the display of a hierarchical index. Setting to False
+            will display each explicit level element in a hierarchical key for each row.
+            Defaults to ``pandas.options.styler.sparse.index``, which is `True`.
+        sparse_columns : bool, optional
+            Whether to sparsify the display of a hierarchical index. Setting to False
+            will display each explicit level element in a hierarchical key for each
+            column. Defaults to ``pandas.options.styler.sparse.columns``, which
+            is `True`.
+        multirow_align : {"c", "t", "b", "naive"}, optional
+            If sparsifying hierarchical MultiIndexes whether to align text centrally,
+            at the top or bottom using the multirow package. If not given defaults to
+            ``pandas.options.styler.latex.multirow_align``, which is `"c"`.
+            If "naive" is given renders without multirow.
+
+            .. versionchanged:: 1.4.0
+        multicol_align : {"r", "c", "l", "naive-l", "naive-r"}, optional
+            If sparsifying hierarchical MultiIndex columns whether to align text at
+            the left, centrally, or at the right. If not given defaults to
+            ``pandas.options.styler.latex.multicol_align``, which is "r".
+            If a naive option is given renders without multicol.
+            Pipe decorators can also be added to non-naive values to draw vertical
+            rules, e.g. "\|r" will draw a rule on the left side of right aligned merged
+            cells.
+
+            .. versionchanged:: 1.4.0
+        siunitx : bool, default False
+            Set to ``True`` to structure LaTeX compatible with the {siunitx} package.
+        environment : str, optional
+            If given, the environment that will replace 'table' in ``\\begin{table}``.
+            If 'longtable' is specified then a more suitable template is
+            rendered. If not given defaults to
+            ``pandas.options.styler.latex.environment``, which is `None`.
+
+            .. versionadded:: 1.4.0
+        encoding : str, optional
+            Character encoding setting. Defaults
+            to ``pandas.options.styler.render.encoding``, which is "utf-8".
+        convert_css : bool, default False
+            Convert simple cell-styles from CSS to LaTeX format. Any CSS not found in
+            conversion table is dropped. A style can be forced by adding option
+            `--latex`. See notes.
+
+        Returns
+        -------
+        str or None
+            If `buf` is None, returns the result as a string. Otherwise returns `None`.
+
+        See Also
+        --------
+        Styler.format: Format the text display value of cells.
+
+        Notes
+        -----
+        **Latex Packages**
+
+        For the following features we recommend the following LaTeX inclusions:
+
+        ===================== ==========================================================
+        Feature               Inclusion
+        ===================== ==========================================================
+        sparse columns        none: included within default {tabular} environment
+        sparse rows           \\usepackage{multirow}
+        hrules                \\usepackage{booktabs}
+        colors                \\usepackage[table]{xcolor}
+        siunitx               \\usepackage{siunitx}
+        bold (with siunitx)   | \\usepackage{etoolbox}
+                              | \\robustify\\bfseries
+                              | \\sisetup{detect-all = true}  *(within {document})*
+        italic (with siunitx) | \\usepackage{etoolbox}
+                              | \\robustify\\itshape
+                              | \\sisetup{detect-all = true}  *(within {document})*
+        environment           \\usepackage{longtable} if arg is "longtable"
+                              | or any other relevant environment package
+        hyperlinks            \\usepackage{hyperref}
+        ===================== ==========================================================
+
+        **Cell Styles**
+
+        LaTeX styling can only be rendered if the accompanying styling functions have
+        been constructed with appropriate LaTeX commands. All styling
+        functionality is built around the concept of a CSS ``(, )``
+        pair (see `Table Visualization <../../user_guide/style.ipynb>`_), and this
+        should be replaced by a LaTeX
+        ``(, )`` approach. Each cell will be styled individually
+        using nested LaTeX commands with their accompanied options.
+
+        For example the following code will highlight and bold a cell in HTML-CSS:
+
+        >>> df = pd.DataFrame([[1,2], [3,4]])
+        >>> s = df.style.highlight_max(axis=None,
+        ...                            props='background-color:red; font-weight:bold;')
+        >>> s.to_html()  # doctest: +SKIP
+
+        The equivalent using LaTeX only commands is the following:
+
+        >>> s = df.style.highlight_max(axis=None,
+        ...                            props='cellcolor:{red}; bfseries: ;')
+        >>> s.to_latex()  # doctest: +SKIP
+
+        Internally these structured LaTeX ``(, )`` pairs
+        are translated to the
+        ``display_value`` with the default structure:
+        ``\ ``.
+        Where there are multiple commands the latter is nested recursively, so that
+        the above example highlighted cell is rendered as
+        ``\cellcolor{red} \bfseries 4``.
+
+        Occasionally this format does not suit the applied command, or
+        combination of LaTeX packages that is in use, so additional flags can be
+        added to the ````, within the tuple, to result in different
+        positions of required braces (the **default** being the same as ``--nowrap``):
+
+        =================================== ============================================
+        Tuple Format                           Output Structure
+        =================================== ============================================
+        (,)               \\ 
+        (, ``--nowrap``)  \\ 
+        (, ``--rwrap``)   \\{}
+        (, ``--wrap``)    {\\ }
+        (, ``--lwrap``)   {\\} 
+        (, ``--dwrap``)   {\\}{}
+        =================================== ============================================
+
+        For example the `textbf` command for font-weight
+        should always be used with `--rwrap` so ``('textbf', '--rwrap')`` will render a
+        working cell, wrapped with braces, as ``\textbf{}``.
+
+        A more comprehensive example is as follows:
+
+        >>> df = pd.DataFrame([[1, 2.2, "dogs"], [3, 4.4, "cats"], [2, 6.6, "cows"]],
+        ...                   index=["ix1", "ix2", "ix3"],
+        ...                   columns=["Integers", "Floats", "Strings"])
+        >>> s = df.style.highlight_max(
+        ...     props='cellcolor:[HTML]{FFFF00}; color:{red};'
+        ...           'textit:--rwrap; textbf:--rwrap;'
+        ... )
+        >>> s.to_latex()  # doctest: +SKIP
+
+        .. figure:: ../../_static/style/latex_1.png
+
+        **Table Styles**
+
+        Internally Styler uses its ``table_styles`` object to parse the
+        ``column_format``, ``position``, ``position_float``, and ``label``
+        input arguments. These arguments are added to table styles in the format:
+
+        .. code-block:: python
+
+            set_table_styles([
+                {"selector": "column_format", "props": f":{column_format};"},
+                {"selector": "position", "props": f":{position};"},
+                {"selector": "position_float", "props": f":{position_float};"},
+                {"selector": "label", "props": f":{{{label.replace(':','§')}}};"}
+            ], overwrite=False)
+
+        Exception is made for the ``hrules`` argument which, in fact, controls all three
+        commands: ``toprule``, ``bottomrule`` and ``midrule`` simultaneously. Instead of
+        setting ``hrules`` to ``True``, it is also possible to set each
+        individual rule definition, by manually setting the ``table_styles``,
+        for example below we set a regular ``toprule``, set an ``hline`` for
+        ``bottomrule`` and exclude the ``midrule``:
+
+        .. code-block:: python
+
+            set_table_styles([
+                {'selector': 'toprule', 'props': ':toprule;'},
+                {'selector': 'bottomrule', 'props': ':hline;'},
+            ], overwrite=False)
+
+        If other ``commands`` are added to table styles they will be detected, and
+        positioned immediately above the '\\begin{tabular}' command. For example to
+        add odd and even row coloring, from the {colortbl} package, in format
+        ``\rowcolors{1}{pink}{red}``, use:
+
+        .. code-block:: python
+
+            set_table_styles([
+                {'selector': 'rowcolors', 'props': ':{1}{pink}{red};'}
+            ], overwrite=False)
+
+        A more comprehensive example using these arguments is as follows:
+
+        >>> df.columns = pd.MultiIndex.from_tuples([
+        ...     ("Numeric", "Integers"),
+        ...     ("Numeric", "Floats"),
+        ...     ("Non-Numeric", "Strings")
+        ... ])
+        >>> df.index = pd.MultiIndex.from_tuples([
+        ...     ("L0", "ix1"), ("L0", "ix2"), ("L1", "ix3")
+        ... ])
+        >>> s = df.style.highlight_max(
+        ...     props='cellcolor:[HTML]{FFFF00}; color:{red}; itshape:; bfseries:;'
+        ... )
+        >>> s.to_latex(
+        ...     column_format="rrrrr", position="h", position_float="centering",
+        ...     hrules=True, label="table:5", caption="Styled LaTeX Table",
+        ...     multirow_align="t", multicol_align="r"
+        ... )  # doctest: +SKIP
+
+        .. figure:: ../../_static/style/latex_2.png
+
+        **Formatting**
+
+        To format values :meth:`Styler.format` should be used prior to calling
+        `Styler.to_latex`, as well as other methods such as :meth:`Styler.hide`
+        for example:
+
+        >>> s.clear()
+        >>> s.table_styles = []
+        >>> s.caption = None
+        >>> s.format({
+        ...    ("Numeric", "Integers"): '\${}',
+        ...    ("Numeric", "Floats"): '{:.3f}',
+        ...    ("Non-Numeric", "Strings"): str.upper
+        ... })  # doctest: +SKIP
+                        Numeric      Non-Numeric
+                  Integers   Floats    Strings
+        L0    ix1       $1   2.200      DOGS
+              ix2       $3   4.400      CATS
+        L1    ix3       $2   6.600      COWS
+
+        >>> s.to_latex()  # doctest: +SKIP
+        \begin{tabular}{llrrl}
+        {} & {} & \multicolumn{2}{r}{Numeric} & {Non-Numeric} \\
+        {} & {} & {Integers} & {Floats} & {Strings} \\
+        \multirow[c]{2}{*}{L0} & ix1 & \\$1 & 2.200 & DOGS \\
+         & ix2 & \$3 & 4.400 & CATS \\
+        L1 & ix3 & \$2 & 6.600 & COWS \\
+        \end{tabular}
+
+        **CSS Conversion**
+
+        This method can convert a Styler constructured with HTML-CSS to LaTeX using
+        the following limited conversions.
+
+        ================== ==================== ============= ==========================
+        CSS Attribute      CSS value            LaTeX Command LaTeX Options
+        ================== ==================== ============= ==========================
+        font-weight        | bold               | bfseries
+                           | bolder             | bfseries
+        font-style         | italic             | itshape
+                           | oblique            | slshape
+        background-color   | red                cellcolor     | {red}--lwrap
+                           | #fe01ea                          | [HTML]{FE01EA}--lwrap
+                           | #f0e                             | [HTML]{FF00EE}--lwrap
+                           | rgb(128,255,0)                   | [rgb]{0.5,1,0}--lwrap
+                           | rgba(128,0,0,0.5)                | [rgb]{0.5,0,0}--lwrap
+                           | rgb(25%,255,50%)                 | [rgb]{0.25,1,0.5}--lwrap
+        color              | red                color         | {red}
+                           | #fe01ea                          | [HTML]{FE01EA}
+                           | #f0e                             | [HTML]{FF00EE}
+                           | rgb(128,255,0)                   | [rgb]{0.5,1,0}
+                           | rgba(128,0,0,0.5)                | [rgb]{0.5,0,0}
+                           | rgb(25%,255,50%)                 | [rgb]{0.25,1,0.5}
+        ================== ==================== ============= ==========================
+
+        It is also possible to add user-defined LaTeX only styles to a HTML-CSS Styler
+        using the ``--latex`` flag, and to add LaTeX parsing options that the
+        converter will detect within a CSS-comment.
+
+        >>> df = pd.DataFrame([[1]])
+        >>> df.style.set_properties(
+        ...     **{"font-weight": "bold /* --dwrap */", "Huge": "--latex--rwrap"}
+        ... ).to_latex(convert_css=True)  # doctest: +SKIP
+        \begin{tabular}{lr}
+        {} & {0} \\
+        0 & {\bfseries}{\Huge{1}} \\
+        \end{tabular}
+
+        Examples
+        --------
+        Below we give a complete step by step example adding some advanced features
+        and noting some common gotchas.
+
+        First we create the DataFrame and Styler as usual, including MultiIndex rows
+        and columns, which allow for more advanced formatting options:
+
+        >>> cidx = pd.MultiIndex.from_arrays([
+        ...     ["Equity", "Equity", "Equity", "Equity",
+        ...      "Stats", "Stats", "Stats", "Stats", "Rating"],
+        ...     ["Energy", "Energy", "Consumer", "Consumer", "", "", "", "", ""],
+        ...     ["BP", "Shell", "H&M", "Unilever",
+        ...      "Std Dev", "Variance", "52w High", "52w Low", ""]
+        ... ])
+        >>> iidx = pd.MultiIndex.from_arrays([
+        ...     ["Equity", "Equity", "Equity", "Equity"],
+        ...     ["Energy", "Energy", "Consumer", "Consumer"],
+        ...     ["BP", "Shell", "H&M", "Unilever"]
+        ... ])
+        >>> styler = pd.DataFrame([
+        ...     [1, 0.8, 0.66, 0.72, 32.1678, 32.1678**2, 335.12, 240.89, "Buy"],
+        ...     [0.8, 1.0, 0.69, 0.79, 1.876, 1.876**2, 14.12, 19.78, "Hold"],
+        ...     [0.66, 0.69, 1.0, 0.86, 7, 7**2, 210.9, 140.6, "Buy"],
+        ...     [0.72, 0.79, 0.86, 1.0, 213.76, 213.76**2, 2807, 3678, "Sell"],
+        ... ], columns=cidx, index=iidx).style
+
+        Second we will format the display and, since our table is quite wide, will
+        hide the repeated level-0 of the index:
+
+        >>> (styler.format(subset="Equity", precision=2)
+        ...       .format(subset="Stats", precision=1, thousands=",")
+        ...       .format(subset="Rating", formatter=str.upper)
+        ...       .format_index(escape="latex", axis=1)
+        ...       .format_index(escape="latex", axis=0)
+        ...       .hide(level=0, axis=0))  # doctest: +SKIP
+
+        Note that one of the string entries of the index and column headers is "H&M".
+        Without applying the `escape="latex"` option to the `format_index` method the
+        resultant LaTeX will fail to render, and the error returned is quite
+        difficult to debug. Using the appropriate escape the "&" is converted to "\\&".
+
+        Thirdly we will apply some (CSS-HTML) styles to our object. We will use a
+        builtin method and also define our own method to highlight the stock
+        recommendation:
+
+        >>> def rating_color(v):
+        ...     if v == "Buy": color = "#33ff85"
+        ...     elif v == "Sell": color = "#ff5933"
+        ...     else: color = "#ffdd33"
+        ...     return f"color: {color}; font-weight: bold;"
+        >>> (styler.background_gradient(cmap="inferno", subset="Equity", vmin=0, vmax=1)
+        ...       .map(rating_color, subset="Rating"))  # doctest: +SKIP
+
+        All the above styles will work with HTML (see below) and LaTeX upon conversion:
+
+        .. figure:: ../../_static/style/latex_stocks_html.png
+
+        However, we finally want to add one LaTeX only style
+        (from the {graphicx} package), that is not easy to convert from CSS and
+        pandas does not support it. Notice the `--latex` flag used here,
+        as well as `--rwrap` to ensure this is formatted correctly and
+        not ignored upon conversion.
+
+        >>> styler.map_index(
+        ...     lambda v: "rotatebox:{45}--rwrap--latex;", level=2, axis=1
+        ... )  # doctest: +SKIP
+
+        Finally we render our LaTeX adding in other options as required:
+
+        >>> styler.to_latex(
+        ...     caption="Selected stock correlation and simple statistics.",
+        ...     clines="skip-last;data",
+        ...     convert_css=True,
+        ...     position_float="centering",
+        ...     multicol_align="|c|",
+        ...     hrules=True,
+        ... )  # doctest: +SKIP
+        \begin{table}
+        \centering
+        \caption{Selected stock correlation and simple statistics.}
+        \begin{tabular}{llrrrrrrrrl}
+        \toprule
+         &  & \multicolumn{4}{|c|}{Equity} & \multicolumn{4}{|c|}{Stats} & Rating \\
+         &  & \multicolumn{2}{|c|}{Energy} & \multicolumn{2}{|c|}{Consumer} &
+        \multicolumn{4}{|c|}{} &  \\
+         &  & \rotatebox{45}{BP} & \rotatebox{45}{Shell} & \rotatebox{45}{H\&M} &
+        \rotatebox{45}{Unilever} & \rotatebox{45}{Std Dev} & \rotatebox{45}{Variance} &
+        \rotatebox{45}{52w High} & \rotatebox{45}{52w Low} & \rotatebox{45}{} \\
+        \midrule
+        \multirow[c]{2}{*}{Energy} & BP & {\cellcolor[HTML]{FCFFA4}}
+        \color[HTML]{000000} 1.00 & {\cellcolor[HTML]{FCA50A}} \color[HTML]{000000}
+        0.80 & {\cellcolor[HTML]{EB6628}} \color[HTML]{F1F1F1} 0.66 &
+        {\cellcolor[HTML]{F68013}} \color[HTML]{F1F1F1} 0.72 & 32.2 & 1,034.8 & 335.1
+        & 240.9 & \color[HTML]{33FF85} \bfseries BUY \\
+         & Shell & {\cellcolor[HTML]{FCA50A}} \color[HTML]{000000} 0.80 &
+        {\cellcolor[HTML]{FCFFA4}} \color[HTML]{000000} 1.00 &
+        {\cellcolor[HTML]{F1731D}} \color[HTML]{F1F1F1} 0.69 &
+        {\cellcolor[HTML]{FCA108}} \color[HTML]{000000} 0.79 & 1.9 & 3.5 & 14.1 &
+        19.8 & \color[HTML]{FFDD33} \bfseries HOLD \\
+        \cline{1-11}
+        \multirow[c]{2}{*}{Consumer} & H\&M & {\cellcolor[HTML]{EB6628}}
+        \color[HTML]{F1F1F1} 0.66 & {\cellcolor[HTML]{F1731D}} \color[HTML]{F1F1F1}
+        0.69 & {\cellcolor[HTML]{FCFFA4}} \color[HTML]{000000} 1.00 &
+        {\cellcolor[HTML]{FAC42A}} \color[HTML]{000000} 0.86 & 7.0 & 49.0 & 210.9 &
+        140.6 & \color[HTML]{33FF85} \bfseries BUY \\
+         & Unilever & {\cellcolor[HTML]{F68013}} \color[HTML]{F1F1F1} 0.72 &
+        {\cellcolor[HTML]{FCA108}} \color[HTML]{000000} 0.79 &
+        {\cellcolor[HTML]{FAC42A}} \color[HTML]{000000} 0.86 &
+        {\cellcolor[HTML]{FCFFA4}} \color[HTML]{000000} 1.00 & 213.8 & 45,693.3 &
+        2,807.0 & 3,678.0 & \color[HTML]{FF5933} \bfseries SELL \\
+        \cline{1-11}
+        \bottomrule
+        \end{tabular}
+        \end{table}
+
+        .. figure:: ../../_static/style/latex_stocks.png
+        """
+        obj = self._copy(deepcopy=True)  # manipulate table_styles on obj, not self
+
+        table_selectors = (
+            [style["selector"] for style in self.table_styles]
+            if self.table_styles is not None
+            else []
+        )
+
+        if column_format is not None:
+            # add more recent setting to table_styles
+            obj.set_table_styles(
+                [{"selector": "column_format", "props": f":{column_format}"}],
+                overwrite=False,
+            )
+        elif "column_format" in table_selectors:
+            pass  # adopt what has been previously set in table_styles
+        else:
+            # create a default: set float, complex, int cols to 'r' ('S'), index to 'l'
+            _original_columns = self.data.columns
+            self.data.columns = RangeIndex(stop=len(self.data.columns))
+            numeric_cols = self.data._get_numeric_data().columns.to_list()
+            self.data.columns = _original_columns
+            column_format = ""
+            for level in range(self.index.nlevels):
+                column_format += "" if self.hide_index_[level] else "l"
+            for ci, _ in enumerate(self.data.columns):
+                if ci not in self.hidden_columns:
+                    column_format += (
+                        ("r" if not siunitx else "S") if ci in numeric_cols else "l"
+                    )
+            obj.set_table_styles(
+                [{"selector": "column_format", "props": f":{column_format}"}],
+                overwrite=False,
+            )
+
+        if position:
+            obj.set_table_styles(
+                [{"selector": "position", "props": f":{position}"}],
+                overwrite=False,
+            )
+
+        if position_float:
+            if environment == "longtable":
+                raise ValueError(
+                    "`position_float` cannot be used in 'longtable' `environment`"
+                )
+            if position_float not in ["raggedright", "raggedleft", "centering"]:
+                raise ValueError(
+                    f"`position_float` should be one of "
+                    f"'raggedright', 'raggedleft', 'centering', "
+                    f"got: '{position_float}'"
+                )
+            obj.set_table_styles(
+                [{"selector": "position_float", "props": f":{position_float}"}],
+                overwrite=False,
+            )
+
+        hrules = get_option("styler.latex.hrules") if hrules is None else hrules
+        if hrules:
+            obj.set_table_styles(
+                [
+                    {"selector": "toprule", "props": ":toprule"},
+                    {"selector": "midrule", "props": ":midrule"},
+                    {"selector": "bottomrule", "props": ":bottomrule"},
+                ],
+                overwrite=False,
+            )
+
+        if label:
+            obj.set_table_styles(
+                [{"selector": "label", "props": f":{{{label.replace(':', '§')}}}"}],
+                overwrite=False,
+            )
+
+        if caption:
+            obj.set_caption(caption)
+
+        if sparse_index is None:
+            sparse_index = get_option("styler.sparse.index")
+        if sparse_columns is None:
+            sparse_columns = get_option("styler.sparse.columns")
+        environment = environment or get_option("styler.latex.environment")
+        multicol_align = multicol_align or get_option("styler.latex.multicol_align")
+        multirow_align = multirow_align or get_option("styler.latex.multirow_align")
+        latex = obj._render_latex(
+            sparse_index=sparse_index,
+            sparse_columns=sparse_columns,
+            multirow_align=multirow_align,
+            multicol_align=multicol_align,
+            environment=environment,
+            convert_css=convert_css,
+            siunitx=siunitx,
+            clines=clines,
+        )
+
+        encoding = (
+            (encoding or get_option("styler.render.encoding"))
+            if isinstance(buf, str)  # i.e. a filepath
+            else encoding
+        )
+        return save_to_buffer(latex, buf=buf, encoding=encoding)
+
+    @overload
+    def to_html(
+        self,
+        buf: FilePath | WriteBuffer[str],
+        *,
+        table_uuid: str | None = ...,
+        table_attributes: str | None = ...,
+        sparse_index: bool | None = ...,
+        sparse_columns: bool | None = ...,
+        bold_headers: bool = ...,
+        caption: str | None = ...,
+        max_rows: int | None = ...,
+        max_columns: int | None = ...,
+        encoding: str | None = ...,
+        doctype_html: bool = ...,
+        exclude_styles: bool = ...,
+        **kwargs,
+    ) -> None:
+        ...
+
+    @overload
+    def to_html(
+        self,
+        buf: None = ...,
+        *,
+        table_uuid: str | None = ...,
+        table_attributes: str | None = ...,
+        sparse_index: bool | None = ...,
+        sparse_columns: bool | None = ...,
+        bold_headers: bool = ...,
+        caption: str | None = ...,
+        max_rows: int | None = ...,
+        max_columns: int | None = ...,
+        encoding: str | None = ...,
+        doctype_html: bool = ...,
+        exclude_styles: bool = ...,
+        **kwargs,
+    ) -> str:
+        ...
+
+    @Substitution(buf=buffering_args, encoding=encoding_args)
+    def to_html(
+        self,
+        buf: FilePath | WriteBuffer[str] | None = None,
+        *,
+        table_uuid: str | None = None,
+        table_attributes: str | None = None,
+        sparse_index: bool | None = None,
+        sparse_columns: bool | None = None,
+        bold_headers: bool = False,
+        caption: str | None = None,
+        max_rows: int | None = None,
+        max_columns: int | None = None,
+        encoding: str | None = None,
+        doctype_html: bool = False,
+        exclude_styles: bool = False,
+        **kwargs,
+    ) -> str | None:
+        """
+        Write Styler to a file, buffer or string in HTML-CSS format.
+
+        .. versionadded:: 1.3.0
+
+        Parameters
+        ----------
+        %(buf)s
+        table_uuid : str, optional
+            Id attribute assigned to the  HTML element in the format:
+
+            ``
``
+
+            If not given uses Styler's initially assigned value.
+        table_attributes : str, optional
+            Attributes to assign within the `
` HTML element in the format:
+
+            ``
 >``
+
+            If not given defaults to Styler's preexisting value.
+        sparse_index : bool, optional
+            Whether to sparsify the display of a hierarchical index. Setting to False
+            will display each explicit level element in a hierarchical key for each row.
+            Defaults to ``pandas.options.styler.sparse.index`` value.
+
+            .. versionadded:: 1.4.0
+        sparse_columns : bool, optional
+            Whether to sparsify the display of a hierarchical index. Setting to False
+            will display each explicit level element in a hierarchical key for each
+            column. Defaults to ``pandas.options.styler.sparse.columns`` value.
+
+            .. versionadded:: 1.4.0
+        bold_headers : bool, optional
+            Adds "font-weight: bold;" as a CSS property to table style header cells.
+
+            .. versionadded:: 1.4.0
+        caption : str, optional
+            Set, or overwrite, the caption on Styler before rendering.
+
+            .. versionadded:: 1.4.0
+        max_rows : int, optional
+            The maximum number of rows that will be rendered. Defaults to
+            ``pandas.options.styler.render.max_rows/max_columns``.
+
+            .. versionadded:: 1.4.0
+        max_columns : int, optional
+            The maximum number of columns that will be rendered. Defaults to
+            ``pandas.options.styler.render.max_columns``, which is None.
+
+            Rows and columns may be reduced if the number of total elements is
+            large. This value is set to ``pandas.options.styler.render.max_elements``,
+            which is 262144 (18 bit browser rendering).
+
+            .. versionadded:: 1.4.0
+        %(encoding)s
+        doctype_html : bool, default False
+            Whether to output a fully structured HTML file including all
+            HTML elements, or just the core ``
+        

+          
+            
+              
+              
+              
+            
+        ...
+        """
+        obj = self._copy(deepcopy=True)  # manipulate table_styles on obj, not self
+
+        if table_uuid:
+            obj.set_uuid(table_uuid)
+
+        if table_attributes:
+            obj.set_table_attributes(table_attributes)
+
+        if sparse_index is None:
+            sparse_index = get_option("styler.sparse.index")
+        if sparse_columns is None:
+            sparse_columns = get_option("styler.sparse.columns")
+
+        if bold_headers:
+            obj.set_table_styles(
+                [{"selector": "th", "props": "font-weight: bold;"}], overwrite=False
+            )
+
+        if caption is not None:
+            obj.set_caption(caption)
+
+        # Build HTML string..
+        html = obj._render_html(
+            sparse_index=sparse_index,
+            sparse_columns=sparse_columns,
+            max_rows=max_rows,
+            max_cols=max_columns,
+            exclude_styles=exclude_styles,
+            encoding=encoding or get_option("styler.render.encoding"),
+            doctype_html=doctype_html,
+            **kwargs,
+        )
+
+        return save_to_buffer(
+            html, buf=buf, encoding=(encoding if buf is not None else None)
+        )
+
+    @overload
+    def to_string(
+        self,
+        buf: FilePath | WriteBuffer[str],
+        *,
+        encoding: str | None = ...,
+        sparse_index: bool | None = ...,
+        sparse_columns: bool | None = ...,
+        max_rows: int | None = ...,
+        max_columns: int | None = ...,
+        delimiter: str = ...,
+    ) -> None:
+        ...
+
+    @overload
+    def to_string(
+        self,
+        buf: None = ...,
+        *,
+        encoding: str | None = ...,
+        sparse_index: bool | None = ...,
+        sparse_columns: bool | None = ...,
+        max_rows: int | None = ...,
+        max_columns: int | None = ...,
+        delimiter: str = ...,
+    ) -> str:
+        ...
+
+    @Substitution(buf=buffering_args, encoding=encoding_args)
+    def to_string(
+        self,
+        buf: FilePath | WriteBuffer[str] | None = None,
+        *,
+        encoding: str | None = None,
+        sparse_index: bool | None = None,
+        sparse_columns: bool | None = None,
+        max_rows: int | None = None,
+        max_columns: int | None = None,
+        delimiter: str = " ",
+    ) -> str | None:
+        """
+        Write Styler to a file, buffer or string in text format.
+
+        .. versionadded:: 1.5.0
+
+        Parameters
+        ----------
+        %(buf)s
+        %(encoding)s
+        sparse_index : bool, optional
+            Whether to sparsify the display of a hierarchical index. Setting to False
+            will display each explicit level element in a hierarchical key for each row.
+            Defaults to ``pandas.options.styler.sparse.index`` value.
+        sparse_columns : bool, optional
+            Whether to sparsify the display of a hierarchical index. Setting to False
+            will display each explicit level element in a hierarchical key for each
+            column. Defaults to ``pandas.options.styler.sparse.columns`` value.
+        max_rows : int, optional
+            The maximum number of rows that will be rendered. Defaults to
+            ``pandas.options.styler.render.max_rows``, which is None.
+        max_columns : int, optional
+            The maximum number of columns that will be rendered. Defaults to
+            ``pandas.options.styler.render.max_columns``, which is None.
+
+            Rows and columns may be reduced if the number of total elements is
+            large. This value is set to ``pandas.options.styler.render.max_elements``,
+            which is 262144 (18 bit browser rendering).
+        delimiter : str, default single space
+            The separator between data elements.
+
+        Returns
+        -------
+        str or None
+            If `buf` is None, returns the result as a string. Otherwise returns `None`.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
+        >>> df.style.to_string()
+        ' A B\\n0 1 3\\n1 2 4\\n'
+        """
+        obj = self._copy(deepcopy=True)
+
+        if sparse_index is None:
+            sparse_index = get_option("styler.sparse.index")
+        if sparse_columns is None:
+            sparse_columns = get_option("styler.sparse.columns")
+
+        text = obj._render_string(
+            sparse_columns=sparse_columns,
+            sparse_index=sparse_index,
+            max_rows=max_rows,
+            max_cols=max_columns,
+            delimiter=delimiter,
+        )
+        return save_to_buffer(
+            text, buf=buf, encoding=(encoding if buf is not None else None)
+        )
+
+    def set_td_classes(self, classes: DataFrame) -> Styler:
+        """
+        Set the ``class`` attribute of ``
 AB
`` HTML elements.
+
+        Parameters
+        ----------
+        classes : DataFrame
+            DataFrame containing strings that will be translated to CSS classes,
+            mapped by identical column and index key values that must exist on the
+            underlying Styler data. None, NaN values, and empty strings will
+            be ignored and not affect the rendered HTML.
+
+        Returns
+        -------
+        Styler
+
+        See Also
+        --------
+        Styler.set_table_styles: Set the table styles included within the ``'
+        ''
+        '  '
+        '    '
+        '  '
+        '  '
+        '    '
+        '  '
+        '
0
1
'
+        """
+        if not classes.index.is_unique or not classes.columns.is_unique:
+            raise KeyError(
+                "Classes render only if `classes` has unique index and columns."
+            )
+        classes = classes.reindex_like(self.data)
+
+        for r, row_tup in enumerate(classes.itertuples()):
+            for c, value in enumerate(row_tup[1:]):
+                if not (pd.isna(value) or value == ""):
+                    self.cell_context[(r, c)] = str(value)
+
+        return self
+
+    def _update_ctx(self, attrs: DataFrame) -> None:
+        """
+        Update the state of the ``Styler`` for data cells.
+
+        Collects a mapping of {index_label: [('', ''), ..]}.
+
+        Parameters
+        ----------
+        attrs : DataFrame
+            should contain strings of ': ;: '
+            Whitespace shouldn't matter and the final trailing ';' shouldn't
+            matter.
+        """
+        if not self.index.is_unique or not self.columns.is_unique:
+            raise KeyError(
+                "`Styler.apply` and `.map` are not compatible "
+                "with non-unique index or columns."
+            )
+
+        for cn in attrs.columns:
+            j = self.columns.get_loc(cn)
+            ser = attrs[cn]
+            for rn, c in ser.items():
+                if not c or pd.isna(c):
+                    continue
+                css_list = maybe_convert_css_to_tuples(c)
+                i = self.index.get_loc(rn)
+                self.ctx[(i, j)].extend(css_list)
+
+    def _update_ctx_header(self, attrs: DataFrame, axis: AxisInt) -> None:
+        """
+        Update the state of the ``Styler`` for header cells.
+
+        Collects a mapping of {index_label: [('', ''), ..]}.
+
+        Parameters
+        ----------
+        attrs : Series
+            Should contain strings of ': ;: ', and an
+            integer index.
+            Whitespace shouldn't matter and the final trailing ';' shouldn't
+            matter.
+        axis : int
+            Identifies whether the ctx object being updated is the index or columns
+        """
+        for j in attrs.columns:
+            ser = attrs[j]
+            for i, c in ser.items():
+                if not c or pd.isna(c):
+                    continue
+                css_list = maybe_convert_css_to_tuples(c)
+                if axis == 0:
+                    self.ctx_index[(i, j)].extend(css_list)
+                else:
+                    self.ctx_columns[(j, i)].extend(css_list)
+
+    def _copy(self, deepcopy: bool = False) -> Styler:
+        """
+        Copies a Styler, allowing for deepcopy or shallow copy
+
+        Copying a Styler aims to recreate a new Styler object which contains the same
+        data and styles as the original.
+
+        Data dependent attributes [copied and NOT exported]:
+          - formatting (._display_funcs)
+          - hidden index values or column values (.hidden_rows, .hidden_columns)
+          - tooltips
+          - cell_context (cell css classes)
+          - ctx (cell css styles)
+          - caption
+          - concatenated stylers
+
+        Non-data dependent attributes [copied and exported]:
+          - css
+          - hidden index state and hidden columns state (.hide_index_, .hide_columns_)
+          - table_attributes
+          - table_styles
+          - applied styles (_todo)
+
+        """
+        # GH 40675, 52728
+        styler = type(self)(
+            self.data,  # populates attributes 'data', 'columns', 'index' as shallow
+        )
+        shallow = [  # simple string or boolean immutables
+            "hide_index_",
+            "hide_columns_",
+            "hide_column_names",
+            "hide_index_names",
+            "table_attributes",
+            "cell_ids",
+            "caption",
+            "uuid",
+            "uuid_len",
+            "template_latex",  # also copy templates if these have been customised
+            "template_html_style",
+            "template_html_table",
+            "template_html",
+        ]
+        deep = [  # nested lists or dicts
+            "css",
+            "concatenated",
+            "_display_funcs",
+            "_display_funcs_index",
+            "_display_funcs_columns",
+            "hidden_rows",
+            "hidden_columns",
+            "ctx",
+            "ctx_index",
+            "ctx_columns",
+            "cell_context",
+            "_todo",
+            "table_styles",
+            "tooltips",
+        ]
+
+        for attr in shallow:
+            setattr(styler, attr, getattr(self, attr))
+
+        for attr in deep:
+            val = getattr(self, attr)
+            setattr(styler, attr, copy.deepcopy(val) if deepcopy else val)
+
+        return styler
+
+    def __copy__(self) -> Styler:
+        return self._copy(deepcopy=False)
+
+    def __deepcopy__(self, memo) -> Styler:
+        return self._copy(deepcopy=True)
+
+    def clear(self) -> None:
+        """
+        Reset the ``Styler``, removing any previously applied styles.
+
+        Returns None.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'A': [1, 2], 'B': [3, np.nan]})
+
+        After any added style:
+
+        >>> df.style.highlight_null(color='yellow')  # doctest: +SKIP
+
+        Remove it with:
+
+        >>> df.style.clear()  # doctest: +SKIP
+
+        Please see:
+        `Table Visualization <../../user_guide/style.ipynb>`_ for more examples.
+        """
+        # create default GH 40675
+        clean_copy = Styler(self.data, uuid=self.uuid)
+        clean_attrs = [a for a in clean_copy.__dict__ if not callable(a)]
+        self_attrs = [a for a in self.__dict__ if not callable(a)]  # maybe more attrs
+        for attr in clean_attrs:
+            setattr(self, attr, getattr(clean_copy, attr))
+        for attr in set(self_attrs).difference(clean_attrs):
+            delattr(self, attr)
+
+    def _apply(
+        self,
+        func: Callable,
+        axis: Axis | None = 0,
+        subset: Subset | None = None,
+        **kwargs,
+    ) -> Styler:
+        subset = slice(None) if subset is None else subset
+        subset = non_reducing_slice(subset)
+        data = self.data.loc[subset]
+        if data.empty:
+            result = DataFrame()
+        elif axis is None:
+            result = func(data, **kwargs)
+            if not isinstance(result, DataFrame):
+                if not isinstance(result, np.ndarray):
+                    raise TypeError(
+                        f"Function {repr(func)} must return a DataFrame or ndarray "
+                        f"when passed to `Styler.apply` with axis=None"
+                    )
+                if data.shape != result.shape:
+                    raise ValueError(
+                        f"Function {repr(func)} returned ndarray with wrong shape.\n"
+                        f"Result has shape: {result.shape}\n"
+                        f"Expected shape: {data.shape}"
+                    )
+                result = DataFrame(result, index=data.index, columns=data.columns)
+        else:
+            axis = self.data._get_axis_number(axis)
+            if axis == 0:
+                result = data.apply(func, axis=0, **kwargs)
+            else:
+                result = data.T.apply(func, axis=0, **kwargs).T  # see GH 42005
+
+        if isinstance(result, Series):
+            raise ValueError(
+                f"Function {repr(func)} resulted in the apply method collapsing to a "
+                f"Series.\nUsually, this is the result of the function returning a "
+                f"single value, instead of list-like."
+            )
+        msg = (
+            f"Function {repr(func)} created invalid {{0}} labels.\nUsually, this is "
+            f"the result of the function returning a "
+            f"{'Series' if axis is not None else 'DataFrame'} which contains invalid "
+            f"labels, or returning an incorrectly shaped, list-like object which "
+            f"cannot be mapped to labels, possibly due to applying the function along "
+            f"the wrong axis.\n"
+            f"Result {{0}} has shape: {{1}}\n"
+            f"Expected {{0}} shape:   {{2}}"
+        )
+        if not all(result.index.isin(data.index)):
+            raise ValueError(msg.format("index", result.index.shape, data.index.shape))
+        if not all(result.columns.isin(data.columns)):
+            raise ValueError(
+                msg.format("columns", result.columns.shape, data.columns.shape)
+            )
+        self._update_ctx(result)
+        return self
+
+    @Substitution(subset=subset_args)
+    def apply(
+        self,
+        func: Callable,
+        axis: Axis | None = 0,
+        subset: Subset | None = None,
+        **kwargs,
+    ) -> Styler:
+        """
+        Apply a CSS-styling function column-wise, row-wise, or table-wise.
+
+        Updates the HTML representation with the result.
+
+        Parameters
+        ----------
+        func : function
+            ``func`` should take a Series if ``axis`` in [0,1] and return a list-like
+            object of same length, or a Series, not necessarily of same length, with
+            valid index labels considering ``subset``.
+            ``func`` should take a DataFrame if ``axis`` is ``None`` and return either
+            an ndarray with the same shape or a DataFrame, not necessarily of the same
+            shape, with valid index and columns labels considering ``subset``.
+
+            .. versionchanged:: 1.3.0
+
+            .. versionchanged:: 1.4.0
+
+        axis : {0 or 'index', 1 or 'columns', None}, default 0
+            Apply to each column (``axis=0`` or ``'index'``), to each row
+            (``axis=1`` or ``'columns'``), or to the entire DataFrame at once
+            with ``axis=None``.
+        %(subset)s
+        **kwargs : dict
+            Pass along to ``func``.
+
+        Returns
+        -------
+        Styler
+
+        See Also
+        --------
+        Styler.map_index: Apply a CSS-styling function to headers elementwise.
+        Styler.apply_index: Apply a CSS-styling function to headers level-wise.
+        Styler.map: Apply a CSS-styling function elementwise.
+
+        Notes
+        -----
+        The elements of the output of ``func`` should be CSS styles as strings, in the
+        format 'attribute: value; attribute2: value2; ...' or,
+        if nothing is to be applied to that element, an empty string or ``None``.
+
+        This is similar to ``DataFrame.apply``, except that ``axis=None``
+        applies the function to the entire DataFrame at once,
+        rather than column-wise or row-wise.
+
+        Examples
+        --------
+        >>> def highlight_max(x, color):
+        ...     return np.where(x == np.nanmax(x.to_numpy()), f"color: {color};", None)
+        >>> df = pd.DataFrame(np.random.randn(5, 2), columns=["A", "B"])
+        >>> df.style.apply(highlight_max, color='red')  # doctest: +SKIP
+        >>> df.style.apply(highlight_max, color='blue', axis=1)  # doctest: +SKIP
+        >>> df.style.apply(highlight_max, color='green', axis=None)  # doctest: +SKIP
+
+        Using ``subset`` to restrict application to a single column or multiple columns
+
+        >>> df.style.apply(highlight_max, color='red', subset="A")
+        ... # doctest: +SKIP
+        >>> df.style.apply(highlight_max, color='red', subset=["A", "B"])
+        ... # doctest: +SKIP
+
+        Using a 2d input to ``subset`` to select rows in addition to columns
+
+        >>> df.style.apply(highlight_max, color='red', subset=([0, 1, 2], slice(None)))
+        ... # doctest: +SKIP
+        >>> df.style.apply(highlight_max, color='red', subset=(slice(0, 5, 2), "A"))
+        ... # doctest: +SKIP
+
+        Using a function which returns a Series / DataFrame of unequal length but
+        containing valid index labels
+
+        >>> df = pd.DataFrame([[1, 2], [3, 4], [4, 6]], index=["A1", "A2", "Total"])
+        >>> total_style = pd.Series("font-weight: bold;", index=["Total"])
+        >>> df.style.apply(lambda s: total_style)  # doctest: +SKIP
+
+        See `Table Visualization <../../user_guide/style.ipynb>`_ user guide for
+        more details.
+        """
+        self._todo.append(
+            (lambda instance: getattr(instance, "_apply"), (func, axis, subset), kwargs)
+        )
+        return self
+
+    def _apply_index(
+        self,
+        func: Callable,
+        axis: Axis = 0,
+        level: Level | list[Level] | None = None,
+        method: str = "apply",
+        **kwargs,
+    ) -> Styler:
+        axis = self.data._get_axis_number(axis)
+        obj = self.index if axis == 0 else self.columns
+
+        levels_ = refactor_levels(level, obj)
+        data = DataFrame(obj.to_list()).loc[:, levels_]
+
+        if method == "apply":
+            result = data.apply(func, axis=0, **kwargs)
+        elif method == "map":
+            result = data.map(func, **kwargs)
+
+        self._update_ctx_header(result, axis)
+        return self
+
+    @doc(
+        this="apply",
+        wise="level-wise",
+        alt="map",
+        altwise="elementwise",
+        func="take a Series and return a string array of the same length",
+        input_note="the index as a Series, if an Index, or a level of a MultiIndex",
+        output_note="an identically sized array of CSS styles as strings",
+        var="s",
+        ret='np.where(s == "B", "background-color: yellow;", "")',
+        ret2='["background-color: yellow;" if "x" in v else "" for v in s]',
+    )
+    def apply_index(
+        self,
+        func: Callable,
+        axis: AxisInt | str = 0,
+        level: Level | list[Level] | None = None,
+        **kwargs,
+    ) -> Styler:
+        """
+        Apply a CSS-styling function to the index or column headers, {wise}.
+
+        Updates the HTML representation with the result.
+
+        .. versionadded:: 1.4.0
+
+        .. versionadded:: 2.1.0
+           Styler.applymap_index was deprecated and renamed to Styler.map_index.
+
+        Parameters
+        ----------
+        func : function
+            ``func`` should {func}.
+        axis : {{0, 1, "index", "columns"}}
+            The headers over which to apply the function.
+        level : int, str, list, optional
+            If index is MultiIndex the level(s) over which to apply the function.
+        **kwargs : dict
+            Pass along to ``func``.
+
+        Returns
+        -------
+        Styler
+
+        See Also
+        --------
+        Styler.{alt}_index: Apply a CSS-styling function to headers {altwise}.
+        Styler.apply: Apply a CSS-styling function column-wise, row-wise, or table-wise.
+        Styler.map: Apply a CSS-styling function elementwise.
+
+        Notes
+        -----
+        Each input to ``func`` will be {input_note}. The output of ``func`` should be
+        {output_note}, in the format 'attribute: value; attribute2: value2; ...'
+        or, if nothing is to be applied to that element, an empty string or ``None``.
+
+        Examples
+        --------
+        Basic usage to conditionally highlight values in the index.
+
+        >>> df = pd.DataFrame([[1,2], [3,4]], index=["A", "B"])
+        >>> def color_b(s):
+        ...     return {ret}
+        >>> df.style.{this}_index(color_b)  # doctest: +SKIP
+
+        .. figure:: ../../_static/style/appmaphead1.png
+
+        Selectively applying to specific levels of MultiIndex columns.
+
+        >>> midx = pd.MultiIndex.from_product([['ix', 'jy'], [0, 1], ['x3', 'z4']])
+        >>> df = pd.DataFrame([np.arange(8)], columns=midx)
+        >>> def highlight_x({var}):
+        ...     return {ret2}
+        >>> df.style.{this}_index(highlight_x, axis="columns", level=[0, 2])
+        ...  # doctest: +SKIP
+
+        .. figure:: ../../_static/style/appmaphead2.png
+        """
+        self._todo.append(
+            (
+                lambda instance: getattr(instance, "_apply_index"),
+                (func, axis, level, "apply"),
+                kwargs,
+            )
+        )
+        return self
+
+    @doc(
+        apply_index,
+        this="map",
+        wise="elementwise",
+        alt="apply",
+        altwise="level-wise",
+        func="take a scalar and return a string",
+        input_note="an index value, if an Index, or a level value of a MultiIndex",
+        output_note="CSS styles as a string",
+        var="v",
+        ret='"background-color: yellow;" if v == "B" else None',
+        ret2='"background-color: yellow;" if "x" in v else None',
+    )
+    def map_index(
+        self,
+        func: Callable,
+        axis: AxisInt | str = 0,
+        level: Level | list[Level] | None = None,
+        **kwargs,
+    ) -> Styler:
+        self._todo.append(
+            (
+                lambda instance: getattr(instance, "_apply_index"),
+                (func, axis, level, "map"),
+                kwargs,
+            )
+        )
+        return self
+
+    def applymap_index(
+        self,
+        func: Callable,
+        axis: AxisInt | str = 0,
+        level: Level | list[Level] | None = None,
+        **kwargs,
+    ) -> Styler:
+        """
+        Apply a CSS-styling function to the index or column headers, elementwise.
+
+        .. deprecated:: 2.1.0
+
+           Styler.applymap_index has been deprecated. Use Styler.map_index instead.
+
+        Parameters
+        ----------
+        func : function
+            ``func`` should take a scalar and return a string.
+        axis : {{0, 1, "index", "columns"}}
+            The headers over which to apply the function.
+        level : int, str, list, optional
+            If index is MultiIndex the level(s) over which to apply the function.
+        **kwargs : dict
+            Pass along to ``func``.
+
+        Returns
+        -------
+        Styler
+        """
+        warnings.warn(
+            "Styler.applymap_index has been deprecated. Use Styler.map_index instead.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        return self.map_index(func, axis, level, **kwargs)
+
+    def _map(self, func: Callable, subset: Subset | None = None, **kwargs) -> Styler:
+        func = partial(func, **kwargs)  # map doesn't take kwargs?
+        if subset is None:
+            subset = IndexSlice[:]
+        subset = non_reducing_slice(subset)
+        result = self.data.loc[subset].map(func)
+        self._update_ctx(result)
+        return self
+
+    @Substitution(subset=subset_args)
+    def map(self, func: Callable, subset: Subset | None = None, **kwargs) -> Styler:
+        """
+        Apply a CSS-styling function elementwise.
+
+        Updates the HTML representation with the result.
+
+        Parameters
+        ----------
+        func : function
+            ``func`` should take a scalar and return a string.
+        %(subset)s
+        **kwargs : dict
+            Pass along to ``func``.
+
+        Returns
+        -------
+        Styler
+
+        See Also
+        --------
+        Styler.map_index: Apply a CSS-styling function to headers elementwise.
+        Styler.apply_index: Apply a CSS-styling function to headers level-wise.
+        Styler.apply: Apply a CSS-styling function column-wise, row-wise, or table-wise.
+
+        Notes
+        -----
+        The elements of the output of ``func`` should be CSS styles as strings, in the
+        format 'attribute: value; attribute2: value2; ...' or,
+        if nothing is to be applied to that element, an empty string or ``None``.
+
+        Examples
+        --------
+        >>> def color_negative(v, color):
+        ...     return f"color: {color};" if v < 0 else None
+        >>> df = pd.DataFrame(np.random.randn(5, 2), columns=["A", "B"])
+        >>> df.style.map(color_negative, color='red')  # doctest: +SKIP
+
+        Using ``subset`` to restrict application to a single column or multiple columns
+
+        >>> df.style.map(color_negative, color='red', subset="A")
+        ...  # doctest: +SKIP
+        >>> df.style.map(color_negative, color='red', subset=["A", "B"])
+        ...  # doctest: +SKIP
+
+        Using a 2d input to ``subset`` to select rows in addition to columns
+
+        >>> df.style.map(color_negative, color='red',
+        ...  subset=([0,1,2], slice(None)))  # doctest: +SKIP
+        >>> df.style.map(color_negative, color='red', subset=(slice(0,5,2), "A"))
+        ...  # doctest: +SKIP
+
+        See `Table Visualization <../../user_guide/style.ipynb>`_ user guide for
+        more details.
+        """
+        self._todo.append(
+            (lambda instance: getattr(instance, "_map"), (func, subset), kwargs)
+        )
+        return self
+
+    @Substitution(subset=subset_args)
+    def applymap(
+        self, func: Callable, subset: Subset | None = None, **kwargs
+    ) -> Styler:
+        """
+        Apply a CSS-styling function elementwise.
+
+        .. deprecated:: 2.1.0
+
+           Styler.applymap has been deprecated. Use Styler.map instead.
+
+        Parameters
+        ----------
+        func : function
+            ``func`` should take a scalar and return a string.
+        %(subset)s
+        **kwargs : dict
+            Pass along to ``func``.
+
+        Returns
+        -------
+        Styler
+        """
+        warnings.warn(
+            "Styler.applymap has been deprecated. Use Styler.map instead.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        return self.map(func, subset, **kwargs)
+
+    def set_table_attributes(self, attributes: str) -> Styler:
+        """
+        Set the table attributes added to the ```` HTML element.
+
+        These are items in addition to automatic (by default) ``id`` attribute.
+
+        Parameters
+        ----------
+        attributes : str
+
+        Returns
+        -------
+        Styler
+
+        See Also
+        --------
+        Styler.set_table_styles: Set the table styles included within the `` block
+
+        Parameters
+        ----------
+        sparsify_index : bool
+            Whether index_headers section will add rowspan attributes (>1) to elements.
+
+        Returns
+        -------
+        body : list
+            The associated HTML elements needed for template rendering.
+        """
+        rlabels = self.data.index.tolist()
+        if not isinstance(self.data.index, MultiIndex):
+            rlabels = [[x] for x in rlabels]
+
+        body: list = []
+        visible_row_count: int = 0
+        for r, row_tup in [
+            z for z in enumerate(self.data.itertuples()) if z[0] not in self.hidden_rows
+        ]:
+            visible_row_count += 1
+            if self._check_trim(
+                visible_row_count,
+                max_rows,
+                body,
+                "row",
+            ):
+                break
+
+            body_row = self._generate_body_row(
+                (r, row_tup, rlabels), max_cols, idx_lengths
+            )
+            body.append(body_row)
+        return body
+
+    def _check_trim(
+        self,
+        count: int,
+        max: int,
+        obj: list,
+        element: str,
+        css: str | None = None,
+        value: str = "...",
+    ) -> bool:
+        """
+        Indicates whether to break render loops and append a trimming indicator
+
+        Parameters
+        ----------
+        count : int
+            The loop count of previous visible items.
+        max : int
+            The allowable rendered items in the loop.
+        obj : list
+            The current render collection of the rendered items.
+        element : str
+            The type of element to append in the case a trimming indicator is needed.
+        css : str, optional
+            The css to add to the trimming indicator element.
+        value : str, optional
+            The value of the elements display if necessary.
+
+        Returns
+        -------
+        result : bool
+            Whether a trimming element was required and appended.
+        """
+        if count > max:
+            if element == "row":
+                obj.append(self._generate_trimmed_row(max))
+            else:
+                obj.append(_element(element, css, value, True, attributes=""))
+            return True
+        return False
+
+    def _generate_trimmed_row(self, max_cols: int) -> list:
+        """
+        When a render has too many rows we generate a trimming row containing "..."
+
+        Parameters
+        ----------
+        max_cols : int
+            Number of permissible columns
+
+        Returns
+        -------
+        list of elements
+        """
+        index_headers = [
+            _element(
+                "th",
+                (
+                    f"{self.css['row_heading']} {self.css['level']}{c} "
+                    f"{self.css['row_trim']}"
+                ),
+                "...",
+                not self.hide_index_[c],
+                attributes="",
+            )
+            for c in range(self.data.index.nlevels)
+        ]
+
+        data: list = []
+        visible_col_count: int = 0
+        for c, _ in enumerate(self.columns):
+            data_element_visible = c not in self.hidden_columns
+            if data_element_visible:
+                visible_col_count += 1
+            if self._check_trim(
+                visible_col_count,
+                max_cols,
+                data,
+                "td",
+                f"{self.css['data']} {self.css['row_trim']} {self.css['col_trim']}",
+            ):
+                break
+
+            data.append(
+                _element(
+                    "td",
+                    f"{self.css['data']} {self.css['col']}{c} {self.css['row_trim']}",
+                    "...",
+                    data_element_visible,
+                    attributes="",
+                )
+            )
+
+        return index_headers + data
+
+    def _generate_body_row(
+        self,
+        iter: tuple,
+        max_cols: int,
+        idx_lengths: dict,
+    ):
+        """
+        Generate a regular row for the body section of appropriate format.
+
+          +--------------------------------------------+---------------------------+
+          |  index_header_0    ...    index_header_n   |  data_by_column   ...     |
+          +--------------------------------------------+---------------------------+
+
+        Parameters
+        ----------
+        iter : tuple
+            Iterable from outer scope: row number, row data tuple, row index labels.
+        max_cols : int
+            Number of permissible columns.
+        idx_lengths : dict
+            A map of the sparsification structure of the index
+
+        Returns
+        -------
+            list of elements
+        """
+        r, row_tup, rlabels = iter
+
+        index_headers = []
+        for c, value in enumerate(rlabels[r]):
+            header_element_visible = (
+                _is_visible(r, c, idx_lengths) and not self.hide_index_[c]
+            )
+            header_element = _element(
+                "th",
+                (
+                    f"{self.css['row_heading']} {self.css['level']}{c} "
+                    f"{self.css['row']}{r}"
+                ),
+                value,
+                header_element_visible,
+                display_value=self._display_funcs_index[(r, c)](value),
+                attributes=(
+                    f'rowspan="{idx_lengths.get((c, r), 0)}"'
+                    if idx_lengths.get((c, r), 0) > 1
+                    else ""
+                ),
+            )
+
+            if self.cell_ids:
+                header_element[
+                    "id"
+                ] = f"{self.css['level']}{c}_{self.css['row']}{r}"  # id is given
+            if (
+                header_element_visible
+                and (r, c) in self.ctx_index
+                and self.ctx_index[r, c]
+            ):
+                # always add id if a style is specified
+                header_element["id"] = f"{self.css['level']}{c}_{self.css['row']}{r}"
+                self.cellstyle_map_index[tuple(self.ctx_index[r, c])].append(
+                    f"{self.css['level']}{c}_{self.css['row']}{r}"
+                )
+
+            index_headers.append(header_element)
+
+        data: list = []
+        visible_col_count: int = 0
+        for c, value in enumerate(row_tup[1:]):
+            data_element_visible = (
+                c not in self.hidden_columns and r not in self.hidden_rows
+            )
+            if data_element_visible:
+                visible_col_count += 1
+            if self._check_trim(
+                visible_col_count,
+                max_cols,
+                data,
+                "td",
+                f"{self.css['data']} {self.css['row']}{r} {self.css['col_trim']}",
+            ):
+                break
+
+            # add custom classes from cell context
+            cls = ""
+            if (r, c) in self.cell_context:
+                cls = " " + self.cell_context[r, c]
+
+            data_element = _element(
+                "td",
+                (
+                    f"{self.css['data']} {self.css['row']}{r} "
+                    f"{self.css['col']}{c}{cls}"
+                ),
+                value,
+                data_element_visible,
+                attributes="",
+                display_value=self._display_funcs[(r, c)](value),
+            )
+
+            if self.cell_ids:
+                data_element["id"] = f"{self.css['row']}{r}_{self.css['col']}{c}"
+            if data_element_visible and (r, c) in self.ctx and self.ctx[r, c]:
+                # always add id if needed due to specified style
+                data_element["id"] = f"{self.css['row']}{r}_{self.css['col']}{c}"
+                self.cellstyle_map[tuple(self.ctx[r, c])].append(
+                    f"{self.css['row']}{r}_{self.css['col']}{c}"
+                )
+
+            data.append(data_element)
+
+        return index_headers + data
+
+    def _translate_latex(self, d: dict, clines: str | None) -> None:
+        r"""
+        Post-process the default render dict for the LaTeX template format.
+
+        Processing items included are:
+          - Remove hidden columns from the non-headers part of the body.
+          - Place cellstyles directly in td cells rather than use cellstyle_map.
+          - Remove hidden indexes or reinsert missing th elements if part of multiindex
+            or multirow sparsification (so that \multirow and \multicol work correctly).
+        """
+        index_levels = self.index.nlevels
+        visible_index_level_n = index_levels - sum(self.hide_index_)
+        d["head"] = [
+            [
+                {**col, "cellstyle": self.ctx_columns[r, c - visible_index_level_n]}
+                for c, col in enumerate(row)
+                if col["is_visible"]
+            ]
+            for r, row in enumerate(d["head"])
+        ]
+
+        def _concatenated_visible_rows(obj, n, row_indices):
+            """
+            Extract all visible row indices recursively from concatenated stylers.
+            """
+            row_indices.extend(
+                [r + n for r in range(len(obj.index)) if r not in obj.hidden_rows]
+            )
+            n += len(obj.index)
+            for concatenated in obj.concatenated:
+                n = _concatenated_visible_rows(concatenated, n, row_indices)
+            return n
+
+        def concatenated_visible_rows(obj):
+            row_indices: list[int] = []
+            _concatenated_visible_rows(obj, 0, row_indices)
+            # TODO try to consolidate the concat visible rows
+            # methods to a single function / recursion for simplicity
+            return row_indices
+
+        body = []
+        for r, row in zip(concatenated_visible_rows(self), d["body"]):
+            # note: cannot enumerate d["body"] because rows were dropped if hidden
+            # during _translate_body so must zip to acquire the true r-index associated
+            # with the ctx obj which contains the cell styles.
+            if all(self.hide_index_):
+                row_body_headers = []
+            else:
+                row_body_headers = [
+                    {
+                        **col,
+                        "display_value": col["display_value"]
+                        if col["is_visible"]
+                        else "",
+                        "cellstyle": self.ctx_index[r, c],
+                    }
+                    for c, col in enumerate(row[:index_levels])
+                    if (col["type"] == "th" and not self.hide_index_[c])
+                ]
+
+            row_body_cells = [
+                {**col, "cellstyle": self.ctx[r, c]}
+                for c, col in enumerate(row[index_levels:])
+                if (col["is_visible"] and col["type"] == "td")
+            ]
+
+            body.append(row_body_headers + row_body_cells)
+        d["body"] = body
+
+        # clines are determined from info on index_lengths and hidden_rows and input
+        # to a dict defining which row clines should be added in the template.
+        if clines not in [
+            None,
+            "all;data",
+            "all;index",
+            "skip-last;data",
+            "skip-last;index",
+        ]:
+            raise ValueError(
+                f"`clines` value of {clines} is invalid. Should either be None or one "
+                f"of 'all;data', 'all;index', 'skip-last;data', 'skip-last;index'."
+            )
+        if clines is not None:
+            data_len = len(row_body_cells) if "data" in clines and d["body"] else 0
+
+            d["clines"] = defaultdict(list)
+            visible_row_indexes: list[int] = [
+                r for r in range(len(self.data.index)) if r not in self.hidden_rows
+            ]
+            visible_index_levels: list[int] = [
+                i for i in range(index_levels) if not self.hide_index_[i]
+            ]
+            for rn, r in enumerate(visible_row_indexes):
+                for lvln, lvl in enumerate(visible_index_levels):
+                    if lvl == index_levels - 1 and "skip-last" in clines:
+                        continue
+                    idx_len = d["index_lengths"].get((lvl, r), None)
+                    if idx_len is not None:  # i.e. not a sparsified entry
+                        d["clines"][rn + idx_len].append(
+                            f"\\cline{{{lvln+1}-{len(visible_index_levels)+data_len}}}"
+                        )
+
+    def format(
+        self,
+        formatter: ExtFormatter | None = None,
+        subset: Subset | None = None,
+        na_rep: str | None = None,
+        precision: int | None = None,
+        decimal: str = ".",
+        thousands: str | None = None,
+        escape: str | None = None,
+        hyperlinks: str | None = None,
+    ) -> StylerRenderer:
+        r"""
+        Format the text display value of cells.
+
+        Parameters
+        ----------
+        formatter : str, callable, dict or None
+            Object to define how values are displayed. See notes.
+        subset : label, array-like, IndexSlice, optional
+            A valid 2d input to `DataFrame.loc[]`, or, in the case of a 1d input
+            or single key, to `DataFrame.loc[:, ]` where the columns are
+            prioritised, to limit ``data`` to *before* applying the function.
+        na_rep : str, optional
+            Representation for missing values.
+            If ``na_rep`` is None, no special formatting is applied.
+        precision : int, optional
+            Floating point precision to use for display purposes, if not determined by
+            the specified ``formatter``.
+
+            .. versionadded:: 1.3.0
+
+        decimal : str, default "."
+            Character used as decimal separator for floats, complex and integers.
+
+            .. versionadded:: 1.3.0
+
+        thousands : str, optional, default None
+            Character used as thousands separator for floats, complex and integers.
+
+            .. versionadded:: 1.3.0
+
+        escape : str, optional
+            Use 'html' to replace the characters ``&``, ``<``, ``>``, ``'``, and ``"``
+            in cell display string with HTML-safe sequences.
+            Use 'latex' to replace the characters ``&``, ``%``, ``$``, ``#``, ``_``,
+            ``{``, ``}``, ``~``, ``^``, and ``\`` in the cell display string with
+            LaTeX-safe sequences.
+            Use 'latex-math' to replace the characters the same way as in 'latex' mode,
+            except for math substrings, which either are surrounded
+            by two characters ``$`` or start with the character ``\(`` and
+            end with ``\)``. Escaping is done before ``formatter``.
+
+            .. versionadded:: 1.3.0
+
+        hyperlinks : {"html", "latex"}, optional
+            Convert string patterns containing https://, http://, ftp:// or www. to
+            HTML  tags as clickable URL hyperlinks if "html", or LaTeX \href
+            commands if "latex".
+
+            .. versionadded:: 1.4.0
+
+        Returns
+        -------
+        Styler
+
+        See Also
+        --------
+        Styler.format_index: Format the text display value of index labels.
+
+        Notes
+        -----
+        This method assigns a formatting function, ``formatter``, to each cell in the
+        DataFrame. If ``formatter`` is ``None``, then the default formatter is used.
+        If a callable then that function should take a data value as input and return
+        a displayable representation, such as a string. If ``formatter`` is
+        given as a string this is assumed to be a valid Python format specification
+        and is wrapped to a callable as ``string.format(x)``. If a ``dict`` is given,
+        keys should correspond to column names, and values should be string or
+        callable, as above.
+
+        The default formatter currently expresses floats and complex numbers with the
+        pandas display precision unless using the ``precision`` argument here. The
+        default formatter does not adjust the representation of missing values unless
+        the ``na_rep`` argument is used.
+
+        The ``subset`` argument defines which region to apply the formatting function
+        to. If the ``formatter`` argument is given in dict form but does not include
+        all columns within the subset then these columns will have the default formatter
+        applied. Any columns in the formatter dict excluded from the subset will
+        be ignored.
+
+        When using a ``formatter`` string the dtypes must be compatible, otherwise a
+        `ValueError` will be raised.
+
+        When instantiating a Styler, default formatting can be applied be setting the
+        ``pandas.options``:
+
+          - ``styler.format.formatter``: default None.
+          - ``styler.format.na_rep``: default None.
+          - ``styler.format.precision``: default 6.
+          - ``styler.format.decimal``: default ".".
+          - ``styler.format.thousands``: default None.
+          - ``styler.format.escape``: default None.
+
+        .. warning::
+           `Styler.format` is ignored when using the output format `Styler.to_excel`,
+           since Excel and Python have inherrently different formatting structures.
+           However, it is possible to use the `number-format` pseudo CSS attribute
+           to force Excel permissible formatting. See examples.
+
+        Examples
+        --------
+        Using ``na_rep`` and ``precision`` with the default ``formatter``
+
+        >>> df = pd.DataFrame([[np.nan, 1.0, 'A'], [2.0, np.nan, 3.0]])
+        >>> df.style.format(na_rep='MISS', precision=3)  # doctest: +SKIP
+                0       1       2
+        0    MISS   1.000       A
+        1   2.000    MISS   3.000
+
+        Using a ``formatter`` specification on consistent column dtypes
+
+        >>> df.style.format('{:.2f}', na_rep='MISS', subset=[0,1])  # doctest: +SKIP
+                0      1          2
+        0    MISS   1.00          A
+        1    2.00   MISS   3.000000
+
+        Using the default ``formatter`` for unspecified columns
+
+        >>> df.style.format({0: '{:.2f}', 1: '£ {:.1f}'}, na_rep='MISS', precision=1)
+        ...  # doctest: +SKIP
+                 0      1     2
+        0    MISS   £ 1.0     A
+        1    2.00    MISS   3.0
+
+        Multiple ``na_rep`` or ``precision`` specifications under the default
+        ``formatter``.
+
+        >>> (df.style.format(na_rep='MISS', precision=1, subset=[0])
+        ...     .format(na_rep='PASS', precision=2, subset=[1, 2]))  # doctest: +SKIP
+                0      1      2
+        0    MISS   1.00      A
+        1     2.0   PASS   3.00
+
+        Using a callable ``formatter`` function.
+
+        >>> func = lambda s: 'STRING' if isinstance(s, str) else 'FLOAT'
+        >>> df.style.format({0: '{:.1f}', 2: func}, precision=4, na_rep='MISS')
+        ...  # doctest: +SKIP
+                0        1        2
+        0    MISS   1.0000   STRING
+        1     2.0     MISS    FLOAT
+
+        Using a ``formatter`` with HTML ``escape`` and ``na_rep``.
+
+        >>> df = pd.DataFrame([['
', '"A&B"', None]])
+        >>> s = df.style.format(
+        ...     '{0}', escape="html", na_rep="NA"
+        ...     )
+        >>> s.to_html()  # doctest: +SKIP
+        ...
+        
+        
+        
+        ...
+
+        Using a ``formatter`` with ``escape`` in 'latex' mode.
+
+        >>> df = pd.DataFrame([["123"], ["~ ^"], ["$%#"]])
+        >>> df.style.format("\\textbf{{{}}}", escape="latex").to_latex()
+        ...  # doctest: +SKIP
+        \begin{tabular}{ll}
+         & 0 \\
+        0 & \textbf{123} \\
+        1 & \textbf{\textasciitilde \space \textasciicircum } \\
+        2 & \textbf{\$\%\#} \\
+        \end{tabular}
+
+        Applying ``escape`` in 'latex-math' mode. In the example below
+        we enter math mode using the character ``$``.
+
+        >>> df = pd.DataFrame([[r"$\sum_{i=1}^{10} a_i$ a~b $\alpha \
+        ...     = \frac{\beta}{\zeta^2}$"], ["%#^ $ \$x^2 $"]])
+        >>> df.style.format(escape="latex-math").to_latex()
+        ...  # doctest: +SKIP
+        \begin{tabular}{ll}
+         & 0 \\
+        0 & $\sum_{i=1}^{10} a_i$ a\textasciitilde b $\alpha = \frac{\beta}{\zeta^2}$ \\
+        1 & \%\#\textasciicircum \space $ \$x^2 $ \\
+        \end{tabular}
+
+        We can use the character ``\(`` to enter math mode and the character ``\)``
+        to close math mode.
+
+        >>> df = pd.DataFrame([[r"\(\sum_{i=1}^{10} a_i\) a~b \(\alpha \
+        ...     = \frac{\beta}{\zeta^2}\)"], ["%#^ \( \$x^2 \)"]])
+        >>> df.style.format(escape="latex-math").to_latex()
+        ...  # doctest: +SKIP
+        \begin{tabular}{ll}
+         & 0 \\
+        0 & \(\sum_{i=1}^{10} a_i\) a\textasciitilde b \(\alpha
+        = \frac{\beta}{\zeta^2}\) \\
+        1 & \%\#\textasciicircum \space \( \$x^2 \) \\
+        \end{tabular}
+
+        If we have in one DataFrame cell a combination of both shorthands
+        for math formulas, the shorthand with the sign ``$`` will be applied.
+
+        >>> df = pd.DataFrame([[r"\( x^2 \)  $x^2$"], \
+        ...     [r"$\frac{\beta}{\zeta}$ \(\frac{\beta}{\zeta}\)"]])
+        >>> df.style.format(escape="latex-math").to_latex()
+        ...  # doctest: +SKIP
+        \begin{tabular}{ll}
+         & 0 \\
+        0 & \textbackslash ( x\textasciicircum 2 \textbackslash )  $x^2$ \\
+        1 & $\frac{\beta}{\zeta}$ \textbackslash (\textbackslash
+        frac\{\textbackslash beta\}\{\textbackslash zeta\}\textbackslash ) \\
+        \end{tabular}
+
+        Pandas defines a `number-format` pseudo CSS attribute instead of the `.format`
+        method to create `to_excel` permissible formatting. Note that semi-colons are
+        CSS protected characters but used as separators in Excel's format string.
+        Replace semi-colons with the section separator character (ASCII-245) when
+        defining the formatting here.
+
+        >>> df = pd.DataFrame({"A": [1, 0, -1]})
+        >>> pseudo_css = "number-format: 0§[Red](0)§-§@;"
+        >>> filename = "formatted_file.xlsx"
+        >>> df.style.map(lambda v: pseudo_css).to_excel(filename) # doctest: +SKIP
+
+        .. figure:: ../../_static/style/format_excel_css.png
+        """
+        if all(
+            (
+                formatter is None,
+                subset is None,
+                precision is None,
+                decimal == ".",
+                thousands is None,
+                na_rep is None,
+                escape is None,
+                hyperlinks is None,
+            )
+        ):
+            self._display_funcs.clear()
+            return self  # clear the formatter / revert to default and avoid looping
+
+        subset = slice(None) if subset is None else subset
+        subset = non_reducing_slice(subset)
+        data = self.data.loc[subset]
+
+        if not isinstance(formatter, dict):
+            formatter = {col: formatter for col in data.columns}
+
+        cis = self.columns.get_indexer_for(data.columns)
+        ris = self.index.get_indexer_for(data.index)
+        for ci in cis:
+            format_func = _maybe_wrap_formatter(
+                formatter.get(self.columns[ci]),
+                na_rep=na_rep,
+                precision=precision,
+                decimal=decimal,
+                thousands=thousands,
+                escape=escape,
+                hyperlinks=hyperlinks,
+            )
+            for ri in ris:
+                self._display_funcs[(ri, ci)] = format_func
+
+        return self
+
+    def format_index(
+        self,
+        formatter: ExtFormatter | None = None,
+        axis: Axis = 0,
+        level: Level | list[Level] | None = None,
+        na_rep: str | None = None,
+        precision: int | None = None,
+        decimal: str = ".",
+        thousands: str | None = None,
+        escape: str | None = None,
+        hyperlinks: str | None = None,
+    ) -> StylerRenderer:
+        r"""
+        Format the text display value of index labels or column headers.
+
+        .. versionadded:: 1.4.0
+
+        Parameters
+        ----------
+        formatter : str, callable, dict or None
+            Object to define how values are displayed. See notes.
+        axis : {0, "index", 1, "columns"}
+            Whether to apply the formatter to the index or column headers.
+        level : int, str, list
+            The level(s) over which to apply the generic formatter.
+        na_rep : str, optional
+            Representation for missing values.
+            If ``na_rep`` is None, no special formatting is applied.
+        precision : int, optional
+            Floating point precision to use for display purposes, if not determined by
+            the specified ``formatter``.
+        decimal : str, default "."
+            Character used as decimal separator for floats, complex and integers.
+        thousands : str, optional, default None
+            Character used as thousands separator for floats, complex and integers.
+        escape : str, optional
+            Use 'html' to replace the characters ``&``, ``<``, ``>``, ``'``, and ``"``
+            in cell display string with HTML-safe sequences.
+            Use 'latex' to replace the characters ``&``, ``%``, ``$``, ``#``, ``_``,
+            ``{``, ``}``, ``~``, ``^``, and ``\`` in the cell display string with
+            LaTeX-safe sequences.
+            Escaping is done before ``formatter``.
+        hyperlinks : {"html", "latex"}, optional
+            Convert string patterns containing https://, http://, ftp:// or www. to
+            HTML  tags as clickable URL hyperlinks if "html", or LaTeX \href
+            commands if "latex".
+
+        Returns
+        -------
+        Styler
+
+        See Also
+        --------
+        Styler.format: Format the text display value of data cells.
+
+        Notes
+        -----
+        This method assigns a formatting function, ``formatter``, to each level label
+        in the DataFrame's index or column headers. If ``formatter`` is ``None``,
+        then the default formatter is used.
+        If a callable then that function should take a label value as input and return
+        a displayable representation, such as a string. If ``formatter`` is
+        given as a string this is assumed to be a valid Python format specification
+        and is wrapped to a callable as ``string.format(x)``. If a ``dict`` is given,
+        keys should correspond to MultiIndex level numbers or names, and values should
+        be string or callable, as above.
+
+        The default formatter currently expresses floats and complex numbers with the
+        pandas display precision unless using the ``precision`` argument here. The
+        default formatter does not adjust the representation of missing values unless
+        the ``na_rep`` argument is used.
+
+        The ``level`` argument defines which levels of a MultiIndex to apply the
+        method to. If the ``formatter`` argument is given in dict form but does
+        not include all levels within the level argument then these unspecified levels
+        will have the default formatter applied. Any levels in the formatter dict
+        specifically excluded from the level argument will be ignored.
+
+        When using a ``formatter`` string the dtypes must be compatible, otherwise a
+        `ValueError` will be raised.
+
+        .. warning::
+           `Styler.format_index` is ignored when using the output format
+           `Styler.to_excel`, since Excel and Python have inherrently different
+           formatting structures.
+           However, it is possible to use the `number-format` pseudo CSS attribute
+           to force Excel permissible formatting. See documentation for `Styler.format`.
+
+        Examples
+        --------
+        Using ``na_rep`` and ``precision`` with the default ``formatter``
+
+        >>> df = pd.DataFrame([[1, 2, 3]], columns=[2.0, np.nan, 4.0])
+        >>> df.style.format_index(axis=1, na_rep='MISS', precision=3)  # doctest: +SKIP
+            2.000    MISS   4.000
+        0       1       2       3
+
+        Using a ``formatter`` specification on consistent dtypes in a level
+
+        >>> df.style.format_index('{:.2f}', axis=1, na_rep='MISS')  # doctest: +SKIP
+             2.00   MISS    4.00
+        0       1      2       3
+
+        Using the default ``formatter`` for unspecified levels
+
+        >>> df = pd.DataFrame([[1, 2, 3]],
+        ...     columns=pd.MultiIndex.from_arrays([["a", "a", "b"],[2, np.nan, 4]]))
+        >>> df.style.format_index({0: lambda v: v.upper()}, axis=1, precision=1)
+        ...  # doctest: +SKIP
+                       A       B
+              2.0    nan     4.0
+        0       1      2       3
+
+        Using a callable ``formatter`` function.
+
+        >>> func = lambda s: 'STRING' if isinstance(s, str) else 'FLOAT'
+        >>> df.style.format_index(func, axis=1, na_rep='MISS')
+        ...  # doctest: +SKIP
+                  STRING  STRING
+            FLOAT   MISS   FLOAT
+        0       1      2       3
+
+        Using a ``formatter`` with HTML ``escape`` and ``na_rep``.
+
+        >>> df = pd.DataFrame([[1, 2, 3]], columns=['"A"', 'A&B', None])
+        >>> s = df.style.format_index('$ {0}', axis=1, escape="html", na_rep="NA")
+        ...  # doctest: +SKIP
+        
+        
+         or  element.
+    """
+    if "display_value" not in kwargs:
+        kwargs["display_value"] = value
+    return {
+        "type": html_element,
+        "value": value,
+        "class": html_class,
+        "is_visible": is_visible,
+        **kwargs,
+    }
+
+
+def _get_trimming_maximums(
+    rn,
+    cn,
+    max_elements,
+    max_rows=None,
+    max_cols=None,
+    scaling_factor: float = 0.8,
+) -> tuple[int, int]:
+    """
+    Recursively reduce the number of rows and columns to satisfy max elements.
+
+    Parameters
+    ----------
+    rn, cn : int
+        The number of input rows / columns
+    max_elements : int
+        The number of allowable elements
+    max_rows, max_cols : int, optional
+        Directly specify an initial maximum rows or columns before compression.
+    scaling_factor : float
+        Factor at which to reduce the number of rows / columns to fit.
+
+    Returns
+    -------
+    rn, cn : tuple
+        New rn and cn values that satisfy the max_elements constraint
+    """
+
+    def scale_down(rn, cn):
+        if cn >= rn:
+            return rn, int(cn * scaling_factor)
+        else:
+            return int(rn * scaling_factor), cn
+
+    if max_rows:
+        rn = max_rows if rn > max_rows else rn
+    if max_cols:
+        cn = max_cols if cn > max_cols else cn
+
+    while rn * cn > max_elements:
+        rn, cn = scale_down(rn, cn)
+
+    return rn, cn
+
+
+def _get_level_lengths(
+    index: Index,
+    sparsify: bool,
+    max_index: int,
+    hidden_elements: Sequence[int] | None = None,
+):
+    """
+    Given an index, find the level length for each element.
+
+    Parameters
+    ----------
+    index : Index
+        Index or columns to determine lengths of each element
+    sparsify : bool
+        Whether to hide or show each distinct element in a MultiIndex
+    max_index : int
+        The maximum number of elements to analyse along the index due to trimming
+    hidden_elements : sequence of int
+        Index positions of elements hidden from display in the index affecting
+        length
+
+    Returns
+    -------
+    Dict :
+        Result is a dictionary of (level, initial_position): span
+    """
+    if isinstance(index, MultiIndex):
+        levels = index._format_multi(sparsify=lib.no_default, include_names=False)
+    else:
+        levels = index._format_flat(include_name=False)
+
+    if hidden_elements is None:
+        hidden_elements = []
+
+    lengths = {}
+    if not isinstance(index, MultiIndex):
+        for i, value in enumerate(levels):
+            if i not in hidden_elements:
+                lengths[(0, i)] = 1
+        return lengths
+
+    for i, lvl in enumerate(levels):
+        visible_row_count = 0  # used to break loop due to display trimming
+        for j, row in enumerate(lvl):
+            if visible_row_count > max_index:
+                break
+            if not sparsify:
+                # then lengths will always equal 1 since no aggregation.
+                if j not in hidden_elements:
+                    lengths[(i, j)] = 1
+                    visible_row_count += 1
+            elif (row is not lib.no_default) and (j not in hidden_elements):
+                # this element has not been sparsified so must be the start of section
+                last_label = j
+                lengths[(i, last_label)] = 1
+                visible_row_count += 1
+            elif row is not lib.no_default:
+                # even if the above is hidden, keep track of it in case length > 1 and
+                # later elements are visible
+                last_label = j
+                lengths[(i, last_label)] = 0
+            elif j not in hidden_elements:
+                # then element must be part of sparsified section and is visible
+                visible_row_count += 1
+                if visible_row_count > max_index:
+                    break  # do not add a length since the render trim limit reached
+                if lengths[(i, last_label)] == 0:
+                    # if previous iteration was first-of-section but hidden then offset
+                    last_label = j
+                    lengths[(i, last_label)] = 1
+                else:
+                    # else add to previous iteration
+                    lengths[(i, last_label)] += 1
+
+    non_zero_lengths = {
+        element: length for element, length in lengths.items() if length >= 1
+    }
+
+    return non_zero_lengths
+
+
+def _is_visible(idx_row, idx_col, lengths) -> bool:
+    """
+    Index -> {(idx_row, idx_col): bool}).
+    """
+    return (idx_col, idx_row) in lengths
+
+
+def format_table_styles(styles: CSSStyles) -> CSSStyles:
+    """
+    looks for multiple CSS selectors and separates them:
+    [{'selector': 'td, th', 'props': 'a:v;'}]
+        ---> [{'selector': 'td', 'props': 'a:v;'},
+              {'selector': 'th', 'props': 'a:v;'}]
+    """
+    return [
+        {"selector": selector, "props": css_dict["props"]}
+        for css_dict in styles
+        for selector in css_dict["selector"].split(",")
+    ]
+
+
+def _default_formatter(x: Any, precision: int, thousands: bool = False) -> Any:
+    """
+    Format the display of a value
+
+    Parameters
+    ----------
+    x : Any
+        Input variable to be formatted
+    precision : Int
+        Floating point precision used if ``x`` is float or complex.
+    thousands : bool, default False
+        Whether to group digits with thousands separated with ",".
+
+    Returns
+    -------
+    value : Any
+        Matches input type, or string if input is float or complex or int with sep.
+    """
+    if is_float(x) or is_complex(x):
+        return f"{x:,.{precision}f}" if thousands else f"{x:.{precision}f}"
+    elif is_integer(x):
+        return f"{x:,}" if thousands else str(x)
+    return x
+
+
+def _wrap_decimal_thousands(
+    formatter: Callable, decimal: str, thousands: str | None
+) -> Callable:
+    """
+    Takes a string formatting function and wraps logic to deal with thousands and
+    decimal parameters, in the case that they are non-standard and that the input
+    is a (float, complex, int).
+    """
+
+    def wrapper(x):
+        if is_float(x) or is_integer(x) or is_complex(x):
+            if decimal != "." and thousands is not None and thousands != ",":
+                return (
+                    formatter(x)
+                    .replace(",", "§_§-")  # rare string to avoid "," <-> "." clash.
+                    .replace(".", decimal)
+                    .replace("§_§-", thousands)
+                )
+            elif decimal != "." and (thousands is None or thousands == ","):
+                return formatter(x).replace(".", decimal)
+            elif decimal == "." and thousands is not None and thousands != ",":
+                return formatter(x).replace(",", thousands)
+        return formatter(x)
+
+    return wrapper
+
+
+def _str_escape(x, escape):
+    """if escaping: only use on str, else return input"""
+    if isinstance(x, str):
+        if escape == "html":
+            return escape_html(x)
+        elif escape == "latex":
+            return _escape_latex(x)
+        elif escape == "latex-math":
+            return _escape_latex_math(x)
+        else:
+            raise ValueError(
+                f"`escape` only permitted in {{'html', 'latex', 'latex-math'}}, \
+got {escape}"
+            )
+    return x
+
+
+def _render_href(x, format):
+    """uses regex to detect a common URL pattern and converts to href tag in format."""
+    if isinstance(x, str):
+        if format == "html":
+            href = '{0}'
+        elif format == "latex":
+            href = r"\href{{{0}}}{{{0}}}"
+        else:
+            raise ValueError("``hyperlinks`` format can only be 'html' or 'latex'")
+        pat = r"((http|ftp)s?:\/\/|www.)[\w/\-?=%.:@]+\.[\w/\-&?=%.,':;~!@#$*()\[\]]+"
+        return re.sub(pat, lambda m: href.format(m.group(0)), x)
+    return x
+
+
+def _maybe_wrap_formatter(
+    formatter: BaseFormatter | None = None,
+    na_rep: str | None = None,
+    precision: int | None = None,
+    decimal: str = ".",
+    thousands: str | None = None,
+    escape: str | None = None,
+    hyperlinks: str | None = None,
+) -> Callable:
+    """
+    Allows formatters to be expressed as str, callable or None, where None returns
+    a default formatting function. wraps with na_rep, and precision where they are
+    available.
+    """
+    # Get initial func from input string, input callable, or from default factory
+    if isinstance(formatter, str):
+        func_0 = lambda x: formatter.format(x)
+    elif callable(formatter):
+        func_0 = formatter
+    elif formatter is None:
+        precision = (
+            get_option("styler.format.precision") if precision is None else precision
+        )
+        func_0 = partial(
+            _default_formatter, precision=precision, thousands=(thousands is not None)
+        )
+    else:
+        raise TypeError(f"'formatter' expected str or callable, got {type(formatter)}")
+
+    # Replace chars if escaping
+    if escape is not None:
+        func_1 = lambda x: func_0(_str_escape(x, escape=escape))
+    else:
+        func_1 = func_0
+
+    # Replace decimals and thousands if non-standard inputs detected
+    if decimal != "." or (thousands is not None and thousands != ","):
+        func_2 = _wrap_decimal_thousands(func_1, decimal=decimal, thousands=thousands)
+    else:
+        func_2 = func_1
+
+    # Render links
+    if hyperlinks is not None:
+        func_3 = lambda x: func_2(_render_href(x, format=hyperlinks))
+    else:
+        func_3 = func_2
+
+    # Replace missing values if na_rep
+    if na_rep is None:
+        return func_3
+    else:
+        return lambda x: na_rep if (isna(x) is True) else func_3(x)
+
+
+def non_reducing_slice(slice_: Subset):
+    """
+    Ensure that a slice doesn't reduce to a Series or Scalar.
+
+    Any user-passed `subset` should have this called on it
+    to make sure we're always working with DataFrames.
+    """
+    # default to column slice, like DataFrame
+    # ['A', 'B'] -> IndexSlices[:, ['A', 'B']]
+    kinds = (ABCSeries, np.ndarray, Index, list, str)
+    if isinstance(slice_, kinds):
+        slice_ = IndexSlice[:, slice_]
+
+    def pred(part) -> bool:
+        """
+        Returns
+        -------
+        bool
+            True if slice does *not* reduce,
+            False if `part` is a tuple.
+        """
+        # true when slice does *not* reduce, False when part is a tuple,
+        # i.e. MultiIndex slice
+        if isinstance(part, tuple):
+            # GH#39421 check for sub-slice:
+            return any((isinstance(s, slice) or is_list_like(s)) for s in part)
+        else:
+            return isinstance(part, slice) or is_list_like(part)
+
+    if not is_list_like(slice_):
+        if not isinstance(slice_, slice):
+            # a 1-d slice, like df.loc[1]
+            slice_ = [[slice_]]
+        else:
+            # slice(a, b, c)
+            slice_ = [slice_]  # to tuplize later
+    else:
+        # error: Item "slice" of "Union[slice, Sequence[Any]]" has no attribute
+        # "__iter__" (not iterable) -> is specifically list_like in conditional
+        slice_ = [p if pred(p) else [p] for p in slice_]  # type: ignore[union-attr]
+    return tuple(slice_)
+
+
+def maybe_convert_css_to_tuples(style: CSSProperties) -> CSSList:
+    """
+    Convert css-string to sequence of tuples format if needed.
+    'color:red; border:1px solid black;' -> [('color', 'red'),
+                                             ('border','1px solid red')]
+    """
+    if isinstance(style, str):
+        s = style.split(";")
+        try:
+            return [
+                (x.split(":")[0].strip(), x.split(":")[1].strip())
+                for x in s
+                if x.strip() != ""
+            ]
+        except IndexError:
+            raise ValueError(
+                "Styles supplied as string must follow CSS rule formats, "
+                f"for example 'attr: val;'. '{style}' was given."
+            )
+    return style
+
+
+def refactor_levels(
+    level: Level | list[Level] | None,
+    obj: Index,
+) -> list[int]:
+    """
+    Returns a consistent levels arg for use in ``hide_index`` or ``hide_columns``.
+
+    Parameters
+    ----------
+    level : int, str, list
+        Original ``level`` arg supplied to above methods.
+    obj:
+        Either ``self.index`` or ``self.columns``
+
+    Returns
+    -------
+    list : refactored arg with a list of levels to hide
+    """
+    if level is None:
+        levels_: list[int] = list(range(obj.nlevels))
+    elif isinstance(level, int):
+        levels_ = [level]
+    elif isinstance(level, str):
+        levels_ = [obj._get_level_number(level)]
+    elif isinstance(level, list):
+        levels_ = [
+            obj._get_level_number(lev) if not isinstance(lev, int) else lev
+            for lev in level
+        ]
+    else:
+        raise ValueError("`level` must be of type `int`, `str` or list of such")
+    return levels_
+
+
+class Tooltips:
+    """
+    An extension to ``Styler`` that allows for and manipulates tooltips on hover
+    of ``
<div></div>"A&B"NA$ "A"$ A&BNA
+        ...
+
+        Using a ``formatter`` with LaTeX ``escape``.
+
+        >>> df = pd.DataFrame([[1, 2, 3]], columns=["123", "~", "$%#"])
+        >>> df.style.format_index("\\textbf{{{}}}", escape="latex", axis=1).to_latex()
+        ...  # doctest: +SKIP
+        \begin{tabular}{lrrr}
+        {} & {\textbf{123}} & {\textbf{\textasciitilde }} & {\textbf{\$\%\#}} \\
+        0 & 1 & 2 & 3 \\
+        \end{tabular}
+        """
+        axis = self.data._get_axis_number(axis)
+        if axis == 0:
+            display_funcs_, obj = self._display_funcs_index, self.index
+        else:
+            display_funcs_, obj = self._display_funcs_columns, self.columns
+        levels_ = refactor_levels(level, obj)
+
+        if all(
+            (
+                formatter is None,
+                level is None,
+                precision is None,
+                decimal == ".",
+                thousands is None,
+                na_rep is None,
+                escape is None,
+                hyperlinks is None,
+            )
+        ):
+            display_funcs_.clear()
+            return self  # clear the formatter / revert to default and avoid looping
+
+        if not isinstance(formatter, dict):
+            formatter = {level: formatter for level in levels_}
+        else:
+            formatter = {
+                obj._get_level_number(level): formatter_
+                for level, formatter_ in formatter.items()
+            }
+
+        for lvl in levels_:
+            format_func = _maybe_wrap_formatter(
+                formatter.get(lvl),
+                na_rep=na_rep,
+                precision=precision,
+                decimal=decimal,
+                thousands=thousands,
+                escape=escape,
+                hyperlinks=hyperlinks,
+            )
+
+            for idx in [(i, lvl) if axis == 0 else (lvl, i) for i in range(len(obj))]:
+                display_funcs_[idx] = format_func
+
+        return self
+
+    def relabel_index(
+        self,
+        labels: Sequence | Index,
+        axis: Axis = 0,
+        level: Level | list[Level] | None = None,
+    ) -> StylerRenderer:
+        r"""
+        Relabel the index, or column header, keys to display a set of specified values.
+
+        .. versionadded:: 1.5.0
+
+        Parameters
+        ----------
+        labels : list-like or Index
+            New labels to display. Must have same length as the underlying values not
+            hidden.
+        axis : {"index", 0, "columns", 1}
+            Apply to the index or columns.
+        level : int, str, list, optional
+            The level(s) over which to apply the new labels. If `None` will apply
+            to all levels of an Index or MultiIndex which are not hidden.
+
+        Returns
+        -------
+        Styler
+
+        See Also
+        --------
+        Styler.format_index: Format the text display value of index or column headers.
+        Styler.hide: Hide the index, column headers, or specified data from display.
+
+        Notes
+        -----
+        As part of Styler, this method allows the display of an index to be
+        completely user-specified without affecting the underlying DataFrame data,
+        index, or column headers. This means that the flexibility of indexing is
+        maintained whilst the final display is customisable.
+
+        Since Styler is designed to be progressively constructed with method chaining,
+        this method is adapted to react to the **currently specified hidden elements**.
+        This is useful because it means one does not have to specify all the new
+        labels if the majority of an index, or column headers, have already been hidden.
+        The following produce equivalent display (note the length of ``labels`` in
+        each case).
+
+        .. code-block:: python
+
+            # relabel first, then hide
+            df = pd.DataFrame({"col": ["a", "b", "c"]})
+            df.style.relabel_index(["A", "B", "C"]).hide([0,1])
+            # hide first, then relabel
+            df = pd.DataFrame({"col": ["a", "b", "c"]})
+            df.style.hide([0,1]).relabel_index(["C"])
+
+        This method should be used, rather than :meth:`Styler.format_index`, in one of
+        the following cases (see examples):
+
+          - A specified set of labels are required which are not a function of the
+            underlying index keys.
+          - The function of the underlying index keys requires a counter variable,
+            such as those available upon enumeration.
+
+        Examples
+        --------
+        Basic use
+
+        >>> df = pd.DataFrame({"col": ["a", "b", "c"]})
+        >>> df.style.relabel_index(["A", "B", "C"])  # doctest: +SKIP
+             col
+        A      a
+        B      b
+        C      c
+
+        Chaining with pre-hidden elements
+
+        >>> df.style.hide([0,1]).relabel_index(["C"])  # doctest: +SKIP
+             col
+        C      c
+
+        Using a MultiIndex
+
+        >>> midx = pd.MultiIndex.from_product([[0, 1], [0, 1], [0, 1]])
+        >>> df = pd.DataFrame({"col": list(range(8))}, index=midx)
+        >>> styler = df.style  # doctest: +SKIP
+                  col
+        0  0  0     0
+              1     1
+           1  0     2
+              1     3
+        1  0  0     4
+              1     5
+           1  0     6
+              1     7
+        >>> styler.hide((midx.get_level_values(0)==0)|(midx.get_level_values(1)==0))
+        ...  # doctest: +SKIP
+        >>> styler.hide(level=[0,1])  # doctest: +SKIP
+        >>> styler.relabel_index(["binary6", "binary7"])  # doctest: +SKIP
+                  col
+        binary6     6
+        binary7     7
+
+        We can also achieve the above by indexing first and then re-labeling
+
+        >>> styler = df.loc[[(1,1,0), (1,1,1)]].style
+        >>> styler.hide(level=[0,1]).relabel_index(["binary6", "binary7"])
+        ...  # doctest: +SKIP
+                  col
+        binary6     6
+        binary7     7
+
+        Defining a formatting function which uses an enumeration counter. Also note
+        that the value of the index key is passed in the case of string labels so it
+        can also be inserted into the label, using curly brackets (or double curly
+        brackets if the string if pre-formatted),
+
+        >>> df = pd.DataFrame({"samples": np.random.rand(10)})
+        >>> styler = df.loc[np.random.randint(0,10,3)].style
+        >>> styler.relabel_index([f"sample{i+1} ({{}})" for i in range(3)])
+        ...  # doctest: +SKIP
+                         samples
+        sample1 (5)     0.315811
+        sample2 (0)     0.495941
+        sample3 (2)     0.067946
+        """
+        axis = self.data._get_axis_number(axis)
+        if axis == 0:
+            display_funcs_, obj = self._display_funcs_index, self.index
+            hidden_labels, hidden_lvls = self.hidden_rows, self.hide_index_
+        else:
+            display_funcs_, obj = self._display_funcs_columns, self.columns
+            hidden_labels, hidden_lvls = self.hidden_columns, self.hide_columns_
+        visible_len = len(obj) - len(set(hidden_labels))
+        if len(labels) != visible_len:
+            raise ValueError(
+                "``labels`` must be of length equal to the number of "
+                f"visible labels along ``axis`` ({visible_len})."
+            )
+
+        if level is None:
+            level = [i for i in range(obj.nlevels) if not hidden_lvls[i]]
+        levels_ = refactor_levels(level, obj)
+
+        def alias_(x, value):
+            if isinstance(value, str):
+                return value.format(x)
+            return value
+
+        for ai, i in enumerate([i for i in range(len(obj)) if i not in hidden_labels]):
+            if len(levels_) == 1:
+                idx = (i, levels_[0]) if axis == 0 else (levels_[0], i)
+                display_funcs_[idx] = partial(alias_, value=labels[ai])
+            else:
+                for aj, lvl in enumerate(levels_):
+                    idx = (i, lvl) if axis == 0 else (lvl, i)
+                    display_funcs_[idx] = partial(alias_, value=labels[ai][aj])
+
+        return self
+
+
+def _element(
+    html_element: str,
+    html_class: str | None,
+    value: Any,
+    is_visible: bool,
+    **kwargs,
+) -> dict:
+    """
+    Template to return container with information for a `` cells in the HTML result.
+
+    Parameters
+    ----------
+    css_name: str, default "pd-t"
+        Name of the CSS class that controls visualisation of tooltips.
+    css_props: list-like, default; see Notes
+        List of (attr, value) tuples defining properties of the CSS class.
+    tooltips: DataFrame, default empty
+        DataFrame of strings aligned with underlying Styler data for tooltip
+        display.
+
+    Notes
+    -----
+    The default properties for the tooltip CSS class are:
+
+        - visibility: hidden
+        - position: absolute
+        - z-index: 1
+        - background-color: black
+        - color: white
+        - transform: translate(-20px, -20px)
+
+    Hidden visibility is a key prerequisite to the hover functionality, and should
+    always be included in any manual properties specification.
+    """
+
+    def __init__(
+        self,
+        css_props: CSSProperties = [
+            ("visibility", "hidden"),
+            ("position", "absolute"),
+            ("z-index", 1),
+            ("background-color", "black"),
+            ("color", "white"),
+            ("transform", "translate(-20px, -20px)"),
+        ],
+        css_name: str = "pd-t",
+        tooltips: DataFrame = DataFrame(),
+    ) -> None:
+        self.class_name = css_name
+        self.class_properties = css_props
+        self.tt_data = tooltips
+        self.table_styles: CSSStyles = []
+
+    @property
+    def _class_styles(self):
+        """
+        Combine the ``_Tooltips`` CSS class name and CSS properties to the format
+        required to extend the underlying ``Styler`` `table_styles` to allow
+        tooltips to render in HTML.
+
+        Returns
+        -------
+        styles : List
+        """
+        return [
+            {
+                "selector": f".{self.class_name}",
+                "props": maybe_convert_css_to_tuples(self.class_properties),
+            }
+        ]
+
+    def _pseudo_css(self, uuid: str, name: str, row: int, col: int, text: str):
+        """
+        For every table data-cell that has a valid tooltip (not None, NaN or
+        empty string) must create two pseudo CSS entries for the specific
+         element id which are added to overall table styles:
+        an on hover visibility change and a content change
+        dependent upon the user's chosen display string.
+
+        For example:
+            [{"selector": "T__row1_col1:hover .pd-t",
+             "props": [("visibility", "visible")]},
+            {"selector": "T__row1_col1 .pd-t::after",
+             "props": [("content", "Some Valid Text String")]}]
+
+        Parameters
+        ----------
+        uuid: str
+            The uuid of the Styler instance
+        name: str
+            The css-name of the class used for styling tooltips
+        row : int
+            The row index of the specified tooltip string data
+        col : int
+            The col index of the specified tooltip string data
+        text : str
+            The textual content of the tooltip to be displayed in HTML.
+
+        Returns
+        -------
+        pseudo_css : List
+        """
+        selector_id = "#T_" + uuid + "_row" + str(row) + "_col" + str(col)
+        return [
+            {
+                "selector": selector_id + f":hover .{name}",
+                "props": [("visibility", "visible")],
+            },
+            {
+                "selector": selector_id + f" .{name}::after",
+                "props": [("content", f'"{text}"')],
+            },
+        ]
+
+    def _translate(self, styler: StylerRenderer, d: dict):
+        """
+        Mutate the render dictionary to allow for tooltips:
+
+        - Add ```` HTML element to each data cells ``display_value``. Ignores
+          headers.
+        - Add table level CSS styles to control pseudo classes.
+
+        Parameters
+        ----------
+        styler_data : DataFrame
+            Underlying ``Styler`` DataFrame used for reindexing.
+        uuid : str
+            The underlying ``Styler`` uuid for CSS id.
+        d : dict
+            The dictionary prior to final render
+
+        Returns
+        -------
+        render_dict : Dict
+        """
+        self.tt_data = self.tt_data.reindex_like(styler.data)
+        if self.tt_data.empty:
+            return d
+
+        name = self.class_name
+        mask = (self.tt_data.isna()) | (self.tt_data.eq(""))  # empty string = no ttip
+        self.table_styles = [
+            style
+            for sublist in [
+                self._pseudo_css(styler.uuid, name, i, j, str(self.tt_data.iloc[i, j]))
+                for i in range(len(self.tt_data.index))
+                for j in range(len(self.tt_data.columns))
+                if not (
+                    mask.iloc[i, j]
+                    or i in styler.hidden_rows
+                    or j in styler.hidden_columns
+                )
+            ]
+            for style in sublist
+        ]
+
+        if self.table_styles:
+            # add span class to every cell only if at least 1 non-empty tooltip
+            for row in d["body"]:
+                for item in row:
+                    if item["type"] == "td":
+                        item["display_value"] = (
+                            str(item["display_value"])
+                            + f''
+                        )
+            d["table_styles"].extend(self._class_styles)
+            d["table_styles"].extend(self.table_styles)
+
+        return d
+
+
+def _parse_latex_table_wrapping(table_styles: CSSStyles, caption: str | None) -> bool:
+    """
+    Indicate whether LaTeX {tabular} should be wrapped with a {table} environment.
+
+    Parses the `table_styles` and detects any selectors which must be included outside
+    of {tabular}, i.e. indicating that wrapping must occur, and therefore return True,
+    or if a caption exists and requires similar.
+    """
+    IGNORED_WRAPPERS = ["toprule", "midrule", "bottomrule", "column_format"]
+    # ignored selectors are included with {tabular} so do not need wrapping
+    return (
+        table_styles is not None
+        and any(d["selector"] not in IGNORED_WRAPPERS for d in table_styles)
+    ) or caption is not None
+
+
+def _parse_latex_table_styles(table_styles: CSSStyles, selector: str) -> str | None:
+    """
+    Return the first 'props' 'value' from ``tables_styles`` identified by ``selector``.
+
+    Examples
+    --------
+    >>> table_styles = [{'selector': 'foo', 'props': [('attr','value')]},
+    ...                 {'selector': 'bar', 'props': [('attr', 'overwritten')]},
+    ...                 {'selector': 'bar', 'props': [('a1', 'baz'), ('a2', 'ignore')]}]
+    >>> _parse_latex_table_styles(table_styles, selector='bar')
+    'baz'
+
+    Notes
+    -----
+    The replacement of "§" with ":" is to avoid the CSS problem where ":" has structural
+    significance and cannot be used in LaTeX labels, but is often required by them.
+    """
+    for style in table_styles[::-1]:  # in reverse for most recently applied style
+        if style["selector"] == selector:
+            return str(style["props"][0][1]).replace("§", ":")
+    return None
+
+
+def _parse_latex_cell_styles(
+    latex_styles: CSSList, display_value: str, convert_css: bool = False
+) -> str:
+    r"""
+    Mutate the ``display_value`` string including LaTeX commands from ``latex_styles``.
+
+    This method builds a recursive latex chain of commands based on the
+    CSSList input, nested around ``display_value``.
+
+    If a CSS style is given as ('', '') this is translated to
+    '\{display_value}', and this value is treated as the
+    display value for the next iteration.
+
+    The most recent style forms the inner component, for example for styles:
+    `[('c1', 'o1'), ('c2', 'o2')]` this returns: `\c1o1{\c2o2{display_value}}`
+
+    Sometimes latex commands have to be wrapped with curly braces in different ways:
+    We create some parsing flags to identify the different behaviours:
+
+     - `--rwrap`        : `\{}`
+     - `--wrap`         : `{\ }`
+     - `--nowrap`       : `\ `
+     - `--lwrap`        : `{\} `
+     - `--dwrap`        : `{\}{}`
+
+    For example for styles:
+    `[('c1', 'o1--wrap'), ('c2', 'o2')]` this returns: `{\c1o1 \c2o2{display_value}}
+    """
+    if convert_css:
+        latex_styles = _parse_latex_css_conversion(latex_styles)
+    for command, options in latex_styles[::-1]:  # in reverse for most recent style
+        formatter = {
+            "--wrap": f"{{\\{command}--to_parse {display_value}}}",
+            "--nowrap": f"\\{command}--to_parse {display_value}",
+            "--lwrap": f"{{\\{command}--to_parse}} {display_value}",
+            "--rwrap": f"\\{command}--to_parse{{{display_value}}}",
+            "--dwrap": f"{{\\{command}--to_parse}}{{{display_value}}}",
+        }
+        display_value = f"\\{command}{options} {display_value}"
+        for arg in ["--nowrap", "--wrap", "--lwrap", "--rwrap", "--dwrap"]:
+            if arg in str(options):
+                display_value = formatter[arg].replace(
+                    "--to_parse", _parse_latex_options_strip(value=options, arg=arg)
+                )
+                break  # only ever one purposeful entry
+    return display_value
+
+
+def _parse_latex_header_span(
+    cell: dict[str, Any],
+    multirow_align: str,
+    multicol_align: str,
+    wrap: bool = False,
+    convert_css: bool = False,
+) -> str:
+    r"""
+    Refactor the cell `display_value` if a 'colspan' or 'rowspan' attribute is present.
+
+    'rowspan' and 'colspan' do not occur simultaneouly. If they are detected then
+    the `display_value` is altered to a LaTeX `multirow` or `multicol` command
+    respectively, with the appropriate cell-span.
+
+    ``wrap`` is used to enclose the `display_value` in braces which is needed for
+    column headers using an siunitx package.
+
+    Requires the package {multirow}, whereas multicol support is usually built in
+    to the {tabular} environment.
+
+    Examples
+    --------
+    >>> cell = {'cellstyle': '', 'display_value':'text', 'attributes': 'colspan="3"'}
+    >>> _parse_latex_header_span(cell, 't', 'c')
+    '\\multicolumn{3}{c}{text}'
+    """
+    display_val = _parse_latex_cell_styles(
+        cell["cellstyle"], cell["display_value"], convert_css
+    )
+    if "attributes" in cell:
+        attrs = cell["attributes"]
+        if 'colspan="' in attrs:
+            colspan = attrs[attrs.find('colspan="') + 9 :]  # len('colspan="') = 9
+            colspan = int(colspan[: colspan.find('"')])
+            if "naive-l" == multicol_align:
+                out = f"{{{display_val}}}" if wrap else f"{display_val}"
+                blanks = " & {}" if wrap else " &"
+                return out + blanks * (colspan - 1)
+            elif "naive-r" == multicol_align:
+                out = f"{{{display_val}}}" if wrap else f"{display_val}"
+                blanks = "{} & " if wrap else "& "
+                return blanks * (colspan - 1) + out
+            return f"\\multicolumn{{{colspan}}}{{{multicol_align}}}{{{display_val}}}"
+        elif 'rowspan="' in attrs:
+            if multirow_align == "naive":
+                return display_val
+            rowspan = attrs[attrs.find('rowspan="') + 9 :]
+            rowspan = int(rowspan[: rowspan.find('"')])
+            return f"\\multirow[{multirow_align}]{{{rowspan}}}{{*}}{{{display_val}}}"
+    if wrap:
+        return f"{{{display_val}}}"
+    else:
+        return display_val
+
+
+def _parse_latex_options_strip(value: str | float, arg: str) -> str:
+    """
+    Strip a css_value which may have latex wrapping arguments, css comment identifiers,
+    and whitespaces, to a valid string for latex options parsing.
+
+    For example: 'red /* --wrap */  ' --> 'red'
+    """
+    return str(value).replace(arg, "").replace("/*", "").replace("*/", "").strip()
+
+
+def _parse_latex_css_conversion(styles: CSSList) -> CSSList:
+    """
+    Convert CSS (attribute,value) pairs to equivalent LaTeX (command,options) pairs.
+
+    Ignore conversion if tagged with `--latex` option, skipped if no conversion found.
+    """
+
+    def font_weight(value, arg):
+        if value in ("bold", "bolder"):
+            return "bfseries", f"{arg}"
+        return None
+
+    def font_style(value, arg):
+        if value == "italic":
+            return "itshape", f"{arg}"
+        if value == "oblique":
+            return "slshape", f"{arg}"
+        return None
+
+    def color(value, user_arg, command, comm_arg):
+        """
+        CSS colors have 5 formats to process:
+
+         - 6 digit hex code: "#ff23ee"     --> [HTML]{FF23EE}
+         - 3 digit hex code: "#f0e"        --> [HTML]{FF00EE}
+         - rgba: rgba(128, 255, 0, 0.5)    --> [rgb]{0.502, 1.000, 0.000}
+         - rgb: rgb(128, 255, 0,)          --> [rbg]{0.502, 1.000, 0.000}
+         - string: red                     --> {red}
+
+        Additionally rgb or rgba can be expressed in % which is also parsed.
+        """
+        arg = user_arg if user_arg != "" else comm_arg
+
+        if value[0] == "#" and len(value) == 7:  # color is hex code
+            return command, f"[HTML]{{{value[1:].upper()}}}{arg}"
+        if value[0] == "#" and len(value) == 4:  # color is short hex code
+            val = f"{value[1].upper()*2}{value[2].upper()*2}{value[3].upper()*2}"
+            return command, f"[HTML]{{{val}}}{arg}"
+        elif value[:3] == "rgb":  # color is rgb or rgba
+            r = re.findall("(?<=\\()[0-9\\s%]+(?=,)", value)[0].strip()
+            r = float(r[:-1]) / 100 if "%" in r else int(r) / 255
+            g = re.findall("(?<=,)[0-9\\s%]+(?=,)", value)[0].strip()
+            g = float(g[:-1]) / 100 if "%" in g else int(g) / 255
+            if value[3] == "a":  # color is rgba
+                b = re.findall("(?<=,)[0-9\\s%]+(?=,)", value)[1].strip()
+            else:  # color is rgb
+                b = re.findall("(?<=,)[0-9\\s%]+(?=\\))", value)[0].strip()
+            b = float(b[:-1]) / 100 if "%" in b else int(b) / 255
+            return command, f"[rgb]{{{r:.3f}, {g:.3f}, {b:.3f}}}{arg}"
+        else:
+            return command, f"{{{value}}}{arg}"  # color is likely string-named
+
+    CONVERTED_ATTRIBUTES: dict[str, Callable] = {
+        "font-weight": font_weight,
+        "background-color": partial(color, command="cellcolor", comm_arg="--lwrap"),
+        "color": partial(color, command="color", comm_arg=""),
+        "font-style": font_style,
+    }
+
+    latex_styles: CSSList = []
+    for attribute, value in styles:
+        if isinstance(value, str) and "--latex" in value:
+            # return the style without conversion but drop '--latex'
+            latex_styles.append((attribute, value.replace("--latex", "")))
+        if attribute in CONVERTED_ATTRIBUTES:
+            arg = ""
+            for x in ["--wrap", "--nowrap", "--lwrap", "--dwrap", "--rwrap"]:
+                if x in str(value):
+                    arg, value = x, _parse_latex_options_strip(value, x)
+                    break
+            latex_style = CONVERTED_ATTRIBUTES[attribute](value, arg)
+            if latex_style is not None:
+                latex_styles.extend([latex_style])
+    return latex_styles
+
+
+def _escape_latex(s: str) -> str:
+    r"""
+    Replace the characters ``&``, ``%``, ``$``, ``#``, ``_``, ``{``, ``}``,
+    ``~``, ``^``, and ``\`` in the string with LaTeX-safe sequences.
+
+    Use this if you need to display text that might contain such characters in LaTeX.
+
+    Parameters
+    ----------
+    s : str
+        Input to be escaped
+
+    Return
+    ------
+    str :
+        Escaped string
+    """
+    return (
+        s.replace("\\", "ab2§=§8yz")  # rare string for final conversion: avoid \\ clash
+        .replace("ab2§=§8yz ", "ab2§=§8yz\\space ")  # since \backslash gobbles spaces
+        .replace("&", "\\&")
+        .replace("%", "\\%")
+        .replace("$", "\\$")
+        .replace("#", "\\#")
+        .replace("_", "\\_")
+        .replace("{", "\\{")
+        .replace("}", "\\}")
+        .replace("~ ", "~\\space ")  # since \textasciitilde gobbles spaces
+        .replace("~", "\\textasciitilde ")
+        .replace("^ ", "^\\space ")  # since \textasciicircum gobbles spaces
+        .replace("^", "\\textasciicircum ")
+        .replace("ab2§=§8yz", "\\textbackslash ")
+    )
+
+
+def _math_mode_with_dollar(s: str) -> str:
+    r"""
+    All characters in LaTeX math mode are preserved.
+
+    The substrings in LaTeX math mode, which start with
+    the character ``$`` and end with ``$``, are preserved
+    without escaping. Otherwise regular LaTeX escaping applies.
+
+    Parameters
+    ----------
+    s : str
+        Input to be escaped
+
+    Return
+    ------
+    str :
+        Escaped string
+    """
+    s = s.replace(r"\$", r"rt8§=§7wz")
+    pattern = re.compile(r"\$.*?\$")
+    pos = 0
+    ps = pattern.search(s, pos)
+    res = []
+    while ps:
+        res.append(_escape_latex(s[pos : ps.span()[0]]))
+        res.append(ps.group())
+        pos = ps.span()[1]
+        ps = pattern.search(s, pos)
+
+    res.append(_escape_latex(s[pos : len(s)]))
+    return "".join(res).replace(r"rt8§=§7wz", r"\$")
+
+
+def _math_mode_with_parentheses(s: str) -> str:
+    r"""
+    All characters in LaTeX math mode are preserved.
+
+    The substrings in LaTeX math mode, which start with
+    the character ``\(`` and end with ``\)``, are preserved
+    without escaping. Otherwise regular LaTeX escaping applies.
+
+    Parameters
+    ----------
+    s : str
+        Input to be escaped
+
+    Return
+    ------
+    str :
+        Escaped string
+    """
+    s = s.replace(r"\(", r"LEFT§=§6yzLEFT").replace(r"\)", r"RIGHTab5§=§RIGHT")
+    res = []
+    for item in re.split(r"LEFT§=§6yz|ab5§=§RIGHT", s):
+        if item.startswith("LEFT") and item.endswith("RIGHT"):
+            res.append(item.replace("LEFT", r"\(").replace("RIGHT", r"\)"))
+        elif "LEFT" in item and "RIGHT" in item:
+            res.append(
+                _escape_latex(item).replace("LEFT", r"\(").replace("RIGHT", r"\)")
+            )
+        else:
+            res.append(
+                _escape_latex(item)
+                .replace("LEFT", r"\textbackslash (")
+                .replace("RIGHT", r"\textbackslash )")
+            )
+    return "".join(res)
+
+
+def _escape_latex_math(s: str) -> str:
+    r"""
+    All characters in LaTeX math mode are preserved.
+
+    The substrings in LaTeX math mode, which either are surrounded
+    by two characters ``$`` or start with the character ``\(`` and end with ``\)``,
+    are preserved without escaping. Otherwise regular LaTeX escaping applies.
+
+    Parameters
+    ----------
+    s : str
+        Input to be escaped
+
+    Return
+    ------
+    str :
+        Escaped string
+    """
+    s = s.replace(r"\$", r"rt8§=§7wz")
+    ps_d = re.compile(r"\$.*?\$").search(s, 0)
+    ps_p = re.compile(r"\(.*?\)").search(s, 0)
+    mode = []
+    if ps_d:
+        mode.append(ps_d.span()[0])
+    if ps_p:
+        mode.append(ps_p.span()[0])
+    if len(mode) == 0:
+        return _escape_latex(s.replace(r"rt8§=§7wz", r"\$"))
+    if s[mode[0]] == r"$":
+        return _math_mode_with_dollar(s.replace(r"rt8§=§7wz", r"\$"))
+    if s[mode[0] - 1 : mode[0] + 1] == r"\(":
+        return _math_mode_with_parentheses(s.replace(r"rt8§=§7wz", r"\$"))
+    else:
+        return _escape_latex(s.replace(r"rt8§=§7wz", r"\$"))
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/formats/templates/html.tpl b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/formats/templates/html.tpl
new file mode 100644
index 0000000000000000000000000000000000000000..8c63be3ad788a8abddf3588b2b9dd6d6126f5df3
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/formats/templates/html.tpl
@@ -0,0 +1,16 @@
+{# Update the html_style/table_structure.html documentation too #}
+{% if doctype_html %}
+
+
+
+
+{% if not exclude_styles %}{% include html_style_tpl %}{% endif %}
+
+
+{% include html_table_tpl %}
+
+
+{% elif not doctype_html %}
+{% if not exclude_styles %}{% include html_style_tpl %}{% endif %}
+{% include html_table_tpl %}
+{% endif %}
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/formats/templates/html_style.tpl b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/formats/templates/html_style.tpl
new file mode 100644
index 0000000000000000000000000000000000000000..5c3fcd97f51bbec263399922579420dfa9ceef9c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/formats/templates/html_style.tpl
@@ -0,0 +1,26 @@
+{%- block before_style -%}{%- endblock before_style -%}
+{% block style %}
+
+{% endblock style %}
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/formats/templates/html_table.tpl b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/formats/templates/html_table.tpl
new file mode 100644
index 0000000000000000000000000000000000000000..17118d2bb21ccd185780d44c83a5242b12bd2a0d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/formats/templates/html_table.tpl
@@ -0,0 +1,63 @@
+{% block before_table %}{% endblock before_table %}
+{% block table %}
+{% if exclude_styles %}
+
+{% else %}
+

+{% endif %}
+{% block caption %}
+{% if caption and caption is string %}
+  
+{% elif caption and caption is sequence %}
+  
+{% endif %}
+{% endblock caption %}
+{% block thead %}
+  
+{% block before_head_rows %}{% endblock %}
+{% for r in head %}
+{% block head_tr scoped %}
+    
+{% if exclude_styles %}
+{% for c in r %}
+{% if c.is_visible != False %}
+      <{{c.type}} {{c.attributes}}>{{c.display_value}}
+{% endif %}
+{% endfor %}
+{% else %}
+{% for c in r %}
+{% if c.is_visible != False %}
+      <{{c.type}} {%- if c.id is defined %} id="T_{{uuid}}_{{c.id}}" {%- endif %} class="{{c.class}}" {{c.attributes}}>{{c.display_value}}
+{% endif %}
+{% endfor %}
+{% endif %}
+    
+{% endblock head_tr %}
+{% endfor %}
+{% block after_head_rows %}{% endblock %}
+  
+{% endblock thead %}
+{% block tbody %}
+  
+{% block before_rows %}{% endblock before_rows %}
+{% for r in body %}
+{% block tr scoped %}
+    
+{% if exclude_styles %}
+{% for c in r %}{% if c.is_visible != False %}
+      <{{c.type}} {{c.attributes}}>{{c.display_value}}
+{% endif %}{% endfor %}
+{% else %}
+{% for c in r %}{% if c.is_visible != False %}
+      <{{c.type}} {%- if c.id is defined %} id="T_{{uuid}}_{{c.id}}" {%- endif %} class="{{c.class}}" {{c.attributes}}>{{c.display_value}}
+{% endif %}{% endfor %}
+{% endif %}
+    
+{% endblock tr %}
+{% endfor %}
+{% block after_rows %}{% endblock after_rows %}
+  
+{% endblock tbody %}
+
{{caption}}{{caption[0]}}

+{% endblock table %}
+{% block after_table %}{% endblock after_table %}
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/formats/templates/latex.tpl b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/formats/templates/latex.tpl
new file mode 100644
index 0000000000000000000000000000000000000000..ae341bbc29823489d9d15e354fae0ce2e10a046d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/formats/templates/latex.tpl
@@ -0,0 +1,5 @@
+{% if environment == "longtable" %}
+{% include "latex_longtable.tpl" %}
+{% else %}
+{% include "latex_table.tpl" %}
+{% endif %}
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/formats/templates/latex_longtable.tpl b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/formats/templates/latex_longtable.tpl
new file mode 100644
index 0000000000000000000000000000000000000000..b97843eeb918da1b12f6f2edd585c8e42d6b7bb5
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/formats/templates/latex_longtable.tpl
@@ -0,0 +1,82 @@
+\begin{longtable}
+{%- set position = parse_table(table_styles, 'position') %}
+{%- if position is not none %}
+[{{position}}]
+{%- endif %}
+{%- set column_format = parse_table(table_styles, 'column_format') %}
+{% raw %}{{% endraw %}{{column_format}}{% raw %}}{% endraw %}
+
+{% for style in table_styles %}
+{% if style['selector'] not in ['position', 'position_float', 'caption', 'toprule', 'midrule', 'bottomrule', 'column_format', 'label'] %}
+\{{style['selector']}}{{parse_table(table_styles, style['selector'])}}
+{% endif %}
+{% endfor %}
+{% if caption and caption is string %}
+\caption{% raw %}{{% endraw %}{{caption}}{% raw %}}{% endraw %}
+{%- set label = parse_table(table_styles, 'label') %}
+{%- if label is not none %}
+ \label{{label}}
+{%- endif %} \\
+{% elif caption and caption is sequence %}
+\caption[{{caption[1]}}]{% raw %}{{% endraw %}{{caption[0]}}{% raw %}}{% endraw %}
+{%- set label = parse_table(table_styles, 'label') %}
+{%- if label is not none %}
+ \label{{label}}
+{%- endif %} \\
+{% else %}
+{%- set label = parse_table(table_styles, 'label') %}
+{%- if label is not none %}
+\label{{label}} \\
+{% endif %}
+{% endif %}
+{% set toprule = parse_table(table_styles, 'toprule') %}
+{% if toprule is not none %}
+\{{toprule}}
+{% endif %}
+{% for row in head %}
+{% for c in row %}{%- if not loop.first %} & {% endif %}{{parse_header(c, multirow_align, multicol_align, siunitx)}}{% endfor %} \\
+{% endfor %}
+{% set midrule = parse_table(table_styles, 'midrule') %}
+{% if midrule is not none %}
+\{{midrule}}
+{% endif %}
+\endfirsthead
+{% if caption and caption is string %}
+\caption[]{% raw %}{{% endraw %}{{caption}}{% raw %}}{% endraw %} \\
+{% elif caption and caption is sequence %}
+\caption[]{% raw %}{{% endraw %}{{caption[0]}}{% raw %}}{% endraw %} \\
+{% endif %}
+{% if toprule is not none %}
+\{{toprule}}
+{% endif %}
+{% for row in head %}
+{% for c in row %}{%- if not loop.first %} & {% endif %}{{parse_header(c, multirow_align, multicol_align, siunitx)}}{% endfor %} \\
+{% endfor %}
+{% if midrule is not none %}
+\{{midrule}}
+{% endif %}
+\endhead
+{% if midrule is not none %}
+\{{midrule}}
+{% endif %}
+\multicolumn{% raw %}{{% endraw %}{{body[0]|length}}{% raw %}}{% endraw %}{r}{Continued on next page} \\
+{% if midrule is not none %}
+\{{midrule}}
+{% endif %}
+\endfoot
+{% set bottomrule = parse_table(table_styles, 'bottomrule') %}
+{% if bottomrule is not none %}
+\{{bottomrule}}
+{% endif %}
+\endlastfoot
+{% for row in body %}
+{% for c in row %}{% if not loop.first %} & {% endif %}
+  {%- if c.type == 'th' %}{{parse_header(c, multirow_align, multicol_align)}}{% else %}{{parse_cell(c.cellstyle, c.display_value, convert_css)}}{% endif %}
+{%- endfor %} \\
+{% if clines and clines[loop.index] | length > 0 %}
+  {%- for cline in clines[loop.index] %}{% if not loop.first %} {% endif %}{{ cline }}{% endfor %}
+
+{% endif %}
+{% endfor %}
+\end{longtable}
+{% raw %}{% endraw %}
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/formats/templates/latex_table.tpl b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/formats/templates/latex_table.tpl
new file mode 100644
index 0000000000000000000000000000000000000000..7858cb4c945534a4d21cd4474460fd1abcf01f82
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/formats/templates/latex_table.tpl
@@ -0,0 +1,57 @@
+{% if environment or parse_wrap(table_styles, caption) %}
+\begin{% raw %}{{% endraw %}{{environment if environment else "table"}}{% raw %}}{% endraw %}
+{%- set position = parse_table(table_styles, 'position') %}
+{%- if position is not none %}
+[{{position}}]
+{%- endif %}
+
+{% set position_float = parse_table(table_styles, 'position_float') %}
+{% if position_float is not none%}
+\{{position_float}}
+{% endif %}
+{% if caption and caption is string %}
+\caption{% raw %}{{% endraw %}{{caption}}{% raw %}}{% endraw %}
+
+{% elif caption and caption is sequence %}
+\caption[{{caption[1]}}]{% raw %}{{% endraw %}{{caption[0]}}{% raw %}}{% endraw %}
+
+{% endif %}
+{% for style in table_styles %}
+{% if style['selector'] not in ['position', 'position_float', 'caption', 'toprule', 'midrule', 'bottomrule', 'column_format'] %}
+\{{style['selector']}}{{parse_table(table_styles, style['selector'])}}
+{% endif %}
+{% endfor %}
+{% endif %}
+\begin{tabular}
+{%- set column_format = parse_table(table_styles, 'column_format') %}
+{% raw %}{{% endraw %}{{column_format}}{% raw %}}{% endraw %}
+
+{% set toprule = parse_table(table_styles, 'toprule') %}
+{% if toprule is not none %}
+\{{toprule}}
+{% endif %}
+{% for row in head %}
+{% for c in row %}{%- if not loop.first %} & {% endif %}{{parse_header(c, multirow_align, multicol_align, siunitx, convert_css)}}{% endfor %} \\
+{% endfor %}
+{% set midrule = parse_table(table_styles, 'midrule') %}
+{% if midrule is not none %}
+\{{midrule}}
+{% endif %}
+{% for row in body %}
+{% for c in row %}{% if not loop.first %} & {% endif %}
+  {%- if c.type == 'th' %}{{parse_header(c, multirow_align, multicol_align, False, convert_css)}}{% else %}{{parse_cell(c.cellstyle, c.display_value, convert_css)}}{% endif %}
+{%- endfor %} \\
+{% if clines and clines[loop.index] | length > 0 %}
+  {%- for cline in clines[loop.index] %}{% if not loop.first %} {% endif %}{{ cline }}{% endfor %}
+
+{% endif %}
+{% endfor %}
+{% set bottomrule = parse_table(table_styles, 'bottomrule') %}
+{% if bottomrule is not none %}
+\{{bottomrule}}
+{% endif %}
+\end{tabular}
+{% if environment or parse_wrap(table_styles, caption) %}
+\end{% raw %}{{% endraw %}{{environment if environment else "table"}}{% raw %}}{% endraw %}
+
+{% endif %}
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/formats/templates/string.tpl b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/formats/templates/string.tpl
new file mode 100644
index 0000000000000000000000000000000000000000..06aeb2b4e413c61a912b535056c19c794d4b9c85
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/formats/templates/string.tpl
@@ -0,0 +1,12 @@
+{% for r in head %}
+{% for c in r %}{% if c["is_visible"] %}
+{{ c["display_value"] }}{% if not loop.last %}{{ delimiter }}{% endif %}
+{% endif %}{% endfor %}
+
+{% endfor %}
+{% for r in body %}
+{% for c in r %}{% if c["is_visible"] %}
+{{ c["display_value"] }}{% if not loop.last %}{{ delimiter }}{% endif %}
+{% endif %}{% endfor %}
+
+{% endfor %}
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/formats/xml.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/formats/xml.py
new file mode 100644
index 0000000000000000000000000000000000000000..f56fca8d7ef4446727bfa34166b0c6b5a2856338
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/formats/xml.py
@@ -0,0 +1,560 @@
+"""
+:mod:`pandas.io.formats.xml` is a module for formatting data in XML.
+"""
+from __future__ import annotations
+
+import codecs
+import io
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    final,
+)
+import warnings
+
+from pandas.errors import AbstractMethodError
+from pandas.util._decorators import (
+    cache_readonly,
+    doc,
+)
+
+from pandas.core.dtypes.common import is_list_like
+from pandas.core.dtypes.missing import isna
+
+from pandas.core.shared_docs import _shared_docs
+
+from pandas.io.common import get_handle
+from pandas.io.xml import (
+    get_data_from_filepath,
+    preprocess_data,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        CompressionOptions,
+        FilePath,
+        ReadBuffer,
+        StorageOptions,
+        WriteBuffer,
+    )
+
+    from pandas import DataFrame
+
+
+@doc(
+    storage_options=_shared_docs["storage_options"],
+    compression_options=_shared_docs["compression_options"] % "path_or_buffer",
+)
+class _BaseXMLFormatter:
+    """
+    Subclass for formatting data in XML.
+
+    Parameters
+    ----------
+    path_or_buffer : str or file-like
+        This can be either a string of raw XML, a valid URL,
+        file or file-like object.
+
+    index : bool
+        Whether to include index in xml document.
+
+    row_name : str
+        Name for root of xml document. Default is 'data'.
+
+    root_name : str
+        Name for row elements of xml document. Default is 'row'.
+
+    na_rep : str
+        Missing data representation.
+
+    attrs_cols : list
+        List of columns to write as attributes in row element.
+
+    elem_cols : list
+        List of columns to write as children in row element.
+
+    namespaces : dict
+        The namespaces to define in XML document as dicts with key
+        being namespace and value the URI.
+
+    prefix : str
+        The prefix for each element in XML document including root.
+
+    encoding : str
+        Encoding of xml object or document.
+
+    xml_declaration : bool
+        Whether to include xml declaration at top line item in xml.
+
+    pretty_print : bool
+        Whether to write xml document with line breaks and indentation.
+
+    stylesheet : str or file-like
+        A URL, file, file-like object, or a raw string containing XSLT.
+
+    {compression_options}
+
+        .. versionchanged:: 1.4.0 Zstandard support.
+
+    {storage_options}
+
+    See also
+    --------
+    pandas.io.formats.xml.EtreeXMLFormatter
+    pandas.io.formats.xml.LxmlXMLFormatter
+
+    """
+
+    def __init__(
+        self,
+        frame: DataFrame,
+        path_or_buffer: FilePath | WriteBuffer[bytes] | WriteBuffer[str] | None = None,
+        index: bool = True,
+        root_name: str | None = "data",
+        row_name: str | None = "row",
+        na_rep: str | None = None,
+        attr_cols: list[str] | None = None,
+        elem_cols: list[str] | None = None,
+        namespaces: dict[str | None, str] | None = None,
+        prefix: str | None = None,
+        encoding: str = "utf-8",
+        xml_declaration: bool | None = True,
+        pretty_print: bool | None = True,
+        stylesheet: FilePath | ReadBuffer[str] | ReadBuffer[bytes] | None = None,
+        compression: CompressionOptions = "infer",
+        storage_options: StorageOptions | None = None,
+    ) -> None:
+        self.frame = frame
+        self.path_or_buffer = path_or_buffer
+        self.index = index
+        self.root_name = root_name
+        self.row_name = row_name
+        self.na_rep = na_rep
+        self.attr_cols = attr_cols
+        self.elem_cols = elem_cols
+        self.namespaces = namespaces
+        self.prefix = prefix
+        self.encoding = encoding
+        self.xml_declaration = xml_declaration
+        self.pretty_print = pretty_print
+        self.stylesheet = stylesheet
+        self.compression: CompressionOptions = compression
+        self.storage_options = storage_options
+
+        self.orig_cols = self.frame.columns.tolist()
+        self.frame_dicts = self._process_dataframe()
+
+        self._validate_columns()
+        self._validate_encoding()
+        self.prefix_uri = self._get_prefix_uri()
+        self._handle_indexes()
+
+    def _build_tree(self) -> bytes:
+        """
+        Build tree from  data.
+
+        This method initializes the root and builds attributes and elements
+        with optional namespaces.
+        """
+        raise AbstractMethodError(self)
+
+    @final
+    def _validate_columns(self) -> None:
+        """
+        Validate elems_cols and attrs_cols.
+
+        This method will check if columns is list-like.
+
+        Raises
+        ------
+        ValueError
+            * If value is not a list and less then length of nodes.
+        """
+        if self.attr_cols and not is_list_like(self.attr_cols):
+            raise TypeError(
+                f"{type(self.attr_cols).__name__} is not a valid type for attr_cols"
+            )
+
+        if self.elem_cols and not is_list_like(self.elem_cols):
+            raise TypeError(
+                f"{type(self.elem_cols).__name__} is not a valid type for elem_cols"
+            )
+
+    @final
+    def _validate_encoding(self) -> None:
+        """
+        Validate encoding.
+
+        This method will check if encoding is among listed under codecs.
+
+        Raises
+        ------
+        LookupError
+            * If encoding is not available in codecs.
+        """
+
+        codecs.lookup(self.encoding)
+
+    @final
+    def _process_dataframe(self) -> dict[int | str, dict[str, Any]]:
+        """
+        Adjust Data Frame to fit xml output.
+
+        This method will adjust underlying data frame for xml output,
+        including optionally replacing missing values and including indexes.
+        """
+
+        df = self.frame
+
+        if self.index:
+            df = df.reset_index()
+
+        if self.na_rep is not None:
+            with warnings.catch_warnings():
+                warnings.filterwarnings(
+                    "ignore",
+                    "Downcasting object dtype arrays",
+                    category=FutureWarning,
+                )
+                df = df.fillna(self.na_rep)
+
+        return df.to_dict(orient="index")
+
+    @final
+    def _handle_indexes(self) -> None:
+        """
+        Handle indexes.
+
+        This method will add indexes into attr_cols or elem_cols.
+        """
+
+        if not self.index:
+            return
+
+        first_key = next(iter(self.frame_dicts))
+        indexes: list[str] = [
+            x for x in self.frame_dicts[first_key].keys() if x not in self.orig_cols
+        ]
+
+        if self.attr_cols:
+            self.attr_cols = indexes + self.attr_cols
+
+        if self.elem_cols:
+            self.elem_cols = indexes + self.elem_cols
+
+    def _get_prefix_uri(self) -> str:
+        """
+        Get uri of namespace prefix.
+
+        This method retrieves corresponding URI to prefix in namespaces.
+
+        Raises
+        ------
+        KeyError
+            *If prefix is not included in namespace dict.
+        """
+
+        raise AbstractMethodError(self)
+
+    @final
+    def _other_namespaces(self) -> dict:
+        """
+        Define other namespaces.
+
+        This method will build dictionary of namespaces attributes
+        for root element, conditionally with optional namespaces and
+        prefix.
+        """
+
+        nmsp_dict: dict[str, str] = {}
+        if self.namespaces:
+            nmsp_dict = {
+                f"xmlns{p if p=='' else f':{p}'}": n
+                for p, n in self.namespaces.items()
+                if n != self.prefix_uri[1:-1]
+            }
+
+        return nmsp_dict
+
+    @final
+    def _build_attribs(self, d: dict[str, Any], elem_row: Any) -> Any:
+        """
+        Create attributes of row.
+
+        This method adds attributes using attr_cols to row element and
+        works with tuples for multindex or hierarchical columns.
+        """
+
+        if not self.attr_cols:
+            return elem_row
+
+        for col in self.attr_cols:
+            attr_name = self._get_flat_col_name(col)
+            try:
+                if not isna(d[col]):
+                    elem_row.attrib[attr_name] = str(d[col])
+            except KeyError:
+                raise KeyError(f"no valid column, {col}")
+        return elem_row
+
+    @final
+    def _get_flat_col_name(self, col: str | tuple) -> str:
+        flat_col = col
+        if isinstance(col, tuple):
+            flat_col = (
+                "".join([str(c) for c in col]).strip()
+                if "" in col
+                else "_".join([str(c) for c in col]).strip()
+            )
+        return f"{self.prefix_uri}{flat_col}"
+
+    @cache_readonly
+    def _sub_element_cls(self):
+        raise AbstractMethodError(self)
+
+    @final
+    def _build_elems(self, d: dict[str, Any], elem_row: Any) -> None:
+        """
+        Create child elements of row.
+
+        This method adds child elements using elem_cols to row element and
+        works with tuples for multindex or hierarchical columns.
+        """
+        sub_element_cls = self._sub_element_cls
+
+        if not self.elem_cols:
+            return
+
+        for col in self.elem_cols:
+            elem_name = self._get_flat_col_name(col)
+            try:
+                val = None if isna(d[col]) or d[col] == "" else str(d[col])
+                sub_element_cls(elem_row, elem_name).text = val
+            except KeyError:
+                raise KeyError(f"no valid column, {col}")
+
+    @final
+    def write_output(self) -> str | None:
+        xml_doc = self._build_tree()
+
+        if self.path_or_buffer is not None:
+            with get_handle(
+                self.path_or_buffer,
+                "wb",
+                compression=self.compression,
+                storage_options=self.storage_options,
+                is_text=False,
+            ) as handles:
+                handles.handle.write(xml_doc)
+            return None
+
+        else:
+            return xml_doc.decode(self.encoding).rstrip()
+
+
+class EtreeXMLFormatter(_BaseXMLFormatter):
+    """
+    Class for formatting data in xml using Python standard library
+    modules: `xml.etree.ElementTree` and `xml.dom.minidom`.
+    """
+
+    def _build_tree(self) -> bytes:
+        from xml.etree.ElementTree import (
+            Element,
+            SubElement,
+            tostring,
+        )
+
+        self.root = Element(
+            f"{self.prefix_uri}{self.root_name}", attrib=self._other_namespaces()
+        )
+
+        for d in self.frame_dicts.values():
+            elem_row = SubElement(self.root, f"{self.prefix_uri}{self.row_name}")
+
+            if not self.attr_cols and not self.elem_cols:
+                self.elem_cols = list(d.keys())
+                self._build_elems(d, elem_row)
+
+            else:
+                elem_row = self._build_attribs(d, elem_row)
+                self._build_elems(d, elem_row)
+
+        self.out_xml = tostring(
+            self.root,
+            method="xml",
+            encoding=self.encoding,
+            xml_declaration=self.xml_declaration,
+        )
+
+        if self.pretty_print:
+            self.out_xml = self._prettify_tree()
+
+        if self.stylesheet is not None:
+            raise ValueError(
+                "To use stylesheet, you need lxml installed and selected as parser."
+            )
+
+        return self.out_xml
+
+    def _get_prefix_uri(self) -> str:
+        from xml.etree.ElementTree import register_namespace
+
+        uri = ""
+        if self.namespaces:
+            for p, n in self.namespaces.items():
+                if isinstance(p, str) and isinstance(n, str):
+                    register_namespace(p, n)
+            if self.prefix:
+                try:
+                    uri = f"{{{self.namespaces[self.prefix]}}}"
+                except KeyError:
+                    raise KeyError(f"{self.prefix} is not included in namespaces")
+            elif "" in self.namespaces:
+                uri = f'{{{self.namespaces[""]}}}'
+            else:
+                uri = ""
+
+        return uri
+
+    @cache_readonly
+    def _sub_element_cls(self):
+        from xml.etree.ElementTree import SubElement
+
+        return SubElement
+
+    def _prettify_tree(self) -> bytes:
+        """
+        Output tree for pretty print format.
+
+        This method will pretty print xml with line breaks and indentation.
+        """
+
+        from xml.dom.minidom import parseString
+
+        dom = parseString(self.out_xml)
+
+        return dom.toprettyxml(indent="  ", encoding=self.encoding)
+
+
+class LxmlXMLFormatter(_BaseXMLFormatter):
+    """
+    Class for formatting data in xml using Python standard library
+    modules: `xml.etree.ElementTree` and `xml.dom.minidom`.
+    """
+
+    def __init__(self, *args, **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+
+        self._convert_empty_str_key()
+
+    def _build_tree(self) -> bytes:
+        """
+        Build tree from  data.
+
+        This method initializes the root and builds attributes and elements
+        with optional namespaces.
+        """
+        from lxml.etree import (
+            Element,
+            SubElement,
+            tostring,
+        )
+
+        self.root = Element(f"{self.prefix_uri}{self.root_name}", nsmap=self.namespaces)
+
+        for d in self.frame_dicts.values():
+            elem_row = SubElement(self.root, f"{self.prefix_uri}{self.row_name}")
+
+            if not self.attr_cols and not self.elem_cols:
+                self.elem_cols = list(d.keys())
+                self._build_elems(d, elem_row)
+
+            else:
+                elem_row = self._build_attribs(d, elem_row)
+                self._build_elems(d, elem_row)
+
+        self.out_xml = tostring(
+            self.root,
+            pretty_print=self.pretty_print,
+            method="xml",
+            encoding=self.encoding,
+            xml_declaration=self.xml_declaration,
+        )
+
+        if self.stylesheet is not None:
+            self.out_xml = self._transform_doc()
+
+        return self.out_xml
+
+    def _convert_empty_str_key(self) -> None:
+        """
+        Replace zero-length string in `namespaces`.
+
+        This method will replace '' with None to align to `lxml`
+        requirement that empty string prefixes are not allowed.
+        """
+
+        if self.namespaces and "" in self.namespaces.keys():
+            self.namespaces[None] = self.namespaces.pop("", "default")
+
+    def _get_prefix_uri(self) -> str:
+        uri = ""
+        if self.namespaces:
+            if self.prefix:
+                try:
+                    uri = f"{{{self.namespaces[self.prefix]}}}"
+                except KeyError:
+                    raise KeyError(f"{self.prefix} is not included in namespaces")
+            elif "" in self.namespaces:
+                uri = f'{{{self.namespaces[""]}}}'
+            else:
+                uri = ""
+
+        return uri
+
+    @cache_readonly
+    def _sub_element_cls(self):
+        from lxml.etree import SubElement
+
+        return SubElement
+
+    def _transform_doc(self) -> bytes:
+        """
+        Parse stylesheet from file or buffer and run it.
+
+        This method will parse stylesheet object into tree for parsing
+        conditionally by its specific object type, then transforms
+        original tree with XSLT script.
+        """
+        from lxml.etree import (
+            XSLT,
+            XMLParser,
+            fromstring,
+            parse,
+        )
+
+        style_doc = self.stylesheet
+        assert style_doc is not None  # is ensured by caller
+
+        handle_data = get_data_from_filepath(
+            filepath_or_buffer=style_doc,
+            encoding=self.encoding,
+            compression=self.compression,
+            storage_options=self.storage_options,
+        )
+
+        with preprocess_data(handle_data) as xml_data:
+            curr_parser = XMLParser(encoding=self.encoding)
+
+            if isinstance(xml_data, io.StringIO):
+                xsl_doc = fromstring(
+                    xml_data.getvalue().encode(self.encoding), parser=curr_parser
+                )
+            else:
+                xsl_doc = parse(xml_data, parser=curr_parser)
+
+        transformer = XSLT(xsl_doc)
+        new_doc = transformer(self.root)
+
+        return bytes(new_doc)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/gbq.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/gbq.py
new file mode 100644
index 0000000000000000000000000000000000000000..24e4e0b7cef0a5fa66a70fa0ad70b52364b02091
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/gbq.py
@@ -0,0 +1,255 @@
+""" Google BigQuery support """
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    Any,
+)
+import warnings
+
+from pandas.compat._optional import import_optional_dependency
+from pandas.util._exceptions import find_stack_level
+
+if TYPE_CHECKING:
+    from google.auth.credentials import Credentials
+
+    from pandas import DataFrame
+
+
+def _try_import():
+    # since pandas is a dependency of pandas-gbq
+    # we need to import on first use
+    msg = (
+        "pandas-gbq is required to load data from Google BigQuery. "
+        "See the docs: https://pandas-gbq.readthedocs.io."
+    )
+    pandas_gbq = import_optional_dependency("pandas_gbq", extra=msg)
+    return pandas_gbq
+
+
+def read_gbq(
+    query: str,
+    project_id: str | None = None,
+    index_col: str | None = None,
+    col_order: list[str] | None = None,
+    reauth: bool = False,
+    auth_local_webserver: bool = True,
+    dialect: str | None = None,
+    location: str | None = None,
+    configuration: dict[str, Any] | None = None,
+    credentials: Credentials | None = None,
+    use_bqstorage_api: bool | None = None,
+    max_results: int | None = None,
+    progress_bar_type: str | None = None,
+) -> DataFrame:
+    """
+    Load data from Google BigQuery.
+
+    .. deprecated:: 2.2.0
+
+       Please use ``pandas_gbq.read_gbq`` instead.
+
+    This function requires the `pandas-gbq package
+    `__.
+
+    See the `How to authenticate with Google BigQuery
+    `__
+    guide for authentication instructions.
+
+    Parameters
+    ----------
+    query : str
+        SQL-Like Query to return data values.
+    project_id : str, optional
+        Google BigQuery Account project ID. Optional when available from
+        the environment.
+    index_col : str, optional
+        Name of result column to use for index in results DataFrame.
+    col_order : list(str), optional
+        List of BigQuery column names in the desired order for results
+        DataFrame.
+    reauth : bool, default False
+        Force Google BigQuery to re-authenticate the user. This is useful
+        if multiple accounts are used.
+    auth_local_webserver : bool, default True
+        Use the `local webserver flow`_ instead of the `console flow`_
+        when getting user credentials.
+
+        .. _local webserver flow:
+            https://google-auth-oauthlib.readthedocs.io/en/latest/reference/google_auth_oauthlib.flow.html#google_auth_oauthlib.flow.InstalledAppFlow.run_local_server
+        .. _console flow:
+            https://google-auth-oauthlib.readthedocs.io/en/latest/reference/google_auth_oauthlib.flow.html#google_auth_oauthlib.flow.InstalledAppFlow.run_console
+
+        *New in version 0.2.0 of pandas-gbq*.
+
+        .. versionchanged:: 1.5.0
+           Default value is changed to ``True``. Google has deprecated the
+           ``auth_local_webserver = False`` `"out of band" (copy-paste)
+           flow
+           `_.
+    dialect : str, default 'legacy'
+        Note: The default value is changing to 'standard' in a future version.
+
+        SQL syntax dialect to use. Value can be one of:
+
+        ``'legacy'``
+            Use BigQuery's legacy SQL dialect. For more information see
+            `BigQuery Legacy SQL Reference
+            `__.
+        ``'standard'``
+            Use BigQuery's standard SQL, which is
+            compliant with the SQL 2011 standard. For more information
+            see `BigQuery Standard SQL Reference
+            `__.
+    location : str, optional
+        Location where the query job should run. See the `BigQuery locations
+        documentation
+        `__ for a
+        list of available locations. The location must match that of any
+        datasets used in the query.
+
+        *New in version 0.5.0 of pandas-gbq*.
+    configuration : dict, optional
+        Query config parameters for job processing.
+        For example:
+
+            configuration = {'query': {'useQueryCache': False}}
+
+        For more information see `BigQuery REST API Reference
+        `__.
+    credentials : google.auth.credentials.Credentials, optional
+        Credentials for accessing Google APIs. Use this parameter to override
+        default credentials, such as to use Compute Engine
+        :class:`google.auth.compute_engine.Credentials` or Service Account
+        :class:`google.oauth2.service_account.Credentials` directly.
+
+        *New in version 0.8.0 of pandas-gbq*.
+    use_bqstorage_api : bool, default False
+        Use the `BigQuery Storage API
+        `__ to
+        download query results quickly, but at an increased cost. To use this
+        API, first `enable it in the Cloud Console
+        `__.
+        You must also have the `bigquery.readsessions.create
+        `__
+        permission on the project you are billing queries to.
+
+        This feature requires version 0.10.0 or later of the ``pandas-gbq``
+        package. It also requires the ``google-cloud-bigquery-storage`` and
+        ``fastavro`` packages.
+
+    max_results : int, optional
+        If set, limit the maximum number of rows to fetch from the query
+        results.
+
+    progress_bar_type : Optional, str
+        If set, use the `tqdm `__ library to
+        display a progress bar while the data downloads. Install the
+        ``tqdm`` package to use this feature.
+
+        Possible values of ``progress_bar_type`` include:
+
+        ``None``
+            No progress bar.
+        ``'tqdm'``
+            Use the :func:`tqdm.tqdm` function to print a progress bar
+            to :data:`sys.stderr`.
+        ``'tqdm_notebook'``
+            Use the :func:`tqdm.tqdm_notebook` function to display a
+            progress bar as a Jupyter notebook widget.
+        ``'tqdm_gui'``
+            Use the :func:`tqdm.tqdm_gui` function to display a
+            progress bar as a graphical dialog box.
+
+    Returns
+    -------
+    df: DataFrame
+        DataFrame representing results of query.
+
+    See Also
+    --------
+    pandas_gbq.read_gbq : This function in the pandas-gbq library.
+    DataFrame.to_gbq : Write a DataFrame to Google BigQuery.
+
+    Examples
+    --------
+    Example taken from `Google BigQuery documentation
+    `_
+
+    >>> sql = "SELECT name FROM table_name WHERE state = 'TX' LIMIT 100;"
+    >>> df = pd.read_gbq(sql, dialect="standard")  # doctest: +SKIP
+    >>> project_id = "your-project-id"  # doctest: +SKIP
+    >>> df = pd.read_gbq(sql,
+    ...                  project_id=project_id,
+    ...                  dialect="standard"
+    ...                  )  # doctest: +SKIP
+    """
+    warnings.warn(
+        "read_gbq is deprecated and will be removed in a future version. "
+        "Please use pandas_gbq.read_gbq instead: "
+        "https://pandas-gbq.readthedocs.io/en/latest/api.html#pandas_gbq.read_gbq",
+        FutureWarning,
+        stacklevel=find_stack_level(),
+    )
+    pandas_gbq = _try_import()
+
+    kwargs: dict[str, str | bool | int | None] = {}
+
+    # START: new kwargs.  Don't populate unless explicitly set.
+    if use_bqstorage_api is not None:
+        kwargs["use_bqstorage_api"] = use_bqstorage_api
+    if max_results is not None:
+        kwargs["max_results"] = max_results
+
+    kwargs["progress_bar_type"] = progress_bar_type
+    # END: new kwargs
+
+    return pandas_gbq.read_gbq(
+        query,
+        project_id=project_id,
+        index_col=index_col,
+        col_order=col_order,
+        reauth=reauth,
+        auth_local_webserver=auth_local_webserver,
+        dialect=dialect,
+        location=location,
+        configuration=configuration,
+        credentials=credentials,
+        **kwargs,
+    )
+
+
+def to_gbq(
+    dataframe: DataFrame,
+    destination_table: str,
+    project_id: str | None = None,
+    chunksize: int | None = None,
+    reauth: bool = False,
+    if_exists: str = "fail",
+    auth_local_webserver: bool = True,
+    table_schema: list[dict[str, str]] | None = None,
+    location: str | None = None,
+    progress_bar: bool = True,
+    credentials: Credentials | None = None,
+) -> None:
+    warnings.warn(
+        "to_gbq is deprecated and will be removed in a future version. "
+        "Please use pandas_gbq.to_gbq instead: "
+        "https://pandas-gbq.readthedocs.io/en/latest/api.html#pandas_gbq.to_gbq",
+        FutureWarning,
+        stacklevel=find_stack_level(),
+    )
+    pandas_gbq = _try_import()
+    pandas_gbq.to_gbq(
+        dataframe,
+        destination_table,
+        project_id=project_id,
+        chunksize=chunksize,
+        reauth=reauth,
+        if_exists=if_exists,
+        auth_local_webserver=auth_local_webserver,
+        table_schema=table_schema,
+        location=location,
+        progress_bar=progress_bar,
+        credentials=credentials,
+    )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/html.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/html.py
new file mode 100644
index 0000000000000000000000000000000000000000..4eeeb1b655f8ac55309edeacd593f5a5c2516678
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/html.py
@@ -0,0 +1,1259 @@
+"""
+:mod:`pandas.io.html` is a module containing functionality for dealing with
+HTML IO.
+
+"""
+
+from __future__ import annotations
+
+from collections import abc
+import numbers
+import re
+from re import Pattern
+from typing import (
+    TYPE_CHECKING,
+    Literal,
+    cast,
+)
+import warnings
+
+from pandas._libs import lib
+from pandas.compat._optional import import_optional_dependency
+from pandas.errors import (
+    AbstractMethodError,
+    EmptyDataError,
+)
+from pandas.util._decorators import doc
+from pandas.util._exceptions import find_stack_level
+from pandas.util._validators import check_dtype_backend
+
+from pandas.core.dtypes.common import is_list_like
+
+from pandas import isna
+from pandas.core.indexes.base import Index
+from pandas.core.indexes.multi import MultiIndex
+from pandas.core.series import Series
+from pandas.core.shared_docs import _shared_docs
+
+from pandas.io.common import (
+    file_exists,
+    get_handle,
+    is_file_like,
+    is_fsspec_url,
+    is_url,
+    stringify_path,
+    validate_header_arg,
+)
+from pandas.io.formats.printing import pprint_thing
+from pandas.io.parsers import TextParser
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Iterable,
+        Sequence,
+    )
+
+    from pandas._typing import (
+        BaseBuffer,
+        DtypeBackend,
+        FilePath,
+        HTMLFlavors,
+        ReadBuffer,
+        StorageOptions,
+    )
+
+    from pandas import DataFrame
+
+#############
+# READ HTML #
+#############
+_RE_WHITESPACE = re.compile(r"[\r\n]+|\s{2,}")
+
+
+def _remove_whitespace(s: str, regex: Pattern = _RE_WHITESPACE) -> str:
+    """
+    Replace extra whitespace inside of a string with a single space.
+
+    Parameters
+    ----------
+    s : str or unicode
+        The string from which to remove extra whitespace.
+    regex : re.Pattern
+        The regular expression to use to remove extra whitespace.
+
+    Returns
+    -------
+    subd : str or unicode
+        `s` with all extra whitespace replaced with a single space.
+    """
+    return regex.sub(" ", s.strip())
+
+
+def _get_skiprows(skiprows: int | Sequence[int] | slice | None) -> int | Sequence[int]:
+    """
+    Get an iterator given an integer, slice or container.
+
+    Parameters
+    ----------
+    skiprows : int, slice, container
+        The iterator to use to skip rows; can also be a slice.
+
+    Raises
+    ------
+    TypeError
+        * If `skiprows` is not a slice, integer, or Container
+
+    Returns
+    -------
+    it : iterable
+        A proper iterator to use to skip rows of a DataFrame.
+    """
+    if isinstance(skiprows, slice):
+        start, step = skiprows.start or 0, skiprows.step or 1
+        return list(range(start, skiprows.stop, step))
+    elif isinstance(skiprows, numbers.Integral) or is_list_like(skiprows):
+        return cast("int | Sequence[int]", skiprows)
+    elif skiprows is None:
+        return 0
+    raise TypeError(f"{type(skiprows).__name__} is not a valid type for skipping rows")
+
+
+def _read(
+    obj: FilePath | BaseBuffer,
+    encoding: str | None,
+    storage_options: StorageOptions | None,
+) -> str | bytes:
+    """
+    Try to read from a url, file or string.
+
+    Parameters
+    ----------
+    obj : str, unicode, path object, or file-like object
+
+    Returns
+    -------
+    raw_text : str
+    """
+    text: str | bytes
+    if (
+        is_url(obj)
+        or hasattr(obj, "read")
+        or (isinstance(obj, str) and file_exists(obj))
+    ):
+        with get_handle(
+            obj, "r", encoding=encoding, storage_options=storage_options
+        ) as handles:
+            text = handles.handle.read()
+    elif isinstance(obj, (str, bytes)):
+        text = obj
+    else:
+        raise TypeError(f"Cannot read object of type '{type(obj).__name__}'")
+    return text
+
+
+class _HtmlFrameParser:
+    """
+    Base class for parsers that parse HTML into DataFrames.
+
+    Parameters
+    ----------
+    io : str or file-like
+        This can be either a string of raw HTML, a valid URL using the HTTP,
+        FTP, or FILE protocols or a file-like object.
+
+    match : str or regex
+        The text to match in the document.
+
+    attrs : dict
+        List of HTML  element attributes to match.
+
+    encoding : str
+        Encoding to be used by parser
+
+    displayed_only : bool
+        Whether or not items with "display:none" should be ignored
+
+    extract_links : {None, "all", "header", "body", "footer"}
+        Table elements in the specified section(s) with  tags will have their
+        href extracted.
+
+        .. versionadded:: 1.5.0
+
+    Attributes
+    ----------
+    io : str or file-like
+        raw HTML, URL, or file-like object
+
+    match : regex
+        The text to match in the raw HTML
+
+    attrs : dict-like
+        A dictionary of valid table attributes to use to search for table
+        elements.
+
+    encoding : str
+        Encoding to be used by parser
+
+    displayed_only : bool
+        Whether or not items with "display:none" should be ignored
+
+    extract_links : {None, "all", "header", "body", "footer"}
+        Table elements in the specified section(s) with  tags will have their
+        href extracted.
+
+        .. versionadded:: 1.5.0
+
+    Notes
+    -----
+    To subclass this class effectively you must override the following methods:
+        * :func:`_build_doc`
+        * :func:`_attr_getter`
+        * :func:`_href_getter`
+        * :func:`_text_getter`
+        * :func:`_parse_td`
+        * :func:`_parse_thead_tr`
+        * :func:`_parse_tbody_tr`
+        * :func:`_parse_tfoot_tr`
+        * :func:`_parse_tables`
+        * :func:`_equals_tag`
+    See each method's respective documentation for details on their
+    functionality.
+    """
+
+    def __init__(
+        self,
+        io: FilePath | ReadBuffer[str] | ReadBuffer[bytes],
+        match: str | Pattern,
+        attrs: dict[str, str] | None,
+        encoding: str,
+        displayed_only: bool,
+        extract_links: Literal[None, "header", "footer", "body", "all"],
+        storage_options: StorageOptions = None,
+    ) -> None:
+        self.io = io
+        self.match = match
+        self.attrs = attrs
+        self.encoding = encoding
+        self.displayed_only = displayed_only
+        self.extract_links = extract_links
+        self.storage_options = storage_options
+
+    def parse_tables(self):
+        """
+        Parse and return all tables from the DOM.
+
+        Returns
+        -------
+        list of parsed (header, body, footer) tuples from tables.
+        """
+        tables = self._parse_tables(self._build_doc(), self.match, self.attrs)
+        return (self._parse_thead_tbody_tfoot(table) for table in tables)
+
+    def _attr_getter(self, obj, attr):
+        """
+        Return the attribute value of an individual DOM node.
+
+        Parameters
+        ----------
+        obj : node-like
+            A DOM node.
+
+        attr : str or unicode
+            The attribute, such as "colspan"
+
+        Returns
+        -------
+        str or unicode
+            The attribute value.
+        """
+        # Both lxml and BeautifulSoup have the same implementation:
+        return obj.get(attr)
+
+    def _href_getter(self, obj) -> str | None:
+        """
+        Return a href if the DOM node contains a child  or None.
+
+        Parameters
+        ----------
+        obj : node-like
+            A DOM node.
+
+        Returns
+        -------
+        href : str or unicode
+            The href from the  child of the DOM node.
+        """
+        raise AbstractMethodError(self)
+
+    def _text_getter(self, obj):
+        """
+        Return the text of an individual DOM node.
+
+        Parameters
+        ----------
+        obj : node-like
+            A DOM node.
+
+        Returns
+        -------
+        text : str or unicode
+            The text from an individual DOM node.
+        """
+        raise AbstractMethodError(self)
+
+    def _parse_td(self, obj):
+        """
+        Return the td elements from a row element.
+
+        Parameters
+        ----------
+        obj : node-like
+            A DOM  node.
+
+        Returns
+        -------
+        list of node-like
+            These are the elements of each row, i.e., the columns.
+        """
+        raise AbstractMethodError(self)
+
+    def _parse_thead_tr(self, table):
+        """
+        Return the list of thead row elements from the parsed table element.
+
+        Parameters
+        ----------
+        table : a table element that contains zero or more thead elements.
+
+        Returns
+        -------
+        list of node-like
+            These are the  row elements of a table.
+        """
+        raise AbstractMethodError(self)
+
+    def _parse_tbody_tr(self, table):
+        """
+        Return the list of tbody row elements from the parsed table element.
+
+        HTML5 table bodies consist of either 0 or more  elements (which
+        only contain  elements) or 0 or more  elements. This method
+        checks for both structures.
+
+        Parameters
+        ----------
+        table : a table element that contains row elements.
+
+        Returns
+        -------
+        list of node-like
+            These are the  row elements of a table.
+        """
+        raise AbstractMethodError(self)
+
+    def _parse_tfoot_tr(self, table):
+        """
+        Return the list of tfoot row elements from the parsed table element.
+
+        Parameters
+        ----------
+        table : a table element that contains row elements.
+
+        Returns
+        -------
+        list of node-like
+            These are the  row elements of a table.
+        """
+        raise AbstractMethodError(self)
+
+    def _parse_tables(self, document, match, attrs):
+        """
+        Return all tables from the parsed DOM.
+
+        Parameters
+        ----------
+        document : the DOM from which to parse the table element.
+
+        match : str or regular expression
+            The text to search for in the DOM tree.
+
+        attrs : dict
+            A dictionary of table attributes that can be used to disambiguate
+            multiple tables on a page.
+
+        Raises
+        ------
+        ValueError : `match` does not match any text in the document.
+
+        Returns
+        -------
+        list of node-like
+            HTML 
 elements to be parsed into raw data.
+        """
+        raise AbstractMethodError(self)
+
+    def _equals_tag(self, obj, tag) -> bool:
+        """
+        Return whether an individual DOM node matches a tag
+
+        Parameters
+        ----------
+        obj : node-like
+            A DOM node.
+
+        tag : str
+            Tag name to be checked for equality.
+
+        Returns
+        -------
+        boolean
+            Whether `obj`'s tag name is `tag`
+        """
+        raise AbstractMethodError(self)
+
+    def _build_doc(self):
+        """
+        Return a tree-like object that can be used to iterate over the DOM.
+
+        Returns
+        -------
+        node-like
+            The DOM from which to parse the table element.
+        """
+        raise AbstractMethodError(self)
+
+    def _parse_thead_tbody_tfoot(self, table_html):
+        """
+        Given a table, return parsed header, body, and foot.
+
+        Parameters
+        ----------
+        table_html : node-like
+
+        Returns
+        -------
+        tuple of (header, body, footer), each a list of list-of-text rows.
+
+        Notes
+        -----
+        Header and body are lists-of-lists. Top level list is a list of
+        rows. Each row is a list of str text.
+
+        Logic: Use , ,  elements to identify
+               header, body, and footer, otherwise:
+               - Put all rows into body
+               - Move rows from top of body to header only if
+                 all elements inside row are . Move the top all- or 
+            while body_rows and row_is_all_th(body_rows[0]):
+                header_rows.append(body_rows.pop(0))
+
+        header = self._expand_colspan_rowspan(header_rows, section="header")
+        body = self._expand_colspan_rowspan(body_rows, section="body")
+        footer = self._expand_colspan_rowspan(footer_rows, section="footer")
+
+        return header, body, footer
+
+    def _expand_colspan_rowspan(
+        self, rows, section: Literal["header", "footer", "body"]
+    ):
+        """
+        Given a list of s, return a list of text rows.
+
+        Parameters
+        ----------
+        rows : list of node-like
+            List of s
+        section : the section that the rows belong to (header, body or footer).
+
+        Returns
+        -------
+        list of list
+            Each returned row is a list of str text, or tuple (text, link)
+            if extract_links is not None.
+
+        Notes
+        -----
+        Any cell with ``rowspan`` or ``colspan`` will have its contents copied
+        to subsequent cells.
+        """
+        all_texts = []  # list of rows, each a list of str
+        text: str | tuple
+        remainder: list[
+            tuple[int, str | tuple, int]
+        ] = []  # list of (index, text, nrows)
+
+        for tr in rows:
+            texts = []  # the output for this row
+            next_remainder = []
+
+            index = 0
+            tds = self._parse_td(tr)
+            for td in tds:
+                # Append texts from previous rows with rowspan>1 that come
+                # before this  or  (see _parse_thead_tr).
+        return row.xpath("./td|./th")
+
+    def _parse_tables(self, document, match, kwargs):
+        pattern = match.pattern
+
+        # 1. check all descendants for the given pattern and only search tables
+        # GH 49929
+        xpath_expr = f"//table[.//text()[re:test(., {repr(pattern)})]]"
+
+        # if any table attributes were given build an xpath expression to
+        # search for them
+        if kwargs:
+            xpath_expr += _build_xpath_expr(kwargs)
+
+        tables = document.xpath(xpath_expr, namespaces=_re_namespace)
+
+        tables = self._handle_hidden_tables(tables, "attrib")
+        if self.displayed_only:
+            for table in tables:
+                # lxml utilizes XPATH 1.0 which does not have regex
+                # support. As a result, we find all elements with a style
+                # attribute and iterate them to check for display:none
+                for elem in table.xpath(".//style"):
+                    elem.drop_tree()
+                for elem in table.xpath(".//*[@style]"):
+                    if "display:none" in elem.attrib.get("style", "").replace(" ", ""):
+                        elem.drop_tree()
+        if not tables:
+            raise ValueError(f"No tables found matching regex {repr(pattern)}")
+        return tables
+
+    def _equals_tag(self, obj, tag) -> bool:
+        return obj.tag == tag
+
+    def _build_doc(self):
+        """
+        Raises
+        ------
+        ValueError
+            * If a URL that lxml cannot parse is passed.
+
+        Exception
+            * Any other ``Exception`` thrown. For example, trying to parse a
+              URL that is syntactically correct on a machine with no internet
+              connection will fail.
+
+        See Also
+        --------
+        pandas.io.html._HtmlFrameParser._build_doc
+        """
+        from lxml.etree import XMLSyntaxError
+        from lxml.html import (
+            HTMLParser,
+            fromstring,
+            parse,
+        )
+
+        parser = HTMLParser(recover=True, encoding=self.encoding)
+
+        try:
+            if is_url(self.io):
+                with get_handle(
+                    self.io, "r", storage_options=self.storage_options
+                ) as f:
+                    r = parse(f.handle, parser=parser)
+            else:
+                # try to parse the input in the simplest way
+                r = parse(self.io, parser=parser)
+            try:
+                r = r.getroot()
+            except AttributeError:
+                pass
+        except (UnicodeDecodeError, OSError) as e:
+            # if the input is a blob of html goop
+            if not is_url(self.io):
+                r = fromstring(self.io, parser=parser)
+
+                try:
+                    r = r.getroot()
+                except AttributeError:
+                    pass
+            else:
+                raise e
+        else:
+            if not hasattr(r, "text_content"):
+                raise XMLSyntaxError("no text parsed from document", 0, 0, 0)
+
+        for br in r.xpath("*//br"):
+            br.tail = "\n" + (br.tail or "")
+
+        return r
+
+    def _parse_thead_tr(self, table):
+        rows = []
+
+        for thead in table.xpath(".//thead"):
+            rows.extend(thead.xpath("./tr"))
+
+            # HACK: lxml does not clean up the clearly-erroneous
+            # . (Missing ). Add
+            # the  and _pretend_ it's a ; _parse_td() will find its
+            # children as though it's a .
+            #
+            # Better solution would be to use html5lib.
+            elements_at_root = thead.xpath("./td|./th")
+            if elements_at_root:
+                rows.append(thead)
+
+        return rows
+
+    def _parse_tbody_tr(self, table):
+        from_tbody = table.xpath(".//tbody//tr")
+        from_root = table.xpath("./tr")
+        # HTML spec: at most one of these lists has content
+        return from_tbody + from_root
+
+    def _parse_tfoot_tr(self, table):
+        return table.xpath(".//tfoot//tr")
+
+
+def _expand_elements(body) -> None:
+    data = [len(elem) for elem in body]
+    lens = Series(data)
+    lens_max = lens.max()
+    not_max = lens[lens != lens_max]
+
+    empty = [""]
+    for ind, length in not_max.items():
+        body[ind] += empty * (lens_max - length)
+
+
+def _data_to_frame(**kwargs):
+    head, body, foot = kwargs.pop("data")
+    header = kwargs.pop("header")
+    kwargs["skiprows"] = _get_skiprows(kwargs["skiprows"])
+    if head:
+        body = head + body
+
+        # Infer header when there is a  or top 

+               - Move rows from bottom of body to footer only if
+                 all elements inside row are 
+        """
+        header_rows = self._parse_thead_tr(table_html)
+        body_rows = self._parse_tbody_tr(table_html)
+        footer_rows = self._parse_tfoot_tr(table_html)
+
+        def row_is_all_th(row):
+            return all(self._equals_tag(t, "th") for t in self._parse_td(row))
+
+        if not header_rows:
+            # The table has no 
 rows from
+            # body_rows to header_rows. (This is a common case because many
+            # tables in the wild have no 

+                while remainder and remainder[0][0] <= index:
+                    prev_i, prev_text, prev_rowspan = remainder.pop(0)
+                    texts.append(prev_text)
+                    if prev_rowspan > 1:
+                        next_remainder.append((prev_i, prev_text, prev_rowspan - 1))
+                    index += 1
+
+                # Append the text from this , colspan times
+                text = _remove_whitespace(self._text_getter(td))
+                if self.extract_links in ("all", section):
+                    href = self._href_getter(td)
+                    text = (text, href)
+                rowspan = int(self._attr_getter(td, "rowspan") or 1)
+                colspan = int(self._attr_getter(td, "colspan") or 1)
+
+                for _ in range(colspan):
+                    texts.append(text)
+                    if rowspan > 1:
+                        next_remainder.append((index, text, rowspan - 1))
+                    index += 1
+
+            # Append texts from previous rows at the final position
+            for prev_i, prev_text, prev_rowspan in remainder:
+                texts.append(prev_text)
+                if prev_rowspan > 1:
+                    next_remainder.append((prev_i, prev_text, prev_rowspan - 1))
+
+            all_texts.append(texts)
+            remainder = next_remainder
+
+        # Append rows that only appear because the previous row had non-1
+        # rowspan
+        while remainder:
+            next_remainder = []
+            texts = []
+            for prev_i, prev_text, prev_rowspan in remainder:
+                texts.append(prev_text)
+                if prev_rowspan > 1:
+                    next_remainder.append((prev_i, prev_text, prev_rowspan - 1))
+            all_texts.append(texts)
+            remainder = next_remainder
+
+        return all_texts
+
+    def _handle_hidden_tables(self, tbl_list, attr_name: str):
+        """
+        Return list of tables, potentially removing hidden elements
+
+        Parameters
+        ----------
+        tbl_list : list of node-like
+            Type of list elements will vary depending upon parser used
+        attr_name : str
+            Name of the accessor for retrieving HTML attributes
+
+        Returns
+        -------
+        list of node-like
+            Return type matches `tbl_list`
+        """
+        if not self.displayed_only:
+            return tbl_list
+
+        return [
+            x
+            for x in tbl_list
+            if "display:none"
+            not in getattr(x, attr_name).get("style", "").replace(" ", "")
+        ]
+
+
+class _BeautifulSoupHtml5LibFrameParser(_HtmlFrameParser):
+    """
+    HTML to DataFrame parser that uses BeautifulSoup under the hood.
+
+    See Also
+    --------
+    pandas.io.html._HtmlFrameParser
+    pandas.io.html._LxmlFrameParser
+
+    Notes
+    -----
+    Documentation strings for this class are in the base class
+    :class:`pandas.io.html._HtmlFrameParser`.
+    """
+
+    def _parse_tables(self, document, match, attrs):
+        element_name = "table"
+        tables = document.find_all(element_name, attrs=attrs)
+        if not tables:
+            raise ValueError("No tables found")
+
+        result = []
+        unique_tables = set()
+        tables = self._handle_hidden_tables(tables, "attrs")
+
+        for table in tables:
+            if self.displayed_only:
+                for elem in table.find_all("style"):
+                    elem.decompose()
+
+                for elem in table.find_all(style=re.compile(r"display:\s*none")):
+                    elem.decompose()
+
+            if table not in unique_tables and table.find(string=match) is not None:
+                result.append(table)
+            unique_tables.add(table)
+        if not result:
+            raise ValueError(f"No tables found matching pattern {repr(match.pattern)}")
+        return result
+
+    def _href_getter(self, obj) -> str | None:
+        a = obj.find("a", href=True)
+        return None if not a else a["href"]
+
+    def _text_getter(self, obj):
+        return obj.text
+
+    def _equals_tag(self, obj, tag) -> bool:
+        return obj.name == tag
+
+    def _parse_td(self, row):
+        return row.find_all(("td", "th"), recursive=False)
+
+    def _parse_thead_tr(self, table):
+        return table.select("thead tr")
+
+    def _parse_tbody_tr(self, table):
+        from_tbody = table.select("tbody tr")
+        from_root = table.find_all("tr", recursive=False)
+        # HTML spec: at most one of these lists has content
+        return from_tbody + from_root
+
+    def _parse_tfoot_tr(self, table):
+        return table.select("tfoot tr")
+
+    def _setup_build_doc(self):
+        raw_text = _read(self.io, self.encoding, self.storage_options)
+        if not raw_text:
+            raise ValueError(f"No text parsed from document: {self.io}")
+        return raw_text
+
+    def _build_doc(self):
+        from bs4 import BeautifulSoup
+
+        bdoc = self._setup_build_doc()
+        if isinstance(bdoc, bytes) and self.encoding is not None:
+            udoc = bdoc.decode(self.encoding)
+            from_encoding = None
+        else:
+            udoc = bdoc
+            from_encoding = self.encoding
+
+        soup = BeautifulSoup(udoc, features="html5lib", from_encoding=from_encoding)
+
+        for br in soup.find_all("br"):
+            br.replace_with("\n" + br.text)
+
+        return soup
+
+
+def _build_xpath_expr(attrs) -> str:
+    """
+    Build an xpath expression to simulate bs4's ability to pass in kwargs to
+    search for attributes when using the lxml parser.
+
+    Parameters
+    ----------
+    attrs : dict
+        A dict of HTML attributes. These are NOT checked for validity.
+
+    Returns
+    -------
+    expr : unicode
+        An XPath expression that checks for the given HTML attributes.
+    """
+    # give class attribute as class_ because class is a python keyword
+    if "class_" in attrs:
+        attrs["class"] = attrs.pop("class_")
+
+    s = " and ".join([f"@{k}={repr(v)}" for k, v in attrs.items()])
+    return f"[{s}]"
+
+
+_re_namespace = {"re": "http://exslt.org/regular-expressions"}
+
+
+class _LxmlFrameParser(_HtmlFrameParser):
+    """
+    HTML to DataFrame parser that uses lxml under the hood.
+
+    Warning
+    -------
+    This parser can only handle HTTP, FTP, and FILE urls.
+
+    See Also
+    --------
+    _HtmlFrameParser
+    _BeautifulSoupLxmlFrameParser
+
+    Notes
+    -----
+    Documentation strings for this class are in the base class
+    :class:`_HtmlFrameParser`.
+    """
+
+    def _href_getter(self, obj) -> str | None:
+        href = obj.xpath(".//a/@href")
+        return None if not href else href[0]
+
+    def _text_getter(self, obj):
+        return obj.text_content()
+
+    def _parse_td(self, row):
+        # Look for direct children only: the "row" element here may be a
+        # 
foobar
-only rows
+        if header is None:
+            if len(head) == 1:
+                header = 0
+            else:
+                # ignore all-empty-text rows
+                header = [i for i, row in enumerate(head) if any(text for text in row)]
+
+    if foot:
+        body += foot
+
+    # fill out elements of body that are "ragged"
+    _expand_elements(body)
+    with TextParser(body, header=header, **kwargs) as tp:
+        return tp.read()
+
+
+_valid_parsers = {
+    "lxml": _LxmlFrameParser,
+    None: _LxmlFrameParser,
+    "html5lib": _BeautifulSoupHtml5LibFrameParser,
+    "bs4": _BeautifulSoupHtml5LibFrameParser,
+}
+
+
+def _parser_dispatch(flavor: HTMLFlavors | None) -> type[_HtmlFrameParser]:
+    """
+    Choose the parser based on the input flavor.
+
+    Parameters
+    ----------
+    flavor : {{"lxml", "html5lib", "bs4"}} or None
+        The type of parser to use. This must be a valid backend.
+
+    Returns
+    -------
+    cls : _HtmlFrameParser subclass
+        The parser class based on the requested input flavor.
+
+    Raises
+    ------
+    ValueError
+        * If `flavor` is not a valid backend.
+    ImportError
+        * If you do not have the requested `flavor`
+    """
+    valid_parsers = list(_valid_parsers.keys())
+    if flavor not in valid_parsers:
+        raise ValueError(
+            f"{repr(flavor)} is not a valid flavor, valid flavors are {valid_parsers}"
+        )
+
+    if flavor in ("bs4", "html5lib"):
+        import_optional_dependency("html5lib")
+        import_optional_dependency("bs4")
+    else:
+        import_optional_dependency("lxml.etree")
+    return _valid_parsers[flavor]
+
+
+def _print_as_set(s) -> str:
+    arg = ", ".join([pprint_thing(el) for el in s])
+    return f"{{{arg}}}"
+
+
+def _validate_flavor(flavor):
+    if flavor is None:
+        flavor = "lxml", "bs4"
+    elif isinstance(flavor, str):
+        flavor = (flavor,)
+    elif isinstance(flavor, abc.Iterable):
+        if not all(isinstance(flav, str) for flav in flavor):
+            raise TypeError(
+                f"Object of type {repr(type(flavor).__name__)} "
+                f"is not an iterable of strings"
+            )
+    else:
+        msg = repr(flavor) if isinstance(flavor, str) else str(flavor)
+        msg += " is not a valid flavor"
+        raise ValueError(msg)
+
+    flavor = tuple(flavor)
+    valid_flavors = set(_valid_parsers)
+    flavor_set = set(flavor)
+
+    if not flavor_set & valid_flavors:
+        raise ValueError(
+            f"{_print_as_set(flavor_set)} is not a valid set of flavors, valid "
+            f"flavors are {_print_as_set(valid_flavors)}"
+        )
+    return flavor
+
+
+def _parse(
+    flavor,
+    io,
+    match,
+    attrs,
+    encoding,
+    displayed_only,
+    extract_links,
+    storage_options,
+    **kwargs,
+):
+    flavor = _validate_flavor(flavor)
+    compiled_match = re.compile(match)  # you can pass a compiled regex here
+
+    retained = None
+    for flav in flavor:
+        parser = _parser_dispatch(flav)
+        p = parser(
+            io,
+            compiled_match,
+            attrs,
+            encoding,
+            displayed_only,
+            extract_links,
+            storage_options,
+        )
+
+        try:
+            tables = p.parse_tables()
+        except ValueError as caught:
+            # if `io` is an io-like object, check if it's seekable
+            # and try to rewind it before trying the next parser
+            if hasattr(io, "seekable") and io.seekable():
+                io.seek(0)
+            elif hasattr(io, "seekable") and not io.seekable():
+                # if we couldn't rewind it, let the user know
+                raise ValueError(
+                    f"The flavor {flav} failed to parse your input. "
+                    "Since you passed a non-rewindable file "
+                    "object, we can't rewind it to try "
+                    "another parser. Try read_html() with a different flavor."
+                ) from caught
+
+            retained = caught
+        else:
+            break
+    else:
+        assert retained is not None  # for mypy
+        raise retained
+
+    ret = []
+    for table in tables:
+        try:
+            df = _data_to_frame(data=table, **kwargs)
+            # Cast MultiIndex header to an Index of tuples when extracting header
+            # links and replace nan with None (therefore can't use mi.to_flat_index()).
+            # This maintains consistency of selection (e.g. df.columns.str[1])
+            if extract_links in ("all", "header") and isinstance(
+                df.columns, MultiIndex
+            ):
+                df.columns = Index(
+                    ((col[0], None if isna(col[1]) else col[1]) for col in df.columns),
+                    tupleize_cols=False,
+                )
+
+            ret.append(df)
+        except EmptyDataError:  # empty table
+            continue
+    return ret
+
+
+@doc(storage_options=_shared_docs["storage_options"])
+def read_html(
+    io: FilePath | ReadBuffer[str],
+    *,
+    match: str | Pattern = ".+",
+    flavor: HTMLFlavors | Sequence[HTMLFlavors] | None = None,
+    header: int | Sequence[int] | None = None,
+    index_col: int | Sequence[int] | None = None,
+    skiprows: int | Sequence[int] | slice | None = None,
+    attrs: dict[str, str] | None = None,
+    parse_dates: bool = False,
+    thousands: str | None = ",",
+    encoding: str | None = None,
+    decimal: str = ".",
+    converters: dict | None = None,
+    na_values: Iterable[object] | None = None,
+    keep_default_na: bool = True,
+    displayed_only: bool = True,
+    extract_links: Literal[None, "header", "footer", "body", "all"] = None,
+    dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
+    storage_options: StorageOptions = None,
+) -> list[DataFrame]:
+    r"""
+    Read HTML tables into a ``list`` of ``DataFrame`` objects.
+
+    Parameters
+    ----------
+    io : str, path object, or file-like object
+        String, path object (implementing ``os.PathLike[str]``), or file-like
+        object implementing a string ``read()`` function.
+        The string can represent a URL or the HTML itself. Note that
+        lxml only accepts the http, ftp and file url protocols. If you have a
+        URL that starts with ``'https'`` you might try removing the ``'s'``.
+
+        .. deprecated:: 2.1.0
+            Passing html literal strings is deprecated.
+            Wrap literal string/bytes input in ``io.StringIO``/``io.BytesIO`` instead.
+
+    match : str or compiled regular expression, optional
+        The set of tables containing text matching this regex or string will be
+        returned. Unless the HTML is extremely simple you will probably need to
+        pass a non-empty string here. Defaults to '.+' (match any non-empty
+        string). The default value will return all tables contained on a page.
+        This value is converted to a regular expression so that there is
+        consistent behavior between Beautiful Soup and lxml.
+
+    flavor : {{"lxml", "html5lib", "bs4"}} or list-like, optional
+        The parsing engine (or list of parsing engines) to use. 'bs4' and
+        'html5lib' are synonymous with each other, they are both there for
+        backwards compatibility. The default of ``None`` tries to use ``lxml``
+        to parse and if that fails it falls back on ``bs4`` + ``html5lib``.
+
+    header : int or list-like, optional
+        The row (or list of rows for a :class:`~pandas.MultiIndex`) to use to
+        make the columns headers.
+
+    index_col : int or list-like, optional
+        The column (or list of columns) to use to create the index.
+
+    skiprows : int, list-like or slice, optional
+        Number of rows to skip after parsing the column integer. 0-based. If a
+        sequence of integers or a slice is given, will skip the rows indexed by
+        that sequence.  Note that a single element sequence means 'skip the nth
+        row' whereas an integer means 'skip n rows'.
+
+    attrs : dict, optional
+        This is a dictionary of attributes that you can pass to use to identify
+        the table in the HTML. These are not checked for validity before being
+        passed to lxml or Beautiful Soup. However, these attributes must be
+        valid HTML table attributes to work correctly. For example, ::
+
+            attrs = {{'id': 'table'}}
+
+        is a valid attribute dictionary because the 'id' HTML tag attribute is
+        a valid HTML attribute for *any* HTML tag as per `this document
+        `__. ::
+
+            attrs = {{'asdf': 'table'}}
+
+        is *not* a valid attribute dictionary because 'asdf' is not a valid
+        HTML attribute even if it is a valid XML attribute.  Valid HTML 4.01
+        table attributes can be found `here
+        `__. A
+        working draft of the HTML 5 spec can be found `here
+        `__. It contains the
+        latest information on table attributes for the modern web.
+
+    parse_dates : bool, optional
+        See :func:`~read_csv` for more details.
+
+    thousands : str, optional
+        Separator to use to parse thousands. Defaults to ``','``.
+
+    encoding : str, optional
+        The encoding used to decode the web page. Defaults to ``None``.``None``
+        preserves the previous encoding behavior, which depends on the
+        underlying parser library (e.g., the parser library will try to use
+        the encoding provided by the document).
+
+    decimal : str, default '.'
+        Character to recognize as decimal point (e.g. use ',' for European
+        data).
+
+    converters : dict, default None
+        Dict of functions for converting values in certain columns. Keys can
+        either be integers or column labels, values are functions that take one
+        input argument, the cell (not column) content, and return the
+        transformed content.
+
+    na_values : iterable, default None
+        Custom NA values.
+
+    keep_default_na : bool, default True
+        If na_values are specified and keep_default_na is False the default NaN
+        values are overridden, otherwise they're appended to.
+
+    displayed_only : bool, default True
+        Whether elements with "display: none" should be parsed.
+
+    extract_links : {{None, "all", "header", "body", "footer"}}
+        Table elements in the specified section(s) with  tags will have their
+        href extracted.
+
+        .. versionadded:: 1.5.0
+
+    dtype_backend : {{'numpy_nullable', 'pyarrow'}}, default 'numpy_nullable'
+        Back-end data type applied to the resultant :class:`DataFrame`
+        (still experimental). Behaviour is as follows:
+
+        * ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame`
+          (default).
+        * ``"pyarrow"``: returns pyarrow-backed nullable :class:`ArrowDtype`
+          DataFrame.
+
+        .. versionadded:: 2.0
+
+    {storage_options}
+
+        .. versionadded:: 2.1.0
+
+    Returns
+    -------
+    dfs
+        A list of DataFrames.
+
+    See Also
+    --------
+    read_csv : Read a comma-separated values (csv) file into DataFrame.
+
+    Notes
+    -----
+    Before using this function you should read the :ref:`gotchas about the
+    HTML parsing libraries `.
+
+    Expect to do some cleanup after you call this function. For example, you
+    might need to manually assign column names if the column names are
+    converted to NaN when you pass the `header=0` argument. We try to assume as
+    little as possible about the structure of the table and push the
+    idiosyncrasies of the HTML contained in the table to the user.
+
+    This function searches for ```` elements and only for ````
+    and ```` or ```` argument, it is used to construct
+    the header, otherwise the function attempts to find the header within
+    the body (by putting rows with only ``
`` rows and ```` elements within each ``
``
+    element in the table. ```` stands for "table data". This function
+    attempts to properly handle ``colspan`` and ``rowspan`` attributes.
+    If the function has a ``
`` elements into the header).
+
+    Similar to :func:`~read_csv` the `header` argument is applied
+    **after** `skiprows` is applied.
+
+    This function will *always* return a list of :class:`DataFrame` *or*
+    it will fail, e.g., it will *not* return an empty list.
+
+    Examples
+    --------
+    See the :ref:`read_html documentation in the IO section of the docs
+    ` for some examples of reading in HTML tables.
+    """
+    # Type check here. We don't want to parse only to fail because of an
+    # invalid value of an integer skiprows.
+    if isinstance(skiprows, numbers.Integral) and skiprows < 0:
+        raise ValueError(
+            "cannot skip rows starting from the end of the "
+            "data (you passed a negative value)"
+        )
+    if extract_links not in [None, "header", "footer", "body", "all"]:
+        raise ValueError(
+            "`extract_links` must be one of "
+            '{None, "header", "footer", "body", "all"}, got '
+            f'"{extract_links}"'
+        )
+
+    validate_header_arg(header)
+    check_dtype_backend(dtype_backend)
+
+    io = stringify_path(io)
+
+    if isinstance(io, str) and not any(
+        [
+            is_file_like(io),
+            file_exists(io),
+            is_url(io),
+            is_fsspec_url(io),
+        ]
+    ):
+        warnings.warn(
+            "Passing literal html to 'read_html' is deprecated and "
+            "will be removed in a future version. To read from a "
+            "literal string, wrap it in a 'StringIO' object.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+
+    return _parse(
+        flavor=flavor,
+        io=io,
+        match=match,
+        header=header,
+        index_col=index_col,
+        skiprows=skiprows,
+        parse_dates=parse_dates,
+        thousands=thousands,
+        attrs=attrs,
+        encoding=encoding,
+        decimal=decimal,
+        converters=converters,
+        na_values=na_values,
+        keep_default_na=keep_default_na,
+        displayed_only=displayed_only,
+        extract_links=extract_links,
+        dtype_backend=dtype_backend,
+        storage_options=storage_options,
+    )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/json/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/json/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..8f4e7a62834b57c151189cdd2994a55d1ad9f7de
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/json/__init__.py
@@ -0,0 +1,15 @@
+from pandas.io.json._json import (
+    read_json,
+    to_json,
+    ujson_dumps,
+    ujson_loads,
+)
+from pandas.io.json._table_schema import build_table_schema
+
+__all__ = [
+    "ujson_dumps",
+    "ujson_loads",
+    "read_json",
+    "to_json",
+    "build_table_schema",
+]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/json/_json.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/json/_json.py
new file mode 100644
index 0000000000000000000000000000000000000000..c0499ce750cf01e7c50a2a117652bae273c5251d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/json/_json.py
@@ -0,0 +1,1494 @@
+from __future__ import annotations
+
+from abc import (
+    ABC,
+    abstractmethod,
+)
+from collections import abc
+from io import StringIO
+from itertools import islice
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Generic,
+    Literal,
+    TypeVar,
+    final,
+    overload,
+)
+import warnings
+
+import numpy as np
+
+from pandas._libs import lib
+from pandas._libs.json import (
+    ujson_dumps,
+    ujson_loads,
+)
+from pandas._libs.tslibs import iNaT
+from pandas.compat._optional import import_optional_dependency
+from pandas.errors import AbstractMethodError
+from pandas.util._decorators import doc
+from pandas.util._exceptions import find_stack_level
+from pandas.util._validators import check_dtype_backend
+
+from pandas.core.dtypes.common import (
+    ensure_str,
+    is_string_dtype,
+)
+from pandas.core.dtypes.dtypes import PeriodDtype
+
+from pandas import (
+    DataFrame,
+    Index,
+    MultiIndex,
+    Series,
+    isna,
+    notna,
+    to_datetime,
+)
+from pandas.core.reshape.concat import concat
+from pandas.core.shared_docs import _shared_docs
+
+from pandas.io._util import arrow_table_to_pandas
+from pandas.io.common import (
+    IOHandles,
+    dedup_names,
+    extension_to_compression,
+    file_exists,
+    get_handle,
+    is_fsspec_url,
+    is_potential_multi_index,
+    is_url,
+    stringify_path,
+)
+from pandas.io.json._normalize import convert_to_line_delimits
+from pandas.io.json._table_schema import (
+    build_table_schema,
+    parse_table_schema,
+)
+from pandas.io.parsers.readers import validate_integer
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Hashable,
+        Mapping,
+    )
+    from types import TracebackType
+
+    from pandas._typing import (
+        CompressionOptions,
+        DtypeArg,
+        DtypeBackend,
+        FilePath,
+        IndexLabel,
+        JSONEngine,
+        JSONSerializable,
+        ReadBuffer,
+        Self,
+        StorageOptions,
+        WriteBuffer,
+    )
+
+    from pandas.core.generic import NDFrame
+
+FrameSeriesStrT = TypeVar("FrameSeriesStrT", bound=Literal["frame", "series"])
+
+
+# interface to/from
+@overload
+def to_json(
+    path_or_buf: FilePath | WriteBuffer[str] | WriteBuffer[bytes],
+    obj: NDFrame,
+    orient: str | None = ...,
+    date_format: str = ...,
+    double_precision: int = ...,
+    force_ascii: bool = ...,
+    date_unit: str = ...,
+    default_handler: Callable[[Any], JSONSerializable] | None = ...,
+    lines: bool = ...,
+    compression: CompressionOptions = ...,
+    index: bool | None = ...,
+    indent: int = ...,
+    storage_options: StorageOptions = ...,
+    mode: Literal["a", "w"] = ...,
+) -> None:
+    ...
+
+
+@overload
+def to_json(
+    path_or_buf: None,
+    obj: NDFrame,
+    orient: str | None = ...,
+    date_format: str = ...,
+    double_precision: int = ...,
+    force_ascii: bool = ...,
+    date_unit: str = ...,
+    default_handler: Callable[[Any], JSONSerializable] | None = ...,
+    lines: bool = ...,
+    compression: CompressionOptions = ...,
+    index: bool | None = ...,
+    indent: int = ...,
+    storage_options: StorageOptions = ...,
+    mode: Literal["a", "w"] = ...,
+) -> str:
+    ...
+
+
+def to_json(
+    path_or_buf: FilePath | WriteBuffer[str] | WriteBuffer[bytes] | None,
+    obj: NDFrame,
+    orient: str | None = None,
+    date_format: str = "epoch",
+    double_precision: int = 10,
+    force_ascii: bool = True,
+    date_unit: str = "ms",
+    default_handler: Callable[[Any], JSONSerializable] | None = None,
+    lines: bool = False,
+    compression: CompressionOptions = "infer",
+    index: bool | None = None,
+    indent: int = 0,
+    storage_options: StorageOptions | None = None,
+    mode: Literal["a", "w"] = "w",
+) -> str | None:
+    if orient in ["records", "values"] and index is True:
+        raise ValueError(
+            "'index=True' is only valid when 'orient' is 'split', 'table', "
+            "'index', or 'columns'."
+        )
+    elif orient in ["index", "columns"] and index is False:
+        raise ValueError(
+            "'index=False' is only valid when 'orient' is 'split', 'table', "
+            "'records', or 'values'."
+        )
+    elif index is None:
+        # will be ignored for orient='records' and 'values'
+        index = True
+
+    if lines and orient != "records":
+        raise ValueError("'lines' keyword only valid when 'orient' is records")
+
+    if mode not in ["a", "w"]:
+        msg = (
+            f"mode={mode} is not a valid option."
+            "Only 'w' and 'a' are currently supported."
+        )
+        raise ValueError(msg)
+
+    if mode == "a" and (not lines or orient != "records"):
+        msg = (
+            "mode='a' (append) is only supported when "
+            "lines is True and orient is 'records'"
+        )
+        raise ValueError(msg)
+
+    if orient == "table" and isinstance(obj, Series):
+        obj = obj.to_frame(name=obj.name or "values")
+
+    writer: type[Writer]
+    if orient == "table" and isinstance(obj, DataFrame):
+        writer = JSONTableWriter
+    elif isinstance(obj, Series):
+        writer = SeriesWriter
+    elif isinstance(obj, DataFrame):
+        writer = FrameWriter
+    else:
+        raise NotImplementedError("'obj' should be a Series or a DataFrame")
+
+    s = writer(
+        obj,
+        orient=orient,
+        date_format=date_format,
+        double_precision=double_precision,
+        ensure_ascii=force_ascii,
+        date_unit=date_unit,
+        default_handler=default_handler,
+        index=index,
+        indent=indent,
+    ).write()
+
+    if lines:
+        s = convert_to_line_delimits(s)
+
+    if path_or_buf is not None:
+        # apply compression and byte/text conversion
+        with get_handle(
+            path_or_buf, mode, compression=compression, storage_options=storage_options
+        ) as handles:
+            handles.handle.write(s)
+    else:
+        return s
+    return None
+
+
+class Writer(ABC):
+    _default_orient: str
+
+    def __init__(
+        self,
+        obj: NDFrame,
+        orient: str | None,
+        date_format: str,
+        double_precision: int,
+        ensure_ascii: bool,
+        date_unit: str,
+        index: bool,
+        default_handler: Callable[[Any], JSONSerializable] | None = None,
+        indent: int = 0,
+    ) -> None:
+        self.obj = obj
+
+        if orient is None:
+            orient = self._default_orient
+
+        self.orient = orient
+        self.date_format = date_format
+        self.double_precision = double_precision
+        self.ensure_ascii = ensure_ascii
+        self.date_unit = date_unit
+        self.default_handler = default_handler
+        self.index = index
+        self.indent = indent
+
+        self.is_copy = None
+        self._format_axes()
+
+    def _format_axes(self) -> None:
+        raise AbstractMethodError(self)
+
+    def write(self) -> str:
+        iso_dates = self.date_format == "iso"
+        return ujson_dumps(
+            self.obj_to_write,
+            orient=self.orient,
+            double_precision=self.double_precision,
+            ensure_ascii=self.ensure_ascii,
+            date_unit=self.date_unit,
+            iso_dates=iso_dates,
+            default_handler=self.default_handler,
+            indent=self.indent,
+        )
+
+    @property
+    @abstractmethod
+    def obj_to_write(self) -> NDFrame | Mapping[IndexLabel, Any]:
+        """Object to write in JSON format."""
+
+
+class SeriesWriter(Writer):
+    _default_orient = "index"
+
+    @property
+    def obj_to_write(self) -> NDFrame | Mapping[IndexLabel, Any]:
+        if not self.index and self.orient == "split":
+            return {"name": self.obj.name, "data": self.obj.values}
+        else:
+            return self.obj
+
+    def _format_axes(self) -> None:
+        if not self.obj.index.is_unique and self.orient == "index":
+            raise ValueError(f"Series index must be unique for orient='{self.orient}'")
+
+
+class FrameWriter(Writer):
+    _default_orient = "columns"
+
+    @property
+    def obj_to_write(self) -> NDFrame | Mapping[IndexLabel, Any]:
+        if not self.index and self.orient == "split":
+            obj_to_write = self.obj.to_dict(orient="split")
+            del obj_to_write["index"]
+        else:
+            obj_to_write = self.obj
+        return obj_to_write
+
+    def _format_axes(self) -> None:
+        """
+        Try to format axes if they are datelike.
+        """
+        if not self.obj.index.is_unique and self.orient in ("index", "columns"):
+            raise ValueError(
+                f"DataFrame index must be unique for orient='{self.orient}'."
+            )
+        if not self.obj.columns.is_unique and self.orient in (
+            "index",
+            "columns",
+            "records",
+        ):
+            raise ValueError(
+                f"DataFrame columns must be unique for orient='{self.orient}'."
+            )
+
+
+class JSONTableWriter(FrameWriter):
+    _default_orient = "records"
+
+    def __init__(
+        self,
+        obj,
+        orient: str | None,
+        date_format: str,
+        double_precision: int,
+        ensure_ascii: bool,
+        date_unit: str,
+        index: bool,
+        default_handler: Callable[[Any], JSONSerializable] | None = None,
+        indent: int = 0,
+    ) -> None:
+        """
+        Adds a `schema` attribute with the Table Schema, resets
+        the index (can't do in caller, because the schema inference needs
+        to know what the index is, forces orient to records, and forces
+        date_format to 'iso'.
+        """
+        super().__init__(
+            obj,
+            orient,
+            date_format,
+            double_precision,
+            ensure_ascii,
+            date_unit,
+            index,
+            default_handler=default_handler,
+            indent=indent,
+        )
+
+        if date_format != "iso":
+            msg = (
+                "Trying to write with `orient='table'` and "
+                f"`date_format='{date_format}'`. Table Schema requires dates "
+                "to be formatted with `date_format='iso'`"
+            )
+            raise ValueError(msg)
+
+        self.schema = build_table_schema(obj, index=self.index)
+
+        # NotImplemented on a column MultiIndex
+        if obj.ndim == 2 and isinstance(obj.columns, MultiIndex):
+            raise NotImplementedError(
+                "orient='table' is not supported for MultiIndex columns"
+            )
+
+        # TODO: Do this timedelta properly in objToJSON.c See GH #15137
+        if (
+            (obj.ndim == 1)
+            and (obj.name in set(obj.index.names))
+            or len(obj.columns.intersection(obj.index.names))
+        ):
+            msg = "Overlapping names between the index and columns"
+            raise ValueError(msg)
+
+        obj = obj.copy()
+        timedeltas = obj.select_dtypes(include=["timedelta"]).columns
+        if len(timedeltas):
+            obj[timedeltas] = obj[timedeltas].map(lambda x: x.isoformat())
+        # Convert PeriodIndex to datetimes before serializing
+        if isinstance(obj.index.dtype, PeriodDtype):
+            obj.index = obj.index.to_timestamp()
+
+        # exclude index from obj if index=False
+        if not self.index:
+            self.obj = obj.reset_index(drop=True)
+        else:
+            self.obj = obj.reset_index(drop=False)
+        self.date_format = "iso"
+        self.orient = "records"
+        self.index = index
+
+    @property
+    def obj_to_write(self) -> NDFrame | Mapping[IndexLabel, Any]:
+        return {"schema": self.schema, "data": self.obj}
+
+
+@overload
+def read_json(
+    path_or_buf: FilePath | ReadBuffer[str] | ReadBuffer[bytes],
+    *,
+    orient: str | None = ...,
+    typ: Literal["frame"] = ...,
+    dtype: DtypeArg | None = ...,
+    convert_axes: bool | None = ...,
+    convert_dates: bool | list[str] = ...,
+    keep_default_dates: bool = ...,
+    precise_float: bool = ...,
+    date_unit: str | None = ...,
+    encoding: str | None = ...,
+    encoding_errors: str | None = ...,
+    lines: bool = ...,
+    chunksize: int,
+    compression: CompressionOptions = ...,
+    nrows: int | None = ...,
+    storage_options: StorageOptions = ...,
+    dtype_backend: DtypeBackend | lib.NoDefault = ...,
+    engine: JSONEngine = ...,
+) -> JsonReader[Literal["frame"]]:
+    ...
+
+
+@overload
+def read_json(
+    path_or_buf: FilePath | ReadBuffer[str] | ReadBuffer[bytes],
+    *,
+    orient: str | None = ...,
+    typ: Literal["series"],
+    dtype: DtypeArg | None = ...,
+    convert_axes: bool | None = ...,
+    convert_dates: bool | list[str] = ...,
+    keep_default_dates: bool = ...,
+    precise_float: bool = ...,
+    date_unit: str | None = ...,
+    encoding: str | None = ...,
+    encoding_errors: str | None = ...,
+    lines: bool = ...,
+    chunksize: int,
+    compression: CompressionOptions = ...,
+    nrows: int | None = ...,
+    storage_options: StorageOptions = ...,
+    dtype_backend: DtypeBackend | lib.NoDefault = ...,
+    engine: JSONEngine = ...,
+) -> JsonReader[Literal["series"]]:
+    ...
+
+
+@overload
+def read_json(
+    path_or_buf: FilePath | ReadBuffer[str] | ReadBuffer[bytes],
+    *,
+    orient: str | None = ...,
+    typ: Literal["series"],
+    dtype: DtypeArg | None = ...,
+    convert_axes: bool | None = ...,
+    convert_dates: bool | list[str] = ...,
+    keep_default_dates: bool = ...,
+    precise_float: bool = ...,
+    date_unit: str | None = ...,
+    encoding: str | None = ...,
+    encoding_errors: str | None = ...,
+    lines: bool = ...,
+    chunksize: None = ...,
+    compression: CompressionOptions = ...,
+    nrows: int | None = ...,
+    storage_options: StorageOptions = ...,
+    dtype_backend: DtypeBackend | lib.NoDefault = ...,
+    engine: JSONEngine = ...,
+) -> Series:
+    ...
+
+
+@overload
+def read_json(
+    path_or_buf: FilePath | ReadBuffer[str] | ReadBuffer[bytes],
+    *,
+    orient: str | None = ...,
+    typ: Literal["frame"] = ...,
+    dtype: DtypeArg | None = ...,
+    convert_axes: bool | None = ...,
+    convert_dates: bool | list[str] = ...,
+    keep_default_dates: bool = ...,
+    precise_float: bool = ...,
+    date_unit: str | None = ...,
+    encoding: str | None = ...,
+    encoding_errors: str | None = ...,
+    lines: bool = ...,
+    chunksize: None = ...,
+    compression: CompressionOptions = ...,
+    nrows: int | None = ...,
+    storage_options: StorageOptions = ...,
+    dtype_backend: DtypeBackend | lib.NoDefault = ...,
+    engine: JSONEngine = ...,
+) -> DataFrame:
+    ...
+
+
+@doc(
+    storage_options=_shared_docs["storage_options"],
+    decompression_options=_shared_docs["decompression_options"] % "path_or_buf",
+)
+def read_json(
+    path_or_buf: FilePath | ReadBuffer[str] | ReadBuffer[bytes],
+    *,
+    orient: str | None = None,
+    typ: Literal["frame", "series"] = "frame",
+    dtype: DtypeArg | None = None,
+    convert_axes: bool | None = None,
+    convert_dates: bool | list[str] = True,
+    keep_default_dates: bool = True,
+    precise_float: bool = False,
+    date_unit: str | None = None,
+    encoding: str | None = None,
+    encoding_errors: str | None = "strict",
+    lines: bool = False,
+    chunksize: int | None = None,
+    compression: CompressionOptions = "infer",
+    nrows: int | None = None,
+    storage_options: StorageOptions | None = None,
+    dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
+    engine: JSONEngine = "ujson",
+) -> DataFrame | Series | JsonReader:
+    """
+    Convert a JSON string to pandas object.
+
+    Parameters
+    ----------
+    path_or_buf : a valid JSON str, path object or file-like object
+        Any valid string path is acceptable. The string could be a URL. Valid
+        URL schemes include http, ftp, s3, and file. For file URLs, a host is
+        expected. A local file could be:
+        ``file://localhost/path/to/table.json``.
+
+        If you want to pass in a path object, pandas accepts any
+        ``os.PathLike``.
+
+        By file-like object, we refer to objects with a ``read()`` method,
+        such as a file handle (e.g. via builtin ``open`` function)
+        or ``StringIO``.
+
+        .. deprecated:: 2.1.0
+            Passing json literal strings is deprecated.
+
+    orient : str, optional
+        Indication of expected JSON string format.
+        Compatible JSON strings can be produced by ``to_json()`` with a
+        corresponding orient value.
+        The set of possible orients is:
+
+        - ``'split'`` : dict like
+          ``{{index -> [index], columns -> [columns], data -> [values]}}``
+        - ``'records'`` : list like
+          ``[{{column -> value}}, ... , {{column -> value}}]``
+        - ``'index'`` : dict like ``{{index -> {{column -> value}}}}``
+        - ``'columns'`` : dict like ``{{column -> {{index -> value}}}}``
+        - ``'values'`` : just the values array
+        - ``'table'`` : dict like ``{{'schema': {{schema}}, 'data': {{data}}}}``
+
+        The allowed and default values depend on the value
+        of the `typ` parameter.
+
+        * when ``typ == 'series'``,
+
+          - allowed orients are ``{{'split','records','index'}}``
+          - default is ``'index'``
+          - The Series index must be unique for orient ``'index'``.
+
+        * when ``typ == 'frame'``,
+
+          - allowed orients are ``{{'split','records','index',
+            'columns','values', 'table'}}``
+          - default is ``'columns'``
+          - The DataFrame index must be unique for orients ``'index'`` and
+            ``'columns'``.
+          - The DataFrame columns must be unique for orients ``'index'``,
+            ``'columns'``, and ``'records'``.
+
+    typ : {{'frame', 'series'}}, default 'frame'
+        The type of object to recover.
+
+    dtype : bool or dict, default None
+        If True, infer dtypes; if a dict of column to dtype, then use those;
+        if False, then don't infer dtypes at all, applies only to the data.
+
+        For all ``orient`` values except ``'table'``, default is True.
+
+    convert_axes : bool, default None
+        Try to convert the axes to the proper dtypes.
+
+        For all ``orient`` values except ``'table'``, default is True.
+
+    convert_dates : bool or list of str, default True
+        If True then default datelike columns may be converted (depending on
+        keep_default_dates).
+        If False, no dates will be converted.
+        If a list of column names, then those columns will be converted and
+        default datelike columns may also be converted (depending on
+        keep_default_dates).
+
+    keep_default_dates : bool, default True
+        If parsing dates (convert_dates is not False), then try to parse the
+        default datelike columns.
+        A column label is datelike if
+
+        * it ends with ``'_at'``,
+
+        * it ends with ``'_time'``,
+
+        * it begins with ``'timestamp'``,
+
+        * it is ``'modified'``, or
+
+        * it is ``'date'``.
+
+    precise_float : bool, default False
+        Set to enable usage of higher precision (strtod) function when
+        decoding string to double values. Default (False) is to use fast but
+        less precise builtin functionality.
+
+    date_unit : str, default None
+        The timestamp unit to detect if converting dates. The default behaviour
+        is to try and detect the correct precision, but if this is not desired
+        then pass one of 's', 'ms', 'us' or 'ns' to force parsing only seconds,
+        milliseconds, microseconds or nanoseconds respectively.
+
+    encoding : str, default is 'utf-8'
+        The encoding to use to decode py3 bytes.
+
+    encoding_errors : str, optional, default "strict"
+        How encoding errors are treated. `List of possible values
+        `_ .
+
+        .. versionadded:: 1.3.0
+
+    lines : bool, default False
+        Read the file as a json object per line.
+
+    chunksize : int, optional
+        Return JsonReader object for iteration.
+        See the `line-delimited json docs
+        `_
+        for more information on ``chunksize``.
+        This can only be passed if `lines=True`.
+        If this is None, the file will be read into memory all at once.
+    {decompression_options}
+
+        .. versionchanged:: 1.4.0 Zstandard support.
+
+    nrows : int, optional
+        The number of lines from the line-delimited jsonfile that has to be read.
+        This can only be passed if `lines=True`.
+        If this is None, all the rows will be returned.
+
+    {storage_options}
+
+    dtype_backend : {{'numpy_nullable', 'pyarrow'}}, default 'numpy_nullable'
+        Back-end data type applied to the resultant :class:`DataFrame`
+        (still experimental). Behaviour is as follows:
+
+        * ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame`
+          (default).
+        * ``"pyarrow"``: returns pyarrow-backed nullable :class:`ArrowDtype`
+          DataFrame.
+
+        .. versionadded:: 2.0
+
+    engine : {{"ujson", "pyarrow"}}, default "ujson"
+        Parser engine to use. The ``"pyarrow"`` engine is only available when
+        ``lines=True``.
+
+        .. versionadded:: 2.0
+
+    Returns
+    -------
+    Series, DataFrame, or pandas.api.typing.JsonReader
+        A JsonReader is returned when ``chunksize`` is not ``0`` or ``None``.
+        Otherwise, the type returned depends on the value of ``typ``.
+
+    See Also
+    --------
+    DataFrame.to_json : Convert a DataFrame to a JSON string.
+    Series.to_json : Convert a Series to a JSON string.
+    json_normalize : Normalize semi-structured JSON data into a flat table.
+
+    Notes
+    -----
+    Specific to ``orient='table'``, if a :class:`DataFrame` with a literal
+    :class:`Index` name of `index` gets written with :func:`to_json`, the
+    subsequent read operation will incorrectly set the :class:`Index` name to
+    ``None``. This is because `index` is also used by :func:`DataFrame.to_json`
+    to denote a missing :class:`Index` name, and the subsequent
+    :func:`read_json` operation cannot distinguish between the two. The same
+    limitation is encountered with a :class:`MultiIndex` and any names
+    beginning with ``'level_'``.
+
+    Examples
+    --------
+    >>> from io import StringIO
+    >>> df = pd.DataFrame([['a', 'b'], ['c', 'd']],
+    ...                   index=['row 1', 'row 2'],
+    ...                   columns=['col 1', 'col 2'])
+
+    Encoding/decoding a Dataframe using ``'split'`` formatted JSON:
+
+    >>> df.to_json(orient='split')
+        '\
+{{\
+"columns":["col 1","col 2"],\
+"index":["row 1","row 2"],\
+"data":[["a","b"],["c","d"]]\
+}}\
+'
+    >>> pd.read_json(StringIO(_), orient='split')
+          col 1 col 2
+    row 1     a     b
+    row 2     c     d
+
+    Encoding/decoding a Dataframe using ``'index'`` formatted JSON:
+
+    >>> df.to_json(orient='index')
+    '{{"row 1":{{"col 1":"a","col 2":"b"}},"row 2":{{"col 1":"c","col 2":"d"}}}}'
+
+    >>> pd.read_json(StringIO(_), orient='index')
+          col 1 col 2
+    row 1     a     b
+    row 2     c     d
+
+    Encoding/decoding a Dataframe using ``'records'`` formatted JSON.
+    Note that index labels are not preserved with this encoding.
+
+    >>> df.to_json(orient='records')
+    '[{{"col 1":"a","col 2":"b"}},{{"col 1":"c","col 2":"d"}}]'
+    >>> pd.read_json(StringIO(_), orient='records')
+      col 1 col 2
+    0     a     b
+    1     c     d
+
+    Encoding with Table Schema
+
+    >>> df.to_json(orient='table')
+        '\
+{{"schema":{{"fields":[\
+{{"name":"index","type":"string"}},\
+{{"name":"col 1","type":"string"}},\
+{{"name":"col 2","type":"string"}}],\
+"primaryKey":["index"],\
+"pandas_version":"1.4.0"}},\
+"data":[\
+{{"index":"row 1","col 1":"a","col 2":"b"}},\
+{{"index":"row 2","col 1":"c","col 2":"d"}}]\
+}}\
+'
+
+    The following example uses ``dtype_backend="numpy_nullable"``
+
+    >>> data = '''{{"index": {{"0": 0, "1": 1}},
+    ...        "a": {{"0": 1, "1": null}},
+    ...        "b": {{"0": 2.5, "1": 4.5}},
+    ...        "c": {{"0": true, "1": false}},
+    ...        "d": {{"0": "a", "1": "b"}},
+    ...        "e": {{"0": 1577.2, "1": 1577.1}}}}'''
+    >>> pd.read_json(StringIO(data), dtype_backend="numpy_nullable")
+       index     a    b      c  d       e
+    0      0     1  2.5   True  a  1577.2
+    1      1    4.5  False  b  1577.1
+    """
+    if orient == "table" and dtype:
+        raise ValueError("cannot pass both dtype and orient='table'")
+    if orient == "table" and convert_axes:
+        raise ValueError("cannot pass both convert_axes and orient='table'")
+
+    check_dtype_backend(dtype_backend)
+
+    if dtype is None and orient != "table":
+        # error: Incompatible types in assignment (expression has type "bool", variable
+        # has type "Union[ExtensionDtype, str, dtype[Any], Type[str], Type[float],
+        # Type[int], Type[complex], Type[bool], Type[object], Dict[Hashable,
+        # Union[ExtensionDtype, Union[str, dtype[Any]], Type[str], Type[float],
+        # Type[int], Type[complex], Type[bool], Type[object]]], None]")
+        dtype = True  # type: ignore[assignment]
+    if convert_axes is None and orient != "table":
+        convert_axes = True
+
+    json_reader = JsonReader(
+        path_or_buf,
+        orient=orient,
+        typ=typ,
+        dtype=dtype,
+        convert_axes=convert_axes,
+        convert_dates=convert_dates,
+        keep_default_dates=keep_default_dates,
+        precise_float=precise_float,
+        date_unit=date_unit,
+        encoding=encoding,
+        lines=lines,
+        chunksize=chunksize,
+        compression=compression,
+        nrows=nrows,
+        storage_options=storage_options,
+        encoding_errors=encoding_errors,
+        dtype_backend=dtype_backend,
+        engine=engine,
+    )
+
+    if chunksize:
+        return json_reader
+    else:
+        return json_reader.read()
+
+
+class JsonReader(abc.Iterator, Generic[FrameSeriesStrT]):
+    """
+    JsonReader provides an interface for reading in a JSON file.
+
+    If initialized with ``lines=True`` and ``chunksize``, can be iterated over
+    ``chunksize`` lines at a time. Otherwise, calling ``read`` reads in the
+    whole document.
+    """
+
+    def __init__(
+        self,
+        filepath_or_buffer,
+        orient,
+        typ: FrameSeriesStrT,
+        dtype,
+        convert_axes: bool | None,
+        convert_dates,
+        keep_default_dates: bool,
+        precise_float: bool,
+        date_unit,
+        encoding,
+        lines: bool,
+        chunksize: int | None,
+        compression: CompressionOptions,
+        nrows: int | None,
+        storage_options: StorageOptions | None = None,
+        encoding_errors: str | None = "strict",
+        dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
+        engine: JSONEngine = "ujson",
+    ) -> None:
+        self.orient = orient
+        self.typ = typ
+        self.dtype = dtype
+        self.convert_axes = convert_axes
+        self.convert_dates = convert_dates
+        self.keep_default_dates = keep_default_dates
+        self.precise_float = precise_float
+        self.date_unit = date_unit
+        self.encoding = encoding
+        self.engine = engine
+        self.compression = compression
+        self.storage_options = storage_options
+        self.lines = lines
+        self.chunksize = chunksize
+        self.nrows_seen = 0
+        self.nrows = nrows
+        self.encoding_errors = encoding_errors
+        self.handles: IOHandles[str] | None = None
+        self.dtype_backend = dtype_backend
+
+        if self.engine not in {"pyarrow", "ujson"}:
+            raise ValueError(
+                f"The engine type {self.engine} is currently not supported."
+            )
+        if self.chunksize is not None:
+            self.chunksize = validate_integer("chunksize", self.chunksize, 1)
+            if not self.lines:
+                raise ValueError("chunksize can only be passed if lines=True")
+            if self.engine == "pyarrow":
+                raise ValueError(
+                    "currently pyarrow engine doesn't support chunksize parameter"
+                )
+        if self.nrows is not None:
+            self.nrows = validate_integer("nrows", self.nrows, 0)
+            if not self.lines:
+                raise ValueError("nrows can only be passed if lines=True")
+        if (
+            isinstance(filepath_or_buffer, str)
+            and not self.lines
+            and "\n" in filepath_or_buffer
+        ):
+            warnings.warn(
+                "Passing literal json to 'read_json' is deprecated and "
+                "will be removed in a future version. To read from a "
+                "literal string, wrap it in a 'StringIO' object.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+        if self.engine == "pyarrow":
+            if not self.lines:
+                raise ValueError(
+                    "currently pyarrow engine only supports "
+                    "the line-delimited JSON format"
+                )
+            self.data = filepath_or_buffer
+        elif self.engine == "ujson":
+            data = self._get_data_from_filepath(filepath_or_buffer)
+            self.data = self._preprocess_data(data)
+
+    def _preprocess_data(self, data):
+        """
+        At this point, the data either has a `read` attribute (e.g. a file
+        object or a StringIO) or is a string that is a JSON document.
+
+        If self.chunksize, we prepare the data for the `__next__` method.
+        Otherwise, we read it into memory for the `read` method.
+        """
+        if hasattr(data, "read") and not (self.chunksize or self.nrows):
+            with self:
+                data = data.read()
+        if not hasattr(data, "read") and (self.chunksize or self.nrows):
+            data = StringIO(data)
+
+        return data
+
+    def _get_data_from_filepath(self, filepath_or_buffer):
+        """
+        The function read_json accepts three input types:
+            1. filepath (string-like)
+            2. file-like object (e.g. open file object, StringIO)
+            3. JSON string
+
+        This method turns (1) into (2) to simplify the rest of the processing.
+        It returns input types (2) and (3) unchanged.
+
+        It raises FileNotFoundError if the input is a string ending in
+        one of .json, .json.gz, .json.bz2, etc. but no such file exists.
+        """
+        # if it is a string but the file does not exist, it might be a JSON string
+        filepath_or_buffer = stringify_path(filepath_or_buffer)
+        if (
+            not isinstance(filepath_or_buffer, str)
+            or is_url(filepath_or_buffer)
+            or is_fsspec_url(filepath_or_buffer)
+            or file_exists(filepath_or_buffer)
+        ):
+            self.handles = get_handle(
+                filepath_or_buffer,
+                "r",
+                encoding=self.encoding,
+                compression=self.compression,
+                storage_options=self.storage_options,
+                errors=self.encoding_errors,
+            )
+            filepath_or_buffer = self.handles.handle
+        elif (
+            isinstance(filepath_or_buffer, str)
+            and filepath_or_buffer.lower().endswith(
+                (".json",) + tuple(f".json{c}" for c in extension_to_compression)
+            )
+            and not file_exists(filepath_or_buffer)
+        ):
+            raise FileNotFoundError(f"File {filepath_or_buffer} does not exist")
+        else:
+            warnings.warn(
+                "Passing literal json to 'read_json' is deprecated and "
+                "will be removed in a future version. To read from a "
+                "literal string, wrap it in a 'StringIO' object.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+        return filepath_or_buffer
+
+    def _combine_lines(self, lines) -> str:
+        """
+        Combines a list of JSON objects into one JSON object.
+        """
+        return (
+            f'[{",".join([line for line in (line.strip() for line in lines) if line])}]'
+        )
+
+    @overload
+    def read(self: JsonReader[Literal["frame"]]) -> DataFrame:
+        ...
+
+    @overload
+    def read(self: JsonReader[Literal["series"]]) -> Series:
+        ...
+
+    @overload
+    def read(self: JsonReader[Literal["frame", "series"]]) -> DataFrame | Series:
+        ...
+
+    def read(self) -> DataFrame | Series:
+        """
+        Read the whole JSON input into a pandas object.
+        """
+        obj: DataFrame | Series
+        with self:
+            if self.engine == "pyarrow":
+                pyarrow_json = import_optional_dependency("pyarrow.json")
+                pa_table = pyarrow_json.read_json(self.data)
+                return arrow_table_to_pandas(pa_table, dtype_backend=self.dtype_backend)
+            elif self.engine == "ujson":
+                if self.lines:
+                    if self.chunksize:
+                        obj = concat(self)
+                    elif self.nrows:
+                        lines = list(islice(self.data, self.nrows))
+                        lines_json = self._combine_lines(lines)
+                        obj = self._get_object_parser(lines_json)
+                    else:
+                        data = ensure_str(self.data)
+                        data_lines = data.split("\n")
+                        obj = self._get_object_parser(self._combine_lines(data_lines))
+                else:
+                    obj = self._get_object_parser(self.data)
+                if self.dtype_backend is not lib.no_default:
+                    return obj.convert_dtypes(
+                        infer_objects=False, dtype_backend=self.dtype_backend
+                    )
+                else:
+                    return obj
+
+    def _get_object_parser(self, json) -> DataFrame | Series:
+        """
+        Parses a json document into a pandas object.
+        """
+        typ = self.typ
+        dtype = self.dtype
+        kwargs = {
+            "orient": self.orient,
+            "dtype": self.dtype,
+            "convert_axes": self.convert_axes,
+            "convert_dates": self.convert_dates,
+            "keep_default_dates": self.keep_default_dates,
+            "precise_float": self.precise_float,
+            "date_unit": self.date_unit,
+            "dtype_backend": self.dtype_backend,
+        }
+        obj = None
+        if typ == "frame":
+            obj = FrameParser(json, **kwargs).parse()
+
+        if typ == "series" or obj is None:
+            if not isinstance(dtype, bool):
+                kwargs["dtype"] = dtype
+            obj = SeriesParser(json, **kwargs).parse()
+
+        return obj
+
+    def close(self) -> None:
+        """
+        If we opened a stream earlier, in _get_data_from_filepath, we should
+        close it.
+
+        If an open stream or file was passed, we leave it open.
+        """
+        if self.handles is not None:
+            self.handles.close()
+
+    def __iter__(self) -> Self:
+        return self
+
+    @overload
+    def __next__(self: JsonReader[Literal["frame"]]) -> DataFrame:
+        ...
+
+    @overload
+    def __next__(self: JsonReader[Literal["series"]]) -> Series:
+        ...
+
+    @overload
+    def __next__(self: JsonReader[Literal["frame", "series"]]) -> DataFrame | Series:
+        ...
+
+    def __next__(self) -> DataFrame | Series:
+        if self.nrows and self.nrows_seen >= self.nrows:
+            self.close()
+            raise StopIteration
+
+        lines = list(islice(self.data, self.chunksize))
+        if not lines:
+            self.close()
+            raise StopIteration
+
+        try:
+            lines_json = self._combine_lines(lines)
+            obj = self._get_object_parser(lines_json)
+
+            # Make sure that the returned objects have the right index.
+            obj.index = range(self.nrows_seen, self.nrows_seen + len(obj))
+            self.nrows_seen += len(obj)
+        except Exception as ex:
+            self.close()
+            raise ex
+
+        if self.dtype_backend is not lib.no_default:
+            return obj.convert_dtypes(
+                infer_objects=False, dtype_backend=self.dtype_backend
+            )
+        else:
+            return obj
+
+    def __enter__(self) -> Self:
+        return self
+
+    def __exit__(
+        self,
+        exc_type: type[BaseException] | None,
+        exc_value: BaseException | None,
+        traceback: TracebackType | None,
+    ) -> None:
+        self.close()
+
+
+class Parser:
+    _split_keys: tuple[str, ...]
+    _default_orient: str
+
+    _STAMP_UNITS = ("s", "ms", "us", "ns")
+    _MIN_STAMPS = {
+        "s": 31536000,
+        "ms": 31536000000,
+        "us": 31536000000000,
+        "ns": 31536000000000000,
+    }
+    json: str
+
+    def __init__(
+        self,
+        json: str,
+        orient,
+        dtype: DtypeArg | None = None,
+        convert_axes: bool = True,
+        convert_dates: bool | list[str] = True,
+        keep_default_dates: bool = False,
+        precise_float: bool = False,
+        date_unit=None,
+        dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
+    ) -> None:
+        self.json = json
+
+        if orient is None:
+            orient = self._default_orient
+
+        self.orient = orient
+
+        self.dtype = dtype
+
+        if date_unit is not None:
+            date_unit = date_unit.lower()
+            if date_unit not in self._STAMP_UNITS:
+                raise ValueError(f"date_unit must be one of {self._STAMP_UNITS}")
+            self.min_stamp = self._MIN_STAMPS[date_unit]
+        else:
+            self.min_stamp = self._MIN_STAMPS["s"]
+
+        self.precise_float = precise_float
+        self.convert_axes = convert_axes
+        self.convert_dates = convert_dates
+        self.date_unit = date_unit
+        self.keep_default_dates = keep_default_dates
+        self.obj: DataFrame | Series | None = None
+        self.dtype_backend = dtype_backend
+
+    @final
+    def check_keys_split(self, decoded: dict) -> None:
+        """
+        Checks that dict has only the appropriate keys for orient='split'.
+        """
+        bad_keys = set(decoded.keys()).difference(set(self._split_keys))
+        if bad_keys:
+            bad_keys_joined = ", ".join(bad_keys)
+            raise ValueError(f"JSON data had unexpected key(s): {bad_keys_joined}")
+
+    @final
+    def parse(self):
+        self._parse()
+
+        if self.obj is None:
+            return None
+        if self.convert_axes:
+            self._convert_axes()
+        self._try_convert_types()
+        return self.obj
+
+    def _parse(self) -> None:
+        raise AbstractMethodError(self)
+
+    @final
+    def _convert_axes(self) -> None:
+        """
+        Try to convert axes.
+        """
+        obj = self.obj
+        assert obj is not None  # for mypy
+        for axis_name in obj._AXIS_ORDERS:
+            ax = obj._get_axis(axis_name)
+            ser = Series(ax, dtype=ax.dtype, copy=False)
+            new_ser, result = self._try_convert_data(
+                name=axis_name,
+                data=ser,
+                use_dtypes=False,
+                convert_dates=True,
+                is_axis=True,
+            )
+            if result:
+                new_axis = Index(new_ser, dtype=new_ser.dtype, copy=False)
+                setattr(self.obj, axis_name, new_axis)
+
+    def _try_convert_types(self) -> None:
+        raise AbstractMethodError(self)
+
+    @final
+    def _try_convert_data(
+        self,
+        name: Hashable,
+        data: Series,
+        use_dtypes: bool = True,
+        convert_dates: bool | list[str] = True,
+        is_axis: bool = False,
+    ) -> tuple[Series, bool]:
+        """
+        Try to parse a Series into a column by inferring dtype.
+        """
+        # don't try to coerce, unless a force conversion
+        if use_dtypes:
+            if not self.dtype:
+                if all(notna(data)):
+                    return data, False
+
+                with warnings.catch_warnings():
+                    warnings.filterwarnings(
+                        "ignore",
+                        "Downcasting object dtype arrays",
+                        category=FutureWarning,
+                    )
+                    filled = data.fillna(np.nan)
+
+                return filled, True
+
+            elif self.dtype is True:
+                pass
+            else:
+                # dtype to force
+                dtype = (
+                    self.dtype.get(name) if isinstance(self.dtype, dict) else self.dtype
+                )
+                if dtype is not None:
+                    try:
+                        return data.astype(dtype), True
+                    except (TypeError, ValueError):
+                        return data, False
+
+        if convert_dates:
+            new_data, result = self._try_convert_to_date(data)
+            if result:
+                return new_data, True
+
+        converted = False
+        if self.dtype_backend is not lib.no_default and not is_axis:
+            # Fall through for conversion later on
+            return data, True
+        elif is_string_dtype(data.dtype):
+            # try float
+            try:
+                data = data.astype("float64")
+                converted = True
+            except (TypeError, ValueError):
+                pass
+
+        if data.dtype.kind == "f" and data.dtype != "float64":
+            # coerce floats to 64
+            try:
+                data = data.astype("float64")
+                converted = True
+            except (TypeError, ValueError):
+                pass
+
+        # don't coerce 0-len data
+        if len(data) and data.dtype in ("float", "object"):
+            # coerce ints if we can
+            try:
+                new_data = data.astype("int64")
+                if (new_data == data).all():
+                    data = new_data
+                    converted = True
+            except (TypeError, ValueError, OverflowError):
+                pass
+
+        if data.dtype == "int" and data.dtype != "int64":
+            # coerce ints to 64
+            try:
+                data = data.astype("int64")
+                converted = True
+            except (TypeError, ValueError):
+                pass
+
+        # if we have an index, we want to preserve dtypes
+        if name == "index" and len(data):
+            if self.orient == "split":
+                return data, False
+
+        return data, converted
+
+    @final
+    def _try_convert_to_date(self, data: Series) -> tuple[Series, bool]:
+        """
+        Try to parse a ndarray like into a date column.
+
+        Try to coerce object in epoch/iso formats and integer/float in epoch
+        formats. Return a boolean if parsing was successful.
+        """
+        # no conversion on empty
+        if not len(data):
+            return data, False
+
+        new_data = data
+
+        if new_data.dtype == "string":
+            new_data = new_data.astype(object)
+
+        if new_data.dtype == "object":
+            try:
+                new_data = data.astype("int64")
+            except OverflowError:
+                return data, False
+            except (TypeError, ValueError):
+                pass
+
+        # ignore numbers that are out of range
+        if issubclass(new_data.dtype.type, np.number):
+            in_range = (
+                isna(new_data._values)
+                | (new_data > self.min_stamp)
+                | (new_data._values == iNaT)
+            )
+            if not in_range.all():
+                return data, False
+
+        date_units = (self.date_unit,) if self.date_unit else self._STAMP_UNITS
+        for date_unit in date_units:
+            try:
+                with warnings.catch_warnings():
+                    warnings.filterwarnings(
+                        "ignore",
+                        ".*parsing datetimes with mixed time "
+                        "zones will raise an error",
+                        category=FutureWarning,
+                    )
+                    new_data = to_datetime(new_data, errors="raise", unit=date_unit)
+            except (ValueError, OverflowError, TypeError):
+                continue
+            return new_data, True
+        return data, False
+
+
+class SeriesParser(Parser):
+    _default_orient = "index"
+    _split_keys = ("name", "index", "data")
+    obj: Series | None
+
+    def _parse(self) -> None:
+        data = ujson_loads(self.json, precise_float=self.precise_float)
+
+        if self.orient == "split":
+            decoded = {str(k): v for k, v in data.items()}
+            self.check_keys_split(decoded)
+            self.obj = Series(**decoded)
+        else:
+            self.obj = Series(data)
+
+    def _try_convert_types(self) -> None:
+        if self.obj is None:
+            return
+        obj, result = self._try_convert_data(
+            "data", self.obj, convert_dates=self.convert_dates
+        )
+        if result:
+            self.obj = obj
+
+
+class FrameParser(Parser):
+    _default_orient = "columns"
+    _split_keys = ("columns", "index", "data")
+    obj: DataFrame | None
+
+    def _parse(self) -> None:
+        json = self.json
+        orient = self.orient
+
+        if orient == "columns":
+            self.obj = DataFrame(
+                ujson_loads(json, precise_float=self.precise_float), dtype=None
+            )
+        elif orient == "split":
+            decoded = {
+                str(k): v
+                for k, v in ujson_loads(json, precise_float=self.precise_float).items()
+            }
+            self.check_keys_split(decoded)
+            orig_names = [
+                (tuple(col) if isinstance(col, list) else col)
+                for col in decoded["columns"]
+            ]
+            decoded["columns"] = dedup_names(
+                orig_names,
+                is_potential_multi_index(orig_names, None),
+            )
+            self.obj = DataFrame(dtype=None, **decoded)
+        elif orient == "index":
+            self.obj = DataFrame.from_dict(
+                ujson_loads(json, precise_float=self.precise_float),
+                dtype=None,
+                orient="index",
+            )
+        elif orient == "table":
+            self.obj = parse_table_schema(json, precise_float=self.precise_float)
+        else:
+            self.obj = DataFrame(
+                ujson_loads(json, precise_float=self.precise_float), dtype=None
+            )
+
+    def _process_converter(
+        self,
+        f: Callable[[Hashable, Series], tuple[Series, bool]],
+        filt: Callable[[Hashable], bool] | None = None,
+    ) -> None:
+        """
+        Take a conversion function and possibly recreate the frame.
+        """
+        if filt is None:
+            filt = lambda col: True
+
+        obj = self.obj
+        assert obj is not None  # for mypy
+
+        needs_new_obj = False
+        new_obj = {}
+        for i, (col, c) in enumerate(obj.items()):
+            if filt(col):
+                new_data, result = f(col, c)
+                if result:
+                    c = new_data
+                    needs_new_obj = True
+            new_obj[i] = c
+
+        if needs_new_obj:
+            # possibly handle dup columns
+            new_frame = DataFrame(new_obj, index=obj.index)
+            new_frame.columns = obj.columns
+            self.obj = new_frame
+
+    def _try_convert_types(self) -> None:
+        if self.obj is None:
+            return
+        if self.convert_dates:
+            self._try_convert_dates()
+
+        self._process_converter(
+            lambda col, c: self._try_convert_data(col, c, convert_dates=False)
+        )
+
+    def _try_convert_dates(self) -> None:
+        if self.obj is None:
+            return
+
+        # our columns to parse
+        convert_dates_list_bool = self.convert_dates
+        if isinstance(convert_dates_list_bool, bool):
+            convert_dates_list_bool = []
+        convert_dates = set(convert_dates_list_bool)
+
+        def is_ok(col) -> bool:
+            """
+            Return if this col is ok to try for a date parse.
+            """
+            if col in convert_dates:
+                return True
+            if not self.keep_default_dates:
+                return False
+            if not isinstance(col, str):
+                return False
+
+            col_lower = col.lower()
+            if (
+                col_lower.endswith(("_at", "_time"))
+                or col_lower == "modified"
+                or col_lower == "date"
+                or col_lower == "datetime"
+                or col_lower.startswith("timestamp")
+            ):
+                return True
+            return False
+
+        self._process_converter(lambda col, c: self._try_convert_to_date(c), filt=is_ok)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/json/_normalize.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/json/_normalize.py
new file mode 100644
index 0000000000000000000000000000000000000000..b1e2210f9d8940a0931b07e1631350089140ff95
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/json/_normalize.py
@@ -0,0 +1,544 @@
+# ---------------------------------------------------------------------
+# JSON normalization routines
+from __future__ import annotations
+
+from collections import (
+    abc,
+    defaultdict,
+)
+import copy
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    DefaultDict,
+)
+
+import numpy as np
+
+from pandas._libs.writers import convert_json_to_lines
+
+import pandas as pd
+from pandas import DataFrame
+
+if TYPE_CHECKING:
+    from collections.abc import Iterable
+
+    from pandas._typing import (
+        IgnoreRaise,
+        Scalar,
+    )
+
+
+def convert_to_line_delimits(s: str) -> str:
+    """
+    Helper function that converts JSON lists to line delimited JSON.
+    """
+    # Determine we have a JSON list to turn to lines otherwise just return the
+    # json object, only lists can
+    if not s[0] == "[" and s[-1] == "]":
+        return s
+    s = s[1:-1]
+
+    return convert_json_to_lines(s)
+
+
+def nested_to_record(
+    ds,
+    prefix: str = "",
+    sep: str = ".",
+    level: int = 0,
+    max_level: int | None = None,
+):
+    """
+    A simplified json_normalize
+
+    Converts a nested dict into a flat dict ("record"), unlike json_normalize,
+    it does not attempt to extract a subset of the data.
+
+    Parameters
+    ----------
+    ds : dict or list of dicts
+    prefix: the prefix, optional, default: ""
+    sep : str, default '.'
+        Nested records will generate names separated by sep,
+        e.g., for sep='.', { 'foo' : { 'bar' : 0 } } -> foo.bar
+    level: int, optional, default: 0
+        The number of levels in the json string.
+
+    max_level: int, optional, default: None
+        The max depth to normalize.
+
+    Returns
+    -------
+    d - dict or list of dicts, matching `ds`
+
+    Examples
+    --------
+    >>> nested_to_record(
+    ...     dict(flat1=1, dict1=dict(c=1, d=2), nested=dict(e=dict(c=1, d=2), d=2))
+    ... )
+    {\
+'flat1': 1, \
+'dict1.c': 1, \
+'dict1.d': 2, \
+'nested.e.c': 1, \
+'nested.e.d': 2, \
+'nested.d': 2\
+}
+    """
+    singleton = False
+    if isinstance(ds, dict):
+        ds = [ds]
+        singleton = True
+    new_ds = []
+    for d in ds:
+        new_d = copy.deepcopy(d)
+        for k, v in d.items():
+            # each key gets renamed with prefix
+            if not isinstance(k, str):
+                k = str(k)
+            if level == 0:
+                newkey = k
+            else:
+                newkey = prefix + sep + k
+
+            # flatten if type is dict and
+            # current dict level  < maximum level provided and
+            # only dicts gets recurse-flattened
+            # only at level>1 do we rename the rest of the keys
+            if not isinstance(v, dict) or (
+                max_level is not None and level >= max_level
+            ):
+                if level != 0:  # so we skip copying for top level, common case
+                    v = new_d.pop(k)
+                    new_d[newkey] = v
+                continue
+
+            v = new_d.pop(k)
+            new_d.update(nested_to_record(v, newkey, sep, level + 1, max_level))
+        new_ds.append(new_d)
+
+    if singleton:
+        return new_ds[0]
+    return new_ds
+
+
+def _normalise_json(
+    data: Any,
+    key_string: str,
+    normalized_dict: dict[str, Any],
+    separator: str,
+) -> dict[str, Any]:
+    """
+    Main recursive function
+    Designed for the most basic use case of pd.json_normalize(data)
+    intended as a performance improvement, see #15621
+
+    Parameters
+    ----------
+    data : Any
+        Type dependent on types contained within nested Json
+    key_string : str
+        New key (with separator(s) in) for data
+    normalized_dict : dict
+        The new normalized/flattened Json dict
+    separator : str, default '.'
+        Nested records will generate names separated by sep,
+        e.g., for sep='.', { 'foo' : { 'bar' : 0 } } -> foo.bar
+    """
+    if isinstance(data, dict):
+        for key, value in data.items():
+            new_key = f"{key_string}{separator}{key}"
+
+            if not key_string:
+                new_key = new_key.removeprefix(separator)
+
+            _normalise_json(
+                data=value,
+                key_string=new_key,
+                normalized_dict=normalized_dict,
+                separator=separator,
+            )
+    else:
+        normalized_dict[key_string] = data
+    return normalized_dict
+
+
+def _normalise_json_ordered(data: dict[str, Any], separator: str) -> dict[str, Any]:
+    """
+    Order the top level keys and then recursively go to depth
+
+    Parameters
+    ----------
+    data : dict or list of dicts
+    separator : str, default '.'
+        Nested records will generate names separated by sep,
+        e.g., for sep='.', { 'foo' : { 'bar' : 0 } } -> foo.bar
+
+    Returns
+    -------
+    dict or list of dicts, matching `normalised_json_object`
+    """
+    top_dict_ = {k: v for k, v in data.items() if not isinstance(v, dict)}
+    nested_dict_ = _normalise_json(
+        data={k: v for k, v in data.items() if isinstance(v, dict)},
+        key_string="",
+        normalized_dict={},
+        separator=separator,
+    )
+    return {**top_dict_, **nested_dict_}
+
+
+def _simple_json_normalize(
+    ds: dict | list[dict],
+    sep: str = ".",
+) -> dict | list[dict] | Any:
+    """
+    A optimized basic json_normalize
+
+    Converts a nested dict into a flat dict ("record"), unlike
+    json_normalize and nested_to_record it doesn't do anything clever.
+    But for the most basic use cases it enhances performance.
+    E.g. pd.json_normalize(data)
+
+    Parameters
+    ----------
+    ds : dict or list of dicts
+    sep : str, default '.'
+        Nested records will generate names separated by sep,
+        e.g., for sep='.', { 'foo' : { 'bar' : 0 } } -> foo.bar
+
+    Returns
+    -------
+    frame : DataFrame
+    d - dict or list of dicts, matching `normalised_json_object`
+
+    Examples
+    --------
+    >>> _simple_json_normalize(
+    ...     {
+    ...         "flat1": 1,
+    ...         "dict1": {"c": 1, "d": 2},
+    ...         "nested": {"e": {"c": 1, "d": 2}, "d": 2},
+    ...     }
+    ... )
+    {\
+'flat1': 1, \
+'dict1.c': 1, \
+'dict1.d': 2, \
+'nested.e.c': 1, \
+'nested.e.d': 2, \
+'nested.d': 2\
+}
+
+    """
+    normalised_json_object = {}
+    # expect a dictionary, as most jsons are. However, lists are perfectly valid
+    if isinstance(ds, dict):
+        normalised_json_object = _normalise_json_ordered(data=ds, separator=sep)
+    elif isinstance(ds, list):
+        normalised_json_list = [_simple_json_normalize(row, sep=sep) for row in ds]
+        return normalised_json_list
+    return normalised_json_object
+
+
+def json_normalize(
+    data: dict | list[dict],
+    record_path: str | list | None = None,
+    meta: str | list[str | list[str]] | None = None,
+    meta_prefix: str | None = None,
+    record_prefix: str | None = None,
+    errors: IgnoreRaise = "raise",
+    sep: str = ".",
+    max_level: int | None = None,
+) -> DataFrame:
+    """
+    Normalize semi-structured JSON data into a flat table.
+
+    Parameters
+    ----------
+    data : dict or list of dicts
+        Unserialized JSON objects.
+    record_path : str or list of str, default None
+        Path in each object to list of records. If not passed, data will be
+        assumed to be an array of records.
+    meta : list of paths (str or list of str), default None
+        Fields to use as metadata for each record in resulting table.
+    meta_prefix : str, default None
+        If True, prefix records with dotted (?) path, e.g. foo.bar.field if
+        meta is ['foo', 'bar'].
+    record_prefix : str, default None
+        If True, prefix records with dotted (?) path, e.g. foo.bar.field if
+        path to records is ['foo', 'bar'].
+    errors : {'raise', 'ignore'}, default 'raise'
+        Configures error handling.
+
+        * 'ignore' : will ignore KeyError if keys listed in meta are not
+          always present.
+        * 'raise' : will raise KeyError if keys listed in meta are not
+          always present.
+    sep : str, default '.'
+        Nested records will generate names separated by sep.
+        e.g., for sep='.', {'foo': {'bar': 0}} -> foo.bar.
+    max_level : int, default None
+        Max number of levels(depth of dict) to normalize.
+        if None, normalizes all levels.
+
+    Returns
+    -------
+    frame : DataFrame
+    Normalize semi-structured JSON data into a flat table.
+
+    Examples
+    --------
+    >>> data = [
+    ...     {"id": 1, "name": {"first": "Coleen", "last": "Volk"}},
+    ...     {"name": {"given": "Mark", "family": "Regner"}},
+    ...     {"id": 2, "name": "Faye Raker"},
+    ... ]
+    >>> pd.json_normalize(data)
+        id name.first name.last name.given name.family        name
+    0  1.0     Coleen      Volk        NaN         NaN         NaN
+    1  NaN        NaN       NaN       Mark      Regner         NaN
+    2  2.0        NaN       NaN        NaN         NaN  Faye Raker
+
+    >>> data = [
+    ...     {
+    ...         "id": 1,
+    ...         "name": "Cole Volk",
+    ...         "fitness": {"height": 130, "weight": 60},
+    ...     },
+    ...     {"name": "Mark Reg", "fitness": {"height": 130, "weight": 60}},
+    ...     {
+    ...         "id": 2,
+    ...         "name": "Faye Raker",
+    ...         "fitness": {"height": 130, "weight": 60},
+    ...     },
+    ... ]
+    >>> pd.json_normalize(data, max_level=0)
+        id        name                        fitness
+    0  1.0   Cole Volk  {'height': 130, 'weight': 60}
+    1  NaN    Mark Reg  {'height': 130, 'weight': 60}
+    2  2.0  Faye Raker  {'height': 130, 'weight': 60}
+
+    Normalizes nested data up to level 1.
+
+    >>> data = [
+    ...     {
+    ...         "id": 1,
+    ...         "name": "Cole Volk",
+    ...         "fitness": {"height": 130, "weight": 60},
+    ...     },
+    ...     {"name": "Mark Reg", "fitness": {"height": 130, "weight": 60}},
+    ...     {
+    ...         "id": 2,
+    ...         "name": "Faye Raker",
+    ...         "fitness": {"height": 130, "weight": 60},
+    ...     },
+    ... ]
+    >>> pd.json_normalize(data, max_level=1)
+        id        name  fitness.height  fitness.weight
+    0  1.0   Cole Volk             130              60
+    1  NaN    Mark Reg             130              60
+    2  2.0  Faye Raker             130              60
+
+    >>> data = [
+    ...     {
+    ...         "state": "Florida",
+    ...         "shortname": "FL",
+    ...         "info": {"governor": "Rick Scott"},
+    ...         "counties": [
+    ...             {"name": "Dade", "population": 12345},
+    ...             {"name": "Broward", "population": 40000},
+    ...             {"name": "Palm Beach", "population": 60000},
+    ...         ],
+    ...     },
+    ...     {
+    ...         "state": "Ohio",
+    ...         "shortname": "OH",
+    ...         "info": {"governor": "John Kasich"},
+    ...         "counties": [
+    ...             {"name": "Summit", "population": 1234},
+    ...             {"name": "Cuyahoga", "population": 1337},
+    ...         ],
+    ...     },
+    ... ]
+    >>> result = pd.json_normalize(
+    ...     data, "counties", ["state", "shortname", ["info", "governor"]]
+    ... )
+    >>> result
+             name  population    state shortname info.governor
+    0        Dade       12345   Florida    FL    Rick Scott
+    1     Broward       40000   Florida    FL    Rick Scott
+    2  Palm Beach       60000   Florida    FL    Rick Scott
+    3      Summit        1234   Ohio       OH    John Kasich
+    4    Cuyahoga        1337   Ohio       OH    John Kasich
+
+    >>> data = {"A": [1, 2]}
+    >>> pd.json_normalize(data, "A", record_prefix="Prefix.")
+        Prefix.0
+    0          1
+    1          2
+
+    Returns normalized data with columns prefixed with the given string.
+    """
+
+    def _pull_field(
+        js: dict[str, Any], spec: list | str, extract_record: bool = False
+    ) -> Scalar | Iterable:
+        """Internal function to pull field"""
+        result = js
+        try:
+            if isinstance(spec, list):
+                for field in spec:
+                    if result is None:
+                        raise KeyError(field)
+                    result = result[field]
+            else:
+                result = result[spec]
+        except KeyError as e:
+            if extract_record:
+                raise KeyError(
+                    f"Key {e} not found. If specifying a record_path, all elements of "
+                    f"data should have the path."
+                ) from e
+            if errors == "ignore":
+                return np.nan
+            else:
+                raise KeyError(
+                    f"Key {e} not found. To replace missing values of {e} with "
+                    f"np.nan, pass in errors='ignore'"
+                ) from e
+
+        return result
+
+    def _pull_records(js: dict[str, Any], spec: list | str) -> list:
+        """
+        Internal function to pull field for records, and similar to
+        _pull_field, but require to return list. And will raise error
+        if has non iterable value.
+        """
+        result = _pull_field(js, spec, extract_record=True)
+
+        # GH 31507 GH 30145, GH 26284 if result is not list, raise TypeError if not
+        # null, otherwise return an empty list
+        if not isinstance(result, list):
+            if pd.isnull(result):
+                result = []
+            else:
+                raise TypeError(
+                    f"{js} has non list value {result} for path {spec}. "
+                    "Must be list or null."
+                )
+        return result
+
+    if isinstance(data, list) and not data:
+        return DataFrame()
+    elif isinstance(data, dict):
+        # A bit of a hackjob
+        data = [data]
+    elif isinstance(data, abc.Iterable) and not isinstance(data, str):
+        # GH35923 Fix pd.json_normalize to not skip the first element of a
+        # generator input
+        data = list(data)
+    else:
+        raise NotImplementedError
+
+    # check to see if a simple recursive function is possible to
+    # improve performance (see #15621) but only for cases such
+    # as pd.Dataframe(data) or pd.Dataframe(data, sep)
+    if (
+        record_path is None
+        and meta is None
+        and meta_prefix is None
+        and record_prefix is None
+        and max_level is None
+    ):
+        return DataFrame(_simple_json_normalize(data, sep=sep))
+
+    if record_path is None:
+        if any([isinstance(x, dict) for x in y.values()] for y in data):
+            # naive normalization, this is idempotent for flat records
+            # and potentially will inflate the data considerably for
+            # deeply nested structures:
+            #  {VeryLong: { b: 1,c:2}} -> {VeryLong.b:1 ,VeryLong.c:@}
+            #
+            # TODO: handle record value which are lists, at least error
+            #       reasonably
+            data = nested_to_record(data, sep=sep, max_level=max_level)
+        return DataFrame(data)
+    elif not isinstance(record_path, list):
+        record_path = [record_path]
+
+    if meta is None:
+        meta = []
+    elif not isinstance(meta, list):
+        meta = [meta]
+
+    _meta = [m if isinstance(m, list) else [m] for m in meta]
+
+    # Disastrously inefficient for now
+    records: list = []
+    lengths = []
+
+    meta_vals: DefaultDict = defaultdict(list)
+    meta_keys = [sep.join(val) for val in _meta]
+
+    def _recursive_extract(data, path, seen_meta, level: int = 0) -> None:
+        if isinstance(data, dict):
+            data = [data]
+        if len(path) > 1:
+            for obj in data:
+                for val, key in zip(_meta, meta_keys):
+                    if level + 1 == len(val):
+                        seen_meta[key] = _pull_field(obj, val[-1])
+
+                _recursive_extract(obj[path[0]], path[1:], seen_meta, level=level + 1)
+        else:
+            for obj in data:
+                recs = _pull_records(obj, path[0])
+                recs = [
+                    nested_to_record(r, sep=sep, max_level=max_level)
+                    if isinstance(r, dict)
+                    else r
+                    for r in recs
+                ]
+
+                # For repeating the metadata later
+                lengths.append(len(recs))
+                for val, key in zip(_meta, meta_keys):
+                    if level + 1 > len(val):
+                        meta_val = seen_meta[key]
+                    else:
+                        meta_val = _pull_field(obj, val[level:])
+                    meta_vals[key].append(meta_val)
+                records.extend(recs)
+
+    _recursive_extract(data, record_path, {}, level=0)
+
+    result = DataFrame(records)
+
+    if record_prefix is not None:
+        result = result.rename(columns=lambda x: f"{record_prefix}{x}")
+
+    # Data types, a problem
+    for k, v in meta_vals.items():
+        if meta_prefix is not None:
+            k = meta_prefix + k
+
+        if k in result:
+            raise ValueError(
+                f"Conflicting metadata name {k}, need distinguishing prefix "
+            )
+        # GH 37782
+
+        values = np.array(v, dtype=object)
+
+        if values.ndim > 1:
+            # GH 37782
+            values = np.empty((len(v),), dtype=object)
+            for i, v in enumerate(v):
+                values[i] = v
+
+        result[k] = values.repeat(lengths)
+    return result
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/json/_table_schema.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/json/_table_schema.py
new file mode 100644
index 0000000000000000000000000000000000000000..c72411d87eabfe2838061a0a99d1e9de9aeed8a5
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/json/_table_schema.py
@@ -0,0 +1,387 @@
+"""
+Table Schema builders
+
+https://specs.frictionlessdata.io/table-schema/
+"""
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    cast,
+)
+import warnings
+
+from pandas._libs import lib
+from pandas._libs.json import ujson_loads
+from pandas._libs.tslibs import timezones
+from pandas._libs.tslibs.dtypes import freq_to_period_freqstr
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.base import _registry as registry
+from pandas.core.dtypes.common import (
+    is_bool_dtype,
+    is_integer_dtype,
+    is_numeric_dtype,
+    is_string_dtype,
+)
+from pandas.core.dtypes.dtypes import (
+    CategoricalDtype,
+    DatetimeTZDtype,
+    ExtensionDtype,
+    PeriodDtype,
+)
+
+from pandas import DataFrame
+import pandas.core.common as com
+
+from pandas.tseries.frequencies import to_offset
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        DtypeObj,
+        JSONSerializable,
+    )
+
+    from pandas import Series
+    from pandas.core.indexes.multi import MultiIndex
+
+
+TABLE_SCHEMA_VERSION = "1.4.0"
+
+
+def as_json_table_type(x: DtypeObj) -> str:
+    """
+    Convert a NumPy / pandas type to its corresponding json_table.
+
+    Parameters
+    ----------
+    x : np.dtype or ExtensionDtype
+
+    Returns
+    -------
+    str
+        the Table Schema data types
+
+    Notes
+    -----
+    This table shows the relationship between NumPy / pandas dtypes,
+    and Table Schema dtypes.
+
+    ==============  =================
+    Pandas type     Table Schema type
+    ==============  =================
+    int64           integer
+    float64         number
+    bool            boolean
+    datetime64[ns]  datetime
+    timedelta64[ns] duration
+    object          str
+    categorical     any
+    =============== =================
+    """
+    if is_integer_dtype(x):
+        return "integer"
+    elif is_bool_dtype(x):
+        return "boolean"
+    elif is_numeric_dtype(x):
+        return "number"
+    elif lib.is_np_dtype(x, "M") or isinstance(x, (DatetimeTZDtype, PeriodDtype)):
+        return "datetime"
+    elif lib.is_np_dtype(x, "m"):
+        return "duration"
+    elif is_string_dtype(x):
+        return "string"
+    else:
+        return "any"
+
+
+def set_default_names(data):
+    """Sets index names to 'index' for regular, or 'level_x' for Multi"""
+    if com.all_not_none(*data.index.names):
+        nms = data.index.names
+        if len(nms) == 1 and data.index.name == "index":
+            warnings.warn(
+                "Index name of 'index' is not round-trippable.",
+                stacklevel=find_stack_level(),
+            )
+        elif len(nms) > 1 and any(x.startswith("level_") for x in nms):
+            warnings.warn(
+                "Index names beginning with 'level_' are not round-trippable.",
+                stacklevel=find_stack_level(),
+            )
+        return data
+
+    data = data.copy()
+    if data.index.nlevels > 1:
+        data.index.names = com.fill_missing_names(data.index.names)
+    else:
+        data.index.name = data.index.name or "index"
+    return data
+
+
+def convert_pandas_type_to_json_field(arr) -> dict[str, JSONSerializable]:
+    dtype = arr.dtype
+    name: JSONSerializable
+    if arr.name is None:
+        name = "values"
+    else:
+        name = arr.name
+    field: dict[str, JSONSerializable] = {
+        "name": name,
+        "type": as_json_table_type(dtype),
+    }
+
+    if isinstance(dtype, CategoricalDtype):
+        cats = dtype.categories
+        ordered = dtype.ordered
+
+        field["constraints"] = {"enum": list(cats)}
+        field["ordered"] = ordered
+    elif isinstance(dtype, PeriodDtype):
+        field["freq"] = dtype.freq.freqstr
+    elif isinstance(dtype, DatetimeTZDtype):
+        if timezones.is_utc(dtype.tz):
+            # timezone.utc has no "zone" attr
+            field["tz"] = "UTC"
+        else:
+            # error: "tzinfo" has no attribute "zone"
+            field["tz"] = dtype.tz.zone  # type: ignore[attr-defined]
+    elif isinstance(dtype, ExtensionDtype):
+        field["extDtype"] = dtype.name
+    return field
+
+
+def convert_json_field_to_pandas_type(field) -> str | CategoricalDtype:
+    """
+    Converts a JSON field descriptor into its corresponding NumPy / pandas type
+
+    Parameters
+    ----------
+    field
+        A JSON field descriptor
+
+    Returns
+    -------
+    dtype
+
+    Raises
+    ------
+    ValueError
+        If the type of the provided field is unknown or currently unsupported
+
+    Examples
+    --------
+    >>> convert_json_field_to_pandas_type({"name": "an_int", "type": "integer"})
+    'int64'
+
+    >>> convert_json_field_to_pandas_type(
+    ...     {
+    ...         "name": "a_categorical",
+    ...         "type": "any",
+    ...         "constraints": {"enum": ["a", "b", "c"]},
+    ...         "ordered": True,
+    ...     }
+    ... )
+    CategoricalDtype(categories=['a', 'b', 'c'], ordered=True, categories_dtype=object)
+
+    >>> convert_json_field_to_pandas_type({"name": "a_datetime", "type": "datetime"})
+    'datetime64[ns]'
+
+    >>> convert_json_field_to_pandas_type(
+    ...     {"name": "a_datetime_with_tz", "type": "datetime", "tz": "US/Central"}
+    ... )
+    'datetime64[ns, US/Central]'
+    """
+    typ = field["type"]
+    if typ == "string":
+        return field.get("extDtype", None)
+    elif typ == "integer":
+        return field.get("extDtype", "int64")
+    elif typ == "number":
+        return field.get("extDtype", "float64")
+    elif typ == "boolean":
+        return field.get("extDtype", "bool")
+    elif typ == "duration":
+        return "timedelta64"
+    elif typ == "datetime":
+        if field.get("tz"):
+            return f"datetime64[ns, {field['tz']}]"
+        elif field.get("freq"):
+            # GH#9586 rename frequency M to ME for offsets
+            offset = to_offset(field["freq"])
+            freq_n, freq_name = offset.n, offset.name
+            freq = freq_to_period_freqstr(freq_n, freq_name)
+            # GH#47747 using datetime over period to minimize the change surface
+            return f"period[{freq}]"
+        else:
+            return "datetime64[ns]"
+    elif typ == "any":
+        if "constraints" in field and "ordered" in field:
+            return CategoricalDtype(
+                categories=field["constraints"]["enum"], ordered=field["ordered"]
+            )
+        elif "extDtype" in field:
+            return registry.find(field["extDtype"])
+        else:
+            return "object"
+
+    raise ValueError(f"Unsupported or invalid field type: {typ}")
+
+
+def build_table_schema(
+    data: DataFrame | Series,
+    index: bool = True,
+    primary_key: bool | None = None,
+    version: bool = True,
+) -> dict[str, JSONSerializable]:
+    """
+    Create a Table schema from ``data``.
+
+    Parameters
+    ----------
+    data : Series, DataFrame
+    index : bool, default True
+        Whether to include ``data.index`` in the schema.
+    primary_key : bool or None, default True
+        Column names to designate as the primary key.
+        The default `None` will set `'primaryKey'` to the index
+        level or levels if the index is unique.
+    version : bool, default True
+        Whether to include a field `pandas_version` with the version
+        of pandas that last revised the table schema. This version
+        can be different from the installed pandas version.
+
+    Returns
+    -------
+    dict
+
+    Notes
+    -----
+    See `Table Schema
+    `__ for
+    conversion types.
+    Timedeltas as converted to ISO8601 duration format with
+    9 decimal places after the seconds field for nanosecond precision.
+
+    Categoricals are converted to the `any` dtype, and use the `enum` field
+    constraint to list the allowed values. The `ordered` attribute is included
+    in an `ordered` field.
+
+    Examples
+    --------
+    >>> from pandas.io.json._table_schema import build_table_schema
+    >>> df = pd.DataFrame(
+    ...     {'A': [1, 2, 3],
+    ...      'B': ['a', 'b', 'c'],
+    ...      'C': pd.date_range('2016-01-01', freq='d', periods=3),
+    ...     }, index=pd.Index(range(3), name='idx'))
+    >>> build_table_schema(df)
+    {'fields': \
+[{'name': 'idx', 'type': 'integer'}, \
+{'name': 'A', 'type': 'integer'}, \
+{'name': 'B', 'type': 'string'}, \
+{'name': 'C', 'type': 'datetime'}], \
+'primaryKey': ['idx'], \
+'pandas_version': '1.4.0'}
+    """
+    if index is True:
+        data = set_default_names(data)
+
+    schema: dict[str, Any] = {}
+    fields = []
+
+    if index:
+        if data.index.nlevels > 1:
+            data.index = cast("MultiIndex", data.index)
+            for level, name in zip(data.index.levels, data.index.names):
+                new_field = convert_pandas_type_to_json_field(level)
+                new_field["name"] = name
+                fields.append(new_field)
+        else:
+            fields.append(convert_pandas_type_to_json_field(data.index))
+
+    if data.ndim > 1:
+        for column, s in data.items():
+            fields.append(convert_pandas_type_to_json_field(s))
+    else:
+        fields.append(convert_pandas_type_to_json_field(data))
+
+    schema["fields"] = fields
+    if index and data.index.is_unique and primary_key is None:
+        if data.index.nlevels == 1:
+            schema["primaryKey"] = [data.index.name]
+        else:
+            schema["primaryKey"] = data.index.names
+    elif primary_key is not None:
+        schema["primaryKey"] = primary_key
+
+    if version:
+        schema["pandas_version"] = TABLE_SCHEMA_VERSION
+    return schema
+
+
+def parse_table_schema(json, precise_float: bool) -> DataFrame:
+    """
+    Builds a DataFrame from a given schema
+
+    Parameters
+    ----------
+    json :
+        A JSON table schema
+    precise_float : bool
+        Flag controlling precision when decoding string to double values, as
+        dictated by ``read_json``
+
+    Returns
+    -------
+    df : DataFrame
+
+    Raises
+    ------
+    NotImplementedError
+        If the JSON table schema contains either timezone or timedelta data
+
+    Notes
+    -----
+        Because :func:`DataFrame.to_json` uses the string 'index' to denote a
+        name-less :class:`Index`, this function sets the name of the returned
+        :class:`DataFrame` to ``None`` when said string is encountered with a
+        normal :class:`Index`. For a :class:`MultiIndex`, the same limitation
+        applies to any strings beginning with 'level_'. Therefore, an
+        :class:`Index` name of 'index'  and :class:`MultiIndex` names starting
+        with 'level_' are not supported.
+
+    See Also
+    --------
+    build_table_schema : Inverse function.
+    pandas.read_json
+    """
+    table = ujson_loads(json, precise_float=precise_float)
+    col_order = [field["name"] for field in table["schema"]["fields"]]
+    df = DataFrame(table["data"], columns=col_order)[col_order]
+
+    dtypes = {
+        field["name"]: convert_json_field_to_pandas_type(field)
+        for field in table["schema"]["fields"]
+    }
+
+    # No ISO constructor for Timedelta as of yet, so need to raise
+    if "timedelta64" in dtypes.values():
+        raise NotImplementedError(
+            'table="orient" can not yet read ISO-formatted Timedelta data'
+        )
+
+    df = df.astype(dtypes)
+
+    if "primaryKey" in table["schema"]:
+        df = df.set_index(table["schema"]["primaryKey"])
+        if len(df.index.names) == 1:
+            if df.index.name == "index":
+                df.index.name = None
+        else:
+            df.index.names = [
+                None if x.startswith("level_") else x for x in df.index.names
+            ]
+
+    return df
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/orc.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/orc.py
new file mode 100644
index 0000000000000000000000000000000000000000..d7f473a929568faa9a00886d627c65fa57a3aa18
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/orc.py
@@ -0,0 +1,228 @@
+""" orc compat """
+from __future__ import annotations
+
+import io
+from types import ModuleType
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Literal,
+)
+
+from pandas._libs import lib
+from pandas.compat._optional import import_optional_dependency
+from pandas.util._validators import check_dtype_backend
+
+from pandas.core.indexes.api import default_index
+
+from pandas.io._util import arrow_table_to_pandas
+from pandas.io.common import (
+    get_handle,
+    is_fsspec_url,
+)
+
+if TYPE_CHECKING:
+    import fsspec
+    import pyarrow.fs
+
+    from pandas._typing import (
+        DtypeBackend,
+        FilePath,
+        ReadBuffer,
+        WriteBuffer,
+    )
+
+    from pandas.core.frame import DataFrame
+
+
+def read_orc(
+    path: FilePath | ReadBuffer[bytes],
+    columns: list[str] | None = None,
+    dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
+    filesystem: pyarrow.fs.FileSystem | fsspec.spec.AbstractFileSystem | None = None,
+    **kwargs: Any,
+) -> DataFrame:
+    """
+    Load an ORC object from the file path, returning a DataFrame.
+
+    Parameters
+    ----------
+    path : str, path object, or file-like object
+        String, path object (implementing ``os.PathLike[str]``), or file-like
+        object implementing a binary ``read()`` function. The string could be a URL.
+        Valid URL schemes include http, ftp, s3, and file. For file URLs, a host is
+        expected. A local file could be:
+        ``file://localhost/path/to/table.orc``.
+    columns : list, default None
+        If not None, only these columns will be read from the file.
+        Output always follows the ordering of the file and not the columns list.
+        This mirrors the original behaviour of
+        :external+pyarrow:py:meth:`pyarrow.orc.ORCFile.read`.
+    dtype_backend : {'numpy_nullable', 'pyarrow'}, default 'numpy_nullable'
+        Back-end data type applied to the resultant :class:`DataFrame`
+        (still experimental). Behaviour is as follows:
+
+        * ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame`
+          (default).
+        * ``"pyarrow"``: returns pyarrow-backed nullable :class:`ArrowDtype`
+          DataFrame.
+
+        .. versionadded:: 2.0
+
+    filesystem : fsspec or pyarrow filesystem, default None
+        Filesystem object to use when reading the parquet file.
+
+        .. versionadded:: 2.1.0
+
+    **kwargs
+        Any additional kwargs are passed to pyarrow.
+
+    Returns
+    -------
+    DataFrame
+
+    Notes
+    -----
+    Before using this function you should read the :ref:`user guide about ORC `
+    and :ref:`install optional dependencies `.
+
+    If ``path`` is a URI scheme pointing to a local or remote file (e.g. "s3://"),
+    a ``pyarrow.fs`` filesystem will be attempted to read the file. You can also pass a
+    pyarrow or fsspec filesystem object into the filesystem keyword to override this
+    behavior.
+
+    Examples
+    --------
+    >>> result = pd.read_orc("example_pa.orc")  # doctest: +SKIP
+    """
+    # we require a newer version of pyarrow than we support for parquet
+
+    orc = import_optional_dependency("pyarrow.orc")
+
+    check_dtype_backend(dtype_backend)
+
+    with get_handle(path, "rb", is_text=False) as handles:
+        source = handles.handle
+        if is_fsspec_url(path) and filesystem is None:
+            pa = import_optional_dependency("pyarrow")
+            pa_fs = import_optional_dependency("pyarrow.fs")
+            try:
+                filesystem, source = pa_fs.FileSystem.from_uri(path)
+            except (TypeError, pa.ArrowInvalid):
+                pass
+
+        pa_table = orc.read_table(
+            source=source, columns=columns, filesystem=filesystem, **kwargs
+        )
+    return arrow_table_to_pandas(pa_table, dtype_backend=dtype_backend)
+
+
+def to_orc(
+    df: DataFrame,
+    path: FilePath | WriteBuffer[bytes] | None = None,
+    *,
+    engine: Literal["pyarrow"] = "pyarrow",
+    index: bool | None = None,
+    engine_kwargs: dict[str, Any] | None = None,
+) -> bytes | None:
+    """
+    Write a DataFrame to the ORC format.
+
+    .. versionadded:: 1.5.0
+
+    Parameters
+    ----------
+    df : DataFrame
+        The dataframe to be written to ORC. Raises NotImplementedError
+        if dtype of one or more columns is category, unsigned integers,
+        intervals, periods or sparse.
+    path : str, file-like object or None, default None
+        If a string, it will be used as Root Directory path
+        when writing a partitioned dataset. By file-like object,
+        we refer to objects with a write() method, such as a file handle
+        (e.g. via builtin open function). If path is None,
+        a bytes object is returned.
+    engine : str, default 'pyarrow'
+        ORC library to use.
+    index : bool, optional
+        If ``True``, include the dataframe's index(es) in the file output. If
+        ``False``, they will not be written to the file.
+        If ``None``, similar to ``infer`` the dataframe's index(es)
+        will be saved. However, instead of being saved as values,
+        the RangeIndex will be stored as a range in the metadata so it
+        doesn't require much space and is faster. Other indexes will
+        be included as columns in the file output.
+    engine_kwargs : dict[str, Any] or None, default None
+        Additional keyword arguments passed to :func:`pyarrow.orc.write_table`.
+
+    Returns
+    -------
+    bytes if no path argument is provided else None
+
+    Raises
+    ------
+    NotImplementedError
+        Dtype of one or more columns is category, unsigned integers, interval,
+        period or sparse.
+    ValueError
+        engine is not pyarrow.
+
+    Notes
+    -----
+    * Before using this function you should read the
+      :ref:`user guide about ORC ` and
+      :ref:`install optional dependencies `.
+    * This function requires `pyarrow `_
+      library.
+    * For supported dtypes please refer to `supported ORC features in Arrow
+      `__.
+    * Currently timezones in datetime columns are not preserved when a
+      dataframe is converted into ORC files.
+    """
+    if index is None:
+        index = df.index.names[0] is not None
+    if engine_kwargs is None:
+        engine_kwargs = {}
+
+    # validate index
+    # --------------
+
+    # validate that we have only a default index
+    # raise on anything else as we don't serialize the index
+
+    if not df.index.equals(default_index(len(df))):
+        raise ValueError(
+            "orc does not support serializing a non-default index for the index; "
+            "you can .reset_index() to make the index into column(s)"
+        )
+
+    if df.index.name is not None:
+        raise ValueError("orc does not serialize index meta-data on a default index")
+
+    if engine != "pyarrow":
+        raise ValueError("engine must be 'pyarrow'")
+    engine = import_optional_dependency(engine, min_version="10.0.1")
+    pa = import_optional_dependency("pyarrow")
+    orc = import_optional_dependency("pyarrow.orc")
+
+    was_none = path is None
+    if was_none:
+        path = io.BytesIO()
+    assert path is not None  # For mypy
+    with get_handle(path, "wb", is_text=False) as handles:
+        assert isinstance(engine, ModuleType)  # For mypy
+        try:
+            orc.write_table(
+                engine.Table.from_pandas(df, preserve_index=index),
+                handles.handle,
+                **engine_kwargs,
+            )
+        except (TypeError, pa.ArrowNotImplementedError) as e:
+            raise NotImplementedError(
+                "The dtype of one or more columns is not supported yet."
+            ) from e
+
+    if was_none:
+        assert isinstance(path, io.BytesIO)  # For mypy
+        return path.getvalue()
+    return None
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/parquet.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/parquet.py
new file mode 100644
index 0000000000000000000000000000000000000000..01e320cdb1b72a4f9425de40721c43189b4c5ed8
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/parquet.py
@@ -0,0 +1,678 @@
+""" parquet compat """
+from __future__ import annotations
+
+import io
+import json
+import os
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Literal,
+)
+import warnings
+from warnings import (
+    catch_warnings,
+    filterwarnings,
+)
+
+from pandas._config.config import _get_option
+
+from pandas._libs import lib
+from pandas.compat._optional import import_optional_dependency
+from pandas.errors import AbstractMethodError
+from pandas.util._decorators import doc
+from pandas.util._exceptions import find_stack_level
+from pandas.util._validators import check_dtype_backend
+
+from pandas import (
+    DataFrame,
+    get_option,
+)
+from pandas.core.shared_docs import _shared_docs
+
+from pandas.io._util import arrow_table_to_pandas
+from pandas.io.common import (
+    IOHandles,
+    get_handle,
+    is_fsspec_url,
+    is_url,
+    stringify_path,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        DtypeBackend,
+        FilePath,
+        ReadBuffer,
+        StorageOptions,
+        WriteBuffer,
+    )
+
+
+def get_engine(engine: str) -> BaseImpl:
+    """return our implementation"""
+    if engine == "auto":
+        engine = get_option("io.parquet.engine")
+
+    if engine == "auto":
+        # try engines in this order
+        engine_classes = [PyArrowImpl, FastParquetImpl]
+
+        error_msgs = ""
+        for engine_class in engine_classes:
+            try:
+                return engine_class()
+            except ImportError as err:
+                error_msgs += "\n - " + str(err)
+
+        raise ImportError(
+            "Unable to find a usable engine; "
+            "tried using: 'pyarrow', 'fastparquet'.\n"
+            "A suitable version of "
+            "pyarrow or fastparquet is required for parquet "
+            "support.\n"
+            "Trying to import the above resulted in these errors:"
+            f"{error_msgs}"
+        )
+
+    if engine == "pyarrow":
+        return PyArrowImpl()
+    elif engine == "fastparquet":
+        return FastParquetImpl()
+
+    raise ValueError("engine must be one of 'pyarrow', 'fastparquet'")
+
+
+def _get_path_or_handle(
+    path: FilePath | ReadBuffer[bytes] | WriteBuffer[bytes],
+    fs: Any,
+    storage_options: StorageOptions | None = None,
+    mode: str = "rb",
+    is_dir: bool = False,
+) -> tuple[
+    FilePath | ReadBuffer[bytes] | WriteBuffer[bytes], IOHandles[bytes] | None, Any
+]:
+    """File handling for PyArrow."""
+    path_or_handle = stringify_path(path)
+    if fs is not None:
+        pa_fs = import_optional_dependency("pyarrow.fs", errors="ignore")
+        fsspec = import_optional_dependency("fsspec", errors="ignore")
+        if pa_fs is not None and isinstance(fs, pa_fs.FileSystem):
+            if storage_options:
+                raise NotImplementedError(
+                    "storage_options not supported with a pyarrow FileSystem."
+                )
+        elif fsspec is not None and isinstance(fs, fsspec.spec.AbstractFileSystem):
+            pass
+        else:
+            raise ValueError(
+                f"filesystem must be a pyarrow or fsspec FileSystem, "
+                f"not a {type(fs).__name__}"
+            )
+    if is_fsspec_url(path_or_handle) and fs is None:
+        if storage_options is None:
+            pa = import_optional_dependency("pyarrow")
+            pa_fs = import_optional_dependency("pyarrow.fs")
+
+            try:
+                fs, path_or_handle = pa_fs.FileSystem.from_uri(path)
+            except (TypeError, pa.ArrowInvalid):
+                pass
+        if fs is None:
+            fsspec = import_optional_dependency("fsspec")
+            fs, path_or_handle = fsspec.core.url_to_fs(
+                path_or_handle, **(storage_options or {})
+            )
+    elif storage_options and (not is_url(path_or_handle) or mode != "rb"):
+        # can't write to a remote url
+        # without making use of fsspec at the moment
+        raise ValueError("storage_options passed with buffer, or non-supported URL")
+
+    handles = None
+    if (
+        not fs
+        and not is_dir
+        and isinstance(path_or_handle, str)
+        and not os.path.isdir(path_or_handle)
+    ):
+        # use get_handle only when we are very certain that it is not a directory
+        # fsspec resources can also point to directories
+        # this branch is used for example when reading from non-fsspec URLs
+        handles = get_handle(
+            path_or_handle, mode, is_text=False, storage_options=storage_options
+        )
+        fs = None
+        path_or_handle = handles.handle
+    return path_or_handle, handles, fs
+
+
+class BaseImpl:
+    @staticmethod
+    def validate_dataframe(df: DataFrame) -> None:
+        if not isinstance(df, DataFrame):
+            raise ValueError("to_parquet only supports IO with DataFrames")
+
+    def write(self, df: DataFrame, path, compression, **kwargs):
+        raise AbstractMethodError(self)
+
+    def read(self, path, columns=None, **kwargs) -> DataFrame:
+        raise AbstractMethodError(self)
+
+
+class PyArrowImpl(BaseImpl):
+    def __init__(self) -> None:
+        import_optional_dependency(
+            "pyarrow", extra="pyarrow is required for parquet support."
+        )
+        import pyarrow.parquet
+
+        # import utils to register the pyarrow extension types
+        import pandas.core.arrays.arrow.extension_types  # pyright: ignore[reportUnusedImport] # noqa: F401
+
+        self.api = pyarrow
+
+    def write(
+        self,
+        df: DataFrame,
+        path: FilePath | WriteBuffer[bytes],
+        compression: str | None = "snappy",
+        index: bool | None = None,
+        storage_options: StorageOptions | None = None,
+        partition_cols: list[str] | None = None,
+        filesystem=None,
+        **kwargs,
+    ) -> None:
+        self.validate_dataframe(df)
+
+        from_pandas_kwargs: dict[str, Any] = {"schema": kwargs.pop("schema", None)}
+        if index is not None:
+            from_pandas_kwargs["preserve_index"] = index
+
+        table = self.api.Table.from_pandas(df, **from_pandas_kwargs)
+
+        if df.attrs:
+            df_metadata = {"PANDAS_ATTRS": json.dumps(df.attrs)}
+            existing_metadata = table.schema.metadata
+            merged_metadata = {**existing_metadata, **df_metadata}
+            table = table.replace_schema_metadata(merged_metadata)
+
+        path_or_handle, handles, filesystem = _get_path_or_handle(
+            path,
+            filesystem,
+            storage_options=storage_options,
+            mode="wb",
+            is_dir=partition_cols is not None,
+        )
+        if (
+            isinstance(path_or_handle, io.BufferedWriter)
+            and hasattr(path_or_handle, "name")
+            and isinstance(path_or_handle.name, (str, bytes))
+        ):
+            if isinstance(path_or_handle.name, bytes):
+                path_or_handle = path_or_handle.name.decode()
+            else:
+                path_or_handle = path_or_handle.name
+
+        try:
+            if partition_cols is not None:
+                # writes to multiple files under the given path
+                self.api.parquet.write_to_dataset(
+                    table,
+                    path_or_handle,
+                    compression=compression,
+                    partition_cols=partition_cols,
+                    filesystem=filesystem,
+                    **kwargs,
+                )
+            else:
+                # write to single output file
+                self.api.parquet.write_table(
+                    table,
+                    path_or_handle,
+                    compression=compression,
+                    filesystem=filesystem,
+                    **kwargs,
+                )
+        finally:
+            if handles is not None:
+                handles.close()
+
+    def read(
+        self,
+        path,
+        columns=None,
+        filters=None,
+        use_nullable_dtypes: bool = False,
+        dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
+        storage_options: StorageOptions | None = None,
+        filesystem=None,
+        **kwargs,
+    ) -> DataFrame:
+        kwargs["use_pandas_metadata"] = True
+
+        to_pandas_kwargs = {}
+
+        manager = _get_option("mode.data_manager", silent=True)
+        if manager == "array":
+            to_pandas_kwargs["split_blocks"] = True
+        path_or_handle, handles, filesystem = _get_path_or_handle(
+            path,
+            filesystem,
+            storage_options=storage_options,
+            mode="rb",
+        )
+        try:
+            pa_table = self.api.parquet.read_table(
+                path_or_handle,
+                columns=columns,
+                filesystem=filesystem,
+                filters=filters,
+                **kwargs,
+            )
+
+            with catch_warnings():
+                filterwarnings(
+                    "ignore",
+                    "make_block is deprecated",
+                    DeprecationWarning,
+                )
+                result = arrow_table_to_pandas(
+                    pa_table,
+                    dtype_backend=dtype_backend,
+                    to_pandas_kwargs=to_pandas_kwargs,
+                )
+
+            if manager == "array":
+                result = result._as_manager("array", copy=False)
+
+            if pa_table.schema.metadata:
+                if b"PANDAS_ATTRS" in pa_table.schema.metadata:
+                    df_metadata = pa_table.schema.metadata[b"PANDAS_ATTRS"]
+                    result.attrs = json.loads(df_metadata)
+            return result
+        finally:
+            if handles is not None:
+                handles.close()
+
+
+class FastParquetImpl(BaseImpl):
+    def __init__(self) -> None:
+        # since pandas is a dependency of fastparquet
+        # we need to import on first use
+        fastparquet = import_optional_dependency(
+            "fastparquet", extra="fastparquet is required for parquet support."
+        )
+        self.api = fastparquet
+
+    def write(
+        self,
+        df: DataFrame,
+        path,
+        compression: Literal["snappy", "gzip", "brotli"] | None = "snappy",
+        index=None,
+        partition_cols=None,
+        storage_options: StorageOptions | None = None,
+        filesystem=None,
+        **kwargs,
+    ) -> None:
+        self.validate_dataframe(df)
+
+        if "partition_on" in kwargs and partition_cols is not None:
+            raise ValueError(
+                "Cannot use both partition_on and "
+                "partition_cols. Use partition_cols for partitioning data"
+            )
+        if "partition_on" in kwargs:
+            partition_cols = kwargs.pop("partition_on")
+
+        if partition_cols is not None:
+            kwargs["file_scheme"] = "hive"
+
+        if filesystem is not None:
+            raise NotImplementedError(
+                "filesystem is not implemented for the fastparquet engine."
+            )
+
+        # cannot use get_handle as write() does not accept file buffers
+        path = stringify_path(path)
+        if is_fsspec_url(path):
+            fsspec = import_optional_dependency("fsspec")
+
+            # if filesystem is provided by fsspec, file must be opened in 'wb' mode.
+            kwargs["open_with"] = lambda path, _: fsspec.open(
+                path, "wb", **(storage_options or {})
+            ).open()
+        elif storage_options:
+            raise ValueError(
+                "storage_options passed with file object or non-fsspec file path"
+            )
+
+        with catch_warnings(record=True):
+            self.api.write(
+                path,
+                df,
+                compression=compression,
+                write_index=index,
+                partition_on=partition_cols,
+                **kwargs,
+            )
+
+    def read(
+        self,
+        path,
+        columns=None,
+        filters=None,
+        storage_options: StorageOptions | None = None,
+        filesystem=None,
+        **kwargs,
+    ) -> DataFrame:
+        parquet_kwargs: dict[str, Any] = {}
+        use_nullable_dtypes = kwargs.pop("use_nullable_dtypes", False)
+        dtype_backend = kwargs.pop("dtype_backend", lib.no_default)
+        # We are disabling nullable dtypes for fastparquet pending discussion
+        parquet_kwargs["pandas_nulls"] = False
+        if use_nullable_dtypes:
+            raise ValueError(
+                "The 'use_nullable_dtypes' argument is not supported for the "
+                "fastparquet engine"
+            )
+        if dtype_backend is not lib.no_default:
+            raise ValueError(
+                "The 'dtype_backend' argument is not supported for the "
+                "fastparquet engine"
+            )
+        if filesystem is not None:
+            raise NotImplementedError(
+                "filesystem is not implemented for the fastparquet engine."
+            )
+        path = stringify_path(path)
+        handles = None
+        if is_fsspec_url(path):
+            fsspec = import_optional_dependency("fsspec")
+
+            parquet_kwargs["fs"] = fsspec.open(path, "rb", **(storage_options or {})).fs
+        elif isinstance(path, str) and not os.path.isdir(path):
+            # use get_handle only when we are very certain that it is not a directory
+            # fsspec resources can also point to directories
+            # this branch is used for example when reading from non-fsspec URLs
+            handles = get_handle(
+                path, "rb", is_text=False, storage_options=storage_options
+            )
+            path = handles.handle
+
+        try:
+            parquet_file = self.api.ParquetFile(path, **parquet_kwargs)
+            return parquet_file.to_pandas(columns=columns, filters=filters, **kwargs)
+        finally:
+            if handles is not None:
+                handles.close()
+
+
+@doc(storage_options=_shared_docs["storage_options"])
+def to_parquet(
+    df: DataFrame,
+    path: FilePath | WriteBuffer[bytes] | None = None,
+    engine: str = "auto",
+    compression: str | None = "snappy",
+    index: bool | None = None,
+    storage_options: StorageOptions | None = None,
+    partition_cols: list[str] | None = None,
+    filesystem: Any = None,
+    **kwargs,
+) -> bytes | None:
+    """
+    Write a DataFrame to the parquet format.
+
+    Parameters
+    ----------
+    df : DataFrame
+    path : str, path object, file-like object, or None, default None
+        String, path object (implementing ``os.PathLike[str]``), or file-like
+        object implementing a binary ``write()`` function. If None, the result is
+        returned as bytes. If a string, it will be used as Root Directory path
+        when writing a partitioned dataset. The engine fastparquet does not
+        accept file-like objects.
+    engine : {{'auto', 'pyarrow', 'fastparquet'}}, default 'auto'
+        Parquet library to use. If 'auto', then the option
+        ``io.parquet.engine`` is used. The default ``io.parquet.engine``
+        behavior is to try 'pyarrow', falling back to 'fastparquet' if
+        'pyarrow' is unavailable.
+
+        When using the ``'pyarrow'`` engine and no storage options are provided
+        and a filesystem is implemented by both ``pyarrow.fs`` and ``fsspec``
+        (e.g. "s3://"), then the ``pyarrow.fs`` filesystem is attempted first.
+        Use the filesystem keyword with an instantiated fsspec filesystem
+        if you wish to use its implementation.
+    compression : {{'snappy', 'gzip', 'brotli', 'lz4', 'zstd', None}},
+        default 'snappy'. Name of the compression to use. Use ``None``
+        for no compression.
+    index : bool, default None
+        If ``True``, include the dataframe's index(es) in the file output. If
+        ``False``, they will not be written to the file.
+        If ``None``, similar to ``True`` the dataframe's index(es)
+        will be saved. However, instead of being saved as values,
+        the RangeIndex will be stored as a range in the metadata so it
+        doesn't require much space and is faster. Other indexes will
+        be included as columns in the file output.
+    partition_cols : str or list, optional, default None
+        Column names by which to partition the dataset.
+        Columns are partitioned in the order they are given.
+        Must be None if path is not a string.
+    {storage_options}
+
+    filesystem : fsspec or pyarrow filesystem, default None
+        Filesystem object to use when reading the parquet file. Only implemented
+        for ``engine="pyarrow"``.
+
+        .. versionadded:: 2.1.0
+
+    kwargs
+        Additional keyword arguments passed to the engine
+
+    Returns
+    -------
+    bytes if no path argument is provided else None
+    """
+    if isinstance(partition_cols, str):
+        partition_cols = [partition_cols]
+    impl = get_engine(engine)
+
+    path_or_buf: FilePath | WriteBuffer[bytes] = io.BytesIO() if path is None else path
+
+    impl.write(
+        df,
+        path_or_buf,
+        compression=compression,
+        index=index,
+        partition_cols=partition_cols,
+        storage_options=storage_options,
+        filesystem=filesystem,
+        **kwargs,
+    )
+
+    if path is None:
+        assert isinstance(path_or_buf, io.BytesIO)
+        return path_or_buf.getvalue()
+    else:
+        return None
+
+
+@doc(storage_options=_shared_docs["storage_options"])
+def read_parquet(
+    path: FilePath | ReadBuffer[bytes],
+    engine: str = "auto",
+    columns: list[str] | None = None,
+    storage_options: StorageOptions | None = None,
+    use_nullable_dtypes: bool | lib.NoDefault = lib.no_default,
+    dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
+    filesystem: Any = None,
+    filters: list[tuple] | list[list[tuple]] | None = None,
+    **kwargs,
+) -> DataFrame:
+    """
+    Load a parquet object from the file path, returning a DataFrame.
+
+    Parameters
+    ----------
+    path : str, path object or file-like object
+        String, path object (implementing ``os.PathLike[str]``), or file-like
+        object implementing a binary ``read()`` function.
+        The string could be a URL. Valid URL schemes include http, ftp, s3,
+        gs, and file. For file URLs, a host is expected. A local file could be:
+        ``file://localhost/path/to/table.parquet``.
+        A file URL can also be a path to a directory that contains multiple
+        partitioned parquet files. Both pyarrow and fastparquet support
+        paths to directories as well as file URLs. A directory path could be:
+        ``file://localhost/path/to/tables`` or ``s3://bucket/partition_dir``.
+    engine : {{'auto', 'pyarrow', 'fastparquet'}}, default 'auto'
+        Parquet library to use. If 'auto', then the option
+        ``io.parquet.engine`` is used. The default ``io.parquet.engine``
+        behavior is to try 'pyarrow', falling back to 'fastparquet' if
+        'pyarrow' is unavailable.
+
+        When using the ``'pyarrow'`` engine and no storage options are provided
+        and a filesystem is implemented by both ``pyarrow.fs`` and ``fsspec``
+        (e.g. "s3://"), then the ``pyarrow.fs`` filesystem is attempted first.
+        Use the filesystem keyword with an instantiated fsspec filesystem
+        if you wish to use its implementation.
+    columns : list, default=None
+        If not None, only these columns will be read from the file.
+    {storage_options}
+
+        .. versionadded:: 1.3.0
+
+    use_nullable_dtypes : bool, default False
+        If True, use dtypes that use ``pd.NA`` as missing value indicator
+        for the resulting DataFrame. (only applicable for the ``pyarrow``
+        engine)
+        As new dtypes are added that support ``pd.NA`` in the future, the
+        output with this option will change to use those dtypes.
+        Note: this is an experimental option, and behaviour (e.g. additional
+        support dtypes) may change without notice.
+
+        .. deprecated:: 2.0
+
+    dtype_backend : {{'numpy_nullable', 'pyarrow'}}, default 'numpy_nullable'
+        Back-end data type applied to the resultant :class:`DataFrame`
+        (still experimental). Behaviour is as follows:
+
+        * ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame`
+          (default).
+        * ``"pyarrow"``: returns pyarrow-backed nullable :class:`ArrowDtype`
+          DataFrame.
+
+        .. versionadded:: 2.0
+
+    filesystem : fsspec or pyarrow filesystem, default None
+        Filesystem object to use when reading the parquet file. Only implemented
+        for ``engine="pyarrow"``.
+
+        .. versionadded:: 2.1.0
+
+    filters : List[Tuple] or List[List[Tuple]], default None
+        To filter out data.
+        Filter syntax: [[(column, op, val), ...],...]
+        where op is [==, =, >, >=, <, <=, !=, in, not in]
+        The innermost tuples are transposed into a set of filters applied
+        through an `AND` operation.
+        The outer list combines these sets of filters through an `OR`
+        operation.
+        A single list of tuples can also be used, meaning that no `OR`
+        operation between set of filters is to be conducted.
+
+        Using this argument will NOT result in row-wise filtering of the final
+        partitions unless ``engine="pyarrow"`` is also specified.  For
+        other engines, filtering is only performed at the partition level, that is,
+        to prevent the loading of some row-groups and/or files.
+
+        .. versionadded:: 2.1.0
+
+    **kwargs
+        Any additional kwargs are passed to the engine.
+
+    Returns
+    -------
+    DataFrame
+
+    See Also
+    --------
+    DataFrame.to_parquet : Create a parquet object that serializes a DataFrame.
+
+    Examples
+    --------
+    >>> original_df = pd.DataFrame(
+    ...     {{"foo": range(5), "bar": range(5, 10)}}
+    ...    )
+    >>> original_df
+       foo  bar
+    0    0    5
+    1    1    6
+    2    2    7
+    3    3    8
+    4    4    9
+    >>> df_parquet_bytes = original_df.to_parquet()
+    >>> from io import BytesIO
+    >>> restored_df = pd.read_parquet(BytesIO(df_parquet_bytes))
+    >>> restored_df
+       foo  bar
+    0    0    5
+    1    1    6
+    2    2    7
+    3    3    8
+    4    4    9
+    >>> restored_df.equals(original_df)
+    True
+    >>> restored_bar = pd.read_parquet(BytesIO(df_parquet_bytes), columns=["bar"])
+    >>> restored_bar
+        bar
+    0    5
+    1    6
+    2    7
+    3    8
+    4    9
+    >>> restored_bar.equals(original_df[['bar']])
+    True
+
+    The function uses `kwargs` that are passed directly to the engine.
+    In the following example, we use the `filters` argument of the pyarrow
+    engine to filter the rows of the DataFrame.
+
+    Since `pyarrow` is the default engine, we can omit the `engine` argument.
+    Note that the `filters` argument is implemented by the `pyarrow` engine,
+    which can benefit from multithreading and also potentially be more
+    economical in terms of memory.
+
+    >>> sel = [("foo", ">", 2)]
+    >>> restored_part = pd.read_parquet(BytesIO(df_parquet_bytes), filters=sel)
+    >>> restored_part
+        foo  bar
+    0    3    8
+    1    4    9
+    """
+
+    impl = get_engine(engine)
+
+    if use_nullable_dtypes is not lib.no_default:
+        msg = (
+            "The argument 'use_nullable_dtypes' is deprecated and will be removed "
+            "in a future version."
+        )
+        if use_nullable_dtypes is True:
+            msg += (
+                "Use dtype_backend='numpy_nullable' instead of use_nullable_dtype=True."
+            )
+        warnings.warn(msg, FutureWarning, stacklevel=find_stack_level())
+    else:
+        use_nullable_dtypes = False
+    check_dtype_backend(dtype_backend)
+
+    return impl.read(
+        path,
+        columns=columns,
+        filters=filters,
+        storage_options=storage_options,
+        use_nullable_dtypes=use_nullable_dtypes,
+        dtype_backend=dtype_backend,
+        filesystem=filesystem,
+        **kwargs,
+    )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/parsers/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/parsers/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ff11968db15f0f7c6057a46c252a91daee7b9cd9
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/parsers/__init__.py
@@ -0,0 +1,9 @@
+from pandas.io.parsers.readers import (
+    TextFileReader,
+    TextParser,
+    read_csv,
+    read_fwf,
+    read_table,
+)
+
+__all__ = ["TextFileReader", "TextParser", "read_csv", "read_fwf", "read_table"]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/parsers/arrow_parser_wrapper.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/parsers/arrow_parser_wrapper.py
new file mode 100644
index 0000000000000000000000000000000000000000..7fe5ecb0e54c2394955101ad13079e721123762d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/parsers/arrow_parser_wrapper.py
@@ -0,0 +1,295 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+import warnings
+
+from pandas._libs import lib
+from pandas.compat._optional import import_optional_dependency
+from pandas.errors import (
+    ParserError,
+    ParserWarning,
+)
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.common import pandas_dtype
+from pandas.core.dtypes.inference import is_integer
+
+from pandas.io._util import arrow_table_to_pandas
+from pandas.io.parsers.base_parser import ParserBase
+
+if TYPE_CHECKING:
+    from pandas._typing import ReadBuffer
+
+    from pandas import DataFrame
+
+
+class ArrowParserWrapper(ParserBase):
+    """
+    Wrapper for the pyarrow engine for read_csv()
+    """
+
+    def __init__(self, src: ReadBuffer[bytes], **kwds) -> None:
+        super().__init__(kwds)
+        self.kwds = kwds
+        self.src = src
+
+        self._parse_kwds()
+
+    def _parse_kwds(self) -> None:
+        """
+        Validates keywords before passing to pyarrow.
+        """
+        encoding: str | None = self.kwds.get("encoding")
+        self.encoding = "utf-8" if encoding is None else encoding
+
+        na_values = self.kwds["na_values"]
+        if isinstance(na_values, dict):
+            raise ValueError(
+                "The pyarrow engine doesn't support passing a dict for na_values"
+            )
+        self.na_values = list(self.kwds["na_values"])
+
+    def _get_pyarrow_options(self) -> None:
+        """
+        Rename some arguments to pass to pyarrow
+        """
+        mapping = {
+            "usecols": "include_columns",
+            "na_values": "null_values",
+            "escapechar": "escape_char",
+            "skip_blank_lines": "ignore_empty_lines",
+            "decimal": "decimal_point",
+            "quotechar": "quote_char",
+        }
+        for pandas_name, pyarrow_name in mapping.items():
+            if pandas_name in self.kwds and self.kwds.get(pandas_name) is not None:
+                self.kwds[pyarrow_name] = self.kwds.pop(pandas_name)
+
+        # Date format handling
+        # If we get a string, we need to convert it into a list for pyarrow
+        # If we get a dict, we want to parse those separately
+        date_format = self.date_format
+        if isinstance(date_format, str):
+            date_format = [date_format]
+        else:
+            # In case of dict, we don't want to propagate through, so
+            # just set to pyarrow default of None
+
+            # Ideally, in future we disable pyarrow dtype inference (read in as string)
+            # to prevent misreads.
+            date_format = None
+        self.kwds["timestamp_parsers"] = date_format
+
+        self.parse_options = {
+            option_name: option_value
+            for option_name, option_value in self.kwds.items()
+            if option_value is not None
+            and option_name
+            in ("delimiter", "quote_char", "escape_char", "ignore_empty_lines")
+        }
+
+        on_bad_lines = self.kwds.get("on_bad_lines")
+        if on_bad_lines is not None:
+            if callable(on_bad_lines):
+                self.parse_options["invalid_row_handler"] = on_bad_lines
+            elif on_bad_lines == ParserBase.BadLineHandleMethod.ERROR:
+                self.parse_options[
+                    "invalid_row_handler"
+                ] = None  # PyArrow raises an exception by default
+            elif on_bad_lines == ParserBase.BadLineHandleMethod.WARN:
+
+                def handle_warning(invalid_row) -> str:
+                    warnings.warn(
+                        f"Expected {invalid_row.expected_columns} columns, but found "
+                        f"{invalid_row.actual_columns}: {invalid_row.text}",
+                        ParserWarning,
+                        stacklevel=find_stack_level(),
+                    )
+                    return "skip"
+
+                self.parse_options["invalid_row_handler"] = handle_warning
+            elif on_bad_lines == ParserBase.BadLineHandleMethod.SKIP:
+                self.parse_options["invalid_row_handler"] = lambda _: "skip"
+
+        self.convert_options = {
+            option_name: option_value
+            for option_name, option_value in self.kwds.items()
+            if option_value is not None
+            and option_name
+            in (
+                "include_columns",
+                "null_values",
+                "true_values",
+                "false_values",
+                "decimal_point",
+                "timestamp_parsers",
+            )
+        }
+        self.convert_options["strings_can_be_null"] = "" in self.kwds["null_values"]
+        # autogenerated column names are prefixed with 'f' in pyarrow.csv
+        if self.header is None and "include_columns" in self.convert_options:
+            self.convert_options["include_columns"] = [
+                f"f{n}" for n in self.convert_options["include_columns"]
+            ]
+
+        self.read_options = {
+            "autogenerate_column_names": self.header is None,
+            "skip_rows": self.header
+            if self.header is not None
+            else self.kwds["skiprows"],
+            "encoding": self.encoding,
+        }
+
+    def _finalize_pandas_output(self, frame: DataFrame) -> DataFrame:
+        """
+        Processes data read in based on kwargs.
+
+        Parameters
+        ----------
+        frame: DataFrame
+            The DataFrame to process.
+
+        Returns
+        -------
+        DataFrame
+            The processed DataFrame.
+        """
+        num_cols = len(frame.columns)
+        multi_index_named = True
+        if self.header is None:
+            if self.names is None:
+                if self.header is None:
+                    self.names = range(num_cols)
+            if len(self.names) != num_cols:
+                # usecols is passed through to pyarrow, we only handle index col here
+                # The only way self.names is not the same length as number of cols is
+                # if we have int index_col. We should just pad the names(they will get
+                # removed anyways) to expected length then.
+                columns_prefix = [str(x) for x in range(num_cols - len(self.names))]
+                self.names = columns_prefix + self.names
+                multi_index_named = False
+            frame.columns = self.names
+        # we only need the frame not the names
+        _, frame = self._do_date_conversions(frame.columns, frame)
+        if self.index_col is not None:
+            index_to_set = self.index_col.copy()
+            for i, item in enumerate(self.index_col):
+                if is_integer(item):
+                    index_to_set[i] = frame.columns[item]
+                # String case
+                elif item not in frame.columns:
+                    raise ValueError(f"Index {item} invalid")
+
+                # Process dtype for index_col and drop from dtypes
+                if self.dtype is not None:
+                    key, new_dtype = (
+                        (item, self.dtype.get(item))
+                        if self.dtype.get(item) is not None
+                        else (frame.columns[item], self.dtype.get(frame.columns[item]))
+                    )
+                    if new_dtype is not None:
+                        frame[key] = frame[key].astype(new_dtype)
+                        del self.dtype[key]
+
+            frame.set_index(index_to_set, drop=True, inplace=True)
+            # Clear names if headerless and no name given
+            if self.header is None and not multi_index_named:
+                frame.index.names = [None] * len(frame.index.names)
+
+        if self.dtype is not None:
+            # Ignore non-existent columns from dtype mapping
+            # like other parsers do
+            if isinstance(self.dtype, dict):
+                self.dtype = {
+                    k: pandas_dtype(v)
+                    for k, v in self.dtype.items()
+                    if k in frame.columns
+                }
+            else:
+                self.dtype = pandas_dtype(self.dtype)
+            try:
+                frame = frame.astype(self.dtype)
+            except TypeError as e:
+                # GH#44901 reraise to keep api consistent
+                raise ValueError(e)
+        return frame
+
+    def _validate_usecols(self, usecols) -> None:
+        if lib.is_list_like(usecols) and not all(isinstance(x, str) for x in usecols):
+            raise ValueError(
+                "The pyarrow engine does not allow 'usecols' to be integer "
+                "column positions. Pass a list of string column names instead."
+            )
+        elif callable(usecols):
+            raise ValueError(
+                "The pyarrow engine does not allow 'usecols' to be a callable."
+            )
+
+    def read(self) -> DataFrame:
+        """
+        Reads the contents of a CSV file into a DataFrame and
+        processes it according to the kwargs passed in the
+        constructor.
+
+        Returns
+        -------
+        DataFrame
+            The DataFrame created from the CSV file.
+        """
+        pa = import_optional_dependency("pyarrow")
+        pyarrow_csv = import_optional_dependency("pyarrow.csv")
+        self._get_pyarrow_options()
+
+        try:
+            convert_options = pyarrow_csv.ConvertOptions(**self.convert_options)
+        except TypeError:
+            include = self.convert_options.get("include_columns", None)
+            if include is not None:
+                self._validate_usecols(include)
+
+            nulls = self.convert_options.get("null_values", set())
+            if not lib.is_list_like(nulls) or not all(
+                isinstance(x, str) for x in nulls
+            ):
+                raise TypeError(
+                    "The 'pyarrow' engine requires all na_values to be strings"
+                )
+
+            raise
+
+        try:
+            table = pyarrow_csv.read_csv(
+                self.src,
+                read_options=pyarrow_csv.ReadOptions(**self.read_options),
+                parse_options=pyarrow_csv.ParseOptions(**self.parse_options),
+                convert_options=convert_options,
+            )
+        except pa.ArrowInvalid as e:
+            raise ParserError(e) from e
+
+        dtype_backend = self.kwds["dtype_backend"]
+
+        # Convert all pa.null() cols -> float64 (non nullable)
+        # else Int64 (nullable case, see below)
+        if dtype_backend is lib.no_default:
+            new_schema = table.schema
+            new_type = pa.float64()
+            for i, arrow_type in enumerate(table.schema.types):
+                if pa.types.is_null(arrow_type):
+                    new_schema = new_schema.set(
+                        i, new_schema.field(i).with_type(new_type)
+                    )
+
+            table = table.cast(new_schema)
+
+        with warnings.catch_warnings():
+            warnings.filterwarnings(
+                "ignore",
+                "make_block is deprecated",
+                DeprecationWarning,
+            )
+            frame = arrow_table_to_pandas(
+                table, dtype_backend=dtype_backend, null_to_int64=True
+            )
+
+        return self._finalize_pandas_output(frame)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/parsers/base_parser.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/parsers/base_parser.py
new file mode 100644
index 0000000000000000000000000000000000000000..40e3ea645064785a965783b2a86ef10b282a7045
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/parsers/base_parser.py
@@ -0,0 +1,1462 @@
+from __future__ import annotations
+
+from collections import defaultdict
+from copy import copy
+import csv
+import datetime
+from enum import Enum
+import itertools
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    cast,
+    final,
+    overload,
+)
+import warnings
+
+import numpy as np
+
+from pandas._libs import (
+    lib,
+    parsers,
+)
+import pandas._libs.ops as libops
+from pandas._libs.parsers import STR_NA_VALUES
+from pandas._libs.tslibs import parsing
+from pandas.compat._optional import import_optional_dependency
+from pandas.errors import (
+    ParserError,
+    ParserWarning,
+)
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.astype import astype_array
+from pandas.core.dtypes.common import (
+    ensure_object,
+    is_bool_dtype,
+    is_dict_like,
+    is_extension_array_dtype,
+    is_float_dtype,
+    is_integer,
+    is_integer_dtype,
+    is_list_like,
+    is_object_dtype,
+    is_scalar,
+    is_string_dtype,
+    pandas_dtype,
+)
+from pandas.core.dtypes.dtypes import (
+    CategoricalDtype,
+    ExtensionDtype,
+)
+from pandas.core.dtypes.missing import isna
+
+from pandas import (
+    ArrowDtype,
+    DataFrame,
+    DatetimeIndex,
+    StringDtype,
+    concat,
+)
+from pandas.core import algorithms
+from pandas.core.arrays import (
+    ArrowExtensionArray,
+    BaseMaskedArray,
+    BooleanArray,
+    Categorical,
+    ExtensionArray,
+    FloatingArray,
+    IntegerArray,
+)
+from pandas.core.arrays.boolean import BooleanDtype
+from pandas.core.indexes.api import (
+    Index,
+    MultiIndex,
+    default_index,
+    ensure_index_from_sequences,
+)
+from pandas.core.series import Series
+from pandas.core.tools import datetimes as tools
+
+from pandas.io.common import is_potential_multi_index
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Hashable,
+        Iterable,
+        Mapping,
+        Sequence,
+    )
+
+    from pandas._typing import (
+        ArrayLike,
+        DtypeArg,
+        DtypeObj,
+        Scalar,
+    )
+
+
+class ParserBase:
+    class BadLineHandleMethod(Enum):
+        ERROR = 0
+        WARN = 1
+        SKIP = 2
+
+    _implicit_index: bool
+    _first_chunk: bool
+    keep_default_na: bool
+    dayfirst: bool
+    cache_dates: bool
+    keep_date_col: bool
+    usecols_dtype: str | None
+
+    def __init__(self, kwds) -> None:
+        self._implicit_index = False
+
+        self.names = kwds.get("names")
+        self.orig_names: Sequence[Hashable] | None = None
+
+        self.index_col = kwds.get("index_col", None)
+        self.unnamed_cols: set = set()
+        self.index_names: Sequence[Hashable] | None = None
+        self.col_names: Sequence[Hashable] | None = None
+
+        self.parse_dates = _validate_parse_dates_arg(kwds.pop("parse_dates", False))
+        self._parse_date_cols: Iterable = []
+        self.date_parser = kwds.pop("date_parser", lib.no_default)
+        self.date_format = kwds.pop("date_format", None)
+        self.dayfirst = kwds.pop("dayfirst", False)
+        self.keep_date_col = kwds.pop("keep_date_col", False)
+
+        self.na_values = kwds.get("na_values")
+        self.na_fvalues = kwds.get("na_fvalues")
+        self.na_filter = kwds.get("na_filter", False)
+        self.keep_default_na = kwds.get("keep_default_na", True)
+
+        self.dtype = copy(kwds.get("dtype", None))
+        self.converters = kwds.get("converters")
+        self.dtype_backend = kwds.get("dtype_backend")
+
+        self.true_values = kwds.get("true_values")
+        self.false_values = kwds.get("false_values")
+        self.cache_dates = kwds.pop("cache_dates", True)
+
+        self._date_conv = _make_date_converter(
+            date_parser=self.date_parser,
+            date_format=self.date_format,
+            dayfirst=self.dayfirst,
+            cache_dates=self.cache_dates,
+        )
+
+        # validate header options for mi
+        self.header = kwds.get("header")
+        if is_list_like(self.header, allow_sets=False):
+            if kwds.get("usecols"):
+                raise ValueError(
+                    "cannot specify usecols when specifying a multi-index header"
+                )
+            if kwds.get("names"):
+                raise ValueError(
+                    "cannot specify names when specifying a multi-index header"
+                )
+
+            # validate index_col that only contains integers
+            if self.index_col is not None:
+                # In this case we can pin down index_col as list[int]
+                if is_integer(self.index_col):
+                    self.index_col = [self.index_col]
+                elif not (
+                    is_list_like(self.index_col, allow_sets=False)
+                    and all(map(is_integer, self.index_col))
+                ):
+                    raise ValueError(
+                        "index_col must only contain row numbers "
+                        "when specifying a multi-index header"
+                    )
+                else:
+                    self.index_col = list(self.index_col)
+
+        self._name_processed = False
+
+        self._first_chunk = True
+
+        self.usecols, self.usecols_dtype = self._validate_usecols_arg(kwds["usecols"])
+
+        # Fallback to error to pass a sketchy test(test_override_set_noconvert_columns)
+        # Normally, this arg would get pre-processed earlier on
+        self.on_bad_lines = kwds.get("on_bad_lines", self.BadLineHandleMethod.ERROR)
+
+    def _validate_parse_dates_presence(self, columns: Sequence[Hashable]) -> Iterable:
+        """
+        Check if parse_dates are in columns.
+
+        If user has provided names for parse_dates, check if those columns
+        are available.
+
+        Parameters
+        ----------
+        columns : list
+            List of names of the dataframe.
+
+        Returns
+        -------
+        The names of the columns which will get parsed later if a dict or list
+        is given as specification.
+
+        Raises
+        ------
+        ValueError
+            If column to parse_date is not in dataframe.
+
+        """
+        cols_needed: Iterable
+        if is_dict_like(self.parse_dates):
+            cols_needed = itertools.chain(*self.parse_dates.values())
+        elif is_list_like(self.parse_dates):
+            # a column in parse_dates could be represented
+            # ColReference = Union[int, str]
+            # DateGroups = List[ColReference]
+            # ParseDates = Union[DateGroups, List[DateGroups],
+            #     Dict[ColReference, DateGroups]]
+            cols_needed = itertools.chain.from_iterable(
+                col if is_list_like(col) and not isinstance(col, tuple) else [col]
+                for col in self.parse_dates
+            )
+        else:
+            cols_needed = []
+
+        cols_needed = list(cols_needed)
+
+        # get only columns that are references using names (str), not by index
+        missing_cols = ", ".join(
+            sorted(
+                {
+                    col
+                    for col in cols_needed
+                    if isinstance(col, str) and col not in columns
+                }
+            )
+        )
+        if missing_cols:
+            raise ValueError(
+                f"Missing column provided to 'parse_dates': '{missing_cols}'"
+            )
+        # Convert positions to actual column names
+        return [
+            col if (isinstance(col, str) or col in columns) else columns[col]
+            for col in cols_needed
+        ]
+
+    def close(self) -> None:
+        pass
+
+    @final
+    @property
+    def _has_complex_date_col(self) -> bool:
+        return isinstance(self.parse_dates, dict) or (
+            isinstance(self.parse_dates, list)
+            and len(self.parse_dates) > 0
+            and isinstance(self.parse_dates[0], list)
+        )
+
+    @final
+    def _should_parse_dates(self, i: int) -> bool:
+        if lib.is_bool(self.parse_dates):
+            return bool(self.parse_dates)
+        else:
+            if self.index_names is not None:
+                name = self.index_names[i]
+            else:
+                name = None
+            j = i if self.index_col is None else self.index_col[i]
+
+            return (j in self.parse_dates) or (
+                name is not None and name in self.parse_dates
+            )
+
+    @final
+    def _extract_multi_indexer_columns(
+        self,
+        header,
+        index_names: Sequence[Hashable] | None,
+        passed_names: bool = False,
+    ) -> tuple[
+        Sequence[Hashable], Sequence[Hashable] | None, Sequence[Hashable] | None, bool
+    ]:
+        """
+        Extract and return the names, index_names, col_names if the column
+        names are a MultiIndex.
+
+        Parameters
+        ----------
+        header: list of lists
+            The header rows
+        index_names: list, optional
+            The names of the future index
+        passed_names: bool, default False
+            A flag specifying if names where passed
+
+        """
+        if len(header) < 2:
+            return header[0], index_names, None, passed_names
+
+        # the names are the tuples of the header that are not the index cols
+        # 0 is the name of the index, assuming index_col is a list of column
+        # numbers
+        ic = self.index_col
+        if ic is None:
+            ic = []
+
+        if not isinstance(ic, (list, tuple, np.ndarray)):
+            ic = [ic]
+        sic = set(ic)
+
+        # clean the index_names
+        index_names = header.pop(-1)
+        index_names, _, _ = self._clean_index_names(index_names, self.index_col)
+
+        # extract the columns
+        field_count = len(header[0])
+
+        # check if header lengths are equal
+        if not all(len(header_iter) == field_count for header_iter in header[1:]):
+            raise ParserError("Header rows must have an equal number of columns.")
+
+        def extract(r):
+            return tuple(r[i] for i in range(field_count) if i not in sic)
+
+        columns = list(zip(*(extract(r) for r in header)))
+        names = columns.copy()
+        for single_ic in sorted(ic):
+            names.insert(single_ic, single_ic)
+
+        # Clean the column names (if we have an index_col).
+        if len(ic):
+            col_names = [
+                r[ic[0]]
+                if ((r[ic[0]] is not None) and r[ic[0]] not in self.unnamed_cols)
+                else None
+                for r in header
+            ]
+        else:
+            col_names = [None] * len(header)
+
+        passed_names = True
+
+        return names, index_names, col_names, passed_names
+
+    @final
+    def _maybe_make_multi_index_columns(
+        self,
+        columns: Sequence[Hashable],
+        col_names: Sequence[Hashable] | None = None,
+    ) -> Sequence[Hashable] | MultiIndex:
+        # possibly create a column mi here
+        if is_potential_multi_index(columns):
+            list_columns = cast(list[tuple], columns)
+            return MultiIndex.from_tuples(list_columns, names=col_names)
+        return columns
+
+    @final
+    def _make_index(
+        self, data, alldata, columns, indexnamerow: list[Scalar] | None = None
+    ) -> tuple[Index | None, Sequence[Hashable] | MultiIndex]:
+        index: Index | None
+        if not is_index_col(self.index_col) or not self.index_col:
+            index = None
+
+        elif not self._has_complex_date_col:
+            simple_index = self._get_simple_index(alldata, columns)
+            index = self._agg_index(simple_index)
+        elif self._has_complex_date_col:
+            if not self._name_processed:
+                (self.index_names, _, self.index_col) = self._clean_index_names(
+                    list(columns), self.index_col
+                )
+                self._name_processed = True
+            date_index = self._get_complex_date_index(data, columns)
+            index = self._agg_index(date_index, try_parse_dates=False)
+
+        # add names for the index
+        if indexnamerow:
+            coffset = len(indexnamerow) - len(columns)
+            assert index is not None
+            index = index.set_names(indexnamerow[:coffset])
+
+        # maybe create a mi on the columns
+        columns = self._maybe_make_multi_index_columns(columns, self.col_names)
+
+        return index, columns
+
+    @final
+    def _get_simple_index(self, data, columns):
+        def ix(col):
+            if not isinstance(col, str):
+                return col
+            raise ValueError(f"Index {col} invalid")
+
+        to_remove = []
+        index = []
+        for idx in self.index_col:
+            i = ix(idx)
+            to_remove.append(i)
+            index.append(data[i])
+
+        # remove index items from content and columns, don't pop in
+        # loop
+        for i in sorted(to_remove, reverse=True):
+            data.pop(i)
+            if not self._implicit_index:
+                columns.pop(i)
+
+        return index
+
+    @final
+    def _get_complex_date_index(self, data, col_names):
+        def _get_name(icol):
+            if isinstance(icol, str):
+                return icol
+
+            if col_names is None:
+                raise ValueError(f"Must supply column order to use {icol!s} as index")
+
+            for i, c in enumerate(col_names):
+                if i == icol:
+                    return c
+
+        to_remove = []
+        index = []
+        for idx in self.index_col:
+            name = _get_name(idx)
+            to_remove.append(name)
+            index.append(data[name])
+
+        # remove index items from content and columns, don't pop in
+        # loop
+        for c in sorted(to_remove, reverse=True):
+            data.pop(c)
+            col_names.remove(c)
+
+        return index
+
+    @final
+    def _clean_mapping(self, mapping):
+        """converts col numbers to names"""
+        if not isinstance(mapping, dict):
+            return mapping
+        clean = {}
+        # for mypy
+        assert self.orig_names is not None
+
+        for col, v in mapping.items():
+            if isinstance(col, int) and col not in self.orig_names:
+                col = self.orig_names[col]
+            clean[col] = v
+        if isinstance(mapping, defaultdict):
+            remaining_cols = set(self.orig_names) - set(clean.keys())
+            clean.update({col: mapping[col] for col in remaining_cols})
+        return clean
+
+    @final
+    def _agg_index(self, index, try_parse_dates: bool = True) -> Index:
+        arrays = []
+        converters = self._clean_mapping(self.converters)
+
+        if self.index_names is not None:
+            names: Iterable = self.index_names
+        else:
+            names = itertools.cycle([None])
+        for i, (arr, name) in enumerate(zip(index, names)):
+            if try_parse_dates and self._should_parse_dates(i):
+                arr = self._date_conv(
+                    arr,
+                    col=self.index_names[i] if self.index_names is not None else None,
+                )
+
+            if self.na_filter:
+                col_na_values = self.na_values
+                col_na_fvalues = self.na_fvalues
+            else:
+                col_na_values = set()
+                col_na_fvalues = set()
+
+            if isinstance(self.na_values, dict):
+                assert self.index_names is not None
+                col_name = self.index_names[i]
+                if col_name is not None:
+                    col_na_values, col_na_fvalues = _get_na_values(
+                        col_name, self.na_values, self.na_fvalues, self.keep_default_na
+                    )
+
+            clean_dtypes = self._clean_mapping(self.dtype)
+
+            cast_type = None
+            index_converter = False
+            if self.index_names is not None:
+                if isinstance(clean_dtypes, dict):
+                    cast_type = clean_dtypes.get(self.index_names[i], None)
+
+                if isinstance(converters, dict):
+                    index_converter = converters.get(self.index_names[i]) is not None
+
+            try_num_bool = not (
+                cast_type and is_string_dtype(cast_type) or index_converter
+            )
+
+            arr, _ = self._infer_types(
+                arr, col_na_values | col_na_fvalues, cast_type is None, try_num_bool
+            )
+            if cast_type is not None:
+                # Don't perform RangeIndex inference
+                idx = Index(arr, name=name, dtype=cast_type)
+            else:
+                idx = ensure_index_from_sequences([arr], [name])
+            arrays.append(idx)
+
+        if len(arrays) == 1:
+            return arrays[0]
+        else:
+            return MultiIndex.from_arrays(arrays)
+
+    @final
+    def _convert_to_ndarrays(
+        self,
+        dct: Mapping,
+        na_values,
+        na_fvalues,
+        verbose: bool = False,
+        converters=None,
+        dtypes=None,
+    ):
+        result = {}
+        for c, values in dct.items():
+            conv_f = None if converters is None else converters.get(c, None)
+            if isinstance(dtypes, dict):
+                cast_type = dtypes.get(c, None)
+            else:
+                # single dtype or None
+                cast_type = dtypes
+
+            if self.na_filter:
+                col_na_values, col_na_fvalues = _get_na_values(
+                    c, na_values, na_fvalues, self.keep_default_na
+                )
+            else:
+                col_na_values, col_na_fvalues = set(), set()
+
+            if c in self._parse_date_cols:
+                # GH#26203 Do not convert columns which get converted to dates
+                # but replace nans to ensure to_datetime works
+                mask = algorithms.isin(values, set(col_na_values) | col_na_fvalues)
+                np.putmask(values, mask, np.nan)
+                result[c] = values
+                continue
+
+            if conv_f is not None:
+                # conv_f applied to data before inference
+                if cast_type is not None:
+                    warnings.warn(
+                        (
+                            "Both a converter and dtype were specified "
+                            f"for column {c} - only the converter will be used."
+                        ),
+                        ParserWarning,
+                        stacklevel=find_stack_level(),
+                    )
+
+                try:
+                    values = lib.map_infer(values, conv_f)
+                except ValueError:
+                    mask = algorithms.isin(values, list(na_values)).view(np.uint8)
+                    values = lib.map_infer_mask(values, conv_f, mask)
+
+                cvals, na_count = self._infer_types(
+                    values,
+                    set(col_na_values) | col_na_fvalues,
+                    cast_type is None,
+                    try_num_bool=False,
+                )
+            else:
+                is_ea = is_extension_array_dtype(cast_type)
+                is_str_or_ea_dtype = is_ea or is_string_dtype(cast_type)
+                # skip inference if specified dtype is object
+                # or casting to an EA
+                try_num_bool = not (cast_type and is_str_or_ea_dtype)
+
+                # general type inference and conversion
+                cvals, na_count = self._infer_types(
+                    values,
+                    set(col_na_values) | col_na_fvalues,
+                    cast_type is None,
+                    try_num_bool,
+                )
+
+                # type specified in dtype param or cast_type is an EA
+                if cast_type is not None:
+                    cast_type = pandas_dtype(cast_type)
+                if cast_type and (cvals.dtype != cast_type or is_ea):
+                    if not is_ea and na_count > 0:
+                        if is_bool_dtype(cast_type):
+                            raise ValueError(f"Bool column has NA values in column {c}")
+                    cvals = self._cast_types(cvals, cast_type, c)
+
+            result[c] = cvals
+            if verbose and na_count:
+                print(f"Filled {na_count} NA values in column {c!s}")
+        return result
+
+    @final
+    def _set_noconvert_dtype_columns(
+        self, col_indices: list[int], names: Sequence[Hashable]
+    ) -> set[int]:
+        """
+        Set the columns that should not undergo dtype conversions.
+
+        Currently, any column that is involved with date parsing will not
+        undergo such conversions. If usecols is specified, the positions of the columns
+        not to cast is relative to the usecols not to all columns.
+
+        Parameters
+        ----------
+        col_indices: The indices specifying order and positions of the columns
+        names: The column names which order is corresponding with the order
+               of col_indices
+
+        Returns
+        -------
+        A set of integers containing the positions of the columns not to convert.
+        """
+        usecols: list[int] | list[str] | None
+        noconvert_columns = set()
+        if self.usecols_dtype == "integer":
+            # A set of integers will be converted to a list in
+            # the correct order every single time.
+            usecols = sorted(self.usecols)
+        elif callable(self.usecols) or self.usecols_dtype not in ("empty", None):
+            # The names attribute should have the correct columns
+            # in the proper order for indexing with parse_dates.
+            usecols = col_indices
+        else:
+            # Usecols is empty.
+            usecols = None
+
+        def _set(x) -> int:
+            if usecols is not None and is_integer(x):
+                x = usecols[x]
+
+            if not is_integer(x):
+                x = col_indices[names.index(x)]
+
+            return x
+
+        if isinstance(self.parse_dates, list):
+            for val in self.parse_dates:
+                if isinstance(val, list):
+                    for k in val:
+                        noconvert_columns.add(_set(k))
+                else:
+                    noconvert_columns.add(_set(val))
+
+        elif isinstance(self.parse_dates, dict):
+            for val in self.parse_dates.values():
+                if isinstance(val, list):
+                    for k in val:
+                        noconvert_columns.add(_set(k))
+                else:
+                    noconvert_columns.add(_set(val))
+
+        elif self.parse_dates:
+            if isinstance(self.index_col, list):
+                for k in self.index_col:
+                    noconvert_columns.add(_set(k))
+            elif self.index_col is not None:
+                noconvert_columns.add(_set(self.index_col))
+
+        return noconvert_columns
+
+    @final
+    def _infer_types(
+        self, values, na_values, no_dtype_specified, try_num_bool: bool = True
+    ) -> tuple[ArrayLike, int]:
+        """
+        Infer types of values, possibly casting
+
+        Parameters
+        ----------
+        values : ndarray
+        na_values : set
+        no_dtype_specified: Specifies if we want to cast explicitly
+        try_num_bool : bool, default try
+           try to cast values to numeric (first preference) or boolean
+
+        Returns
+        -------
+        converted : ndarray or ExtensionArray
+        na_count : int
+        """
+        na_count = 0
+        if issubclass(values.dtype.type, (np.number, np.bool_)):
+            # If our array has numeric dtype, we don't have to check for strings in isin
+            na_values = np.array([val for val in na_values if not isinstance(val, str)])
+            mask = algorithms.isin(values, na_values)
+            na_count = mask.astype("uint8", copy=False).sum()
+            if na_count > 0:
+                if is_integer_dtype(values):
+                    values = values.astype(np.float64)
+                np.putmask(values, mask, np.nan)
+            return values, na_count
+
+        dtype_backend = self.dtype_backend
+        non_default_dtype_backend = (
+            no_dtype_specified and dtype_backend is not lib.no_default
+        )
+        result: ArrayLike
+
+        if try_num_bool and is_object_dtype(values.dtype):
+            # exclude e.g DatetimeIndex here
+            try:
+                result, result_mask = lib.maybe_convert_numeric(
+                    values,
+                    na_values,
+                    False,
+                    convert_to_masked_nullable=non_default_dtype_backend,  # type: ignore[arg-type]
+                )
+            except (ValueError, TypeError):
+                # e.g. encountering datetime string gets ValueError
+                #  TypeError can be raised in floatify
+                na_count = parsers.sanitize_objects(values, na_values)
+                result = values
+            else:
+                if non_default_dtype_backend:
+                    if result_mask is None:
+                        result_mask = np.zeros(result.shape, dtype=np.bool_)
+
+                    if result_mask.all():
+                        result = IntegerArray(
+                            np.ones(result_mask.shape, dtype=np.int64), result_mask
+                        )
+                    elif is_integer_dtype(result):
+                        result = IntegerArray(result, result_mask)
+                    elif is_bool_dtype(result):
+                        result = BooleanArray(result, result_mask)
+                    elif is_float_dtype(result):
+                        result = FloatingArray(result, result_mask)
+
+                    na_count = result_mask.sum()
+                else:
+                    na_count = isna(result).sum()
+        else:
+            result = values
+            if values.dtype == np.object_:
+                na_count = parsers.sanitize_objects(values, na_values)
+
+        if result.dtype == np.object_ and try_num_bool:
+            result, bool_mask = libops.maybe_convert_bool(
+                np.asarray(values),
+                true_values=self.true_values,
+                false_values=self.false_values,
+                convert_to_masked_nullable=non_default_dtype_backend,  # type: ignore[arg-type]
+            )
+            if result.dtype == np.bool_ and non_default_dtype_backend:
+                if bool_mask is None:
+                    bool_mask = np.zeros(result.shape, dtype=np.bool_)
+                result = BooleanArray(result, bool_mask)
+            elif result.dtype == np.object_ and non_default_dtype_backend:
+                # read_excel sends array of datetime objects
+                if not lib.is_datetime_array(result, skipna=True):
+                    dtype = StringDtype()
+                    cls = dtype.construct_array_type()
+                    result = cls._from_sequence(values, dtype=dtype)
+
+        if dtype_backend == "pyarrow":
+            pa = import_optional_dependency("pyarrow")
+            if isinstance(result, np.ndarray):
+                result = ArrowExtensionArray(pa.array(result, from_pandas=True))
+            elif isinstance(result, BaseMaskedArray):
+                if result._mask.all():
+                    # We want an arrow null array here
+                    result = ArrowExtensionArray(pa.array([None] * len(result)))
+                else:
+                    result = ArrowExtensionArray(
+                        pa.array(result._data, mask=result._mask)
+                    )
+            else:
+                result = ArrowExtensionArray(
+                    pa.array(result.to_numpy(), from_pandas=True)
+                )
+
+        return result, na_count
+
+    @final
+    def _cast_types(self, values: ArrayLike, cast_type: DtypeObj, column) -> ArrayLike:
+        """
+        Cast values to specified type
+
+        Parameters
+        ----------
+        values : ndarray or ExtensionArray
+        cast_type : np.dtype or ExtensionDtype
+           dtype to cast values to
+        column : string
+            column name - used only for error reporting
+
+        Returns
+        -------
+        converted : ndarray or ExtensionArray
+        """
+        if isinstance(cast_type, CategoricalDtype):
+            known_cats = cast_type.categories is not None
+
+            if not is_object_dtype(values.dtype) and not known_cats:
+                # TODO: this is for consistency with
+                # c-parser which parses all categories
+                # as strings
+                values = lib.ensure_string_array(
+                    values, skipna=False, convert_na_value=False
+                )
+
+            cats = Index(values).unique().dropna()
+            values = Categorical._from_inferred_categories(
+                cats, cats.get_indexer(values), cast_type, true_values=self.true_values
+            )
+
+        # use the EA's implementation of casting
+        elif isinstance(cast_type, ExtensionDtype):
+            array_type = cast_type.construct_array_type()
+            try:
+                if isinstance(cast_type, BooleanDtype):
+                    # error: Unexpected keyword argument "true_values" for
+                    # "_from_sequence_of_strings" of "ExtensionArray"
+                    return array_type._from_sequence_of_strings(  # type: ignore[call-arg]
+                        values,
+                        dtype=cast_type,
+                        true_values=self.true_values,
+                        false_values=self.false_values,
+                    )
+                else:
+                    return array_type._from_sequence_of_strings(values, dtype=cast_type)
+            except NotImplementedError as err:
+                raise NotImplementedError(
+                    f"Extension Array: {array_type} must implement "
+                    "_from_sequence_of_strings in order to be used in parser methods"
+                ) from err
+
+        elif isinstance(values, ExtensionArray):
+            values = values.astype(cast_type, copy=False)
+        elif issubclass(cast_type.type, str):
+            # TODO: why skipna=True here and False above? some tests depend
+            #  on it here, but nothing fails if we change it above
+            #  (as no tests get there as of 2022-12-06)
+            values = lib.ensure_string_array(
+                values, skipna=True, convert_na_value=False
+            )
+        else:
+            try:
+                values = astype_array(values, cast_type, copy=True)
+            except ValueError as err:
+                raise ValueError(
+                    f"Unable to convert column {column} to type {cast_type}"
+                ) from err
+        return values
+
+    @overload
+    def _do_date_conversions(
+        self,
+        names: Index,
+        data: DataFrame,
+    ) -> tuple[Sequence[Hashable] | Index, DataFrame]:
+        ...
+
+    @overload
+    def _do_date_conversions(
+        self,
+        names: Sequence[Hashable],
+        data: Mapping[Hashable, ArrayLike],
+    ) -> tuple[Sequence[Hashable], Mapping[Hashable, ArrayLike]]:
+        ...
+
+    @final
+    def _do_date_conversions(
+        self,
+        names: Sequence[Hashable] | Index,
+        data: Mapping[Hashable, ArrayLike] | DataFrame,
+    ) -> tuple[Sequence[Hashable] | Index, Mapping[Hashable, ArrayLike] | DataFrame]:
+        # returns data, columns
+
+        if self.parse_dates is not None:
+            data, names = _process_date_conversion(
+                data,
+                self._date_conv,
+                self.parse_dates,
+                self.index_col,
+                self.index_names,
+                names,
+                keep_date_col=self.keep_date_col,
+                dtype_backend=self.dtype_backend,
+            )
+
+        return names, data
+
+    @final
+    def _check_data_length(
+        self,
+        columns: Sequence[Hashable],
+        data: Sequence[ArrayLike],
+    ) -> None:
+        """Checks if length of data is equal to length of column names.
+
+        One set of trailing commas is allowed. self.index_col not False
+        results in a ParserError previously when lengths do not match.
+
+        Parameters
+        ----------
+        columns: list of column names
+        data: list of array-likes containing the data column-wise.
+        """
+        if not self.index_col and len(columns) != len(data) and columns:
+            empty_str = is_object_dtype(data[-1]) and data[-1] == ""
+            # error: No overload variant of "__ror__" of "ndarray" matches
+            # argument type "ExtensionArray"
+            empty_str_or_na = empty_str | isna(data[-1])  # type: ignore[operator]
+            if len(columns) == len(data) - 1 and np.all(empty_str_or_na):
+                return
+            warnings.warn(
+                "Length of header or names does not match length of data. This leads "
+                "to a loss of data with index_col=False.",
+                ParserWarning,
+                stacklevel=find_stack_level(),
+            )
+
+    @overload
+    def _evaluate_usecols(
+        self,
+        usecols: set[int] | Callable[[Hashable], object],
+        names: Sequence[Hashable],
+    ) -> set[int]:
+        ...
+
+    @overload
+    def _evaluate_usecols(
+        self, usecols: set[str], names: Sequence[Hashable]
+    ) -> set[str]:
+        ...
+
+    @final
+    def _evaluate_usecols(
+        self,
+        usecols: Callable[[Hashable], object] | set[str] | set[int],
+        names: Sequence[Hashable],
+    ) -> set[str] | set[int]:
+        """
+        Check whether or not the 'usecols' parameter
+        is a callable.  If so, enumerates the 'names'
+        parameter and returns a set of indices for
+        each entry in 'names' that evaluates to True.
+        If not a callable, returns 'usecols'.
+        """
+        if callable(usecols):
+            return {i for i, name in enumerate(names) if usecols(name)}
+        return usecols
+
+    @final
+    def _validate_usecols_names(self, usecols, names: Sequence):
+        """
+        Validates that all usecols are present in a given
+        list of names. If not, raise a ValueError that
+        shows what usecols are missing.
+
+        Parameters
+        ----------
+        usecols : iterable of usecols
+            The columns to validate are present in names.
+        names : iterable of names
+            The column names to check against.
+
+        Returns
+        -------
+        usecols : iterable of usecols
+            The `usecols` parameter if the validation succeeds.
+
+        Raises
+        ------
+        ValueError : Columns were missing. Error message will list them.
+        """
+        missing = [c for c in usecols if c not in names]
+        if len(missing) > 0:
+            raise ValueError(
+                f"Usecols do not match columns, columns expected but not found: "
+                f"{missing}"
+            )
+
+        return usecols
+
+    @final
+    def _validate_usecols_arg(self, usecols):
+        """
+        Validate the 'usecols' parameter.
+
+        Checks whether or not the 'usecols' parameter contains all integers
+        (column selection by index), strings (column by name) or is a callable.
+        Raises a ValueError if that is not the case.
+
+        Parameters
+        ----------
+        usecols : list-like, callable, or None
+            List of columns to use when parsing or a callable that can be used
+            to filter a list of table columns.
+
+        Returns
+        -------
+        usecols_tuple : tuple
+            A tuple of (verified_usecols, usecols_dtype).
+
+            'verified_usecols' is either a set if an array-like is passed in or
+            'usecols' if a callable or None is passed in.
+
+            'usecols_dtype` is the inferred dtype of 'usecols' if an array-like
+            is passed in or None if a callable or None is passed in.
+        """
+        msg = (
+            "'usecols' must either be list-like of all strings, all unicode, "
+            "all integers or a callable."
+        )
+        if usecols is not None:
+            if callable(usecols):
+                return usecols, None
+
+            if not is_list_like(usecols):
+                # see gh-20529
+                #
+                # Ensure it is iterable container but not string.
+                raise ValueError(msg)
+
+            usecols_dtype = lib.infer_dtype(usecols, skipna=False)
+
+            if usecols_dtype not in ("empty", "integer", "string"):
+                raise ValueError(msg)
+
+            usecols = set(usecols)
+
+            return usecols, usecols_dtype
+        return usecols, None
+
+    @final
+    def _clean_index_names(self, columns, index_col) -> tuple[list | None, list, list]:
+        if not is_index_col(index_col):
+            return None, columns, index_col
+
+        columns = list(columns)
+
+        # In case of no rows and multiindex columns we have to set index_names to
+        # list of Nones GH#38292
+        if not columns:
+            return [None] * len(index_col), columns, index_col
+
+        cp_cols = list(columns)
+        index_names: list[str | int | None] = []
+
+        # don't mutate
+        index_col = list(index_col)
+
+        for i, c in enumerate(index_col):
+            if isinstance(c, str):
+                index_names.append(c)
+                for j, name in enumerate(cp_cols):
+                    if name == c:
+                        index_col[i] = j
+                        columns.remove(name)
+                        break
+            else:
+                name = cp_cols[c]
+                columns.remove(name)
+                index_names.append(name)
+
+        # Only clean index names that were placeholders.
+        for i, name in enumerate(index_names):
+            if isinstance(name, str) and name in self.unnamed_cols:
+                index_names[i] = None
+
+        return index_names, columns, index_col
+
+    @final
+    def _get_empty_meta(self, columns, dtype: DtypeArg | None = None):
+        columns = list(columns)
+
+        index_col = self.index_col
+        index_names = self.index_names
+
+        # Convert `dtype` to a defaultdict of some kind.
+        # This will enable us to write `dtype[col_name]`
+        # without worrying about KeyError issues later on.
+        dtype_dict: defaultdict[Hashable, Any]
+        if not is_dict_like(dtype):
+            # if dtype == None, default will be object.
+            dtype_dict = defaultdict(lambda: dtype)
+        else:
+            dtype = cast(dict, dtype)
+            dtype_dict = defaultdict(
+                lambda: None,
+                {columns[k] if is_integer(k) else k: v for k, v in dtype.items()},
+            )
+
+        # Even though we have no data, the "index" of the empty DataFrame
+        # could for example still be an empty MultiIndex. Thus, we need to
+        # check whether we have any index columns specified, via either:
+        #
+        # 1) index_col (column indices)
+        # 2) index_names (column names)
+        #
+        # Both must be non-null to ensure a successful construction. Otherwise,
+        # we have to create a generic empty Index.
+        index: Index
+        if (index_col is None or index_col is False) or index_names is None:
+            index = default_index(0)
+        else:
+            # TODO: We could return default_index(0) if dtype_dict[name] is None
+            data = [
+                Index([], name=name, dtype=dtype_dict[name]) for name in index_names
+            ]
+            if len(data) == 1:
+                index = data[0]
+            else:
+                index = MultiIndex.from_arrays(data)
+            index_col.sort()
+
+            for i, n in enumerate(index_col):
+                columns.pop(n - i)
+
+        col_dict = {
+            col_name: Series([], dtype=dtype_dict[col_name]) for col_name in columns
+        }
+
+        return index, columns, col_dict
+
+
+def _make_date_converter(
+    date_parser=lib.no_default,
+    dayfirst: bool = False,
+    cache_dates: bool = True,
+    date_format: dict[Hashable, str] | str | None = None,
+):
+    if date_parser is not lib.no_default:
+        warnings.warn(
+            "The argument 'date_parser' is deprecated and will "
+            "be removed in a future version. "
+            "Please use 'date_format' instead, or read your data in as 'object' dtype "
+            "and then call 'to_datetime'.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+    if date_parser is not lib.no_default and date_format is not None:
+        raise TypeError("Cannot use both 'date_parser' and 'date_format'")
+
+    def unpack_if_single_element(arg):
+        # NumPy 1.25 deprecation: https://github.com/numpy/numpy/pull/10615
+        if isinstance(arg, np.ndarray) and arg.ndim == 1 and len(arg) == 1:
+            return arg[0]
+        return arg
+
+    def converter(*date_cols, col: Hashable):
+        if len(date_cols) == 1 and date_cols[0].dtype.kind in "Mm":
+            return date_cols[0]
+
+        if date_parser is lib.no_default:
+            strs = parsing.concat_date_cols(date_cols)
+            date_fmt = (
+                date_format.get(col) if isinstance(date_format, dict) else date_format
+            )
+
+            with warnings.catch_warnings():
+                warnings.filterwarnings(
+                    "ignore",
+                    ".*parsing datetimes with mixed time zones will raise an error",
+                    category=FutureWarning,
+                )
+                str_objs = ensure_object(strs)
+                try:
+                    result = tools.to_datetime(
+                        str_objs,
+                        format=date_fmt,
+                        utc=False,
+                        dayfirst=dayfirst,
+                        cache=cache_dates,
+                    )
+                except (ValueError, TypeError):
+                    # test_usecols_with_parse_dates4
+                    return str_objs
+
+            if isinstance(result, DatetimeIndex):
+                arr = result.to_numpy()
+                arr.flags.writeable = True
+                return arr
+            return result._values
+        else:
+            try:
+                with warnings.catch_warnings():
+                    warnings.filterwarnings(
+                        "ignore",
+                        ".*parsing datetimes with mixed time zones "
+                        "will raise an error",
+                        category=FutureWarning,
+                    )
+                    pre_parsed = date_parser(
+                        *(unpack_if_single_element(arg) for arg in date_cols)
+                    )
+                    try:
+                        result = tools.to_datetime(
+                            pre_parsed,
+                            cache=cache_dates,
+                        )
+                    except (ValueError, TypeError):
+                        # test_read_csv_with_custom_date_parser
+                        result = pre_parsed
+                if isinstance(result, datetime.datetime):
+                    raise Exception("scalar parser")
+                return result
+            except Exception:
+                # e.g. test_datetime_fractional_seconds
+                with warnings.catch_warnings():
+                    warnings.filterwarnings(
+                        "ignore",
+                        ".*parsing datetimes with mixed time zones "
+                        "will raise an error",
+                        category=FutureWarning,
+                    )
+                    pre_parsed = parsing.try_parse_dates(
+                        parsing.concat_date_cols(date_cols),
+                        parser=date_parser,
+                    )
+                    try:
+                        return tools.to_datetime(pre_parsed)
+                    except (ValueError, TypeError):
+                        # TODO: not reached in tests 2023-10-27; needed?
+                        return pre_parsed
+
+    return converter
+
+
+parser_defaults = {
+    "delimiter": None,
+    "escapechar": None,
+    "quotechar": '"',
+    "quoting": csv.QUOTE_MINIMAL,
+    "doublequote": True,
+    "skipinitialspace": False,
+    "lineterminator": None,
+    "header": "infer",
+    "index_col": None,
+    "names": None,
+    "skiprows": None,
+    "skipfooter": 0,
+    "nrows": None,
+    "na_values": None,
+    "keep_default_na": True,
+    "true_values": None,
+    "false_values": None,
+    "converters": None,
+    "dtype": None,
+    "cache_dates": True,
+    "thousands": None,
+    "comment": None,
+    "decimal": ".",
+    # 'engine': 'c',
+    "parse_dates": False,
+    "keep_date_col": False,
+    "dayfirst": False,
+    "date_parser": lib.no_default,
+    "date_format": None,
+    "usecols": None,
+    # 'iterator': False,
+    "chunksize": None,
+    "verbose": False,
+    "encoding": None,
+    "compression": None,
+    "skip_blank_lines": True,
+    "encoding_errors": "strict",
+    "on_bad_lines": ParserBase.BadLineHandleMethod.ERROR,
+    "dtype_backend": lib.no_default,
+}
+
+
+def _process_date_conversion(
+    data_dict,
+    converter: Callable,
+    parse_spec,
+    index_col,
+    index_names,
+    columns,
+    keep_date_col: bool = False,
+    dtype_backend=lib.no_default,
+):
+    def _isindex(colspec):
+        return (isinstance(index_col, list) and colspec in index_col) or (
+            isinstance(index_names, list) and colspec in index_names
+        )
+
+    new_cols = []
+    new_data = {}
+
+    orig_names = columns
+    columns = list(columns)
+
+    date_cols = set()
+
+    if parse_spec is None or isinstance(parse_spec, bool):
+        return data_dict, columns
+
+    if isinstance(parse_spec, list):
+        # list of column lists
+        for colspec in parse_spec:
+            if is_scalar(colspec) or isinstance(colspec, tuple):
+                if isinstance(colspec, int) and colspec not in data_dict:
+                    colspec = orig_names[colspec]
+                if _isindex(colspec):
+                    continue
+                elif dtype_backend == "pyarrow":
+                    import pyarrow as pa
+
+                    dtype = data_dict[colspec].dtype
+                    if isinstance(dtype, ArrowDtype) and (
+                        pa.types.is_timestamp(dtype.pyarrow_dtype)
+                        or pa.types.is_date(dtype.pyarrow_dtype)
+                    ):
+                        continue
+
+                # Pyarrow engine returns Series which we need to convert to
+                # numpy array before converter, its a no-op for other parsers
+                data_dict[colspec] = converter(
+                    np.asarray(data_dict[colspec]), col=colspec
+                )
+            else:
+                new_name, col, old_names = _try_convert_dates(
+                    converter, colspec, data_dict, orig_names
+                )
+                if new_name in data_dict:
+                    raise ValueError(f"New date column already in dict {new_name}")
+                new_data[new_name] = col
+                new_cols.append(new_name)
+                date_cols.update(old_names)
+
+    elif isinstance(parse_spec, dict):
+        # dict of new name to column list
+        for new_name, colspec in parse_spec.items():
+            if new_name in data_dict:
+                raise ValueError(f"Date column {new_name} already in dict")
+
+            _, col, old_names = _try_convert_dates(
+                converter,
+                colspec,
+                data_dict,
+                orig_names,
+                target_name=new_name,
+            )
+
+            new_data[new_name] = col
+
+            # If original column can be converted to date we keep the converted values
+            # This can only happen if values are from single column
+            if len(colspec) == 1:
+                new_data[colspec[0]] = col
+
+            new_cols.append(new_name)
+            date_cols.update(old_names)
+
+    if isinstance(data_dict, DataFrame):
+        data_dict = concat([DataFrame(new_data), data_dict], axis=1, copy=False)
+    else:
+        data_dict.update(new_data)
+    new_cols.extend(columns)
+
+    if not keep_date_col:
+        for c in list(date_cols):
+            data_dict.pop(c)
+            new_cols.remove(c)
+
+    return data_dict, new_cols
+
+
+def _try_convert_dates(
+    parser: Callable, colspec, data_dict, columns, target_name: str | None = None
+):
+    colset = set(columns)
+    colnames = []
+
+    for c in colspec:
+        if c in colset:
+            colnames.append(c)
+        elif isinstance(c, int) and c not in columns:
+            colnames.append(columns[c])
+        else:
+            colnames.append(c)
+
+    new_name: tuple | str
+    if all(isinstance(x, tuple) for x in colnames):
+        new_name = tuple(map("_".join, zip(*colnames)))
+    else:
+        new_name = "_".join([str(x) for x in colnames])
+    to_parse = [np.asarray(data_dict[c]) for c in colnames if c in data_dict]
+
+    new_col = parser(*to_parse, col=new_name if target_name is None else target_name)
+    return new_name, new_col, colnames
+
+
+def _get_na_values(col, na_values, na_fvalues, keep_default_na: bool):
+    """
+    Get the NaN values for a given column.
+
+    Parameters
+    ----------
+    col : str
+        The name of the column.
+    na_values : array-like, dict
+        The object listing the NaN values as strings.
+    na_fvalues : array-like, dict
+        The object listing the NaN values as floats.
+    keep_default_na : bool
+        If `na_values` is a dict, and the column is not mapped in the
+        dictionary, whether to return the default NaN values or the empty set.
+
+    Returns
+    -------
+    nan_tuple : A length-two tuple composed of
+
+        1) na_values : the string NaN values for that column.
+        2) na_fvalues : the float NaN values for that column.
+    """
+    if isinstance(na_values, dict):
+        if col in na_values:
+            return na_values[col], na_fvalues[col]
+        else:
+            if keep_default_na:
+                return STR_NA_VALUES, set()
+
+            return set(), set()
+    else:
+        return na_values, na_fvalues
+
+
+def _validate_parse_dates_arg(parse_dates):
+    """
+    Check whether or not the 'parse_dates' parameter
+    is a non-boolean scalar. Raises a ValueError if
+    that is the case.
+    """
+    msg = (
+        "Only booleans, lists, and dictionaries are accepted "
+        "for the 'parse_dates' parameter"
+    )
+
+    if not (
+        parse_dates is None
+        or lib.is_bool(parse_dates)
+        or isinstance(parse_dates, (list, dict))
+    ):
+        raise TypeError(msg)
+
+    return parse_dates
+
+
+def is_index_col(col) -> bool:
+    return col is not None and col is not False
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/parsers/c_parser_wrapper.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/parsers/c_parser_wrapper.py
new file mode 100644
index 0000000000000000000000000000000000000000..0cd788c5e57399597e3fe4ee1b1bf2af4bffd74b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/parsers/c_parser_wrapper.py
@@ -0,0 +1,410 @@
+from __future__ import annotations
+
+from collections import defaultdict
+from typing import TYPE_CHECKING
+import warnings
+
+import numpy as np
+
+from pandas._libs import (
+    lib,
+    parsers,
+)
+from pandas.compat._optional import import_optional_dependency
+from pandas.errors import DtypeWarning
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.common import pandas_dtype
+from pandas.core.dtypes.concat import (
+    concat_compat,
+    union_categoricals,
+)
+from pandas.core.dtypes.dtypes import CategoricalDtype
+
+from pandas.core.indexes.api import ensure_index_from_sequences
+
+from pandas.io.common import (
+    dedup_names,
+    is_potential_multi_index,
+)
+from pandas.io.parsers.base_parser import (
+    ParserBase,
+    ParserError,
+    is_index_col,
+)
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Hashable,
+        Mapping,
+        Sequence,
+    )
+
+    from pandas._typing import (
+        ArrayLike,
+        DtypeArg,
+        DtypeObj,
+        ReadCsvBuffer,
+    )
+
+    from pandas import (
+        Index,
+        MultiIndex,
+    )
+
+
+class CParserWrapper(ParserBase):
+    low_memory: bool
+    _reader: parsers.TextReader
+
+    def __init__(self, src: ReadCsvBuffer[str], **kwds) -> None:
+        super().__init__(kwds)
+        self.kwds = kwds
+        kwds = kwds.copy()
+
+        self.low_memory = kwds.pop("low_memory", False)
+
+        # #2442
+        # error: Cannot determine type of 'index_col'
+        kwds["allow_leading_cols"] = (
+            self.index_col is not False  # type: ignore[has-type]
+        )
+
+        # GH20529, validate usecol arg before TextReader
+        kwds["usecols"] = self.usecols
+
+        # Have to pass int, would break tests using TextReader directly otherwise :(
+        kwds["on_bad_lines"] = self.on_bad_lines.value
+
+        for key in (
+            "storage_options",
+            "encoding",
+            "memory_map",
+            "compression",
+        ):
+            kwds.pop(key, None)
+
+        kwds["dtype"] = ensure_dtype_objs(kwds.get("dtype", None))
+        if "dtype_backend" not in kwds or kwds["dtype_backend"] is lib.no_default:
+            kwds["dtype_backend"] = "numpy"
+        if kwds["dtype_backend"] == "pyarrow":
+            # Fail here loudly instead of in cython after reading
+            import_optional_dependency("pyarrow")
+        self._reader = parsers.TextReader(src, **kwds)
+
+        self.unnamed_cols = self._reader.unnamed_cols
+
+        # error: Cannot determine type of 'names'
+        passed_names = self.names is None  # type: ignore[has-type]
+
+        if self._reader.header is None:
+            self.names = None
+        else:
+            # error: Cannot determine type of 'names'
+            # error: Cannot determine type of 'index_names'
+            (
+                self.names,  # type: ignore[has-type]
+                self.index_names,
+                self.col_names,
+                passed_names,
+            ) = self._extract_multi_indexer_columns(
+                self._reader.header,
+                self.index_names,  # type: ignore[has-type]
+                passed_names,
+            )
+
+        # error: Cannot determine type of 'names'
+        if self.names is None:  # type: ignore[has-type]
+            self.names = list(range(self._reader.table_width))
+
+        # gh-9755
+        #
+        # need to set orig_names here first
+        # so that proper indexing can be done
+        # with _set_noconvert_columns
+        #
+        # once names has been filtered, we will
+        # then set orig_names again to names
+        # error: Cannot determine type of 'names'
+        self.orig_names = self.names[:]  # type: ignore[has-type]
+
+        if self.usecols:
+            usecols = self._evaluate_usecols(self.usecols, self.orig_names)
+
+            # GH 14671
+            # assert for mypy, orig_names is List or None, None would error in issubset
+            assert self.orig_names is not None
+            if self.usecols_dtype == "string" and not set(usecols).issubset(
+                self.orig_names
+            ):
+                self._validate_usecols_names(usecols, self.orig_names)
+
+            # error: Cannot determine type of 'names'
+            if len(self.names) > len(usecols):  # type: ignore[has-type]
+                # error: Cannot determine type of 'names'
+                self.names = [  # type: ignore[has-type]
+                    n
+                    # error: Cannot determine type of 'names'
+                    for i, n in enumerate(self.names)  # type: ignore[has-type]
+                    if (i in usecols or n in usecols)
+                ]
+
+            # error: Cannot determine type of 'names'
+            if len(self.names) < len(usecols):  # type: ignore[has-type]
+                # error: Cannot determine type of 'names'
+                self._validate_usecols_names(
+                    usecols,
+                    self.names,  # type: ignore[has-type]
+                )
+
+        # error: Cannot determine type of 'names'
+        self._validate_parse_dates_presence(self.names)  # type: ignore[has-type]
+        self._set_noconvert_columns()
+
+        # error: Cannot determine type of 'names'
+        self.orig_names = self.names  # type: ignore[has-type]
+
+        if not self._has_complex_date_col:
+            # error: Cannot determine type of 'index_col'
+            if self._reader.leading_cols == 0 and is_index_col(
+                self.index_col  # type: ignore[has-type]
+            ):
+                self._name_processed = True
+                (
+                    index_names,
+                    # error: Cannot determine type of 'names'
+                    self.names,  # type: ignore[has-type]
+                    self.index_col,
+                ) = self._clean_index_names(
+                    # error: Cannot determine type of 'names'
+                    self.names,  # type: ignore[has-type]
+                    # error: Cannot determine type of 'index_col'
+                    self.index_col,  # type: ignore[has-type]
+                )
+
+                if self.index_names is None:
+                    self.index_names = index_names
+
+            if self._reader.header is None and not passed_names:
+                assert self.index_names is not None
+                self.index_names = [None] * len(self.index_names)
+
+        self._implicit_index = self._reader.leading_cols > 0
+
+    def close(self) -> None:
+        # close handles opened by C parser
+        try:
+            self._reader.close()
+        except ValueError:
+            pass
+
+    def _set_noconvert_columns(self) -> None:
+        """
+        Set the columns that should not undergo dtype conversions.
+
+        Currently, any column that is involved with date parsing will not
+        undergo such conversions.
+        """
+        assert self.orig_names is not None
+        # error: Cannot determine type of 'names'
+
+        # much faster than using orig_names.index(x) xref GH#44106
+        names_dict = {x: i for i, x in enumerate(self.orig_names)}
+        col_indices = [names_dict[x] for x in self.names]  # type: ignore[has-type]
+        # error: Cannot determine type of 'names'
+        noconvert_columns = self._set_noconvert_dtype_columns(
+            col_indices,
+            self.names,  # type: ignore[has-type]
+        )
+        for col in noconvert_columns:
+            self._reader.set_noconvert(col)
+
+    def read(
+        self,
+        nrows: int | None = None,
+    ) -> tuple[
+        Index | MultiIndex | None,
+        Sequence[Hashable] | MultiIndex,
+        Mapping[Hashable, ArrayLike],
+    ]:
+        index: Index | MultiIndex | None
+        column_names: Sequence[Hashable] | MultiIndex
+        try:
+            if self.low_memory:
+                chunks = self._reader.read_low_memory(nrows)
+                # destructive to chunks
+                data = _concatenate_chunks(chunks)
+
+            else:
+                data = self._reader.read(nrows)
+        except StopIteration:
+            if self._first_chunk:
+                self._first_chunk = False
+                names = dedup_names(
+                    self.orig_names,
+                    is_potential_multi_index(self.orig_names, self.index_col),
+                )
+                index, columns, col_dict = self._get_empty_meta(
+                    names,
+                    dtype=self.dtype,
+                )
+                columns = self._maybe_make_multi_index_columns(columns, self.col_names)
+
+                if self.usecols is not None:
+                    columns = self._filter_usecols(columns)
+
+                col_dict = {k: v for k, v in col_dict.items() if k in columns}
+
+                return index, columns, col_dict
+
+            else:
+                self.close()
+                raise
+
+        # Done with first read, next time raise StopIteration
+        self._first_chunk = False
+
+        # error: Cannot determine type of 'names'
+        names = self.names  # type: ignore[has-type]
+
+        if self._reader.leading_cols:
+            if self._has_complex_date_col:
+                raise NotImplementedError("file structure not yet supported")
+
+            # implicit index, no index names
+            arrays = []
+
+            if self.index_col and self._reader.leading_cols != len(self.index_col):
+                raise ParserError(
+                    "Could not construct index. Requested to use "
+                    f"{len(self.index_col)} number of columns, but "
+                    f"{self._reader.leading_cols} left to parse."
+                )
+
+            for i in range(self._reader.leading_cols):
+                if self.index_col is None:
+                    values = data.pop(i)
+                else:
+                    values = data.pop(self.index_col[i])
+
+                values = self._maybe_parse_dates(values, i, try_parse_dates=True)
+                arrays.append(values)
+
+            index = ensure_index_from_sequences(arrays)
+
+            if self.usecols is not None:
+                names = self._filter_usecols(names)
+
+            names = dedup_names(names, is_potential_multi_index(names, self.index_col))
+
+            # rename dict keys
+            data_tups = sorted(data.items())
+            data = {k: v for k, (i, v) in zip(names, data_tups)}
+
+            column_names, date_data = self._do_date_conversions(names, data)
+
+            # maybe create a mi on the columns
+            column_names = self._maybe_make_multi_index_columns(
+                column_names, self.col_names
+            )
+
+        else:
+            # rename dict keys
+            data_tups = sorted(data.items())
+
+            # ugh, mutation
+
+            # assert for mypy, orig_names is List or None, None would error in list(...)
+            assert self.orig_names is not None
+            names = list(self.orig_names)
+            names = dedup_names(names, is_potential_multi_index(names, self.index_col))
+
+            if self.usecols is not None:
+                names = self._filter_usecols(names)
+
+            # columns as list
+            alldata = [x[1] for x in data_tups]
+            if self.usecols is None:
+                self._check_data_length(names, alldata)
+
+            data = {k: v for k, (i, v) in zip(names, data_tups)}
+
+            names, date_data = self._do_date_conversions(names, data)
+            index, column_names = self._make_index(date_data, alldata, names)
+
+        return index, column_names, date_data
+
+    def _filter_usecols(self, names: Sequence[Hashable]) -> Sequence[Hashable]:
+        # hackish
+        usecols = self._evaluate_usecols(self.usecols, names)
+        if usecols is not None and len(names) != len(usecols):
+            names = [
+                name for i, name in enumerate(names) if i in usecols or name in usecols
+            ]
+        return names
+
+    def _maybe_parse_dates(self, values, index: int, try_parse_dates: bool = True):
+        if try_parse_dates and self._should_parse_dates(index):
+            values = self._date_conv(
+                values,
+                col=self.index_names[index] if self.index_names is not None else None,
+            )
+        return values
+
+
+def _concatenate_chunks(chunks: list[dict[int, ArrayLike]]) -> dict:
+    """
+    Concatenate chunks of data read with low_memory=True.
+
+    The tricky part is handling Categoricals, where different chunks
+    may have different inferred categories.
+    """
+    names = list(chunks[0].keys())
+    warning_columns = []
+
+    result: dict = {}
+    for name in names:
+        arrs = [chunk.pop(name) for chunk in chunks]
+        # Check each arr for consistent types.
+        dtypes = {a.dtype for a in arrs}
+        non_cat_dtypes = {x for x in dtypes if not isinstance(x, CategoricalDtype)}
+
+        dtype = dtypes.pop()
+        if isinstance(dtype, CategoricalDtype):
+            result[name] = union_categoricals(arrs, sort_categories=False)
+        else:
+            result[name] = concat_compat(arrs)
+            if len(non_cat_dtypes) > 1 and result[name].dtype == np.dtype(object):
+                warning_columns.append(str(name))
+
+    if warning_columns:
+        warning_names = ",".join(warning_columns)
+        warning_message = " ".join(
+            [
+                f"Columns ({warning_names}) have mixed types. "
+                f"Specify dtype option on import or set low_memory=False."
+            ]
+        )
+        warnings.warn(warning_message, DtypeWarning, stacklevel=find_stack_level())
+    return result
+
+
+def ensure_dtype_objs(
+    dtype: DtypeArg | dict[Hashable, DtypeArg] | None
+) -> DtypeObj | dict[Hashable, DtypeObj] | None:
+    """
+    Ensure we have either None, a dtype object, or a dictionary mapping to
+    dtype objects.
+    """
+    if isinstance(dtype, defaultdict):
+        # "None" not callable  [misc]
+        default_dtype = pandas_dtype(dtype.default_factory())  # type: ignore[misc]
+        dtype_converted: defaultdict = defaultdict(lambda: default_dtype)
+        for key in dtype.keys():
+            dtype_converted[key] = pandas_dtype(dtype[key])
+        return dtype_converted
+    elif isinstance(dtype, dict):
+        return {k: pandas_dtype(dtype[k]) for k in dtype}
+    elif dtype is not None:
+        return pandas_dtype(dtype)
+    return dtype
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/parsers/python_parser.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/parsers/python_parser.py
new file mode 100644
index 0000000000000000000000000000000000000000..79e7554a5744cf439a65e9fd1e18782a0fa71548
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/parsers/python_parser.py
@@ -0,0 +1,1387 @@
+from __future__ import annotations
+
+from collections import (
+    abc,
+    defaultdict,
+)
+from collections.abc import (
+    Hashable,
+    Iterator,
+    Mapping,
+    Sequence,
+)
+import csv
+from io import StringIO
+import re
+from typing import (
+    IO,
+    TYPE_CHECKING,
+    DefaultDict,
+    Literal,
+    cast,
+)
+import warnings
+
+import numpy as np
+
+from pandas._libs import lib
+from pandas.errors import (
+    EmptyDataError,
+    ParserError,
+    ParserWarning,
+)
+from pandas.util._decorators import cache_readonly
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.common import (
+    is_bool_dtype,
+    is_integer,
+    is_numeric_dtype,
+)
+from pandas.core.dtypes.inference import is_dict_like
+
+from pandas.io.common import (
+    dedup_names,
+    is_potential_multi_index,
+)
+from pandas.io.parsers.base_parser import (
+    ParserBase,
+    parser_defaults,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        ArrayLike,
+        ReadCsvBuffer,
+        Scalar,
+    )
+
+    from pandas import (
+        Index,
+        MultiIndex,
+    )
+
+# BOM character (byte order mark)
+# This exists at the beginning of a file to indicate endianness
+# of a file (stream). Unfortunately, this marker screws up parsing,
+# so we need to remove it if we see it.
+_BOM = "\ufeff"
+
+
+class PythonParser(ParserBase):
+    _no_thousands_columns: set[int]
+
+    def __init__(self, f: ReadCsvBuffer[str] | list, **kwds) -> None:
+        """
+        Workhorse function for processing nested list into DataFrame
+        """
+        super().__init__(kwds)
+
+        self.data: Iterator[str] | None = None
+        self.buf: list = []
+        self.pos = 0
+        self.line_pos = 0
+
+        self.skiprows = kwds["skiprows"]
+
+        if callable(self.skiprows):
+            self.skipfunc = self.skiprows
+        else:
+            self.skipfunc = lambda x: x in self.skiprows
+
+        self.skipfooter = _validate_skipfooter_arg(kwds["skipfooter"])
+        self.delimiter = kwds["delimiter"]
+
+        self.quotechar = kwds["quotechar"]
+        if isinstance(self.quotechar, str):
+            self.quotechar = str(self.quotechar)
+
+        self.escapechar = kwds["escapechar"]
+        self.doublequote = kwds["doublequote"]
+        self.skipinitialspace = kwds["skipinitialspace"]
+        self.lineterminator = kwds["lineterminator"]
+        self.quoting = kwds["quoting"]
+        self.skip_blank_lines = kwds["skip_blank_lines"]
+
+        self.has_index_names = False
+        if "has_index_names" in kwds:
+            self.has_index_names = kwds["has_index_names"]
+
+        self.verbose = kwds["verbose"]
+
+        self.thousands = kwds["thousands"]
+        self.decimal = kwds["decimal"]
+
+        self.comment = kwds["comment"]
+
+        # Set self.data to something that can read lines.
+        if isinstance(f, list):
+            # read_excel: f is a list
+            self.data = cast(Iterator[str], f)
+        else:
+            assert hasattr(f, "readline")
+            self.data = self._make_reader(f)
+
+        # Get columns in two steps: infer from data, then
+        # infer column indices from self.usecols if it is specified.
+        self._col_indices: list[int] | None = None
+        columns: list[list[Scalar | None]]
+        (
+            columns,
+            self.num_original_columns,
+            self.unnamed_cols,
+        ) = self._infer_columns()
+
+        # Now self.columns has the set of columns that we will process.
+        # The original set is stored in self.original_columns.
+        # error: Cannot determine type of 'index_names'
+        (
+            self.columns,
+            self.index_names,
+            self.col_names,
+            _,
+        ) = self._extract_multi_indexer_columns(
+            columns,
+            self.index_names,  # type: ignore[has-type]
+        )
+
+        # get popped off for index
+        self.orig_names: list[Hashable] = list(self.columns)
+
+        # needs to be cleaned/refactored
+        # multiple date column thing turning into a real spaghetti factory
+
+        if not self._has_complex_date_col:
+            (index_names, self.orig_names, self.columns) = self._get_index_name()
+            self._name_processed = True
+            if self.index_names is None:
+                self.index_names = index_names
+
+        if self._col_indices is None:
+            self._col_indices = list(range(len(self.columns)))
+
+        self._parse_date_cols = self._validate_parse_dates_presence(self.columns)
+        self._no_thousands_columns = self._set_no_thousand_columns()
+
+        if len(self.decimal) != 1:
+            raise ValueError("Only length-1 decimal markers supported")
+
+    @cache_readonly
+    def num(self) -> re.Pattern:
+        decimal = re.escape(self.decimal)
+        if self.thousands is None:
+            regex = rf"^[\-\+]?[0-9]*({decimal}[0-9]*)?([0-9]?(E|e)\-?[0-9]+)?$"
+        else:
+            thousands = re.escape(self.thousands)
+            regex = (
+                rf"^[\-\+]?([0-9]+{thousands}|[0-9])*({decimal}[0-9]*)?"
+                rf"([0-9]?(E|e)\-?[0-9]+)?$"
+            )
+        return re.compile(regex)
+
+    def _make_reader(self, f: IO[str] | ReadCsvBuffer[str]):
+        sep = self.delimiter
+
+        if sep is None or len(sep) == 1:
+            if self.lineterminator:
+                raise ValueError(
+                    "Custom line terminators not supported in python parser (yet)"
+                )
+
+            class MyDialect(csv.Dialect):
+                delimiter = self.delimiter
+                quotechar = self.quotechar
+                escapechar = self.escapechar
+                doublequote = self.doublequote
+                skipinitialspace = self.skipinitialspace
+                quoting = self.quoting
+                lineterminator = "\n"
+
+            dia = MyDialect
+
+            if sep is not None:
+                dia.delimiter = sep
+            else:
+                # attempt to sniff the delimiter from the first valid line,
+                # i.e. no comment line and not in skiprows
+                line = f.readline()
+                lines = self._check_comments([[line]])[0]
+                while self.skipfunc(self.pos) or not lines:
+                    self.pos += 1
+                    line = f.readline()
+                    lines = self._check_comments([[line]])[0]
+                lines_str = cast(list[str], lines)
+
+                # since `line` was a string, lines will be a list containing
+                # only a single string
+                line = lines_str[0]
+
+                self.pos += 1
+                self.line_pos += 1
+                sniffed = csv.Sniffer().sniff(line)
+                dia.delimiter = sniffed.delimiter
+
+                # Note: encoding is irrelevant here
+                line_rdr = csv.reader(StringIO(line), dialect=dia)
+                self.buf.extend(list(line_rdr))
+
+            # Note: encoding is irrelevant here
+            reader = csv.reader(f, dialect=dia, strict=True)
+
+        else:
+
+            def _read():
+                line = f.readline()
+                pat = re.compile(sep)
+
+                yield pat.split(line.strip())
+
+                for line in f:
+                    yield pat.split(line.strip())
+
+            reader = _read()
+
+        return reader
+
+    def read(
+        self, rows: int | None = None
+    ) -> tuple[
+        Index | None, Sequence[Hashable] | MultiIndex, Mapping[Hashable, ArrayLike]
+    ]:
+        try:
+            content = self._get_lines(rows)
+        except StopIteration:
+            if self._first_chunk:
+                content = []
+            else:
+                self.close()
+                raise
+
+        # done with first read, next time raise StopIteration
+        self._first_chunk = False
+
+        columns: Sequence[Hashable] = list(self.orig_names)
+        if not len(content):  # pragma: no cover
+            # DataFrame with the right metadata, even though it's length 0
+            # error: Cannot determine type of 'index_col'
+            names = dedup_names(
+                self.orig_names,
+                is_potential_multi_index(
+                    self.orig_names,
+                    self.index_col,  # type: ignore[has-type]
+                ),
+            )
+            index, columns, col_dict = self._get_empty_meta(
+                names,
+                self.dtype,
+            )
+            conv_columns = self._maybe_make_multi_index_columns(columns, self.col_names)
+            return index, conv_columns, col_dict
+
+        # handle new style for names in index
+        count_empty_content_vals = count_empty_vals(content[0])
+        indexnamerow = None
+        if self.has_index_names and count_empty_content_vals == len(columns):
+            indexnamerow = content[0]
+            content = content[1:]
+
+        alldata = self._rows_to_cols(content)
+        data, columns = self._exclude_implicit_index(alldata)
+
+        conv_data = self._convert_data(data)
+        columns, conv_data = self._do_date_conversions(columns, conv_data)
+
+        index, result_columns = self._make_index(
+            conv_data, alldata, columns, indexnamerow
+        )
+
+        return index, result_columns, conv_data
+
+    def _exclude_implicit_index(
+        self,
+        alldata: list[np.ndarray],
+    ) -> tuple[Mapping[Hashable, np.ndarray], Sequence[Hashable]]:
+        # error: Cannot determine type of 'index_col'
+        names = dedup_names(
+            self.orig_names,
+            is_potential_multi_index(
+                self.orig_names,
+                self.index_col,  # type: ignore[has-type]
+            ),
+        )
+
+        offset = 0
+        if self._implicit_index:
+            # error: Cannot determine type of 'index_col'
+            offset = len(self.index_col)  # type: ignore[has-type]
+
+        len_alldata = len(alldata)
+        self._check_data_length(names, alldata)
+
+        return {
+            name: alldata[i + offset] for i, name in enumerate(names) if i < len_alldata
+        }, names
+
+    # legacy
+    def get_chunk(
+        self, size: int | None = None
+    ) -> tuple[
+        Index | None, Sequence[Hashable] | MultiIndex, Mapping[Hashable, ArrayLike]
+    ]:
+        if size is None:
+            # error: "PythonParser" has no attribute "chunksize"
+            size = self.chunksize  # type: ignore[attr-defined]
+        return self.read(rows=size)
+
+    def _convert_data(
+        self,
+        data: Mapping[Hashable, np.ndarray],
+    ) -> Mapping[Hashable, ArrayLike]:
+        # apply converters
+        clean_conv = self._clean_mapping(self.converters)
+        clean_dtypes = self._clean_mapping(self.dtype)
+
+        # Apply NA values.
+        clean_na_values = {}
+        clean_na_fvalues = {}
+
+        if isinstance(self.na_values, dict):
+            for col in self.na_values:
+                na_value = self.na_values[col]
+                na_fvalue = self.na_fvalues[col]
+
+                if isinstance(col, int) and col not in self.orig_names:
+                    col = self.orig_names[col]
+
+                clean_na_values[col] = na_value
+                clean_na_fvalues[col] = na_fvalue
+        else:
+            clean_na_values = self.na_values
+            clean_na_fvalues = self.na_fvalues
+
+        return self._convert_to_ndarrays(
+            data,
+            clean_na_values,
+            clean_na_fvalues,
+            self.verbose,
+            clean_conv,
+            clean_dtypes,
+        )
+
+    @cache_readonly
+    def _have_mi_columns(self) -> bool:
+        if self.header is None:
+            return False
+
+        header = self.header
+        if isinstance(header, (list, tuple, np.ndarray)):
+            return len(header) > 1
+        else:
+            return False
+
+    def _infer_columns(
+        self,
+    ) -> tuple[list[list[Scalar | None]], int, set[Scalar | None]]:
+        names = self.names
+        num_original_columns = 0
+        clear_buffer = True
+        unnamed_cols: set[Scalar | None] = set()
+
+        if self.header is not None:
+            header = self.header
+            have_mi_columns = self._have_mi_columns
+
+            if isinstance(header, (list, tuple, np.ndarray)):
+                # we have a mi columns, so read an extra line
+                if have_mi_columns:
+                    header = list(header) + [header[-1] + 1]
+            else:
+                header = [header]
+
+            columns: list[list[Scalar | None]] = []
+            for level, hr in enumerate(header):
+                try:
+                    line = self._buffered_line()
+
+                    while self.line_pos <= hr:
+                        line = self._next_line()
+
+                except StopIteration as err:
+                    if 0 < self.line_pos <= hr and (
+                        not have_mi_columns or hr != header[-1]
+                    ):
+                        # If no rows we want to raise a different message and if
+                        # we have mi columns, the last line is not part of the header
+                        joi = list(map(str, header[:-1] if have_mi_columns else header))
+                        msg = f"[{','.join(joi)}], len of {len(joi)}, "
+                        raise ValueError(
+                            f"Passed header={msg}"
+                            f"but only {self.line_pos} lines in file"
+                        ) from err
+
+                    # We have an empty file, so check
+                    # if columns are provided. That will
+                    # serve as the 'line' for parsing
+                    if have_mi_columns and hr > 0:
+                        if clear_buffer:
+                            self._clear_buffer()
+                        columns.append([None] * len(columns[-1]))
+                        return columns, num_original_columns, unnamed_cols
+
+                    if not self.names:
+                        raise EmptyDataError("No columns to parse from file") from err
+
+                    line = self.names[:]
+
+                this_columns: list[Scalar | None] = []
+                this_unnamed_cols = []
+
+                for i, c in enumerate(line):
+                    if c == "":
+                        if have_mi_columns:
+                            col_name = f"Unnamed: {i}_level_{level}"
+                        else:
+                            col_name = f"Unnamed: {i}"
+
+                        this_unnamed_cols.append(i)
+                        this_columns.append(col_name)
+                    else:
+                        this_columns.append(c)
+
+                if not have_mi_columns:
+                    counts: DefaultDict = defaultdict(int)
+                    # Ensure that regular columns are used before unnamed ones
+                    # to keep given names and mangle unnamed columns
+                    col_loop_order = [
+                        i
+                        for i in range(len(this_columns))
+                        if i not in this_unnamed_cols
+                    ] + this_unnamed_cols
+
+                    # TODO: Use pandas.io.common.dedup_names instead (see #50371)
+                    for i in col_loop_order:
+                        col = this_columns[i]
+                        old_col = col
+                        cur_count = counts[col]
+
+                        if cur_count > 0:
+                            while cur_count > 0:
+                                counts[old_col] = cur_count + 1
+                                col = f"{old_col}.{cur_count}"
+                                if col in this_columns:
+                                    cur_count += 1
+                                else:
+                                    cur_count = counts[col]
+
+                            if (
+                                self.dtype is not None
+                                and is_dict_like(self.dtype)
+                                and self.dtype.get(old_col) is not None
+                                and self.dtype.get(col) is None
+                            ):
+                                self.dtype.update({col: self.dtype.get(old_col)})
+                        this_columns[i] = col
+                        counts[col] = cur_count + 1
+                elif have_mi_columns:
+                    # if we have grabbed an extra line, but its not in our
+                    # format so save in the buffer, and create an blank extra
+                    # line for the rest of the parsing code
+                    if hr == header[-1]:
+                        lc = len(this_columns)
+                        # error: Cannot determine type of 'index_col'
+                        sic = self.index_col  # type: ignore[has-type]
+                        ic = len(sic) if sic is not None else 0
+                        unnamed_count = len(this_unnamed_cols)
+
+                        # if wrong number of blanks or no index, not our format
+                        if (lc != unnamed_count and lc - ic > unnamed_count) or ic == 0:
+                            clear_buffer = False
+                            this_columns = [None] * lc
+                            self.buf = [self.buf[-1]]
+
+                columns.append(this_columns)
+                unnamed_cols.update({this_columns[i] for i in this_unnamed_cols})
+
+                if len(columns) == 1:
+                    num_original_columns = len(this_columns)
+
+            if clear_buffer:
+                self._clear_buffer()
+
+            first_line: list[Scalar] | None
+            if names is not None:
+                # Read first row after header to check if data are longer
+                try:
+                    first_line = self._next_line()
+                except StopIteration:
+                    first_line = None
+
+                len_first_data_row = 0 if first_line is None else len(first_line)
+
+                if len(names) > len(columns[0]) and len(names) > len_first_data_row:
+                    raise ValueError(
+                        "Number of passed names did not match "
+                        "number of header fields in the file"
+                    )
+                if len(columns) > 1:
+                    raise TypeError("Cannot pass names with multi-index columns")
+
+                if self.usecols is not None:
+                    # Set _use_cols. We don't store columns because they are
+                    # overwritten.
+                    self._handle_usecols(columns, names, num_original_columns)
+                else:
+                    num_original_columns = len(names)
+                if self._col_indices is not None and len(names) != len(
+                    self._col_indices
+                ):
+                    columns = [[names[i] for i in sorted(self._col_indices)]]
+                else:
+                    columns = [names]
+            else:
+                columns = self._handle_usecols(
+                    columns, columns[0], num_original_columns
+                )
+        else:
+            ncols = len(self._header_line)
+            num_original_columns = ncols
+
+            if not names:
+                columns = [list(range(ncols))]
+                columns = self._handle_usecols(columns, columns[0], ncols)
+            elif self.usecols is None or len(names) >= ncols:
+                columns = self._handle_usecols([names], names, ncols)
+                num_original_columns = len(names)
+            elif not callable(self.usecols) and len(names) != len(self.usecols):
+                raise ValueError(
+                    "Number of passed names did not match number of "
+                    "header fields in the file"
+                )
+            else:
+                # Ignore output but set used columns.
+                columns = [names]
+                self._handle_usecols(columns, columns[0], ncols)
+
+        return columns, num_original_columns, unnamed_cols
+
+    @cache_readonly
+    def _header_line(self):
+        # Store line for reuse in _get_index_name
+        if self.header is not None:
+            return None
+
+        try:
+            line = self._buffered_line()
+        except StopIteration as err:
+            if not self.names:
+                raise EmptyDataError("No columns to parse from file") from err
+
+            line = self.names[:]
+        return line
+
+    def _handle_usecols(
+        self,
+        columns: list[list[Scalar | None]],
+        usecols_key: list[Scalar | None],
+        num_original_columns: int,
+    ) -> list[list[Scalar | None]]:
+        """
+        Sets self._col_indices
+
+        usecols_key is used if there are string usecols.
+        """
+        col_indices: set[int] | list[int]
+        if self.usecols is not None:
+            if callable(self.usecols):
+                col_indices = self._evaluate_usecols(self.usecols, usecols_key)
+            elif any(isinstance(u, str) for u in self.usecols):
+                if len(columns) > 1:
+                    raise ValueError(
+                        "If using multiple headers, usecols must be integers."
+                    )
+                col_indices = []
+
+                for col in self.usecols:
+                    if isinstance(col, str):
+                        try:
+                            col_indices.append(usecols_key.index(col))
+                        except ValueError:
+                            self._validate_usecols_names(self.usecols, usecols_key)
+                    else:
+                        col_indices.append(col)
+            else:
+                missing_usecols = [
+                    col for col in self.usecols if col >= num_original_columns
+                ]
+                if missing_usecols:
+                    raise ParserError(
+                        "Defining usecols with out-of-bounds indices is not allowed. "
+                        f"{missing_usecols} are out-of-bounds.",
+                    )
+                col_indices = self.usecols
+
+            columns = [
+                [n for i, n in enumerate(column) if i in col_indices]
+                for column in columns
+            ]
+            self._col_indices = sorted(col_indices)
+        return columns
+
+    def _buffered_line(self) -> list[Scalar]:
+        """
+        Return a line from buffer, filling buffer if required.
+        """
+        if len(self.buf) > 0:
+            return self.buf[0]
+        else:
+            return self._next_line()
+
+    def _check_for_bom(self, first_row: list[Scalar]) -> list[Scalar]:
+        """
+        Checks whether the file begins with the BOM character.
+        If it does, remove it. In addition, if there is quoting
+        in the field subsequent to the BOM, remove it as well
+        because it technically takes place at the beginning of
+        the name, not the middle of it.
+        """
+        # first_row will be a list, so we need to check
+        # that that list is not empty before proceeding.
+        if not first_row:
+            return first_row
+
+        # The first element of this row is the one that could have the
+        # BOM that we want to remove. Check that the first element is a
+        # string before proceeding.
+        if not isinstance(first_row[0], str):
+            return first_row
+
+        # Check that the string is not empty, as that would
+        # obviously not have a BOM at the start of it.
+        if not first_row[0]:
+            return first_row
+
+        # Since the string is non-empty, check that it does
+        # in fact begin with a BOM.
+        first_elt = first_row[0][0]
+        if first_elt != _BOM:
+            return first_row
+
+        first_row_bom = first_row[0]
+        new_row: str
+
+        if len(first_row_bom) > 1 and first_row_bom[1] == self.quotechar:
+            start = 2
+            quote = first_row_bom[1]
+            end = first_row_bom[2:].index(quote) + 2
+
+            # Extract the data between the quotation marks
+            new_row = first_row_bom[start:end]
+
+            # Extract any remaining data after the second
+            # quotation mark.
+            if len(first_row_bom) > end + 1:
+                new_row += first_row_bom[end + 1 :]
+
+        else:
+            # No quotation so just remove BOM from first element
+            new_row = first_row_bom[1:]
+
+        new_row_list: list[Scalar] = [new_row]
+        return new_row_list + first_row[1:]
+
+    def _is_line_empty(self, line: list[Scalar]) -> bool:
+        """
+        Check if a line is empty or not.
+
+        Parameters
+        ----------
+        line : str, array-like
+            The line of data to check.
+
+        Returns
+        -------
+        boolean : Whether or not the line is empty.
+        """
+        return not line or all(not x for x in line)
+
+    def _next_line(self) -> list[Scalar]:
+        if isinstance(self.data, list):
+            while self.skipfunc(self.pos):
+                if self.pos >= len(self.data):
+                    break
+                self.pos += 1
+
+            while True:
+                try:
+                    line = self._check_comments([self.data[self.pos]])[0]
+                    self.pos += 1
+                    # either uncommented or blank to begin with
+                    if not self.skip_blank_lines and (
+                        self._is_line_empty(self.data[self.pos - 1]) or line
+                    ):
+                        break
+                    if self.skip_blank_lines:
+                        ret = self._remove_empty_lines([line])
+                        if ret:
+                            line = ret[0]
+                            break
+                except IndexError:
+                    raise StopIteration
+        else:
+            while self.skipfunc(self.pos):
+                self.pos += 1
+                # assert for mypy, data is Iterator[str] or None, would error in next
+                assert self.data is not None
+                next(self.data)
+
+            while True:
+                orig_line = self._next_iter_line(row_num=self.pos + 1)
+                self.pos += 1
+
+                if orig_line is not None:
+                    line = self._check_comments([orig_line])[0]
+
+                    if self.skip_blank_lines:
+                        ret = self._remove_empty_lines([line])
+
+                        if ret:
+                            line = ret[0]
+                            break
+                    elif self._is_line_empty(orig_line) or line:
+                        break
+
+        # This was the first line of the file,
+        # which could contain the BOM at the
+        # beginning of it.
+        if self.pos == 1:
+            line = self._check_for_bom(line)
+
+        self.line_pos += 1
+        self.buf.append(line)
+        return line
+
+    def _alert_malformed(self, msg: str, row_num: int) -> None:
+        """
+        Alert a user about a malformed row, depending on value of
+        `self.on_bad_lines` enum.
+
+        If `self.on_bad_lines` is ERROR, the alert will be `ParserError`.
+        If `self.on_bad_lines` is WARN, the alert will be printed out.
+
+        Parameters
+        ----------
+        msg: str
+            The error message to display.
+        row_num: int
+            The row number where the parsing error occurred.
+            Because this row number is displayed, we 1-index,
+            even though we 0-index internally.
+        """
+        if self.on_bad_lines == self.BadLineHandleMethod.ERROR:
+            raise ParserError(msg)
+        if self.on_bad_lines == self.BadLineHandleMethod.WARN:
+            warnings.warn(
+                f"Skipping line {row_num}: {msg}\n",
+                ParserWarning,
+                stacklevel=find_stack_level(),
+            )
+
+    def _next_iter_line(self, row_num: int) -> list[Scalar] | None:
+        """
+        Wrapper around iterating through `self.data` (CSV source).
+
+        When a CSV error is raised, we check for specific
+        error messages that allow us to customize the
+        error message displayed to the user.
+
+        Parameters
+        ----------
+        row_num: int
+            The row number of the line being parsed.
+        """
+        try:
+            # assert for mypy, data is Iterator[str] or None, would error in next
+            assert self.data is not None
+            line = next(self.data)
+            # for mypy
+            assert isinstance(line, list)
+            return line
+        except csv.Error as e:
+            if self.on_bad_lines in (
+                self.BadLineHandleMethod.ERROR,
+                self.BadLineHandleMethod.WARN,
+            ):
+                msg = str(e)
+
+                if "NULL byte" in msg or "line contains NUL" in msg:
+                    msg = (
+                        "NULL byte detected. This byte "
+                        "cannot be processed in Python's "
+                        "native csv library at the moment, "
+                        "so please pass in engine='c' instead"
+                    )
+
+                if self.skipfooter > 0:
+                    reason = (
+                        "Error could possibly be due to "
+                        "parsing errors in the skipped footer rows "
+                        "(the skipfooter keyword is only applied "
+                        "after Python's csv library has parsed "
+                        "all rows)."
+                    )
+                    msg += ". " + reason
+
+                self._alert_malformed(msg, row_num)
+            return None
+
+    def _check_comments(self, lines: list[list[Scalar]]) -> list[list[Scalar]]:
+        if self.comment is None:
+            return lines
+        ret = []
+        for line in lines:
+            rl = []
+            for x in line:
+                if (
+                    not isinstance(x, str)
+                    or self.comment not in x
+                    or x in self.na_values
+                ):
+                    rl.append(x)
+                else:
+                    x = x[: x.find(self.comment)]
+                    if len(x) > 0:
+                        rl.append(x)
+                    break
+            ret.append(rl)
+        return ret
+
+    def _remove_empty_lines(self, lines: list[list[Scalar]]) -> list[list[Scalar]]:
+        """
+        Iterate through the lines and remove any that are
+        either empty or contain only one whitespace value
+
+        Parameters
+        ----------
+        lines : list of list of Scalars
+            The array of lines that we are to filter.
+
+        Returns
+        -------
+        filtered_lines : list of list of Scalars
+            The same array of lines with the "empty" ones removed.
+        """
+        # Remove empty lines and lines with only one whitespace value
+        ret = [
+            line
+            for line in lines
+            if (
+                len(line) > 1
+                or len(line) == 1
+                and (not isinstance(line[0], str) or line[0].strip())
+            )
+        ]
+        return ret
+
+    def _check_thousands(self, lines: list[list[Scalar]]) -> list[list[Scalar]]:
+        if self.thousands is None:
+            return lines
+
+        return self._search_replace_num_columns(
+            lines=lines, search=self.thousands, replace=""
+        )
+
+    def _search_replace_num_columns(
+        self, lines: list[list[Scalar]], search: str, replace: str
+    ) -> list[list[Scalar]]:
+        ret = []
+        for line in lines:
+            rl = []
+            for i, x in enumerate(line):
+                if (
+                    not isinstance(x, str)
+                    or search not in x
+                    or i in self._no_thousands_columns
+                    or not self.num.search(x.strip())
+                ):
+                    rl.append(x)
+                else:
+                    rl.append(x.replace(search, replace))
+            ret.append(rl)
+        return ret
+
+    def _check_decimal(self, lines: list[list[Scalar]]) -> list[list[Scalar]]:
+        if self.decimal == parser_defaults["decimal"]:
+            return lines
+
+        return self._search_replace_num_columns(
+            lines=lines, search=self.decimal, replace="."
+        )
+
+    def _clear_buffer(self) -> None:
+        self.buf = []
+
+    def _get_index_name(
+        self,
+    ) -> tuple[Sequence[Hashable] | None, list[Hashable], list[Hashable]]:
+        """
+        Try several cases to get lines:
+
+        0) There are headers on row 0 and row 1 and their
+        total summed lengths equals the length of the next line.
+        Treat row 0 as columns and row 1 as indices
+        1) Look for implicit index: there are more columns
+        on row 1 than row 0. If this is true, assume that row
+        1 lists index columns and row 0 lists normal columns.
+        2) Get index from the columns if it was listed.
+        """
+        columns: Sequence[Hashable] = self.orig_names
+        orig_names = list(columns)
+        columns = list(columns)
+
+        line: list[Scalar] | None
+        if self._header_line is not None:
+            line = self._header_line
+        else:
+            try:
+                line = self._next_line()
+            except StopIteration:
+                line = None
+
+        next_line: list[Scalar] | None
+        try:
+            next_line = self._next_line()
+        except StopIteration:
+            next_line = None
+
+        # implicitly index_col=0 b/c 1 fewer column names
+        implicit_first_cols = 0
+        if line is not None:
+            # leave it 0, #2442
+            # Case 1
+            # error: Cannot determine type of 'index_col'
+            index_col = self.index_col  # type: ignore[has-type]
+            if index_col is not False:
+                implicit_first_cols = len(line) - self.num_original_columns
+
+            # Case 0
+            if (
+                next_line is not None
+                and self.header is not None
+                and index_col is not False
+            ):
+                if len(next_line) == len(line) + self.num_original_columns:
+                    # column and index names on diff rows
+                    self.index_col = list(range(len(line)))
+                    self.buf = self.buf[1:]
+
+                    for c in reversed(line):
+                        columns.insert(0, c)
+
+                    # Update list of original names to include all indices.
+                    orig_names = list(columns)
+                    self.num_original_columns = len(columns)
+                    return line, orig_names, columns
+
+        if implicit_first_cols > 0:
+            # Case 1
+            self._implicit_index = True
+            if self.index_col is None:
+                self.index_col = list(range(implicit_first_cols))
+
+            index_name = None
+
+        else:
+            # Case 2
+            (index_name, _, self.index_col) = self._clean_index_names(
+                columns, self.index_col
+            )
+
+        return index_name, orig_names, columns
+
+    def _rows_to_cols(self, content: list[list[Scalar]]) -> list[np.ndarray]:
+        col_len = self.num_original_columns
+
+        if self._implicit_index:
+            col_len += len(self.index_col)
+
+        max_len = max(len(row) for row in content)
+
+        # Check that there are no rows with too many
+        # elements in their row (rows with too few
+        # elements are padded with NaN).
+        # error: Non-overlapping identity check (left operand type: "List[int]",
+        # right operand type: "Literal[False]")
+        if (
+            max_len > col_len
+            and self.index_col is not False  # type: ignore[comparison-overlap]
+            and self.usecols is None
+        ):
+            footers = self.skipfooter if self.skipfooter else 0
+            bad_lines = []
+
+            iter_content = enumerate(content)
+            content_len = len(content)
+            content = []
+
+            for i, _content in iter_content:
+                actual_len = len(_content)
+
+                if actual_len > col_len:
+                    if callable(self.on_bad_lines):
+                        new_l = self.on_bad_lines(_content)
+                        if new_l is not None:
+                            content.append(new_l)
+                    elif self.on_bad_lines in (
+                        self.BadLineHandleMethod.ERROR,
+                        self.BadLineHandleMethod.WARN,
+                    ):
+                        row_num = self.pos - (content_len - i + footers)
+                        bad_lines.append((row_num, actual_len))
+
+                        if self.on_bad_lines == self.BadLineHandleMethod.ERROR:
+                            break
+                else:
+                    content.append(_content)
+
+            for row_num, actual_len in bad_lines:
+                msg = (
+                    f"Expected {col_len} fields in line {row_num + 1}, saw "
+                    f"{actual_len}"
+                )
+                if (
+                    self.delimiter
+                    and len(self.delimiter) > 1
+                    and self.quoting != csv.QUOTE_NONE
+                ):
+                    # see gh-13374
+                    reason = (
+                        "Error could possibly be due to quotes being "
+                        "ignored when a multi-char delimiter is used."
+                    )
+                    msg += ". " + reason
+
+                self._alert_malformed(msg, row_num + 1)
+
+        # see gh-13320
+        zipped_content = list(lib.to_object_array(content, min_width=col_len).T)
+
+        if self.usecols:
+            assert self._col_indices is not None
+            col_indices = self._col_indices
+
+            if self._implicit_index:
+                zipped_content = [
+                    a
+                    for i, a in enumerate(zipped_content)
+                    if (
+                        i < len(self.index_col)
+                        or i - len(self.index_col) in col_indices
+                    )
+                ]
+            else:
+                zipped_content = [
+                    a for i, a in enumerate(zipped_content) if i in col_indices
+                ]
+        return zipped_content
+
+    def _get_lines(self, rows: int | None = None) -> list[list[Scalar]]:
+        lines = self.buf
+        new_rows = None
+
+        # already fetched some number
+        if rows is not None:
+            # we already have the lines in the buffer
+            if len(self.buf) >= rows:
+                new_rows, self.buf = self.buf[:rows], self.buf[rows:]
+
+            # need some lines
+            else:
+                rows -= len(self.buf)
+
+        if new_rows is None:
+            if isinstance(self.data, list):
+                if self.pos > len(self.data):
+                    raise StopIteration
+                if rows is None:
+                    new_rows = self.data[self.pos :]
+                    new_pos = len(self.data)
+                else:
+                    new_rows = self.data[self.pos : self.pos + rows]
+                    new_pos = self.pos + rows
+
+                new_rows = self._remove_skipped_rows(new_rows)
+                lines.extend(new_rows)
+                self.pos = new_pos
+
+            else:
+                new_rows = []
+                try:
+                    if rows is not None:
+                        row_index = 0
+                        row_ct = 0
+                        offset = self.pos if self.pos is not None else 0
+                        while row_ct < rows:
+                            # assert for mypy, data is Iterator[str] or None, would
+                            # error in next
+                            assert self.data is not None
+                            new_row = next(self.data)
+                            if not self.skipfunc(offset + row_index):
+                                row_ct += 1
+                            row_index += 1
+                            new_rows.append(new_row)
+
+                        len_new_rows = len(new_rows)
+                        new_rows = self._remove_skipped_rows(new_rows)
+                        lines.extend(new_rows)
+                    else:
+                        rows = 0
+
+                        while True:
+                            next_row = self._next_iter_line(row_num=self.pos + rows + 1)
+                            rows += 1
+
+                            if next_row is not None:
+                                new_rows.append(next_row)
+                        len_new_rows = len(new_rows)
+
+                except StopIteration:
+                    len_new_rows = len(new_rows)
+                    new_rows = self._remove_skipped_rows(new_rows)
+                    lines.extend(new_rows)
+                    if len(lines) == 0:
+                        raise
+                self.pos += len_new_rows
+
+            self.buf = []
+        else:
+            lines = new_rows
+
+        if self.skipfooter:
+            lines = lines[: -self.skipfooter]
+
+        lines = self._check_comments(lines)
+        if self.skip_blank_lines:
+            lines = self._remove_empty_lines(lines)
+        lines = self._check_thousands(lines)
+        return self._check_decimal(lines)
+
+    def _remove_skipped_rows(self, new_rows: list[list[Scalar]]) -> list[list[Scalar]]:
+        if self.skiprows:
+            return [
+                row for i, row in enumerate(new_rows) if not self.skipfunc(i + self.pos)
+            ]
+        return new_rows
+
+    def _set_no_thousand_columns(self) -> set[int]:
+        no_thousands_columns: set[int] = set()
+        if self.columns and self.parse_dates:
+            assert self._col_indices is not None
+            no_thousands_columns = self._set_noconvert_dtype_columns(
+                self._col_indices, self.columns
+            )
+        if self.columns and self.dtype:
+            assert self._col_indices is not None
+            for i, col in zip(self._col_indices, self.columns):
+                if not isinstance(self.dtype, dict) and not is_numeric_dtype(
+                    self.dtype
+                ):
+                    no_thousands_columns.add(i)
+                if (
+                    isinstance(self.dtype, dict)
+                    and col in self.dtype
+                    and (
+                        not is_numeric_dtype(self.dtype[col])
+                        or is_bool_dtype(self.dtype[col])
+                    )
+                ):
+                    no_thousands_columns.add(i)
+        return no_thousands_columns
+
+
+class FixedWidthReader(abc.Iterator):
+    """
+    A reader of fixed-width lines.
+    """
+
+    def __init__(
+        self,
+        f: IO[str] | ReadCsvBuffer[str],
+        colspecs: list[tuple[int, int]] | Literal["infer"],
+        delimiter: str | None,
+        comment: str | None,
+        skiprows: set[int] | None = None,
+        infer_nrows: int = 100,
+    ) -> None:
+        self.f = f
+        self.buffer: Iterator | None = None
+        self.delimiter = "\r\n" + delimiter if delimiter else "\n\r\t "
+        self.comment = comment
+        if colspecs == "infer":
+            self.colspecs = self.detect_colspecs(
+                infer_nrows=infer_nrows, skiprows=skiprows
+            )
+        else:
+            self.colspecs = colspecs
+
+        if not isinstance(self.colspecs, (tuple, list)):
+            raise TypeError(
+                "column specifications must be a list or tuple, "
+                f"input was a {type(colspecs).__name__}"
+            )
+
+        for colspec in self.colspecs:
+            if not (
+                isinstance(colspec, (tuple, list))
+                and len(colspec) == 2
+                and isinstance(colspec[0], (int, np.integer, type(None)))
+                and isinstance(colspec[1], (int, np.integer, type(None)))
+            ):
+                raise TypeError(
+                    "Each column specification must be "
+                    "2 element tuple or list of integers"
+                )
+
+    def get_rows(self, infer_nrows: int, skiprows: set[int] | None = None) -> list[str]:
+        """
+        Read rows from self.f, skipping as specified.
+
+        We distinguish buffer_rows (the first <= infer_nrows
+        lines) from the rows returned to detect_colspecs
+        because it's simpler to leave the other locations
+        with skiprows logic alone than to modify them to
+        deal with the fact we skipped some rows here as
+        well.
+
+        Parameters
+        ----------
+        infer_nrows : int
+            Number of rows to read from self.f, not counting
+            rows that are skipped.
+        skiprows: set, optional
+            Indices of rows to skip.
+
+        Returns
+        -------
+        detect_rows : list of str
+            A list containing the rows to read.
+
+        """
+        if skiprows is None:
+            skiprows = set()
+        buffer_rows = []
+        detect_rows = []
+        for i, row in enumerate(self.f):
+            if i not in skiprows:
+                detect_rows.append(row)
+            buffer_rows.append(row)
+            if len(detect_rows) >= infer_nrows:
+                break
+        self.buffer = iter(buffer_rows)
+        return detect_rows
+
+    def detect_colspecs(
+        self, infer_nrows: int = 100, skiprows: set[int] | None = None
+    ) -> list[tuple[int, int]]:
+        # Regex escape the delimiters
+        delimiters = "".join([rf"\{x}" for x in self.delimiter])
+        pattern = re.compile(f"([^{delimiters}]+)")
+        rows = self.get_rows(infer_nrows, skiprows)
+        if not rows:
+            raise EmptyDataError("No rows from which to infer column width")
+        max_len = max(map(len, rows))
+        mask = np.zeros(max_len + 1, dtype=int)
+        if self.comment is not None:
+            rows = [row.partition(self.comment)[0] for row in rows]
+        for row in rows:
+            for m in pattern.finditer(row):
+                mask[m.start() : m.end()] = 1
+        shifted = np.roll(mask, 1)
+        shifted[0] = 0
+        edges = np.where((mask ^ shifted) == 1)[0]
+        edge_pairs = list(zip(edges[::2], edges[1::2]))
+        return edge_pairs
+
+    def __next__(self) -> list[str]:
+        # Argument 1 to "next" has incompatible type "Union[IO[str],
+        # ReadCsvBuffer[str]]"; expected "SupportsNext[str]"
+        if self.buffer is not None:
+            try:
+                line = next(self.buffer)
+            except StopIteration:
+                self.buffer = None
+                line = next(self.f)  # type: ignore[arg-type]
+        else:
+            line = next(self.f)  # type: ignore[arg-type]
+        # Note: 'colspecs' is a sequence of half-open intervals.
+        return [line[from_:to].strip(self.delimiter) for (from_, to) in self.colspecs]
+
+
+class FixedWidthFieldParser(PythonParser):
+    """
+    Specialization that Converts fixed-width fields into DataFrames.
+    See PythonParser for details.
+    """
+
+    def __init__(self, f: ReadCsvBuffer[str], **kwds) -> None:
+        # Support iterators, convert to a list.
+        self.colspecs = kwds.pop("colspecs")
+        self.infer_nrows = kwds.pop("infer_nrows")
+        PythonParser.__init__(self, f, **kwds)
+
+    def _make_reader(self, f: IO[str] | ReadCsvBuffer[str]) -> FixedWidthReader:
+        return FixedWidthReader(
+            f,
+            self.colspecs,
+            self.delimiter,
+            self.comment,
+            self.skiprows,
+            self.infer_nrows,
+        )
+
+    def _remove_empty_lines(self, lines: list[list[Scalar]]) -> list[list[Scalar]]:
+        """
+        Returns the list of lines without the empty ones. With fixed-width
+        fields, empty lines become arrays of empty strings.
+
+        See PythonParser._remove_empty_lines.
+        """
+        return [
+            line
+            for line in lines
+            if any(not isinstance(e, str) or e.strip() for e in line)
+        ]
+
+
+def count_empty_vals(vals) -> int:
+    return sum(1 for v in vals if v == "" or v is None)
+
+
+def _validate_skipfooter_arg(skipfooter: int) -> int:
+    """
+    Validate the 'skipfooter' parameter.
+
+    Checks whether 'skipfooter' is a non-negative integer.
+    Raises a ValueError if that is not the case.
+
+    Parameters
+    ----------
+    skipfooter : non-negative integer
+        The number of rows to skip at the end of the file.
+
+    Returns
+    -------
+    validated_skipfooter : non-negative integer
+        The original input if the validation succeeds.
+
+    Raises
+    ------
+    ValueError : 'skipfooter' was not a non-negative integer.
+    """
+    if not is_integer(skipfooter):
+        raise ValueError("skipfooter must be an integer")
+
+    if skipfooter < 0:
+        raise ValueError("skipfooter cannot be negative")
+
+    # Incompatible return value type (got "Union[int, integer[Any]]", expected "int")
+    return skipfooter  # type: ignore[return-value]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/parsers/readers.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/parsers/readers.py
new file mode 100644
index 0000000000000000000000000000000000000000..e04f27b56061030d19081d87439f0461fa53cc76
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/parsers/readers.py
@@ -0,0 +1,2383 @@
+"""
+Module contains tools for processing files into DataFrames or other objects
+
+GH#48849 provides a convenient way of deprecating keyword arguments
+"""
+from __future__ import annotations
+
+from collections import (
+    abc,
+    defaultdict,
+)
+import csv
+import sys
+from textwrap import fill
+from typing import (
+    IO,
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Literal,
+    NamedTuple,
+    TypedDict,
+    overload,
+)
+import warnings
+
+import numpy as np
+
+from pandas._config import using_copy_on_write
+
+from pandas._libs import lib
+from pandas._libs.parsers import STR_NA_VALUES
+from pandas.errors import (
+    AbstractMethodError,
+    ParserWarning,
+)
+from pandas.util._decorators import Appender
+from pandas.util._exceptions import find_stack_level
+from pandas.util._validators import check_dtype_backend
+
+from pandas.core.dtypes.common import (
+    is_file_like,
+    is_float,
+    is_hashable,
+    is_integer,
+    is_list_like,
+    pandas_dtype,
+)
+
+from pandas import Series
+from pandas.core.frame import DataFrame
+from pandas.core.indexes.api import RangeIndex
+from pandas.core.shared_docs import _shared_docs
+
+from pandas.io.common import (
+    IOHandles,
+    get_handle,
+    stringify_path,
+    validate_header_arg,
+)
+from pandas.io.parsers.arrow_parser_wrapper import ArrowParserWrapper
+from pandas.io.parsers.base_parser import (
+    ParserBase,
+    is_index_col,
+    parser_defaults,
+)
+from pandas.io.parsers.c_parser_wrapper import CParserWrapper
+from pandas.io.parsers.python_parser import (
+    FixedWidthFieldParser,
+    PythonParser,
+)
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Hashable,
+        Iterable,
+        Mapping,
+        Sequence,
+    )
+    from types import TracebackType
+
+    from pandas._typing import (
+        CompressionOptions,
+        CSVEngine,
+        DtypeArg,
+        DtypeBackend,
+        FilePath,
+        IndexLabel,
+        ReadCsvBuffer,
+        Self,
+        StorageOptions,
+        UsecolsArgType,
+    )
+_doc_read_csv_and_table = (
+    r"""
+{summary}
+
+Also supports optionally iterating or breaking of the file
+into chunks.
+
+Additional help can be found in the online docs for
+`IO Tools `_.
+
+Parameters
+----------
+filepath_or_buffer : str, path object or file-like object
+    Any valid string path is acceptable. The string could be a URL. Valid
+    URL schemes include http, ftp, s3, gs, and file. For file URLs, a host is
+    expected. A local file could be: file://localhost/path/to/table.csv.
+
+    If you want to pass in a path object, pandas accepts any ``os.PathLike``.
+
+    By file-like object, we refer to objects with a ``read()`` method, such as
+    a file handle (e.g. via builtin ``open`` function) or ``StringIO``.
+sep : str, default {_default_sep}
+    Character or regex pattern to treat as the delimiter. If ``sep=None``, the
+    C engine cannot automatically detect
+    the separator, but the Python parsing engine can, meaning the latter will
+    be used and automatically detect the separator from only the first valid
+    row of the file by Python's builtin sniffer tool, ``csv.Sniffer``.
+    In addition, separators longer than 1 character and different from
+    ``'\s+'`` will be interpreted as regular expressions and will also force
+    the use of the Python parsing engine. Note that regex delimiters are prone
+    to ignoring quoted data. Regex example: ``'\r\t'``.
+delimiter : str, optional
+    Alias for ``sep``.
+header : int, Sequence of int, 'infer' or None, default 'infer'
+    Row number(s) containing column labels and marking the start of the
+    data (zero-indexed). Default behavior is to infer the column names: if no ``names``
+    are passed the behavior is identical to ``header=0`` and column
+    names are inferred from the first line of the file, if column
+    names are passed explicitly to ``names`` then the behavior is identical to
+    ``header=None``. Explicitly pass ``header=0`` to be able to
+    replace existing names. The header can be a list of integers that
+    specify row locations for a :class:`~pandas.MultiIndex` on the columns
+    e.g. ``[0, 1, 3]``. Intervening rows that are not specified will be
+    skipped (e.g. 2 in this example is skipped). Note that this
+    parameter ignores commented lines and empty lines if
+    ``skip_blank_lines=True``, so ``header=0`` denotes the first line of
+    data rather than the first line of the file.
+names : Sequence of Hashable, optional
+    Sequence of column labels to apply. If the file contains a header row,
+    then you should explicitly pass ``header=0`` to override the column names.
+    Duplicates in this list are not allowed.
+index_col : Hashable, Sequence of Hashable or False, optional
+  Column(s) to use as row label(s), denoted either by column labels or column
+  indices.  If a sequence of labels or indices is given, :class:`~pandas.MultiIndex`
+  will be formed for the row labels.
+
+  Note: ``index_col=False`` can be used to force pandas to *not* use the first
+  column as the index, e.g., when you have a malformed file with delimiters at
+  the end of each line.
+usecols : Sequence of Hashable or Callable, optional
+    Subset of columns to select, denoted either by column labels or column indices.
+    If list-like, all elements must either
+    be positional (i.e. integer indices into the document columns) or strings
+    that correspond to column names provided either by the user in ``names`` or
+    inferred from the document header row(s). If ``names`` are given, the document
+    header row(s) are not taken into account. For example, a valid list-like
+    ``usecols`` parameter would be ``[0, 1, 2]`` or ``['foo', 'bar', 'baz']``.
+    Element order is ignored, so ``usecols=[0, 1]`` is the same as ``[1, 0]``.
+    To instantiate a :class:`~pandas.DataFrame` from ``data`` with element order
+    preserved use ``pd.read_csv(data, usecols=['foo', 'bar'])[['foo', 'bar']]``
+    for columns in ``['foo', 'bar']`` order or
+    ``pd.read_csv(data, usecols=['foo', 'bar'])[['bar', 'foo']]``
+    for ``['bar', 'foo']`` order.
+
+    If callable, the callable function will be evaluated against the column
+    names, returning names where the callable function evaluates to ``True``. An
+    example of a valid callable argument would be ``lambda x: x.upper() in
+    ['AAA', 'BBB', 'DDD']``. Using this parameter results in much faster
+    parsing time and lower memory usage.
+dtype : dtype or dict of {{Hashable : dtype}}, optional
+    Data type(s) to apply to either the whole dataset or individual columns.
+    E.g., ``{{'a': np.float64, 'b': np.int32, 'c': 'Int64'}}``
+    Use ``str`` or ``object`` together with suitable ``na_values`` settings
+    to preserve and not interpret ``dtype``.
+    If ``converters`` are specified, they will be applied INSTEAD
+    of ``dtype`` conversion.
+
+    .. versionadded:: 1.5.0
+
+        Support for ``defaultdict`` was added. Specify a ``defaultdict`` as input where
+        the default determines the ``dtype`` of the columns which are not explicitly
+        listed.
+engine : {{'c', 'python', 'pyarrow'}}, optional
+    Parser engine to use. The C and pyarrow engines are faster, while the python engine
+    is currently more feature-complete. Multithreading is currently only supported by
+    the pyarrow engine.
+
+    .. versionadded:: 1.4.0
+
+        The 'pyarrow' engine was added as an *experimental* engine, and some features
+        are unsupported, or may not work correctly, with this engine.
+converters : dict of {{Hashable : Callable}}, optional
+    Functions for converting values in specified columns. Keys can either
+    be column labels or column indices.
+true_values : list, optional
+    Values to consider as ``True`` in addition to case-insensitive variants of 'True'.
+false_values : list, optional
+    Values to consider as ``False`` in addition to case-insensitive variants of 'False'.
+skipinitialspace : bool, default False
+    Skip spaces after delimiter.
+skiprows : int, list of int or Callable, optional
+    Line numbers to skip (0-indexed) or number of lines to skip (``int``)
+    at the start of the file.
+
+    If callable, the callable function will be evaluated against the row
+    indices, returning ``True`` if the row should be skipped and ``False`` otherwise.
+    An example of a valid callable argument would be ``lambda x: x in [0, 2]``.
+skipfooter : int, default 0
+    Number of lines at bottom of file to skip (Unsupported with ``engine='c'``).
+nrows : int, optional
+    Number of rows of file to read. Useful for reading pieces of large files.
+na_values : Hashable, Iterable of Hashable or dict of {{Hashable : Iterable}}, optional
+    Additional strings to recognize as ``NA``/``NaN``. If ``dict`` passed, specific
+    per-column ``NA`` values.  By default the following values are interpreted as
+    ``NaN``: " """
+    + fill('", "'.join(sorted(STR_NA_VALUES)), 70, subsequent_indent="    ")
+    + """ ".
+
+keep_default_na : bool, default True
+    Whether or not to include the default ``NaN`` values when parsing the data.
+    Depending on whether ``na_values`` is passed in, the behavior is as follows:
+
+    * If ``keep_default_na`` is ``True``, and ``na_values`` are specified, ``na_values``
+      is appended to the default ``NaN`` values used for parsing.
+    * If ``keep_default_na`` is ``True``, and ``na_values`` are not specified, only
+      the default ``NaN`` values are used for parsing.
+    * If ``keep_default_na`` is ``False``, and ``na_values`` are specified, only
+      the ``NaN`` values specified ``na_values`` are used for parsing.
+    * If ``keep_default_na`` is ``False``, and ``na_values`` are not specified, no
+      strings will be parsed as ``NaN``.
+
+    Note that if ``na_filter`` is passed in as ``False``, the ``keep_default_na`` and
+    ``na_values`` parameters will be ignored.
+na_filter : bool, default True
+    Detect missing value markers (empty strings and the value of ``na_values``). In
+    data without any ``NA`` values, passing ``na_filter=False`` can improve the
+    performance of reading a large file.
+verbose : bool, default False
+    Indicate number of ``NA`` values placed in non-numeric columns.
+
+    .. deprecated:: 2.2.0
+skip_blank_lines : bool, default True
+    If ``True``, skip over blank lines rather than interpreting as ``NaN`` values.
+parse_dates : bool, list of Hashable, list of lists or dict of {{Hashable : list}}, \
+default False
+    The behavior is as follows:
+
+    * ``bool``. If ``True`` -> try parsing the index. Note: Automatically set to
+      ``True`` if ``date_format`` or ``date_parser`` arguments have been passed.
+    * ``list`` of ``int`` or names. e.g. If ``[1, 2, 3]`` -> try parsing columns 1, 2, 3
+      each as a separate date column.
+    * ``list`` of ``list``. e.g.  If ``[[1, 3]]`` -> combine columns 1 and 3 and parse
+      as a single date column. Values are joined with a space before parsing.
+    * ``dict``, e.g. ``{{'foo' : [1, 3]}}`` -> parse columns 1, 3 as date and call
+      result 'foo'. Values are joined with a space before parsing.
+
+    If a column or index cannot be represented as an array of ``datetime``,
+    say because of an unparsable value or a mixture of timezones, the column
+    or index will be returned unaltered as an ``object`` data type. For
+    non-standard ``datetime`` parsing, use :func:`~pandas.to_datetime` after
+    :func:`~pandas.read_csv`.
+
+    Note: A fast-path exists for iso8601-formatted dates.
+infer_datetime_format : bool, default False
+    If ``True`` and ``parse_dates`` is enabled, pandas will attempt to infer the
+    format of the ``datetime`` strings in the columns, and if it can be inferred,
+    switch to a faster method of parsing them. In some cases this can increase
+    the parsing speed by 5-10x.
+
+    .. deprecated:: 2.0.0
+        A strict version of this argument is now the default, passing it has no effect.
+
+keep_date_col : bool, default False
+    If ``True`` and ``parse_dates`` specifies combining multiple columns then
+    keep the original columns.
+date_parser : Callable, optional
+    Function to use for converting a sequence of string columns to an array of
+    ``datetime`` instances. The default uses ``dateutil.parser.parser`` to do the
+    conversion. pandas will try to call ``date_parser`` in three different ways,
+    advancing to the next if an exception occurs: 1) Pass one or more arrays
+    (as defined by ``parse_dates``) as arguments; 2) concatenate (row-wise) the
+    string values from the columns defined by ``parse_dates`` into a single array
+    and pass that; and 3) call ``date_parser`` once for each row using one or
+    more strings (corresponding to the columns defined by ``parse_dates``) as
+    arguments.
+
+    .. deprecated:: 2.0.0
+       Use ``date_format`` instead, or read in as ``object`` and then apply
+       :func:`~pandas.to_datetime` as-needed.
+date_format : str or dict of column -> format, optional
+    Format to use for parsing dates when used in conjunction with ``parse_dates``.
+    The strftime to parse time, e.g. :const:`"%d/%m/%Y"`. See
+    `strftime documentation
+    `_ for more information on choices, though
+    note that :const:`"%f"` will parse all the way up to nanoseconds.
+    You can also pass:
+
+    - "ISO8601", to parse any `ISO8601 `_
+        time string (not necessarily in exactly the same format);
+    - "mixed", to infer the format for each element individually. This is risky,
+        and you should probably use it along with `dayfirst`.
+
+    .. versionadded:: 2.0.0
+dayfirst : bool, default False
+    DD/MM format dates, international and European format.
+cache_dates : bool, default True
+    If ``True``, use a cache of unique, converted dates to apply the ``datetime``
+    conversion. May produce significant speed-up when parsing duplicate
+    date strings, especially ones with timezone offsets.
+
+iterator : bool, default False
+    Return ``TextFileReader`` object for iteration or getting chunks with
+    ``get_chunk()``.
+chunksize : int, optional
+    Number of lines to read from the file per chunk. Passing a value will cause the
+    function to return a ``TextFileReader`` object for iteration.
+    See the `IO Tools docs
+    `_
+    for more information on ``iterator`` and ``chunksize``.
+
+{decompression_options}
+
+    .. versionchanged:: 1.4.0 Zstandard support.
+
+thousands : str (length 1), optional
+    Character acting as the thousands separator in numerical values.
+decimal : str (length 1), default '.'
+    Character to recognize as decimal point (e.g., use ',' for European data).
+lineterminator : str (length 1), optional
+    Character used to denote a line break. Only valid with C parser.
+quotechar : str (length 1), optional
+    Character used to denote the start and end of a quoted item. Quoted
+    items can include the ``delimiter`` and it will be ignored.
+quoting : {{0 or csv.QUOTE_MINIMAL, 1 or csv.QUOTE_ALL, 2 or csv.QUOTE_NONNUMERIC, \
+3 or csv.QUOTE_NONE}}, default csv.QUOTE_MINIMAL
+    Control field quoting behavior per ``csv.QUOTE_*`` constants. Default is
+    ``csv.QUOTE_MINIMAL`` (i.e., 0) which implies that only fields containing special
+    characters are quoted (e.g., characters defined in ``quotechar``, ``delimiter``,
+    or ``lineterminator``.
+doublequote : bool, default True
+   When ``quotechar`` is specified and ``quoting`` is not ``QUOTE_NONE``, indicate
+   whether or not to interpret two consecutive ``quotechar`` elements INSIDE a
+   field as a single ``quotechar`` element.
+escapechar : str (length 1), optional
+    Character used to escape other characters.
+comment : str (length 1), optional
+    Character indicating that the remainder of line should not be parsed.
+    If found at the beginning
+    of a line, the line will be ignored altogether. This parameter must be a
+    single character. Like empty lines (as long as ``skip_blank_lines=True``),
+    fully commented lines are ignored by the parameter ``header`` but not by
+    ``skiprows``. For example, if ``comment='#'``, parsing
+    ``#empty\\na,b,c\\n1,2,3`` with ``header=0`` will result in ``'a,b,c'`` being
+    treated as the header.
+encoding : str, optional, default 'utf-8'
+    Encoding to use for UTF when reading/writing (ex. ``'utf-8'``). `List of Python
+    standard encodings
+    `_ .
+
+encoding_errors : str, optional, default 'strict'
+    How encoding errors are treated. `List of possible values
+    `_ .
+
+    .. versionadded:: 1.3.0
+
+dialect : str or csv.Dialect, optional
+    If provided, this parameter will override values (default or not) for the
+    following parameters: ``delimiter``, ``doublequote``, ``escapechar``,
+    ``skipinitialspace``, ``quotechar``, and ``quoting``. If it is necessary to
+    override values, a ``ParserWarning`` will be issued. See ``csv.Dialect``
+    documentation for more details.
+on_bad_lines : {{'error', 'warn', 'skip'}} or Callable, default 'error'
+    Specifies what to do upon encountering a bad line (a line with too many fields).
+    Allowed values are :
+
+    - ``'error'``, raise an Exception when a bad line is encountered.
+    - ``'warn'``, raise a warning when a bad line is encountered and skip that line.
+    - ``'skip'``, skip bad lines without raising or warning when they are encountered.
+
+    .. versionadded:: 1.3.0
+
+    .. versionadded:: 1.4.0
+
+        - Callable, function with signature
+          ``(bad_line: list[str]) -> list[str] | None`` that will process a single
+          bad line. ``bad_line`` is a list of strings split by the ``sep``.
+          If the function returns ``None``, the bad line will be ignored.
+          If the function returns a new ``list`` of strings with more elements than
+          expected, a ``ParserWarning`` will be emitted while dropping extra elements.
+          Only supported when ``engine='python'``
+
+    .. versionchanged:: 2.2.0
+
+        - Callable, function with signature
+          as described in `pyarrow documentation
+          `_ when ``engine='pyarrow'``
+
+delim_whitespace : bool, default False
+    Specifies whether or not whitespace (e.g. ``' '`` or ``'\\t'``) will be
+    used as the ``sep`` delimiter. Equivalent to setting ``sep='\\s+'``. If this option
+    is set to ``True``, nothing should be passed in for the ``delimiter``
+    parameter.
+
+    .. deprecated:: 2.2.0
+        Use ``sep="\\s+"`` instead.
+low_memory : bool, default True
+    Internally process the file in chunks, resulting in lower memory use
+    while parsing, but possibly mixed type inference.  To ensure no mixed
+    types either set ``False``, or specify the type with the ``dtype`` parameter.
+    Note that the entire file is read into a single :class:`~pandas.DataFrame`
+    regardless, use the ``chunksize`` or ``iterator`` parameter to return the data in
+    chunks. (Only valid with C parser).
+memory_map : bool, default False
+    If a filepath is provided for ``filepath_or_buffer``, map the file object
+    directly onto memory and access the data directly from there. Using this
+    option can improve performance because there is no longer any I/O overhead.
+float_precision : {{'high', 'legacy', 'round_trip'}}, optional
+    Specifies which converter the C engine should use for floating-point
+    values. The options are ``None`` or ``'high'`` for the ordinary converter,
+    ``'legacy'`` for the original lower precision pandas converter, and
+    ``'round_trip'`` for the round-trip converter.
+
+{storage_options}
+
+dtype_backend : {{'numpy_nullable', 'pyarrow'}}, default 'numpy_nullable'
+    Back-end data type applied to the resultant :class:`DataFrame`
+    (still experimental). Behaviour is as follows:
+
+    * ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame`
+      (default).
+    * ``"pyarrow"``: returns pyarrow-backed nullable :class:`ArrowDtype`
+      DataFrame.
+
+    .. versionadded:: 2.0
+
+Returns
+-------
+DataFrame or TextFileReader
+    A comma-separated values (csv) file is returned as two-dimensional
+    data structure with labeled axes.
+
+See Also
+--------
+DataFrame.to_csv : Write DataFrame to a comma-separated values (csv) file.
+{see_also_func_name} : {see_also_func_summary}
+read_fwf : Read a table of fixed-width formatted lines into DataFrame.
+
+Examples
+--------
+>>> pd.{func_name}('data.csv')  # doctest: +SKIP
+"""
+)
+
+
+class _C_Parser_Defaults(TypedDict):
+    delim_whitespace: Literal[False]
+    na_filter: Literal[True]
+    low_memory: Literal[True]
+    memory_map: Literal[False]
+    float_precision: None
+
+
+_c_parser_defaults: _C_Parser_Defaults = {
+    "delim_whitespace": False,
+    "na_filter": True,
+    "low_memory": True,
+    "memory_map": False,
+    "float_precision": None,
+}
+
+
+class _Fwf_Defaults(TypedDict):
+    colspecs: Literal["infer"]
+    infer_nrows: Literal[100]
+    widths: None
+
+
+_fwf_defaults: _Fwf_Defaults = {"colspecs": "infer", "infer_nrows": 100, "widths": None}
+_c_unsupported = {"skipfooter"}
+_python_unsupported = {"low_memory", "float_precision"}
+_pyarrow_unsupported = {
+    "skipfooter",
+    "float_precision",
+    "chunksize",
+    "comment",
+    "nrows",
+    "thousands",
+    "memory_map",
+    "dialect",
+    "delim_whitespace",
+    "quoting",
+    "lineterminator",
+    "converters",
+    "iterator",
+    "dayfirst",
+    "verbose",
+    "skipinitialspace",
+    "low_memory",
+}
+
+
+class _DeprecationConfig(NamedTuple):
+    default_value: Any
+    msg: str | None
+
+
+@overload
+def validate_integer(name: str, val: None, min_val: int = ...) -> None:
+    ...
+
+
+@overload
+def validate_integer(name: str, val: float, min_val: int = ...) -> int:
+    ...
+
+
+@overload
+def validate_integer(name: str, val: int | None, min_val: int = ...) -> int | None:
+    ...
+
+
+def validate_integer(
+    name: str, val: int | float | None, min_val: int = 0
+) -> int | None:
+    """
+    Checks whether the 'name' parameter for parsing is either
+    an integer OR float that can SAFELY be cast to an integer
+    without losing accuracy. Raises a ValueError if that is
+    not the case.
+
+    Parameters
+    ----------
+    name : str
+        Parameter name (used for error reporting)
+    val : int or float
+        The value to check
+    min_val : int
+        Minimum allowed value (val < min_val will result in a ValueError)
+    """
+    if val is None:
+        return val
+
+    msg = f"'{name:s}' must be an integer >={min_val:d}"
+    if is_float(val):
+        if int(val) != val:
+            raise ValueError(msg)
+        val = int(val)
+    elif not (is_integer(val) and val >= min_val):
+        raise ValueError(msg)
+
+    return int(val)
+
+
+def _validate_names(names: Sequence[Hashable] | None) -> None:
+    """
+    Raise ValueError if the `names` parameter contains duplicates or has an
+    invalid data type.
+
+    Parameters
+    ----------
+    names : array-like or None
+        An array containing a list of the names used for the output DataFrame.
+
+    Raises
+    ------
+    ValueError
+        If names are not unique or are not ordered (e.g. set).
+    """
+    if names is not None:
+        if len(names) != len(set(names)):
+            raise ValueError("Duplicate names are not allowed.")
+        if not (
+            is_list_like(names, allow_sets=False) or isinstance(names, abc.KeysView)
+        ):
+            raise ValueError("Names should be an ordered collection.")
+
+
+def _read(
+    filepath_or_buffer: FilePath | ReadCsvBuffer[bytes] | ReadCsvBuffer[str], kwds
+) -> DataFrame | TextFileReader:
+    """Generic reader of line files."""
+    # if we pass a date_parser and parse_dates=False, we should not parse the
+    # dates GH#44366
+    if kwds.get("parse_dates", None) is None:
+        if (
+            kwds.get("date_parser", lib.no_default) is lib.no_default
+            and kwds.get("date_format", None) is None
+        ):
+            kwds["parse_dates"] = False
+        else:
+            kwds["parse_dates"] = True
+
+    # Extract some of the arguments (pass chunksize on).
+    iterator = kwds.get("iterator", False)
+    chunksize = kwds.get("chunksize", None)
+    if kwds.get("engine") == "pyarrow":
+        if iterator:
+            raise ValueError(
+                "The 'iterator' option is not supported with the 'pyarrow' engine"
+            )
+
+        if chunksize is not None:
+            raise ValueError(
+                "The 'chunksize' option is not supported with the 'pyarrow' engine"
+            )
+    else:
+        chunksize = validate_integer("chunksize", chunksize, 1)
+
+    nrows = kwds.get("nrows", None)
+
+    # Check for duplicates in names.
+    _validate_names(kwds.get("names", None))
+
+    # Create the parser.
+    parser = TextFileReader(filepath_or_buffer, **kwds)
+
+    if chunksize or iterator:
+        return parser
+
+    with parser:
+        return parser.read(nrows)
+
+
+# iterator=True -> TextFileReader
+@overload
+def read_csv(
+    filepath_or_buffer: FilePath | ReadCsvBuffer[bytes] | ReadCsvBuffer[str],
+    *,
+    sep: str | None | lib.NoDefault = ...,
+    delimiter: str | None | lib.NoDefault = ...,
+    header: int | Sequence[int] | None | Literal["infer"] = ...,
+    names: Sequence[Hashable] | None | lib.NoDefault = ...,
+    index_col: IndexLabel | Literal[False] | None = ...,
+    usecols: UsecolsArgType = ...,
+    dtype: DtypeArg | None = ...,
+    engine: CSVEngine | None = ...,
+    converters: Mapping[Hashable, Callable] | None = ...,
+    true_values: list | None = ...,
+    false_values: list | None = ...,
+    skipinitialspace: bool = ...,
+    skiprows: list[int] | int | Callable[[Hashable], bool] | None = ...,
+    skipfooter: int = ...,
+    nrows: int | None = ...,
+    na_values: Hashable
+    | Iterable[Hashable]
+    | Mapping[Hashable, Iterable[Hashable]]
+    | None = ...,
+    na_filter: bool = ...,
+    verbose: bool | lib.NoDefault = ...,
+    skip_blank_lines: bool = ...,
+    parse_dates: bool | Sequence[Hashable] | None = ...,
+    infer_datetime_format: bool | lib.NoDefault = ...,
+    keep_date_col: bool | lib.NoDefault = ...,
+    date_parser: Callable | lib.NoDefault = ...,
+    date_format: str | dict[Hashable, str] | None = ...,
+    dayfirst: bool = ...,
+    cache_dates: bool = ...,
+    iterator: Literal[True],
+    chunksize: int | None = ...,
+    compression: CompressionOptions = ...,
+    thousands: str | None = ...,
+    decimal: str = ...,
+    lineterminator: str | None = ...,
+    quotechar: str = ...,
+    quoting: int = ...,
+    doublequote: bool = ...,
+    escapechar: str | None = ...,
+    comment: str | None = ...,
+    encoding: str | None = ...,
+    encoding_errors: str | None = ...,
+    dialect: str | csv.Dialect | None = ...,
+    on_bad_lines=...,
+    delim_whitespace: bool | lib.NoDefault = ...,
+    low_memory: bool = ...,
+    memory_map: bool = ...,
+    float_precision: Literal["high", "legacy"] | None = ...,
+    storage_options: StorageOptions = ...,
+    dtype_backend: DtypeBackend | lib.NoDefault = ...,
+) -> TextFileReader:
+    ...
+
+
+# chunksize=int -> TextFileReader
+@overload
+def read_csv(
+    filepath_or_buffer: FilePath | ReadCsvBuffer[bytes] | ReadCsvBuffer[str],
+    *,
+    sep: str | None | lib.NoDefault = ...,
+    delimiter: str | None | lib.NoDefault = ...,
+    header: int | Sequence[int] | None | Literal["infer"] = ...,
+    names: Sequence[Hashable] | None | lib.NoDefault = ...,
+    index_col: IndexLabel | Literal[False] | None = ...,
+    usecols: UsecolsArgType = ...,
+    dtype: DtypeArg | None = ...,
+    engine: CSVEngine | None = ...,
+    converters: Mapping[Hashable, Callable] | None = ...,
+    true_values: list | None = ...,
+    false_values: list | None = ...,
+    skipinitialspace: bool = ...,
+    skiprows: list[int] | int | Callable[[Hashable], bool] | None = ...,
+    skipfooter: int = ...,
+    nrows: int | None = ...,
+    na_values: Hashable
+    | Iterable[Hashable]
+    | Mapping[Hashable, Iterable[Hashable]]
+    | None = ...,
+    keep_default_na: bool = ...,
+    na_filter: bool = ...,
+    verbose: bool | lib.NoDefault = ...,
+    skip_blank_lines: bool = ...,
+    parse_dates: bool | Sequence[Hashable] | None = ...,
+    infer_datetime_format: bool | lib.NoDefault = ...,
+    keep_date_col: bool | lib.NoDefault = ...,
+    date_parser: Callable | lib.NoDefault = ...,
+    date_format: str | dict[Hashable, str] | None = ...,
+    dayfirst: bool = ...,
+    cache_dates: bool = ...,
+    iterator: bool = ...,
+    chunksize: int,
+    compression: CompressionOptions = ...,
+    thousands: str | None = ...,
+    decimal: str = ...,
+    lineterminator: str | None = ...,
+    quotechar: str = ...,
+    quoting: int = ...,
+    doublequote: bool = ...,
+    escapechar: str | None = ...,
+    comment: str | None = ...,
+    encoding: str | None = ...,
+    encoding_errors: str | None = ...,
+    dialect: str | csv.Dialect | None = ...,
+    on_bad_lines=...,
+    delim_whitespace: bool | lib.NoDefault = ...,
+    low_memory: bool = ...,
+    memory_map: bool = ...,
+    float_precision: Literal["high", "legacy"] | None = ...,
+    storage_options: StorageOptions = ...,
+    dtype_backend: DtypeBackend | lib.NoDefault = ...,
+) -> TextFileReader:
+    ...
+
+
+# default case -> DataFrame
+@overload
+def read_csv(
+    filepath_or_buffer: FilePath | ReadCsvBuffer[bytes] | ReadCsvBuffer[str],
+    *,
+    sep: str | None | lib.NoDefault = ...,
+    delimiter: str | None | lib.NoDefault = ...,
+    header: int | Sequence[int] | None | Literal["infer"] = ...,
+    names: Sequence[Hashable] | None | lib.NoDefault = ...,
+    index_col: IndexLabel | Literal[False] | None = ...,
+    usecols: UsecolsArgType = ...,
+    dtype: DtypeArg | None = ...,
+    engine: CSVEngine | None = ...,
+    converters: Mapping[Hashable, Callable] | None = ...,
+    true_values: list | None = ...,
+    false_values: list | None = ...,
+    skipinitialspace: bool = ...,
+    skiprows: list[int] | int | Callable[[Hashable], bool] | None = ...,
+    skipfooter: int = ...,
+    nrows: int | None = ...,
+    na_values: Hashable
+    | Iterable[Hashable]
+    | Mapping[Hashable, Iterable[Hashable]]
+    | None = ...,
+    keep_default_na: bool = ...,
+    na_filter: bool = ...,
+    verbose: bool | lib.NoDefault = ...,
+    skip_blank_lines: bool = ...,
+    parse_dates: bool | Sequence[Hashable] | None = ...,
+    infer_datetime_format: bool | lib.NoDefault = ...,
+    keep_date_col: bool | lib.NoDefault = ...,
+    date_parser: Callable | lib.NoDefault = ...,
+    date_format: str | dict[Hashable, str] | None = ...,
+    dayfirst: bool = ...,
+    cache_dates: bool = ...,
+    iterator: Literal[False] = ...,
+    chunksize: None = ...,
+    compression: CompressionOptions = ...,
+    thousands: str | None = ...,
+    decimal: str = ...,
+    lineterminator: str | None = ...,
+    quotechar: str = ...,
+    quoting: int = ...,
+    doublequote: bool = ...,
+    escapechar: str | None = ...,
+    comment: str | None = ...,
+    encoding: str | None = ...,
+    encoding_errors: str | None = ...,
+    dialect: str | csv.Dialect | None = ...,
+    on_bad_lines=...,
+    delim_whitespace: bool | lib.NoDefault = ...,
+    low_memory: bool = ...,
+    memory_map: bool = ...,
+    float_precision: Literal["high", "legacy"] | None = ...,
+    storage_options: StorageOptions = ...,
+    dtype_backend: DtypeBackend | lib.NoDefault = ...,
+) -> DataFrame:
+    ...
+
+
+# Unions -> DataFrame | TextFileReader
+@overload
+def read_csv(
+    filepath_or_buffer: FilePath | ReadCsvBuffer[bytes] | ReadCsvBuffer[str],
+    *,
+    sep: str | None | lib.NoDefault = ...,
+    delimiter: str | None | lib.NoDefault = ...,
+    header: int | Sequence[int] | None | Literal["infer"] = ...,
+    names: Sequence[Hashable] | None | lib.NoDefault = ...,
+    index_col: IndexLabel | Literal[False] | None = ...,
+    usecols: UsecolsArgType = ...,
+    dtype: DtypeArg | None = ...,
+    engine: CSVEngine | None = ...,
+    converters: Mapping[Hashable, Callable] | None = ...,
+    true_values: list | None = ...,
+    false_values: list | None = ...,
+    skipinitialspace: bool = ...,
+    skiprows: list[int] | int | Callable[[Hashable], bool] | None = ...,
+    skipfooter: int = ...,
+    nrows: int | None = ...,
+    na_values: Hashable
+    | Iterable[Hashable]
+    | Mapping[Hashable, Iterable[Hashable]]
+    | None = ...,
+    keep_default_na: bool = ...,
+    na_filter: bool = ...,
+    verbose: bool | lib.NoDefault = ...,
+    skip_blank_lines: bool = ...,
+    parse_dates: bool | Sequence[Hashable] | None = ...,
+    infer_datetime_format: bool | lib.NoDefault = ...,
+    keep_date_col: bool | lib.NoDefault = ...,
+    date_parser: Callable | lib.NoDefault = ...,
+    date_format: str | dict[Hashable, str] | None = ...,
+    dayfirst: bool = ...,
+    cache_dates: bool = ...,
+    iterator: bool = ...,
+    chunksize: int | None = ...,
+    compression: CompressionOptions = ...,
+    thousands: str | None = ...,
+    decimal: str = ...,
+    lineterminator: str | None = ...,
+    quotechar: str = ...,
+    quoting: int = ...,
+    doublequote: bool = ...,
+    escapechar: str | None = ...,
+    comment: str | None = ...,
+    encoding: str | None = ...,
+    encoding_errors: str | None = ...,
+    dialect: str | csv.Dialect | None = ...,
+    on_bad_lines=...,
+    delim_whitespace: bool | lib.NoDefault = ...,
+    low_memory: bool = ...,
+    memory_map: bool = ...,
+    float_precision: Literal["high", "legacy"] | None = ...,
+    storage_options: StorageOptions = ...,
+    dtype_backend: DtypeBackend | lib.NoDefault = ...,
+) -> DataFrame | TextFileReader:
+    ...
+
+
+@Appender(
+    _doc_read_csv_and_table.format(
+        func_name="read_csv",
+        summary="Read a comma-separated values (csv) file into DataFrame.",
+        see_also_func_name="read_table",
+        see_also_func_summary="Read general delimited file into DataFrame.",
+        _default_sep="','",
+        storage_options=_shared_docs["storage_options"],
+        decompression_options=_shared_docs["decompression_options"]
+        % "filepath_or_buffer",
+    )
+)
+def read_csv(
+    filepath_or_buffer: FilePath | ReadCsvBuffer[bytes] | ReadCsvBuffer[str],
+    *,
+    sep: str | None | lib.NoDefault = lib.no_default,
+    delimiter: str | None | lib.NoDefault = None,
+    # Column and Index Locations and Names
+    header: int | Sequence[int] | None | Literal["infer"] = "infer",
+    names: Sequence[Hashable] | None | lib.NoDefault = lib.no_default,
+    index_col: IndexLabel | Literal[False] | None = None,
+    usecols: UsecolsArgType = None,
+    # General Parsing Configuration
+    dtype: DtypeArg | None = None,
+    engine: CSVEngine | None = None,
+    converters: Mapping[Hashable, Callable] | None = None,
+    true_values: list | None = None,
+    false_values: list | None = None,
+    skipinitialspace: bool = False,
+    skiprows: list[int] | int | Callable[[Hashable], bool] | None = None,
+    skipfooter: int = 0,
+    nrows: int | None = None,
+    # NA and Missing Data Handling
+    na_values: Hashable
+    | Iterable[Hashable]
+    | Mapping[Hashable, Iterable[Hashable]]
+    | None = None,
+    keep_default_na: bool = True,
+    na_filter: bool = True,
+    verbose: bool | lib.NoDefault = lib.no_default,
+    skip_blank_lines: bool = True,
+    # Datetime Handling
+    parse_dates: bool | Sequence[Hashable] | None = None,
+    infer_datetime_format: bool | lib.NoDefault = lib.no_default,
+    keep_date_col: bool | lib.NoDefault = lib.no_default,
+    date_parser: Callable | lib.NoDefault = lib.no_default,
+    date_format: str | dict[Hashable, str] | None = None,
+    dayfirst: bool = False,
+    cache_dates: bool = True,
+    # Iteration
+    iterator: bool = False,
+    chunksize: int | None = None,
+    # Quoting, Compression, and File Format
+    compression: CompressionOptions = "infer",
+    thousands: str | None = None,
+    decimal: str = ".",
+    lineterminator: str | None = None,
+    quotechar: str = '"',
+    quoting: int = csv.QUOTE_MINIMAL,
+    doublequote: bool = True,
+    escapechar: str | None = None,
+    comment: str | None = None,
+    encoding: str | None = None,
+    encoding_errors: str | None = "strict",
+    dialect: str | csv.Dialect | None = None,
+    # Error Handling
+    on_bad_lines: str = "error",
+    # Internal
+    delim_whitespace: bool | lib.NoDefault = lib.no_default,
+    low_memory: bool = _c_parser_defaults["low_memory"],
+    memory_map: bool = False,
+    float_precision: Literal["high", "legacy"] | None = None,
+    storage_options: StorageOptions | None = None,
+    dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
+) -> DataFrame | TextFileReader:
+    if keep_date_col is not lib.no_default:
+        # GH#55569
+        warnings.warn(
+            "The 'keep_date_col' keyword in pd.read_csv is deprecated and "
+            "will be removed in a future version. Explicitly remove unwanted "
+            "columns after parsing instead.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+    else:
+        keep_date_col = False
+
+    if lib.is_list_like(parse_dates):
+        # GH#55569
+        depr = False
+        # error: Item "bool" of "bool | Sequence[Hashable] | None" has no
+        # attribute "__iter__" (not iterable)
+        if not all(is_hashable(x) for x in parse_dates):  # type: ignore[union-attr]
+            depr = True
+        elif isinstance(parse_dates, dict) and any(
+            lib.is_list_like(x) for x in parse_dates.values()
+        ):
+            depr = True
+        if depr:
+            warnings.warn(
+                "Support for nested sequences for 'parse_dates' in pd.read_csv "
+                "is deprecated. Combine the desired columns with pd.to_datetime "
+                "after parsing instead.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+
+    if infer_datetime_format is not lib.no_default:
+        warnings.warn(
+            "The argument 'infer_datetime_format' is deprecated and will "
+            "be removed in a future version. "
+            "A strict version of it is now the default, see "
+            "https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. "
+            "You can safely remove this argument.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+
+    if delim_whitespace is not lib.no_default:
+        # GH#55569
+        warnings.warn(
+            "The 'delim_whitespace' keyword in pd.read_csv is deprecated and "
+            "will be removed in a future version. Use ``sep='\\s+'`` instead",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+    else:
+        delim_whitespace = False
+
+    if verbose is not lib.no_default:
+        # GH#55569
+        warnings.warn(
+            "The 'verbose' keyword in pd.read_csv is deprecated and "
+            "will be removed in a future version.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+    else:
+        verbose = False
+
+    # locals() should never be modified
+    kwds = locals().copy()
+    del kwds["filepath_or_buffer"]
+    del kwds["sep"]
+
+    kwds_defaults = _refine_defaults_read(
+        dialect,
+        delimiter,
+        delim_whitespace,
+        engine,
+        sep,
+        on_bad_lines,
+        names,
+        defaults={"delimiter": ","},
+        dtype_backend=dtype_backend,
+    )
+    kwds.update(kwds_defaults)
+
+    return _read(filepath_or_buffer, kwds)
+
+
+# iterator=True -> TextFileReader
+@overload
+def read_table(
+    filepath_or_buffer: FilePath | ReadCsvBuffer[bytes] | ReadCsvBuffer[str],
+    *,
+    sep: str | None | lib.NoDefault = ...,
+    delimiter: str | None | lib.NoDefault = ...,
+    header: int | Sequence[int] | None | Literal["infer"] = ...,
+    names: Sequence[Hashable] | None | lib.NoDefault = ...,
+    index_col: IndexLabel | Literal[False] | None = ...,
+    usecols: UsecolsArgType = ...,
+    dtype: DtypeArg | None = ...,
+    engine: CSVEngine | None = ...,
+    converters: Mapping[Hashable, Callable] | None = ...,
+    true_values: list | None = ...,
+    false_values: list | None = ...,
+    skipinitialspace: bool = ...,
+    skiprows: list[int] | int | Callable[[Hashable], bool] | None = ...,
+    skipfooter: int = ...,
+    nrows: int | None = ...,
+    na_values: Sequence[str] | Mapping[str, Sequence[str]] | None = ...,
+    keep_default_na: bool = ...,
+    na_filter: bool = ...,
+    verbose: bool | lib.NoDefault = ...,
+    skip_blank_lines: bool = ...,
+    parse_dates: bool | Sequence[Hashable] = ...,
+    infer_datetime_format: bool | lib.NoDefault = ...,
+    keep_date_col: bool | lib.NoDefault = ...,
+    date_parser: Callable | lib.NoDefault = ...,
+    date_format: str | dict[Hashable, str] | None = ...,
+    dayfirst: bool = ...,
+    cache_dates: bool = ...,
+    iterator: Literal[True],
+    chunksize: int | None = ...,
+    compression: CompressionOptions = ...,
+    thousands: str | None = ...,
+    decimal: str = ...,
+    lineterminator: str | None = ...,
+    quotechar: str = ...,
+    quoting: int = ...,
+    doublequote: bool = ...,
+    escapechar: str | None = ...,
+    comment: str | None = ...,
+    encoding: str | None = ...,
+    encoding_errors: str | None = ...,
+    dialect: str | csv.Dialect | None = ...,
+    on_bad_lines=...,
+    delim_whitespace: bool = ...,
+    low_memory: bool = ...,
+    memory_map: bool = ...,
+    float_precision: str | None = ...,
+    storage_options: StorageOptions = ...,
+    dtype_backend: DtypeBackend | lib.NoDefault = ...,
+) -> TextFileReader:
+    ...
+
+
+# chunksize=int -> TextFileReader
+@overload
+def read_table(
+    filepath_or_buffer: FilePath | ReadCsvBuffer[bytes] | ReadCsvBuffer[str],
+    *,
+    sep: str | None | lib.NoDefault = ...,
+    delimiter: str | None | lib.NoDefault = ...,
+    header: int | Sequence[int] | None | Literal["infer"] = ...,
+    names: Sequence[Hashable] | None | lib.NoDefault = ...,
+    index_col: IndexLabel | Literal[False] | None = ...,
+    usecols: UsecolsArgType = ...,
+    dtype: DtypeArg | None = ...,
+    engine: CSVEngine | None = ...,
+    converters: Mapping[Hashable, Callable] | None = ...,
+    true_values: list | None = ...,
+    false_values: list | None = ...,
+    skipinitialspace: bool = ...,
+    skiprows: list[int] | int | Callable[[Hashable], bool] | None = ...,
+    skipfooter: int = ...,
+    nrows: int | None = ...,
+    na_values: Sequence[str] | Mapping[str, Sequence[str]] | None = ...,
+    keep_default_na: bool = ...,
+    na_filter: bool = ...,
+    verbose: bool | lib.NoDefault = ...,
+    skip_blank_lines: bool = ...,
+    parse_dates: bool | Sequence[Hashable] = ...,
+    infer_datetime_format: bool | lib.NoDefault = ...,
+    keep_date_col: bool | lib.NoDefault = ...,
+    date_parser: Callable | lib.NoDefault = ...,
+    date_format: str | dict[Hashable, str] | None = ...,
+    dayfirst: bool = ...,
+    cache_dates: bool = ...,
+    iterator: bool = ...,
+    chunksize: int,
+    compression: CompressionOptions = ...,
+    thousands: str | None = ...,
+    decimal: str = ...,
+    lineterminator: str | None = ...,
+    quotechar: str = ...,
+    quoting: int = ...,
+    doublequote: bool = ...,
+    escapechar: str | None = ...,
+    comment: str | None = ...,
+    encoding: str | None = ...,
+    encoding_errors: str | None = ...,
+    dialect: str | csv.Dialect | None = ...,
+    on_bad_lines=...,
+    delim_whitespace: bool = ...,
+    low_memory: bool = ...,
+    memory_map: bool = ...,
+    float_precision: str | None = ...,
+    storage_options: StorageOptions = ...,
+    dtype_backend: DtypeBackend | lib.NoDefault = ...,
+) -> TextFileReader:
+    ...
+
+
+# default -> DataFrame
+@overload
+def read_table(
+    filepath_or_buffer: FilePath | ReadCsvBuffer[bytes] | ReadCsvBuffer[str],
+    *,
+    sep: str | None | lib.NoDefault = ...,
+    delimiter: str | None | lib.NoDefault = ...,
+    header: int | Sequence[int] | None | Literal["infer"] = ...,
+    names: Sequence[Hashable] | None | lib.NoDefault = ...,
+    index_col: IndexLabel | Literal[False] | None = ...,
+    usecols: UsecolsArgType = ...,
+    dtype: DtypeArg | None = ...,
+    engine: CSVEngine | None = ...,
+    converters: Mapping[Hashable, Callable] | None = ...,
+    true_values: list | None = ...,
+    false_values: list | None = ...,
+    skipinitialspace: bool = ...,
+    skiprows: list[int] | int | Callable[[Hashable], bool] | None = ...,
+    skipfooter: int = ...,
+    nrows: int | None = ...,
+    na_values: Sequence[str] | Mapping[str, Sequence[str]] | None = ...,
+    keep_default_na: bool = ...,
+    na_filter: bool = ...,
+    verbose: bool | lib.NoDefault = ...,
+    skip_blank_lines: bool = ...,
+    parse_dates: bool | Sequence[Hashable] = ...,
+    infer_datetime_format: bool | lib.NoDefault = ...,
+    keep_date_col: bool | lib.NoDefault = ...,
+    date_parser: Callable | lib.NoDefault = ...,
+    date_format: str | dict[Hashable, str] | None = ...,
+    dayfirst: bool = ...,
+    cache_dates: bool = ...,
+    iterator: Literal[False] = ...,
+    chunksize: None = ...,
+    compression: CompressionOptions = ...,
+    thousands: str | None = ...,
+    decimal: str = ...,
+    lineterminator: str | None = ...,
+    quotechar: str = ...,
+    quoting: int = ...,
+    doublequote: bool = ...,
+    escapechar: str | None = ...,
+    comment: str | None = ...,
+    encoding: str | None = ...,
+    encoding_errors: str | None = ...,
+    dialect: str | csv.Dialect | None = ...,
+    on_bad_lines=...,
+    delim_whitespace: bool = ...,
+    low_memory: bool = ...,
+    memory_map: bool = ...,
+    float_precision: str | None = ...,
+    storage_options: StorageOptions = ...,
+    dtype_backend: DtypeBackend | lib.NoDefault = ...,
+) -> DataFrame:
+    ...
+
+
+# Unions -> DataFrame | TextFileReader
+@overload
+def read_table(
+    filepath_or_buffer: FilePath | ReadCsvBuffer[bytes] | ReadCsvBuffer[str],
+    *,
+    sep: str | None | lib.NoDefault = ...,
+    delimiter: str | None | lib.NoDefault = ...,
+    header: int | Sequence[int] | None | Literal["infer"] = ...,
+    names: Sequence[Hashable] | None | lib.NoDefault = ...,
+    index_col: IndexLabel | Literal[False] | None = ...,
+    usecols: UsecolsArgType = ...,
+    dtype: DtypeArg | None = ...,
+    engine: CSVEngine | None = ...,
+    converters: Mapping[Hashable, Callable] | None = ...,
+    true_values: list | None = ...,
+    false_values: list | None = ...,
+    skipinitialspace: bool = ...,
+    skiprows: list[int] | int | Callable[[Hashable], bool] | None = ...,
+    skipfooter: int = ...,
+    nrows: int | None = ...,
+    na_values: Sequence[str] | Mapping[str, Sequence[str]] | None = ...,
+    keep_default_na: bool = ...,
+    na_filter: bool = ...,
+    verbose: bool | lib.NoDefault = ...,
+    skip_blank_lines: bool = ...,
+    parse_dates: bool | Sequence[Hashable] = ...,
+    infer_datetime_format: bool | lib.NoDefault = ...,
+    keep_date_col: bool | lib.NoDefault = ...,
+    date_parser: Callable | lib.NoDefault = ...,
+    date_format: str | dict[Hashable, str] | None = ...,
+    dayfirst: bool = ...,
+    cache_dates: bool = ...,
+    iterator: bool = ...,
+    chunksize: int | None = ...,
+    compression: CompressionOptions = ...,
+    thousands: str | None = ...,
+    decimal: str = ...,
+    lineterminator: str | None = ...,
+    quotechar: str = ...,
+    quoting: int = ...,
+    doublequote: bool = ...,
+    escapechar: str | None = ...,
+    comment: str | None = ...,
+    encoding: str | None = ...,
+    encoding_errors: str | None = ...,
+    dialect: str | csv.Dialect | None = ...,
+    on_bad_lines=...,
+    delim_whitespace: bool = ...,
+    low_memory: bool = ...,
+    memory_map: bool = ...,
+    float_precision: str | None = ...,
+    storage_options: StorageOptions = ...,
+    dtype_backend: DtypeBackend | lib.NoDefault = ...,
+) -> DataFrame | TextFileReader:
+    ...
+
+
+@Appender(
+    _doc_read_csv_and_table.format(
+        func_name="read_table",
+        summary="Read general delimited file into DataFrame.",
+        see_also_func_name="read_csv",
+        see_also_func_summary=(
+            "Read a comma-separated values (csv) file into DataFrame."
+        ),
+        _default_sep=r"'\\t' (tab-stop)",
+        storage_options=_shared_docs["storage_options"],
+        decompression_options=_shared_docs["decompression_options"]
+        % "filepath_or_buffer",
+    )
+)
+def read_table(
+    filepath_or_buffer: FilePath | ReadCsvBuffer[bytes] | ReadCsvBuffer[str],
+    *,
+    sep: str | None | lib.NoDefault = lib.no_default,
+    delimiter: str | None | lib.NoDefault = None,
+    # Column and Index Locations and Names
+    header: int | Sequence[int] | None | Literal["infer"] = "infer",
+    names: Sequence[Hashable] | None | lib.NoDefault = lib.no_default,
+    index_col: IndexLabel | Literal[False] | None = None,
+    usecols: UsecolsArgType = None,
+    # General Parsing Configuration
+    dtype: DtypeArg | None = None,
+    engine: CSVEngine | None = None,
+    converters: Mapping[Hashable, Callable] | None = None,
+    true_values: list | None = None,
+    false_values: list | None = None,
+    skipinitialspace: bool = False,
+    skiprows: list[int] | int | Callable[[Hashable], bool] | None = None,
+    skipfooter: int = 0,
+    nrows: int | None = None,
+    # NA and Missing Data Handling
+    na_values: Sequence[str] | Mapping[str, Sequence[str]] | None = None,
+    keep_default_na: bool = True,
+    na_filter: bool = True,
+    verbose: bool | lib.NoDefault = lib.no_default,
+    skip_blank_lines: bool = True,
+    # Datetime Handling
+    parse_dates: bool | Sequence[Hashable] = False,
+    infer_datetime_format: bool | lib.NoDefault = lib.no_default,
+    keep_date_col: bool | lib.NoDefault = lib.no_default,
+    date_parser: Callable | lib.NoDefault = lib.no_default,
+    date_format: str | dict[Hashable, str] | None = None,
+    dayfirst: bool = False,
+    cache_dates: bool = True,
+    # Iteration
+    iterator: bool = False,
+    chunksize: int | None = None,
+    # Quoting, Compression, and File Format
+    compression: CompressionOptions = "infer",
+    thousands: str | None = None,
+    decimal: str = ".",
+    lineterminator: str | None = None,
+    quotechar: str = '"',
+    quoting: int = csv.QUOTE_MINIMAL,
+    doublequote: bool = True,
+    escapechar: str | None = None,
+    comment: str | None = None,
+    encoding: str | None = None,
+    encoding_errors: str | None = "strict",
+    dialect: str | csv.Dialect | None = None,
+    # Error Handling
+    on_bad_lines: str = "error",
+    # Internal
+    delim_whitespace: bool | lib.NoDefault = lib.no_default,
+    low_memory: bool = _c_parser_defaults["low_memory"],
+    memory_map: bool = False,
+    float_precision: str | None = None,
+    storage_options: StorageOptions | None = None,
+    dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
+) -> DataFrame | TextFileReader:
+    if keep_date_col is not lib.no_default:
+        # GH#55569
+        warnings.warn(
+            "The 'keep_date_col' keyword in pd.read_table is deprecated and "
+            "will be removed in a future version. Explicitly remove unwanted "
+            "columns after parsing instead.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+    else:
+        keep_date_col = False
+
+    # error: Item "bool" of "bool | Sequence[Hashable]" has no attribute "__iter__"
+    if lib.is_list_like(parse_dates) and not all(is_hashable(x) for x in parse_dates):  # type: ignore[union-attr]
+        # GH#55569
+        warnings.warn(
+            "Support for nested sequences for 'parse_dates' in pd.read_table "
+            "is deprecated. Combine the desired columns with pd.to_datetime "
+            "after parsing instead.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+
+    if infer_datetime_format is not lib.no_default:
+        warnings.warn(
+            "The argument 'infer_datetime_format' is deprecated and will "
+            "be removed in a future version. "
+            "A strict version of it is now the default, see "
+            "https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. "
+            "You can safely remove this argument.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+
+    if delim_whitespace is not lib.no_default:
+        # GH#55569
+        warnings.warn(
+            "The 'delim_whitespace' keyword in pd.read_table is deprecated and "
+            "will be removed in a future version. Use ``sep='\\s+'`` instead",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+    else:
+        delim_whitespace = False
+
+    if verbose is not lib.no_default:
+        # GH#55569
+        warnings.warn(
+            "The 'verbose' keyword in pd.read_table is deprecated and "
+            "will be removed in a future version.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+    else:
+        verbose = False
+
+    # locals() should never be modified
+    kwds = locals().copy()
+    del kwds["filepath_or_buffer"]
+    del kwds["sep"]
+
+    kwds_defaults = _refine_defaults_read(
+        dialect,
+        delimiter,
+        delim_whitespace,
+        engine,
+        sep,
+        on_bad_lines,
+        names,
+        defaults={"delimiter": "\t"},
+        dtype_backend=dtype_backend,
+    )
+    kwds.update(kwds_defaults)
+
+    return _read(filepath_or_buffer, kwds)
+
+
+@overload
+def read_fwf(
+    filepath_or_buffer: FilePath | ReadCsvBuffer[bytes] | ReadCsvBuffer[str],
+    *,
+    colspecs: Sequence[tuple[int, int]] | str | None = ...,
+    widths: Sequence[int] | None = ...,
+    infer_nrows: int = ...,
+    dtype_backend: DtypeBackend | lib.NoDefault = ...,
+    iterator: Literal[True],
+    chunksize: int | None = ...,
+    **kwds,
+) -> TextFileReader:
+    ...
+
+
+@overload
+def read_fwf(
+    filepath_or_buffer: FilePath | ReadCsvBuffer[bytes] | ReadCsvBuffer[str],
+    *,
+    colspecs: Sequence[tuple[int, int]] | str | None = ...,
+    widths: Sequence[int] | None = ...,
+    infer_nrows: int = ...,
+    dtype_backend: DtypeBackend | lib.NoDefault = ...,
+    iterator: bool = ...,
+    chunksize: int,
+    **kwds,
+) -> TextFileReader:
+    ...
+
+
+@overload
+def read_fwf(
+    filepath_or_buffer: FilePath | ReadCsvBuffer[bytes] | ReadCsvBuffer[str],
+    *,
+    colspecs: Sequence[tuple[int, int]] | str | None = ...,
+    widths: Sequence[int] | None = ...,
+    infer_nrows: int = ...,
+    dtype_backend: DtypeBackend | lib.NoDefault = ...,
+    iterator: Literal[False] = ...,
+    chunksize: None = ...,
+    **kwds,
+) -> DataFrame:
+    ...
+
+
+def read_fwf(
+    filepath_or_buffer: FilePath | ReadCsvBuffer[bytes] | ReadCsvBuffer[str],
+    *,
+    colspecs: Sequence[tuple[int, int]] | str | None = "infer",
+    widths: Sequence[int] | None = None,
+    infer_nrows: int = 100,
+    dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
+    iterator: bool = False,
+    chunksize: int | None = None,
+    **kwds,
+) -> DataFrame | TextFileReader:
+    r"""
+    Read a table of fixed-width formatted lines into DataFrame.
+
+    Also supports optionally iterating or breaking of the file
+    into chunks.
+
+    Additional help can be found in the `online docs for IO Tools
+    `_.
+
+    Parameters
+    ----------
+    filepath_or_buffer : str, path object, or file-like object
+        String, path object (implementing ``os.PathLike[str]``), or file-like
+        object implementing a text ``read()`` function.The string could be a URL.
+        Valid URL schemes include http, ftp, s3, and file. For file URLs, a host is
+        expected. A local file could be:
+        ``file://localhost/path/to/table.csv``.
+    colspecs : list of tuple (int, int) or 'infer'. optional
+        A list of tuples giving the extents of the fixed-width
+        fields of each line as half-open intervals (i.e.,  [from, to[ ).
+        String value 'infer' can be used to instruct the parser to try
+        detecting the column specifications from the first 100 rows of
+        the data which are not being skipped via skiprows (default='infer').
+    widths : list of int, optional
+        A list of field widths which can be used instead of 'colspecs' if
+        the intervals are contiguous.
+    infer_nrows : int, default 100
+        The number of rows to consider when letting the parser determine the
+        `colspecs`.
+    dtype_backend : {'numpy_nullable', 'pyarrow'}, default 'numpy_nullable'
+        Back-end data type applied to the resultant :class:`DataFrame`
+        (still experimental). Behaviour is as follows:
+
+        * ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame`
+          (default).
+        * ``"pyarrow"``: returns pyarrow-backed nullable :class:`ArrowDtype`
+          DataFrame.
+
+        .. versionadded:: 2.0
+
+    **kwds : optional
+        Optional keyword arguments can be passed to ``TextFileReader``.
+
+    Returns
+    -------
+    DataFrame or TextFileReader
+        A comma-separated values (csv) file is returned as two-dimensional
+        data structure with labeled axes.
+
+    See Also
+    --------
+    DataFrame.to_csv : Write DataFrame to a comma-separated values (csv) file.
+    read_csv : Read a comma-separated values (csv) file into DataFrame.
+
+    Examples
+    --------
+    >>> pd.read_fwf('data.csv')  # doctest: +SKIP
+    """
+    # Check input arguments.
+    if colspecs is None and widths is None:
+        raise ValueError("Must specify either colspecs or widths")
+    if colspecs not in (None, "infer") and widths is not None:
+        raise ValueError("You must specify only one of 'widths' and 'colspecs'")
+
+    # Compute 'colspecs' from 'widths', if specified.
+    if widths is not None:
+        colspecs, col = [], 0
+        for w in widths:
+            colspecs.append((col, col + w))
+            col += w
+
+    # for mypy
+    assert colspecs is not None
+
+    # GH#40830
+    # Ensure length of `colspecs` matches length of `names`
+    names = kwds.get("names")
+    if names is not None:
+        if len(names) != len(colspecs) and colspecs != "infer":
+            # need to check len(index_col) as it might contain
+            # unnamed indices, in which case it's name is not required
+            len_index = 0
+            if kwds.get("index_col") is not None:
+                index_col: Any = kwds.get("index_col")
+                if index_col is not False:
+                    if not is_list_like(index_col):
+                        len_index = 1
+                    else:
+                        len_index = len(index_col)
+            if kwds.get("usecols") is None and len(names) + len_index != len(colspecs):
+                # If usecols is used colspec may be longer than names
+                raise ValueError("Length of colspecs must match length of names")
+
+    kwds["colspecs"] = colspecs
+    kwds["infer_nrows"] = infer_nrows
+    kwds["engine"] = "python-fwf"
+    kwds["iterator"] = iterator
+    kwds["chunksize"] = chunksize
+
+    check_dtype_backend(dtype_backend)
+    kwds["dtype_backend"] = dtype_backend
+    return _read(filepath_or_buffer, kwds)
+
+
+class TextFileReader(abc.Iterator):
+    """
+
+    Passed dialect overrides any of the related parser options
+
+    """
+
+    def __init__(
+        self,
+        f: FilePath | ReadCsvBuffer[bytes] | ReadCsvBuffer[str] | list,
+        engine: CSVEngine | None = None,
+        **kwds,
+    ) -> None:
+        if engine is not None:
+            engine_specified = True
+        else:
+            engine = "python"
+            engine_specified = False
+        self.engine = engine
+        self._engine_specified = kwds.get("engine_specified", engine_specified)
+
+        _validate_skipfooter(kwds)
+
+        dialect = _extract_dialect(kwds)
+        if dialect is not None:
+            if engine == "pyarrow":
+                raise ValueError(
+                    "The 'dialect' option is not supported with the 'pyarrow' engine"
+                )
+            kwds = _merge_with_dialect_properties(dialect, kwds)
+
+        if kwds.get("header", "infer") == "infer":
+            kwds["header"] = 0 if kwds.get("names") is None else None
+
+        self.orig_options = kwds
+
+        # miscellanea
+        self._currow = 0
+
+        options = self._get_options_with_defaults(engine)
+        options["storage_options"] = kwds.get("storage_options", None)
+
+        self.chunksize = options.pop("chunksize", None)
+        self.nrows = options.pop("nrows", None)
+
+        self._check_file_or_buffer(f, engine)
+        self.options, self.engine = self._clean_options(options, engine)
+
+        if "has_index_names" in kwds:
+            self.options["has_index_names"] = kwds["has_index_names"]
+
+        self.handles: IOHandles | None = None
+        self._engine = self._make_engine(f, self.engine)
+
+    def close(self) -> None:
+        if self.handles is not None:
+            self.handles.close()
+        self._engine.close()
+
+    def _get_options_with_defaults(self, engine: CSVEngine) -> dict[str, Any]:
+        kwds = self.orig_options
+
+        options = {}
+        default: object | None
+
+        for argname, default in parser_defaults.items():
+            value = kwds.get(argname, default)
+
+            # see gh-12935
+            if (
+                engine == "pyarrow"
+                and argname in _pyarrow_unsupported
+                and value != default
+                and value != getattr(value, "value", default)
+            ):
+                raise ValueError(
+                    f"The {repr(argname)} option is not supported with the "
+                    f"'pyarrow' engine"
+                )
+            options[argname] = value
+
+        for argname, default in _c_parser_defaults.items():
+            if argname in kwds:
+                value = kwds[argname]
+
+                if engine != "c" and value != default:
+                    # TODO: Refactor this logic, its pretty convoluted
+                    if "python" in engine and argname not in _python_unsupported:
+                        pass
+                    elif "pyarrow" in engine and argname not in _pyarrow_unsupported:
+                        pass
+                    else:
+                        raise ValueError(
+                            f"The {repr(argname)} option is not supported with the "
+                            f"{repr(engine)} engine"
+                        )
+            else:
+                value = default
+            options[argname] = value
+
+        if engine == "python-fwf":
+            for argname, default in _fwf_defaults.items():
+                options[argname] = kwds.get(argname, default)
+
+        return options
+
+    def _check_file_or_buffer(self, f, engine: CSVEngine) -> None:
+        # see gh-16530
+        if is_file_like(f) and engine != "c" and not hasattr(f, "__iter__"):
+            # The C engine doesn't need the file-like to have the "__iter__"
+            # attribute. However, the Python engine needs "__iter__(...)"
+            # when iterating through such an object, meaning it
+            # needs to have that attribute
+            raise ValueError(
+                "The 'python' engine cannot iterate through this file buffer."
+            )
+
+    def _clean_options(
+        self, options: dict[str, Any], engine: CSVEngine
+    ) -> tuple[dict[str, Any], CSVEngine]:
+        result = options.copy()
+
+        fallback_reason = None
+
+        # C engine not supported yet
+        if engine == "c":
+            if options["skipfooter"] > 0:
+                fallback_reason = "the 'c' engine does not support skipfooter"
+                engine = "python"
+
+        sep = options["delimiter"]
+        delim_whitespace = options["delim_whitespace"]
+
+        if sep is None and not delim_whitespace:
+            if engine in ("c", "pyarrow"):
+                fallback_reason = (
+                    f"the '{engine}' engine does not support "
+                    "sep=None with delim_whitespace=False"
+                )
+                engine = "python"
+        elif sep is not None and len(sep) > 1:
+            if engine == "c" and sep == r"\s+":
+                result["delim_whitespace"] = True
+                del result["delimiter"]
+            elif engine not in ("python", "python-fwf"):
+                # wait until regex engine integrated
+                fallback_reason = (
+                    f"the '{engine}' engine does not support "
+                    "regex separators (separators > 1 char and "
+                    r"different from '\s+' are interpreted as regex)"
+                )
+                engine = "python"
+        elif delim_whitespace:
+            if "python" in engine:
+                result["delimiter"] = r"\s+"
+        elif sep is not None:
+            encodeable = True
+            encoding = sys.getfilesystemencoding() or "utf-8"
+            try:
+                if len(sep.encode(encoding)) > 1:
+                    encodeable = False
+            except UnicodeDecodeError:
+                encodeable = False
+            if not encodeable and engine not in ("python", "python-fwf"):
+                fallback_reason = (
+                    f"the separator encoded in {encoding} "
+                    f"is > 1 char long, and the '{engine}' engine "
+                    "does not support such separators"
+                )
+                engine = "python"
+
+        quotechar = options["quotechar"]
+        if quotechar is not None and isinstance(quotechar, (str, bytes)):
+            if (
+                len(quotechar) == 1
+                and ord(quotechar) > 127
+                and engine not in ("python", "python-fwf")
+            ):
+                fallback_reason = (
+                    "ord(quotechar) > 127, meaning the "
+                    "quotechar is larger than one byte, "
+                    f"and the '{engine}' engine does not support such quotechars"
+                )
+                engine = "python"
+
+        if fallback_reason and self._engine_specified:
+            raise ValueError(fallback_reason)
+
+        if engine == "c":
+            for arg in _c_unsupported:
+                del result[arg]
+
+        if "python" in engine:
+            for arg in _python_unsupported:
+                if fallback_reason and result[arg] != _c_parser_defaults.get(arg):
+                    raise ValueError(
+                        "Falling back to the 'python' engine because "
+                        f"{fallback_reason}, but this causes {repr(arg)} to be "
+                        "ignored as it is not supported by the 'python' engine."
+                    )
+                del result[arg]
+
+        if fallback_reason:
+            warnings.warn(
+                (
+                    "Falling back to the 'python' engine because "
+                    f"{fallback_reason}; you can avoid this warning by specifying "
+                    "engine='python'."
+                ),
+                ParserWarning,
+                stacklevel=find_stack_level(),
+            )
+
+        index_col = options["index_col"]
+        names = options["names"]
+        converters = options["converters"]
+        na_values = options["na_values"]
+        skiprows = options["skiprows"]
+
+        validate_header_arg(options["header"])
+
+        if index_col is True:
+            raise ValueError("The value of index_col couldn't be 'True'")
+        if is_index_col(index_col):
+            if not isinstance(index_col, (list, tuple, np.ndarray)):
+                index_col = [index_col]
+        result["index_col"] = index_col
+
+        names = list(names) if names is not None else names
+
+        # type conversion-related
+        if converters is not None:
+            if not isinstance(converters, dict):
+                raise TypeError(
+                    "Type converters must be a dict or subclass, "
+                    f"input was a {type(converters).__name__}"
+                )
+        else:
+            converters = {}
+
+        # Converting values to NA
+        keep_default_na = options["keep_default_na"]
+        floatify = engine != "pyarrow"
+        na_values, na_fvalues = _clean_na_values(
+            na_values, keep_default_na, floatify=floatify
+        )
+
+        # handle skiprows; this is internally handled by the
+        # c-engine, so only need for python and pyarrow parsers
+        if engine == "pyarrow":
+            if not is_integer(skiprows) and skiprows is not None:
+                # pyarrow expects skiprows to be passed as an integer
+                raise ValueError(
+                    "skiprows argument must be an integer when using "
+                    "engine='pyarrow'"
+                )
+        else:
+            if is_integer(skiprows):
+                skiprows = list(range(skiprows))
+            if skiprows is None:
+                skiprows = set()
+            elif not callable(skiprows):
+                skiprows = set(skiprows)
+
+        # put stuff back
+        result["names"] = names
+        result["converters"] = converters
+        result["na_values"] = na_values
+        result["na_fvalues"] = na_fvalues
+        result["skiprows"] = skiprows
+
+        return result, engine
+
+    def __next__(self) -> DataFrame:
+        try:
+            return self.get_chunk()
+        except StopIteration:
+            self.close()
+            raise
+
+    def _make_engine(
+        self,
+        f: FilePath | ReadCsvBuffer[bytes] | ReadCsvBuffer[str] | list | IO,
+        engine: CSVEngine = "c",
+    ) -> ParserBase:
+        mapping: dict[str, type[ParserBase]] = {
+            "c": CParserWrapper,
+            "python": PythonParser,
+            "pyarrow": ArrowParserWrapper,
+            "python-fwf": FixedWidthFieldParser,
+        }
+        if engine not in mapping:
+            raise ValueError(
+                f"Unknown engine: {engine} (valid options are {mapping.keys()})"
+            )
+        if not isinstance(f, list):
+            # open file here
+            is_text = True
+            mode = "r"
+            if engine == "pyarrow":
+                is_text = False
+                mode = "rb"
+            elif (
+                engine == "c"
+                and self.options.get("encoding", "utf-8") == "utf-8"
+                and isinstance(stringify_path(f), str)
+            ):
+                # c engine can decode utf-8 bytes, adding TextIOWrapper makes
+                # the c-engine especially for memory_map=True far slower
+                is_text = False
+                if "b" not in mode:
+                    mode += "b"
+            self.handles = get_handle(
+                f,
+                mode,
+                encoding=self.options.get("encoding", None),
+                compression=self.options.get("compression", None),
+                memory_map=self.options.get("memory_map", False),
+                is_text=is_text,
+                errors=self.options.get("encoding_errors", "strict"),
+                storage_options=self.options.get("storage_options", None),
+            )
+            assert self.handles is not None
+            f = self.handles.handle
+
+        elif engine != "python":
+            msg = f"Invalid file path or buffer object type: {type(f)}"
+            raise ValueError(msg)
+
+        try:
+            return mapping[engine](f, **self.options)
+        except Exception:
+            if self.handles is not None:
+                self.handles.close()
+            raise
+
+    def _failover_to_python(self) -> None:
+        raise AbstractMethodError(self)
+
+    def read(self, nrows: int | None = None) -> DataFrame:
+        if self.engine == "pyarrow":
+            try:
+                # error: "ParserBase" has no attribute "read"
+                df = self._engine.read()  # type: ignore[attr-defined]
+            except Exception:
+                self.close()
+                raise
+        else:
+            nrows = validate_integer("nrows", nrows)
+            try:
+                # error: "ParserBase" has no attribute "read"
+                (
+                    index,
+                    columns,
+                    col_dict,
+                ) = self._engine.read(  # type: ignore[attr-defined]
+                    nrows
+                )
+            except Exception:
+                self.close()
+                raise
+
+            if index is None:
+                if col_dict:
+                    # Any column is actually fine:
+                    new_rows = len(next(iter(col_dict.values())))
+                    index = RangeIndex(self._currow, self._currow + new_rows)
+                else:
+                    new_rows = 0
+            else:
+                new_rows = len(index)
+
+            if hasattr(self, "orig_options"):
+                dtype_arg = self.orig_options.get("dtype", None)
+            else:
+                dtype_arg = None
+
+            if isinstance(dtype_arg, dict):
+                dtype = defaultdict(lambda: None)  # type: ignore[var-annotated]
+                dtype.update(dtype_arg)
+            elif dtype_arg is not None and pandas_dtype(dtype_arg) in (
+                np.str_,
+                np.object_,
+            ):
+                dtype = defaultdict(lambda: dtype_arg)
+            else:
+                dtype = None
+
+            if dtype is not None:
+                new_col_dict = {}
+                for k, v in col_dict.items():
+                    d = (
+                        dtype[k]
+                        if pandas_dtype(dtype[k]) in (np.str_, np.object_)
+                        else None
+                    )
+                    new_col_dict[k] = Series(v, index=index, dtype=d, copy=False)
+            else:
+                new_col_dict = col_dict
+
+            df = DataFrame(
+                new_col_dict,
+                columns=columns,
+                index=index,
+                copy=not using_copy_on_write(),
+            )
+
+            self._currow += new_rows
+        return df
+
+    def get_chunk(self, size: int | None = None) -> DataFrame:
+        if size is None:
+            size = self.chunksize
+        if self.nrows is not None:
+            if self._currow >= self.nrows:
+                raise StopIteration
+            size = min(size, self.nrows - self._currow)
+        return self.read(nrows=size)
+
+    def __enter__(self) -> Self:
+        return self
+
+    def __exit__(
+        self,
+        exc_type: type[BaseException] | None,
+        exc_value: BaseException | None,
+        traceback: TracebackType | None,
+    ) -> None:
+        self.close()
+
+
+def TextParser(*args, **kwds) -> TextFileReader:
+    """
+    Converts lists of lists/tuples into DataFrames with proper type inference
+    and optional (e.g. string to datetime) conversion. Also enables iterating
+    lazily over chunks of large files
+
+    Parameters
+    ----------
+    data : file-like object or list
+    delimiter : separator character to use
+    dialect : str or csv.Dialect instance, optional
+        Ignored if delimiter is longer than 1 character
+    names : sequence, default
+    header : int, default 0
+        Row to use to parse column labels. Defaults to the first row. Prior
+        rows will be discarded
+    index_col : int or list, optional
+        Column or columns to use as the (possibly hierarchical) index
+    has_index_names: bool, default False
+        True if the cols defined in index_col have an index name and are
+        not in the header.
+    na_values : scalar, str, list-like, or dict, optional
+        Additional strings to recognize as NA/NaN.
+    keep_default_na : bool, default True
+    thousands : str, optional
+        Thousands separator
+    comment : str, optional
+        Comment out remainder of line
+    parse_dates : bool, default False
+    keep_date_col : bool, default False
+    date_parser : function, optional
+
+        .. deprecated:: 2.0.0
+    date_format : str or dict of column -> format, default ``None``
+
+        .. versionadded:: 2.0.0
+    skiprows : list of integers
+        Row numbers to skip
+    skipfooter : int
+        Number of line at bottom of file to skip
+    converters : dict, optional
+        Dict of functions for converting values in certain columns. Keys can
+        either be integers or column labels, values are functions that take one
+        input argument, the cell (not column) content, and return the
+        transformed content.
+    encoding : str, optional
+        Encoding to use for UTF when reading/writing (ex. 'utf-8')
+    float_precision : str, optional
+        Specifies which converter the C engine should use for floating-point
+        values. The options are `None` or `high` for the ordinary converter,
+        `legacy` for the original lower precision pandas converter, and
+        `round_trip` for the round-trip converter.
+    """
+    kwds["engine"] = "python"
+    return TextFileReader(*args, **kwds)
+
+
+def _clean_na_values(na_values, keep_default_na: bool = True, floatify: bool = True):
+    na_fvalues: set | dict
+    if na_values is None:
+        if keep_default_na:
+            na_values = STR_NA_VALUES
+        else:
+            na_values = set()
+        na_fvalues = set()
+    elif isinstance(na_values, dict):
+        old_na_values = na_values.copy()
+        na_values = {}  # Prevent aliasing.
+
+        # Convert the values in the na_values dictionary
+        # into array-likes for further use. This is also
+        # where we append the default NaN values, provided
+        # that `keep_default_na=True`.
+        for k, v in old_na_values.items():
+            if not is_list_like(v):
+                v = [v]
+
+            if keep_default_na:
+                v = set(v) | STR_NA_VALUES
+
+            na_values[k] = v
+        na_fvalues = {k: _floatify_na_values(v) for k, v in na_values.items()}
+    else:
+        if not is_list_like(na_values):
+            na_values = [na_values]
+        na_values = _stringify_na_values(na_values, floatify)
+        if keep_default_na:
+            na_values = na_values | STR_NA_VALUES
+
+        na_fvalues = _floatify_na_values(na_values)
+
+    return na_values, na_fvalues
+
+
+def _floatify_na_values(na_values):
+    # create float versions of the na_values
+    result = set()
+    for v in na_values:
+        try:
+            v = float(v)
+            if not np.isnan(v):
+                result.add(v)
+        except (TypeError, ValueError, OverflowError):
+            pass
+    return result
+
+
+def _stringify_na_values(na_values, floatify: bool):
+    """return a stringified and numeric for these values"""
+    result: list[str | float] = []
+    for x in na_values:
+        result.append(str(x))
+        result.append(x)
+        try:
+            v = float(x)
+
+            # we are like 999 here
+            if v == int(v):
+                v = int(v)
+                result.append(f"{v}.0")
+                result.append(str(v))
+
+            if floatify:
+                result.append(v)
+        except (TypeError, ValueError, OverflowError):
+            pass
+        if floatify:
+            try:
+                result.append(int(x))
+            except (TypeError, ValueError, OverflowError):
+                pass
+    return set(result)
+
+
+def _refine_defaults_read(
+    dialect: str | csv.Dialect | None,
+    delimiter: str | None | lib.NoDefault,
+    delim_whitespace: bool,
+    engine: CSVEngine | None,
+    sep: str | None | lib.NoDefault,
+    on_bad_lines: str | Callable,
+    names: Sequence[Hashable] | None | lib.NoDefault,
+    defaults: dict[str, Any],
+    dtype_backend: DtypeBackend | lib.NoDefault,
+):
+    """Validate/refine default values of input parameters of read_csv, read_table.
+
+    Parameters
+    ----------
+    dialect : str or csv.Dialect
+        If provided, this parameter will override values (default or not) for the
+        following parameters: `delimiter`, `doublequote`, `escapechar`,
+        `skipinitialspace`, `quotechar`, and `quoting`. If it is necessary to
+        override values, a ParserWarning will be issued. See csv.Dialect
+        documentation for more details.
+    delimiter : str or object
+        Alias for sep.
+    delim_whitespace : bool
+        Specifies whether or not whitespace (e.g. ``' '`` or ``'\t'``) will be
+        used as the sep. Equivalent to setting ``sep='\\s+'``. If this option
+        is set to True, nothing should be passed in for the ``delimiter``
+        parameter.
+
+        .. deprecated:: 2.2.0
+            Use ``sep="\\s+"`` instead.
+    engine : {{'c', 'python'}}
+        Parser engine to use. The C engine is faster while the python engine is
+        currently more feature-complete.
+    sep : str or object
+        A delimiter provided by the user (str) or a sentinel value, i.e.
+        pandas._libs.lib.no_default.
+    on_bad_lines : str, callable
+        An option for handling bad lines or a sentinel value(None).
+    names : array-like, optional
+        List of column names to use. If the file contains a header row,
+        then you should explicitly pass ``header=0`` to override the column names.
+        Duplicates in this list are not allowed.
+    defaults: dict
+        Default values of input parameters.
+
+    Returns
+    -------
+    kwds : dict
+        Input parameters with correct values.
+
+    Raises
+    ------
+    ValueError :
+        If a delimiter was specified with ``sep`` (or ``delimiter``) and
+        ``delim_whitespace=True``.
+    """
+    # fix types for sep, delimiter to Union(str, Any)
+    delim_default = defaults["delimiter"]
+    kwds: dict[str, Any] = {}
+    # gh-23761
+    #
+    # When a dialect is passed, it overrides any of the overlapping
+    # parameters passed in directly. We don't want to warn if the
+    # default parameters were passed in (since it probably means
+    # that the user didn't pass them in explicitly in the first place).
+    #
+    # "delimiter" is the annoying corner case because we alias it to
+    # "sep" before doing comparison to the dialect values later on.
+    # Thus, we need a flag to indicate that we need to "override"
+    # the comparison to dialect values by checking if default values
+    # for BOTH "delimiter" and "sep" were provided.
+    if dialect is not None:
+        kwds["sep_override"] = delimiter is None and (
+            sep is lib.no_default or sep == delim_default
+        )
+
+    if delimiter and (sep is not lib.no_default):
+        raise ValueError("Specified a sep and a delimiter; you can only specify one.")
+
+    kwds["names"] = None if names is lib.no_default else names
+
+    # Alias sep -> delimiter.
+    if delimiter is None:
+        delimiter = sep
+
+    if delim_whitespace and (delimiter is not lib.no_default):
+        raise ValueError(
+            "Specified a delimiter with both sep and "
+            "delim_whitespace=True; you can only specify one."
+        )
+
+    if delimiter == "\n":
+        raise ValueError(
+            r"Specified \n as separator or delimiter. This forces the python engine "
+            "which does not accept a line terminator. Hence it is not allowed to use "
+            "the line terminator as separator.",
+        )
+
+    if delimiter is lib.no_default:
+        # assign default separator value
+        kwds["delimiter"] = delim_default
+    else:
+        kwds["delimiter"] = delimiter
+
+    if engine is not None:
+        kwds["engine_specified"] = True
+    else:
+        kwds["engine"] = "c"
+        kwds["engine_specified"] = False
+
+    if on_bad_lines == "error":
+        kwds["on_bad_lines"] = ParserBase.BadLineHandleMethod.ERROR
+    elif on_bad_lines == "warn":
+        kwds["on_bad_lines"] = ParserBase.BadLineHandleMethod.WARN
+    elif on_bad_lines == "skip":
+        kwds["on_bad_lines"] = ParserBase.BadLineHandleMethod.SKIP
+    elif callable(on_bad_lines):
+        if engine not in ["python", "pyarrow"]:
+            raise ValueError(
+                "on_bad_line can only be a callable function "
+                "if engine='python' or 'pyarrow'"
+            )
+        kwds["on_bad_lines"] = on_bad_lines
+    else:
+        raise ValueError(f"Argument {on_bad_lines} is invalid for on_bad_lines")
+
+    check_dtype_backend(dtype_backend)
+
+    kwds["dtype_backend"] = dtype_backend
+
+    return kwds
+
+
+def _extract_dialect(kwds: dict[str, Any]) -> csv.Dialect | None:
+    """
+    Extract concrete csv dialect instance.
+
+    Returns
+    -------
+    csv.Dialect or None
+    """
+    if kwds.get("dialect") is None:
+        return None
+
+    dialect = kwds["dialect"]
+    if dialect in csv.list_dialects():
+        dialect = csv.get_dialect(dialect)
+
+    _validate_dialect(dialect)
+
+    return dialect
+
+
+MANDATORY_DIALECT_ATTRS = (
+    "delimiter",
+    "doublequote",
+    "escapechar",
+    "skipinitialspace",
+    "quotechar",
+    "quoting",
+)
+
+
+def _validate_dialect(dialect: csv.Dialect) -> None:
+    """
+    Validate csv dialect instance.
+
+    Raises
+    ------
+    ValueError
+        If incorrect dialect is provided.
+    """
+    for param in MANDATORY_DIALECT_ATTRS:
+        if not hasattr(dialect, param):
+            raise ValueError(f"Invalid dialect {dialect} provided")
+
+
+def _merge_with_dialect_properties(
+    dialect: csv.Dialect,
+    defaults: dict[str, Any],
+) -> dict[str, Any]:
+    """
+    Merge default kwargs in TextFileReader with dialect parameters.
+
+    Parameters
+    ----------
+    dialect : csv.Dialect
+        Concrete csv dialect. See csv.Dialect documentation for more details.
+    defaults : dict
+        Keyword arguments passed to TextFileReader.
+
+    Returns
+    -------
+    kwds : dict
+        Updated keyword arguments, merged with dialect parameters.
+    """
+    kwds = defaults.copy()
+
+    for param in MANDATORY_DIALECT_ATTRS:
+        dialect_val = getattr(dialect, param)
+
+        parser_default = parser_defaults[param]
+        provided = kwds.get(param, parser_default)
+
+        # Messages for conflicting values between the dialect
+        # instance and the actual parameters provided.
+        conflict_msgs = []
+
+        # Don't warn if the default parameter was passed in,
+        # even if it conflicts with the dialect (gh-23761).
+        if provided not in (parser_default, dialect_val):
+            msg = (
+                f"Conflicting values for '{param}': '{provided}' was "
+                f"provided, but the dialect specifies '{dialect_val}'. "
+                "Using the dialect-specified value."
+            )
+
+            # Annoying corner case for not warning about
+            # conflicts between dialect and delimiter parameter.
+            # Refer to the outer "_read_" function for more info.
+            if not (param == "delimiter" and kwds.pop("sep_override", False)):
+                conflict_msgs.append(msg)
+
+        if conflict_msgs:
+            warnings.warn(
+                "\n\n".join(conflict_msgs), ParserWarning, stacklevel=find_stack_level()
+            )
+        kwds[param] = dialect_val
+    return kwds
+
+
+def _validate_skipfooter(kwds: dict[str, Any]) -> None:
+    """
+    Check whether skipfooter is compatible with other kwargs in TextFileReader.
+
+    Parameters
+    ----------
+    kwds : dict
+        Keyword arguments passed to TextFileReader.
+
+    Raises
+    ------
+    ValueError
+        If skipfooter is not compatible with other parameters.
+    """
+    if kwds.get("skipfooter"):
+        if kwds.get("iterator") or kwds.get("chunksize"):
+            raise ValueError("'skipfooter' not supported for iteration")
+        if kwds.get("nrows"):
+            raise ValueError("'skipfooter' not supported with 'nrows'")
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/pickle.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/pickle.py
new file mode 100644
index 0000000000000000000000000000000000000000..0dae0e7106b69a471f0c2702158cfe0f11f0389c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/pickle.py
@@ -0,0 +1,210 @@
+""" pickle compat """
+from __future__ import annotations
+
+import pickle
+from typing import (
+    TYPE_CHECKING,
+    Any,
+)
+import warnings
+
+from pandas.compat import pickle_compat as pc
+from pandas.util._decorators import doc
+
+from pandas.core.shared_docs import _shared_docs
+
+from pandas.io.common import get_handle
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        CompressionOptions,
+        FilePath,
+        ReadPickleBuffer,
+        StorageOptions,
+        WriteBuffer,
+    )
+
+    from pandas import (
+        DataFrame,
+        Series,
+    )
+
+
+@doc(
+    storage_options=_shared_docs["storage_options"],
+    compression_options=_shared_docs["compression_options"] % "filepath_or_buffer",
+)
+def to_pickle(
+    obj: Any,
+    filepath_or_buffer: FilePath | WriteBuffer[bytes],
+    compression: CompressionOptions = "infer",
+    protocol: int = pickle.HIGHEST_PROTOCOL,
+    storage_options: StorageOptions | None = None,
+) -> None:
+    """
+    Pickle (serialize) object to file.
+
+    Parameters
+    ----------
+    obj : any object
+        Any python object.
+    filepath_or_buffer : str, path object, or file-like object
+        String, path object (implementing ``os.PathLike[str]``), or file-like
+        object implementing a binary ``write()`` function.
+        Also accepts URL. URL has to be of S3 or GCS.
+    {compression_options}
+
+        .. versionchanged:: 1.4.0 Zstandard support.
+
+    protocol : int
+        Int which indicates which protocol should be used by the pickler,
+        default HIGHEST_PROTOCOL (see [1], paragraph 12.1.2). The possible
+        values for this parameter depend on the version of Python. For Python
+        2.x, possible values are 0, 1, 2. For Python>=3.0, 3 is a valid value.
+        For Python >= 3.4, 4 is a valid value. A negative value for the
+        protocol parameter is equivalent to setting its value to
+        HIGHEST_PROTOCOL.
+
+    {storage_options}
+
+        .. [1] https://docs.python.org/3/library/pickle.html
+
+    See Also
+    --------
+    read_pickle : Load pickled pandas object (or any object) from file.
+    DataFrame.to_hdf : Write DataFrame to an HDF5 file.
+    DataFrame.to_sql : Write DataFrame to a SQL database.
+    DataFrame.to_parquet : Write a DataFrame to the binary parquet format.
+
+    Examples
+    --------
+    >>> original_df = pd.DataFrame({{"foo": range(5), "bar": range(5, 10)}})  # doctest: +SKIP
+    >>> original_df  # doctest: +SKIP
+       foo  bar
+    0    0    5
+    1    1    6
+    2    2    7
+    3    3    8
+    4    4    9
+    >>> pd.to_pickle(original_df, "./dummy.pkl")  # doctest: +SKIP
+
+    >>> unpickled_df = pd.read_pickle("./dummy.pkl")  # doctest: +SKIP
+    >>> unpickled_df  # doctest: +SKIP
+       foo  bar
+    0    0    5
+    1    1    6
+    2    2    7
+    3    3    8
+    4    4    9
+    """  # noqa: E501
+    if protocol < 0:
+        protocol = pickle.HIGHEST_PROTOCOL
+
+    with get_handle(
+        filepath_or_buffer,
+        "wb",
+        compression=compression,
+        is_text=False,
+        storage_options=storage_options,
+    ) as handles:
+        # letting pickle write directly to the buffer is more memory-efficient
+        pickle.dump(obj, handles.handle, protocol=protocol)
+
+
+@doc(
+    storage_options=_shared_docs["storage_options"],
+    decompression_options=_shared_docs["decompression_options"] % "filepath_or_buffer",
+)
+def read_pickle(
+    filepath_or_buffer: FilePath | ReadPickleBuffer,
+    compression: CompressionOptions = "infer",
+    storage_options: StorageOptions | None = None,
+) -> DataFrame | Series:
+    """
+    Load pickled pandas object (or any object) from file.
+
+    .. warning::
+
+       Loading pickled data received from untrusted sources can be
+       unsafe. See `here `__.
+
+    Parameters
+    ----------
+    filepath_or_buffer : str, path object, or file-like object
+        String, path object (implementing ``os.PathLike[str]``), or file-like
+        object implementing a binary ``readlines()`` function.
+        Also accepts URL. URL is not limited to S3 and GCS.
+
+    {decompression_options}
+
+        .. versionchanged:: 1.4.0 Zstandard support.
+
+    {storage_options}
+
+    Returns
+    -------
+    same type as object stored in file
+
+    See Also
+    --------
+    DataFrame.to_pickle : Pickle (serialize) DataFrame object to file.
+    Series.to_pickle : Pickle (serialize) Series object to file.
+    read_hdf : Read HDF5 file into a DataFrame.
+    read_sql : Read SQL query or database table into a DataFrame.
+    read_parquet : Load a parquet object, returning a DataFrame.
+
+    Notes
+    -----
+    read_pickle is only guaranteed to be backwards compatible to pandas 0.20.3
+    provided the object was serialized with to_pickle.
+
+    Examples
+    --------
+    >>> original_df = pd.DataFrame(
+    ...     {{"foo": range(5), "bar": range(5, 10)}}
+    ...    )  # doctest: +SKIP
+    >>> original_df  # doctest: +SKIP
+       foo  bar
+    0    0    5
+    1    1    6
+    2    2    7
+    3    3    8
+    4    4    9
+    >>> pd.to_pickle(original_df, "./dummy.pkl")  # doctest: +SKIP
+
+    >>> unpickled_df = pd.read_pickle("./dummy.pkl")  # doctest: +SKIP
+    >>> unpickled_df  # doctest: +SKIP
+       foo  bar
+    0    0    5
+    1    1    6
+    2    2    7
+    3    3    8
+    4    4    9
+    """
+    excs_to_catch = (AttributeError, ImportError, ModuleNotFoundError, TypeError)
+    with get_handle(
+        filepath_or_buffer,
+        "rb",
+        compression=compression,
+        is_text=False,
+        storage_options=storage_options,
+    ) as handles:
+        # 1) try standard library Pickle
+        # 2) try pickle_compat (older pandas version) to handle subclass changes
+        # 3) try pickle_compat with latin-1 encoding upon a UnicodeDecodeError
+
+        try:
+            # TypeError for Cython complaints about object.__new__ vs Tick.__new__
+            try:
+                with warnings.catch_warnings(record=True):
+                    # We want to silence any warnings about, e.g. moved modules.
+                    warnings.simplefilter("ignore", Warning)
+                    return pickle.load(handles.handle)
+            except excs_to_catch:
+                # e.g.
+                #  "No module named 'pandas.core.sparse.series'"
+                #  "Can't get attribute '__nat_unpickle' on  str:
+    # set the encoding if we need
+    if encoding is None:
+        encoding = _default_encoding
+
+    return encoding
+
+
+def _ensure_str(name):
+    """
+    Ensure that an index / column name is a str (python 3); otherwise they
+    may be np.string dtype. Non-string dtypes are passed through unchanged.
+
+    https://github.com/pandas-dev/pandas/issues/13492
+    """
+    if isinstance(name, str):
+        name = str(name)
+    return name
+
+
+Term = PyTablesExpr
+
+
+def _ensure_term(where, scope_level: int):
+    """
+    Ensure that the where is a Term or a list of Term.
+
+    This makes sure that we are capturing the scope of variables that are
+    passed create the terms here with a frame_level=2 (we are 2 levels down)
+    """
+    # only consider list/tuple here as an ndarray is automatically a coordinate
+    # list
+    level = scope_level + 1
+    if isinstance(where, (list, tuple)):
+        where = [
+            Term(term, scope_level=level + 1) if maybe_expression(term) else term
+            for term in where
+            if term is not None
+        ]
+    elif maybe_expression(where):
+        where = Term(where, scope_level=level)
+    return where if where is None or len(where) else None
+
+
+incompatibility_doc: Final = """
+where criteria is being ignored as this version [%s] is too old (or
+not-defined), read the file in and write it out to a new file to upgrade (with
+the copy_to method)
+"""
+
+attribute_conflict_doc: Final = """
+the [%s] attribute of the existing index is [%s] which conflicts with the new
+[%s], resetting the attribute to None
+"""
+
+performance_doc: Final = """
+your performance may suffer as PyTables will pickle object types that it cannot
+map directly to c-types [inferred_type->%s,key->%s] [items->%s]
+"""
+
+# formats
+_FORMAT_MAP = {"f": "fixed", "fixed": "fixed", "t": "table", "table": "table"}
+
+# axes map
+_AXES_MAP = {DataFrame: [0]}
+
+# register our configuration options
+dropna_doc: Final = """
+: boolean
+    drop ALL nan rows when appending to a table
+"""
+format_doc: Final = """
+: format
+    default format writing format, if None, then
+    put will default to 'fixed' and append will default to 'table'
+"""
+
+with config.config_prefix("io.hdf"):
+    config.register_option("dropna_table", False, dropna_doc, validator=config.is_bool)
+    config.register_option(
+        "default_format",
+        None,
+        format_doc,
+        validator=config.is_one_of_factory(["fixed", "table", None]),
+    )
+
+# oh the troubles to reduce import time
+_table_mod = None
+_table_file_open_policy_is_strict = False
+
+
+def _tables():
+    global _table_mod
+    global _table_file_open_policy_is_strict
+    if _table_mod is None:
+        import tables
+
+        _table_mod = tables
+
+        # set the file open policy
+        # return the file open policy; this changes as of pytables 3.1
+        # depending on the HDF5 version
+        with suppress(AttributeError):
+            _table_file_open_policy_is_strict = (
+                tables.file._FILE_OPEN_POLICY == "strict"
+            )
+
+    return _table_mod
+
+
+# interface to/from ###
+
+
+def to_hdf(
+    path_or_buf: FilePath | HDFStore,
+    key: str,
+    value: DataFrame | Series,
+    mode: str = "a",
+    complevel: int | None = None,
+    complib: str | None = None,
+    append: bool = False,
+    format: str | None = None,
+    index: bool = True,
+    min_itemsize: int | dict[str, int] | None = None,
+    nan_rep=None,
+    dropna: bool | None = None,
+    data_columns: Literal[True] | list[str] | None = None,
+    errors: str = "strict",
+    encoding: str = "UTF-8",
+) -> None:
+    """store this object, close it if we opened it"""
+    if append:
+        f = lambda store: store.append(
+            key,
+            value,
+            format=format,
+            index=index,
+            min_itemsize=min_itemsize,
+            nan_rep=nan_rep,
+            dropna=dropna,
+            data_columns=data_columns,
+            errors=errors,
+            encoding=encoding,
+        )
+    else:
+        # NB: dropna is not passed to `put`
+        f = lambda store: store.put(
+            key,
+            value,
+            format=format,
+            index=index,
+            min_itemsize=min_itemsize,
+            nan_rep=nan_rep,
+            data_columns=data_columns,
+            errors=errors,
+            encoding=encoding,
+            dropna=dropna,
+        )
+
+    path_or_buf = stringify_path(path_or_buf)
+    if isinstance(path_or_buf, str):
+        with HDFStore(
+            path_or_buf, mode=mode, complevel=complevel, complib=complib
+        ) as store:
+            f(store)
+    else:
+        f(path_or_buf)
+
+
+def read_hdf(
+    path_or_buf: FilePath | HDFStore,
+    key=None,
+    mode: str = "r",
+    errors: str = "strict",
+    where: str | list | None = None,
+    start: int | None = None,
+    stop: int | None = None,
+    columns: list[str] | None = None,
+    iterator: bool = False,
+    chunksize: int | None = None,
+    **kwargs,
+):
+    """
+    Read from the store, close it if we opened it.
+
+    Retrieve pandas object stored in file, optionally based on where
+    criteria.
+
+    .. warning::
+
+       Pandas uses PyTables for reading and writing HDF5 files, which allows
+       serializing object-dtype data with pickle when using the "fixed" format.
+       Loading pickled data received from untrusted sources can be unsafe.
+
+       See: https://docs.python.org/3/library/pickle.html for more.
+
+    Parameters
+    ----------
+    path_or_buf : str, path object, pandas.HDFStore
+        Any valid string path is acceptable. Only supports the local file system,
+        remote URLs and file-like objects are not supported.
+
+        If you want to pass in a path object, pandas accepts any
+        ``os.PathLike``.
+
+        Alternatively, pandas accepts an open :class:`pandas.HDFStore` object.
+
+    key : object, optional
+        The group identifier in the store. Can be omitted if the HDF file
+        contains a single pandas object.
+    mode : {'r', 'r+', 'a'}, default 'r'
+        Mode to use when opening the file. Ignored if path_or_buf is a
+        :class:`pandas.HDFStore`. Default is 'r'.
+    errors : str, default 'strict'
+        Specifies how encoding and decoding errors are to be handled.
+        See the errors argument for :func:`open` for a full list
+        of options.
+    where : list, optional
+        A list of Term (or convertible) objects.
+    start : int, optional
+        Row number to start selection.
+    stop  : int, optional
+        Row number to stop selection.
+    columns : list, optional
+        A list of columns names to return.
+    iterator : bool, optional
+        Return an iterator object.
+    chunksize : int, optional
+        Number of rows to include in an iteration when using an iterator.
+    **kwargs
+        Additional keyword arguments passed to HDFStore.
+
+    Returns
+    -------
+    object
+        The selected object. Return type depends on the object stored.
+
+    See Also
+    --------
+    DataFrame.to_hdf : Write a HDF file from a DataFrame.
+    HDFStore : Low-level access to HDF files.
+
+    Notes
+    -----
+    When ``errors="surrogatepass"``, ``pd.options.future.infer_string`` is true,
+    and PyArrow is installed, if a UTF-16 surrogate is encountered when decoding
+    to UTF-8, the resulting dtype will be
+    ``pd.StringDtype(storage="python", na_value=np.nan)``.
+
+    Examples
+    --------
+    >>> df = pd.DataFrame([[1, 1.0, 'a']], columns=['x', 'y', 'z'])  # doctest: +SKIP
+    >>> df.to_hdf('./store.h5', 'data')  # doctest: +SKIP
+    >>> reread = pd.read_hdf('./store.h5')  # doctest: +SKIP
+    """
+    if mode not in ["r", "r+", "a"]:
+        raise ValueError(
+            f"mode {mode} is not allowed while performing a read. "
+            f"Allowed modes are r, r+ and a."
+        )
+    # grab the scope
+    if where is not None:
+        where = _ensure_term(where, scope_level=1)
+
+    if isinstance(path_or_buf, HDFStore):
+        if not path_or_buf.is_open:
+            raise OSError("The HDFStore must be open for reading.")
+
+        store = path_or_buf
+        auto_close = False
+    else:
+        path_or_buf = stringify_path(path_or_buf)
+        if not isinstance(path_or_buf, str):
+            raise NotImplementedError(
+                "Support for generic buffers has not been implemented."
+            )
+        try:
+            exists = os.path.exists(path_or_buf)
+
+        # if filepath is too long
+        except (TypeError, ValueError):
+            exists = False
+
+        if not exists:
+            raise FileNotFoundError(f"File {path_or_buf} does not exist")
+
+        store = HDFStore(path_or_buf, mode=mode, errors=errors, **kwargs)
+        # can't auto open/close if we are using an iterator
+        # so delegate to the iterator
+        auto_close = True
+
+    try:
+        if key is None:
+            groups = store.groups()
+            if len(groups) == 0:
+                raise ValueError(
+                    "Dataset(s) incompatible with Pandas data types, "
+                    "not table, or no datasets found in HDF5 file."
+                )
+            candidate_only_group = groups[0]
+
+            # For the HDF file to have only one dataset, all other groups
+            # should then be metadata groups for that candidate group. (This
+            # assumes that the groups() method enumerates parent groups
+            # before their children.)
+            for group_to_check in groups[1:]:
+                if not _is_metadata_of(group_to_check, candidate_only_group):
+                    raise ValueError(
+                        "key must be provided when HDF5 "
+                        "file contains multiple datasets."
+                    )
+            key = candidate_only_group._v_pathname
+        return store.select(
+            key,
+            where=where,
+            start=start,
+            stop=stop,
+            columns=columns,
+            iterator=iterator,
+            chunksize=chunksize,
+            auto_close=auto_close,
+        )
+    except (ValueError, TypeError, LookupError):
+        if not isinstance(path_or_buf, HDFStore):
+            # if there is an error, close the store if we opened it.
+            with suppress(AttributeError):
+                store.close()
+
+        raise
+
+
+def _is_metadata_of(group: Node, parent_group: Node) -> bool:
+    """Check if a given group is a metadata group for a given parent_group."""
+    if group._v_depth <= parent_group._v_depth:
+        return False
+
+    current = group
+    while current._v_depth > 1:
+        parent = current._v_parent
+        if parent == parent_group and current._v_name == "meta":
+            return True
+        current = current._v_parent
+    return False
+
+
+class HDFStore:
+    """
+    Dict-like IO interface for storing pandas objects in PyTables.
+
+    Either Fixed or Table format.
+
+    .. warning::
+
+       Pandas uses PyTables for reading and writing HDF5 files, which allows
+       serializing object-dtype data with pickle when using the "fixed" format.
+       Loading pickled data received from untrusted sources can be unsafe.
+
+       See: https://docs.python.org/3/library/pickle.html for more.
+
+    Parameters
+    ----------
+    path : str
+        File path to HDF5 file.
+    mode : {'a', 'w', 'r', 'r+'}, default 'a'
+
+        ``'r'``
+            Read-only; no data can be modified.
+        ``'w'``
+            Write; a new file is created (an existing file with the same
+            name would be deleted).
+        ``'a'``
+            Append; an existing file is opened for reading and writing,
+            and if the file does not exist it is created.
+        ``'r+'``
+            It is similar to ``'a'``, but the file must already exist.
+    complevel : int, 0-9, default None
+        Specifies a compression level for data.
+        A value of 0 or None disables compression.
+    complib : {'zlib', 'lzo', 'bzip2', 'blosc'}, default 'zlib'
+        Specifies the compression library to be used.
+        These additional compressors for Blosc are supported
+        (default if no compressor specified: 'blosc:blosclz'):
+        {'blosc:blosclz', 'blosc:lz4', 'blosc:lz4hc', 'blosc:snappy',
+         'blosc:zlib', 'blosc:zstd'}.
+        Specifying a compression library which is not available issues
+        a ValueError.
+    fletcher32 : bool, default False
+        If applying compression use the fletcher32 checksum.
+    **kwargs
+        These parameters will be passed to the PyTables open_file method.
+
+    Examples
+    --------
+    >>> bar = pd.DataFrame(np.random.randn(10, 4))
+    >>> store = pd.HDFStore('test.h5')
+    >>> store['foo'] = bar   # write to HDF5
+    >>> bar = store['foo']   # retrieve
+    >>> store.close()
+
+    **Create or load HDF5 file in-memory**
+
+    When passing the `driver` option to the PyTables open_file method through
+    **kwargs, the HDF5 file is loaded or created in-memory and will only be
+    written when closed:
+
+    >>> bar = pd.DataFrame(np.random.randn(10, 4))
+    >>> store = pd.HDFStore('test.h5', driver='H5FD_CORE')
+    >>> store['foo'] = bar
+    >>> store.close()   # only now, data is written to disk
+    """
+
+    _handle: File | None
+    _mode: str
+
+    def __init__(
+        self,
+        path,
+        mode: str = "a",
+        complevel: int | None = None,
+        complib=None,
+        fletcher32: bool = False,
+        **kwargs,
+    ) -> None:
+        if "format" in kwargs:
+            raise ValueError("format is not a defined argument for HDFStore")
+
+        tables = import_optional_dependency("tables")
+
+        if complib is not None and complib not in tables.filters.all_complibs:
+            raise ValueError(
+                f"complib only supports {tables.filters.all_complibs} compression."
+            )
+
+        if complib is None and complevel is not None:
+            complib = tables.filters.default_complib
+
+        self._path = stringify_path(path)
+        if mode is None:
+            mode = "a"
+        self._mode = mode
+        self._handle = None
+        self._complevel = complevel if complevel else 0
+        self._complib = complib
+        self._fletcher32 = fletcher32
+        self._filters = None
+        self.open(mode=mode, **kwargs)
+
+    def __fspath__(self) -> str:
+        return self._path
+
+    @property
+    def root(self):
+        """return the root node"""
+        self._check_if_open()
+        assert self._handle is not None  # for mypy
+        return self._handle.root
+
+    @property
+    def filename(self) -> str:
+        return self._path
+
+    def __getitem__(self, key: str):
+        return self.get(key)
+
+    def __setitem__(self, key: str, value) -> None:
+        self.put(key, value)
+
+    def __delitem__(self, key: str) -> None:
+        return self.remove(key)
+
+    def __getattr__(self, name: str):
+        """allow attribute access to get stores"""
+        try:
+            return self.get(name)
+        except (KeyError, ClosedFileError):
+            pass
+        raise AttributeError(
+            f"'{type(self).__name__}' object has no attribute '{name}'"
+        )
+
+    def __contains__(self, key: str) -> bool:
+        """
+        check for existence of this key
+        can match the exact pathname or the pathnm w/o the leading '/'
+        """
+        node = self.get_node(key)
+        if node is not None:
+            name = node._v_pathname
+            if key in (name, name[1:]):
+                return True
+        return False
+
+    def __len__(self) -> int:
+        return len(self.groups())
+
+    def __repr__(self) -> str:
+        pstr = pprint_thing(self._path)
+        return f"{type(self)}\nFile path: {pstr}\n"
+
+    def __enter__(self) -> Self:
+        return self
+
+    def __exit__(
+        self,
+        exc_type: type[BaseException] | None,
+        exc_value: BaseException | None,
+        traceback: TracebackType | None,
+    ) -> None:
+        self.close()
+
+    def keys(self, include: str = "pandas") -> list[str]:
+        """
+        Return a list of keys corresponding to objects stored in HDFStore.
+
+        Parameters
+        ----------
+
+        include : str, default 'pandas'
+                When kind equals 'pandas' return pandas objects.
+                When kind equals 'native' return native HDF5 Table objects.
+
+        Returns
+        -------
+        list
+            List of ABSOLUTE path-names (e.g. have the leading '/').
+
+        Raises
+        ------
+        raises ValueError if kind has an illegal value
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'])
+        >>> store = pd.HDFStore("store.h5", 'w')  # doctest: +SKIP
+        >>> store.put('data', df)  # doctest: +SKIP
+        >>> store.get('data')  # doctest: +SKIP
+        >>> print(store.keys())  # doctest: +SKIP
+        ['/data1', '/data2']
+        >>> store.close()  # doctest: +SKIP
+        """
+        if include == "pandas":
+            return [n._v_pathname for n in self.groups()]
+
+        elif include == "native":
+            assert self._handle is not None  # mypy
+            return [
+                n._v_pathname for n in self._handle.walk_nodes("/", classname="Table")
+            ]
+        raise ValueError(
+            f"`include` should be either 'pandas' or 'native' but is '{include}'"
+        )
+
+    def __iter__(self) -> Iterator[str]:
+        return iter(self.keys())
+
+    def items(self) -> Iterator[tuple[str, list]]:
+        """
+        iterate on key->group
+        """
+        for g in self.groups():
+            yield g._v_pathname, g
+
+    def open(self, mode: str = "a", **kwargs) -> None:
+        """
+        Open the file in the specified mode
+
+        Parameters
+        ----------
+        mode : {'a', 'w', 'r', 'r+'}, default 'a'
+            See HDFStore docstring or tables.open_file for info about modes
+        **kwargs
+            These parameters will be passed to the PyTables open_file method.
+        """
+        tables = _tables()
+
+        if self._mode != mode:
+            # if we are changing a write mode to read, ok
+            if self._mode in ["a", "w"] and mode in ["r", "r+"]:
+                pass
+            elif mode in ["w"]:
+                # this would truncate, raise here
+                if self.is_open:
+                    raise PossibleDataLossError(
+                        f"Re-opening the file [{self._path}] with mode [{self._mode}] "
+                        "will delete the current file!"
+                    )
+
+            self._mode = mode
+
+        # close and reopen the handle
+        if self.is_open:
+            self.close()
+
+        if self._complevel and self._complevel > 0:
+            self._filters = _tables().Filters(
+                self._complevel, self._complib, fletcher32=self._fletcher32
+            )
+
+        if _table_file_open_policy_is_strict and self.is_open:
+            msg = (
+                "Cannot open HDF5 file, which is already opened, "
+                "even in read-only mode."
+            )
+            raise ValueError(msg)
+
+        self._handle = tables.open_file(self._path, self._mode, **kwargs)
+
+    def close(self) -> None:
+        """
+        Close the PyTables file handle
+        """
+        if self._handle is not None:
+            self._handle.close()
+        self._handle = None
+
+    @property
+    def is_open(self) -> bool:
+        """
+        return a boolean indicating whether the file is open
+        """
+        if self._handle is None:
+            return False
+        return bool(self._handle.isopen)
+
+    def flush(self, fsync: bool = False) -> None:
+        """
+        Force all buffered modifications to be written to disk.
+
+        Parameters
+        ----------
+        fsync : bool (default False)
+          call ``os.fsync()`` on the file handle to force writing to disk.
+
+        Notes
+        -----
+        Without ``fsync=True``, flushing may not guarantee that the OS writes
+        to disk. With fsync, the operation will block until the OS claims the
+        file has been written; however, other caching layers may still
+        interfere.
+        """
+        if self._handle is not None:
+            self._handle.flush()
+            if fsync:
+                with suppress(OSError):
+                    os.fsync(self._handle.fileno())
+
+    def get(self, key: str):
+        """
+        Retrieve pandas object stored in file.
+
+        Parameters
+        ----------
+        key : str
+
+        Returns
+        -------
+        object
+            Same type as object stored in file.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'])
+        >>> store = pd.HDFStore("store.h5", 'w')  # doctest: +SKIP
+        >>> store.put('data', df)  # doctest: +SKIP
+        >>> store.get('data')  # doctest: +SKIP
+        >>> store.close()  # doctest: +SKIP
+        """
+        with patch_pickle():
+            # GH#31167 Without this patch, pickle doesn't know how to unpickle
+            #  old DateOffset objects now that they are cdef classes.
+            group = self.get_node(key)
+            if group is None:
+                raise KeyError(f"No object named {key} in the file")
+            return self._read_group(group)
+
+    def select(
+        self,
+        key: str,
+        where=None,
+        start=None,
+        stop=None,
+        columns=None,
+        iterator: bool = False,
+        chunksize: int | None = None,
+        auto_close: bool = False,
+    ):
+        """
+        Retrieve pandas object stored in file, optionally based on where criteria.
+
+        .. warning::
+
+           Pandas uses PyTables for reading and writing HDF5 files, which allows
+           serializing object-dtype data with pickle when using the "fixed" format.
+           Loading pickled data received from untrusted sources can be unsafe.
+
+           See: https://docs.python.org/3/library/pickle.html for more.
+
+        Parameters
+        ----------
+        key : str
+            Object being retrieved from file.
+        where : list or None
+            List of Term (or convertible) objects, optional.
+        start : int or None
+            Row number to start selection.
+        stop : int, default None
+            Row number to stop selection.
+        columns : list or None
+            A list of columns that if not None, will limit the return columns.
+        iterator : bool or False
+            Returns an iterator.
+        chunksize : int or None
+            Number or rows to include in iteration, return an iterator.
+        auto_close : bool or False
+            Should automatically close the store when finished.
+
+        Returns
+        -------
+        object
+            Retrieved object from file.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'])
+        >>> store = pd.HDFStore("store.h5", 'w')  # doctest: +SKIP
+        >>> store.put('data', df)  # doctest: +SKIP
+        >>> store.get('data')  # doctest: +SKIP
+        >>> print(store.keys())  # doctest: +SKIP
+        ['/data1', '/data2']
+        >>> store.select('/data1')  # doctest: +SKIP
+           A  B
+        0  1  2
+        1  3  4
+        >>> store.select('/data1', where='columns == A')  # doctest: +SKIP
+           A
+        0  1
+        1  3
+        >>> store.close()  # doctest: +SKIP
+        """
+        group = self.get_node(key)
+        if group is None:
+            raise KeyError(f"No object named {key} in the file")
+
+        # create the storer and axes
+        where = _ensure_term(where, scope_level=1)
+        s = self._create_storer(group)
+        s.infer_axes()
+
+        # function to call on iteration
+        def func(_start, _stop, _where):
+            return s.read(start=_start, stop=_stop, where=_where, columns=columns)
+
+        # create the iterator
+        it = TableIterator(
+            self,
+            s,
+            func,
+            where=where,
+            nrows=s.nrows,
+            start=start,
+            stop=stop,
+            iterator=iterator,
+            chunksize=chunksize,
+            auto_close=auto_close,
+        )
+
+        return it.get_result()
+
+    def select_as_coordinates(
+        self,
+        key: str,
+        where=None,
+        start: int | None = None,
+        stop: int | None = None,
+    ):
+        """
+        return the selection as an Index
+
+        .. warning::
+
+           Pandas uses PyTables for reading and writing HDF5 files, which allows
+           serializing object-dtype data with pickle when using the "fixed" format.
+           Loading pickled data received from untrusted sources can be unsafe.
+
+           See: https://docs.python.org/3/library/pickle.html for more.
+
+
+        Parameters
+        ----------
+        key : str
+        where : list of Term (or convertible) objects, optional
+        start : integer (defaults to None), row number to start selection
+        stop  : integer (defaults to None), row number to stop selection
+        """
+        where = _ensure_term(where, scope_level=1)
+        tbl = self.get_storer(key)
+        if not isinstance(tbl, Table):
+            raise TypeError("can only read_coordinates with a table")
+        return tbl.read_coordinates(where=where, start=start, stop=stop)
+
+    def select_column(
+        self,
+        key: str,
+        column: str,
+        start: int | None = None,
+        stop: int | None = None,
+    ):
+        """
+        return a single column from the table. This is generally only useful to
+        select an indexable
+
+        .. warning::
+
+           Pandas uses PyTables for reading and writing HDF5 files, which allows
+           serializing object-dtype data with pickle when using the "fixed" format.
+           Loading pickled data received from untrusted sources can be unsafe.
+
+           See: https://docs.python.org/3/library/pickle.html for more.
+
+        Parameters
+        ----------
+        key : str
+        column : str
+            The column of interest.
+        start : int or None, default None
+        stop : int or None, default None
+
+        Raises
+        ------
+        raises KeyError if the column is not found (or key is not a valid
+            store)
+        raises ValueError if the column can not be extracted individually (it
+            is part of a data block)
+
+        """
+        tbl = self.get_storer(key)
+        if not isinstance(tbl, Table):
+            raise TypeError("can only read_column with a table")
+        return tbl.read_column(column=column, start=start, stop=stop)
+
+    def select_as_multiple(
+        self,
+        keys,
+        where=None,
+        selector=None,
+        columns=None,
+        start=None,
+        stop=None,
+        iterator: bool = False,
+        chunksize: int | None = None,
+        auto_close: bool = False,
+    ):
+        """
+        Retrieve pandas objects from multiple tables.
+
+        .. warning::
+
+           Pandas uses PyTables for reading and writing HDF5 files, which allows
+           serializing object-dtype data with pickle when using the "fixed" format.
+           Loading pickled data received from untrusted sources can be unsafe.
+
+           See: https://docs.python.org/3/library/pickle.html for more.
+
+        Parameters
+        ----------
+        keys : a list of the tables
+        selector : the table to apply the where criteria (defaults to keys[0]
+            if not supplied)
+        columns : the columns I want back
+        start : integer (defaults to None), row number to start selection
+        stop  : integer (defaults to None), row number to stop selection
+        iterator : bool, return an iterator, default False
+        chunksize : nrows to include in iteration, return an iterator
+        auto_close : bool, default False
+            Should automatically close the store when finished.
+
+        Raises
+        ------
+        raises KeyError if keys or selector is not found or keys is empty
+        raises TypeError if keys is not a list or tuple
+        raises ValueError if the tables are not ALL THE SAME DIMENSIONS
+        """
+        # default to single select
+        where = _ensure_term(where, scope_level=1)
+        if isinstance(keys, (list, tuple)) and len(keys) == 1:
+            keys = keys[0]
+        if isinstance(keys, str):
+            return self.select(
+                key=keys,
+                where=where,
+                columns=columns,
+                start=start,
+                stop=stop,
+                iterator=iterator,
+                chunksize=chunksize,
+                auto_close=auto_close,
+            )
+
+        if not isinstance(keys, (list, tuple)):
+            raise TypeError("keys must be a list/tuple")
+
+        if not len(keys):
+            raise ValueError("keys must have a non-zero length")
+
+        if selector is None:
+            selector = keys[0]
+
+        # collect the tables
+        tbls = [self.get_storer(k) for k in keys]
+        s = self.get_storer(selector)
+
+        # validate rows
+        nrows = None
+        for t, k in itertools.chain([(s, selector)], zip(tbls, keys)):
+            if t is None:
+                raise KeyError(f"Invalid table [{k}]")
+            if not t.is_table:
+                raise TypeError(
+                    f"object [{t.pathname}] is not a table, and cannot be used in all "
+                    "select as multiple"
+                )
+
+            if nrows is None:
+                nrows = t.nrows
+            elif t.nrows != nrows:
+                raise ValueError("all tables must have exactly the same nrows!")
+
+        # The isinstance checks here are redundant with the check above,
+        #  but necessary for mypy; see GH#29757
+        _tbls = [x for x in tbls if isinstance(x, Table)]
+
+        # axis is the concentration axes
+        axis = {t.non_index_axes[0][0] for t in _tbls}.pop()
+
+        def func(_start, _stop, _where):
+            # retrieve the objs, _where is always passed as a set of
+            # coordinates here
+            objs = [
+                t.read(where=_where, columns=columns, start=_start, stop=_stop)
+                for t in tbls
+            ]
+
+            # concat and return
+            return concat(objs, axis=axis, verify_integrity=False)._consolidate()
+
+        # create the iterator
+        it = TableIterator(
+            self,
+            s,
+            func,
+            where=where,
+            nrows=nrows,
+            start=start,
+            stop=stop,
+            iterator=iterator,
+            chunksize=chunksize,
+            auto_close=auto_close,
+        )
+
+        return it.get_result(coordinates=True)
+
+    def put(
+        self,
+        key: str,
+        value: DataFrame | Series,
+        format=None,
+        index: bool = True,
+        append: bool = False,
+        complib=None,
+        complevel: int | None = None,
+        min_itemsize: int | dict[str, int] | None = None,
+        nan_rep=None,
+        data_columns: Literal[True] | list[str] | None = None,
+        encoding=None,
+        errors: str = "strict",
+        track_times: bool = True,
+        dropna: bool = False,
+    ) -> None:
+        """
+        Store object in HDFStore.
+
+        Parameters
+        ----------
+        key : str
+        value : {Series, DataFrame}
+        format : 'fixed(f)|table(t)', default is 'fixed'
+            Format to use when storing object in HDFStore. Value can be one of:
+
+            ``'fixed'``
+                Fixed format.  Fast writing/reading. Not-appendable, nor searchable.
+            ``'table'``
+                Table format.  Write as a PyTables Table structure which may perform
+                worse but allow more flexible operations like searching / selecting
+                subsets of the data.
+        index : bool, default True
+            Write DataFrame index as a column.
+        append : bool, default False
+            This will force Table format, append the input data to the existing.
+        data_columns : list of columns or True, default None
+            List of columns to create as data columns, or True to use all columns.
+            See `here
+            `__.
+        encoding : str, default None
+            Provide an encoding for strings.
+        track_times : bool, default True
+            Parameter is propagated to 'create_table' method of 'PyTables'.
+            If set to False it enables to have the same h5 files (same hashes)
+            independent on creation time.
+        dropna : bool, default False, optional
+            Remove missing values.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'])
+        >>> store = pd.HDFStore("store.h5", 'w')  # doctest: +SKIP
+        >>> store.put('data', df)  # doctest: +SKIP
+        """
+        if format is None:
+            format = get_option("io.hdf.default_format") or "fixed"
+        format = self._validate_format(format)
+        self._write_to_group(
+            key,
+            value,
+            format=format,
+            index=index,
+            append=append,
+            complib=complib,
+            complevel=complevel,
+            min_itemsize=min_itemsize,
+            nan_rep=nan_rep,
+            data_columns=data_columns,
+            encoding=encoding,
+            errors=errors,
+            track_times=track_times,
+            dropna=dropna,
+        )
+
+    def remove(self, key: str, where=None, start=None, stop=None) -> None:
+        """
+        Remove pandas object partially by specifying the where condition
+
+        Parameters
+        ----------
+        key : str
+            Node to remove or delete rows from
+        where : list of Term (or convertible) objects, optional
+        start : integer (defaults to None), row number to start selection
+        stop  : integer (defaults to None), row number to stop selection
+
+        Returns
+        -------
+        number of rows removed (or None if not a Table)
+
+        Raises
+        ------
+        raises KeyError if key is not a valid store
+
+        """
+        where = _ensure_term(where, scope_level=1)
+        try:
+            s = self.get_storer(key)
+        except KeyError:
+            # the key is not a valid store, re-raising KeyError
+            raise
+        except AssertionError:
+            # surface any assertion errors for e.g. debugging
+            raise
+        except Exception as err:
+            # In tests we get here with ClosedFileError, TypeError, and
+            #  _table_mod.NoSuchNodeError.  TODO: Catch only these?
+
+            if where is not None:
+                raise ValueError(
+                    "trying to remove a node with a non-None where clause!"
+                ) from err
+
+            # we are actually trying to remove a node (with children)
+            node = self.get_node(key)
+            if node is not None:
+                node._f_remove(recursive=True)
+                return None
+
+        # remove the node
+        if com.all_none(where, start, stop):
+            s.group._f_remove(recursive=True)
+
+        # delete from the table
+        else:
+            if not s.is_table:
+                raise ValueError(
+                    "can only remove with where on objects written as tables"
+                )
+            return s.delete(where=where, start=start, stop=stop)
+
+    def append(
+        self,
+        key: str,
+        value: DataFrame | Series,
+        format=None,
+        axes=None,
+        index: bool | list[str] = True,
+        append: bool = True,
+        complib=None,
+        complevel: int | None = None,
+        columns=None,
+        min_itemsize: int | dict[str, int] | None = None,
+        nan_rep=None,
+        chunksize: int | None = None,
+        expectedrows=None,
+        dropna: bool | None = None,
+        data_columns: Literal[True] | list[str] | None = None,
+        encoding=None,
+        errors: str = "strict",
+    ) -> None:
+        """
+        Append to Table in file.
+
+        Node must already exist and be Table format.
+
+        Parameters
+        ----------
+        key : str
+        value : {Series, DataFrame}
+        format : 'table' is the default
+            Format to use when storing object in HDFStore.  Value can be one of:
+
+            ``'table'``
+                Table format. Write as a PyTables Table structure which may perform
+                worse but allow more flexible operations like searching / selecting
+                subsets of the data.
+        index : bool, default True
+            Write DataFrame index as a column.
+        append       : bool, default True
+            Append the input data to the existing.
+        data_columns : list of columns, or True, default None
+            List of columns to create as indexed data columns for on-disk
+            queries, or True to use all columns. By default only the axes
+            of the object are indexed. See `here
+            `__.
+        min_itemsize : dict of columns that specify minimum str sizes
+        nan_rep      : str to use as str nan representation
+        chunksize    : size to chunk the writing
+        expectedrows : expected TOTAL row size of this table
+        encoding     : default None, provide an encoding for str
+        dropna : bool, default False, optional
+            Do not write an ALL nan row to the store settable
+            by the option 'io.hdf.dropna_table'.
+
+        Notes
+        -----
+        Does *not* check if data being appended overlaps with existing
+        data in the table, so be careful
+
+        Examples
+        --------
+        >>> df1 = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'])
+        >>> store = pd.HDFStore("store.h5", 'w')  # doctest: +SKIP
+        >>> store.put('data', df1, format='table')  # doctest: +SKIP
+        >>> df2 = pd.DataFrame([[5, 6], [7, 8]], columns=['A', 'B'])
+        >>> store.append('data', df2)  # doctest: +SKIP
+        >>> store.close()  # doctest: +SKIP
+           A  B
+        0  1  2
+        1  3  4
+        0  5  6
+        1  7  8
+        """
+        if columns is not None:
+            raise TypeError(
+                "columns is not a supported keyword in append, try data_columns"
+            )
+
+        if dropna is None:
+            dropna = get_option("io.hdf.dropna_table")
+        if format is None:
+            format = get_option("io.hdf.default_format") or "table"
+        format = self._validate_format(format)
+        self._write_to_group(
+            key,
+            value,
+            format=format,
+            axes=axes,
+            index=index,
+            append=append,
+            complib=complib,
+            complevel=complevel,
+            min_itemsize=min_itemsize,
+            nan_rep=nan_rep,
+            chunksize=chunksize,
+            expectedrows=expectedrows,
+            dropna=dropna,
+            data_columns=data_columns,
+            encoding=encoding,
+            errors=errors,
+        )
+
+    def append_to_multiple(
+        self,
+        d: dict,
+        value,
+        selector,
+        data_columns=None,
+        axes=None,
+        dropna: bool = False,
+        **kwargs,
+    ) -> None:
+        """
+        Append to multiple tables
+
+        Parameters
+        ----------
+        d : a dict of table_name to table_columns, None is acceptable as the
+            values of one node (this will get all the remaining columns)
+        value : a pandas object
+        selector : a string that designates the indexable table; all of its
+            columns will be designed as data_columns, unless data_columns is
+            passed, in which case these are used
+        data_columns : list of columns to create as data columns, or True to
+            use all columns
+        dropna : if evaluates to True, drop rows from all tables if any single
+                 row in each table has all NaN. Default False.
+
+        Notes
+        -----
+        axes parameter is currently not accepted
+
+        """
+        if axes is not None:
+            raise TypeError(
+                "axes is currently not accepted as a parameter to append_to_multiple; "
+                "you can create the tables independently instead"
+            )
+
+        if not isinstance(d, dict):
+            raise ValueError(
+                "append_to_multiple must have a dictionary specified as the "
+                "way to split the value"
+            )
+
+        if selector not in d:
+            raise ValueError(
+                "append_to_multiple requires a selector that is in passed dict"
+            )
+
+        # figure out the splitting axis (the non_index_axis)
+        axis = next(iter(set(range(value.ndim)) - set(_AXES_MAP[type(value)])))
+
+        # figure out how to split the value
+        remain_key = None
+        remain_values: list = []
+        for k, v in d.items():
+            if v is None:
+                if remain_key is not None:
+                    raise ValueError(
+                        "append_to_multiple can only have one value in d that is None"
+                    )
+                remain_key = k
+            else:
+                remain_values.extend(v)
+        if remain_key is not None:
+            ordered = value.axes[axis]
+            ordd = ordered.difference(Index(remain_values))
+            ordd = sorted(ordered.get_indexer(ordd))
+            d[remain_key] = ordered.take(ordd)
+
+        # data_columns
+        if data_columns is None:
+            data_columns = d[selector]
+
+        # ensure rows are synchronized across the tables
+        if dropna:
+            idxs = (value[cols].dropna(how="all").index for cols in d.values())
+            valid_index = next(idxs)
+            for index in idxs:
+                valid_index = valid_index.intersection(index)
+            value = value.loc[valid_index]
+
+        min_itemsize = kwargs.pop("min_itemsize", None)
+
+        # append
+        for k, v in d.items():
+            dc = data_columns if k == selector else None
+
+            # compute the val
+            val = value.reindex(v, axis=axis)
+
+            filtered = (
+                {key: value for (key, value) in min_itemsize.items() if key in v}
+                if min_itemsize is not None
+                else None
+            )
+            self.append(k, val, data_columns=dc, min_itemsize=filtered, **kwargs)
+
+    def create_table_index(
+        self,
+        key: str,
+        columns=None,
+        optlevel: int | None = None,
+        kind: str | None = None,
+    ) -> None:
+        """
+        Create a pytables index on the table.
+
+        Parameters
+        ----------
+        key : str
+        columns : None, bool, or listlike[str]
+            Indicate which columns to create an index on.
+
+            * False : Do not create any indexes.
+            * True : Create indexes on all columns.
+            * None : Create indexes on all columns.
+            * listlike : Create indexes on the given columns.
+
+        optlevel : int or None, default None
+            Optimization level, if None, pytables defaults to 6.
+        kind : str or None, default None
+            Kind of index, if None, pytables defaults to "medium".
+
+        Raises
+        ------
+        TypeError: raises if the node is not a table
+        """
+        # version requirements
+        _tables()
+        s = self.get_storer(key)
+        if s is None:
+            return
+
+        if not isinstance(s, Table):
+            raise TypeError("cannot create table index on a Fixed format store")
+        s.create_index(columns=columns, optlevel=optlevel, kind=kind)
+
+    def groups(self) -> list:
+        """
+        Return a list of all the top-level nodes.
+
+        Each node returned is not a pandas storage object.
+
+        Returns
+        -------
+        list
+            List of objects.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'])
+        >>> store = pd.HDFStore("store.h5", 'w')  # doctest: +SKIP
+        >>> store.put('data', df)  # doctest: +SKIP
+        >>> print(store.groups())  # doctest: +SKIP
+        >>> store.close()  # doctest: +SKIP
+        [/data (Group) ''
+          children := ['axis0' (Array), 'axis1' (Array), 'block0_values' (Array),
+          'block0_items' (Array)]]
+        """
+        _tables()
+        self._check_if_open()
+        assert self._handle is not None  # for mypy
+        assert _table_mod is not None  # for mypy
+        return [
+            g
+            for g in self._handle.walk_groups()
+            if (
+                not isinstance(g, _table_mod.link.Link)
+                and (
+                    getattr(g._v_attrs, "pandas_type", None)
+                    or getattr(g, "table", None)
+                    or (isinstance(g, _table_mod.table.Table) and g._v_name != "table")
+                )
+            )
+        ]
+
+    def walk(self, where: str = "/") -> Iterator[tuple[str, list[str], list[str]]]:
+        """
+        Walk the pytables group hierarchy for pandas objects.
+
+        This generator will yield the group path, subgroups and pandas object
+        names for each group.
+
+        Any non-pandas PyTables objects that are not a group will be ignored.
+
+        The `where` group itself is listed first (preorder), then each of its
+        child groups (following an alphanumerical order) is also traversed,
+        following the same procedure.
+
+        Parameters
+        ----------
+        where : str, default "/"
+            Group where to start walking.
+
+        Yields
+        ------
+        path : str
+            Full path to a group (without trailing '/').
+        groups : list
+            Names (strings) of the groups contained in `path`.
+        leaves : list
+            Names (strings) of the pandas objects contained in `path`.
+
+        Examples
+        --------
+        >>> df1 = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'])
+        >>> store = pd.HDFStore("store.h5", 'w')  # doctest: +SKIP
+        >>> store.put('data', df1, format='table')  # doctest: +SKIP
+        >>> df2 = pd.DataFrame([[5, 6], [7, 8]], columns=['A', 'B'])
+        >>> store.append('data', df2)  # doctest: +SKIP
+        >>> store.close()  # doctest: +SKIP
+        >>> for group in store.walk():  # doctest: +SKIP
+        ...     print(group)  # doctest: +SKIP
+        >>> store.close()  # doctest: +SKIP
+        """
+        _tables()
+        self._check_if_open()
+        assert self._handle is not None  # for mypy
+        assert _table_mod is not None  # for mypy
+
+        for g in self._handle.walk_groups(where):
+            if getattr(g._v_attrs, "pandas_type", None) is not None:
+                continue
+
+            groups = []
+            leaves = []
+            for child in g._v_children.values():
+                pandas_type = getattr(child._v_attrs, "pandas_type", None)
+                if pandas_type is None:
+                    if isinstance(child, _table_mod.group.Group):
+                        groups.append(child._v_name)
+                else:
+                    leaves.append(child._v_name)
+
+            yield (g._v_pathname.rstrip("/"), groups, leaves)
+
+    def get_node(self, key: str) -> Node | None:
+        """return the node with the key or None if it does not exist"""
+        self._check_if_open()
+        if not key.startswith("/"):
+            key = "/" + key
+
+        assert self._handle is not None
+        assert _table_mod is not None  # for mypy
+        try:
+            node = self._handle.get_node(self.root, key)
+        except _table_mod.exceptions.NoSuchNodeError:
+            return None
+
+        assert isinstance(node, _table_mod.Node), type(node)
+        return node
+
+    def get_storer(self, key: str) -> GenericFixed | Table:
+        """return the storer object for a key, raise if not in the file"""
+        group = self.get_node(key)
+        if group is None:
+            raise KeyError(f"No object named {key} in the file")
+
+        s = self._create_storer(group)
+        s.infer_axes()
+        return s
+
+    def copy(
+        self,
+        file,
+        mode: str = "w",
+        propindexes: bool = True,
+        keys=None,
+        complib=None,
+        complevel: int | None = None,
+        fletcher32: bool = False,
+        overwrite: bool = True,
+    ) -> HDFStore:
+        """
+        Copy the existing store to a new file, updating in place.
+
+        Parameters
+        ----------
+        propindexes : bool, default True
+            Restore indexes in copied file.
+        keys : list, optional
+            List of keys to include in the copy (defaults to all).
+        overwrite : bool, default True
+            Whether to overwrite (remove and replace) existing nodes in the new store.
+        mode, complib, complevel, fletcher32 same as in HDFStore.__init__
+
+        Returns
+        -------
+        open file handle of the new store
+        """
+        new_store = HDFStore(
+            file, mode=mode, complib=complib, complevel=complevel, fletcher32=fletcher32
+        )
+        if keys is None:
+            keys = list(self.keys())
+        if not isinstance(keys, (tuple, list)):
+            keys = [keys]
+        for k in keys:
+            s = self.get_storer(k)
+            if s is not None:
+                if k in new_store:
+                    if overwrite:
+                        new_store.remove(k)
+
+                data = self.select(k)
+                if isinstance(s, Table):
+                    index: bool | list[str] = False
+                    if propindexes:
+                        index = [a.name for a in s.axes if a.is_indexed]
+                    new_store.append(
+                        k,
+                        data,
+                        index=index,
+                        data_columns=getattr(s, "data_columns", None),
+                        encoding=s.encoding,
+                    )
+                else:
+                    new_store.put(k, data, encoding=s.encoding)
+
+        return new_store
+
+    def info(self) -> str:
+        """
+        Print detailed information on the store.
+
+        Returns
+        -------
+        str
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'])
+        >>> store = pd.HDFStore("store.h5", 'w')  # doctest: +SKIP
+        >>> store.put('data', df)  # doctest: +SKIP
+        >>> print(store.info())  # doctest: +SKIP
+        >>> store.close()  # doctest: +SKIP
+        
+        File path: store.h5
+        /data    frame    (shape->[2,2])
+        """
+        path = pprint_thing(self._path)
+        output = f"{type(self)}\nFile path: {path}\n"
+
+        if self.is_open:
+            lkeys = sorted(self.keys())
+            if len(lkeys):
+                keys = []
+                values = []
+
+                for k in lkeys:
+                    try:
+                        s = self.get_storer(k)
+                        if s is not None:
+                            keys.append(pprint_thing(s.pathname or k))
+                            values.append(pprint_thing(s or "invalid_HDFStore node"))
+                    except AssertionError:
+                        # surface any assertion errors for e.g. debugging
+                        raise
+                    except Exception as detail:
+                        keys.append(k)
+                        dstr = pprint_thing(detail)
+                        values.append(f"[invalid_HDFStore node: {dstr}]")
+
+                output += adjoin(12, keys, values)
+            else:
+                output += "Empty"
+        else:
+            output += "File is CLOSED"
+
+        return output
+
+    # ------------------------------------------------------------------------
+    # private methods
+
+    def _check_if_open(self) -> None:
+        if not self.is_open:
+            raise ClosedFileError(f"{self._path} file is not open!")
+
+    def _validate_format(self, format: str) -> str:
+        """validate / deprecate formats"""
+        # validate
+        try:
+            format = _FORMAT_MAP[format.lower()]
+        except KeyError as err:
+            raise TypeError(f"invalid HDFStore format specified [{format}]") from err
+
+        return format
+
+    def _create_storer(
+        self,
+        group,
+        format=None,
+        value: DataFrame | Series | None = None,
+        encoding: str = "UTF-8",
+        errors: str = "strict",
+    ) -> GenericFixed | Table:
+        """return a suitable class to operate"""
+        cls: type[GenericFixed | Table]
+
+        if value is not None and not isinstance(value, (Series, DataFrame)):
+            raise TypeError("value must be None, Series, or DataFrame")
+
+        pt = _ensure_decoded(getattr(group._v_attrs, "pandas_type", None))
+        tt = _ensure_decoded(getattr(group._v_attrs, "table_type", None))
+
+        # infer the pt from the passed value
+        if pt is None:
+            if value is None:
+                _tables()
+                assert _table_mod is not None  # for mypy
+                if getattr(group, "table", None) or isinstance(
+                    group, _table_mod.table.Table
+                ):
+                    pt = "frame_table"
+                    tt = "generic_table"
+                else:
+                    raise TypeError(
+                        "cannot create a storer if the object is not existing "
+                        "nor a value are passed"
+                    )
+            else:
+                if isinstance(value, Series):
+                    pt = "series"
+                else:
+                    pt = "frame"
+
+                # we are actually a table
+                if format == "table":
+                    pt += "_table"
+
+        # a storer node
+        if "table" not in pt:
+            _STORER_MAP = {"series": SeriesFixed, "frame": FrameFixed}
+            try:
+                cls = _STORER_MAP[pt]
+            except KeyError as err:
+                raise TypeError(
+                    f"cannot properly create the storer for: [_STORER_MAP] [group->"
+                    f"{group},value->{type(value)},format->{format}"
+                ) from err
+            return cls(self, group, encoding=encoding, errors=errors)
+
+        # existing node (and must be a table)
+        if tt is None:
+            # if we are a writer, determine the tt
+            if value is not None:
+                if pt == "series_table":
+                    index = getattr(value, "index", None)
+                    if index is not None:
+                        if index.nlevels == 1:
+                            tt = "appendable_series"
+                        elif index.nlevels > 1:
+                            tt = "appendable_multiseries"
+                elif pt == "frame_table":
+                    index = getattr(value, "index", None)
+                    if index is not None:
+                        if index.nlevels == 1:
+                            tt = "appendable_frame"
+                        elif index.nlevels > 1:
+                            tt = "appendable_multiframe"
+
+        _TABLE_MAP = {
+            "generic_table": GenericTable,
+            "appendable_series": AppendableSeriesTable,
+            "appendable_multiseries": AppendableMultiSeriesTable,
+            "appendable_frame": AppendableFrameTable,
+            "appendable_multiframe": AppendableMultiFrameTable,
+            "worm": WORMTable,
+        }
+        try:
+            cls = _TABLE_MAP[tt]
+        except KeyError as err:
+            raise TypeError(
+                f"cannot properly create the storer for: [_TABLE_MAP] [group->"
+                f"{group},value->{type(value)},format->{format}"
+            ) from err
+
+        return cls(self, group, encoding=encoding, errors=errors)
+
+    def _write_to_group(
+        self,
+        key: str,
+        value: DataFrame | Series,
+        format,
+        axes=None,
+        index: bool | list[str] = True,
+        append: bool = False,
+        complib=None,
+        complevel: int | None = None,
+        fletcher32=None,
+        min_itemsize: int | dict[str, int] | None = None,
+        chunksize: int | None = None,
+        expectedrows=None,
+        dropna: bool = False,
+        nan_rep=None,
+        data_columns=None,
+        encoding=None,
+        errors: str = "strict",
+        track_times: bool = True,
+    ) -> None:
+        # we don't want to store a table node at all if our object is 0-len
+        # as there are not dtypes
+        if getattr(value, "empty", None) and (format == "table" or append):
+            return
+
+        group = self._identify_group(key, append)
+
+        s = self._create_storer(group, format, value, encoding=encoding, errors=errors)
+        if append:
+            # raise if we are trying to append to a Fixed format,
+            #       or a table that exists (and we are putting)
+            if not s.is_table or (s.is_table and format == "fixed" and s.is_exists):
+                raise ValueError("Can only append to Tables")
+            if not s.is_exists:
+                s.set_object_info()
+        else:
+            s.set_object_info()
+
+        if not s.is_table and complib:
+            raise ValueError("Compression not supported on Fixed format stores")
+
+        # write the object
+        s.write(
+            obj=value,
+            axes=axes,
+            append=append,
+            complib=complib,
+            complevel=complevel,
+            fletcher32=fletcher32,
+            min_itemsize=min_itemsize,
+            chunksize=chunksize,
+            expectedrows=expectedrows,
+            dropna=dropna,
+            nan_rep=nan_rep,
+            data_columns=data_columns,
+            track_times=track_times,
+        )
+
+        if isinstance(s, Table) and index:
+            s.create_index(columns=index)
+
+    def _read_group(self, group: Node):
+        s = self._create_storer(group)
+        s.infer_axes()
+        return s.read()
+
+    def _identify_group(self, key: str, append: bool) -> Node:
+        """Identify HDF5 group based on key, delete/create group if needed."""
+        group = self.get_node(key)
+
+        # we make this assertion for mypy; the get_node call will already
+        # have raised if this is incorrect
+        assert self._handle is not None
+
+        # remove the node if we are not appending
+        if group is not None and not append:
+            self._handle.remove_node(group, recursive=True)
+            group = None
+
+        if group is None:
+            group = self._create_nodes_and_group(key)
+
+        return group
+
+    def _create_nodes_and_group(self, key: str) -> Node:
+        """Create nodes from key and return group name."""
+        # assertion for mypy
+        assert self._handle is not None
+
+        paths = key.split("/")
+        # recursively create the groups
+        path = "/"
+        for p in paths:
+            if not len(p):
+                continue
+            new_path = path
+            if not path.endswith("/"):
+                new_path += "/"
+            new_path += p
+            group = self.get_node(new_path)
+            if group is None:
+                group = self._handle.create_group(path, p)
+            path = new_path
+        return group
+
+
+class TableIterator:
+    """
+    Define the iteration interface on a table
+
+    Parameters
+    ----------
+    store : HDFStore
+    s     : the referred storer
+    func  : the function to execute the query
+    where : the where of the query
+    nrows : the rows to iterate on
+    start : the passed start value (default is None)
+    stop  : the passed stop value (default is None)
+    iterator : bool, default False
+        Whether to use the default iterator.
+    chunksize : the passed chunking value (default is 100000)
+    auto_close : bool, default False
+        Whether to automatically close the store at the end of iteration.
+    """
+
+    chunksize: int | None
+    store: HDFStore
+    s: GenericFixed | Table
+
+    def __init__(
+        self,
+        store: HDFStore,
+        s: GenericFixed | Table,
+        func,
+        where,
+        nrows,
+        start=None,
+        stop=None,
+        iterator: bool = False,
+        chunksize: int | None = None,
+        auto_close: bool = False,
+    ) -> None:
+        self.store = store
+        self.s = s
+        self.func = func
+        self.where = where
+
+        # set start/stop if they are not set if we are a table
+        if self.s.is_table:
+            if nrows is None:
+                nrows = 0
+            if start is None:
+                start = 0
+            if stop is None:
+                stop = nrows
+            stop = min(nrows, stop)
+
+        self.nrows = nrows
+        self.start = start
+        self.stop = stop
+
+        self.coordinates = None
+        if iterator or chunksize is not None:
+            if chunksize is None:
+                chunksize = 100000
+            self.chunksize = int(chunksize)
+        else:
+            self.chunksize = None
+
+        self.auto_close = auto_close
+
+    def __iter__(self) -> Iterator:
+        # iterate
+        current = self.start
+        if self.coordinates is None:
+            raise ValueError("Cannot iterate until get_result is called.")
+        while current < self.stop:
+            stop = min(current + self.chunksize, self.stop)
+            value = self.func(None, None, self.coordinates[current:stop])
+            current = stop
+            if value is None or not len(value):
+                continue
+
+            yield value
+
+        self.close()
+
+    def close(self) -> None:
+        if self.auto_close:
+            self.store.close()
+
+    def get_result(self, coordinates: bool = False):
+        #  return the actual iterator
+        if self.chunksize is not None:
+            if not isinstance(self.s, Table):
+                raise TypeError("can only use an iterator or chunksize on a table")
+
+            self.coordinates = self.s.read_coordinates(where=self.where)
+
+            return self
+
+        # if specified read via coordinates (necessary for multiple selections
+        if coordinates:
+            if not isinstance(self.s, Table):
+                raise TypeError("can only read_coordinates on a table")
+            where = self.s.read_coordinates(
+                where=self.where, start=self.start, stop=self.stop
+            )
+        else:
+            where = self.where
+
+        # directly return the result
+        results = self.func(self.start, self.stop, where)
+        self.close()
+        return results
+
+
+class IndexCol:
+    """
+    an index column description class
+
+    Parameters
+    ----------
+    axis   : axis which I reference
+    values : the ndarray like converted values
+    kind   : a string description of this type
+    typ    : the pytables type
+    pos    : the position in the pytables
+
+    """
+
+    is_an_indexable: bool = True
+    is_data_indexable: bool = True
+    _info_fields = ["freq", "tz", "index_name"]
+
+    def __init__(
+        self,
+        name: str,
+        values=None,
+        kind=None,
+        typ=None,
+        cname: str | None = None,
+        axis=None,
+        pos=None,
+        freq=None,
+        tz=None,
+        index_name=None,
+        ordered=None,
+        table=None,
+        meta=None,
+        metadata=None,
+    ) -> None:
+        if not isinstance(name, str):
+            raise ValueError("`name` must be a str.")
+
+        self.values = values
+        self.kind = kind
+        self.typ = typ
+        self.name = name
+        self.cname = cname or name
+        self.axis = axis
+        self.pos = pos
+        self.freq = freq
+        self.tz = tz
+        self.index_name = index_name
+        self.ordered = ordered
+        self.table = table
+        self.meta = meta
+        self.metadata = metadata
+
+        if pos is not None:
+            self.set_pos(pos)
+
+        # These are ensured as long as the passed arguments match the
+        #  constructor annotations.
+        assert isinstance(self.name, str)
+        assert isinstance(self.cname, str)
+
+    @property
+    def itemsize(self) -> int:
+        # Assumes self.typ has already been initialized
+        return self.typ.itemsize
+
+    @property
+    def kind_attr(self) -> str:
+        return f"{self.name}_kind"
+
+    def set_pos(self, pos: int) -> None:
+        """set the position of this column in the Table"""
+        self.pos = pos
+        if pos is not None and self.typ is not None:
+            self.typ._v_pos = pos
+
+    def __repr__(self) -> str:
+        temp = tuple(
+            map(pprint_thing, (self.name, self.cname, self.axis, self.pos, self.kind))
+        )
+        return ",".join(
+            [
+                f"{key}->{value}"
+                for key, value in zip(["name", "cname", "axis", "pos", "kind"], temp)
+            ]
+        )
+
+    def __eq__(self, other: object) -> bool:
+        """compare 2 col items"""
+        return all(
+            getattr(self, a, None) == getattr(other, a, None)
+            for a in ["name", "cname", "axis", "pos"]
+        )
+
+    def __ne__(self, other) -> bool:
+        return not self.__eq__(other)
+
+    @property
+    def is_indexed(self) -> bool:
+        """return whether I am an indexed column"""
+        if not hasattr(self.table, "cols"):
+            # e.g. if infer hasn't been called yet, self.table will be None.
+            return False
+        return getattr(self.table.cols, self.cname).is_indexed
+
+    def convert(
+        self, values: np.ndarray, nan_rep, encoding: str, errors: str
+    ) -> tuple[np.ndarray, np.ndarray] | tuple[Index, Index]:
+        """
+        Convert the data from this selection to the appropriate pandas type.
+        """
+        assert isinstance(values, np.ndarray), type(values)
+
+        # values is a recarray
+        if values.dtype.fields is not None:
+            # Copy, otherwise values will be a view
+            # preventing the original recarry from being free'ed
+            values = values[self.cname].copy()
+
+        val_kind = _ensure_decoded(self.kind)
+        values = _maybe_convert(values, val_kind, encoding, errors)
+        kwargs = {}
+        kwargs["name"] = _ensure_decoded(self.index_name)
+
+        if self.freq is not None:
+            kwargs["freq"] = _ensure_decoded(self.freq)
+
+        factory: type[Index | DatetimeIndex] = Index
+        if lib.is_np_dtype(values.dtype, "M") or isinstance(
+            values.dtype, DatetimeTZDtype
+        ):
+            factory = DatetimeIndex
+        elif values.dtype == "i8" and "freq" in kwargs:
+            # PeriodIndex data is stored as i8
+            # error: Incompatible types in assignment (expression has type
+            # "Callable[[Any, KwArg(Any)], PeriodIndex]", variable has type
+            # "Union[Type[Index], Type[DatetimeIndex]]")
+            factory = lambda x, **kwds: PeriodIndex.from_ordinals(  # type: ignore[assignment]
+                x, freq=kwds.get("freq", None)
+            )._rename(
+                kwds["name"]
+            )
+
+        # making an Index instance could throw a number of different errors
+        try:
+            new_pd_index = factory(values, **kwargs)
+        except UnicodeEncodeError as err:
+            if (
+                errors == "surrogatepass"
+                and get_option("future.infer_string")
+                and str(err).endswith("surrogates not allowed")
+                and HAS_PYARROW
+            ):
+                new_pd_index = factory(
+                    values,
+                    dtype=StringDtype(storage="python", na_value=np.nan),
+                    **kwargs,
+                )
+            else:
+                raise
+        except ValueError:
+            # if the output freq is different that what we recorded,
+            # it should be None (see also 'doc example part 2')
+            if "freq" in kwargs:
+                kwargs["freq"] = None
+            new_pd_index = factory(values, **kwargs)
+        final_pd_index = _set_tz(new_pd_index, self.tz)
+        return final_pd_index, final_pd_index
+
+    def take_data(self):
+        """return the values"""
+        return self.values
+
+    @property
+    def attrs(self):
+        return self.table._v_attrs
+
+    @property
+    def description(self):
+        return self.table.description
+
+    @property
+    def col(self):
+        """return my current col description"""
+        return getattr(self.description, self.cname, None)
+
+    @property
+    def cvalues(self):
+        """return my cython values"""
+        return self.values
+
+    def __iter__(self) -> Iterator:
+        return iter(self.values)
+
+    def maybe_set_size(self, min_itemsize=None) -> None:
+        """
+        maybe set a string col itemsize:
+            min_itemsize can be an integer or a dict with this columns name
+            with an integer size
+        """
+        if _ensure_decoded(self.kind) == "string":
+            if isinstance(min_itemsize, dict):
+                min_itemsize = min_itemsize.get(self.name)
+
+            if min_itemsize is not None and self.typ.itemsize < min_itemsize:
+                self.typ = _tables().StringCol(itemsize=min_itemsize, pos=self.pos)
+
+    def validate_names(self) -> None:
+        pass
+
+    def validate_and_set(self, handler: AppendableTable, append: bool) -> None:
+        self.table = handler.table
+        self.validate_col()
+        self.validate_attr(append)
+        self.validate_metadata(handler)
+        self.write_metadata(handler)
+        self.set_attr()
+
+    def validate_col(self, itemsize=None):
+        """validate this column: return the compared against itemsize"""
+        # validate this column for string truncation (or reset to the max size)
+        if _ensure_decoded(self.kind) == "string":
+            c = self.col
+            if c is not None:
+                if itemsize is None:
+                    itemsize = self.itemsize
+                if c.itemsize < itemsize:
+                    raise ValueError(
+                        f"Trying to store a string with len [{itemsize}] in "
+                        f"[{self.cname}] column but\nthis column has a limit of "
+                        f"[{c.itemsize}]!\nConsider using min_itemsize to "
+                        "preset the sizes on these columns"
+                    )
+                return c.itemsize
+
+        return None
+
+    def validate_attr(self, append: bool) -> None:
+        # check for backwards incompatibility
+        if append:
+            existing_kind = getattr(self.attrs, self.kind_attr, None)
+            if existing_kind is not None and existing_kind != self.kind:
+                raise TypeError(
+                    f"incompatible kind in col [{existing_kind} - {self.kind}]"
+                )
+
+    def update_info(self, info) -> None:
+        """
+        set/update the info for this indexable with the key/value
+        if there is a conflict raise/warn as needed
+        """
+        for key in self._info_fields:
+            value = getattr(self, key, None)
+            idx = info.setdefault(self.name, {})
+
+            existing_value = idx.get(key)
+            if key in idx and value is not None and existing_value != value:
+                # frequency/name just warn
+                if key in ["freq", "index_name"]:
+                    ws = attribute_conflict_doc % (key, existing_value, value)
+                    warnings.warn(
+                        ws, AttributeConflictWarning, stacklevel=find_stack_level()
+                    )
+
+                    # reset
+                    idx[key] = None
+                    setattr(self, key, None)
+
+                else:
+                    raise ValueError(
+                        f"invalid info for [{self.name}] for [{key}], "
+                        f"existing_value [{existing_value}] conflicts with "
+                        f"new value [{value}]"
+                    )
+            elif value is not None or existing_value is not None:
+                idx[key] = value
+
+    def set_info(self, info) -> None:
+        """set my state from the passed info"""
+        idx = info.get(self.name)
+        if idx is not None:
+            self.__dict__.update(idx)
+
+    def set_attr(self) -> None:
+        """set the kind for this column"""
+        setattr(self.attrs, self.kind_attr, self.kind)
+
+    def validate_metadata(self, handler: AppendableTable) -> None:
+        """validate that kind=category does not change the categories"""
+        if self.meta == "category":
+            new_metadata = self.metadata
+            cur_metadata = handler.read_metadata(self.cname)
+            if (
+                new_metadata is not None
+                and cur_metadata is not None
+                and not array_equivalent(
+                    new_metadata, cur_metadata, strict_nan=True, dtype_equal=True
+                )
+            ):
+                raise ValueError(
+                    "cannot append a categorical with "
+                    "different categories to the existing"
+                )
+
+    def write_metadata(self, handler: AppendableTable) -> None:
+        """set the meta data"""
+        if self.metadata is not None:
+            handler.write_metadata(self.cname, self.metadata)
+
+
+class GenericIndexCol(IndexCol):
+    """an index which is not represented in the data of the table"""
+
+    @property
+    def is_indexed(self) -> bool:
+        return False
+
+    def convert(
+        self, values: np.ndarray, nan_rep, encoding: str, errors: str
+    ) -> tuple[Index, Index]:
+        """
+        Convert the data from this selection to the appropriate pandas type.
+
+        Parameters
+        ----------
+        values : np.ndarray
+        nan_rep : str
+        encoding : str
+        errors : str
+        """
+        assert isinstance(values, np.ndarray), type(values)
+
+        index = RangeIndex(len(values))
+        return index, index
+
+    def set_attr(self) -> None:
+        pass
+
+
+class DataCol(IndexCol):
+    """
+    a data holding column, by definition this is not indexable
+
+    Parameters
+    ----------
+    data   : the actual data
+    cname  : the column name in the table to hold the data (typically
+                values)
+    meta   : a string description of the metadata
+    metadata : the actual metadata
+    """
+
+    is_an_indexable = False
+    is_data_indexable = False
+    _info_fields = ["tz", "ordered"]
+
+    def __init__(
+        self,
+        name: str,
+        values=None,
+        kind=None,
+        typ=None,
+        cname: str | None = None,
+        pos=None,
+        tz=None,
+        ordered=None,
+        table=None,
+        meta=None,
+        metadata=None,
+        dtype: DtypeArg | None = None,
+        data=None,
+    ) -> None:
+        super().__init__(
+            name=name,
+            values=values,
+            kind=kind,
+            typ=typ,
+            pos=pos,
+            cname=cname,
+            tz=tz,
+            ordered=ordered,
+            table=table,
+            meta=meta,
+            metadata=metadata,
+        )
+        self.dtype = dtype
+        self.data = data
+
+    @property
+    def dtype_attr(self) -> str:
+        return f"{self.name}_dtype"
+
+    @property
+    def meta_attr(self) -> str:
+        return f"{self.name}_meta"
+
+    def __repr__(self) -> str:
+        temp = tuple(
+            map(
+                pprint_thing, (self.name, self.cname, self.dtype, self.kind, self.shape)
+            )
+        )
+        return ",".join(
+            [
+                f"{key}->{value}"
+                for key, value in zip(["name", "cname", "dtype", "kind", "shape"], temp)
+            ]
+        )
+
+    def __eq__(self, other: object) -> bool:
+        """compare 2 col items"""
+        return all(
+            getattr(self, a, None) == getattr(other, a, None)
+            for a in ["name", "cname", "dtype", "pos"]
+        )
+
+    def set_data(self, data: ArrayLike) -> None:
+        assert data is not None
+        assert self.dtype is None
+
+        data, dtype_name = _get_data_and_dtype_name(data)
+
+        self.data = data
+        self.dtype = dtype_name
+        self.kind = _dtype_to_kind(dtype_name)
+
+    def take_data(self):
+        """return the data"""
+        return self.data
+
+    @classmethod
+    def _get_atom(cls, values: ArrayLike) -> Col:
+        """
+        Get an appropriately typed and shaped pytables.Col object for values.
+        """
+        dtype = values.dtype
+        # error: Item "ExtensionDtype" of "Union[ExtensionDtype, dtype[Any]]" has no
+        # attribute "itemsize"
+        itemsize = dtype.itemsize  # type: ignore[union-attr]
+
+        shape = values.shape
+        if values.ndim == 1:
+            # EA, use block shape pretending it is 2D
+            # TODO(EA2D): not necessary with 2D EAs
+            shape = (1, values.size)
+
+        if isinstance(values, Categorical):
+            codes = values.codes
+            atom = cls.get_atom_data(shape, kind=codes.dtype.name)
+        elif lib.is_np_dtype(dtype, "M") or isinstance(dtype, DatetimeTZDtype):
+            atom = cls.get_atom_datetime64(shape)
+        elif lib.is_np_dtype(dtype, "m"):
+            atom = cls.get_atom_timedelta64(shape)
+        elif is_complex_dtype(dtype):
+            atom = _tables().ComplexCol(itemsize=itemsize, shape=shape[0])
+        elif is_string_dtype(dtype):
+            atom = cls.get_atom_string(shape, itemsize)
+        else:
+            atom = cls.get_atom_data(shape, kind=dtype.name)
+
+        return atom
+
+    @classmethod
+    def get_atom_string(cls, shape, itemsize):
+        return _tables().StringCol(itemsize=itemsize, shape=shape[0])
+
+    @classmethod
+    def get_atom_coltype(cls, kind: str) -> type[Col]:
+        """return the PyTables column class for this column"""
+        if kind.startswith("uint"):
+            k4 = kind[4:]
+            col_name = f"UInt{k4}Col"
+        elif kind.startswith("period"):
+            # we store as integer
+            col_name = "Int64Col"
+        else:
+            kcap = kind.capitalize()
+            col_name = f"{kcap}Col"
+
+        return getattr(_tables(), col_name)
+
+    @classmethod
+    def get_atom_data(cls, shape, kind: str) -> Col:
+        return cls.get_atom_coltype(kind=kind)(shape=shape[0])
+
+    @classmethod
+    def get_atom_datetime64(cls, shape):
+        return _tables().Int64Col(shape=shape[0])
+
+    @classmethod
+    def get_atom_timedelta64(cls, shape):
+        return _tables().Int64Col(shape=shape[0])
+
+    @property
+    def shape(self):
+        return getattr(self.data, "shape", None)
+
+    @property
+    def cvalues(self):
+        """return my cython values"""
+        return self.data
+
+    def validate_attr(self, append) -> None:
+        """validate that we have the same order as the existing & same dtype"""
+        if append:
+            existing_fields = getattr(self.attrs, self.kind_attr, None)
+            if existing_fields is not None and existing_fields != list(self.values):
+                raise ValueError("appended items do not match existing items in table!")
+
+            existing_dtype = getattr(self.attrs, self.dtype_attr, None)
+            if existing_dtype is not None and existing_dtype != self.dtype:
+                raise ValueError(
+                    "appended items dtype do not match existing items dtype in table!"
+                )
+
+    def convert(self, values: np.ndarray, nan_rep, encoding: str, errors: str):
+        """
+        Convert the data from this selection to the appropriate pandas type.
+
+        Parameters
+        ----------
+        values : np.ndarray
+        nan_rep :
+        encoding : str
+        errors : str
+
+        Returns
+        -------
+        index : listlike to become an Index
+        data : ndarraylike to become a column
+        """
+        assert isinstance(values, np.ndarray), type(values)
+
+        # values is a recarray
+        if values.dtype.fields is not None:
+            values = values[self.cname]
+
+        assert self.typ is not None
+        if self.dtype is None:
+            # Note: in tests we never have timedelta64 or datetime64,
+            #  so the _get_data_and_dtype_name may be unnecessary
+            converted, dtype_name = _get_data_and_dtype_name(values)
+            kind = _dtype_to_kind(dtype_name)
+        else:
+            converted = values
+            dtype_name = self.dtype
+            kind = self.kind
+
+        assert isinstance(converted, np.ndarray)  # for mypy
+
+        # use the meta if needed
+        meta = _ensure_decoded(self.meta)
+        metadata = self.metadata
+        ordered = self.ordered
+        tz = self.tz
+
+        assert dtype_name is not None
+        # convert to the correct dtype
+        dtype = _ensure_decoded(dtype_name)
+
+        # reverse converts
+        if dtype.startswith("datetime64"):
+            # recreate with tz if indicated
+            converted = _set_tz(converted, tz, coerce=True)
+
+        elif dtype == "timedelta64":
+            converted = np.asarray(converted, dtype="m8[ns]")
+        elif dtype == "date":
+            try:
+                converted = np.asarray(
+                    [date.fromordinal(v) for v in converted], dtype=object
+                )
+            except ValueError:
+                converted = np.asarray(
+                    [date.fromtimestamp(v) for v in converted], dtype=object
+                )
+
+        elif meta == "category":
+            # we have a categorical
+            categories = metadata
+            codes = converted.ravel()
+
+            # if we have stored a NaN in the categories
+            # then strip it; in theory we could have BOTH
+            # -1s in the codes and nulls :<
+            if categories is None:
+                # Handle case of NaN-only categorical columns in which case
+                # the categories are an empty array; when this is stored,
+                # pytables cannot write a zero-len array, so on readback
+                # the categories would be None and `read_hdf()` would fail.
+                categories = Index([], dtype=np.float64)
+            else:
+                mask = isna(categories)
+                if mask.any():
+                    categories = categories[~mask]
+                    codes[codes != -1] -= mask.astype(int).cumsum()._values
+
+            converted = Categorical.from_codes(
+                codes, categories=categories, ordered=ordered, validate=False
+            )
+
+        else:
+            try:
+                converted = converted.astype(dtype, copy=False)
+            except TypeError:
+                converted = converted.astype("O", copy=False)
+
+        # convert nans / decode
+        if _ensure_decoded(kind) == "string":
+            converted = _unconvert_string_array(
+                converted, nan_rep=nan_rep, encoding=encoding, errors=errors
+            )
+
+        return self.values, converted
+
+    def set_attr(self) -> None:
+        """set the data for this column"""
+        setattr(self.attrs, self.kind_attr, self.values)
+        setattr(self.attrs, self.meta_attr, self.meta)
+        assert self.dtype is not None
+        setattr(self.attrs, self.dtype_attr, self.dtype)
+
+
+class DataIndexableCol(DataCol):
+    """represent a data column that can be indexed"""
+
+    is_data_indexable = True
+
+    def validate_names(self) -> None:
+        if not is_string_dtype(Index(self.values).dtype):
+            # TODO: should the message here be more specifically non-str?
+            raise ValueError("cannot have non-object label DataIndexableCol")
+
+    @classmethod
+    def get_atom_string(cls, shape, itemsize):
+        return _tables().StringCol(itemsize=itemsize)
+
+    @classmethod
+    def get_atom_data(cls, shape, kind: str) -> Col:
+        return cls.get_atom_coltype(kind=kind)()
+
+    @classmethod
+    def get_atom_datetime64(cls, shape):
+        return _tables().Int64Col()
+
+    @classmethod
+    def get_atom_timedelta64(cls, shape):
+        return _tables().Int64Col()
+
+
+class GenericDataIndexableCol(DataIndexableCol):
+    """represent a generic pytables data column"""
+
+
+class Fixed:
+    """
+    represent an object in my store
+    facilitate read/write of various types of objects
+    this is an abstract base class
+
+    Parameters
+    ----------
+    parent : HDFStore
+    group : Node
+        The group node where the table resides.
+    """
+
+    pandas_kind: str
+    format_type: str = "fixed"  # GH#30962 needed by dask
+    obj_type: type[DataFrame | Series]
+    ndim: int
+    parent: HDFStore
+    is_table: bool = False
+
+    def __init__(
+        self,
+        parent: HDFStore,
+        group: Node,
+        encoding: str | None = "UTF-8",
+        errors: str = "strict",
+    ) -> None:
+        assert isinstance(parent, HDFStore), type(parent)
+        assert _table_mod is not None  # needed for mypy
+        assert isinstance(group, _table_mod.Node), type(group)
+        self.parent = parent
+        self.group = group
+        self.encoding = _ensure_encoding(encoding)
+        self.errors = errors
+
+    @property
+    def is_old_version(self) -> bool:
+        return self.version[0] <= 0 and self.version[1] <= 10 and self.version[2] < 1
+
+    @property
+    def version(self) -> tuple[int, int, int]:
+        """compute and set our version"""
+        version = _ensure_decoded(getattr(self.group._v_attrs, "pandas_version", None))
+        try:
+            version = tuple(int(x) for x in version.split("."))
+            if len(version) == 2:
+                version = version + (0,)
+        except AttributeError:
+            version = (0, 0, 0)
+        return version
+
+    @property
+    def pandas_type(self):
+        return _ensure_decoded(getattr(self.group._v_attrs, "pandas_type", None))
+
+    def __repr__(self) -> str:
+        """return a pretty representation of myself"""
+        self.infer_axes()
+        s = self.shape
+        if s is not None:
+            if isinstance(s, (list, tuple)):
+                jshape = ",".join([pprint_thing(x) for x in s])
+                s = f"[{jshape}]"
+            return f"{self.pandas_type:12.12} (shape->{s})"
+        return self.pandas_type
+
+    def set_object_info(self) -> None:
+        """set my pandas type & version"""
+        self.attrs.pandas_type = str(self.pandas_kind)
+        self.attrs.pandas_version = str(_version)
+
+    def copy(self) -> Fixed:
+        new_self = copy.copy(self)
+        return new_self
+
+    @property
+    def shape(self):
+        return self.nrows
+
+    @property
+    def pathname(self):
+        return self.group._v_pathname
+
+    @property
+    def _handle(self):
+        return self.parent._handle
+
+    @property
+    def _filters(self):
+        return self.parent._filters
+
+    @property
+    def _complevel(self) -> int:
+        return self.parent._complevel
+
+    @property
+    def _fletcher32(self) -> bool:
+        return self.parent._fletcher32
+
+    @property
+    def attrs(self):
+        return self.group._v_attrs
+
+    def set_attrs(self) -> None:
+        """set our object attributes"""
+
+    def get_attrs(self) -> None:
+        """get our object attributes"""
+
+    @property
+    def storable(self):
+        """return my storable"""
+        return self.group
+
+    @property
+    def is_exists(self) -> bool:
+        return False
+
+    @property
+    def nrows(self):
+        return getattr(self.storable, "nrows", None)
+
+    def validate(self, other) -> Literal[True] | None:
+        """validate against an existing storable"""
+        if other is None:
+            return None
+        return True
+
+    def validate_version(self, where=None) -> None:
+        """are we trying to operate on an old version?"""
+
+    def infer_axes(self) -> bool:
+        """
+        infer the axes of my storer
+        return a boolean indicating if we have a valid storer or not
+        """
+        s = self.storable
+        if s is None:
+            return False
+        self.get_attrs()
+        return True
+
+    def read(
+        self,
+        where=None,
+        columns=None,
+        start: int | None = None,
+        stop: int | None = None,
+    ):
+        raise NotImplementedError(
+            "cannot read on an abstract storer: subclasses should implement"
+        )
+
+    def write(self, obj, **kwargs) -> None:
+        raise NotImplementedError(
+            "cannot write on an abstract storer: subclasses should implement"
+        )
+
+    def delete(
+        self, where=None, start: int | None = None, stop: int | None = None
+    ) -> None:
+        """
+        support fully deleting the node in its entirety (only) - where
+        specification must be None
+        """
+        if com.all_none(where, start, stop):
+            self._handle.remove_node(self.group, recursive=True)
+            return None
+
+        raise TypeError("cannot delete on an abstract storer")
+
+
+class GenericFixed(Fixed):
+    """a generified fixed version"""
+
+    _index_type_map = {DatetimeIndex: "datetime", PeriodIndex: "period"}
+    _reverse_index_map = {v: k for k, v in _index_type_map.items()}
+    attributes: list[str] = []
+
+    # indexer helpers
+    def _class_to_alias(self, cls) -> str:
+        return self._index_type_map.get(cls, "")
+
+    def _alias_to_class(self, alias):
+        if isinstance(alias, type):  # pragma: no cover
+            # compat: for a short period of time master stored types
+            return alias
+        return self._reverse_index_map.get(alias, Index)
+
+    def _get_index_factory(self, attrs):
+        index_class = self._alias_to_class(
+            _ensure_decoded(getattr(attrs, "index_class", ""))
+        )
+
+        factory: Callable
+
+        if index_class == DatetimeIndex:
+
+            def f(values, freq=None, tz=None):
+                # data are already in UTC, localize and convert if tz present
+                dta = DatetimeArray._simple_new(
+                    values.values, dtype=values.dtype, freq=freq
+                )
+                result = DatetimeIndex._simple_new(dta, name=None)
+                if tz is not None:
+                    result = result.tz_localize("UTC").tz_convert(tz)
+                return result
+
+            factory = f
+        elif index_class == PeriodIndex:
+
+            def f(values, freq=None, tz=None):
+                dtype = PeriodDtype(freq)
+                parr = PeriodArray._simple_new(values, dtype=dtype)
+                return PeriodIndex._simple_new(parr, name=None)
+
+            factory = f
+        else:
+            factory = index_class
+
+        kwargs = {}
+        if "freq" in attrs:
+            kwargs["freq"] = attrs["freq"]
+            if index_class is Index:
+                # DTI/PI would be gotten by _alias_to_class
+                factory = TimedeltaIndex
+
+        if "tz" in attrs:
+            if isinstance(attrs["tz"], bytes):
+                # created by python2
+                kwargs["tz"] = attrs["tz"].decode("utf-8")
+            else:
+                # created by python3
+                kwargs["tz"] = attrs["tz"]
+            assert index_class is DatetimeIndex  # just checking
+
+        return factory, kwargs
+
+    def validate_read(self, columns, where) -> None:
+        """
+        raise if any keywords are passed which are not-None
+        """
+        if columns is not None:
+            raise TypeError(
+                "cannot pass a column specification when reading "
+                "a Fixed format store. this store must be selected in its entirety"
+            )
+        if where is not None:
+            raise TypeError(
+                "cannot pass a where specification when reading "
+                "from a Fixed format store. this store must be selected in its entirety"
+            )
+
+    @property
+    def is_exists(self) -> bool:
+        return True
+
+    def set_attrs(self) -> None:
+        """set our object attributes"""
+        self.attrs.encoding = self.encoding
+        self.attrs.errors = self.errors
+
+    def get_attrs(self) -> None:
+        """retrieve our attributes"""
+        self.encoding = _ensure_encoding(getattr(self.attrs, "encoding", None))
+        self.errors = _ensure_decoded(getattr(self.attrs, "errors", "strict"))
+        for n in self.attributes:
+            setattr(self, n, _ensure_decoded(getattr(self.attrs, n, None)))
+
+    def write(self, obj, **kwargs) -> None:
+        self.set_attrs()
+
+    def read_array(self, key: str, start: int | None = None, stop: int | None = None):
+        """read an array for the specified node (off of group"""
+        import tables
+
+        node = getattr(self.group, key)
+        attrs = node._v_attrs
+
+        transposed = getattr(attrs, "transposed", False)
+
+        if isinstance(node, tables.VLArray):
+            ret = node[0][start:stop]
+            dtype = getattr(attrs, "value_type", None)
+            if dtype is not None:
+                ret = pd_array(ret, dtype=dtype)
+        else:
+            dtype = _ensure_decoded(getattr(attrs, "value_type", None))
+            shape = getattr(attrs, "shape", None)
+
+            if shape is not None:
+                # length 0 axis
+                ret = np.empty(shape, dtype=dtype)
+            else:
+                ret = node[start:stop]
+
+            if dtype and dtype.startswith("datetime64"):
+                # reconstruct a timezone if indicated
+                tz = getattr(attrs, "tz", None)
+                ret = _set_tz(ret, tz, coerce=True)
+
+            elif dtype == "timedelta64":
+                ret = np.asarray(ret, dtype="m8[ns]")
+
+        if transposed:
+            return ret.T
+        else:
+            return ret
+
+    def read_index(
+        self, key: str, start: int | None = None, stop: int | None = None
+    ) -> Index:
+        variety = _ensure_decoded(getattr(self.attrs, f"{key}_variety"))
+
+        if variety == "multi":
+            return self.read_multi_index(key, start=start, stop=stop)
+        elif variety == "regular":
+            node = getattr(self.group, key)
+            index = self.read_index_node(node, start=start, stop=stop)
+            return index
+        else:  # pragma: no cover
+            raise TypeError(f"unrecognized index variety: {variety}")
+
+    def write_index(self, key: str, index: Index) -> None:
+        if isinstance(index, MultiIndex):
+            setattr(self.attrs, f"{key}_variety", "multi")
+            self.write_multi_index(key, index)
+        else:
+            setattr(self.attrs, f"{key}_variety", "regular")
+            converted = _convert_index("index", index, self.encoding, self.errors)
+
+            self.write_array(key, converted.values)
+
+            node = getattr(self.group, key)
+            node._v_attrs.kind = converted.kind
+            node._v_attrs.name = index.name
+
+            if isinstance(index, (DatetimeIndex, PeriodIndex)):
+                node._v_attrs.index_class = self._class_to_alias(type(index))
+
+            if isinstance(index, (DatetimeIndex, PeriodIndex, TimedeltaIndex)):
+                node._v_attrs.freq = index.freq
+
+            if isinstance(index, DatetimeIndex) and index.tz is not None:
+                node._v_attrs.tz = _get_tz(index.tz)
+
+    def write_multi_index(self, key: str, index: MultiIndex) -> None:
+        setattr(self.attrs, f"{key}_nlevels", index.nlevels)
+
+        for i, (lev, level_codes, name) in enumerate(
+            zip(index.levels, index.codes, index.names)
+        ):
+            # write the level
+            if isinstance(lev.dtype, ExtensionDtype):
+                raise NotImplementedError(
+                    "Saving a MultiIndex with an extension dtype is not supported."
+                )
+            level_key = f"{key}_level{i}"
+            conv_level = _convert_index(level_key, lev, self.encoding, self.errors)
+            self.write_array(level_key, conv_level.values)
+            node = getattr(self.group, level_key)
+            node._v_attrs.kind = conv_level.kind
+            node._v_attrs.name = name
+
+            # write the name
+            setattr(node._v_attrs, f"{key}_name{name}", name)
+
+            # write the labels
+            label_key = f"{key}_label{i}"
+            self.write_array(label_key, level_codes)
+
+    def read_multi_index(
+        self, key: str, start: int | None = None, stop: int | None = None
+    ) -> MultiIndex:
+        nlevels = getattr(self.attrs, f"{key}_nlevels")
+
+        levels = []
+        codes = []
+        names: list[Hashable] = []
+        for i in range(nlevels):
+            level_key = f"{key}_level{i}"
+            node = getattr(self.group, level_key)
+            lev = self.read_index_node(node, start=start, stop=stop)
+            levels.append(lev)
+            names.append(lev.name)
+
+            label_key = f"{key}_label{i}"
+            level_codes = self.read_array(label_key, start=start, stop=stop)
+            codes.append(level_codes)
+
+        return MultiIndex(
+            levels=levels, codes=codes, names=names, verify_integrity=True
+        )
+
+    def read_index_node(
+        self, node: Node, start: int | None = None, stop: int | None = None
+    ) -> Index:
+        data = node[start:stop]
+        # If the index was an empty array write_array_empty() will
+        # have written a sentinel. Here we replace it with the original.
+        if "shape" in node._v_attrs and np.prod(node._v_attrs.shape) == 0:
+            data = np.empty(node._v_attrs.shape, dtype=node._v_attrs.value_type)
+        kind = _ensure_decoded(node._v_attrs.kind)
+        name = None
+
+        if "name" in node._v_attrs:
+            name = _ensure_str(node._v_attrs.name)
+            name = _ensure_decoded(name)
+
+        attrs = node._v_attrs
+        factory, kwargs = self._get_index_factory(attrs)
+
+        if kind in ("date", "object"):
+            index = factory(
+                _unconvert_index(
+                    data, kind, encoding=self.encoding, errors=self.errors
+                ),
+                dtype=object,
+                **kwargs,
+            )
+        else:
+            try:
+                index = factory(
+                    _unconvert_index(
+                        data, kind, encoding=self.encoding, errors=self.errors
+                    ),
+                    **kwargs,
+                )
+            except UnicodeEncodeError as err:
+                if (
+                    self.errors == "surrogatepass"
+                    and get_option("future.infer_string")
+                    and str(err).endswith("surrogates not allowed")
+                    and HAS_PYARROW
+                ):
+                    index = factory(
+                        _unconvert_index(
+                            data, kind, encoding=self.encoding, errors=self.errors
+                        ),
+                        dtype=StringDtype(storage="python", na_value=np.nan),
+                        **kwargs,
+                    )
+                else:
+                    raise
+
+        index.name = name
+
+        return index
+
+    def write_array_empty(self, key: str, value: ArrayLike) -> None:
+        """write a 0-len array"""
+        # ugly hack for length 0 axes
+        arr = np.empty((1,) * value.ndim)
+        self._handle.create_array(self.group, key, arr)
+        node = getattr(self.group, key)
+        node._v_attrs.value_type = str(value.dtype)
+        node._v_attrs.shape = value.shape
+
+    def write_array(
+        self, key: str, obj: AnyArrayLike, items: Index | None = None
+    ) -> None:
+        # TODO: we only have a few tests that get here, the only EA
+        #  that gets passed is DatetimeArray, and we never have
+        #  both self._filters and EA
+
+        value = extract_array(obj, extract_numpy=True)
+
+        if key in self.group:
+            self._handle.remove_node(self.group, key)
+
+        # Transform needed to interface with pytables row/col notation
+        empty_array = value.size == 0
+        transposed = False
+
+        if isinstance(value.dtype, CategoricalDtype):
+            raise NotImplementedError(
+                "Cannot store a category dtype in a HDF5 dataset that uses format="
+                '"fixed". Use format="table".'
+            )
+        if not empty_array:
+            if hasattr(value, "T"):
+                # ExtensionArrays (1d) may not have transpose.
+                value = value.T
+                transposed = True
+
+        atom = None
+        if self._filters is not None:
+            with suppress(ValueError):
+                # get the atom for this datatype
+                atom = _tables().Atom.from_dtype(value.dtype)
+
+        if atom is not None:
+            # We only get here if self._filters is non-None and
+            #  the Atom.from_dtype call succeeded
+
+            # create an empty chunked array and fill it from value
+            if not empty_array:
+                ca = self._handle.create_carray(
+                    self.group, key, atom, value.shape, filters=self._filters
+                )
+                ca[:] = value
+
+            else:
+                self.write_array_empty(key, value)
+
+        elif value.dtype.type == np.object_:
+            # infer the type, warn if we have a non-string type here (for
+            # performance)
+            inferred_type = lib.infer_dtype(value, skipna=False)
+            if empty_array:
+                pass
+            elif inferred_type == "string":
+                pass
+            else:
+                ws = performance_doc % (inferred_type, key, items)
+                warnings.warn(ws, PerformanceWarning, stacklevel=find_stack_level())
+
+            vlarr = self._handle.create_vlarray(self.group, key, _tables().ObjectAtom())
+            vlarr.append(value)
+
+        elif lib.is_np_dtype(value.dtype, "M"):
+            self._handle.create_array(self.group, key, value.view("i8"))
+            getattr(self.group, key)._v_attrs.value_type = str(value.dtype)
+        elif isinstance(value.dtype, DatetimeTZDtype):
+            # store as UTC
+            # with a zone
+
+            # error: Item "ExtensionArray" of "Union[Any, ExtensionArray]" has no
+            # attribute "asi8"
+            self._handle.create_array(
+                self.group, key, value.asi8  # type: ignore[union-attr]
+            )
+
+            node = getattr(self.group, key)
+            # error: Item "ExtensionArray" of "Union[Any, ExtensionArray]" has no
+            # attribute "tz"
+            node._v_attrs.tz = _get_tz(value.tz)  # type: ignore[union-attr]
+            node._v_attrs.value_type = f"datetime64[{value.dtype.unit}]"
+        elif lib.is_np_dtype(value.dtype, "m"):
+            self._handle.create_array(self.group, key, value.view("i8"))
+            getattr(self.group, key)._v_attrs.value_type = "timedelta64"
+        elif isinstance(value, BaseStringArray):
+            vlarr = self._handle.create_vlarray(self.group, key, _tables().ObjectAtom())
+            vlarr.append(value.to_numpy())
+            node = getattr(self.group, key)
+            node._v_attrs.value_type = str(value.dtype)
+        elif empty_array:
+            self.write_array_empty(key, value)
+        else:
+            self._handle.create_array(self.group, key, value)
+
+        getattr(self.group, key)._v_attrs.transposed = transposed
+
+
+class SeriesFixed(GenericFixed):
+    pandas_kind = "series"
+    attributes = ["name"]
+
+    name: Hashable
+
+    @property
+    def shape(self):
+        try:
+            return (len(self.group.values),)
+        except (TypeError, AttributeError):
+            return None
+
+    def read(
+        self,
+        where=None,
+        columns=None,
+        start: int | None = None,
+        stop: int | None = None,
+    ) -> Series:
+        self.validate_read(columns, where)
+        index = self.read_index("index", start=start, stop=stop)
+        values = self.read_array("values", start=start, stop=stop)
+        try:
+            result = Series(values, index=index, name=self.name, copy=False)
+        except UnicodeEncodeError as err:
+            if (
+                self.errors == "surrogatepass"
+                and get_option("future.infer_string")
+                and str(err).endswith("surrogates not allowed")
+                and HAS_PYARROW
+            ):
+                result = Series(
+                    values,
+                    index=index,
+                    name=self.name,
+                    copy=False,
+                    dtype=StringDtype(storage="python", na_value=np.nan),
+                )
+            else:
+                raise
+        return result
+
+    def write(self, obj, **kwargs) -> None:
+        super().write(obj, **kwargs)
+        self.write_index("index", obj.index)
+        self.write_array("values", obj)
+        self.attrs.name = obj.name
+
+
+class BlockManagerFixed(GenericFixed):
+    attributes = ["ndim", "nblocks"]
+
+    nblocks: int
+
+    @property
+    def shape(self) -> Shape | None:
+        try:
+            ndim = self.ndim
+
+            # items
+            items = 0
+            for i in range(self.nblocks):
+                node = getattr(self.group, f"block{i}_items")
+                shape = getattr(node, "shape", None)
+                if shape is not None:
+                    items += shape[0]
+
+            # data shape
+            node = self.group.block0_values
+            shape = getattr(node, "shape", None)
+            if shape is not None:
+                shape = list(shape[0 : (ndim - 1)])
+            else:
+                shape = []
+
+            shape.append(items)
+
+            return shape
+        except AttributeError:
+            return None
+
+    def read(
+        self,
+        where=None,
+        columns=None,
+        start: int | None = None,
+        stop: int | None = None,
+    ) -> DataFrame:
+        # start, stop applied to rows, so 0th axis only
+        self.validate_read(columns, where)
+        select_axis = self.obj_type()._get_block_manager_axis(0)
+
+        axes = []
+        for i in range(self.ndim):
+            _start, _stop = (start, stop) if i == select_axis else (None, None)
+            ax = self.read_index(f"axis{i}", start=_start, stop=_stop)
+            axes.append(ax)
+
+        items = axes[0]
+        dfs = []
+
+        for i in range(self.nblocks):
+            blk_items = self.read_index(f"block{i}_items")
+            values = self.read_array(f"block{i}_values", start=_start, stop=_stop)
+
+            columns = items[items.get_indexer(blk_items)]
+            df = DataFrame(values.T, columns=columns, index=axes[1], copy=False)
+            if (
+                using_string_dtype()
+                and isinstance(values, np.ndarray)
+                and is_string_array(values, skipna=True)
+            ):
+                df = df.astype(StringDtype(na_value=np.nan))
+            dfs.append(df)
+
+        if len(dfs) > 0:
+            out = concat(dfs, axis=1, copy=True)
+            if using_copy_on_write():
+                # with CoW, concat ignores the copy keyword. Here, we still want
+                # to copy to enforce optimized column-major layout
+                out = out.copy()
+            out = out.reindex(columns=items, copy=False)
+            return out
+
+        return DataFrame(columns=axes[0], index=axes[1])
+
+    def write(self, obj, **kwargs) -> None:
+        super().write(obj, **kwargs)
+
+        # TODO(ArrayManager) HDFStore relies on accessing the blocks
+        if isinstance(obj._mgr, ArrayManager):
+            obj = obj._as_manager("block")
+
+        data = obj._mgr
+        if not data.is_consolidated():
+            data = data.consolidate()
+
+        self.attrs.ndim = data.ndim
+        for i, ax in enumerate(data.axes):
+            if i == 0 and (not ax.is_unique):
+                raise ValueError("Columns index has to be unique for fixed format")
+            self.write_index(f"axis{i}", ax)
+
+        # Supporting mixed-type DataFrame objects...nontrivial
+        self.attrs.nblocks = len(data.blocks)
+        for i, blk in enumerate(data.blocks):
+            # I have no idea why, but writing values before items fixed #2299
+            blk_items = data.items.take(blk.mgr_locs)
+            self.write_array(f"block{i}_values", blk.values, items=blk_items)
+            self.write_index(f"block{i}_items", blk_items)
+
+
+class FrameFixed(BlockManagerFixed):
+    pandas_kind = "frame"
+    obj_type = DataFrame
+
+
+class Table(Fixed):
+    """
+    represent a table:
+        facilitate read/write of various types of tables
+
+    Attrs in Table Node
+    -------------------
+    These are attributes that are store in the main table node, they are
+    necessary to recreate these tables when read back in.
+
+    index_axes    : a list of tuples of the (original indexing axis and
+        index column)
+    non_index_axes: a list of tuples of the (original index axis and
+        columns on a non-indexing axis)
+    values_axes   : a list of the columns which comprise the data of this
+        table
+    data_columns  : a list of the columns that we are allowing indexing
+        (these become single columns in values_axes)
+    nan_rep       : the string to use for nan representations for string
+        objects
+    levels        : the names of levels
+    metadata      : the names of the metadata columns
+    """
+
+    pandas_kind = "wide_table"
+    format_type: str = "table"  # GH#30962 needed by dask
+    table_type: str
+    levels: int | list[Hashable] = 1
+    is_table = True
+
+    metadata: list
+
+    def __init__(
+        self,
+        parent: HDFStore,
+        group: Node,
+        encoding: str | None = None,
+        errors: str = "strict",
+        index_axes: list[IndexCol] | None = None,
+        non_index_axes: list[tuple[AxisInt, Any]] | None = None,
+        values_axes: list[DataCol] | None = None,
+        data_columns: list | None = None,
+        info: dict | None = None,
+        nan_rep=None,
+    ) -> None:
+        super().__init__(parent, group, encoding=encoding, errors=errors)
+        self.index_axes = index_axes or []
+        self.non_index_axes = non_index_axes or []
+        self.values_axes = values_axes or []
+        self.data_columns = data_columns or []
+        self.info = info or {}
+        self.nan_rep = nan_rep
+
+    @property
+    def table_type_short(self) -> str:
+        return self.table_type.split("_")[0]
+
+    def __repr__(self) -> str:
+        """return a pretty representation of myself"""
+        self.infer_axes()
+        jdc = ",".join(self.data_columns) if len(self.data_columns) else ""
+        dc = f",dc->[{jdc}]"
+
+        ver = ""
+        if self.is_old_version:
+            jver = ".".join([str(x) for x in self.version])
+            ver = f"[{jver}]"
+
+        jindex_axes = ",".join([a.name for a in self.index_axes])
+        return (
+            f"{self.pandas_type:12.12}{ver} "
+            f"(typ->{self.table_type_short},nrows->{self.nrows},"
+            f"ncols->{self.ncols},indexers->[{jindex_axes}]{dc})"
+        )
+
+    def __getitem__(self, c: str):
+        """return the axis for c"""
+        for a in self.axes:
+            if c == a.name:
+                return a
+        return None
+
+    def validate(self, other) -> None:
+        """validate against an existing table"""
+        if other is None:
+            return
+
+        if other.table_type != self.table_type:
+            raise TypeError(
+                "incompatible table_type with existing "
+                f"[{other.table_type} - {self.table_type}]"
+            )
+
+        for c in ["index_axes", "non_index_axes", "values_axes"]:
+            sv = getattr(self, c, None)
+            ov = getattr(other, c, None)
+            if sv != ov:
+                # show the error for the specific axes
+                # Argument 1 to "enumerate" has incompatible type
+                # "Optional[Any]"; expected "Iterable[Any]"  [arg-type]
+                for i, sax in enumerate(sv):  # type: ignore[arg-type]
+                    # Value of type "Optional[Any]" is not indexable  [index]
+                    oax = ov[i]  # type: ignore[index]
+                    if sax != oax:
+                        if c == "values_axes" and sax.kind != oax.kind:
+                            raise ValueError(
+                                f"Cannot serialize the column [{oax.values[0]}] "
+                                f"because its data contents are not [{sax.kind}] "
+                                f"but [{oax.kind}] object dtype"
+                            )
+                        raise ValueError(
+                            f"invalid combination of [{c}] on appending data "
+                            f"[{sax}] vs current table [{oax}]"
+                        )
+
+                # should never get here
+                raise Exception(
+                    f"invalid combination of [{c}] on appending data [{sv}] vs "
+                    f"current table [{ov}]"
+                )
+
+    @property
+    def is_multi_index(self) -> bool:
+        """the levels attribute is 1 or a list in the case of a multi-index"""
+        return isinstance(self.levels, list)
+
+    def validate_multiindex(
+        self, obj: DataFrame | Series
+    ) -> tuple[DataFrame, list[Hashable]]:
+        """
+        validate that we can store the multi-index; reset and return the
+        new object
+        """
+        levels = com.fill_missing_names(obj.index.names)
+        try:
+            reset_obj = obj.reset_index()
+        except ValueError as err:
+            raise ValueError(
+                "duplicate names/columns in the multi-index when storing as a table"
+            ) from err
+        assert isinstance(reset_obj, DataFrame)  # for mypy
+        return reset_obj, levels
+
+    @property
+    def nrows_expected(self) -> int:
+        """based on our axes, compute the expected nrows"""
+        return np.prod([i.cvalues.shape[0] for i in self.index_axes])
+
+    @property
+    def is_exists(self) -> bool:
+        """has this table been created"""
+        return "table" in self.group
+
+    @property
+    def storable(self):
+        return getattr(self.group, "table", None)
+
+    @property
+    def table(self):
+        """return the table group (this is my storable)"""
+        return self.storable
+
+    @property
+    def dtype(self):
+        return self.table.dtype
+
+    @property
+    def description(self):
+        return self.table.description
+
+    @property
+    def axes(self) -> itertools.chain[IndexCol]:
+        return itertools.chain(self.index_axes, self.values_axes)
+
+    @property
+    def ncols(self) -> int:
+        """the number of total columns in the values axes"""
+        return sum(len(a.values) for a in self.values_axes)
+
+    @property
+    def is_transposed(self) -> bool:
+        return False
+
+    @property
+    def data_orientation(self) -> tuple[int, ...]:
+        """return a tuple of my permutated axes, non_indexable at the front"""
+        return tuple(
+            itertools.chain(
+                [int(a[0]) for a in self.non_index_axes],
+                [int(a.axis) for a in self.index_axes],
+            )
+        )
+
+    def queryables(self) -> dict[str, Any]:
+        """return a dict of the kinds allowable columns for this object"""
+        # mypy doesn't recognize DataFrame._AXIS_NAMES, so we re-write it here
+        axis_names = {0: "index", 1: "columns"}
+
+        # compute the values_axes queryables
+        d1 = [(a.cname, a) for a in self.index_axes]
+        d2 = [(axis_names[axis], None) for axis, values in self.non_index_axes]
+        d3 = [
+            (v.cname, v) for v in self.values_axes if v.name in set(self.data_columns)
+        ]
+
+        return dict(d1 + d2 + d3)
+
+    def index_cols(self):
+        """return a list of my index cols"""
+        # Note: each `i.cname` below is assured to be a str.
+        return [(i.axis, i.cname) for i in self.index_axes]
+
+    def values_cols(self) -> list[str]:
+        """return a list of my values cols"""
+        return [i.cname for i in self.values_axes]
+
+    def _get_metadata_path(self, key: str) -> str:
+        """return the metadata pathname for this key"""
+        group = self.group._v_pathname
+        return f"{group}/meta/{key}/meta"
+
+    def write_metadata(self, key: str, values: np.ndarray) -> None:
+        """
+        Write out a metadata array to the key as a fixed-format Series.
+
+        Parameters
+        ----------
+        key : str
+        values : ndarray
+        """
+        self.parent.put(
+            self._get_metadata_path(key),
+            Series(values, copy=False),
+            format="table",
+            encoding=self.encoding,
+            errors=self.errors,
+            nan_rep=self.nan_rep,
+        )
+
+    def read_metadata(self, key: str):
+        """return the meta data array for this key"""
+        if getattr(getattr(self.group, "meta", None), key, None) is not None:
+            return self.parent.select(self._get_metadata_path(key))
+        return None
+
+    def set_attrs(self) -> None:
+        """set our table type & indexables"""
+        self.attrs.table_type = str(self.table_type)
+        self.attrs.index_cols = self.index_cols()
+        self.attrs.values_cols = self.values_cols()
+        self.attrs.non_index_axes = self.non_index_axes
+        self.attrs.data_columns = self.data_columns
+        self.attrs.nan_rep = self.nan_rep
+        self.attrs.encoding = self.encoding
+        self.attrs.errors = self.errors
+        self.attrs.levels = self.levels
+        self.attrs.info = self.info
+
+    def get_attrs(self) -> None:
+        """retrieve our attributes"""
+        self.non_index_axes = getattr(self.attrs, "non_index_axes", None) or []
+        self.data_columns = getattr(self.attrs, "data_columns", None) or []
+        self.info = getattr(self.attrs, "info", None) or {}
+        self.nan_rep = getattr(self.attrs, "nan_rep", None)
+        self.encoding = _ensure_encoding(getattr(self.attrs, "encoding", None))
+        self.errors = _ensure_decoded(getattr(self.attrs, "errors", "strict"))
+        self.levels: list[Hashable] = getattr(self.attrs, "levels", None) or []
+        self.index_axes = [a for a in self.indexables if a.is_an_indexable]
+        self.values_axes = [a for a in self.indexables if not a.is_an_indexable]
+
+    def validate_version(self, where=None) -> None:
+        """are we trying to operate on an old version?"""
+        if where is not None:
+            if self.is_old_version:
+                ws = incompatibility_doc % ".".join([str(x) for x in self.version])
+                warnings.warn(
+                    ws,
+                    IncompatibilityWarning,
+                    stacklevel=find_stack_level(),
+                )
+
+    def validate_min_itemsize(self, min_itemsize) -> None:
+        """
+        validate the min_itemsize doesn't contain items that are not in the
+        axes this needs data_columns to be defined
+        """
+        if min_itemsize is None:
+            return
+        if not isinstance(min_itemsize, dict):
+            return
+
+        q = self.queryables()
+        for k in min_itemsize:
+            # ok, apply generally
+            if k == "values":
+                continue
+            if k not in q:
+                raise ValueError(
+                    f"min_itemsize has the key [{k}] which is not an axis or "
+                    "data_column"
+                )
+
+    @cache_readonly
+    def indexables(self):
+        """create/cache the indexables if they don't exist"""
+        _indexables = []
+
+        desc = self.description
+        table_attrs = self.table.attrs
+
+        # Note: each of the `name` kwargs below are str, ensured
+        #  by the definition in index_cols.
+        # index columns
+        for i, (axis, name) in enumerate(self.attrs.index_cols):
+            atom = getattr(desc, name)
+            md = self.read_metadata(name)
+            meta = "category" if md is not None else None
+
+            kind_attr = f"{name}_kind"
+            kind = getattr(table_attrs, kind_attr, None)
+
+            index_col = IndexCol(
+                name=name,
+                axis=axis,
+                pos=i,
+                kind=kind,
+                typ=atom,
+                table=self.table,
+                meta=meta,
+                metadata=md,
+            )
+            _indexables.append(index_col)
+
+        # values columns
+        dc = set(self.data_columns)
+        base_pos = len(_indexables)
+
+        def f(i, c):
+            assert isinstance(c, str)
+            klass = DataCol
+            if c in dc:
+                klass = DataIndexableCol
+
+            atom = getattr(desc, c)
+            adj_name = _maybe_adjust_name(c, self.version)
+
+            # TODO: why kind_attr here?
+            values = getattr(table_attrs, f"{adj_name}_kind", None)
+            dtype = getattr(table_attrs, f"{adj_name}_dtype", None)
+            # Argument 1 to "_dtype_to_kind" has incompatible type
+            # "Optional[Any]"; expected "str"  [arg-type]
+            kind = _dtype_to_kind(dtype)  # type: ignore[arg-type]
+
+            md = self.read_metadata(c)
+            # TODO: figure out why these two versions of `meta` dont always match.
+            #  meta = "category" if md is not None else None
+            meta = getattr(table_attrs, f"{adj_name}_meta", None)
+
+            obj = klass(
+                name=adj_name,
+                cname=c,
+                values=values,
+                kind=kind,
+                pos=base_pos + i,
+                typ=atom,
+                table=self.table,
+                meta=meta,
+                metadata=md,
+                dtype=dtype,
+            )
+            return obj
+
+        # Note: the definition of `values_cols` ensures that each
+        #  `c` below is a str.
+        _indexables.extend([f(i, c) for i, c in enumerate(self.attrs.values_cols)])
+
+        return _indexables
+
+    def create_index(
+        self, columns=None, optlevel=None, kind: str | None = None
+    ) -> None:
+        """
+        Create a pytables index on the specified columns.
+
+        Parameters
+        ----------
+        columns : None, bool, or listlike[str]
+            Indicate which columns to create an index on.
+
+            * False : Do not create any indexes.
+            * True : Create indexes on all columns.
+            * None : Create indexes on all columns.
+            * listlike : Create indexes on the given columns.
+
+        optlevel : int or None, default None
+            Optimization level, if None, pytables defaults to 6.
+        kind : str or None, default None
+            Kind of index, if None, pytables defaults to "medium".
+
+        Raises
+        ------
+        TypeError if trying to create an index on a complex-type column.
+
+        Notes
+        -----
+        Cannot index Time64Col or ComplexCol.
+        Pytables must be >= 3.0.
+        """
+        if not self.infer_axes():
+            return
+        if columns is False:
+            return
+
+        # index all indexables and data_columns
+        if columns is None or columns is True:
+            columns = [a.cname for a in self.axes if a.is_data_indexable]
+        if not isinstance(columns, (tuple, list)):
+            columns = [columns]
+
+        kw = {}
+        if optlevel is not None:
+            kw["optlevel"] = optlevel
+        if kind is not None:
+            kw["kind"] = kind
+
+        table = self.table
+        for c in columns:
+            v = getattr(table.cols, c, None)
+            if v is not None:
+                # remove the index if the kind/optlevel have changed
+                if v.is_indexed:
+                    index = v.index
+                    cur_optlevel = index.optlevel
+                    cur_kind = index.kind
+
+                    if kind is not None and cur_kind != kind:
+                        v.remove_index()
+                    else:
+                        kw["kind"] = cur_kind
+
+                    if optlevel is not None and cur_optlevel != optlevel:
+                        v.remove_index()
+                    else:
+                        kw["optlevel"] = cur_optlevel
+
+                # create the index
+                if not v.is_indexed:
+                    if v.type.startswith("complex"):
+                        raise TypeError(
+                            "Columns containing complex values can be stored but "
+                            "cannot be indexed when using table format. Either use "
+                            "fixed format, set index=False, or do not include "
+                            "the columns containing complex values to "
+                            "data_columns when initializing the table."
+                        )
+                    v.create_index(**kw)
+            elif c in self.non_index_axes[0][1]:
+                # GH 28156
+                raise AttributeError(
+                    f"column {c} is not a data_column.\n"
+                    f"In order to read column {c} you must reload the dataframe \n"
+                    f"into HDFStore and include {c} with the data_columns argument."
+                )
+
+    def _read_axes(
+        self, where, start: int | None = None, stop: int | None = None
+    ) -> list[tuple[np.ndarray, np.ndarray] | tuple[Index, Index]]:
+        """
+        Create the axes sniffed from the table.
+
+        Parameters
+        ----------
+        where : ???
+        start : int or None, default None
+        stop : int or None, default None
+
+        Returns
+        -------
+        List[Tuple[index_values, column_values]]
+        """
+        # create the selection
+        selection = Selection(self, where=where, start=start, stop=stop)
+        values = selection.select()
+
+        results = []
+        # convert the data
+        for a in self.axes:
+            a.set_info(self.info)
+            res = a.convert(
+                values,
+                nan_rep=self.nan_rep,
+                encoding=self.encoding,
+                errors=self.errors,
+            )
+            results.append(res)
+
+        return results
+
+    @classmethod
+    def get_object(cls, obj, transposed: bool):
+        """return the data for this obj"""
+        return obj
+
+    def validate_data_columns(self, data_columns, min_itemsize, non_index_axes):
+        """
+        take the input data_columns and min_itemize and create a data
+        columns spec
+        """
+        if not len(non_index_axes):
+            return []
+
+        axis, axis_labels = non_index_axes[0]
+        info = self.info.get(axis, {})
+        if info.get("type") == "MultiIndex" and data_columns:
+            raise ValueError(
+                f"cannot use a multi-index on axis [{axis}] with "
+                f"data_columns {data_columns}"
+            )
+
+        # evaluate the passed data_columns, True == use all columns
+        # take only valid axis labels
+        if data_columns is True:
+            data_columns = list(axis_labels)
+        elif data_columns is None:
+            data_columns = []
+
+        # if min_itemsize is a dict, add the keys (exclude 'values')
+        if isinstance(min_itemsize, dict):
+            existing_data_columns = set(data_columns)
+            data_columns = list(data_columns)  # ensure we do not modify
+            data_columns.extend(
+                [
+                    k
+                    for k in min_itemsize.keys()
+                    if k != "values" and k not in existing_data_columns
+                ]
+            )
+
+        # return valid columns in the order of our axis
+        return [c for c in data_columns if c in axis_labels]
+
+    def _create_axes(
+        self,
+        axes,
+        obj: DataFrame,
+        validate: bool = True,
+        nan_rep=None,
+        data_columns=None,
+        min_itemsize=None,
+    ):
+        """
+        Create and return the axes.
+
+        Parameters
+        ----------
+        axes: list or None
+            The names or numbers of the axes to create.
+        obj : DataFrame
+            The object to create axes on.
+        validate: bool, default True
+            Whether to validate the obj against an existing object already written.
+        nan_rep :
+            A value to use for string column nan_rep.
+        data_columns : List[str], True, or None, default None
+            Specify the columns that we want to create to allow indexing on.
+
+            * True : Use all available columns.
+            * None : Use no columns.
+            * List[str] : Use the specified columns.
+
+        min_itemsize: Dict[str, int] or None, default None
+            The min itemsize for a column in bytes.
+        """
+        if not isinstance(obj, DataFrame):
+            group = self.group._v_name
+            raise TypeError(
+                f"cannot properly create the storer for: [group->{group},"
+                f"value->{type(obj)}]"
+            )
+
+        # set the default axes if needed
+        if axes is None:
+            axes = [0]
+
+        # map axes to numbers
+        axes = [obj._get_axis_number(a) for a in axes]
+
+        # do we have an existing table (if so, use its axes & data_columns)
+        if self.infer_axes():
+            table_exists = True
+            axes = [a.axis for a in self.index_axes]
+            data_columns = list(self.data_columns)
+            nan_rep = self.nan_rep
+            # TODO: do we always have validate=True here?
+        else:
+            table_exists = False
+
+        new_info = self.info
+
+        assert self.ndim == 2  # with next check, we must have len(axes) == 1
+        # currently support on ndim-1 axes
+        if len(axes) != self.ndim - 1:
+            raise ValueError(
+                "currently only support ndim-1 indexers in an AppendableTable"
+            )
+
+        # create according to the new data
+        new_non_index_axes: list = []
+
+        # nan_representation
+        if nan_rep is None:
+            nan_rep = "nan"
+
+        # We construct the non-index-axis first, since that alters new_info
+        idx = next(x for x in [0, 1] if x not in axes)
+
+        a = obj.axes[idx]
+        # we might be able to change the axes on the appending data if necessary
+        append_axis = list(a)
+        if table_exists:
+            indexer = len(new_non_index_axes)  # i.e. 0
+            exist_axis = self.non_index_axes[indexer][1]
+            if not array_equivalent(
+                np.array(append_axis),
+                np.array(exist_axis),
+                strict_nan=True,
+                dtype_equal=True,
+            ):
+                # ahah! -> reindex
+                if array_equivalent(
+                    np.array(sorted(append_axis)),
+                    np.array(sorted(exist_axis)),
+                    strict_nan=True,
+                    dtype_equal=True,
+                ):
+                    append_axis = exist_axis
+
+        # the non_index_axes info
+        info = new_info.setdefault(idx, {})
+        info["names"] = list(a.names)
+        info["type"] = type(a).__name__
+
+        new_non_index_axes.append((idx, append_axis))
+
+        # Now we can construct our new index axis
+        idx = axes[0]
+        a = obj.axes[idx]
+        axis_name = obj._get_axis_name(idx)
+        new_index = _convert_index(axis_name, a, self.encoding, self.errors)
+        new_index.axis = idx
+
+        # Because we are always 2D, there is only one new_index, so
+        #  we know it will have pos=0
+        new_index.set_pos(0)
+        new_index.update_info(new_info)
+        new_index.maybe_set_size(min_itemsize)  # check for column conflicts
+
+        new_index_axes = [new_index]
+        j = len(new_index_axes)  # i.e. 1
+        assert j == 1
+
+        # reindex by our non_index_axes & compute data_columns
+        assert len(new_non_index_axes) == 1
+        for a in new_non_index_axes:
+            obj = _reindex_axis(obj, a[0], a[1])
+
+        transposed = new_index.axis == 1
+
+        # figure out data_columns and get out blocks
+        data_columns = self.validate_data_columns(
+            data_columns, min_itemsize, new_non_index_axes
+        )
+
+        frame = self.get_object(obj, transposed)._consolidate()
+
+        blocks, blk_items = self._get_blocks_and_items(
+            frame, table_exists, new_non_index_axes, self.values_axes, data_columns
+        )
+
+        # add my values
+        vaxes = []
+        for i, (blk, b_items) in enumerate(zip(blocks, blk_items)):
+            # shape of the data column are the indexable axes
+            klass = DataCol
+            name = None
+
+            # we have a data_column
+            if data_columns and len(b_items) == 1 and b_items[0] in data_columns:
+                klass = DataIndexableCol
+                name = b_items[0]
+                if not (name is None or isinstance(name, str)):
+                    # TODO: should the message here be more specifically non-str?
+                    raise ValueError("cannot have non-object label DataIndexableCol")
+
+            # make sure that we match up the existing columns
+            # if we have an existing table
+            existing_col: DataCol | None
+
+            if table_exists and validate:
+                try:
+                    existing_col = self.values_axes[i]
+                except (IndexError, KeyError) as err:
+                    raise ValueError(
+                        f"Incompatible appended table [{blocks}]"
+                        f"with existing table [{self.values_axes}]"
+                    ) from err
+            else:
+                existing_col = None
+
+            new_name = name or f"values_block_{i}"
+            data_converted = _maybe_convert_for_string_atom(
+                new_name,
+                blk.values,
+                existing_col=existing_col,
+                min_itemsize=min_itemsize,
+                nan_rep=nan_rep,
+                encoding=self.encoding,
+                errors=self.errors,
+                columns=b_items,
+            )
+            adj_name = _maybe_adjust_name(new_name, self.version)
+
+            typ = klass._get_atom(data_converted)
+            kind = _dtype_to_kind(data_converted.dtype.name)
+            tz = None
+            if getattr(data_converted, "tz", None) is not None:
+                tz = _get_tz(data_converted.tz)
+
+            meta = metadata = ordered = None
+            if isinstance(data_converted.dtype, CategoricalDtype):
+                ordered = data_converted.ordered
+                meta = "category"
+                metadata = np.asarray(data_converted.categories).ravel()
+            elif isinstance(blk.dtype, StringDtype):
+                meta = str(blk.dtype)
+
+            data, dtype_name = _get_data_and_dtype_name(data_converted)
+
+            col = klass(
+                name=adj_name,
+                cname=new_name,
+                values=list(b_items),
+                typ=typ,
+                pos=j,
+                kind=kind,
+                tz=tz,
+                ordered=ordered,
+                meta=meta,
+                metadata=metadata,
+                dtype=dtype_name,
+                data=data,
+            )
+            col.update_info(new_info)
+
+            vaxes.append(col)
+
+            j += 1
+
+        dcs = [col.name for col in vaxes if col.is_data_indexable]
+
+        new_table = type(self)(
+            parent=self.parent,
+            group=self.group,
+            encoding=self.encoding,
+            errors=self.errors,
+            index_axes=new_index_axes,
+            non_index_axes=new_non_index_axes,
+            values_axes=vaxes,
+            data_columns=dcs,
+            info=new_info,
+            nan_rep=nan_rep,
+        )
+        if hasattr(self, "levels"):
+            # TODO: get this into constructor, only for appropriate subclass
+            new_table.levels = self.levels
+
+        new_table.validate_min_itemsize(min_itemsize)
+
+        if validate and table_exists:
+            new_table.validate(self)
+
+        return new_table
+
+    @staticmethod
+    def _get_blocks_and_items(
+        frame: DataFrame,
+        table_exists: bool,
+        new_non_index_axes,
+        values_axes,
+        data_columns,
+    ):
+        # Helper to clarify non-state-altering parts of _create_axes
+
+        # TODO(ArrayManager) HDFStore relies on accessing the blocks
+        if isinstance(frame._mgr, ArrayManager):
+            frame = frame._as_manager("block")
+
+        def get_blk_items(mgr):
+            return [mgr.items.take(blk.mgr_locs) for blk in mgr.blocks]
+
+        mgr = frame._mgr
+        mgr = cast(BlockManager, mgr)
+        blocks: list[Block] = list(mgr.blocks)
+        blk_items: list[Index] = get_blk_items(mgr)
+
+        if len(data_columns):
+            # TODO: prove that we only get here with axis == 1?
+            #  It is the case in all extant tests, but NOT the case
+            #  outside this `if len(data_columns)` check.
+
+            axis, axis_labels = new_non_index_axes[0]
+            new_labels = Index(axis_labels).difference(Index(data_columns))
+            mgr = frame.reindex(new_labels, axis=axis)._mgr
+            mgr = cast(BlockManager, mgr)
+
+            blocks = list(mgr.blocks)
+            blk_items = get_blk_items(mgr)
+            for c in data_columns:
+                # This reindex would raise ValueError if we had a duplicate
+                #  index, so we can infer that (as long as axis==1) we
+                #  get a single column back, so a single block.
+                mgr = frame.reindex([c], axis=axis)._mgr
+                mgr = cast(BlockManager, mgr)
+                blocks.extend(mgr.blocks)
+                blk_items.extend(get_blk_items(mgr))
+
+        # reorder the blocks in the same order as the existing table if we can
+        if table_exists:
+            by_items = {
+                tuple(b_items.tolist()): (b, b_items)
+                for b, b_items in zip(blocks, blk_items)
+            }
+            new_blocks: list[Block] = []
+            new_blk_items = []
+            for ea in values_axes:
+                items = tuple(ea.values)
+                try:
+                    b, b_items = by_items.pop(items)
+                    new_blocks.append(b)
+                    new_blk_items.append(b_items)
+                except (IndexError, KeyError) as err:
+                    jitems = ",".join([pprint_thing(item) for item in items])
+                    raise ValueError(
+                        f"cannot match existing table structure for [{jitems}] "
+                        "on appending data"
+                    ) from err
+            blocks = new_blocks
+            blk_items = new_blk_items
+
+        return blocks, blk_items
+
+    def process_axes(self, obj, selection: Selection, columns=None) -> DataFrame:
+        """process axes filters"""
+        # make a copy to avoid side effects
+        if columns is not None:
+            columns = list(columns)
+
+        # make sure to include levels if we have them
+        if columns is not None and self.is_multi_index:
+            assert isinstance(self.levels, list)  # assured by is_multi_index
+            for n in self.levels:
+                if n not in columns:
+                    columns.insert(0, n)
+
+        # reorder by any non_index_axes & limit to the select columns
+        for axis, labels in self.non_index_axes:
+            obj = _reindex_axis(obj, axis, labels, columns)
+
+            def process_filter(field, filt, op):
+                for axis_name in obj._AXIS_ORDERS:
+                    axis_number = obj._get_axis_number(axis_name)
+                    axis_values = obj._get_axis(axis_name)
+                    assert axis_number is not None
+
+                    # see if the field is the name of an axis
+                    if field == axis_name:
+                        # if we have a multi-index, then need to include
+                        # the levels
+                        if self.is_multi_index:
+                            filt = filt.union(Index(self.levels))
+
+                        takers = op(axis_values, filt)
+                        return obj.loc(axis=axis_number)[takers]
+
+                    # this might be the name of a file IN an axis
+                    elif field in axis_values:
+                        # we need to filter on this dimension
+                        values = ensure_index(getattr(obj, field).values)
+                        filt = ensure_index(filt)
+
+                        # hack until we support reversed dim flags
+                        if isinstance(obj, DataFrame):
+                            axis_number = 1 - axis_number
+
+                        takers = op(values, filt)
+                        return obj.loc(axis=axis_number)[takers]
+
+                raise ValueError(f"cannot find the field [{field}] for filtering!")
+
+        # apply the selection filters (but keep in the same order)
+        if selection.filter is not None:
+            for field, op, filt in selection.filter.format():
+                obj = process_filter(field, filt, op)
+
+        return obj
+
+    def create_description(
+        self,
+        complib,
+        complevel: int | None,
+        fletcher32: bool,
+        expectedrows: int | None,
+    ) -> dict[str, Any]:
+        """create the description of the table from the axes & values"""
+        # provided expected rows if its passed
+        if expectedrows is None:
+            expectedrows = max(self.nrows_expected, 10000)
+
+        d = {"name": "table", "expectedrows": expectedrows}
+
+        # description from the axes & values
+        d["description"] = {a.cname: a.typ for a in self.axes}
+
+        if complib:
+            if complevel is None:
+                complevel = self._complevel or 9
+            filters = _tables().Filters(
+                complevel=complevel,
+                complib=complib,
+                fletcher32=fletcher32 or self._fletcher32,
+            )
+            d["filters"] = filters
+        elif self._filters is not None:
+            d["filters"] = self._filters
+
+        return d
+
+    def read_coordinates(
+        self, where=None, start: int | None = None, stop: int | None = None
+    ):
+        """
+        select coordinates (row numbers) from a table; return the
+        coordinates object
+        """
+        # validate the version
+        self.validate_version(where)
+
+        # infer the data kind
+        if not self.infer_axes():
+            return False
+
+        # create the selection
+        selection = Selection(self, where=where, start=start, stop=stop)
+        coords = selection.select_coords()
+        if selection.filter is not None:
+            for field, op, filt in selection.filter.format():
+                data = self.read_column(
+                    field, start=coords.min(), stop=coords.max() + 1
+                )
+                coords = coords[op(data.iloc[coords - coords.min()], filt).values]
+
+        return Index(coords)
+
+    def read_column(
+        self,
+        column: str,
+        where=None,
+        start: int | None = None,
+        stop: int | None = None,
+    ):
+        """
+        return a single column from the table, generally only indexables
+        are interesting
+        """
+        # validate the version
+        self.validate_version()
+
+        # infer the data kind
+        if not self.infer_axes():
+            return False
+
+        if where is not None:
+            raise TypeError("read_column does not currently accept a where clause")
+
+        # find the axes
+        for a in self.axes:
+            if column == a.name:
+                if not a.is_data_indexable:
+                    raise ValueError(
+                        f"column [{column}] can not be extracted individually; "
+                        "it is not data indexable"
+                    )
+
+                # column must be an indexable or a data column
+                c = getattr(self.table.cols, column)
+                a.set_info(self.info)
+                col_values = a.convert(
+                    c[start:stop],
+                    nan_rep=self.nan_rep,
+                    encoding=self.encoding,
+                    errors=self.errors,
+                )
+                cvs = _set_tz(col_values[1], a.tz)
+                dtype = getattr(self.table.attrs, f"{column}_meta", None)
+                return Series(cvs, name=column, copy=False, dtype=dtype)
+
+        raise KeyError(f"column [{column}] not found in the table")
+
+
+class WORMTable(Table):
+    """
+    a write-once read-many table: this format DOES NOT ALLOW appending to a
+    table. writing is a one-time operation the data are stored in a format
+    that allows for searching the data on disk
+    """
+
+    table_type = "worm"
+
+    def read(
+        self,
+        where=None,
+        columns=None,
+        start: int | None = None,
+        stop: int | None = None,
+    ):
+        """
+        read the indices and the indexing array, calculate offset rows and return
+        """
+        raise NotImplementedError("WORMTable needs to implement read")
+
+    def write(self, obj, **kwargs) -> None:
+        """
+        write in a format that we can search later on (but cannot append
+        to): write out the indices and the values using _write_array
+        (e.g. a CArray) create an indexing table so that we can search
+        """
+        raise NotImplementedError("WORMTable needs to implement write")
+
+
+class AppendableTable(Table):
+    """support the new appendable table formats"""
+
+    table_type = "appendable"
+
+    # error: Signature of "write" incompatible with supertype "Fixed"
+    def write(  # type: ignore[override]
+        self,
+        obj,
+        axes=None,
+        append: bool = False,
+        complib=None,
+        complevel=None,
+        fletcher32=None,
+        min_itemsize=None,
+        chunksize: int | None = None,
+        expectedrows=None,
+        dropna: bool = False,
+        nan_rep=None,
+        data_columns=None,
+        track_times: bool = True,
+    ) -> None:
+        if not append and self.is_exists:
+            self._handle.remove_node(self.group, "table")
+
+        # create the axes
+        table = self._create_axes(
+            axes=axes,
+            obj=obj,
+            validate=append,
+            min_itemsize=min_itemsize,
+            nan_rep=nan_rep,
+            data_columns=data_columns,
+        )
+
+        for a in table.axes:
+            a.validate_names()
+
+        if not table.is_exists:
+            # create the table
+            options = table.create_description(
+                complib=complib,
+                complevel=complevel,
+                fletcher32=fletcher32,
+                expectedrows=expectedrows,
+            )
+
+            # set the table attributes
+            table.set_attrs()
+
+            options["track_times"] = track_times
+
+            # create the table
+            table._handle.create_table(table.group, **options)
+
+        # update my info
+        table.attrs.info = table.info
+
+        # validate the axes and set the kinds
+        for a in table.axes:
+            a.validate_and_set(table, append)
+
+        # add the rows
+        table.write_data(chunksize, dropna=dropna)
+
+    def write_data(self, chunksize: int | None, dropna: bool = False) -> None:
+        """
+        we form the data into a 2-d including indexes,values,mask write chunk-by-chunk
+        """
+        names = self.dtype.names
+        nrows = self.nrows_expected
+
+        # if dropna==True, then drop ALL nan rows
+        masks = []
+        if dropna:
+            for a in self.values_axes:
+                # figure the mask: only do if we can successfully process this
+                # column, otherwise ignore the mask
+                mask = isna(a.data).all(axis=0)
+                if isinstance(mask, np.ndarray):
+                    masks.append(mask.astype("u1", copy=False))
+
+        # consolidate masks
+        if len(masks):
+            mask = masks[0]
+            for m in masks[1:]:
+                mask = mask & m
+            mask = mask.ravel()
+        else:
+            mask = None
+
+        # broadcast the indexes if needed
+        indexes = [a.cvalues for a in self.index_axes]
+        nindexes = len(indexes)
+        assert nindexes == 1, nindexes  # ensures we dont need to broadcast
+
+        # transpose the values so first dimension is last
+        # reshape the values if needed
+        values = [a.take_data() for a in self.values_axes]
+        values = [v.transpose(np.roll(np.arange(v.ndim), v.ndim - 1)) for v in values]
+        bvalues = []
+        for i, v in enumerate(values):
+            new_shape = (nrows,) + self.dtype[names[nindexes + i]].shape
+            bvalues.append(v.reshape(new_shape))
+
+        # write the chunks
+        if chunksize is None:
+            chunksize = 100000
+
+        rows = np.empty(min(chunksize, nrows), dtype=self.dtype)
+        chunks = nrows // chunksize + 1
+        for i in range(chunks):
+            start_i = i * chunksize
+            end_i = min((i + 1) * chunksize, nrows)
+            if start_i >= end_i:
+                break
+
+            self.write_data_chunk(
+                rows,
+                indexes=[a[start_i:end_i] for a in indexes],
+                mask=mask[start_i:end_i] if mask is not None else None,
+                values=[v[start_i:end_i] for v in bvalues],
+            )
+
+    def write_data_chunk(
+        self,
+        rows: np.ndarray,
+        indexes: list[np.ndarray],
+        mask: npt.NDArray[np.bool_] | None,
+        values: list[np.ndarray],
+    ) -> None:
+        """
+        Parameters
+        ----------
+        rows : an empty memory space where we are putting the chunk
+        indexes : an array of the indexes
+        mask : an array of the masks
+        values : an array of the values
+        """
+        # 0 len
+        for v in values:
+            if not np.prod(v.shape):
+                return
+
+        nrows = indexes[0].shape[0]
+        if nrows != len(rows):
+            rows = np.empty(nrows, dtype=self.dtype)
+        names = self.dtype.names
+        nindexes = len(indexes)
+
+        # indexes
+        for i, idx in enumerate(indexes):
+            rows[names[i]] = idx
+
+        # values
+        for i, v in enumerate(values):
+            rows[names[i + nindexes]] = v
+
+        # mask
+        if mask is not None:
+            m = ~mask.ravel().astype(bool, copy=False)
+            if not m.all():
+                rows = rows[m]
+
+        if len(rows):
+            self.table.append(rows)
+            self.table.flush()
+
+    def delete(self, where=None, start: int | None = None, stop: int | None = None):
+        # delete all rows (and return the nrows)
+        if where is None or not len(where):
+            if start is None and stop is None:
+                nrows = self.nrows
+                self._handle.remove_node(self.group, recursive=True)
+            else:
+                # pytables<3.0 would remove a single row with stop=None
+                if stop is None:
+                    stop = self.nrows
+                nrows = self.table.remove_rows(start=start, stop=stop)
+                self.table.flush()
+            return nrows
+
+        # infer the data kind
+        if not self.infer_axes():
+            return None
+
+        # create the selection
+        table = self.table
+        selection = Selection(self, where, start=start, stop=stop)
+        values = selection.select_coords()
+
+        # delete the rows in reverse order
+        sorted_series = Series(values, copy=False).sort_values()
+        ln = len(sorted_series)
+
+        if ln:
+            # construct groups of consecutive rows
+            diff = sorted_series.diff()
+            groups = list(diff[diff > 1].index)
+
+            # 1 group
+            if not len(groups):
+                groups = [0]
+
+            # final element
+            if groups[-1] != ln:
+                groups.append(ln)
+
+            # initial element
+            if groups[0] != 0:
+                groups.insert(0, 0)
+
+            # we must remove in reverse order!
+            pg = groups.pop()
+            for g in reversed(groups):
+                rows = sorted_series.take(range(g, pg))
+                table.remove_rows(
+                    start=rows[rows.index[0]], stop=rows[rows.index[-1]] + 1
+                )
+                pg = g
+
+            self.table.flush()
+
+        # return the number of rows removed
+        return ln
+
+
+class AppendableFrameTable(AppendableTable):
+    """support the new appendable table formats"""
+
+    pandas_kind = "frame_table"
+    table_type = "appendable_frame"
+    ndim = 2
+    obj_type: type[DataFrame | Series] = DataFrame
+
+    @property
+    def is_transposed(self) -> bool:
+        return self.index_axes[0].axis == 1
+
+    @classmethod
+    def get_object(cls, obj, transposed: bool):
+        """these are written transposed"""
+        if transposed:
+            obj = obj.T
+        return obj
+
+    def read(
+        self,
+        where=None,
+        columns=None,
+        start: int | None = None,
+        stop: int | None = None,
+    ):
+        # validate the version
+        self.validate_version(where)
+
+        # infer the data kind
+        if not self.infer_axes():
+            return None
+
+        result = self._read_axes(where=where, start=start, stop=stop)
+
+        info = (
+            self.info.get(self.non_index_axes[0][0], {})
+            if len(self.non_index_axes)
+            else {}
+        )
+
+        inds = [i for i, ax in enumerate(self.axes) if ax is self.index_axes[0]]
+        assert len(inds) == 1
+        ind = inds[0]
+
+        index = result[ind][0]
+
+        frames = []
+        for i, a in enumerate(self.axes):
+            if a not in self.values_axes:
+                continue
+            index_vals, cvalues = result[i]
+
+            # we could have a multi-index constructor here
+            # ensure_index doesn't recognized our list-of-tuples here
+            if info.get("type") != "MultiIndex":
+                cols = Index(index_vals)
+            else:
+                cols = MultiIndex.from_tuples(index_vals)
+
+            names = info.get("names")
+            if names is not None:
+                cols.set_names(names, inplace=True)
+
+            if self.is_transposed:
+                values = cvalues
+                index_ = cols
+                cols_ = Index(index, name=getattr(index, "name", None))
+            else:
+                values = cvalues.T
+                index_ = Index(index, name=getattr(index, "name", None))
+                cols_ = cols
+
+            # if we have a DataIndexableCol, its shape will only be 1 dim
+            if values.ndim == 1 and isinstance(values, np.ndarray):
+                values = values.reshape((1, values.shape[0]))
+
+            if isinstance(values, np.ndarray):
+                try:
+                    df = DataFrame(values.T, columns=cols_, index=index_, copy=False)
+                except UnicodeEncodeError as err:
+                    if (
+                        self.errors == "surrogatepass"
+                        and get_option("future.infer_string")
+                        and str(err).endswith("surrogates not allowed")
+                        and HAS_PYARROW
+                    ):
+                        df = DataFrame(
+                            values.T,
+                            columns=cols_,
+                            index=index_,
+                            copy=False,
+                            dtype=StringDtype(storage="python", na_value=np.nan),
+                        )
+                    else:
+                        raise
+            elif isinstance(values, Index):
+                df = DataFrame(values, columns=cols_, index=index_)
+            else:
+                # Categorical
+                df = DataFrame._from_arrays([values], columns=cols_, index=index_)
+            if not (using_string_dtype() and values.dtype.kind == "O"):
+                assert (df.dtypes == values.dtype).all(), (df.dtypes, values.dtype)
+
+            # If str / string dtype is stored in meta, use that.
+            for column in cols_:
+                dtype = getattr(self.table.attrs, f"{column}_meta", None)
+                if dtype in ["str", "string"]:
+                    df[column] = df[column].astype(dtype)
+            frames.append(df)
+
+        if len(frames) == 1:
+            df = frames[0]
+        else:
+            df = concat(frames, axis=1)
+
+        selection = Selection(self, where=where, start=start, stop=stop)
+        # apply the selection filters & axis orderings
+        df = self.process_axes(df, selection=selection, columns=columns)
+        return df
+
+
+class AppendableSeriesTable(AppendableFrameTable):
+    """support the new appendable table formats"""
+
+    pandas_kind = "series_table"
+    table_type = "appendable_series"
+    ndim = 2
+    obj_type = Series
+
+    @property
+    def is_transposed(self) -> bool:
+        return False
+
+    @classmethod
+    def get_object(cls, obj, transposed: bool):
+        return obj
+
+    # error: Signature of "write" incompatible with supertype "Fixed"
+    def write(self, obj, data_columns=None, **kwargs) -> None:  # type: ignore[override]
+        """we are going to write this as a frame table"""
+        if not isinstance(obj, DataFrame):
+            name = obj.name or "values"
+            obj = obj.to_frame(name)
+        super().write(obj=obj, data_columns=obj.columns.tolist(), **kwargs)
+
+    def read(
+        self,
+        where=None,
+        columns=None,
+        start: int | None = None,
+        stop: int | None = None,
+    ) -> Series:
+        is_multi_index = self.is_multi_index
+        if columns is not None and is_multi_index:
+            assert isinstance(self.levels, list)  # needed for mypy
+            for n in self.levels:
+                if n not in columns:
+                    columns.insert(0, n)
+        s = super().read(where=where, columns=columns, start=start, stop=stop)
+        if is_multi_index:
+            s.set_index(self.levels, inplace=True)
+
+        s = s.iloc[:, 0]
+
+        # remove the default name
+        if s.name == "values":
+            s.name = None
+        return s
+
+
+class AppendableMultiSeriesTable(AppendableSeriesTable):
+    """support the new appendable table formats"""
+
+    pandas_kind = "series_table"
+    table_type = "appendable_multiseries"
+
+    #  error: Signature of "write" incompatible with supertype "Fixed"
+    def write(self, obj, **kwargs) -> None:  # type: ignore[override]
+        """we are going to write this as a frame table"""
+        name = obj.name or "values"
+        newobj, self.levels = self.validate_multiindex(obj)
+        assert isinstance(self.levels, list)  # for mypy
+        cols = list(self.levels)
+        cols.append(name)
+        newobj.columns = Index(cols)
+        super().write(obj=newobj, **kwargs)
+
+
+class GenericTable(AppendableFrameTable):
+    """a table that read/writes the generic pytables table format"""
+
+    pandas_kind = "frame_table"
+    table_type = "generic_table"
+    ndim = 2
+    obj_type = DataFrame
+    levels: list[Hashable]
+
+    @property
+    def pandas_type(self) -> str:
+        return self.pandas_kind
+
+    @property
+    def storable(self):
+        return getattr(self.group, "table", None) or self.group
+
+    def get_attrs(self) -> None:
+        """retrieve our attributes"""
+        self.non_index_axes = []
+        self.nan_rep = None
+        self.levels = []
+
+        self.index_axes = [a for a in self.indexables if a.is_an_indexable]
+        self.values_axes = [a for a in self.indexables if not a.is_an_indexable]
+        self.data_columns = [a.name for a in self.values_axes]
+
+    @cache_readonly
+    def indexables(self):
+        """create the indexables from the table description"""
+        d = self.description
+
+        # TODO: can we get a typ for this?  AFAICT it is the only place
+        #  where we aren't passing one
+        # the index columns is just a simple index
+        md = self.read_metadata("index")
+        meta = "category" if md is not None else None
+        index_col = GenericIndexCol(
+            name="index", axis=0, table=self.table, meta=meta, metadata=md
+        )
+
+        _indexables: list[GenericIndexCol | GenericDataIndexableCol] = [index_col]
+
+        for i, n in enumerate(d._v_names):
+            assert isinstance(n, str)
+
+            atom = getattr(d, n)
+            md = self.read_metadata(n)
+            meta = "category" if md is not None else None
+            dc = GenericDataIndexableCol(
+                name=n,
+                pos=i,
+                values=[n],
+                typ=atom,
+                table=self.table,
+                meta=meta,
+                metadata=md,
+            )
+            _indexables.append(dc)
+
+        return _indexables
+
+    # error: Signature of "write" incompatible with supertype "AppendableTable"
+    def write(self, **kwargs) -> None:  # type: ignore[override]
+        raise NotImplementedError("cannot write on an generic table")
+
+
+class AppendableMultiFrameTable(AppendableFrameTable):
+    """a frame with a multi-index"""
+
+    table_type = "appendable_multiframe"
+    obj_type = DataFrame
+    ndim = 2
+    _re_levels = re.compile(r"^level_\d+$")
+
+    @property
+    def table_type_short(self) -> str:
+        return "appendable_multi"
+
+    # error: Signature of "write" incompatible with supertype "Fixed"
+    def write(self, obj, data_columns=None, **kwargs) -> None:  # type: ignore[override]
+        if data_columns is None:
+            data_columns = []
+        elif data_columns is True:
+            data_columns = obj.columns.tolist()
+        obj, self.levels = self.validate_multiindex(obj)
+        assert isinstance(self.levels, list)  # for mypy
+        for n in self.levels:
+            if n not in data_columns:
+                data_columns.insert(0, n)
+        super().write(obj=obj, data_columns=data_columns, **kwargs)
+
+    def read(
+        self,
+        where=None,
+        columns=None,
+        start: int | None = None,
+        stop: int | None = None,
+    ):
+        df = super().read(where=where, columns=columns, start=start, stop=stop)
+        df = df.set_index(self.levels)
+
+        # remove names for 'level_%d'
+        df.index = df.index.set_names(
+            [None if self._re_levels.search(name) else name for name in df.index.names]
+        )
+
+        return df
+
+
+def _reindex_axis(
+    obj: DataFrame, axis: AxisInt, labels: Index, other=None
+) -> DataFrame:
+    ax = obj._get_axis(axis)
+    labels = ensure_index(labels)
+
+    # try not to reindex even if other is provided
+    # if it equals our current index
+    if other is not None:
+        other = ensure_index(other)
+    if (other is None or labels.equals(other)) and labels.equals(ax):
+        return obj
+
+    labels = ensure_index(labels.unique())
+    if other is not None:
+        labels = ensure_index(other.unique()).intersection(labels, sort=False)
+    if not labels.equals(ax):
+        slicer: list[slice | Index] = [slice(None, None)] * obj.ndim
+        slicer[axis] = labels
+        obj = obj.loc[tuple(slicer)]
+    return obj
+
+
+# tz to/from coercion
+
+
+def _get_tz(tz: tzinfo) -> str | tzinfo:
+    """for a tz-aware type, return an encoded zone"""
+    zone = timezones.get_timezone(tz)
+    return zone
+
+
+@overload
+def _set_tz(
+    values: np.ndarray | Index, tz: str | tzinfo, coerce: bool = False
+) -> DatetimeIndex:
+    ...
+
+
+@overload
+def _set_tz(values: np.ndarray | Index, tz: None, coerce: bool = False) -> np.ndarray:
+    ...
+
+
+def _set_tz(
+    values: np.ndarray | Index, tz: str | tzinfo | None, coerce: bool = False
+) -> np.ndarray | DatetimeIndex:
+    """
+    coerce the values to a DatetimeIndex if tz is set
+    preserve the input shape if possible
+
+    Parameters
+    ----------
+    values : ndarray or Index
+    tz : str or tzinfo
+    coerce : if we do not have a passed timezone, coerce to M8[ns] ndarray
+    """
+    if isinstance(values, DatetimeIndex):
+        # If values is tzaware, the tz gets dropped in the values.ravel()
+        #  call below (which returns an ndarray).  So we are only non-lossy
+        #  if `tz` matches `values.tz`.
+        assert values.tz is None or values.tz == tz
+        if values.tz is not None:
+            return values
+
+    if tz is not None:
+        if isinstance(values, DatetimeIndex):
+            name = values.name
+        else:
+            name = None
+            values = values.ravel()
+
+        tz = _ensure_decoded(tz)
+        values = DatetimeIndex(values, name=name)
+        values = values.tz_localize("UTC").tz_convert(tz)
+    elif coerce:
+        values = np.asarray(values, dtype="M8[ns]")
+
+    # error: Incompatible return value type (got "Union[ndarray, Index]",
+    # expected "Union[ndarray, DatetimeIndex]")
+    return values  # type: ignore[return-value]
+
+
+def _convert_index(name: str, index: Index, encoding: str, errors: str) -> IndexCol:
+    assert isinstance(name, str)
+
+    index_name = index.name
+    # error: Argument 1 to "_get_data_and_dtype_name" has incompatible type "Index";
+    # expected "Union[ExtensionArray, ndarray]"
+    converted, dtype_name = _get_data_and_dtype_name(index)  # type: ignore[arg-type]
+    kind = _dtype_to_kind(dtype_name)
+    atom = DataIndexableCol._get_atom(converted)
+
+    if (
+        lib.is_np_dtype(index.dtype, "iu")
+        or needs_i8_conversion(index.dtype)
+        or is_bool_dtype(index.dtype)
+    ):
+        # Includes Index, RangeIndex, DatetimeIndex, TimedeltaIndex, PeriodIndex,
+        #  in which case "kind" is "integer", "integer", "datetime64",
+        #  "timedelta64", and "integer", respectively.
+        return IndexCol(
+            name,
+            values=converted,
+            kind=kind,
+            typ=atom,
+            freq=getattr(index, "freq", None),
+            tz=getattr(index, "tz", None),
+            index_name=index_name,
+        )
+
+    if isinstance(index, MultiIndex):
+        raise TypeError("MultiIndex not supported here!")
+
+    inferred_type = lib.infer_dtype(index, skipna=False)
+    # we won't get inferred_type of "datetime64" or "timedelta64" as these
+    #  would go through the DatetimeIndex/TimedeltaIndex paths above
+
+    values = np.asarray(index)
+
+    if inferred_type == "date":
+        converted = np.asarray([v.toordinal() for v in values], dtype=np.int32)
+        return IndexCol(
+            name, converted, "date", _tables().Time32Col(), index_name=index_name
+        )
+    elif inferred_type == "string":
+        converted = _convert_string_array(values, encoding, errors)
+        itemsize = converted.dtype.itemsize
+        return IndexCol(
+            name,
+            converted,
+            "string",
+            _tables().StringCol(itemsize),
+            index_name=index_name,
+        )
+
+    elif inferred_type in ["integer", "floating"]:
+        return IndexCol(
+            name, values=converted, kind=kind, typ=atom, index_name=index_name
+        )
+    else:
+        assert isinstance(converted, np.ndarray) and converted.dtype == object
+        assert kind == "object", kind
+        atom = _tables().ObjectAtom()
+        return IndexCol(name, converted, kind, atom, index_name=index_name)
+
+
+def _unconvert_index(data, kind: str, encoding: str, errors: str) -> np.ndarray | Index:
+    index: Index | np.ndarray
+
+    if kind.startswith("datetime64"):
+        if kind == "datetime64":
+            # created before we stored resolution information
+            index = DatetimeIndex(data)
+        else:
+            index = DatetimeIndex(data.view(kind))
+    elif kind == "timedelta64":
+        index = TimedeltaIndex(data)
+    elif kind == "date":
+        try:
+            index = np.asarray([date.fromordinal(v) for v in data], dtype=object)
+        except ValueError:
+            index = np.asarray([date.fromtimestamp(v) for v in data], dtype=object)
+    elif kind in ("integer", "float", "bool"):
+        index = np.asarray(data)
+    elif kind in ("string"):
+        index = _unconvert_string_array(
+            data, nan_rep=None, encoding=encoding, errors=errors
+        )
+    elif kind == "object":
+        index = np.asarray(data[0])
+    else:  # pragma: no cover
+        raise ValueError(f"unrecognized index type {kind}")
+    return index
+
+
+def _maybe_convert_for_string_atom(
+    name: str,
+    bvalues: ArrayLike,
+    existing_col,
+    min_itemsize,
+    nan_rep,
+    encoding,
+    errors,
+    columns: list[str],
+):
+    if isinstance(bvalues.dtype, StringDtype):
+        # "ndarray[Any, Any]" has no attribute "to_numpy"
+        bvalues = bvalues.to_numpy()  # type: ignore[union-attr]
+    if bvalues.dtype != object:
+        return bvalues
+
+    bvalues = cast(np.ndarray, bvalues)
+
+    dtype_name = bvalues.dtype.name
+    inferred_type = lib.infer_dtype(bvalues, skipna=False)
+
+    if inferred_type == "date":
+        raise TypeError("[date] is not implemented as a table column")
+    if inferred_type == "datetime":
+        # after GH#8260
+        # this only would be hit for a multi-timezone dtype which is an error
+        raise TypeError(
+            "too many timezones in this block, create separate data columns"
+        )
+
+    if not (inferred_type == "string" or dtype_name == "object"):
+        return bvalues
+
+    mask = isna(bvalues)
+    data = bvalues.copy()
+    data[mask] = nan_rep
+
+    if existing_col and mask.any() and len(nan_rep) > existing_col.itemsize:
+        raise ValueError("NaN representation is too large for existing column size")
+
+    # see if we have a valid string type
+    inferred_type = lib.infer_dtype(data, skipna=False)
+    if inferred_type != "string":
+        # we cannot serialize this data, so report an exception on a column
+        # by column basis
+
+        # expected behaviour:
+        # search block for a non-string object column by column
+        for i in range(data.shape[0]):
+            col = data[i]
+            inferred_type = lib.infer_dtype(col, skipna=False)
+            if inferred_type != "string":
+                error_column_label = columns[i] if len(columns) > i else f"No.{i}"
+                raise TypeError(
+                    f"Cannot serialize the column [{error_column_label}]\n"
+                    f"because its data contents are not [string] but "
+                    f"[{inferred_type}] object dtype"
+                )
+
+    # itemsize is the maximum length of a string (along any dimension)
+
+    data_converted = _convert_string_array(data, encoding, errors).reshape(data.shape)
+    itemsize = data_converted.itemsize
+
+    # specified min_itemsize?
+    if isinstance(min_itemsize, dict):
+        min_itemsize = int(min_itemsize.get(name) or min_itemsize.get("values") or 0)
+    itemsize = max(min_itemsize or 0, itemsize)
+
+    # check for column in the values conflicts
+    if existing_col is not None:
+        eci = existing_col.validate_col(itemsize)
+        if eci is not None and eci > itemsize:
+            itemsize = eci
+
+    data_converted = data_converted.astype(f"|S{itemsize}", copy=False)
+    return data_converted
+
+
+def _convert_string_array(data: np.ndarray, encoding: str, errors: str) -> np.ndarray:
+    """
+    Take a string-like that is object dtype and coerce to a fixed size string type.
+
+    Parameters
+    ----------
+    data : np.ndarray[object]
+    encoding : str
+    errors : str
+        Handler for encoding errors.
+
+    Returns
+    -------
+    np.ndarray[fixed-length-string]
+    """
+    # encode if needed
+    if len(data):
+        data = (
+            Series(data.ravel(), copy=False, dtype="object")
+            .str.encode(encoding, errors)
+            ._values.reshape(data.shape)
+        )
+
+    # create the sized dtype
+    ensured = ensure_object(data.ravel())
+    itemsize = max(1, libwriters.max_len_string_array(ensured))
+
+    data = np.asarray(data, dtype=f"S{itemsize}")
+    return data
+
+
+def _unconvert_string_array(
+    data: np.ndarray, nan_rep, encoding: str, errors: str
+) -> np.ndarray:
+    """
+    Inverse of _convert_string_array.
+
+    Parameters
+    ----------
+    data : np.ndarray[fixed-length-string]
+    nan_rep : the storage repr of NaN
+    encoding : str
+    errors : str
+        Handler for encoding errors.
+
+    Returns
+    -------
+    np.ndarray[object]
+        Decoded data.
+    """
+    shape = data.shape
+    data = np.asarray(data.ravel(), dtype=object)
+
+    if len(data):
+        itemsize = libwriters.max_len_string_array(ensure_object(data))
+        dtype = f"U{itemsize}"
+
+        if isinstance(data[0], bytes):
+            ser = Series(data, copy=False).str.decode(
+                encoding, errors=errors, dtype="object"
+            )
+            data = ser.to_numpy()
+            data.flags.writeable = True
+        else:
+            data = data.astype(dtype, copy=False).astype(object, copy=False)
+
+    if nan_rep is None:
+        nan_rep = "nan"
+
+    libwriters.string_array_replace_from_nan_rep(data, nan_rep)
+    return data.reshape(shape)
+
+
+def _maybe_convert(values: np.ndarray, val_kind: str, encoding: str, errors: str):
+    assert isinstance(val_kind, str), type(val_kind)
+    if _need_convert(val_kind):
+        conv = _get_converter(val_kind, encoding, errors)
+        values = conv(values)
+    return values
+
+
+def _get_converter(kind: str, encoding: str, errors: str):
+    if kind == "datetime64":
+        return lambda x: np.asarray(x, dtype="M8[ns]")
+    elif "datetime64" in kind:
+        return lambda x: np.asarray(x, dtype=kind)
+    elif kind == "string":
+        return lambda x: _unconvert_string_array(
+            x, nan_rep=None, encoding=encoding, errors=errors
+        )
+    else:  # pragma: no cover
+        raise ValueError(f"invalid kind {kind}")
+
+
+def _need_convert(kind: str) -> bool:
+    if kind in ("datetime64", "string") or "datetime64" in kind:
+        return True
+    return False
+
+
+def _maybe_adjust_name(name: str, version: Sequence[int]) -> str:
+    """
+    Prior to 0.10.1, we named values blocks like: values_block_0 an the
+    name values_0, adjust the given name if necessary.
+
+    Parameters
+    ----------
+    name : str
+    version : Tuple[int, int, int]
+
+    Returns
+    -------
+    str
+    """
+    if isinstance(version, str) or len(version) < 3:
+        raise ValueError("Version is incorrect, expected sequence of 3 integers.")
+
+    if version[0] == 0 and version[1] <= 10 and version[2] == 0:
+        m = re.search(r"values_block_(\d+)", name)
+        if m:
+            grp = m.groups()[0]
+            name = f"values_{grp}"
+    return name
+
+
+def _dtype_to_kind(dtype_str: str) -> str:
+    """
+    Find the "kind" string describing the given dtype name.
+    """
+    dtype_str = _ensure_decoded(dtype_str)
+
+    if dtype_str.startswith(("string", "bytes")):
+        kind = "string"
+    elif dtype_str.startswith("float"):
+        kind = "float"
+    elif dtype_str.startswith("complex"):
+        kind = "complex"
+    elif dtype_str.startswith(("int", "uint")):
+        kind = "integer"
+    elif dtype_str.startswith("datetime64"):
+        kind = dtype_str
+    elif dtype_str.startswith("timedelta"):
+        kind = "timedelta64"
+    elif dtype_str.startswith("bool"):
+        kind = "bool"
+    elif dtype_str.startswith("category"):
+        kind = "category"
+    elif dtype_str.startswith("period"):
+        # We store the `freq` attr so we can restore from integers
+        kind = "integer"
+    elif dtype_str == "object":
+        kind = "object"
+    elif dtype_str == "str":
+        kind = "str"
+    else:
+        raise ValueError(f"cannot interpret dtype of [{dtype_str}]")
+
+    return kind
+
+
+def _get_data_and_dtype_name(data: ArrayLike):
+    """
+    Convert the passed data into a storable form and a dtype string.
+    """
+    if isinstance(data, Categorical):
+        data = data.codes
+
+    if isinstance(data.dtype, DatetimeTZDtype):
+        # For datetime64tz we need to drop the TZ in tests TODO: why?
+        dtype_name = f"datetime64[{data.dtype.unit}]"
+    else:
+        dtype_name = data.dtype.name
+
+    if data.dtype.kind in "mM":
+        data = np.asarray(data.view("i8"))
+        # TODO: we used to reshape for the dt64tz case, but no longer
+        #  doing that doesn't seem to break anything.  why?
+
+    elif isinstance(data, PeriodIndex):
+        data = data.asi8
+
+    data = np.asarray(data)
+    return data, dtype_name
+
+
+class Selection:
+    """
+    Carries out a selection operation on a tables.Table object.
+
+    Parameters
+    ----------
+    table : a Table object
+    where : list of Terms (or convertible to)
+    start, stop: indices to start and/or stop selection
+
+    """
+
+    def __init__(
+        self,
+        table: Table,
+        where=None,
+        start: int | None = None,
+        stop: int | None = None,
+    ) -> None:
+        self.table = table
+        self.where = where
+        self.start = start
+        self.stop = stop
+        self.condition = None
+        self.filter = None
+        self.terms = None
+        self.coordinates = None
+
+        if is_list_like(where):
+            # see if we have a passed coordinate like
+            with suppress(ValueError):
+                inferred = lib.infer_dtype(where, skipna=False)
+                if inferred in ("integer", "boolean"):
+                    where = np.asarray(where)
+                    if where.dtype == np.bool_:
+                        start, stop = self.start, self.stop
+                        if start is None:
+                            start = 0
+                        if stop is None:
+                            stop = self.table.nrows
+                        self.coordinates = np.arange(start, stop)[where]
+                    elif issubclass(where.dtype.type, np.integer):
+                        if (self.start is not None and (where < self.start).any()) or (
+                            self.stop is not None and (where >= self.stop).any()
+                        ):
+                            raise ValueError(
+                                "where must have index locations >= start and < stop"
+                            )
+                        self.coordinates = where
+
+        if self.coordinates is None:
+            self.terms = self.generate(where)
+
+            # create the numexpr & the filter
+            if self.terms is not None:
+                self.condition, self.filter = self.terms.evaluate()
+
+    def generate(self, where):
+        """where can be a : dict,list,tuple,string"""
+        if where is None:
+            return None
+
+        q = self.table.queryables()
+        try:
+            return PyTablesExpr(where, queryables=q, encoding=self.table.encoding)
+        except NameError as err:
+            # raise a nice message, suggesting that the user should use
+            # data_columns
+            qkeys = ",".join(q.keys())
+            msg = dedent(
+                f"""\
+                The passed where expression: {where}
+                            contains an invalid variable reference
+                            all of the variable references must be a reference to
+                            an axis (e.g. 'index' or 'columns'), or a data_column
+                            The currently defined references are: {qkeys}
+                """
+            )
+            raise ValueError(msg) from err
+
+    def select(self):
+        """
+        generate the selection
+        """
+        if self.condition is not None:
+            return self.table.table.read_where(
+                self.condition.format(), start=self.start, stop=self.stop
+            )
+        elif self.coordinates is not None:
+            return self.table.table.read_coordinates(self.coordinates)
+        return self.table.table.read(start=self.start, stop=self.stop)
+
+    def select_coords(self):
+        """
+        generate the selection
+        """
+        start, stop = self.start, self.stop
+        nrows = self.table.nrows
+        if start is None:
+            start = 0
+        elif start < 0:
+            start += nrows
+        if stop is None:
+            stop = nrows
+        elif stop < 0:
+            stop += nrows
+
+        if self.condition is not None:
+            return self.table.table.get_where_list(
+                self.condition.format(), start=start, stop=stop, sort=True
+            )
+        elif self.coordinates is not None:
+            return self.coordinates
+
+        return np.arange(start, stop)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/sas/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/sas/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..317730745b6e3a0278a48b7bb810cf43e718e787
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/sas/__init__.py
@@ -0,0 +1,3 @@
+from pandas.io.sas.sasreader import read_sas
+
+__all__ = ["read_sas"]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/sas/sas7bdat.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/sas/sas7bdat.py
new file mode 100644
index 0000000000000000000000000000000000000000..1d424425cd927784ea2f16c41f635d71143995f9
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/sas/sas7bdat.py
@@ -0,0 +1,762 @@
+"""
+Read SAS7BDAT files
+
+Based on code written by Jared Hobbs:
+  https://bitbucket.org/jaredhobbs/sas7bdat
+
+See also:
+  https://github.com/BioStatMatt/sas7bdat
+
+Partial documentation of the file format:
+  https://cran.r-project.org/package=sas7bdat/vignettes/sas7bdat.pdf
+
+Reference for binary data compression:
+  http://collaboration.cmc.ec.gc.ca/science/rpn/biblio/ddj/Website/articles/CUJ/1992/9210/ross/ross.htm
+"""
+from __future__ import annotations
+
+from collections import abc
+from datetime import (
+    datetime,
+    timedelta,
+)
+import sys
+from typing import TYPE_CHECKING
+
+import numpy as np
+
+from pandas._config import get_option
+
+from pandas._libs.byteswap import (
+    read_double_with_byteswap,
+    read_float_with_byteswap,
+    read_uint16_with_byteswap,
+    read_uint32_with_byteswap,
+    read_uint64_with_byteswap,
+)
+from pandas._libs.sas import (
+    Parser,
+    get_subheader_index,
+)
+from pandas._libs.tslibs.conversion import cast_from_unit_vectorized
+from pandas.errors import EmptyDataError
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Timestamp,
+    isna,
+)
+
+from pandas.io.common import get_handle
+import pandas.io.sas.sas_constants as const
+from pandas.io.sas.sasreader import ReaderBase
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        CompressionOptions,
+        FilePath,
+        ReadBuffer,
+    )
+
+
+_unix_origin = Timestamp("1970-01-01")
+_sas_origin = Timestamp("1960-01-01")
+
+
+def _parse_datetime(sas_datetime: float, unit: str):
+    if isna(sas_datetime):
+        return pd.NaT
+
+    if unit == "s":
+        return datetime(1960, 1, 1) + timedelta(seconds=sas_datetime)
+
+    elif unit == "d":
+        return datetime(1960, 1, 1) + timedelta(days=sas_datetime)
+
+    else:
+        raise ValueError("unit must be 'd' or 's'")
+
+
+def _convert_datetimes(sas_datetimes: pd.Series, unit: str) -> pd.Series:
+    """
+    Convert to Timestamp if possible, otherwise to datetime.datetime.
+    SAS float64 lacks precision for more than ms resolution so the fit
+    to datetime.datetime is ok.
+
+    Parameters
+    ----------
+    sas_datetimes : {Series, Sequence[float]}
+       Dates or datetimes in SAS
+    unit : {'d', 's'}
+       "d" if the floats represent dates, "s" for datetimes
+
+    Returns
+    -------
+    Series
+       Series of datetime64 dtype or datetime.datetime.
+    """
+    td = (_sas_origin - _unix_origin).as_unit("s")
+    if unit == "s":
+        millis = cast_from_unit_vectorized(
+            sas_datetimes._values, unit="s", out_unit="ms"
+        )
+        dt64ms = millis.view("M8[ms]") + td
+        return pd.Series(dt64ms, index=sas_datetimes.index, copy=False)
+    else:
+        vals = np.array(sas_datetimes, dtype="M8[D]") + td
+        return pd.Series(vals, dtype="M8[s]", index=sas_datetimes.index, copy=False)
+
+
+class _Column:
+    col_id: int
+    name: str | bytes
+    label: str | bytes
+    format: str | bytes
+    ctype: bytes
+    length: int
+
+    def __init__(
+        self,
+        col_id: int,
+        # These can be bytes when convert_header_text is False
+        name: str | bytes,
+        label: str | bytes,
+        format: str | bytes,
+        ctype: bytes,
+        length: int,
+    ) -> None:
+        self.col_id = col_id
+        self.name = name
+        self.label = label
+        self.format = format
+        self.ctype = ctype
+        self.length = length
+
+
+# SAS7BDAT represents a SAS data file in SAS7BDAT format.
+class SAS7BDATReader(ReaderBase, abc.Iterator):
+    """
+    Read SAS files in SAS7BDAT format.
+
+    Parameters
+    ----------
+    path_or_buf : path name or buffer
+        Name of SAS file or file-like object pointing to SAS file
+        contents.
+    index : column identifier, defaults to None
+        Column to use as index.
+    convert_dates : bool, defaults to True
+        Attempt to convert dates to Pandas datetime values.  Note that
+        some rarely used SAS date formats may be unsupported.
+    blank_missing : bool, defaults to True
+        Convert empty strings to missing values (SAS uses blanks to
+        indicate missing character variables).
+    chunksize : int, defaults to None
+        Return SAS7BDATReader object for iterations, returns chunks
+        with given number of lines.
+    encoding : str, 'infer', defaults to None
+        String encoding acc. to Python standard encodings,
+        encoding='infer' tries to detect the encoding from the file header,
+        encoding=None will leave the data in binary format.
+    convert_text : bool, defaults to True
+        If False, text variables are left as raw bytes.
+    convert_header_text : bool, defaults to True
+        If False, header text, including column names, are left as raw
+        bytes.
+    """
+
+    _int_length: int
+    _cached_page: bytes | None
+
+    def __init__(
+        self,
+        path_or_buf: FilePath | ReadBuffer[bytes],
+        index=None,
+        convert_dates: bool = True,
+        blank_missing: bool = True,
+        chunksize: int | None = None,
+        encoding: str | None = None,
+        convert_text: bool = True,
+        convert_header_text: bool = True,
+        compression: CompressionOptions = "infer",
+    ) -> None:
+        self.index = index
+        self.convert_dates = convert_dates
+        self.blank_missing = blank_missing
+        self.chunksize = chunksize
+        self.encoding = encoding
+        self.convert_text = convert_text
+        self.convert_header_text = convert_header_text
+
+        self.default_encoding = "latin-1"
+        self.compression = b""
+        self.column_names_raw: list[bytes] = []
+        self.column_names: list[str | bytes] = []
+        self.column_formats: list[str | bytes] = []
+        self.columns: list[_Column] = []
+
+        self._current_page_data_subheader_pointers: list[tuple[int, int]] = []
+        self._cached_page = None
+        self._column_data_lengths: list[int] = []
+        self._column_data_offsets: list[int] = []
+        self._column_types: list[bytes] = []
+
+        self._current_row_in_file_index = 0
+        self._current_row_on_page_index = 0
+        self._current_row_in_file_index = 0
+
+        self.handles = get_handle(
+            path_or_buf, "rb", is_text=False, compression=compression
+        )
+
+        self._path_or_buf = self.handles.handle
+
+        # Same order as const.SASIndex
+        self._subheader_processors = [
+            self._process_rowsize_subheader,
+            self._process_columnsize_subheader,
+            self._process_subheader_counts,
+            self._process_columntext_subheader,
+            self._process_columnname_subheader,
+            self._process_columnattributes_subheader,
+            self._process_format_subheader,
+            self._process_columnlist_subheader,
+            None,  # Data
+        ]
+
+        try:
+            self._get_properties()
+            self._parse_metadata()
+        except Exception:
+            self.close()
+            raise
+
+    def column_data_lengths(self) -> np.ndarray:
+        """Return a numpy int64 array of the column data lengths"""
+        return np.asarray(self._column_data_lengths, dtype=np.int64)
+
+    def column_data_offsets(self) -> np.ndarray:
+        """Return a numpy int64 array of the column offsets"""
+        return np.asarray(self._column_data_offsets, dtype=np.int64)
+
+    def column_types(self) -> np.ndarray:
+        """
+        Returns a numpy character array of the column types:
+           s (string) or d (double)
+        """
+        return np.asarray(self._column_types, dtype=np.dtype("S1"))
+
+    def close(self) -> None:
+        self.handles.close()
+
+    def _get_properties(self) -> None:
+        # Check magic number
+        self._path_or_buf.seek(0)
+        self._cached_page = self._path_or_buf.read(288)
+        if self._cached_page[0 : len(const.magic)] != const.magic:
+            raise ValueError("magic number mismatch (not a SAS file?)")
+
+        # Get alignment information
+        buf = self._read_bytes(const.align_1_offset, const.align_1_length)
+        if buf == const.u64_byte_checker_value:
+            self.U64 = True
+            self._int_length = 8
+            self._page_bit_offset = const.page_bit_offset_x64
+            self._subheader_pointer_length = const.subheader_pointer_length_x64
+        else:
+            self.U64 = False
+            self._page_bit_offset = const.page_bit_offset_x86
+            self._subheader_pointer_length = const.subheader_pointer_length_x86
+            self._int_length = 4
+        buf = self._read_bytes(const.align_2_offset, const.align_2_length)
+        if buf == const.align_1_checker_value:
+            align1 = const.align_2_value
+        else:
+            align1 = 0
+
+        # Get endianness information
+        buf = self._read_bytes(const.endianness_offset, const.endianness_length)
+        if buf == b"\x01":
+            self.byte_order = "<"
+            self.need_byteswap = sys.byteorder == "big"
+        else:
+            self.byte_order = ">"
+            self.need_byteswap = sys.byteorder == "little"
+
+        # Get encoding information
+        buf = self._read_bytes(const.encoding_offset, const.encoding_length)[0]
+        if buf in const.encoding_names:
+            self.inferred_encoding = const.encoding_names[buf]
+            if self.encoding == "infer":
+                self.encoding = self.inferred_encoding
+        else:
+            self.inferred_encoding = f"unknown (code={buf})"
+
+        # Timestamp is epoch 01/01/1960
+        epoch = datetime(1960, 1, 1)
+        x = self._read_float(
+            const.date_created_offset + align1, const.date_created_length
+        )
+        self.date_created = epoch + pd.to_timedelta(x, unit="s")
+        x = self._read_float(
+            const.date_modified_offset + align1, const.date_modified_length
+        )
+        self.date_modified = epoch + pd.to_timedelta(x, unit="s")
+
+        self.header_length = self._read_uint(
+            const.header_size_offset + align1, const.header_size_length
+        )
+
+        # Read the rest of the header into cached_page.
+        buf = self._path_or_buf.read(self.header_length - 288)
+        self._cached_page += buf
+        # error: Argument 1 to "len" has incompatible type "Optional[bytes]";
+        #  expected "Sized"
+        if len(self._cached_page) != self.header_length:  # type: ignore[arg-type]
+            raise ValueError("The SAS7BDAT file appears to be truncated.")
+
+        self._page_length = self._read_uint(
+            const.page_size_offset + align1, const.page_size_length
+        )
+
+    def __next__(self) -> DataFrame:
+        da = self.read(nrows=self.chunksize or 1)
+        if da.empty:
+            self.close()
+            raise StopIteration
+        return da
+
+    # Read a single float of the given width (4 or 8).
+    def _read_float(self, offset: int, width: int):
+        assert self._cached_page is not None
+        if width == 4:
+            return read_float_with_byteswap(
+                self._cached_page, offset, self.need_byteswap
+            )
+        elif width == 8:
+            return read_double_with_byteswap(
+                self._cached_page, offset, self.need_byteswap
+            )
+        else:
+            self.close()
+            raise ValueError("invalid float width")
+
+    # Read a single unsigned integer of the given width (1, 2, 4 or 8).
+    def _read_uint(self, offset: int, width: int) -> int:
+        assert self._cached_page is not None
+        if width == 1:
+            return self._read_bytes(offset, 1)[0]
+        elif width == 2:
+            return read_uint16_with_byteswap(
+                self._cached_page, offset, self.need_byteswap
+            )
+        elif width == 4:
+            return read_uint32_with_byteswap(
+                self._cached_page, offset, self.need_byteswap
+            )
+        elif width == 8:
+            return read_uint64_with_byteswap(
+                self._cached_page, offset, self.need_byteswap
+            )
+        else:
+            self.close()
+            raise ValueError("invalid int width")
+
+    def _read_bytes(self, offset: int, length: int):
+        assert self._cached_page is not None
+        if offset + length > len(self._cached_page):
+            self.close()
+            raise ValueError("The cached page is too small.")
+        return self._cached_page[offset : offset + length]
+
+    def _read_and_convert_header_text(self, offset: int, length: int) -> str | bytes:
+        return self._convert_header_text(
+            self._read_bytes(offset, length).rstrip(b"\x00 ")
+        )
+
+    def _parse_metadata(self) -> None:
+        done = False
+        while not done:
+            self._cached_page = self._path_or_buf.read(self._page_length)
+            if len(self._cached_page) <= 0:
+                break
+            if len(self._cached_page) != self._page_length:
+                raise ValueError("Failed to read a meta data page from the SAS file.")
+            done = self._process_page_meta()
+
+    def _process_page_meta(self) -> bool:
+        self._read_page_header()
+        pt = const.page_meta_types + [const.page_amd_type, const.page_mix_type]
+        if self._current_page_type in pt:
+            self._process_page_metadata()
+        is_data_page = self._current_page_type == const.page_data_type
+        is_mix_page = self._current_page_type == const.page_mix_type
+        return bool(
+            is_data_page
+            or is_mix_page
+            or self._current_page_data_subheader_pointers != []
+        )
+
+    def _read_page_header(self) -> None:
+        bit_offset = self._page_bit_offset
+        tx = const.page_type_offset + bit_offset
+        self._current_page_type = (
+            self._read_uint(tx, const.page_type_length) & const.page_type_mask2
+        )
+        tx = const.block_count_offset + bit_offset
+        self._current_page_block_count = self._read_uint(tx, const.block_count_length)
+        tx = const.subheader_count_offset + bit_offset
+        self._current_page_subheaders_count = self._read_uint(
+            tx, const.subheader_count_length
+        )
+
+    def _process_page_metadata(self) -> None:
+        bit_offset = self._page_bit_offset
+
+        for i in range(self._current_page_subheaders_count):
+            offset = const.subheader_pointers_offset + bit_offset
+            total_offset = offset + self._subheader_pointer_length * i
+
+            subheader_offset = self._read_uint(total_offset, self._int_length)
+            total_offset += self._int_length
+
+            subheader_length = self._read_uint(total_offset, self._int_length)
+            total_offset += self._int_length
+
+            subheader_compression = self._read_uint(total_offset, 1)
+            total_offset += 1
+
+            subheader_type = self._read_uint(total_offset, 1)
+
+            if (
+                subheader_length == 0
+                or subheader_compression == const.truncated_subheader_id
+            ):
+                continue
+
+            subheader_signature = self._read_bytes(subheader_offset, self._int_length)
+            subheader_index = get_subheader_index(subheader_signature)
+            subheader_processor = self._subheader_processors[subheader_index]
+
+            if subheader_processor is None:
+                f1 = subheader_compression in (const.compressed_subheader_id, 0)
+                f2 = subheader_type == const.compressed_subheader_type
+                if self.compression and f1 and f2:
+                    self._current_page_data_subheader_pointers.append(
+                        (subheader_offset, subheader_length)
+                    )
+                else:
+                    self.close()
+                    raise ValueError(
+                        f"Unknown subheader signature {subheader_signature}"
+                    )
+            else:
+                subheader_processor(subheader_offset, subheader_length)
+
+    def _process_rowsize_subheader(self, offset: int, length: int) -> None:
+        int_len = self._int_length
+        lcs_offset = offset
+        lcp_offset = offset
+        if self.U64:
+            lcs_offset += 682
+            lcp_offset += 706
+        else:
+            lcs_offset += 354
+            lcp_offset += 378
+
+        self.row_length = self._read_uint(
+            offset + const.row_length_offset_multiplier * int_len,
+            int_len,
+        )
+        self.row_count = self._read_uint(
+            offset + const.row_count_offset_multiplier * int_len,
+            int_len,
+        )
+        self.col_count_p1 = self._read_uint(
+            offset + const.col_count_p1_multiplier * int_len, int_len
+        )
+        self.col_count_p2 = self._read_uint(
+            offset + const.col_count_p2_multiplier * int_len, int_len
+        )
+        mx = const.row_count_on_mix_page_offset_multiplier * int_len
+        self._mix_page_row_count = self._read_uint(offset + mx, int_len)
+        self._lcs = self._read_uint(lcs_offset, 2)
+        self._lcp = self._read_uint(lcp_offset, 2)
+
+    def _process_columnsize_subheader(self, offset: int, length: int) -> None:
+        int_len = self._int_length
+        offset += int_len
+        self.column_count = self._read_uint(offset, int_len)
+        if self.col_count_p1 + self.col_count_p2 != self.column_count:
+            print(
+                f"Warning: column count mismatch ({self.col_count_p1} + "
+                f"{self.col_count_p2} != {self.column_count})\n"
+            )
+
+    # Unknown purpose
+    def _process_subheader_counts(self, offset: int, length: int) -> None:
+        pass
+
+    def _process_columntext_subheader(self, offset: int, length: int) -> None:
+        offset += self._int_length
+        text_block_size = self._read_uint(offset, const.text_block_size_length)
+
+        buf = self._read_bytes(offset, text_block_size)
+        cname_raw = buf[0:text_block_size].rstrip(b"\x00 ")
+        self.column_names_raw.append(cname_raw)
+
+        if len(self.column_names_raw) == 1:
+            compression_literal = b""
+            for cl in const.compression_literals:
+                if cl in cname_raw:
+                    compression_literal = cl
+            self.compression = compression_literal
+            offset -= self._int_length
+
+            offset1 = offset + 16
+            if self.U64:
+                offset1 += 4
+
+            buf = self._read_bytes(offset1, self._lcp)
+            compression_literal = buf.rstrip(b"\x00")
+            if compression_literal == b"":
+                self._lcs = 0
+                offset1 = offset + 32
+                if self.U64:
+                    offset1 += 4
+                buf = self._read_bytes(offset1, self._lcp)
+                self.creator_proc = buf[0 : self._lcp]
+            elif compression_literal == const.rle_compression:
+                offset1 = offset + 40
+                if self.U64:
+                    offset1 += 4
+                buf = self._read_bytes(offset1, self._lcp)
+                self.creator_proc = buf[0 : self._lcp]
+            elif self._lcs > 0:
+                self._lcp = 0
+                offset1 = offset + 16
+                if self.U64:
+                    offset1 += 4
+                buf = self._read_bytes(offset1, self._lcs)
+                self.creator_proc = buf[0 : self._lcp]
+            if hasattr(self, "creator_proc"):
+                self.creator_proc = self._convert_header_text(self.creator_proc)
+
+    def _process_columnname_subheader(self, offset: int, length: int) -> None:
+        int_len = self._int_length
+        offset += int_len
+        column_name_pointers_count = (length - 2 * int_len - 12) // 8
+        for i in range(column_name_pointers_count):
+            text_subheader = (
+                offset
+                + const.column_name_pointer_length * (i + 1)
+                + const.column_name_text_subheader_offset
+            )
+            col_name_offset = (
+                offset
+                + const.column_name_pointer_length * (i + 1)
+                + const.column_name_offset_offset
+            )
+            col_name_length = (
+                offset
+                + const.column_name_pointer_length * (i + 1)
+                + const.column_name_length_offset
+            )
+
+            idx = self._read_uint(
+                text_subheader, const.column_name_text_subheader_length
+            )
+            col_offset = self._read_uint(
+                col_name_offset, const.column_name_offset_length
+            )
+            col_len = self._read_uint(col_name_length, const.column_name_length_length)
+
+            name_raw = self.column_names_raw[idx]
+            cname = name_raw[col_offset : col_offset + col_len]
+            self.column_names.append(self._convert_header_text(cname))
+
+    def _process_columnattributes_subheader(self, offset: int, length: int) -> None:
+        int_len = self._int_length
+        column_attributes_vectors_count = (length - 2 * int_len - 12) // (int_len + 8)
+        for i in range(column_attributes_vectors_count):
+            col_data_offset = (
+                offset + int_len + const.column_data_offset_offset + i * (int_len + 8)
+            )
+            col_data_len = (
+                offset
+                + 2 * int_len
+                + const.column_data_length_offset
+                + i * (int_len + 8)
+            )
+            col_types = (
+                offset + 2 * int_len + const.column_type_offset + i * (int_len + 8)
+            )
+
+            x = self._read_uint(col_data_offset, int_len)
+            self._column_data_offsets.append(x)
+
+            x = self._read_uint(col_data_len, const.column_data_length_length)
+            self._column_data_lengths.append(x)
+
+            x = self._read_uint(col_types, const.column_type_length)
+            self._column_types.append(b"d" if x == 1 else b"s")
+
+    def _process_columnlist_subheader(self, offset: int, length: int) -> None:
+        # unknown purpose
+        pass
+
+    def _process_format_subheader(self, offset: int, length: int) -> None:
+        int_len = self._int_length
+        text_subheader_format = (
+            offset + const.column_format_text_subheader_index_offset + 3 * int_len
+        )
+        col_format_offset = offset + const.column_format_offset_offset + 3 * int_len
+        col_format_len = offset + const.column_format_length_offset + 3 * int_len
+        text_subheader_label = (
+            offset + const.column_label_text_subheader_index_offset + 3 * int_len
+        )
+        col_label_offset = offset + const.column_label_offset_offset + 3 * int_len
+        col_label_len = offset + const.column_label_length_offset + 3 * int_len
+
+        x = self._read_uint(
+            text_subheader_format, const.column_format_text_subheader_index_length
+        )
+        format_idx = min(x, len(self.column_names_raw) - 1)
+
+        format_start = self._read_uint(
+            col_format_offset, const.column_format_offset_length
+        )
+        format_len = self._read_uint(col_format_len, const.column_format_length_length)
+
+        label_idx = self._read_uint(
+            text_subheader_label, const.column_label_text_subheader_index_length
+        )
+        label_idx = min(label_idx, len(self.column_names_raw) - 1)
+
+        label_start = self._read_uint(
+            col_label_offset, const.column_label_offset_length
+        )
+        label_len = self._read_uint(col_label_len, const.column_label_length_length)
+
+        label_names = self.column_names_raw[label_idx]
+        column_label = self._convert_header_text(
+            label_names[label_start : label_start + label_len]
+        )
+        format_names = self.column_names_raw[format_idx]
+        column_format = self._convert_header_text(
+            format_names[format_start : format_start + format_len]
+        )
+        current_column_number = len(self.columns)
+
+        col = _Column(
+            current_column_number,
+            self.column_names[current_column_number],
+            column_label,
+            column_format,
+            self._column_types[current_column_number],
+            self._column_data_lengths[current_column_number],
+        )
+
+        self.column_formats.append(column_format)
+        self.columns.append(col)
+
+    def read(self, nrows: int | None = None) -> DataFrame:
+        if (nrows is None) and (self.chunksize is not None):
+            nrows = self.chunksize
+        elif nrows is None:
+            nrows = self.row_count
+
+        if len(self._column_types) == 0:
+            self.close()
+            raise EmptyDataError("No columns to parse from file")
+
+        if nrows > 0 and self._current_row_in_file_index >= self.row_count:
+            return DataFrame()
+
+        nrows = min(nrows, self.row_count - self._current_row_in_file_index)
+
+        nd = self._column_types.count(b"d")
+        ns = self._column_types.count(b"s")
+
+        self._string_chunk = np.empty((ns, nrows), dtype=object)
+        self._byte_chunk = np.zeros((nd, 8 * nrows), dtype=np.uint8)
+
+        self._current_row_in_chunk_index = 0
+        p = Parser(self)
+        p.read(nrows)
+
+        rslt = self._chunk_to_dataframe()
+        if self.index is not None:
+            rslt = rslt.set_index(self.index)
+
+        return rslt
+
+    def _read_next_page(self):
+        self._current_page_data_subheader_pointers = []
+        self._cached_page = self._path_or_buf.read(self._page_length)
+        if len(self._cached_page) <= 0:
+            return True
+        elif len(self._cached_page) != self._page_length:
+            self.close()
+            msg = (
+                "failed to read complete page from file (read "
+                f"{len(self._cached_page):d} of {self._page_length:d} bytes)"
+            )
+            raise ValueError(msg)
+
+        self._read_page_header()
+        if self._current_page_type in const.page_meta_types:
+            self._process_page_metadata()
+
+        if self._current_page_type not in const.page_meta_types + [
+            const.page_data_type,
+            const.page_mix_type,
+        ]:
+            return self._read_next_page()
+
+        return False
+
+    def _chunk_to_dataframe(self) -> DataFrame:
+        n = self._current_row_in_chunk_index
+        m = self._current_row_in_file_index
+        ix = range(m - n, m)
+        rslt = {}
+
+        js, jb = 0, 0
+        infer_string = get_option("future.infer_string")
+        for j in range(self.column_count):
+            name = self.column_names[j]
+
+            if self._column_types[j] == b"d":
+                col_arr = self._byte_chunk[jb, :].view(dtype=self.byte_order + "d")
+                rslt[name] = pd.Series(col_arr, dtype=np.float64, index=ix, copy=False)
+                if self.convert_dates:
+                    if self.column_formats[j] in const.sas_date_formats:
+                        rslt[name] = _convert_datetimes(rslt[name], "d")
+                    elif self.column_formats[j] in const.sas_datetime_formats:
+                        rslt[name] = _convert_datetimes(rslt[name], "s")
+                jb += 1
+            elif self._column_types[j] == b"s":
+                rslt[name] = pd.Series(self._string_chunk[js, :], index=ix, copy=False)
+                if self.convert_text and (self.encoding is not None):
+                    rslt[name] = self._decode_string(rslt[name].str)
+                    if infer_string:
+                        rslt[name] = rslt[name].astype("str")
+
+                js += 1
+            else:
+                self.close()
+                raise ValueError(f"unknown column type {repr(self._column_types[j])}")
+
+        df = DataFrame(rslt, columns=self.column_names, index=ix, copy=False)
+        return df
+
+    def _decode_string(self, b):
+        return b.decode(self.encoding or self.default_encoding)
+
+    def _convert_header_text(self, b: bytes) -> str | bytes:
+        if self.convert_header_text:
+            return self._decode_string(b)
+        else:
+            return b
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/sas/sas_constants.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/sas/sas_constants.py
new file mode 100644
index 0000000000000000000000000000000000000000..62c17bd03927e5f852af708e6b9ef6cf7e74d57c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/sas/sas_constants.py
@@ -0,0 +1,310 @@
+from __future__ import annotations
+
+from typing import Final
+
+magic: Final = (
+    b"\x00\x00\x00\x00\x00\x00\x00\x00"
+    b"\x00\x00\x00\x00\xc2\xea\x81\x60"
+    b"\xb3\x14\x11\xcf\xbd\x92\x08\x00"
+    b"\x09\xc7\x31\x8c\x18\x1f\x10\x11"
+)
+
+align_1_checker_value: Final = b"3"
+align_1_offset: Final = 32
+align_1_length: Final = 1
+align_1_value: Final = 4
+u64_byte_checker_value: Final = b"3"
+align_2_offset: Final = 35
+align_2_length: Final = 1
+align_2_value: Final = 4
+endianness_offset: Final = 37
+endianness_length: Final = 1
+platform_offset: Final = 39
+platform_length: Final = 1
+encoding_offset: Final = 70
+encoding_length: Final = 1
+dataset_offset: Final = 92
+dataset_length: Final = 64
+file_type_offset: Final = 156
+file_type_length: Final = 8
+date_created_offset: Final = 164
+date_created_length: Final = 8
+date_modified_offset: Final = 172
+date_modified_length: Final = 8
+header_size_offset: Final = 196
+header_size_length: Final = 4
+page_size_offset: Final = 200
+page_size_length: Final = 4
+page_count_offset: Final = 204
+page_count_length: Final = 4
+sas_release_offset: Final = 216
+sas_release_length: Final = 8
+sas_server_type_offset: Final = 224
+sas_server_type_length: Final = 16
+os_version_number_offset: Final = 240
+os_version_number_length: Final = 16
+os_maker_offset: Final = 256
+os_maker_length: Final = 16
+os_name_offset: Final = 272
+os_name_length: Final = 16
+page_bit_offset_x86: Final = 16
+page_bit_offset_x64: Final = 32
+subheader_pointer_length_x86: Final = 12
+subheader_pointer_length_x64: Final = 24
+page_type_offset: Final = 0
+page_type_length: Final = 2
+block_count_offset: Final = 2
+block_count_length: Final = 2
+subheader_count_offset: Final = 4
+subheader_count_length: Final = 2
+page_type_mask: Final = 0x0F00
+# Keep "page_comp_type" bits
+page_type_mask2: Final = 0xF000 | page_type_mask
+page_meta_type: Final = 0x0000
+page_data_type: Final = 0x0100
+page_mix_type: Final = 0x0200
+page_amd_type: Final = 0x0400
+page_meta2_type: Final = 0x4000
+page_comp_type: Final = 0x9000
+page_meta_types: Final = [page_meta_type, page_meta2_type]
+subheader_pointers_offset: Final = 8
+truncated_subheader_id: Final = 1
+compressed_subheader_id: Final = 4
+compressed_subheader_type: Final = 1
+text_block_size_length: Final = 2
+row_length_offset_multiplier: Final = 5
+row_count_offset_multiplier: Final = 6
+col_count_p1_multiplier: Final = 9
+col_count_p2_multiplier: Final = 10
+row_count_on_mix_page_offset_multiplier: Final = 15
+column_name_pointer_length: Final = 8
+column_name_text_subheader_offset: Final = 0
+column_name_text_subheader_length: Final = 2
+column_name_offset_offset: Final = 2
+column_name_offset_length: Final = 2
+column_name_length_offset: Final = 4
+column_name_length_length: Final = 2
+column_data_offset_offset: Final = 8
+column_data_length_offset: Final = 8
+column_data_length_length: Final = 4
+column_type_offset: Final = 14
+column_type_length: Final = 1
+column_format_text_subheader_index_offset: Final = 22
+column_format_text_subheader_index_length: Final = 2
+column_format_offset_offset: Final = 24
+column_format_offset_length: Final = 2
+column_format_length_offset: Final = 26
+column_format_length_length: Final = 2
+column_label_text_subheader_index_offset: Final = 28
+column_label_text_subheader_index_length: Final = 2
+column_label_offset_offset: Final = 30
+column_label_offset_length: Final = 2
+column_label_length_offset: Final = 32
+column_label_length_length: Final = 2
+rle_compression: Final = b"SASYZCRL"
+rdc_compression: Final = b"SASYZCR2"
+
+compression_literals: Final = [rle_compression, rdc_compression]
+
+# Incomplete list of encodings, using SAS nomenclature:
+# https://support.sas.com/documentation/onlinedoc/dfdmstudio/2.6/dmpdmsug/Content/dfU_Encodings_SAS.html
+# corresponding to the Python documentation of standard encodings
+# https://docs.python.org/3/library/codecs.html#standard-encodings
+encoding_names: Final = {
+    20: "utf-8",
+    29: "latin1",
+    30: "latin2",
+    31: "latin3",
+    32: "latin4",
+    33: "cyrillic",
+    34: "arabic",
+    35: "greek",
+    36: "hebrew",
+    37: "latin5",
+    38: "latin6",
+    39: "cp874",
+    40: "latin9",
+    41: "cp437",
+    42: "cp850",
+    43: "cp852",
+    44: "cp857",
+    45: "cp858",
+    46: "cp862",
+    47: "cp864",
+    48: "cp865",
+    49: "cp866",
+    50: "cp869",
+    51: "cp874",
+    # 52: "",  # not found
+    # 53: "",  # not found
+    # 54: "",  # not found
+    55: "cp720",
+    56: "cp737",
+    57: "cp775",
+    58: "cp860",
+    59: "cp863",
+    60: "cp1250",
+    61: "cp1251",
+    62: "cp1252",
+    63: "cp1253",
+    64: "cp1254",
+    65: "cp1255",
+    66: "cp1256",
+    67: "cp1257",
+    68: "cp1258",
+    118: "cp950",
+    # 119: "",  # not found
+    123: "big5",
+    125: "gb2312",
+    126: "cp936",
+    134: "euc_jp",
+    136: "cp932",
+    138: "shift_jis",
+    140: "euc-kr",
+    141: "cp949",
+    227: "latin8",
+    # 228: "", # not found
+    # 229: ""  # not found
+}
+
+
+class SASIndex:
+    row_size_index: Final = 0
+    column_size_index: Final = 1
+    subheader_counts_index: Final = 2
+    column_text_index: Final = 3
+    column_name_index: Final = 4
+    column_attributes_index: Final = 5
+    format_and_label_index: Final = 6
+    column_list_index: Final = 7
+    data_subheader_index: Final = 8
+
+
+subheader_signature_to_index: Final = {
+    b"\xF7\xF7\xF7\xF7": SASIndex.row_size_index,
+    b"\x00\x00\x00\x00\xF7\xF7\xF7\xF7": SASIndex.row_size_index,
+    b"\xF7\xF7\xF7\xF7\x00\x00\x00\x00": SASIndex.row_size_index,
+    b"\xF7\xF7\xF7\xF7\xFF\xFF\xFB\xFE": SASIndex.row_size_index,
+    b"\xF6\xF6\xF6\xF6": SASIndex.column_size_index,
+    b"\x00\x00\x00\x00\xF6\xF6\xF6\xF6": SASIndex.column_size_index,
+    b"\xF6\xF6\xF6\xF6\x00\x00\x00\x00": SASIndex.column_size_index,
+    b"\xF6\xF6\xF6\xF6\xFF\xFF\xFB\xFE": SASIndex.column_size_index,
+    b"\x00\xFC\xFF\xFF": SASIndex.subheader_counts_index,
+    b"\xFF\xFF\xFC\x00": SASIndex.subheader_counts_index,
+    b"\x00\xFC\xFF\xFF\xFF\xFF\xFF\xFF": SASIndex.subheader_counts_index,
+    b"\xFF\xFF\xFF\xFF\xFF\xFF\xFC\x00": SASIndex.subheader_counts_index,
+    b"\xFD\xFF\xFF\xFF": SASIndex.column_text_index,
+    b"\xFF\xFF\xFF\xFD": SASIndex.column_text_index,
+    b"\xFD\xFF\xFF\xFF\xFF\xFF\xFF\xFF": SASIndex.column_text_index,
+    b"\xFF\xFF\xFF\xFF\xFF\xFF\xFF\xFD": SASIndex.column_text_index,
+    b"\xFF\xFF\xFF\xFF": SASIndex.column_name_index,
+    b"\xFF\xFF\xFF\xFF\xFF\xFF\xFF\xFF": SASIndex.column_name_index,
+    b"\xFC\xFF\xFF\xFF": SASIndex.column_attributes_index,
+    b"\xFF\xFF\xFF\xFC": SASIndex.column_attributes_index,
+    b"\xFC\xFF\xFF\xFF\xFF\xFF\xFF\xFF": SASIndex.column_attributes_index,
+    b"\xFF\xFF\xFF\xFF\xFF\xFF\xFF\xFC": SASIndex.column_attributes_index,
+    b"\xFE\xFB\xFF\xFF": SASIndex.format_and_label_index,
+    b"\xFF\xFF\xFB\xFE": SASIndex.format_and_label_index,
+    b"\xFE\xFB\xFF\xFF\xFF\xFF\xFF\xFF": SASIndex.format_and_label_index,
+    b"\xFF\xFF\xFF\xFF\xFF\xFF\xFB\xFE": SASIndex.format_and_label_index,
+    b"\xFE\xFF\xFF\xFF": SASIndex.column_list_index,
+    b"\xFF\xFF\xFF\xFE": SASIndex.column_list_index,
+    b"\xFE\xFF\xFF\xFF\xFF\xFF\xFF\xFF": SASIndex.column_list_index,
+    b"\xFF\xFF\xFF\xFF\xFF\xFF\xFF\xFE": SASIndex.column_list_index,
+}
+
+
+# List of frequently used SAS date and datetime formats
+# http://support.sas.com/documentation/cdl/en/etsug/60372/HTML/default/viewer.htm#etsug_intervals_sect009.htm
+# https://github.com/epam/parso/blob/master/src/main/java/com/epam/parso/impl/SasFileConstants.java
+sas_date_formats: Final = (
+    "DATE",
+    "DAY",
+    "DDMMYY",
+    "DOWNAME",
+    "JULDAY",
+    "JULIAN",
+    "MMDDYY",
+    "MMYY",
+    "MMYYC",
+    "MMYYD",
+    "MMYYP",
+    "MMYYS",
+    "MMYYN",
+    "MONNAME",
+    "MONTH",
+    "MONYY",
+    "QTR",
+    "QTRR",
+    "NENGO",
+    "WEEKDATE",
+    "WEEKDATX",
+    "WEEKDAY",
+    "WEEKV",
+    "WORDDATE",
+    "WORDDATX",
+    "YEAR",
+    "YYMM",
+    "YYMMC",
+    "YYMMD",
+    "YYMMP",
+    "YYMMS",
+    "YYMMN",
+    "YYMON",
+    "YYMMDD",
+    "YYQ",
+    "YYQC",
+    "YYQD",
+    "YYQP",
+    "YYQS",
+    "YYQN",
+    "YYQR",
+    "YYQRC",
+    "YYQRD",
+    "YYQRP",
+    "YYQRS",
+    "YYQRN",
+    "YYMMDDP",
+    "YYMMDDC",
+    "E8601DA",
+    "YYMMDDN",
+    "MMDDYYC",
+    "MMDDYYS",
+    "MMDDYYD",
+    "YYMMDDS",
+    "B8601DA",
+    "DDMMYYN",
+    "YYMMDDD",
+    "DDMMYYB",
+    "DDMMYYP",
+    "MMDDYYP",
+    "YYMMDDB",
+    "MMDDYYN",
+    "DDMMYYC",
+    "DDMMYYD",
+    "DDMMYYS",
+    "MINGUO",
+)
+
+sas_datetime_formats: Final = (
+    "DATETIME",
+    "DTWKDATX",
+    "B8601DN",
+    "B8601DT",
+    "B8601DX",
+    "B8601DZ",
+    "B8601LX",
+    "E8601DN",
+    "E8601DT",
+    "E8601DX",
+    "E8601DZ",
+    "E8601LX",
+    "DATEAMPM",
+    "DTDATE",
+    "DTMONYY",
+    "DTMONYY",
+    "DTWKDATX",
+    "DTYEAR",
+    "TOD",
+    "MDYAMPM",
+)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/sas/sas_xport.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/sas/sas_xport.py
new file mode 100644
index 0000000000000000000000000000000000000000..11b2ed0ee73168ba82e3b8d312f96bcea9398e49
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/sas/sas_xport.py
@@ -0,0 +1,508 @@
+"""
+Read a SAS XPort format file into a Pandas DataFrame.
+
+Based on code from Jack Cushman (github.com/jcushman/xport).
+
+The file format is defined here:
+
+https://support.sas.com/content/dam/SAS/support/en/technical-papers/record-layout-of-a-sas-version-5-or-6-data-set-in-sas-transport-xport-format.pdf
+"""
+from __future__ import annotations
+
+from collections import abc
+from datetime import datetime
+import struct
+from typing import TYPE_CHECKING
+import warnings
+
+import numpy as np
+
+from pandas.util._decorators import Appender
+from pandas.util._exceptions import find_stack_level
+
+import pandas as pd
+
+from pandas.io.common import get_handle
+from pandas.io.sas.sasreader import ReaderBase
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        CompressionOptions,
+        DatetimeNaTType,
+        FilePath,
+        ReadBuffer,
+    )
+_correct_line1 = (
+    "HEADER RECORD*******LIBRARY HEADER RECORD!!!!!!!"
+    "000000000000000000000000000000  "
+)
+_correct_header1 = (
+    "HEADER RECORD*******MEMBER  HEADER RECORD!!!!!!!000000000000000001600000000"
+)
+_correct_header2 = (
+    "HEADER RECORD*******DSCRPTR HEADER RECORD!!!!!!!"
+    "000000000000000000000000000000  "
+)
+_correct_obs_header = (
+    "HEADER RECORD*******OBS     HEADER RECORD!!!!!!!"
+    "000000000000000000000000000000  "
+)
+_fieldkeys = [
+    "ntype",
+    "nhfun",
+    "field_length",
+    "nvar0",
+    "name",
+    "label",
+    "nform",
+    "nfl",
+    "num_decimals",
+    "nfj",
+    "nfill",
+    "niform",
+    "nifl",
+    "nifd",
+    "npos",
+    "_",
+]
+
+
+_base_params_doc = """\
+Parameters
+----------
+filepath_or_buffer : str or file-like object
+    Path to SAS file or object implementing binary read method."""
+
+_params2_doc = """\
+index : identifier of index column
+    Identifier of column that should be used as index of the DataFrame.
+encoding : str
+    Encoding for text data.
+chunksize : int
+    Read file `chunksize` lines at a time, returns iterator."""
+
+_format_params_doc = """\
+format : str
+    File format, only `xport` is currently supported."""
+
+_iterator_doc = """\
+iterator : bool, default False
+    Return XportReader object for reading file incrementally."""
+
+
+_read_sas_doc = f"""Read a SAS file into a DataFrame.
+
+{_base_params_doc}
+{_format_params_doc}
+{_params2_doc}
+{_iterator_doc}
+
+Returns
+-------
+DataFrame or XportReader
+
+Examples
+--------
+Read a SAS Xport file:
+
+>>> df = pd.read_sas('filename.XPT')
+
+Read a Xport file in 10,000 line chunks:
+
+>>> itr = pd.read_sas('filename.XPT', chunksize=10000)
+>>> for chunk in itr:
+>>>     do_something(chunk)
+
+"""
+
+_xport_reader_doc = f"""\
+Class for reading SAS Xport files.
+
+{_base_params_doc}
+{_params2_doc}
+
+Attributes
+----------
+member_info : list
+    Contains information about the file
+fields : list
+    Contains information about the variables in the file
+"""
+
+_read_method_doc = """\
+Read observations from SAS Xport file, returning as data frame.
+
+Parameters
+----------
+nrows : int
+    Number of rows to read from data file; if None, read whole
+    file.
+
+Returns
+-------
+A DataFrame.
+"""
+
+
+def _parse_date(datestr: str) -> DatetimeNaTType:
+    """Given a date in xport format, return Python date."""
+    try:
+        # e.g. "16FEB11:10:07:55"
+        return datetime.strptime(datestr, "%d%b%y:%H:%M:%S")
+    except ValueError:
+        return pd.NaT
+
+
+def _split_line(s: str, parts):
+    """
+    Parameters
+    ----------
+    s: str
+        Fixed-length string to split
+    parts: list of (name, length) pairs
+        Used to break up string, name '_' will be filtered from output.
+
+    Returns
+    -------
+    Dict of name:contents of string at given location.
+    """
+    out = {}
+    start = 0
+    for name, length in parts:
+        out[name] = s[start : start + length].strip()
+        start += length
+    del out["_"]
+    return out
+
+
+def _handle_truncated_float_vec(vec, nbytes):
+    # This feature is not well documented, but some SAS XPORT files
+    # have 2-7 byte "truncated" floats.  To read these truncated
+    # floats, pad them with zeros on the right to make 8 byte floats.
+    #
+    # References:
+    # https://github.com/jcushman/xport/pull/3
+    # The R "foreign" library
+
+    if nbytes != 8:
+        vec1 = np.zeros(len(vec), np.dtype("S8"))
+        dtype = np.dtype(f"S{nbytes},S{8 - nbytes}")
+        vec2 = vec1.view(dtype=dtype)
+        vec2["f0"] = vec
+        return vec2
+
+    return vec
+
+
+def _parse_float_vec(vec):
+    """
+    Parse a vector of float values representing IBM 8 byte floats into
+    native 8 byte floats.
+    """
+    dtype = np.dtype(">u4,>u4")
+    vec1 = vec.view(dtype=dtype)
+    xport1 = vec1["f0"]
+    xport2 = vec1["f1"]
+
+    # Start by setting first half of ieee number to first half of IBM
+    # number sans exponent
+    ieee1 = xport1 & 0x00FFFFFF
+
+    # The fraction bit to the left of the binary point in the ieee
+    # format was set and the number was shifted 0, 1, 2, or 3
+    # places. This will tell us how to adjust the ibm exponent to be a
+    # power of 2 ieee exponent and how to shift the fraction bits to
+    # restore the correct magnitude.
+    shift = np.zeros(len(vec), dtype=np.uint8)
+    shift[np.where(xport1 & 0x00200000)] = 1
+    shift[np.where(xport1 & 0x00400000)] = 2
+    shift[np.where(xport1 & 0x00800000)] = 3
+
+    # shift the ieee number down the correct number of places then
+    # set the second half of the ieee number to be the second half
+    # of the ibm number shifted appropriately, ored with the bits
+    # from the first half that would have been shifted in if we
+    # could shift a double. All we are worried about are the low
+    # order 3 bits of the first half since we're only shifting by
+    # 1, 2, or 3.
+    ieee1 >>= shift
+    ieee2 = (xport2 >> shift) | ((xport1 & 0x00000007) << (29 + (3 - shift)))
+
+    # clear the 1 bit to the left of the binary point
+    ieee1 &= 0xFFEFFFFF
+
+    # set the exponent of the ieee number to be the actual exponent
+    # plus the shift count + 1023. Or this into the first half of the
+    # ieee number. The ibm exponent is excess 64 but is adjusted by 65
+    # since during conversion to ibm format the exponent is
+    # incremented by 1 and the fraction bits left 4 positions to the
+    # right of the radix point.  (had to add >> 24 because C treats &
+    # 0x7f as 0x7f000000 and Python doesn't)
+    ieee1 |= ((((((xport1 >> 24) & 0x7F) - 65) << 2) + shift + 1023) << 20) | (
+        xport1 & 0x80000000
+    )
+
+    ieee = np.empty((len(ieee1),), dtype=">u4,>u4")
+    ieee["f0"] = ieee1
+    ieee["f1"] = ieee2
+    ieee = ieee.view(dtype=">f8")
+    ieee = ieee.astype("f8")
+
+    return ieee
+
+
+class XportReader(ReaderBase, abc.Iterator):
+    __doc__ = _xport_reader_doc
+
+    def __init__(
+        self,
+        filepath_or_buffer: FilePath | ReadBuffer[bytes],
+        index=None,
+        encoding: str | None = "ISO-8859-1",
+        chunksize: int | None = None,
+        compression: CompressionOptions = "infer",
+    ) -> None:
+        self._encoding = encoding
+        self._lines_read = 0
+        self._index = index
+        self._chunksize = chunksize
+
+        self.handles = get_handle(
+            filepath_or_buffer,
+            "rb",
+            encoding=encoding,
+            is_text=False,
+            compression=compression,
+        )
+        self.filepath_or_buffer = self.handles.handle
+
+        try:
+            self._read_header()
+        except Exception:
+            self.close()
+            raise
+
+    def close(self) -> None:
+        self.handles.close()
+
+    def _get_row(self):
+        return self.filepath_or_buffer.read(80).decode()
+
+    def _read_header(self) -> None:
+        self.filepath_or_buffer.seek(0)
+
+        # read file header
+        line1 = self._get_row()
+        if line1 != _correct_line1:
+            if "**COMPRESSED**" in line1:
+                # this was created with the PROC CPORT method and can't be read
+                # https://documentation.sas.com/doc/en/pgmsascdc/9.4_3.5/movefile/p1bm6aqp3fw4uin1hucwh718f6kp.htm
+                raise ValueError(
+                    "Header record indicates a CPORT file, which is not readable."
+                )
+            raise ValueError("Header record is not an XPORT file.")
+
+        line2 = self._get_row()
+        fif = [["prefix", 24], ["version", 8], ["OS", 8], ["_", 24], ["created", 16]]
+        file_info = _split_line(line2, fif)
+        if file_info["prefix"] != "SAS     SAS     SASLIB":
+            raise ValueError("Header record has invalid prefix.")
+        file_info["created"] = _parse_date(file_info["created"])
+        self.file_info = file_info
+
+        line3 = self._get_row()
+        file_info["modified"] = _parse_date(line3[:16])
+
+        # read member header
+        header1 = self._get_row()
+        header2 = self._get_row()
+        headflag1 = header1.startswith(_correct_header1)
+        headflag2 = header2 == _correct_header2
+        if not (headflag1 and headflag2):
+            raise ValueError("Member header not found")
+        # usually 140, could be 135
+        fieldnamelength = int(header1[-5:-2])
+
+        # member info
+        mem = [
+            ["prefix", 8],
+            ["set_name", 8],
+            ["sasdata", 8],
+            ["version", 8],
+            ["OS", 8],
+            ["_", 24],
+            ["created", 16],
+        ]
+        member_info = _split_line(self._get_row(), mem)
+        mem = [["modified", 16], ["_", 16], ["label", 40], ["type", 8]]
+        member_info.update(_split_line(self._get_row(), mem))
+        member_info["modified"] = _parse_date(member_info["modified"])
+        member_info["created"] = _parse_date(member_info["created"])
+        self.member_info = member_info
+
+        # read field names
+        types = {1: "numeric", 2: "char"}
+        fieldcount = int(self._get_row()[54:58])
+        datalength = fieldnamelength * fieldcount
+        # round up to nearest 80
+        if datalength % 80:
+            datalength += 80 - datalength % 80
+        fielddata = self.filepath_or_buffer.read(datalength)
+        fields = []
+        obs_length = 0
+        while len(fielddata) >= fieldnamelength:
+            # pull data for one field
+            fieldbytes, fielddata = (
+                fielddata[:fieldnamelength],
+                fielddata[fieldnamelength:],
+            )
+
+            # rest at end gets ignored, so if field is short, pad out
+            # to match struct pattern below
+            fieldbytes = fieldbytes.ljust(140)
+
+            fieldstruct = struct.unpack(">hhhh8s40s8shhh2s8shhl52s", fieldbytes)
+            field = dict(zip(_fieldkeys, fieldstruct))
+            del field["_"]
+            field["ntype"] = types[field["ntype"]]
+            fl = field["field_length"]
+            if field["ntype"] == "numeric" and ((fl < 2) or (fl > 8)):
+                msg = f"Floating field width {fl} is not between 2 and 8."
+                raise TypeError(msg)
+
+            for k, v in field.items():
+                try:
+                    field[k] = v.strip()
+                except AttributeError:
+                    pass
+
+            obs_length += field["field_length"]
+            fields += [field]
+
+        header = self._get_row()
+        if not header == _correct_obs_header:
+            raise ValueError("Observation header not found.")
+
+        self.fields = fields
+        self.record_length = obs_length
+        self.record_start = self.filepath_or_buffer.tell()
+
+        self.nobs = self._record_count()
+        self.columns = [x["name"].decode() for x in self.fields]
+
+        # Setup the dtype.
+        dtypel = [
+            ("s" + str(i), "S" + str(field["field_length"]))
+            for i, field in enumerate(self.fields)
+        ]
+        dtype = np.dtype(dtypel)
+        self._dtype = dtype
+
+    def __next__(self) -> pd.DataFrame:
+        return self.read(nrows=self._chunksize or 1)
+
+    def _record_count(self) -> int:
+        """
+        Get number of records in file.
+
+        This is maybe suboptimal because we have to seek to the end of
+        the file.
+
+        Side effect: returns file position to record_start.
+        """
+        self.filepath_or_buffer.seek(0, 2)
+        total_records_length = self.filepath_or_buffer.tell() - self.record_start
+
+        if total_records_length % 80 != 0:
+            warnings.warn(
+                "xport file may be corrupted.",
+                stacklevel=find_stack_level(),
+            )
+
+        if self.record_length > 80:
+            self.filepath_or_buffer.seek(self.record_start)
+            return total_records_length // self.record_length
+
+        self.filepath_or_buffer.seek(-80, 2)
+        last_card_bytes = self.filepath_or_buffer.read(80)
+        last_card = np.frombuffer(last_card_bytes, dtype=np.uint64)
+
+        # 8 byte blank
+        ix = np.flatnonzero(last_card == 2314885530818453536)
+
+        if len(ix) == 0:
+            tail_pad = 0
+        else:
+            tail_pad = 8 * len(ix)
+
+        self.filepath_or_buffer.seek(self.record_start)
+
+        return (total_records_length - tail_pad) // self.record_length
+
+    def get_chunk(self, size: int | None = None) -> pd.DataFrame:
+        """
+        Reads lines from Xport file and returns as dataframe
+
+        Parameters
+        ----------
+        size : int, defaults to None
+            Number of lines to read.  If None, reads whole file.
+
+        Returns
+        -------
+        DataFrame
+        """
+        if size is None:
+            size = self._chunksize
+        return self.read(nrows=size)
+
+    def _missing_double(self, vec):
+        v = vec.view(dtype="u1,u1,u2,u4")
+        miss = (v["f1"] == 0) & (v["f2"] == 0) & (v["f3"] == 0)
+        miss1 = (
+            ((v["f0"] >= 0x41) & (v["f0"] <= 0x5A))
+            | (v["f0"] == 0x5F)
+            | (v["f0"] == 0x2E)
+        )
+        miss &= miss1
+        return miss
+
+    @Appender(_read_method_doc)
+    def read(self, nrows: int | None = None) -> pd.DataFrame:
+        if nrows is None:
+            nrows = self.nobs
+
+        read_lines = min(nrows, self.nobs - self._lines_read)
+        read_len = read_lines * self.record_length
+        if read_len <= 0:
+            self.close()
+            raise StopIteration
+        raw = self.filepath_or_buffer.read(read_len)
+        data = np.frombuffer(raw, dtype=self._dtype, count=read_lines)
+
+        df_data = {}
+        for j, x in enumerate(self.columns):
+            vec = data["s" + str(j)]
+            ntype = self.fields[j]["ntype"]
+            if ntype == "numeric":
+                vec = _handle_truncated_float_vec(vec, self.fields[j]["field_length"])
+                miss = self._missing_double(vec)
+                v = _parse_float_vec(vec)
+                v[miss] = np.nan
+            elif self.fields[j]["ntype"] == "char":
+                v = [y.rstrip() for y in vec]
+
+                if self._encoding is not None:
+                    v = [y.decode(self._encoding) for y in v]
+
+            df_data.update({x: v})
+        df = pd.DataFrame(df_data)
+
+        if self._index is None:
+            df.index = pd.Index(range(self._lines_read, self._lines_read + read_lines))
+        else:
+            df = df.set_index(self._index)
+
+        self._lines_read += read_lines
+
+        return df
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/sas/sasreader.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/sas/sasreader.py
new file mode 100644
index 0000000000000000000000000000000000000000..c39313d5dc6548fcc014f7a886988a2b9d9001ed
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/sas/sasreader.py
@@ -0,0 +1,178 @@
+"""
+Read SAS sas7bdat or xport files.
+"""
+from __future__ import annotations
+
+from abc import (
+    ABC,
+    abstractmethod,
+)
+from typing import (
+    TYPE_CHECKING,
+    overload,
+)
+
+from pandas.util._decorators import doc
+
+from pandas.core.shared_docs import _shared_docs
+
+from pandas.io.common import stringify_path
+
+if TYPE_CHECKING:
+    from collections.abc import Hashable
+    from types import TracebackType
+
+    from pandas._typing import (
+        CompressionOptions,
+        FilePath,
+        ReadBuffer,
+        Self,
+    )
+
+    from pandas import DataFrame
+
+
+class ReaderBase(ABC):
+    """
+    Protocol for XportReader and SAS7BDATReader classes.
+    """
+
+    @abstractmethod
+    def read(self, nrows: int | None = None) -> DataFrame:
+        ...
+
+    @abstractmethod
+    def close(self) -> None:
+        ...
+
+    def __enter__(self) -> Self:
+        return self
+
+    def __exit__(
+        self,
+        exc_type: type[BaseException] | None,
+        exc_value: BaseException | None,
+        traceback: TracebackType | None,
+    ) -> None:
+        self.close()
+
+
+@overload
+def read_sas(
+    filepath_or_buffer: FilePath | ReadBuffer[bytes],
+    *,
+    format: str | None = ...,
+    index: Hashable | None = ...,
+    encoding: str | None = ...,
+    chunksize: int = ...,
+    iterator: bool = ...,
+    compression: CompressionOptions = ...,
+) -> ReaderBase:
+    ...
+
+
+@overload
+def read_sas(
+    filepath_or_buffer: FilePath | ReadBuffer[bytes],
+    *,
+    format: str | None = ...,
+    index: Hashable | None = ...,
+    encoding: str | None = ...,
+    chunksize: None = ...,
+    iterator: bool = ...,
+    compression: CompressionOptions = ...,
+) -> DataFrame | ReaderBase:
+    ...
+
+
+@doc(decompression_options=_shared_docs["decompression_options"] % "filepath_or_buffer")
+def read_sas(
+    filepath_or_buffer: FilePath | ReadBuffer[bytes],
+    *,
+    format: str | None = None,
+    index: Hashable | None = None,
+    encoding: str | None = None,
+    chunksize: int | None = None,
+    iterator: bool = False,
+    compression: CompressionOptions = "infer",
+) -> DataFrame | ReaderBase:
+    """
+    Read SAS files stored as either XPORT or SAS7BDAT format files.
+
+    Parameters
+    ----------
+    filepath_or_buffer : str, path object, or file-like object
+        String, path object (implementing ``os.PathLike[str]``), or file-like
+        object implementing a binary ``read()`` function. The string could be a URL.
+        Valid URL schemes include http, ftp, s3, and file. For file URLs, a host is
+        expected. A local file could be:
+        ``file://localhost/path/to/table.sas7bdat``.
+    format : str {{'xport', 'sas7bdat'}} or None
+        If None, file format is inferred from file extension. If 'xport' or
+        'sas7bdat', uses the corresponding format.
+    index : identifier of index column, defaults to None
+        Identifier of column that should be used as index of the DataFrame.
+    encoding : str, default is None
+        Encoding for text data.  If None, text data are stored as raw bytes.
+    chunksize : int
+        Read file `chunksize` lines at a time, returns iterator.
+    iterator : bool, defaults to False
+        If True, returns an iterator for reading the file incrementally.
+    {decompression_options}
+
+    Returns
+    -------
+    DataFrame if iterator=False and chunksize=None, else SAS7BDATReader
+    or XportReader
+
+    Examples
+    --------
+    >>> df = pd.read_sas("sas_data.sas7bdat")  # doctest: +SKIP
+    """
+    if format is None:
+        buffer_error_msg = (
+            "If this is a buffer object rather "
+            "than a string name, you must specify a format string"
+        )
+        filepath_or_buffer = stringify_path(filepath_or_buffer)
+        if not isinstance(filepath_or_buffer, str):
+            raise ValueError(buffer_error_msg)
+        fname = filepath_or_buffer.lower()
+        if ".xpt" in fname:
+            format = "xport"
+        elif ".sas7bdat" in fname:
+            format = "sas7bdat"
+        else:
+            raise ValueError(
+                f"unable to infer format of SAS file from filename: {repr(fname)}"
+            )
+
+    reader: ReaderBase
+    if format.lower() == "xport":
+        from pandas.io.sas.sas_xport import XportReader
+
+        reader = XportReader(
+            filepath_or_buffer,
+            index=index,
+            encoding=encoding,
+            chunksize=chunksize,
+            compression=compression,
+        )
+    elif format.lower() == "sas7bdat":
+        from pandas.io.sas.sas7bdat import SAS7BDATReader
+
+        reader = SAS7BDATReader(
+            filepath_or_buffer,
+            index=index,
+            encoding=encoding,
+            chunksize=chunksize,
+            compression=compression,
+        )
+    else:
+        raise ValueError("unknown SAS format")
+
+    if iterator or chunksize:
+        return reader
+
+    with reader:
+        return reader.read()
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/spss.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/spss.py
new file mode 100644
index 0000000000000000000000000000000000000000..db31a07df79e6de2862e57fd75de0bd4b9c2455d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/spss.py
@@ -0,0 +1,72 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+from pandas._libs import lib
+from pandas.compat._optional import import_optional_dependency
+from pandas.util._validators import check_dtype_backend
+
+from pandas.core.dtypes.inference import is_list_like
+
+from pandas.io.common import stringify_path
+
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+    from pathlib import Path
+
+    from pandas._typing import DtypeBackend
+
+    from pandas import DataFrame
+
+
+def read_spss(
+    path: str | Path,
+    usecols: Sequence[str] | None = None,
+    convert_categoricals: bool = True,
+    dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
+) -> DataFrame:
+    """
+    Load an SPSS file from the file path, returning a DataFrame.
+
+    Parameters
+    ----------
+    path : str or Path
+        File path.
+    usecols : list-like, optional
+        Return a subset of the columns. If None, return all columns.
+    convert_categoricals : bool, default is True
+        Convert categorical columns into pd.Categorical.
+    dtype_backend : {'numpy_nullable', 'pyarrow'}, default 'numpy_nullable'
+        Back-end data type applied to the resultant :class:`DataFrame`
+        (still experimental). Behaviour is as follows:
+
+        * ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame`
+          (default).
+        * ``"pyarrow"``: returns pyarrow-backed nullable :class:`ArrowDtype`
+          DataFrame.
+
+        .. versionadded:: 2.0
+
+    Returns
+    -------
+    DataFrame
+
+    Examples
+    --------
+    >>> df = pd.read_spss("spss_data.sav")  # doctest: +SKIP
+    """
+    pyreadstat = import_optional_dependency("pyreadstat")
+    check_dtype_backend(dtype_backend)
+
+    if usecols is not None:
+        if not is_list_like(usecols):
+            raise TypeError("usecols must be list-like.")
+        usecols = list(usecols)  # pyreadstat requires a list
+
+    df, metadata = pyreadstat.read_sav(
+        stringify_path(path), usecols=usecols, apply_value_formats=convert_categoricals
+    )
+    df.attrs = metadata.__dict__
+    if dtype_backend is not lib.no_default:
+        df = df.convert_dtypes(dtype_backend=dtype_backend)
+    return df
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/sql.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/sql.py
new file mode 100644
index 0000000000000000000000000000000000000000..7027702a696feda183995a6346238e4076dbc069
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/sql.py
@@ -0,0 +1,2916 @@
+"""
+Collection of query wrappers / abstractions to both facilitate data
+retrieval and to reduce dependency on DB-specific API.
+"""
+
+from __future__ import annotations
+
+from abc import (
+    ABC,
+    abstractmethod,
+)
+from contextlib import (
+    ExitStack,
+    contextmanager,
+)
+from datetime import (
+    date,
+    datetime,
+    time,
+)
+from functools import partial
+import re
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Literal,
+    cast,
+    overload,
+)
+import warnings
+
+import numpy as np
+
+from pandas._config import using_string_dtype
+
+from pandas._libs import lib
+from pandas.compat._optional import import_optional_dependency
+from pandas.errors import (
+    AbstractMethodError,
+    DatabaseError,
+)
+from pandas.util._exceptions import find_stack_level
+from pandas.util._validators import check_dtype_backend
+
+from pandas.core.dtypes.common import (
+    is_dict_like,
+    is_list_like,
+    is_object_dtype,
+    is_string_dtype,
+)
+from pandas.core.dtypes.dtypes import DatetimeTZDtype
+from pandas.core.dtypes.missing import isna
+
+from pandas import get_option
+from pandas.core.api import (
+    DataFrame,
+    Series,
+)
+from pandas.core.arrays import ArrowExtensionArray
+from pandas.core.arrays.string_ import StringDtype
+from pandas.core.base import PandasObject
+import pandas.core.common as com
+from pandas.core.common import maybe_make_list
+from pandas.core.internals.construction import convert_object_array
+from pandas.core.tools.datetimes import to_datetime
+
+from pandas.io._util import arrow_table_to_pandas
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Iterator,
+        Mapping,
+    )
+
+    from sqlalchemy import Table
+    from sqlalchemy.sql.expression import (
+        Select,
+        TextClause,
+    )
+
+    from pandas._typing import (
+        DateTimeErrorChoices,
+        DtypeArg,
+        DtypeBackend,
+        IndexLabel,
+        Self,
+    )
+
+    from pandas import Index
+
+# -----------------------------------------------------------------------------
+# -- Helper functions
+
+
+def _process_parse_dates_argument(parse_dates):
+    """Process parse_dates argument for read_sql functions"""
+    # handle non-list entries for parse_dates gracefully
+    if parse_dates is True or parse_dates is None or parse_dates is False:
+        parse_dates = []
+
+    elif not hasattr(parse_dates, "__iter__"):
+        parse_dates = [parse_dates]
+    return parse_dates
+
+
+def _handle_date_column(
+    col, utc: bool = False, format: str | dict[str, Any] | None = None
+):
+    if isinstance(format, dict):
+        # GH35185 Allow custom error values in parse_dates argument of
+        # read_sql like functions.
+        # Format can take on custom to_datetime argument values such as
+        # {"errors": "coerce"} or {"dayfirst": True}
+        error: DateTimeErrorChoices = format.pop("errors", None) or "ignore"
+        if error == "ignore":
+            try:
+                return to_datetime(col, **format)
+            except (TypeError, ValueError):
+                # TODO: not reached 2023-10-27; needed?
+                return col
+        return to_datetime(col, errors=error, **format)
+    else:
+        # Allow passing of formatting string for integers
+        # GH17855
+        if format is None and (
+            issubclass(col.dtype.type, np.floating)
+            or issubclass(col.dtype.type, np.integer)
+        ):
+            format = "s"
+        if format in ["D", "d", "h", "m", "s", "ms", "us", "ns"]:
+            return to_datetime(col, errors="coerce", unit=format, utc=utc)
+        elif isinstance(col.dtype, DatetimeTZDtype):
+            # coerce to UTC timezone
+            # GH11216
+            return to_datetime(col, utc=True)
+        else:
+            return to_datetime(col, errors="coerce", format=format, utc=utc)
+
+
+def _parse_date_columns(data_frame, parse_dates):
+    """
+    Force non-datetime columns to be read as such.
+    Supports both string formatted and integer timestamp columns.
+    """
+    parse_dates = _process_parse_dates_argument(parse_dates)
+
+    # we want to coerce datetime64_tz dtypes for now to UTC
+    # we could in theory do a 'nice' conversion from a FixedOffset tz
+    # GH11216
+    for i, (col_name, df_col) in enumerate(data_frame.items()):
+        if isinstance(df_col.dtype, DatetimeTZDtype) or col_name in parse_dates:
+            try:
+                fmt = parse_dates[col_name]
+            except (KeyError, TypeError):
+                fmt = None
+            data_frame.isetitem(i, _handle_date_column(df_col, format=fmt))
+
+    return data_frame
+
+
+def _convert_arrays_to_dataframe(
+    data,
+    columns,
+    coerce_float: bool = True,
+    dtype_backend: DtypeBackend | Literal["numpy"] = "numpy",
+) -> DataFrame:
+    content = lib.to_object_array_tuples(data)
+    arrays = convert_object_array(
+        list(content.T),
+        dtype=None,
+        coerce_float=coerce_float,
+        dtype_backend=dtype_backend,
+    )
+    if dtype_backend == "pyarrow":
+        pa = import_optional_dependency("pyarrow")
+
+        result_arrays = []
+        for arr in arrays:
+            pa_array = pa.array(arr, from_pandas=True)
+            if arr.dtype == "string":
+                # TODO: Arrow still infers strings arrays as regular strings instead
+                # of large_string, which is what we preserver everywhere else for
+                # dtype_backend="pyarrow". We may want to reconsider this
+                pa_array = pa_array.cast(pa.string())
+            result_arrays.append(ArrowExtensionArray(pa_array))
+        arrays = result_arrays  # type: ignore[assignment]
+    if arrays:
+        df = DataFrame(dict(zip(list(range(len(columns))), arrays)))
+        df.columns = columns
+        return df
+    else:
+        return DataFrame(columns=columns)
+
+
+def _wrap_result(
+    data,
+    columns,
+    index_col=None,
+    coerce_float: bool = True,
+    parse_dates=None,
+    dtype: DtypeArg | None = None,
+    dtype_backend: DtypeBackend | Literal["numpy"] = "numpy",
+):
+    """Wrap result set of a SQLAlchemy query in a DataFrame."""
+    frame = _convert_arrays_to_dataframe(data, columns, coerce_float, dtype_backend)
+
+    if dtype:
+        frame = frame.astype(dtype)
+
+    frame = _parse_date_columns(frame, parse_dates)
+
+    if index_col is not None:
+        frame = frame.set_index(index_col)
+
+    return frame
+
+
+def _wrap_result_adbc(
+    df: DataFrame,
+    *,
+    index_col=None,
+    parse_dates=None,
+    dtype: DtypeArg | None = None,
+    dtype_backend: DtypeBackend | Literal["numpy"] = "numpy",
+) -> DataFrame:
+    """Wrap result set of a SQLAlchemy query in a DataFrame."""
+    if dtype:
+        df = df.astype(dtype)
+
+    df = _parse_date_columns(df, parse_dates)
+
+    if index_col is not None:
+        df = df.set_index(index_col)
+
+    return df
+
+
+def execute(sql, con, params=None):
+    """
+    Execute the given SQL query using the provided connection object.
+
+    Parameters
+    ----------
+    sql : string
+        SQL query to be executed.
+    con : SQLAlchemy connection or sqlite3 connection
+        If a DBAPI2 object, only sqlite3 is supported.
+    params : list or tuple, optional, default: None
+        List of parameters to pass to execute method.
+
+    Returns
+    -------
+    Results Iterable
+    """
+    warnings.warn(
+        "`pandas.io.sql.execute` is deprecated and "
+        "will be removed in the future version.",
+        FutureWarning,
+        stacklevel=find_stack_level(),
+    )  # GH50185
+    sqlalchemy = import_optional_dependency("sqlalchemy", errors="ignore")
+
+    if sqlalchemy is not None and isinstance(con, (str, sqlalchemy.engine.Engine)):
+        raise TypeError("pandas.io.sql.execute requires a connection")  # GH50185
+    with pandasSQL_builder(con, need_transaction=True) as pandas_sql:
+        return pandas_sql.execute(sql, params)
+
+
+# -----------------------------------------------------------------------------
+# -- Read and write to DataFrames
+
+
+@overload
+def read_sql_table(
+    table_name: str,
+    con,
+    schema=...,
+    index_col: str | list[str] | None = ...,
+    coerce_float=...,
+    parse_dates: list[str] | dict[str, str] | None = ...,
+    columns: list[str] | None = ...,
+    chunksize: None = ...,
+    dtype_backend: DtypeBackend | lib.NoDefault = ...,
+) -> DataFrame:
+    ...
+
+
+@overload
+def read_sql_table(
+    table_name: str,
+    con,
+    schema=...,
+    index_col: str | list[str] | None = ...,
+    coerce_float=...,
+    parse_dates: list[str] | dict[str, str] | None = ...,
+    columns: list[str] | None = ...,
+    chunksize: int = ...,
+    dtype_backend: DtypeBackend | lib.NoDefault = ...,
+) -> Iterator[DataFrame]:
+    ...
+
+
+def read_sql_table(
+    table_name: str,
+    con,
+    schema: str | None = None,
+    index_col: str | list[str] | None = None,
+    coerce_float: bool = True,
+    parse_dates: list[str] | dict[str, str] | None = None,
+    columns: list[str] | None = None,
+    chunksize: int | None = None,
+    dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
+) -> DataFrame | Iterator[DataFrame]:
+    """
+    Read SQL database table into a DataFrame.
+
+    Given a table name and a SQLAlchemy connectable, returns a DataFrame.
+    This function does not support DBAPI connections.
+
+    Parameters
+    ----------
+    table_name : str
+        Name of SQL table in database.
+    con : SQLAlchemy connectable or str
+        A database URI could be provided as str.
+        SQLite DBAPI connection mode not supported.
+    schema : str, default None
+        Name of SQL schema in database to query (if database flavor
+        supports this). Uses default schema if None (default).
+    index_col : str or list of str, optional, default: None
+        Column(s) to set as index(MultiIndex).
+    coerce_float : bool, default True
+        Attempts to convert values of non-string, non-numeric objects (like
+        decimal.Decimal) to floating point. Can result in loss of Precision.
+    parse_dates : list or dict, default None
+        - List of column names to parse as dates.
+        - Dict of ``{column_name: format string}`` where format string is
+          strftime compatible in case of parsing string times or is one of
+          (D, s, ns, ms, us) in case of parsing integer timestamps.
+        - Dict of ``{column_name: arg dict}``, where the arg dict corresponds
+          to the keyword arguments of :func:`pandas.to_datetime`
+          Especially useful with databases without native Datetime support,
+          such as SQLite.
+    columns : list, default None
+        List of column names to select from SQL table.
+    chunksize : int, default None
+        If specified, returns an iterator where `chunksize` is the number of
+        rows to include in each chunk.
+    dtype_backend : {'numpy_nullable', 'pyarrow'}, default 'numpy_nullable'
+        Back-end data type applied to the resultant :class:`DataFrame`
+        (still experimental). Behaviour is as follows:
+
+        * ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame`
+          (default).
+        * ``"pyarrow"``: returns pyarrow-backed nullable :class:`ArrowDtype`
+          DataFrame.
+
+        .. versionadded:: 2.0
+
+    Returns
+    -------
+    DataFrame or Iterator[DataFrame]
+        A SQL table is returned as two-dimensional data structure with labeled
+        axes.
+
+    See Also
+    --------
+    read_sql_query : Read SQL query into a DataFrame.
+    read_sql : Read SQL query or database table into a DataFrame.
+
+    Notes
+    -----
+    Any datetime values with time zone information will be converted to UTC.
+
+    Examples
+    --------
+    >>> pd.read_sql_table('table_name', 'postgres:///db_name')  # doctest:+SKIP
+    """
+
+    check_dtype_backend(dtype_backend)
+    if dtype_backend is lib.no_default:
+        dtype_backend = "numpy"  # type: ignore[assignment]
+    assert dtype_backend is not lib.no_default
+
+    with pandasSQL_builder(con, schema=schema, need_transaction=True) as pandas_sql:
+        if not pandas_sql.has_table(table_name):
+            raise ValueError(f"Table {table_name} not found")
+
+        table = pandas_sql.read_table(
+            table_name,
+            index_col=index_col,
+            coerce_float=coerce_float,
+            parse_dates=parse_dates,
+            columns=columns,
+            chunksize=chunksize,
+            dtype_backend=dtype_backend,
+        )
+
+    if table is not None:
+        return table
+    else:
+        raise ValueError(f"Table {table_name} not found", con)
+
+
+@overload
+def read_sql_query(
+    sql,
+    con,
+    index_col: str | list[str] | None = ...,
+    coerce_float=...,
+    params: list[Any] | Mapping[str, Any] | None = ...,
+    parse_dates: list[str] | dict[str, str] | None = ...,
+    chunksize: None = ...,
+    dtype: DtypeArg | None = ...,
+    dtype_backend: DtypeBackend | lib.NoDefault = ...,
+) -> DataFrame:
+    ...
+
+
+@overload
+def read_sql_query(
+    sql,
+    con,
+    index_col: str | list[str] | None = ...,
+    coerce_float=...,
+    params: list[Any] | Mapping[str, Any] | None = ...,
+    parse_dates: list[str] | dict[str, str] | None = ...,
+    chunksize: int = ...,
+    dtype: DtypeArg | None = ...,
+    dtype_backend: DtypeBackend | lib.NoDefault = ...,
+) -> Iterator[DataFrame]:
+    ...
+
+
+def read_sql_query(
+    sql,
+    con,
+    index_col: str | list[str] | None = None,
+    coerce_float: bool = True,
+    params: list[Any] | Mapping[str, Any] | None = None,
+    parse_dates: list[str] | dict[str, str] | None = None,
+    chunksize: int | None = None,
+    dtype: DtypeArg | None = None,
+    dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
+) -> DataFrame | Iterator[DataFrame]:
+    """
+    Read SQL query into a DataFrame.
+
+    Returns a DataFrame corresponding to the result set of the query
+    string. Optionally provide an `index_col` parameter to use one of the
+    columns as the index, otherwise default integer index will be used.
+
+    Parameters
+    ----------
+    sql : str SQL query or SQLAlchemy Selectable (select or text object)
+        SQL query to be executed.
+    con : SQLAlchemy connectable, str, or sqlite3 connection
+        Using SQLAlchemy makes it possible to use any DB supported by that
+        library. If a DBAPI2 object, only sqlite3 is supported.
+    index_col : str or list of str, optional, default: None
+        Column(s) to set as index(MultiIndex).
+    coerce_float : bool, default True
+        Attempts to convert values of non-string, non-numeric objects (like
+        decimal.Decimal) to floating point. Useful for SQL result sets.
+    params : list, tuple or mapping, optional, default: None
+        List of parameters to pass to execute method.  The syntax used
+        to pass parameters is database driver dependent. Check your
+        database driver documentation for which of the five syntax styles,
+        described in PEP 249's paramstyle, is supported.
+        Eg. for psycopg2, uses %(name)s so use params={'name' : 'value'}.
+    parse_dates : list or dict, default: None
+        - List of column names to parse as dates.
+        - Dict of ``{column_name: format string}`` where format string is
+          strftime compatible in case of parsing string times, or is one of
+          (D, s, ns, ms, us) in case of parsing integer timestamps.
+        - Dict of ``{column_name: arg dict}``, where the arg dict corresponds
+          to the keyword arguments of :func:`pandas.to_datetime`
+          Especially useful with databases without native Datetime support,
+          such as SQLite.
+    chunksize : int, default None
+        If specified, return an iterator where `chunksize` is the number of
+        rows to include in each chunk.
+    dtype : Type name or dict of columns
+        Data type for data or columns. E.g. np.float64 or
+        {'a': np.float64, 'b': np.int32, 'c': 'Int64'}.
+
+        .. versionadded:: 1.3.0
+    dtype_backend : {'numpy_nullable', 'pyarrow'}, default 'numpy_nullable'
+        Back-end data type applied to the resultant :class:`DataFrame`
+        (still experimental). Behaviour is as follows:
+
+        * ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame`
+          (default).
+        * ``"pyarrow"``: returns pyarrow-backed nullable :class:`ArrowDtype`
+          DataFrame.
+
+        .. versionadded:: 2.0
+
+    Returns
+    -------
+    DataFrame or Iterator[DataFrame]
+
+    See Also
+    --------
+    read_sql_table : Read SQL database table into a DataFrame.
+    read_sql : Read SQL query or database table into a DataFrame.
+
+    Notes
+    -----
+    Any datetime values with time zone information parsed via the `parse_dates`
+    parameter will be converted to UTC.
+
+    Examples
+    --------
+    >>> from sqlalchemy import create_engine  # doctest: +SKIP
+    >>> engine = create_engine("sqlite:///database.db")  # doctest: +SKIP
+    >>> with engine.connect() as conn, conn.begin():  # doctest: +SKIP
+    ...     data = pd.read_sql_table("data", conn)  # doctest: +SKIP
+    """
+
+    check_dtype_backend(dtype_backend)
+    if dtype_backend is lib.no_default:
+        dtype_backend = "numpy"  # type: ignore[assignment]
+    assert dtype_backend is not lib.no_default
+
+    with pandasSQL_builder(con) as pandas_sql:
+        return pandas_sql.read_query(
+            sql,
+            index_col=index_col,
+            params=params,
+            coerce_float=coerce_float,
+            parse_dates=parse_dates,
+            chunksize=chunksize,
+            dtype=dtype,
+            dtype_backend=dtype_backend,
+        )
+
+
+@overload
+def read_sql(
+    sql,
+    con,
+    index_col: str | list[str] | None = ...,
+    coerce_float=...,
+    params=...,
+    parse_dates=...,
+    columns: list[str] = ...,
+    chunksize: None = ...,
+    dtype_backend: DtypeBackend | lib.NoDefault = ...,
+    dtype: DtypeArg | None = None,
+) -> DataFrame:
+    ...
+
+
+@overload
+def read_sql(
+    sql,
+    con,
+    index_col: str | list[str] | None = ...,
+    coerce_float=...,
+    params=...,
+    parse_dates=...,
+    columns: list[str] = ...,
+    chunksize: int = ...,
+    dtype_backend: DtypeBackend | lib.NoDefault = ...,
+    dtype: DtypeArg | None = None,
+) -> Iterator[DataFrame]:
+    ...
+
+
+def read_sql(
+    sql,
+    con,
+    index_col: str | list[str] | None = None,
+    coerce_float: bool = True,
+    params=None,
+    parse_dates=None,
+    columns: list[str] | None = None,
+    chunksize: int | None = None,
+    dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
+    dtype: DtypeArg | None = None,
+) -> DataFrame | Iterator[DataFrame]:
+    """
+    Read SQL query or database table into a DataFrame.
+
+    This function is a convenience wrapper around ``read_sql_table`` and
+    ``read_sql_query`` (for backward compatibility). It will delegate
+    to the specific function depending on the provided input. A SQL query
+    will be routed to ``read_sql_query``, while a database table name will
+    be routed to ``read_sql_table``. Note that the delegated function might
+    have more specific notes about their functionality not listed here.
+
+    Parameters
+    ----------
+    sql : str or SQLAlchemy Selectable (select or text object)
+        SQL query to be executed or a table name.
+    con : ADBC Connection, SQLAlchemy connectable, str, or sqlite3 connection
+        ADBC provides high performance I/O with native type support, where available.
+        Using SQLAlchemy makes it possible to use any DB supported by that
+        library. If a DBAPI2 object, only sqlite3 is supported. The user is responsible
+        for engine disposal and connection closure for the ADBC connection and
+        SQLAlchemy connectable; str connections are closed automatically. See
+        `here `_.
+    index_col : str or list of str, optional, default: None
+        Column(s) to set as index(MultiIndex).
+    coerce_float : bool, default True
+        Attempts to convert values of non-string, non-numeric objects (like
+        decimal.Decimal) to floating point, useful for SQL result sets.
+    params : list, tuple or dict, optional, default: None
+        List of parameters to pass to execute method.  The syntax used
+        to pass parameters is database driver dependent. Check your
+        database driver documentation for which of the five syntax styles,
+        described in PEP 249's paramstyle, is supported.
+        Eg. for psycopg2, uses %(name)s so use params={'name' : 'value'}.
+    parse_dates : list or dict, default: None
+        - List of column names to parse as dates.
+        - Dict of ``{column_name: format string}`` where format string is
+          strftime compatible in case of parsing string times, or is one of
+          (D, s, ns, ms, us) in case of parsing integer timestamps.
+        - Dict of ``{column_name: arg dict}``, where the arg dict corresponds
+          to the keyword arguments of :func:`pandas.to_datetime`
+          Especially useful with databases without native Datetime support,
+          such as SQLite.
+    columns : list, default: None
+        List of column names to select from SQL table (only used when reading
+        a table).
+    chunksize : int, default None
+        If specified, return an iterator where `chunksize` is the
+        number of rows to include in each chunk.
+    dtype_backend : {'numpy_nullable', 'pyarrow'}, default 'numpy_nullable'
+        Back-end data type applied to the resultant :class:`DataFrame`
+        (still experimental). Behaviour is as follows:
+
+        * ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame`
+          (default).
+        * ``"pyarrow"``: returns pyarrow-backed nullable :class:`ArrowDtype`
+          DataFrame.
+
+        .. versionadded:: 2.0
+    dtype : Type name or dict of columns
+        Data type for data or columns. E.g. np.float64 or
+        {'a': np.float64, 'b': np.int32, 'c': 'Int64'}.
+        The argument is ignored if a table is passed instead of a query.
+
+        .. versionadded:: 2.0.0
+
+    Returns
+    -------
+    DataFrame or Iterator[DataFrame]
+
+    See Also
+    --------
+    read_sql_table : Read SQL database table into a DataFrame.
+    read_sql_query : Read SQL query into a DataFrame.
+
+    Examples
+    --------
+    Read data from SQL via either a SQL query or a SQL tablename.
+    When using a SQLite database only SQL queries are accepted,
+    providing only the SQL tablename will result in an error.
+
+    >>> from sqlite3 import connect
+    >>> conn = connect(':memory:')
+    >>> df = pd.DataFrame(data=[[0, '10/11/12'], [1, '12/11/10']],
+    ...                   columns=['int_column', 'date_column'])
+    >>> df.to_sql(name='test_data', con=conn)
+    2
+
+    >>> pd.read_sql('SELECT int_column, date_column FROM test_data', conn)
+       int_column date_column
+    0           0    10/11/12
+    1           1    12/11/10
+
+    >>> pd.read_sql('test_data', 'postgres:///db_name')  # doctest:+SKIP
+
+    Apply date parsing to columns through the ``parse_dates`` argument
+    The ``parse_dates`` argument calls ``pd.to_datetime`` on the provided columns.
+    Custom argument values for applying ``pd.to_datetime`` on a column are specified
+    via a dictionary format:
+
+    >>> pd.read_sql('SELECT int_column, date_column FROM test_data',
+    ...             conn,
+    ...             parse_dates={"date_column": {"format": "%d/%m/%y"}})
+       int_column date_column
+    0           0  2012-11-10
+    1           1  2010-11-12
+
+    .. versionadded:: 2.2.0
+
+       pandas now supports reading via ADBC drivers
+
+    >>> from adbc_driver_postgresql import dbapi  # doctest:+SKIP
+    >>> with dbapi.connect('postgres:///db_name') as conn:  # doctest:+SKIP
+    ...     pd.read_sql('SELECT int_column FROM test_data', conn)
+       int_column
+    0           0
+    1           1
+    """
+
+    check_dtype_backend(dtype_backend)
+    if dtype_backend is lib.no_default:
+        dtype_backend = "numpy"  # type: ignore[assignment]
+    assert dtype_backend is not lib.no_default
+
+    with pandasSQL_builder(con) as pandas_sql:
+        if isinstance(pandas_sql, SQLiteDatabase):
+            return pandas_sql.read_query(
+                sql,
+                index_col=index_col,
+                params=params,
+                coerce_float=coerce_float,
+                parse_dates=parse_dates,
+                chunksize=chunksize,
+                dtype_backend=dtype_backend,
+                dtype=dtype,
+            )
+
+        try:
+            _is_table_name = pandas_sql.has_table(sql)
+        except Exception:
+            # using generic exception to catch errors from sql drivers (GH24988)
+            _is_table_name = False
+
+        if _is_table_name:
+            return pandas_sql.read_table(
+                sql,
+                index_col=index_col,
+                coerce_float=coerce_float,
+                parse_dates=parse_dates,
+                columns=columns,
+                chunksize=chunksize,
+                dtype_backend=dtype_backend,
+            )
+        else:
+            return pandas_sql.read_query(
+                sql,
+                index_col=index_col,
+                params=params,
+                coerce_float=coerce_float,
+                parse_dates=parse_dates,
+                chunksize=chunksize,
+                dtype_backend=dtype_backend,
+                dtype=dtype,
+            )
+
+
+def to_sql(
+    frame,
+    name: str,
+    con,
+    schema: str | None = None,
+    if_exists: Literal["fail", "replace", "append"] = "fail",
+    index: bool = True,
+    index_label: IndexLabel | None = None,
+    chunksize: int | None = None,
+    dtype: DtypeArg | None = None,
+    method: Literal["multi"] | Callable | None = None,
+    engine: str = "auto",
+    **engine_kwargs,
+) -> int | None:
+    """
+    Write records stored in a DataFrame to a SQL database.
+
+    Parameters
+    ----------
+    frame : DataFrame, Series
+    name : str
+        Name of SQL table.
+    con : ADBC Connection, SQLAlchemy connectable, str, or sqlite3 connection
+        or sqlite3 DBAPI2 connection
+        ADBC provides high performance I/O with native type support, where available.
+        Using SQLAlchemy makes it possible to use any DB supported by that
+        library.
+        If a DBAPI2 object, only sqlite3 is supported.
+    schema : str, optional
+        Name of SQL schema in database to write to (if database flavor
+        supports this). If None, use default schema (default).
+    if_exists : {'fail', 'replace', 'append'}, default 'fail'
+        - fail: If table exists, do nothing.
+        - replace: If table exists, drop it, recreate it, and insert data.
+        - append: If table exists, insert data. Create if does not exist.
+    index : bool, default True
+        Write DataFrame index as a column.
+    index_label : str or sequence, optional
+        Column label for index column(s). If None is given (default) and
+        `index` is True, then the index names are used.
+        A sequence should be given if the DataFrame uses MultiIndex.
+    chunksize : int, optional
+        Specify the number of rows in each batch to be written at a time.
+        By default, all rows will be written at once.
+    dtype : dict or scalar, optional
+        Specifying the datatype for columns. If a dictionary is used, the
+        keys should be the column names and the values should be the
+        SQLAlchemy types or strings for the sqlite3 fallback mode. If a
+        scalar is provided, it will be applied to all columns.
+    method : {None, 'multi', callable}, optional
+        Controls the SQL insertion clause used:
+
+        - None : Uses standard SQL ``INSERT`` clause (one per row).
+        - ``'multi'``: Pass multiple values in a single ``INSERT`` clause.
+        - callable with signature ``(pd_table, conn, keys, data_iter) -> int | None``.
+
+        Details and a sample callable implementation can be found in the
+        section :ref:`insert method `.
+    engine : {'auto', 'sqlalchemy'}, default 'auto'
+        SQL engine library to use. If 'auto', then the option
+        ``io.sql.engine`` is used. The default ``io.sql.engine``
+        behavior is 'sqlalchemy'
+
+        .. versionadded:: 1.3.0
+
+    **engine_kwargs
+        Any additional kwargs are passed to the engine.
+
+    Returns
+    -------
+    None or int
+        Number of rows affected by to_sql. None is returned if the callable
+        passed into ``method`` does not return an integer number of rows.
+
+        .. versionadded:: 1.4.0
+
+    Notes
+    -----
+    The returned rows affected is the sum of the ``rowcount`` attribute of ``sqlite3.Cursor``
+    or SQLAlchemy connectable. If using ADBC the returned rows are the result
+    of ``Cursor.adbc_ingest``. The returned value may not reflect the exact number of written
+    rows as stipulated in the
+    `sqlite3 `__ or
+    `SQLAlchemy `__
+    """  # noqa: E501
+    if if_exists not in ("fail", "replace", "append"):
+        raise ValueError(f"'{if_exists}' is not valid for if_exists")
+
+    if isinstance(frame, Series):
+        frame = frame.to_frame()
+    elif not isinstance(frame, DataFrame):
+        raise NotImplementedError(
+            "'frame' argument should be either a Series or a DataFrame"
+        )
+
+    with pandasSQL_builder(con, schema=schema, need_transaction=True) as pandas_sql:
+        return pandas_sql.to_sql(
+            frame,
+            name,
+            if_exists=if_exists,
+            index=index,
+            index_label=index_label,
+            schema=schema,
+            chunksize=chunksize,
+            dtype=dtype,
+            method=method,
+            engine=engine,
+            **engine_kwargs,
+        )
+
+
+def has_table(table_name: str, con, schema: str | None = None) -> bool:
+    """
+    Check if DataBase has named table.
+
+    Parameters
+    ----------
+    table_name: string
+        Name of SQL table.
+    con: ADBC Connection, SQLAlchemy connectable, str, or sqlite3 connection
+        ADBC provides high performance I/O with native type support, where available.
+        Using SQLAlchemy makes it possible to use any DB supported by that
+        library.
+        If a DBAPI2 object, only sqlite3 is supported.
+    schema : string, default None
+        Name of SQL schema in database to write to (if database flavor supports
+        this). If None, use default schema (default).
+
+    Returns
+    -------
+    boolean
+    """
+    with pandasSQL_builder(con, schema=schema) as pandas_sql:
+        return pandas_sql.has_table(table_name)
+
+
+table_exists = has_table
+
+
+def pandasSQL_builder(
+    con,
+    schema: str | None = None,
+    need_transaction: bool = False,
+) -> PandasSQL:
+    """
+    Convenience function to return the correct PandasSQL subclass based on the
+    provided parameters.  Also creates a sqlalchemy connection and transaction
+    if necessary.
+    """
+    import sqlite3
+
+    if isinstance(con, sqlite3.Connection) or con is None:
+        return SQLiteDatabase(con)
+
+    sqlalchemy = import_optional_dependency("sqlalchemy", errors="ignore")
+
+    if isinstance(con, str) and sqlalchemy is None:
+        raise ImportError("Using URI string without sqlalchemy installed.")
+
+    if sqlalchemy is not None and isinstance(con, (str, sqlalchemy.engine.Connectable)):
+        return SQLDatabase(con, schema, need_transaction)
+
+    adbc = import_optional_dependency("adbc_driver_manager.dbapi", errors="ignore")
+    if adbc and isinstance(con, adbc.Connection):
+        return ADBCDatabase(con)
+
+    warnings.warn(
+        "pandas only supports SQLAlchemy connectable (engine/connection) or "
+        "database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 "
+        "objects are not tested. Please consider using SQLAlchemy.",
+        UserWarning,
+        stacklevel=find_stack_level(),
+    )
+    return SQLiteDatabase(con)
+
+
+class SQLTable(PandasObject):
+    """
+    For mapping Pandas tables to SQL tables.
+    Uses fact that table is reflected by SQLAlchemy to
+    do better type conversions.
+    Also holds various flags needed to avoid having to
+    pass them between functions all the time.
+    """
+
+    # TODO: support for multiIndex
+
+    def __init__(
+        self,
+        name: str,
+        pandas_sql_engine,
+        frame=None,
+        index: bool | str | list[str] | None = True,
+        if_exists: Literal["fail", "replace", "append"] = "fail",
+        prefix: str = "pandas",
+        index_label=None,
+        schema=None,
+        keys=None,
+        dtype: DtypeArg | None = None,
+    ) -> None:
+        self.name = name
+        self.pd_sql = pandas_sql_engine
+        self.prefix = prefix
+        self.frame = frame
+        self.index = self._index_name(index, index_label)
+        self.schema = schema
+        self.if_exists = if_exists
+        self.keys = keys
+        self.dtype = dtype
+
+        if frame is not None:
+            # We want to initialize based on a dataframe
+            self.table = self._create_table_setup()
+        else:
+            # no data provided, read-only mode
+            self.table = self.pd_sql.get_table(self.name, self.schema)
+
+        if self.table is None:
+            raise ValueError(f"Could not init table '{name}'")
+
+        if not len(self.name):
+            raise ValueError("Empty table name specified")
+
+    def exists(self):
+        return self.pd_sql.has_table(self.name, self.schema)
+
+    def sql_schema(self) -> str:
+        from sqlalchemy.schema import CreateTable
+
+        return str(CreateTable(self.table).compile(self.pd_sql.con))
+
+    def _execute_create(self) -> None:
+        # Inserting table into database, add to MetaData object
+        self.table = self.table.to_metadata(self.pd_sql.meta)
+        with self.pd_sql.run_transaction():
+            self.table.create(bind=self.pd_sql.con)
+
+    def create(self) -> None:
+        if self.exists():
+            if self.if_exists == "fail":
+                raise ValueError(f"Table '{self.name}' already exists.")
+            if self.if_exists == "replace":
+                self.pd_sql.drop_table(self.name, self.schema)
+                self._execute_create()
+            elif self.if_exists == "append":
+                pass
+            else:
+                raise ValueError(f"'{self.if_exists}' is not valid for if_exists")
+        else:
+            self._execute_create()
+
+    def _execute_insert(self, conn, keys: list[str], data_iter) -> int:
+        """
+        Execute SQL statement inserting data
+
+        Parameters
+        ----------
+        conn : sqlalchemy.engine.Engine or sqlalchemy.engine.Connection
+        keys : list of str
+           Column names
+        data_iter : generator of list
+           Each item contains a list of values to be inserted
+        """
+        data = [dict(zip(keys, row)) for row in data_iter]
+        result = conn.execute(self.table.insert(), data)
+        return result.rowcount
+
+    def _execute_insert_multi(self, conn, keys: list[str], data_iter) -> int:
+        """
+        Alternative to _execute_insert for DBs support multi-value INSERT.
+
+        Note: multi-value insert is usually faster for analytics DBs
+        and tables containing a few columns
+        but performance degrades quickly with increase of columns.
+
+        """
+
+        from sqlalchemy import insert
+
+        data = [dict(zip(keys, row)) for row in data_iter]
+        stmt = insert(self.table).values(data)
+        result = conn.execute(stmt)
+        return result.rowcount
+
+    def insert_data(self) -> tuple[list[str], list[np.ndarray]]:
+        if self.index is not None:
+            temp = self.frame.copy()
+            temp.index.names = self.index
+            try:
+                temp.reset_index(inplace=True)
+            except ValueError as err:
+                raise ValueError(f"duplicate name in index/columns: {err}") from err
+        else:
+            temp = self.frame
+
+        column_names = list(map(str, temp.columns))
+        ncols = len(column_names)
+        # this just pre-allocates the list: None's will be replaced with ndarrays
+        # error: List item 0 has incompatible type "None"; expected "ndarray"
+        data_list: list[np.ndarray] = [None] * ncols  # type: ignore[list-item]
+
+        for i, (_, ser) in enumerate(temp.items()):
+            if ser.dtype.kind == "M":
+                if isinstance(ser._values, ArrowExtensionArray):
+                    import pyarrow as pa
+
+                    if pa.types.is_date(ser.dtype.pyarrow_dtype):
+                        # GH#53854 to_pydatetime not supported for pyarrow date dtypes
+                        d = ser._values.to_numpy(dtype=object)
+                    else:
+                        with warnings.catch_warnings():
+                            warnings.filterwarnings("ignore", category=FutureWarning)
+                            # GH#52459 to_pydatetime will return Index[object]
+                            d = np.asarray(ser.dt.to_pydatetime(), dtype=object)
+                else:
+                    d = ser._values.to_pydatetime()
+            elif ser.dtype.kind == "m":
+                vals = ser._values
+                if isinstance(vals, ArrowExtensionArray):
+                    vals = vals.to_numpy(dtype=np.dtype("m8[ns]"))
+                # store as integers, see GH#6921, GH#7076
+                d = vals.view("i8").astype(object)
+            else:
+                d = ser._values.astype(object)
+
+            assert isinstance(d, np.ndarray), type(d)
+
+            if ser._can_hold_na:
+                # Note: this will miss timedeltas since they are converted to int
+                mask = isna(d)
+                d[mask] = None
+
+            data_list[i] = d
+
+        return column_names, data_list
+
+    def insert(
+        self,
+        chunksize: int | None = None,
+        method: Literal["multi"] | Callable | None = None,
+    ) -> int | None:
+        # set insert method
+        if method is None:
+            exec_insert = self._execute_insert
+        elif method == "multi":
+            exec_insert = self._execute_insert_multi
+        elif callable(method):
+            exec_insert = partial(method, self)
+        else:
+            raise ValueError(f"Invalid parameter `method`: {method}")
+
+        keys, data_list = self.insert_data()
+
+        nrows = len(self.frame)
+
+        if nrows == 0:
+            return 0
+
+        if chunksize is None:
+            chunksize = nrows
+        elif chunksize == 0:
+            raise ValueError("chunksize argument should be non-zero")
+
+        chunks = (nrows // chunksize) + 1
+        total_inserted = None
+        with self.pd_sql.run_transaction() as conn:
+            for i in range(chunks):
+                start_i = i * chunksize
+                end_i = min((i + 1) * chunksize, nrows)
+                if start_i >= end_i:
+                    break
+
+                chunk_iter = zip(*(arr[start_i:end_i] for arr in data_list))
+                num_inserted = exec_insert(conn, keys, chunk_iter)
+                # GH 46891
+                if num_inserted is not None:
+                    if total_inserted is None:
+                        total_inserted = num_inserted
+                    else:
+                        total_inserted += num_inserted
+        return total_inserted
+
+    def _query_iterator(
+        self,
+        result,
+        exit_stack: ExitStack,
+        chunksize: int | None,
+        columns,
+        coerce_float: bool = True,
+        parse_dates=None,
+        dtype_backend: DtypeBackend | Literal["numpy"] = "numpy",
+    ):
+        """Return generator through chunked result set."""
+        has_read_data = False
+        with exit_stack:
+            while True:
+                data = result.fetchmany(chunksize)
+                if not data:
+                    if not has_read_data:
+                        yield DataFrame.from_records(
+                            [], columns=columns, coerce_float=coerce_float
+                        )
+                    break
+
+                has_read_data = True
+                self.frame = _convert_arrays_to_dataframe(
+                    data, columns, coerce_float, dtype_backend
+                )
+
+                self._harmonize_columns(
+                    parse_dates=parse_dates, dtype_backend=dtype_backend
+                )
+
+                if self.index is not None:
+                    self.frame.set_index(self.index, inplace=True)
+
+                yield self.frame
+
+    def read(
+        self,
+        exit_stack: ExitStack,
+        coerce_float: bool = True,
+        parse_dates=None,
+        columns=None,
+        chunksize: int | None = None,
+        dtype_backend: DtypeBackend | Literal["numpy"] = "numpy",
+    ) -> DataFrame | Iterator[DataFrame]:
+        from sqlalchemy import select
+
+        if columns is not None and len(columns) > 0:
+            cols = [self.table.c[n] for n in columns]
+            if self.index is not None:
+                for idx in self.index[::-1]:
+                    cols.insert(0, self.table.c[idx])
+            sql_select = select(*cols)
+        else:
+            sql_select = select(self.table)
+        result = self.pd_sql.execute(sql_select)
+        column_names = result.keys()
+
+        if chunksize is not None:
+            return self._query_iterator(
+                result,
+                exit_stack,
+                chunksize,
+                column_names,
+                coerce_float=coerce_float,
+                parse_dates=parse_dates,
+                dtype_backend=dtype_backend,
+            )
+        else:
+            data = result.fetchall()
+            self.frame = _convert_arrays_to_dataframe(
+                data, column_names, coerce_float, dtype_backend
+            )
+
+            self._harmonize_columns(
+                parse_dates=parse_dates, dtype_backend=dtype_backend
+            )
+
+            if self.index is not None:
+                self.frame.set_index(self.index, inplace=True)
+
+            return self.frame
+
+    def _index_name(self, index, index_label):
+        # for writing: index=True to include index in sql table
+        if index is True:
+            nlevels = self.frame.index.nlevels
+            # if index_label is specified, set this as index name(s)
+            if index_label is not None:
+                if not isinstance(index_label, list):
+                    index_label = [index_label]
+                if len(index_label) != nlevels:
+                    raise ValueError(
+                        "Length of 'index_label' should match number of "
+                        f"levels, which is {nlevels}"
+                    )
+                return index_label
+            # return the used column labels for the index columns
+            if (
+                nlevels == 1
+                and "index" not in self.frame.columns
+                and self.frame.index.name is None
+            ):
+                return ["index"]
+            else:
+                return com.fill_missing_names(self.frame.index.names)
+
+        # for reading: index=(list of) string to specify column to set as index
+        elif isinstance(index, str):
+            return [index]
+        elif isinstance(index, list):
+            return index
+        else:
+            return None
+
+    def _get_column_names_and_types(self, dtype_mapper):
+        column_names_and_types = []
+        if self.index is not None:
+            for i, idx_label in enumerate(self.index):
+                idx_type = dtype_mapper(self.frame.index._get_level_values(i))
+                column_names_and_types.append((str(idx_label), idx_type, True))
+
+        column_names_and_types += [
+            (str(self.frame.columns[i]), dtype_mapper(self.frame.iloc[:, i]), False)
+            for i in range(len(self.frame.columns))
+        ]
+
+        return column_names_and_types
+
+    def _create_table_setup(self):
+        from sqlalchemy import (
+            Column,
+            PrimaryKeyConstraint,
+            Table,
+        )
+        from sqlalchemy.schema import MetaData
+
+        column_names_and_types = self._get_column_names_and_types(self._sqlalchemy_type)
+
+        columns: list[Any] = [
+            Column(name, typ, index=is_index)
+            for name, typ, is_index in column_names_and_types
+        ]
+
+        if self.keys is not None:
+            if not is_list_like(self.keys):
+                keys = [self.keys]
+            else:
+                keys = self.keys
+            pkc = PrimaryKeyConstraint(*keys, name=self.name + "_pk")
+            columns.append(pkc)
+
+        schema = self.schema or self.pd_sql.meta.schema
+
+        # At this point, attach to new metadata, only attach to self.meta
+        # once table is created.
+        meta = MetaData()
+        return Table(self.name, meta, *columns, schema=schema)
+
+    def _harmonize_columns(
+        self,
+        parse_dates=None,
+        dtype_backend: DtypeBackend | Literal["numpy"] = "numpy",
+    ) -> None:
+        """
+        Make the DataFrame's column types align with the SQL table
+        column types.
+        Need to work around limited NA value support. Floats are always
+        fine, ints must always be floats if there are Null values.
+        Booleans are hard because converting bool column with None replaces
+        all Nones with false. Therefore only convert bool if there are no
+        NA values.
+        Datetimes should already be converted to np.datetime64 if supported,
+        but here we also force conversion if required.
+        """
+        parse_dates = _process_parse_dates_argument(parse_dates)
+
+        for sql_col in self.table.columns:
+            col_name = sql_col.name
+            try:
+                df_col = self.frame[col_name]
+
+                # Handle date parsing upfront; don't try to convert columns
+                # twice
+                if col_name in parse_dates:
+                    try:
+                        fmt = parse_dates[col_name]
+                    except TypeError:
+                        fmt = None
+                    self.frame[col_name] = _handle_date_column(df_col, format=fmt)
+                    continue
+
+                # the type the dataframe column should have
+                col_type = self._get_dtype(sql_col.type)
+
+                if (
+                    col_type is datetime
+                    or col_type is date
+                    or col_type is DatetimeTZDtype
+                ):
+                    # Convert tz-aware Datetime SQL columns to UTC
+                    utc = col_type is DatetimeTZDtype
+                    self.frame[col_name] = _handle_date_column(df_col, utc=utc)
+                elif dtype_backend == "numpy" and col_type is float:
+                    # floats support NA, can always convert!
+                    self.frame[col_name] = df_col.astype(col_type, copy=False)
+                elif (
+                    using_string_dtype()
+                    and is_string_dtype(col_type)
+                    and is_object_dtype(self.frame[col_name])
+                ):
+                    self.frame[col_name] = df_col.astype(col_type, copy=False)
+                elif dtype_backend == "numpy" and len(df_col) == df_col.count():
+                    # No NA values, can convert ints and bools
+                    if col_type is np.dtype("int64") or col_type is bool:
+                        self.frame[col_name] = df_col.astype(col_type, copy=False)
+            except KeyError:
+                pass  # this column not in results
+
+    def _sqlalchemy_type(self, col: Index | Series):
+        dtype: DtypeArg = self.dtype or {}
+        if is_dict_like(dtype):
+            dtype = cast(dict, dtype)
+            if col.name in dtype:
+                return dtype[col.name]
+
+        # Infer type of column, while ignoring missing values.
+        # Needed for inserting typed data containing NULLs, GH 8778.
+        col_type = lib.infer_dtype(col, skipna=True)
+
+        from sqlalchemy.types import (
+            TIMESTAMP,
+            BigInteger,
+            Boolean,
+            Date,
+            DateTime,
+            Float,
+            Integer,
+            SmallInteger,
+            Text,
+            Time,
+        )
+
+        if col_type in ("datetime64", "datetime"):
+            # GH 9086: TIMESTAMP is the suggested type if the column contains
+            # timezone information
+            try:
+                # error: Item "Index" of "Union[Index, Series]" has no attribute "dt"
+                if col.dt.tz is not None:  # type: ignore[union-attr]
+                    return TIMESTAMP(timezone=True)
+            except AttributeError:
+                # The column is actually a DatetimeIndex
+                # GH 26761 or an Index with date-like data e.g. 9999-01-01
+                if getattr(col, "tz", None) is not None:
+                    return TIMESTAMP(timezone=True)
+            return DateTime
+        if col_type == "timedelta64":
+            warnings.warn(
+                "the 'timedelta' type is not supported, and will be "
+                "written as integer values (ns frequency) to the database.",
+                UserWarning,
+                stacklevel=find_stack_level(),
+            )
+            return BigInteger
+        elif col_type == "floating":
+            if col.dtype == "float32":
+                return Float(precision=23)
+            else:
+                return Float(precision=53)
+        elif col_type == "integer":
+            # GH35076 Map pandas integer to optimal SQLAlchemy integer type
+            if col.dtype.name.lower() in ("int8", "uint8", "int16"):
+                return SmallInteger
+            elif col.dtype.name.lower() in ("uint16", "int32"):
+                return Integer
+            elif col.dtype.name.lower() == "uint64":
+                raise ValueError("Unsigned 64 bit integer datatype is not supported")
+            else:
+                return BigInteger
+        elif col_type == "boolean":
+            return Boolean
+        elif col_type == "date":
+            return Date
+        elif col_type == "time":
+            return Time
+        elif col_type == "complex":
+            raise ValueError("Complex datatypes not supported")
+
+        return Text
+
+    def _get_dtype(self, sqltype):
+        from sqlalchemy.types import (
+            TIMESTAMP,
+            Boolean,
+            Date,
+            DateTime,
+            Float,
+            Integer,
+            String,
+        )
+
+        if isinstance(sqltype, Float):
+            return float
+        elif isinstance(sqltype, Integer):
+            # TODO: Refine integer size.
+            return np.dtype("int64")
+        elif isinstance(sqltype, TIMESTAMP):
+            # we have a timezone capable type
+            if not sqltype.timezone:
+                return datetime
+            return DatetimeTZDtype
+        elif isinstance(sqltype, DateTime):
+            # Caution: np.datetime64 is also a subclass of np.number.
+            return datetime
+        elif isinstance(sqltype, Date):
+            return date
+        elif isinstance(sqltype, Boolean):
+            return bool
+        elif isinstance(sqltype, String):
+            if using_string_dtype():
+                return StringDtype(na_value=np.nan)
+
+        return object
+
+
+class PandasSQL(PandasObject, ABC):
+    """
+    Subclasses Should define read_query and to_sql.
+    """
+
+    def __enter__(self) -> Self:
+        return self
+
+    def __exit__(self, *args) -> None:
+        pass
+
+    def read_table(
+        self,
+        table_name: str,
+        index_col: str | list[str] | None = None,
+        coerce_float: bool = True,
+        parse_dates=None,
+        columns=None,
+        schema: str | None = None,
+        chunksize: int | None = None,
+        dtype_backend: DtypeBackend | Literal["numpy"] = "numpy",
+    ) -> DataFrame | Iterator[DataFrame]:
+        raise NotImplementedError
+
+    @abstractmethod
+    def read_query(
+        self,
+        sql: str,
+        index_col: str | list[str] | None = None,
+        coerce_float: bool = True,
+        parse_dates=None,
+        params=None,
+        chunksize: int | None = None,
+        dtype: DtypeArg | None = None,
+        dtype_backend: DtypeBackend | Literal["numpy"] = "numpy",
+    ) -> DataFrame | Iterator[DataFrame]:
+        pass
+
+    @abstractmethod
+    def to_sql(
+        self,
+        frame,
+        name: str,
+        if_exists: Literal["fail", "replace", "append"] = "fail",
+        index: bool = True,
+        index_label=None,
+        schema=None,
+        chunksize: int | None = None,
+        dtype: DtypeArg | None = None,
+        method: Literal["multi"] | Callable | None = None,
+        engine: str = "auto",
+        **engine_kwargs,
+    ) -> int | None:
+        pass
+
+    @abstractmethod
+    def execute(self, sql: str | Select | TextClause, params=None):
+        pass
+
+    @abstractmethod
+    def has_table(self, name: str, schema: str | None = None) -> bool:
+        pass
+
+    @abstractmethod
+    def _create_sql_schema(
+        self,
+        frame: DataFrame,
+        table_name: str,
+        keys: list[str] | None = None,
+        dtype: DtypeArg | None = None,
+        schema: str | None = None,
+    ) -> str:
+        pass
+
+
+class BaseEngine:
+    def insert_records(
+        self,
+        table: SQLTable,
+        con,
+        frame,
+        name: str,
+        index: bool | str | list[str] | None = True,
+        schema=None,
+        chunksize: int | None = None,
+        method=None,
+        **engine_kwargs,
+    ) -> int | None:
+        """
+        Inserts data into already-prepared table
+        """
+        raise AbstractMethodError(self)
+
+
+class SQLAlchemyEngine(BaseEngine):
+    def __init__(self) -> None:
+        import_optional_dependency(
+            "sqlalchemy", extra="sqlalchemy is required for SQL support."
+        )
+
+    def insert_records(
+        self,
+        table: SQLTable,
+        con,
+        frame,
+        name: str,
+        index: bool | str | list[str] | None = True,
+        schema=None,
+        chunksize: int | None = None,
+        method=None,
+        **engine_kwargs,
+    ) -> int | None:
+        from sqlalchemy import exc
+
+        try:
+            return table.insert(chunksize=chunksize, method=method)
+        except exc.StatementError as err:
+            # GH34431
+            # https://stackoverflow.com/a/67358288/6067848
+            msg = r"""(\(1054, "Unknown column 'inf(e0)?' in 'field list'"\))(?#
+            )|inf can not be used with MySQL"""
+            err_text = str(err.orig)
+            if re.search(msg, err_text):
+                raise ValueError("inf cannot be used with MySQL") from err
+            raise err
+
+
+def get_engine(engine: str) -> BaseEngine:
+    """return our implementation"""
+    if engine == "auto":
+        engine = get_option("io.sql.engine")
+
+    if engine == "auto":
+        # try engines in this order
+        engine_classes = [SQLAlchemyEngine]
+
+        error_msgs = ""
+        for engine_class in engine_classes:
+            try:
+                return engine_class()
+            except ImportError as err:
+                error_msgs += "\n - " + str(err)
+
+        raise ImportError(
+            "Unable to find a usable engine; "
+            "tried using: 'sqlalchemy'.\n"
+            "A suitable version of "
+            "sqlalchemy is required for sql I/O "
+            "support.\n"
+            "Trying to import the above resulted in these errors:"
+            f"{error_msgs}"
+        )
+
+    if engine == "sqlalchemy":
+        return SQLAlchemyEngine()
+
+    raise ValueError("engine must be one of 'auto', 'sqlalchemy'")
+
+
+class SQLDatabase(PandasSQL):
+    """
+    This class enables conversion between DataFrame and SQL databases
+    using SQLAlchemy to handle DataBase abstraction.
+
+    Parameters
+    ----------
+    con : SQLAlchemy Connectable or URI string.
+        Connectable to connect with the database. Using SQLAlchemy makes it
+        possible to use any DB supported by that library.
+    schema : string, default None
+        Name of SQL schema in database to write to (if database flavor
+        supports this). If None, use default schema (default).
+    need_transaction : bool, default False
+        If True, SQLDatabase will create a transaction.
+
+    """
+
+    def __init__(
+        self, con, schema: str | None = None, need_transaction: bool = False
+    ) -> None:
+        from sqlalchemy import create_engine
+        from sqlalchemy.engine import Engine
+        from sqlalchemy.schema import MetaData
+
+        # self.exit_stack cleans up the Engine and Connection and commits the
+        # transaction if any of those objects was created below.
+        # Cleanup happens either in self.__exit__ or at the end of the iterator
+        # returned by read_sql when chunksize is not None.
+        self.exit_stack = ExitStack()
+        if isinstance(con, str):
+            con = create_engine(con)
+            self.exit_stack.callback(con.dispose)
+        if isinstance(con, Engine):
+            con = self.exit_stack.enter_context(con.connect())
+        if need_transaction and not con.in_transaction():
+            self.exit_stack.enter_context(con.begin())
+        self.con = con
+        self.meta = MetaData(schema=schema)
+        self.returns_generator = False
+
+    def __exit__(self, *args) -> None:
+        if not self.returns_generator:
+            self.exit_stack.close()
+
+    @contextmanager
+    def run_transaction(self):
+        if not self.con.in_transaction():
+            with self.con.begin():
+                yield self.con
+        else:
+            yield self.con
+
+    def execute(self, sql: str | Select | TextClause, params=None):
+        """Simple passthrough to SQLAlchemy connectable"""
+        args = [] if params is None else [params]
+        if isinstance(sql, str):
+            return self.con.exec_driver_sql(sql, *args)
+        return self.con.execute(sql, *args)
+
+    def read_table(
+        self,
+        table_name: str,
+        index_col: str | list[str] | None = None,
+        coerce_float: bool = True,
+        parse_dates=None,
+        columns=None,
+        schema: str | None = None,
+        chunksize: int | None = None,
+        dtype_backend: DtypeBackend | Literal["numpy"] = "numpy",
+    ) -> DataFrame | Iterator[DataFrame]:
+        """
+        Read SQL database table into a DataFrame.
+
+        Parameters
+        ----------
+        table_name : str
+            Name of SQL table in database.
+        index_col : string, optional, default: None
+            Column to set as index.
+        coerce_float : bool, default True
+            Attempts to convert values of non-string, non-numeric objects
+            (like decimal.Decimal) to floating point. This can result in
+            loss of precision.
+        parse_dates : list or dict, default: None
+            - List of column names to parse as dates.
+            - Dict of ``{column_name: format string}`` where format string is
+              strftime compatible in case of parsing string times, or is one of
+              (D, s, ns, ms, us) in case of parsing integer timestamps.
+            - Dict of ``{column_name: arg}``, where the arg corresponds
+              to the keyword arguments of :func:`pandas.to_datetime`.
+              Especially useful with databases without native Datetime support,
+              such as SQLite.
+        columns : list, default: None
+            List of column names to select from SQL table.
+        schema : string, default None
+            Name of SQL schema in database to query (if database flavor
+            supports this).  If specified, this overwrites the default
+            schema of the SQL database object.
+        chunksize : int, default None
+            If specified, return an iterator where `chunksize` is the number
+            of rows to include in each chunk.
+        dtype_backend : {'numpy_nullable', 'pyarrow'}, default 'numpy_nullable'
+            Back-end data type applied to the resultant :class:`DataFrame`
+            (still experimental). Behaviour is as follows:
+
+            * ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame`
+              (default).
+            * ``"pyarrow"``: returns pyarrow-backed nullable :class:`ArrowDtype`
+              DataFrame.
+
+            .. versionadded:: 2.0
+
+        Returns
+        -------
+        DataFrame
+
+        See Also
+        --------
+        pandas.read_sql_table
+        SQLDatabase.read_query
+
+        """
+        self.meta.reflect(bind=self.con, only=[table_name], views=True)
+        table = SQLTable(table_name, self, index=index_col, schema=schema)
+        if chunksize is not None:
+            self.returns_generator = True
+        return table.read(
+            self.exit_stack,
+            coerce_float=coerce_float,
+            parse_dates=parse_dates,
+            columns=columns,
+            chunksize=chunksize,
+            dtype_backend=dtype_backend,
+        )
+
+    @staticmethod
+    def _query_iterator(
+        result,
+        exit_stack: ExitStack,
+        chunksize: int,
+        columns,
+        index_col=None,
+        coerce_float: bool = True,
+        parse_dates=None,
+        dtype: DtypeArg | None = None,
+        dtype_backend: DtypeBackend | Literal["numpy"] = "numpy",
+    ):
+        """Return generator through chunked result set"""
+        has_read_data = False
+        with exit_stack:
+            while True:
+                data = result.fetchmany(chunksize)
+                if not data:
+                    if not has_read_data:
+                        yield _wrap_result(
+                            [],
+                            columns,
+                            index_col=index_col,
+                            coerce_float=coerce_float,
+                            parse_dates=parse_dates,
+                            dtype=dtype,
+                            dtype_backend=dtype_backend,
+                        )
+                    break
+
+                has_read_data = True
+                yield _wrap_result(
+                    data,
+                    columns,
+                    index_col=index_col,
+                    coerce_float=coerce_float,
+                    parse_dates=parse_dates,
+                    dtype=dtype,
+                    dtype_backend=dtype_backend,
+                )
+
+    def read_query(
+        self,
+        sql: str,
+        index_col: str | list[str] | None = None,
+        coerce_float: bool = True,
+        parse_dates=None,
+        params=None,
+        chunksize: int | None = None,
+        dtype: DtypeArg | None = None,
+        dtype_backend: DtypeBackend | Literal["numpy"] = "numpy",
+    ) -> DataFrame | Iterator[DataFrame]:
+        """
+        Read SQL query into a DataFrame.
+
+        Parameters
+        ----------
+        sql : str
+            SQL query to be executed.
+        index_col : string, optional, default: None
+            Column name to use as index for the returned DataFrame object.
+        coerce_float : bool, default True
+            Attempt to convert values of non-string, non-numeric objects (like
+            decimal.Decimal) to floating point, useful for SQL result sets.
+        params : list, tuple or dict, optional, default: None
+            List of parameters to pass to execute method.  The syntax used
+            to pass parameters is database driver dependent. Check your
+            database driver documentation for which of the five syntax styles,
+            described in PEP 249's paramstyle, is supported.
+            Eg. for psycopg2, uses %(name)s so use params={'name' : 'value'}
+        parse_dates : list or dict, default: None
+            - List of column names to parse as dates.
+            - Dict of ``{column_name: format string}`` where format string is
+              strftime compatible in case of parsing string times, or is one of
+              (D, s, ns, ms, us) in case of parsing integer timestamps.
+            - Dict of ``{column_name: arg dict}``, where the arg dict
+              corresponds to the keyword arguments of
+              :func:`pandas.to_datetime` Especially useful with databases
+              without native Datetime support, such as SQLite.
+        chunksize : int, default None
+            If specified, return an iterator where `chunksize` is the number
+            of rows to include in each chunk.
+        dtype : Type name or dict of columns
+            Data type for data or columns. E.g. np.float64 or
+            {'a': np.float64, 'b': np.int32, 'c': 'Int64'}
+
+            .. versionadded:: 1.3.0
+
+        Returns
+        -------
+        DataFrame
+
+        See Also
+        --------
+        read_sql_table : Read SQL database table into a DataFrame.
+        read_sql
+
+        """
+        result = self.execute(sql, params)
+        columns = result.keys()
+
+        if chunksize is not None:
+            self.returns_generator = True
+            return self._query_iterator(
+                result,
+                self.exit_stack,
+                chunksize,
+                columns,
+                index_col=index_col,
+                coerce_float=coerce_float,
+                parse_dates=parse_dates,
+                dtype=dtype,
+                dtype_backend=dtype_backend,
+            )
+        else:
+            data = result.fetchall()
+            frame = _wrap_result(
+                data,
+                columns,
+                index_col=index_col,
+                coerce_float=coerce_float,
+                parse_dates=parse_dates,
+                dtype=dtype,
+                dtype_backend=dtype_backend,
+            )
+            return frame
+
+    read_sql = read_query
+
+    def prep_table(
+        self,
+        frame,
+        name: str,
+        if_exists: Literal["fail", "replace", "append"] = "fail",
+        index: bool | str | list[str] | None = True,
+        index_label=None,
+        schema=None,
+        dtype: DtypeArg | None = None,
+    ) -> SQLTable:
+        """
+        Prepares table in the database for data insertion. Creates it if needed, etc.
+        """
+        if dtype:
+            if not is_dict_like(dtype):
+                # error: Value expression in dictionary comprehension has incompatible
+                # type "Union[ExtensionDtype, str, dtype[Any], Type[object],
+                # Dict[Hashable, Union[ExtensionDtype, Union[str, dtype[Any]],
+                # Type[str], Type[float], Type[int], Type[complex], Type[bool],
+                # Type[object]]]]"; expected type "Union[ExtensionDtype, str,
+                # dtype[Any], Type[object]]"
+                dtype = {col_name: dtype for col_name in frame}  # type: ignore[misc]
+            else:
+                dtype = cast(dict, dtype)
+
+            from sqlalchemy.types import TypeEngine
+
+            for col, my_type in dtype.items():
+                if isinstance(my_type, type) and issubclass(my_type, TypeEngine):
+                    pass
+                elif isinstance(my_type, TypeEngine):
+                    pass
+                else:
+                    raise ValueError(f"The type of {col} is not a SQLAlchemy type")
+
+        table = SQLTable(
+            name,
+            self,
+            frame=frame,
+            index=index,
+            if_exists=if_exists,
+            index_label=index_label,
+            schema=schema,
+            dtype=dtype,
+        )
+        table.create()
+        return table
+
+    def check_case_sensitive(
+        self,
+        name: str,
+        schema: str | None,
+    ) -> None:
+        """
+        Checks table name for issues with case-sensitivity.
+        Method is called after data is inserted.
+        """
+        if not name.isdigit() and not name.islower():
+            # check for potentially case sensitivity issues (GH7815)
+            # Only check when name is not a number and name is not lower case
+            from sqlalchemy import inspect as sqlalchemy_inspect
+
+            insp = sqlalchemy_inspect(self.con)
+            table_names = insp.get_table_names(schema=schema or self.meta.schema)
+            if name not in table_names:
+                msg = (
+                    f"The provided table name '{name}' is not found exactly as "
+                    "such in the database after writing the table, possibly "
+                    "due to case sensitivity issues. Consider using lower "
+                    "case table names."
+                )
+                warnings.warn(
+                    msg,
+                    UserWarning,
+                    stacklevel=find_stack_level(),
+                )
+
+    def to_sql(
+        self,
+        frame,
+        name: str,
+        if_exists: Literal["fail", "replace", "append"] = "fail",
+        index: bool = True,
+        index_label=None,
+        schema: str | None = None,
+        chunksize: int | None = None,
+        dtype: DtypeArg | None = None,
+        method: Literal["multi"] | Callable | None = None,
+        engine: str = "auto",
+        **engine_kwargs,
+    ) -> int | None:
+        """
+        Write records stored in a DataFrame to a SQL database.
+
+        Parameters
+        ----------
+        frame : DataFrame
+        name : string
+            Name of SQL table.
+        if_exists : {'fail', 'replace', 'append'}, default 'fail'
+            - fail: If table exists, do nothing.
+            - replace: If table exists, drop it, recreate it, and insert data.
+            - append: If table exists, insert data. Create if does not exist.
+        index : boolean, default True
+            Write DataFrame index as a column.
+        index_label : string or sequence, default None
+            Column label for index column(s). If None is given (default) and
+            `index` is True, then the index names are used.
+            A sequence should be given if the DataFrame uses MultiIndex.
+        schema : string, default None
+            Name of SQL schema in database to write to (if database flavor
+            supports this). If specified, this overwrites the default
+            schema of the SQLDatabase object.
+        chunksize : int, default None
+            If not None, then rows will be written in batches of this size at a
+            time.  If None, all rows will be written at once.
+        dtype : single type or dict of column name to SQL type, default None
+            Optional specifying the datatype for columns. The SQL type should
+            be a SQLAlchemy type. If all columns are of the same type, one
+            single value can be used.
+        method : {None', 'multi', callable}, default None
+            Controls the SQL insertion clause used:
+
+            * None : Uses standard SQL ``INSERT`` clause (one per row).
+            * 'multi': Pass multiple values in a single ``INSERT`` clause.
+            * callable with signature ``(pd_table, conn, keys, data_iter)``.
+
+            Details and a sample callable implementation can be found in the
+            section :ref:`insert method `.
+        engine : {'auto', 'sqlalchemy'}, default 'auto'
+            SQL engine library to use. If 'auto', then the option
+            ``io.sql.engine`` is used. The default ``io.sql.engine``
+            behavior is 'sqlalchemy'
+
+            .. versionadded:: 1.3.0
+
+        **engine_kwargs
+            Any additional kwargs are passed to the engine.
+        """
+        sql_engine = get_engine(engine)
+
+        table = self.prep_table(
+            frame=frame,
+            name=name,
+            if_exists=if_exists,
+            index=index,
+            index_label=index_label,
+            schema=schema,
+            dtype=dtype,
+        )
+
+        total_inserted = sql_engine.insert_records(
+            table=table,
+            con=self.con,
+            frame=frame,
+            name=name,
+            index=index,
+            schema=schema,
+            chunksize=chunksize,
+            method=method,
+            **engine_kwargs,
+        )
+
+        self.check_case_sensitive(name=name, schema=schema)
+        return total_inserted
+
+    @property
+    def tables(self):
+        return self.meta.tables
+
+    def has_table(self, name: str, schema: str | None = None) -> bool:
+        from sqlalchemy import inspect as sqlalchemy_inspect
+
+        insp = sqlalchemy_inspect(self.con)
+        return insp.has_table(name, schema or self.meta.schema)
+
+    def get_table(self, table_name: str, schema: str | None = None) -> Table:
+        from sqlalchemy import (
+            Numeric,
+            Table,
+        )
+
+        schema = schema or self.meta.schema
+        tbl = Table(table_name, self.meta, autoload_with=self.con, schema=schema)
+        for column in tbl.columns:
+            if isinstance(column.type, Numeric):
+                column.type.asdecimal = False
+        return tbl
+
+    def drop_table(self, table_name: str, schema: str | None = None) -> None:
+        schema = schema or self.meta.schema
+        if self.has_table(table_name, schema):
+            self.meta.reflect(
+                bind=self.con, only=[table_name], schema=schema, views=True
+            )
+            with self.run_transaction():
+                self.get_table(table_name, schema).drop(bind=self.con)
+            self.meta.clear()
+
+    def _create_sql_schema(
+        self,
+        frame: DataFrame,
+        table_name: str,
+        keys: list[str] | None = None,
+        dtype: DtypeArg | None = None,
+        schema: str | None = None,
+    ) -> str:
+        table = SQLTable(
+            table_name,
+            self,
+            frame=frame,
+            index=False,
+            keys=keys,
+            dtype=dtype,
+            schema=schema,
+        )
+        return str(table.sql_schema())
+
+
+# ---- SQL without SQLAlchemy ---
+
+
+class ADBCDatabase(PandasSQL):
+    """
+    This class enables conversion between DataFrame and SQL databases
+    using ADBC to handle DataBase abstraction.
+
+    Parameters
+    ----------
+    con : adbc_driver_manager.dbapi.Connection
+    """
+
+    def __init__(self, con) -> None:
+        self.con = con
+
+    @contextmanager
+    def run_transaction(self):
+        with self.con.cursor() as cur:
+            try:
+                yield cur
+            except Exception:
+                self.con.rollback()
+                raise
+            self.con.commit()
+
+    def execute(self, sql: str | Select | TextClause, params=None):
+        if not isinstance(sql, str):
+            raise TypeError("Query must be a string unless using sqlalchemy.")
+        args = [] if params is None else [params]
+        cur = self.con.cursor()
+        try:
+            cur.execute(sql, *args)
+            return cur
+        except Exception as exc:
+            try:
+                self.con.rollback()
+            except Exception as inner_exc:  # pragma: no cover
+                ex = DatabaseError(
+                    f"Execution failed on sql: {sql}\n{exc}\nunable to rollback"
+                )
+                raise ex from inner_exc
+
+            ex = DatabaseError(f"Execution failed on sql '{sql}': {exc}")
+            raise ex from exc
+
+    def read_table(
+        self,
+        table_name: str,
+        index_col: str | list[str] | None = None,
+        coerce_float: bool = True,
+        parse_dates=None,
+        columns=None,
+        schema: str | None = None,
+        chunksize: int | None = None,
+        dtype_backend: DtypeBackend | Literal["numpy"] = "numpy",
+    ) -> DataFrame | Iterator[DataFrame]:
+        """
+        Read SQL database table into a DataFrame.
+
+        Parameters
+        ----------
+        table_name : str
+            Name of SQL table in database.
+        coerce_float : bool, default True
+            Raises NotImplementedError
+        parse_dates : list or dict, default: None
+            - List of column names to parse as dates.
+            - Dict of ``{column_name: format string}`` where format string is
+              strftime compatible in case of parsing string times, or is one of
+              (D, s, ns, ms, us) in case of parsing integer timestamps.
+            - Dict of ``{column_name: arg}``, where the arg corresponds
+              to the keyword arguments of :func:`pandas.to_datetime`.
+              Especially useful with databases without native Datetime support,
+              such as SQLite.
+        columns : list, default: None
+            List of column names to select from SQL table.
+        schema : string, default None
+            Name of SQL schema in database to query (if database flavor
+            supports this).  If specified, this overwrites the default
+            schema of the SQL database object.
+        chunksize : int, default None
+            Raises NotImplementedError
+        dtype_backend : {'numpy_nullable', 'pyarrow'}, default 'numpy_nullable'
+            Back-end data type applied to the resultant :class:`DataFrame`
+            (still experimental). Behaviour is as follows:
+
+            * ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame`
+              (default).
+            * ``"pyarrow"``: returns pyarrow-backed nullable :class:`ArrowDtype`
+              DataFrame.
+
+            .. versionadded:: 2.0
+
+        Returns
+        -------
+        DataFrame
+
+        See Also
+        --------
+        pandas.read_sql_table
+        SQLDatabase.read_query
+
+        """
+        if coerce_float is not True:
+            raise NotImplementedError(
+                "'coerce_float' is not implemented for ADBC drivers"
+            )
+        if chunksize:
+            raise NotImplementedError("'chunksize' is not implemented for ADBC drivers")
+
+        if columns:
+            if index_col:
+                index_select = maybe_make_list(index_col)
+            else:
+                index_select = []
+            to_select = index_select + columns
+            select_list = ", ".join(f'"{x}"' for x in to_select)
+        else:
+            select_list = "*"
+        if schema:
+            stmt = f"SELECT {select_list} FROM {schema}.{table_name}"
+        else:
+            stmt = f"SELECT {select_list} FROM {table_name}"
+
+        with self.con.cursor() as cur:
+            cur.execute(stmt)
+            pa_table = cur.fetch_arrow_table()
+            df = arrow_table_to_pandas(pa_table, dtype_backend=dtype_backend)
+
+        return _wrap_result_adbc(
+            df,
+            index_col=index_col,
+            parse_dates=parse_dates,
+        )
+
+    def read_query(
+        self,
+        sql: str,
+        index_col: str | list[str] | None = None,
+        coerce_float: bool = True,
+        parse_dates=None,
+        params=None,
+        chunksize: int | None = None,
+        dtype: DtypeArg | None = None,
+        dtype_backend: DtypeBackend | Literal["numpy"] = "numpy",
+    ) -> DataFrame | Iterator[DataFrame]:
+        """
+        Read SQL query into a DataFrame.
+
+        Parameters
+        ----------
+        sql : str
+            SQL query to be executed.
+        index_col : string, optional, default: None
+            Column name to use as index for the returned DataFrame object.
+        coerce_float : bool, default True
+            Raises NotImplementedError
+        params : list, tuple or dict, optional, default: None
+            Raises NotImplementedError
+        parse_dates : list or dict, default: None
+            - List of column names to parse as dates.
+            - Dict of ``{column_name: format string}`` where format string is
+              strftime compatible in case of parsing string times, or is one of
+              (D, s, ns, ms, us) in case of parsing integer timestamps.
+            - Dict of ``{column_name: arg dict}``, where the arg dict
+              corresponds to the keyword arguments of
+              :func:`pandas.to_datetime` Especially useful with databases
+              without native Datetime support, such as SQLite.
+        chunksize : int, default None
+            Raises NotImplementedError
+        dtype : Type name or dict of columns
+            Data type for data or columns. E.g. np.float64 or
+            {'a': np.float64, 'b': np.int32, 'c': 'Int64'}
+
+            .. versionadded:: 1.3.0
+
+        Returns
+        -------
+        DataFrame
+
+        See Also
+        --------
+        read_sql_table : Read SQL database table into a DataFrame.
+        read_sql
+
+        """
+        if coerce_float is not True:
+            raise NotImplementedError(
+                "'coerce_float' is not implemented for ADBC drivers"
+            )
+        if params:
+            raise NotImplementedError("'params' is not implemented for ADBC drivers")
+        if chunksize:
+            raise NotImplementedError("'chunksize' is not implemented for ADBC drivers")
+
+        with self.con.cursor() as cur:
+            cur.execute(sql)
+            pa_table = cur.fetch_arrow_table()
+            df = arrow_table_to_pandas(pa_table, dtype_backend=dtype_backend)
+
+        return _wrap_result_adbc(
+            df,
+            index_col=index_col,
+            parse_dates=parse_dates,
+            dtype=dtype,
+        )
+
+    read_sql = read_query
+
+    def to_sql(
+        self,
+        frame,
+        name: str,
+        if_exists: Literal["fail", "replace", "append"] = "fail",
+        index: bool = True,
+        index_label=None,
+        schema: str | None = None,
+        chunksize: int | None = None,
+        dtype: DtypeArg | None = None,
+        method: Literal["multi"] | Callable | None = None,
+        engine: str = "auto",
+        **engine_kwargs,
+    ) -> int | None:
+        """
+        Write records stored in a DataFrame to a SQL database.
+
+        Parameters
+        ----------
+        frame : DataFrame
+        name : string
+            Name of SQL table.
+        if_exists : {'fail', 'replace', 'append'}, default 'fail'
+            - fail: If table exists, do nothing.
+            - replace: If table exists, drop it, recreate it, and insert data.
+            - append: If table exists, insert data. Create if does not exist.
+        index : boolean, default True
+            Write DataFrame index as a column.
+        index_label : string or sequence, default None
+            Raises NotImplementedError
+        schema : string, default None
+            Name of SQL schema in database to write to (if database flavor
+            supports this). If specified, this overwrites the default
+            schema of the SQLDatabase object.
+        chunksize : int, default None
+            Raises NotImplementedError
+        dtype : single type or dict of column name to SQL type, default None
+            Raises NotImplementedError
+        method : {None', 'multi', callable}, default None
+            Raises NotImplementedError
+        engine : {'auto', 'sqlalchemy'}, default 'auto'
+            Raises NotImplementedError if not set to 'auto'
+        """
+        if index_label:
+            raise NotImplementedError(
+                "'index_label' is not implemented for ADBC drivers"
+            )
+        if chunksize:
+            raise NotImplementedError("'chunksize' is not implemented for ADBC drivers")
+        if dtype:
+            raise NotImplementedError("'dtype' is not implemented for ADBC drivers")
+        if method:
+            raise NotImplementedError("'method' is not implemented for ADBC drivers")
+        if engine != "auto":
+            raise NotImplementedError(
+                "engine != 'auto' not implemented for ADBC drivers"
+            )
+
+        if schema:
+            table_name = f"{schema}.{name}"
+        else:
+            table_name = name
+
+        # pandas if_exists="append" will still create the
+        # table if it does not exist; ADBC is more explicit with append/create
+        # as applicable modes, so the semantics get blurred across
+        # the libraries
+        mode = "create"
+        if self.has_table(name, schema):
+            if if_exists == "fail":
+                raise ValueError(f"Table '{table_name}' already exists.")
+            elif if_exists == "replace":
+                with self.con.cursor() as cur:
+                    cur.execute(f"DROP TABLE {table_name}")
+            elif if_exists == "append":
+                mode = "append"
+
+        import pyarrow as pa
+
+        try:
+            tbl = pa.Table.from_pandas(frame, preserve_index=index)
+        except pa.ArrowNotImplementedError as exc:
+            raise ValueError("datatypes not supported") from exc
+
+        with self.con.cursor() as cur:
+            total_inserted = cur.adbc_ingest(
+                table_name=name, data=tbl, mode=mode, db_schema_name=schema
+            )
+
+        self.con.commit()
+        return total_inserted
+
+    def has_table(self, name: str, schema: str | None = None) -> bool:
+        meta = self.con.adbc_get_objects(
+            db_schema_filter=schema, table_name_filter=name
+        ).read_all()
+
+        for catalog_schema in meta["catalog_db_schemas"].to_pylist():
+            if not catalog_schema:
+                continue
+            for schema_record in catalog_schema:
+                if not schema_record:
+                    continue
+
+                for table_record in schema_record["db_schema_tables"]:
+                    if table_record["table_name"] == name:
+                        return True
+
+        return False
+
+    def _create_sql_schema(
+        self,
+        frame: DataFrame,
+        table_name: str,
+        keys: list[str] | None = None,
+        dtype: DtypeArg | None = None,
+        schema: str | None = None,
+    ) -> str:
+        raise NotImplementedError("not implemented for adbc")
+
+
+# sqlite-specific sql strings and handler class
+# dictionary used for readability purposes
+_SQL_TYPES = {
+    "string": "TEXT",
+    "floating": "REAL",
+    "integer": "INTEGER",
+    "datetime": "TIMESTAMP",
+    "date": "DATE",
+    "time": "TIME",
+    "boolean": "INTEGER",
+}
+
+
+def _get_unicode_name(name: object):
+    try:
+        uname = str(name).encode("utf-8", "strict").decode("utf-8")
+    except UnicodeError as err:
+        raise ValueError(f"Cannot convert identifier to UTF-8: '{name}'") from err
+    return uname
+
+
+def _get_valid_sqlite_name(name: object):
+    # See https://stackoverflow.com/questions/6514274/how-do-you-escape-strings\
+    # -for-sqlite-table-column-names-in-python
+    # Ensure the string can be encoded as UTF-8.
+    # Ensure the string does not include any NUL characters.
+    # Replace all " with "".
+    # Wrap the entire thing in double quotes.
+
+    uname = _get_unicode_name(name)
+    if not len(uname):
+        raise ValueError("Empty table or column name specified")
+
+    nul_index = uname.find("\x00")
+    if nul_index >= 0:
+        raise ValueError("SQLite identifier cannot contain NULs")
+    return '"' + uname.replace('"', '""') + '"'
+
+
+class SQLiteTable(SQLTable):
+    """
+    Patch the SQLTable for fallback support.
+    Instead of a table variable just use the Create Table statement.
+    """
+
+    def __init__(self, *args, **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+
+        self._register_date_adapters()
+
+    def _register_date_adapters(self) -> None:
+        # GH 8341
+        # register an adapter callable for datetime.time object
+        import sqlite3
+
+        # this will transform time(12,34,56,789) into '12:34:56.000789'
+        # (this is what sqlalchemy does)
+        def _adapt_time(t) -> str:
+            # This is faster than strftime
+            return f"{t.hour:02d}:{t.minute:02d}:{t.second:02d}.{t.microsecond:06d}"
+
+        # Also register adapters for date/datetime and co
+        # xref https://docs.python.org/3.12/library/sqlite3.html#adapter-and-converter-recipes
+        # Python 3.12+ doesn't auto-register adapters for us anymore
+
+        adapt_date_iso = lambda val: val.isoformat()
+        adapt_datetime_iso = lambda val: val.isoformat(" ")
+
+        sqlite3.register_adapter(time, _adapt_time)
+
+        sqlite3.register_adapter(date, adapt_date_iso)
+        sqlite3.register_adapter(datetime, adapt_datetime_iso)
+
+        convert_date = lambda val: date.fromisoformat(val.decode())
+        convert_timestamp = lambda val: datetime.fromisoformat(val.decode())
+
+        sqlite3.register_converter("date", convert_date)
+        sqlite3.register_converter("timestamp", convert_timestamp)
+
+    def sql_schema(self) -> str:
+        return str(";\n".join(self.table))
+
+    def _execute_create(self) -> None:
+        with self.pd_sql.run_transaction() as conn:
+            for stmt in self.table:
+                conn.execute(stmt)
+
+    def insert_statement(self, *, num_rows: int) -> str:
+        names = list(map(str, self.frame.columns))
+        wld = "?"  # wildcard char
+        escape = _get_valid_sqlite_name
+
+        if self.index is not None:
+            for idx in self.index[::-1]:
+                names.insert(0, idx)
+
+        bracketed_names = [escape(column) for column in names]
+        col_names = ",".join(bracketed_names)
+
+        row_wildcards = ",".join([wld] * len(names))
+        wildcards = ",".join([f"({row_wildcards})" for _ in range(num_rows)])
+        insert_statement = (
+            f"INSERT INTO {escape(self.name)} ({col_names}) VALUES {wildcards}"
+        )
+        return insert_statement
+
+    def _execute_insert(self, conn, keys, data_iter) -> int:
+        data_list = list(data_iter)
+        conn.executemany(self.insert_statement(num_rows=1), data_list)
+        return conn.rowcount
+
+    def _execute_insert_multi(self, conn, keys, data_iter) -> int:
+        data_list = list(data_iter)
+        flattened_data = [x for row in data_list for x in row]
+        conn.execute(self.insert_statement(num_rows=len(data_list)), flattened_data)
+        return conn.rowcount
+
+    def _create_table_setup(self):
+        """
+        Return a list of SQL statements that creates a table reflecting the
+        structure of a DataFrame.  The first entry will be a CREATE TABLE
+        statement while the rest will be CREATE INDEX statements.
+        """
+        column_names_and_types = self._get_column_names_and_types(self._sql_type_name)
+        escape = _get_valid_sqlite_name
+
+        create_tbl_stmts = [
+            escape(cname) + " " + ctype for cname, ctype, _ in column_names_and_types
+        ]
+
+        if self.keys is not None and len(self.keys):
+            if not is_list_like(self.keys):
+                keys = [self.keys]
+            else:
+                keys = self.keys
+            cnames_br = ", ".join([escape(c) for c in keys])
+            create_tbl_stmts.append(
+                f"CONSTRAINT {self.name}_pk PRIMARY KEY ({cnames_br})"
+            )
+        if self.schema:
+            schema_name = self.schema + "."
+        else:
+            schema_name = ""
+        create_stmts = [
+            "CREATE TABLE "
+            + schema_name
+            + escape(self.name)
+            + " (\n"
+            + ",\n  ".join(create_tbl_stmts)
+            + "\n)"
+        ]
+
+        ix_cols = [cname for cname, _, is_index in column_names_and_types if is_index]
+        if len(ix_cols):
+            cnames = "_".join(ix_cols)
+            cnames_br = ",".join([escape(c) for c in ix_cols])
+            create_stmts.append(
+                "CREATE INDEX "
+                + escape("ix_" + self.name + "_" + cnames)
+                + "ON "
+                + escape(self.name)
+                + " ("
+                + cnames_br
+                + ")"
+            )
+
+        return create_stmts
+
+    def _sql_type_name(self, col):
+        dtype: DtypeArg = self.dtype or {}
+        if is_dict_like(dtype):
+            dtype = cast(dict, dtype)
+            if col.name in dtype:
+                return dtype[col.name]
+
+        # Infer type of column, while ignoring missing values.
+        # Needed for inserting typed data containing NULLs, GH 8778.
+        col_type = lib.infer_dtype(col, skipna=True)
+
+        if col_type == "timedelta64":
+            warnings.warn(
+                "the 'timedelta' type is not supported, and will be "
+                "written as integer values (ns frequency) to the database.",
+                UserWarning,
+                stacklevel=find_stack_level(),
+            )
+            col_type = "integer"
+
+        elif col_type == "datetime64":
+            col_type = "datetime"
+
+        elif col_type == "empty":
+            col_type = "string"
+
+        elif col_type == "complex":
+            raise ValueError("Complex datatypes not supported")
+
+        if col_type not in _SQL_TYPES:
+            col_type = "string"
+
+        return _SQL_TYPES[col_type]
+
+
+class SQLiteDatabase(PandasSQL):
+    """
+    Version of SQLDatabase to support SQLite connections (fallback without
+    SQLAlchemy). This should only be used internally.
+
+    Parameters
+    ----------
+    con : sqlite connection object
+
+    """
+
+    def __init__(self, con) -> None:
+        self.con = con
+
+    @contextmanager
+    def run_transaction(self):
+        cur = self.con.cursor()
+        try:
+            yield cur
+            self.con.commit()
+        except Exception:
+            self.con.rollback()
+            raise
+        finally:
+            cur.close()
+
+    def execute(self, sql: str | Select | TextClause, params=None):
+        if not isinstance(sql, str):
+            raise TypeError("Query must be a string unless using sqlalchemy.")
+        args = [] if params is None else [params]
+        cur = self.con.cursor()
+        try:
+            cur.execute(sql, *args)
+            return cur
+        except Exception as exc:
+            try:
+                self.con.rollback()
+            except Exception as inner_exc:  # pragma: no cover
+                ex = DatabaseError(
+                    f"Execution failed on sql: {sql}\n{exc}\nunable to rollback"
+                )
+                raise ex from inner_exc
+
+            ex = DatabaseError(f"Execution failed on sql '{sql}': {exc}")
+            raise ex from exc
+
+    @staticmethod
+    def _query_iterator(
+        cursor,
+        chunksize: int,
+        columns,
+        index_col=None,
+        coerce_float: bool = True,
+        parse_dates=None,
+        dtype: DtypeArg | None = None,
+        dtype_backend: DtypeBackend | Literal["numpy"] = "numpy",
+    ):
+        """Return generator through chunked result set"""
+        has_read_data = False
+        while True:
+            data = cursor.fetchmany(chunksize)
+            if type(data) == tuple:
+                data = list(data)
+            if not data:
+                cursor.close()
+                if not has_read_data:
+                    result = DataFrame.from_records(
+                        [], columns=columns, coerce_float=coerce_float
+                    )
+                    if dtype:
+                        result = result.astype(dtype)
+                    yield result
+                break
+
+            has_read_data = True
+            yield _wrap_result(
+                data,
+                columns,
+                index_col=index_col,
+                coerce_float=coerce_float,
+                parse_dates=parse_dates,
+                dtype=dtype,
+                dtype_backend=dtype_backend,
+            )
+
+    def read_query(
+        self,
+        sql,
+        index_col=None,
+        coerce_float: bool = True,
+        parse_dates=None,
+        params=None,
+        chunksize: int | None = None,
+        dtype: DtypeArg | None = None,
+        dtype_backend: DtypeBackend | Literal["numpy"] = "numpy",
+    ) -> DataFrame | Iterator[DataFrame]:
+        cursor = self.execute(sql, params)
+        columns = [col_desc[0] for col_desc in cursor.description]
+
+        if chunksize is not None:
+            return self._query_iterator(
+                cursor,
+                chunksize,
+                columns,
+                index_col=index_col,
+                coerce_float=coerce_float,
+                parse_dates=parse_dates,
+                dtype=dtype,
+                dtype_backend=dtype_backend,
+            )
+        else:
+            data = self._fetchall_as_list(cursor)
+            cursor.close()
+
+            frame = _wrap_result(
+                data,
+                columns,
+                index_col=index_col,
+                coerce_float=coerce_float,
+                parse_dates=parse_dates,
+                dtype=dtype,
+                dtype_backend=dtype_backend,
+            )
+            return frame
+
+    def _fetchall_as_list(self, cur):
+        result = cur.fetchall()
+        if not isinstance(result, list):
+            result = list(result)
+        return result
+
+    def to_sql(
+        self,
+        frame,
+        name: str,
+        if_exists: str = "fail",
+        index: bool = True,
+        index_label=None,
+        schema=None,
+        chunksize: int | None = None,
+        dtype: DtypeArg | None = None,
+        method: Literal["multi"] | Callable | None = None,
+        engine: str = "auto",
+        **engine_kwargs,
+    ) -> int | None:
+        """
+        Write records stored in a DataFrame to a SQL database.
+
+        Parameters
+        ----------
+        frame: DataFrame
+        name: string
+            Name of SQL table.
+        if_exists: {'fail', 'replace', 'append'}, default 'fail'
+            fail: If table exists, do nothing.
+            replace: If table exists, drop it, recreate it, and insert data.
+            append: If table exists, insert data. Create if it does not exist.
+        index : bool, default True
+            Write DataFrame index as a column
+        index_label : string or sequence, default None
+            Column label for index column(s). If None is given (default) and
+            `index` is True, then the index names are used.
+            A sequence should be given if the DataFrame uses MultiIndex.
+        schema : string, default None
+            Ignored parameter included for compatibility with SQLAlchemy
+            version of ``to_sql``.
+        chunksize : int, default None
+            If not None, then rows will be written in batches of this
+            size at a time. If None, all rows will be written at once.
+        dtype : single type or dict of column name to SQL type, default None
+            Optional specifying the datatype for columns. The SQL type should
+            be a string. If all columns are of the same type, one single value
+            can be used.
+        method : {None, 'multi', callable}, default None
+            Controls the SQL insertion clause used:
+
+            * None : Uses standard SQL ``INSERT`` clause (one per row).
+            * 'multi': Pass multiple values in a single ``INSERT`` clause.
+            * callable with signature ``(pd_table, conn, keys, data_iter)``.
+
+            Details and a sample callable implementation can be found in the
+            section :ref:`insert method `.
+        """
+        if dtype:
+            if not is_dict_like(dtype):
+                # error: Value expression in dictionary comprehension has incompatible
+                # type "Union[ExtensionDtype, str, dtype[Any], Type[object],
+                # Dict[Hashable, Union[ExtensionDtype, Union[str, dtype[Any]],
+                # Type[str], Type[float], Type[int], Type[complex], Type[bool],
+                # Type[object]]]]"; expected type "Union[ExtensionDtype, str,
+                # dtype[Any], Type[object]]"
+                dtype = {col_name: dtype for col_name in frame}  # type: ignore[misc]
+            else:
+                dtype = cast(dict, dtype)
+
+            for col, my_type in dtype.items():
+                if not isinstance(my_type, str):
+                    raise ValueError(f"{col} ({my_type}) not a string")
+
+        table = SQLiteTable(
+            name,
+            self,
+            frame=frame,
+            index=index,
+            if_exists=if_exists,
+            index_label=index_label,
+            dtype=dtype,
+        )
+        table.create()
+        return table.insert(chunksize, method)
+
+    def has_table(self, name: str, schema: str | None = None) -> bool:
+        wld = "?"
+        query = f"""
+        SELECT
+            name
+        FROM
+            sqlite_master
+        WHERE
+            type IN ('table', 'view')
+            AND name={wld};
+        """
+
+        return len(self.execute(query, [name]).fetchall()) > 0
+
+    def get_table(self, table_name: str, schema: str | None = None) -> None:
+        return None  # not supported in fallback mode
+
+    def drop_table(self, name: str, schema: str | None = None) -> None:
+        drop_sql = f"DROP TABLE {_get_valid_sqlite_name(name)}"
+        self.execute(drop_sql)
+
+    def _create_sql_schema(
+        self,
+        frame,
+        table_name: str,
+        keys=None,
+        dtype: DtypeArg | None = None,
+        schema: str | None = None,
+    ) -> str:
+        table = SQLiteTable(
+            table_name,
+            self,
+            frame=frame,
+            index=False,
+            keys=keys,
+            dtype=dtype,
+            schema=schema,
+        )
+        return str(table.sql_schema())
+
+
+def get_schema(
+    frame,
+    name: str,
+    keys=None,
+    con=None,
+    dtype: DtypeArg | None = None,
+    schema: str | None = None,
+) -> str:
+    """
+    Get the SQL db table schema for the given frame.
+
+    Parameters
+    ----------
+    frame : DataFrame
+    name : str
+        name of SQL table
+    keys : string or sequence, default: None
+        columns to use a primary key
+    con: ADBC Connection, SQLAlchemy connectable, sqlite3 connection, default: None
+        ADBC provides high performance I/O with native type support, where available.
+        Using SQLAlchemy makes it possible to use any DB supported by that
+        library
+        If a DBAPI2 object, only sqlite3 is supported.
+    dtype : dict of column name to SQL type, default None
+        Optional specifying the datatype for columns. The SQL type should
+        be a SQLAlchemy type, or a string for sqlite3 fallback connection.
+    schema: str, default: None
+        Optional specifying the schema to be used in creating the table.
+    """
+    with pandasSQL_builder(con=con) as pandas_sql:
+        return pandas_sql._create_sql_schema(
+            frame, name, keys=keys, dtype=dtype, schema=schema
+        )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/stata.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/stata.py
new file mode 100644
index 0000000000000000000000000000000000000000..b5057a66816386d344a3c9d2ced174ae1b4642e1
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/stata.py
@@ -0,0 +1,3768 @@
+"""
+Module contains tools for processing Stata files into DataFrames
+
+The StataReader below was originally written by Joe Presbrey as part of PyDTA.
+It has been extended and improved by Skipper Seabold from the Statsmodels
+project who also developed the StataWriter and was finally added to pandas in
+a once again improved version.
+
+You can find more information on http://presbrey.mit.edu/PyDTA and
+https://www.statsmodels.org/devel/
+"""
+from __future__ import annotations
+
+from collections import abc
+from datetime import (
+    datetime,
+    timedelta,
+)
+from io import BytesIO
+import os
+import struct
+import sys
+from typing import (
+    IO,
+    TYPE_CHECKING,
+    AnyStr,
+    Callable,
+    Final,
+    cast,
+)
+import warnings
+
+import numpy as np
+
+from pandas._libs import lib
+from pandas._libs.lib import infer_dtype
+from pandas._libs.writers import max_len_string_array
+from pandas.errors import (
+    CategoricalConversionWarning,
+    InvalidColumnName,
+    PossiblePrecisionLoss,
+    ValueLabelTypeMismatch,
+)
+from pandas.util._decorators import (
+    Appender,
+    doc,
+)
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.base import ExtensionDtype
+from pandas.core.dtypes.common import (
+    ensure_object,
+    is_numeric_dtype,
+    is_string_dtype,
+)
+from pandas.core.dtypes.dtypes import CategoricalDtype
+
+from pandas import (
+    Categorical,
+    DatetimeIndex,
+    NaT,
+    Timestamp,
+    isna,
+    to_datetime,
+    to_timedelta,
+)
+from pandas.core.frame import DataFrame
+from pandas.core.indexes.base import Index
+from pandas.core.indexes.range import RangeIndex
+from pandas.core.series import Series
+from pandas.core.shared_docs import _shared_docs
+
+from pandas.io.common import get_handle
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Hashable,
+        Sequence,
+    )
+    from types import TracebackType
+    from typing import Literal
+
+    from pandas._typing import (
+        CompressionOptions,
+        FilePath,
+        ReadBuffer,
+        Self,
+        StorageOptions,
+        WriteBuffer,
+    )
+
+_version_error = (
+    "Version of given Stata file is {version}. pandas supports importing "
+    "versions 105, 108, 111 (Stata 7SE), 113 (Stata 8/9), "
+    "114 (Stata 10/11), 115 (Stata 12), 117 (Stata 13), 118 (Stata 14/15/16),"
+    "and 119 (Stata 15/16, over 32,767 variables)."
+)
+
+_statafile_processing_params1 = """\
+convert_dates : bool, default True
+    Convert date variables to DataFrame time values.
+convert_categoricals : bool, default True
+    Read value labels and convert columns to Categorical/Factor variables."""
+
+_statafile_processing_params2 = """\
+index_col : str, optional
+    Column to set as index.
+convert_missing : bool, default False
+    Flag indicating whether to convert missing values to their Stata
+    representations.  If False, missing values are replaced with nan.
+    If True, columns containing missing values are returned with
+    object data types and missing values are represented by
+    StataMissingValue objects.
+preserve_dtypes : bool, default True
+    Preserve Stata datatypes. If False, numeric data are upcast to pandas
+    default types for foreign data (float64 or int64).
+columns : list or None
+    Columns to retain.  Columns will be returned in the given order.  None
+    returns all columns.
+order_categoricals : bool, default True
+    Flag indicating whether converted categorical data are ordered."""
+
+_chunksize_params = """\
+chunksize : int, default None
+    Return StataReader object for iterations, returns chunks with
+    given number of lines."""
+
+_iterator_params = """\
+iterator : bool, default False
+    Return StataReader object."""
+
+_reader_notes = """\
+Notes
+-----
+Categorical variables read through an iterator may not have the same
+categories and dtype. This occurs when  a variable stored in a DTA
+file is associated to an incomplete set of value labels that only
+label a strict subset of the values."""
+
+_read_stata_doc = f"""
+Read Stata file into DataFrame.
+
+Parameters
+----------
+filepath_or_buffer : str, path object or file-like object
+    Any valid string path is acceptable. The string could be a URL. Valid
+    URL schemes include http, ftp, s3, and file. For file URLs, a host is
+    expected. A local file could be: ``file://localhost/path/to/table.dta``.
+
+    If you want to pass in a path object, pandas accepts any ``os.PathLike``.
+
+    By file-like object, we refer to objects with a ``read()`` method,
+    such as a file handle (e.g. via builtin ``open`` function)
+    or ``StringIO``.
+{_statafile_processing_params1}
+{_statafile_processing_params2}
+{_chunksize_params}
+{_iterator_params}
+{_shared_docs["decompression_options"] % "filepath_or_buffer"}
+{_shared_docs["storage_options"]}
+
+Returns
+-------
+DataFrame or pandas.api.typing.StataReader
+
+See Also
+--------
+io.stata.StataReader : Low-level reader for Stata data files.
+DataFrame.to_stata: Export Stata data files.
+
+{_reader_notes}
+
+Examples
+--------
+
+Creating a dummy stata for this example
+
+>>> df = pd.DataFrame({{'animal': ['falcon', 'parrot', 'falcon', 'parrot'],
+...                     'speed': [350, 18, 361, 15]}})  # doctest: +SKIP
+>>> df.to_stata('animals.dta')  # doctest: +SKIP
+
+Read a Stata dta file:
+
+>>> df = pd.read_stata('animals.dta')  # doctest: +SKIP
+
+Read a Stata dta file in 10,000 line chunks:
+
+>>> values = np.random.randint(0, 10, size=(20_000, 1), dtype="uint8")  # doctest: +SKIP
+>>> df = pd.DataFrame(values, columns=["i"])  # doctest: +SKIP
+>>> df.to_stata('filename.dta')  # doctest: +SKIP
+
+>>> with pd.read_stata('filename.dta', chunksize=10000) as itr: # doctest: +SKIP
+>>>     for chunk in itr:
+...         # Operate on a single chunk, e.g., chunk.mean()
+...         pass  # doctest: +SKIP
+"""
+
+_read_method_doc = f"""\
+Reads observations from Stata file, converting them into a dataframe
+
+Parameters
+----------
+nrows : int
+    Number of lines to read from data file, if None read whole file.
+{_statafile_processing_params1}
+{_statafile_processing_params2}
+
+Returns
+-------
+DataFrame
+"""
+
+_stata_reader_doc = f"""\
+Class for reading Stata dta files.
+
+Parameters
+----------
+path_or_buf : path (string), buffer or path object
+    string, path object (pathlib.Path or py._path.local.LocalPath) or object
+    implementing a binary read() functions.
+{_statafile_processing_params1}
+{_statafile_processing_params2}
+{_chunksize_params}
+{_shared_docs["decompression_options"]}
+{_shared_docs["storage_options"]}
+
+{_reader_notes}
+"""
+
+
+_date_formats = ["%tc", "%tC", "%td", "%d", "%tw", "%tm", "%tq", "%th", "%ty"]
+
+
+stata_epoch: Final = datetime(1960, 1, 1)
+
+
+def _stata_elapsed_date_to_datetime_vec(dates: Series, fmt: str) -> Series:
+    """
+    Convert from SIF to datetime. https://www.stata.com/help.cgi?datetime
+
+    Parameters
+    ----------
+    dates : Series
+        The Stata Internal Format date to convert to datetime according to fmt
+    fmt : str
+        The format to convert to. Can be, tc, td, tw, tm, tq, th, ty
+        Returns
+
+    Returns
+    -------
+    converted : Series
+        The converted dates
+
+    Examples
+    --------
+    >>> dates = pd.Series([52])
+    >>> _stata_elapsed_date_to_datetime_vec(dates , "%tw")
+    0   1961-01-01
+    dtype: datetime64[ns]
+
+    Notes
+    -----
+    datetime/c - tc
+        milliseconds since 01jan1960 00:00:00.000, assuming 86,400 s/day
+    datetime/C - tC - NOT IMPLEMENTED
+        milliseconds since 01jan1960 00:00:00.000, adjusted for leap seconds
+    date - td
+        days since 01jan1960 (01jan1960 = 0)
+    weekly date - tw
+        weeks since 1960w1
+        This assumes 52 weeks in a year, then adds 7 * remainder of the weeks.
+        The datetime value is the start of the week in terms of days in the
+        year, not ISO calendar weeks.
+    monthly date - tm
+        months since 1960m1
+    quarterly date - tq
+        quarters since 1960q1
+    half-yearly date - th
+        half-years since 1960h1 yearly
+    date - ty
+        years since 0000
+    """
+    MIN_YEAR, MAX_YEAR = Timestamp.min.year, Timestamp.max.year
+    MAX_DAY_DELTA = (Timestamp.max - datetime(1960, 1, 1)).days
+    MIN_DAY_DELTA = (Timestamp.min - datetime(1960, 1, 1)).days
+    MIN_MS_DELTA = MIN_DAY_DELTA * 24 * 3600 * 1000
+    MAX_MS_DELTA = MAX_DAY_DELTA * 24 * 3600 * 1000
+
+    def convert_year_month_safe(year, month) -> Series:
+        """
+        Convert year and month to datetimes, using pandas vectorized versions
+        when the date range falls within the range supported by pandas.
+        Otherwise it falls back to a slower but more robust method
+        using datetime.
+        """
+        if year.max() < MAX_YEAR and year.min() > MIN_YEAR:
+            return to_datetime(100 * year + month, format="%Y%m")
+        else:
+            index = getattr(year, "index", None)
+            return Series([datetime(y, m, 1) for y, m in zip(year, month)], index=index)
+
+    def convert_year_days_safe(year, days) -> Series:
+        """
+        Converts year (e.g. 1999) and days since the start of the year to a
+        datetime or datetime64 Series
+        """
+        if year.max() < (MAX_YEAR - 1) and year.min() > MIN_YEAR:
+            return to_datetime(year, format="%Y") + to_timedelta(days, unit="d")
+        else:
+            index = getattr(year, "index", None)
+            value = [
+                datetime(y, 1, 1) + timedelta(days=int(d)) for y, d in zip(year, days)
+            ]
+            return Series(value, index=index)
+
+    def convert_delta_safe(base, deltas, unit) -> Series:
+        """
+        Convert base dates and deltas to datetimes, using pandas vectorized
+        versions if the deltas satisfy restrictions required to be expressed
+        as dates in pandas.
+        """
+        index = getattr(deltas, "index", None)
+        if unit == "d":
+            if deltas.max() > MAX_DAY_DELTA or deltas.min() < MIN_DAY_DELTA:
+                values = [base + timedelta(days=int(d)) for d in deltas]
+                return Series(values, index=index)
+        elif unit == "ms":
+            if deltas.max() > MAX_MS_DELTA or deltas.min() < MIN_MS_DELTA:
+                values = [
+                    base + timedelta(microseconds=(int(d) * 1000)) for d in deltas
+                ]
+                return Series(values, index=index)
+        else:
+            raise ValueError("format not understood")
+        base = to_datetime(base)
+        deltas = to_timedelta(deltas, unit=unit)
+        return base + deltas
+
+    # TODO(non-nano): If/when pandas supports more than datetime64[ns], this
+    #  should be improved to use correct range, e.g. datetime[Y] for yearly
+    bad_locs = np.isnan(dates)
+    has_bad_values = False
+    if bad_locs.any():
+        has_bad_values = True
+        dates._values[bad_locs] = 1.0  # Replace with NaT
+    dates = dates.astype(np.int64)
+
+    if fmt.startswith(("%tc", "tc")):  # Delta ms relative to base
+        base = stata_epoch
+        ms = dates
+        conv_dates = convert_delta_safe(base, ms, "ms")
+    elif fmt.startswith(("%tC", "tC")):
+        warnings.warn(
+            "Encountered %tC format. Leaving in Stata Internal Format.",
+            stacklevel=find_stack_level(),
+        )
+        conv_dates = Series(dates, dtype=object)
+        if has_bad_values:
+            conv_dates[bad_locs] = NaT
+        return conv_dates
+    # Delta days relative to base
+    elif fmt.startswith(("%td", "td", "%d", "d")):
+        base = stata_epoch
+        days = dates
+        conv_dates = convert_delta_safe(base, days, "d")
+    # does not count leap days - 7 days is a week.
+    # 52nd week may have more than 7 days
+    elif fmt.startswith(("%tw", "tw")):
+        year = stata_epoch.year + dates // 52
+        days = (dates % 52) * 7
+        conv_dates = convert_year_days_safe(year, days)
+    elif fmt.startswith(("%tm", "tm")):  # Delta months relative to base
+        year = stata_epoch.year + dates // 12
+        month = (dates % 12) + 1
+        conv_dates = convert_year_month_safe(year, month)
+    elif fmt.startswith(("%tq", "tq")):  # Delta quarters relative to base
+        year = stata_epoch.year + dates // 4
+        quarter_month = (dates % 4) * 3 + 1
+        conv_dates = convert_year_month_safe(year, quarter_month)
+    elif fmt.startswith(("%th", "th")):  # Delta half-years relative to base
+        year = stata_epoch.year + dates // 2
+        month = (dates % 2) * 6 + 1
+        conv_dates = convert_year_month_safe(year, month)
+    elif fmt.startswith(("%ty", "ty")):  # Years -- not delta
+        year = dates
+        first_month = np.ones_like(dates)
+        conv_dates = convert_year_month_safe(year, first_month)
+    else:
+        raise ValueError(f"Date fmt {fmt} not understood")
+
+    if has_bad_values:  # Restore NaT for bad values
+        conv_dates[bad_locs] = NaT
+
+    return conv_dates
+
+
+def _datetime_to_stata_elapsed_vec(dates: Series, fmt: str) -> Series:
+    """
+    Convert from datetime to SIF. https://www.stata.com/help.cgi?datetime
+
+    Parameters
+    ----------
+    dates : Series
+        Series or array containing datetime or datetime64[ns] to
+        convert to the Stata Internal Format given by fmt
+    fmt : str
+        The format to convert to. Can be, tc, td, tw, tm, tq, th, ty
+    """
+    index = dates.index
+    NS_PER_DAY = 24 * 3600 * 1000 * 1000 * 1000
+    US_PER_DAY = NS_PER_DAY / 1000
+
+    def parse_dates_safe(
+        dates: Series, delta: bool = False, year: bool = False, days: bool = False
+    ):
+        d = {}
+        if lib.is_np_dtype(dates.dtype, "M"):
+            if delta:
+                time_delta = dates - Timestamp(stata_epoch).as_unit("ns")
+                d["delta"] = time_delta._values.view(np.int64) // 1000  # microseconds
+            if days or year:
+                date_index = DatetimeIndex(dates)
+                d["year"] = date_index._data.year
+                d["month"] = date_index._data.month
+            if days:
+                days_in_ns = dates._values.view(np.int64) - to_datetime(
+                    d["year"], format="%Y"
+                )._values.view(np.int64)
+                d["days"] = days_in_ns // NS_PER_DAY
+
+        elif infer_dtype(dates, skipna=False) == "datetime":
+            if delta:
+                delta = dates._values - stata_epoch
+
+                def f(x: timedelta) -> float:
+                    return US_PER_DAY * x.days + 1000000 * x.seconds + x.microseconds
+
+                v = np.vectorize(f)
+                d["delta"] = v(delta)
+            if year:
+                year_month = dates.apply(lambda x: 100 * x.year + x.month)
+                d["year"] = year_month._values // 100
+                d["month"] = year_month._values - d["year"] * 100
+            if days:
+
+                def g(x: datetime) -> int:
+                    return (x - datetime(x.year, 1, 1)).days
+
+                v = np.vectorize(g)
+                d["days"] = v(dates)
+        else:
+            raise ValueError(
+                "Columns containing dates must contain either "
+                "datetime64, datetime or null values."
+            )
+
+        return DataFrame(d, index=index)
+
+    bad_loc = isna(dates)
+    index = dates.index
+    if bad_loc.any():
+        if lib.is_np_dtype(dates.dtype, "M"):
+            dates._values[bad_loc] = to_datetime(stata_epoch)
+        else:
+            dates._values[bad_loc] = stata_epoch
+
+    if fmt in ["%tc", "tc"]:
+        d = parse_dates_safe(dates, delta=True)
+        conv_dates = d.delta / 1000
+    elif fmt in ["%tC", "tC"]:
+        warnings.warn(
+            "Stata Internal Format tC not supported.",
+            stacklevel=find_stack_level(),
+        )
+        conv_dates = dates
+    elif fmt in ["%td", "td"]:
+        d = parse_dates_safe(dates, delta=True)
+        conv_dates = d.delta // US_PER_DAY
+    elif fmt in ["%tw", "tw"]:
+        d = parse_dates_safe(dates, year=True, days=True)
+        conv_dates = 52 * (d.year - stata_epoch.year) + d.days // 7
+    elif fmt in ["%tm", "tm"]:
+        d = parse_dates_safe(dates, year=True)
+        conv_dates = 12 * (d.year - stata_epoch.year) + d.month - 1
+    elif fmt in ["%tq", "tq"]:
+        d = parse_dates_safe(dates, year=True)
+        conv_dates = 4 * (d.year - stata_epoch.year) + (d.month - 1) // 3
+    elif fmt in ["%th", "th"]:
+        d = parse_dates_safe(dates, year=True)
+        conv_dates = 2 * (d.year - stata_epoch.year) + (d.month > 6).astype(int)
+    elif fmt in ["%ty", "ty"]:
+        d = parse_dates_safe(dates, year=True)
+        conv_dates = d.year
+    else:
+        raise ValueError(f"Format {fmt} is not a known Stata date format")
+
+    conv_dates = Series(conv_dates, dtype=np.float64, copy=False)
+    missing_value = struct.unpack(" DataFrame:
+    """
+    Checks the dtypes of the columns of a pandas DataFrame for
+    compatibility with the data types and ranges supported by Stata, and
+    converts if necessary.
+
+    Parameters
+    ----------
+    data : DataFrame
+        The DataFrame to check and convert
+
+    Notes
+    -----
+    Numeric columns in Stata must be one of int8, int16, int32, float32 or
+    float64, with some additional value restrictions.  int8 and int16 columns
+    are checked for violations of the value restrictions and upcast if needed.
+    int64 data is not usable in Stata, and so it is downcast to int32 whenever
+    the value are in the int32 range, and sidecast to float64 when larger than
+    this range.  If the int64 values are outside of the range of those
+    perfectly representable as float64 values, a warning is raised.
+
+    bool columns are cast to int8.  uint columns are converted to int of the
+    same size if there is no loss in precision, otherwise are upcast to a
+    larger type.  uint64 is currently not supported since it is concerted to
+    object in a DataFrame.
+    """
+    ws = ""
+    # original, if small, if large
+    conversion_data: tuple[
+        tuple[type, type, type],
+        tuple[type, type, type],
+        tuple[type, type, type],
+        tuple[type, type, type],
+        tuple[type, type, type],
+    ] = (
+        (np.bool_, np.int8, np.int8),
+        (np.uint8, np.int8, np.int16),
+        (np.uint16, np.int16, np.int32),
+        (np.uint32, np.int32, np.int64),
+        (np.uint64, np.int64, np.float64),
+    )
+
+    float32_max = struct.unpack("= 2**53:
+                        ws = precision_loss_doc.format("uint64", "float64")
+
+                data[col] = data[col].astype(dtype)
+
+        # Check values and upcast if necessary
+
+        if dtype == np.int8 and not empty_df:
+            if data[col].max() > 100 or data[col].min() < -127:
+                data[col] = data[col].astype(np.int16)
+        elif dtype == np.int16 and not empty_df:
+            if data[col].max() > 32740 or data[col].min() < -32767:
+                data[col] = data[col].astype(np.int32)
+        elif dtype == np.int64:
+            if empty_df or (
+                data[col].max() <= 2147483620 and data[col].min() >= -2147483647
+            ):
+                data[col] = data[col].astype(np.int32)
+            else:
+                data[col] = data[col].astype(np.float64)
+                if data[col].max() >= 2**53 or data[col].min() <= -(2**53):
+                    ws = precision_loss_doc.format("int64", "float64")
+        elif dtype in (np.float32, np.float64):
+            if np.isinf(data[col]).any():
+                raise ValueError(
+                    f"Column {col} contains infinity or -infinity"
+                    "which is outside the range supported by Stata."
+                )
+            value = data[col].max()
+            if dtype == np.float32 and value > float32_max:
+                data[col] = data[col].astype(np.float64)
+            elif dtype == np.float64:
+                if value > float64_max:
+                    raise ValueError(
+                        f"Column {col} has a maximum value ({value}) outside the range "
+                        f"supported by Stata ({float64_max})"
+                    )
+        if is_nullable_int:
+            if orig_missing.any():
+                # Replace missing by Stata sentinel value
+                sentinel = StataMissingValue.BASE_MISSING_VALUES[data[col].dtype.name]
+                data.loc[orig_missing, col] = sentinel
+    if ws:
+        warnings.warn(
+            ws,
+            PossiblePrecisionLoss,
+            stacklevel=find_stack_level(),
+        )
+
+    return data
+
+
+class StataValueLabel:
+    """
+    Parse a categorical column and prepare formatted output
+
+    Parameters
+    ----------
+    catarray : Series
+        Categorical Series to encode
+    encoding : {"latin-1", "utf-8"}
+        Encoding to use for value labels.
+    """
+
+    def __init__(
+        self, catarray: Series, encoding: Literal["latin-1", "utf-8"] = "latin-1"
+    ) -> None:
+        if encoding not in ("latin-1", "utf-8"):
+            raise ValueError("Only latin-1 and utf-8 are supported.")
+        self.labname = catarray.name
+        self._encoding = encoding
+        categories = catarray.cat.categories
+        self.value_labels = enumerate(categories)
+
+        self._prepare_value_labels()
+
+    def _prepare_value_labels(self) -> None:
+        """Encode value labels."""
+
+        self.text_len = 0
+        self.txt: list[bytes] = []
+        self.n = 0
+        # Offsets (length of categories), converted to int32
+        self.off = np.array([], dtype=np.int32)
+        # Values, converted to int32
+        self.val = np.array([], dtype=np.int32)
+        self.len = 0
+
+        # Compute lengths and setup lists of offsets and labels
+        offsets: list[int] = []
+        values: list[float] = []
+        for vl in self.value_labels:
+            category: str | bytes = vl[1]
+            if not isinstance(category, str):
+                category = str(category)
+                warnings.warn(
+                    value_label_mismatch_doc.format(self.labname),
+                    ValueLabelTypeMismatch,
+                    stacklevel=find_stack_level(),
+                )
+            category = category.encode(self._encoding)
+            offsets.append(self.text_len)
+            self.text_len += len(category) + 1  # +1 for the padding
+            values.append(vl[0])
+            self.txt.append(category)
+            self.n += 1
+
+        if self.text_len > 32000:
+            raise ValueError(
+                "Stata value labels for a single variable must "
+                "have a combined length less than 32,000 characters."
+            )
+
+        # Ensure int32
+        self.off = np.array(offsets, dtype=np.int32)
+        self.val = np.array(values, dtype=np.int32)
+
+        # Total length
+        self.len = 4 + 4 + 4 * self.n + 4 * self.n + self.text_len
+
+    def generate_value_label(self, byteorder: str) -> bytes:
+        """
+        Generate the binary representation of the value labels.
+
+        Parameters
+        ----------
+        byteorder : str
+            Byte order of the output
+
+        Returns
+        -------
+        value_label : bytes
+            Bytes containing the formatted value label
+        """
+        encoding = self._encoding
+        bio = BytesIO()
+        null_byte = b"\x00"
+
+        # len
+        bio.write(struct.pack(byteorder + "i", self.len))
+
+        # labname
+        labname = str(self.labname)[:32].encode(encoding)
+        lab_len = 32 if encoding not in ("utf-8", "utf8") else 128
+        labname = _pad_bytes(labname, lab_len + 1)
+        bio.write(labname)
+
+        # padding - 3 bytes
+        for i in range(3):
+            bio.write(struct.pack("c", null_byte))
+
+        # value_label_table
+        # n - int32
+        bio.write(struct.pack(byteorder + "i", self.n))
+
+        # textlen  - int32
+        bio.write(struct.pack(byteorder + "i", self.text_len))
+
+        # off - int32 array (n elements)
+        for offset in self.off:
+            bio.write(struct.pack(byteorder + "i", offset))
+
+        # val - int32 array (n elements)
+        for value in self.val:
+            bio.write(struct.pack(byteorder + "i", value))
+
+        # txt - Text labels, null terminated
+        for text in self.txt:
+            bio.write(text + null_byte)
+
+        return bio.getvalue()
+
+
+class StataNonCatValueLabel(StataValueLabel):
+    """
+    Prepare formatted version of value labels
+
+    Parameters
+    ----------
+    labname : str
+        Value label name
+    value_labels: Dictionary
+        Mapping of values to labels
+    encoding : {"latin-1", "utf-8"}
+        Encoding to use for value labels.
+    """
+
+    def __init__(
+        self,
+        labname: str,
+        value_labels: dict[float, str],
+        encoding: Literal["latin-1", "utf-8"] = "latin-1",
+    ) -> None:
+        if encoding not in ("latin-1", "utf-8"):
+            raise ValueError("Only latin-1 and utf-8 are supported.")
+
+        self.labname = labname
+        self._encoding = encoding
+        self.value_labels = sorted(  # type: ignore[assignment]
+            value_labels.items(), key=lambda x: x[0]
+        )
+        self._prepare_value_labels()
+
+
+class StataMissingValue:
+    """
+    An observation's missing value.
+
+    Parameters
+    ----------
+    value : {int, float}
+        The Stata missing value code
+
+    Notes
+    -----
+    More information: 
+
+    Integer missing values make the code '.', '.a', ..., '.z' to the ranges
+    101 ... 127 (for int8), 32741 ... 32767  (for int16) and 2147483621 ...
+    2147483647 (for int32).  Missing values for floating point data types are
+    more complex but the pattern is simple to discern from the following table.
+
+    np.float32 missing values (float in Stata)
+    0000007f    .
+    0008007f    .a
+    0010007f    .b
+    ...
+    00c0007f    .x
+    00c8007f    .y
+    00d0007f    .z
+
+    np.float64 missing values (double in Stata)
+    000000000000e07f    .
+    000000000001e07f    .a
+    000000000002e07f    .b
+    ...
+    000000000018e07f    .x
+    000000000019e07f    .y
+    00000000001ae07f    .z
+    """
+
+    # Construct a dictionary of missing values
+    MISSING_VALUES: dict[float, str] = {}
+    bases: Final = (101, 32741, 2147483621)
+    for b in bases:
+        # Conversion to long to avoid hash issues on 32 bit platforms #8968
+        MISSING_VALUES[b] = "."
+        for i in range(1, 27):
+            MISSING_VALUES[i + b] = "." + chr(96 + i)
+
+    float32_base: bytes = b"\x00\x00\x00\x7f"
+    increment_32: int = struct.unpack(" 0:
+            MISSING_VALUES[key] += chr(96 + i)
+        int_value = struct.unpack(" 0:
+            MISSING_VALUES[key] += chr(96 + i)
+        int_value = struct.unpack("q", struct.pack(" None:
+        self._value = value
+        # Conversion to int to avoid hash issues on 32 bit platforms #8968
+        value = int(value) if value < 2147483648 else float(value)
+        self._str = self.MISSING_VALUES[value]
+
+    @property
+    def string(self) -> str:
+        """
+        The Stata representation of the missing value: '.', '.a'..'.z'
+
+        Returns
+        -------
+        str
+            The representation of the missing value.
+        """
+        return self._str
+
+    @property
+    def value(self) -> float:
+        """
+        The binary representation of the missing value.
+
+        Returns
+        -------
+        {int, float}
+            The binary representation of the missing value.
+        """
+        return self._value
+
+    def __str__(self) -> str:
+        return self.string
+
+    def __repr__(self) -> str:
+        return f"{type(self)}({self})"
+
+    def __eq__(self, other: object) -> bool:
+        return (
+            isinstance(other, type(self))
+            and self.string == other.string
+            and self.value == other.value
+        )
+
+    @classmethod
+    def get_base_missing_value(cls, dtype: np.dtype) -> float:
+        if dtype.type is np.int8:
+            value = cls.BASE_MISSING_VALUES["int8"]
+        elif dtype.type is np.int16:
+            value = cls.BASE_MISSING_VALUES["int16"]
+        elif dtype.type is np.int32:
+            value = cls.BASE_MISSING_VALUES["int32"]
+        elif dtype.type is np.float32:
+            value = cls.BASE_MISSING_VALUES["float32"]
+        elif dtype.type is np.float64:
+            value = cls.BASE_MISSING_VALUES["float64"]
+        else:
+            raise ValueError("Unsupported dtype")
+        return value
+
+
+class StataParser:
+    def __init__(self) -> None:
+        # type          code.
+        # --------------------
+        # str1        1 = 0x01
+        # str2        2 = 0x02
+        # ...
+        # str244    244 = 0xf4
+        # byte      251 = 0xfb  (sic)
+        # int       252 = 0xfc
+        # long      253 = 0xfd
+        # float     254 = 0xfe
+        # double    255 = 0xff
+        # --------------------
+        # NOTE: the byte type seems to be reserved for categorical variables
+        # with a label, but the underlying variable is -127 to 100
+        # we're going to drop the label and cast to int
+        self.DTYPE_MAP = dict(
+            [(i, np.dtype(f"S{i}")) for i in range(1, 245)]
+            + [
+                (251, np.dtype(np.int8)),
+                (252, np.dtype(np.int16)),
+                (253, np.dtype(np.int32)),
+                (254, np.dtype(np.float32)),
+                (255, np.dtype(np.float64)),
+            ]
+        )
+        self.DTYPE_MAP_XML: dict[int, np.dtype] = {
+            32768: np.dtype(np.uint8),  # Keys to GSO
+            65526: np.dtype(np.float64),
+            65527: np.dtype(np.float32),
+            65528: np.dtype(np.int32),
+            65529: np.dtype(np.int16),
+            65530: np.dtype(np.int8),
+        }
+        self.TYPE_MAP = list(tuple(range(251)) + tuple("bhlfd"))
+        self.TYPE_MAP_XML = {
+            # Not really a Q, unclear how to handle byteswap
+            32768: "Q",
+            65526: "d",
+            65527: "f",
+            65528: "l",
+            65529: "h",
+            65530: "b",
+        }
+        # NOTE: technically, some of these are wrong. there are more numbers
+        # that can be represented. it's the 27 ABOVE and BELOW the max listed
+        # numeric data type in [U] 12.2.2 of the 11.2 manual
+        float32_min = b"\xff\xff\xff\xfe"
+        float32_max = b"\xff\xff\xff\x7e"
+        float64_min = b"\xff\xff\xff\xff\xff\xff\xef\xff"
+        float64_max = b"\xff\xff\xff\xff\xff\xff\xdf\x7f"
+        self.VALID_RANGE = {
+            "b": (-127, 100),
+            "h": (-32767, 32740),
+            "l": (-2147483647, 2147483620),
+            "f": (
+                np.float32(struct.unpack(" None:
+        super().__init__()
+
+        # Arguments to the reader (can be temporarily overridden in
+        # calls to read).
+        self._convert_dates = convert_dates
+        self._convert_categoricals = convert_categoricals
+        self._index_col = index_col
+        self._convert_missing = convert_missing
+        self._preserve_dtypes = preserve_dtypes
+        self._columns = columns
+        self._order_categoricals = order_categoricals
+        self._original_path_or_buf = path_or_buf
+        self._compression = compression
+        self._storage_options = storage_options
+        self._encoding = ""
+        self._chunksize = chunksize
+        self._using_iterator = False
+        self._entered = False
+        if self._chunksize is None:
+            self._chunksize = 1
+        elif not isinstance(chunksize, int) or chunksize <= 0:
+            raise ValueError("chunksize must be a positive integer when set.")
+
+        # State variables for the file
+        self._close_file: Callable[[], None] | None = None
+        self._missing_values = False
+        self._can_read_value_labels = False
+        self._column_selector_set = False
+        self._value_labels_read = False
+        self._data_read = False
+        self._dtype: np.dtype | None = None
+        self._lines_read = 0
+
+        self._native_byteorder = _set_endianness(sys.byteorder)
+
+    def _ensure_open(self) -> None:
+        """
+        Ensure the file has been opened and its header data read.
+        """
+        if not hasattr(self, "_path_or_buf"):
+            self._open_file()
+
+    def _open_file(self) -> None:
+        """
+        Open the file (with compression options, etc.), and read header information.
+        """
+        if not self._entered:
+            warnings.warn(
+                "StataReader is being used without using a context manager. "
+                "Using StataReader as a context manager is the only supported method.",
+                ResourceWarning,
+                stacklevel=find_stack_level(),
+            )
+        handles = get_handle(
+            self._original_path_or_buf,
+            "rb",
+            storage_options=self._storage_options,
+            is_text=False,
+            compression=self._compression,
+        )
+        if hasattr(handles.handle, "seekable") and handles.handle.seekable():
+            # If the handle is directly seekable, use it without an extra copy.
+            self._path_or_buf = handles.handle
+            self._close_file = handles.close
+        else:
+            # Copy to memory, and ensure no encoding.
+            with handles:
+                self._path_or_buf = BytesIO(handles.handle.read())
+            self._close_file = self._path_or_buf.close
+
+        self._read_header()
+        self._setup_dtype()
+
+    def __enter__(self) -> Self:
+        """enter context manager"""
+        self._entered = True
+        return self
+
+    def __exit__(
+        self,
+        exc_type: type[BaseException] | None,
+        exc_value: BaseException | None,
+        traceback: TracebackType | None,
+    ) -> None:
+        if self._close_file:
+            self._close_file()
+
+    def close(self) -> None:
+        """Close the handle if its open.
+
+        .. deprecated: 2.0.0
+
+           The close method is not part of the public API.
+           The only supported way to use StataReader is to use it as a context manager.
+        """
+        warnings.warn(
+            "The StataReader.close() method is not part of the public API and "
+            "will be removed in a future version without notice. "
+            "Using StataReader as a context manager is the only supported method.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        if self._close_file:
+            self._close_file()
+
+    def _set_encoding(self) -> None:
+        """
+        Set string encoding which depends on file version
+        """
+        if self._format_version < 118:
+            self._encoding = "latin-1"
+        else:
+            self._encoding = "utf-8"
+
+    def _read_int8(self) -> int:
+        return struct.unpack("b", self._path_or_buf.read(1))[0]
+
+    def _read_uint8(self) -> int:
+        return struct.unpack("B", self._path_or_buf.read(1))[0]
+
+    def _read_uint16(self) -> int:
+        return struct.unpack(f"{self._byteorder}H", self._path_or_buf.read(2))[0]
+
+    def _read_uint32(self) -> int:
+        return struct.unpack(f"{self._byteorder}I", self._path_or_buf.read(4))[0]
+
+    def _read_uint64(self) -> int:
+        return struct.unpack(f"{self._byteorder}Q", self._path_or_buf.read(8))[0]
+
+    def _read_int16(self) -> int:
+        return struct.unpack(f"{self._byteorder}h", self._path_or_buf.read(2))[0]
+
+    def _read_int32(self) -> int:
+        return struct.unpack(f"{self._byteorder}i", self._path_or_buf.read(4))[0]
+
+    def _read_int64(self) -> int:
+        return struct.unpack(f"{self._byteorder}q", self._path_or_buf.read(8))[0]
+
+    def _read_char8(self) -> bytes:
+        return struct.unpack("c", self._path_or_buf.read(1))[0]
+
+    def _read_int16_count(self, count: int) -> tuple[int, ...]:
+        return struct.unpack(
+            f"{self._byteorder}{'h' * count}",
+            self._path_or_buf.read(2 * count),
+        )
+
+    def _read_header(self) -> None:
+        first_char = self._read_char8()
+        if first_char == b"<":
+            self._read_new_header()
+        else:
+            self._read_old_header(first_char)
+
+    def _read_new_header(self) -> None:
+        # The first part of the header is common to 117 - 119.
+        self._path_or_buf.read(27)  # stata_dta>

+        self._format_version = int(self._path_or_buf.read(3))
+        if self._format_version not in [117, 118, 119]:
+            raise ValueError(_version_error.format(version=self._format_version))
+        self._set_encoding()
+        self._path_or_buf.read(21)  # 
+        self._byteorder = ">" if self._path_or_buf.read(3) == b"MSF" else "<"
+        self._path_or_buf.read(15)  # 
+        self._nvar = (
+            self._read_uint16() if self._format_version <= 118 else self._read_uint32()
+        )
+        self._path_or_buf.read(7)  # 
+
+        self._nobs = self._get_nobs()
+        self._path_or_buf.read(11)  # 
+        self._time_stamp = self._get_time_stamp()
+        self._path_or_buf.read(26)  # 

+        self._path_or_buf.read(8)  # 0x0000000000000000
+        self._path_or_buf.read(8)  # position of 
+
+        self._seek_vartypes = self._read_int64() + 16
+        self._seek_varnames = self._read_int64() + 10
+        self._seek_sortlist = self._read_int64() + 10
+        self._seek_formats = self._read_int64() + 9
+        self._seek_value_label_names = self._read_int64() + 19
+
+        # Requires version-specific treatment
+        self._seek_variable_labels = self._get_seek_variable_labels()
+
+        self._path_or_buf.read(8)  # 
+        self._data_location = self._read_int64() + 6
+        self._seek_strls = self._read_int64() + 7
+        self._seek_value_labels = self._read_int64() + 14
+
+        self._typlist, self._dtyplist = self._get_dtypes(self._seek_vartypes)
+
+        self._path_or_buf.seek(self._seek_varnames)
+        self._varlist = self._get_varlist()
+
+        self._path_or_buf.seek(self._seek_sortlist)
+        self._srtlist = self._read_int16_count(self._nvar + 1)[:-1]
+
+        self._path_or_buf.seek(self._seek_formats)
+        self._fmtlist = self._get_fmtlist()
+
+        self._path_or_buf.seek(self._seek_value_label_names)
+        self._lbllist = self._get_lbllist()
+
+        self._path_or_buf.seek(self._seek_variable_labels)
+        self._variable_labels = self._get_variable_labels()
+
+    # Get data type information, works for versions 117-119.
+    def _get_dtypes(
+        self, seek_vartypes: int
+    ) -> tuple[list[int | str], list[str | np.dtype]]:
+        self._path_or_buf.seek(seek_vartypes)
+        typlist = []
+        dtyplist = []
+        for _ in range(self._nvar):
+            typ = self._read_uint16()
+            if typ <= 2045:
+                typlist.append(typ)
+                dtyplist.append(str(typ))
+            else:
+                try:
+                    typlist.append(self.TYPE_MAP_XML[typ])  # type: ignore[arg-type]
+                    dtyplist.append(self.DTYPE_MAP_XML[typ])  # type: ignore[arg-type]
+                except KeyError as err:
+                    raise ValueError(f"cannot convert stata types [{typ}]") from err
+
+        return typlist, dtyplist  # type: ignore[return-value]
+
+    def _get_varlist(self) -> list[str]:
+        # 33 in order formats, 129 in formats 118 and 119
+        b = 33 if self._format_version < 118 else 129
+        return [self._decode(self._path_or_buf.read(b)) for _ in range(self._nvar)]
+
+    # Returns the format list
+    def _get_fmtlist(self) -> list[str]:
+        if self._format_version >= 118:
+            b = 57
+        elif self._format_version > 113:
+            b = 49
+        elif self._format_version > 104:
+            b = 12
+        else:
+            b = 7
+
+        return [self._decode(self._path_or_buf.read(b)) for _ in range(self._nvar)]
+
+    # Returns the label list
+    def _get_lbllist(self) -> list[str]:
+        if self._format_version >= 118:
+            b = 129
+        elif self._format_version > 108:
+            b = 33
+        else:
+            b = 9
+        return [self._decode(self._path_or_buf.read(b)) for _ in range(self._nvar)]
+
+    def _get_variable_labels(self) -> list[str]:
+        if self._format_version >= 118:
+            vlblist = [
+                self._decode(self._path_or_buf.read(321)) for _ in range(self._nvar)
+            ]
+        elif self._format_version > 105:
+            vlblist = [
+                self._decode(self._path_or_buf.read(81)) for _ in range(self._nvar)
+            ]
+        else:
+            vlblist = [
+                self._decode(self._path_or_buf.read(32)) for _ in range(self._nvar)
+            ]
+        return vlblist
+
+    def _get_nobs(self) -> int:
+        if self._format_version >= 118:
+            return self._read_uint64()
+        else:
+            return self._read_uint32()
+
+    def _get_data_label(self) -> str:
+        if self._format_version >= 118:
+            strlen = self._read_uint16()
+            return self._decode(self._path_or_buf.read(strlen))
+        elif self._format_version == 117:
+            strlen = self._read_int8()
+            return self._decode(self._path_or_buf.read(strlen))
+        elif self._format_version > 105:
+            return self._decode(self._path_or_buf.read(81))
+        else:
+            return self._decode(self._path_or_buf.read(32))
+
+    def _get_time_stamp(self) -> str:
+        if self._format_version >= 118:
+            strlen = self._read_int8()
+            return self._path_or_buf.read(strlen).decode("utf-8")
+        elif self._format_version == 117:
+            strlen = self._read_int8()
+            return self._decode(self._path_or_buf.read(strlen))
+        elif self._format_version > 104:
+            return self._decode(self._path_or_buf.read(18))
+        else:
+            raise ValueError()
+
+    def _get_seek_variable_labels(self) -> int:
+        if self._format_version == 117:
+            self._path_or_buf.read(8)  # , throw away
+            # Stata 117 data files do not follow the described format.  This is
+            # a work around that uses the previous label, 33 bytes for each
+            # variable, 20 for the closing tag and 17 for the opening tag
+            return self._seek_value_label_names + (33 * self._nvar) + 20 + 17
+        elif self._format_version >= 118:
+            return self._read_int64() + 17
+        else:
+            raise ValueError()
+
+    def _read_old_header(self, first_char: bytes) -> None:
+        self._format_version = int(first_char[0])
+        if self._format_version not in [104, 105, 108, 111, 113, 114, 115]:
+            raise ValueError(_version_error.format(version=self._format_version))
+        self._set_encoding()
+        self._byteorder = ">" if self._read_int8() == 0x1 else "<"
+        self._filetype = self._read_int8()
+        self._path_or_buf.read(1)  # unused
+
+        self._nvar = self._read_uint16()
+        self._nobs = self._get_nobs()
+
+        self._data_label = self._get_data_label()
+
+        self._time_stamp = self._get_time_stamp()
+
+        # descriptors
+        if self._format_version > 108:
+            typlist = [int(c) for c in self._path_or_buf.read(self._nvar)]
+        else:
+            buf = self._path_or_buf.read(self._nvar)
+            typlistb = np.frombuffer(buf, dtype=np.uint8)
+            typlist = []
+            for tp in typlistb:
+                if tp in self.OLD_TYPE_MAPPING:
+                    typlist.append(self.OLD_TYPE_MAPPING[tp])
+                else:
+                    typlist.append(tp - 127)  # bytes
+
+        try:
+            self._typlist = [self.TYPE_MAP[typ] for typ in typlist]
+        except ValueError as err:
+            invalid_types = ",".join([str(x) for x in typlist])
+            raise ValueError(f"cannot convert stata types [{invalid_types}]") from err
+        try:
+            self._dtyplist = [self.DTYPE_MAP[typ] for typ in typlist]
+        except ValueError as err:
+            invalid_dtypes = ",".join([str(x) for x in typlist])
+            raise ValueError(f"cannot convert stata dtypes [{invalid_dtypes}]") from err
+
+        if self._format_version > 108:
+            self._varlist = [
+                self._decode(self._path_or_buf.read(33)) for _ in range(self._nvar)
+            ]
+        else:
+            self._varlist = [
+                self._decode(self._path_or_buf.read(9)) for _ in range(self._nvar)
+            ]
+        self._srtlist = self._read_int16_count(self._nvar + 1)[:-1]
+
+        self._fmtlist = self._get_fmtlist()
+
+        self._lbllist = self._get_lbllist()
+
+        self._variable_labels = self._get_variable_labels()
+
+        # ignore expansion fields (Format 105 and later)
+        # When reading, read five bytes; the last four bytes now tell you
+        # the size of the next read, which you discard.  You then continue
+        # like this until you read 5 bytes of zeros.
+
+        if self._format_version > 104:
+            while True:
+                data_type = self._read_int8()
+                if self._format_version > 108:
+                    data_len = self._read_int32()
+                else:
+                    data_len = self._read_int16()
+                if data_type == 0:
+                    break
+                self._path_or_buf.read(data_len)
+
+        # necessary data to continue parsing
+        self._data_location = self._path_or_buf.tell()
+
+    def _setup_dtype(self) -> np.dtype:
+        """Map between numpy and state dtypes"""
+        if self._dtype is not None:
+            return self._dtype
+
+        dtypes = []  # Convert struct data types to numpy data type
+        for i, typ in enumerate(self._typlist):
+            if typ in self.NUMPY_TYPE_MAP:
+                typ = cast(str, typ)  # only strs in NUMPY_TYPE_MAP
+                dtypes.append((f"s{i}", f"{self._byteorder}{self.NUMPY_TYPE_MAP[typ]}"))
+            else:
+                dtypes.append((f"s{i}", f"S{typ}"))
+        self._dtype = np.dtype(dtypes)
+
+        return self._dtype
+
+    def _decode(self, s: bytes) -> str:
+        # have bytes not strings, so must decode
+        s = s.partition(b"\0")[0]
+        try:
+            return s.decode(self._encoding)
+        except UnicodeDecodeError:
+            # GH 25960, fallback to handle incorrect format produced when 117
+            # files are converted to 118 files in Stata
+            encoding = self._encoding
+            msg = f"""
+One or more strings in the dta file could not be decoded using {encoding}, and
+so the fallback encoding of latin-1 is being used.  This can happen when a file
+has been incorrectly encoded by Stata or some other software. You should verify
+the string values returned are correct."""
+            warnings.warn(
+                msg,
+                UnicodeWarning,
+                stacklevel=find_stack_level(),
+            )
+            return s.decode("latin-1")
+
+    def _read_value_labels(self) -> None:
+        self._ensure_open()
+        if self._value_labels_read:
+            # Don't read twice
+            return
+        if self._format_version <= 108:
+            # Value labels are not supported in version 108 and earlier.
+            self._value_labels_read = True
+            self._value_label_dict: dict[str, dict[float, str]] = {}
+            return
+
+        if self._format_version >= 117:
+            self._path_or_buf.seek(self._seek_value_labels)
+        else:
+            assert self._dtype is not None
+            offset = self._nobs * self._dtype.itemsize
+            self._path_or_buf.seek(self._data_location + offset)
+
+        self._value_labels_read = True
+        self._value_label_dict = {}
+
+        while True:
+            if self._format_version >= 117:
+                if self._path_or_buf.read(5) == b"
+                    break  # end of value label table
+
+            slength = self._path_or_buf.read(4)
+            if not slength:
+                break  # end of value label table (format < 117)
+            if self._format_version <= 117:
+                labname = self._decode(self._path_or_buf.read(33))
+            else:
+                labname = self._decode(self._path_or_buf.read(129))
+            self._path_or_buf.read(3)  # padding
+
+            n = self._read_uint32()
+            txtlen = self._read_uint32()
+            off = np.frombuffer(
+                self._path_or_buf.read(4 * n), dtype=f"{self._byteorder}i4", count=n
+            )
+            val = np.frombuffer(
+                self._path_or_buf.read(4 * n), dtype=f"{self._byteorder}i4", count=n
+            )
+            ii = np.argsort(off)
+            off = off[ii]
+            val = val[ii]
+            txt = self._path_or_buf.read(txtlen)
+            self._value_label_dict[labname] = {}
+            for i in range(n):
+                end = off[i + 1] if i < n - 1 else txtlen
+                self._value_label_dict[labname][val[i]] = self._decode(
+                    txt[off[i] : end]
+                )
+            if self._format_version >= 117:
+                self._path_or_buf.read(6)  # 
+        self._value_labels_read = True
+
+    def _read_strls(self) -> None:
+        self._path_or_buf.seek(self._seek_strls)
+        # Wrap v_o in a string to allow uint64 values as keys on 32bit OS
+        self.GSO = {"0": ""}
+        while True:
+            if self._path_or_buf.read(3) != b"GSO":
+                break
+
+            if self._format_version == 117:
+                v_o = self._read_uint64()
+            else:
+                buf = self._path_or_buf.read(12)
+                # Only tested on little endian file on little endian machine.
+                v_size = 2 if self._format_version == 118 else 3
+                if self._byteorder == "<":
+                    buf = buf[0:v_size] + buf[4 : (12 - v_size)]
+                else:
+                    # This path may not be correct, impossible to test
+                    buf = buf[0:v_size] + buf[(4 + v_size) :]
+                v_o = struct.unpack("Q", buf)[0]
+            typ = self._read_uint8()
+            length = self._read_uint32()
+            va = self._path_or_buf.read(length)
+            if typ == 130:
+                decoded_va = va[0:-1].decode(self._encoding)
+            else:
+                # Stata says typ 129 can be binary, so use str
+                decoded_va = str(va)
+                # Wrap v_o in a string to allow uint64 values as keys on 32bit OS
+            self.GSO[str(v_o)] = decoded_va
+
+    def __next__(self) -> DataFrame:
+        self._using_iterator = True
+        return self.read(nrows=self._chunksize)
+
+    def get_chunk(self, size: int | None = None) -> DataFrame:
+        """
+        Reads lines from Stata file and returns as dataframe
+
+        Parameters
+        ----------
+        size : int, defaults to None
+            Number of lines to read.  If None, reads whole file.
+
+        Returns
+        -------
+        DataFrame
+        """
+        if size is None:
+            size = self._chunksize
+        return self.read(nrows=size)
+
+    @Appender(_read_method_doc)
+    def read(
+        self,
+        nrows: int | None = None,
+        convert_dates: bool | None = None,
+        convert_categoricals: bool | None = None,
+        index_col: str | None = None,
+        convert_missing: bool | None = None,
+        preserve_dtypes: bool | None = None,
+        columns: Sequence[str] | None = None,
+        order_categoricals: bool | None = None,
+    ) -> DataFrame:
+        self._ensure_open()
+
+        # Handle options
+        if convert_dates is None:
+            convert_dates = self._convert_dates
+        if convert_categoricals is None:
+            convert_categoricals = self._convert_categoricals
+        if convert_missing is None:
+            convert_missing = self._convert_missing
+        if preserve_dtypes is None:
+            preserve_dtypes = self._preserve_dtypes
+        if columns is None:
+            columns = self._columns
+        if order_categoricals is None:
+            order_categoricals = self._order_categoricals
+        if index_col is None:
+            index_col = self._index_col
+        if nrows is None:
+            nrows = self._nobs
+
+        # Handle empty file or chunk.  If reading incrementally raise
+        # StopIteration.  If reading the whole thing return an empty
+        # data frame.
+        if (self._nobs == 0) and nrows == 0:
+            self._can_read_value_labels = True
+            self._data_read = True
+            data = DataFrame(columns=self._varlist)
+            # Apply dtypes correctly
+            for i, col in enumerate(data.columns):
+                dt = self._dtyplist[i]
+                if isinstance(dt, np.dtype):
+                    if dt.char != "S":
+                        data[col] = data[col].astype(dt)
+            if columns is not None:
+                data = self._do_select_columns(data, columns)
+            return data
+
+        if (self._format_version >= 117) and (not self._value_labels_read):
+            self._can_read_value_labels = True
+            self._read_strls()
+
+        # Read data
+        assert self._dtype is not None
+        dtype = self._dtype
+        max_read_len = (self._nobs - self._lines_read) * dtype.itemsize
+        read_len = nrows * dtype.itemsize
+        read_len = min(read_len, max_read_len)
+        if read_len <= 0:
+            # Iterator has finished, should never be here unless
+            # we are reading the file incrementally
+            if convert_categoricals:
+                self._read_value_labels()
+            raise StopIteration
+        offset = self._lines_read * dtype.itemsize
+        self._path_or_buf.seek(self._data_location + offset)
+        read_lines = min(nrows, self._nobs - self._lines_read)
+        raw_data = np.frombuffer(
+            self._path_or_buf.read(read_len), dtype=dtype, count=read_lines
+        )
+
+        self._lines_read += read_lines
+        if self._lines_read == self._nobs:
+            self._can_read_value_labels = True
+            self._data_read = True
+        # if necessary, swap the byte order to native here
+        if self._byteorder != self._native_byteorder:
+            raw_data = raw_data.byteswap().view(raw_data.dtype.newbyteorder())
+
+        if convert_categoricals:
+            self._read_value_labels()
+
+        if len(raw_data) == 0:
+            data = DataFrame(columns=self._varlist)
+        else:
+            data = DataFrame.from_records(raw_data)
+            data.columns = Index(self._varlist)
+
+        # If index is not specified, use actual row number rather than
+        # restarting at 0 for each chunk.
+        if index_col is None:
+            data.index = RangeIndex(
+                self._lines_read - read_lines, self._lines_read
+            )  # set attr instead of set_index to avoid copy
+
+        if columns is not None:
+            data = self._do_select_columns(data, columns)
+
+        # Decode strings
+        for col, typ in zip(data, self._typlist):
+            if isinstance(typ, int):
+                data[col] = data[col].apply(self._decode)
+
+        data = self._insert_strls(data)
+
+        # Convert columns (if needed) to match input type
+        valid_dtypes = [i for i, dtyp in enumerate(self._dtyplist) if dtyp is not None]
+        object_type = np.dtype(object)
+        for idx in valid_dtypes:
+            dtype = data.iloc[:, idx].dtype
+            if dtype not in (object_type, self._dtyplist[idx]):
+                data.isetitem(idx, data.iloc[:, idx].astype(dtype))
+
+        data = self._do_convert_missing(data, convert_missing)
+
+        if convert_dates:
+            for i, fmt in enumerate(self._fmtlist):
+                if any(fmt.startswith(date_fmt) for date_fmt in _date_formats):
+                    data.isetitem(
+                        i, _stata_elapsed_date_to_datetime_vec(data.iloc[:, i], fmt)
+                    )
+
+        if convert_categoricals and self._format_version > 108:
+            data = self._do_convert_categoricals(
+                data, self._value_label_dict, self._lbllist, order_categoricals
+            )
+
+        if not preserve_dtypes:
+            retyped_data = []
+            convert = False
+            for col in data:
+                dtype = data[col].dtype
+                if dtype in (np.dtype(np.float16), np.dtype(np.float32)):
+                    dtype = np.dtype(np.float64)
+                    convert = True
+                elif dtype in (
+                    np.dtype(np.int8),
+                    np.dtype(np.int16),
+                    np.dtype(np.int32),
+                ):
+                    dtype = np.dtype(np.int64)
+                    convert = True
+                retyped_data.append((col, data[col].astype(dtype)))
+            if convert:
+                data = DataFrame.from_dict(dict(retyped_data))
+
+        if index_col is not None:
+            data = data.set_index(data.pop(index_col))
+
+        return data
+
+    def _do_convert_missing(self, data: DataFrame, convert_missing: bool) -> DataFrame:
+        # Check for missing values, and replace if found
+        replacements = {}
+        for i in range(len(data.columns)):
+            fmt = self._typlist[i]
+            if fmt not in self.VALID_RANGE:
+                continue
+
+            fmt = cast(str, fmt)  # only strs in VALID_RANGE
+            nmin, nmax = self.VALID_RANGE[fmt]
+            series = data.iloc[:, i]
+
+            # appreciably faster to do this with ndarray instead of Series
+            svals = series._values
+            missing = (svals < nmin) | (svals > nmax)
+
+            if not missing.any():
+                continue
+
+            if convert_missing:  # Replacement follows Stata notation
+                missing_loc = np.nonzero(np.asarray(missing))[0]
+                umissing, umissing_loc = np.unique(series[missing], return_inverse=True)
+                replacement = Series(series, dtype=object)
+                for j, um in enumerate(umissing):
+                    missing_value = StataMissingValue(um)
+
+                    loc = missing_loc[umissing_loc == j]
+                    replacement.iloc[loc] = missing_value
+            else:  # All replacements are identical
+                dtype = series.dtype
+                if dtype not in (np.float32, np.float64):
+                    dtype = np.float64
+                replacement = Series(series, dtype=dtype)
+                if not replacement._values.flags["WRITEABLE"]:
+                    # only relevant for ArrayManager; construction
+                    #  path for BlockManager ensures writeability
+                    replacement = replacement.copy()
+                # Note: operating on ._values is much faster than directly
+                # TODO: can we fix that?
+                replacement._values[missing] = np.nan
+            replacements[i] = replacement
+        if replacements:
+            for idx, value in replacements.items():
+                data.isetitem(idx, value)
+        return data
+
+    def _insert_strls(self, data: DataFrame) -> DataFrame:
+        if not hasattr(self, "GSO") or len(self.GSO) == 0:
+            return data
+        for i, typ in enumerate(self._typlist):
+            if typ != "Q":
+                continue
+            # Wrap v_o in a string to allow uint64 values as keys on 32bit OS
+            data.isetitem(i, [self.GSO[str(k)] for k in data.iloc[:, i]])
+        return data
+
+    def _do_select_columns(self, data: DataFrame, columns: Sequence[str]) -> DataFrame:
+        if not self._column_selector_set:
+            column_set = set(columns)
+            if len(column_set) != len(columns):
+                raise ValueError("columns contains duplicate entries")
+            unmatched = column_set.difference(data.columns)
+            if unmatched:
+                joined = ", ".join(list(unmatched))
+                raise ValueError(
+                    "The following columns were not "
+                    f"found in the Stata data set: {joined}"
+                )
+            # Copy information for retained columns for later processing
+            dtyplist = []
+            typlist = []
+            fmtlist = []
+            lbllist = []
+            for col in columns:
+                i = data.columns.get_loc(col)
+                dtyplist.append(self._dtyplist[i])
+                typlist.append(self._typlist[i])
+                fmtlist.append(self._fmtlist[i])
+                lbllist.append(self._lbllist[i])
+
+            self._dtyplist = dtyplist
+            self._typlist = typlist
+            self._fmtlist = fmtlist
+            self._lbllist = lbllist
+            self._column_selector_set = True
+
+        return data[columns]
+
+    def _do_convert_categoricals(
+        self,
+        data: DataFrame,
+        value_label_dict: dict[str, dict[float, str]],
+        lbllist: Sequence[str],
+        order_categoricals: bool,
+    ) -> DataFrame:
+        """
+        Converts categorical columns to Categorical type.
+        """
+        if not value_label_dict:
+            return data
+        cat_converted_data = []
+        for col, label in zip(data, lbllist):
+            if label in value_label_dict:
+                # Explicit call with ordered=True
+                vl = value_label_dict[label]
+                keys = np.array(list(vl.keys()))
+                column = data[col]
+                key_matches = column.isin(keys)
+                if self._using_iterator and key_matches.all():
+                    initial_categories: np.ndarray | None = keys
+                    # If all categories are in the keys and we are iterating,
+                    # use the same keys for all chunks. If some are missing
+                    # value labels, then we will fall back to the categories
+                    # varying across chunks.
+                else:
+                    if self._using_iterator:
+                        # warn is using an iterator
+                        warnings.warn(
+                            categorical_conversion_warning,
+                            CategoricalConversionWarning,
+                            stacklevel=find_stack_level(),
+                        )
+                    initial_categories = None
+                cat_data = Categorical(
+                    column, categories=initial_categories, ordered=order_categoricals
+                )
+                if initial_categories is None:
+                    # If None here, then we need to match the cats in the Categorical
+                    categories = []
+                    for category in cat_data.categories:
+                        if category in vl:
+                            categories.append(vl[category])
+                        else:
+                            categories.append(category)
+                else:
+                    # If all cats are matched, we can use the values
+                    categories = list(vl.values())
+                try:
+                    # Try to catch duplicate categories
+                    # TODO: if we get a non-copying rename_categories, use that
+                    cat_data = cat_data.rename_categories(categories)
+                except ValueError as err:
+                    vc = Series(categories, copy=False).value_counts()
+                    repeated_cats = list(vc.index[vc > 1])
+                    repeats = "-" * 80 + "\n" + "\n".join(repeated_cats)
+                    # GH 25772
+                    msg = f"""
+Value labels for column {col} are not unique. These cannot be converted to
+pandas categoricals.
+
+Either read the file with `convert_categoricals` set to False or use the
+low level interface in `StataReader` to separately read the values and the
+value_labels.
+
+The repeated labels are:
+{repeats}
+"""
+                    raise ValueError(msg) from err
+                # TODO: is the next line needed above in the data(...) method?
+                cat_series = Series(cat_data, index=data.index, copy=False)
+                cat_converted_data.append((col, cat_series))
+            else:
+                cat_converted_data.append((col, data[col]))
+        data = DataFrame(dict(cat_converted_data), copy=False)
+        return data
+
+    @property
+    def data_label(self) -> str:
+        """
+        Return data label of Stata file.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([(1,)], columns=["variable"])
+        >>> time_stamp = pd.Timestamp(2000, 2, 29, 14, 21)
+        >>> data_label = "This is a data file."
+        >>> path = "/My_path/filename.dta"
+        >>> df.to_stata(path, time_stamp=time_stamp,    # doctest: +SKIP
+        ...             data_label=data_label,  # doctest: +SKIP
+        ...             version=None)  # doctest: +SKIP
+        >>> with pd.io.stata.StataReader(path) as reader:  # doctest: +SKIP
+        ...     print(reader.data_label)  # doctest: +SKIP
+        This is a data file.
+        """
+        self._ensure_open()
+        return self._data_label
+
+    @property
+    def time_stamp(self) -> str:
+        """
+        Return time stamp of Stata file.
+        """
+        self._ensure_open()
+        return self._time_stamp
+
+    def variable_labels(self) -> dict[str, str]:
+        """
+        Return a dict associating each variable name with corresponding label.
+
+        Returns
+        -------
+        dict
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([[1, 2], [3, 4]], columns=["col_1", "col_2"])
+        >>> time_stamp = pd.Timestamp(2000, 2, 29, 14, 21)
+        >>> path = "/My_path/filename.dta"
+        >>> variable_labels = {"col_1": "This is an example"}
+        >>> df.to_stata(path, time_stamp=time_stamp,  # doctest: +SKIP
+        ...             variable_labels=variable_labels, version=None)  # doctest: +SKIP
+        >>> with pd.io.stata.StataReader(path) as reader:  # doctest: +SKIP
+        ...     print(reader.variable_labels())  # doctest: +SKIP
+        {'index': '', 'col_1': 'This is an example', 'col_2': ''}
+        >>> pd.read_stata(path)  # doctest: +SKIP
+            index col_1 col_2
+        0       0    1    2
+        1       1    3    4
+        """
+        self._ensure_open()
+        return dict(zip(self._varlist, self._variable_labels))
+
+    def value_labels(self) -> dict[str, dict[float, str]]:
+        """
+        Return a nested dict associating each variable name to its value and label.
+
+        Returns
+        -------
+        dict
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([[1, 2], [3, 4]], columns=["col_1", "col_2"])
+        >>> time_stamp = pd.Timestamp(2000, 2, 29, 14, 21)
+        >>> path = "/My_path/filename.dta"
+        >>> value_labels = {"col_1": {3: "x"}}
+        >>> df.to_stata(path, time_stamp=time_stamp,  # doctest: +SKIP
+        ...             value_labels=value_labels, version=None)  # doctest: +SKIP
+        >>> with pd.io.stata.StataReader(path) as reader:  # doctest: +SKIP
+        ...     print(reader.value_labels())  # doctest: +SKIP
+        {'col_1': {3: 'x'}}
+        >>> pd.read_stata(path)  # doctest: +SKIP
+            index col_1 col_2
+        0       0    1    2
+        1       1    x    4
+        """
+        if not self._value_labels_read:
+            self._read_value_labels()
+
+        return self._value_label_dict
+
+
+@Appender(_read_stata_doc)
+def read_stata(
+    filepath_or_buffer: FilePath | ReadBuffer[bytes],
+    *,
+    convert_dates: bool = True,
+    convert_categoricals: bool = True,
+    index_col: str | None = None,
+    convert_missing: bool = False,
+    preserve_dtypes: bool = True,
+    columns: Sequence[str] | None = None,
+    order_categoricals: bool = True,
+    chunksize: int | None = None,
+    iterator: bool = False,
+    compression: CompressionOptions = "infer",
+    storage_options: StorageOptions | None = None,
+) -> DataFrame | StataReader:
+    reader = StataReader(
+        filepath_or_buffer,
+        convert_dates=convert_dates,
+        convert_categoricals=convert_categoricals,
+        index_col=index_col,
+        convert_missing=convert_missing,
+        preserve_dtypes=preserve_dtypes,
+        columns=columns,
+        order_categoricals=order_categoricals,
+        chunksize=chunksize,
+        storage_options=storage_options,
+        compression=compression,
+    )
+
+    if iterator or chunksize:
+        return reader
+
+    with reader:
+        return reader.read()
+
+
+def _set_endianness(endianness: str) -> str:
+    if endianness.lower() in ["<", "little"]:
+        return "<"
+    elif endianness.lower() in [">", "big"]:
+        return ">"
+    else:  # pragma : no cover
+        raise ValueError(f"Endianness {endianness} not understood")
+
+
+def _pad_bytes(name: AnyStr, length: int) -> AnyStr:
+    """
+    Take a char string and pads it with null bytes until it's length chars.
+    """
+    if isinstance(name, bytes):
+        return name + b"\x00" * (length - len(name))
+    return name + "\x00" * (length - len(name))
+
+
+def _convert_datetime_to_stata_type(fmt: str) -> np.dtype:
+    """
+    Convert from one of the stata date formats to a type in TYPE_MAP.
+    """
+    if fmt in [
+        "tc",
+        "%tc",
+        "td",
+        "%td",
+        "tw",
+        "%tw",
+        "tm",
+        "%tm",
+        "tq",
+        "%tq",
+        "th",
+        "%th",
+        "ty",
+        "%ty",
+    ]:
+        return np.dtype(np.float64)  # Stata expects doubles for SIFs
+    else:
+        raise NotImplementedError(f"Format {fmt} not implemented")
+
+
+def _maybe_convert_to_int_keys(convert_dates: dict, varlist: list[Hashable]) -> dict:
+    new_dict = {}
+    for key in convert_dates:
+        if not convert_dates[key].startswith("%"):  # make sure proper fmts
+            convert_dates[key] = "%" + convert_dates[key]
+        if key in varlist:
+            new_dict.update({varlist.index(key): convert_dates[key]})
+        else:
+            if not isinstance(key, int):
+                raise ValueError("convert_dates key must be a column or an integer")
+            new_dict.update({key: convert_dates[key]})
+    return new_dict
+
+
+def _dtype_to_stata_type(dtype: np.dtype, column: Series) -> int:
+    """
+    Convert dtype types to stata types. Returns the byte of the given ordinal.
+    See TYPE_MAP and comments for an explanation. This is also explained in
+    the dta spec.
+    1 - 244 are strings of this length
+                         Pandas    Stata
+    251 - for int8      byte
+    252 - for int16     int
+    253 - for int32     long
+    254 - for float32   float
+    255 - for double    double
+
+    If there are dates to convert, then dtype will already have the correct
+    type inserted.
+    """
+    # TODO: expand to handle datetime to integer conversion
+    if dtype.type is np.object_:  # try to coerce it to the biggest string
+        # not memory efficient, what else could we
+        # do?
+        itemsize = max_len_string_array(ensure_object(column._values))
+        return max(itemsize, 1)
+    elif dtype.type is np.float64:
+        return 255
+    elif dtype.type is np.float32:
+        return 254
+    elif dtype.type is np.int32:
+        return 253
+    elif dtype.type is np.int16:
+        return 252
+    elif dtype.type is np.int8:
+        return 251
+    else:  # pragma : no cover
+        raise NotImplementedError(f"Data type {dtype} not supported.")
+
+
+def _dtype_to_default_stata_fmt(
+    dtype, column: Series, dta_version: int = 114, force_strl: bool = False
+) -> str:
+    """
+    Map numpy dtype to stata's default format for this type. Not terribly
+    important since users can change this in Stata. Semantics are
+
+    object  -> "%DDs" where DD is the length of the string.  If not a string,
+                raise ValueError
+    float64 -> "%10.0g"
+    float32 -> "%9.0g"
+    int64   -> "%9.0g"
+    int32   -> "%12.0g"
+    int16   -> "%8.0g"
+    int8    -> "%8.0g"
+    strl    -> "%9s"
+    """
+    # TODO: Refactor to combine type with format
+    # TODO: expand this to handle a default datetime format?
+    if dta_version < 117:
+        max_str_len = 244
+    else:
+        max_str_len = 2045
+        if force_strl:
+            return "%9s"
+    if dtype.type is np.object_:
+        itemsize = max_len_string_array(ensure_object(column._values))
+        if itemsize > max_str_len:
+            if dta_version >= 117:
+                return "%9s"
+            else:
+                raise ValueError(excessive_string_length_error.format(column.name))
+        return "%" + str(max(itemsize, 1)) + "s"
+    elif dtype == np.float64:
+        return "%10.0g"
+    elif dtype == np.float32:
+        return "%9.0g"
+    elif dtype == np.int32:
+        return "%12.0g"
+    elif dtype in (np.int8, np.int16):
+        return "%8.0g"
+    else:  # pragma : no cover
+        raise NotImplementedError(f"Data type {dtype} not supported.")
+
+
+@doc(
+    storage_options=_shared_docs["storage_options"],
+    compression_options=_shared_docs["compression_options"] % "fname",
+)
+class StataWriter(StataParser):
+    """
+    A class for writing Stata binary dta files
+
+    Parameters
+    ----------
+    fname : path (string), buffer or path object
+        string, path object (pathlib.Path or py._path.local.LocalPath) or
+        object implementing a binary write() functions. If using a buffer
+        then the buffer will not be automatically closed after the file
+        is written.
+    data : DataFrame
+        Input to save
+    convert_dates : dict
+        Dictionary mapping columns containing datetime types to stata internal
+        format to use when writing the dates. Options are 'tc', 'td', 'tm',
+        'tw', 'th', 'tq', 'ty'. Column can be either an integer or a name.
+        Datetime columns that do not have a conversion type specified will be
+        converted to 'tc'. Raises NotImplementedError if a datetime column has
+        timezone information
+    write_index : bool
+        Write the index to Stata dataset.
+    byteorder : str
+        Can be ">", "<", "little", or "big". default is `sys.byteorder`
+    time_stamp : datetime
+        A datetime to use as file creation date.  Default is the current time
+    data_label : str
+        A label for the data set.  Must be 80 characters or smaller.
+    variable_labels : dict
+        Dictionary containing columns as keys and variable labels as values.
+        Each label must be 80 characters or smaller.
+    {compression_options}
+
+        .. versionchanged:: 1.4.0 Zstandard support.
+
+    {storage_options}
+
+    value_labels : dict of dicts
+        Dictionary containing columns as keys and dictionaries of column value
+        to labels as values. The combined length of all labels for a single
+        variable must be 32,000 characters or smaller.
+
+        .. versionadded:: 1.4.0
+
+    Returns
+    -------
+    writer : StataWriter instance
+        The StataWriter instance has a write_file method, which will
+        write the file to the given `fname`.
+
+    Raises
+    ------
+    NotImplementedError
+        * If datetimes contain timezone information
+    ValueError
+        * Columns listed in convert_dates are neither datetime64[ns]
+          or datetime
+        * Column dtype is not representable in Stata
+        * Column listed in convert_dates is not in DataFrame
+        * Categorical label contains more than 32,000 characters
+
+    Examples
+    --------
+    >>> data = pd.DataFrame([[1.0, 1]], columns=['a', 'b'])
+    >>> writer = StataWriter('./data_file.dta', data)
+    >>> writer.write_file()
+
+    Directly write a zip file
+    >>> compression = {{"method": "zip", "archive_name": "data_file.dta"}}
+    >>> writer = StataWriter('./data_file.zip', data, compression=compression)
+    >>> writer.write_file()
+
+    Save a DataFrame with dates
+    >>> from datetime import datetime
+    >>> data = pd.DataFrame([[datetime(2000,1,1)]], columns=['date'])
+    >>> writer = StataWriter('./date_data_file.dta', data, {{'date' : 'tw'}})
+    >>> writer.write_file()
+    """
+
+    _max_string_length = 244
+    _encoding: Literal["latin-1", "utf-8"] = "latin-1"
+
+    def __init__(
+        self,
+        fname: FilePath | WriteBuffer[bytes],
+        data: DataFrame,
+        convert_dates: dict[Hashable, str] | None = None,
+        write_index: bool = True,
+        byteorder: str | None = None,
+        time_stamp: datetime | None = None,
+        data_label: str | None = None,
+        variable_labels: dict[Hashable, str] | None = None,
+        compression: CompressionOptions = "infer",
+        storage_options: StorageOptions | None = None,
+        *,
+        value_labels: dict[Hashable, dict[float, str]] | None = None,
+    ) -> None:
+        super().__init__()
+        self.data = data
+        self._convert_dates = {} if convert_dates is None else convert_dates
+        self._write_index = write_index
+        self._time_stamp = time_stamp
+        self._data_label = data_label
+        self._variable_labels = variable_labels
+        self._non_cat_value_labels = value_labels
+        self._value_labels: list[StataValueLabel] = []
+        self._has_value_labels = np.array([], dtype=bool)
+        self._compression = compression
+        self._output_file: IO[bytes] | None = None
+        self._converted_names: dict[Hashable, str] = {}
+        # attach nobs, nvars, data, varlist, typlist
+        self._prepare_pandas(data)
+        self.storage_options = storage_options
+
+        if byteorder is None:
+            byteorder = sys.byteorder
+        self._byteorder = _set_endianness(byteorder)
+        self._fname = fname
+        self.type_converters = {253: np.int32, 252: np.int16, 251: np.int8}
+
+    def _write(self, to_write: str) -> None:
+        """
+        Helper to call encode before writing to file for Python 3 compat.
+        """
+        self.handles.handle.write(to_write.encode(self._encoding))
+
+    def _write_bytes(self, value: bytes) -> None:
+        """
+        Helper to assert file is open before writing.
+        """
+        self.handles.handle.write(value)
+
+    def _prepare_non_cat_value_labels(
+        self, data: DataFrame
+    ) -> list[StataNonCatValueLabel]:
+        """
+        Check for value labels provided for non-categorical columns. Value
+        labels
+        """
+        non_cat_value_labels: list[StataNonCatValueLabel] = []
+        if self._non_cat_value_labels is None:
+            return non_cat_value_labels
+
+        for labname, labels in self._non_cat_value_labels.items():
+            if labname in self._converted_names:
+                colname = self._converted_names[labname]
+            elif labname in data.columns:
+                colname = str(labname)
+            else:
+                raise KeyError(
+                    f"Can't create value labels for {labname}, it wasn't "
+                    "found in the dataset."
+                )
+
+            if not is_numeric_dtype(data[colname].dtype):
+                # Labels should not be passed explicitly for categorical
+                # columns that will be converted to int
+                raise ValueError(
+                    f"Can't create value labels for {labname}, value labels "
+                    "can only be applied to numeric columns."
+                )
+            svl = StataNonCatValueLabel(colname, labels, self._encoding)
+            non_cat_value_labels.append(svl)
+        return non_cat_value_labels
+
+    def _prepare_categoricals(self, data: DataFrame) -> DataFrame:
+        """
+        Check for categorical columns, retain categorical information for
+        Stata file and convert categorical data to int
+        """
+        is_cat = [isinstance(dtype, CategoricalDtype) for dtype in data.dtypes]
+        if not any(is_cat):
+            return data
+
+        self._has_value_labels |= np.array(is_cat)
+
+        get_base_missing_value = StataMissingValue.get_base_missing_value
+        data_formatted = []
+        for col, col_is_cat in zip(data, is_cat):
+            if col_is_cat:
+                svl = StataValueLabel(data[col], encoding=self._encoding)
+                self._value_labels.append(svl)
+                dtype = data[col].cat.codes.dtype
+                if dtype == np.int64:
+                    raise ValueError(
+                        "It is not possible to export "
+                        "int64-based categorical data to Stata."
+                    )
+                values = data[col].cat.codes._values.copy()
+
+                # Upcast if needed so that correct missing values can be set
+                if values.max() >= get_base_missing_value(dtype):
+                    if dtype == np.int8:
+                        dtype = np.dtype(np.int16)
+                    elif dtype == np.int16:
+                        dtype = np.dtype(np.int32)
+                    else:
+                        dtype = np.dtype(np.float64)
+                    values = np.array(values, dtype=dtype)
+
+                # Replace missing values with Stata missing value for type
+                values[values == -1] = get_base_missing_value(dtype)
+                data_formatted.append((col, values))
+            else:
+                data_formatted.append((col, data[col]))
+        return DataFrame.from_dict(dict(data_formatted))
+
+    def _replace_nans(self, data: DataFrame) -> DataFrame:
+        # return data
+        """
+        Checks floating point data columns for nans, and replaces these with
+        the generic Stata for missing value (.)
+        """
+        for c in data:
+            dtype = data[c].dtype
+            if dtype in (np.float32, np.float64):
+                if dtype == np.float32:
+                    replacement = self.MISSING_VALUES["f"]
+                else:
+                    replacement = self.MISSING_VALUES["d"]
+                data[c] = data[c].fillna(replacement)
+
+        return data
+
+    def _update_strl_names(self) -> None:
+        """No-op, forward compatibility"""
+
+    def _validate_variable_name(self, name: str) -> str:
+        """
+        Validate variable names for Stata export.
+
+        Parameters
+        ----------
+        name : str
+            Variable name
+
+        Returns
+        -------
+        str
+            The validated name with invalid characters replaced with
+            underscores.
+
+        Notes
+        -----
+        Stata 114 and 117 support ascii characters in a-z, A-Z, 0-9
+        and _.
+        """
+        for c in name:
+            if (
+                (c < "A" or c > "Z")
+                and (c < "a" or c > "z")
+                and (c < "0" or c > "9")
+                and c != "_"
+            ):
+                name = name.replace(c, "_")
+        return name
+
+    def _check_column_names(self, data: DataFrame) -> DataFrame:
+        """
+        Checks column names to ensure that they are valid Stata column names.
+        This includes checks for:
+            * Non-string names
+            * Stata keywords
+            * Variables that start with numbers
+            * Variables with names that are too long
+
+        When an illegal variable name is detected, it is converted, and if
+        dates are exported, the variable name is propagated to the date
+        conversion dictionary
+        """
+        converted_names: dict[Hashable, str] = {}
+        columns = list(data.columns)
+        original_columns = columns[:]
+
+        duplicate_var_id = 0
+        for j, name in enumerate(columns):
+            orig_name = name
+            if not isinstance(name, str):
+                name = str(name)
+
+            name = self._validate_variable_name(name)
+
+            # Variable name must not be a reserved word
+            if name in self.RESERVED_WORDS:
+                name = "_" + name
+
+            # Variable name may not start with a number
+            if "0" <= name[0] <= "9":
+                name = "_" + name
+
+            name = name[: min(len(name), 32)]
+
+            if not name == orig_name:
+                # check for duplicates
+                while columns.count(name) > 0:
+                    # prepend ascending number to avoid duplicates
+                    name = "_" + str(duplicate_var_id) + name
+                    name = name[: min(len(name), 32)]
+                    duplicate_var_id += 1
+                converted_names[orig_name] = name
+
+            columns[j] = name
+
+        data.columns = Index(columns)
+
+        # Check date conversion, and fix key if needed
+        if self._convert_dates:
+            for c, o in zip(columns, original_columns):
+                if c != o:
+                    self._convert_dates[c] = self._convert_dates[o]
+                    del self._convert_dates[o]
+
+        if converted_names:
+            conversion_warning = []
+            for orig_name, name in converted_names.items():
+                msg = f"{orig_name}   ->   {name}"
+                conversion_warning.append(msg)
+
+            ws = invalid_name_doc.format("\n    ".join(conversion_warning))
+            warnings.warn(
+                ws,
+                InvalidColumnName,
+                stacklevel=find_stack_level(),
+            )
+
+        self._converted_names = converted_names
+        self._update_strl_names()
+
+        return data
+
+    def _set_formats_and_types(self, dtypes: Series) -> None:
+        self.fmtlist: list[str] = []
+        self.typlist: list[int] = []
+        for col, dtype in dtypes.items():
+            self.fmtlist.append(_dtype_to_default_stata_fmt(dtype, self.data[col]))
+            self.typlist.append(_dtype_to_stata_type(dtype, self.data[col]))
+
+    def _prepare_pandas(self, data: DataFrame) -> None:
+        # NOTE: we might need a different API / class for pandas objects so
+        # we can set different semantics - handle this with a PR to pandas.io
+
+        data = data.copy()
+
+        if self._write_index:
+            temp = data.reset_index()
+            if isinstance(temp, DataFrame):
+                data = temp
+
+        # Ensure column names are strings
+        data = self._check_column_names(data)
+
+        # Check columns for compatibility with stata, upcast if necessary
+        # Raise if outside the supported range
+        data = _cast_to_stata_types(data)
+
+        # Replace NaNs with Stata missing values
+        data = self._replace_nans(data)
+
+        # Set all columns to initially unlabelled
+        self._has_value_labels = np.repeat(False, data.shape[1])
+
+        # Create value labels for non-categorical data
+        non_cat_value_labels = self._prepare_non_cat_value_labels(data)
+
+        non_cat_columns = [svl.labname for svl in non_cat_value_labels]
+        has_non_cat_val_labels = data.columns.isin(non_cat_columns)
+        self._has_value_labels |= has_non_cat_val_labels
+        self._value_labels.extend(non_cat_value_labels)
+
+        # Convert categoricals to int data, and strip labels
+        data = self._prepare_categoricals(data)
+
+        self.nobs, self.nvar = data.shape
+        self.data = data
+        self.varlist = data.columns.tolist()
+
+        dtypes = data.dtypes
+
+        # Ensure all date columns are converted
+        for col in data:
+            if col in self._convert_dates:
+                continue
+            if lib.is_np_dtype(data[col].dtype, "M"):
+                self._convert_dates[col] = "tc"
+
+        self._convert_dates = _maybe_convert_to_int_keys(
+            self._convert_dates, self.varlist
+        )
+        for key in self._convert_dates:
+            new_type = _convert_datetime_to_stata_type(self._convert_dates[key])
+            dtypes.iloc[key] = np.dtype(new_type)
+
+        # Verify object arrays are strings and encode to bytes
+        self._encode_strings()
+
+        self._set_formats_and_types(dtypes)
+
+        # set the given format for the datetime cols
+        if self._convert_dates is not None:
+            for key in self._convert_dates:
+                if isinstance(key, int):
+                    self.fmtlist[key] = self._convert_dates[key]
+
+    def _encode_strings(self) -> None:
+        """
+        Encode strings in dta-specific encoding
+
+        Do not encode columns marked for date conversion or for strL
+        conversion. The strL converter independently handles conversion and
+        also accepts empty string arrays.
+        """
+        convert_dates = self._convert_dates
+        # _convert_strl is not available in dta 114
+        convert_strl = getattr(self, "_convert_strl", [])
+        for i, col in enumerate(self.data):
+            # Skip columns marked for date conversion or strl conversion
+            if i in convert_dates or col in convert_strl:
+                continue
+            column = self.data[col]
+            dtype = column.dtype
+            # TODO could also handle string dtype here specifically
+            if dtype.type is np.object_:
+                inferred_dtype = infer_dtype(column, skipna=True)
+                if not ((inferred_dtype == "string") or len(column) == 0):
+                    col = column.name
+                    raise ValueError(
+                        f"""\
+Column `{col}` cannot be exported.\n\nOnly string-like object arrays
+containing all strings or a mix of strings and None can be exported.
+Object arrays containing only null values are prohibited. Other object
+types cannot be exported and must first be converted to one of the
+supported types."""
+                    )
+                encoded = self.data[col].str.encode(self._encoding)
+                # If larger than _max_string_length do nothing
+                if (
+                    max_len_string_array(ensure_object(encoded._values))
+                    <= self._max_string_length
+                ):
+                    self.data[col] = encoded
+
+    def write_file(self) -> None:
+        """
+        Export DataFrame object to Stata dta format.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({"fully_labelled": [1, 2, 3, 3, 1],
+        ...                    "partially_labelled": [1.0, 2.0, np.nan, 9.0, np.nan],
+        ...                    "Y": [7, 7, 9, 8, 10],
+        ...                    "Z": pd.Categorical(["j", "k", "l", "k", "j"]),
+        ...                    })
+        >>> path = "/My_path/filename.dta"
+        >>> labels = {"fully_labelled": {1: "one", 2: "two", 3: "three"},
+        ...           "partially_labelled": {1.0: "one", 2.0: "two"},
+        ...           }
+        >>> writer = pd.io.stata.StataWriter(path,
+        ...                                  df,
+        ...                                  value_labels=labels)  # doctest: +SKIP
+        >>> writer.write_file()  # doctest: +SKIP
+        >>> df = pd.read_stata(path)  # doctest: +SKIP
+        >>> df  # doctest: +SKIP
+            index fully_labelled  partially_labeled  Y  Z
+        0       0            one                one  7  j
+        1       1            two                two  7  k
+        2       2          three                NaN  9  l
+        3       3          three                9.0  8  k
+        4       4            one                NaN 10  j
+        """
+        with get_handle(
+            self._fname,
+            "wb",
+            compression=self._compression,
+            is_text=False,
+            storage_options=self.storage_options,
+        ) as self.handles:
+            if self.handles.compression["method"] is not None:
+                # ZipFile creates a file (with the same name) for each write call.
+                # Write it first into a buffer and then write the buffer to the ZipFile.
+                self._output_file, self.handles.handle = self.handles.handle, BytesIO()
+                self.handles.created_handles.append(self.handles.handle)
+
+            try:
+                self._write_header(
+                    data_label=self._data_label, time_stamp=self._time_stamp
+                )
+                self._write_map()
+                self._write_variable_types()
+                self._write_varnames()
+                self._write_sortlist()
+                self._write_formats()
+                self._write_value_label_names()
+                self._write_variable_labels()
+                self._write_expansion_fields()
+                self._write_characteristics()
+                records = self._prepare_data()
+                self._write_data(records)
+                self._write_strls()
+                self._write_value_labels()
+                self._write_file_close_tag()
+                self._write_map()
+                self._close()
+            except Exception as exc:
+                self.handles.close()
+                if isinstance(self._fname, (str, os.PathLike)) and os.path.isfile(
+                    self._fname
+                ):
+                    try:
+                        os.unlink(self._fname)
+                    except OSError:
+                        warnings.warn(
+                            f"This save was not successful but {self._fname} could not "
+                            "be deleted. This file is not valid.",
+                            ResourceWarning,
+                            stacklevel=find_stack_level(),
+                        )
+                raise exc
+
+    def _close(self) -> None:
+        """
+        Close the file if it was created by the writer.
+
+        If a buffer or file-like object was passed in, for example a GzipFile,
+        then leave this file open for the caller to close.
+        """
+        # write compression
+        if self._output_file is not None:
+            assert isinstance(self.handles.handle, BytesIO)
+            bio, self.handles.handle = self.handles.handle, self._output_file
+            self.handles.handle.write(bio.getvalue())
+
+    def _write_map(self) -> None:
+        """No-op, future compatibility"""
+
+    def _write_file_close_tag(self) -> None:
+        """No-op, future compatibility"""
+
+    def _write_characteristics(self) -> None:
+        """No-op, future compatibility"""
+
+    def _write_strls(self) -> None:
+        """No-op, future compatibility"""
+
+    def _write_expansion_fields(self) -> None:
+        """Write 5 zeros for expansion fields"""
+        self._write(_pad_bytes("", 5))
+
+    def _write_value_labels(self) -> None:
+        for vl in self._value_labels:
+            self._write_bytes(vl.generate_value_label(self._byteorder))
+
+    def _write_header(
+        self,
+        data_label: str | None = None,
+        time_stamp: datetime | None = None,
+    ) -> None:
+        byteorder = self._byteorder
+        # ds_format - just use 114
+        self._write_bytes(struct.pack("b", 114))
+        # byteorder
+        self._write(byteorder == ">" and "\x01" or "\x02")
+        # filetype
+        self._write("\x01")
+        # unused
+        self._write("\x00")
+        # number of vars, 2 bytes
+        self._write_bytes(struct.pack(byteorder + "h", self.nvar)[:2])
+        # number of obs, 4 bytes
+        self._write_bytes(struct.pack(byteorder + "i", self.nobs)[:4])
+        # data label 81 bytes, char, null terminated
+        if data_label is None:
+            self._write_bytes(self._null_terminate_bytes(_pad_bytes("", 80)))
+        else:
+            self._write_bytes(
+                self._null_terminate_bytes(_pad_bytes(data_label[:80], 80))
+            )
+        # time stamp, 18 bytes, char, null terminated
+        # format dd Mon yyyy hh:mm
+        if time_stamp is None:
+            time_stamp = datetime.now()
+        elif not isinstance(time_stamp, datetime):
+            raise ValueError("time_stamp should be datetime type")
+        # GH #13856
+        # Avoid locale-specific month conversion
+        months = [
+            "Jan",
+            "Feb",
+            "Mar",
+            "Apr",
+            "May",
+            "Jun",
+            "Jul",
+            "Aug",
+            "Sep",
+            "Oct",
+            "Nov",
+            "Dec",
+        ]
+        month_lookup = {i + 1: month for i, month in enumerate(months)}
+        ts = (
+            time_stamp.strftime("%d ")
+            + month_lookup[time_stamp.month]
+            + time_stamp.strftime(" %Y %H:%M")
+        )
+        self._write_bytes(self._null_terminate_bytes(ts))
+
+    def _write_variable_types(self) -> None:
+        for typ in self.typlist:
+            self._write_bytes(struct.pack("B", typ))
+
+    def _write_varnames(self) -> None:
+        # varlist names are checked by _check_column_names
+        # varlist, requires null terminated
+        for name in self.varlist:
+            name = self._null_terminate_str(name)
+            name = _pad_bytes(name[:32], 33)
+            self._write(name)
+
+    def _write_sortlist(self) -> None:
+        # srtlist, 2*(nvar+1), int array, encoded by byteorder
+        srtlist = _pad_bytes("", 2 * (self.nvar + 1))
+        self._write(srtlist)
+
+    def _write_formats(self) -> None:
+        # fmtlist, 49*nvar, char array
+        for fmt in self.fmtlist:
+            self._write(_pad_bytes(fmt, 49))
+
+    def _write_value_label_names(self) -> None:
+        # lbllist, 33*nvar, char array
+        for i in range(self.nvar):
+            # Use variable name when categorical
+            if self._has_value_labels[i]:
+                name = self.varlist[i]
+                name = self._null_terminate_str(name)
+                name = _pad_bytes(name[:32], 33)
+                self._write(name)
+            else:  # Default is empty label
+                self._write(_pad_bytes("", 33))
+
+    def _write_variable_labels(self) -> None:
+        # Missing labels are 80 blank characters plus null termination
+        blank = _pad_bytes("", 81)
+
+        if self._variable_labels is None:
+            for i in range(self.nvar):
+                self._write(blank)
+            return
+
+        for col in self.data:
+            if col in self._variable_labels:
+                label = self._variable_labels[col]
+                if len(label) > 80:
+                    raise ValueError("Variable labels must be 80 characters or fewer")
+                is_latin1 = all(ord(c) < 256 for c in label)
+                if not is_latin1:
+                    raise ValueError(
+                        "Variable labels must contain only characters that "
+                        "can be encoded in Latin-1"
+                    )
+                self._write(_pad_bytes(label, 81))
+            else:
+                self._write(blank)
+
+    def _convert_strls(self, data: DataFrame) -> DataFrame:
+        """No-op, future compatibility"""
+        return data
+
+    def _prepare_data(self) -> np.rec.recarray:
+        data = self.data
+        typlist = self.typlist
+        convert_dates = self._convert_dates
+        # 1. Convert dates
+        if self._convert_dates is not None:
+            for i, col in enumerate(data):
+                if i in convert_dates:
+                    data[col] = _datetime_to_stata_elapsed_vec(
+                        data[col], self.fmtlist[i]
+                    )
+        # 2. Convert strls
+        data = self._convert_strls(data)
+
+        # 3. Convert bad string data to '' and pad to correct length
+        dtypes = {}
+        native_byteorder = self._byteorder == _set_endianness(sys.byteorder)
+        for i, col in enumerate(data):
+            typ = typlist[i]
+            if typ <= self._max_string_length:
+                with warnings.catch_warnings():
+                    warnings.filterwarnings(
+                        "ignore",
+                        "Downcasting object dtype arrays",
+                        category=FutureWarning,
+                    )
+                    dc = data[col].fillna("")
+                data[col] = dc.apply(_pad_bytes, args=(typ,))
+                stype = f"S{typ}"
+                dtypes[col] = stype
+                data[col] = data[col].astype(stype)
+            else:
+                dtype = data[col].dtype
+                if not native_byteorder:
+                    dtype = dtype.newbyteorder(self._byteorder)
+                dtypes[col] = dtype
+
+        return data.to_records(index=False, column_dtypes=dtypes)
+
+    def _write_data(self, records: np.rec.recarray) -> None:
+        self._write_bytes(records.tobytes())
+
+    @staticmethod
+    def _null_terminate_str(s: str) -> str:
+        s += "\x00"
+        return s
+
+    def _null_terminate_bytes(self, s: str) -> bytes:
+        return self._null_terminate_str(s).encode(self._encoding)
+
+
+def _dtype_to_stata_type_117(dtype: np.dtype, column: Series, force_strl: bool) -> int:
+    """
+    Converts dtype types to stata types. Returns the byte of the given ordinal.
+    See TYPE_MAP and comments for an explanation. This is also explained in
+    the dta spec.
+    1 - 2045 are strings of this length
+                Pandas    Stata
+    32768 - for object    strL
+    65526 - for int8      byte
+    65527 - for int16     int
+    65528 - for int32     long
+    65529 - for float32   float
+    65530 - for double    double
+
+    If there are dates to convert, then dtype will already have the correct
+    type inserted.
+    """
+    # TODO: expand to handle datetime to integer conversion
+    if force_strl:
+        return 32768
+    if dtype.type is np.object_:  # try to coerce it to the biggest string
+        # not memory efficient, what else could we
+        # do?
+        itemsize = max_len_string_array(ensure_object(column._values))
+        itemsize = max(itemsize, 1)
+        if itemsize <= 2045:
+            return itemsize
+        return 32768
+    elif dtype.type is np.float64:
+        return 65526
+    elif dtype.type is np.float32:
+        return 65527
+    elif dtype.type is np.int32:
+        return 65528
+    elif dtype.type is np.int16:
+        return 65529
+    elif dtype.type is np.int8:
+        return 65530
+    else:  # pragma : no cover
+        raise NotImplementedError(f"Data type {dtype} not supported.")
+
+
+def _pad_bytes_new(name: str | bytes, length: int) -> bytes:
+    """
+    Takes a bytes instance and pads it with null bytes until it's length chars.
+    """
+    if isinstance(name, str):
+        name = bytes(name, "utf-8")
+    return name + b"\x00" * (length - len(name))
+
+
+class StataStrLWriter:
+    """
+    Converter for Stata StrLs
+
+    Stata StrLs map 8 byte values to strings which are stored using a
+    dictionary-like format where strings are keyed to two values.
+
+    Parameters
+    ----------
+    df : DataFrame
+        DataFrame to convert
+    columns : Sequence[str]
+        List of columns names to convert to StrL
+    version : int, optional
+        dta version.  Currently supports 117, 118 and 119
+    byteorder : str, optional
+        Can be ">", "<", "little", or "big". default is `sys.byteorder`
+
+    Notes
+    -----
+    Supports creation of the StrL block of a dta file for dta versions
+    117, 118 and 119.  These differ in how the GSO is stored.  118 and
+    119 store the GSO lookup value as a uint32 and a uint64, while 117
+    uses two uint32s. 118 and 119 also encode all strings as unicode
+    which is required by the format.  117 uses 'latin-1' a fixed width
+    encoding that extends the 7-bit ascii table with an additional 128
+    characters.
+    """
+
+    def __init__(
+        self,
+        df: DataFrame,
+        columns: Sequence[str],
+        version: int = 117,
+        byteorder: str | None = None,
+    ) -> None:
+        if version not in (117, 118, 119):
+            raise ValueError("Only dta versions 117, 118 and 119 supported")
+        self._dta_ver = version
+
+        self.df = df
+        self.columns = columns
+        self._gso_table = {"": (0, 0)}
+        if byteorder is None:
+            byteorder = sys.byteorder
+        self._byteorder = _set_endianness(byteorder)
+
+        gso_v_type = "I"  # uint32
+        gso_o_type = "Q"  # uint64
+        self._encoding = "utf-8"
+        if version == 117:
+            o_size = 4
+            gso_o_type = "I"  # 117 used uint32
+            self._encoding = "latin-1"
+        elif version == 118:
+            o_size = 6
+        else:  # version == 119
+            o_size = 5
+        self._o_offet = 2 ** (8 * (8 - o_size))
+        self._gso_o_type = gso_o_type
+        self._gso_v_type = gso_v_type
+
+    def _convert_key(self, key: tuple[int, int]) -> int:
+        v, o = key
+        return v + self._o_offet * o
+
+    def generate_table(self) -> tuple[dict[str, tuple[int, int]], DataFrame]:
+        """
+        Generates the GSO lookup table for the DataFrame
+
+        Returns
+        -------
+        gso_table : dict
+            Ordered dictionary using the string found as keys
+            and their lookup position (v,o) as values
+        gso_df : DataFrame
+            DataFrame where strl columns have been converted to
+            (v,o) values
+
+        Notes
+        -----
+        Modifies the DataFrame in-place.
+
+        The DataFrame returned encodes the (v,o) values as uint64s. The
+        encoding depends on the dta version, and can be expressed as
+
+        enc = v + o * 2 ** (o_size * 8)
+
+        so that v is stored in the lower bits and o is in the upper
+        bits. o_size is
+
+          * 117: 4
+          * 118: 6
+          * 119: 5
+        """
+        gso_table = self._gso_table
+        gso_df = self.df
+        columns = list(gso_df.columns)
+        selected = gso_df[self.columns]
+        col_index = [(col, columns.index(col)) for col in self.columns]
+        keys = np.empty(selected.shape, dtype=np.uint64)
+        for o, (idx, row) in enumerate(selected.iterrows()):
+            for j, (col, v) in enumerate(col_index):
+                val = row[col]
+                # Allow columns with mixed str and None (GH 23633)
+                val = "" if val is None else val
+                key = gso_table.get(val, None)
+                if key is None:
+                    # Stata prefers human numbers
+                    key = (v + 1, o + 1)
+                    gso_table[val] = key
+                keys[o, j] = self._convert_key(key)
+        for i, col in enumerate(self.columns):
+            gso_df[col] = keys[:, i]
+
+        return gso_table, gso_df
+
+    def generate_blob(self, gso_table: dict[str, tuple[int, int]]) -> bytes:
+        """
+        Generates the binary blob of GSOs that is written to the dta file.
+
+        Parameters
+        ----------
+        gso_table : dict
+            Ordered dictionary (str, vo)
+
+        Returns
+        -------
+        gso : bytes
+            Binary content of dta file to be placed between strl tags
+
+        Notes
+        -----
+        Output format depends on dta version.  117 uses two uint32s to
+        express v and o while 118+ uses a uint32 for v and a uint64 for o.
+        """
+        # Format information
+        # Length includes null term
+        # 117
+        # GSOvvvvooootllllxxxxxxxxxxxxxxx...x
+        #  3  u4  u4 u1 u4  string + null term
+        #
+        # 118, 119
+        # GSOvvvvooooooootllllxxxxxxxxxxxxxxx...x
+        #  3  u4   u8   u1 u4    string + null term
+
+        bio = BytesIO()
+        gso = bytes("GSO", "ascii")
+        gso_type = struct.pack(self._byteorder + "B", 130)
+        null = struct.pack(self._byteorder + "B", 0)
+        v_type = self._byteorder + self._gso_v_type
+        o_type = self._byteorder + self._gso_o_type
+        len_type = self._byteorder + "I"
+        for strl, vo in gso_table.items():
+            if vo == (0, 0):
+                continue
+            v, o = vo
+
+            # GSO
+            bio.write(gso)
+
+            # vvvv
+            bio.write(struct.pack(v_type, v))
+
+            # oooo / oooooooo
+            bio.write(struct.pack(o_type, o))
+
+            # t
+            bio.write(gso_type)
+
+            # llll
+            utf8_string = bytes(strl, "utf-8")
+            bio.write(struct.pack(len_type, len(utf8_string) + 1))
+
+            # xxx...xxx
+            bio.write(utf8_string)
+            bio.write(null)
+
+        return bio.getvalue()
+
+
+class StataWriter117(StataWriter):
+    """
+    A class for writing Stata binary dta files in Stata 13 format (117)
+
+    Parameters
+    ----------
+    fname : path (string), buffer or path object
+        string, path object (pathlib.Path or py._path.local.LocalPath) or
+        object implementing a binary write() functions. If using a buffer
+        then the buffer will not be automatically closed after the file
+        is written.
+    data : DataFrame
+        Input to save
+    convert_dates : dict
+        Dictionary mapping columns containing datetime types to stata internal
+        format to use when writing the dates. Options are 'tc', 'td', 'tm',
+        'tw', 'th', 'tq', 'ty'. Column can be either an integer or a name.
+        Datetime columns that do not have a conversion type specified will be
+        converted to 'tc'. Raises NotImplementedError if a datetime column has
+        timezone information
+    write_index : bool
+        Write the index to Stata dataset.
+    byteorder : str
+        Can be ">", "<", "little", or "big". default is `sys.byteorder`
+    time_stamp : datetime
+        A datetime to use as file creation date.  Default is the current time
+    data_label : str
+        A label for the data set.  Must be 80 characters or smaller.
+    variable_labels : dict
+        Dictionary containing columns as keys and variable labels as values.
+        Each label must be 80 characters or smaller.
+    convert_strl : list
+        List of columns names to convert to Stata StrL format.  Columns with
+        more than 2045 characters are automatically written as StrL.
+        Smaller columns can be converted by including the column name.  Using
+        StrLs can reduce output file size when strings are longer than 8
+        characters, and either frequently repeated or sparse.
+    {compression_options}
+
+        .. versionchanged:: 1.4.0 Zstandard support.
+
+    value_labels : dict of dicts
+        Dictionary containing columns as keys and dictionaries of column value
+        to labels as values. The combined length of all labels for a single
+        variable must be 32,000 characters or smaller.
+
+        .. versionadded:: 1.4.0
+
+    Returns
+    -------
+    writer : StataWriter117 instance
+        The StataWriter117 instance has a write_file method, which will
+        write the file to the given `fname`.
+
+    Raises
+    ------
+    NotImplementedError
+        * If datetimes contain timezone information
+    ValueError
+        * Columns listed in convert_dates are neither datetime64[ns]
+          or datetime
+        * Column dtype is not representable in Stata
+        * Column listed in convert_dates is not in DataFrame
+        * Categorical label contains more than 32,000 characters
+
+    Examples
+    --------
+    >>> data = pd.DataFrame([[1.0, 1, 'a']], columns=['a', 'b', 'c'])
+    >>> writer = pd.io.stata.StataWriter117('./data_file.dta', data)
+    >>> writer.write_file()
+
+    Directly write a zip file
+    >>> compression = {"method": "zip", "archive_name": "data_file.dta"}
+    >>> writer = pd.io.stata.StataWriter117(
+    ...     './data_file.zip', data, compression=compression
+    ...     )
+    >>> writer.write_file()
+
+    Or with long strings stored in strl format
+    >>> data = pd.DataFrame([['A relatively long string'], [''], ['']],
+    ...                     columns=['strls'])
+    >>> writer = pd.io.stata.StataWriter117(
+    ...     './data_file_with_long_strings.dta', data, convert_strl=['strls'])
+    >>> writer.write_file()
+    """
+
+    _max_string_length = 2045
+    _dta_version = 117
+
+    def __init__(
+        self,
+        fname: FilePath | WriteBuffer[bytes],
+        data: DataFrame,
+        convert_dates: dict[Hashable, str] | None = None,
+        write_index: bool = True,
+        byteorder: str | None = None,
+        time_stamp: datetime | None = None,
+        data_label: str | None = None,
+        variable_labels: dict[Hashable, str] | None = None,
+        convert_strl: Sequence[Hashable] | None = None,
+        compression: CompressionOptions = "infer",
+        storage_options: StorageOptions | None = None,
+        *,
+        value_labels: dict[Hashable, dict[float, str]] | None = None,
+    ) -> None:
+        # Copy to new list since convert_strl might be modified later
+        self._convert_strl: list[Hashable] = []
+        if convert_strl is not None:
+            self._convert_strl.extend(convert_strl)
+
+        super().__init__(
+            fname,
+            data,
+            convert_dates,
+            write_index,
+            byteorder=byteorder,
+            time_stamp=time_stamp,
+            data_label=data_label,
+            variable_labels=variable_labels,
+            value_labels=value_labels,
+            compression=compression,
+            storage_options=storage_options,
+        )
+        self._map: dict[str, int] = {}
+        self._strl_blob = b""
+
+    @staticmethod
+    def _tag(val: str | bytes, tag: str) -> bytes:
+        """Surround val with """
+        if isinstance(val, str):
+            val = bytes(val, "utf-8")
+        return bytes("<" + tag + ">", "utf-8") + val + bytes("", "utf-8")
+
+    def _update_map(self, tag: str) -> None:
+        """Update map location for tag with file position"""
+        assert self.handles.handle is not None
+        self._map[tag] = self.handles.handle.tell()
+
+    def _write_header(
+        self,
+        data_label: str | None = None,
+        time_stamp: datetime | None = None,
+    ) -> None:
+        """Write the file header"""
+        byteorder = self._byteorder
+        self._write_bytes(bytes("", "utf-8"))
+        bio = BytesIO()
+        # ds_format - 117
+        bio.write(self._tag(bytes(str(self._dta_version), "utf-8"), "release"))
+        # byteorder
+        bio.write(self._tag(byteorder == ">" and "MSF" or "LSF", "byteorder"))
+        # number of vars, 2 bytes in 117 and 118, 4 byte in 119
+        nvar_type = "H" if self._dta_version <= 118 else "I"
+        bio.write(self._tag(struct.pack(byteorder + nvar_type, self.nvar), "K"))
+        # 117 uses 4 bytes, 118 uses 8
+        nobs_size = "I" if self._dta_version == 117 else "Q"
+        bio.write(self._tag(struct.pack(byteorder + nobs_size, self.nobs), "N"))
+        # data label 81 bytes, char, null terminated
+        label = data_label[:80] if data_label is not None else ""
+        encoded_label = label.encode(self._encoding)
+        label_size = "B" if self._dta_version == 117 else "H"
+        label_len = struct.pack(byteorder + label_size, len(encoded_label))
+        encoded_label = label_len + encoded_label
+        bio.write(self._tag(encoded_label, "label"))
+        # time stamp, 18 bytes, char, null terminated
+        # format dd Mon yyyy hh:mm
+        if time_stamp is None:
+            time_stamp = datetime.now()
+        elif not isinstance(time_stamp, datetime):
+            raise ValueError("time_stamp should be datetime type")
+        # Avoid locale-specific month conversion
+        months = [
+            "Jan",
+            "Feb",
+            "Mar",
+            "Apr",
+            "May",
+            "Jun",
+            "Jul",
+            "Aug",
+            "Sep",
+            "Oct",
+            "Nov",
+            "Dec",
+        ]
+        month_lookup = {i + 1: month for i, month in enumerate(months)}
+        ts = (
+            time_stamp.strftime("%d ")
+            + month_lookup[time_stamp.month]
+            + time_stamp.strftime(" %Y %H:%M")
+        )
+        # '\x11' added due to inspection of Stata file
+        stata_ts = b"\x11" + bytes(ts, "utf-8")
+        bio.write(self._tag(stata_ts, "timestamp"))
+        self._write_bytes(self._tag(bio.getvalue(), "header"))
+
+    def _write_map(self) -> None:
+        """
+        Called twice during file write. The first populates the values in
+        the map with 0s.  The second call writes the final map locations when
+        all blocks have been written.
+        """
+        if not self._map:
+            self._map = {
+                "stata_data": 0,
+                "map": self.handles.handle.tell(),
+                "variable_types": 0,
+                "varnames": 0,
+                "sortlist": 0,
+                "formats": 0,
+                "value_label_names": 0,
+                "variable_labels": 0,
+                "characteristics": 0,
+                "data": 0,
+                "strls": 0,
+                "value_labels": 0,
+                "stata_data_close": 0,
+                "end-of-file": 0,
+            }
+        # Move to start of map
+        self.handles.handle.seek(self._map["map"])
+        bio = BytesIO()
+        for val in self._map.values():
+            bio.write(struct.pack(self._byteorder + "Q", val))
+        self._write_bytes(self._tag(bio.getvalue(), "map"))
+
+    def _write_variable_types(self) -> None:
+        self._update_map("variable_types")
+        bio = BytesIO()
+        for typ in self.typlist:
+            bio.write(struct.pack(self._byteorder + "H", typ))
+        self._write_bytes(self._tag(bio.getvalue(), "variable_types"))
+
+    def _write_varnames(self) -> None:
+        self._update_map("varnames")
+        bio = BytesIO()
+        # 118 scales by 4 to accommodate utf-8 data worst case encoding
+        vn_len = 32 if self._dta_version == 117 else 128
+        for name in self.varlist:
+            name = self._null_terminate_str(name)
+            name = _pad_bytes_new(name[:32].encode(self._encoding), vn_len + 1)
+            bio.write(name)
+        self._write_bytes(self._tag(bio.getvalue(), "varnames"))
+
+    def _write_sortlist(self) -> None:
+        self._update_map("sortlist")
+        sort_size = 2 if self._dta_version < 119 else 4
+        self._write_bytes(self._tag(b"\x00" * sort_size * (self.nvar + 1), "sortlist"))
+
+    def _write_formats(self) -> None:
+        self._update_map("formats")
+        bio = BytesIO()
+        fmt_len = 49 if self._dta_version == 117 else 57
+        for fmt in self.fmtlist:
+            bio.write(_pad_bytes_new(fmt.encode(self._encoding), fmt_len))
+        self._write_bytes(self._tag(bio.getvalue(), "formats"))
+
+    def _write_value_label_names(self) -> None:
+        self._update_map("value_label_names")
+        bio = BytesIO()
+        # 118 scales by 4 to accommodate utf-8 data worst case encoding
+        vl_len = 32 if self._dta_version == 117 else 128
+        for i in range(self.nvar):
+            # Use variable name when categorical
+            name = ""  # default name
+            if self._has_value_labels[i]:
+                name = self.varlist[i]
+            name = self._null_terminate_str(name)
+            encoded_name = _pad_bytes_new(name[:32].encode(self._encoding), vl_len + 1)
+            bio.write(encoded_name)
+        self._write_bytes(self._tag(bio.getvalue(), "value_label_names"))
+
+    def _write_variable_labels(self) -> None:
+        # Missing labels are 80 blank characters plus null termination
+        self._update_map("variable_labels")
+        bio = BytesIO()
+        # 118 scales by 4 to accommodate utf-8 data worst case encoding
+        vl_len = 80 if self._dta_version == 117 else 320
+        blank = _pad_bytes_new("", vl_len + 1)
+
+        if self._variable_labels is None:
+            for _ in range(self.nvar):
+                bio.write(blank)
+            self._write_bytes(self._tag(bio.getvalue(), "variable_labels"))
+            return
+
+        for col in self.data:
+            if col in self._variable_labels:
+                label = self._variable_labels[col]
+                if len(label) > 80:
+                    raise ValueError("Variable labels must be 80 characters or fewer")
+                try:
+                    encoded = label.encode(self._encoding)
+                except UnicodeEncodeError as err:
+                    raise ValueError(
+                        "Variable labels must contain only characters that "
+                        f"can be encoded in {self._encoding}"
+                    ) from err
+
+                bio.write(_pad_bytes_new(encoded, vl_len + 1))
+            else:
+                bio.write(blank)
+        self._write_bytes(self._tag(bio.getvalue(), "variable_labels"))
+
+    def _write_characteristics(self) -> None:
+        self._update_map("characteristics")
+        self._write_bytes(self._tag(b"", "characteristics"))
+
+    def _write_data(self, records) -> None:
+        self._update_map("data")
+        self._write_bytes(b"")
+        self._write_bytes(records.tobytes())
+        self._write_bytes(b"")
+
+    def _write_strls(self) -> None:
+        self._update_map("strls")
+        self._write_bytes(self._tag(self._strl_blob, "strls"))
+
+    def _write_expansion_fields(self) -> None:
+        """No-op in dta 117+"""
+
+    def _write_value_labels(self) -> None:
+        self._update_map("value_labels")
+        bio = BytesIO()
+        for vl in self._value_labels:
+            lab = vl.generate_value_label(self._byteorder)
+            lab = self._tag(lab, "lbl")
+            bio.write(lab)
+        self._write_bytes(self._tag(bio.getvalue(), "value_labels"))
+
+    def _write_file_close_tag(self) -> None:
+        self._update_map("stata_data_close")
+        self._write_bytes(bytes("", "utf-8"))
+        self._update_map("end-of-file")
+
+    def _update_strl_names(self) -> None:
+        """
+        Update column names for conversion to strl if they might have been
+        changed to comply with Stata naming rules
+        """
+        # Update convert_strl if names changed
+        for orig, new in self._converted_names.items():
+            if orig in self._convert_strl:
+                idx = self._convert_strl.index(orig)
+                self._convert_strl[idx] = new
+
+    def _convert_strls(self, data: DataFrame) -> DataFrame:
+        """
+        Convert columns to StrLs if either very large or in the
+        convert_strl variable
+        """
+        convert_cols = [
+            col
+            for i, col in enumerate(data)
+            if self.typlist[i] == 32768 or col in self._convert_strl
+        ]
+
+        if convert_cols:
+            ssw = StataStrLWriter(data, convert_cols, version=self._dta_version)
+            tab, new_data = ssw.generate_table()
+            data = new_data
+            self._strl_blob = ssw.generate_blob(tab)
+        return data
+
+    def _set_formats_and_types(self, dtypes: Series) -> None:
+        self.typlist = []
+        self.fmtlist = []
+        for col, dtype in dtypes.items():
+            force_strl = col in self._convert_strl
+            fmt = _dtype_to_default_stata_fmt(
+                dtype,
+                self.data[col],
+                dta_version=self._dta_version,
+                force_strl=force_strl,
+            )
+            self.fmtlist.append(fmt)
+            self.typlist.append(
+                _dtype_to_stata_type_117(dtype, self.data[col], force_strl)
+            )
+
+
+class StataWriterUTF8(StataWriter117):
+    """
+    Stata binary dta file writing in Stata 15 (118) and 16 (119) formats
+
+    DTA 118 and 119 format files support unicode string data (both fixed
+    and strL) format. Unicode is also supported in value labels, variable
+    labels and the dataset label. Format 119 is automatically used if the
+    file contains more than 32,767 variables.
+
+    Parameters
+    ----------
+    fname : path (string), buffer or path object
+        string, path object (pathlib.Path or py._path.local.LocalPath) or
+        object implementing a binary write() functions. If using a buffer
+        then the buffer will not be automatically closed after the file
+        is written.
+    data : DataFrame
+        Input to save
+    convert_dates : dict, default None
+        Dictionary mapping columns containing datetime types to stata internal
+        format to use when writing the dates. Options are 'tc', 'td', 'tm',
+        'tw', 'th', 'tq', 'ty'. Column can be either an integer or a name.
+        Datetime columns that do not have a conversion type specified will be
+        converted to 'tc'. Raises NotImplementedError if a datetime column has
+        timezone information
+    write_index : bool, default True
+        Write the index to Stata dataset.
+    byteorder : str, default None
+        Can be ">", "<", "little", or "big". default is `sys.byteorder`
+    time_stamp : datetime, default None
+        A datetime to use as file creation date.  Default is the current time
+    data_label : str, default None
+        A label for the data set.  Must be 80 characters or smaller.
+    variable_labels : dict, default None
+        Dictionary containing columns as keys and variable labels as values.
+        Each label must be 80 characters or smaller.
+    convert_strl : list, default None
+        List of columns names to convert to Stata StrL format.  Columns with
+        more than 2045 characters are automatically written as StrL.
+        Smaller columns can be converted by including the column name.  Using
+        StrLs can reduce output file size when strings are longer than 8
+        characters, and either frequently repeated or sparse.
+    version : int, default None
+        The dta version to use. By default, uses the size of data to determine
+        the version. 118 is used if data.shape[1] <= 32767, and 119 is used
+        for storing larger DataFrames.
+    {compression_options}
+
+        .. versionchanged:: 1.4.0 Zstandard support.
+
+    value_labels : dict of dicts
+        Dictionary containing columns as keys and dictionaries of column value
+        to labels as values. The combined length of all labels for a single
+        variable must be 32,000 characters or smaller.
+
+        .. versionadded:: 1.4.0
+
+    Returns
+    -------
+    StataWriterUTF8
+        The instance has a write_file method, which will write the file to the
+        given `fname`.
+
+    Raises
+    ------
+    NotImplementedError
+        * If datetimes contain timezone information
+    ValueError
+        * Columns listed in convert_dates are neither datetime64[ns]
+          or datetime
+        * Column dtype is not representable in Stata
+        * Column listed in convert_dates is not in DataFrame
+        * Categorical label contains more than 32,000 characters
+
+    Examples
+    --------
+    Using Unicode data and column names
+
+    >>> from pandas.io.stata import StataWriterUTF8
+    >>> data = pd.DataFrame([[1.0, 1, 'ᴬ']], columns=['a', 'β', 'ĉ'])
+    >>> writer = StataWriterUTF8('./data_file.dta', data)
+    >>> writer.write_file()
+
+    Directly write a zip file
+    >>> compression = {"method": "zip", "archive_name": "data_file.dta"}
+    >>> writer = StataWriterUTF8('./data_file.zip', data, compression=compression)
+    >>> writer.write_file()
+
+    Or with long strings stored in strl format
+
+    >>> data = pd.DataFrame([['ᴀ relatively long ŝtring'], [''], ['']],
+    ...                     columns=['strls'])
+    >>> writer = StataWriterUTF8('./data_file_with_long_strings.dta', data,
+    ...                          convert_strl=['strls'])
+    >>> writer.write_file()
+    """
+
+    _encoding: Literal["utf-8"] = "utf-8"
+
+    def __init__(
+        self,
+        fname: FilePath | WriteBuffer[bytes],
+        data: DataFrame,
+        convert_dates: dict[Hashable, str] | None = None,
+        write_index: bool = True,
+        byteorder: str | None = None,
+        time_stamp: datetime | None = None,
+        data_label: str | None = None,
+        variable_labels: dict[Hashable, str] | None = None,
+        convert_strl: Sequence[Hashable] | None = None,
+        version: int | None = None,
+        compression: CompressionOptions = "infer",
+        storage_options: StorageOptions | None = None,
+        *,
+        value_labels: dict[Hashable, dict[float, str]] | None = None,
+    ) -> None:
+        if version is None:
+            version = 118 if data.shape[1] <= 32767 else 119
+        elif version not in (118, 119):
+            raise ValueError("version must be either 118 or 119.")
+        elif version == 118 and data.shape[1] > 32767:
+            raise ValueError(
+                "You must use version 119 for data sets containing more than"
+                "32,767 variables"
+            )
+
+        super().__init__(
+            fname,
+            data,
+            convert_dates=convert_dates,
+            write_index=write_index,
+            byteorder=byteorder,
+            time_stamp=time_stamp,
+            data_label=data_label,
+            variable_labels=variable_labels,
+            value_labels=value_labels,
+            convert_strl=convert_strl,
+            compression=compression,
+            storage_options=storage_options,
+        )
+        # Override version set in StataWriter117 init
+        self._dta_version = version
+
+    def _validate_variable_name(self, name: str) -> str:
+        """
+        Validate variable names for Stata export.
+
+        Parameters
+        ----------
+        name : str
+            Variable name
+
+        Returns
+        -------
+        str
+            The validated name with invalid characters replaced with
+            underscores.
+
+        Notes
+        -----
+        Stata 118+ support most unicode characters. The only limitation is in
+        the ascii range where the characters supported are a-z, A-Z, 0-9 and _.
+        """
+        # High code points appear to be acceptable
+        for c in name:
+            if (
+                (
+                    ord(c) < 128
+                    and (c < "A" or c > "Z")
+                    and (c < "a" or c > "z")
+                    and (c < "0" or c > "9")
+                    and c != "_"
+                )
+                or 128 <= ord(c) < 192
+                or c in {"×", "÷"}  # noqa: RUF001
+            ):
+                name = name.replace(c, "_")
+
+        return name
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/xml.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/xml.py
new file mode 100644
index 0000000000000000000000000000000000000000..ac497cd266027f7af71884996182e1725baba361
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/io/xml.py
@@ -0,0 +1,1177 @@
+"""
+:mod:``pandas.io.xml`` is a module for reading XML.
+"""
+
+from __future__ import annotations
+
+import io
+from os import PathLike
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+)
+import warnings
+
+from pandas._libs import lib
+from pandas.compat._optional import import_optional_dependency
+from pandas.errors import (
+    AbstractMethodError,
+    ParserError,
+)
+from pandas.util._decorators import doc
+from pandas.util._exceptions import find_stack_level
+from pandas.util._validators import check_dtype_backend
+
+from pandas.core.dtypes.common import is_list_like
+
+from pandas.core.shared_docs import _shared_docs
+
+from pandas.io.common import (
+    file_exists,
+    get_handle,
+    infer_compression,
+    is_file_like,
+    is_fsspec_url,
+    is_url,
+    stringify_path,
+)
+from pandas.io.parsers import TextParser
+
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+    from xml.etree.ElementTree import Element
+
+    from lxml import etree
+
+    from pandas._typing import (
+        CompressionOptions,
+        ConvertersArg,
+        DtypeArg,
+        DtypeBackend,
+        FilePath,
+        ParseDatesArg,
+        ReadBuffer,
+        StorageOptions,
+        XMLParsers,
+    )
+
+    from pandas import DataFrame
+
+
+@doc(
+    storage_options=_shared_docs["storage_options"],
+    decompression_options=_shared_docs["decompression_options"] % "path_or_buffer",
+)
+class _XMLFrameParser:
+    """
+    Internal subclass to parse XML into DataFrames.
+
+    Parameters
+    ----------
+    path_or_buffer : a valid JSON ``str``, path object or file-like object
+        Any valid string path is acceptable. The string could be a URL. Valid
+        URL schemes include http, ftp, s3, and file.
+
+    xpath : str or regex
+        The ``XPath`` expression to parse required set of nodes for
+        migration to :class:`~pandas.DataFrame`. ``etree`` supports limited ``XPath``.
+
+    namespaces : dict
+        The namespaces defined in XML document (``xmlns:namespace='URI'``)
+        as dicts with key being namespace and value the URI.
+
+    elems_only : bool
+        Parse only the child elements at the specified ``xpath``.
+
+    attrs_only : bool
+        Parse only the attributes at the specified ``xpath``.
+
+    names : list
+        Column names for :class:`~pandas.DataFrame` of parsed XML data.
+
+    dtype : dict
+        Data type for data or columns. E.g. {{'a': np.float64,
+        'b': np.int32, 'c': 'Int64'}}
+
+        .. versionadded:: 1.5.0
+
+    converters : dict, optional
+        Dict of functions for converting values in certain columns. Keys can
+        either be integers or column labels.
+
+        .. versionadded:: 1.5.0
+
+    parse_dates : bool or list of int or names or list of lists or dict
+        Converts either index or select columns to datetimes
+
+        .. versionadded:: 1.5.0
+
+    encoding : str
+        Encoding of xml object or document.
+
+    stylesheet : str or file-like
+        URL, file, file-like object, or a raw string containing XSLT,
+        ``etree`` does not support XSLT but retained for consistency.
+
+    iterparse : dict, optional
+        Dict with row element as key and list of descendant elements
+        and/or attributes as value to be retrieved in iterparsing of
+        XML document.
+
+        .. versionadded:: 1.5.0
+
+    {decompression_options}
+
+        .. versionchanged:: 1.4.0 Zstandard support.
+
+    {storage_options}
+
+    See also
+    --------
+    pandas.io.xml._EtreeFrameParser
+    pandas.io.xml._LxmlFrameParser
+
+    Notes
+    -----
+    To subclass this class effectively you must override the following methods:`
+        * :func:`parse_data`
+        * :func:`_parse_nodes`
+        * :func:`_iterparse_nodes`
+        * :func:`_parse_doc`
+        * :func:`_validate_names`
+        * :func:`_validate_path`
+
+
+    See each method's respective documentation for details on their
+    functionality.
+    """
+
+    def __init__(
+        self,
+        path_or_buffer: FilePath | ReadBuffer[bytes] | ReadBuffer[str],
+        xpath: str,
+        namespaces: dict[str, str] | None,
+        elems_only: bool,
+        attrs_only: bool,
+        names: Sequence[str] | None,
+        dtype: DtypeArg | None,
+        converters: ConvertersArg | None,
+        parse_dates: ParseDatesArg | None,
+        encoding: str | None,
+        stylesheet: FilePath | ReadBuffer[bytes] | ReadBuffer[str] | None,
+        iterparse: dict[str, list[str]] | None,
+        compression: CompressionOptions,
+        storage_options: StorageOptions,
+    ) -> None:
+        self.path_or_buffer = path_or_buffer
+        self.xpath = xpath
+        self.namespaces = namespaces
+        self.elems_only = elems_only
+        self.attrs_only = attrs_only
+        self.names = names
+        self.dtype = dtype
+        self.converters = converters
+        self.parse_dates = parse_dates
+        self.encoding = encoding
+        self.stylesheet = stylesheet
+        self.iterparse = iterparse
+        self.is_style = None
+        self.compression: CompressionOptions = compression
+        self.storage_options = storage_options
+
+    def parse_data(self) -> list[dict[str, str | None]]:
+        """
+        Parse xml data.
+
+        This method will call the other internal methods to
+        validate ``xpath``, names, parse and return specific nodes.
+        """
+
+        raise AbstractMethodError(self)
+
+    def _parse_nodes(self, elems: list[Any]) -> list[dict[str, str | None]]:
+        """
+        Parse xml nodes.
+
+        This method will parse the children and attributes of elements
+        in ``xpath``, conditionally for only elements, only attributes
+        or both while optionally renaming node names.
+
+        Raises
+        ------
+        ValueError
+            * If only elements and only attributes are specified.
+
+        Notes
+        -----
+        Namespace URIs will be removed from return node values. Also,
+        elements with missing children or attributes compared to siblings
+        will have optional keys filled with None values.
+        """
+
+        dicts: list[dict[str, str | None]]
+
+        if self.elems_only and self.attrs_only:
+            raise ValueError("Either element or attributes can be parsed not both.")
+        if self.elems_only:
+            if self.names:
+                dicts = [
+                    {
+                        **(
+                            {el.tag: el.text}
+                            if el.text and not el.text.isspace()
+                            else {}
+                        ),
+                        **{
+                            nm: ch.text if ch.text else None
+                            for nm, ch in zip(self.names, el.findall("*"))
+                        },
+                    }
+                    for el in elems
+                ]
+            else:
+                dicts = [
+                    {ch.tag: ch.text if ch.text else None for ch in el.findall("*")}
+                    for el in elems
+                ]
+
+        elif self.attrs_only:
+            dicts = [
+                {k: v if v else None for k, v in el.attrib.items()} for el in elems
+            ]
+
+        elif self.names:
+            dicts = [
+                {
+                    **el.attrib,
+                    **({el.tag: el.text} if el.text and not el.text.isspace() else {}),
+                    **{
+                        nm: ch.text if ch.text else None
+                        for nm, ch in zip(self.names, el.findall("*"))
+                    },
+                }
+                for el in elems
+            ]
+
+        else:
+            dicts = [
+                {
+                    **el.attrib,
+                    **({el.tag: el.text} if el.text and not el.text.isspace() else {}),
+                    **{ch.tag: ch.text if ch.text else None for ch in el.findall("*")},
+                }
+                for el in elems
+            ]
+
+        dicts = [
+            {k.split("}")[1] if "}" in k else k: v for k, v in d.items()} for d in dicts
+        ]
+
+        keys = list(dict.fromkeys([k for d in dicts for k in d.keys()]))
+        dicts = [{k: d[k] if k in d.keys() else None for k in keys} for d in dicts]
+
+        if self.names:
+            dicts = [dict(zip(self.names, d.values())) for d in dicts]
+
+        return dicts
+
+    def _iterparse_nodes(self, iterparse: Callable) -> list[dict[str, str | None]]:
+        """
+        Iterparse xml nodes.
+
+        This method will read in local disk, decompressed XML files for elements
+        and underlying descendants using iterparse, a method to iterate through
+        an XML tree without holding entire XML tree in memory.
+
+        Raises
+        ------
+        TypeError
+            * If ``iterparse`` is not a dict or its dict value is not list-like.
+        ParserError
+            * If ``path_or_buffer`` is not a physical file on disk or file-like object.
+            * If no data is returned from selected items in ``iterparse``.
+
+        Notes
+        -----
+        Namespace URIs will be removed from return node values. Also,
+        elements with missing children or attributes in submitted list
+        will have optional keys filled with None values.
+        """
+
+        dicts: list[dict[str, str | None]] = []
+        row: dict[str, str | None] | None = None
+
+        if not isinstance(self.iterparse, dict):
+            raise TypeError(
+                f"{type(self.iterparse).__name__} is not a valid type for iterparse"
+            )
+
+        row_node = next(iter(self.iterparse.keys())) if self.iterparse else ""
+        if not is_list_like(self.iterparse[row_node]):
+            raise TypeError(
+                f"{type(self.iterparse[row_node])} is not a valid type "
+                "for value in iterparse"
+            )
+
+        if (not hasattr(self.path_or_buffer, "read")) and (
+            not isinstance(self.path_or_buffer, (str, PathLike))
+            or is_url(self.path_or_buffer)
+            or is_fsspec_url(self.path_or_buffer)
+            or (
+                isinstance(self.path_or_buffer, str)
+                and self.path_or_buffer.startswith((" list[Any]:
+        """
+        Validate ``xpath``.
+
+        This method checks for syntax, evaluation, or empty nodes return.
+
+        Raises
+        ------
+        SyntaxError
+            * If xpah is not supported or issues with namespaces.
+
+        ValueError
+            * If xpah does not return any nodes.
+        """
+
+        raise AbstractMethodError(self)
+
+    def _validate_names(self) -> None:
+        """
+        Validate names.
+
+        This method will check if names is a list-like and aligns
+        with length of parse nodes.
+
+        Raises
+        ------
+        ValueError
+            * If value is not a list and less then length of nodes.
+        """
+        raise AbstractMethodError(self)
+
+    def _parse_doc(
+        self, raw_doc: FilePath | ReadBuffer[bytes] | ReadBuffer[str]
+    ) -> Element | etree._Element:
+        """
+        Build tree from path_or_buffer.
+
+        This method will parse XML object into tree
+        either from string/bytes or file location.
+        """
+        raise AbstractMethodError(self)
+
+
+class _EtreeFrameParser(_XMLFrameParser):
+    """
+    Internal class to parse XML into DataFrames with the Python
+    standard library XML module: `xml.etree.ElementTree`.
+    """
+
+    def parse_data(self) -> list[dict[str, str | None]]:
+        from xml.etree.ElementTree import iterparse
+
+        if self.stylesheet is not None:
+            raise ValueError(
+                "To use stylesheet, you need lxml installed and selected as parser."
+            )
+
+        if self.iterparse is None:
+            self.xml_doc = self._parse_doc(self.path_or_buffer)
+            elems = self._validate_path()
+
+        self._validate_names()
+
+        xml_dicts: list[dict[str, str | None]] = (
+            self._parse_nodes(elems)
+            if self.iterparse is None
+            else self._iterparse_nodes(iterparse)
+        )
+
+        return xml_dicts
+
+    def _validate_path(self) -> list[Any]:
+        """
+        Notes
+        -----
+        ``etree`` supports limited ``XPath``. If user attempts a more complex
+        expression syntax error will raise.
+        """
+
+        msg = (
+            "xpath does not return any nodes or attributes. "
+            "Be sure to specify in `xpath` the parent nodes of "
+            "children and attributes to parse. "
+            "If document uses namespaces denoted with "
+            "xmlns, be sure to define namespaces and "
+            "use them in xpath."
+        )
+        try:
+            elems = self.xml_doc.findall(self.xpath, namespaces=self.namespaces)
+            children = [ch for el in elems for ch in el.findall("*")]
+            attrs = {k: v for el in elems for k, v in el.attrib.items()}
+
+            if elems is None:
+                raise ValueError(msg)
+
+            if elems is not None:
+                if self.elems_only and children == []:
+                    raise ValueError(msg)
+                if self.attrs_only and attrs == {}:
+                    raise ValueError(msg)
+                if children == [] and attrs == {}:
+                    raise ValueError(msg)
+
+        except (KeyError, SyntaxError):
+            raise SyntaxError(
+                "You have used an incorrect or unsupported XPath "
+                "expression for etree library or you used an "
+                "undeclared namespace prefix."
+            )
+
+        return elems
+
+    def _validate_names(self) -> None:
+        children: list[Any]
+
+        if self.names:
+            if self.iterparse:
+                children = self.iterparse[next(iter(self.iterparse))]
+            else:
+                parent = self.xml_doc.find(self.xpath, namespaces=self.namespaces)
+                children = parent.findall("*") if parent is not None else []
+
+            if is_list_like(self.names):
+                if len(self.names) < len(children):
+                    raise ValueError(
+                        "names does not match length of child elements in xpath."
+                    )
+            else:
+                raise TypeError(
+                    f"{type(self.names).__name__} is not a valid type for names"
+                )
+
+    def _parse_doc(
+        self, raw_doc: FilePath | ReadBuffer[bytes] | ReadBuffer[str]
+    ) -> Element:
+        from xml.etree.ElementTree import (
+            XMLParser,
+            parse,
+        )
+
+        handle_data = get_data_from_filepath(
+            filepath_or_buffer=raw_doc,
+            encoding=self.encoding,
+            compression=self.compression,
+            storage_options=self.storage_options,
+        )
+
+        with preprocess_data(handle_data) as xml_data:
+            curr_parser = XMLParser(encoding=self.encoding)
+            document = parse(xml_data, parser=curr_parser)
+
+        return document.getroot()
+
+
+class _LxmlFrameParser(_XMLFrameParser):
+    """
+    Internal class to parse XML into :class:`~pandas.DataFrame` with third-party
+    full-featured XML library, ``lxml``, that supports
+    ``XPath`` 1.0 and XSLT 1.0.
+    """
+
+    def parse_data(self) -> list[dict[str, str | None]]:
+        """
+        Parse xml data.
+
+        This method will call the other internal methods to
+        validate ``xpath``, names, optionally parse and run XSLT,
+        and parse original or transformed XML and return specific nodes.
+        """
+        from lxml.etree import iterparse
+
+        if self.iterparse is None:
+            self.xml_doc = self._parse_doc(self.path_or_buffer)
+
+            if self.stylesheet:
+                self.xsl_doc = self._parse_doc(self.stylesheet)
+                self.xml_doc = self._transform_doc()
+
+            elems = self._validate_path()
+
+        self._validate_names()
+
+        xml_dicts: list[dict[str, str | None]] = (
+            self._parse_nodes(elems)
+            if self.iterparse is None
+            else self._iterparse_nodes(iterparse)
+        )
+
+        return xml_dicts
+
+    def _validate_path(self) -> list[Any]:
+        msg = (
+            "xpath does not return any nodes or attributes. "
+            "Be sure to specify in `xpath` the parent nodes of "
+            "children and attributes to parse. "
+            "If document uses namespaces denoted with "
+            "xmlns, be sure to define namespaces and "
+            "use them in xpath."
+        )
+
+        elems = self.xml_doc.xpath(self.xpath, namespaces=self.namespaces)
+        children = [ch for el in elems for ch in el.xpath("*")]
+        attrs = {k: v for el in elems for k, v in el.attrib.items()}
+
+        if elems == []:
+            raise ValueError(msg)
+
+        if elems != []:
+            if self.elems_only and children == []:
+                raise ValueError(msg)
+            if self.attrs_only and attrs == {}:
+                raise ValueError(msg)
+            if children == [] and attrs == {}:
+                raise ValueError(msg)
+
+        return elems
+
+    def _validate_names(self) -> None:
+        children: list[Any]
+
+        if self.names:
+            if self.iterparse:
+                children = self.iterparse[next(iter(self.iterparse))]
+            else:
+                children = self.xml_doc.xpath(
+                    self.xpath + "[1]/*", namespaces=self.namespaces
+                )
+
+            if is_list_like(self.names):
+                if len(self.names) < len(children):
+                    raise ValueError(
+                        "names does not match length of child elements in xpath."
+                    )
+            else:
+                raise TypeError(
+                    f"{type(self.names).__name__} is not a valid type for names"
+                )
+
+    def _parse_doc(
+        self, raw_doc: FilePath | ReadBuffer[bytes] | ReadBuffer[str]
+    ) -> etree._Element:
+        from lxml.etree import (
+            XMLParser,
+            fromstring,
+            parse,
+        )
+
+        handle_data = get_data_from_filepath(
+            filepath_or_buffer=raw_doc,
+            encoding=self.encoding,
+            compression=self.compression,
+            storage_options=self.storage_options,
+        )
+
+        with preprocess_data(handle_data) as xml_data:
+            curr_parser = XMLParser(encoding=self.encoding)
+
+            if isinstance(xml_data, io.StringIO):
+                if self.encoding is None:
+                    raise TypeError(
+                        "Can not pass encoding None when input is StringIO."
+                    )
+
+                document = fromstring(
+                    xml_data.getvalue().encode(self.encoding), parser=curr_parser
+                )
+            else:
+                document = parse(xml_data, parser=curr_parser)
+
+        return document
+
+    def _transform_doc(self) -> etree._XSLTResultTree:
+        """
+        Transform original tree using stylesheet.
+
+        This method will transform original xml using XSLT script into
+        am ideally flatter xml document for easier parsing and migration
+        to Data Frame.
+        """
+        from lxml.etree import XSLT
+
+        transformer = XSLT(self.xsl_doc)
+        new_doc = transformer(self.xml_doc)
+
+        return new_doc
+
+
+def get_data_from_filepath(
+    filepath_or_buffer: FilePath | bytes | ReadBuffer[bytes] | ReadBuffer[str],
+    encoding: str | None,
+    compression: CompressionOptions,
+    storage_options: StorageOptions,
+) -> str | bytes | ReadBuffer[bytes] | ReadBuffer[str]:
+    """
+    Extract raw XML data.
+
+    The method accepts three input types:
+        1. filepath (string-like)
+        2. file-like object (e.g. open file object, StringIO)
+        3. XML string or bytes
+
+    This method turns (1) into (2) to simplify the rest of the processing.
+    It returns input types (2) and (3) unchanged.
+    """
+    if not isinstance(filepath_or_buffer, bytes):
+        filepath_or_buffer = stringify_path(filepath_or_buffer)
+
+    if (
+        isinstance(filepath_or_buffer, str)
+        and not filepath_or_buffer.startswith((" io.StringIO | io.BytesIO:
+    """
+    Convert extracted raw data.
+
+    This method will return underlying data of extracted XML content.
+    The data either has a `read` attribute (e.g. a file object or a
+    StringIO/BytesIO) or is a string or bytes that is an XML document.
+    """
+
+    if isinstance(data, str):
+        data = io.StringIO(data)
+
+    elif isinstance(data, bytes):
+        data = io.BytesIO(data)
+
+    return data
+
+
+def _data_to_frame(data, **kwargs) -> DataFrame:
+    """
+    Convert parsed data to Data Frame.
+
+    This method will bind xml dictionary data of keys and values
+    into named columns of Data Frame using the built-in TextParser
+    class that build Data Frame and infers specific dtypes.
+    """
+
+    tags = next(iter(data))
+    nodes = [list(d.values()) for d in data]
+
+    try:
+        with TextParser(nodes, names=tags, **kwargs) as tp:
+            return tp.read()
+    except ParserError:
+        raise ParserError(
+            "XML document may be too complex for import. "
+            "Try to flatten document and use distinct "
+            "element and attribute names."
+        )
+
+
+def _parse(
+    path_or_buffer: FilePath | ReadBuffer[bytes] | ReadBuffer[str],
+    xpath: str,
+    namespaces: dict[str, str] | None,
+    elems_only: bool,
+    attrs_only: bool,
+    names: Sequence[str] | None,
+    dtype: DtypeArg | None,
+    converters: ConvertersArg | None,
+    parse_dates: ParseDatesArg | None,
+    encoding: str | None,
+    parser: XMLParsers,
+    stylesheet: FilePath | ReadBuffer[bytes] | ReadBuffer[str] | None,
+    iterparse: dict[str, list[str]] | None,
+    compression: CompressionOptions,
+    storage_options: StorageOptions,
+    dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
+    **kwargs,
+) -> DataFrame:
+    """
+    Call internal parsers.
+
+    This method will conditionally call internal parsers:
+    LxmlFrameParser and/or EtreeParser.
+
+    Raises
+    ------
+    ImportError
+        * If lxml is not installed if selected as parser.
+
+    ValueError
+        * If parser is not lxml or etree.
+    """
+
+    p: _EtreeFrameParser | _LxmlFrameParser
+
+    if isinstance(path_or_buffer, str) and not any(
+        [
+            is_file_like(path_or_buffer),
+            file_exists(path_or_buffer),
+            is_url(path_or_buffer),
+            is_fsspec_url(path_or_buffer),
+        ]
+    ):
+        warnings.warn(
+            "Passing literal xml to 'read_xml' is deprecated and "
+            "will be removed in a future version. To read from a "
+            "literal string, wrap it in a 'StringIO' object.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+
+    if parser == "lxml":
+        lxml = import_optional_dependency("lxml.etree", errors="ignore")
+
+        if lxml is not None:
+            p = _LxmlFrameParser(
+                path_or_buffer,
+                xpath,
+                namespaces,
+                elems_only,
+                attrs_only,
+                names,
+                dtype,
+                converters,
+                parse_dates,
+                encoding,
+                stylesheet,
+                iterparse,
+                compression,
+                storage_options,
+            )
+        else:
+            raise ImportError("lxml not found, please install or use the etree parser.")
+
+    elif parser == "etree":
+        p = _EtreeFrameParser(
+            path_or_buffer,
+            xpath,
+            namespaces,
+            elems_only,
+            attrs_only,
+            names,
+            dtype,
+            converters,
+            parse_dates,
+            encoding,
+            stylesheet,
+            iterparse,
+            compression,
+            storage_options,
+        )
+    else:
+        raise ValueError("Values for parser can only be lxml or etree.")
+
+    data_dicts = p.parse_data()
+
+    return _data_to_frame(
+        data=data_dicts,
+        dtype=dtype,
+        converters=converters,
+        parse_dates=parse_dates,
+        dtype_backend=dtype_backend,
+        **kwargs,
+    )
+
+
+@doc(
+    storage_options=_shared_docs["storage_options"],
+    decompression_options=_shared_docs["decompression_options"] % "path_or_buffer",
+)
+def read_xml(
+    path_or_buffer: FilePath | ReadBuffer[bytes] | ReadBuffer[str],
+    *,
+    xpath: str = "./*",
+    namespaces: dict[str, str] | None = None,
+    elems_only: bool = False,
+    attrs_only: bool = False,
+    names: Sequence[str] | None = None,
+    dtype: DtypeArg | None = None,
+    converters: ConvertersArg | None = None,
+    parse_dates: ParseDatesArg | None = None,
+    # encoding can not be None for lxml and StringIO input
+    encoding: str | None = "utf-8",
+    parser: XMLParsers = "lxml",
+    stylesheet: FilePath | ReadBuffer[bytes] | ReadBuffer[str] | None = None,
+    iterparse: dict[str, list[str]] | None = None,
+    compression: CompressionOptions = "infer",
+    storage_options: StorageOptions | None = None,
+    dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
+) -> DataFrame:
+    r"""
+    Read XML document into a :class:`~pandas.DataFrame` object.
+
+    .. versionadded:: 1.3.0
+
+    Parameters
+    ----------
+    path_or_buffer : str, path object, or file-like object
+        String, path object (implementing ``os.PathLike[str]``), or file-like
+        object implementing a ``read()`` function. The string can be any valid XML
+        string or a path. The string can further be a URL. Valid URL schemes
+        include http, ftp, s3, and file.
+
+        .. deprecated:: 2.1.0
+            Passing xml literal strings is deprecated.
+            Wrap literal xml input in ``io.StringIO`` or ``io.BytesIO`` instead.
+
+    xpath : str, optional, default './\*'
+        The ``XPath`` to parse required set of nodes for migration to
+        :class:`~pandas.DataFrame`.``XPath`` should return a collection of elements
+        and not a single element. Note: The ``etree`` parser supports limited ``XPath``
+        expressions. For more complex ``XPath``, use ``lxml`` which requires
+        installation.
+
+    namespaces : dict, optional
+        The namespaces defined in XML document as dicts with key being
+        namespace prefix and value the URI. There is no need to include all
+        namespaces in XML, only the ones used in ``xpath`` expression.
+        Note: if XML document uses default namespace denoted as
+        `xmlns=''` without a prefix, you must assign any temporary
+        namespace prefix such as 'doc' to the URI in order to parse
+        underlying nodes and/or attributes. For example, ::
+
+            namespaces = {{"doc": "https://example.com"}}
+
+    elems_only : bool, optional, default False
+        Parse only the child elements at the specified ``xpath``. By default,
+        all child elements and non-empty text nodes are returned.
+
+    attrs_only :  bool, optional, default False
+        Parse only the attributes at the specified ``xpath``.
+        By default, all attributes are returned.
+
+    names :  list-like, optional
+        Column names for DataFrame of parsed XML data. Use this parameter to
+        rename original element names and distinguish same named elements and
+        attributes.
+
+    dtype : Type name or dict of column -> type, optional
+        Data type for data or columns. E.g. {{'a': np.float64, 'b': np.int32,
+        'c': 'Int64'}}
+        Use `str` or `object` together with suitable `na_values` settings
+        to preserve and not interpret dtype.
+        If converters are specified, they will be applied INSTEAD
+        of dtype conversion.
+
+        .. versionadded:: 1.5.0
+
+    converters : dict, optional
+        Dict of functions for converting values in certain columns. Keys can either
+        be integers or column labels.
+
+        .. versionadded:: 1.5.0
+
+    parse_dates : bool or list of int or names or list of lists or dict, default False
+        Identifiers to parse index or columns to datetime. The behavior is as follows:
+
+        * boolean. If True -> try parsing the index.
+        * list of int or names. e.g. If [1, 2, 3] -> try parsing columns 1, 2, 3
+          each as a separate date column.
+        * list of lists. e.g.  If [[1, 3]] -> combine columns 1 and 3 and parse as
+          a single date column.
+        * dict, e.g. {{'foo' : [1, 3]}} -> parse columns 1, 3 as date and call
+          result 'foo'
+
+        .. versionadded:: 1.5.0
+
+    encoding : str, optional, default 'utf-8'
+        Encoding of XML document.
+
+    parser : {{'lxml','etree'}}, default 'lxml'
+        Parser module to use for retrieval of data. Only 'lxml' and
+        'etree' are supported. With 'lxml' more complex ``XPath`` searches
+        and ability to use XSLT stylesheet are supported.
+
+    stylesheet : str, path object or file-like object
+        A URL, file-like object, or a raw string containing an XSLT script.
+        This stylesheet should flatten complex, deeply nested XML documents
+        for easier parsing. To use this feature you must have ``lxml`` module
+        installed and specify 'lxml' as ``parser``. The ``xpath`` must
+        reference nodes of transformed XML document generated after XSLT
+        transformation and not the original XML document. Only XSLT 1.0
+        scripts and not later versions is currently supported.
+
+    iterparse : dict, optional
+        The nodes or attributes to retrieve in iterparsing of XML document
+        as a dict with key being the name of repeating element and value being
+        list of elements or attribute names that are descendants of the repeated
+        element. Note: If this option is used, it will replace ``xpath`` parsing
+        and unlike ``xpath``, descendants do not need to relate to each other but can
+        exist any where in document under the repeating element. This memory-
+        efficient method should be used for very large XML files (500MB, 1GB, or 5GB+).
+        For example, ::
+
+            iterparse = {{"row_element": ["child_elem", "attr", "grandchild_elem"]}}
+
+        .. versionadded:: 1.5.0
+
+    {decompression_options}
+
+        .. versionchanged:: 1.4.0 Zstandard support.
+
+    {storage_options}
+
+    dtype_backend : {{'numpy_nullable', 'pyarrow'}}, default 'numpy_nullable'
+        Back-end data type applied to the resultant :class:`DataFrame`
+        (still experimental). Behaviour is as follows:
+
+        * ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame`
+          (default).
+        * ``"pyarrow"``: returns pyarrow-backed nullable :class:`ArrowDtype`
+          DataFrame.
+
+        .. versionadded:: 2.0
+
+    Returns
+    -------
+    df
+        A DataFrame.
+
+    See Also
+    --------
+    read_json : Convert a JSON string to pandas object.
+    read_html : Read HTML tables into a list of DataFrame objects.
+
+    Notes
+    -----
+    This method is best designed to import shallow XML documents in
+    following format which is the ideal fit for the two-dimensions of a
+    ``DataFrame`` (row by column). ::
+
+            
+                
+                  data
+                  data
+                  data
+                  ...
+               
+               
+                  ...
+               
+               ...
+            
+
+    As a file format, XML documents can be designed any way including
+    layout of elements and attributes as long as it conforms to W3C
+    specifications. Therefore, this method is a convenience handler for
+    a specific flatter design and not all possible XML structures.
+
+    However, for more complex XML documents, ``stylesheet`` allows you to
+    temporarily redesign original document with XSLT (a special purpose
+    language) for a flatter version for migration to a DataFrame.
+
+    This function will *always* return a single :class:`DataFrame` or raise
+    exceptions due to issues with XML document, ``xpath``, or other
+    parameters.
+
+    See the :ref:`read_xml documentation in the IO section of the docs
+    ` for more information in using this method to parse XML
+    files to DataFrames.
+
+    Examples
+    --------
+    >>> from io import StringIO
+    >>> xml = '''
+    ... 
+    ...  
+    ...    square
+    ...    360
+    ...    4.0
+    ...  
+    ...  
+    ...    circle
+    ...    360
+    ...    
+    ...  
+    ...  
+    ...    triangle
+    ...    180
+    ...    3.0
+    ...  
+    ... '''
+
+    >>> df = pd.read_xml(StringIO(xml))
+    >>> df
+          shape  degrees  sides
+    0    square      360    4.0
+    1    circle      360    NaN
+    2  triangle      180    3.0
+
+    >>> xml = '''
+    ... 
+    ...   
+    ...   
+    ...   
+    ... '''
+
+    >>> df = pd.read_xml(StringIO(xml), xpath=".//row")
+    >>> df
+          shape  degrees  sides
+    0    square      360    4.0
+    1    circle      360    NaN
+    2  triangle      180    3.0
+
+    >>> xml = '''
+    ... 
+    ...   
+    ...     square
+    ...     360
+    ...     4.0
+    ...   
+    ...   
+    ...     circle
+    ...     360
+    ...     
+    ...   
+    ...   
+    ...     triangle
+    ...     180
+    ...     3.0
+    ...   
+    ... '''
+
+    >>> df = pd.read_xml(StringIO(xml),
+    ...                  xpath="//doc:row",
+    ...                  namespaces={{"doc": "https://example.com"}})
+    >>> df
+          shape  degrees  sides
+    0    square      360    4.0
+    1    circle      360    NaN
+    2  triangle      180    3.0
+
+    >>> xml_data = '''
+    ...         
+    ...            
+    ...               0
+    ...               1
+    ...               2.5
+    ...               True
+    ...               a
+    ...               2019-12-31 00:00:00
+    ...            
+    ...            
+    ...               1
+    ...               4.5
+    ...               False
+    ...               b
+    ...               2019-12-31 00:00:00
+    ...            
+    ...         
+    ...         '''
+
+    >>> df = pd.read_xml(StringIO(xml_data),
+    ...                  dtype_backend="numpy_nullable",
+    ...                  parse_dates=["e"])
+    >>> df
+       index     a    b      c  d          e
+    0      0     1  2.5   True  a 2019-12-31
+    1      1    4.5  False  b 2019-12-31
+    """
+    check_dtype_backend(dtype_backend)
+
+    return _parse(
+        path_or_buffer=path_or_buffer,
+        xpath=xpath,
+        namespaces=namespaces,
+        elems_only=elems_only,
+        attrs_only=attrs_only,
+        names=names,
+        dtype=dtype,
+        converters=converters,
+        parse_dates=parse_dates,
+        encoding=encoding,
+        parser=parser,
+        stylesheet=stylesheet,
+        iterparse=iterparse,
+        compression=compression,
+        storage_options=storage_options,
+        dtype_backend=dtype_backend,
+    )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/plotting/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/plotting/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..55c861e384d679654b8615d4cb5808f536fd8f2e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/plotting/__init__.py
@@ -0,0 +1,98 @@
+"""
+Plotting public API.
+
+Authors of third-party plotting backends should implement a module with a
+public ``plot(data, kind, **kwargs)``. The parameter `data` will contain
+the data structure and can be a `Series` or a `DataFrame`. For example,
+for ``df.plot()`` the parameter `data` will contain the DataFrame `df`.
+In some cases, the data structure is transformed before being sent to
+the backend (see PlotAccessor.__call__ in pandas/plotting/_core.py for
+the exact transformations).
+
+The parameter `kind` will be one of:
+
+- line
+- bar
+- barh
+- box
+- hist
+- kde
+- area
+- pie
+- scatter
+- hexbin
+
+See the pandas API reference for documentation on each kind of plot.
+
+Any other keyword argument is currently assumed to be backend specific,
+but some parameters may be unified and added to the signature in the
+future (e.g. `title` which should be useful for any backend).
+
+Currently, all the Matplotlib functions in pandas are accessed through
+the selected backend. For example, `pandas.plotting.boxplot` (equivalent
+to `DataFrame.boxplot`) is also accessed in the selected backend. This
+is expected to change, and the exact API is under discussion. But with
+the current version, backends are expected to implement the next functions:
+
+- plot (describe above, used for `Series.plot` and `DataFrame.plot`)
+- hist_series and hist_frame (for `Series.hist` and `DataFrame.hist`)
+- boxplot (`pandas.plotting.boxplot(df)` equivalent to `DataFrame.boxplot`)
+- boxplot_frame and boxplot_frame_groupby
+- register and deregister (register converters for the tick formats)
+- Plots not called as `Series` and `DataFrame` methods:
+  - table
+  - andrews_curves
+  - autocorrelation_plot
+  - bootstrap_plot
+  - lag_plot
+  - parallel_coordinates
+  - radviz
+  - scatter_matrix
+
+Use the code in pandas/plotting/_matplotib.py and
+https://github.com/pyviz/hvplot as a reference on how to write a backend.
+
+For the discussion about the API see
+https://github.com/pandas-dev/pandas/issues/26747.
+"""
+from pandas.plotting._core import (
+    PlotAccessor,
+    boxplot,
+    boxplot_frame,
+    boxplot_frame_groupby,
+    hist_frame,
+    hist_series,
+)
+from pandas.plotting._misc import (
+    andrews_curves,
+    autocorrelation_plot,
+    bootstrap_plot,
+    deregister as deregister_matplotlib_converters,
+    lag_plot,
+    parallel_coordinates,
+    plot_params,
+    radviz,
+    register as register_matplotlib_converters,
+    scatter_matrix,
+    table,
+)
+
+__all__ = [
+    "PlotAccessor",
+    "boxplot",
+    "boxplot_frame",
+    "boxplot_frame_groupby",
+    "hist_frame",
+    "hist_series",
+    "scatter_matrix",
+    "radviz",
+    "andrews_curves",
+    "bootstrap_plot",
+    "parallel_coordinates",
+    "lag_plot",
+    "autocorrelation_plot",
+    "table",
+    "plot_params",
+    "register_matplotlib_converters",
+    "deregister_matplotlib_converters",
+]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/plotting/_core.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/plotting/_core.py
new file mode 100644
index 0000000000000000000000000000000000000000..cb5598a98d5afbc93954d74e3ecc78b4e572606d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/plotting/_core.py
@@ -0,0 +1,1946 @@
+from __future__ import annotations
+
+import importlib
+from typing import (
+    TYPE_CHECKING,
+    Callable,
+    Literal,
+)
+
+from pandas._config import get_option
+
+from pandas.util._decorators import (
+    Appender,
+    Substitution,
+)
+
+from pandas.core.dtypes.common import (
+    is_integer,
+    is_list_like,
+)
+from pandas.core.dtypes.generic import (
+    ABCDataFrame,
+    ABCSeries,
+)
+
+from pandas.core.base import PandasObject
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Hashable,
+        Sequence,
+    )
+    import types
+
+    from matplotlib.axes import Axes
+    import numpy as np
+
+    from pandas._typing import IndexLabel
+
+    from pandas import (
+        DataFrame,
+        Series,
+    )
+    from pandas.core.groupby.generic import DataFrameGroupBy
+
+
+def hist_series(
+    self: Series,
+    by=None,
+    ax=None,
+    grid: bool = True,
+    xlabelsize: int | None = None,
+    xrot: float | None = None,
+    ylabelsize: int | None = None,
+    yrot: float | None = None,
+    figsize: tuple[int, int] | None = None,
+    bins: int | Sequence[int] = 10,
+    backend: str | None = None,
+    legend: bool = False,
+    **kwargs,
+):
+    """
+    Draw histogram of the input series using matplotlib.
+
+    Parameters
+    ----------
+    by : object, optional
+        If passed, then used to form histograms for separate groups.
+    ax : matplotlib axis object
+        If not passed, uses gca().
+    grid : bool, default True
+        Whether to show axis grid lines.
+    xlabelsize : int, default None
+        If specified changes the x-axis label size.
+    xrot : float, default None
+        Rotation of x axis labels.
+    ylabelsize : int, default None
+        If specified changes the y-axis label size.
+    yrot : float, default None
+        Rotation of y axis labels.
+    figsize : tuple, default None
+        Figure size in inches by default.
+    bins : int or sequence, default 10
+        Number of histogram bins to be used. If an integer is given, bins + 1
+        bin edges are calculated and returned. If bins is a sequence, gives
+        bin edges, including left edge of first bin and right edge of last
+        bin. In this case, bins is returned unmodified.
+    backend : str, default None
+        Backend to use instead of the backend specified in the option
+        ``plotting.backend``. For instance, 'matplotlib'. Alternatively, to
+        specify the ``plotting.backend`` for the whole session, set
+        ``pd.options.plotting.backend``.
+    legend : bool, default False
+        Whether to show the legend.
+
+    **kwargs
+        To be passed to the actual plotting function.
+
+    Returns
+    -------
+    matplotlib.AxesSubplot
+        A histogram plot.
+
+    See Also
+    --------
+    matplotlib.axes.Axes.hist : Plot a histogram using matplotlib.
+
+    Examples
+    --------
+    For Series:
+
+    .. plot::
+        :context: close-figs
+
+        >>> lst = ['a', 'a', 'a', 'b', 'b', 'b']
+        >>> ser = pd.Series([1, 2, 2, 4, 6, 6], index=lst)
+        >>> hist = ser.hist()
+
+    For Groupby:
+
+    .. plot::
+        :context: close-figs
+
+        >>> lst = ['a', 'a', 'a', 'b', 'b', 'b']
+        >>> ser = pd.Series([1, 2, 2, 4, 6, 6], index=lst)
+        >>> hist = ser.groupby(level=0).hist()
+    """
+    plot_backend = _get_plot_backend(backend)
+    return plot_backend.hist_series(
+        self,
+        by=by,
+        ax=ax,
+        grid=grid,
+        xlabelsize=xlabelsize,
+        xrot=xrot,
+        ylabelsize=ylabelsize,
+        yrot=yrot,
+        figsize=figsize,
+        bins=bins,
+        legend=legend,
+        **kwargs,
+    )
+
+
+def hist_frame(
+    data: DataFrame,
+    column: IndexLabel | None = None,
+    by=None,
+    grid: bool = True,
+    xlabelsize: int | None = None,
+    xrot: float | None = None,
+    ylabelsize: int | None = None,
+    yrot: float | None = None,
+    ax=None,
+    sharex: bool = False,
+    sharey: bool = False,
+    figsize: tuple[int, int] | None = None,
+    layout: tuple[int, int] | None = None,
+    bins: int | Sequence[int] = 10,
+    backend: str | None = None,
+    legend: bool = False,
+    **kwargs,
+):
+    """
+    Make a histogram of the DataFrame's columns.
+
+    A `histogram`_ is a representation of the distribution of data.
+    This function calls :meth:`matplotlib.pyplot.hist`, on each series in
+    the DataFrame, resulting in one histogram per column.
+
+    .. _histogram: https://en.wikipedia.org/wiki/Histogram
+
+    Parameters
+    ----------
+    data : DataFrame
+        The pandas object holding the data.
+    column : str or sequence, optional
+        If passed, will be used to limit data to a subset of columns.
+    by : object, optional
+        If passed, then used to form histograms for separate groups.
+    grid : bool, default True
+        Whether to show axis grid lines.
+    xlabelsize : int, default None
+        If specified changes the x-axis label size.
+    xrot : float, default None
+        Rotation of x axis labels. For example, a value of 90 displays the
+        x labels rotated 90 degrees clockwise.
+    ylabelsize : int, default None
+        If specified changes the y-axis label size.
+    yrot : float, default None
+        Rotation of y axis labels. For example, a value of 90 displays the
+        y labels rotated 90 degrees clockwise.
+    ax : Matplotlib axes object, default None
+        The axes to plot the histogram on.
+    sharex : bool, default True if ax is None else False
+        In case subplots=True, share x axis and set some x axis labels to
+        invisible; defaults to True if ax is None otherwise False if an ax
+        is passed in.
+        Note that passing in both an ax and sharex=True will alter all x axis
+        labels for all subplots in a figure.
+    sharey : bool, default False
+        In case subplots=True, share y axis and set some y axis labels to
+        invisible.
+    figsize : tuple, optional
+        The size in inches of the figure to create. Uses the value in
+        `matplotlib.rcParams` by default.
+    layout : tuple, optional
+        Tuple of (rows, columns) for the layout of the histograms.
+    bins : int or sequence, default 10
+        Number of histogram bins to be used. If an integer is given, bins + 1
+        bin edges are calculated and returned. If bins is a sequence, gives
+        bin edges, including left edge of first bin and right edge of last
+        bin. In this case, bins is returned unmodified.
+
+    backend : str, default None
+        Backend to use instead of the backend specified in the option
+        ``plotting.backend``. For instance, 'matplotlib'. Alternatively, to
+        specify the ``plotting.backend`` for the whole session, set
+        ``pd.options.plotting.backend``.
+
+    legend : bool, default False
+        Whether to show the legend.
+
+    **kwargs
+        All other plotting keyword arguments to be passed to
+        :meth:`matplotlib.pyplot.hist`.
+
+    Returns
+    -------
+    matplotlib.AxesSubplot or numpy.ndarray of them
+
+    See Also
+    --------
+    matplotlib.pyplot.hist : Plot a histogram using matplotlib.
+
+    Examples
+    --------
+    This example draws a histogram based on the length and width of
+    some animals, displayed in three bins
+
+    .. plot::
+        :context: close-figs
+
+        >>> data = {'length': [1.5, 0.5, 1.2, 0.9, 3],
+        ...         'width': [0.7, 0.2, 0.15, 0.2, 1.1]}
+        >>> index = ['pig', 'rabbit', 'duck', 'chicken', 'horse']
+        >>> df = pd.DataFrame(data, index=index)
+        >>> hist = df.hist(bins=3)
+    """
+    plot_backend = _get_plot_backend(backend)
+    return plot_backend.hist_frame(
+        data,
+        column=column,
+        by=by,
+        grid=grid,
+        xlabelsize=xlabelsize,
+        xrot=xrot,
+        ylabelsize=ylabelsize,
+        yrot=yrot,
+        ax=ax,
+        sharex=sharex,
+        sharey=sharey,
+        figsize=figsize,
+        layout=layout,
+        legend=legend,
+        bins=bins,
+        **kwargs,
+    )
+
+
+_boxplot_doc = """
+Make a box plot from DataFrame columns.
+
+Make a box-and-whisker plot from DataFrame columns, optionally grouped
+by some other columns. A box plot is a method for graphically depicting
+groups of numerical data through their quartiles.
+The box extends from the Q1 to Q3 quartile values of the data,
+with a line at the median (Q2). The whiskers extend from the edges
+of box to show the range of the data. By default, they extend no more than
+`1.5 * IQR (IQR = Q3 - Q1)` from the edges of the box, ending at the farthest
+data point within that interval. Outliers are plotted as separate dots.
+
+For further details see
+Wikipedia's entry for `boxplot `_.
+
+Parameters
+----------
+%(data)s\
+column : str or list of str, optional
+    Column name or list of names, or vector.
+    Can be any valid input to :meth:`pandas.DataFrame.groupby`.
+by : str or array-like, optional
+    Column in the DataFrame to :meth:`pandas.DataFrame.groupby`.
+    One box-plot will be done per value of columns in `by`.
+ax : object of class matplotlib.axes.Axes, optional
+    The matplotlib axes to be used by boxplot.
+fontsize : float or str
+    Tick label font size in points or as a string (e.g., `large`).
+rot : float, default 0
+    The rotation angle of labels (in degrees)
+    with respect to the screen coordinate system.
+grid : bool, default True
+    Setting this to True will show the grid.
+figsize : A tuple (width, height) in inches
+    The size of the figure to create in matplotlib.
+layout : tuple (rows, columns), optional
+    For example, (3, 5) will display the subplots
+    using 3 rows and 5 columns, starting from the top-left.
+return_type : {'axes', 'dict', 'both'} or None, default 'axes'
+    The kind of object to return. The default is ``axes``.
+
+    * 'axes' returns the matplotlib axes the boxplot is drawn on.
+    * 'dict' returns a dictionary whose values are the matplotlib
+      Lines of the boxplot.
+    * 'both' returns a namedtuple with the axes and dict.
+    * when grouping with ``by``, a Series mapping columns to
+      ``return_type`` is returned.
+
+      If ``return_type`` is `None`, a NumPy array
+      of axes with the same shape as ``layout`` is returned.
+%(backend)s\
+
+**kwargs
+    All other plotting keyword arguments to be passed to
+    :func:`matplotlib.pyplot.boxplot`.
+
+Returns
+-------
+result
+    See Notes.
+
+See Also
+--------
+pandas.Series.plot.hist: Make a histogram.
+matplotlib.pyplot.boxplot : Matplotlib equivalent plot.
+
+Notes
+-----
+The return type depends on the `return_type` parameter:
+
+* 'axes' : object of class matplotlib.axes.Axes
+* 'dict' : dict of matplotlib.lines.Line2D objects
+* 'both' : a namedtuple with structure (ax, lines)
+
+For data grouped with ``by``, return a Series of the above or a numpy
+array:
+
+* :class:`~pandas.Series`
+* :class:`~numpy.array` (for ``return_type = None``)
+
+Use ``return_type='dict'`` when you want to tweak the appearance
+of the lines after plotting. In this case a dict containing the Lines
+making up the boxes, caps, fliers, medians, and whiskers is returned.
+
+Examples
+--------
+
+Boxplots can be created for every column in the dataframe
+by ``df.boxplot()`` or indicating the columns to be used:
+
+.. plot::
+    :context: close-figs
+
+    >>> np.random.seed(1234)
+    >>> df = pd.DataFrame(np.random.randn(10, 4),
+    ...                   columns=['Col1', 'Col2', 'Col3', 'Col4'])
+    >>> boxplot = df.boxplot(column=['Col1', 'Col2', 'Col3'])  # doctest: +SKIP
+
+Boxplots of variables distributions grouped by the values of a third
+variable can be created using the option ``by``. For instance:
+
+.. plot::
+    :context: close-figs
+
+    >>> df = pd.DataFrame(np.random.randn(10, 2),
+    ...                   columns=['Col1', 'Col2'])
+    >>> df['X'] = pd.Series(['A', 'A', 'A', 'A', 'A',
+    ...                      'B', 'B', 'B', 'B', 'B'])
+    >>> boxplot = df.boxplot(by='X')
+
+A list of strings (i.e. ``['X', 'Y']``) can be passed to boxplot
+in order to group the data by combination of the variables in the x-axis:
+
+.. plot::
+    :context: close-figs
+
+    >>> df = pd.DataFrame(np.random.randn(10, 3),
+    ...                   columns=['Col1', 'Col2', 'Col3'])
+    >>> df['X'] = pd.Series(['A', 'A', 'A', 'A', 'A',
+    ...                      'B', 'B', 'B', 'B', 'B'])
+    >>> df['Y'] = pd.Series(['A', 'B', 'A', 'B', 'A',
+    ...                      'B', 'A', 'B', 'A', 'B'])
+    >>> boxplot = df.boxplot(column=['Col1', 'Col2'], by=['X', 'Y'])
+
+The layout of boxplot can be adjusted giving a tuple to ``layout``:
+
+.. plot::
+    :context: close-figs
+
+    >>> boxplot = df.boxplot(column=['Col1', 'Col2'], by='X',
+    ...                      layout=(2, 1))
+
+Additional formatting can be done to the boxplot, like suppressing the grid
+(``grid=False``), rotating the labels in the x-axis (i.e. ``rot=45``)
+or changing the fontsize (i.e. ``fontsize=15``):
+
+.. plot::
+    :context: close-figs
+
+    >>> boxplot = df.boxplot(grid=False, rot=45, fontsize=15)  # doctest: +SKIP
+
+The parameter ``return_type`` can be used to select the type of element
+returned by `boxplot`.  When ``return_type='axes'`` is selected,
+the matplotlib axes on which the boxplot is drawn are returned:
+
+    >>> boxplot = df.boxplot(column=['Col1', 'Col2'], return_type='axes')
+    >>> type(boxplot)
+    
+
+When grouping with ``by``, a Series mapping columns to ``return_type``
+is returned:
+
+    >>> boxplot = df.boxplot(column=['Col1', 'Col2'], by='X',
+    ...                      return_type='axes')
+    >>> type(boxplot)
+    
+
+If ``return_type`` is `None`, a NumPy array of axes with the same shape
+as ``layout`` is returned:
+
+    >>> boxplot = df.boxplot(column=['Col1', 'Col2'], by='X',
+    ...                      return_type=None)
+    >>> type(boxplot)
+    
+"""
+
+_backend_doc = """\
+backend : str, default None
+    Backend to use instead of the backend specified in the option
+    ``plotting.backend``. For instance, 'matplotlib'. Alternatively, to
+    specify the ``plotting.backend`` for the whole session, set
+    ``pd.options.plotting.backend``.
+"""
+
+
+_bar_or_line_doc = """
+        Parameters
+        ----------
+        x : label or position, optional
+            Allows plotting of one column versus another. If not specified,
+            the index of the DataFrame is used.
+        y : label or position, optional
+            Allows plotting of one column versus another. If not specified,
+            all numerical columns are used.
+        color : str, array-like, or dict, optional
+            The color for each of the DataFrame's columns. Possible values are:
+
+            - A single color string referred to by name, RGB or RGBA code,
+                for instance 'red' or '#a98d19'.
+
+            - A sequence of color strings referred to by name, RGB or RGBA
+                code, which will be used for each column recursively. For
+                instance ['green','yellow'] each column's %(kind)s will be filled in
+                green or yellow, alternatively. If there is only a single column to
+                be plotted, then only the first color from the color list will be
+                used.
+
+            - A dict of the form {column name : color}, so that each column will be
+                colored accordingly. For example, if your columns are called `a` and
+                `b`, then passing {'a': 'green', 'b': 'red'} will color %(kind)ss for
+                column `a` in green and %(kind)ss for column `b` in red.
+
+        **kwargs
+            Additional keyword arguments are documented in
+            :meth:`DataFrame.plot`.
+
+        Returns
+        -------
+        matplotlib.axes.Axes or np.ndarray of them
+            An ndarray is returned with one :class:`matplotlib.axes.Axes`
+            per column when ``subplots=True``.
+"""
+
+
+@Substitution(data="data : DataFrame\n    The data to visualize.\n", backend="")
+@Appender(_boxplot_doc)
+def boxplot(
+    data: DataFrame,
+    column: str | list[str] | None = None,
+    by: str | list[str] | None = None,
+    ax: Axes | None = None,
+    fontsize: float | str | None = None,
+    rot: int = 0,
+    grid: bool = True,
+    figsize: tuple[float, float] | None = None,
+    layout: tuple[int, int] | None = None,
+    return_type: str | None = None,
+    **kwargs,
+):
+    plot_backend = _get_plot_backend("matplotlib")
+    return plot_backend.boxplot(
+        data,
+        column=column,
+        by=by,
+        ax=ax,
+        fontsize=fontsize,
+        rot=rot,
+        grid=grid,
+        figsize=figsize,
+        layout=layout,
+        return_type=return_type,
+        **kwargs,
+    )
+
+
+@Substitution(data="", backend=_backend_doc)
+@Appender(_boxplot_doc)
+def boxplot_frame(
+    self: DataFrame,
+    column=None,
+    by=None,
+    ax=None,
+    fontsize: int | None = None,
+    rot: int = 0,
+    grid: bool = True,
+    figsize: tuple[float, float] | None = None,
+    layout=None,
+    return_type=None,
+    backend=None,
+    **kwargs,
+):
+    plot_backend = _get_plot_backend(backend)
+    return plot_backend.boxplot_frame(
+        self,
+        column=column,
+        by=by,
+        ax=ax,
+        fontsize=fontsize,
+        rot=rot,
+        grid=grid,
+        figsize=figsize,
+        layout=layout,
+        return_type=return_type,
+        **kwargs,
+    )
+
+
+def boxplot_frame_groupby(
+    grouped: DataFrameGroupBy,
+    subplots: bool = True,
+    column=None,
+    fontsize: int | None = None,
+    rot: int = 0,
+    grid: bool = True,
+    ax=None,
+    figsize: tuple[float, float] | None = None,
+    layout=None,
+    sharex: bool = False,
+    sharey: bool = True,
+    backend=None,
+    **kwargs,
+):
+    """
+    Make box plots from DataFrameGroupBy data.
+
+    Parameters
+    ----------
+    grouped : Grouped DataFrame
+    subplots : bool
+        * ``False`` - no subplots will be used
+        * ``True`` - create a subplot for each group.
+
+    column : column name or list of names, or vector
+        Can be any valid input to groupby.
+    fontsize : float or str
+    rot : label rotation angle
+    grid : Setting this to True will show the grid
+    ax : Matplotlib axis object, default None
+    figsize : A tuple (width, height) in inches
+    layout : tuple (optional)
+        The layout of the plot: (rows, columns).
+    sharex : bool, default False
+        Whether x-axes will be shared among subplots.
+    sharey : bool, default True
+        Whether y-axes will be shared among subplots.
+    backend : str, default None
+        Backend to use instead of the backend specified in the option
+        ``plotting.backend``. For instance, 'matplotlib'. Alternatively, to
+        specify the ``plotting.backend`` for the whole session, set
+        ``pd.options.plotting.backend``.
+    **kwargs
+        All other plotting keyword arguments to be passed to
+        matplotlib's boxplot function.
+
+    Returns
+    -------
+    dict of key/value = group key/DataFrame.boxplot return value
+    or DataFrame.boxplot return value in case subplots=figures=False
+
+    Examples
+    --------
+    You can create boxplots for grouped data and show them as separate subplots:
+
+    .. plot::
+        :context: close-figs
+
+        >>> import itertools
+        >>> tuples = [t for t in itertools.product(range(1000), range(4))]
+        >>> index = pd.MultiIndex.from_tuples(tuples, names=['lvl0', 'lvl1'])
+        >>> data = np.random.randn(len(index), 4)
+        >>> df = pd.DataFrame(data, columns=list('ABCD'), index=index)
+        >>> grouped = df.groupby(level='lvl1')
+        >>> grouped.boxplot(rot=45, fontsize=12, figsize=(8, 10))  # doctest: +SKIP
+
+    The ``subplots=False`` option shows the boxplots in a single figure.
+
+    .. plot::
+        :context: close-figs
+
+        >>> grouped.boxplot(subplots=False, rot=45, fontsize=12)  # doctest: +SKIP
+    """
+    plot_backend = _get_plot_backend(backend)
+    return plot_backend.boxplot_frame_groupby(
+        grouped,
+        subplots=subplots,
+        column=column,
+        fontsize=fontsize,
+        rot=rot,
+        grid=grid,
+        ax=ax,
+        figsize=figsize,
+        layout=layout,
+        sharex=sharex,
+        sharey=sharey,
+        **kwargs,
+    )
+
+
+class PlotAccessor(PandasObject):
+    """
+    Make plots of Series or DataFrame.
+
+    Uses the backend specified by the
+    option ``plotting.backend``. By default, matplotlib is used.
+
+    Parameters
+    ----------
+    data : Series or DataFrame
+        The object for which the method is called.
+    x : label or position, default None
+        Only used if data is a DataFrame.
+    y : label, position or list of label, positions, default None
+        Allows plotting of one column versus another. Only used if data is a
+        DataFrame.
+    kind : str
+        The kind of plot to produce:
+
+        - 'line' : line plot (default)
+        - 'bar' : vertical bar plot
+        - 'barh' : horizontal bar plot
+        - 'hist' : histogram
+        - 'box' : boxplot
+        - 'kde' : Kernel Density Estimation plot
+        - 'density' : same as 'kde'
+        - 'area' : area plot
+        - 'pie' : pie plot
+        - 'scatter' : scatter plot (DataFrame only)
+        - 'hexbin' : hexbin plot (DataFrame only)
+    ax : matplotlib axes object, default None
+        An axes of the current figure.
+    subplots : bool or sequence of iterables, default False
+        Whether to group columns into subplots:
+
+        - ``False`` : No subplots will be used
+        - ``True`` : Make separate subplots for each column.
+        - sequence of iterables of column labels: Create a subplot for each
+          group of columns. For example `[('a', 'c'), ('b', 'd')]` will
+          create 2 subplots: one with columns 'a' and 'c', and one
+          with columns 'b' and 'd'. Remaining columns that aren't specified
+          will be plotted in additional subplots (one per column).
+
+          .. versionadded:: 1.5.0
+
+    sharex : bool, default True if ax is None else False
+        In case ``subplots=True``, share x axis and set some x axis labels
+        to invisible; defaults to True if ax is None otherwise False if
+        an ax is passed in; Be aware, that passing in both an ax and
+        ``sharex=True`` will alter all x axis labels for all axis in a figure.
+    sharey : bool, default False
+        In case ``subplots=True``, share y axis and set some y axis labels to invisible.
+    layout : tuple, optional
+        (rows, columns) for the layout of subplots.
+    figsize : a tuple (width, height) in inches
+        Size of a figure object.
+    use_index : bool, default True
+        Use index as ticks for x axis.
+    title : str or list
+        Title to use for the plot. If a string is passed, print the string
+        at the top of the figure. If a list is passed and `subplots` is
+        True, print each item in the list above the corresponding subplot.
+    grid : bool, default None (matlab style default)
+        Axis grid lines.
+    legend : bool or {'reverse'}
+        Place legend on axis subplots.
+    style : list or dict
+        The matplotlib line style per column.
+    logx : bool or 'sym', default False
+        Use log scaling or symlog scaling on x axis.
+
+    logy : bool or 'sym' default False
+        Use log scaling or symlog scaling on y axis.
+
+    loglog : bool or 'sym', default False
+        Use log scaling or symlog scaling on both x and y axes.
+
+    xticks : sequence
+        Values to use for the xticks.
+    yticks : sequence
+        Values to use for the yticks.
+    xlim : 2-tuple/list
+        Set the x limits of the current axes.
+    ylim : 2-tuple/list
+        Set the y limits of the current axes.
+    xlabel : label, optional
+        Name to use for the xlabel on x-axis. Default uses index name as xlabel, or the
+        x-column name for planar plots.
+
+        .. versionchanged:: 2.0.0
+
+            Now applicable to histograms.
+
+    ylabel : label, optional
+        Name to use for the ylabel on y-axis. Default will show no ylabel, or the
+        y-column name for planar plots.
+
+        .. versionchanged:: 2.0.0
+
+            Now applicable to histograms.
+
+    rot : float, default None
+        Rotation for ticks (xticks for vertical, yticks for horizontal
+        plots).
+    fontsize : float, default None
+        Font size for xticks and yticks.
+    colormap : str or matplotlib colormap object, default None
+        Colormap to select colors from. If string, load colormap with that
+        name from matplotlib.
+    colorbar : bool, optional
+        If True, plot colorbar (only relevant for 'scatter' and 'hexbin'
+        plots).
+    position : float
+        Specify relative alignments for bar plot layout.
+        From 0 (left/bottom-end) to 1 (right/top-end). Default is 0.5
+        (center).
+    table : bool, Series or DataFrame, default False
+        If True, draw a table using the data in the DataFrame and the data
+        will be transposed to meet matplotlib's default layout.
+        If a Series or DataFrame is passed, use passed data to draw a
+        table.
+    yerr : DataFrame, Series, array-like, dict and str
+        See :ref:`Plotting with Error Bars ` for
+        detail.
+    xerr : DataFrame, Series, array-like, dict and str
+        Equivalent to yerr.
+    stacked : bool, default False in line and bar plots, and True in area plot
+        If True, create stacked plot.
+    secondary_y : bool or sequence, default False
+        Whether to plot on the secondary y-axis if a list/tuple, which
+        columns to plot on secondary y-axis.
+    mark_right : bool, default True
+        When using a secondary_y axis, automatically mark the column
+        labels with "(right)" in the legend.
+    include_bool : bool, default is False
+        If True, boolean values can be plotted.
+    backend : str, default None
+        Backend to use instead of the backend specified in the option
+        ``plotting.backend``. For instance, 'matplotlib'. Alternatively, to
+        specify the ``plotting.backend`` for the whole session, set
+        ``pd.options.plotting.backend``.
+    **kwargs
+        Options to pass to matplotlib plotting method.
+
+    Returns
+    -------
+    :class:`matplotlib.axes.Axes` or numpy.ndarray of them
+        If the backend is not the default matplotlib one, the return value
+        will be the object returned by the backend.
+
+    Notes
+    -----
+    - See matplotlib documentation online for more on this subject
+    - If `kind` = 'bar' or 'barh', you can specify relative alignments
+      for bar plot layout by `position` keyword.
+      From 0 (left/bottom-end) to 1 (right/top-end). Default is 0.5
+      (center)
+
+    Examples
+    --------
+    For Series:
+
+    .. plot::
+        :context: close-figs
+
+        >>> ser = pd.Series([1, 2, 3, 3])
+        >>> plot = ser.plot(kind='hist', title="My plot")
+
+    For DataFrame:
+
+    .. plot::
+        :context: close-figs
+
+        >>> df = pd.DataFrame({'length': [1.5, 0.5, 1.2, 0.9, 3],
+        ...                   'width': [0.7, 0.2, 0.15, 0.2, 1.1]},
+        ...                   index=['pig', 'rabbit', 'duck', 'chicken', 'horse'])
+        >>> plot = df.plot(title="DataFrame Plot")
+
+    For SeriesGroupBy:
+
+    .. plot::
+        :context: close-figs
+
+        >>> lst = [-1, -2, -3, 1, 2, 3]
+        >>> ser = pd.Series([1, 2, 2, 4, 6, 6], index=lst)
+        >>> plot = ser.groupby(lambda x: x > 0).plot(title="SeriesGroupBy Plot")
+
+    For DataFrameGroupBy:
+
+    .. plot::
+        :context: close-figs
+
+        >>> df = pd.DataFrame({"col1" : [1, 2, 3, 4],
+        ...                   "col2" : ["A", "B", "A", "B"]})
+        >>> plot = df.groupby("col2").plot(kind="bar", title="DataFrameGroupBy Plot")
+    """
+
+    _common_kinds = ("line", "bar", "barh", "kde", "density", "area", "hist", "box")
+    _series_kinds = ("pie",)
+    _dataframe_kinds = ("scatter", "hexbin")
+    _kind_aliases = {"density": "kde"}
+    _all_kinds = _common_kinds + _series_kinds + _dataframe_kinds
+
+    def __init__(self, data: Series | DataFrame) -> None:
+        self._parent = data
+
+    @staticmethod
+    def _get_call_args(backend_name: str, data: Series | DataFrame, args, kwargs):
+        """
+        This function makes calls to this accessor `__call__` method compatible
+        with the previous `SeriesPlotMethods.__call__` and
+        `DataFramePlotMethods.__call__`. Those had slightly different
+        signatures, since `DataFramePlotMethods` accepted `x` and `y`
+        parameters.
+        """
+        if isinstance(data, ABCSeries):
+            arg_def = [
+                ("kind", "line"),
+                ("ax", None),
+                ("figsize", None),
+                ("use_index", True),
+                ("title", None),
+                ("grid", None),
+                ("legend", False),
+                ("style", None),
+                ("logx", False),
+                ("logy", False),
+                ("loglog", False),
+                ("xticks", None),
+                ("yticks", None),
+                ("xlim", None),
+                ("ylim", None),
+                ("rot", None),
+                ("fontsize", None),
+                ("colormap", None),
+                ("table", False),
+                ("yerr", None),
+                ("xerr", None),
+                ("label", None),
+                ("secondary_y", False),
+                ("xlabel", None),
+                ("ylabel", None),
+            ]
+        elif isinstance(data, ABCDataFrame):
+            arg_def = [
+                ("x", None),
+                ("y", None),
+                ("kind", "line"),
+                ("ax", None),
+                ("subplots", False),
+                ("sharex", None),
+                ("sharey", False),
+                ("layout", None),
+                ("figsize", None),
+                ("use_index", True),
+                ("title", None),
+                ("grid", None),
+                ("legend", True),
+                ("style", None),
+                ("logx", False),
+                ("logy", False),
+                ("loglog", False),
+                ("xticks", None),
+                ("yticks", None),
+                ("xlim", None),
+                ("ylim", None),
+                ("rot", None),
+                ("fontsize", None),
+                ("colormap", None),
+                ("table", False),
+                ("yerr", None),
+                ("xerr", None),
+                ("secondary_y", False),
+                ("xlabel", None),
+                ("ylabel", None),
+            ]
+        else:
+            raise TypeError(
+                f"Called plot accessor for type {type(data).__name__}, "
+                "expected Series or DataFrame"
+            )
+
+        if args and isinstance(data, ABCSeries):
+            positional_args = str(args)[1:-1]
+            keyword_args = ", ".join(
+                [f"{name}={repr(value)}" for (name, _), value in zip(arg_def, args)]
+            )
+            msg = (
+                "`Series.plot()` should not be called with positional "
+                "arguments, only keyword arguments. The order of "
+                "positional arguments will change in the future. "
+                f"Use `Series.plot({keyword_args})` instead of "
+                f"`Series.plot({positional_args})`."
+            )
+            raise TypeError(msg)
+
+        pos_args = {name: value for (name, _), value in zip(arg_def, args)}
+        if backend_name == "pandas.plotting._matplotlib":
+            kwargs = dict(arg_def, **pos_args, **kwargs)
+        else:
+            kwargs = dict(pos_args, **kwargs)
+
+        x = kwargs.pop("x", None)
+        y = kwargs.pop("y", None)
+        kind = kwargs.pop("kind", "line")
+        return x, y, kind, kwargs
+
+    def __call__(self, *args, **kwargs):
+        plot_backend = _get_plot_backend(kwargs.pop("backend", None))
+
+        x, y, kind, kwargs = self._get_call_args(
+            plot_backend.__name__, self._parent, args, kwargs
+        )
+
+        kind = self._kind_aliases.get(kind, kind)
+
+        # when using another backend, get out of the way
+        if plot_backend.__name__ != "pandas.plotting._matplotlib":
+            return plot_backend.plot(self._parent, x=x, y=y, kind=kind, **kwargs)
+
+        if kind not in self._all_kinds:
+            raise ValueError(
+                f"{kind} is not a valid plot kind "
+                f"Valid plot kinds: {self._all_kinds}"
+            )
+
+        # The original data structured can be transformed before passed to the
+        # backend. For example, for DataFrame is common to set the index as the
+        # `x` parameter, and return a Series with the parameter `y` as values.
+        data = self._parent.copy()
+
+        if isinstance(data, ABCSeries):
+            kwargs["reuse_plot"] = True
+
+        if kind in self._dataframe_kinds:
+            if isinstance(data, ABCDataFrame):
+                return plot_backend.plot(data, x=x, y=y, kind=kind, **kwargs)
+            else:
+                raise ValueError(f"plot kind {kind} can only be used for data frames")
+        elif kind in self._series_kinds:
+            if isinstance(data, ABCDataFrame):
+                if y is None and kwargs.get("subplots") is False:
+                    raise ValueError(
+                        f"{kind} requires either y column or 'subplots=True'"
+                    )
+                if y is not None:
+                    if is_integer(y) and not data.columns._holds_integer():
+                        y = data.columns[y]
+                    # converted to series actually. copy to not modify
+                    data = data[y].copy()
+                    data.index.name = y
+        elif isinstance(data, ABCDataFrame):
+            data_cols = data.columns
+            if x is not None:
+                if is_integer(x) and not data.columns._holds_integer():
+                    x = data_cols[x]
+                elif not isinstance(data[x], ABCSeries):
+                    raise ValueError("x must be a label or position")
+                data = data.set_index(x)
+            if y is not None:
+                # check if we have y as int or list of ints
+                int_ylist = is_list_like(y) and all(is_integer(c) for c in y)
+                int_y_arg = is_integer(y) or int_ylist
+                if int_y_arg and not data.columns._holds_integer():
+                    y = data_cols[y]
+
+                label_kw = kwargs["label"] if "label" in kwargs else False
+                for kw in ["xerr", "yerr"]:
+                    if kw in kwargs and (
+                        isinstance(kwargs[kw], str) or is_integer(kwargs[kw])
+                    ):
+                        try:
+                            kwargs[kw] = data[kwargs[kw]]
+                        except (IndexError, KeyError, TypeError):
+                            pass
+
+                # don't overwrite
+                data = data[y].copy()
+
+                if isinstance(data, ABCSeries):
+                    label_name = label_kw or y
+                    data.name = label_name
+                else:
+                    match = is_list_like(label_kw) and len(label_kw) == len(y)
+                    if label_kw and not match:
+                        raise ValueError(
+                            "label should be list-like and same length as y"
+                        )
+                    label_name = label_kw or data.columns
+                    data.columns = label_name
+
+        return plot_backend.plot(data, kind=kind, **kwargs)
+
+    __call__.__doc__ = __doc__
+
+    @Appender(
+        """
+        See Also
+        --------
+        matplotlib.pyplot.plot : Plot y versus x as lines and/or markers.
+
+        Examples
+        --------
+
+        .. plot::
+            :context: close-figs
+
+            >>> s = pd.Series([1, 3, 2])
+            >>> s.plot.line()  # doctest: +SKIP
+
+        .. plot::
+            :context: close-figs
+
+            The following example shows the populations for some animals
+            over the years.
+
+            >>> df = pd.DataFrame({
+            ...    'pig': [20, 18, 489, 675, 1776],
+            ...    'horse': [4, 25, 281, 600, 1900]
+            ...    }, index=[1990, 1997, 2003, 2009, 2014])
+            >>> lines = df.plot.line()
+
+        .. plot::
+           :context: close-figs
+
+           An example with subplots, so an array of axes is returned.
+
+           >>> axes = df.plot.line(subplots=True)
+           >>> type(axes)
+           
+
+        .. plot::
+           :context: close-figs
+
+           Let's repeat the same example, but specifying colors for
+           each column (in this case, for each animal).
+
+           >>> axes = df.plot.line(
+           ...     subplots=True, color={"pig": "pink", "horse": "#742802"}
+           ... )
+
+        .. plot::
+            :context: close-figs
+
+            The following example shows the relationship between both
+            populations.
+
+            >>> lines = df.plot.line(x='pig', y='horse')
+        """
+    )
+    @Substitution(kind="line")
+    @Appender(_bar_or_line_doc)
+    def line(
+        self, x: Hashable | None = None, y: Hashable | None = None, **kwargs
+    ) -> PlotAccessor:
+        """
+        Plot Series or DataFrame as lines.
+
+        This function is useful to plot lines using DataFrame's values
+        as coordinates.
+        """
+        return self(kind="line", x=x, y=y, **kwargs)
+
+    @Appender(
+        """
+        See Also
+        --------
+        DataFrame.plot.barh : Horizontal bar plot.
+        DataFrame.plot : Make plots of a DataFrame.
+        matplotlib.pyplot.bar : Make a bar plot with matplotlib.
+
+        Examples
+        --------
+        Basic plot.
+
+        .. plot::
+            :context: close-figs
+
+            >>> df = pd.DataFrame({'lab':['A', 'B', 'C'], 'val':[10, 30, 20]})
+            >>> ax = df.plot.bar(x='lab', y='val', rot=0)
+
+        Plot a whole dataframe to a bar plot. Each column is assigned a
+        distinct color, and each row is nested in a group along the
+        horizontal axis.
+
+        .. plot::
+            :context: close-figs
+
+            >>> speed = [0.1, 17.5, 40, 48, 52, 69, 88]
+            >>> lifespan = [2, 8, 70, 1.5, 25, 12, 28]
+            >>> index = ['snail', 'pig', 'elephant',
+            ...          'rabbit', 'giraffe', 'coyote', 'horse']
+            >>> df = pd.DataFrame({'speed': speed,
+            ...                    'lifespan': lifespan}, index=index)
+            >>> ax = df.plot.bar(rot=0)
+
+        Plot stacked bar charts for the DataFrame
+
+        .. plot::
+            :context: close-figs
+
+            >>> ax = df.plot.bar(stacked=True)
+
+        Instead of nesting, the figure can be split by column with
+        ``subplots=True``. In this case, a :class:`numpy.ndarray` of
+        :class:`matplotlib.axes.Axes` are returned.
+
+        .. plot::
+            :context: close-figs
+
+            >>> axes = df.plot.bar(rot=0, subplots=True)
+            >>> axes[1].legend(loc=2)  # doctest: +SKIP
+
+        If you don't like the default colours, you can specify how you'd
+        like each column to be colored.
+
+        .. plot::
+            :context: close-figs
+
+            >>> axes = df.plot.bar(
+            ...     rot=0, subplots=True, color={"speed": "red", "lifespan": "green"}
+            ... )
+            >>> axes[1].legend(loc=2)  # doctest: +SKIP
+
+        Plot a single column.
+
+        .. plot::
+            :context: close-figs
+
+            >>> ax = df.plot.bar(y='speed', rot=0)
+
+        Plot only selected categories for the DataFrame.
+
+        .. plot::
+            :context: close-figs
+
+            >>> ax = df.plot.bar(x='lifespan', rot=0)
+    """
+    )
+    @Substitution(kind="bar")
+    @Appender(_bar_or_line_doc)
+    def bar(  # pylint: disable=disallowed-name
+        self, x: Hashable | None = None, y: Hashable | None = None, **kwargs
+    ) -> PlotAccessor:
+        """
+        Vertical bar plot.
+
+        A bar plot is a plot that presents categorical data with
+        rectangular bars with lengths proportional to the values that they
+        represent. A bar plot shows comparisons among discrete categories. One
+        axis of the plot shows the specific categories being compared, and the
+        other axis represents a measured value.
+        """
+        return self(kind="bar", x=x, y=y, **kwargs)
+
+    @Appender(
+        """
+        See Also
+        --------
+        DataFrame.plot.bar: Vertical bar plot.
+        DataFrame.plot : Make plots of DataFrame using matplotlib.
+        matplotlib.axes.Axes.bar : Plot a vertical bar plot using matplotlib.
+
+        Examples
+        --------
+        Basic example
+
+        .. plot::
+            :context: close-figs
+
+            >>> df = pd.DataFrame({'lab': ['A', 'B', 'C'], 'val': [10, 30, 20]})
+            >>> ax = df.plot.barh(x='lab', y='val')
+
+        Plot a whole DataFrame to a horizontal bar plot
+
+        .. plot::
+            :context: close-figs
+
+            >>> speed = [0.1, 17.5, 40, 48, 52, 69, 88]
+            >>> lifespan = [2, 8, 70, 1.5, 25, 12, 28]
+            >>> index = ['snail', 'pig', 'elephant',
+            ...          'rabbit', 'giraffe', 'coyote', 'horse']
+            >>> df = pd.DataFrame({'speed': speed,
+            ...                    'lifespan': lifespan}, index=index)
+            >>> ax = df.plot.barh()
+
+        Plot stacked barh charts for the DataFrame
+
+        .. plot::
+            :context: close-figs
+
+            >>> ax = df.plot.barh(stacked=True)
+
+        We can specify colors for each column
+
+        .. plot::
+            :context: close-figs
+
+            >>> ax = df.plot.barh(color={"speed": "red", "lifespan": "green"})
+
+        Plot a column of the DataFrame to a horizontal bar plot
+
+        .. plot::
+            :context: close-figs
+
+            >>> speed = [0.1, 17.5, 40, 48, 52, 69, 88]
+            >>> lifespan = [2, 8, 70, 1.5, 25, 12, 28]
+            >>> index = ['snail', 'pig', 'elephant',
+            ...          'rabbit', 'giraffe', 'coyote', 'horse']
+            >>> df = pd.DataFrame({'speed': speed,
+            ...                    'lifespan': lifespan}, index=index)
+            >>> ax = df.plot.barh(y='speed')
+
+        Plot DataFrame versus the desired column
+
+        .. plot::
+            :context: close-figs
+
+            >>> speed = [0.1, 17.5, 40, 48, 52, 69, 88]
+            >>> lifespan = [2, 8, 70, 1.5, 25, 12, 28]
+            >>> index = ['snail', 'pig', 'elephant',
+            ...          'rabbit', 'giraffe', 'coyote', 'horse']
+            >>> df = pd.DataFrame({'speed': speed,
+            ...                    'lifespan': lifespan}, index=index)
+            >>> ax = df.plot.barh(x='lifespan')
+    """
+    )
+    @Substitution(kind="bar")
+    @Appender(_bar_or_line_doc)
+    def barh(
+        self, x: Hashable | None = None, y: Hashable | None = None, **kwargs
+    ) -> PlotAccessor:
+        """
+        Make a horizontal bar plot.
+
+        A horizontal bar plot is a plot that presents quantitative data with
+        rectangular bars with lengths proportional to the values that they
+        represent. A bar plot shows comparisons among discrete categories. One
+        axis of the plot shows the specific categories being compared, and the
+        other axis represents a measured value.
+        """
+        return self(kind="barh", x=x, y=y, **kwargs)
+
+    def box(self, by: IndexLabel | None = None, **kwargs) -> PlotAccessor:
+        r"""
+        Make a box plot of the DataFrame columns.
+
+        A box plot is a method for graphically depicting groups of numerical
+        data through their quartiles.
+        The box extends from the Q1 to Q3 quartile values of the data,
+        with a line at the median (Q2). The whiskers extend from the edges
+        of box to show the range of the data. The position of the whiskers
+        is set by default to 1.5*IQR (IQR = Q3 - Q1) from the edges of the
+        box. Outlier points are those past the end of the whiskers.
+
+        For further details see Wikipedia's
+        entry for `boxplot `__.
+
+        A consideration when using this chart is that the box and the whiskers
+        can overlap, which is very common when plotting small sets of data.
+
+        Parameters
+        ----------
+        by : str or sequence
+            Column in the DataFrame to group by.
+
+            .. versionchanged:: 1.4.0
+
+               Previously, `by` is silently ignore and makes no groupings
+
+        **kwargs
+            Additional keywords are documented in
+            :meth:`DataFrame.plot`.
+
+        Returns
+        -------
+        :class:`matplotlib.axes.Axes` or numpy.ndarray of them
+
+        See Also
+        --------
+        DataFrame.boxplot: Another method to draw a box plot.
+        Series.plot.box: Draw a box plot from a Series object.
+        matplotlib.pyplot.boxplot: Draw a box plot in matplotlib.
+
+        Examples
+        --------
+        Draw a box plot from a DataFrame with four columns of randomly
+        generated data.
+
+        .. plot::
+            :context: close-figs
+
+            >>> data = np.random.randn(25, 4)
+            >>> df = pd.DataFrame(data, columns=list('ABCD'))
+            >>> ax = df.plot.box()
+
+        You can also generate groupings if you specify the `by` parameter (which
+        can take a column name, or a list or tuple of column names):
+
+        .. versionchanged:: 1.4.0
+
+        .. plot::
+            :context: close-figs
+
+            >>> age_list = [8, 10, 12, 14, 72, 74, 76, 78, 20, 25, 30, 35, 60, 85]
+            >>> df = pd.DataFrame({"gender": list("MMMMMMMMFFFFFF"), "age": age_list})
+            >>> ax = df.plot.box(column="age", by="gender", figsize=(10, 8))
+        """
+        return self(kind="box", by=by, **kwargs)
+
+    def hist(
+        self, by: IndexLabel | None = None, bins: int = 10, **kwargs
+    ) -> PlotAccessor:
+        """
+        Draw one histogram of the DataFrame's columns.
+
+        A histogram is a representation of the distribution of data.
+        This function groups the values of all given Series in the DataFrame
+        into bins and draws all bins in one :class:`matplotlib.axes.Axes`.
+        This is useful when the DataFrame's Series are in a similar scale.
+
+        Parameters
+        ----------
+        by : str or sequence, optional
+            Column in the DataFrame to group by.
+
+            .. versionchanged:: 1.4.0
+
+               Previously, `by` is silently ignore and makes no groupings
+
+        bins : int, default 10
+            Number of histogram bins to be used.
+        **kwargs
+            Additional keyword arguments are documented in
+            :meth:`DataFrame.plot`.
+
+        Returns
+        -------
+        class:`matplotlib.AxesSubplot`
+            Return a histogram plot.
+
+        See Also
+        --------
+        DataFrame.hist : Draw histograms per DataFrame's Series.
+        Series.hist : Draw a histogram with Series' data.
+
+        Examples
+        --------
+        When we roll a die 6000 times, we expect to get each value around 1000
+        times. But when we roll two dice and sum the result, the distribution
+        is going to be quite different. A histogram illustrates those
+        distributions.
+
+        .. plot::
+            :context: close-figs
+
+            >>> df = pd.DataFrame(np.random.randint(1, 7, 6000), columns=['one'])
+            >>> df['two'] = df['one'] + np.random.randint(1, 7, 6000)
+            >>> ax = df.plot.hist(bins=12, alpha=0.5)
+
+        A grouped histogram can be generated by providing the parameter `by` (which
+        can be a column name, or a list of column names):
+
+        .. plot::
+            :context: close-figs
+
+            >>> age_list = [8, 10, 12, 14, 72, 74, 76, 78, 20, 25, 30, 35, 60, 85]
+            >>> df = pd.DataFrame({"gender": list("MMMMMMMMFFFFFF"), "age": age_list})
+            >>> ax = df.plot.hist(column=["age"], by="gender", figsize=(10, 8))
+        """
+        return self(kind="hist", by=by, bins=bins, **kwargs)
+
+    def kde(
+        self,
+        bw_method: Literal["scott", "silverman"] | float | Callable | None = None,
+        ind: np.ndarray | int | None = None,
+        **kwargs,
+    ) -> PlotAccessor:
+        """
+        Generate Kernel Density Estimate plot using Gaussian kernels.
+
+        In statistics, `kernel density estimation`_ (KDE) is a non-parametric
+        way to estimate the probability density function (PDF) of a random
+        variable. This function uses Gaussian kernels and includes automatic
+        bandwidth determination.
+
+        .. _kernel density estimation:
+            https://en.wikipedia.org/wiki/Kernel_density_estimation
+
+        Parameters
+        ----------
+        bw_method : str, scalar or callable, optional
+            The method used to calculate the estimator bandwidth. This can be
+            'scott', 'silverman', a scalar constant or a callable.
+            If None (default), 'scott' is used.
+            See :class:`scipy.stats.gaussian_kde` for more information.
+        ind : NumPy array or int, optional
+            Evaluation points for the estimated PDF. If None (default),
+            1000 equally spaced points are used. If `ind` is a NumPy array, the
+            KDE is evaluated at the points passed. If `ind` is an integer,
+            `ind` number of equally spaced points are used.
+        **kwargs
+            Additional keyword arguments are documented in
+            :meth:`DataFrame.plot`.
+
+        Returns
+        -------
+        matplotlib.axes.Axes or numpy.ndarray of them
+
+        See Also
+        --------
+        scipy.stats.gaussian_kde : Representation of a kernel-density
+            estimate using Gaussian kernels. This is the function used
+            internally to estimate the PDF.
+
+        Examples
+        --------
+        Given a Series of points randomly sampled from an unknown
+        distribution, estimate its PDF using KDE with automatic
+        bandwidth determination and plot the results, evaluating them at
+        1000 equally spaced points (default):
+
+        .. plot::
+            :context: close-figs
+
+            >>> s = pd.Series([1, 2, 2.5, 3, 3.5, 4, 5])
+            >>> ax = s.plot.kde()
+
+        A scalar bandwidth can be specified. Using a small bandwidth value can
+        lead to over-fitting, while using a large bandwidth value may result
+        in under-fitting:
+
+        .. plot::
+            :context: close-figs
+
+            >>> ax = s.plot.kde(bw_method=0.3)
+
+        .. plot::
+            :context: close-figs
+
+            >>> ax = s.plot.kde(bw_method=3)
+
+        Finally, the `ind` parameter determines the evaluation points for the
+        plot of the estimated PDF:
+
+        .. plot::
+            :context: close-figs
+
+            >>> ax = s.plot.kde(ind=[1, 2, 3, 4, 5])
+
+        For DataFrame, it works in the same way:
+
+        .. plot::
+            :context: close-figs
+
+            >>> df = pd.DataFrame({
+            ...     'x': [1, 2, 2.5, 3, 3.5, 4, 5],
+            ...     'y': [4, 4, 4.5, 5, 5.5, 6, 6],
+            ... })
+            >>> ax = df.plot.kde()
+
+        A scalar bandwidth can be specified. Using a small bandwidth value can
+        lead to over-fitting, while using a large bandwidth value may result
+        in under-fitting:
+
+        .. plot::
+            :context: close-figs
+
+            >>> ax = df.plot.kde(bw_method=0.3)
+
+        .. plot::
+            :context: close-figs
+
+            >>> ax = df.plot.kde(bw_method=3)
+
+        Finally, the `ind` parameter determines the evaluation points for the
+        plot of the estimated PDF:
+
+        .. plot::
+            :context: close-figs
+
+            >>> ax = df.plot.kde(ind=[1, 2, 3, 4, 5, 6])
+        """
+        return self(kind="kde", bw_method=bw_method, ind=ind, **kwargs)
+
+    density = kde
+
+    def area(
+        self,
+        x: Hashable | None = None,
+        y: Hashable | None = None,
+        stacked: bool = True,
+        **kwargs,
+    ) -> PlotAccessor:
+        """
+        Draw a stacked area plot.
+
+        An area plot displays quantitative data visually.
+        This function wraps the matplotlib area function.
+
+        Parameters
+        ----------
+        x : label or position, optional
+            Coordinates for the X axis. By default uses the index.
+        y : label or position, optional
+            Column to plot. By default uses all columns.
+        stacked : bool, default True
+            Area plots are stacked by default. Set to False to create a
+            unstacked plot.
+        **kwargs
+            Additional keyword arguments are documented in
+            :meth:`DataFrame.plot`.
+
+        Returns
+        -------
+        matplotlib.axes.Axes or numpy.ndarray
+            Area plot, or array of area plots if subplots is True.
+
+        See Also
+        --------
+        DataFrame.plot : Make plots of DataFrame using matplotlib / pylab.
+
+        Examples
+        --------
+        Draw an area plot based on basic business metrics:
+
+        .. plot::
+            :context: close-figs
+
+            >>> df = pd.DataFrame({
+            ...     'sales': [3, 2, 3, 9, 10, 6],
+            ...     'signups': [5, 5, 6, 12, 14, 13],
+            ...     'visits': [20, 42, 28, 62, 81, 50],
+            ... }, index=pd.date_range(start='2018/01/01', end='2018/07/01',
+            ...                        freq='ME'))
+            >>> ax = df.plot.area()
+
+        Area plots are stacked by default. To produce an unstacked plot,
+        pass ``stacked=False``:
+
+        .. plot::
+            :context: close-figs
+
+            >>> ax = df.plot.area(stacked=False)
+
+        Draw an area plot for a single column:
+
+        .. plot::
+            :context: close-figs
+
+            >>> ax = df.plot.area(y='sales')
+
+        Draw with a different `x`:
+
+        .. plot::
+            :context: close-figs
+
+            >>> df = pd.DataFrame({
+            ...     'sales': [3, 2, 3],
+            ...     'visits': [20, 42, 28],
+            ...     'day': [1, 2, 3],
+            ... })
+            >>> ax = df.plot.area(x='day')
+        """
+        return self(kind="area", x=x, y=y, stacked=stacked, **kwargs)
+
+    def pie(self, **kwargs) -> PlotAccessor:
+        """
+        Generate a pie plot.
+
+        A pie plot is a proportional representation of the numerical data in a
+        column. This function wraps :meth:`matplotlib.pyplot.pie` for the
+        specified column. If no column reference is passed and
+        ``subplots=True`` a pie plot is drawn for each numerical column
+        independently.
+
+        Parameters
+        ----------
+        y : int or label, optional
+            Label or position of the column to plot.
+            If not provided, ``subplots=True`` argument must be passed.
+        **kwargs
+            Keyword arguments to pass on to :meth:`DataFrame.plot`.
+
+        Returns
+        -------
+        matplotlib.axes.Axes or np.ndarray of them
+            A NumPy array is returned when `subplots` is True.
+
+        See Also
+        --------
+        Series.plot.pie : Generate a pie plot for a Series.
+        DataFrame.plot : Make plots of a DataFrame.
+
+        Examples
+        --------
+        In the example below we have a DataFrame with the information about
+        planet's mass and radius. We pass the 'mass' column to the
+        pie function to get a pie plot.
+
+        .. plot::
+            :context: close-figs
+
+            >>> df = pd.DataFrame({'mass': [0.330, 4.87 , 5.97],
+            ...                    'radius': [2439.7, 6051.8, 6378.1]},
+            ...                   index=['Mercury', 'Venus', 'Earth'])
+            >>> plot = df.plot.pie(y='mass', figsize=(5, 5))
+
+        .. plot::
+            :context: close-figs
+
+            >>> plot = df.plot.pie(subplots=True, figsize=(11, 6))
+        """
+        if (
+            isinstance(self._parent, ABCDataFrame)
+            and kwargs.get("y", None) is None
+            and not kwargs.get("subplots", False)
+        ):
+            raise ValueError("pie requires either y column or 'subplots=True'")
+        return self(kind="pie", **kwargs)
+
+    def scatter(
+        self,
+        x: Hashable,
+        y: Hashable,
+        s: Hashable | Sequence[Hashable] | None = None,
+        c: Hashable | Sequence[Hashable] | None = None,
+        **kwargs,
+    ) -> PlotAccessor:
+        """
+        Create a scatter plot with varying marker point size and color.
+
+        The coordinates of each point are defined by two dataframe columns and
+        filled circles are used to represent each point. This kind of plot is
+        useful to see complex correlations between two variables. Points could
+        be for instance natural 2D coordinates like longitude and latitude in
+        a map or, in general, any pair of metrics that can be plotted against
+        each other.
+
+        Parameters
+        ----------
+        x : int or str
+            The column name or column position to be used as horizontal
+            coordinates for each point.
+        y : int or str
+            The column name or column position to be used as vertical
+            coordinates for each point.
+        s : str, scalar or array-like, optional
+            The size of each point. Possible values are:
+
+            - A string with the name of the column to be used for marker's size.
+
+            - A single scalar so all points have the same size.
+
+            - A sequence of scalars, which will be used for each point's size
+              recursively. For instance, when passing [2,14] all points size
+              will be either 2 or 14, alternatively.
+
+        c : str, int or array-like, optional
+            The color of each point. Possible values are:
+
+            - A single color string referred to by name, RGB or RGBA code,
+              for instance 'red' or '#a98d19'.
+
+            - A sequence of color strings referred to by name, RGB or RGBA
+              code, which will be used for each point's color recursively. For
+              instance ['green','yellow'] all points will be filled in green or
+              yellow, alternatively.
+
+            - A column name or position whose values will be used to color the
+              marker points according to a colormap.
+
+        **kwargs
+            Keyword arguments to pass on to :meth:`DataFrame.plot`.
+
+        Returns
+        -------
+        :class:`matplotlib.axes.Axes` or numpy.ndarray of them
+
+        See Also
+        --------
+        matplotlib.pyplot.scatter : Scatter plot using multiple input data
+            formats.
+
+        Examples
+        --------
+        Let's see how to draw a scatter plot using coordinates from the values
+        in a DataFrame's columns.
+
+        .. plot::
+            :context: close-figs
+
+            >>> df = pd.DataFrame([[5.1, 3.5, 0], [4.9, 3.0, 0], [7.0, 3.2, 1],
+            ...                    [6.4, 3.2, 1], [5.9, 3.0, 2]],
+            ...                   columns=['length', 'width', 'species'])
+            >>> ax1 = df.plot.scatter(x='length',
+            ...                       y='width',
+            ...                       c='DarkBlue')
+
+        And now with the color determined by a column as well.
+
+        .. plot::
+            :context: close-figs
+
+            >>> ax2 = df.plot.scatter(x='length',
+            ...                       y='width',
+            ...                       c='species',
+            ...                       colormap='viridis')
+        """
+        return self(kind="scatter", x=x, y=y, s=s, c=c, **kwargs)
+
+    def hexbin(
+        self,
+        x: Hashable,
+        y: Hashable,
+        C: Hashable | None = None,
+        reduce_C_function: Callable | None = None,
+        gridsize: int | tuple[int, int] | None = None,
+        **kwargs,
+    ) -> PlotAccessor:
+        """
+        Generate a hexagonal binning plot.
+
+        Generate a hexagonal binning plot of `x` versus `y`. If `C` is `None`
+        (the default), this is a histogram of the number of occurrences
+        of the observations at ``(x[i], y[i])``.
+
+        If `C` is specified, specifies values at given coordinates
+        ``(x[i], y[i])``. These values are accumulated for each hexagonal
+        bin and then reduced according to `reduce_C_function`,
+        having as default the NumPy's mean function (:meth:`numpy.mean`).
+        (If `C` is specified, it must also be a 1-D sequence
+        of the same length as `x` and `y`, or a column label.)
+
+        Parameters
+        ----------
+        x : int or str
+            The column label or position for x points.
+        y : int or str
+            The column label or position for y points.
+        C : int or str, optional
+            The column label or position for the value of `(x, y)` point.
+        reduce_C_function : callable, default `np.mean`
+            Function of one argument that reduces all the values in a bin to
+            a single number (e.g. `np.mean`, `np.max`, `np.sum`, `np.std`).
+        gridsize : int or tuple of (int, int), default 100
+            The number of hexagons in the x-direction.
+            The corresponding number of hexagons in the y-direction is
+            chosen in a way that the hexagons are approximately regular.
+            Alternatively, gridsize can be a tuple with two elements
+            specifying the number of hexagons in the x-direction and the
+            y-direction.
+        **kwargs
+            Additional keyword arguments are documented in
+            :meth:`DataFrame.plot`.
+
+        Returns
+        -------
+        matplotlib.AxesSubplot
+            The matplotlib ``Axes`` on which the hexbin is plotted.
+
+        See Also
+        --------
+        DataFrame.plot : Make plots of a DataFrame.
+        matplotlib.pyplot.hexbin : Hexagonal binning plot using matplotlib,
+            the matplotlib function that is used under the hood.
+
+        Examples
+        --------
+        The following examples are generated with random data from
+        a normal distribution.
+
+        .. plot::
+            :context: close-figs
+
+            >>> n = 10000
+            >>> df = pd.DataFrame({'x': np.random.randn(n),
+            ...                    'y': np.random.randn(n)})
+            >>> ax = df.plot.hexbin(x='x', y='y', gridsize=20)
+
+        The next example uses `C` and `np.sum` as `reduce_C_function`.
+        Note that `'observations'` values ranges from 1 to 5 but the result
+        plot shows values up to more than 25. This is because of the
+        `reduce_C_function`.
+
+        .. plot::
+            :context: close-figs
+
+            >>> n = 500
+            >>> df = pd.DataFrame({
+            ...     'coord_x': np.random.uniform(-3, 3, size=n),
+            ...     'coord_y': np.random.uniform(30, 50, size=n),
+            ...     'observations': np.random.randint(1,5, size=n)
+            ...     })
+            >>> ax = df.plot.hexbin(x='coord_x',
+            ...                     y='coord_y',
+            ...                     C='observations',
+            ...                     reduce_C_function=np.sum,
+            ...                     gridsize=10,
+            ...                     cmap="viridis")
+        """
+        if reduce_C_function is not None:
+            kwargs["reduce_C_function"] = reduce_C_function
+        if gridsize is not None:
+            kwargs["gridsize"] = gridsize
+
+        return self(kind="hexbin", x=x, y=y, C=C, **kwargs)
+
+
+_backends: dict[str, types.ModuleType] = {}
+
+
+def _load_backend(backend: str) -> types.ModuleType:
+    """
+    Load a pandas plotting backend.
+
+    Parameters
+    ----------
+    backend : str
+        The identifier for the backend. Either an entrypoint item registered
+        with importlib.metadata, "matplotlib", or a module name.
+
+    Returns
+    -------
+    types.ModuleType
+        The imported backend.
+    """
+    from importlib.metadata import entry_points
+
+    if backend == "matplotlib":
+        # Because matplotlib is an optional dependency and first-party backend,
+        # we need to attempt an import here to raise an ImportError if needed.
+        try:
+            module = importlib.import_module("pandas.plotting._matplotlib")
+        except ImportError:
+            raise ImportError(
+                "matplotlib is required for plotting when the "
+                'default backend "matplotlib" is selected.'
+            ) from None
+        return module
+
+    found_backend = False
+
+    eps = entry_points()
+    key = "pandas_plotting_backends"
+    # entry_points lost dict API ~ PY 3.10
+    # https://github.com/python/importlib_metadata/issues/298
+    if hasattr(eps, "select"):
+        entry = eps.select(group=key)
+    else:
+        # Argument 2 to "get" of "dict" has incompatible type "Tuple[]";
+        # expected "EntryPoints"  [arg-type]
+        entry = eps.get(key, ())  # type: ignore[arg-type]
+    for entry_point in entry:
+        found_backend = entry_point.name == backend
+        if found_backend:
+            module = entry_point.load()
+            break
+
+    if not found_backend:
+        # Fall back to unregistered, module name approach.
+        try:
+            module = importlib.import_module(backend)
+            found_backend = True
+        except ImportError:
+            # We re-raise later on.
+            pass
+
+    if found_backend:
+        if hasattr(module, "plot"):
+            # Validate that the interface is implemented when the option is set,
+            # rather than at plot time.
+            return module
+
+    raise ValueError(
+        f"Could not find plotting backend '{backend}'. Ensure that you've "
+        f"installed the package providing the '{backend}' entrypoint, or that "
+        "the package has a top-level `.plot` method."
+    )
+
+
+def _get_plot_backend(backend: str | None = None):
+    """
+    Return the plotting backend to use (e.g. `pandas.plotting._matplotlib`).
+
+    The plotting system of pandas uses matplotlib by default, but the idea here
+    is that it can also work with other third-party backends. This function
+    returns the module which provides a top-level `.plot` method that will
+    actually do the plotting. The backend is specified from a string, which
+    either comes from the keyword argument `backend`, or, if not specified, from
+    the option `pandas.options.plotting.backend`. All the rest of the code in
+    this file uses the backend specified there for the plotting.
+
+    The backend is imported lazily, as matplotlib is a soft dependency, and
+    pandas can be used without it being installed.
+
+    Notes
+    -----
+    Modifies `_backends` with imported backend as a side effect.
+    """
+    backend_str: str = backend or get_option("plotting.backend")
+
+    if backend_str in _backends:
+        return _backends[backend_str]
+
+    module = _load_backend(backend_str)
+    _backends[backend_str] = module
+    return module
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/plotting/_matplotlib/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/plotting/_matplotlib/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..75c61da03795af0d4f60cd4d4a8b8e0dd45e3d5e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/plotting/_matplotlib/__init__.py
@@ -0,0 +1,93 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+from pandas.plotting._matplotlib.boxplot import (
+    BoxPlot,
+    boxplot,
+    boxplot_frame,
+    boxplot_frame_groupby,
+)
+from pandas.plotting._matplotlib.converter import (
+    deregister,
+    register,
+)
+from pandas.plotting._matplotlib.core import (
+    AreaPlot,
+    BarhPlot,
+    BarPlot,
+    HexBinPlot,
+    LinePlot,
+    PiePlot,
+    ScatterPlot,
+)
+from pandas.plotting._matplotlib.hist import (
+    HistPlot,
+    KdePlot,
+    hist_frame,
+    hist_series,
+)
+from pandas.plotting._matplotlib.misc import (
+    andrews_curves,
+    autocorrelation_plot,
+    bootstrap_plot,
+    lag_plot,
+    parallel_coordinates,
+    radviz,
+    scatter_matrix,
+)
+from pandas.plotting._matplotlib.tools import table
+
+if TYPE_CHECKING:
+    from pandas.plotting._matplotlib.core import MPLPlot
+
+PLOT_CLASSES: dict[str, type[MPLPlot]] = {
+    "line": LinePlot,
+    "bar": BarPlot,
+    "barh": BarhPlot,
+    "box": BoxPlot,
+    "hist": HistPlot,
+    "kde": KdePlot,
+    "area": AreaPlot,
+    "pie": PiePlot,
+    "scatter": ScatterPlot,
+    "hexbin": HexBinPlot,
+}
+
+
+def plot(data, kind, **kwargs):
+    # Importing pyplot at the top of the file (before the converters are
+    # registered) causes problems in matplotlib 2 (converters seem to not
+    # work)
+    import matplotlib.pyplot as plt
+
+    if kwargs.pop("reuse_plot", False):
+        ax = kwargs.get("ax")
+        if ax is None and len(plt.get_fignums()) > 0:
+            with plt.rc_context():
+                ax = plt.gca()
+            kwargs["ax"] = getattr(ax, "left_ax", ax)
+    plot_obj = PLOT_CLASSES[kind](data, **kwargs)
+    plot_obj.generate()
+    plot_obj.draw()
+    return plot_obj.result
+
+
+__all__ = [
+    "plot",
+    "hist_series",
+    "hist_frame",
+    "boxplot",
+    "boxplot_frame",
+    "boxplot_frame_groupby",
+    "table",
+    "andrews_curves",
+    "autocorrelation_plot",
+    "bootstrap_plot",
+    "lag_plot",
+    "parallel_coordinates",
+    "radviz",
+    "scatter_matrix",
+    "register",
+    "deregister",
+]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/plotting/_matplotlib/boxplot.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/plotting/_matplotlib/boxplot.py
new file mode 100644
index 0000000000000000000000000000000000000000..80f0349b205e6072abeb63c4727a1efa060b2d36
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/plotting/_matplotlib/boxplot.py
@@ -0,0 +1,575 @@
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    Literal,
+    NamedTuple,
+)
+import warnings
+
+import matplotlib as mpl
+from matplotlib.artist import setp
+import numpy as np
+
+from pandas._libs import lib
+from pandas.util._decorators import cache_readonly
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.common import is_dict_like
+from pandas.core.dtypes.generic import ABCSeries
+from pandas.core.dtypes.missing import remove_na_arraylike
+
+import pandas as pd
+import pandas.core.common as com
+from pandas.util.version import Version
+
+from pandas.io.formats.printing import pprint_thing
+from pandas.plotting._matplotlib.core import (
+    LinePlot,
+    MPLPlot,
+)
+from pandas.plotting._matplotlib.groupby import create_iter_data_given_by
+from pandas.plotting._matplotlib.style import get_standard_colors
+from pandas.plotting._matplotlib.tools import (
+    create_subplots,
+    flatten_axes,
+    maybe_adjust_figure,
+)
+
+if TYPE_CHECKING:
+    from collections.abc import Collection
+
+    from matplotlib.axes import Axes
+    from matplotlib.figure import Figure
+    from matplotlib.lines import Line2D
+
+    from pandas._typing import MatplotlibColor
+
+
+def _set_ticklabels(ax: Axes, labels: list[str], is_vertical: bool, **kwargs) -> None:
+    """Set the tick labels of a given axis.
+
+    Due to https://github.com/matplotlib/matplotlib/pull/17266, we need to handle the
+    case of repeated ticks (due to `FixedLocator`) and thus we duplicate the number of
+    labels.
+    """
+    ticks = ax.get_xticks() if is_vertical else ax.get_yticks()
+    if len(ticks) != len(labels):
+        i, remainder = divmod(len(ticks), len(labels))
+        if Version(mpl.__version__) < Version("3.10"):
+            assert remainder == 0, remainder
+        labels *= i
+    if is_vertical:
+        ax.set_xticklabels(labels, **kwargs)
+    else:
+        ax.set_yticklabels(labels, **kwargs)
+
+
+class BoxPlot(LinePlot):
+    @property
+    def _kind(self) -> Literal["box"]:
+        return "box"
+
+    _layout_type = "horizontal"
+
+    _valid_return_types = (None, "axes", "dict", "both")
+
+    class BP(NamedTuple):
+        # namedtuple to hold results
+        ax: Axes
+        lines: dict[str, list[Line2D]]
+
+    def __init__(self, data, return_type: str = "axes", **kwargs) -> None:
+        if return_type not in self._valid_return_types:
+            raise ValueError("return_type must be {None, 'axes', 'dict', 'both'}")
+
+        self.return_type = return_type
+        # Do not call LinePlot.__init__ which may fill nan
+        MPLPlot.__init__(self, data, **kwargs)  # pylint: disable=non-parent-init-called
+
+        if self.subplots:
+            # Disable label ax sharing. Otherwise, all subplots shows last
+            # column label
+            if self.orientation == "vertical":
+                self.sharex = False
+            else:
+                self.sharey = False
+
+    # error: Signature of "_plot" incompatible with supertype "MPLPlot"
+    @classmethod
+    def _plot(  # type: ignore[override]
+        cls, ax: Axes, y: np.ndarray, column_num=None, return_type: str = "axes", **kwds
+    ):
+        ys: np.ndarray | list[np.ndarray]
+        if y.ndim == 2:
+            ys = [remove_na_arraylike(v) for v in y]
+            # Boxplot fails with empty arrays, so need to add a NaN
+            #   if any cols are empty
+            # GH 8181
+            ys = [v if v.size > 0 else np.array([np.nan]) for v in ys]
+        else:
+            ys = remove_na_arraylike(y)
+        bp = ax.boxplot(ys, **kwds)
+
+        if return_type == "dict":
+            return bp, bp
+        elif return_type == "both":
+            return cls.BP(ax=ax, lines=bp), bp
+        else:
+            return ax, bp
+
+    def _validate_color_args(self, color, colormap):
+        if color is lib.no_default:
+            return None
+
+        if colormap is not None:
+            warnings.warn(
+                "'color' and 'colormap' cannot be used "
+                "simultaneously. Using 'color'",
+                stacklevel=find_stack_level(),
+            )
+
+        if isinstance(color, dict):
+            valid_keys = ["boxes", "whiskers", "medians", "caps"]
+            for key in color:
+                if key not in valid_keys:
+                    raise ValueError(
+                        f"color dict contains invalid key '{key}'. "
+                        f"The key must be either {valid_keys}"
+                    )
+        return color
+
+    @cache_readonly
+    def _color_attrs(self):
+        # get standard colors for default
+        # use 2 colors by default, for box/whisker and median
+        # flier colors isn't needed here
+        # because it can be specified by ``sym`` kw
+        return get_standard_colors(num_colors=3, colormap=self.colormap, color=None)
+
+    @cache_readonly
+    def _boxes_c(self):
+        return self._color_attrs[0]
+
+    @cache_readonly
+    def _whiskers_c(self):
+        return self._color_attrs[0]
+
+    @cache_readonly
+    def _medians_c(self):
+        return self._color_attrs[2]
+
+    @cache_readonly
+    def _caps_c(self):
+        return self._color_attrs[0]
+
+    def _get_colors(
+        self,
+        num_colors=None,
+        color_kwds: dict[str, MatplotlibColor]
+        | MatplotlibColor
+        | Collection[MatplotlibColor]
+        | None = "color",
+    ) -> None:
+        pass
+
+    def maybe_color_bp(self, bp) -> None:
+        if isinstance(self.color, dict):
+            boxes = self.color.get("boxes", self._boxes_c)
+            whiskers = self.color.get("whiskers", self._whiskers_c)
+            medians = self.color.get("medians", self._medians_c)
+            caps = self.color.get("caps", self._caps_c)
+        else:
+            # Other types are forwarded to matplotlib
+            # If None, use default colors
+            boxes = self.color or self._boxes_c
+            whiskers = self.color or self._whiskers_c
+            medians = self.color or self._medians_c
+            caps = self.color or self._caps_c
+
+        color_tup = (boxes, whiskers, medians, caps)
+        maybe_color_bp(bp, color_tup=color_tup, **self.kwds)
+
+    def _make_plot(self, fig: Figure) -> None:
+        if self.subplots:
+            self._return_obj = pd.Series(dtype=object)
+
+            # Re-create iterated data if `by` is assigned by users
+            data = (
+                create_iter_data_given_by(self.data, self._kind)
+                if self.by is not None
+                else self.data
+            )
+
+            # error: Argument "data" to "_iter_data" of "MPLPlot" has
+            # incompatible type "object"; expected "DataFrame |
+            # dict[Hashable, Series | DataFrame]"
+            for i, (label, y) in enumerate(self._iter_data(data=data)):  # type: ignore[arg-type]
+                ax = self._get_ax(i)
+                kwds = self.kwds.copy()
+
+                # When by is applied, show title for subplots to know which group it is
+                # just like df.boxplot, and need to apply T on y to provide right input
+                if self.by is not None:
+                    y = y.T
+                    ax.set_title(pprint_thing(label))
+
+                    # When `by` is assigned, the ticklabels will become unique grouped
+                    # values, instead of label which is used as subtitle in this case.
+                    # error: "Index" has no attribute "levels"; maybe "nlevels"?
+                    levels = self.data.columns.levels  # type: ignore[attr-defined]
+                    ticklabels = [pprint_thing(col) for col in levels[0]]
+                else:
+                    ticklabels = [pprint_thing(label)]
+
+                ret, bp = self._plot(
+                    ax, y, column_num=i, return_type=self.return_type, **kwds
+                )
+                self.maybe_color_bp(bp)
+                self._return_obj[label] = ret
+                _set_ticklabels(
+                    ax=ax, labels=ticklabels, is_vertical=self.orientation == "vertical"
+                )
+        else:
+            y = self.data.values.T
+            ax = self._get_ax(0)
+            kwds = self.kwds.copy()
+
+            ret, bp = self._plot(
+                ax, y, column_num=0, return_type=self.return_type, **kwds
+            )
+            self.maybe_color_bp(bp)
+            self._return_obj = ret
+
+            labels = [pprint_thing(left) for left in self.data.columns]
+            if not self.use_index:
+                labels = [pprint_thing(key) for key in range(len(labels))]
+            _set_ticklabels(
+                ax=ax, labels=labels, is_vertical=self.orientation == "vertical"
+            )
+
+    def _make_legend(self) -> None:
+        pass
+
+    def _post_plot_logic(self, ax: Axes, data) -> None:
+        # GH 45465: make sure that the boxplot doesn't ignore xlabel/ylabel
+        if self.xlabel:
+            ax.set_xlabel(pprint_thing(self.xlabel))
+        if self.ylabel:
+            ax.set_ylabel(pprint_thing(self.ylabel))
+
+    @property
+    def orientation(self) -> Literal["horizontal", "vertical"]:
+        if self.kwds.get("vert", True):
+            return "vertical"
+        else:
+            return "horizontal"
+
+    @property
+    def result(self):
+        if self.return_type is None:
+            return super().result
+        else:
+            return self._return_obj
+
+
+def maybe_color_bp(bp, color_tup, **kwds) -> None:
+    # GH#30346, when users specifying those arguments explicitly, our defaults
+    # for these four kwargs should be overridden; if not, use Pandas settings
+    if not kwds.get("boxprops"):
+        setp(bp["boxes"], color=color_tup[0], alpha=1)
+    if not kwds.get("whiskerprops"):
+        setp(bp["whiskers"], color=color_tup[1], alpha=1)
+    if not kwds.get("medianprops"):
+        setp(bp["medians"], color=color_tup[2], alpha=1)
+    if not kwds.get("capprops"):
+        setp(bp["caps"], color=color_tup[3], alpha=1)
+
+
+def _grouped_plot_by_column(
+    plotf,
+    data,
+    columns=None,
+    by=None,
+    numeric_only: bool = True,
+    grid: bool = False,
+    figsize: tuple[float, float] | None = None,
+    ax=None,
+    layout=None,
+    return_type=None,
+    **kwargs,
+):
+    grouped = data.groupby(by, observed=False)
+    if columns is None:
+        if not isinstance(by, (list, tuple)):
+            by = [by]
+        columns = data._get_numeric_data().columns.difference(by)
+    naxes = len(columns)
+    fig, axes = create_subplots(
+        naxes=naxes,
+        sharex=kwargs.pop("sharex", True),
+        sharey=kwargs.pop("sharey", True),
+        figsize=figsize,
+        ax=ax,
+        layout=layout,
+    )
+
+    _axes = flatten_axes(axes)
+
+    # GH 45465: move the "by" label based on "vert"
+    xlabel, ylabel = kwargs.pop("xlabel", None), kwargs.pop("ylabel", None)
+    if kwargs.get("vert", True):
+        xlabel = xlabel or by
+    else:
+        ylabel = ylabel or by
+
+    ax_values = []
+
+    for i, col in enumerate(columns):
+        ax = _axes[i]
+        gp_col = grouped[col]
+        keys, values = zip(*gp_col)
+        re_plotf = plotf(keys, values, ax, xlabel=xlabel, ylabel=ylabel, **kwargs)
+        ax.set_title(col)
+        ax_values.append(re_plotf)
+        ax.grid(grid)
+
+    result = pd.Series(ax_values, index=columns, copy=False)
+
+    # Return axes in multiplot case, maybe revisit later # 985
+    if return_type is None:
+        result = axes
+
+    byline = by[0] if len(by) == 1 else by
+    fig.suptitle(f"Boxplot grouped by {byline}")
+    maybe_adjust_figure(fig, bottom=0.15, top=0.9, left=0.1, right=0.9, wspace=0.2)
+
+    return result
+
+
+def boxplot(
+    data,
+    column=None,
+    by=None,
+    ax=None,
+    fontsize: int | None = None,
+    rot: int = 0,
+    grid: bool = True,
+    figsize: tuple[float, float] | None = None,
+    layout=None,
+    return_type=None,
+    **kwds,
+):
+    import matplotlib.pyplot as plt
+
+    # validate return_type:
+    if return_type not in BoxPlot._valid_return_types:
+        raise ValueError("return_type must be {'axes', 'dict', 'both'}")
+
+    if isinstance(data, ABCSeries):
+        data = data.to_frame("x")
+        column = "x"
+
+    def _get_colors():
+        #  num_colors=3 is required as method maybe_color_bp takes the colors
+        #  in positions 0 and 2.
+        #  if colors not provided, use same defaults as DataFrame.plot.box
+        result = get_standard_colors(num_colors=3)
+        result = np.take(result, [0, 0, 2])
+        result = np.append(result, "k")
+
+        colors = kwds.pop("color", None)
+        if colors:
+            if is_dict_like(colors):
+                # replace colors in result array with user-specified colors
+                # taken from the colors dict parameter
+                # "boxes" value placed in position 0, "whiskers" in 1, etc.
+                valid_keys = ["boxes", "whiskers", "medians", "caps"]
+                key_to_index = dict(zip(valid_keys, range(4)))
+                for key, value in colors.items():
+                    if key in valid_keys:
+                        result[key_to_index[key]] = value
+                    else:
+                        raise ValueError(
+                            f"color dict contains invalid key '{key}'. "
+                            f"The key must be either {valid_keys}"
+                        )
+            else:
+                result.fill(colors)
+
+        return result
+
+    def plot_group(keys, values, ax: Axes, **kwds):
+        # GH 45465: xlabel/ylabel need to be popped out before plotting happens
+        xlabel, ylabel = kwds.pop("xlabel", None), kwds.pop("ylabel", None)
+        if xlabel:
+            ax.set_xlabel(pprint_thing(xlabel))
+        if ylabel:
+            ax.set_ylabel(pprint_thing(ylabel))
+
+        keys = [pprint_thing(x) for x in keys]
+        values = [np.asarray(remove_na_arraylike(v), dtype=object) for v in values]
+        bp = ax.boxplot(values, **kwds)
+        if fontsize is not None:
+            ax.tick_params(axis="both", labelsize=fontsize)
+
+        # GH 45465: x/y are flipped when "vert" changes
+        _set_ticklabels(
+            ax=ax, labels=keys, is_vertical=kwds.get("vert", True), rotation=rot
+        )
+        maybe_color_bp(bp, color_tup=colors, **kwds)
+
+        # Return axes in multiplot case, maybe revisit later # 985
+        if return_type == "dict":
+            return bp
+        elif return_type == "both":
+            return BoxPlot.BP(ax=ax, lines=bp)
+        else:
+            return ax
+
+    colors = _get_colors()
+    if column is None:
+        columns = None
+    elif isinstance(column, (list, tuple)):
+        columns = column
+    else:
+        columns = [column]
+
+    if by is not None:
+        # Prefer array return type for 2-D plots to match the subplot layout
+        # https://github.com/pandas-dev/pandas/pull/12216#issuecomment-241175580
+        result = _grouped_plot_by_column(
+            plot_group,
+            data,
+            columns=columns,
+            by=by,
+            grid=grid,
+            figsize=figsize,
+            ax=ax,
+            layout=layout,
+            return_type=return_type,
+            **kwds,
+        )
+    else:
+        if return_type is None:
+            return_type = "axes"
+        if layout is not None:
+            raise ValueError("The 'layout' keyword is not supported when 'by' is None")
+
+        if ax is None:
+            rc = {"figure.figsize": figsize} if figsize is not None else {}
+            with plt.rc_context(rc):
+                ax = plt.gca()
+        data = data._get_numeric_data()
+        naxes = len(data.columns)
+        if naxes == 0:
+            raise ValueError(
+                "boxplot method requires numerical columns, nothing to plot."
+            )
+        if columns is None:
+            columns = data.columns
+        else:
+            data = data[columns]
+
+        result = plot_group(columns, data.values.T, ax, **kwds)
+        ax.grid(grid)
+
+    return result
+
+
+def boxplot_frame(
+    self,
+    column=None,
+    by=None,
+    ax=None,
+    fontsize: int | None = None,
+    rot: int = 0,
+    grid: bool = True,
+    figsize: tuple[float, float] | None = None,
+    layout=None,
+    return_type=None,
+    **kwds,
+):
+    import matplotlib.pyplot as plt
+
+    ax = boxplot(
+        self,
+        column=column,
+        by=by,
+        ax=ax,
+        fontsize=fontsize,
+        grid=grid,
+        rot=rot,
+        figsize=figsize,
+        layout=layout,
+        return_type=return_type,
+        **kwds,
+    )
+    plt.draw_if_interactive()
+    return ax
+
+
+def boxplot_frame_groupby(
+    grouped,
+    subplots: bool = True,
+    column=None,
+    fontsize: int | None = None,
+    rot: int = 0,
+    grid: bool = True,
+    ax=None,
+    figsize: tuple[float, float] | None = None,
+    layout=None,
+    sharex: bool = False,
+    sharey: bool = True,
+    **kwds,
+):
+    if subplots is True:
+        naxes = len(grouped)
+        fig, axes = create_subplots(
+            naxes=naxes,
+            squeeze=False,
+            ax=ax,
+            sharex=sharex,
+            sharey=sharey,
+            figsize=figsize,
+            layout=layout,
+        )
+        axes = flatten_axes(axes)
+
+        ret = pd.Series(dtype=object)
+
+        for (key, group), ax in zip(grouped, axes):
+            d = group.boxplot(
+                ax=ax, column=column, fontsize=fontsize, rot=rot, grid=grid, **kwds
+            )
+            ax.set_title(pprint_thing(key))
+            ret.loc[key] = d
+        maybe_adjust_figure(fig, bottom=0.15, top=0.9, left=0.1, right=0.9, wspace=0.2)
+    else:
+        keys, frames = zip(*grouped)
+        if grouped.axis == 0:
+            df = pd.concat(frames, keys=keys, axis=1)
+        elif len(frames) > 1:
+            df = frames[0].join(frames[1::])
+        else:
+            df = frames[0]
+
+        # GH 16748, DataFrameGroupby fails when subplots=False and `column` argument
+        # is assigned, and in this case, since `df` here becomes MI after groupby,
+        # so we need to couple the keys (grouped values) and column (original df
+        # column) together to search for subset to plot
+        if column is not None:
+            column = com.convert_to_list_like(column)
+            multi_key = pd.MultiIndex.from_product([keys, column])
+            column = list(multi_key.values)
+        ret = df.boxplot(
+            column=column,
+            fontsize=fontsize,
+            rot=rot,
+            grid=grid,
+            ax=ax,
+            figsize=figsize,
+            layout=layout,
+            **kwds,
+        )
+    return ret
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/plotting/_matplotlib/converter.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/plotting/_matplotlib/converter.py
new file mode 100644
index 0000000000000000000000000000000000000000..9acb93ce69a9ca25962139891e6bb1e5e163add8
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/plotting/_matplotlib/converter.py
@@ -0,0 +1,1139 @@
+from __future__ import annotations
+
+import contextlib
+import datetime as pydt
+from datetime import (
+    datetime,
+    timedelta,
+    tzinfo,
+)
+import functools
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    cast,
+)
+import warnings
+
+import matplotlib.dates as mdates
+from matplotlib.ticker import (
+    AutoLocator,
+    Formatter,
+    Locator,
+)
+from matplotlib.transforms import nonsingular
+import matplotlib.units as munits
+import numpy as np
+
+from pandas._libs import lib
+from pandas._libs.tslibs import (
+    Timestamp,
+    to_offset,
+)
+from pandas._libs.tslibs.dtypes import (
+    FreqGroup,
+    periods_per_day,
+)
+from pandas._typing import (
+    F,
+    npt,
+)
+
+from pandas.core.dtypes.common import (
+    is_float,
+    is_float_dtype,
+    is_integer,
+    is_integer_dtype,
+    is_nested_list_like,
+)
+
+from pandas import (
+    Index,
+    Series,
+    get_option,
+)
+import pandas.core.common as com
+from pandas.core.indexes.datetimes import date_range
+from pandas.core.indexes.period import (
+    Period,
+    PeriodIndex,
+    period_range,
+)
+import pandas.core.tools.datetimes as tools
+
+if TYPE_CHECKING:
+    from collections.abc import Generator
+
+    from matplotlib.axis import Axis
+
+    from pandas._libs.tslibs.offsets import BaseOffset
+
+
+_mpl_units = {}  # Cache for units overwritten by us
+
+
+def get_pairs():
+    pairs = [
+        (Timestamp, DatetimeConverter),
+        (Period, PeriodConverter),
+        (pydt.datetime, DatetimeConverter),
+        (pydt.date, DatetimeConverter),
+        (pydt.time, TimeConverter),
+        (np.datetime64, DatetimeConverter),
+    ]
+    return pairs
+
+
+def register_pandas_matplotlib_converters(func: F) -> F:
+    """
+    Decorator applying pandas_converters.
+    """
+
+    @functools.wraps(func)
+    def wrapper(*args, **kwargs):
+        with pandas_converters():
+            return func(*args, **kwargs)
+
+    return cast(F, wrapper)
+
+
+@contextlib.contextmanager
+def pandas_converters() -> Generator[None, None, None]:
+    """
+    Context manager registering pandas' converters for a plot.
+
+    See Also
+    --------
+    register_pandas_matplotlib_converters : Decorator that applies this.
+    """
+    value = get_option("plotting.matplotlib.register_converters")
+
+    if value:
+        # register for True or "auto"
+        register()
+    try:
+        yield
+    finally:
+        if value == "auto":
+            # only deregister for "auto"
+            deregister()
+
+
+def register() -> None:
+    pairs = get_pairs()
+    for type_, cls in pairs:
+        # Cache previous converter if present
+        if type_ in munits.registry and not isinstance(munits.registry[type_], cls):
+            previous = munits.registry[type_]
+            _mpl_units[type_] = previous
+        # Replace with pandas converter
+        munits.registry[type_] = cls()
+
+
+def deregister() -> None:
+    # Renamed in pandas.plotting.__init__
+    for type_, cls in get_pairs():
+        # We use type to catch our classes directly, no inheritance
+        if type(munits.registry.get(type_)) is cls:
+            munits.registry.pop(type_)
+
+    # restore the old keys
+    for unit, formatter in _mpl_units.items():
+        if type(formatter) not in {DatetimeConverter, PeriodConverter, TimeConverter}:
+            # make it idempotent by excluding ours.
+            munits.registry[unit] = formatter
+
+
+def _to_ordinalf(tm: pydt.time) -> float:
+    tot_sec = tm.hour * 3600 + tm.minute * 60 + tm.second + tm.microsecond / 10**6
+    return tot_sec
+
+
+def time2num(d):
+    if isinstance(d, str):
+        parsed = Timestamp(d)
+        return _to_ordinalf(parsed.time())
+    if isinstance(d, pydt.time):
+        return _to_ordinalf(d)
+    return d
+
+
+class TimeConverter(munits.ConversionInterface):
+    @staticmethod
+    def convert(value, unit, axis):
+        valid_types = (str, pydt.time)
+        if isinstance(value, valid_types) or is_integer(value) or is_float(value):
+            return time2num(value)
+        if isinstance(value, Index):
+            return value.map(time2num)
+        if isinstance(value, (list, tuple, np.ndarray, Index)):
+            return [time2num(x) for x in value]
+        return value
+
+    @staticmethod
+    def axisinfo(unit, axis) -> munits.AxisInfo | None:
+        if unit != "time":
+            return None
+
+        majloc = AutoLocator()
+        majfmt = TimeFormatter(majloc)
+        return munits.AxisInfo(majloc=majloc, majfmt=majfmt, label="time")
+
+    @staticmethod
+    def default_units(x, axis) -> str:
+        return "time"
+
+
+# time formatter
+class TimeFormatter(Formatter):
+    def __init__(self, locs) -> None:
+        self.locs = locs
+
+    def __call__(self, x, pos: int | None = 0) -> str:
+        """
+        Return the time of day as a formatted string.
+
+        Parameters
+        ----------
+        x : float
+            The time of day specified as seconds since 00:00 (midnight),
+            with up to microsecond precision.
+        pos
+            Unused
+
+        Returns
+        -------
+        str
+            A string in HH:MM:SS.mmmuuu format. Microseconds,
+            milliseconds and seconds are only displayed if non-zero.
+        """
+        fmt = "%H:%M:%S.%f"
+        s = int(x)
+        msus = round((x - s) * 10**6)
+        ms = msus // 1000
+        us = msus % 1000
+        m, s = divmod(s, 60)
+        h, m = divmod(m, 60)
+        _, h = divmod(h, 24)
+        if us != 0:
+            return pydt.time(h, m, s, msus).strftime(fmt)
+        elif ms != 0:
+            return pydt.time(h, m, s, msus).strftime(fmt)[:-3]
+        elif s != 0:
+            return pydt.time(h, m, s).strftime("%H:%M:%S")
+
+        return pydt.time(h, m).strftime("%H:%M")
+
+
+# Period Conversion
+
+
+class PeriodConverter(mdates.DateConverter):
+    @staticmethod
+    def convert(values, units, axis):
+        if is_nested_list_like(values):
+            values = [PeriodConverter._convert_1d(v, units, axis) for v in values]
+        else:
+            values = PeriodConverter._convert_1d(values, units, axis)
+        return values
+
+    @staticmethod
+    def _convert_1d(values, units, axis):
+        if not hasattr(axis, "freq"):
+            raise TypeError("Axis must have `freq` set to convert to Periods")
+        valid_types = (str, datetime, Period, pydt.date, pydt.time, np.datetime64)
+        with warnings.catch_warnings():
+            warnings.filterwarnings(
+                "ignore", "Period with BDay freq is deprecated", category=FutureWarning
+            )
+            warnings.filterwarnings(
+                "ignore", r"PeriodDtype\[B\] is deprecated", category=FutureWarning
+            )
+            if (
+                isinstance(values, valid_types)
+                or is_integer(values)
+                or is_float(values)
+            ):
+                return get_datevalue(values, axis.freq)
+            elif isinstance(values, PeriodIndex):
+                return values.asfreq(axis.freq).asi8
+            elif isinstance(values, Index):
+                return values.map(lambda x: get_datevalue(x, axis.freq))
+            elif lib.infer_dtype(values, skipna=False) == "period":
+                # https://github.com/pandas-dev/pandas/issues/24304
+                # convert ndarray[period] -> PeriodIndex
+                return PeriodIndex(values, freq=axis.freq).asi8
+            elif isinstance(values, (list, tuple, np.ndarray, Index)):
+                return [get_datevalue(x, axis.freq) for x in values]
+        return values
+
+
+def get_datevalue(date, freq):
+    if isinstance(date, Period):
+        return date.asfreq(freq).ordinal
+    elif isinstance(date, (str, datetime, pydt.date, pydt.time, np.datetime64)):
+        return Period(date, freq).ordinal
+    elif (
+        is_integer(date)
+        or is_float(date)
+        or (isinstance(date, (np.ndarray, Index)) and (date.size == 1))
+    ):
+        return date
+    elif date is None:
+        return None
+    raise ValueError(f"Unrecognizable date '{date}'")
+
+
+# Datetime Conversion
+class DatetimeConverter(mdates.DateConverter):
+    @staticmethod
+    def convert(values, unit, axis):
+        # values might be a 1-d array, or a list-like of arrays.
+        if is_nested_list_like(values):
+            values = [DatetimeConverter._convert_1d(v, unit, axis) for v in values]
+        else:
+            values = DatetimeConverter._convert_1d(values, unit, axis)
+        return values
+
+    @staticmethod
+    def _convert_1d(values, unit, axis):
+        def try_parse(values):
+            try:
+                return mdates.date2num(tools.to_datetime(values))
+            except Exception:
+                return values
+
+        if isinstance(values, (datetime, pydt.date, np.datetime64, pydt.time)):
+            return mdates.date2num(values)
+        elif is_integer(values) or is_float(values):
+            return values
+        elif isinstance(values, str):
+            return try_parse(values)
+        elif isinstance(values, (list, tuple, np.ndarray, Index, Series)):
+            if isinstance(values, Series):
+                # https://github.com/matplotlib/matplotlib/issues/11391
+                # Series was skipped. Convert to DatetimeIndex to get asi8
+                values = Index(values)
+            if isinstance(values, Index):
+                values = values.values
+            if not isinstance(values, np.ndarray):
+                values = com.asarray_tuplesafe(values)
+
+            if is_integer_dtype(values) or is_float_dtype(values):
+                return values
+
+            try:
+                values = tools.to_datetime(values)
+            except Exception:
+                pass
+
+            values = mdates.date2num(values)
+
+        return values
+
+    @staticmethod
+    def axisinfo(unit: tzinfo | None, axis) -> munits.AxisInfo:
+        """
+        Return the :class:`~matplotlib.units.AxisInfo` for *unit*.
+
+        *unit* is a tzinfo instance or None.
+        The *axis* argument is required but not used.
+        """
+        tz = unit
+
+        majloc = PandasAutoDateLocator(tz=tz)
+        majfmt = PandasAutoDateFormatter(majloc, tz=tz)
+        datemin = pydt.date(2000, 1, 1)
+        datemax = pydt.date(2010, 1, 1)
+
+        return munits.AxisInfo(
+            majloc=majloc, majfmt=majfmt, label="", default_limits=(datemin, datemax)
+        )
+
+
+class PandasAutoDateFormatter(mdates.AutoDateFormatter):
+    def __init__(self, locator, tz=None, defaultfmt: str = "%Y-%m-%d") -> None:
+        mdates.AutoDateFormatter.__init__(self, locator, tz, defaultfmt)
+
+
+class PandasAutoDateLocator(mdates.AutoDateLocator):
+    def get_locator(self, dmin, dmax):
+        """Pick the best locator based on a distance."""
+        tot_sec = (dmax - dmin).total_seconds()
+
+        if abs(tot_sec) < self.minticks:
+            self._freq = -1
+            locator = MilliSecondLocator(self.tz)
+            locator.set_axis(self.axis)
+
+            # error: Item "None" of "Axis | _DummyAxis | _AxisWrapper | None"
+            # has no attribute "get_data_interval"
+            locator.axis.set_view_interval(  # type: ignore[union-attr]
+                *self.axis.get_view_interval()  # type: ignore[union-attr]
+            )
+            locator.axis.set_data_interval(  # type: ignore[union-attr]
+                *self.axis.get_data_interval()  # type: ignore[union-attr]
+            )
+            return locator
+
+        return mdates.AutoDateLocator.get_locator(self, dmin, dmax)
+
+    def _get_unit(self):
+        return MilliSecondLocator.get_unit_generic(self._freq)
+
+
+class MilliSecondLocator(mdates.DateLocator):
+    UNIT = 1.0 / (24 * 3600 * 1000)
+
+    def __init__(self, tz) -> None:
+        mdates.DateLocator.__init__(self, tz)
+        self._interval = 1.0
+
+    def _get_unit(self):
+        return self.get_unit_generic(-1)
+
+    @staticmethod
+    def get_unit_generic(freq):
+        unit = mdates.RRuleLocator.get_unit_generic(freq)
+        if unit < 0:
+            return MilliSecondLocator.UNIT
+        return unit
+
+    def __call__(self):
+        # if no data have been set, this will tank with a ValueError
+        try:
+            dmin, dmax = self.viewlim_to_dt()
+        except ValueError:
+            return []
+
+        # We need to cap at the endpoints of valid datetime
+        nmax, nmin = mdates.date2num((dmax, dmin))
+
+        num = (nmax - nmin) * 86400 * 1000
+        max_millis_ticks = 6
+        for interval in [1, 10, 50, 100, 200, 500]:
+            if num <= interval * (max_millis_ticks - 1):
+                self._interval = interval
+                break
+            # We went through the whole loop without breaking, default to 1
+            self._interval = 1000.0
+
+        estimate = (nmax - nmin) / (self._get_unit() * self._get_interval())
+
+        if estimate > self.MAXTICKS * 2:
+            raise RuntimeError(
+                "MillisecondLocator estimated to generate "
+                f"{estimate:d} ticks from {dmin} to {dmax}: exceeds Locator.MAXTICKS"
+                f"* 2 ({self.MAXTICKS * 2:d}) "
+            )
+
+        interval = self._get_interval()
+        freq = f"{interval}ms"
+        tz = self.tz.tzname(None)
+        st = dmin.replace(tzinfo=None)
+        ed = dmin.replace(tzinfo=None)
+        all_dates = date_range(start=st, end=ed, freq=freq, tz=tz).astype(object)
+
+        try:
+            if len(all_dates) > 0:
+                locs = self.raise_if_exceeds(mdates.date2num(all_dates))
+                return locs
+        except Exception:  # pragma: no cover
+            pass
+
+        lims = mdates.date2num([dmin, dmax])
+        return lims
+
+    def _get_interval(self):
+        return self._interval
+
+    def autoscale(self):
+        """
+        Set the view limits to include the data range.
+        """
+        # We need to cap at the endpoints of valid datetime
+        dmin, dmax = self.datalim_to_dt()
+
+        vmin = mdates.date2num(dmin)
+        vmax = mdates.date2num(dmax)
+
+        return self.nonsingular(vmin, vmax)
+
+
+def _from_ordinal(x, tz: tzinfo | None = None) -> datetime:
+    ix = int(x)
+    dt = datetime.fromordinal(ix)
+    remainder = float(x) - ix
+    hour, remainder = divmod(24 * remainder, 1)
+    minute, remainder = divmod(60 * remainder, 1)
+    second, remainder = divmod(60 * remainder, 1)
+    microsecond = int(1_000_000 * remainder)
+    if microsecond < 10:
+        microsecond = 0  # compensate for rounding errors
+    dt = datetime(
+        dt.year, dt.month, dt.day, int(hour), int(minute), int(second), microsecond
+    )
+    if tz is not None:
+        dt = dt.astimezone(tz)
+
+    if microsecond > 999990:  # compensate for rounding errors
+        dt += timedelta(microseconds=1_000_000 - microsecond)
+
+    return dt
+
+
+# Fixed frequency dynamic tick locators and formatters
+
+# -------------------------------------------------------------------------
+# --- Locators ---
+# -------------------------------------------------------------------------
+
+
+def _get_default_annual_spacing(nyears) -> tuple[int, int]:
+    """
+    Returns a default spacing between consecutive ticks for annual data.
+    """
+    if nyears < 11:
+        (min_spacing, maj_spacing) = (1, 1)
+    elif nyears < 20:
+        (min_spacing, maj_spacing) = (1, 2)
+    elif nyears < 50:
+        (min_spacing, maj_spacing) = (1, 5)
+    elif nyears < 100:
+        (min_spacing, maj_spacing) = (5, 10)
+    elif nyears < 200:
+        (min_spacing, maj_spacing) = (5, 25)
+    elif nyears < 600:
+        (min_spacing, maj_spacing) = (10, 50)
+    else:
+        factor = nyears // 1000 + 1
+        (min_spacing, maj_spacing) = (factor * 20, factor * 100)
+    return (min_spacing, maj_spacing)
+
+
+def _period_break(dates: PeriodIndex, period: str) -> npt.NDArray[np.intp]:
+    """
+    Returns the indices where the given period changes.
+
+    Parameters
+    ----------
+    dates : PeriodIndex
+        Array of intervals to monitor.
+    period : str
+        Name of the period to monitor.
+    """
+    mask = _period_break_mask(dates, period)
+    return np.nonzero(mask)[0]
+
+
+def _period_break_mask(dates: PeriodIndex, period: str) -> npt.NDArray[np.bool_]:
+    current = getattr(dates, period)
+    previous = getattr(dates - 1 * dates.freq, period)
+    return current != previous
+
+
+def has_level_label(label_flags: npt.NDArray[np.intp], vmin: float) -> bool:
+    """
+    Returns true if the ``label_flags`` indicate there is at least one label
+    for this level.
+
+    if the minimum view limit is not an exact integer, then the first tick
+    label won't be shown, so we must adjust for that.
+    """
+    if label_flags.size == 0 or (
+        label_flags.size == 1 and label_flags[0] == 0 and vmin % 1 > 0.0
+    ):
+        return False
+    else:
+        return True
+
+
+def _get_periods_per_ymd(freq: BaseOffset) -> tuple[int, int, int]:
+    # error: "BaseOffset" has no attribute "_period_dtype_code"
+    dtype_code = freq._period_dtype_code  # type: ignore[attr-defined]
+    freq_group = FreqGroup.from_period_dtype_code(dtype_code)
+
+    ppd = -1  # placeholder for above-day freqs
+
+    if dtype_code >= FreqGroup.FR_HR.value:
+        # error: "BaseOffset" has no attribute "_creso"
+        ppd = periods_per_day(freq._creso)  # type: ignore[attr-defined]
+        ppm = 28 * ppd
+        ppy = 365 * ppd
+    elif freq_group == FreqGroup.FR_BUS:
+        ppm = 19
+        ppy = 261
+    elif freq_group == FreqGroup.FR_DAY:
+        ppm = 28
+        ppy = 365
+    elif freq_group == FreqGroup.FR_WK:
+        ppm = 3
+        ppy = 52
+    elif freq_group == FreqGroup.FR_MTH:
+        ppm = 1
+        ppy = 12
+    elif freq_group == FreqGroup.FR_QTR:
+        ppm = -1  # placerholder
+        ppy = 4
+    elif freq_group == FreqGroup.FR_ANN:
+        ppm = -1  # placeholder
+        ppy = 1
+    else:
+        raise NotImplementedError(f"Unsupported frequency: {dtype_code}")
+
+    return ppd, ppm, ppy
+
+
+@functools.cache
+def _daily_finder(vmin: float, vmax: float, freq: BaseOffset) -> np.ndarray:
+    # error: "BaseOffset" has no attribute "_period_dtype_code"
+    dtype_code = freq._period_dtype_code  # type: ignore[attr-defined]
+
+    periodsperday, periodspermonth, periodsperyear = _get_periods_per_ymd(freq)
+
+    # save this for later usage
+    vmin_orig = vmin
+    (vmin, vmax) = (int(vmin), int(vmax))
+    span = vmax - vmin + 1
+
+    with warnings.catch_warnings():
+        warnings.filterwarnings(
+            "ignore", "Period with BDay freq is deprecated", category=FutureWarning
+        )
+        warnings.filterwarnings(
+            "ignore", r"PeriodDtype\[B\] is deprecated", category=FutureWarning
+        )
+        dates_ = period_range(
+            start=Period(ordinal=vmin, freq=freq),
+            end=Period(ordinal=vmax, freq=freq),
+            freq=freq,
+        )
+
+    # Initialize the output
+    info = np.zeros(
+        span, dtype=[("val", np.int64), ("maj", bool), ("min", bool), ("fmt", "|S20")]
+    )
+    info["val"][:] = dates_.asi8
+    info["fmt"][:] = ""
+    info["maj"][[0, -1]] = True
+    # .. and set some shortcuts
+    info_maj = info["maj"]
+    info_min = info["min"]
+    info_fmt = info["fmt"]
+
+    def first_label(label_flags):
+        if (label_flags[0] == 0) and (label_flags.size > 1) and ((vmin_orig % 1) > 0.0):
+            return label_flags[1]
+        else:
+            return label_flags[0]
+
+    # Case 1. Less than a month
+    if span <= periodspermonth:
+        day_start = _period_break(dates_, "day")
+        month_start = _period_break(dates_, "month")
+        year_start = _period_break(dates_, "year")
+
+        def _hour_finder(label_interval: int, force_year_start: bool) -> None:
+            target = dates_.hour
+            mask = _period_break_mask(dates_, "hour")
+            info_maj[day_start] = True
+            info_min[mask & (target % label_interval == 0)] = True
+            info_fmt[mask & (target % label_interval == 0)] = "%H:%M"
+            info_fmt[day_start] = "%H:%M\n%d-%b"
+            info_fmt[year_start] = "%H:%M\n%d-%b\n%Y"
+            if force_year_start and not has_level_label(year_start, vmin_orig):
+                info_fmt[first_label(day_start)] = "%H:%M\n%d-%b\n%Y"
+
+        def _minute_finder(label_interval: int) -> None:
+            target = dates_.minute
+            hour_start = _period_break(dates_, "hour")
+            mask = _period_break_mask(dates_, "minute")
+            info_maj[hour_start] = True
+            info_min[mask & (target % label_interval == 0)] = True
+            info_fmt[mask & (target % label_interval == 0)] = "%H:%M"
+            info_fmt[day_start] = "%H:%M\n%d-%b"
+            info_fmt[year_start] = "%H:%M\n%d-%b\n%Y"
+
+        def _second_finder(label_interval: int) -> None:
+            target = dates_.second
+            minute_start = _period_break(dates_, "minute")
+            mask = _period_break_mask(dates_, "second")
+            info_maj[minute_start] = True
+            info_min[mask & (target % label_interval == 0)] = True
+            info_fmt[mask & (target % label_interval == 0)] = "%H:%M:%S"
+            info_fmt[day_start] = "%H:%M:%S\n%d-%b"
+            info_fmt[year_start] = "%H:%M:%S\n%d-%b\n%Y"
+
+        if span < periodsperday / 12000:
+            _second_finder(1)
+        elif span < periodsperday / 6000:
+            _second_finder(2)
+        elif span < periodsperday / 2400:
+            _second_finder(5)
+        elif span < periodsperday / 1200:
+            _second_finder(10)
+        elif span < periodsperday / 800:
+            _second_finder(15)
+        elif span < periodsperday / 400:
+            _second_finder(30)
+        elif span < periodsperday / 150:
+            _minute_finder(1)
+        elif span < periodsperday / 70:
+            _minute_finder(2)
+        elif span < periodsperday / 24:
+            _minute_finder(5)
+        elif span < periodsperday / 12:
+            _minute_finder(15)
+        elif span < periodsperday / 6:
+            _minute_finder(30)
+        elif span < periodsperday / 2.5:
+            _hour_finder(1, False)
+        elif span < periodsperday / 1.5:
+            _hour_finder(2, False)
+        elif span < periodsperday * 1.25:
+            _hour_finder(3, False)
+        elif span < periodsperday * 2.5:
+            _hour_finder(6, True)
+        elif span < periodsperday * 4:
+            _hour_finder(12, True)
+        else:
+            info_maj[month_start] = True
+            info_min[day_start] = True
+            info_fmt[day_start] = "%d"
+            info_fmt[month_start] = "%d\n%b"
+            info_fmt[year_start] = "%d\n%b\n%Y"
+            if not has_level_label(year_start, vmin_orig):
+                if not has_level_label(month_start, vmin_orig):
+                    info_fmt[first_label(day_start)] = "%d\n%b\n%Y"
+                else:
+                    info_fmt[first_label(month_start)] = "%d\n%b\n%Y"
+
+    # Case 2. Less than three months
+    elif span <= periodsperyear // 4:
+        month_start = _period_break(dates_, "month")
+        info_maj[month_start] = True
+        if dtype_code < FreqGroup.FR_HR.value:
+            info["min"] = True
+        else:
+            day_start = _period_break(dates_, "day")
+            info["min"][day_start] = True
+        week_start = _period_break(dates_, "week")
+        year_start = _period_break(dates_, "year")
+        info_fmt[week_start] = "%d"
+        info_fmt[month_start] = "\n\n%b"
+        info_fmt[year_start] = "\n\n%b\n%Y"
+        if not has_level_label(year_start, vmin_orig):
+            if not has_level_label(month_start, vmin_orig):
+                info_fmt[first_label(week_start)] = "\n\n%b\n%Y"
+            else:
+                info_fmt[first_label(month_start)] = "\n\n%b\n%Y"
+    # Case 3. Less than 14 months ...............
+    elif span <= 1.15 * periodsperyear:
+        year_start = _period_break(dates_, "year")
+        month_start = _period_break(dates_, "month")
+        week_start = _period_break(dates_, "week")
+        info_maj[month_start] = True
+        info_min[week_start] = True
+        info_min[year_start] = False
+        info_min[month_start] = False
+        info_fmt[month_start] = "%b"
+        info_fmt[year_start] = "%b\n%Y"
+        if not has_level_label(year_start, vmin_orig):
+            info_fmt[first_label(month_start)] = "%b\n%Y"
+    # Case 4. Less than 2.5 years ...............
+    elif span <= 2.5 * periodsperyear:
+        year_start = _period_break(dates_, "year")
+        quarter_start = _period_break(dates_, "quarter")
+        month_start = _period_break(dates_, "month")
+        info_maj[quarter_start] = True
+        info_min[month_start] = True
+        info_fmt[quarter_start] = "%b"
+        info_fmt[year_start] = "%b\n%Y"
+    # Case 4. Less than 4 years .................
+    elif span <= 4 * periodsperyear:
+        year_start = _period_break(dates_, "year")
+        month_start = _period_break(dates_, "month")
+        info_maj[year_start] = True
+        info_min[month_start] = True
+        info_min[year_start] = False
+
+        month_break = dates_[month_start].month
+        jan_or_jul = month_start[(month_break == 1) | (month_break == 7)]
+        info_fmt[jan_or_jul] = "%b"
+        info_fmt[year_start] = "%b\n%Y"
+    # Case 5. Less than 11 years ................
+    elif span <= 11 * periodsperyear:
+        year_start = _period_break(dates_, "year")
+        quarter_start = _period_break(dates_, "quarter")
+        info_maj[year_start] = True
+        info_min[quarter_start] = True
+        info_min[year_start] = False
+        info_fmt[year_start] = "%Y"
+    # Case 6. More than 12 years ................
+    else:
+        year_start = _period_break(dates_, "year")
+        year_break = dates_[year_start].year
+        nyears = span / periodsperyear
+        (min_anndef, maj_anndef) = _get_default_annual_spacing(nyears)
+        major_idx = year_start[(year_break % maj_anndef == 0)]
+        info_maj[major_idx] = True
+        minor_idx = year_start[(year_break % min_anndef == 0)]
+        info_min[minor_idx] = True
+        info_fmt[major_idx] = "%Y"
+
+    return info
+
+
+@functools.cache
+def _monthly_finder(vmin: float, vmax: float, freq: BaseOffset) -> np.ndarray:
+    _, _, periodsperyear = _get_periods_per_ymd(freq)
+
+    vmin_orig = vmin
+    (vmin, vmax) = (int(vmin), int(vmax))
+    span = vmax - vmin + 1
+
+    # Initialize the output
+    info = np.zeros(
+        span, dtype=[("val", int), ("maj", bool), ("min", bool), ("fmt", "|S8")]
+    )
+    info["val"] = np.arange(vmin, vmax + 1)
+    dates_ = info["val"]
+    info["fmt"] = ""
+    year_start = (dates_ % 12 == 0).nonzero()[0]
+    info_maj = info["maj"]
+    info_fmt = info["fmt"]
+
+    if span <= 1.15 * periodsperyear:
+        info_maj[year_start] = True
+        info["min"] = True
+
+        info_fmt[:] = "%b"
+        info_fmt[year_start] = "%b\n%Y"
+
+        if not has_level_label(year_start, vmin_orig):
+            if dates_.size > 1:
+                idx = 1
+            else:
+                idx = 0
+            info_fmt[idx] = "%b\n%Y"
+
+    elif span <= 2.5 * periodsperyear:
+        quarter_start = (dates_ % 3 == 0).nonzero()
+        info_maj[year_start] = True
+        # TODO: Check the following : is it really info['fmt'] ?
+        #  2023-09-15 this is reached in test_finder_monthly
+        info["fmt"][quarter_start] = True
+        info["min"] = True
+
+        info_fmt[quarter_start] = "%b"
+        info_fmt[year_start] = "%b\n%Y"
+
+    elif span <= 4 * periodsperyear:
+        info_maj[year_start] = True
+        info["min"] = True
+
+        jan_or_jul = (dates_ % 12 == 0) | (dates_ % 12 == 6)
+        info_fmt[jan_or_jul] = "%b"
+        info_fmt[year_start] = "%b\n%Y"
+
+    elif span <= 11 * periodsperyear:
+        quarter_start = (dates_ % 3 == 0).nonzero()
+        info_maj[year_start] = True
+        info["min"][quarter_start] = True
+
+        info_fmt[year_start] = "%Y"
+
+    else:
+        nyears = span / periodsperyear
+        (min_anndef, maj_anndef) = _get_default_annual_spacing(nyears)
+        years = dates_[year_start] // 12 + 1
+        major_idx = year_start[(years % maj_anndef == 0)]
+        info_maj[major_idx] = True
+        info["min"][year_start[(years % min_anndef == 0)]] = True
+
+        info_fmt[major_idx] = "%Y"
+
+    return info
+
+
+@functools.cache
+def _quarterly_finder(vmin: float, vmax: float, freq: BaseOffset) -> np.ndarray:
+    _, _, periodsperyear = _get_periods_per_ymd(freq)
+    vmin_orig = vmin
+    (vmin, vmax) = (int(vmin), int(vmax))
+    span = vmax - vmin + 1
+
+    info = np.zeros(
+        span, dtype=[("val", int), ("maj", bool), ("min", bool), ("fmt", "|S8")]
+    )
+    info["val"] = np.arange(vmin, vmax + 1)
+    info["fmt"] = ""
+    dates_ = info["val"]
+    info_maj = info["maj"]
+    info_fmt = info["fmt"]
+    year_start = (dates_ % 4 == 0).nonzero()[0]
+
+    if span <= 3.5 * periodsperyear:
+        info_maj[year_start] = True
+        info["min"] = True
+
+        info_fmt[:] = "Q%q"
+        info_fmt[year_start] = "Q%q\n%F"
+        if not has_level_label(year_start, vmin_orig):
+            if dates_.size > 1:
+                idx = 1
+            else:
+                idx = 0
+            info_fmt[idx] = "Q%q\n%F"
+
+    elif span <= 11 * periodsperyear:
+        info_maj[year_start] = True
+        info["min"] = True
+        info_fmt[year_start] = "%F"
+
+    else:
+        # https://github.com/pandas-dev/pandas/pull/47602
+        years = dates_[year_start] // 4 + 1970
+        nyears = span / periodsperyear
+        (min_anndef, maj_anndef) = _get_default_annual_spacing(nyears)
+        major_idx = year_start[(years % maj_anndef == 0)]
+        info_maj[major_idx] = True
+        info["min"][year_start[(years % min_anndef == 0)]] = True
+        info_fmt[major_idx] = "%F"
+
+    return info
+
+
+@functools.cache
+def _annual_finder(vmin: float, vmax: float, freq: BaseOffset) -> np.ndarray:
+    # Note: small difference here vs other finders in adding 1 to vmax
+    (vmin, vmax) = (int(vmin), int(vmax + 1))
+    span = vmax - vmin + 1
+
+    info = np.zeros(
+        span, dtype=[("val", int), ("maj", bool), ("min", bool), ("fmt", "|S8")]
+    )
+    info["val"] = np.arange(vmin, vmax + 1)
+    info["fmt"] = ""
+    dates_ = info["val"]
+
+    (min_anndef, maj_anndef) = _get_default_annual_spacing(span)
+    major_idx = dates_ % maj_anndef == 0
+    minor_idx = dates_ % min_anndef == 0
+    info["maj"][major_idx] = True
+    info["min"][minor_idx] = True
+    info["fmt"][major_idx] = "%Y"
+
+    return info
+
+
+def get_finder(freq: BaseOffset):
+    # error: "BaseOffset" has no attribute "_period_dtype_code"
+    dtype_code = freq._period_dtype_code  # type: ignore[attr-defined]
+    fgroup = FreqGroup.from_period_dtype_code(dtype_code)
+
+    if fgroup == FreqGroup.FR_ANN:
+        return _annual_finder
+    elif fgroup == FreqGroup.FR_QTR:
+        return _quarterly_finder
+    elif fgroup == FreqGroup.FR_MTH:
+        return _monthly_finder
+    elif (dtype_code >= FreqGroup.FR_BUS.value) or fgroup == FreqGroup.FR_WK:
+        return _daily_finder
+    else:  # pragma: no cover
+        raise NotImplementedError(f"Unsupported frequency: {dtype_code}")
+
+
+class TimeSeries_DateLocator(Locator):
+    """
+    Locates the ticks along an axis controlled by a :class:`Series`.
+
+    Parameters
+    ----------
+    freq : BaseOffset
+        Valid frequency specifier.
+    minor_locator : {False, True}, optional
+        Whether the locator is for minor ticks (True) or not.
+    dynamic_mode : {True, False}, optional
+        Whether the locator should work in dynamic mode.
+    base : {int}, optional
+    quarter : {int}, optional
+    month : {int}, optional
+    day : {int}, optional
+    """
+
+    axis: Axis
+
+    def __init__(
+        self,
+        freq: BaseOffset,
+        minor_locator: bool = False,
+        dynamic_mode: bool = True,
+        base: int = 1,
+        quarter: int = 1,
+        month: int = 1,
+        day: int = 1,
+        plot_obj=None,
+    ) -> None:
+        freq = to_offset(freq, is_period=True)
+        self.freq = freq
+        self.base = base
+        (self.quarter, self.month, self.day) = (quarter, month, day)
+        self.isminor = minor_locator
+        self.isdynamic = dynamic_mode
+        self.offset = 0
+        self.plot_obj = plot_obj
+        self.finder = get_finder(freq)
+
+    def _get_default_locs(self, vmin, vmax):
+        """Returns the default locations of ticks."""
+        locator = self.finder(vmin, vmax, self.freq)
+
+        if self.isminor:
+            return np.compress(locator["min"], locator["val"])
+        return np.compress(locator["maj"], locator["val"])
+
+    def __call__(self):
+        """Return the locations of the ticks."""
+        # axis calls Locator.set_axis inside set_m_formatter
+
+        vi = tuple(self.axis.get_view_interval())
+        vmin, vmax = vi
+        if vmax < vmin:
+            vmin, vmax = vmax, vmin
+        if self.isdynamic:
+            locs = self._get_default_locs(vmin, vmax)
+        else:  # pragma: no cover
+            base = self.base
+            (d, m) = divmod(vmin, base)
+            vmin = (d + 1) * base
+            # error: No overload variant of "range" matches argument types "float",
+            # "float", "int"
+            locs = list(range(vmin, vmax + 1, base))  # type: ignore[call-overload]
+        return locs
+
+    def autoscale(self):
+        """
+        Sets the view limits to the nearest multiples of base that contain the
+        data.
+        """
+        # requires matplotlib >= 0.98.0
+        (vmin, vmax) = self.axis.get_data_interval()
+
+        locs = self._get_default_locs(vmin, vmax)
+        (vmin, vmax) = locs[[0, -1]]
+        if vmin == vmax:
+            vmin -= 1
+            vmax += 1
+        return nonsingular(vmin, vmax)
+
+
+# -------------------------------------------------------------------------
+# --- Formatter ---
+# -------------------------------------------------------------------------
+
+
+class TimeSeries_DateFormatter(Formatter):
+    """
+    Formats the ticks along an axis controlled by a :class:`PeriodIndex`.
+
+    Parameters
+    ----------
+    freq : BaseOffset
+        Valid frequency specifier.
+    minor_locator : bool, default False
+        Whether the current formatter should apply to minor ticks (True) or
+        major ticks (False).
+    dynamic_mode : bool, default True
+        Whether the formatter works in dynamic mode or not.
+    """
+
+    axis: Axis
+
+    def __init__(
+        self,
+        freq: BaseOffset,
+        minor_locator: bool = False,
+        dynamic_mode: bool = True,
+        plot_obj=None,
+    ) -> None:
+        freq = to_offset(freq, is_period=True)
+        self.format = None
+        self.freq = freq
+        self.locs: list[Any] = []  # unused, for matplotlib compat
+        self.formatdict: dict[Any, Any] | None = None
+        self.isminor = minor_locator
+        self.isdynamic = dynamic_mode
+        self.offset = 0
+        self.plot_obj = plot_obj
+        self.finder = get_finder(freq)
+
+    def _set_default_format(self, vmin, vmax):
+        """Returns the default ticks spacing."""
+        info = self.finder(vmin, vmax, self.freq)
+
+        if self.isminor:
+            format = np.compress(info["min"] & np.logical_not(info["maj"]), info)
+        else:
+            format = np.compress(info["maj"], info)
+        self.formatdict = {x: f for (x, _, _, f) in format}
+        return self.formatdict
+
+    def set_locs(self, locs) -> None:
+        """Sets the locations of the ticks"""
+        # don't actually use the locs. This is just needed to work with
+        # matplotlib. Force to use vmin, vmax
+
+        self.locs = locs
+
+        (vmin, vmax) = tuple(self.axis.get_view_interval())
+        if vmax < vmin:
+            (vmin, vmax) = (vmax, vmin)
+        self._set_default_format(vmin, vmax)
+
+    def __call__(self, x, pos: int | None = 0) -> str:
+        if self.formatdict is None:
+            return ""
+        else:
+            fmt = self.formatdict.pop(x, "")
+            if isinstance(fmt, np.bytes_):
+                fmt = fmt.decode("utf-8")
+            with warnings.catch_warnings():
+                warnings.filterwarnings(
+                    "ignore",
+                    "Period with BDay freq is deprecated",
+                    category=FutureWarning,
+                )
+                period = Period(ordinal=int(x), freq=self.freq)
+            assert isinstance(period, Period)
+            return period.strftime(fmt)
+
+
+class TimeSeries_TimedeltaFormatter(Formatter):
+    """
+    Formats the ticks along an axis controlled by a :class:`TimedeltaIndex`.
+    """
+
+    axis: Axis
+
+    @staticmethod
+    def format_timedelta_ticks(x, pos, n_decimals: int) -> str:
+        """
+        Convert seconds to 'D days HH:MM:SS.F'
+        """
+        s, ns = divmod(x, 10**9)  # TODO(non-nano): this looks like it assumes ns
+        m, s = divmod(s, 60)
+        h, m = divmod(m, 60)
+        d, h = divmod(h, 24)
+        decimals = int(ns * 10 ** (n_decimals - 9))
+        s = f"{int(h):02d}:{int(m):02d}:{int(s):02d}"
+        if n_decimals > 0:
+            s += f".{decimals:0{n_decimals}d}"
+        if d != 0:
+            s = f"{int(d):d} days {s}"
+        return s
+
+    def __call__(self, x, pos: int | None = 0) -> str:
+        (vmin, vmax) = tuple(self.axis.get_view_interval())
+        n_decimals = min(int(np.ceil(np.log10(100 * 10**9 / abs(vmax - vmin)))), 9)
+        return self.format_timedelta_ticks(x, pos, n_decimals)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/plotting/_matplotlib/core.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/plotting/_matplotlib/core.py
new file mode 100644
index 0000000000000000000000000000000000000000..3a1e589c2279bdadb736ce85312bc2c84f5793eb
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/plotting/_matplotlib/core.py
@@ -0,0 +1,2125 @@
+from __future__ import annotations
+
+from abc import (
+    ABC,
+    abstractmethod,
+)
+from collections.abc import (
+    Hashable,
+    Iterable,
+    Iterator,
+    Sequence,
+)
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Literal,
+    cast,
+    final,
+)
+import warnings
+
+import matplotlib as mpl
+import numpy as np
+
+from pandas._libs import lib
+from pandas.errors import AbstractMethodError
+from pandas.util._decorators import cache_readonly
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.common import (
+    is_any_real_numeric_dtype,
+    is_bool,
+    is_float,
+    is_float_dtype,
+    is_hashable,
+    is_integer,
+    is_integer_dtype,
+    is_iterator,
+    is_list_like,
+    is_number,
+    is_numeric_dtype,
+)
+from pandas.core.dtypes.dtypes import (
+    CategoricalDtype,
+    ExtensionDtype,
+)
+from pandas.core.dtypes.generic import (
+    ABCDataFrame,
+    ABCDatetimeIndex,
+    ABCIndex,
+    ABCMultiIndex,
+    ABCPeriodIndex,
+    ABCSeries,
+)
+from pandas.core.dtypes.missing import isna
+
+import pandas.core.common as com
+from pandas.core.frame import DataFrame
+from pandas.util.version import Version
+
+from pandas.io.formats.printing import pprint_thing
+from pandas.plotting._matplotlib import tools
+from pandas.plotting._matplotlib.converter import register_pandas_matplotlib_converters
+from pandas.plotting._matplotlib.groupby import reconstruct_data_with_by
+from pandas.plotting._matplotlib.misc import unpack_single_str_list
+from pandas.plotting._matplotlib.style import get_standard_colors
+from pandas.plotting._matplotlib.timeseries import (
+    decorate_axes,
+    format_dateaxis,
+    maybe_convert_index,
+    maybe_resample,
+    use_dynamic_x,
+)
+from pandas.plotting._matplotlib.tools import (
+    create_subplots,
+    flatten_axes,
+    format_date_labels,
+    get_all_lines,
+    get_xlim,
+    handle_shared_axes,
+)
+
+if TYPE_CHECKING:
+    from matplotlib.artist import Artist
+    from matplotlib.axes import Axes
+    from matplotlib.axis import Axis
+    from matplotlib.figure import Figure
+
+    from pandas._typing import (
+        IndexLabel,
+        NDFrameT,
+        PlottingOrientation,
+        npt,
+    )
+
+    from pandas import Series
+
+
+def _color_in_style(style: str) -> bool:
+    """
+    Check if there is a color letter in the style string.
+    """
+    from matplotlib.colors import BASE_COLORS
+
+    return not set(BASE_COLORS).isdisjoint(style)
+
+
+class MPLPlot(ABC):
+    """
+    Base class for assembling a pandas plot using matplotlib
+
+    Parameters
+    ----------
+    data :
+
+    """
+
+    @property
+    @abstractmethod
+    def _kind(self) -> str:
+        """Specify kind str. Must be overridden in child class"""
+        raise NotImplementedError
+
+    _layout_type = "vertical"
+    _default_rot = 0
+
+    @property
+    def orientation(self) -> str | None:
+        return None
+
+    data: DataFrame
+
+    def __init__(
+        self,
+        data,
+        kind=None,
+        by: IndexLabel | None = None,
+        subplots: bool | Sequence[Sequence[str]] = False,
+        sharex: bool | None = None,
+        sharey: bool = False,
+        use_index: bool = True,
+        figsize: tuple[float, float] | None = None,
+        grid=None,
+        legend: bool | str = True,
+        rot=None,
+        ax=None,
+        fig=None,
+        title=None,
+        xlim=None,
+        ylim=None,
+        xticks=None,
+        yticks=None,
+        xlabel: Hashable | None = None,
+        ylabel: Hashable | None = None,
+        fontsize: int | None = None,
+        secondary_y: bool | tuple | list | np.ndarray = False,
+        colormap=None,
+        table: bool = False,
+        layout=None,
+        include_bool: bool = False,
+        column: IndexLabel | None = None,
+        *,
+        logx: bool | None | Literal["sym"] = False,
+        logy: bool | None | Literal["sym"] = False,
+        loglog: bool | None | Literal["sym"] = False,
+        mark_right: bool = True,
+        stacked: bool = False,
+        label: Hashable | None = None,
+        style=None,
+        **kwds,
+    ) -> None:
+        import matplotlib.pyplot as plt
+
+        # if users assign an empty list or tuple, raise `ValueError`
+        # similar to current `df.box` and `df.hist` APIs.
+        if by in ([], ()):
+            raise ValueError("No group keys passed!")
+        self.by = com.maybe_make_list(by)
+
+        # Assign the rest of columns into self.columns if by is explicitly defined
+        # while column is not, only need `columns` in hist/box plot when it's DF
+        # TODO: Might deprecate `column` argument in future PR (#28373)
+        if isinstance(data, DataFrame):
+            if column:
+                self.columns = com.maybe_make_list(column)
+            elif self.by is None:
+                self.columns = [
+                    col for col in data.columns if is_numeric_dtype(data[col])
+                ]
+            else:
+                self.columns = [
+                    col
+                    for col in data.columns
+                    if col not in self.by and is_numeric_dtype(data[col])
+                ]
+
+        # For `hist` plot, need to get grouped original data before `self.data` is
+        # updated later
+        if self.by is not None and self._kind == "hist":
+            self._grouped = data.groupby(unpack_single_str_list(self.by))
+
+        self.kind = kind
+
+        self.subplots = type(self)._validate_subplots_kwarg(
+            subplots, data, kind=self._kind
+        )
+
+        self.sharex = type(self)._validate_sharex(sharex, ax, by)
+        self.sharey = sharey
+        self.figsize = figsize
+        self.layout = layout
+
+        self.xticks = xticks
+        self.yticks = yticks
+        self.xlim = xlim
+        self.ylim = ylim
+        self.title = title
+        self.use_index = use_index
+        self.xlabel = xlabel
+        self.ylabel = ylabel
+
+        self.fontsize = fontsize
+
+        if rot is not None:
+            self.rot = rot
+            # need to know for format_date_labels since it's rotated to 30 by
+            # default
+            self._rot_set = True
+        else:
+            self._rot_set = False
+            self.rot = self._default_rot
+
+        if grid is None:
+            grid = False if secondary_y else plt.rcParams["axes.grid"]
+
+        self.grid = grid
+        self.legend = legend
+        self.legend_handles: list[Artist] = []
+        self.legend_labels: list[Hashable] = []
+
+        self.logx = type(self)._validate_log_kwd("logx", logx)
+        self.logy = type(self)._validate_log_kwd("logy", logy)
+        self.loglog = type(self)._validate_log_kwd("loglog", loglog)
+        self.label = label
+        self.style = style
+        self.mark_right = mark_right
+        self.stacked = stacked
+
+        # ax may be an Axes object or (if self.subplots) an ndarray of
+        #  Axes objects
+        self.ax = ax
+        # TODO: deprecate fig keyword as it is ignored, not passed in tests
+        #  as of 2023-11-05
+
+        # parse errorbar input if given
+        xerr = kwds.pop("xerr", None)
+        yerr = kwds.pop("yerr", None)
+        nseries = self._get_nseries(data)
+        xerr, data = type(self)._parse_errorbars("xerr", xerr, data, nseries)
+        yerr, data = type(self)._parse_errorbars("yerr", yerr, data, nseries)
+        self.errors = {"xerr": xerr, "yerr": yerr}
+        self.data = data
+
+        if not isinstance(secondary_y, (bool, tuple, list, np.ndarray, ABCIndex)):
+            secondary_y = [secondary_y]
+        self.secondary_y = secondary_y
+
+        # ugly TypeError if user passes matplotlib's `cmap` name.
+        # Probably better to accept either.
+        if "cmap" in kwds and colormap:
+            raise TypeError("Only specify one of `cmap` and `colormap`.")
+        if "cmap" in kwds:
+            self.colormap = kwds.pop("cmap")
+        else:
+            self.colormap = colormap
+
+        self.table = table
+        self.include_bool = include_bool
+
+        self.kwds = kwds
+
+        color = kwds.pop("color", lib.no_default)
+        self.color = self._validate_color_args(color, self.colormap)
+        assert "color" not in self.kwds
+
+        self.data = self._ensure_frame(self.data)
+
+    @final
+    @staticmethod
+    def _validate_sharex(sharex: bool | None, ax, by) -> bool:
+        if sharex is None:
+            # if by is defined, subplots are used and sharex should be False
+            if ax is None and by is None:  # pylint: disable=simplifiable-if-statement
+                sharex = True
+            else:
+                # if we get an axis, the users should do the visibility
+                # setting...
+                sharex = False
+        elif not is_bool(sharex):
+            raise TypeError("sharex must be a bool or None")
+        return bool(sharex)
+
+    @classmethod
+    def _validate_log_kwd(
+        cls,
+        kwd: str,
+        value: bool | None | Literal["sym"],
+    ) -> bool | None | Literal["sym"]:
+        if (
+            value is None
+            or isinstance(value, bool)
+            or (isinstance(value, str) and value == "sym")
+        ):
+            return value
+        raise ValueError(
+            f"keyword '{kwd}' should be bool, None, or 'sym', not '{value}'"
+        )
+
+    @final
+    @staticmethod
+    def _validate_subplots_kwarg(
+        subplots: bool | Sequence[Sequence[str]], data: Series | DataFrame, kind: str
+    ) -> bool | list[tuple[int, ...]]:
+        """
+        Validate the subplots parameter
+
+        - check type and content
+        - check for duplicate columns
+        - check for invalid column names
+        - convert column names into indices
+        - add missing columns in a group of their own
+        See comments in code below for more details.
+
+        Parameters
+        ----------
+        subplots : subplots parameters as passed to PlotAccessor
+
+        Returns
+        -------
+        validated subplots : a bool or a list of tuples of column indices. Columns
+        in the same tuple will be grouped together in the resulting plot.
+        """
+
+        if isinstance(subplots, bool):
+            return subplots
+        elif not isinstance(subplots, Iterable):
+            raise ValueError("subplots should be a bool or an iterable")
+
+        supported_kinds = (
+            "line",
+            "bar",
+            "barh",
+            "hist",
+            "kde",
+            "density",
+            "area",
+            "pie",
+        )
+        if kind not in supported_kinds:
+            raise ValueError(
+                "When subplots is an iterable, kind must be "
+                f"one of {', '.join(supported_kinds)}. Got {kind}."
+            )
+
+        if isinstance(data, ABCSeries):
+            raise NotImplementedError(
+                "An iterable subplots for a Series is not supported."
+            )
+
+        columns = data.columns
+        if isinstance(columns, ABCMultiIndex):
+            raise NotImplementedError(
+                "An iterable subplots for a DataFrame with a MultiIndex column "
+                "is not supported."
+            )
+
+        if columns.nunique() != len(columns):
+            raise NotImplementedError(
+                "An iterable subplots for a DataFrame with non-unique column "
+                "labels is not supported."
+            )
+
+        # subplots is a list of tuples where each tuple is a group of
+        # columns to be grouped together (one ax per group).
+        # we consolidate the subplots list such that:
+        # - the tuples contain indices instead of column names
+        # - the columns that aren't yet in the list are added in a group
+        #   of their own.
+        # For example with columns from a to g, and
+        # subplots = [(a, c), (b, f, e)],
+        # we end up with [(ai, ci), (bi, fi, ei), (di,), (gi,)]
+        # This way, we can handle self.subplots in a homogeneous manner
+        # later.
+        # TODO: also accept indices instead of just names?
+
+        out = []
+        seen_columns: set[Hashable] = set()
+        for group in subplots:
+            if not is_list_like(group):
+                raise ValueError(
+                    "When subplots is an iterable, each entry "
+                    "should be a list/tuple of column names."
+                )
+            idx_locs = columns.get_indexer_for(group)
+            if (idx_locs == -1).any():
+                bad_labels = np.extract(idx_locs == -1, group)
+                raise ValueError(
+                    f"Column label(s) {list(bad_labels)} not found in the DataFrame."
+                )
+            unique_columns = set(group)
+            duplicates = seen_columns.intersection(unique_columns)
+            if duplicates:
+                raise ValueError(
+                    "Each column should be in only one subplot. "
+                    f"Columns {duplicates} were found in multiple subplots."
+                )
+            seen_columns = seen_columns.union(unique_columns)
+            out.append(tuple(idx_locs))
+
+        unseen_columns = columns.difference(seen_columns)
+        for column in unseen_columns:
+            idx_loc = columns.get_loc(column)
+            out.append((idx_loc,))
+        return out
+
+    def _validate_color_args(self, color, colormap):
+        if color is lib.no_default:
+            # It was not provided by the user
+            if "colors" in self.kwds and colormap is not None:
+                warnings.warn(
+                    "'color' and 'colormap' cannot be used simultaneously. "
+                    "Using 'color'",
+                    stacklevel=find_stack_level(),
+                )
+            return None
+        if self.nseries == 1 and color is not None and not is_list_like(color):
+            # support series.plot(color='green')
+            color = [color]
+
+        if isinstance(color, tuple) and self.nseries == 1 and len(color) in (3, 4):
+            # support RGB and RGBA tuples in series plot
+            color = [color]
+
+        if colormap is not None:
+            warnings.warn(
+                "'color' and 'colormap' cannot be used simultaneously. Using 'color'",
+                stacklevel=find_stack_level(),
+            )
+
+        if self.style is not None:
+            if is_list_like(self.style):
+                styles = self.style
+            else:
+                styles = [self.style]
+            # need only a single match
+            for s in styles:
+                if _color_in_style(s):
+                    raise ValueError(
+                        "Cannot pass 'style' string with a color symbol and "
+                        "'color' keyword argument. Please use one or the "
+                        "other or pass 'style' without a color symbol"
+                    )
+        return color
+
+    @final
+    @staticmethod
+    def _iter_data(
+        data: DataFrame | dict[Hashable, Series | DataFrame]
+    ) -> Iterator[tuple[Hashable, np.ndarray]]:
+        for col, values in data.items():
+            # This was originally written to use values.values before EAs
+            #  were implemented; adding np.asarray(...) to keep consistent
+            #  typing.
+            yield col, np.asarray(values.values)
+
+    def _get_nseries(self, data: Series | DataFrame) -> int:
+        # When `by` is explicitly assigned, grouped data size will be defined, and
+        # this will determine number of subplots to have, aka `self.nseries`
+        if data.ndim == 1:
+            return 1
+        elif self.by is not None and self._kind == "hist":
+            return len(self._grouped)
+        elif self.by is not None and self._kind == "box":
+            return len(self.columns)
+        else:
+            return data.shape[1]
+
+    @final
+    @property
+    def nseries(self) -> int:
+        return self._get_nseries(self.data)
+
+    @final
+    def draw(self) -> None:
+        self.plt.draw_if_interactive()
+
+    @final
+    def generate(self) -> None:
+        self._compute_plot_data()
+        fig = self.fig
+        self._make_plot(fig)
+        self._add_table()
+        self._make_legend()
+        self._adorn_subplots(fig)
+
+        for ax in self.axes:
+            self._post_plot_logic_common(ax)
+            self._post_plot_logic(ax, self.data)
+
+    @final
+    @staticmethod
+    def _has_plotted_object(ax: Axes) -> bool:
+        """check whether ax has data"""
+        return len(ax.lines) != 0 or len(ax.artists) != 0 or len(ax.containers) != 0
+
+    @final
+    def _maybe_right_yaxis(self, ax: Axes, axes_num: int) -> Axes:
+        if not self.on_right(axes_num):
+            # secondary axes may be passed via ax kw
+            return self._get_ax_layer(ax)
+
+        if hasattr(ax, "right_ax"):
+            # if it has right_ax property, ``ax`` must be left axes
+            return ax.right_ax
+        elif hasattr(ax, "left_ax"):
+            # if it has left_ax property, ``ax`` must be right axes
+            return ax
+        else:
+            # otherwise, create twin axes
+            orig_ax, new_ax = ax, ax.twinx()
+            # TODO: use Matplotlib public API when available
+            new_ax._get_lines = orig_ax._get_lines  # type: ignore[attr-defined]
+            # TODO #54485
+            new_ax._get_patches_for_fill = (  # type: ignore[attr-defined]
+                orig_ax._get_patches_for_fill  # type: ignore[attr-defined]
+            )
+            # TODO #54485
+            orig_ax.right_ax, new_ax.left_ax = (  # type: ignore[attr-defined]
+                new_ax,
+                orig_ax,
+            )
+
+            if not self._has_plotted_object(orig_ax):  # no data on left y
+                orig_ax.get_yaxis().set_visible(False)
+
+            if self.logy is True or self.loglog is True:
+                new_ax.set_yscale("log")
+            elif self.logy == "sym" or self.loglog == "sym":
+                new_ax.set_yscale("symlog")
+            return new_ax
+
+    @final
+    @cache_readonly
+    def fig(self) -> Figure:
+        return self._axes_and_fig[1]
+
+    @final
+    @cache_readonly
+    # TODO: can we annotate this as both a Sequence[Axes] and ndarray[object]?
+    def axes(self) -> Sequence[Axes]:
+        return self._axes_and_fig[0]
+
+    @final
+    @cache_readonly
+    def _axes_and_fig(self) -> tuple[Sequence[Axes], Figure]:
+        if self.subplots:
+            naxes = (
+                self.nseries if isinstance(self.subplots, bool) else len(self.subplots)
+            )
+            fig, axes = create_subplots(
+                naxes=naxes,
+                sharex=self.sharex,
+                sharey=self.sharey,
+                figsize=self.figsize,
+                ax=self.ax,
+                layout=self.layout,
+                layout_type=self._layout_type,
+            )
+        elif self.ax is None:
+            fig = self.plt.figure(figsize=self.figsize)
+            axes = fig.add_subplot(111)
+        else:
+            fig = self.ax.get_figure()
+            if self.figsize is not None:
+                fig.set_size_inches(self.figsize)
+            axes = self.ax
+
+        axes = flatten_axes(axes)
+
+        if self.logx is True or self.loglog is True:
+            [a.set_xscale("log") for a in axes]
+        elif self.logx == "sym" or self.loglog == "sym":
+            [a.set_xscale("symlog") for a in axes]
+
+        if self.logy is True or self.loglog is True:
+            [a.set_yscale("log") for a in axes]
+        elif self.logy == "sym" or self.loglog == "sym":
+            [a.set_yscale("symlog") for a in axes]
+
+        axes_seq = cast(Sequence["Axes"], axes)
+        return axes_seq, fig
+
+    @property
+    def result(self):
+        """
+        Return result axes
+        """
+        if self.subplots:
+            if self.layout is not None and not is_list_like(self.ax):
+                # error: "Sequence[Any]" has no attribute "reshape"
+                return self.axes.reshape(*self.layout)  # type: ignore[attr-defined]
+            else:
+                return self.axes
+        else:
+            sec_true = isinstance(self.secondary_y, bool) and self.secondary_y
+            # error: Argument 1 to "len" has incompatible type "Union[bool,
+            # Tuple[Any, ...], List[Any], ndarray[Any, Any]]"; expected "Sized"
+            all_sec = (
+                is_list_like(self.secondary_y)
+                and len(self.secondary_y) == self.nseries  # type: ignore[arg-type]
+            )
+            if sec_true or all_sec:
+                # if all data is plotted on secondary, return right axes
+                return self._get_ax_layer(self.axes[0], primary=False)
+            else:
+                return self.axes[0]
+
+    @final
+    @staticmethod
+    def _convert_to_ndarray(data):
+        # GH31357: categorical columns are processed separately
+        if isinstance(data.dtype, CategoricalDtype):
+            return data
+
+        # GH32073: cast to float if values contain nulled integers
+        if (is_integer_dtype(data.dtype) or is_float_dtype(data.dtype)) and isinstance(
+            data.dtype, ExtensionDtype
+        ):
+            return data.to_numpy(dtype="float", na_value=np.nan)
+
+        # GH25587: cast ExtensionArray of pandas (IntegerArray, etc.) to
+        # np.ndarray before plot.
+        if len(data) > 0:
+            return np.asarray(data)
+
+        return data
+
+    @final
+    def _ensure_frame(self, data) -> DataFrame:
+        if isinstance(data, ABCSeries):
+            label = self.label
+            if label is None and data.name is None:
+                label = ""
+            if label is None:
+                # We'll end up with columns of [0] instead of [None]
+                data = data.to_frame()
+            else:
+                data = data.to_frame(name=label)
+        elif self._kind in ("hist", "box"):
+            cols = self.columns if self.by is None else self.columns + self.by
+            data = data.loc[:, cols]
+        return data
+
+    @final
+    def _compute_plot_data(self) -> None:
+        data = self.data
+
+        # GH15079 reconstruct data if by is defined
+        if self.by is not None:
+            self.subplots = True
+            data = reconstruct_data_with_by(self.data, by=self.by, cols=self.columns)
+
+        # GH16953, infer_objects is needed as fallback, for ``Series``
+        # with ``dtype == object``
+        data = data.infer_objects(copy=False)
+        include_type = [np.number, "datetime", "datetimetz", "timedelta"]
+
+        # GH23719, allow plotting boolean
+        if self.include_bool is True:
+            include_type.append(np.bool_)
+
+        # GH22799, exclude datetime-like type for boxplot
+        exclude_type = None
+        if self._kind == "box":
+            # TODO: change after solving issue 27881
+            include_type = [np.number]
+            exclude_type = ["timedelta"]
+
+        # GH 18755, include object and category type for scatter plot
+        if self._kind == "scatter":
+            include_type.extend(["object", "category", "string"])
+
+        numeric_data = data.select_dtypes(include=include_type, exclude=exclude_type)
+
+        is_empty = numeric_data.shape[-1] == 0
+        # no non-numeric frames or series allowed
+        if is_empty:
+            raise TypeError("no numeric data to plot")
+
+        self.data = numeric_data.apply(type(self)._convert_to_ndarray)
+
+    def _make_plot(self, fig: Figure) -> None:
+        raise AbstractMethodError(self)
+
+    @final
+    def _add_table(self) -> None:
+        if self.table is False:
+            return
+        elif self.table is True:
+            data = self.data.transpose()
+        else:
+            data = self.table
+        ax = self._get_ax(0)
+        tools.table(ax, data)
+
+    @final
+    def _post_plot_logic_common(self, ax: Axes) -> None:
+        """Common post process for each axes"""
+        if self.orientation == "vertical" or self.orientation is None:
+            type(self)._apply_axis_properties(
+                ax.xaxis, rot=self.rot, fontsize=self.fontsize
+            )
+            type(self)._apply_axis_properties(ax.yaxis, fontsize=self.fontsize)
+
+            if hasattr(ax, "right_ax"):
+                type(self)._apply_axis_properties(
+                    ax.right_ax.yaxis, fontsize=self.fontsize
+                )
+
+        elif self.orientation == "horizontal":
+            type(self)._apply_axis_properties(
+                ax.yaxis, rot=self.rot, fontsize=self.fontsize
+            )
+            type(self)._apply_axis_properties(ax.xaxis, fontsize=self.fontsize)
+
+            if hasattr(ax, "right_ax"):
+                type(self)._apply_axis_properties(
+                    ax.right_ax.yaxis, fontsize=self.fontsize
+                )
+        else:  # pragma no cover
+            raise ValueError
+
+    @abstractmethod
+    def _post_plot_logic(self, ax: Axes, data) -> None:
+        """Post process for each axes. Overridden in child classes"""
+
+    @final
+    def _adorn_subplots(self, fig: Figure) -> None:
+        """Common post process unrelated to data"""
+        if len(self.axes) > 0:
+            all_axes = self._get_subplots(fig)
+            nrows, ncols = self._get_axes_layout(fig)
+            handle_shared_axes(
+                axarr=all_axes,
+                nplots=len(all_axes),
+                naxes=nrows * ncols,
+                nrows=nrows,
+                ncols=ncols,
+                sharex=self.sharex,
+                sharey=self.sharey,
+            )
+
+        for ax in self.axes:
+            ax = getattr(ax, "right_ax", ax)
+            if self.yticks is not None:
+                ax.set_yticks(self.yticks)
+
+            if self.xticks is not None:
+                ax.set_xticks(self.xticks)
+
+            if self.ylim is not None:
+                ax.set_ylim(self.ylim)
+
+            if self.xlim is not None:
+                ax.set_xlim(self.xlim)
+
+            # GH9093, currently Pandas does not show ylabel, so if users provide
+            # ylabel will set it as ylabel in the plot.
+            if self.ylabel is not None:
+                ax.set_ylabel(pprint_thing(self.ylabel))
+
+            ax.grid(self.grid)
+
+        if self.title:
+            if self.subplots:
+                if is_list_like(self.title):
+                    if len(self.title) != self.nseries:
+                        raise ValueError(
+                            "The length of `title` must equal the number "
+                            "of columns if using `title` of type `list` "
+                            "and `subplots=True`.\n"
+                            f"length of title = {len(self.title)}\n"
+                            f"number of columns = {self.nseries}"
+                        )
+
+                    for ax, title in zip(self.axes, self.title):
+                        ax.set_title(title)
+                else:
+                    fig.suptitle(self.title)
+            else:
+                if is_list_like(self.title):
+                    msg = (
+                        "Using `title` of type `list` is not supported "
+                        "unless `subplots=True` is passed"
+                    )
+                    raise ValueError(msg)
+                self.axes[0].set_title(self.title)
+
+    @final
+    @staticmethod
+    def _apply_axis_properties(
+        axis: Axis, rot=None, fontsize: int | None = None
+    ) -> None:
+        """
+        Tick creation within matplotlib is reasonably expensive and is
+        internally deferred until accessed as Ticks are created/destroyed
+        multiple times per draw. It's therefore beneficial for us to avoid
+        accessing unless we will act on the Tick.
+        """
+        if rot is not None or fontsize is not None:
+            # rot=0 is a valid setting, hence the explicit None check
+            labels = axis.get_majorticklabels() + axis.get_minorticklabels()
+            for label in labels:
+                if rot is not None:
+                    label.set_rotation(rot)
+                if fontsize is not None:
+                    label.set_fontsize(fontsize)
+
+    @final
+    @property
+    def legend_title(self) -> str | None:
+        if not isinstance(self.data.columns, ABCMultiIndex):
+            name = self.data.columns.name
+            if name is not None:
+                name = pprint_thing(name)
+            return name
+        else:
+            stringified = map(pprint_thing, self.data.columns.names)
+            return ",".join(stringified)
+
+    @final
+    def _mark_right_label(self, label: str, index: int) -> str:
+        """
+        Append ``(right)`` to the label of a line if it's plotted on the right axis.
+
+        Note that ``(right)`` is only appended when ``subplots=False``.
+        """
+        if not self.subplots and self.mark_right and self.on_right(index):
+            label += " (right)"
+        return label
+
+    @final
+    def _append_legend_handles_labels(self, handle: Artist, label: str) -> None:
+        """
+        Append current handle and label to ``legend_handles`` and ``legend_labels``.
+
+        These will be used to make the legend.
+        """
+        self.legend_handles.append(handle)
+        self.legend_labels.append(label)
+
+    def _make_legend(self) -> None:
+        ax, leg = self._get_ax_legend(self.axes[0])
+
+        handles = []
+        labels = []
+        title = ""
+
+        if not self.subplots:
+            if leg is not None:
+                title = leg.get_title().get_text()
+                # Replace leg.legend_handles because it misses marker info
+                if Version(mpl.__version__) < Version("3.7"):
+                    handles = leg.legendHandles
+                else:
+                    handles = leg.legend_handles
+                labels = [x.get_text() for x in leg.get_texts()]
+
+            if self.legend:
+                if self.legend == "reverse":
+                    handles += reversed(self.legend_handles)
+                    labels += reversed(self.legend_labels)
+                else:
+                    handles += self.legend_handles
+                    labels += self.legend_labels
+
+                if self.legend_title is not None:
+                    title = self.legend_title
+
+            if len(handles) > 0:
+                ax.legend(handles, labels, loc="best", title=title)
+
+        elif self.subplots and self.legend:
+            for ax in self.axes:
+                if ax.get_visible():
+                    with warnings.catch_warnings():
+                        warnings.filterwarnings(
+                            "ignore",
+                            "No artists with labels found to put in legend.",
+                            UserWarning,
+                        )
+                        ax.legend(loc="best")
+
+    @final
+    @staticmethod
+    def _get_ax_legend(ax: Axes):
+        """
+        Take in axes and return ax and legend under different scenarios
+        """
+        leg = ax.get_legend()
+
+        other_ax = getattr(ax, "left_ax", None) or getattr(ax, "right_ax", None)
+        other_leg = None
+        if other_ax is not None:
+            other_leg = other_ax.get_legend()
+        if leg is None and other_leg is not None:
+            leg = other_leg
+            ax = other_ax
+        return ax, leg
+
+    @final
+    @cache_readonly
+    def plt(self):
+        import matplotlib.pyplot as plt
+
+        return plt
+
+    _need_to_set_index = False
+
+    @final
+    def _get_xticks(self):
+        index = self.data.index
+        is_datetype = index.inferred_type in ("datetime", "date", "datetime64", "time")
+
+        # TODO: be stricter about x?
+        x: list[int] | np.ndarray
+        if self.use_index:
+            if isinstance(index, ABCPeriodIndex):
+                # test_mixed_freq_irreg_period
+                x = index.to_timestamp()._mpl_repr()
+                # TODO: why do we need to do to_timestamp() here but not other
+                #  places where we call mpl_repr?
+            elif is_any_real_numeric_dtype(index.dtype):
+                # Matplotlib supports numeric values or datetime objects as
+                # xaxis values. Taking LBYL approach here, by the time
+                # matplotlib raises exception when using non numeric/datetime
+                # values for xaxis, several actions are already taken by plt.
+                x = index._mpl_repr()
+            elif isinstance(index, ABCDatetimeIndex) or is_datetype:
+                x = index._mpl_repr()
+            else:
+                self._need_to_set_index = True
+                x = list(range(len(index)))
+        else:
+            x = list(range(len(index)))
+
+        return x
+
+    @classmethod
+    @register_pandas_matplotlib_converters
+    def _plot(
+        cls, ax: Axes, x, y: np.ndarray, style=None, is_errorbar: bool = False, **kwds
+    ):
+        mask = isna(y)
+        if mask.any():
+            y = np.ma.array(y)
+            y = np.ma.masked_where(mask, y)
+
+        if isinstance(x, ABCIndex):
+            x = x._mpl_repr()
+
+        if is_errorbar:
+            if "xerr" in kwds:
+                kwds["xerr"] = np.array(kwds.get("xerr"))
+            if "yerr" in kwds:
+                kwds["yerr"] = np.array(kwds.get("yerr"))
+            return ax.errorbar(x, y, **kwds)
+        else:
+            # prevent style kwarg from going to errorbar, where it is unsupported
+            args = (x, y, style) if style is not None else (x, y)
+            return ax.plot(*args, **kwds)
+
+    def _get_custom_index_name(self):
+        """Specify whether xlabel/ylabel should be used to override index name"""
+        return self.xlabel
+
+    @final
+    def _get_index_name(self) -> str | None:
+        if isinstance(self.data.index, ABCMultiIndex):
+            name = self.data.index.names
+            if com.any_not_none(*name):
+                name = ",".join([pprint_thing(x) for x in name])
+            else:
+                name = None
+        else:
+            name = self.data.index.name
+            if name is not None:
+                name = pprint_thing(name)
+
+        # GH 45145, override the default axis label if one is provided.
+        index_name = self._get_custom_index_name()
+        if index_name is not None:
+            name = pprint_thing(index_name)
+
+        return name
+
+    @final
+    @classmethod
+    def _get_ax_layer(cls, ax, primary: bool = True):
+        """get left (primary) or right (secondary) axes"""
+        if primary:
+            return getattr(ax, "left_ax", ax)
+        else:
+            return getattr(ax, "right_ax", ax)
+
+    @final
+    def _col_idx_to_axis_idx(self, col_idx: int) -> int:
+        """Return the index of the axis where the column at col_idx should be plotted"""
+        if isinstance(self.subplots, list):
+            # Subplots is a list: some columns will be grouped together in the same ax
+            return next(
+                group_idx
+                for (group_idx, group) in enumerate(self.subplots)
+                if col_idx in group
+            )
+        else:
+            # subplots is True: one ax per column
+            return col_idx
+
+    @final
+    def _get_ax(self, i: int):
+        # get the twinx ax if appropriate
+        if self.subplots:
+            i = self._col_idx_to_axis_idx(i)
+            ax = self.axes[i]
+            ax = self._maybe_right_yaxis(ax, i)
+            # error: Unsupported target for indexed assignment ("Sequence[Any]")
+            self.axes[i] = ax  # type: ignore[index]
+        else:
+            ax = self.axes[0]
+            ax = self._maybe_right_yaxis(ax, i)
+
+        ax.get_yaxis().set_visible(True)
+        return ax
+
+    @final
+    def on_right(self, i: int):
+        if isinstance(self.secondary_y, bool):
+            return self.secondary_y
+
+        if isinstance(self.secondary_y, (tuple, list, np.ndarray, ABCIndex)):
+            return self.data.columns[i] in self.secondary_y
+
+    @final
+    def _apply_style_colors(
+        self, colors, kwds: dict[str, Any], col_num: int, label: str
+    ):
+        """
+        Manage style and color based on column number and its label.
+        Returns tuple of appropriate style and kwds which "color" may be added.
+        """
+        style = None
+        if self.style is not None:
+            if isinstance(self.style, list):
+                try:
+                    style = self.style[col_num]
+                except IndexError:
+                    pass
+            elif isinstance(self.style, dict):
+                style = self.style.get(label, style)
+            else:
+                style = self.style
+
+        has_color = "color" in kwds or self.colormap is not None
+        nocolor_style = style is None or not _color_in_style(style)
+        if (has_color or self.subplots) and nocolor_style:
+            if isinstance(colors, dict):
+                kwds["color"] = colors[label]
+            else:
+                kwds["color"] = colors[col_num % len(colors)]
+        return style, kwds
+
+    def _get_colors(
+        self,
+        num_colors: int | None = None,
+        color_kwds: str = "color",
+    ):
+        if num_colors is None:
+            num_colors = self.nseries
+        if color_kwds == "color":
+            color = self.color
+        else:
+            color = self.kwds.get(color_kwds)
+        return get_standard_colors(
+            num_colors=num_colors,
+            colormap=self.colormap,
+            color=color,
+        )
+
+    # TODO: tighter typing for first return?
+    @final
+    @staticmethod
+    def _parse_errorbars(
+        label: str, err, data: NDFrameT, nseries: int
+    ) -> tuple[Any, NDFrameT]:
+        """
+        Look for error keyword arguments and return the actual errorbar data
+        or return the error DataFrame/dict
+
+        Error bars can be specified in several ways:
+            Series: the user provides a pandas.Series object of the same
+                    length as the data
+            ndarray: provides a np.ndarray of the same length as the data
+            DataFrame/dict: error values are paired with keys matching the
+                    key in the plotted DataFrame
+            str: the name of the column within the plotted DataFrame
+
+        Asymmetrical error bars are also supported, however raw error values
+        must be provided in this case. For a ``N`` length :class:`Series`, a
+        ``2xN`` array should be provided indicating lower and upper (or left
+        and right) errors. For a ``MxN`` :class:`DataFrame`, asymmetrical errors
+        should be in a ``Mx2xN`` array.
+        """
+        if err is None:
+            return None, data
+
+        def match_labels(data, e):
+            e = e.reindex(data.index)
+            return e
+
+        # key-matched DataFrame
+        if isinstance(err, ABCDataFrame):
+            err = match_labels(data, err)
+        # key-matched dict
+        elif isinstance(err, dict):
+            pass
+
+        # Series of error values
+        elif isinstance(err, ABCSeries):
+            # broadcast error series across data
+            err = match_labels(data, err)
+            err = np.atleast_2d(err)
+            err = np.tile(err, (nseries, 1))
+
+        # errors are a column in the dataframe
+        elif isinstance(err, str):
+            evalues = data[err].values
+            data = data[data.columns.drop(err)]
+            err = np.atleast_2d(evalues)
+            err = np.tile(err, (nseries, 1))
+
+        elif is_list_like(err):
+            if is_iterator(err):
+                err = np.atleast_2d(list(err))
+            else:
+                # raw error values
+                err = np.atleast_2d(err)
+
+            err_shape = err.shape
+
+            # asymmetrical error bars
+            if isinstance(data, ABCSeries) and err_shape[0] == 2:
+                err = np.expand_dims(err, 0)
+                err_shape = err.shape
+                if err_shape[2] != len(data):
+                    raise ValueError(
+                        "Asymmetrical error bars should be provided "
+                        f"with the shape (2, {len(data)})"
+                    )
+            elif isinstance(data, ABCDataFrame) and err.ndim == 3:
+                if (
+                    (err_shape[0] != nseries)
+                    or (err_shape[1] != 2)
+                    or (err_shape[2] != len(data))
+                ):
+                    raise ValueError(
+                        "Asymmetrical error bars should be provided "
+                        f"with the shape ({nseries}, 2, {len(data)})"
+                    )
+
+            # broadcast errors to each data series
+            if len(err) == 1:
+                err = np.tile(err, (nseries, 1))
+
+        elif is_number(err):
+            err = np.tile(
+                [err],
+                (nseries, len(data)),
+            )
+
+        else:
+            msg = f"No valid {label} detected"
+            raise ValueError(msg)
+
+        return err, data
+
+    @final
+    def _get_errorbars(
+        self, label=None, index=None, xerr: bool = True, yerr: bool = True
+    ) -> dict[str, Any]:
+        errors = {}
+
+        for kw, flag in zip(["xerr", "yerr"], [xerr, yerr]):
+            if flag:
+                err = self.errors[kw]
+                # user provided label-matched dataframe of errors
+                if isinstance(err, (ABCDataFrame, dict)):
+                    if label is not None and label in err.keys():
+                        err = err[label]
+                    else:
+                        err = None
+                elif index is not None and err is not None:
+                    err = err[index]
+
+                if err is not None:
+                    errors[kw] = err
+        return errors
+
+    @final
+    def _get_subplots(self, fig: Figure):
+        if Version(mpl.__version__) < Version("3.8"):
+            from matplotlib.axes import Subplot as Klass
+        else:
+            from matplotlib.axes import Axes as Klass
+
+        return [
+            ax
+            for ax in fig.get_axes()
+            if (isinstance(ax, Klass) and ax.get_subplotspec() is not None)
+        ]
+
+    @final
+    def _get_axes_layout(self, fig: Figure) -> tuple[int, int]:
+        axes = self._get_subplots(fig)
+        x_set = set()
+        y_set = set()
+        for ax in axes:
+            # check axes coordinates to estimate layout
+            points = ax.get_position().get_points()
+            x_set.add(points[0][0])
+            y_set.add(points[0][1])
+        return (len(y_set), len(x_set))
+
+
+class PlanePlot(MPLPlot, ABC):
+    """
+    Abstract class for plotting on plane, currently scatter and hexbin.
+    """
+
+    _layout_type = "single"
+
+    def __init__(self, data, x, y, **kwargs) -> None:
+        MPLPlot.__init__(self, data, **kwargs)
+        if x is None or y is None:
+            raise ValueError(self._kind + " requires an x and y column")
+        if is_integer(x) and not self.data.columns._holds_integer():
+            x = self.data.columns[x]
+        if is_integer(y) and not self.data.columns._holds_integer():
+            y = self.data.columns[y]
+
+        self.x = x
+        self.y = y
+
+    @final
+    def _get_nseries(self, data: Series | DataFrame) -> int:
+        return 1
+
+    @final
+    def _post_plot_logic(self, ax: Axes, data) -> None:
+        x, y = self.x, self.y
+        xlabel = self.xlabel if self.xlabel is not None else pprint_thing(x)
+        ylabel = self.ylabel if self.ylabel is not None else pprint_thing(y)
+        # error: Argument 1 to "set_xlabel" of "_AxesBase" has incompatible
+        # type "Hashable"; expected "str"
+        ax.set_xlabel(xlabel)  # type: ignore[arg-type]
+        ax.set_ylabel(ylabel)  # type: ignore[arg-type]
+
+    @final
+    def _plot_colorbar(self, ax: Axes, *, fig: Figure, **kwds):
+        # Addresses issues #10611 and #10678:
+        # When plotting scatterplots and hexbinplots in IPython
+        # inline backend the colorbar axis height tends not to
+        # exactly match the parent axis height.
+        # The difference is due to small fractional differences
+        # in floating points with similar representation.
+        # To deal with this, this method forces the colorbar
+        # height to take the height of the parent axes.
+        # For a more detailed description of the issue
+        # see the following link:
+        # https://github.com/ipython/ipython/issues/11215
+
+        # GH33389, if ax is used multiple times, we should always
+        # use the last one which contains the latest information
+        # about the ax
+        img = ax.collections[-1]
+        return fig.colorbar(img, ax=ax, **kwds)
+
+
+class ScatterPlot(PlanePlot):
+    @property
+    def _kind(self) -> Literal["scatter"]:
+        return "scatter"
+
+    def __init__(
+        self,
+        data,
+        x,
+        y,
+        s=None,
+        c=None,
+        *,
+        colorbar: bool | lib.NoDefault = lib.no_default,
+        norm=None,
+        **kwargs,
+    ) -> None:
+        if s is None:
+            # hide the matplotlib default for size, in case we want to change
+            # the handling of this argument later
+            s = 20
+        elif is_hashable(s) and s in data.columns:
+            s = data[s]
+        self.s = s
+
+        self.colorbar = colorbar
+        self.norm = norm
+
+        super().__init__(data, x, y, **kwargs)
+        if is_integer(c) and not self.data.columns._holds_integer():
+            c = self.data.columns[c]
+        self.c = c
+
+    def _make_plot(self, fig: Figure) -> None:
+        x, y, c, data = self.x, self.y, self.c, self.data
+        ax = self.axes[0]
+
+        c_is_column = is_hashable(c) and c in self.data.columns
+
+        color_by_categorical = c_is_column and isinstance(
+            self.data[c].dtype, CategoricalDtype
+        )
+
+        color = self.color
+        c_values = self._get_c_values(color, color_by_categorical, c_is_column)
+        norm, cmap = self._get_norm_and_cmap(c_values, color_by_categorical)
+        cb = self._get_colorbar(c_values, c_is_column)
+
+        if self.legend:
+            label = self.label
+        else:
+            label = None
+        scatter = ax.scatter(
+            data[x].values,
+            data[y].values,
+            c=c_values,
+            label=label,
+            cmap=cmap,
+            norm=norm,
+            s=self.s,
+            **self.kwds,
+        )
+        if cb:
+            cbar_label = c if c_is_column else ""
+            cbar = self._plot_colorbar(ax, fig=fig, label=cbar_label)
+            if color_by_categorical:
+                n_cats = len(self.data[c].cat.categories)
+                cbar.set_ticks(np.linspace(0.5, n_cats - 0.5, n_cats))
+                cbar.ax.set_yticklabels(self.data[c].cat.categories)
+
+        if label is not None:
+            self._append_legend_handles_labels(
+                # error: Argument 2 to "_append_legend_handles_labels" of
+                # "MPLPlot" has incompatible type "Hashable"; expected "str"
+                scatter,
+                label,  # type: ignore[arg-type]
+            )
+
+        errors_x = self._get_errorbars(label=x, index=0, yerr=False)
+        errors_y = self._get_errorbars(label=y, index=0, xerr=False)
+        if len(errors_x) > 0 or len(errors_y) > 0:
+            err_kwds = dict(errors_x, **errors_y)
+            err_kwds["ecolor"] = scatter.get_facecolor()[0]
+            ax.errorbar(data[x].values, data[y].values, linestyle="none", **err_kwds)
+
+    def _get_c_values(self, color, color_by_categorical: bool, c_is_column: bool):
+        c = self.c
+        if c is not None and color is not None:
+            raise TypeError("Specify exactly one of `c` and `color`")
+        if c is None and color is None:
+            c_values = self.plt.rcParams["patch.facecolor"]
+        elif color is not None:
+            c_values = color
+        elif color_by_categorical:
+            c_values = self.data[c].cat.codes
+        elif c_is_column:
+            c_values = self.data[c].values
+        else:
+            c_values = c
+        return c_values
+
+    def _get_norm_and_cmap(self, c_values, color_by_categorical: bool):
+        c = self.c
+        if self.colormap is not None:
+            cmap = mpl.colormaps.get_cmap(self.colormap)
+        # cmap is only used if c_values are integers, otherwise UserWarning.
+        # GH-53908: additionally call isinstance() because is_integer_dtype
+        # returns True for "b" (meaning "blue" and not int8 in this context)
+        elif not isinstance(c_values, str) and is_integer_dtype(c_values):
+            # pandas uses colormap, matplotlib uses cmap.
+            cmap = mpl.colormaps["Greys"]
+        else:
+            cmap = None
+
+        if color_by_categorical and cmap is not None:
+            from matplotlib import colors
+
+            n_cats = len(self.data[c].cat.categories)
+            cmap = colors.ListedColormap([cmap(i) for i in range(cmap.N)])
+            bounds = np.linspace(0, n_cats, n_cats + 1)
+            norm = colors.BoundaryNorm(bounds, cmap.N)
+            # TODO: warn that we are ignoring self.norm if user specified it?
+            #  Doesn't happen in any tests 2023-11-09
+        else:
+            norm = self.norm
+        return norm, cmap
+
+    def _get_colorbar(self, c_values, c_is_column: bool) -> bool:
+        # plot colorbar if
+        # 1. colormap is assigned, and
+        # 2.`c` is a column containing only numeric values
+        plot_colorbar = self.colormap or c_is_column
+        cb = self.colorbar
+        if cb is lib.no_default:
+            return is_numeric_dtype(c_values) and plot_colorbar
+        return cb
+
+
+class HexBinPlot(PlanePlot):
+    @property
+    def _kind(self) -> Literal["hexbin"]:
+        return "hexbin"
+
+    def __init__(self, data, x, y, C=None, *, colorbar: bool = True, **kwargs) -> None:
+        super().__init__(data, x, y, **kwargs)
+        if is_integer(C) and not self.data.columns._holds_integer():
+            C = self.data.columns[C]
+        self.C = C
+
+        self.colorbar = colorbar
+
+        # Scatter plot allows to plot objects data
+        if len(self.data[self.x]._get_numeric_data()) == 0:
+            raise ValueError(self._kind + " requires x column to be numeric")
+        if len(self.data[self.y]._get_numeric_data()) == 0:
+            raise ValueError(self._kind + " requires y column to be numeric")
+
+    def _make_plot(self, fig: Figure) -> None:
+        x, y, data, C = self.x, self.y, self.data, self.C
+        ax = self.axes[0]
+        # pandas uses colormap, matplotlib uses cmap.
+        cmap = self.colormap or "BuGn"
+        cmap = mpl.colormaps.get_cmap(cmap)
+        cb = self.colorbar
+
+        if C is None:
+            c_values = None
+        else:
+            c_values = data[C].values
+
+        ax.hexbin(data[x].values, data[y].values, C=c_values, cmap=cmap, **self.kwds)
+        if cb:
+            self._plot_colorbar(ax, fig=fig)
+
+    def _make_legend(self) -> None:
+        pass
+
+
+class LinePlot(MPLPlot):
+    _default_rot = 0
+
+    @property
+    def orientation(self) -> PlottingOrientation:
+        return "vertical"
+
+    @property
+    def _kind(self) -> Literal["line", "area", "hist", "kde", "box"]:
+        return "line"
+
+    def __init__(self, data, **kwargs) -> None:
+        from pandas.plotting import plot_params
+
+        MPLPlot.__init__(self, data, **kwargs)
+        if self.stacked:
+            self.data = self.data.fillna(value=0)
+        self.x_compat = plot_params["x_compat"]
+        if "x_compat" in self.kwds:
+            self.x_compat = bool(self.kwds.pop("x_compat"))
+
+    @final
+    def _is_ts_plot(self) -> bool:
+        # this is slightly deceptive
+        return not self.x_compat and self.use_index and self._use_dynamic_x()
+
+    @final
+    def _use_dynamic_x(self) -> bool:
+        return use_dynamic_x(self._get_ax(0), self.data)
+
+    def _make_plot(self, fig: Figure) -> None:
+        if self._is_ts_plot():
+            data = maybe_convert_index(self._get_ax(0), self.data)
+
+            x = data.index  # dummy, not used
+            plotf = self._ts_plot
+            it = data.items()
+        else:
+            x = self._get_xticks()
+            # error: Incompatible types in assignment (expression has type
+            # "Callable[[Any, Any, Any, Any, Any, Any, KwArg(Any)], Any]", variable has
+            # type "Callable[[Any, Any, Any, Any, KwArg(Any)], Any]")
+            plotf = self._plot  # type: ignore[assignment]
+            # error: Incompatible types in assignment (expression has type
+            # "Iterator[tuple[Hashable, ndarray[Any, Any]]]", variable has
+            # type "Iterable[tuple[Hashable, Series]]")
+            it = self._iter_data(data=self.data)  # type: ignore[assignment]
+
+        stacking_id = self._get_stacking_id()
+        is_errorbar = com.any_not_none(*self.errors.values())
+
+        colors = self._get_colors()
+        for i, (label, y) in enumerate(it):
+            ax = self._get_ax(i)
+            kwds = self.kwds.copy()
+            if self.color is not None:
+                kwds["color"] = self.color
+            style, kwds = self._apply_style_colors(
+                colors,
+                kwds,
+                i,
+                # error: Argument 4 to "_apply_style_colors" of "MPLPlot" has
+                # incompatible type "Hashable"; expected "str"
+                label,  # type: ignore[arg-type]
+            )
+
+            errors = self._get_errorbars(label=label, index=i)
+            kwds = dict(kwds, **errors)
+
+            label = pprint_thing(label)
+            label = self._mark_right_label(label, index=i)
+            kwds["label"] = label
+
+            newlines = plotf(
+                ax,
+                x,
+                y,
+                style=style,
+                column_num=i,
+                stacking_id=stacking_id,
+                is_errorbar=is_errorbar,
+                **kwds,
+            )
+            self._append_legend_handles_labels(newlines[0], label)
+
+            if self._is_ts_plot():
+                # reset of xlim should be used for ts data
+                # TODO: GH28021, should find a way to change view limit on xaxis
+                lines = get_all_lines(ax)
+                left, right = get_xlim(lines)
+                ax.set_xlim(left, right)
+
+    # error: Signature of "_plot" incompatible with supertype "MPLPlot"
+    @classmethod
+    def _plot(  # type: ignore[override]
+        cls,
+        ax: Axes,
+        x,
+        y: np.ndarray,
+        style=None,
+        column_num=None,
+        stacking_id=None,
+        **kwds,
+    ):
+        # column_num is used to get the target column from plotf in line and
+        # area plots
+        if column_num == 0:
+            cls._initialize_stacker(ax, stacking_id, len(y))
+        y_values = cls._get_stacked_values(ax, stacking_id, y, kwds["label"])
+        lines = MPLPlot._plot(ax, x, y_values, style=style, **kwds)
+        cls._update_stacker(ax, stacking_id, y)
+        return lines
+
+    @final
+    def _ts_plot(self, ax: Axes, x, data: Series, style=None, **kwds):
+        # accept x to be consistent with normal plot func,
+        # x is not passed to tsplot as it uses data.index as x coordinate
+        # column_num must be in kwds for stacking purpose
+        freq, data = maybe_resample(data, ax, kwds)
+
+        # Set ax with freq info
+        decorate_axes(ax, freq)
+        # digging deeper
+        if hasattr(ax, "left_ax"):
+            decorate_axes(ax.left_ax, freq)
+        if hasattr(ax, "right_ax"):
+            decorate_axes(ax.right_ax, freq)
+        # TODO #54485
+        ax._plot_data.append((data, self._kind, kwds))  # type: ignore[attr-defined]
+
+        lines = self._plot(ax, data.index, np.asarray(data.values), style=style, **kwds)
+        # set date formatter, locators and rescale limits
+        # TODO #54485
+        format_dateaxis(ax, ax.freq, data.index)  # type: ignore[arg-type, attr-defined]
+        return lines
+
+    @final
+    def _get_stacking_id(self) -> int | None:
+        if self.stacked:
+            return id(self.data)
+        else:
+            return None
+
+    @final
+    @classmethod
+    def _initialize_stacker(cls, ax: Axes, stacking_id, n: int) -> None:
+        if stacking_id is None:
+            return
+        if not hasattr(ax, "_stacker_pos_prior"):
+            # TODO #54485
+            ax._stacker_pos_prior = {}  # type: ignore[attr-defined]
+        if not hasattr(ax, "_stacker_neg_prior"):
+            # TODO #54485
+            ax._stacker_neg_prior = {}  # type: ignore[attr-defined]
+        # TODO #54485
+        ax._stacker_pos_prior[stacking_id] = np.zeros(n)  # type: ignore[attr-defined]
+        # TODO #54485
+        ax._stacker_neg_prior[stacking_id] = np.zeros(n)  # type: ignore[attr-defined]
+
+    @final
+    @classmethod
+    def _get_stacked_values(
+        cls, ax: Axes, stacking_id: int | None, values: np.ndarray, label
+    ) -> np.ndarray:
+        if stacking_id is None:
+            return values
+        if not hasattr(ax, "_stacker_pos_prior"):
+            # stacker may not be initialized for subplots
+            cls._initialize_stacker(ax, stacking_id, len(values))
+
+        if (values >= 0).all():
+            # TODO #54485
+            return (
+                ax._stacker_pos_prior[stacking_id]  # type: ignore[attr-defined]
+                + values
+            )
+        elif (values <= 0).all():
+            # TODO #54485
+            return (
+                ax._stacker_neg_prior[stacking_id]  # type: ignore[attr-defined]
+                + values
+            )
+
+        raise ValueError(
+            "When stacked is True, each column must be either "
+            "all positive or all negative. "
+            f"Column '{label}' contains both positive and negative values"
+        )
+
+    @final
+    @classmethod
+    def _update_stacker(cls, ax: Axes, stacking_id: int | None, values) -> None:
+        if stacking_id is None:
+            return
+        if (values >= 0).all():
+            # TODO #54485
+            ax._stacker_pos_prior[stacking_id] += values  # type: ignore[attr-defined]
+        elif (values <= 0).all():
+            # TODO #54485
+            ax._stacker_neg_prior[stacking_id] += values  # type: ignore[attr-defined]
+
+    def _post_plot_logic(self, ax: Axes, data) -> None:
+        from matplotlib.ticker import FixedLocator
+
+        def get_label(i):
+            if is_float(i) and i.is_integer():
+                i = int(i)
+            try:
+                return pprint_thing(data.index[i])
+            except Exception:
+                return ""
+
+        if self._need_to_set_index:
+            xticks = ax.get_xticks()
+            xticklabels = [get_label(x) for x in xticks]
+            # error: Argument 1 to "FixedLocator" has incompatible type "ndarray[Any,
+            # Any]"; expected "Sequence[float]"
+            ax.xaxis.set_major_locator(FixedLocator(xticks))  # type: ignore[arg-type]
+            ax.set_xticklabels(xticklabels)
+
+        # If the index is an irregular time series, then by default
+        # we rotate the tick labels. The exception is if there are
+        # subplots which don't share their x-axes, in which we case
+        # we don't rotate the ticklabels as by default the subplots
+        # would be too close together.
+        condition = (
+            not self._use_dynamic_x()
+            and (data.index._is_all_dates and self.use_index)
+            and (not self.subplots or (self.subplots and self.sharex))
+        )
+
+        index_name = self._get_index_name()
+
+        if condition:
+            # irregular TS rotated 30 deg. by default
+            # probably a better place to check / set this.
+            if not self._rot_set:
+                self.rot = 30
+            format_date_labels(ax, rot=self.rot)
+
+        if index_name is not None and self.use_index:
+            ax.set_xlabel(index_name)
+
+
+class AreaPlot(LinePlot):
+    @property
+    def _kind(self) -> Literal["area"]:
+        return "area"
+
+    def __init__(self, data, **kwargs) -> None:
+        kwargs.setdefault("stacked", True)
+        with warnings.catch_warnings():
+            warnings.filterwarnings(
+                "ignore",
+                "Downcasting object dtype arrays",
+                category=FutureWarning,
+            )
+            data = data.fillna(value=0)
+        LinePlot.__init__(self, data, **kwargs)
+
+        if not self.stacked:
+            # use smaller alpha to distinguish overlap
+            self.kwds.setdefault("alpha", 0.5)
+
+        if self.logy or self.loglog:
+            raise ValueError("Log-y scales are not supported in area plot")
+
+    # error: Signature of "_plot" incompatible with supertype "MPLPlot"
+    @classmethod
+    def _plot(  # type: ignore[override]
+        cls,
+        ax: Axes,
+        x,
+        y: np.ndarray,
+        style=None,
+        column_num=None,
+        stacking_id=None,
+        is_errorbar: bool = False,
+        **kwds,
+    ):
+        if column_num == 0:
+            cls._initialize_stacker(ax, stacking_id, len(y))
+        y_values = cls._get_stacked_values(ax, stacking_id, y, kwds["label"])
+
+        # need to remove label, because subplots uses mpl legend as it is
+        line_kwds = kwds.copy()
+        line_kwds.pop("label")
+        lines = MPLPlot._plot(ax, x, y_values, style=style, **line_kwds)
+
+        # get data from the line to get coordinates for fill_between
+        xdata, y_values = lines[0].get_data(orig=False)
+
+        # unable to use ``_get_stacked_values`` here to get starting point
+        if stacking_id is None:
+            start = np.zeros(len(y))
+        elif (y >= 0).all():
+            # TODO #54485
+            start = ax._stacker_pos_prior[stacking_id]  # type: ignore[attr-defined]
+        elif (y <= 0).all():
+            # TODO #54485
+            start = ax._stacker_neg_prior[stacking_id]  # type: ignore[attr-defined]
+        else:
+            start = np.zeros(len(y))
+
+        if "color" not in kwds:
+            kwds["color"] = lines[0].get_color()
+
+        rect = ax.fill_between(xdata, start, y_values, **kwds)
+        cls._update_stacker(ax, stacking_id, y)
+
+        # LinePlot expects list of artists
+        res = [rect]
+        return res
+
+    def _post_plot_logic(self, ax: Axes, data) -> None:
+        LinePlot._post_plot_logic(self, ax, data)
+
+        is_shared_y = len(list(ax.get_shared_y_axes())) > 0
+        # do not override the default axis behaviour in case of shared y axes
+        if self.ylim is None and not is_shared_y:
+            if (data >= 0).all().all():
+                ax.set_ylim(0, None)
+            elif (data <= 0).all().all():
+                ax.set_ylim(None, 0)
+
+
+class BarPlot(MPLPlot):
+    @property
+    def _kind(self) -> Literal["bar", "barh"]:
+        return "bar"
+
+    _default_rot = 90
+
+    @property
+    def orientation(self) -> PlottingOrientation:
+        return "vertical"
+
+    def __init__(
+        self,
+        data,
+        *,
+        align="center",
+        bottom=0,
+        left=0,
+        width=0.5,
+        position=0.5,
+        log=False,
+        **kwargs,
+    ) -> None:
+        # we have to treat a series differently than a
+        # 1-column DataFrame w.r.t. color handling
+        self._is_series = isinstance(data, ABCSeries)
+        self.bar_width = width
+        self._align = align
+        self._position = position
+        self.tick_pos = np.arange(len(data))
+
+        if is_list_like(bottom):
+            bottom = np.array(bottom)
+        if is_list_like(left):
+            left = np.array(left)
+        self.bottom = bottom
+        self.left = left
+
+        self.log = log
+
+        MPLPlot.__init__(self, data, **kwargs)
+
+    @cache_readonly
+    def ax_pos(self) -> np.ndarray:
+        return self.tick_pos - self.tickoffset
+
+    @cache_readonly
+    def tickoffset(self):
+        if self.stacked or self.subplots:
+            return self.bar_width * self._position
+        elif self._align == "edge":
+            w = self.bar_width / self.nseries
+            return self.bar_width * (self._position - 0.5) + w * 0.5
+        else:
+            return self.bar_width * self._position
+
+    @cache_readonly
+    def lim_offset(self):
+        if self.stacked or self.subplots:
+            if self._align == "edge":
+                return self.bar_width / 2
+            else:
+                return 0
+        elif self._align == "edge":
+            w = self.bar_width / self.nseries
+            return w * 0.5
+        else:
+            return 0
+
+    # error: Signature of "_plot" incompatible with supertype "MPLPlot"
+    @classmethod
+    def _plot(  # type: ignore[override]
+        cls,
+        ax: Axes,
+        x,
+        y: np.ndarray,
+        w,
+        start: int | npt.NDArray[np.intp] = 0,
+        log: bool = False,
+        **kwds,
+    ):
+        return ax.bar(x, y, w, bottom=start, log=log, **kwds)
+
+    @property
+    def _start_base(self):
+        return self.bottom
+
+    def _make_plot(self, fig: Figure) -> None:
+        colors = self._get_colors()
+        ncolors = len(colors)
+
+        pos_prior = neg_prior = np.zeros(len(self.data))
+        K = self.nseries
+
+        data = self.data.fillna(0)
+        for i, (label, y) in enumerate(self._iter_data(data=data)):
+            ax = self._get_ax(i)
+            kwds = self.kwds.copy()
+            if self._is_series:
+                kwds["color"] = colors
+            elif isinstance(colors, dict):
+                kwds["color"] = colors[label]
+            else:
+                kwds["color"] = colors[i % ncolors]
+
+            errors = self._get_errorbars(label=label, index=i)
+            kwds = dict(kwds, **errors)
+
+            label = pprint_thing(label)
+            label = self._mark_right_label(label, index=i)
+
+            if (("yerr" in kwds) or ("xerr" in kwds)) and (kwds.get("ecolor") is None):
+                kwds["ecolor"] = mpl.rcParams["xtick.color"]
+
+            start = 0
+            if self.log and (y >= 1).all():
+                start = 1
+            start = start + self._start_base
+
+            kwds["align"] = self._align
+            if self.subplots:
+                w = self.bar_width / 2
+                rect = self._plot(
+                    ax,
+                    self.ax_pos + w,
+                    y,
+                    self.bar_width,
+                    start=start,
+                    label=label,
+                    log=self.log,
+                    **kwds,
+                )
+                ax.set_title(label)
+            elif self.stacked:
+                mask = y > 0
+                start = np.where(mask, pos_prior, neg_prior) + self._start_base
+                w = self.bar_width / 2
+                rect = self._plot(
+                    ax,
+                    self.ax_pos + w,
+                    y,
+                    self.bar_width,
+                    start=start,
+                    label=label,
+                    log=self.log,
+                    **kwds,
+                )
+                pos_prior = pos_prior + np.where(mask, y, 0)
+                neg_prior = neg_prior + np.where(mask, 0, y)
+            else:
+                w = self.bar_width / K
+                rect = self._plot(
+                    ax,
+                    self.ax_pos + (i + 0.5) * w,
+                    y,
+                    w,
+                    start=start,
+                    label=label,
+                    log=self.log,
+                    **kwds,
+                )
+            self._append_legend_handles_labels(rect, label)
+
+    def _post_plot_logic(self, ax: Axes, data) -> None:
+        if self.use_index:
+            str_index = [pprint_thing(key) for key in data.index]
+        else:
+            str_index = [pprint_thing(key) for key in range(data.shape[0])]
+
+        s_edge = self.ax_pos[0] - 0.25 + self.lim_offset
+        e_edge = self.ax_pos[-1] + 0.25 + self.bar_width + self.lim_offset
+
+        self._decorate_ticks(ax, self._get_index_name(), str_index, s_edge, e_edge)
+
+    def _decorate_ticks(
+        self,
+        ax: Axes,
+        name: str | None,
+        ticklabels: list[str],
+        start_edge: float,
+        end_edge: float,
+    ) -> None:
+        ax.set_xlim((start_edge, end_edge))
+
+        if self.xticks is not None:
+            ax.set_xticks(np.array(self.xticks))
+        else:
+            ax.set_xticks(self.tick_pos)
+            ax.set_xticklabels(ticklabels)
+
+        if name is not None and self.use_index:
+            ax.set_xlabel(name)
+
+
+class BarhPlot(BarPlot):
+    @property
+    def _kind(self) -> Literal["barh"]:
+        return "barh"
+
+    _default_rot = 0
+
+    @property
+    def orientation(self) -> Literal["horizontal"]:
+        return "horizontal"
+
+    @property
+    def _start_base(self):
+        return self.left
+
+    # error: Signature of "_plot" incompatible with supertype "MPLPlot"
+    @classmethod
+    def _plot(  # type: ignore[override]
+        cls,
+        ax: Axes,
+        x,
+        y: np.ndarray,
+        w,
+        start: int | npt.NDArray[np.intp] = 0,
+        log: bool = False,
+        **kwds,
+    ):
+        return ax.barh(x, y, w, left=start, log=log, **kwds)
+
+    def _get_custom_index_name(self):
+        return self.ylabel
+
+    def _decorate_ticks(
+        self,
+        ax: Axes,
+        name: str | None,
+        ticklabels: list[str],
+        start_edge: float,
+        end_edge: float,
+    ) -> None:
+        # horizontal bars
+        ax.set_ylim((start_edge, end_edge))
+        ax.set_yticks(self.tick_pos)
+        ax.set_yticklabels(ticklabels)
+        if name is not None and self.use_index:
+            ax.set_ylabel(name)
+        # error: Argument 1 to "set_xlabel" of "_AxesBase" has incompatible type
+        # "Hashable | None"; expected "str"
+        ax.set_xlabel(self.xlabel)  # type: ignore[arg-type]
+
+
+class PiePlot(MPLPlot):
+    @property
+    def _kind(self) -> Literal["pie"]:
+        return "pie"
+
+    _layout_type = "horizontal"
+
+    def __init__(self, data, kind=None, **kwargs) -> None:
+        data = data.fillna(value=0)
+        if (data < 0).any().any():
+            raise ValueError(f"{self._kind} plot doesn't allow negative values")
+        MPLPlot.__init__(self, data, kind=kind, **kwargs)
+
+    @classmethod
+    def _validate_log_kwd(
+        cls,
+        kwd: str,
+        value: bool | None | Literal["sym"],
+    ) -> bool | None | Literal["sym"]:
+        super()._validate_log_kwd(kwd=kwd, value=value)
+        if value is not False:
+            warnings.warn(
+                f"PiePlot ignores the '{kwd}' keyword",
+                UserWarning,
+                stacklevel=find_stack_level(),
+            )
+        return False
+
+    def _validate_color_args(self, color, colormap) -> None:
+        # TODO: warn if color is passed and ignored?
+        return None
+
+    def _make_plot(self, fig: Figure) -> None:
+        colors = self._get_colors(num_colors=len(self.data), color_kwds="colors")
+        self.kwds.setdefault("colors", colors)
+
+        for i, (label, y) in enumerate(self._iter_data(data=self.data)):
+            ax = self._get_ax(i)
+            if label is not None:
+                label = pprint_thing(label)
+                ax.set_ylabel(label)
+
+            kwds = self.kwds.copy()
+
+            def blank_labeler(label, value):
+                if value == 0:
+                    return ""
+                else:
+                    return label
+
+            idx = [pprint_thing(v) for v in self.data.index]
+            labels = kwds.pop("labels", idx)
+            # labels is used for each wedge's labels
+            # Blank out labels for values of 0 so they don't overlap
+            # with nonzero wedges
+            if labels is not None:
+                blabels = [blank_labeler(left, value) for left, value in zip(labels, y)]
+            else:
+                blabels = None
+            results = ax.pie(y, labels=blabels, **kwds)
+
+            if kwds.get("autopct", None) is not None:
+                patches, texts, autotexts = results
+            else:
+                patches, texts = results
+                autotexts = []
+
+            if self.fontsize is not None:
+                for t in texts + autotexts:
+                    t.set_fontsize(self.fontsize)
+
+            # leglabels is used for legend labels
+            leglabels = labels if labels is not None else idx
+            for _patch, _leglabel in zip(patches, leglabels):
+                self._append_legend_handles_labels(_patch, _leglabel)
+
+    def _post_plot_logic(self, ax: Axes, data) -> None:
+        pass
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/plotting/_matplotlib/groupby.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/plotting/_matplotlib/groupby.py
new file mode 100644
index 0000000000000000000000000000000000000000..cbb66065a8039c63b7181619aea3aa74277da4a5
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/plotting/_matplotlib/groupby.py
@@ -0,0 +1,142 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import numpy as np
+
+from pandas.core.dtypes.missing import remove_na_arraylike
+
+from pandas import (
+    MultiIndex,
+    concat,
+)
+
+from pandas.plotting._matplotlib.misc import unpack_single_str_list
+
+if TYPE_CHECKING:
+    from collections.abc import Hashable
+
+    from pandas._typing import IndexLabel
+
+    from pandas import (
+        DataFrame,
+        Series,
+    )
+
+
+def create_iter_data_given_by(
+    data: DataFrame, kind: str = "hist"
+) -> dict[Hashable, DataFrame | Series]:
+    """
+    Create data for iteration given `by` is assigned or not, and it is only
+    used in both hist and boxplot.
+
+    If `by` is assigned, return a dictionary of DataFrames in which the key of
+    dictionary is the values in groups.
+    If `by` is not assigned, return input as is, and this preserves current
+    status of iter_data.
+
+    Parameters
+    ----------
+    data : reformatted grouped data from `_compute_plot_data` method.
+    kind : str, plot kind. This function is only used for `hist` and `box` plots.
+
+    Returns
+    -------
+    iter_data : DataFrame or Dictionary of DataFrames
+
+    Examples
+    --------
+    If `by` is assigned:
+
+    >>> import numpy as np
+    >>> tuples = [('h1', 'a'), ('h1', 'b'), ('h2', 'a'), ('h2', 'b')]
+    >>> mi = pd.MultiIndex.from_tuples(tuples)
+    >>> value = [[1, 3, np.nan, np.nan],
+    ...          [3, 4, np.nan, np.nan], [np.nan, np.nan, 5, 6]]
+    >>> data = pd.DataFrame(value, columns=mi)
+    >>> create_iter_data_given_by(data)
+    {'h1':     h1
+         a    b
+    0  1.0  3.0
+    1  3.0  4.0
+    2  NaN  NaN, 'h2':     h2
+         a    b
+    0  NaN  NaN
+    1  NaN  NaN
+    2  5.0  6.0}
+    """
+
+    # For `hist` plot, before transformation, the values in level 0 are values
+    # in groups and subplot titles, and later used for column subselection and
+    # iteration; For `box` plot, values in level 1 are column names to show,
+    # and are used for iteration and as subplots titles.
+    if kind == "hist":
+        level = 0
+    else:
+        level = 1
+
+    # Select sub-columns based on the value of level of MI, and if `by` is
+    # assigned, data must be a MI DataFrame
+    assert isinstance(data.columns, MultiIndex)
+    return {
+        col: data.loc[:, data.columns.get_level_values(level) == col]
+        for col in data.columns.levels[level]
+    }
+
+
+def reconstruct_data_with_by(
+    data: DataFrame, by: IndexLabel, cols: IndexLabel
+) -> DataFrame:
+    """
+    Internal function to group data, and reassign multiindex column names onto the
+    result in order to let grouped data be used in _compute_plot_data method.
+
+    Parameters
+    ----------
+    data : Original DataFrame to plot
+    by : grouped `by` parameter selected by users
+    cols : columns of data set (excluding columns used in `by`)
+
+    Returns
+    -------
+    Output is the reconstructed DataFrame with MultiIndex columns. The first level
+    of MI is unique values of groups, and second level of MI is the columns
+    selected by users.
+
+    Examples
+    --------
+    >>> d = {'h': ['h1', 'h1', 'h2'], 'a': [1, 3, 5], 'b': [3, 4, 6]}
+    >>> df = pd.DataFrame(d)
+    >>> reconstruct_data_with_by(df, by='h', cols=['a', 'b'])
+       h1      h2
+       a     b     a     b
+    0  1.0   3.0   NaN   NaN
+    1  3.0   4.0   NaN   NaN
+    2  NaN   NaN   5.0   6.0
+    """
+    by_modified = unpack_single_str_list(by)
+    grouped = data.groupby(by_modified)
+
+    data_list = []
+    for key, group in grouped:
+        # error: List item 1 has incompatible type "Union[Hashable,
+        # Sequence[Hashable]]"; expected "Iterable[Hashable]"
+        columns = MultiIndex.from_product([[key], cols])  # type: ignore[list-item]
+        sub_group = group[cols]
+        sub_group.columns = columns
+        data_list.append(sub_group)
+
+    data = concat(data_list, axis=1)
+    return data
+
+
+def reformat_hist_y_given_by(y: np.ndarray, by: IndexLabel | None) -> np.ndarray:
+    """Internal function to reformat y given `by` is applied or not for hist plot.
+
+    If by is None, input y is 1-d with NaN removed; and if by is not None, groupby
+    will take place and input y is multi-dimensional array.
+    """
+    if by is not None and len(y.shape) > 1:
+        return np.array([remove_na_arraylike(col) for col in y.T]).T
+    return remove_na_arraylike(y)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/plotting/_matplotlib/hist.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/plotting/_matplotlib/hist.py
new file mode 100644
index 0000000000000000000000000000000000000000..e610f1adb602c46ffd7affa50c0f857ad7d030e4
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/plotting/_matplotlib/hist.py
@@ -0,0 +1,581 @@
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Literal,
+    final,
+)
+
+import numpy as np
+
+from pandas.core.dtypes.common import (
+    is_integer,
+    is_list_like,
+)
+from pandas.core.dtypes.generic import (
+    ABCDataFrame,
+    ABCIndex,
+)
+from pandas.core.dtypes.missing import (
+    isna,
+    remove_na_arraylike,
+)
+
+from pandas.io.formats.printing import pprint_thing
+from pandas.plotting._matplotlib.core import (
+    LinePlot,
+    MPLPlot,
+)
+from pandas.plotting._matplotlib.groupby import (
+    create_iter_data_given_by,
+    reformat_hist_y_given_by,
+)
+from pandas.plotting._matplotlib.misc import unpack_single_str_list
+from pandas.plotting._matplotlib.tools import (
+    create_subplots,
+    flatten_axes,
+    maybe_adjust_figure,
+    set_ticks_props,
+)
+
+if TYPE_CHECKING:
+    from matplotlib.axes import Axes
+    from matplotlib.figure import Figure
+
+    from pandas._typing import PlottingOrientation
+
+    from pandas import (
+        DataFrame,
+        Series,
+    )
+
+
+class HistPlot(LinePlot):
+    @property
+    def _kind(self) -> Literal["hist", "kde"]:
+        return "hist"
+
+    def __init__(
+        self,
+        data,
+        bins: int | np.ndarray | list[np.ndarray] = 10,
+        bottom: int | np.ndarray = 0,
+        *,
+        range=None,
+        weights=None,
+        **kwargs,
+    ) -> None:
+        if is_list_like(bottom):
+            bottom = np.array(bottom)
+        self.bottom = bottom
+
+        self._bin_range = range
+        self.weights = weights
+
+        self.xlabel = kwargs.get("xlabel")
+        self.ylabel = kwargs.get("ylabel")
+        # Do not call LinePlot.__init__ which may fill nan
+        MPLPlot.__init__(self, data, **kwargs)  # pylint: disable=non-parent-init-called
+
+        self.bins = self._adjust_bins(bins)
+
+    def _adjust_bins(self, bins: int | np.ndarray | list[np.ndarray]):
+        if is_integer(bins):
+            if self.by is not None:
+                by_modified = unpack_single_str_list(self.by)
+                grouped = self.data.groupby(by_modified)[self.columns]
+                bins = [self._calculate_bins(group, bins) for key, group in grouped]
+            else:
+                bins = self._calculate_bins(self.data, bins)
+        return bins
+
+    def _calculate_bins(self, data: Series | DataFrame, bins) -> np.ndarray:
+        """Calculate bins given data"""
+        nd_values = data.infer_objects(copy=False)._get_numeric_data()
+        values = np.ravel(nd_values)
+        values = values[~isna(values)]
+
+        hist, bins = np.histogram(values, bins=bins, range=self._bin_range)
+        return bins
+
+    # error: Signature of "_plot" incompatible with supertype "LinePlot"
+    @classmethod
+    def _plot(  # type: ignore[override]
+        cls,
+        ax: Axes,
+        y: np.ndarray,
+        style=None,
+        bottom: int | np.ndarray = 0,
+        column_num: int = 0,
+        stacking_id=None,
+        *,
+        bins,
+        **kwds,
+    ):
+        if column_num == 0:
+            cls._initialize_stacker(ax, stacking_id, len(bins) - 1)
+
+        base = np.zeros(len(bins) - 1)
+        bottom = bottom + cls._get_stacked_values(ax, stacking_id, base, kwds["label"])
+        # ignore style
+        n, bins, patches = ax.hist(y, bins=bins, bottom=bottom, **kwds)
+        cls._update_stacker(ax, stacking_id, n)
+        return patches
+
+    def _make_plot(self, fig: Figure) -> None:
+        colors = self._get_colors()
+        stacking_id = self._get_stacking_id()
+
+        # Re-create iterated data if `by` is assigned by users
+        data = (
+            create_iter_data_given_by(self.data, self._kind)
+            if self.by is not None
+            else self.data
+        )
+
+        # error: Argument "data" to "_iter_data" of "MPLPlot" has incompatible
+        # type "object"; expected "DataFrame | dict[Hashable, Series | DataFrame]"
+        for i, (label, y) in enumerate(self._iter_data(data=data)):  # type: ignore[arg-type]
+            ax = self._get_ax(i)
+
+            kwds = self.kwds.copy()
+            if self.color is not None:
+                kwds["color"] = self.color
+
+            label = pprint_thing(label)
+            label = self._mark_right_label(label, index=i)
+            kwds["label"] = label
+
+            style, kwds = self._apply_style_colors(colors, kwds, i, label)
+            if style is not None:
+                kwds["style"] = style
+
+            self._make_plot_keywords(kwds, y)
+
+            # the bins is multi-dimension array now and each plot need only 1-d and
+            # when by is applied, label should be columns that are grouped
+            if self.by is not None:
+                kwds["bins"] = kwds["bins"][i]
+                kwds["label"] = self.columns
+                kwds.pop("color")
+
+            if self.weights is not None:
+                kwds["weights"] = type(self)._get_column_weights(self.weights, i, y)
+
+            y = reformat_hist_y_given_by(y, self.by)
+
+            artists = self._plot(ax, y, column_num=i, stacking_id=stacking_id, **kwds)
+
+            # when by is applied, show title for subplots to know which group it is
+            if self.by is not None:
+                ax.set_title(pprint_thing(label))
+
+            self._append_legend_handles_labels(artists[0], label)
+
+    def _make_plot_keywords(self, kwds: dict[str, Any], y: np.ndarray) -> None:
+        """merge BoxPlot/KdePlot properties to passed kwds"""
+        # y is required for KdePlot
+        kwds["bottom"] = self.bottom
+        kwds["bins"] = self.bins
+
+    @final
+    @staticmethod
+    def _get_column_weights(weights, i: int, y):
+        # We allow weights to be a multi-dimensional array, e.g. a (10, 2) array,
+        # and each sub-array (10,) will be called in each iteration. If users only
+        # provide 1D array, we assume the same weights is used for all iterations
+        if weights is not None:
+            if np.ndim(weights) != 1 and np.shape(weights)[-1] != 1:
+                try:
+                    weights = weights[:, i]
+                except IndexError as err:
+                    raise ValueError(
+                        "weights must have the same shape as data, "
+                        "or be a single column"
+                    ) from err
+            weights = weights[~isna(y)]
+        return weights
+
+    def _post_plot_logic(self, ax: Axes, data) -> None:
+        if self.orientation == "horizontal":
+            # error: Argument 1 to "set_xlabel" of "_AxesBase" has incompatible
+            # type "Hashable"; expected "str"
+            ax.set_xlabel(
+                "Frequency"
+                if self.xlabel is None
+                else self.xlabel  # type: ignore[arg-type]
+            )
+            ax.set_ylabel(self.ylabel)  # type: ignore[arg-type]
+        else:
+            ax.set_xlabel(self.xlabel)  # type: ignore[arg-type]
+            ax.set_ylabel(
+                "Frequency"
+                if self.ylabel is None
+                else self.ylabel  # type: ignore[arg-type]
+            )
+
+    @property
+    def orientation(self) -> PlottingOrientation:
+        if self.kwds.get("orientation", None) == "horizontal":
+            return "horizontal"
+        else:
+            return "vertical"
+
+
+class KdePlot(HistPlot):
+    @property
+    def _kind(self) -> Literal["kde"]:
+        return "kde"
+
+    @property
+    def orientation(self) -> Literal["vertical"]:
+        return "vertical"
+
+    def __init__(
+        self, data, bw_method=None, ind=None, *, weights=None, **kwargs
+    ) -> None:
+        # Do not call LinePlot.__init__ which may fill nan
+        MPLPlot.__init__(self, data, **kwargs)  # pylint: disable=non-parent-init-called
+        self.bw_method = bw_method
+        self.ind = ind
+        self.weights = weights
+
+    @staticmethod
+    def _get_ind(y: np.ndarray, ind):
+        if ind is None:
+            # np.nanmax() and np.nanmin() ignores the missing values
+            sample_range = np.nanmax(y) - np.nanmin(y)
+            ind = np.linspace(
+                np.nanmin(y) - 0.5 * sample_range,
+                np.nanmax(y) + 0.5 * sample_range,
+                1000,
+            )
+        elif is_integer(ind):
+            sample_range = np.nanmax(y) - np.nanmin(y)
+            ind = np.linspace(
+                np.nanmin(y) - 0.5 * sample_range,
+                np.nanmax(y) + 0.5 * sample_range,
+                ind,
+            )
+        return ind
+
+    @classmethod
+    # error: Signature of "_plot" incompatible with supertype "MPLPlot"
+    def _plot(  #  type: ignore[override]
+        cls,
+        ax: Axes,
+        y: np.ndarray,
+        style=None,
+        bw_method=None,
+        ind=None,
+        column_num=None,
+        stacking_id: int | None = None,
+        **kwds,
+    ):
+        from scipy.stats import gaussian_kde
+
+        y = remove_na_arraylike(y)
+        gkde = gaussian_kde(y, bw_method=bw_method)
+
+        y = gkde.evaluate(ind)
+        lines = MPLPlot._plot(ax, ind, y, style=style, **kwds)
+        return lines
+
+    def _make_plot_keywords(self, kwds: dict[str, Any], y: np.ndarray) -> None:
+        kwds["bw_method"] = self.bw_method
+        kwds["ind"] = type(self)._get_ind(y, ind=self.ind)
+
+    def _post_plot_logic(self, ax: Axes, data) -> None:
+        ax.set_ylabel("Density")
+
+
+def _grouped_plot(
+    plotf,
+    data: Series | DataFrame,
+    column=None,
+    by=None,
+    numeric_only: bool = True,
+    figsize: tuple[float, float] | None = None,
+    sharex: bool = True,
+    sharey: bool = True,
+    layout=None,
+    rot: float = 0,
+    ax=None,
+    **kwargs,
+):
+    # error: Non-overlapping equality check (left operand type: "Optional[Tuple[float,
+    # float]]", right operand type: "Literal['default']")
+    if figsize == "default":  # type: ignore[comparison-overlap]
+        # allowed to specify mpl default with 'default'
+        raise ValueError(
+            "figsize='default' is no longer supported. "
+            "Specify figure size by tuple instead"
+        )
+
+    grouped = data.groupby(by)
+    if column is not None:
+        grouped = grouped[column]
+
+    naxes = len(grouped)
+    fig, axes = create_subplots(
+        naxes=naxes, figsize=figsize, sharex=sharex, sharey=sharey, ax=ax, layout=layout
+    )
+
+    _axes = flatten_axes(axes)
+
+    for i, (key, group) in enumerate(grouped):
+        ax = _axes[i]
+        if numeric_only and isinstance(group, ABCDataFrame):
+            group = group._get_numeric_data()
+        plotf(group, ax, **kwargs)
+        ax.set_title(pprint_thing(key))
+
+    return fig, axes
+
+
+def _grouped_hist(
+    data: Series | DataFrame,
+    column=None,
+    by=None,
+    ax=None,
+    bins: int = 50,
+    figsize: tuple[float, float] | None = None,
+    layout=None,
+    sharex: bool = False,
+    sharey: bool = False,
+    rot: float = 90,
+    grid: bool = True,
+    xlabelsize: int | None = None,
+    xrot=None,
+    ylabelsize: int | None = None,
+    yrot=None,
+    legend: bool = False,
+    **kwargs,
+):
+    """
+    Grouped histogram
+
+    Parameters
+    ----------
+    data : Series/DataFrame
+    column : object, optional
+    by : object, optional
+    ax : axes, optional
+    bins : int, default 50
+    figsize : tuple, optional
+    layout : optional
+    sharex : bool, default False
+    sharey : bool, default False
+    rot : float, default 90
+    grid : bool, default True
+    legend: : bool, default False
+    kwargs : dict, keyword arguments passed to matplotlib.Axes.hist
+
+    Returns
+    -------
+    collection of Matplotlib Axes
+    """
+    if legend:
+        assert "label" not in kwargs
+        if data.ndim == 1:
+            kwargs["label"] = data.name
+        elif column is None:
+            kwargs["label"] = data.columns
+        else:
+            kwargs["label"] = column
+
+    def plot_group(group, ax) -> None:
+        ax.hist(group.dropna().values, bins=bins, **kwargs)
+        if legend:
+            ax.legend()
+
+    if xrot is None:
+        xrot = rot
+
+    fig, axes = _grouped_plot(
+        plot_group,
+        data,
+        column=column,
+        by=by,
+        sharex=sharex,
+        sharey=sharey,
+        ax=ax,
+        figsize=figsize,
+        layout=layout,
+        rot=rot,
+    )
+
+    set_ticks_props(
+        axes, xlabelsize=xlabelsize, xrot=xrot, ylabelsize=ylabelsize, yrot=yrot
+    )
+
+    maybe_adjust_figure(
+        fig, bottom=0.15, top=0.9, left=0.1, right=0.9, hspace=0.5, wspace=0.3
+    )
+    return axes
+
+
+def hist_series(
+    self: Series,
+    by=None,
+    ax=None,
+    grid: bool = True,
+    xlabelsize: int | None = None,
+    xrot=None,
+    ylabelsize: int | None = None,
+    yrot=None,
+    figsize: tuple[float, float] | None = None,
+    bins: int = 10,
+    legend: bool = False,
+    **kwds,
+):
+    import matplotlib.pyplot as plt
+
+    if legend and "label" in kwds:
+        raise ValueError("Cannot use both legend and label")
+
+    if by is None:
+        if kwds.get("layout", None) is not None:
+            raise ValueError("The 'layout' keyword is not supported when 'by' is None")
+        # hack until the plotting interface is a bit more unified
+        fig = kwds.pop(
+            "figure", plt.gcf() if plt.get_fignums() else plt.figure(figsize=figsize)
+        )
+        if figsize is not None and tuple(figsize) != tuple(fig.get_size_inches()):
+            fig.set_size_inches(*figsize, forward=True)
+        if ax is None:
+            ax = fig.gca()
+        elif ax.get_figure() != fig:
+            raise AssertionError("passed axis not bound to passed figure")
+        values = self.dropna().values
+        if legend:
+            kwds["label"] = self.name
+        ax.hist(values, bins=bins, **kwds)
+        if legend:
+            ax.legend()
+        ax.grid(grid)
+        axes = np.array([ax])
+
+        # error: Argument 1 to "set_ticks_props" has incompatible type "ndarray[Any,
+        # dtype[Any]]"; expected "Axes | Sequence[Axes]"
+        set_ticks_props(
+            axes,  # type: ignore[arg-type]
+            xlabelsize=xlabelsize,
+            xrot=xrot,
+            ylabelsize=ylabelsize,
+            yrot=yrot,
+        )
+
+    else:
+        if "figure" in kwds:
+            raise ValueError(
+                "Cannot pass 'figure' when using the "
+                "'by' argument, since a new 'Figure' instance will be created"
+            )
+        axes = _grouped_hist(
+            self,
+            by=by,
+            ax=ax,
+            grid=grid,
+            figsize=figsize,
+            bins=bins,
+            xlabelsize=xlabelsize,
+            xrot=xrot,
+            ylabelsize=ylabelsize,
+            yrot=yrot,
+            legend=legend,
+            **kwds,
+        )
+
+    if hasattr(axes, "ndim"):
+        if axes.ndim == 1 and len(axes) == 1:
+            return axes[0]
+    return axes
+
+
+def hist_frame(
+    data: DataFrame,
+    column=None,
+    by=None,
+    grid: bool = True,
+    xlabelsize: int | None = None,
+    xrot=None,
+    ylabelsize: int | None = None,
+    yrot=None,
+    ax=None,
+    sharex: bool = False,
+    sharey: bool = False,
+    figsize: tuple[float, float] | None = None,
+    layout=None,
+    bins: int = 10,
+    legend: bool = False,
+    **kwds,
+):
+    if legend and "label" in kwds:
+        raise ValueError("Cannot use both legend and label")
+    if by is not None:
+        axes = _grouped_hist(
+            data,
+            column=column,
+            by=by,
+            ax=ax,
+            grid=grid,
+            figsize=figsize,
+            sharex=sharex,
+            sharey=sharey,
+            layout=layout,
+            bins=bins,
+            xlabelsize=xlabelsize,
+            xrot=xrot,
+            ylabelsize=ylabelsize,
+            yrot=yrot,
+            legend=legend,
+            **kwds,
+        )
+        return axes
+
+    if column is not None:
+        if not isinstance(column, (list, np.ndarray, ABCIndex)):
+            column = [column]
+        data = data[column]
+    # GH32590
+    data = data.select_dtypes(
+        include=(np.number, "datetime64", "datetimetz"), exclude="timedelta"
+    )
+    naxes = len(data.columns)
+
+    if naxes == 0:
+        raise ValueError(
+            "hist method requires numerical or datetime columns, nothing to plot."
+        )
+
+    fig, axes = create_subplots(
+        naxes=naxes,
+        ax=ax,
+        squeeze=False,
+        sharex=sharex,
+        sharey=sharey,
+        figsize=figsize,
+        layout=layout,
+    )
+    _axes = flatten_axes(axes)
+
+    can_set_label = "label" not in kwds
+
+    for i, col in enumerate(data.columns):
+        ax = _axes[i]
+        if legend and can_set_label:
+            kwds["label"] = col
+        ax.hist(data[col].dropna().values, bins=bins, **kwds)
+        ax.set_title(col)
+        ax.grid(grid)
+        if legend:
+            ax.legend()
+
+    set_ticks_props(
+        axes, xlabelsize=xlabelsize, xrot=xrot, ylabelsize=ylabelsize, yrot=yrot
+    )
+    maybe_adjust_figure(fig, wspace=0.3, hspace=0.3)
+
+    return axes
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/plotting/_matplotlib/misc.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/plotting/_matplotlib/misc.py
new file mode 100644
index 0000000000000000000000000000000000000000..1f9212587e05e2e3689b680ff01ae7780230657e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/plotting/_matplotlib/misc.py
@@ -0,0 +1,481 @@
+from __future__ import annotations
+
+import random
+from typing import TYPE_CHECKING
+
+from matplotlib import patches
+import matplotlib.lines as mlines
+import numpy as np
+
+from pandas.core.dtypes.missing import notna
+
+from pandas.io.formats.printing import pprint_thing
+from pandas.plotting._matplotlib.style import get_standard_colors
+from pandas.plotting._matplotlib.tools import (
+    create_subplots,
+    do_adjust_figure,
+    maybe_adjust_figure,
+    set_ticks_props,
+)
+
+if TYPE_CHECKING:
+    from collections.abc import Hashable
+
+    from matplotlib.axes import Axes
+    from matplotlib.figure import Figure
+
+    from pandas import (
+        DataFrame,
+        Index,
+        Series,
+    )
+
+
+def scatter_matrix(
+    frame: DataFrame,
+    alpha: float = 0.5,
+    figsize: tuple[float, float] | None = None,
+    ax=None,
+    grid: bool = False,
+    diagonal: str = "hist",
+    marker: str = ".",
+    density_kwds=None,
+    hist_kwds=None,
+    range_padding: float = 0.05,
+    **kwds,
+):
+    df = frame._get_numeric_data()
+    n = df.columns.size
+    naxes = n * n
+    fig, axes = create_subplots(naxes=naxes, figsize=figsize, ax=ax, squeeze=False)
+
+    # no gaps between subplots
+    maybe_adjust_figure(fig, wspace=0, hspace=0)
+
+    mask = notna(df)
+
+    marker = _get_marker_compat(marker)
+
+    hist_kwds = hist_kwds or {}
+    density_kwds = density_kwds or {}
+
+    # GH 14855
+    kwds.setdefault("edgecolors", "none")
+
+    boundaries_list = []
+    for a in df.columns:
+        values = df[a].values[mask[a].values]
+        rmin_, rmax_ = np.min(values), np.max(values)
+        rdelta_ext = (rmax_ - rmin_) * range_padding / 2
+        boundaries_list.append((rmin_ - rdelta_ext, rmax_ + rdelta_ext))
+
+    for i, a in enumerate(df.columns):
+        for j, b in enumerate(df.columns):
+            ax = axes[i, j]
+
+            if i == j:
+                values = df[a].values[mask[a].values]
+
+                # Deal with the diagonal by drawing a histogram there.
+                if diagonal == "hist":
+                    ax.hist(values, **hist_kwds)
+
+                elif diagonal in ("kde", "density"):
+                    from scipy.stats import gaussian_kde
+
+                    y = values
+                    gkde = gaussian_kde(y)
+                    ind = np.linspace(y.min(), y.max(), 1000)
+                    ax.plot(ind, gkde.evaluate(ind), **density_kwds)
+
+                ax.set_xlim(boundaries_list[i])
+
+            else:
+                common = (mask[a] & mask[b]).values
+
+                ax.scatter(
+                    df[b][common], df[a][common], marker=marker, alpha=alpha, **kwds
+                )
+
+                ax.set_xlim(boundaries_list[j])
+                ax.set_ylim(boundaries_list[i])
+
+            ax.set_xlabel(b)
+            ax.set_ylabel(a)
+
+            if j != 0:
+                ax.yaxis.set_visible(False)
+            if i != n - 1:
+                ax.xaxis.set_visible(False)
+
+    if len(df.columns) > 1:
+        lim1 = boundaries_list[0]
+        locs = axes[0][1].yaxis.get_majorticklocs()
+        locs = locs[(lim1[0] <= locs) & (locs <= lim1[1])]
+        adj = (locs - lim1[0]) / (lim1[1] - lim1[0])
+
+        lim0 = axes[0][0].get_ylim()
+        adj = adj * (lim0[1] - lim0[0]) + lim0[0]
+        axes[0][0].yaxis.set_ticks(adj)
+
+        if np.all(locs == locs.astype(int)):
+            # if all ticks are int
+            locs = locs.astype(int)
+        axes[0][0].yaxis.set_ticklabels(locs)
+
+    set_ticks_props(axes, xlabelsize=8, xrot=90, ylabelsize=8, yrot=0)
+
+    return axes
+
+
+def _get_marker_compat(marker):
+    if marker not in mlines.lineMarkers:
+        return "o"
+    return marker
+
+
+def radviz(
+    frame: DataFrame,
+    class_column,
+    ax: Axes | None = None,
+    color=None,
+    colormap=None,
+    **kwds,
+) -> Axes:
+    import matplotlib.pyplot as plt
+
+    def normalize(series):
+        a = min(series)
+        b = max(series)
+        return (series - a) / (b - a)
+
+    n = len(frame)
+    classes = frame[class_column].drop_duplicates()
+    class_col = frame[class_column]
+    df = frame.drop(class_column, axis=1).apply(normalize)
+
+    if ax is None:
+        ax = plt.gca()
+        ax.set_xlim(-1, 1)
+        ax.set_ylim(-1, 1)
+
+    to_plot: dict[Hashable, list[list]] = {}
+    colors = get_standard_colors(
+        num_colors=len(classes), colormap=colormap, color_type="random", color=color
+    )
+
+    for kls in classes:
+        to_plot[kls] = [[], []]
+
+    m = len(frame.columns) - 1
+    s = np.array(
+        [(np.cos(t), np.sin(t)) for t in [2 * np.pi * (i / m) for i in range(m)]]
+    )
+
+    for i in range(n):
+        row = df.iloc[i].values
+        row_ = np.repeat(np.expand_dims(row, axis=1), 2, axis=1)
+        y = (s * row_).sum(axis=0) / row.sum()
+        kls = class_col.iat[i]
+        to_plot[kls][0].append(y[0])
+        to_plot[kls][1].append(y[1])
+
+    for i, kls in enumerate(classes):
+        ax.scatter(
+            to_plot[kls][0],
+            to_plot[kls][1],
+            color=colors[i],
+            label=pprint_thing(kls),
+            **kwds,
+        )
+    ax.legend()
+
+    ax.add_patch(patches.Circle((0.0, 0.0), radius=1.0, facecolor="none"))
+
+    for xy, name in zip(s, df.columns):
+        ax.add_patch(patches.Circle(xy, radius=0.025, facecolor="gray"))
+
+        if xy[0] < 0.0 and xy[1] < 0.0:
+            ax.text(
+                xy[0] - 0.025, xy[1] - 0.025, name, ha="right", va="top", size="small"
+            )
+        elif xy[0] < 0.0 <= xy[1]:
+            ax.text(
+                xy[0] - 0.025,
+                xy[1] + 0.025,
+                name,
+                ha="right",
+                va="bottom",
+                size="small",
+            )
+        elif xy[1] < 0.0 <= xy[0]:
+            ax.text(
+                xy[0] + 0.025, xy[1] - 0.025, name, ha="left", va="top", size="small"
+            )
+        elif xy[0] >= 0.0 and xy[1] >= 0.0:
+            ax.text(
+                xy[0] + 0.025, xy[1] + 0.025, name, ha="left", va="bottom", size="small"
+            )
+
+    ax.axis("equal")
+    return ax
+
+
+def andrews_curves(
+    frame: DataFrame,
+    class_column,
+    ax: Axes | None = None,
+    samples: int = 200,
+    color=None,
+    colormap=None,
+    **kwds,
+) -> Axes:
+    import matplotlib.pyplot as plt
+
+    def function(amplitudes):
+        def f(t):
+            x1 = amplitudes[0]
+            result = x1 / np.sqrt(2.0)
+
+            # Take the rest of the coefficients and resize them
+            # appropriately. Take a copy of amplitudes as otherwise numpy
+            # deletes the element from amplitudes itself.
+            coeffs = np.delete(np.copy(amplitudes), 0)
+            coeffs = np.resize(coeffs, (int((coeffs.size + 1) / 2), 2))
+
+            # Generate the harmonics and arguments for the sin and cos
+            # functions.
+            harmonics = np.arange(0, coeffs.shape[0]) + 1
+            trig_args = np.outer(harmonics, t)
+
+            result += np.sum(
+                coeffs[:, 0, np.newaxis] * np.sin(trig_args)
+                + coeffs[:, 1, np.newaxis] * np.cos(trig_args),
+                axis=0,
+            )
+            return result
+
+        return f
+
+    n = len(frame)
+    class_col = frame[class_column]
+    classes = frame[class_column].drop_duplicates()
+    df = frame.drop(class_column, axis=1)
+    t = np.linspace(-np.pi, np.pi, samples)
+    used_legends: set[str] = set()
+
+    color_values = get_standard_colors(
+        num_colors=len(classes), colormap=colormap, color_type="random", color=color
+    )
+    colors = dict(zip(classes, color_values))
+    if ax is None:
+        ax = plt.gca()
+        ax.set_xlim(-np.pi, np.pi)
+    for i in range(n):
+        row = df.iloc[i].values
+        f = function(row)
+        y = f(t)
+        kls = class_col.iat[i]
+        label = pprint_thing(kls)
+        if label not in used_legends:
+            used_legends.add(label)
+            ax.plot(t, y, color=colors[kls], label=label, **kwds)
+        else:
+            ax.plot(t, y, color=colors[kls], **kwds)
+
+    ax.legend(loc="upper right")
+    ax.grid()
+    return ax
+
+
+def bootstrap_plot(
+    series: Series,
+    fig: Figure | None = None,
+    size: int = 50,
+    samples: int = 500,
+    **kwds,
+) -> Figure:
+    import matplotlib.pyplot as plt
+
+    # TODO: is the failure mentioned below still relevant?
+    # random.sample(ndarray, int) fails on python 3.3, sigh
+    data = list(series.values)
+    samplings = [random.sample(data, size) for _ in range(samples)]
+
+    means = np.array([np.mean(sampling) for sampling in samplings])
+    medians = np.array([np.median(sampling) for sampling in samplings])
+    midranges = np.array(
+        [(min(sampling) + max(sampling)) * 0.5 for sampling in samplings]
+    )
+    if fig is None:
+        fig = plt.figure()
+    x = list(range(samples))
+    axes = []
+    ax1 = fig.add_subplot(2, 3, 1)
+    ax1.set_xlabel("Sample")
+    axes.append(ax1)
+    ax1.plot(x, means, **kwds)
+    ax2 = fig.add_subplot(2, 3, 2)
+    ax2.set_xlabel("Sample")
+    axes.append(ax2)
+    ax2.plot(x, medians, **kwds)
+    ax3 = fig.add_subplot(2, 3, 3)
+    ax3.set_xlabel("Sample")
+    axes.append(ax3)
+    ax3.plot(x, midranges, **kwds)
+    ax4 = fig.add_subplot(2, 3, 4)
+    ax4.set_xlabel("Mean")
+    axes.append(ax4)
+    ax4.hist(means, **kwds)
+    ax5 = fig.add_subplot(2, 3, 5)
+    ax5.set_xlabel("Median")
+    axes.append(ax5)
+    ax5.hist(medians, **kwds)
+    ax6 = fig.add_subplot(2, 3, 6)
+    ax6.set_xlabel("Midrange")
+    axes.append(ax6)
+    ax6.hist(midranges, **kwds)
+    for axis in axes:
+        plt.setp(axis.get_xticklabels(), fontsize=8)
+        plt.setp(axis.get_yticklabels(), fontsize=8)
+    if do_adjust_figure(fig):
+        plt.tight_layout()
+    return fig
+
+
+def parallel_coordinates(
+    frame: DataFrame,
+    class_column,
+    cols=None,
+    ax: Axes | None = None,
+    color=None,
+    use_columns: bool = False,
+    xticks=None,
+    colormap=None,
+    axvlines: bool = True,
+    axvlines_kwds=None,
+    sort_labels: bool = False,
+    **kwds,
+) -> Axes:
+    import matplotlib.pyplot as plt
+
+    if axvlines_kwds is None:
+        axvlines_kwds = {"linewidth": 1, "color": "black"}
+
+    n = len(frame)
+    classes = frame[class_column].drop_duplicates()
+    class_col = frame[class_column]
+
+    if cols is None:
+        df = frame.drop(class_column, axis=1)
+    else:
+        df = frame[cols]
+
+    used_legends: set[str] = set()
+
+    ncols = len(df.columns)
+
+    # determine values to use for xticks
+    x: list[int] | Index
+    if use_columns is True:
+        if not np.all(np.isreal(list(df.columns))):
+            raise ValueError("Columns must be numeric to be used as xticks")
+        x = df.columns
+    elif xticks is not None:
+        if not np.all(np.isreal(xticks)):
+            raise ValueError("xticks specified must be numeric")
+        if len(xticks) != ncols:
+            raise ValueError("Length of xticks must match number of columns")
+        x = xticks
+    else:
+        x = list(range(ncols))
+
+    if ax is None:
+        ax = plt.gca()
+
+    color_values = get_standard_colors(
+        num_colors=len(classes), colormap=colormap, color_type="random", color=color
+    )
+
+    if sort_labels:
+        classes = sorted(classes)
+        color_values = sorted(color_values)
+    colors = dict(zip(classes, color_values))
+
+    for i in range(n):
+        y = df.iloc[i].values
+        kls = class_col.iat[i]
+        label = pprint_thing(kls)
+        if label not in used_legends:
+            used_legends.add(label)
+            ax.plot(x, y, color=colors[kls], label=label, **kwds)
+        else:
+            ax.plot(x, y, color=colors[kls], **kwds)
+
+    if axvlines:
+        for i in x:
+            ax.axvline(i, **axvlines_kwds)
+
+    ax.set_xticks(x)
+    ax.set_xticklabels(df.columns)
+    ax.set_xlim(x[0], x[-1])
+    ax.legend(loc="upper right")
+    ax.grid()
+    return ax
+
+
+def lag_plot(series: Series, lag: int = 1, ax: Axes | None = None, **kwds) -> Axes:
+    # workaround because `c='b'` is hardcoded in matplotlib's scatter method
+    import matplotlib.pyplot as plt
+
+    kwds.setdefault("c", plt.rcParams["patch.facecolor"])
+
+    data = series.values
+    y1 = data[:-lag]
+    y2 = data[lag:]
+    if ax is None:
+        ax = plt.gca()
+    ax.set_xlabel("y(t)")
+    ax.set_ylabel(f"y(t + {lag})")
+    ax.scatter(y1, y2, **kwds)
+    return ax
+
+
+def autocorrelation_plot(series: Series, ax: Axes | None = None, **kwds) -> Axes:
+    import matplotlib.pyplot as plt
+
+    n = len(series)
+    data = np.asarray(series)
+    if ax is None:
+        ax = plt.gca()
+        ax.set_xlim(1, n)
+        ax.set_ylim(-1.0, 1.0)
+    mean = np.mean(data)
+    c0 = np.sum((data - mean) ** 2) / n
+
+    def r(h):
+        return ((data[: n - h] - mean) * (data[h:] - mean)).sum() / n / c0
+
+    x = np.arange(n) + 1
+    y = [r(loc) for loc in x]
+    z95 = 1.959963984540054
+    z99 = 2.5758293035489004
+    ax.axhline(y=z99 / np.sqrt(n), linestyle="--", color="grey")
+    ax.axhline(y=z95 / np.sqrt(n), color="grey")
+    ax.axhline(y=0.0, color="black")
+    ax.axhline(y=-z95 / np.sqrt(n), color="grey")
+    ax.axhline(y=-z99 / np.sqrt(n), linestyle="--", color="grey")
+    ax.set_xlabel("Lag")
+    ax.set_ylabel("Autocorrelation")
+    ax.plot(x, y, **kwds)
+    if "label" in kwds:
+        ax.legend()
+    ax.grid()
+    return ax
+
+
+def unpack_single_str_list(keys):
+    # GH 42795
+    if isinstance(keys, list) and len(keys) == 1:
+        keys = keys[0]
+    return keys
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/plotting/_matplotlib/style.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/plotting/_matplotlib/style.py
new file mode 100644
index 0000000000000000000000000000000000000000..bf4e4be3bfd82e6ce89d526aa0da555f67b9f565
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/plotting/_matplotlib/style.py
@@ -0,0 +1,278 @@
+from __future__ import annotations
+
+from collections.abc import (
+    Collection,
+    Iterator,
+)
+import itertools
+from typing import (
+    TYPE_CHECKING,
+    cast,
+)
+import warnings
+
+import matplotlib as mpl
+import matplotlib.colors
+import numpy as np
+
+from pandas._typing import MatplotlibColor as Color
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.common import is_list_like
+
+import pandas.core.common as com
+
+if TYPE_CHECKING:
+    from matplotlib.colors import Colormap
+
+
+def get_standard_colors(
+    num_colors: int,
+    colormap: Colormap | None = None,
+    color_type: str = "default",
+    color: dict[str, Color] | Color | Collection[Color] | None = None,
+):
+    """
+    Get standard colors based on `colormap`, `color_type` or `color` inputs.
+
+    Parameters
+    ----------
+    num_colors : int
+        Minimum number of colors to be returned.
+        Ignored if `color` is a dictionary.
+    colormap : :py:class:`matplotlib.colors.Colormap`, optional
+        Matplotlib colormap.
+        When provided, the resulting colors will be derived from the colormap.
+    color_type : {"default", "random"}, optional
+        Type of colors to derive. Used if provided `color` and `colormap` are None.
+        Ignored if either `color` or `colormap` are not None.
+    color : dict or str or sequence, optional
+        Color(s) to be used for deriving sequence of colors.
+        Can be either be a dictionary, or a single color (single color string,
+        or sequence of floats representing a single color),
+        or a sequence of colors.
+
+    Returns
+    -------
+    dict or list
+        Standard colors. Can either be a mapping if `color` was a dictionary,
+        or a list of colors with a length of `num_colors` or more.
+
+    Warns
+    -----
+    UserWarning
+        If both `colormap` and `color` are provided.
+        Parameter `color` will override.
+    """
+    if isinstance(color, dict):
+        return color
+
+    colors = _derive_colors(
+        color=color,
+        colormap=colormap,
+        color_type=color_type,
+        num_colors=num_colors,
+    )
+
+    return list(_cycle_colors(colors, num_colors=num_colors))
+
+
+def _derive_colors(
+    *,
+    color: Color | Collection[Color] | None,
+    colormap: str | Colormap | None,
+    color_type: str,
+    num_colors: int,
+) -> list[Color]:
+    """
+    Derive colors from either `colormap`, `color_type` or `color` inputs.
+
+    Get a list of colors either from `colormap`, or from `color`,
+    or from `color_type` (if both `colormap` and `color` are None).
+
+    Parameters
+    ----------
+    color : str or sequence, optional
+        Color(s) to be used for deriving sequence of colors.
+        Can be either be a single color (single color string, or sequence of floats
+        representing a single color), or a sequence of colors.
+    colormap : :py:class:`matplotlib.colors.Colormap`, optional
+        Matplotlib colormap.
+        When provided, the resulting colors will be derived from the colormap.
+    color_type : {"default", "random"}, optional
+        Type of colors to derive. Used if provided `color` and `colormap` are None.
+        Ignored if either `color` or `colormap`` are not None.
+    num_colors : int
+        Number of colors to be extracted.
+
+    Returns
+    -------
+    list
+        List of colors extracted.
+
+    Warns
+    -----
+    UserWarning
+        If both `colormap` and `color` are provided.
+        Parameter `color` will override.
+    """
+    if color is None and colormap is not None:
+        return _get_colors_from_colormap(colormap, num_colors=num_colors)
+    elif color is not None:
+        if colormap is not None:
+            warnings.warn(
+                "'color' and 'colormap' cannot be used simultaneously. Using 'color'",
+                stacklevel=find_stack_level(),
+            )
+        return _get_colors_from_color(color)
+    else:
+        return _get_colors_from_color_type(color_type, num_colors=num_colors)
+
+
+def _cycle_colors(colors: list[Color], num_colors: int) -> Iterator[Color]:
+    """Cycle colors until achieving max of `num_colors` or length of `colors`.
+
+    Extra colors will be ignored by matplotlib if there are more colors
+    than needed and nothing needs to be done here.
+    """
+    max_colors = max(num_colors, len(colors))
+    yield from itertools.islice(itertools.cycle(colors), max_colors)
+
+
+def _get_colors_from_colormap(
+    colormap: str | Colormap,
+    num_colors: int,
+) -> list[Color]:
+    """Get colors from colormap."""
+    cmap = _get_cmap_instance(colormap)
+    return [cmap(num) for num in np.linspace(0, 1, num=num_colors)]
+
+
+def _get_cmap_instance(colormap: str | Colormap) -> Colormap:
+    """Get instance of matplotlib colormap."""
+    if isinstance(colormap, str):
+        cmap = colormap
+        colormap = mpl.colormaps[colormap]
+        if colormap is None:
+            raise ValueError(f"Colormap {cmap} is not recognized")
+    return colormap
+
+
+def _get_colors_from_color(
+    color: Color | Collection[Color],
+) -> list[Color]:
+    """Get colors from user input color."""
+    if len(color) == 0:
+        raise ValueError(f"Invalid color argument: {color}")
+
+    if _is_single_color(color):
+        color = cast(Color, color)
+        return [color]
+
+    color = cast(Collection[Color], color)
+    return list(_gen_list_of_colors_from_iterable(color))
+
+
+def _is_single_color(color: Color | Collection[Color]) -> bool:
+    """Check if `color` is a single color, not a sequence of colors.
+
+    Single color is of these kinds:
+        - Named color "red", "C0", "firebrick"
+        - Alias "g"
+        - Sequence of floats, such as (0.1, 0.2, 0.3) or (0.1, 0.2, 0.3, 0.4).
+
+    See Also
+    --------
+    _is_single_string_color
+    """
+    if isinstance(color, str) and _is_single_string_color(color):
+        # GH #36972
+        return True
+
+    if _is_floats_color(color):
+        return True
+
+    return False
+
+
+def _gen_list_of_colors_from_iterable(color: Collection[Color]) -> Iterator[Color]:
+    """
+    Yield colors from string of several letters or from collection of colors.
+    """
+    for x in color:
+        if _is_single_color(x):
+            yield x
+        else:
+            raise ValueError(f"Invalid color {x}")
+
+
+def _is_floats_color(color: Color | Collection[Color]) -> bool:
+    """Check if color comprises a sequence of floats representing color."""
+    return bool(
+        is_list_like(color)
+        and (len(color) == 3 or len(color) == 4)
+        and all(isinstance(x, (int, float)) for x in color)
+    )
+
+
+def _get_colors_from_color_type(color_type: str, num_colors: int) -> list[Color]:
+    """Get colors from user input color type."""
+    if color_type == "default":
+        return _get_default_colors(num_colors)
+    elif color_type == "random":
+        return _get_random_colors(num_colors)
+    else:
+        raise ValueError("color_type must be either 'default' or 'random'")
+
+
+def _get_default_colors(num_colors: int) -> list[Color]:
+    """Get `num_colors` of default colors from matplotlib rc params."""
+    import matplotlib.pyplot as plt
+
+    colors = [c["color"] for c in plt.rcParams["axes.prop_cycle"]]
+    return colors[0:num_colors]
+
+
+def _get_random_colors(num_colors: int) -> list[Color]:
+    """Get `num_colors` of random colors."""
+    return [_random_color(num) for num in range(num_colors)]
+
+
+def _random_color(column: int) -> list[float]:
+    """Get a random color represented as a list of length 3"""
+    # GH17525 use common._random_state to avoid resetting the seed
+    rs = com.random_state(column)
+    return rs.rand(3).tolist()
+
+
+def _is_single_string_color(color: Color) -> bool:
+    """Check if `color` is a single string color.
+
+    Examples of single string colors:
+        - 'r'
+        - 'g'
+        - 'red'
+        - 'green'
+        - 'C3'
+        - 'firebrick'
+
+    Parameters
+    ----------
+    color : Color
+        Color string or sequence of floats.
+
+    Returns
+    -------
+    bool
+        True if `color` looks like a valid color.
+        False otherwise.
+    """
+    conv = matplotlib.colors.ColorConverter()
+    try:
+        # error: Argument 1 to "to_rgba" of "ColorConverter" has incompatible type
+        # "str | Sequence[float]"; expected "tuple[float, float, float] | ..."
+        conv.to_rgba(color)  # type: ignore[arg-type]
+    except ValueError:
+        return False
+    else:
+        return True
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/plotting/_matplotlib/timeseries.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/plotting/_matplotlib/timeseries.py
new file mode 100644
index 0000000000000000000000000000000000000000..accf418526d9be36f6c613ebad312109727648de
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/plotting/_matplotlib/timeseries.py
@@ -0,0 +1,367 @@
+# TODO: Use the fact that axis can have units to simplify the process
+
+from __future__ import annotations
+
+import functools
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    cast,
+)
+import warnings
+
+import numpy as np
+
+from pandas._libs.tslibs import (
+    BaseOffset,
+    Period,
+    to_offset,
+)
+from pandas._libs.tslibs.dtypes import (
+    OFFSET_TO_PERIOD_FREQSTR,
+    FreqGroup,
+)
+
+from pandas.core.dtypes.generic import (
+    ABCDatetimeIndex,
+    ABCPeriodIndex,
+    ABCTimedeltaIndex,
+)
+
+from pandas.io.formats.printing import pprint_thing
+from pandas.plotting._matplotlib.converter import (
+    TimeSeries_DateFormatter,
+    TimeSeries_DateLocator,
+    TimeSeries_TimedeltaFormatter,
+)
+from pandas.tseries.frequencies import (
+    get_period_alias,
+    is_subperiod,
+    is_superperiod,
+)
+
+if TYPE_CHECKING:
+    from datetime import timedelta
+
+    from matplotlib.axes import Axes
+
+    from pandas._typing import NDFrameT
+
+    from pandas import (
+        DataFrame,
+        DatetimeIndex,
+        Index,
+        PeriodIndex,
+        Series,
+    )
+
+# ---------------------------------------------------------------------
+# Plotting functions and monkey patches
+
+
+def maybe_resample(series: Series, ax: Axes, kwargs: dict[str, Any]):
+    # resample against axes freq if necessary
+
+    if "how" in kwargs:
+        raise ValueError(
+            "'how' is not a valid keyword for plotting functions. If plotting "
+            "multiple objects on shared axes, resample manually first."
+        )
+
+    freq, ax_freq = _get_freq(ax, series)
+
+    if freq is None:  # pragma: no cover
+        raise ValueError("Cannot use dynamic axis without frequency info")
+
+    # Convert DatetimeIndex to PeriodIndex
+    if isinstance(series.index, ABCDatetimeIndex):
+        series = series.to_period(freq=freq)
+
+    if ax_freq is not None and freq != ax_freq:
+        if is_superperiod(freq, ax_freq):  # upsample input
+            series = series.copy()
+            # error: "Index" has no attribute "asfreq"
+            series.index = series.index.asfreq(  # type: ignore[attr-defined]
+                ax_freq, how="s"
+            )
+            freq = ax_freq
+        elif _is_sup(freq, ax_freq):  # one is weekly
+            how = "last"
+            series = getattr(series.resample("D"), how)().dropna()
+            series = getattr(series.resample(ax_freq), how)().dropna()
+            freq = ax_freq
+        elif is_subperiod(freq, ax_freq) or _is_sub(freq, ax_freq):
+            _upsample_others(ax, freq, kwargs)
+        else:  # pragma: no cover
+            raise ValueError("Incompatible frequency conversion")
+    return freq, series
+
+
+def _is_sub(f1: str, f2: str) -> bool:
+    return (f1.startswith("W") and is_subperiod("D", f2)) or (
+        f2.startswith("W") and is_subperiod(f1, "D")
+    )
+
+
+def _is_sup(f1: str, f2: str) -> bool:
+    return (f1.startswith("W") and is_superperiod("D", f2)) or (
+        f2.startswith("W") and is_superperiod(f1, "D")
+    )
+
+
+def _upsample_others(ax: Axes, freq: BaseOffset, kwargs: dict[str, Any]) -> None:
+    legend = ax.get_legend()
+    lines, labels = _replot_ax(ax, freq)
+    _replot_ax(ax, freq)
+
+    other_ax = None
+    if hasattr(ax, "left_ax"):
+        other_ax = ax.left_ax
+    if hasattr(ax, "right_ax"):
+        other_ax = ax.right_ax
+
+    if other_ax is not None:
+        rlines, rlabels = _replot_ax(other_ax, freq)
+        lines.extend(rlines)
+        labels.extend(rlabels)
+
+    if legend is not None and kwargs.get("legend", True) and len(lines) > 0:
+        title: str | None = legend.get_title().get_text()
+        if title == "None":
+            title = None
+        ax.legend(lines, labels, loc="best", title=title)
+
+
+def _replot_ax(ax: Axes, freq: BaseOffset):
+    data = getattr(ax, "_plot_data", None)
+
+    # clear current axes and data
+    # TODO #54485
+    ax._plot_data = []  # type: ignore[attr-defined]
+    ax.clear()
+
+    decorate_axes(ax, freq)
+
+    lines = []
+    labels = []
+    if data is not None:
+        for series, plotf, kwds in data:
+            series = series.copy()
+            idx = series.index.asfreq(freq, how="S")
+            series.index = idx
+            # TODO #54485
+            ax._plot_data.append((series, plotf, kwds))  # type: ignore[attr-defined]
+
+            # for tsplot
+            if isinstance(plotf, str):
+                from pandas.plotting._matplotlib import PLOT_CLASSES
+
+                plotf = PLOT_CLASSES[plotf]._plot
+
+            lines.append(plotf(ax, series.index._mpl_repr(), series.values, **kwds)[0])
+            labels.append(pprint_thing(series.name))
+
+    return lines, labels
+
+
+def decorate_axes(ax: Axes, freq: BaseOffset) -> None:
+    """Initialize axes for time-series plotting"""
+    if not hasattr(ax, "_plot_data"):
+        # TODO #54485
+        ax._plot_data = []  # type: ignore[attr-defined]
+
+    # TODO #54485
+    ax.freq = freq  # type: ignore[attr-defined]
+    xaxis = ax.get_xaxis()
+    # TODO #54485
+    xaxis.freq = freq  # type: ignore[attr-defined]
+
+
+def _get_ax_freq(ax: Axes):
+    """
+    Get the freq attribute of the ax object if set.
+    Also checks shared axes (eg when using secondary yaxis, sharex=True
+    or twinx)
+    """
+    ax_freq = getattr(ax, "freq", None)
+    if ax_freq is None:
+        # check for left/right ax in case of secondary yaxis
+        if hasattr(ax, "left_ax"):
+            ax_freq = getattr(ax.left_ax, "freq", None)
+        elif hasattr(ax, "right_ax"):
+            ax_freq = getattr(ax.right_ax, "freq", None)
+    if ax_freq is None:
+        # check if a shared ax (sharex/twinx) has already freq set
+        shared_axes = ax.get_shared_x_axes().get_siblings(ax)
+        if len(shared_axes) > 1:
+            for shared_ax in shared_axes:
+                ax_freq = getattr(shared_ax, "freq", None)
+                if ax_freq is not None:
+                    break
+    return ax_freq
+
+
+def _get_period_alias(freq: timedelta | BaseOffset | str) -> str | None:
+    if isinstance(freq, BaseOffset):
+        freqstr = freq.name
+    else:
+        freqstr = to_offset(freq, is_period=True).rule_code
+
+    return get_period_alias(freqstr)
+
+
+def _get_freq(ax: Axes, series: Series):
+    # get frequency from data
+    freq = getattr(series.index, "freq", None)
+    if freq is None:
+        freq = getattr(series.index, "inferred_freq", None)
+        freq = to_offset(freq, is_period=True)
+
+    ax_freq = _get_ax_freq(ax)
+
+    # use axes freq if no data freq
+    if freq is None:
+        freq = ax_freq
+
+    # get the period frequency
+    freq = _get_period_alias(freq)
+    return freq, ax_freq
+
+
+def use_dynamic_x(ax: Axes, data: DataFrame | Series) -> bool:
+    freq = _get_index_freq(data.index)
+    ax_freq = _get_ax_freq(ax)
+
+    if freq is None:  # convert irregular if axes has freq info
+        freq = ax_freq
+    # do not use tsplot if irregular was plotted first
+    elif (ax_freq is None) and (len(ax.get_lines()) > 0):
+        return False
+
+    if freq is None:
+        return False
+
+    freq_str = _get_period_alias(freq)
+
+    if freq_str is None:
+        return False
+
+    # FIXME: hack this for 0.10.1, creating more technical debt...sigh
+    if isinstance(data.index, ABCDatetimeIndex):
+        # error: "BaseOffset" has no attribute "_period_dtype_code"
+        freq_str = OFFSET_TO_PERIOD_FREQSTR.get(freq_str, freq_str)
+        base = to_offset(
+            freq_str, is_period=True
+        )._period_dtype_code  # type: ignore[attr-defined]
+        x = data.index
+        if base <= FreqGroup.FR_DAY.value:
+            return x[:1].is_normalized
+        period = Period(x[0], freq_str)
+        assert isinstance(period, Period)
+        return period.to_timestamp().tz_localize(x.tz) == x[0]
+    return True
+
+
+def _get_index_freq(index: Index) -> BaseOffset | None:
+    freq = getattr(index, "freq", None)
+    if freq is None:
+        freq = getattr(index, "inferred_freq", None)
+        if freq == "B":
+            # error: "Index" has no attribute "dayofweek"
+            weekdays = np.unique(index.dayofweek)  # type: ignore[attr-defined]
+            if (5 in weekdays) or (6 in weekdays):
+                freq = None
+
+    freq = to_offset(freq)
+    return freq
+
+
+def maybe_convert_index(ax: Axes, data: NDFrameT) -> NDFrameT:
+    # tsplot converts automatically, but don't want to convert index
+    # over and over for DataFrames
+    if isinstance(data.index, (ABCDatetimeIndex, ABCPeriodIndex)):
+        freq: str | BaseOffset | None = data.index.freq
+
+        if freq is None:
+            # We only get here for DatetimeIndex
+            data.index = cast("DatetimeIndex", data.index)
+            freq = data.index.inferred_freq
+            freq = to_offset(freq)
+
+        if freq is None:
+            freq = _get_ax_freq(ax)
+
+        if freq is None:
+            raise ValueError("Could not get frequency alias for plotting")
+
+        freq_str = _get_period_alias(freq)
+
+        with warnings.catch_warnings():
+            # suppress Period[B] deprecation warning
+            # TODO: need to find an alternative to this before the deprecation
+            #  is enforced!
+            warnings.filterwarnings(
+                "ignore",
+                r"PeriodDtype\[B\] is deprecated",
+                category=FutureWarning,
+            )
+
+            if isinstance(data.index, ABCDatetimeIndex):
+                data = data.tz_localize(None).to_period(freq=freq_str)
+            elif isinstance(data.index, ABCPeriodIndex):
+                data.index = data.index.asfreq(freq=freq_str)
+    return data
+
+
+# Patch methods for subplot.
+
+
+def _format_coord(freq, t, y) -> str:
+    time_period = Period(ordinal=int(t), freq=freq)
+    return f"t = {time_period}  y = {y:8f}"
+
+
+def format_dateaxis(
+    subplot, freq: BaseOffset, index: DatetimeIndex | PeriodIndex
+) -> None:
+    """
+    Pretty-formats the date axis (x-axis).
+
+    Major and minor ticks are automatically set for the frequency of the
+    current underlying series.  As the dynamic mode is activated by
+    default, changing the limits of the x axis will intelligently change
+    the positions of the ticks.
+    """
+    from matplotlib import pylab
+
+    # handle index specific formatting
+    # Note: DatetimeIndex does not use this
+    # interface. DatetimeIndex uses matplotlib.date directly
+    if isinstance(index, ABCPeriodIndex):
+        majlocator = TimeSeries_DateLocator(
+            freq, dynamic_mode=True, minor_locator=False, plot_obj=subplot
+        )
+        minlocator = TimeSeries_DateLocator(
+            freq, dynamic_mode=True, minor_locator=True, plot_obj=subplot
+        )
+        subplot.xaxis.set_major_locator(majlocator)
+        subplot.xaxis.set_minor_locator(minlocator)
+
+        majformatter = TimeSeries_DateFormatter(
+            freq, dynamic_mode=True, minor_locator=False, plot_obj=subplot
+        )
+        minformatter = TimeSeries_DateFormatter(
+            freq, dynamic_mode=True, minor_locator=True, plot_obj=subplot
+        )
+        subplot.xaxis.set_major_formatter(majformatter)
+        subplot.xaxis.set_minor_formatter(minformatter)
+
+        # x and y coord info
+        subplot.format_coord = functools.partial(_format_coord, freq)
+
+    elif isinstance(index, ABCTimedeltaIndex):
+        subplot.xaxis.set_major_formatter(TimeSeries_TimedeltaFormatter())
+    else:
+        raise TypeError("index type not supported")
+
+    pylab.draw_if_interactive()
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/plotting/_matplotlib/tools.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/plotting/_matplotlib/tools.py
new file mode 100644
index 0000000000000000000000000000000000000000..98441c5afbaa47bfdfd7db27f33ef91c90332088
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/plotting/_matplotlib/tools.py
@@ -0,0 +1,492 @@
+# being a bit too dynamic
+from __future__ import annotations
+
+from math import ceil
+from typing import TYPE_CHECKING
+import warnings
+
+from matplotlib import ticker
+import matplotlib.table
+import numpy as np
+
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.common import is_list_like
+from pandas.core.dtypes.generic import (
+    ABCDataFrame,
+    ABCIndex,
+    ABCSeries,
+)
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Iterable,
+        Sequence,
+    )
+
+    from matplotlib.axes import Axes
+    from matplotlib.axis import Axis
+    from matplotlib.figure import Figure
+    from matplotlib.lines import Line2D
+    from matplotlib.table import Table
+
+    from pandas import (
+        DataFrame,
+        Series,
+    )
+
+
+def do_adjust_figure(fig: Figure) -> bool:
+    """Whether fig has constrained_layout enabled."""
+    if not hasattr(fig, "get_constrained_layout"):
+        return False
+    return not fig.get_constrained_layout()
+
+
+def maybe_adjust_figure(fig: Figure, *args, **kwargs) -> None:
+    """Call fig.subplots_adjust unless fig has constrained_layout enabled."""
+    if do_adjust_figure(fig):
+        fig.subplots_adjust(*args, **kwargs)
+
+
+def format_date_labels(ax: Axes, rot) -> None:
+    # mini version of autofmt_xdate
+    for label in ax.get_xticklabels():
+        label.set_horizontalalignment("right")
+        label.set_rotation(rot)
+    fig = ax.get_figure()
+    if fig is not None:
+        # should always be a Figure but can technically be None
+        maybe_adjust_figure(fig, bottom=0.2)  # type: ignore[arg-type]
+
+
+def table(
+    ax, data: DataFrame | Series, rowLabels=None, colLabels=None, **kwargs
+) -> Table:
+    if isinstance(data, ABCSeries):
+        data = data.to_frame()
+    elif isinstance(data, ABCDataFrame):
+        pass
+    else:
+        raise ValueError("Input data must be DataFrame or Series")
+
+    if rowLabels is None:
+        rowLabels = data.index
+
+    if colLabels is None:
+        colLabels = data.columns
+
+    cellText = data.values
+
+    # error: Argument "cellText" to "table" has incompatible type "ndarray[Any,
+    # Any]"; expected "Sequence[Sequence[str]] | None"
+    return matplotlib.table.table(
+        ax,
+        cellText=cellText,  # type: ignore[arg-type]
+        rowLabels=rowLabels,
+        colLabels=colLabels,
+        **kwargs,
+    )
+
+
+def _get_layout(
+    nplots: int,
+    layout: tuple[int, int] | None = None,
+    layout_type: str = "box",
+) -> tuple[int, int]:
+    if layout is not None:
+        if not isinstance(layout, (tuple, list)) or len(layout) != 2:
+            raise ValueError("Layout must be a tuple of (rows, columns)")
+
+        nrows, ncols = layout
+
+        if nrows == -1 and ncols > 0:
+            layout = nrows, ncols = (ceil(nplots / ncols), ncols)
+        elif ncols == -1 and nrows > 0:
+            layout = nrows, ncols = (nrows, ceil(nplots / nrows))
+        elif ncols <= 0 and nrows <= 0:
+            msg = "At least one dimension of layout must be positive"
+            raise ValueError(msg)
+
+        if nrows * ncols < nplots:
+            raise ValueError(
+                f"Layout of {nrows}x{ncols} must be larger than required size {nplots}"
+            )
+
+        return layout
+
+    if layout_type == "single":
+        return (1, 1)
+    elif layout_type == "horizontal":
+        return (1, nplots)
+    elif layout_type == "vertical":
+        return (nplots, 1)
+
+    layouts = {1: (1, 1), 2: (1, 2), 3: (2, 2), 4: (2, 2)}
+    try:
+        return layouts[nplots]
+    except KeyError:
+        k = 1
+        while k**2 < nplots:
+            k += 1
+
+        if (k - 1) * k >= nplots:
+            return k, (k - 1)
+        else:
+            return k, k
+
+
+# copied from matplotlib/pyplot.py and modified for pandas.plotting
+
+
+def create_subplots(
+    naxes: int,
+    sharex: bool = False,
+    sharey: bool = False,
+    squeeze: bool = True,
+    subplot_kw=None,
+    ax=None,
+    layout=None,
+    layout_type: str = "box",
+    **fig_kw,
+):
+    """
+    Create a figure with a set of subplots already made.
+
+    This utility wrapper makes it convenient to create common layouts of
+    subplots, including the enclosing figure object, in a single call.
+
+    Parameters
+    ----------
+    naxes : int
+      Number of required axes. Exceeded axes are set invisible. Default is
+      nrows * ncols.
+
+    sharex : bool
+      If True, the X axis will be shared amongst all subplots.
+
+    sharey : bool
+      If True, the Y axis will be shared amongst all subplots.
+
+    squeeze : bool
+
+      If True, extra dimensions are squeezed out from the returned axis object:
+        - if only one subplot is constructed (nrows=ncols=1), the resulting
+        single Axis object is returned as a scalar.
+        - for Nx1 or 1xN subplots, the returned object is a 1-d numpy object
+        array of Axis objects are returned as numpy 1-d arrays.
+        - for NxM subplots with N>1 and M>1 are returned as a 2d array.
+
+      If False, no squeezing is done: the returned axis object is always
+      a 2-d array containing Axis instances, even if it ends up being 1x1.
+
+    subplot_kw : dict
+      Dict with keywords passed to the add_subplot() call used to create each
+      subplots.
+
+    ax : Matplotlib axis object, optional
+
+    layout : tuple
+      Number of rows and columns of the subplot grid.
+      If not specified, calculated from naxes and layout_type
+
+    layout_type : {'box', 'horizontal', 'vertical'}, default 'box'
+      Specify how to layout the subplot grid.
+
+    fig_kw : Other keyword arguments to be passed to the figure() call.
+        Note that all keywords not recognized above will be
+        automatically included here.
+
+    Returns
+    -------
+    fig, ax : tuple
+      - fig is the Matplotlib Figure object
+      - ax can be either a single axis object or an array of axis objects if
+      more than one subplot was created.  The dimensions of the resulting array
+      can be controlled with the squeeze keyword, see above.
+
+    Examples
+    --------
+    x = np.linspace(0, 2*np.pi, 400)
+    y = np.sin(x**2)
+
+    # Just a figure and one subplot
+    f, ax = plt.subplots()
+    ax.plot(x, y)
+    ax.set_title('Simple plot')
+
+    # Two subplots, unpack the output array immediately
+    f, (ax1, ax2) = plt.subplots(1, 2, sharey=True)
+    ax1.plot(x, y)
+    ax1.set_title('Sharing Y axis')
+    ax2.scatter(x, y)
+
+    # Four polar axes
+    plt.subplots(2, 2, subplot_kw=dict(polar=True))
+    """
+    import matplotlib.pyplot as plt
+
+    if subplot_kw is None:
+        subplot_kw = {}
+
+    if ax is None:
+        fig = plt.figure(**fig_kw)
+    else:
+        if is_list_like(ax):
+            if squeeze:
+                ax = flatten_axes(ax)
+            if layout is not None:
+                warnings.warn(
+                    "When passing multiple axes, layout keyword is ignored.",
+                    UserWarning,
+                    stacklevel=find_stack_level(),
+                )
+            if sharex or sharey:
+                warnings.warn(
+                    "When passing multiple axes, sharex and sharey "
+                    "are ignored. These settings must be specified when creating axes.",
+                    UserWarning,
+                    stacklevel=find_stack_level(),
+                )
+            if ax.size == naxes:
+                fig = ax.flat[0].get_figure()
+                return fig, ax
+            else:
+                raise ValueError(
+                    f"The number of passed axes must be {naxes}, the "
+                    "same as the output plot"
+                )
+
+        fig = ax.get_figure()
+        # if ax is passed and a number of subplots is 1, return ax as it is
+        if naxes == 1:
+            if squeeze:
+                return fig, ax
+            else:
+                return fig, flatten_axes(ax)
+        else:
+            warnings.warn(
+                "To output multiple subplots, the figure containing "
+                "the passed axes is being cleared.",
+                UserWarning,
+                stacklevel=find_stack_level(),
+            )
+            fig.clear()
+
+    nrows, ncols = _get_layout(naxes, layout=layout, layout_type=layout_type)
+    nplots = nrows * ncols
+
+    # Create empty object array to hold all axes.  It's easiest to make it 1-d
+    # so we can just append subplots upon creation, and then
+    axarr = np.empty(nplots, dtype=object)
+
+    # Create first subplot separately, so we can share it if requested
+    ax0 = fig.add_subplot(nrows, ncols, 1, **subplot_kw)
+
+    if sharex:
+        subplot_kw["sharex"] = ax0
+    if sharey:
+        subplot_kw["sharey"] = ax0
+    axarr[0] = ax0
+
+    # Note off-by-one counting because add_subplot uses the MATLAB 1-based
+    # convention.
+    for i in range(1, nplots):
+        kwds = subplot_kw.copy()
+        # Set sharex and sharey to None for blank/dummy axes, these can
+        # interfere with proper axis limits on the visible axes if
+        # they share axes e.g. issue #7528
+        if i >= naxes:
+            kwds["sharex"] = None
+            kwds["sharey"] = None
+        ax = fig.add_subplot(nrows, ncols, i + 1, **kwds)
+        axarr[i] = ax
+
+    if naxes != nplots:
+        for ax in axarr[naxes:]:
+            ax.set_visible(False)
+
+    handle_shared_axes(axarr, nplots, naxes, nrows, ncols, sharex, sharey)
+
+    if squeeze:
+        # Reshape the array to have the final desired dimension (nrow,ncol),
+        # though discarding unneeded dimensions that equal 1.  If we only have
+        # one subplot, just return it instead of a 1-element array.
+        if nplots == 1:
+            axes = axarr[0]
+        else:
+            axes = axarr.reshape(nrows, ncols).squeeze()
+    else:
+        # returned axis array will be always 2-d, even if nrows=ncols=1
+        axes = axarr.reshape(nrows, ncols)
+
+    return fig, axes
+
+
+def _remove_labels_from_axis(axis: Axis) -> None:
+    for t in axis.get_majorticklabels():
+        t.set_visible(False)
+
+    # set_visible will not be effective if
+    # minor axis has NullLocator and NullFormatter (default)
+    if isinstance(axis.get_minor_locator(), ticker.NullLocator):
+        axis.set_minor_locator(ticker.AutoLocator())
+    if isinstance(axis.get_minor_formatter(), ticker.NullFormatter):
+        axis.set_minor_formatter(ticker.FormatStrFormatter(""))
+    for t in axis.get_minorticklabels():
+        t.set_visible(False)
+
+    axis.get_label().set_visible(False)
+
+
+def _has_externally_shared_axis(ax1: Axes, compare_axis: str) -> bool:
+    """
+    Return whether an axis is externally shared.
+
+    Parameters
+    ----------
+    ax1 : matplotlib.axes.Axes
+        Axis to query.
+    compare_axis : str
+        `"x"` or `"y"` according to whether the X-axis or Y-axis is being
+        compared.
+
+    Returns
+    -------
+    bool
+        `True` if the axis is externally shared. Otherwise `False`.
+
+    Notes
+    -----
+    If two axes with different positions are sharing an axis, they can be
+    referred to as *externally* sharing the common axis.
+
+    If two axes sharing an axis also have the same position, they can be
+    referred to as *internally* sharing the common axis (a.k.a twinning).
+
+    _handle_shared_axes() is only interested in axes externally sharing an
+    axis, regardless of whether either of the axes is also internally sharing
+    with a third axis.
+    """
+    if compare_axis == "x":
+        axes = ax1.get_shared_x_axes()
+    elif compare_axis == "y":
+        axes = ax1.get_shared_y_axes()
+    else:
+        raise ValueError(
+            "_has_externally_shared_axis() needs 'x' or 'y' as a second parameter"
+        )
+
+    axes_siblings = axes.get_siblings(ax1)
+
+    # Retain ax1 and any of its siblings which aren't in the same position as it
+    ax1_points = ax1.get_position().get_points()
+
+    for ax2 in axes_siblings:
+        if not np.array_equal(ax1_points, ax2.get_position().get_points()):
+            return True
+
+    return False
+
+
+def handle_shared_axes(
+    axarr: Iterable[Axes],
+    nplots: int,
+    naxes: int,
+    nrows: int,
+    ncols: int,
+    sharex: bool,
+    sharey: bool,
+) -> None:
+    if nplots > 1:
+        row_num = lambda x: x.get_subplotspec().rowspan.start
+        col_num = lambda x: x.get_subplotspec().colspan.start
+
+        is_first_col = lambda x: x.get_subplotspec().is_first_col()
+
+        if nrows > 1:
+            try:
+                # first find out the ax layout,
+                # so that we can correctly handle 'gaps"
+                layout = np.zeros((nrows + 1, ncols + 1), dtype=np.bool_)
+                for ax in axarr:
+                    layout[row_num(ax), col_num(ax)] = ax.get_visible()
+
+                for ax in axarr:
+                    # only the last row of subplots should get x labels -> all
+                    # other off layout handles the case that the subplot is
+                    # the last in the column, because below is no subplot/gap.
+                    if not layout[row_num(ax) + 1, col_num(ax)]:
+                        continue
+                    if sharex or _has_externally_shared_axis(ax, "x"):
+                        _remove_labels_from_axis(ax.xaxis)
+
+            except IndexError:
+                # if gridspec is used, ax.rowNum and ax.colNum may different
+                # from layout shape. in this case, use last_row logic
+                is_last_row = lambda x: x.get_subplotspec().is_last_row()
+                for ax in axarr:
+                    if is_last_row(ax):
+                        continue
+                    if sharex or _has_externally_shared_axis(ax, "x"):
+                        _remove_labels_from_axis(ax.xaxis)
+
+        if ncols > 1:
+            for ax in axarr:
+                # only the first column should get y labels -> set all other to
+                # off as we only have labels in the first column and we always
+                # have a subplot there, we can skip the layout test
+                if is_first_col(ax):
+                    continue
+                if sharey or _has_externally_shared_axis(ax, "y"):
+                    _remove_labels_from_axis(ax.yaxis)
+
+
+def flatten_axes(axes: Axes | Sequence[Axes]) -> np.ndarray:
+    if not is_list_like(axes):
+        return np.array([axes])
+    elif isinstance(axes, (np.ndarray, ABCIndex)):
+        return np.asarray(axes).ravel()
+    return np.array(axes)
+
+
+def set_ticks_props(
+    axes: Axes | Sequence[Axes],
+    xlabelsize: int | None = None,
+    xrot=None,
+    ylabelsize: int | None = None,
+    yrot=None,
+):
+    import matplotlib.pyplot as plt
+
+    for ax in flatten_axes(axes):
+        if xlabelsize is not None:
+            plt.setp(ax.get_xticklabels(), fontsize=xlabelsize)
+        if xrot is not None:
+            plt.setp(ax.get_xticklabels(), rotation=xrot)
+        if ylabelsize is not None:
+            plt.setp(ax.get_yticklabels(), fontsize=ylabelsize)
+        if yrot is not None:
+            plt.setp(ax.get_yticklabels(), rotation=yrot)
+    return axes
+
+
+def get_all_lines(ax: Axes) -> list[Line2D]:
+    lines = ax.get_lines()
+
+    if hasattr(ax, "right_ax"):
+        lines += ax.right_ax.get_lines()
+
+    if hasattr(ax, "left_ax"):
+        lines += ax.left_ax.get_lines()
+
+    return lines
+
+
+def get_xlim(lines: Iterable[Line2D]) -> tuple[float, float]:
+    left, right = np.inf, -np.inf
+    for line in lines:
+        x = line.get_xdata(orig=False)
+        left = min(np.nanmin(x), left)
+        right = max(np.nanmax(x), right)
+    return left, right
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/plotting/_misc.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/plotting/_misc.py
new file mode 100644
index 0000000000000000000000000000000000000000..18db460d388a4b748f91282ae42875206ba36cc6
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/plotting/_misc.py
@@ -0,0 +1,688 @@
+from __future__ import annotations
+
+from contextlib import contextmanager
+from typing import (
+    TYPE_CHECKING,
+    Any,
+)
+
+from pandas.plotting._core import _get_plot_backend
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Generator,
+        Mapping,
+    )
+
+    from matplotlib.axes import Axes
+    from matplotlib.colors import Colormap
+    from matplotlib.figure import Figure
+    from matplotlib.table import Table
+    import numpy as np
+
+    from pandas import (
+        DataFrame,
+        Series,
+    )
+
+
+def table(ax: Axes, data: DataFrame | Series, **kwargs) -> Table:
+    """
+    Helper function to convert DataFrame and Series to matplotlib.table.
+
+    Parameters
+    ----------
+    ax : Matplotlib axes object
+    data : DataFrame or Series
+        Data for table contents.
+    **kwargs
+        Keyword arguments to be passed to matplotlib.table.table.
+        If `rowLabels` or `colLabels` is not specified, data index or column
+        name will be used.
+
+    Returns
+    -------
+    matplotlib table object
+
+    Examples
+    --------
+
+    .. plot::
+            :context: close-figs
+
+            >>> import matplotlib.pyplot as plt
+            >>> df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
+            >>> fix, ax = plt.subplots()
+            >>> ax.axis('off')
+            (0.0, 1.0, 0.0, 1.0)
+            >>> table = pd.plotting.table(ax, df, loc='center',
+            ...                           cellLoc='center', colWidths=list([.2, .2]))
+    """
+    plot_backend = _get_plot_backend("matplotlib")
+    return plot_backend.table(
+        ax=ax, data=data, rowLabels=None, colLabels=None, **kwargs
+    )
+
+
+def register() -> None:
+    """
+    Register pandas formatters and converters with matplotlib.
+
+    This function modifies the global ``matplotlib.units.registry``
+    dictionary. pandas adds custom converters for
+
+    * pd.Timestamp
+    * pd.Period
+    * np.datetime64
+    * datetime.datetime
+    * datetime.date
+    * datetime.time
+
+    See Also
+    --------
+    deregister_matplotlib_converters : Remove pandas formatters and converters.
+
+    Examples
+    --------
+    .. plot::
+       :context: close-figs
+
+        The following line is done automatically by pandas so
+        the plot can be rendered:
+
+        >>> pd.plotting.register_matplotlib_converters()
+
+        >>> df = pd.DataFrame({'ts': pd.period_range('2020', periods=2, freq='M'),
+        ...                    'y': [1, 2]
+        ...                    })
+        >>> plot = df.plot.line(x='ts', y='y')
+
+    Unsetting the register manually an error will be raised:
+
+    >>> pd.set_option("plotting.matplotlib.register_converters",
+    ...               False)  # doctest: +SKIP
+    >>> df.plot.line(x='ts', y='y')  # doctest: +SKIP
+    Traceback (most recent call last):
+    TypeError: float() argument must be a string or a real number, not 'Period'
+    """
+    plot_backend = _get_plot_backend("matplotlib")
+    plot_backend.register()
+
+
+def deregister() -> None:
+    """
+    Remove pandas formatters and converters.
+
+    Removes the custom converters added by :func:`register`. This
+    attempts to set the state of the registry back to the state before
+    pandas registered its own units. Converters for pandas' own types like
+    Timestamp and Period are removed completely. Converters for types
+    pandas overwrites, like ``datetime.datetime``, are restored to their
+    original value.
+
+    See Also
+    --------
+    register_matplotlib_converters : Register pandas formatters and converters
+        with matplotlib.
+
+    Examples
+    --------
+    .. plot::
+       :context: close-figs
+
+        The following line is done automatically by pandas so
+        the plot can be rendered:
+
+        >>> pd.plotting.register_matplotlib_converters()
+
+        >>> df = pd.DataFrame({'ts': pd.period_range('2020', periods=2, freq='M'),
+        ...                    'y': [1, 2]
+        ...                    })
+        >>> plot = df.plot.line(x='ts', y='y')
+
+    Unsetting the register manually an error will be raised:
+
+    >>> pd.set_option("plotting.matplotlib.register_converters",
+    ...               False)  # doctest: +SKIP
+    >>> df.plot.line(x='ts', y='y')  # doctest: +SKIP
+    Traceback (most recent call last):
+    TypeError: float() argument must be a string or a real number, not 'Period'
+    """
+    plot_backend = _get_plot_backend("matplotlib")
+    plot_backend.deregister()
+
+
+def scatter_matrix(
+    frame: DataFrame,
+    alpha: float = 0.5,
+    figsize: tuple[float, float] | None = None,
+    ax: Axes | None = None,
+    grid: bool = False,
+    diagonal: str = "hist",
+    marker: str = ".",
+    density_kwds: Mapping[str, Any] | None = None,
+    hist_kwds: Mapping[str, Any] | None = None,
+    range_padding: float = 0.05,
+    **kwargs,
+) -> np.ndarray:
+    """
+    Draw a matrix of scatter plots.
+
+    Parameters
+    ----------
+    frame : DataFrame
+    alpha : float, optional
+        Amount of transparency applied.
+    figsize : (float,float), optional
+        A tuple (width, height) in inches.
+    ax : Matplotlib axis object, optional
+    grid : bool, optional
+        Setting this to True will show the grid.
+    diagonal : {'hist', 'kde'}
+        Pick between 'kde' and 'hist' for either Kernel Density Estimation or
+        Histogram plot in the diagonal.
+    marker : str, optional
+        Matplotlib marker type, default '.'.
+    density_kwds : keywords
+        Keyword arguments to be passed to kernel density estimate plot.
+    hist_kwds : keywords
+        Keyword arguments to be passed to hist function.
+    range_padding : float, default 0.05
+        Relative extension of axis range in x and y with respect to
+        (x_max - x_min) or (y_max - y_min).
+    **kwargs
+        Keyword arguments to be passed to scatter function.
+
+    Returns
+    -------
+    numpy.ndarray
+        A matrix of scatter plots.
+
+    Examples
+    --------
+
+    .. plot::
+        :context: close-figs
+
+        >>> df = pd.DataFrame(np.random.randn(1000, 4), columns=['A','B','C','D'])
+        >>> pd.plotting.scatter_matrix(df, alpha=0.2)
+        array([[, ,
+                , ],
+               [, ,
+                , ],
+               [, ,
+                , ],
+               [, ,
+                , ]],
+              dtype=object)
+    """
+    plot_backend = _get_plot_backend("matplotlib")
+    return plot_backend.scatter_matrix(
+        frame=frame,
+        alpha=alpha,
+        figsize=figsize,
+        ax=ax,
+        grid=grid,
+        diagonal=diagonal,
+        marker=marker,
+        density_kwds=density_kwds,
+        hist_kwds=hist_kwds,
+        range_padding=range_padding,
+        **kwargs,
+    )
+
+
+def radviz(
+    frame: DataFrame,
+    class_column: str,
+    ax: Axes | None = None,
+    color: list[str] | tuple[str, ...] | None = None,
+    colormap: Colormap | str | None = None,
+    **kwds,
+) -> Axes:
+    """
+    Plot a multidimensional dataset in 2D.
+
+    Each Series in the DataFrame is represented as a evenly distributed
+    slice on a circle. Each data point is rendered in the circle according to
+    the value on each Series. Highly correlated `Series` in the `DataFrame`
+    are placed closer on the unit circle.
+
+    RadViz allow to project a N-dimensional data set into a 2D space where the
+    influence of each dimension can be interpreted as a balance between the
+    influence of all dimensions.
+
+    More info available at the `original article
+    `_
+    describing RadViz.
+
+    Parameters
+    ----------
+    frame : `DataFrame`
+        Object holding the data.
+    class_column : str
+        Column name containing the name of the data point category.
+    ax : :class:`matplotlib.axes.Axes`, optional
+        A plot instance to which to add the information.
+    color : list[str] or tuple[str], optional
+        Assign a color to each category. Example: ['blue', 'green'].
+    colormap : str or :class:`matplotlib.colors.Colormap`, default None
+        Colormap to select colors from. If string, load colormap with that
+        name from matplotlib.
+    **kwds
+        Options to pass to matplotlib scatter plotting method.
+
+    Returns
+    -------
+    :class:`matplotlib.axes.Axes`
+
+    See Also
+    --------
+    pandas.plotting.andrews_curves : Plot clustering visualization.
+
+    Examples
+    --------
+
+    .. plot::
+        :context: close-figs
+
+        >>> df = pd.DataFrame(
+        ...     {
+        ...         'SepalLength': [6.5, 7.7, 5.1, 5.8, 7.6, 5.0, 5.4, 4.6, 6.7, 4.6],
+        ...         'SepalWidth': [3.0, 3.8, 3.8, 2.7, 3.0, 2.3, 3.0, 3.2, 3.3, 3.6],
+        ...         'PetalLength': [5.5, 6.7, 1.9, 5.1, 6.6, 3.3, 4.5, 1.4, 5.7, 1.0],
+        ...         'PetalWidth': [1.8, 2.2, 0.4, 1.9, 2.1, 1.0, 1.5, 0.2, 2.1, 0.2],
+        ...         'Category': [
+        ...             'virginica',
+        ...             'virginica',
+        ...             'setosa',
+        ...             'virginica',
+        ...             'virginica',
+        ...             'versicolor',
+        ...             'versicolor',
+        ...             'setosa',
+        ...             'virginica',
+        ...             'setosa'
+        ...         ]
+        ...     }
+        ... )
+        >>> pd.plotting.radviz(df, 'Category')  # doctest: +SKIP
+    """
+    plot_backend = _get_plot_backend("matplotlib")
+    return plot_backend.radviz(
+        frame=frame,
+        class_column=class_column,
+        ax=ax,
+        color=color,
+        colormap=colormap,
+        **kwds,
+    )
+
+
+def andrews_curves(
+    frame: DataFrame,
+    class_column: str,
+    ax: Axes | None = None,
+    samples: int = 200,
+    color: list[str] | tuple[str, ...] | None = None,
+    colormap: Colormap | str | None = None,
+    **kwargs,
+) -> Axes:
+    """
+    Generate a matplotlib plot for visualizing clusters of multivariate data.
+
+    Andrews curves have the functional form:
+
+    .. math::
+        f(t) = \\frac{x_1}{\\sqrt{2}} + x_2 \\sin(t) + x_3 \\cos(t) +
+        x_4 \\sin(2t) + x_5 \\cos(2t) + \\cdots
+
+    Where :math:`x` coefficients correspond to the values of each dimension
+    and :math:`t` is linearly spaced between :math:`-\\pi` and :math:`+\\pi`.
+    Each row of frame then corresponds to a single curve.
+
+    Parameters
+    ----------
+    frame : DataFrame
+        Data to be plotted, preferably normalized to (0.0, 1.0).
+    class_column : label
+        Name of the column containing class names.
+    ax : axes object, default None
+        Axes to use.
+    samples : int
+        Number of points to plot in each curve.
+    color : str, list[str] or tuple[str], optional
+        Colors to use for the different classes. Colors can be strings
+        or 3-element floating point RGB values.
+    colormap : str or matplotlib colormap object, default None
+        Colormap to select colors from. If a string, load colormap with that
+        name from matplotlib.
+    **kwargs
+        Options to pass to matplotlib plotting method.
+
+    Returns
+    -------
+    :class:`matplotlib.axes.Axes`
+
+    Examples
+    --------
+
+    .. plot::
+        :context: close-figs
+
+        >>> df = pd.read_csv(
+        ...     'https://raw.githubusercontent.com/pandas-dev/'
+        ...     'pandas/main/pandas/tests/io/data/csv/iris.csv'
+        ... )
+        >>> pd.plotting.andrews_curves(df, 'Name')  # doctest: +SKIP
+    """
+    plot_backend = _get_plot_backend("matplotlib")
+    return plot_backend.andrews_curves(
+        frame=frame,
+        class_column=class_column,
+        ax=ax,
+        samples=samples,
+        color=color,
+        colormap=colormap,
+        **kwargs,
+    )
+
+
+def bootstrap_plot(
+    series: Series,
+    fig: Figure | None = None,
+    size: int = 50,
+    samples: int = 500,
+    **kwds,
+) -> Figure:
+    """
+    Bootstrap plot on mean, median and mid-range statistics.
+
+    The bootstrap plot is used to estimate the uncertainty of a statistic
+    by relying on random sampling with replacement [1]_. This function will
+    generate bootstrapping plots for mean, median and mid-range statistics
+    for the given number of samples of the given size.
+
+    .. [1] "Bootstrapping (statistics)" in \
+    https://en.wikipedia.org/wiki/Bootstrapping_%28statistics%29
+
+    Parameters
+    ----------
+    series : pandas.Series
+        Series from where to get the samplings for the bootstrapping.
+    fig : matplotlib.figure.Figure, default None
+        If given, it will use the `fig` reference for plotting instead of
+        creating a new one with default parameters.
+    size : int, default 50
+        Number of data points to consider during each sampling. It must be
+        less than or equal to the length of the `series`.
+    samples : int, default 500
+        Number of times the bootstrap procedure is performed.
+    **kwds
+        Options to pass to matplotlib plotting method.
+
+    Returns
+    -------
+    matplotlib.figure.Figure
+        Matplotlib figure.
+
+    See Also
+    --------
+    pandas.DataFrame.plot : Basic plotting for DataFrame objects.
+    pandas.Series.plot : Basic plotting for Series objects.
+
+    Examples
+    --------
+    This example draws a basic bootstrap plot for a Series.
+
+    .. plot::
+        :context: close-figs
+
+        >>> s = pd.Series(np.random.uniform(size=100))
+        >>> pd.plotting.bootstrap_plot(s)  # doctest: +SKIP
+        

+    """
+    plot_backend = _get_plot_backend("matplotlib")
+    return plot_backend.bootstrap_plot(
+        series=series, fig=fig, size=size, samples=samples, **kwds
+    )
+
+
+def parallel_coordinates(
+    frame: DataFrame,
+    class_column: str,
+    cols: list[str] | None = None,
+    ax: Axes | None = None,
+    color: list[str] | tuple[str, ...] | None = None,
+    use_columns: bool = False,
+    xticks: list | tuple | None = None,
+    colormap: Colormap | str | None = None,
+    axvlines: bool = True,
+    axvlines_kwds: Mapping[str, Any] | None = None,
+    sort_labels: bool = False,
+    **kwargs,
+) -> Axes:
+    """
+    Parallel coordinates plotting.
+
+    Parameters
+    ----------
+    frame : DataFrame
+    class_column : str
+        Column name containing class names.
+    cols : list, optional
+        A list of column names to use.
+    ax : matplotlib.axis, optional
+        Matplotlib axis object.
+    color : list or tuple, optional
+        Colors to use for the different classes.
+    use_columns : bool, optional
+        If true, columns will be used as xticks.
+    xticks : list or tuple, optional
+        A list of values to use for xticks.
+    colormap : str or matplotlib colormap, default None
+        Colormap to use for line colors.
+    axvlines : bool, optional
+        If true, vertical lines will be added at each xtick.
+    axvlines_kwds : keywords, optional
+        Options to be passed to axvline method for vertical lines.
+    sort_labels : bool, default False
+        Sort class_column labels, useful when assigning colors.
+    **kwargs
+        Options to pass to matplotlib plotting method.
+
+    Returns
+    -------
+    matplotlib.axes.Axes
+
+    Examples
+    --------
+
+    .. plot::
+        :context: close-figs
+
+        >>> df = pd.read_csv(
+        ...     'https://raw.githubusercontent.com/pandas-dev/'
+        ...     'pandas/main/pandas/tests/io/data/csv/iris.csv'
+        ... )
+        >>> pd.plotting.parallel_coordinates(
+        ...     df, 'Name', color=('#556270', '#4ECDC4', '#C7F464')
+        ... )  # doctest: +SKIP
+    """
+    plot_backend = _get_plot_backend("matplotlib")
+    return plot_backend.parallel_coordinates(
+        frame=frame,
+        class_column=class_column,
+        cols=cols,
+        ax=ax,
+        color=color,
+        use_columns=use_columns,
+        xticks=xticks,
+        colormap=colormap,
+        axvlines=axvlines,
+        axvlines_kwds=axvlines_kwds,
+        sort_labels=sort_labels,
+        **kwargs,
+    )
+
+
+def lag_plot(series: Series, lag: int = 1, ax: Axes | None = None, **kwds) -> Axes:
+    """
+    Lag plot for time series.
+
+    Parameters
+    ----------
+    series : Series
+        The time series to visualize.
+    lag : int, default 1
+        Lag length of the scatter plot.
+    ax : Matplotlib axis object, optional
+        The matplotlib axis object to use.
+    **kwds
+        Matplotlib scatter method keyword arguments.
+
+    Returns
+    -------
+    matplotlib.axes.Axes
+
+    Examples
+    --------
+    Lag plots are most commonly used to look for patterns in time series data.
+
+    Given the following time series
+
+    .. plot::
+        :context: close-figs
+
+        >>> np.random.seed(5)
+        >>> x = np.cumsum(np.random.normal(loc=1, scale=5, size=50))
+        >>> s = pd.Series(x)
+        >>> s.plot()  # doctest: +SKIP
+
+    A lag plot with ``lag=1`` returns
+
+    .. plot::
+        :context: close-figs
+
+        >>> pd.plotting.lag_plot(s, lag=1)
+        
+    """
+    plot_backend = _get_plot_backend("matplotlib")
+    return plot_backend.lag_plot(series=series, lag=lag, ax=ax, **kwds)
+
+
+def autocorrelation_plot(series: Series, ax: Axes | None = None, **kwargs) -> Axes:
+    """
+    Autocorrelation plot for time series.
+
+    Parameters
+    ----------
+    series : Series
+        The time series to visualize.
+    ax : Matplotlib axis object, optional
+        The matplotlib axis object to use.
+    **kwargs
+        Options to pass to matplotlib plotting method.
+
+    Returns
+    -------
+    matplotlib.axes.Axes
+
+    Examples
+    --------
+    The horizontal lines in the plot correspond to 95% and 99% confidence bands.
+
+    The dashed line is 99% confidence band.
+
+    .. plot::
+        :context: close-figs
+
+        >>> spacing = np.linspace(-9 * np.pi, 9 * np.pi, num=1000)
+        >>> s = pd.Series(0.7 * np.random.rand(1000) + 0.3 * np.sin(spacing))
+        >>> pd.plotting.autocorrelation_plot(s)  # doctest: +SKIP
+    """
+    plot_backend = _get_plot_backend("matplotlib")
+    return plot_backend.autocorrelation_plot(series=series, ax=ax, **kwargs)
+
+
+class _Options(dict):
+    """
+    Stores pandas plotting options.
+
+    Allows for parameter aliasing so you can just use parameter names that are
+    the same as the plot function parameters, but is stored in a canonical
+    format that makes it easy to breakdown into groups later.
+
+    Examples
+    --------
+
+    .. plot::
+            :context: close-figs
+
+             >>> np.random.seed(42)
+             >>> df = pd.DataFrame({'A': np.random.randn(10),
+             ...                   'B': np.random.randn(10)},
+             ...                   index=pd.date_range("1/1/2000",
+             ...                   freq='4MS', periods=10))
+             >>> with pd.plotting.plot_params.use("x_compat", True):
+             ...     _ = df["A"].plot(color="r")
+             ...     _ = df["B"].plot(color="g")
+    """
+
+    # alias so the names are same as plotting method parameter names
+    _ALIASES = {"x_compat": "xaxis.compat"}
+    _DEFAULT_KEYS = ["xaxis.compat"]
+
+    def __init__(self, deprecated: bool = False) -> None:
+        self._deprecated = deprecated
+        super().__setitem__("xaxis.compat", False)
+
+    def __getitem__(self, key):
+        key = self._get_canonical_key(key)
+        if key not in self:
+            raise ValueError(f"{key} is not a valid pandas plotting option")
+        return super().__getitem__(key)
+
+    def __setitem__(self, key, value) -> None:
+        key = self._get_canonical_key(key)
+        super().__setitem__(key, value)
+
+    def __delitem__(self, key) -> None:
+        key = self._get_canonical_key(key)
+        if key in self._DEFAULT_KEYS:
+            raise ValueError(f"Cannot remove default parameter {key}")
+        super().__delitem__(key)
+
+    def __contains__(self, key) -> bool:
+        key = self._get_canonical_key(key)
+        return super().__contains__(key)
+
+    def reset(self) -> None:
+        """
+        Reset the option store to its initial state
+
+        Returns
+        -------
+        None
+        """
+        # error: Cannot access "__init__" directly
+        self.__init__()  # type: ignore[misc]
+
+    def _get_canonical_key(self, key):
+        return self._ALIASES.get(key, key)
+
+    @contextmanager
+    def use(self, key, value) -> Generator[_Options, None, None]:
+        """
+        Temporarily set a parameter value using the with statement.
+        Aliasing allowed.
+        """
+        old_value = self[key]
+        try:
+            self[key] = value
+            yield self
+        finally:
+            self[key] = old_value
+
+
+plot_params = _Options()
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/pyproject.toml b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/pyproject.toml
new file mode 100644
index 0000000000000000000000000000000000000000..d5a7f71391fef6e9797fb82700bb4021ab906984
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/pyproject.toml
@@ -0,0 +1,819 @@
+[build-system]
+# Minimum requirements for the build system to execute.
+# See https://github.com/scipy/scipy/pull/12940 for the AIX issue.
+requires = [
+    "meson-python>=0.13.1",
+    "meson>=1.2.1,<2",
+    "wheel",
+    "Cython<4.0.0a0",  # Note: sync with setup.py, environment.yml and asv.conf.json
+    # Force numpy higher than 2.0rc1, so that built wheels are compatible
+    # with both numpy 1 and 2
+    "numpy>=2.0",
+    "versioneer[toml]"
+]
+
+build-backend = "mesonpy"
+
+[project]
+name = 'pandas'
+dynamic = [
+  'version'
+]
+description = 'Powerful data structures for data analysis, time series, and statistics'
+readme = 'README.md'
+authors = [
+  { name = 'The Pandas Development Team', email='pandas-dev@python.org' },
+]
+license = {file = 'LICENSE'}
+requires-python = '>=3.9'
+dependencies = [
+  "numpy>=1.22.4; python_version<'3.11'",
+  "numpy>=1.23.2; python_version=='3.11'",
+  "numpy>=1.26.0; python_version>='3.12'",
+  "python-dateutil>=2.8.2",
+  "pytz>=2020.1",
+  "tzdata>=2022.7"
+]
+classifiers = [
+    'Development Status :: 5 - Production/Stable',
+    'Environment :: Console',
+    'Intended Audience :: Science/Research',
+    'License :: OSI Approved :: BSD License',
+    'Operating System :: OS Independent',
+    'Programming Language :: Cython',
+    'Programming Language :: Python',
+    'Programming Language :: Python :: 3',
+    'Programming Language :: Python :: 3 :: Only',
+    'Programming Language :: Python :: 3.9',
+    'Programming Language :: Python :: 3.10',
+    'Programming Language :: Python :: 3.11',
+    'Programming Language :: Python :: 3.12',
+    'Programming Language :: Python :: 3.13',
+    'Programming Language :: Python :: 3.14',
+    'Topic :: Scientific/Engineering'
+]
+
+[project.urls]
+homepage = 'https://pandas.pydata.org'
+documentation = 'https://pandas.pydata.org/docs/'
+repository = 'https://github.com/pandas-dev/pandas'
+
+[project.entry-points."pandas_plotting_backends"]
+matplotlib = "pandas:plotting._matplotlib"
+
+[project.optional-dependencies]
+test = ['hypothesis>=6.46.1', 'pytest>=7.3.2', 'pytest-xdist>=2.2.0']
+pyarrow = ['pyarrow>=10.0.1']
+performance = ['bottleneck>=1.3.6', 'numba>=0.56.4', 'numexpr>=2.8.4']
+computation = ['scipy>=1.10.0', 'xarray>=2022.12.0']
+fss = ['fsspec>=2022.11.0']
+aws = ['s3fs>=2022.11.0']
+gcp = ['gcsfs>=2022.11.0', 'pandas-gbq>=0.19.0']
+excel = ['odfpy>=1.4.1', 'openpyxl>=3.1.0', 'python-calamine>=0.1.7', 'pyxlsb>=1.0.10', 'xlrd>=2.0.1', 'xlsxwriter>=3.0.5']
+parquet = ['pyarrow>=10.0.1']
+feather = ['pyarrow>=10.0.1']
+hdf5 = [# blosc only available on conda (https://github.com/Blosc/python-blosc/issues/297)
+        #'blosc>=1.20.1',
+        'tables>=3.8.0']
+spss = ['pyreadstat>=1.2.0']
+postgresql = ['SQLAlchemy>=2.0.0', 'psycopg2>=2.9.6', 'adbc-driver-postgresql>=0.8.0']
+mysql = ['SQLAlchemy>=2.0.0', 'pymysql>=1.0.2']
+sql-other = ['SQLAlchemy>=2.0.0', 'adbc-driver-postgresql>=0.8.0', 'adbc-driver-sqlite>=0.8.0']
+html = ['beautifulsoup4>=4.11.2', 'html5lib>=1.1', 'lxml>=4.9.2']
+xml = ['lxml>=4.9.2']
+plot = ['matplotlib>=3.6.3']
+output-formatting = ['jinja2>=3.1.2', 'tabulate>=0.9.0']
+clipboard = ['PyQt5>=5.15.9', 'qtpy>=2.3.0']
+compression = ['zstandard>=0.19.0']
+consortium-standard = ['dataframe-api-compat>=0.1.7']
+all = ['adbc-driver-postgresql>=0.8.0',
+       'adbc-driver-sqlite>=0.8.0',
+       'beautifulsoup4>=4.11.2',
+       # blosc only available on conda (https://github.com/Blosc/python-blosc/issues/297)
+       #'blosc>=1.21.3',
+       'bottleneck>=1.3.6',
+       'dataframe-api-compat>=0.1.7',
+       'fastparquet>=2022.12.0',
+       'fsspec>=2022.11.0',
+       'gcsfs>=2022.11.0',
+       'html5lib>=1.1',
+       'hypothesis>=6.46.1',
+       'jinja2>=3.1.2',
+       'lxml>=4.9.2',
+       'matplotlib>=3.6.3',
+       'numba>=0.56.4',
+       'numexpr>=2.8.4',
+       'odfpy>=1.4.1',
+       'openpyxl>=3.1.0',
+       'pandas-gbq>=0.19.0',
+       'psycopg2>=2.9.6',
+       'pyarrow>=10.0.1',
+       'pymysql>=1.0.2',
+       'PyQt5>=5.15.9',
+       'pyreadstat>=1.2.0',
+       'pytest>=7.3.2',
+       'pytest-xdist>=2.2.0',
+       'python-calamine>=0.1.7',
+       'pyxlsb>=1.0.10',
+       'qtpy>=2.3.0',
+       'scipy>=1.10.0',
+       's3fs>=2022.11.0',
+       'SQLAlchemy>=2.0.0',
+       'tables>=3.8.0',
+       'tabulate>=0.9.0',
+       'xarray>=2022.12.0',
+       'xlrd>=2.0.1',
+       'xlsxwriter>=3.0.5',
+       'zstandard>=0.19.0']
+
+# TODO: Remove after setuptools support is dropped.
+[tool.setuptools]
+include-package-data = true
+
+[tool.setuptools.packages.find]
+include = ["pandas", "pandas.*"]
+namespaces = false
+
+[tool.setuptools.exclude-package-data]
+"*" = ["*.c", "*.h"]
+
+# See the docstring in versioneer.py for instructions. Note that you must
+# re-run 'versioneer.py setup' after changing this section, and commit the
+# resulting files.
+[tool.versioneer]
+VCS = "git"
+style = "pep440"
+versionfile_source = "pandas/_version.py"
+versionfile_build = "pandas/_version.py"
+tag_prefix = "v"
+parentdir_prefix = "pandas-"
+
+[tool.meson-python.args]
+setup = ['--vsenv'] # For Windows
+
+[tool.cibuildwheel]
+skip = "cp38-* *_i686 *_ppc64le *_s390x"
+build-verbosity = "3"
+environment = {LDFLAGS="-Wl,--strip-all"}
+# pytz 2024.2 causing some failures
+test-requires = "hypothesis>=6.46.1 pytest>=7.3.2 pytest-xdist>=2.2.0 pytz<2024.2"
+test-command = """
+  PANDAS_CI='1' python -c 'import pandas as pd; \
+  pd.test(extra_args=["-m not clipboard and not single_cpu and not slow and not network and not db", "-n 2", "--no-strict-data-files"]); \
+  pd.test(extra_args=["-m not clipboard and single_cpu and not slow and not network and not db", "--no-strict-data-files"]);' \
+  """
+enable = ["cpython-freethreading"]
+before-build = "PACKAGE_DIR={package} bash {package}/scripts/cibw_before_build.sh"
+
+[tool.cibuildwheel.windows]
+before-build = "pip install delvewheel"
+repair-wheel-command = "delvewheel repair -w {dest_dir} {wheel}"
+
+[[tool.cibuildwheel.overrides]]
+select = "*-manylinux_aarch64*"
+test-command = """
+  PANDAS_CI='1' python -c 'import pandas as pd; \
+  pd.test(extra_args=["-m not clipboard and not single_cpu and not slow and not network and not db and not fails_arm_wheels", "-n 2", "--no-strict-data-files"]); \
+  pd.test(extra_args=["-m not clipboard and single_cpu and not slow and not network and not db", "--no-strict-data-files"]);' \
+  """
+
+[[tool.cibuildwheel.overrides]]
+select = "*-musllinux*"
+before-test = "apk update && apk add musl-locales"
+
+[[tool.cibuildwheel.overrides]]
+select = "*-win*"
+# We test separately for Windows, since we use
+# the windowsservercore docker image to check if any dlls are
+# missing from the wheel
+test-command = ""
+
+[[tool.cibuildwheel.overrides]]
+# Don't strip wheels on macOS.
+# macOS doesn't support stripping wheels with linker
+# https://github.com/MacPython/numpy-wheels/pull/87#issuecomment-624878264
+select = "*-macosx*"
+environment = {CFLAGS="-g0"}
+
+[tool.black]
+target-version = ['py39', 'py310']
+required-version = '23.11.0'
+exclude = '''
+(
+    asv_bench/env
+  | \.egg
+  | \.git
+  | \.hg
+  | \.mypy_cache
+  | \.nox
+  | \.tox
+  | \.venv
+  | _build
+  | buck-out
+  | build
+  | dist
+  | setup.py
+)
+'''
+
+[tool.ruff]
+line-length = 88
+target-version = "py310"
+fix = true
+unfixable = []
+typing-modules = ["pandas._typing"]
+
+select = [
+  # pyflakes
+  "F",
+  # pycodestyle
+  "E", "W",
+  # flake8-2020
+  "YTT",
+  # flake8-bugbear
+  "B",
+  # flake8-quotes
+  "Q",
+  # flake8-debugger
+  "T10",
+  # flake8-gettext
+  "INT",
+  # pylint
+  "PL",
+  # misc lints
+  "PIE",
+  # flake8-pyi
+  "PYI",
+  # tidy imports
+  "TID",
+  # implicit string concatenation
+  "ISC",
+  # type-checking imports
+  "TCH",
+  # comprehensions
+  "C4",
+  # pygrep-hooks
+  "PGH",
+  # Ruff-specific rules
+  "RUF",
+  # flake8-bandit: exec-builtin
+  "S102",
+  # numpy-legacy-random
+  "NPY002",
+  # Perflint
+  "PERF",
+  # flynt
+  "FLY",
+  # flake8-logging-format
+  "G",
+  # flake8-future-annotations
+  "FA",
+]
+
+ignore = [
+  ### Intentionally disabled
+  # space before : (needed for how black formats slicing)
+  "E203",
+  # module level import not at top of file
+  "E402",
+  # do not assign a lambda expression, use a def
+  "E731",
+  # line break before binary operator
+  # "W503",  # not yet implemented
+  # line break after binary operator
+  # "W504",  # not yet implemented
+  # controversial
+  "B006",
+  # controversial
+  "B007",
+  # controversial
+  "B008",
+  # setattr is used to side-step mypy
+  "B009",
+  # getattr is used to side-step mypy
+  "B010",
+  # tests use assert False
+  "B011",
+  # tests use comparisons but not their returned value
+  "B015",
+  # false positives
+  "B019",
+  # Loop control variable overrides iterable it iterates
+  "B020",
+  # Function definition does not bind loop variable
+  "B023",
+  # Functions defined inside a loop must not use variables redefined in the loop
+  # "B301",  # not yet implemented
+  # Only works with python >=3.10
+  "B905",
+  # Too many arguments to function call
+  "PLR0913",
+  # Too many returns
+  "PLR0911",
+  # Too many branches
+  "PLR0912",
+  # Too many statements
+  "PLR0915",
+  # Redefined loop name
+  "PLW2901",
+  # Global statements are discouraged
+  "PLW0603",
+  # Docstrings should not be included in stubs
+  "PYI021",
+  # Use `typing.NamedTuple` instead of `collections.namedtuple`
+  "PYI024",
+  # No builtin `eval()` allowed
+  "PGH001",
+  # compare-to-empty-string
+  "PLC1901",
+  # while int | float can be shortened to float, the former is more explicit
+  "PYI041",
+  # incorrect-dict-iterator, flags valid Series.items usage
+  "PERF102",
+  # try-except-in-loop, becomes useless in Python 3.11
+  "PERF203",
+
+
+  ### TODO: Enable gradually
+  # Useless statement
+  "B018",
+  # Within an except clause, raise exceptions with ...
+  "B904",
+  # Magic number
+  "PLR2004",
+  # comparison-with-itself
+  "PLR0124",
+  # Consider `elif` instead of `else` then `if` to remove indentation level
+  "PLR5501",
+  # collection-literal-concatenation
+  "RUF005",
+  # pairwise-over-zipped (>=PY310 only)
+  "RUF007",
+  # explicit-f-string-type-conversion
+  "RUF010",
+  # mutable-class-default
+  "RUF012"
+]
+
+exclude = [
+  "doc/sphinxext/*.py",
+  "doc/build/*.py",
+  "doc/temp/*.py",
+  ".eggs/*.py",
+  # vendored files
+  "pandas/util/version/*",
+  "pandas/io/clipboard/__init__.py",
+  # exclude asv benchmark environments from linting
+  "env",
+]
+
+[tool.ruff.per-file-ignores]
+# relative imports allowed for asv_bench
+"asv_bench/*" = ["TID", "NPY002"]
+# to be enabled gradually
+"pandas/core/*" = ["PLR5501"]
+"pandas/tests/*" = ["B028", "FLY"]
+"scripts/*" = ["B028"]
+# Keep this one enabled
+"pandas/_typing.py" = ["TCH"]
+
+[tool.pylint.messages_control]
+max-line-length = 88
+disable = [
+ # intentionally turned off
+  "bad-mcs-classmethod-argument",
+  "broad-except",
+  "c-extension-no-member",
+  "comparison-with-itself",
+  "consider-using-enumerate",
+  "import-error",
+  "import-outside-toplevel",
+  "invalid-name",
+  "invalid-unary-operand-type",
+  "line-too-long",
+  "no-else-continue",
+  "no-else-raise",
+  "no-else-return",
+  "no-member",
+  "no-name-in-module",
+  "not-an-iterable",
+  "overridden-final-method",
+  "pointless-statement",
+  "redundant-keyword-arg",
+  "singleton-comparison",
+  "too-many-ancestors",
+  "too-many-arguments",
+  "too-many-boolean-expressions",
+  "too-many-branches",
+  "too-many-function-args",
+  "too-many-instance-attributes",
+  "too-many-locals",
+  "too-many-nested-blocks",
+  "too-many-public-methods",
+  "too-many-return-statements",
+  "too-many-statements",
+  "unexpected-keyword-arg",
+  "ungrouped-imports",
+  "unsubscriptable-object",
+  "unsupported-assignment-operation",
+  "unsupported-membership-test",
+  "unused-import",
+  "use-dict-literal",
+  "use-implicit-booleaness-not-comparison",
+  "use-implicit-booleaness-not-len",
+  "wrong-import-order",
+  "wrong-import-position",
+  "redefined-loop-name",
+
+ # misc
+  "abstract-class-instantiated",
+  "no-value-for-parameter",
+  "undefined-variable",
+  "unpacking-non-sequence",
+  "used-before-assignment",
+
+ # pylint type "C": convention, for programming standard violation
+  "missing-class-docstring",
+  "missing-function-docstring",
+  "missing-module-docstring",
+  "superfluous-parens",
+  "too-many-lines",
+  "unidiomatic-typecheck",
+  "unnecessary-dunder-call",
+  "unnecessary-lambda-assignment",
+
+  # pylint type "R": refactor, for bad code smell
+  "consider-using-with",
+  "cyclic-import",
+  "duplicate-code",
+  "inconsistent-return-statements",
+  "redefined-argument-from-local",
+  "too-few-public-methods",
+
+  # pylint type "W": warning, for python specific problems
+  "abstract-method",
+  "arguments-differ",
+  "arguments-out-of-order",
+  "arguments-renamed",
+  "attribute-defined-outside-init",
+  "broad-exception-raised",
+  "comparison-with-callable",
+  "dangerous-default-value",
+  "deprecated-module",
+  "eval-used",
+  "expression-not-assigned",
+  "fixme",
+  "global-statement",
+  "invalid-overridden-method",
+  "keyword-arg-before-vararg",
+  "possibly-unused-variable",
+  "protected-access",
+  "raise-missing-from",
+  "redefined-builtin",
+  "redefined-outer-name",
+  "self-cls-assignment",
+  "signature-differs",
+  "super-init-not-called",
+  "try-except-raise",
+  "unnecessary-lambda",
+  "unused-argument",
+  "unused-variable",
+  "using-constant-test",
+
+  # disabled on 2.3.x branch
+  "consider-using-in",
+  "simplifiable-if-expression",
+]
+
+[tool.pytest.ini_options]
+# sync minversion with pyproject.toml & install.rst
+minversion = "7.3.2"
+addopts = "--strict-markers --strict-config --capture=no --durations=30 --junitxml=test-data.xml"
+empty_parameter_set_mark = "fail_at_collect"
+xfail_strict = true
+testpaths = "pandas"
+doctest_optionflags = [
+  "NORMALIZE_WHITESPACE",
+  "IGNORE_EXCEPTION_DETAIL",
+  "ELLIPSIS",
+]
+filterwarnings = [
+  "error:::pandas",
+  "error::ResourceWarning",
+  "error::pytest.PytestUnraisableExceptionWarning",
+  # TODO(PY311-minimum): Specify EncodingWarning
+  # Ignore 3rd party EncodingWarning but raise on pandas'
+  "ignore:.*encoding.* argument not specified",
+  "error:.*encoding.* argument not specified::pandas",
+  "ignore:.*ssl.SSLSocket:pytest.PytestUnraisableExceptionWarning",
+  "ignore:.*ssl.SSLSocket:ResourceWarning",
+  # GH 44844: Can remove once minimum matplotlib version >= 3.7
+  "ignore:.*FileIO:pytest.PytestUnraisableExceptionWarning",
+  "ignore:.*BufferedRandom:ResourceWarning",
+  "ignore::ResourceWarning:asyncio",
+  # From plotting doctests
+  "ignore:More than 20 figures have been opened:RuntimeWarning",
+  # Will be fixed in numba 0.56: https://github.com/numba/numba/issues/7758
+  "ignore:`np.MachAr` is deprecated:DeprecationWarning:numba",
+  "ignore:.*urllib3:DeprecationWarning:botocore",
+  "ignore:Setuptools is replacing distutils.:UserWarning:_distutils_hack",
+  # https://github.com/PyTables/PyTables/issues/822
+  "ignore:a closed node found in the registry:UserWarning:tables",
+  "ignore:`np.object` is a deprecated:DeprecationWarning:tables",
+  "ignore:tostring:DeprecationWarning:tables",
+  "ignore:distutils Version classes are deprecated:DeprecationWarning:pandas_datareader",
+  "ignore:distutils Version classes are deprecated:DeprecationWarning:numexpr",
+  "ignore:distutils Version classes are deprecated:DeprecationWarning:fastparquet",
+  "ignore:distutils Version classes are deprecated:DeprecationWarning:fsspec",
+  # Can be removed once https://github.com/numpy/numpy/pull/24794 is merged
+  "ignore:.*In the future `np.long` will be defined as.*:FutureWarning",
+  # https://github.com/numpy/numpy/pull/29301
+  "ignore:.*align should be passed:",
+]
+junit_family = "xunit2"
+markers = [
+  "single_cpu: tests that should run on a single cpu only",
+  "slow: mark a test as slow",
+  "network: mark a test as network",
+  "db: tests requiring a database (mysql or postgres)",
+  "clipboard: mark a pd.read_clipboard test",
+  "arm_slow: mark a test as slow for arm64 architecture",
+  "skip_ubsan: Tests known to fail UBSAN check",
+  # TODO: someone should investigate this ...
+  # these tests only fail in the wheel builder and don't fail in regular
+  # ARM CI
+  "fails_arm_wheels: Tests that fail in the ARM wheel build only",
+]
+
+[tool.mypy]
+# Import discovery
+mypy_path = "typings"
+files = ["pandas", "typings"]
+namespace_packages = false
+explicit_package_bases = false
+ignore_missing_imports = true
+follow_imports = "normal"
+follow_imports_for_stubs = false
+no_site_packages = false
+no_silence_site_packages = false
+# Platform configuration
+python_version = "3.11"
+platform = "linux-64"
+# Disallow dynamic typing
+disallow_any_unimported = false # TODO
+disallow_any_expr = false # TODO
+disallow_any_decorated = false # TODO
+disallow_any_explicit = false # TODO
+disallow_any_generics = false # TODO
+disallow_subclassing_any = false # TODO
+# Untyped definitions and calls
+disallow_untyped_calls = true
+disallow_untyped_defs = true
+disallow_incomplete_defs = true
+check_untyped_defs = true
+disallow_untyped_decorators = true
+# None and Optional handling
+no_implicit_optional = true
+strict_optional = true
+# Configuring warnings
+warn_redundant_casts = true
+warn_unused_ignores = true
+warn_no_return = true
+warn_return_any = false # TODO
+warn_unreachable = false # GH#27396
+# Suppressing errors
+ignore_errors = false
+enable_error_code = "ignore-without-code"
+# Miscellaneous strictness flags
+allow_untyped_globals = false
+allow_redefinition = false
+local_partial_types = false
+implicit_reexport = true
+strict_equality = true
+# Configuring error messages
+show_error_context = false
+show_column_numbers = false
+show_error_codes = true
+
+[[tool.mypy.overrides]]
+module = [
+  "pandas._config.config", # TODO
+  "pandas._libs.*",
+  "pandas._testing.*", # TODO
+  "pandas.arrays", # TODO
+  "pandas.compat.numpy.function", # TODO
+  "pandas.compat._optional", # TODO
+  "pandas.compat.compressors", # TODO
+  "pandas.compat.pickle_compat", # TODO
+  "pandas.core._numba.executor", # TODO
+  "pandas.core.array_algos.datetimelike_accumulations", # TODO
+  "pandas.core.array_algos.masked_accumulations", # TODO
+  "pandas.core.array_algos.masked_reductions", # TODO
+  "pandas.core.array_algos.putmask", # TODO
+  "pandas.core.array_algos.quantile", # TODO
+  "pandas.core.array_algos.replace", # TODO
+  "pandas.core.array_algos.take", # TODO
+  "pandas.core.arrays.*", # TODO
+  "pandas.core.computation.*", # TODO
+  "pandas.core.dtypes.astype", # TODO
+  "pandas.core.dtypes.cast", # TODO
+  "pandas.core.dtypes.common", # TODO
+  "pandas.core.dtypes.concat", # TODO
+  "pandas.core.dtypes.dtypes", # TODO
+  "pandas.core.dtypes.generic", # TODO
+  "pandas.core.dtypes.inference", # TODO
+  "pandas.core.dtypes.missing", # TODO
+  "pandas.core.groupby.categorical", # TODO
+  "pandas.core.groupby.generic", # TODO
+  "pandas.core.groupby.grouper", # TODO
+  "pandas.core.groupby.groupby", # TODO
+  "pandas.core.groupby.ops", # TODO
+  "pandas.core.indexers.*", # TODO
+  "pandas.core.indexes.*", # TODO
+  "pandas.core.interchange.column", # TODO
+  "pandas.core.interchange.dataframe_protocol", # TODO
+  "pandas.core.interchange.from_dataframe", # TODO
+  "pandas.core.internals.*", # TODO
+  "pandas.core.methods.*", # TODO
+  "pandas.core.ops.array_ops", # TODO
+  "pandas.core.ops.common", # TODO
+  "pandas.core.ops.invalid", # TODO
+  "pandas.core.ops.mask_ops", # TODO
+  "pandas.core.ops.missing", # TODO
+  "pandas.core.reshape.*", # TODO
+  "pandas.core.strings.*", # TODO
+  "pandas.core.tools.*", # TODO
+  "pandas.core.window.common", # TODO
+  "pandas.core.window.ewm", # TODO
+  "pandas.core.window.expanding", # TODO
+  "pandas.core.window.numba_", # TODO
+  "pandas.core.window.online", # TODO
+  "pandas.core.window.rolling", # TODO
+  "pandas.core.accessor", # TODO
+  "pandas.core.algorithms", # TODO
+  "pandas.core.apply", # TODO
+  "pandas.core.arraylike", # TODO
+  "pandas.core.base", # TODO
+  "pandas.core.common", # TODO
+  "pandas.core.config_init", # TODO
+  "pandas.core.construction", # TODO
+  "pandas.core.flags", # TODO
+  "pandas.core.frame", # TODO
+  "pandas.core.generic", # TODO
+  "pandas.core.indexing", # TODO
+  "pandas.core.missing", # TODO
+  "pandas.core.nanops", # TODO
+  "pandas.core.resample", # TODO
+  "pandas.core.roperator", # TODO
+  "pandas.core.sample", # TODO
+  "pandas.core.series", # TODO
+  "pandas.core.sorting", # TODO
+  "pandas.errors", # TODO
+  "pandas.io.clipboard", # TODO
+  "pandas.io.excel._base", # TODO
+  "pandas.io.excel._odfreader", # TODO
+  "pandas.io.excel._odswriter", # TODO
+  "pandas.io.excel._openpyxl", # TODO
+  "pandas.io.excel._pyxlsb", # TODO
+  "pandas.io.excel._xlrd", # TODO
+  "pandas.io.excel._xlsxwriter", # TODO
+  "pandas.io.formats.console", # TODO
+  "pandas.io.formats.css", # TODO
+  "pandas.io.formats.excel", # TODO
+  "pandas.io.formats.format", # TODO
+  "pandas.io.formats.info", # TODO
+  "pandas.io.formats.printing", # TODO
+  "pandas.io.formats.style", # TODO
+  "pandas.io.formats.style_render", # TODO
+  "pandas.io.formats.xml", # TODO
+  "pandas.io.json.*", # TODO
+  "pandas.io.parsers.*", # TODO
+  "pandas.io.sas.sas_xport", # TODO
+  "pandas.io.sas.sas7bdat", # TODO
+  "pandas.io.clipboards", # TODO
+  "pandas.io.common", # TODO
+  "pandas.io.gbq", # TODO
+  "pandas.io.html", # TODO
+  "pandas.io.gbq", # TODO
+  "pandas.io.parquet", # TODO
+  "pandas.io.pytables", # TODO
+  "pandas.io.sql", # TODO
+  "pandas.io.stata", # TODO
+  "pandas.io.xml", # TODO
+  "pandas.plotting.*", # TODO
+  "pandas.tests.*",
+  "pandas.tseries.frequencies", # TODO
+  "pandas.tseries.holiday", # TODO
+  "pandas.util._decorators", # TODO
+  "pandas.util._doctools", # TODO
+  "pandas.util._print_versions", # TODO
+  "pandas.util._test_decorators", # TODO
+  "pandas.util._validators", # TODO
+  "pandas.util", # TODO
+  "pandas._version",
+  "pandas.conftest",
+  "pandas"
+]
+disallow_untyped_calls = false
+disallow_untyped_defs = false
+disallow_incomplete_defs = false
+
+[[tool.mypy.overrides]]
+module = [
+  "pandas.tests.*",
+  "pandas._version",
+  "pandas.io.clipboard",
+]
+check_untyped_defs = false
+
+[[tool.mypy.overrides]]
+module = [
+  "pandas.tests.apply.test_series_apply",
+  "pandas.tests.arithmetic.conftest",
+  "pandas.tests.arrays.sparse.test_combine_concat",
+  "pandas.tests.dtypes.test_common",
+  "pandas.tests.frame.methods.test_to_records",
+  "pandas.tests.groupby.test_rank",
+  "pandas.tests.groupby.transform.test_transform",
+  "pandas.tests.indexes.interval.test_interval",
+  "pandas.tests.indexing.test_categorical",
+  "pandas.tests.io.excel.test_writers",
+  "pandas.tests.reductions.test_reductions",
+  "pandas.tests.test_expressions",
+]
+ignore_errors = true
+
+# To be kept consistent with "Import Formatting" section in contributing.rst
+[tool.isort]
+known_pre_libs = "pandas._config"
+known_pre_core = ["pandas._libs", "pandas._typing", "pandas.util._*", "pandas.compat", "pandas.errors"]
+known_dtypes = "pandas.core.dtypes"
+known_post_core = ["pandas.tseries", "pandas.io", "pandas.plotting"]
+sections = ["FUTURE", "STDLIB", "THIRDPARTY" ,"PRE_LIBS" , "PRE_CORE", "DTYPES", "FIRSTPARTY", "POST_CORE", "LOCALFOLDER"]
+profile = "black"
+combine_as_imports = true
+force_grid_wrap = 2
+force_sort_within_sections = true
+skip_glob = "env"
+skip = "pandas/__init__.py"
+
+[tool.pyright]
+pythonVersion = "3.11"
+typeCheckingMode = "basic"
+useLibraryCodeForTypes = false
+include = ["pandas", "typings"]
+exclude = ["pandas/tests", "pandas/io/clipboard", "pandas/util/version", "pandas/core/_numba/extensions.py"]
+# enable subset of "strict"
+reportDuplicateImport = true
+reportInconsistentConstructor = true
+reportInvalidStubStatement = true
+reportOverlappingOverload = true
+reportPropertyTypeMismatch = true
+reportUntypedClassDecorator = true
+reportUntypedFunctionDecorator = true
+reportUntypedNamedTuple = true
+reportUnusedImport = true
+disableBytesTypePromotions = true
+# disable subset of "basic"
+reportGeneralTypeIssues = false
+reportMissingModuleSource = false
+reportOptionalCall = false
+reportOptionalIterable = false
+reportOptionalMemberAccess = false
+reportOptionalOperand = false
+reportOptionalSubscript = false
+reportPrivateImportUsage = false
+reportUnboundVariable = false
+
+[tool.coverage.run]
+branch = true
+omit = ["pandas/_typing.py", "pandas/_version.py"]
+plugins = ["Cython.Coverage"]
+source = ["pandas"]
+
+[tool.coverage.report]
+ignore_errors = false
+show_missing = true
+omit = ["pandas/_version.py"]
+exclude_lines = [
+  # Have to re-enable the standard pragma
+  "pragma: no cover",
+  # Don't complain about missing debug-only code:s
+  "def __repr__",
+  "if self.debug",
+  # Don't complain if tests don't hit defensive assertion code:
+  "raise AssertionError",
+  "raise NotImplementedError",
+  "AbstractMethodError",
+  # Don't complain if non-runnable code isn't run:
+  "if 0:",
+  "if __name__ == .__main__.:",
+  "if TYPE_CHECKING:",
+]
+
+[tool.coverage.html]
+directory = "coverage_html_report"
+
+[tool.codespell]
+ignore-words-list = "blocs, coo, hist, nd, sav, ser, recuse, nin, timere, expec, expecs"
+ignore-regex = 'https://([\w/\.])+'
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/testing.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/testing.py
new file mode 100644
index 0000000000000000000000000000000000000000..841b55df48556561904b9144a05f747d889ea621
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/testing.py
@@ -0,0 +1,18 @@
+"""
+Public testing utility functions.
+"""
+
+
+from pandas._testing import (
+    assert_extension_array_equal,
+    assert_frame_equal,
+    assert_index_equal,
+    assert_series_equal,
+)
+
+__all__ = [
+    "assert_extension_array_equal",
+    "assert_frame_equal",
+    "assert_series_equal",
+    "assert_index_equal",
+]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/api/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/api/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/api/test_api.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/api/test_api.py
new file mode 100644
index 0000000000000000000000000000000000000000..60bcb97aaa3642be064bcacd130edf2084c4a55c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/api/test_api.py
@@ -0,0 +1,383 @@
+from __future__ import annotations
+
+import pytest
+
+import pandas as pd
+from pandas import api
+import pandas._testing as tm
+from pandas.api import (
+    extensions as api_extensions,
+    indexers as api_indexers,
+    interchange as api_interchange,
+    types as api_types,
+    typing as api_typing,
+)
+
+
+class Base:
+    def check(self, namespace, expected, ignored=None):
+        # see which names are in the namespace, minus optional
+        # ignored ones
+        # compare vs the expected
+
+        result = sorted(
+            f for f in dir(namespace) if not f.startswith("__") and f != "annotations"
+        )
+        if ignored is not None:
+            result = sorted(set(result) - set(ignored))
+
+        expected = sorted(expected)
+        tm.assert_almost_equal(result, expected)
+
+
+class TestPDApi(Base):
+    # these are optionally imported based on testing
+    # & need to be ignored
+    ignored = ["tests", "locale", "conftest", "_version_meson"]
+
+    # top-level sub-packages
+    public_lib = [
+        "api",
+        "arrays",
+        "options",
+        "test",
+        "testing",
+        "errors",
+        "plotting",
+        "io",
+        "tseries",
+    ]
+    private_lib = ["compat", "core", "pandas", "util", "_built_with_meson"]
+
+    # misc
+    misc = ["IndexSlice", "NaT", "NA"]
+
+    # top-level classes
+    classes = [
+        "ArrowDtype",
+        "Categorical",
+        "CategoricalIndex",
+        "DataFrame",
+        "DateOffset",
+        "DatetimeIndex",
+        "ExcelFile",
+        "ExcelWriter",
+        "Flags",
+        "Grouper",
+        "HDFStore",
+        "Index",
+        "MultiIndex",
+        "Period",
+        "PeriodIndex",
+        "RangeIndex",
+        "Series",
+        "SparseDtype",
+        "StringDtype",
+        "Timedelta",
+        "TimedeltaIndex",
+        "Timestamp",
+        "Interval",
+        "IntervalIndex",
+        "CategoricalDtype",
+        "PeriodDtype",
+        "IntervalDtype",
+        "DatetimeTZDtype",
+        "BooleanDtype",
+        "Int8Dtype",
+        "Int16Dtype",
+        "Int32Dtype",
+        "Int64Dtype",
+        "UInt8Dtype",
+        "UInt16Dtype",
+        "UInt32Dtype",
+        "UInt64Dtype",
+        "Float32Dtype",
+        "Float64Dtype",
+        "NamedAgg",
+    ]
+
+    # these are already deprecated; awaiting removal
+    deprecated_classes: list[str] = []
+
+    # external modules exposed in pandas namespace
+    modules: list[str] = []
+
+    # top-level functions
+    funcs = [
+        "array",
+        "bdate_range",
+        "concat",
+        "crosstab",
+        "cut",
+        "date_range",
+        "interval_range",
+        "eval",
+        "factorize",
+        "get_dummies",
+        "from_dummies",
+        "infer_freq",
+        "isna",
+        "isnull",
+        "lreshape",
+        "melt",
+        "notna",
+        "notnull",
+        "offsets",
+        "merge",
+        "merge_ordered",
+        "merge_asof",
+        "period_range",
+        "pivot",
+        "pivot_table",
+        "qcut",
+        "show_versions",
+        "timedelta_range",
+        "unique",
+        "value_counts",
+        "wide_to_long",
+    ]
+
+    # top-level option funcs
+    funcs_option = [
+        "reset_option",
+        "describe_option",
+        "get_option",
+        "option_context",
+        "set_option",
+        "set_eng_float_format",
+    ]
+
+    # top-level read_* funcs
+    funcs_read = [
+        "read_clipboard",
+        "read_csv",
+        "read_excel",
+        "read_fwf",
+        "read_gbq",
+        "read_hdf",
+        "read_html",
+        "read_xml",
+        "read_json",
+        "read_pickle",
+        "read_sas",
+        "read_sql",
+        "read_sql_query",
+        "read_sql_table",
+        "read_stata",
+        "read_table",
+        "read_feather",
+        "read_parquet",
+        "read_orc",
+        "read_spss",
+    ]
+
+    # top-level json funcs
+    funcs_json = ["json_normalize"]
+
+    # top-level to_* funcs
+    funcs_to = ["to_datetime", "to_numeric", "to_pickle", "to_timedelta"]
+
+    # top-level to deprecate in the future
+    deprecated_funcs_in_future: list[str] = []
+
+    # these are already deprecated; awaiting removal
+    deprecated_funcs: list[str] = []
+
+    # private modules in pandas namespace
+    private_modules = [
+        "_config",
+        "_libs",
+        "_is_numpy_dev",
+        "_pandas_datetime_CAPI",
+        "_pandas_parser_CAPI",
+        "_testing",
+        "_typing",
+    ]
+    if not pd._built_with_meson:
+        private_modules.append("_version")
+
+    def test_api(self):
+        checkthese = (
+            self.public_lib
+            + self.private_lib
+            + self.misc
+            + self.modules
+            + self.classes
+            + self.funcs
+            + self.funcs_option
+            + self.funcs_read
+            + self.funcs_json
+            + self.funcs_to
+            + self.private_modules
+        )
+        self.check(namespace=pd, expected=checkthese, ignored=self.ignored)
+
+    def test_api_all(self):
+        expected = set(
+            self.public_lib
+            + self.misc
+            + self.modules
+            + self.classes
+            + self.funcs
+            + self.funcs_option
+            + self.funcs_read
+            + self.funcs_json
+            + self.funcs_to
+        ) - set(self.deprecated_classes)
+        actual = set(pd.__all__)
+
+        extraneous = actual - expected
+        assert not extraneous
+
+        missing = expected - actual
+        assert not missing
+
+    def test_depr(self):
+        deprecated_list = (
+            self.deprecated_classes
+            + self.deprecated_funcs
+            + self.deprecated_funcs_in_future
+        )
+        for depr in deprecated_list:
+            with tm.assert_produces_warning(FutureWarning):
+                _ = getattr(pd, depr)
+
+
+class TestApi(Base):
+    allowed_api_dirs = [
+        "types",
+        "extensions",
+        "indexers",
+        "interchange",
+        "typing",
+    ]
+    allowed_typing = [
+        "DataFrameGroupBy",
+        "DatetimeIndexResamplerGroupby",
+        "Expanding",
+        "ExpandingGroupby",
+        "ExponentialMovingWindow",
+        "ExponentialMovingWindowGroupby",
+        "JsonReader",
+        "NaTType",
+        "NAType",
+        "PeriodIndexResamplerGroupby",
+        "Resampler",
+        "Rolling",
+        "RollingGroupby",
+        "SeriesGroupBy",
+        "StataReader",
+        "TimedeltaIndexResamplerGroupby",
+        "TimeGrouper",
+        "Window",
+    ]
+    allowed_api_types = [
+        "is_any_real_numeric_dtype",
+        "is_array_like",
+        "is_bool",
+        "is_bool_dtype",
+        "is_categorical_dtype",
+        "is_complex",
+        "is_complex_dtype",
+        "is_datetime64_any_dtype",
+        "is_datetime64_dtype",
+        "is_datetime64_ns_dtype",
+        "is_datetime64tz_dtype",
+        "is_dict_like",
+        "is_dtype_equal",
+        "is_extension_array_dtype",
+        "is_file_like",
+        "is_float",
+        "is_float_dtype",
+        "is_hashable",
+        "is_int64_dtype",
+        "is_integer",
+        "is_integer_dtype",
+        "is_interval",
+        "is_interval_dtype",
+        "is_iterator",
+        "is_list_like",
+        "is_named_tuple",
+        "is_number",
+        "is_numeric_dtype",
+        "is_object_dtype",
+        "is_period_dtype",
+        "is_re",
+        "is_re_compilable",
+        "is_scalar",
+        "is_signed_integer_dtype",
+        "is_sparse",
+        "is_string_dtype",
+        "is_timedelta64_dtype",
+        "is_timedelta64_ns_dtype",
+        "is_unsigned_integer_dtype",
+        "pandas_dtype",
+        "infer_dtype",
+        "union_categoricals",
+        "CategoricalDtype",
+        "DatetimeTZDtype",
+        "IntervalDtype",
+        "PeriodDtype",
+    ]
+    allowed_api_interchange = ["from_dataframe", "DataFrame"]
+    allowed_api_indexers = [
+        "check_array_indexer",
+        "BaseIndexer",
+        "FixedForwardWindowIndexer",
+        "VariableOffsetWindowIndexer",
+    ]
+    allowed_api_extensions = [
+        "no_default",
+        "ExtensionDtype",
+        "register_extension_dtype",
+        "register_dataframe_accessor",
+        "register_index_accessor",
+        "register_series_accessor",
+        "take",
+        "ExtensionArray",
+        "ExtensionScalarOpsMixin",
+    ]
+
+    def test_api(self):
+        self.check(api, self.allowed_api_dirs)
+
+    def test_api_typing(self):
+        self.check(api_typing, self.allowed_typing)
+
+    def test_api_types(self):
+        self.check(api_types, self.allowed_api_types)
+
+    def test_api_interchange(self):
+        self.check(api_interchange, self.allowed_api_interchange)
+
+    def test_api_indexers(self):
+        self.check(api_indexers, self.allowed_api_indexers)
+
+    def test_api_extensions(self):
+        self.check(api_extensions, self.allowed_api_extensions)
+
+
+class TestTesting(Base):
+    funcs = [
+        "assert_frame_equal",
+        "assert_series_equal",
+        "assert_index_equal",
+        "assert_extension_array_equal",
+    ]
+
+    def test_testing(self):
+        from pandas import testing
+
+        self.check(testing, self.funcs)
+
+    def test_util_in_top_level(self):
+        with pytest.raises(AttributeError, match="foo"):
+            pd.util.foo
+
+
+def test_pandas_array_alias():
+    msg = "PandasArray has been renamed NumpyExtensionArray"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        res = pd.arrays.PandasArray
+
+    assert res is pd.arrays.NumpyExtensionArray
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/api/test_types.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/api/test_types.py
new file mode 100644
index 0000000000000000000000000000000000000000..fbaa6e7e18bcaa9a574b741b5361818f1be01ecf
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/api/test_types.py
@@ -0,0 +1,62 @@
+from __future__ import annotations
+
+import pandas._testing as tm
+from pandas.api import types
+from pandas.tests.api.test_api import Base
+
+
+class TestTypes(Base):
+    allowed = [
+        "is_any_real_numeric_dtype",
+        "is_bool",
+        "is_bool_dtype",
+        "is_categorical_dtype",
+        "is_complex",
+        "is_complex_dtype",
+        "is_datetime64_any_dtype",
+        "is_datetime64_dtype",
+        "is_datetime64_ns_dtype",
+        "is_datetime64tz_dtype",
+        "is_dtype_equal",
+        "is_float",
+        "is_float_dtype",
+        "is_int64_dtype",
+        "is_integer",
+        "is_integer_dtype",
+        "is_number",
+        "is_numeric_dtype",
+        "is_object_dtype",
+        "is_scalar",
+        "is_sparse",
+        "is_string_dtype",
+        "is_signed_integer_dtype",
+        "is_timedelta64_dtype",
+        "is_timedelta64_ns_dtype",
+        "is_unsigned_integer_dtype",
+        "is_period_dtype",
+        "is_interval",
+        "is_interval_dtype",
+        "is_re",
+        "is_re_compilable",
+        "is_dict_like",
+        "is_iterator",
+        "is_file_like",
+        "is_list_like",
+        "is_hashable",
+        "is_array_like",
+        "is_named_tuple",
+        "pandas_dtype",
+        "union_categoricals",
+        "infer_dtype",
+        "is_extension_array_dtype",
+    ]
+    deprecated: list[str] = []
+    dtypes = ["CategoricalDtype", "DatetimeTZDtype", "PeriodDtype", "IntervalDtype"]
+
+    def test_types(self):
+        self.check(types, self.allowed + self.dtypes + self.deprecated)
+
+    def test_deprecated_from_api_types(self):
+        for t in self.deprecated:
+            with tm.assert_produces_warning(FutureWarning):
+                getattr(types, t)(1)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/apply/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/apply/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/apply/common.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/apply/common.py
new file mode 100644
index 0000000000000000000000000000000000000000..b4d153df54059ca2a82f336e19afb4297eb218a2
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/apply/common.py
@@ -0,0 +1,7 @@
+from pandas.core.groupby.base import transformation_kernels
+
+# There is no Series.cumcount or DataFrame.cumcount
+series_transform_kernels = [
+    x for x in sorted(transformation_kernels) if x != "cumcount"
+]
+frame_transform_kernels = [x for x in sorted(transformation_kernels) if x != "cumcount"]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/apply/test_frame_apply.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/apply/test_frame_apply.py
new file mode 100644
index 0000000000000000000000000000000000000000..1a776892b7bb754e16aaa34e6dbe281b354ee751
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/apply/test_frame_apply.py
@@ -0,0 +1,1739 @@
+from datetime import datetime
+import warnings
+
+import numpy as np
+import pytest
+
+from pandas.compat import is_platform_arm
+
+from pandas.core.dtypes.dtypes import CategoricalDtype
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    MultiIndex,
+    Series,
+    Timestamp,
+    date_range,
+)
+import pandas._testing as tm
+from pandas.tests.frame.common import zip_frames
+from pandas.util.version import Version
+
+
+@pytest.fixture
+def int_frame_const_col():
+    """
+    Fixture for DataFrame of ints which are constant per column
+
+    Columns are ['A', 'B', 'C'], with values (per column): [1, 2, 3]
+    """
+    df = DataFrame(
+        np.tile(np.arange(3, dtype="int64"), 6).reshape(6, -1) + 1,
+        columns=["A", "B", "C"],
+    )
+    return df
+
+
+@pytest.fixture(params=["python", pytest.param("numba", marks=pytest.mark.single_cpu)])
+def engine(request):
+    if request.param == "numba":
+        pytest.importorskip("numba")
+    return request.param
+
+
+def test_apply(float_frame, engine, request):
+    if engine == "numba":
+        mark = pytest.mark.xfail(reason="numba engine not supporting numpy ufunc yet")
+        request.node.add_marker(mark)
+    with np.errstate(all="ignore"):
+        # ufunc
+        result = np.sqrt(float_frame["A"])
+        expected = float_frame.apply(np.sqrt, engine=engine)["A"]
+        tm.assert_series_equal(result, expected)
+
+        # aggregator
+        result = float_frame.apply(np.mean, engine=engine)["A"]
+        expected = np.mean(float_frame["A"])
+        assert result == expected
+
+        d = float_frame.index[0]
+        result = float_frame.apply(np.mean, axis=1, engine=engine)
+        expected = np.mean(float_frame.xs(d))
+        assert result[d] == expected
+        assert result.index is float_frame.index
+
+
+@pytest.mark.parametrize("axis", [0, 1])
+@pytest.mark.parametrize("raw", [True, False])
+def test_apply_args(float_frame, axis, raw, engine, request):
+    if engine == "numba":
+        numba = pytest.importorskip("numba")
+        if Version(numba.__version__) == Version("0.61") and is_platform_arm():
+            pytest.skip(f"Segfaults on ARM platforms with numba {numba.__version__}")
+        mark = pytest.mark.xfail(reason="numba engine doesn't support args")
+        request.node.add_marker(mark)
+    result = float_frame.apply(
+        lambda x, y: x + y, axis, args=(1,), raw=raw, engine=engine
+    )
+    expected = float_frame + 1
+    tm.assert_frame_equal(result, expected)
+
+
+def test_apply_categorical_func():
+    # GH 9573
+    df = DataFrame({"c0": ["A", "A", "B", "B"], "c1": ["C", "C", "D", "D"]})
+    result = df.apply(lambda ts: ts.astype("category"))
+
+    assert result.shape == (4, 2)
+    assert isinstance(result["c0"].dtype, CategoricalDtype)
+    assert isinstance(result["c1"].dtype, CategoricalDtype)
+
+
+def test_apply_axis1_with_ea():
+    # GH#36785
+    expected = DataFrame({"A": [Timestamp("2013-01-01", tz="UTC")]})
+    result = expected.apply(lambda x: x, axis=1)
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "data, dtype",
+    [(1, None), (1, CategoricalDtype([1])), (Timestamp("2013-01-01", tz="UTC"), None)],
+)
+def test_agg_axis1_duplicate_index(data, dtype):
+    # GH 42380
+    expected = DataFrame([[data], [data]], index=["a", "a"], dtype=dtype)
+    result = expected.agg(lambda x: x, axis=1)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_apply_mixed_datetimelike():
+    # mixed datetimelike
+    # GH 7778
+    expected = DataFrame(
+        {
+            "A": date_range("20130101", periods=3),
+            "B": pd.to_timedelta(np.arange(3), unit="s"),
+        }
+    )
+    result = expected.apply(lambda x: x, axis=1)
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("func", [np.sqrt, np.mean])
+def test_apply_empty(func, engine):
+    # empty
+    empty_frame = DataFrame()
+
+    result = empty_frame.apply(func, engine=engine)
+    assert result.empty
+
+
+def test_apply_float_frame(float_frame, engine):
+    no_rows = float_frame[:0]
+    result = no_rows.apply(lambda x: x.mean(), engine=engine)
+    expected = Series(np.nan, index=float_frame.columns)
+    tm.assert_series_equal(result, expected)
+
+    no_cols = float_frame.loc[:, []]
+    result = no_cols.apply(lambda x: x.mean(), axis=1, engine=engine)
+    expected = Series(np.nan, index=float_frame.index)
+    tm.assert_series_equal(result, expected)
+
+
+def test_apply_empty_except_index(engine):
+    # GH 2476
+    expected = DataFrame(index=["a"])
+    result = expected.apply(lambda x: x["a"], axis=1, engine=engine)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_apply_with_reduce_empty():
+    # reduce with an empty DataFrame
+    empty_frame = DataFrame()
+
+    x = []
+    result = empty_frame.apply(x.append, axis=1, result_type="expand")
+    tm.assert_frame_equal(result, empty_frame)
+    result = empty_frame.apply(x.append, axis=1, result_type="reduce")
+    expected = Series([], dtype=np.float64)
+    tm.assert_series_equal(result, expected)
+
+    empty_with_cols = DataFrame(columns=["a", "b", "c"])
+    result = empty_with_cols.apply(x.append, axis=1, result_type="expand")
+    tm.assert_frame_equal(result, empty_with_cols)
+    result = empty_with_cols.apply(x.append, axis=1, result_type="reduce")
+    expected = Series([], dtype=np.float64)
+    tm.assert_series_equal(result, expected)
+
+    # Ensure that x.append hasn't been called
+    assert x == []
+
+
+@pytest.mark.parametrize("func", ["sum", "prod", "any", "all"])
+def test_apply_funcs_over_empty(func):
+    # GH 28213
+    df = DataFrame(columns=["a", "b", "c"])
+
+    result = df.apply(getattr(np, func))
+    expected = getattr(df, func)()
+    if func in ("sum", "prod"):
+        expected = expected.astype(float)
+    tm.assert_series_equal(result, expected)
+
+
+def test_nunique_empty():
+    # GH 28213
+    df = DataFrame(columns=["a", "b", "c"])
+
+    result = df.nunique()
+    expected = Series(0, index=df.columns)
+    tm.assert_series_equal(result, expected)
+
+    result = df.T.nunique()
+    expected = Series([], dtype=np.float64)
+    tm.assert_series_equal(result, expected)
+
+
+def test_apply_standard_nonunique():
+    df = DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]], index=["a", "a", "c"])
+
+    result = df.apply(lambda s: s[0], axis=1)
+    expected = Series([1, 4, 7], ["a", "a", "c"])
+    tm.assert_series_equal(result, expected)
+
+    result = df.T.apply(lambda s: s[0], axis=0)
+    tm.assert_series_equal(result, expected)
+
+
+def test_apply_broadcast_scalars(float_frame):
+    # scalars
+    result = float_frame.apply(np.mean, result_type="broadcast")
+    expected = DataFrame([float_frame.mean()], index=float_frame.index)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_apply_broadcast_scalars_axis1(float_frame):
+    result = float_frame.apply(np.mean, axis=1, result_type="broadcast")
+    m = float_frame.mean(axis=1)
+    expected = DataFrame({c: m for c in float_frame.columns})
+    tm.assert_frame_equal(result, expected)
+
+
+def test_apply_broadcast_lists_columns(float_frame):
+    # lists
+    result = float_frame.apply(
+        lambda x: list(range(len(float_frame.columns))),
+        axis=1,
+        result_type="broadcast",
+    )
+    m = list(range(len(float_frame.columns)))
+    expected = DataFrame(
+        [m] * len(float_frame.index),
+        dtype="float64",
+        index=float_frame.index,
+        columns=float_frame.columns,
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_apply_broadcast_lists_index(float_frame):
+    result = float_frame.apply(
+        lambda x: list(range(len(float_frame.index))), result_type="broadcast"
+    )
+    m = list(range(len(float_frame.index)))
+    expected = DataFrame(
+        {c: m for c in float_frame.columns},
+        dtype="float64",
+        index=float_frame.index,
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_apply_broadcast_list_lambda_func(int_frame_const_col):
+    # preserve columns
+    df = int_frame_const_col
+    result = df.apply(lambda x: [1, 2, 3], axis=1, result_type="broadcast")
+    tm.assert_frame_equal(result, df)
+
+
+def test_apply_broadcast_series_lambda_func(int_frame_const_col):
+    df = int_frame_const_col
+    result = df.apply(
+        lambda x: Series([1, 2, 3], index=list("abc")),
+        axis=1,
+        result_type="broadcast",
+    )
+    expected = df.copy()
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("axis", [0, 1])
+def test_apply_raw_float_frame(float_frame, axis, engine):
+    if engine == "numba":
+        pytest.skip("numba can't handle when UDF returns None.")
+
+    def _assert_raw(x):
+        assert isinstance(x, np.ndarray)
+        assert x.ndim == 1
+
+    float_frame.apply(_assert_raw, axis=axis, engine=engine, raw=True)
+
+
+@pytest.mark.parametrize("axis", [0, 1])
+def test_apply_raw_float_frame_lambda(float_frame, axis, engine):
+    result = float_frame.apply(np.mean, axis=axis, engine=engine, raw=True)
+    expected = float_frame.apply(lambda x: x.values.mean(), axis=axis)
+    tm.assert_series_equal(result, expected)
+
+
+def test_apply_raw_float_frame_no_reduction(float_frame, engine):
+    # no reduction
+    result = float_frame.apply(lambda x: x * 2, engine=engine, raw=True)
+    expected = float_frame * 2
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("axis", [0, 1])
+def test_apply_raw_mixed_type_frame(axis, engine):
+    if engine == "numba":
+        pytest.skip("isinstance check doesn't work with numba")
+
+    def _assert_raw(x):
+        assert isinstance(x, np.ndarray)
+        assert x.ndim == 1
+
+    # Mixed dtype (GH-32423)
+    df = DataFrame(
+        {
+            "a": 1.0,
+            "b": 2,
+            "c": "foo",
+            "float32": np.array([1.0] * 10, dtype="float32"),
+            "int32": np.array([1] * 10, dtype="int32"),
+        },
+        index=np.arange(10),
+    )
+    df.apply(_assert_raw, axis=axis, engine=engine, raw=True)
+
+
+def test_apply_axis1(float_frame):
+    d = float_frame.index[0]
+    result = float_frame.apply(np.mean, axis=1)[d]
+    expected = np.mean(float_frame.xs(d))
+    assert result == expected
+
+
+def test_apply_mixed_dtype_corner():
+    df = DataFrame({"A": ["foo"], "B": [1.0]})
+    result = df[:0].apply(np.mean, axis=1)
+    # the result here is actually kind of ambiguous, should it be a Series
+    # or a DataFrame?
+    expected = Series(np.nan, index=pd.Index([], dtype="int64"))
+    tm.assert_series_equal(result, expected)
+
+
+def test_apply_mixed_dtype_corner_indexing():
+    df = DataFrame({"A": ["foo"], "B": [1.0]})
+    result = df.apply(lambda x: x["A"], axis=1)
+    expected = Series(["foo"], index=[0])
+    tm.assert_series_equal(result, expected)
+
+    result = df.apply(lambda x: x["B"], axis=1)
+    expected = Series([1.0], index=[0])
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.filterwarnings("ignore::RuntimeWarning")
+@pytest.mark.parametrize("ax", ["index", "columns"])
+@pytest.mark.parametrize(
+    "func", [lambda x: x, lambda x: x.mean()], ids=["identity", "mean"]
+)
+@pytest.mark.parametrize("raw", [True, False])
+@pytest.mark.parametrize("axis", [0, 1])
+def test_apply_empty_infer_type(ax, func, raw, axis, engine, request):
+    df = DataFrame(**{ax: ["a", "b", "c"]})
+
+    with np.errstate(all="ignore"):
+        test_res = func(np.array([], dtype="f8"))
+        is_reduction = not isinstance(test_res, np.ndarray)
+
+        result = df.apply(func, axis=axis, engine=engine, raw=raw)
+        if is_reduction:
+            agg_axis = df._get_agg_axis(axis)
+            assert isinstance(result, Series)
+            assert result.index is agg_axis
+        else:
+            assert isinstance(result, DataFrame)
+
+
+def test_apply_empty_infer_type_broadcast():
+    no_cols = DataFrame(index=["a", "b", "c"])
+    result = no_cols.apply(lambda x: x.mean(), result_type="broadcast")
+    assert isinstance(result, DataFrame)
+
+
+def test_apply_with_args_kwds_add_some(float_frame):
+    def add_some(x, howmuch=0):
+        return x + howmuch
+
+    result = float_frame.apply(add_some, howmuch=2)
+    expected = float_frame.apply(lambda x: x + 2)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_apply_with_args_kwds_agg_and_add(float_frame):
+    def agg_and_add(x, howmuch=0):
+        return x.mean() + howmuch
+
+    result = float_frame.apply(agg_and_add, howmuch=2)
+    expected = float_frame.apply(lambda x: x.mean() + 2)
+    tm.assert_series_equal(result, expected)
+
+
+def test_apply_with_args_kwds_subtract_and_divide(float_frame):
+    def subtract_and_divide(x, sub, divide=1):
+        return (x - sub) / divide
+
+    result = float_frame.apply(subtract_and_divide, args=(2,), divide=2)
+    expected = float_frame.apply(lambda x: (x - 2.0) / 2.0)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_apply_yield_list(float_frame):
+    result = float_frame.apply(list)
+    tm.assert_frame_equal(result, float_frame)
+
+
+def test_apply_reduce_Series(float_frame):
+    float_frame.iloc[::2, float_frame.columns.get_loc("A")] = np.nan
+    expected = float_frame.mean(1)
+    result = float_frame.apply(np.mean, axis=1)
+    tm.assert_series_equal(result, expected)
+
+
+def test_apply_reduce_to_dict():
+    # GH 25196 37544
+    data = DataFrame([[1, 2], [3, 4]], columns=["c0", "c1"], index=["i0", "i1"])
+
+    result = data.apply(dict, axis=0)
+    expected = Series([{"i0": 1, "i1": 3}, {"i0": 2, "i1": 4}], index=data.columns)
+    tm.assert_series_equal(result, expected)
+
+    result = data.apply(dict, axis=1)
+    expected = Series([{"c0": 1, "c1": 2}, {"c0": 3, "c1": 4}], index=data.index)
+    tm.assert_series_equal(result, expected)
+
+
+def test_apply_differently_indexed():
+    df = DataFrame(np.random.default_rng(2).standard_normal((20, 10)))
+
+    result = df.apply(Series.describe, axis=0)
+    expected = DataFrame({i: v.describe() for i, v in df.items()}, columns=df.columns)
+    tm.assert_frame_equal(result, expected)
+
+    result = df.apply(Series.describe, axis=1)
+    expected = DataFrame({i: v.describe() for i, v in df.T.items()}, columns=df.index).T
+    tm.assert_frame_equal(result, expected)
+
+
+def test_apply_bug():
+    # GH 6125
+    positions = DataFrame(
+        [
+            [1, "ABC0", 50],
+            [1, "YUM0", 20],
+            [1, "DEF0", 20],
+            [2, "ABC1", 50],
+            [2, "YUM1", 20],
+            [2, "DEF1", 20],
+        ],
+        columns=["a", "market", "position"],
+    )
+
+    def f(r):
+        return r["market"]
+
+    expected = positions.apply(f, axis=1)
+
+    positions = DataFrame(
+        [
+            [datetime(2013, 1, 1), "ABC0", 50],
+            [datetime(2013, 1, 2), "YUM0", 20],
+            [datetime(2013, 1, 3), "DEF0", 20],
+            [datetime(2013, 1, 4), "ABC1", 50],
+            [datetime(2013, 1, 5), "YUM1", 20],
+            [datetime(2013, 1, 6), "DEF1", 20],
+        ],
+        columns=["a", "market", "position"],
+    )
+    result = positions.apply(f, axis=1)
+    tm.assert_series_equal(result, expected)
+
+
+def test_apply_convert_objects():
+    expected = DataFrame(
+        {
+            "A": [
+                "foo",
+                "foo",
+                "foo",
+                "foo",
+                "bar",
+                "bar",
+                "bar",
+                "bar",
+                "foo",
+                "foo",
+                "foo",
+            ],
+            "B": [
+                "one",
+                "one",
+                "one",
+                "two",
+                "one",
+                "one",
+                "one",
+                "two",
+                "two",
+                "two",
+                "one",
+            ],
+            "C": [
+                "dull",
+                "dull",
+                "shiny",
+                "dull",
+                "dull",
+                "shiny",
+                "shiny",
+                "dull",
+                "shiny",
+                "shiny",
+                "shiny",
+            ],
+            "D": np.random.default_rng(2).standard_normal(11),
+            "E": np.random.default_rng(2).standard_normal(11),
+            "F": np.random.default_rng(2).standard_normal(11),
+        }
+    )
+
+    result = expected.apply(lambda x: x, axis=1)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_apply_attach_name(float_frame):
+    result = float_frame.apply(lambda x: x.name)
+    expected = Series(float_frame.columns, index=float_frame.columns)
+    tm.assert_series_equal(result, expected)
+
+
+def test_apply_attach_name_axis1(float_frame):
+    result = float_frame.apply(lambda x: x.name, axis=1)
+    expected = Series(float_frame.index, index=float_frame.index)
+    tm.assert_series_equal(result, expected)
+
+
+def test_apply_attach_name_non_reduction(float_frame):
+    # non-reductions
+    result = float_frame.apply(lambda x: np.repeat(x.name, len(x)))
+    expected = DataFrame(
+        np.tile(float_frame.columns, (len(float_frame.index), 1)),
+        index=float_frame.index,
+        columns=float_frame.columns,
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_apply_attach_name_non_reduction_axis1(float_frame):
+    result = float_frame.apply(lambda x: np.repeat(x.name, len(x)), axis=1)
+    expected = Series(
+        np.repeat(t[0], len(float_frame.columns)) for t in float_frame.itertuples()
+    )
+    expected.index = float_frame.index
+    tm.assert_series_equal(result, expected)
+
+
+def test_apply_multi_index():
+    index = MultiIndex.from_arrays([["a", "a", "b"], ["c", "d", "d"]])
+    s = DataFrame([[1, 2], [3, 4], [5, 6]], index=index, columns=["col1", "col2"])
+    result = s.apply(lambda x: Series({"min": min(x), "max": max(x)}), 1)
+    expected = DataFrame([[1, 2], [3, 4], [5, 6]], index=index, columns=["min", "max"])
+    tm.assert_frame_equal(result, expected, check_like=True)
+
+
+@pytest.mark.parametrize(
+    "df, dicts",
+    [
+        [
+            DataFrame([["foo", "bar"], ["spam", "eggs"]]),
+            Series([{0: "foo", 1: "spam"}, {0: "bar", 1: "eggs"}]),
+        ],
+        [DataFrame([[0, 1], [2, 3]]), Series([{0: 0, 1: 2}, {0: 1, 1: 3}])],
+    ],
+)
+def test_apply_dict(df, dicts):
+    # GH 8735
+    fn = lambda x: x.to_dict()
+    reduce_true = df.apply(fn, result_type="reduce")
+    reduce_false = df.apply(fn, result_type="expand")
+    reduce_none = df.apply(fn)
+
+    tm.assert_series_equal(reduce_true, dicts)
+    tm.assert_frame_equal(reduce_false, df)
+    tm.assert_series_equal(reduce_none, dicts)
+
+
+def test_apply_non_numpy_dtype():
+    # GH 12244
+    df = DataFrame({"dt": date_range("2015-01-01", periods=3, tz="Europe/Brussels")})
+    result = df.apply(lambda x: x)
+    tm.assert_frame_equal(result, df)
+
+    result = df.apply(lambda x: x + pd.Timedelta("1day"))
+    expected = DataFrame(
+        {"dt": date_range("2015-01-02", periods=3, tz="Europe/Brussels")}
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_apply_non_numpy_dtype_category():
+    df = DataFrame({"dt": ["a", "b", "c", "a"]}, dtype="category")
+    result = df.apply(lambda x: x)
+    tm.assert_frame_equal(result, df)
+
+
+def test_apply_dup_names_multi_agg():
+    # GH 21063
+    df = DataFrame([[0, 1], [2, 3]], columns=["a", "a"])
+    expected = DataFrame([[0, 1]], columns=["a", "a"], index=["min"])
+    result = df.agg(["min"])
+
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("op", ["apply", "agg"])
+def test_apply_nested_result_axis_1(op):
+    # GH 13820
+    def apply_list(row):
+        return [2 * row["A"], 2 * row["C"], 2 * row["B"]]
+
+    df = DataFrame(np.zeros((4, 4)), columns=list("ABCD"))
+    result = getattr(df, op)(apply_list, axis=1)
+    expected = Series(
+        [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]
+    )
+    tm.assert_series_equal(result, expected)
+
+
+def test_apply_noreduction_tzaware_object():
+    # https://github.com/pandas-dev/pandas/issues/31505
+    expected = DataFrame(
+        {"foo": [Timestamp("2020", tz="UTC")]}, dtype="datetime64[ns, UTC]"
+    )
+    result = expected.apply(lambda x: x)
+    tm.assert_frame_equal(result, expected)
+    result = expected.apply(lambda x: x.copy())
+    tm.assert_frame_equal(result, expected)
+
+
+def test_apply_function_runs_once():
+    # https://github.com/pandas-dev/pandas/issues/30815
+
+    df = DataFrame({"a": [1, 2, 3]})
+    names = []  # Save row names function is applied to
+
+    def reducing_function(row):
+        names.append(row.name)
+
+    def non_reducing_function(row):
+        names.append(row.name)
+        return row
+
+    for func in [reducing_function, non_reducing_function]:
+        del names[:]
+
+        df.apply(func, axis=1)
+        assert names == list(df.index)
+
+
+def test_apply_raw_function_runs_once(engine):
+    # https://github.com/pandas-dev/pandas/issues/34506
+    if engine == "numba":
+        pytest.skip("appending to list outside of numba func is not supported")
+
+    df = DataFrame({"a": [1, 2, 3]})
+    values = []  # Save row values function is applied to
+
+    def reducing_function(row):
+        values.extend(row)
+
+    def non_reducing_function(row):
+        values.extend(row)
+        return row
+
+    for func in [reducing_function, non_reducing_function]:
+        del values[:]
+
+        df.apply(func, engine=engine, raw=True, axis=1)
+        assert values == list(df.a.to_list())
+
+
+def test_apply_with_byte_string():
+    # GH 34529
+    df = DataFrame(np.array([b"abcd", b"efgh"]), columns=["col"])
+    expected = DataFrame(np.array([b"abcd", b"efgh"]), columns=["col"], dtype=object)
+    # After we make the apply we expect a dataframe just
+    # like the original but with the object datatype
+    result = df.apply(lambda x: x.astype("object"))
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("val", ["asd", 12, None, np.nan])
+def test_apply_category_equalness(val):
+    # Check if categorical comparisons on apply, GH 21239
+    df_values = ["asd", None, 12, "asd", "cde", np.nan]
+    df = DataFrame({"a": df_values}, dtype="category")
+
+    result = df.a.apply(lambda x: x == val)
+    expected = Series(
+        [np.nan if pd.isnull(x) else x == val for x in df_values], name="a"
+    )
+    tm.assert_series_equal(result, expected)
+
+
+# the user has supplied an opaque UDF where
+# they are transforming the input that requires
+# us to infer the output
+
+
+def test_infer_row_shape():
+    # GH 17437
+    # if row shape is changing, infer it
+    df = DataFrame(np.random.default_rng(2).random((10, 2)))
+    result = df.apply(np.fft.fft, axis=0).shape
+    assert result == (10, 2)
+
+    result = df.apply(np.fft.rfft, axis=0).shape
+    assert result == (6, 2)
+
+
+@pytest.mark.parametrize(
+    "ops, by_row, expected",
+    [
+        ({"a": lambda x: x + 1}, "compat", DataFrame({"a": [2, 3]})),
+        ({"a": lambda x: x + 1}, False, DataFrame({"a": [2, 3]})),
+        ({"a": lambda x: x.sum()}, "compat", Series({"a": 3})),
+        ({"a": lambda x: x.sum()}, False, Series({"a": 3})),
+        (
+            {"a": ["sum", np.sum, lambda x: x.sum()]},
+            "compat",
+            DataFrame({"a": [3, 3, 3]}, index=["sum", "sum", ""]),
+        ),
+        (
+            {"a": ["sum", np.sum, lambda x: x.sum()]},
+            False,
+            DataFrame({"a": [3, 3, 3]}, index=["sum", "sum", ""]),
+        ),
+        ({"a": lambda x: 1}, "compat", DataFrame({"a": [1, 1]})),
+        ({"a": lambda x: 1}, False, Series({"a": 1})),
+    ],
+)
+def test_dictlike_lambda(ops, by_row, expected):
+    # GH53601
+    df = DataFrame({"a": [1, 2]})
+    result = df.apply(ops, by_row=by_row)
+    tm.assert_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "ops",
+    [
+        {"a": lambda x: x + 1},
+        {"a": lambda x: x.sum()},
+        {"a": ["sum", np.sum, lambda x: x.sum()]},
+        {"a": lambda x: 1},
+    ],
+)
+def test_dictlike_lambda_raises(ops):
+    # GH53601
+    df = DataFrame({"a": [1, 2]})
+    with pytest.raises(ValueError, match="by_row=True not allowed"):
+        df.apply(ops, by_row=True)
+
+
+def test_with_dictlike_columns():
+    # GH 17602
+    df = DataFrame([[1, 2], [1, 2]], columns=["a", "b"])
+    result = df.apply(lambda x: {"s": x["a"] + x["b"]}, axis=1)
+    expected = Series([{"s": 3} for t in df.itertuples()])
+    tm.assert_series_equal(result, expected)
+
+    df["tm"] = [
+        Timestamp("2017-05-01 00:00:00"),
+        Timestamp("2017-05-02 00:00:00"),
+    ]
+    result = df.apply(lambda x: {"s": x["a"] + x["b"]}, axis=1)
+    tm.assert_series_equal(result, expected)
+
+    # compose a series
+    result = (df["a"] + df["b"]).apply(lambda x: {"s": x})
+    expected = Series([{"s": 3}, {"s": 3}])
+    tm.assert_series_equal(result, expected)
+
+
+def test_with_dictlike_columns_with_datetime():
+    # GH 18775
+    df = DataFrame()
+    df["author"] = ["X", "Y", "Z"]
+    df["publisher"] = ["BBC", "NBC", "N24"]
+    df["date"] = pd.to_datetime(
+        ["17-10-2010 07:15:30", "13-05-2011 08:20:35", "15-01-2013 09:09:09"],
+        dayfirst=True,
+    )
+    result = df.apply(lambda x: {}, axis=1)
+    expected = Series([{}, {}, {}])
+    tm.assert_series_equal(result, expected)
+
+
+def test_with_dictlike_columns_with_infer():
+    # GH 17602
+    df = DataFrame([[1, 2], [1, 2]], columns=["a", "b"])
+    result = df.apply(lambda x: {"s": x["a"] + x["b"]}, axis=1, result_type="expand")
+    expected = DataFrame({"s": [3, 3]})
+    tm.assert_frame_equal(result, expected)
+
+    df["tm"] = [
+        Timestamp("2017-05-01 00:00:00"),
+        Timestamp("2017-05-02 00:00:00"),
+    ]
+    result = df.apply(lambda x: {"s": x["a"] + x["b"]}, axis=1, result_type="expand")
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "ops, by_row, expected",
+    [
+        ([lambda x: x + 1], "compat", DataFrame({("a", ""): [2, 3]})),
+        ([lambda x: x + 1], False, DataFrame({("a", ""): [2, 3]})),
+        ([lambda x: x.sum()], "compat", DataFrame({"a": [3]}, index=[""])),
+        ([lambda x: x.sum()], False, DataFrame({"a": [3]}, index=[""])),
+        (
+            ["sum", np.sum, lambda x: x.sum()],
+            "compat",
+            DataFrame({"a": [3, 3, 3]}, index=["sum", "sum", ""]),
+        ),
+        (
+            ["sum", np.sum, lambda x: x.sum()],
+            False,
+            DataFrame({"a": [3, 3, 3]}, index=["sum", "sum", ""]),
+        ),
+        (
+            [lambda x: x + 1, lambda x: 3],
+            "compat",
+            DataFrame([[2, 3], [3, 3]], columns=[["a", "a"], ["", ""]]),
+        ),
+        (
+            [lambda x: 2, lambda x: 3],
+            False,
+            DataFrame({"a": [2, 3]}, ["", ""]),
+        ),
+    ],
+)
+def test_listlike_lambda(ops, by_row, expected):
+    # GH53601
+    df = DataFrame({"a": [1, 2]})
+    result = df.apply(ops, by_row=by_row)
+    tm.assert_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "ops",
+    [
+        [lambda x: x + 1],
+        [lambda x: x.sum()],
+        ["sum", np.sum, lambda x: x.sum()],
+        [lambda x: x + 1, lambda x: 3],
+    ],
+)
+def test_listlike_lambda_raises(ops):
+    # GH53601
+    df = DataFrame({"a": [1, 2]})
+    with pytest.raises(ValueError, match="by_row=True not allowed"):
+        df.apply(ops, by_row=True)
+
+
+def test_with_listlike_columns():
+    # GH 17348
+    df = DataFrame(
+        {
+            "a": Series(np.random.default_rng(2).standard_normal(4)),
+            "b": ["a", "list", "of", "words"],
+            "ts": date_range("2016-10-01", periods=4, freq="h"),
+        }
+    )
+
+    result = df[["a", "b"]].apply(tuple, axis=1)
+    expected = Series([t[1:] for t in df[["a", "b"]].itertuples()])
+    tm.assert_series_equal(result, expected)
+
+    result = df[["a", "ts"]].apply(tuple, axis=1)
+    expected = Series([t[1:] for t in df[["a", "ts"]].itertuples()])
+    tm.assert_series_equal(result, expected)
+
+
+def test_with_listlike_columns_returning_list():
+    # GH 18919
+    df = DataFrame({"x": Series([["a", "b"], ["q"]]), "y": Series([["z"], ["q", "t"]])})
+    df.index = MultiIndex.from_tuples([("i0", "j0"), ("i1", "j1")])
+
+    result = df.apply(lambda row: [el for el in row["x"] if el in row["y"]], axis=1)
+    expected = Series([[], ["q"]], index=df.index)
+    tm.assert_series_equal(result, expected)
+
+
+def test_infer_output_shape_columns():
+    # GH 18573
+
+    df = DataFrame(
+        {
+            "number": [1.0, 2.0],
+            "string": ["foo", "bar"],
+            "datetime": [
+                Timestamp("2017-11-29 03:30:00"),
+                Timestamp("2017-11-29 03:45:00"),
+            ],
+        }
+    )
+    result = df.apply(lambda row: (row.number, row.string), axis=1)
+    expected = Series([(t.number, t.string) for t in df.itertuples()])
+    tm.assert_series_equal(result, expected)
+
+
+def test_infer_output_shape_listlike_columns():
+    # GH 16353
+
+    df = DataFrame(
+        np.random.default_rng(2).standard_normal((6, 3)), columns=["A", "B", "C"]
+    )
+
+    result = df.apply(lambda x: [1, 2, 3], axis=1)
+    expected = Series([[1, 2, 3] for t in df.itertuples()])
+    tm.assert_series_equal(result, expected)
+
+    result = df.apply(lambda x: [1, 2], axis=1)
+    expected = Series([[1, 2] for t in df.itertuples()])
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("val", [1, 2])
+def test_infer_output_shape_listlike_columns_np_func(val):
+    # GH 17970
+    df = DataFrame({"a": [1, 2, 3]}, index=list("abc"))
+
+    result = df.apply(lambda row: np.ones(val), axis=1)
+    expected = Series([np.ones(val) for t in df.itertuples()], index=df.index)
+    tm.assert_series_equal(result, expected)
+
+
+def test_infer_output_shape_listlike_columns_with_timestamp():
+    # GH 17892
+    df = DataFrame(
+        {
+            "a": [
+                Timestamp("2010-02-01"),
+                Timestamp("2010-02-04"),
+                Timestamp("2010-02-05"),
+                Timestamp("2010-02-06"),
+            ],
+            "b": [9, 5, 4, 3],
+            "c": [5, 3, 4, 2],
+            "d": [1, 2, 3, 4],
+        }
+    )
+
+    def fun(x):
+        return (1, 2)
+
+    result = df.apply(fun, axis=1)
+    expected = Series([(1, 2) for t in df.itertuples()])
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("lst", [[1, 2, 3], [1, 2]])
+def test_consistent_coerce_for_shapes(lst):
+    # we want column names to NOT be propagated
+    # just because the shape matches the input shape
+    df = DataFrame(
+        np.random.default_rng(2).standard_normal((4, 3)), columns=["A", "B", "C"]
+    )
+
+    result = df.apply(lambda x: lst, axis=1)
+    expected = Series([lst for t in df.itertuples()])
+    tm.assert_series_equal(result, expected)
+
+
+def test_consistent_names(int_frame_const_col):
+    # if a Series is returned, we should use the resulting index names
+    df = int_frame_const_col
+
+    result = df.apply(
+        lambda x: Series([1, 2, 3], index=["test", "other", "cols"]), axis=1
+    )
+    expected = int_frame_const_col.rename(
+        columns={"A": "test", "B": "other", "C": "cols"}
+    )
+    tm.assert_frame_equal(result, expected)
+
+    result = df.apply(lambda x: Series([1, 2], index=["test", "other"]), axis=1)
+    expected = expected[["test", "other"]]
+    tm.assert_frame_equal(result, expected)
+
+
+def test_result_type(int_frame_const_col):
+    # result_type should be consistent no matter which
+    # path we take in the code
+    df = int_frame_const_col
+
+    result = df.apply(lambda x: [1, 2, 3], axis=1, result_type="expand")
+    expected = df.copy()
+    expected.columns = [0, 1, 2]
+    tm.assert_frame_equal(result, expected)
+
+
+def test_result_type_shorter_list(int_frame_const_col):
+    # result_type should be consistent no matter which
+    # path we take in the code
+    df = int_frame_const_col
+    result = df.apply(lambda x: [1, 2], axis=1, result_type="expand")
+    expected = df[["A", "B"]].copy()
+    expected.columns = [0, 1]
+    tm.assert_frame_equal(result, expected)
+
+
+def test_result_type_broadcast(int_frame_const_col, request, engine):
+    # result_type should be consistent no matter which
+    # path we take in the code
+    if engine == "numba":
+        mark = pytest.mark.xfail(reason="numba engine doesn't support list return")
+        request.node.add_marker(mark)
+    df = int_frame_const_col
+    # broadcast result
+    result = df.apply(
+        lambda x: [1, 2, 3], axis=1, result_type="broadcast", engine=engine
+    )
+    expected = df.copy()
+    tm.assert_frame_equal(result, expected)
+
+
+def test_result_type_broadcast_series_func(int_frame_const_col, engine, request):
+    # result_type should be consistent no matter which
+    # path we take in the code
+    if engine == "numba":
+        mark = pytest.mark.xfail(
+            reason="numba Series constructor only support ndarrays not list data"
+        )
+        request.node.add_marker(mark)
+    df = int_frame_const_col
+    columns = ["other", "col", "names"]
+    result = df.apply(
+        lambda x: Series([1, 2, 3], index=columns),
+        axis=1,
+        result_type="broadcast",
+        engine=engine,
+    )
+    expected = df.copy()
+    tm.assert_frame_equal(result, expected)
+
+
+def test_result_type_series_result(int_frame_const_col, engine, request):
+    # result_type should be consistent no matter which
+    # path we take in the code
+    if engine == "numba":
+        mark = pytest.mark.xfail(
+            reason="numba Series constructor only support ndarrays not list data"
+        )
+        request.node.add_marker(mark)
+    df = int_frame_const_col
+    # series result
+    result = df.apply(lambda x: Series([1, 2, 3], index=x.index), axis=1, engine=engine)
+    expected = df.copy()
+    tm.assert_frame_equal(result, expected)
+
+
+def test_result_type_series_result_other_index(int_frame_const_col, engine, request):
+    # result_type should be consistent no matter which
+    # path we take in the code
+
+    if engine == "numba":
+        mark = pytest.mark.xfail(
+            reason="no support in numba Series constructor for list of columns"
+        )
+        request.node.add_marker(mark)
+    df = int_frame_const_col
+    # series result with other index
+    columns = ["other", "col", "names"]
+    result = df.apply(lambda x: Series([1, 2, 3], index=columns), axis=1, engine=engine)
+    expected = df.copy()
+    expected.columns = columns
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "box",
+    [lambda x: list(x), lambda x: tuple(x), lambda x: np.array(x, dtype="int64")],
+    ids=["list", "tuple", "array"],
+)
+def test_consistency_for_boxed(box, int_frame_const_col):
+    # passing an array or list should not affect the output shape
+    df = int_frame_const_col
+
+    result = df.apply(lambda x: box([1, 2]), axis=1)
+    expected = Series([box([1, 2]) for t in df.itertuples()])
+    tm.assert_series_equal(result, expected)
+
+    result = df.apply(lambda x: box([1, 2]), axis=1, result_type="expand")
+    expected = int_frame_const_col[["A", "B"]].rename(columns={"A": 0, "B": 1})
+    tm.assert_frame_equal(result, expected)
+
+
+def test_agg_transform(axis, float_frame):
+    other_axis = 1 if axis in {0, "index"} else 0
+
+    with np.errstate(all="ignore"):
+        f_abs = np.abs(float_frame)
+        f_sqrt = np.sqrt(float_frame)
+
+        # ufunc
+        expected = f_sqrt.copy()
+        result = float_frame.apply(np.sqrt, axis=axis)
+        tm.assert_frame_equal(result, expected)
+
+        # list-like
+        result = float_frame.apply([np.sqrt], axis=axis)
+        expected = f_sqrt.copy()
+        if axis in {0, "index"}:
+            expected.columns = MultiIndex.from_product([float_frame.columns, ["sqrt"]])
+        else:
+            expected.index = MultiIndex.from_product([float_frame.index, ["sqrt"]])
+        tm.assert_frame_equal(result, expected)
+
+        # multiple items in list
+        # these are in the order as if we are applying both
+        # functions per series and then concatting
+        result = float_frame.apply([np.abs, np.sqrt], axis=axis)
+        expected = zip_frames([f_abs, f_sqrt], axis=other_axis)
+        if axis in {0, "index"}:
+            expected.columns = MultiIndex.from_product(
+                [float_frame.columns, ["absolute", "sqrt"]]
+            )
+        else:
+            expected.index = MultiIndex.from_product(
+                [float_frame.index, ["absolute", "sqrt"]]
+            )
+        tm.assert_frame_equal(result, expected)
+
+
+def test_demo():
+    # demonstration tests
+    df = DataFrame({"A": range(5), "B": 5})
+
+    result = df.agg(["min", "max"])
+    expected = DataFrame(
+        {"A": [0, 4], "B": [5, 5]}, columns=["A", "B"], index=["min", "max"]
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_demo_dict_agg():
+    # demonstration tests
+    df = DataFrame({"A": range(5), "B": 5})
+    result = df.agg({"A": ["min", "max"], "B": ["sum", "max"]})
+    expected = DataFrame(
+        {"A": [4.0, 0.0, np.nan], "B": [5.0, np.nan, 25.0]},
+        columns=["A", "B"],
+        index=["max", "min", "sum"],
+    )
+    tm.assert_frame_equal(result.reindex_like(expected), expected)
+
+
+def test_agg_with_name_as_column_name():
+    # GH 36212 - Column name is "name"
+    data = {"name": ["foo", "bar"]}
+    df = DataFrame(data)
+
+    # result's name should be None
+    result = df.agg({"name": "count"})
+    expected = Series({"name": 2})
+    tm.assert_series_equal(result, expected)
+
+    # Check if name is still preserved when aggregating series instead
+    result = df["name"].agg({"name": "count"})
+    expected = Series({"name": 2}, name="name")
+    tm.assert_series_equal(result, expected)
+
+
+def test_agg_multiple_mixed():
+    # GH 20909
+    mdf = DataFrame(
+        {
+            "A": [1, 2, 3],
+            "B": [1.0, 2.0, 3.0],
+            "C": ["foo", "bar", "baz"],
+        }
+    )
+    expected = DataFrame(
+        {
+            "A": [1, 6],
+            "B": [1.0, 6.0],
+            "C": ["bar", "foobarbaz"],
+        },
+        index=["min", "sum"],
+    )
+    # sorted index
+    result = mdf.agg(["min", "sum"])
+    tm.assert_frame_equal(result, expected)
+
+    result = mdf[["C", "B", "A"]].agg(["sum", "min"])
+    # GH40420: the result of .agg should have an index that is sorted
+    # according to the arguments provided to agg.
+    expected = expected[["C", "B", "A"]].reindex(["sum", "min"])
+    tm.assert_frame_equal(result, expected)
+
+
+def test_agg_multiple_mixed_raises():
+    # GH 20909
+    mdf = DataFrame(
+        {
+            "A": [1, 2, 3],
+            "B": [1.0, 2.0, 3.0],
+            "C": ["foo", "bar", "baz"],
+            "D": date_range("20130101", periods=3),
+        }
+    )
+
+    # sorted index
+    msg = "does not support reduction"
+    with pytest.raises(TypeError, match=msg):
+        mdf.agg(["min", "sum"])
+
+    with pytest.raises(TypeError, match=msg):
+        mdf[["D", "C", "B", "A"]].agg(["sum", "min"])
+
+
+def test_agg_reduce(axis, float_frame):
+    other_axis = 1 if axis in {0, "index"} else 0
+    name1, name2 = float_frame.axes[other_axis].unique()[:2].sort_values()
+
+    # all reducers
+    expected = pd.concat(
+        [
+            float_frame.mean(axis=axis),
+            float_frame.max(axis=axis),
+            float_frame.sum(axis=axis),
+        ],
+        axis=1,
+    )
+    expected.columns = ["mean", "max", "sum"]
+    expected = expected.T if axis in {0, "index"} else expected
+
+    result = float_frame.agg(["mean", "max", "sum"], axis=axis)
+    tm.assert_frame_equal(result, expected)
+
+    # dict input with scalars
+    func = {name1: "mean", name2: "sum"}
+    result = float_frame.agg(func, axis=axis)
+    expected = Series(
+        [
+            float_frame.loc(other_axis)[name1].mean(),
+            float_frame.loc(other_axis)[name2].sum(),
+        ],
+        index=[name1, name2],
+    )
+    tm.assert_series_equal(result, expected)
+
+    # dict input with lists
+    func = {name1: ["mean"], name2: ["sum"]}
+    result = float_frame.agg(func, axis=axis)
+    expected = DataFrame(
+        {
+            name1: Series([float_frame.loc(other_axis)[name1].mean()], index=["mean"]),
+            name2: Series([float_frame.loc(other_axis)[name2].sum()], index=["sum"]),
+        }
+    )
+    expected = expected.T if axis in {1, "columns"} else expected
+    tm.assert_frame_equal(result, expected)
+
+    # dict input with lists with multiple
+    func = {name1: ["mean", "sum"], name2: ["sum", "max"]}
+    result = float_frame.agg(func, axis=axis)
+    expected = pd.concat(
+        {
+            name1: Series(
+                [
+                    float_frame.loc(other_axis)[name1].mean(),
+                    float_frame.loc(other_axis)[name1].sum(),
+                ],
+                index=["mean", "sum"],
+            ),
+            name2: Series(
+                [
+                    float_frame.loc(other_axis)[name2].sum(),
+                    float_frame.loc(other_axis)[name2].max(),
+                ],
+                index=["sum", "max"],
+            ),
+        },
+        axis=1,
+    )
+    expected = expected.T if axis in {1, "columns"} else expected
+    tm.assert_frame_equal(result, expected)
+
+
+def test_nuiscance_columns():
+    # GH 15015
+    df = DataFrame(
+        {
+            "A": [1, 2, 3],
+            "B": [1.0, 2.0, 3.0],
+            "C": ["foo", "bar", "baz"],
+            "D": date_range("20130101", periods=3),
+        }
+    )
+
+    result = df.agg("min")
+    expected = Series([1, 1.0, "bar", Timestamp("20130101")], index=df.columns)
+    tm.assert_series_equal(result, expected)
+
+    result = df.agg(["min"])
+    expected = DataFrame(
+        [[1, 1.0, "bar", Timestamp("20130101").as_unit("ns")]],
+        index=["min"],
+        columns=df.columns,
+    )
+    tm.assert_frame_equal(result, expected)
+
+    msg = "does not support reduction"
+    with pytest.raises(TypeError, match=msg):
+        df.agg("sum")
+
+    result = df[["A", "B", "C"]].agg("sum")
+    expected = Series([6, 6.0, "foobarbaz"], index=["A", "B", "C"])
+    tm.assert_series_equal(result, expected)
+
+    msg = "does not support reduction"
+    with pytest.raises(TypeError, match=msg):
+        df.agg(["sum"])
+
+
+@pytest.mark.parametrize("how", ["agg", "apply"])
+def test_non_callable_aggregates(how):
+    # GH 16405
+    # 'size' is a property of frame/series
+    # validate that this is working
+    # GH 39116 - expand to apply
+    df = DataFrame(
+        {"A": [None, 2, 3], "B": [1.0, np.nan, 3.0], "C": ["foo", None, "bar"]}
+    )
+
+    # Function aggregate
+    result = getattr(df, how)({"A": "count"})
+    expected = Series({"A": 2})
+
+    tm.assert_series_equal(result, expected)
+
+    # Non-function aggregate
+    result = getattr(df, how)({"A": "size"})
+    expected = Series({"A": 3})
+
+    tm.assert_series_equal(result, expected)
+
+    # Mix function and non-function aggs
+    result1 = getattr(df, how)(["count", "size"])
+    result2 = getattr(df, how)(
+        {"A": ["count", "size"], "B": ["count", "size"], "C": ["count", "size"]}
+    )
+    expected = DataFrame(
+        {
+            "A": {"count": 2, "size": 3},
+            "B": {"count": 2, "size": 3},
+            "C": {"count": 2, "size": 3},
+        }
+    )
+
+    tm.assert_frame_equal(result1, result2, check_like=True)
+    tm.assert_frame_equal(result2, expected, check_like=True)
+
+    # Just functional string arg is same as calling df.arg()
+    result = getattr(df, how)("count")
+    expected = df.count()
+
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("how", ["agg", "apply"])
+def test_size_as_str(how, axis):
+    # GH 39934
+    df = DataFrame(
+        {"A": [None, 2, 3], "B": [1.0, np.nan, 3.0], "C": ["foo", None, "bar"]}
+    )
+    # Just a string attribute arg same as calling df.arg
+    # on the columns
+    result = getattr(df, how)("size", axis=axis)
+    if axis in (0, "index"):
+        expected = Series(df.shape[0], index=df.columns)
+    else:
+        expected = Series(df.shape[1], index=df.index)
+    tm.assert_series_equal(result, expected)
+
+
+def test_agg_listlike_result():
+    # GH-29587 user defined function returning list-likes
+    df = DataFrame({"A": [2, 2, 3], "B": [1.5, np.nan, 1.5], "C": ["foo", None, "bar"]})
+
+    def func(group_col):
+        return list(group_col.dropna().unique())
+
+    result = df.agg(func)
+    expected = Series([[2, 3], [1.5], ["foo", "bar"]], index=["A", "B", "C"])
+    tm.assert_series_equal(result, expected)
+
+    result = df.agg([func])
+    expected = expected.to_frame("func").T
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("axis", [0, 1])
+@pytest.mark.parametrize(
+    "args, kwargs",
+    [
+        ((1, 2, 3), {}),
+        ((8, 7, 15), {}),
+        ((1, 2), {}),
+        ((1,), {"b": 2}),
+        ((), {"a": 1, "b": 2}),
+        ((), {"a": 2, "b": 1}),
+        ((), {"a": 1, "b": 2, "c": 3}),
+    ],
+)
+def test_agg_args_kwargs(axis, args, kwargs):
+    def f(x, a, b, c=3):
+        return x.sum() + (a + b) / c
+
+    df = DataFrame([[1, 2], [3, 4]])
+
+    if axis == 0:
+        expected = Series([5.0, 7.0])
+    else:
+        expected = Series([4.0, 8.0])
+
+    result = df.agg(f, axis, *args, **kwargs)
+
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("num_cols", [2, 3, 5])
+def test_frequency_is_original(num_cols, engine, request):
+    # GH 22150
+    if engine == "numba":
+        mark = pytest.mark.xfail(reason="numba engine only supports numeric indices")
+        request.node.add_marker(mark)
+    index = pd.DatetimeIndex(["1950-06-30", "1952-10-24", "1953-05-29"])
+    original = index.copy()
+    df = DataFrame(1, index=index, columns=range(num_cols))
+    df.apply(lambda x: x, engine=engine)
+    assert index.freq == original.freq
+
+
+def test_apply_datetime_tz_issue(engine, request):
+    # GH 29052
+
+    if engine == "numba":
+        mark = pytest.mark.xfail(
+            reason="numba engine doesn't support non-numeric indexes"
+        )
+        request.node.add_marker(mark)
+
+    timestamps = [
+        Timestamp("2019-03-15 12:34:31.909000+0000", tz="UTC"),
+        Timestamp("2019-03-15 12:34:34.359000+0000", tz="UTC"),
+        Timestamp("2019-03-15 12:34:34.660000+0000", tz="UTC"),
+    ]
+    df = DataFrame(data=[0, 1, 2], index=timestamps)
+    result = df.apply(lambda x: x.name, axis=1, engine=engine)
+    expected = Series(index=timestamps, data=timestamps)
+
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("df", [DataFrame({"A": ["a", None], "B": ["c", "d"]})])
+@pytest.mark.parametrize("method", ["min", "max", "sum"])
+def test_mixed_column_raises(df, method, using_infer_string):
+    # GH 16832
+    if method == "sum":
+        msg = r'can only concatenate str \(not "int"\) to str|does not support'
+    else:
+        msg = "not supported between instances of 'str' and 'float'"
+    if not using_infer_string:
+        with pytest.raises(TypeError, match=msg):
+            getattr(df, method)()
+    else:
+        getattr(df, method)()
+
+
+@pytest.mark.parametrize("col", [1, 1.0, True, "a", np.nan])
+def test_apply_dtype(col):
+    # GH 31466
+    df = DataFrame([[1.0, col]], columns=["a", "b"])
+    result = df.apply(lambda x: x.dtype)
+    expected = df.dtypes
+
+    tm.assert_series_equal(result, expected)
+
+
+def test_apply_mutating(using_array_manager, using_copy_on_write, warn_copy_on_write):
+    # GH#35462 case where applied func pins a new BlockManager to a row
+    df = DataFrame({"a": range(100), "b": range(100, 200)})
+    df_orig = df.copy()
+
+    def func(row):
+        mgr = row._mgr
+        row.loc["a"] += 1
+        assert row._mgr is not mgr
+        return row
+
+    expected = df.copy()
+    expected["a"] += 1
+
+    with tm.assert_cow_warning(warn_copy_on_write):
+        result = df.apply(func, axis=1)
+
+    tm.assert_frame_equal(result, expected)
+    if using_copy_on_write or using_array_manager:
+        # INFO(CoW) With copy on write, mutating a viewing row doesn't mutate the parent
+        # INFO(ArrayManager) With BlockManager, the row is a view and mutated in place,
+        # with ArrayManager the row is not a view, and thus not mutated in place
+        tm.assert_frame_equal(df, df_orig)
+    else:
+        tm.assert_frame_equal(df, result)
+
+
+def test_apply_empty_list_reduce():
+    # GH#35683 get columns correct
+    df = DataFrame([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]], columns=["a", "b"])
+
+    result = df.apply(lambda x: [], result_type="reduce")
+    expected = Series({"a": [], "b": []}, dtype=object)
+    tm.assert_series_equal(result, expected)
+
+
+def test_apply_no_suffix_index(engine, request):
+    # GH36189
+    if engine == "numba":
+        mark = pytest.mark.xfail(
+            reason="numba engine doesn't support list-likes/dict-like callables"
+        )
+        request.node.add_marker(mark)
+    pdf = DataFrame([[4, 9]] * 3, columns=["A", "B"])
+    result = pdf.apply(["sum", lambda x: x.sum(), lambda x: x.sum()], engine=engine)
+    expected = DataFrame(
+        {"A": [12, 12, 12], "B": [27, 27, 27]}, index=["sum", "", ""]
+    )
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_apply_raw_returns_string(engine):
+    # https://github.com/pandas-dev/pandas/issues/35940
+    if engine == "numba":
+        pytest.skip("No object dtype support in numba")
+    df = DataFrame({"A": ["aa", "bbb"]})
+    result = df.apply(lambda x: x[0], engine=engine, axis=1, raw=True)
+    expected = Series(["aa", "bbb"])
+    tm.assert_series_equal(result, expected)
+
+
+def test_aggregation_func_column_order():
+    # GH40420: the result of .agg should have an index that is sorted
+    # according to the arguments provided to agg.
+    df = DataFrame(
+        [
+            (1, 0, 0),
+            (2, 0, 0),
+            (3, 0, 0),
+            (4, 5, 4),
+            (5, 6, 6),
+            (6, 7, 7),
+        ],
+        columns=("att1", "att2", "att3"),
+    )
+
+    def sum_div2(s):
+        return s.sum() / 2
+
+    aggs = ["sum", sum_div2, "count", "min"]
+    result = df.agg(aggs)
+    expected = DataFrame(
+        {
+            "att1": [21.0, 10.5, 6.0, 1.0],
+            "att2": [18.0, 9.0, 6.0, 0.0],
+            "att3": [17.0, 8.5, 6.0, 0.0],
+        },
+        index=["sum", "sum_div2", "count", "min"],
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_apply_getitem_axis_1(engine, request):
+    # GH 13427
+    if engine == "numba":
+        mark = pytest.mark.xfail(
+            reason="numba engine not supporting duplicate index values"
+        )
+        request.node.add_marker(mark)
+    df = DataFrame({"a": [0, 1, 2], "b": [1, 2, 3]})
+    result = df[["a", "a"]].apply(
+        lambda x: x.iloc[0] + x.iloc[1], axis=1, engine=engine
+    )
+    expected = Series([0, 2, 4])
+    tm.assert_series_equal(result, expected)
+
+
+def test_nuisance_depr_passes_through_warnings():
+    # GH 43740
+    # DataFrame.agg with list-likes may emit warnings for both individual
+    # args and for entire columns, but we only want to emit once. We
+    # catch and suppress the warnings for individual args, but need to make
+    # sure if some other warnings were raised, they get passed through to
+    # the user.
+
+    def expected_warning(x):
+        warnings.warn("Hello, World!")
+        return x.sum()
+
+    df = DataFrame({"a": [1, 2, 3]})
+    with tm.assert_produces_warning(UserWarning, match="Hello, World!"):
+        df.agg([expected_warning])
+
+
+def test_apply_type():
+    # GH 46719
+    df = DataFrame(
+        {"col1": [3, "string", float], "col2": [0.25, datetime(2020, 1, 1), np.nan]},
+        index=["a", "b", "c"],
+    )
+
+    # axis=0
+    result = df.apply(type, axis=0)
+    expected = Series({"col1": Series, "col2": Series})
+    tm.assert_series_equal(result, expected)
+
+    # axis=1
+    result = df.apply(type, axis=1)
+    expected = Series({"a": Series, "b": Series, "c": Series})
+    tm.assert_series_equal(result, expected)
+
+
+def test_apply_on_empty_dataframe(engine):
+    # GH 39111
+    df = DataFrame({"a": [1, 2], "b": [3, 0]})
+    result = df.head(0).apply(lambda x: max(x["a"], x["b"]), axis=1, engine=engine)
+    expected = Series([], dtype=np.float64)
+    tm.assert_series_equal(result, expected)
+
+
+def test_apply_return_list():
+    df = DataFrame({"a": [1, 2], "b": [2, 3]})
+    result = df.apply(lambda x: [x.values])
+    expected = DataFrame({"a": [[1, 2]], "b": [[2, 3]]})
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "test, constant",
+    [
+        ({"a": [1, 2, 3], "b": [1, 1, 1]}, {"a": [1, 2, 3], "b": [1]}),
+        ({"a": [2, 2, 2], "b": [1, 1, 1]}, {"a": [2], "b": [1]}),
+    ],
+)
+def test_unique_agg_type_is_series(test, constant):
+    # GH#22558
+    df1 = DataFrame(test)
+    expected = Series(data=constant, index=["a", "b"], dtype="object")
+    aggregation = {"a": "unique", "b": "unique"}
+
+    result = df1.agg(aggregation)
+
+    tm.assert_series_equal(result, expected)
+
+
+def test_any_apply_keyword_non_zero_axis_regression():
+    # https://github.com/pandas-dev/pandas/issues/48656
+    df = DataFrame({"A": [1, 2, 0], "B": [0, 2, 0], "C": [0, 0, 0]})
+    expected = Series([True, True, False])
+    tm.assert_series_equal(df.any(axis=1), expected)
+
+    result = df.apply("any", axis=1)
+    tm.assert_series_equal(result, expected)
+
+    result = df.apply("any", 1)
+    tm.assert_series_equal(result, expected)
+
+
+def test_agg_mapping_func_deprecated():
+    # GH 53325
+    df = DataFrame({"x": [1, 2, 3]})
+
+    def foo1(x, a=1, c=0):
+        return x + a + c
+
+    def foo2(x, b=2, c=0):
+        return x + b + c
+
+    # single func already takes the vectorized path
+    result = df.agg(foo1, 0, 3, c=4)
+    expected = df + 7
+    tm.assert_frame_equal(result, expected)
+
+    msg = "using .+ in Series.agg cannot aggregate and"
+
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        result = df.agg([foo1, foo2], 0, 3, c=4)
+    expected = DataFrame(
+        [[8, 8], [9, 9], [10, 10]], columns=[["x", "x"], ["foo1", "foo2"]]
+    )
+    tm.assert_frame_equal(result, expected)
+
+    # TODO: the result below is wrong, should be fixed (GH53325)
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        result = df.agg({"x": foo1}, 0, 3, c=4)
+    expected = DataFrame([2, 3, 4], columns=["x"])
+    tm.assert_frame_equal(result, expected)
+
+
+def test_agg_std():
+    df = DataFrame(np.arange(6).reshape(3, 2), columns=["A", "B"])
+
+    with tm.assert_produces_warning(FutureWarning, match="using DataFrame.std"):
+        result = df.agg(np.std)
+    expected = Series({"A": 2.0, "B": 2.0}, dtype=float)
+    tm.assert_series_equal(result, expected)
+
+    with tm.assert_produces_warning(FutureWarning, match="using Series.std"):
+        result = df.agg([np.std])
+    expected = DataFrame({"A": 2.0, "B": 2.0}, index=["std"])
+    tm.assert_frame_equal(result, expected)
+
+
+def test_agg_dist_like_and_nonunique_columns():
+    # GH#51099
+    df = DataFrame(
+        {"A": [None, 2, 3], "B": [1.0, np.nan, 3.0], "C": ["foo", None, "bar"]}
+    )
+    df.columns = ["A", "A", "C"]
+
+    result = df.agg({"A": "count"})
+    expected = df["A"].count()
+    tm.assert_series_equal(result, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/apply/test_frame_apply_relabeling.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/apply/test_frame_apply_relabeling.py
new file mode 100644
index 0000000000000000000000000000000000000000..723bdd349c0cb8a8f3fe73ded665b6d22260ffb5
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/apply/test_frame_apply_relabeling.py
@@ -0,0 +1,113 @@
+import numpy as np
+import pytest
+
+from pandas.compat.numpy import np_version_gte1p25
+
+import pandas as pd
+import pandas._testing as tm
+
+
+def test_agg_relabel():
+    # GH 26513
+    df = pd.DataFrame({"A": [1, 2, 1, 2], "B": [1, 2, 3, 4], "C": [3, 4, 5, 6]})
+
+    # simplest case with one column, one func
+    result = df.agg(foo=("B", "sum"))
+    expected = pd.DataFrame({"B": [10]}, index=pd.Index(["foo"]))
+    tm.assert_frame_equal(result, expected)
+
+    # test on same column with different methods
+    result = df.agg(foo=("B", "sum"), bar=("B", "min"))
+    expected = pd.DataFrame({"B": [10, 1]}, index=pd.Index(["foo", "bar"]))
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_agg_relabel_multi_columns_multi_methods():
+    # GH 26513, test on multiple columns with multiple methods
+    df = pd.DataFrame({"A": [1, 2, 1, 2], "B": [1, 2, 3, 4], "C": [3, 4, 5, 6]})
+    result = df.agg(
+        foo=("A", "sum"),
+        bar=("B", "mean"),
+        cat=("A", "min"),
+        dat=("B", "max"),
+        f=("A", "max"),
+        g=("C", "min"),
+    )
+    expected = pd.DataFrame(
+        {
+            "A": [6.0, np.nan, 1.0, np.nan, 2.0, np.nan],
+            "B": [np.nan, 2.5, np.nan, 4.0, np.nan, np.nan],
+            "C": [np.nan, np.nan, np.nan, np.nan, np.nan, 3.0],
+        },
+        index=pd.Index(["foo", "bar", "cat", "dat", "f", "g"]),
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.xfail(np_version_gte1p25, reason="name of min now equals name of np.min")
+def test_agg_relabel_partial_functions():
+    # GH 26513, test on partial, functools or more complex cases
+    df = pd.DataFrame({"A": [1, 2, 1, 2], "B": [1, 2, 3, 4], "C": [3, 4, 5, 6]})
+    msg = "using Series.[mean|min]"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        result = df.agg(foo=("A", np.mean), bar=("A", "mean"), cat=("A", min))
+    expected = pd.DataFrame(
+        {"A": [1.5, 1.5, 1.0]}, index=pd.Index(["foo", "bar", "cat"])
+    )
+    tm.assert_frame_equal(result, expected)
+
+    msg = "using Series.[mean|min|max|sum]"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        result = df.agg(
+            foo=("A", min),
+            bar=("A", np.min),
+            cat=("B", max),
+            dat=("C", "min"),
+            f=("B", np.sum),
+            kk=("B", lambda x: min(x)),
+        )
+    expected = pd.DataFrame(
+        {
+            "A": [1.0, 1.0, np.nan, np.nan, np.nan, np.nan],
+            "B": [np.nan, np.nan, 4.0, np.nan, 10.0, 1.0],
+            "C": [np.nan, np.nan, np.nan, 3.0, np.nan, np.nan],
+        },
+        index=pd.Index(["foo", "bar", "cat", "dat", "f", "kk"]),
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_agg_namedtuple():
+    # GH 26513
+    df = pd.DataFrame({"A": [0, 1], "B": [1, 2]})
+    result = df.agg(
+        foo=pd.NamedAgg("B", "sum"),
+        bar=pd.NamedAgg("B", "min"),
+        cat=pd.NamedAgg(column="B", aggfunc="count"),
+        fft=pd.NamedAgg("B", aggfunc="max"),
+    )
+
+    expected = pd.DataFrame(
+        {"B": [3, 1, 2, 2]}, index=pd.Index(["foo", "bar", "cat", "fft"])
+    )
+    tm.assert_frame_equal(result, expected)
+
+    result = df.agg(
+        foo=pd.NamedAgg("A", "min"),
+        bar=pd.NamedAgg(column="B", aggfunc="max"),
+        cat=pd.NamedAgg(column="A", aggfunc="max"),
+    )
+    expected = pd.DataFrame(
+        {"A": [0.0, np.nan, 1.0], "B": [np.nan, 2.0, np.nan]},
+        index=pd.Index(["foo", "bar", "cat"]),
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_reconstruct_func():
+    # GH 28472, test to ensure reconstruct_func isn't moved;
+    # This method is used by other libraries (e.g. dask)
+    result = pd.core.apply.reconstruct_func("min")
+    expected = (False, "min", None, None)
+    tm.assert_equal(result, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/apply/test_frame_transform.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/apply/test_frame_transform.py
new file mode 100644
index 0000000000000000000000000000000000000000..558d76ae8fdc4b95d46bbe94e15822779bd7c53f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/apply/test_frame_transform.py
@@ -0,0 +1,264 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    DataFrame,
+    MultiIndex,
+    Series,
+)
+import pandas._testing as tm
+from pandas.tests.apply.common import frame_transform_kernels
+from pandas.tests.frame.common import zip_frames
+
+
+def unpack_obj(obj, klass, axis):
+    """
+    Helper to ensure we have the right type of object for a test parametrized
+    over frame_or_series.
+    """
+    if klass is not DataFrame:
+        obj = obj["A"]
+        if axis != 0:
+            pytest.skip(f"Test is only for DataFrame with axis={axis}")
+    return obj
+
+
+def test_transform_ufunc(axis, float_frame, frame_or_series):
+    # GH 35964
+    obj = unpack_obj(float_frame, frame_or_series, axis)
+
+    with np.errstate(all="ignore"):
+        f_sqrt = np.sqrt(obj)
+
+    # ufunc
+    result = obj.transform(np.sqrt, axis=axis)
+    expected = f_sqrt
+    tm.assert_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "ops, names",
+    [
+        ([np.sqrt], ["sqrt"]),
+        ([np.abs, np.sqrt], ["absolute", "sqrt"]),
+        (np.array([np.sqrt]), ["sqrt"]),
+        (np.array([np.abs, np.sqrt]), ["absolute", "sqrt"]),
+    ],
+)
+def test_transform_listlike(axis, float_frame, ops, names):
+    # GH 35964
+    other_axis = 1 if axis in {0, "index"} else 0
+    with np.errstate(all="ignore"):
+        expected = zip_frames([op(float_frame) for op in ops], axis=other_axis)
+    if axis in {0, "index"}:
+        expected.columns = MultiIndex.from_product([float_frame.columns, names])
+    else:
+        expected.index = MultiIndex.from_product([float_frame.index, names])
+    result = float_frame.transform(ops, axis=axis)
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("ops", [[], np.array([])])
+def test_transform_empty_listlike(float_frame, ops, frame_or_series):
+    obj = unpack_obj(float_frame, frame_or_series, 0)
+
+    with pytest.raises(ValueError, match="No transform functions were provided"):
+        obj.transform(ops)
+
+
+def test_transform_listlike_func_with_args():
+    # GH 50624
+    df = DataFrame({"x": [1, 2, 3]})
+
+    def foo1(x, a=1, c=0):
+        return x + a + c
+
+    def foo2(x, b=2, c=0):
+        return x + b + c
+
+    msg = r"foo1\(\) got an unexpected keyword argument 'b'"
+    with pytest.raises(TypeError, match=msg):
+        df.transform([foo1, foo2], 0, 3, b=3, c=4)
+
+    result = df.transform([foo1, foo2], 0, 3, c=4)
+    expected = DataFrame(
+        [[8, 8], [9, 9], [10, 10]],
+        columns=MultiIndex.from_tuples([("x", "foo1"), ("x", "foo2")]),
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("box", [dict, Series])
+def test_transform_dictlike(axis, float_frame, box):
+    # GH 35964
+    if axis in (0, "index"):
+        e = float_frame.columns[0]
+        expected = float_frame[[e]].transform(np.abs)
+    else:
+        e = float_frame.index[0]
+        expected = float_frame.iloc[[0]].transform(np.abs)
+    result = float_frame.transform(box({e: np.abs}), axis=axis)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_transform_dictlike_mixed():
+    # GH 40018 - mix of lists and non-lists in values of a dictionary
+    df = DataFrame({"a": [1, 2], "b": [1, 4], "c": [1, 4]})
+    result = df.transform({"b": ["sqrt", "abs"], "c": "sqrt"})
+    expected = DataFrame(
+        [[1.0, 1, 1.0], [2.0, 4, 2.0]],
+        columns=MultiIndex([("b", "c"), ("sqrt", "abs")], [(0, 0, 1), (0, 1, 0)]),
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "ops",
+    [
+        {},
+        {"A": []},
+        {"A": [], "B": "cumsum"},
+        {"A": "cumsum", "B": []},
+        {"A": [], "B": ["cumsum"]},
+        {"A": ["cumsum"], "B": []},
+    ],
+)
+def test_transform_empty_dictlike(float_frame, ops, frame_or_series):
+    obj = unpack_obj(float_frame, frame_or_series, 0)
+
+    with pytest.raises(ValueError, match="No transform functions were provided"):
+        obj.transform(ops)
+
+
+@pytest.mark.parametrize("use_apply", [True, False])
+def test_transform_udf(axis, float_frame, use_apply, frame_or_series):
+    # GH 35964
+    obj = unpack_obj(float_frame, frame_or_series, axis)
+
+    # transform uses UDF either via apply or passing the entire DataFrame
+    def func(x):
+        # transform is using apply iff x is not a DataFrame
+        if use_apply == isinstance(x, frame_or_series):
+            # Force transform to fallback
+            raise ValueError
+        return x + 1
+
+    result = obj.transform(func, axis=axis)
+    expected = obj + 1
+    tm.assert_equal(result, expected)
+
+
+wont_fail = ["ffill", "bfill", "fillna", "pad", "backfill", "shift"]
+frame_kernels_raise = [x for x in frame_transform_kernels if x not in wont_fail]
+
+
+@pytest.mark.parametrize("op", [*frame_kernels_raise, lambda x: x + 1])
+def test_transform_bad_dtype(op, frame_or_series, request):
+    # GH 35964
+    if op == "ngroup":
+        request.applymarker(
+            pytest.mark.xfail(raises=ValueError, reason="ngroup not valid for NDFrame")
+        )
+
+    obj = DataFrame({"A": 3 * [object]})  # DataFrame that will fail on most transforms
+    obj = tm.get_obj(obj, frame_or_series)
+    error = TypeError
+    msg = "|".join(
+        [
+            "not supported between instances of 'type' and 'type'",
+            "unsupported operand type",
+        ]
+    )
+
+    with pytest.raises(error, match=msg):
+        obj.transform(op)
+    with pytest.raises(error, match=msg):
+        obj.transform([op])
+    with pytest.raises(error, match=msg):
+        obj.transform({"A": op})
+    with pytest.raises(error, match=msg):
+        obj.transform({"A": [op]})
+
+
+@pytest.mark.parametrize("op", frame_kernels_raise)
+def test_transform_failure_typeerror(request, op):
+    # GH 35964
+
+    if op == "ngroup":
+        request.applymarker(
+            pytest.mark.xfail(raises=ValueError, reason="ngroup not valid for NDFrame")
+        )
+
+    # Using object makes most transform kernels fail
+    df = DataFrame({"A": 3 * [object], "B": [1, 2, 3]})
+    error = TypeError
+    msg = "|".join(
+        [
+            "not supported between instances of 'type' and 'type'",
+            "unsupported operand type",
+        ]
+    )
+
+    with pytest.raises(error, match=msg):
+        df.transform([op])
+
+    with pytest.raises(error, match=msg):
+        df.transform({"A": op, "B": op})
+
+    with pytest.raises(error, match=msg):
+        df.transform({"A": [op], "B": [op]})
+
+    with pytest.raises(error, match=msg):
+        df.transform({"A": [op, "shift"], "B": [op]})
+
+
+def test_transform_failure_valueerror():
+    # GH 40211
+    def op(x):
+        if np.sum(np.sum(x)) < 10:
+            raise ValueError
+        return x
+
+    df = DataFrame({"A": [1, 2, 3], "B": [400, 500, 600]})
+    msg = "Transform function failed"
+
+    with pytest.raises(ValueError, match=msg):
+        df.transform([op])
+
+    with pytest.raises(ValueError, match=msg):
+        df.transform({"A": op, "B": op})
+
+    with pytest.raises(ValueError, match=msg):
+        df.transform({"A": [op], "B": [op]})
+
+    with pytest.raises(ValueError, match=msg):
+        df.transform({"A": [op, "shift"], "B": [op]})
+
+
+@pytest.mark.parametrize("use_apply", [True, False])
+def test_transform_passes_args(use_apply, frame_or_series):
+    # GH 35964
+    # transform uses UDF either via apply or passing the entire DataFrame
+    expected_args = [1, 2]
+    expected_kwargs = {"c": 3}
+
+    def f(x, a, b, c):
+        # transform is using apply iff x is not a DataFrame
+        if use_apply == isinstance(x, frame_or_series):
+            # Force transform to fallback
+            raise ValueError
+        assert [a, b] == expected_args
+        assert c == expected_kwargs["c"]
+        return x
+
+    frame_or_series([1]).transform(f, 0, *expected_args, **expected_kwargs)
+
+
+def test_transform_empty_dataframe():
+    # https://github.com/pandas-dev/pandas/issues/39636
+    df = DataFrame([], columns=["col1", "col2"])
+    result = df.transform(lambda x: x + 10)
+    tm.assert_frame_equal(result, df)
+
+    result = df["col1"].transform(lambda x: x + 10)
+    tm.assert_series_equal(result, df["col1"])
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/apply/test_invalid_arg.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/apply/test_invalid_arg.py
new file mode 100644
index 0000000000000000000000000000000000000000..68f3fe36546a09404f4f390ace4f6266c1512abe
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/apply/test_invalid_arg.py
@@ -0,0 +1,363 @@
+# Tests specifically aimed at detecting bad arguments.
+# This file is organized by reason for exception.
+#     1. always invalid argument values
+#     2. missing column(s)
+#     3. incompatible ops/dtype/args/kwargs
+#     4. invalid result shape/type
+# If your test does not fit into one of these categories, add to this list.
+
+from itertools import chain
+import re
+
+import numpy as np
+import pytest
+
+from pandas.errors import SpecificationError
+
+from pandas import (
+    DataFrame,
+    Series,
+    date_range,
+)
+import pandas._testing as tm
+
+
+@pytest.mark.parametrize("result_type", ["foo", 1])
+def test_result_type_error(result_type):
+    # allowed result_type
+    df = DataFrame(
+        np.tile(np.arange(3, dtype="int64"), 6).reshape(6, -1) + 1,
+        columns=["A", "B", "C"],
+    )
+
+    msg = (
+        "invalid value for result_type, must be one of "
+        "{None, 'reduce', 'broadcast', 'expand'}"
+    )
+    with pytest.raises(ValueError, match=msg):
+        df.apply(lambda x: [1, 2, 3], axis=1, result_type=result_type)
+
+
+def test_apply_invalid_axis_value():
+    df = DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]], index=["a", "a", "c"])
+    msg = "No axis named 2 for object type DataFrame"
+    with pytest.raises(ValueError, match=msg):
+        df.apply(lambda x: x, 2)
+
+
+def test_agg_raises():
+    # GH 26513
+    df = DataFrame({"A": [0, 1], "B": [1, 2]})
+    msg = "Must provide"
+
+    with pytest.raises(TypeError, match=msg):
+        df.agg()
+
+
+def test_map_with_invalid_na_action_raises():
+    # https://github.com/pandas-dev/pandas/issues/32815
+    s = Series([1, 2, 3])
+    msg = "na_action must either be 'ignore' or None"
+    with pytest.raises(ValueError, match=msg):
+        s.map(lambda x: x, na_action="____")
+
+
+@pytest.mark.parametrize("input_na_action", ["____", True])
+def test_map_arg_is_dict_with_invalid_na_action_raises(input_na_action):
+    # https://github.com/pandas-dev/pandas/issues/46588
+    s = Series([1, 2, 3])
+    msg = f"na_action must either be 'ignore' or None, {input_na_action} was passed"
+    with pytest.raises(ValueError, match=msg):
+        s.map({1: 2}, na_action=input_na_action)
+
+
+@pytest.mark.parametrize("method", ["apply", "agg", "transform"])
+@pytest.mark.parametrize("func", [{"A": {"B": "sum"}}, {"A": {"B": ["sum"]}}])
+def test_nested_renamer(frame_or_series, method, func):
+    # GH 35964
+    obj = frame_or_series({"A": [1]})
+    match = "nested renamer is not supported"
+    with pytest.raises(SpecificationError, match=match):
+        getattr(obj, method)(func)
+
+
+@pytest.mark.parametrize(
+    "renamer",
+    [{"foo": ["min", "max"]}, {"foo": ["min", "max"], "bar": ["sum", "mean"]}],
+)
+def test_series_nested_renamer(renamer):
+    s = Series(range(6), dtype="int64", name="series")
+    msg = "nested renamer is not supported"
+    with pytest.raises(SpecificationError, match=msg):
+        s.agg(renamer)
+
+
+def test_apply_dict_depr():
+    tsdf = DataFrame(
+        np.random.default_rng(2).standard_normal((10, 3)),
+        columns=["A", "B", "C"],
+        index=date_range("1/1/2000", periods=10),
+    )
+    msg = "nested renamer is not supported"
+    with pytest.raises(SpecificationError, match=msg):
+        tsdf.A.agg({"foo": ["sum", "mean"]})
+
+
+@pytest.mark.parametrize("method", ["agg", "transform"])
+def test_dict_nested_renaming_depr(method):
+    df = DataFrame({"A": range(5), "B": 5})
+
+    # nested renaming
+    msg = r"nested renamer is not supported"
+    with pytest.raises(SpecificationError, match=msg):
+        getattr(df, method)({"A": {"foo": "min"}, "B": {"bar": "max"}})
+
+
+@pytest.mark.parametrize("method", ["apply", "agg", "transform"])
+@pytest.mark.parametrize("func", [{"B": "sum"}, {"B": ["sum"]}])
+def test_missing_column(method, func):
+    # GH 40004
+    obj = DataFrame({"A": [1]})
+    match = re.escape("Column(s) ['B'] do not exist")
+    with pytest.raises(KeyError, match=match):
+        getattr(obj, method)(func)
+
+
+def test_transform_mixed_column_name_dtypes():
+    # GH39025
+    df = DataFrame({"a": ["1"]})
+    msg = r"Column\(s\) \[1, 'b'\] do not exist"
+    with pytest.raises(KeyError, match=msg):
+        df.transform({"a": int, 1: str, "b": int})
+
+
+@pytest.mark.parametrize(
+    "how, args", [("pct_change", ()), ("nsmallest", (1, ["a", "b"])), ("tail", 1)]
+)
+def test_apply_str_axis_1_raises(how, args):
+    # GH 39211 - some ops don't support axis=1
+    df = DataFrame({"a": [1, 2], "b": [3, 4]})
+    msg = f"Operation {how} does not support axis=1"
+    with pytest.raises(ValueError, match=msg):
+        df.apply(how, axis=1, args=args)
+
+
+def test_transform_axis_1_raises():
+    # GH 35964
+    msg = "No axis named 1 for object type Series"
+    with pytest.raises(ValueError, match=msg):
+        Series([1]).transform("sum", axis=1)
+
+
+def test_apply_modify_traceback():
+    data = DataFrame(
+        {
+            "A": [
+                "foo",
+                "foo",
+                "foo",
+                "foo",
+                "bar",
+                "bar",
+                "bar",
+                "bar",
+                "foo",
+                "foo",
+                "foo",
+            ],
+            "B": [
+                "one",
+                "one",
+                "one",
+                "two",
+                "one",
+                "one",
+                "one",
+                "two",
+                "two",
+                "two",
+                "one",
+            ],
+            "C": [
+                "dull",
+                "dull",
+                "shiny",
+                "dull",
+                "dull",
+                "shiny",
+                "shiny",
+                "dull",
+                "shiny",
+                "shiny",
+                "shiny",
+            ],
+            "D": np.random.default_rng(2).standard_normal(11),
+            "E": np.random.default_rng(2).standard_normal(11),
+            "F": np.random.default_rng(2).standard_normal(11),
+        }
+    )
+
+    data.loc[4, "C"] = np.nan
+
+    def transform(row):
+        if row["C"].startswith("shin") and row["A"] == "foo":
+            row["D"] = 7
+        return row
+
+    msg = "'float' object has no attribute 'startswith'"
+    with pytest.raises(AttributeError, match=msg):
+        data.apply(transform, axis=1)
+
+
+@pytest.mark.parametrize(
+    "df, func, expected",
+    tm.get_cython_table_params(
+        DataFrame([["a", "b"], ["b", "a"]]), [["cumprod", TypeError]]
+    ),
+)
+def test_agg_cython_table_raises_frame(df, func, expected, axis, using_infer_string):
+    # GH 21224
+    if using_infer_string:
+        expected = (expected, NotImplementedError)
+
+    msg = (
+        "can't multiply sequence by non-int of type 'str'"
+        "|cannot perform cumprod with type str"  # NotImplementedError python backend
+        "|operation 'cumprod' not supported for dtype 'str'"  # TypeError pyarrow
+    )
+    warn = None if isinstance(func, str) else FutureWarning
+    with pytest.raises(expected, match=msg):
+        with tm.assert_produces_warning(warn, match="using DataFrame.cumprod"):
+            df.agg(func, axis=axis)
+
+
+@pytest.mark.parametrize(
+    "series, func, expected",
+    chain(
+        tm.get_cython_table_params(
+            Series("a b c".split()),
+            [
+                ("mean", TypeError),  # mean raises TypeError
+                ("prod", TypeError),
+                ("std", TypeError),
+                ("var", TypeError),
+                ("median", TypeError),
+                ("cumprod", TypeError),
+            ],
+        )
+    ),
+)
+def test_agg_cython_table_raises_series(series, func, expected, using_infer_string):
+    # GH21224
+    msg = r"[Cc]ould not convert|can't multiply sequence by non-int of type"
+    if func == "median" or func is np.nanmedian or func is np.median:
+        msg = r"Cannot convert \['a' 'b' 'c'\] to numeric"
+
+    if using_infer_string and func in ("cumprod", np.cumprod, np.nancumprod):
+        expected = (expected, NotImplementedError)
+
+    msg = (
+        msg + "|does not support|has no kernel|Cannot perform|cannot perform|operation"
+    )
+    warn = None if isinstance(func, str) else FutureWarning
+
+    with pytest.raises(expected, match=msg):
+        # e.g. Series('a b'.split()).cumprod() will raise
+        with tm.assert_produces_warning(warn, match="is currently using Series.*"):
+            series.agg(func)
+
+
+def test_agg_none_to_type():
+    # GH 40543
+    df = DataFrame({"a": [None]})
+    msg = re.escape("int() argument must be a string")
+    with pytest.raises(TypeError, match=msg):
+        df.agg({"a": lambda x: int(x.iloc[0])})
+
+
+def test_transform_none_to_type():
+    # GH#34377
+    df = DataFrame({"a": [None]})
+    msg = "argument must be a"
+    with pytest.raises(TypeError, match=msg):
+        df.transform({"a": lambda x: int(x.iloc[0])})
+
+
+@pytest.mark.parametrize(
+    "func",
+    [
+        lambda x: np.array([1, 2]).reshape(-1, 2),
+        lambda x: [1, 2],
+        lambda x: Series([1, 2]),
+    ],
+)
+def test_apply_broadcast_error(func):
+    df = DataFrame(
+        np.tile(np.arange(3, dtype="int64"), 6).reshape(6, -1) + 1,
+        columns=["A", "B", "C"],
+    )
+
+    # > 1 ndim
+    msg = "too many dims to broadcast|cannot broadcast result"
+    with pytest.raises(ValueError, match=msg):
+        df.apply(func, axis=1, result_type="broadcast")
+
+
+def test_transform_and_agg_err_agg(axis, float_frame):
+    # cannot both transform and agg
+    msg = "cannot combine transform and aggregation operations"
+    with pytest.raises(ValueError, match=msg):
+        with np.errstate(all="ignore"):
+            float_frame.agg(["max", "sqrt"], axis=axis)
+
+
+@pytest.mark.filterwarnings("ignore::FutureWarning")  # GH53325
+@pytest.mark.parametrize(
+    "func, msg",
+    [
+        (["sqrt", "max"], "cannot combine transform and aggregation"),
+        (
+            {"foo": np.sqrt, "bar": "sum"},
+            "cannot perform both aggregation and transformation",
+        ),
+    ],
+)
+def test_transform_and_agg_err_series(string_series, func, msg):
+    # we are trying to transform with an aggregator
+    with pytest.raises(ValueError, match=msg):
+        with np.errstate(all="ignore"):
+            string_series.agg(func)
+
+
+@pytest.mark.parametrize("func", [["max", "min"], ["max", "sqrt"]])
+def test_transform_wont_agg_frame(axis, float_frame, func):
+    # GH 35964
+    # cannot both transform and agg
+    msg = "Function did not transform"
+    with pytest.raises(ValueError, match=msg):
+        float_frame.transform(func, axis=axis)
+
+
+@pytest.mark.parametrize("func", [["min", "max"], ["sqrt", "max"]])
+def test_transform_wont_agg_series(string_series, func):
+    # GH 35964
+    # we are trying to transform with an aggregator
+    msg = "Function did not transform"
+
+    with pytest.raises(ValueError, match=msg):
+        string_series.transform(func)
+
+
+@pytest.mark.parametrize(
+    "op_wrapper", [lambda x: x, lambda x: [x], lambda x: {"A": x}, lambda x: {"A": [x]}]
+)
+def test_transform_reducer_raises(all_reductions, frame_or_series, op_wrapper):
+    # GH 35964
+    op = op_wrapper(all_reductions)
+
+    obj = DataFrame({"A": [1, 2, 3]})
+    obj = tm.get_obj(obj, frame_or_series)
+
+    msg = "Function did not transform"
+    with pytest.raises(ValueError, match=msg):
+        obj.transform(op)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/apply/test_numba.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/apply/test_numba.py
new file mode 100644
index 0000000000000000000000000000000000000000..c211073f758881fdd9e2acf72b30e86b9aa49cb2
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/apply/test_numba.py
@@ -0,0 +1,129 @@
+import numpy as np
+import pytest
+
+from pandas.compat import is_platform_arm
+import pandas.util._test_decorators as td
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Index,
+)
+import pandas._testing as tm
+from pandas.util.version import Version
+
+pytestmark = [td.skip_if_no("numba"), pytest.mark.single_cpu, pytest.mark.skipif()]
+
+numba = pytest.importorskip("numba")
+pytestmark.append(
+    pytest.mark.skipif(
+        Version(numba.__version__) == Version("0.61") and is_platform_arm(),
+        reason=f"Segfaults on ARM platforms with numba {numba.__version__}",
+    )
+)
+
+
+@pytest.fixture(params=[0, 1])
+def apply_axis(request):
+    return request.param
+
+
+def test_numba_vs_python_noop(float_frame, apply_axis):
+    func = lambda x: x
+    result = float_frame.apply(func, engine="numba", axis=apply_axis)
+    expected = float_frame.apply(func, engine="python", axis=apply_axis)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_numba_vs_python_string_index():
+    # GH#56189
+    df = DataFrame(
+        1,
+        index=Index(["a", "b"], dtype=pd.StringDtype(na_value=np.nan)),
+        columns=Index(["x", "y"], dtype=pd.StringDtype(na_value=np.nan)),
+    )
+    func = lambda x: x
+    result = df.apply(func, engine="numba", axis=0)
+    expected = df.apply(func, engine="python", axis=0)
+    tm.assert_frame_equal(
+        result, expected, check_column_type=False, check_index_type=False
+    )
+
+
+def test_numba_vs_python_indexing():
+    frame = DataFrame(
+        {"a": [1, 2, 3], "b": [4, 5, 6], "c": [7.0, 8.0, 9.0]},
+        index=Index(["A", "B", "C"]),
+    )
+    row_func = lambda x: x["c"]
+    result = frame.apply(row_func, engine="numba", axis=1)
+    expected = frame.apply(row_func, engine="python", axis=1)
+    tm.assert_series_equal(result, expected)
+
+    col_func = lambda x: x["A"]
+    result = frame.apply(col_func, engine="numba", axis=0)
+    expected = frame.apply(col_func, engine="python", axis=0)
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "reduction",
+    [lambda x: x.mean(), lambda x: x.min(), lambda x: x.max(), lambda x: x.sum()],
+)
+def test_numba_vs_python_reductions(reduction, apply_axis):
+    df = DataFrame(np.ones((4, 4), dtype=np.float64))
+    result = df.apply(reduction, engine="numba", axis=apply_axis)
+    expected = df.apply(reduction, engine="python", axis=apply_axis)
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("colnames", [[1, 2, 3], [1.0, 2.0, 3.0]])
+def test_numba_numeric_colnames(colnames):
+    # Check that numeric column names lower properly and can be indxed on
+    df = DataFrame(
+        np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.int64), columns=colnames
+    )
+    first_col = colnames[0]
+    f = lambda x: x[first_col]  # Get the first column
+    result = df.apply(f, engine="numba", axis=1)
+    expected = df.apply(f, engine="python", axis=1)
+    tm.assert_series_equal(result, expected)
+
+
+def test_numba_parallel_unsupported(float_frame):
+    f = lambda x: x
+    with pytest.raises(
+        NotImplementedError,
+        match="Parallel apply is not supported when raw=False and engine='numba'",
+    ):
+        float_frame.apply(f, engine="numba", engine_kwargs={"parallel": True})
+
+
+def test_numba_nonunique_unsupported(apply_axis):
+    f = lambda x: x
+    df = DataFrame({"a": [1, 2]}, index=Index(["a", "a"]))
+    with pytest.raises(
+        NotImplementedError,
+        match="The index/columns must be unique when raw=False and engine='numba'",
+    ):
+        df.apply(f, engine="numba", axis=apply_axis)
+
+
+def test_numba_unsupported_dtypes(apply_axis):
+    pytest.importorskip("pyarrow")
+    f = lambda x: x
+    df = DataFrame({"a": [1, 2], "b": ["a", "b"], "c": [4, 5]})
+    df["c"] = df["c"].astype("double[pyarrow]")
+
+    with pytest.raises(
+        ValueError,
+        match="Column b must have a numeric dtype. Found 'object|str' instead",
+    ):
+        df.apply(f, engine="numba", axis=apply_axis)
+
+    with pytest.raises(
+        ValueError,
+        match="Column c is backed by an extension array, "
+        "which is not supported by the numba engine.",
+    ):
+        df["c"].to_frame().apply(f, engine="numba", axis=apply_axis)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/apply/test_series_apply.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/apply/test_series_apply.py
new file mode 100644
index 0000000000000000000000000000000000000000..69f84ca74ab0b44e177a4a79a1ef5a6c893efbe8
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/apply/test_series_apply.py
@@ -0,0 +1,701 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Index,
+    MultiIndex,
+    Series,
+    concat,
+    date_range,
+    timedelta_range,
+)
+import pandas._testing as tm
+from pandas.tests.apply.common import series_transform_kernels
+
+
+@pytest.fixture(params=[False, "compat"])
+def by_row(request):
+    return request.param
+
+
+def test_series_map_box_timedelta(by_row):
+    # GH#11349
+    ser = Series(timedelta_range("1 day 1 s", periods=3, freq="h"))
+
+    def f(x):
+        return x.total_seconds() if by_row else x.dt.total_seconds()
+
+    result = ser.apply(f, by_row=by_row)
+
+    expected = ser.map(lambda x: x.total_seconds())
+    tm.assert_series_equal(result, expected)
+
+    expected = Series([86401.0, 90001.0, 93601.0])
+    tm.assert_series_equal(result, expected)
+
+
+def test_apply(datetime_series, by_row):
+    result = datetime_series.apply(np.sqrt, by_row=by_row)
+    with np.errstate(all="ignore"):
+        expected = np.sqrt(datetime_series)
+    tm.assert_series_equal(result, expected)
+
+    # element-wise apply (ufunc)
+    result = datetime_series.apply(np.exp, by_row=by_row)
+    expected = np.exp(datetime_series)
+    tm.assert_series_equal(result, expected)
+
+    # empty series
+    s = Series(dtype=object, name="foo", index=Index([], name="bar"))
+    rs = s.apply(lambda x: x, by_row=by_row)
+    tm.assert_series_equal(s, rs)
+
+    # check all metadata (GH 9322)
+    assert s is not rs
+    assert s.index is rs.index
+    assert s.dtype == rs.dtype
+    assert s.name == rs.name
+
+    # index but no data
+    s = Series(index=[1, 2, 3], dtype=np.float64)
+    rs = s.apply(lambda x: x, by_row=by_row)
+    tm.assert_series_equal(s, rs)
+
+
+def test_apply_map_same_length_inference_bug():
+    s = Series([1, 2])
+
+    def f(x):
+        return (x, x + 1)
+
+    result = s.apply(f, by_row="compat")
+    expected = s.map(f)
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("convert_dtype", [True, False])
+def test_apply_convert_dtype_deprecated(convert_dtype):
+    ser = Series(np.random.default_rng(2).standard_normal(10))
+
+    def func(x):
+        return x if x > 0 else np.nan
+
+    with tm.assert_produces_warning(FutureWarning):
+        ser.apply(func, convert_dtype=convert_dtype, by_row="compat")
+
+
+def test_apply_args():
+    s = Series(["foo,bar"])
+
+    result = s.apply(str.split, args=(",",))
+    assert result[0] == ["foo", "bar"]
+    assert isinstance(result[0], list)
+
+
+@pytest.mark.parametrize(
+    "args, kwargs, increment",
+    [((), {}, 0), ((), {"a": 1}, 1), ((2, 3), {}, 32), ((1,), {"c": 2}, 201)],
+)
+def test_agg_args(args, kwargs, increment):
+    # GH 43357
+    def f(x, a=0, b=0, c=0):
+        return x + a + 10 * b + 100 * c
+
+    s = Series([1, 2])
+    msg = (
+        "in Series.agg cannot aggregate and has been deprecated. "
+        "Use Series.transform to keep behavior unchanged."
+    )
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        result = s.agg(f, 0, *args, **kwargs)
+    expected = s + increment
+    tm.assert_series_equal(result, expected)
+
+
+def test_agg_mapping_func_deprecated():
+    # GH 53325
+    s = Series([1, 2, 3])
+
+    def foo1(x, a=1, c=0):
+        return x + a + c
+
+    def foo2(x, b=2, c=0):
+        return x + b + c
+
+    msg = "using .+ in Series.agg cannot aggregate and"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        s.agg(foo1, 0, 3, c=4)
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        s.agg([foo1, foo2], 0, 3, c=4)
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        s.agg({"a": foo1, "b": foo2}, 0, 3, c=4)
+
+
+def test_series_apply_map_box_timestamps(by_row):
+    # GH#2689, GH#2627
+    ser = Series(date_range("1/1/2000", periods=10))
+
+    def func(x):
+        return (x.hour, x.day, x.month)
+
+    if not by_row:
+        msg = "Series' object has no attribute 'hour'"
+        with pytest.raises(AttributeError, match=msg):
+            ser.apply(func, by_row=by_row)
+        return
+
+    result = ser.apply(func, by_row=by_row)
+    expected = ser.map(func)
+    tm.assert_series_equal(result, expected)
+
+
+def test_apply_box_dt64():
+    # ufunc will not be boxed. Same test cases as the test_map_box
+    vals = [pd.Timestamp("2011-01-01"), pd.Timestamp("2011-01-02")]
+    ser = Series(vals, dtype="M8[ns]")
+    assert ser.dtype == "datetime64[ns]"
+    # boxed value must be Timestamp instance
+    res = ser.apply(lambda x: f"{type(x).__name__}_{x.day}_{x.tz}", by_row="compat")
+    exp = Series(["Timestamp_1_None", "Timestamp_2_None"])
+    tm.assert_series_equal(res, exp)
+
+
+def test_apply_box_dt64tz():
+    vals = [
+        pd.Timestamp("2011-01-01", tz="US/Eastern"),
+        pd.Timestamp("2011-01-02", tz="US/Eastern"),
+    ]
+    ser = Series(vals, dtype="M8[ns, US/Eastern]")
+    assert ser.dtype == "datetime64[ns, US/Eastern]"
+    res = ser.apply(lambda x: f"{type(x).__name__}_{x.day}_{x.tz}", by_row="compat")
+    exp = Series(["Timestamp_1_US/Eastern", "Timestamp_2_US/Eastern"])
+    tm.assert_series_equal(res, exp)
+
+
+def test_apply_box_td64():
+    # timedelta
+    vals = [pd.Timedelta("1 days"), pd.Timedelta("2 days")]
+    ser = Series(vals)
+    assert ser.dtype == "timedelta64[ns]"
+    res = ser.apply(lambda x: f"{type(x).__name__}_{x.days}", by_row="compat")
+    exp = Series(["Timedelta_1", "Timedelta_2"])
+    tm.assert_series_equal(res, exp)
+
+
+def test_apply_box_period():
+    # period
+    vals = [pd.Period("2011-01-01", freq="M"), pd.Period("2011-01-02", freq="M")]
+    ser = Series(vals)
+    assert ser.dtype == "Period[M]"
+    res = ser.apply(lambda x: f"{type(x).__name__}_{x.freqstr}", by_row="compat")
+    exp = Series(["Period_M", "Period_M"])
+    tm.assert_series_equal(res, exp)
+
+
+def test_apply_datetimetz(by_row):
+    values = date_range("2011-01-01", "2011-01-02", freq="h").tz_localize("Asia/Tokyo")
+    s = Series(values, name="XX")
+
+    result = s.apply(lambda x: x + pd.offsets.Day(), by_row=by_row)
+    exp_values = date_range("2011-01-02", "2011-01-03", freq="h").tz_localize(
+        "Asia/Tokyo"
+    )
+    exp = Series(exp_values, name="XX")
+    tm.assert_series_equal(result, exp)
+
+    result = s.apply(lambda x: x.hour if by_row else x.dt.hour, by_row=by_row)
+    exp = Series(list(range(24)) + [0], name="XX", dtype="int64" if by_row else "int32")
+    tm.assert_series_equal(result, exp)
+
+    # not vectorized
+    def f(x):
+        return str(x.tz) if by_row else str(x.dt.tz)
+
+    result = s.apply(f, by_row=by_row)
+    if by_row:
+        exp = Series(["Asia/Tokyo"] * 25, name="XX")
+        tm.assert_series_equal(result, exp)
+    else:
+        assert result == "Asia/Tokyo"
+
+
+def test_apply_categorical(by_row, using_infer_string):
+    values = pd.Categorical(list("ABBABCD"), categories=list("DCBA"), ordered=True)
+    ser = Series(values, name="XX", index=list("abcdefg"))
+
+    if not by_row:
+        msg = "Series' object has no attribute 'lower"
+        with pytest.raises(AttributeError, match=msg):
+            ser.apply(lambda x: x.lower(), by_row=by_row)
+        assert ser.apply(lambda x: "A", by_row=by_row) == "A"
+        return
+
+    result = ser.apply(lambda x: x.lower(), by_row=by_row)
+
+    # should be categorical dtype when the number of categories are
+    # the same
+    values = pd.Categorical(list("abbabcd"), categories=list("dcba"), ordered=True)
+    exp = Series(values, name="XX", index=list("abcdefg"))
+    tm.assert_series_equal(result, exp)
+    tm.assert_categorical_equal(result.values, exp.values)
+
+    result = ser.apply(lambda x: "A")
+    exp = Series(["A"] * 7, name="XX", index=list("abcdefg"))
+    tm.assert_series_equal(result, exp)
+    assert result.dtype == object if not using_infer_string else "str"
+
+
+@pytest.mark.parametrize("series", [["1-1", "1-1", np.nan], ["1-1", "1-2", np.nan]])
+def test_apply_categorical_with_nan_values(series, by_row):
+    # GH 20714 bug fixed in: GH 24275
+    s = Series(series, dtype="category")
+    if not by_row:
+        msg = "'Series' object has no attribute 'split'"
+        with pytest.raises(AttributeError, match=msg):
+            s.apply(lambda x: x.split("-")[0], by_row=by_row)
+        return
+
+    result = s.apply(lambda x: x.split("-")[0], by_row=by_row)
+    result = result.astype(object)
+    expected = Series(["1", "1", np.nan], dtype="category")
+    expected = expected.astype(object)
+    tm.assert_series_equal(result, expected)
+
+
+def test_apply_empty_integer_series_with_datetime_index(by_row):
+    # GH 21245
+    s = Series([], index=date_range(start="2018-01-01", periods=0), dtype=int)
+    result = s.apply(lambda x: x, by_row=by_row)
+    tm.assert_series_equal(result, s)
+
+
+def test_apply_dataframe_iloc():
+    uintDF = DataFrame(np.uint64([1, 2, 3, 4, 5]), columns=["Numbers"])
+    indexDF = DataFrame([2, 3, 2, 1, 2], columns=["Indices"])
+
+    def retrieve(targetRow, targetDF):
+        val = targetDF["Numbers"].iloc[targetRow]
+        return val
+
+    result = indexDF["Indices"].apply(retrieve, args=(uintDF,))
+    expected = Series([3, 4, 3, 2, 3], name="Indices", dtype="uint64")
+    tm.assert_series_equal(result, expected)
+
+
+def test_transform(string_series, by_row):
+    # transforming functions
+
+    with np.errstate(all="ignore"):
+        f_sqrt = np.sqrt(string_series)
+        f_abs = np.abs(string_series)
+
+        # ufunc
+        result = string_series.apply(np.sqrt, by_row=by_row)
+        expected = f_sqrt.copy()
+        tm.assert_series_equal(result, expected)
+
+        # list-like
+        result = string_series.apply([np.sqrt], by_row=by_row)
+        expected = f_sqrt.to_frame().copy()
+        expected.columns = ["sqrt"]
+        tm.assert_frame_equal(result, expected)
+
+        result = string_series.apply(["sqrt"], by_row=by_row)
+        tm.assert_frame_equal(result, expected)
+
+        # multiple items in list
+        # these are in the order as if we are applying both functions per
+        # series and then concatting
+        expected = concat([f_sqrt, f_abs], axis=1)
+        expected.columns = ["sqrt", "absolute"]
+        result = string_series.apply([np.sqrt, np.abs], by_row=by_row)
+        tm.assert_frame_equal(result, expected)
+
+        # dict, provide renaming
+        expected = concat([f_sqrt, f_abs], axis=1)
+        expected.columns = ["foo", "bar"]
+        expected = expected.unstack().rename("series")
+
+        result = string_series.apply({"foo": np.sqrt, "bar": np.abs}, by_row=by_row)
+        tm.assert_series_equal(result.reindex_like(expected), expected)
+
+
+@pytest.mark.parametrize("op", series_transform_kernels)
+def test_transform_partial_failure(op, request):
+    # GH 35964
+    if op in ("ffill", "bfill", "pad", "backfill", "shift"):
+        request.applymarker(
+            pytest.mark.xfail(reason=f"{op} is successful on any dtype")
+        )
+
+    # Using object makes most transform kernels fail
+    ser = Series(3 * [object])
+
+    if op in ("fillna", "ngroup"):
+        error = ValueError
+        msg = "Transform function failed"
+    else:
+        error = TypeError
+        msg = "|".join(
+            [
+                "not supported between instances of 'type' and 'type'",
+                "unsupported operand type",
+            ]
+        )
+
+    with pytest.raises(error, match=msg):
+        ser.transform([op, "shift"])
+
+    with pytest.raises(error, match=msg):
+        ser.transform({"A": op, "B": "shift"})
+
+    with pytest.raises(error, match=msg):
+        ser.transform({"A": [op], "B": ["shift"]})
+
+    with pytest.raises(error, match=msg):
+        ser.transform({"A": [op, "shift"], "B": [op]})
+
+
+def test_transform_partial_failure_valueerror():
+    # GH 40211
+    def noop(x):
+        return x
+
+    def raising_op(_):
+        raise ValueError
+
+    ser = Series(3 * [object])
+    msg = "Transform function failed"
+
+    with pytest.raises(ValueError, match=msg):
+        ser.transform([noop, raising_op])
+
+    with pytest.raises(ValueError, match=msg):
+        ser.transform({"A": raising_op, "B": noop})
+
+    with pytest.raises(ValueError, match=msg):
+        ser.transform({"A": [raising_op], "B": [noop]})
+
+    with pytest.raises(ValueError, match=msg):
+        ser.transform({"A": [noop, raising_op], "B": [noop]})
+
+
+def test_demo():
+    # demonstration tests
+    s = Series(range(6), dtype="int64", name="series")
+
+    result = s.agg(["min", "max"])
+    expected = Series([0, 5], index=["min", "max"], name="series")
+    tm.assert_series_equal(result, expected)
+
+    result = s.agg({"foo": "min"})
+    expected = Series([0], index=["foo"], name="series")
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("func", [str, lambda x: str(x)])
+def test_apply_map_evaluate_lambdas_the_same(string_series, func, by_row):
+    # test that we are evaluating row-by-row first if by_row="compat"
+    # else vectorized evaluation
+    result = string_series.apply(func, by_row=by_row)
+
+    if by_row:
+        expected = string_series.map(func)
+        tm.assert_series_equal(result, expected)
+    else:
+        assert result == str(string_series)
+
+
+def test_agg_evaluate_lambdas(string_series):
+    # GH53325
+    # in the future, the result will be a Series class.
+
+    with tm.assert_produces_warning(FutureWarning):
+        result = string_series.agg(lambda x: type(x))
+    assert isinstance(result, Series) and len(result) == len(string_series)
+
+    with tm.assert_produces_warning(FutureWarning):
+        result = string_series.agg(type)
+    assert isinstance(result, Series) and len(result) == len(string_series)
+
+
+@pytest.mark.parametrize("op_name", ["agg", "apply"])
+def test_with_nested_series(datetime_series, op_name):
+    # GH 2316
+    # .agg with a reducer and a transform, what to do
+    msg = "cannot aggregate"
+    warning = FutureWarning if op_name == "agg" else None
+    with tm.assert_produces_warning(warning, match=msg):
+        # GH52123
+        result = getattr(datetime_series, op_name)(
+            lambda x: Series([x, x**2], index=["x", "x^2"])
+        )
+    expected = DataFrame({"x": datetime_series, "x^2": datetime_series**2})
+    tm.assert_frame_equal(result, expected)
+
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        result = datetime_series.agg(lambda x: Series([x, x**2], index=["x", "x^2"]))
+    tm.assert_frame_equal(result, expected)
+
+
+def test_replicate_describe(string_series):
+    # this also tests a result set that is all scalars
+    expected = string_series.describe()
+    result = string_series.apply(
+        {
+            "count": "count",
+            "mean": "mean",
+            "std": "std",
+            "min": "min",
+            "25%": lambda x: x.quantile(0.25),
+            "50%": "median",
+            "75%": lambda x: x.quantile(0.75),
+            "max": "max",
+        },
+    )
+    tm.assert_series_equal(result, expected)
+
+
+def test_reduce(string_series):
+    # reductions with named functions
+    result = string_series.agg(["sum", "mean"])
+    expected = Series(
+        [string_series.sum(), string_series.mean()],
+        ["sum", "mean"],
+        name=string_series.name,
+    )
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "how, kwds",
+    [("agg", {}), ("apply", {"by_row": "compat"}), ("apply", {"by_row": False})],
+)
+def test_non_callable_aggregates(how, kwds):
+    # test agg using non-callable series attributes
+    # GH 39116 - expand to apply
+    s = Series([1, 2, None])
+
+    # Calling agg w/ just a string arg same as calling s.arg
+    result = getattr(s, how)("size", **kwds)
+    expected = s.size
+    assert result == expected
+
+    # test when mixed w/ callable reducers
+    result = getattr(s, how)(["size", "count", "mean"], **kwds)
+    expected = Series({"size": 3.0, "count": 2.0, "mean": 1.5})
+    tm.assert_series_equal(result, expected)
+
+    result = getattr(s, how)({"size": "size", "count": "count", "mean": "mean"}, **kwds)
+    tm.assert_series_equal(result, expected)
+
+
+def test_series_apply_no_suffix_index(by_row):
+    # GH36189
+    s = Series([4] * 3)
+    result = s.apply(["sum", lambda x: x.sum(), lambda x: x.sum()], by_row=by_row)
+    expected = Series([12, 12, 12], index=["sum", "", ""])
+
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "dti,exp",
+    [
+        (
+            Series([1, 2], index=pd.DatetimeIndex([0, 31536000000])),
+            DataFrame(np.repeat([[1, 2]], 2, axis=0), dtype="int64"),
+        ),
+        (
+            Series(
+                np.arange(10, dtype=np.float64),
+                index=date_range("2020-01-01", periods=10),
+                name="ts",
+            ),
+            DataFrame(np.repeat([[1, 2]], 10, axis=0), dtype="int64"),
+        ),
+    ],
+)
+@pytest.mark.parametrize("aware", [True, False])
+def test_apply_series_on_date_time_index_aware_series(dti, exp, aware):
+    # GH 25959
+    # Calling apply on a localized time series should not cause an error
+    if aware:
+        index = dti.tz_localize("UTC").index
+    else:
+        index = dti.index
+    result = Series(index).apply(lambda x: Series([1, 2]))
+    tm.assert_frame_equal(result, exp)
+
+
+@pytest.mark.parametrize(
+    "by_row, expected", [("compat", Series(np.ones(10), dtype="int64")), (False, 1)]
+)
+def test_apply_scalar_on_date_time_index_aware_series(by_row, expected):
+    # GH 25959
+    # Calling apply on a localized time series should not cause an error
+    series = Series(
+        np.arange(10, dtype=np.float64),
+        index=date_range("2020-01-01", periods=10, tz="UTC"),
+    )
+    result = Series(series.index).apply(lambda x: 1, by_row=by_row)
+    tm.assert_equal(result, expected)
+
+
+def test_apply_to_timedelta(by_row):
+    list_of_valid_strings = ["00:00:01", "00:00:02"]
+    a = pd.to_timedelta(list_of_valid_strings)
+    b = Series(list_of_valid_strings).apply(pd.to_timedelta, by_row=by_row)
+    tm.assert_series_equal(Series(a), b)
+
+    list_of_strings = ["00:00:01", np.nan, pd.NaT, pd.NaT]
+
+    a = pd.to_timedelta(list_of_strings)
+    ser = Series(list_of_strings)
+    b = ser.apply(pd.to_timedelta, by_row=by_row)
+    tm.assert_series_equal(Series(a), b)
+
+
+@pytest.mark.parametrize(
+    "ops, names",
+    [
+        ([np.sum], ["sum"]),
+        ([np.sum, np.mean], ["sum", "mean"]),
+        (np.array([np.sum]), ["sum"]),
+        (np.array([np.sum, np.mean]), ["sum", "mean"]),
+    ],
+)
+@pytest.mark.parametrize(
+    "how, kwargs",
+    [["agg", {}], ["apply", {"by_row": "compat"}], ["apply", {"by_row": False}]],
+)
+def test_apply_listlike_reducer(string_series, ops, names, how, kwargs):
+    # GH 39140
+    expected = Series({name: op(string_series) for name, op in zip(names, ops)})
+    expected.name = "series"
+    warn = FutureWarning if how == "agg" else None
+    msg = f"using Series.[{'|'.join(names)}]"
+    with tm.assert_produces_warning(warn, match=msg):
+        result = getattr(string_series, how)(ops, **kwargs)
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "ops",
+    [
+        {"A": np.sum},
+        {"A": np.sum, "B": np.mean},
+        Series({"A": np.sum}),
+        Series({"A": np.sum, "B": np.mean}),
+    ],
+)
+@pytest.mark.parametrize(
+    "how, kwargs",
+    [["agg", {}], ["apply", {"by_row": "compat"}], ["apply", {"by_row": False}]],
+)
+def test_apply_dictlike_reducer(string_series, ops, how, kwargs, by_row):
+    # GH 39140
+    expected = Series({name: op(string_series) for name, op in ops.items()})
+    expected.name = string_series.name
+    warn = FutureWarning if how == "agg" else None
+    msg = "using Series.[sum|mean]"
+    with tm.assert_produces_warning(warn, match=msg):
+        result = getattr(string_series, how)(ops, **kwargs)
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "ops, names",
+    [
+        ([np.sqrt], ["sqrt"]),
+        ([np.abs, np.sqrt], ["absolute", "sqrt"]),
+        (np.array([np.sqrt]), ["sqrt"]),
+        (np.array([np.abs, np.sqrt]), ["absolute", "sqrt"]),
+    ],
+)
+def test_apply_listlike_transformer(string_series, ops, names, by_row):
+    # GH 39140
+    with np.errstate(all="ignore"):
+        expected = concat([op(string_series) for op in ops], axis=1)
+        expected.columns = names
+        result = string_series.apply(ops, by_row=by_row)
+        tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "ops, expected",
+    [
+        ([lambda x: x], DataFrame({"": [1, 2, 3]})),
+        ([lambda x: x.sum()], Series([6], index=[""])),
+    ],
+)
+def test_apply_listlike_lambda(ops, expected, by_row):
+    # GH53400
+    ser = Series([1, 2, 3])
+    result = ser.apply(ops, by_row=by_row)
+    tm.assert_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "ops",
+    [
+        {"A": np.sqrt},
+        {"A": np.sqrt, "B": np.exp},
+        Series({"A": np.sqrt}),
+        Series({"A": np.sqrt, "B": np.exp}),
+    ],
+)
+def test_apply_dictlike_transformer(string_series, ops, by_row):
+    # GH 39140
+    with np.errstate(all="ignore"):
+        expected = concat({name: op(string_series) for name, op in ops.items()})
+        expected.name = string_series.name
+        result = string_series.apply(ops, by_row=by_row)
+        tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "ops, expected",
+    [
+        (
+            {"a": lambda x: x},
+            Series([1, 2, 3], index=MultiIndex.from_arrays([["a"] * 3, range(3)])),
+        ),
+        ({"a": lambda x: x.sum()}, Series([6], index=["a"])),
+    ],
+)
+def test_apply_dictlike_lambda(ops, by_row, expected):
+    # GH53400
+    ser = Series([1, 2, 3])
+    result = ser.apply(ops, by_row=by_row)
+    tm.assert_equal(result, expected)
+
+
+def test_apply_retains_column_name(by_row):
+    # GH 16380
+    df = DataFrame({"x": range(3)}, Index(range(3), name="x"))
+    result = df.x.apply(lambda x: Series(range(x + 1), Index(range(x + 1), name="y")))
+    expected = DataFrame(
+        [[0.0, np.nan, np.nan], [0.0, 1.0, np.nan], [0.0, 1.0, 2.0]],
+        columns=Index(range(3), name="y"),
+        index=Index(range(3), name="x"),
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_apply_type():
+    # GH 46719
+    s = Series([3, "string", float], index=["a", "b", "c"])
+    result = s.apply(type)
+    expected = Series([int, str, type], index=["a", "b", "c"])
+    tm.assert_series_equal(result, expected)
+
+
+def test_series_apply_unpack_nested_data():
+    # GH#55189
+    ser = Series([[1, 2, 3], [4, 5, 6, 7]])
+    result = ser.apply(lambda x: Series(x))
+    expected = DataFrame({0: [1.0, 4.0], 1: [2.0, 5.0], 2: [3.0, 6.0], 3: [np.nan, 7]})
+    tm.assert_frame_equal(result, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/apply/test_series_apply_relabeling.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/apply/test_series_apply_relabeling.py
new file mode 100644
index 0000000000000000000000000000000000000000..cdfa054f91c9b67261d715cd7812a53d1b2d4b2f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/apply/test_series_apply_relabeling.py
@@ -0,0 +1,39 @@
+import pandas as pd
+import pandas._testing as tm
+
+
+def test_relabel_no_duplicated_method():
+    # this is to test there is no duplicated method used in agg
+    df = pd.DataFrame({"A": [1, 2, 1, 2], "B": [1, 2, 3, 4]})
+
+    result = df["A"].agg(foo="sum")
+    expected = df["A"].agg({"foo": "sum"})
+    tm.assert_series_equal(result, expected)
+
+    result = df["B"].agg(foo="min", bar="max")
+    expected = df["B"].agg({"foo": "min", "bar": "max"})
+    tm.assert_series_equal(result, expected)
+
+    msg = "using Series.[sum|min|max]"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        result = df["B"].agg(foo=sum, bar=min, cat="max")
+    msg = "using Series.[sum|min|max]"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        expected = df["B"].agg({"foo": sum, "bar": min, "cat": "max"})
+    tm.assert_series_equal(result, expected)
+
+
+def test_relabel_duplicated_method():
+    # this is to test with nested renaming, duplicated method can be used
+    # if they are assigned with different new names
+    df = pd.DataFrame({"A": [1, 2, 1, 2], "B": [1, 2, 3, 4]})
+
+    result = df["A"].agg(foo="sum", bar="sum")
+    expected = pd.Series([6, 6], index=["foo", "bar"], name="A")
+    tm.assert_series_equal(result, expected)
+
+    msg = "using Series.min"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        result = df["B"].agg(foo=min, bar="min")
+    expected = pd.Series([1, 1], index=["foo", "bar"], name="B")
+    tm.assert_series_equal(result, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/apply/test_series_transform.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/apply/test_series_transform.py
new file mode 100644
index 0000000000000000000000000000000000000000..82592c4711ece5a7f4b6d421d743e1adbd78c345
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/apply/test_series_transform.py
@@ -0,0 +1,84 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    DataFrame,
+    MultiIndex,
+    Series,
+    concat,
+)
+import pandas._testing as tm
+
+
+@pytest.mark.parametrize(
+    "args, kwargs, increment",
+    [((), {}, 0), ((), {"a": 1}, 1), ((2, 3), {}, 32), ((1,), {"c": 2}, 201)],
+)
+def test_agg_args(args, kwargs, increment):
+    # GH 43357
+    def f(x, a=0, b=0, c=0):
+        return x + a + 10 * b + 100 * c
+
+    s = Series([1, 2])
+    result = s.transform(f, 0, *args, **kwargs)
+    expected = s + increment
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "ops, names",
+    [
+        ([np.sqrt], ["sqrt"]),
+        ([np.abs, np.sqrt], ["absolute", "sqrt"]),
+        (np.array([np.sqrt]), ["sqrt"]),
+        (np.array([np.abs, np.sqrt]), ["absolute", "sqrt"]),
+    ],
+)
+def test_transform_listlike(string_series, ops, names):
+    # GH 35964
+    with np.errstate(all="ignore"):
+        expected = concat([op(string_series) for op in ops], axis=1)
+        expected.columns = names
+        result = string_series.transform(ops)
+        tm.assert_frame_equal(result, expected)
+
+
+def test_transform_listlike_func_with_args():
+    # GH 50624
+
+    s = Series([1, 2, 3])
+
+    def foo1(x, a=1, c=0):
+        return x + a + c
+
+    def foo2(x, b=2, c=0):
+        return x + b + c
+
+    msg = r"foo1\(\) got an unexpected keyword argument 'b'"
+    with pytest.raises(TypeError, match=msg):
+        s.transform([foo1, foo2], 0, 3, b=3, c=4)
+
+    result = s.transform([foo1, foo2], 0, 3, c=4)
+    expected = DataFrame({"foo1": [8, 9, 10], "foo2": [8, 9, 10]})
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("box", [dict, Series])
+def test_transform_dictlike(string_series, box):
+    # GH 35964
+    with np.errstate(all="ignore"):
+        expected = concat([np.sqrt(string_series), np.abs(string_series)], axis=1)
+    expected.columns = ["foo", "bar"]
+    result = string_series.transform(box({"foo": np.sqrt, "bar": np.abs}))
+    tm.assert_frame_equal(result, expected)
+
+
+def test_transform_dictlike_mixed():
+    # GH 40018 - mix of lists and non-lists in values of a dictionary
+    df = Series([1, 4])
+    result = df.transform({"b": ["sqrt", "abs"], "c": "sqrt"})
+    expected = DataFrame(
+        [[1.0, 1, 1.0], [2.0, 4, 2.0]],
+        columns=MultiIndex([("b", "c"), ("sqrt", "abs")], [(0, 0, 1), (0, 1, 0)]),
+    )
+    tm.assert_frame_equal(result, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/apply/test_str.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/apply/test_str.py
new file mode 100644
index 0000000000000000000000000000000000000000..17e8322dc40e1ef0e65ed6d63a6e4af3a373e29b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/apply/test_str.py
@@ -0,0 +1,326 @@
+from itertools import chain
+import operator
+
+import numpy as np
+import pytest
+
+from pandas.core.dtypes.common import is_number
+
+from pandas import (
+    DataFrame,
+    Series,
+)
+import pandas._testing as tm
+from pandas.tests.apply.common import (
+    frame_transform_kernels,
+    series_transform_kernels,
+)
+
+
+@pytest.mark.parametrize("func", ["sum", "mean", "min", "max", "std"])
+@pytest.mark.parametrize(
+    "args,kwds",
+    [
+        pytest.param([], {}, id="no_args_or_kwds"),
+        pytest.param([1], {}, id="axis_from_args"),
+        pytest.param([], {"axis": 1}, id="axis_from_kwds"),
+        pytest.param([], {"numeric_only": True}, id="optional_kwds"),
+        pytest.param([1, True], {"numeric_only": True}, id="args_and_kwds"),
+    ],
+)
+@pytest.mark.parametrize("how", ["agg", "apply"])
+def test_apply_with_string_funcs(request, float_frame, func, args, kwds, how):
+    if len(args) > 1 and how == "agg":
+        request.applymarker(
+            pytest.mark.xfail(
+                raises=TypeError,
+                reason="agg/apply signature mismatch - agg passes 2nd "
+                "argument to func",
+            )
+        )
+    result = getattr(float_frame, how)(func, *args, **kwds)
+    expected = getattr(float_frame, func)(*args, **kwds)
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("arg", ["sum", "mean", "min", "max", "std"])
+def test_with_string_args(datetime_series, arg):
+    result = datetime_series.apply(arg)
+    expected = getattr(datetime_series, arg)()
+    assert result == expected
+
+
+@pytest.mark.parametrize("op", ["mean", "median", "std", "var"])
+@pytest.mark.parametrize("how", ["agg", "apply"])
+def test_apply_np_reducer(op, how):
+    # GH 39116
+    float_frame = DataFrame({"a": [1, 2], "b": [3, 4]})
+    result = getattr(float_frame, how)(op)
+    # pandas ddof defaults to 1, numpy to 0
+    kwargs = {"ddof": 1} if op in ("std", "var") else {}
+    expected = Series(
+        getattr(np, op)(float_frame, axis=0, **kwargs), index=float_frame.columns
+    )
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "op", ["abs", "ceil", "cos", "cumsum", "exp", "log", "sqrt", "square"]
+)
+@pytest.mark.parametrize("how", ["transform", "apply"])
+def test_apply_np_transformer(float_frame, op, how):
+    # GH 39116
+
+    # float_frame will _usually_ have negative values, which will
+    #  trigger the warning here, but let's put one in just to be sure
+    float_frame.iloc[0, 0] = -1.0
+    warn = None
+    if op in ["log", "sqrt"]:
+        warn = RuntimeWarning
+
+    with tm.assert_produces_warning(warn, check_stacklevel=False):
+        # float_frame fixture is defined in conftest.py, so we don't check the
+        # stacklevel as otherwise the test would fail.
+        result = getattr(float_frame, how)(op)
+        expected = getattr(np, op)(float_frame)
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "series, func, expected",
+    chain(
+        tm.get_cython_table_params(
+            Series(dtype=np.float64),
+            [
+                ("sum", 0),
+                ("max", np.nan),
+                ("min", np.nan),
+                ("all", True),
+                ("any", False),
+                ("mean", np.nan),
+                ("prod", 1),
+                ("std", np.nan),
+                ("var", np.nan),
+                ("median", np.nan),
+            ],
+        ),
+        tm.get_cython_table_params(
+            Series([np.nan, 1, 2, 3]),
+            [
+                ("sum", 6),
+                ("max", 3),
+                ("min", 1),
+                ("all", True),
+                ("any", True),
+                ("mean", 2),
+                ("prod", 6),
+                ("std", 1),
+                ("var", 1),
+                ("median", 2),
+            ],
+        ),
+        tm.get_cython_table_params(
+            Series("a b c".split()),
+            [
+                ("sum", "abc"),
+                ("max", "c"),
+                ("min", "a"),
+                ("all", True),
+                ("any", True),
+            ],
+        ),
+    ),
+)
+def test_agg_cython_table_series(series, func, expected):
+    # GH21224
+    # test reducing functions in
+    # pandas.core.base.SelectionMixin._cython_table
+    warn = None if isinstance(func, str) else FutureWarning
+    with tm.assert_produces_warning(warn, match="is currently using Series.*"):
+        result = series.agg(func)
+    if is_number(expected):
+        assert np.isclose(result, expected, equal_nan=True)
+    else:
+        assert result == expected
+
+
+@pytest.mark.parametrize(
+    "series, func, expected",
+    chain(
+        tm.get_cython_table_params(
+            Series(dtype=np.float64),
+            [
+                ("cumprod", Series([], dtype=np.float64)),
+                ("cumsum", Series([], dtype=np.float64)),
+            ],
+        ),
+        tm.get_cython_table_params(
+            Series([np.nan, 1, 2, 3]),
+            [
+                ("cumprod", Series([np.nan, 1, 2, 6])),
+                ("cumsum", Series([np.nan, 1, 3, 6])),
+            ],
+        ),
+        tm.get_cython_table_params(
+            Series("a b c".split()), [("cumsum", Series(["a", "ab", "abc"]))]
+        ),
+    ),
+)
+def test_agg_cython_table_transform_series(series, func, expected):
+    # GH21224
+    # test transforming functions in
+    # pandas.core.base.SelectionMixin._cython_table (cumprod, cumsum)
+    warn = None if isinstance(func, str) else FutureWarning
+    with tm.assert_produces_warning(warn, match="is currently using Series.*"):
+        result = series.agg(func)
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "df, func, expected",
+    chain(
+        tm.get_cython_table_params(
+            DataFrame(),
+            [
+                ("sum", Series(dtype="float64")),
+                ("max", Series(dtype="float64")),
+                ("min", Series(dtype="float64")),
+                ("all", Series(dtype=bool)),
+                ("any", Series(dtype=bool)),
+                ("mean", Series(dtype="float64")),
+                ("prod", Series(dtype="float64")),
+                ("std", Series(dtype="float64")),
+                ("var", Series(dtype="float64")),
+                ("median", Series(dtype="float64")),
+            ],
+        ),
+        tm.get_cython_table_params(
+            DataFrame([[np.nan, 1], [1, 2]]),
+            [
+                ("sum", Series([1.0, 3])),
+                ("max", Series([1.0, 2])),
+                ("min", Series([1.0, 1])),
+                ("all", Series([True, True])),
+                ("any", Series([True, True])),
+                ("mean", Series([1, 1.5])),
+                ("prod", Series([1.0, 2])),
+                ("std", Series([np.nan, 0.707107])),
+                ("var", Series([np.nan, 0.5])),
+                ("median", Series([1, 1.5])),
+            ],
+        ),
+    ),
+)
+def test_agg_cython_table_frame(df, func, expected, axis):
+    # GH 21224
+    # test reducing functions in
+    # pandas.core.base.SelectionMixin._cython_table
+    warn = None if isinstance(func, str) else FutureWarning
+    with tm.assert_produces_warning(warn, match="is currently using DataFrame.*"):
+        # GH#53425
+        result = df.agg(func, axis=axis)
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "df, func, expected",
+    chain(
+        tm.get_cython_table_params(
+            DataFrame(), [("cumprod", DataFrame()), ("cumsum", DataFrame())]
+        ),
+        tm.get_cython_table_params(
+            DataFrame([[np.nan, 1], [1, 2]]),
+            [
+                ("cumprod", DataFrame([[np.nan, 1], [1, 2]])),
+                ("cumsum", DataFrame([[np.nan, 1], [1, 3]])),
+            ],
+        ),
+    ),
+)
+def test_agg_cython_table_transform_frame(df, func, expected, axis):
+    # GH 21224
+    # test transforming functions in
+    # pandas.core.base.SelectionMixin._cython_table (cumprod, cumsum)
+    if axis in ("columns", 1):
+        # operating blockwise doesn't let us preserve dtypes
+        expected = expected.astype("float64")
+
+    warn = None if isinstance(func, str) else FutureWarning
+    with tm.assert_produces_warning(warn, match="is currently using DataFrame.*"):
+        # GH#53425
+        result = df.agg(func, axis=axis)
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("op", series_transform_kernels)
+def test_transform_groupby_kernel_series(request, string_series, op):
+    # GH 35964
+    if op == "ngroup":
+        request.applymarker(
+            pytest.mark.xfail(raises=ValueError, reason="ngroup not valid for NDFrame")
+        )
+    args = [0.0] if op == "fillna" else []
+    ones = np.ones(string_series.shape[0])
+
+    warn = FutureWarning if op == "fillna" else None
+    msg = "SeriesGroupBy.fillna is deprecated"
+    with tm.assert_produces_warning(warn, match=msg):
+        expected = string_series.groupby(ones).transform(op, *args)
+    result = string_series.transform(op, 0, *args)
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("op", frame_transform_kernels)
+def test_transform_groupby_kernel_frame(request, axis, float_frame, op):
+    if op == "ngroup":
+        request.applymarker(
+            pytest.mark.xfail(raises=ValueError, reason="ngroup not valid for NDFrame")
+        )
+
+    # GH 35964
+
+    args = [0.0] if op == "fillna" else []
+    if axis in (0, "index"):
+        ones = np.ones(float_frame.shape[0])
+        msg = "The 'axis' keyword in DataFrame.groupby is deprecated"
+    else:
+        ones = np.ones(float_frame.shape[1])
+        msg = "DataFrame.groupby with axis=1 is deprecated"
+
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        gb = float_frame.groupby(ones, axis=axis)
+
+    warn = FutureWarning if op == "fillna" else None
+    op_msg = "DataFrameGroupBy.fillna is deprecated"
+    with tm.assert_produces_warning(warn, match=op_msg):
+        expected = gb.transform(op, *args)
+
+    result = float_frame.transform(op, axis, *args)
+    tm.assert_frame_equal(result, expected)
+
+    # same thing, but ensuring we have multiple blocks
+    assert "E" not in float_frame.columns
+    float_frame["E"] = float_frame["A"].copy()
+    assert len(float_frame._mgr.arrays) > 1
+
+    if axis in (0, "index"):
+        ones = np.ones(float_frame.shape[0])
+    else:
+        ones = np.ones(float_frame.shape[1])
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        gb2 = float_frame.groupby(ones, axis=axis)
+    warn = FutureWarning if op == "fillna" else None
+    op_msg = "DataFrameGroupBy.fillna is deprecated"
+    with tm.assert_produces_warning(warn, match=op_msg):
+        expected2 = gb2.transform(op, *args)
+    result2 = float_frame.transform(op, axis, *args)
+    tm.assert_frame_equal(result2, expected2)
+
+
+@pytest.mark.parametrize("method", ["abs", "shift", "pct_change", "cumsum", "rank"])
+def test_transform_method_name(method):
+    # GH 19760
+    df = DataFrame({"A": [-1, 2]})
+    result = df.transform(method)
+    expected = operator.methodcaller(method)(df)
+    tm.assert_frame_equal(result, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arithmetic/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arithmetic/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arithmetic/common.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arithmetic/common.py
new file mode 100644
index 0000000000000000000000000000000000000000..b608df1554154f4723a0147ea02c04c780839c65
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arithmetic/common.py
@@ -0,0 +1,155 @@
+"""
+Assertion helpers for arithmetic tests.
+"""
+import numpy as np
+import pytest
+
+from pandas import (
+    DataFrame,
+    Index,
+    Series,
+    array,
+)
+import pandas._testing as tm
+from pandas.core.arrays import (
+    BooleanArray,
+    NumpyExtensionArray,
+)
+
+
+def assert_cannot_add(left, right, msg="cannot add"):
+    """
+    Helper to assert that left and right cannot be added.
+
+    Parameters
+    ----------
+    left : object
+    right : object
+    msg : str, default "cannot add"
+    """
+    with pytest.raises(TypeError, match=msg):
+        left + right
+    with pytest.raises(TypeError, match=msg):
+        right + left
+
+
+def assert_invalid_addsub_type(left, right, msg=None):
+    """
+    Helper to assert that left and right can be neither added nor subtracted.
+
+    Parameters
+    ----------
+    left : object
+    right : object
+    msg : str or None, default None
+    """
+    with pytest.raises(TypeError, match=msg):
+        left + right
+    with pytest.raises(TypeError, match=msg):
+        right + left
+    with pytest.raises(TypeError, match=msg):
+        left - right
+    with pytest.raises(TypeError, match=msg):
+        right - left
+
+
+def get_upcast_box(left, right, is_cmp: bool = False):
+    """
+    Get the box to use for 'expected' in an arithmetic or comparison operation.
+
+    Parameters
+    left : Any
+    right : Any
+    is_cmp : bool, default False
+        Whether the operation is a comparison method.
+    """
+
+    if isinstance(left, DataFrame) or isinstance(right, DataFrame):
+        return DataFrame
+    if isinstance(left, Series) or isinstance(right, Series):
+        if is_cmp and isinstance(left, Index):
+            # Index does not defer for comparisons
+            return np.array
+        return Series
+    if isinstance(left, Index) or isinstance(right, Index):
+        if is_cmp:
+            return np.array
+        return Index
+    return tm.to_array
+
+
+def assert_invalid_comparison(left, right, box):
+    """
+    Assert that comparison operations with mismatched types behave correctly.
+
+    Parameters
+    ----------
+    left : np.ndarray, ExtensionArray, Index, or Series
+    right : object
+    box : {pd.DataFrame, pd.Series, pd.Index, pd.array, tm.to_array}
+    """
+    # Not for tznaive-tzaware comparison
+
+    # Note: not quite the same as how we do this for tm.box_expected
+    xbox = box if box not in [Index, array] else np.array
+
+    def xbox2(x):
+        # Eventually we'd like this to be tighter, but for now we'll
+        #  just exclude NumpyExtensionArray[bool]
+        if isinstance(x, NumpyExtensionArray):
+            return x._ndarray
+        if isinstance(x, BooleanArray):
+            # NB: we are assuming no pd.NAs for now
+            return x.astype(bool)
+        return x
+
+    # rev_box: box to use for reversed comparisons
+    rev_box = xbox
+    if isinstance(right, Index) and isinstance(left, Series):
+        rev_box = np.array
+
+    result = xbox2(left == right)
+    expected = xbox(np.zeros(result.shape, dtype=np.bool_))
+
+    tm.assert_equal(result, expected)
+
+    result = xbox2(right == left)
+    tm.assert_equal(result, rev_box(expected))
+
+    result = xbox2(left != right)
+    tm.assert_equal(result, ~expected)
+
+    result = xbox2(right != left)
+    tm.assert_equal(result, rev_box(~expected))
+
+    msg = "|".join(
+        [
+            "Invalid comparison between",
+            "Cannot compare type",
+            "not supported between",
+            "invalid type promotion",
+            (
+                # GH#36706 npdev 1.20.0 2020-09-28
+                r"The DTypes  and "
+                r" do not have a common DType. "
+                "For example they cannot be stored in a single array unless the "
+                "dtype is `object`."
+            ),
+        ]
+    )
+    with pytest.raises(TypeError, match=msg):
+        left < right
+    with pytest.raises(TypeError, match=msg):
+        left <= right
+    with pytest.raises(TypeError, match=msg):
+        left > right
+    with pytest.raises(TypeError, match=msg):
+        left >= right
+    with pytest.raises(TypeError, match=msg):
+        right < left
+    with pytest.raises(TypeError, match=msg):
+        right <= left
+    with pytest.raises(TypeError, match=msg):
+        right > left
+    with pytest.raises(TypeError, match=msg):
+        right >= left
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arithmetic/conftest.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arithmetic/conftest.py
new file mode 100644
index 0000000000000000000000000000000000000000..c7703b34a5e38e7a3887d727b0a8c954016ad836
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arithmetic/conftest.py
@@ -0,0 +1,139 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import Index
+
+
+@pytest.fixture(params=[1, np.array(1, dtype=np.int64)])
+def one(request):
+    """
+    Several variants of integer value 1. The zero-dim integer array
+    behaves like an integer.
+
+    This fixture can be used to check that datetimelike indexes handle
+    addition and subtraction of integers and zero-dimensional arrays
+    of integers.
+
+    Examples
+    --------
+    dti = pd.date_range('2016-01-01', periods=2, freq='h')
+    dti
+    DatetimeIndex(['2016-01-01 00:00:00', '2016-01-01 01:00:00'],
+    dtype='datetime64[ns]', freq='h')
+    dti + one
+    DatetimeIndex(['2016-01-01 01:00:00', '2016-01-01 02:00:00'],
+    dtype='datetime64[ns]', freq='h')
+    """
+    return request.param
+
+
+zeros = [
+    box_cls([0] * 5, dtype=dtype)
+    for box_cls in [Index, np.array, pd.array]
+    for dtype in [np.int64, np.uint64, np.float64]
+]
+zeros.extend([box_cls([-0.0] * 5, dtype=np.float64) for box_cls in [Index, np.array]])
+zeros.extend([np.array(0, dtype=dtype) for dtype in [np.int64, np.uint64, np.float64]])
+zeros.extend([np.array(-0.0, dtype=np.float64)])
+zeros.extend([0, 0.0, -0.0])
+
+
+@pytest.fixture(params=zeros)
+def zero(request):
+    """
+    Several types of scalar zeros and length 5 vectors of zeros.
+
+    This fixture can be used to check that numeric-dtype indexes handle
+    division by any zero numeric-dtype.
+
+    Uses vector of length 5 for broadcasting with `numeric_idx` fixture,
+    which creates numeric-dtype vectors also of length 5.
+
+    Examples
+    --------
+    arr = RangeIndex(5)
+    arr / zeros
+    Index([nan, inf, inf, inf, inf], dtype='float64')
+    """
+    return request.param
+
+
+# ------------------------------------------------------------------
+# Scalar Fixtures
+
+
+@pytest.fixture(
+    params=[
+        pd.Timedelta("10m7s").to_pytimedelta(),
+        pd.Timedelta("10m7s"),
+        pd.Timedelta("10m7s").to_timedelta64(),
+    ],
+    ids=lambda x: type(x).__name__,
+)
+def scalar_td(request):
+    """
+    Several variants of Timedelta scalars representing 10 minutes and 7 seconds.
+    """
+    return request.param
+
+
+@pytest.fixture(
+    params=[
+        pd.offsets.Day(3),
+        pd.offsets.Hour(72),
+        pd.Timedelta(days=3).to_pytimedelta(),
+        pd.Timedelta("72:00:00"),
+        np.timedelta64(3, "D"),
+        np.timedelta64(72, "h"),
+    ],
+    ids=lambda x: type(x).__name__,
+)
+def three_days(request):
+    """
+    Several timedelta-like and DateOffset objects that each represent
+    a 3-day timedelta
+    """
+    return request.param
+
+
+@pytest.fixture(
+    params=[
+        pd.offsets.Hour(2),
+        pd.offsets.Minute(120),
+        pd.Timedelta(hours=2).to_pytimedelta(),
+        pd.Timedelta(seconds=2 * 3600),
+        np.timedelta64(2, "h"),
+        np.timedelta64(120, "m"),
+    ],
+    ids=lambda x: type(x).__name__,
+)
+def two_hours(request):
+    """
+    Several timedelta-like and DateOffset objects that each represent
+    a 2-hour timedelta
+    """
+    return request.param
+
+
+_common_mismatch = [
+    pd.offsets.YearBegin(2),
+    pd.offsets.MonthBegin(1),
+    pd.offsets.Minute(),
+]
+
+
+@pytest.fixture(
+    params=[
+        np.timedelta64(4, "h"),
+        pd.Timedelta(hours=23).to_pytimedelta(),
+        pd.Timedelta("23:00:00"),
+    ]
+    + _common_mismatch
+)
+def not_daily(request):
+    """
+    Several timedelta-like and DateOffset instances that are _not_
+    compatible with Daily frequencies.
+    """
+    return request.param
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arithmetic/test_array_ops.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arithmetic/test_array_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..2c347d965bbf7353a6a4e81ca955341f8041b6de
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arithmetic/test_array_ops.py
@@ -0,0 +1,39 @@
+import operator
+
+import numpy as np
+import pytest
+
+import pandas._testing as tm
+from pandas.core.ops.array_ops import (
+    comparison_op,
+    na_logical_op,
+)
+
+
+def test_na_logical_op_2d():
+    left = np.arange(8).reshape(4, 2)
+    right = left.astype(object)
+    right[0, 0] = np.nan
+
+    # Check that we fall back to the vec_binop branch
+    with pytest.raises(TypeError, match="unsupported operand type"):
+        operator.or_(left, right)
+
+    result = na_logical_op(left, right, operator.or_)
+    expected = right
+    tm.assert_numpy_array_equal(result, expected)
+
+
+def test_object_comparison_2d():
+    left = np.arange(9).reshape(3, 3).astype(object)
+    right = left.T
+
+    result = comparison_op(left, right, operator.eq)
+    expected = np.eye(3).astype(bool)
+    tm.assert_numpy_array_equal(result, expected)
+
+    # Ensure that cython doesn't raise on non-writeable arg, which
+    #  we can get from np.broadcast_to
+    right.flags.writeable = False
+    result = comparison_op(left, right, operator.ne)
+    tm.assert_numpy_array_equal(result, ~expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arithmetic/test_categorical.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arithmetic/test_categorical.py
new file mode 100644
index 0000000000000000000000000000000000000000..d6f3a13ce670596a12ca10b9e8d02d69d63c96fb
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arithmetic/test_categorical.py
@@ -0,0 +1,25 @@
+import numpy as np
+
+from pandas import (
+    Categorical,
+    Series,
+)
+import pandas._testing as tm
+
+
+class TestCategoricalComparisons:
+    def test_categorical_nan_equality(self):
+        cat = Series(Categorical(["a", "b", "c", np.nan]))
+        expected = Series([True, True, True, False])
+        result = cat == cat
+        tm.assert_series_equal(result, expected)
+
+    def test_categorical_tuple_equality(self):
+        # GH 18050
+        ser = Series([(0, 0), (0, 1), (0, 0), (1, 0), (1, 1)])
+        expected = Series([True, False, True, False, False])
+        result = ser == (0, 0)
+        tm.assert_series_equal(result, expected)
+
+        result = ser.astype("category") == (0, 0)
+        tm.assert_series_equal(result, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arithmetic/test_datetime64.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arithmetic/test_datetime64.py
new file mode 100644
index 0000000000000000000000000000000000000000..a468449efd507fae37f3fcb15f64a3e1bf551f93
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arithmetic/test_datetime64.py
@@ -0,0 +1,2469 @@
+# Arithmetic tests for DataFrame/Series/Index/Array classes that should
+# behave identically.
+# Specifically for datetime64 and datetime64tz dtypes
+from datetime import (
+    datetime,
+    time,
+    timedelta,
+)
+from itertools import (
+    product,
+    starmap,
+)
+import operator
+
+import numpy as np
+import pytest
+import pytz
+
+from pandas._libs.tslibs.conversion import localize_pydatetime
+from pandas._libs.tslibs.offsets import shift_months
+from pandas.errors import PerformanceWarning
+
+import pandas as pd
+from pandas import (
+    DateOffset,
+    DatetimeIndex,
+    NaT,
+    Period,
+    Series,
+    Timedelta,
+    TimedeltaIndex,
+    Timestamp,
+    date_range,
+)
+import pandas._testing as tm
+from pandas.core import roperator
+from pandas.tests.arithmetic.common import (
+    assert_cannot_add,
+    assert_invalid_addsub_type,
+    assert_invalid_comparison,
+    get_upcast_box,
+)
+
+# ------------------------------------------------------------------
+# Comparisons
+
+
+class TestDatetime64ArrayLikeComparisons:
+    # Comparison tests for datetime64 vectors fully parametrized over
+    #  DataFrame/Series/DatetimeIndex/DatetimeArray.  Ideally all comparison
+    #  tests will eventually end up here.
+
+    def test_compare_zerodim(self, tz_naive_fixture, box_with_array):
+        # Test comparison with zero-dimensional array is unboxed
+        tz = tz_naive_fixture
+        box = box_with_array
+        dti = date_range("20130101", periods=3, tz=tz)
+
+        other = np.array(dti.to_numpy()[0])
+
+        dtarr = tm.box_expected(dti, box)
+        xbox = get_upcast_box(dtarr, other, True)
+        result = dtarr <= other
+        expected = np.array([True, False, False])
+        expected = tm.box_expected(expected, xbox)
+        tm.assert_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "other",
+        [
+            "foo",
+            -1,
+            99,
+            4.0,
+            object(),
+            timedelta(days=2),
+            # GH#19800, GH#19301 datetime.date comparison raises to
+            #  match DatetimeIndex/Timestamp.  This also matches the behavior
+            #  of stdlib datetime.datetime
+            datetime(2001, 1, 1).date(),
+            # GH#19301 None and NaN are *not* cast to NaT for comparisons
+            None,
+            np.nan,
+        ],
+    )
+    def test_dt64arr_cmp_scalar_invalid(self, other, tz_naive_fixture, box_with_array):
+        # GH#22074, GH#15966
+        tz = tz_naive_fixture
+
+        rng = date_range("1/1/2000", periods=10, tz=tz)
+        dtarr = tm.box_expected(rng, box_with_array)
+        assert_invalid_comparison(dtarr, other, box_with_array)
+
+    @pytest.mark.parametrize(
+        "other",
+        [
+            # GH#4968 invalid date/int comparisons
+            list(range(10)),
+            np.arange(10),
+            np.arange(10).astype(np.float32),
+            np.arange(10).astype(object),
+            pd.timedelta_range("1ns", periods=10).array,
+            np.array(pd.timedelta_range("1ns", periods=10)),
+            list(pd.timedelta_range("1ns", periods=10)),
+            pd.timedelta_range("1 Day", periods=10).astype(object),
+            pd.period_range("1971-01-01", freq="D", periods=10).array,
+            pd.period_range("1971-01-01", freq="D", periods=10).astype(object),
+        ],
+    )
+    def test_dt64arr_cmp_arraylike_invalid(
+        self, other, tz_naive_fixture, box_with_array
+    ):
+        tz = tz_naive_fixture
+
+        dta = date_range("1970-01-01", freq="ns", periods=10, tz=tz)._data
+        obj = tm.box_expected(dta, box_with_array)
+        assert_invalid_comparison(obj, other, box_with_array)
+
+    def test_dt64arr_cmp_mixed_invalid(self, tz_naive_fixture):
+        tz = tz_naive_fixture
+
+        dta = date_range("1970-01-01", freq="h", periods=5, tz=tz)._data
+
+        other = np.array([0, 1, 2, dta[3], Timedelta(days=1)])
+        result = dta == other
+        expected = np.array([False, False, False, True, False])
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = dta != other
+        tm.assert_numpy_array_equal(result, ~expected)
+
+        msg = "Invalid comparison between|Cannot compare type|not supported between"
+        with pytest.raises(TypeError, match=msg):
+            dta < other
+        with pytest.raises(TypeError, match=msg):
+            dta > other
+        with pytest.raises(TypeError, match=msg):
+            dta <= other
+        with pytest.raises(TypeError, match=msg):
+            dta >= other
+
+    def test_dt64arr_nat_comparison(self, tz_naive_fixture, box_with_array):
+        # GH#22242, GH#22163 DataFrame considered NaT == ts incorrectly
+        tz = tz_naive_fixture
+        box = box_with_array
+
+        ts = Timestamp("2021-01-01", tz=tz)
+        ser = Series([ts, NaT])
+
+        obj = tm.box_expected(ser, box)
+        xbox = get_upcast_box(obj, ts, True)
+
+        expected = Series([True, False], dtype=np.bool_)
+        expected = tm.box_expected(expected, xbox)
+
+        result = obj == ts
+        tm.assert_equal(result, expected)
+
+
+class TestDatetime64SeriesComparison:
+    # TODO: moved from tests.series.test_operators; needs cleanup
+
+    @pytest.mark.parametrize(
+        "pair",
+        [
+            (
+                [Timestamp("2011-01-01"), NaT, Timestamp("2011-01-03")],
+                [NaT, NaT, Timestamp("2011-01-03")],
+            ),
+            (
+                [Timedelta("1 days"), NaT, Timedelta("3 days")],
+                [NaT, NaT, Timedelta("3 days")],
+            ),
+            (
+                [Period("2011-01", freq="M"), NaT, Period("2011-03", freq="M")],
+                [NaT, NaT, Period("2011-03", freq="M")],
+            ),
+        ],
+    )
+    @pytest.mark.parametrize("reverse", [True, False])
+    @pytest.mark.parametrize("dtype", [None, object])
+    @pytest.mark.parametrize(
+        "op, expected",
+        [
+            (operator.eq, Series([False, False, True])),
+            (operator.ne, Series([True, True, False])),
+            (operator.lt, Series([False, False, False])),
+            (operator.gt, Series([False, False, False])),
+            (operator.ge, Series([False, False, True])),
+            (operator.le, Series([False, False, True])),
+        ],
+    )
+    def test_nat_comparisons(
+        self,
+        dtype,
+        index_or_series,
+        reverse,
+        pair,
+        op,
+        expected,
+    ):
+        box = index_or_series
+        lhs, rhs = pair
+        if reverse:
+            # add lhs / rhs switched data
+            lhs, rhs = rhs, lhs
+
+        left = Series(lhs, dtype=dtype)
+        right = box(rhs, dtype=dtype)
+
+        result = op(left, right)
+
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "data",
+        [
+            [Timestamp("2011-01-01"), NaT, Timestamp("2011-01-03")],
+            [Timedelta("1 days"), NaT, Timedelta("3 days")],
+            [Period("2011-01", freq="M"), NaT, Period("2011-03", freq="M")],
+        ],
+    )
+    @pytest.mark.parametrize("dtype", [None, object])
+    def test_nat_comparisons_scalar(self, dtype, data, box_with_array):
+        box = box_with_array
+
+        left = Series(data, dtype=dtype)
+        left = tm.box_expected(left, box)
+        xbox = get_upcast_box(left, NaT, True)
+
+        expected = [False, False, False]
+        expected = tm.box_expected(expected, xbox)
+        if box is pd.array and dtype is object:
+            expected = pd.array(expected, dtype="bool")
+
+        tm.assert_equal(left == NaT, expected)
+        tm.assert_equal(NaT == left, expected)
+
+        expected = [True, True, True]
+        expected = tm.box_expected(expected, xbox)
+        if box is pd.array and dtype is object:
+            expected = pd.array(expected, dtype="bool")
+        tm.assert_equal(left != NaT, expected)
+        tm.assert_equal(NaT != left, expected)
+
+        expected = [False, False, False]
+        expected = tm.box_expected(expected, xbox)
+        if box is pd.array and dtype is object:
+            expected = pd.array(expected, dtype="bool")
+        tm.assert_equal(left < NaT, expected)
+        tm.assert_equal(NaT > left, expected)
+        tm.assert_equal(left <= NaT, expected)
+        tm.assert_equal(NaT >= left, expected)
+
+        tm.assert_equal(left > NaT, expected)
+        tm.assert_equal(NaT < left, expected)
+        tm.assert_equal(left >= NaT, expected)
+        tm.assert_equal(NaT <= left, expected)
+
+    @pytest.mark.parametrize("val", [datetime(2000, 1, 4), datetime(2000, 1, 5)])
+    def test_series_comparison_scalars(self, val):
+        series = Series(date_range("1/1/2000", periods=10))
+
+        result = series > val
+        expected = Series([x > val for x in series])
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "left,right", [("lt", "gt"), ("le", "ge"), ("eq", "eq"), ("ne", "ne")]
+    )
+    def test_timestamp_compare_series(self, left, right):
+        # see gh-4982
+        # Make sure we can compare Timestamps on the right AND left hand side.
+        ser = Series(date_range("20010101", periods=10), name="dates")
+        s_nat = ser.copy(deep=True)
+
+        ser[0] = Timestamp("nat")
+        ser[3] = Timestamp("nat")
+
+        left_f = getattr(operator, left)
+        right_f = getattr(operator, right)
+
+        # No NaT
+        expected = left_f(ser, Timestamp("20010109"))
+        result = right_f(Timestamp("20010109"), ser)
+        tm.assert_series_equal(result, expected)
+
+        # NaT
+        expected = left_f(ser, Timestamp("nat"))
+        result = right_f(Timestamp("nat"), ser)
+        tm.assert_series_equal(result, expected)
+
+        # Compare to Timestamp with series containing NaT
+        expected = left_f(s_nat, Timestamp("20010109"))
+        result = right_f(Timestamp("20010109"), s_nat)
+        tm.assert_series_equal(result, expected)
+
+        # Compare to NaT with series containing NaT
+        expected = left_f(s_nat, NaT)
+        result = right_f(NaT, s_nat)
+        tm.assert_series_equal(result, expected)
+
+    def test_dt64arr_timestamp_equality(self, box_with_array):
+        # GH#11034
+        box = box_with_array
+
+        ser = Series([Timestamp("2000-01-29 01:59:00"), Timestamp("2000-01-30"), NaT])
+        ser = tm.box_expected(ser, box)
+        xbox = get_upcast_box(ser, ser, True)
+
+        result = ser != ser
+        expected = tm.box_expected([False, False, True], xbox)
+        tm.assert_equal(result, expected)
+
+        if box is pd.DataFrame:
+            # alignment for frame vs series comparisons deprecated
+            #  in GH#46795 enforced 2.0
+            with pytest.raises(ValueError, match="not aligned"):
+                ser != ser[0]
+
+        else:
+            result = ser != ser[0]
+            expected = tm.box_expected([False, True, True], xbox)
+            tm.assert_equal(result, expected)
+
+        if box is pd.DataFrame:
+            # alignment for frame vs series comparisons deprecated
+            #  in GH#46795 enforced 2.0
+            with pytest.raises(ValueError, match="not aligned"):
+                ser != ser[2]
+        else:
+            result = ser != ser[2]
+            expected = tm.box_expected([True, True, True], xbox)
+            tm.assert_equal(result, expected)
+
+        result = ser == ser
+        expected = tm.box_expected([True, True, False], xbox)
+        tm.assert_equal(result, expected)
+
+        if box is pd.DataFrame:
+            # alignment for frame vs series comparisons deprecated
+            #  in GH#46795 enforced 2.0
+            with pytest.raises(ValueError, match="not aligned"):
+                ser == ser[0]
+        else:
+            result = ser == ser[0]
+            expected = tm.box_expected([True, False, False], xbox)
+            tm.assert_equal(result, expected)
+
+        if box is pd.DataFrame:
+            # alignment for frame vs series comparisons deprecated
+            #  in GH#46795 enforced 2.0
+            with pytest.raises(ValueError, match="not aligned"):
+                ser == ser[2]
+        else:
+            result = ser == ser[2]
+            expected = tm.box_expected([False, False, False], xbox)
+            tm.assert_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "datetimelike",
+        [
+            Timestamp("20130101"),
+            datetime(2013, 1, 1),
+            np.datetime64("2013-01-01T00:00", "ns"),
+        ],
+    )
+    @pytest.mark.parametrize(
+        "op,expected",
+        [
+            (operator.lt, [True, False, False, False]),
+            (operator.le, [True, True, False, False]),
+            (operator.eq, [False, True, False, False]),
+            (operator.gt, [False, False, False, True]),
+        ],
+    )
+    def test_dt64_compare_datetime_scalar(self, datetimelike, op, expected):
+        # GH#17965, test for ability to compare datetime64[ns] columns
+        #  to datetimelike
+        ser = Series(
+            [
+                Timestamp("20120101"),
+                Timestamp("20130101"),
+                np.nan,
+                Timestamp("20130103"),
+            ],
+            name="A",
+        )
+        result = op(ser, datetimelike)
+        expected = Series(expected, name="A")
+        tm.assert_series_equal(result, expected)
+
+
+class TestDatetimeIndexComparisons:
+    # TODO: moved from tests.indexes.test_base; parametrize and de-duplicate
+    def test_comparators(self, comparison_op):
+        index = date_range("2020-01-01", periods=10)
+        element = index[len(index) // 2]
+        element = Timestamp(element).to_datetime64()
+
+        arr = np.array(index)
+        arr_result = comparison_op(arr, element)
+        index_result = comparison_op(index, element)
+
+        assert isinstance(index_result, np.ndarray)
+        tm.assert_numpy_array_equal(arr_result, index_result)
+
+    @pytest.mark.parametrize(
+        "other",
+        [datetime(2016, 1, 1), Timestamp("2016-01-01"), np.datetime64("2016-01-01")],
+    )
+    def test_dti_cmp_datetimelike(self, other, tz_naive_fixture):
+        tz = tz_naive_fixture
+        dti = date_range("2016-01-01", periods=2, tz=tz)
+        if tz is not None:
+            if isinstance(other, np.datetime64):
+                pytest.skip(f"{type(other).__name__} is not tz aware")
+            other = localize_pydatetime(other, dti.tzinfo)
+
+        result = dti == other
+        expected = np.array([True, False])
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = dti > other
+        expected = np.array([False, True])
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = dti >= other
+        expected = np.array([True, True])
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = dti < other
+        expected = np.array([False, False])
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = dti <= other
+        expected = np.array([True, False])
+        tm.assert_numpy_array_equal(result, expected)
+
+    @pytest.mark.parametrize("dtype", [None, object])
+    def test_dti_cmp_nat(self, dtype, box_with_array):
+        left = DatetimeIndex([Timestamp("2011-01-01"), NaT, Timestamp("2011-01-03")])
+        right = DatetimeIndex([NaT, NaT, Timestamp("2011-01-03")])
+
+        left = tm.box_expected(left, box_with_array)
+        right = tm.box_expected(right, box_with_array)
+        xbox = get_upcast_box(left, right, True)
+
+        lhs, rhs = left, right
+        if dtype is object:
+            lhs, rhs = left.astype(object), right.astype(object)
+
+        result = rhs == lhs
+        expected = np.array([False, False, True])
+        expected = tm.box_expected(expected, xbox)
+        tm.assert_equal(result, expected)
+
+        result = lhs != rhs
+        expected = np.array([True, True, False])
+        expected = tm.box_expected(expected, xbox)
+        tm.assert_equal(result, expected)
+
+        expected = np.array([False, False, False])
+        expected = tm.box_expected(expected, xbox)
+        tm.assert_equal(lhs == NaT, expected)
+        tm.assert_equal(NaT == rhs, expected)
+
+        expected = np.array([True, True, True])
+        expected = tm.box_expected(expected, xbox)
+        tm.assert_equal(lhs != NaT, expected)
+        tm.assert_equal(NaT != lhs, expected)
+
+        expected = np.array([False, False, False])
+        expected = tm.box_expected(expected, xbox)
+        tm.assert_equal(lhs < NaT, expected)
+        tm.assert_equal(NaT > lhs, expected)
+
+    def test_dti_cmp_nat_behaves_like_float_cmp_nan(self):
+        fidx1 = pd.Index([1.0, np.nan, 3.0, np.nan, 5.0, 7.0])
+        fidx2 = pd.Index([2.0, 3.0, np.nan, np.nan, 6.0, 7.0])
+
+        didx1 = DatetimeIndex(
+            ["2014-01-01", NaT, "2014-03-01", NaT, "2014-05-01", "2014-07-01"]
+        )
+        didx2 = DatetimeIndex(
+            ["2014-02-01", "2014-03-01", NaT, NaT, "2014-06-01", "2014-07-01"]
+        )
+        darr = np.array(
+            [
+                np.datetime64("2014-02-01 00:00"),
+                np.datetime64("2014-03-01 00:00"),
+                np.datetime64("nat"),
+                np.datetime64("nat"),
+                np.datetime64("2014-06-01 00:00"),
+                np.datetime64("2014-07-01 00:00"),
+            ]
+        )
+
+        cases = [(fidx1, fidx2), (didx1, didx2), (didx1, darr)]
+
+        # Check pd.NaT is handles as the same as np.nan
+        with tm.assert_produces_warning(None):
+            for idx1, idx2 in cases:
+                result = idx1 < idx2
+                expected = np.array([True, False, False, False, True, False])
+                tm.assert_numpy_array_equal(result, expected)
+
+                result = idx2 > idx1
+                expected = np.array([True, False, False, False, True, False])
+                tm.assert_numpy_array_equal(result, expected)
+
+                result = idx1 <= idx2
+                expected = np.array([True, False, False, False, True, True])
+                tm.assert_numpy_array_equal(result, expected)
+
+                result = idx2 >= idx1
+                expected = np.array([True, False, False, False, True, True])
+                tm.assert_numpy_array_equal(result, expected)
+
+                result = idx1 == idx2
+                expected = np.array([False, False, False, False, False, True])
+                tm.assert_numpy_array_equal(result, expected)
+
+                result = idx1 != idx2
+                expected = np.array([True, True, True, True, True, False])
+                tm.assert_numpy_array_equal(result, expected)
+
+        with tm.assert_produces_warning(None):
+            for idx1, val in [(fidx1, np.nan), (didx1, NaT)]:
+                result = idx1 < val
+                expected = np.array([False, False, False, False, False, False])
+                tm.assert_numpy_array_equal(result, expected)
+                result = idx1 > val
+                tm.assert_numpy_array_equal(result, expected)
+
+                result = idx1 <= val
+                tm.assert_numpy_array_equal(result, expected)
+                result = idx1 >= val
+                tm.assert_numpy_array_equal(result, expected)
+
+                result = idx1 == val
+                tm.assert_numpy_array_equal(result, expected)
+
+                result = idx1 != val
+                expected = np.array([True, True, True, True, True, True])
+                tm.assert_numpy_array_equal(result, expected)
+
+        # Check pd.NaT is handles as the same as np.nan
+        with tm.assert_produces_warning(None):
+            for idx1, val in [(fidx1, 3), (didx1, datetime(2014, 3, 1))]:
+                result = idx1 < val
+                expected = np.array([True, False, False, False, False, False])
+                tm.assert_numpy_array_equal(result, expected)
+                result = idx1 > val
+                expected = np.array([False, False, False, False, True, True])
+                tm.assert_numpy_array_equal(result, expected)
+
+                result = idx1 <= val
+                expected = np.array([True, False, True, False, False, False])
+                tm.assert_numpy_array_equal(result, expected)
+                result = idx1 >= val
+                expected = np.array([False, False, True, False, True, True])
+                tm.assert_numpy_array_equal(result, expected)
+
+                result = idx1 == val
+                expected = np.array([False, False, True, False, False, False])
+                tm.assert_numpy_array_equal(result, expected)
+
+                result = idx1 != val
+                expected = np.array([True, True, False, True, True, True])
+                tm.assert_numpy_array_equal(result, expected)
+
+    def test_comparison_tzawareness_compat(self, comparison_op, box_with_array):
+        # GH#18162
+        op = comparison_op
+        box = box_with_array
+
+        dr = date_range("2016-01-01", periods=6)
+        dz = dr.tz_localize("US/Pacific")
+
+        dr = tm.box_expected(dr, box)
+        dz = tm.box_expected(dz, box)
+
+        if box is pd.DataFrame:
+            tolist = lambda x: x.astype(object).values.tolist()[0]
+        else:
+            tolist = list
+
+        if op not in [operator.eq, operator.ne]:
+            msg = (
+                r"Invalid comparison between dtype=datetime64\[ns.*\] "
+                "and (Timestamp|DatetimeArray|list|ndarray)"
+            )
+            with pytest.raises(TypeError, match=msg):
+                op(dr, dz)
+
+            with pytest.raises(TypeError, match=msg):
+                op(dr, tolist(dz))
+            with pytest.raises(TypeError, match=msg):
+                op(dr, np.array(tolist(dz), dtype=object))
+            with pytest.raises(TypeError, match=msg):
+                op(dz, dr)
+
+            with pytest.raises(TypeError, match=msg):
+                op(dz, tolist(dr))
+            with pytest.raises(TypeError, match=msg):
+                op(dz, np.array(tolist(dr), dtype=object))
+
+        # The aware==aware and naive==naive comparisons should *not* raise
+        assert np.all(dr == dr)
+        assert np.all(dr == tolist(dr))
+        assert np.all(tolist(dr) == dr)
+        assert np.all(np.array(tolist(dr), dtype=object) == dr)
+        assert np.all(dr == np.array(tolist(dr), dtype=object))
+
+        assert np.all(dz == dz)
+        assert np.all(dz == tolist(dz))
+        assert np.all(tolist(dz) == dz)
+        assert np.all(np.array(tolist(dz), dtype=object) == dz)
+        assert np.all(dz == np.array(tolist(dz), dtype=object))
+
+    def test_comparison_tzawareness_compat_scalars(self, comparison_op, box_with_array):
+        # GH#18162
+        op = comparison_op
+
+        dr = date_range("2016-01-01", periods=6)
+        dz = dr.tz_localize("US/Pacific")
+
+        dr = tm.box_expected(dr, box_with_array)
+        dz = tm.box_expected(dz, box_with_array)
+
+        # Check comparisons against scalar Timestamps
+        ts = Timestamp("2000-03-14 01:59")
+        ts_tz = Timestamp("2000-03-14 01:59", tz="Europe/Amsterdam")
+
+        assert np.all(dr > ts)
+        msg = r"Invalid comparison between dtype=datetime64\[ns.*\] and Timestamp"
+        if op not in [operator.eq, operator.ne]:
+            with pytest.raises(TypeError, match=msg):
+                op(dr, ts_tz)
+
+        assert np.all(dz > ts_tz)
+        if op not in [operator.eq, operator.ne]:
+            with pytest.raises(TypeError, match=msg):
+                op(dz, ts)
+
+        if op not in [operator.eq, operator.ne]:
+            # GH#12601: Check comparison against Timestamps and DatetimeIndex
+            with pytest.raises(TypeError, match=msg):
+                op(ts, dz)
+
+    @pytest.mark.parametrize(
+        "other",
+        [datetime(2016, 1, 1), Timestamp("2016-01-01"), np.datetime64("2016-01-01")],
+    )
+    # Bug in NumPy? https://github.com/numpy/numpy/issues/13841
+    # Raising in __eq__ will fallback to NumPy, which warns, fails,
+    # then re-raises the original exception. So we just need to ignore.
+    @pytest.mark.filterwarnings("ignore:elementwise comp:DeprecationWarning")
+    def test_scalar_comparison_tzawareness(
+        self, comparison_op, other, tz_aware_fixture, box_with_array
+    ):
+        op = comparison_op
+        tz = tz_aware_fixture
+        dti = date_range("2016-01-01", periods=2, tz=tz)
+
+        dtarr = tm.box_expected(dti, box_with_array)
+        xbox = get_upcast_box(dtarr, other, True)
+        if op in [operator.eq, operator.ne]:
+            exbool = op is operator.ne
+            expected = np.array([exbool, exbool], dtype=bool)
+            expected = tm.box_expected(expected, xbox)
+
+            result = op(dtarr, other)
+            tm.assert_equal(result, expected)
+
+            result = op(other, dtarr)
+            tm.assert_equal(result, expected)
+        else:
+            msg = (
+                r"Invalid comparison between dtype=datetime64\[ns, .*\] "
+                f"and {type(other).__name__}"
+            )
+            with pytest.raises(TypeError, match=msg):
+                op(dtarr, other)
+            with pytest.raises(TypeError, match=msg):
+                op(other, dtarr)
+
+    def test_nat_comparison_tzawareness(self, comparison_op):
+        # GH#19276
+        # tzaware DatetimeIndex should not raise when compared to NaT
+        op = comparison_op
+
+        dti = DatetimeIndex(
+            ["2014-01-01", NaT, "2014-03-01", NaT, "2014-05-01", "2014-07-01"]
+        )
+        expected = np.array([op == operator.ne] * len(dti))
+        result = op(dti, NaT)
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = op(dti.tz_localize("US/Pacific"), NaT)
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_dti_cmp_str(self, tz_naive_fixture):
+        # GH#22074
+        # regardless of tz, we expect these comparisons are valid
+        tz = tz_naive_fixture
+        rng = date_range("1/1/2000", periods=10, tz=tz)
+        other = "1/1/2000"
+
+        result = rng == other
+        expected = np.array([True] + [False] * 9)
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = rng != other
+        expected = np.array([False] + [True] * 9)
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = rng < other
+        expected = np.array([False] * 10)
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = rng <= other
+        expected = np.array([True] + [False] * 9)
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = rng > other
+        expected = np.array([False] + [True] * 9)
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = rng >= other
+        expected = np.array([True] * 10)
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_dti_cmp_list(self):
+        rng = date_range("1/1/2000", periods=10)
+
+        result = rng == list(rng)
+        expected = rng == rng
+        tm.assert_numpy_array_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "other",
+        [
+            pd.timedelta_range("1D", periods=10),
+            pd.timedelta_range("1D", periods=10).to_series(),
+            pd.timedelta_range("1D", periods=10).asi8.view("m8[ns]"),
+        ],
+        ids=lambda x: type(x).__name__,
+    )
+    def test_dti_cmp_tdi_tzawareness(self, other):
+        # GH#22074
+        # reversion test that we _don't_ call _assert_tzawareness_compat
+        # when comparing against TimedeltaIndex
+        dti = date_range("2000-01-01", periods=10, tz="Asia/Tokyo")
+
+        result = dti == other
+        expected = np.array([False] * 10)
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = dti != other
+        expected = np.array([True] * 10)
+        tm.assert_numpy_array_equal(result, expected)
+        msg = "Invalid comparison between"
+        with pytest.raises(TypeError, match=msg):
+            dti < other
+        with pytest.raises(TypeError, match=msg):
+            dti <= other
+        with pytest.raises(TypeError, match=msg):
+            dti > other
+        with pytest.raises(TypeError, match=msg):
+            dti >= other
+
+    def test_dti_cmp_object_dtype(self):
+        # GH#22074
+        dti = date_range("2000-01-01", periods=10, tz="Asia/Tokyo")
+
+        other = dti.astype("O")
+
+        result = dti == other
+        expected = np.array([True] * 10)
+        tm.assert_numpy_array_equal(result, expected)
+
+        other = dti.tz_localize(None)
+        result = dti != other
+        tm.assert_numpy_array_equal(result, expected)
+
+        other = np.array(list(dti[:5]) + [Timedelta(days=1)] * 5)
+        result = dti == other
+        expected = np.array([True] * 5 + [False] * 5)
+        tm.assert_numpy_array_equal(result, expected)
+        msg = ">=' not supported between instances of 'Timestamp' and 'Timedelta'"
+        with pytest.raises(TypeError, match=msg):
+            dti >= other
+
+
+# ------------------------------------------------------------------
+# Arithmetic
+
+
+class TestDatetime64Arithmetic:
+    # This class is intended for "finished" tests that are fully parametrized
+    #  over DataFrame/Series/Index/DatetimeArray
+
+    # -------------------------------------------------------------
+    # Addition/Subtraction of timedelta-like
+
+    @pytest.mark.arm_slow
+    def test_dt64arr_add_timedeltalike_scalar(
+        self, tz_naive_fixture, two_hours, box_with_array
+    ):
+        # GH#22005, GH#22163 check DataFrame doesn't raise TypeError
+        tz = tz_naive_fixture
+
+        rng = date_range("2000-01-01", "2000-02-01", tz=tz)
+        expected = date_range("2000-01-01 02:00", "2000-02-01 02:00", tz=tz)
+
+        rng = tm.box_expected(rng, box_with_array)
+        expected = tm.box_expected(expected, box_with_array)
+
+        result = rng + two_hours
+        tm.assert_equal(result, expected)
+
+        result = two_hours + rng
+        tm.assert_equal(result, expected)
+
+        rng += two_hours
+        tm.assert_equal(rng, expected)
+
+    def test_dt64arr_sub_timedeltalike_scalar(
+        self, tz_naive_fixture, two_hours, box_with_array
+    ):
+        tz = tz_naive_fixture
+
+        rng = date_range("2000-01-01", "2000-02-01", tz=tz)
+        expected = date_range("1999-12-31 22:00", "2000-01-31 22:00", tz=tz)
+
+        rng = tm.box_expected(rng, box_with_array)
+        expected = tm.box_expected(expected, box_with_array)
+
+        result = rng - two_hours
+        tm.assert_equal(result, expected)
+
+        rng -= two_hours
+        tm.assert_equal(rng, expected)
+
+    def test_dt64_array_sub_dt_with_different_timezone(self, box_with_array):
+        t1 = date_range("20130101", periods=3).tz_localize("US/Eastern")
+        t1 = tm.box_expected(t1, box_with_array)
+        t2 = Timestamp("20130101").tz_localize("CET")
+        tnaive = Timestamp(20130101)
+
+        result = t1 - t2
+        expected = TimedeltaIndex(
+            ["0 days 06:00:00", "1 days 06:00:00", "2 days 06:00:00"]
+        )
+        expected = tm.box_expected(expected, box_with_array)
+        tm.assert_equal(result, expected)
+
+        result = t2 - t1
+        expected = TimedeltaIndex(
+            ["-1 days +18:00:00", "-2 days +18:00:00", "-3 days +18:00:00"]
+        )
+        expected = tm.box_expected(expected, box_with_array)
+        tm.assert_equal(result, expected)
+
+        msg = "Cannot subtract tz-naive and tz-aware datetime-like objects"
+        with pytest.raises(TypeError, match=msg):
+            t1 - tnaive
+
+        with pytest.raises(TypeError, match=msg):
+            tnaive - t1
+
+    def test_dt64_array_sub_dt64_array_with_different_timezone(self, box_with_array):
+        t1 = date_range("20130101", periods=3).tz_localize("US/Eastern")
+        t1 = tm.box_expected(t1, box_with_array)
+        t2 = date_range("20130101", periods=3).tz_localize("CET")
+        t2 = tm.box_expected(t2, box_with_array)
+        tnaive = date_range("20130101", periods=3)
+
+        result = t1 - t2
+        expected = TimedeltaIndex(
+            ["0 days 06:00:00", "0 days 06:00:00", "0 days 06:00:00"]
+        )
+        expected = tm.box_expected(expected, box_with_array)
+        tm.assert_equal(result, expected)
+
+        result = t2 - t1
+        expected = TimedeltaIndex(
+            ["-1 days +18:00:00", "-1 days +18:00:00", "-1 days +18:00:00"]
+        )
+        expected = tm.box_expected(expected, box_with_array)
+        tm.assert_equal(result, expected)
+
+        msg = "Cannot subtract tz-naive and tz-aware datetime-like objects"
+        with pytest.raises(TypeError, match=msg):
+            t1 - tnaive
+
+        with pytest.raises(TypeError, match=msg):
+            tnaive - t1
+
+    def test_dt64arr_add_sub_td64_nat(self, box_with_array, tz_naive_fixture):
+        # GH#23320 special handling for timedelta64("NaT")
+        tz = tz_naive_fixture
+
+        dti = date_range("1994-04-01", periods=9, tz=tz, freq="QS")
+        other = np.timedelta64("NaT")
+        expected = DatetimeIndex(["NaT"] * 9, tz=tz).as_unit("ns")
+
+        obj = tm.box_expected(dti, box_with_array)
+        expected = tm.box_expected(expected, box_with_array)
+
+        result = obj + other
+        tm.assert_equal(result, expected)
+        result = other + obj
+        tm.assert_equal(result, expected)
+        result = obj - other
+        tm.assert_equal(result, expected)
+        msg = "cannot subtract"
+        with pytest.raises(TypeError, match=msg):
+            other - obj
+
+    def test_dt64arr_add_sub_td64ndarray(self, tz_naive_fixture, box_with_array):
+        tz = tz_naive_fixture
+        dti = date_range("2016-01-01", periods=3, tz=tz)
+        tdi = TimedeltaIndex(["-1 Day", "-1 Day", "-1 Day"])
+        tdarr = tdi.values
+
+        expected = date_range("2015-12-31", "2016-01-02", periods=3, tz=tz)
+
+        dtarr = tm.box_expected(dti, box_with_array)
+        expected = tm.box_expected(expected, box_with_array)
+
+        result = dtarr + tdarr
+        tm.assert_equal(result, expected)
+        result = tdarr + dtarr
+        tm.assert_equal(result, expected)
+
+        expected = date_range("2016-01-02", "2016-01-04", periods=3, tz=tz)
+        expected = tm.box_expected(expected, box_with_array)
+
+        result = dtarr - tdarr
+        tm.assert_equal(result, expected)
+        msg = "cannot subtract|(bad|unsupported) operand type for unary"
+        with pytest.raises(TypeError, match=msg):
+            tdarr - dtarr
+
+    # -----------------------------------------------------------------
+    # Subtraction of datetime-like scalars
+
+    @pytest.mark.parametrize(
+        "ts",
+        [
+            Timestamp("2013-01-01"),
+            Timestamp("2013-01-01").to_pydatetime(),
+            Timestamp("2013-01-01").to_datetime64(),
+            # GH#7996, GH#22163 ensure non-nano datetime64 is converted to nano
+            #  for DataFrame operation
+            np.datetime64("2013-01-01", "D"),
+        ],
+    )
+    def test_dt64arr_sub_dtscalar(self, box_with_array, ts):
+        # GH#8554, GH#22163 DataFrame op should _not_ return dt64 dtype
+        idx = date_range("2013-01-01", periods=3)._with_freq(None)
+        idx = tm.box_expected(idx, box_with_array)
+
+        expected = TimedeltaIndex(["0 Days", "1 Day", "2 Days"])
+        expected = tm.box_expected(expected, box_with_array)
+
+        result = idx - ts
+        tm.assert_equal(result, expected)
+
+        result = ts - idx
+        tm.assert_equal(result, -expected)
+        tm.assert_equal(result, -expected)
+
+    def test_dt64arr_sub_timestamp_tzaware(self, box_with_array):
+        ser = date_range("2014-03-17", periods=2, freq="D", tz="US/Eastern")
+        ser = ser._with_freq(None)
+        ts = ser[0]
+
+        ser = tm.box_expected(ser, box_with_array)
+
+        delta_series = Series([np.timedelta64(0, "D"), np.timedelta64(1, "D")])
+        expected = tm.box_expected(delta_series, box_with_array)
+
+        tm.assert_equal(ser - ts, expected)
+        tm.assert_equal(ts - ser, -expected)
+
+    def test_dt64arr_sub_NaT(self, box_with_array, unit):
+        # GH#18808
+        dti = DatetimeIndex([NaT, Timestamp("19900315")]).as_unit(unit)
+        ser = tm.box_expected(dti, box_with_array)
+
+        result = ser - NaT
+        expected = Series([NaT, NaT], dtype=f"timedelta64[{unit}]")
+        expected = tm.box_expected(expected, box_with_array)
+        tm.assert_equal(result, expected)
+
+        dti_tz = dti.tz_localize("Asia/Tokyo")
+        ser_tz = tm.box_expected(dti_tz, box_with_array)
+
+        result = ser_tz - NaT
+        expected = Series([NaT, NaT], dtype=f"timedelta64[{unit}]")
+        expected = tm.box_expected(expected, box_with_array)
+        tm.assert_equal(result, expected)
+
+    # -------------------------------------------------------------
+    # Subtraction of datetime-like array-like
+
+    def test_dt64arr_sub_dt64object_array(self, box_with_array, tz_naive_fixture):
+        dti = date_range("2016-01-01", periods=3, tz=tz_naive_fixture)
+        expected = dti - dti
+
+        obj = tm.box_expected(dti, box_with_array)
+        expected = tm.box_expected(expected, box_with_array).astype(object)
+
+        with tm.assert_produces_warning(PerformanceWarning):
+            result = obj - obj.astype(object)
+        tm.assert_equal(result, expected)
+
+    def test_dt64arr_naive_sub_dt64ndarray(self, box_with_array):
+        dti = date_range("2016-01-01", periods=3, tz=None)
+        dt64vals = dti.values
+
+        dtarr = tm.box_expected(dti, box_with_array)
+
+        expected = dtarr - dtarr
+        result = dtarr - dt64vals
+        tm.assert_equal(result, expected)
+        result = dt64vals - dtarr
+        tm.assert_equal(result, expected)
+
+    def test_dt64arr_aware_sub_dt64ndarray_raises(
+        self, tz_aware_fixture, box_with_array
+    ):
+        tz = tz_aware_fixture
+        dti = date_range("2016-01-01", periods=3, tz=tz)
+        dt64vals = dti.values
+
+        dtarr = tm.box_expected(dti, box_with_array)
+        msg = "Cannot subtract tz-naive and tz-aware datetime"
+        with pytest.raises(TypeError, match=msg):
+            dtarr - dt64vals
+        with pytest.raises(TypeError, match=msg):
+            dt64vals - dtarr
+
+    # -------------------------------------------------------------
+    # Addition of datetime-like others (invalid)
+
+    def test_dt64arr_add_dtlike_raises(self, tz_naive_fixture, box_with_array):
+        # GH#22163 ensure DataFrame doesn't cast Timestamp to i8
+        # GH#9631
+        tz = tz_naive_fixture
+
+        dti = date_range("2016-01-01", periods=3, tz=tz)
+        if tz is None:
+            dti2 = dti.tz_localize("US/Eastern")
+        else:
+            dti2 = dti.tz_localize(None)
+        dtarr = tm.box_expected(dti, box_with_array)
+
+        assert_cannot_add(dtarr, dti.values)
+        assert_cannot_add(dtarr, dti)
+        assert_cannot_add(dtarr, dtarr)
+        assert_cannot_add(dtarr, dti[0])
+        assert_cannot_add(dtarr, dti[0].to_pydatetime())
+        assert_cannot_add(dtarr, dti[0].to_datetime64())
+        assert_cannot_add(dtarr, dti2[0])
+        assert_cannot_add(dtarr, dti2[0].to_pydatetime())
+        assert_cannot_add(dtarr, np.datetime64("2011-01-01", "D"))
+
+    # -------------------------------------------------------------
+    # Other Invalid Addition/Subtraction
+
+    # Note: freq here includes both Tick and non-Tick offsets; this is
+    #  relevant because historically integer-addition was allowed if we had
+    #  a freq.
+    @pytest.mark.parametrize("freq", ["h", "D", "W", "2ME", "MS", "QE", "B", None])
+    @pytest.mark.parametrize("dtype", [None, "uint8"])
+    def test_dt64arr_addsub_intlike(
+        self, request, dtype, index_or_series_or_array, freq, tz_naive_fixture
+    ):
+        # GH#19959, GH#19123, GH#19012
+        # GH#55860 use index_or_series_or_array instead of box_with_array
+        #  bc DataFrame alignment makes it inapplicable
+        tz = tz_naive_fixture
+
+        if freq is None:
+            dti = DatetimeIndex(["NaT", "2017-04-05 06:07:08"], tz=tz)
+        else:
+            dti = date_range("2016-01-01", periods=2, freq=freq, tz=tz)
+
+        obj = index_or_series_or_array(dti)
+        other = np.array([4, -1])
+        if dtype is not None:
+            other = other.astype(dtype)
+
+        msg = "|".join(
+            [
+                "Addition/subtraction of integers",
+                "cannot subtract DatetimeArray from",
+                # IntegerArray
+                "can only perform ops with numeric values",
+                "unsupported operand type.*Categorical",
+                r"unsupported operand type\(s\) for -: 'int' and 'Timestamp'",
+            ]
+        )
+        assert_invalid_addsub_type(obj, 1, msg)
+        assert_invalid_addsub_type(obj, np.int64(2), msg)
+        assert_invalid_addsub_type(obj, np.array(3, dtype=np.int64), msg)
+        assert_invalid_addsub_type(obj, other, msg)
+        assert_invalid_addsub_type(obj, np.array(other), msg)
+        assert_invalid_addsub_type(obj, pd.array(other), msg)
+        assert_invalid_addsub_type(obj, pd.Categorical(other), msg)
+        assert_invalid_addsub_type(obj, pd.Index(other), msg)
+        assert_invalid_addsub_type(obj, Series(other), msg)
+
+    @pytest.mark.parametrize(
+        "other",
+        [
+            3.14,
+            np.array([2.0, 3.0]),
+            # GH#13078 datetime +/- Period is invalid
+            Period("2011-01-01", freq="D"),
+            # https://github.com/pandas-dev/pandas/issues/10329
+            time(1, 2, 3),
+        ],
+    )
+    @pytest.mark.parametrize("dti_freq", [None, "D"])
+    def test_dt64arr_add_sub_invalid(self, dti_freq, other, box_with_array):
+        dti = DatetimeIndex(["2011-01-01", "2011-01-02"], freq=dti_freq)
+        dtarr = tm.box_expected(dti, box_with_array)
+        msg = "|".join(
+            [
+                "unsupported operand type",
+                "cannot (add|subtract)",
+                "cannot use operands with types",
+                "ufunc '?(add|subtract)'? cannot use operands with types",
+                "Concatenation operation is not implemented for NumPy arrays",
+            ]
+        )
+        assert_invalid_addsub_type(dtarr, other, msg)
+
+    @pytest.mark.parametrize("pi_freq", ["D", "W", "Q", "h"])
+    @pytest.mark.parametrize("dti_freq", [None, "D"])
+    def test_dt64arr_add_sub_parr(
+        self, dti_freq, pi_freq, box_with_array, box_with_array2
+    ):
+        # GH#20049 subtracting PeriodIndex should raise TypeError
+        dti = DatetimeIndex(["2011-01-01", "2011-01-02"], freq=dti_freq)
+        pi = dti.to_period(pi_freq)
+
+        dtarr = tm.box_expected(dti, box_with_array)
+        parr = tm.box_expected(pi, box_with_array2)
+        msg = "|".join(
+            [
+                "cannot (add|subtract)",
+                "unsupported operand",
+                "descriptor.*requires",
+                "ufunc.*cannot use operands",
+            ]
+        )
+        assert_invalid_addsub_type(dtarr, parr, msg)
+
+    @pytest.mark.filterwarnings("ignore::pandas.errors.PerformanceWarning")
+    def test_dt64arr_addsub_time_objects_raises(self, box_with_array, tz_naive_fixture):
+        # https://github.com/pandas-dev/pandas/issues/10329
+
+        tz = tz_naive_fixture
+
+        obj1 = date_range("2012-01-01", periods=3, tz=tz)
+        obj2 = [time(i, i, i) for i in range(3)]
+
+        obj1 = tm.box_expected(obj1, box_with_array)
+        obj2 = tm.box_expected(obj2, box_with_array)
+
+        msg = "|".join(
+            [
+                "unsupported operand",
+                "cannot subtract DatetimeArray from ndarray",
+            ]
+        )
+        # pandas.errors.PerformanceWarning: Non-vectorized DateOffset being
+        # applied to Series or DatetimeIndex
+        # we aren't testing that here, so ignore.
+        assert_invalid_addsub_type(obj1, obj2, msg=msg)
+
+    # -------------------------------------------------------------
+    # Other invalid operations
+
+    @pytest.mark.parametrize(
+        "dt64_series",
+        [
+            Series([Timestamp("19900315"), Timestamp("19900315")]),
+            Series([NaT, Timestamp("19900315")]),
+            Series([NaT, NaT], dtype="datetime64[ns]"),
+        ],
+    )
+    @pytest.mark.parametrize("one", [1, 1.0, np.array(1)])
+    def test_dt64_mul_div_numeric_invalid(self, one, dt64_series, box_with_array):
+        obj = tm.box_expected(dt64_series, box_with_array)
+
+        msg = "cannot perform .* with this index type"
+
+        # multiplication
+        with pytest.raises(TypeError, match=msg):
+            obj * one
+        with pytest.raises(TypeError, match=msg):
+            one * obj
+
+        # division
+        with pytest.raises(TypeError, match=msg):
+            obj / one
+        with pytest.raises(TypeError, match=msg):
+            one / obj
+
+
+class TestDatetime64DateOffsetArithmetic:
+    # -------------------------------------------------------------
+    # Tick DateOffsets
+
+    # TODO: parametrize over timezone?
+    @pytest.mark.parametrize("unit", ["s", "ms", "us", "ns"])
+    def test_dt64arr_series_add_tick_DateOffset(self, box_with_array, unit):
+        # GH#4532
+        # operate with pd.offsets
+        ser = Series(
+            [Timestamp("20130101 9:01"), Timestamp("20130101 9:02")]
+        ).dt.as_unit(unit)
+        expected = Series(
+            [Timestamp("20130101 9:01:05"), Timestamp("20130101 9:02:05")]
+        ).dt.as_unit(unit)
+
+        ser = tm.box_expected(ser, box_with_array)
+        expected = tm.box_expected(expected, box_with_array)
+
+        result = ser + pd.offsets.Second(5)
+        tm.assert_equal(result, expected)
+
+        result2 = pd.offsets.Second(5) + ser
+        tm.assert_equal(result2, expected)
+
+    def test_dt64arr_series_sub_tick_DateOffset(self, box_with_array):
+        # GH#4532
+        # operate with pd.offsets
+        ser = Series([Timestamp("20130101 9:01"), Timestamp("20130101 9:02")])
+        expected = Series(
+            [Timestamp("20130101 9:00:55"), Timestamp("20130101 9:01:55")]
+        )
+
+        ser = tm.box_expected(ser, box_with_array)
+        expected = tm.box_expected(expected, box_with_array)
+
+        result = ser - pd.offsets.Second(5)
+        tm.assert_equal(result, expected)
+
+        result2 = -pd.offsets.Second(5) + ser
+        tm.assert_equal(result2, expected)
+        msg = "(bad|unsupported) operand type for unary"
+        with pytest.raises(TypeError, match=msg):
+            pd.offsets.Second(5) - ser
+
+    @pytest.mark.parametrize(
+        "cls_name", ["Day", "Hour", "Minute", "Second", "Milli", "Micro", "Nano"]
+    )
+    def test_dt64arr_add_sub_tick_DateOffset_smoke(self, cls_name, box_with_array):
+        # GH#4532
+        # smoke tests for valid DateOffsets
+        ser = Series([Timestamp("20130101 9:01"), Timestamp("20130101 9:02")])
+        ser = tm.box_expected(ser, box_with_array)
+
+        offset_cls = getattr(pd.offsets, cls_name)
+        ser + offset_cls(5)
+        offset_cls(5) + ser
+        ser - offset_cls(5)
+
+    def test_dti_add_tick_tzaware(self, tz_aware_fixture, box_with_array):
+        # GH#21610, GH#22163 ensure DataFrame doesn't return object-dtype
+        tz = tz_aware_fixture
+        if tz == "US/Pacific":
+            dates = date_range("2012-11-01", periods=3, tz=tz)
+            offset = dates + pd.offsets.Hour(5)
+            assert dates[0] + pd.offsets.Hour(5) == offset[0]
+
+        dates = date_range("2010-11-01 00:00", periods=3, tz=tz, freq="h")
+        expected = DatetimeIndex(
+            ["2010-11-01 05:00", "2010-11-01 06:00", "2010-11-01 07:00"],
+            freq="h",
+            tz=tz,
+        ).as_unit("ns")
+
+        dates = tm.box_expected(dates, box_with_array)
+        expected = tm.box_expected(expected, box_with_array)
+
+        for scalar in [pd.offsets.Hour(5), np.timedelta64(5, "h"), timedelta(hours=5)]:
+            offset = dates + scalar
+            tm.assert_equal(offset, expected)
+            offset = scalar + dates
+            tm.assert_equal(offset, expected)
+
+            roundtrip = offset - scalar
+            tm.assert_equal(roundtrip, dates)
+
+            msg = "|".join(
+                ["bad operand type for unary -", "cannot subtract DatetimeArray"]
+            )
+            with pytest.raises(TypeError, match=msg):
+                scalar - dates
+
+    # -------------------------------------------------------------
+    # RelativeDelta DateOffsets
+
+    @pytest.mark.parametrize("unit", ["s", "ms", "us", "ns"])
+    def test_dt64arr_add_sub_relativedelta_offsets(self, box_with_array, unit):
+        # GH#10699
+        vec = DatetimeIndex(
+            [
+                Timestamp("2000-01-05 00:15:00"),
+                Timestamp("2000-01-31 00:23:00"),
+                Timestamp("2000-01-01"),
+                Timestamp("2000-03-31"),
+                Timestamp("2000-02-29"),
+                Timestamp("2000-12-31"),
+                Timestamp("2000-05-15"),
+                Timestamp("2001-06-15"),
+            ]
+        ).as_unit(unit)
+        vec = tm.box_expected(vec, box_with_array)
+        vec_items = vec.iloc[0] if box_with_array is pd.DataFrame else vec
+
+        # DateOffset relativedelta fastpath
+        relative_kwargs = [
+            ("years", 2),
+            ("months", 5),
+            ("days", 3),
+            ("hours", 5),
+            ("minutes", 10),
+            ("seconds", 2),
+            ("microseconds", 5),
+        ]
+        for i, (offset_unit, value) in enumerate(relative_kwargs):
+            off = DateOffset(**{offset_unit: value})
+
+            exp_unit = unit
+            if offset_unit == "microseconds" and unit != "ns":
+                exp_unit = "us"
+
+            # TODO(GH#55564): as_unit will be unnecessary
+            expected = DatetimeIndex([x + off for x in vec_items]).as_unit(exp_unit)
+            expected = tm.box_expected(expected, box_with_array)
+            tm.assert_equal(expected, vec + off)
+
+            expected = DatetimeIndex([x - off for x in vec_items]).as_unit(exp_unit)
+            expected = tm.box_expected(expected, box_with_array)
+            tm.assert_equal(expected, vec - off)
+
+            off = DateOffset(**dict(relative_kwargs[: i + 1]))
+
+            expected = DatetimeIndex([x + off for x in vec_items]).as_unit(exp_unit)
+            expected = tm.box_expected(expected, box_with_array)
+            tm.assert_equal(expected, vec + off)
+
+            expected = DatetimeIndex([x - off for x in vec_items]).as_unit(exp_unit)
+            expected = tm.box_expected(expected, box_with_array)
+            tm.assert_equal(expected, vec - off)
+            msg = "(bad|unsupported) operand type for unary"
+            with pytest.raises(TypeError, match=msg):
+                off - vec
+
+    # -------------------------------------------------------------
+    # Non-Tick, Non-RelativeDelta DateOffsets
+
+    # TODO: redundant with test_dt64arr_add_sub_DateOffset?  that includes
+    #  tz-aware cases which this does not
+    @pytest.mark.filterwarnings("ignore::pandas.errors.PerformanceWarning")
+    @pytest.mark.parametrize(
+        "cls_and_kwargs",
+        [
+            "YearBegin",
+            ("YearBegin", {"month": 5}),
+            "YearEnd",
+            ("YearEnd", {"month": 5}),
+            "MonthBegin",
+            "MonthEnd",
+            "SemiMonthEnd",
+            "SemiMonthBegin",
+            "Week",
+            ("Week", {"weekday": 3}),
+            "Week",
+            ("Week", {"weekday": 6}),
+            "BusinessDay",
+            "BDay",
+            "QuarterEnd",
+            "QuarterBegin",
+            "CustomBusinessDay",
+            "CDay",
+            "CBMonthEnd",
+            "CBMonthBegin",
+            "BMonthBegin",
+            "BMonthEnd",
+            "BusinessHour",
+            "BYearBegin",
+            "BYearEnd",
+            "BQuarterBegin",
+            ("LastWeekOfMonth", {"weekday": 2}),
+            (
+                "FY5253Quarter",
+                {
+                    "qtr_with_extra_week": 1,
+                    "startingMonth": 1,
+                    "weekday": 2,
+                    "variation": "nearest",
+                },
+            ),
+            ("FY5253", {"weekday": 0, "startingMonth": 2, "variation": "nearest"}),
+            ("WeekOfMonth", {"weekday": 2, "week": 2}),
+            "Easter",
+            ("DateOffset", {"day": 4}),
+            ("DateOffset", {"month": 5}),
+        ],
+    )
+    @pytest.mark.parametrize("normalize", [True, False])
+    @pytest.mark.parametrize("n", [0, 5])
+    @pytest.mark.parametrize("unit", ["s", "ms", "us", "ns"])
+    @pytest.mark.parametrize("tz", [None, "US/Central"])
+    def test_dt64arr_add_sub_DateOffsets(
+        self, box_with_array, n, normalize, cls_and_kwargs, unit, tz
+    ):
+        # GH#10699
+        # assert vectorized operation matches pointwise operations
+
+        if isinstance(cls_and_kwargs, tuple):
+            # If cls_name param is a tuple, then 2nd entry is kwargs for
+            # the offset constructor
+            cls_name, kwargs = cls_and_kwargs
+        else:
+            cls_name = cls_and_kwargs
+            kwargs = {}
+
+        if n == 0 and cls_name in [
+            "WeekOfMonth",
+            "LastWeekOfMonth",
+            "FY5253Quarter",
+            "FY5253",
+        ]:
+            # passing n = 0 is invalid for these offset classes
+            return
+
+        vec = (
+            DatetimeIndex(
+                [
+                    Timestamp("2000-01-05 00:15:00"),
+                    Timestamp("2000-01-31 00:23:00"),
+                    Timestamp("2000-01-01"),
+                    Timestamp("2000-03-31"),
+                    Timestamp("2000-02-29"),
+                    Timestamp("2000-12-31"),
+                    Timestamp("2000-05-15"),
+                    Timestamp("2001-06-15"),
+                ]
+            )
+            .as_unit(unit)
+            .tz_localize(tz)
+        )
+        vec = tm.box_expected(vec, box_with_array)
+        vec_items = vec.iloc[0] if box_with_array is pd.DataFrame else vec
+
+        offset_cls = getattr(pd.offsets, cls_name)
+        offset = offset_cls(n, normalize=normalize, **kwargs)
+
+        # TODO(GH#55564): as_unit will be unnecessary
+        expected = DatetimeIndex([x + offset for x in vec_items]).as_unit(unit)
+        expected = tm.box_expected(expected, box_with_array)
+        tm.assert_equal(expected, vec + offset)
+        tm.assert_equal(expected, offset + vec)
+
+        expected = DatetimeIndex([x - offset for x in vec_items]).as_unit(unit)
+        expected = tm.box_expected(expected, box_with_array)
+        tm.assert_equal(expected, vec - offset)
+
+        expected = DatetimeIndex([offset + x for x in vec_items]).as_unit(unit)
+        expected = tm.box_expected(expected, box_with_array)
+        tm.assert_equal(expected, offset + vec)
+        msg = "(bad|unsupported) operand type for unary"
+        with pytest.raises(TypeError, match=msg):
+            offset - vec
+
+    @pytest.mark.parametrize(
+        "other",
+        [
+            np.array([pd.offsets.MonthEnd(), pd.offsets.Day(n=2)]),
+            np.array([pd.offsets.DateOffset(years=1), pd.offsets.MonthEnd()]),
+            np.array(  # matching offsets
+                [pd.offsets.DateOffset(years=1), pd.offsets.DateOffset(years=1)]
+            ),
+        ],
+    )
+    @pytest.mark.parametrize("op", [operator.add, roperator.radd, operator.sub])
+    def test_dt64arr_add_sub_offset_array(
+        self, tz_naive_fixture, box_with_array, op, other
+    ):
+        # GH#18849
+        # GH#10699 array of offsets
+
+        tz = tz_naive_fixture
+        dti = date_range("2017-01-01", periods=2, tz=tz)
+        dtarr = tm.box_expected(dti, box_with_array)
+
+        expected = DatetimeIndex([op(dti[n], other[n]) for n in range(len(dti))])
+        expected = tm.box_expected(expected, box_with_array).astype(object)
+
+        with tm.assert_produces_warning(PerformanceWarning):
+            res = op(dtarr, other)
+        tm.assert_equal(res, expected)
+
+        # Same thing but boxing other
+        other = tm.box_expected(other, box_with_array)
+        if box_with_array is pd.array and op is roperator.radd:
+            # We expect a NumpyExtensionArray, not ndarray[object] here
+            expected = pd.array(expected, dtype=object)
+        with tm.assert_produces_warning(PerformanceWarning):
+            res = op(dtarr, other)
+        tm.assert_equal(res, expected)
+
+    @pytest.mark.parametrize(
+        "op, offset, exp, exp_freq",
+        [
+            (
+                "__add__",
+                DateOffset(months=3, days=10),
+                [
+                    Timestamp("2014-04-11"),
+                    Timestamp("2015-04-11"),
+                    Timestamp("2016-04-11"),
+                    Timestamp("2017-04-11"),
+                ],
+                None,
+            ),
+            (
+                "__add__",
+                DateOffset(months=3),
+                [
+                    Timestamp("2014-04-01"),
+                    Timestamp("2015-04-01"),
+                    Timestamp("2016-04-01"),
+                    Timestamp("2017-04-01"),
+                ],
+                "YS-APR",
+            ),
+            (
+                "__sub__",
+                DateOffset(months=3, days=10),
+                [
+                    Timestamp("2013-09-21"),
+                    Timestamp("2014-09-21"),
+                    Timestamp("2015-09-21"),
+                    Timestamp("2016-09-21"),
+                ],
+                None,
+            ),
+            (
+                "__sub__",
+                DateOffset(months=3),
+                [
+                    Timestamp("2013-10-01"),
+                    Timestamp("2014-10-01"),
+                    Timestamp("2015-10-01"),
+                    Timestamp("2016-10-01"),
+                ],
+                "YS-OCT",
+            ),
+        ],
+    )
+    def test_dti_add_sub_nonzero_mth_offset(
+        self, op, offset, exp, exp_freq, tz_aware_fixture, box_with_array
+    ):
+        # GH 26258
+        tz = tz_aware_fixture
+        date = date_range(start="01 Jan 2014", end="01 Jan 2017", freq="YS", tz=tz)
+        date = tm.box_expected(date, box_with_array, False)
+        mth = getattr(date, op)
+        result = mth(offset)
+
+        expected = DatetimeIndex(exp, tz=tz).as_unit("ns")
+        expected = tm.box_expected(expected, box_with_array, False)
+        tm.assert_equal(result, expected)
+
+    def test_dt64arr_series_add_DateOffset_with_milli(self):
+        # GH 57529
+        dti = DatetimeIndex(
+            [
+                "2000-01-01 00:00:00.012345678",
+                "2000-01-31 00:00:00.012345678",
+                "2000-02-29 00:00:00.012345678",
+            ],
+            dtype="datetime64[ns]",
+        )
+        result = dti + DateOffset(milliseconds=4)
+        expected = DatetimeIndex(
+            [
+                "2000-01-01 00:00:00.016345678",
+                "2000-01-31 00:00:00.016345678",
+                "2000-02-29 00:00:00.016345678",
+            ],
+            dtype="datetime64[ns]",
+        )
+        tm.assert_index_equal(result, expected)
+
+        result = dti + DateOffset(days=1, milliseconds=4)
+        expected = DatetimeIndex(
+            [
+                "2000-01-02 00:00:00.016345678",
+                "2000-02-01 00:00:00.016345678",
+                "2000-03-01 00:00:00.016345678",
+            ],
+            dtype="datetime64[ns]",
+        )
+        tm.assert_index_equal(result, expected)
+
+
+class TestDatetime64OverflowHandling:
+    # TODO: box + de-duplicate
+
+    def test_dt64_overflow_masking(self, box_with_array):
+        # GH#25317
+        left = Series([Timestamp("1969-12-31")], dtype="M8[ns]")
+        right = Series([NaT])
+
+        left = tm.box_expected(left, box_with_array)
+        right = tm.box_expected(right, box_with_array)
+
+        expected = TimedeltaIndex([NaT], dtype="m8[ns]")
+        expected = tm.box_expected(expected, box_with_array)
+
+        result = left - right
+        tm.assert_equal(result, expected)
+
+    def test_dt64_series_arith_overflow(self):
+        # GH#12534, fixed by GH#19024
+        dt = Timestamp("1700-01-31")
+        td = Timedelta("20000 Days")
+        dti = date_range("1949-09-30", freq="100YE", periods=4)
+        ser = Series(dti)
+        msg = "Overflow in int64 addition"
+        with pytest.raises(OverflowError, match=msg):
+            ser - dt
+        with pytest.raises(OverflowError, match=msg):
+            dt - ser
+        with pytest.raises(OverflowError, match=msg):
+            ser + td
+        with pytest.raises(OverflowError, match=msg):
+            td + ser
+
+        ser.iloc[-1] = NaT
+        expected = Series(
+            ["2004-10-03", "2104-10-04", "2204-10-04", "NaT"], dtype="datetime64[ns]"
+        )
+        res = ser + td
+        tm.assert_series_equal(res, expected)
+        res = td + ser
+        tm.assert_series_equal(res, expected)
+
+        ser.iloc[1:] = NaT
+        expected = Series(["91279 Days", "NaT", "NaT", "NaT"], dtype="timedelta64[ns]")
+        res = ser - dt
+        tm.assert_series_equal(res, expected)
+        res = dt - ser
+        tm.assert_series_equal(res, -expected)
+
+    def test_datetimeindex_sub_timestamp_overflow(self):
+        dtimax = pd.to_datetime(["2021-12-28 17:19", Timestamp.max]).as_unit("ns")
+        dtimin = pd.to_datetime(["2021-12-28 17:19", Timestamp.min]).as_unit("ns")
+
+        tsneg = Timestamp("1950-01-01").as_unit("ns")
+        ts_neg_variants = [
+            tsneg,
+            tsneg.to_pydatetime(),
+            tsneg.to_datetime64().astype("datetime64[ns]"),
+            tsneg.to_datetime64().astype("datetime64[D]"),
+        ]
+
+        tspos = Timestamp("1980-01-01").as_unit("ns")
+        ts_pos_variants = [
+            tspos,
+            tspos.to_pydatetime(),
+            tspos.to_datetime64().astype("datetime64[ns]"),
+            tspos.to_datetime64().astype("datetime64[D]"),
+        ]
+        msg = "Overflow in int64 addition"
+        for variant in ts_neg_variants:
+            with pytest.raises(OverflowError, match=msg):
+                dtimax - variant
+
+        expected = Timestamp.max._value - tspos._value
+        for variant in ts_pos_variants:
+            res = dtimax - variant
+            assert res[1]._value == expected
+
+        expected = Timestamp.min._value - tsneg._value
+        for variant in ts_neg_variants:
+            res = dtimin - variant
+            assert res[1]._value == expected
+
+        for variant in ts_pos_variants:
+            with pytest.raises(OverflowError, match=msg):
+                dtimin - variant
+
+    def test_datetimeindex_sub_datetimeindex_overflow(self):
+        # GH#22492, GH#22508
+        dtimax = pd.to_datetime(["2021-12-28 17:19", Timestamp.max]).as_unit("ns")
+        dtimin = pd.to_datetime(["2021-12-28 17:19", Timestamp.min]).as_unit("ns")
+
+        ts_neg = pd.to_datetime(["1950-01-01", "1950-01-01"]).as_unit("ns")
+        ts_pos = pd.to_datetime(["1980-01-01", "1980-01-01"]).as_unit("ns")
+
+        # General tests
+        expected = Timestamp.max._value - ts_pos[1]._value
+        result = dtimax - ts_pos
+        assert result[1]._value == expected
+
+        expected = Timestamp.min._value - ts_neg[1]._value
+        result = dtimin - ts_neg
+        assert result[1]._value == expected
+        msg = "Overflow in int64 addition"
+        with pytest.raises(OverflowError, match=msg):
+            dtimax - ts_neg
+
+        with pytest.raises(OverflowError, match=msg):
+            dtimin - ts_pos
+
+        # Edge cases
+        tmin = pd.to_datetime([Timestamp.min])
+        t1 = tmin + Timedelta.max + Timedelta("1us")
+        with pytest.raises(OverflowError, match=msg):
+            t1 - tmin
+
+        tmax = pd.to_datetime([Timestamp.max])
+        t2 = tmax + Timedelta.min - Timedelta("1us")
+        with pytest.raises(OverflowError, match=msg):
+            tmax - t2
+
+
+class TestTimestampSeriesArithmetic:
+    def test_empty_series_add_sub(self, box_with_array):
+        # GH#13844
+        a = Series(dtype="M8[ns]")
+        b = Series(dtype="m8[ns]")
+        a = box_with_array(a)
+        b = box_with_array(b)
+        tm.assert_equal(a, a + b)
+        tm.assert_equal(a, a - b)
+        tm.assert_equal(a, b + a)
+        msg = "cannot subtract"
+        with pytest.raises(TypeError, match=msg):
+            b - a
+
+    def test_operators_datetimelike(self):
+        # ## timedelta64 ###
+        td1 = Series([timedelta(minutes=5, seconds=3)] * 3)
+        td1.iloc[2] = np.nan
+
+        # ## datetime64 ###
+        dt1 = Series(
+            [
+                Timestamp("20111230"),
+                Timestamp("20120101"),
+                Timestamp("20120103"),
+            ]
+        )
+        dt1.iloc[2] = np.nan
+        dt2 = Series(
+            [
+                Timestamp("20111231"),
+                Timestamp("20120102"),
+                Timestamp("20120104"),
+            ]
+        )
+        dt1 - dt2
+        dt2 - dt1
+
+        # datetime64 with timetimedelta
+        dt1 + td1
+        td1 + dt1
+        dt1 - td1
+
+        # timetimedelta with datetime64
+        td1 + dt1
+        dt1 + td1
+
+    def test_dt64ser_sub_datetime_dtype(self, unit):
+        ts = Timestamp(datetime(1993, 1, 7, 13, 30, 00))
+        dt = datetime(1993, 6, 22, 13, 30)
+        ser = Series([ts], dtype=f"M8[{unit}]")
+        result = ser - dt
+
+        # the expected unit is the max of `unit` and the unit imputed to `dt`,
+        #  which is "us"
+        exp_unit = tm.get_finest_unit(unit, "us")
+        assert result.dtype == f"timedelta64[{exp_unit}]"
+
+    # -------------------------------------------------------------
+    # TODO: This next block of tests came from tests.series.test_operators,
+    # needs to be de-duplicated and parametrized over `box` classes
+
+    @pytest.mark.parametrize(
+        "left, right, op_fail",
+        [
+            [
+                [Timestamp("20111230"), Timestamp("20120101"), NaT],
+                [Timestamp("20111231"), Timestamp("20120102"), Timestamp("20120104")],
+                ["__sub__", "__rsub__"],
+            ],
+            [
+                [Timestamp("20111230"), Timestamp("20120101"), NaT],
+                [timedelta(minutes=5, seconds=3), timedelta(minutes=5, seconds=3), NaT],
+                ["__add__", "__radd__", "__sub__"],
+            ],
+            [
+                [
+                    Timestamp("20111230", tz="US/Eastern"),
+                    Timestamp("20111230", tz="US/Eastern"),
+                    NaT,
+                ],
+                [timedelta(minutes=5, seconds=3), NaT, timedelta(minutes=5, seconds=3)],
+                ["__add__", "__radd__", "__sub__"],
+            ],
+        ],
+    )
+    def test_operators_datetimelike_invalid(
+        self, left, right, op_fail, all_arithmetic_operators
+    ):
+        # these are all TypeError ops
+        op_str = all_arithmetic_operators
+        arg1 = Series(left)
+        arg2 = Series(right)
+        # check that we are getting a TypeError
+        # with 'operate' (from core/ops.py) for the ops that are not
+        # defined
+        op = getattr(arg1, op_str, None)
+        # Previously, _validate_for_numeric_binop in core/indexes/base.py
+        # did this for us.
+        if op_str not in op_fail:
+            with pytest.raises(
+                TypeError, match="operate|[cC]annot|unsupported operand"
+            ):
+                op(arg2)
+        else:
+            # Smoke test
+            op(arg2)
+
+    def test_sub_single_tz(self, unit):
+        # GH#12290
+        s1 = Series([Timestamp("2016-02-10", tz="America/Sao_Paulo")]).dt.as_unit(unit)
+        s2 = Series([Timestamp("2016-02-08", tz="America/Sao_Paulo")]).dt.as_unit(unit)
+        result = s1 - s2
+        expected = Series([Timedelta("2days")]).dt.as_unit(unit)
+        tm.assert_series_equal(result, expected)
+        result = s2 - s1
+        expected = Series([Timedelta("-2days")]).dt.as_unit(unit)
+        tm.assert_series_equal(result, expected)
+
+    def test_dt64tz_series_sub_dtitz(self):
+        # GH#19071 subtracting tzaware DatetimeIndex from tzaware Series
+        # (with same tz) raises, fixed by #19024
+        dti = date_range("1999-09-30", periods=10, tz="US/Pacific")
+        ser = Series(dti)
+        expected = Series(TimedeltaIndex(["0days"] * 10))
+
+        res = dti - ser
+        tm.assert_series_equal(res, expected)
+        res = ser - dti
+        tm.assert_series_equal(res, expected)
+
+    def test_sub_datetime_compat(self, unit):
+        # see GH#14088
+        ser = Series([datetime(2016, 8, 23, 12, tzinfo=pytz.utc), NaT]).dt.as_unit(unit)
+        dt = datetime(2016, 8, 22, 12, tzinfo=pytz.utc)
+        # The datetime object has "us" so we upcast lower units
+        exp_unit = tm.get_finest_unit(unit, "us")
+        exp = Series([Timedelta("1 days"), NaT]).dt.as_unit(exp_unit)
+        result = ser - dt
+        tm.assert_series_equal(result, exp)
+        result2 = ser - Timestamp(dt)
+        tm.assert_series_equal(result2, exp)
+
+    def test_dt64_series_add_mixed_tick_DateOffset(self):
+        # GH#4532
+        # operate with pd.offsets
+        s = Series([Timestamp("20130101 9:01"), Timestamp("20130101 9:02")])
+
+        result = s + pd.offsets.Milli(5)
+        result2 = pd.offsets.Milli(5) + s
+        expected = Series(
+            [Timestamp("20130101 9:01:00.005"), Timestamp("20130101 9:02:00.005")]
+        )
+        tm.assert_series_equal(result, expected)
+        tm.assert_series_equal(result2, expected)
+
+        result = s + pd.offsets.Minute(5) + pd.offsets.Milli(5)
+        expected = Series(
+            [Timestamp("20130101 9:06:00.005"), Timestamp("20130101 9:07:00.005")]
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_datetime64_ops_nat(self, unit):
+        # GH#11349
+        datetime_series = Series([NaT, Timestamp("19900315")]).dt.as_unit(unit)
+        nat_series_dtype_timestamp = Series([NaT, NaT], dtype=f"datetime64[{unit}]")
+        single_nat_dtype_datetime = Series([NaT], dtype=f"datetime64[{unit}]")
+
+        # subtraction
+        tm.assert_series_equal(-NaT + datetime_series, nat_series_dtype_timestamp)
+        msg = "bad operand type for unary -: 'DatetimeArray'"
+        with pytest.raises(TypeError, match=msg):
+            -single_nat_dtype_datetime + datetime_series
+
+        tm.assert_series_equal(
+            -NaT + nat_series_dtype_timestamp, nat_series_dtype_timestamp
+        )
+        with pytest.raises(TypeError, match=msg):
+            -single_nat_dtype_datetime + nat_series_dtype_timestamp
+
+        # addition
+        tm.assert_series_equal(
+            nat_series_dtype_timestamp + NaT, nat_series_dtype_timestamp
+        )
+        tm.assert_series_equal(
+            NaT + nat_series_dtype_timestamp, nat_series_dtype_timestamp
+        )
+
+        tm.assert_series_equal(
+            nat_series_dtype_timestamp + NaT, nat_series_dtype_timestamp
+        )
+        tm.assert_series_equal(
+            NaT + nat_series_dtype_timestamp, nat_series_dtype_timestamp
+        )
+
+    # -------------------------------------------------------------
+    # Timezone-Centric Tests
+
+    def test_operators_datetimelike_with_timezones(self):
+        tz = "US/Eastern"
+        dt1 = Series(date_range("2000-01-01 09:00:00", periods=5, tz=tz), name="foo")
+        dt2 = dt1.copy()
+        dt2.iloc[2] = np.nan
+
+        td1 = Series(pd.timedelta_range("1 days 1 min", periods=5, freq="h"))
+        td2 = td1.copy()
+        td2.iloc[1] = np.nan
+        assert td2._values.freq is None
+
+        result = dt1 + td1[0]
+        exp = (dt1.dt.tz_localize(None) + td1[0]).dt.tz_localize(tz)
+        tm.assert_series_equal(result, exp)
+
+        result = dt2 + td2[0]
+        exp = (dt2.dt.tz_localize(None) + td2[0]).dt.tz_localize(tz)
+        tm.assert_series_equal(result, exp)
+
+        # odd numpy behavior with scalar timedeltas
+        result = td1[0] + dt1
+        exp = (dt1.dt.tz_localize(None) + td1[0]).dt.tz_localize(tz)
+        tm.assert_series_equal(result, exp)
+
+        result = td2[0] + dt2
+        exp = (dt2.dt.tz_localize(None) + td2[0]).dt.tz_localize(tz)
+        tm.assert_series_equal(result, exp)
+
+        result = dt1 - td1[0]
+        exp = (dt1.dt.tz_localize(None) - td1[0]).dt.tz_localize(tz)
+        tm.assert_series_equal(result, exp)
+        msg = "(bad|unsupported) operand type for unary"
+        with pytest.raises(TypeError, match=msg):
+            td1[0] - dt1
+
+        result = dt2 - td2[0]
+        exp = (dt2.dt.tz_localize(None) - td2[0]).dt.tz_localize(tz)
+        tm.assert_series_equal(result, exp)
+        with pytest.raises(TypeError, match=msg):
+            td2[0] - dt2
+
+        result = dt1 + td1
+        exp = (dt1.dt.tz_localize(None) + td1).dt.tz_localize(tz)
+        tm.assert_series_equal(result, exp)
+
+        result = dt2 + td2
+        exp = (dt2.dt.tz_localize(None) + td2).dt.tz_localize(tz)
+        tm.assert_series_equal(result, exp)
+
+        result = dt1 - td1
+        exp = (dt1.dt.tz_localize(None) - td1).dt.tz_localize(tz)
+        tm.assert_series_equal(result, exp)
+
+        result = dt2 - td2
+        exp = (dt2.dt.tz_localize(None) - td2).dt.tz_localize(tz)
+        tm.assert_series_equal(result, exp)
+        msg = "cannot (add|subtract)"
+        with pytest.raises(TypeError, match=msg):
+            td1 - dt1
+        with pytest.raises(TypeError, match=msg):
+            td2 - dt2
+
+
+class TestDatetimeIndexArithmetic:
+    # -------------------------------------------------------------
+    # Binary operations DatetimeIndex and TimedeltaIndex/array
+
+    def test_dti_add_tdi(self, tz_naive_fixture):
+        # GH#17558
+        tz = tz_naive_fixture
+        dti = DatetimeIndex([Timestamp("2017-01-01", tz=tz)] * 10)
+        tdi = pd.timedelta_range("0 days", periods=10)
+        expected = date_range("2017-01-01", periods=10, tz=tz)
+        expected = expected._with_freq(None)
+
+        # add with TimedeltaIndex
+        result = dti + tdi
+        tm.assert_index_equal(result, expected)
+
+        result = tdi + dti
+        tm.assert_index_equal(result, expected)
+
+        # add with timedelta64 array
+        result = dti + tdi.values
+        tm.assert_index_equal(result, expected)
+
+        result = tdi.values + dti
+        tm.assert_index_equal(result, expected)
+
+    def test_dti_iadd_tdi(self, tz_naive_fixture):
+        # GH#17558
+        tz = tz_naive_fixture
+        dti = DatetimeIndex([Timestamp("2017-01-01", tz=tz)] * 10)
+        tdi = pd.timedelta_range("0 days", periods=10)
+        expected = date_range("2017-01-01", periods=10, tz=tz)
+        expected = expected._with_freq(None)
+
+        # iadd with TimedeltaIndex
+        result = DatetimeIndex([Timestamp("2017-01-01", tz=tz)] * 10)
+        result += tdi
+        tm.assert_index_equal(result, expected)
+
+        result = pd.timedelta_range("0 days", periods=10)
+        result += dti
+        tm.assert_index_equal(result, expected)
+
+        # iadd with timedelta64 array
+        result = DatetimeIndex([Timestamp("2017-01-01", tz=tz)] * 10)
+        result += tdi.values
+        tm.assert_index_equal(result, expected)
+
+        result = pd.timedelta_range("0 days", periods=10)
+        result += dti
+        tm.assert_index_equal(result, expected)
+
+    def test_dti_sub_tdi(self, tz_naive_fixture):
+        # GH#17558
+        tz = tz_naive_fixture
+        dti = DatetimeIndex([Timestamp("2017-01-01", tz=tz)] * 10)
+        tdi = pd.timedelta_range("0 days", periods=10)
+        expected = date_range("2017-01-01", periods=10, tz=tz, freq="-1D")
+        expected = expected._with_freq(None)
+
+        # sub with TimedeltaIndex
+        result = dti - tdi
+        tm.assert_index_equal(result, expected)
+
+        msg = "cannot subtract .*TimedeltaArray"
+        with pytest.raises(TypeError, match=msg):
+            tdi - dti
+
+        # sub with timedelta64 array
+        result = dti - tdi.values
+        tm.assert_index_equal(result, expected)
+
+        msg = "cannot subtract a datelike from a TimedeltaArray"
+        with pytest.raises(TypeError, match=msg):
+            tdi.values - dti
+
+    def test_dti_isub_tdi(self, tz_naive_fixture, unit):
+        # GH#17558
+        tz = tz_naive_fixture
+        dti = DatetimeIndex([Timestamp("2017-01-01", tz=tz)] * 10).as_unit(unit)
+        tdi = pd.timedelta_range("0 days", periods=10, unit=unit)
+        expected = date_range("2017-01-01", periods=10, tz=tz, freq="-1D", unit=unit)
+        expected = expected._with_freq(None)
+
+        # isub with TimedeltaIndex
+        result = DatetimeIndex([Timestamp("2017-01-01", tz=tz)] * 10).as_unit(unit)
+        result -= tdi
+        tm.assert_index_equal(result, expected)
+
+        # DTA.__isub__ GH#43904
+        dta = dti._data.copy()
+        dta -= tdi
+        tm.assert_datetime_array_equal(dta, expected._data)
+
+        out = dti._data.copy()
+        np.subtract(out, tdi, out=out)
+        tm.assert_datetime_array_equal(out, expected._data)
+
+        msg = "cannot subtract a datelike from a TimedeltaArray"
+        with pytest.raises(TypeError, match=msg):
+            tdi -= dti
+
+        # isub with timedelta64 array
+        result = DatetimeIndex([Timestamp("2017-01-01", tz=tz)] * 10).as_unit(unit)
+        result -= tdi.values
+        tm.assert_index_equal(result, expected)
+
+        with pytest.raises(TypeError, match=msg):
+            tdi.values -= dti
+
+        with pytest.raises(TypeError, match=msg):
+            tdi._values -= dti
+
+    # -------------------------------------------------------------
+    # Binary Operations DatetimeIndex and datetime-like
+    # TODO: A couple other tests belong in this section.  Move them in
+    # A PR where there isn't already a giant diff.
+
+    # -------------------------------------------------------------
+
+    def test_dta_add_sub_index(self, tz_naive_fixture):
+        # Check that DatetimeArray defers to Index classes
+        dti = date_range("20130101", periods=3, tz=tz_naive_fixture)
+        dta = dti.array
+        result = dta - dti
+        expected = dti - dti
+        tm.assert_index_equal(result, expected)
+
+        tdi = result
+        result = dta + tdi
+        expected = dti + tdi
+        tm.assert_index_equal(result, expected)
+
+        result = dta - tdi
+        expected = dti - tdi
+        tm.assert_index_equal(result, expected)
+
+    def test_sub_dti_dti(self, unit):
+        # previously performed setop (deprecated in 0.16.0), now changed to
+        # return subtraction -> TimeDeltaIndex (GH ...)
+
+        dti = date_range("20130101", periods=3, unit=unit)
+        dti_tz = date_range("20130101", periods=3, unit=unit).tz_localize("US/Eastern")
+        expected = TimedeltaIndex([0, 0, 0]).as_unit(unit)
+
+        result = dti - dti
+        tm.assert_index_equal(result, expected)
+
+        result = dti_tz - dti_tz
+        tm.assert_index_equal(result, expected)
+        msg = "Cannot subtract tz-naive and tz-aware datetime-like objects"
+        with pytest.raises(TypeError, match=msg):
+            dti_tz - dti
+
+        with pytest.raises(TypeError, match=msg):
+            dti - dti_tz
+
+        # isub
+        dti -= dti
+        tm.assert_index_equal(dti, expected)
+
+        # different length raises ValueError
+        dti1 = date_range("20130101", periods=3, unit=unit)
+        dti2 = date_range("20130101", periods=4, unit=unit)
+        msg = "cannot add indices of unequal length"
+        with pytest.raises(ValueError, match=msg):
+            dti1 - dti2
+
+        # NaN propagation
+        dti1 = DatetimeIndex(["2012-01-01", np.nan, "2012-01-03"]).as_unit(unit)
+        dti2 = DatetimeIndex(["2012-01-02", "2012-01-03", np.nan]).as_unit(unit)
+        expected = TimedeltaIndex(["1 days", np.nan, np.nan]).as_unit(unit)
+        result = dti2 - dti1
+        tm.assert_index_equal(result, expected)
+
+    # -------------------------------------------------------------------
+    # TODO: Most of this block is moved from series or frame tests, needs
+    # cleanup, box-parametrization, and de-duplication
+
+    @pytest.mark.parametrize("op", [operator.add, operator.sub])
+    def test_timedelta64_equal_timedelta_supported_ops(self, op, box_with_array):
+        ser = Series(
+            [
+                Timestamp("20130301"),
+                Timestamp("20130228 23:00:00"),
+                Timestamp("20130228 22:00:00"),
+                Timestamp("20130228 21:00:00"),
+            ]
+        )
+        obj = box_with_array(ser)
+
+        intervals = ["D", "h", "m", "s", "us"]
+
+        def timedelta64(*args):
+            # see casting notes in NumPy gh-12927
+            return np.sum(list(starmap(np.timedelta64, zip(args, intervals))))
+
+        for d, h, m, s, us in product(*([range(2)] * 5)):
+            nptd = timedelta64(d, h, m, s, us)
+            pytd = timedelta(days=d, hours=h, minutes=m, seconds=s, microseconds=us)
+            lhs = op(obj, nptd)
+            rhs = op(obj, pytd)
+
+            tm.assert_equal(lhs, rhs)
+
+    def test_ops_nat_mixed_datetime64_timedelta64(self):
+        # GH#11349
+        timedelta_series = Series([NaT, Timedelta("1s")])
+        datetime_series = Series([NaT, Timestamp("19900315")])
+        nat_series_dtype_timedelta = Series([NaT, NaT], dtype="timedelta64[ns]")
+        nat_series_dtype_timestamp = Series([NaT, NaT], dtype="datetime64[ns]")
+        single_nat_dtype_datetime = Series([NaT], dtype="datetime64[ns]")
+        single_nat_dtype_timedelta = Series([NaT], dtype="timedelta64[ns]")
+
+        # subtraction
+        tm.assert_series_equal(
+            datetime_series - single_nat_dtype_datetime, nat_series_dtype_timedelta
+        )
+
+        tm.assert_series_equal(
+            datetime_series - single_nat_dtype_timedelta, nat_series_dtype_timestamp
+        )
+        tm.assert_series_equal(
+            -single_nat_dtype_timedelta + datetime_series, nat_series_dtype_timestamp
+        )
+
+        # without a Series wrapping the NaT, it is ambiguous
+        # whether it is a datetime64 or timedelta64
+        # defaults to interpreting it as timedelta64
+        tm.assert_series_equal(
+            nat_series_dtype_timestamp - single_nat_dtype_datetime,
+            nat_series_dtype_timedelta,
+        )
+
+        tm.assert_series_equal(
+            nat_series_dtype_timestamp - single_nat_dtype_timedelta,
+            nat_series_dtype_timestamp,
+        )
+        tm.assert_series_equal(
+            -single_nat_dtype_timedelta + nat_series_dtype_timestamp,
+            nat_series_dtype_timestamp,
+        )
+        msg = "cannot subtract a datelike"
+        with pytest.raises(TypeError, match=msg):
+            timedelta_series - single_nat_dtype_datetime
+
+        # addition
+        tm.assert_series_equal(
+            nat_series_dtype_timestamp + single_nat_dtype_timedelta,
+            nat_series_dtype_timestamp,
+        )
+        tm.assert_series_equal(
+            single_nat_dtype_timedelta + nat_series_dtype_timestamp,
+            nat_series_dtype_timestamp,
+        )
+
+        tm.assert_series_equal(
+            nat_series_dtype_timestamp + single_nat_dtype_timedelta,
+            nat_series_dtype_timestamp,
+        )
+        tm.assert_series_equal(
+            single_nat_dtype_timedelta + nat_series_dtype_timestamp,
+            nat_series_dtype_timestamp,
+        )
+
+        tm.assert_series_equal(
+            nat_series_dtype_timedelta + single_nat_dtype_datetime,
+            nat_series_dtype_timestamp,
+        )
+        tm.assert_series_equal(
+            single_nat_dtype_datetime + nat_series_dtype_timedelta,
+            nat_series_dtype_timestamp,
+        )
+
+    def test_ufunc_coercions(self, unit):
+        idx = date_range("2011-01-01", periods=3, freq="2D", name="x", unit=unit)
+
+        delta = np.timedelta64(1, "D")
+        exp = date_range("2011-01-02", periods=3, freq="2D", name="x", unit=unit)
+        for result in [idx + delta, np.add(idx, delta)]:
+            assert isinstance(result, DatetimeIndex)
+            tm.assert_index_equal(result, exp)
+            assert result.freq == "2D"
+
+        exp = date_range("2010-12-31", periods=3, freq="2D", name="x", unit=unit)
+
+        for result in [idx - delta, np.subtract(idx, delta)]:
+            assert isinstance(result, DatetimeIndex)
+            tm.assert_index_equal(result, exp)
+            assert result.freq == "2D"
+
+        # When adding/subtracting an ndarray (which has no .freq), the result
+        #  does not infer freq
+        idx = idx._with_freq(None)
+        delta = np.array(
+            [np.timedelta64(1, "D"), np.timedelta64(2, "D"), np.timedelta64(3, "D")]
+        )
+        exp = DatetimeIndex(
+            ["2011-01-02", "2011-01-05", "2011-01-08"], name="x"
+        ).as_unit(unit)
+
+        for result in [idx + delta, np.add(idx, delta)]:
+            tm.assert_index_equal(result, exp)
+            assert result.freq == exp.freq
+
+        exp = DatetimeIndex(
+            ["2010-12-31", "2011-01-01", "2011-01-02"], name="x"
+        ).as_unit(unit)
+        for result in [idx - delta, np.subtract(idx, delta)]:
+            assert isinstance(result, DatetimeIndex)
+            tm.assert_index_equal(result, exp)
+            assert result.freq == exp.freq
+
+    def test_dti_add_series(self, tz_naive_fixture, names):
+        # GH#13905
+        tz = tz_naive_fixture
+        index = DatetimeIndex(
+            ["2016-06-28 05:30", "2016-06-28 05:31"], tz=tz, name=names[0]
+        ).as_unit("ns")
+        ser = Series([Timedelta(seconds=5)] * 2, index=index, name=names[1])
+        expected = Series(index + Timedelta(seconds=5), index=index, name=names[2])
+
+        # passing name arg isn't enough when names[2] is None
+        expected.name = names[2]
+        assert expected.dtype == index.dtype
+        result = ser + index
+        tm.assert_series_equal(result, expected)
+        result2 = index + ser
+        tm.assert_series_equal(result2, expected)
+
+        expected = index + Timedelta(seconds=5)
+        result3 = ser.values + index
+        tm.assert_index_equal(result3, expected)
+        result4 = index + ser.values
+        tm.assert_index_equal(result4, expected)
+
+    @pytest.mark.parametrize("op", [operator.add, roperator.radd, operator.sub])
+    def test_dti_addsub_offset_arraylike(
+        self, tz_naive_fixture, names, op, index_or_series
+    ):
+        # GH#18849, GH#19744
+        other_box = index_or_series
+
+        tz = tz_naive_fixture
+        dti = date_range("2017-01-01", periods=2, tz=tz, name=names[0])
+        other = other_box([pd.offsets.MonthEnd(), pd.offsets.Day(n=2)], name=names[1])
+
+        xbox = get_upcast_box(dti, other)
+
+        with tm.assert_produces_warning(PerformanceWarning):
+            res = op(dti, other)
+
+        expected = DatetimeIndex(
+            [op(dti[n], other[n]) for n in range(len(dti))], name=names[2], freq="infer"
+        )
+        expected = tm.box_expected(expected, xbox).astype(object)
+        tm.assert_equal(res, expected)
+
+    @pytest.mark.parametrize("other_box", [pd.Index, np.array])
+    def test_dti_addsub_object_arraylike(
+        self, tz_naive_fixture, box_with_array, other_box
+    ):
+        tz = tz_naive_fixture
+
+        dti = date_range("2017-01-01", periods=2, tz=tz)
+        dtarr = tm.box_expected(dti, box_with_array)
+        other = other_box([pd.offsets.MonthEnd(), Timedelta(days=4)])
+        xbox = get_upcast_box(dtarr, other)
+
+        expected = DatetimeIndex(["2017-01-31", "2017-01-06"], tz=tz_naive_fixture)
+        expected = tm.box_expected(expected, xbox).astype(object)
+
+        with tm.assert_produces_warning(PerformanceWarning):
+            result = dtarr + other
+        tm.assert_equal(result, expected)
+
+        expected = DatetimeIndex(["2016-12-31", "2016-12-29"], tz=tz_naive_fixture)
+        expected = tm.box_expected(expected, xbox).astype(object)
+
+        with tm.assert_produces_warning(PerformanceWarning):
+            result = dtarr - other
+        tm.assert_equal(result, expected)
+
+
+@pytest.mark.parametrize("years", [-1, 0, 1])
+@pytest.mark.parametrize("months", [-2, 0, 2])
+@pytest.mark.parametrize("unit", ["s", "ms", "us", "ns"])
+def test_shift_months(years, months, unit):
+    dti = DatetimeIndex(
+        [
+            Timestamp("2000-01-05 00:15:00"),
+            Timestamp("2000-01-31 00:23:00"),
+            Timestamp("2000-01-01"),
+            Timestamp("2000-02-29"),
+            Timestamp("2000-12-31"),
+        ]
+    ).as_unit(unit)
+    shifted = shift_months(dti.asi8, years * 12 + months, reso=dti._data._creso)
+    shifted_dt64 = shifted.view(f"M8[{dti.unit}]")
+    actual = DatetimeIndex(shifted_dt64)
+
+    raw = [x + pd.offsets.DateOffset(years=years, months=months) for x in dti]
+    expected = DatetimeIndex(raw).as_unit(dti.unit)
+    tm.assert_index_equal(actual, expected)
+
+
+def test_dt64arr_addsub_object_dtype_2d():
+    # block-wise DataFrame operations will require operating on 2D
+    #  DatetimeArray/TimedeltaArray, so check that specifically.
+    dti = date_range("1994-02-13", freq="2W", periods=4)
+    dta = dti._data.reshape((4, 1))
+
+    other = np.array([[pd.offsets.Day(n)] for n in range(4)])
+    assert other.shape == dta.shape
+
+    with tm.assert_produces_warning(PerformanceWarning):
+        result = dta + other
+    with tm.assert_produces_warning(PerformanceWarning):
+        expected = (dta[:, 0] + other[:, 0]).reshape(-1, 1)
+
+    tm.assert_numpy_array_equal(result, expected)
+
+    with tm.assert_produces_warning(PerformanceWarning):
+        # Case where we expect to get a TimedeltaArray back
+        result2 = dta - dta.astype(object)
+
+    assert result2.shape == (4, 1)
+    assert all(td._value == 0 for td in result2.ravel())
+
+
+def test_non_nano_dt64_addsub_np_nat_scalars():
+    # GH 52295
+    ser = Series([1233242342344, 232432434324, 332434242344], dtype="datetime64[ms]")
+    result = ser - np.datetime64("nat", "ms")
+    expected = Series([NaT] * 3, dtype="timedelta64[ms]")
+    tm.assert_series_equal(result, expected)
+
+    result = ser + np.timedelta64("nat", "ms")
+    expected = Series([NaT] * 3, dtype="datetime64[ms]")
+    tm.assert_series_equal(result, expected)
+
+
+def test_non_nano_dt64_addsub_np_nat_scalars_unitless():
+    # GH 52295
+    # TODO: Can we default to the ser unit?
+    ser = Series([1233242342344, 232432434324, 332434242344], dtype="datetime64[ms]")
+    result = ser - np.datetime64("nat")
+    expected = Series([NaT] * 3, dtype="timedelta64[ns]")
+    tm.assert_series_equal(result, expected)
+
+    result = ser + np.timedelta64("nat")
+    expected = Series([NaT] * 3, dtype="datetime64[ns]")
+    tm.assert_series_equal(result, expected)
+
+
+def test_non_nano_dt64_addsub_np_nat_scalars_unsupported_unit():
+    # GH 52295
+    ser = Series([12332, 23243, 33243], dtype="datetime64[s]")
+    result = ser - np.datetime64("nat", "D")
+    expected = Series([NaT] * 3, dtype="timedelta64[s]")
+    tm.assert_series_equal(result, expected)
+
+    result = ser + np.timedelta64("nat", "D")
+    expected = Series([NaT] * 3, dtype="datetime64[s]")
+    tm.assert_series_equal(result, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arithmetic/test_interval.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arithmetic/test_interval.py
new file mode 100644
index 0000000000000000000000000000000000000000..0e316cf419cb0d3be489f474a9c6d889e668e7c9
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arithmetic/test_interval.py
@@ -0,0 +1,306 @@
+import operator
+
+import numpy as np
+import pytest
+
+from pandas.core.dtypes.common import is_list_like
+
+import pandas as pd
+from pandas import (
+    Categorical,
+    Index,
+    Interval,
+    IntervalIndex,
+    Period,
+    Series,
+    Timedelta,
+    Timestamp,
+    date_range,
+    period_range,
+    timedelta_range,
+)
+import pandas._testing as tm
+from pandas.core.arrays import (
+    BooleanArray,
+    IntervalArray,
+)
+from pandas.tests.arithmetic.common import get_upcast_box
+
+
+@pytest.fixture(
+    params=[
+        (Index([0, 2, 4, 4]), Index([1, 3, 5, 8])),
+        (Index([0.0, 1.0, 2.0, np.nan]), Index([1.0, 2.0, 3.0, np.nan])),
+        (
+            timedelta_range("0 days", periods=3).insert(3, pd.NaT),
+            timedelta_range("1 day", periods=3).insert(3, pd.NaT),
+        ),
+        (
+            date_range("20170101", periods=3).insert(3, pd.NaT),
+            date_range("20170102", periods=3).insert(3, pd.NaT),
+        ),
+        (
+            date_range("20170101", periods=3, tz="US/Eastern").insert(3, pd.NaT),
+            date_range("20170102", periods=3, tz="US/Eastern").insert(3, pd.NaT),
+        ),
+    ],
+    ids=lambda x: str(x[0].dtype),
+)
+def left_right_dtypes(request):
+    """
+    Fixture for building an IntervalArray from various dtypes
+    """
+    return request.param
+
+
+@pytest.fixture
+def interval_array(left_right_dtypes):
+    """
+    Fixture to generate an IntervalArray of various dtypes containing NA if possible
+    """
+    left, right = left_right_dtypes
+    return IntervalArray.from_arrays(left, right)
+
+
+def create_categorical_intervals(left, right, closed="right"):
+    return Categorical(IntervalIndex.from_arrays(left, right, closed))
+
+
+def create_series_intervals(left, right, closed="right"):
+    return Series(IntervalArray.from_arrays(left, right, closed))
+
+
+def create_series_categorical_intervals(left, right, closed="right"):
+    return Series(Categorical(IntervalIndex.from_arrays(left, right, closed)))
+
+
+class TestComparison:
+    @pytest.fixture(params=[operator.eq, operator.ne])
+    def op(self, request):
+        return request.param
+
+    @pytest.fixture(
+        params=[
+            IntervalArray.from_arrays,
+            IntervalIndex.from_arrays,
+            create_categorical_intervals,
+            create_series_intervals,
+            create_series_categorical_intervals,
+        ],
+        ids=[
+            "IntervalArray",
+            "IntervalIndex",
+            "Categorical[Interval]",
+            "Series[Interval]",
+            "Series[Categorical[Interval]]",
+        ],
+    )
+    def interval_constructor(self, request):
+        """
+        Fixture for all pandas native interval constructors.
+        To be used as the LHS of IntervalArray comparisons.
+        """
+        return request.param
+
+    def elementwise_comparison(self, op, interval_array, other):
+        """
+        Helper that performs elementwise comparisons between `array` and `other`
+        """
+        other = other if is_list_like(other) else [other] * len(interval_array)
+        expected = np.array([op(x, y) for x, y in zip(interval_array, other)])
+        if isinstance(other, Series):
+            return Series(expected, index=other.index)
+        return expected
+
+    def test_compare_scalar_interval(self, op, interval_array):
+        # matches first interval
+        other = interval_array[0]
+        result = op(interval_array, other)
+        expected = self.elementwise_comparison(op, interval_array, other)
+        tm.assert_numpy_array_equal(result, expected)
+
+        # matches on a single endpoint but not both
+        other = Interval(interval_array.left[0], interval_array.right[1])
+        result = op(interval_array, other)
+        expected = self.elementwise_comparison(op, interval_array, other)
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_compare_scalar_interval_mixed_closed(self, op, closed, other_closed):
+        interval_array = IntervalArray.from_arrays(range(2), range(1, 3), closed=closed)
+        other = Interval(0, 1, closed=other_closed)
+
+        result = op(interval_array, other)
+        expected = self.elementwise_comparison(op, interval_array, other)
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_compare_scalar_na(self, op, interval_array, nulls_fixture, box_with_array):
+        box = box_with_array
+        obj = tm.box_expected(interval_array, box)
+        result = op(obj, nulls_fixture)
+
+        if nulls_fixture is pd.NA:
+            # GH#31882
+            exp = np.ones(interval_array.shape, dtype=bool)
+            expected = BooleanArray(exp, exp)
+        else:
+            expected = self.elementwise_comparison(op, interval_array, nulls_fixture)
+
+        if not (box is Index and nulls_fixture is pd.NA):
+            # don't cast expected from BooleanArray to ndarray[object]
+            xbox = get_upcast_box(obj, nulls_fixture, True)
+            expected = tm.box_expected(expected, xbox)
+
+        tm.assert_equal(result, expected)
+
+        rev = op(nulls_fixture, obj)
+        tm.assert_equal(rev, expected)
+
+    @pytest.mark.parametrize(
+        "other",
+        [
+            0,
+            1.0,
+            True,
+            "foo",
+            Timestamp("2017-01-01"),
+            Timestamp("2017-01-01", tz="US/Eastern"),
+            Timedelta("0 days"),
+            Period("2017-01-01", "D"),
+        ],
+    )
+    def test_compare_scalar_other(self, op, interval_array, other):
+        result = op(interval_array, other)
+        expected = self.elementwise_comparison(op, interval_array, other)
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_compare_list_like_interval(self, op, interval_array, interval_constructor):
+        # same endpoints
+        other = interval_constructor(interval_array.left, interval_array.right)
+        result = op(interval_array, other)
+        expected = self.elementwise_comparison(op, interval_array, other)
+        tm.assert_equal(result, expected)
+
+        # different endpoints
+        other = interval_constructor(
+            interval_array.left[::-1], interval_array.right[::-1]
+        )
+        result = op(interval_array, other)
+        expected = self.elementwise_comparison(op, interval_array, other)
+        tm.assert_equal(result, expected)
+
+        # all nan endpoints
+        other = interval_constructor([np.nan] * 4, [np.nan] * 4)
+        result = op(interval_array, other)
+        expected = self.elementwise_comparison(op, interval_array, other)
+        tm.assert_equal(result, expected)
+
+    def test_compare_list_like_interval_mixed_closed(
+        self, op, interval_constructor, closed, other_closed
+    ):
+        interval_array = IntervalArray.from_arrays(range(2), range(1, 3), closed=closed)
+        other = interval_constructor(range(2), range(1, 3), closed=other_closed)
+
+        result = op(interval_array, other)
+        expected = self.elementwise_comparison(op, interval_array, other)
+        tm.assert_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "other",
+        [
+            (
+                Interval(0, 1),
+                Interval(Timedelta("1 day"), Timedelta("2 days")),
+                Interval(4, 5, "both"),
+                Interval(10, 20, "neither"),
+            ),
+            (0, 1.5, Timestamp("20170103"), np.nan),
+            (
+                Timestamp("20170102", tz="US/Eastern"),
+                Timedelta("2 days"),
+                "baz",
+                pd.NaT,
+            ),
+        ],
+    )
+    def test_compare_list_like_object(self, op, interval_array, other):
+        result = op(interval_array, other)
+        expected = self.elementwise_comparison(op, interval_array, other)
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_compare_list_like_nan(self, op, interval_array, nulls_fixture):
+        other = [nulls_fixture] * 4
+        result = op(interval_array, other)
+        expected = self.elementwise_comparison(op, interval_array, other)
+
+        tm.assert_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "other",
+        [
+            np.arange(4, dtype="int64"),
+            np.arange(4, dtype="float64"),
+            date_range("2017-01-01", periods=4),
+            date_range("2017-01-01", periods=4, tz="US/Eastern"),
+            timedelta_range("0 days", periods=4),
+            period_range("2017-01-01", periods=4, freq="D"),
+            Categorical(list("abab")),
+            Categorical(date_range("2017-01-01", periods=4)),
+            pd.array(list("abcd")),
+            pd.array(["foo", 3.14, None, object()], dtype=object),
+        ],
+        ids=lambda x: str(x.dtype),
+    )
+    def test_compare_list_like_other(self, op, interval_array, other):
+        result = op(interval_array, other)
+        expected = self.elementwise_comparison(op, interval_array, other)
+        tm.assert_numpy_array_equal(result, expected)
+
+    @pytest.mark.parametrize("length", [1, 3, 5])
+    @pytest.mark.parametrize("other_constructor", [IntervalArray, list])
+    def test_compare_length_mismatch_errors(self, op, other_constructor, length):
+        interval_array = IntervalArray.from_arrays(range(4), range(1, 5))
+        other = other_constructor([Interval(0, 1)] * length)
+        with pytest.raises(ValueError, match="Lengths must match to compare"):
+            op(interval_array, other)
+
+    @pytest.mark.parametrize(
+        "constructor, expected_type, assert_func",
+        [
+            (IntervalIndex, np.array, tm.assert_numpy_array_equal),
+            (Series, Series, tm.assert_series_equal),
+        ],
+    )
+    def test_index_series_compat(self, op, constructor, expected_type, assert_func):
+        # IntervalIndex/Series that rely on IntervalArray for comparisons
+        breaks = range(4)
+        index = constructor(IntervalIndex.from_breaks(breaks))
+
+        # scalar comparisons
+        other = index[0]
+        result = op(index, other)
+        expected = expected_type(self.elementwise_comparison(op, index, other))
+        assert_func(result, expected)
+
+        other = breaks[0]
+        result = op(index, other)
+        expected = expected_type(self.elementwise_comparison(op, index, other))
+        assert_func(result, expected)
+
+        # list-like comparisons
+        other = IntervalArray.from_breaks(breaks)
+        result = op(index, other)
+        expected = expected_type(self.elementwise_comparison(op, index, other))
+        assert_func(result, expected)
+
+        other = [index[0], breaks[0], "foo"]
+        result = op(index, other)
+        expected = expected_type(self.elementwise_comparison(op, index, other))
+        assert_func(result, expected)
+
+    @pytest.mark.parametrize("scalars", ["a", False, 1, 1.0, None])
+    def test_comparison_operations(self, scalars):
+        # GH #28981
+        expected = Series([False, False])
+        s = Series([Interval(0, 1), Interval(1, 2)], dtype="interval")
+        result = s == scalars
+        tm.assert_series_equal(result, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arithmetic/test_numeric.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arithmetic/test_numeric.py
new file mode 100644
index 0000000000000000000000000000000000000000..d8c1786b6b422c32a0f396b43f51d75b2b3ffe25
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arithmetic/test_numeric.py
@@ -0,0 +1,1567 @@
+# Arithmetic tests for DataFrame/Series/Index/Array classes that should
+# behave identically.
+# Specifically for numeric dtypes
+from __future__ import annotations
+
+from collections import abc
+from datetime import timedelta
+from decimal import Decimal
+import operator
+
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    Index,
+    RangeIndex,
+    Series,
+    Timedelta,
+    TimedeltaIndex,
+    array,
+    date_range,
+)
+import pandas._testing as tm
+from pandas.core import ops
+from pandas.core.computation import expressions as expr
+from pandas.tests.arithmetic.common import (
+    assert_invalid_addsub_type,
+    assert_invalid_comparison,
+)
+
+
+@pytest.fixture(autouse=True, params=[0, 1000000], ids=["numexpr", "python"])
+def switch_numexpr_min_elements(request, monkeypatch):
+    with monkeypatch.context() as m:
+        m.setattr(expr, "_MIN_ELEMENTS", request.param)
+        yield request.param
+
+
+@pytest.fixture(params=[Index, Series, tm.to_array])
+def box_pandas_1d_array(request):
+    """
+    Fixture to test behavior for Index, Series and tm.to_array classes
+    """
+    return request.param
+
+
+@pytest.fixture(
+    params=[
+        # TODO: add more  dtypes here
+        Index(np.arange(5, dtype="float64")),
+        Index(np.arange(5, dtype="int64")),
+        Index(np.arange(5, dtype="uint64")),
+        RangeIndex(5),
+    ],
+    ids=lambda x: type(x).__name__,
+)
+def numeric_idx(request):
+    """
+    Several types of numeric-dtypes Index objects
+    """
+    return request.param
+
+
+@pytest.fixture(
+    params=[Index, Series, tm.to_array, np.array, list], ids=lambda x: x.__name__
+)
+def box_1d_array(request):
+    """
+    Fixture to test behavior for Index, Series, tm.to_array, numpy Array and list
+    classes
+    """
+    return request.param
+
+
+def adjust_negative_zero(zero, expected):
+    """
+    Helper to adjust the expected result if we are dividing by -0.0
+    as opposed to 0.0
+    """
+    if np.signbit(np.array(zero)).any():
+        # All entries in the `zero` fixture should be either
+        #  all-negative or no-negative.
+        assert np.signbit(np.array(zero)).all()
+
+        expected *= -1
+
+    return expected
+
+
+def compare_op(series, other, op):
+    left = np.abs(series) if op in (ops.rpow, operator.pow) else series
+    right = np.abs(other) if op in (ops.rpow, operator.pow) else other
+
+    cython_or_numpy = op(left, right)
+    python = left.combine(right, op)
+    if isinstance(other, Series) and not other.index.equals(series.index):
+        python.index = python.index._with_freq(None)
+    tm.assert_series_equal(cython_or_numpy, python)
+
+
+# TODO: remove this kludge once mypy stops giving false positives here
+# List comprehension has incompatible type List[PandasObject]; expected List[RangeIndex]
+#  See GH#29725
+_ldtypes = ["i1", "i2", "i4", "i8", "u1", "u2", "u4", "u8", "f2", "f4", "f8"]
+lefts: list[Index | Series] = [RangeIndex(10, 40, 10)]
+lefts.extend([Series([10, 20, 30], dtype=dtype) for dtype in _ldtypes])
+lefts.extend([Index([10, 20, 30], dtype=dtype) for dtype in _ldtypes if dtype != "f2"])
+
+# ------------------------------------------------------------------
+# Comparisons
+
+
+class TestNumericComparisons:
+    def test_operator_series_comparison_zerorank(self):
+        # GH#13006
+        result = np.float64(0) > Series([1, 2, 3])
+        expected = 0.0 > Series([1, 2, 3])
+        tm.assert_series_equal(result, expected)
+        result = Series([1, 2, 3]) < np.float64(0)
+        expected = Series([1, 2, 3]) < 0.0
+        tm.assert_series_equal(result, expected)
+        result = np.array([0, 1, 2])[0] > Series([0, 1, 2])
+        expected = 0.0 > Series([1, 2, 3])
+        tm.assert_series_equal(result, expected)
+
+    def test_df_numeric_cmp_dt64_raises(self, box_with_array, fixed_now_ts):
+        # GH#8932, GH#22163
+        ts = fixed_now_ts
+        obj = np.array(range(5))
+        obj = tm.box_expected(obj, box_with_array)
+
+        assert_invalid_comparison(obj, ts, box_with_array)
+
+    def test_compare_invalid(self):
+        # GH#8058
+        # ops testing
+        a = Series(np.random.default_rng(2).standard_normal(5), name=0)
+        b = Series(np.random.default_rng(2).standard_normal(5))
+        b.name = pd.Timestamp("2000-01-01")
+        tm.assert_series_equal(a / b, 1 / (b / a))
+
+    def test_numeric_cmp_string_numexpr_path(self, box_with_array, monkeypatch):
+        # GH#36377, GH#35700
+        box = box_with_array
+        xbox = box if box is not Index else np.ndarray
+
+        obj = Series(np.random.default_rng(2).standard_normal(51))
+        obj = tm.box_expected(obj, box, transpose=False)
+        with monkeypatch.context() as m:
+            m.setattr(expr, "_MIN_ELEMENTS", 50)
+            result = obj == "a"
+
+        expected = Series(np.zeros(51, dtype=bool))
+        expected = tm.box_expected(expected, xbox, transpose=False)
+        tm.assert_equal(result, expected)
+
+        with monkeypatch.context() as m:
+            m.setattr(expr, "_MIN_ELEMENTS", 50)
+            result = obj != "a"
+        tm.assert_equal(result, ~expected)
+
+        msg = "Invalid comparison between dtype=float64 and str"
+        with pytest.raises(TypeError, match=msg):
+            obj < "a"
+
+
+# ------------------------------------------------------------------
+# Numeric dtypes Arithmetic with Datetime/Timedelta Scalar
+
+
+class TestNumericArraylikeArithmeticWithDatetimeLike:
+    @pytest.mark.parametrize("box_cls", [np.array, Index, Series])
+    @pytest.mark.parametrize(
+        "left", lefts, ids=lambda x: type(x).__name__ + str(x.dtype)
+    )
+    def test_mul_td64arr(self, left, box_cls):
+        # GH#22390
+        right = np.array([1, 2, 3], dtype="m8[s]")
+        right = box_cls(right)
+
+        expected = TimedeltaIndex(["10s", "40s", "90s"], dtype=right.dtype)
+
+        if isinstance(left, Series) or box_cls is Series:
+            expected = Series(expected)
+        assert expected.dtype == right.dtype
+
+        result = left * right
+        tm.assert_equal(result, expected)
+
+        result = right * left
+        tm.assert_equal(result, expected)
+
+    @pytest.mark.parametrize("box_cls", [np.array, Index, Series])
+    @pytest.mark.parametrize(
+        "left", lefts, ids=lambda x: type(x).__name__ + str(x.dtype)
+    )
+    def test_div_td64arr(self, left, box_cls):
+        # GH#22390
+        right = np.array([10, 40, 90], dtype="m8[s]")
+        right = box_cls(right)
+
+        expected = TimedeltaIndex(["1s", "2s", "3s"], dtype=right.dtype)
+        if isinstance(left, Series) or box_cls is Series:
+            expected = Series(expected)
+        assert expected.dtype == right.dtype
+
+        result = right / left
+        tm.assert_equal(result, expected)
+
+        result = right // left
+        tm.assert_equal(result, expected)
+
+        # (true_) needed for min-versions build 2022-12-26
+        msg = "ufunc '(true_)?divide' cannot use operands with types"
+        with pytest.raises(TypeError, match=msg):
+            left / right
+
+        msg = "ufunc 'floor_divide' cannot use operands with types"
+        with pytest.raises(TypeError, match=msg):
+            left // right
+
+    # TODO: also test Tick objects;
+    #  see test_numeric_arr_rdiv_tdscalar for note on these failing
+    @pytest.mark.parametrize(
+        "scalar_td",
+        [
+            Timedelta(days=1),
+            Timedelta(days=1).to_timedelta64(),
+            Timedelta(days=1).to_pytimedelta(),
+            Timedelta(days=1).to_timedelta64().astype("timedelta64[s]"),
+            Timedelta(days=1).to_timedelta64().astype("timedelta64[ms]"),
+        ],
+        ids=lambda x: type(x).__name__,
+    )
+    def test_numeric_arr_mul_tdscalar(self, scalar_td, numeric_idx, box_with_array):
+        # GH#19333
+        box = box_with_array
+        index = numeric_idx
+        expected = TimedeltaIndex([Timedelta(days=n) for n in range(len(index))])
+        if isinstance(scalar_td, np.timedelta64):
+            dtype = scalar_td.dtype
+            expected = expected.astype(dtype)
+        elif type(scalar_td) is timedelta:
+            expected = expected.astype("m8[us]")
+
+        index = tm.box_expected(index, box)
+        expected = tm.box_expected(expected, box)
+
+        result = index * scalar_td
+        tm.assert_equal(result, expected)
+
+        commute = scalar_td * index
+        tm.assert_equal(commute, expected)
+
+    @pytest.mark.parametrize(
+        "scalar_td",
+        [
+            Timedelta(days=1),
+            Timedelta(days=1).to_timedelta64(),
+            Timedelta(days=1).to_pytimedelta(),
+        ],
+        ids=lambda x: type(x).__name__,
+    )
+    @pytest.mark.parametrize("dtype", [np.int64, np.float64])
+    def test_numeric_arr_mul_tdscalar_numexpr_path(
+        self, dtype, scalar_td, box_with_array
+    ):
+        # GH#44772 for the float64 case
+        box = box_with_array
+
+        arr_i8 = np.arange(2 * 10**4).astype(np.int64, copy=False)
+        arr = arr_i8.astype(dtype, copy=False)
+        obj = tm.box_expected(arr, box, transpose=False)
+
+        expected = arr_i8.view("timedelta64[D]").astype("timedelta64[ns]")
+        if type(scalar_td) is timedelta:
+            expected = expected.astype("timedelta64[us]")
+
+        expected = tm.box_expected(expected, box, transpose=False)
+
+        result = obj * scalar_td
+        tm.assert_equal(result, expected)
+
+        result = scalar_td * obj
+        tm.assert_equal(result, expected)
+
+    def test_numeric_arr_rdiv_tdscalar(self, three_days, numeric_idx, box_with_array):
+        box = box_with_array
+
+        index = numeric_idx[1:3]
+
+        expected = TimedeltaIndex(["3 Days", "36 Hours"])
+        if isinstance(three_days, np.timedelta64):
+            dtype = three_days.dtype
+            if dtype < np.dtype("m8[s]"):
+                # i.e. resolution is lower -> use lowest supported resolution
+                dtype = np.dtype("m8[s]")
+            expected = expected.astype(dtype)
+        elif type(three_days) is timedelta:
+            expected = expected.astype("m8[us]")
+        elif isinstance(
+            three_days,
+            (pd.offsets.Day, pd.offsets.Hour, pd.offsets.Minute, pd.offsets.Second),
+        ):
+            # closest reso is Second
+            expected = expected.astype("m8[s]")
+
+        index = tm.box_expected(index, box)
+        expected = tm.box_expected(expected, box)
+
+        result = three_days / index
+        tm.assert_equal(result, expected)
+
+        msg = "cannot use operands with types dtype"
+        with pytest.raises(TypeError, match=msg):
+            index / three_days
+
+    @pytest.mark.parametrize(
+        "other",
+        [
+            Timedelta(hours=31),
+            Timedelta(hours=31).to_pytimedelta(),
+            Timedelta(hours=31).to_timedelta64(),
+            Timedelta(hours=31).to_timedelta64().astype("m8[h]"),
+            np.timedelta64("NaT"),
+            np.timedelta64("NaT", "D"),
+            pd.offsets.Minute(3),
+            pd.offsets.Second(0),
+            # GH#28080 numeric+datetimelike should raise; Timestamp used
+            #  to raise NullFrequencyError but that behavior was removed in 1.0
+            pd.Timestamp("2021-01-01", tz="Asia/Tokyo"),
+            pd.Timestamp("2021-01-01"),
+            pd.Timestamp("2021-01-01").to_pydatetime(),
+            pd.Timestamp("2021-01-01", tz="UTC").to_pydatetime(),
+            pd.Timestamp("2021-01-01").to_datetime64(),
+            np.datetime64("NaT", "ns"),
+            pd.NaT,
+        ],
+        ids=repr,
+    )
+    def test_add_sub_datetimedeltalike_invalid(
+        self, numeric_idx, other, box_with_array
+    ):
+        box = box_with_array
+
+        left = tm.box_expected(numeric_idx, box)
+        msg = "|".join(
+            [
+                "unsupported operand type",
+                "Addition/subtraction of integers and integer-arrays",
+                "Instead of adding/subtracting",
+                "cannot use operands with types dtype",
+                "Concatenation operation is not implemented for NumPy arrays",
+                "Cannot (add|subtract) NaT (to|from) ndarray",
+                # pd.array vs np.datetime64 case
+                r"operand type\(s\) all returned NotImplemented from __array_ufunc__",
+                "can only perform ops with numeric values",
+                "cannot subtract DatetimeArray from ndarray",
+                # pd.Timedelta(1) + Index([0, 1, 2])
+                "Cannot add or subtract Timedelta from integers",
+            ]
+        )
+        assert_invalid_addsub_type(left, other, msg)
+
+
+# ------------------------------------------------------------------
+# Arithmetic
+
+
+class TestDivisionByZero:
+    def test_div_zero(self, zero, numeric_idx):
+        idx = numeric_idx
+
+        expected = Index([np.nan, np.inf, np.inf, np.inf, np.inf], dtype=np.float64)
+        # We only adjust for Index, because Series does not yet apply
+        #  the adjustment correctly.
+        expected2 = adjust_negative_zero(zero, expected)
+
+        result = idx / zero
+        tm.assert_index_equal(result, expected2)
+        ser_compat = Series(idx).astype("i8") / np.array(zero).astype("i8")
+        tm.assert_series_equal(ser_compat, Series(expected))
+
+    def test_floordiv_zero(self, zero, numeric_idx):
+        idx = numeric_idx
+
+        expected = Index([np.nan, np.inf, np.inf, np.inf, np.inf], dtype=np.float64)
+        # We only adjust for Index, because Series does not yet apply
+        #  the adjustment correctly.
+        expected2 = adjust_negative_zero(zero, expected)
+
+        result = idx // zero
+        tm.assert_index_equal(result, expected2)
+        ser_compat = Series(idx).astype("i8") // np.array(zero).astype("i8")
+        tm.assert_series_equal(ser_compat, Series(expected))
+
+    def test_mod_zero(self, zero, numeric_idx):
+        idx = numeric_idx
+
+        expected = Index([np.nan, np.nan, np.nan, np.nan, np.nan], dtype=np.float64)
+        result = idx % zero
+        tm.assert_index_equal(result, expected)
+        ser_compat = Series(idx).astype("i8") % np.array(zero).astype("i8")
+        tm.assert_series_equal(ser_compat, Series(result))
+
+    def test_divmod_zero(self, zero, numeric_idx):
+        idx = numeric_idx
+
+        exleft = Index([np.nan, np.inf, np.inf, np.inf, np.inf], dtype=np.float64)
+        exright = Index([np.nan, np.nan, np.nan, np.nan, np.nan], dtype=np.float64)
+        exleft = adjust_negative_zero(zero, exleft)
+
+        result = divmod(idx, zero)
+        tm.assert_index_equal(result[0], exleft)
+        tm.assert_index_equal(result[1], exright)
+
+    @pytest.mark.parametrize("op", [operator.truediv, operator.floordiv])
+    def test_div_negative_zero(self, zero, numeric_idx, op):
+        # Check that -1 / -0.0 returns np.inf, not -np.inf
+        if numeric_idx.dtype == np.uint64:
+            pytest.skip(f"Div by negative 0 not relevant for {numeric_idx.dtype}")
+        idx = numeric_idx - 3
+
+        expected = Index([-np.inf, -np.inf, -np.inf, np.nan, np.inf], dtype=np.float64)
+        expected = adjust_negative_zero(zero, expected)
+
+        result = op(idx, zero)
+        tm.assert_index_equal(result, expected)
+
+    # ------------------------------------------------------------------
+
+    @pytest.mark.parametrize("dtype1", [np.int64, np.float64, np.uint64])
+    def test_ser_div_ser(
+        self,
+        switch_numexpr_min_elements,
+        dtype1,
+        any_real_numpy_dtype,
+    ):
+        # no longer do integer div for any ops, but deal with the 0's
+        dtype2 = any_real_numpy_dtype
+
+        first = Series([3, 4, 5, 8], name="first").astype(dtype1)
+        second = Series([0, 0, 0, 3], name="second").astype(dtype2)
+
+        with np.errstate(all="ignore"):
+            expected = Series(
+                first.values.astype(np.float64) / second.values,
+                dtype="float64",
+                name=None,
+            )
+        expected.iloc[0:3] = np.inf
+        if first.dtype == "int64" and second.dtype == "float32":
+            # when using numexpr, the casting rules are slightly different
+            # and int64/float32 combo results in float32 instead of float64
+            if expr.USE_NUMEXPR and switch_numexpr_min_elements == 0:
+                expected = expected.astype("float32")
+
+        result = first / second
+        tm.assert_series_equal(result, expected)
+        assert not result.equals(second / first)
+
+    @pytest.mark.parametrize("dtype1", [np.int64, np.float64, np.uint64])
+    def test_ser_divmod_zero(self, dtype1, any_real_numpy_dtype):
+        # GH#26987
+        dtype2 = any_real_numpy_dtype
+        left = Series([1, 1]).astype(dtype1)
+        right = Series([0, 2]).astype(dtype2)
+
+        # GH#27321 pandas convention is to set 1 // 0 to np.inf, as opposed
+        #  to numpy which sets to np.nan; patch `expected[0]` below
+        expected = left // right, left % right
+        expected = list(expected)
+        expected[0] = expected[0].astype(np.float64)
+        expected[0][0] = np.inf
+        result = divmod(left, right)
+
+        tm.assert_series_equal(result[0], expected[0])
+        tm.assert_series_equal(result[1], expected[1])
+
+        # rdivmod case
+        result = divmod(left.values, right)
+        tm.assert_series_equal(result[0], expected[0])
+        tm.assert_series_equal(result[1], expected[1])
+
+    def test_ser_divmod_inf(self):
+        left = Series([np.inf, 1.0])
+        right = Series([np.inf, 2.0])
+
+        expected = left // right, left % right
+        result = divmod(left, right)
+
+        tm.assert_series_equal(result[0], expected[0])
+        tm.assert_series_equal(result[1], expected[1])
+
+        # rdivmod case
+        result = divmod(left.values, right)
+        tm.assert_series_equal(result[0], expected[0])
+        tm.assert_series_equal(result[1], expected[1])
+
+    def test_rdiv_zero_compat(self):
+        # GH#8674
+        zero_array = np.array([0] * 5)
+        data = np.random.default_rng(2).standard_normal(5)
+        expected = Series([0.0] * 5)
+
+        result = zero_array / Series(data)
+        tm.assert_series_equal(result, expected)
+
+        result = Series(zero_array) / data
+        tm.assert_series_equal(result, expected)
+
+        result = Series(zero_array) / Series(data)
+        tm.assert_series_equal(result, expected)
+
+    def test_div_zero_inf_signs(self):
+        # GH#9144, inf signing
+        ser = Series([-1, 0, 1], name="first")
+        expected = Series([-np.inf, np.nan, np.inf], name="first")
+
+        result = ser / 0
+        tm.assert_series_equal(result, expected)
+
+    def test_rdiv_zero(self):
+        # GH#9144
+        ser = Series([-1, 0, 1], name="first")
+        expected = Series([0.0, np.nan, 0.0], name="first")
+
+        result = 0 / ser
+        tm.assert_series_equal(result, expected)
+
+    def test_floordiv_div(self):
+        # GH#9144
+        ser = Series([-1, 0, 1], name="first")
+
+        result = ser // 0
+        expected = Series([-np.inf, np.nan, np.inf], name="first")
+        tm.assert_series_equal(result, expected)
+
+    def test_df_div_zero_df(self):
+        # integer div, but deal with the 0's (GH#9144)
+        df = pd.DataFrame({"first": [3, 4, 5, 8], "second": [0, 0, 0, 3]})
+        result = df / df
+
+        first = Series([1.0, 1.0, 1.0, 1.0])
+        second = Series([np.nan, np.nan, np.nan, 1])
+        expected = pd.DataFrame({"first": first, "second": second})
+        tm.assert_frame_equal(result, expected)
+
+    def test_df_div_zero_array(self):
+        # integer div, but deal with the 0's (GH#9144)
+        df = pd.DataFrame({"first": [3, 4, 5, 8], "second": [0, 0, 0, 3]})
+
+        first = Series([1.0, 1.0, 1.0, 1.0])
+        second = Series([np.nan, np.nan, np.nan, 1])
+        expected = pd.DataFrame({"first": first, "second": second})
+
+        with np.errstate(all="ignore"):
+            arr = df.values.astype("float") / df.values
+        result = pd.DataFrame(arr, index=df.index, columns=df.columns)
+        tm.assert_frame_equal(result, expected)
+
+    def test_df_div_zero_int(self):
+        # integer div, but deal with the 0's (GH#9144)
+        df = pd.DataFrame({"first": [3, 4, 5, 8], "second": [0, 0, 0, 3]})
+
+        result = df / 0
+        expected = pd.DataFrame(np.inf, index=df.index, columns=df.columns)
+        expected.iloc[0:3, 1] = np.nan
+        tm.assert_frame_equal(result, expected)
+
+        # numpy has a slightly different (wrong) treatment
+        with np.errstate(all="ignore"):
+            arr = df.values.astype("float64") / 0
+        result2 = pd.DataFrame(arr, index=df.index, columns=df.columns)
+        tm.assert_frame_equal(result2, expected)
+
+    def test_df_div_zero_series_does_not_commute(self):
+        # integer div, but deal with the 0's (GH#9144)
+        df = pd.DataFrame(np.random.default_rng(2).standard_normal((10, 5)))
+        ser = df[0]
+        res = ser / df
+        res2 = df / ser
+        assert not res.fillna(0).equals(res2.fillna(0))
+
+    # ------------------------------------------------------------------
+    # Mod By Zero
+
+    def test_df_mod_zero_df(self, using_array_manager):
+        # GH#3590, modulo as ints
+        df = pd.DataFrame({"first": [3, 4, 5, 8], "second": [0, 0, 0, 3]})
+        # this is technically wrong, as the integer portion is coerced to float
+        first = Series([0, 0, 0, 0])
+        if not using_array_manager:
+            # INFO(ArrayManager) BlockManager doesn't preserve dtype per column
+            # while ArrayManager performs op column-wisedoes and thus preserves
+            # dtype if possible
+            first = first.astype("float64")
+        second = Series([np.nan, np.nan, np.nan, 0])
+        expected = pd.DataFrame({"first": first, "second": second})
+        result = df % df
+        tm.assert_frame_equal(result, expected)
+
+        # GH#38939 If we dont pass copy=False, df is consolidated and
+        #  result["first"] is float64 instead of int64
+        df = pd.DataFrame({"first": [3, 4, 5, 8], "second": [0, 0, 0, 3]}, copy=False)
+        first = Series([0, 0, 0, 0], dtype="int64")
+        second = Series([np.nan, np.nan, np.nan, 0])
+        expected = pd.DataFrame({"first": first, "second": second})
+        result = df % df
+        tm.assert_frame_equal(result, expected)
+
+    def test_df_mod_zero_array(self):
+        # GH#3590, modulo as ints
+        df = pd.DataFrame({"first": [3, 4, 5, 8], "second": [0, 0, 0, 3]})
+
+        # this is technically wrong, as the integer portion is coerced to float
+        # ###
+        first = Series([0, 0, 0, 0], dtype="float64")
+        second = Series([np.nan, np.nan, np.nan, 0])
+        expected = pd.DataFrame({"first": first, "second": second})
+
+        # numpy has a slightly different (wrong) treatment
+        with np.errstate(all="ignore"):
+            arr = df.values % df.values
+        result2 = pd.DataFrame(arr, index=df.index, columns=df.columns, dtype="float64")
+        result2.iloc[0:3, 1] = np.nan
+        tm.assert_frame_equal(result2, expected)
+
+    def test_df_mod_zero_int(self):
+        # GH#3590, modulo as ints
+        df = pd.DataFrame({"first": [3, 4, 5, 8], "second": [0, 0, 0, 3]})
+
+        result = df % 0
+        expected = pd.DataFrame(np.nan, index=df.index, columns=df.columns)
+        tm.assert_frame_equal(result, expected)
+
+        # numpy has a slightly different (wrong) treatment
+        with np.errstate(all="ignore"):
+            arr = df.values.astype("float64") % 0
+        result2 = pd.DataFrame(arr, index=df.index, columns=df.columns)
+        tm.assert_frame_equal(result2, expected)
+
+    def test_df_mod_zero_series_does_not_commute(self):
+        # GH#3590, modulo as ints
+        # not commutative with series
+        df = pd.DataFrame(np.random.default_rng(2).standard_normal((10, 5)))
+        ser = df[0]
+        res = ser % df
+        res2 = df % ser
+        assert not res.fillna(0).equals(res2.fillna(0))
+
+
+class TestMultiplicationDivision:
+    # __mul__, __rmul__, __div__, __rdiv__, __floordiv__, __rfloordiv__
+    # for non-timestamp/timedelta/period dtypes
+
+    def test_divide_decimal(self, box_with_array):
+        # resolves issue GH#9787
+        box = box_with_array
+        ser = Series([Decimal(10)])
+        expected = Series([Decimal(5)])
+
+        ser = tm.box_expected(ser, box)
+        expected = tm.box_expected(expected, box)
+
+        result = ser / Decimal(2)
+
+        tm.assert_equal(result, expected)
+
+        result = ser // Decimal(2)
+        tm.assert_equal(result, expected)
+
+    def test_div_equiv_binop(self):
+        # Test Series.div as well as Series.__div__
+        # float/integer issue
+        # GH#7785
+        first = Series([1, 0], name="first")
+        second = Series([-0.01, -0.02], name="second")
+        expected = Series([-0.01, -np.inf])
+
+        result = second.div(first)
+        tm.assert_series_equal(result, expected, check_names=False)
+
+        result = second / first
+        tm.assert_series_equal(result, expected)
+
+    def test_div_int(self, numeric_idx):
+        idx = numeric_idx
+        result = idx / 1
+        expected = idx.astype("float64")
+        tm.assert_index_equal(result, expected)
+
+        result = idx / 2
+        expected = Index(idx.values / 2)
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize("op", [operator.mul, ops.rmul, operator.floordiv])
+    def test_mul_int_identity(self, op, numeric_idx, box_with_array):
+        idx = numeric_idx
+        idx = tm.box_expected(idx, box_with_array)
+
+        result = op(idx, 1)
+        tm.assert_equal(result, idx)
+
+    def test_mul_int_array(self, numeric_idx):
+        idx = numeric_idx
+        didx = idx * idx
+
+        result = idx * np.array(5, dtype="int64")
+        tm.assert_index_equal(result, idx * 5)
+
+        arr_dtype = "uint64" if idx.dtype == np.uint64 else "int64"
+        result = idx * np.arange(5, dtype=arr_dtype)
+        tm.assert_index_equal(result, didx)
+
+    def test_mul_int_series(self, numeric_idx):
+        idx = numeric_idx
+        didx = idx * idx
+
+        arr_dtype = "uint64" if idx.dtype == np.uint64 else "int64"
+        result = idx * Series(np.arange(5, dtype=arr_dtype))
+        tm.assert_series_equal(result, Series(didx))
+
+    def test_mul_float_series(self, numeric_idx):
+        idx = numeric_idx
+        rng5 = np.arange(5, dtype="float64")
+
+        result = idx * Series(rng5 + 0.1)
+        expected = Series(rng5 * (rng5 + 0.1))
+        tm.assert_series_equal(result, expected)
+
+    def test_mul_index(self, numeric_idx):
+        idx = numeric_idx
+
+        result = idx * idx
+        tm.assert_index_equal(result, idx**2)
+
+    def test_mul_datelike_raises(self, numeric_idx):
+        idx = numeric_idx
+        msg = "cannot perform __rmul__ with this index type"
+        with pytest.raises(TypeError, match=msg):
+            idx * date_range("20130101", periods=5)
+
+    def test_mul_size_mismatch_raises(self, numeric_idx):
+        idx = numeric_idx
+        msg = "operands could not be broadcast together"
+        with pytest.raises(ValueError, match=msg):
+            idx * idx[0:3]
+        with pytest.raises(ValueError, match=msg):
+            idx * np.array([1, 2])
+
+    @pytest.mark.parametrize("op", [operator.pow, ops.rpow])
+    def test_pow_float(self, op, numeric_idx, box_with_array):
+        # test power calculations both ways, GH#14973
+        box = box_with_array
+        idx = numeric_idx
+        expected = Index(op(idx.values, 2.0))
+
+        idx = tm.box_expected(idx, box)
+        expected = tm.box_expected(expected, box)
+
+        result = op(idx, 2.0)
+        tm.assert_equal(result, expected)
+
+    def test_modulo(self, numeric_idx, box_with_array):
+        # GH#9244
+        box = box_with_array
+        idx = numeric_idx
+        expected = Index(idx.values % 2)
+
+        idx = tm.box_expected(idx, box)
+        expected = tm.box_expected(expected, box)
+
+        result = idx % 2
+        tm.assert_equal(result, expected)
+
+    def test_divmod_scalar(self, numeric_idx):
+        idx = numeric_idx
+
+        result = divmod(idx, 2)
+        with np.errstate(all="ignore"):
+            div, mod = divmod(idx.values, 2)
+
+        expected = Index(div), Index(mod)
+        for r, e in zip(result, expected):
+            tm.assert_index_equal(r, e)
+
+    def test_divmod_ndarray(self, numeric_idx):
+        idx = numeric_idx
+        other = np.ones(idx.values.shape, dtype=idx.values.dtype) * 2
+
+        result = divmod(idx, other)
+        with np.errstate(all="ignore"):
+            div, mod = divmod(idx.values, other)
+
+        expected = Index(div), Index(mod)
+        for r, e in zip(result, expected):
+            tm.assert_index_equal(r, e)
+
+    def test_divmod_series(self, numeric_idx):
+        idx = numeric_idx
+        other = np.ones(idx.values.shape, dtype=idx.values.dtype) * 2
+
+        result = divmod(idx, Series(other))
+        with np.errstate(all="ignore"):
+            div, mod = divmod(idx.values, other)
+
+        expected = Series(div), Series(mod)
+        for r, e in zip(result, expected):
+            tm.assert_series_equal(r, e)
+
+    @pytest.mark.parametrize("other", [np.nan, 7, -23, 2.718, -3.14, np.inf])
+    def test_ops_np_scalar(self, other):
+        vals = np.random.default_rng(2).standard_normal((5, 3))
+        f = lambda x: pd.DataFrame(
+            x, index=list("ABCDE"), columns=["jim", "joe", "jolie"]
+        )
+
+        df = f(vals)
+
+        tm.assert_frame_equal(df / np.array(other), f(vals / other))
+        tm.assert_frame_equal(np.array(other) * df, f(vals * other))
+        tm.assert_frame_equal(df + np.array(other), f(vals + other))
+        tm.assert_frame_equal(np.array(other) - df, f(other - vals))
+
+    # TODO: This came from series.test.test_operators, needs cleanup
+    def test_operators_frame(self):
+        # rpow does not work with DataFrame
+        ts = Series(
+            np.arange(10, dtype=np.float64),
+            index=date_range("2020-01-01", periods=10),
+            name="ts",
+        )
+        ts.name = "ts"
+
+        df = pd.DataFrame({"A": ts})
+
+        tm.assert_series_equal(ts + ts, ts + df["A"], check_names=False)
+        tm.assert_series_equal(ts**ts, ts ** df["A"], check_names=False)
+        tm.assert_series_equal(ts < ts, ts < df["A"], check_names=False)
+        tm.assert_series_equal(ts / ts, ts / df["A"], check_names=False)
+
+    # TODO: this came from tests.series.test_analytics, needs cleanup and
+    #  de-duplication with test_modulo above
+    def test_modulo2(self):
+        with np.errstate(all="ignore"):
+            # GH#3590, modulo as ints
+            p = pd.DataFrame({"first": [3, 4, 5, 8], "second": [0, 0, 0, 3]})
+            result = p["first"] % p["second"]
+            expected = Series(p["first"].values % p["second"].values, dtype="float64")
+            expected.iloc[0:3] = np.nan
+            tm.assert_series_equal(result, expected)
+
+            result = p["first"] % 0
+            expected = Series(np.nan, index=p.index, name="first")
+            tm.assert_series_equal(result, expected)
+
+            p = p.astype("float64")
+            result = p["first"] % p["second"]
+            expected = Series(p["first"].values % p["second"].values)
+            tm.assert_series_equal(result, expected)
+
+            p = p.astype("float64")
+            result = p["first"] % p["second"]
+            result2 = p["second"] % p["first"]
+            assert not result.equals(result2)
+
+    def test_modulo_zero_int(self):
+        # GH#9144
+        with np.errstate(all="ignore"):
+            s = Series([0, 1])
+
+            result = s % 0
+            expected = Series([np.nan, np.nan])
+            tm.assert_series_equal(result, expected)
+
+            result = 0 % s
+            expected = Series([np.nan, 0.0])
+            tm.assert_series_equal(result, expected)
+
+
+class TestAdditionSubtraction:
+    # __add__, __sub__, __radd__, __rsub__, __iadd__, __isub__
+    # for non-timestamp/timedelta/period dtypes
+
+    @pytest.mark.parametrize(
+        "first, second, expected",
+        [
+            (
+                Series([1, 2, 3], index=list("ABC"), name="x"),
+                Series([2, 2, 2], index=list("ABD"), name="x"),
+                Series([3.0, 4.0, np.nan, np.nan], index=list("ABCD"), name="x"),
+            ),
+            (
+                Series([1, 2, 3], index=list("ABC"), name="x"),
+                Series([2, 2, 2, 2], index=list("ABCD"), name="x"),
+                Series([3, 4, 5, np.nan], index=list("ABCD"), name="x"),
+            ),
+        ],
+    )
+    def test_add_series(self, first, second, expected):
+        # GH#1134
+        tm.assert_series_equal(first + second, expected)
+        tm.assert_series_equal(second + first, expected)
+
+    @pytest.mark.parametrize(
+        "first, second, expected",
+        [
+            (
+                pd.DataFrame({"x": [1, 2, 3]}, index=list("ABC")),
+                pd.DataFrame({"x": [2, 2, 2]}, index=list("ABD")),
+                pd.DataFrame({"x": [3.0, 4.0, np.nan, np.nan]}, index=list("ABCD")),
+            ),
+            (
+                pd.DataFrame({"x": [1, 2, 3]}, index=list("ABC")),
+                pd.DataFrame({"x": [2, 2, 2, 2]}, index=list("ABCD")),
+                pd.DataFrame({"x": [3, 4, 5, np.nan]}, index=list("ABCD")),
+            ),
+        ],
+    )
+    def test_add_frames(self, first, second, expected):
+        # GH#1134
+        tm.assert_frame_equal(first + second, expected)
+        tm.assert_frame_equal(second + first, expected)
+
+    # TODO: This came from series.test.test_operators, needs cleanup
+    def test_series_frame_radd_bug(self, fixed_now_ts):
+        # GH#353
+        vals = Series([str(i) for i in range(5)])
+        result = "foo_" + vals
+        expected = vals.map(lambda x: "foo_" + x)
+        tm.assert_series_equal(result, expected)
+
+        frame = pd.DataFrame({"vals": vals})
+        result = "foo_" + frame
+        expected = pd.DataFrame({"vals": vals.map(lambda x: "foo_" + x)})
+        tm.assert_frame_equal(result, expected)
+
+        ts = Series(
+            np.arange(10, dtype=np.float64),
+            index=date_range("2020-01-01", periods=10),
+            name="ts",
+        )
+
+        # really raise this time
+        fix_now = fixed_now_ts.to_pydatetime()
+        msg = "|".join(
+            [
+                "unsupported operand type",
+                # wrong error message, see https://github.com/numpy/numpy/issues/18832
+                "Concatenation operation",
+            ]
+        )
+        with pytest.raises(TypeError, match=msg):
+            fix_now + ts
+
+        with pytest.raises(TypeError, match=msg):
+            ts + fix_now
+
+    # TODO: This came from series.test.test_operators, needs cleanup
+    def test_datetime64_with_index(self):
+        # arithmetic integer ops with an index
+        ser = Series(np.random.default_rng(2).standard_normal(5))
+        expected = ser - ser.index.to_series()
+        result = ser - ser.index
+        tm.assert_series_equal(result, expected)
+
+        # GH#4629
+        # arithmetic datetime64 ops with an index
+        ser = Series(
+            date_range("20130101", periods=5),
+            index=date_range("20130101", periods=5),
+        )
+        expected = ser - ser.index.to_series()
+        result = ser - ser.index
+        tm.assert_series_equal(result, expected)
+
+        msg = "cannot subtract PeriodArray from DatetimeArray"
+        with pytest.raises(TypeError, match=msg):
+            # GH#18850
+            result = ser - ser.index.to_period()
+
+        df = pd.DataFrame(
+            np.random.default_rng(2).standard_normal((5, 2)),
+            index=date_range("20130101", periods=5),
+        )
+        df["date"] = pd.Timestamp("20130102")
+        df["expected"] = df["date"] - df.index.to_series()
+        df["result"] = df["date"] - df.index
+        tm.assert_series_equal(df["result"], df["expected"], check_names=False)
+
+    # TODO: taken from tests.frame.test_operators, needs cleanup
+    def test_frame_operators(self, float_frame):
+        frame = float_frame
+
+        garbage = np.random.default_rng(2).random(4)
+        colSeries = Series(garbage, index=np.array(frame.columns))
+
+        idSum = frame + frame
+        seriesSum = frame + colSeries
+
+        for col, series in idSum.items():
+            for idx, val in series.items():
+                origVal = frame[col][idx] * 2
+                if not np.isnan(val):
+                    assert val == origVal
+                else:
+                    assert np.isnan(origVal)
+
+        for col, series in seriesSum.items():
+            for idx, val in series.items():
+                origVal = frame[col][idx] + colSeries[col]
+                if not np.isnan(val):
+                    assert val == origVal
+                else:
+                    assert np.isnan(origVal)
+
+    def test_frame_operators_col_align(self, float_frame):
+        frame2 = pd.DataFrame(float_frame, columns=["D", "C", "B", "A"])
+        added = frame2 + frame2
+        expected = frame2 * 2
+        tm.assert_frame_equal(added, expected)
+
+    def test_frame_operators_none_to_nan(self):
+        df = pd.DataFrame({"a": ["a", None, "b"]})
+        tm.assert_frame_equal(df + df, pd.DataFrame({"a": ["aa", np.nan, "bb"]}))
+
+    @pytest.mark.parametrize("dtype", ("float", "int64"))
+    def test_frame_operators_empty_like(self, dtype):
+        # Test for issue #10181
+        frames = [
+            pd.DataFrame(dtype=dtype),
+            pd.DataFrame(columns=["A"], dtype=dtype),
+            pd.DataFrame(index=[0], dtype=dtype),
+        ]
+        for df in frames:
+            assert (df + df).equals(df)
+            tm.assert_frame_equal(df + df, df)
+
+    @pytest.mark.parametrize(
+        "func",
+        [lambda x: x * 2, lambda x: x[::2], lambda x: 5],
+        ids=["multiply", "slice", "constant"],
+    )
+    def test_series_operators_arithmetic(self, all_arithmetic_functions, func):
+        op = all_arithmetic_functions
+        series = Series(
+            np.arange(10, dtype=np.float64),
+            index=date_range("2020-01-01", periods=10),
+            name="ts",
+        )
+        other = func(series)
+        compare_op(series, other, op)
+
+    @pytest.mark.parametrize(
+        "func", [lambda x: x + 1, lambda x: 5], ids=["add", "constant"]
+    )
+    def test_series_operators_compare(self, comparison_op, func):
+        op = comparison_op
+        series = Series(
+            np.arange(10, dtype=np.float64),
+            index=date_range("2020-01-01", periods=10),
+            name="ts",
+        )
+        other = func(series)
+        compare_op(series, other, op)
+
+    @pytest.mark.parametrize(
+        "func",
+        [lambda x: x * 2, lambda x: x[::2], lambda x: 5],
+        ids=["multiply", "slice", "constant"],
+    )
+    def test_divmod(self, func):
+        series = Series(
+            np.arange(10, dtype=np.float64),
+            index=date_range("2020-01-01", periods=10),
+            name="ts",
+        )
+        other = func(series)
+        results = divmod(series, other)
+        if isinstance(other, abc.Iterable) and len(series) != len(other):
+            # if the lengths don't match, this is the test where we use
+            # `tser[::2]`. Pad every other value in `other_np` with nan.
+            other_np = []
+            for n in other:
+                other_np.append(n)
+                other_np.append(np.nan)
+        else:
+            other_np = other
+        other_np = np.asarray(other_np)
+        with np.errstate(all="ignore"):
+            expecteds = divmod(series.values, np.asarray(other_np))
+
+        for result, expected in zip(results, expecteds):
+            # check the values, name, and index separately
+            tm.assert_almost_equal(np.asarray(result), expected)
+
+            assert result.name == series.name
+            tm.assert_index_equal(result.index, series.index._with_freq(None))
+
+    def test_series_divmod_zero(self):
+        # Check that divmod uses pandas convention for division by zero,
+        #  which does not match numpy.
+        # pandas convention has
+        #  1/0 == np.inf
+        #  -1/0 == -np.inf
+        #  1/-0.0 == -np.inf
+        #  -1/-0.0 == np.inf
+        tser = Series(
+            np.arange(1, 11, dtype=np.float64),
+            index=date_range("2020-01-01", periods=10),
+            name="ts",
+        )
+        other = tser * 0
+
+        result = divmod(tser, other)
+        exp1 = Series([np.inf] * len(tser), index=tser.index, name="ts")
+        exp2 = Series([np.nan] * len(tser), index=tser.index, name="ts")
+        tm.assert_series_equal(result[0], exp1)
+        tm.assert_series_equal(result[1], exp2)
+
+
+class TestUFuncCompat:
+    # TODO: add more dtypes
+    @pytest.mark.parametrize("holder", [Index, RangeIndex, Series])
+    @pytest.mark.parametrize("dtype", [np.int64, np.uint64, np.float64])
+    def test_ufunc_compat(self, holder, dtype):
+        box = Series if holder is Series else Index
+
+        if holder is RangeIndex:
+            if dtype != np.int64:
+                pytest.skip(f"dtype {dtype} not relevant for RangeIndex")
+            idx = RangeIndex(0, 5, name="foo")
+        else:
+            idx = holder(np.arange(5, dtype=dtype), name="foo")
+        result = np.sin(idx)
+        expected = box(np.sin(np.arange(5, dtype=dtype)), name="foo")
+        tm.assert_equal(result, expected)
+
+    # TODO: add more dtypes
+    @pytest.mark.parametrize("holder", [Index, Series])
+    @pytest.mark.parametrize("dtype", [np.int64, np.uint64, np.float64])
+    def test_ufunc_coercions(self, holder, dtype):
+        idx = holder([1, 2, 3, 4, 5], dtype=dtype, name="x")
+        box = Series if holder is Series else Index
+
+        result = np.sqrt(idx)
+        assert result.dtype == "f8" and isinstance(result, box)
+        exp = Index(np.sqrt(np.array([1, 2, 3, 4, 5], dtype=np.float64)), name="x")
+        exp = tm.box_expected(exp, box)
+        tm.assert_equal(result, exp)
+
+        result = np.divide(idx, 2.0)
+        assert result.dtype == "f8" and isinstance(result, box)
+        exp = Index([0.5, 1.0, 1.5, 2.0, 2.5], dtype=np.float64, name="x")
+        exp = tm.box_expected(exp, box)
+        tm.assert_equal(result, exp)
+
+        # _evaluate_numeric_binop
+        result = idx + 2.0
+        assert result.dtype == "f8" and isinstance(result, box)
+        exp = Index([3.0, 4.0, 5.0, 6.0, 7.0], dtype=np.float64, name="x")
+        exp = tm.box_expected(exp, box)
+        tm.assert_equal(result, exp)
+
+        result = idx - 2.0
+        assert result.dtype == "f8" and isinstance(result, box)
+        exp = Index([-1.0, 0.0, 1.0, 2.0, 3.0], dtype=np.float64, name="x")
+        exp = tm.box_expected(exp, box)
+        tm.assert_equal(result, exp)
+
+        result = idx * 1.0
+        assert result.dtype == "f8" and isinstance(result, box)
+        exp = Index([1.0, 2.0, 3.0, 4.0, 5.0], dtype=np.float64, name="x")
+        exp = tm.box_expected(exp, box)
+        tm.assert_equal(result, exp)
+
+        result = idx / 2.0
+        assert result.dtype == "f8" and isinstance(result, box)
+        exp = Index([0.5, 1.0, 1.5, 2.0, 2.5], dtype=np.float64, name="x")
+        exp = tm.box_expected(exp, box)
+        tm.assert_equal(result, exp)
+
+    # TODO: add more dtypes
+    @pytest.mark.parametrize("holder", [Index, Series])
+    @pytest.mark.parametrize("dtype", [np.int64, np.uint64, np.float64])
+    def test_ufunc_multiple_return_values(self, holder, dtype):
+        obj = holder([1, 2, 3], dtype=dtype, name="x")
+        box = Series if holder is Series else Index
+
+        result = np.modf(obj)
+        assert isinstance(result, tuple)
+        exp1 = Index([0.0, 0.0, 0.0], dtype=np.float64, name="x")
+        exp2 = Index([1.0, 2.0, 3.0], dtype=np.float64, name="x")
+        tm.assert_equal(result[0], tm.box_expected(exp1, box))
+        tm.assert_equal(result[1], tm.box_expected(exp2, box))
+
+    def test_ufunc_at(self):
+        s = Series([0, 1, 2], index=[1, 2, 3], name="x")
+        np.add.at(s, [0, 2], 10)
+        expected = Series([10, 1, 12], index=[1, 2, 3], name="x")
+        tm.assert_series_equal(s, expected)
+
+
+class TestObjectDtypeEquivalence:
+    # Tests that arithmetic operations match operations executed elementwise
+
+    @pytest.mark.parametrize("dtype", [None, object])
+    def test_numarr_with_dtype_add_nan(self, dtype, box_with_array):
+        box = box_with_array
+        ser = Series([1, 2, 3], dtype=dtype)
+        expected = Series([np.nan, np.nan, np.nan], dtype=dtype)
+
+        ser = tm.box_expected(ser, box)
+        expected = tm.box_expected(expected, box)
+
+        result = np.nan + ser
+        tm.assert_equal(result, expected)
+
+        result = ser + np.nan
+        tm.assert_equal(result, expected)
+
+    @pytest.mark.parametrize("dtype", [None, object])
+    def test_numarr_with_dtype_add_int(self, dtype, box_with_array):
+        box = box_with_array
+        ser = Series([1, 2, 3], dtype=dtype)
+        expected = Series([2, 3, 4], dtype=dtype)
+
+        ser = tm.box_expected(ser, box)
+        expected = tm.box_expected(expected, box)
+
+        result = 1 + ser
+        tm.assert_equal(result, expected)
+
+        result = ser + 1
+        tm.assert_equal(result, expected)
+
+    # TODO: moved from tests.series.test_operators; needs cleanup
+    @pytest.mark.parametrize(
+        "op",
+        [operator.add, operator.sub, operator.mul, operator.truediv, operator.floordiv],
+    )
+    def test_operators_reverse_object(self, op):
+        # GH#56
+        arr = Series(
+            np.random.default_rng(2).standard_normal(10),
+            index=np.arange(10),
+            dtype=object,
+        )
+
+        result = op(1.0, arr)
+        expected = op(1.0, arr.astype(float))
+        tm.assert_series_equal(result.astype(float), expected)
+
+
+class TestNumericArithmeticUnsorted:
+    # Tests in this class have been moved from type-specific test modules
+    #  but not yet sorted, parametrized, and de-duplicated
+    @pytest.mark.parametrize(
+        "op",
+        [
+            operator.add,
+            operator.sub,
+            operator.mul,
+            operator.floordiv,
+            operator.truediv,
+        ],
+    )
+    @pytest.mark.parametrize(
+        "idx1",
+        [
+            RangeIndex(0, 10, 1),
+            RangeIndex(0, 20, 2),
+            RangeIndex(-10, 10, 2),
+            RangeIndex(5, -5, -1),
+        ],
+    )
+    @pytest.mark.parametrize(
+        "idx2",
+        [
+            RangeIndex(0, 10, 1),
+            RangeIndex(0, 20, 2),
+            RangeIndex(-10, 10, 2),
+            RangeIndex(5, -5, -1),
+        ],
+    )
+    def test_binops_index(self, op, idx1, idx2):
+        idx1 = idx1._rename("foo")
+        idx2 = idx2._rename("bar")
+        result = op(idx1, idx2)
+        expected = op(Index(idx1.to_numpy()), Index(idx2.to_numpy()))
+        tm.assert_index_equal(result, expected, exact="equiv")
+
+    @pytest.mark.parametrize(
+        "op",
+        [
+            operator.add,
+            operator.sub,
+            operator.mul,
+            operator.floordiv,
+            operator.truediv,
+        ],
+    )
+    @pytest.mark.parametrize(
+        "idx",
+        [
+            RangeIndex(0, 10, 1),
+            RangeIndex(0, 20, 2),
+            RangeIndex(-10, 10, 2),
+            RangeIndex(5, -5, -1),
+        ],
+    )
+    @pytest.mark.parametrize("scalar", [-1, 1, 2])
+    def test_binops_index_scalar(self, op, idx, scalar):
+        result = op(idx, scalar)
+        expected = op(Index(idx.to_numpy()), scalar)
+        tm.assert_index_equal(result, expected, exact="equiv")
+
+    @pytest.mark.parametrize("idx1", [RangeIndex(0, 10, 1), RangeIndex(0, 20, 2)])
+    @pytest.mark.parametrize("idx2", [RangeIndex(0, 10, 1), RangeIndex(0, 20, 2)])
+    def test_binops_index_pow(self, idx1, idx2):
+        # numpy does not allow powers of negative integers so test separately
+        # https://github.com/numpy/numpy/pull/8127
+        idx1 = idx1._rename("foo")
+        idx2 = idx2._rename("bar")
+        result = pow(idx1, idx2)
+        expected = pow(Index(idx1.to_numpy()), Index(idx2.to_numpy()))
+        tm.assert_index_equal(result, expected, exact="equiv")
+
+    @pytest.mark.parametrize("idx", [RangeIndex(0, 10, 1), RangeIndex(0, 20, 2)])
+    @pytest.mark.parametrize("scalar", [1, 2])
+    def test_binops_index_scalar_pow(self, idx, scalar):
+        # numpy does not allow powers of negative integers so test separately
+        # https://github.com/numpy/numpy/pull/8127
+        result = pow(idx, scalar)
+        expected = pow(Index(idx.to_numpy()), scalar)
+        tm.assert_index_equal(result, expected, exact="equiv")
+
+    # TODO: divmod?
+    @pytest.mark.parametrize(
+        "op",
+        [
+            operator.add,
+            operator.sub,
+            operator.mul,
+            operator.floordiv,
+            operator.truediv,
+            operator.pow,
+            operator.mod,
+        ],
+    )
+    def test_arithmetic_with_frame_or_series(self, op):
+        # check that we return NotImplemented when operating with Series
+        # or DataFrame
+        index = RangeIndex(5)
+        other = Series(np.random.default_rng(2).standard_normal(5))
+
+        expected = op(Series(index), other)
+        result = op(index, other)
+        tm.assert_series_equal(result, expected)
+
+        other = pd.DataFrame(np.random.default_rng(2).standard_normal((2, 5)))
+        expected = op(pd.DataFrame([index, index]), other)
+        result = op(index, other)
+        tm.assert_frame_equal(result, expected)
+
+    def test_numeric_compat2(self):
+        # validate that we are handling the RangeIndex overrides to numeric ops
+        # and returning RangeIndex where possible
+
+        idx = RangeIndex(0, 10, 2)
+
+        result = idx * 2
+        expected = RangeIndex(0, 20, 4)
+        tm.assert_index_equal(result, expected, exact=True)
+
+        result = idx + 2
+        expected = RangeIndex(2, 12, 2)
+        tm.assert_index_equal(result, expected, exact=True)
+
+        result = idx - 2
+        expected = RangeIndex(-2, 8, 2)
+        tm.assert_index_equal(result, expected, exact=True)
+
+        result = idx / 2
+        expected = RangeIndex(0, 5, 1).astype("float64")
+        tm.assert_index_equal(result, expected, exact=True)
+
+        result = idx / 4
+        expected = RangeIndex(0, 10, 2) / 4
+        tm.assert_index_equal(result, expected, exact=True)
+
+        result = idx // 1
+        expected = idx
+        tm.assert_index_equal(result, expected, exact=True)
+
+        # __mul__
+        result = idx * idx
+        expected = Index(idx.values * idx.values)
+        tm.assert_index_equal(result, expected, exact=True)
+
+        # __pow__
+        idx = RangeIndex(0, 1000, 2)
+        result = idx**2
+        expected = Index(idx._values) ** 2
+        tm.assert_index_equal(Index(result.values), expected, exact=True)
+
+    @pytest.mark.parametrize(
+        "idx, div, expected",
+        [
+            # TODO: add more dtypes
+            (RangeIndex(0, 1000, 2), 2, RangeIndex(0, 500, 1)),
+            (RangeIndex(-99, -201, -3), -3, RangeIndex(33, 67, 1)),
+            (
+                RangeIndex(0, 1000, 1),
+                2,
+                Index(RangeIndex(0, 1000, 1)._values) // 2,
+            ),
+            (
+                RangeIndex(0, 100, 1),
+                2.0,
+                Index(RangeIndex(0, 100, 1)._values) // 2.0,
+            ),
+            (RangeIndex(0), 50, RangeIndex(0)),
+            (RangeIndex(2, 4, 2), 3, RangeIndex(0, 1, 1)),
+            (RangeIndex(-5, -10, -6), 4, RangeIndex(-2, -1, 1)),
+            (RangeIndex(-100, -200, 3), 2, RangeIndex(0)),
+        ],
+    )
+    def test_numeric_compat2_floordiv(self, idx, div, expected):
+        # __floordiv__
+        tm.assert_index_equal(idx // div, expected, exact=True)
+
+    @pytest.mark.parametrize("dtype", [np.int64, np.float64])
+    @pytest.mark.parametrize("delta", [1, 0, -1])
+    def test_addsub_arithmetic(self, dtype, delta):
+        # GH#8142
+        delta = dtype(delta)
+        index = Index([10, 11, 12], dtype=dtype)
+        result = index + delta
+        expected = Index(index.values + delta, dtype=dtype)
+        tm.assert_index_equal(result, expected)
+
+        # this subtraction used to fail
+        result = index - delta
+        expected = Index(index.values - delta, dtype=dtype)
+        tm.assert_index_equal(result, expected)
+
+        tm.assert_index_equal(index + index, 2 * index)
+        tm.assert_index_equal(index - index, 0 * index)
+        assert not (index - index).empty
+
+    def test_pow_nan_with_zero(self, box_with_array):
+        left = Index([np.nan, np.nan, np.nan])
+        right = Index([0, 0, 0])
+        expected = Index([1.0, 1.0, 1.0])
+
+        left = tm.box_expected(left, box_with_array)
+        right = tm.box_expected(right, box_with_array)
+        expected = tm.box_expected(expected, box_with_array)
+
+        result = left**right
+        tm.assert_equal(result, expected)
+
+
+def test_fill_value_inf_masking():
+    # GH #27464 make sure we mask 0/1 with Inf and not NaN
+    df = pd.DataFrame({"A": [0, 1, 2], "B": [1.1, None, 1.1]})
+
+    other = pd.DataFrame({"A": [1.1, 1.2, 1.3]}, index=[0, 2, 3])
+
+    result = df.rfloordiv(other, fill_value=1)
+
+    expected = pd.DataFrame(
+        {"A": [np.inf, 1.0, 0.0, 1.0], "B": [0.0, np.nan, 0.0, np.nan]}
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_dataframe_div_silenced():
+    # GH#26793
+    pdf1 = pd.DataFrame(
+        {
+            "A": np.arange(10),
+            "B": [np.nan, 1, 2, 3, 4] * 2,
+            "C": [np.nan] * 10,
+            "D": np.arange(10),
+        },
+        index=list("abcdefghij"),
+        columns=list("ABCD"),
+    )
+    pdf2 = pd.DataFrame(
+        np.random.default_rng(2).standard_normal((10, 4)),
+        index=list("abcdefghjk"),
+        columns=list("ABCX"),
+    )
+    with tm.assert_produces_warning(None):
+        pdf1.div(pdf2, fill_value=0)
+
+
+@pytest.mark.parametrize(
+    "data, expected_data",
+    [([0, 1, 2], [0, 2, 4])],
+)
+def test_integer_array_add_list_like(
+    box_pandas_1d_array, box_1d_array, data, expected_data
+):
+    # GH22606 Verify operators with IntegerArray and list-likes
+    arr = array(data, dtype="Int64")
+    container = box_pandas_1d_array(arr)
+    left = container + box_1d_array(data)
+    right = box_1d_array(data) + container
+
+    if Series in [box_1d_array, box_pandas_1d_array]:
+        cls = Series
+    elif Index in [box_1d_array, box_pandas_1d_array]:
+        cls = Index
+    else:
+        cls = array
+
+    expected = cls(expected_data, dtype="Int64")
+
+    tm.assert_equal(left, expected)
+    tm.assert_equal(right, expected)
+
+
+def test_sub_multiindex_swapped_levels():
+    # GH 9952
+    df = pd.DataFrame(
+        {"a": np.random.default_rng(2).standard_normal(6)},
+        index=pd.MultiIndex.from_product(
+            [["a", "b"], [0, 1, 2]], names=["levA", "levB"]
+        ),
+    )
+    df2 = df.copy()
+    df2.index = df2.index.swaplevel(0, 1)
+    result = df - df2
+    expected = pd.DataFrame([0.0] * 6, columns=["a"], index=df.index)
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("power", [1, 2, 5])
+@pytest.mark.parametrize("string_size", [0, 1, 2, 5])
+def test_empty_str_comparison(power, string_size):
+    # GH 37348
+    a = np.array(range(10**power))
+    right = pd.DataFrame(a, dtype=np.int64)
+    left = " " * string_size
+
+    result = right == left
+    expected = pd.DataFrame(np.zeros(right.shape, dtype=bool))
+    tm.assert_frame_equal(result, expected)
+
+
+def test_series_add_sub_with_UInt64():
+    # GH 22023
+    series1 = Series([1, 2, 3])
+    series2 = Series([2, 1, 3], dtype="UInt64")
+
+    result = series1 + series2
+    expected = Series([3, 3, 6], dtype="Float64")
+    tm.assert_series_equal(result, expected)
+
+    result = series1 - series2
+    expected = Series([-1, 1, 0], dtype="Float64")
+    tm.assert_series_equal(result, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arithmetic/test_object.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arithmetic/test_object.py
new file mode 100644
index 0000000000000000000000000000000000000000..44e485d40ba536ba08fabb8d2f5aa4e439177010
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arithmetic/test_object.py
@@ -0,0 +1,414 @@
+# Arithmetic tests for DataFrame/Series/Index/Array classes that should
+# behave identically.
+# Specifically for object dtype
+import datetime
+from decimal import Decimal
+import operator
+
+import numpy as np
+import pytest
+
+import pandas.util._test_decorators as td
+
+import pandas as pd
+from pandas import (
+    Series,
+    Timestamp,
+    option_context,
+)
+import pandas._testing as tm
+from pandas.core import ops
+
+# ------------------------------------------------------------------
+# Comparisons
+
+
+class TestObjectComparisons:
+    def test_comparison_object_numeric_nas(self, comparison_op):
+        ser = Series(np.random.default_rng(2).standard_normal(10), dtype=object)
+        shifted = ser.shift(2)
+
+        func = comparison_op
+
+        result = func(ser, shifted)
+        expected = func(ser.astype(float), shifted.astype(float))
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "infer_string", [False, pytest.param(True, marks=td.skip_if_no("pyarrow"))]
+    )
+    def test_object_comparisons(self, infer_string):
+        with option_context("future.infer_string", infer_string):
+            ser = Series(["a", "b", np.nan, "c", "a"])
+
+            result = ser == "a"
+            expected = Series([True, False, False, False, True])
+            tm.assert_series_equal(result, expected)
+
+            result = ser < "a"
+            expected = Series([False, False, False, False, False])
+            tm.assert_series_equal(result, expected)
+
+            result = ser != "a"
+            expected = -(ser == "a")
+            tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("dtype", [None, object])
+    def test_more_na_comparisons(self, dtype):
+        left = Series(["a", np.nan, "c"], dtype=dtype)
+        right = Series(["a", np.nan, "d"], dtype=dtype)
+
+        result = left == right
+        expected = Series([True, False, False])
+        tm.assert_series_equal(result, expected)
+
+        result = left != right
+        expected = Series([False, True, True])
+        tm.assert_series_equal(result, expected)
+
+        result = left == np.nan
+        expected = Series([False, False, False])
+        tm.assert_series_equal(result, expected)
+
+        result = left != np.nan
+        expected = Series([True, True, True])
+        tm.assert_series_equal(result, expected)
+
+
+# ------------------------------------------------------------------
+# Arithmetic
+
+
+class TestArithmetic:
+    def test_add_period_to_array_of_offset(self):
+        # GH#50162
+        per = pd.Period("2012-1-1", freq="D")
+        pi = pd.period_range("2012-1-1", periods=10, freq="D")
+        idx = per - pi
+
+        expected = pd.Index([x + per for x in idx], dtype=object)
+        result = idx + per
+        tm.assert_index_equal(result, expected)
+
+        result = per + idx
+        tm.assert_index_equal(result, expected)
+
+    # TODO: parametrize
+    def test_pow_ops_object(self):
+        # GH#22922
+        # pow is weird with masking & 1, so testing here
+        a = Series([1, np.nan, 1, np.nan], dtype=object)
+        b = Series([1, np.nan, np.nan, 1], dtype=object)
+        result = a**b
+        expected = Series(a.values**b.values, dtype=object)
+        tm.assert_series_equal(result, expected)
+
+        result = b**a
+        expected = Series(b.values**a.values, dtype=object)
+
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("op", [operator.add, ops.radd])
+    @pytest.mark.parametrize("other", ["category", "Int64"])
+    def test_add_extension_scalar(self, other, box_with_array, op):
+        # GH#22378
+        # Check that scalars satisfying is_extension_array_dtype(obj)
+        # do not incorrectly try to dispatch to an ExtensionArray operation
+
+        arr = Series(["a", "b", "c"])
+        expected = Series([op(x, other) for x in arr])
+
+        arr = tm.box_expected(arr, box_with_array)
+        expected = tm.box_expected(expected, box_with_array)
+
+        result = op(arr, other)
+        tm.assert_equal(result, expected)
+
+    def test_objarr_add_str(self, box_with_array):
+        ser = Series(["x", np.nan, "x"])
+        expected = Series(["xa", np.nan, "xa"])
+
+        ser = tm.box_expected(ser, box_with_array)
+        expected = tm.box_expected(expected, box_with_array)
+
+        result = ser + "a"
+        tm.assert_equal(result, expected)
+
+    def test_objarr_radd_str(self, box_with_array):
+        ser = Series(["x", np.nan, "x"])
+        expected = Series(["ax", np.nan, "ax"])
+
+        ser = tm.box_expected(ser, box_with_array)
+        expected = tm.box_expected(expected, box_with_array)
+
+        result = "a" + ser
+        tm.assert_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "data",
+        [
+            [1, 2, 3],
+            [1.1, 2.2, 3.3],
+            [Timestamp("2011-01-01"), Timestamp("2011-01-02"), pd.NaT],
+            ["x", "y", 1],
+        ],
+    )
+    @pytest.mark.parametrize("dtype", [None, object])
+    def test_objarr_radd_str_invalid(self, dtype, data, box_with_array):
+        ser = Series(data, dtype=dtype)
+
+        ser = tm.box_expected(ser, box_with_array)
+        msg = "|".join(
+            [
+                "can only concatenate str",
+                "did not contain a loop with signature matching types",
+                "unsupported operand type",
+                "must be str",
+            ]
+        )
+        with pytest.raises(TypeError, match=msg):
+            "foo_" + ser
+
+    @pytest.mark.parametrize("op", [operator.add, ops.radd, operator.sub, ops.rsub])
+    def test_objarr_add_invalid(self, op, box_with_array):
+        # invalid ops
+        box = box_with_array
+
+        obj_ser = Series(list("abc"), dtype=object, name="objects")
+
+        obj_ser = tm.box_expected(obj_ser, box)
+        msg = "|".join(
+            [
+                "can only concatenate str",
+                "unsupported operand type",
+                "must be str",
+                "has no kernel",
+                "operation 'add' not supported",
+                "operation 'radd' not supported",
+                "operation 'sub' not supported",
+                "operation 'rsub' not supported",
+            ]
+        )
+        with pytest.raises(Exception, match=msg):
+            op(obj_ser, 1)
+        with pytest.raises(Exception, match=msg):
+            op(obj_ser, np.array(1, dtype=np.int64))
+
+    # TODO: Moved from tests.series.test_operators; needs cleanup
+    def test_operators_na_handling(self):
+        ser = Series(["foo", "bar", "baz", np.nan])
+        result = "prefix_" + ser
+        expected = Series(["prefix_foo", "prefix_bar", "prefix_baz", np.nan])
+        tm.assert_series_equal(result, expected)
+
+        result = ser + "_suffix"
+        expected = Series(["foo_suffix", "bar_suffix", "baz_suffix", np.nan])
+        tm.assert_series_equal(result, expected)
+
+    # TODO: parametrize over box
+    @pytest.mark.parametrize("dtype", [None, object])
+    def test_series_with_dtype_radd_timedelta(self, dtype):
+        # note this test is _not_ aimed at timedelta64-dtyped Series
+        # as of 2.0 we retain object dtype when ser.dtype == object
+        ser = Series(
+            [pd.Timedelta("1 days"), pd.Timedelta("2 days"), pd.Timedelta("3 days")],
+            dtype=dtype,
+        )
+        expected = Series(
+            [pd.Timedelta("4 days"), pd.Timedelta("5 days"), pd.Timedelta("6 days")],
+            dtype=dtype,
+        )
+
+        result = pd.Timedelta("3 days") + ser
+        tm.assert_series_equal(result, expected)
+
+        result = ser + pd.Timedelta("3 days")
+        tm.assert_series_equal(result, expected)
+
+    # TODO: cleanup & parametrize over box
+    def test_mixed_timezone_series_ops_object(self):
+        # GH#13043
+        ser = Series(
+            [
+                Timestamp("2015-01-01", tz="US/Eastern"),
+                Timestamp("2015-01-01", tz="Asia/Tokyo"),
+            ],
+            name="xxx",
+        )
+        assert ser.dtype == object
+
+        exp = Series(
+            [
+                Timestamp("2015-01-02", tz="US/Eastern"),
+                Timestamp("2015-01-02", tz="Asia/Tokyo"),
+            ],
+            name="xxx",
+        )
+        tm.assert_series_equal(ser + pd.Timedelta("1 days"), exp)
+        tm.assert_series_equal(pd.Timedelta("1 days") + ser, exp)
+
+        # object series & object series
+        ser2 = Series(
+            [
+                Timestamp("2015-01-03", tz="US/Eastern"),
+                Timestamp("2015-01-05", tz="Asia/Tokyo"),
+            ],
+            name="xxx",
+        )
+        assert ser2.dtype == object
+        exp = Series(
+            [pd.Timedelta("2 days"), pd.Timedelta("4 days")], name="xxx", dtype=object
+        )
+        tm.assert_series_equal(ser2 - ser, exp)
+        tm.assert_series_equal(ser - ser2, -exp)
+
+        ser = Series(
+            [pd.Timedelta("01:00:00"), pd.Timedelta("02:00:00")],
+            name="xxx",
+            dtype=object,
+        )
+        assert ser.dtype == object
+
+        exp = Series(
+            [pd.Timedelta("01:30:00"), pd.Timedelta("02:30:00")],
+            name="xxx",
+            dtype=object,
+        )
+        tm.assert_series_equal(ser + pd.Timedelta("00:30:00"), exp)
+        tm.assert_series_equal(pd.Timedelta("00:30:00") + ser, exp)
+
+    # TODO: cleanup & parametrize over box
+    def test_iadd_preserves_name(self):
+        # GH#17067, GH#19723 __iadd__ and __isub__ should preserve index name
+        ser = Series([1, 2, 3])
+        ser.index.name = "foo"
+
+        ser.index += 1
+        assert ser.index.name == "foo"
+
+        ser.index -= 1
+        assert ser.index.name == "foo"
+
+    def test_add_string(self):
+        # from bug report
+        index = pd.Index(["a", "b", "c"])
+        index2 = index + "foo"
+
+        assert "a" not in index2
+        assert "afoo" in index2
+
+    def test_iadd_string(self):
+        index = pd.Index(["a", "b", "c"])
+        # doesn't fail test unless there is a check before `+=`
+        assert "a" in index
+
+        index += "_x"
+        assert "a_x" in index
+
+    def test_add(self):
+        index = pd.Index([str(i) for i in range(10)])
+        expected = pd.Index(index.values * 2)
+        tm.assert_index_equal(index + index, expected)
+        tm.assert_index_equal(index + index.tolist(), expected)
+        tm.assert_index_equal(index.tolist() + index, expected)
+
+        # test add and radd
+        index = pd.Index(list("abc"))
+        expected = pd.Index(["a1", "b1", "c1"])
+        tm.assert_index_equal(index + "1", expected)
+        expected = pd.Index(["1a", "1b", "1c"])
+        tm.assert_index_equal("1" + index, expected)
+
+    def test_sub_fail(self):
+        index = pd.Index([str(i) for i in range(10)])
+
+        msg = "unsupported operand type|Cannot broadcast|sub' not supported"
+        with pytest.raises(TypeError, match=msg):
+            index - "a"
+        with pytest.raises(TypeError, match=msg):
+            index - index
+        with pytest.raises(TypeError, match=msg):
+            index - index.tolist()
+        with pytest.raises(TypeError, match=msg):
+            index.tolist() - index
+
+    def test_sub_object(self):
+        # GH#19369
+        index = pd.Index([Decimal(1), Decimal(2)])
+        expected = pd.Index([Decimal(0), Decimal(1)])
+
+        result = index - Decimal(1)
+        tm.assert_index_equal(result, expected)
+
+        result = index - pd.Index([Decimal(1), Decimal(1)])
+        tm.assert_index_equal(result, expected)
+
+        msg = "unsupported operand type"
+        with pytest.raises(TypeError, match=msg):
+            index - "foo"
+
+        with pytest.raises(TypeError, match=msg):
+            index - np.array([2, "foo"], dtype=object)
+
+    def test_rsub_object(self, fixed_now_ts):
+        # GH#19369
+        index = pd.Index([Decimal(1), Decimal(2)])
+        expected = pd.Index([Decimal(1), Decimal(0)])
+
+        result = Decimal(2) - index
+        tm.assert_index_equal(result, expected)
+
+        result = np.array([Decimal(2), Decimal(2)]) - index
+        tm.assert_index_equal(result, expected)
+
+        msg = "unsupported operand type"
+        with pytest.raises(TypeError, match=msg):
+            "foo" - index
+
+        with pytest.raises(TypeError, match=msg):
+            np.array([True, fixed_now_ts]) - index
+
+
+class MyIndex(pd.Index):
+    # Simple index subclass that tracks ops calls.
+
+    _calls: int
+
+    @classmethod
+    def _simple_new(cls, values, name=None, dtype=None):
+        result = object.__new__(cls)
+        result._data = values
+        result._name = name
+        result._calls = 0
+        result._reset_identity()
+
+        return result
+
+    def __add__(self, other):
+        self._calls += 1
+        return self._simple_new(self._data)
+
+    def __radd__(self, other):
+        return self.__add__(other)
+
+
+@pytest.mark.parametrize(
+    "other",
+    [
+        [datetime.timedelta(1), datetime.timedelta(2)],
+        [datetime.datetime(2000, 1, 1), datetime.datetime(2000, 1, 2)],
+        [pd.Period("2000"), pd.Period("2001")],
+        ["a", "b"],
+    ],
+    ids=["timedelta", "datetime", "period", "object"],
+)
+def test_index_ops_defer_to_unknown_subclasses(other):
+    # https://github.com/pandas-dev/pandas/issues/31109
+    values = np.array(
+        [datetime.date(2000, 1, 1), datetime.date(2000, 1, 2)], dtype=object
+    )
+    a = MyIndex._simple_new(values)
+    other = pd.Index(other)
+    result = other + a
+    assert isinstance(result, MyIndex)
+    assert a._calls == 1
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arithmetic/test_period.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arithmetic/test_period.py
new file mode 100644
index 0000000000000000000000000000000000000000..5535fe8ff928d10b994bd6556229e0163a358ab0
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arithmetic/test_period.py
@@ -0,0 +1,1675 @@
+# Arithmetic tests for DataFrame/Series/Index/Array classes that should
+# behave identically.
+# Specifically for Period dtype
+import operator
+
+import numpy as np
+import pytest
+
+from pandas._libs.tslibs import (
+    IncompatibleFrequency,
+    Period,
+    Timestamp,
+    to_offset,
+)
+from pandas.errors import PerformanceWarning
+
+import pandas as pd
+from pandas import (
+    PeriodIndex,
+    Series,
+    Timedelta,
+    TimedeltaIndex,
+    period_range,
+)
+import pandas._testing as tm
+from pandas.core import ops
+from pandas.core.arrays import TimedeltaArray
+from pandas.tests.arithmetic.common import (
+    assert_invalid_addsub_type,
+    assert_invalid_comparison,
+    get_upcast_box,
+)
+
+_common_mismatch = [
+    pd.offsets.YearBegin(2),
+    pd.offsets.MonthBegin(1),
+    pd.offsets.Minute(),
+]
+
+
+@pytest.fixture(
+    params=[
+        Timedelta(minutes=30).to_pytimedelta(),
+        np.timedelta64(30, "s"),
+        Timedelta(seconds=30),
+    ]
+    + _common_mismatch
+)
+def not_hourly(request):
+    """
+    Several timedelta-like and DateOffset instances that are _not_
+    compatible with Hourly frequencies.
+    """
+    return request.param
+
+
+@pytest.fixture(
+    params=[
+        np.timedelta64(365, "D"),
+        Timedelta(days=365).to_pytimedelta(),
+        Timedelta(days=365),
+    ]
+    + _common_mismatch
+)
+def mismatched_freq(request):
+    """
+    Several timedelta-like and DateOffset instances that are _not_
+    compatible with Monthly or Annual frequencies.
+    """
+    return request.param
+
+
+# ------------------------------------------------------------------
+# Comparisons
+
+
+class TestPeriodArrayLikeComparisons:
+    # Comparison tests for PeriodDtype vectors fully parametrized over
+    #  DataFrame/Series/PeriodIndex/PeriodArray.  Ideally all comparison
+    #  tests will eventually end up here.
+
+    @pytest.mark.parametrize("other", ["2017", Period("2017", freq="D")])
+    def test_eq_scalar(self, other, box_with_array):
+        idx = PeriodIndex(["2017", "2017", "2018"], freq="D")
+        idx = tm.box_expected(idx, box_with_array)
+        xbox = get_upcast_box(idx, other, True)
+
+        expected = np.array([True, True, False])
+        expected = tm.box_expected(expected, xbox)
+
+        result = idx == other
+
+        tm.assert_equal(result, expected)
+
+    def test_compare_zerodim(self, box_with_array):
+        # GH#26689 make sure we unbox zero-dimensional arrays
+
+        pi = period_range("2000", periods=4)
+        other = np.array(pi.to_numpy()[0])
+
+        pi = tm.box_expected(pi, box_with_array)
+        xbox = get_upcast_box(pi, other, True)
+
+        result = pi <= other
+        expected = np.array([True, False, False, False])
+        expected = tm.box_expected(expected, xbox)
+        tm.assert_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "scalar",
+        [
+            "foo",
+            Timestamp("2021-01-01"),
+            Timedelta(days=4),
+            9,
+            9.5,
+            2000,  # specifically don't consider 2000 to match Period("2000", "D")
+            False,
+            None,
+        ],
+    )
+    def test_compare_invalid_scalar(self, box_with_array, scalar):
+        # GH#28980
+        # comparison with scalar that cannot be interpreted as a Period
+        pi = period_range("2000", periods=4)
+        parr = tm.box_expected(pi, box_with_array)
+        assert_invalid_comparison(parr, scalar, box_with_array)
+
+    @pytest.mark.parametrize(
+        "other",
+        [
+            pd.date_range("2000", periods=4).array,
+            pd.timedelta_range("1D", periods=4).array,
+            np.arange(4),
+            np.arange(4).astype(np.float64),
+            list(range(4)),
+            # match Period semantics by not treating integers as Periods
+            [2000, 2001, 2002, 2003],
+            np.arange(2000, 2004),
+            np.arange(2000, 2004).astype(object),
+            pd.Index([2000, 2001, 2002, 2003]),
+        ],
+    )
+    def test_compare_invalid_listlike(self, box_with_array, other):
+        pi = period_range("2000", periods=4)
+        parr = tm.box_expected(pi, box_with_array)
+        assert_invalid_comparison(parr, other, box_with_array)
+
+    @pytest.mark.parametrize("other_box", [list, np.array, lambda x: x.astype(object)])
+    def test_compare_object_dtype(self, box_with_array, other_box):
+        pi = period_range("2000", periods=5)
+        parr = tm.box_expected(pi, box_with_array)
+
+        other = other_box(pi)
+        xbox = get_upcast_box(parr, other, True)
+
+        expected = np.array([True, True, True, True, True])
+        expected = tm.box_expected(expected, xbox)
+
+        result = parr == other
+        tm.assert_equal(result, expected)
+        result = parr <= other
+        tm.assert_equal(result, expected)
+        result = parr >= other
+        tm.assert_equal(result, expected)
+
+        result = parr != other
+        tm.assert_equal(result, ~expected)
+        result = parr < other
+        tm.assert_equal(result, ~expected)
+        result = parr > other
+        tm.assert_equal(result, ~expected)
+
+        other = other_box(pi[::-1])
+
+        expected = np.array([False, False, True, False, False])
+        expected = tm.box_expected(expected, xbox)
+        result = parr == other
+        tm.assert_equal(result, expected)
+
+        expected = np.array([True, True, True, False, False])
+        expected = tm.box_expected(expected, xbox)
+        result = parr <= other
+        tm.assert_equal(result, expected)
+
+        expected = np.array([False, False, True, True, True])
+        expected = tm.box_expected(expected, xbox)
+        result = parr >= other
+        tm.assert_equal(result, expected)
+
+        expected = np.array([True, True, False, True, True])
+        expected = tm.box_expected(expected, xbox)
+        result = parr != other
+        tm.assert_equal(result, expected)
+
+        expected = np.array([True, True, False, False, False])
+        expected = tm.box_expected(expected, xbox)
+        result = parr < other
+        tm.assert_equal(result, expected)
+
+        expected = np.array([False, False, False, True, True])
+        expected = tm.box_expected(expected, xbox)
+        result = parr > other
+        tm.assert_equal(result, expected)
+
+
+class TestPeriodIndexComparisons:
+    # TODO: parameterize over boxes
+
+    def test_pi_cmp_period(self):
+        idx = period_range("2007-01", periods=20, freq="M")
+        per = idx[10]
+
+        result = idx < per
+        exp = idx.values < idx.values[10]
+        tm.assert_numpy_array_equal(result, exp)
+
+        # Tests Period.__richcmp__ against ndarray[object, ndim=2]
+        result = idx.values.reshape(10, 2) < per
+        tm.assert_numpy_array_equal(result, exp.reshape(10, 2))
+
+        # Tests Period.__richcmp__ against ndarray[object, ndim=0]
+        result = idx < np.array(per)
+        tm.assert_numpy_array_equal(result, exp)
+
+    # TODO: moved from test_datetime64; de-duplicate with version below
+    def test_parr_cmp_period_scalar2(self, box_with_array):
+        pi = period_range("2000-01-01", periods=10, freq="D")
+
+        val = pi[3]
+        expected = [x > val for x in pi]
+
+        ser = tm.box_expected(pi, box_with_array)
+        xbox = get_upcast_box(ser, val, True)
+
+        expected = tm.box_expected(expected, xbox)
+        result = ser > val
+        tm.assert_equal(result, expected)
+
+        val = pi[5]
+        result = ser > val
+        expected = [x > val for x in pi]
+        expected = tm.box_expected(expected, xbox)
+        tm.assert_equal(result, expected)
+
+    @pytest.mark.parametrize("freq", ["M", "2M", "3M"])
+    def test_parr_cmp_period_scalar(self, freq, box_with_array):
+        # GH#13200
+        base = PeriodIndex(["2011-01", "2011-02", "2011-03", "2011-04"], freq=freq)
+        base = tm.box_expected(base, box_with_array)
+        per = Period("2011-02", freq=freq)
+        xbox = get_upcast_box(base, per, True)
+
+        exp = np.array([False, True, False, False])
+        exp = tm.box_expected(exp, xbox)
+        tm.assert_equal(base == per, exp)
+        tm.assert_equal(per == base, exp)
+
+        exp = np.array([True, False, True, True])
+        exp = tm.box_expected(exp, xbox)
+        tm.assert_equal(base != per, exp)
+        tm.assert_equal(per != base, exp)
+
+        exp = np.array([False, False, True, True])
+        exp = tm.box_expected(exp, xbox)
+        tm.assert_equal(base > per, exp)
+        tm.assert_equal(per < base, exp)
+
+        exp = np.array([True, False, False, False])
+        exp = tm.box_expected(exp, xbox)
+        tm.assert_equal(base < per, exp)
+        tm.assert_equal(per > base, exp)
+
+        exp = np.array([False, True, True, True])
+        exp = tm.box_expected(exp, xbox)
+        tm.assert_equal(base >= per, exp)
+        tm.assert_equal(per <= base, exp)
+
+        exp = np.array([True, True, False, False])
+        exp = tm.box_expected(exp, xbox)
+        tm.assert_equal(base <= per, exp)
+        tm.assert_equal(per >= base, exp)
+
+    @pytest.mark.parametrize("freq", ["M", "2M", "3M"])
+    def test_parr_cmp_pi(self, freq, box_with_array):
+        # GH#13200
+        base = PeriodIndex(["2011-01", "2011-02", "2011-03", "2011-04"], freq=freq)
+        base = tm.box_expected(base, box_with_array)
+
+        # TODO: could also box idx?
+        idx = PeriodIndex(["2011-02", "2011-01", "2011-03", "2011-05"], freq=freq)
+
+        xbox = get_upcast_box(base, idx, True)
+
+        exp = np.array([False, False, True, False])
+        exp = tm.box_expected(exp, xbox)
+        tm.assert_equal(base == idx, exp)
+
+        exp = np.array([True, True, False, True])
+        exp = tm.box_expected(exp, xbox)
+        tm.assert_equal(base != idx, exp)
+
+        exp = np.array([False, True, False, False])
+        exp = tm.box_expected(exp, xbox)
+        tm.assert_equal(base > idx, exp)
+
+        exp = np.array([True, False, False, True])
+        exp = tm.box_expected(exp, xbox)
+        tm.assert_equal(base < idx, exp)
+
+        exp = np.array([False, True, True, False])
+        exp = tm.box_expected(exp, xbox)
+        tm.assert_equal(base >= idx, exp)
+
+        exp = np.array([True, False, True, True])
+        exp = tm.box_expected(exp, xbox)
+        tm.assert_equal(base <= idx, exp)
+
+    @pytest.mark.parametrize("freq", ["M", "2M", "3M"])
+    def test_parr_cmp_pi_mismatched_freq(self, freq, box_with_array):
+        # GH#13200
+        # different base freq
+        base = PeriodIndex(["2011-01", "2011-02", "2011-03", "2011-04"], freq=freq)
+        base = tm.box_expected(base, box_with_array)
+
+        msg = rf"Invalid comparison between dtype=period\[{freq}\] and Period"
+        with pytest.raises(TypeError, match=msg):
+            base <= Period("2011", freq="Y")
+
+        with pytest.raises(TypeError, match=msg):
+            Period("2011", freq="Y") >= base
+
+        # TODO: Could parametrize over boxes for idx?
+        idx = PeriodIndex(["2011", "2012", "2013", "2014"], freq="Y")
+        rev_msg = r"Invalid comparison between dtype=period\[Y-DEC\] and PeriodArray"
+        idx_msg = rev_msg if box_with_array in [tm.to_array, pd.array] else msg
+        with pytest.raises(TypeError, match=idx_msg):
+            base <= idx
+
+        # Different frequency
+        msg = rf"Invalid comparison between dtype=period\[{freq}\] and Period"
+        with pytest.raises(TypeError, match=msg):
+            base <= Period("2011", freq="4M")
+
+        with pytest.raises(TypeError, match=msg):
+            Period("2011", freq="4M") >= base
+
+        idx = PeriodIndex(["2011", "2012", "2013", "2014"], freq="4M")
+        rev_msg = r"Invalid comparison between dtype=period\[4M\] and PeriodArray"
+        idx_msg = rev_msg if box_with_array in [tm.to_array, pd.array] else msg
+        with pytest.raises(TypeError, match=idx_msg):
+            base <= idx
+
+    @pytest.mark.parametrize("freq", ["M", "2M", "3M"])
+    def test_pi_cmp_nat(self, freq):
+        idx1 = PeriodIndex(["2011-01", "2011-02", "NaT", "2011-05"], freq=freq)
+        per = idx1[1]
+
+        result = idx1 > per
+        exp = np.array([False, False, False, True])
+        tm.assert_numpy_array_equal(result, exp)
+        result = per < idx1
+        tm.assert_numpy_array_equal(result, exp)
+
+        result = idx1 == pd.NaT
+        exp = np.array([False, False, False, False])
+        tm.assert_numpy_array_equal(result, exp)
+        result = pd.NaT == idx1
+        tm.assert_numpy_array_equal(result, exp)
+
+        result = idx1 != pd.NaT
+        exp = np.array([True, True, True, True])
+        tm.assert_numpy_array_equal(result, exp)
+        result = pd.NaT != idx1
+        tm.assert_numpy_array_equal(result, exp)
+
+        idx2 = PeriodIndex(["2011-02", "2011-01", "2011-04", "NaT"], freq=freq)
+        result = idx1 < idx2
+        exp = np.array([True, False, False, False])
+        tm.assert_numpy_array_equal(result, exp)
+
+        result = idx1 == idx2
+        exp = np.array([False, False, False, False])
+        tm.assert_numpy_array_equal(result, exp)
+
+        result = idx1 != idx2
+        exp = np.array([True, True, True, True])
+        tm.assert_numpy_array_equal(result, exp)
+
+        result = idx1 == idx1
+        exp = np.array([True, True, False, True])
+        tm.assert_numpy_array_equal(result, exp)
+
+        result = idx1 != idx1
+        exp = np.array([False, False, True, False])
+        tm.assert_numpy_array_equal(result, exp)
+
+    @pytest.mark.parametrize("freq", ["M", "2M", "3M"])
+    def test_pi_cmp_nat_mismatched_freq_raises(self, freq):
+        idx1 = PeriodIndex(["2011-01", "2011-02", "NaT", "2011-05"], freq=freq)
+
+        diff = PeriodIndex(["2011-02", "2011-01", "2011-04", "NaT"], freq="4M")
+        msg = rf"Invalid comparison between dtype=period\[{freq}\] and PeriodArray"
+        with pytest.raises(TypeError, match=msg):
+            idx1 > diff
+
+        result = idx1 == diff
+        expected = np.array([False, False, False, False], dtype=bool)
+        tm.assert_numpy_array_equal(result, expected)
+
+    # TODO: De-duplicate with test_pi_cmp_nat
+    @pytest.mark.parametrize("dtype", [object, None])
+    def test_comp_nat(self, dtype):
+        left = PeriodIndex([Period("2011-01-01"), pd.NaT, Period("2011-01-03")])
+        right = PeriodIndex([pd.NaT, pd.NaT, Period("2011-01-03")])
+
+        if dtype is not None:
+            left = left.astype(dtype)
+            right = right.astype(dtype)
+
+        result = left == right
+        expected = np.array([False, False, True])
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = left != right
+        expected = np.array([True, True, False])
+        tm.assert_numpy_array_equal(result, expected)
+
+        expected = np.array([False, False, False])
+        tm.assert_numpy_array_equal(left == pd.NaT, expected)
+        tm.assert_numpy_array_equal(pd.NaT == right, expected)
+
+        expected = np.array([True, True, True])
+        tm.assert_numpy_array_equal(left != pd.NaT, expected)
+        tm.assert_numpy_array_equal(pd.NaT != left, expected)
+
+        expected = np.array([False, False, False])
+        tm.assert_numpy_array_equal(left < pd.NaT, expected)
+        tm.assert_numpy_array_equal(pd.NaT > left, expected)
+
+
+class TestPeriodSeriesComparisons:
+    def test_cmp_series_period_series_mixed_freq(self):
+        # GH#13200
+        base = Series(
+            [
+                Period("2011", freq="Y"),
+                Period("2011-02", freq="M"),
+                Period("2013", freq="Y"),
+                Period("2011-04", freq="M"),
+            ]
+        )
+
+        ser = Series(
+            [
+                Period("2012", freq="Y"),
+                Period("2011-01", freq="M"),
+                Period("2013", freq="Y"),
+                Period("2011-05", freq="M"),
+            ]
+        )
+
+        exp = Series([False, False, True, False])
+        tm.assert_series_equal(base == ser, exp)
+
+        exp = Series([True, True, False, True])
+        tm.assert_series_equal(base != ser, exp)
+
+        exp = Series([False, True, False, False])
+        tm.assert_series_equal(base > ser, exp)
+
+        exp = Series([True, False, False, True])
+        tm.assert_series_equal(base < ser, exp)
+
+        exp = Series([False, True, True, False])
+        tm.assert_series_equal(base >= ser, exp)
+
+        exp = Series([True, False, True, True])
+        tm.assert_series_equal(base <= ser, exp)
+
+
+class TestPeriodIndexSeriesComparisonConsistency:
+    """Test PeriodIndex and Period Series Ops consistency"""
+
+    # TODO: needs parametrization+de-duplication
+
+    def _check(self, values, func, expected):
+        # Test PeriodIndex and Period Series Ops consistency
+
+        idx = PeriodIndex(values)
+        result = func(idx)
+
+        # check that we don't pass an unwanted type to tm.assert_equal
+        assert isinstance(expected, (pd.Index, np.ndarray))
+        tm.assert_equal(result, expected)
+
+        s = Series(values)
+        result = func(s)
+
+        exp = Series(expected, name=values.name)
+        tm.assert_series_equal(result, exp)
+
+    def test_pi_comp_period(self):
+        idx = PeriodIndex(
+            ["2011-01", "2011-02", "2011-03", "2011-04"], freq="M", name="idx"
+        )
+        per = idx[2]
+
+        f = lambda x: x == per
+        exp = np.array([False, False, True, False], dtype=np.bool_)
+        self._check(idx, f, exp)
+        f = lambda x: per == x
+        self._check(idx, f, exp)
+
+        f = lambda x: x != per
+        exp = np.array([True, True, False, True], dtype=np.bool_)
+        self._check(idx, f, exp)
+        f = lambda x: per != x
+        self._check(idx, f, exp)
+
+        f = lambda x: per >= x
+        exp = np.array([True, True, True, False], dtype=np.bool_)
+        self._check(idx, f, exp)
+
+        f = lambda x: x > per
+        exp = np.array([False, False, False, True], dtype=np.bool_)
+        self._check(idx, f, exp)
+
+        f = lambda x: per >= x
+        exp = np.array([True, True, True, False], dtype=np.bool_)
+        self._check(idx, f, exp)
+
+    def test_pi_comp_period_nat(self):
+        idx = PeriodIndex(
+            ["2011-01", "NaT", "2011-03", "2011-04"], freq="M", name="idx"
+        )
+        per = idx[2]
+
+        f = lambda x: x == per
+        exp = np.array([False, False, True, False], dtype=np.bool_)
+        self._check(idx, f, exp)
+        f = lambda x: per == x
+        self._check(idx, f, exp)
+
+        f = lambda x: x == pd.NaT
+        exp = np.array([False, False, False, False], dtype=np.bool_)
+        self._check(idx, f, exp)
+        f = lambda x: pd.NaT == x
+        self._check(idx, f, exp)
+
+        f = lambda x: x != per
+        exp = np.array([True, True, False, True], dtype=np.bool_)
+        self._check(idx, f, exp)
+        f = lambda x: per != x
+        self._check(idx, f, exp)
+
+        f = lambda x: x != pd.NaT
+        exp = np.array([True, True, True, True], dtype=np.bool_)
+        self._check(idx, f, exp)
+        f = lambda x: pd.NaT != x
+        self._check(idx, f, exp)
+
+        f = lambda x: per >= x
+        exp = np.array([True, False, True, False], dtype=np.bool_)
+        self._check(idx, f, exp)
+
+        f = lambda x: x < per
+        exp = np.array([True, False, False, False], dtype=np.bool_)
+        self._check(idx, f, exp)
+
+        f = lambda x: x > pd.NaT
+        exp = np.array([False, False, False, False], dtype=np.bool_)
+        self._check(idx, f, exp)
+
+        f = lambda x: pd.NaT >= x
+        exp = np.array([False, False, False, False], dtype=np.bool_)
+        self._check(idx, f, exp)
+
+
+# ------------------------------------------------------------------
+# Arithmetic
+
+
+class TestPeriodFrameArithmetic:
+    def test_ops_frame_period(self):
+        # GH#13043
+        df = pd.DataFrame(
+            {
+                "A": [Period("2015-01", freq="M"), Period("2015-02", freq="M")],
+                "B": [Period("2014-01", freq="M"), Period("2014-02", freq="M")],
+            }
+        )
+        assert df["A"].dtype == "Period[M]"
+        assert df["B"].dtype == "Period[M]"
+
+        p = Period("2015-03", freq="M")
+        off = p.freq
+        # dtype will be object because of original dtype
+        exp = pd.DataFrame(
+            {
+                "A": np.array([2 * off, 1 * off], dtype=object),
+                "B": np.array([14 * off, 13 * off], dtype=object),
+            }
+        )
+        tm.assert_frame_equal(p - df, exp)
+        tm.assert_frame_equal(df - p, -1 * exp)
+
+        df2 = pd.DataFrame(
+            {
+                "A": [Period("2015-05", freq="M"), Period("2015-06", freq="M")],
+                "B": [Period("2015-05", freq="M"), Period("2015-06", freq="M")],
+            }
+        )
+        assert df2["A"].dtype == "Period[M]"
+        assert df2["B"].dtype == "Period[M]"
+
+        exp = pd.DataFrame(
+            {
+                "A": np.array([4 * off, 4 * off], dtype=object),
+                "B": np.array([16 * off, 16 * off], dtype=object),
+            }
+        )
+        tm.assert_frame_equal(df2 - df, exp)
+        tm.assert_frame_equal(df - df2, -1 * exp)
+
+
+class TestPeriodIndexArithmetic:
+    # ---------------------------------------------------------------
+    # __add__/__sub__ with PeriodIndex
+    # PeriodIndex + other is defined for integers and timedelta-like others
+    # PeriodIndex - other is defined for integers, timedelta-like others,
+    #   and PeriodIndex (with matching freq)
+
+    def test_parr_add_iadd_parr_raises(self, box_with_array):
+        rng = period_range("1/1/2000", freq="D", periods=5)
+        other = period_range("1/6/2000", freq="D", periods=5)
+        # TODO: parametrize over boxes for other?
+
+        rng = tm.box_expected(rng, box_with_array)
+        # An earlier implementation of PeriodIndex addition performed
+        # a set operation (union).  This has since been changed to
+        # raise a TypeError. See GH#14164 and GH#13077 for historical
+        # reference.
+        msg = r"unsupported operand type\(s\) for \+: .* and .*"
+        with pytest.raises(TypeError, match=msg):
+            rng + other
+
+        with pytest.raises(TypeError, match=msg):
+            rng += other
+
+    def test_pi_sub_isub_pi(self):
+        # GH#20049
+        # For historical reference see GH#14164, GH#13077.
+        # PeriodIndex subtraction originally performed set difference,
+        # then changed to raise TypeError before being implemented in GH#20049
+        rng = period_range("1/1/2000", freq="D", periods=5)
+        other = period_range("1/6/2000", freq="D", periods=5)
+
+        off = rng.freq
+        expected = pd.Index([-5 * off] * 5)
+        result = rng - other
+        tm.assert_index_equal(result, expected)
+
+        rng -= other
+        tm.assert_index_equal(rng, expected)
+
+    def test_pi_sub_pi_with_nat(self):
+        rng = period_range("1/1/2000", freq="D", periods=5)
+        other = rng[1:].insert(0, pd.NaT)
+        assert other[1:].equals(rng[1:])
+
+        result = rng - other
+        off = rng.freq
+        expected = pd.Index([pd.NaT, 0 * off, 0 * off, 0 * off, 0 * off])
+        tm.assert_index_equal(result, expected)
+
+    def test_parr_sub_pi_mismatched_freq(self, box_with_array, box_with_array2):
+        rng = period_range("1/1/2000", freq="D", periods=5)
+        other = period_range("1/6/2000", freq="h", periods=5)
+
+        rng = tm.box_expected(rng, box_with_array)
+        other = tm.box_expected(other, box_with_array2)
+        msg = r"Input has different freq=[hD] from PeriodArray\(freq=[Dh]\)"
+        with pytest.raises(IncompatibleFrequency, match=msg):
+            rng - other
+
+    @pytest.mark.parametrize("n", [1, 2, 3, 4])
+    def test_sub_n_gt_1_ticks(self, tick_classes, n):
+        # GH 23878
+        p1_d = "19910905"
+        p2_d = "19920406"
+        p1 = PeriodIndex([p1_d], freq=tick_classes(n))
+        p2 = PeriodIndex([p2_d], freq=tick_classes(n))
+
+        expected = PeriodIndex([p2_d], freq=p2.freq.base) - PeriodIndex(
+            [p1_d], freq=p1.freq.base
+        )
+
+        tm.assert_index_equal((p2 - p1), expected)
+
+    @pytest.mark.parametrize("n", [1, 2, 3, 4])
+    @pytest.mark.parametrize(
+        "offset, kwd_name",
+        [
+            (pd.offsets.YearEnd, "month"),
+            (pd.offsets.QuarterEnd, "startingMonth"),
+            (pd.offsets.MonthEnd, None),
+            (pd.offsets.Week, "weekday"),
+        ],
+    )
+    def test_sub_n_gt_1_offsets(self, offset, kwd_name, n):
+        # GH 23878
+        kwds = {kwd_name: 3} if kwd_name is not None else {}
+        p1_d = "19910905"
+        p2_d = "19920406"
+        freq = offset(n, normalize=False, **kwds)
+        p1 = PeriodIndex([p1_d], freq=freq)
+        p2 = PeriodIndex([p2_d], freq=freq)
+
+        result = p2 - p1
+        expected = PeriodIndex([p2_d], freq=freq.base) - PeriodIndex(
+            [p1_d], freq=freq.base
+        )
+
+        tm.assert_index_equal(result, expected)
+
+    # -------------------------------------------------------------
+    # Invalid Operations
+
+    @pytest.mark.parametrize(
+        "other",
+        [
+            # datetime scalars
+            Timestamp("2016-01-01"),
+            Timestamp("2016-01-01").to_pydatetime(),
+            Timestamp("2016-01-01").to_datetime64(),
+            # datetime-like arrays
+            pd.date_range("2016-01-01", periods=3, freq="h"),
+            pd.date_range("2016-01-01", periods=3, tz="Europe/Brussels"),
+            pd.date_range("2016-01-01", periods=3, freq="s")._data,
+            pd.date_range("2016-01-01", periods=3, tz="Asia/Tokyo")._data,
+            # Miscellaneous invalid types
+            3.14,
+            np.array([2.0, 3.0, 4.0]),
+        ],
+    )
+    def test_parr_add_sub_invalid(self, other, box_with_array):
+        # GH#23215
+        rng = period_range("1/1/2000", freq="D", periods=3)
+        rng = tm.box_expected(rng, box_with_array)
+
+        msg = "|".join(
+            [
+                r"(:?cannot add PeriodArray and .*)",
+                r"(:?cannot subtract .* from (:?a\s)?.*)",
+                r"(:?unsupported operand type\(s\) for \+: .* and .*)",
+                r"unsupported operand type\(s\) for [+-]: .* and .*",
+            ]
+        )
+        assert_invalid_addsub_type(rng, other, msg)
+        with pytest.raises(TypeError, match=msg):
+            rng + other
+        with pytest.raises(TypeError, match=msg):
+            other + rng
+        with pytest.raises(TypeError, match=msg):
+            rng - other
+        with pytest.raises(TypeError, match=msg):
+            other - rng
+
+    # -----------------------------------------------------------------
+    # __add__/__sub__ with ndarray[datetime64] and ndarray[timedelta64]
+
+    def test_pi_add_sub_td64_array_non_tick_raises(self):
+        rng = period_range("1/1/2000", freq="Q", periods=3)
+        tdi = TimedeltaIndex(["-1 Day", "-1 Day", "-1 Day"])
+        tdarr = tdi.values
+
+        msg = r"Cannot add or subtract timedelta64\[ns\] dtype from period\[Q-DEC\]"
+        with pytest.raises(TypeError, match=msg):
+            rng + tdarr
+        with pytest.raises(TypeError, match=msg):
+            tdarr + rng
+
+        with pytest.raises(TypeError, match=msg):
+            rng - tdarr
+        msg = r"cannot subtract PeriodArray from TimedeltaArray"
+        with pytest.raises(TypeError, match=msg):
+            tdarr - rng
+
+    def test_pi_add_sub_td64_array_tick(self):
+        # PeriodIndex + Timedelta-like is allowed only with
+        #   tick-like frequencies
+        rng = period_range("1/1/2000", freq="90D", periods=3)
+        tdi = TimedeltaIndex(["-1 Day", "-1 Day", "-1 Day"])
+        tdarr = tdi.values
+
+        expected = period_range("12/31/1999", freq="90D", periods=3)
+        result = rng + tdi
+        tm.assert_index_equal(result, expected)
+        result = rng + tdarr
+        tm.assert_index_equal(result, expected)
+        result = tdi + rng
+        tm.assert_index_equal(result, expected)
+        result = tdarr + rng
+        tm.assert_index_equal(result, expected)
+
+        expected = period_range("1/2/2000", freq="90D", periods=3)
+
+        result = rng - tdi
+        tm.assert_index_equal(result, expected)
+        result = rng - tdarr
+        tm.assert_index_equal(result, expected)
+
+        msg = r"cannot subtract .* from .*"
+        with pytest.raises(TypeError, match=msg):
+            tdarr - rng
+
+        with pytest.raises(TypeError, match=msg):
+            tdi - rng
+
+    @pytest.mark.parametrize("pi_freq", ["D", "W", "Q", "h"])
+    @pytest.mark.parametrize("tdi_freq", [None, "h"])
+    def test_parr_sub_td64array(self, box_with_array, tdi_freq, pi_freq):
+        box = box_with_array
+        xbox = box if box not in [pd.array, tm.to_array] else pd.Index
+
+        tdi = TimedeltaIndex(["1 hours", "2 hours"], freq=tdi_freq)
+        dti = Timestamp("2018-03-07 17:16:40") + tdi
+        pi = dti.to_period(pi_freq)
+
+        # TODO: parametrize over box for pi?
+        td64obj = tm.box_expected(tdi, box)
+
+        if pi_freq == "h":
+            result = pi - td64obj
+            expected = (pi.to_timestamp("s") - tdi).to_period(pi_freq)
+            expected = tm.box_expected(expected, xbox)
+            tm.assert_equal(result, expected)
+
+            # Subtract from scalar
+            result = pi[0] - td64obj
+            expected = (pi[0].to_timestamp("s") - tdi).to_period(pi_freq)
+            expected = tm.box_expected(expected, box)
+            tm.assert_equal(result, expected)
+
+        elif pi_freq == "D":
+            # Tick, but non-compatible
+            msg = (
+                "Cannot add/subtract timedelta-like from PeriodArray that is "
+                "not an integer multiple of the PeriodArray's freq."
+            )
+            with pytest.raises(IncompatibleFrequency, match=msg):
+                pi - td64obj
+
+            with pytest.raises(IncompatibleFrequency, match=msg):
+                pi[0] - td64obj
+
+        else:
+            # With non-Tick freq, we could not add timedelta64 array regardless
+            #  of what its resolution is
+            msg = "Cannot add or subtract timedelta64"
+            with pytest.raises(TypeError, match=msg):
+                pi - td64obj
+            with pytest.raises(TypeError, match=msg):
+                pi[0] - td64obj
+
+    # -----------------------------------------------------------------
+    # operations with array/Index of DateOffset objects
+
+    @pytest.mark.parametrize("box", [np.array, pd.Index])
+    def test_pi_add_offset_array(self, box):
+        # GH#18849
+        pi = PeriodIndex([Period("2015Q1"), Period("2016Q2")])
+        offs = box(
+            [
+                pd.offsets.QuarterEnd(n=1, startingMonth=12),
+                pd.offsets.QuarterEnd(n=-2, startingMonth=12),
+            ]
+        )
+        expected = PeriodIndex([Period("2015Q2"), Period("2015Q4")]).astype(object)
+
+        with tm.assert_produces_warning(PerformanceWarning):
+            res = pi + offs
+        tm.assert_index_equal(res, expected)
+
+        with tm.assert_produces_warning(PerformanceWarning):
+            res2 = offs + pi
+        tm.assert_index_equal(res2, expected)
+
+        unanchored = np.array([pd.offsets.Hour(n=1), pd.offsets.Minute(n=-2)])
+        # addition/subtraction ops with incompatible offsets should issue
+        # a PerformanceWarning and _then_ raise a TypeError.
+        msg = r"Input cannot be converted to Period\(freq=Q-DEC\)"
+        with pytest.raises(IncompatibleFrequency, match=msg):
+            with tm.assert_produces_warning(PerformanceWarning):
+                pi + unanchored
+        with pytest.raises(IncompatibleFrequency, match=msg):
+            with tm.assert_produces_warning(PerformanceWarning):
+                unanchored + pi
+
+    @pytest.mark.parametrize("box", [np.array, pd.Index])
+    def test_pi_sub_offset_array(self, box):
+        # GH#18824
+        pi = PeriodIndex([Period("2015Q1"), Period("2016Q2")])
+        other = box(
+            [
+                pd.offsets.QuarterEnd(n=1, startingMonth=12),
+                pd.offsets.QuarterEnd(n=-2, startingMonth=12),
+            ]
+        )
+
+        expected = PeriodIndex([pi[n] - other[n] for n in range(len(pi))])
+        expected = expected.astype(object)
+
+        with tm.assert_produces_warning(PerformanceWarning):
+            res = pi - other
+        tm.assert_index_equal(res, expected)
+
+        anchored = box([pd.offsets.MonthEnd(), pd.offsets.Day(n=2)])
+
+        # addition/subtraction ops with anchored offsets should issue
+        # a PerformanceWarning and _then_ raise a TypeError.
+        msg = r"Input has different freq=-1M from Period\(freq=Q-DEC\)"
+        with pytest.raises(IncompatibleFrequency, match=msg):
+            with tm.assert_produces_warning(PerformanceWarning):
+                pi - anchored
+        with pytest.raises(IncompatibleFrequency, match=msg):
+            with tm.assert_produces_warning(PerformanceWarning):
+                anchored - pi
+
+    def test_pi_add_iadd_int(self, one):
+        # Variants of `one` for #19012
+        rng = period_range("2000-01-01 09:00", freq="h", periods=10)
+        result = rng + one
+        expected = period_range("2000-01-01 10:00", freq="h", periods=10)
+        tm.assert_index_equal(result, expected)
+        rng += one
+        tm.assert_index_equal(rng, expected)
+
+    def test_pi_sub_isub_int(self, one):
+        """
+        PeriodIndex.__sub__ and __isub__ with several representations of
+        the integer 1, e.g. int, np.int64, np.uint8, ...
+        """
+        rng = period_range("2000-01-01 09:00", freq="h", periods=10)
+        result = rng - one
+        expected = period_range("2000-01-01 08:00", freq="h", periods=10)
+        tm.assert_index_equal(result, expected)
+        rng -= one
+        tm.assert_index_equal(rng, expected)
+
+    @pytest.mark.parametrize("five", [5, np.array(5, dtype=np.int64)])
+    def test_pi_sub_intlike(self, five):
+        rng = period_range("2007-01", periods=50)
+
+        result = rng - five
+        exp = rng + (-five)
+        tm.assert_index_equal(result, exp)
+
+    def test_pi_add_sub_int_array_freqn_gt1(self):
+        # GH#47209 test adding array of ints when freq.n > 1 matches
+        #  scalar behavior
+        pi = period_range("2016-01-01", periods=10, freq="2D")
+        arr = np.arange(10)
+        result = pi + arr
+        expected = pd.Index([x + y for x, y in zip(pi, arr)])
+        tm.assert_index_equal(result, expected)
+
+        result = pi - arr
+        expected = pd.Index([x - y for x, y in zip(pi, arr)])
+        tm.assert_index_equal(result, expected)
+
+    def test_pi_sub_isub_offset(self):
+        # offset
+        # DateOffset
+        rng = period_range("2014", "2024", freq="Y")
+        result = rng - pd.offsets.YearEnd(5)
+        expected = period_range("2009", "2019", freq="Y")
+        tm.assert_index_equal(result, expected)
+        rng -= pd.offsets.YearEnd(5)
+        tm.assert_index_equal(rng, expected)
+
+        rng = period_range("2014-01", "2016-12", freq="M")
+        result = rng - pd.offsets.MonthEnd(5)
+        expected = period_range("2013-08", "2016-07", freq="M")
+        tm.assert_index_equal(result, expected)
+
+        rng -= pd.offsets.MonthEnd(5)
+        tm.assert_index_equal(rng, expected)
+
+    @pytest.mark.parametrize("transpose", [True, False])
+    def test_pi_add_offset_n_gt1(self, box_with_array, transpose):
+        # GH#23215
+        # add offset to PeriodIndex with freq.n > 1
+
+        per = Period("2016-01", freq="2M")
+        pi = PeriodIndex([per])
+
+        expected = PeriodIndex(["2016-03"], freq="2M")
+
+        pi = tm.box_expected(pi, box_with_array, transpose=transpose)
+        expected = tm.box_expected(expected, box_with_array, transpose=transpose)
+
+        result = pi + per.freq
+        tm.assert_equal(result, expected)
+
+        result = per.freq + pi
+        tm.assert_equal(result, expected)
+
+    def test_pi_add_offset_n_gt1_not_divisible(self, box_with_array):
+        # GH#23215
+        # PeriodIndex with freq.n > 1 add offset with offset.n % freq.n != 0
+        pi = PeriodIndex(["2016-01"], freq="2M")
+        expected = PeriodIndex(["2016-04"], freq="2M")
+
+        pi = tm.box_expected(pi, box_with_array)
+        expected = tm.box_expected(expected, box_with_array)
+
+        result = pi + to_offset("3ME")
+        tm.assert_equal(result, expected)
+
+        result = to_offset("3ME") + pi
+        tm.assert_equal(result, expected)
+
+    # ---------------------------------------------------------------
+    # __add__/__sub__ with integer arrays
+
+    @pytest.mark.parametrize("int_holder", [np.array, pd.Index])
+    @pytest.mark.parametrize("op", [operator.add, ops.radd])
+    def test_pi_add_intarray(self, int_holder, op):
+        # GH#19959
+        pi = PeriodIndex([Period("2015Q1"), Period("NaT")])
+        other = int_holder([4, -1])
+
+        result = op(pi, other)
+        expected = PeriodIndex([Period("2016Q1"), Period("NaT")])
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize("int_holder", [np.array, pd.Index])
+    def test_pi_sub_intarray(self, int_holder):
+        # GH#19959
+        pi = PeriodIndex([Period("2015Q1"), Period("NaT")])
+        other = int_holder([4, -1])
+
+        result = pi - other
+        expected = PeriodIndex([Period("2014Q1"), Period("NaT")])
+        tm.assert_index_equal(result, expected)
+
+        msg = r"bad operand type for unary -: 'PeriodArray'"
+        with pytest.raises(TypeError, match=msg):
+            other - pi
+
+    # ---------------------------------------------------------------
+    # Timedelta-like (timedelta, timedelta64, Timedelta, Tick)
+    # TODO: Some of these are misnomers because of non-Tick DateOffsets
+
+    def test_parr_add_timedeltalike_minute_gt1(self, three_days, box_with_array):
+        # GH#23031 adding a time-delta-like offset to a PeriodArray that has
+        # minute frequency with n != 1.  A more general case is tested below
+        # in test_pi_add_timedeltalike_tick_gt1, but here we write out the
+        # expected result more explicitly.
+        other = three_days
+        rng = period_range("2014-05-01", periods=3, freq="2D")
+        rng = tm.box_expected(rng, box_with_array)
+
+        expected = PeriodIndex(["2014-05-04", "2014-05-06", "2014-05-08"], freq="2D")
+        expected = tm.box_expected(expected, box_with_array)
+
+        result = rng + other
+        tm.assert_equal(result, expected)
+
+        result = other + rng
+        tm.assert_equal(result, expected)
+
+        # subtraction
+        expected = PeriodIndex(["2014-04-28", "2014-04-30", "2014-05-02"], freq="2D")
+        expected = tm.box_expected(expected, box_with_array)
+        result = rng - other
+        tm.assert_equal(result, expected)
+
+        msg = "|".join(
+            [
+                r"bad operand type for unary -: 'PeriodArray'",
+                r"cannot subtract PeriodArray from timedelta64\[[hD]\]",
+            ]
+        )
+        with pytest.raises(TypeError, match=msg):
+            other - rng
+
+    @pytest.mark.parametrize("freqstr", ["5ns", "5us", "5ms", "5s", "5min", "5h", "5d"])
+    def test_parr_add_timedeltalike_tick_gt1(self, three_days, freqstr, box_with_array):
+        # GH#23031 adding a time-delta-like offset to a PeriodArray that has
+        # tick-like frequency with n != 1
+        other = three_days
+        rng = period_range("2014-05-01", periods=6, freq=freqstr)
+        first = rng[0]
+        rng = tm.box_expected(rng, box_with_array)
+
+        expected = period_range(first + other, periods=6, freq=freqstr)
+        expected = tm.box_expected(expected, box_with_array)
+
+        result = rng + other
+        tm.assert_equal(result, expected)
+
+        result = other + rng
+        tm.assert_equal(result, expected)
+
+        # subtraction
+        expected = period_range(first - other, periods=6, freq=freqstr)
+        expected = tm.box_expected(expected, box_with_array)
+        result = rng - other
+        tm.assert_equal(result, expected)
+        msg = "|".join(
+            [
+                r"bad operand type for unary -: 'PeriodArray'",
+                r"cannot subtract PeriodArray from timedelta64\[[hD]\]",
+            ]
+        )
+        with pytest.raises(TypeError, match=msg):
+            other - rng
+
+    def test_pi_add_iadd_timedeltalike_daily(self, three_days):
+        # Tick
+        other = three_days
+        rng = period_range("2014-05-01", "2014-05-15", freq="D")
+        expected = period_range("2014-05-04", "2014-05-18", freq="D")
+
+        result = rng + other
+        tm.assert_index_equal(result, expected)
+
+        rng += other
+        tm.assert_index_equal(rng, expected)
+
+    def test_pi_sub_isub_timedeltalike_daily(self, three_days):
+        # Tick-like 3 Days
+        other = three_days
+        rng = period_range("2014-05-01", "2014-05-15", freq="D")
+        expected = period_range("2014-04-28", "2014-05-12", freq="D")
+
+        result = rng - other
+        tm.assert_index_equal(result, expected)
+
+        rng -= other
+        tm.assert_index_equal(rng, expected)
+
+    def test_parr_add_sub_timedeltalike_freq_mismatch_daily(
+        self, not_daily, box_with_array
+    ):
+        other = not_daily
+        rng = period_range("2014-05-01", "2014-05-15", freq="D")
+        rng = tm.box_expected(rng, box_with_array)
+
+        msg = "|".join(
+            [
+                # non-timedelta-like DateOffset
+                "Input has different freq(=.+)? from Period.*?\\(freq=D\\)",
+                # timedelta/td64/Timedelta but not a multiple of 24H
+                "Cannot add/subtract timedelta-like from PeriodArray that is "
+                "not an integer multiple of the PeriodArray's freq.",
+            ]
+        )
+        with pytest.raises(IncompatibleFrequency, match=msg):
+            rng + other
+        with pytest.raises(IncompatibleFrequency, match=msg):
+            rng += other
+        with pytest.raises(IncompatibleFrequency, match=msg):
+            rng - other
+        with pytest.raises(IncompatibleFrequency, match=msg):
+            rng -= other
+
+    def test_pi_add_iadd_timedeltalike_hourly(self, two_hours):
+        other = two_hours
+        rng = period_range("2014-01-01 10:00", "2014-01-05 10:00", freq="h")
+        expected = period_range("2014-01-01 12:00", "2014-01-05 12:00", freq="h")
+
+        result = rng + other
+        tm.assert_index_equal(result, expected)
+
+        rng += other
+        tm.assert_index_equal(rng, expected)
+
+    def test_parr_add_timedeltalike_mismatched_freq_hourly(
+        self, not_hourly, box_with_array
+    ):
+        other = not_hourly
+        rng = period_range("2014-01-01 10:00", "2014-01-05 10:00", freq="h")
+        rng = tm.box_expected(rng, box_with_array)
+        msg = "|".join(
+            [
+                # non-timedelta-like DateOffset
+                "Input has different freq(=.+)? from Period.*?\\(freq=h\\)",
+                # timedelta/td64/Timedelta but not a multiple of 24H
+                "Cannot add/subtract timedelta-like from PeriodArray that is "
+                "not an integer multiple of the PeriodArray's freq.",
+            ]
+        )
+
+        with pytest.raises(IncompatibleFrequency, match=msg):
+            rng + other
+
+        with pytest.raises(IncompatibleFrequency, match=msg):
+            rng += other
+
+    def test_pi_sub_isub_timedeltalike_hourly(self, two_hours):
+        other = two_hours
+        rng = period_range("2014-01-01 10:00", "2014-01-05 10:00", freq="h")
+        expected = period_range("2014-01-01 08:00", "2014-01-05 08:00", freq="h")
+
+        result = rng - other
+        tm.assert_index_equal(result, expected)
+
+        rng -= other
+        tm.assert_index_equal(rng, expected)
+
+    def test_add_iadd_timedeltalike_annual(self):
+        # offset
+        # DateOffset
+        rng = period_range("2014", "2024", freq="Y")
+        result = rng + pd.offsets.YearEnd(5)
+        expected = period_range("2019", "2029", freq="Y")
+        tm.assert_index_equal(result, expected)
+        rng += pd.offsets.YearEnd(5)
+        tm.assert_index_equal(rng, expected)
+
+    def test_pi_add_sub_timedeltalike_freq_mismatch_annual(self, mismatched_freq):
+        other = mismatched_freq
+        rng = period_range("2014", "2024", freq="Y")
+        msg = "Input has different freq(=.+)? from Period.*?\\(freq=Y-DEC\\)"
+        with pytest.raises(IncompatibleFrequency, match=msg):
+            rng + other
+        with pytest.raises(IncompatibleFrequency, match=msg):
+            rng += other
+        with pytest.raises(IncompatibleFrequency, match=msg):
+            rng - other
+        with pytest.raises(IncompatibleFrequency, match=msg):
+            rng -= other
+
+    def test_pi_add_iadd_timedeltalike_M(self):
+        rng = period_range("2014-01", "2016-12", freq="M")
+        expected = period_range("2014-06", "2017-05", freq="M")
+
+        result = rng + pd.offsets.MonthEnd(5)
+        tm.assert_index_equal(result, expected)
+
+        rng += pd.offsets.MonthEnd(5)
+        tm.assert_index_equal(rng, expected)
+
+    def test_pi_add_sub_timedeltalike_freq_mismatch_monthly(self, mismatched_freq):
+        other = mismatched_freq
+        rng = period_range("2014-01", "2016-12", freq="M")
+        msg = "Input has different freq(=.+)? from Period.*?\\(freq=M\\)"
+        with pytest.raises(IncompatibleFrequency, match=msg):
+            rng + other
+        with pytest.raises(IncompatibleFrequency, match=msg):
+            rng += other
+        with pytest.raises(IncompatibleFrequency, match=msg):
+            rng - other
+        with pytest.raises(IncompatibleFrequency, match=msg):
+            rng -= other
+
+    @pytest.mark.parametrize("transpose", [True, False])
+    def test_parr_add_sub_td64_nat(self, box_with_array, transpose):
+        # GH#23320 special handling for timedelta64("NaT")
+        pi = period_range("1994-04-01", periods=9, freq="19D")
+        other = np.timedelta64("NaT")
+        expected = PeriodIndex(["NaT"] * 9, freq="19D")
+
+        obj = tm.box_expected(pi, box_with_array, transpose=transpose)
+        expected = tm.box_expected(expected, box_with_array, transpose=transpose)
+
+        result = obj + other
+        tm.assert_equal(result, expected)
+        result = other + obj
+        tm.assert_equal(result, expected)
+        result = obj - other
+        tm.assert_equal(result, expected)
+        msg = r"cannot subtract .* from .*"
+        with pytest.raises(TypeError, match=msg):
+            other - obj
+
+    @pytest.mark.parametrize(
+        "other",
+        [
+            np.array(["NaT"] * 9, dtype="m8[ns]"),
+            TimedeltaArray._from_sequence(["NaT"] * 9, dtype="m8[ns]"),
+        ],
+    )
+    def test_parr_add_sub_tdt64_nat_array(self, box_with_array, other):
+        pi = period_range("1994-04-01", periods=9, freq="19D")
+        expected = PeriodIndex(["NaT"] * 9, freq="19D")
+
+        obj = tm.box_expected(pi, box_with_array)
+        expected = tm.box_expected(expected, box_with_array)
+
+        result = obj + other
+        tm.assert_equal(result, expected)
+        result = other + obj
+        tm.assert_equal(result, expected)
+        result = obj - other
+        tm.assert_equal(result, expected)
+        msg = r"cannot subtract .* from .*"
+        with pytest.raises(TypeError, match=msg):
+            other - obj
+
+        # some but not *all* NaT
+        other = other.copy()
+        other[0] = np.timedelta64(0, "ns")
+        expected = PeriodIndex([pi[0]] + ["NaT"] * 8, freq="19D")
+        expected = tm.box_expected(expected, box_with_array)
+
+        result = obj + other
+        tm.assert_equal(result, expected)
+        result = other + obj
+        tm.assert_equal(result, expected)
+        result = obj - other
+        tm.assert_equal(result, expected)
+        with pytest.raises(TypeError, match=msg):
+            other - obj
+
+    # ---------------------------------------------------------------
+    # Unsorted
+
+    def test_parr_add_sub_index(self):
+        # Check that PeriodArray defers to Index on arithmetic ops
+        pi = period_range("2000-12-31", periods=3)
+        parr = pi.array
+
+        result = parr - pi
+        expected = pi - pi
+        tm.assert_index_equal(result, expected)
+
+    def test_parr_add_sub_object_array(self):
+        pi = period_range("2000-12-31", periods=3, freq="D")
+        parr = pi.array
+
+        other = np.array([Timedelta(days=1), pd.offsets.Day(2), 3])
+
+        with tm.assert_produces_warning(PerformanceWarning):
+            result = parr + other
+
+        expected = PeriodIndex(
+            ["2001-01-01", "2001-01-03", "2001-01-05"], freq="D"
+        )._data.astype(object)
+        tm.assert_equal(result, expected)
+
+        with tm.assert_produces_warning(PerformanceWarning):
+            result = parr - other
+
+        expected = PeriodIndex(["2000-12-30"] * 3, freq="D")._data.astype(object)
+        tm.assert_equal(result, expected)
+
+    def test_period_add_timestamp_raises(self, box_with_array):
+        # GH#17983
+        ts = Timestamp("2017")
+        per = Period("2017", freq="M")
+
+        arr = pd.Index([per], dtype="Period[M]")
+        arr = tm.box_expected(arr, box_with_array)
+
+        msg = "cannot add PeriodArray and Timestamp"
+        with pytest.raises(TypeError, match=msg):
+            arr + ts
+        with pytest.raises(TypeError, match=msg):
+            ts + arr
+        msg = "cannot add PeriodArray and DatetimeArray"
+        with pytest.raises(TypeError, match=msg):
+            arr + Series([ts])
+        with pytest.raises(TypeError, match=msg):
+            Series([ts]) + arr
+        with pytest.raises(TypeError, match=msg):
+            arr + pd.Index([ts])
+        with pytest.raises(TypeError, match=msg):
+            pd.Index([ts]) + arr
+
+        if box_with_array is pd.DataFrame:
+            msg = "cannot add PeriodArray and DatetimeArray"
+        else:
+            msg = r"unsupported operand type\(s\) for \+: 'Period' and 'DatetimeArray"
+        with pytest.raises(TypeError, match=msg):
+            arr + pd.DataFrame([ts])
+        if box_with_array is pd.DataFrame:
+            msg = "cannot add PeriodArray and DatetimeArray"
+        else:
+            msg = r"unsupported operand type\(s\) for \+: 'DatetimeArray' and 'Period'"
+        with pytest.raises(TypeError, match=msg):
+            pd.DataFrame([ts]) + arr
+
+
+class TestPeriodSeriesArithmetic:
+    def test_parr_add_timedeltalike_scalar(self, three_days, box_with_array):
+        # GH#13043
+        ser = Series(
+            [Period("2015-01-01", freq="D"), Period("2015-01-02", freq="D")],
+            name="xxx",
+        )
+        assert ser.dtype == "Period[D]"
+
+        expected = Series(
+            [Period("2015-01-04", freq="D"), Period("2015-01-05", freq="D")],
+            name="xxx",
+        )
+
+        obj = tm.box_expected(ser, box_with_array)
+        if box_with_array is pd.DataFrame:
+            assert (obj.dtypes == "Period[D]").all()
+
+        expected = tm.box_expected(expected, box_with_array)
+
+        result = obj + three_days
+        tm.assert_equal(result, expected)
+
+        result = three_days + obj
+        tm.assert_equal(result, expected)
+
+    def test_ops_series_period(self):
+        # GH#13043
+        ser = Series(
+            [Period("2015-01-01", freq="D"), Period("2015-01-02", freq="D")],
+            name="xxx",
+        )
+        assert ser.dtype == "Period[D]"
+
+        per = Period("2015-01-10", freq="D")
+        off = per.freq
+        # dtype will be object because of original dtype
+        expected = Series([9 * off, 8 * off], name="xxx", dtype=object)
+        tm.assert_series_equal(per - ser, expected)
+        tm.assert_series_equal(ser - per, -1 * expected)
+
+        s2 = Series(
+            [Period("2015-01-05", freq="D"), Period("2015-01-04", freq="D")],
+            name="xxx",
+        )
+        assert s2.dtype == "Period[D]"
+
+        expected = Series([4 * off, 2 * off], name="xxx", dtype=object)
+        tm.assert_series_equal(s2 - ser, expected)
+        tm.assert_series_equal(ser - s2, -1 * expected)
+
+
+class TestPeriodIndexSeriesMethods:
+    """Test PeriodIndex and Period Series Ops consistency"""
+
+    def _check(self, values, func, expected):
+        idx = PeriodIndex(values)
+        result = func(idx)
+        tm.assert_equal(result, expected)
+
+        ser = Series(values)
+        result = func(ser)
+
+        exp = Series(expected, name=values.name)
+        tm.assert_series_equal(result, exp)
+
+    def test_pi_ops(self):
+        idx = PeriodIndex(
+            ["2011-01", "2011-02", "2011-03", "2011-04"], freq="M", name="idx"
+        )
+
+        expected = PeriodIndex(
+            ["2011-03", "2011-04", "2011-05", "2011-06"], freq="M", name="idx"
+        )
+
+        self._check(idx, lambda x: x + 2, expected)
+        self._check(idx, lambda x: 2 + x, expected)
+
+        self._check(idx + 2, lambda x: x - 2, idx)
+
+        result = idx - Period("2011-01", freq="M")
+        off = idx.freq
+        exp = pd.Index([0 * off, 1 * off, 2 * off, 3 * off], name="idx")
+        tm.assert_index_equal(result, exp)
+
+        result = Period("2011-01", freq="M") - idx
+        exp = pd.Index([0 * off, -1 * off, -2 * off, -3 * off], name="idx")
+        tm.assert_index_equal(result, exp)
+
+    @pytest.mark.parametrize("ng", ["str", 1.5])
+    @pytest.mark.parametrize(
+        "func",
+        [
+            lambda obj, ng: obj + ng,
+            lambda obj, ng: ng + obj,
+            lambda obj, ng: obj - ng,
+            lambda obj, ng: ng - obj,
+            lambda obj, ng: np.add(obj, ng),
+            lambda obj, ng: np.add(ng, obj),
+            lambda obj, ng: np.subtract(obj, ng),
+            lambda obj, ng: np.subtract(ng, obj),
+        ],
+    )
+    def test_parr_ops_errors(self, ng, func, box_with_array):
+        idx = PeriodIndex(
+            ["2011-01", "2011-02", "2011-03", "2011-04"], freq="M", name="idx"
+        )
+        obj = tm.box_expected(idx, box_with_array)
+        msg = "|".join(
+            [
+                r"unsupported operand type\(s\)",
+                "can only concatenate",
+                r"must be str",
+                "object to str implicitly",
+            ]
+        )
+
+        with pytest.raises(TypeError, match=msg):
+            func(obj, ng)
+
+    def test_pi_ops_nat(self):
+        idx = PeriodIndex(
+            ["2011-01", "2011-02", "NaT", "2011-04"], freq="M", name="idx"
+        )
+        expected = PeriodIndex(
+            ["2011-03", "2011-04", "NaT", "2011-06"], freq="M", name="idx"
+        )
+
+        self._check(idx, lambda x: x + 2, expected)
+        self._check(idx, lambda x: 2 + x, expected)
+        self._check(idx, lambda x: np.add(x, 2), expected)
+
+        self._check(idx + 2, lambda x: x - 2, idx)
+        self._check(idx + 2, lambda x: np.subtract(x, 2), idx)
+
+        # freq with mult
+        idx = PeriodIndex(
+            ["2011-01", "2011-02", "NaT", "2011-04"], freq="2M", name="idx"
+        )
+        expected = PeriodIndex(
+            ["2011-07", "2011-08", "NaT", "2011-10"], freq="2M", name="idx"
+        )
+
+        self._check(idx, lambda x: x + 3, expected)
+        self._check(idx, lambda x: 3 + x, expected)
+        self._check(idx, lambda x: np.add(x, 3), expected)
+
+        self._check(idx + 3, lambda x: x - 3, idx)
+        self._check(idx + 3, lambda x: np.subtract(x, 3), idx)
+
+    def test_pi_ops_array_int(self):
+        idx = PeriodIndex(
+            ["2011-01", "2011-02", "NaT", "2011-04"], freq="M", name="idx"
+        )
+        f = lambda x: x + np.array([1, 2, 3, 4])
+        exp = PeriodIndex(
+            ["2011-02", "2011-04", "NaT", "2011-08"], freq="M", name="idx"
+        )
+        self._check(idx, f, exp)
+
+        f = lambda x: np.add(x, np.array([4, -1, 1, 2]))
+        exp = PeriodIndex(
+            ["2011-05", "2011-01", "NaT", "2011-06"], freq="M", name="idx"
+        )
+        self._check(idx, f, exp)
+
+        f = lambda x: x - np.array([1, 2, 3, 4])
+        exp = PeriodIndex(
+            ["2010-12", "2010-12", "NaT", "2010-12"], freq="M", name="idx"
+        )
+        self._check(idx, f, exp)
+
+        f = lambda x: np.subtract(x, np.array([3, 2, 3, -2]))
+        exp = PeriodIndex(
+            ["2010-10", "2010-12", "NaT", "2011-06"], freq="M", name="idx"
+        )
+        self._check(idx, f, exp)
+
+    def test_pi_ops_offset(self):
+        idx = PeriodIndex(
+            ["2011-01-01", "2011-02-01", "2011-03-01", "2011-04-01"],
+            freq="D",
+            name="idx",
+        )
+        f = lambda x: x + pd.offsets.Day()
+        exp = PeriodIndex(
+            ["2011-01-02", "2011-02-02", "2011-03-02", "2011-04-02"],
+            freq="D",
+            name="idx",
+        )
+        self._check(idx, f, exp)
+
+        f = lambda x: x + pd.offsets.Day(2)
+        exp = PeriodIndex(
+            ["2011-01-03", "2011-02-03", "2011-03-03", "2011-04-03"],
+            freq="D",
+            name="idx",
+        )
+        self._check(idx, f, exp)
+
+        f = lambda x: x - pd.offsets.Day(2)
+        exp = PeriodIndex(
+            ["2010-12-30", "2011-01-30", "2011-02-27", "2011-03-30"],
+            freq="D",
+            name="idx",
+        )
+        self._check(idx, f, exp)
+
+    def test_pi_offset_errors(self):
+        idx = PeriodIndex(
+            ["2011-01-01", "2011-02-01", "2011-03-01", "2011-04-01"],
+            freq="D",
+            name="idx",
+        )
+        ser = Series(idx)
+
+        msg = (
+            "Cannot add/subtract timedelta-like from PeriodArray that is not "
+            "an integer multiple of the PeriodArray's freq"
+        )
+        for obj in [idx, ser]:
+            with pytest.raises(IncompatibleFrequency, match=msg):
+                obj + pd.offsets.Hour(2)
+
+            with pytest.raises(IncompatibleFrequency, match=msg):
+                pd.offsets.Hour(2) + obj
+
+            with pytest.raises(IncompatibleFrequency, match=msg):
+                obj - pd.offsets.Hour(2)
+
+    def test_pi_sub_period(self):
+        # GH#13071
+        idx = PeriodIndex(
+            ["2011-01", "2011-02", "2011-03", "2011-04"], freq="M", name="idx"
+        )
+
+        result = idx - Period("2012-01", freq="M")
+        off = idx.freq
+        exp = pd.Index([-12 * off, -11 * off, -10 * off, -9 * off], name="idx")
+        tm.assert_index_equal(result, exp)
+
+        result = np.subtract(idx, Period("2012-01", freq="M"))
+        tm.assert_index_equal(result, exp)
+
+        result = Period("2012-01", freq="M") - idx
+        exp = pd.Index([12 * off, 11 * off, 10 * off, 9 * off], name="idx")
+        tm.assert_index_equal(result, exp)
+
+        result = np.subtract(Period("2012-01", freq="M"), idx)
+        tm.assert_index_equal(result, exp)
+
+        exp = TimedeltaIndex([np.nan, np.nan, np.nan, np.nan], name="idx")
+        result = idx - Period("NaT", freq="M")
+        tm.assert_index_equal(result, exp)
+        assert result.freq == exp.freq
+
+        result = Period("NaT", freq="M") - idx
+        tm.assert_index_equal(result, exp)
+        assert result.freq == exp.freq
+
+    def test_pi_sub_pdnat(self):
+        # GH#13071, GH#19389
+        idx = PeriodIndex(
+            ["2011-01", "2011-02", "NaT", "2011-04"], freq="M", name="idx"
+        )
+        exp = TimedeltaIndex([pd.NaT] * 4, name="idx")
+        tm.assert_index_equal(pd.NaT - idx, exp)
+        tm.assert_index_equal(idx - pd.NaT, exp)
+
+    def test_pi_sub_period_nat(self):
+        # GH#13071
+        idx = PeriodIndex(
+            ["2011-01", "NaT", "2011-03", "2011-04"], freq="M", name="idx"
+        )
+
+        result = idx - Period("2012-01", freq="M")
+        off = idx.freq
+        exp = pd.Index([-12 * off, pd.NaT, -10 * off, -9 * off], name="idx")
+        tm.assert_index_equal(result, exp)
+
+        result = Period("2012-01", freq="M") - idx
+        exp = pd.Index([12 * off, pd.NaT, 10 * off, 9 * off], name="idx")
+        tm.assert_index_equal(result, exp)
+
+        exp = TimedeltaIndex([np.nan, np.nan, np.nan, np.nan], name="idx")
+        tm.assert_index_equal(idx - Period("NaT", freq="M"), exp)
+        tm.assert_index_equal(Period("NaT", freq="M") - idx, exp)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arithmetic/test_timedelta64.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arithmetic/test_timedelta64.py
new file mode 100644
index 0000000000000000000000000000000000000000..d02e827d435cf16c806b5130f5949143f51c15e3
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arithmetic/test_timedelta64.py
@@ -0,0 +1,2179 @@
+# Arithmetic tests for DataFrame/Series/Index/Array classes that should
+# behave identically.
+from datetime import (
+    datetime,
+    timedelta,
+)
+
+import numpy as np
+import pytest
+
+from pandas.errors import (
+    OutOfBoundsDatetime,
+    PerformanceWarning,
+)
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    DatetimeIndex,
+    Index,
+    NaT,
+    Series,
+    Timedelta,
+    TimedeltaIndex,
+    Timestamp,
+    offsets,
+    timedelta_range,
+)
+import pandas._testing as tm
+from pandas.core.arrays import NumpyExtensionArray
+from pandas.tests.arithmetic.common import (
+    assert_invalid_addsub_type,
+    assert_invalid_comparison,
+    get_upcast_box,
+)
+
+
+def assert_dtype(obj, expected_dtype):
+    """
+    Helper to check the dtype for a Series, Index, or single-column DataFrame.
+    """
+    dtype = tm.get_dtype(obj)
+
+    assert dtype == expected_dtype
+
+
+def get_expected_name(box, names):
+    if box is DataFrame:
+        # Since we are operating with a DataFrame and a non-DataFrame,
+        # the non-DataFrame is cast to Series and its name ignored.
+        exname = names[0]
+    elif box in [tm.to_array, pd.array]:
+        exname = names[1]
+    else:
+        exname = names[2]
+    return exname
+
+
+# ------------------------------------------------------------------
+# Timedelta64[ns] dtype Comparisons
+
+
+class TestTimedelta64ArrayLikeComparisons:
+    # Comparison tests for timedelta64[ns] vectors fully parametrized over
+    #  DataFrame/Series/TimedeltaIndex/TimedeltaArray.  Ideally all comparison
+    #  tests will eventually end up here.
+
+    def test_compare_timedelta64_zerodim(self, box_with_array):
+        # GH#26689 should unbox when comparing with zerodim array
+        box = box_with_array
+        xbox = box_with_array if box_with_array not in [Index, pd.array] else np.ndarray
+
+        tdi = timedelta_range("2h", periods=4)
+        other = np.array(tdi.to_numpy()[0])
+
+        tdi = tm.box_expected(tdi, box)
+        res = tdi <= other
+        expected = np.array([True, False, False, False])
+        expected = tm.box_expected(expected, xbox)
+        tm.assert_equal(res, expected)
+
+    @pytest.mark.parametrize(
+        "td_scalar",
+        [
+            timedelta(days=1),
+            Timedelta(days=1),
+            Timedelta(days=1).to_timedelta64(),
+            offsets.Hour(24),
+        ],
+    )
+    def test_compare_timedeltalike_scalar(self, box_with_array, td_scalar):
+        # regression test for GH#5963
+        box = box_with_array
+        xbox = box if box not in [Index, pd.array] else np.ndarray
+
+        ser = Series([timedelta(days=1), timedelta(days=2)])
+        ser = tm.box_expected(ser, box)
+        actual = ser > td_scalar
+        expected = Series([False, True])
+        expected = tm.box_expected(expected, xbox)
+        tm.assert_equal(actual, expected)
+
+    @pytest.mark.parametrize(
+        "invalid",
+        [
+            345600000000000,
+            "a",
+            Timestamp("2021-01-01"),
+            Timestamp("2021-01-01").now("UTC"),
+            Timestamp("2021-01-01").now().to_datetime64(),
+            Timestamp("2021-01-01").now().to_pydatetime(),
+            Timestamp("2021-01-01").date(),
+            np.array(4),  # zero-dim mismatched dtype
+        ],
+    )
+    def test_td64_comparisons_invalid(self, box_with_array, invalid):
+        # GH#13624 for str
+        box = box_with_array
+
+        rng = timedelta_range("1 days", periods=10)
+        obj = tm.box_expected(rng, box)
+
+        assert_invalid_comparison(obj, invalid, box)
+
+    @pytest.mark.parametrize(
+        "other",
+        [
+            list(range(10)),
+            np.arange(10),
+            np.arange(10).astype(np.float32),
+            np.arange(10).astype(object),
+            pd.date_range("1970-01-01", periods=10, tz="UTC").array,
+            np.array(pd.date_range("1970-01-01", periods=10)),
+            list(pd.date_range("1970-01-01", periods=10)),
+            pd.date_range("1970-01-01", periods=10).astype(object),
+            pd.period_range("1971-01-01", freq="D", periods=10).array,
+            pd.period_range("1971-01-01", freq="D", periods=10).astype(object),
+        ],
+    )
+    def test_td64arr_cmp_arraylike_invalid(self, other, box_with_array):
+        # We don't parametrize this over box_with_array because listlike
+        #  other plays poorly with assert_invalid_comparison reversed checks
+
+        rng = timedelta_range("1 days", periods=10)._data
+        rng = tm.box_expected(rng, box_with_array)
+        assert_invalid_comparison(rng, other, box_with_array)
+
+    def test_td64arr_cmp_mixed_invalid(self):
+        rng = timedelta_range("1 days", periods=5)._data
+        other = np.array([0, 1, 2, rng[3], Timestamp("2021-01-01")])
+
+        result = rng == other
+        expected = np.array([False, False, False, True, False])
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = rng != other
+        tm.assert_numpy_array_equal(result, ~expected)
+
+        msg = "Invalid comparison between|Cannot compare type|not supported between"
+        with pytest.raises(TypeError, match=msg):
+            rng < other
+        with pytest.raises(TypeError, match=msg):
+            rng > other
+        with pytest.raises(TypeError, match=msg):
+            rng <= other
+        with pytest.raises(TypeError, match=msg):
+            rng >= other
+
+
+class TestTimedelta64ArrayComparisons:
+    # TODO: All of these need to be parametrized over box
+
+    @pytest.mark.parametrize("dtype", [None, object])
+    def test_comp_nat(self, dtype):
+        left = TimedeltaIndex([Timedelta("1 days"), NaT, Timedelta("3 days")])
+        right = TimedeltaIndex([NaT, NaT, Timedelta("3 days")])
+
+        lhs, rhs = left, right
+        if dtype is object:
+            lhs, rhs = left.astype(object), right.astype(object)
+
+        result = rhs == lhs
+        expected = np.array([False, False, True])
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = rhs != lhs
+        expected = np.array([True, True, False])
+        tm.assert_numpy_array_equal(result, expected)
+
+        expected = np.array([False, False, False])
+        tm.assert_numpy_array_equal(lhs == NaT, expected)
+        tm.assert_numpy_array_equal(NaT == rhs, expected)
+
+        expected = np.array([True, True, True])
+        tm.assert_numpy_array_equal(lhs != NaT, expected)
+        tm.assert_numpy_array_equal(NaT != lhs, expected)
+
+        expected = np.array([False, False, False])
+        tm.assert_numpy_array_equal(lhs < NaT, expected)
+        tm.assert_numpy_array_equal(NaT > lhs, expected)
+
+    @pytest.mark.parametrize(
+        "idx2",
+        [
+            TimedeltaIndex(
+                ["2 day", "2 day", NaT, NaT, "1 day 00:00:02", "5 days 00:00:03"]
+            ),
+            np.array(
+                [
+                    np.timedelta64(2, "D"),
+                    np.timedelta64(2, "D"),
+                    np.timedelta64("nat"),
+                    np.timedelta64("nat"),
+                    np.timedelta64(1, "D") + np.timedelta64(2, "s"),
+                    np.timedelta64(5, "D") + np.timedelta64(3, "s"),
+                ]
+            ),
+        ],
+    )
+    def test_comparisons_nat(self, idx2):
+        idx1 = TimedeltaIndex(
+            [
+                "1 day",
+                NaT,
+                "1 day 00:00:01",
+                NaT,
+                "1 day 00:00:01",
+                "5 day 00:00:03",
+            ]
+        )
+        # Check pd.NaT is handles as the same as np.nan
+        result = idx1 < idx2
+        expected = np.array([True, False, False, False, True, False])
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = idx2 > idx1
+        expected = np.array([True, False, False, False, True, False])
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = idx1 <= idx2
+        expected = np.array([True, False, False, False, True, True])
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = idx2 >= idx1
+        expected = np.array([True, False, False, False, True, True])
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = idx1 == idx2
+        expected = np.array([False, False, False, False, False, True])
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = idx1 != idx2
+        expected = np.array([True, True, True, True, True, False])
+        tm.assert_numpy_array_equal(result, expected)
+
+    # TODO: better name
+    def test_comparisons_coverage(self):
+        rng = timedelta_range("1 days", periods=10)
+
+        result = rng < rng[3]
+        expected = np.array([True, True, True] + [False] * 7)
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = rng == list(rng)
+        exp = rng == rng
+        tm.assert_numpy_array_equal(result, exp)
+
+
+# ------------------------------------------------------------------
+# Timedelta64[ns] dtype Arithmetic Operations
+
+
+class TestTimedelta64ArithmeticUnsorted:
+    # Tests moved from type-specific test files but not
+    #  yet sorted/parametrized/de-duplicated
+
+    def test_ufunc_coercions(self):
+        # normal ops are also tested in tseries/test_timedeltas.py
+        idx = TimedeltaIndex(["2h", "4h", "6h", "8h", "10h"], freq="2h", name="x")
+
+        for result in [idx * 2, np.multiply(idx, 2)]:
+            assert isinstance(result, TimedeltaIndex)
+            exp = TimedeltaIndex(["4h", "8h", "12h", "16h", "20h"], freq="4h", name="x")
+            tm.assert_index_equal(result, exp)
+            assert result.freq == "4h"
+
+        for result in [idx / 2, np.divide(idx, 2)]:
+            assert isinstance(result, TimedeltaIndex)
+            exp = TimedeltaIndex(["1h", "2h", "3h", "4h", "5h"], freq="h", name="x")
+            tm.assert_index_equal(result, exp)
+            assert result.freq == "h"
+
+        for result in [-idx, np.negative(idx)]:
+            assert isinstance(result, TimedeltaIndex)
+            exp = TimedeltaIndex(
+                ["-2h", "-4h", "-6h", "-8h", "-10h"], freq="-2h", name="x"
+            )
+            tm.assert_index_equal(result, exp)
+            assert result.freq == "-2h"
+
+        idx = TimedeltaIndex(["-2h", "-1h", "0h", "1h", "2h"], freq="h", name="x")
+        for result in [abs(idx), np.absolute(idx)]:
+            assert isinstance(result, TimedeltaIndex)
+            exp = TimedeltaIndex(["2h", "1h", "0h", "1h", "2h"], freq=None, name="x")
+            tm.assert_index_equal(result, exp)
+            assert result.freq is None
+
+    def test_subtraction_ops(self):
+        # with datetimes/timedelta and tdi/dti
+        tdi = TimedeltaIndex(["1 days", NaT, "2 days"], name="foo")
+        dti = pd.date_range("20130101", periods=3, name="bar")
+        td = Timedelta("1 days")
+        dt = Timestamp("20130101")
+
+        msg = "cannot subtract a datelike from a TimedeltaArray"
+        with pytest.raises(TypeError, match=msg):
+            tdi - dt
+        with pytest.raises(TypeError, match=msg):
+            tdi - dti
+
+        msg = r"unsupported operand type\(s\) for -"
+        with pytest.raises(TypeError, match=msg):
+            td - dt
+
+        msg = "(bad|unsupported) operand type for unary"
+        with pytest.raises(TypeError, match=msg):
+            td - dti
+
+        result = dt - dti
+        expected = TimedeltaIndex(["0 days", "-1 days", "-2 days"], name="bar")
+        tm.assert_index_equal(result, expected)
+
+        result = dti - dt
+        expected = TimedeltaIndex(["0 days", "1 days", "2 days"], name="bar")
+        tm.assert_index_equal(result, expected)
+
+        result = tdi - td
+        expected = TimedeltaIndex(["0 days", NaT, "1 days"], name="foo")
+        tm.assert_index_equal(result, expected)
+
+        result = td - tdi
+        expected = TimedeltaIndex(["0 days", NaT, "-1 days"], name="foo")
+        tm.assert_index_equal(result, expected)
+
+        result = dti - td
+        expected = DatetimeIndex(
+            ["20121231", "20130101", "20130102"], dtype="M8[ns]", freq="D", name="bar"
+        )
+        tm.assert_index_equal(result, expected)
+
+        result = dt - tdi
+        expected = DatetimeIndex(
+            ["20121231", NaT, "20121230"], dtype="M8[ns]", name="foo"
+        )
+        tm.assert_index_equal(result, expected)
+
+    def test_subtraction_ops_with_tz(self, box_with_array):
+        # check that dt/dti subtraction ops with tz are validated
+        dti = pd.date_range("20130101", periods=3)
+        dti = tm.box_expected(dti, box_with_array)
+        ts = Timestamp("20130101")
+        dt = ts.to_pydatetime()
+        dti_tz = pd.date_range("20130101", periods=3).tz_localize("US/Eastern")
+        dti_tz = tm.box_expected(dti_tz, box_with_array)
+        ts_tz = Timestamp("20130101").tz_localize("US/Eastern")
+        ts_tz2 = Timestamp("20130101").tz_localize("CET")
+        dt_tz = ts_tz.to_pydatetime()
+        td = Timedelta("1 days")
+
+        def _check(result, expected):
+            assert result == expected
+            assert isinstance(result, Timedelta)
+
+        # scalars
+        result = ts - ts
+        expected = Timedelta("0 days")
+        _check(result, expected)
+
+        result = dt_tz - ts_tz
+        expected = Timedelta("0 days")
+        _check(result, expected)
+
+        result = ts_tz - dt_tz
+        expected = Timedelta("0 days")
+        _check(result, expected)
+
+        # tz mismatches
+        msg = "Cannot subtract tz-naive and tz-aware datetime-like objects."
+        with pytest.raises(TypeError, match=msg):
+            dt_tz - ts
+        msg = "can't subtract offset-naive and offset-aware datetimes"
+        with pytest.raises(TypeError, match=msg):
+            dt_tz - dt
+        msg = "can't subtract offset-naive and offset-aware datetimes"
+        with pytest.raises(TypeError, match=msg):
+            dt - dt_tz
+        msg = "Cannot subtract tz-naive and tz-aware datetime-like objects."
+        with pytest.raises(TypeError, match=msg):
+            ts - dt_tz
+        with pytest.raises(TypeError, match=msg):
+            ts_tz2 - ts
+        with pytest.raises(TypeError, match=msg):
+            ts_tz2 - dt
+
+        msg = "Cannot subtract tz-naive and tz-aware"
+        # with dti
+        with pytest.raises(TypeError, match=msg):
+            dti - ts_tz
+        with pytest.raises(TypeError, match=msg):
+            dti_tz - ts
+
+        result = dti_tz - dt_tz
+        expected = TimedeltaIndex(["0 days", "1 days", "2 days"])
+        expected = tm.box_expected(expected, box_with_array)
+        tm.assert_equal(result, expected)
+
+        result = dt_tz - dti_tz
+        expected = TimedeltaIndex(["0 days", "-1 days", "-2 days"])
+        expected = tm.box_expected(expected, box_with_array)
+        tm.assert_equal(result, expected)
+
+        result = dti_tz - ts_tz
+        expected = TimedeltaIndex(["0 days", "1 days", "2 days"])
+        expected = tm.box_expected(expected, box_with_array)
+        tm.assert_equal(result, expected)
+
+        result = ts_tz - dti_tz
+        expected = TimedeltaIndex(["0 days", "-1 days", "-2 days"])
+        expected = tm.box_expected(expected, box_with_array)
+        tm.assert_equal(result, expected)
+
+        result = td - td
+        expected = Timedelta("0 days")
+        _check(result, expected)
+
+        result = dti_tz - td
+        expected = DatetimeIndex(
+            ["20121231", "20130101", "20130102"], tz="US/Eastern"
+        ).as_unit("ns")
+        expected = tm.box_expected(expected, box_with_array)
+        tm.assert_equal(result, expected)
+
+    def test_dti_tdi_numeric_ops(self):
+        # These are normally union/diff set-like ops
+        tdi = TimedeltaIndex(["1 days", NaT, "2 days"], name="foo")
+        dti = pd.date_range("20130101", periods=3, name="bar")
+
+        result = tdi - tdi
+        expected = TimedeltaIndex(["0 days", NaT, "0 days"], name="foo")
+        tm.assert_index_equal(result, expected)
+
+        result = tdi + tdi
+        expected = TimedeltaIndex(["2 days", NaT, "4 days"], name="foo")
+        tm.assert_index_equal(result, expected)
+
+        result = dti - tdi  # name will be reset
+        expected = DatetimeIndex(["20121231", NaT, "20130101"], dtype="M8[ns]")
+        tm.assert_index_equal(result, expected)
+
+    def test_addition_ops(self):
+        # with datetimes/timedelta and tdi/dti
+        tdi = TimedeltaIndex(["1 days", NaT, "2 days"], name="foo")
+        dti = pd.date_range("20130101", periods=3, name="bar")
+        td = Timedelta("1 days")
+        dt = Timestamp("20130101")
+
+        result = tdi + dt
+        expected = DatetimeIndex(
+            ["20130102", NaT, "20130103"], dtype="M8[ns]", name="foo"
+        )
+        tm.assert_index_equal(result, expected)
+
+        result = dt + tdi
+        expected = DatetimeIndex(
+            ["20130102", NaT, "20130103"], dtype="M8[ns]", name="foo"
+        )
+        tm.assert_index_equal(result, expected)
+
+        result = td + tdi
+        expected = TimedeltaIndex(["2 days", NaT, "3 days"], name="foo")
+        tm.assert_index_equal(result, expected)
+
+        result = tdi + td
+        expected = TimedeltaIndex(["2 days", NaT, "3 days"], name="foo")
+        tm.assert_index_equal(result, expected)
+
+        # unequal length
+        msg = "cannot add indices of unequal length"
+        with pytest.raises(ValueError, match=msg):
+            tdi + dti[0:1]
+        with pytest.raises(ValueError, match=msg):
+            tdi[0:1] + dti
+
+        # random indexes
+        msg = "Addition/subtraction of integers and integer-arrays"
+        with pytest.raises(TypeError, match=msg):
+            tdi + Index([1, 2, 3], dtype=np.int64)
+
+        # this is a union!
+        # FIXME: don't leave commented-out
+        # pytest.raises(TypeError, lambda : Index([1,2,3]) + tdi)
+
+        result = tdi + dti  # name will be reset
+        expected = DatetimeIndex(["20130102", NaT, "20130105"], dtype="M8[ns]")
+        tm.assert_index_equal(result, expected)
+
+        result = dti + tdi  # name will be reset
+        expected = DatetimeIndex(["20130102", NaT, "20130105"], dtype="M8[ns]")
+        tm.assert_index_equal(result, expected)
+
+        result = dt + td
+        expected = Timestamp("20130102")
+        assert result == expected
+
+        result = td + dt
+        expected = Timestamp("20130102")
+        assert result == expected
+
+    # TODO: Needs more informative name, probably split up into
+    # more targeted tests
+    @pytest.mark.parametrize("freq", ["D", "B"])
+    def test_timedelta(self, freq):
+        index = pd.date_range("1/1/2000", periods=50, freq=freq)
+
+        shifted = index + timedelta(1)
+        back = shifted + timedelta(-1)
+        back = back._with_freq("infer")
+        tm.assert_index_equal(index, back)
+
+        if freq == "D":
+            expected = pd.tseries.offsets.Day(1)
+            assert index.freq == expected
+            assert shifted.freq == expected
+            assert back.freq == expected
+        else:  # freq == 'B'
+            assert index.freq == pd.tseries.offsets.BusinessDay(1)
+            assert shifted.freq is None
+            assert back.freq == pd.tseries.offsets.BusinessDay(1)
+
+        result = index - timedelta(1)
+        expected = index + timedelta(-1)
+        tm.assert_index_equal(result, expected)
+
+    def test_timedelta_tick_arithmetic(self):
+        # GH#4134, buggy with timedeltas
+        rng = pd.date_range("2013", "2014")
+        s = Series(rng)
+        result1 = rng - offsets.Hour(1)
+        result2 = DatetimeIndex(s - np.timedelta64(100000000))
+        result3 = rng - np.timedelta64(100000000)
+        result4 = DatetimeIndex(s - offsets.Hour(1))
+
+        assert result1.freq == rng.freq
+        result1 = result1._with_freq(None)
+        tm.assert_index_equal(result1, result4)
+
+        assert result3.freq == rng.freq
+        result3 = result3._with_freq(None)
+        tm.assert_index_equal(result2, result3)
+
+    def test_tda_add_sub_index(self):
+        # Check that TimedeltaArray defers to Index on arithmetic ops
+        tdi = TimedeltaIndex(["1 days", NaT, "2 days"])
+        tda = tdi.array
+
+        dti = pd.date_range("1999-12-31", periods=3, freq="D")
+
+        result = tda + dti
+        expected = tdi + dti
+        tm.assert_index_equal(result, expected)
+
+        result = tda + tdi
+        expected = tdi + tdi
+        tm.assert_index_equal(result, expected)
+
+        result = tda - tdi
+        expected = tdi - tdi
+        tm.assert_index_equal(result, expected)
+
+    def test_tda_add_dt64_object_array(self, box_with_array, tz_naive_fixture):
+        # Result should be cast back to DatetimeArray
+        box = box_with_array
+
+        dti = pd.date_range("2016-01-01", periods=3, tz=tz_naive_fixture)
+        dti = dti._with_freq(None)
+        tdi = dti - dti
+
+        obj = tm.box_expected(tdi, box)
+        other = tm.box_expected(dti, box)
+
+        with tm.assert_produces_warning(PerformanceWarning):
+            result = obj + other.astype(object)
+        tm.assert_equal(result, other.astype(object))
+
+    # -------------------------------------------------------------
+    # Binary operations TimedeltaIndex and timedelta-like
+
+    def test_tdi_iadd_timedeltalike(self, two_hours, box_with_array):
+        # only test adding/sub offsets as + is now numeric
+        rng = timedelta_range("1 days", "10 days")
+        expected = timedelta_range("1 days 02:00:00", "10 days 02:00:00", freq="D")
+
+        rng = tm.box_expected(rng, box_with_array)
+        expected = tm.box_expected(expected, box_with_array)
+
+        orig_rng = rng
+        rng += two_hours
+        tm.assert_equal(rng, expected)
+        if box_with_array is not Index:
+            # Check that operation is actually inplace
+            tm.assert_equal(orig_rng, expected)
+
+    def test_tdi_isub_timedeltalike(self, two_hours, box_with_array):
+        # only test adding/sub offsets as - is now numeric
+        rng = timedelta_range("1 days", "10 days")
+        expected = timedelta_range("0 days 22:00:00", "9 days 22:00:00")
+
+        rng = tm.box_expected(rng, box_with_array)
+        expected = tm.box_expected(expected, box_with_array)
+
+        orig_rng = rng
+        rng -= two_hours
+        tm.assert_equal(rng, expected)
+        if box_with_array is not Index:
+            # Check that operation is actually inplace
+            tm.assert_equal(orig_rng, expected)
+
+    # -------------------------------------------------------------
+
+    def test_tdi_ops_attributes(self):
+        rng = timedelta_range("2 days", periods=5, freq="2D", name="x")
+
+        result = rng + 1 * rng.freq
+        exp = timedelta_range("4 days", periods=5, freq="2D", name="x")
+        tm.assert_index_equal(result, exp)
+        assert result.freq == "2D"
+
+        result = rng - 2 * rng.freq
+        exp = timedelta_range("-2 days", periods=5, freq="2D", name="x")
+        tm.assert_index_equal(result, exp)
+        assert result.freq == "2D"
+
+        result = rng * 2
+        exp = timedelta_range("4 days", periods=5, freq="4D", name="x")
+        tm.assert_index_equal(result, exp)
+        assert result.freq == "4D"
+
+        result = rng / 2
+        exp = timedelta_range("1 days", periods=5, freq="D", name="x")
+        tm.assert_index_equal(result, exp)
+        assert result.freq == "D"
+
+        result = -rng
+        exp = timedelta_range("-2 days", periods=5, freq="-2D", name="x")
+        tm.assert_index_equal(result, exp)
+        assert result.freq == "-2D"
+
+        rng = timedelta_range("-2 days", periods=5, freq="D", name="x")
+
+        result = abs(rng)
+        exp = TimedeltaIndex(
+            ["2 days", "1 days", "0 days", "1 days", "2 days"], name="x"
+        )
+        tm.assert_index_equal(result, exp)
+        assert result.freq is None
+
+
+class TestAddSubNaTMasking:
+    # TODO: parametrize over boxes
+
+    @pytest.mark.parametrize("str_ts", ["1950-01-01", "1980-01-01"])
+    def test_tdarr_add_timestamp_nat_masking(self, box_with_array, str_ts):
+        # GH#17991 checking for overflow-masking with NaT
+        tdinat = pd.to_timedelta(["24658 days 11:15:00", "NaT"])
+        tdobj = tm.box_expected(tdinat, box_with_array)
+
+        ts = Timestamp(str_ts)
+        ts_variants = [
+            ts,
+            ts.to_pydatetime(),
+            ts.to_datetime64().astype("datetime64[ns]"),
+            ts.to_datetime64().astype("datetime64[D]"),
+        ]
+
+        for variant in ts_variants:
+            res = tdobj + variant
+            if box_with_array is DataFrame:
+                assert res.iloc[1, 1] is NaT
+            else:
+                assert res[1] is NaT
+
+    def test_tdi_add_overflow(self):
+        # See GH#14068
+        # preliminary test scalar analogue of vectorized tests below
+        # TODO: Make raised error message more informative and test
+        with pytest.raises(OutOfBoundsDatetime, match="10155196800000000000"):
+            pd.to_timedelta(106580, "D") + Timestamp("2000")
+        with pytest.raises(OutOfBoundsDatetime, match="10155196800000000000"):
+            Timestamp("2000") + pd.to_timedelta(106580, "D")
+
+        _NaT = NaT._value + 1
+        msg = "Overflow in int64 addition"
+        with pytest.raises(OverflowError, match=msg):
+            pd.to_timedelta([106580], "D") + Timestamp("2000")
+        with pytest.raises(OverflowError, match=msg):
+            Timestamp("2000") + pd.to_timedelta([106580], "D")
+        with pytest.raises(OverflowError, match=msg):
+            pd.to_timedelta([_NaT]) - Timedelta("1 days")
+        with pytest.raises(OverflowError, match=msg):
+            pd.to_timedelta(["5 days", _NaT]) - Timedelta("1 days")
+        with pytest.raises(OverflowError, match=msg):
+            (
+                pd.to_timedelta([_NaT, "5 days", "1 hours"])
+                - pd.to_timedelta(["7 seconds", _NaT, "4 hours"])
+            )
+
+        # These should not overflow!
+        exp = TimedeltaIndex([NaT])
+        result = pd.to_timedelta([NaT]) - Timedelta("1 days")
+        tm.assert_index_equal(result, exp)
+
+        exp = TimedeltaIndex(["4 days", NaT])
+        result = pd.to_timedelta(["5 days", NaT]) - Timedelta("1 days")
+        tm.assert_index_equal(result, exp)
+
+        exp = TimedeltaIndex([NaT, NaT, "5 hours"])
+        result = pd.to_timedelta([NaT, "5 days", "1 hours"]) + pd.to_timedelta(
+            ["7 seconds", NaT, "4 hours"]
+        )
+        tm.assert_index_equal(result, exp)
+
+
+class TestTimedeltaArraylikeAddSubOps:
+    # Tests for timedelta64[ns] __add__, __sub__, __radd__, __rsub__
+
+    def test_sub_nat_retain_unit(self):
+        ser = pd.to_timedelta(Series(["00:00:01"])).astype("m8[s]")
+
+        result = ser - NaT
+        expected = Series([NaT], dtype="m8[s]")
+        tm.assert_series_equal(result, expected)
+
+    # TODO: moved from tests.indexes.timedeltas.test_arithmetic; needs
+    #  parametrization+de-duplication
+    def test_timedelta_ops_with_missing_values(self):
+        # setup
+        s1 = pd.to_timedelta(Series(["00:00:01"]))
+        s2 = pd.to_timedelta(Series(["00:00:02"]))
+
+        sn = pd.to_timedelta(Series([NaT], dtype="m8[ns]"))
+
+        df1 = DataFrame(["00:00:01"]).apply(pd.to_timedelta)
+        df2 = DataFrame(["00:00:02"]).apply(pd.to_timedelta)
+
+        dfn = DataFrame([NaT._value]).apply(pd.to_timedelta)
+
+        scalar1 = pd.to_timedelta("00:00:01")
+        scalar2 = pd.to_timedelta("00:00:02")
+        timedelta_NaT = pd.to_timedelta("NaT")
+
+        actual = scalar1 + scalar1
+        assert actual == scalar2
+        actual = scalar2 - scalar1
+        assert actual == scalar1
+
+        actual = s1 + s1
+        tm.assert_series_equal(actual, s2)
+        actual = s2 - s1
+        tm.assert_series_equal(actual, s1)
+
+        actual = s1 + scalar1
+        tm.assert_series_equal(actual, s2)
+        actual = scalar1 + s1
+        tm.assert_series_equal(actual, s2)
+        actual = s2 - scalar1
+        tm.assert_series_equal(actual, s1)
+        actual = -scalar1 + s2
+        tm.assert_series_equal(actual, s1)
+
+        actual = s1 + timedelta_NaT
+        tm.assert_series_equal(actual, sn)
+        actual = timedelta_NaT + s1
+        tm.assert_series_equal(actual, sn)
+        actual = s1 - timedelta_NaT
+        tm.assert_series_equal(actual, sn)
+        actual = -timedelta_NaT + s1
+        tm.assert_series_equal(actual, sn)
+
+        msg = "unsupported operand type"
+        with pytest.raises(TypeError, match=msg):
+            s1 + np.nan
+        with pytest.raises(TypeError, match=msg):
+            np.nan + s1
+        with pytest.raises(TypeError, match=msg):
+            s1 - np.nan
+        with pytest.raises(TypeError, match=msg):
+            -np.nan + s1
+
+        actual = s1 + NaT
+        tm.assert_series_equal(actual, sn)
+        actual = s2 - NaT
+        tm.assert_series_equal(actual, sn)
+
+        actual = s1 + df1
+        tm.assert_frame_equal(actual, df2)
+        actual = s2 - df1
+        tm.assert_frame_equal(actual, df1)
+        actual = df1 + s1
+        tm.assert_frame_equal(actual, df2)
+        actual = df2 - s1
+        tm.assert_frame_equal(actual, df1)
+
+        actual = df1 + df1
+        tm.assert_frame_equal(actual, df2)
+        actual = df2 - df1
+        tm.assert_frame_equal(actual, df1)
+
+        actual = df1 + scalar1
+        tm.assert_frame_equal(actual, df2)
+        actual = df2 - scalar1
+        tm.assert_frame_equal(actual, df1)
+
+        actual = df1 + timedelta_NaT
+        tm.assert_frame_equal(actual, dfn)
+        actual = df1 - timedelta_NaT
+        tm.assert_frame_equal(actual, dfn)
+
+        msg = "cannot subtract a datelike from|unsupported operand type"
+        with pytest.raises(TypeError, match=msg):
+            df1 + np.nan
+        with pytest.raises(TypeError, match=msg):
+            df1 - np.nan
+
+        actual = df1 + NaT  # NaT is datetime, not timedelta
+        tm.assert_frame_equal(actual, dfn)
+        actual = df1 - NaT
+        tm.assert_frame_equal(actual, dfn)
+
+    # TODO: moved from tests.series.test_operators, needs splitting, cleanup,
+    # de-duplication, box-parametrization...
+    def test_operators_timedelta64(self):
+        # series ops
+        v1 = pd.date_range("2012-1-1", periods=3, freq="D")
+        v2 = pd.date_range("2012-1-2", periods=3, freq="D")
+        rs = Series(v2) - Series(v1)
+        xp = Series(1e9 * 3600 * 24, rs.index).astype("int64").astype("timedelta64[ns]")
+        tm.assert_series_equal(rs, xp)
+        assert rs.dtype == "timedelta64[ns]"
+
+        df = DataFrame({"A": v1})
+        td = Series([timedelta(days=i) for i in range(3)])
+        assert td.dtype == "timedelta64[ns]"
+
+        # series on the rhs
+        result = df["A"] - df["A"].shift()
+        assert result.dtype == "timedelta64[ns]"
+
+        result = df["A"] + td
+        assert result.dtype == "M8[ns]"
+
+        # scalar Timestamp on rhs
+        maxa = df["A"].max()
+        assert isinstance(maxa, Timestamp)
+
+        resultb = df["A"] - df["A"].max()
+        assert resultb.dtype == "timedelta64[ns]"
+
+        # timestamp on lhs
+        result = resultb + df["A"]
+        values = [Timestamp("20111230"), Timestamp("20120101"), Timestamp("20120103")]
+        expected = Series(values, dtype="M8[ns]", name="A")
+        tm.assert_series_equal(result, expected)
+
+        # datetimes on rhs
+        result = df["A"] - datetime(2001, 1, 1)
+        expected = Series([timedelta(days=4017 + i) for i in range(3)], name="A")
+        tm.assert_series_equal(result, expected)
+        assert result.dtype == "m8[ns]"
+
+        d = datetime(2001, 1, 1, 3, 4)
+        resulta = df["A"] - d
+        assert resulta.dtype == "m8[ns]"
+
+        # roundtrip
+        resultb = resulta + d
+        tm.assert_series_equal(df["A"], resultb)
+
+        # timedeltas on rhs
+        td = timedelta(days=1)
+        resulta = df["A"] + td
+        resultb = resulta - td
+        tm.assert_series_equal(resultb, df["A"])
+        assert resultb.dtype == "M8[ns]"
+
+        # roundtrip
+        td = timedelta(minutes=5, seconds=3)
+        resulta = df["A"] + td
+        resultb = resulta - td
+        tm.assert_series_equal(df["A"], resultb)
+        assert resultb.dtype == "M8[ns]"
+
+        # inplace
+        value = rs[2] + np.timedelta64(timedelta(minutes=5, seconds=1))
+        rs[2] += np.timedelta64(timedelta(minutes=5, seconds=1))
+        assert rs[2] == value
+
+    def test_timedelta64_ops_nat(self):
+        # GH 11349
+        timedelta_series = Series([NaT, Timedelta("1s")])
+        nat_series_dtype_timedelta = Series([NaT, NaT], dtype="timedelta64[ns]")
+        single_nat_dtype_timedelta = Series([NaT], dtype="timedelta64[ns]")
+
+        # subtraction
+        tm.assert_series_equal(timedelta_series - NaT, nat_series_dtype_timedelta)
+        tm.assert_series_equal(-NaT + timedelta_series, nat_series_dtype_timedelta)
+
+        tm.assert_series_equal(
+            timedelta_series - single_nat_dtype_timedelta, nat_series_dtype_timedelta
+        )
+        tm.assert_series_equal(
+            -single_nat_dtype_timedelta + timedelta_series, nat_series_dtype_timedelta
+        )
+
+        # addition
+        tm.assert_series_equal(
+            nat_series_dtype_timedelta + NaT, nat_series_dtype_timedelta
+        )
+        tm.assert_series_equal(
+            NaT + nat_series_dtype_timedelta, nat_series_dtype_timedelta
+        )
+
+        tm.assert_series_equal(
+            nat_series_dtype_timedelta + single_nat_dtype_timedelta,
+            nat_series_dtype_timedelta,
+        )
+        tm.assert_series_equal(
+            single_nat_dtype_timedelta + nat_series_dtype_timedelta,
+            nat_series_dtype_timedelta,
+        )
+
+        tm.assert_series_equal(timedelta_series + NaT, nat_series_dtype_timedelta)
+        tm.assert_series_equal(NaT + timedelta_series, nat_series_dtype_timedelta)
+
+        tm.assert_series_equal(
+            timedelta_series + single_nat_dtype_timedelta, nat_series_dtype_timedelta
+        )
+        tm.assert_series_equal(
+            single_nat_dtype_timedelta + timedelta_series, nat_series_dtype_timedelta
+        )
+
+        tm.assert_series_equal(
+            nat_series_dtype_timedelta + NaT, nat_series_dtype_timedelta
+        )
+        tm.assert_series_equal(
+            NaT + nat_series_dtype_timedelta, nat_series_dtype_timedelta
+        )
+
+        tm.assert_series_equal(
+            nat_series_dtype_timedelta + single_nat_dtype_timedelta,
+            nat_series_dtype_timedelta,
+        )
+        tm.assert_series_equal(
+            single_nat_dtype_timedelta + nat_series_dtype_timedelta,
+            nat_series_dtype_timedelta,
+        )
+
+        # multiplication
+        tm.assert_series_equal(
+            nat_series_dtype_timedelta * 1.0, nat_series_dtype_timedelta
+        )
+        tm.assert_series_equal(
+            1.0 * nat_series_dtype_timedelta, nat_series_dtype_timedelta
+        )
+
+        tm.assert_series_equal(timedelta_series * 1, timedelta_series)
+        tm.assert_series_equal(1 * timedelta_series, timedelta_series)
+
+        tm.assert_series_equal(timedelta_series * 1.5, Series([NaT, Timedelta("1.5s")]))
+        tm.assert_series_equal(1.5 * timedelta_series, Series([NaT, Timedelta("1.5s")]))
+
+        tm.assert_series_equal(timedelta_series * np.nan, nat_series_dtype_timedelta)
+        tm.assert_series_equal(np.nan * timedelta_series, nat_series_dtype_timedelta)
+
+        # division
+        tm.assert_series_equal(timedelta_series / 2, Series([NaT, Timedelta("0.5s")]))
+        tm.assert_series_equal(timedelta_series / 2.0, Series([NaT, Timedelta("0.5s")]))
+        tm.assert_series_equal(timedelta_series / np.nan, nat_series_dtype_timedelta)
+
+    # -------------------------------------------------------------
+    # Binary operations td64 arraylike and datetime-like
+
+    @pytest.mark.parametrize("cls", [Timestamp, datetime, np.datetime64])
+    def test_td64arr_add_sub_datetimelike_scalar(
+        self, cls, box_with_array, tz_naive_fixture
+    ):
+        # GH#11925, GH#29558, GH#23215
+        tz = tz_naive_fixture
+
+        dt_scalar = Timestamp("2012-01-01", tz=tz)
+        if cls is datetime:
+            ts = dt_scalar.to_pydatetime()
+        elif cls is np.datetime64:
+            if tz_naive_fixture is not None:
+                pytest.skip(f"{cls} doesn support {tz_naive_fixture}")
+            ts = dt_scalar.to_datetime64()
+        else:
+            ts = dt_scalar
+
+        tdi = timedelta_range("1 day", periods=3)
+        expected = pd.date_range("2012-01-02", periods=3, tz=tz)
+
+        tdarr = tm.box_expected(tdi, box_with_array)
+        expected = tm.box_expected(expected, box_with_array)
+
+        tm.assert_equal(ts + tdarr, expected)
+        tm.assert_equal(tdarr + ts, expected)
+
+        expected2 = pd.date_range("2011-12-31", periods=3, freq="-1D", tz=tz)
+        expected2 = tm.box_expected(expected2, box_with_array)
+
+        tm.assert_equal(ts - tdarr, expected2)
+        tm.assert_equal(ts + (-tdarr), expected2)
+
+        msg = "cannot subtract a datelike"
+        with pytest.raises(TypeError, match=msg):
+            tdarr - ts
+
+    def test_td64arr_add_datetime64_nat(self, box_with_array):
+        # GH#23215
+        other = np.datetime64("NaT")
+
+        tdi = timedelta_range("1 day", periods=3)
+        expected = DatetimeIndex(["NaT", "NaT", "NaT"], dtype="M8[ns]")
+
+        tdser = tm.box_expected(tdi, box_with_array)
+        expected = tm.box_expected(expected, box_with_array)
+
+        tm.assert_equal(tdser + other, expected)
+        tm.assert_equal(other + tdser, expected)
+
+    def test_td64arr_sub_dt64_array(self, box_with_array):
+        dti = pd.date_range("2016-01-01", periods=3)
+        tdi = TimedeltaIndex(["-1 Day"] * 3)
+        dtarr = dti.values
+        expected = DatetimeIndex(dtarr) - tdi
+
+        tdi = tm.box_expected(tdi, box_with_array)
+        expected = tm.box_expected(expected, box_with_array)
+
+        msg = "cannot subtract a datelike from"
+        with pytest.raises(TypeError, match=msg):
+            tdi - dtarr
+
+        # TimedeltaIndex.__rsub__
+        result = dtarr - tdi
+        tm.assert_equal(result, expected)
+
+    def test_td64arr_add_dt64_array(self, box_with_array):
+        dti = pd.date_range("2016-01-01", periods=3)
+        tdi = TimedeltaIndex(["-1 Day"] * 3)
+        dtarr = dti.values
+        expected = DatetimeIndex(dtarr) + tdi
+
+        tdi = tm.box_expected(tdi, box_with_array)
+        expected = tm.box_expected(expected, box_with_array)
+
+        result = tdi + dtarr
+        tm.assert_equal(result, expected)
+        result = dtarr + tdi
+        tm.assert_equal(result, expected)
+
+    # ------------------------------------------------------------------
+    # Invalid __add__/__sub__ operations
+
+    @pytest.mark.parametrize("pi_freq", ["D", "W", "Q", "h"])
+    @pytest.mark.parametrize("tdi_freq", [None, "h"])
+    def test_td64arr_sub_periodlike(
+        self, box_with_array, box_with_array2, tdi_freq, pi_freq
+    ):
+        # GH#20049 subtracting PeriodIndex should raise TypeError
+        tdi = TimedeltaIndex(["1 hours", "2 hours"], freq=tdi_freq)
+        dti = Timestamp("2018-03-07 17:16:40") + tdi
+        pi = dti.to_period(pi_freq)
+        per = pi[0]
+
+        tdi = tm.box_expected(tdi, box_with_array)
+        pi = tm.box_expected(pi, box_with_array2)
+        msg = "cannot subtract|unsupported operand type"
+        with pytest.raises(TypeError, match=msg):
+            tdi - pi
+
+        # GH#13078 subtraction of Period scalar not supported
+        with pytest.raises(TypeError, match=msg):
+            tdi - per
+
+    @pytest.mark.parametrize(
+        "other",
+        [
+            # GH#12624 for str case
+            "a",
+            # GH#19123
+            1,
+            1.5,
+            np.array(2),
+        ],
+    )
+    def test_td64arr_addsub_numeric_scalar_invalid(self, box_with_array, other):
+        # vector-like others are tested in test_td64arr_add_sub_numeric_arr_invalid
+        tdser = Series(["59 Days", "59 Days", "NaT"], dtype="m8[ns]")
+        tdarr = tm.box_expected(tdser, box_with_array)
+
+        assert_invalid_addsub_type(tdarr, other)
+
+    @pytest.mark.parametrize(
+        "vec",
+        [
+            np.array([1, 2, 3]),
+            Index([1, 2, 3]),
+            Series([1, 2, 3]),
+            DataFrame([[1, 2, 3]]),
+        ],
+        ids=lambda x: type(x).__name__,
+    )
+    def test_td64arr_addsub_numeric_arr_invalid(
+        self, box_with_array, vec, any_real_numpy_dtype
+    ):
+        tdser = Series(["59 Days", "59 Days", "NaT"], dtype="m8[ns]")
+        tdarr = tm.box_expected(tdser, box_with_array)
+
+        vector = vec.astype(any_real_numpy_dtype)
+        assert_invalid_addsub_type(tdarr, vector)
+
+    def test_td64arr_add_sub_int(self, box_with_array, one):
+        # Variants of `one` for #19012, deprecated GH#22535
+        rng = timedelta_range("1 days 09:00:00", freq="h", periods=10)
+        tdarr = tm.box_expected(rng, box_with_array)
+
+        msg = "Addition/subtraction of integers"
+        assert_invalid_addsub_type(tdarr, one, msg)
+
+        # TODO: get inplace ops into assert_invalid_addsub_type
+        with pytest.raises(TypeError, match=msg):
+            tdarr += one
+        with pytest.raises(TypeError, match=msg):
+            tdarr -= one
+
+    def test_td64arr_add_sub_integer_array(self, box_with_array):
+        # GH#19959, deprecated GH#22535
+        # GH#22696 for DataFrame case, check that we don't dispatch to numpy
+        #  implementation, which treats int64 as m8[ns]
+        box = box_with_array
+        xbox = np.ndarray if box is pd.array else box
+
+        rng = timedelta_range("1 days 09:00:00", freq="h", periods=3)
+        tdarr = tm.box_expected(rng, box)
+        other = tm.box_expected([4, 3, 2], xbox)
+
+        msg = "Addition/subtraction of integers and integer-arrays"
+        assert_invalid_addsub_type(tdarr, other, msg)
+
+    def test_td64arr_addsub_integer_array_no_freq(self, box_with_array):
+        # GH#19959
+        box = box_with_array
+        xbox = np.ndarray if box is pd.array else box
+
+        tdi = TimedeltaIndex(["1 Day", "NaT", "3 Hours"])
+        tdarr = tm.box_expected(tdi, box)
+        other = tm.box_expected([14, -1, 16], xbox)
+
+        msg = "Addition/subtraction of integers"
+        assert_invalid_addsub_type(tdarr, other, msg)
+
+    # ------------------------------------------------------------------
+    # Operations with timedelta-like others
+
+    def test_td64arr_add_sub_td64_array(self, box_with_array):
+        box = box_with_array
+        dti = pd.date_range("2016-01-01", periods=3)
+        tdi = dti - dti.shift(1)
+        tdarr = tdi.values
+
+        expected = 2 * tdi
+        tdi = tm.box_expected(tdi, box)
+        expected = tm.box_expected(expected, box)
+
+        result = tdi + tdarr
+        tm.assert_equal(result, expected)
+        result = tdarr + tdi
+        tm.assert_equal(result, expected)
+
+        expected_sub = 0 * tdi
+        result = tdi - tdarr
+        tm.assert_equal(result, expected_sub)
+        result = tdarr - tdi
+        tm.assert_equal(result, expected_sub)
+
+    def test_td64arr_add_sub_tdi(self, box_with_array, names):
+        # GH#17250 make sure result dtype is correct
+        # GH#19043 make sure names are propagated correctly
+        box = box_with_array
+        exname = get_expected_name(box, names)
+
+        tdi = TimedeltaIndex(["0 days", "1 day"], name=names[1])
+        tdi = np.array(tdi) if box in [tm.to_array, pd.array] else tdi
+        ser = Series([Timedelta(hours=3), Timedelta(hours=4)], name=names[0])
+        expected = Series([Timedelta(hours=3), Timedelta(days=1, hours=4)], name=exname)
+
+        ser = tm.box_expected(ser, box)
+        expected = tm.box_expected(expected, box)
+
+        result = tdi + ser
+        tm.assert_equal(result, expected)
+        assert_dtype(result, "timedelta64[ns]")
+
+        result = ser + tdi
+        tm.assert_equal(result, expected)
+        assert_dtype(result, "timedelta64[ns]")
+
+        expected = Series(
+            [Timedelta(hours=-3), Timedelta(days=1, hours=-4)], name=exname
+        )
+        expected = tm.box_expected(expected, box)
+
+        result = tdi - ser
+        tm.assert_equal(result, expected)
+        assert_dtype(result, "timedelta64[ns]")
+
+        result = ser - tdi
+        tm.assert_equal(result, -expected)
+        assert_dtype(result, "timedelta64[ns]")
+
+    @pytest.mark.parametrize("tdnat", [np.timedelta64("NaT"), NaT])
+    def test_td64arr_add_sub_td64_nat(self, box_with_array, tdnat):
+        # GH#18808, GH#23320 special handling for timedelta64("NaT")
+        box = box_with_array
+        tdi = TimedeltaIndex([NaT, Timedelta("1s")])
+        expected = TimedeltaIndex(["NaT"] * 2)
+
+        obj = tm.box_expected(tdi, box)
+        expected = tm.box_expected(expected, box)
+
+        result = obj + tdnat
+        tm.assert_equal(result, expected)
+        result = tdnat + obj
+        tm.assert_equal(result, expected)
+        result = obj - tdnat
+        tm.assert_equal(result, expected)
+        result = tdnat - obj
+        tm.assert_equal(result, expected)
+
+    def test_td64arr_add_timedeltalike(self, two_hours, box_with_array):
+        # only test adding/sub offsets as + is now numeric
+        # GH#10699 for Tick cases
+        box = box_with_array
+        rng = timedelta_range("1 days", "10 days")
+        expected = timedelta_range("1 days 02:00:00", "10 days 02:00:00", freq="D")
+        rng = tm.box_expected(rng, box)
+        expected = tm.box_expected(expected, box)
+
+        result = rng + two_hours
+        tm.assert_equal(result, expected)
+
+        result = two_hours + rng
+        tm.assert_equal(result, expected)
+
+    def test_td64arr_sub_timedeltalike(self, two_hours, box_with_array):
+        # only test adding/sub offsets as - is now numeric
+        # GH#10699 for Tick cases
+        box = box_with_array
+        rng = timedelta_range("1 days", "10 days")
+        expected = timedelta_range("0 days 22:00:00", "9 days 22:00:00")
+
+        rng = tm.box_expected(rng, box)
+        expected = tm.box_expected(expected, box)
+
+        result = rng - two_hours
+        tm.assert_equal(result, expected)
+
+        result = two_hours - rng
+        tm.assert_equal(result, -expected)
+
+    # ------------------------------------------------------------------
+    # __add__/__sub__ with DateOffsets and arrays of DateOffsets
+
+    def test_td64arr_add_sub_offset_index(self, names, box_with_array):
+        # GH#18849, GH#19744
+        box = box_with_array
+        exname = get_expected_name(box, names)
+
+        tdi = TimedeltaIndex(["1 days 00:00:00", "3 days 04:00:00"], name=names[0])
+        other = Index([offsets.Hour(n=1), offsets.Minute(n=-2)], name=names[1])
+        other = np.array(other) if box in [tm.to_array, pd.array] else other
+
+        expected = TimedeltaIndex(
+            [tdi[n] + other[n] for n in range(len(tdi))], freq="infer", name=exname
+        )
+        expected_sub = TimedeltaIndex(
+            [tdi[n] - other[n] for n in range(len(tdi))], freq="infer", name=exname
+        )
+
+        tdi = tm.box_expected(tdi, box)
+        expected = tm.box_expected(expected, box).astype(object, copy=False)
+        expected_sub = tm.box_expected(expected_sub, box).astype(object, copy=False)
+
+        with tm.assert_produces_warning(PerformanceWarning):
+            res = tdi + other
+        tm.assert_equal(res, expected)
+
+        with tm.assert_produces_warning(PerformanceWarning):
+            res2 = other + tdi
+        tm.assert_equal(res2, expected)
+
+        with tm.assert_produces_warning(PerformanceWarning):
+            res_sub = tdi - other
+        tm.assert_equal(res_sub, expected_sub)
+
+    def test_td64arr_add_sub_offset_array(self, box_with_array):
+        # GH#18849, GH#18824
+        box = box_with_array
+        tdi = TimedeltaIndex(["1 days 00:00:00", "3 days 04:00:00"])
+        other = np.array([offsets.Hour(n=1), offsets.Minute(n=-2)])
+
+        expected = TimedeltaIndex(
+            [tdi[n] + other[n] for n in range(len(tdi))], freq="infer"
+        )
+        expected_sub = TimedeltaIndex(
+            [tdi[n] - other[n] for n in range(len(tdi))], freq="infer"
+        )
+
+        tdi = tm.box_expected(tdi, box)
+        expected = tm.box_expected(expected, box).astype(object)
+
+        with tm.assert_produces_warning(PerformanceWarning):
+            res = tdi + other
+        tm.assert_equal(res, expected)
+
+        with tm.assert_produces_warning(PerformanceWarning):
+            res2 = other + tdi
+        tm.assert_equal(res2, expected)
+
+        expected_sub = tm.box_expected(expected_sub, box_with_array).astype(object)
+        with tm.assert_produces_warning(PerformanceWarning):
+            res_sub = tdi - other
+        tm.assert_equal(res_sub, expected_sub)
+
+    def test_td64arr_with_offset_series(self, names, box_with_array):
+        # GH#18849
+        box = box_with_array
+        box2 = Series if box in [Index, tm.to_array, pd.array] else box
+        exname = get_expected_name(box, names)
+
+        tdi = TimedeltaIndex(["1 days 00:00:00", "3 days 04:00:00"], name=names[0])
+        other = Series([offsets.Hour(n=1), offsets.Minute(n=-2)], name=names[1])
+
+        expected_add = Series(
+            [tdi[n] + other[n] for n in range(len(tdi))], name=exname, dtype=object
+        )
+        obj = tm.box_expected(tdi, box)
+        expected_add = tm.box_expected(expected_add, box2).astype(object)
+
+        with tm.assert_produces_warning(PerformanceWarning):
+            res = obj + other
+        tm.assert_equal(res, expected_add)
+
+        with tm.assert_produces_warning(PerformanceWarning):
+            res2 = other + obj
+        tm.assert_equal(res2, expected_add)
+
+        expected_sub = Series(
+            [tdi[n] - other[n] for n in range(len(tdi))], name=exname, dtype=object
+        )
+        expected_sub = tm.box_expected(expected_sub, box2).astype(object)
+
+        with tm.assert_produces_warning(PerformanceWarning):
+            res3 = obj - other
+        tm.assert_equal(res3, expected_sub)
+
+    @pytest.mark.parametrize("obox", [np.array, Index, Series])
+    def test_td64arr_addsub_anchored_offset_arraylike(self, obox, box_with_array):
+        # GH#18824
+        tdi = TimedeltaIndex(["1 days 00:00:00", "3 days 04:00:00"])
+        tdi = tm.box_expected(tdi, box_with_array)
+
+        anchored = obox([offsets.MonthEnd(), offsets.Day(n=2)])
+
+        # addition/subtraction ops with anchored offsets should issue
+        # a PerformanceWarning and _then_ raise a TypeError.
+        msg = "has incorrect type|cannot add the type MonthEnd"
+        with pytest.raises(TypeError, match=msg):
+            with tm.assert_produces_warning(PerformanceWarning):
+                tdi + anchored
+        with pytest.raises(TypeError, match=msg):
+            with tm.assert_produces_warning(PerformanceWarning):
+                anchored + tdi
+        with pytest.raises(TypeError, match=msg):
+            with tm.assert_produces_warning(PerformanceWarning):
+                tdi - anchored
+        with pytest.raises(TypeError, match=msg):
+            with tm.assert_produces_warning(PerformanceWarning):
+                anchored - tdi
+
+    # ------------------------------------------------------------------
+    # Unsorted
+
+    def test_td64arr_add_sub_object_array(self, box_with_array):
+        box = box_with_array
+        xbox = np.ndarray if box is pd.array else box
+
+        tdi = timedelta_range("1 day", periods=3, freq="D")
+        tdarr = tm.box_expected(tdi, box)
+
+        other = np.array([Timedelta(days=1), offsets.Day(2), Timestamp("2000-01-04")])
+
+        with tm.assert_produces_warning(PerformanceWarning):
+            result = tdarr + other
+
+        expected = Index(
+            [Timedelta(days=2), Timedelta(days=4), Timestamp("2000-01-07")]
+        )
+        expected = tm.box_expected(expected, xbox).astype(object)
+        tm.assert_equal(result, expected)
+
+        msg = "unsupported operand type|cannot subtract a datelike"
+        with pytest.raises(TypeError, match=msg):
+            with tm.assert_produces_warning(PerformanceWarning):
+                tdarr - other
+
+        with tm.assert_produces_warning(PerformanceWarning):
+            result = other - tdarr
+
+        expected = Index([Timedelta(0), Timedelta(0), Timestamp("2000-01-01")])
+        expected = tm.box_expected(expected, xbox).astype(object)
+        tm.assert_equal(result, expected)
+
+
+class TestTimedeltaArraylikeMulDivOps:
+    # Tests for timedelta64[ns]
+    # __mul__, __rmul__, __div__, __rdiv__, __floordiv__, __rfloordiv__
+
+    # ------------------------------------------------------------------
+    # Multiplication
+    # organized with scalar others first, then array-like
+
+    def test_td64arr_mul_int(self, box_with_array):
+        idx = TimedeltaIndex(np.arange(5, dtype="int64"))
+        idx = tm.box_expected(idx, box_with_array)
+
+        result = idx * 1
+        tm.assert_equal(result, idx)
+
+        result = 1 * idx
+        tm.assert_equal(result, idx)
+
+    def test_td64arr_mul_tdlike_scalar_raises(self, two_hours, box_with_array):
+        rng = timedelta_range("1 days", "10 days", name="foo")
+        rng = tm.box_expected(rng, box_with_array)
+        msg = "|".join(
+            [
+                "argument must be an integer",
+                "cannot use operands with types dtype",
+                "Cannot multiply with",
+            ]
+        )
+        with pytest.raises(TypeError, match=msg):
+            rng * two_hours
+
+    def test_tdi_mul_int_array_zerodim(self, box_with_array):
+        rng5 = np.arange(5, dtype="int64")
+        idx = TimedeltaIndex(rng5)
+        expected = TimedeltaIndex(rng5 * 5)
+
+        idx = tm.box_expected(idx, box_with_array)
+        expected = tm.box_expected(expected, box_with_array)
+
+        result = idx * np.array(5, dtype="int64")
+        tm.assert_equal(result, expected)
+
+    def test_tdi_mul_int_array(self, box_with_array):
+        rng5 = np.arange(5, dtype="int64")
+        idx = TimedeltaIndex(rng5)
+        expected = TimedeltaIndex(rng5**2)
+
+        idx = tm.box_expected(idx, box_with_array)
+        expected = tm.box_expected(expected, box_with_array)
+
+        result = idx * rng5
+        tm.assert_equal(result, expected)
+
+    def test_tdi_mul_int_series(self, box_with_array):
+        box = box_with_array
+        xbox = Series if box in [Index, tm.to_array, pd.array] else box
+
+        idx = TimedeltaIndex(np.arange(5, dtype="int64"))
+        expected = TimedeltaIndex(np.arange(5, dtype="int64") ** 2)
+
+        idx = tm.box_expected(idx, box)
+        expected = tm.box_expected(expected, xbox)
+
+        result = idx * Series(np.arange(5, dtype="int64"))
+        tm.assert_equal(result, expected)
+
+    def test_tdi_mul_float_series(self, box_with_array):
+        box = box_with_array
+        xbox = Series if box in [Index, tm.to_array, pd.array] else box
+
+        idx = TimedeltaIndex(np.arange(5, dtype="int64"))
+        idx = tm.box_expected(idx, box)
+
+        rng5f = np.arange(5, dtype="float64")
+        expected = TimedeltaIndex(rng5f * (rng5f + 1.0))
+        expected = tm.box_expected(expected, xbox)
+
+        result = idx * Series(rng5f + 1.0)
+        tm.assert_equal(result, expected)
+
+    # TODO: Put Series/DataFrame in others?
+    @pytest.mark.parametrize(
+        "other",
+        [
+            np.arange(1, 11),
+            Index(np.arange(1, 11), np.int64),
+            Index(range(1, 11), np.uint64),
+            Index(range(1, 11), np.float64),
+            pd.RangeIndex(1, 11),
+        ],
+        ids=lambda x: type(x).__name__,
+    )
+    def test_tdi_rmul_arraylike(self, other, box_with_array):
+        box = box_with_array
+
+        tdi = TimedeltaIndex(["1 Day"] * 10)
+        expected = timedelta_range("1 days", "10 days")._with_freq(None)
+
+        tdi = tm.box_expected(tdi, box)
+        xbox = get_upcast_box(tdi, other)
+
+        expected = tm.box_expected(expected, xbox)
+
+        result = other * tdi
+        tm.assert_equal(result, expected)
+        commute = tdi * other
+        tm.assert_equal(commute, expected)
+
+    # ------------------------------------------------------------------
+    # __div__, __rdiv__
+
+    def test_td64arr_div_nat_invalid(self, box_with_array):
+        # don't allow division by NaT (maybe could in the future)
+        rng = timedelta_range("1 days", "10 days", name="foo")
+        rng = tm.box_expected(rng, box_with_array)
+
+        with pytest.raises(TypeError, match="unsupported operand type"):
+            rng / NaT
+        with pytest.raises(TypeError, match="Cannot divide NaTType by"):
+            NaT / rng
+
+        dt64nat = np.datetime64("NaT", "ns")
+        msg = "|".join(
+            [
+                # 'divide' on npdev as of 2021-12-18
+                "ufunc '(true_divide|divide)' cannot use operands",
+                "cannot perform __r?truediv__",
+                "Cannot divide datetime64 by TimedeltaArray",
+            ]
+        )
+        with pytest.raises(TypeError, match=msg):
+            rng / dt64nat
+        with pytest.raises(TypeError, match=msg):
+            dt64nat / rng
+
+    def test_td64arr_div_td64nat(self, box_with_array):
+        # GH#23829
+        box = box_with_array
+        xbox = np.ndarray if box is pd.array else box
+
+        rng = timedelta_range("1 days", "10 days")
+        rng = tm.box_expected(rng, box)
+
+        other = np.timedelta64("NaT")
+
+        expected = np.array([np.nan] * 10)
+        expected = tm.box_expected(expected, xbox)
+
+        result = rng / other
+        tm.assert_equal(result, expected)
+
+        result = other / rng
+        tm.assert_equal(result, expected)
+
+    def test_td64arr_div_int(self, box_with_array):
+        idx = TimedeltaIndex(np.arange(5, dtype="int64"))
+        idx = tm.box_expected(idx, box_with_array)
+
+        result = idx / 1
+        tm.assert_equal(result, idx)
+
+        with pytest.raises(TypeError, match="Cannot divide"):
+            # GH#23829
+            1 / idx
+
+    def test_td64arr_div_tdlike_scalar(self, two_hours, box_with_array):
+        # GH#20088, GH#22163 ensure DataFrame returns correct dtype
+        box = box_with_array
+        xbox = np.ndarray if box is pd.array else box
+
+        rng = timedelta_range("1 days", "10 days", name="foo")
+        expected = Index((np.arange(10) + 1) * 12, dtype=np.float64, name="foo")
+
+        rng = tm.box_expected(rng, box)
+        expected = tm.box_expected(expected, xbox)
+
+        result = rng / two_hours
+        tm.assert_equal(result, expected)
+
+        result = two_hours / rng
+        expected = 1 / expected
+        tm.assert_equal(result, expected)
+
+    @pytest.mark.parametrize("m", [1, 3, 10])
+    @pytest.mark.parametrize("unit", ["D", "h", "m", "s", "ms", "us", "ns"])
+    def test_td64arr_div_td64_scalar(self, m, unit, box_with_array):
+        box = box_with_array
+        xbox = np.ndarray if box is pd.array else box
+
+        ser = Series([Timedelta(days=59)] * 3)
+        ser[2] = np.nan
+        flat = ser
+        ser = tm.box_expected(ser, box)
+
+        # op
+        expected = Series([x / np.timedelta64(m, unit) for x in flat])
+        expected = tm.box_expected(expected, xbox)
+        result = ser / np.timedelta64(m, unit)
+        tm.assert_equal(result, expected)
+
+        # reverse op
+        expected = Series([Timedelta(np.timedelta64(m, unit)) / x for x in flat])
+        expected = tm.box_expected(expected, xbox)
+        result = np.timedelta64(m, unit) / ser
+        tm.assert_equal(result, expected)
+
+    def test_td64arr_div_tdlike_scalar_with_nat(self, two_hours, box_with_array):
+        box = box_with_array
+        xbox = np.ndarray if box is pd.array else box
+
+        rng = TimedeltaIndex(["1 days", NaT, "2 days"], name="foo")
+        expected = Index([12, np.nan, 24], dtype=np.float64, name="foo")
+
+        rng = tm.box_expected(rng, box)
+        expected = tm.box_expected(expected, xbox)
+
+        result = rng / two_hours
+        tm.assert_equal(result, expected)
+
+        result = two_hours / rng
+        expected = 1 / expected
+        tm.assert_equal(result, expected)
+
+    def test_td64arr_div_td64_ndarray(self, box_with_array):
+        # GH#22631
+        box = box_with_array
+        xbox = np.ndarray if box is pd.array else box
+
+        rng = TimedeltaIndex(["1 days", NaT, "2 days"])
+        expected = Index([12, np.nan, 24], dtype=np.float64)
+
+        rng = tm.box_expected(rng, box)
+        expected = tm.box_expected(expected, xbox)
+
+        other = np.array([2, 4, 2], dtype="m8[h]")
+        result = rng / other
+        tm.assert_equal(result, expected)
+
+        result = rng / tm.box_expected(other, box)
+        tm.assert_equal(result, expected)
+
+        result = rng / other.astype(object)
+        tm.assert_equal(result, expected.astype(object))
+
+        result = rng / list(other)
+        tm.assert_equal(result, expected)
+
+        # reversed op
+        expected = 1 / expected
+        result = other / rng
+        tm.assert_equal(result, expected)
+
+        result = tm.box_expected(other, box) / rng
+        tm.assert_equal(result, expected)
+
+        result = other.astype(object) / rng
+        tm.assert_equal(result, expected)
+
+        result = list(other) / rng
+        tm.assert_equal(result, expected)
+
+    def test_tdarr_div_length_mismatch(self, box_with_array):
+        rng = TimedeltaIndex(["1 days", NaT, "2 days"])
+        mismatched = [1, 2, 3, 4]
+
+        rng = tm.box_expected(rng, box_with_array)
+        msg = "Cannot divide vectors|Unable to coerce to Series"
+        for obj in [mismatched, mismatched[:2]]:
+            # one shorter, one longer
+            for other in [obj, np.array(obj), Index(obj)]:
+                with pytest.raises(ValueError, match=msg):
+                    rng / other
+                with pytest.raises(ValueError, match=msg):
+                    other / rng
+
+    def test_td64_div_object_mixed_result(self, box_with_array):
+        # Case where we having a NaT in the result inseat of timedelta64("NaT")
+        #  is misleading
+        orig = timedelta_range("1 Day", periods=3).insert(1, NaT)
+        tdi = tm.box_expected(orig, box_with_array, transpose=False)
+
+        other = np.array([orig[0], 1.5, 2.0, orig[2]], dtype=object)
+        other = tm.box_expected(other, box_with_array, transpose=False)
+
+        res = tdi / other
+
+        expected = Index([1.0, np.timedelta64("NaT", "ns"), orig[0], 1.5], dtype=object)
+        expected = tm.box_expected(expected, box_with_array, transpose=False)
+        if isinstance(expected, NumpyExtensionArray):
+            expected = expected.to_numpy()
+        tm.assert_equal(res, expected)
+        if box_with_array is DataFrame:
+            # We have a np.timedelta64(NaT), not pd.NaT
+            assert isinstance(res.iloc[1, 0], np.timedelta64)
+
+        res = tdi // other
+
+        expected = Index([1, np.timedelta64("NaT", "ns"), orig[0], 1], dtype=object)
+        expected = tm.box_expected(expected, box_with_array, transpose=False)
+        if isinstance(expected, NumpyExtensionArray):
+            expected = expected.to_numpy()
+        tm.assert_equal(res, expected)
+        if box_with_array is DataFrame:
+            # We have a np.timedelta64(NaT), not pd.NaT
+            assert isinstance(res.iloc[1, 0], np.timedelta64)
+
+    # ------------------------------------------------------------------
+    # __floordiv__, __rfloordiv__
+
+    def test_td64arr_floordiv_td64arr_with_nat(
+        self, box_with_array, using_array_manager
+    ):
+        # GH#35529
+        box = box_with_array
+        xbox = np.ndarray if box is pd.array else box
+
+        left = Series([1000, 222330, 30], dtype="timedelta64[ns]")
+        right = Series([1000, 222330, None], dtype="timedelta64[ns]")
+
+        left = tm.box_expected(left, box)
+        right = tm.box_expected(right, box)
+
+        expected = np.array([1.0, 1.0, np.nan], dtype=np.float64)
+        expected = tm.box_expected(expected, xbox)
+        if box is DataFrame and using_array_manager:
+            # INFO(ArrayManager) floordiv returns integer, and ArrayManager
+            # performs ops column-wise and thus preserves int64 dtype for
+            # columns without missing values
+            expected[[0, 1]] = expected[[0, 1]].astype("int64")
+
+        with tm.maybe_produces_warning(
+            RuntimeWarning, box is pd.array, check_stacklevel=False
+        ):
+            result = left // right
+
+        tm.assert_equal(result, expected)
+
+        # case that goes through __rfloordiv__ with arraylike
+        with tm.maybe_produces_warning(
+            RuntimeWarning, box is pd.array, check_stacklevel=False
+        ):
+            result = np.asarray(left) // right
+        tm.assert_equal(result, expected)
+
+    @pytest.mark.filterwarnings("ignore:invalid value encountered:RuntimeWarning")
+    def test_td64arr_floordiv_tdscalar(self, box_with_array, scalar_td):
+        # GH#18831, GH#19125
+        box = box_with_array
+        xbox = np.ndarray if box is pd.array else box
+        td = Timedelta("5m3s")  # i.e. (scalar_td - 1sec) / 2
+
+        td1 = Series([td, td, NaT], dtype="m8[ns]")
+        td1 = tm.box_expected(td1, box, transpose=False)
+
+        expected = Series([0, 0, np.nan])
+        expected = tm.box_expected(expected, xbox, transpose=False)
+
+        result = td1 // scalar_td
+        tm.assert_equal(result, expected)
+
+        # Reversed op
+        expected = Series([2, 2, np.nan])
+        expected = tm.box_expected(expected, xbox, transpose=False)
+
+        result = scalar_td // td1
+        tm.assert_equal(result, expected)
+
+        # same thing buts let's be explicit about calling __rfloordiv__
+        result = td1.__rfloordiv__(scalar_td)
+        tm.assert_equal(result, expected)
+
+    def test_td64arr_floordiv_int(self, box_with_array):
+        idx = TimedeltaIndex(np.arange(5, dtype="int64"))
+        idx = tm.box_expected(idx, box_with_array)
+        result = idx // 1
+        tm.assert_equal(result, idx)
+
+        pattern = "floor_divide cannot use operands|Cannot divide int by Timedelta*"
+        with pytest.raises(TypeError, match=pattern):
+            1 // idx
+
+    # ------------------------------------------------------------------
+    # mod, divmod
+    # TODO: operations with timedelta-like arrays, numeric arrays,
+    #  reversed ops
+
+    def test_td64arr_mod_tdscalar(self, box_with_array, three_days):
+        tdi = timedelta_range("1 Day", "9 days")
+        tdarr = tm.box_expected(tdi, box_with_array)
+
+        expected = TimedeltaIndex(["1 Day", "2 Days", "0 Days"] * 3)
+        expected = tm.box_expected(expected, box_with_array)
+
+        result = tdarr % three_days
+        tm.assert_equal(result, expected)
+
+        warn = None
+        if box_with_array is DataFrame and isinstance(three_days, pd.DateOffset):
+            warn = PerformanceWarning
+            # TODO: making expected be object here a result of DataFrame.__divmod__
+            #  being defined in a naive way that does not dispatch to the underlying
+            #  array's __divmod__
+            expected = expected.astype(object)
+
+        with tm.assert_produces_warning(warn):
+            result = divmod(tdarr, three_days)
+
+        tm.assert_equal(result[1], expected)
+        tm.assert_equal(result[0], tdarr // three_days)
+
+    def test_td64arr_mod_int(self, box_with_array):
+        tdi = timedelta_range("1 ns", "10 ns", periods=10)
+        tdarr = tm.box_expected(tdi, box_with_array)
+
+        expected = TimedeltaIndex(["1 ns", "0 ns"] * 5)
+        expected = tm.box_expected(expected, box_with_array)
+
+        result = tdarr % 2
+        tm.assert_equal(result, expected)
+
+        msg = "Cannot divide int by"
+        with pytest.raises(TypeError, match=msg):
+            2 % tdarr
+
+        result = divmod(tdarr, 2)
+        tm.assert_equal(result[1], expected)
+        tm.assert_equal(result[0], tdarr // 2)
+
+    def test_td64arr_rmod_tdscalar(self, box_with_array, three_days):
+        tdi = timedelta_range("1 Day", "9 days")
+        tdarr = tm.box_expected(tdi, box_with_array)
+
+        expected = ["0 Days", "1 Day", "0 Days"] + ["3 Days"] * 6
+        expected = TimedeltaIndex(expected)
+        expected = tm.box_expected(expected, box_with_array)
+
+        result = three_days % tdarr
+        tm.assert_equal(result, expected)
+
+        result = divmod(three_days, tdarr)
+        tm.assert_equal(result[1], expected)
+        tm.assert_equal(result[0], three_days // tdarr)
+
+    # ------------------------------------------------------------------
+    # Operations with invalid others
+
+    def test_td64arr_mul_tdscalar_invalid(self, box_with_array, scalar_td):
+        td1 = Series([timedelta(minutes=5, seconds=3)] * 3)
+        td1.iloc[2] = np.nan
+
+        td1 = tm.box_expected(td1, box_with_array)
+
+        # check that we are getting a TypeError
+        # with 'operate' (from core/ops.py) for the ops that are not
+        # defined
+        pattern = "operate|unsupported|cannot|not supported"
+        with pytest.raises(TypeError, match=pattern):
+            td1 * scalar_td
+        with pytest.raises(TypeError, match=pattern):
+            scalar_td * td1
+
+    def test_td64arr_mul_too_short_raises(self, box_with_array):
+        idx = TimedeltaIndex(np.arange(5, dtype="int64"))
+        idx = tm.box_expected(idx, box_with_array)
+        msg = "|".join(
+            [
+                "cannot use operands with types dtype",
+                "Cannot multiply with unequal lengths",
+                "Unable to coerce to Series",
+            ]
+        )
+        with pytest.raises(TypeError, match=msg):
+            # length check before dtype check
+            idx * idx[:3]
+        with pytest.raises(ValueError, match=msg):
+            idx * np.array([1, 2])
+
+    def test_td64arr_mul_td64arr_raises(self, box_with_array):
+        idx = TimedeltaIndex(np.arange(5, dtype="int64"))
+        idx = tm.box_expected(idx, box_with_array)
+        msg = "cannot use operands with types dtype"
+        with pytest.raises(TypeError, match=msg):
+            idx * idx
+
+    # ------------------------------------------------------------------
+    # Operations with numeric others
+
+    def test_td64arr_mul_numeric_scalar(self, box_with_array, one):
+        # GH#4521
+        # divide/multiply by integers
+        tdser = Series(["59 Days", "59 Days", "NaT"], dtype="m8[ns]")
+        expected = Series(["-59 Days", "-59 Days", "NaT"], dtype="timedelta64[ns]")
+
+        tdser = tm.box_expected(tdser, box_with_array)
+        expected = tm.box_expected(expected, box_with_array)
+
+        result = tdser * (-one)
+        tm.assert_equal(result, expected)
+        result = (-one) * tdser
+        tm.assert_equal(result, expected)
+
+        expected = Series(["118 Days", "118 Days", "NaT"], dtype="timedelta64[ns]")
+        expected = tm.box_expected(expected, box_with_array)
+
+        result = tdser * (2 * one)
+        tm.assert_equal(result, expected)
+        result = (2 * one) * tdser
+        tm.assert_equal(result, expected)
+
+    @pytest.mark.parametrize("two", [2, 2.0, np.array(2), np.array(2.0)])
+    def test_td64arr_div_numeric_scalar(self, box_with_array, two):
+        # GH#4521
+        # divide/multiply by integers
+        tdser = Series(["59 Days", "59 Days", "NaT"], dtype="m8[ns]")
+        expected = Series(["29.5D", "29.5D", "NaT"], dtype="timedelta64[ns]")
+
+        tdser = tm.box_expected(tdser, box_with_array)
+        expected = tm.box_expected(expected, box_with_array)
+
+        result = tdser / two
+        tm.assert_equal(result, expected)
+
+        with pytest.raises(TypeError, match="Cannot divide"):
+            two / tdser
+
+    @pytest.mark.parametrize("two", [2, 2.0, np.array(2), np.array(2.0)])
+    def test_td64arr_floordiv_numeric_scalar(self, box_with_array, two):
+        tdser = Series(["59 Days", "59 Days", "NaT"], dtype="m8[ns]")
+        expected = Series(["29.5D", "29.5D", "NaT"], dtype="timedelta64[ns]")
+
+        tdser = tm.box_expected(tdser, box_with_array)
+        expected = tm.box_expected(expected, box_with_array)
+
+        result = tdser // two
+        tm.assert_equal(result, expected)
+
+        with pytest.raises(TypeError, match="Cannot divide"):
+            two // tdser
+
+    @pytest.mark.parametrize(
+        "vector",
+        [np.array([20, 30, 40]), Index([20, 30, 40]), Series([20, 30, 40])],
+        ids=lambda x: type(x).__name__,
+    )
+    def test_td64arr_rmul_numeric_array(
+        self,
+        box_with_array,
+        vector,
+        any_real_numpy_dtype,
+    ):
+        # GH#4521
+        # divide/multiply by integers
+
+        tdser = Series(["59 Days", "59 Days", "NaT"], dtype="m8[ns]")
+        vector = vector.astype(any_real_numpy_dtype)
+
+        expected = Series(["1180 Days", "1770 Days", "NaT"], dtype="timedelta64[ns]")
+
+        tdser = tm.box_expected(tdser, box_with_array)
+        xbox = get_upcast_box(tdser, vector)
+
+        expected = tm.box_expected(expected, xbox)
+
+        result = tdser * vector
+        tm.assert_equal(result, expected)
+
+        result = vector * tdser
+        tm.assert_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "vector",
+        [np.array([20, 30, 40]), Index([20, 30, 40]), Series([20, 30, 40])],
+        ids=lambda x: type(x).__name__,
+    )
+    def test_td64arr_div_numeric_array(
+        self, box_with_array, vector, any_real_numpy_dtype
+    ):
+        # GH#4521
+        # divide/multiply by integers
+
+        tdser = Series(["59 Days", "59 Days", "NaT"], dtype="m8[ns]")
+        vector = vector.astype(any_real_numpy_dtype)
+
+        expected = Series(["2.95D", "1D 23h 12m", "NaT"], dtype="timedelta64[ns]")
+
+        tdser = tm.box_expected(tdser, box_with_array)
+        xbox = get_upcast_box(tdser, vector)
+        expected = tm.box_expected(expected, xbox)
+
+        result = tdser / vector
+        tm.assert_equal(result, expected)
+
+        pattern = "|".join(
+            [
+                "true_divide'? cannot use operands",
+                "cannot perform __div__",
+                "cannot perform __truediv__",
+                "unsupported operand",
+                "Cannot divide",
+                "ufunc 'divide' cannot use operands with types",
+            ]
+        )
+        with pytest.raises(TypeError, match=pattern):
+            vector / tdser
+
+        result = tdser / vector.astype(object)
+        if box_with_array is DataFrame:
+            expected = [tdser.iloc[0, n] / vector[n] for n in range(len(vector))]
+            expected = tm.box_expected(expected, xbox).astype(object)
+            # We specifically expect timedelta64("NaT") here, not pd.NA
+            msg = "The 'downcast' keyword in fillna"
+            with tm.assert_produces_warning(FutureWarning, match=msg):
+                expected[2] = expected[2].fillna(
+                    np.timedelta64("NaT", "ns"), downcast=False
+                )
+        else:
+            expected = [tdser[n] / vector[n] for n in range(len(tdser))]
+            expected = [
+                x if x is not NaT else np.timedelta64("NaT", "ns") for x in expected
+            ]
+            if xbox is tm.to_array:
+                expected = tm.to_array(expected).astype(object)
+            else:
+                expected = xbox(expected, dtype=object)
+
+        tm.assert_equal(result, expected)
+
+        with pytest.raises(TypeError, match=pattern):
+            vector.astype(object) / tdser
+
+    def test_td64arr_mul_int_series(self, box_with_array, names):
+        # GH#19042 test for correct name attachment
+        box = box_with_array
+        exname = get_expected_name(box, names)
+
+        tdi = TimedeltaIndex(
+            ["0days", "1day", "2days", "3days", "4days"], name=names[0]
+        )
+        # TODO: Should we be parametrizing over types for `ser` too?
+        ser = Series([0, 1, 2, 3, 4], dtype=np.int64, name=names[1])
+
+        expected = Series(
+            ["0days", "1day", "4days", "9days", "16days"],
+            dtype="timedelta64[ns]",
+            name=exname,
+        )
+
+        tdi = tm.box_expected(tdi, box)
+        xbox = get_upcast_box(tdi, ser)
+
+        expected = tm.box_expected(expected, xbox)
+
+        result = ser * tdi
+        tm.assert_equal(result, expected)
+
+        result = tdi * ser
+        tm.assert_equal(result, expected)
+
+    # TODO: Should we be parametrizing over types for `ser` too?
+    def test_float_series_rdiv_td64arr(self, box_with_array, names):
+        # GH#19042 test for correct name attachment
+        box = box_with_array
+        tdi = TimedeltaIndex(
+            ["0days", "1day", "2days", "3days", "4days"], name=names[0]
+        )
+        ser = Series([1.5, 3, 4.5, 6, 7.5], dtype=np.float64, name=names[1])
+
+        xname = names[2] if box not in [tm.to_array, pd.array] else names[1]
+        expected = Series(
+            [tdi[n] / ser[n] for n in range(len(ser))],
+            dtype="timedelta64[ns]",
+            name=xname,
+        )
+
+        tdi = tm.box_expected(tdi, box)
+        xbox = get_upcast_box(tdi, ser)
+        expected = tm.box_expected(expected, xbox)
+
+        result = ser.__rtruediv__(tdi)
+        if box is DataFrame:
+            assert result is NotImplemented
+        else:
+            tm.assert_equal(result, expected)
+
+    def test_td64arr_all_nat_div_object_dtype_numeric(self, box_with_array):
+        # GH#39750 make sure we infer the result as td64
+        tdi = TimedeltaIndex([NaT, NaT])
+
+        left = tm.box_expected(tdi, box_with_array)
+        right = np.array([2, 2.0], dtype=object)
+
+        tdnat = np.timedelta64("NaT", "ns")
+        expected = Index([tdnat] * 2, dtype=object)
+        if box_with_array is not Index:
+            expected = tm.box_expected(expected, box_with_array).astype(object)
+            if box_with_array in [Series, DataFrame]:
+                msg = "The 'downcast' keyword in fillna is deprecated"
+                with tm.assert_produces_warning(FutureWarning, match=msg):
+                    expected = expected.fillna(tdnat, downcast=False)  # GH#18463
+
+        result = left / right
+        tm.assert_equal(result, expected)
+
+        result = left // right
+        tm.assert_equal(result, expected)
+
+
+class TestTimedelta64ArrayLikeArithmetic:
+    # Arithmetic tests for timedelta64[ns] vectors fully parametrized over
+    #  DataFrame/Series/TimedeltaIndex/TimedeltaArray.  Ideally all arithmetic
+    #  tests will eventually end up here.
+
+    def test_td64arr_pow_invalid(self, scalar_td, box_with_array):
+        td1 = Series([timedelta(minutes=5, seconds=3)] * 3)
+        td1.iloc[2] = np.nan
+
+        td1 = tm.box_expected(td1, box_with_array)
+
+        # check that we are getting a TypeError
+        # with 'operate' (from core/ops.py) for the ops that are not
+        # defined
+        pattern = "operate|unsupported|cannot|not supported"
+        with pytest.raises(TypeError, match=pattern):
+            scalar_td**td1
+
+        with pytest.raises(TypeError, match=pattern):
+            td1**scalar_td
+
+
+def test_add_timestamp_to_timedelta():
+    # GH: 35897
+    timestamp = Timestamp("2021-01-01")
+    result = timestamp + timedelta_range("0s", "1s", periods=31)
+    expected = DatetimeIndex(
+        [
+            timestamp
+            + (
+                pd.to_timedelta("0.033333333s") * i
+                + pd.to_timedelta("0.000000001s") * divmod(i, 3)[0]
+            )
+            for i in range(31)
+        ]
+    )
+    tm.assert_index_equal(result, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/boolean/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/boolean/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/boolean/test_arithmetic.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/boolean/test_arithmetic.py
new file mode 100644
index 0000000000000000000000000000000000000000..9ff690cdc914d7f81d134a2bc93287fb914067e2
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/boolean/test_arithmetic.py
@@ -0,0 +1,134 @@
+import operator
+
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+
+
+@pytest.fixture
+def data():
+    """Fixture returning boolean array with valid and missing values."""
+    return pd.array(
+        [True, False] * 4 + [np.nan] + [True, False] * 44 + [np.nan] + [True, False],
+        dtype="boolean",
+    )
+
+
+@pytest.fixture
+def left_array():
+    """Fixture returning boolean array with valid and missing values."""
+    return pd.array([True] * 3 + [False] * 3 + [None] * 3, dtype="boolean")
+
+
+@pytest.fixture
+def right_array():
+    """Fixture returning boolean array with valid and missing values."""
+    return pd.array([True, False, None] * 3, dtype="boolean")
+
+
+# Basic test for the arithmetic array ops
+# -----------------------------------------------------------------------------
+
+
+@pytest.mark.parametrize(
+    "opname, exp",
+    [
+        ("add", [True, True, None, True, False, None, None, None, None]),
+        ("mul", [True, False, None, False, False, None, None, None, None]),
+    ],
+    ids=["add", "mul"],
+)
+def test_add_mul(left_array, right_array, opname, exp):
+    op = getattr(operator, opname)
+    result = op(left_array, right_array)
+    expected = pd.array(exp, dtype="boolean")
+    tm.assert_extension_array_equal(result, expected)
+
+
+def test_sub(left_array, right_array):
+    msg = (
+        r"numpy boolean subtract, the `-` operator, is (?:deprecated|not supported), "
+        r"use the bitwise_xor, the `\^` operator, or the logical_xor function instead\."
+    )
+    with pytest.raises(TypeError, match=msg):
+        left_array - right_array
+
+
+def test_div(left_array, right_array):
+    msg = "operator '.*' not implemented for bool dtypes"
+    with pytest.raises(NotImplementedError, match=msg):
+        # check that we are matching the non-masked Series behavior
+        pd.Series(left_array._data) / pd.Series(right_array._data)
+
+    with pytest.raises(NotImplementedError, match=msg):
+        left_array / right_array
+
+
+@pytest.mark.parametrize(
+    "opname",
+    [
+        "floordiv",
+        "mod",
+        "pow",
+    ],
+)
+def test_op_int8(left_array, right_array, opname):
+    op = getattr(operator, opname)
+    if opname != "mod":
+        msg = "operator '.*' not implemented for bool dtypes"
+        with pytest.raises(NotImplementedError, match=msg):
+            result = op(left_array, right_array)
+        return
+    result = op(left_array, right_array)
+    expected = op(left_array.astype("Int8"), right_array.astype("Int8"))
+    tm.assert_extension_array_equal(result, expected)
+
+
+# Test generic characteristics / errors
+# -----------------------------------------------------------------------------
+
+
+def test_error_invalid_values(data, all_arithmetic_operators):
+    # invalid ops
+    op = all_arithmetic_operators
+    s = pd.Series(data)
+    ops = getattr(s, op)
+
+    # invalid scalars
+    msg = (
+        "did not contain a loop with signature matching types|"
+        "BooleanArray cannot perform the operation|"
+        "not supported for the input types, and the inputs could not be safely coerced "
+        "to any supported types according to the casting rule ''safe''|"
+        "not supported for dtype"
+    )
+    with pytest.raises(TypeError, match=msg):
+        ops("foo")
+    msg = "|".join(
+        [
+            r"unsupported operand type\(s\) for",
+            "Concatenation operation is not implemented for NumPy arrays",
+            "has no kernel",
+            "not supported for dtype",
+        ]
+    )
+    with pytest.raises(TypeError, match=msg):
+        ops(pd.Timestamp("20180101"))
+
+    # invalid array-likes
+    if op not in ("__mul__", "__rmul__"):
+        # TODO(extension) numpy's mul with object array sees booleans as numbers
+        msg = "|".join(
+            [
+                r"unsupported operand type\(s\) for",
+                "can only concatenate str",
+                "not all arguments converted during string formatting",
+                "has no kernel",
+                "not implemented",
+                "not supported for dtype",
+            ]
+        )
+        with pytest.raises(TypeError, match=msg):
+            ops(pd.Series("foo", index=s.index))
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/boolean/test_astype.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/boolean/test_astype.py
new file mode 100644
index 0000000000000000000000000000000000000000..8c2672218f273c6ff39ae0a0b9c86f21879e45f3
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/boolean/test_astype.py
@@ -0,0 +1,59 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+
+
+def test_astype(using_infer_string):
+    # with missing values
+    arr = pd.array([True, False, None], dtype="boolean")
+
+    with pytest.raises(ValueError, match="cannot convert NA to integer"):
+        arr.astype("int64")
+
+    with pytest.raises(ValueError, match="cannot convert float NaN to"):
+        arr.astype("bool")
+
+    result = arr.astype("float64")
+    expected = np.array([1, 0, np.nan], dtype="float64")
+    tm.assert_numpy_array_equal(result, expected)
+
+    result = arr.astype("str")
+    if using_infer_string:
+        expected = pd.array(
+            ["True", "False", None], dtype=pd.StringDtype(na_value=np.nan)
+        )
+        tm.assert_extension_array_equal(result, expected)
+    else:
+        expected = np.array(["True", "False", ""], dtype=f"{tm.ENDIAN}U5")
+        tm.assert_numpy_array_equal(result, expected)
+
+    # no missing values
+    arr = pd.array([True, False, True], dtype="boolean")
+    result = arr.astype("int64")
+    expected = np.array([1, 0, 1], dtype="int64")
+    tm.assert_numpy_array_equal(result, expected)
+
+    result = arr.astype("bool")
+    expected = np.array([True, False, True], dtype="bool")
+    tm.assert_numpy_array_equal(result, expected)
+
+
+def test_astype_to_boolean_array():
+    # astype to BooleanArray
+    arr = pd.array([True, False, None], dtype="boolean")
+
+    result = arr.astype("boolean")
+    tm.assert_extension_array_equal(result, arr)
+    result = arr.astype(pd.BooleanDtype())
+    tm.assert_extension_array_equal(result, arr)
+
+
+def test_astype_to_integer_array():
+    # astype to IntegerArray
+    arr = pd.array([True, False, None], dtype="boolean")
+
+    result = arr.astype("Int64")
+    expected = pd.array([1, 0, None], dtype="Int64")
+    tm.assert_extension_array_equal(result, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/boolean/test_comparison.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/boolean/test_comparison.py
new file mode 100644
index 0000000000000000000000000000000000000000..2eeb9da574b1e7973d98390ada40f23f57526203
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/boolean/test_comparison.py
@@ -0,0 +1,60 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.arrays import BooleanArray
+from pandas.tests.arrays.masked_shared import ComparisonOps
+
+
+@pytest.fixture
+def data():
+    """Fixture returning boolean array with valid and missing data"""
+    return pd.array(
+        [True, False] * 4 + [np.nan] + [True, False] * 44 + [np.nan] + [True, False],
+        dtype="boolean",
+    )
+
+
+@pytest.fixture
+def dtype():
+    """Fixture returning BooleanDtype"""
+    return pd.BooleanDtype()
+
+
+class TestComparisonOps(ComparisonOps):
+    def test_compare_scalar(self, data, comparison_op):
+        self._compare_other(data, comparison_op, True)
+
+    def test_compare_array(self, data, comparison_op):
+        other = pd.array([True] * len(data), dtype="boolean")
+        self._compare_other(data, comparison_op, other)
+        other = np.array([True] * len(data))
+        self._compare_other(data, comparison_op, other)
+        other = pd.Series([True] * len(data))
+        self._compare_other(data, comparison_op, other)
+
+    @pytest.mark.parametrize("other", [True, False, pd.NA])
+    def test_scalar(self, other, comparison_op, dtype):
+        ComparisonOps.test_scalar(self, other, comparison_op, dtype)
+
+    def test_array(self, comparison_op):
+        op = comparison_op
+        a = pd.array([True] * 3 + [False] * 3 + [None] * 3, dtype="boolean")
+        b = pd.array([True, False, None] * 3, dtype="boolean")
+
+        result = op(a, b)
+
+        values = op(a._data, b._data)
+        mask = a._mask | b._mask
+        expected = BooleanArray(values, mask)
+        tm.assert_extension_array_equal(result, expected)
+
+        # ensure we haven't mutated anything inplace
+        result[0] = None
+        tm.assert_extension_array_equal(
+            a, pd.array([True] * 3 + [False] * 3 + [None] * 3, dtype="boolean")
+        )
+        tm.assert_extension_array_equal(
+            b, pd.array([True, False, None] * 3, dtype="boolean")
+        )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/boolean/test_construction.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/boolean/test_construction.py
new file mode 100644
index 0000000000000000000000000000000000000000..645e763fbf00cec4f62474fc7d2d2be564a4e4ba
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/boolean/test_construction.py
@@ -0,0 +1,325 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.arrays import BooleanArray
+from pandas.core.arrays.boolean import coerce_to_array
+
+
+def test_boolean_array_constructor():
+    values = np.array([True, False, True, False], dtype="bool")
+    mask = np.array([False, False, False, True], dtype="bool")
+
+    result = BooleanArray(values, mask)
+    expected = pd.array([True, False, True, None], dtype="boolean")
+    tm.assert_extension_array_equal(result, expected)
+
+    with pytest.raises(TypeError, match="values should be boolean numpy array"):
+        BooleanArray(values.tolist(), mask)
+
+    with pytest.raises(TypeError, match="mask should be boolean numpy array"):
+        BooleanArray(values, mask.tolist())
+
+    with pytest.raises(TypeError, match="values should be boolean numpy array"):
+        BooleanArray(values.astype(int), mask)
+
+    with pytest.raises(TypeError, match="mask should be boolean numpy array"):
+        BooleanArray(values, None)
+
+    with pytest.raises(ValueError, match="values.shape must match mask.shape"):
+        BooleanArray(values.reshape(1, -1), mask)
+
+    with pytest.raises(ValueError, match="values.shape must match mask.shape"):
+        BooleanArray(values, mask.reshape(1, -1))
+
+
+def test_boolean_array_constructor_copy():
+    values = np.array([True, False, True, False], dtype="bool")
+    mask = np.array([False, False, False, True], dtype="bool")
+
+    result = BooleanArray(values, mask)
+    assert result._data is values
+    assert result._mask is mask
+
+    result = BooleanArray(values, mask, copy=True)
+    assert result._data is not values
+    assert result._mask is not mask
+
+
+def test_to_boolean_array():
+    expected = BooleanArray(
+        np.array([True, False, True]), np.array([False, False, False])
+    )
+
+    result = pd.array([True, False, True], dtype="boolean")
+    tm.assert_extension_array_equal(result, expected)
+    result = pd.array(np.array([True, False, True]), dtype="boolean")
+    tm.assert_extension_array_equal(result, expected)
+    result = pd.array(np.array([True, False, True], dtype=object), dtype="boolean")
+    tm.assert_extension_array_equal(result, expected)
+
+    # with missing values
+    expected = BooleanArray(
+        np.array([True, False, True]), np.array([False, False, True])
+    )
+
+    result = pd.array([True, False, None], dtype="boolean")
+    tm.assert_extension_array_equal(result, expected)
+    result = pd.array(np.array([True, False, None], dtype=object), dtype="boolean")
+    tm.assert_extension_array_equal(result, expected)
+
+
+def test_to_boolean_array_all_none():
+    expected = BooleanArray(np.array([True, True, True]), np.array([True, True, True]))
+
+    result = pd.array([None, None, None], dtype="boolean")
+    tm.assert_extension_array_equal(result, expected)
+    result = pd.array(np.array([None, None, None], dtype=object), dtype="boolean")
+    tm.assert_extension_array_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "a, b",
+    [
+        ([True, False, None, np.nan, pd.NA], [True, False, None, None, None]),
+        ([True, np.nan], [True, None]),
+        ([True, pd.NA], [True, None]),
+        ([np.nan, np.nan], [None, None]),
+        (np.array([np.nan, np.nan], dtype=float), [None, None]),
+    ],
+)
+def test_to_boolean_array_missing_indicators(a, b):
+    result = pd.array(a, dtype="boolean")
+    expected = pd.array(b, dtype="boolean")
+    tm.assert_extension_array_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "values",
+    [
+        ["foo", "bar"],
+        ["1", "2"],
+        # "foo",
+        [1, 2],
+        [1.0, 2.0],
+        pd.date_range("20130101", periods=2),
+        np.array(["foo"]),
+        np.array([1, 2]),
+        np.array([1.0, 2.0]),
+        [np.nan, {"a": 1}],
+    ],
+)
+def test_to_boolean_array_error(values):
+    # error in converting existing arrays to BooleanArray
+    msg = "Need to pass bool-like value"
+    with pytest.raises(TypeError, match=msg):
+        pd.array(values, dtype="boolean")
+
+
+def test_to_boolean_array_from_integer_array():
+    result = pd.array(np.array([1, 0, 1, 0]), dtype="boolean")
+    expected = pd.array([True, False, True, False], dtype="boolean")
+    tm.assert_extension_array_equal(result, expected)
+
+    # with missing values
+    result = pd.array(np.array([1, 0, 1, None]), dtype="boolean")
+    expected = pd.array([True, False, True, None], dtype="boolean")
+    tm.assert_extension_array_equal(result, expected)
+
+
+def test_to_boolean_array_from_float_array():
+    result = pd.array(np.array([1.0, 0.0, 1.0, 0.0]), dtype="boolean")
+    expected = pd.array([True, False, True, False], dtype="boolean")
+    tm.assert_extension_array_equal(result, expected)
+
+    # with missing values
+    result = pd.array(np.array([1.0, 0.0, 1.0, np.nan]), dtype="boolean")
+    expected = pd.array([True, False, True, None], dtype="boolean")
+    tm.assert_extension_array_equal(result, expected)
+
+
+def test_to_boolean_array_integer_like():
+    # integers of 0's and 1's
+    result = pd.array([1, 0, 1, 0], dtype="boolean")
+    expected = pd.array([True, False, True, False], dtype="boolean")
+    tm.assert_extension_array_equal(result, expected)
+
+    # with missing values
+    result = pd.array([1, 0, 1, None], dtype="boolean")
+    expected = pd.array([True, False, True, None], dtype="boolean")
+    tm.assert_extension_array_equal(result, expected)
+
+
+def test_coerce_to_array():
+    # TODO this is currently not public API
+    values = np.array([True, False, True, False], dtype="bool")
+    mask = np.array([False, False, False, True], dtype="bool")
+    result = BooleanArray(*coerce_to_array(values, mask=mask))
+    expected = BooleanArray(values, mask)
+    tm.assert_extension_array_equal(result, expected)
+    assert result._data is values
+    assert result._mask is mask
+    result = BooleanArray(*coerce_to_array(values, mask=mask, copy=True))
+    expected = BooleanArray(values, mask)
+    tm.assert_extension_array_equal(result, expected)
+    assert result._data is not values
+    assert result._mask is not mask
+
+    # mixed missing from values and mask
+    values = [True, False, None, False]
+    mask = np.array([False, False, False, True], dtype="bool")
+    result = BooleanArray(*coerce_to_array(values, mask=mask))
+    expected = BooleanArray(
+        np.array([True, False, True, True]), np.array([False, False, True, True])
+    )
+    tm.assert_extension_array_equal(result, expected)
+    result = BooleanArray(*coerce_to_array(np.array(values, dtype=object), mask=mask))
+    tm.assert_extension_array_equal(result, expected)
+    result = BooleanArray(*coerce_to_array(values, mask=mask.tolist()))
+    tm.assert_extension_array_equal(result, expected)
+
+    # raise errors for wrong dimension
+    values = np.array([True, False, True, False], dtype="bool")
+    mask = np.array([False, False, False, True], dtype="bool")
+
+    # passing 2D values is OK as long as no mask
+    coerce_to_array(values.reshape(1, -1))
+
+    with pytest.raises(ValueError, match="values.shape and mask.shape must match"):
+        coerce_to_array(values.reshape(1, -1), mask=mask)
+
+    with pytest.raises(ValueError, match="values.shape and mask.shape must match"):
+        coerce_to_array(values, mask=mask.reshape(1, -1))
+
+
+def test_coerce_to_array_from_boolean_array():
+    # passing BooleanArray to coerce_to_array
+    values = np.array([True, False, True, False], dtype="bool")
+    mask = np.array([False, False, False, True], dtype="bool")
+    arr = BooleanArray(values, mask)
+    result = BooleanArray(*coerce_to_array(arr))
+    tm.assert_extension_array_equal(result, arr)
+    # no copy
+    assert result._data is arr._data
+    assert result._mask is arr._mask
+
+    result = BooleanArray(*coerce_to_array(arr), copy=True)
+    tm.assert_extension_array_equal(result, arr)
+    assert result._data is not arr._data
+    assert result._mask is not arr._mask
+
+    with pytest.raises(ValueError, match="cannot pass mask for BooleanArray input"):
+        coerce_to_array(arr, mask=mask)
+
+
+def test_coerce_to_numpy_array():
+    # with missing values -> object dtype
+    arr = pd.array([True, False, None], dtype="boolean")
+    result = np.array(arr)
+    expected = np.array([True, False, pd.NA], dtype="object")
+    tm.assert_numpy_array_equal(result, expected)
+
+    # also with no missing values -> object dtype
+    arr = pd.array([True, False, True], dtype="boolean")
+    result = np.array(arr)
+    expected = np.array([True, False, True], dtype="bool")
+    tm.assert_numpy_array_equal(result, expected)
+
+    # force bool dtype
+    result = np.array(arr, dtype="bool")
+    expected = np.array([True, False, True], dtype="bool")
+    tm.assert_numpy_array_equal(result, expected)
+    # with missing values will raise error
+    arr = pd.array([True, False, None], dtype="boolean")
+    msg = (
+        "cannot convert to 'bool'-dtype NumPy array with missing values. "
+        "Specify an appropriate 'na_value' for this dtype."
+    )
+    with pytest.raises(ValueError, match=msg):
+        np.array(arr, dtype="bool")
+
+
+def test_to_boolean_array_from_strings():
+    result = BooleanArray._from_sequence_of_strings(
+        np.array(["True", "False", "1", "1.0", "0", "0.0", np.nan], dtype=object),
+        dtype="boolean",
+    )
+    expected = BooleanArray(
+        np.array([True, False, True, True, False, False, False]),
+        np.array([False, False, False, False, False, False, True]),
+    )
+
+    tm.assert_extension_array_equal(result, expected)
+
+
+def test_to_boolean_array_from_strings_invalid_string():
+    with pytest.raises(ValueError, match="cannot be cast"):
+        BooleanArray._from_sequence_of_strings(["donkey"], dtype="boolean")
+
+
+@pytest.mark.parametrize("box", [True, False], ids=["series", "array"])
+def test_to_numpy(box):
+    con = pd.Series if box else pd.array
+    # default (with or without missing values) -> object dtype
+    arr = con([True, False, True], dtype="boolean")
+    result = arr.to_numpy()
+    expected = np.array([True, False, True], dtype="bool")
+    tm.assert_numpy_array_equal(result, expected)
+
+    arr = con([True, False, None], dtype="boolean")
+    result = arr.to_numpy()
+    expected = np.array([True, False, pd.NA], dtype="object")
+    tm.assert_numpy_array_equal(result, expected)
+
+    arr = con([True, False, None], dtype="boolean")
+    result = arr.to_numpy(dtype="str")
+    expected = np.array([True, False, pd.NA], dtype=f"{tm.ENDIAN}U5")
+    tm.assert_numpy_array_equal(result, expected)
+
+    # no missing values -> can convert to bool, otherwise raises
+    arr = con([True, False, True], dtype="boolean")
+    result = arr.to_numpy(dtype="bool")
+    expected = np.array([True, False, True], dtype="bool")
+    tm.assert_numpy_array_equal(result, expected)
+
+    arr = con([True, False, None], dtype="boolean")
+    with pytest.raises(ValueError, match="cannot convert to 'bool'-dtype"):
+        result = arr.to_numpy(dtype="bool")
+
+    # specify dtype and na_value
+    arr = con([True, False, None], dtype="boolean")
+    result = arr.to_numpy(dtype=object, na_value=None)
+    expected = np.array([True, False, None], dtype="object")
+    tm.assert_numpy_array_equal(result, expected)
+
+    result = arr.to_numpy(dtype=bool, na_value=False)
+    expected = np.array([True, False, False], dtype="bool")
+    tm.assert_numpy_array_equal(result, expected)
+
+    result = arr.to_numpy(dtype="int64", na_value=-99)
+    expected = np.array([1, 0, -99], dtype="int64")
+    tm.assert_numpy_array_equal(result, expected)
+
+    result = arr.to_numpy(dtype="float64", na_value=np.nan)
+    expected = np.array([1, 0, np.nan], dtype="float64")
+    tm.assert_numpy_array_equal(result, expected)
+
+    # converting to int or float without specifying na_value raises
+    with pytest.raises(ValueError, match="cannot convert to 'int64'-dtype"):
+        arr.to_numpy(dtype="int64")
+
+
+def test_to_numpy_copy():
+    # to_numpy can be zero-copy if no missing values
+    arr = pd.array([True, False, True], dtype="boolean")
+    result = arr.to_numpy(dtype=bool)
+    result[0] = False
+    tm.assert_extension_array_equal(
+        arr, pd.array([False, False, True], dtype="boolean")
+    )
+
+    arr = pd.array([True, False, True], dtype="boolean")
+    result = arr.to_numpy(dtype=bool, copy=True)
+    result[0] = False
+    tm.assert_extension_array_equal(arr, pd.array([True, False, True], dtype="boolean"))
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/boolean/test_function.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/boolean/test_function.py
new file mode 100644
index 0000000000000000000000000000000000000000..2b3f3d3d16ac6c49d231ac526fa89570975e4bfb
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/boolean/test_function.py
@@ -0,0 +1,126 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+
+
+@pytest.mark.parametrize(
+    "ufunc", [np.add, np.logical_or, np.logical_and, np.logical_xor]
+)
+def test_ufuncs_binary(ufunc):
+    # two BooleanArrays
+    a = pd.array([True, False, None], dtype="boolean")
+    result = ufunc(a, a)
+    expected = pd.array(ufunc(a._data, a._data), dtype="boolean")
+    expected[a._mask] = np.nan
+    tm.assert_extension_array_equal(result, expected)
+
+    s = pd.Series(a)
+    result = ufunc(s, a)
+    expected = pd.Series(ufunc(a._data, a._data), dtype="boolean")
+    expected[a._mask] = np.nan
+    tm.assert_series_equal(result, expected)
+
+    # Boolean with numpy array
+    arr = np.array([True, True, False])
+    result = ufunc(a, arr)
+    expected = pd.array(ufunc(a._data, arr), dtype="boolean")
+    expected[a._mask] = np.nan
+    tm.assert_extension_array_equal(result, expected)
+
+    result = ufunc(arr, a)
+    expected = pd.array(ufunc(arr, a._data), dtype="boolean")
+    expected[a._mask] = np.nan
+    tm.assert_extension_array_equal(result, expected)
+
+    # BooleanArray with scalar
+    result = ufunc(a, True)
+    expected = pd.array(ufunc(a._data, True), dtype="boolean")
+    expected[a._mask] = np.nan
+    tm.assert_extension_array_equal(result, expected)
+
+    result = ufunc(True, a)
+    expected = pd.array(ufunc(True, a._data), dtype="boolean")
+    expected[a._mask] = np.nan
+    tm.assert_extension_array_equal(result, expected)
+
+    # not handled types
+    msg = r"operand type\(s\) all returned NotImplemented from __array_ufunc__"
+    with pytest.raises(TypeError, match=msg):
+        ufunc(a, "test")
+
+
+@pytest.mark.parametrize("ufunc", [np.logical_not])
+def test_ufuncs_unary(ufunc):
+    a = pd.array([True, False, None], dtype="boolean")
+    result = ufunc(a)
+    expected = pd.array(ufunc(a._data), dtype="boolean")
+    expected[a._mask] = np.nan
+    tm.assert_extension_array_equal(result, expected)
+
+    ser = pd.Series(a)
+    result = ufunc(ser)
+    expected = pd.Series(ufunc(a._data), dtype="boolean")
+    expected[a._mask] = np.nan
+    tm.assert_series_equal(result, expected)
+
+
+def test_ufunc_numeric():
+    # np.sqrt on np.bool_ returns float16, which we upcast to Float32
+    #  bc we do not have Float16
+    arr = pd.array([True, False, None], dtype="boolean")
+
+    res = np.sqrt(arr)
+
+    expected = pd.array([1, 0, None], dtype="Float32")
+    tm.assert_extension_array_equal(res, expected)
+
+
+@pytest.mark.parametrize("values", [[True, False], [True, None]])
+def test_ufunc_reduce_raises(values):
+    arr = pd.array(values, dtype="boolean")
+
+    res = np.add.reduce(arr)
+    if arr[-1] is pd.NA:
+        expected = pd.NA
+    else:
+        expected = arr._data.sum()
+    tm.assert_almost_equal(res, expected)
+
+
+def test_value_counts_na():
+    arr = pd.array([True, False, pd.NA], dtype="boolean")
+    result = arr.value_counts(dropna=False)
+    expected = pd.Series([1, 1, 1], index=arr, dtype="Int64", name="count")
+    assert expected.index.dtype == arr.dtype
+    tm.assert_series_equal(result, expected)
+
+    result = arr.value_counts(dropna=True)
+    expected = pd.Series([1, 1], index=arr[:-1], dtype="Int64", name="count")
+    assert expected.index.dtype == arr.dtype
+    tm.assert_series_equal(result, expected)
+
+
+def test_value_counts_with_normalize():
+    ser = pd.Series([True, False, pd.NA], dtype="boolean")
+    result = ser.value_counts(normalize=True)
+    expected = pd.Series([1, 1], index=ser[:-1], dtype="Float64", name="proportion") / 2
+    assert expected.index.dtype == "boolean"
+    tm.assert_series_equal(result, expected)
+
+
+def test_diff():
+    a = pd.array(
+        [True, True, False, False, True, None, True, None, False], dtype="boolean"
+    )
+    result = pd.core.algorithms.diff(a, 1)
+    expected = pd.array(
+        [None, False, True, False, True, None, None, None, None], dtype="boolean"
+    )
+    tm.assert_extension_array_equal(result, expected)
+
+    ser = pd.Series(a)
+    result = ser.diff()
+    expected = pd.Series(expected)
+    tm.assert_series_equal(result, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/boolean/test_indexing.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/boolean/test_indexing.py
new file mode 100644
index 0000000000000000000000000000000000000000..6a7daea16963c99fb7c4bbcd4b122d6af53d2576
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/boolean/test_indexing.py
@@ -0,0 +1,13 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+
+
+@pytest.mark.parametrize("na", [None, np.nan, pd.NA])
+def test_setitem_missing_values(na):
+    arr = pd.array([True, False, None], dtype="boolean")
+    expected = pd.array([True, None, None], dtype="boolean")
+    arr[1] = na
+    tm.assert_extension_array_equal(arr, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/boolean/test_logical.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/boolean/test_logical.py
new file mode 100644
index 0000000000000000000000000000000000000000..66c117ea3fc66cbc5f847cc96c23e80a98329c5d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/boolean/test_logical.py
@@ -0,0 +1,254 @@
+import operator
+
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.arrays import BooleanArray
+from pandas.core.ops.mask_ops import (
+    kleene_and,
+    kleene_or,
+    kleene_xor,
+)
+from pandas.tests.extension.base import BaseOpsUtil
+
+
+class TestLogicalOps(BaseOpsUtil):
+    def test_numpy_scalars_ok(self, all_logical_operators):
+        a = pd.array([True, False, None], dtype="boolean")
+        op = getattr(a, all_logical_operators)
+
+        tm.assert_extension_array_equal(op(True), op(np.bool_(True)))
+        tm.assert_extension_array_equal(op(False), op(np.bool_(False)))
+
+    def get_op_from_name(self, op_name):
+        short_opname = op_name.strip("_")
+        short_opname = short_opname if "xor" in short_opname else short_opname + "_"
+        try:
+            op = getattr(operator, short_opname)
+        except AttributeError:
+            # Assume it is the reverse operator
+            rop = getattr(operator, short_opname[1:])
+            op = lambda x, y: rop(y, x)
+
+        return op
+
+    def test_empty_ok(self, all_logical_operators):
+        a = pd.array([], dtype="boolean")
+        op_name = all_logical_operators
+        result = getattr(a, op_name)(True)
+        tm.assert_extension_array_equal(a, result)
+
+        result = getattr(a, op_name)(False)
+        tm.assert_extension_array_equal(a, result)
+
+        result = getattr(a, op_name)(pd.NA)
+        tm.assert_extension_array_equal(a, result)
+
+    @pytest.mark.parametrize(
+        "other", ["a", pd.Timestamp(2017, 1, 1, 12), np.timedelta64(4)]
+    )
+    def test_eq_mismatched_type(self, other):
+        # GH-44499
+        arr = pd.array([True, False])
+        result = arr == other
+        expected = pd.array([False, False])
+        tm.assert_extension_array_equal(result, expected)
+
+        result = arr != other
+        expected = pd.array([True, True])
+        tm.assert_extension_array_equal(result, expected)
+
+    def test_logical_length_mismatch_raises(self, all_logical_operators):
+        op_name = all_logical_operators
+        a = pd.array([True, False, None], dtype="boolean")
+        msg = "Lengths must match"
+
+        with pytest.raises(ValueError, match=msg):
+            getattr(a, op_name)([True, False])
+
+        with pytest.raises(ValueError, match=msg):
+            getattr(a, op_name)(np.array([True, False]))
+
+        with pytest.raises(ValueError, match=msg):
+            getattr(a, op_name)(pd.array([True, False], dtype="boolean"))
+
+    def test_logical_nan_raises(self, all_logical_operators):
+        op_name = all_logical_operators
+        a = pd.array([True, False, None], dtype="boolean")
+        msg = "Got float instead"
+
+        with pytest.raises(TypeError, match=msg):
+            getattr(a, op_name)(np.nan)
+
+    @pytest.mark.parametrize("other", ["a", 1])
+    def test_non_bool_or_na_other_raises(self, other, all_logical_operators):
+        a = pd.array([True, False], dtype="boolean")
+        with pytest.raises(TypeError, match=str(type(other).__name__)):
+            getattr(a, all_logical_operators)(other)
+
+    def test_kleene_or(self):
+        # A clear test of behavior.
+        a = pd.array([True] * 3 + [False] * 3 + [None] * 3, dtype="boolean")
+        b = pd.array([True, False, None] * 3, dtype="boolean")
+        result = a | b
+        expected = pd.array(
+            [True, True, True, True, False, None, True, None, None], dtype="boolean"
+        )
+        tm.assert_extension_array_equal(result, expected)
+
+        result = b | a
+        tm.assert_extension_array_equal(result, expected)
+
+        # ensure we haven't mutated anything inplace
+        tm.assert_extension_array_equal(
+            a, pd.array([True] * 3 + [False] * 3 + [None] * 3, dtype="boolean")
+        )
+        tm.assert_extension_array_equal(
+            b, pd.array([True, False, None] * 3, dtype="boolean")
+        )
+
+    @pytest.mark.parametrize(
+        "other, expected",
+        [
+            (pd.NA, [True, None, None]),
+            (True, [True, True, True]),
+            (np.bool_(True), [True, True, True]),
+            (False, [True, False, None]),
+            (np.bool_(False), [True, False, None]),
+        ],
+    )
+    def test_kleene_or_scalar(self, other, expected):
+        # TODO: test True & False
+        a = pd.array([True, False, None], dtype="boolean")
+        result = a | other
+        expected = pd.array(expected, dtype="boolean")
+        tm.assert_extension_array_equal(result, expected)
+
+        result = other | a
+        tm.assert_extension_array_equal(result, expected)
+
+        # ensure we haven't mutated anything inplace
+        tm.assert_extension_array_equal(
+            a, pd.array([True, False, None], dtype="boolean")
+        )
+
+    def test_kleene_and(self):
+        # A clear test of behavior.
+        a = pd.array([True] * 3 + [False] * 3 + [None] * 3, dtype="boolean")
+        b = pd.array([True, False, None] * 3, dtype="boolean")
+        result = a & b
+        expected = pd.array(
+            [True, False, None, False, False, False, None, False, None], dtype="boolean"
+        )
+        tm.assert_extension_array_equal(result, expected)
+
+        result = b & a
+        tm.assert_extension_array_equal(result, expected)
+
+        # ensure we haven't mutated anything inplace
+        tm.assert_extension_array_equal(
+            a, pd.array([True] * 3 + [False] * 3 + [None] * 3, dtype="boolean")
+        )
+        tm.assert_extension_array_equal(
+            b, pd.array([True, False, None] * 3, dtype="boolean")
+        )
+
+    @pytest.mark.parametrize(
+        "other, expected",
+        [
+            (pd.NA, [None, False, None]),
+            (True, [True, False, None]),
+            (False, [False, False, False]),
+            (np.bool_(True), [True, False, None]),
+            (np.bool_(False), [False, False, False]),
+        ],
+    )
+    def test_kleene_and_scalar(self, other, expected):
+        a = pd.array([True, False, None], dtype="boolean")
+        result = a & other
+        expected = pd.array(expected, dtype="boolean")
+        tm.assert_extension_array_equal(result, expected)
+
+        result = other & a
+        tm.assert_extension_array_equal(result, expected)
+
+        # ensure we haven't mutated anything inplace
+        tm.assert_extension_array_equal(
+            a, pd.array([True, False, None], dtype="boolean")
+        )
+
+    def test_kleene_xor(self):
+        a = pd.array([True] * 3 + [False] * 3 + [None] * 3, dtype="boolean")
+        b = pd.array([True, False, None] * 3, dtype="boolean")
+        result = a ^ b
+        expected = pd.array(
+            [False, True, None, True, False, None, None, None, None], dtype="boolean"
+        )
+        tm.assert_extension_array_equal(result, expected)
+
+        result = b ^ a
+        tm.assert_extension_array_equal(result, expected)
+
+        # ensure we haven't mutated anything inplace
+        tm.assert_extension_array_equal(
+            a, pd.array([True] * 3 + [False] * 3 + [None] * 3, dtype="boolean")
+        )
+        tm.assert_extension_array_equal(
+            b, pd.array([True, False, None] * 3, dtype="boolean")
+        )
+
+    @pytest.mark.parametrize(
+        "other, expected",
+        [
+            (pd.NA, [None, None, None]),
+            (True, [False, True, None]),
+            (np.bool_(True), [False, True, None]),
+            (np.bool_(False), [True, False, None]),
+        ],
+    )
+    def test_kleene_xor_scalar(self, other, expected):
+        a = pd.array([True, False, None], dtype="boolean")
+        result = a ^ other
+        expected = pd.array(expected, dtype="boolean")
+        tm.assert_extension_array_equal(result, expected)
+
+        result = other ^ a
+        tm.assert_extension_array_equal(result, expected)
+
+        # ensure we haven't mutated anything inplace
+        tm.assert_extension_array_equal(
+            a, pd.array([True, False, None], dtype="boolean")
+        )
+
+    @pytest.mark.parametrize("other", [True, False, pd.NA, [True, False, None] * 3])
+    def test_no_masked_assumptions(self, other, all_logical_operators):
+        # The logical operations should not assume that masked values are False!
+        a = pd.arrays.BooleanArray(
+            np.array([True, True, True, False, False, False, True, False, True]),
+            np.array([False] * 6 + [True, True, True]),
+        )
+        b = pd.array([True] * 3 + [False] * 3 + [None] * 3, dtype="boolean")
+        if isinstance(other, list):
+            other = pd.array(other, dtype="boolean")
+
+        result = getattr(a, all_logical_operators)(other)
+        expected = getattr(b, all_logical_operators)(other)
+        tm.assert_extension_array_equal(result, expected)
+
+        if isinstance(other, BooleanArray):
+            other._data[other._mask] = True
+            a._data[a._mask] = False
+
+            result = getattr(a, all_logical_operators)(other)
+            expected = getattr(b, all_logical_operators)(other)
+            tm.assert_extension_array_equal(result, expected)
+
+
+@pytest.mark.parametrize("operation", [kleene_or, kleene_xor, kleene_and])
+def test_error_both_scalar(operation):
+    msg = r"Either `left` or `right` need to be a np\.ndarray."
+    with pytest.raises(TypeError, match=msg):
+        # masks need to be non-None, otherwise it ends up in an infinite recursion
+        operation(True, True, np.zeros(1), np.zeros(1))
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/boolean/test_ops.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/boolean/test_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..95ebe8528c2e5fec1a580b00bd79e0617fe7609f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/boolean/test_ops.py
@@ -0,0 +1,27 @@
+import pandas as pd
+import pandas._testing as tm
+
+
+class TestUnaryOps:
+    def test_invert(self):
+        a = pd.array([True, False, None], dtype="boolean")
+        expected = pd.array([False, True, None], dtype="boolean")
+        tm.assert_extension_array_equal(~a, expected)
+
+        expected = pd.Series(expected, index=["a", "b", "c"], name="name")
+        result = ~pd.Series(a, index=["a", "b", "c"], name="name")
+        tm.assert_series_equal(result, expected)
+
+        df = pd.DataFrame({"A": a, "B": [True, False, False]}, index=["a", "b", "c"])
+        result = ~df
+        expected = pd.DataFrame(
+            {"A": expected, "B": [False, True, True]}, index=["a", "b", "c"]
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_abs(self):
+        # matching numpy behavior, abs is the identity function
+        arr = pd.array([True, False, None], dtype="boolean")
+        result = abs(arr)
+
+        tm.assert_extension_array_equal(result, arr)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/boolean/test_reduction.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/boolean/test_reduction.py
new file mode 100644
index 0000000000000000000000000000000000000000..dd8c3eda9ed05b6844c90024631a1e90f755069e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/boolean/test_reduction.py
@@ -0,0 +1,62 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+
+
+@pytest.fixture
+def data():
+    """Fixture returning boolean array, with valid and missing values."""
+    return pd.array(
+        [True, False] * 4 + [np.nan] + [True, False] * 44 + [np.nan] + [True, False],
+        dtype="boolean",
+    )
+
+
+@pytest.mark.parametrize(
+    "values, exp_any, exp_all, exp_any_noskip, exp_all_noskip",
+    [
+        ([True, pd.NA], True, True, True, pd.NA),
+        ([False, pd.NA], False, False, pd.NA, False),
+        ([pd.NA], False, True, pd.NA, pd.NA),
+        ([], False, True, False, True),
+        # GH-33253: all True / all False values buggy with skipna=False
+        ([True, True], True, True, True, True),
+        ([False, False], False, False, False, False),
+    ],
+)
+def test_any_all(values, exp_any, exp_all, exp_any_noskip, exp_all_noskip):
+    # the methods return numpy scalars
+    exp_any = pd.NA if exp_any is pd.NA else np.bool_(exp_any)
+    exp_all = pd.NA if exp_all is pd.NA else np.bool_(exp_all)
+    exp_any_noskip = pd.NA if exp_any_noskip is pd.NA else np.bool_(exp_any_noskip)
+    exp_all_noskip = pd.NA if exp_all_noskip is pd.NA else np.bool_(exp_all_noskip)
+
+    for con in [pd.array, pd.Series]:
+        a = con(values, dtype="boolean")
+        assert a.any() is exp_any
+        assert a.all() is exp_all
+        assert a.any(skipna=False) is exp_any_noskip
+        assert a.all(skipna=False) is exp_all_noskip
+
+        assert np.any(a.any()) is exp_any
+        assert np.all(a.all()) is exp_all
+
+
+@pytest.mark.parametrize("dropna", [True, False])
+def test_reductions_return_types(dropna, data, all_numeric_reductions):
+    op = all_numeric_reductions
+    s = pd.Series(data)
+    if dropna:
+        s = s.dropna()
+
+    if op in ("sum", "prod"):
+        assert isinstance(getattr(s, op)(), np.int_)
+    elif op == "count":
+        # Oddly on the 32 bit build (but not Windows), this is intc (!= intp)
+        assert isinstance(getattr(s, op)(), np.integer)
+    elif op in ("min", "max"):
+        assert isinstance(getattr(s, op)(), np.bool_)
+    else:
+        # "mean", "std", "var", "median", "kurt", "skew"
+        assert isinstance(getattr(s, op)(), np.float64)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/boolean/test_repr.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/boolean/test_repr.py
new file mode 100644
index 0000000000000000000000000000000000000000..0ee904b18cc9ec6197ed3ad009fae1da593c5219
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/boolean/test_repr.py
@@ -0,0 +1,13 @@
+import pandas as pd
+
+
+def test_repr():
+    df = pd.DataFrame({"A": pd.array([True, False, None], dtype="boolean")})
+    expected = "       A\n0   True\n1  False\n2   "
+    assert repr(df) == expected
+
+    expected = "0     True\n1    False\n2     \nName: A, dtype: boolean"
+    assert repr(df.A) == expected
+
+    expected = "\n[True, False, ]\nLength: 3, dtype: boolean"
+    assert repr(df.A.array) == expected
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/categorical/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/categorical/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_algos.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_algos.py
new file mode 100644
index 0000000000000000000000000000000000000000..d4c19a4970135cfb1865eaa0fae0845dc7d17971
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_algos.py
@@ -0,0 +1,89 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+
+
+@pytest.mark.parametrize("ordered", [True, False])
+@pytest.mark.parametrize("categories", [["b", "a", "c"], ["a", "b", "c", "d"]])
+def test_factorize(categories, ordered):
+    cat = pd.Categorical(
+        ["b", "b", "a", "c", None], categories=categories, ordered=ordered
+    )
+    codes, uniques = pd.factorize(cat)
+    expected_codes = np.array([0, 0, 1, 2, -1], dtype=np.intp)
+    expected_uniques = pd.Categorical(
+        ["b", "a", "c"], categories=categories, ordered=ordered
+    )
+
+    tm.assert_numpy_array_equal(codes, expected_codes)
+    tm.assert_categorical_equal(uniques, expected_uniques)
+
+
+def test_factorized_sort():
+    cat = pd.Categorical(["b", "b", None, "a"])
+    codes, uniques = pd.factorize(cat, sort=True)
+    expected_codes = np.array([1, 1, -1, 0], dtype=np.intp)
+    expected_uniques = pd.Categorical(["a", "b"])
+
+    tm.assert_numpy_array_equal(codes, expected_codes)
+    tm.assert_categorical_equal(uniques, expected_uniques)
+
+
+def test_factorized_sort_ordered():
+    cat = pd.Categorical(
+        ["b", "b", None, "a"], categories=["c", "b", "a"], ordered=True
+    )
+
+    codes, uniques = pd.factorize(cat, sort=True)
+    expected_codes = np.array([0, 0, -1, 1], dtype=np.intp)
+    expected_uniques = pd.Categorical(
+        ["b", "a"], categories=["c", "b", "a"], ordered=True
+    )
+
+    tm.assert_numpy_array_equal(codes, expected_codes)
+    tm.assert_categorical_equal(uniques, expected_uniques)
+
+
+def test_isin_cats():
+    # GH2003
+    cat = pd.Categorical(["a", "b", np.nan])
+
+    result = cat.isin(["a", np.nan])
+    expected = np.array([True, False, True], dtype=bool)
+    tm.assert_numpy_array_equal(expected, result)
+
+    result = cat.isin(["a", "c"])
+    expected = np.array([True, False, False], dtype=bool)
+    tm.assert_numpy_array_equal(expected, result)
+
+
+@pytest.mark.parametrize("value", [[""], [None, ""], [pd.NaT, ""]])
+def test_isin_cats_corner_cases(value):
+    # GH36550
+    cat = pd.Categorical([""])
+    result = cat.isin(value)
+    expected = np.array([True], dtype=bool)
+    tm.assert_numpy_array_equal(expected, result)
+
+
+@pytest.mark.parametrize("empty", [[], pd.Series(dtype=object), np.array([])])
+def test_isin_empty(empty):
+    s = pd.Categorical(["a", "b"])
+    expected = np.array([False, False], dtype=bool)
+
+    result = s.isin(empty)
+    tm.assert_numpy_array_equal(expected, result)
+
+
+def test_diff():
+    ser = pd.Series([1, 2, 3], dtype="category")
+
+    msg = "Convert to a suitable dtype"
+    with pytest.raises(TypeError, match=msg):
+        ser.diff()
+
+    df = ser.to_frame(name="A")
+    with pytest.raises(TypeError, match=msg):
+        df.diff()
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_analytics.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_analytics.py
new file mode 100644
index 0000000000000000000000000000000000000000..9a0356cbc422bc534b0cef5aad79ba304e7a77b2
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_analytics.py
@@ -0,0 +1,355 @@
+import re
+import sys
+
+import numpy as np
+import pytest
+
+from pandas.compat import PYPY
+
+from pandas import (
+    Categorical,
+    CategoricalDtype,
+    DataFrame,
+    Index,
+    NaT,
+    Series,
+    date_range,
+)
+import pandas._testing as tm
+from pandas.api.types import is_scalar
+
+
+class TestCategoricalAnalytics:
+    @pytest.mark.parametrize("aggregation", ["min", "max"])
+    def test_min_max_not_ordered_raises(self, aggregation):
+        # unordered cats have no min/max
+        cat = Categorical(["a", "b", "c", "d"], ordered=False)
+        msg = f"Categorical is not ordered for operation {aggregation}"
+        agg_func = getattr(cat, aggregation)
+
+        with pytest.raises(TypeError, match=msg):
+            agg_func()
+
+        ufunc = np.minimum if aggregation == "min" else np.maximum
+        with pytest.raises(TypeError, match=msg):
+            ufunc.reduce(cat)
+
+    def test_min_max_ordered(self, index_or_series_or_array):
+        cat = Categorical(["a", "b", "c", "d"], ordered=True)
+        obj = index_or_series_or_array(cat)
+        _min = obj.min()
+        _max = obj.max()
+        assert _min == "a"
+        assert _max == "d"
+
+        assert np.minimum.reduce(obj) == "a"
+        assert np.maximum.reduce(obj) == "d"
+        # TODO: raises if we pass axis=0  (on Index and Categorical, not Series)
+
+        cat = Categorical(
+            ["a", "b", "c", "d"], categories=["d", "c", "b", "a"], ordered=True
+        )
+        obj = index_or_series_or_array(cat)
+        _min = obj.min()
+        _max = obj.max()
+        assert _min == "d"
+        assert _max == "a"
+        assert np.minimum.reduce(obj) == "d"
+        assert np.maximum.reduce(obj) == "a"
+
+    def test_min_max_reduce(self):
+        # GH52788
+        cat = Categorical(["a", "b", "c", "d"], ordered=True)
+        df = DataFrame(cat)
+
+        result_max = df.agg("max")
+        expected_max = Series(Categorical(["d"], dtype=cat.dtype))
+        tm.assert_series_equal(result_max, expected_max)
+
+        result_min = df.agg("min")
+        expected_min = Series(Categorical(["a"], dtype=cat.dtype))
+        tm.assert_series_equal(result_min, expected_min)
+
+    @pytest.mark.parametrize(
+        "categories,expected",
+        [
+            (list("ABC"), np.nan),
+            ([1, 2, 3], np.nan),
+            pytest.param(
+                Series(date_range("2020-01-01", periods=3), dtype="category"),
+                NaT,
+                marks=pytest.mark.xfail(
+                    reason="https://github.com/pandas-dev/pandas/issues/29962"
+                ),
+            ),
+        ],
+    )
+    @pytest.mark.parametrize("aggregation", ["min", "max"])
+    def test_min_max_ordered_empty(self, categories, expected, aggregation):
+        # GH 30227
+        cat = Categorical([], categories=categories, ordered=True)
+
+        agg_func = getattr(cat, aggregation)
+        result = agg_func()
+        assert result is expected
+
+    @pytest.mark.parametrize(
+        "values, categories",
+        [(["a", "b", "c", np.nan], list("cba")), ([1, 2, 3, np.nan], [3, 2, 1])],
+    )
+    @pytest.mark.parametrize("skipna", [True, False])
+    @pytest.mark.parametrize("function", ["min", "max"])
+    def test_min_max_with_nan(self, values, categories, function, skipna):
+        # GH 25303
+        cat = Categorical(values, categories=categories, ordered=True)
+        result = getattr(cat, function)(skipna=skipna)
+
+        if skipna is False:
+            assert result is np.nan
+        else:
+            expected = categories[0] if function == "min" else categories[2]
+            assert result == expected
+
+    @pytest.mark.parametrize("function", ["min", "max"])
+    @pytest.mark.parametrize("skipna", [True, False])
+    def test_min_max_only_nan(self, function, skipna):
+        # https://github.com/pandas-dev/pandas/issues/33450
+        cat = Categorical([np.nan], categories=[1, 2], ordered=True)
+        result = getattr(cat, function)(skipna=skipna)
+        assert result is np.nan
+
+    @pytest.mark.parametrize("method", ["min", "max"])
+    def test_numeric_only_min_max_raises(self, method):
+        # GH 25303
+        cat = Categorical(
+            [np.nan, 1, 2, np.nan], categories=[5, 4, 3, 2, 1], ordered=True
+        )
+        with pytest.raises(TypeError, match=".* got an unexpected keyword"):
+            getattr(cat, method)(numeric_only=True)
+
+    @pytest.mark.parametrize("method", ["min", "max"])
+    def test_numpy_min_max_raises(self, method):
+        cat = Categorical(["a", "b", "c", "b"], ordered=False)
+        msg = (
+            f"Categorical is not ordered for operation {method}\n"
+            "you can use .as_ordered() to change the Categorical to an ordered one"
+        )
+        method = getattr(np, method)
+        with pytest.raises(TypeError, match=re.escape(msg)):
+            method(cat)
+
+    @pytest.mark.parametrize("kwarg", ["axis", "out", "keepdims"])
+    @pytest.mark.parametrize("method", ["min", "max"])
+    def test_numpy_min_max_unsupported_kwargs_raises(self, method, kwarg):
+        cat = Categorical(["a", "b", "c", "b"], ordered=True)
+        msg = (
+            f"the '{kwarg}' parameter is not supported in the pandas implementation "
+            f"of {method}"
+        )
+        if kwarg == "axis":
+            msg = r"`axis` must be fewer than the number of dimensions \(1\)"
+        kwargs = {kwarg: 42}
+        method = getattr(np, method)
+        with pytest.raises(ValueError, match=msg):
+            method(cat, **kwargs)
+
+    @pytest.mark.parametrize("method, expected", [("min", "a"), ("max", "c")])
+    def test_numpy_min_max_axis_equals_none(self, method, expected):
+        cat = Categorical(["a", "b", "c", "b"], ordered=True)
+        method = getattr(np, method)
+        result = method(cat, axis=None)
+        assert result == expected
+
+    @pytest.mark.parametrize(
+        "values,categories,exp_mode",
+        [
+            ([1, 1, 2, 4, 5, 5, 5], [5, 4, 3, 2, 1], [5]),
+            ([1, 1, 1, 4, 5, 5, 5], [5, 4, 3, 2, 1], [5, 1]),
+            ([1, 2, 3, 4, 5], [5, 4, 3, 2, 1], [5, 4, 3, 2, 1]),
+            ([np.nan, np.nan, np.nan, 4, 5], [5, 4, 3, 2, 1], [5, 4]),
+            ([np.nan, np.nan, np.nan, 4, 5, 4], [5, 4, 3, 2, 1], [4]),
+            ([np.nan, np.nan, 4, 5, 4], [5, 4, 3, 2, 1], [4]),
+        ],
+    )
+    def test_mode(self, values, categories, exp_mode):
+        cat = Categorical(values, categories=categories, ordered=True)
+        res = Series(cat).mode()._values
+        exp = Categorical(exp_mode, categories=categories, ordered=True)
+        tm.assert_categorical_equal(res, exp)
+
+    def test_searchsorted(self, ordered):
+        # https://github.com/pandas-dev/pandas/issues/8420
+        # https://github.com/pandas-dev/pandas/issues/14522
+
+        cat = Categorical(
+            ["cheese", "milk", "apple", "bread", "bread"],
+            categories=["cheese", "milk", "apple", "bread"],
+            ordered=ordered,
+        )
+        ser = Series(cat)
+
+        # Searching for single item argument, side='left' (default)
+        res_cat = cat.searchsorted("apple")
+        assert res_cat == 2
+        assert is_scalar(res_cat)
+
+        res_ser = ser.searchsorted("apple")
+        assert res_ser == 2
+        assert is_scalar(res_ser)
+
+        # Searching for single item array, side='left' (default)
+        res_cat = cat.searchsorted(["bread"])
+        res_ser = ser.searchsorted(["bread"])
+        exp = np.array([3], dtype=np.intp)
+        tm.assert_numpy_array_equal(res_cat, exp)
+        tm.assert_numpy_array_equal(res_ser, exp)
+
+        # Searching for several items array, side='right'
+        res_cat = cat.searchsorted(["apple", "bread"], side="right")
+        res_ser = ser.searchsorted(["apple", "bread"], side="right")
+        exp = np.array([3, 5], dtype=np.intp)
+        tm.assert_numpy_array_equal(res_cat, exp)
+        tm.assert_numpy_array_equal(res_ser, exp)
+
+        # Searching for a single value that is not from the Categorical
+        with pytest.raises(TypeError, match="cucumber"):
+            cat.searchsorted("cucumber")
+        with pytest.raises(TypeError, match="cucumber"):
+            ser.searchsorted("cucumber")
+
+        # Searching for multiple values one of each is not from the Categorical
+        msg = (
+            "Cannot setitem on a Categorical with a new category, "
+            "set the categories first"
+        )
+        with pytest.raises(TypeError, match=msg):
+            cat.searchsorted(["bread", "cucumber"])
+        with pytest.raises(TypeError, match=msg):
+            ser.searchsorted(["bread", "cucumber"])
+
+    def test_unique(self, ordered):
+        # GH38140
+        dtype = CategoricalDtype(["a", "b", "c"], ordered=ordered)
+
+        # categories are reordered based on value when ordered=False
+        cat = Categorical(["a", "b", "c"], dtype=dtype)
+        res = cat.unique()
+        tm.assert_categorical_equal(res, cat)
+
+        cat = Categorical(["a", "b", "a", "a"], dtype=dtype)
+        res = cat.unique()
+        tm.assert_categorical_equal(res, Categorical(["a", "b"], dtype=dtype))
+
+        cat = Categorical(["c", "a", "b", "a", "a"], dtype=dtype)
+        res = cat.unique()
+        exp_cat = Categorical(["c", "a", "b"], dtype=dtype)
+        tm.assert_categorical_equal(res, exp_cat)
+
+        # nan must be removed
+        cat = Categorical(["b", np.nan, "b", np.nan, "a"], dtype=dtype)
+        res = cat.unique()
+        exp_cat = Categorical(["b", np.nan, "a"], dtype=dtype)
+        tm.assert_categorical_equal(res, exp_cat)
+
+    def test_unique_index_series(self, ordered):
+        # GH38140
+        dtype = CategoricalDtype([3, 2, 1], ordered=ordered)
+
+        c = Categorical([3, 1, 2, 2, 1], dtype=dtype)
+        # Categorical.unique sorts categories by appearance order
+        # if ordered=False
+        exp = Categorical([3, 1, 2], dtype=dtype)
+        tm.assert_categorical_equal(c.unique(), exp)
+
+        tm.assert_index_equal(Index(c).unique(), Index(exp))
+        tm.assert_categorical_equal(Series(c).unique(), exp)
+
+        c = Categorical([1, 1, 2, 2], dtype=dtype)
+        exp = Categorical([1, 2], dtype=dtype)
+        tm.assert_categorical_equal(c.unique(), exp)
+        tm.assert_index_equal(Index(c).unique(), Index(exp))
+        tm.assert_categorical_equal(Series(c).unique(), exp)
+
+    def test_shift(self):
+        # GH 9416
+        cat = Categorical(["a", "b", "c", "d", "a"])
+
+        # shift forward
+        sp1 = cat.shift(1)
+        xp1 = Categorical([np.nan, "a", "b", "c", "d"])
+        tm.assert_categorical_equal(sp1, xp1)
+        tm.assert_categorical_equal(cat[:-1], sp1[1:])
+
+        # shift back
+        sn2 = cat.shift(-2)
+        xp2 = Categorical(
+            ["c", "d", "a", np.nan, np.nan], categories=["a", "b", "c", "d"]
+        )
+        tm.assert_categorical_equal(sn2, xp2)
+        tm.assert_categorical_equal(cat[2:], sn2[:-2])
+
+        # shift by zero
+        tm.assert_categorical_equal(cat, cat.shift(0))
+
+    def test_nbytes(self):
+        cat = Categorical([1, 2, 3])
+        exp = 3 + 3 * 8  # 3 int8s for values + 3 int64s for categories
+        assert cat.nbytes == exp
+
+    def test_memory_usage(self, using_infer_string):
+        cat = Categorical([1, 2, 3])
+
+        # .categories is an index, so we include the hashtable
+        assert 0 < cat.nbytes <= cat.memory_usage()
+        assert 0 < cat.nbytes <= cat.memory_usage(deep=True)
+
+        cat = Categorical(["foo", "foo", "bar"])
+        if using_infer_string:
+            if cat.categories.dtype.storage == "python":
+                assert cat.memory_usage(deep=True) > cat.nbytes
+            else:
+                assert cat.memory_usage(deep=True) >= cat.nbytes
+        else:
+            assert cat.memory_usage(deep=True) > cat.nbytes
+
+        if not PYPY:
+            # sys.getsizeof will call the .memory_usage with
+            # deep=True, and add on some GC overhead
+            diff = cat.memory_usage(deep=True) - sys.getsizeof(cat)
+            assert abs(diff) < 100
+
+    def test_map(self):
+        c = Categorical(list("ABABC"), categories=list("CBA"), ordered=True)
+        result = c.map(lambda x: x.lower(), na_action=None)
+        exp = Categorical(list("ababc"), categories=list("cba"), ordered=True)
+        tm.assert_categorical_equal(result, exp)
+
+        c = Categorical(list("ABABC"), categories=list("ABC"), ordered=False)
+        result = c.map(lambda x: x.lower(), na_action=None)
+        exp = Categorical(list("ababc"), categories=list("abc"), ordered=False)
+        tm.assert_categorical_equal(result, exp)
+
+        result = c.map(lambda x: 1, na_action=None)
+        # GH 12766: Return an index not an array
+        tm.assert_index_equal(result, Index(np.array([1] * 5, dtype=np.int64)))
+
+    @pytest.mark.parametrize("value", [1, "True", [1, 2, 3], 5.0])
+    def test_validate_inplace_raises(self, value):
+        cat = Categorical(["A", "B", "B", "C", "A"])
+        msg = (
+            'For argument "inplace" expected type bool, '
+            f"received type {type(value).__name__}"
+        )
+
+        with pytest.raises(ValueError, match=msg):
+            cat.sort_values(inplace=value)
+
+    def test_quantile_empty(self):
+        # make sure we have correct itemsize on resulting codes
+        cat = Categorical(["A", "B"])
+        idx = Index([0.0, 0.5])
+        result = cat[:0]._quantile(idx, interpolation="linear")
+        assert result._codes.dtype == np.int8
+
+        expected = cat.take([-1, -1], allow_fill=True)
+        tm.assert_extension_array_equal(result, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_api.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_api.py
new file mode 100644
index 0000000000000000000000000000000000000000..a939ee5f6f53f805211d46773c625c8361203991
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_api.py
@@ -0,0 +1,501 @@
+import re
+
+import numpy as np
+import pytest
+
+from pandas.compat import PY311
+
+from pandas import (
+    Categorical,
+    CategoricalIndex,
+    DataFrame,
+    Index,
+    Series,
+    StringDtype,
+)
+import pandas._testing as tm
+from pandas.core.arrays.categorical import recode_for_categories
+
+
+class TestCategoricalAPI:
+    def test_to_list_deprecated(self):
+        # GH#51254
+        cat1 = Categorical(list("acb"), ordered=False)
+        msg = "Categorical.to_list is deprecated and will be removed"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            cat1.to_list()
+
+    def test_ordered_api(self):
+        # GH 9347
+        cat1 = Categorical(list("acb"), ordered=False)
+        tm.assert_index_equal(cat1.categories, Index(["a", "b", "c"]))
+        assert not cat1.ordered
+
+        cat2 = Categorical(list("acb"), categories=list("bca"), ordered=False)
+        tm.assert_index_equal(cat2.categories, Index(["b", "c", "a"]))
+        assert not cat2.ordered
+
+        cat3 = Categorical(list("acb"), ordered=True)
+        tm.assert_index_equal(cat3.categories, Index(["a", "b", "c"]))
+        assert cat3.ordered
+
+        cat4 = Categorical(list("acb"), categories=list("bca"), ordered=True)
+        tm.assert_index_equal(cat4.categories, Index(["b", "c", "a"]))
+        assert cat4.ordered
+
+    def test_set_ordered(self):
+        cat = Categorical(["a", "b", "c", "a"], ordered=True)
+        cat2 = cat.as_unordered()
+        assert not cat2.ordered
+        cat2 = cat.as_ordered()
+        assert cat2.ordered
+
+        assert cat2.set_ordered(True).ordered
+        assert not cat2.set_ordered(False).ordered
+
+        # removed in 0.19.0
+        msg = (
+            "property 'ordered' of 'Categorical' object has no setter"
+            if PY311
+            else "can't set attribute"
+        )
+        with pytest.raises(AttributeError, match=msg):
+            cat.ordered = True
+        with pytest.raises(AttributeError, match=msg):
+            cat.ordered = False
+
+    def test_rename_categories(self):
+        cat = Categorical(["a", "b", "c", "a"])
+
+        # inplace=False: the old one must not be changed
+        res = cat.rename_categories([1, 2, 3])
+        tm.assert_numpy_array_equal(
+            res.__array__(), np.array([1, 2, 3, 1], dtype=np.int64)
+        )
+        tm.assert_index_equal(res.categories, Index([1, 2, 3]))
+
+        exp_cat = np.array(["a", "b", "c", "a"], dtype=np.object_)
+        tm.assert_numpy_array_equal(cat.__array__(), exp_cat)
+
+        exp_cat = Index(["a", "b", "c"])
+        tm.assert_index_equal(cat.categories, exp_cat)
+
+        # GH18862 (let rename_categories take callables)
+        result = cat.rename_categories(lambda x: x.upper())
+        expected = Categorical(["A", "B", "C", "A"])
+        tm.assert_categorical_equal(result, expected)
+
+    @pytest.mark.parametrize("new_categories", [[1, 2, 3, 4], [1, 2]])
+    def test_rename_categories_wrong_length_raises(self, new_categories):
+        cat = Categorical(["a", "b", "c", "a"])
+        msg = (
+            "new categories need to have the same number of items as the "
+            "old categories!"
+        )
+        with pytest.raises(ValueError, match=msg):
+            cat.rename_categories(new_categories)
+
+    def test_rename_categories_series(self):
+        # https://github.com/pandas-dev/pandas/issues/17981
+        c = Categorical(["a", "b"])
+        result = c.rename_categories(Series([0, 1], index=["a", "b"]))
+        expected = Categorical([0, 1])
+        tm.assert_categorical_equal(result, expected)
+
+    def test_rename_categories_dict(self):
+        # GH 17336
+        cat = Categorical(["a", "b", "c", "d"])
+        res = cat.rename_categories({"a": 4, "b": 3, "c": 2, "d": 1})
+        expected = Index([4, 3, 2, 1])
+        tm.assert_index_equal(res.categories, expected)
+
+        # Test for dicts of smaller length
+        cat = Categorical(["a", "b", "c", "d"])
+        res = cat.rename_categories({"a": 1, "c": 3})
+
+        expected = Index([1, "b", 3, "d"])
+        tm.assert_index_equal(res.categories, expected)
+
+        # Test for dicts with bigger length
+        cat = Categorical(["a", "b", "c", "d"])
+        res = cat.rename_categories({"a": 1, "b": 2, "c": 3, "d": 4, "e": 5, "f": 6})
+        expected = Index([1, 2, 3, 4])
+        tm.assert_index_equal(res.categories, expected)
+
+        # Test for dicts with no items from old categories
+        cat = Categorical(["a", "b", "c", "d"])
+        res = cat.rename_categories({"f": 1, "g": 3})
+
+        expected = Index(["a", "b", "c", "d"])
+        tm.assert_index_equal(res.categories, expected)
+
+    def test_reorder_categories(self):
+        cat = Categorical(["a", "b", "c", "a"], ordered=True)
+        old = cat.copy()
+        new = Categorical(
+            ["a", "b", "c", "a"], categories=["c", "b", "a"], ordered=True
+        )
+
+        res = cat.reorder_categories(["c", "b", "a"])
+        # cat must be the same as before
+        tm.assert_categorical_equal(cat, old)
+        # only res is changed
+        tm.assert_categorical_equal(res, new)
+
+    @pytest.mark.parametrize(
+        "new_categories",
+        [
+            ["a"],  # not all "old" included in "new"
+            ["a", "b", "d"],  # still not all "old" in "new"
+            ["a", "b", "c", "d"],  # all "old" included in "new", but too long
+        ],
+    )
+    def test_reorder_categories_raises(self, new_categories):
+        cat = Categorical(["a", "b", "c", "a"], ordered=True)
+        msg = "items in new_categories are not the same as in old categories"
+        with pytest.raises(ValueError, match=msg):
+            cat.reorder_categories(new_categories)
+
+    def test_add_categories(self):
+        cat = Categorical(["a", "b", "c", "a"], ordered=True)
+        old = cat.copy()
+        new = Categorical(
+            ["a", "b", "c", "a"], categories=["a", "b", "c", "d"], ordered=True
+        )
+
+        res = cat.add_categories("d")
+        tm.assert_categorical_equal(cat, old)
+        tm.assert_categorical_equal(res, new)
+
+        res = cat.add_categories(["d"])
+        tm.assert_categorical_equal(cat, old)
+        tm.assert_categorical_equal(res, new)
+
+        # GH 9927
+        cat = Categorical(list("abc"), ordered=True)
+        expected = Categorical(list("abc"), categories=list("abcde"), ordered=True)
+        # test with Series, np.array, index, list
+        res = cat.add_categories(Series(["d", "e"]))
+        tm.assert_categorical_equal(res, expected)
+        res = cat.add_categories(np.array(["d", "e"]))
+        tm.assert_categorical_equal(res, expected)
+        res = cat.add_categories(Index(["d", "e"]))
+        tm.assert_categorical_equal(res, expected)
+        res = cat.add_categories(["d", "e"])
+        tm.assert_categorical_equal(res, expected)
+
+    def test_add_categories_existing_raises(self):
+        # new is in old categories
+        cat = Categorical(["a", "b", "c", "d"], ordered=True)
+        msg = re.escape("new categories must not include old categories: {'d'}")
+        with pytest.raises(ValueError, match=msg):
+            cat.add_categories(["d"])
+
+    def test_add_categories_losing_dtype_information(self):
+        # GH#48812
+        cat = Categorical(Series([1, 2], dtype="Int64"))
+        ser = Series([4], dtype="Int64")
+        result = cat.add_categories(ser)
+        expected = Categorical(
+            Series([1, 2], dtype="Int64"), categories=Series([1, 2, 4], dtype="Int64")
+        )
+        tm.assert_categorical_equal(result, expected)
+
+        cat = Categorical(Series(["a", "b", "a"], dtype=StringDtype()))
+        ser = Series(["d"], dtype=StringDtype())
+        result = cat.add_categories(ser)
+        expected = Categorical(
+            Series(["a", "b", "a"], dtype=StringDtype()),
+            categories=Series(["a", "b", "d"], dtype=StringDtype()),
+        )
+        tm.assert_categorical_equal(result, expected)
+
+    def test_set_categories(self):
+        cat = Categorical(["a", "b", "c", "a"], ordered=True)
+        exp_categories = Index(["c", "b", "a"])
+        exp_values = np.array(["a", "b", "c", "a"], dtype=np.object_)
+
+        cat = cat.set_categories(["c", "b", "a"])
+        res = cat.set_categories(["a", "b", "c"])
+        # cat must be the same as before
+        tm.assert_index_equal(cat.categories, exp_categories)
+        tm.assert_numpy_array_equal(cat.__array__(), exp_values)
+        # only res is changed
+        exp_categories_back = Index(["a", "b", "c"])
+        tm.assert_index_equal(res.categories, exp_categories_back)
+        tm.assert_numpy_array_equal(res.__array__(), exp_values)
+
+        # not all "old" included in "new" -> all not included ones are now
+        # np.nan
+        cat = Categorical(["a", "b", "c", "a"], ordered=True)
+        res = cat.set_categories(["a"])
+        tm.assert_numpy_array_equal(res.codes, np.array([0, -1, -1, 0], dtype=np.int8))
+
+        # still not all "old" in "new"
+        res = cat.set_categories(["a", "b", "d"])
+        tm.assert_numpy_array_equal(res.codes, np.array([0, 1, -1, 0], dtype=np.int8))
+        tm.assert_index_equal(res.categories, Index(["a", "b", "d"]))
+
+        # all "old" included in "new"
+        cat = cat.set_categories(["a", "b", "c", "d"])
+        exp_categories = Index(["a", "b", "c", "d"])
+        tm.assert_index_equal(cat.categories, exp_categories)
+
+        # internals...
+        c = Categorical([1, 2, 3, 4, 1], categories=[1, 2, 3, 4], ordered=True)
+        tm.assert_numpy_array_equal(c._codes, np.array([0, 1, 2, 3, 0], dtype=np.int8))
+        tm.assert_index_equal(c.categories, Index([1, 2, 3, 4]))
+
+        exp = np.array([1, 2, 3, 4, 1], dtype=np.int64)
+        tm.assert_numpy_array_equal(np.asarray(c), exp)
+
+        # all "pointers" to '4' must be changed from 3 to 0,...
+        c = c.set_categories([4, 3, 2, 1])
+
+        # positions are changed
+        tm.assert_numpy_array_equal(c._codes, np.array([3, 2, 1, 0, 3], dtype=np.int8))
+
+        # categories are now in new order
+        tm.assert_index_equal(c.categories, Index([4, 3, 2, 1]))
+
+        # output is the same
+        exp = np.array([1, 2, 3, 4, 1], dtype=np.int64)
+        tm.assert_numpy_array_equal(np.asarray(c), exp)
+        assert c.min() == 4
+        assert c.max() == 1
+
+        # set_categories should set the ordering if specified
+        c2 = c.set_categories([4, 3, 2, 1], ordered=False)
+        assert not c2.ordered
+
+        tm.assert_numpy_array_equal(np.asarray(c), np.asarray(c2))
+
+        # set_categories should pass thru the ordering
+        c2 = c.set_ordered(False).set_categories([4, 3, 2, 1])
+        assert not c2.ordered
+
+        tm.assert_numpy_array_equal(np.asarray(c), np.asarray(c2))
+
+    @pytest.mark.parametrize(
+        "values, categories, new_categories",
+        [
+            # No NaNs, same cats, same order
+            (["a", "b", "a"], ["a", "b"], ["a", "b"]),
+            # No NaNs, same cats, different order
+            (["a", "b", "a"], ["a", "b"], ["b", "a"]),
+            # Same, unsorted
+            (["b", "a", "a"], ["a", "b"], ["a", "b"]),
+            # No NaNs, same cats, different order
+            (["b", "a", "a"], ["a", "b"], ["b", "a"]),
+            # NaNs
+            (["a", "b", "c"], ["a", "b"], ["a", "b"]),
+            (["a", "b", "c"], ["a", "b"], ["b", "a"]),
+            (["b", "a", "c"], ["a", "b"], ["a", "b"]),
+            (["b", "a", "c"], ["a", "b"], ["a", "b"]),
+            # Introduce NaNs
+            (["a", "b", "c"], ["a", "b"], ["a"]),
+            (["a", "b", "c"], ["a", "b"], ["b"]),
+            (["b", "a", "c"], ["a", "b"], ["a"]),
+            (["b", "a", "c"], ["a", "b"], ["a"]),
+            # No overlap
+            (["a", "b", "c"], ["a", "b"], ["d", "e"]),
+        ],
+    )
+    @pytest.mark.parametrize("ordered", [True, False])
+    def test_set_categories_many(self, values, categories, new_categories, ordered):
+        c = Categorical(values, categories)
+        expected = Categorical(values, new_categories, ordered)
+        result = c.set_categories(new_categories, ordered=ordered)
+        tm.assert_categorical_equal(result, expected)
+
+    def test_set_categories_rename_less(self):
+        # GH 24675
+        cat = Categorical(["A", "B"])
+        result = cat.set_categories(["A"], rename=True)
+        expected = Categorical(["A", np.nan])
+        tm.assert_categorical_equal(result, expected)
+
+    def test_set_categories_private(self):
+        cat = Categorical(["a", "b", "c"], categories=["a", "b", "c", "d"])
+        cat._set_categories(["a", "c", "d", "e"])
+        expected = Categorical(["a", "c", "d"], categories=list("acde"))
+        tm.assert_categorical_equal(cat, expected)
+
+        # fastpath
+        cat = Categorical(["a", "b", "c"], categories=["a", "b", "c", "d"])
+        cat._set_categories(["a", "c", "d", "e"], fastpath=True)
+        expected = Categorical(["a", "c", "d"], categories=list("acde"))
+        tm.assert_categorical_equal(cat, expected)
+
+    def test_remove_categories(self):
+        cat = Categorical(["a", "b", "c", "a"], ordered=True)
+        old = cat.copy()
+        new = Categorical(["a", "b", np.nan, "a"], categories=["a", "b"], ordered=True)
+
+        res = cat.remove_categories("c")
+        tm.assert_categorical_equal(cat, old)
+        tm.assert_categorical_equal(res, new)
+
+        res = cat.remove_categories(["c"])
+        tm.assert_categorical_equal(cat, old)
+        tm.assert_categorical_equal(res, new)
+
+    @pytest.mark.parametrize("removals", [["c"], ["c", np.nan], "c", ["c", "c"]])
+    def test_remove_categories_raises(self, removals):
+        cat = Categorical(["a", "b", "a"])
+        message = re.escape("removals must all be in old categories: {'c'}")
+
+        with pytest.raises(ValueError, match=message):
+            cat.remove_categories(removals)
+
+    def test_remove_unused_categories(self):
+        c = Categorical(["a", "b", "c", "d", "a"], categories=["a", "b", "c", "d", "e"])
+        exp_categories_all = Index(["a", "b", "c", "d", "e"])
+        exp_categories_dropped = Index(["a", "b", "c", "d"])
+
+        tm.assert_index_equal(c.categories, exp_categories_all)
+
+        res = c.remove_unused_categories()
+        tm.assert_index_equal(res.categories, exp_categories_dropped)
+        tm.assert_index_equal(c.categories, exp_categories_all)
+
+        # with NaN values (GH11599)
+        c = Categorical(["a", "b", "c", np.nan], categories=["a", "b", "c", "d", "e"])
+        res = c.remove_unused_categories()
+        tm.assert_index_equal(res.categories, Index(np.array(["a", "b", "c"])))
+        exp_codes = np.array([0, 1, 2, -1], dtype=np.int8)
+        tm.assert_numpy_array_equal(res.codes, exp_codes)
+        tm.assert_index_equal(c.categories, exp_categories_all)
+
+        val = ["F", np.nan, "D", "B", "D", "F", np.nan]
+        cat = Categorical(values=val, categories=list("ABCDEFG"))
+        out = cat.remove_unused_categories()
+        tm.assert_index_equal(out.categories, Index(["B", "D", "F"]))
+        exp_codes = np.array([2, -1, 1, 0, 1, 2, -1], dtype=np.int8)
+        tm.assert_numpy_array_equal(out.codes, exp_codes)
+        assert out.tolist() == val
+
+        alpha = list("abcdefghijklmnopqrstuvwxyz")
+        val = np.random.default_rng(2).choice(alpha[::2], 10000).astype("object")
+        val[np.random.default_rng(2).choice(len(val), 100)] = np.nan
+
+        cat = Categorical(values=val, categories=alpha)
+        out = cat.remove_unused_categories()
+        assert out.tolist() == val.tolist()
+
+
+class TestCategoricalAPIWithFactor:
+    def test_describe(self):
+        factor = Categorical(["a", "b", "b", "a", "a", "c", "c", "c"], ordered=True)
+        # string type
+        desc = factor.describe()
+        assert factor.ordered
+        exp_index = CategoricalIndex(
+            ["a", "b", "c"], name="categories", ordered=factor.ordered
+        )
+        expected = DataFrame(
+            {"counts": [3, 2, 3], "freqs": [3 / 8.0, 2 / 8.0, 3 / 8.0]}, index=exp_index
+        )
+        tm.assert_frame_equal(desc, expected)
+
+        # check unused categories
+        cat = factor.copy()
+        cat = cat.set_categories(["a", "b", "c", "d"])
+        desc = cat.describe()
+
+        exp_index = CategoricalIndex(
+            list("abcd"), ordered=factor.ordered, name="categories"
+        )
+        expected = DataFrame(
+            {"counts": [3, 2, 3, 0], "freqs": [3 / 8.0, 2 / 8.0, 3 / 8.0, 0]},
+            index=exp_index,
+        )
+        tm.assert_frame_equal(desc, expected)
+
+        # check an integer one
+        cat = Categorical([1, 2, 3, 1, 2, 3, 3, 2, 1, 1, 1])
+        desc = cat.describe()
+        exp_index = CategoricalIndex([1, 2, 3], ordered=cat.ordered, name="categories")
+        expected = DataFrame(
+            {"counts": [5, 3, 3], "freqs": [5 / 11.0, 3 / 11.0, 3 / 11.0]},
+            index=exp_index,
+        )
+        tm.assert_frame_equal(desc, expected)
+
+        # https://github.com/pandas-dev/pandas/issues/3678
+        # describe should work with NaN
+        cat = Categorical([np.nan, 1, 2, 2])
+        desc = cat.describe()
+        expected = DataFrame(
+            {"counts": [1, 2, 1], "freqs": [1 / 4.0, 2 / 4.0, 1 / 4.0]},
+            index=CategoricalIndex(
+                [1, 2, np.nan], categories=[1, 2], name="categories"
+            ),
+        )
+        tm.assert_frame_equal(desc, expected)
+
+
+class TestPrivateCategoricalAPI:
+    def test_codes_immutable(self):
+        # Codes should be read only
+        c = Categorical(["a", "b", "c", "a", np.nan])
+        exp = np.array([0, 1, 2, 0, -1], dtype="int8")
+        tm.assert_numpy_array_equal(c.codes, exp)
+
+        # Assignments to codes should raise
+        msg = (
+            "property 'codes' of 'Categorical' object has no setter"
+            if PY311
+            else "can't set attribute"
+        )
+        with pytest.raises(AttributeError, match=msg):
+            c.codes = np.array([0, 1, 2, 0, 1], dtype="int8")
+
+        # changes in the codes array should raise
+        codes = c.codes
+
+        with pytest.raises(ValueError, match="assignment destination is read-only"):
+            codes[4] = 1
+
+        # But even after getting the codes, the original array should still be
+        # writeable!
+        c[4] = "a"
+        exp = np.array([0, 1, 2, 0, 0], dtype="int8")
+        tm.assert_numpy_array_equal(c.codes, exp)
+        c._codes[4] = 2
+        exp = np.array([0, 1, 2, 0, 2], dtype="int8")
+        tm.assert_numpy_array_equal(c.codes, exp)
+
+    @pytest.mark.parametrize(
+        "codes, old, new, expected",
+        [
+            ([0, 1], ["a", "b"], ["a", "b"], [0, 1]),
+            ([0, 1], ["b", "a"], ["b", "a"], [0, 1]),
+            ([0, 1], ["a", "b"], ["b", "a"], [1, 0]),
+            ([0, 1], ["b", "a"], ["a", "b"], [1, 0]),
+            ([0, 1, 0, 1], ["a", "b"], ["a", "b", "c"], [0, 1, 0, 1]),
+            ([0, 1, 2, 2], ["a", "b", "c"], ["a", "b"], [0, 1, -1, -1]),
+            ([0, 1, -1], ["a", "b", "c"], ["a", "b", "c"], [0, 1, -1]),
+            ([0, 1, -1], ["a", "b", "c"], ["b"], [-1, 0, -1]),
+            ([0, 1, -1], ["a", "b", "c"], ["d"], [-1, -1, -1]),
+            ([0, 1, -1], ["a", "b", "c"], [], [-1, -1, -1]),
+            ([-1, -1], [], ["a", "b"], [-1, -1]),
+            ([1, 0], ["b", "a"], ["a", "b"], [0, 1]),
+        ],
+    )
+    def test_recode_to_categories(self, codes, old, new, expected):
+        codes = np.asanyarray(codes, dtype=np.int8)
+        expected = np.asanyarray(expected, dtype=np.int8)
+        old = Index(old)
+        new = Index(new)
+        result = recode_for_categories(codes, old, new)
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_recode_to_categories_large(self):
+        N = 1000
+        codes = np.arange(N)
+        old = Index(codes)
+        expected = np.arange(N - 1, -1, -1, dtype=np.int16)
+        new = Index(expected)
+        result = recode_for_categories(codes, old, new)
+        tm.assert_numpy_array_equal(result, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_astype.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_astype.py
new file mode 100644
index 0000000000000000000000000000000000000000..ee930ac84aaf246c6e79cb1c7eb7a7dfe179ff8c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_astype.py
@@ -0,0 +1,155 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    Categorical,
+    CategoricalDtype,
+    CategoricalIndex,
+    DatetimeIndex,
+    Interval,
+    NaT,
+    Period,
+    Timestamp,
+    array,
+    to_datetime,
+)
+import pandas._testing as tm
+
+
+class TestAstype:
+    @pytest.mark.parametrize("cls", [Categorical, CategoricalIndex])
+    @pytest.mark.parametrize("values", [[1, np.nan], [Timestamp("2000"), NaT]])
+    def test_astype_nan_to_int(self, cls, values):
+        # GH#28406
+        obj = cls(values)
+
+        msg = "Cannot (cast|convert)"
+        with pytest.raises((ValueError, TypeError), match=msg):
+            obj.astype(int)
+
+    @pytest.mark.parametrize(
+        "expected",
+        [
+            array(["2019", "2020"], dtype="datetime64[ns, UTC]"),
+            array([0, 0], dtype="timedelta64[ns]"),
+            array([Period("2019"), Period("2020")], dtype="period[Y-DEC]"),
+            array([Interval(0, 1), Interval(1, 2)], dtype="interval"),
+            array([1, np.nan], dtype="Int64"),
+        ],
+    )
+    def test_astype_category_to_extension_dtype(self, expected):
+        # GH#28668
+        result = expected.astype("category").astype(expected.dtype)
+
+        tm.assert_extension_array_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "dtype, expected",
+        [
+            (
+                "datetime64[ns]",
+                np.array(["2015-01-01T00:00:00.000000000"], dtype="datetime64[ns]"),
+            ),
+            (
+                "datetime64[ns, MET]",
+                DatetimeIndex([Timestamp("2015-01-01 00:00:00+0100", tz="MET")]).array,
+            ),
+        ],
+    )
+    def test_astype_to_datetime64(self, dtype, expected):
+        # GH#28448
+        result = Categorical(["2015-01-01"]).astype(dtype)
+        assert result == expected
+
+    def test_astype_str_int_categories_to_nullable_int(self):
+        # GH#39616
+        dtype = CategoricalDtype([str(i) for i in range(5)])
+        codes = np.random.default_rng(2).integers(5, size=20)
+        arr = Categorical.from_codes(codes, dtype=dtype)
+
+        res = arr.astype("Int64")
+        expected = array(codes, dtype="Int64")
+        tm.assert_extension_array_equal(res, expected)
+
+    def test_astype_str_int_categories_to_nullable_float(self):
+        # GH#39616
+        dtype = CategoricalDtype([str(i / 2) for i in range(5)])
+        codes = np.random.default_rng(2).integers(5, size=20)
+        arr = Categorical.from_codes(codes, dtype=dtype)
+
+        res = arr.astype("Float64")
+        expected = array(codes, dtype="Float64") / 2
+        tm.assert_extension_array_equal(res, expected)
+
+    @pytest.mark.parametrize("ordered", [True, False])
+    def test_astype(self, ordered):
+        # string
+        cat = Categorical(list("abbaaccc"), ordered=ordered)
+        result = cat.astype(object)
+        expected = np.array(cat)
+        tm.assert_numpy_array_equal(result, expected)
+
+        msg = r"Cannot cast object|str dtype to float64"
+        with pytest.raises(ValueError, match=msg):
+            cat.astype(float)
+
+        # numeric
+        cat = Categorical([0, 1, 2, 2, 1, 0, 1, 0, 2], ordered=ordered)
+        result = cat.astype(object)
+        expected = np.array(cat, dtype=object)
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = cat.astype(int)
+        expected = np.array(cat, dtype="int")
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = cat.astype(float)
+        expected = np.array(cat, dtype=float)
+        tm.assert_numpy_array_equal(result, expected)
+
+    @pytest.mark.parametrize("dtype_ordered", [True, False])
+    @pytest.mark.parametrize("cat_ordered", [True, False])
+    def test_astype_category(self, dtype_ordered, cat_ordered):
+        # GH#10696/GH#18593
+        data = list("abcaacbab")
+        cat = Categorical(data, categories=list("bac"), ordered=cat_ordered)
+
+        # standard categories
+        dtype = CategoricalDtype(ordered=dtype_ordered)
+        result = cat.astype(dtype)
+        expected = Categorical(data, categories=cat.categories, ordered=dtype_ordered)
+        tm.assert_categorical_equal(result, expected)
+
+        # non-standard categories
+        dtype = CategoricalDtype(list("adc"), dtype_ordered)
+        result = cat.astype(dtype)
+        expected = Categorical(data, dtype=dtype)
+        tm.assert_categorical_equal(result, expected)
+
+        if dtype_ordered is False:
+            # dtype='category' can't specify ordered, so only test once
+            result = cat.astype("category")
+            expected = cat
+            tm.assert_categorical_equal(result, expected)
+
+    def test_astype_object_datetime_categories(self):
+        # GH#40754
+        cat = Categorical(to_datetime(["2021-03-27", NaT]))
+        result = cat.astype(object)
+        expected = np.array([Timestamp("2021-03-27 00:00:00"), NaT], dtype="object")
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_astype_object_timestamp_categories(self):
+        # GH#18024
+        cat = Categorical([Timestamp("2014-01-01")])
+        result = cat.astype(object)
+        expected = np.array([Timestamp("2014-01-01 00:00:00")], dtype="object")
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_astype_category_readonly_mask_values(self):
+        # GH#53658
+        arr = array([0, 1, 2], dtype="Int64")
+        arr._mask.flags["WRITEABLE"] = False
+        result = arr.astype("category")
+        expected = array([0, 1, 2], dtype="Int64").astype("category")
+        tm.assert_extension_array_equal(result, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_constructors.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_constructors.py
new file mode 100644
index 0000000000000000000000000000000000000000..8ac479cf8a0a4a7fb0c11f94f04bad496f7a6343
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_constructors.py
@@ -0,0 +1,787 @@
+from datetime import (
+    date,
+    datetime,
+)
+
+import numpy as np
+import pytest
+
+from pandas._config import using_string_dtype
+
+from pandas.compat import HAS_PYARROW
+
+from pandas.core.dtypes.common import (
+    is_float_dtype,
+    is_integer_dtype,
+)
+from pandas.core.dtypes.dtypes import CategoricalDtype
+
+import pandas as pd
+from pandas import (
+    Categorical,
+    CategoricalIndex,
+    DatetimeIndex,
+    Index,
+    Interval,
+    IntervalIndex,
+    MultiIndex,
+    NaT,
+    Series,
+    Timestamp,
+    date_range,
+    period_range,
+    timedelta_range,
+)
+import pandas._testing as tm
+
+
+class TestCategoricalConstructors:
+    def test_fastpath_deprecated(self):
+        codes = np.array([1, 2, 3])
+        dtype = CategoricalDtype(categories=["a", "b", "c", "d"], ordered=False)
+        msg = "The 'fastpath' keyword in Categorical is deprecated"
+        with tm.assert_produces_warning(DeprecationWarning, match=msg):
+            Categorical(codes, dtype=dtype, fastpath=True)
+
+    def test_categorical_from_cat_and_dtype_str_preserve_ordered(self):
+        # GH#49309 we should preserve orderedness in `res`
+        cat = Categorical([3, 1], categories=[3, 2, 1], ordered=True)
+
+        res = Categorical(cat, dtype="category")
+        assert res.dtype.ordered
+
+    def test_categorical_disallows_scalar(self):
+        # GH#38433
+        with pytest.raises(TypeError, match="Categorical input must be list-like"):
+            Categorical("A", categories=["A", "B"])
+
+    def test_categorical_1d_only(self):
+        # ndim > 1
+        msg = "> 1 ndim Categorical are not supported at this time"
+        with pytest.raises(NotImplementedError, match=msg):
+            Categorical(np.array([list("abcd")]))
+
+    def test_validate_ordered(self):
+        # see gh-14058
+        exp_msg = "'ordered' must either be 'True' or 'False'"
+        exp_err = TypeError
+
+        # This should be a boolean.
+        ordered = np.array([0, 1, 2])
+
+        with pytest.raises(exp_err, match=exp_msg):
+            Categorical([1, 2, 3], ordered=ordered)
+
+        with pytest.raises(exp_err, match=exp_msg):
+            Categorical.from_codes(
+                [0, 0, 1], categories=["a", "b", "c"], ordered=ordered
+            )
+
+    def test_constructor_empty(self):
+        # GH 17248
+        c = Categorical([])
+        expected = Index([])
+        tm.assert_index_equal(c.categories, expected)
+
+        c = Categorical([], categories=[1, 2, 3])
+        expected = Index([1, 2, 3], dtype=np.int64)
+        tm.assert_index_equal(c.categories, expected)
+
+    def test_constructor_empty_boolean(self):
+        # see gh-22702
+        cat = Categorical([], categories=[True, False])
+        categories = sorted(cat.categories.tolist())
+        assert categories == [False, True]
+
+    def test_constructor_tuples(self):
+        values = np.array([(1,), (1, 2), (1,), (1, 2)], dtype=object)
+        result = Categorical(values)
+        expected = Index([(1,), (1, 2)], tupleize_cols=False)
+        tm.assert_index_equal(result.categories, expected)
+        assert result.ordered is False
+
+    def test_constructor_tuples_datetimes(self):
+        # numpy will auto reshape when all of the tuples are the
+        # same len, so add an extra one with 2 items and slice it off
+        values = np.array(
+            [
+                (Timestamp("2010-01-01"),),
+                (Timestamp("2010-01-02"),),
+                (Timestamp("2010-01-01"),),
+                (Timestamp("2010-01-02"),),
+                ("a", "b"),
+            ],
+            dtype=object,
+        )[:-1]
+        result = Categorical(values)
+        expected = Index(
+            [(Timestamp("2010-01-01"),), (Timestamp("2010-01-02"),)],
+            tupleize_cols=False,
+        )
+        tm.assert_index_equal(result.categories, expected)
+
+    def test_constructor_unsortable(self):
+        # it works!
+        arr = np.array([1, 2, 3, datetime.now()], dtype="O")
+        factor = Categorical(arr, ordered=False)
+        assert not factor.ordered
+
+        # this however will raise as cannot be sorted
+        msg = (
+            "'values' is not ordered, please explicitly specify the "
+            "categories order by passing in a categories argument."
+        )
+        with pytest.raises(TypeError, match=msg):
+            Categorical(arr, ordered=True)
+
+    def test_constructor_interval(self):
+        result = Categorical(
+            [Interval(1, 2), Interval(2, 3), Interval(3, 6)], ordered=True
+        )
+        ii = IntervalIndex([Interval(1, 2), Interval(2, 3), Interval(3, 6)])
+        exp = Categorical(ii, ordered=True)
+        tm.assert_categorical_equal(result, exp)
+        tm.assert_index_equal(result.categories, ii)
+
+    def test_constructor(self):
+        exp_arr = np.array(["a", "b", "c", "a", "b", "c"], dtype=np.object_)
+        c1 = Categorical(exp_arr)
+        tm.assert_numpy_array_equal(c1.__array__(), exp_arr)
+        c2 = Categorical(exp_arr, categories=["a", "b", "c"])
+        tm.assert_numpy_array_equal(c2.__array__(), exp_arr)
+        c2 = Categorical(exp_arr, categories=["c", "b", "a"])
+        tm.assert_numpy_array_equal(c2.__array__(), exp_arr)
+
+        # categories must be unique
+        msg = "Categorical categories must be unique"
+        with pytest.raises(ValueError, match=msg):
+            Categorical([1, 2], [1, 2, 2])
+
+        with pytest.raises(ValueError, match=msg):
+            Categorical(["a", "b"], ["a", "b", "b"])
+
+        # The default should be unordered
+        c1 = Categorical(["a", "b", "c", "a"])
+        assert not c1.ordered
+
+        # Categorical as input
+        c1 = Categorical(["a", "b", "c", "a"])
+        c2 = Categorical(c1)
+        tm.assert_categorical_equal(c1, c2)
+
+        c1 = Categorical(["a", "b", "c", "a"], categories=["a", "b", "c", "d"])
+        c2 = Categorical(c1)
+        tm.assert_categorical_equal(c1, c2)
+
+        c1 = Categorical(["a", "b", "c", "a"], categories=["a", "c", "b"])
+        c2 = Categorical(c1)
+        tm.assert_categorical_equal(c1, c2)
+
+        c1 = Categorical(["a", "b", "c", "a"], categories=["a", "c", "b"])
+        c2 = Categorical(c1, categories=["a", "b", "c"])
+        tm.assert_numpy_array_equal(c1.__array__(), c2.__array__())
+        tm.assert_index_equal(c2.categories, Index(["a", "b", "c"]))
+
+        # Series of dtype category
+        c1 = Categorical(["a", "b", "c", "a"], categories=["a", "b", "c", "d"])
+        c2 = Categorical(Series(c1))
+        tm.assert_categorical_equal(c1, c2)
+
+        c1 = Categorical(["a", "b", "c", "a"], categories=["a", "c", "b"])
+        c2 = Categorical(Series(c1))
+        tm.assert_categorical_equal(c1, c2)
+
+        # Series
+        c1 = Categorical(["a", "b", "c", "a"])
+        c2 = Categorical(Series(["a", "b", "c", "a"]))
+        tm.assert_categorical_equal(c1, c2)
+
+        c1 = Categorical(["a", "b", "c", "a"], categories=["a", "b", "c", "d"])
+        c2 = Categorical(Series(["a", "b", "c", "a"]), categories=["a", "b", "c", "d"])
+        tm.assert_categorical_equal(c1, c2)
+
+        # This should result in integer categories, not float!
+        cat = Categorical([1, 2, 3, np.nan], categories=[1, 2, 3])
+        assert is_integer_dtype(cat.categories)
+
+        # https://github.com/pandas-dev/pandas/issues/3678
+        cat = Categorical([np.nan, 1, 2, 3])
+        assert is_integer_dtype(cat.categories)
+
+        # this should result in floats
+        cat = Categorical([np.nan, 1, 2.0, 3])
+        assert is_float_dtype(cat.categories)
+
+        cat = Categorical([np.nan, 1.0, 2.0, 3.0])
+        assert is_float_dtype(cat.categories)
+
+        # This doesn't work -> this would probably need some kind of "remember
+        # the original type" feature to try to cast the array interface result
+        # to...
+
+        # vals = np.asarray(cat[cat.notna()])
+        # assert is_integer_dtype(vals)
+
+        # corner cases
+        cat = Categorical([1])
+        assert len(cat.categories) == 1
+        assert cat.categories[0] == 1
+        assert len(cat.codes) == 1
+        assert cat.codes[0] == 0
+
+        cat = Categorical(["a"])
+        assert len(cat.categories) == 1
+        assert cat.categories[0] == "a"
+        assert len(cat.codes) == 1
+        assert cat.codes[0] == 0
+
+        # two arrays
+        #  - when the first is an integer dtype and the second is not
+        #  - when the resulting codes are all -1/NaN
+        with tm.assert_produces_warning(None):
+            Categorical([0, 1, 2, 0, 1, 2], categories=["a", "b", "c"])
+
+        with tm.assert_produces_warning(None):
+            Categorical([0, 1, 2, 0, 1, 2], categories=[3, 4, 5])
+
+        # the next one are from the old docs
+        with tm.assert_produces_warning(None):
+            Categorical([0, 1, 2, 0, 1, 2], [1, 2, 3])
+            cat = Categorical([1, 2], categories=[1, 2, 3])
+
+        # this is a legitimate constructor
+        with tm.assert_produces_warning(None):
+            Categorical(np.array([], dtype="int64"), categories=[3, 2, 1], ordered=True)
+
+    def test_constructor_with_existing_categories(self):
+        # GH25318: constructing with pd.Series used to bogusly skip recoding
+        # categories
+        c0 = Categorical(["a", "b", "c", "a"])
+        c1 = Categorical(["a", "b", "c", "a"], categories=["b", "c"])
+
+        c2 = Categorical(c0, categories=c1.categories)
+        tm.assert_categorical_equal(c1, c2)
+
+        c3 = Categorical(Series(c0), categories=c1.categories)
+        tm.assert_categorical_equal(c1, c3)
+
+    def test_constructor_not_sequence(self):
+        # https://github.com/pandas-dev/pandas/issues/16022
+        msg = r"^Parameter 'categories' must be list-like, was"
+        with pytest.raises(TypeError, match=msg):
+            Categorical(["a", "b"], categories="a")
+
+    def test_constructor_with_null(self):
+        # Cannot have NaN in categories
+        msg = "Categorical categories cannot be null"
+        with pytest.raises(ValueError, match=msg):
+            Categorical([np.nan, "a", "b", "c"], categories=[np.nan, "a", "b", "c"])
+
+        with pytest.raises(ValueError, match=msg):
+            Categorical([None, "a", "b", "c"], categories=[None, "a", "b", "c"])
+
+        with pytest.raises(ValueError, match=msg):
+            Categorical(
+                DatetimeIndex(["nat", "20160101"]),
+                categories=[NaT, Timestamp("20160101")],
+            )
+
+    def test_constructor_with_index(self):
+        ci = CategoricalIndex(list("aabbca"), categories=list("cab"))
+        tm.assert_categorical_equal(ci.values, Categorical(ci))
+
+        ci = CategoricalIndex(list("aabbca"), categories=list("cab"))
+        tm.assert_categorical_equal(
+            ci.values, Categorical(ci.astype(object), categories=ci.categories)
+        )
+
+    def test_constructor_with_generator(self):
+        # This was raising an Error in isna(single_val).any() because isna
+        # returned a scalar for a generator
+
+        exp = Categorical([0, 1, 2])
+        cat = Categorical(x for x in [0, 1, 2])
+        tm.assert_categorical_equal(cat, exp)
+        cat = Categorical(range(3))
+        tm.assert_categorical_equal(cat, exp)
+
+        MultiIndex.from_product([range(5), ["a", "b", "c"]])
+
+        # check that categories accept generators and sequences
+        cat = Categorical([0, 1, 2], categories=(x for x in [0, 1, 2]))
+        tm.assert_categorical_equal(cat, exp)
+        cat = Categorical([0, 1, 2], categories=range(3))
+        tm.assert_categorical_equal(cat, exp)
+
+    def test_constructor_with_rangeindex(self):
+        # RangeIndex is preserved in Categories
+        rng = Index(range(3))
+
+        cat = Categorical(rng)
+        tm.assert_index_equal(cat.categories, rng, exact=True)
+
+        cat = Categorical([1, 2, 0], categories=rng)
+        tm.assert_index_equal(cat.categories, rng, exact=True)
+
+    @pytest.mark.parametrize(
+        "dtl",
+        [
+            date_range("1995-01-01 00:00:00", periods=5, freq="s"),
+            date_range("1995-01-01 00:00:00", periods=5, freq="s", tz="US/Eastern"),
+            timedelta_range("1 day", periods=5, freq="s"),
+        ],
+    )
+    def test_constructor_with_datetimelike(self, dtl):
+        # see gh-12077
+        # constructor with a datetimelike and NaT
+
+        s = Series(dtl)
+        c = Categorical(s)
+
+        expected = type(dtl)(s)
+        expected._data.freq = None
+
+        tm.assert_index_equal(c.categories, expected)
+        tm.assert_numpy_array_equal(c.codes, np.arange(5, dtype="int8"))
+
+        # with NaT
+        s2 = s.copy()
+        s2.iloc[-1] = NaT
+        c = Categorical(s2)
+
+        expected = type(dtl)(s2.dropna())
+        expected._data.freq = None
+
+        tm.assert_index_equal(c.categories, expected)
+
+        exp = np.array([0, 1, 2, 3, -1], dtype=np.int8)
+        tm.assert_numpy_array_equal(c.codes, exp)
+
+        result = repr(c)
+        assert "NaT" in result
+
+    def test_constructor_from_index_series_datetimetz(self):
+        idx = date_range("2015-01-01 10:00", freq="D", periods=3, tz="US/Eastern")
+        idx = idx._with_freq(None)  # freq not preserved in result.categories
+        result = Categorical(idx)
+        tm.assert_index_equal(result.categories, idx)
+
+        result = Categorical(Series(idx))
+        tm.assert_index_equal(result.categories, idx)
+
+    def test_constructor_date_objects(self):
+        # we dont cast date objects to timestamps, matching Index constructor
+        v = date.today()
+
+        cat = Categorical([v, v])
+        assert cat.categories.dtype == object
+        assert type(cat.categories[0]) is date
+
+    def test_constructor_from_index_series_timedelta(self):
+        idx = timedelta_range("1 days", freq="D", periods=3)
+        idx = idx._with_freq(None)  # freq not preserved in result.categories
+        result = Categorical(idx)
+        tm.assert_index_equal(result.categories, idx)
+
+        result = Categorical(Series(idx))
+        tm.assert_index_equal(result.categories, idx)
+
+    def test_constructor_from_index_series_period(self):
+        idx = period_range("2015-01-01", freq="D", periods=3)
+        result = Categorical(idx)
+        tm.assert_index_equal(result.categories, idx)
+
+        result = Categorical(Series(idx))
+        tm.assert_index_equal(result.categories, idx)
+
+    @pytest.mark.parametrize(
+        "values",
+        [
+            np.array([1.0, 1.2, 1.8, np.nan]),
+            np.array([1, 2, 3], dtype="int64"),
+            ["a", "b", "c", np.nan],
+            [pd.Period("2014-01"), pd.Period("2014-02"), NaT],
+            [Timestamp("2014-01-01"), Timestamp("2014-01-02"), NaT],
+            [
+                Timestamp("2014-01-01", tz="US/Eastern"),
+                Timestamp("2014-01-02", tz="US/Eastern"),
+                NaT,
+            ],
+        ],
+    )
+    def test_constructor_invariant(self, values):
+        # GH 14190
+        c = Categorical(values)
+        c2 = Categorical(c)
+        tm.assert_categorical_equal(c, c2)
+
+    @pytest.mark.parametrize("ordered", [True, False])
+    def test_constructor_with_dtype(self, ordered):
+        categories = ["b", "a", "c"]
+        dtype = CategoricalDtype(categories, ordered=ordered)
+        result = Categorical(["a", "b", "a", "c"], dtype=dtype)
+        expected = Categorical(
+            ["a", "b", "a", "c"], categories=categories, ordered=ordered
+        )
+        tm.assert_categorical_equal(result, expected)
+        assert result.ordered is ordered
+
+    def test_constructor_dtype_and_others_raises(self):
+        dtype = CategoricalDtype(["a", "b"], ordered=True)
+        msg = "Cannot specify `categories` or `ordered` together with `dtype`."
+        with pytest.raises(ValueError, match=msg):
+            Categorical(["a", "b"], categories=["a", "b"], dtype=dtype)
+
+        with pytest.raises(ValueError, match=msg):
+            Categorical(["a", "b"], ordered=True, dtype=dtype)
+
+        with pytest.raises(ValueError, match=msg):
+            Categorical(["a", "b"], ordered=False, dtype=dtype)
+
+    @pytest.mark.parametrize("categories", [None, ["a", "b"], ["a", "c"]])
+    @pytest.mark.parametrize("ordered", [True, False])
+    def test_constructor_str_category(self, categories, ordered):
+        result = Categorical(
+            ["a", "b"], categories=categories, ordered=ordered, dtype="category"
+        )
+        expected = Categorical(["a", "b"], categories=categories, ordered=ordered)
+        tm.assert_categorical_equal(result, expected)
+
+    def test_constructor_str_unknown(self):
+        with pytest.raises(ValueError, match="Unknown dtype"):
+            Categorical([1, 2], dtype="foo")
+
+    @pytest.mark.xfail(
+        using_string_dtype() and HAS_PYARROW, reason="Can't be NumPy strings"
+    )
+    def test_constructor_np_strs(self):
+        # GH#31499 Hashtable.map_locations needs to work on np.str_ objects
+        cat = Categorical(["1", "0", "1"], [np.str_("0"), np.str_("1")])
+        assert all(isinstance(x, np.str_) for x in cat.categories)
+
+    def test_constructor_from_categorical_with_dtype(self):
+        dtype = CategoricalDtype(["a", "b", "c"], ordered=True)
+        values = Categorical(["a", "b", "d"])
+        result = Categorical(values, dtype=dtype)
+        # We use dtype.categories, not values.categories
+        expected = Categorical(
+            ["a", "b", "d"], categories=["a", "b", "c"], ordered=True
+        )
+        tm.assert_categorical_equal(result, expected)
+
+    def test_constructor_from_categorical_with_unknown_dtype(self):
+        dtype = CategoricalDtype(None, ordered=True)
+        values = Categorical(["a", "b", "d"])
+        result = Categorical(values, dtype=dtype)
+        # We use values.categories, not dtype.categories
+        expected = Categorical(
+            ["a", "b", "d"], categories=["a", "b", "d"], ordered=True
+        )
+        tm.assert_categorical_equal(result, expected)
+
+    def test_constructor_from_categorical_string(self):
+        values = Categorical(["a", "b", "d"])
+        # use categories, ordered
+        result = Categorical(
+            values, categories=["a", "b", "c"], ordered=True, dtype="category"
+        )
+        expected = Categorical(
+            ["a", "b", "d"], categories=["a", "b", "c"], ordered=True
+        )
+        tm.assert_categorical_equal(result, expected)
+
+        # No string
+        result = Categorical(values, categories=["a", "b", "c"], ordered=True)
+        tm.assert_categorical_equal(result, expected)
+
+    def test_constructor_with_categorical_categories(self):
+        # GH17884
+        expected = Categorical(["a", "b"], categories=["a", "b", "c"])
+
+        result = Categorical(["a", "b"], categories=Categorical(["a", "b", "c"]))
+        tm.assert_categorical_equal(result, expected)
+
+        result = Categorical(["a", "b"], categories=CategoricalIndex(["a", "b", "c"]))
+        tm.assert_categorical_equal(result, expected)
+
+    @pytest.mark.parametrize("klass", [lambda x: np.array(x, dtype=object), list])
+    def test_construction_with_null(self, klass, nulls_fixture):
+        # https://github.com/pandas-dev/pandas/issues/31927
+        values = klass(["a", nulls_fixture, "b"])
+        result = Categorical(values)
+
+        dtype = CategoricalDtype(["a", "b"])
+        codes = [0, -1, 1]
+        expected = Categorical.from_codes(codes=codes, dtype=dtype)
+
+        tm.assert_categorical_equal(result, expected)
+
+    @pytest.mark.parametrize("validate", [True, False])
+    def test_from_codes_nullable_int_categories(self, any_numeric_ea_dtype, validate):
+        # GH#39649
+        cats = pd.array(range(5), dtype=any_numeric_ea_dtype)
+        codes = np.random.default_rng(2).integers(5, size=3)
+        dtype = CategoricalDtype(cats)
+        arr = Categorical.from_codes(codes, dtype=dtype, validate=validate)
+        assert arr.categories.dtype == cats.dtype
+        tm.assert_index_equal(arr.categories, Index(cats))
+
+    def test_from_codes_empty(self):
+        cat = ["a", "b", "c"]
+        result = Categorical.from_codes([], categories=cat)
+        expected = Categorical([], categories=cat)
+
+        tm.assert_categorical_equal(result, expected)
+
+    @pytest.mark.parametrize("validate", [True, False])
+    def test_from_codes_validate(self, validate):
+        # GH53122
+        dtype = CategoricalDtype(["a", "b"])
+        if validate:
+            with pytest.raises(ValueError, match="codes need to be between "):
+                Categorical.from_codes([4, 5], dtype=dtype, validate=validate)
+        else:
+            # passes, though has incorrect codes, but that's the user responsibility
+            Categorical.from_codes([4, 5], dtype=dtype, validate=validate)
+
+    def test_from_codes_too_few_categories(self):
+        dtype = CategoricalDtype(categories=[1, 2])
+        msg = "codes need to be between "
+        with pytest.raises(ValueError, match=msg):
+            Categorical.from_codes([1, 2], categories=dtype.categories)
+        with pytest.raises(ValueError, match=msg):
+            Categorical.from_codes([1, 2], dtype=dtype)
+
+    def test_from_codes_non_int_codes(self):
+        dtype = CategoricalDtype(categories=[1, 2])
+        msg = "codes need to be array-like integers"
+        with pytest.raises(ValueError, match=msg):
+            Categorical.from_codes(["a"], categories=dtype.categories)
+        with pytest.raises(ValueError, match=msg):
+            Categorical.from_codes(["a"], dtype=dtype)
+
+    def test_from_codes_non_unique_categories(self):
+        with pytest.raises(ValueError, match="Categorical categories must be unique"):
+            Categorical.from_codes([0, 1, 2], categories=["a", "a", "b"])
+
+    def test_from_codes_nan_cat_included(self):
+        with pytest.raises(ValueError, match="Categorical categories cannot be null"):
+            Categorical.from_codes([0, 1, 2], categories=["a", "b", np.nan])
+
+    def test_from_codes_too_negative(self):
+        dtype = CategoricalDtype(categories=["a", "b", "c"])
+        msg = r"codes need to be between -1 and len\(categories\)-1"
+        with pytest.raises(ValueError, match=msg):
+            Categorical.from_codes([-2, 1, 2], categories=dtype.categories)
+        with pytest.raises(ValueError, match=msg):
+            Categorical.from_codes([-2, 1, 2], dtype=dtype)
+
+    def test_from_codes(self):
+        dtype = CategoricalDtype(categories=["a", "b", "c"])
+        exp = Categorical(["a", "b", "c"], ordered=False)
+        res = Categorical.from_codes([0, 1, 2], categories=dtype.categories)
+        tm.assert_categorical_equal(exp, res)
+
+        res = Categorical.from_codes([0, 1, 2], dtype=dtype)
+        tm.assert_categorical_equal(exp, res)
+
+    @pytest.mark.parametrize("klass", [Categorical, CategoricalIndex])
+    def test_from_codes_with_categorical_categories(self, klass):
+        # GH17884
+        expected = Categorical(["a", "b"], categories=["a", "b", "c"])
+
+        result = Categorical.from_codes([0, 1], categories=klass(["a", "b", "c"]))
+        tm.assert_categorical_equal(result, expected)
+
+    @pytest.mark.parametrize("klass", [Categorical, CategoricalIndex])
+    def test_from_codes_with_non_unique_categorical_categories(self, klass):
+        with pytest.raises(ValueError, match="Categorical categories must be unique"):
+            Categorical.from_codes([0, 1], klass(["a", "b", "a"]))
+
+    def test_from_codes_with_nan_code(self):
+        # GH21767
+        codes = [1, 2, np.nan]
+        dtype = CategoricalDtype(categories=["a", "b", "c"])
+        with pytest.raises(ValueError, match="codes need to be array-like integers"):
+            Categorical.from_codes(codes, categories=dtype.categories)
+        with pytest.raises(ValueError, match="codes need to be array-like integers"):
+            Categorical.from_codes(codes, dtype=dtype)
+
+    @pytest.mark.parametrize("codes", [[1.0, 2.0, 0], [1.1, 2.0, 0]])
+    def test_from_codes_with_float(self, codes):
+        # GH21767
+        # float codes should raise even if values are equal to integers
+        dtype = CategoricalDtype(categories=["a", "b", "c"])
+
+        msg = "codes need to be array-like integers"
+        with pytest.raises(ValueError, match=msg):
+            Categorical.from_codes(codes, dtype.categories)
+        with pytest.raises(ValueError, match=msg):
+            Categorical.from_codes(codes, dtype=dtype)
+
+    def test_from_codes_with_dtype_raises(self):
+        msg = "Cannot specify"
+        with pytest.raises(ValueError, match=msg):
+            Categorical.from_codes(
+                [0, 1], categories=["a", "b"], dtype=CategoricalDtype(["a", "b"])
+            )
+
+        with pytest.raises(ValueError, match=msg):
+            Categorical.from_codes(
+                [0, 1], ordered=True, dtype=CategoricalDtype(["a", "b"])
+            )
+
+    def test_from_codes_neither(self):
+        msg = "Both were None"
+        with pytest.raises(ValueError, match=msg):
+            Categorical.from_codes([0, 1])
+
+    def test_from_codes_with_nullable_int(self):
+        codes = pd.array([0, 1], dtype="Int64")
+        categories = ["a", "b"]
+
+        result = Categorical.from_codes(codes, categories=categories)
+        expected = Categorical.from_codes(codes.to_numpy(int), categories=categories)
+
+        tm.assert_categorical_equal(result, expected)
+
+    def test_from_codes_with_nullable_int_na_raises(self):
+        codes = pd.array([0, None], dtype="Int64")
+        categories = ["a", "b"]
+
+        msg = "codes cannot contain NA values"
+        with pytest.raises(ValueError, match=msg):
+            Categorical.from_codes(codes, categories=categories)
+
+    @pytest.mark.parametrize("dtype", [None, "category"])
+    def test_from_inferred_categories(self, dtype):
+        cats = ["a", "b"]
+        codes = np.array([0, 0, 1, 1], dtype="i8")
+        result = Categorical._from_inferred_categories(cats, codes, dtype)
+        expected = Categorical.from_codes(codes, cats)
+        tm.assert_categorical_equal(result, expected)
+
+    @pytest.mark.parametrize("dtype", [None, "category"])
+    def test_from_inferred_categories_sorts(self, dtype):
+        cats = ["b", "a"]
+        codes = np.array([0, 1, 1, 1], dtype="i8")
+        result = Categorical._from_inferred_categories(cats, codes, dtype)
+        expected = Categorical.from_codes([1, 0, 0, 0], ["a", "b"])
+        tm.assert_categorical_equal(result, expected)
+
+    def test_from_inferred_categories_dtype(self):
+        cats = ["a", "b", "d"]
+        codes = np.array([0, 1, 0, 2], dtype="i8")
+        dtype = CategoricalDtype(["c", "b", "a"], ordered=True)
+        result = Categorical._from_inferred_categories(cats, codes, dtype)
+        expected = Categorical(
+            ["a", "b", "a", "d"], categories=["c", "b", "a"], ordered=True
+        )
+        tm.assert_categorical_equal(result, expected)
+
+    def test_from_inferred_categories_coerces(self):
+        cats = ["1", "2", "bad"]
+        codes = np.array([0, 0, 1, 2], dtype="i8")
+        dtype = CategoricalDtype([1, 2])
+        result = Categorical._from_inferred_categories(cats, codes, dtype)
+        expected = Categorical([1, 1, 2, np.nan])
+        tm.assert_categorical_equal(result, expected)
+
+    @pytest.mark.parametrize("ordered", [None, True, False])
+    def test_construction_with_ordered(self, ordered):
+        # GH 9347, 9190
+        cat = Categorical([0, 1, 2], ordered=ordered)
+        assert cat.ordered == bool(ordered)
+
+    def test_constructor_imaginary(self):
+        values = [1, 2, 3 + 1j]
+        c1 = Categorical(values)
+        tm.assert_index_equal(c1.categories, Index(values))
+        tm.assert_numpy_array_equal(np.array(c1), np.array(values))
+
+    def test_constructor_string_and_tuples(self):
+        # GH 21416
+        c = Categorical(np.array(["c", ("a", "b"), ("b", "a"), "c"], dtype=object))
+        expected_index = Index([("a", "b"), ("b", "a"), "c"])
+        assert c.categories.equals(expected_index)
+
+    def test_interval(self):
+        idx = pd.interval_range(0, 10, periods=10)
+        cat = Categorical(idx, categories=idx)
+        expected_codes = np.arange(10, dtype="int8")
+        tm.assert_numpy_array_equal(cat.codes, expected_codes)
+        tm.assert_index_equal(cat.categories, idx)
+
+        # infer categories
+        cat = Categorical(idx)
+        tm.assert_numpy_array_equal(cat.codes, expected_codes)
+        tm.assert_index_equal(cat.categories, idx)
+
+        # list values
+        cat = Categorical(list(idx))
+        tm.assert_numpy_array_equal(cat.codes, expected_codes)
+        tm.assert_index_equal(cat.categories, idx)
+
+        # list values, categories
+        cat = Categorical(list(idx), categories=list(idx))
+        tm.assert_numpy_array_equal(cat.codes, expected_codes)
+        tm.assert_index_equal(cat.categories, idx)
+
+        # shuffled
+        values = idx.take([1, 2, 0])
+        cat = Categorical(values, categories=idx)
+        tm.assert_numpy_array_equal(cat.codes, np.array([1, 2, 0], dtype="int8"))
+        tm.assert_index_equal(cat.categories, idx)
+
+        # extra
+        values = pd.interval_range(8, 11, periods=3)
+        cat = Categorical(values, categories=idx)
+        expected_codes = np.array([8, 9, -1], dtype="int8")
+        tm.assert_numpy_array_equal(cat.codes, expected_codes)
+        tm.assert_index_equal(cat.categories, idx)
+
+        # overlapping
+        idx = IntervalIndex([Interval(0, 2), Interval(0, 1)])
+        cat = Categorical(idx, categories=idx)
+        expected_codes = np.array([0, 1], dtype="int8")
+        tm.assert_numpy_array_equal(cat.codes, expected_codes)
+        tm.assert_index_equal(cat.categories, idx)
+
+    def test_categorical_extension_array_nullable(self, nulls_fixture):
+        # GH:
+        arr = pd.arrays.StringArray._from_sequence(
+            [nulls_fixture] * 2, dtype=pd.StringDtype()
+        )
+        result = Categorical(arr)
+        assert arr.dtype == result.categories.dtype
+        expected = Categorical(Series([pd.NA, pd.NA], dtype=arr.dtype))
+        tm.assert_categorical_equal(result, expected)
+
+    def test_from_sequence_copy(self):
+        cat = Categorical(np.arange(5).repeat(2))
+        result = Categorical._from_sequence(cat, dtype=cat.dtype, copy=False)
+
+        # more generally, we'd be OK with a view
+        assert result._codes is cat._codes
+
+        result = Categorical._from_sequence(cat, dtype=cat.dtype, copy=True)
+
+        assert not tm.shares_memory(result, cat)
+
+    def test_constructor_datetime64_non_nano(self):
+        categories = np.arange(10).view("M8[D]")
+        values = categories[::2].copy()
+
+        cat = Categorical(values, categories=categories)
+        assert (cat == values).all()
+
+    def test_constructor_preserves_freq(self):
+        # GH33830 freq retention in categorical
+        dti = date_range("2016-01-01", periods=5)
+
+        expected = dti.freq
+
+        cat = Categorical(dti)
+        result = cat.categories.freq
+
+        assert expected == result
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_dtypes.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_dtypes.py
new file mode 100644
index 0000000000000000000000000000000000000000..525663cad1745880bc5e683e7302afdc2c06a527
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_dtypes.py
@@ -0,0 +1,139 @@
+import numpy as np
+import pytest
+
+from pandas.core.dtypes.dtypes import CategoricalDtype
+
+from pandas import (
+    Categorical,
+    CategoricalIndex,
+    Index,
+    IntervalIndex,
+    Series,
+    Timestamp,
+)
+import pandas._testing as tm
+
+
+class TestCategoricalDtypes:
+    def test_categories_match_up_to_permutation(self):
+        # test dtype comparisons between cats
+
+        c1 = Categorical(list("aabca"), categories=list("abc"), ordered=False)
+        c2 = Categorical(list("aabca"), categories=list("cab"), ordered=False)
+        c3 = Categorical(list("aabca"), categories=list("cab"), ordered=True)
+        assert c1._categories_match_up_to_permutation(c1)
+        assert c2._categories_match_up_to_permutation(c2)
+        assert c3._categories_match_up_to_permutation(c3)
+        assert c1._categories_match_up_to_permutation(c2)
+        assert not c1._categories_match_up_to_permutation(c3)
+        assert not c1._categories_match_up_to_permutation(Index(list("aabca")))
+        assert not c1._categories_match_up_to_permutation(c1.astype(object))
+        assert c1._categories_match_up_to_permutation(CategoricalIndex(c1))
+        assert c1._categories_match_up_to_permutation(
+            CategoricalIndex(c1, categories=list("cab"))
+        )
+        assert not c1._categories_match_up_to_permutation(
+            CategoricalIndex(c1, ordered=True)
+        )
+
+        # GH 16659
+        s1 = Series(c1)
+        s2 = Series(c2)
+        s3 = Series(c3)
+        assert c1._categories_match_up_to_permutation(s1)
+        assert c2._categories_match_up_to_permutation(s2)
+        assert c3._categories_match_up_to_permutation(s3)
+        assert c1._categories_match_up_to_permutation(s2)
+        assert not c1._categories_match_up_to_permutation(s3)
+        assert not c1._categories_match_up_to_permutation(s1.astype(object))
+
+    def test_set_dtype_same(self):
+        c = Categorical(["a", "b", "c"])
+        result = c._set_dtype(CategoricalDtype(["a", "b", "c"]))
+        tm.assert_categorical_equal(result, c)
+
+    def test_set_dtype_new_categories(self):
+        c = Categorical(["a", "b", "c"])
+        result = c._set_dtype(CategoricalDtype(list("abcd")))
+        tm.assert_numpy_array_equal(result.codes, c.codes)
+        tm.assert_index_equal(result.dtype.categories, Index(list("abcd")))
+
+    @pytest.mark.parametrize(
+        "values, categories, new_categories",
+        [
+            # No NaNs, same cats, same order
+            (["a", "b", "a"], ["a", "b"], ["a", "b"]),
+            # No NaNs, same cats, different order
+            (["a", "b", "a"], ["a", "b"], ["b", "a"]),
+            # Same, unsorted
+            (["b", "a", "a"], ["a", "b"], ["a", "b"]),
+            # No NaNs, same cats, different order
+            (["b", "a", "a"], ["a", "b"], ["b", "a"]),
+            # NaNs
+            (["a", "b", "c"], ["a", "b"], ["a", "b"]),
+            (["a", "b", "c"], ["a", "b"], ["b", "a"]),
+            (["b", "a", "c"], ["a", "b"], ["a", "b"]),
+            (["b", "a", "c"], ["a", "b"], ["a", "b"]),
+            # Introduce NaNs
+            (["a", "b", "c"], ["a", "b"], ["a"]),
+            (["a", "b", "c"], ["a", "b"], ["b"]),
+            (["b", "a", "c"], ["a", "b"], ["a"]),
+            (["b", "a", "c"], ["a", "b"], ["a"]),
+            # No overlap
+            (["a", "b", "c"], ["a", "b"], ["d", "e"]),
+        ],
+    )
+    @pytest.mark.parametrize("ordered", [True, False])
+    def test_set_dtype_many(self, values, categories, new_categories, ordered):
+        c = Categorical(values, categories)
+        expected = Categorical(values, new_categories, ordered)
+        result = c._set_dtype(expected.dtype)
+        tm.assert_categorical_equal(result, expected)
+
+    def test_set_dtype_no_overlap(self):
+        c = Categorical(["a", "b", "c"], ["d", "e"])
+        result = c._set_dtype(CategoricalDtype(["a", "b"]))
+        expected = Categorical([None, None, None], categories=["a", "b"])
+        tm.assert_categorical_equal(result, expected)
+
+    def test_codes_dtypes(self):
+        # GH 8453
+        result = Categorical(["foo", "bar", "baz"])
+        assert result.codes.dtype == "int8"
+
+        result = Categorical([f"foo{i:05d}" for i in range(400)])
+        assert result.codes.dtype == "int16"
+
+        result = Categorical([f"foo{i:05d}" for i in range(40000)])
+        assert result.codes.dtype == "int32"
+
+        # adding cats
+        result = Categorical(["foo", "bar", "baz"])
+        assert result.codes.dtype == "int8"
+        result = result.add_categories([f"foo{i:05d}" for i in range(400)])
+        assert result.codes.dtype == "int16"
+
+        # removing cats
+        result = result.remove_categories([f"foo{i:05d}" for i in range(300)])
+        assert result.codes.dtype == "int8"
+
+    def test_iter_python_types(self):
+        # GH-19909
+        cat = Categorical([1, 2])
+        assert isinstance(next(iter(cat)), int)
+        assert isinstance(cat.tolist()[0], int)
+
+    def test_iter_python_types_datetime(self):
+        cat = Categorical([Timestamp("2017-01-01"), Timestamp("2017-01-02")])
+        assert isinstance(next(iter(cat)), Timestamp)
+        assert isinstance(cat.tolist()[0], Timestamp)
+
+    def test_interval_index_category(self):
+        # GH 38316
+        index = IntervalIndex.from_breaks(np.arange(3, dtype="uint64"))
+
+        result = CategoricalIndex(index).dtype.categories
+        expected = IntervalIndex.from_arrays(
+            [0, 1], [1, 2], dtype="interval[uint64, right]"
+        )
+        tm.assert_index_equal(result, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_indexing.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_indexing.py
new file mode 100644
index 0000000000000000000000000000000000000000..5e1c5c64fa660f501d2b9d77c9181f47e013267f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_indexing.py
@@ -0,0 +1,388 @@
+import math
+
+import numpy as np
+import pytest
+
+from pandas import (
+    NA,
+    Categorical,
+    CategoricalIndex,
+    Index,
+    Interval,
+    IntervalIndex,
+    NaT,
+    PeriodIndex,
+    Series,
+    Timedelta,
+    Timestamp,
+)
+import pandas._testing as tm
+import pandas.core.common as com
+
+
+class TestCategoricalIndexingWithFactor:
+    def test_getitem(self):
+        factor = Categorical(["a", "b", "b", "a", "a", "c", "c", "c"], ordered=True)
+        assert factor[0] == "a"
+        assert factor[-1] == "c"
+
+        subf = factor[[0, 1, 2]]
+        tm.assert_numpy_array_equal(subf._codes, np.array([0, 1, 1], dtype=np.int8))
+
+        subf = factor[np.asarray(factor) == "c"]
+        tm.assert_numpy_array_equal(subf._codes, np.array([2, 2, 2], dtype=np.int8))
+
+    def test_setitem(self):
+        factor = Categorical(["a", "b", "b", "a", "a", "c", "c", "c"], ordered=True)
+        # int/positional
+        c = factor.copy()
+        c[0] = "b"
+        assert c[0] == "b"
+        c[-1] = "a"
+        assert c[-1] == "a"
+
+        # boolean
+        c = factor.copy()
+        indexer = np.zeros(len(c), dtype="bool")
+        indexer[0] = True
+        indexer[-1] = True
+        c[indexer] = "c"
+        expected = Categorical(["c", "b", "b", "a", "a", "c", "c", "c"], ordered=True)
+
+        tm.assert_categorical_equal(c, expected)
+
+    @pytest.mark.parametrize(
+        "other",
+        [Categorical(["b", "a"]), Categorical(["b", "a"], categories=["b", "a"])],
+    )
+    def test_setitem_same_but_unordered(self, other):
+        # GH-24142
+        target = Categorical(["a", "b"], categories=["a", "b"])
+        mask = np.array([True, False])
+        target[mask] = other[mask]
+        expected = Categorical(["b", "b"], categories=["a", "b"])
+        tm.assert_categorical_equal(target, expected)
+
+    @pytest.mark.parametrize(
+        "other",
+        [
+            Categorical(["b", "a"], categories=["b", "a", "c"]),
+            Categorical(["b", "a"], categories=["a", "b", "c"]),
+            Categorical(["a", "a"], categories=["a"]),
+            Categorical(["b", "b"], categories=["b"]),
+        ],
+    )
+    def test_setitem_different_unordered_raises(self, other):
+        # GH-24142
+        target = Categorical(["a", "b"], categories=["a", "b"])
+        mask = np.array([True, False])
+        msg = "Cannot set a Categorical with another, without identical categories"
+        with pytest.raises(TypeError, match=msg):
+            target[mask] = other[mask]
+
+    @pytest.mark.parametrize(
+        "other",
+        [
+            Categorical(["b", "a"]),
+            Categorical(["b", "a"], categories=["b", "a"], ordered=True),
+            Categorical(["b", "a"], categories=["a", "b", "c"], ordered=True),
+        ],
+    )
+    def test_setitem_same_ordered_raises(self, other):
+        # Gh-24142
+        target = Categorical(["a", "b"], categories=["a", "b"], ordered=True)
+        mask = np.array([True, False])
+        msg = "Cannot set a Categorical with another, without identical categories"
+        with pytest.raises(TypeError, match=msg):
+            target[mask] = other[mask]
+
+    def test_setitem_tuple(self):
+        # GH#20439
+        cat = Categorical([(0, 1), (0, 2), (0, 1)])
+
+        # This should not raise
+        cat[1] = cat[0]
+        assert cat[1] == (0, 1)
+
+    def test_setitem_listlike(self):
+        # GH#9469
+        # properly coerce the input indexers
+
+        cat = Categorical(
+            np.random.default_rng(2).integers(0, 5, size=150000).astype(np.int8)
+        ).add_categories([-1000])
+        indexer = np.array([100000]).astype(np.int64)
+        cat[indexer] = -1000
+
+        # we are asserting the code result here
+        # which maps to the -1000 category
+        result = cat.codes[np.array([100000]).astype(np.int64)]
+        tm.assert_numpy_array_equal(result, np.array([5], dtype="int8"))
+
+
+class TestCategoricalIndexing:
+    def test_getitem_slice(self):
+        cat = Categorical(["a", "b", "c", "d", "a", "b", "c"])
+        sliced = cat[3]
+        assert sliced == "d"
+
+        sliced = cat[3:5]
+        expected = Categorical(["d", "a"], categories=["a", "b", "c", "d"])
+        tm.assert_categorical_equal(sliced, expected)
+
+    def test_getitem_listlike(self):
+        # GH 9469
+        # properly coerce the input indexers
+
+        c = Categorical(
+            np.random.default_rng(2).integers(0, 5, size=150000).astype(np.int8)
+        )
+        result = c.codes[np.array([100000]).astype(np.int64)]
+        expected = c[np.array([100000]).astype(np.int64)].codes
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_periodindex(self):
+        idx1 = PeriodIndex(
+            ["2014-01", "2014-01", "2014-02", "2014-02", "2014-03", "2014-03"],
+            freq="M",
+        )
+
+        cat1 = Categorical(idx1)
+        str(cat1)
+        exp_arr = np.array([0, 0, 1, 1, 2, 2], dtype=np.int8)
+        exp_idx = PeriodIndex(["2014-01", "2014-02", "2014-03"], freq="M")
+        tm.assert_numpy_array_equal(cat1._codes, exp_arr)
+        tm.assert_index_equal(cat1.categories, exp_idx)
+
+        idx2 = PeriodIndex(
+            ["2014-03", "2014-03", "2014-02", "2014-01", "2014-03", "2014-01"],
+            freq="M",
+        )
+        cat2 = Categorical(idx2, ordered=True)
+        str(cat2)
+        exp_arr = np.array([2, 2, 1, 0, 2, 0], dtype=np.int8)
+        exp_idx2 = PeriodIndex(["2014-01", "2014-02", "2014-03"], freq="M")
+        tm.assert_numpy_array_equal(cat2._codes, exp_arr)
+        tm.assert_index_equal(cat2.categories, exp_idx2)
+
+        idx3 = PeriodIndex(
+            [
+                "2013-12",
+                "2013-11",
+                "2013-10",
+                "2013-09",
+                "2013-08",
+                "2013-07",
+                "2013-05",
+            ],
+            freq="M",
+        )
+        cat3 = Categorical(idx3, ordered=True)
+        exp_arr = np.array([6, 5, 4, 3, 2, 1, 0], dtype=np.int8)
+        exp_idx = PeriodIndex(
+            [
+                "2013-05",
+                "2013-07",
+                "2013-08",
+                "2013-09",
+                "2013-10",
+                "2013-11",
+                "2013-12",
+            ],
+            freq="M",
+        )
+        tm.assert_numpy_array_equal(cat3._codes, exp_arr)
+        tm.assert_index_equal(cat3.categories, exp_idx)
+
+    @pytest.mark.parametrize(
+        "null_val",
+        [None, np.nan, NaT, NA, math.nan, "NaT", "nat", "NAT", "nan", "NaN", "NAN"],
+    )
+    def test_periodindex_on_null_types(self, null_val):
+        # GH 46673
+        result = PeriodIndex(["2022-04-06", "2022-04-07", null_val], freq="D")
+        expected = PeriodIndex(["2022-04-06", "2022-04-07", "NaT"], dtype="period[D]")
+        assert result[2] is NaT
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize("new_categories", [[1, 2, 3, 4], [1, 2]])
+    def test_categories_assignments_wrong_length_raises(self, new_categories):
+        cat = Categorical(["a", "b", "c", "a"])
+        msg = (
+            "new categories need to have the same number of items "
+            "as the old categories!"
+        )
+        with pytest.raises(ValueError, match=msg):
+            cat.rename_categories(new_categories)
+
+    # Combinations of sorted/unique:
+    @pytest.mark.parametrize(
+        "idx_values", [[1, 2, 3, 4], [1, 3, 2, 4], [1, 3, 3, 4], [1, 2, 2, 4]]
+    )
+    # Combinations of missing/unique
+    @pytest.mark.parametrize("key_values", [[1, 2], [1, 5], [1, 1], [5, 5]])
+    @pytest.mark.parametrize("key_class", [Categorical, CategoricalIndex])
+    @pytest.mark.parametrize("dtype", [None, "category", "key"])
+    def test_get_indexer_non_unique(self, idx_values, key_values, key_class, dtype):
+        # GH 21448
+        key = key_class(key_values, categories=range(1, 5))
+
+        if dtype == "key":
+            dtype = key.dtype
+
+        # Test for flat index and CategoricalIndex with same/different cats:
+        idx = Index(idx_values, dtype=dtype)
+        expected, exp_miss = idx.get_indexer_non_unique(key_values)
+        result, res_miss = idx.get_indexer_non_unique(key)
+
+        tm.assert_numpy_array_equal(expected, result)
+        tm.assert_numpy_array_equal(exp_miss, res_miss)
+
+        exp_unique = idx.unique().get_indexer(key_values)
+        res_unique = idx.unique().get_indexer(key)
+        tm.assert_numpy_array_equal(res_unique, exp_unique)
+
+    def test_where_unobserved_nan(self):
+        ser = Series(Categorical(["a", "b"]))
+        result = ser.where([True, False])
+        expected = Series(Categorical(["a", None], categories=["a", "b"]))
+        tm.assert_series_equal(result, expected)
+
+        # all NA
+        ser = Series(Categorical(["a", "b"]))
+        result = ser.where([False, False])
+        expected = Series(Categorical([None, None], categories=["a", "b"]))
+        tm.assert_series_equal(result, expected)
+
+    def test_where_unobserved_categories(self):
+        ser = Series(Categorical(["a", "b", "c"], categories=["d", "c", "b", "a"]))
+        result = ser.where([True, True, False], other="b")
+        expected = Series(Categorical(["a", "b", "b"], categories=ser.cat.categories))
+        tm.assert_series_equal(result, expected)
+
+    def test_where_other_categorical(self):
+        ser = Series(Categorical(["a", "b", "c"], categories=["d", "c", "b", "a"]))
+        other = Categorical(["b", "c", "a"], categories=["a", "c", "b", "d"])
+        result = ser.where([True, False, True], other)
+        expected = Series(Categorical(["a", "c", "c"], dtype=ser.dtype))
+        tm.assert_series_equal(result, expected)
+
+    def test_where_new_category_raises(self):
+        ser = Series(Categorical(["a", "b", "c"]))
+        msg = "Cannot setitem on a Categorical with a new category"
+        with pytest.raises(TypeError, match=msg):
+            ser.where([True, False, True], "d")
+
+    def test_where_ordered_differs_rasies(self):
+        ser = Series(
+            Categorical(["a", "b", "c"], categories=["d", "c", "b", "a"], ordered=True)
+        )
+        other = Categorical(
+            ["b", "c", "a"], categories=["a", "c", "b", "d"], ordered=True
+        )
+        with pytest.raises(TypeError, match="without identical categories"):
+            ser.where([True, False, True], other)
+
+
+class TestContains:
+    def test_contains(self):
+        # GH#21508
+        cat = Categorical(list("aabbca"), categories=list("cab"))
+
+        assert "b" in cat
+        assert "z" not in cat
+        assert np.nan not in cat
+        with pytest.raises(TypeError, match="unhashable type: 'list'"):
+            assert [1] in cat
+
+        # assert codes NOT in index
+        assert 0 not in cat
+        assert 1 not in cat
+
+        cat = Categorical(list("aabbca") + [np.nan], categories=list("cab"))
+        assert np.nan in cat
+
+    @pytest.mark.parametrize(
+        "item, expected",
+        [
+            (Interval(0, 1), True),
+            (1.5, True),
+            (Interval(0.5, 1.5), False),
+            ("a", False),
+            (Timestamp(1), False),
+            (Timedelta(1), False),
+        ],
+        ids=str,
+    )
+    def test_contains_interval(self, item, expected):
+        # GH#23705
+        cat = Categorical(IntervalIndex.from_breaks(range(3)))
+        result = item in cat
+        assert result is expected
+
+    def test_contains_list(self):
+        # GH#21729
+        cat = Categorical([1, 2, 3])
+
+        assert "a" not in cat
+
+        with pytest.raises(TypeError, match="unhashable type"):
+            ["a"] in cat
+
+        with pytest.raises(TypeError, match="unhashable type"):
+            ["a", "b"] in cat
+
+
+@pytest.mark.parametrize("index", [True, False])
+def test_mask_with_boolean(index):
+    ser = Series(range(3))
+    idx = Categorical([True, False, True])
+    if index:
+        idx = CategoricalIndex(idx)
+
+    assert com.is_bool_indexer(idx)
+    result = ser[idx]
+    expected = ser[idx.astype("object")]
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("index", [True, False])
+def test_mask_with_boolean_na_treated_as_false(index):
+    # https://github.com/pandas-dev/pandas/issues/31503
+    ser = Series(range(3))
+    idx = Categorical([True, False, None])
+    if index:
+        idx = CategoricalIndex(idx)
+
+    result = ser[idx]
+    expected = ser[idx.fillna(False)]
+
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.fixture
+def non_coercible_categorical(monkeypatch):
+    """
+    Monkeypatch Categorical.__array__ to ensure no implicit conversion.
+
+    Raises
+    ------
+    ValueError
+        When Categorical.__array__ is called.
+    """
+
+    # TODO(Categorical): identify other places where this may be
+    # useful and move to a conftest.py
+    def array(self, dtype=None):
+        raise ValueError("I cannot be converted.")
+
+    with monkeypatch.context() as m:
+        m.setattr(Categorical, "__array__", array)
+        yield
+
+
+def test_series_at():
+    arr = Categorical(["a", "b", "c"])
+    ser = Series(arr)
+    result = ser.at[0]
+    assert result == "a"
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_map.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_map.py
new file mode 100644
index 0000000000000000000000000000000000000000..3d41b7cc7094d237fa8d31501ce90a99b04fe4e6
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_map.py
@@ -0,0 +1,154 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    Categorical,
+    Index,
+    Series,
+)
+import pandas._testing as tm
+
+
+@pytest.fixture(params=[None, "ignore"])
+def na_action(request):
+    return request.param
+
+
+@pytest.mark.parametrize(
+    "data, categories",
+    [
+        (list("abcbca"), list("cab")),
+        (pd.interval_range(0, 3).repeat(3), pd.interval_range(0, 3)),
+    ],
+    ids=["string", "interval"],
+)
+def test_map_str(data, categories, ordered, na_action):
+    # GH 31202 - override base class since we want to maintain categorical/ordered
+    cat = Categorical(data, categories=categories, ordered=ordered)
+    result = cat.map(str, na_action=na_action)
+    expected = Categorical(
+        map(str, data), categories=map(str, categories), ordered=ordered
+    )
+    tm.assert_categorical_equal(result, expected)
+
+
+def test_map(na_action):
+    cat = Categorical(list("ABABC"), categories=list("CBA"), ordered=True)
+    result = cat.map(lambda x: x.lower(), na_action=na_action)
+    exp = Categorical(list("ababc"), categories=list("cba"), ordered=True)
+    tm.assert_categorical_equal(result, exp)
+
+    cat = Categorical(list("ABABC"), categories=list("BAC"), ordered=False)
+    result = cat.map(lambda x: x.lower(), na_action=na_action)
+    exp = Categorical(list("ababc"), categories=list("bac"), ordered=False)
+    tm.assert_categorical_equal(result, exp)
+
+    # GH 12766: Return an index not an array
+    result = cat.map(lambda x: 1, na_action=na_action)
+    exp = Index(np.array([1] * 5, dtype=np.int64))
+    tm.assert_index_equal(result, exp)
+
+    # change categories dtype
+    cat = Categorical(list("ABABC"), categories=list("BAC"), ordered=False)
+
+    def f(x):
+        return {"A": 10, "B": 20, "C": 30}.get(x)
+
+    result = cat.map(f, na_action=na_action)
+    exp = Categorical([10, 20, 10, 20, 30], categories=[20, 10, 30], ordered=False)
+    tm.assert_categorical_equal(result, exp)
+
+    mapper = Series([10, 20, 30], index=["A", "B", "C"])
+    result = cat.map(mapper, na_action=na_action)
+    tm.assert_categorical_equal(result, exp)
+
+    result = cat.map({"A": 10, "B": 20, "C": 30}, na_action=na_action)
+    tm.assert_categorical_equal(result, exp)
+
+
+@pytest.mark.parametrize(
+    ("data", "f", "expected"),
+    (
+        ([1, 1, np.nan], pd.isna, Index([False, False, True])),
+        ([1, 2, np.nan], pd.isna, Index([False, False, True])),
+        ([1, 1, np.nan], {1: False}, Categorical([False, False, np.nan])),
+        ([1, 2, np.nan], {1: False, 2: False}, Index([False, False, np.nan])),
+        (
+            [1, 1, np.nan],
+            Series([False, False]),
+            Categorical([False, False, np.nan]),
+        ),
+        (
+            [1, 2, np.nan],
+            Series([False] * 3),
+            Index([False, False, np.nan]),
+        ),
+    ),
+)
+def test_map_with_nan_none(data, f, expected):  # GH 24241
+    values = Categorical(data)
+    result = values.map(f, na_action=None)
+    if isinstance(expected, Categorical):
+        tm.assert_categorical_equal(result, expected)
+    else:
+        tm.assert_index_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    ("data", "f", "expected"),
+    (
+        ([1, 1, np.nan], pd.isna, Categorical([False, False, np.nan])),
+        ([1, 2, np.nan], pd.isna, Index([False, False, np.nan])),
+        ([1, 1, np.nan], {1: False}, Categorical([False, False, np.nan])),
+        ([1, 2, np.nan], {1: False, 2: False}, Index([False, False, np.nan])),
+        (
+            [1, 1, np.nan],
+            Series([False, False]),
+            Categorical([False, False, np.nan]),
+        ),
+        (
+            [1, 2, np.nan],
+            Series([False, False, False]),
+            Index([False, False, np.nan]),
+        ),
+    ),
+)
+def test_map_with_nan_ignore(data, f, expected):  # GH 24241
+    values = Categorical(data)
+    result = values.map(f, na_action="ignore")
+    if data[1] == 1:
+        tm.assert_categorical_equal(result, expected)
+    else:
+        tm.assert_index_equal(result, expected)
+
+
+def test_map_with_dict_or_series(na_action):
+    orig_values = ["a", "B", 1, "a"]
+    new_values = ["one", 2, 3.0, "one"]
+    cat = Categorical(orig_values)
+
+    mapper = Series(new_values[:-1], index=orig_values[:-1])
+    result = cat.map(mapper, na_action=na_action)
+
+    # Order of categories in result can be different
+    expected = Categorical(new_values, categories=[3.0, 2, "one"])
+    tm.assert_categorical_equal(result, expected)
+
+    mapper = dict(zip(orig_values[:-1], new_values[:-1]))
+    result = cat.map(mapper, na_action=na_action)
+    # Order of categories in result can be different
+    tm.assert_categorical_equal(result, expected)
+
+
+def test_map_na_action_no_default_deprecated():
+    # GH51645
+    cat = Categorical(["a", "b", "c"])
+    msg = (
+        "The default value of 'ignore' for the `na_action` parameter in "
+        "pandas.Categorical.map is deprecated and will be "
+        "changed to 'None' in a future version. Please set na_action to the "
+        "desired value to avoid seeing this warning"
+    )
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        cat.map(lambda x: x)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_missing.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_missing.py
new file mode 100644
index 0000000000000000000000000000000000000000..0eeb01b74608890daf81fef083adb29e797e57ce
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_missing.py
@@ -0,0 +1,216 @@
+import collections
+
+import numpy as np
+import pytest
+
+from pandas.core.dtypes.dtypes import CategoricalDtype
+
+import pandas as pd
+from pandas import (
+    Categorical,
+    DataFrame,
+    Index,
+    Series,
+    isna,
+)
+import pandas._testing as tm
+
+
+class TestCategoricalMissing:
+    def test_isna(self):
+        exp = np.array([False, False, True])
+        cat = Categorical(["a", "b", np.nan])
+        res = cat.isna()
+
+        tm.assert_numpy_array_equal(res, exp)
+
+    def test_na_flags_int_categories(self):
+        # #1457
+
+        categories = list(range(10))
+        labels = np.random.default_rng(2).integers(0, 10, 20)
+        labels[::5] = -1
+
+        cat = Categorical(labels, categories)
+        repr(cat)
+
+        tm.assert_numpy_array_equal(isna(cat), labels == -1)
+
+    def test_nan_handling(self):
+        # Nans are represented as -1 in codes
+        c = Categorical(["a", "b", np.nan, "a"])
+        tm.assert_index_equal(c.categories, Index(["a", "b"]))
+        tm.assert_numpy_array_equal(c._codes, np.array([0, 1, -1, 0], dtype=np.int8))
+        c[1] = np.nan
+        tm.assert_index_equal(c.categories, Index(["a", "b"]))
+        tm.assert_numpy_array_equal(c._codes, np.array([0, -1, -1, 0], dtype=np.int8))
+
+        # Adding nan to categories should make assigned nan point to the
+        # category!
+        c = Categorical(["a", "b", np.nan, "a"])
+        tm.assert_index_equal(c.categories, Index(["a", "b"]))
+        tm.assert_numpy_array_equal(c._codes, np.array([0, 1, -1, 0], dtype=np.int8))
+
+    def test_set_dtype_nans(self):
+        c = Categorical(["a", "b", np.nan])
+        result = c._set_dtype(CategoricalDtype(["a", "c"]))
+        tm.assert_numpy_array_equal(result.codes, np.array([0, -1, -1], dtype="int8"))
+
+    def test_set_item_nan(self):
+        cat = Categorical([1, 2, 3])
+        cat[1] = np.nan
+
+        exp = Categorical([1, np.nan, 3], categories=[1, 2, 3])
+        tm.assert_categorical_equal(cat, exp)
+
+    @pytest.mark.parametrize(
+        "fillna_kwargs, msg",
+        [
+            (
+                {"value": 1, "method": "ffill"},
+                "Cannot specify both 'value' and 'method'.",
+            ),
+            ({}, "Must specify a fill 'value' or 'method'."),
+            ({"method": "bad"}, "Invalid fill method. Expecting .* bad"),
+            (
+                {"value": Series([1, 2, 3, 4, "a"])},
+                "Cannot setitem on a Categorical with a new category",
+            ),
+        ],
+    )
+    def test_fillna_raises(self, fillna_kwargs, msg):
+        # https://github.com/pandas-dev/pandas/issues/19682
+        # https://github.com/pandas-dev/pandas/issues/13628
+        cat = Categorical([1, 2, 3, None, None])
+
+        if len(fillna_kwargs) == 1 and "value" in fillna_kwargs:
+            err = TypeError
+        else:
+            err = ValueError
+
+        with pytest.raises(err, match=msg):
+            cat.fillna(**fillna_kwargs)
+
+    @pytest.mark.parametrize("named", [True, False])
+    def test_fillna_iterable_category(self, named):
+        # https://github.com/pandas-dev/pandas/issues/21097
+        if named:
+            Point = collections.namedtuple("Point", "x y")
+        else:
+            Point = lambda *args: args  # tuple
+        cat = Categorical(np.array([Point(0, 0), Point(0, 1), None], dtype=object))
+        result = cat.fillna(Point(0, 0))
+        expected = Categorical([Point(0, 0), Point(0, 1), Point(0, 0)])
+
+        tm.assert_categorical_equal(result, expected)
+
+        # Case where the Point is not among our categories; we want ValueError,
+        #  not NotImplementedError GH#41914
+        cat = Categorical(np.array([Point(1, 0), Point(0, 1), None], dtype=object))
+        msg = "Cannot setitem on a Categorical with a new category"
+        with pytest.raises(TypeError, match=msg):
+            cat.fillna(Point(0, 0))
+
+    def test_fillna_array(self):
+        # accept Categorical or ndarray value if it holds appropriate values
+        cat = Categorical(["A", "B", "C", None, None])
+
+        other = cat.fillna("C")
+        result = cat.fillna(other)
+        tm.assert_categorical_equal(result, other)
+        assert isna(cat[-1])  # didn't modify original inplace
+
+        other = np.array(["A", "B", "C", "B", "A"])
+        result = cat.fillna(other)
+        expected = Categorical(["A", "B", "C", "B", "A"], dtype=cat.dtype)
+        tm.assert_categorical_equal(result, expected)
+        assert isna(cat[-1])  # didn't modify original inplace
+
+    @pytest.mark.parametrize(
+        "values, expected",
+        [
+            ([1, 2, 3], np.array([False, False, False])),
+            ([1, 2, np.nan], np.array([False, False, True])),
+            ([1, 2, np.inf], np.array([False, False, True])),
+            ([1, 2, pd.NA], np.array([False, False, True])),
+        ],
+    )
+    def test_use_inf_as_na(self, values, expected):
+        # https://github.com/pandas-dev/pandas/issues/33594
+        msg = "use_inf_as_na option is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            with pd.option_context("mode.use_inf_as_na", True):
+                cat = Categorical(values)
+                result = cat.isna()
+                tm.assert_numpy_array_equal(result, expected)
+
+                result = Series(cat).isna()
+                expected = Series(expected)
+                tm.assert_series_equal(result, expected)
+
+                result = DataFrame(cat).isna()
+                expected = DataFrame(expected)
+                tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "values, expected",
+        [
+            ([1, 2, 3], np.array([False, False, False])),
+            ([1, 2, np.nan], np.array([False, False, True])),
+            ([1, 2, np.inf], np.array([False, False, True])),
+            ([1, 2, pd.NA], np.array([False, False, True])),
+        ],
+    )
+    def test_use_inf_as_na_outside_context(self, values, expected):
+        # https://github.com/pandas-dev/pandas/issues/33594
+        # Using isna directly for Categorical will fail in general here
+        cat = Categorical(values)
+
+        msg = "use_inf_as_na option is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            with pd.option_context("mode.use_inf_as_na", True):
+                result = isna(cat)
+                tm.assert_numpy_array_equal(result, expected)
+
+                result = isna(Series(cat))
+                expected = Series(expected)
+                tm.assert_series_equal(result, expected)
+
+                result = isna(DataFrame(cat))
+                expected = DataFrame(expected)
+                tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "a1, a2, categories",
+        [
+            (["a", "b", "c"], [np.nan, "a", "b"], ["a", "b", "c"]),
+            ([1, 2, 3], [np.nan, 1, 2], [1, 2, 3]),
+        ],
+    )
+    def test_compare_categorical_with_missing(self, a1, a2, categories):
+        # GH 28384
+        cat_type = CategoricalDtype(categories)
+
+        # !=
+        result = Series(a1, dtype=cat_type) != Series(a2, dtype=cat_type)
+        expected = Series(a1) != Series(a2)
+        tm.assert_series_equal(result, expected)
+
+        # ==
+        result = Series(a1, dtype=cat_type) == Series(a2, dtype=cat_type)
+        expected = Series(a1) == Series(a2)
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "na_value, dtype",
+        [
+            (pd.NaT, "datetime64[ns]"),
+            (None, "float64"),
+            (np.nan, "float64"),
+            (pd.NA, "float64"),
+        ],
+    )
+    def test_categorical_only_missing_values_no_cast(self, na_value, dtype):
+        # GH#44900
+        result = Categorical([na_value, na_value])
+        tm.assert_index_equal(result.categories, Index([], dtype=dtype))
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_operators.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_operators.py
new file mode 100644
index 0000000000000000000000000000000000000000..4174d2adc810b872e7ec0b1e3ca820e3d2c3920d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_operators.py
@@ -0,0 +1,414 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    Categorical,
+    DataFrame,
+    Series,
+    Timestamp,
+    date_range,
+)
+import pandas._testing as tm
+
+
+class TestCategoricalOpsWithFactor:
+    def test_categories_none_comparisons(self):
+        factor = Categorical(["a", "b", "b", "a", "a", "c", "c", "c"], ordered=True)
+        tm.assert_categorical_equal(factor, factor)
+
+    def test_comparisons(self):
+        factor = Categorical(["a", "b", "b", "a", "a", "c", "c", "c"], ordered=True)
+        result = factor[factor == "a"]
+        expected = factor[np.asarray(factor) == "a"]
+        tm.assert_categorical_equal(result, expected)
+
+        result = factor[factor != "a"]
+        expected = factor[np.asarray(factor) != "a"]
+        tm.assert_categorical_equal(result, expected)
+
+        result = factor[factor < "c"]
+        expected = factor[np.asarray(factor) < "c"]
+        tm.assert_categorical_equal(result, expected)
+
+        result = factor[factor > "a"]
+        expected = factor[np.asarray(factor) > "a"]
+        tm.assert_categorical_equal(result, expected)
+
+        result = factor[factor >= "b"]
+        expected = factor[np.asarray(factor) >= "b"]
+        tm.assert_categorical_equal(result, expected)
+
+        result = factor[factor <= "b"]
+        expected = factor[np.asarray(factor) <= "b"]
+        tm.assert_categorical_equal(result, expected)
+
+        n = len(factor)
+
+        other = factor[np.random.default_rng(2).permutation(n)]
+        result = factor == other
+        expected = np.asarray(factor) == np.asarray(other)
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = factor == "d"
+        expected = np.zeros(len(factor), dtype=bool)
+        tm.assert_numpy_array_equal(result, expected)
+
+        # comparisons with categoricals
+        cat_rev = Categorical(["a", "b", "c"], categories=["c", "b", "a"], ordered=True)
+        cat_rev_base = Categorical(
+            ["b", "b", "b"], categories=["c", "b", "a"], ordered=True
+        )
+        cat = Categorical(["a", "b", "c"], ordered=True)
+        cat_base = Categorical(["b", "b", "b"], categories=cat.categories, ordered=True)
+
+        # comparisons need to take categories ordering into account
+        res_rev = cat_rev > cat_rev_base
+        exp_rev = np.array([True, False, False])
+        tm.assert_numpy_array_equal(res_rev, exp_rev)
+
+        res_rev = cat_rev < cat_rev_base
+        exp_rev = np.array([False, False, True])
+        tm.assert_numpy_array_equal(res_rev, exp_rev)
+
+        res = cat > cat_base
+        exp = np.array([False, False, True])
+        tm.assert_numpy_array_equal(res, exp)
+
+        # Only categories with same categories can be compared
+        msg = "Categoricals can only be compared if 'categories' are the same"
+        with pytest.raises(TypeError, match=msg):
+            cat > cat_rev
+
+        cat_rev_base2 = Categorical(["b", "b", "b"], categories=["c", "b", "a", "d"])
+
+        with pytest.raises(TypeError, match=msg):
+            cat_rev > cat_rev_base2
+
+        # Only categories with same ordering information can be compared
+        cat_unordered = cat.set_ordered(False)
+        assert not (cat > cat).any()
+
+        with pytest.raises(TypeError, match=msg):
+            cat > cat_unordered
+
+        # comparison (in both directions) with Series will raise
+        s = Series(["b", "b", "b"], dtype=object)
+        msg = (
+            "Cannot compare a Categorical for op __gt__ with type "
+            r""
+        )
+        with pytest.raises(TypeError, match=msg):
+            cat > s
+        with pytest.raises(TypeError, match=msg):
+            cat_rev > s
+        with pytest.raises(TypeError, match=msg):
+            s < cat
+        with pytest.raises(TypeError, match=msg):
+            s < cat_rev
+
+        # comparison with numpy.array will raise in both direction, but only on
+        # newer numpy versions
+        a = np.array(["b", "b", "b"], dtype=object)
+        with pytest.raises(TypeError, match=msg):
+            cat > a
+        with pytest.raises(TypeError, match=msg):
+            cat_rev > a
+
+        # Make sure that unequal comparison take the categories order in
+        # account
+        cat_rev = Categorical(list("abc"), categories=list("cba"), ordered=True)
+        exp = np.array([True, False, False])
+        res = cat_rev > "b"
+        tm.assert_numpy_array_equal(res, exp)
+
+        # check that zero-dim array gets unboxed
+        res = cat_rev > np.array("b")
+        tm.assert_numpy_array_equal(res, exp)
+
+
+class TestCategoricalOps:
+    @pytest.mark.parametrize(
+        "categories",
+        [["a", "b"], [0, 1], [Timestamp("2019"), Timestamp("2020")]],
+    )
+    def test_not_equal_with_na(self, categories):
+        # https://github.com/pandas-dev/pandas/issues/32276
+        c1 = Categorical.from_codes([-1, 0], categories=categories)
+        c2 = Categorical.from_codes([0, 1], categories=categories)
+
+        result = c1 != c2
+
+        assert result.all()
+
+    def test_compare_frame(self):
+        # GH#24282 check that Categorical.__cmp__(DataFrame) defers to frame
+        data = ["a", "b", 2, "a"]
+        cat = Categorical(data)
+
+        df = DataFrame(cat)
+
+        result = cat == df.T
+        expected = DataFrame([[True, True, True, True]])
+        tm.assert_frame_equal(result, expected)
+
+        result = cat[::-1] != df.T
+        expected = DataFrame([[False, True, True, False]])
+        tm.assert_frame_equal(result, expected)
+
+    def test_compare_frame_raises(self, comparison_op):
+        # alignment raises unless we transpose
+        op = comparison_op
+        cat = Categorical(["a", "b", 2, "a"])
+        df = DataFrame(cat)
+        msg = "Unable to coerce to Series, length must be 1: given 4"
+        with pytest.raises(ValueError, match=msg):
+            op(cat, df)
+
+    def test_datetime_categorical_comparison(self):
+        dt_cat = Categorical(date_range("2014-01-01", periods=3), ordered=True)
+        tm.assert_numpy_array_equal(dt_cat > dt_cat[0], np.array([False, True, True]))
+        tm.assert_numpy_array_equal(dt_cat[0] < dt_cat, np.array([False, True, True]))
+
+    def test_reflected_comparison_with_scalars(self):
+        # GH8658
+        cat = Categorical([1, 2, 3], ordered=True)
+        tm.assert_numpy_array_equal(cat > cat[0], np.array([False, True, True]))
+        tm.assert_numpy_array_equal(cat[0] < cat, np.array([False, True, True]))
+
+    def test_comparison_with_unknown_scalars(self):
+        # https://github.com/pandas-dev/pandas/issues/9836#issuecomment-92123057
+        # and following comparisons with scalars not in categories should raise
+        # for unequal comps, but not for equal/not equal
+        cat = Categorical([1, 2, 3], ordered=True)
+
+        msg = "Invalid comparison between dtype=category and int"
+        with pytest.raises(TypeError, match=msg):
+            cat < 4
+        with pytest.raises(TypeError, match=msg):
+            cat > 4
+        with pytest.raises(TypeError, match=msg):
+            4 < cat
+        with pytest.raises(TypeError, match=msg):
+            4 > cat
+
+        tm.assert_numpy_array_equal(cat == 4, np.array([False, False, False]))
+        tm.assert_numpy_array_equal(cat != 4, np.array([True, True, True]))
+
+    def test_comparison_with_tuple(self):
+        cat = Categorical(np.array(["foo", (0, 1), 3, (0, 1)], dtype=object))
+
+        result = cat == "foo"
+        expected = np.array([True, False, False, False], dtype=bool)
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = cat == (0, 1)
+        expected = np.array([False, True, False, True], dtype=bool)
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = cat != (0, 1)
+        tm.assert_numpy_array_equal(result, ~expected)
+
+    @pytest.mark.filterwarnings("ignore::RuntimeWarning")
+    def test_comparison_of_ordered_categorical_with_nan_to_scalar(
+        self, compare_operators_no_eq_ne
+    ):
+        # https://github.com/pandas-dev/pandas/issues/26504
+        # BUG: fix ordered categorical comparison with missing values (#26504 )
+        # and following comparisons with scalars in categories with missing
+        # values should be evaluated as False
+
+        cat = Categorical([1, 2, 3, None], categories=[1, 2, 3], ordered=True)
+        scalar = 2
+        expected = getattr(np.array(cat), compare_operators_no_eq_ne)(scalar)
+        actual = getattr(cat, compare_operators_no_eq_ne)(scalar)
+        tm.assert_numpy_array_equal(actual, expected)
+
+    @pytest.mark.filterwarnings("ignore::RuntimeWarning")
+    def test_comparison_of_ordered_categorical_with_nan_to_listlike(
+        self, compare_operators_no_eq_ne
+    ):
+        # https://github.com/pandas-dev/pandas/issues/26504
+        # and following comparisons of missing values in ordered Categorical
+        # with listlike should be evaluated as False
+
+        cat = Categorical([1, 2, 3, None], categories=[1, 2, 3], ordered=True)
+        other = Categorical([2, 2, 2, 2], categories=[1, 2, 3], ordered=True)
+        expected = getattr(np.array(cat), compare_operators_no_eq_ne)(2)
+        actual = getattr(cat, compare_operators_no_eq_ne)(other)
+        tm.assert_numpy_array_equal(actual, expected)
+
+    @pytest.mark.parametrize(
+        "data,reverse,base",
+        [(list("abc"), list("cba"), list("bbb")), ([1, 2, 3], [3, 2, 1], [2, 2, 2])],
+    )
+    def test_comparisons(self, data, reverse, base):
+        cat_rev = Series(Categorical(data, categories=reverse, ordered=True))
+        cat_rev_base = Series(Categorical(base, categories=reverse, ordered=True))
+        cat = Series(Categorical(data, ordered=True))
+        cat_base = Series(
+            Categorical(base, categories=cat.cat.categories, ordered=True)
+        )
+        s = Series(base, dtype=object if base == list("bbb") else None)
+        a = np.array(base)
+
+        # comparisons need to take categories ordering into account
+        res_rev = cat_rev > cat_rev_base
+        exp_rev = Series([True, False, False])
+        tm.assert_series_equal(res_rev, exp_rev)
+
+        res_rev = cat_rev < cat_rev_base
+        exp_rev = Series([False, False, True])
+        tm.assert_series_equal(res_rev, exp_rev)
+
+        res = cat > cat_base
+        exp = Series([False, False, True])
+        tm.assert_series_equal(res, exp)
+
+        scalar = base[1]
+        res = cat > scalar
+        exp = Series([False, False, True])
+        exp2 = cat.values > scalar
+        tm.assert_series_equal(res, exp)
+        tm.assert_numpy_array_equal(res.values, exp2)
+        res_rev = cat_rev > scalar
+        exp_rev = Series([True, False, False])
+        exp_rev2 = cat_rev.values > scalar
+        tm.assert_series_equal(res_rev, exp_rev)
+        tm.assert_numpy_array_equal(res_rev.values, exp_rev2)
+
+        # Only categories with same categories can be compared
+        msg = "Categoricals can only be compared if 'categories' are the same"
+        with pytest.raises(TypeError, match=msg):
+            cat > cat_rev
+
+        # categorical cannot be compared to Series or numpy array, and also
+        # not the other way around
+        msg = (
+            "Cannot compare a Categorical for op __gt__ with type "
+            r""
+        )
+        with pytest.raises(TypeError, match=msg):
+            cat > s
+        with pytest.raises(TypeError, match=msg):
+            cat_rev > s
+        with pytest.raises(TypeError, match=msg):
+            cat > a
+        with pytest.raises(TypeError, match=msg):
+            cat_rev > a
+
+        with pytest.raises(TypeError, match=msg):
+            s < cat
+        with pytest.raises(TypeError, match=msg):
+            s < cat_rev
+
+        with pytest.raises(TypeError, match=msg):
+            a < cat
+        with pytest.raises(TypeError, match=msg):
+            a < cat_rev
+
+    @pytest.mark.parametrize(
+        "ctor",
+        [
+            lambda *args, **kwargs: Categorical(*args, **kwargs),
+            lambda *args, **kwargs: Series(Categorical(*args, **kwargs)),
+        ],
+    )
+    def test_unordered_different_order_equal(self, ctor):
+        # https://github.com/pandas-dev/pandas/issues/16014
+        c1 = ctor(["a", "b"], categories=["a", "b"], ordered=False)
+        c2 = ctor(["a", "b"], categories=["b", "a"], ordered=False)
+        assert (c1 == c2).all()
+
+        c1 = ctor(["a", "b"], categories=["a", "b"], ordered=False)
+        c2 = ctor(["b", "a"], categories=["b", "a"], ordered=False)
+        assert (c1 != c2).all()
+
+        c1 = ctor(["a", "a"], categories=["a", "b"], ordered=False)
+        c2 = ctor(["b", "b"], categories=["b", "a"], ordered=False)
+        assert (c1 != c2).all()
+
+        c1 = ctor(["a", "a"], categories=["a", "b"], ordered=False)
+        c2 = ctor(["a", "b"], categories=["b", "a"], ordered=False)
+        result = c1 == c2
+        tm.assert_numpy_array_equal(np.array(result), np.array([True, False]))
+
+    def test_unordered_different_categories_raises(self):
+        c1 = Categorical(["a", "b"], categories=["a", "b"], ordered=False)
+        c2 = Categorical(["a", "c"], categories=["c", "a"], ordered=False)
+
+        with pytest.raises(TypeError, match=("Categoricals can only be compared")):
+            c1 == c2
+
+    def test_compare_different_lengths(self):
+        c1 = Categorical([], categories=["a", "b"])
+        c2 = Categorical([], categories=["a"])
+
+        msg = "Categoricals can only be compared if 'categories' are the same."
+        with pytest.raises(TypeError, match=msg):
+            c1 == c2
+
+    def test_compare_unordered_different_order(self):
+        # https://github.com/pandas-dev/pandas/issues/16603#issuecomment-
+        # 349290078
+        a = Categorical(["a"], categories=["a", "b"])
+        b = Categorical(["b"], categories=["b", "a"])
+        assert not a.equals(b)
+
+    def test_numeric_like_ops(self):
+        df = DataFrame({"value": np.random.default_rng(2).integers(0, 10000, 100)})
+        labels = [f"{i} - {i + 499}" for i in range(0, 10000, 500)]
+        cat_labels = Categorical(labels, labels)
+
+        df = df.sort_values(by=["value"], ascending=True)
+        df["value_group"] = pd.cut(
+            df.value, range(0, 10500, 500), right=False, labels=cat_labels
+        )
+
+        # numeric ops should not succeed
+        for op, str_rep in [
+            ("__add__", r"\+"),
+            ("__sub__", "-"),
+            ("__mul__", r"\*"),
+            ("__truediv__", "/"),
+        ]:
+            msg = f"Series cannot perform the operation {str_rep}|unsupported operand"
+            with pytest.raises(TypeError, match=msg):
+                getattr(df, op)(df)
+
+        # reduction ops should not succeed (unless specifically defined, e.g.
+        # min/max)
+        s = df["value_group"]
+        for op in ["kurt", "skew", "var", "std", "mean", "sum", "median"]:
+            msg = f"does not support reduction '{op}'"
+            with pytest.raises(TypeError, match=msg):
+                getattr(s, op)(numeric_only=False)
+
+    def test_numeric_like_ops_series(self):
+        # numpy ops
+        s = Series(Categorical([1, 2, 3, 4]))
+        with pytest.raises(TypeError, match="does not support reduction 'sum'"):
+            np.sum(s)
+
+    @pytest.mark.parametrize(
+        "op, str_rep",
+        [
+            ("__add__", r"\+"),
+            ("__sub__", "-"),
+            ("__mul__", r"\*"),
+            ("__truediv__", "/"),
+        ],
+    )
+    def test_numeric_like_ops_series_arith(self, op, str_rep):
+        # numeric ops on a Series
+        s = Series(Categorical([1, 2, 3, 4]))
+        msg = f"Series cannot perform the operation {str_rep}|unsupported operand"
+        with pytest.raises(TypeError, match=msg):
+            getattr(s, op)(2)
+
+    def test_numeric_like_ops_series_invalid(self):
+        # invalid ufunc
+        s = Series(Categorical([1, 2, 3, 4]))
+        msg = "Object with dtype category cannot perform the numpy op log"
+        with pytest.raises(TypeError, match=msg):
+            np.log(s)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_replace.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_replace.py
new file mode 100644
index 0000000000000000000000000000000000000000..3c677142846d73f7cfd08c6681ff0d7814b55bd1
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_replace.py
@@ -0,0 +1,111 @@
+import pytest
+
+import pandas as pd
+from pandas import Categorical
+import pandas._testing as tm
+
+
+@pytest.mark.parametrize(
+    "to_replace,value,expected,flip_categories",
+    [
+        # one-to-one
+        (1, 2, [2, 2, 3], False),
+        (1, 4, [4, 2, 3], False),
+        (4, 1, [1, 2, 3], False),
+        (5, 6, [1, 2, 3], False),
+        # many-to-one
+        ([1], 2, [2, 2, 3], False),
+        ([1, 2], 3, [3, 3, 3], False),
+        ([1, 2], 4, [4, 4, 3], False),
+        ((1, 2, 4), 5, [5, 5, 3], False),
+        ((5, 6), 2, [1, 2, 3], False),
+        ([1], [2], [2, 2, 3], False),
+        ([1, 4], [5, 2], [5, 2, 3], False),
+        # GH49404: overlap between to_replace and value
+        ([1, 2, 3], [2, 3, 4], [2, 3, 4], False),
+        # GH50872, GH46884: replace with null
+        (1, None, [None, 2, 3], False),
+        (1, pd.NA, [None, 2, 3], False),
+        # check_categorical sorts categories, which crashes on mixed dtypes
+        (3, "4", [1, 2, "4"], False),
+        ([1, 2, "3"], "5", ["5", "5", 3], True),
+    ],
+)
+@pytest.mark.filterwarnings(
+    "ignore:.*with CategoricalDtype is deprecated:FutureWarning"
+)
+def test_replace_categorical_series(to_replace, value, expected, flip_categories):
+    # GH 31720
+
+    ser = pd.Series([1, 2, 3], dtype="category")
+    result = ser.replace(to_replace, value)
+    expected = pd.Series(expected, dtype="category")
+    ser.replace(to_replace, value, inplace=True)
+
+    if flip_categories:
+        expected = expected.cat.set_categories(expected.cat.categories[::-1])
+
+    tm.assert_series_equal(expected, result, check_category_order=False)
+    tm.assert_series_equal(expected, ser, check_category_order=False)
+
+
+@pytest.mark.parametrize(
+    "to_replace, value, result, expected_error_msg",
+    [
+        ("b", "c", ["a", "c"], "Categorical.categories are different"),
+        ("c", "d", ["a", "b"], None),
+        # https://github.com/pandas-dev/pandas/issues/33288
+        ("a", "a", ["a", "b"], None),
+        ("b", None, ["a", None], "Categorical.categories length are different"),
+    ],
+)
+def test_replace_categorical(to_replace, value, result, expected_error_msg):
+    # GH#26988
+    cat = Categorical(["a", "b"])
+    expected = Categorical(result)
+    msg = (
+        r"The behavior of Series\.replace \(and DataFrame.replace\) "
+        "with CategoricalDtype"
+    )
+    warn = FutureWarning if expected_error_msg is not None else None
+    with tm.assert_produces_warning(warn, match=msg):
+        result = pd.Series(cat, copy=False).replace(to_replace, value)._values
+
+    tm.assert_categorical_equal(result, expected)
+    if to_replace == "b":  # the "c" test is supposed to be unchanged
+        with pytest.raises(AssertionError, match=expected_error_msg):
+            # ensure non-inplace call does not affect original
+            tm.assert_categorical_equal(cat, expected)
+
+    ser = pd.Series(cat, copy=False)
+    with tm.assert_produces_warning(warn, match=msg):
+        ser.replace(to_replace, value, inplace=True)
+    tm.assert_categorical_equal(cat, expected)
+
+
+def test_replace_categorical_ea_dtype():
+    # GH49404
+    cat = Categorical(pd.array(["a", "b"], dtype="string"))
+    msg = (
+        r"The behavior of Series\.replace \(and DataFrame.replace\) "
+        "with CategoricalDtype"
+    )
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        result = pd.Series(cat).replace(["a", "b"], ["c", pd.NA])._values
+    expected = Categorical(pd.array(["c", pd.NA], dtype="string"))
+    tm.assert_categorical_equal(result, expected)
+
+
+def test_replace_maintain_ordering():
+    # GH51016
+    dtype = pd.CategoricalDtype([0, 1, 2], ordered=True)
+    ser = pd.Series([0, 1, 2], dtype=dtype)
+    msg = (
+        r"The behavior of Series\.replace \(and DataFrame.replace\) "
+        "with CategoricalDtype"
+    )
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        result = ser.replace(0, 2)
+    expected_dtype = pd.CategoricalDtype([1, 2], ordered=True)
+    expected = pd.Series([2, 1, 2], dtype=expected_dtype)
+    tm.assert_series_equal(expected, result, check_category_order=True)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_repr.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_repr.py
new file mode 100644
index 0000000000000000000000000000000000000000..7929dfc9270342f188493cc9d51cc8ac013a43b5
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_repr.py
@@ -0,0 +1,545 @@
+import numpy as np
+import pytest
+
+from pandas._config import using_string_dtype
+
+from pandas import (
+    Categorical,
+    CategoricalDtype,
+    CategoricalIndex,
+    Index,
+    Series,
+    date_range,
+    option_context,
+    period_range,
+    timedelta_range,
+)
+
+
+class TestCategoricalReprWithFactor:
+    def test_print(self, using_infer_string):
+        factor = Categorical(["a", "b", "b", "a", "a", "c", "c", "c"], ordered=True)
+        dtype = "str" if using_infer_string else "object"
+        expected = [
+            "['a', 'b', 'b', 'a', 'a', 'c', 'c', 'c']",
+            f"Categories (3, {dtype}): ['a' < 'b' < 'c']",
+        ]
+        expected = "\n".join(expected)
+        actual = repr(factor)
+        assert actual == expected
+
+
+class TestCategoricalRepr:
+    def test_big_print(self):
+        codes = np.array([0, 1, 2, 0, 1, 2] * 100)
+        dtype = CategoricalDtype(categories=Index(["a", "b", "c"], dtype=object))
+        factor = Categorical.from_codes(codes, dtype=dtype)
+        expected = [
+            "['a', 'b', 'c', 'a', 'b', ..., 'b', 'c', 'a', 'b', 'c']",
+            "Length: 600",
+            "Categories (3, object): ['a', 'b', 'c']",
+        ]
+        expected = "\n".join(expected)
+
+        actual = repr(factor)
+
+        assert actual == expected
+
+    def test_empty_print(self):
+        factor = Categorical([], Index(["a", "b", "c"], dtype=object))
+        expected = "[], Categories (3, object): ['a', 'b', 'c']"
+        actual = repr(factor)
+        assert actual == expected
+
+        assert expected == actual
+        factor = Categorical([], Index(["a", "b", "c"], dtype=object), ordered=True)
+        expected = "[], Categories (3, object): ['a' < 'b' < 'c']"
+        actual = repr(factor)
+        assert expected == actual
+
+        factor = Categorical([], [])
+        expected = "[], Categories (0, object): []"
+        assert expected == repr(factor)
+
+    def test_print_none_width(self):
+        # GH10087
+        a = Series(Categorical([1, 2, 3, 4]))
+        exp = (
+            "0    1\n1    2\n2    3\n3    4\n"
+            "dtype: category\nCategories (4, int64): [1, 2, 3, 4]"
+        )
+
+        with option_context("display.width", None):
+            assert exp == repr(a)
+
+    @pytest.mark.skipif(
+        using_string_dtype(),
+        reason="Change once infer_string is set to True by default",
+    )
+    def test_unicode_print(self):
+        c = Categorical(["aaaaa", "bb", "cccc"] * 20)
+        expected = """\
+['aaaaa', 'bb', 'cccc', 'aaaaa', 'bb', ..., 'bb', 'cccc', 'aaaaa', 'bb', 'cccc']
+Length: 60
+Categories (3, object): ['aaaaa', 'bb', 'cccc']"""
+
+        assert repr(c) == expected
+
+        c = Categorical(["ああああ", "いいいいい", "ううううううう"] * 20)
+        expected = """\
+['ああああ', 'いいいいい', 'ううううううう', 'ああああ', 'いいいいい', ..., 'いいいいい', 'ううううううう', 'ああああ', 'いいいいい', 'ううううううう']
+Length: 60
+Categories (3, object): ['ああああ', 'いいいいい', 'ううううううう']"""  # noqa: E501
+
+        assert repr(c) == expected
+
+        # unicode option should not affect to Categorical, as it doesn't care
+        # the repr width
+        with option_context("display.unicode.east_asian_width", True):
+            c = Categorical(["ああああ", "いいいいい", "ううううううう"] * 20)
+            expected = """['ああああ', 'いいいいい', 'ううううううう', 'ああああ', 'いいいいい', ..., 'いいいいい', 'ううううううう', 'ああああ', 'いいいいい', 'ううううううう']
+Length: 60
+Categories (3, object): ['ああああ', 'いいいいい', 'ううううううう']"""  # noqa: E501
+
+            assert repr(c) == expected
+
+    def test_categorical_repr(self):
+        c = Categorical([1, 2, 3])
+        exp = """[1, 2, 3]
+Categories (3, int64): [1, 2, 3]"""
+
+        assert repr(c) == exp
+
+        c = Categorical([1, 2, 3, 1, 2, 3], categories=[1, 2, 3])
+        exp = """[1, 2, 3, 1, 2, 3]
+Categories (3, int64): [1, 2, 3]"""
+
+        assert repr(c) == exp
+
+        c = Categorical([1, 2, 3, 4, 5] * 10)
+        exp = """[1, 2, 3, 4, 5, ..., 1, 2, 3, 4, 5]
+Length: 50
+Categories (5, int64): [1, 2, 3, 4, 5]"""
+
+        assert repr(c) == exp
+
+        c = Categorical(np.arange(20, dtype=np.int64))
+        exp = """[0, 1, 2, 3, 4, ..., 15, 16, 17, 18, 19]
+Length: 20
+Categories (20, int64): [0, 1, 2, 3, ..., 16, 17, 18, 19]"""
+
+        assert repr(c) == exp
+
+    def test_categorical_repr_ordered(self):
+        c = Categorical([1, 2, 3], ordered=True)
+        exp = """[1, 2, 3]
+Categories (3, int64): [1 < 2 < 3]"""
+
+        assert repr(c) == exp
+
+        c = Categorical([1, 2, 3, 1, 2, 3], categories=[1, 2, 3], ordered=True)
+        exp = """[1, 2, 3, 1, 2, 3]
+Categories (3, int64): [1 < 2 < 3]"""
+
+        assert repr(c) == exp
+
+        c = Categorical([1, 2, 3, 4, 5] * 10, ordered=True)
+        exp = """[1, 2, 3, 4, 5, ..., 1, 2, 3, 4, 5]
+Length: 50
+Categories (5, int64): [1 < 2 < 3 < 4 < 5]"""
+
+        assert repr(c) == exp
+
+        c = Categorical(np.arange(20, dtype=np.int64), ordered=True)
+        exp = """[0, 1, 2, 3, 4, ..., 15, 16, 17, 18, 19]
+Length: 20
+Categories (20, int64): [0 < 1 < 2 < 3 ... 16 < 17 < 18 < 19]"""
+
+        assert repr(c) == exp
+
+    def test_categorical_repr_datetime(self):
+        idx = date_range("2011-01-01 09:00", freq="h", periods=5)
+        c = Categorical(idx)
+
+        exp = (
+            "[2011-01-01 09:00:00, 2011-01-01 10:00:00, 2011-01-01 11:00:00, "
+            "2011-01-01 12:00:00, 2011-01-01 13:00:00]\n"
+            "Categories (5, datetime64[ns]): [2011-01-01 09:00:00, "
+            "2011-01-01 10:00:00, 2011-01-01 11:00:00,\n"
+            "                                 2011-01-01 12:00:00, "
+            "2011-01-01 13:00:00]"
+            ""
+        )
+        assert repr(c) == exp
+
+        c = Categorical(idx.append(idx), categories=idx)
+        exp = (
+            "[2011-01-01 09:00:00, 2011-01-01 10:00:00, 2011-01-01 11:00:00, "
+            "2011-01-01 12:00:00, 2011-01-01 13:00:00, 2011-01-01 09:00:00, "
+            "2011-01-01 10:00:00, 2011-01-01 11:00:00, 2011-01-01 12:00:00, "
+            "2011-01-01 13:00:00]\n"
+            "Categories (5, datetime64[ns]): [2011-01-01 09:00:00, "
+            "2011-01-01 10:00:00, 2011-01-01 11:00:00,\n"
+            "                                 2011-01-01 12:00:00, "
+            "2011-01-01 13:00:00]"
+        )
+
+        assert repr(c) == exp
+
+        idx = date_range("2011-01-01 09:00", freq="h", periods=5, tz="US/Eastern")
+        c = Categorical(idx)
+        exp = (
+            "[2011-01-01 09:00:00-05:00, 2011-01-01 10:00:00-05:00, "
+            "2011-01-01 11:00:00-05:00, 2011-01-01 12:00:00-05:00, "
+            "2011-01-01 13:00:00-05:00]\n"
+            "Categories (5, datetime64[ns, US/Eastern]): "
+            "[2011-01-01 09:00:00-05:00, 2011-01-01 10:00:00-05:00,\n"
+            "                                             "
+            "2011-01-01 11:00:00-05:00, 2011-01-01 12:00:00-05:00,\n"
+            "                                             "
+            "2011-01-01 13:00:00-05:00]"
+        )
+
+        assert repr(c) == exp
+
+        c = Categorical(idx.append(idx), categories=idx)
+        exp = (
+            "[2011-01-01 09:00:00-05:00, 2011-01-01 10:00:00-05:00, "
+            "2011-01-01 11:00:00-05:00, 2011-01-01 12:00:00-05:00, "
+            "2011-01-01 13:00:00-05:00, 2011-01-01 09:00:00-05:00, "
+            "2011-01-01 10:00:00-05:00, 2011-01-01 11:00:00-05:00, "
+            "2011-01-01 12:00:00-05:00, 2011-01-01 13:00:00-05:00]\n"
+            "Categories (5, datetime64[ns, US/Eastern]): "
+            "[2011-01-01 09:00:00-05:00, 2011-01-01 10:00:00-05:00,\n"
+            "                                             "
+            "2011-01-01 11:00:00-05:00, 2011-01-01 12:00:00-05:00,\n"
+            "                                             "
+            "2011-01-01 13:00:00-05:00]"
+        )
+
+        assert repr(c) == exp
+
+    def test_categorical_repr_datetime_ordered(self):
+        idx = date_range("2011-01-01 09:00", freq="h", periods=5)
+        c = Categorical(idx, ordered=True)
+        exp = """[2011-01-01 09:00:00, 2011-01-01 10:00:00, 2011-01-01 11:00:00, 2011-01-01 12:00:00, 2011-01-01 13:00:00]
+Categories (5, datetime64[ns]): [2011-01-01 09:00:00 < 2011-01-01 10:00:00 < 2011-01-01 11:00:00 <
+                                 2011-01-01 12:00:00 < 2011-01-01 13:00:00]"""  # noqa: E501
+
+        assert repr(c) == exp
+
+        c = Categorical(idx.append(idx), categories=idx, ordered=True)
+        exp = """[2011-01-01 09:00:00, 2011-01-01 10:00:00, 2011-01-01 11:00:00, 2011-01-01 12:00:00, 2011-01-01 13:00:00, 2011-01-01 09:00:00, 2011-01-01 10:00:00, 2011-01-01 11:00:00, 2011-01-01 12:00:00, 2011-01-01 13:00:00]
+Categories (5, datetime64[ns]): [2011-01-01 09:00:00 < 2011-01-01 10:00:00 < 2011-01-01 11:00:00 <
+                                 2011-01-01 12:00:00 < 2011-01-01 13:00:00]"""  # noqa: E501
+
+        assert repr(c) == exp
+
+        idx = date_range("2011-01-01 09:00", freq="h", periods=5, tz="US/Eastern")
+        c = Categorical(idx, ordered=True)
+        exp = """[2011-01-01 09:00:00-05:00, 2011-01-01 10:00:00-05:00, 2011-01-01 11:00:00-05:00, 2011-01-01 12:00:00-05:00, 2011-01-01 13:00:00-05:00]
+Categories (5, datetime64[ns, US/Eastern]): [2011-01-01 09:00:00-05:00 < 2011-01-01 10:00:00-05:00 <
+                                             2011-01-01 11:00:00-05:00 < 2011-01-01 12:00:00-05:00 <
+                                             2011-01-01 13:00:00-05:00]"""  # noqa: E501
+
+        assert repr(c) == exp
+
+        c = Categorical(idx.append(idx), categories=idx, ordered=True)
+        exp = """[2011-01-01 09:00:00-05:00, 2011-01-01 10:00:00-05:00, 2011-01-01 11:00:00-05:00, 2011-01-01 12:00:00-05:00, 2011-01-01 13:00:00-05:00, 2011-01-01 09:00:00-05:00, 2011-01-01 10:00:00-05:00, 2011-01-01 11:00:00-05:00, 2011-01-01 12:00:00-05:00, 2011-01-01 13:00:00-05:00]
+Categories (5, datetime64[ns, US/Eastern]): [2011-01-01 09:00:00-05:00 < 2011-01-01 10:00:00-05:00 <
+                                             2011-01-01 11:00:00-05:00 < 2011-01-01 12:00:00-05:00 <
+                                             2011-01-01 13:00:00-05:00]"""  # noqa: E501
+
+        assert repr(c) == exp
+
+    def test_categorical_repr_int_with_nan(self):
+        c = Categorical([1, 2, np.nan])
+        c_exp = """[1, 2, NaN]\nCategories (2, int64): [1, 2]"""
+        assert repr(c) == c_exp
+
+        s = Series([1, 2, np.nan], dtype="object").astype("category")
+        s_exp = """0      1\n1      2\n2    NaN
+dtype: category
+Categories (2, int64): [1, 2]"""
+        assert repr(s) == s_exp
+
+    def test_categorical_repr_period(self):
+        idx = period_range("2011-01-01 09:00", freq="h", periods=5)
+        c = Categorical(idx)
+        exp = """[2011-01-01 09:00, 2011-01-01 10:00, 2011-01-01 11:00, 2011-01-01 12:00, 2011-01-01 13:00]
+Categories (5, period[h]): [2011-01-01 09:00, 2011-01-01 10:00, 2011-01-01 11:00, 2011-01-01 12:00,
+                            2011-01-01 13:00]"""  # noqa: E501
+
+        assert repr(c) == exp
+
+        c = Categorical(idx.append(idx), categories=idx)
+        exp = """[2011-01-01 09:00, 2011-01-01 10:00, 2011-01-01 11:00, 2011-01-01 12:00, 2011-01-01 13:00, 2011-01-01 09:00, 2011-01-01 10:00, 2011-01-01 11:00, 2011-01-01 12:00, 2011-01-01 13:00]
+Categories (5, period[h]): [2011-01-01 09:00, 2011-01-01 10:00, 2011-01-01 11:00, 2011-01-01 12:00,
+                            2011-01-01 13:00]"""  # noqa: E501
+
+        assert repr(c) == exp
+
+        idx = period_range("2011-01", freq="M", periods=5)
+        c = Categorical(idx)
+        exp = """[2011-01, 2011-02, 2011-03, 2011-04, 2011-05]
+Categories (5, period[M]): [2011-01, 2011-02, 2011-03, 2011-04, 2011-05]"""
+
+        assert repr(c) == exp
+
+        c = Categorical(idx.append(idx), categories=idx)
+        exp = """[2011-01, 2011-02, 2011-03, 2011-04, 2011-05, 2011-01, 2011-02, 2011-03, 2011-04, 2011-05]
+Categories (5, period[M]): [2011-01, 2011-02, 2011-03, 2011-04, 2011-05]"""  # noqa: E501
+
+        assert repr(c) == exp
+
+    def test_categorical_repr_period_ordered(self):
+        idx = period_range("2011-01-01 09:00", freq="h", periods=5)
+        c = Categorical(idx, ordered=True)
+        exp = """[2011-01-01 09:00, 2011-01-01 10:00, 2011-01-01 11:00, 2011-01-01 12:00, 2011-01-01 13:00]
+Categories (5, period[h]): [2011-01-01 09:00 < 2011-01-01 10:00 < 2011-01-01 11:00 < 2011-01-01 12:00 <
+                            2011-01-01 13:00]"""  # noqa: E501
+
+        assert repr(c) == exp
+
+        c = Categorical(idx.append(idx), categories=idx, ordered=True)
+        exp = """[2011-01-01 09:00, 2011-01-01 10:00, 2011-01-01 11:00, 2011-01-01 12:00, 2011-01-01 13:00, 2011-01-01 09:00, 2011-01-01 10:00, 2011-01-01 11:00, 2011-01-01 12:00, 2011-01-01 13:00]
+Categories (5, period[h]): [2011-01-01 09:00 < 2011-01-01 10:00 < 2011-01-01 11:00 < 2011-01-01 12:00 <
+                            2011-01-01 13:00]"""  # noqa: E501
+
+        assert repr(c) == exp
+
+        idx = period_range("2011-01", freq="M", periods=5)
+        c = Categorical(idx, ordered=True)
+        exp = """[2011-01, 2011-02, 2011-03, 2011-04, 2011-05]
+Categories (5, period[M]): [2011-01 < 2011-02 < 2011-03 < 2011-04 < 2011-05]"""
+
+        assert repr(c) == exp
+
+        c = Categorical(idx.append(idx), categories=idx, ordered=True)
+        exp = """[2011-01, 2011-02, 2011-03, 2011-04, 2011-05, 2011-01, 2011-02, 2011-03, 2011-04, 2011-05]
+Categories (5, period[M]): [2011-01 < 2011-02 < 2011-03 < 2011-04 < 2011-05]"""  # noqa: E501
+
+        assert repr(c) == exp
+
+    def test_categorical_repr_timedelta(self):
+        idx = timedelta_range("1 days", periods=5)
+        c = Categorical(idx)
+        exp = """[1 days, 2 days, 3 days, 4 days, 5 days]
+Categories (5, timedelta64[ns]): [1 days, 2 days, 3 days, 4 days, 5 days]"""
+
+        assert repr(c) == exp
+
+        c = Categorical(idx.append(idx), categories=idx)
+        exp = """[1 days, 2 days, 3 days, 4 days, 5 days, 1 days, 2 days, 3 days, 4 days, 5 days]
+Categories (5, timedelta64[ns]): [1 days, 2 days, 3 days, 4 days, 5 days]"""  # noqa: E501
+
+        assert repr(c) == exp
+
+        idx = timedelta_range("1 hours", periods=20)
+        c = Categorical(idx)
+        exp = """[0 days 01:00:00, 1 days 01:00:00, 2 days 01:00:00, 3 days 01:00:00, 4 days 01:00:00, ..., 15 days 01:00:00, 16 days 01:00:00, 17 days 01:00:00, 18 days 01:00:00, 19 days 01:00:00]
+Length: 20
+Categories (20, timedelta64[ns]): [0 days 01:00:00, 1 days 01:00:00, 2 days 01:00:00,
+                                   3 days 01:00:00, ..., 16 days 01:00:00, 17 days 01:00:00,
+                                   18 days 01:00:00, 19 days 01:00:00]"""  # noqa: E501
+
+        assert repr(c) == exp
+
+        c = Categorical(idx.append(idx), categories=idx)
+        exp = """[0 days 01:00:00, 1 days 01:00:00, 2 days 01:00:00, 3 days 01:00:00, 4 days 01:00:00, ..., 15 days 01:00:00, 16 days 01:00:00, 17 days 01:00:00, 18 days 01:00:00, 19 days 01:00:00]
+Length: 40
+Categories (20, timedelta64[ns]): [0 days 01:00:00, 1 days 01:00:00, 2 days 01:00:00,
+                                   3 days 01:00:00, ..., 16 days 01:00:00, 17 days 01:00:00,
+                                   18 days 01:00:00, 19 days 01:00:00]"""  # noqa: E501
+
+        assert repr(c) == exp
+
+    def test_categorical_repr_timedelta_ordered(self):
+        idx = timedelta_range("1 days", periods=5)
+        c = Categorical(idx, ordered=True)
+        exp = """[1 days, 2 days, 3 days, 4 days, 5 days]
+Categories (5, timedelta64[ns]): [1 days < 2 days < 3 days < 4 days < 5 days]"""
+
+        assert repr(c) == exp
+
+        c = Categorical(idx.append(idx), categories=idx, ordered=True)
+        exp = """[1 days, 2 days, 3 days, 4 days, 5 days, 1 days, 2 days, 3 days, 4 days, 5 days]
+Categories (5, timedelta64[ns]): [1 days < 2 days < 3 days < 4 days < 5 days]"""  # noqa: E501
+
+        assert repr(c) == exp
+
+        idx = timedelta_range("1 hours", periods=20)
+        c = Categorical(idx, ordered=True)
+        exp = """[0 days 01:00:00, 1 days 01:00:00, 2 days 01:00:00, 3 days 01:00:00, 4 days 01:00:00, ..., 15 days 01:00:00, 16 days 01:00:00, 17 days 01:00:00, 18 days 01:00:00, 19 days 01:00:00]
+Length: 20
+Categories (20, timedelta64[ns]): [0 days 01:00:00 < 1 days 01:00:00 < 2 days 01:00:00 <
+                                   3 days 01:00:00 ... 16 days 01:00:00 < 17 days 01:00:00 <
+                                   18 days 01:00:00 < 19 days 01:00:00]"""  # noqa: E501
+
+        assert repr(c) == exp
+
+        c = Categorical(idx.append(idx), categories=idx, ordered=True)
+        exp = """[0 days 01:00:00, 1 days 01:00:00, 2 days 01:00:00, 3 days 01:00:00, 4 days 01:00:00, ..., 15 days 01:00:00, 16 days 01:00:00, 17 days 01:00:00, 18 days 01:00:00, 19 days 01:00:00]
+Length: 40
+Categories (20, timedelta64[ns]): [0 days 01:00:00 < 1 days 01:00:00 < 2 days 01:00:00 <
+                                   3 days 01:00:00 ... 16 days 01:00:00 < 17 days 01:00:00 <
+                                   18 days 01:00:00 < 19 days 01:00:00]"""  # noqa: E501
+
+        assert repr(c) == exp
+
+    def test_categorical_index_repr(self):
+        idx = CategoricalIndex(Categorical([1, 2, 3]))
+        exp = """CategoricalIndex([1, 2, 3], categories=[1, 2, 3], ordered=False, dtype='category')"""  # noqa: E501
+        assert repr(idx) == exp
+
+        i = CategoricalIndex(Categorical(np.arange(10, dtype=np.int64)))
+        exp = """CategoricalIndex([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], categories=[0, 1, 2, 3, ..., 6, 7, 8, 9], ordered=False, dtype='category')"""  # noqa: E501
+        assert repr(i) == exp
+
+    def test_categorical_index_repr_ordered(self):
+        i = CategoricalIndex(Categorical([1, 2, 3], ordered=True))
+        exp = """CategoricalIndex([1, 2, 3], categories=[1, 2, 3], ordered=True, dtype='category')"""  # noqa: E501
+        assert repr(i) == exp
+
+        i = CategoricalIndex(Categorical(np.arange(10, dtype=np.int64), ordered=True))
+        exp = """CategoricalIndex([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], categories=[0, 1, 2, 3, ..., 6, 7, 8, 9], ordered=True, dtype='category')"""  # noqa: E501
+        assert repr(i) == exp
+
+    def test_categorical_index_repr_datetime(self):
+        idx = date_range("2011-01-01 09:00", freq="h", periods=5)
+        i = CategoricalIndex(Categorical(idx))
+        exp = """CategoricalIndex(['2011-01-01 09:00:00', '2011-01-01 10:00:00',
+                  '2011-01-01 11:00:00', '2011-01-01 12:00:00',
+                  '2011-01-01 13:00:00'],
+                 categories=[2011-01-01 09:00:00, 2011-01-01 10:00:00, 2011-01-01 11:00:00, 2011-01-01 12:00:00, 2011-01-01 13:00:00], ordered=False, dtype='category')"""  # noqa: E501
+
+        assert repr(i) == exp
+
+        idx = date_range("2011-01-01 09:00", freq="h", periods=5, tz="US/Eastern")
+        i = CategoricalIndex(Categorical(idx))
+        exp = """CategoricalIndex(['2011-01-01 09:00:00-05:00', '2011-01-01 10:00:00-05:00',
+                  '2011-01-01 11:00:00-05:00', '2011-01-01 12:00:00-05:00',
+                  '2011-01-01 13:00:00-05:00'],
+                 categories=[2011-01-01 09:00:00-05:00, 2011-01-01 10:00:00-05:00, 2011-01-01 11:00:00-05:00, 2011-01-01 12:00:00-05:00, 2011-01-01 13:00:00-05:00], ordered=False, dtype='category')"""  # noqa: E501
+
+        assert repr(i) == exp
+
+    def test_categorical_index_repr_datetime_ordered(self):
+        idx = date_range("2011-01-01 09:00", freq="h", periods=5)
+        i = CategoricalIndex(Categorical(idx, ordered=True))
+        exp = """CategoricalIndex(['2011-01-01 09:00:00', '2011-01-01 10:00:00',
+                  '2011-01-01 11:00:00', '2011-01-01 12:00:00',
+                  '2011-01-01 13:00:00'],
+                 categories=[2011-01-01 09:00:00, 2011-01-01 10:00:00, 2011-01-01 11:00:00, 2011-01-01 12:00:00, 2011-01-01 13:00:00], ordered=True, dtype='category')"""  # noqa: E501
+
+        assert repr(i) == exp
+
+        idx = date_range("2011-01-01 09:00", freq="h", periods=5, tz="US/Eastern")
+        i = CategoricalIndex(Categorical(idx, ordered=True))
+        exp = """CategoricalIndex(['2011-01-01 09:00:00-05:00', '2011-01-01 10:00:00-05:00',
+                  '2011-01-01 11:00:00-05:00', '2011-01-01 12:00:00-05:00',
+                  '2011-01-01 13:00:00-05:00'],
+                 categories=[2011-01-01 09:00:00-05:00, 2011-01-01 10:00:00-05:00, 2011-01-01 11:00:00-05:00, 2011-01-01 12:00:00-05:00, 2011-01-01 13:00:00-05:00], ordered=True, dtype='category')"""  # noqa: E501
+
+        assert repr(i) == exp
+
+        i = CategoricalIndex(Categorical(idx.append(idx), ordered=True))
+        exp = """CategoricalIndex(['2011-01-01 09:00:00-05:00', '2011-01-01 10:00:00-05:00',
+                  '2011-01-01 11:00:00-05:00', '2011-01-01 12:00:00-05:00',
+                  '2011-01-01 13:00:00-05:00', '2011-01-01 09:00:00-05:00',
+                  '2011-01-01 10:00:00-05:00', '2011-01-01 11:00:00-05:00',
+                  '2011-01-01 12:00:00-05:00', '2011-01-01 13:00:00-05:00'],
+                 categories=[2011-01-01 09:00:00-05:00, 2011-01-01 10:00:00-05:00, 2011-01-01 11:00:00-05:00, 2011-01-01 12:00:00-05:00, 2011-01-01 13:00:00-05:00], ordered=True, dtype='category')"""  # noqa: E501
+
+        assert repr(i) == exp
+
+    def test_categorical_index_repr_period(self):
+        # test all length
+        idx = period_range("2011-01-01 09:00", freq="h", periods=1)
+        i = CategoricalIndex(Categorical(idx))
+        exp = """CategoricalIndex(['2011-01-01 09:00'], categories=[2011-01-01 09:00], ordered=False, dtype='category')"""  # noqa: E501
+        assert repr(i) == exp
+
+        idx = period_range("2011-01-01 09:00", freq="h", periods=2)
+        i = CategoricalIndex(Categorical(idx))
+        exp = """CategoricalIndex(['2011-01-01 09:00', '2011-01-01 10:00'], categories=[2011-01-01 09:00, 2011-01-01 10:00], ordered=False, dtype='category')"""  # noqa: E501
+        assert repr(i) == exp
+
+        idx = period_range("2011-01-01 09:00", freq="h", periods=3)
+        i = CategoricalIndex(Categorical(idx))
+        exp = """CategoricalIndex(['2011-01-01 09:00', '2011-01-01 10:00', '2011-01-01 11:00'], categories=[2011-01-01 09:00, 2011-01-01 10:00, 2011-01-01 11:00], ordered=False, dtype='category')"""  # noqa: E501
+        assert repr(i) == exp
+
+        idx = period_range("2011-01-01 09:00", freq="h", periods=5)
+        i = CategoricalIndex(Categorical(idx))
+        exp = """CategoricalIndex(['2011-01-01 09:00', '2011-01-01 10:00', '2011-01-01 11:00',
+                  '2011-01-01 12:00', '2011-01-01 13:00'],
+                 categories=[2011-01-01 09:00, 2011-01-01 10:00, 2011-01-01 11:00, 2011-01-01 12:00, 2011-01-01 13:00], ordered=False, dtype='category')"""  # noqa: E501
+
+        assert repr(i) == exp
+
+        i = CategoricalIndex(Categorical(idx.append(idx)))
+        exp = """CategoricalIndex(['2011-01-01 09:00', '2011-01-01 10:00', '2011-01-01 11:00',
+                  '2011-01-01 12:00', '2011-01-01 13:00', '2011-01-01 09:00',
+                  '2011-01-01 10:00', '2011-01-01 11:00', '2011-01-01 12:00',
+                  '2011-01-01 13:00'],
+                 categories=[2011-01-01 09:00, 2011-01-01 10:00, 2011-01-01 11:00, 2011-01-01 12:00, 2011-01-01 13:00], ordered=False, dtype='category')"""  # noqa: E501
+
+        assert repr(i) == exp
+
+        idx = period_range("2011-01", freq="M", periods=5)
+        i = CategoricalIndex(Categorical(idx))
+        exp = """CategoricalIndex(['2011-01', '2011-02', '2011-03', '2011-04', '2011-05'], categories=[2011-01, 2011-02, 2011-03, 2011-04, 2011-05], ordered=False, dtype='category')"""  # noqa: E501
+        assert repr(i) == exp
+
+    def test_categorical_index_repr_period_ordered(self):
+        idx = period_range("2011-01-01 09:00", freq="h", periods=5)
+        i = CategoricalIndex(Categorical(idx, ordered=True))
+        exp = """CategoricalIndex(['2011-01-01 09:00', '2011-01-01 10:00', '2011-01-01 11:00',
+                  '2011-01-01 12:00', '2011-01-01 13:00'],
+                 categories=[2011-01-01 09:00, 2011-01-01 10:00, 2011-01-01 11:00, 2011-01-01 12:00, 2011-01-01 13:00], ordered=True, dtype='category')"""  # noqa: E501
+
+        assert repr(i) == exp
+
+        idx = period_range("2011-01", freq="M", periods=5)
+        i = CategoricalIndex(Categorical(idx, ordered=True))
+        exp = """CategoricalIndex(['2011-01', '2011-02', '2011-03', '2011-04', '2011-05'], categories=[2011-01, 2011-02, 2011-03, 2011-04, 2011-05], ordered=True, dtype='category')"""  # noqa: E501
+        assert repr(i) == exp
+
+    def test_categorical_index_repr_timedelta(self):
+        idx = timedelta_range("1 days", periods=5)
+        i = CategoricalIndex(Categorical(idx))
+        exp = """CategoricalIndex(['1 days', '2 days', '3 days', '4 days', '5 days'], categories=[1 days, 2 days, 3 days, 4 days, 5 days], ordered=False, dtype='category')"""  # noqa: E501
+        assert repr(i) == exp
+
+        idx = timedelta_range("1 hours", periods=10)
+        i = CategoricalIndex(Categorical(idx))
+        exp = """CategoricalIndex(['0 days 01:00:00', '1 days 01:00:00', '2 days 01:00:00',
+                  '3 days 01:00:00', '4 days 01:00:00', '5 days 01:00:00',
+                  '6 days 01:00:00', '7 days 01:00:00', '8 days 01:00:00',
+                  '9 days 01:00:00'],
+                 categories=[0 days 01:00:00, 1 days 01:00:00, 2 days 01:00:00, 3 days 01:00:00, ..., 6 days 01:00:00, 7 days 01:00:00, 8 days 01:00:00, 9 days 01:00:00], ordered=False, dtype='category')"""  # noqa: E501
+
+        assert repr(i) == exp
+
+    def test_categorical_index_repr_timedelta_ordered(self):
+        idx = timedelta_range("1 days", periods=5)
+        i = CategoricalIndex(Categorical(idx, ordered=True))
+        exp = """CategoricalIndex(['1 days', '2 days', '3 days', '4 days', '5 days'], categories=[1 days, 2 days, 3 days, 4 days, 5 days], ordered=True, dtype='category')"""  # noqa: E501
+        assert repr(i) == exp
+
+        idx = timedelta_range("1 hours", periods=10)
+        i = CategoricalIndex(Categorical(idx, ordered=True))
+        exp = """CategoricalIndex(['0 days 01:00:00', '1 days 01:00:00', '2 days 01:00:00',
+                  '3 days 01:00:00', '4 days 01:00:00', '5 days 01:00:00',
+                  '6 days 01:00:00', '7 days 01:00:00', '8 days 01:00:00',
+                  '9 days 01:00:00'],
+                 categories=[0 days 01:00:00, 1 days 01:00:00, 2 days 01:00:00, 3 days 01:00:00, ..., 6 days 01:00:00, 7 days 01:00:00, 8 days 01:00:00, 9 days 01:00:00], ordered=True, dtype='category')"""  # noqa: E501
+
+        assert repr(i) == exp
+
+    def test_categorical_str_repr(self):
+        # GH 33676
+        result = repr(Categorical([1, "2", 3, 4]))
+        expected = "[1, '2', 3, 4]\nCategories (4, object): [1, 3, 4, '2']"
+        assert result == expected
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_sorting.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_sorting.py
new file mode 100644
index 0000000000000000000000000000000000000000..ae527065b3fb970263609881d217f5c6d2761231
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_sorting.py
@@ -0,0 +1,128 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    Categorical,
+    Index,
+)
+import pandas._testing as tm
+
+
+class TestCategoricalSort:
+    def test_argsort(self):
+        c = Categorical([5, 3, 1, 4, 2], ordered=True)
+
+        expected = np.array([2, 4, 1, 3, 0])
+        tm.assert_numpy_array_equal(
+            c.argsort(ascending=True), expected, check_dtype=False
+        )
+
+        expected = expected[::-1]
+        tm.assert_numpy_array_equal(
+            c.argsort(ascending=False), expected, check_dtype=False
+        )
+
+    def test_numpy_argsort(self):
+        c = Categorical([5, 3, 1, 4, 2], ordered=True)
+
+        expected = np.array([2, 4, 1, 3, 0])
+        tm.assert_numpy_array_equal(np.argsort(c), expected, check_dtype=False)
+
+        tm.assert_numpy_array_equal(
+            np.argsort(c, kind="mergesort"), expected, check_dtype=False
+        )
+
+        msg = "the 'axis' parameter is not supported"
+        with pytest.raises(ValueError, match=msg):
+            np.argsort(c, axis=0)
+
+        msg = "the 'order' parameter is not supported"
+        with pytest.raises(ValueError, match=msg):
+            np.argsort(c, order="C")
+
+    def test_sort_values(self):
+        # unordered cats are sortable
+        cat = Categorical(["a", "b", "b", "a"], ordered=False)
+        cat.sort_values()
+
+        cat = Categorical(["a", "c", "b", "d"], ordered=True)
+
+        # sort_values
+        res = cat.sort_values()
+        exp = np.array(["a", "b", "c", "d"], dtype=object)
+        tm.assert_numpy_array_equal(res.__array__(), exp)
+        tm.assert_index_equal(res.categories, cat.categories)
+
+        cat = Categorical(
+            ["a", "c", "b", "d"], categories=["a", "b", "c", "d"], ordered=True
+        )
+        res = cat.sort_values()
+        exp = np.array(["a", "b", "c", "d"], dtype=object)
+        tm.assert_numpy_array_equal(res.__array__(), exp)
+        tm.assert_index_equal(res.categories, cat.categories)
+
+        res = cat.sort_values(ascending=False)
+        exp = np.array(["d", "c", "b", "a"], dtype=object)
+        tm.assert_numpy_array_equal(res.__array__(), exp)
+        tm.assert_index_equal(res.categories, cat.categories)
+
+        # sort (inplace order)
+        cat1 = cat.copy()
+        orig_codes = cat1._codes
+        cat1.sort_values(inplace=True)
+        assert cat1._codes is orig_codes
+        exp = np.array(["a", "b", "c", "d"], dtype=object)
+        tm.assert_numpy_array_equal(cat1.__array__(), exp)
+        tm.assert_index_equal(res.categories, cat.categories)
+
+        # reverse
+        cat = Categorical(["a", "c", "c", "b", "d"], ordered=True)
+        res = cat.sort_values(ascending=False)
+        exp_val = np.array(["d", "c", "c", "b", "a"], dtype=object)
+        exp_categories = Index(["a", "b", "c", "d"])
+        tm.assert_numpy_array_equal(res.__array__(), exp_val)
+        tm.assert_index_equal(res.categories, exp_categories)
+
+    def test_sort_values_na_position(self):
+        # see gh-12882
+        cat = Categorical([5, 2, np.nan, 2, np.nan], ordered=True)
+        exp_categories = Index([2, 5])
+
+        exp = np.array([2.0, 2.0, 5.0, np.nan, np.nan])
+        res = cat.sort_values()  # default arguments
+        tm.assert_numpy_array_equal(res.__array__(), exp)
+        tm.assert_index_equal(res.categories, exp_categories)
+
+        exp = np.array([np.nan, np.nan, 2.0, 2.0, 5.0])
+        res = cat.sort_values(ascending=True, na_position="first")
+        tm.assert_numpy_array_equal(res.__array__(), exp)
+        tm.assert_index_equal(res.categories, exp_categories)
+
+        exp = np.array([np.nan, np.nan, 5.0, 2.0, 2.0])
+        res = cat.sort_values(ascending=False, na_position="first")
+        tm.assert_numpy_array_equal(res.__array__(), exp)
+        tm.assert_index_equal(res.categories, exp_categories)
+
+        exp = np.array([2.0, 2.0, 5.0, np.nan, np.nan])
+        res = cat.sort_values(ascending=True, na_position="last")
+        tm.assert_numpy_array_equal(res.__array__(), exp)
+        tm.assert_index_equal(res.categories, exp_categories)
+
+        exp = np.array([5.0, 2.0, 2.0, np.nan, np.nan])
+        res = cat.sort_values(ascending=False, na_position="last")
+        tm.assert_numpy_array_equal(res.__array__(), exp)
+        tm.assert_index_equal(res.categories, exp_categories)
+
+        cat = Categorical(["a", "c", "b", "d", np.nan], ordered=True)
+        res = cat.sort_values(ascending=False, na_position="last")
+        exp_val = np.array(["d", "c", "b", "a", np.nan], dtype=object)
+        exp_categories = Index(["a", "b", "c", "d"])
+        tm.assert_numpy_array_equal(res.__array__(), exp_val)
+        tm.assert_index_equal(res.categories, exp_categories)
+
+        cat = Categorical(["a", "c", "b", "d", np.nan], ordered=True)
+        res = cat.sort_values(ascending=False, na_position="first")
+        exp_val = np.array([np.nan, "d", "c", "b", "a"], dtype=object)
+        exp_categories = Index(["a", "b", "c", "d"])
+        tm.assert_numpy_array_equal(res.__array__(), exp_val)
+        tm.assert_index_equal(res.categories, exp_categories)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_subclass.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_subclass.py
new file mode 100644
index 0000000000000000000000000000000000000000..5b0c0a44e655d5dd943f95415336204aa12f0b67
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_subclass.py
@@ -0,0 +1,26 @@
+from pandas import Categorical
+import pandas._testing as tm
+
+
+class SubclassedCategorical(Categorical):
+    pass
+
+
+class TestCategoricalSubclassing:
+    def test_constructor(self):
+        sc = SubclassedCategorical(["a", "b", "c"])
+        assert isinstance(sc, SubclassedCategorical)
+        tm.assert_categorical_equal(sc, Categorical(["a", "b", "c"]))
+
+    def test_from_codes(self):
+        sc = SubclassedCategorical.from_codes([1, 0, 2], ["a", "b", "c"])
+        assert isinstance(sc, SubclassedCategorical)
+        exp = Categorical.from_codes([1, 0, 2], ["a", "b", "c"])
+        tm.assert_categorical_equal(sc, exp)
+
+    def test_map(self):
+        sc = SubclassedCategorical(["a", "b", "c"])
+        res = sc.map(lambda x: x.upper(), na_action=None)
+        assert isinstance(res, SubclassedCategorical)
+        exp = Categorical(["A", "B", "C"])
+        tm.assert_categorical_equal(res, exp)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_take.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_take.py
new file mode 100644
index 0000000000000000000000000000000000000000..373f1b30a13c2daff23e14a3e0640e7a716cceb3
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_take.py
@@ -0,0 +1,89 @@
+import numpy as np
+import pytest
+
+from pandas import Categorical
+import pandas._testing as tm
+
+
+@pytest.fixture(params=[True, False])
+def allow_fill(request):
+    """Boolean 'allow_fill' parameter for Categorical.take"""
+    return request.param
+
+
+class TestTake:
+    # https://github.com/pandas-dev/pandas/issues/20664
+
+    def test_take_default_allow_fill(self):
+        cat = Categorical(["a", "b"])
+        with tm.assert_produces_warning(None):
+            result = cat.take([0, -1])
+
+        assert result.equals(cat)
+
+    def test_take_positive_no_warning(self):
+        cat = Categorical(["a", "b"])
+        with tm.assert_produces_warning(None):
+            cat.take([0, 0])
+
+    def test_take_bounds(self, allow_fill):
+        # https://github.com/pandas-dev/pandas/issues/20664
+        cat = Categorical(["a", "b", "a"])
+        if allow_fill:
+            msg = "indices are out-of-bounds"
+        else:
+            msg = "index 4 is out of bounds for( axis 0 with)? size 3"
+        with pytest.raises(IndexError, match=msg):
+            cat.take([4, 5], allow_fill=allow_fill)
+
+    def test_take_empty(self, allow_fill):
+        # https://github.com/pandas-dev/pandas/issues/20664
+        cat = Categorical([], categories=["a", "b"])
+        if allow_fill:
+            msg = "indices are out-of-bounds"
+        else:
+            msg = "cannot do a non-empty take from an empty axes"
+        with pytest.raises(IndexError, match=msg):
+            cat.take([0], allow_fill=allow_fill)
+
+    def test_positional_take(self, ordered):
+        cat = Categorical(["a", "a", "b", "b"], categories=["b", "a"], ordered=ordered)
+        result = cat.take([0, 1, 2], allow_fill=False)
+        expected = Categorical(
+            ["a", "a", "b"], categories=cat.categories, ordered=ordered
+        )
+        tm.assert_categorical_equal(result, expected)
+
+    def test_positional_take_unobserved(self, ordered):
+        cat = Categorical(["a", "b"], categories=["a", "b", "c"], ordered=ordered)
+        result = cat.take([1, 0], allow_fill=False)
+        expected = Categorical(["b", "a"], categories=cat.categories, ordered=ordered)
+        tm.assert_categorical_equal(result, expected)
+
+    def test_take_allow_fill(self):
+        # https://github.com/pandas-dev/pandas/issues/23296
+        cat = Categorical(["a", "a", "b"])
+        result = cat.take([0, -1, -1], allow_fill=True)
+        expected = Categorical(["a", np.nan, np.nan], categories=["a", "b"])
+        tm.assert_categorical_equal(result, expected)
+
+    def test_take_fill_with_negative_one(self):
+        # -1 was a category
+        cat = Categorical([-1, 0, 1])
+        result = cat.take([0, -1, 1], allow_fill=True, fill_value=-1)
+        expected = Categorical([-1, -1, 0], categories=[-1, 0, 1])
+        tm.assert_categorical_equal(result, expected)
+
+    def test_take_fill_value(self):
+        # https://github.com/pandas-dev/pandas/issues/23296
+        cat = Categorical(["a", "b", "c"])
+        result = cat.take([0, 1, -1], fill_value="a", allow_fill=True)
+        expected = Categorical(["a", "b", "a"], categories=["a", "b", "c"])
+        tm.assert_categorical_equal(result, expected)
+
+    def test_take_fill_value_new_raises(self):
+        # https://github.com/pandas-dev/pandas/issues/23296
+        cat = Categorical(["a", "b", "c"])
+        xpr = r"Cannot setitem on a Categorical with a new category \(d\)"
+        with pytest.raises(TypeError, match=xpr):
+            cat.take([0, 1, -1], fill_value="d", allow_fill=True)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_warnings.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_warnings.py
new file mode 100644
index 0000000000000000000000000000000000000000..68c59706a6c3bf93908108c337b51c8da187cbb4
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_warnings.py
@@ -0,0 +1,19 @@
+import pytest
+
+import pandas._testing as tm
+
+
+class TestCategoricalWarnings:
+    def test_tab_complete_warning(self, ip):
+        # https://github.com/pandas-dev/pandas/issues/16409
+        pytest.importorskip("IPython", minversion="6.0.0")
+        from IPython.core.completer import provisionalcompleter
+
+        code = "import pandas as pd; c = pd.Categorical([])"
+        ip.run_cell(code)
+
+        # GH 31324 newer jedi version raises Deprecation warning;
+        #  appears resolved 2021-02-02
+        with tm.assert_produces_warning(None, raise_on_extra_warnings=False):
+            with provisionalcompleter("ignore"):
+                list(ip.Completer.completions("c.", 1))
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/datetimes/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/datetimes/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/datetimes/test_constructors.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/datetimes/test_constructors.py
new file mode 100644
index 0000000000000000000000000000000000000000..3652b5fec46bbe7a519dd2c3a196ac87bd74784f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/datetimes/test_constructors.py
@@ -0,0 +1,284 @@
+import numpy as np
+import pytest
+
+from pandas._libs import iNaT
+
+from pandas.core.dtypes.dtypes import DatetimeTZDtype
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.core.arrays import DatetimeArray
+
+
+class TestDatetimeArrayConstructor:
+    def test_from_sequence_invalid_type(self):
+        mi = pd.MultiIndex.from_product([np.arange(5), np.arange(5)])
+        with pytest.raises(TypeError, match="Cannot create a DatetimeArray"):
+            DatetimeArray._from_sequence(mi, dtype="M8[ns]")
+
+    def test_only_1dim_accepted(self):
+        arr = np.array([0, 1, 2, 3], dtype="M8[h]").astype("M8[ns]")
+
+        depr_msg = "DatetimeArray.__init__ is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=depr_msg):
+            with pytest.raises(ValueError, match="Only 1-dimensional"):
+                # 3-dim, we allow 2D to sneak in for ops purposes GH#29853
+                DatetimeArray(arr.reshape(2, 2, 1))
+
+        with tm.assert_produces_warning(FutureWarning, match=depr_msg):
+            with pytest.raises(ValueError, match="Only 1-dimensional"):
+                # 0-dim
+                DatetimeArray(arr[[0]].squeeze())
+
+    def test_freq_validation(self):
+        # GH#24623 check that invalid instances cannot be created with the
+        #  public constructor
+        arr = np.arange(5, dtype=np.int64) * 3600 * 10**9
+
+        msg = (
+            "Inferred frequency h from passed values does not "
+            "conform to passed frequency W-SUN"
+        )
+        depr_msg = "DatetimeArray.__init__ is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=depr_msg):
+            with pytest.raises(ValueError, match=msg):
+                DatetimeArray(arr, freq="W")
+
+    @pytest.mark.parametrize(
+        "meth",
+        [
+            DatetimeArray._from_sequence,
+            pd.to_datetime,
+            pd.DatetimeIndex,
+        ],
+    )
+    def test_mixing_naive_tzaware_raises(self, meth):
+        # GH#24569
+        arr = np.array([pd.Timestamp("2000"), pd.Timestamp("2000", tz="CET")])
+
+        msg = (
+            "Cannot mix tz-aware with tz-naive values|"
+            "Tz-aware datetime.datetime cannot be converted "
+            "to datetime64 unless utc=True"
+        )
+
+        for obj in [arr, arr[::-1]]:
+            # check that we raise regardless of whether naive is found
+            #  before aware or vice-versa
+            with pytest.raises(ValueError, match=msg):
+                meth(obj)
+
+    def test_from_pandas_array(self):
+        arr = pd.array(np.arange(5, dtype=np.int64)) * 3600 * 10**9
+
+        result = DatetimeArray._from_sequence(arr, dtype="M8[ns]")._with_freq("infer")
+
+        expected = pd.date_range("1970-01-01", periods=5, freq="h")._data
+        tm.assert_datetime_array_equal(result, expected)
+
+    def test_mismatched_timezone_raises(self):
+        depr_msg = "DatetimeArray.__init__ is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=depr_msg):
+            arr = DatetimeArray(
+                np.array(["2000-01-01T06:00:00"], dtype="M8[ns]"),
+                dtype=DatetimeTZDtype(tz="US/Central"),
+            )
+        dtype = DatetimeTZDtype(tz="US/Eastern")
+        msg = r"dtype=datetime64\[ns.*\] does not match data dtype datetime64\[ns.*\]"
+        with tm.assert_produces_warning(FutureWarning, match=depr_msg):
+            with pytest.raises(TypeError, match=msg):
+                DatetimeArray(arr, dtype=dtype)
+
+        # also with mismatched tzawareness
+        with tm.assert_produces_warning(FutureWarning, match=depr_msg):
+            with pytest.raises(TypeError, match=msg):
+                DatetimeArray(arr, dtype=np.dtype("M8[ns]"))
+        with tm.assert_produces_warning(FutureWarning, match=depr_msg):
+            with pytest.raises(TypeError, match=msg):
+                DatetimeArray(arr.tz_localize(None), dtype=arr.dtype)
+
+    def test_non_array_raises(self):
+        depr_msg = "DatetimeArray.__init__ is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=depr_msg):
+            with pytest.raises(ValueError, match="list"):
+                DatetimeArray([1, 2, 3])
+
+    def test_bool_dtype_raises(self):
+        arr = np.array([1, 2, 3], dtype="bool")
+
+        depr_msg = "DatetimeArray.__init__ is deprecated"
+        msg = "Unexpected value for 'dtype': 'bool'. Must be"
+        with tm.assert_produces_warning(FutureWarning, match=depr_msg):
+            with pytest.raises(ValueError, match=msg):
+                DatetimeArray(arr)
+
+        msg = r"dtype bool cannot be converted to datetime64\[ns\]"
+        with pytest.raises(TypeError, match=msg):
+            DatetimeArray._from_sequence(arr, dtype="M8[ns]")
+
+        with pytest.raises(TypeError, match=msg):
+            pd.DatetimeIndex(arr)
+
+        with pytest.raises(TypeError, match=msg):
+            pd.to_datetime(arr)
+
+    def test_incorrect_dtype_raises(self):
+        depr_msg = "DatetimeArray.__init__ is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=depr_msg):
+            with pytest.raises(ValueError, match="Unexpected value for 'dtype'."):
+                DatetimeArray(np.array([1, 2, 3], dtype="i8"), dtype="category")
+
+        with tm.assert_produces_warning(FutureWarning, match=depr_msg):
+            with pytest.raises(ValueError, match="Unexpected value for 'dtype'."):
+                DatetimeArray(np.array([1, 2, 3], dtype="i8"), dtype="m8[s]")
+
+        with tm.assert_produces_warning(FutureWarning, match=depr_msg):
+            with pytest.raises(ValueError, match="Unexpected value for 'dtype'."):
+                DatetimeArray(np.array([1, 2, 3], dtype="i8"), dtype="M8[D]")
+
+    def test_mismatched_values_dtype_units(self):
+        arr = np.array([1, 2, 3], dtype="M8[s]")
+        dtype = np.dtype("M8[ns]")
+        msg = "Values resolution does not match dtype."
+        depr_msg = "DatetimeArray.__init__ is deprecated"
+
+        with tm.assert_produces_warning(FutureWarning, match=depr_msg):
+            with pytest.raises(ValueError, match=msg):
+                DatetimeArray(arr, dtype=dtype)
+
+        dtype2 = DatetimeTZDtype(tz="UTC", unit="ns")
+        with tm.assert_produces_warning(FutureWarning, match=depr_msg):
+            with pytest.raises(ValueError, match=msg):
+                DatetimeArray(arr, dtype=dtype2)
+
+    def test_freq_infer_raises(self):
+        depr_msg = "DatetimeArray.__init__ is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=depr_msg):
+            with pytest.raises(ValueError, match="Frequency inference"):
+                DatetimeArray(np.array([1, 2, 3], dtype="i8"), freq="infer")
+
+    def test_copy(self):
+        data = np.array([1, 2, 3], dtype="M8[ns]")
+        arr = DatetimeArray._from_sequence(data, copy=False)
+        assert arr._ndarray is data
+
+        arr = DatetimeArray._from_sequence(data, copy=True)
+        assert arr._ndarray is not data
+
+    @pytest.mark.parametrize("unit", ["s", "ms", "us", "ns"])
+    def test_numpy_datetime_unit(self, unit):
+        data = np.array([1, 2, 3], dtype=f"M8[{unit}]")
+        arr = DatetimeArray._from_sequence(data)
+        assert arr.unit == unit
+        assert arr[0].unit == unit
+
+
+class TestSequenceToDT64NS:
+    def test_tz_dtype_mismatch_raises(self):
+        arr = DatetimeArray._from_sequence(
+            ["2000"], dtype=DatetimeTZDtype(tz="US/Central")
+        )
+        with pytest.raises(TypeError, match="data is already tz-aware"):
+            DatetimeArray._from_sequence(arr, dtype=DatetimeTZDtype(tz="UTC"))
+
+    def test_tz_dtype_matches(self):
+        dtype = DatetimeTZDtype(tz="US/Central")
+        arr = DatetimeArray._from_sequence(["2000"], dtype=dtype)
+        result = DatetimeArray._from_sequence(arr, dtype=dtype)
+        tm.assert_equal(arr, result)
+
+    @pytest.mark.parametrize("order", ["F", "C"])
+    def test_2d(self, order):
+        dti = pd.date_range("2016-01-01", periods=6, tz="US/Pacific")
+        arr = np.array(dti, dtype=object).reshape(3, 2)
+        if order == "F":
+            arr = arr.T
+
+        res = DatetimeArray._from_sequence(arr, dtype=dti.dtype)
+        expected = DatetimeArray._from_sequence(arr.ravel(), dtype=dti.dtype).reshape(
+            arr.shape
+        )
+        tm.assert_datetime_array_equal(res, expected)
+
+
+# ----------------------------------------------------------------------------
+# Arrow interaction
+
+
+EXTREME_VALUES = [0, 123456789, None, iNaT, 2**63 - 1, -(2**63) + 1]
+FINE_TO_COARSE_SAFE = [123_000_000_000, None, -123_000_000_000]
+COARSE_TO_FINE_SAFE = [123, None, -123]
+
+
+@pytest.mark.parametrize(
+    ("pa_unit", "pd_unit", "pa_tz", "pd_tz", "data"),
+    [
+        ("s", "s", "UTC", "UTC", EXTREME_VALUES),
+        ("ms", "ms", "UTC", "Europe/Berlin", EXTREME_VALUES),
+        ("us", "us", "US/Eastern", "UTC", EXTREME_VALUES),
+        ("ns", "ns", "US/Central", "Asia/Kolkata", EXTREME_VALUES),
+        ("ns", "s", "UTC", "UTC", FINE_TO_COARSE_SAFE),
+        ("us", "ms", "UTC", "Europe/Berlin", FINE_TO_COARSE_SAFE),
+        ("ms", "us", "US/Eastern", "UTC", COARSE_TO_FINE_SAFE),
+        ("s", "ns", "US/Central", "Asia/Kolkata", COARSE_TO_FINE_SAFE),
+    ],
+)
+def test_from_arrow_with_different_units_and_timezones_with(
+    pa_unit, pd_unit, pa_tz, pd_tz, data
+):
+    pa = pytest.importorskip("pyarrow")
+
+    pa_type = pa.timestamp(pa_unit, tz=pa_tz)
+    arr = pa.array(data, type=pa_type)
+    dtype = DatetimeTZDtype(unit=pd_unit, tz=pd_tz)
+
+    result = dtype.__from_arrow__(arr)
+    expected = DatetimeArray._from_sequence(data, dtype=f"M8[{pa_unit}, UTC]").astype(
+        dtype, copy=False
+    )
+    tm.assert_extension_array_equal(result, expected)
+
+    result = dtype.__from_arrow__(pa.chunked_array([arr]))
+    tm.assert_extension_array_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    ("unit", "tz"),
+    [
+        ("s", "UTC"),
+        ("ms", "Europe/Berlin"),
+        ("us", "US/Eastern"),
+        ("ns", "Asia/Kolkata"),
+        ("ns", "UTC"),
+    ],
+)
+def test_from_arrow_from_empty(unit, tz):
+    pa = pytest.importorskip("pyarrow")
+
+    data = []
+    arr = pa.array(data)
+    dtype = DatetimeTZDtype(unit=unit, tz=tz)
+
+    result = dtype.__from_arrow__(arr)
+    expected = DatetimeArray._from_sequence(np.array(data, dtype=f"datetime64[{unit}]"))
+    expected = expected.tz_localize(tz=tz)
+    tm.assert_extension_array_equal(result, expected)
+
+    result = dtype.__from_arrow__(pa.chunked_array([arr]))
+    tm.assert_extension_array_equal(result, expected)
+
+
+def test_from_arrow_from_integers():
+    pa = pytest.importorskip("pyarrow")
+
+    data = [0, 123456789, None, 2**63 - 1, iNaT, -123456789]
+    arr = pa.array(data)
+    dtype = DatetimeTZDtype(unit="ns", tz="UTC")
+
+    result = dtype.__from_arrow__(arr)
+    expected = DatetimeArray._from_sequence(np.array(data, dtype="datetime64[ns]"))
+    expected = expected.tz_localize("UTC")
+    tm.assert_extension_array_equal(result, expected)
+
+    result = dtype.__from_arrow__(pa.chunked_array([arr]))
+    tm.assert_extension_array_equal(result, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/datetimes/test_cumulative.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/datetimes/test_cumulative.py
new file mode 100644
index 0000000000000000000000000000000000000000..e9d2dfdd0048a42a3f23e41be1d45a89aae11d23
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/datetimes/test_cumulative.py
@@ -0,0 +1,44 @@
+import pytest
+
+import pandas._testing as tm
+from pandas.core.arrays import DatetimeArray
+
+
+class TestAccumulator:
+    def test_accumulators_freq(self):
+        # GH#50297
+        arr = DatetimeArray._from_sequence(
+            [
+                "2000-01-01",
+                "2000-01-02",
+                "2000-01-03",
+            ],
+            dtype="M8[ns]",
+        )._with_freq("infer")
+        result = arr._accumulate("cummin")
+        expected = DatetimeArray._from_sequence(["2000-01-01"] * 3, dtype="M8[ns]")
+        tm.assert_datetime_array_equal(result, expected)
+
+        result = arr._accumulate("cummax")
+        expected = DatetimeArray._from_sequence(
+            [
+                "2000-01-01",
+                "2000-01-02",
+                "2000-01-03",
+            ],
+            dtype="M8[ns]",
+        )
+        tm.assert_datetime_array_equal(result, expected)
+
+    @pytest.mark.parametrize("func", ["cumsum", "cumprod"])
+    def test_accumulators_disallowed(self, func):
+        # GH#50297
+        arr = DatetimeArray._from_sequence(
+            [
+                "2000-01-01",
+                "2000-01-02",
+            ],
+            dtype="M8[ns]",
+        )._with_freq("infer")
+        with pytest.raises(TypeError, match=f"Accumulation {func}"):
+            arr._accumulate(func)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/datetimes/test_reductions.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/datetimes/test_reductions.py
new file mode 100644
index 0000000000000000000000000000000000000000..a941546b13a567b705f61a3a667119cd55a2f0e4
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/datetimes/test_reductions.py
@@ -0,0 +1,183 @@
+import numpy as np
+import pytest
+
+from pandas.core.dtypes.dtypes import DatetimeTZDtype
+
+import pandas as pd
+from pandas import NaT
+import pandas._testing as tm
+from pandas.core.arrays import DatetimeArray
+
+
+class TestReductions:
+    @pytest.fixture(params=["s", "ms", "us", "ns"])
+    def unit(self, request):
+        return request.param
+
+    @pytest.fixture
+    def arr1d(self, tz_naive_fixture):
+        """Fixture returning DatetimeArray with parametrized timezones"""
+        tz = tz_naive_fixture
+        dtype = DatetimeTZDtype(tz=tz) if tz is not None else np.dtype("M8[ns]")
+        arr = DatetimeArray._from_sequence(
+            [
+                "2000-01-03",
+                "2000-01-03",
+                "NaT",
+                "2000-01-02",
+                "2000-01-05",
+                "2000-01-04",
+            ],
+            dtype=dtype,
+        )
+        return arr
+
+    def test_min_max(self, arr1d, unit):
+        arr = arr1d
+        arr = arr.as_unit(unit)
+        tz = arr.tz
+
+        result = arr.min()
+        expected = pd.Timestamp("2000-01-02", tz=tz).as_unit(unit)
+        assert result == expected
+        assert result.unit == expected.unit
+
+        result = arr.max()
+        expected = pd.Timestamp("2000-01-05", tz=tz).as_unit(unit)
+        assert result == expected
+        assert result.unit == expected.unit
+
+        result = arr.min(skipna=False)
+        assert result is NaT
+
+        result = arr.max(skipna=False)
+        assert result is NaT
+
+    @pytest.mark.parametrize("tz", [None, "US/Central"])
+    @pytest.mark.parametrize("skipna", [True, False])
+    def test_min_max_empty(self, skipna, tz):
+        dtype = DatetimeTZDtype(tz=tz) if tz is not None else np.dtype("M8[ns]")
+        arr = DatetimeArray._from_sequence([], dtype=dtype)
+        result = arr.min(skipna=skipna)
+        assert result is NaT
+
+        result = arr.max(skipna=skipna)
+        assert result is NaT
+
+    @pytest.mark.parametrize("tz", [None, "US/Central"])
+    @pytest.mark.parametrize("skipna", [True, False])
+    def test_median_empty(self, skipna, tz):
+        dtype = DatetimeTZDtype(tz=tz) if tz is not None else np.dtype("M8[ns]")
+        arr = DatetimeArray._from_sequence([], dtype=dtype)
+        result = arr.median(skipna=skipna)
+        assert result is NaT
+
+        arr = arr.reshape(0, 3)
+        result = arr.median(axis=0, skipna=skipna)
+        expected = type(arr)._from_sequence([NaT, NaT, NaT], dtype=arr.dtype)
+        tm.assert_equal(result, expected)
+
+        result = arr.median(axis=1, skipna=skipna)
+        expected = type(arr)._from_sequence([], dtype=arr.dtype)
+        tm.assert_equal(result, expected)
+
+    def test_median(self, arr1d):
+        arr = arr1d
+
+        result = arr.median()
+        assert result == arr[0]
+        result = arr.median(skipna=False)
+        assert result is NaT
+
+        result = arr.dropna().median(skipna=False)
+        assert result == arr[0]
+
+        result = arr.median(axis=0)
+        assert result == arr[0]
+
+    def test_median_axis(self, arr1d):
+        arr = arr1d
+        assert arr.median(axis=0) == arr.median()
+        assert arr.median(axis=0, skipna=False) is NaT
+
+        msg = r"abs\(axis\) must be less than ndim"
+        with pytest.raises(ValueError, match=msg):
+            arr.median(axis=1)
+
+    @pytest.mark.filterwarnings("ignore:All-NaN slice encountered:RuntimeWarning")
+    def test_median_2d(self, arr1d):
+        arr = arr1d.reshape(1, -1)
+
+        # axis = None
+        assert arr.median() == arr1d.median()
+        assert arr.median(skipna=False) is NaT
+
+        # axis = 0
+        result = arr.median(axis=0)
+        expected = arr1d
+        tm.assert_equal(result, expected)
+
+        # Since column 3 is all-NaT, we get NaT there with or without skipna
+        result = arr.median(axis=0, skipna=False)
+        expected = arr1d
+        tm.assert_equal(result, expected)
+
+        # axis = 1
+        result = arr.median(axis=1)
+        expected = type(arr)._from_sequence([arr1d.median()], dtype=arr.dtype)
+        tm.assert_equal(result, expected)
+
+        result = arr.median(axis=1, skipna=False)
+        expected = type(arr)._from_sequence([NaT], dtype=arr.dtype)
+        tm.assert_equal(result, expected)
+
+    def test_mean(self, arr1d):
+        arr = arr1d
+
+        # manually verified result
+        expected = arr[0] + 0.4 * pd.Timedelta(days=1)
+
+        result = arr.mean()
+        assert result == expected
+        result = arr.mean(skipna=False)
+        assert result is NaT
+
+        result = arr.dropna().mean(skipna=False)
+        assert result == expected
+
+        result = arr.mean(axis=0)
+        assert result == expected
+
+    def test_mean_2d(self):
+        dti = pd.date_range("2016-01-01", periods=6, tz="US/Pacific")
+        dta = dti._data.reshape(3, 2)
+
+        result = dta.mean(axis=0)
+        expected = dta[1]
+        tm.assert_datetime_array_equal(result, expected)
+
+        result = dta.mean(axis=1)
+        expected = dta[:, 0] + pd.Timedelta(hours=12)
+        tm.assert_datetime_array_equal(result, expected)
+
+        result = dta.mean(axis=None)
+        expected = dti.mean()
+        assert result == expected
+
+    @pytest.mark.parametrize("skipna", [True, False])
+    def test_mean_empty(self, arr1d, skipna):
+        arr = arr1d[:0]
+
+        assert arr.mean(skipna=skipna) is NaT
+
+        arr2d = arr.reshape(0, 3)
+        result = arr2d.mean(axis=0, skipna=skipna)
+        expected = DatetimeArray._from_sequence([NaT, NaT, NaT], dtype=arr.dtype)
+        tm.assert_datetime_array_equal(result, expected)
+
+        result = arr2d.mean(axis=1, skipna=skipna)
+        expected = arr  # i.e. 1D, empty
+        tm.assert_datetime_array_equal(result, expected)
+
+        result = arr2d.mean(axis=None, skipna=skipna)
+        assert result is NaT
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/floating/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/floating/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/floating/conftest.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/floating/conftest.py
new file mode 100644
index 0000000000000000000000000000000000000000..5e971c66029d5ba90ecaa5eb3437246f1548557a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/floating/conftest.py
@@ -0,0 +1,48 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas.core.arrays.floating import (
+    Float32Dtype,
+    Float64Dtype,
+)
+
+
+@pytest.fixture(params=[Float32Dtype, Float64Dtype])
+def dtype(request):
+    """Parametrized fixture returning a float 'dtype'"""
+    return request.param()
+
+
+@pytest.fixture
+def data(dtype):
+    """Fixture returning 'data' array according to parametrized float 'dtype'"""
+    return pd.array(
+        list(np.arange(0.1, 0.9, 0.1))
+        + [pd.NA]
+        + list(np.arange(1, 9.8, 0.1))
+        + [pd.NA]
+        + [9.9, 10.0],
+        dtype=dtype,
+    )
+
+
+@pytest.fixture
+def data_missing(dtype):
+    """
+    Fixture returning array with missing data according to parametrized float
+    'dtype'.
+    """
+    return pd.array([np.nan, 0.1], dtype=dtype)
+
+
+@pytest.fixture(params=["data", "data_missing"])
+def all_data(request, data, data_missing):
+    """Parametrized fixture returning 'data' or 'data_missing' float arrays.
+
+    Used to test dtype conversion with and without missing values.
+    """
+    if request.param == "data":
+        return data
+    elif request.param == "data_missing":
+        return data_missing
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/floating/test_arithmetic.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/floating/test_arithmetic.py
new file mode 100644
index 0000000000000000000000000000000000000000..009fac4c2f5ed4af079024aea35f60337f85989b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/floating/test_arithmetic.py
@@ -0,0 +1,240 @@
+import operator
+
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.core.arrays import FloatingArray
+
+# Basic test for the arithmetic array ops
+# -----------------------------------------------------------------------------
+
+
+@pytest.mark.parametrize(
+    "opname, exp",
+    [
+        ("add", [1.1, 2.2, None, None, 5.5]),
+        ("mul", [0.1, 0.4, None, None, 2.5]),
+        ("sub", [0.9, 1.8, None, None, 4.5]),
+        ("truediv", [10.0, 10.0, None, None, 10.0]),
+        ("floordiv", [9.0, 9.0, None, None, 10.0]),
+        ("mod", [0.1, 0.2, None, None, 0.0]),
+    ],
+    ids=["add", "mul", "sub", "div", "floordiv", "mod"],
+)
+def test_array_op(dtype, opname, exp):
+    a = pd.array([1.0, 2.0, None, 4.0, 5.0], dtype=dtype)
+    b = pd.array([0.1, 0.2, 0.3, None, 0.5], dtype=dtype)
+
+    op = getattr(operator, opname)
+
+    result = op(a, b)
+    expected = pd.array(exp, dtype=dtype)
+    tm.assert_extension_array_equal(result, expected)
+
+
+@pytest.mark.parametrize("zero, negative", [(0, False), (0.0, False), (-0.0, True)])
+def test_divide_by_zero(dtype, zero, negative):
+    # TODO pending NA/NaN discussion
+    # https://github.com/pandas-dev/pandas/issues/32265/
+    a = pd.array([0, 1, -1, None], dtype=dtype)
+    result = a / zero
+    expected = FloatingArray(
+        np.array([np.nan, np.inf, -np.inf, np.nan], dtype=dtype.numpy_dtype),
+        np.array([False, False, False, True]),
+    )
+    if negative:
+        expected *= -1
+    tm.assert_extension_array_equal(result, expected)
+
+
+def test_pow_scalar(dtype):
+    a = pd.array([-1, 0, 1, None, 2], dtype=dtype)
+    result = a**0
+    expected = pd.array([1, 1, 1, 1, 1], dtype=dtype)
+    tm.assert_extension_array_equal(result, expected)
+
+    result = a**1
+    expected = pd.array([-1, 0, 1, None, 2], dtype=dtype)
+    tm.assert_extension_array_equal(result, expected)
+
+    result = a**pd.NA
+    expected = pd.array([None, None, 1, None, None], dtype=dtype)
+    tm.assert_extension_array_equal(result, expected)
+
+    result = a**np.nan
+    # TODO np.nan should be converted to pd.NA / missing before operation?
+    expected = FloatingArray(
+        np.array([np.nan, np.nan, 1, np.nan, np.nan], dtype=dtype.numpy_dtype),
+        mask=a._mask,
+    )
+    tm.assert_extension_array_equal(result, expected)
+
+    # reversed
+    a = a[1:]  # Can't raise integers to negative powers.
+
+    result = 0**a
+    expected = pd.array([1, 0, None, 0], dtype=dtype)
+    tm.assert_extension_array_equal(result, expected)
+
+    result = 1**a
+    expected = pd.array([1, 1, 1, 1], dtype=dtype)
+    tm.assert_extension_array_equal(result, expected)
+
+    result = pd.NA**a
+    expected = pd.array([1, None, None, None], dtype=dtype)
+    tm.assert_extension_array_equal(result, expected)
+
+    result = np.nan**a
+    expected = FloatingArray(
+        np.array([1, np.nan, np.nan, np.nan], dtype=dtype.numpy_dtype), mask=a._mask
+    )
+    tm.assert_extension_array_equal(result, expected)
+
+
+def test_pow_array(dtype):
+    a = pd.array([0, 0, 0, 1, 1, 1, None, None, None], dtype=dtype)
+    b = pd.array([0, 1, None, 0, 1, None, 0, 1, None], dtype=dtype)
+    result = a**b
+    expected = pd.array([1, 0, None, 1, 1, 1, 1, None, None], dtype=dtype)
+    tm.assert_extension_array_equal(result, expected)
+
+
+def test_rpow_one_to_na():
+    # https://github.com/pandas-dev/pandas/issues/22022
+    # https://github.com/pandas-dev/pandas/issues/29997
+    arr = pd.array([np.nan, np.nan], dtype="Float64")
+    result = np.array([1.0, 2.0]) ** arr
+    expected = pd.array([1.0, np.nan], dtype="Float64")
+    tm.assert_extension_array_equal(result, expected)
+
+
+@pytest.mark.parametrize("other", [0, 0.5])
+def test_arith_zero_dim_ndarray(other):
+    arr = pd.array([1, None, 2], dtype="Float64")
+    result = arr + np.array(other)
+    expected = arr + other
+    tm.assert_equal(result, expected)
+
+
+# Test generic characteristics / errors
+# -----------------------------------------------------------------------------
+
+
+def test_error_invalid_values(data, all_arithmetic_operators):
+    op = all_arithmetic_operators
+    s = pd.Series(data)
+    ops = getattr(s, op)
+
+    # invalid scalars
+    msg = "|".join(
+        [
+            r"can only perform ops with numeric values",
+            r"FloatingArray cannot perform the operation mod",
+            "unsupported operand type",
+            "not all arguments converted during string formatting",
+            "can't multiply sequence by non-int of type 'float'",
+            "ufunc 'subtract' cannot use operands with types dtype",
+            r"can only concatenate str \(not \"float\"\) to str",
+            "ufunc '.*' not supported for the input types, and the inputs could not",
+            "ufunc '.*' did not contain a loop with signature matching types",
+            "Concatenation operation is not implemented for NumPy arrays",
+            "has no kernel",
+            "not implemented",
+            "not supported for dtype",
+            "Can only string multiply by an integer",
+        ]
+    )
+    with pytest.raises(TypeError, match=msg):
+        ops("foo")
+    with pytest.raises(TypeError, match=msg):
+        ops(pd.Timestamp("20180101"))
+
+    # invalid array-likes
+    with pytest.raises(TypeError, match=msg):
+        ops(pd.Series("foo", index=s.index))
+
+    msg = "|".join(
+        [
+            "can only perform ops with numeric values",
+            "cannot perform .* with this index type: DatetimeArray",
+            "Addition/subtraction of integers and integer-arrays "
+            "with DatetimeArray is no longer supported. *",
+            "unsupported operand type",
+            "not all arguments converted during string formatting",
+            "can't multiply sequence by non-int of type 'float'",
+            "ufunc 'subtract' cannot use operands with types dtype",
+            (
+                "ufunc 'add' cannot use operands with types "
+                rf"dtype\('{tm.ENDIAN}M8\[ns\]'\)"
+            ),
+            r"ufunc 'add' cannot use operands with types dtype\('float\d{2}'\)",
+            "cannot subtract DatetimeArray from ndarray",
+            "has no kernel",
+            "not implemented",
+            "not supported for dtype",
+        ]
+    )
+    with pytest.raises(TypeError, match=msg):
+        ops(pd.Series(pd.date_range("20180101", periods=len(s))))
+
+
+# Various
+# -----------------------------------------------------------------------------
+
+
+def test_cross_type_arithmetic():
+    df = pd.DataFrame(
+        {
+            "A": pd.array([1, 2, np.nan], dtype="Float64"),
+            "B": pd.array([1, np.nan, 3], dtype="Float32"),
+            "C": np.array([1, 2, 3], dtype="float64"),
+        }
+    )
+
+    result = df.A + df.C
+    expected = pd.Series([2, 4, np.nan], dtype="Float64")
+    tm.assert_series_equal(result, expected)
+
+    result = (df.A + df.C) * 3 == 12
+    expected = pd.Series([False, True, None], dtype="boolean")
+    tm.assert_series_equal(result, expected)
+
+    result = df.A + df.B
+    expected = pd.Series([2, np.nan, np.nan], dtype="Float64")
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "source, neg_target, abs_target",
+    [
+        ([1.1, 2.2, 3.3], [-1.1, -2.2, -3.3], [1.1, 2.2, 3.3]),
+        ([1.1, 2.2, None], [-1.1, -2.2, None], [1.1, 2.2, None]),
+        ([-1.1, 0.0, 1.1], [1.1, 0.0, -1.1], [1.1, 0.0, 1.1]),
+    ],
+)
+def test_unary_float_operators(float_ea_dtype, source, neg_target, abs_target):
+    # GH38794
+    dtype = float_ea_dtype
+    arr = pd.array(source, dtype=dtype)
+    neg_result, pos_result, abs_result = -arr, +arr, abs(arr)
+    neg_target = pd.array(neg_target, dtype=dtype)
+    abs_target = pd.array(abs_target, dtype=dtype)
+
+    tm.assert_extension_array_equal(neg_result, neg_target)
+    tm.assert_extension_array_equal(pos_result, arr)
+    assert not tm.shares_memory(pos_result, arr)
+    tm.assert_extension_array_equal(abs_result, abs_target)
+
+
+def test_bitwise(dtype):
+    left = pd.array([1, None, 3, 4], dtype=dtype)
+    right = pd.array([None, 3, 5, 4], dtype=dtype)
+
+    with pytest.raises(TypeError, match="unsupported operand type"):
+        left | right
+    with pytest.raises(TypeError, match="unsupported operand type"):
+        left & right
+    with pytest.raises(TypeError, match="unsupported operand type"):
+        left ^ right
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/floating/test_astype.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/floating/test_astype.py
new file mode 100644
index 0000000000000000000000000000000000000000..752ebe194ffcfdccf491d22320a8edcae5a8adab
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/floating/test_astype.py
@@ -0,0 +1,135 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+
+
+def test_astype():
+    # with missing values
+    arr = pd.array([0.1, 0.2, None], dtype="Float64")
+
+    with pytest.raises(ValueError, match="cannot convert NA to integer"):
+        arr.astype("int64")
+
+    with pytest.raises(ValueError, match="cannot convert float NaN to bool"):
+        arr.astype("bool")
+
+    result = arr.astype("float64")
+    expected = np.array([0.1, 0.2, np.nan], dtype="float64")
+    tm.assert_numpy_array_equal(result, expected)
+
+    # no missing values
+    arr = pd.array([0.0, 1.0, 0.5], dtype="Float64")
+    result = arr.astype("int64")
+    expected = np.array([0, 1, 0], dtype="int64")
+    tm.assert_numpy_array_equal(result, expected)
+
+    result = arr.astype("bool")
+    expected = np.array([False, True, True], dtype="bool")
+    tm.assert_numpy_array_equal(result, expected)
+
+
+def test_astype_to_floating_array():
+    # astype to FloatingArray
+    arr = pd.array([0.0, 1.0, None], dtype="Float64")
+
+    result = arr.astype("Float64")
+    tm.assert_extension_array_equal(result, arr)
+    result = arr.astype(pd.Float64Dtype())
+    tm.assert_extension_array_equal(result, arr)
+    result = arr.astype("Float32")
+    expected = pd.array([0.0, 1.0, None], dtype="Float32")
+    tm.assert_extension_array_equal(result, expected)
+
+
+def test_astype_to_boolean_array():
+    # astype to BooleanArray
+    arr = pd.array([0.0, 1.0, None], dtype="Float64")
+
+    result = arr.astype("boolean")
+    expected = pd.array([False, True, None], dtype="boolean")
+    tm.assert_extension_array_equal(result, expected)
+    result = arr.astype(pd.BooleanDtype())
+    tm.assert_extension_array_equal(result, expected)
+
+
+def test_astype_to_integer_array():
+    # astype to IntegerArray
+    arr = pd.array([0.0, 1.5, None], dtype="Float64")
+
+    result = arr.astype("Int64")
+    expected = pd.array([0, 1, None], dtype="Int64")
+    tm.assert_extension_array_equal(result, expected)
+
+
+def test_astype_str(using_infer_string):
+    a = pd.array([0.1, 0.2, None], dtype="Float64")
+
+    if using_infer_string:
+        expected = pd.array(["0.1", "0.2", None], dtype=pd.StringDtype(na_value=np.nan))
+
+        tm.assert_extension_array_equal(a.astype(str), expected)
+        tm.assert_extension_array_equal(a.astype("str"), expected)
+    else:
+        expected = np.array(["0.1", "0.2", ""], dtype="U32")
+
+        tm.assert_numpy_array_equal(a.astype(str), expected)
+        tm.assert_numpy_array_equal(a.astype("str"), expected)
+
+
+def test_astype_copy():
+    arr = pd.array([0.1, 0.2, None], dtype="Float64")
+    orig = pd.array([0.1, 0.2, None], dtype="Float64")
+
+    # copy=True -> ensure both data and mask are actual copies
+    result = arr.astype("Float64", copy=True)
+    assert result is not arr
+    assert not tm.shares_memory(result, arr)
+    result[0] = 10
+    tm.assert_extension_array_equal(arr, orig)
+    result[0] = pd.NA
+    tm.assert_extension_array_equal(arr, orig)
+
+    # copy=False
+    result = arr.astype("Float64", copy=False)
+    assert result is arr
+    assert np.shares_memory(result._data, arr._data)
+    assert np.shares_memory(result._mask, arr._mask)
+    result[0] = 10
+    assert arr[0] == 10
+    result[0] = pd.NA
+    assert arr[0] is pd.NA
+
+    # astype to different dtype -> always needs a copy -> even with copy=False
+    # we need to ensure that also the mask is actually copied
+    arr = pd.array([0.1, 0.2, None], dtype="Float64")
+    orig = pd.array([0.1, 0.2, None], dtype="Float64")
+
+    result = arr.astype("Float32", copy=False)
+    assert not tm.shares_memory(result, arr)
+    result[0] = 10
+    tm.assert_extension_array_equal(arr, orig)
+    result[0] = pd.NA
+    tm.assert_extension_array_equal(arr, orig)
+
+
+def test_astype_object(dtype):
+    arr = pd.array([1.0, pd.NA], dtype=dtype)
+
+    result = arr.astype(object)
+    expected = np.array([1.0, pd.NA], dtype=object)
+    tm.assert_numpy_array_equal(result, expected)
+    # check exact element types
+    assert isinstance(result[0], float)
+    assert result[1] is pd.NA
+
+
+def test_Float64_conversion():
+    # GH#40729
+    testseries = pd.Series(["1", "2", "3", "4"], dtype="object")
+    result = testseries.astype(pd.Float64Dtype())
+
+    expected = pd.Series([1.0, 2.0, 3.0, 4.0], dtype=pd.Float64Dtype())
+
+    tm.assert_series_equal(result, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/floating/test_comparison.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/floating/test_comparison.py
new file mode 100644
index 0000000000000000000000000000000000000000..a429649f1ce1dc10fc9610faa73a81dd94255b37
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/floating/test_comparison.py
@@ -0,0 +1,65 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.core.arrays import FloatingArray
+from pandas.tests.arrays.masked_shared import (
+    ComparisonOps,
+    NumericOps,
+)
+
+
+class TestComparisonOps(NumericOps, ComparisonOps):
+    @pytest.mark.parametrize("other", [True, False, pd.NA, -1.0, 0.0, 1])
+    def test_scalar(self, other, comparison_op, dtype):
+        ComparisonOps.test_scalar(self, other, comparison_op, dtype)
+
+    def test_compare_with_integerarray(self, comparison_op):
+        op = comparison_op
+        a = pd.array([0, 1, None] * 3, dtype="Int64")
+        b = pd.array([0] * 3 + [1] * 3 + [None] * 3, dtype="Float64")
+        other = b.astype("Int64")
+        expected = op(a, other)
+        result = op(a, b)
+        tm.assert_extension_array_equal(result, expected)
+        expected = op(other, a)
+        result = op(b, a)
+        tm.assert_extension_array_equal(result, expected)
+
+
+def test_equals():
+    # GH-30652
+    # equals is generally tested in /tests/extension/base/methods, but this
+    # specifically tests that two arrays of the same class but different dtype
+    # do not evaluate equal
+    a1 = pd.array([1, 2, None], dtype="Float64")
+    a2 = pd.array([1, 2, None], dtype="Float32")
+    assert a1.equals(a2) is False
+
+
+def test_equals_nan_vs_na():
+    # GH#44382
+
+    mask = np.zeros(3, dtype=bool)
+    data = np.array([1.0, np.nan, 3.0], dtype=np.float64)
+
+    left = FloatingArray(data, mask)
+    assert left.equals(left)
+    tm.assert_extension_array_equal(left, left)
+
+    assert left.equals(left.copy())
+    assert left.equals(FloatingArray(data.copy(), mask.copy()))
+
+    mask2 = np.array([False, True, False], dtype=bool)
+    data2 = np.array([1.0, 2.0, 3.0], dtype=np.float64)
+    right = FloatingArray(data2, mask2)
+    assert right.equals(right)
+    tm.assert_extension_array_equal(right, right)
+
+    assert not left.equals(right)
+
+    # with mask[1] = True, the only difference is data[1], which should
+    #  not matter for equals
+    mask[1] = True
+    assert left.equals(right)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/floating/test_concat.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/floating/test_concat.py
new file mode 100644
index 0000000000000000000000000000000000000000..2174a834aa959b88d899971f83247258a94476e3
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/floating/test_concat.py
@@ -0,0 +1,20 @@
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+
+
+@pytest.mark.parametrize(
+    "to_concat_dtypes, result_dtype",
+    [
+        (["Float64", "Float64"], "Float64"),
+        (["Float32", "Float64"], "Float64"),
+        (["Float32", "Float32"], "Float32"),
+    ],
+)
+def test_concat_series(to_concat_dtypes, result_dtype):
+    result = pd.concat([pd.Series([1, 2, pd.NA], dtype=t) for t in to_concat_dtypes])
+    expected = pd.concat([pd.Series([1, 2, pd.NA], dtype=object)] * 2).astype(
+        result_dtype
+    )
+    tm.assert_series_equal(result, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/floating/test_construction.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/floating/test_construction.py
new file mode 100644
index 0000000000000000000000000000000000000000..4007ee6b415c9b0f21f580f6240ed85ba1889781
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/floating/test_construction.py
@@ -0,0 +1,204 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.core.arrays import FloatingArray
+from pandas.core.arrays.floating import (
+    Float32Dtype,
+    Float64Dtype,
+)
+
+
+def test_uses_pandas_na():
+    a = pd.array([1, None], dtype=Float64Dtype())
+    assert a[1] is pd.NA
+
+
+def test_floating_array_constructor():
+    values = np.array([1, 2, 3, 4], dtype="float64")
+    mask = np.array([False, False, False, True], dtype="bool")
+
+    result = FloatingArray(values, mask)
+    expected = pd.array([1, 2, 3, np.nan], dtype="Float64")
+    tm.assert_extension_array_equal(result, expected)
+    tm.assert_numpy_array_equal(result._data, values)
+    tm.assert_numpy_array_equal(result._mask, mask)
+
+    msg = r".* should be .* numpy array. Use the 'pd.array' function instead"
+    with pytest.raises(TypeError, match=msg):
+        FloatingArray(values.tolist(), mask)
+
+    with pytest.raises(TypeError, match=msg):
+        FloatingArray(values, mask.tolist())
+
+    with pytest.raises(TypeError, match=msg):
+        FloatingArray(values.astype(int), mask)
+
+    msg = r"__init__\(\) missing 1 required positional argument: 'mask'"
+    with pytest.raises(TypeError, match=msg):
+        FloatingArray(values)
+
+
+def test_floating_array_disallows_float16():
+    # GH#44715
+    arr = np.array([1, 2], dtype=np.float16)
+    mask = np.array([False, False])
+
+    msg = "FloatingArray does not support np.float16 dtype"
+    with pytest.raises(TypeError, match=msg):
+        FloatingArray(arr, mask)
+
+
+def test_floating_array_disallows_Float16_dtype(request):
+    # GH#44715
+    with pytest.raises(TypeError, match="data type 'Float16' not understood"):
+        pd.array([1.0, 2.0], dtype="Float16")
+
+
+def test_floating_array_constructor_copy():
+    values = np.array([1, 2, 3, 4], dtype="float64")
+    mask = np.array([False, False, False, True], dtype="bool")
+
+    result = FloatingArray(values, mask)
+    assert result._data is values
+    assert result._mask is mask
+
+    result = FloatingArray(values, mask, copy=True)
+    assert result._data is not values
+    assert result._mask is not mask
+
+
+def test_to_array():
+    result = pd.array([0.1, 0.2, 0.3, 0.4])
+    expected = pd.array([0.1, 0.2, 0.3, 0.4], dtype="Float64")
+    tm.assert_extension_array_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "a, b",
+    [
+        ([1, None], [1, pd.NA]),
+        ([None], [pd.NA]),
+        ([None, np.nan], [pd.NA, pd.NA]),
+        ([1, np.nan], [1, pd.NA]),
+        ([np.nan], [pd.NA]),
+    ],
+)
+def test_to_array_none_is_nan(a, b):
+    result = pd.array(a, dtype="Float64")
+    expected = pd.array(b, dtype="Float64")
+    tm.assert_extension_array_equal(result, expected)
+
+
+def test_to_array_mixed_integer_float():
+    result = pd.array([1, 2.0])
+    expected = pd.array([1.0, 2.0], dtype="Float64")
+    tm.assert_extension_array_equal(result, expected)
+
+    result = pd.array([1, None, 2.0])
+    expected = pd.array([1.0, None, 2.0], dtype="Float64")
+    tm.assert_extension_array_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "values",
+    [
+        ["foo", "bar"],
+        "foo",
+        1,
+        1.0,
+        pd.date_range("20130101", periods=2),
+        np.array(["foo"]),
+        [[1, 2], [3, 4]],
+        [np.nan, {"a": 1}],
+        # GH#44514 all-NA case used to get quietly swapped out before checking ndim
+        np.array([pd.NA] * 6, dtype=object).reshape(3, 2),
+    ],
+)
+def test_to_array_error(values):
+    # error in converting existing arrays to FloatingArray
+    msg = "|".join(
+        [
+            "cannot be converted to FloatingDtype",
+            "values must be a 1D list-like",
+            "Cannot pass scalar",
+            r"float\(\) argument must be a string or a (real )?number, not 'dict'",
+            "could not convert string to float: 'foo'",
+            r"could not convert string to float: np\.str_\('foo'\)",
+        ]
+    )
+    with pytest.raises((TypeError, ValueError), match=msg):
+        pd.array(values, dtype="Float64")
+
+
+@pytest.mark.parametrize("values", [["1", "2", None], ["1.5", "2", None]])
+def test_construct_from_float_strings(values):
+    # see also test_to_integer_array_str
+    expected = pd.array([float(values[0]), 2, None], dtype="Float64")
+
+    res = pd.array(values, dtype="Float64")
+    tm.assert_extension_array_equal(res, expected)
+
+    res = FloatingArray._from_sequence(values)
+    tm.assert_extension_array_equal(res, expected)
+
+
+def test_to_array_inferred_dtype():
+    # if values has dtype -> respect it
+    result = pd.array(np.array([1, 2], dtype="float32"))
+    assert result.dtype == Float32Dtype()
+
+    # if values have no dtype -> always float64
+    result = pd.array([1.0, 2.0])
+    assert result.dtype == Float64Dtype()
+
+
+def test_to_array_dtype_keyword():
+    result = pd.array([1, 2], dtype="Float32")
+    assert result.dtype == Float32Dtype()
+
+    # if values has dtype -> override it
+    result = pd.array(np.array([1, 2], dtype="float32"), dtype="Float64")
+    assert result.dtype == Float64Dtype()
+
+
+def test_to_array_integer():
+    result = pd.array([1, 2], dtype="Float64")
+    expected = pd.array([1.0, 2.0], dtype="Float64")
+    tm.assert_extension_array_equal(result, expected)
+
+    # for integer dtypes, the itemsize is not preserved
+    # TODO can we specify "floating" in general?
+    result = pd.array(np.array([1, 2], dtype="int32"), dtype="Float64")
+    assert result.dtype == Float64Dtype()
+
+
+@pytest.mark.parametrize(
+    "bool_values, values, target_dtype, expected_dtype",
+    [
+        ([False, True], [0, 1], Float64Dtype(), Float64Dtype()),
+        ([False, True], [0, 1], "Float64", Float64Dtype()),
+        ([False, True, np.nan], [0, 1, np.nan], Float64Dtype(), Float64Dtype()),
+    ],
+)
+def test_to_array_bool(bool_values, values, target_dtype, expected_dtype):
+    result = pd.array(bool_values, dtype=target_dtype)
+    assert result.dtype == expected_dtype
+    expected = pd.array(values, dtype=target_dtype)
+    tm.assert_extension_array_equal(result, expected)
+
+
+def test_series_from_float(data):
+    # construct from our dtype & string dtype
+    dtype = data.dtype
+
+    # from float
+    expected = pd.Series(data)
+    result = pd.Series(data.to_numpy(na_value=np.nan, dtype="float"), dtype=str(dtype))
+    tm.assert_series_equal(result, expected)
+
+    # from list
+    expected = pd.Series(data)
+    result = pd.Series(np.array(data).tolist(), dtype=str(dtype))
+    tm.assert_series_equal(result, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/floating/test_contains.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/floating/test_contains.py
new file mode 100644
index 0000000000000000000000000000000000000000..956642697bf3285e5c661c43047a5f0dafa83144
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/floating/test_contains.py
@@ -0,0 +1,12 @@
+import numpy as np
+
+import pandas as pd
+
+
+def test_contains_nan():
+    # GH#52840
+    arr = pd.array(range(5)) / 0
+
+    assert np.isnan(arr._data[0])
+    assert not arr.isna()[0]
+    assert np.nan in arr
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/floating/test_function.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/floating/test_function.py
new file mode 100644
index 0000000000000000000000000000000000000000..40fd66fd049a621138c2cda074a08a1a94967bb5
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/floating/test_function.py
@@ -0,0 +1,194 @@
+import numpy as np
+import pytest
+
+from pandas.compat import IS64
+
+import pandas as pd
+import pandas._testing as tm
+
+
+@pytest.mark.parametrize("ufunc", [np.abs, np.sign])
+# np.sign emits a warning with nans, 
+@pytest.mark.filterwarnings("ignore:invalid value encountered in sign:RuntimeWarning")
+def test_ufuncs_single(ufunc):
+    a = pd.array([1, 2, -3, np.nan], dtype="Float64")
+    result = ufunc(a)
+    expected = pd.array(ufunc(a.astype(float)), dtype="Float64")
+    tm.assert_extension_array_equal(result, expected)
+
+    s = pd.Series(a)
+    result = ufunc(s)
+    expected = pd.Series(expected)
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("ufunc", [np.log, np.exp, np.sin, np.cos, np.sqrt])
+def test_ufuncs_single_float(ufunc):
+    a = pd.array([1.0, 0.2, 3.0, np.nan], dtype="Float64")
+    with np.errstate(invalid="ignore"):
+        result = ufunc(a)
+        expected = pd.array(ufunc(a.astype(float)), dtype="Float64")
+    tm.assert_extension_array_equal(result, expected)
+
+    s = pd.Series(a)
+    with np.errstate(invalid="ignore"):
+        result = ufunc(s)
+        expected = pd.Series(ufunc(s.astype(float)), dtype="Float64")
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("ufunc", [np.add, np.subtract])
+def test_ufuncs_binary_float(ufunc):
+    # two FloatingArrays
+    a = pd.array([1, 0.2, -3, np.nan], dtype="Float64")
+    result = ufunc(a, a)
+    expected = pd.array(ufunc(a.astype(float), a.astype(float)), dtype="Float64")
+    tm.assert_extension_array_equal(result, expected)
+
+    # FloatingArray with numpy array
+    arr = np.array([1, 2, 3, 4])
+    result = ufunc(a, arr)
+    expected = pd.array(ufunc(a.astype(float), arr), dtype="Float64")
+    tm.assert_extension_array_equal(result, expected)
+
+    result = ufunc(arr, a)
+    expected = pd.array(ufunc(arr, a.astype(float)), dtype="Float64")
+    tm.assert_extension_array_equal(result, expected)
+
+    # FloatingArray with scalar
+    result = ufunc(a, 1)
+    expected = pd.array(ufunc(a.astype(float), 1), dtype="Float64")
+    tm.assert_extension_array_equal(result, expected)
+
+    result = ufunc(1, a)
+    expected = pd.array(ufunc(1, a.astype(float)), dtype="Float64")
+    tm.assert_extension_array_equal(result, expected)
+
+
+@pytest.mark.parametrize("values", [[0, 1], [0, None]])
+def test_ufunc_reduce_raises(values):
+    arr = pd.array(values, dtype="Float64")
+
+    res = np.add.reduce(arr)
+    expected = arr.sum(skipna=False)
+    tm.assert_almost_equal(res, expected)
+
+
+@pytest.mark.skipif(not IS64, reason="GH 36579: fail on 32-bit system")
+@pytest.mark.parametrize(
+    "pandasmethname, kwargs",
+    [
+        ("var", {"ddof": 0}),
+        ("var", {"ddof": 1}),
+        ("std", {"ddof": 0}),
+        ("std", {"ddof": 1}),
+        ("kurtosis", {}),
+        ("skew", {}),
+        ("sem", {}),
+    ],
+)
+def test_stat_method(pandasmethname, kwargs):
+    s = pd.Series(data=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, np.nan, np.nan], dtype="Float64")
+    pandasmeth = getattr(s, pandasmethname)
+    result = pandasmeth(**kwargs)
+    s2 = pd.Series(data=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6], dtype="float64")
+    pandasmeth = getattr(s2, pandasmethname)
+    expected = pandasmeth(**kwargs)
+    assert expected == result
+
+
+def test_value_counts_na():
+    arr = pd.array([0.1, 0.2, 0.1, pd.NA], dtype="Float64")
+    result = arr.value_counts(dropna=False)
+    idx = pd.Index([0.1, 0.2, pd.NA], dtype=arr.dtype)
+    assert idx.dtype == arr.dtype
+    expected = pd.Series([2, 1, 1], index=idx, dtype="Int64", name="count")
+    tm.assert_series_equal(result, expected)
+
+    result = arr.value_counts(dropna=True)
+    expected = pd.Series([2, 1], index=idx[:-1], dtype="Int64", name="count")
+    tm.assert_series_equal(result, expected)
+
+
+def test_value_counts_empty():
+    ser = pd.Series([], dtype="Float64")
+    result = ser.value_counts()
+    idx = pd.Index([], dtype="Float64")
+    assert idx.dtype == "Float64"
+    expected = pd.Series([], index=idx, dtype="Int64", name="count")
+    tm.assert_series_equal(result, expected)
+
+
+def test_value_counts_with_normalize():
+    ser = pd.Series([0.1, 0.2, 0.1, pd.NA], dtype="Float64")
+    result = ser.value_counts(normalize=True)
+    expected = pd.Series([2, 1], index=ser[:2], dtype="Float64", name="proportion") / 3
+    assert expected.index.dtype == ser.dtype
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("skipna", [True, False])
+@pytest.mark.parametrize("min_count", [0, 4])
+def test_floating_array_sum(skipna, min_count, dtype):
+    arr = pd.array([1, 2, 3, None], dtype=dtype)
+    result = arr.sum(skipna=skipna, min_count=min_count)
+    if skipna and min_count == 0:
+        assert result == 6.0
+    else:
+        assert result is pd.NA
+
+
+@pytest.mark.parametrize(
+    "values, expected", [([1, 2, 3], 6.0), ([1, 2, 3, None], 6.0), ([None], 0.0)]
+)
+def test_floating_array_numpy_sum(values, expected):
+    arr = pd.array(values, dtype="Float64")
+    result = np.sum(arr)
+    assert result == expected
+
+
+@pytest.mark.parametrize("op", ["sum", "min", "max", "prod"])
+def test_preserve_dtypes(op):
+    df = pd.DataFrame(
+        {
+            "A": ["a", "b", "b"],
+            "B": [1, None, 3],
+            "C": pd.array([0.1, None, 3.0], dtype="Float64"),
+        }
+    )
+
+    # op
+    result = getattr(df.C, op)()
+    assert isinstance(result, np.float64)
+
+    # groupby
+    result = getattr(df.groupby("A"), op)()
+
+    expected = pd.DataFrame(
+        {"B": np.array([1.0, 3.0]), "C": pd.array([0.1, 3], dtype="Float64")},
+        index=pd.Index(["a", "b"], name="A"),
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("skipna", [True, False])
+@pytest.mark.parametrize("method", ["min", "max"])
+def test_floating_array_min_max(skipna, method, dtype):
+    arr = pd.array([0.0, 1.0, None], dtype=dtype)
+    func = getattr(arr, method)
+    result = func(skipna=skipna)
+    if skipna:
+        assert result == (0 if method == "min" else 1)
+    else:
+        assert result is pd.NA
+
+
+@pytest.mark.parametrize("skipna", [True, False])
+@pytest.mark.parametrize("min_count", [0, 9])
+def test_floating_array_prod(skipna, min_count, dtype):
+    arr = pd.array([1.0, 2.0, None], dtype=dtype)
+    result = arr.prod(skipna=skipna, min_count=min_count)
+    if skipna and min_count == 0:
+        assert result == 2
+    else:
+        assert result is pd.NA
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/floating/test_repr.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/floating/test_repr.py
new file mode 100644
index 0000000000000000000000000000000000000000..ea2cdd4fab86ada36d6d5804204c4a479a3e1603
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/floating/test_repr.py
@@ -0,0 +1,47 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas.core.arrays.floating import (
+    Float32Dtype,
+    Float64Dtype,
+)
+
+
+def test_dtypes(dtype):
+    # smoke tests on auto dtype construction
+
+    np.dtype(dtype.type).kind == "f"
+    assert dtype.name is not None
+
+
+@pytest.mark.parametrize(
+    "dtype, expected",
+    [(Float32Dtype(), "Float32Dtype()"), (Float64Dtype(), "Float64Dtype()")],
+)
+def test_repr_dtype(dtype, expected):
+    assert repr(dtype) == expected
+
+
+def test_repr_array():
+    result = repr(pd.array([1.0, None, 3.0]))
+    expected = "\n[1.0, , 3.0]\nLength: 3, dtype: Float64"
+    assert result == expected
+
+
+def test_repr_array_long():
+    data = pd.array([1.0, 2.0, None] * 1000)
+    expected = """
+[ 1.0,  2.0, ,  1.0,  2.0, ,  1.0,  2.0, ,  1.0,
+ ...
+ ,  1.0,  2.0, ,  1.0,  2.0, ,  1.0,  2.0, ]
+Length: 3000, dtype: Float64"""
+    result = repr(data)
+    assert result == expected
+
+
+def test_frame_repr(data_missing):
+    df = pd.DataFrame({"A": data_missing})
+    result = repr(df)
+    expected = "      A\n0  \n1   0.1"
+    assert result == expected
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/floating/test_to_numpy.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/floating/test_to_numpy.py
new file mode 100644
index 0000000000000000000000000000000000000000..e954cecba417afd71059a35f7506c650eb780373
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/floating/test_to_numpy.py
@@ -0,0 +1,132 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.core.arrays import FloatingArray
+
+
+@pytest.mark.parametrize("box", [True, False], ids=["series", "array"])
+def test_to_numpy(box):
+    con = pd.Series if box else pd.array
+
+    # default (with or without missing values) -> object dtype
+    arr = con([0.1, 0.2, 0.3], dtype="Float64")
+    result = arr.to_numpy()
+    expected = np.array([0.1, 0.2, 0.3], dtype="float64")
+    tm.assert_numpy_array_equal(result, expected)
+
+    arr = con([0.1, 0.2, None], dtype="Float64")
+    result = arr.to_numpy()
+    expected = np.array([0.1, 0.2, np.nan], dtype="float64")
+    tm.assert_numpy_array_equal(result, expected)
+
+
+@pytest.mark.parametrize("box", [True, False], ids=["series", "array"])
+def test_to_numpy_float(box):
+    con = pd.Series if box else pd.array
+
+    # no missing values -> can convert to float, otherwise raises
+    arr = con([0.1, 0.2, 0.3], dtype="Float64")
+    result = arr.to_numpy(dtype="float64")
+    expected = np.array([0.1, 0.2, 0.3], dtype="float64")
+    tm.assert_numpy_array_equal(result, expected)
+
+    arr = con([0.1, 0.2, None], dtype="Float64")
+    result = arr.to_numpy(dtype="float64")
+    expected = np.array([0.1, 0.2, np.nan], dtype="float64")
+    tm.assert_numpy_array_equal(result, expected)
+
+    result = arr.to_numpy(dtype="float64", na_value=np.nan)
+    expected = np.array([0.1, 0.2, np.nan], dtype="float64")
+    tm.assert_numpy_array_equal(result, expected)
+
+
+@pytest.mark.parametrize("box", [True, False], ids=["series", "array"])
+def test_to_numpy_int(box):
+    con = pd.Series if box else pd.array
+
+    # no missing values -> can convert to int, otherwise raises
+    arr = con([1.0, 2.0, 3.0], dtype="Float64")
+    result = arr.to_numpy(dtype="int64")
+    expected = np.array([1, 2, 3], dtype="int64")
+    tm.assert_numpy_array_equal(result, expected)
+
+    arr = con([1.0, 2.0, None], dtype="Float64")
+    with pytest.raises(ValueError, match="cannot convert to 'int64'-dtype"):
+        result = arr.to_numpy(dtype="int64")
+
+    # automatic casting (floors the values)
+    arr = con([0.1, 0.9, 1.1], dtype="Float64")
+    result = arr.to_numpy(dtype="int64")
+    expected = np.array([0, 0, 1], dtype="int64")
+    tm.assert_numpy_array_equal(result, expected)
+
+
+@pytest.mark.parametrize("box", [True, False], ids=["series", "array"])
+def test_to_numpy_na_value(box):
+    con = pd.Series if box else pd.array
+
+    arr = con([0.0, 1.0, None], dtype="Float64")
+    result = arr.to_numpy(dtype=object, na_value=None)
+    expected = np.array([0.0, 1.0, None], dtype="object")
+    tm.assert_numpy_array_equal(result, expected)
+
+    result = arr.to_numpy(dtype=bool, na_value=False)
+    expected = np.array([False, True, False], dtype="bool")
+    tm.assert_numpy_array_equal(result, expected)
+
+    result = arr.to_numpy(dtype="int64", na_value=-99)
+    expected = np.array([0, 1, -99], dtype="int64")
+    tm.assert_numpy_array_equal(result, expected)
+
+
+def test_to_numpy_na_value_with_nan():
+    # array with both NaN and NA -> only fill NA with `na_value`
+    arr = FloatingArray(np.array([0.0, np.nan, 0.0]), np.array([False, False, True]))
+    result = arr.to_numpy(dtype="float64", na_value=-1)
+    expected = np.array([0.0, np.nan, -1.0], dtype="float64")
+    tm.assert_numpy_array_equal(result, expected)
+
+
+@pytest.mark.parametrize("dtype", ["float64", "float32", "int32", "int64", "bool"])
+@pytest.mark.parametrize("box", [True, False], ids=["series", "array"])
+def test_to_numpy_dtype(box, dtype):
+    con = pd.Series if box else pd.array
+    arr = con([0.0, 1.0], dtype="Float64")
+
+    result = arr.to_numpy(dtype=dtype)
+    expected = np.array([0, 1], dtype=dtype)
+    tm.assert_numpy_array_equal(result, expected)
+
+
+@pytest.mark.parametrize("dtype", ["int32", "int64", "bool"])
+@pytest.mark.parametrize("box", [True, False], ids=["series", "array"])
+def test_to_numpy_na_raises(box, dtype):
+    con = pd.Series if box else pd.array
+    arr = con([0.0, 1.0, None], dtype="Float64")
+    with pytest.raises(ValueError, match=dtype):
+        arr.to_numpy(dtype=dtype)
+
+
+@pytest.mark.parametrize("box", [True, False], ids=["series", "array"])
+def test_to_numpy_string(box, dtype):
+    con = pd.Series if box else pd.array
+    arr = con([0.0, 1.0, None], dtype="Float64")
+
+    result = arr.to_numpy(dtype="str")
+    expected = np.array([0.0, 1.0, pd.NA], dtype=f"{tm.ENDIAN}U32")
+    tm.assert_numpy_array_equal(result, expected)
+
+
+def test_to_numpy_copy():
+    # to_numpy can be zero-copy if no missing values
+    arr = pd.array([0.1, 0.2, 0.3], dtype="Float64")
+    result = arr.to_numpy(dtype="float64")
+    result[0] = 10
+    tm.assert_extension_array_equal(arr, pd.array([10, 0.2, 0.3], dtype="Float64"))
+
+    arr = pd.array([0.1, 0.2, 0.3], dtype="Float64")
+    result = arr.to_numpy(dtype="float64", copy=True)
+    result[0] = 10
+    tm.assert_extension_array_equal(arr, pd.array([0.1, 0.2, 0.3], dtype="Float64"))
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/integer/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/integer/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/integer/conftest.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/integer/conftest.py
new file mode 100644
index 0000000000000000000000000000000000000000..f73400dfe689e91c4c2b457c4be1a0a41380fd6a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/integer/conftest.py
@@ -0,0 +1,68 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas.core.arrays.integer import (
+    Int8Dtype,
+    Int16Dtype,
+    Int32Dtype,
+    Int64Dtype,
+    UInt8Dtype,
+    UInt16Dtype,
+    UInt32Dtype,
+    UInt64Dtype,
+)
+
+
+@pytest.fixture(
+    params=[
+        Int8Dtype,
+        Int16Dtype,
+        Int32Dtype,
+        Int64Dtype,
+        UInt8Dtype,
+        UInt16Dtype,
+        UInt32Dtype,
+        UInt64Dtype,
+    ]
+)
+def dtype(request):
+    """Parametrized fixture returning integer 'dtype'"""
+    return request.param()
+
+
+@pytest.fixture
+def data(dtype):
+    """
+    Fixture returning 'data' array with valid and missing values according to
+    parametrized integer 'dtype'.
+
+    Used to test dtype conversion with and without missing values.
+    """
+    return pd.array(
+        list(range(8)) + [np.nan] + list(range(10, 98)) + [np.nan] + [99, 100],
+        dtype=dtype,
+    )
+
+
+@pytest.fixture
+def data_missing(dtype):
+    """
+    Fixture returning array with exactly one NaN and one valid integer,
+    according to parametrized integer 'dtype'.
+
+    Used to test dtype conversion with and without missing values.
+    """
+    return pd.array([np.nan, 1], dtype=dtype)
+
+
+@pytest.fixture(params=["data", "data_missing"])
+def all_data(request, data, data_missing):
+    """Parametrized fixture returning 'data' or 'data_missing' integer arrays.
+
+    Used to test dtype conversion with and without missing values.
+    """
+    if request.param == "data":
+        return data
+    elif request.param == "data_missing":
+        return data_missing
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/integer/test_arithmetic.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/integer/test_arithmetic.py
new file mode 100644
index 0000000000000000000000000000000000000000..9fbea2022c87b36a44acdab9e42ac36f0018ae06
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/integer/test_arithmetic.py
@@ -0,0 +1,345 @@
+import operator
+
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.core import ops
+from pandas.core.arrays import FloatingArray
+
+# Basic test for the arithmetic array ops
+# -----------------------------------------------------------------------------
+
+
+@pytest.mark.parametrize(
+    "opname, exp",
+    [("add", [1, 3, None, None, 9]), ("mul", [0, 2, None, None, 20])],
+    ids=["add", "mul"],
+)
+def test_add_mul(dtype, opname, exp):
+    a = pd.array([0, 1, None, 3, 4], dtype=dtype)
+    b = pd.array([1, 2, 3, None, 5], dtype=dtype)
+
+    # array / array
+    expected = pd.array(exp, dtype=dtype)
+
+    op = getattr(operator, opname)
+    result = op(a, b)
+    tm.assert_extension_array_equal(result, expected)
+
+    op = getattr(ops, "r" + opname)
+    result = op(a, b)
+    tm.assert_extension_array_equal(result, expected)
+
+
+def test_sub(dtype):
+    a = pd.array([1, 2, 3, None, 5], dtype=dtype)
+    b = pd.array([0, 1, None, 3, 4], dtype=dtype)
+
+    result = a - b
+    expected = pd.array([1, 1, None, None, 1], dtype=dtype)
+    tm.assert_extension_array_equal(result, expected)
+
+
+def test_div(dtype):
+    a = pd.array([1, 2, 3, None, 5], dtype=dtype)
+    b = pd.array([0, 1, None, 3, 4], dtype=dtype)
+
+    result = a / b
+    expected = pd.array([np.inf, 2, None, None, 1.25], dtype="Float64")
+    tm.assert_extension_array_equal(result, expected)
+
+
+@pytest.mark.parametrize("zero, negative", [(0, False), (0.0, False), (-0.0, True)])
+def test_divide_by_zero(zero, negative):
+    # https://github.com/pandas-dev/pandas/issues/27398, GH#22793
+    a = pd.array([0, 1, -1, None], dtype="Int64")
+    result = a / zero
+    expected = FloatingArray(
+        np.array([np.nan, np.inf, -np.inf, 1], dtype="float64"),
+        np.array([False, False, False, True]),
+    )
+    if negative:
+        expected *= -1
+    tm.assert_extension_array_equal(result, expected)
+
+
+def test_floordiv(dtype):
+    a = pd.array([1, 2, 3, None, 5], dtype=dtype)
+    b = pd.array([0, 1, None, 3, 4], dtype=dtype)
+
+    result = a // b
+    # Series op sets 1//0 to np.inf, which IntegerArray does not do (yet)
+    expected = pd.array([0, 2, None, None, 1], dtype=dtype)
+    tm.assert_extension_array_equal(result, expected)
+
+
+def test_floordiv_by_int_zero_no_mask(any_int_ea_dtype):
+    # GH 48223: Aligns with non-masked floordiv
+    # but differs from numpy
+    # https://github.com/pandas-dev/pandas/issues/30188#issuecomment-564452740
+    ser = pd.Series([0, 1], dtype=any_int_ea_dtype)
+    result = 1 // ser
+    expected = pd.Series([np.inf, 1.0], dtype="Float64")
+    tm.assert_series_equal(result, expected)
+
+    ser_non_nullable = ser.astype(ser.dtype.numpy_dtype)
+    result = 1 // ser_non_nullable
+    expected = expected.astype(np.float64)
+    tm.assert_series_equal(result, expected)
+
+
+def test_mod(dtype):
+    a = pd.array([1, 2, 3, None, 5], dtype=dtype)
+    b = pd.array([0, 1, None, 3, 4], dtype=dtype)
+
+    result = a % b
+    expected = pd.array([0, 0, None, None, 1], dtype=dtype)
+    tm.assert_extension_array_equal(result, expected)
+
+
+def test_pow_scalar():
+    a = pd.array([-1, 0, 1, None, 2], dtype="Int64")
+    result = a**0
+    expected = pd.array([1, 1, 1, 1, 1], dtype="Int64")
+    tm.assert_extension_array_equal(result, expected)
+
+    result = a**1
+    expected = pd.array([-1, 0, 1, None, 2], dtype="Int64")
+    tm.assert_extension_array_equal(result, expected)
+
+    result = a**pd.NA
+    expected = pd.array([None, None, 1, None, None], dtype="Int64")
+    tm.assert_extension_array_equal(result, expected)
+
+    result = a**np.nan
+    expected = FloatingArray(
+        np.array([np.nan, np.nan, 1, np.nan, np.nan], dtype="float64"),
+        np.array([False, False, False, True, False]),
+    )
+    tm.assert_extension_array_equal(result, expected)
+
+    # reversed
+    a = a[1:]  # Can't raise integers to negative powers.
+
+    result = 0**a
+    expected = pd.array([1, 0, None, 0], dtype="Int64")
+    tm.assert_extension_array_equal(result, expected)
+
+    result = 1**a
+    expected = pd.array([1, 1, 1, 1], dtype="Int64")
+    tm.assert_extension_array_equal(result, expected)
+
+    result = pd.NA**a
+    expected = pd.array([1, None, None, None], dtype="Int64")
+    tm.assert_extension_array_equal(result, expected)
+
+    result = np.nan**a
+    expected = FloatingArray(
+        np.array([1, np.nan, np.nan, np.nan], dtype="float64"),
+        np.array([False, False, True, False]),
+    )
+    tm.assert_extension_array_equal(result, expected)
+
+
+def test_pow_array():
+    a = pd.array([0, 0, 0, 1, 1, 1, None, None, None])
+    b = pd.array([0, 1, None, 0, 1, None, 0, 1, None])
+    result = a**b
+    expected = pd.array([1, 0, None, 1, 1, 1, 1, None, None])
+    tm.assert_extension_array_equal(result, expected)
+
+
+def test_rpow_one_to_na():
+    # https://github.com/pandas-dev/pandas/issues/22022
+    # https://github.com/pandas-dev/pandas/issues/29997
+    arr = pd.array([np.nan, np.nan], dtype="Int64")
+    result = np.array([1.0, 2.0]) ** arr
+    expected = pd.array([1.0, np.nan], dtype="Float64")
+    tm.assert_extension_array_equal(result, expected)
+
+
+@pytest.mark.parametrize("other", [0, 0.5])
+def test_numpy_zero_dim_ndarray(other):
+    arr = pd.array([1, None, 2])
+    result = arr + np.array(other)
+    expected = arr + other
+    tm.assert_equal(result, expected)
+
+
+# Test generic characteristics / errors
+# -----------------------------------------------------------------------------
+
+
+def test_error_invalid_values(data, all_arithmetic_operators):
+    op = all_arithmetic_operators
+    s = pd.Series(data)
+    ops = getattr(s, op)
+
+    # invalid scalars
+    with tm.external_error_raised(TypeError):
+        ops("foo")
+    with tm.external_error_raised(TypeError):
+        ops(pd.Timestamp("20180101"))
+
+    # invalid array-likes
+    str_ser = pd.Series("foo", index=s.index)
+    # with pytest.raises(TypeError, match=msg):
+    if all_arithmetic_operators in [
+        "__mul__",
+        "__rmul__",
+    ]:  # (data[~data.isna()] >= 0).all():
+        res = ops(str_ser)
+        expected = pd.Series(["foo" * x for x in data], index=s.index)
+        expected = expected.fillna(np.nan)
+        # TODO: doing this fillna to keep tests passing as we make
+        #  assert_almost_equal stricter, but the expected with pd.NA seems
+        #  more-correct than np.nan here.
+        tm.assert_series_equal(res, expected)
+    else:
+        with tm.external_error_raised(TypeError):
+            ops(str_ser)
+
+    with tm.external_error_raised(TypeError):
+        ops(pd.Series(pd.date_range("20180101", periods=len(s))))
+
+
+# Various
+# -----------------------------------------------------------------------------
+
+
+# TODO test unsigned overflow
+
+
+def test_arith_coerce_scalar(data, all_arithmetic_operators):
+    op = tm.get_op_from_name(all_arithmetic_operators)
+    s = pd.Series(data)
+    other = 0.01
+
+    result = op(s, other)
+    expected = op(s.astype(float), other)
+    expected = expected.astype("Float64")
+
+    # rmod results in NaN that wasn't NA in original nullable Series -> unmask it
+    if all_arithmetic_operators == "__rmod__":
+        mask = (s == 0).fillna(False).to_numpy(bool)
+        expected.array._mask[mask] = False
+
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("other", [1.0, np.array(1.0)])
+def test_arithmetic_conversion(all_arithmetic_operators, other):
+    # if we have a float operand we should have a float result
+    # if that is equal to an integer
+    op = tm.get_op_from_name(all_arithmetic_operators)
+
+    s = pd.Series([1, 2, 3], dtype="Int64")
+    result = op(s, other)
+    assert result.dtype == "Float64"
+
+
+def test_cross_type_arithmetic():
+    df = pd.DataFrame(
+        {
+            "A": pd.Series([1, 2, np.nan], dtype="Int64"),
+            "B": pd.Series([1, np.nan, 3], dtype="UInt8"),
+            "C": [1, 2, 3],
+        }
+    )
+
+    result = df.A + df.C
+    expected = pd.Series([2, 4, np.nan], dtype="Int64")
+    tm.assert_series_equal(result, expected)
+
+    result = (df.A + df.C) * 3 == 12
+    expected = pd.Series([False, True, None], dtype="boolean")
+    tm.assert_series_equal(result, expected)
+
+    result = df.A + df.B
+    expected = pd.Series([2, np.nan, np.nan], dtype="Int64")
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("op", ["mean"])
+def test_reduce_to_float(op):
+    # some reduce ops always return float, even if the result
+    # is a rounded number
+    df = pd.DataFrame(
+        {
+            "A": ["a", "b", "b"],
+            "B": [1, None, 3],
+            "C": pd.array([1, None, 3], dtype="Int64"),
+        }
+    )
+
+    # op
+    result = getattr(df.C, op)()
+    assert isinstance(result, float)
+
+    # groupby
+    result = getattr(df.groupby("A"), op)()
+
+    expected = pd.DataFrame(
+        {"B": np.array([1.0, 3.0]), "C": pd.array([1, 3], dtype="Float64")},
+        index=pd.Index(["a", "b"], name="A"),
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "source, neg_target, abs_target",
+    [
+        ([1, 2, 3], [-1, -2, -3], [1, 2, 3]),
+        ([1, 2, None], [-1, -2, None], [1, 2, None]),
+        ([-1, 0, 1], [1, 0, -1], [1, 0, 1]),
+    ],
+)
+def test_unary_int_operators(any_signed_int_ea_dtype, source, neg_target, abs_target):
+    dtype = any_signed_int_ea_dtype
+    arr = pd.array(source, dtype=dtype)
+    neg_result, pos_result, abs_result = -arr, +arr, abs(arr)
+    neg_target = pd.array(neg_target, dtype=dtype)
+    abs_target = pd.array(abs_target, dtype=dtype)
+
+    tm.assert_extension_array_equal(neg_result, neg_target)
+    tm.assert_extension_array_equal(pos_result, arr)
+    assert not tm.shares_memory(pos_result, arr)
+    tm.assert_extension_array_equal(abs_result, abs_target)
+
+
+def test_values_multiplying_large_series_by_NA():
+    # GH#33701
+
+    result = pd.NA * pd.Series(np.zeros(10001))
+    expected = pd.Series([pd.NA] * 10001)
+
+    tm.assert_series_equal(result, expected)
+
+
+def test_bitwise(dtype):
+    left = pd.array([1, None, 3, 4], dtype=dtype)
+    right = pd.array([None, 3, 5, 4], dtype=dtype)
+
+    result = left | right
+    expected = pd.array([None, None, 3 | 5, 4 | 4], dtype=dtype)
+    tm.assert_extension_array_equal(result, expected)
+
+    result = left & right
+    expected = pd.array([None, None, 3 & 5, 4 & 4], dtype=dtype)
+    tm.assert_extension_array_equal(result, expected)
+
+    result = left ^ right
+    expected = pd.array([None, None, 3 ^ 5, 4 ^ 4], dtype=dtype)
+    tm.assert_extension_array_equal(result, expected)
+
+    # TODO: desired behavior when operating with boolean?  defer?
+
+    floats = right.astype("Float64")
+    with pytest.raises(TypeError, match="unsupported operand type"):
+        left | floats
+    with pytest.raises(TypeError, match="unsupported operand type"):
+        left & floats
+    with pytest.raises(TypeError, match="unsupported operand type"):
+        left ^ floats
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/integer/test_comparison.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/integer/test_comparison.py
new file mode 100644
index 0000000000000000000000000000000000000000..568b0b087bf1db9610960dba12ea2e0bab8f1729
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/integer/test_comparison.py
@@ -0,0 +1,39 @@
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.tests.arrays.masked_shared import (
+    ComparisonOps,
+    NumericOps,
+)
+
+
+class TestComparisonOps(NumericOps, ComparisonOps):
+    @pytest.mark.parametrize("other", [True, False, pd.NA, -1, 0, 1])
+    def test_scalar(self, other, comparison_op, dtype):
+        ComparisonOps.test_scalar(self, other, comparison_op, dtype)
+
+    def test_compare_to_int(self, dtype, comparison_op):
+        # GH 28930
+        op_name = f"__{comparison_op.__name__}__"
+        s1 = pd.Series([1, None, 3], dtype=dtype)
+        s2 = pd.Series([1, None, 3], dtype="float")
+
+        method = getattr(s1, op_name)
+        result = method(2)
+
+        method = getattr(s2, op_name)
+        expected = method(2).astype("boolean")
+        expected[s2.isna()] = pd.NA
+
+        tm.assert_series_equal(result, expected)
+
+
+def test_equals():
+    # GH-30652
+    # equals is generally tested in /tests/extension/base/methods, but this
+    # specifically tests that two arrays of the same class but different dtype
+    # do not evaluate equal
+    a1 = pd.array([1, 2, None], dtype="Int64")
+    a2 = pd.array([1, 2, None], dtype="Int32")
+    assert a1.equals(a2) is False
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/integer/test_concat.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/integer/test_concat.py
new file mode 100644
index 0000000000000000000000000000000000000000..feba574da548fd597c25103f67821145bccec9ed
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/integer/test_concat.py
@@ -0,0 +1,69 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+
+
+@pytest.mark.parametrize(
+    "to_concat_dtypes, result_dtype",
+    [
+        (["Int64", "Int64"], "Int64"),
+        (["UInt64", "UInt64"], "UInt64"),
+        (["Int8", "Int8"], "Int8"),
+        (["Int8", "Int16"], "Int16"),
+        (["UInt8", "Int8"], "Int16"),
+        (["Int32", "UInt32"], "Int64"),
+        (["Int64", "UInt64"], "Float64"),
+        (["Int64", "boolean"], "object"),
+        (["UInt8", "boolean"], "object"),
+    ],
+)
+def test_concat_series(to_concat_dtypes, result_dtype):
+    # we expect the same dtypes as we would get with non-masked inputs,
+    #  just masked where available.
+
+    result = pd.concat([pd.Series([0, 1, pd.NA], dtype=t) for t in to_concat_dtypes])
+    expected = pd.concat([pd.Series([0, 1, pd.NA], dtype=object)] * 2).astype(
+        result_dtype
+    )
+    tm.assert_series_equal(result, expected)
+
+    # order doesn't matter for result
+    result = pd.concat(
+        [pd.Series([0, 1, pd.NA], dtype=t) for t in to_concat_dtypes[::-1]]
+    )
+    expected = pd.concat([pd.Series([0, 1, pd.NA], dtype=object)] * 2).astype(
+        result_dtype
+    )
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "to_concat_dtypes, result_dtype",
+    [
+        (["Int64", "int64"], "Int64"),
+        (["UInt64", "uint64"], "UInt64"),
+        (["Int8", "int8"], "Int8"),
+        (["Int8", "int16"], "Int16"),
+        (["UInt8", "int8"], "Int16"),
+        (["Int32", "uint32"], "Int64"),
+        (["Int64", "uint64"], "Float64"),
+        (["Int64", "bool"], "object"),
+        (["UInt8", "bool"], "object"),
+    ],
+)
+def test_concat_series_with_numpy(to_concat_dtypes, result_dtype):
+    # we expect the same dtypes as we would get with non-masked inputs,
+    #  just masked where available.
+
+    s1 = pd.Series([0, 1, pd.NA], dtype=to_concat_dtypes[0])
+    s2 = pd.Series(np.array([0, 1], dtype=to_concat_dtypes[1]))
+    result = pd.concat([s1, s2], ignore_index=True)
+    expected = pd.Series([0, 1, pd.NA, 0, 1], dtype=object).astype(result_dtype)
+    tm.assert_series_equal(result, expected)
+
+    # order doesn't matter for result
+    result = pd.concat([s2, s1], ignore_index=True)
+    expected = pd.Series([0, 1, 0, 1, pd.NA], dtype=object).astype(result_dtype)
+    tm.assert_series_equal(result, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/integer/test_construction.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/integer/test_construction.py
new file mode 100644
index 0000000000000000000000000000000000000000..64fe40e53a9d287b6240a908b7ed9d0cf7ec1396
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/integer/test_construction.py
@@ -0,0 +1,245 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.api.types import is_integer
+from pandas.core.arrays import IntegerArray
+from pandas.core.arrays.integer import (
+    Int8Dtype,
+    Int32Dtype,
+    Int64Dtype,
+)
+
+
+@pytest.fixture(params=[pd.array, IntegerArray._from_sequence])
+def constructor(request):
+    """Fixture returning parametrized IntegerArray from given sequence.
+
+    Used to test dtype conversions.
+    """
+    return request.param
+
+
+def test_uses_pandas_na():
+    a = pd.array([1, None], dtype=Int64Dtype())
+    assert a[1] is pd.NA
+
+
+def test_from_dtype_from_float(data):
+    # construct from our dtype & string dtype
+    dtype = data.dtype
+
+    # from float
+    expected = pd.Series(data)
+    result = pd.Series(data.to_numpy(na_value=np.nan, dtype="float"), dtype=str(dtype))
+    tm.assert_series_equal(result, expected)
+
+    # from int / list
+    expected = pd.Series(data)
+    result = pd.Series(np.array(data).tolist(), dtype=str(dtype))
+    tm.assert_series_equal(result, expected)
+
+    # from int / array
+    expected = pd.Series(data).dropna().reset_index(drop=True)
+    dropped = np.array(data.dropna()).astype(np.dtype(dtype.type))
+    result = pd.Series(dropped, dtype=str(dtype))
+    tm.assert_series_equal(result, expected)
+
+
+def test_conversions(data_missing):
+    # astype to object series
+    df = pd.DataFrame({"A": data_missing})
+    result = df["A"].astype("object")
+    expected = pd.Series(np.array([pd.NA, 1], dtype=object), name="A")
+    tm.assert_series_equal(result, expected)
+
+    # convert to object ndarray
+    # we assert that we are exactly equal
+    # including type conversions of scalars
+    result = df["A"].astype("object").values
+    expected = np.array([pd.NA, 1], dtype=object)
+    tm.assert_numpy_array_equal(result, expected)
+
+    for r, e in zip(result, expected):
+        if pd.isnull(r):
+            assert pd.isnull(e)
+        elif is_integer(r):
+            assert r == e
+            assert is_integer(e)
+        else:
+            assert r == e
+            assert type(r) == type(e)
+
+
+def test_integer_array_constructor():
+    values = np.array([1, 2, 3, 4], dtype="int64")
+    mask = np.array([False, False, False, True], dtype="bool")
+
+    result = IntegerArray(values, mask)
+    expected = pd.array([1, 2, 3, np.nan], dtype="Int64")
+    tm.assert_extension_array_equal(result, expected)
+
+    msg = r".* should be .* numpy array. Use the 'pd.array' function instead"
+    with pytest.raises(TypeError, match=msg):
+        IntegerArray(values.tolist(), mask)
+
+    with pytest.raises(TypeError, match=msg):
+        IntegerArray(values, mask.tolist())
+
+    with pytest.raises(TypeError, match=msg):
+        IntegerArray(values.astype(float), mask)
+    msg = r"__init__\(\) missing 1 required positional argument: 'mask'"
+    with pytest.raises(TypeError, match=msg):
+        IntegerArray(values)
+
+
+def test_integer_array_constructor_copy():
+    values = np.array([1, 2, 3, 4], dtype="int64")
+    mask = np.array([False, False, False, True], dtype="bool")
+
+    result = IntegerArray(values, mask)
+    assert result._data is values
+    assert result._mask is mask
+
+    result = IntegerArray(values, mask, copy=True)
+    assert result._data is not values
+    assert result._mask is not mask
+
+
+@pytest.mark.parametrize(
+    "a, b",
+    [
+        ([1, None], [1, np.nan]),
+        ([None], [np.nan]),
+        ([None, np.nan], [np.nan, np.nan]),
+        ([np.nan, np.nan], [np.nan, np.nan]),
+    ],
+)
+def test_to_integer_array_none_is_nan(a, b):
+    result = pd.array(a, dtype="Int64")
+    expected = pd.array(b, dtype="Int64")
+    tm.assert_extension_array_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "values",
+    [
+        ["foo", "bar"],
+        "foo",
+        1,
+        1.0,
+        pd.date_range("20130101", periods=2),
+        np.array(["foo"]),
+        [[1, 2], [3, 4]],
+        [np.nan, {"a": 1}],
+    ],
+)
+def test_to_integer_array_error(values):
+    # error in converting existing arrays to IntegerArrays
+    msg = "|".join(
+        [
+            r"cannot be converted to IntegerDtype",
+            r"invalid literal for int\(\) with base 10:",
+            r"values must be a 1D list-like",
+            r"Cannot pass scalar",
+            r"int\(\) argument must be a string",
+        ]
+    )
+    with pytest.raises((ValueError, TypeError), match=msg):
+        pd.array(values, dtype="Int64")
+
+    with pytest.raises((ValueError, TypeError), match=msg):
+        IntegerArray._from_sequence(values)
+
+
+def test_to_integer_array_inferred_dtype(constructor):
+    # if values has dtype -> respect it
+    result = constructor(np.array([1, 2], dtype="int8"))
+    assert result.dtype == Int8Dtype()
+    result = constructor(np.array([1, 2], dtype="int32"))
+    assert result.dtype == Int32Dtype()
+
+    # if values have no dtype -> always int64
+    result = constructor([1, 2])
+    assert result.dtype == Int64Dtype()
+
+
+def test_to_integer_array_dtype_keyword(constructor):
+    result = constructor([1, 2], dtype="Int8")
+    assert result.dtype == Int8Dtype()
+
+    # if values has dtype -> override it
+    result = constructor(np.array([1, 2], dtype="int8"), dtype="Int32")
+    assert result.dtype == Int32Dtype()
+
+
+def test_to_integer_array_float():
+    result = IntegerArray._from_sequence([1.0, 2.0], dtype="Int64")
+    expected = pd.array([1, 2], dtype="Int64")
+    tm.assert_extension_array_equal(result, expected)
+
+    with pytest.raises(TypeError, match="cannot safely cast non-equivalent"):
+        IntegerArray._from_sequence([1.5, 2.0], dtype="Int64")
+
+    # for float dtypes, the itemsize is not preserved
+    result = IntegerArray._from_sequence(
+        np.array([1.0, 2.0], dtype="float32"), dtype="Int64"
+    )
+    assert result.dtype == Int64Dtype()
+
+
+def test_to_integer_array_str():
+    result = IntegerArray._from_sequence(["1", "2", None], dtype="Int64")
+    expected = pd.array([1, 2, np.nan], dtype="Int64")
+    tm.assert_extension_array_equal(result, expected)
+
+    with pytest.raises(
+        ValueError, match=r"invalid literal for int\(\) with base 10: .*"
+    ):
+        IntegerArray._from_sequence(["1", "2", ""], dtype="Int64")
+
+    with pytest.raises(
+        ValueError, match=r"invalid literal for int\(\) with base 10: .*"
+    ):
+        IntegerArray._from_sequence(["1.5", "2.0"], dtype="Int64")
+
+
+@pytest.mark.parametrize(
+    "bool_values, int_values, target_dtype, expected_dtype",
+    [
+        ([False, True], [0, 1], Int64Dtype(), Int64Dtype()),
+        ([False, True], [0, 1], "Int64", Int64Dtype()),
+        ([False, True, np.nan], [0, 1, np.nan], Int64Dtype(), Int64Dtype()),
+    ],
+)
+def test_to_integer_array_bool(
+    constructor, bool_values, int_values, target_dtype, expected_dtype
+):
+    result = constructor(bool_values, dtype=target_dtype)
+    assert result.dtype == expected_dtype
+    expected = pd.array(int_values, dtype=target_dtype)
+    tm.assert_extension_array_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "values, to_dtype, result_dtype",
+    [
+        (np.array([1], dtype="int64"), None, Int64Dtype),
+        (np.array([1, np.nan]), None, Int64Dtype),
+        (np.array([1, np.nan]), "int8", Int8Dtype),
+    ],
+)
+def test_to_integer_array(values, to_dtype, result_dtype):
+    # convert existing arrays to IntegerArrays
+    result = IntegerArray._from_sequence(values, dtype=to_dtype)
+    assert result.dtype == result_dtype()
+    expected = pd.array(values, dtype=result_dtype())
+    tm.assert_extension_array_equal(result, expected)
+
+
+def test_integer_array_from_boolean():
+    # GH31104
+    expected = pd.array(np.array([True, False]), dtype="Int64")
+    result = pd.array(np.array([True, False], dtype=object), dtype="Int64")
+    tm.assert_extension_array_equal(result, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/integer/test_dtypes.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/integer/test_dtypes.py
new file mode 100644
index 0000000000000000000000000000000000000000..90879d8bd30633246415acccd2d4d94e7c46124c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/integer/test_dtypes.py
@@ -0,0 +1,301 @@
+import numpy as np
+import pytest
+
+from pandas.core.dtypes.generic import ABCIndex
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.core.arrays.integer import (
+    Int8Dtype,
+    UInt32Dtype,
+)
+
+
+def test_dtypes(dtype):
+    # smoke tests on auto dtype construction
+
+    if dtype.is_signed_integer:
+        assert np.dtype(dtype.type).kind == "i"
+    else:
+        assert np.dtype(dtype.type).kind == "u"
+    assert dtype.name is not None
+
+
+@pytest.mark.parametrize("op", ["sum", "min", "max", "prod"])
+def test_preserve_dtypes(op):
+    # for ops that enable (mean would actually work here
+    # but generally it is a float return value)
+    df = pd.DataFrame(
+        {
+            "A": ["a", "b", "b"],
+            "B": [1, None, 3],
+            "C": pd.array([1, None, 3], dtype="Int64"),
+        }
+    )
+
+    # op
+    result = getattr(df.C, op)()
+    if op in {"sum", "prod", "min", "max"}:
+        assert isinstance(result, np.int64)
+    else:
+        assert isinstance(result, int)
+
+    # groupby
+    result = getattr(df.groupby("A"), op)()
+
+    expected = pd.DataFrame(
+        {"B": np.array([1.0, 3.0]), "C": pd.array([1, 3], dtype="Int64")},
+        index=pd.Index(["a", "b"], name="A"),
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_astype_nansafe():
+    # see gh-22343
+    arr = pd.array([np.nan, 1, 2], dtype="Int8")
+    msg = "cannot convert NA to integer"
+
+    with pytest.raises(ValueError, match=msg):
+        arr.astype("uint32")
+
+
+@pytest.mark.parametrize("dropna", [True, False])
+def test_construct_index(all_data, dropna):
+    # ensure that we do not coerce to different Index dtype or non-index
+
+    all_data = all_data[:10]
+    if dropna:
+        other = np.array(all_data[~all_data.isna()])
+    else:
+        other = all_data
+
+    result = pd.Index(pd.array(other, dtype=all_data.dtype))
+    expected = pd.Index(other, dtype=all_data.dtype)
+    assert all_data.dtype == expected.dtype  # dont coerce to object
+
+    tm.assert_index_equal(result, expected)
+
+
+@pytest.mark.parametrize("dropna", [True, False])
+def test_astype_index(all_data, dropna):
+    # as an int/uint index to Index
+
+    all_data = all_data[:10]
+    if dropna:
+        other = all_data[~all_data.isna()]
+    else:
+        other = all_data
+
+    dtype = all_data.dtype
+    idx = pd.Index(np.array(other))
+    assert isinstance(idx, ABCIndex)
+
+    result = idx.astype(dtype)
+    expected = idx.astype(object).astype(dtype)
+    tm.assert_index_equal(result, expected)
+
+
+def test_astype(all_data):
+    all_data = all_data[:10]
+
+    ints = all_data[~all_data.isna()]
+    mixed = all_data
+    dtype = Int8Dtype()
+
+    # coerce to same type - ints
+    s = pd.Series(ints)
+    result = s.astype(all_data.dtype)
+    expected = pd.Series(ints)
+    tm.assert_series_equal(result, expected)
+
+    # coerce to same other - ints
+    s = pd.Series(ints)
+    result = s.astype(dtype)
+    expected = pd.Series(ints, dtype=dtype)
+    tm.assert_series_equal(result, expected)
+
+    # coerce to same numpy_dtype - ints
+    s = pd.Series(ints)
+    result = s.astype(all_data.dtype.numpy_dtype)
+    expected = pd.Series(ints._data.astype(all_data.dtype.numpy_dtype))
+    tm.assert_series_equal(result, expected)
+
+    # coerce to same type - mixed
+    s = pd.Series(mixed)
+    result = s.astype(all_data.dtype)
+    expected = pd.Series(mixed)
+    tm.assert_series_equal(result, expected)
+
+    # coerce to same other - mixed
+    s = pd.Series(mixed)
+    result = s.astype(dtype)
+    expected = pd.Series(mixed, dtype=dtype)
+    tm.assert_series_equal(result, expected)
+
+    # coerce to same numpy_dtype - mixed
+    s = pd.Series(mixed)
+    msg = "cannot convert NA to integer"
+    with pytest.raises(ValueError, match=msg):
+        s.astype(all_data.dtype.numpy_dtype)
+
+    # coerce to object
+    s = pd.Series(mixed)
+    result = s.astype("object")
+    expected = pd.Series(np.asarray(mixed, dtype=object))
+    tm.assert_series_equal(result, expected)
+
+
+def test_astype_copy():
+    arr = pd.array([1, 2, 3, None], dtype="Int64")
+    orig = pd.array([1, 2, 3, None], dtype="Int64")
+
+    # copy=True -> ensure both data and mask are actual copies
+    result = arr.astype("Int64", copy=True)
+    assert result is not arr
+    assert not tm.shares_memory(result, arr)
+    result[0] = 10
+    tm.assert_extension_array_equal(arr, orig)
+    result[0] = pd.NA
+    tm.assert_extension_array_equal(arr, orig)
+
+    # copy=False
+    result = arr.astype("Int64", copy=False)
+    assert result is arr
+    assert np.shares_memory(result._data, arr._data)
+    assert np.shares_memory(result._mask, arr._mask)
+    result[0] = 10
+    assert arr[0] == 10
+    result[0] = pd.NA
+    assert arr[0] is pd.NA
+
+    # astype to different dtype -> always needs a copy -> even with copy=False
+    # we need to ensure that also the mask is actually copied
+    arr = pd.array([1, 2, 3, None], dtype="Int64")
+    orig = pd.array([1, 2, 3, None], dtype="Int64")
+
+    result = arr.astype("Int32", copy=False)
+    assert not tm.shares_memory(result, arr)
+    result[0] = 10
+    tm.assert_extension_array_equal(arr, orig)
+    result[0] = pd.NA
+    tm.assert_extension_array_equal(arr, orig)
+
+
+def test_astype_to_larger_numpy():
+    a = pd.array([1, 2], dtype="Int32")
+    result = a.astype("int64")
+    expected = np.array([1, 2], dtype="int64")
+    tm.assert_numpy_array_equal(result, expected)
+
+    a = pd.array([1, 2], dtype="UInt32")
+    result = a.astype("uint64")
+    expected = np.array([1, 2], dtype="uint64")
+    tm.assert_numpy_array_equal(result, expected)
+
+
+@pytest.mark.parametrize("dtype", [Int8Dtype(), "Int8", UInt32Dtype(), "UInt32"])
+def test_astype_specific_casting(dtype):
+    s = pd.Series([1, 2, 3], dtype="Int64")
+    result = s.astype(dtype)
+    expected = pd.Series([1, 2, 3], dtype=dtype)
+    tm.assert_series_equal(result, expected)
+
+    s = pd.Series([1, 2, 3, None], dtype="Int64")
+    result = s.astype(dtype)
+    expected = pd.Series([1, 2, 3, None], dtype=dtype)
+    tm.assert_series_equal(result, expected)
+
+
+def test_astype_floating():
+    arr = pd.array([1, 2, None], dtype="Int64")
+    result = arr.astype("Float64")
+    expected = pd.array([1.0, 2.0, None], dtype="Float64")
+    tm.assert_extension_array_equal(result, expected)
+
+
+def test_astype_dt64():
+    # GH#32435
+    arr = pd.array([1, 2, 3, pd.NA]) * 10**9
+
+    result = arr.astype("datetime64[ns]")
+
+    expected = np.array([1, 2, 3, "NaT"], dtype="M8[s]").astype("M8[ns]")
+    tm.assert_numpy_array_equal(result, expected)
+
+
+def test_construct_cast_invalid(dtype):
+    msg = "cannot safely"
+    arr = [1.2, 2.3, 3.7]
+    with pytest.raises(TypeError, match=msg):
+        pd.array(arr, dtype=dtype)
+
+    with pytest.raises(TypeError, match=msg):
+        pd.Series(arr).astype(dtype)
+
+    arr = [1.2, 2.3, 3.7, np.nan]
+    with pytest.raises(TypeError, match=msg):
+        pd.array(arr, dtype=dtype)
+
+    with pytest.raises(TypeError, match=msg):
+        pd.Series(arr).astype(dtype)
+
+
+@pytest.mark.parametrize("in_series", [True, False])
+def test_to_numpy_na_nan(in_series):
+    a = pd.array([0, 1, None], dtype="Int64")
+    if in_series:
+        a = pd.Series(a)
+
+    result = a.to_numpy(dtype="float64", na_value=np.nan)
+    expected = np.array([0.0, 1.0, np.nan], dtype="float64")
+    tm.assert_numpy_array_equal(result, expected)
+
+    result = a.to_numpy(dtype="int64", na_value=-1)
+    expected = np.array([0, 1, -1], dtype="int64")
+    tm.assert_numpy_array_equal(result, expected)
+
+    result = a.to_numpy(dtype="bool", na_value=False)
+    expected = np.array([False, True, False], dtype="bool")
+    tm.assert_numpy_array_equal(result, expected)
+
+
+@pytest.mark.parametrize("in_series", [True, False])
+@pytest.mark.parametrize("dtype", ["int32", "int64", "bool"])
+def test_to_numpy_dtype(dtype, in_series):
+    a = pd.array([0, 1], dtype="Int64")
+    if in_series:
+        a = pd.Series(a)
+
+    result = a.to_numpy(dtype=dtype)
+    expected = np.array([0, 1], dtype=dtype)
+    tm.assert_numpy_array_equal(result, expected)
+
+
+@pytest.mark.parametrize("dtype", ["int64", "bool"])
+def test_to_numpy_na_raises(dtype):
+    a = pd.array([0, 1, None], dtype="Int64")
+    with pytest.raises(ValueError, match=dtype):
+        a.to_numpy(dtype=dtype)
+
+
+def test_astype_str(using_infer_string):
+    a = pd.array([1, 2, None], dtype="Int64")
+
+    if using_infer_string:
+        expected = pd.array(["1", "2", None], dtype=pd.StringDtype(na_value=np.nan))
+
+        tm.assert_extension_array_equal(a.astype(str), expected)
+        tm.assert_extension_array_equal(a.astype("str"), expected)
+    else:
+        expected = np.array(["1", "2", ""], dtype=f"{tm.ENDIAN}U21")
+
+        tm.assert_numpy_array_equal(a.astype(str), expected)
+        tm.assert_numpy_array_equal(a.astype("str"), expected)
+
+
+def test_astype_boolean():
+    # https://github.com/pandas-dev/pandas/issues/31102
+    a = pd.array([1, 0, -1, 2, None], dtype="Int64")
+    result = a.astype("boolean")
+    expected = pd.array([True, False, True, True, None], dtype="boolean")
+    tm.assert_extension_array_equal(result, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/integer/test_function.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/integer/test_function.py
new file mode 100644
index 0000000000000000000000000000000000000000..d48b636a98feb94c5eaeee9eefbf5730fe9c6f79
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/integer/test_function.py
@@ -0,0 +1,203 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.core.arrays import FloatingArray
+
+
+@pytest.mark.parametrize("ufunc", [np.abs, np.sign])
+# np.sign emits a warning with nans, 
+@pytest.mark.filterwarnings("ignore:invalid value encountered in sign:RuntimeWarning")
+def test_ufuncs_single_int(ufunc):
+    a = pd.array([1, 2, -3, np.nan])
+    result = ufunc(a)
+    expected = pd.array(ufunc(a.astype(float)), dtype="Int64")
+    tm.assert_extension_array_equal(result, expected)
+
+    s = pd.Series(a)
+    result = ufunc(s)
+    expected = pd.Series(pd.array(ufunc(a.astype(float)), dtype="Int64"))
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("ufunc", [np.log, np.exp, np.sin, np.cos, np.sqrt])
+def test_ufuncs_single_float(ufunc):
+    a = pd.array([1, 2, -3, np.nan])
+    with np.errstate(invalid="ignore"):
+        result = ufunc(a)
+        expected = FloatingArray(ufunc(a.astype(float)), mask=a._mask)
+    tm.assert_extension_array_equal(result, expected)
+
+    s = pd.Series(a)
+    with np.errstate(invalid="ignore"):
+        result = ufunc(s)
+    expected = pd.Series(expected)
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("ufunc", [np.add, np.subtract])
+def test_ufuncs_binary_int(ufunc):
+    # two IntegerArrays
+    a = pd.array([1, 2, -3, np.nan])
+    result = ufunc(a, a)
+    expected = pd.array(ufunc(a.astype(float), a.astype(float)), dtype="Int64")
+    tm.assert_extension_array_equal(result, expected)
+
+    # IntegerArray with numpy array
+    arr = np.array([1, 2, 3, 4])
+    result = ufunc(a, arr)
+    expected = pd.array(ufunc(a.astype(float), arr), dtype="Int64")
+    tm.assert_extension_array_equal(result, expected)
+
+    result = ufunc(arr, a)
+    expected = pd.array(ufunc(arr, a.astype(float)), dtype="Int64")
+    tm.assert_extension_array_equal(result, expected)
+
+    # IntegerArray with scalar
+    result = ufunc(a, 1)
+    expected = pd.array(ufunc(a.astype(float), 1), dtype="Int64")
+    tm.assert_extension_array_equal(result, expected)
+
+    result = ufunc(1, a)
+    expected = pd.array(ufunc(1, a.astype(float)), dtype="Int64")
+    tm.assert_extension_array_equal(result, expected)
+
+
+def test_ufunc_binary_output():
+    a = pd.array([1, 2, np.nan])
+    result = np.modf(a)
+    expected = np.modf(a.to_numpy(na_value=np.nan, dtype="float"))
+    expected = (pd.array(expected[0]), pd.array(expected[1]))
+
+    assert isinstance(result, tuple)
+    assert len(result) == 2
+
+    for x, y in zip(result, expected):
+        tm.assert_extension_array_equal(x, y)
+
+
+@pytest.mark.parametrize("values", [[0, 1], [0, None]])
+def test_ufunc_reduce_raises(values):
+    arr = pd.array(values)
+
+    res = np.add.reduce(arr)
+    expected = arr.sum(skipna=False)
+    tm.assert_almost_equal(res, expected)
+
+
+@pytest.mark.parametrize(
+    "pandasmethname, kwargs",
+    [
+        ("var", {"ddof": 0}),
+        ("var", {"ddof": 1}),
+        ("std", {"ddof": 0}),
+        ("std", {"ddof": 1}),
+        ("kurtosis", {}),
+        ("skew", {}),
+        ("sem", {}),
+    ],
+)
+def test_stat_method(pandasmethname, kwargs):
+    s = pd.Series(data=[1, 2, 3, 4, 5, 6, np.nan, np.nan], dtype="Int64")
+    pandasmeth = getattr(s, pandasmethname)
+    result = pandasmeth(**kwargs)
+    s2 = pd.Series(data=[1, 2, 3, 4, 5, 6], dtype="Int64")
+    pandasmeth = getattr(s2, pandasmethname)
+    expected = pandasmeth(**kwargs)
+    assert expected == result
+
+
+def test_value_counts_na():
+    arr = pd.array([1, 2, 1, pd.NA], dtype="Int64")
+    result = arr.value_counts(dropna=False)
+    ex_index = pd.Index([1, 2, pd.NA], dtype="Int64")
+    assert ex_index.dtype == "Int64"
+    expected = pd.Series([2, 1, 1], index=ex_index, dtype="Int64", name="count")
+    tm.assert_series_equal(result, expected)
+
+    result = arr.value_counts(dropna=True)
+    expected = pd.Series([2, 1], index=arr[:2], dtype="Int64", name="count")
+    assert expected.index.dtype == arr.dtype
+    tm.assert_series_equal(result, expected)
+
+
+def test_value_counts_empty():
+    # https://github.com/pandas-dev/pandas/issues/33317
+    ser = pd.Series([], dtype="Int64")
+    result = ser.value_counts()
+    idx = pd.Index([], dtype=ser.dtype)
+    assert idx.dtype == ser.dtype
+    expected = pd.Series([], index=idx, dtype="Int64", name="count")
+    tm.assert_series_equal(result, expected)
+
+
+def test_value_counts_with_normalize():
+    # GH 33172
+    ser = pd.Series([1, 2, 1, pd.NA], dtype="Int64")
+    result = ser.value_counts(normalize=True)
+    expected = pd.Series([2, 1], index=ser[:2], dtype="Float64", name="proportion") / 3
+    assert expected.index.dtype == ser.dtype
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("skipna", [True, False])
+@pytest.mark.parametrize("min_count", [0, 4])
+def test_integer_array_sum(skipna, min_count, any_int_ea_dtype):
+    dtype = any_int_ea_dtype
+    arr = pd.array([1, 2, 3, None], dtype=dtype)
+    result = arr.sum(skipna=skipna, min_count=min_count)
+    if skipna and min_count == 0:
+        assert result == 6
+    else:
+        assert result is pd.NA
+
+
+@pytest.mark.parametrize("skipna", [True, False])
+@pytest.mark.parametrize("method", ["min", "max"])
+def test_integer_array_min_max(skipna, method, any_int_ea_dtype):
+    dtype = any_int_ea_dtype
+    arr = pd.array([0, 1, None], dtype=dtype)
+    func = getattr(arr, method)
+    result = func(skipna=skipna)
+    if skipna:
+        assert result == (0 if method == "min" else 1)
+    else:
+        assert result is pd.NA
+
+
+@pytest.mark.parametrize("skipna", [True, False])
+@pytest.mark.parametrize("min_count", [0, 9])
+def test_integer_array_prod(skipna, min_count, any_int_ea_dtype):
+    dtype = any_int_ea_dtype
+    arr = pd.array([1, 2, None], dtype=dtype)
+    result = arr.prod(skipna=skipna, min_count=min_count)
+    if skipna and min_count == 0:
+        assert result == 2
+    else:
+        assert result is pd.NA
+
+
+@pytest.mark.parametrize(
+    "values, expected", [([1, 2, 3], 6), ([1, 2, 3, None], 6), ([None], 0)]
+)
+def test_integer_array_numpy_sum(values, expected):
+    arr = pd.array(values, dtype="Int64")
+    result = np.sum(arr)
+    assert result == expected
+
+
+@pytest.mark.parametrize("op", ["sum", "prod", "min", "max"])
+def test_dataframe_reductions(op):
+    # https://github.com/pandas-dev/pandas/pull/32867
+    # ensure the integers are not cast to float during reductions
+    df = pd.DataFrame({"a": pd.array([1, 2], dtype="Int64")})
+    result = df.max()
+    assert isinstance(result["a"], np.int64)
+
+
+# TODO(jreback) - these need testing / are broken
+
+# shift
+
+# set_index (destroys type)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/integer/test_indexing.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/integer/test_indexing.py
new file mode 100644
index 0000000000000000000000000000000000000000..4b953d699108b2aed1c992cf3a33f3013b298254
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/integer/test_indexing.py
@@ -0,0 +1,19 @@
+import pandas as pd
+import pandas._testing as tm
+
+
+def test_array_setitem_nullable_boolean_mask():
+    # GH 31446
+    ser = pd.Series([1, 2], dtype="Int64")
+    result = ser.where(ser > 1)
+    expected = pd.Series([pd.NA, 2], dtype="Int64")
+    tm.assert_series_equal(result, expected)
+
+
+def test_array_setitem():
+    # GH 31446
+    arr = pd.Series([1, 2], dtype="Int64").array
+    arr[arr > 1] = 1
+
+    expected = pd.array([1, 1], dtype="Int64")
+    tm.assert_extension_array_equal(arr, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/integer/test_reduction.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/integer/test_reduction.py
new file mode 100644
index 0000000000000000000000000000000000000000..1c91cd25ba69ce207c6be3f6dd438a4ae4f37980
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/integer/test_reduction.py
@@ -0,0 +1,123 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Series,
+    array,
+)
+import pandas._testing as tm
+
+
+@pytest.mark.parametrize(
+    "op, expected",
+    [
+        ["sum", np.int64(3)],
+        ["prod", np.int64(2)],
+        ["min", np.int64(1)],
+        ["max", np.int64(2)],
+        ["mean", np.float64(1.5)],
+        ["median", np.float64(1.5)],
+        ["var", np.float64(0.5)],
+        ["std", np.float64(0.5**0.5)],
+        ["skew", pd.NA],
+        ["kurt", pd.NA],
+        ["any", True],
+        ["all", True],
+    ],
+)
+def test_series_reductions(op, expected):
+    ser = Series([1, 2], dtype="Int64")
+    result = getattr(ser, op)()
+    tm.assert_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "op, expected",
+    [
+        ["sum", Series([3], index=["a"], dtype="Int64")],
+        ["prod", Series([2], index=["a"], dtype="Int64")],
+        ["min", Series([1], index=["a"], dtype="Int64")],
+        ["max", Series([2], index=["a"], dtype="Int64")],
+        ["mean", Series([1.5], index=["a"], dtype="Float64")],
+        ["median", Series([1.5], index=["a"], dtype="Float64")],
+        ["var", Series([0.5], index=["a"], dtype="Float64")],
+        ["std", Series([0.5**0.5], index=["a"], dtype="Float64")],
+        ["skew", Series([pd.NA], index=["a"], dtype="Float64")],
+        ["kurt", Series([pd.NA], index=["a"], dtype="Float64")],
+        ["any", Series([True], index=["a"], dtype="boolean")],
+        ["all", Series([True], index=["a"], dtype="boolean")],
+    ],
+)
+def test_dataframe_reductions(op, expected):
+    df = DataFrame({"a": array([1, 2], dtype="Int64")})
+    result = getattr(df, op)()
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "op, expected",
+    [
+        ["sum", array([1, 3], dtype="Int64")],
+        ["prod", array([1, 3], dtype="Int64")],
+        ["min", array([1, 3], dtype="Int64")],
+        ["max", array([1, 3], dtype="Int64")],
+        ["mean", array([1, 3], dtype="Float64")],
+        ["median", array([1, 3], dtype="Float64")],
+        ["var", array([pd.NA], dtype="Float64")],
+        ["std", array([pd.NA], dtype="Float64")],
+        ["skew", array([pd.NA], dtype="Float64")],
+        ["any", array([True, True], dtype="boolean")],
+        ["all", array([True, True], dtype="boolean")],
+    ],
+)
+def test_groupby_reductions(op, expected):
+    df = DataFrame(
+        {
+            "A": ["a", "b", "b"],
+            "B": array([1, None, 3], dtype="Int64"),
+        }
+    )
+    result = getattr(df.groupby("A"), op)()
+    expected = DataFrame(expected, index=pd.Index(["a", "b"], name="A"), columns=["B"])
+
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "op, expected",
+    [
+        ["sum", Series([4, 4], index=["B", "C"], dtype="Float64")],
+        ["prod", Series([3, 3], index=["B", "C"], dtype="Float64")],
+        ["min", Series([1, 1], index=["B", "C"], dtype="Float64")],
+        ["max", Series([3, 3], index=["B", "C"], dtype="Float64")],
+        ["mean", Series([2, 2], index=["B", "C"], dtype="Float64")],
+        ["median", Series([2, 2], index=["B", "C"], dtype="Float64")],
+        ["var", Series([2, 2], index=["B", "C"], dtype="Float64")],
+        ["std", Series([2**0.5, 2**0.5], index=["B", "C"], dtype="Float64")],
+        ["skew", Series([pd.NA, pd.NA], index=["B", "C"], dtype="Float64")],
+        ["kurt", Series([pd.NA, pd.NA], index=["B", "C"], dtype="Float64")],
+        ["any", Series([True, True, True], index=["A", "B", "C"], dtype="boolean")],
+        ["all", Series([True, True, True], index=["A", "B", "C"], dtype="boolean")],
+    ],
+)
+def test_mixed_reductions(op, expected):
+    df = DataFrame(
+        {
+            "A": ["a", "b", "b"],
+            "B": [1, None, 3],
+            "C": array([1, None, 3], dtype="Int64"),
+        }
+    )
+
+    # series
+    result = getattr(df.C, op)()
+    tm.assert_equal(result, expected["C"])
+
+    # frame
+    if op in ["any", "all"]:
+        result = getattr(df, op)()
+    else:
+        result = getattr(df, op)(numeric_only=True)
+    tm.assert_series_equal(result, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/integer/test_repr.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/integer/test_repr.py
new file mode 100644
index 0000000000000000000000000000000000000000..168210eed5d06a461bbf42dd1e1fae3db0fd851c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/integer/test_repr.py
@@ -0,0 +1,67 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas.core.arrays.integer import (
+    Int8Dtype,
+    Int16Dtype,
+    Int32Dtype,
+    Int64Dtype,
+    UInt8Dtype,
+    UInt16Dtype,
+    UInt32Dtype,
+    UInt64Dtype,
+)
+
+
+def test_dtypes(dtype):
+    # smoke tests on auto dtype construction
+
+    if dtype.is_signed_integer:
+        assert np.dtype(dtype.type).kind == "i"
+    else:
+        assert np.dtype(dtype.type).kind == "u"
+    assert dtype.name is not None
+
+
+@pytest.mark.parametrize(
+    "dtype, expected",
+    [
+        (Int8Dtype(), "Int8Dtype()"),
+        (Int16Dtype(), "Int16Dtype()"),
+        (Int32Dtype(), "Int32Dtype()"),
+        (Int64Dtype(), "Int64Dtype()"),
+        (UInt8Dtype(), "UInt8Dtype()"),
+        (UInt16Dtype(), "UInt16Dtype()"),
+        (UInt32Dtype(), "UInt32Dtype()"),
+        (UInt64Dtype(), "UInt64Dtype()"),
+    ],
+)
+def test_repr_dtype(dtype, expected):
+    assert repr(dtype) == expected
+
+
+def test_repr_array():
+    result = repr(pd.array([1, None, 3]))
+    expected = "\n[1, , 3]\nLength: 3, dtype: Int64"
+    assert result == expected
+
+
+def test_repr_array_long():
+    data = pd.array([1, 2, None] * 1000)
+    expected = (
+        "\n"
+        "[   1,    2, ,    1,    2, ,    1,    2, ,    1,\n"
+        " ...\n"
+        " ,    1,    2, ,    1,    2, ,    1,    2, ]\n"
+        "Length: 3000, dtype: Int64"
+    )
+    result = repr(data)
+    assert result == expected
+
+
+def test_frame_repr(data_missing):
+    df = pd.DataFrame({"A": data_missing})
+    result = repr(df)
+    expected = "      A\n0  \n1     1"
+    assert result == expected
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/interval/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/interval/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/interval/test_astype.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/interval/test_astype.py
new file mode 100644
index 0000000000000000000000000000000000000000..d7a2140f817f3a8e5689d001768cf5642118b105
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/interval/test_astype.py
@@ -0,0 +1,28 @@
+import pytest
+
+from pandas import (
+    Categorical,
+    CategoricalDtype,
+    Index,
+    IntervalIndex,
+)
+import pandas._testing as tm
+
+
+class TestAstype:
+    @pytest.mark.parametrize("ordered", [True, False])
+    def test_astype_categorical_retains_ordered(self, ordered):
+        index = IntervalIndex.from_breaks(range(5))
+        arr = index._data
+
+        dtype = CategoricalDtype(None, ordered=ordered)
+
+        expected = Categorical(list(arr), ordered=ordered)
+        result = arr.astype(dtype)
+        assert result.ordered is ordered
+        tm.assert_categorical_equal(result, expected)
+
+        # test IntervalIndex.astype while we're at it.
+        result = index.astype(dtype)
+        expected = Index(expected)
+        tm.assert_index_equal(result, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/interval/test_formats.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/interval/test_formats.py
new file mode 100644
index 0000000000000000000000000000000000000000..535efee51937473071c9490330eb68364769d5aa
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/interval/test_formats.py
@@ -0,0 +1,13 @@
+from pandas.core.arrays import IntervalArray
+
+
+def test_repr():
+    # GH#25022
+    arr = IntervalArray.from_tuples([(0, 1), (1, 2)])
+    result = repr(arr)
+    expected = (
+        "\n"
+        "[(0, 1], (1, 2]]\n"
+        "Length: 2, dtype: interval[int64, right]"
+    )
+    assert result == expected
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/interval/test_interval.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/interval/test_interval.py
new file mode 100644
index 0000000000000000000000000000000000000000..be4b2c3e7e74cddfaf8b2efa08879d3a6d8f1757
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/interval/test_interval.py
@@ -0,0 +1,231 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    Index,
+    Interval,
+    IntervalIndex,
+    Timedelta,
+    Timestamp,
+    date_range,
+    timedelta_range,
+)
+import pandas._testing as tm
+from pandas.core.arrays import IntervalArray
+
+
+@pytest.fixture(
+    params=[
+        (Index([0, 2, 4]), Index([1, 3, 5])),
+        (Index([0.0, 1.0, 2.0]), Index([1.0, 2.0, 3.0])),
+        (timedelta_range("0 days", periods=3), timedelta_range("1 day", periods=3)),
+        (date_range("20170101", periods=3), date_range("20170102", periods=3)),
+        (
+            date_range("20170101", periods=3, tz="US/Eastern"),
+            date_range("20170102", periods=3, tz="US/Eastern"),
+        ),
+    ],
+    ids=lambda x: str(x[0].dtype),
+)
+def left_right_dtypes(request):
+    """
+    Fixture for building an IntervalArray from various dtypes
+    """
+    return request.param
+
+
+class TestAttributes:
+    @pytest.mark.parametrize(
+        "left, right",
+        [
+            (0, 1),
+            (Timedelta("0 days"), Timedelta("1 day")),
+            (Timestamp("2018-01-01"), Timestamp("2018-01-02")),
+            (
+                Timestamp("2018-01-01", tz="US/Eastern"),
+                Timestamp("2018-01-02", tz="US/Eastern"),
+            ),
+        ],
+    )
+    @pytest.mark.parametrize("constructor", [IntervalArray, IntervalIndex])
+    def test_is_empty(self, constructor, left, right, closed):
+        # GH27219
+        tuples = [(left, left), (left, right), np.nan]
+        expected = np.array([closed != "both", False, False])
+        result = constructor.from_tuples(tuples, closed=closed).is_empty
+        tm.assert_numpy_array_equal(result, expected)
+
+
+class TestMethods:
+    @pytest.mark.parametrize("new_closed", ["left", "right", "both", "neither"])
+    def test_set_closed(self, closed, new_closed):
+        # GH 21670
+        array = IntervalArray.from_breaks(range(10), closed=closed)
+        result = array.set_closed(new_closed)
+        expected = IntervalArray.from_breaks(range(10), closed=new_closed)
+        tm.assert_extension_array_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "other",
+        [
+            Interval(0, 1, closed="right"),
+            IntervalArray.from_breaks([1, 2, 3, 4], closed="right"),
+        ],
+    )
+    def test_where_raises(self, other):
+        # GH#45768 The IntervalArray methods raises; the Series method coerces
+        ser = pd.Series(IntervalArray.from_breaks([1, 2, 3, 4], closed="left"))
+        mask = np.array([True, False, True])
+        match = "'value.closed' is 'right', expected 'left'."
+        with pytest.raises(ValueError, match=match):
+            ser.array._where(mask, other)
+
+        res = ser.where(mask, other=other)
+        expected = ser.astype(object).where(mask, other)
+        tm.assert_series_equal(res, expected)
+
+    def test_shift(self):
+        # https://github.com/pandas-dev/pandas/issues/31495, GH#22428, GH#31502
+        a = IntervalArray.from_breaks([1, 2, 3])
+        result = a.shift()
+        # int -> float
+        expected = IntervalArray.from_tuples([(np.nan, np.nan), (1.0, 2.0)])
+        tm.assert_interval_array_equal(result, expected)
+
+        msg = "can only insert Interval objects and NA into an IntervalArray"
+        with pytest.raises(TypeError, match=msg):
+            a.shift(1, fill_value=pd.NaT)
+
+    def test_shift_datetime(self):
+        # GH#31502, GH#31504
+        a = IntervalArray.from_breaks(date_range("2000", periods=4))
+        result = a.shift(2)
+        expected = a.take([-1, -1, 0], allow_fill=True)
+        tm.assert_interval_array_equal(result, expected)
+
+        result = a.shift(-1)
+        expected = a.take([1, 2, -1], allow_fill=True)
+        tm.assert_interval_array_equal(result, expected)
+
+        msg = "can only insert Interval objects and NA into an IntervalArray"
+        with pytest.raises(TypeError, match=msg):
+            a.shift(1, fill_value=np.timedelta64("NaT", "ns"))
+
+
+class TestSetitem:
+    def test_set_na(self, left_right_dtypes):
+        left, right = left_right_dtypes
+        left = left.copy(deep=True)
+        right = right.copy(deep=True)
+        result = IntervalArray.from_arrays(left, right)
+
+        if result.dtype.subtype.kind not in ["m", "M"]:
+            msg = "'value' should be an interval type, got <.*NaTType'> instead."
+            with pytest.raises(TypeError, match=msg):
+                result[0] = pd.NaT
+        if result.dtype.subtype.kind in ["i", "u"]:
+            msg = "Cannot set float NaN to integer-backed IntervalArray"
+            # GH#45484 TypeError, not ValueError, matches what we get with
+            # non-NA un-holdable value.
+            with pytest.raises(TypeError, match=msg):
+                result[0] = np.nan
+            return
+
+        result[0] = np.nan
+
+        expected_left = Index([left._na_value] + list(left[1:]))
+        expected_right = Index([right._na_value] + list(right[1:]))
+        expected = IntervalArray.from_arrays(expected_left, expected_right)
+
+        tm.assert_extension_array_equal(result, expected)
+
+    def test_setitem_mismatched_closed(self):
+        arr = IntervalArray.from_breaks(range(4))
+        orig = arr.copy()
+        other = arr.set_closed("both")
+
+        msg = "'value.closed' is 'both', expected 'right'"
+        with pytest.raises(ValueError, match=msg):
+            arr[0] = other[0]
+        with pytest.raises(ValueError, match=msg):
+            arr[:1] = other[:1]
+        with pytest.raises(ValueError, match=msg):
+            arr[:0] = other[:0]
+        with pytest.raises(ValueError, match=msg):
+            arr[:] = other[::-1]
+        with pytest.raises(ValueError, match=msg):
+            arr[:] = list(other[::-1])
+        with pytest.raises(ValueError, match=msg):
+            arr[:] = other[::-1].astype(object)
+        with pytest.raises(ValueError, match=msg):
+            arr[:] = other[::-1].astype("category")
+
+        # empty list should be no-op
+        arr[:0] = []
+        tm.assert_interval_array_equal(arr, orig)
+
+
+class TestReductions:
+    def test_min_max_invalid_axis(self, left_right_dtypes):
+        left, right = left_right_dtypes
+        left = left.copy(deep=True)
+        right = right.copy(deep=True)
+        arr = IntervalArray.from_arrays(left, right)
+
+        msg = "`axis` must be fewer than the number of dimensions"
+        for axis in [-2, 1]:
+            with pytest.raises(ValueError, match=msg):
+                arr.min(axis=axis)
+            with pytest.raises(ValueError, match=msg):
+                arr.max(axis=axis)
+
+        msg = "'>=' not supported between"
+        with pytest.raises(TypeError, match=msg):
+            arr.min(axis="foo")
+        with pytest.raises(TypeError, match=msg):
+            arr.max(axis="foo")
+
+    def test_min_max(self, left_right_dtypes, index_or_series_or_array):
+        # GH#44746
+        left, right = left_right_dtypes
+        left = left.copy(deep=True)
+        right = right.copy(deep=True)
+        arr = IntervalArray.from_arrays(left, right)
+
+        # The expected results below are only valid if monotonic
+        assert left.is_monotonic_increasing
+        assert Index(arr).is_monotonic_increasing
+
+        MIN = arr[0]
+        MAX = arr[-1]
+
+        indexer = np.arange(len(arr))
+        np.random.default_rng(2).shuffle(indexer)
+        arr = arr.take(indexer)
+
+        arr_na = arr.insert(2, np.nan)
+
+        arr = index_or_series_or_array(arr)
+        arr_na = index_or_series_or_array(arr_na)
+
+        for skipna in [True, False]:
+            res = arr.min(skipna=skipna)
+            assert res == MIN
+            assert type(res) == type(MIN)
+
+            res = arr.max(skipna=skipna)
+            assert res == MAX
+            assert type(res) == type(MAX)
+
+        res = arr_na.min(skipna=False)
+        assert np.isnan(res)
+        res = arr_na.max(skipna=False)
+        assert np.isnan(res)
+
+        res = arr_na.min(skipna=True)
+        assert res == MIN
+        assert type(res) == type(MIN)
+        res = arr_na.max(skipna=True)
+        assert res == MAX
+        assert type(res) == type(MAX)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/interval/test_interval_pyarrow.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/interval/test_interval_pyarrow.py
new file mode 100644
index 0000000000000000000000000000000000000000..ef8701be81e2b9248c29fc4e901161fd18d72bbe
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/interval/test_interval_pyarrow.py
@@ -0,0 +1,160 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.core.arrays import IntervalArray
+
+
+def test_arrow_extension_type():
+    pa = pytest.importorskip("pyarrow")
+
+    from pandas.core.arrays.arrow.extension_types import ArrowIntervalType
+
+    p1 = ArrowIntervalType(pa.int64(), "left")
+    p2 = ArrowIntervalType(pa.int64(), "left")
+    p3 = ArrowIntervalType(pa.int64(), "right")
+
+    assert p1.closed == "left"
+    assert p1 == p2
+    assert p1 != p3
+    assert hash(p1) == hash(p2)
+    assert hash(p1) != hash(p3)
+
+
+def test_arrow_array():
+    pa = pytest.importorskip("pyarrow")
+
+    from pandas.core.arrays.arrow.extension_types import ArrowIntervalType
+
+    intervals = pd.interval_range(1, 5, freq=1).array
+
+    result = pa.array(intervals)
+    assert isinstance(result.type, ArrowIntervalType)
+    assert result.type.closed == intervals.closed
+    assert result.type.subtype == pa.int64()
+    assert result.storage.field("left").equals(pa.array([1, 2, 3, 4], type="int64"))
+    assert result.storage.field("right").equals(pa.array([2, 3, 4, 5], type="int64"))
+
+    expected = pa.array([{"left": i, "right": i + 1} for i in range(1, 5)])
+    assert result.storage.equals(expected)
+
+    # convert to its storage type
+    result = pa.array(intervals, type=expected.type)
+    assert result.equals(expected)
+
+    # unsupported conversions
+    with pytest.raises(TypeError, match="Not supported to convert IntervalArray"):
+        pa.array(intervals, type="float64")
+
+    with pytest.raises(TypeError, match="Not supported to convert IntervalArray"):
+        pa.array(intervals, type=ArrowIntervalType(pa.float64(), "left"))
+
+
+def test_arrow_array_missing():
+    pa = pytest.importorskip("pyarrow")
+
+    from pandas.core.arrays.arrow.extension_types import ArrowIntervalType
+
+    arr = IntervalArray.from_breaks([0.0, 1.0, 2.0, 3.0])
+    arr[1] = None
+
+    result = pa.array(arr)
+    assert isinstance(result.type, ArrowIntervalType)
+    assert result.type.closed == arr.closed
+    assert result.type.subtype == pa.float64()
+
+    # fields have missing values (not NaN)
+    left = pa.array([0.0, None, 2.0], type="float64")
+    right = pa.array([1.0, None, 3.0], type="float64")
+    assert result.storage.field("left").equals(left)
+    assert result.storage.field("right").equals(right)
+
+    # structarray itself also has missing values on the array level
+    vals = [
+        {"left": 0.0, "right": 1.0},
+        {"left": None, "right": None},
+        {"left": 2.0, "right": 3.0},
+    ]
+    expected = pa.StructArray.from_pandas(vals, mask=np.array([False, True, False]))
+    assert result.storage.equals(expected)
+
+
+@pytest.mark.filterwarnings(
+    "ignore:Passing a BlockManager to DataFrame:DeprecationWarning"
+)
+@pytest.mark.parametrize(
+    "breaks",
+    [[0.0, 1.0, 2.0, 3.0], pd.date_range("2017", periods=4, freq="D")],
+    ids=["float", "datetime64[ns]"],
+)
+def test_arrow_table_roundtrip(breaks):
+    pa = pytest.importorskip("pyarrow")
+
+    from pandas.core.arrays.arrow.extension_types import ArrowIntervalType
+
+    arr = IntervalArray.from_breaks(breaks)
+    arr[1] = None
+    df = pd.DataFrame({"a": arr})
+
+    table = pa.table(df)
+    assert isinstance(table.field("a").type, ArrowIntervalType)
+    result = table.to_pandas()
+    assert isinstance(result["a"].dtype, pd.IntervalDtype)
+    tm.assert_frame_equal(result, df)
+
+    table2 = pa.concat_tables([table, table])
+    result = table2.to_pandas()
+    expected = pd.concat([df, df], ignore_index=True)
+    tm.assert_frame_equal(result, expected)
+
+    # GH#41040
+    table = pa.table(
+        [pa.chunked_array([], type=table.column(0).type)], schema=table.schema
+    )
+    result = table.to_pandas()
+    tm.assert_frame_equal(result, expected[0:0])
+
+
+@pytest.mark.filterwarnings(
+    "ignore:Passing a BlockManager to DataFrame:DeprecationWarning"
+)
+@pytest.mark.parametrize(
+    "breaks",
+    [[0.0, 1.0, 2.0, 3.0], pd.date_range("2017", periods=4, freq="D")],
+    ids=["float", "datetime64[ns]"],
+)
+def test_arrow_table_roundtrip_without_metadata(breaks):
+    pa = pytest.importorskip("pyarrow")
+
+    arr = IntervalArray.from_breaks(breaks)
+    arr[1] = None
+    df = pd.DataFrame({"a": arr})
+
+    table = pa.table(df)
+    # remove the metadata
+    table = table.replace_schema_metadata()
+    assert table.schema.metadata is None
+
+    result = table.to_pandas()
+    assert isinstance(result["a"].dtype, pd.IntervalDtype)
+    tm.assert_frame_equal(result, df)
+
+
+def test_from_arrow_from_raw_struct_array():
+    # in case pyarrow lost the Interval extension type (eg on parquet roundtrip
+    # with datetime64[ns] subtype, see GH-45881), still allow conversion
+    # from arrow to IntervalArray
+    pa = pytest.importorskip("pyarrow")
+
+    arr = pa.array([{"left": 0, "right": 1}, {"left": 1, "right": 2}])
+    dtype = pd.IntervalDtype(np.dtype("int64"), closed="neither")
+
+    result = dtype.__from_arrow__(arr)
+    expected = IntervalArray.from_breaks(
+        np.array([0, 1, 2], dtype="int64"), closed="neither"
+    )
+    tm.assert_extension_array_equal(result, expected)
+
+    result = dtype.__from_arrow__(pa.chunked_array([arr]))
+    tm.assert_extension_array_equal(result, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/interval/test_overlaps.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/interval/test_overlaps.py
new file mode 100644
index 0000000000000000000000000000000000000000..4853bec51106c05781a4c11921f0e082934147ec
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/interval/test_overlaps.py
@@ -0,0 +1,93 @@
+"""Tests for Interval-Interval operations, such as overlaps, contains, etc."""
+import numpy as np
+import pytest
+
+from pandas import (
+    Interval,
+    IntervalIndex,
+    Timedelta,
+    Timestamp,
+)
+import pandas._testing as tm
+from pandas.core.arrays import IntervalArray
+
+
+@pytest.fixture(params=[IntervalArray, IntervalIndex])
+def constructor(request):
+    """
+    Fixture for testing both interval container classes.
+    """
+    return request.param
+
+
+@pytest.fixture(
+    params=[
+        (Timedelta("0 days"), Timedelta("1 day")),
+        (Timestamp("2018-01-01"), Timedelta("1 day")),
+        (0, 1),
+    ],
+    ids=lambda x: type(x[0]).__name__,
+)
+def start_shift(request):
+    """
+    Fixture for generating intervals of different types from a start value
+    and a shift value that can be added to start to generate an endpoint.
+    """
+    return request.param
+
+
+class TestOverlaps:
+    def test_overlaps_interval(self, constructor, start_shift, closed, other_closed):
+        start, shift = start_shift
+        interval = Interval(start, start + 3 * shift, other_closed)
+
+        # intervals: identical, nested, spanning, partial, adjacent, disjoint
+        tuples = [
+            (start, start + 3 * shift),
+            (start + shift, start + 2 * shift),
+            (start - shift, start + 4 * shift),
+            (start + 2 * shift, start + 4 * shift),
+            (start + 3 * shift, start + 4 * shift),
+            (start + 4 * shift, start + 5 * shift),
+        ]
+        interval_container = constructor.from_tuples(tuples, closed)
+
+        adjacent = interval.closed_right and interval_container.closed_left
+        expected = np.array([True, True, True, True, adjacent, False])
+        result = interval_container.overlaps(interval)
+        tm.assert_numpy_array_equal(result, expected)
+
+    @pytest.mark.parametrize("other_constructor", [IntervalArray, IntervalIndex])
+    def test_overlaps_interval_container(self, constructor, other_constructor):
+        # TODO: modify this test when implemented
+        interval_container = constructor.from_breaks(range(5))
+        other_container = other_constructor.from_breaks(range(5))
+        with pytest.raises(NotImplementedError, match="^$"):
+            interval_container.overlaps(other_container)
+
+    def test_overlaps_na(self, constructor, start_shift):
+        """NA values are marked as False"""
+        start, shift = start_shift
+        interval = Interval(start, start + shift)
+
+        tuples = [
+            (start, start + shift),
+            np.nan,
+            (start + 2 * shift, start + 3 * shift),
+        ]
+        interval_container = constructor.from_tuples(tuples)
+
+        expected = np.array([True, False, False])
+        result = interval_container.overlaps(interval)
+        tm.assert_numpy_array_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "other",
+        [10, True, "foo", Timedelta("1 day"), Timestamp("2018-01-01")],
+        ids=lambda x: type(x).__name__,
+    )
+    def test_overlaps_invalid_type(self, constructor, other):
+        interval_container = constructor.from_breaks(range(5))
+        msg = f"`other` must be Interval-like, got {type(other).__name__}"
+        with pytest.raises(TypeError, match=msg):
+            interval_container.overlaps(other)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/masked/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/masked/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/masked/test_arithmetic.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/masked/test_arithmetic.py
new file mode 100644
index 0000000000000000000000000000000000000000..f4b571ca627b3da34e943972ba70b03bae74417a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/masked/test_arithmetic.py
@@ -0,0 +1,248 @@
+from __future__ import annotations
+
+from typing import Any
+
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+
+# integer dtypes
+arrays = [pd.array([1, 2, 3, None], dtype=dtype) for dtype in tm.ALL_INT_EA_DTYPES]
+scalars: list[Any] = [2] * len(arrays)
+# floating dtypes
+arrays += [pd.array([0.1, 0.2, 0.3, None], dtype=dtype) for dtype in tm.FLOAT_EA_DTYPES]
+scalars += [0.2, 0.2]
+# boolean
+arrays += [pd.array([True, False, True, None], dtype="boolean")]
+scalars += [False]
+
+
+@pytest.fixture(params=zip(arrays, scalars), ids=[a.dtype.name for a in arrays])
+def data(request):
+    """Fixture returning parametrized (array, scalar) tuple.
+
+    Used to test equivalence of scalars, numpy arrays with array ops, and the
+    equivalence of DataFrame and Series ops.
+    """
+    return request.param
+
+
+def check_skip(data, op_name):
+    if isinstance(data.dtype, pd.BooleanDtype) and "sub" in op_name:
+        pytest.skip("subtract not implemented for boolean")
+
+
+def is_bool_not_implemented(data, op_name):
+    # match non-masked behavior
+    return data.dtype.kind == "b" and op_name.strip("_").lstrip("r") in [
+        "pow",
+        "truediv",
+        "floordiv",
+    ]
+
+
+# Test equivalence of scalars, numpy arrays with array ops
+# -----------------------------------------------------------------------------
+
+
+def test_array_scalar_like_equivalence(data, all_arithmetic_operators):
+    data, scalar = data
+    op = tm.get_op_from_name(all_arithmetic_operators)
+    check_skip(data, all_arithmetic_operators)
+
+    scalar_array = pd.array([scalar] * len(data), dtype=data.dtype)
+
+    # TODO also add len-1 array (np.array([scalar], dtype=data.dtype.numpy_dtype))
+    for scalar in [scalar, data.dtype.type(scalar)]:
+        if is_bool_not_implemented(data, all_arithmetic_operators):
+            msg = "operator '.*' not implemented for bool dtypes"
+            with pytest.raises(NotImplementedError, match=msg):
+                op(data, scalar)
+            with pytest.raises(NotImplementedError, match=msg):
+                op(data, scalar_array)
+        else:
+            result = op(data, scalar)
+            expected = op(data, scalar_array)
+            tm.assert_extension_array_equal(result, expected)
+
+
+def test_array_NA(data, all_arithmetic_operators):
+    data, _ = data
+    op = tm.get_op_from_name(all_arithmetic_operators)
+    check_skip(data, all_arithmetic_operators)
+
+    scalar = pd.NA
+    scalar_array = pd.array([pd.NA] * len(data), dtype=data.dtype)
+
+    mask = data._mask.copy()
+
+    if is_bool_not_implemented(data, all_arithmetic_operators):
+        msg = "operator '.*' not implemented for bool dtypes"
+        with pytest.raises(NotImplementedError, match=msg):
+            op(data, scalar)
+        # GH#45421 check op doesn't alter data._mask inplace
+        tm.assert_numpy_array_equal(mask, data._mask)
+        return
+
+    result = op(data, scalar)
+    # GH#45421 check op doesn't alter data._mask inplace
+    tm.assert_numpy_array_equal(mask, data._mask)
+
+    expected = op(data, scalar_array)
+    tm.assert_numpy_array_equal(mask, data._mask)
+
+    tm.assert_extension_array_equal(result, expected)
+
+
+def test_numpy_array_equivalence(data, all_arithmetic_operators):
+    data, scalar = data
+    op = tm.get_op_from_name(all_arithmetic_operators)
+    check_skip(data, all_arithmetic_operators)
+
+    numpy_array = np.array([scalar] * len(data), dtype=data.dtype.numpy_dtype)
+    pd_array = pd.array(numpy_array, dtype=data.dtype)
+
+    if is_bool_not_implemented(data, all_arithmetic_operators):
+        msg = "operator '.*' not implemented for bool dtypes"
+        with pytest.raises(NotImplementedError, match=msg):
+            op(data, numpy_array)
+        with pytest.raises(NotImplementedError, match=msg):
+            op(data, pd_array)
+        return
+
+    result = op(data, numpy_array)
+    expected = op(data, pd_array)
+    tm.assert_extension_array_equal(result, expected)
+
+
+# Test equivalence with Series and DataFrame ops
+# -----------------------------------------------------------------------------
+
+
+def test_frame(data, all_arithmetic_operators):
+    data, scalar = data
+    op = tm.get_op_from_name(all_arithmetic_operators)
+    check_skip(data, all_arithmetic_operators)
+
+    # DataFrame with scalar
+    df = pd.DataFrame({"A": data})
+
+    if is_bool_not_implemented(data, all_arithmetic_operators):
+        msg = "operator '.*' not implemented for bool dtypes"
+        with pytest.raises(NotImplementedError, match=msg):
+            op(df, scalar)
+        with pytest.raises(NotImplementedError, match=msg):
+            op(data, scalar)
+        return
+
+    result = op(df, scalar)
+    expected = pd.DataFrame({"A": op(data, scalar)})
+    tm.assert_frame_equal(result, expected)
+
+
+def test_series(data, all_arithmetic_operators):
+    data, scalar = data
+    op = tm.get_op_from_name(all_arithmetic_operators)
+    check_skip(data, all_arithmetic_operators)
+
+    ser = pd.Series(data)
+
+    others = [
+        scalar,
+        np.array([scalar] * len(data), dtype=data.dtype.numpy_dtype),
+        pd.array([scalar] * len(data), dtype=data.dtype),
+        pd.Series([scalar] * len(data), dtype=data.dtype),
+    ]
+
+    for other in others:
+        if is_bool_not_implemented(data, all_arithmetic_operators):
+            msg = "operator '.*' not implemented for bool dtypes"
+            with pytest.raises(NotImplementedError, match=msg):
+                op(ser, other)
+
+        else:
+            result = op(ser, other)
+            expected = pd.Series(op(data, other))
+            tm.assert_series_equal(result, expected)
+
+
+# Test generic characteristics / errors
+# -----------------------------------------------------------------------------
+
+
+def test_error_invalid_object(data, all_arithmetic_operators):
+    data, _ = data
+
+    op = all_arithmetic_operators
+    opa = getattr(data, op)
+
+    # 2d -> return NotImplemented
+    result = opa(pd.DataFrame({"A": data}))
+    assert result is NotImplemented
+
+    msg = r"can only perform ops with 1-d structures"
+    with pytest.raises(NotImplementedError, match=msg):
+        opa(np.arange(len(data)).reshape(-1, len(data)))
+
+
+def test_error_len_mismatch(data, all_arithmetic_operators):
+    # operating with a list-like with non-matching length raises
+    data, scalar = data
+    op = tm.get_op_from_name(all_arithmetic_operators)
+
+    other = [scalar] * (len(data) - 1)
+
+    err = ValueError
+    msg = "|".join(
+        [
+            r"operands could not be broadcast together with shapes \(3,\) \(4,\)",
+            r"operands could not be broadcast together with shapes \(4,\) \(3,\)",
+        ]
+    )
+    if data.dtype.kind == "b" and all_arithmetic_operators.strip("_") in [
+        "sub",
+        "rsub",
+    ]:
+        err = TypeError
+        msg = (
+            r"numpy boolean subtract, the `\-` operator, is not supported, use "
+            r"the bitwise_xor, the `\^` operator, or the logical_xor function instead"
+        )
+    elif is_bool_not_implemented(data, all_arithmetic_operators):
+        msg = "operator '.*' not implemented for bool dtypes"
+        err = NotImplementedError
+
+    for other in [other, np.array(other)]:
+        with pytest.raises(err, match=msg):
+            op(data, other)
+
+        s = pd.Series(data)
+        with pytest.raises(err, match=msg):
+            op(s, other)
+
+
+@pytest.mark.parametrize("op", ["__neg__", "__abs__", "__invert__"])
+def test_unary_op_does_not_propagate_mask(data, op):
+    # https://github.com/pandas-dev/pandas/issues/39943
+    data, _ = data
+    ser = pd.Series(data)
+
+    if op == "__invert__" and data.dtype.kind == "f":
+        # we follow numpy in raising
+        msg = "ufunc 'invert' not supported for the input types"
+        with pytest.raises(TypeError, match=msg):
+            getattr(ser, op)()
+        with pytest.raises(TypeError, match=msg):
+            getattr(data, op)()
+        with pytest.raises(TypeError, match=msg):
+            # Check that this is still the numpy behavior
+            getattr(data._data, op)()
+
+        return
+
+    result = getattr(ser, op)()
+    expected = result.copy(deep=True)
+    ser[0] = None
+    tm.assert_series_equal(result, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/masked/test_arrow_compat.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/masked/test_arrow_compat.py
new file mode 100644
index 0000000000000000000000000000000000000000..293ee4095d02e1b35ace33458e38c61d87181373
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/masked/test_arrow_compat.py
@@ -0,0 +1,210 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+
+pytestmark = pytest.mark.filterwarnings(
+    "ignore:Passing a BlockManager to DataFrame:DeprecationWarning"
+)
+
+
+pa = pytest.importorskip("pyarrow")
+
+from pandas.core.arrays.arrow._arrow_utils import pyarrow_array_to_numpy_and_mask
+
+arrays = [pd.array([1, 2, 3, None], dtype=dtype) for dtype in tm.ALL_INT_EA_DTYPES]
+arrays += [pd.array([0.1, 0.2, 0.3, None], dtype=dtype) for dtype in tm.FLOAT_EA_DTYPES]
+arrays += [pd.array([True, False, True, None], dtype="boolean")]
+
+
+@pytest.fixture(params=arrays, ids=[a.dtype.name for a in arrays])
+def data(request):
+    """
+    Fixture returning parametrized array from given dtype, including integer,
+    float and boolean
+    """
+    return request.param
+
+
+def test_arrow_array(data):
+    arr = pa.array(data)
+    expected = pa.array(
+        data.to_numpy(object, na_value=None),
+        type=pa.from_numpy_dtype(data.dtype.numpy_dtype),
+    )
+    assert arr.equals(expected)
+
+
+def test_arrow_roundtrip(data):
+    df = pd.DataFrame({"a": data})
+    table = pa.table(df)
+    assert table.field("a").type == str(data.dtype.numpy_dtype)
+
+    result = table.to_pandas()
+    assert result["a"].dtype == data.dtype
+    tm.assert_frame_equal(result, df)
+
+
+def test_dataframe_from_arrow_types_mapper():
+    def types_mapper(arrow_type):
+        if pa.types.is_boolean(arrow_type):
+            return pd.BooleanDtype()
+        elif pa.types.is_integer(arrow_type):
+            return pd.Int64Dtype()
+
+    bools_array = pa.array([True, None, False], type=pa.bool_())
+    ints_array = pa.array([1, None, 2], type=pa.int64())
+    small_ints_array = pa.array([-1, 0, 7], type=pa.int8())
+    record_batch = pa.RecordBatch.from_arrays(
+        [bools_array, ints_array, small_ints_array], ["bools", "ints", "small_ints"]
+    )
+    result = record_batch.to_pandas(types_mapper=types_mapper)
+    bools = pd.Series([True, None, False], dtype="boolean")
+    ints = pd.Series([1, None, 2], dtype="Int64")
+    small_ints = pd.Series([-1, 0, 7], dtype="Int64")
+    expected = pd.DataFrame({"bools": bools, "ints": ints, "small_ints": small_ints})
+    tm.assert_frame_equal(result, expected)
+
+
+def test_arrow_load_from_zero_chunks(data):
+    # GH-41040
+
+    df = pd.DataFrame({"a": data[0:0]})
+    table = pa.table(df)
+    assert table.field("a").type == str(data.dtype.numpy_dtype)
+    table = pa.table(
+        [pa.chunked_array([], type=table.field("a").type)], schema=table.schema
+    )
+    result = table.to_pandas()
+    assert result["a"].dtype == data.dtype
+    tm.assert_frame_equal(result, df)
+
+
+def test_arrow_from_arrow_uint():
+    # https://github.com/pandas-dev/pandas/issues/31896
+    # possible mismatch in types
+
+    dtype = pd.UInt32Dtype()
+    result = dtype.__from_arrow__(pa.array([1, 2, 3, 4, None], type="int64"))
+    expected = pd.array([1, 2, 3, 4, None], dtype="UInt32")
+
+    tm.assert_extension_array_equal(result, expected)
+
+
+def test_arrow_sliced(data):
+    # https://github.com/pandas-dev/pandas/issues/38525
+
+    df = pd.DataFrame({"a": data})
+    table = pa.table(df)
+    result = table.slice(2, None).to_pandas()
+    expected = df.iloc[2:].reset_index(drop=True)
+    tm.assert_frame_equal(result, expected)
+
+    # no missing values
+    df2 = df.fillna(data[0])
+    table = pa.table(df2)
+    result = table.slice(2, None).to_pandas()
+    expected = df2.iloc[2:].reset_index(drop=True)
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.fixture
+def np_dtype_to_arrays(any_real_numpy_dtype):
+    """
+    Fixture returning actual and expected dtype, pandas and numpy arrays and
+    mask from a given numpy dtype
+    """
+    np_dtype = np.dtype(any_real_numpy_dtype)
+    pa_type = pa.from_numpy_dtype(np_dtype)
+
+    # None ensures the creation of a bitmask buffer.
+    pa_array = pa.array([0, 1, 2, None], type=pa_type)
+    # Since masked Arrow buffer slots are not required to contain a specific
+    # value, assert only the first three values of the created np.array
+    np_expected = np.array([0, 1, 2], dtype=np_dtype)
+    mask_expected = np.array([True, True, True, False])
+    return np_dtype, pa_array, np_expected, mask_expected
+
+
+def test_pyarrow_array_to_numpy_and_mask(np_dtype_to_arrays):
+    """
+    Test conversion from pyarrow array to numpy array.
+
+    Modifies the pyarrow buffer to contain padding and offset, which are
+    considered valid buffers by pyarrow.
+
+    Also tests empty pyarrow arrays with non empty buffers.
+    See https://github.com/pandas-dev/pandas/issues/40896
+    """
+    np_dtype, pa_array, np_expected, mask_expected = np_dtype_to_arrays
+    data, mask = pyarrow_array_to_numpy_and_mask(pa_array, np_dtype)
+    tm.assert_numpy_array_equal(data[:3], np_expected)
+    tm.assert_numpy_array_equal(mask, mask_expected)
+
+    mask_buffer = pa_array.buffers()[0]
+    data_buffer = pa_array.buffers()[1]
+    data_buffer_bytes = pa_array.buffers()[1].to_pybytes()
+
+    # Add trailing padding to the buffer.
+    data_buffer_trail = pa.py_buffer(data_buffer_bytes + b"\x00")
+    pa_array_trail = pa.Array.from_buffers(
+        type=pa_array.type,
+        length=len(pa_array),
+        buffers=[mask_buffer, data_buffer_trail],
+        offset=pa_array.offset,
+    )
+    pa_array_trail.validate()
+    data, mask = pyarrow_array_to_numpy_and_mask(pa_array_trail, np_dtype)
+    tm.assert_numpy_array_equal(data[:3], np_expected)
+    tm.assert_numpy_array_equal(mask, mask_expected)
+
+    # Add offset to the buffer.
+    offset = b"\x00" * (pa_array.type.bit_width // 8)
+    data_buffer_offset = pa.py_buffer(offset + data_buffer_bytes)
+    mask_buffer_offset = pa.py_buffer(b"\x0E")
+    pa_array_offset = pa.Array.from_buffers(
+        type=pa_array.type,
+        length=len(pa_array),
+        buffers=[mask_buffer_offset, data_buffer_offset],
+        offset=pa_array.offset + 1,
+    )
+    pa_array_offset.validate()
+    data, mask = pyarrow_array_to_numpy_and_mask(pa_array_offset, np_dtype)
+    tm.assert_numpy_array_equal(data[:3], np_expected)
+    tm.assert_numpy_array_equal(mask, mask_expected)
+
+    # Empty array
+    np_expected_empty = np.array([], dtype=np_dtype)
+    mask_expected_empty = np.array([], dtype=np.bool_)
+
+    pa_array_offset = pa.Array.from_buffers(
+        type=pa_array.type,
+        length=0,
+        buffers=[mask_buffer, data_buffer],
+        offset=pa_array.offset,
+    )
+    pa_array_offset.validate()
+    data, mask = pyarrow_array_to_numpy_and_mask(pa_array_offset, np_dtype)
+    tm.assert_numpy_array_equal(data[:3], np_expected_empty)
+    tm.assert_numpy_array_equal(mask, mask_expected_empty)
+
+
+@pytest.mark.parametrize(
+    "arr", [pa.nulls(10), pa.chunked_array([pa.nulls(4), pa.nulls(6)])]
+)
+def test_from_arrow_null(data, arr):
+    res = data.dtype.__from_arrow__(arr)
+    assert res.isna().all()
+    assert len(res) == 10
+
+
+def test_from_arrow_type_error(data):
+    # ensure that __from_arrow__ returns a TypeError when getting a wrong
+    # array type
+
+    arr = pa.array(data).cast("string")
+    with pytest.raises(TypeError, match=None):
+        # we don't test the exact error message, only the fact that it raises
+        # a TypeError is relevant
+        data.dtype.__from_arrow__(arr)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/masked/test_function.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/masked/test_function.py
new file mode 100644
index 0000000000000000000000000000000000000000..b259018cd6121c53c767e36e3c757211643262d6
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/masked/test_function.py
@@ -0,0 +1,74 @@
+import numpy as np
+import pytest
+
+from pandas.core.dtypes.common import is_integer_dtype
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.core.arrays import BaseMaskedArray
+
+arrays = [pd.array([1, 2, 3, None], dtype=dtype) for dtype in tm.ALL_INT_EA_DTYPES]
+arrays += [
+    pd.array([0.141, -0.268, 5.895, None], dtype=dtype) for dtype in tm.FLOAT_EA_DTYPES
+]
+
+
+@pytest.fixture(params=arrays, ids=[a.dtype.name for a in arrays])
+def data(request):
+    """
+    Fixture returning parametrized 'data' array with different integer and
+    floating point types
+    """
+    return request.param
+
+
+@pytest.fixture()
+def numpy_dtype(data):
+    """
+    Fixture returning numpy dtype from 'data' input array.
+    """
+    # For integer dtype, the numpy conversion must be done to float
+    if is_integer_dtype(data):
+        numpy_dtype = float
+    else:
+        numpy_dtype = data.dtype.type
+    return numpy_dtype
+
+
+def test_round(data, numpy_dtype):
+    # No arguments
+    result = data.round()
+    expected = pd.array(
+        np.round(data.to_numpy(dtype=numpy_dtype, na_value=None)), dtype=data.dtype
+    )
+    tm.assert_extension_array_equal(result, expected)
+
+    # Decimals argument
+    result = data.round(decimals=2)
+    expected = pd.array(
+        np.round(data.to_numpy(dtype=numpy_dtype, na_value=None), decimals=2),
+        dtype=data.dtype,
+    )
+    tm.assert_extension_array_equal(result, expected)
+
+
+def test_tolist(data):
+    result = data.tolist()
+    expected = list(data)
+    tm.assert_equal(result, expected)
+
+
+def test_to_numpy():
+    # GH#56991
+
+    class MyStringArray(BaseMaskedArray):
+        dtype = pd.StringDtype()
+        _dtype_cls = pd.StringDtype
+        _internal_fill_value = pd.NA
+
+    arr = MyStringArray(
+        values=np.array(["a", "b", "c"]), mask=np.array([False, True, False])
+    )
+    result = arr.to_numpy()
+    expected = np.array(["a", pd.NA, "c"])
+    tm.assert_numpy_array_equal(result, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/masked/test_indexing.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/masked/test_indexing.py
new file mode 100644
index 0000000000000000000000000000000000000000..753d562c87ffa86bbbf665bf3dbbd409eddfdd88
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/masked/test_indexing.py
@@ -0,0 +1,60 @@
+import re
+
+import numpy as np
+import pytest
+
+import pandas as pd
+
+
+class TestSetitemValidation:
+    def _check_setitem_invalid(self, arr, invalid):
+        msg = f"Invalid value '{invalid!s}' for dtype '{arr.dtype}'"
+        msg = re.escape(msg)
+        with pytest.raises(TypeError, match=msg):
+            arr[0] = invalid
+
+        with pytest.raises(TypeError, match=msg):
+            arr[:] = invalid
+
+        with pytest.raises(TypeError, match=msg):
+            arr[[0]] = invalid
+
+        # FIXME: don't leave commented-out
+        # with pytest.raises(TypeError):
+        #    arr[[0]] = [invalid]
+
+        # with pytest.raises(TypeError):
+        #    arr[[0]] = np.array([invalid], dtype=object)
+
+        # Series non-coercion, behavior subject to change
+        ser = pd.Series(arr)
+        with pytest.raises(TypeError, match=msg):
+            ser[0] = invalid
+            # TODO: so, so many other variants of this...
+
+    _invalid_scalars = [
+        1 + 2j,
+        "True",
+        "1",
+        "1.0",
+        pd.NaT,
+        np.datetime64("NaT"),
+        np.timedelta64("NaT"),
+    ]
+
+    @pytest.mark.parametrize(
+        "invalid", _invalid_scalars + [1, 1.0, np.int64(1), np.float64(1)]
+    )
+    def test_setitem_validation_scalar_bool(self, invalid):
+        arr = pd.array([True, False, None], dtype="boolean")
+        self._check_setitem_invalid(arr, invalid)
+
+    @pytest.mark.parametrize("invalid", _invalid_scalars + [True, 1.5, np.float64(1.5)])
+    def test_setitem_validation_scalar_int(self, invalid, any_int_ea_dtype):
+        arr = pd.array([1, 2, None], dtype=any_int_ea_dtype)
+        self._check_setitem_invalid(arr, invalid)
+
+    @pytest.mark.parametrize("invalid", _invalid_scalars + [True])
+    def test_setitem_validation_scalar_float(self, invalid, float_ea_dtype):
+        arr = pd.array([1, 2, None], dtype=float_ea_dtype)
+        self._check_setitem_invalid(arr, invalid)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/masked_shared.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/masked_shared.py
new file mode 100644
index 0000000000000000000000000000000000000000..3e74402263cf9c119ec344c5da48dd8598970f69
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/masked_shared.py
@@ -0,0 +1,154 @@
+"""
+Tests shared by MaskedArray subclasses.
+"""
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.tests.extension.base import BaseOpsUtil
+
+
+class ComparisonOps(BaseOpsUtil):
+    def _compare_other(self, data, op, other):
+        # array
+        result = pd.Series(op(data, other))
+        expected = pd.Series(op(data._data, other), dtype="boolean")
+
+        # fill the nan locations
+        expected[data._mask] = pd.NA
+
+        tm.assert_series_equal(result, expected)
+
+        # series
+        ser = pd.Series(data)
+        result = op(ser, other)
+
+        # Set nullable dtype here to avoid upcasting when setting to pd.NA below
+        expected = op(pd.Series(data._data), other).astype("boolean")
+
+        # fill the nan locations
+        expected[data._mask] = pd.NA
+
+        tm.assert_series_equal(result, expected)
+
+    # subclass will override to parametrize 'other'
+    def test_scalar(self, other, comparison_op, dtype):
+        op = comparison_op
+        left = pd.array([1, 0, None], dtype=dtype)
+
+        result = op(left, other)
+
+        if other is pd.NA:
+            expected = pd.array([None, None, None], dtype="boolean")
+        else:
+            values = op(left._data, other)
+            expected = pd.arrays.BooleanArray(values, left._mask, copy=True)
+        tm.assert_extension_array_equal(result, expected)
+
+        # ensure we haven't mutated anything inplace
+        result[0] = pd.NA
+        tm.assert_extension_array_equal(left, pd.array([1, 0, None], dtype=dtype))
+
+
+class NumericOps:
+    # Shared by IntegerArray and FloatingArray, not BooleanArray
+
+    def test_searchsorted_nan(self, dtype):
+        # The base class casts to object dtype, for which searchsorted returns
+        #  0 from the left and 10 from the right.
+        arr = pd.array(range(10), dtype=dtype)
+
+        assert arr.searchsorted(np.nan, side="left") == 10
+        assert arr.searchsorted(np.nan, side="right") == 10
+
+    def test_no_shared_mask(self, data):
+        result = data + 1
+        assert not tm.shares_memory(result, data)
+
+    def test_array(self, comparison_op, dtype):
+        op = comparison_op
+
+        left = pd.array([0, 1, 2, None, None, None], dtype=dtype)
+        right = pd.array([0, 1, None, 0, 1, None], dtype=dtype)
+
+        result = op(left, right)
+        values = op(left._data, right._data)
+        mask = left._mask | right._mask
+
+        expected = pd.arrays.BooleanArray(values, mask)
+        tm.assert_extension_array_equal(result, expected)
+
+        # ensure we haven't mutated anything inplace
+        result[0] = pd.NA
+        tm.assert_extension_array_equal(
+            left, pd.array([0, 1, 2, None, None, None], dtype=dtype)
+        )
+        tm.assert_extension_array_equal(
+            right, pd.array([0, 1, None, 0, 1, None], dtype=dtype)
+        )
+
+    def test_compare_with_booleanarray(self, comparison_op, dtype):
+        op = comparison_op
+
+        left = pd.array([True, False, None] * 3, dtype="boolean")
+        right = pd.array([0] * 3 + [1] * 3 + [None] * 3, dtype=dtype)
+        other = pd.array([False] * 3 + [True] * 3 + [None] * 3, dtype="boolean")
+
+        expected = op(left, other)
+        result = op(left, right)
+        tm.assert_extension_array_equal(result, expected)
+
+        # reversed op
+        expected = op(other, left)
+        result = op(right, left)
+        tm.assert_extension_array_equal(result, expected)
+
+    def test_compare_to_string(self, dtype):
+        # GH#28930
+        ser = pd.Series([1, None], dtype=dtype)
+        result = ser == "a"
+        expected = pd.Series([False, pd.NA], dtype="boolean")
+
+        tm.assert_series_equal(result, expected)
+
+    def test_ufunc_with_out(self, dtype):
+        arr = pd.array([1, 2, 3], dtype=dtype)
+        arr2 = pd.array([1, 2, pd.NA], dtype=dtype)
+
+        mask = arr == arr
+        mask2 = arr2 == arr2
+
+        result = np.zeros(3, dtype=bool)
+        result |= mask
+        # If MaskedArray.__array_ufunc__ handled "out" appropriately,
+        #  `result` should still be an ndarray.
+        assert isinstance(result, np.ndarray)
+        assert result.all()
+
+        # result |= mask worked because mask could be cast losslessly to
+        #  boolean ndarray. mask2 can't, so this raises
+        result = np.zeros(3, dtype=bool)
+        msg = "Specify an appropriate 'na_value' for this dtype"
+        with pytest.raises(ValueError, match=msg):
+            result |= mask2
+
+        # addition
+        res = np.add(arr, arr2)
+        expected = pd.array([2, 4, pd.NA], dtype=dtype)
+        tm.assert_extension_array_equal(res, expected)
+
+        # when passing out=arr, we will modify 'arr' inplace.
+        res = np.add(arr, arr2, out=arr)
+        assert res is arr
+        tm.assert_extension_array_equal(res, expected)
+        tm.assert_extension_array_equal(arr, expected)
+
+    def test_mul_td64_array(self, dtype):
+        # GH#45622
+        arr = pd.array([1, 2, pd.NA], dtype=dtype)
+        other = np.arange(3, dtype=np.int64).view("m8[ns]")
+
+        result = arr * other
+        expected = pd.array([pd.Timedelta(0), pd.Timedelta(2), pd.NaT])
+        tm.assert_extension_array_equal(result, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/numpy_/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/numpy_/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/numpy_/test_indexing.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/numpy_/test_indexing.py
new file mode 100644
index 0000000000000000000000000000000000000000..225d64ad7d2580f877505f0ac3a459e2ea4f0f53
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/numpy_/test_indexing.py
@@ -0,0 +1,41 @@
+import numpy as np
+
+from pandas.core.dtypes.common import is_scalar
+
+import pandas as pd
+import pandas._testing as tm
+
+
+class TestSearchsorted:
+    def test_searchsorted_string(self, string_dtype):
+        arr = pd.array(["a", "b", "c"], dtype=string_dtype)
+
+        result = arr.searchsorted("a", side="left")
+        assert is_scalar(result)
+        assert result == 0
+
+        result = arr.searchsorted("a", side="right")
+        assert is_scalar(result)
+        assert result == 1
+
+    def test_searchsorted_numeric_dtypes_scalar(self, any_real_numpy_dtype):
+        arr = pd.array([1, 3, 90], dtype=any_real_numpy_dtype)
+        result = arr.searchsorted(30)
+        assert is_scalar(result)
+        assert result == 2
+
+        result = arr.searchsorted([30])
+        expected = np.array([2], dtype=np.intp)
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_searchsorted_numeric_dtypes_vector(self, any_real_numpy_dtype):
+        arr = pd.array([1, 3, 90], dtype=any_real_numpy_dtype)
+        result = arr.searchsorted([2, 30])
+        expected = np.array([1, 2], dtype=np.intp)
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_searchsorted_sorter(self, any_real_numpy_dtype):
+        arr = pd.array([3, 1, 2], dtype=any_real_numpy_dtype)
+        result = arr.searchsorted([0, 3], sorter=np.argsort(arr))
+        expected = np.array([0, 2], dtype=np.intp)
+        tm.assert_numpy_array_equal(result, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/numpy_/test_numpy.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/numpy_/test_numpy.py
new file mode 100644
index 0000000000000000000000000000000000000000..f21fb4ccfba075acf2771ac5c9d1056aa50274bd
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/numpy_/test_numpy.py
@@ -0,0 +1,351 @@
+"""
+Additional tests for NumpyExtensionArray that aren't covered by
+the interface tests.
+"""
+import numpy as np
+import pytest
+
+from pandas.core.dtypes.dtypes import NumpyEADtype
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.arrays import NumpyExtensionArray
+
+
+@pytest.fixture(
+    params=[
+        np.array(["a", "b"], dtype=object),
+        np.array([0, 1], dtype=float),
+        np.array([0, 1], dtype=int),
+        np.array([0, 1 + 2j], dtype=complex),
+        np.array([True, False], dtype=bool),
+        np.array([0, 1], dtype="datetime64[ns]"),
+        np.array([0, 1], dtype="timedelta64[ns]"),
+    ],
+)
+def any_numpy_array(request):
+    """
+    Parametrized fixture for NumPy arrays with different dtypes.
+
+    This excludes string and bytes.
+    """
+    return request.param.copy()
+
+
+# ----------------------------------------------------------------------------
+# NumpyEADtype
+
+
+@pytest.mark.parametrize(
+    "dtype, expected",
+    [
+        ("bool", True),
+        ("int", True),
+        ("uint", True),
+        ("float", True),
+        ("complex", True),
+        ("str", False),
+        ("bytes", False),
+        ("datetime64[ns]", False),
+        ("object", False),
+        ("void", False),
+    ],
+)
+def test_is_numeric(dtype, expected):
+    dtype = NumpyEADtype(dtype)
+    assert dtype._is_numeric is expected
+
+
+@pytest.mark.parametrize(
+    "dtype, expected",
+    [
+        ("bool", True),
+        ("int", False),
+        ("uint", False),
+        ("float", False),
+        ("complex", False),
+        ("str", False),
+        ("bytes", False),
+        ("datetime64[ns]", False),
+        ("object", False),
+        ("void", False),
+    ],
+)
+def test_is_boolean(dtype, expected):
+    dtype = NumpyEADtype(dtype)
+    assert dtype._is_boolean is expected
+
+
+def test_repr():
+    dtype = NumpyEADtype(np.dtype("int64"))
+    assert repr(dtype) == "NumpyEADtype('int64')"
+
+
+def test_constructor_from_string():
+    result = NumpyEADtype.construct_from_string("int64")
+    expected = NumpyEADtype(np.dtype("int64"))
+    assert result == expected
+
+
+def test_dtype_idempotent(any_numpy_dtype):
+    dtype = NumpyEADtype(any_numpy_dtype)
+
+    result = NumpyEADtype(dtype)
+    assert result == dtype
+
+
+# ----------------------------------------------------------------------------
+# Construction
+
+
+def test_constructor_no_coercion():
+    with pytest.raises(ValueError, match="NumPy array"):
+        NumpyExtensionArray([1, 2, 3])
+
+
+def test_series_constructor_with_copy():
+    ndarray = np.array([1, 2, 3])
+    ser = pd.Series(NumpyExtensionArray(ndarray), copy=True)
+
+    assert ser.values is not ndarray
+
+
+def test_series_constructor_with_astype():
+    ndarray = np.array([1, 2, 3])
+    result = pd.Series(NumpyExtensionArray(ndarray), dtype="float64")
+    expected = pd.Series([1.0, 2.0, 3.0], dtype="float64")
+    tm.assert_series_equal(result, expected)
+
+
+def test_from_sequence_dtype():
+    arr = np.array([1, 2, 3], dtype="int64")
+    result = NumpyExtensionArray._from_sequence(arr, dtype="uint64")
+    expected = NumpyExtensionArray(np.array([1, 2, 3], dtype="uint64"))
+    tm.assert_extension_array_equal(result, expected)
+
+
+def test_constructor_copy():
+    arr = np.array([0, 1])
+    result = NumpyExtensionArray(arr, copy=True)
+
+    assert not tm.shares_memory(result, arr)
+
+
+def test_constructor_with_data(any_numpy_array):
+    nparr = any_numpy_array
+    arr = NumpyExtensionArray(nparr)
+    assert arr.dtype.numpy_dtype == nparr.dtype
+
+
+# ----------------------------------------------------------------------------
+# Conversion
+
+
+def test_to_numpy():
+    arr = NumpyExtensionArray(np.array([1, 2, 3]))
+    result = arr.to_numpy()
+    assert result is arr._ndarray
+
+    result = arr.to_numpy(copy=True)
+    assert result is not arr._ndarray
+
+    result = arr.to_numpy(dtype="f8")
+    expected = np.array([1, 2, 3], dtype="f8")
+    tm.assert_numpy_array_equal(result, expected)
+
+
+# ----------------------------------------------------------------------------
+# Setitem
+
+
+def test_setitem_series():
+    ser = pd.Series([1, 2, 3])
+    ser.array[0] = 10
+    expected = pd.Series([10, 2, 3])
+    tm.assert_series_equal(ser, expected)
+
+
+def test_setitem(any_numpy_array):
+    nparr = any_numpy_array
+    arr = NumpyExtensionArray(nparr, copy=True)
+
+    arr[0] = arr[1]
+    nparr[0] = nparr[1]
+
+    tm.assert_numpy_array_equal(arr.to_numpy(), nparr)
+
+
+# ----------------------------------------------------------------------------
+# Reductions
+
+
+def test_bad_reduce_raises():
+    arr = np.array([1, 2, 3], dtype="int64")
+    arr = NumpyExtensionArray(arr)
+    msg = "cannot perform not_a_method with type int"
+    with pytest.raises(TypeError, match=msg):
+        arr._reduce(msg)
+
+
+def test_validate_reduction_keyword_args():
+    arr = NumpyExtensionArray(np.array([1, 2, 3]))
+    msg = "the 'keepdims' parameter is not supported .*all"
+    with pytest.raises(ValueError, match=msg):
+        arr.all(keepdims=True)
+
+
+def test_np_max_nested_tuples():
+    # case where checking in ufunc.nout works while checking for tuples
+    #  does not
+    vals = [
+        (("j", "k"), ("l", "m")),
+        (("l", "m"), ("o", "p")),
+        (("o", "p"), ("j", "k")),
+    ]
+    ser = pd.Series(vals)
+    arr = ser.array
+
+    assert arr.max() is arr[2]
+    assert ser.max() is arr[2]
+
+    result = np.maximum.reduce(arr)
+    assert result == arr[2]
+
+    result = np.maximum.reduce(ser)
+    assert result == arr[2]
+
+
+def test_np_reduce_2d():
+    raw = np.arange(12).reshape(4, 3)
+    arr = NumpyExtensionArray(raw)
+
+    res = np.maximum.reduce(arr, axis=0)
+    tm.assert_extension_array_equal(res, arr[-1])
+
+    alt = arr.max(axis=0)
+    tm.assert_extension_array_equal(alt, arr[-1])
+
+
+# ----------------------------------------------------------------------------
+# Ops
+
+
+@pytest.mark.parametrize("ufunc", [np.abs, np.negative, np.positive])
+def test_ufunc_unary(ufunc):
+    arr = NumpyExtensionArray(np.array([-1.0, 0.0, 1.0]))
+    result = ufunc(arr)
+    expected = NumpyExtensionArray(ufunc(arr._ndarray))
+    tm.assert_extension_array_equal(result, expected)
+
+    # same thing but with the 'out' keyword
+    out = NumpyExtensionArray(np.array([-9.0, -9.0, -9.0]))
+    ufunc(arr, out=out)
+    tm.assert_extension_array_equal(out, expected)
+
+
+def test_ufunc():
+    arr = NumpyExtensionArray(np.array([-1.0, 0.0, 1.0]))
+
+    r1, r2 = np.divmod(arr, np.add(arr, 2))
+    e1, e2 = np.divmod(arr._ndarray, np.add(arr._ndarray, 2))
+    e1 = NumpyExtensionArray(e1)
+    e2 = NumpyExtensionArray(e2)
+    tm.assert_extension_array_equal(r1, e1)
+    tm.assert_extension_array_equal(r2, e2)
+
+
+def test_basic_binop():
+    # Just a basic smoke test. The EA interface tests exercise this
+    # more thoroughly.
+    x = NumpyExtensionArray(np.array([1, 2, 3]))
+    result = x + x
+    expected = NumpyExtensionArray(np.array([2, 4, 6]))
+    tm.assert_extension_array_equal(result, expected)
+
+
+@pytest.mark.parametrize("dtype", [None, object])
+def test_setitem_object_typecode(dtype):
+    arr = NumpyExtensionArray(np.array(["a", "b", "c"], dtype=dtype))
+    arr[0] = "t"
+    expected = NumpyExtensionArray(np.array(["t", "b", "c"], dtype=dtype))
+    tm.assert_extension_array_equal(arr, expected)
+
+
+def test_setitem_no_coercion():
+    # https://github.com/pandas-dev/pandas/issues/28150
+    arr = NumpyExtensionArray(np.array([1, 2, 3]))
+    with pytest.raises(ValueError, match="int"):
+        arr[0] = "a"
+
+    # With a value that we do coerce, check that we coerce the value
+    #  and not the underlying array.
+    arr[0] = 2.5
+    assert isinstance(arr[0], (int, np.integer)), type(arr[0])
+
+
+def test_setitem_preserves_views():
+    # GH#28150, see also extension test of the same name
+    arr = NumpyExtensionArray(np.array([1, 2, 3]))
+    view1 = arr.view()
+    view2 = arr[:]
+    view3 = np.asarray(arr)
+
+    arr[0] = 9
+    assert view1[0] == 9
+    assert view2[0] == 9
+    assert view3[0] == 9
+
+    arr[-1] = 2.5
+    view1[-1] = 5
+    assert arr[-1] == 5
+
+
+@pytest.mark.parametrize("dtype", [np.int64, np.uint64])
+def test_quantile_empty(dtype):
+    # we should get back np.nans, not -1s
+    arr = NumpyExtensionArray(np.array([], dtype=dtype))
+    idx = pd.Index([0.0, 0.5])
+
+    result = arr._quantile(idx, interpolation="linear")
+    expected = NumpyExtensionArray(np.array([np.nan, np.nan]))
+    tm.assert_extension_array_equal(result, expected)
+
+
+def test_factorize_unsigned():
+    # don't raise when calling factorize on unsigned int NumpyExtensionArray
+    arr = np.array([1, 2, 3], dtype=np.uint64)
+    obj = NumpyExtensionArray(arr)
+
+    res_codes, res_unique = obj.factorize()
+    exp_codes, exp_unique = pd.factorize(arr)
+
+    tm.assert_numpy_array_equal(res_codes, exp_codes)
+
+    tm.assert_extension_array_equal(res_unique, NumpyExtensionArray(exp_unique))
+
+
+# ----------------------------------------------------------------------------
+# Output formatting
+
+
+def test_array_repr(any_numpy_array):
+    # GH#61085
+    nparray = any_numpy_array
+    arr = NumpyExtensionArray(nparray)
+    if nparray.dtype == "object":
+        values = "['a', 'b']"
+    elif nparray.dtype == "float64":
+        values = "[0.0, 1.0]"
+    elif str(nparray.dtype).startswith("int"):
+        values = "[0, 1]"
+    elif nparray.dtype == "complex128":
+        values = "[0j, (1+2j)]"
+    elif nparray.dtype == "bool":
+        values = "[True, False]"
+    elif nparray.dtype == "datetime64[ns]":
+        values = "[1970-01-01T00:00:00.000000000, 1970-01-01T00:00:00.000000001]"
+    elif nparray.dtype == "timedelta64[ns]":
+        values = "[0 nanoseconds, 1 nanoseconds]"
+    expected = f"\n{values}\nLength: 2, dtype: {nparray.dtype}"
+    result = repr(arr)
+    assert result == expected, f"{result} vs {expected}"
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/period/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/period/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/period/test_arrow_compat.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/period/test_arrow_compat.py
new file mode 100644
index 0000000000000000000000000000000000000000..431309aca0df21dbe885ae015b10c3c21f0134a2
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/period/test_arrow_compat.py
@@ -0,0 +1,130 @@
+import pytest
+
+from pandas.compat.pyarrow import pa_version_under10p1
+
+from pandas.core.dtypes.dtypes import PeriodDtype
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.core.arrays import (
+    PeriodArray,
+    period_array,
+)
+
+pytestmark = pytest.mark.filterwarnings(
+    "ignore:Passing a BlockManager to DataFrame:DeprecationWarning"
+)
+
+
+pa = pytest.importorskip("pyarrow")
+
+
+def test_arrow_extension_type():
+    from pandas.core.arrays.arrow.extension_types import ArrowPeriodType
+
+    p1 = ArrowPeriodType("D")
+    p2 = ArrowPeriodType("D")
+    p3 = ArrowPeriodType("M")
+
+    assert p1.freq == "D"
+    assert p1 == p2
+    assert p1 != p3
+    assert hash(p1) == hash(p2)
+    assert hash(p1) != hash(p3)
+
+
+@pytest.mark.xfail(not pa_version_under10p1, reason="Wrong behavior with pyarrow 10")
+@pytest.mark.parametrize(
+    "data, freq",
+    [
+        (pd.date_range("2017", periods=3), "D"),
+        (pd.date_range("2017", periods=3, freq="YE"), "Y-DEC"),
+    ],
+)
+def test_arrow_array(data, freq):
+    from pandas.core.arrays.arrow.extension_types import ArrowPeriodType
+
+    periods = period_array(data, freq=freq)
+    result = pa.array(periods)
+    assert isinstance(result.type, ArrowPeriodType)
+    assert result.type.freq == freq
+    expected = pa.array(periods.asi8, type="int64")
+    assert result.storage.equals(expected)
+
+    # convert to its storage type
+    result = pa.array(periods, type=pa.int64())
+    assert result.equals(expected)
+
+    # unsupported conversions
+    msg = "Not supported to convert PeriodArray to 'double' type"
+    with pytest.raises(TypeError, match=msg):
+        pa.array(periods, type="float64")
+
+    with pytest.raises(TypeError, match="different 'freq'"):
+        pa.array(periods, type=ArrowPeriodType("T"))
+
+
+def test_arrow_array_missing():
+    from pandas.core.arrays.arrow.extension_types import ArrowPeriodType
+
+    arr = PeriodArray([1, 2, 3], dtype="period[D]")
+    arr[1] = pd.NaT
+
+    result = pa.array(arr)
+    assert isinstance(result.type, ArrowPeriodType)
+    assert result.type.freq == "D"
+    expected = pa.array([1, None, 3], type="int64")
+    assert result.storage.equals(expected)
+
+
+def test_arrow_table_roundtrip():
+    from pandas.core.arrays.arrow.extension_types import ArrowPeriodType
+
+    arr = PeriodArray([1, 2, 3], dtype="period[D]")
+    arr[1] = pd.NaT
+    df = pd.DataFrame({"a": arr})
+
+    table = pa.table(df)
+    assert isinstance(table.field("a").type, ArrowPeriodType)
+    result = table.to_pandas()
+    assert isinstance(result["a"].dtype, PeriodDtype)
+    tm.assert_frame_equal(result, df)
+
+    table2 = pa.concat_tables([table, table])
+    result = table2.to_pandas()
+    expected = pd.concat([df, df], ignore_index=True)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_arrow_load_from_zero_chunks():
+    # GH-41040
+
+    from pandas.core.arrays.arrow.extension_types import ArrowPeriodType
+
+    arr = PeriodArray([], dtype="period[D]")
+    df = pd.DataFrame({"a": arr})
+
+    table = pa.table(df)
+    assert isinstance(table.field("a").type, ArrowPeriodType)
+    table = pa.table(
+        [pa.chunked_array([], type=table.column(0).type)], schema=table.schema
+    )
+
+    result = table.to_pandas()
+    assert isinstance(result["a"].dtype, PeriodDtype)
+    tm.assert_frame_equal(result, df)
+
+
+def test_arrow_table_roundtrip_without_metadata():
+    arr = PeriodArray([1, 2, 3], dtype="period[h]")
+    arr[1] = pd.NaT
+    df = pd.DataFrame({"a": arr})
+
+    table = pa.table(df)
+    # remove the metadata
+    table = table.replace_schema_metadata()
+    assert table.schema.metadata is None
+
+    result = table.to_pandas()
+    assert isinstance(result["a"].dtype, PeriodDtype)
+    tm.assert_frame_equal(result, df)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/period/test_astype.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/period/test_astype.py
new file mode 100644
index 0000000000000000000000000000000000000000..9976c3a32580da0b5b237eaa2b839b2337363f51
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/period/test_astype.py
@@ -0,0 +1,67 @@
+import numpy as np
+import pytest
+
+from pandas.core.dtypes.dtypes import PeriodDtype
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.core.arrays import period_array
+
+
+@pytest.mark.parametrize("dtype", [int, np.int32, np.int64, "uint32", "uint64"])
+def test_astype_int(dtype):
+    # We choose to ignore the sign and size of integers for
+    # Period/Datetime/Timedelta astype
+    arr = period_array(["2000", "2001", None], freq="D")
+
+    if np.dtype(dtype) != np.int64:
+        with pytest.raises(TypeError, match=r"Do obj.astype\('int64'\)"):
+            arr.astype(dtype)
+        return
+
+    result = arr.astype(dtype)
+    expected = arr._ndarray.view("i8")
+    tm.assert_numpy_array_equal(result, expected)
+
+
+def test_astype_copies():
+    arr = period_array(["2000", "2001", None], freq="D")
+    result = arr.astype(np.int64, copy=False)
+
+    # Add the `.base`, since we now use `.asi8` which returns a view.
+    # We could maybe override it in PeriodArray to return ._ndarray directly.
+    assert result.base is arr._ndarray
+
+    result = arr.astype(np.int64, copy=True)
+    assert result is not arr._ndarray
+    tm.assert_numpy_array_equal(result, arr._ndarray.view("i8"))
+
+
+def test_astype_categorical():
+    arr = period_array(["2000", "2001", "2001", None], freq="D")
+    result = arr.astype("category")
+    categories = pd.PeriodIndex(["2000", "2001"], freq="D")
+    expected = pd.Categorical.from_codes([0, 1, 1, -1], categories=categories)
+    tm.assert_categorical_equal(result, expected)
+
+
+def test_astype_period():
+    arr = period_array(["2000", "2001", None], freq="D")
+    result = arr.astype(PeriodDtype("M"))
+    expected = period_array(["2000", "2001", None], freq="M")
+    tm.assert_period_array_equal(result, expected)
+
+
+@pytest.mark.parametrize("dtype", ["datetime64[ns]", "timedelta64[ns]"])
+def test_astype_datetime(dtype):
+    arr = period_array(["2000", "2001", None], freq="D")
+    # slice off the [ns] so that the regex matches.
+    if dtype == "timedelta64[ns]":
+        with pytest.raises(TypeError, match=dtype[:-4]):
+            arr.astype(dtype)
+
+    else:
+        # GH#45038 allow period->dt64 because we allow dt64->period
+        result = arr.astype(dtype)
+        expected = pd.DatetimeIndex(["2000", "2001", pd.NaT], dtype=dtype)._data
+        tm.assert_datetime_array_equal(result, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/period/test_constructors.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/period/test_constructors.py
new file mode 100644
index 0000000000000000000000000000000000000000..d034162f1b46e11bd06204de7707c7343fd9b1b2
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/period/test_constructors.py
@@ -0,0 +1,156 @@
+import numpy as np
+import pytest
+
+from pandas._libs.tslibs import iNaT
+from pandas._libs.tslibs.offsets import MonthEnd
+from pandas._libs.tslibs.period import IncompatibleFrequency
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.core.arrays import (
+    PeriodArray,
+    period_array,
+)
+
+
+@pytest.mark.parametrize(
+    "data, freq, expected",
+    [
+        ([pd.Period("2017", "D")], None, [17167]),
+        ([pd.Period("2017", "D")], "D", [17167]),
+        ([2017], "D", [17167]),
+        (["2017"], "D", [17167]),
+        ([pd.Period("2017", "D")], pd.tseries.offsets.Day(), [17167]),
+        ([pd.Period("2017", "D"), None], None, [17167, iNaT]),
+        (pd.Series(pd.date_range("2017", periods=3)), None, [17167, 17168, 17169]),
+        (pd.date_range("2017", periods=3), None, [17167, 17168, 17169]),
+        (pd.period_range("2017", periods=4, freq="Q"), None, [188, 189, 190, 191]),
+    ],
+)
+def test_period_array_ok(data, freq, expected):
+    result = period_array(data, freq=freq).asi8
+    expected = np.asarray(expected, dtype=np.int64)
+    tm.assert_numpy_array_equal(result, expected)
+
+
+def test_period_array_readonly_object():
+    # https://github.com/pandas-dev/pandas/issues/25403
+    pa = period_array([pd.Period("2019-01-01")])
+    arr = np.asarray(pa, dtype="object")
+    arr.setflags(write=False)
+
+    result = period_array(arr)
+    tm.assert_period_array_equal(result, pa)
+
+    result = pd.Series(arr)
+    tm.assert_series_equal(result, pd.Series(pa))
+
+    result = pd.DataFrame({"A": arr})
+    tm.assert_frame_equal(result, pd.DataFrame({"A": pa}))
+
+
+def test_from_datetime64_freq_changes():
+    # https://github.com/pandas-dev/pandas/issues/23438
+    arr = pd.date_range("2017", periods=3, freq="D")
+    result = PeriodArray._from_datetime64(arr, freq="M")
+    expected = period_array(["2017-01-01", "2017-01-01", "2017-01-01"], freq="M")
+    tm.assert_period_array_equal(result, expected)
+
+
+@pytest.mark.parametrize("freq", ["2M", MonthEnd(2)])
+def test_from_datetime64_freq_2M(freq):
+    arr = np.array(
+        ["2020-01-01T00:00:00", "2020-01-02T00:00:00"], dtype="datetime64[ns]"
+    )
+    result = PeriodArray._from_datetime64(arr, freq)
+    expected = period_array(["2020-01", "2020-01"], freq=freq)
+    tm.assert_period_array_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "data, freq, msg",
+    [
+        (
+            [pd.Period("2017", "D"), pd.Period("2017", "Y")],
+            None,
+            "Input has different freq",
+        ),
+        ([pd.Period("2017", "D")], "Y", "Input has different freq"),
+    ],
+)
+def test_period_array_raises(data, freq, msg):
+    with pytest.raises(IncompatibleFrequency, match=msg):
+        period_array(data, freq)
+
+
+def test_period_array_non_period_series_raies():
+    ser = pd.Series([1, 2, 3])
+    with pytest.raises(TypeError, match="dtype"):
+        PeriodArray(ser, dtype="period[D]")
+
+
+def test_period_array_freq_mismatch():
+    arr = period_array(["2000", "2001"], freq="D")
+    with pytest.raises(IncompatibleFrequency, match="freq"):
+        PeriodArray(arr, dtype="period[M]")
+
+    dtype = pd.PeriodDtype(pd.tseries.offsets.MonthEnd())
+    with pytest.raises(IncompatibleFrequency, match="freq"):
+        PeriodArray(arr, dtype=dtype)
+
+
+def test_from_sequence_disallows_i8():
+    arr = period_array(["2000", "2001"], freq="D")
+
+    msg = str(arr[0].ordinal)
+    with pytest.raises(TypeError, match=msg):
+        PeriodArray._from_sequence(arr.asi8, dtype=arr.dtype)
+
+    with pytest.raises(TypeError, match=msg):
+        PeriodArray._from_sequence(list(arr.asi8), dtype=arr.dtype)
+
+
+def test_from_td64nat_sequence_raises():
+    # GH#44507
+    td = pd.NaT.to_numpy("m8[ns]")
+
+    dtype = pd.period_range("2005-01-01", periods=3, freq="D").dtype
+
+    arr = np.array([None], dtype=object)
+    arr[0] = td
+
+    msg = "Value must be Period, string, integer, or datetime"
+    with pytest.raises(ValueError, match=msg):
+        PeriodArray._from_sequence(arr, dtype=dtype)
+
+    with pytest.raises(ValueError, match=msg):
+        pd.PeriodIndex(arr, dtype=dtype)
+    with pytest.raises(ValueError, match=msg):
+        pd.Index(arr, dtype=dtype)
+    with pytest.raises(ValueError, match=msg):
+        pd.array(arr, dtype=dtype)
+    with pytest.raises(ValueError, match=msg):
+        pd.Series(arr, dtype=dtype)
+    with pytest.raises(ValueError, match=msg):
+        pd.DataFrame(arr, dtype=dtype)
+
+
+def test_freq_deprecated():
+    # GH#52462
+    data = np.arange(5).astype(np.int64)
+    msg = "The 'freq' keyword in the PeriodArray constructor is deprecated"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        res = PeriodArray(data, freq="M")
+
+    expected = PeriodArray(data, dtype="period[M]")
+    tm.assert_equal(res, expected)
+
+
+def test_period_array_from_datetime64():
+    arr = np.array(
+        ["2020-01-01T00:00:00", "2020-02-02T00:00:00"], dtype="datetime64[ns]"
+    )
+    result = PeriodArray._from_datetime64(arr, freq=MonthEnd(2))
+
+    expected = period_array(["2020-01-01", "2020-02-01"], freq=MonthEnd(2))
+    tm.assert_period_array_equal(result, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/period/test_reductions.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/period/test_reductions.py
new file mode 100644
index 0000000000000000000000000000000000000000..2889cc786dd71583ca345ad206553907af3a13fa
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/period/test_reductions.py
@@ -0,0 +1,42 @@
+import pytest
+
+import pandas as pd
+from pandas.core.arrays import period_array
+
+
+class TestReductions:
+    def test_min_max(self):
+        arr = period_array(
+            [
+                "2000-01-03",
+                "2000-01-03",
+                "NaT",
+                "2000-01-02",
+                "2000-01-05",
+                "2000-01-04",
+            ],
+            freq="D",
+        )
+
+        result = arr.min()
+        expected = pd.Period("2000-01-02", freq="D")
+        assert result == expected
+
+        result = arr.max()
+        expected = pd.Period("2000-01-05", freq="D")
+        assert result == expected
+
+        result = arr.min(skipna=False)
+        assert result is pd.NaT
+
+        result = arr.max(skipna=False)
+        assert result is pd.NaT
+
+    @pytest.mark.parametrize("skipna", [True, False])
+    def test_min_max_empty(self, skipna):
+        arr = period_array([], freq="D")
+        result = arr.min(skipna=skipna)
+        assert result is pd.NaT
+
+        result = arr.max(skipna=skipna)
+        assert result is pd.NaT
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/sparse/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/sparse/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_accessor.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_accessor.py
new file mode 100644
index 0000000000000000000000000000000000000000..87eb7bcfa9cee3e92386ad0f148b896c0e682b07
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_accessor.py
@@ -0,0 +1,253 @@
+import string
+
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import SparseDtype
+import pandas._testing as tm
+from pandas.core.arrays.sparse import SparseArray
+
+
+class TestSeriesAccessor:
+    def test_to_dense(self):
+        ser = pd.Series([0, 1, 0, 10], dtype="Sparse[int64]")
+        result = ser.sparse.to_dense()
+        expected = pd.Series([0, 1, 0, 10])
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("attr", ["npoints", "density", "fill_value", "sp_values"])
+    def test_get_attributes(self, attr):
+        arr = SparseArray([0, 1])
+        ser = pd.Series(arr)
+
+        result = getattr(ser.sparse, attr)
+        expected = getattr(arr, attr)
+        assert result == expected
+
+    def test_from_coo(self):
+        scipy_sparse = pytest.importorskip("scipy.sparse")
+
+        row = [0, 3, 1, 0]
+        col = [0, 3, 1, 2]
+        data = [4, 5, 7, 9]
+
+        sp_array = scipy_sparse.coo_matrix((data, (row, col)))
+        result = pd.Series.sparse.from_coo(sp_array)
+
+        index = pd.MultiIndex.from_arrays(
+            [
+                np.array([0, 0, 1, 3], dtype=np.int32),
+                np.array([0, 2, 1, 3], dtype=np.int32),
+            ],
+        )
+        expected = pd.Series([4, 9, 7, 5], index=index, dtype="Sparse[int]")
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "sort_labels, expected_rows, expected_cols, expected_values_pos",
+        [
+            (
+                False,
+                [("b", 2), ("a", 2), ("b", 1), ("a", 1)],
+                [("z", 1), ("z", 2), ("x", 2), ("z", 0)],
+                {1: (1, 0), 3: (3, 3)},
+            ),
+            (
+                True,
+                [("a", 1), ("a", 2), ("b", 1), ("b", 2)],
+                [("x", 2), ("z", 0), ("z", 1), ("z", 2)],
+                {1: (1, 2), 3: (0, 1)},
+            ),
+        ],
+    )
+    def test_to_coo(
+        self, sort_labels, expected_rows, expected_cols, expected_values_pos
+    ):
+        sp_sparse = pytest.importorskip("scipy.sparse")
+
+        values = SparseArray([0, np.nan, 1, 0, None, 3], fill_value=0)
+        index = pd.MultiIndex.from_tuples(
+            [
+                ("b", 2, "z", 1),
+                ("a", 2, "z", 2),
+                ("a", 2, "z", 1),
+                ("a", 2, "x", 2),
+                ("b", 1, "z", 1),
+                ("a", 1, "z", 0),
+            ]
+        )
+        ss = pd.Series(values, index=index)
+
+        expected_A = np.zeros((4, 4))
+        for value, (row, col) in expected_values_pos.items():
+            expected_A[row, col] = value
+
+        A, rows, cols = ss.sparse.to_coo(
+            row_levels=(0, 1), column_levels=(2, 3), sort_labels=sort_labels
+        )
+        assert isinstance(A, sp_sparse.coo_matrix)
+        tm.assert_numpy_array_equal(A.toarray(), expected_A)
+        assert rows == expected_rows
+        assert cols == expected_cols
+
+    def test_non_sparse_raises(self):
+        ser = pd.Series([1, 2, 3])
+        with pytest.raises(AttributeError, match=".sparse"):
+            ser.sparse.density
+
+
+class TestFrameAccessor:
+    def test_accessor_raises(self):
+        df = pd.DataFrame({"A": [0, 1]})
+        with pytest.raises(AttributeError, match="sparse"):
+            df.sparse
+
+    @pytest.mark.parametrize("format", ["csc", "csr", "coo"])
+    @pytest.mark.parametrize("labels", [None, list(string.ascii_letters[:10])])
+    @pytest.mark.parametrize("dtype", ["float64", "int64"])
+    def test_from_spmatrix(self, format, labels, dtype):
+        sp_sparse = pytest.importorskip("scipy.sparse")
+
+        sp_dtype = SparseDtype(dtype, np.array(0, dtype=dtype).item())
+
+        mat = sp_sparse.eye(10, format=format, dtype=dtype)
+        result = pd.DataFrame.sparse.from_spmatrix(mat, index=labels, columns=labels)
+        expected = pd.DataFrame(
+            np.eye(10, dtype=dtype), index=labels, columns=labels
+        ).astype(sp_dtype)
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("format", ["csc", "csr", "coo"])
+    def test_from_spmatrix_including_explicit_zero(self, format):
+        sp_sparse = pytest.importorskip("scipy.sparse")
+
+        mat = sp_sparse.random(10, 2, density=0.5, format=format)
+        mat.data[0] = 0
+        result = pd.DataFrame.sparse.from_spmatrix(mat)
+        dtype = SparseDtype("float64", 0.0)
+        expected = pd.DataFrame(mat.todense()).astype(dtype)
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "columns",
+        [["a", "b"], pd.MultiIndex.from_product([["A"], ["a", "b"]]), ["a", "a"]],
+    )
+    def test_from_spmatrix_columns(self, columns):
+        sp_sparse = pytest.importorskip("scipy.sparse")
+
+        dtype = SparseDtype("float64", 0.0)
+
+        mat = sp_sparse.random(10, 2, density=0.5)
+        result = pd.DataFrame.sparse.from_spmatrix(mat, columns=columns)
+        expected = pd.DataFrame(mat.toarray(), columns=columns).astype(dtype)
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "colnames", [("A", "B"), (1, 2), (1, pd.NA), (0.1, 0.2), ("x", "x"), (0, 0)]
+    )
+    def test_to_coo(self, colnames):
+        sp_sparse = pytest.importorskip("scipy.sparse")
+
+        df = pd.DataFrame(
+            {colnames[0]: [0, 1, 0], colnames[1]: [1, 0, 0]}, dtype="Sparse[int64, 0]"
+        )
+        result = df.sparse.to_coo()
+        expected = sp_sparse.coo_matrix(np.asarray(df))
+        assert (result != expected).nnz == 0
+
+    @pytest.mark.parametrize("fill_value", [1, np.nan])
+    def test_to_coo_nonzero_fill_val_raises(self, fill_value):
+        pytest.importorskip("scipy")
+        df = pd.DataFrame(
+            {
+                "A": SparseArray(
+                    [fill_value, fill_value, fill_value, 2], fill_value=fill_value
+                ),
+                "B": SparseArray(
+                    [fill_value, 2, fill_value, fill_value], fill_value=fill_value
+                ),
+            }
+        )
+        with pytest.raises(ValueError, match="fill value must be 0"):
+            df.sparse.to_coo()
+
+    def test_to_coo_midx_categorical(self):
+        # GH#50996
+        sp_sparse = pytest.importorskip("scipy.sparse")
+
+        midx = pd.MultiIndex.from_arrays(
+            [
+                pd.CategoricalIndex(list("ab"), name="x"),
+                pd.CategoricalIndex([0, 1], name="y"),
+            ]
+        )
+
+        ser = pd.Series(1, index=midx, dtype="Sparse[int]")
+        result = ser.sparse.to_coo(row_levels=["x"], column_levels=["y"])[0]
+        expected = sp_sparse.coo_matrix(
+            (np.array([1, 1]), (np.array([0, 1]), np.array([0, 1]))), shape=(2, 2)
+        )
+        assert (result != expected).nnz == 0
+
+    def test_to_dense(self):
+        df = pd.DataFrame(
+            {
+                "A": SparseArray([1, 0], dtype=SparseDtype("int64", 0)),
+                "B": SparseArray([1, 0], dtype=SparseDtype("int64", 1)),
+                "C": SparseArray([1.0, 0.0], dtype=SparseDtype("float64", 0.0)),
+            },
+            index=["b", "a"],
+        )
+        result = df.sparse.to_dense()
+        expected = pd.DataFrame(
+            {"A": [1, 0], "B": [1, 0], "C": [1.0, 0.0]}, index=["b", "a"]
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_density(self):
+        df = pd.DataFrame(
+            {
+                "A": SparseArray([1, 0, 2, 1], fill_value=0),
+                "B": SparseArray([0, 1, 1, 1], fill_value=0),
+            }
+        )
+        res = df.sparse.density
+        expected = 0.75
+        assert res == expected
+
+    @pytest.mark.parametrize("dtype", ["int64", "float64"])
+    @pytest.mark.parametrize("dense_index", [True, False])
+    def test_series_from_coo(self, dtype, dense_index):
+        sp_sparse = pytest.importorskip("scipy.sparse")
+
+        A = sp_sparse.eye(3, format="coo", dtype=dtype)
+        result = pd.Series.sparse.from_coo(A, dense_index=dense_index)
+
+        index = pd.MultiIndex.from_tuples(
+            [
+                np.array([0, 0], dtype=np.int32),
+                np.array([1, 1], dtype=np.int32),
+                np.array([2, 2], dtype=np.int32),
+            ],
+        )
+        expected = pd.Series(SparseArray(np.array([1, 1, 1], dtype=dtype)), index=index)
+        if dense_index:
+            expected = expected.reindex(pd.MultiIndex.from_product(index.levels))
+
+        tm.assert_series_equal(result, expected)
+
+    def test_series_from_coo_incorrect_format_raises(self):
+        # gh-26554
+        sp_sparse = pytest.importorskip("scipy.sparse")
+
+        m = sp_sparse.csr_matrix(np.array([[0, 1], [0, 0]]))
+        with pytest.raises(
+            TypeError, match="Expected coo_matrix. Got csr_matrix instead."
+        ):
+            pd.Series.sparse.from_coo(m)
+
+    def test_with_column_named_sparse(self):
+        # https://github.com/pandas-dev/pandas/issues/30758
+        df = pd.DataFrame({"sparse": pd.arrays.SparseArray([1, 2])})
+        assert isinstance(df.sparse, pd.core.arrays.sparse.accessor.SparseFrameAccessor)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_arithmetics.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_arithmetics.py
new file mode 100644
index 0000000000000000000000000000000000000000..ffc93b4e4f176385ac7b2b8a0b51027cb0bad9f6
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_arithmetics.py
@@ -0,0 +1,514 @@
+import operator
+
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import SparseDtype
+import pandas._testing as tm
+from pandas.core.arrays.sparse import SparseArray
+
+
+@pytest.fixture(params=["integer", "block"])
+def kind(request):
+    """kind kwarg to pass to SparseArray"""
+    return request.param
+
+
+@pytest.fixture(params=[True, False])
+def mix(request):
+    """
+    Fixture returning True or False, determining whether to operate
+    op(sparse, dense) instead of op(sparse, sparse)
+    """
+    return request.param
+
+
+class TestSparseArrayArithmetics:
+    def _assert(self, a, b):
+        # We have to use tm.assert_sp_array_equal. See GH #45126
+        tm.assert_numpy_array_equal(a, b)
+
+    def _check_numeric_ops(self, a, b, a_dense, b_dense, mix: bool, op):
+        # Check that arithmetic behavior matches non-Sparse Series arithmetic
+
+        if isinstance(a_dense, np.ndarray):
+            expected = op(pd.Series(a_dense), b_dense).values
+        elif isinstance(b_dense, np.ndarray):
+            expected = op(a_dense, pd.Series(b_dense)).values
+        else:
+            raise NotImplementedError
+
+        with np.errstate(invalid="ignore", divide="ignore"):
+            if mix:
+                result = op(a, b_dense).to_dense()
+            else:
+                result = op(a, b).to_dense()
+
+        self._assert(result, expected)
+
+    def _check_bool_result(self, res):
+        assert isinstance(res, SparseArray)
+        assert isinstance(res.dtype, SparseDtype)
+        assert res.dtype.subtype == np.bool_
+        assert isinstance(res.fill_value, bool)
+
+    def _check_comparison_ops(self, a, b, a_dense, b_dense):
+        with np.errstate(invalid="ignore"):
+            # Unfortunately, trying to wrap the computation of each expected
+            # value is with np.errstate() is too tedious.
+            #
+            # sparse & sparse
+            self._check_bool_result(a == b)
+            self._assert((a == b).to_dense(), a_dense == b_dense)
+
+            self._check_bool_result(a != b)
+            self._assert((a != b).to_dense(), a_dense != b_dense)
+
+            self._check_bool_result(a >= b)
+            self._assert((a >= b).to_dense(), a_dense >= b_dense)
+
+            self._check_bool_result(a <= b)
+            self._assert((a <= b).to_dense(), a_dense <= b_dense)
+
+            self._check_bool_result(a > b)
+            self._assert((a > b).to_dense(), a_dense > b_dense)
+
+            self._check_bool_result(a < b)
+            self._assert((a < b).to_dense(), a_dense < b_dense)
+
+            # sparse & dense
+            self._check_bool_result(a == b_dense)
+            self._assert((a == b_dense).to_dense(), a_dense == b_dense)
+
+            self._check_bool_result(a != b_dense)
+            self._assert((a != b_dense).to_dense(), a_dense != b_dense)
+
+            self._check_bool_result(a >= b_dense)
+            self._assert((a >= b_dense).to_dense(), a_dense >= b_dense)
+
+            self._check_bool_result(a <= b_dense)
+            self._assert((a <= b_dense).to_dense(), a_dense <= b_dense)
+
+            self._check_bool_result(a > b_dense)
+            self._assert((a > b_dense).to_dense(), a_dense > b_dense)
+
+            self._check_bool_result(a < b_dense)
+            self._assert((a < b_dense).to_dense(), a_dense < b_dense)
+
+    def _check_logical_ops(self, a, b, a_dense, b_dense):
+        # sparse & sparse
+        self._check_bool_result(a & b)
+        self._assert((a & b).to_dense(), a_dense & b_dense)
+
+        self._check_bool_result(a | b)
+        self._assert((a | b).to_dense(), a_dense | b_dense)
+        # sparse & dense
+        self._check_bool_result(a & b_dense)
+        self._assert((a & b_dense).to_dense(), a_dense & b_dense)
+
+        self._check_bool_result(a | b_dense)
+        self._assert((a | b_dense).to_dense(), a_dense | b_dense)
+
+    @pytest.mark.parametrize("scalar", [0, 1, 3])
+    @pytest.mark.parametrize("fill_value", [None, 0, 2])
+    def test_float_scalar(
+        self, kind, mix, all_arithmetic_functions, fill_value, scalar, request
+    ):
+        op = all_arithmetic_functions
+        values = np.array([np.nan, 1, 2, 0, np.nan, 0, 1, 2, 1, np.nan])
+        a = SparseArray(values, kind=kind, fill_value=fill_value)
+        self._check_numeric_ops(a, scalar, values, scalar, mix, op)
+
+    def test_float_scalar_comparison(self, kind):
+        values = np.array([np.nan, 1, 2, 0, np.nan, 0, 1, 2, 1, np.nan])
+
+        a = SparseArray(values, kind=kind)
+        self._check_comparison_ops(a, 1, values, 1)
+        self._check_comparison_ops(a, 0, values, 0)
+        self._check_comparison_ops(a, 3, values, 3)
+
+        a = SparseArray(values, kind=kind, fill_value=0)
+        self._check_comparison_ops(a, 1, values, 1)
+        self._check_comparison_ops(a, 0, values, 0)
+        self._check_comparison_ops(a, 3, values, 3)
+
+        a = SparseArray(values, kind=kind, fill_value=2)
+        self._check_comparison_ops(a, 1, values, 1)
+        self._check_comparison_ops(a, 0, values, 0)
+        self._check_comparison_ops(a, 3, values, 3)
+
+    def test_float_same_index_without_nans(self, kind, mix, all_arithmetic_functions):
+        # when sp_index are the same
+        op = all_arithmetic_functions
+
+        values = np.array([0.0, 1.0, 2.0, 6.0, 0.0, 0.0, 1.0, 2.0, 1.0, 0.0])
+        rvalues = np.array([0.0, 2.0, 3.0, 4.0, 0.0, 0.0, 1.0, 3.0, 2.0, 0.0])
+
+        a = SparseArray(values, kind=kind, fill_value=0)
+        b = SparseArray(rvalues, kind=kind, fill_value=0)
+        self._check_numeric_ops(a, b, values, rvalues, mix, op)
+
+    def test_float_same_index_with_nans(
+        self, kind, mix, all_arithmetic_functions, request
+    ):
+        # when sp_index are the same
+        op = all_arithmetic_functions
+        values = np.array([np.nan, 1, 2, 0, np.nan, 0, 1, 2, 1, np.nan])
+        rvalues = np.array([np.nan, 2, 3, 4, np.nan, 0, 1, 3, 2, np.nan])
+
+        a = SparseArray(values, kind=kind)
+        b = SparseArray(rvalues, kind=kind)
+        self._check_numeric_ops(a, b, values, rvalues, mix, op)
+
+    def test_float_same_index_comparison(self, kind):
+        # when sp_index are the same
+        values = np.array([np.nan, 1, 2, 0, np.nan, 0, 1, 2, 1, np.nan])
+        rvalues = np.array([np.nan, 2, 3, 4, np.nan, 0, 1, 3, 2, np.nan])
+
+        a = SparseArray(values, kind=kind)
+        b = SparseArray(rvalues, kind=kind)
+        self._check_comparison_ops(a, b, values, rvalues)
+
+        values = np.array([0.0, 1.0, 2.0, 6.0, 0.0, 0.0, 1.0, 2.0, 1.0, 0.0])
+        rvalues = np.array([0.0, 2.0, 3.0, 4.0, 0.0, 0.0, 1.0, 3.0, 2.0, 0.0])
+
+        a = SparseArray(values, kind=kind, fill_value=0)
+        b = SparseArray(rvalues, kind=kind, fill_value=0)
+        self._check_comparison_ops(a, b, values, rvalues)
+
+    def test_float_array(self, kind, mix, all_arithmetic_functions):
+        op = all_arithmetic_functions
+
+        values = np.array([np.nan, 1, 2, 0, np.nan, 0, 1, 2, 1, np.nan])
+        rvalues = np.array([2, np.nan, 2, 3, np.nan, 0, 1, 5, 2, np.nan])
+
+        a = SparseArray(values, kind=kind)
+        b = SparseArray(rvalues, kind=kind)
+        self._check_numeric_ops(a, b, values, rvalues, mix, op)
+        self._check_numeric_ops(a, b * 0, values, rvalues * 0, mix, op)
+
+        a = SparseArray(values, kind=kind, fill_value=0)
+        b = SparseArray(rvalues, kind=kind)
+        self._check_numeric_ops(a, b, values, rvalues, mix, op)
+
+        a = SparseArray(values, kind=kind, fill_value=0)
+        b = SparseArray(rvalues, kind=kind, fill_value=0)
+        self._check_numeric_ops(a, b, values, rvalues, mix, op)
+
+        a = SparseArray(values, kind=kind, fill_value=1)
+        b = SparseArray(rvalues, kind=kind, fill_value=2)
+        self._check_numeric_ops(a, b, values, rvalues, mix, op)
+
+    def test_float_array_different_kind(self, mix, all_arithmetic_functions):
+        op = all_arithmetic_functions
+
+        values = np.array([np.nan, 1, 2, 0, np.nan, 0, 1, 2, 1, np.nan])
+        rvalues = np.array([2, np.nan, 2, 3, np.nan, 0, 1, 5, 2, np.nan])
+
+        a = SparseArray(values, kind="integer")
+        b = SparseArray(rvalues, kind="block")
+        self._check_numeric_ops(a, b, values, rvalues, mix, op)
+        self._check_numeric_ops(a, b * 0, values, rvalues * 0, mix, op)
+
+        a = SparseArray(values, kind="integer", fill_value=0)
+        b = SparseArray(rvalues, kind="block")
+        self._check_numeric_ops(a, b, values, rvalues, mix, op)
+
+        a = SparseArray(values, kind="integer", fill_value=0)
+        b = SparseArray(rvalues, kind="block", fill_value=0)
+        self._check_numeric_ops(a, b, values, rvalues, mix, op)
+
+        a = SparseArray(values, kind="integer", fill_value=1)
+        b = SparseArray(rvalues, kind="block", fill_value=2)
+        self._check_numeric_ops(a, b, values, rvalues, mix, op)
+
+    def test_float_array_comparison(self, kind):
+        values = np.array([np.nan, 1, 2, 0, np.nan, 0, 1, 2, 1, np.nan])
+        rvalues = np.array([2, np.nan, 2, 3, np.nan, 0, 1, 5, 2, np.nan])
+
+        a = SparseArray(values, kind=kind)
+        b = SparseArray(rvalues, kind=kind)
+        self._check_comparison_ops(a, b, values, rvalues)
+        self._check_comparison_ops(a, b * 0, values, rvalues * 0)
+
+        a = SparseArray(values, kind=kind, fill_value=0)
+        b = SparseArray(rvalues, kind=kind)
+        self._check_comparison_ops(a, b, values, rvalues)
+
+        a = SparseArray(values, kind=kind, fill_value=0)
+        b = SparseArray(rvalues, kind=kind, fill_value=0)
+        self._check_comparison_ops(a, b, values, rvalues)
+
+        a = SparseArray(values, kind=kind, fill_value=1)
+        b = SparseArray(rvalues, kind=kind, fill_value=2)
+        self._check_comparison_ops(a, b, values, rvalues)
+
+    def test_int_array(self, kind, mix, all_arithmetic_functions):
+        op = all_arithmetic_functions
+
+        # have to specify dtype explicitly until fixing GH 667
+        dtype = np.int64
+
+        values = np.array([0, 1, 2, 0, 0, 0, 1, 2, 1, 0], dtype=dtype)
+        rvalues = np.array([2, 0, 2, 3, 0, 0, 1, 5, 2, 0], dtype=dtype)
+
+        a = SparseArray(values, dtype=dtype, kind=kind)
+        assert a.dtype == SparseDtype(dtype)
+        b = SparseArray(rvalues, dtype=dtype, kind=kind)
+        assert b.dtype == SparseDtype(dtype)
+
+        self._check_numeric_ops(a, b, values, rvalues, mix, op)
+        self._check_numeric_ops(a, b * 0, values, rvalues * 0, mix, op)
+
+        a = SparseArray(values, fill_value=0, dtype=dtype, kind=kind)
+        assert a.dtype == SparseDtype(dtype)
+        b = SparseArray(rvalues, dtype=dtype, kind=kind)
+        assert b.dtype == SparseDtype(dtype)
+
+        self._check_numeric_ops(a, b, values, rvalues, mix, op)
+
+        a = SparseArray(values, fill_value=0, dtype=dtype, kind=kind)
+        assert a.dtype == SparseDtype(dtype)
+        b = SparseArray(rvalues, fill_value=0, dtype=dtype, kind=kind)
+        assert b.dtype == SparseDtype(dtype)
+        self._check_numeric_ops(a, b, values, rvalues, mix, op)
+
+        a = SparseArray(values, fill_value=1, dtype=dtype, kind=kind)
+        assert a.dtype == SparseDtype(dtype, fill_value=1)
+        b = SparseArray(rvalues, fill_value=2, dtype=dtype, kind=kind)
+        assert b.dtype == SparseDtype(dtype, fill_value=2)
+        self._check_numeric_ops(a, b, values, rvalues, mix, op)
+
+    def test_int_array_comparison(self, kind):
+        dtype = "int64"
+        # int32 NI ATM
+
+        values = np.array([0, 1, 2, 0, 0, 0, 1, 2, 1, 0], dtype=dtype)
+        rvalues = np.array([2, 0, 2, 3, 0, 0, 1, 5, 2, 0], dtype=dtype)
+
+        a = SparseArray(values, dtype=dtype, kind=kind)
+        b = SparseArray(rvalues, dtype=dtype, kind=kind)
+        self._check_comparison_ops(a, b, values, rvalues)
+        self._check_comparison_ops(a, b * 0, values, rvalues * 0)
+
+        a = SparseArray(values, dtype=dtype, kind=kind, fill_value=0)
+        b = SparseArray(rvalues, dtype=dtype, kind=kind)
+        self._check_comparison_ops(a, b, values, rvalues)
+
+        a = SparseArray(values, dtype=dtype, kind=kind, fill_value=0)
+        b = SparseArray(rvalues, dtype=dtype, kind=kind, fill_value=0)
+        self._check_comparison_ops(a, b, values, rvalues)
+
+        a = SparseArray(values, dtype=dtype, kind=kind, fill_value=1)
+        b = SparseArray(rvalues, dtype=dtype, kind=kind, fill_value=2)
+        self._check_comparison_ops(a, b, values, rvalues)
+
+    @pytest.mark.parametrize("fill_value", [True, False, np.nan])
+    def test_bool_same_index(self, kind, fill_value):
+        # GH 14000
+        # when sp_index are the same
+        values = np.array([True, False, True, True], dtype=np.bool_)
+        rvalues = np.array([True, False, True, True], dtype=np.bool_)
+
+        a = SparseArray(values, kind=kind, dtype=np.bool_, fill_value=fill_value)
+        b = SparseArray(rvalues, kind=kind, dtype=np.bool_, fill_value=fill_value)
+        self._check_logical_ops(a, b, values, rvalues)
+
+    @pytest.mark.parametrize("fill_value", [True, False, np.nan])
+    def test_bool_array_logical(self, kind, fill_value):
+        # GH 14000
+        # when sp_index are the same
+        values = np.array([True, False, True, False, True, True], dtype=np.bool_)
+        rvalues = np.array([True, False, False, True, False, True], dtype=np.bool_)
+
+        a = SparseArray(values, kind=kind, dtype=np.bool_, fill_value=fill_value)
+        b = SparseArray(rvalues, kind=kind, dtype=np.bool_, fill_value=fill_value)
+        self._check_logical_ops(a, b, values, rvalues)
+
+    def test_mixed_array_float_int(self, kind, mix, all_arithmetic_functions, request):
+        op = all_arithmetic_functions
+        rdtype = "int64"
+        values = np.array([np.nan, 1, 2, 0, np.nan, 0, 1, 2, 1, np.nan])
+        rvalues = np.array([2, 0, 2, 3, 0, 0, 1, 5, 2, 0], dtype=rdtype)
+
+        a = SparseArray(values, kind=kind)
+        b = SparseArray(rvalues, kind=kind)
+        assert b.dtype == SparseDtype(rdtype)
+
+        self._check_numeric_ops(a, b, values, rvalues, mix, op)
+        self._check_numeric_ops(a, b * 0, values, rvalues * 0, mix, op)
+
+        a = SparseArray(values, kind=kind, fill_value=0)
+        b = SparseArray(rvalues, kind=kind)
+        assert b.dtype == SparseDtype(rdtype)
+        self._check_numeric_ops(a, b, values, rvalues, mix, op)
+
+        a = SparseArray(values, kind=kind, fill_value=0)
+        b = SparseArray(rvalues, kind=kind, fill_value=0)
+        assert b.dtype == SparseDtype(rdtype)
+        self._check_numeric_ops(a, b, values, rvalues, mix, op)
+
+        a = SparseArray(values, kind=kind, fill_value=1)
+        b = SparseArray(rvalues, kind=kind, fill_value=2)
+        assert b.dtype == SparseDtype(rdtype, fill_value=2)
+        self._check_numeric_ops(a, b, values, rvalues, mix, op)
+
+    def test_mixed_array_comparison(self, kind):
+        rdtype = "int64"
+        # int32 NI ATM
+
+        values = np.array([np.nan, 1, 2, 0, np.nan, 0, 1, 2, 1, np.nan])
+        rvalues = np.array([2, 0, 2, 3, 0, 0, 1, 5, 2, 0], dtype=rdtype)
+
+        a = SparseArray(values, kind=kind)
+        b = SparseArray(rvalues, kind=kind)
+        assert b.dtype == SparseDtype(rdtype)
+
+        self._check_comparison_ops(a, b, values, rvalues)
+        self._check_comparison_ops(a, b * 0, values, rvalues * 0)
+
+        a = SparseArray(values, kind=kind, fill_value=0)
+        b = SparseArray(rvalues, kind=kind)
+        assert b.dtype == SparseDtype(rdtype)
+        self._check_comparison_ops(a, b, values, rvalues)
+
+        a = SparseArray(values, kind=kind, fill_value=0)
+        b = SparseArray(rvalues, kind=kind, fill_value=0)
+        assert b.dtype == SparseDtype(rdtype)
+        self._check_comparison_ops(a, b, values, rvalues)
+
+        a = SparseArray(values, kind=kind, fill_value=1)
+        b = SparseArray(rvalues, kind=kind, fill_value=2)
+        assert b.dtype == SparseDtype(rdtype, fill_value=2)
+        self._check_comparison_ops(a, b, values, rvalues)
+
+    def test_xor(self):
+        s = SparseArray([True, True, False, False])
+        t = SparseArray([True, False, True, False])
+        result = s ^ t
+        sp_index = pd.core.arrays.sparse.IntIndex(4, np.array([0, 1, 2], dtype="int32"))
+        expected = SparseArray([False, True, True], sparse_index=sp_index)
+        tm.assert_sp_array_equal(result, expected)
+
+
+@pytest.mark.parametrize("op", [operator.eq, operator.add])
+def test_with_list(op):
+    arr = SparseArray([0, 1], fill_value=0)
+    result = op(arr, [0, 1])
+    expected = op(arr, SparseArray([0, 1]))
+    tm.assert_sp_array_equal(result, expected)
+
+
+def test_with_dataframe():
+    # GH#27910
+    arr = SparseArray([0, 1], fill_value=0)
+    df = pd.DataFrame([[1, 2], [3, 4]])
+    result = arr.__add__(df)
+    assert result is NotImplemented
+
+
+def test_with_zerodim_ndarray():
+    # GH#27910
+    arr = SparseArray([0, 1], fill_value=0)
+
+    result = arr * np.array(2)
+    expected = arr * 2
+    tm.assert_sp_array_equal(result, expected)
+
+
+@pytest.mark.parametrize("ufunc", [np.abs, np.exp])
+@pytest.mark.parametrize(
+    "arr", [SparseArray([0, 0, -1, 1]), SparseArray([None, None, -1, 1])]
+)
+def test_ufuncs(ufunc, arr):
+    result = ufunc(arr)
+    fill_value = ufunc(arr.fill_value)
+    expected = SparseArray(ufunc(np.asarray(arr)), fill_value=fill_value)
+    tm.assert_sp_array_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "a, b",
+    [
+        (SparseArray([0, 0, 0]), np.array([0, 1, 2])),
+        (SparseArray([0, 0, 0], fill_value=1), np.array([0, 1, 2])),
+        (SparseArray([0, 0, 0], fill_value=1), np.array([0, 1, 2])),
+        (SparseArray([0, 0, 0], fill_value=1), np.array([0, 1, 2])),
+        (SparseArray([0, 0, 0], fill_value=1), np.array([0, 1, 2])),
+    ],
+)
+@pytest.mark.parametrize("ufunc", [np.add, np.greater])
+def test_binary_ufuncs(ufunc, a, b):
+    # can't say anything about fill value here.
+    result = ufunc(a, b)
+    expected = ufunc(np.asarray(a), np.asarray(b))
+    assert isinstance(result, SparseArray)
+    tm.assert_numpy_array_equal(np.asarray(result), expected)
+
+
+def test_ndarray_inplace():
+    sparray = SparseArray([0, 2, 0, 0])
+    ndarray = np.array([0, 1, 2, 3])
+    ndarray += sparray
+    expected = np.array([0, 3, 2, 3])
+    tm.assert_numpy_array_equal(ndarray, expected)
+
+
+def test_sparray_inplace():
+    sparray = SparseArray([0, 2, 0, 0])
+    ndarray = np.array([0, 1, 2, 3])
+    sparray += ndarray
+    expected = SparseArray([0, 3, 2, 3], fill_value=0)
+    tm.assert_sp_array_equal(sparray, expected)
+
+
+@pytest.mark.parametrize("cons", [list, np.array, SparseArray])
+def test_mismatched_length_cmp_op(cons):
+    left = SparseArray([True, True])
+    right = cons([True, True, True])
+    with pytest.raises(ValueError, match="operands have mismatched length"):
+        left & right
+
+
+@pytest.mark.parametrize("op", ["add", "sub", "mul", "truediv", "floordiv", "pow"])
+@pytest.mark.parametrize("fill_value", [np.nan, 3])
+def test_binary_operators(op, fill_value):
+    op = getattr(operator, op)
+    data1 = np.random.default_rng(2).standard_normal(20)
+    data2 = np.random.default_rng(2).standard_normal(20)
+
+    data1[::2] = fill_value
+    data2[::3] = fill_value
+
+    first = SparseArray(data1, fill_value=fill_value)
+    second = SparseArray(data2, fill_value=fill_value)
+
+    with np.errstate(all="ignore"):
+        res = op(first, second)
+        exp = SparseArray(
+            op(first.to_dense(), second.to_dense()), fill_value=first.fill_value
+        )
+        assert isinstance(res, SparseArray)
+        tm.assert_almost_equal(res.to_dense(), exp.to_dense())
+
+        res2 = op(first, second.to_dense())
+        assert isinstance(res2, SparseArray)
+        tm.assert_sp_array_equal(res, res2)
+
+        res3 = op(first.to_dense(), second)
+        assert isinstance(res3, SparseArray)
+        tm.assert_sp_array_equal(res, res3)
+
+        res4 = op(first, 4)
+        assert isinstance(res4, SparseArray)
+
+        # Ignore this if the actual op raises (e.g. pow).
+        try:
+            exp = op(first.to_dense(), 4)
+            exp_fv = op(first.fill_value, 4)
+        except ValueError:
+            pass
+        else:
+            tm.assert_almost_equal(res4.fill_value, exp_fv)
+            tm.assert_almost_equal(res4.to_dense(), exp)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_array.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_array.py
new file mode 100644
index 0000000000000000000000000000000000000000..b2a570b14df3c980a6fe5f32773bf4e8ed02da60
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_array.py
@@ -0,0 +1,511 @@
+import re
+
+import numpy as np
+import pytest
+
+from pandas._libs.sparse import IntIndex
+from pandas.compat.numpy import np_version_gt2
+
+import pandas as pd
+from pandas import (
+    SparseDtype,
+    isna,
+)
+import pandas._testing as tm
+from pandas.core.arrays.sparse import SparseArray
+
+
+@pytest.fixture
+def arr_data():
+    """Fixture returning numpy array with valid and missing entries"""
+    return np.array([np.nan, np.nan, 1, 2, 3, np.nan, 4, 5, np.nan, 6])
+
+
+@pytest.fixture
+def arr(arr_data):
+    """Fixture returning SparseArray from 'arr_data'"""
+    return SparseArray(arr_data)
+
+
+@pytest.fixture
+def zarr():
+    """Fixture returning SparseArray with integer entries and 'fill_value=0'"""
+    return SparseArray([0, 0, 1, 2, 3, 0, 4, 5, 0, 6], fill_value=0)
+
+
+class TestSparseArray:
+    @pytest.mark.parametrize("fill_value", [0, None, np.nan])
+    def test_shift_fill_value(self, fill_value):
+        # GH #24128
+        sparse = SparseArray(np.array([1, 0, 0, 3, 0]), fill_value=8.0)
+        res = sparse.shift(1, fill_value=fill_value)
+        if isna(fill_value):
+            fill_value = res.dtype.na_value
+        exp = SparseArray(np.array([fill_value, 1, 0, 0, 3]), fill_value=8.0)
+        tm.assert_sp_array_equal(res, exp)
+
+    def test_set_fill_value(self):
+        arr = SparseArray([1.0, np.nan, 2.0], fill_value=np.nan)
+        arr.fill_value = 2
+        assert arr.fill_value == 2
+
+        arr = SparseArray([1, 0, 2], fill_value=0, dtype=np.int64)
+        arr.fill_value = 2
+        assert arr.fill_value == 2
+
+        msg = "Allowing arbitrary scalar fill_value in SparseDtype is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            arr.fill_value = 3.1
+        assert arr.fill_value == 3.1
+
+        arr.fill_value = np.nan
+        assert np.isnan(arr.fill_value)
+
+        arr = SparseArray([True, False, True], fill_value=False, dtype=np.bool_)
+        arr.fill_value = True
+        assert arr.fill_value is True
+
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            arr.fill_value = 0
+
+        arr.fill_value = np.nan
+        assert np.isnan(arr.fill_value)
+
+    @pytest.mark.parametrize("val", [[1, 2, 3], np.array([1, 2]), (1, 2, 3)])
+    def test_set_fill_invalid_non_scalar(self, val):
+        arr = SparseArray([True, False, True], fill_value=False, dtype=np.bool_)
+        msg = "fill_value must be a scalar"
+
+        with pytest.raises(ValueError, match=msg):
+            arr.fill_value = val
+
+    def test_copy(self, arr):
+        arr2 = arr.copy()
+        assert arr2.sp_values is not arr.sp_values
+        assert arr2.sp_index is arr.sp_index
+
+    def test_values_asarray(self, arr_data, arr):
+        tm.assert_almost_equal(arr.to_dense(), arr_data)
+
+    @pytest.mark.parametrize(
+        "data,shape,dtype",
+        [
+            ([0, 0, 0, 0, 0], (5,), None),
+            ([], (0,), None),
+            ([0], (1,), None),
+            (["A", "A", np.nan, "B"], (4,), object),
+        ],
+    )
+    def test_shape(self, data, shape, dtype):
+        # GH 21126
+        out = SparseArray(data, dtype=dtype)
+        assert out.shape == shape
+
+    @pytest.mark.parametrize(
+        "vals",
+        [
+            [np.nan, np.nan, np.nan, np.nan, np.nan],
+            [1, np.nan, np.nan, 3, np.nan],
+            [1, np.nan, 0, 3, 0],
+        ],
+    )
+    @pytest.mark.parametrize("fill_value", [None, 0])
+    def test_dense_repr(self, vals, fill_value):
+        vals = np.array(vals)
+        arr = SparseArray(vals, fill_value=fill_value)
+
+        res = arr.to_dense()
+        tm.assert_numpy_array_equal(res, vals)
+
+    @pytest.mark.parametrize("fix", ["arr", "zarr"])
+    def test_pickle(self, fix, request):
+        obj = request.getfixturevalue(fix)
+        unpickled = tm.round_trip_pickle(obj)
+        tm.assert_sp_array_equal(unpickled, obj)
+
+    def test_generator_warnings(self):
+        sp_arr = SparseArray([1, 2, 3])
+        with tm.assert_produces_warning(None):
+            for _ in sp_arr:
+                pass
+
+    def test_where_retain_fill_value(self):
+        # GH#45691 don't lose fill_value on _where
+        arr = SparseArray([np.nan, 1.0], fill_value=0)
+
+        mask = np.array([True, False])
+
+        res = arr._where(~mask, 1)
+        exp = SparseArray([1, 1.0], fill_value=0)
+        tm.assert_sp_array_equal(res, exp)
+
+        ser = pd.Series(arr)
+        res = ser.where(~mask, 1)
+        tm.assert_series_equal(res, pd.Series(exp))
+
+    def test_fillna(self):
+        s = SparseArray([1, np.nan, np.nan, 3, np.nan])
+        res = s.fillna(-1)
+        exp = SparseArray([1, -1, -1, 3, -1], fill_value=-1, dtype=np.float64)
+        tm.assert_sp_array_equal(res, exp)
+
+        s = SparseArray([1, np.nan, np.nan, 3, np.nan], fill_value=0)
+        res = s.fillna(-1)
+        exp = SparseArray([1, -1, -1, 3, -1], fill_value=0, dtype=np.float64)
+        tm.assert_sp_array_equal(res, exp)
+
+        s = SparseArray([1, np.nan, 0, 3, 0])
+        res = s.fillna(-1)
+        exp = SparseArray([1, -1, 0, 3, 0], fill_value=-1, dtype=np.float64)
+        tm.assert_sp_array_equal(res, exp)
+
+        s = SparseArray([1, np.nan, 0, 3, 0], fill_value=0)
+        res = s.fillna(-1)
+        exp = SparseArray([1, -1, 0, 3, 0], fill_value=0, dtype=np.float64)
+        tm.assert_sp_array_equal(res, exp)
+
+        s = SparseArray([np.nan, np.nan, np.nan, np.nan])
+        res = s.fillna(-1)
+        exp = SparseArray([-1, -1, -1, -1], fill_value=-1, dtype=np.float64)
+        tm.assert_sp_array_equal(res, exp)
+
+        s = SparseArray([np.nan, np.nan, np.nan, np.nan], fill_value=0)
+        res = s.fillna(-1)
+        exp = SparseArray([-1, -1, -1, -1], fill_value=0, dtype=np.float64)
+        tm.assert_sp_array_equal(res, exp)
+
+        # float dtype's fill_value is np.nan, replaced by -1
+        s = SparseArray([0.0, 0.0, 0.0, 0.0])
+        res = s.fillna(-1)
+        exp = SparseArray([0.0, 0.0, 0.0, 0.0], fill_value=-1)
+        tm.assert_sp_array_equal(res, exp)
+
+        # int dtype shouldn't have missing. No changes.
+        s = SparseArray([0, 0, 0, 0])
+        assert s.dtype == SparseDtype(np.int64)
+        assert s.fill_value == 0
+        res = s.fillna(-1)
+        tm.assert_sp_array_equal(res, s)
+
+        s = SparseArray([0, 0, 0, 0], fill_value=0)
+        assert s.dtype == SparseDtype(np.int64)
+        assert s.fill_value == 0
+        res = s.fillna(-1)
+        exp = SparseArray([0, 0, 0, 0], fill_value=0)
+        tm.assert_sp_array_equal(res, exp)
+
+        # fill_value can be nan if there is no missing hole.
+        # only fill_value will be changed
+        s = SparseArray([0, 0, 0, 0], fill_value=np.nan)
+        assert s.dtype == SparseDtype(np.int64, fill_value=np.nan)
+        assert np.isnan(s.fill_value)
+        res = s.fillna(-1)
+        exp = SparseArray([0, 0, 0, 0], fill_value=-1)
+        tm.assert_sp_array_equal(res, exp)
+
+    def test_fillna_overlap(self):
+        s = SparseArray([1, np.nan, np.nan, 3, np.nan])
+        # filling with existing value doesn't replace existing value with
+        # fill_value, i.e. existing 3 remains in sp_values
+        res = s.fillna(3)
+        exp = np.array([1, 3, 3, 3, 3], dtype=np.float64)
+        tm.assert_numpy_array_equal(res.to_dense(), exp)
+
+        s = SparseArray([1, np.nan, np.nan, 3, np.nan], fill_value=0)
+        res = s.fillna(3)
+        exp = SparseArray([1, 3, 3, 3, 3], fill_value=0, dtype=np.float64)
+        tm.assert_sp_array_equal(res, exp)
+
+    def test_nonzero(self):
+        # Tests regression #21172.
+        sa = SparseArray([float("nan"), float("nan"), 1, 0, 0, 2, 0, 0, 0, 3, 0, 0])
+        expected = np.array([2, 5, 9], dtype=np.int32)
+        (result,) = sa.nonzero()
+        tm.assert_numpy_array_equal(expected, result)
+
+        sa = SparseArray([0, 0, 1, 0, 0, 2, 0, 0, 0, 3, 0, 0])
+        (result,) = sa.nonzero()
+        tm.assert_numpy_array_equal(expected, result)
+
+
+class TestSparseArrayAnalytics:
+    @pytest.mark.parametrize(
+        "data,expected",
+        [
+            (
+                np.array([1, 2, 3, 4, 5], dtype=float),  # non-null data
+                SparseArray(np.array([1.0, 3.0, 6.0, 10.0, 15.0])),
+            ),
+            (
+                np.array([1, 2, np.nan, 4, 5], dtype=float),  # null data
+                SparseArray(np.array([1.0, 3.0, np.nan, 7.0, 12.0])),
+            ),
+        ],
+    )
+    @pytest.mark.parametrize("numpy", [True, False])
+    def test_cumsum(self, data, expected, numpy):
+        cumsum = np.cumsum if numpy else lambda s: s.cumsum()
+
+        out = cumsum(SparseArray(data))
+        tm.assert_sp_array_equal(out, expected)
+
+        out = cumsum(SparseArray(data, fill_value=np.nan))
+        tm.assert_sp_array_equal(out, expected)
+
+        out = cumsum(SparseArray(data, fill_value=2))
+        tm.assert_sp_array_equal(out, expected)
+
+        if numpy:  # numpy compatibility checks.
+            msg = "the 'dtype' parameter is not supported"
+            with pytest.raises(ValueError, match=msg):
+                np.cumsum(SparseArray(data), dtype=np.int64)
+
+            msg = "the 'out' parameter is not supported"
+            with pytest.raises(ValueError, match=msg):
+                np.cumsum(SparseArray(data), out=out)
+        else:
+            axis = 1  # SparseArray currently 1-D, so only axis = 0 is valid.
+            msg = re.escape(f"axis(={axis}) out of bounds")
+            with pytest.raises(ValueError, match=msg):
+                SparseArray(data).cumsum(axis=axis)
+
+    def test_ufunc(self):
+        # GH 13853 make sure ufunc is applied to fill_value
+        sparse = SparseArray([1, np.nan, 2, np.nan, -2])
+        result = SparseArray([1, np.nan, 2, np.nan, 2])
+        tm.assert_sp_array_equal(abs(sparse), result)
+        tm.assert_sp_array_equal(np.abs(sparse), result)
+
+        sparse = SparseArray([1, -1, 2, -2], fill_value=1)
+        result = SparseArray([1, 2, 2], sparse_index=sparse.sp_index, fill_value=1)
+        tm.assert_sp_array_equal(abs(sparse), result)
+        tm.assert_sp_array_equal(np.abs(sparse), result)
+
+        sparse = SparseArray([1, -1, 2, -2], fill_value=-1)
+        exp = SparseArray([1, 1, 2, 2], fill_value=1)
+        tm.assert_sp_array_equal(abs(sparse), exp)
+        tm.assert_sp_array_equal(np.abs(sparse), exp)
+
+        sparse = SparseArray([1, np.nan, 2, np.nan, -2])
+        result = SparseArray(np.sin([1, np.nan, 2, np.nan, -2]))
+        tm.assert_sp_array_equal(np.sin(sparse), result)
+
+        sparse = SparseArray([1, -1, 2, -2], fill_value=1)
+        result = SparseArray(np.sin([1, -1, 2, -2]), fill_value=np.sin(1))
+        tm.assert_sp_array_equal(np.sin(sparse), result)
+
+        sparse = SparseArray([1, -1, 0, -2], fill_value=0)
+        result = SparseArray(np.sin([1, -1, 0, -2]), fill_value=np.sin(0))
+        tm.assert_sp_array_equal(np.sin(sparse), result)
+
+    def test_ufunc_args(self):
+        # GH 13853 make sure ufunc is applied to fill_value, including its arg
+        sparse = SparseArray([1, np.nan, 2, np.nan, -2])
+        result = SparseArray([2, np.nan, 3, np.nan, -1])
+        tm.assert_sp_array_equal(np.add(sparse, 1), result)
+
+        sparse = SparseArray([1, -1, 2, -2], fill_value=1)
+        result = SparseArray([2, 0, 3, -1], fill_value=2)
+        tm.assert_sp_array_equal(np.add(sparse, 1), result)
+
+        sparse = SparseArray([1, -1, 0, -2], fill_value=0)
+        result = SparseArray([2, 0, 1, -1], fill_value=1)
+        tm.assert_sp_array_equal(np.add(sparse, 1), result)
+
+    @pytest.mark.parametrize("fill_value", [0.0, np.nan])
+    def test_modf(self, fill_value):
+        # https://github.com/pandas-dev/pandas/issues/26946
+        sparse = SparseArray([fill_value] * 10 + [1.1, 2.2], fill_value=fill_value)
+        r1, r2 = np.modf(sparse)
+        e1, e2 = np.modf(np.asarray(sparse))
+        tm.assert_sp_array_equal(r1, SparseArray(e1, fill_value=fill_value))
+        tm.assert_sp_array_equal(r2, SparseArray(e2, fill_value=fill_value))
+
+    def test_nbytes_integer(self):
+        arr = SparseArray([1, 0, 0, 0, 2], kind="integer")
+        result = arr.nbytes
+        # (2 * 8) + 2 * 4
+        assert result == 24
+
+    def test_nbytes_block(self):
+        arr = SparseArray([1, 2, 0, 0, 0], kind="block")
+        result = arr.nbytes
+        # (2 * 8) + 4 + 4
+        # sp_values, blocs, blengths
+        assert result == 24
+
+    def test_asarray_datetime64(self):
+        s = SparseArray(pd.to_datetime(["2012", None, None, "2013"]))
+        np.asarray(s)
+
+    def test_density(self):
+        arr = SparseArray([0, 1])
+        assert arr.density == 0.5
+
+    def test_npoints(self):
+        arr = SparseArray([0, 1])
+        assert arr.npoints == 1
+
+
+def test_setting_fill_value_fillna_still_works():
+    # This is why letting users update fill_value / dtype is bad
+    # astype has the same problem.
+    arr = SparseArray([1.0, np.nan, 1.0], fill_value=0.0)
+    arr.fill_value = np.nan
+    result = arr.isna()
+    # Can't do direct comparison, since the sp_index will be different
+    # So let's convert to ndarray and check there.
+    result = np.asarray(result)
+
+    expected = np.array([False, True, False])
+    tm.assert_numpy_array_equal(result, expected)
+
+
+def test_setting_fill_value_updates():
+    arr = SparseArray([0.0, np.nan], fill_value=0)
+    arr.fill_value = np.nan
+    # use private constructor to get the index right
+    # otherwise both nans would be un-stored.
+    expected = SparseArray._simple_new(
+        sparse_array=np.array([np.nan]),
+        sparse_index=IntIndex(2, [1]),
+        dtype=SparseDtype(float, np.nan),
+    )
+    tm.assert_sp_array_equal(arr, expected)
+
+
+@pytest.mark.parametrize(
+    "arr,fill_value,loc",
+    [
+        ([None, 1, 2], None, 0),
+        ([0, None, 2], None, 1),
+        ([0, 1, None], None, 2),
+        ([0, 1, 1, None, None], None, 3),
+        ([1, 1, 1, 2], None, -1),
+        ([], None, -1),
+        ([None, 1, 0, 0, None, 2], None, 0),
+        ([None, 1, 0, 0, None, 2], 1, 1),
+        ([None, 1, 0, 0, None, 2], 2, 5),
+        ([None, 1, 0, 0, None, 2], 3, -1),
+        ([None, 0, 0, 1, 2, 1], 0, 1),
+        ([None, 0, 0, 1, 2, 1], 1, 3),
+    ],
+)
+def test_first_fill_value_loc(arr, fill_value, loc):
+    result = SparseArray(arr, fill_value=fill_value)._first_fill_value_loc()
+    assert result == loc
+
+
+@pytest.mark.parametrize(
+    "arr",
+    [
+        [1, 2, np.nan, np.nan],
+        [1, np.nan, 2, np.nan],
+        [1, 2, np.nan],
+        [np.nan, 1, 0, 0, np.nan, 2],
+        [np.nan, 0, 0, 1, 2, 1],
+    ],
+)
+@pytest.mark.parametrize("fill_value", [np.nan, 0, 1])
+def test_unique_na_fill(arr, fill_value):
+    a = SparseArray(arr, fill_value=fill_value).unique()
+    b = pd.Series(arr).unique()
+    assert isinstance(a, SparseArray)
+    a = np.asarray(a)
+    tm.assert_numpy_array_equal(a, b)
+
+
+def test_unique_all_sparse():
+    # https://github.com/pandas-dev/pandas/issues/23168
+    arr = SparseArray([0, 0])
+    result = arr.unique()
+    expected = SparseArray([0])
+    tm.assert_sp_array_equal(result, expected)
+
+
+def test_map():
+    arr = SparseArray([0, 1, 2])
+    expected = SparseArray([10, 11, 12], fill_value=10)
+
+    # dict
+    result = arr.map({0: 10, 1: 11, 2: 12})
+    tm.assert_sp_array_equal(result, expected)
+
+    # series
+    result = arr.map(pd.Series({0: 10, 1: 11, 2: 12}))
+    tm.assert_sp_array_equal(result, expected)
+
+    # function
+    result = arr.map(pd.Series({0: 10, 1: 11, 2: 12}))
+    expected = SparseArray([10, 11, 12], fill_value=10)
+    tm.assert_sp_array_equal(result, expected)
+
+
+def test_map_missing():
+    arr = SparseArray([0, 1, 2])
+    expected = SparseArray([10, 11, None], fill_value=10)
+
+    result = arr.map({0: 10, 1: 11})
+    tm.assert_sp_array_equal(result, expected)
+
+
+@pytest.mark.parametrize("fill_value", [np.nan, 1])
+def test_dropna(fill_value):
+    # GH-28287
+    arr = SparseArray([np.nan, 1], fill_value=fill_value)
+    exp = SparseArray([1.0], fill_value=fill_value)
+    tm.assert_sp_array_equal(arr.dropna(), exp)
+
+    df = pd.DataFrame({"a": [0, 1], "b": arr})
+    expected_df = pd.DataFrame({"a": [1], "b": exp}, index=pd.Index([1]))
+    tm.assert_equal(df.dropna(), expected_df)
+
+
+def test_drop_duplicates_fill_value():
+    # GH 11726
+    df = pd.DataFrame(np.zeros((5, 5))).apply(lambda x: SparseArray(x, fill_value=0))
+    result = df.drop_duplicates()
+    expected = pd.DataFrame({i: SparseArray([0.0], fill_value=0) for i in range(5)})
+    tm.assert_frame_equal(result, expected)
+
+
+def test_zero_sparse_column():
+    # GH 27781
+    df1 = pd.DataFrame({"A": SparseArray([0, 0, 0]), "B": [1, 2, 3]})
+    df2 = pd.DataFrame({"A": SparseArray([0, 1, 0]), "B": [1, 2, 3]})
+    result = df1.loc[df1["B"] != 2]
+    expected = df2.loc[df2["B"] != 2]
+    tm.assert_frame_equal(result, expected)
+
+    expected = pd.DataFrame({"A": SparseArray([0, 0]), "B": [1, 3]}, index=[0, 2])
+    tm.assert_frame_equal(result, expected)
+
+
+def test_array_interface(arr_data, arr):
+    # https://github.com/pandas-dev/pandas/pull/60046
+    result = np.asarray(arr)
+    tm.assert_numpy_array_equal(result, arr_data)
+
+    # it always gives a copy by default
+    result_copy1 = np.asarray(arr)
+    result_copy2 = np.asarray(arr)
+    assert not np.may_share_memory(result_copy1, result_copy2)
+
+    # or with explicit copy=True
+    result_copy1 = np.array(arr, copy=True)
+    result_copy2 = np.array(arr, copy=True)
+    assert not np.may_share_memory(result_copy1, result_copy2)
+
+    if not np_version_gt2:
+        # copy=False semantics are only supported in NumPy>=2.
+        return
+
+    msg = "Starting with NumPy 2.0, the behavior of the 'copy' keyword has changed"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        np.array(arr, copy=False)
+
+    # except when there are actually no sparse filled values
+    arr2 = SparseArray(np.array([1, 2, 3]))
+    result_nocopy1 = np.array(arr2, copy=False)
+    result_nocopy2 = np.array(arr2, copy=False)
+    assert np.may_share_memory(result_nocopy1, result_nocopy2)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_astype.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_astype.py
new file mode 100644
index 0000000000000000000000000000000000000000..e6e4a11a0f5ab4056606a127f8ed61ee3a4456a8
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_astype.py
@@ -0,0 +1,133 @@
+import numpy as np
+import pytest
+
+from pandas._libs.sparse import IntIndex
+
+from pandas import (
+    SparseDtype,
+    Timestamp,
+)
+import pandas._testing as tm
+from pandas.core.arrays.sparse import SparseArray
+
+
+class TestAstype:
+    def test_astype(self):
+        # float -> float
+        arr = SparseArray([None, None, 0, 2])
+        result = arr.astype("Sparse[float32]")
+        expected = SparseArray([None, None, 0, 2], dtype=np.dtype("float32"))
+        tm.assert_sp_array_equal(result, expected)
+
+        dtype = SparseDtype("float64", fill_value=0)
+        result = arr.astype(dtype)
+        expected = SparseArray._simple_new(
+            np.array([0.0, 2.0], dtype=dtype.subtype), IntIndex(4, [2, 3]), dtype
+        )
+        tm.assert_sp_array_equal(result, expected)
+
+        dtype = SparseDtype("int64", 0)
+        result = arr.astype(dtype)
+        expected = SparseArray._simple_new(
+            np.array([0, 2], dtype=np.int64), IntIndex(4, [2, 3]), dtype
+        )
+        tm.assert_sp_array_equal(result, expected)
+
+        arr = SparseArray([0, np.nan, 0, 1], fill_value=0)
+        with pytest.raises(ValueError, match="NA"):
+            arr.astype("Sparse[i8]")
+
+    def test_astype_bool(self):
+        a = SparseArray([1, 0, 0, 1], dtype=SparseDtype(int, 0))
+        result = a.astype(bool)
+        expected = np.array([1, 0, 0, 1], dtype=bool)
+        tm.assert_numpy_array_equal(result, expected)
+
+        # update fill value
+        result = a.astype(SparseDtype(bool, False))
+        expected = SparseArray(
+            [True, False, False, True], dtype=SparseDtype(bool, False)
+        )
+        tm.assert_sp_array_equal(result, expected)
+
+    def test_astype_all(self, any_real_numpy_dtype):
+        vals = np.array([1, 2, 3])
+        arr = SparseArray(vals, fill_value=1)
+        typ = np.dtype(any_real_numpy_dtype)
+        res = arr.astype(typ)
+        tm.assert_numpy_array_equal(res, vals.astype(any_real_numpy_dtype))
+
+    @pytest.mark.parametrize(
+        "arr, dtype, expected",
+        [
+            (
+                SparseArray([0, 1]),
+                "float",
+                SparseArray([0.0, 1.0], dtype=SparseDtype(float, 0.0)),
+            ),
+            (SparseArray([0, 1]), bool, SparseArray([False, True])),
+            (
+                SparseArray([0, 1], fill_value=1),
+                bool,
+                SparseArray([False, True], dtype=SparseDtype(bool, True)),
+            ),
+            pytest.param(
+                SparseArray([0, 1]),
+                "datetime64[ns]",
+                SparseArray(
+                    np.array([0, 1], dtype="datetime64[ns]"),
+                    dtype=SparseDtype("datetime64[ns]", Timestamp("1970")),
+                ),
+            ),
+            (
+                SparseArray([0, 1, 10]),
+                np.str_,
+                SparseArray(["0", "1", "10"], dtype=SparseDtype(np.str_, "0")),
+            ),
+            (SparseArray(["10", "20"]), float, SparseArray([10.0, 20.0])),
+            (
+                SparseArray([0, 1, 0]),
+                object,
+                SparseArray([0, 1, 0], dtype=SparseDtype(object, 0)),
+            ),
+        ],
+    )
+    def test_astype_more(self, arr, dtype, expected):
+        result = arr.astype(arr.dtype.update_dtype(dtype))
+        tm.assert_sp_array_equal(result, expected)
+
+    def test_astype_nan_raises(self):
+        arr = SparseArray([1.0, np.nan])
+        with pytest.raises(ValueError, match="Cannot convert non-finite"):
+            arr.astype(int)
+
+    def test_astype_copy_false(self):
+        # GH#34456 bug caused by using .view instead of .astype in astype_nansafe
+        arr = SparseArray([1, 2, 3])
+
+        dtype = SparseDtype(float, 0)
+
+        result = arr.astype(dtype, copy=False)
+        expected = SparseArray([1.0, 2.0, 3.0], fill_value=0.0)
+        tm.assert_sp_array_equal(result, expected)
+
+    def test_astype_dt64_to_int64(self):
+        # GH#49631 match non-sparse behavior
+        values = np.array(["NaT", "2016-01-02", "2016-01-03"], dtype="M8[ns]")
+
+        arr = SparseArray(values)
+        result = arr.astype("int64")
+        expected = values.astype("int64")
+        tm.assert_numpy_array_equal(result, expected)
+
+        # we should also be able to cast to equivalent Sparse[int64]
+        dtype_int64 = SparseDtype("int64", np.iinfo(np.int64).min)
+        result2 = arr.astype(dtype_int64)
+        tm.assert_numpy_array_equal(result2.to_numpy(), expected)
+
+        # GH#50087 we should match the non-sparse behavior regardless of
+        #  if we have a fill_value other than NaT
+        dtype = SparseDtype("datetime64[ns]", values[1])
+        arr3 = SparseArray(values, dtype=dtype)
+        result3 = arr3.astype("int64")
+        tm.assert_numpy_array_equal(result3, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_combine_concat.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_combine_concat.py
new file mode 100644
index 0000000000000000000000000000000000000000..0f09af269148bc6fec712b9b1df63cca6f44d248
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_combine_concat.py
@@ -0,0 +1,62 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.core.arrays.sparse import SparseArray
+
+
+class TestSparseArrayConcat:
+    @pytest.mark.parametrize("kind", ["integer", "block"])
+    def test_basic(self, kind):
+        a = SparseArray([1, 0, 0, 2], kind=kind)
+        b = SparseArray([1, 0, 2, 2], kind=kind)
+
+        result = SparseArray._concat_same_type([a, b])
+        # Can't make any assertions about the sparse index itself
+        # since we aren't don't merge sparse blocs across arrays
+        # in to_concat
+        expected = np.array([1, 2, 1, 2, 2], dtype="int64")
+        tm.assert_numpy_array_equal(result.sp_values, expected)
+        assert result.kind == kind
+
+    @pytest.mark.parametrize("kind", ["integer", "block"])
+    def test_uses_first_kind(self, kind):
+        other = "integer" if kind == "block" else "block"
+        a = SparseArray([1, 0, 0, 2], kind=kind)
+        b = SparseArray([1, 0, 2, 2], kind=other)
+
+        result = SparseArray._concat_same_type([a, b])
+        expected = np.array([1, 2, 1, 2, 2], dtype="int64")
+        tm.assert_numpy_array_equal(result.sp_values, expected)
+        assert result.kind == kind
+
+
+@pytest.mark.parametrize(
+    "other, expected_dtype",
+    [
+        # compatible dtype -> preserve sparse
+        (pd.Series([3, 4, 5], dtype="int64"), pd.SparseDtype("int64", 0)),
+        # (pd.Series([3, 4, 5], dtype="Int64"), pd.SparseDtype("int64", 0)),
+        # incompatible dtype -> Sparse[common dtype]
+        (pd.Series([1.5, 2.5, 3.5], dtype="float64"), pd.SparseDtype("float64", 0)),
+        # incompatible dtype -> Sparse[object] dtype
+        (pd.Series(["a", "b", "c"], dtype=object), pd.SparseDtype(object, 0)),
+        # categorical with compatible categories -> dtype of the categories
+        (pd.Series([3, 4, 5], dtype="category"), np.dtype("int64")),
+        (pd.Series([1.5, 2.5, 3.5], dtype="category"), np.dtype("float64")),
+        # categorical with incompatible categories -> object dtype
+        (pd.Series(["a", "b", "c"], dtype="category"), np.dtype(object)),
+    ],
+)
+def test_concat_with_non_sparse(other, expected_dtype):
+    # https://github.com/pandas-dev/pandas/issues/34336
+    s_sparse = pd.Series([1, 0, 2], dtype=pd.SparseDtype("int64", 0))
+
+    result = pd.concat([s_sparse, other], ignore_index=True)
+    expected = pd.Series(list(s_sparse) + list(other)).astype(expected_dtype)
+    tm.assert_series_equal(result, expected)
+
+    result = pd.concat([other, s_sparse], ignore_index=True)
+    expected = pd.Series(list(other) + list(s_sparse)).astype(expected_dtype)
+    tm.assert_series_equal(result, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_constructors.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_constructors.py
new file mode 100644
index 0000000000000000000000000000000000000000..2831c8abdaf137b6454ea8f73bff7e94a3ec1b2b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_constructors.py
@@ -0,0 +1,285 @@
+import numpy as np
+import pytest
+
+from pandas._libs.sparse import IntIndex
+
+import pandas as pd
+from pandas import (
+    SparseDtype,
+    isna,
+)
+import pandas._testing as tm
+from pandas.core.arrays.sparse import SparseArray
+
+
+class TestConstructors:
+    def test_constructor_dtype(self):
+        arr = SparseArray([np.nan, 1, 2, np.nan])
+        assert arr.dtype == SparseDtype(np.float64, np.nan)
+        assert arr.dtype.subtype == np.float64
+        assert np.isnan(arr.fill_value)
+
+        arr = SparseArray([np.nan, 1, 2, np.nan], fill_value=0)
+        assert arr.dtype == SparseDtype(np.float64, 0)
+        assert arr.fill_value == 0
+
+        arr = SparseArray([0, 1, 2, 4], dtype=np.float64)
+        assert arr.dtype == SparseDtype(np.float64, np.nan)
+        assert np.isnan(arr.fill_value)
+
+        arr = SparseArray([0, 1, 2, 4], dtype=np.int64)
+        assert arr.dtype == SparseDtype(np.int64, 0)
+        assert arr.fill_value == 0
+
+        arr = SparseArray([0, 1, 2, 4], fill_value=0, dtype=np.int64)
+        assert arr.dtype == SparseDtype(np.int64, 0)
+        assert arr.fill_value == 0
+
+        arr = SparseArray([0, 1, 2, 4], dtype=None)
+        assert arr.dtype == SparseDtype(np.int64, 0)
+        assert arr.fill_value == 0
+
+        arr = SparseArray([0, 1, 2, 4], fill_value=0, dtype=None)
+        assert arr.dtype == SparseDtype(np.int64, 0)
+        assert arr.fill_value == 0
+
+    def test_constructor_dtype_str(self):
+        result = SparseArray([1, 2, 3], dtype="int")
+        expected = SparseArray([1, 2, 3], dtype=int)
+        tm.assert_sp_array_equal(result, expected)
+
+    def test_constructor_sparse_dtype(self):
+        result = SparseArray([1, 0, 0, 1], dtype=SparseDtype("int64", -1))
+        expected = SparseArray([1, 0, 0, 1], fill_value=-1, dtype=np.int64)
+        tm.assert_sp_array_equal(result, expected)
+        assert result.sp_values.dtype == np.dtype("int64")
+
+    def test_constructor_sparse_dtype_str(self):
+        result = SparseArray([1, 0, 0, 1], dtype="Sparse[int32]")
+        expected = SparseArray([1, 0, 0, 1], dtype=np.int32)
+        tm.assert_sp_array_equal(result, expected)
+        assert result.sp_values.dtype == np.dtype("int32")
+
+    def test_constructor_object_dtype(self):
+        # GH#11856
+        arr = SparseArray(["A", "A", np.nan, "B"], dtype=object)
+        assert arr.dtype == SparseDtype(object)
+        assert np.isnan(arr.fill_value)
+
+        arr = SparseArray(["A", "A", np.nan, "B"], dtype=object, fill_value="A")
+        assert arr.dtype == SparseDtype(object, "A")
+        assert arr.fill_value == "A"
+
+    def test_constructor_object_dtype_bool_fill(self):
+        # GH#17574
+        data = [False, 0, 100.0, 0.0]
+        arr = SparseArray(data, dtype=object, fill_value=False)
+        assert arr.dtype == SparseDtype(object, False)
+        assert arr.fill_value is False
+        arr_expected = np.array(data, dtype=object)
+        it = (type(x) == type(y) and x == y for x, y in zip(arr, arr_expected))
+        assert np.fromiter(it, dtype=np.bool_).all()
+
+    @pytest.mark.parametrize("dtype", [SparseDtype(int, 0), int])
+    def test_constructor_na_dtype(self, dtype):
+        with pytest.raises(ValueError, match="Cannot convert"):
+            SparseArray([0, 1, np.nan], dtype=dtype)
+
+    def test_constructor_warns_when_losing_timezone(self):
+        # GH#32501 warn when losing timezone information
+        dti = pd.date_range("2016-01-01", periods=3, tz="US/Pacific")
+
+        expected = SparseArray(np.asarray(dti, dtype="datetime64[ns]"))
+
+        with tm.assert_produces_warning(UserWarning):
+            result = SparseArray(dti)
+
+        tm.assert_sp_array_equal(result, expected)
+
+        with tm.assert_produces_warning(UserWarning):
+            result = SparseArray(pd.Series(dti))
+
+        tm.assert_sp_array_equal(result, expected)
+
+    def test_constructor_spindex_dtype(self):
+        arr = SparseArray(data=[1, 2], sparse_index=IntIndex(4, [1, 2]))
+        # TODO: actionable?
+        # XXX: Behavior change: specifying SparseIndex no longer changes the
+        # fill_value
+        expected = SparseArray([0, 1, 2, 0], kind="integer")
+        tm.assert_sp_array_equal(arr, expected)
+        assert arr.dtype == SparseDtype(np.int64)
+        assert arr.fill_value == 0
+
+        arr = SparseArray(
+            data=[1, 2, 3],
+            sparse_index=IntIndex(4, [1, 2, 3]),
+            dtype=np.int64,
+            fill_value=0,
+        )
+        exp = SparseArray([0, 1, 2, 3], dtype=np.int64, fill_value=0)
+        tm.assert_sp_array_equal(arr, exp)
+        assert arr.dtype == SparseDtype(np.int64)
+        assert arr.fill_value == 0
+
+        arr = SparseArray(
+            data=[1, 2], sparse_index=IntIndex(4, [1, 2]), fill_value=0, dtype=np.int64
+        )
+        exp = SparseArray([0, 1, 2, 0], fill_value=0, dtype=np.int64)
+        tm.assert_sp_array_equal(arr, exp)
+        assert arr.dtype == SparseDtype(np.int64)
+        assert arr.fill_value == 0
+
+        arr = SparseArray(
+            data=[1, 2, 3],
+            sparse_index=IntIndex(4, [1, 2, 3]),
+            dtype=None,
+            fill_value=0,
+        )
+        exp = SparseArray([0, 1, 2, 3], dtype=None)
+        tm.assert_sp_array_equal(arr, exp)
+        assert arr.dtype == SparseDtype(np.int64)
+        assert arr.fill_value == 0
+
+    @pytest.mark.parametrize("sparse_index", [None, IntIndex(1, [0])])
+    def test_constructor_spindex_dtype_scalar(self, sparse_index):
+        # scalar input
+        msg = "Constructing SparseArray with scalar data is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            arr = SparseArray(data=1, sparse_index=sparse_index, dtype=None)
+        exp = SparseArray([1], dtype=None)
+        tm.assert_sp_array_equal(arr, exp)
+        assert arr.dtype == SparseDtype(np.int64)
+        assert arr.fill_value == 0
+
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            arr = SparseArray(data=1, sparse_index=IntIndex(1, [0]), dtype=None)
+        exp = SparseArray([1], dtype=None)
+        tm.assert_sp_array_equal(arr, exp)
+        assert arr.dtype == SparseDtype(np.int64)
+        assert arr.fill_value == 0
+
+    def test_constructor_spindex_dtype_scalar_broadcasts(self):
+        arr = SparseArray(
+            data=[1, 2], sparse_index=IntIndex(4, [1, 2]), fill_value=0, dtype=None
+        )
+        exp = SparseArray([0, 1, 2, 0], fill_value=0, dtype=None)
+        tm.assert_sp_array_equal(arr, exp)
+        assert arr.dtype == SparseDtype(np.int64)
+        assert arr.fill_value == 0
+
+    @pytest.mark.parametrize(
+        "data, fill_value",
+        [
+            (np.array([1, 2]), 0),
+            (np.array([1.0, 2.0]), np.nan),
+            ([True, False], False),
+            ([pd.Timestamp("2017-01-01")], pd.NaT),
+        ],
+    )
+    def test_constructor_inferred_fill_value(self, data, fill_value):
+        result = SparseArray(data).fill_value
+
+        if isna(fill_value):
+            assert isna(result)
+        else:
+            assert result == fill_value
+
+    @pytest.mark.parametrize("format", ["coo", "csc", "csr"])
+    @pytest.mark.parametrize("size", [0, 10])
+    def test_from_spmatrix(self, size, format):
+        sp_sparse = pytest.importorskip("scipy.sparse")
+
+        mat = sp_sparse.random(size, 1, density=0.5, format=format)
+        result = SparseArray.from_spmatrix(mat)
+
+        result = np.asarray(result)
+        expected = mat.toarray().ravel()
+        tm.assert_numpy_array_equal(result, expected)
+
+    @pytest.mark.parametrize("format", ["coo", "csc", "csr"])
+    def test_from_spmatrix_including_explicit_zero(self, format):
+        sp_sparse = pytest.importorskip("scipy.sparse")
+
+        mat = sp_sparse.random(10, 1, density=0.5, format=format)
+        mat.data[0] = 0
+        result = SparseArray.from_spmatrix(mat)
+
+        result = np.asarray(result)
+        expected = mat.toarray().ravel()
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_from_spmatrix_raises(self):
+        sp_sparse = pytest.importorskip("scipy.sparse")
+
+        mat = sp_sparse.eye(5, 4, format="csc")
+
+        with pytest.raises(ValueError, match="not '4'"):
+            SparseArray.from_spmatrix(mat)
+
+    def test_constructor_from_too_large_array(self):
+        with pytest.raises(TypeError, match="expected dimension <= 1 data"):
+            SparseArray(np.arange(10).reshape((2, 5)))
+
+    def test_constructor_from_sparse(self):
+        zarr = SparseArray([0, 0, 1, 2, 3, 0, 4, 5, 0, 6], fill_value=0)
+        res = SparseArray(zarr)
+        assert res.fill_value == 0
+        tm.assert_almost_equal(res.sp_values, zarr.sp_values)
+
+    def test_constructor_copy(self):
+        arr_data = np.array([np.nan, np.nan, 1, 2, 3, np.nan, 4, 5, np.nan, 6])
+        arr = SparseArray(arr_data)
+
+        cp = SparseArray(arr, copy=True)
+        cp.sp_values[:3] = 0
+        assert not (arr.sp_values[:3] == 0).any()
+
+        not_copy = SparseArray(arr)
+        not_copy.sp_values[:3] = 0
+        assert (arr.sp_values[:3] == 0).all()
+
+    def test_constructor_bool(self):
+        # GH#10648
+        data = np.array([False, False, True, True, False, False])
+        arr = SparseArray(data, fill_value=False, dtype=bool)
+
+        assert arr.dtype == SparseDtype(bool)
+        tm.assert_numpy_array_equal(arr.sp_values, np.array([True, True]))
+        # Behavior change: np.asarray densifies.
+        # tm.assert_numpy_array_equal(arr.sp_values, np.asarray(arr))
+        tm.assert_numpy_array_equal(arr.sp_index.indices, np.array([2, 3], np.int32))
+
+        dense = arr.to_dense()
+        assert dense.dtype == bool
+        tm.assert_numpy_array_equal(dense, data)
+
+    def test_constructor_bool_fill_value(self):
+        arr = SparseArray([True, False, True], dtype=None)
+        assert arr.dtype == SparseDtype(np.bool_)
+        assert not arr.fill_value
+
+        arr = SparseArray([True, False, True], dtype=np.bool_)
+        assert arr.dtype == SparseDtype(np.bool_)
+        assert not arr.fill_value
+
+        arr = SparseArray([True, False, True], dtype=np.bool_, fill_value=True)
+        assert arr.dtype == SparseDtype(np.bool_, True)
+        assert arr.fill_value
+
+    def test_constructor_float32(self):
+        # GH#10648
+        data = np.array([1.0, np.nan, 3], dtype=np.float32)
+        arr = SparseArray(data, dtype=np.float32)
+
+        assert arr.dtype == SparseDtype(np.float32)
+        tm.assert_numpy_array_equal(arr.sp_values, np.array([1, 3], dtype=np.float32))
+        # Behavior change: np.asarray densifies.
+        # tm.assert_numpy_array_equal(arr.sp_values, np.asarray(arr))
+        tm.assert_numpy_array_equal(
+            arr.sp_index.indices, np.array([0, 2], dtype=np.int32)
+        )
+
+        dense = arr.to_dense()
+        assert dense.dtype == np.float32
+        tm.assert_numpy_array_equal(dense, data)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_dtype.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_dtype.py
new file mode 100644
index 0000000000000000000000000000000000000000..149c28341ba3d8b83b490361f7cfb4f9df0994ea
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_dtype.py
@@ -0,0 +1,224 @@
+import re
+import warnings
+
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import SparseDtype
+
+
+@pytest.mark.parametrize(
+    "dtype, fill_value",
+    [
+        ("int", 0),
+        ("float", np.nan),
+        ("bool", False),
+        ("object", np.nan),
+        ("datetime64[ns]", np.datetime64("NaT", "ns")),
+        ("timedelta64[ns]", np.timedelta64("NaT", "ns")),
+    ],
+)
+def test_inferred_dtype(dtype, fill_value):
+    sparse_dtype = SparseDtype(dtype)
+    result = sparse_dtype.fill_value
+    if pd.isna(fill_value):
+        assert pd.isna(result) and type(result) == type(fill_value)
+    else:
+        assert result == fill_value
+
+
+def test_from_sparse_dtype():
+    dtype = SparseDtype("float", 0)
+    result = SparseDtype(dtype)
+    assert result.fill_value == 0
+
+
+def test_from_sparse_dtype_fill_value():
+    dtype = SparseDtype("int", 1)
+    result = SparseDtype(dtype, fill_value=2)
+    expected = SparseDtype("int", 2)
+    assert result == expected
+
+
+@pytest.mark.parametrize(
+    "dtype, fill_value",
+    [
+        ("int", None),
+        ("float", None),
+        ("bool", None),
+        ("object", None),
+        ("datetime64[ns]", None),
+        ("timedelta64[ns]", None),
+        ("int", np.nan),
+        ("float", 0),
+    ],
+)
+def test_equal(dtype, fill_value):
+    a = SparseDtype(dtype, fill_value)
+    b = SparseDtype(dtype, fill_value)
+    assert a == b
+    assert b == a
+
+
+def test_nans_equal():
+    a = SparseDtype(float, float("nan"))
+    b = SparseDtype(float, np.nan)
+    assert a == b
+    assert b == a
+
+
+with warnings.catch_warnings():
+    msg = "Allowing arbitrary scalar fill_value in SparseDtype is deprecated"
+    warnings.filterwarnings("ignore", msg, category=FutureWarning)
+
+    tups = [
+        (SparseDtype("float64"), SparseDtype("float32")),
+        (SparseDtype("float64"), SparseDtype("float64", 0)),
+        (SparseDtype("float64"), SparseDtype("datetime64[ns]", np.nan)),
+        (SparseDtype(int, pd.NaT), SparseDtype(float, pd.NaT)),
+        (SparseDtype("float64"), np.dtype("float64")),
+    ]
+
+
+@pytest.mark.parametrize(
+    "a, b",
+    tups,
+)
+def test_not_equal(a, b):
+    assert a != b
+
+
+def test_construct_from_string_raises():
+    with pytest.raises(
+        TypeError, match="Cannot construct a 'SparseDtype' from 'not a dtype'"
+    ):
+        SparseDtype.construct_from_string("not a dtype")
+
+
+@pytest.mark.parametrize(
+    "dtype, expected",
+    [
+        (SparseDtype(int), True),
+        (SparseDtype(float), True),
+        (SparseDtype(bool), True),
+        (SparseDtype(object), False),
+        (SparseDtype(str), False),
+    ],
+)
+def test_is_numeric(dtype, expected):
+    assert dtype._is_numeric is expected
+
+
+def test_str_uses_object():
+    result = SparseDtype(str).subtype
+    assert result == np.dtype("object")
+
+
+@pytest.mark.parametrize(
+    "string, expected",
+    [
+        ("Sparse[float64]", SparseDtype(np.dtype("float64"))),
+        ("Sparse[float32]", SparseDtype(np.dtype("float32"))),
+        ("Sparse[int]", SparseDtype(np.dtype("int"))),
+        ("Sparse[str]", SparseDtype(np.dtype("str"))),
+        ("Sparse[datetime64[ns]]", SparseDtype(np.dtype("datetime64[ns]"))),
+        ("Sparse", SparseDtype(np.dtype("float"), np.nan)),
+    ],
+)
+def test_construct_from_string(string, expected):
+    result = SparseDtype.construct_from_string(string)
+    assert result == expected
+
+
+@pytest.mark.parametrize(
+    "a, b, expected",
+    [
+        (SparseDtype(float, 0.0), SparseDtype(np.dtype("float"), 0.0), True),
+        (SparseDtype(int, 0), SparseDtype(int, 0), True),
+        (SparseDtype(float, float("nan")), SparseDtype(float, np.nan), True),
+        (SparseDtype(float, 0), SparseDtype(float, np.nan), False),
+        (SparseDtype(int, 0.0), SparseDtype(float, 0.0), False),
+    ],
+)
+def test_hash_equal(a, b, expected):
+    result = a == b
+    assert result is expected
+
+    result = hash(a) == hash(b)
+    assert result is expected
+
+
+@pytest.mark.parametrize(
+    "string, expected",
+    [
+        ("Sparse[int]", "int"),
+        ("Sparse[int, 0]", "int"),
+        ("Sparse[int64]", "int64"),
+        ("Sparse[int64, 0]", "int64"),
+        ("Sparse[datetime64[ns], 0]", "datetime64[ns]"),
+    ],
+)
+def test_parse_subtype(string, expected):
+    subtype, _ = SparseDtype._parse_subtype(string)
+    assert subtype == expected
+
+
+@pytest.mark.parametrize(
+    "string", ["Sparse[int, 1]", "Sparse[float, 0.0]", "Sparse[bool, True]"]
+)
+def test_construct_from_string_fill_value_raises(string):
+    with pytest.raises(TypeError, match="fill_value in the string is not"):
+        SparseDtype.construct_from_string(string)
+
+
+@pytest.mark.parametrize(
+    "original, dtype, expected",
+    [
+        (SparseDtype(int, 0), float, SparseDtype(float, 0.0)),
+        (SparseDtype(int, 1), float, SparseDtype(float, 1.0)),
+        (SparseDtype(int, 1), np.str_, SparseDtype(object, "1")),
+        (SparseDtype(float, 1.5), int, SparseDtype(int, 1)),
+    ],
+)
+def test_update_dtype(original, dtype, expected):
+    result = original.update_dtype(dtype)
+    assert result == expected
+
+
+@pytest.mark.parametrize(
+    "original, dtype, expected_error_msg",
+    [
+        (
+            SparseDtype(float, np.nan),
+            int,
+            re.escape("Cannot convert non-finite values (NA or inf) to integer"),
+        ),
+        (
+            SparseDtype(str, "abc"),
+            int,
+            r"invalid literal for int\(\) with base 10: ('abc'|np\.str_\('abc'\))",
+        ),
+    ],
+)
+def test_update_dtype_raises(original, dtype, expected_error_msg):
+    with pytest.raises(ValueError, match=expected_error_msg):
+        original.update_dtype(dtype)
+
+
+def test_repr():
+    # GH-34352
+    result = str(SparseDtype("int64", fill_value=0))
+    expected = "Sparse[int64, 0]"
+    assert result == expected
+
+    result = str(SparseDtype(object, fill_value="0"))
+    expected = "Sparse[object, '0']"
+    assert result == expected
+
+
+def test_sparse_dtype_subtype_must_be_numpy_dtype():
+    # GH#53160
+    msg = "SparseDtype subtype must be a numpy dtype"
+    with pytest.raises(TypeError, match=msg):
+        SparseDtype("category", fill_value="c")
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_indexing.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_indexing.py
new file mode 100644
index 0000000000000000000000000000000000000000..60029ac06ddb47ef0ad4ee35a75fd09ca12f7f53
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_indexing.py
@@ -0,0 +1,302 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import SparseDtype
+import pandas._testing as tm
+from pandas.core.arrays.sparse import SparseArray
+
+
+@pytest.fixture
+def arr_data():
+    return np.array([np.nan, np.nan, 1, 2, 3, np.nan, 4, 5, np.nan, 6])
+
+
+@pytest.fixture
+def arr(arr_data):
+    return SparseArray(arr_data)
+
+
+class TestGetitem:
+    def test_getitem(self, arr):
+        dense = arr.to_dense()
+        for i, value in enumerate(arr):
+            tm.assert_almost_equal(value, dense[i])
+            tm.assert_almost_equal(arr[-i], dense[-i])
+
+    def test_getitem_arraylike_mask(self, arr):
+        arr = SparseArray([0, 1, 2])
+        result = arr[[True, False, True]]
+        expected = SparseArray([0, 2])
+        tm.assert_sp_array_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "slc",
+        [
+            np.s_[:],
+            np.s_[1:10],
+            np.s_[1:100],
+            np.s_[10:1],
+            np.s_[:-3],
+            np.s_[-5:-4],
+            np.s_[:-12],
+            np.s_[-12:],
+            np.s_[2:],
+            np.s_[2::3],
+            np.s_[::2],
+            np.s_[::-1],
+            np.s_[::-2],
+            np.s_[1:6:2],
+            np.s_[:-6:-2],
+        ],
+    )
+    @pytest.mark.parametrize(
+        "as_dense", [[np.nan] * 10, [1] * 10, [np.nan] * 5 + [1] * 5, []]
+    )
+    def test_getslice(self, slc, as_dense):
+        as_dense = np.array(as_dense)
+        arr = SparseArray(as_dense)
+
+        result = arr[slc]
+        expected = SparseArray(as_dense[slc])
+
+        tm.assert_sp_array_equal(result, expected)
+
+    def test_getslice_tuple(self):
+        dense = np.array([np.nan, 0, 3, 4, 0, 5, np.nan, np.nan, 0])
+
+        sparse = SparseArray(dense)
+        res = sparse[(slice(4, None),)]
+        exp = SparseArray(dense[4:])
+        tm.assert_sp_array_equal(res, exp)
+
+        sparse = SparseArray(dense, fill_value=0)
+        res = sparse[(slice(4, None),)]
+        exp = SparseArray(dense[4:], fill_value=0)
+        tm.assert_sp_array_equal(res, exp)
+
+        msg = "too many indices for array"
+        with pytest.raises(IndexError, match=msg):
+            sparse[4:, :]
+
+        with pytest.raises(IndexError, match=msg):
+            # check numpy compat
+            dense[4:, :]
+
+    def test_boolean_slice_empty(self):
+        arr = SparseArray([0, 1, 2])
+        res = arr[[False, False, False]]
+        assert res.dtype == arr.dtype
+
+    def test_getitem_bool_sparse_array(self, arr):
+        # GH 23122
+        spar_bool = SparseArray([False, True] * 5, dtype=np.bool_, fill_value=True)
+        exp = SparseArray([np.nan, 2, np.nan, 5, 6])
+        tm.assert_sp_array_equal(arr[spar_bool], exp)
+
+        spar_bool = ~spar_bool
+        res = arr[spar_bool]
+        exp = SparseArray([np.nan, 1, 3, 4, np.nan])
+        tm.assert_sp_array_equal(res, exp)
+
+        spar_bool = SparseArray(
+            [False, True, np.nan] * 3, dtype=np.bool_, fill_value=np.nan
+        )
+        res = arr[spar_bool]
+        exp = SparseArray([np.nan, 3, 5])
+        tm.assert_sp_array_equal(res, exp)
+
+    def test_getitem_bool_sparse_array_as_comparison(self):
+        # GH 45110
+        arr = SparseArray([1, 2, 3, 4, np.nan, np.nan], fill_value=np.nan)
+        res = arr[arr > 2]
+        exp = SparseArray([3.0, 4.0], fill_value=np.nan)
+        tm.assert_sp_array_equal(res, exp)
+
+    def test_get_item(self, arr):
+        zarr = SparseArray([0, 0, 1, 2, 3, 0, 4, 5, 0, 6], fill_value=0)
+
+        assert np.isnan(arr[1])
+        assert arr[2] == 1
+        assert arr[7] == 5
+
+        assert zarr[0] == 0
+        assert zarr[2] == 1
+        assert zarr[7] == 5
+
+        errmsg = "must be an integer between -10 and 10"
+
+        with pytest.raises(IndexError, match=errmsg):
+            arr[11]
+
+        with pytest.raises(IndexError, match=errmsg):
+            arr[-11]
+
+        assert arr[-1] == arr[len(arr) - 1]
+
+
+class TestSetitem:
+    def test_set_item(self, arr_data):
+        arr = SparseArray(arr_data).copy()
+
+        def setitem():
+            arr[5] = 3
+
+        def setslice():
+            arr[1:5] = 2
+
+        with pytest.raises(TypeError, match="assignment via setitem"):
+            setitem()
+
+        with pytest.raises(TypeError, match="assignment via setitem"):
+            setslice()
+
+
+class TestTake:
+    def test_take_scalar_raises(self, arr):
+        msg = "'indices' must be an array, not a scalar '2'."
+        with pytest.raises(ValueError, match=msg):
+            arr.take(2)
+
+    def test_take(self, arr_data, arr):
+        exp = SparseArray(np.take(arr_data, [2, 3]))
+        tm.assert_sp_array_equal(arr.take([2, 3]), exp)
+
+        exp = SparseArray(np.take(arr_data, [0, 1, 2]))
+        tm.assert_sp_array_equal(arr.take([0, 1, 2]), exp)
+
+    def test_take_all_empty(self):
+        sparse = pd.array([0, 0], dtype=SparseDtype("int64"))
+        result = sparse.take([0, 1], allow_fill=True, fill_value=np.nan)
+        tm.assert_sp_array_equal(sparse, result)
+
+    def test_take_different_fill_value(self):
+        # Take with a different fill value shouldn't overwrite the original
+        sparse = pd.array([0.0], dtype=SparseDtype("float64", fill_value=0.0))
+        result = sparse.take([0, -1], allow_fill=True, fill_value=np.nan)
+        expected = pd.array([0, np.nan], dtype=sparse.dtype)
+        tm.assert_sp_array_equal(expected, result)
+
+    def test_take_fill_value(self):
+        data = np.array([1, np.nan, 0, 3, 0])
+        sparse = SparseArray(data, fill_value=0)
+
+        exp = SparseArray(np.take(data, [0]), fill_value=0)
+        tm.assert_sp_array_equal(sparse.take([0]), exp)
+
+        exp = SparseArray(np.take(data, [1, 3, 4]), fill_value=0)
+        tm.assert_sp_array_equal(sparse.take([1, 3, 4]), exp)
+
+    def test_take_negative(self, arr_data, arr):
+        exp = SparseArray(np.take(arr_data, [-1]))
+        tm.assert_sp_array_equal(arr.take([-1]), exp)
+
+        exp = SparseArray(np.take(arr_data, [-4, -3, -2]))
+        tm.assert_sp_array_equal(arr.take([-4, -3, -2]), exp)
+
+    def test_bad_take(self, arr):
+        with pytest.raises(IndexError, match="bounds"):
+            arr.take([11])
+
+    def test_take_filling(self):
+        # similar tests as GH 12631
+        sparse = SparseArray([np.nan, np.nan, 1, np.nan, 4])
+        result = sparse.take(np.array([1, 0, -1]))
+        expected = SparseArray([np.nan, np.nan, 4])
+        tm.assert_sp_array_equal(result, expected)
+
+        # TODO: actionable?
+        # XXX: test change: fill_value=True -> allow_fill=True
+        result = sparse.take(np.array([1, 0, -1]), allow_fill=True)
+        expected = SparseArray([np.nan, np.nan, np.nan])
+        tm.assert_sp_array_equal(result, expected)
+
+        # allow_fill=False
+        result = sparse.take(np.array([1, 0, -1]), allow_fill=False, fill_value=True)
+        expected = SparseArray([np.nan, np.nan, 4])
+        tm.assert_sp_array_equal(result, expected)
+
+        msg = "Invalid value in 'indices'"
+        with pytest.raises(ValueError, match=msg):
+            sparse.take(np.array([1, 0, -2]), allow_fill=True)
+
+        with pytest.raises(ValueError, match=msg):
+            sparse.take(np.array([1, 0, -5]), allow_fill=True)
+
+        msg = "out of bounds value in 'indices'"
+        with pytest.raises(IndexError, match=msg):
+            sparse.take(np.array([1, -6]))
+        with pytest.raises(IndexError, match=msg):
+            sparse.take(np.array([1, 5]))
+        with pytest.raises(IndexError, match=msg):
+            sparse.take(np.array([1, 5]), allow_fill=True)
+
+    def test_take_filling_fill_value(self):
+        # same tests as GH#12631
+        sparse = SparseArray([np.nan, 0, 1, 0, 4], fill_value=0)
+        result = sparse.take(np.array([1, 0, -1]))
+        expected = SparseArray([0, np.nan, 4], fill_value=0)
+        tm.assert_sp_array_equal(result, expected)
+
+        # fill_value
+        result = sparse.take(np.array([1, 0, -1]), allow_fill=True)
+        # TODO: actionable?
+        # XXX: behavior change.
+        # the old way of filling self.fill_value doesn't follow EA rules.
+        # It's supposed to be self.dtype.na_value (nan in this case)
+        expected = SparseArray([0, np.nan, np.nan], fill_value=0)
+        tm.assert_sp_array_equal(result, expected)
+
+        # allow_fill=False
+        result = sparse.take(np.array([1, 0, -1]), allow_fill=False, fill_value=True)
+        expected = SparseArray([0, np.nan, 4], fill_value=0)
+        tm.assert_sp_array_equal(result, expected)
+
+        msg = "Invalid value in 'indices'."
+        with pytest.raises(ValueError, match=msg):
+            sparse.take(np.array([1, 0, -2]), allow_fill=True)
+        with pytest.raises(ValueError, match=msg):
+            sparse.take(np.array([1, 0, -5]), allow_fill=True)
+
+        msg = "out of bounds value in 'indices'"
+        with pytest.raises(IndexError, match=msg):
+            sparse.take(np.array([1, -6]))
+        with pytest.raises(IndexError, match=msg):
+            sparse.take(np.array([1, 5]))
+        with pytest.raises(IndexError, match=msg):
+            sparse.take(np.array([1, 5]), fill_value=True)
+
+    @pytest.mark.parametrize("kind", ["block", "integer"])
+    def test_take_filling_all_nan(self, kind):
+        sparse = SparseArray([np.nan, np.nan, np.nan, np.nan, np.nan], kind=kind)
+        result = sparse.take(np.array([1, 0, -1]))
+        expected = SparseArray([np.nan, np.nan, np.nan], kind=kind)
+        tm.assert_sp_array_equal(result, expected)
+
+        result = sparse.take(np.array([1, 0, -1]), fill_value=True)
+        expected = SparseArray([np.nan, np.nan, np.nan], kind=kind)
+        tm.assert_sp_array_equal(result, expected)
+
+        msg = "out of bounds value in 'indices'"
+        with pytest.raises(IndexError, match=msg):
+            sparse.take(np.array([1, -6]))
+        with pytest.raises(IndexError, match=msg):
+            sparse.take(np.array([1, 5]))
+        with pytest.raises(IndexError, match=msg):
+            sparse.take(np.array([1, 5]), fill_value=True)
+
+
+class TestWhere:
+    def test_where_retain_fill_value(self):
+        # GH#45691 don't lose fill_value on _where
+        arr = SparseArray([np.nan, 1.0], fill_value=0)
+
+        mask = np.array([True, False])
+
+        res = arr._where(~mask, 1)
+        exp = SparseArray([1, 1.0], fill_value=0)
+        tm.assert_sp_array_equal(res, exp)
+
+        ser = pd.Series(arr)
+        res = ser.where(~mask, 1)
+        tm.assert_series_equal(res, pd.Series(exp))
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_libsparse.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_libsparse.py
new file mode 100644
index 0000000000000000000000000000000000000000..7a77a2064e7e097f924a3901994d131d98164ad6
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_libsparse.py
@@ -0,0 +1,551 @@
+import operator
+
+import numpy as np
+import pytest
+
+import pandas._libs.sparse as splib
+import pandas.util._test_decorators as td
+
+from pandas import Series
+import pandas._testing as tm
+from pandas.core.arrays.sparse import (
+    BlockIndex,
+    IntIndex,
+    make_sparse_index,
+)
+
+
+@pytest.fixture
+def test_length():
+    return 20
+
+
+@pytest.fixture(
+    params=[
+        [
+            [0, 7, 15],
+            [3, 5, 5],
+            [2, 9, 14],
+            [2, 3, 5],
+            [2, 9, 15],
+            [1, 3, 4],
+        ],
+        [
+            [0, 5],
+            [4, 4],
+            [1],
+            [4],
+            [1],
+            [3],
+        ],
+        [
+            [0],
+            [10],
+            [0, 5],
+            [3, 7],
+            [0, 5],
+            [3, 5],
+        ],
+        [
+            [10],
+            [5],
+            [0, 12],
+            [5, 3],
+            [12],
+            [3],
+        ],
+        [
+            [0, 10],
+            [4, 6],
+            [5, 17],
+            [4, 2],
+            [],
+            [],
+        ],
+        [
+            [0],
+            [5],
+            [],
+            [],
+            [],
+            [],
+        ],
+    ],
+    ids=[
+        "plain_case",
+        "delete_blocks",
+        "split_blocks",
+        "skip_block",
+        "no_intersect",
+        "one_empty",
+    ],
+)
+def cases(request):
+    return request.param
+
+
+class TestSparseIndexUnion:
+    @pytest.mark.parametrize(
+        "xloc, xlen, yloc, ylen, eloc, elen",
+        [
+            [[0], [5], [5], [4], [0], [9]],
+            [[0, 10], [5, 5], [2, 17], [5, 2], [0, 10, 17], [7, 5, 2]],
+            [[1], [5], [3], [5], [1], [7]],
+            [[2, 10], [4, 4], [4], [8], [2], [12]],
+            [[0, 5], [3, 5], [0], [7], [0], [10]],
+            [[2, 10], [4, 4], [4, 13], [8, 4], [2], [15]],
+            [[2], [15], [4, 9, 14], [3, 2, 2], [2], [15]],
+            [[0, 10], [3, 3], [5, 15], [2, 2], [0, 5, 10, 15], [3, 2, 3, 2]],
+        ],
+    )
+    def test_index_make_union(self, xloc, xlen, yloc, ylen, eloc, elen, test_length):
+        # Case 1
+        # x: ----
+        # y:     ----
+        # r: --------
+        # Case 2
+        # x: -----     -----
+        # y:   -----          --
+        # Case 3
+        # x: ------
+        # y:    -------
+        # r: ----------
+        # Case 4
+        # x: ------  -----
+        # y:    -------
+        # r: -------------
+        # Case 5
+        # x: ---  -----
+        # y: -------
+        # r: -------------
+        # Case 6
+        # x: ------  -----
+        # y:    -------  ---
+        # r: -------------
+        # Case 7
+        # x: ----------------------
+        # y:   ----  ----   ---
+        # r: ----------------------
+        # Case 8
+        # x: ----       ---
+        # y:       ---       ---
+        xindex = BlockIndex(test_length, xloc, xlen)
+        yindex = BlockIndex(test_length, yloc, ylen)
+        bresult = xindex.make_union(yindex)
+        assert isinstance(bresult, BlockIndex)
+        tm.assert_numpy_array_equal(bresult.blocs, np.array(eloc, dtype=np.int32))
+        tm.assert_numpy_array_equal(bresult.blengths, np.array(elen, dtype=np.int32))
+
+        ixindex = xindex.to_int_index()
+        iyindex = yindex.to_int_index()
+        iresult = ixindex.make_union(iyindex)
+        assert isinstance(iresult, IntIndex)
+        tm.assert_numpy_array_equal(iresult.indices, bresult.to_int_index().indices)
+
+    def test_int_index_make_union(self):
+        a = IntIndex(5, np.array([0, 3, 4], dtype=np.int32))
+        b = IntIndex(5, np.array([0, 2], dtype=np.int32))
+        res = a.make_union(b)
+        exp = IntIndex(5, np.array([0, 2, 3, 4], np.int32))
+        assert res.equals(exp)
+
+        a = IntIndex(5, np.array([], dtype=np.int32))
+        b = IntIndex(5, np.array([0, 2], dtype=np.int32))
+        res = a.make_union(b)
+        exp = IntIndex(5, np.array([0, 2], np.int32))
+        assert res.equals(exp)
+
+        a = IntIndex(5, np.array([], dtype=np.int32))
+        b = IntIndex(5, np.array([], dtype=np.int32))
+        res = a.make_union(b)
+        exp = IntIndex(5, np.array([], np.int32))
+        assert res.equals(exp)
+
+        a = IntIndex(5, np.array([0, 1, 2, 3, 4], dtype=np.int32))
+        b = IntIndex(5, np.array([0, 1, 2, 3, 4], dtype=np.int32))
+        res = a.make_union(b)
+        exp = IntIndex(5, np.array([0, 1, 2, 3, 4], np.int32))
+        assert res.equals(exp)
+
+        a = IntIndex(5, np.array([0, 1], dtype=np.int32))
+        b = IntIndex(4, np.array([0, 1], dtype=np.int32))
+
+        msg = "Indices must reference same underlying length"
+        with pytest.raises(ValueError, match=msg):
+            a.make_union(b)
+
+
+class TestSparseIndexIntersect:
+    @td.skip_if_windows
+    def test_intersect(self, cases, test_length):
+        xloc, xlen, yloc, ylen, eloc, elen = cases
+        xindex = BlockIndex(test_length, xloc, xlen)
+        yindex = BlockIndex(test_length, yloc, ylen)
+        expected = BlockIndex(test_length, eloc, elen)
+        longer_index = BlockIndex(test_length + 1, yloc, ylen)
+
+        result = xindex.intersect(yindex)
+        assert result.equals(expected)
+        result = xindex.to_int_index().intersect(yindex.to_int_index())
+        assert result.equals(expected.to_int_index())
+
+        msg = "Indices must reference same underlying length"
+        with pytest.raises(Exception, match=msg):
+            xindex.intersect(longer_index)
+        with pytest.raises(Exception, match=msg):
+            xindex.to_int_index().intersect(longer_index.to_int_index())
+
+    def test_intersect_empty(self):
+        xindex = IntIndex(4, np.array([], dtype=np.int32))
+        yindex = IntIndex(4, np.array([2, 3], dtype=np.int32))
+        assert xindex.intersect(yindex).equals(xindex)
+        assert yindex.intersect(xindex).equals(xindex)
+
+        xindex = xindex.to_block_index()
+        yindex = yindex.to_block_index()
+        assert xindex.intersect(yindex).equals(xindex)
+        assert yindex.intersect(xindex).equals(xindex)
+
+    @pytest.mark.parametrize(
+        "case",
+        [
+            # Argument 2 to "IntIndex" has incompatible type "ndarray[Any,
+            # dtype[signedinteger[_32Bit]]]"; expected "Sequence[int]"
+            IntIndex(5, np.array([1, 2], dtype=np.int32)),  # type: ignore[arg-type]
+            IntIndex(5, np.array([0, 2, 4], dtype=np.int32)),  # type: ignore[arg-type]
+            IntIndex(0, np.array([], dtype=np.int32)),  # type: ignore[arg-type]
+            IntIndex(5, np.array([], dtype=np.int32)),  # type: ignore[arg-type]
+        ],
+    )
+    def test_intersect_identical(self, case):
+        assert case.intersect(case).equals(case)
+        case = case.to_block_index()
+        assert case.intersect(case).equals(case)
+
+
+class TestSparseIndexCommon:
+    def test_int_internal(self):
+        idx = make_sparse_index(4, np.array([2, 3], dtype=np.int32), kind="integer")
+        assert isinstance(idx, IntIndex)
+        assert idx.npoints == 2
+        tm.assert_numpy_array_equal(idx.indices, np.array([2, 3], dtype=np.int32))
+
+        idx = make_sparse_index(4, np.array([], dtype=np.int32), kind="integer")
+        assert isinstance(idx, IntIndex)
+        assert idx.npoints == 0
+        tm.assert_numpy_array_equal(idx.indices, np.array([], dtype=np.int32))
+
+        idx = make_sparse_index(
+            4, np.array([0, 1, 2, 3], dtype=np.int32), kind="integer"
+        )
+        assert isinstance(idx, IntIndex)
+        assert idx.npoints == 4
+        tm.assert_numpy_array_equal(idx.indices, np.array([0, 1, 2, 3], dtype=np.int32))
+
+    def test_block_internal(self):
+        idx = make_sparse_index(4, np.array([2, 3], dtype=np.int32), kind="block")
+        assert isinstance(idx, BlockIndex)
+        assert idx.npoints == 2
+        tm.assert_numpy_array_equal(idx.blocs, np.array([2], dtype=np.int32))
+        tm.assert_numpy_array_equal(idx.blengths, np.array([2], dtype=np.int32))
+
+        idx = make_sparse_index(4, np.array([], dtype=np.int32), kind="block")
+        assert isinstance(idx, BlockIndex)
+        assert idx.npoints == 0
+        tm.assert_numpy_array_equal(idx.blocs, np.array([], dtype=np.int32))
+        tm.assert_numpy_array_equal(idx.blengths, np.array([], dtype=np.int32))
+
+        idx = make_sparse_index(4, np.array([0, 1, 2, 3], dtype=np.int32), kind="block")
+        assert isinstance(idx, BlockIndex)
+        assert idx.npoints == 4
+        tm.assert_numpy_array_equal(idx.blocs, np.array([0], dtype=np.int32))
+        tm.assert_numpy_array_equal(idx.blengths, np.array([4], dtype=np.int32))
+
+        idx = make_sparse_index(4, np.array([0, 2, 3], dtype=np.int32), kind="block")
+        assert isinstance(idx, BlockIndex)
+        assert idx.npoints == 3
+        tm.assert_numpy_array_equal(idx.blocs, np.array([0, 2], dtype=np.int32))
+        tm.assert_numpy_array_equal(idx.blengths, np.array([1, 2], dtype=np.int32))
+
+    @pytest.mark.parametrize("kind", ["integer", "block"])
+    def test_lookup(self, kind):
+        idx = make_sparse_index(4, np.array([2, 3], dtype=np.int32), kind=kind)
+        assert idx.lookup(-1) == -1
+        assert idx.lookup(0) == -1
+        assert idx.lookup(1) == -1
+        assert idx.lookup(2) == 0
+        assert idx.lookup(3) == 1
+        assert idx.lookup(4) == -1
+
+        idx = make_sparse_index(4, np.array([], dtype=np.int32), kind=kind)
+
+        for i in range(-1, 5):
+            assert idx.lookup(i) == -1
+
+        idx = make_sparse_index(4, np.array([0, 1, 2, 3], dtype=np.int32), kind=kind)
+        assert idx.lookup(-1) == -1
+        assert idx.lookup(0) == 0
+        assert idx.lookup(1) == 1
+        assert idx.lookup(2) == 2
+        assert idx.lookup(3) == 3
+        assert idx.lookup(4) == -1
+
+        idx = make_sparse_index(4, np.array([0, 2, 3], dtype=np.int32), kind=kind)
+        assert idx.lookup(-1) == -1
+        assert idx.lookup(0) == 0
+        assert idx.lookup(1) == -1
+        assert idx.lookup(2) == 1
+        assert idx.lookup(3) == 2
+        assert idx.lookup(4) == -1
+
+    @pytest.mark.parametrize("kind", ["integer", "block"])
+    def test_lookup_array(self, kind):
+        idx = make_sparse_index(4, np.array([2, 3], dtype=np.int32), kind=kind)
+
+        res = idx.lookup_array(np.array([-1, 0, 2], dtype=np.int32))
+        exp = np.array([-1, -1, 0], dtype=np.int32)
+        tm.assert_numpy_array_equal(res, exp)
+
+        res = idx.lookup_array(np.array([4, 2, 1, 3], dtype=np.int32))
+        exp = np.array([-1, 0, -1, 1], dtype=np.int32)
+        tm.assert_numpy_array_equal(res, exp)
+
+        idx = make_sparse_index(4, np.array([], dtype=np.int32), kind=kind)
+        res = idx.lookup_array(np.array([-1, 0, 2, 4], dtype=np.int32))
+        exp = np.array([-1, -1, -1, -1], dtype=np.int32)
+        tm.assert_numpy_array_equal(res, exp)
+
+        idx = make_sparse_index(4, np.array([0, 1, 2, 3], dtype=np.int32), kind=kind)
+        res = idx.lookup_array(np.array([-1, 0, 2], dtype=np.int32))
+        exp = np.array([-1, 0, 2], dtype=np.int32)
+        tm.assert_numpy_array_equal(res, exp)
+
+        res = idx.lookup_array(np.array([4, 2, 1, 3], dtype=np.int32))
+        exp = np.array([-1, 2, 1, 3], dtype=np.int32)
+        tm.assert_numpy_array_equal(res, exp)
+
+        idx = make_sparse_index(4, np.array([0, 2, 3], dtype=np.int32), kind=kind)
+        res = idx.lookup_array(np.array([2, 1, 3, 0], dtype=np.int32))
+        exp = np.array([1, -1, 2, 0], dtype=np.int32)
+        tm.assert_numpy_array_equal(res, exp)
+
+        res = idx.lookup_array(np.array([1, 4, 2, 5], dtype=np.int32))
+        exp = np.array([-1, -1, 1, -1], dtype=np.int32)
+        tm.assert_numpy_array_equal(res, exp)
+
+    @pytest.mark.parametrize(
+        "idx, expected",
+        [
+            [0, -1],
+            [5, 0],
+            [7, 2],
+            [8, -1],
+            [9, -1],
+            [10, -1],
+            [11, -1],
+            [12, 3],
+            [17, 8],
+            [18, -1],
+        ],
+    )
+    def test_lookup_basics(self, idx, expected):
+        bindex = BlockIndex(20, [5, 12], [3, 6])
+        assert bindex.lookup(idx) == expected
+
+        iindex = bindex.to_int_index()
+        assert iindex.lookup(idx) == expected
+
+
+class TestBlockIndex:
+    def test_block_internal(self):
+        idx = make_sparse_index(4, np.array([2, 3], dtype=np.int32), kind="block")
+        assert isinstance(idx, BlockIndex)
+        assert idx.npoints == 2
+        tm.assert_numpy_array_equal(idx.blocs, np.array([2], dtype=np.int32))
+        tm.assert_numpy_array_equal(idx.blengths, np.array([2], dtype=np.int32))
+
+        idx = make_sparse_index(4, np.array([], dtype=np.int32), kind="block")
+        assert isinstance(idx, BlockIndex)
+        assert idx.npoints == 0
+        tm.assert_numpy_array_equal(idx.blocs, np.array([], dtype=np.int32))
+        tm.assert_numpy_array_equal(idx.blengths, np.array([], dtype=np.int32))
+
+        idx = make_sparse_index(4, np.array([0, 1, 2, 3], dtype=np.int32), kind="block")
+        assert isinstance(idx, BlockIndex)
+        assert idx.npoints == 4
+        tm.assert_numpy_array_equal(idx.blocs, np.array([0], dtype=np.int32))
+        tm.assert_numpy_array_equal(idx.blengths, np.array([4], dtype=np.int32))
+
+        idx = make_sparse_index(4, np.array([0, 2, 3], dtype=np.int32), kind="block")
+        assert isinstance(idx, BlockIndex)
+        assert idx.npoints == 3
+        tm.assert_numpy_array_equal(idx.blocs, np.array([0, 2], dtype=np.int32))
+        tm.assert_numpy_array_equal(idx.blengths, np.array([1, 2], dtype=np.int32))
+
+    @pytest.mark.parametrize("i", [5, 10, 100, 101])
+    def test_make_block_boundary(self, i):
+        idx = make_sparse_index(i, np.arange(0, i, 2, dtype=np.int32), kind="block")
+
+        exp = np.arange(0, i, 2, dtype=np.int32)
+        tm.assert_numpy_array_equal(idx.blocs, exp)
+        tm.assert_numpy_array_equal(idx.blengths, np.ones(len(exp), dtype=np.int32))
+
+    def test_equals(self):
+        index = BlockIndex(10, [0, 4], [2, 5])
+
+        assert index.equals(index)
+        assert not index.equals(BlockIndex(10, [0, 4], [2, 6]))
+
+    def test_check_integrity(self):
+        locs = []
+        lengths = []
+
+        # 0-length OK
+        BlockIndex(0, locs, lengths)
+
+        # also OK even though empty
+        BlockIndex(1, locs, lengths)
+
+        msg = "Block 0 extends beyond end"
+        with pytest.raises(ValueError, match=msg):
+            BlockIndex(10, [5], [10])
+
+        msg = "Block 0 overlaps"
+        with pytest.raises(ValueError, match=msg):
+            BlockIndex(10, [2, 5], [5, 3])
+
+    def test_to_int_index(self):
+        locs = [0, 10]
+        lengths = [4, 6]
+        exp_inds = [0, 1, 2, 3, 10, 11, 12, 13, 14, 15]
+
+        block = BlockIndex(20, locs, lengths)
+        dense = block.to_int_index()
+
+        tm.assert_numpy_array_equal(dense.indices, np.array(exp_inds, dtype=np.int32))
+
+    def test_to_block_index(self):
+        index = BlockIndex(10, [0, 5], [4, 5])
+        assert index.to_block_index() is index
+
+
+class TestIntIndex:
+    def test_check_integrity(self):
+        # Too many indices than specified in self.length
+        msg = "Too many indices"
+
+        with pytest.raises(ValueError, match=msg):
+            IntIndex(length=1, indices=[1, 2, 3])
+
+        # No index can be negative.
+        msg = "No index can be less than zero"
+
+        with pytest.raises(ValueError, match=msg):
+            IntIndex(length=5, indices=[1, -2, 3])
+
+        # No index can be negative.
+        msg = "No index can be less than zero"
+
+        with pytest.raises(ValueError, match=msg):
+            IntIndex(length=5, indices=[1, -2, 3])
+
+        # All indices must be less than the length.
+        msg = "All indices must be less than the length"
+
+        with pytest.raises(ValueError, match=msg):
+            IntIndex(length=5, indices=[1, 2, 5])
+
+        with pytest.raises(ValueError, match=msg):
+            IntIndex(length=5, indices=[1, 2, 6])
+
+        # Indices must be strictly ascending.
+        msg = "Indices must be strictly increasing"
+
+        with pytest.raises(ValueError, match=msg):
+            IntIndex(length=5, indices=[1, 3, 2])
+
+        with pytest.raises(ValueError, match=msg):
+            IntIndex(length=5, indices=[1, 3, 3])
+
+    def test_int_internal(self):
+        idx = make_sparse_index(4, np.array([2, 3], dtype=np.int32), kind="integer")
+        assert isinstance(idx, IntIndex)
+        assert idx.npoints == 2
+        tm.assert_numpy_array_equal(idx.indices, np.array([2, 3], dtype=np.int32))
+
+        idx = make_sparse_index(4, np.array([], dtype=np.int32), kind="integer")
+        assert isinstance(idx, IntIndex)
+        assert idx.npoints == 0
+        tm.assert_numpy_array_equal(idx.indices, np.array([], dtype=np.int32))
+
+        idx = make_sparse_index(
+            4, np.array([0, 1, 2, 3], dtype=np.int32), kind="integer"
+        )
+        assert isinstance(idx, IntIndex)
+        assert idx.npoints == 4
+        tm.assert_numpy_array_equal(idx.indices, np.array([0, 1, 2, 3], dtype=np.int32))
+
+    def test_equals(self):
+        index = IntIndex(10, [0, 1, 2, 3, 4])
+        assert index.equals(index)
+        assert not index.equals(IntIndex(10, [0, 1, 2, 3]))
+
+    def test_to_block_index(self, cases, test_length):
+        xloc, xlen, yloc, ylen, _, _ = cases
+        xindex = BlockIndex(test_length, xloc, xlen)
+        yindex = BlockIndex(test_length, yloc, ylen)
+
+        # see if survive the round trip
+        xbindex = xindex.to_int_index().to_block_index()
+        ybindex = yindex.to_int_index().to_block_index()
+        assert isinstance(xbindex, BlockIndex)
+        assert xbindex.equals(xindex)
+        assert ybindex.equals(yindex)
+
+    def test_to_int_index(self):
+        index = IntIndex(10, [2, 3, 4, 5, 6])
+        assert index.to_int_index() is index
+
+
+class TestSparseOperators:
+    @pytest.mark.parametrize("opname", ["add", "sub", "mul", "truediv", "floordiv"])
+    def test_op(self, opname, cases, test_length):
+        xloc, xlen, yloc, ylen, _, _ = cases
+        sparse_op = getattr(splib, f"sparse_{opname}_float64")
+        python_op = getattr(operator, opname)
+
+        xindex = BlockIndex(test_length, xloc, xlen)
+        yindex = BlockIndex(test_length, yloc, ylen)
+
+        xdindex = xindex.to_int_index()
+        ydindex = yindex.to_int_index()
+
+        x = np.arange(xindex.npoints) * 10.0 + 1
+        y = np.arange(yindex.npoints) * 100.0 + 1
+
+        xfill = 0
+        yfill = 2
+
+        result_block_vals, rb_index, bfill = sparse_op(
+            x, xindex, xfill, y, yindex, yfill
+        )
+        result_int_vals, ri_index, ifill = sparse_op(
+            x, xdindex, xfill, y, ydindex, yfill
+        )
+
+        assert rb_index.to_int_index().equals(ri_index)
+        tm.assert_numpy_array_equal(result_block_vals, result_int_vals)
+        assert bfill == ifill
+
+        # check versus Series...
+        xseries = Series(x, xdindex.indices)
+        xseries = xseries.reindex(np.arange(test_length)).fillna(xfill)
+
+        yseries = Series(y, ydindex.indices)
+        yseries = yseries.reindex(np.arange(test_length)).fillna(yfill)
+
+        series_result = python_op(xseries, yseries)
+        series_result = series_result.reindex(ri_index.indices)
+
+        tm.assert_numpy_array_equal(result_block_vals, series_result.values)
+        tm.assert_numpy_array_equal(result_int_vals, series_result.values)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_reductions.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_reductions.py
new file mode 100644
index 0000000000000000000000000000000000000000..f44423d5e635c3f74725db219d48fcaca27c4d53
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_reductions.py
@@ -0,0 +1,306 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    NaT,
+    SparseDtype,
+    Timestamp,
+    isna,
+)
+from pandas.core.arrays.sparse import SparseArray
+
+
+class TestReductions:
+    @pytest.mark.parametrize(
+        "data,pos,neg",
+        [
+            ([True, True, True], True, False),
+            ([1, 2, 1], 1, 0),
+            ([1.0, 2.0, 1.0], 1.0, 0.0),
+        ],
+    )
+    def test_all(self, data, pos, neg):
+        # GH#17570
+        out = SparseArray(data).all()
+        assert out
+
+        out = SparseArray(data, fill_value=pos).all()
+        assert out
+
+        data[1] = neg
+        out = SparseArray(data).all()
+        assert not out
+
+        out = SparseArray(data, fill_value=pos).all()
+        assert not out
+
+    @pytest.mark.parametrize(
+        "data,pos,neg",
+        [
+            ([True, True, True], True, False),
+            ([1, 2, 1], 1, 0),
+            ([1.0, 2.0, 1.0], 1.0, 0.0),
+        ],
+    )
+    def test_numpy_all(self, data, pos, neg):
+        # GH#17570
+        out = np.all(SparseArray(data))
+        assert out
+
+        out = np.all(SparseArray(data, fill_value=pos))
+        assert out
+
+        data[1] = neg
+        out = np.all(SparseArray(data))
+        assert not out
+
+        out = np.all(SparseArray(data, fill_value=pos))
+        assert not out
+
+        # raises with a different message on py2.
+        msg = "the 'out' parameter is not supported"
+        with pytest.raises(ValueError, match=msg):
+            np.all(SparseArray(data), out=np.array([]))
+
+    @pytest.mark.parametrize(
+        "data,pos,neg",
+        [
+            ([False, True, False], True, False),
+            ([0, 2, 0], 2, 0),
+            ([0.0, 2.0, 0.0], 2.0, 0.0),
+        ],
+    )
+    def test_any(self, data, pos, neg):
+        # GH#17570
+        out = SparseArray(data).any()
+        assert out
+
+        out = SparseArray(data, fill_value=pos).any()
+        assert out
+
+        data[1] = neg
+        out = SparseArray(data).any()
+        assert not out
+
+        out = SparseArray(data, fill_value=pos).any()
+        assert not out
+
+    @pytest.mark.parametrize(
+        "data,pos,neg",
+        [
+            ([False, True, False], True, False),
+            ([0, 2, 0], 2, 0),
+            ([0.0, 2.0, 0.0], 2.0, 0.0),
+        ],
+    )
+    def test_numpy_any(self, data, pos, neg):
+        # GH#17570
+        out = np.any(SparseArray(data))
+        assert out
+
+        out = np.any(SparseArray(data, fill_value=pos))
+        assert out
+
+        data[1] = neg
+        out = np.any(SparseArray(data))
+        assert not out
+
+        out = np.any(SparseArray(data, fill_value=pos))
+        assert not out
+
+        msg = "the 'out' parameter is not supported"
+        with pytest.raises(ValueError, match=msg):
+            np.any(SparseArray(data), out=out)
+
+    def test_sum(self):
+        data = np.arange(10).astype(float)
+        out = SparseArray(data).sum()
+        assert out == 45.0
+
+        data[5] = np.nan
+        out = SparseArray(data, fill_value=2).sum()
+        assert out == 40.0
+
+        out = SparseArray(data, fill_value=np.nan).sum()
+        assert out == 40.0
+
+    @pytest.mark.parametrize(
+        "arr",
+        [np.array([0, 1, np.nan, 1]), np.array([0, 1, 1])],
+    )
+    @pytest.mark.parametrize("fill_value", [0, 1, np.nan])
+    @pytest.mark.parametrize("min_count, expected", [(3, 2), (4, np.nan)])
+    def test_sum_min_count(self, arr, fill_value, min_count, expected):
+        # GH#25777
+        sparray = SparseArray(arr, fill_value=fill_value)
+        result = sparray.sum(min_count=min_count)
+        if np.isnan(expected):
+            assert np.isnan(result)
+        else:
+            assert result == expected
+
+    def test_bool_sum_min_count(self):
+        spar_bool = SparseArray([False, True] * 5, dtype=np.bool_, fill_value=True)
+        res = spar_bool.sum(min_count=1)
+        assert res == 5
+        res = spar_bool.sum(min_count=11)
+        assert isna(res)
+
+    def test_numpy_sum(self):
+        data = np.arange(10).astype(float)
+        out = np.sum(SparseArray(data))
+        assert out == 45.0
+
+        data[5] = np.nan
+        out = np.sum(SparseArray(data, fill_value=2))
+        assert out == 40.0
+
+        out = np.sum(SparseArray(data, fill_value=np.nan))
+        assert out == 40.0
+
+        msg = "the 'dtype' parameter is not supported"
+        with pytest.raises(ValueError, match=msg):
+            np.sum(SparseArray(data), dtype=np.int64)
+
+        msg = "the 'out' parameter is not supported"
+        with pytest.raises(ValueError, match=msg):
+            np.sum(SparseArray(data), out=out)
+
+    def test_mean(self):
+        data = np.arange(10).astype(float)
+        out = SparseArray(data).mean()
+        assert out == 4.5
+
+        data[5] = np.nan
+        out = SparseArray(data).mean()
+        assert out == 40.0 / 9
+
+    def test_numpy_mean(self):
+        data = np.arange(10).astype(float)
+        out = np.mean(SparseArray(data))
+        assert out == 4.5
+
+        data[5] = np.nan
+        out = np.mean(SparseArray(data))
+        assert out == 40.0 / 9
+
+        msg = "the 'dtype' parameter is not supported"
+        with pytest.raises(ValueError, match=msg):
+            np.mean(SparseArray(data), dtype=np.int64)
+
+        msg = "the 'out' parameter is not supported"
+        with pytest.raises(ValueError, match=msg):
+            np.mean(SparseArray(data), out=out)
+
+
+class TestMinMax:
+    @pytest.mark.parametrize(
+        "raw_data,max_expected,min_expected",
+        [
+            (np.arange(5.0), [4], [0]),
+            (-np.arange(5.0), [0], [-4]),
+            (np.array([0, 1, 2, np.nan, 4]), [4], [0]),
+            (np.array([np.nan] * 5), [np.nan], [np.nan]),
+            (np.array([]), [np.nan], [np.nan]),
+        ],
+    )
+    def test_nan_fill_value(self, raw_data, max_expected, min_expected):
+        arr = SparseArray(raw_data)
+        max_result = arr.max()
+        min_result = arr.min()
+        assert max_result in max_expected
+        assert min_result in min_expected
+
+        max_result = arr.max(skipna=False)
+        min_result = arr.min(skipna=False)
+        if np.isnan(raw_data).any():
+            assert np.isnan(max_result)
+            assert np.isnan(min_result)
+        else:
+            assert max_result in max_expected
+            assert min_result in min_expected
+
+    @pytest.mark.parametrize(
+        "fill_value,max_expected,min_expected",
+        [
+            (100, 100, 0),
+            (-100, 1, -100),
+        ],
+    )
+    def test_fill_value(self, fill_value, max_expected, min_expected):
+        arr = SparseArray(
+            np.array([fill_value, 0, 1]), dtype=SparseDtype("int", fill_value)
+        )
+        max_result = arr.max()
+        assert max_result == max_expected
+
+        min_result = arr.min()
+        assert min_result == min_expected
+
+    def test_only_fill_value(self):
+        fv = 100
+        arr = SparseArray(np.array([fv, fv, fv]), dtype=SparseDtype("int", fv))
+        assert len(arr._valid_sp_values) == 0
+
+        assert arr.max() == fv
+        assert arr.min() == fv
+        assert arr.max(skipna=False) == fv
+        assert arr.min(skipna=False) == fv
+
+    @pytest.mark.parametrize("func", ["min", "max"])
+    @pytest.mark.parametrize("data", [np.array([]), np.array([np.nan, np.nan])])
+    @pytest.mark.parametrize(
+        "dtype,expected",
+        [
+            (SparseDtype(np.float64, np.nan), np.nan),
+            (SparseDtype(np.float64, 5.0), np.nan),
+            (SparseDtype("datetime64[ns]", NaT), NaT),
+            (SparseDtype("datetime64[ns]", Timestamp("2018-05-05")), NaT),
+        ],
+    )
+    def test_na_value_if_no_valid_values(self, func, data, dtype, expected):
+        arr = SparseArray(data, dtype=dtype)
+        result = getattr(arr, func)()
+        if expected is NaT:
+            # TODO: pin down whether we wrap datetime64("NaT")
+            assert result is NaT or np.isnat(result)
+        else:
+            assert np.isnan(result)
+
+
+class TestArgmaxArgmin:
+    @pytest.mark.parametrize(
+        "arr,argmax_expected,argmin_expected",
+        [
+            (SparseArray([1, 2, 0, 1, 2]), 1, 2),
+            (SparseArray([-1, -2, 0, -1, -2]), 2, 1),
+            (SparseArray([np.nan, 1, 0, 0, np.nan, -1]), 1, 5),
+            (SparseArray([np.nan, 1, 0, 0, np.nan, 2]), 5, 2),
+            (SparseArray([np.nan, 1, 0, 0, np.nan, 2], fill_value=-1), 5, 2),
+            (SparseArray([np.nan, 1, 0, 0, np.nan, 2], fill_value=0), 5, 2),
+            (SparseArray([np.nan, 1, 0, 0, np.nan, 2], fill_value=1), 5, 2),
+            (SparseArray([np.nan, 1, 0, 0, np.nan, 2], fill_value=2), 5, 2),
+            (SparseArray([np.nan, 1, 0, 0, np.nan, 2], fill_value=3), 5, 2),
+            (SparseArray([0] * 10 + [-1], fill_value=0), 0, 10),
+            (SparseArray([0] * 10 + [-1], fill_value=-1), 0, 10),
+            (SparseArray([0] * 10 + [-1], fill_value=1), 0, 10),
+            (SparseArray([-1] + [0] * 10, fill_value=0), 1, 0),
+            (SparseArray([1] + [0] * 10, fill_value=0), 0, 1),
+            (SparseArray([-1] + [0] * 10, fill_value=-1), 1, 0),
+            (SparseArray([1] + [0] * 10, fill_value=1), 0, 1),
+        ],
+    )
+    def test_argmax_argmin(self, arr, argmax_expected, argmin_expected):
+        argmax_result = arr.argmax()
+        argmin_result = arr.argmin()
+        assert argmax_result == argmax_expected
+        assert argmin_result == argmin_expected
+
+    @pytest.mark.parametrize(
+        "arr,method",
+        [(SparseArray([]), "argmax"), (SparseArray([]), "argmin")],
+    )
+    def test_empty_array(self, arr, method):
+        msg = f"attempt to get {method} of an empty sequence"
+        with pytest.raises(ValueError, match=msg):
+            arr.argmax() if method == "argmax" else arr.argmin()
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_unary.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_unary.py
new file mode 100644
index 0000000000000000000000000000000000000000..c00a73773fdd4795e3d5d7f030a591a060dc3bfc
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_unary.py
@@ -0,0 +1,79 @@
+import operator
+
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.core.arrays import SparseArray
+
+
+@pytest.mark.filterwarnings("ignore:invalid value encountered in cast:RuntimeWarning")
+@pytest.mark.parametrize("fill_value", [0, np.nan])
+@pytest.mark.parametrize("op", [operator.pos, operator.neg])
+def test_unary_op(op, fill_value):
+    arr = np.array([0, 1, np.nan, 2])
+    sparray = SparseArray(arr, fill_value=fill_value)
+    result = op(sparray)
+    expected = SparseArray(op(arr), fill_value=op(fill_value))
+    tm.assert_sp_array_equal(result, expected)
+
+
+@pytest.mark.parametrize("fill_value", [True, False])
+def test_invert(fill_value):
+    arr = np.array([True, False, False, True])
+    sparray = SparseArray(arr, fill_value=fill_value)
+    result = ~sparray
+    expected = SparseArray(~arr, fill_value=not fill_value)
+    tm.assert_sp_array_equal(result, expected)
+
+    result = ~pd.Series(sparray)
+    expected = pd.Series(expected)
+    tm.assert_series_equal(result, expected)
+
+    result = ~pd.DataFrame({"A": sparray})
+    expected = pd.DataFrame({"A": expected})
+    tm.assert_frame_equal(result, expected)
+
+
+class TestUnaryMethods:
+    @pytest.mark.filterwarnings(
+        "ignore:invalid value encountered in cast:RuntimeWarning"
+    )
+    def test_neg_operator(self):
+        arr = SparseArray([-1, -2, np.nan, 3], fill_value=np.nan, dtype=np.int8)
+        res = -arr
+        exp = SparseArray([1, 2, np.nan, -3], fill_value=np.nan, dtype=np.int8)
+        tm.assert_sp_array_equal(exp, res)
+
+        arr = SparseArray([-1, -2, 1, 3], fill_value=-1, dtype=np.int8)
+        res = -arr
+        exp = SparseArray([1, 2, -1, -3], fill_value=1, dtype=np.int8)
+        tm.assert_sp_array_equal(exp, res)
+
+    @pytest.mark.filterwarnings(
+        "ignore:invalid value encountered in cast:RuntimeWarning"
+    )
+    def test_abs_operator(self):
+        arr = SparseArray([-1, -2, np.nan, 3], fill_value=np.nan, dtype=np.int8)
+        res = abs(arr)
+        exp = SparseArray([1, 2, np.nan, 3], fill_value=np.nan, dtype=np.int8)
+        tm.assert_sp_array_equal(exp, res)
+
+        arr = SparseArray([-1, -2, 1, 3], fill_value=-1, dtype=np.int8)
+        res = abs(arr)
+        exp = SparseArray([1, 2, 1, 3], fill_value=1, dtype=np.int8)
+        tm.assert_sp_array_equal(exp, res)
+
+    def test_invert_operator(self):
+        arr = SparseArray([False, True, False, True], fill_value=False, dtype=np.bool_)
+        exp = SparseArray(
+            np.invert([False, True, False, True]), fill_value=True, dtype=np.bool_
+        )
+        res = ~arr
+        tm.assert_sp_array_equal(exp, res)
+
+        arr = SparseArray([0, 1, 0, 2, 3, 0], fill_value=0, dtype=np.int32)
+        res = ~arr
+        exp = SparseArray([-1, -2, -1, -3, -4, -1], fill_value=-1, dtype=np.int32)
+        tm.assert_sp_array_equal(exp, res)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/string_/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/string_/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/string_/test_concat.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/string_/test_concat.py
new file mode 100644
index 0000000000000000000000000000000000000000..320d700b2b6c340d1cb52e708aa8defcad7e60bb
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/string_/test_concat.py
@@ -0,0 +1,73 @@
+import numpy as np
+import pytest
+
+from pandas.compat import HAS_PYARROW
+
+from pandas.core.dtypes.cast import find_common_type
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.util.version import Version
+
+
+@pytest.mark.parametrize(
+    "to_concat_dtypes, result_dtype",
+    [
+        # same types
+        ([("pyarrow", pd.NA), ("pyarrow", pd.NA)], ("pyarrow", pd.NA)),
+        ([("pyarrow", np.nan), ("pyarrow", np.nan)], ("pyarrow", np.nan)),
+        ([("python", pd.NA), ("python", pd.NA)], ("python", pd.NA)),
+        ([("python", np.nan), ("python", np.nan)], ("python", np.nan)),
+        # pyarrow preference
+        ([("pyarrow", pd.NA), ("python", pd.NA)], ("pyarrow", pd.NA)),
+        # NA preference
+        ([("python", pd.NA), ("python", np.nan)], ("python", pd.NA)),
+    ],
+)
+def test_concat_series(request, to_concat_dtypes, result_dtype):
+    if any(storage == "pyarrow" for storage, _ in to_concat_dtypes) and not HAS_PYARROW:
+        pytest.skip("Could not import 'pyarrow'")
+
+    ser_list = [
+        pd.Series(["a", "b", None], dtype=pd.StringDtype(storage, na_value))
+        for storage, na_value in to_concat_dtypes
+    ]
+
+    result = pd.concat(ser_list, ignore_index=True)
+    expected = pd.Series(
+        ["a", "b", None, "a", "b", None], dtype=pd.StringDtype(*result_dtype)
+    )
+    tm.assert_series_equal(result, expected)
+
+    # order doesn't matter for result
+    result = pd.concat(ser_list[::1], ignore_index=True)
+    tm.assert_series_equal(result, expected)
+
+
+def test_concat_with_object(string_dtype_arguments):
+    # _get_common_dtype cannot inspect values, so object dtype with strings still
+    # results in object dtype
+    result = pd.concat(
+        [
+            pd.Series(["a", "b", None], dtype=pd.StringDtype(*string_dtype_arguments)),
+            pd.Series(["a", "b", None], dtype=object),
+        ]
+    )
+    assert result.dtype == np.dtype("object")
+
+
+def test_concat_with_numpy(string_dtype_arguments):
+    # common type with a numpy string dtype always preserves the pandas string dtype
+    dtype = pd.StringDtype(*string_dtype_arguments)
+    assert find_common_type([dtype, np.dtype("U")]) == dtype
+    assert find_common_type([np.dtype("U"), dtype]) == dtype
+    assert find_common_type([dtype, np.dtype("U10")]) == dtype
+    assert find_common_type([np.dtype("U10"), dtype]) == dtype
+
+    # with any other numpy dtype -> object
+    assert find_common_type([dtype, np.dtype("S")]) == np.dtype("object")
+    assert find_common_type([dtype, np.dtype("int64")]) == np.dtype("object")
+
+    if Version(np.__version__) >= Version("2"):
+        assert find_common_type([dtype, np.dtypes.StringDType()]) == dtype
+        assert find_common_type([np.dtypes.StringDType(), dtype]) == dtype
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/string_/test_string.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/string_/test_string.py
new file mode 100644
index 0000000000000000000000000000000000000000..b468480cf5f865461c196756c979c8dfd9fa4b2b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/string_/test_string.py
@@ -0,0 +1,893 @@
+"""
+This module tests the functionality of StringArray and ArrowStringArray.
+Tests for the str accessors are in pandas/tests/strings/test_string_array.py
+"""
+import operator
+
+import numpy as np
+import pytest
+
+from pandas._config import using_string_dtype
+
+from pandas.compat import HAS_PYARROW
+from pandas.compat.pyarrow import (
+    pa_version_under12p0,
+    pa_version_under19p0,
+)
+import pandas.util._test_decorators as td
+
+from pandas.core.dtypes.common import is_dtype_equal
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.core.arrays.string_ import StringArrayNumpySemantics
+from pandas.core.arrays.string_arrow import (
+    ArrowStringArray,
+    ArrowStringArrayNumpySemantics,
+)
+
+
+@pytest.fixture
+def dtype(string_dtype_arguments):
+    """Fixture giving StringDtype from parametrized storage and na_value arguments"""
+    storage, na_value = string_dtype_arguments
+    return pd.StringDtype(storage=storage, na_value=na_value)
+
+
+@pytest.fixture
+def dtype2(string_dtype_arguments2):
+    storage, na_value = string_dtype_arguments2
+    return pd.StringDtype(storage=storage, na_value=na_value)
+
+
+@pytest.fixture
+def cls(dtype):
+    """Fixture giving array type from parametrized 'dtype'"""
+    return dtype.construct_array_type()
+
+
+def string_dtype_highest_priority(dtype1, dtype2):
+    if HAS_PYARROW:
+        DTYPE_HIERARCHY = [
+            pd.StringDtype("python", na_value=np.nan),
+            pd.StringDtype("pyarrow", na_value=np.nan),
+            pd.StringDtype("python", na_value=pd.NA),
+            pd.StringDtype("pyarrow", na_value=pd.NA),
+        ]
+    else:
+        DTYPE_HIERARCHY = [
+            pd.StringDtype("python", na_value=np.nan),
+            pd.StringDtype("python", na_value=pd.NA),
+        ]
+
+    h1 = DTYPE_HIERARCHY.index(dtype1)
+    h2 = DTYPE_HIERARCHY.index(dtype2)
+    return DTYPE_HIERARCHY[max(h1, h2)]
+
+
+def test_dtype_constructor():
+    pytest.importorskip("pyarrow")
+
+    with tm.assert_produces_warning(FutureWarning):
+        dtype = pd.StringDtype("pyarrow_numpy")
+    assert dtype == pd.StringDtype("pyarrow", na_value=np.nan)
+
+
+def test_dtype_equality():
+    pytest.importorskip("pyarrow")
+
+    dtype1 = pd.StringDtype("python")
+    dtype2 = pd.StringDtype("pyarrow")
+    dtype3 = pd.StringDtype("pyarrow", na_value=np.nan)
+
+    assert dtype1 == pd.StringDtype("python", na_value=pd.NA)
+    assert dtype1 != dtype2
+    assert dtype1 != dtype3
+
+    assert dtype2 == pd.StringDtype("pyarrow", na_value=pd.NA)
+    assert dtype2 != dtype1
+    assert dtype2 != dtype3
+
+    assert dtype3 == pd.StringDtype("pyarrow", na_value=np.nan)
+    assert dtype3 == pd.StringDtype("pyarrow", na_value=float("nan"))
+    assert dtype3 != dtype1
+    assert dtype3 != dtype2
+
+
+def test_repr(dtype):
+    df = pd.DataFrame({"A": pd.array(["a", pd.NA, "b"], dtype=dtype)})
+    if dtype.na_value is np.nan:
+        expected = "     A\n0    a\n1  NaN\n2    b"
+    else:
+        expected = "      A\n0     a\n1  \n2     b"
+    assert repr(df) == expected
+
+    if dtype.na_value is np.nan:
+        expected = "0      a\n1    NaN\n2      b\nName: A, dtype: str"
+    else:
+        expected = "0       a\n1    \n2       b\nName: A, dtype: string"
+    assert repr(df.A) == expected
+
+    if dtype.storage == "pyarrow" and dtype.na_value is pd.NA:
+        arr_name = "ArrowStringArray"
+        expected = f"<{arr_name}>\n['a', , 'b']\nLength: 3, dtype: string"
+    elif dtype.storage == "pyarrow" and dtype.na_value is np.nan:
+        arr_name = "ArrowStringArrayNumpySemantics"
+        expected = f"<{arr_name}>\n['a', nan, 'b']\nLength: 3, dtype: str"
+    elif dtype.storage == "python" and dtype.na_value is np.nan:
+        arr_name = "StringArrayNumpySemantics"
+        expected = f"<{arr_name}>\n['a', nan, 'b']\nLength: 3, dtype: str"
+    else:
+        arr_name = "StringArray"
+        expected = f"<{arr_name}>\n['a', , 'b']\nLength: 3, dtype: string"
+    assert repr(df.A.array) == expected
+
+
+def test_dtype_repr(dtype):
+    if dtype.storage == "pyarrow":
+        if dtype.na_value is pd.NA:
+            assert repr(dtype) == "string[pyarrow]"
+        else:
+            assert repr(dtype) == ""
+    elif dtype.na_value is pd.NA:
+        assert repr(dtype) == "string[python]"
+    else:
+        assert repr(dtype) == ""
+
+
+def test_none_to_nan(cls, dtype):
+    a = cls._from_sequence(["a", None, "b"], dtype=dtype)
+    assert a[1] is not None
+    assert a[1] is a.dtype.na_value
+
+
+def test_setitem_validates(cls, dtype):
+    arr = cls._from_sequence(["a", "b"], dtype=dtype)
+
+    msg = "Invalid value '10' for dtype 'str"
+    with pytest.raises(TypeError, match=msg):
+        arr[0] = 10
+
+    msg = "Invalid value for dtype 'str"
+    with pytest.raises(TypeError, match=msg):
+        arr[:] = np.array([1, 2])
+
+
+def test_setitem_with_scalar_string(dtype):
+    # is_float_dtype considers some strings, like 'd', to be floats
+    # which can cause issues.
+    arr = pd.array(["a", "c"], dtype=dtype)
+    arr[0] = "d"
+    expected = pd.array(["d", "c"], dtype=dtype)
+    tm.assert_extension_array_equal(arr, expected)
+
+
+def test_setitem_with_array_with_missing(dtype):
+    # ensure that when setting with an array of values, we don't mutate the
+    # array `value` in __setitem__(self, key, value)
+    arr = pd.array(["a", "b", "c"], dtype=dtype)
+    value = np.array(["A", None])
+    value_orig = value.copy()
+    arr[[0, 1]] = value
+
+    expected = pd.array(["A", pd.NA, "c"], dtype=dtype)
+    tm.assert_extension_array_equal(arr, expected)
+    tm.assert_numpy_array_equal(value, value_orig)
+
+
+def test_astype_roundtrip(dtype):
+    ser = pd.Series(pd.date_range("2000", periods=12))
+    ser[0] = None
+
+    casted = ser.astype(dtype)
+    assert is_dtype_equal(casted.dtype, dtype)
+
+    result = casted.astype("datetime64[ns]")
+    tm.assert_series_equal(result, ser)
+
+    # GH#38509 same thing for timedelta64
+    ser2 = ser - ser.iloc[-1]
+    casted2 = ser2.astype(dtype)
+    assert is_dtype_equal(casted2.dtype, dtype)
+
+    result2 = casted2.astype(ser2.dtype)
+    tm.assert_series_equal(result2, ser2)
+
+
+def test_add(dtype):
+    a = pd.Series(["a", "b", "c", None, None], dtype=dtype)
+    b = pd.Series(["x", "y", None, "z", None], dtype=dtype)
+
+    result = a + b
+    expected = pd.Series(["ax", "by", None, None, None], dtype=dtype)
+    tm.assert_series_equal(result, expected)
+
+    result = a.add(b)
+    tm.assert_series_equal(result, expected)
+
+    result = a.radd(b)
+    expected = pd.Series(["xa", "yb", None, None, None], dtype=dtype)
+    tm.assert_series_equal(result, expected)
+
+    result = a.add(b, fill_value="-")
+    expected = pd.Series(["ax", "by", "c-", "-z", None], dtype=dtype)
+    tm.assert_series_equal(result, expected)
+
+
+def test_add_2d(dtype, request):
+    if dtype.storage == "pyarrow":
+        reason = "Failed: DID NOT RAISE "
+        mark = pytest.mark.xfail(raises=None, reason=reason)
+        request.applymarker(mark)
+
+    a = pd.array(["a", "b", "c"], dtype=dtype)
+    b = np.array([["a", "b", "c"]], dtype=object)
+    with pytest.raises(ValueError, match="3 != 1"):
+        a + b
+
+    s = pd.Series(a)
+    with pytest.raises(ValueError, match="3 != 1"):
+        s + b
+
+
+def test_add_sequence(dtype):
+    a = pd.array(["a", "b", None, None], dtype=dtype)
+    other = ["x", None, "y", None]
+
+    result = a + other
+    expected = pd.array(["ax", None, None, None], dtype=dtype)
+    tm.assert_extension_array_equal(result, expected)
+
+    result = other + a
+    expected = pd.array(["xa", None, None, None], dtype=dtype)
+    tm.assert_extension_array_equal(result, expected)
+
+
+def test_mul(dtype):
+    a = pd.array(["a", "b", None], dtype=dtype)
+    result = a * 2
+    expected = pd.array(["aa", "bb", None], dtype=dtype)
+    tm.assert_extension_array_equal(result, expected)
+
+    result = 2 * a
+    tm.assert_extension_array_equal(result, expected)
+
+
+@pytest.mark.xfail(reason="GH-28527")
+def test_add_strings(dtype):
+    arr = pd.array(["a", "b", "c", "d"], dtype=dtype)
+    df = pd.DataFrame([["t", "y", "v", "w"]], dtype=object)
+    assert arr.__add__(df) is NotImplemented
+
+    result = arr + df
+    expected = pd.DataFrame([["at", "by", "cv", "dw"]]).astype(dtype)
+    tm.assert_frame_equal(result, expected)
+
+    result = df + arr
+    expected = pd.DataFrame([["ta", "yb", "vc", "wd"]]).astype(dtype)
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.xfail(reason="GH-28527")
+def test_add_frame(dtype):
+    arr = pd.array(["a", "b", np.nan, np.nan], dtype=dtype)
+    df = pd.DataFrame([["x", np.nan, "y", np.nan]])
+
+    assert arr.__add__(df) is NotImplemented
+
+    result = arr + df
+    expected = pd.DataFrame([["ax", np.nan, np.nan, np.nan]]).astype(dtype)
+    tm.assert_frame_equal(result, expected)
+
+    result = df + arr
+    expected = pd.DataFrame([["xa", np.nan, np.nan, np.nan]]).astype(dtype)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_comparison_methods_scalar(comparison_op, dtype):
+    op_name = f"__{comparison_op.__name__}__"
+    a = pd.array(["a", None, "c"], dtype=dtype)
+    other = "a"
+    result = getattr(a, op_name)(other)
+    if dtype.na_value is np.nan:
+        expected = np.array([getattr(item, op_name)(other) for item in a])
+        if comparison_op == operator.ne:
+            expected[1] = True
+        else:
+            expected[1] = False
+        tm.assert_numpy_array_equal(result, expected.astype(np.bool_))
+    else:
+        expected_dtype = "boolean[pyarrow]" if dtype.storage == "pyarrow" else "boolean"
+        expected = np.array([getattr(item, op_name)(other) for item in a], dtype=object)
+        expected = pd.array(expected, dtype=expected_dtype)
+        tm.assert_extension_array_equal(result, expected)
+
+
+def test_comparison_methods_scalar_pd_na(comparison_op, dtype):
+    op_name = f"__{comparison_op.__name__}__"
+    a = pd.array(["a", None, "c"], dtype=dtype)
+    result = getattr(a, op_name)(pd.NA)
+
+    if dtype.na_value is np.nan:
+        if operator.ne == comparison_op:
+            expected = np.array([True, True, True])
+        else:
+            expected = np.array([False, False, False])
+        tm.assert_numpy_array_equal(result, expected)
+    else:
+        expected_dtype = "boolean[pyarrow]" if dtype.storage == "pyarrow" else "boolean"
+        expected = pd.array([None, None, None], dtype=expected_dtype)
+        tm.assert_extension_array_equal(result, expected)
+        tm.assert_extension_array_equal(result, expected)
+
+
+def test_comparison_methods_scalar_not_string(comparison_op, dtype):
+    op_name = f"__{comparison_op.__name__}__"
+
+    a = pd.array(["a", None, "c"], dtype=dtype)
+    other = 42
+
+    if op_name not in ["__eq__", "__ne__"]:
+        with pytest.raises(TypeError, match="Invalid comparison|not supported between"):
+            getattr(a, op_name)(other)
+
+        return
+
+    result = getattr(a, op_name)(other)
+
+    if dtype.na_value is np.nan:
+        expected_data = {
+            "__eq__": [False, False, False],
+            "__ne__": [True, True, True],
+        }[op_name]
+        expected = np.array(expected_data)
+        tm.assert_numpy_array_equal(result, expected)
+    else:
+        expected_data = {"__eq__": [False, None, False], "__ne__": [True, None, True]}[
+            op_name
+        ]
+        expected_dtype = "boolean[pyarrow]" if dtype.storage == "pyarrow" else "boolean"
+        expected = pd.array(expected_data, dtype=expected_dtype)
+        tm.assert_extension_array_equal(result, expected)
+
+
+def test_comparison_methods_array(comparison_op, dtype, dtype2):
+    op_name = f"__{comparison_op.__name__}__"
+
+    a = pd.array(["a", None, "c"], dtype=dtype)
+    other = pd.array([None, None, "c"], dtype=dtype2)
+    result = comparison_op(a, other)
+
+    # ensure operation is commutative
+    result2 = comparison_op(other, a)
+    tm.assert_equal(result, result2)
+
+    if dtype.na_value is np.nan and dtype2.na_value is np.nan:
+        if operator.ne == comparison_op:
+            expected = np.array([True, True, False])
+        else:
+            expected = np.array([False, False, False])
+            expected[-1] = getattr(other[-1], op_name)(a[-1])
+        tm.assert_numpy_array_equal(result, expected)
+
+    else:
+        max_dtype = string_dtype_highest_priority(dtype, dtype2)
+        if max_dtype.storage == "python":
+            expected_dtype = "boolean"
+        else:
+            expected_dtype = "bool[pyarrow]"
+
+        expected = np.full(len(a), fill_value=None, dtype="object")
+        expected[-1] = getattr(other[-1], op_name)(a[-1])
+        expected = pd.array(expected, dtype=expected_dtype)
+        tm.assert_extension_array_equal(result, expected)
+
+
+@td.skip_if_no("pyarrow")
+def test_comparison_methods_array_arrow_extension(comparison_op, dtype2):
+    # Test pd.ArrowDtype(pa.string()) against other string arrays
+    import pyarrow as pa
+
+    op_name = f"__{comparison_op.__name__}__"
+    dtype = pd.ArrowDtype(pa.string())
+    a = pd.array(["a", None, "c"], dtype=dtype)
+    other = pd.array([None, None, "c"], dtype=dtype2)
+    result = comparison_op(a, other)
+
+    # ensure operation is commutative
+    result2 = comparison_op(other, a)
+    tm.assert_equal(result, result2)
+
+    expected = pd.array([None, None, True], dtype="bool[pyarrow]")
+    expected[-1] = getattr(other[-1], op_name)(a[-1])
+    tm.assert_extension_array_equal(result, expected)
+
+
+def test_comparison_methods_list(comparison_op, dtype):
+    op_name = f"__{comparison_op.__name__}__"
+
+    a = pd.array(["a", None, "c"], dtype=dtype)
+    other = [None, None, "c"]
+    result = comparison_op(a, other)
+
+    # ensure operation is commutative
+    result2 = comparison_op(other, a)
+    tm.assert_equal(result, result2)
+
+    if dtype.na_value is np.nan:
+        if operator.ne == comparison_op:
+            expected = np.array([True, True, False])
+        else:
+            expected = np.array([False, False, False])
+            expected[-1] = getattr(other[-1], op_name)(a[-1])
+        tm.assert_numpy_array_equal(result, expected)
+
+    else:
+        expected_dtype = "boolean[pyarrow]" if dtype.storage == "pyarrow" else "boolean"
+        expected = np.full(len(a), fill_value=None, dtype="object")
+        expected[-1] = getattr(other[-1], op_name)(a[-1])
+        expected = pd.array(expected, dtype=expected_dtype)
+        tm.assert_extension_array_equal(result, expected)
+
+
+def test_constructor_raises(cls):
+    if cls is pd.arrays.StringArray:
+        msg = "StringArray requires a sequence of strings or pandas.NA"
+    elif cls is StringArrayNumpySemantics:
+        msg = "StringArrayNumpySemantics requires a sequence of strings or NaN"
+    else:
+        msg = "Unsupported type '' for ArrowExtensionArray"
+
+    with pytest.raises(ValueError, match=msg):
+        cls(np.array(["a", "b"], dtype="S1"))
+
+    with pytest.raises(ValueError, match=msg):
+        cls(np.array([]))
+
+    if cls is pd.arrays.StringArray or cls is StringArrayNumpySemantics:
+        # GH#45057 np.nan and None do NOT raise, as they are considered valid NAs
+        #  for string dtype
+        cls(np.array(["a", np.nan], dtype=object))
+        cls(np.array(["a", None], dtype=object))
+    else:
+        with pytest.raises(ValueError, match=msg):
+            cls(np.array(["a", np.nan], dtype=object))
+        with pytest.raises(ValueError, match=msg):
+            cls(np.array(["a", None], dtype=object))
+
+    with pytest.raises(ValueError, match=msg):
+        cls(np.array(["a", pd.NaT], dtype=object))
+
+    with pytest.raises(ValueError, match=msg):
+        cls(np.array(["a", np.datetime64("NaT", "ns")], dtype=object))
+
+    with pytest.raises(ValueError, match=msg):
+        cls(np.array(["a", np.timedelta64("NaT", "ns")], dtype=object))
+
+
+@pytest.mark.parametrize("na", [np.nan, np.float64("nan"), float("nan"), None, pd.NA])
+def test_constructor_nan_like(na):
+    expected = pd.arrays.StringArray(np.array(["a", pd.NA]))
+    tm.assert_extension_array_equal(
+        pd.arrays.StringArray(np.array(["a", na], dtype="object")), expected
+    )
+
+
+@pytest.mark.parametrize("copy", [True, False])
+def test_from_sequence_no_mutate(copy, cls, dtype):
+    nan_arr = np.array(["a", np.nan], dtype=object)
+    expected_input = nan_arr.copy()
+    na_arr = np.array(["a", pd.NA], dtype=object)
+
+    result = cls._from_sequence(nan_arr, dtype=dtype, copy=copy)
+
+    if cls in (ArrowStringArray, ArrowStringArrayNumpySemantics):
+        import pyarrow as pa
+
+        expected = cls(pa.array(na_arr, type=pa.string(), from_pandas=True))
+    elif cls is StringArrayNumpySemantics:
+        expected = cls(nan_arr)
+    else:
+        expected = cls(na_arr)
+
+    tm.assert_extension_array_equal(result, expected)
+    tm.assert_numpy_array_equal(nan_arr, expected_input)
+
+
+def test_astype_int(dtype):
+    arr = pd.array(["1", "2", "3"], dtype=dtype)
+    result = arr.astype("int64")
+    expected = np.array([1, 2, 3], dtype="int64")
+    tm.assert_numpy_array_equal(result, expected)
+
+    arr = pd.array(["1", pd.NA, "3"], dtype=dtype)
+    if dtype.na_value is np.nan:
+        err = ValueError
+        msg = "cannot convert float NaN to integer"
+    else:
+        err = TypeError
+        msg = (
+            r"int\(\) argument must be a string, a bytes-like "
+            r"object or a( real)? number"
+        )
+    with pytest.raises(err, match=msg):
+        arr.astype("int64")
+
+
+def test_astype_nullable_int(dtype):
+    arr = pd.array(["1", pd.NA, "3"], dtype=dtype)
+
+    result = arr.astype("Int64")
+    expected = pd.array([1, pd.NA, 3], dtype="Int64")
+    tm.assert_extension_array_equal(result, expected)
+
+
+def test_astype_float(dtype, any_float_dtype):
+    # Don't compare arrays (37974)
+    ser = pd.Series(["1.1", pd.NA, "3.3"], dtype=dtype)
+    result = ser.astype(any_float_dtype)
+    expected = pd.Series([1.1, np.nan, 3.3], dtype=any_float_dtype)
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("skipna", [True, False])
+def test_reduce(skipna, dtype):
+    arr = pd.Series(["a", "b", "c"], dtype=dtype)
+    result = arr.sum(skipna=skipna)
+    assert result == "abc"
+
+
+@pytest.mark.parametrize("skipna", [True, False])
+def test_reduce_missing(skipna, dtype):
+    arr = pd.Series([None, "a", None, "b", "c", None], dtype=dtype)
+    result = arr.sum(skipna=skipna)
+    if skipna:
+        assert result == "abc"
+    else:
+        assert pd.isna(result)
+
+
+@pytest.mark.parametrize("method", ["min", "max"])
+@pytest.mark.parametrize("skipna", [True, False])
+def test_min_max(method, skipna, dtype):
+    arr = pd.Series(["a", "b", "c", None], dtype=dtype)
+    result = getattr(arr, method)(skipna=skipna)
+    if skipna:
+        expected = "a" if method == "min" else "c"
+        assert result == expected
+    else:
+        assert result is arr.dtype.na_value
+
+
+@pytest.mark.parametrize("method", ["min", "max"])
+@pytest.mark.parametrize("box", [pd.Series, pd.array])
+def test_min_max_numpy(method, box, dtype, request):
+    if dtype.storage == "pyarrow" and box is pd.array:
+        if box is pd.array:
+            reason = "'<=' not supported between instances of 'str' and 'NoneType'"
+        else:
+            reason = "'ArrowStringArray' object has no attribute 'max'"
+        mark = pytest.mark.xfail(raises=TypeError, reason=reason)
+        request.applymarker(mark)
+
+    arr = box(["a", "b", "c", None], dtype=dtype)
+    result = getattr(np, method)(arr)
+    expected = "a" if method == "min" else "c"
+    assert result == expected
+
+
+def test_fillna_args(dtype):
+    # GH 37987
+
+    arr = pd.array(["a", pd.NA], dtype=dtype)
+
+    res = arr.fillna(value="b")
+    expected = pd.array(["a", "b"], dtype=dtype)
+    tm.assert_extension_array_equal(res, expected)
+
+    res = arr.fillna(value=np.str_("b"))
+    expected = pd.array(["a", "b"], dtype=dtype)
+    tm.assert_extension_array_equal(res, expected)
+
+    msg = "Invalid value '1' for dtype 'str"
+    with pytest.raises(TypeError, match=msg):
+        arr.fillna(value=1)
+
+
+def test_arrow_array(dtype):
+    # protocol added in 0.15.0
+    pa = pytest.importorskip("pyarrow")
+    import pyarrow.compute as pc
+
+    data = pd.array(["a", "b", "c"], dtype=dtype)
+    arr = pa.array(data)
+    expected = pa.array(list(data), type=pa.large_string(), from_pandas=True)
+    if dtype.storage == "pyarrow" and pa_version_under12p0:
+        expected = pa.chunked_array(expected)
+    if dtype.storage == "python":
+        expected = pc.cast(expected, pa.string())
+    assert arr.equals(expected)
+
+
+@pytest.mark.filterwarnings("ignore:Passing a BlockManager:DeprecationWarning")
+def test_arrow_roundtrip(dtype, string_storage, using_infer_string):
+    # roundtrip possible from arrow 1.0.0
+    pa = pytest.importorskip("pyarrow")
+
+    data = pd.array(["a", "b", None], dtype=dtype)
+    df = pd.DataFrame({"a": data})
+    table = pa.table(df)
+    if dtype.storage == "python":
+        assert table.field("a").type == "string"
+    else:
+        assert table.field("a").type == "large_string"
+    with pd.option_context("string_storage", string_storage):
+        result = table.to_pandas()
+    if dtype.na_value is np.nan and not using_infer_string:
+        assert result["a"].dtype == "object"
+    else:
+        assert isinstance(result["a"].dtype, pd.StringDtype)
+        expected = df.astype(pd.StringDtype(string_storage, na_value=dtype.na_value))
+        if using_infer_string:
+            expected.columns = expected.columns.astype(
+                pd.StringDtype(string_storage, na_value=np.nan)
+            )
+        tm.assert_frame_equal(result, expected)
+        # ensure the missing value is represented by NA and not np.nan or None
+        assert result.loc[2, "a"] is result["a"].dtype.na_value
+
+
+@pytest.mark.filterwarnings("ignore:Passing a BlockManager:DeprecationWarning")
+def test_arrow_from_string(using_infer_string):
+    # not roundtrip,  but starting with pyarrow table without pandas metadata
+    pa = pytest.importorskip("pyarrow")
+    table = pa.table({"a": pa.array(["a", "b", None], type=pa.string())})
+
+    result = table.to_pandas()
+
+    if using_infer_string and not pa_version_under19p0:
+        expected = pd.DataFrame({"a": ["a", "b", None]}, dtype="str")
+    else:
+        expected = pd.DataFrame({"a": ["a", "b", None]}, dtype="object")
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.filterwarnings("ignore:Passing a BlockManager:DeprecationWarning")
+def test_arrow_load_from_zero_chunks(dtype, string_storage, using_infer_string):
+    # GH-41040
+    pa = pytest.importorskip("pyarrow")
+
+    data = pd.array([], dtype=dtype)
+    df = pd.DataFrame({"a": data})
+    table = pa.table(df)
+    if dtype.storage == "python":
+        assert table.field("a").type == "string"
+    else:
+        assert table.field("a").type == "large_string"
+    # Instantiate the same table with no chunks at all
+    table = pa.table([pa.chunked_array([], type=pa.string())], schema=table.schema)
+    with pd.option_context("string_storage", string_storage):
+        result = table.to_pandas()
+
+    if dtype.na_value is np.nan and not using_string_dtype():
+        assert result["a"].dtype == "object"
+    else:
+        assert isinstance(result["a"].dtype, pd.StringDtype)
+        expected = df.astype(pd.StringDtype(string_storage, na_value=dtype.na_value))
+        if using_infer_string:
+            expected.columns = expected.columns.astype(
+                pd.StringDtype(string_storage, na_value=np.nan)
+            )
+        tm.assert_frame_equal(result, expected)
+
+
+def test_value_counts_na(dtype):
+    if dtype.na_value is np.nan:
+        exp_dtype = "int64"
+    elif dtype.storage == "pyarrow":
+        exp_dtype = "int64[pyarrow]"
+    else:
+        exp_dtype = "Int64"
+    arr = pd.array(["a", "b", "a", pd.NA], dtype=dtype)
+    result = arr.value_counts(dropna=False)
+    expected = pd.Series([2, 1, 1], index=arr[[0, 1, 3]], dtype=exp_dtype, name="count")
+    tm.assert_series_equal(result, expected)
+
+    result = arr.value_counts(dropna=True)
+    expected = pd.Series([2, 1], index=arr[:2], dtype=exp_dtype, name="count")
+    tm.assert_series_equal(result, expected)
+
+
+def test_value_counts_with_normalize(dtype):
+    if dtype.na_value is np.nan:
+        exp_dtype = np.float64
+    elif dtype.storage == "pyarrow":
+        exp_dtype = "double[pyarrow]"
+    else:
+        exp_dtype = "Float64"
+    ser = pd.Series(["a", "b", "a", pd.NA], dtype=dtype)
+    result = ser.value_counts(normalize=True)
+    expected = pd.Series([2, 1], index=ser[:2], dtype=exp_dtype, name="proportion") / 3
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "values, expected",
+    [
+        (["a", "b", "c"], np.array([False, False, False])),
+        (["a", "b", None], np.array([False, False, True])),
+    ],
+)
+def test_use_inf_as_na(values, expected, dtype):
+    # https://github.com/pandas-dev/pandas/issues/33655
+    values = pd.array(values, dtype=dtype)
+    msg = "use_inf_as_na option is deprecated"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        with pd.option_context("mode.use_inf_as_na", True):
+            result = values.isna()
+            tm.assert_numpy_array_equal(result, expected)
+
+            result = pd.Series(values).isna()
+            expected = pd.Series(expected)
+            tm.assert_series_equal(result, expected)
+
+            result = pd.DataFrame(values).isna()
+            expected = pd.DataFrame(expected)
+            tm.assert_frame_equal(result, expected)
+
+
+def test_value_counts_sort_false(dtype):
+    if dtype.na_value is np.nan:
+        exp_dtype = "int64"
+    elif dtype.storage == "pyarrow":
+        exp_dtype = "int64[pyarrow]"
+    else:
+        exp_dtype = "Int64"
+    ser = pd.Series(["a", "b", "c", "b"], dtype=dtype)
+    result = ser.value_counts(sort=False)
+    expected = pd.Series([1, 2, 1], index=ser[:3], dtype=exp_dtype, name="count")
+    tm.assert_series_equal(result, expected)
+
+
+def test_memory_usage(dtype):
+    # GH 33963
+
+    if dtype.storage == "pyarrow":
+        pytest.skip(f"not applicable for {dtype.storage}")
+
+    series = pd.Series(["a", "b", "c"], dtype=dtype)
+
+    assert 0 < series.nbytes <= series.memory_usage() < series.memory_usage(deep=True)
+
+
+@pytest.mark.parametrize("float_dtype", [np.float16, np.float32, np.float64])
+def test_astype_from_float_dtype(float_dtype, dtype):
+    # https://github.com/pandas-dev/pandas/issues/36451
+    ser = pd.Series([0.1], dtype=float_dtype)
+    result = ser.astype(dtype)
+    expected = pd.Series(["0.1"], dtype=dtype)
+    tm.assert_series_equal(result, expected)
+
+
+def test_to_numpy_returns_pdna_default(dtype):
+    arr = pd.array(["a", pd.NA, "b"], dtype=dtype)
+    result = np.array(arr)
+    expected = np.array(["a", dtype.na_value, "b"], dtype=object)
+    tm.assert_numpy_array_equal(result, expected)
+
+
+def test_to_numpy_na_value(dtype, nulls_fixture):
+    na_value = nulls_fixture
+    arr = pd.array(["a", pd.NA, "b"], dtype=dtype)
+    result = arr.to_numpy(na_value=na_value)
+    expected = np.array(["a", na_value, "b"], dtype=object)
+    tm.assert_numpy_array_equal(result, expected)
+
+
+def test_isin(dtype, fixed_now_ts):
+    s = pd.Series(["a", "b", None], dtype=dtype)
+
+    result = s.isin(["a", "c"])
+    expected = pd.Series([True, False, False])
+    tm.assert_series_equal(result, expected)
+
+    result = s.isin(["a", pd.NA])
+    expected = pd.Series([True, False, True])
+    tm.assert_series_equal(result, expected)
+
+    result = s.isin([])
+    expected = pd.Series([False, False, False])
+    tm.assert_series_equal(result, expected)
+
+    result = s.isin(["a", fixed_now_ts])
+    expected = pd.Series([True, False, False])
+    tm.assert_series_equal(result, expected)
+
+    result = s.isin([fixed_now_ts])
+    expected = pd.Series([False, False, False])
+    tm.assert_series_equal(result, expected)
+
+
+def test_isin_string_array(dtype, dtype2):
+    s = pd.Series(["a", "b", None], dtype=dtype)
+
+    result = s.isin(pd.array(["a", "c"], dtype=dtype2))
+    expected = pd.Series([True, False, False])
+    tm.assert_series_equal(result, expected)
+
+    result = s.isin(pd.array(["a", None], dtype=dtype2))
+    expected = pd.Series([True, False, True])
+    tm.assert_series_equal(result, expected)
+
+
+def test_isin_arrow_string_array(dtype):
+    pa = pytest.importorskip("pyarrow")
+    s = pd.Series(["a", "b", None], dtype=dtype)
+
+    result = s.isin(pd.array(["a", "c"], dtype=pd.ArrowDtype(pa.string())))
+    expected = pd.Series([True, False, False])
+    tm.assert_series_equal(result, expected)
+
+    result = s.isin(pd.array(["a", None], dtype=pd.ArrowDtype(pa.string())))
+    expected = pd.Series([True, False, True])
+    tm.assert_series_equal(result, expected)
+
+
+def test_setitem_scalar_with_mask_validation(dtype):
+    # https://github.com/pandas-dev/pandas/issues/47628
+    # setting None with a boolean mask (through _putmaks) should still result
+    # in pd.NA values in the underlying array
+    ser = pd.Series(["a", "b", "c"], dtype=dtype)
+    mask = np.array([False, True, False])
+
+    ser[mask] = None
+    assert ser.array[1] is ser.dtype.na_value
+
+    # for other non-string we should also raise an error
+    ser = pd.Series(["a", "b", "c"], dtype=dtype)
+    msg = "Invalid value '1' for dtype 'str"
+    with pytest.raises(TypeError, match=msg):
+        ser[mask] = 1
+
+
+def test_from_numpy_str(dtype):
+    vals = ["a", "b", "c"]
+    arr = np.array(vals, dtype=np.str_)
+    result = pd.array(arr, dtype=dtype)
+    expected = pd.array(vals, dtype=dtype)
+    tm.assert_extension_array_equal(result, expected)
+
+
+def test_tolist(dtype):
+    vals = ["a", "b", "c"]
+    arr = pd.array(vals, dtype=dtype)
+    result = arr.tolist()
+    expected = vals
+    tm.assert_equal(result, expected)
+
+
+@pytest.mark.parametrize("box", [pd.Series, pd.array])
+def test_numpy_array_ufunc(dtype, box):
+    arr = box(["a", "bb", "ccc"], dtype=dtype)
+
+    # custom ufunc that works with string (object) input -> returning numeric
+    str_len_ufunc = np.frompyfunc(lambda x: len(x), 1, 1)
+    result = str_len_ufunc(arr)
+    expected_cls = pd.Series if box is pd.Series else np.array
+    # TODO we should infer int64 dtype here?
+    expected = expected_cls([1, 2, 3], dtype=object)
+    tm.assert_equal(result, expected)
+
+    # custom ufunc returning strings
+    str_multiply_ufunc = np.frompyfunc(lambda x: x * 2, 1, 1)
+    result = str_multiply_ufunc(arr)
+    expected = box(["aa", "bbbb", "cccccc"], dtype=dtype)
+    if dtype.storage == "pyarrow":
+        # TODO ArrowStringArray should also preserve the class / dtype
+        if box is pd.array:
+            expected = np.array(["aa", "bbbb", "cccccc"], dtype=object)
+        else:
+            # not specifying the dtype because the exact dtype is not yet preserved
+            expected = pd.Series(["aa", "bbbb", "cccccc"])
+
+    tm.assert_equal(result, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/string_/test_string_arrow.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/string_/test_string_arrow.py
new file mode 100644
index 0000000000000000000000000000000000000000..aa87f5fc0f49a07b40174ef7aad5b12349d28a5b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/string_/test_string_arrow.py
@@ -0,0 +1,282 @@
+import pickle
+import re
+
+import numpy as np
+import pytest
+
+import pandas.util._test_decorators as td
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.core.arrays.string_ import (
+    StringArray,
+    StringDtype,
+)
+from pandas.core.arrays.string_arrow import (
+    ArrowStringArray,
+    ArrowStringArrayNumpySemantics,
+)
+
+
+def test_eq_all_na():
+    pytest.importorskip("pyarrow")
+    a = pd.array([pd.NA, pd.NA], dtype=StringDtype("pyarrow"))
+    result = a == a
+    expected = pd.array([pd.NA, pd.NA], dtype="boolean[pyarrow]")
+    tm.assert_extension_array_equal(result, expected)
+
+
+def test_config(string_storage, using_infer_string):
+    # with the default string_storage setting
+    # always "python" at the moment
+    assert StringDtype().storage == "python"
+
+    with pd.option_context("string_storage", string_storage):
+        assert StringDtype().storage == string_storage
+        result = pd.array(["a", "b"])
+        assert result.dtype.storage == string_storage
+
+    # pd.array(..) by default always returns the NA-variant
+    dtype = StringDtype(string_storage, na_value=pd.NA)
+    expected = dtype.construct_array_type()._from_sequence(["a", "b"], dtype=dtype)
+    tm.assert_equal(result, expected)
+
+
+def test_config_bad_storage_raises():
+    msg = re.escape("Value must be one of python|pyarrow")
+    with pytest.raises(ValueError, match=msg):
+        pd.options.mode.string_storage = "foo"
+
+
+@pytest.mark.parametrize("chunked", [True, False])
+@pytest.mark.parametrize("array_lib", ["numpy", "pyarrow"])
+def test_constructor_not_string_type_raises(array_lib, chunked):
+    pa = pytest.importorskip("pyarrow")
+
+    array_lib = pa if array_lib == "pyarrow" else np
+
+    arr = array_lib.array([1, 2, 3])
+    if chunked:
+        if array_lib is np:
+            pytest.skip("chunked not applicable to numpy array")
+        arr = pa.chunked_array(arr)
+    if array_lib is np:
+        msg = "Unsupported type '' for ArrowExtensionArray"
+    else:
+        msg = re.escape(
+            "ArrowStringArray requires a PyArrow (chunked) array of large_string type"
+        )
+    with pytest.raises(ValueError, match=msg):
+        ArrowStringArray(arr)
+
+
+@pytest.mark.parametrize("chunked", [True, False])
+def test_constructor_not_string_type_value_dictionary_raises(chunked):
+    pa = pytest.importorskip("pyarrow")
+
+    arr = pa.array([1, 2, 3], pa.dictionary(pa.int32(), pa.int32()))
+    if chunked:
+        arr = pa.chunked_array(arr)
+
+    msg = re.escape(
+        "ArrowStringArray requires a PyArrow (chunked) array of large_string type"
+    )
+    with pytest.raises(ValueError, match=msg):
+        ArrowStringArray(arr)
+
+
+@pytest.mark.parametrize("string_type", ["string", "large_string"])
+@pytest.mark.parametrize("chunked", [True, False])
+def test_constructor_valid_string_type_value_dictionary(string_type, chunked):
+    pa = pytest.importorskip("pyarrow")
+
+    arr = pa.array(["1", "2", "3"], getattr(pa, string_type)()).dictionary_encode()
+    if chunked:
+        arr = pa.chunked_array(arr)
+
+    arr = ArrowStringArray(arr)
+    # dictionary type get converted to dense large string array
+    assert pa.types.is_large_string(arr._pa_array.type)
+
+
+@pytest.mark.parametrize("chunked", [True, False])
+def test_constructor_valid_string_view(chunked):
+    # requires pyarrow>=18 for casting string_view to string
+    pa = pytest.importorskip("pyarrow", minversion="18")
+
+    arr = pa.array(["1", "2", "3"], pa.string_view())
+    if chunked:
+        arr = pa.chunked_array(arr)
+
+    arr = ArrowStringArray(arr)
+    # dictionary type get converted to dense large string array
+    assert pa.types.is_large_string(arr._pa_array.type)
+
+
+def test_constructor_from_list():
+    # GH#27673
+    pytest.importorskip("pyarrow")
+    result = pd.Series(["E"], dtype=StringDtype(storage="pyarrow"))
+    assert isinstance(result.dtype, StringDtype)
+    assert result.dtype.storage == "pyarrow"
+
+
+def test_from_sequence_wrong_dtype_raises(using_infer_string):
+    pytest.importorskip("pyarrow")
+    with pd.option_context("string_storage", "python"):
+        ArrowStringArray._from_sequence(["a", None, "c"], dtype="string")
+
+    with pd.option_context("string_storage", "pyarrow"):
+        ArrowStringArray._from_sequence(["a", None, "c"], dtype="string")
+
+    with pytest.raises(AssertionError, match=None):
+        ArrowStringArray._from_sequence(["a", None, "c"], dtype="string[python]")
+
+    ArrowStringArray._from_sequence(["a", None, "c"], dtype="string[pyarrow]")
+
+    if not using_infer_string:
+        with pytest.raises(AssertionError, match=None):
+            with pd.option_context("string_storage", "python"):
+                ArrowStringArray._from_sequence(["a", None, "c"], dtype=StringDtype())
+
+    with pd.option_context("string_storage", "pyarrow"):
+        ArrowStringArray._from_sequence(["a", None, "c"], dtype=StringDtype())
+
+    if not using_infer_string:
+        with pytest.raises(AssertionError, match=None):
+            ArrowStringArray._from_sequence(
+                ["a", None, "c"], dtype=StringDtype("python")
+            )
+
+    ArrowStringArray._from_sequence(["a", None, "c"], dtype=StringDtype("pyarrow"))
+
+    with pd.option_context("string_storage", "python"):
+        StringArray._from_sequence(["a", None, "c"], dtype="string")
+
+    with pd.option_context("string_storage", "pyarrow"):
+        StringArray._from_sequence(["a", None, "c"], dtype="string")
+
+    StringArray._from_sequence(["a", None, "c"], dtype="string[python]")
+
+    with pytest.raises(AssertionError, match=None):
+        StringArray._from_sequence(["a", None, "c"], dtype="string[pyarrow]")
+
+    if not using_infer_string:
+        with pd.option_context("string_storage", "python"):
+            StringArray._from_sequence(["a", None, "c"], dtype=StringDtype())
+
+    if not using_infer_string:
+        with pytest.raises(AssertionError, match=None):
+            with pd.option_context("string_storage", "pyarrow"):
+                StringArray._from_sequence(["a", None, "c"], dtype=StringDtype())
+
+    StringArray._from_sequence(["a", None, "c"], dtype=StringDtype("python"))
+
+    with pytest.raises(AssertionError, match=None):
+        StringArray._from_sequence(["a", None, "c"], dtype=StringDtype("pyarrow"))
+
+
+@td.skip_if_installed("pyarrow")
+def test_pyarrow_not_installed_raises():
+    msg = re.escape("pyarrow>=10.0.1 is required for PyArrow backed")
+
+    with pytest.raises(ImportError, match=msg):
+        StringDtype(storage="pyarrow")
+
+    with pytest.raises(ImportError, match=msg):
+        ArrowStringArray([])
+
+    with pytest.raises(ImportError, match=msg):
+        ArrowStringArrayNumpySemantics([])
+
+    with pytest.raises(ImportError, match=msg):
+        ArrowStringArray._from_sequence(["a", None, "b"])
+
+
+@pytest.mark.parametrize("multiple_chunks", [False, True])
+@pytest.mark.parametrize(
+    "key, value, expected",
+    [
+        (-1, "XX", ["a", "b", "c", "d", "XX"]),
+        (1, "XX", ["a", "XX", "c", "d", "e"]),
+        (1, None, ["a", None, "c", "d", "e"]),
+        (1, pd.NA, ["a", None, "c", "d", "e"]),
+        ([1, 3], "XX", ["a", "XX", "c", "XX", "e"]),
+        ([1, 3], ["XX", "YY"], ["a", "XX", "c", "YY", "e"]),
+        ([1, 3], ["XX", None], ["a", "XX", "c", None, "e"]),
+        ([1, 3], ["XX", pd.NA], ["a", "XX", "c", None, "e"]),
+        ([0, -1], ["XX", "YY"], ["XX", "b", "c", "d", "YY"]),
+        ([-1, 0], ["XX", "YY"], ["YY", "b", "c", "d", "XX"]),
+        (slice(3, None), "XX", ["a", "b", "c", "XX", "XX"]),
+        (slice(2, 4), ["XX", "YY"], ["a", "b", "XX", "YY", "e"]),
+        (slice(3, 1, -1), ["XX", "YY"], ["a", "b", "YY", "XX", "e"]),
+        (slice(None), "XX", ["XX", "XX", "XX", "XX", "XX"]),
+        ([False, True, False, True, False], ["XX", "YY"], ["a", "XX", "c", "YY", "e"]),
+    ],
+)
+def test_setitem(multiple_chunks, key, value, expected):
+    pa = pytest.importorskip("pyarrow")
+
+    result = pa.array(list("abcde"))
+    expected = pa.array(expected)
+
+    if multiple_chunks:
+        result = pa.chunked_array([result[:3], result[3:]])
+        expected = pa.chunked_array([expected[:3], expected[3:]])
+
+    result = ArrowStringArray(result)
+    expected = ArrowStringArray(expected)
+
+    result[key] = value
+    tm.assert_equal(result, expected)
+
+
+def test_setitem_invalid_indexer_raises():
+    pa = pytest.importorskip("pyarrow")
+
+    arr = ArrowStringArray(pa.array(list("abcde")))
+
+    with pytest.raises(IndexError, match=None):
+        arr[5] = "foo"
+
+    with pytest.raises(IndexError, match=None):
+        arr[-6] = "foo"
+
+    with pytest.raises(IndexError, match=None):
+        arr[[0, 5]] = "foo"
+
+    with pytest.raises(IndexError, match=None):
+        arr[[0, -6]] = "foo"
+
+    with pytest.raises(IndexError, match=None):
+        arr[[True, True, False]] = "foo"
+
+    with pytest.raises(ValueError, match=None):
+        arr[[0, 1]] = ["foo", "bar", "baz"]
+
+
+@pytest.mark.parametrize("na_value", [pd.NA, np.nan])
+def test_pickle_roundtrip(na_value):
+    # GH 42600
+    pytest.importorskip("pyarrow")
+    dtype = StringDtype("pyarrow", na_value=na_value)
+    expected = pd.Series(range(10), dtype=dtype)
+    expected_sliced = expected.head(2)
+    full_pickled = pickle.dumps(expected)
+    sliced_pickled = pickle.dumps(expected_sliced)
+
+    assert len(full_pickled) > len(sliced_pickled)
+
+    result = pickle.loads(full_pickled)
+    tm.assert_series_equal(result, expected)
+
+    result_sliced = pickle.loads(sliced_pickled)
+    tm.assert_series_equal(result_sliced, expected_sliced)
+
+
+def test_string_dtype_error_message():
+    # GH#55051
+    pytest.importorskip("pyarrow")
+    msg = "Storage must be 'python' or 'pyarrow'."
+    with pytest.raises(ValueError, match=msg):
+        StringDtype("bla")
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/test_array.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/test_array.py
new file mode 100644
index 0000000000000000000000000000000000000000..158a963845b066139868de6905f45c83da1ca4bb
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/test_array.py
@@ -0,0 +1,519 @@
+import datetime
+import decimal
+import re
+
+import numpy as np
+import pytest
+import pytz
+
+from pandas._config import using_string_dtype
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.api.extensions import register_extension_dtype
+from pandas.arrays import (
+    BooleanArray,
+    DatetimeArray,
+    FloatingArray,
+    IntegerArray,
+    IntervalArray,
+    SparseArray,
+    TimedeltaArray,
+)
+from pandas.core.arrays import (
+    NumpyExtensionArray,
+    period_array,
+)
+from pandas.tests.extension.decimal import (
+    DecimalArray,
+    DecimalDtype,
+    to_decimal,
+)
+
+
+@pytest.mark.parametrize("dtype_unit", ["M8[h]", "M8[m]", "m8[h]", "M8[m]"])
+def test_dt64_array(dtype_unit):
+    # PR 53817
+    dtype_var = np.dtype(dtype_unit)
+    msg = (
+        r"datetime64 and timedelta64 dtype resolutions other than "
+        r"'s', 'ms', 'us', and 'ns' are deprecated. "
+        r"In future releases passing unsupported resolutions will "
+        r"raise an exception."
+    )
+    with tm.assert_produces_warning(FutureWarning, match=re.escape(msg)):
+        pd.array([], dtype=dtype_var)
+
+
+@pytest.mark.parametrize(
+    "data, dtype, expected",
+    [
+        # Basic NumPy defaults.
+        ([], None, FloatingArray._from_sequence([], dtype="Float64")),
+        ([1, 2], None, IntegerArray._from_sequence([1, 2], dtype="Int64")),
+        ([1, 2], object, NumpyExtensionArray(np.array([1, 2], dtype=object))),
+        (
+            [1, 2],
+            np.dtype("float32"),
+            NumpyExtensionArray(np.array([1.0, 2.0], dtype=np.dtype("float32"))),
+        ),
+        (
+            np.array([], dtype=object),
+            None,
+            NumpyExtensionArray(np.array([], dtype=object)),
+        ),
+        (
+            np.array([1, 2], dtype="int64"),
+            None,
+            IntegerArray._from_sequence([1, 2], dtype="Int64"),
+        ),
+        (
+            np.array([1.0, 2.0], dtype="float64"),
+            None,
+            FloatingArray._from_sequence([1.0, 2.0], dtype="Float64"),
+        ),
+        # String alias passes through to NumPy
+        ([1, 2], "float32", NumpyExtensionArray(np.array([1, 2], dtype="float32"))),
+        ([1, 2], "int64", NumpyExtensionArray(np.array([1, 2], dtype=np.int64))),
+        # GH#44715 FloatingArray does not support float16, so fall
+        #  back to NumpyExtensionArray
+        (
+            np.array([1, 2], dtype=np.float16),
+            None,
+            NumpyExtensionArray(np.array([1, 2], dtype=np.float16)),
+        ),
+        # idempotency with e.g. pd.array(pd.array([1, 2], dtype="int64"))
+        (
+            NumpyExtensionArray(np.array([1, 2], dtype=np.int32)),
+            None,
+            NumpyExtensionArray(np.array([1, 2], dtype=np.int32)),
+        ),
+        # Period alias
+        (
+            [pd.Period("2000", "D"), pd.Period("2001", "D")],
+            "Period[D]",
+            period_array(["2000", "2001"], freq="D"),
+        ),
+        # Period dtype
+        (
+            [pd.Period("2000", "D")],
+            pd.PeriodDtype("D"),
+            period_array(["2000"], freq="D"),
+        ),
+        # Datetime (naive)
+        (
+            [1, 2],
+            np.dtype("datetime64[ns]"),
+            DatetimeArray._from_sequence(
+                np.array([1, 2], dtype="M8[ns]"), dtype="M8[ns]"
+            ),
+        ),
+        (
+            [1, 2],
+            np.dtype("datetime64[s]"),
+            DatetimeArray._from_sequence(
+                np.array([1, 2], dtype="M8[s]"), dtype="M8[s]"
+            ),
+        ),
+        (
+            np.array([1, 2], dtype="datetime64[ns]"),
+            None,
+            DatetimeArray._from_sequence(
+                np.array([1, 2], dtype="M8[ns]"), dtype="M8[ns]"
+            ),
+        ),
+        (
+            pd.DatetimeIndex(["2000", "2001"]),
+            np.dtype("datetime64[ns]"),
+            DatetimeArray._from_sequence(["2000", "2001"], dtype="M8[ns]"),
+        ),
+        (
+            pd.DatetimeIndex(["2000", "2001"]),
+            None,
+            DatetimeArray._from_sequence(["2000", "2001"], dtype="M8[ns]"),
+        ),
+        (
+            ["2000", "2001"],
+            np.dtype("datetime64[ns]"),
+            DatetimeArray._from_sequence(["2000", "2001"], dtype="M8[ns]"),
+        ),
+        # Datetime (tz-aware)
+        (
+            ["2000", "2001"],
+            pd.DatetimeTZDtype(tz="CET"),
+            DatetimeArray._from_sequence(
+                ["2000", "2001"], dtype=pd.DatetimeTZDtype(tz="CET")
+            ),
+        ),
+        # Timedelta
+        (
+            ["1h", "2h"],
+            np.dtype("timedelta64[ns]"),
+            TimedeltaArray._from_sequence(["1h", "2h"], dtype="m8[ns]"),
+        ),
+        (
+            pd.TimedeltaIndex(["1h", "2h"]),
+            np.dtype("timedelta64[ns]"),
+            TimedeltaArray._from_sequence(["1h", "2h"], dtype="m8[ns]"),
+        ),
+        (
+            np.array([1, 2], dtype="m8[s]"),
+            np.dtype("timedelta64[s]"),
+            TimedeltaArray._from_sequence(
+                np.array([1, 2], dtype="m8[s]"), dtype="m8[s]"
+            ),
+        ),
+        (
+            pd.TimedeltaIndex(["1h", "2h"]),
+            None,
+            TimedeltaArray._from_sequence(["1h", "2h"], dtype="m8[ns]"),
+        ),
+        (
+            # preserve non-nano, i.e. don't cast to NumpyExtensionArray
+            TimedeltaArray._simple_new(
+                np.arange(5, dtype=np.int64).view("m8[s]"), dtype=np.dtype("m8[s]")
+            ),
+            None,
+            TimedeltaArray._simple_new(
+                np.arange(5, dtype=np.int64).view("m8[s]"), dtype=np.dtype("m8[s]")
+            ),
+        ),
+        (
+            # preserve non-nano, i.e. don't cast to NumpyExtensionArray
+            TimedeltaArray._simple_new(
+                np.arange(5, dtype=np.int64).view("m8[s]"), dtype=np.dtype("m8[s]")
+            ),
+            np.dtype("m8[s]"),
+            TimedeltaArray._simple_new(
+                np.arange(5, dtype=np.int64).view("m8[s]"), dtype=np.dtype("m8[s]")
+            ),
+        ),
+        # Category
+        (["a", "b"], "category", pd.Categorical(["a", "b"])),
+        (
+            ["a", "b"],
+            pd.CategoricalDtype(None, ordered=True),
+            pd.Categorical(["a", "b"], ordered=True),
+        ),
+        # Interval
+        (
+            [pd.Interval(1, 2), pd.Interval(3, 4)],
+            "interval",
+            IntervalArray.from_tuples([(1, 2), (3, 4)]),
+        ),
+        # Sparse
+        ([0, 1], "Sparse[int64]", SparseArray([0, 1], dtype="int64")),
+        # IntegerNA
+        ([1, None], "Int16", pd.array([1, None], dtype="Int16")),
+        (
+            pd.Series([1, 2]),
+            None,
+            NumpyExtensionArray(np.array([1, 2], dtype=np.int64)),
+        ),
+        # String
+        (
+            ["a", None],
+            "string",
+            pd.StringDtype()
+            .construct_array_type()
+            ._from_sequence(["a", None], dtype=pd.StringDtype()),
+        ),
+        (
+            ["a", None],
+            "str",
+            pd.StringDtype(na_value=np.nan)
+            .construct_array_type()
+            ._from_sequence(["a", None], dtype=pd.StringDtype(na_value=np.nan))
+            if using_string_dtype()
+            else NumpyExtensionArray(np.array(["a", "None"])),
+        ),
+        (
+            ["a", None],
+            pd.StringDtype(),
+            pd.StringDtype()
+            .construct_array_type()
+            ._from_sequence(["a", None], dtype=pd.StringDtype()),
+        ),
+        (
+            ["a", None],
+            pd.StringDtype(na_value=np.nan),
+            pd.StringDtype(na_value=np.nan)
+            .construct_array_type()
+            ._from_sequence(["a", None], dtype=pd.StringDtype(na_value=np.nan)),
+        ),
+        (
+            # numpy array with string dtype
+            np.array(["a", "b"], dtype=str),
+            pd.StringDtype(),
+            pd.StringDtype()
+            .construct_array_type()
+            ._from_sequence(["a", "b"], dtype=pd.StringDtype()),
+        ),
+        (
+            # numpy array with string dtype
+            np.array(["a", "b"], dtype=str),
+            pd.StringDtype(na_value=np.nan),
+            pd.StringDtype(na_value=np.nan)
+            .construct_array_type()
+            ._from_sequence(["a", "b"], dtype=pd.StringDtype(na_value=np.nan)),
+        ),
+        # Boolean
+        (
+            [True, None],
+            "boolean",
+            BooleanArray._from_sequence([True, None], dtype="boolean"),
+        ),
+        (
+            [True, None],
+            pd.BooleanDtype(),
+            BooleanArray._from_sequence([True, None], dtype="boolean"),
+        ),
+        # Index
+        (pd.Index([1, 2]), None, NumpyExtensionArray(np.array([1, 2], dtype=np.int64))),
+        # Series[EA] returns the EA
+        (
+            pd.Series(pd.Categorical(["a", "b"], categories=["a", "b", "c"])),
+            None,
+            pd.Categorical(["a", "b"], categories=["a", "b", "c"]),
+        ),
+        # "3rd party" EAs work
+        ([decimal.Decimal(0), decimal.Decimal(1)], "decimal", to_decimal([0, 1])),
+        # pass an ExtensionArray, but a different dtype
+        (
+            period_array(["2000", "2001"], freq="D"),
+            "category",
+            pd.Categorical([pd.Period("2000", "D"), pd.Period("2001", "D")]),
+        ),
+    ],
+)
+def test_array(data, dtype, expected):
+    result = pd.array(data, dtype=dtype)
+    tm.assert_equal(result, expected)
+
+
+def test_array_copy():
+    a = np.array([1, 2])
+    # default is to copy
+    b = pd.array(a, dtype=a.dtype)
+    assert not tm.shares_memory(a, b)
+
+    # copy=True
+    b = pd.array(a, dtype=a.dtype, copy=True)
+    assert not tm.shares_memory(a, b)
+
+    # copy=False
+    b = pd.array(a, dtype=a.dtype, copy=False)
+    assert tm.shares_memory(a, b)
+
+
+cet = pytz.timezone("CET")
+
+
+@pytest.mark.parametrize(
+    "data, expected",
+    [
+        # period
+        (
+            [pd.Period("2000", "D"), pd.Period("2001", "D")],
+            period_array(["2000", "2001"], freq="D"),
+        ),
+        # interval
+        ([pd.Interval(0, 1), pd.Interval(1, 2)], IntervalArray.from_breaks([0, 1, 2])),
+        # datetime
+        (
+            [pd.Timestamp("2000"), pd.Timestamp("2001")],
+            DatetimeArray._from_sequence(["2000", "2001"], dtype="M8[ns]"),
+        ),
+        (
+            [datetime.datetime(2000, 1, 1), datetime.datetime(2001, 1, 1)],
+            DatetimeArray._from_sequence(["2000", "2001"], dtype="M8[ns]"),
+        ),
+        (
+            np.array([1, 2], dtype="M8[ns]"),
+            DatetimeArray._from_sequence(np.array([1, 2], dtype="M8[ns]")),
+        ),
+        (
+            np.array([1, 2], dtype="M8[us]"),
+            DatetimeArray._simple_new(
+                np.array([1, 2], dtype="M8[us]"), dtype=np.dtype("M8[us]")
+            ),
+        ),
+        # datetimetz
+        (
+            [pd.Timestamp("2000", tz="CET"), pd.Timestamp("2001", tz="CET")],
+            DatetimeArray._from_sequence(
+                ["2000", "2001"], dtype=pd.DatetimeTZDtype(tz="CET", unit="ns")
+            ),
+        ),
+        (
+            [
+                datetime.datetime(2000, 1, 1, tzinfo=cet),
+                datetime.datetime(2001, 1, 1, tzinfo=cet),
+            ],
+            DatetimeArray._from_sequence(
+                ["2000", "2001"], dtype=pd.DatetimeTZDtype(tz=cet, unit="ns")
+            ),
+        ),
+        # timedelta
+        (
+            [pd.Timedelta("1h"), pd.Timedelta("2h")],
+            TimedeltaArray._from_sequence(["1h", "2h"], dtype="m8[ns]"),
+        ),
+        (
+            np.array([1, 2], dtype="m8[ns]"),
+            TimedeltaArray._from_sequence(np.array([1, 2], dtype="m8[ns]")),
+        ),
+        (
+            np.array([1, 2], dtype="m8[us]"),
+            TimedeltaArray._from_sequence(np.array([1, 2], dtype="m8[us]")),
+        ),
+        # integer
+        ([1, 2], IntegerArray._from_sequence([1, 2], dtype="Int64")),
+        ([1, None], IntegerArray._from_sequence([1, None], dtype="Int64")),
+        ([1, pd.NA], IntegerArray._from_sequence([1, pd.NA], dtype="Int64")),
+        ([1, np.nan], IntegerArray._from_sequence([1, np.nan], dtype="Int64")),
+        # float
+        ([0.1, 0.2], FloatingArray._from_sequence([0.1, 0.2], dtype="Float64")),
+        ([0.1, None], FloatingArray._from_sequence([0.1, pd.NA], dtype="Float64")),
+        ([0.1, np.nan], FloatingArray._from_sequence([0.1, pd.NA], dtype="Float64")),
+        ([0.1, pd.NA], FloatingArray._from_sequence([0.1, pd.NA], dtype="Float64")),
+        # integer-like float
+        ([1.0, 2.0], FloatingArray._from_sequence([1.0, 2.0], dtype="Float64")),
+        ([1.0, None], FloatingArray._from_sequence([1.0, pd.NA], dtype="Float64")),
+        ([1.0, np.nan], FloatingArray._from_sequence([1.0, pd.NA], dtype="Float64")),
+        ([1.0, pd.NA], FloatingArray._from_sequence([1.0, pd.NA], dtype="Float64")),
+        # mixed-integer-float
+        ([1, 2.0], FloatingArray._from_sequence([1.0, 2.0], dtype="Float64")),
+        (
+            [1, np.nan, 2.0],
+            FloatingArray._from_sequence([1.0, None, 2.0], dtype="Float64"),
+        ),
+        # string
+        (
+            ["a", "b"],
+            pd.StringDtype()
+            .construct_array_type()
+            ._from_sequence(["a", "b"], dtype=pd.StringDtype()),
+        ),
+        (
+            ["a", None],
+            pd.StringDtype()
+            .construct_array_type()
+            ._from_sequence(["a", None], dtype=pd.StringDtype()),
+        ),
+        (
+            # numpy array with string dtype
+            np.array(["a", "b"], dtype=str),
+            pd.StringDtype()
+            .construct_array_type()
+            ._from_sequence(["a", "b"], dtype=pd.StringDtype()),
+        ),
+        # Boolean
+        ([True, False], BooleanArray._from_sequence([True, False], dtype="boolean")),
+        ([True, None], BooleanArray._from_sequence([True, None], dtype="boolean")),
+    ],
+)
+def test_array_inference(data, expected):
+    result = pd.array(data)
+    tm.assert_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        # mix of frequencies
+        [pd.Period("2000", "D"), pd.Period("2001", "Y")],
+        # mix of closed
+        [pd.Interval(0, 1, closed="left"), pd.Interval(1, 2, closed="right")],
+        # Mix of timezones
+        [pd.Timestamp("2000", tz="CET"), pd.Timestamp("2000", tz="UTC")],
+        # Mix of tz-aware and tz-naive
+        [pd.Timestamp("2000", tz="CET"), pd.Timestamp("2000")],
+        np.array([pd.Timestamp("2000"), pd.Timestamp("2000", tz="CET")]),
+    ],
+)
+def test_array_inference_fails(data):
+    result = pd.array(data)
+    expected = NumpyExtensionArray(np.array(data, dtype=object))
+    tm.assert_extension_array_equal(result, expected)
+
+
+@pytest.mark.parametrize("data", [np.array(0)])
+def test_nd_raises(data):
+    with pytest.raises(ValueError, match="NumpyExtensionArray must be 1-dimensional"):
+        pd.array(data, dtype="int64")
+
+
+def test_scalar_raises():
+    with pytest.raises(ValueError, match="Cannot pass scalar '1'"):
+        pd.array(1)
+
+
+def test_dataframe_raises():
+    # GH#51167 don't accidentally cast to StringArray by doing inference on columns
+    df = pd.DataFrame([[1, 2], [3, 4]], columns=["A", "B"])
+    msg = "Cannot pass DataFrame to 'pandas.array'"
+    with pytest.raises(TypeError, match=msg):
+        pd.array(df)
+
+
+def test_bounds_check():
+    # GH21796
+    with pytest.raises(
+        TypeError, match=r"cannot safely cast non-equivalent int(32|64) to uint16"
+    ):
+        pd.array([-1, 2, 3], dtype="UInt16")
+
+
+# ---------------------------------------------------------------------------
+# A couple dummy classes to ensure that Series and Indexes are unboxed before
+# getting to the EA classes.
+
+
+@register_extension_dtype
+class DecimalDtype2(DecimalDtype):
+    name = "decimal2"
+
+    @classmethod
+    def construct_array_type(cls):
+        """
+        Return the array type associated with this dtype.
+
+        Returns
+        -------
+        type
+        """
+        return DecimalArray2
+
+
+class DecimalArray2(DecimalArray):
+    @classmethod
+    def _from_sequence(cls, scalars, *, dtype=None, copy=False):
+        if isinstance(scalars, (pd.Series, pd.Index)):
+            raise TypeError("scalars should not be of type pd.Series or pd.Index")
+
+        return super()._from_sequence(scalars, dtype=dtype, copy=copy)
+
+
+def test_array_unboxes(index_or_series):
+    box = index_or_series
+
+    data = box([decimal.Decimal("1"), decimal.Decimal("2")])
+    dtype = DecimalDtype2()
+    # make sure it works
+    with pytest.raises(
+        TypeError, match="scalars should not be of type pd.Series or pd.Index"
+    ):
+        DecimalArray2._from_sequence(data, dtype=dtype)
+
+    result = pd.array(data, dtype="decimal2")
+    expected = DecimalArray2._from_sequence(data.values, dtype=dtype)
+    tm.assert_equal(result, expected)
+
+
+def test_array_to_numpy_na():
+    # GH#40638
+    arr = pd.array([pd.NA, 1], dtype="string[python]")
+    result = arr.to_numpy(na_value=True, dtype=bool)
+    expected = np.array([True, True])
+    tm.assert_numpy_array_equal(result, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/test_datetimelike.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/test_datetimelike.py
new file mode 100644
index 0000000000000000000000000000000000000000..0397913b69b26833c394fb25c427130cea098674
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/test_datetimelike.py
@@ -0,0 +1,1360 @@
+from __future__ import annotations
+
+import re
+import warnings
+
+import numpy as np
+import pytest
+
+from pandas._libs import (
+    NaT,
+    OutOfBoundsDatetime,
+    Timestamp,
+)
+from pandas._libs.tslibs.dtypes import freq_to_period_freqstr
+from pandas.compat.numpy import np_version_gt2
+
+import pandas as pd
+from pandas import (
+    DatetimeIndex,
+    Period,
+    PeriodIndex,
+    TimedeltaIndex,
+)
+import pandas._testing as tm
+from pandas.core.arrays import (
+    DatetimeArray,
+    NumpyExtensionArray,
+    PeriodArray,
+    TimedeltaArray,
+)
+
+
+# TODO: more freq variants
+@pytest.fixture(params=["D", "B", "W", "ME", "QE", "YE"])
+def freqstr(request):
+    """Fixture returning parametrized frequency in string format."""
+    return request.param
+
+
+@pytest.fixture
+def period_index(freqstr):
+    """
+    A fixture to provide PeriodIndex objects with different frequencies.
+
+    Most PeriodArray behavior is already tested in PeriodIndex tests,
+    so here we just test that the PeriodArray behavior matches
+    the PeriodIndex behavior.
+    """
+    # TODO: non-monotone indexes; NaTs, different start dates
+    with warnings.catch_warnings():
+        # suppress deprecation of Period[B]
+        warnings.filterwarnings(
+            "ignore", message="Period with BDay freq", category=FutureWarning
+        )
+        freqstr = freq_to_period_freqstr(1, freqstr)
+        pi = pd.period_range(start=Timestamp("2000-01-01"), periods=100, freq=freqstr)
+    return pi
+
+
+@pytest.fixture
+def datetime_index(freqstr):
+    """
+    A fixture to provide DatetimeIndex objects with different frequencies.
+
+    Most DatetimeArray behavior is already tested in DatetimeIndex tests,
+    so here we just test that the DatetimeArray behavior matches
+    the DatetimeIndex behavior.
+    """
+    # TODO: non-monotone indexes; NaTs, different start dates, timezones
+    dti = pd.date_range(start=Timestamp("2000-01-01"), periods=100, freq=freqstr)
+    return dti
+
+
+@pytest.fixture
+def timedelta_index():
+    """
+    A fixture to provide TimedeltaIndex objects with different frequencies.
+     Most TimedeltaArray behavior is already tested in TimedeltaIndex tests,
+    so here we just test that the TimedeltaArray behavior matches
+    the TimedeltaIndex behavior.
+    """
+    # TODO: flesh this out
+    return TimedeltaIndex(["1 Day", "3 Hours", "NaT"])
+
+
+class SharedTests:
+    index_cls: type[DatetimeIndex | PeriodIndex | TimedeltaIndex]
+
+    @pytest.fixture
+    def arr1d(self):
+        """Fixture returning DatetimeArray with daily frequency."""
+        data = np.arange(10, dtype="i8") * 24 * 3600 * 10**9
+        if self.array_cls is PeriodArray:
+            arr = self.array_cls(data, freq="D")
+        else:
+            arr = self.index_cls(data, freq="D")._data
+        return arr
+
+    def test_compare_len1_raises(self, arr1d):
+        # make sure we raise when comparing with different lengths, specific
+        #  to the case where one has length-1, which numpy would broadcast
+        arr = arr1d
+        idx = self.index_cls(arr)
+
+        with pytest.raises(ValueError, match="Lengths must match"):
+            arr == arr[:1]
+
+        # test the index classes while we're at it, GH#23078
+        with pytest.raises(ValueError, match="Lengths must match"):
+            idx <= idx[[0]]
+
+    @pytest.mark.parametrize(
+        "result",
+        [
+            pd.date_range("2020", periods=3),
+            pd.date_range("2020", periods=3, tz="UTC"),
+            pd.timedelta_range("0 days", periods=3),
+            pd.period_range("2020Q1", periods=3, freq="Q"),
+        ],
+    )
+    def test_compare_with_Categorical(self, result):
+        expected = pd.Categorical(result)
+        assert all(result == expected)
+        assert not any(result != expected)
+
+    @pytest.mark.parametrize("reverse", [True, False])
+    @pytest.mark.parametrize("as_index", [True, False])
+    def test_compare_categorical_dtype(self, arr1d, as_index, reverse, ordered):
+        other = pd.Categorical(arr1d, ordered=ordered)
+        if as_index:
+            other = pd.CategoricalIndex(other)
+
+        left, right = arr1d, other
+        if reverse:
+            left, right = right, left
+
+        ones = np.ones(arr1d.shape, dtype=bool)
+        zeros = ~ones
+
+        result = left == right
+        tm.assert_numpy_array_equal(result, ones)
+
+        result = left != right
+        tm.assert_numpy_array_equal(result, zeros)
+
+        if not reverse and not as_index:
+            # Otherwise Categorical raises TypeError bc it is not ordered
+            # TODO: we should probably get the same behavior regardless?
+            result = left < right
+            tm.assert_numpy_array_equal(result, zeros)
+
+            result = left <= right
+            tm.assert_numpy_array_equal(result, ones)
+
+            result = left > right
+            tm.assert_numpy_array_equal(result, zeros)
+
+            result = left >= right
+            tm.assert_numpy_array_equal(result, ones)
+
+    def test_take(self):
+        data = np.arange(100, dtype="i8") * 24 * 3600 * 10**9
+        np.random.default_rng(2).shuffle(data)
+
+        if self.array_cls is PeriodArray:
+            arr = PeriodArray(data, dtype="period[D]")
+        else:
+            arr = self.index_cls(data)._data
+        idx = self.index_cls._simple_new(arr)
+
+        takers = [1, 4, 94]
+        result = arr.take(takers)
+        expected = idx.take(takers)
+
+        tm.assert_index_equal(self.index_cls(result), expected)
+
+        takers = np.array([1, 4, 94])
+        result = arr.take(takers)
+        expected = idx.take(takers)
+
+        tm.assert_index_equal(self.index_cls(result), expected)
+
+    @pytest.mark.parametrize("fill_value", [2, 2.0, Timestamp(2021, 1, 1, 12).time])
+    def test_take_fill_raises(self, fill_value, arr1d):
+        msg = f"value should be a '{arr1d._scalar_type.__name__}' or 'NaT'. Got"
+        with pytest.raises(TypeError, match=msg):
+            arr1d.take([0, 1], allow_fill=True, fill_value=fill_value)
+
+    def test_take_fill(self, arr1d):
+        arr = arr1d
+
+        result = arr.take([-1, 1], allow_fill=True, fill_value=None)
+        assert result[0] is NaT
+
+        result = arr.take([-1, 1], allow_fill=True, fill_value=np.nan)
+        assert result[0] is NaT
+
+        result = arr.take([-1, 1], allow_fill=True, fill_value=NaT)
+        assert result[0] is NaT
+
+    @pytest.mark.filterwarnings(
+        "ignore:Period with BDay freq is deprecated:FutureWarning"
+    )
+    def test_take_fill_str(self, arr1d):
+        # Cast str fill_value matching other fill_value-taking methods
+        result = arr1d.take([-1, 1], allow_fill=True, fill_value=str(arr1d[-1]))
+        expected = arr1d[[-1, 1]]
+        tm.assert_equal(result, expected)
+
+        msg = f"value should be a '{arr1d._scalar_type.__name__}' or 'NaT'. Got"
+        with pytest.raises(TypeError, match=msg):
+            arr1d.take([-1, 1], allow_fill=True, fill_value="foo")
+
+    def test_concat_same_type(self, arr1d):
+        arr = arr1d
+        idx = self.index_cls(arr)
+        idx = idx.insert(0, NaT)
+        arr = arr1d
+
+        result = arr._concat_same_type([arr[:-1], arr[1:], arr])
+        arr2 = arr.astype(object)
+        expected = self.index_cls(np.concatenate([arr2[:-1], arr2[1:], arr2]))
+
+        tm.assert_index_equal(self.index_cls(result), expected)
+
+    def test_unbox_scalar(self, arr1d):
+        result = arr1d._unbox_scalar(arr1d[0])
+        expected = arr1d._ndarray.dtype.type
+        assert isinstance(result, expected)
+
+        result = arr1d._unbox_scalar(NaT)
+        assert isinstance(result, expected)
+
+        msg = f"'value' should be a {self.scalar_type.__name__}."
+        with pytest.raises(ValueError, match=msg):
+            arr1d._unbox_scalar("foo")
+
+    def test_check_compatible_with(self, arr1d):
+        arr1d._check_compatible_with(arr1d[0])
+        arr1d._check_compatible_with(arr1d[:1])
+        arr1d._check_compatible_with(NaT)
+
+    def test_scalar_from_string(self, arr1d):
+        result = arr1d._scalar_from_string(str(arr1d[0]))
+        assert result == arr1d[0]
+
+    def test_reduce_invalid(self, arr1d):
+        msg = "does not support reduction 'not a method'"
+        with pytest.raises(TypeError, match=msg):
+            arr1d._reduce("not a method")
+
+    @pytest.mark.parametrize("method", ["pad", "backfill"])
+    def test_fillna_method_doesnt_change_orig(self, method):
+        data = np.arange(10, dtype="i8") * 24 * 3600 * 10**9
+        if self.array_cls is PeriodArray:
+            arr = self.array_cls(data, dtype="period[D]")
+        else:
+            arr = self.array_cls._from_sequence(data)
+        arr[4] = NaT
+
+        fill_value = arr[3] if method == "pad" else arr[5]
+
+        result = arr._pad_or_backfill(method=method)
+        assert result[4] == fill_value
+
+        # check that the original was not changed
+        assert arr[4] is NaT
+
+    def test_searchsorted(self):
+        data = np.arange(10, dtype="i8") * 24 * 3600 * 10**9
+        if self.array_cls is PeriodArray:
+            arr = self.array_cls(data, dtype="period[D]")
+        else:
+            arr = self.array_cls._from_sequence(data)
+
+        # scalar
+        result = arr.searchsorted(arr[1])
+        assert result == 1
+
+        result = arr.searchsorted(arr[2], side="right")
+        assert result == 3
+
+        # own-type
+        result = arr.searchsorted(arr[1:3])
+        expected = np.array([1, 2], dtype=np.intp)
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = arr.searchsorted(arr[1:3], side="right")
+        expected = np.array([2, 3], dtype=np.intp)
+        tm.assert_numpy_array_equal(result, expected)
+
+        # GH#29884 match numpy convention on whether NaT goes
+        #  at the end or the beginning
+        result = arr.searchsorted(NaT)
+        assert result == 10
+
+    @pytest.mark.parametrize("box", [None, "index", "series"])
+    def test_searchsorted_castable_strings(self, arr1d, box, string_storage):
+        arr = arr1d
+        if box is None:
+            pass
+        elif box == "index":
+            # Test the equivalent Index.searchsorted method while we're here
+            arr = self.index_cls(arr)
+        else:
+            # Test the equivalent Series.searchsorted method while we're here
+            arr = pd.Series(arr)
+
+        # scalar
+        result = arr.searchsorted(str(arr[1]))
+        assert result == 1
+
+        result = arr.searchsorted(str(arr[2]), side="right")
+        assert result == 3
+
+        result = arr.searchsorted([str(x) for x in arr[1:3]])
+        expected = np.array([1, 2], dtype=np.intp)
+        tm.assert_numpy_array_equal(result, expected)
+
+        with pytest.raises(
+            TypeError,
+            match=re.escape(
+                f"value should be a '{arr1d._scalar_type.__name__}', 'NaT', "
+                "or array of those. Got 'str' instead."
+            ),
+        ):
+            arr.searchsorted("foo")
+
+        with pd.option_context("string_storage", string_storage):
+            with pytest.raises(
+                TypeError,
+                match=re.escape(
+                    f"value should be a '{arr1d._scalar_type.__name__}', 'NaT', "
+                    "or array of those. Got string array instead."
+                ),
+            ):
+                arr.searchsorted([str(arr[1]), "baz"])
+
+    def test_getitem_near_implementation_bounds(self):
+        # We only check tz-naive for DTA bc the bounds are slightly different
+        #  for other tzs
+        i8vals = np.asarray([NaT._value + n for n in range(1, 5)], dtype="i8")
+        if self.array_cls is PeriodArray:
+            arr = self.array_cls(i8vals, dtype="period[ns]")
+        else:
+            arr = self.index_cls(i8vals, freq="ns")._data
+        arr[0]  # should not raise OutOfBoundsDatetime
+
+        index = pd.Index(arr)
+        index[0]  # should not raise OutOfBoundsDatetime
+
+        ser = pd.Series(arr)
+        ser[0]  # should not raise OutOfBoundsDatetime
+
+    def test_getitem_2d(self, arr1d):
+        # 2d slicing on a 1D array
+        expected = type(arr1d)._simple_new(
+            arr1d._ndarray[:, np.newaxis], dtype=arr1d.dtype
+        )
+        result = arr1d[:, np.newaxis]
+        tm.assert_equal(result, expected)
+
+        # Lookup on a 2D array
+        arr2d = expected
+        expected = type(arr2d)._simple_new(arr2d._ndarray[:3, 0], dtype=arr2d.dtype)
+        result = arr2d[:3, 0]
+        tm.assert_equal(result, expected)
+
+        # Scalar lookup
+        result = arr2d[-1, 0]
+        expected = arr1d[-1]
+        assert result == expected
+
+    def test_iter_2d(self, arr1d):
+        data2d = arr1d._ndarray[:3, np.newaxis]
+        arr2d = type(arr1d)._simple_new(data2d, dtype=arr1d.dtype)
+        result = list(arr2d)
+        assert len(result) == 3
+        for x in result:
+            assert isinstance(x, type(arr1d))
+            assert x.ndim == 1
+            assert x.dtype == arr1d.dtype
+
+    def test_repr_2d(self, arr1d):
+        data2d = arr1d._ndarray[:3, np.newaxis]
+        arr2d = type(arr1d)._simple_new(data2d, dtype=arr1d.dtype)
+
+        result = repr(arr2d)
+
+        if isinstance(arr2d, TimedeltaArray):
+            expected = (
+                f"<{type(arr2d).__name__}>\n"
+                "[\n"
+                f"['{arr1d[0]._repr_base()}'],\n"
+                f"['{arr1d[1]._repr_base()}'],\n"
+                f"['{arr1d[2]._repr_base()}']\n"
+                "]\n"
+                f"Shape: (3, 1), dtype: {arr1d.dtype}"
+            )
+        else:
+            expected = (
+                f"<{type(arr2d).__name__}>\n"
+                "[\n"
+                f"['{arr1d[0]}'],\n"
+                f"['{arr1d[1]}'],\n"
+                f"['{arr1d[2]}']\n"
+                "]\n"
+                f"Shape: (3, 1), dtype: {arr1d.dtype}"
+            )
+
+        assert result == expected
+
+    def test_setitem(self):
+        data = np.arange(10, dtype="i8") * 24 * 3600 * 10**9
+        if self.array_cls is PeriodArray:
+            arr = self.array_cls(data, dtype="period[D]")
+        else:
+            arr = self.index_cls(data, freq="D")._data
+
+        arr[0] = arr[1]
+        expected = np.arange(10, dtype="i8") * 24 * 3600 * 10**9
+        expected[0] = expected[1]
+
+        tm.assert_numpy_array_equal(arr.asi8, expected)
+
+        arr[:2] = arr[-2:]
+        expected[:2] = expected[-2:]
+        tm.assert_numpy_array_equal(arr.asi8, expected)
+
+    @pytest.mark.parametrize(
+        "box",
+        [
+            pd.Index,
+            pd.Series,
+            np.array,
+            list,
+            NumpyExtensionArray,
+        ],
+    )
+    def test_setitem_object_dtype(self, box, arr1d):
+        expected = arr1d.copy()[::-1]
+        if expected.dtype.kind in ["m", "M"]:
+            expected = expected._with_freq(None)
+
+        vals = expected
+        if box is list:
+            vals = list(vals)
+        elif box is np.array:
+            # if we do np.array(x).astype(object) then dt64 and td64 cast to ints
+            vals = np.array(vals.astype(object))
+        elif box is NumpyExtensionArray:
+            vals = box(np.asarray(vals, dtype=object))
+        else:
+            vals = box(vals).astype(object)
+
+        arr1d[:] = vals
+
+        tm.assert_equal(arr1d, expected)
+
+    def test_setitem_strs(self, arr1d):
+        # Check that we parse strs in both scalar and listlike
+
+        # Setting list-like of strs
+        expected = arr1d.copy()
+        expected[[0, 1]] = arr1d[-2:]
+
+        result = arr1d.copy()
+        result[:2] = [str(x) for x in arr1d[-2:]]
+        tm.assert_equal(result, expected)
+
+        # Same thing but now for just a scalar str
+        expected = arr1d.copy()
+        expected[0] = arr1d[-1]
+
+        result = arr1d.copy()
+        result[0] = str(arr1d[-1])
+        tm.assert_equal(result, expected)
+
+    @pytest.mark.parametrize("as_index", [True, False])
+    def test_setitem_categorical(self, arr1d, as_index):
+        expected = arr1d.copy()[::-1]
+        if not isinstance(expected, PeriodArray):
+            expected = expected._with_freq(None)
+
+        cat = pd.Categorical(arr1d)
+        if as_index:
+            cat = pd.CategoricalIndex(cat)
+
+        arr1d[:] = cat[::-1]
+
+        tm.assert_equal(arr1d, expected)
+
+    def test_setitem_raises(self, arr1d):
+        arr = arr1d[:10]
+        val = arr[0]
+
+        with pytest.raises(IndexError, match="index 12 is out of bounds"):
+            arr[12] = val
+
+        with pytest.raises(TypeError, match="value should be a.* 'object'"):
+            arr[0] = object()
+
+        msg = "cannot set using a list-like indexer with a different length"
+        with pytest.raises(ValueError, match=msg):
+            # GH#36339
+            arr[[]] = [arr[1]]
+
+        msg = "cannot set using a slice indexer with a different length than"
+        with pytest.raises(ValueError, match=msg):
+            # GH#36339
+            arr[1:1] = arr[:3]
+
+    @pytest.mark.parametrize("box", [list, np.array, pd.Index, pd.Series])
+    def test_setitem_numeric_raises(self, arr1d, box):
+        # We dont case e.g. int64 to our own dtype for setitem
+
+        msg = (
+            f"value should be a '{arr1d._scalar_type.__name__}', "
+            "'NaT', or array of those. Got"
+        )
+        with pytest.raises(TypeError, match=msg):
+            arr1d[:2] = box([0, 1])
+
+        with pytest.raises(TypeError, match=msg):
+            arr1d[:2] = box([0.0, 1.0])
+
+    def test_inplace_arithmetic(self):
+        # GH#24115 check that iadd and isub are actually in-place
+        data = np.arange(10, dtype="i8") * 24 * 3600 * 10**9
+        if self.array_cls is PeriodArray:
+            arr = self.array_cls(data, dtype="period[D]")
+        else:
+            arr = self.index_cls(data, freq="D")._data
+
+        expected = arr + pd.Timedelta(days=1)
+        arr += pd.Timedelta(days=1)
+        tm.assert_equal(arr, expected)
+
+        expected = arr - pd.Timedelta(days=1)
+        arr -= pd.Timedelta(days=1)
+        tm.assert_equal(arr, expected)
+
+    def test_shift_fill_int_deprecated(self, arr1d):
+        # GH#31971, enforced in 2.0
+        with pytest.raises(TypeError, match="value should be a"):
+            arr1d.shift(1, fill_value=1)
+
+    def test_median(self, arr1d):
+        arr = arr1d
+        if len(arr) % 2 == 0:
+            # make it easier to define `expected`
+            arr = arr[:-1]
+
+        expected = arr[len(arr) // 2]
+
+        result = arr.median()
+        assert type(result) is type(expected)
+        assert result == expected
+
+        arr[len(arr) // 2] = NaT
+        if not isinstance(expected, Period):
+            expected = arr[len(arr) // 2 - 1 : len(arr) // 2 + 2].mean()
+
+        assert arr.median(skipna=False) is NaT
+
+        result = arr.median()
+        assert type(result) is type(expected)
+        assert result == expected
+
+        assert arr[:0].median() is NaT
+        assert arr[:0].median(skipna=False) is NaT
+
+        # 2d Case
+        arr2 = arr.reshape(-1, 1)
+
+        result = arr2.median(axis=None)
+        assert type(result) is type(expected)
+        assert result == expected
+
+        assert arr2.median(axis=None, skipna=False) is NaT
+
+        result = arr2.median(axis=0)
+        expected2 = type(arr)._from_sequence([expected], dtype=arr.dtype)
+        tm.assert_equal(result, expected2)
+
+        result = arr2.median(axis=0, skipna=False)
+        expected2 = type(arr)._from_sequence([NaT], dtype=arr.dtype)
+        tm.assert_equal(result, expected2)
+
+        result = arr2.median(axis=1)
+        tm.assert_equal(result, arr)
+
+        result = arr2.median(axis=1, skipna=False)
+        tm.assert_equal(result, arr)
+
+    def test_from_integer_array(self):
+        arr = np.array([1, 2, 3], dtype=np.int64)
+        data = pd.array(arr, dtype="Int64")
+        if self.array_cls is PeriodArray:
+            expected = self.array_cls(arr, dtype=self.example_dtype)
+            result = self.array_cls(data, dtype=self.example_dtype)
+        else:
+            expected = self.array_cls._from_sequence(arr, dtype=self.example_dtype)
+            result = self.array_cls._from_sequence(data, dtype=self.example_dtype)
+
+        tm.assert_extension_array_equal(result, expected)
+
+
+class TestDatetimeArray(SharedTests):
+    index_cls = DatetimeIndex
+    array_cls = DatetimeArray
+    scalar_type = Timestamp
+    example_dtype = "M8[ns]"
+
+    @pytest.fixture
+    def arr1d(self, tz_naive_fixture, freqstr):
+        """
+        Fixture returning DatetimeArray with parametrized frequency and
+        timezones
+        """
+        tz = tz_naive_fixture
+        dti = pd.date_range("2016-01-01 01:01:00", periods=5, freq=freqstr, tz=tz)
+        dta = dti._data
+        return dta
+
+    def test_round(self, arr1d):
+        # GH#24064
+        dti = self.index_cls(arr1d)
+
+        result = dti.round(freq="2min")
+        expected = dti - pd.Timedelta(minutes=1)
+        expected = expected._with_freq(None)
+        tm.assert_index_equal(result, expected)
+
+        dta = dti._data
+        result = dta.round(freq="2min")
+        expected = expected._data._with_freq(None)
+        tm.assert_datetime_array_equal(result, expected)
+
+    def test_array_interface(self, datetime_index):
+        arr = datetime_index._data
+        copy_false = None if np_version_gt2 else False
+
+        # default asarray gives the same underlying data (for tz naive)
+        result = np.asarray(arr)
+        expected = arr._ndarray
+        assert result is expected
+        tm.assert_numpy_array_equal(result, expected)
+        result = np.array(arr, copy=copy_false)
+        assert result is expected
+        tm.assert_numpy_array_equal(result, expected)
+
+        # specifying M8[ns] gives the same result as default
+        result = np.asarray(arr, dtype="datetime64[ns]")
+        expected = arr._ndarray
+        assert result is expected
+        tm.assert_numpy_array_equal(result, expected)
+        result = np.array(arr, dtype="datetime64[ns]", copy=copy_false)
+        assert result is expected
+        tm.assert_numpy_array_equal(result, expected)
+        result = np.array(arr, dtype="datetime64[ns]")
+        if not np_version_gt2:
+            # TODO: GH 57739
+            assert result is not expected
+        tm.assert_numpy_array_equal(result, expected)
+
+        # to object dtype
+        result = np.asarray(arr, dtype=object)
+        expected = np.array(list(arr), dtype=object)
+        tm.assert_numpy_array_equal(result, expected)
+
+        # to other dtype always copies
+        result = np.asarray(arr, dtype="int64")
+        assert result is not arr.asi8
+        assert not np.may_share_memory(arr, result)
+        expected = arr.asi8.copy()
+        tm.assert_numpy_array_equal(result, expected)
+
+        # other dtypes handled by numpy
+        for dtype in ["float64", str]:
+            result = np.asarray(arr, dtype=dtype)
+            expected = np.asarray(arr).astype(dtype)
+            tm.assert_numpy_array_equal(result, expected)
+
+    def test_array_object_dtype(self, arr1d):
+        # GH#23524
+        arr = arr1d
+        dti = self.index_cls(arr1d)
+
+        expected = np.array(list(dti))
+
+        result = np.array(arr, dtype=object)
+        tm.assert_numpy_array_equal(result, expected)
+
+        # also test the DatetimeIndex method while we're at it
+        result = np.array(dti, dtype=object)
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_array_tz(self, arr1d):
+        # GH#23524
+        arr = arr1d
+        dti = self.index_cls(arr1d)
+        copy_false = None if np_version_gt2 else False
+
+        expected = dti.asi8.view("M8[ns]")
+        result = np.array(arr, dtype="M8[ns]")
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = np.array(arr, dtype="datetime64[ns]")
+        tm.assert_numpy_array_equal(result, expected)
+
+        # check that we are not making copies when setting copy=copy_false
+        result = np.array(arr, dtype="M8[ns]", copy=copy_false)
+        assert result.base is expected.base
+        assert result.base is not None
+        result = np.array(arr, dtype="datetime64[ns]", copy=copy_false)
+        assert result.base is expected.base
+        assert result.base is not None
+
+    def test_array_i8_dtype(self, arr1d):
+        arr = arr1d
+        dti = self.index_cls(arr1d)
+        copy_false = None if np_version_gt2 else False
+
+        expected = dti.asi8
+        result = np.array(arr, dtype="i8")
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = np.array(arr, dtype=np.int64)
+        tm.assert_numpy_array_equal(result, expected)
+
+        # check that we are still making copies when setting copy=copy_false
+        result = np.array(arr, dtype="i8", copy=copy_false)
+        assert result.base is not expected.base
+        assert result.base is None
+
+    def test_from_array_keeps_base(self):
+        # Ensure that DatetimeArray._ndarray.base isn't lost.
+        arr = np.array(["2000-01-01", "2000-01-02"], dtype="M8[ns]")
+        dta = DatetimeArray._from_sequence(arr)
+
+        assert dta._ndarray is arr
+        dta = DatetimeArray._from_sequence(arr[:0])
+        assert dta._ndarray.base is arr
+
+    def test_from_dti(self, arr1d):
+        arr = arr1d
+        dti = self.index_cls(arr1d)
+        assert list(dti) == list(arr)
+
+        # Check that Index.__new__ knows what to do with DatetimeArray
+        dti2 = pd.Index(arr)
+        assert isinstance(dti2, DatetimeIndex)
+        assert list(dti2) == list(arr)
+
+    def test_astype_object(self, arr1d):
+        arr = arr1d
+        dti = self.index_cls(arr1d)
+
+        asobj = arr.astype("O")
+        assert isinstance(asobj, np.ndarray)
+        assert asobj.dtype == "O"
+        assert list(asobj) == list(dti)
+
+    @pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning")
+    def test_to_period(self, datetime_index, freqstr):
+        dti = datetime_index
+        arr = dti._data
+
+        freqstr = freq_to_period_freqstr(1, freqstr)
+        expected = dti.to_period(freq=freqstr)
+        result = arr.to_period(freq=freqstr)
+        assert isinstance(result, PeriodArray)
+
+        tm.assert_equal(result, expected._data)
+
+    def test_to_period_2d(self, arr1d):
+        arr2d = arr1d.reshape(1, -1)
+
+        warn = None if arr1d.tz is None else UserWarning
+        with tm.assert_produces_warning(warn):
+            result = arr2d.to_period("D")
+            expected = arr1d.to_period("D").reshape(1, -1)
+        tm.assert_period_array_equal(result, expected)
+
+    @pytest.mark.parametrize("propname", DatetimeArray._bool_ops)
+    def test_bool_properties(self, arr1d, propname):
+        # in this case _bool_ops is just `is_leap_year`
+        dti = self.index_cls(arr1d)
+        arr = arr1d
+        assert dti.freq == arr.freq
+
+        result = getattr(arr, propname)
+        expected = np.array(getattr(dti, propname), dtype=result.dtype)
+
+        tm.assert_numpy_array_equal(result, expected)
+
+    @pytest.mark.parametrize("propname", DatetimeArray._field_ops)
+    def test_int_properties(self, arr1d, propname):
+        dti = self.index_cls(arr1d)
+        arr = arr1d
+
+        result = getattr(arr, propname)
+        expected = np.array(getattr(dti, propname), dtype=result.dtype)
+
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_take_fill_valid(self, arr1d, fixed_now_ts):
+        arr = arr1d
+        dti = self.index_cls(arr1d)
+
+        now = fixed_now_ts.tz_localize(dti.tz)
+        result = arr.take([-1, 1], allow_fill=True, fill_value=now)
+        assert result[0] == now
+
+        msg = f"value should be a '{arr1d._scalar_type.__name__}' or 'NaT'. Got"
+        with pytest.raises(TypeError, match=msg):
+            # fill_value Timedelta invalid
+            arr.take([-1, 1], allow_fill=True, fill_value=now - now)
+
+        with pytest.raises(TypeError, match=msg):
+            # fill_value Period invalid
+            arr.take([-1, 1], allow_fill=True, fill_value=Period("2014Q1"))
+
+        tz = None if dti.tz is not None else "US/Eastern"
+        now = fixed_now_ts.tz_localize(tz)
+        msg = "Cannot compare tz-naive and tz-aware datetime-like objects"
+        with pytest.raises(TypeError, match=msg):
+            # Timestamp with mismatched tz-awareness
+            arr.take([-1, 1], allow_fill=True, fill_value=now)
+
+        value = NaT._value
+        msg = f"value should be a '{arr1d._scalar_type.__name__}' or 'NaT'. Got"
+        with pytest.raises(TypeError, match=msg):
+            # require NaT, not iNaT, as it could be confused with an integer
+            arr.take([-1, 1], allow_fill=True, fill_value=value)
+
+        value = np.timedelta64("NaT", "ns")
+        with pytest.raises(TypeError, match=msg):
+            # require appropriate-dtype if we have a NA value
+            arr.take([-1, 1], allow_fill=True, fill_value=value)
+
+        if arr.tz is not None:
+            # GH#37356
+            # Assuming here that arr1d fixture does not include Australia/Melbourne
+            value = fixed_now_ts.tz_localize("Australia/Melbourne")
+            result = arr.take([-1, 1], allow_fill=True, fill_value=value)
+
+            expected = arr.take(
+                [-1, 1],
+                allow_fill=True,
+                fill_value=value.tz_convert(arr.dtype.tz),
+            )
+            tm.assert_equal(result, expected)
+
+    def test_concat_same_type_invalid(self, arr1d):
+        # different timezones
+        arr = arr1d
+
+        if arr.tz is None:
+            other = arr.tz_localize("UTC")
+        else:
+            other = arr.tz_localize(None)
+
+        with pytest.raises(ValueError, match="to_concat must have the same"):
+            arr._concat_same_type([arr, other])
+
+    def test_concat_same_type_different_freq(self, unit):
+        # we *can* concatenate DTI with different freqs.
+        a = pd.date_range("2000", periods=2, freq="D", tz="US/Central", unit=unit)._data
+        b = pd.date_range("2000", periods=2, freq="h", tz="US/Central", unit=unit)._data
+        result = DatetimeArray._concat_same_type([a, b])
+        expected = (
+            pd.to_datetime(
+                [
+                    "2000-01-01 00:00:00",
+                    "2000-01-02 00:00:00",
+                    "2000-01-01 00:00:00",
+                    "2000-01-01 01:00:00",
+                ]
+            )
+            .tz_localize("US/Central")
+            .as_unit(unit)
+            ._data
+        )
+
+        tm.assert_datetime_array_equal(result, expected)
+
+    def test_strftime(self, arr1d, using_infer_string):
+        arr = arr1d
+
+        result = arr.strftime("%Y %b")
+        expected = np.array([ts.strftime("%Y %b") for ts in arr], dtype=object)
+        if using_infer_string:
+            expected = pd.array(expected, dtype=pd.StringDtype(na_value=np.nan))
+        tm.assert_equal(result, expected)
+
+    def test_strftime_nat(self, using_infer_string):
+        # GH 29578
+        arr = DatetimeIndex(["2019-01-01", NaT])._data
+
+        result = arr.strftime("%Y-%m-%d")
+        expected = np.array(["2019-01-01", np.nan], dtype=object)
+        if using_infer_string:
+            expected = pd.array(expected, dtype=pd.StringDtype(na_value=np.nan))
+        tm.assert_equal(result, expected)
+
+
+class TestTimedeltaArray(SharedTests):
+    index_cls = TimedeltaIndex
+    array_cls = TimedeltaArray
+    scalar_type = pd.Timedelta
+    example_dtype = "m8[ns]"
+
+    def test_from_tdi(self):
+        tdi = TimedeltaIndex(["1 Day", "3 Hours"])
+        arr = tdi._data
+        assert list(arr) == list(tdi)
+
+        # Check that Index.__new__ knows what to do with TimedeltaArray
+        tdi2 = pd.Index(arr)
+        assert isinstance(tdi2, TimedeltaIndex)
+        assert list(tdi2) == list(arr)
+
+    def test_astype_object(self):
+        tdi = TimedeltaIndex(["1 Day", "3 Hours"])
+        arr = tdi._data
+        asobj = arr.astype("O")
+        assert isinstance(asobj, np.ndarray)
+        assert asobj.dtype == "O"
+        assert list(asobj) == list(tdi)
+
+    def test_to_pytimedelta(self, timedelta_index):
+        tdi = timedelta_index
+        arr = tdi._data
+
+        expected = tdi.to_pytimedelta()
+        result = arr.to_pytimedelta()
+
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_total_seconds(self, timedelta_index):
+        tdi = timedelta_index
+        arr = tdi._data
+
+        expected = tdi.total_seconds()
+        result = arr.total_seconds()
+
+        tm.assert_numpy_array_equal(result, expected.values)
+
+    @pytest.mark.parametrize("propname", TimedeltaArray._field_ops)
+    def test_int_properties(self, timedelta_index, propname):
+        tdi = timedelta_index
+        arr = tdi._data
+
+        result = getattr(arr, propname)
+        expected = np.array(getattr(tdi, propname), dtype=result.dtype)
+
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_array_interface(self, timedelta_index):
+        arr = timedelta_index._data
+        copy_false = None if np_version_gt2 else False
+
+        # default asarray gives the same underlying data
+        result = np.asarray(arr)
+        expected = arr._ndarray
+        assert result is expected
+        tm.assert_numpy_array_equal(result, expected)
+        result = np.array(arr, copy=copy_false)
+        assert result is expected
+        tm.assert_numpy_array_equal(result, expected)
+
+        # specifying m8[ns] gives the same result as default
+        result = np.asarray(arr, dtype="timedelta64[ns]")
+        expected = arr._ndarray
+        assert result is expected
+        tm.assert_numpy_array_equal(result, expected)
+        result = np.array(arr, dtype="timedelta64[ns]", copy=copy_false)
+        assert result is expected
+        tm.assert_numpy_array_equal(result, expected)
+        result = np.array(arr, dtype="timedelta64[ns]")
+        if not np_version_gt2:
+            # TODO: GH 57739
+            assert result is not expected
+        tm.assert_numpy_array_equal(result, expected)
+
+        # to object dtype
+        result = np.asarray(arr, dtype=object)
+        expected = np.array(list(arr), dtype=object)
+        tm.assert_numpy_array_equal(result, expected)
+
+        # to other dtype always copies
+        result = np.asarray(arr, dtype="int64")
+        assert result is not arr.asi8
+        assert not np.may_share_memory(arr, result)
+        expected = arr.asi8.copy()
+        tm.assert_numpy_array_equal(result, expected)
+
+        # other dtypes handled by numpy
+        for dtype in ["float64", str]:
+            result = np.asarray(arr, dtype=dtype)
+            expected = np.asarray(arr).astype(dtype)
+            tm.assert_numpy_array_equal(result, expected)
+
+    def test_take_fill_valid(self, timedelta_index, fixed_now_ts):
+        tdi = timedelta_index
+        arr = tdi._data
+
+        td1 = pd.Timedelta(days=1)
+        result = arr.take([-1, 1], allow_fill=True, fill_value=td1)
+        assert result[0] == td1
+
+        value = fixed_now_ts
+        msg = f"value should be a '{arr._scalar_type.__name__}' or 'NaT'. Got"
+        with pytest.raises(TypeError, match=msg):
+            # fill_value Timestamp invalid
+            arr.take([0, 1], allow_fill=True, fill_value=value)
+
+        value = fixed_now_ts.to_period("D")
+        with pytest.raises(TypeError, match=msg):
+            # fill_value Period invalid
+            arr.take([0, 1], allow_fill=True, fill_value=value)
+
+        value = np.datetime64("NaT", "ns")
+        with pytest.raises(TypeError, match=msg):
+            # require appropriate-dtype if we have a NA value
+            arr.take([-1, 1], allow_fill=True, fill_value=value)
+
+
+@pytest.mark.filterwarnings(r"ignore:Period with BDay freq is deprecated:FutureWarning")
+@pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning")
+class TestPeriodArray(SharedTests):
+    index_cls = PeriodIndex
+    array_cls = PeriodArray
+    scalar_type = Period
+    example_dtype = PeriodIndex([], freq="W").dtype
+
+    @pytest.fixture
+    def arr1d(self, period_index):
+        """
+        Fixture returning DatetimeArray from parametrized PeriodIndex objects
+        """
+        return period_index._data
+
+    def test_from_pi(self, arr1d):
+        pi = self.index_cls(arr1d)
+        arr = arr1d
+        assert list(arr) == list(pi)
+
+        # Check that Index.__new__ knows what to do with PeriodArray
+        pi2 = pd.Index(arr)
+        assert isinstance(pi2, PeriodIndex)
+        assert list(pi2) == list(arr)
+
+    def test_astype_object(self, arr1d):
+        pi = self.index_cls(arr1d)
+        arr = arr1d
+        asobj = arr.astype("O")
+        assert isinstance(asobj, np.ndarray)
+        assert asobj.dtype == "O"
+        assert list(asobj) == list(pi)
+
+    def test_take_fill_valid(self, arr1d):
+        arr = arr1d
+
+        value = NaT._value
+        msg = f"value should be a '{arr1d._scalar_type.__name__}' or 'NaT'. Got"
+        with pytest.raises(TypeError, match=msg):
+            # require NaT, not iNaT, as it could be confused with an integer
+            arr.take([-1, 1], allow_fill=True, fill_value=value)
+
+        value = np.timedelta64("NaT", "ns")
+        with pytest.raises(TypeError, match=msg):
+            # require appropriate-dtype if we have a NA value
+            arr.take([-1, 1], allow_fill=True, fill_value=value)
+
+    @pytest.mark.parametrize("how", ["S", "E"])
+    def test_to_timestamp(self, how, arr1d):
+        pi = self.index_cls(arr1d)
+        arr = arr1d
+
+        expected = DatetimeIndex(pi.to_timestamp(how=how))._data
+        result = arr.to_timestamp(how=how)
+        assert isinstance(result, DatetimeArray)
+
+        tm.assert_equal(result, expected)
+
+    def test_to_timestamp_roundtrip_bday(self):
+        # Case where infer_freq inside would choose "D" instead of "B"
+        dta = pd.date_range("2021-10-18", periods=3, freq="B")._data
+        parr = dta.to_period()
+        result = parr.to_timestamp()
+        assert result.freq == "B"
+        tm.assert_extension_array_equal(result, dta)
+
+        dta2 = dta[::2]
+        parr2 = dta2.to_period()
+        result2 = parr2.to_timestamp()
+        assert result2.freq == "2B"
+        tm.assert_extension_array_equal(result2, dta2)
+
+        parr3 = dta.to_period("2B")
+        result3 = parr3.to_timestamp()
+        assert result3.freq == "B"
+        tm.assert_extension_array_equal(result3, dta)
+
+    def test_to_timestamp_out_of_bounds(self):
+        # GH#19643 previously overflowed silently
+        pi = pd.period_range("1500", freq="Y", periods=3)
+        msg = "Out of bounds nanosecond timestamp: 1500-01-01 00:00:00"
+        with pytest.raises(OutOfBoundsDatetime, match=msg):
+            pi.to_timestamp()
+
+        with pytest.raises(OutOfBoundsDatetime, match=msg):
+            pi._data.to_timestamp()
+
+    @pytest.mark.parametrize("propname", PeriodArray._bool_ops)
+    def test_bool_properties(self, arr1d, propname):
+        # in this case _bool_ops is just `is_leap_year`
+        pi = self.index_cls(arr1d)
+        arr = arr1d
+
+        result = getattr(arr, propname)
+        expected = np.array(getattr(pi, propname))
+
+        tm.assert_numpy_array_equal(result, expected)
+
+    @pytest.mark.parametrize("propname", PeriodArray._field_ops)
+    def test_int_properties(self, arr1d, propname):
+        pi = self.index_cls(arr1d)
+        arr = arr1d
+
+        result = getattr(arr, propname)
+        expected = np.array(getattr(pi, propname))
+
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_array_interface(self, arr1d):
+        arr = arr1d
+
+        # default asarray gives objects
+        result = np.asarray(arr)
+        expected = np.array(list(arr), dtype=object)
+        tm.assert_numpy_array_equal(result, expected)
+
+        # to object dtype (same as default)
+        result = np.asarray(arr, dtype=object)
+        tm.assert_numpy_array_equal(result, expected)
+
+        # to int64 gives the underlying representation
+        result = np.asarray(arr, dtype="int64")
+        tm.assert_numpy_array_equal(result, arr.asi8)
+
+        result2 = np.asarray(arr, dtype="int64")
+        assert np.may_share_memory(result, result2)
+
+        result_copy1 = np.array(arr, dtype="int64", copy=True)
+        result_copy2 = np.array(arr, dtype="int64", copy=True)
+        assert not np.may_share_memory(result_copy1, result_copy2)
+
+        # to other dtypes
+        msg = r"float\(\) argument must be a string or a( real)? number, not 'Period'"
+        with pytest.raises(TypeError, match=msg):
+            np.asarray(arr, dtype="float64")
+
+        result = np.asarray(arr, dtype="S20")
+        expected = np.asarray(arr).astype("S20")
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_strftime(self, arr1d, using_infer_string):
+        arr = arr1d
+
+        result = arr.strftime("%Y")
+        expected = np.array([per.strftime("%Y") for per in arr], dtype=object)
+        if using_infer_string:
+            expected = pd.array(expected, dtype=pd.StringDtype(na_value=np.nan))
+        tm.assert_equal(result, expected)
+
+    def test_strftime_nat(self, using_infer_string):
+        # GH 29578
+        arr = PeriodArray(PeriodIndex(["2019-01-01", NaT], dtype="period[D]"))
+
+        result = arr.strftime("%Y-%m-%d")
+        expected = np.array(["2019-01-01", np.nan], dtype=object)
+        if using_infer_string:
+            expected = pd.array(expected, dtype=pd.StringDtype(na_value=np.nan))
+        tm.assert_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "arr,casting_nats",
+    [
+        (
+            TimedeltaIndex(["1 Day", "3 Hours", "NaT"])._data,
+            (NaT, np.timedelta64("NaT", "ns")),
+        ),
+        (
+            pd.date_range("2000-01-01", periods=3, freq="D")._data,
+            (NaT, np.datetime64("NaT", "ns")),
+        ),
+        (pd.period_range("2000-01-01", periods=3, freq="D")._data, (NaT,)),
+    ],
+    ids=lambda x: type(x).__name__,
+)
+def test_casting_nat_setitem_array(arr, casting_nats):
+    expected = type(arr)._from_sequence([NaT, arr[1], arr[2]], dtype=arr.dtype)
+
+    for nat in casting_nats:
+        arr = arr.copy()
+        arr[0] = nat
+        tm.assert_equal(arr, expected)
+
+
+@pytest.mark.parametrize(
+    "arr,non_casting_nats",
+    [
+        (
+            TimedeltaIndex(["1 Day", "3 Hours", "NaT"])._data,
+            (np.datetime64("NaT", "ns"), NaT._value),
+        ),
+        (
+            pd.date_range("2000-01-01", periods=3, freq="D")._data,
+            (np.timedelta64("NaT", "ns"), NaT._value),
+        ),
+        (
+            pd.period_range("2000-01-01", periods=3, freq="D")._data,
+            (np.datetime64("NaT", "ns"), np.timedelta64("NaT", "ns"), NaT._value),
+        ),
+    ],
+    ids=lambda x: type(x).__name__,
+)
+def test_invalid_nat_setitem_array(arr, non_casting_nats):
+    msg = (
+        "value should be a '(Timestamp|Timedelta|Period)', 'NaT', or array of those. "
+        "Got '(timedelta64|datetime64|int)' instead."
+    )
+
+    for nat in non_casting_nats:
+        with pytest.raises(TypeError, match=msg):
+            arr[0] = nat
+
+
+@pytest.mark.parametrize(
+    "arr",
+    [
+        pd.date_range("2000", periods=4).array,
+        pd.timedelta_range("2000", periods=4).array,
+    ],
+)
+def test_to_numpy_extra(arr):
+    arr[0] = NaT
+    original = arr.copy()
+
+    result = arr.to_numpy()
+    assert np.isnan(result[0])
+
+    result = arr.to_numpy(dtype="int64")
+    assert result[0] == -9223372036854775808
+
+    result = arr.to_numpy(dtype="int64", na_value=0)
+    assert result[0] == 0
+
+    result = arr.to_numpy(na_value=arr[1].to_numpy())
+    assert result[0] == result[1]
+
+    result = arr.to_numpy(na_value=arr[1].to_numpy(copy=False))
+    assert result[0] == result[1]
+
+    tm.assert_equal(arr, original)
+
+
+@pytest.mark.parametrize("as_index", [True, False])
+@pytest.mark.parametrize(
+    "values",
+    [
+        pd.to_datetime(["2020-01-01", "2020-02-01"]),
+        pd.to_timedelta([1, 2], unit="D"),
+        PeriodIndex(["2020-01-01", "2020-02-01"], freq="D"),
+    ],
+)
+@pytest.mark.parametrize(
+    "klass",
+    [
+        list,
+        np.array,
+        pd.array,
+        pd.Series,
+        pd.Index,
+        pd.Categorical,
+        pd.CategoricalIndex,
+    ],
+)
+def test_searchsorted_datetimelike_with_listlike(values, klass, as_index):
+    # https://github.com/pandas-dev/pandas/issues/32762
+    if not as_index:
+        values = values._data
+
+    result = values.searchsorted(klass(values))
+    expected = np.array([0, 1], dtype=result.dtype)
+
+    tm.assert_numpy_array_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "values",
+    [
+        pd.to_datetime(["2020-01-01", "2020-02-01"]),
+        pd.to_timedelta([1, 2], unit="D"),
+        PeriodIndex(["2020-01-01", "2020-02-01"], freq="D"),
+    ],
+)
+@pytest.mark.parametrize(
+    "arg", [[1, 2], ["a", "b"], [Timestamp("2020-01-01", tz="Europe/London")] * 2]
+)
+def test_searchsorted_datetimelike_with_listlike_invalid_dtype(values, arg):
+    # https://github.com/pandas-dev/pandas/issues/32762
+    msg = "[Unexpected type|Cannot compare]"
+    with pytest.raises(TypeError, match=msg):
+        values.searchsorted(arg)
+
+
+@pytest.mark.parametrize("klass", [list, tuple, np.array, pd.Series])
+def test_period_index_construction_from_strings(klass):
+    # https://github.com/pandas-dev/pandas/issues/26109
+    strings = ["2020Q1", "2020Q2"] * 2
+    data = klass(strings)
+    result = PeriodIndex(data, freq="Q")
+    expected = PeriodIndex([Period(s) for s in strings])
+    tm.assert_index_equal(result, expected)
+
+
+@pytest.mark.parametrize("dtype", ["M8[ns]", "m8[ns]"])
+def test_from_pandas_array(dtype):
+    # GH#24615
+    data = np.array([1, 2, 3], dtype=dtype)
+    arr = NumpyExtensionArray(data)
+
+    cls = {"M8[ns]": DatetimeArray, "m8[ns]": TimedeltaArray}[dtype]
+
+    depr_msg = f"{cls.__name__}.__init__ is deprecated"
+    with tm.assert_produces_warning(FutureWarning, match=depr_msg):
+        result = cls(arr)
+        expected = cls(data)
+    tm.assert_extension_array_equal(result, expected)
+
+    result = cls._from_sequence(arr, dtype=dtype)
+    expected = cls._from_sequence(data, dtype=dtype)
+    tm.assert_extension_array_equal(result, expected)
+
+    func = {"M8[ns]": pd.to_datetime, "m8[ns]": pd.to_timedelta}[dtype]
+    result = func(arr).array
+    expected = func(data).array
+    tm.assert_equal(result, expected)
+
+    # Let's check the Indexes while we're here
+    idx_cls = {"M8[ns]": DatetimeIndex, "m8[ns]": TimedeltaIndex}[dtype]
+    result = idx_cls(arr)
+    expected = idx_cls(data)
+    tm.assert_index_equal(result, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/test_datetimes.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/test_datetimes.py
new file mode 100644
index 0000000000000000000000000000000000000000..8f0576cc65a2787edacdb1e377a02287d1caaff1
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/test_datetimes.py
@@ -0,0 +1,840 @@
+"""
+Tests for DatetimeArray
+"""
+from __future__ import annotations
+
+from datetime import timedelta
+import operator
+
+try:
+    from zoneinfo import ZoneInfo
+except ImportError:
+    # Cannot assign to a type
+    ZoneInfo = None  # type: ignore[misc, assignment]
+
+import numpy as np
+import pytest
+
+from pandas._libs.tslibs import tz_compare
+
+from pandas.core.dtypes.dtypes import DatetimeTZDtype
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.core.arrays import (
+    DatetimeArray,
+    TimedeltaArray,
+)
+
+
+class TestNonNano:
+    @pytest.fixture(params=["s", "ms", "us"])
+    def unit(self, request):
+        """Fixture returning parametrized time units"""
+        return request.param
+
+    @pytest.fixture
+    def dtype(self, unit, tz_naive_fixture):
+        tz = tz_naive_fixture
+        if tz is None:
+            return np.dtype(f"datetime64[{unit}]")
+        else:
+            return DatetimeTZDtype(unit=unit, tz=tz)
+
+    @pytest.fixture
+    def dta_dti(self, unit, dtype):
+        tz = getattr(dtype, "tz", None)
+
+        dti = pd.date_range("2016-01-01", periods=55, freq="D", tz=tz)
+        if tz is None:
+            arr = np.asarray(dti).astype(f"M8[{unit}]")
+        else:
+            arr = np.asarray(dti.tz_convert("UTC").tz_localize(None)).astype(
+                f"M8[{unit}]"
+            )
+
+        dta = DatetimeArray._simple_new(arr, dtype=dtype)
+        return dta, dti
+
+    @pytest.fixture
+    def dta(self, dta_dti):
+        dta, dti = dta_dti
+        return dta
+
+    def test_non_nano(self, unit, dtype):
+        arr = np.arange(5, dtype=np.int64).view(f"M8[{unit}]")
+        dta = DatetimeArray._simple_new(arr, dtype=dtype)
+
+        assert dta.dtype == dtype
+        assert dta[0].unit == unit
+        assert tz_compare(dta.tz, dta[0].tz)
+        assert (dta[0] == dta[:1]).all()
+
+    @pytest.mark.parametrize(
+        "field", DatetimeArray._field_ops + DatetimeArray._bool_ops
+    )
+    def test_fields(self, unit, field, dtype, dta_dti):
+        dta, dti = dta_dti
+
+        assert (dti == dta).all()
+
+        res = getattr(dta, field)
+        expected = getattr(dti._data, field)
+        tm.assert_numpy_array_equal(res, expected)
+
+    def test_normalize(self, unit):
+        dti = pd.date_range("2016-01-01 06:00:00", periods=55, freq="D")
+        arr = np.asarray(dti).astype(f"M8[{unit}]")
+
+        dta = DatetimeArray._simple_new(arr, dtype=arr.dtype)
+
+        assert not dta.is_normalized
+
+        # TODO: simplify once we can just .astype to other unit
+        exp = np.asarray(dti.normalize()).astype(f"M8[{unit}]")
+        expected = DatetimeArray._simple_new(exp, dtype=exp.dtype)
+
+        res = dta.normalize()
+        tm.assert_extension_array_equal(res, expected)
+
+    def test_simple_new_requires_match(self, unit):
+        arr = np.arange(5, dtype=np.int64).view(f"M8[{unit}]")
+        dtype = DatetimeTZDtype(unit, "UTC")
+
+        dta = DatetimeArray._simple_new(arr, dtype=dtype)
+        assert dta.dtype == dtype
+
+        wrong = DatetimeTZDtype("ns", "UTC")
+        with pytest.raises(AssertionError, match=""):
+            DatetimeArray._simple_new(arr, dtype=wrong)
+
+    def test_std_non_nano(self, unit):
+        dti = pd.date_range("2016-01-01", periods=55, freq="D")
+        arr = np.asarray(dti).astype(f"M8[{unit}]")
+
+        dta = DatetimeArray._simple_new(arr, dtype=arr.dtype)
+
+        # we should match the nano-reso std, but floored to our reso.
+        res = dta.std()
+        assert res._creso == dta._creso
+        assert res == dti.std().floor(unit)
+
+    @pytest.mark.filterwarnings("ignore:Converting to PeriodArray.*:UserWarning")
+    def test_to_period(self, dta_dti):
+        dta, dti = dta_dti
+        result = dta.to_period("D")
+        expected = dti._data.to_period("D")
+
+        tm.assert_extension_array_equal(result, expected)
+
+    def test_iter(self, dta):
+        res = next(iter(dta))
+        expected = dta[0]
+
+        assert type(res) is pd.Timestamp
+        assert res._value == expected._value
+        assert res._creso == expected._creso
+        assert res == expected
+
+    def test_astype_object(self, dta):
+        result = dta.astype(object)
+        assert all(x._creso == dta._creso for x in result)
+        assert all(x == y for x, y in zip(result, dta))
+
+    def test_to_pydatetime(self, dta_dti):
+        dta, dti = dta_dti
+
+        result = dta.to_pydatetime()
+        expected = dti.to_pydatetime()
+        tm.assert_numpy_array_equal(result, expected)
+
+    @pytest.mark.parametrize("meth", ["time", "timetz", "date"])
+    def test_time_date(self, dta_dti, meth):
+        dta, dti = dta_dti
+
+        result = getattr(dta, meth)
+        expected = getattr(dti, meth)
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_format_native_types(self, unit, dtype, dta_dti):
+        # In this case we should get the same formatted values with our nano
+        #  version dti._data as we do with the non-nano dta
+        dta, dti = dta_dti
+
+        res = dta._format_native_types()
+        exp = dti._data._format_native_types()
+        tm.assert_numpy_array_equal(res, exp)
+
+    def test_repr(self, dta_dti, unit):
+        dta, dti = dta_dti
+
+        assert repr(dta) == repr(dti._data).replace("[ns", f"[{unit}")
+
+    # TODO: tests with td64
+    def test_compare_mismatched_resolutions(self, comparison_op):
+        # comparison that numpy gets wrong bc of silent overflows
+        op = comparison_op
+
+        iinfo = np.iinfo(np.int64)
+        vals = np.array([iinfo.min, iinfo.min + 1, iinfo.max], dtype=np.int64)
+
+        # Construct so that arr2[1] < arr[1] < arr[2] < arr2[2]
+        arr = np.array(vals).view("M8[ns]")
+        arr2 = arr.view("M8[s]")
+
+        left = DatetimeArray._simple_new(arr, dtype=arr.dtype)
+        right = DatetimeArray._simple_new(arr2, dtype=arr2.dtype)
+
+        if comparison_op is operator.eq:
+            expected = np.array([False, False, False])
+        elif comparison_op is operator.ne:
+            expected = np.array([True, True, True])
+        elif comparison_op in [operator.lt, operator.le]:
+            expected = np.array([False, False, True])
+        else:
+            expected = np.array([False, True, False])
+
+        result = op(left, right)
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = op(left[1], right)
+        tm.assert_numpy_array_equal(result, expected)
+
+        if op not in [operator.eq, operator.ne]:
+            # check that numpy still gets this wrong; if it is fixed we may be
+            #  able to remove compare_mismatched_resolutions
+            np_res = op(left._ndarray, right._ndarray)
+            tm.assert_numpy_array_equal(np_res[1:], ~expected[1:])
+
+    def test_add_mismatched_reso_doesnt_downcast(self):
+        # https://github.com/pandas-dev/pandas/pull/48748#issuecomment-1260181008
+        td = pd.Timedelta(microseconds=1)
+        dti = pd.date_range("2016-01-01", periods=3) - td
+        dta = dti._data.as_unit("us")
+
+        res = dta + td.as_unit("us")
+        # even though the result is an even number of days
+        #  (so we _could_ downcast to unit="s"), we do not.
+        assert res.unit == "us"
+
+    @pytest.mark.parametrize(
+        "scalar",
+        [
+            timedelta(hours=2),
+            pd.Timedelta(hours=2),
+            np.timedelta64(2, "h"),
+            np.timedelta64(2 * 3600 * 1000, "ms"),
+            pd.offsets.Minute(120),
+            pd.offsets.Hour(2),
+        ],
+    )
+    def test_add_timedeltalike_scalar_mismatched_reso(self, dta_dti, scalar):
+        dta, dti = dta_dti
+
+        td = pd.Timedelta(scalar)
+        exp_unit = tm.get_finest_unit(dta.unit, td.unit)
+
+        expected = (dti + td)._data.as_unit(exp_unit)
+        result = dta + scalar
+        tm.assert_extension_array_equal(result, expected)
+
+        result = scalar + dta
+        tm.assert_extension_array_equal(result, expected)
+
+        expected = (dti - td)._data.as_unit(exp_unit)
+        result = dta - scalar
+        tm.assert_extension_array_equal(result, expected)
+
+    def test_sub_datetimelike_scalar_mismatch(self):
+        dti = pd.date_range("2016-01-01", periods=3)
+        dta = dti._data.as_unit("us")
+
+        ts = dta[0].as_unit("s")
+
+        result = dta - ts
+        expected = (dti - dti[0])._data.as_unit("us")
+        assert result.dtype == "m8[us]"
+        tm.assert_extension_array_equal(result, expected)
+
+    def test_sub_datetime64_reso_mismatch(self):
+        dti = pd.date_range("2016-01-01", periods=3)
+        left = dti._data.as_unit("s")
+        right = left.as_unit("ms")
+
+        result = left - right
+        exp_values = np.array([0, 0, 0], dtype="m8[ms]")
+        expected = TimedeltaArray._simple_new(
+            exp_values,
+            dtype=exp_values.dtype,
+        )
+        tm.assert_extension_array_equal(result, expected)
+        result2 = right - left
+        tm.assert_extension_array_equal(result2, expected)
+
+
+class TestDatetimeArrayComparisons:
+    # TODO: merge this into tests/arithmetic/test_datetime64 once it is
+    #  sufficiently robust
+
+    def test_cmp_dt64_arraylike_tznaive(self, comparison_op):
+        # arbitrary tz-naive DatetimeIndex
+        op = comparison_op
+
+        dti = pd.date_range("2016-01-1", freq="MS", periods=9, tz=None)
+        arr = dti._data
+        assert arr.freq == dti.freq
+        assert arr.tz == dti.tz
+
+        right = dti
+
+        expected = np.ones(len(arr), dtype=bool)
+        if comparison_op.__name__ in ["ne", "gt", "lt"]:
+            # for these the comparisons should be all-False
+            expected = ~expected
+
+        result = op(arr, arr)
+        tm.assert_numpy_array_equal(result, expected)
+        for other in [
+            right,
+            np.array(right),
+            list(right),
+            tuple(right),
+            right.astype(object),
+        ]:
+            result = op(arr, other)
+            tm.assert_numpy_array_equal(result, expected)
+
+            result = op(other, arr)
+            tm.assert_numpy_array_equal(result, expected)
+
+
+class TestDatetimeArray:
+    def test_astype_ns_to_ms_near_bounds(self):
+        # GH#55979
+        ts = pd.Timestamp("1677-09-21 00:12:43.145225")
+        target = ts.as_unit("ms")
+
+        dta = DatetimeArray._from_sequence([ts], dtype="M8[ns]")
+        assert (dta.view("i8") == ts.as_unit("ns").value).all()
+
+        result = dta.astype("M8[ms]")
+        assert result[0] == target
+
+        expected = DatetimeArray._from_sequence([ts], dtype="M8[ms]")
+        assert (expected.view("i8") == target._value).all()
+
+        tm.assert_datetime_array_equal(result, expected)
+
+    def test_astype_non_nano_tznaive(self):
+        dti = pd.date_range("2016-01-01", periods=3)
+
+        res = dti.astype("M8[s]")
+        assert res.dtype == "M8[s]"
+
+        dta = dti._data
+        res = dta.astype("M8[s]")
+        assert res.dtype == "M8[s]"
+        assert isinstance(res, pd.core.arrays.DatetimeArray)  # used to be ndarray
+
+    def test_astype_non_nano_tzaware(self):
+        dti = pd.date_range("2016-01-01", periods=3, tz="UTC")
+
+        res = dti.astype("M8[s, US/Pacific]")
+        assert res.dtype == "M8[s, US/Pacific]"
+
+        dta = dti._data
+        res = dta.astype("M8[s, US/Pacific]")
+        assert res.dtype == "M8[s, US/Pacific]"
+
+        # from non-nano to non-nano, preserving reso
+        res2 = res.astype("M8[s, UTC]")
+        assert res2.dtype == "M8[s, UTC]"
+        assert not tm.shares_memory(res2, res)
+
+        res3 = res.astype("M8[s, UTC]", copy=False)
+        assert res2.dtype == "M8[s, UTC]"
+        assert tm.shares_memory(res3, res)
+
+    def test_astype_to_same(self):
+        arr = DatetimeArray._from_sequence(
+            ["2000"], dtype=DatetimeTZDtype(tz="US/Central")
+        )
+        result = arr.astype(DatetimeTZDtype(tz="US/Central"), copy=False)
+        assert result is arr
+
+    @pytest.mark.parametrize("dtype", ["datetime64[ns]", "datetime64[ns, UTC]"])
+    @pytest.mark.parametrize(
+        "other", ["datetime64[ns]", "datetime64[ns, UTC]", "datetime64[ns, CET]"]
+    )
+    def test_astype_copies(self, dtype, other):
+        # https://github.com/pandas-dev/pandas/pull/32490
+        ser = pd.Series([1, 2], dtype=dtype)
+        orig = ser.copy()
+
+        err = False
+        if (dtype == "datetime64[ns]") ^ (other == "datetime64[ns]"):
+            # deprecated in favor of tz_localize
+            err = True
+
+        if err:
+            if dtype == "datetime64[ns]":
+                msg = "Use obj.tz_localize instead or series.dt.tz_localize instead"
+            else:
+                msg = "from timezone-aware dtype to timezone-naive dtype"
+            with pytest.raises(TypeError, match=msg):
+                ser.astype(other)
+        else:
+            t = ser.astype(other)
+            t[:] = pd.NaT
+            tm.assert_series_equal(ser, orig)
+
+    @pytest.mark.parametrize("dtype", [int, np.int32, np.int64, "uint32", "uint64"])
+    def test_astype_int(self, dtype):
+        arr = DatetimeArray._from_sequence(
+            [pd.Timestamp("2000"), pd.Timestamp("2001")], dtype="M8[ns]"
+        )
+
+        if np.dtype(dtype) != np.int64:
+            with pytest.raises(TypeError, match=r"Do obj.astype\('int64'\)"):
+                arr.astype(dtype)
+            return
+
+        result = arr.astype(dtype)
+        expected = arr._ndarray.view("i8")
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_astype_to_sparse_dt64(self):
+        # GH#50082
+        dti = pd.date_range("2016-01-01", periods=4)
+        dta = dti._data
+        result = dta.astype("Sparse[datetime64[ns]]")
+
+        assert result.dtype == "Sparse[datetime64[ns]]"
+        assert (result == dta).all()
+
+    def test_tz_setter_raises(self):
+        arr = DatetimeArray._from_sequence(
+            ["2000"], dtype=DatetimeTZDtype(tz="US/Central")
+        )
+        with pytest.raises(AttributeError, match="tz_localize"):
+            arr.tz = "UTC"
+
+    def test_setitem_str_impute_tz(self, tz_naive_fixture):
+        # Like for getitem, if we are passed a naive-like string, we impute
+        #  our own timezone.
+        tz = tz_naive_fixture
+
+        data = np.array([1, 2, 3], dtype="M8[ns]")
+        dtype = data.dtype if tz is None else DatetimeTZDtype(tz=tz)
+        arr = DatetimeArray._from_sequence(data, dtype=dtype)
+        expected = arr.copy()
+
+        ts = pd.Timestamp("2020-09-08 16:50").tz_localize(tz)
+        setter = str(ts.tz_localize(None))
+
+        # Setting a scalar tznaive string
+        expected[0] = ts
+        arr[0] = setter
+        tm.assert_equal(arr, expected)
+
+        # Setting a listlike of tznaive strings
+        expected[1] = ts
+        arr[:2] = [setter, setter]
+        tm.assert_equal(arr, expected)
+
+    def test_setitem_different_tz_raises(self):
+        # pre-2.0 we required exact tz match, in 2.0 we require only
+        #  tzawareness-match
+        data = np.array([1, 2, 3], dtype="M8[ns]")
+        arr = DatetimeArray._from_sequence(
+            data, copy=False, dtype=DatetimeTZDtype(tz="US/Central")
+        )
+        with pytest.raises(TypeError, match="Cannot compare tz-naive and tz-aware"):
+            arr[0] = pd.Timestamp("2000")
+
+        ts = pd.Timestamp("2000", tz="US/Eastern")
+        arr[0] = ts
+        assert arr[0] == ts.tz_convert("US/Central")
+
+    def test_setitem_clears_freq(self):
+        a = pd.date_range("2000", periods=2, freq="D", tz="US/Central")._data
+        a[0] = pd.Timestamp("2000", tz="US/Central")
+        assert a.freq is None
+
+    @pytest.mark.parametrize(
+        "obj",
+        [
+            pd.Timestamp("2021-01-01"),
+            pd.Timestamp("2021-01-01").to_datetime64(),
+            pd.Timestamp("2021-01-01").to_pydatetime(),
+        ],
+    )
+    def test_setitem_objects(self, obj):
+        # make sure we accept datetime64 and datetime in addition to Timestamp
+        dti = pd.date_range("2000", periods=2, freq="D")
+        arr = dti._data
+
+        arr[0] = obj
+        assert arr[0] == obj
+
+    def test_repeat_preserves_tz(self):
+        dti = pd.date_range("2000", periods=2, freq="D", tz="US/Central")
+        arr = dti._data
+
+        repeated = arr.repeat([1, 1])
+
+        # preserves tz and values, but not freq
+        expected = DatetimeArray._from_sequence(arr.asi8, dtype=arr.dtype)
+        tm.assert_equal(repeated, expected)
+
+    def test_value_counts_preserves_tz(self):
+        dti = pd.date_range("2000", periods=2, freq="D", tz="US/Central")
+        arr = dti._data.repeat([4, 3])
+
+        result = arr.value_counts()
+
+        # Note: not tm.assert_index_equal, since `freq`s do not match
+        assert result.index.equals(dti)
+
+        arr[-2] = pd.NaT
+        result = arr.value_counts(dropna=False)
+        expected = pd.Series([4, 2, 1], index=[dti[0], dti[1], pd.NaT], name="count")
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("method", ["pad", "backfill"])
+    def test_fillna_preserves_tz(self, method):
+        dti = pd.date_range("2000-01-01", periods=5, freq="D", tz="US/Central")
+        arr = DatetimeArray._from_sequence(dti, copy=True)
+        arr[2] = pd.NaT
+
+        fill_val = dti[1] if method == "pad" else dti[3]
+        expected = DatetimeArray._from_sequence(
+            [dti[0], dti[1], fill_val, dti[3], dti[4]],
+            dtype=DatetimeTZDtype(tz="US/Central"),
+        )
+
+        result = arr._pad_or_backfill(method=method)
+        tm.assert_extension_array_equal(result, expected)
+
+        # assert that arr and dti were not modified in-place
+        assert arr[2] is pd.NaT
+        assert dti[2] == pd.Timestamp("2000-01-03", tz="US/Central")
+
+    def test_fillna_2d(self):
+        dti = pd.date_range("2016-01-01", periods=6, tz="US/Pacific")
+        dta = dti._data.reshape(3, 2).copy()
+        dta[0, 1] = pd.NaT
+        dta[1, 0] = pd.NaT
+
+        res1 = dta._pad_or_backfill(method="pad")
+        expected1 = dta.copy()
+        expected1[1, 0] = dta[0, 0]
+        tm.assert_extension_array_equal(res1, expected1)
+
+        res2 = dta._pad_or_backfill(method="backfill")
+        expected2 = dta.copy()
+        expected2 = dta.copy()
+        expected2[1, 0] = dta[2, 0]
+        expected2[0, 1] = dta[1, 1]
+        tm.assert_extension_array_equal(res2, expected2)
+
+        # with different ordering for underlying ndarray; behavior should
+        #  be unchanged
+        dta2 = dta._from_backing_data(dta._ndarray.copy(order="F"))
+        assert dta2._ndarray.flags["F_CONTIGUOUS"]
+        assert not dta2._ndarray.flags["C_CONTIGUOUS"]
+        tm.assert_extension_array_equal(dta, dta2)
+
+        res3 = dta2._pad_or_backfill(method="pad")
+        tm.assert_extension_array_equal(res3, expected1)
+
+        res4 = dta2._pad_or_backfill(method="backfill")
+        tm.assert_extension_array_equal(res4, expected2)
+
+        # test the DataFrame method while we're here
+        df = pd.DataFrame(dta)
+        res = df.ffill()
+        expected = pd.DataFrame(expected1)
+        tm.assert_frame_equal(res, expected)
+
+        res = df.bfill()
+        expected = pd.DataFrame(expected2)
+        tm.assert_frame_equal(res, expected)
+
+    def test_array_interface_tz(self):
+        tz = "US/Central"
+        data = pd.date_range("2017", periods=2, tz=tz)._data
+        result = np.asarray(data)
+
+        expected = np.array(
+            [
+                pd.Timestamp("2017-01-01T00:00:00", tz=tz),
+                pd.Timestamp("2017-01-02T00:00:00", tz=tz),
+            ],
+            dtype=object,
+        )
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = np.asarray(data, dtype=object)
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = np.asarray(data, dtype="M8[ns]")
+
+        expected = np.array(
+            ["2017-01-01T06:00:00", "2017-01-02T06:00:00"], dtype="M8[ns]"
+        )
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_array_interface(self):
+        data = pd.date_range("2017", periods=2)._data
+        expected = np.array(
+            ["2017-01-01T00:00:00", "2017-01-02T00:00:00"], dtype="datetime64[ns]"
+        )
+
+        result = np.asarray(data)
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = np.asarray(data, dtype=object)
+        expected = np.array(
+            [pd.Timestamp("2017-01-01T00:00:00"), pd.Timestamp("2017-01-02T00:00:00")],
+            dtype=object,
+        )
+        tm.assert_numpy_array_equal(result, expected)
+
+    @pytest.mark.parametrize("index", [True, False])
+    def test_searchsorted_different_tz(self, index):
+        data = np.arange(10, dtype="i8") * 24 * 3600 * 10**9
+        arr = pd.DatetimeIndex(data, freq="D")._data.tz_localize("Asia/Tokyo")
+        if index:
+            arr = pd.Index(arr)
+
+        expected = arr.searchsorted(arr[2])
+        result = arr.searchsorted(arr[2].tz_convert("UTC"))
+        assert result == expected
+
+        expected = arr.searchsorted(arr[2:6])
+        result = arr.searchsorted(arr[2:6].tz_convert("UTC"))
+        tm.assert_equal(result, expected)
+
+    @pytest.mark.parametrize("index", [True, False])
+    def test_searchsorted_tzawareness_compat(self, index):
+        data = np.arange(10, dtype="i8") * 24 * 3600 * 10**9
+        arr = pd.DatetimeIndex(data, freq="D")._data
+        if index:
+            arr = pd.Index(arr)
+
+        mismatch = arr.tz_localize("Asia/Tokyo")
+
+        msg = "Cannot compare tz-naive and tz-aware datetime-like objects"
+        with pytest.raises(TypeError, match=msg):
+            arr.searchsorted(mismatch[0])
+        with pytest.raises(TypeError, match=msg):
+            arr.searchsorted(mismatch)
+
+        with pytest.raises(TypeError, match=msg):
+            mismatch.searchsorted(arr[0])
+        with pytest.raises(TypeError, match=msg):
+            mismatch.searchsorted(arr)
+
+    @pytest.mark.parametrize(
+        "other",
+        [
+            1,
+            np.int64(1),
+            1.0,
+            np.timedelta64("NaT"),
+            pd.Timedelta(days=2),
+            "invalid",
+            np.arange(10, dtype="i8") * 24 * 3600 * 10**9,
+            np.arange(10).view("timedelta64[ns]") * 24 * 3600 * 10**9,
+            pd.Timestamp("2021-01-01").to_period("D"),
+        ],
+    )
+    @pytest.mark.parametrize("index", [True, False])
+    def test_searchsorted_invalid_types(self, other, index):
+        data = np.arange(10, dtype="i8") * 24 * 3600 * 10**9
+        arr = pd.DatetimeIndex(data, freq="D")._data
+        if index:
+            arr = pd.Index(arr)
+
+        msg = "|".join(
+            [
+                "searchsorted requires compatible dtype or scalar",
+                "value should be a 'Timestamp', 'NaT', or array of those. Got",
+            ]
+        )
+        with pytest.raises(TypeError, match=msg):
+            arr.searchsorted(other)
+
+    def test_shift_fill_value(self):
+        dti = pd.date_range("2016-01-01", periods=3)
+
+        dta = dti._data
+        expected = DatetimeArray._from_sequence(np.roll(dta._ndarray, 1))
+
+        fv = dta[-1]
+        for fill_value in [fv, fv.to_pydatetime(), fv.to_datetime64()]:
+            result = dta.shift(1, fill_value=fill_value)
+            tm.assert_datetime_array_equal(result, expected)
+
+        dta = dta.tz_localize("UTC")
+        expected = expected.tz_localize("UTC")
+        fv = dta[-1]
+        for fill_value in [fv, fv.to_pydatetime()]:
+            result = dta.shift(1, fill_value=fill_value)
+            tm.assert_datetime_array_equal(result, expected)
+
+    def test_shift_value_tzawareness_mismatch(self):
+        dti = pd.date_range("2016-01-01", periods=3)
+
+        dta = dti._data
+
+        fv = dta[-1].tz_localize("UTC")
+        for invalid in [fv, fv.to_pydatetime()]:
+            with pytest.raises(TypeError, match="Cannot compare"):
+                dta.shift(1, fill_value=invalid)
+
+        dta = dta.tz_localize("UTC")
+        fv = dta[-1].tz_localize(None)
+        for invalid in [fv, fv.to_pydatetime(), fv.to_datetime64()]:
+            with pytest.raises(TypeError, match="Cannot compare"):
+                dta.shift(1, fill_value=invalid)
+
+    def test_shift_requires_tzmatch(self):
+        # pre-2.0 we required exact tz match, in 2.0 we require just
+        #  matching tzawareness
+        dti = pd.date_range("2016-01-01", periods=3, tz="UTC")
+        dta = dti._data
+
+        fill_value = pd.Timestamp("2020-10-18 18:44", tz="US/Pacific")
+
+        result = dta.shift(1, fill_value=fill_value)
+        expected = dta.shift(1, fill_value=fill_value.tz_convert("UTC"))
+        tm.assert_equal(result, expected)
+
+    def test_tz_localize_t2d(self):
+        dti = pd.date_range("1994-05-12", periods=12, tz="US/Pacific")
+        dta = dti._data.reshape(3, 4)
+        result = dta.tz_localize(None)
+
+        expected = dta.ravel().tz_localize(None).reshape(dta.shape)
+        tm.assert_datetime_array_equal(result, expected)
+
+        roundtrip = expected.tz_localize("US/Pacific")
+        tm.assert_datetime_array_equal(roundtrip, dta)
+
+    easts = ["US/Eastern", "dateutil/US/Eastern"]
+    if ZoneInfo is not None:
+        try:
+            tz = ZoneInfo("US/Eastern")
+        except KeyError:
+            # no tzdata
+            pass
+        else:
+            # Argument 1 to "append" of "list" has incompatible type "ZoneInfo";
+            # expected "str"
+            easts.append(tz)  # type: ignore[arg-type]
+
+    @pytest.mark.parametrize("tz", easts)
+    def test_iter_zoneinfo_fold(self, tz):
+        # GH#49684
+        utc_vals = np.array(
+            [1320552000, 1320555600, 1320559200, 1320562800], dtype=np.int64
+        )
+        utc_vals *= 1_000_000_000
+
+        dta = DatetimeArray._from_sequence(utc_vals).tz_localize("UTC").tz_convert(tz)
+
+        left = dta[2]
+        right = list(dta)[2]
+        assert str(left) == str(right)
+        # previously there was a bug where with non-pytz right would be
+        #  Timestamp('2011-11-06 01:00:00-0400', tz='US/Eastern')
+        # while left would be
+        #  Timestamp('2011-11-06 01:00:00-0500', tz='US/Eastern')
+        # The .value's would match (so they would compare as equal),
+        #  but the folds would not
+        assert left.utcoffset() == right.utcoffset()
+
+        # The same bug in ints_to_pydatetime affected .astype, so we test
+        #  that here.
+        right2 = dta.astype(object)[2]
+        assert str(left) == str(right2)
+        assert left.utcoffset() == right2.utcoffset()
+
+    @pytest.mark.parametrize(
+        "freq, freq_depr",
+        [
+            ("2ME", "2M"),
+            ("2SME", "2SM"),
+            ("2SME", "2sm"),
+            ("2QE", "2Q"),
+            ("2QE-SEP", "2Q-SEP"),
+            ("1YE", "1Y"),
+            ("2YE-MAR", "2Y-MAR"),
+            ("1YE", "1A"),
+            ("2YE-MAR", "2A-MAR"),
+            ("2ME", "2m"),
+            ("2QE-SEP", "2q-sep"),
+            ("2YE-MAR", "2a-mar"),
+            ("2YE", "2y"),
+        ],
+    )
+    def test_date_range_frequency_M_Q_Y_A_deprecated(self, freq, freq_depr):
+        # GH#9586, GH#54275
+        depr_msg = f"'{freq_depr[1:]}' is deprecated and will be removed "
+        f"in a future version, please use '{freq[1:]}' instead."
+
+        expected = pd.date_range("1/1/2000", periods=4, freq=freq)
+        with tm.assert_produces_warning(FutureWarning, match=depr_msg):
+            result = pd.date_range("1/1/2000", periods=4, freq=freq_depr)
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize("freq_depr", ["2H", "2CBH", "2MIN", "2S", "2mS", "2Us"])
+    def test_date_range_uppercase_frequency_deprecated(self, freq_depr):
+        # GH#9586, GH#54939
+        depr_msg = f"'{freq_depr[1:]}' is deprecated and will be removed in a "
+        f"future version. Please use '{freq_depr.lower()[1:]}' instead."
+
+        expected = pd.date_range("1/1/2000", periods=4, freq=freq_depr.lower())
+        with tm.assert_produces_warning(FutureWarning, match=depr_msg):
+            result = pd.date_range("1/1/2000", periods=4, freq=freq_depr)
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "freq_depr",
+        [
+            "2ye-mar",
+            "2ys",
+            "2qe",
+            "2qs-feb",
+            "2bqs",
+            "2sms",
+            "2bms",
+            "2cbme",
+            "2me",
+            "2w",
+        ],
+    )
+    def test_date_range_lowercase_frequency_deprecated(self, freq_depr):
+        # GH#9586, GH#54939
+        depr_msg = f"'{freq_depr[1:]}' is deprecated and will be removed in a "
+        f"future version, please use '{freq_depr.upper()[1:]}' instead."
+
+        expected = pd.date_range("1/1/2000", periods=4, freq=freq_depr.upper())
+        with tm.assert_produces_warning(FutureWarning, match=depr_msg):
+            result = pd.date_range("1/1/2000", periods=4, freq=freq_depr)
+        tm.assert_index_equal(result, expected)
+
+
+def test_factorize_sort_without_freq():
+    dta = DatetimeArray._from_sequence([0, 2, 1], dtype="M8[ns]")
+
+    msg = r"call pd.factorize\(obj, sort=True\) instead"
+    with pytest.raises(NotImplementedError, match=msg):
+        dta.factorize(sort=True)
+
+    # Do TimedeltaArray while we're here
+    tda = dta - dta[0]
+    with pytest.raises(NotImplementedError, match=msg):
+        tda.factorize(sort=True)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/test_ndarray_backed.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/test_ndarray_backed.py
new file mode 100644
index 0000000000000000000000000000000000000000..1fe7cc9b03e8a6cef04558958ed949a0239a96cc
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/test_ndarray_backed.py
@@ -0,0 +1,75 @@
+"""
+Tests for subclasses of NDArrayBackedExtensionArray
+"""
+import numpy as np
+
+from pandas import (
+    CategoricalIndex,
+    date_range,
+)
+from pandas.core.arrays import (
+    Categorical,
+    DatetimeArray,
+    NumpyExtensionArray,
+    TimedeltaArray,
+)
+
+
+class TestEmpty:
+    def test_empty_categorical(self):
+        ci = CategoricalIndex(["a", "b", "c"], ordered=True)
+        dtype = ci.dtype
+
+        # case with int8 codes
+        shape = (4,)
+        result = Categorical._empty(shape, dtype=dtype)
+        assert isinstance(result, Categorical)
+        assert result.shape == shape
+        assert result._ndarray.dtype == np.int8
+
+        # case where repr would segfault if we didn't override base implementation
+        result = Categorical._empty((4096,), dtype=dtype)
+        assert isinstance(result, Categorical)
+        assert result.shape == (4096,)
+        assert result._ndarray.dtype == np.int8
+        repr(result)
+
+        # case with int16 codes
+        ci = CategoricalIndex(list(range(512)) * 4, ordered=False)
+        dtype = ci.dtype
+        result = Categorical._empty(shape, dtype=dtype)
+        assert isinstance(result, Categorical)
+        assert result.shape == shape
+        assert result._ndarray.dtype == np.int16
+
+    def test_empty_dt64tz(self):
+        dti = date_range("2016-01-01", periods=2, tz="Asia/Tokyo")
+        dtype = dti.dtype
+
+        shape = (0,)
+        result = DatetimeArray._empty(shape, dtype=dtype)
+        assert result.dtype == dtype
+        assert isinstance(result, DatetimeArray)
+        assert result.shape == shape
+
+    def test_empty_dt64(self):
+        shape = (3, 9)
+        result = DatetimeArray._empty(shape, dtype="datetime64[ns]")
+        assert isinstance(result, DatetimeArray)
+        assert result.shape == shape
+
+    def test_empty_td64(self):
+        shape = (3, 9)
+        result = TimedeltaArray._empty(shape, dtype="m8[ns]")
+        assert isinstance(result, TimedeltaArray)
+        assert result.shape == shape
+
+    def test_empty_pandas_array(self):
+        arr = NumpyExtensionArray(np.array([1, 2]))
+        dtype = arr.dtype
+
+        shape = (3, 9)
+        result = NumpyExtensionArray._empty(shape, dtype=dtype)
+        assert isinstance(result, NumpyExtensionArray)
+        assert result.dtype == dtype
+        assert result.shape == shape
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/test_period.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/test_period.py
new file mode 100644
index 0000000000000000000000000000000000000000..48453ba19e9a1f6971a2e56872ec42f1856d1dd0
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/test_period.py
@@ -0,0 +1,184 @@
+import numpy as np
+import pytest
+
+from pandas._libs.tslibs import iNaT
+from pandas._libs.tslibs.period import IncompatibleFrequency
+
+from pandas.core.dtypes.base import _registry as registry
+from pandas.core.dtypes.dtypes import PeriodDtype
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.core.arrays import PeriodArray
+
+# ----------------------------------------------------------------------------
+# Dtype
+
+
+def test_registered():
+    assert PeriodDtype in registry.dtypes
+    result = registry.find("Period[D]")
+    expected = PeriodDtype("D")
+    assert result == expected
+
+
+# ----------------------------------------------------------------------------
+# period_array
+
+
+def test_asi8():
+    result = PeriodArray._from_sequence(["2000", "2001", None], dtype="period[D]").asi8
+    expected = np.array([10957, 11323, iNaT])
+    tm.assert_numpy_array_equal(result, expected)
+
+
+def test_take_raises():
+    arr = PeriodArray._from_sequence(["2000", "2001"], dtype="period[D]")
+    with pytest.raises(IncompatibleFrequency, match="freq"):
+        arr.take([0, -1], allow_fill=True, fill_value=pd.Period("2000", freq="W"))
+
+    msg = "value should be a 'Period' or 'NaT'. Got 'str' instead"
+    with pytest.raises(TypeError, match=msg):
+        arr.take([0, -1], allow_fill=True, fill_value="foo")
+
+
+def test_fillna_raises():
+    arr = PeriodArray._from_sequence(["2000", "2001", "2002"], dtype="period[D]")
+    with pytest.raises(ValueError, match="Length"):
+        arr.fillna(arr[:2])
+
+
+def test_fillna_copies():
+    arr = PeriodArray._from_sequence(["2000", "2001", "2002"], dtype="period[D]")
+    result = arr.fillna(pd.Period("2000", "D"))
+    assert result is not arr
+
+
+# ----------------------------------------------------------------------------
+# setitem
+
+
+@pytest.mark.parametrize(
+    "key, value, expected",
+    [
+        ([0], pd.Period("2000", "D"), [10957, 1, 2]),
+        ([0], None, [iNaT, 1, 2]),
+        ([0], np.nan, [iNaT, 1, 2]),
+        ([0, 1, 2], pd.Period("2000", "D"), [10957] * 3),
+        (
+            [0, 1, 2],
+            [pd.Period("2000", "D"), pd.Period("2001", "D"), pd.Period("2002", "D")],
+            [10957, 11323, 11688],
+        ),
+    ],
+)
+def test_setitem(key, value, expected):
+    arr = PeriodArray(np.arange(3), dtype="period[D]")
+    expected = PeriodArray(expected, dtype="period[D]")
+    arr[key] = value
+    tm.assert_period_array_equal(arr, expected)
+
+
+def test_setitem_raises_incompatible_freq():
+    arr = PeriodArray(np.arange(3), dtype="period[D]")
+    with pytest.raises(IncompatibleFrequency, match="freq"):
+        arr[0] = pd.Period("2000", freq="Y")
+
+    other = PeriodArray._from_sequence(["2000", "2001"], dtype="period[Y]")
+    with pytest.raises(IncompatibleFrequency, match="freq"):
+        arr[[0, 1]] = other
+
+
+def test_setitem_raises_length():
+    arr = PeriodArray(np.arange(3), dtype="period[D]")
+    with pytest.raises(ValueError, match="length"):
+        arr[[0, 1]] = [pd.Period("2000", freq="D")]
+
+
+def test_setitem_raises_type():
+    arr = PeriodArray(np.arange(3), dtype="period[D]")
+    with pytest.raises(TypeError, match="int"):
+        arr[0] = 1
+
+
+# ----------------------------------------------------------------------------
+# Ops
+
+
+def test_sub_period():
+    arr = PeriodArray._from_sequence(["2000", "2001"], dtype="period[D]")
+    other = pd.Period("2000", freq="M")
+    with pytest.raises(IncompatibleFrequency, match="freq"):
+        arr - other
+
+
+def test_sub_period_overflow():
+    # GH#47538
+    dti = pd.date_range("1677-09-22", periods=2, freq="D")
+    pi = dti.to_period("ns")
+
+    per = pd.Period._from_ordinal(10**14, pi.freq)
+
+    with pytest.raises(OverflowError, match="Overflow in int64 addition"):
+        pi - per
+
+    with pytest.raises(OverflowError, match="Overflow in int64 addition"):
+        per - pi
+
+
+# ----------------------------------------------------------------------------
+# Methods
+
+
+@pytest.mark.parametrize(
+    "other",
+    [
+        pd.Period("2000", freq="h"),
+        PeriodArray._from_sequence(["2000", "2001", "2000"], dtype="period[h]"),
+    ],
+)
+def test_where_different_freq_raises(other):
+    # GH#45768 The PeriodArray method raises, the Series method coerces
+    ser = pd.Series(
+        PeriodArray._from_sequence(["2000", "2001", "2002"], dtype="period[D]")
+    )
+    cond = np.array([True, False, True])
+
+    with pytest.raises(IncompatibleFrequency, match="freq"):
+        ser.array._where(cond, other)
+
+    res = ser.where(cond, other)
+    expected = ser.astype(object).where(cond, other)
+    tm.assert_series_equal(res, expected)
+
+
+# ----------------------------------------------------------------------------
+# Printing
+
+
+def test_repr_small():
+    arr = PeriodArray._from_sequence(["2000", "2001"], dtype="period[D]")
+    result = str(arr)
+    expected = (
+        "\n['2000-01-01', '2001-01-01']\nLength: 2, dtype: period[D]"
+    )
+    assert result == expected
+
+
+def test_repr_large():
+    arr = PeriodArray._from_sequence(["2000", "2001"] * 500, dtype="period[D]")
+    result = str(arr)
+    expected = (
+        "\n"
+        "['2000-01-01', '2001-01-01', '2000-01-01', '2001-01-01', "
+        "'2000-01-01',\n"
+        " '2001-01-01', '2000-01-01', '2001-01-01', '2000-01-01', "
+        "'2001-01-01',\n"
+        " ...\n"
+        " '2000-01-01', '2001-01-01', '2000-01-01', '2001-01-01', "
+        "'2000-01-01',\n"
+        " '2001-01-01', '2000-01-01', '2001-01-01', '2000-01-01', "
+        "'2001-01-01']\n"
+        "Length: 1000, dtype: period[D]"
+    )
+    assert result == expected
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/test_timedeltas.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/test_timedeltas.py
new file mode 100644
index 0000000000000000000000000000000000000000..a3f15467feb144ee21883a0a2a777e3b5e0cdf42
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/test_timedeltas.py
@@ -0,0 +1,313 @@
+from datetime import timedelta
+
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import Timedelta
+import pandas._testing as tm
+from pandas.core.arrays import (
+    DatetimeArray,
+    TimedeltaArray,
+)
+
+
+class TestNonNano:
+    @pytest.fixture(params=["s", "ms", "us"])
+    def unit(self, request):
+        return request.param
+
+    @pytest.fixture
+    def tda(self, unit):
+        arr = np.arange(5, dtype=np.int64).view(f"m8[{unit}]")
+        return TimedeltaArray._simple_new(arr, dtype=arr.dtype)
+
+    def test_non_nano(self, unit):
+        arr = np.arange(5, dtype=np.int64).view(f"m8[{unit}]")
+        tda = TimedeltaArray._simple_new(arr, dtype=arr.dtype)
+
+        assert tda.dtype == arr.dtype
+        assert tda[0].unit == unit
+
+    def test_as_unit_raises(self, tda):
+        # GH#50616
+        with pytest.raises(ValueError, match="Supported units"):
+            tda.as_unit("D")
+
+        tdi = pd.Index(tda)
+        with pytest.raises(ValueError, match="Supported units"):
+            tdi.as_unit("D")
+
+    @pytest.mark.parametrize("field", TimedeltaArray._field_ops)
+    def test_fields(self, tda, field):
+        as_nano = tda._ndarray.astype("m8[ns]")
+        tda_nano = TimedeltaArray._simple_new(as_nano, dtype=as_nano.dtype)
+
+        result = getattr(tda, field)
+        expected = getattr(tda_nano, field)
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_to_pytimedelta(self, tda):
+        as_nano = tda._ndarray.astype("m8[ns]")
+        tda_nano = TimedeltaArray._simple_new(as_nano, dtype=as_nano.dtype)
+
+        result = tda.to_pytimedelta()
+        expected = tda_nano.to_pytimedelta()
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_total_seconds(self, unit, tda):
+        as_nano = tda._ndarray.astype("m8[ns]")
+        tda_nano = TimedeltaArray._simple_new(as_nano, dtype=as_nano.dtype)
+
+        result = tda.total_seconds()
+        expected = tda_nano.total_seconds()
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_timedelta_array_total_seconds(self):
+        # GH34290
+        expected = Timedelta("2 min").total_seconds()
+
+        result = pd.array([Timedelta("2 min")]).total_seconds()[0]
+        assert result == expected
+
+    def test_total_seconds_nanoseconds(self):
+        # issue #48521
+        start_time = pd.Series(["2145-11-02 06:00:00"]).astype("datetime64[ns]")
+        end_time = pd.Series(["2145-11-02 07:06:00"]).astype("datetime64[ns]")
+        expected = (end_time - start_time).values / np.timedelta64(1, "s")
+        result = (end_time - start_time).dt.total_seconds().values
+        assert result == expected
+
+    @pytest.mark.parametrize(
+        "nat", [np.datetime64("NaT", "ns"), np.datetime64("NaT", "us")]
+    )
+    def test_add_nat_datetimelike_scalar(self, nat, tda):
+        result = tda + nat
+        assert isinstance(result, DatetimeArray)
+        assert result._creso == tda._creso
+        assert result.isna().all()
+
+        result = nat + tda
+        assert isinstance(result, DatetimeArray)
+        assert result._creso == tda._creso
+        assert result.isna().all()
+
+    def test_add_pdnat(self, tda):
+        result = tda + pd.NaT
+        assert isinstance(result, TimedeltaArray)
+        assert result._creso == tda._creso
+        assert result.isna().all()
+
+        result = pd.NaT + tda
+        assert isinstance(result, TimedeltaArray)
+        assert result._creso == tda._creso
+        assert result.isna().all()
+
+    # TODO: 2022-07-11 this is the only test that gets to DTA.tz_convert
+    #  or tz_localize with non-nano; implement tests specific to that.
+    def test_add_datetimelike_scalar(self, tda, tz_naive_fixture):
+        ts = pd.Timestamp("2016-01-01", tz=tz_naive_fixture).as_unit("ns")
+
+        expected = tda.as_unit("ns") + ts
+        res = tda + ts
+        tm.assert_extension_array_equal(res, expected)
+        res = ts + tda
+        tm.assert_extension_array_equal(res, expected)
+
+        ts += Timedelta(1)  # case where we can't cast losslessly
+
+        exp_values = tda._ndarray + ts.asm8
+        expected = (
+            DatetimeArray._simple_new(exp_values, dtype=exp_values.dtype)
+            .tz_localize("UTC")
+            .tz_convert(ts.tz)
+        )
+
+        result = tda + ts
+        tm.assert_extension_array_equal(result, expected)
+
+        result = ts + tda
+        tm.assert_extension_array_equal(result, expected)
+
+    def test_mul_scalar(self, tda):
+        other = 2
+        result = tda * other
+        expected = TimedeltaArray._simple_new(tda._ndarray * other, dtype=tda.dtype)
+        tm.assert_extension_array_equal(result, expected)
+        assert result._creso == tda._creso
+
+    def test_mul_listlike(self, tda):
+        other = np.arange(len(tda))
+        result = tda * other
+        expected = TimedeltaArray._simple_new(tda._ndarray * other, dtype=tda.dtype)
+        tm.assert_extension_array_equal(result, expected)
+        assert result._creso == tda._creso
+
+    def test_mul_listlike_object(self, tda):
+        other = np.arange(len(tda))
+        result = tda * other.astype(object)
+        expected = TimedeltaArray._simple_new(tda._ndarray * other, dtype=tda.dtype)
+        tm.assert_extension_array_equal(result, expected)
+        assert result._creso == tda._creso
+
+    def test_div_numeric_scalar(self, tda):
+        other = 2
+        result = tda / other
+        expected = TimedeltaArray._simple_new(tda._ndarray / other, dtype=tda.dtype)
+        tm.assert_extension_array_equal(result, expected)
+        assert result._creso == tda._creso
+
+    def test_div_td_scalar(self, tda):
+        other = timedelta(seconds=1)
+        result = tda / other
+        expected = tda._ndarray / np.timedelta64(1, "s")
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_div_numeric_array(self, tda):
+        other = np.arange(len(tda))
+        result = tda / other
+        expected = TimedeltaArray._simple_new(tda._ndarray / other, dtype=tda.dtype)
+        tm.assert_extension_array_equal(result, expected)
+        assert result._creso == tda._creso
+
+    def test_div_td_array(self, tda):
+        other = tda._ndarray + tda._ndarray[-1]
+        result = tda / other
+        expected = tda._ndarray / other
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_add_timedeltaarraylike(self, tda):
+        tda_nano = tda.astype("m8[ns]")
+
+        expected = tda_nano * 2
+        res = tda_nano + tda
+        tm.assert_extension_array_equal(res, expected)
+        res = tda + tda_nano
+        tm.assert_extension_array_equal(res, expected)
+
+        expected = tda_nano * 0
+        res = tda - tda_nano
+        tm.assert_extension_array_equal(res, expected)
+
+        res = tda_nano - tda
+        tm.assert_extension_array_equal(res, expected)
+
+
+class TestTimedeltaArray:
+    @pytest.mark.parametrize("dtype", [int, np.int32, np.int64, "uint32", "uint64"])
+    def test_astype_int(self, dtype):
+        arr = TimedeltaArray._from_sequence(
+            [Timedelta("1h"), Timedelta("2h")], dtype="m8[ns]"
+        )
+
+        if np.dtype(dtype) != np.int64:
+            with pytest.raises(TypeError, match=r"Do obj.astype\('int64'\)"):
+                arr.astype(dtype)
+            return
+
+        result = arr.astype(dtype)
+        expected = arr._ndarray.view("i8")
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_setitem_clears_freq(self):
+        a = pd.timedelta_range("1h", periods=2, freq="h")._data
+        a[0] = Timedelta("1h")
+        assert a.freq is None
+
+    @pytest.mark.parametrize(
+        "obj",
+        [
+            Timedelta(seconds=1),
+            Timedelta(seconds=1).to_timedelta64(),
+            Timedelta(seconds=1).to_pytimedelta(),
+        ],
+    )
+    def test_setitem_objects(self, obj):
+        # make sure we accept timedelta64 and timedelta in addition to Timedelta
+        tdi = pd.timedelta_range("2 Days", periods=4, freq="h")
+        arr = tdi._data
+
+        arr[0] = obj
+        assert arr[0] == Timedelta(seconds=1)
+
+    @pytest.mark.parametrize(
+        "other",
+        [
+            1,
+            np.int64(1),
+            1.0,
+            np.datetime64("NaT"),
+            pd.Timestamp("2021-01-01"),
+            "invalid",
+            np.arange(10, dtype="i8") * 24 * 3600 * 10**9,
+            (np.arange(10) * 24 * 3600 * 10**9).view("datetime64[ns]"),
+            pd.Timestamp("2021-01-01").to_period("D"),
+        ],
+    )
+    @pytest.mark.parametrize("index", [True, False])
+    def test_searchsorted_invalid_types(self, other, index):
+        data = np.arange(10, dtype="i8") * 24 * 3600 * 10**9
+        arr = pd.TimedeltaIndex(data, freq="D")._data
+        if index:
+            arr = pd.Index(arr)
+
+        msg = "|".join(
+            [
+                "searchsorted requires compatible dtype or scalar",
+                "value should be a 'Timedelta', 'NaT', or array of those. Got",
+            ]
+        )
+        with pytest.raises(TypeError, match=msg):
+            arr.searchsorted(other)
+
+
+class TestUnaryOps:
+    def test_abs(self):
+        vals = np.array([-3600 * 10**9, "NaT", 7200 * 10**9], dtype="m8[ns]")
+        arr = TimedeltaArray._from_sequence(vals)
+
+        evals = np.array([3600 * 10**9, "NaT", 7200 * 10**9], dtype="m8[ns]")
+        expected = TimedeltaArray._from_sequence(evals)
+
+        result = abs(arr)
+        tm.assert_timedelta_array_equal(result, expected)
+
+        result2 = np.abs(arr)
+        tm.assert_timedelta_array_equal(result2, expected)
+
+    def test_pos(self):
+        vals = np.array([-3600 * 10**9, "NaT", 7200 * 10**9], dtype="m8[ns]")
+        arr = TimedeltaArray._from_sequence(vals)
+
+        result = +arr
+        tm.assert_timedelta_array_equal(result, arr)
+        assert not tm.shares_memory(result, arr)
+
+        result2 = np.positive(arr)
+        tm.assert_timedelta_array_equal(result2, arr)
+        assert not tm.shares_memory(result2, arr)
+
+    def test_neg(self):
+        vals = np.array([-3600 * 10**9, "NaT", 7200 * 10**9], dtype="m8[ns]")
+        arr = TimedeltaArray._from_sequence(vals)
+
+        evals = np.array([3600 * 10**9, "NaT", -7200 * 10**9], dtype="m8[ns]")
+        expected = TimedeltaArray._from_sequence(evals)
+
+        result = -arr
+        tm.assert_timedelta_array_equal(result, expected)
+
+        result2 = np.negative(arr)
+        tm.assert_timedelta_array_equal(result2, expected)
+
+    def test_neg_freq(self):
+        tdi = pd.timedelta_range("2 Days", periods=4, freq="h")
+        arr = tdi._data
+
+        expected = -tdi._data
+
+        result = -arr
+        tm.assert_timedelta_array_equal(result, expected)
+
+        result2 = np.negative(arr)
+        tm.assert_timedelta_array_equal(result2, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/timedeltas/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/timedeltas/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/timedeltas/test_constructors.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/timedeltas/test_constructors.py
new file mode 100644
index 0000000000000000000000000000000000000000..91b6f7fa222f9a668092a99a8371753e914008c8
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/timedeltas/test_constructors.py
@@ -0,0 +1,103 @@
+import numpy as np
+import pytest
+
+import pandas._testing as tm
+from pandas.core.arrays import TimedeltaArray
+
+
+class TestTimedeltaArrayConstructor:
+    def test_only_1dim_accepted(self):
+        # GH#25282
+        arr = np.array([0, 1, 2, 3], dtype="m8[h]").astype("m8[ns]")
+
+        depr_msg = "TimedeltaArray.__init__ is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=depr_msg):
+            with pytest.raises(ValueError, match="Only 1-dimensional"):
+                # 3-dim, we allow 2D to sneak in for ops purposes GH#29853
+                TimedeltaArray(arr.reshape(2, 2, 1))
+
+        with tm.assert_produces_warning(FutureWarning, match=depr_msg):
+            with pytest.raises(ValueError, match="Only 1-dimensional"):
+                # 0-dim
+                TimedeltaArray(arr[[0]].squeeze())
+
+    def test_freq_validation(self):
+        # ensure that the public constructor cannot create an invalid instance
+        arr = np.array([0, 0, 1], dtype=np.int64) * 3600 * 10**9
+
+        msg = (
+            "Inferred frequency None from passed values does not "
+            "conform to passed frequency D"
+        )
+        depr_msg = "TimedeltaArray.__init__ is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=depr_msg):
+            with pytest.raises(ValueError, match=msg):
+                TimedeltaArray(arr.view("timedelta64[ns]"), freq="D")
+
+    def test_non_array_raises(self):
+        depr_msg = "TimedeltaArray.__init__ is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=depr_msg):
+            with pytest.raises(ValueError, match="list"):
+                TimedeltaArray([1, 2, 3])
+
+    def test_other_type_raises(self):
+        msg = r"dtype bool cannot be converted to timedelta64\[ns\]"
+        with pytest.raises(TypeError, match=msg):
+            TimedeltaArray._from_sequence(np.array([1, 2, 3], dtype="bool"))
+
+    def test_incorrect_dtype_raises(self):
+        msg = "dtype 'category' is invalid, should be np.timedelta64 dtype"
+        with pytest.raises(ValueError, match=msg):
+            TimedeltaArray._from_sequence(
+                np.array([1, 2, 3], dtype="i8"), dtype="category"
+            )
+
+        msg = "dtype 'int64' is invalid, should be np.timedelta64 dtype"
+        with pytest.raises(ValueError, match=msg):
+            TimedeltaArray._from_sequence(
+                np.array([1, 2, 3], dtype="i8"), dtype=np.dtype("int64")
+            )
+
+        msg = r"dtype 'datetime64\[ns\]' is invalid, should be np.timedelta64 dtype"
+        with pytest.raises(ValueError, match=msg):
+            TimedeltaArray._from_sequence(
+                np.array([1, 2, 3], dtype="i8"), dtype=np.dtype("M8[ns]")
+            )
+
+        msg = (
+            r"dtype 'datetime64\[us, UTC\]' is invalid, should be np.timedelta64 dtype"
+        )
+        with pytest.raises(ValueError, match=msg):
+            TimedeltaArray._from_sequence(
+                np.array([1, 2, 3], dtype="i8"), dtype="M8[us, UTC]"
+            )
+
+        msg = "Supported timedelta64 resolutions are 's', 'ms', 'us', 'ns'"
+        with pytest.raises(ValueError, match=msg):
+            TimedeltaArray._from_sequence(
+                np.array([1, 2, 3], dtype="i8"), dtype=np.dtype("m8[Y]")
+            )
+
+    def test_mismatched_values_dtype_units(self):
+        arr = np.array([1, 2, 3], dtype="m8[s]")
+        dtype = np.dtype("m8[ns]")
+        msg = r"Values resolution does not match dtype"
+        depr_msg = "TimedeltaArray.__init__ is deprecated"
+
+        with tm.assert_produces_warning(FutureWarning, match=depr_msg):
+            with pytest.raises(ValueError, match=msg):
+                TimedeltaArray(arr, dtype=dtype)
+
+    def test_copy(self):
+        data = np.array([1, 2, 3], dtype="m8[ns]")
+        arr = TimedeltaArray._from_sequence(data, copy=False)
+        assert arr._ndarray is data
+
+        arr = TimedeltaArray._from_sequence(data, copy=True)
+        assert arr._ndarray is not data
+        assert arr._ndarray.base is not data
+
+    def test_from_sequence_dtype(self):
+        msg = "dtype 'object' is invalid, should be np.timedelta64 dtype"
+        with pytest.raises(ValueError, match=msg):
+            TimedeltaArray._from_sequence([], dtype=object)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/timedeltas/test_cumulative.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/timedeltas/test_cumulative.py
new file mode 100644
index 0000000000000000000000000000000000000000..2d8fe65f807e431d788e526eee058780b5bf979c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/timedeltas/test_cumulative.py
@@ -0,0 +1,20 @@
+import pytest
+
+import pandas._testing as tm
+from pandas.core.arrays import TimedeltaArray
+
+
+class TestAccumulator:
+    def test_accumulators_disallowed(self):
+        # GH#50297
+        arr = TimedeltaArray._from_sequence(["1D", "2D"], dtype="m8[ns]")
+        with pytest.raises(TypeError, match="cumprod not supported"):
+            arr._accumulate("cumprod")
+
+    def test_cumsum(self, unit):
+        # GH#50297
+        dtype = f"m8[{unit}]"
+        arr = TimedeltaArray._from_sequence(["1D", "2D"], dtype=dtype)
+        result = arr._accumulate("cumsum")
+        expected = TimedeltaArray._from_sequence(["1D", "3D"], dtype=dtype)
+        tm.assert_timedelta_array_equal(result, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/timedeltas/test_reductions.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/timedeltas/test_reductions.py
new file mode 100644
index 0000000000000000000000000000000000000000..991dbf41c808794f9a53a3f3351a9b6e79a7c6fd
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/arrays/timedeltas/test_reductions.py
@@ -0,0 +1,218 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import Timedelta
+import pandas._testing as tm
+from pandas.core import nanops
+from pandas.core.arrays import TimedeltaArray
+
+
+class TestReductions:
+    @pytest.mark.parametrize("name", ["std", "min", "max", "median", "mean"])
+    @pytest.mark.parametrize("skipna", [True, False])
+    def test_reductions_empty(self, name, skipna):
+        tdi = pd.TimedeltaIndex([])
+        arr = tdi.array
+
+        result = getattr(tdi, name)(skipna=skipna)
+        assert result is pd.NaT
+
+        result = getattr(arr, name)(skipna=skipna)
+        assert result is pd.NaT
+
+    @pytest.mark.parametrize("skipna", [True, False])
+    def test_sum_empty(self, skipna):
+        tdi = pd.TimedeltaIndex([])
+        arr = tdi.array
+
+        result = tdi.sum(skipna=skipna)
+        assert isinstance(result, Timedelta)
+        assert result == Timedelta(0)
+
+        result = arr.sum(skipna=skipna)
+        assert isinstance(result, Timedelta)
+        assert result == Timedelta(0)
+
+    def test_min_max(self, unit):
+        dtype = f"m8[{unit}]"
+        arr = TimedeltaArray._from_sequence(
+            ["3h", "3h", "NaT", "2h", "5h", "4h"], dtype=dtype
+        )
+
+        result = arr.min()
+        expected = Timedelta("2h")
+        assert result == expected
+
+        result = arr.max()
+        expected = Timedelta("5h")
+        assert result == expected
+
+        result = arr.min(skipna=False)
+        assert result is pd.NaT
+
+        result = arr.max(skipna=False)
+        assert result is pd.NaT
+
+    def test_sum(self):
+        tdi = pd.TimedeltaIndex(["3h", "3h", "NaT", "2h", "5h", "4h"])
+        arr = tdi.array
+
+        result = arr.sum(skipna=True)
+        expected = Timedelta(hours=17)
+        assert isinstance(result, Timedelta)
+        assert result == expected
+
+        result = tdi.sum(skipna=True)
+        assert isinstance(result, Timedelta)
+        assert result == expected
+
+        result = arr.sum(skipna=False)
+        assert result is pd.NaT
+
+        result = tdi.sum(skipna=False)
+        assert result is pd.NaT
+
+        result = arr.sum(min_count=9)
+        assert result is pd.NaT
+
+        result = tdi.sum(min_count=9)
+        assert result is pd.NaT
+
+        result = arr.sum(min_count=1)
+        assert isinstance(result, Timedelta)
+        assert result == expected
+
+        result = tdi.sum(min_count=1)
+        assert isinstance(result, Timedelta)
+        assert result == expected
+
+    def test_npsum(self):
+        # GH#25282, GH#25335 np.sum should return a Timedelta, not timedelta64
+        tdi = pd.TimedeltaIndex(["3h", "3h", "2h", "5h", "4h"])
+        arr = tdi.array
+
+        result = np.sum(tdi)
+        expected = Timedelta(hours=17)
+        assert isinstance(result, Timedelta)
+        assert result == expected
+
+        result = np.sum(arr)
+        assert isinstance(result, Timedelta)
+        assert result == expected
+
+    def test_sum_2d_skipna_false(self):
+        arr = np.arange(8).astype(np.int64).view("m8[s]").astype("m8[ns]").reshape(4, 2)
+        arr[-1, -1] = "Nat"
+
+        tda = TimedeltaArray._from_sequence(arr)
+
+        result = tda.sum(skipna=False)
+        assert result is pd.NaT
+
+        result = tda.sum(axis=0, skipna=False)
+        expected = pd.TimedeltaIndex([Timedelta(seconds=12), pd.NaT])._values
+        tm.assert_timedelta_array_equal(result, expected)
+
+        result = tda.sum(axis=1, skipna=False)
+        expected = pd.TimedeltaIndex(
+            [
+                Timedelta(seconds=1),
+                Timedelta(seconds=5),
+                Timedelta(seconds=9),
+                pd.NaT,
+            ]
+        )._values
+        tm.assert_timedelta_array_equal(result, expected)
+
+    # Adding a Timestamp makes this a test for DatetimeArray.std
+    @pytest.mark.parametrize(
+        "add",
+        [
+            Timedelta(0),
+            pd.Timestamp("2021-01-01"),
+            pd.Timestamp("2021-01-01", tz="UTC"),
+            pd.Timestamp("2021-01-01", tz="Asia/Tokyo"),
+        ],
+    )
+    def test_std(self, add):
+        tdi = pd.TimedeltaIndex(["0h", "4h", "NaT", "4h", "0h", "2h"]) + add
+        arr = tdi.array
+
+        result = arr.std(skipna=True)
+        expected = Timedelta(hours=2)
+        assert isinstance(result, Timedelta)
+        assert result == expected
+
+        result = tdi.std(skipna=True)
+        assert isinstance(result, Timedelta)
+        assert result == expected
+
+        if getattr(arr, "tz", None) is None:
+            result = nanops.nanstd(np.asarray(arr), skipna=True)
+            assert isinstance(result, np.timedelta64)
+            assert result == expected
+
+        result = arr.std(skipna=False)
+        assert result is pd.NaT
+
+        result = tdi.std(skipna=False)
+        assert result is pd.NaT
+
+        if getattr(arr, "tz", None) is None:
+            result = nanops.nanstd(np.asarray(arr), skipna=False)
+            assert isinstance(result, np.timedelta64)
+            assert np.isnat(result)
+
+    def test_median(self):
+        tdi = pd.TimedeltaIndex(["0h", "3h", "NaT", "5h06m", "0h", "2h"])
+        arr = tdi.array
+
+        result = arr.median(skipna=True)
+        expected = Timedelta(hours=2)
+        assert isinstance(result, Timedelta)
+        assert result == expected
+
+        result = tdi.median(skipna=True)
+        assert isinstance(result, Timedelta)
+        assert result == expected
+
+        result = arr.median(skipna=False)
+        assert result is pd.NaT
+
+        result = tdi.median(skipna=False)
+        assert result is pd.NaT
+
+    def test_mean(self):
+        tdi = pd.TimedeltaIndex(["0h", "3h", "NaT", "5h06m", "0h", "2h"])
+        arr = tdi._data
+
+        # manually verified result
+        expected = Timedelta(arr.dropna()._ndarray.mean())
+
+        result = arr.mean()
+        assert result == expected
+        result = arr.mean(skipna=False)
+        assert result is pd.NaT
+
+        result = arr.dropna().mean(skipna=False)
+        assert result == expected
+
+        result = arr.mean(axis=0)
+        assert result == expected
+
+    def test_mean_2d(self):
+        tdi = pd.timedelta_range("14 days", periods=6)
+        tda = tdi._data.reshape(3, 2)
+
+        result = tda.mean(axis=0)
+        expected = tda[1]
+        tm.assert_timedelta_array_equal(result, expected)
+
+        result = tda.mean(axis=1)
+        expected = tda[:, 0] + Timedelta(hours=12)
+        tm.assert_timedelta_array_equal(result, expected)
+
+        result = tda.mean(axis=None)
+        expected = tdi.mean()
+        assert result == expected
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/base/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/base/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/base/common.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/base/common.py
new file mode 100644
index 0000000000000000000000000000000000000000..ad0b394105742ca5de92a03a3da2c569c38da469
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/base/common.py
@@ -0,0 +1,9 @@
+from typing import Any
+
+from pandas import Index
+
+
+def allow_na_ops(obj: Any) -> bool:
+    """Whether to skip test cases including NaN"""
+    is_bool_index = isinstance(obj, Index) and obj.inferred_type == "boolean"
+    return not is_bool_index and obj._can_hold_na
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/base/test_constructors.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/base/test_constructors.py
new file mode 100644
index 0000000000000000000000000000000000000000..3434c8110a79c3de126463651eecb93990f65c7c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/base/test_constructors.py
@@ -0,0 +1,190 @@
+from datetime import datetime
+import sys
+
+import numpy as np
+import pytest
+
+from pandas.compat import PYPY
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Index,
+    Series,
+)
+import pandas._testing as tm
+from pandas.core.accessor import PandasDelegate
+from pandas.core.base import (
+    NoNewAttributesMixin,
+    PandasObject,
+)
+
+
+def series_via_frame_from_dict(x, **kwargs):
+    return DataFrame({"a": x}, **kwargs)["a"]
+
+
+def series_via_frame_from_scalar(x, **kwargs):
+    return DataFrame(x, **kwargs)[0]
+
+
+@pytest.fixture(
+    params=[
+        Series,
+        series_via_frame_from_dict,
+        series_via_frame_from_scalar,
+        Index,
+    ],
+    ids=["Series", "DataFrame-dict", "DataFrame-array", "Index"],
+)
+def constructor(request):
+    return request.param
+
+
+class TestPandasDelegate:
+    class Delegator:
+        _properties = ["prop"]
+        _methods = ["test_method"]
+
+        def _set_prop(self, value):
+            self.prop = value
+
+        def _get_prop(self):
+            return self.prop
+
+        prop = property(_get_prop, _set_prop, doc="foo property")
+
+        def test_method(self, *args, **kwargs):
+            """a test method"""
+
+    class Delegate(PandasDelegate, PandasObject):
+        def __init__(self, obj) -> None:
+            self.obj = obj
+
+    def test_invalid_delegation(self):
+        # these show that in order for the delegation to work
+        # the _delegate_* methods need to be overridden to not raise
+        # a TypeError
+
+        self.Delegate._add_delegate_accessors(
+            delegate=self.Delegator,
+            accessors=self.Delegator._properties,
+            typ="property",
+        )
+        self.Delegate._add_delegate_accessors(
+            delegate=self.Delegator, accessors=self.Delegator._methods, typ="method"
+        )
+
+        delegate = self.Delegate(self.Delegator())
+
+        msg = "You cannot access the property prop"
+        with pytest.raises(TypeError, match=msg):
+            delegate.prop
+
+        msg = "The property prop cannot be set"
+        with pytest.raises(TypeError, match=msg):
+            delegate.prop = 5
+
+        msg = "You cannot access the property prop"
+        with pytest.raises(TypeError, match=msg):
+            delegate.prop
+
+    @pytest.mark.skipif(PYPY, reason="not relevant for PyPy")
+    def test_memory_usage(self):
+        # Delegate does not implement memory_usage.
+        # Check that we fall back to in-built `__sizeof__`
+        # GH 12924
+        delegate = self.Delegate(self.Delegator())
+        sys.getsizeof(delegate)
+
+
+class TestNoNewAttributesMixin:
+    def test_mixin(self):
+        class T(NoNewAttributesMixin):
+            pass
+
+        t = T()
+        assert not hasattr(t, "__frozen")
+
+        t.a = "test"
+        assert t.a == "test"
+
+        t._freeze()
+        assert "__frozen" in dir(t)
+        assert getattr(t, "__frozen")
+        msg = "You cannot add any new attribute"
+        with pytest.raises(AttributeError, match=msg):
+            t.b = "test"
+
+        assert not hasattr(t, "b")
+
+
+class TestConstruction:
+    # test certain constructor behaviours on dtype inference across Series,
+    # Index and DataFrame
+
+    @pytest.mark.parametrize(
+        "a",
+        [
+            np.array(["2263-01-01"], dtype="datetime64[D]"),
+            np.array([datetime(2263, 1, 1)], dtype=object),
+            np.array([np.datetime64("2263-01-01", "D")], dtype=object),
+            np.array(["2263-01-01"], dtype=object),
+        ],
+        ids=[
+            "datetime64[D]",
+            "object-datetime.datetime",
+            "object-numpy-scalar",
+            "object-string",
+        ],
+    )
+    def test_constructor_datetime_outofbound(
+        self, a, constructor, request, using_infer_string
+    ):
+        # GH-26853 (+ bug GH-26206 out of bound non-ns unit)
+
+        # No dtype specified (dtype inference)
+        # datetime64[non-ns] raise error, other cases result in object dtype
+        # and preserve original data
+        if a.dtype.kind == "M":
+            # Can't fit in nanosecond bounds -> get the nearest supported unit
+            result = constructor(a)
+            assert result.dtype == "M8[s]"
+        else:
+            result = constructor(a)
+            if using_infer_string and "object-string" in request.node.callspec.id:
+                assert result.dtype == "string"
+            else:
+                assert result.dtype == "object"
+            tm.assert_numpy_array_equal(result.to_numpy(), a)
+
+        # Explicit dtype specified
+        # Forced conversion fails for all -> all cases raise error
+        msg = "Out of bounds|Out of bounds .* present at position 0"
+        with pytest.raises(pd.errors.OutOfBoundsDatetime, match=msg):
+            constructor(a, dtype="datetime64[ns]")
+
+    def test_constructor_datetime_nonns(self, constructor):
+        arr = np.array(["2020-01-01T00:00:00.000000"], dtype="datetime64[us]")
+        dta = pd.core.arrays.DatetimeArray._simple_new(arr, dtype=arr.dtype)
+        expected = constructor(dta)
+        assert expected.dtype == arr.dtype
+
+        result = constructor(arr)
+        tm.assert_equal(result, expected)
+
+        # https://github.com/pandas-dev/pandas/issues/34843
+        arr.flags.writeable = False
+        result = constructor(arr)
+        tm.assert_equal(result, expected)
+
+    def test_constructor_from_dict_keys(self, constructor, using_infer_string):
+        # https://github.com/pandas-dev/pandas/issues/60343
+        d = {"a": 1, "b": 2}
+        result = constructor(d.keys(), dtype="str")
+        if using_infer_string:
+            assert result.dtype == "str"
+        else:
+            assert result.dtype == "object"
+        expected = constructor(list(d.keys()), dtype="str")
+        tm.assert_equal(result, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/base/test_conversion.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/base/test_conversion.py
new file mode 100644
index 0000000000000000000000000000000000000000..4d0e2d1ce0e07ecb100870ffa784d3ecc54e0f37
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/base/test_conversion.py
@@ -0,0 +1,596 @@
+import numpy as np
+import pytest
+
+from pandas.compat import HAS_PYARROW
+from pandas.compat.numpy import np_version_gt2
+
+from pandas.core.dtypes.dtypes import DatetimeTZDtype
+
+import pandas as pd
+from pandas import (
+    CategoricalIndex,
+    Series,
+    Timedelta,
+    Timestamp,
+    date_range,
+)
+import pandas._testing as tm
+from pandas.core.arrays import (
+    DatetimeArray,
+    IntervalArray,
+    NumpyExtensionArray,
+    PeriodArray,
+    SparseArray,
+    TimedeltaArray,
+)
+from pandas.core.arrays.string_ import StringArrayNumpySemantics
+from pandas.core.arrays.string_arrow import ArrowStringArrayNumpySemantics
+
+
+class TestToIterable:
+    # test that we convert an iterable to python types
+
+    dtypes = [
+        ("int8", int),
+        ("int16", int),
+        ("int32", int),
+        ("int64", int),
+        ("uint8", int),
+        ("uint16", int),
+        ("uint32", int),
+        ("uint64", int),
+        ("float16", float),
+        ("float32", float),
+        ("float64", float),
+        ("datetime64[ns]", Timestamp),
+        ("datetime64[ns, US/Eastern]", Timestamp),
+        ("timedelta64[ns]", Timedelta),
+    ]
+
+    @pytest.mark.parametrize("dtype, rdtype", dtypes)
+    @pytest.mark.parametrize(
+        "method",
+        [
+            lambda x: x.tolist(),
+            lambda x: x.to_list(),
+            lambda x: list(x),
+            lambda x: list(x.__iter__()),
+        ],
+        ids=["tolist", "to_list", "list", "iter"],
+    )
+    def test_iterable(self, index_or_series, method, dtype, rdtype):
+        # gh-10904
+        # gh-13258
+        # coerce iteration to underlying python / pandas types
+        typ = index_or_series
+        if dtype == "float16" and issubclass(typ, pd.Index):
+            with pytest.raises(NotImplementedError, match="float16 indexes are not "):
+                typ([1], dtype=dtype)
+            return
+        s = typ([1], dtype=dtype)
+        result = method(s)[0]
+        assert isinstance(result, rdtype)
+
+    @pytest.mark.parametrize(
+        "dtype, rdtype, obj",
+        [
+            ("object", object, "a"),
+            ("object", int, 1),
+            ("category", object, "a"),
+            ("category", int, 1),
+        ],
+    )
+    @pytest.mark.parametrize(
+        "method",
+        [
+            lambda x: x.tolist(),
+            lambda x: x.to_list(),
+            lambda x: list(x),
+            lambda x: list(x.__iter__()),
+        ],
+        ids=["tolist", "to_list", "list", "iter"],
+    )
+    def test_iterable_object_and_category(
+        self, index_or_series, method, dtype, rdtype, obj
+    ):
+        # gh-10904
+        # gh-13258
+        # coerce iteration to underlying python / pandas types
+        typ = index_or_series
+        s = typ([obj], dtype=dtype)
+        result = method(s)[0]
+        assert isinstance(result, rdtype)
+
+    @pytest.mark.parametrize("dtype, rdtype", dtypes)
+    def test_iterable_items(self, dtype, rdtype):
+        # gh-13258
+        # test if items yields the correct boxed scalars
+        # this only applies to series
+        s = Series([1], dtype=dtype)
+        _, result = next(iter(s.items()))
+        assert isinstance(result, rdtype)
+
+        _, result = next(iter(s.items()))
+        assert isinstance(result, rdtype)
+
+    @pytest.mark.parametrize(
+        "dtype, rdtype", dtypes + [("object", int), ("category", int)]
+    )
+    def test_iterable_map(self, index_or_series, dtype, rdtype):
+        # gh-13236
+        # coerce iteration to underlying python / pandas types
+        typ = index_or_series
+        if dtype == "float16" and issubclass(typ, pd.Index):
+            with pytest.raises(NotImplementedError, match="float16 indexes are not "):
+                typ([1], dtype=dtype)
+            return
+        s = typ([1], dtype=dtype)
+        result = s.map(type)[0]
+        if not isinstance(rdtype, tuple):
+            rdtype = (rdtype,)
+        assert result in rdtype
+
+    @pytest.mark.parametrize(
+        "method",
+        [
+            lambda x: x.tolist(),
+            lambda x: x.to_list(),
+            lambda x: list(x),
+            lambda x: list(x.__iter__()),
+        ],
+        ids=["tolist", "to_list", "list", "iter"],
+    )
+    def test_categorial_datetimelike(self, method):
+        i = CategoricalIndex([Timestamp("1999-12-31"), Timestamp("2000-12-31")])
+
+        result = method(i)[0]
+        assert isinstance(result, Timestamp)
+
+    def test_iter_box_dt64(self, unit):
+        vals = [Timestamp("2011-01-01"), Timestamp("2011-01-02")]
+        ser = Series(vals).dt.as_unit(unit)
+        assert ser.dtype == f"datetime64[{unit}]"
+        for res, exp in zip(ser, vals):
+            assert isinstance(res, Timestamp)
+            assert res.tz is None
+            assert res == exp
+            assert res.unit == unit
+
+    def test_iter_box_dt64tz(self, unit):
+        vals = [
+            Timestamp("2011-01-01", tz="US/Eastern"),
+            Timestamp("2011-01-02", tz="US/Eastern"),
+        ]
+        ser = Series(vals).dt.as_unit(unit)
+
+        assert ser.dtype == f"datetime64[{unit}, US/Eastern]"
+        for res, exp in zip(ser, vals):
+            assert isinstance(res, Timestamp)
+            assert res.tz == exp.tz
+            assert res == exp
+            assert res.unit == unit
+
+    def test_iter_box_timedelta64(self, unit):
+        # timedelta
+        vals = [Timedelta("1 days"), Timedelta("2 days")]
+        ser = Series(vals).dt.as_unit(unit)
+        assert ser.dtype == f"timedelta64[{unit}]"
+        for res, exp in zip(ser, vals):
+            assert isinstance(res, Timedelta)
+            assert res == exp
+            assert res.unit == unit
+
+    def test_iter_box_period(self):
+        # period
+        vals = [pd.Period("2011-01-01", freq="M"), pd.Period("2011-01-02", freq="M")]
+        s = Series(vals)
+        assert s.dtype == "Period[M]"
+        for res, exp in zip(s, vals):
+            assert isinstance(res, pd.Period)
+            assert res.freq == "ME"
+            assert res == exp
+
+
+@pytest.mark.parametrize(
+    "arr, expected_type, dtype",
+    [
+        (np.array([0, 1], dtype=np.int64), np.ndarray, "int64"),
+        (np.array(["a", "b"]), np.ndarray, "object"),
+        (pd.Categorical(["a", "b"]), pd.Categorical, "category"),
+        (
+            pd.DatetimeIndex(["2017", "2018"], tz="US/Central"),
+            DatetimeArray,
+            "datetime64[ns, US/Central]",
+        ),
+        (
+            pd.PeriodIndex([2018, 2019], freq="Y"),
+            PeriodArray,
+            pd.core.dtypes.dtypes.PeriodDtype("Y-DEC"),
+        ),
+        (pd.IntervalIndex.from_breaks([0, 1, 2]), IntervalArray, "interval"),
+        (
+            pd.DatetimeIndex(["2017", "2018"]),
+            DatetimeArray,
+            "datetime64[ns]",
+        ),
+        (
+            pd.TimedeltaIndex([10**10]),
+            TimedeltaArray,
+            "m8[ns]",
+        ),
+    ],
+)
+def test_values_consistent(arr, expected_type, dtype, using_infer_string):
+    if using_infer_string and dtype == "object":
+        expected_type = (
+            ArrowStringArrayNumpySemantics if HAS_PYARROW else StringArrayNumpySemantics
+        )
+    l_values = Series(arr)._values
+    r_values = pd.Index(arr)._values
+    assert type(l_values) is expected_type
+    assert type(l_values) is type(r_values)
+
+    tm.assert_equal(l_values, r_values)
+
+
+@pytest.mark.parametrize("arr", [np.array([1, 2, 3])])
+def test_numpy_array(arr):
+    ser = Series(arr)
+    result = ser.array
+    expected = NumpyExtensionArray(arr)
+    tm.assert_extension_array_equal(result, expected)
+
+
+def test_numpy_array_all_dtypes(any_numpy_dtype):
+    ser = Series(dtype=any_numpy_dtype)
+    result = ser.array
+    if np.dtype(any_numpy_dtype).kind == "M":
+        assert isinstance(result, DatetimeArray)
+    elif np.dtype(any_numpy_dtype).kind == "m":
+        assert isinstance(result, TimedeltaArray)
+    else:
+        assert isinstance(result, NumpyExtensionArray)
+
+
+@pytest.mark.parametrize(
+    "arr, attr",
+    [
+        (pd.Categorical(["a", "b"]), "_codes"),
+        (PeriodArray._from_sequence(["2000", "2001"], dtype="period[D]"), "_ndarray"),
+        (pd.array([0, np.nan], dtype="Int64"), "_data"),
+        (IntervalArray.from_breaks([0, 1]), "_left"),
+        (SparseArray([0, 1]), "_sparse_values"),
+        (
+            DatetimeArray._from_sequence(np.array([1, 2], dtype="datetime64[ns]")),
+            "_ndarray",
+        ),
+        # tz-aware Datetime
+        (
+            DatetimeArray._from_sequence(
+                np.array(
+                    ["2000-01-01T12:00:00", "2000-01-02T12:00:00"], dtype="M8[ns]"
+                ),
+                dtype=DatetimeTZDtype(tz="US/Central"),
+            ),
+            "_ndarray",
+        ),
+    ],
+)
+def test_array(arr, attr, index_or_series, request):
+    box = index_or_series
+
+    result = box(arr, copy=False).array
+
+    if attr:
+        arr = getattr(arr, attr)
+        result = getattr(result, attr)
+
+    assert result is arr
+
+
+def test_array_multiindex_raises():
+    idx = pd.MultiIndex.from_product([["A"], ["a", "b"]])
+    msg = "MultiIndex has no single backing array"
+    with pytest.raises(ValueError, match=msg):
+        idx.array
+
+
+@pytest.mark.parametrize(
+    "arr, expected, zero_copy",
+    [
+        (np.array([1, 2], dtype=np.int64), np.array([1, 2], dtype=np.int64), True),
+        (pd.Categorical(["a", "b"]), np.array(["a", "b"], dtype=object), False),
+        (
+            pd.core.arrays.period_array(["2000", "2001"], freq="D"),
+            np.array([pd.Period("2000", freq="D"), pd.Period("2001", freq="D")]),
+            False,
+        ),
+        (pd.array([0, np.nan], dtype="Int64"), np.array([0, np.nan]), False),
+        (
+            IntervalArray.from_breaks([0, 1, 2]),
+            np.array([pd.Interval(0, 1), pd.Interval(1, 2)], dtype=object),
+            False,
+        ),
+        (SparseArray([0, 1]), np.array([0, 1], dtype=np.int64), False),
+        # tz-naive datetime
+        (
+            DatetimeArray._from_sequence(np.array(["2000", "2001"], dtype="M8[ns]")),
+            np.array(["2000", "2001"], dtype="M8[ns]"),
+            True,
+        ),
+        # tz-aware stays tz`-aware
+        (
+            DatetimeArray._from_sequence(
+                np.array(["2000-01-01T06:00:00", "2000-01-02T06:00:00"], dtype="M8[ns]")
+            )
+            .tz_localize("UTC")
+            .tz_convert("US/Central"),
+            np.array(
+                [
+                    Timestamp("2000-01-01", tz="US/Central"),
+                    Timestamp("2000-01-02", tz="US/Central"),
+                ]
+            ),
+            False,
+        ),
+        # Timedelta
+        (
+            TimedeltaArray._from_sequence(
+                np.array([0, 3600000000000], dtype="i8").view("m8[ns]")
+            ),
+            np.array([0, 3600000000000], dtype="m8[ns]"),
+            True,
+        ),
+        # GH#26406 tz is preserved in Categorical[dt64tz]
+        (
+            pd.Categorical(date_range("2016-01-01", periods=2, tz="US/Pacific")),
+            np.array(
+                [
+                    Timestamp("2016-01-01", tz="US/Pacific"),
+                    Timestamp("2016-01-02", tz="US/Pacific"),
+                ]
+            ),
+            False,
+        ),
+    ],
+)
+def test_to_numpy(arr, expected, zero_copy, index_or_series_or_array):
+    box = index_or_series_or_array
+
+    with tm.assert_produces_warning(None):
+        thing = box(arr)
+
+    result = thing.to_numpy()
+    tm.assert_numpy_array_equal(result, expected)
+
+    result = np.asarray(thing)
+    tm.assert_numpy_array_equal(result, expected)
+
+    # Additionally, we check the `copy=` semantics for array/asarray
+    # (these are implemented by us via `__array__`).
+    result_cp1 = np.array(thing, copy=True)
+    result_cp2 = np.array(thing, copy=True)
+    # When called with `copy=True` NumPy/we should ensure a copy was made
+    assert not np.may_share_memory(result_cp1, result_cp2)
+
+    if not np_version_gt2:
+        # copy=False semantics are only supported in NumPy>=2.
+        return
+
+    if not zero_copy:
+        msg = "Starting with NumPy 2.0, the behavior of the 'copy' keyword has changed"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            np.array(thing, copy=False)
+
+    else:
+        result_nocopy1 = np.array(thing, copy=False)
+        result_nocopy2 = np.array(thing, copy=False)
+        # If copy=False was given, these must share the same data
+        assert np.may_share_memory(result_nocopy1, result_nocopy2)
+
+
+@pytest.mark.parametrize("as_series", [True, False])
+@pytest.mark.parametrize(
+    "arr", [np.array([1, 2, 3], dtype="int64"), np.array(["a", "b", "c"], dtype=object)]
+)
+def test_to_numpy_copy(arr, as_series, using_infer_string):
+    obj = pd.Index(arr, copy=False)
+    if as_series:
+        obj = Series(obj.values, copy=False)
+
+    # no copy by default
+    result = obj.to_numpy()
+    if using_infer_string and arr.dtype == object and obj.dtype.storage == "pyarrow":
+        assert np.shares_memory(arr, result) is False
+    else:
+        assert np.shares_memory(arr, result) is True
+
+    result = obj.to_numpy(copy=False)
+    if using_infer_string and arr.dtype == object and obj.dtype.storage == "pyarrow":
+        assert np.shares_memory(arr, result) is False
+    else:
+        assert np.shares_memory(arr, result) is True
+
+    # copy=True
+    result = obj.to_numpy(copy=True)
+    assert np.shares_memory(arr, result) is False
+
+
+@pytest.mark.parametrize("as_series", [True, False])
+def test_to_numpy_dtype(as_series, unit):
+    tz = "US/Eastern"
+    obj = pd.DatetimeIndex(["2000", "2001"], tz=tz)
+    if as_series:
+        obj = Series(obj)
+
+    # preserve tz by default
+    result = obj.to_numpy()
+    expected = np.array(
+        [Timestamp("2000", tz=tz), Timestamp("2001", tz=tz)], dtype=object
+    )
+    tm.assert_numpy_array_equal(result, expected)
+
+    result = obj.to_numpy(dtype="object")
+    tm.assert_numpy_array_equal(result, expected)
+
+    result = obj.to_numpy(dtype="M8[ns]")
+    expected = np.array(["2000-01-01T05", "2001-01-01T05"], dtype="M8[ns]")
+    tm.assert_numpy_array_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "values, dtype, na_value, expected",
+    [
+        ([1, 2, None], "float64", 0, [1.0, 2.0, 0.0]),
+        (
+            [Timestamp("2000"), Timestamp("2000"), pd.NaT],
+            None,
+            Timestamp("2000"),
+            [np.datetime64("2000-01-01T00:00:00.000000000")] * 3,
+        ),
+    ],
+)
+def test_to_numpy_na_value_numpy_dtype(
+    index_or_series, values, dtype, na_value, expected
+):
+    obj = index_or_series(values)
+    result = obj.to_numpy(dtype=dtype, na_value=na_value)
+    expected = np.array(expected)
+    tm.assert_numpy_array_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "data, multiindex, dtype, na_value, expected",
+    [
+        (
+            [1, 2, None, 4],
+            [(0, "a"), (0, "b"), (1, "b"), (1, "c")],
+            float,
+            None,
+            [1.0, 2.0, np.nan, 4.0],
+        ),
+        (
+            [1, 2, None, 4],
+            [(0, "a"), (0, "b"), (1, "b"), (1, "c")],
+            float,
+            np.nan,
+            [1.0, 2.0, np.nan, 4.0],
+        ),
+        (
+            [1.0, 2.0, np.nan, 4.0],
+            [("a", 0), ("a", 1), ("a", 2), ("b", 0)],
+            int,
+            0,
+            [1, 2, 0, 4],
+        ),
+        (
+            [Timestamp("2000"), Timestamp("2000"), pd.NaT],
+            [(0, Timestamp("2021")), (0, Timestamp("2022")), (1, Timestamp("2000"))],
+            None,
+            Timestamp("2000"),
+            [np.datetime64("2000-01-01T00:00:00.000000000")] * 3,
+        ),
+    ],
+)
+def test_to_numpy_multiindex_series_na_value(
+    data, multiindex, dtype, na_value, expected
+):
+    index = pd.MultiIndex.from_tuples(multiindex)
+    series = Series(data, index=index)
+    result = series.to_numpy(dtype=dtype, na_value=na_value)
+    expected = np.array(expected)
+    tm.assert_numpy_array_equal(result, expected)
+
+
+def test_to_numpy_kwargs_raises():
+    # numpy
+    s = Series([1, 2, 3])
+    msg = r"to_numpy\(\) got an unexpected keyword argument 'foo'"
+    with pytest.raises(TypeError, match=msg):
+        s.to_numpy(foo=True)
+
+    # extension
+    s = Series([1, 2, 3], dtype="Int64")
+    with pytest.raises(TypeError, match=msg):
+        s.to_numpy(foo=True)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        {"a": [1, 2, 3], "b": [1, 2, None]},
+        {"a": np.array([1, 2, 3]), "b": np.array([1, 2, np.nan])},
+        {"a": pd.array([1, 2, 3]), "b": pd.array([1, 2, None])},
+    ],
+)
+@pytest.mark.parametrize("dtype, na_value", [(float, np.nan), (object, None)])
+def test_to_numpy_dataframe_na_value(data, dtype, na_value):
+    # https://github.com/pandas-dev/pandas/issues/33820
+    df = pd.DataFrame(data)
+    result = df.to_numpy(dtype=dtype, na_value=na_value)
+    expected = np.array([[1, 1], [2, 2], [3, na_value]], dtype=dtype)
+    tm.assert_numpy_array_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "data, expected",
+    [
+        (
+            {"a": pd.array([1, 2, None])},
+            np.array([[1.0], [2.0], [np.nan]], dtype=float),
+        ),
+        (
+            {"a": [1, 2, 3], "b": [1, 2, 3]},
+            np.array([[1, 1], [2, 2], [3, 3]], dtype=float),
+        ),
+    ],
+)
+def test_to_numpy_dataframe_single_block(data, expected):
+    # https://github.com/pandas-dev/pandas/issues/33820
+    df = pd.DataFrame(data)
+    result = df.to_numpy(dtype=float, na_value=np.nan)
+    tm.assert_numpy_array_equal(result, expected)
+
+
+def test_to_numpy_dataframe_single_block_no_mutate():
+    # https://github.com/pandas-dev/pandas/issues/33820
+    result = pd.DataFrame(np.array([1.0, 2.0, np.nan]))
+    expected = pd.DataFrame(np.array([1.0, 2.0, np.nan]))
+    result.to_numpy(na_value=0.0)
+    tm.assert_frame_equal(result, expected)
+
+
+class TestAsArray:
+    @pytest.mark.parametrize("tz", [None, "US/Central"])
+    def test_asarray_object_dt64(self, tz):
+        ser = Series(date_range("2000", periods=2, tz=tz))
+
+        with tm.assert_produces_warning(None):
+            # Future behavior (for tzaware case) with no warning
+            result = np.asarray(ser, dtype=object)
+
+        expected = np.array(
+            [Timestamp("2000-01-01", tz=tz), Timestamp("2000-01-02", tz=tz)]
+        )
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_asarray_tz_naive(self):
+        # This shouldn't produce a warning.
+        ser = Series(date_range("2000", periods=2))
+        expected = np.array(["2000-01-01", "2000-01-02"], dtype="M8[ns]")
+        result = np.asarray(ser)
+
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_asarray_tz_aware(self):
+        tz = "US/Central"
+        ser = Series(date_range("2000", periods=2, tz=tz))
+        expected = np.array(["2000-01-01T06", "2000-01-02T06"], dtype="M8[ns]")
+        result = np.asarray(ser, dtype="datetime64[ns]")
+
+        tm.assert_numpy_array_equal(result, expected)
+
+        # Old behavior with no warning
+        result = np.asarray(ser, dtype="M8[ns]")
+
+        tm.assert_numpy_array_equal(result, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/base/test_fillna.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/base/test_fillna.py
new file mode 100644
index 0000000000000000000000000000000000000000..7300d3013305a7ca08312ae85cc42ae8950acf23
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/base/test_fillna.py
@@ -0,0 +1,60 @@
+"""
+Though Index.fillna and Series.fillna has separate impl,
+test here to confirm these works as the same
+"""
+
+import numpy as np
+import pytest
+
+from pandas import MultiIndex
+import pandas._testing as tm
+from pandas.tests.base.common import allow_na_ops
+
+
+def test_fillna(index_or_series_obj):
+    # GH 11343
+    obj = index_or_series_obj
+
+    if isinstance(obj, MultiIndex):
+        msg = "isna is not defined for MultiIndex"
+        with pytest.raises(NotImplementedError, match=msg):
+            obj.fillna(0)
+        return
+
+    # values will not be changed
+    fill_value = obj.values[0] if len(obj) > 0 else 0
+    result = obj.fillna(fill_value)
+
+    tm.assert_equal(obj, result)
+
+    # check shallow_copied
+    assert obj is not result
+
+
+@pytest.mark.parametrize("null_obj", [np.nan, None])
+def test_fillna_null(null_obj, index_or_series_obj):
+    # GH 11343
+    obj = index_or_series_obj
+    klass = type(obj)
+
+    if not allow_na_ops(obj):
+        pytest.skip(f"{klass} doesn't allow for NA operations")
+    elif len(obj) < 1:
+        pytest.skip("Test doesn't make sense on empty data")
+    elif isinstance(obj, MultiIndex):
+        pytest.skip(f"MultiIndex can't hold '{null_obj}'")
+
+    values = obj._values
+    fill_value = values[0]
+    expected = values.copy()
+    values[0:2] = null_obj
+    expected[0:2] = fill_value
+
+    expected = klass(expected)
+    obj = klass(values)
+
+    result = obj.fillna(fill_value)
+    tm.assert_equal(result, expected)
+
+    # check shallow_copied
+    assert obj is not result
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/base/test_misc.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/base/test_misc.py
new file mode 100644
index 0000000000000000000000000000000000000000..1bf0a8d75dd4f688a5776cc3be5523d997518a85
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/base/test_misc.py
@@ -0,0 +1,190 @@
+import sys
+
+import numpy as np
+import pytest
+
+from pandas._config import using_string_dtype
+
+from pandas.compat import PYPY
+
+from pandas.core.dtypes.common import (
+    is_dtype_equal,
+    is_object_dtype,
+)
+
+import pandas as pd
+from pandas import (
+    Index,
+    Series,
+)
+import pandas._testing as tm
+
+
+def test_isnull_notnull_docstrings():
+    # GH#41855 make sure its clear these are aliases
+    doc = pd.DataFrame.notnull.__doc__
+    assert doc.startswith("\nDataFrame.notnull is an alias for DataFrame.notna.\n")
+    doc = pd.DataFrame.isnull.__doc__
+    assert doc.startswith("\nDataFrame.isnull is an alias for DataFrame.isna.\n")
+
+    doc = Series.notnull.__doc__
+    assert doc.startswith("\nSeries.notnull is an alias for Series.notna.\n")
+    doc = Series.isnull.__doc__
+    assert doc.startswith("\nSeries.isnull is an alias for Series.isna.\n")
+
+
+@pytest.mark.parametrize(
+    "op_name, op",
+    [
+        ("add", "+"),
+        ("sub", "-"),
+        ("mul", "*"),
+        ("mod", "%"),
+        ("pow", "**"),
+        ("truediv", "/"),
+        ("floordiv", "//"),
+    ],
+)
+def test_binary_ops_docstring(frame_or_series, op_name, op):
+    # not using the all_arithmetic_functions fixture with _get_opstr
+    # as _get_opstr is used internally in the dynamic implementation of the docstring
+    klass = frame_or_series
+
+    operand1 = klass.__name__.lower()
+    operand2 = "other"
+    expected_str = " ".join([operand1, op, operand2])
+    assert expected_str in getattr(klass, op_name).__doc__
+
+    # reverse version of the binary ops
+    expected_str = " ".join([operand2, op, operand1])
+    assert expected_str in getattr(klass, "r" + op_name).__doc__
+
+
+def test_ndarray_compat_properties(index_or_series_obj):
+    obj = index_or_series_obj
+
+    # Check that we work.
+    for p in ["shape", "dtype", "T", "nbytes"]:
+        assert getattr(obj, p, None) is not None
+
+    # deprecated properties
+    for p in ["strides", "itemsize", "base", "data"]:
+        assert not hasattr(obj, p)
+
+    msg = "can only convert an array of size 1 to a Python scalar"
+    with pytest.raises(ValueError, match=msg):
+        obj.item()  # len > 1
+
+    assert obj.ndim == 1
+    assert obj.size == len(obj)
+
+    assert Index([1]).item() == 1
+    assert Series([1]).item() == 1
+
+
+@pytest.mark.skipif(
+    PYPY or using_string_dtype(),
+    reason="not relevant for PyPy doesn't work properly for arrow strings",
+)
+def test_memory_usage(index_or_series_memory_obj):
+    obj = index_or_series_memory_obj
+    # Clear index caches so that len(obj) == 0 report 0 memory usage
+    if isinstance(obj, Series):
+        is_ser = True
+        obj.index._engine.clear_mapping()
+    else:
+        is_ser = False
+        obj._engine.clear_mapping()
+
+    res = obj.memory_usage()
+    res_deep = obj.memory_usage(deep=True)
+
+    is_object = is_object_dtype(obj) or (is_ser and is_object_dtype(obj.index))
+    is_categorical = isinstance(obj.dtype, pd.CategoricalDtype) or (
+        is_ser and isinstance(obj.index.dtype, pd.CategoricalDtype)
+    )
+    is_object_string = is_dtype_equal(obj, "string[python]") or (
+        is_ser and is_dtype_equal(obj.index.dtype, "string[python]")
+    )
+
+    if len(obj) == 0:
+        expected = 0
+        assert res_deep == res == expected
+    elif is_object or is_categorical or is_object_string:
+        # only deep will pick them up
+        assert res_deep > res
+    else:
+        assert res == res_deep
+
+    # sys.getsizeof will call the .memory_usage with
+    # deep=True, and add on some GC overhead
+    diff = res_deep - sys.getsizeof(obj)
+    assert abs(diff) < 100
+
+
+def test_memory_usage_components_series(series_with_simple_index):
+    series = series_with_simple_index
+    total_usage = series.memory_usage(index=True)
+    non_index_usage = series.memory_usage(index=False)
+    index_usage = series.index.memory_usage()
+    assert total_usage == non_index_usage + index_usage
+
+
+@pytest.mark.parametrize("dtype", tm.NARROW_NP_DTYPES)
+def test_memory_usage_components_narrow_series(dtype):
+    series = Series(range(5), dtype=dtype, index=[f"i-{i}" for i in range(5)], name="a")
+    total_usage = series.memory_usage(index=True)
+    non_index_usage = series.memory_usage(index=False)
+    index_usage = series.index.memory_usage()
+    assert total_usage == non_index_usage + index_usage
+
+
+def test_searchsorted(request, index_or_series_obj):
+    # numpy.searchsorted calls obj.searchsorted under the hood.
+    # See gh-12238
+    obj = index_or_series_obj
+
+    if isinstance(obj, pd.MultiIndex):
+        # See gh-14833
+        request.applymarker(
+            pytest.mark.xfail(
+                reason="np.searchsorted doesn't work on pd.MultiIndex: GH 14833"
+            )
+        )
+    elif obj.dtype.kind == "c" and isinstance(obj, Index):
+        # TODO: Should Series cases also raise? Looks like they use numpy
+        #  comparison semantics https://github.com/numpy/numpy/issues/15981
+        mark = pytest.mark.xfail(reason="complex objects are not comparable")
+        request.applymarker(mark)
+
+    max_obj = max(obj, default=0)
+    index = np.searchsorted(obj, max_obj)
+    assert 0 <= index <= len(obj)
+
+    index = np.searchsorted(obj, max_obj, sorter=range(len(obj)))
+    assert 0 <= index <= len(obj)
+
+
+@pytest.mark.filterwarnings(r"ignore:Dtype inference:FutureWarning")
+def test_access_by_position(index_flat):
+    index = index_flat
+
+    if len(index) == 0:
+        pytest.skip("Test doesn't make sense on empty data")
+
+    series = Series(index)
+    assert index[0] == series.iloc[0]
+    assert index[5] == series.iloc[5]
+    assert index[-1] == series.iloc[-1]
+
+    size = len(index)
+    assert index[-1] == index[size - 1]
+
+    msg = f"index {size} is out of bounds for axis 0 with size {size}"
+    if isinstance(index.dtype, pd.StringDtype) and index.dtype.storage == "pyarrow":
+        msg = "index out of bounds"
+    with pytest.raises(IndexError, match=msg):
+        index[size]
+    msg = "single positional indexer is out-of-bounds"
+    with pytest.raises(IndexError, match=msg):
+        series.iloc[size]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/base/test_transpose.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/base/test_transpose.py
new file mode 100644
index 0000000000000000000000000000000000000000..246f33d27476cb419620fb8571984619785f9b62
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/base/test_transpose.py
@@ -0,0 +1,56 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    CategoricalDtype,
+    DataFrame,
+)
+import pandas._testing as tm
+
+
+def test_transpose(index_or_series_obj):
+    obj = index_or_series_obj
+    tm.assert_equal(obj.transpose(), obj)
+
+
+def test_transpose_non_default_axes(index_or_series_obj):
+    msg = "the 'axes' parameter is not supported"
+    obj = index_or_series_obj
+    with pytest.raises(ValueError, match=msg):
+        obj.transpose(1)
+    with pytest.raises(ValueError, match=msg):
+        obj.transpose(axes=1)
+
+
+def test_numpy_transpose(index_or_series_obj):
+    msg = "the 'axes' parameter is not supported"
+    obj = index_or_series_obj
+    tm.assert_equal(np.transpose(obj), obj)
+
+    with pytest.raises(ValueError, match=msg):
+        np.transpose(obj, axes=1)
+
+
+@pytest.mark.parametrize(
+    "data, transposed_data, index, columns, dtype",
+    [
+        ([[1], [2]], [[1, 2]], ["a", "a"], ["b"], int),
+        ([[1], [2]], [[1, 2]], ["a", "a"], ["b"], CategoricalDtype([1, 2])),
+        ([[1, 2]], [[1], [2]], ["b"], ["a", "a"], int),
+        ([[1, 2]], [[1], [2]], ["b"], ["a", "a"], CategoricalDtype([1, 2])),
+        ([[1, 2], [3, 4]], [[1, 3], [2, 4]], ["a", "a"], ["b", "b"], int),
+        (
+            [[1, 2], [3, 4]],
+            [[1, 3], [2, 4]],
+            ["a", "a"],
+            ["b", "b"],
+            CategoricalDtype([1, 2, 3, 4]),
+        ),
+    ],
+)
+def test_duplicate_labels(data, transposed_data, index, columns, dtype):
+    # GH 42380
+    df = DataFrame(data, index=index, columns=columns, dtype=dtype)
+    result = df.T
+    expected = DataFrame(transposed_data, index=columns, columns=index, dtype=dtype)
+    tm.assert_frame_equal(result, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/base/test_unique.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/base/test_unique.py
new file mode 100644
index 0000000000000000000000000000000000000000..1add56b47b36399960f16724fa8ff14e7bfd4f0e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/base/test_unique.py
@@ -0,0 +1,121 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.tests.base.common import allow_na_ops
+
+
+@pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning")
+def test_unique(index_or_series_obj):
+    obj = index_or_series_obj
+    obj = np.repeat(obj, range(1, len(obj) + 1))
+    result = obj.unique()
+
+    # dict.fromkeys preserves the order
+    unique_values = list(dict.fromkeys(obj.values))
+    if isinstance(obj, pd.MultiIndex):
+        expected = pd.MultiIndex.from_tuples(unique_values)
+        expected.names = obj.names
+        tm.assert_index_equal(result, expected, exact=True)
+    elif isinstance(obj, pd.Index):
+        expected = pd.Index(unique_values, dtype=obj.dtype)
+        if isinstance(obj.dtype, pd.DatetimeTZDtype):
+            expected = expected.normalize()
+        tm.assert_index_equal(result, expected, exact=True)
+    else:
+        expected = np.array(unique_values)
+        tm.assert_numpy_array_equal(result, expected)
+
+
+@pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning")
+@pytest.mark.parametrize("null_obj", [np.nan, None])
+def test_unique_null(null_obj, index_or_series_obj):
+    obj = index_or_series_obj
+
+    if not allow_na_ops(obj):
+        pytest.skip("type doesn't allow for NA operations")
+    elif len(obj) < 1:
+        pytest.skip("Test doesn't make sense on empty data")
+    elif isinstance(obj, pd.MultiIndex):
+        pytest.skip(f"MultiIndex can't hold '{null_obj}'")
+
+    values = obj._values
+    values[0:2] = null_obj
+
+    klass = type(obj)
+    repeated_values = np.repeat(values, range(1, len(values) + 1))
+    obj = klass(repeated_values, dtype=obj.dtype)
+    result = obj.unique()
+
+    unique_values_raw = dict.fromkeys(obj.values)
+    # because np.nan == np.nan is False, but None == None is True
+    # np.nan would be duplicated, whereas None wouldn't
+    unique_values_not_null = [val for val in unique_values_raw if not pd.isnull(val)]
+    unique_values = [null_obj] + unique_values_not_null
+
+    if isinstance(obj, pd.Index):
+        expected = pd.Index(unique_values, dtype=obj.dtype)
+        if isinstance(obj.dtype, pd.DatetimeTZDtype):
+            result = result.normalize()
+            expected = expected.normalize()
+        tm.assert_index_equal(result, expected, exact=True)
+    else:
+        expected = np.array(unique_values, dtype=obj.dtype)
+        tm.assert_numpy_array_equal(result, expected)
+
+
+def test_nunique(index_or_series_obj):
+    obj = index_or_series_obj
+    obj = np.repeat(obj, range(1, len(obj) + 1))
+    expected = len(obj.unique())
+    assert obj.nunique(dropna=False) == expected
+
+
+@pytest.mark.parametrize("null_obj", [np.nan, None])
+def test_nunique_null(null_obj, index_or_series_obj):
+    obj = index_or_series_obj
+
+    if not allow_na_ops(obj):
+        pytest.skip("type doesn't allow for NA operations")
+    elif isinstance(obj, pd.MultiIndex):
+        pytest.skip(f"MultiIndex can't hold '{null_obj}'")
+
+    values = obj._values
+    values[0:2] = null_obj
+
+    klass = type(obj)
+    repeated_values = np.repeat(values, range(1, len(values) + 1))
+    obj = klass(repeated_values, dtype=obj.dtype)
+
+    if isinstance(obj, pd.CategoricalIndex):
+        assert obj.nunique() == len(obj.categories)
+        assert obj.nunique(dropna=False) == len(obj.categories) + 1
+    else:
+        num_unique_values = len(obj.unique())
+        assert obj.nunique() == max(0, num_unique_values - 1)
+        assert obj.nunique(dropna=False) == max(0, num_unique_values)
+
+
+@pytest.mark.single_cpu
+def test_unique_bad_unicode(index_or_series):
+    # regression test for #34550
+    uval = "\ud83d"  # smiley emoji
+
+    obj = index_or_series([uval] * 2, dtype=object)
+    result = obj.unique()
+
+    if isinstance(obj, pd.Index):
+        expected = pd.Index(["\ud83d"], dtype=object)
+        tm.assert_index_equal(result, expected, exact=True)
+    else:
+        expected = np.array(["\ud83d"], dtype=object)
+        tm.assert_numpy_array_equal(result, expected)
+
+
+@pytest.mark.parametrize("dropna", [True, False])
+def test_nunique_dropna(dropna):
+    # GH37566
+    ser = pd.Series(["yes", "yes", pd.NA, np.nan, None, pd.NaT])
+    res = ser.nunique(dropna)
+    assert res == 1 if dropna else 5
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/base/test_value_counts.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/base/test_value_counts.py
new file mode 100644
index 0000000000000000000000000000000000000000..1f643f24ed5f773b605f52b4e257ab5747da538b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/base/test_value_counts.py
@@ -0,0 +1,356 @@
+import collections
+from datetime import timedelta
+
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    DatetimeIndex,
+    Index,
+    Interval,
+    IntervalIndex,
+    MultiIndex,
+    Series,
+    Timedelta,
+    TimedeltaIndex,
+    array,
+)
+import pandas._testing as tm
+from pandas.tests.base.common import allow_na_ops
+
+
+@pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning")
+def test_value_counts(index_or_series_obj):
+    obj = index_or_series_obj
+    obj = np.repeat(obj, range(1, len(obj) + 1))
+    result = obj.value_counts()
+
+    counter = collections.Counter(obj)
+    expected = Series(dict(counter.most_common()), dtype=np.int64, name="count")
+
+    if obj.dtype != np.float16:
+        expected.index = expected.index.astype(obj.dtype)
+    else:
+        with pytest.raises(NotImplementedError, match="float16 indexes are not "):
+            expected.index.astype(obj.dtype)
+        return
+    if isinstance(expected.index, MultiIndex):
+        expected.index.names = obj.names
+    else:
+        expected.index.name = obj.name
+
+    if not isinstance(result.dtype, np.dtype):
+        if getattr(obj.dtype, "storage", "") == "pyarrow":
+            expected = expected.astype("int64[pyarrow]")
+        else:
+            # i.e IntegerDtype
+            expected = expected.astype("Int64")
+
+    # TODO(GH#32514): Order of entries with the same count is inconsistent
+    #  on CI (gh-32449)
+    if obj.duplicated().any():
+        result = result.sort_index()
+        expected = expected.sort_index()
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("null_obj", [np.nan, None])
+@pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning")
+def test_value_counts_null(null_obj, index_or_series_obj):
+    orig = index_or_series_obj
+    obj = orig.copy()
+
+    if not allow_na_ops(obj):
+        pytest.skip("type doesn't allow for NA operations")
+    elif len(obj) < 1:
+        pytest.skip("Test doesn't make sense on empty data")
+    elif isinstance(orig, MultiIndex):
+        pytest.skip(f"MultiIndex can't hold '{null_obj}'")
+
+    values = obj._values
+    values[0:2] = null_obj
+
+    klass = type(obj)
+    repeated_values = np.repeat(values, range(1, len(values) + 1))
+    obj = klass(repeated_values, dtype=obj.dtype)
+
+    # because np.nan == np.nan is False, but None == None is True
+    # np.nan would be duplicated, whereas None wouldn't
+    counter = collections.Counter(obj.dropna())
+    expected = Series(dict(counter.most_common()), dtype=np.int64, name="count")
+
+    if obj.dtype != np.float16:
+        expected.index = expected.index.astype(obj.dtype)
+    else:
+        with pytest.raises(NotImplementedError, match="float16 indexes are not "):
+            expected.index.astype(obj.dtype)
+        return
+    expected.index.name = obj.name
+
+    result = obj.value_counts()
+    if obj.duplicated().any():
+        # TODO(GH#32514):
+        #  Order of entries with the same count is inconsistent on CI (gh-32449)
+        expected = expected.sort_index()
+        result = result.sort_index()
+
+    if not isinstance(result.dtype, np.dtype):
+        if getattr(obj.dtype, "storage", "") == "pyarrow":
+            expected = expected.astype("int64[pyarrow]")
+        else:
+            # i.e IntegerDtype
+            expected = expected.astype("Int64")
+    tm.assert_series_equal(result, expected)
+
+    expected[null_obj] = 3
+
+    result = obj.value_counts(dropna=False)
+    if obj.duplicated().any():
+        # TODO(GH#32514):
+        #  Order of entries with the same count is inconsistent on CI (gh-32449)
+        expected = expected.sort_index()
+        result = result.sort_index()
+    tm.assert_series_equal(result, expected)
+
+
+def test_value_counts_inferred(index_or_series, using_infer_string):
+    klass = index_or_series
+    s_values = ["a", "b", "b", "b", "b", "c", "d", "d", "a", "a"]
+    s = klass(s_values)
+    expected = Series([4, 3, 2, 1], index=["b", "a", "d", "c"], name="count")
+    tm.assert_series_equal(s.value_counts(), expected)
+
+    if isinstance(s, Index):
+        exp = Index(np.unique(np.array(s_values, dtype=np.object_)))
+        tm.assert_index_equal(s.unique(), exp)
+    else:
+        exp = np.unique(np.array(s_values, dtype=np.object_))
+        if using_infer_string:
+            exp = array(exp, dtype="str")
+        tm.assert_equal(s.unique(), exp)
+
+    assert s.nunique() == 4
+    # don't sort, have to sort after the fact as not sorting is
+    # platform-dep
+    hist = s.value_counts(sort=False).sort_values()
+    expected = Series([3, 1, 4, 2], index=list("acbd"), name="count").sort_values()
+    tm.assert_series_equal(hist, expected)
+
+    # sort ascending
+    hist = s.value_counts(ascending=True)
+    expected = Series([1, 2, 3, 4], index=list("cdab"), name="count")
+    tm.assert_series_equal(hist, expected)
+
+    # relative histogram.
+    hist = s.value_counts(normalize=True)
+    expected = Series(
+        [0.4, 0.3, 0.2, 0.1], index=["b", "a", "d", "c"], name="proportion"
+    )
+    tm.assert_series_equal(hist, expected)
+
+
+def test_value_counts_bins(index_or_series, using_infer_string):
+    klass = index_or_series
+    s_values = ["a", "b", "b", "b", "b", "c", "d", "d", "a", "a"]
+    s = klass(s_values)
+
+    # bins
+    msg = "bins argument only works with numeric data"
+    with pytest.raises(TypeError, match=msg):
+        s.value_counts(bins=1)
+
+    s1 = Series([1, 1, 2, 3])
+    res1 = s1.value_counts(bins=1)
+    exp1 = Series({Interval(0.997, 3.0): 4}, name="count")
+    tm.assert_series_equal(res1, exp1)
+    res1n = s1.value_counts(bins=1, normalize=True)
+    exp1n = Series({Interval(0.997, 3.0): 1.0}, name="proportion")
+    tm.assert_series_equal(res1n, exp1n)
+
+    if isinstance(s1, Index):
+        tm.assert_index_equal(s1.unique(), Index([1, 2, 3]))
+    else:
+        exp = np.array([1, 2, 3], dtype=np.int64)
+        tm.assert_numpy_array_equal(s1.unique(), exp)
+
+    assert s1.nunique() == 3
+
+    # these return the same
+    res4 = s1.value_counts(bins=4, dropna=True)
+    intervals = IntervalIndex.from_breaks([0.997, 1.5, 2.0, 2.5, 3.0])
+    exp4 = Series([2, 1, 1, 0], index=intervals.take([0, 1, 3, 2]), name="count")
+    tm.assert_series_equal(res4, exp4)
+
+    res4 = s1.value_counts(bins=4, dropna=False)
+    intervals = IntervalIndex.from_breaks([0.997, 1.5, 2.0, 2.5, 3.0])
+    exp4 = Series([2, 1, 1, 0], index=intervals.take([0, 1, 3, 2]), name="count")
+    tm.assert_series_equal(res4, exp4)
+
+    res4n = s1.value_counts(bins=4, normalize=True)
+    exp4n = Series(
+        [0.5, 0.25, 0.25, 0], index=intervals.take([0, 1, 3, 2]), name="proportion"
+    )
+    tm.assert_series_equal(res4n, exp4n)
+
+    # handle NA's properly
+    s_values = ["a", "b", "b", "b", np.nan, np.nan, "d", "d", "a", "a", "b"]
+    s = klass(s_values)
+    expected = Series([4, 3, 2], index=["b", "a", "d"], name="count")
+    tm.assert_series_equal(s.value_counts(), expected)
+
+    if isinstance(s, Index):
+        exp = Index(["a", "b", np.nan, "d"])
+        tm.assert_index_equal(s.unique(), exp)
+    else:
+        exp = np.array(["a", "b", np.nan, "d"], dtype=object)
+        if using_infer_string:
+            exp = array(exp, dtype="str")
+        tm.assert_equal(s.unique(), exp)
+    assert s.nunique() == 3
+
+    s = klass({}) if klass is dict else klass({}, dtype=object)
+    expected = Series([], dtype=np.int64, name="count")
+    tm.assert_series_equal(s.value_counts(), expected, check_index_type=False)
+    # returned dtype differs depending on original
+    if isinstance(s, Index):
+        tm.assert_index_equal(s.unique(), Index([]), exact=False)
+    else:
+        tm.assert_numpy_array_equal(s.unique(), np.array([]), check_dtype=False)
+
+    assert s.nunique() == 0
+
+
+def test_value_counts_datetime64(index_or_series, unit):
+    klass = index_or_series
+
+    # GH 3002, datetime64[ns]
+    # don't test names though
+    df = pd.DataFrame(
+        {
+            "person_id": ["xxyyzz", "xxyyzz", "xxyyzz", "xxyyww", "foofoo", "foofoo"],
+            "dt": pd.to_datetime(
+                [
+                    "2010-01-01",
+                    "2010-01-01",
+                    "2010-01-01",
+                    "2009-01-01",
+                    "2008-09-09",
+                    "2008-09-09",
+                ]
+            ).as_unit(unit),
+            "food": ["PIE", "GUM", "EGG", "EGG", "PIE", "GUM"],
+        }
+    )
+
+    s = klass(df["dt"].copy())
+    s.name = None
+    idx = pd.to_datetime(
+        ["2010-01-01 00:00:00", "2008-09-09 00:00:00", "2009-01-01 00:00:00"]
+    ).as_unit(unit)
+    expected_s = Series([3, 2, 1], index=idx, name="count")
+    tm.assert_series_equal(s.value_counts(), expected_s)
+
+    expected = array(
+        np.array(
+            ["2010-01-01 00:00:00", "2009-01-01 00:00:00", "2008-09-09 00:00:00"],
+            dtype=f"datetime64[{unit}]",
+        )
+    )
+    result = s.unique()
+    if isinstance(s, Index):
+        tm.assert_index_equal(result, DatetimeIndex(expected))
+    else:
+        tm.assert_extension_array_equal(result, expected)
+
+    assert s.nunique() == 3
+
+    # with NaT
+    s = df["dt"].copy()
+    s = klass(list(s.values) + [pd.NaT] * 4)
+    if klass is Series:
+        s = s.dt.as_unit(unit)
+    else:
+        s = s.as_unit(unit)
+
+    result = s.value_counts()
+    assert result.index.dtype == f"datetime64[{unit}]"
+    tm.assert_series_equal(result, expected_s)
+
+    result = s.value_counts(dropna=False)
+    expected_s = pd.concat(
+        [
+            Series([4], index=DatetimeIndex([pd.NaT]).as_unit(unit), name="count"),
+            expected_s,
+        ]
+    )
+    tm.assert_series_equal(result, expected_s)
+
+    assert s.dtype == f"datetime64[{unit}]"
+    unique = s.unique()
+    assert unique.dtype == f"datetime64[{unit}]"
+
+    # numpy_array_equal cannot compare pd.NaT
+    if isinstance(s, Index):
+        exp_idx = DatetimeIndex(expected.tolist() + [pd.NaT]).as_unit(unit)
+        tm.assert_index_equal(unique, exp_idx)
+    else:
+        tm.assert_extension_array_equal(unique[:3], expected)
+        assert pd.isna(unique[3])
+
+    assert s.nunique() == 3
+    assert s.nunique(dropna=False) == 4
+
+
+def test_value_counts_timedelta64(index_or_series, unit):
+    # timedelta64[ns]
+    klass = index_or_series
+
+    day = Timedelta(timedelta(1)).as_unit(unit)
+    tdi = TimedeltaIndex([day], name="dt").as_unit(unit)
+
+    tdvals = np.zeros(6, dtype=f"m8[{unit}]") + day
+    td = klass(tdvals, name="dt")
+
+    result = td.value_counts()
+    expected_s = Series([6], index=tdi, name="count")
+    tm.assert_series_equal(result, expected_s)
+
+    expected = tdi
+    result = td.unique()
+    if isinstance(td, Index):
+        tm.assert_index_equal(result, expected)
+    else:
+        tm.assert_extension_array_equal(result, expected._values)
+
+    td2 = day + np.zeros(6, dtype=f"m8[{unit}]")
+    td2 = klass(td2, name="dt")
+    result2 = td2.value_counts()
+    tm.assert_series_equal(result2, expected_s)
+
+
+@pytest.mark.parametrize("dropna", [True, False])
+def test_value_counts_with_nan(dropna, index_or_series):
+    # GH31944
+    klass = index_or_series
+    values = [True, pd.NA, np.nan]
+    obj = klass(values)
+    res = obj.value_counts(dropna=dropna)
+    if dropna is True:
+        expected = Series([1], index=Index([True], dtype=obj.dtype), name="count")
+    else:
+        expected = Series([1, 1, 1], index=[True, pd.NA, np.nan], name="count")
+    tm.assert_series_equal(res, expected)
+
+
+def test_value_counts_object_inference_deprecated():
+    # GH#56161
+    dti = pd.date_range("2016-01-01", periods=3, tz="UTC")
+
+    idx = dti.astype(object)
+    msg = "The behavior of value_counts with object-dtype is deprecated"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        res = idx.value_counts()
+
+    exp = dti.value_counts()
+    tm.assert_series_equal(res, exp)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/computation/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/computation/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/computation/test_compat.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/computation/test_compat.py
new file mode 100644
index 0000000000000000000000000000000000000000..856a5b3a22a95d35cc577050f52d762b065e3ddf
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/computation/test_compat.py
@@ -0,0 +1,32 @@
+import pytest
+
+from pandas.compat._optional import VERSIONS
+
+import pandas as pd
+from pandas.core.computation import expr
+from pandas.core.computation.engines import ENGINES
+from pandas.util.version import Version
+
+
+def test_compat():
+    # test we have compat with our version of numexpr
+
+    from pandas.core.computation.check import NUMEXPR_INSTALLED
+
+    ne = pytest.importorskip("numexpr")
+
+    ver = ne.__version__
+    if Version(ver) < Version(VERSIONS["numexpr"]):
+        assert not NUMEXPR_INSTALLED
+    else:
+        assert NUMEXPR_INSTALLED
+
+
+@pytest.mark.parametrize("engine", ENGINES)
+@pytest.mark.parametrize("parser", expr.PARSERS)
+def test_invalid_numexpr_version(engine, parser):
+    if engine == "numexpr":
+        pytest.importorskip("numexpr")
+    a, b = 1, 2  # noqa: F841
+    res = pd.eval("a + b", engine=engine, parser=parser)
+    assert res == 3
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/computation/test_eval.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/computation/test_eval.py
new file mode 100644
index 0000000000000000000000000000000000000000..859cbd30cfeadb29add9cabd770be0d6bd67d2d1
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/computation/test_eval.py
@@ -0,0 +1,2006 @@
+from __future__ import annotations
+
+from functools import reduce
+from itertools import product
+import operator
+
+import numpy as np
+import pytest
+
+from pandas.compat import PY312
+from pandas.compat._optional import import_optional_dependency
+from pandas.errors import (
+    NumExprClobberingError,
+    PerformanceWarning,
+    UndefinedVariableError,
+)
+import pandas.util._test_decorators as td
+
+from pandas.core.dtypes.common import (
+    is_bool,
+    is_float,
+    is_list_like,
+    is_scalar,
+)
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Index,
+    Series,
+    date_range,
+    period_range,
+    timedelta_range,
+)
+import pandas._testing as tm
+from pandas.core.computation import (
+    expr,
+    pytables,
+)
+from pandas.core.computation.engines import ENGINES
+from pandas.core.computation.expr import (
+    BaseExprVisitor,
+    PandasExprVisitor,
+    PythonExprVisitor,
+)
+from pandas.core.computation.expressions import (
+    NUMEXPR_INSTALLED,
+    USE_NUMEXPR,
+)
+from pandas.core.computation.ops import (
+    ARITH_OPS_SYMS,
+    SPECIAL_CASE_ARITH_OPS_SYMS,
+    _binary_math_ops,
+    _binary_ops_dict,
+    _unary_math_ops,
+)
+from pandas.core.computation.scope import DEFAULT_GLOBALS
+from pandas.util.version import Version
+
+numexpr = import_optional_dependency("numexpr", errors="ignore")
+
+
+@pytest.fixture(
+    params=(
+        pytest.param(
+            engine,
+            marks=[
+                pytest.mark.skipif(
+                    engine == "numexpr" and not USE_NUMEXPR,
+                    reason=f"numexpr enabled->{USE_NUMEXPR}, "
+                    f"installed->{NUMEXPR_INSTALLED}",
+                ),
+                td.skip_if_no("numexpr"),
+            ],
+        )
+        for engine in ENGINES
+    )
+)
+def engine(request):
+    return request.param
+
+
+@pytest.fixture(params=expr.PARSERS)
+def parser(request):
+    return request.param
+
+
+def _eval_single_bin(lhs, cmp1, rhs, engine):
+    c = _binary_ops_dict[cmp1]
+    if ENGINES[engine].has_neg_frac:
+        try:
+            return c(lhs, rhs)
+        except ValueError as e:
+            if str(e).startswith(
+                "negative number cannot be raised to a fractional power"
+            ):
+                return np.nan
+            raise
+    return c(lhs, rhs)
+
+
+# TODO: using range(5) here is a kludge
+@pytest.fixture(
+    params=list(range(5)),
+    ids=["DataFrame", "Series", "SeriesNaN", "DataFrameNaN", "float"],
+)
+def lhs(request):
+    nan_df1 = DataFrame(np.random.default_rng(2).standard_normal((10, 5)))
+    nan_df1[nan_df1 > 0.5] = np.nan
+
+    opts = (
+        DataFrame(np.random.default_rng(2).standard_normal((10, 5))),
+        Series(np.random.default_rng(2).standard_normal(5)),
+        Series([1, 2, np.nan, np.nan, 5]),
+        nan_df1,
+        np.random.default_rng(2).standard_normal(),
+    )
+    return opts[request.param]
+
+
+rhs = lhs
+midhs = lhs
+
+
+@pytest.fixture
+def idx_func_dict():
+    return {
+        "i": lambda n: Index(np.arange(n), dtype=np.int64),
+        "f": lambda n: Index(np.arange(n), dtype=np.float64),
+        "s": lambda n: Index([f"{i}_{chr(i)}" for i in range(97, 97 + n)]),
+        "dt": lambda n: date_range("2020-01-01", periods=n),
+        "td": lambda n: timedelta_range("1 day", periods=n),
+        "p": lambda n: period_range("2020-01-01", periods=n, freq="D"),
+    }
+
+
+class TestEval:
+    @pytest.mark.parametrize(
+        "cmp1",
+        ["!=", "==", "<=", ">=", "<", ">"],
+        ids=["ne", "eq", "le", "ge", "lt", "gt"],
+    )
+    @pytest.mark.parametrize("cmp2", [">", "<"], ids=["gt", "lt"])
+    @pytest.mark.parametrize("binop", expr.BOOL_OPS_SYMS)
+    def test_complex_cmp_ops(self, cmp1, cmp2, binop, lhs, rhs, engine, parser):
+        if parser == "python" and binop in ["and", "or"]:
+            msg = "'BoolOp' nodes are not implemented"
+            with pytest.raises(NotImplementedError, match=msg):
+                ex = f"(lhs {cmp1} rhs) {binop} (lhs {cmp2} rhs)"
+                pd.eval(ex, engine=engine, parser=parser)
+            return
+
+        lhs_new = _eval_single_bin(lhs, cmp1, rhs, engine)
+        rhs_new = _eval_single_bin(lhs, cmp2, rhs, engine)
+        expected = _eval_single_bin(lhs_new, binop, rhs_new, engine)
+
+        ex = f"(lhs {cmp1} rhs) {binop} (lhs {cmp2} rhs)"
+        result = pd.eval(ex, engine=engine, parser=parser)
+        tm.assert_equal(result, expected)
+
+    @pytest.mark.parametrize("cmp_op", expr.CMP_OPS_SYMS)
+    def test_simple_cmp_ops(self, cmp_op, lhs, rhs, engine, parser):
+        lhs = lhs < 0
+        rhs = rhs < 0
+
+        if parser == "python" and cmp_op in ["in", "not in"]:
+            msg = "'(In|NotIn)' nodes are not implemented"
+
+            with pytest.raises(NotImplementedError, match=msg):
+                ex = f"lhs {cmp_op} rhs"
+                pd.eval(ex, engine=engine, parser=parser)
+            return
+
+        ex = f"lhs {cmp_op} rhs"
+        msg = "|".join(
+            [
+                r"only list-like( or dict-like)? objects are allowed to be "
+                r"passed to (DataFrame\.)?isin\(\), you passed a "
+                r"(`|')bool(`|')",
+                "argument of type 'bool' is not iterable",
+            ]
+        )
+        if cmp_op in ("in", "not in") and not is_list_like(rhs):
+            with pytest.raises(TypeError, match=msg):
+                pd.eval(
+                    ex,
+                    engine=engine,
+                    parser=parser,
+                    local_dict={"lhs": lhs, "rhs": rhs},
+                )
+        else:
+            expected = _eval_single_bin(lhs, cmp_op, rhs, engine)
+            result = pd.eval(ex, engine=engine, parser=parser)
+            tm.assert_equal(result, expected)
+
+    @pytest.mark.parametrize("op", expr.CMP_OPS_SYMS)
+    def test_compound_invert_op(self, op, lhs, rhs, request, engine, parser):
+        if parser == "python" and op in ["in", "not in"]:
+            msg = "'(In|NotIn)' nodes are not implemented"
+            with pytest.raises(NotImplementedError, match=msg):
+                ex = f"~(lhs {op} rhs)"
+                pd.eval(ex, engine=engine, parser=parser)
+            return
+
+        if (
+            is_float(lhs)
+            and not is_float(rhs)
+            and op in ["in", "not in"]
+            and engine == "python"
+            and parser == "pandas"
+        ):
+            mark = pytest.mark.xfail(
+                reason="Looks like expected is negative, unclear whether "
+                "expected is incorrect or result is incorrect"
+            )
+            request.applymarker(mark)
+        skip_these = ["in", "not in"]
+        ex = f"~(lhs {op} rhs)"
+
+        msg = "|".join(
+            [
+                r"only list-like( or dict-like)? objects are allowed to be "
+                r"passed to (DataFrame\.)?isin\(\), you passed a "
+                r"(`|')float(`|')",
+                "argument of type 'float' is not iterable",
+            ]
+        )
+        if is_scalar(rhs) and op in skip_these:
+            with pytest.raises(TypeError, match=msg):
+                pd.eval(
+                    ex,
+                    engine=engine,
+                    parser=parser,
+                    local_dict={"lhs": lhs, "rhs": rhs},
+                )
+        else:
+            # compound
+            if is_scalar(lhs) and is_scalar(rhs):
+                lhs, rhs = (np.array([x]) for x in (lhs, rhs))
+            expected = _eval_single_bin(lhs, op, rhs, engine)
+            if is_scalar(expected):
+                expected = not expected
+            else:
+                expected = ~expected
+            result = pd.eval(ex, engine=engine, parser=parser)
+            tm.assert_almost_equal(expected, result)
+
+    @pytest.mark.parametrize("cmp1", ["<", ">"])
+    @pytest.mark.parametrize("cmp2", ["<", ">"])
+    def test_chained_cmp_op(self, cmp1, cmp2, lhs, midhs, rhs, engine, parser):
+        mid = midhs
+        if parser == "python":
+            ex1 = f"lhs {cmp1} mid {cmp2} rhs"
+            msg = "'BoolOp' nodes are not implemented"
+            with pytest.raises(NotImplementedError, match=msg):
+                pd.eval(ex1, engine=engine, parser=parser)
+            return
+
+        lhs_new = _eval_single_bin(lhs, cmp1, mid, engine)
+        rhs_new = _eval_single_bin(mid, cmp2, rhs, engine)
+
+        if lhs_new is not None and rhs_new is not None:
+            ex1 = f"lhs {cmp1} mid {cmp2} rhs"
+            ex2 = f"lhs {cmp1} mid and mid {cmp2} rhs"
+            ex3 = f"(lhs {cmp1} mid) & (mid {cmp2} rhs)"
+            expected = _eval_single_bin(lhs_new, "&", rhs_new, engine)
+
+            for ex in (ex1, ex2, ex3):
+                result = pd.eval(ex, engine=engine, parser=parser)
+
+                tm.assert_almost_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "arith1", sorted(set(ARITH_OPS_SYMS).difference(SPECIAL_CASE_ARITH_OPS_SYMS))
+    )
+    def test_binary_arith_ops(self, arith1, lhs, rhs, engine, parser):
+        ex = f"lhs {arith1} rhs"
+        result = pd.eval(ex, engine=engine, parser=parser)
+        expected = _eval_single_bin(lhs, arith1, rhs, engine)
+
+        tm.assert_almost_equal(result, expected)
+        ex = f"lhs {arith1} rhs {arith1} rhs"
+        result = pd.eval(ex, engine=engine, parser=parser)
+        nlhs = _eval_single_bin(lhs, arith1, rhs, engine)
+        try:
+            nlhs, ghs = nlhs.align(rhs)
+        except (ValueError, TypeError, AttributeError):
+            # ValueError: series frame or frame series align
+            # TypeError, AttributeError: series or frame with scalar align
+            return
+        else:
+            if engine == "numexpr":
+                import numexpr as ne
+
+                # direct numpy comparison
+                expected = ne.evaluate(f"nlhs {arith1} ghs")
+                # Update assert statement due to unreliable numerical
+                # precision component (GH37328)
+                # TODO: update testing code so that assert_almost_equal statement
+                #  can be replaced again by the assert_numpy_array_equal statement
+                tm.assert_almost_equal(result.values, expected)
+            else:
+                expected = eval(f"nlhs {arith1} ghs")
+                tm.assert_almost_equal(result, expected)
+
+    # modulus, pow, and floor division require special casing
+
+    def test_modulus(self, lhs, rhs, engine, parser):
+        ex = r"lhs % rhs"
+        result = pd.eval(ex, engine=engine, parser=parser)
+        expected = lhs % rhs
+        tm.assert_almost_equal(result, expected)
+
+        if engine == "numexpr":
+            import numexpr as ne
+
+            expected = ne.evaluate(r"expected % rhs")
+            if isinstance(result, (DataFrame, Series)):
+                tm.assert_almost_equal(result.values, expected)
+            else:
+                tm.assert_almost_equal(result, expected.item())
+        else:
+            expected = _eval_single_bin(expected, "%", rhs, engine)
+            tm.assert_almost_equal(result, expected)
+
+    def test_floor_division(self, lhs, rhs, engine, parser):
+        ex = "lhs // rhs"
+
+        if engine == "python" or (
+            engine == "numexpr" and Version(numexpr.__version__) >= Version("2.13.0")
+        ):
+            res = pd.eval(ex, engine=engine, parser=parser)
+            expected = lhs // rhs
+            tm.assert_equal(res, expected)
+        else:
+            msg = (
+                r"unsupported operand type\(s\) for //: 'VariableNode' and "
+                "'VariableNode'"
+            )
+            with pytest.raises(TypeError, match=msg):
+                pd.eval(
+                    ex,
+                    local_dict={"lhs": lhs, "rhs": rhs},
+                    engine=engine,
+                    parser=parser,
+                )
+
+    @td.skip_if_windows
+    def test_pow(self, lhs, rhs, engine, parser):
+        # odd failure on win32 platform, so skip
+        ex = "lhs ** rhs"
+        expected = _eval_single_bin(lhs, "**", rhs, engine)
+        result = pd.eval(ex, engine=engine, parser=parser)
+
+        if (
+            is_scalar(lhs)
+            and is_scalar(rhs)
+            and isinstance(expected, (complex, np.complexfloating))
+            and np.isnan(result)
+        ):
+            msg = "(DataFrame.columns|numpy array) are different"
+            with pytest.raises(AssertionError, match=msg):
+                tm.assert_numpy_array_equal(result, expected)
+        else:
+            tm.assert_almost_equal(result, expected)
+
+            ex = "(lhs ** rhs) ** rhs"
+            result = pd.eval(ex, engine=engine, parser=parser)
+
+            middle = _eval_single_bin(lhs, "**", rhs, engine)
+            expected = _eval_single_bin(middle, "**", rhs, engine)
+            tm.assert_almost_equal(result, expected)
+
+    def test_check_single_invert_op(self, lhs, engine, parser):
+        # simple
+        try:
+            elb = lhs.astype(bool)
+        except AttributeError:
+            elb = np.array([bool(lhs)])
+        expected = ~elb
+        result = pd.eval("~elb", engine=engine, parser=parser)
+        tm.assert_almost_equal(expected, result)
+
+    def test_frame_invert(self, engine, parser):
+        expr = "~lhs"
+
+        # ~ ##
+        # frame
+        # float always raises
+        lhs = DataFrame(np.random.default_rng(2).standard_normal((5, 2)))
+        if engine == "numexpr":
+            msg = "couldn't find matching opcode for 'invert_dd'"
+            with pytest.raises(NotImplementedError, match=msg):
+                pd.eval(expr, engine=engine, parser=parser)
+        else:
+            msg = "ufunc 'invert' not supported for the input types"
+            with pytest.raises(TypeError, match=msg):
+                pd.eval(expr, engine=engine, parser=parser)
+
+        # int raises on numexpr
+        lhs = DataFrame(np.random.default_rng(2).integers(5, size=(5, 2)))
+        if engine == "numexpr" and Version(numexpr.__version__) < Version("2.13.0"):
+            msg = "couldn't find matching opcode for 'invert"
+            with pytest.raises(NotImplementedError, match=msg):
+                pd.eval(expr, engine=engine, parser=parser)
+        else:
+            expect = ~lhs
+            result = pd.eval(expr, engine=engine, parser=parser)
+            tm.assert_frame_equal(expect, result)
+
+        # bool always works
+        lhs = DataFrame(np.random.default_rng(2).standard_normal((5, 2)) > 0.5)
+        expect = ~lhs
+        result = pd.eval(expr, engine=engine, parser=parser)
+        tm.assert_frame_equal(expect, result)
+
+        # object raises
+        lhs = DataFrame(
+            {"b": ["a", 1, 2.0], "c": np.random.default_rng(2).standard_normal(3) > 0.5}
+        )
+        if engine == "numexpr":
+            with pytest.raises(ValueError, match="unknown type object"):
+                pd.eval(expr, engine=engine, parser=parser)
+        else:
+            msg = "bad operand type for unary ~: 'str'"
+            with pytest.raises(TypeError, match=msg):
+                pd.eval(expr, engine=engine, parser=parser)
+
+    def test_series_invert(self, engine, parser):
+        # ~ ####
+        expr = "~lhs"
+
+        # series
+        # float raises
+        lhs = Series(np.random.default_rng(2).standard_normal(5))
+        if engine == "numexpr":
+            msg = "couldn't find matching opcode for 'invert_dd'"
+            with pytest.raises(NotImplementedError, match=msg):
+                result = pd.eval(expr, engine=engine, parser=parser)
+        else:
+            msg = "ufunc 'invert' not supported for the input types"
+            with pytest.raises(TypeError, match=msg):
+                pd.eval(expr, engine=engine, parser=parser)
+
+        # int raises on numexpr
+        lhs = Series(np.random.default_rng(2).integers(5, size=5))
+        if engine == "numexpr" and Version(numexpr.__version__) < Version("2.13.0"):
+            msg = "couldn't find matching opcode for 'invert"
+            with pytest.raises(NotImplementedError, match=msg):
+                pd.eval(expr, engine=engine, parser=parser)
+        else:
+            expect = ~lhs
+            result = pd.eval(expr, engine=engine, parser=parser)
+            tm.assert_series_equal(expect, result)
+
+        # bool
+        lhs = Series(np.random.default_rng(2).standard_normal(5) > 0.5)
+        expect = ~lhs
+        result = pd.eval(expr, engine=engine, parser=parser)
+        tm.assert_series_equal(expect, result)
+
+        # float
+        # int
+        # bool
+
+        # object
+        lhs = Series(["a", 1, 2.0])
+        if engine == "numexpr":
+            with pytest.raises(ValueError, match="unknown type object"):
+                pd.eval(expr, engine=engine, parser=parser)
+        else:
+            msg = "bad operand type for unary ~: 'str'"
+            with pytest.raises(TypeError, match=msg):
+                pd.eval(expr, engine=engine, parser=parser)
+
+    def test_frame_negate(self, engine, parser):
+        expr = "-lhs"
+
+        # float
+        lhs = DataFrame(np.random.default_rng(2).standard_normal((5, 2)))
+        expect = -lhs
+        result = pd.eval(expr, engine=engine, parser=parser)
+        tm.assert_frame_equal(expect, result)
+
+        # int
+        lhs = DataFrame(np.random.default_rng(2).integers(5, size=(5, 2)))
+        expect = -lhs
+        result = pd.eval(expr, engine=engine, parser=parser)
+        tm.assert_frame_equal(expect, result)
+
+        # bool doesn't work with numexpr but works elsewhere
+        lhs = DataFrame(np.random.default_rng(2).standard_normal((5, 2)) > 0.5)
+        if engine == "numexpr":
+            msg = "couldn't find matching opcode for 'neg_bb'"
+            with pytest.raises(NotImplementedError, match=msg):
+                pd.eval(expr, engine=engine, parser=parser)
+        else:
+            expect = -lhs
+            result = pd.eval(expr, engine=engine, parser=parser)
+            tm.assert_frame_equal(expect, result)
+
+    def test_series_negate(self, engine, parser):
+        expr = "-lhs"
+
+        # float
+        lhs = Series(np.random.default_rng(2).standard_normal(5))
+        expect = -lhs
+        result = pd.eval(expr, engine=engine, parser=parser)
+        tm.assert_series_equal(expect, result)
+
+        # int
+        lhs = Series(np.random.default_rng(2).integers(5, size=5))
+        expect = -lhs
+        result = pd.eval(expr, engine=engine, parser=parser)
+        tm.assert_series_equal(expect, result)
+
+        # bool doesn't work with numexpr but works elsewhere
+        lhs = Series(np.random.default_rng(2).standard_normal(5) > 0.5)
+        if engine == "numexpr":
+            msg = "couldn't find matching opcode for 'neg_bb'"
+            with pytest.raises(NotImplementedError, match=msg):
+                pd.eval(expr, engine=engine, parser=parser)
+        else:
+            expect = -lhs
+            result = pd.eval(expr, engine=engine, parser=parser)
+            tm.assert_series_equal(expect, result)
+
+    @pytest.mark.parametrize(
+        "lhs",
+        [
+            # Float
+            DataFrame(np.random.default_rng(2).standard_normal((5, 2))),
+            # Int
+            DataFrame(np.random.default_rng(2).integers(5, size=(5, 2))),
+            # bool doesn't work with numexpr but works elsewhere
+            DataFrame(np.random.default_rng(2).standard_normal((5, 2)) > 0.5),
+        ],
+    )
+    def test_frame_pos(self, lhs, engine, parser):
+        expr = "+lhs"
+        expect = lhs
+
+        result = pd.eval(expr, engine=engine, parser=parser)
+        tm.assert_frame_equal(expect, result)
+
+    @pytest.mark.parametrize(
+        "lhs",
+        [
+            # Float
+            Series(np.random.default_rng(2).standard_normal(5)),
+            # Int
+            Series(np.random.default_rng(2).integers(5, size=5)),
+            # bool doesn't work with numexpr but works elsewhere
+            Series(np.random.default_rng(2).standard_normal(5) > 0.5),
+        ],
+    )
+    def test_series_pos(self, lhs, engine, parser):
+        expr = "+lhs"
+        expect = lhs
+
+        result = pd.eval(expr, engine=engine, parser=parser)
+        tm.assert_series_equal(expect, result)
+
+    def test_scalar_unary(self, engine, parser):
+        msg = "bad operand type for unary ~: 'float'"
+        warn = None
+        if PY312 and not (engine == "numexpr" and parser == "pandas"):
+            warn = DeprecationWarning
+        with pytest.raises(TypeError, match=msg):
+            pd.eval("~1.0", engine=engine, parser=parser)
+
+        assert pd.eval("-1.0", parser=parser, engine=engine) == -1.0
+        assert pd.eval("+1.0", parser=parser, engine=engine) == +1.0
+        assert pd.eval("~1", parser=parser, engine=engine) == ~1
+        assert pd.eval("-1", parser=parser, engine=engine) == -1
+        assert pd.eval("+1", parser=parser, engine=engine) == +1
+        with tm.assert_produces_warning(
+            warn, match="Bitwise inversion", check_stacklevel=False
+        ):
+            assert pd.eval("~True", parser=parser, engine=engine) == ~True
+        with tm.assert_produces_warning(
+            warn, match="Bitwise inversion", check_stacklevel=False
+        ):
+            assert pd.eval("~False", parser=parser, engine=engine) == ~False
+        assert pd.eval("-True", parser=parser, engine=engine) == -True
+        assert pd.eval("-False", parser=parser, engine=engine) == -False
+        assert pd.eval("+True", parser=parser, engine=engine) == +True
+        assert pd.eval("+False", parser=parser, engine=engine) == +False
+
+    def test_unary_in_array(self):
+        # GH 11235
+        # TODO: 2022-01-29: result return list with numexpr 2.7.3 in CI
+        # but cannot reproduce locally
+        result = np.array(
+            pd.eval("[-True, True, +True, -False, False, +False, -37, 37, ~37, +37]"),
+            dtype=np.object_,
+        )
+        expected = np.array(
+            [
+                -True,
+                True,
+                +True,
+                -False,
+                False,
+                +False,
+                -37,
+                37,
+                ~37,
+                +37,
+            ],
+            dtype=np.object_,
+        )
+        tm.assert_numpy_array_equal(result, expected)
+
+    @pytest.mark.parametrize("expr", ["x < -0.1", "-5 > x"])
+    def test_float_comparison_bin_op(self, float_numpy_dtype, expr):
+        # GH 16363
+        df = DataFrame({"x": np.array([0], dtype=float_numpy_dtype)})
+        res = df.eval(expr)
+        assert res.values == np.array([False])
+
+    def test_unary_in_function(self):
+        # GH 46471
+        df = DataFrame({"x": [0, 1, np.nan]})
+
+        result = df.eval("x.fillna(-1)")
+        expected = df.x.fillna(-1)
+        # column name becomes None if using numexpr
+        # only check names when the engine is not numexpr
+        tm.assert_series_equal(result, expected, check_names=not USE_NUMEXPR)
+
+        result = df.eval("x.shift(1, fill_value=-1)")
+        expected = df.x.shift(1, fill_value=-1)
+        tm.assert_series_equal(result, expected, check_names=not USE_NUMEXPR)
+
+    @pytest.mark.parametrize(
+        "ex",
+        (
+            "1 or 2",
+            "1 and 2",
+            "a and b",
+            "a or b",
+            "1 or 2 and (3 + 2) > 3",
+            "2 * x > 2 or 1 and 2",
+            "2 * df > 3 and 1 or a",
+        ),
+    )
+    def test_disallow_scalar_bool_ops(self, ex, engine, parser):
+        x, a, b = np.random.default_rng(2).standard_normal(3), 1, 2  # noqa: F841
+        df = DataFrame(np.random.default_rng(2).standard_normal((3, 2)))  # noqa: F841
+
+        msg = "cannot evaluate scalar only bool ops|'BoolOp' nodes are not"
+        with pytest.raises(NotImplementedError, match=msg):
+            pd.eval(ex, engine=engine, parser=parser)
+
+    def test_identical(self, engine, parser):
+        # see gh-10546
+        x = 1
+        result = pd.eval("x", engine=engine, parser=parser)
+        assert result == 1
+        assert is_scalar(result)
+
+        x = 1.5
+        result = pd.eval("x", engine=engine, parser=parser)
+        assert result == 1.5
+        assert is_scalar(result)
+
+        x = False
+        result = pd.eval("x", engine=engine, parser=parser)
+        assert not result
+        assert is_bool(result)
+        assert is_scalar(result)
+
+        x = np.array([1])
+        result = pd.eval("x", engine=engine, parser=parser)
+        tm.assert_numpy_array_equal(result, np.array([1]))
+        assert result.shape == (1,)
+
+        x = np.array([1.5])
+        result = pd.eval("x", engine=engine, parser=parser)
+        tm.assert_numpy_array_equal(result, np.array([1.5]))
+        assert result.shape == (1,)
+
+        x = np.array([False])  # noqa: F841
+        result = pd.eval("x", engine=engine, parser=parser)
+        tm.assert_numpy_array_equal(result, np.array([False]))
+        assert result.shape == (1,)
+
+    def test_line_continuation(self, engine, parser):
+        # GH 11149
+        exp = """1 + 2 * \
+        5 - 1 + 2 """
+        result = pd.eval(exp, engine=engine, parser=parser)
+        assert result == 12
+
+    def test_float_truncation(self, engine, parser):
+        # GH 14241
+        exp = "1000000000.006"
+        result = pd.eval(exp, engine=engine, parser=parser)
+        expected = np.float64(exp)
+        assert result == expected
+
+        df = DataFrame({"A": [1000000000.0009, 1000000000.0011, 1000000000.0015]})
+        cutoff = 1000000000.0006
+        result = df.query(f"A < {cutoff:.4f}")
+        assert result.empty
+
+        cutoff = 1000000000.0010
+        result = df.query(f"A > {cutoff:.4f}")
+        expected = df.loc[[1, 2], :]
+        tm.assert_frame_equal(expected, result)
+
+        exact = 1000000000.0011
+        result = df.query(f"A == {exact:.4f}")
+        expected = df.loc[[1], :]
+        tm.assert_frame_equal(expected, result)
+
+    def test_disallow_python_keywords(self):
+        # GH 18221
+        df = DataFrame([[0, 0, 0]], columns=["foo", "bar", "class"])
+        msg = "Python keyword not valid identifier in numexpr query"
+        with pytest.raises(SyntaxError, match=msg):
+            df.query("class == 0")
+
+        df = DataFrame()
+        df.index.name = "lambda"
+        with pytest.raises(SyntaxError, match=msg):
+            df.query("lambda == 0")
+
+    def test_true_false_logic(self):
+        # GH 25823
+        # This behavior is deprecated in Python 3.12
+        with tm.maybe_produces_warning(
+            DeprecationWarning, PY312, check_stacklevel=False
+        ):
+            assert pd.eval("not True") == -2
+            assert pd.eval("not False") == -1
+            assert pd.eval("True and not True") == 0
+
+    def test_and_logic_string_match(self):
+        # GH 25823
+        event = Series({"a": "hello"})
+        assert pd.eval(f"{event.str.match('hello').a}")
+        assert pd.eval(f"{event.str.match('hello').a and event.str.match('hello').a}")
+
+
+# -------------------------------------
+# gh-12388: Typecasting rules consistency with python
+
+
+class TestTypeCasting:
+    @pytest.mark.parametrize("op", ["+", "-", "*", "**", "/"])
+    # maybe someday... numexpr has too many upcasting rules now
+    # chain(*(np.core.sctypes[x] for x in ['uint', 'int', 'float']))
+    @pytest.mark.parametrize("left_right", [("df", "3"), ("3", "df")])
+    def test_binop_typecasting(
+        self, engine, parser, op, complex_or_float_dtype, left_right, request
+    ):
+        # GH#21374
+        dtype = complex_or_float_dtype
+        df = DataFrame(np.random.default_rng(2).standard_normal((5, 3)), dtype=dtype)
+        left, right = left_right
+        s = f"{left} {op} {right}"
+        res = pd.eval(s, engine=engine, parser=parser)
+        if dtype == "complex64" and engine == "numexpr":
+            mark = pytest.mark.xfail(
+                reason="numexpr issue with complex that are upcast "
+                "to complex 128 "
+                "https://github.com/pydata/numexpr/issues/492"
+            )
+            request.applymarker(mark)
+        assert df.values.dtype == dtype
+        assert res.values.dtype == dtype
+        tm.assert_frame_equal(res, eval(s), check_exact=False)
+
+
+# -------------------------------------
+# Basic and complex alignment
+
+
+def should_warn(*args):
+    not_mono = not any(map(operator.attrgetter("is_monotonic_increasing"), args))
+    only_one_dt = reduce(
+        operator.xor, (issubclass(x.dtype.type, np.datetime64) for x in args)
+    )
+    return not_mono and only_one_dt
+
+
+class TestAlignment:
+    index_types = ["i", "s", "dt"]
+    lhs_index_types = index_types + ["s"]  # 'p'
+
+    def test_align_nested_unary_op(self, engine, parser):
+        s = "df * ~2"
+        df = DataFrame(np.random.default_rng(2).standard_normal((5, 3)))
+        res = pd.eval(s, engine=engine, parser=parser)
+        tm.assert_frame_equal(res, df * ~2)
+
+    @pytest.mark.filterwarnings("always::RuntimeWarning")
+    @pytest.mark.parametrize("lr_idx_type", lhs_index_types)
+    @pytest.mark.parametrize("rr_idx_type", index_types)
+    @pytest.mark.parametrize("c_idx_type", index_types)
+    def test_basic_frame_alignment(
+        self, engine, parser, lr_idx_type, rr_idx_type, c_idx_type, idx_func_dict
+    ):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((10, 10)),
+            index=idx_func_dict[lr_idx_type](10),
+            columns=idx_func_dict[c_idx_type](10),
+        )
+        df2 = DataFrame(
+            np.random.default_rng(2).standard_normal((20, 10)),
+            index=idx_func_dict[rr_idx_type](20),
+            columns=idx_func_dict[c_idx_type](10),
+        )
+        # only warns if not monotonic and not sortable
+        if should_warn(df.index, df2.index):
+            with tm.assert_produces_warning(RuntimeWarning):
+                res = pd.eval("df + df2", engine=engine, parser=parser)
+        else:
+            res = pd.eval("df + df2", engine=engine, parser=parser)
+        tm.assert_frame_equal(res, df + df2)
+
+    @pytest.mark.parametrize("r_idx_type", lhs_index_types)
+    @pytest.mark.parametrize("c_idx_type", lhs_index_types)
+    def test_frame_comparison(
+        self, engine, parser, r_idx_type, c_idx_type, idx_func_dict
+    ):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((10, 10)),
+            index=idx_func_dict[r_idx_type](10),
+            columns=idx_func_dict[c_idx_type](10),
+        )
+        res = pd.eval("df < 2", engine=engine, parser=parser)
+        tm.assert_frame_equal(res, df < 2)
+
+        df3 = DataFrame(
+            np.random.default_rng(2).standard_normal(df.shape),
+            index=df.index,
+            columns=df.columns,
+        )
+        res = pd.eval("df < df3", engine=engine, parser=parser)
+        tm.assert_frame_equal(res, df < df3)
+
+    @pytest.mark.filterwarnings("ignore::RuntimeWarning")
+    @pytest.mark.parametrize("r1", lhs_index_types)
+    @pytest.mark.parametrize("c1", index_types)
+    @pytest.mark.parametrize("r2", index_types)
+    @pytest.mark.parametrize("c2", index_types)
+    def test_medium_complex_frame_alignment(
+        self, engine, parser, r1, c1, r2, c2, idx_func_dict
+    ):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((3, 2)),
+            index=idx_func_dict[r1](3),
+            columns=idx_func_dict[c1](2),
+        )
+        df2 = DataFrame(
+            np.random.default_rng(2).standard_normal((4, 2)),
+            index=idx_func_dict[r2](4),
+            columns=idx_func_dict[c2](2),
+        )
+        df3 = DataFrame(
+            np.random.default_rng(2).standard_normal((5, 2)),
+            index=idx_func_dict[r2](5),
+            columns=idx_func_dict[c2](2),
+        )
+        if should_warn(df.index, df2.index, df3.index):
+            with tm.assert_produces_warning(RuntimeWarning):
+                res = pd.eval("df + df2 + df3", engine=engine, parser=parser)
+        else:
+            res = pd.eval("df + df2 + df3", engine=engine, parser=parser)
+        tm.assert_frame_equal(res, df + df2 + df3)
+
+    @pytest.mark.filterwarnings("ignore::RuntimeWarning")
+    @pytest.mark.parametrize("index_name", ["index", "columns"])
+    @pytest.mark.parametrize("c_idx_type", index_types)
+    @pytest.mark.parametrize("r_idx_type", lhs_index_types)
+    def test_basic_frame_series_alignment(
+        self, engine, parser, index_name, r_idx_type, c_idx_type, idx_func_dict
+    ):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((10, 10)),
+            index=idx_func_dict[r_idx_type](10),
+            columns=idx_func_dict[c_idx_type](10),
+        )
+        index = getattr(df, index_name)
+        s = Series(np.random.default_rng(2).standard_normal(5), index[:5])
+
+        if should_warn(df.index, s.index):
+            with tm.assert_produces_warning(RuntimeWarning):
+                res = pd.eval("df + s", engine=engine, parser=parser)
+        else:
+            res = pd.eval("df + s", engine=engine, parser=parser)
+
+        if r_idx_type == "dt" or c_idx_type == "dt":
+            expected = df.add(s) if engine == "numexpr" else df + s
+        else:
+            expected = df + s
+        tm.assert_frame_equal(res, expected)
+
+    @pytest.mark.parametrize("index_name", ["index", "columns"])
+    @pytest.mark.parametrize(
+        "r_idx_type, c_idx_type",
+        list(product(["i", "s"], ["i", "s"])) + [("dt", "dt")],
+    )
+    @pytest.mark.filterwarnings("ignore::RuntimeWarning")
+    def test_basic_series_frame_alignment(
+        self, request, engine, parser, index_name, r_idx_type, c_idx_type, idx_func_dict
+    ):
+        if (
+            engine == "numexpr"
+            and parser in ("pandas", "python")
+            and index_name == "index"
+            and r_idx_type == "i"
+            and c_idx_type == "s"
+        ):
+            reason = (
+                f"Flaky column ordering when engine={engine}, "
+                f"parser={parser}, index_name={index_name}, "
+                f"r_idx_type={r_idx_type}, c_idx_type={c_idx_type}"
+            )
+            request.applymarker(pytest.mark.xfail(reason=reason, strict=False))
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((10, 7)),
+            index=idx_func_dict[r_idx_type](10),
+            columns=idx_func_dict[c_idx_type](7),
+        )
+        index = getattr(df, index_name)
+        s = Series(np.random.default_rng(2).standard_normal(5), index[:5])
+        if should_warn(s.index, df.index):
+            with tm.assert_produces_warning(RuntimeWarning):
+                res = pd.eval("s + df", engine=engine, parser=parser)
+        else:
+            res = pd.eval("s + df", engine=engine, parser=parser)
+
+        if r_idx_type == "dt" or c_idx_type == "dt":
+            expected = df.add(s) if engine == "numexpr" else s + df
+        else:
+            expected = s + df
+        tm.assert_frame_equal(res, expected)
+
+    @pytest.mark.filterwarnings("ignore::RuntimeWarning")
+    @pytest.mark.parametrize("c_idx_type", index_types)
+    @pytest.mark.parametrize("r_idx_type", lhs_index_types)
+    @pytest.mark.parametrize("index_name", ["index", "columns"])
+    @pytest.mark.parametrize("op", ["+", "*"])
+    def test_series_frame_commutativity(
+        self, engine, parser, index_name, op, r_idx_type, c_idx_type, idx_func_dict
+    ):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((10, 10)),
+            index=idx_func_dict[r_idx_type](10),
+            columns=idx_func_dict[c_idx_type](10),
+        )
+        index = getattr(df, index_name)
+        s = Series(np.random.default_rng(2).standard_normal(5), index[:5])
+
+        lhs = f"s {op} df"
+        rhs = f"df {op} s"
+        if should_warn(df.index, s.index):
+            with tm.assert_produces_warning(RuntimeWarning):
+                a = pd.eval(lhs, engine=engine, parser=parser)
+            with tm.assert_produces_warning(RuntimeWarning):
+                b = pd.eval(rhs, engine=engine, parser=parser)
+        else:
+            a = pd.eval(lhs, engine=engine, parser=parser)
+            b = pd.eval(rhs, engine=engine, parser=parser)
+
+        if r_idx_type != "dt" and c_idx_type != "dt":
+            if engine == "numexpr":
+                tm.assert_frame_equal(a, b)
+
+    @pytest.mark.filterwarnings("always::RuntimeWarning")
+    @pytest.mark.parametrize("r1", lhs_index_types)
+    @pytest.mark.parametrize("c1", index_types)
+    @pytest.mark.parametrize("r2", index_types)
+    @pytest.mark.parametrize("c2", index_types)
+    def test_complex_series_frame_alignment(
+        self, engine, parser, r1, c1, r2, c2, idx_func_dict
+    ):
+        n = 3
+        m1 = 5
+        m2 = 2 * m1
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((m1, n)),
+            index=idx_func_dict[r1](m1),
+            columns=idx_func_dict[c1](n),
+        )
+        df2 = DataFrame(
+            np.random.default_rng(2).standard_normal((m2, n)),
+            index=idx_func_dict[r2](m2),
+            columns=idx_func_dict[c2](n),
+        )
+        index = df2.columns
+        ser = Series(np.random.default_rng(2).standard_normal(n), index[:n])
+
+        if r2 == "dt" or c2 == "dt":
+            if engine == "numexpr":
+                expected2 = df2.add(ser)
+            else:
+                expected2 = df2 + ser
+        else:
+            expected2 = df2 + ser
+
+        if r1 == "dt" or c1 == "dt":
+            if engine == "numexpr":
+                expected = expected2.add(df)
+            else:
+                expected = expected2 + df
+        else:
+            expected = expected2 + df
+
+        if should_warn(df2.index, ser.index, df.index):
+            with tm.assert_produces_warning(RuntimeWarning):
+                res = pd.eval("df2 + ser + df", engine=engine, parser=parser)
+        else:
+            res = pd.eval("df2 + ser + df", engine=engine, parser=parser)
+        assert res.shape == expected.shape
+        tm.assert_frame_equal(res, expected)
+
+    def test_performance_warning_for_poor_alignment(self, engine, parser):
+        df = DataFrame(np.random.default_rng(2).standard_normal((1000, 10)))
+        s = Series(np.random.default_rng(2).standard_normal(10000))
+        if engine == "numexpr":
+            seen = PerformanceWarning
+        else:
+            seen = False
+
+        with tm.assert_produces_warning(seen):
+            pd.eval("df + s", engine=engine, parser=parser)
+
+        s = Series(np.random.default_rng(2).standard_normal(1000))
+        with tm.assert_produces_warning(False):
+            pd.eval("df + s", engine=engine, parser=parser)
+
+        df = DataFrame(np.random.default_rng(2).standard_normal((10, 10000)))
+        s = Series(np.random.default_rng(2).standard_normal(10000))
+        with tm.assert_produces_warning(False):
+            pd.eval("df + s", engine=engine, parser=parser)
+
+        df = DataFrame(np.random.default_rng(2).standard_normal((10, 10)))
+        s = Series(np.random.default_rng(2).standard_normal(10000))
+
+        is_python_engine = engine == "python"
+
+        if not is_python_engine:
+            wrn = PerformanceWarning
+        else:
+            wrn = False
+
+        with tm.assert_produces_warning(wrn) as w:
+            pd.eval("df + s", engine=engine, parser=parser)
+
+            if not is_python_engine:
+                assert len(w) == 1
+                msg = str(w[0].message)
+                logged = np.log10(s.size - df.shape[1])
+                expected = (
+                    f"Alignment difference on axis 1 is larger "
+                    f"than an order of magnitude on term 'df', "
+                    f"by more than {logged:.4g}; performance may suffer."
+                )
+                assert msg == expected
+
+
+# ------------------------------------
+# Slightly more complex ops
+
+
+class TestOperations:
+    def eval(self, *args, **kwargs):
+        kwargs["level"] = kwargs.pop("level", 0) + 1
+        return pd.eval(*args, **kwargs)
+
+    def test_simple_arith_ops(self, engine, parser):
+        exclude_arith = []
+        if parser == "python":
+            exclude_arith = ["in", "not in"]
+
+        arith_ops = [
+            op
+            for op in expr.ARITH_OPS_SYMS + expr.CMP_OPS_SYMS
+            if op not in exclude_arith
+        ]
+
+        ops = (op for op in arith_ops if op != "//")
+
+        for op in ops:
+            ex = f"1 {op} 1"
+            ex2 = f"x {op} 1"
+            ex3 = f"1 {op} (x + 1)"
+
+            if op in ("in", "not in"):
+                msg = "argument of type 'int' is not iterable"
+                with pytest.raises(TypeError, match=msg):
+                    pd.eval(ex, engine=engine, parser=parser)
+            else:
+                expec = _eval_single_bin(1, op, 1, engine)
+                x = self.eval(ex, engine=engine, parser=parser)
+                assert x == expec
+
+                expec = _eval_single_bin(x, op, 1, engine)
+                y = self.eval(ex2, local_dict={"x": x}, engine=engine, parser=parser)
+                assert y == expec
+
+                expec = _eval_single_bin(1, op, x + 1, engine)
+                y = self.eval(ex3, local_dict={"x": x}, engine=engine, parser=parser)
+                assert y == expec
+
+    @pytest.mark.parametrize("rhs", [True, False])
+    @pytest.mark.parametrize("lhs", [True, False])
+    @pytest.mark.parametrize("op", expr.BOOL_OPS_SYMS)
+    def test_simple_bool_ops(self, rhs, lhs, op):
+        ex = f"{lhs} {op} {rhs}"
+
+        if parser == "python" and op in ["and", "or"]:
+            msg = "'BoolOp' nodes are not implemented"
+            with pytest.raises(NotImplementedError, match=msg):
+                self.eval(ex)
+            return
+
+        res = self.eval(ex)
+        exp = eval(ex)
+        assert res == exp
+
+    @pytest.mark.parametrize("rhs", [True, False])
+    @pytest.mark.parametrize("lhs", [True, False])
+    @pytest.mark.parametrize("op", expr.BOOL_OPS_SYMS)
+    def test_bool_ops_with_constants(self, rhs, lhs, op):
+        ex = f"{lhs} {op} {rhs}"
+
+        if parser == "python" and op in ["and", "or"]:
+            msg = "'BoolOp' nodes are not implemented"
+            with pytest.raises(NotImplementedError, match=msg):
+                self.eval(ex)
+            return
+
+        res = self.eval(ex)
+        exp = eval(ex)
+        assert res == exp
+
+    def test_4d_ndarray_fails(self):
+        x = np.random.default_rng(2).standard_normal((3, 4, 5, 6))
+        y = Series(np.random.default_rng(2).standard_normal(10))
+        msg = "N-dimensional objects, where N > 2, are not supported with eval"
+        with pytest.raises(NotImplementedError, match=msg):
+            self.eval("x + y", local_dict={"x": x, "y": y})
+
+    def test_constant(self):
+        x = self.eval("1")
+        assert x == 1
+
+    def test_single_variable(self):
+        df = DataFrame(np.random.default_rng(2).standard_normal((10, 2)))
+        df2 = self.eval("df", local_dict={"df": df})
+        tm.assert_frame_equal(df, df2)
+
+    def test_failing_subscript_with_name_error(self):
+        df = DataFrame(np.random.default_rng(2).standard_normal((5, 3)))  # noqa: F841
+        with pytest.raises(NameError, match="name 'x' is not defined"):
+            self.eval("df[x > 2] > 2")
+
+    def test_lhs_expression_subscript(self):
+        df = DataFrame(np.random.default_rng(2).standard_normal((5, 3)))
+        result = self.eval("(df + 1)[df > 2]", local_dict={"df": df})
+        expected = (df + 1)[df > 2]
+        tm.assert_frame_equal(result, expected)
+
+    def test_attr_expression(self):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((5, 3)), columns=list("abc")
+        )
+        expr1 = "df.a < df.b"
+        expec1 = df.a < df.b
+        expr2 = "df.a + df.b + df.c"
+        expec2 = df.a + df.b + df.c
+        expr3 = "df.a + df.b + df.c[df.b < 0]"
+        expec3 = df.a + df.b + df.c[df.b < 0]
+        exprs = expr1, expr2, expr3
+        expecs = expec1, expec2, expec3
+        for e, expec in zip(exprs, expecs):
+            tm.assert_series_equal(expec, self.eval(e, local_dict={"df": df}))
+
+    def test_assignment_fails(self):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((5, 3)), columns=list("abc")
+        )
+        df2 = DataFrame(np.random.default_rng(2).standard_normal((5, 3)))
+        expr1 = "df = df2"
+        msg = "cannot assign without a target object"
+        with pytest.raises(ValueError, match=msg):
+            self.eval(expr1, local_dict={"df": df, "df2": df2})
+
+    def test_assignment_column_multiple_raise(self):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((5, 2)), columns=list("ab")
+        )
+        # multiple assignees
+        with pytest.raises(SyntaxError, match="invalid syntax"):
+            df.eval("d c = a + b")
+
+    def test_assignment_column_invalid_assign(self):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((5, 2)), columns=list("ab")
+        )
+        # invalid assignees
+        msg = "left hand side of an assignment must be a single name"
+        with pytest.raises(SyntaxError, match=msg):
+            df.eval("d,c = a + b")
+
+    def test_assignment_column_invalid_assign_function_call(self):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((5, 2)), columns=list("ab")
+        )
+        msg = "cannot assign to function call"
+        with pytest.raises(SyntaxError, match=msg):
+            df.eval('Timestamp("20131001") = a + b')
+
+    def test_assignment_single_assign_existing(self):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((5, 2)), columns=list("ab")
+        )
+        # single assignment - existing variable
+        expected = df.copy()
+        expected["a"] = expected["a"] + expected["b"]
+        df.eval("a = a + b", inplace=True)
+        tm.assert_frame_equal(df, expected)
+
+    def test_assignment_single_assign_new(self):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((5, 2)), columns=list("ab")
+        )
+        # single assignment - new variable
+        expected = df.copy()
+        expected["c"] = expected["a"] + expected["b"]
+        df.eval("c = a + b", inplace=True)
+        tm.assert_frame_equal(df, expected)
+
+    def test_assignment_single_assign_local_overlap(self):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((5, 2)), columns=list("ab")
+        )
+        df = df.copy()
+        a = 1  # noqa: F841
+        df.eval("a = 1 + b", inplace=True)
+
+        expected = df.copy()
+        expected["a"] = 1 + expected["b"]
+        tm.assert_frame_equal(df, expected)
+
+    def test_assignment_single_assign_name(self):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((5, 2)), columns=list("ab")
+        )
+
+        a = 1  # noqa: F841
+        old_a = df.a.copy()
+        df.eval("a = a + b", inplace=True)
+        result = old_a + df.b
+        tm.assert_series_equal(result, df.a, check_names=False)
+        assert result.name is None
+
+    def test_assignment_multiple_raises(self):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((5, 2)), columns=list("ab")
+        )
+        # multiple assignment
+        df.eval("c = a + b", inplace=True)
+        msg = "can only assign a single expression"
+        with pytest.raises(SyntaxError, match=msg):
+            df.eval("c = a = b")
+
+    def test_assignment_explicit(self):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((5, 2)), columns=list("ab")
+        )
+        # explicit targets
+        self.eval("c = df.a + df.b", local_dict={"df": df}, target=df, inplace=True)
+        expected = df.copy()
+        expected["c"] = expected["a"] + expected["b"]
+        tm.assert_frame_equal(df, expected)
+
+    def test_column_in(self):
+        # GH 11235
+        df = DataFrame({"a": [11], "b": [-32]})
+        result = df.eval("a in [11, -32]")
+        expected = Series([True])
+        # TODO: 2022-01-29: Name check failed with numexpr 2.7.3 in CI
+        # but cannot reproduce locally
+        tm.assert_series_equal(result, expected, check_names=False)
+
+    @pytest.mark.xfail(reason="Unknown: Omitted test_ in name prior.")
+    def test_assignment_not_inplace(self):
+        # see gh-9297
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((5, 2)), columns=list("ab")
+        )
+
+        actual = df.eval("c = a + b", inplace=False)
+        assert actual is not None
+
+        expected = df.copy()
+        expected["c"] = expected["a"] + expected["b"]
+        tm.assert_frame_equal(df, expected)
+
+    def test_multi_line_expression(self, warn_copy_on_write):
+        # GH 11149
+        df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
+        expected = df.copy()
+
+        expected["c"] = expected["a"] + expected["b"]
+        expected["d"] = expected["c"] + expected["b"]
+        answer = df.eval(
+            """
+        c = a + b
+        d = c + b""",
+            inplace=True,
+        )
+        tm.assert_frame_equal(expected, df)
+        assert answer is None
+
+        expected["a"] = expected["a"] - 1
+        expected["e"] = expected["a"] + 2
+        answer = df.eval(
+            """
+        a = a - 1
+        e = a + 2""",
+            inplace=True,
+        )
+        tm.assert_frame_equal(expected, df)
+        assert answer is None
+
+        # multi-line not valid if not all assignments
+        msg = "Multi-line expressions are only valid if all expressions contain"
+        with pytest.raises(ValueError, match=msg):
+            df.eval(
+                """
+            a = b + 2
+            b - 2""",
+                inplace=False,
+            )
+
+    def test_multi_line_expression_not_inplace(self):
+        # GH 11149
+        df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
+        expected = df.copy()
+
+        expected["c"] = expected["a"] + expected["b"]
+        expected["d"] = expected["c"] + expected["b"]
+        df = df.eval(
+            """
+        c = a + b
+        d = c + b""",
+            inplace=False,
+        )
+        tm.assert_frame_equal(expected, df)
+
+        expected["a"] = expected["a"] - 1
+        expected["e"] = expected["a"] + 2
+        df = df.eval(
+            """
+        a = a - 1
+        e = a + 2""",
+            inplace=False,
+        )
+        tm.assert_frame_equal(expected, df)
+
+    def test_multi_line_expression_local_variable(self):
+        # GH 15342
+        df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
+        expected = df.copy()
+
+        local_var = 7
+        expected["c"] = expected["a"] * local_var
+        expected["d"] = expected["c"] + local_var
+        answer = df.eval(
+            """
+        c = a * @local_var
+        d = c + @local_var
+        """,
+            inplace=True,
+        )
+        tm.assert_frame_equal(expected, df)
+        assert answer is None
+
+    def test_multi_line_expression_callable_local_variable(self):
+        # 26426
+        df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
+
+        def local_func(a, b):
+            return b
+
+        expected = df.copy()
+        expected["c"] = expected["a"] * local_func(1, 7)
+        expected["d"] = expected["c"] + local_func(1, 7)
+        answer = df.eval(
+            """
+        c = a * @local_func(1, 7)
+        d = c + @local_func(1, 7)
+        """,
+            inplace=True,
+        )
+        tm.assert_frame_equal(expected, df)
+        assert answer is None
+
+    def test_multi_line_expression_callable_local_variable_with_kwargs(self):
+        # 26426
+        df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
+
+        def local_func(a, b):
+            return b
+
+        expected = df.copy()
+        expected["c"] = expected["a"] * local_func(b=7, a=1)
+        expected["d"] = expected["c"] + local_func(b=7, a=1)
+        answer = df.eval(
+            """
+        c = a * @local_func(b=7, a=1)
+        d = c + @local_func(b=7, a=1)
+        """,
+            inplace=True,
+        )
+        tm.assert_frame_equal(expected, df)
+        assert answer is None
+
+    def test_assignment_in_query(self):
+        # GH 8664
+        df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
+        df_orig = df.copy()
+        msg = "cannot assign without a target object"
+        with pytest.raises(ValueError, match=msg):
+            df.query("a = 1")
+        tm.assert_frame_equal(df, df_orig)
+
+    def test_query_inplace(self):
+        # see gh-11149
+        df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
+        expected = df.copy()
+        expected = expected[expected["a"] == 2]
+        df.query("a == 2", inplace=True)
+        tm.assert_frame_equal(expected, df)
+
+        df = {}
+        expected = {"a": 3}
+
+        self.eval("a = 1 + 2", target=df, inplace=True)
+        tm.assert_dict_equal(df, expected)
+
+    @pytest.mark.parametrize("invalid_target", [1, "cat", [1, 2], np.array([]), (1, 3)])
+    def test_cannot_item_assign(self, invalid_target):
+        msg = "Cannot assign expression output to target"
+        expression = "a = 1 + 2"
+
+        with pytest.raises(ValueError, match=msg):
+            self.eval(expression, target=invalid_target, inplace=True)
+
+        if hasattr(invalid_target, "copy"):
+            with pytest.raises(ValueError, match=msg):
+                self.eval(expression, target=invalid_target, inplace=False)
+
+    @pytest.mark.parametrize("invalid_target", [1, "cat", (1, 3)])
+    def test_cannot_copy_item(self, invalid_target):
+        msg = "Cannot return a copy of the target"
+        expression = "a = 1 + 2"
+
+        with pytest.raises(ValueError, match=msg):
+            self.eval(expression, target=invalid_target, inplace=False)
+
+    @pytest.mark.parametrize("target", [1, "cat", [1, 2], np.array([]), (1, 3), {1: 2}])
+    def test_inplace_no_assignment(self, target):
+        expression = "1 + 2"
+
+        assert self.eval(expression, target=target, inplace=False) == 3
+
+        msg = "Cannot operate inplace if there is no assignment"
+        with pytest.raises(ValueError, match=msg):
+            self.eval(expression, target=target, inplace=True)
+
+    def test_basic_period_index_boolean_expression(self):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((2, 2)),
+            columns=period_range("2020-01-01", freq="D", periods=2),
+        )
+        e = df < 2
+        r = self.eval("df < 2", local_dict={"df": df})
+        x = df < 2
+
+        tm.assert_frame_equal(r, e)
+        tm.assert_frame_equal(x, e)
+
+    def test_basic_period_index_subscript_expression(self):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((2, 2)),
+            columns=period_range("2020-01-01", freq="D", periods=2),
+        )
+        r = self.eval("df[df < 2 + 3]", local_dict={"df": df})
+        e = df[df < 2 + 3]
+        tm.assert_frame_equal(r, e)
+
+    def test_nested_period_index_subscript_expression(self):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((2, 2)),
+            columns=period_range("2020-01-01", freq="D", periods=2),
+        )
+        r = self.eval("df[df[df < 2] < 2] + df * 2", local_dict={"df": df})
+        e = df[df[df < 2] < 2] + df * 2
+        tm.assert_frame_equal(r, e)
+
+    def test_date_boolean(self, engine, parser):
+        df = DataFrame(np.random.default_rng(2).standard_normal((5, 3)))
+        df["dates1"] = date_range("1/1/2012", periods=5)
+        res = self.eval(
+            "df.dates1 < 20130101",
+            local_dict={"df": df},
+            engine=engine,
+            parser=parser,
+        )
+        expec = df.dates1 < "20130101"
+        tm.assert_series_equal(res, expec, check_names=False)
+
+    def test_simple_in_ops(self, engine, parser):
+        if parser != "python":
+            res = pd.eval("1 in [1, 2]", engine=engine, parser=parser)
+            assert res
+
+            res = pd.eval("2 in (1, 2)", engine=engine, parser=parser)
+            assert res
+
+            res = pd.eval("3 in (1, 2)", engine=engine, parser=parser)
+            assert not res
+
+            res = pd.eval("3 not in (1, 2)", engine=engine, parser=parser)
+            assert res
+
+            res = pd.eval("[3] not in (1, 2)", engine=engine, parser=parser)
+            assert res
+
+            res = pd.eval("[3] in ([3], 2)", engine=engine, parser=parser)
+            assert res
+
+            res = pd.eval("[[3]] in [[[3]], 2]", engine=engine, parser=parser)
+            assert res
+
+            res = pd.eval("(3,) in [(3,), 2]", engine=engine, parser=parser)
+            assert res
+
+            res = pd.eval("(3,) not in [(3,), 2]", engine=engine, parser=parser)
+            assert not res
+
+            res = pd.eval("[(3,)] in [[(3,)], 2]", engine=engine, parser=parser)
+            assert res
+        else:
+            msg = "'In' nodes are not implemented"
+            with pytest.raises(NotImplementedError, match=msg):
+                pd.eval("1 in [1, 2]", engine=engine, parser=parser)
+            with pytest.raises(NotImplementedError, match=msg):
+                pd.eval("2 in (1, 2)", engine=engine, parser=parser)
+            with pytest.raises(NotImplementedError, match=msg):
+                pd.eval("3 in (1, 2)", engine=engine, parser=parser)
+            with pytest.raises(NotImplementedError, match=msg):
+                pd.eval("[(3,)] in (1, 2, [(3,)])", engine=engine, parser=parser)
+            msg = "'NotIn' nodes are not implemented"
+            with pytest.raises(NotImplementedError, match=msg):
+                pd.eval("3 not in (1, 2)", engine=engine, parser=parser)
+            with pytest.raises(NotImplementedError, match=msg):
+                pd.eval("[3] not in (1, 2, [[3]])", engine=engine, parser=parser)
+
+    def test_check_many_exprs(self, engine, parser):
+        a = 1  # noqa: F841
+        expr = " * ".join("a" * 33)
+        expected = 1
+        res = pd.eval(expr, engine=engine, parser=parser)
+        assert res == expected
+
+    @pytest.mark.parametrize(
+        "expr",
+        [
+            "df > 2 and df > 3",
+            "df > 2 or df > 3",
+            "not df > 2",
+        ],
+    )
+    def test_fails_and_or_not(self, expr, engine, parser):
+        df = DataFrame(np.random.default_rng(2).standard_normal((5, 3)))
+        if parser == "python":
+            msg = "'BoolOp' nodes are not implemented"
+            if "not" in expr:
+                msg = "'Not' nodes are not implemented"
+
+            with pytest.raises(NotImplementedError, match=msg):
+                pd.eval(
+                    expr,
+                    local_dict={"df": df},
+                    parser=parser,
+                    engine=engine,
+                )
+        else:
+            # smoke-test, should not raise
+            pd.eval(
+                expr,
+                local_dict={"df": df},
+                parser=parser,
+                engine=engine,
+            )
+
+    @pytest.mark.parametrize("char", ["|", "&"])
+    def test_fails_ampersand_pipe(self, char, engine, parser):
+        df = DataFrame(np.random.default_rng(2).standard_normal((5, 3)))  # noqa: F841
+        ex = f"(df + 2)[df > 1] > 0 {char} (df > 0)"
+        if parser == "python":
+            msg = "cannot evaluate scalar only bool ops"
+            with pytest.raises(NotImplementedError, match=msg):
+                pd.eval(ex, parser=parser, engine=engine)
+        else:
+            # smoke-test, should not raise
+            pd.eval(ex, parser=parser, engine=engine)
+
+
+class TestMath:
+    def eval(self, *args, **kwargs):
+        kwargs["level"] = kwargs.pop("level", 0) + 1
+        return pd.eval(*args, **kwargs)
+
+    @pytest.mark.skipif(
+        not NUMEXPR_INSTALLED, reason="Unary ops only implemented for numexpr"
+    )
+    @pytest.mark.parametrize("fn", _unary_math_ops)
+    def test_unary_functions(self, fn):
+        df = DataFrame({"a": np.random.default_rng(2).standard_normal(10)})
+        a = df.a
+
+        expr = f"{fn}(a)"
+        got = self.eval(expr)
+        with np.errstate(all="ignore"):
+            expect = getattr(np, fn)(a)
+        tm.assert_series_equal(got, expect, check_names=False)
+
+    @pytest.mark.parametrize("fn", _binary_math_ops)
+    def test_binary_functions(self, fn):
+        df = DataFrame(
+            {
+                "a": np.random.default_rng(2).standard_normal(10),
+                "b": np.random.default_rng(2).standard_normal(10),
+            }
+        )
+        a = df.a
+        b = df.b
+
+        expr = f"{fn}(a, b)"
+        got = self.eval(expr)
+        with np.errstate(all="ignore"):
+            expect = getattr(np, fn)(a, b)
+        tm.assert_almost_equal(got, expect, check_names=False)
+
+    def test_df_use_case(self, engine, parser):
+        df = DataFrame(
+            {
+                "a": np.random.default_rng(2).standard_normal(10),
+                "b": np.random.default_rng(2).standard_normal(10),
+            }
+        )
+        df.eval(
+            "e = arctan2(sin(a), b)",
+            engine=engine,
+            parser=parser,
+            inplace=True,
+        )
+        got = df.e
+        expect = np.arctan2(np.sin(df.a), df.b)
+        tm.assert_series_equal(got, expect, check_names=False)
+
+    def test_df_arithmetic_subexpression(self, engine, parser):
+        df = DataFrame(
+            {
+                "a": np.random.default_rng(2).standard_normal(10),
+                "b": np.random.default_rng(2).standard_normal(10),
+            }
+        )
+        df.eval("e = sin(a + b)", engine=engine, parser=parser, inplace=True)
+        got = df.e
+        expect = np.sin(df.a + df.b)
+        tm.assert_series_equal(got, expect, check_names=False)
+
+    @pytest.mark.parametrize(
+        "dtype, expect_dtype",
+        [
+            (np.int32, np.float64),
+            (np.int64, np.float64),
+            (np.float32, np.float32),
+            (np.float64, np.float64),
+            pytest.param(np.complex128, np.complex128, marks=td.skip_if_windows),
+        ],
+    )
+    def test_result_types(self, dtype, expect_dtype, engine, parser):
+        # xref https://github.com/pandas-dev/pandas/issues/12293
+        #  this fails on Windows, apparently a floating point precision issue
+
+        # Did not test complex64 because DataFrame is converting it to
+        # complex128. Due to https://github.com/pandas-dev/pandas/issues/10952
+        df = DataFrame(
+            {"a": np.random.default_rng(2).standard_normal(10).astype(dtype)}
+        )
+        assert df.a.dtype == dtype
+        df.eval("b = sin(a)", engine=engine, parser=parser, inplace=True)
+        got = df.b
+        expect = np.sin(df.a)
+        assert expect.dtype == got.dtype
+        assert expect_dtype == got.dtype
+        tm.assert_series_equal(got, expect, check_names=False)
+
+    def test_undefined_func(self, engine, parser):
+        df = DataFrame({"a": np.random.default_rng(2).standard_normal(10)})
+        msg = '"mysin" is not a supported function'
+
+        with pytest.raises(ValueError, match=msg):
+            df.eval("mysin(a)", engine=engine, parser=parser)
+
+    def test_keyword_arg(self, engine, parser):
+        df = DataFrame({"a": np.random.default_rng(2).standard_normal(10)})
+        msg = 'Function "sin" does not support keyword arguments'
+
+        with pytest.raises(TypeError, match=msg):
+            df.eval("sin(x=a)", engine=engine, parser=parser)
+
+
+_var_s = np.random.default_rng(2).standard_normal(10)
+
+
+class TestScope:
+    def test_global_scope(self, engine, parser):
+        e = "_var_s * 2"
+        tm.assert_numpy_array_equal(
+            _var_s * 2, pd.eval(e, engine=engine, parser=parser)
+        )
+
+    def test_no_new_locals(self, engine, parser):
+        x = 1
+        lcls = locals().copy()
+        pd.eval("x + 1", local_dict=lcls, engine=engine, parser=parser)
+        lcls2 = locals().copy()
+        lcls2.pop("lcls")
+        assert lcls == lcls2
+
+    def test_no_new_globals(self, engine, parser):
+        x = 1  # noqa: F841
+        gbls = globals().copy()
+        pd.eval("x + 1", engine=engine, parser=parser)
+        gbls2 = globals().copy()
+        assert gbls == gbls2
+
+    def test_empty_locals(self, engine, parser):
+        # GH 47084
+        x = 1  # noqa: F841
+        msg = "name 'x' is not defined"
+        with pytest.raises(UndefinedVariableError, match=msg):
+            pd.eval("x + 1", engine=engine, parser=parser, local_dict={})
+
+    def test_empty_globals(self, engine, parser):
+        # GH 47084
+        msg = "name '_var_s' is not defined"
+        e = "_var_s * 2"
+        with pytest.raises(UndefinedVariableError, match=msg):
+            pd.eval(e, engine=engine, parser=parser, global_dict={})
+
+
+@td.skip_if_no("numexpr")
+def test_invalid_engine():
+    msg = "Invalid engine 'asdf' passed"
+    with pytest.raises(KeyError, match=msg):
+        pd.eval("x + y", local_dict={"x": 1, "y": 2}, engine="asdf")
+
+
+@td.skip_if_no("numexpr")
+@pytest.mark.parametrize(
+    ("use_numexpr", "expected"),
+    (
+        (True, "numexpr"),
+        (False, "python"),
+    ),
+)
+def test_numexpr_option_respected(use_numexpr, expected):
+    # GH 32556
+    from pandas.core.computation.eval import _check_engine
+
+    with pd.option_context("compute.use_numexpr", use_numexpr):
+        result = _check_engine(None)
+        assert result == expected
+
+
+@td.skip_if_no("numexpr")
+def test_numexpr_option_incompatible_op():
+    # GH 32556
+    with pd.option_context("compute.use_numexpr", False):
+        df = DataFrame(
+            {"A": [True, False, True, False, None, None], "B": [1, 2, 3, 4, 5, 6]}
+        )
+        result = df.query("A.isnull()")
+        expected = DataFrame({"A": [None, None], "B": [5, 6]}, index=[4, 5])
+        tm.assert_frame_equal(result, expected)
+
+
+@td.skip_if_no("numexpr")
+def test_invalid_parser():
+    msg = "Invalid parser 'asdf' passed"
+    with pytest.raises(KeyError, match=msg):
+        pd.eval("x + y", local_dict={"x": 1, "y": 2}, parser="asdf")
+
+
+_parsers: dict[str, type[BaseExprVisitor]] = {
+    "python": PythonExprVisitor,
+    "pytables": pytables.PyTablesExprVisitor,
+    "pandas": PandasExprVisitor,
+}
+
+
+@pytest.mark.parametrize("engine", ENGINES)
+@pytest.mark.parametrize("parser", _parsers)
+def test_disallowed_nodes(engine, parser):
+    VisitorClass = _parsers[parser]
+    inst = VisitorClass("x + 1", engine, parser)
+
+    for ops in VisitorClass.unsupported_nodes:
+        msg = "nodes are not implemented"
+        with pytest.raises(NotImplementedError, match=msg):
+            getattr(inst, ops)()
+
+
+def test_syntax_error_exprs(engine, parser):
+    e = "s +"
+    with pytest.raises(SyntaxError, match="invalid syntax"):
+        pd.eval(e, engine=engine, parser=parser)
+
+
+def test_name_error_exprs(engine, parser):
+    e = "s + t"
+    msg = "name 's' is not defined"
+    with pytest.raises(NameError, match=msg):
+        pd.eval(e, engine=engine, parser=parser)
+
+
+@pytest.mark.parametrize("express", ["a + @b", "@a + b", "@a + @b"])
+def test_invalid_local_variable_reference(engine, parser, express):
+    a, b = 1, 2  # noqa: F841
+
+    if parser != "pandas":
+        with pytest.raises(SyntaxError, match="The '@' prefix is only"):
+            pd.eval(express, engine=engine, parser=parser)
+    else:
+        with pytest.raises(SyntaxError, match="The '@' prefix is not"):
+            pd.eval(express, engine=engine, parser=parser)
+
+
+def test_numexpr_builtin_raises(engine, parser):
+    sin, dotted_line = 1, 2
+    if engine == "numexpr":
+        msg = "Variables in expression .+"
+        with pytest.raises(NumExprClobberingError, match=msg):
+            pd.eval("sin + dotted_line", engine=engine, parser=parser)
+    else:
+        res = pd.eval("sin + dotted_line", engine=engine, parser=parser)
+        assert res == sin + dotted_line
+
+
+def test_bad_resolver_raises(engine, parser):
+    cannot_resolve = 42, 3.0
+    with pytest.raises(TypeError, match="Resolver of type .+"):
+        pd.eval("1 + 2", resolvers=cannot_resolve, engine=engine, parser=parser)
+
+
+def test_empty_string_raises(engine, parser):
+    # GH 13139
+    with pytest.raises(ValueError, match="expr cannot be an empty string"):
+        pd.eval("", engine=engine, parser=parser)
+
+
+def test_more_than_one_expression_raises(engine, parser):
+    with pytest.raises(SyntaxError, match="only a single expression is allowed"):
+        pd.eval("1 + 1; 2 + 2", engine=engine, parser=parser)
+
+
+@pytest.mark.parametrize("cmp", ("and", "or"))
+@pytest.mark.parametrize("lhs", (int, float))
+@pytest.mark.parametrize("rhs", (int, float))
+def test_bool_ops_fails_on_scalars(lhs, cmp, rhs, engine, parser):
+    gen = {
+        int: lambda: np.random.default_rng(2).integers(10),
+        float: np.random.default_rng(2).standard_normal,
+    }
+
+    mid = gen[lhs]()  # noqa: F841
+    lhs = gen[lhs]()
+    rhs = gen[rhs]()
+
+    ex1 = f"lhs {cmp} mid {cmp} rhs"
+    ex2 = f"lhs {cmp} mid and mid {cmp} rhs"
+    ex3 = f"(lhs {cmp} mid) & (mid {cmp} rhs)"
+    for ex in (ex1, ex2, ex3):
+        msg = "cannot evaluate scalar only bool ops|'BoolOp' nodes are not"
+        with pytest.raises(NotImplementedError, match=msg):
+            pd.eval(ex, engine=engine, parser=parser)
+
+
+@pytest.mark.parametrize(
+    "other",
+    [
+        "'x'",
+        "...",
+    ],
+)
+def test_equals_various(other):
+    df = DataFrame({"A": ["a", "b", "c"]}, dtype=object)
+    result = df.eval(f"A == {other}")
+    expected = Series([False, False, False], name="A")
+    if USE_NUMEXPR:
+        # https://github.com/pandas-dev/pandas/issues/10239
+        # lose name with numexpr engine. Remove when that's fixed.
+        expected.name = None
+    tm.assert_series_equal(result, expected)
+
+
+def test_inf(engine, parser):
+    s = "inf + 1"
+    expected = np.inf
+    result = pd.eval(s, engine=engine, parser=parser)
+    assert result == expected
+
+
+@pytest.mark.parametrize("column", ["Temp(°C)", "Capacitance(μF)"])
+def test_query_token(engine, column):
+    # See: https://github.com/pandas-dev/pandas/pull/42826
+    df = DataFrame(
+        np.random.default_rng(2).standard_normal((5, 2)), columns=[column, "b"]
+    )
+    expected = df[df[column] > 5]
+    query_string = f"`{column}` > 5"
+    result = df.query(query_string, engine=engine)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_negate_lt_eq_le(engine, parser):
+    df = DataFrame([[0, 10], [1, 20]], columns=["cat", "count"])
+    expected = df[~(df.cat > 0)]
+
+    result = df.query("~(cat > 0)", engine=engine, parser=parser)
+    tm.assert_frame_equal(result, expected)
+
+    if parser == "python":
+        msg = "'Not' nodes are not implemented"
+        with pytest.raises(NotImplementedError, match=msg):
+            df.query("not (cat > 0)", engine=engine, parser=parser)
+    else:
+        result = df.query("not (cat > 0)", engine=engine, parser=parser)
+        tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "column",
+    DEFAULT_GLOBALS.keys(),
+)
+def test_eval_no_support_column_name(request, column):
+    # GH 44603
+    if column in ["True", "False", "inf", "Inf"]:
+        request.applymarker(
+            pytest.mark.xfail(
+                raises=KeyError,
+                reason=f"GH 47859 DataFrame eval not supported with {column}",
+            )
+        )
+
+    df = DataFrame(
+        np.random.default_rng(2).integers(0, 100, size=(10, 2)),
+        columns=[column, "col1"],
+    )
+    expected = df[df[column] > 6]
+    result = df.query(f"{column}>6")
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_set_inplace(using_copy_on_write, warn_copy_on_write):
+    # https://github.com/pandas-dev/pandas/issues/47449
+    # Ensure we don't only update the DataFrame inplace, but also the actual
+    # column values, such that references to this column also get updated
+    df = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6], "C": [7, 8, 9]})
+    result_view = df[:]
+    ser = df["A"]
+    with tm.assert_cow_warning(warn_copy_on_write):
+        df.eval("A = B + C", inplace=True)
+    expected = DataFrame({"A": [11, 13, 15], "B": [4, 5, 6], "C": [7, 8, 9]})
+    tm.assert_frame_equal(df, expected)
+    if not using_copy_on_write:
+        tm.assert_series_equal(ser, expected["A"])
+        tm.assert_series_equal(result_view["A"], expected["A"])
+    else:
+        expected = Series([1, 2, 3], name="A")
+        tm.assert_series_equal(ser, expected)
+        tm.assert_series_equal(result_view["A"], expected)
+
+
+class TestValidate:
+    @pytest.mark.parametrize("value", [1, "True", [1, 2, 3], 5.0])
+    def test_validate_bool_args(self, value):
+        msg = 'For argument "inplace" expected type bool, received type'
+        with pytest.raises(ValueError, match=msg):
+            pd.eval("2+2", inplace=value)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/config/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/config/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/config/test_config.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/config/test_config.py
new file mode 100644
index 0000000000000000000000000000000000000000..f49ae942423992f6dbb209e8f931f091e900ba12
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/config/test_config.py
@@ -0,0 +1,437 @@
+import pytest
+
+from pandas._config import config as cf
+from pandas._config.config import OptionError
+
+import pandas as pd
+import pandas._testing as tm
+
+
+class TestConfig:
+    @pytest.fixture(autouse=True)
+    def clean_config(self, monkeypatch):
+        with monkeypatch.context() as m:
+            m.setattr(cf, "_global_config", {})
+            m.setattr(cf, "options", cf.DictWrapper(cf._global_config))
+            m.setattr(cf, "_deprecated_options", {})
+            m.setattr(cf, "_registered_options", {})
+
+            # Our test fixture in conftest.py sets "chained_assignment"
+            # to "raise" only after all test methods have been setup.
+            # However, after this setup, there is no longer any
+            # "chained_assignment" option, so re-register it.
+            cf.register_option("chained_assignment", "raise")
+            yield
+
+    def test_api(self):
+        # the pandas object exposes the user API
+        assert hasattr(pd, "get_option")
+        assert hasattr(pd, "set_option")
+        assert hasattr(pd, "reset_option")
+        assert hasattr(pd, "describe_option")
+
+    def test_is_one_of_factory(self):
+        v = cf.is_one_of_factory([None, 12])
+
+        v(12)
+        v(None)
+        msg = r"Value must be one of None\|12"
+        with pytest.raises(ValueError, match=msg):
+            v(1.1)
+
+    def test_register_option(self):
+        cf.register_option("a", 1, "doc")
+
+        # can't register an already registered option
+        msg = "Option 'a' has already been registered"
+        with pytest.raises(OptionError, match=msg):
+            cf.register_option("a", 1, "doc")
+
+        # can't register an already registered option
+        msg = "Path prefix to option 'a' is already an option"
+        with pytest.raises(OptionError, match=msg):
+            cf.register_option("a.b.c.d1", 1, "doc")
+        with pytest.raises(OptionError, match=msg):
+            cf.register_option("a.b.c.d2", 1, "doc")
+
+        # no python keywords
+        msg = "for is a python keyword"
+        with pytest.raises(ValueError, match=msg):
+            cf.register_option("for", 0)
+        with pytest.raises(ValueError, match=msg):
+            cf.register_option("a.for.b", 0)
+        # must be valid identifier (ensure attribute access works)
+        msg = "oh my goddess! is not a valid identifier"
+        with pytest.raises(ValueError, match=msg):
+            cf.register_option("Oh my Goddess!", 0)
+
+        # we can register options several levels deep
+        # without predefining the intermediate steps
+        # and we can define differently named options
+        # in the same namespace
+        cf.register_option("k.b.c.d1", 1, "doc")
+        cf.register_option("k.b.c.d2", 1, "doc")
+
+    def test_describe_option(self):
+        cf.register_option("a", 1, "doc")
+        cf.register_option("b", 1, "doc2")
+        cf.deprecate_option("b")
+
+        cf.register_option("c.d.e1", 1, "doc3")
+        cf.register_option("c.d.e2", 1, "doc4")
+        cf.register_option("f", 1)
+        cf.register_option("g.h", 1)
+        cf.register_option("k", 2)
+        cf.deprecate_option("g.h", rkey="k")
+        cf.register_option("l", "foo")
+
+        # non-existent keys raise KeyError
+        msg = r"No such keys\(s\)"
+        with pytest.raises(OptionError, match=msg):
+            cf.describe_option("no.such.key")
+
+        # we can get the description for any key we registered
+        assert "doc" in cf.describe_option("a", _print_desc=False)
+        assert "doc2" in cf.describe_option("b", _print_desc=False)
+        assert "precated" in cf.describe_option("b", _print_desc=False)
+        assert "doc3" in cf.describe_option("c.d.e1", _print_desc=False)
+        assert "doc4" in cf.describe_option("c.d.e2", _print_desc=False)
+
+        # if no doc is specified we get a default message
+        # saying "description not available"
+        assert "available" in cf.describe_option("f", _print_desc=False)
+        assert "available" in cf.describe_option("g.h", _print_desc=False)
+        assert "precated" in cf.describe_option("g.h", _print_desc=False)
+        assert "k" in cf.describe_option("g.h", _print_desc=False)
+
+        # default is reported
+        assert "foo" in cf.describe_option("l", _print_desc=False)
+        # current value is reported
+        assert "bar" not in cf.describe_option("l", _print_desc=False)
+        cf.set_option("l", "bar")
+        assert "bar" in cf.describe_option("l", _print_desc=False)
+
+    def test_case_insensitive(self):
+        cf.register_option("KanBAN", 1, "doc")
+
+        assert "doc" in cf.describe_option("kanbaN", _print_desc=False)
+        assert cf.get_option("kanBaN") == 1
+        cf.set_option("KanBan", 2)
+        assert cf.get_option("kAnBaN") == 2
+
+        # gets of non-existent keys fail
+        msg = r"No such keys\(s\): 'no_such_option'"
+        with pytest.raises(OptionError, match=msg):
+            cf.get_option("no_such_option")
+        cf.deprecate_option("KanBan")
+
+        assert cf._is_deprecated("kAnBaN")
+
+    def test_get_option(self):
+        cf.register_option("a", 1, "doc")
+        cf.register_option("b.c", "hullo", "doc2")
+        cf.register_option("b.b", None, "doc2")
+
+        # gets of existing keys succeed
+        assert cf.get_option("a") == 1
+        assert cf.get_option("b.c") == "hullo"
+        assert cf.get_option("b.b") is None
+
+        # gets of non-existent keys fail
+        msg = r"No such keys\(s\): 'no_such_option'"
+        with pytest.raises(OptionError, match=msg):
+            cf.get_option("no_such_option")
+
+    def test_set_option(self):
+        cf.register_option("a", 1, "doc")
+        cf.register_option("b.c", "hullo", "doc2")
+        cf.register_option("b.b", None, "doc2")
+
+        assert cf.get_option("a") == 1
+        assert cf.get_option("b.c") == "hullo"
+        assert cf.get_option("b.b") is None
+
+        cf.set_option("a", 2)
+        cf.set_option("b.c", "wurld")
+        cf.set_option("b.b", 1.1)
+
+        assert cf.get_option("a") == 2
+        assert cf.get_option("b.c") == "wurld"
+        assert cf.get_option("b.b") == 1.1
+
+        msg = r"No such keys\(s\): 'no.such.key'"
+        with pytest.raises(OptionError, match=msg):
+            cf.set_option("no.such.key", None)
+
+    def test_set_option_empty_args(self):
+        msg = "Must provide an even number of non-keyword arguments"
+        with pytest.raises(ValueError, match=msg):
+            cf.set_option()
+
+    def test_set_option_uneven_args(self):
+        msg = "Must provide an even number of non-keyword arguments"
+        with pytest.raises(ValueError, match=msg):
+            cf.set_option("a.b", 2, "b.c")
+
+    def test_set_option_invalid_single_argument_type(self):
+        msg = "Must provide an even number of non-keyword arguments"
+        with pytest.raises(ValueError, match=msg):
+            cf.set_option(2)
+
+    def test_set_option_multiple(self):
+        cf.register_option("a", 1, "doc")
+        cf.register_option("b.c", "hullo", "doc2")
+        cf.register_option("b.b", None, "doc2")
+
+        assert cf.get_option("a") == 1
+        assert cf.get_option("b.c") == "hullo"
+        assert cf.get_option("b.b") is None
+
+        cf.set_option("a", "2", "b.c", None, "b.b", 10.0)
+
+        assert cf.get_option("a") == "2"
+        assert cf.get_option("b.c") is None
+        assert cf.get_option("b.b") == 10.0
+
+    def test_validation(self):
+        cf.register_option("a", 1, "doc", validator=cf.is_int)
+        cf.register_option("d", 1, "doc", validator=cf.is_nonnegative_int)
+        cf.register_option("b.c", "hullo", "doc2", validator=cf.is_text)
+
+        msg = "Value must have type ''"
+        with pytest.raises(ValueError, match=msg):
+            cf.register_option("a.b.c.d2", "NO", "doc", validator=cf.is_int)
+
+        cf.set_option("a", 2)  # int is_int
+        cf.set_option("b.c", "wurld")  # str is_str
+        cf.set_option("d", 2)
+        cf.set_option("d", None)  # non-negative int can be None
+
+        # None not is_int
+        with pytest.raises(ValueError, match=msg):
+            cf.set_option("a", None)
+        with pytest.raises(ValueError, match=msg):
+            cf.set_option("a", "ab")
+
+        msg = "Value must be a nonnegative integer or None"
+        with pytest.raises(ValueError, match=msg):
+            cf.register_option("a.b.c.d3", "NO", "doc", validator=cf.is_nonnegative_int)
+        with pytest.raises(ValueError, match=msg):
+            cf.register_option("a.b.c.d3", -2, "doc", validator=cf.is_nonnegative_int)
+
+        msg = r"Value must be an instance of \|"
+        with pytest.raises(ValueError, match=msg):
+            cf.set_option("b.c", 1)
+
+        validator = cf.is_one_of_factory([None, cf.is_callable])
+        cf.register_option("b", lambda: None, "doc", validator=validator)
+        # pylint: disable-next=consider-using-f-string
+        cf.set_option("b", "%.1f".format)  # Formatter is callable
+        cf.set_option("b", None)  # Formatter is none (default)
+        with pytest.raises(ValueError, match="Value must be a callable"):
+            cf.set_option("b", "%.1f")
+
+    def test_reset_option(self):
+        cf.register_option("a", 1, "doc", validator=cf.is_int)
+        cf.register_option("b.c", "hullo", "doc2", validator=cf.is_str)
+        assert cf.get_option("a") == 1
+        assert cf.get_option("b.c") == "hullo"
+
+        cf.set_option("a", 2)
+        cf.set_option("b.c", "wurld")
+        assert cf.get_option("a") == 2
+        assert cf.get_option("b.c") == "wurld"
+
+        cf.reset_option("a")
+        assert cf.get_option("a") == 1
+        assert cf.get_option("b.c") == "wurld"
+        cf.reset_option("b.c")
+        assert cf.get_option("a") == 1
+        assert cf.get_option("b.c") == "hullo"
+
+    def test_reset_option_all(self):
+        cf.register_option("a", 1, "doc", validator=cf.is_int)
+        cf.register_option("b.c", "hullo", "doc2", validator=cf.is_str)
+        assert cf.get_option("a") == 1
+        assert cf.get_option("b.c") == "hullo"
+
+        cf.set_option("a", 2)
+        cf.set_option("b.c", "wurld")
+        assert cf.get_option("a") == 2
+        assert cf.get_option("b.c") == "wurld"
+
+        cf.reset_option("all")
+        assert cf.get_option("a") == 1
+        assert cf.get_option("b.c") == "hullo"
+
+    def test_deprecate_option(self):
+        # we can deprecate non-existent options
+        cf.deprecate_option("foo")
+
+        assert cf._is_deprecated("foo")
+        with tm.assert_produces_warning(FutureWarning, match="deprecated"):
+            with pytest.raises(KeyError, match="No such keys.s.: 'foo'"):
+                cf.get_option("foo")
+
+        cf.register_option("a", 1, "doc", validator=cf.is_int)
+        cf.register_option("b.c", "hullo", "doc2")
+        cf.register_option("foo", "hullo", "doc2")
+
+        cf.deprecate_option("a", removal_ver="nifty_ver")
+        with tm.assert_produces_warning(FutureWarning, match="eprecated.*nifty_ver"):
+            cf.get_option("a")
+
+            msg = "Option 'a' has already been defined as deprecated"
+            with pytest.raises(OptionError, match=msg):
+                cf.deprecate_option("a")
+
+        cf.deprecate_option("b.c", "zounds!")
+        with tm.assert_produces_warning(FutureWarning, match="zounds!"):
+            cf.get_option("b.c")
+
+        # test rerouting keys
+        cf.register_option("d.a", "foo", "doc2")
+        cf.register_option("d.dep", "bar", "doc2")
+        assert cf.get_option("d.a") == "foo"
+        assert cf.get_option("d.dep") == "bar"
+
+        cf.deprecate_option("d.dep", rkey="d.a")  # reroute d.dep to d.a
+        with tm.assert_produces_warning(FutureWarning, match="eprecated"):
+            assert cf.get_option("d.dep") == "foo"
+
+        with tm.assert_produces_warning(FutureWarning, match="eprecated"):
+            cf.set_option("d.dep", "baz")  # should overwrite "d.a"
+
+        with tm.assert_produces_warning(FutureWarning, match="eprecated"):
+            assert cf.get_option("d.dep") == "baz"
+
+    def test_config_prefix(self):
+        with cf.config_prefix("base"):
+            cf.register_option("a", 1, "doc1")
+            cf.register_option("b", 2, "doc2")
+            assert cf.get_option("a") == 1
+            assert cf.get_option("b") == 2
+
+            cf.set_option("a", 3)
+            cf.set_option("b", 4)
+            assert cf.get_option("a") == 3
+            assert cf.get_option("b") == 4
+
+        assert cf.get_option("base.a") == 3
+        assert cf.get_option("base.b") == 4
+        assert "doc1" in cf.describe_option("base.a", _print_desc=False)
+        assert "doc2" in cf.describe_option("base.b", _print_desc=False)
+
+        cf.reset_option("base.a")
+        cf.reset_option("base.b")
+
+        with cf.config_prefix("base"):
+            assert cf.get_option("a") == 1
+            assert cf.get_option("b") == 2
+
+    def test_callback(self):
+        k = [None]
+        v = [None]
+
+        def callback(key):
+            k.append(key)
+            v.append(cf.get_option(key))
+
+        cf.register_option("d.a", "foo", cb=callback)
+        cf.register_option("d.b", "foo", cb=callback)
+
+        del k[-1], v[-1]
+        cf.set_option("d.a", "fooz")
+        assert k[-1] == "d.a"
+        assert v[-1] == "fooz"
+
+        del k[-1], v[-1]
+        cf.set_option("d.b", "boo")
+        assert k[-1] == "d.b"
+        assert v[-1] == "boo"
+
+        del k[-1], v[-1]
+        cf.reset_option("d.b")
+        assert k[-1] == "d.b"
+
+    def test_set_ContextManager(self):
+        def eq(val):
+            assert cf.get_option("a") == val
+
+        cf.register_option("a", 0)
+        eq(0)
+        with cf.option_context("a", 15):
+            eq(15)
+            with cf.option_context("a", 25):
+                eq(25)
+            eq(15)
+        eq(0)
+
+        cf.set_option("a", 17)
+        eq(17)
+
+        # Test that option_context can be used as a decorator too (#34253).
+        @cf.option_context("a", 123)
+        def f():
+            eq(123)
+
+        f()
+
+    def test_attribute_access(self):
+        holder = []
+
+        def f3(key):
+            holder.append(True)
+
+        cf.register_option("a", 0)
+        cf.register_option("c", 0, cb=f3)
+        options = cf.options
+
+        assert options.a == 0
+        with cf.option_context("a", 15):
+            assert options.a == 15
+
+        options.a = 500
+        assert cf.get_option("a") == 500
+
+        cf.reset_option("a")
+        assert options.a == cf.get_option("a", 0)
+
+        msg = "You can only set the value of existing options"
+        with pytest.raises(OptionError, match=msg):
+            options.b = 1
+        with pytest.raises(OptionError, match=msg):
+            options.display = 1
+
+        # make sure callback kicks when using this form of setting
+        options.c = 1
+        assert len(holder) == 1
+
+    def test_option_context_scope(self):
+        # Ensure that creating a context does not affect the existing
+        # environment as it is supposed to be used with the `with` statement.
+        # See https://github.com/pandas-dev/pandas/issues/8514
+
+        original_value = 60
+        context_value = 10
+        option_name = "a"
+
+        cf.register_option(option_name, original_value)
+
+        # Ensure creating contexts didn't affect the current context.
+        ctx = cf.option_context(option_name, context_value)
+        assert cf.get_option(option_name) == original_value
+
+        # Ensure the correct value is available inside the context.
+        with ctx:
+            assert cf.get_option(option_name) == context_value
+
+        # Ensure the current context is reset
+        assert cf.get_option(option_name) == original_value
+
+    def test_dictwrapper_getattr(self):
+        options = cf.options
+        # GH 19789
+        with pytest.raises(OptionError, match="No such option"):
+            options.bananas
+        assert not hasattr(options, "bananas")
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/config/test_localization.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/config/test_localization.py
new file mode 100644
index 0000000000000000000000000000000000000000..3907f557d1075536e46d12f219dc9b0c3f3f32c1
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/config/test_localization.py
@@ -0,0 +1,156 @@
+import codecs
+import locale
+import os
+
+import pytest
+
+from pandas._config.localization import (
+    can_set_locale,
+    get_locales,
+    set_locale,
+)
+
+from pandas.compat import ISMUSL
+
+import pandas as pd
+
+_all_locales = get_locales()
+_current_locale = locale.setlocale(locale.LC_ALL)  # getlocale() is wrong, see GH#46595
+
+# Don't run any of these tests if we have no locales.
+pytestmark = pytest.mark.skipif(not _all_locales, reason="Need locales")
+
+_skip_if_only_one_locale = pytest.mark.skipif(
+    len(_all_locales) <= 1, reason="Need multiple locales for meaningful test"
+)
+
+
+def _get_current_locale(lc_var: int = locale.LC_ALL) -> str:
+    # getlocale is not always compliant with setlocale, use setlocale. GH#46595
+    return locale.setlocale(lc_var)
+
+
+@pytest.mark.parametrize("lc_var", (locale.LC_ALL, locale.LC_CTYPE, locale.LC_TIME))
+def test_can_set_current_locale(lc_var):
+    # Can set the current locale
+    before_locale = _get_current_locale(lc_var)
+    assert can_set_locale(before_locale, lc_var=lc_var)
+    after_locale = _get_current_locale(lc_var)
+    assert before_locale == after_locale
+
+
+@pytest.mark.parametrize("lc_var", (locale.LC_ALL, locale.LC_CTYPE, locale.LC_TIME))
+def test_can_set_locale_valid_set(lc_var):
+    # Can set the default locale.
+    before_locale = _get_current_locale(lc_var)
+    assert can_set_locale("", lc_var=lc_var)
+    after_locale = _get_current_locale(lc_var)
+    assert before_locale == after_locale
+
+
+@pytest.mark.parametrize(
+    "lc_var",
+    (
+        locale.LC_ALL,
+        locale.LC_CTYPE,
+        pytest.param(
+            locale.LC_TIME,
+            marks=pytest.mark.skipif(
+                ISMUSL, reason="MUSL allows setting invalid LC_TIME."
+            ),
+        ),
+    ),
+)
+def test_can_set_locale_invalid_set(lc_var):
+    # Cannot set an invalid locale.
+    before_locale = _get_current_locale(lc_var)
+    assert not can_set_locale("non-existent_locale", lc_var=lc_var)
+    after_locale = _get_current_locale(lc_var)
+    assert before_locale == after_locale
+
+
+@pytest.mark.parametrize(
+    "lang,enc",
+    [
+        ("it_CH", "UTF-8"),
+        ("en_US", "ascii"),
+        ("zh_CN", "GB2312"),
+        ("it_IT", "ISO-8859-1"),
+    ],
+)
+@pytest.mark.parametrize("lc_var", (locale.LC_ALL, locale.LC_CTYPE, locale.LC_TIME))
+def test_can_set_locale_no_leak(lang, enc, lc_var):
+    # Test that can_set_locale does not leak even when returning False. See GH#46595
+    before_locale = _get_current_locale(lc_var)
+    can_set_locale((lang, enc), locale.LC_ALL)
+    after_locale = _get_current_locale(lc_var)
+    assert before_locale == after_locale
+
+
+def test_can_set_locale_invalid_get(monkeypatch):
+    # see GH#22129
+    # In some cases, an invalid locale can be set,
+    #  but a subsequent getlocale() raises a ValueError.
+
+    def mock_get_locale():
+        raise ValueError()
+
+    with monkeypatch.context() as m:
+        m.setattr(locale, "getlocale", mock_get_locale)
+        assert not can_set_locale("")
+
+
+def test_get_locales_at_least_one():
+    # see GH#9744
+    assert len(_all_locales) > 0
+
+
+@_skip_if_only_one_locale
+def test_get_locales_prefix():
+    first_locale = _all_locales[0]
+    assert len(get_locales(prefix=first_locale[:2])) > 0
+
+
+@_skip_if_only_one_locale
+@pytest.mark.parametrize(
+    "lang,enc",
+    [
+        ("it_CH", "UTF-8"),
+        ("en_US", "ascii"),
+        ("zh_CN", "GB2312"),
+        ("it_IT", "ISO-8859-1"),
+    ],
+)
+def test_set_locale(lang, enc):
+    before_locale = _get_current_locale()
+
+    enc = codecs.lookup(enc).name
+    new_locale = lang, enc
+
+    if not can_set_locale(new_locale):
+        msg = "unsupported locale setting"
+
+        with pytest.raises(locale.Error, match=msg):
+            with set_locale(new_locale):
+                pass
+    else:
+        with set_locale(new_locale) as normalized_locale:
+            new_lang, new_enc = normalized_locale.split(".")
+            new_enc = codecs.lookup(enc).name
+
+            normalized_locale = new_lang, new_enc
+            assert normalized_locale == new_locale
+
+    # Once we exit the "with" statement, locale should be back to what it was.
+    after_locale = _get_current_locale()
+    assert before_locale == after_locale
+
+
+def test_encoding_detected():
+    system_locale = os.environ.get("LC_ALL")
+    system_encoding = system_locale.split(".")[-1] if system_locale else "utf-8"
+
+    assert (
+        codecs.lookup(pd.options.display.encoding).name
+        == codecs.lookup(system_encoding).name
+    )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/construction/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/construction/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/construction/test_extract_array.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/construction/test_extract_array.py
new file mode 100644
index 0000000000000000000000000000000000000000..4dd3eda8c995ce022e9d46b907323e79bcd679f8
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/construction/test_extract_array.py
@@ -0,0 +1,18 @@
+from pandas import Index
+import pandas._testing as tm
+from pandas.core.construction import extract_array
+
+
+def test_extract_array_rangeindex():
+    ri = Index(range(5))
+
+    expected = ri._values
+    res = extract_array(ri, extract_numpy=True, extract_range=True)
+    tm.assert_numpy_array_equal(res, expected)
+    res = extract_array(ri, extract_numpy=False, extract_range=True)
+    tm.assert_numpy_array_equal(res, expected)
+
+    res = extract_array(ri, extract_numpy=True, extract_range=False)
+    tm.assert_index_equal(res, ri)
+    res = extract_array(ri, extract_numpy=False, extract_range=False)
+    tm.assert_index_equal(res, ri)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/copy_view/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/copy_view/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/copy_view/index/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/copy_view/index/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/copy_view/index/test_datetimeindex.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/copy_view/index/test_datetimeindex.py
new file mode 100644
index 0000000000000000000000000000000000000000..b023297c9549d88f6e1c493e50f148a74f26cea6
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/copy_view/index/test_datetimeindex.py
@@ -0,0 +1,69 @@
+import pytest
+
+from pandas import (
+    DatetimeIndex,
+    Series,
+    Timestamp,
+    date_range,
+)
+import pandas._testing as tm
+
+pytestmark = pytest.mark.filterwarnings(
+    "ignore:Setting a value on a view:FutureWarning"
+)
+
+
+@pytest.mark.parametrize(
+    "cons",
+    [
+        lambda x: DatetimeIndex(x),
+        lambda x: DatetimeIndex(DatetimeIndex(x)),
+    ],
+)
+def test_datetimeindex(using_copy_on_write, cons):
+    dt = date_range("2019-12-31", periods=3, freq="D")
+    ser = Series(dt)
+    idx = cons(ser)
+    expected = idx.copy(deep=True)
+    ser.iloc[0] = Timestamp("2020-12-31")
+    if using_copy_on_write:
+        tm.assert_index_equal(idx, expected)
+
+
+def test_datetimeindex_tz_convert(using_copy_on_write):
+    dt = date_range("2019-12-31", periods=3, freq="D", tz="Europe/Berlin")
+    ser = Series(dt)
+    idx = DatetimeIndex(ser).tz_convert("US/Eastern")
+    expected = idx.copy(deep=True)
+    ser.iloc[0] = Timestamp("2020-12-31", tz="Europe/Berlin")
+    if using_copy_on_write:
+        tm.assert_index_equal(idx, expected)
+
+
+def test_datetimeindex_tz_localize(using_copy_on_write):
+    dt = date_range("2019-12-31", periods=3, freq="D")
+    ser = Series(dt)
+    idx = DatetimeIndex(ser).tz_localize("Europe/Berlin")
+    expected = idx.copy(deep=True)
+    ser.iloc[0] = Timestamp("2020-12-31")
+    if using_copy_on_write:
+        tm.assert_index_equal(idx, expected)
+
+
+def test_datetimeindex_isocalendar(using_copy_on_write):
+    dt = date_range("2019-12-31", periods=3, freq="D")
+    ser = Series(dt)
+    df = DatetimeIndex(ser).isocalendar()
+    expected = df.index.copy(deep=True)
+    ser.iloc[0] = Timestamp("2020-12-31")
+    if using_copy_on_write:
+        tm.assert_index_equal(df.index, expected)
+
+
+def test_index_values(using_copy_on_write):
+    idx = date_range("2019-12-31", periods=3, freq="D")
+    result = idx.values
+    if using_copy_on_write:
+        assert result.flags.writeable is False
+    else:
+        assert result.flags.writeable is True
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/copy_view/index/test_index.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/copy_view/index/test_index.py
new file mode 100644
index 0000000000000000000000000000000000000000..49d756cf32d34306fbb4eb3525f1c5b70d5f155c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/copy_view/index/test_index.py
@@ -0,0 +1,184 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    DataFrame,
+    Index,
+    Series,
+)
+import pandas._testing as tm
+from pandas.tests.copy_view.util import get_array
+
+
+def index_view(index_data=[1, 2]):
+    df = DataFrame({"a": index_data, "b": 1.5})
+    view = df[:]
+    df = df.set_index("a", drop=True)
+    idx = df.index
+    # df = None
+    return idx, view
+
+
+def test_set_index_update_column(using_copy_on_write, warn_copy_on_write):
+    df = DataFrame({"a": [1, 2], "b": 1})
+    df = df.set_index("a", drop=False)
+    expected = df.index.copy(deep=True)
+    with tm.assert_cow_warning(warn_copy_on_write):
+        df.iloc[0, 0] = 100
+    if using_copy_on_write:
+        tm.assert_index_equal(df.index, expected)
+    else:
+        tm.assert_index_equal(df.index, Index([100, 2], name="a"))
+
+
+def test_set_index_drop_update_column(using_copy_on_write):
+    df = DataFrame({"a": [1, 2], "b": 1.5})
+    view = df[:]
+    df = df.set_index("a", drop=True)
+    expected = df.index.copy(deep=True)
+    view.iloc[0, 0] = 100
+    tm.assert_index_equal(df.index, expected)
+
+
+def test_set_index_series(using_copy_on_write, warn_copy_on_write):
+    df = DataFrame({"a": [1, 2], "b": 1.5})
+    ser = Series([10, 11])
+    df = df.set_index(ser)
+    expected = df.index.copy(deep=True)
+    with tm.assert_cow_warning(warn_copy_on_write):
+        ser.iloc[0] = 100
+    if using_copy_on_write:
+        tm.assert_index_equal(df.index, expected)
+    else:
+        tm.assert_index_equal(df.index, Index([100, 11]))
+
+
+def test_assign_index_as_series(using_copy_on_write, warn_copy_on_write):
+    df = DataFrame({"a": [1, 2], "b": 1.5})
+    ser = Series([10, 11])
+    df.index = ser
+    expected = df.index.copy(deep=True)
+    with tm.assert_cow_warning(warn_copy_on_write):
+        ser.iloc[0] = 100
+    if using_copy_on_write:
+        tm.assert_index_equal(df.index, expected)
+    else:
+        tm.assert_index_equal(df.index, Index([100, 11]))
+
+
+def test_assign_index_as_index(using_copy_on_write, warn_copy_on_write):
+    df = DataFrame({"a": [1, 2], "b": 1.5})
+    ser = Series([10, 11])
+    rhs_index = Index(ser)
+    df.index = rhs_index
+    rhs_index = None  # overwrite to clear reference
+    expected = df.index.copy(deep=True)
+    with tm.assert_cow_warning(warn_copy_on_write):
+        ser.iloc[0] = 100
+    if using_copy_on_write:
+        tm.assert_index_equal(df.index, expected)
+    else:
+        tm.assert_index_equal(df.index, Index([100, 11]))
+
+
+def test_index_from_series(using_copy_on_write, warn_copy_on_write):
+    ser = Series([1, 2])
+    idx = Index(ser)
+    expected = idx.copy(deep=True)
+    with tm.assert_cow_warning(warn_copy_on_write):
+        ser.iloc[0] = 100
+    if using_copy_on_write:
+        tm.assert_index_equal(idx, expected)
+    else:
+        tm.assert_index_equal(idx, Index([100, 2]))
+
+
+def test_index_from_series_copy(using_copy_on_write):
+    ser = Series([1, 2])
+    idx = Index(ser, copy=True)  # noqa: F841
+    arr = get_array(ser)
+    ser.iloc[0] = 100
+    assert np.shares_memory(get_array(ser), arr)
+
+
+def test_index_from_index(using_copy_on_write, warn_copy_on_write):
+    ser = Series([1, 2])
+    idx = Index(ser)
+    idx = Index(idx)
+    expected = idx.copy(deep=True)
+    with tm.assert_cow_warning(warn_copy_on_write):
+        ser.iloc[0] = 100
+    if using_copy_on_write:
+        tm.assert_index_equal(idx, expected)
+    else:
+        tm.assert_index_equal(idx, Index([100, 2]))
+
+
+@pytest.mark.parametrize(
+    "func",
+    [
+        lambda x: x._shallow_copy(x._values),
+        lambda x: x.view(),
+        lambda x: x.take([0, 1]),
+        lambda x: x.repeat([1, 1]),
+        lambda x: x[slice(0, 2)],
+        lambda x: x[[0, 1]],
+        lambda x: x._getitem_slice(slice(0, 2)),
+        lambda x: x.delete([]),
+        lambda x: x.rename("b"),
+        lambda x: x.astype("Int64", copy=False),
+    ],
+    ids=[
+        "_shallow_copy",
+        "view",
+        "take",
+        "repeat",
+        "getitem_slice",
+        "getitem_list",
+        "_getitem_slice",
+        "delete",
+        "rename",
+        "astype",
+    ],
+)
+def test_index_ops(using_copy_on_write, func, request):
+    idx, view_ = index_view()
+    expected = idx.copy(deep=True)
+    if "astype" in request.node.callspec.id:
+        expected = expected.astype("Int64")
+    idx = func(idx)
+    view_.iloc[0, 0] = 100
+    if using_copy_on_write:
+        tm.assert_index_equal(idx, expected, check_names=False)
+
+
+def test_infer_objects(using_copy_on_write):
+    idx, view_ = index_view(["a", "b"])
+    expected = idx.copy(deep=True)
+    idx = idx.infer_objects(copy=False)
+    view_.iloc[0, 0] = "aaaa"
+    if using_copy_on_write:
+        tm.assert_index_equal(idx, expected, check_names=False)
+
+
+def test_index_to_frame(using_copy_on_write):
+    idx = Index([1, 2, 3], name="a")
+    expected = idx.copy(deep=True)
+    df = idx.to_frame()
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(df, "a"), idx._values)
+        assert not df._mgr._has_no_reference(0)
+    else:
+        assert not np.shares_memory(get_array(df, "a"), idx._values)
+
+    df.iloc[0, 0] = 100
+    tm.assert_index_equal(idx, expected)
+
+
+def test_index_values(using_copy_on_write):
+    idx = Index([1, 2, 3])
+    result = idx.values
+    if using_copy_on_write:
+        assert result.flags.writeable is False
+    else:
+        assert result.flags.writeable is True
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/copy_view/index/test_periodindex.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/copy_view/index/test_periodindex.py
new file mode 100644
index 0000000000000000000000000000000000000000..b80ce1d3d838fc0f517089d452221ac19363a9b8
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/copy_view/index/test_periodindex.py
@@ -0,0 +1,30 @@
+import pytest
+
+from pandas import (
+    Period,
+    PeriodIndex,
+    Series,
+    period_range,
+)
+import pandas._testing as tm
+
+pytestmark = pytest.mark.filterwarnings(
+    "ignore:Setting a value on a view:FutureWarning"
+)
+
+
+@pytest.mark.parametrize(
+    "cons",
+    [
+        lambda x: PeriodIndex(x),
+        lambda x: PeriodIndex(PeriodIndex(x)),
+    ],
+)
+def test_periodindex(using_copy_on_write, cons):
+    dt = period_range("2019-12-31", periods=3, freq="D")
+    ser = Series(dt)
+    idx = cons(ser)
+    expected = idx.copy(deep=True)
+    ser.iloc[0] = Period("2020-12-31")
+    if using_copy_on_write:
+        tm.assert_index_equal(idx, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/copy_view/index/test_timedeltaindex.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/copy_view/index/test_timedeltaindex.py
new file mode 100644
index 0000000000000000000000000000000000000000..5b9832093fded0f48c523bdbc363d043a871eb60
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/copy_view/index/test_timedeltaindex.py
@@ -0,0 +1,30 @@
+import pytest
+
+from pandas import (
+    Series,
+    Timedelta,
+    TimedeltaIndex,
+    timedelta_range,
+)
+import pandas._testing as tm
+
+pytestmark = pytest.mark.filterwarnings(
+    "ignore:Setting a value on a view:FutureWarning"
+)
+
+
+@pytest.mark.parametrize(
+    "cons",
+    [
+        lambda x: TimedeltaIndex(x),
+        lambda x: TimedeltaIndex(TimedeltaIndex(x)),
+    ],
+)
+def test_timedeltaindex(using_copy_on_write, cons):
+    dt = timedelta_range("1 day", periods=3)
+    ser = Series(dt)
+    idx = cons(ser)
+    expected = idx.copy(deep=True)
+    ser.iloc[0] = Timedelta("5 days")
+    if using_copy_on_write:
+        tm.assert_index_equal(idx, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/copy_view/test_array.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/copy_view/test_array.py
new file mode 100644
index 0000000000000000000000000000000000000000..0dabec6014b0dfff04874ab67f0c7bd830dad3f3
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/copy_view/test_array.py
@@ -0,0 +1,218 @@
+import numpy as np
+import pytest
+
+from pandas.compat.numpy import np_version_gt2
+
+from pandas import (
+    DataFrame,
+    Series,
+    date_range,
+)
+import pandas._testing as tm
+from pandas.tests.copy_view.util import get_array
+
+# -----------------------------------------------------------------------------
+# Copy/view behaviour for accessing underlying array of Series/DataFrame
+
+
+@pytest.mark.parametrize(
+    "method",
+    [
+        lambda ser: ser.values,
+        lambda ser: np.asarray(ser),
+        lambda ser: np.array(ser, copy=False),
+    ],
+    ids=["values", "asarray", "array"],
+)
+def test_series_values(using_copy_on_write, method):
+    ser = Series([1, 2, 3], name="name")
+    ser_orig = ser.copy()
+
+    arr = method(ser)
+
+    if using_copy_on_write:
+        # .values still gives a view but is read-only
+        assert np.shares_memory(arr, get_array(ser, "name"))
+        assert arr.flags.writeable is False
+
+        # mutating series through arr therefore doesn't work
+        with pytest.raises(ValueError, match="read-only"):
+            arr[0] = 0
+        tm.assert_series_equal(ser, ser_orig)
+
+        # mutating the series itself still works
+        ser.iloc[0] = 0
+        assert ser.values[0] == 0
+    else:
+        assert arr.flags.writeable is True
+        arr[0] = 0
+        assert ser.iloc[0] == 0
+
+
+@pytest.mark.parametrize(
+    "method",
+    [
+        lambda df: df.values,
+        lambda df: np.asarray(df),
+        lambda ser: np.array(ser, copy=False),
+    ],
+    ids=["values", "asarray", "array"],
+)
+def test_dataframe_values(using_copy_on_write, using_array_manager, method):
+    df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
+    df_orig = df.copy()
+
+    arr = method(df)
+
+    if using_copy_on_write:
+        # .values still gives a view but is read-only
+        assert np.shares_memory(arr, get_array(df, "a"))
+        assert arr.flags.writeable is False
+
+        # mutating series through arr therefore doesn't work
+        with pytest.raises(ValueError, match="read-only"):
+            arr[0, 0] = 0
+        tm.assert_frame_equal(df, df_orig)
+
+        # mutating the series itself still works
+        df.iloc[0, 0] = 0
+        assert df.values[0, 0] == 0
+    else:
+        assert arr.flags.writeable is True
+        arr[0, 0] = 0
+        if not using_array_manager:
+            assert df.iloc[0, 0] == 0
+        else:
+            tm.assert_frame_equal(df, df_orig)
+
+
+def test_series_to_numpy(using_copy_on_write):
+    ser = Series([1, 2, 3], name="name")
+    ser_orig = ser.copy()
+
+    # default: copy=False, no dtype or NAs
+    arr = ser.to_numpy()
+    if using_copy_on_write:
+        # to_numpy still gives a view but is read-only
+        assert np.shares_memory(arr, get_array(ser, "name"))
+        assert arr.flags.writeable is False
+
+        # mutating series through arr therefore doesn't work
+        with pytest.raises(ValueError, match="read-only"):
+            arr[0] = 0
+        tm.assert_series_equal(ser, ser_orig)
+
+        # mutating the series itself still works
+        ser.iloc[0] = 0
+        assert ser.values[0] == 0
+    else:
+        assert arr.flags.writeable is True
+        arr[0] = 0
+        assert ser.iloc[0] == 0
+
+    # specify copy=True gives a writeable array
+    ser = Series([1, 2, 3], name="name")
+    arr = ser.to_numpy(copy=True)
+    assert not np.shares_memory(arr, get_array(ser, "name"))
+    assert arr.flags.writeable is True
+
+    # specifying a dtype that already causes a copy also gives a writeable array
+    ser = Series([1, 2, 3], name="name")
+    arr = ser.to_numpy(dtype="float64")
+    assert not np.shares_memory(arr, get_array(ser, "name"))
+    assert arr.flags.writeable is True
+
+
+@pytest.mark.parametrize("order", ["F", "C"])
+def test_ravel_read_only(using_copy_on_write, order):
+    ser = Series([1, 2, 3])
+    with tm.assert_produces_warning(FutureWarning, match="is deprecated"):
+        arr = ser.ravel(order=order)
+    if using_copy_on_write:
+        assert arr.flags.writeable is False
+    assert np.shares_memory(get_array(ser), arr)
+
+
+def test_series_array_ea_dtypes(using_copy_on_write):
+    ser = Series([1, 2, 3], dtype="Int64")
+    arr = np.asarray(ser, dtype="int64")
+    assert np.shares_memory(arr, get_array(ser))
+    if using_copy_on_write:
+        assert arr.flags.writeable is False
+    else:
+        assert arr.flags.writeable is True
+
+    arr = np.asarray(ser)
+    assert np.shares_memory(arr, get_array(ser))
+    if using_copy_on_write:
+        assert arr.flags.writeable is False
+    else:
+        assert arr.flags.writeable is True
+
+
+def test_dataframe_array_ea_dtypes(using_copy_on_write):
+    df = DataFrame({"a": [1, 2, 3]}, dtype="Int64")
+    arr = np.asarray(df, dtype="int64")
+    assert np.shares_memory(arr, get_array(df, "a"))
+    if using_copy_on_write:
+        assert arr.flags.writeable is False
+    else:
+        assert arr.flags.writeable is True
+
+    arr = np.asarray(df)
+    assert np.shares_memory(arr, get_array(df, "a"))
+    if using_copy_on_write:
+        assert arr.flags.writeable is False
+    else:
+        assert arr.flags.writeable is True
+
+
+def test_dataframe_array_string_dtype(using_copy_on_write, using_array_manager):
+    df = DataFrame({"a": ["a", "b"]}, dtype="string")
+    arr = np.asarray(df)
+    if not using_array_manager:
+        assert np.shares_memory(arr, get_array(df, "a"))
+    if using_copy_on_write:
+        assert arr.flags.writeable is False
+    else:
+        assert arr.flags.writeable is True
+
+
+def test_dataframe_multiple_numpy_dtypes():
+    df = DataFrame({"a": [1, 2, 3], "b": 1.5})
+    arr = np.asarray(df)
+    assert not np.shares_memory(arr, get_array(df, "a"))
+    assert arr.flags.writeable is True
+
+    if np_version_gt2:
+        # copy=False semantics are only supported in NumPy>=2.
+
+        msg = "Starting with NumPy 2.0, the behavior of the 'copy' keyword has changed"
+        with pytest.raises(FutureWarning, match=msg):
+            arr = np.array(df, copy=False)
+
+    arr = np.array(df, copy=True)
+    assert arr.flags.writeable is True
+
+
+def test_dataframe_single_block_copy_true():
+    # the copy=False/None cases are tested above in test_dataframe_values
+    df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
+    arr = np.array(df, copy=True)
+    assert not np.shares_memory(arr, get_array(df, "a"))
+    assert arr.flags.writeable is True
+
+
+def test_values_is_ea(using_copy_on_write):
+    df = DataFrame({"a": date_range("2012-01-01", periods=3)})
+    arr = np.asarray(df)
+    if using_copy_on_write:
+        assert arr.flags.writeable is False
+    else:
+        assert arr.flags.writeable is True
+
+
+def test_empty_dataframe():
+    df = DataFrame()
+    arr = np.asarray(df)
+    assert arr.flags.writeable is True
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/copy_view/test_astype.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/copy_view/test_astype.py
new file mode 100644
index 0000000000000000000000000000000000000000..45fc3333c49a7722db200985d8232b9470baa205
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/copy_view/test_astype.py
@@ -0,0 +1,287 @@
+import pickle
+
+import numpy as np
+import pytest
+
+from pandas.compat import HAS_PYARROW
+from pandas.compat.pyarrow import pa_version_under12p0
+import pandas.util._test_decorators as td
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Series,
+    Timestamp,
+    date_range,
+)
+import pandas._testing as tm
+from pandas.tests.copy_view.util import get_array
+
+
+def test_astype_single_dtype(using_copy_on_write):
+    df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": 1.5})
+    df_orig = df.copy()
+    df2 = df.astype("float64")
+
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(df2, "c"), get_array(df, "c"))
+        assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+    else:
+        assert not np.shares_memory(get_array(df2, "c"), get_array(df, "c"))
+        assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+
+    # mutating df2 triggers a copy-on-write for that column/block
+    df2.iloc[0, 2] = 5.5
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(df2, "c"), get_array(df, "c"))
+    tm.assert_frame_equal(df, df_orig)
+
+    # mutating parent also doesn't update result
+    df2 = df.astype("float64")
+    df.iloc[0, 2] = 5.5
+    tm.assert_frame_equal(df2, df_orig.astype("float64"))
+
+
+@pytest.mark.parametrize("dtype", ["int64", "Int64"])
+@pytest.mark.parametrize("new_dtype", ["int64", "Int64", "int64[pyarrow]"])
+def test_astype_avoids_copy(using_copy_on_write, dtype, new_dtype):
+    if new_dtype == "int64[pyarrow]":
+        pytest.importorskip("pyarrow")
+    df = DataFrame({"a": [1, 2, 3]}, dtype=dtype)
+    df_orig = df.copy()
+    df2 = df.astype(new_dtype)
+
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+    else:
+        assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+
+    # mutating df2 triggers a copy-on-write for that column/block
+    df2.iloc[0, 0] = 10
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+    tm.assert_frame_equal(df, df_orig)
+
+    # mutating parent also doesn't update result
+    df2 = df.astype(new_dtype)
+    df.iloc[0, 0] = 100
+    tm.assert_frame_equal(df2, df_orig.astype(new_dtype))
+
+
+@pytest.mark.parametrize("dtype", ["float64", "int32", "Int32", "int32[pyarrow]"])
+def test_astype_different_target_dtype(using_copy_on_write, dtype):
+    if dtype == "int32[pyarrow]":
+        pytest.importorskip("pyarrow")
+    df = DataFrame({"a": [1, 2, 3]})
+    df_orig = df.copy()
+    df2 = df.astype(dtype)
+
+    assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+    if using_copy_on_write:
+        assert df2._mgr._has_no_reference(0)
+
+    df2.iloc[0, 0] = 5
+    tm.assert_frame_equal(df, df_orig)
+
+    # mutating parent also doesn't update result
+    df2 = df.astype(dtype)
+    df.iloc[0, 0] = 100
+    tm.assert_frame_equal(df2, df_orig.astype(dtype))
+
+
+@td.skip_array_manager_invalid_test
+def test_astype_numpy_to_ea():
+    ser = Series([1, 2, 3])
+    with pd.option_context("mode.copy_on_write", True):
+        result = ser.astype("Int64")
+    assert np.shares_memory(get_array(ser), get_array(result))
+
+
+@pytest.mark.parametrize(
+    "dtype, new_dtype", [("object", "string"), ("string", "object")]
+)
+def test_astype_string_and_object(using_copy_on_write, dtype, new_dtype):
+    df = DataFrame({"a": ["a", "b", "c"]}, dtype=dtype)
+    df_orig = df.copy()
+    df2 = df.astype(new_dtype)
+
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+    else:
+        assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+
+    df2.iloc[0, 0] = "x"
+    tm.assert_frame_equal(df, df_orig)
+
+
+@pytest.mark.parametrize(
+    "dtype, new_dtype", [("object", "string"), ("string", "object")]
+)
+def test_astype_string_and_object_update_original(
+    using_copy_on_write, dtype, new_dtype
+):
+    df = DataFrame({"a": ["a", "b", "c"]}, dtype=dtype)
+    df2 = df.astype(new_dtype)
+    df_orig = df2.copy()
+
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+    else:
+        assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+
+    df.iloc[0, 0] = "x"
+    tm.assert_frame_equal(df2, df_orig)
+
+
+def test_astype_str_copy_on_pickle_roundrip():
+    # TODO(infer_string) this test can be removed after 3.0 (once str is the default)
+    # https://github.com/pandas-dev/pandas/issues/54654
+    # ensure_string_array may alter array inplace
+    base = Series(np.array([(1, 2), None, 1], dtype="object"))
+    base_copy = pickle.loads(pickle.dumps(base))
+    base_copy.astype(str)
+    tm.assert_series_equal(base, base_copy)
+
+
+def test_astype_string_copy_on_pickle_roundrip(any_string_dtype):
+    # https://github.com/pandas-dev/pandas/issues/54654
+    # ensure_string_array may alter array inplace
+    base = Series(np.array([(1, 2), None, 1], dtype="object"))
+    base_copy = pickle.loads(pickle.dumps(base))
+    base_copy.astype(any_string_dtype)
+    tm.assert_series_equal(base, base_copy)
+
+
+def test_astype_string_read_only_on_pickle_roundrip(any_string_dtype):
+    # https://github.com/pandas-dev/pandas/issues/54654
+    # ensure_string_array may alter read-only array inplace
+    base = Series(np.array([(1, 2), None, 1], dtype="object"))
+    base_copy = pickle.loads(pickle.dumps(base))
+    base_copy._values.flags.writeable = False
+    base_copy.astype(any_string_dtype)
+    tm.assert_series_equal(base, base_copy)
+
+
+def test_astype_dict_dtypes(using_copy_on_write):
+    df = DataFrame(
+        {"a": [1, 2, 3], "b": [4, 5, 6], "c": Series([1.5, 1.5, 1.5], dtype="float64")}
+    )
+    df_orig = df.copy()
+    df2 = df.astype({"a": "float64", "c": "float64"})
+
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(df2, "c"), get_array(df, "c"))
+        assert np.shares_memory(get_array(df2, "b"), get_array(df, "b"))
+        assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+    else:
+        assert not np.shares_memory(get_array(df2, "c"), get_array(df, "c"))
+        assert not np.shares_memory(get_array(df2, "b"), get_array(df, "b"))
+        assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+
+    # mutating df2 triggers a copy-on-write for that column/block
+    df2.iloc[0, 2] = 5.5
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(df2, "c"), get_array(df, "c"))
+
+    df2.iloc[0, 1] = 10
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(df2, "b"), get_array(df, "b"))
+    tm.assert_frame_equal(df, df_orig)
+
+
+def test_astype_different_datetime_resos(using_copy_on_write):
+    df = DataFrame({"a": date_range("2019-12-31", periods=2, freq="D")})
+    result = df.astype("datetime64[ms]")
+
+    assert not np.shares_memory(get_array(df, "a"), get_array(result, "a"))
+    if using_copy_on_write:
+        assert result._mgr._has_no_reference(0)
+
+
+def test_astype_different_timezones(using_copy_on_write):
+    df = DataFrame(
+        {"a": date_range("2019-12-31", periods=5, freq="D", tz="US/Pacific")}
+    )
+    result = df.astype("datetime64[ns, Europe/Berlin]")
+    if using_copy_on_write:
+        assert not result._mgr._has_no_reference(0)
+        assert np.shares_memory(get_array(df, "a"), get_array(result, "a"))
+
+
+def test_astype_different_timezones_different_reso(using_copy_on_write):
+    df = DataFrame(
+        {"a": date_range("2019-12-31", periods=5, freq="D", tz="US/Pacific")}
+    )
+    result = df.astype("datetime64[ms, Europe/Berlin]")
+    if using_copy_on_write:
+        assert result._mgr._has_no_reference(0)
+        assert not np.shares_memory(get_array(df, "a"), get_array(result, "a"))
+
+
+def test_astype_arrow_timestamp(using_copy_on_write):
+    pytest.importorskip("pyarrow")
+    df = DataFrame(
+        {
+            "a": [
+                Timestamp("2020-01-01 01:01:01.000001"),
+                Timestamp("2020-01-01 01:01:01.000001"),
+            ]
+        },
+        dtype="M8[ns]",
+    )
+    result = df.astype("timestamp[ns][pyarrow]")
+    if using_copy_on_write:
+        assert not result._mgr._has_no_reference(0)
+        if pa_version_under12p0:
+            assert not np.shares_memory(
+                get_array(df, "a"), get_array(result, "a")._pa_array
+            )
+        else:
+            assert np.shares_memory(
+                get_array(df, "a"), get_array(result, "a")._pa_array
+            )
+
+
+def test_convert_dtypes_infer_objects(using_copy_on_write):
+    ser = Series(["a", "b", "c"])
+    ser_orig = ser.copy()
+    result = ser.convert_dtypes(
+        convert_integer=False,
+        convert_boolean=False,
+        convert_floating=False,
+        convert_string=False,
+    )
+
+    if using_copy_on_write:
+        assert tm.shares_memory(get_array(ser), get_array(result))
+    else:
+        assert not np.shares_memory(get_array(ser), get_array(result))
+
+    result.iloc[0] = "x"
+    tm.assert_series_equal(ser, ser_orig)
+
+
+def test_convert_dtypes(using_copy_on_write, using_infer_string):
+    df = DataFrame({"a": ["a", "b"], "b": [1, 2], "c": [1.5, 2.5], "d": [True, False]})
+    df_orig = df.copy()
+    df2 = df.convert_dtypes()
+
+    if using_copy_on_write:
+        if using_infer_string and HAS_PYARROW:
+            # TODO the default nullable string dtype still uses python storage
+            # this should be changed to pyarrow if installed
+            assert not tm.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+        else:
+            assert tm.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+        assert tm.shares_memory(get_array(df2, "d"), get_array(df, "d"))
+        assert tm.shares_memory(get_array(df2, "b"), get_array(df, "b"))
+        assert tm.shares_memory(get_array(df2, "c"), get_array(df, "c"))
+    else:
+        assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+        assert not np.shares_memory(get_array(df2, "b"), get_array(df, "b"))
+        assert not np.shares_memory(get_array(df2, "c"), get_array(df, "c"))
+        assert not np.shares_memory(get_array(df2, "d"), get_array(df, "d"))
+
+    df2.iloc[0, 0] = "x"
+    df2.iloc[0, 1] = 10
+    tm.assert_frame_equal(df, df_orig)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/copy_view/test_chained_assignment_deprecation.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/copy_view/test_chained_assignment_deprecation.py
new file mode 100644
index 0000000000000000000000000000000000000000..49f5254f5bab44fa871b80eb898bde689974aff7
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/copy_view/test_chained_assignment_deprecation.py
@@ -0,0 +1,260 @@
+import numpy as np
+import pytest
+
+from pandas.compat import (
+    PY311,
+    WARNING_CHECK_DISABLED,
+)
+from pandas.errors import (
+    ChainedAssignmentError,
+    SettingWithCopyWarning,
+)
+
+from pandas import (
+    DataFrame,
+    option_context,
+)
+import pandas._testing as tm
+
+
+def test_methods_iloc_warn(using_copy_on_write):
+    if not using_copy_on_write:
+        df = DataFrame({"a": [1, 2, 3], "b": 1})
+        with tm.assert_cow_warning(match="A value"):
+            df.iloc[:, 0].replace(1, 5, inplace=True)
+
+        with tm.assert_cow_warning(match="A value"):
+            df.iloc[:, 0].fillna(1, inplace=True)
+
+        with tm.assert_cow_warning(match="A value"):
+            df.iloc[:, 0].interpolate(inplace=True)
+
+        with tm.assert_cow_warning(match="A value"):
+            df.iloc[:, 0].ffill(inplace=True)
+
+        with tm.assert_cow_warning(match="A value"):
+            df.iloc[:, 0].bfill(inplace=True)
+
+
+@pytest.mark.parametrize(
+    "func, args",
+    [
+        ("replace", (4, 5)),
+        ("fillna", (1,)),
+        ("interpolate", ()),
+        ("bfill", ()),
+        ("ffill", ()),
+    ],
+)
+def test_methods_iloc_getitem_item_cache(
+    func, args, using_copy_on_write, warn_copy_on_write
+):
+    # ensure we don't incorrectly raise chained assignment warning because
+    # of the item cache / iloc not setting the item cache
+    df_orig = DataFrame({"a": [1, 2, 3], "b": 1})
+
+    df = df_orig.copy()
+    ser = df.iloc[:, 0]
+    getattr(ser, func)(*args, inplace=True)
+
+    # parent that holds item_cache is dead, so don't increase ref count
+    df = df_orig.copy()
+    ser = df.copy()["a"]
+    getattr(ser, func)(*args, inplace=True)
+
+    df = df_orig.copy()
+    df["a"]  # populate the item_cache
+    ser = df.iloc[:, 0]  # iloc creates a new object
+    getattr(ser, func)(*args, inplace=True)
+
+    df = df_orig.copy()
+    df["a"]  # populate the item_cache
+    ser = df["a"]
+    getattr(ser, func)(*args, inplace=True)
+
+    df = df_orig.copy()
+    df["a"]  # populate the item_cache
+    # TODO(CoW-warn) because of the usage of *args, this doesn't warn on Py3.11+
+    if using_copy_on_write:
+        with tm.raises_chained_assignment_error(not PY311):
+            getattr(df["a"], func)(*args, inplace=True)
+    else:
+        with tm.assert_cow_warning(not PY311, match="A value"):
+            getattr(df["a"], func)(*args, inplace=True)
+
+    df = df_orig.copy()
+    ser = df["a"]  # populate the item_cache and keep ref
+    if using_copy_on_write:
+        with tm.raises_chained_assignment_error(not PY311):
+            getattr(df["a"], func)(*args, inplace=True)
+    else:
+        # ideally also warns on the default mode, but the ser' _cacher
+        # messes up the refcount + even in warning mode this doesn't trigger
+        # the warning of Py3.1+ (see above)
+        with tm.assert_cow_warning(warn_copy_on_write and not PY311, match="A value"):
+            getattr(df["a"], func)(*args, inplace=True)
+
+
+def test_methods_iloc_getitem_item_cache_fillna(
+    using_copy_on_write, warn_copy_on_write
+):
+    # ensure we don't incorrectly raise chained assignment warning because
+    # of the item cache / iloc not setting the item cache
+    df_orig = DataFrame({"a": [1, 2, 3], "b": 1})
+
+    df = df_orig.copy()
+    ser = df.iloc[:, 0]
+    ser.fillna(1, inplace=True)
+
+    # parent that holds item_cache is dead, so don't increase ref count
+    df = df_orig.copy()
+    ser = df.copy()["a"]
+    ser.fillna(1, inplace=True)
+
+    df = df_orig.copy()
+    df["a"]  # populate the item_cache
+    ser = df.iloc[:, 0]  # iloc creates a new object
+    ser.fillna(1, inplace=True)
+
+    df = df_orig.copy()
+    df["a"]  # populate the item_cache
+    ser = df["a"]
+    ser.fillna(1, inplace=True)
+
+    df = df_orig.copy()
+    df["a"]  # populate the item_cache
+    if using_copy_on_write:
+        with tm.raises_chained_assignment_error():
+            df["a"].fillna(1, inplace=True)
+    else:
+        with tm.assert_cow_warning(match="A value"):
+            df["a"].fillna(1, inplace=True)
+
+    df = df_orig.copy()
+    ser = df["a"]  # populate the item_cache and keep ref
+    if using_copy_on_write:
+        with tm.raises_chained_assignment_error():
+            df["a"].fillna(1, inplace=True)
+    else:
+        # TODO(CoW-warn) ideally also warns on the default mode, but the ser' _cacher
+        # messes up the refcount
+        with tm.assert_cow_warning(warn_copy_on_write, match="A value"):
+            df["a"].fillna(1, inplace=True)
+
+
+# TODO(CoW-warn) expand the cases
+@pytest.mark.parametrize(
+    "indexer", [0, [0, 1], slice(0, 2), np.array([True, False, True])]
+)
+def test_series_setitem(indexer, using_copy_on_write, warn_copy_on_write):
+    # ensure we only get a single warning for those typical cases of chained
+    # assignment
+    df = DataFrame({"a": [1, 2, 3], "b": 1})
+
+    # using custom check instead of tm.assert_produces_warning because that doesn't
+    # fail if multiple warnings are raised
+    if WARNING_CHECK_DISABLED:
+        return
+
+    with pytest.warns() as record:
+        df["a"][indexer] = 0
+    assert len(record) == 1
+    if using_copy_on_write:
+        assert record[0].category == ChainedAssignmentError
+    else:
+        assert record[0].category == FutureWarning
+        assert "ChainedAssignmentError" in record[0].message.args[0]
+
+
+@pytest.mark.filterwarnings("ignore::pandas.errors.SettingWithCopyWarning")
+@pytest.mark.parametrize(
+    "indexer", ["a", ["a", "b"], slice(0, 2), np.array([True, False, True])]
+)
+def test_frame_setitem(indexer, using_copy_on_write):
+    df = DataFrame({"a": [1, 2, 3, 4, 5], "b": 1})
+
+    extra_warnings = () if using_copy_on_write else (SettingWithCopyWarning,)
+
+    with option_context("chained_assignment", "warn"):
+        with tm.raises_chained_assignment_error(extra_warnings=extra_warnings):
+            df[0:3][indexer] = 10
+
+
+@pytest.mark.parametrize(
+    "indexer", [0, [0, 1], slice(0, 2), np.array([True, False, True])]
+)
+def test_series_iloc_setitem(indexer):
+    df = DataFrame({"a": [1, 2, 3], "b": 1})
+
+    with option_context("chained_assignment", "warn"):
+        with tm.raises_chained_assignment_error():
+            df["a"].iloc[indexer] = 0
+
+
+@pytest.mark.parametrize(
+    "indexer", [0, [0, 1], slice(0, 2), np.array([True, False, True])]
+)
+def test_frame_iloc_setitem(indexer, using_copy_on_write):
+    df = DataFrame({"a": [1, 2, 3, 4, 5], "b": 1})
+
+    extra_warnings = () if using_copy_on_write else (SettingWithCopyWarning,)
+
+    with option_context("chained_assignment", "warn"):
+        with tm.raises_chained_assignment_error(extra_warnings=extra_warnings):
+            df[0:3].iloc[indexer] = 10
+
+
+@pytest.mark.parametrize(
+    "indexer", [0, [0, 1], slice(0, 2), np.array([True, False, True])]
+)
+def test_series_loc_setitem(indexer):
+    df = DataFrame({"a": [1, 2, 3], "b": 1})
+
+    with option_context("chained_assignment", "warn"):
+        with tm.raises_chained_assignment_error():
+            df["a"].loc[indexer] = 0
+
+
+@pytest.mark.parametrize(
+    "indexer", [0, [0, 1], (0, "a"), slice(0, 2), np.array([True, False, True])]
+)
+def test_frame_loc_setitem(indexer, using_copy_on_write):
+    df = DataFrame({"a": [1, 2, 3, 4, 5], "b": 1})
+
+    extra_warnings = () if using_copy_on_write else (SettingWithCopyWarning,)
+
+    with option_context("chained_assignment", "warn"):
+        with tm.raises_chained_assignment_error(extra_warnings=extra_warnings):
+            df[0:3].loc[indexer] = 10
+
+
+def test_series_at_setitem():
+    df = DataFrame({"a": [1, 2, 3], "b": 1})
+
+    with option_context("chained_assignment", "warn"):
+        with tm.raises_chained_assignment_error():
+            df["a"].at[0] = 0
+
+
+def test_frame_at_setitem():
+    df = DataFrame({"a": [1, 2, 3, 4, 5], "b": 1})
+
+    with option_context("chained_assignment", "warn"):
+        with tm.raises_chained_assignment_error():
+            df[0:3].at[0, "a"] = 10
+
+
+def test_series_iat_setitem():
+    df = DataFrame({"a": [1, 2, 3], "b": 1})
+
+    with option_context("chained_assignment", "warn"):
+        with tm.raises_chained_assignment_error():
+            df["a"].iat[0] = 0
+
+
+def test_frame_iat_setitem():
+    df = DataFrame({"a": [1, 2, 3, 4, 5], "b": 1})
+
+    with option_context("chained_assignment", "warn"):
+        with tm.raises_chained_assignment_error():
+            df[0:3].iat[0, 0] = 10
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/copy_view/test_clip.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/copy_view/test_clip.py
new file mode 100644
index 0000000000000000000000000000000000000000..5c5abdae7a021497d69c61c6b1e75e47a39d00b2
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/copy_view/test_clip.py
@@ -0,0 +1,106 @@
+import numpy as np
+
+from pandas.compat import WARNING_CHECK_DISABLED
+
+from pandas import (
+    DataFrame,
+    option_context,
+)
+import pandas._testing as tm
+from pandas.tests.copy_view.util import get_array
+
+
+def test_clip_inplace_reference(using_copy_on_write, warn_copy_on_write):
+    df = DataFrame({"a": [1.5, 2, 3]})
+    df_copy = df.copy()
+    arr_a = get_array(df, "a")
+    view = df[:]
+    if warn_copy_on_write:
+        with tm.assert_cow_warning():
+            df.clip(lower=2, inplace=True)
+    else:
+        df.clip(lower=2, inplace=True)
+
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(df, "a"), arr_a)
+        assert df._mgr._has_no_reference(0)
+        assert view._mgr._has_no_reference(0)
+        tm.assert_frame_equal(df_copy, view)
+    else:
+        assert np.shares_memory(get_array(df, "a"), arr_a)
+
+
+def test_clip_inplace_reference_no_op(using_copy_on_write):
+    df = DataFrame({"a": [1.5, 2, 3]})
+    df_copy = df.copy()
+    arr_a = get_array(df, "a")
+    view = df[:]
+    df.clip(lower=0, inplace=True)
+
+    assert np.shares_memory(get_array(df, "a"), arr_a)
+
+    if using_copy_on_write:
+        assert not df._mgr._has_no_reference(0)
+        assert not view._mgr._has_no_reference(0)
+        tm.assert_frame_equal(df_copy, view)
+
+
+def test_clip_inplace(using_copy_on_write):
+    df = DataFrame({"a": [1.5, 2, 3]})
+    arr_a = get_array(df, "a")
+    df.clip(lower=2, inplace=True)
+
+    assert np.shares_memory(get_array(df, "a"), arr_a)
+
+    if using_copy_on_write:
+        assert df._mgr._has_no_reference(0)
+
+
+def test_clip(using_copy_on_write):
+    df = DataFrame({"a": [1.5, 2, 3]})
+    df_orig = df.copy()
+    df2 = df.clip(lower=2)
+
+    assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+
+    if using_copy_on_write:
+        assert df._mgr._has_no_reference(0)
+    tm.assert_frame_equal(df_orig, df)
+
+
+def test_clip_no_op(using_copy_on_write):
+    df = DataFrame({"a": [1.5, 2, 3]})
+    df2 = df.clip(lower=0)
+
+    if using_copy_on_write:
+        assert not df._mgr._has_no_reference(0)
+        assert np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+    else:
+        assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+
+
+def test_clip_chained_inplace(using_copy_on_write):
+    df = DataFrame({"a": [1, 4, 2], "b": 1})
+    df_orig = df.copy()
+    if using_copy_on_write:
+        with tm.raises_chained_assignment_error():
+            df["a"].clip(1, 2, inplace=True)
+        tm.assert_frame_equal(df, df_orig)
+
+        with tm.raises_chained_assignment_error():
+            df[["a"]].clip(1, 2, inplace=True)
+        tm.assert_frame_equal(df, df_orig)
+    else:
+        with tm.assert_produces_warning(
+            FutureWarning if not WARNING_CHECK_DISABLED else None,
+            match="inplace method",
+        ):
+            df["a"].clip(1, 2, inplace=True)
+
+        with tm.assert_produces_warning(None):
+            with option_context("mode.chained_assignment", None):
+                df[["a"]].clip(1, 2, inplace=True)
+
+        with tm.assert_produces_warning(None):
+            with option_context("mode.chained_assignment", None):
+                df[df["a"] > 1].clip(1, 2, inplace=True)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/copy_view/test_constructors.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/copy_view/test_constructors.py
new file mode 100644
index 0000000000000000000000000000000000000000..66c9b456f18adc6824333e46f8dbc3e3f806221e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/copy_view/test_constructors.py
@@ -0,0 +1,382 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    DatetimeIndex,
+    Index,
+    Period,
+    PeriodIndex,
+    Series,
+    Timedelta,
+    TimedeltaIndex,
+    Timestamp,
+)
+import pandas._testing as tm
+from pandas.tests.copy_view.util import get_array
+
+# -----------------------------------------------------------------------------
+# Copy/view behaviour for Series / DataFrame constructors
+
+
+@pytest.mark.parametrize("dtype", [None, "int64"])
+def test_series_from_series(dtype, using_copy_on_write, warn_copy_on_write):
+    # Case: constructing a Series from another Series object follows CoW rules:
+    # a new object is returned and thus mutations are not propagated
+    ser = Series([1, 2, 3], name="name")
+
+    # default is copy=False -> new Series is a shallow copy / view of original
+    result = Series(ser, dtype=dtype)
+
+    # the shallow copy still shares memory
+    assert np.shares_memory(get_array(ser), get_array(result))
+
+    if using_copy_on_write:
+        assert result._mgr.blocks[0].refs.has_reference()
+
+    if using_copy_on_write:
+        # mutating new series copy doesn't mutate original
+        result.iloc[0] = 0
+        assert ser.iloc[0] == 1
+        # mutating triggered a copy-on-write -> no longer shares memory
+        assert not np.shares_memory(get_array(ser), get_array(result))
+    else:
+        # mutating shallow copy does mutate original
+        with tm.assert_cow_warning(warn_copy_on_write):
+            result.iloc[0] = 0
+        assert ser.iloc[0] == 0
+        # and still shares memory
+        assert np.shares_memory(get_array(ser), get_array(result))
+
+    # the same when modifying the parent
+    result = Series(ser, dtype=dtype)
+
+    if using_copy_on_write:
+        # mutating original doesn't mutate new series
+        ser.iloc[0] = 0
+        assert result.iloc[0] == 1
+    else:
+        # mutating original does mutate shallow copy
+        with tm.assert_cow_warning(warn_copy_on_write):
+            ser.iloc[0] = 0
+        assert result.iloc[0] == 0
+
+
+def test_series_from_series_with_reindex(using_copy_on_write, warn_copy_on_write):
+    # Case: constructing a Series from another Series with specifying an index
+    # that potentially requires a reindex of the values
+    ser = Series([1, 2, 3], name="name")
+
+    # passing an index that doesn't actually require a reindex of the values
+    # -> without CoW we get an actual mutating view
+    for index in [
+        ser.index,
+        ser.index.copy(),
+        list(ser.index),
+        ser.index.rename("idx"),
+    ]:
+        result = Series(ser, index=index)
+        assert np.shares_memory(ser.values, result.values)
+        with tm.assert_cow_warning(warn_copy_on_write):
+            result.iloc[0] = 0
+        if using_copy_on_write:
+            assert ser.iloc[0] == 1
+        else:
+            assert ser.iloc[0] == 0
+
+    # ensure that if an actual reindex is needed, we don't have any refs
+    # (mutating the result wouldn't trigger CoW)
+    result = Series(ser, index=[0, 1, 2, 3])
+    assert not np.shares_memory(ser.values, result.values)
+    if using_copy_on_write:
+        assert not result._mgr.blocks[0].refs.has_reference()
+
+
+@pytest.mark.parametrize("fastpath", [False, True])
+@pytest.mark.parametrize("dtype", [None, "int64"])
+@pytest.mark.parametrize("idx", [None, pd.RangeIndex(start=0, stop=3, step=1)])
+@pytest.mark.parametrize(
+    "arr", [np.array([1, 2, 3], dtype="int64"), pd.array([1, 2, 3], dtype="Int64")]
+)
+def test_series_from_array(using_copy_on_write, idx, dtype, fastpath, arr):
+    if idx is None or dtype is not None:
+        fastpath = False
+    msg = "The 'fastpath' keyword in pd.Series is deprecated"
+    with tm.assert_produces_warning(DeprecationWarning, match=msg):
+        ser = Series(arr, dtype=dtype, index=idx, fastpath=fastpath)
+    ser_orig = ser.copy()
+    data = getattr(arr, "_data", arr)
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(ser), data)
+    else:
+        assert np.shares_memory(get_array(ser), data)
+
+    arr[0] = 100
+    if using_copy_on_write:
+        tm.assert_series_equal(ser, ser_orig)
+    else:
+        expected = Series([100, 2, 3], dtype=dtype if dtype is not None else arr.dtype)
+        tm.assert_series_equal(ser, expected)
+
+
+@pytest.mark.parametrize("copy", [True, False, None])
+def test_series_from_array_different_dtype(using_copy_on_write, copy):
+    arr = np.array([1, 2, 3], dtype="int64")
+    ser = Series(arr, dtype="int32", copy=copy)
+    assert not np.shares_memory(get_array(ser), arr)
+
+
+@pytest.mark.parametrize(
+    "idx",
+    [
+        Index([1, 2]),
+        DatetimeIndex([Timestamp("2019-12-31"), Timestamp("2020-12-31")]),
+        PeriodIndex([Period("2019-12-31"), Period("2020-12-31")]),
+        TimedeltaIndex([Timedelta("1 days"), Timedelta("2 days")]),
+    ],
+)
+def test_series_from_index(using_copy_on_write, idx):
+    ser = Series(idx)
+    expected = idx.copy(deep=True)
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(ser), get_array(idx))
+        assert not ser._mgr._has_no_reference(0)
+    else:
+        assert not np.shares_memory(get_array(ser), get_array(idx))
+    ser.iloc[0] = ser.iloc[1]
+    tm.assert_index_equal(idx, expected)
+
+
+def test_series_from_index_different_dtypes(using_copy_on_write):
+    idx = Index([1, 2, 3], dtype="int64")
+    ser = Series(idx, dtype="int32")
+    assert not np.shares_memory(get_array(ser), get_array(idx))
+    if using_copy_on_write:
+        assert ser._mgr._has_no_reference(0)
+
+
+@pytest.mark.filterwarnings("ignore:Setting a value on a view:FutureWarning")
+@pytest.mark.parametrize("fastpath", [False, True])
+@pytest.mark.parametrize("dtype", [None, "int64"])
+@pytest.mark.parametrize("idx", [None, pd.RangeIndex(start=0, stop=3, step=1)])
+def test_series_from_block_manager(using_copy_on_write, idx, dtype, fastpath):
+    ser = Series([1, 2, 3], dtype="int64")
+    ser_orig = ser.copy()
+    msg = "The 'fastpath' keyword in pd.Series is deprecated"
+    with tm.assert_produces_warning(DeprecationWarning, match=msg):
+        ser2 = Series(ser._mgr, dtype=dtype, fastpath=fastpath, index=idx)
+    assert np.shares_memory(get_array(ser), get_array(ser2))
+    if using_copy_on_write:
+        assert not ser2._mgr._has_no_reference(0)
+
+    ser2.iloc[0] = 100
+    if using_copy_on_write:
+        tm.assert_series_equal(ser, ser_orig)
+    else:
+        expected = Series([100, 2, 3])
+        tm.assert_series_equal(ser, expected)
+
+
+def test_series_from_block_manager_different_dtype(using_copy_on_write):
+    ser = Series([1, 2, 3], dtype="int64")
+    msg = "Passing a SingleBlockManager to Series"
+    with tm.assert_produces_warning(DeprecationWarning, match=msg):
+        ser2 = Series(ser._mgr, dtype="int32")
+    assert not np.shares_memory(get_array(ser), get_array(ser2))
+    if using_copy_on_write:
+        assert ser2._mgr._has_no_reference(0)
+
+
+@pytest.mark.parametrize("use_mgr", [True, False])
+@pytest.mark.parametrize("columns", [None, ["a"]])
+def test_dataframe_constructor_mgr_or_df(
+    using_copy_on_write, warn_copy_on_write, columns, use_mgr
+):
+    df = DataFrame({"a": [1, 2, 3]})
+    df_orig = df.copy()
+
+    if use_mgr:
+        data = df._mgr
+        warn = DeprecationWarning
+    else:
+        data = df
+        warn = None
+    msg = "Passing a BlockManager to DataFrame"
+    with tm.assert_produces_warning(warn, match=msg, check_stacklevel=False):
+        new_df = DataFrame(data)
+
+    assert np.shares_memory(get_array(df, "a"), get_array(new_df, "a"))
+    with tm.assert_cow_warning(warn_copy_on_write and not use_mgr):
+        new_df.iloc[0] = 100
+
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(df, "a"), get_array(new_df, "a"))
+        tm.assert_frame_equal(df, df_orig)
+    else:
+        assert np.shares_memory(get_array(df, "a"), get_array(new_df, "a"))
+        tm.assert_frame_equal(df, new_df)
+
+
+@pytest.mark.parametrize("dtype", [None, "int64", "Int64"])
+@pytest.mark.parametrize("index", [None, [0, 1, 2]])
+@pytest.mark.parametrize("columns", [None, ["a", "b"], ["a", "b", "c"]])
+def test_dataframe_from_dict_of_series(
+    request, using_copy_on_write, warn_copy_on_write, columns, index, dtype
+):
+    # Case: constructing a DataFrame from Series objects with copy=False
+    # has to do a lazy following CoW rules
+    # (the default for DataFrame(dict) is still to copy to ensure consolidation)
+    s1 = Series([1, 2, 3])
+    s2 = Series([4, 5, 6])
+    s1_orig = s1.copy()
+    expected = DataFrame(
+        {"a": [1, 2, 3], "b": [4, 5, 6]}, index=index, columns=columns, dtype=dtype
+    )
+
+    result = DataFrame(
+        {"a": s1, "b": s2}, index=index, columns=columns, dtype=dtype, copy=False
+    )
+
+    # the shallow copy still shares memory
+    assert np.shares_memory(get_array(result, "a"), get_array(s1))
+
+    # mutating the new dataframe doesn't mutate original
+    with tm.assert_cow_warning(warn_copy_on_write):
+        result.iloc[0, 0] = 10
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(result, "a"), get_array(s1))
+        tm.assert_series_equal(s1, s1_orig)
+    else:
+        assert s1.iloc[0] == 10
+
+    # the same when modifying the parent series
+    s1 = Series([1, 2, 3])
+    s2 = Series([4, 5, 6])
+    result = DataFrame(
+        {"a": s1, "b": s2}, index=index, columns=columns, dtype=dtype, copy=False
+    )
+    with tm.assert_cow_warning(warn_copy_on_write):
+        s1.iloc[0] = 10
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(result, "a"), get_array(s1))
+        tm.assert_frame_equal(result, expected)
+    else:
+        assert result.iloc[0, 0] == 10
+
+
+@pytest.mark.parametrize("dtype", [None, "int64"])
+def test_dataframe_from_dict_of_series_with_reindex(dtype):
+    # Case: constructing a DataFrame from Series objects with copy=False
+    # and passing an index that requires an actual (no-view) reindex -> need
+    # to ensure the result doesn't have refs set up to unnecessarily trigger
+    # a copy on write
+    s1 = Series([1, 2, 3])
+    s2 = Series([4, 5, 6])
+    df = DataFrame({"a": s1, "b": s2}, index=[1, 2, 3], dtype=dtype, copy=False)
+
+    # df should own its memory, so mutating shouldn't trigger a copy
+    arr_before = get_array(df, "a")
+    assert not np.shares_memory(arr_before, get_array(s1))
+    df.iloc[0, 0] = 100
+    arr_after = get_array(df, "a")
+    assert np.shares_memory(arr_before, arr_after)
+
+
+@pytest.mark.parametrize("cons", [Series, Index])
+@pytest.mark.parametrize(
+    "data, dtype", [([1, 2], None), ([1, 2], "int64"), (["a", "b"], object)]
+)
+def test_dataframe_from_series_or_index(
+    using_copy_on_write, warn_copy_on_write, data, dtype, cons
+):
+    obj = cons(data, dtype=dtype)
+    obj_orig = obj.copy()
+    df = DataFrame(obj, dtype=dtype)
+    assert np.shares_memory(get_array(obj), get_array(df, 0))
+    if using_copy_on_write:
+        assert not df._mgr._has_no_reference(0)
+
+    with tm.assert_cow_warning(warn_copy_on_write):
+        df.iloc[0, 0] = data[-1]
+    if using_copy_on_write:
+        tm.assert_equal(obj, obj_orig)
+
+
+@pytest.mark.parametrize("cons", [Series, Index])
+def test_dataframe_from_series_or_index_different_dtype(using_copy_on_write, cons):
+    obj = cons([1, 2], dtype="int64")
+    df = DataFrame(obj, dtype="int32")
+    assert not np.shares_memory(get_array(obj), get_array(df, 0))
+    if using_copy_on_write:
+        assert df._mgr._has_no_reference(0)
+
+
+def test_dataframe_from_series_infer_datetime(using_copy_on_write):
+    ser = Series([Timestamp("2019-12-31"), Timestamp("2020-12-31")], dtype=object)
+    with tm.assert_produces_warning(FutureWarning, match="Dtype inference"):
+        df = DataFrame(ser)
+    assert not np.shares_memory(get_array(ser), get_array(df, 0))
+    if using_copy_on_write:
+        assert df._mgr._has_no_reference(0)
+
+
+@pytest.mark.parametrize("index", [None, [0, 1, 2]])
+def test_dataframe_from_dict_of_series_with_dtype(index):
+    # Variant of above, but now passing a dtype that causes a copy
+    # -> need to ensure the result doesn't have refs set up to unnecessarily
+    # trigger a copy on write
+    s1 = Series([1.0, 2.0, 3.0])
+    s2 = Series([4, 5, 6])
+    df = DataFrame({"a": s1, "b": s2}, index=index, dtype="int64", copy=False)
+
+    # df should own its memory, so mutating shouldn't trigger a copy
+    arr_before = get_array(df, "a")
+    assert not np.shares_memory(arr_before, get_array(s1))
+    df.iloc[0, 0] = 100
+    arr_after = get_array(df, "a")
+    assert np.shares_memory(arr_before, arr_after)
+
+
+@pytest.mark.parametrize("copy", [False, None, True])
+def test_frame_from_numpy_array(using_copy_on_write, copy, using_array_manager):
+    arr = np.array([[1, 2], [3, 4]])
+    df = DataFrame(arr, copy=copy)
+
+    if (
+        using_copy_on_write
+        and copy is not False
+        or copy is True
+        or (using_array_manager and copy is None)
+    ):
+        assert not np.shares_memory(get_array(df, 0), arr)
+    else:
+        assert np.shares_memory(get_array(df, 0), arr)
+
+
+def test_dataframe_from_records_with_dataframe(using_copy_on_write, warn_copy_on_write):
+    df = DataFrame({"a": [1, 2, 3]})
+    df_orig = df.copy()
+    with tm.assert_produces_warning(FutureWarning):
+        df2 = DataFrame.from_records(df)
+    if using_copy_on_write:
+        assert not df._mgr._has_no_reference(0)
+    assert np.shares_memory(get_array(df, "a"), get_array(df2, "a"))
+    with tm.assert_cow_warning(warn_copy_on_write):
+        df2.iloc[0, 0] = 100
+    if using_copy_on_write:
+        tm.assert_frame_equal(df, df_orig)
+    else:
+        tm.assert_frame_equal(df, df2)
+
+
+def test_frame_from_dict_of_index(using_copy_on_write):
+    idx = Index([1, 2, 3])
+    expected = idx.copy(deep=True)
+    df = DataFrame({"a": idx}, copy=False)
+    assert np.shares_memory(get_array(df, "a"), idx._values)
+    if using_copy_on_write:
+        assert not df._mgr._has_no_reference(0)
+
+        df.iloc[0, 0] = 100
+        tm.assert_index_equal(idx, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/copy_view/test_core_functionalities.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/copy_view/test_core_functionalities.py
new file mode 100644
index 0000000000000000000000000000000000000000..8dc80c5cc0e0eadbe792e114d48593d95df17907
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/copy_view/test_core_functionalities.py
@@ -0,0 +1,106 @@
+import numpy as np
+import pytest
+
+from pandas import DataFrame
+import pandas._testing as tm
+from pandas.tests.copy_view.util import get_array
+
+
+def test_assigning_to_same_variable_removes_references(using_copy_on_write):
+    df = DataFrame({"a": [1, 2, 3]})
+    df = df.reset_index()
+    if using_copy_on_write:
+        assert df._mgr._has_no_reference(1)
+    arr = get_array(df, "a")
+    df.iloc[0, 1] = 100  # Write into a
+
+    assert np.shares_memory(arr, get_array(df, "a"))
+
+
+def test_setitem_dont_track_unnecessary_references(using_copy_on_write):
+    df = DataFrame({"a": [1, 2, 3], "b": 1, "c": 1})
+
+    df["b"] = 100
+    arr = get_array(df, "a")
+    # We split the block in setitem, if we are not careful the new blocks will
+    # reference each other triggering a copy
+    df.iloc[0, 0] = 100
+    assert np.shares_memory(arr, get_array(df, "a"))
+
+
+def test_setitem_with_view_copies(using_copy_on_write, warn_copy_on_write):
+    df = DataFrame({"a": [1, 2, 3], "b": 1, "c": 1})
+    view = df[:]
+    expected = df.copy()
+
+    df["b"] = 100
+    arr = get_array(df, "a")
+    with tm.assert_cow_warning(warn_copy_on_write):
+        df.iloc[0, 0] = 100  # Check that we correctly track reference
+    if using_copy_on_write:
+        assert not np.shares_memory(arr, get_array(df, "a"))
+        tm.assert_frame_equal(view, expected)
+
+
+def test_setitem_with_view_invalidated_does_not_copy(
+    using_copy_on_write, warn_copy_on_write, request
+):
+    df = DataFrame({"a": [1, 2, 3], "b": 1, "c": 1})
+    view = df[:]
+
+    df["b"] = 100
+    arr = get_array(df, "a")
+    view = None  # noqa: F841
+    # TODO(CoW-warn) false positive? -> block gets split because of `df["b"] = 100`
+    # which introduces additional refs, even when those of `view` go out of scopes
+    with tm.assert_cow_warning(warn_copy_on_write):
+        df.iloc[0, 0] = 100
+    if using_copy_on_write:
+        # Setitem split the block. Since the old block shared data with view
+        # all the new blocks are referencing view and each other. When view
+        # goes out of scope, they don't share data with any other block,
+        # so we should not trigger a copy
+        mark = pytest.mark.xfail(
+            reason="blk.delete does not track references correctly"
+        )
+        request.applymarker(mark)
+        assert np.shares_memory(arr, get_array(df, "a"))
+
+
+def test_out_of_scope(using_copy_on_write):
+    def func():
+        df = DataFrame({"a": [1, 2], "b": 1.5, "c": 1})
+        # create some subset
+        result = df[["a", "b"]]
+        return result
+
+    result = func()
+    if using_copy_on_write:
+        assert not result._mgr.blocks[0].refs.has_reference()
+        assert not result._mgr.blocks[1].refs.has_reference()
+
+
+def test_delete(using_copy_on_write):
+    df = DataFrame(
+        np.random.default_rng(2).standard_normal((4, 3)), columns=["a", "b", "c"]
+    )
+    del df["b"]
+    if using_copy_on_write:
+        assert not df._mgr.blocks[0].refs.has_reference()
+        assert not df._mgr.blocks[1].refs.has_reference()
+
+    df = df[["a"]]
+    if using_copy_on_write:
+        assert not df._mgr.blocks[0].refs.has_reference()
+
+
+def test_delete_reference(using_copy_on_write):
+    df = DataFrame(
+        np.random.default_rng(2).standard_normal((4, 3)), columns=["a", "b", "c"]
+    )
+    x = df[:]
+    del df["b"]
+    if using_copy_on_write:
+        assert df._mgr.blocks[0].refs.has_reference()
+        assert df._mgr.blocks[1].refs.has_reference()
+        assert x._mgr.blocks[0].refs.has_reference()
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/copy_view/test_functions.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/copy_view/test_functions.py
new file mode 100644
index 0000000000000000000000000000000000000000..ce444ac3571fa64866e43fc982577e6f6f28fe5d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/copy_view/test_functions.py
@@ -0,0 +1,397 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    DataFrame,
+    Index,
+    Series,
+    concat,
+    merge,
+)
+import pandas._testing as tm
+from pandas.tests.copy_view.util import get_array
+
+
+def test_concat_frames(using_copy_on_write):
+    df = DataFrame({"b": ["a"] * 3}, dtype=object)
+    df2 = DataFrame({"a": ["a"] * 3}, dtype=object)
+    df_orig = df.copy()
+    result = concat([df, df2], axis=1)
+
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(result, "b"), get_array(df, "b"))
+        assert np.shares_memory(get_array(result, "a"), get_array(df2, "a"))
+    else:
+        assert not np.shares_memory(get_array(result, "b"), get_array(df, "b"))
+        assert not np.shares_memory(get_array(result, "a"), get_array(df2, "a"))
+
+    result.iloc[0, 0] = "d"
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(result, "b"), get_array(df, "b"))
+        assert np.shares_memory(get_array(result, "a"), get_array(df2, "a"))
+
+    result.iloc[0, 1] = "d"
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(result, "a"), get_array(df2, "a"))
+    tm.assert_frame_equal(df, df_orig)
+
+
+def test_concat_frames_updating_input(using_copy_on_write):
+    df = DataFrame({"b": ["a"] * 3}, dtype=object)
+    df2 = DataFrame({"a": ["a"] * 3}, dtype=object)
+    result = concat([df, df2], axis=1)
+
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(result, "b"), get_array(df, "b"))
+        assert np.shares_memory(get_array(result, "a"), get_array(df2, "a"))
+    else:
+        assert not np.shares_memory(get_array(result, "b"), get_array(df, "b"))
+        assert not np.shares_memory(get_array(result, "a"), get_array(df2, "a"))
+
+    expected = result.copy()
+    df.iloc[0, 0] = "d"
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(result, "b"), get_array(df, "b"))
+        assert np.shares_memory(get_array(result, "a"), get_array(df2, "a"))
+
+    df2.iloc[0, 0] = "d"
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(result, "a"), get_array(df2, "a"))
+    tm.assert_frame_equal(result, expected)
+
+
+def test_concat_series(using_copy_on_write):
+    ser = Series([1, 2], name="a")
+    ser2 = Series([3, 4], name="b")
+    ser_orig = ser.copy()
+    ser2_orig = ser2.copy()
+    result = concat([ser, ser2], axis=1)
+
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(result, "a"), ser.values)
+        assert np.shares_memory(get_array(result, "b"), ser2.values)
+    else:
+        assert not np.shares_memory(get_array(result, "a"), ser.values)
+        assert not np.shares_memory(get_array(result, "b"), ser2.values)
+
+    result.iloc[0, 0] = 100
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(result, "a"), ser.values)
+        assert np.shares_memory(get_array(result, "b"), ser2.values)
+
+    result.iloc[0, 1] = 1000
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(result, "b"), ser2.values)
+    tm.assert_series_equal(ser, ser_orig)
+    tm.assert_series_equal(ser2, ser2_orig)
+
+
+def test_concat_frames_chained(using_copy_on_write):
+    df1 = DataFrame({"a": [1, 2, 3], "b": [0.1, 0.2, 0.3]})
+    df2 = DataFrame({"c": [4, 5, 6]})
+    df3 = DataFrame({"d": [4, 5, 6]})
+    result = concat([concat([df1, df2], axis=1), df3], axis=1)
+    expected = result.copy()
+
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(result, "a"), get_array(df1, "a"))
+        assert np.shares_memory(get_array(result, "c"), get_array(df2, "c"))
+        assert np.shares_memory(get_array(result, "d"), get_array(df3, "d"))
+    else:
+        assert not np.shares_memory(get_array(result, "a"), get_array(df1, "a"))
+        assert not np.shares_memory(get_array(result, "c"), get_array(df2, "c"))
+        assert not np.shares_memory(get_array(result, "d"), get_array(df3, "d"))
+
+    df1.iloc[0, 0] = 100
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(result, "a"), get_array(df1, "a"))
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_concat_series_chained(using_copy_on_write):
+    ser1 = Series([1, 2, 3], name="a")
+    ser2 = Series([4, 5, 6], name="c")
+    ser3 = Series([4, 5, 6], name="d")
+    result = concat([concat([ser1, ser2], axis=1), ser3], axis=1)
+    expected = result.copy()
+
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(result, "a"), get_array(ser1, "a"))
+        assert np.shares_memory(get_array(result, "c"), get_array(ser2, "c"))
+        assert np.shares_memory(get_array(result, "d"), get_array(ser3, "d"))
+    else:
+        assert not np.shares_memory(get_array(result, "a"), get_array(ser1, "a"))
+        assert not np.shares_memory(get_array(result, "c"), get_array(ser2, "c"))
+        assert not np.shares_memory(get_array(result, "d"), get_array(ser3, "d"))
+
+    ser1.iloc[0] = 100
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(result, "a"), get_array(ser1, "a"))
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_concat_series_updating_input(using_copy_on_write):
+    ser = Series([1, 2], name="a")
+    ser2 = Series([3, 4], name="b")
+    expected = DataFrame({"a": [1, 2], "b": [3, 4]})
+    result = concat([ser, ser2], axis=1)
+
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(result, "a"), get_array(ser, "a"))
+        assert np.shares_memory(get_array(result, "b"), get_array(ser2, "b"))
+    else:
+        assert not np.shares_memory(get_array(result, "a"), get_array(ser, "a"))
+        assert not np.shares_memory(get_array(result, "b"), get_array(ser2, "b"))
+
+    ser.iloc[0] = 100
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(result, "a"), get_array(ser, "a"))
+        assert np.shares_memory(get_array(result, "b"), get_array(ser2, "b"))
+    tm.assert_frame_equal(result, expected)
+
+    ser2.iloc[0] = 1000
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(result, "b"), get_array(ser2, "b"))
+    tm.assert_frame_equal(result, expected)
+
+
+def test_concat_mixed_series_frame(using_copy_on_write):
+    df = DataFrame({"a": [1, 2, 3], "c": 1})
+    ser = Series([4, 5, 6], name="d")
+    result = concat([df, ser], axis=1)
+    expected = result.copy()
+
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(result, "a"), get_array(df, "a"))
+        assert np.shares_memory(get_array(result, "c"), get_array(df, "c"))
+        assert np.shares_memory(get_array(result, "d"), get_array(ser, "d"))
+    else:
+        assert not np.shares_memory(get_array(result, "a"), get_array(df, "a"))
+        assert not np.shares_memory(get_array(result, "c"), get_array(df, "c"))
+        assert not np.shares_memory(get_array(result, "d"), get_array(ser, "d"))
+
+    ser.iloc[0] = 100
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(result, "d"), get_array(ser, "d"))
+
+    df.iloc[0, 0] = 100
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(result, "a"), get_array(df, "a"))
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("copy", [True, None, False])
+def test_concat_copy_keyword(using_copy_on_write, copy):
+    df = DataFrame({"a": [1, 2]})
+    df2 = DataFrame({"b": [1.5, 2.5]})
+
+    result = concat([df, df2], axis=1, copy=copy)
+
+    if using_copy_on_write or copy is False:
+        assert np.shares_memory(get_array(df, "a"), get_array(result, "a"))
+        assert np.shares_memory(get_array(df2, "b"), get_array(result, "b"))
+    else:
+        assert not np.shares_memory(get_array(df, "a"), get_array(result, "a"))
+        assert not np.shares_memory(get_array(df2, "b"), get_array(result, "b"))
+
+
+@pytest.mark.parametrize(
+    "func",
+    [
+        lambda df1, df2, **kwargs: df1.merge(df2, **kwargs),
+        lambda df1, df2, **kwargs: merge(df1, df2, **kwargs),
+    ],
+)
+def test_merge_on_key(using_copy_on_write, func):
+    df1 = DataFrame({"key": Series(["a", "b", "c"], dtype=object), "a": [1, 2, 3]})
+    df2 = DataFrame({"key": Series(["a", "b", "c"], dtype=object), "b": [4, 5, 6]})
+    df1_orig = df1.copy()
+    df2_orig = df2.copy()
+
+    result = func(df1, df2, on="key")
+
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(result, "a"), get_array(df1, "a"))
+        assert np.shares_memory(get_array(result, "b"), get_array(df2, "b"))
+        assert np.shares_memory(get_array(result, "key"), get_array(df1, "key"))
+        assert not np.shares_memory(get_array(result, "key"), get_array(df2, "key"))
+    else:
+        assert not np.shares_memory(get_array(result, "a"), get_array(df1, "a"))
+        assert not np.shares_memory(get_array(result, "b"), get_array(df2, "b"))
+
+    result.iloc[0, 1] = 0
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(result, "a"), get_array(df1, "a"))
+        assert np.shares_memory(get_array(result, "b"), get_array(df2, "b"))
+
+    result.iloc[0, 2] = 0
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(result, "b"), get_array(df2, "b"))
+    tm.assert_frame_equal(df1, df1_orig)
+    tm.assert_frame_equal(df2, df2_orig)
+
+
+def test_merge_on_index(using_copy_on_write):
+    df1 = DataFrame({"a": [1, 2, 3]})
+    df2 = DataFrame({"b": [4, 5, 6]})
+    df1_orig = df1.copy()
+    df2_orig = df2.copy()
+
+    result = merge(df1, df2, left_index=True, right_index=True)
+
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(result, "a"), get_array(df1, "a"))
+        assert np.shares_memory(get_array(result, "b"), get_array(df2, "b"))
+    else:
+        assert not np.shares_memory(get_array(result, "a"), get_array(df1, "a"))
+        assert not np.shares_memory(get_array(result, "b"), get_array(df2, "b"))
+
+    result.iloc[0, 0] = 0
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(result, "a"), get_array(df1, "a"))
+        assert np.shares_memory(get_array(result, "b"), get_array(df2, "b"))
+
+    result.iloc[0, 1] = 0
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(result, "b"), get_array(df2, "b"))
+    tm.assert_frame_equal(df1, df1_orig)
+    tm.assert_frame_equal(df2, df2_orig)
+
+
+@pytest.mark.parametrize(
+    "func, how",
+    [
+        (lambda df1, df2, **kwargs: merge(df2, df1, on="key", **kwargs), "right"),
+        (lambda df1, df2, **kwargs: merge(df1, df2, on="key", **kwargs), "left"),
+    ],
+)
+def test_merge_on_key_enlarging_one(using_copy_on_write, func, how):
+    df1 = DataFrame({"key": Series(["a", "b", "c"], dtype=object), "a": [1, 2, 3]})
+    df2 = DataFrame({"key": Series(["a", "b"], dtype=object), "b": [4, 5]})
+    df1_orig = df1.copy()
+    df2_orig = df2.copy()
+
+    result = func(df1, df2, how=how)
+
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(result, "a"), get_array(df1, "a"))
+        assert not np.shares_memory(get_array(result, "b"), get_array(df2, "b"))
+        assert df2._mgr._has_no_reference(1)
+        assert df2._mgr._has_no_reference(0)
+        assert np.shares_memory(get_array(result, "key"), get_array(df1, "key")) is (
+            how == "left"
+        )
+        assert not np.shares_memory(get_array(result, "key"), get_array(df2, "key"))
+    else:
+        assert not np.shares_memory(get_array(result, "a"), get_array(df1, "a"))
+        assert not np.shares_memory(get_array(result, "b"), get_array(df2, "b"))
+
+    if how == "left":
+        result.iloc[0, 1] = 0
+    else:
+        result.iloc[0, 2] = 0
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(result, "a"), get_array(df1, "a"))
+    tm.assert_frame_equal(df1, df1_orig)
+    tm.assert_frame_equal(df2, df2_orig)
+
+
+@pytest.mark.parametrize("copy", [True, None, False])
+def test_merge_copy_keyword(using_copy_on_write, copy):
+    df = DataFrame({"a": [1, 2]})
+    df2 = DataFrame({"b": [3, 4.5]})
+
+    result = df.merge(df2, copy=copy, left_index=True, right_index=True)
+
+    if using_copy_on_write or copy is False:
+        assert np.shares_memory(get_array(df, "a"), get_array(result, "a"))
+        assert np.shares_memory(get_array(df2, "b"), get_array(result, "b"))
+    else:
+        assert not np.shares_memory(get_array(df, "a"), get_array(result, "a"))
+        assert not np.shares_memory(get_array(df2, "b"), get_array(result, "b"))
+
+
+@pytest.mark.parametrize("dtype", [object, "str"])
+def test_join_on_key(dtype, using_copy_on_write):
+    df_index = Index(["a", "b", "c"], name="key", dtype=dtype)
+
+    df1 = DataFrame({"a": [1, 2, 3]}, index=df_index.copy(deep=True))
+    df2 = DataFrame({"b": [4, 5, 6]}, index=df_index.copy(deep=True))
+
+    df1_orig = df1.copy()
+    df2_orig = df2.copy()
+
+    result = df1.join(df2, on="key")
+
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(result, "a"), get_array(df1, "a"))
+        assert np.shares_memory(get_array(result, "b"), get_array(df2, "b"))
+        assert tm.shares_memory(get_array(result.index), get_array(df1.index))
+        assert not np.shares_memory(get_array(result.index), get_array(df2.index))
+    else:
+        assert not np.shares_memory(get_array(result, "a"), get_array(df1, "a"))
+        assert not np.shares_memory(get_array(result, "b"), get_array(df2, "b"))
+
+    result.iloc[0, 0] = 0
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(result, "a"), get_array(df1, "a"))
+        assert np.shares_memory(get_array(result, "b"), get_array(df2, "b"))
+
+    result.iloc[0, 1] = 0
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(result, "b"), get_array(df2, "b"))
+
+    tm.assert_frame_equal(df1, df1_orig)
+    tm.assert_frame_equal(df2, df2_orig)
+
+
+def test_join_multiple_dataframes_on_key(using_copy_on_write):
+    df_index = Index(["a", "b", "c"], name="key", dtype=object)
+
+    df1 = DataFrame({"a": [1, 2, 3]}, index=df_index.copy(deep=True))
+    dfs_list = [
+        DataFrame({"b": [4, 5, 6]}, index=df_index.copy(deep=True)),
+        DataFrame({"c": [7, 8, 9]}, index=df_index.copy(deep=True)),
+    ]
+
+    df1_orig = df1.copy()
+    dfs_list_orig = [df.copy() for df in dfs_list]
+
+    result = df1.join(dfs_list)
+
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(result, "a"), get_array(df1, "a"))
+        assert np.shares_memory(get_array(result, "b"), get_array(dfs_list[0], "b"))
+        assert np.shares_memory(get_array(result, "c"), get_array(dfs_list[1], "c"))
+        assert np.shares_memory(get_array(result.index), get_array(df1.index))
+        assert not np.shares_memory(
+            get_array(result.index), get_array(dfs_list[0].index)
+        )
+        assert not np.shares_memory(
+            get_array(result.index), get_array(dfs_list[1].index)
+        )
+    else:
+        assert not np.shares_memory(get_array(result, "a"), get_array(df1, "a"))
+        assert not np.shares_memory(get_array(result, "b"), get_array(dfs_list[0], "b"))
+        assert not np.shares_memory(get_array(result, "c"), get_array(dfs_list[1], "c"))
+
+    result.iloc[0, 0] = 0
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(result, "a"), get_array(df1, "a"))
+        assert np.shares_memory(get_array(result, "b"), get_array(dfs_list[0], "b"))
+        assert np.shares_memory(get_array(result, "c"), get_array(dfs_list[1], "c"))
+
+    result.iloc[0, 1] = 0
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(result, "b"), get_array(dfs_list[0], "b"))
+        assert np.shares_memory(get_array(result, "c"), get_array(dfs_list[1], "c"))
+
+    result.iloc[0, 2] = 0
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(result, "c"), get_array(dfs_list[1], "c"))
+
+    tm.assert_frame_equal(df1, df1_orig)
+    for df, df_orig in zip(dfs_list, dfs_list_orig):
+        tm.assert_frame_equal(df, df_orig)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/copy_view/test_indexing.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/copy_view/test_indexing.py
new file mode 100644
index 0000000000000000000000000000000000000000..479fa148f994a74eb205e3fa19ba957504744a54
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/copy_view/test_indexing.py
@@ -0,0 +1,1266 @@
+import numpy as np
+import pytest
+
+from pandas.errors import SettingWithCopyWarning
+
+from pandas.core.dtypes.common import is_float_dtype
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Series,
+)
+import pandas._testing as tm
+from pandas.tests.copy_view.util import get_array
+
+
+@pytest.fixture(params=["numpy", "nullable"])
+def backend(request):
+    if request.param == "numpy":
+
+        def make_dataframe(*args, **kwargs):
+            return DataFrame(*args, **kwargs)
+
+        def make_series(*args, **kwargs):
+            return Series(*args, **kwargs)
+
+    elif request.param == "nullable":
+
+        def make_dataframe(*args, **kwargs):
+            df = DataFrame(*args, **kwargs)
+            df_nullable = df.convert_dtypes()
+            # convert_dtypes will try to cast float to int if there is no loss in
+            # precision -> undo that change
+            for col in df.columns:
+                if is_float_dtype(df[col].dtype) and not is_float_dtype(
+                    df_nullable[col].dtype
+                ):
+                    df_nullable[col] = df_nullable[col].astype("Float64")
+            # copy final result to ensure we start with a fully self-owning DataFrame
+            return df_nullable.copy()
+
+        def make_series(*args, **kwargs):
+            ser = Series(*args, **kwargs)
+            return ser.convert_dtypes().copy()
+
+    return request.param, make_dataframe, make_series
+
+
+# -----------------------------------------------------------------------------
+# Indexing operations taking subset + modifying the subset/parent
+
+
+def test_subset_column_selection(backend, using_copy_on_write):
+    # Case: taking a subset of the columns of a DataFrame
+    # + afterwards modifying the subset
+    _, DataFrame, _ = backend
+    df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [0.1, 0.2, 0.3]})
+    df_orig = df.copy()
+
+    subset = df[["a", "c"]]
+
+    if using_copy_on_write:
+        # the subset shares memory ...
+        assert np.shares_memory(get_array(subset, "a"), get_array(df, "a"))
+        # ... but uses CoW when being modified
+        subset.iloc[0, 0] = 0
+    else:
+        assert not np.shares_memory(get_array(subset, "a"), get_array(df, "a"))
+        # INFO this no longer raise warning since pandas 1.4
+        # with pd.option_context("chained_assignment", "warn"):
+        #     with tm.assert_produces_warning(SettingWithCopyWarning):
+        subset.iloc[0, 0] = 0
+
+    assert not np.shares_memory(get_array(subset, "a"), get_array(df, "a"))
+
+    expected = DataFrame({"a": [0, 2, 3], "c": [0.1, 0.2, 0.3]})
+    tm.assert_frame_equal(subset, expected)
+    tm.assert_frame_equal(df, df_orig)
+
+
+def test_subset_column_selection_modify_parent(backend, using_copy_on_write):
+    # Case: taking a subset of the columns of a DataFrame
+    # + afterwards modifying the parent
+    _, DataFrame, _ = backend
+    df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [0.1, 0.2, 0.3]})
+
+    subset = df[["a", "c"]]
+
+    if using_copy_on_write:
+        # the subset shares memory ...
+        assert np.shares_memory(get_array(subset, "a"), get_array(df, "a"))
+        # ... but parent uses CoW parent when it is modified
+    df.iloc[0, 0] = 0
+
+    assert not np.shares_memory(get_array(subset, "a"), get_array(df, "a"))
+    if using_copy_on_write:
+        # different column/block still shares memory
+        assert np.shares_memory(get_array(subset, "c"), get_array(df, "c"))
+
+    expected = DataFrame({"a": [1, 2, 3], "c": [0.1, 0.2, 0.3]})
+    tm.assert_frame_equal(subset, expected)
+
+
+def test_subset_row_slice(backend, using_copy_on_write, warn_copy_on_write):
+    # Case: taking a subset of the rows of a DataFrame using a slice
+    # + afterwards modifying the subset
+    _, DataFrame, _ = backend
+    df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [0.1, 0.2, 0.3]})
+    df_orig = df.copy()
+
+    subset = df[1:3]
+    subset._mgr._verify_integrity()
+
+    assert np.shares_memory(get_array(subset, "a"), get_array(df, "a"))
+
+    if using_copy_on_write:
+        subset.iloc[0, 0] = 0
+        assert not np.shares_memory(get_array(subset, "a"), get_array(df, "a"))
+
+    else:
+        # INFO this no longer raise warning since pandas 1.4
+        # with pd.option_context("chained_assignment", "warn"):
+        #     with tm.assert_produces_warning(SettingWithCopyWarning):
+        with tm.assert_cow_warning(warn_copy_on_write):
+            subset.iloc[0, 0] = 0
+
+    subset._mgr._verify_integrity()
+
+    expected = DataFrame({"a": [0, 3], "b": [5, 6], "c": [0.2, 0.3]}, index=range(1, 3))
+    tm.assert_frame_equal(subset, expected)
+    if using_copy_on_write:
+        # original parent dataframe is not modified (CoW)
+        tm.assert_frame_equal(df, df_orig)
+    else:
+        # original parent dataframe is actually updated
+        df_orig.iloc[1, 0] = 0
+        tm.assert_frame_equal(df, df_orig)
+
+
+@pytest.mark.parametrize(
+    "dtype", ["int64", "float64"], ids=["single-block", "mixed-block"]
+)
+def test_subset_column_slice(
+    backend, using_copy_on_write, warn_copy_on_write, using_array_manager, dtype
+):
+    # Case: taking a subset of the columns of a DataFrame using a slice
+    # + afterwards modifying the subset
+    dtype_backend, DataFrame, _ = backend
+    single_block = (
+        dtype == "int64" and dtype_backend == "numpy"
+    ) and not using_array_manager
+    df = DataFrame(
+        {"a": [1, 2, 3], "b": [4, 5, 6], "c": np.array([7, 8, 9], dtype=dtype)}
+    )
+    df_orig = df.copy()
+
+    subset = df.iloc[:, 1:]
+    subset._mgr._verify_integrity()
+
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(subset, "b"), get_array(df, "b"))
+
+        subset.iloc[0, 0] = 0
+        assert not np.shares_memory(get_array(subset, "b"), get_array(df, "b"))
+    elif warn_copy_on_write:
+        with tm.assert_cow_warning(single_block):
+            subset.iloc[0, 0] = 0
+    else:
+        # we only get a warning in case of a single block
+        warn = SettingWithCopyWarning if single_block else None
+        with pd.option_context("chained_assignment", "warn"):
+            with tm.assert_produces_warning(warn):
+                subset.iloc[0, 0] = 0
+
+    expected = DataFrame({"b": [0, 5, 6], "c": np.array([7, 8, 9], dtype=dtype)})
+    tm.assert_frame_equal(subset, expected)
+    # original parent dataframe is not modified (also not for BlockManager case,
+    # except for single block)
+    if not using_copy_on_write and (using_array_manager or single_block):
+        df_orig.iloc[0, 1] = 0
+        tm.assert_frame_equal(df, df_orig)
+    else:
+        tm.assert_frame_equal(df, df_orig)
+
+
+@pytest.mark.parametrize(
+    "dtype", ["int64", "float64"], ids=["single-block", "mixed-block"]
+)
+@pytest.mark.parametrize(
+    "row_indexer",
+    [slice(1, 2), np.array([False, True, True]), np.array([1, 2])],
+    ids=["slice", "mask", "array"],
+)
+@pytest.mark.parametrize(
+    "column_indexer",
+    [slice("b", "c"), np.array([False, True, True]), ["b", "c"]],
+    ids=["slice", "mask", "array"],
+)
+def test_subset_loc_rows_columns(
+    backend,
+    dtype,
+    row_indexer,
+    column_indexer,
+    using_array_manager,
+    using_copy_on_write,
+    warn_copy_on_write,
+):
+    # Case: taking a subset of the rows+columns of a DataFrame using .loc
+    # + afterwards modifying the subset
+    # Generic test for several combinations of row/column indexers, not all
+    # of those could actually return a view / need CoW (so this test is not
+    # checking memory sharing, only ensuring subsequent mutation doesn't
+    # affect the parent dataframe)
+    dtype_backend, DataFrame, _ = backend
+    df = DataFrame(
+        {"a": [1, 2, 3], "b": [4, 5, 6], "c": np.array([7, 8, 9], dtype=dtype)}
+    )
+    df_orig = df.copy()
+
+    subset = df.loc[row_indexer, column_indexer]
+
+    # a few corner cases _do_ actually modify the parent (with both row and column
+    # slice, and in case of ArrayManager or BlockManager with single block)
+    mutate_parent = (
+        isinstance(row_indexer, slice)
+        and isinstance(column_indexer, slice)
+        and (
+            using_array_manager
+            or (
+                dtype == "int64"
+                and dtype_backend == "numpy"
+                and not using_copy_on_write
+            )
+        )
+    )
+
+    # modifying the subset never modifies the parent
+    with tm.assert_cow_warning(warn_copy_on_write and mutate_parent):
+        subset.iloc[0, 0] = 0
+
+    expected = DataFrame(
+        {"b": [0, 6], "c": np.array([8, 9], dtype=dtype)}, index=range(1, 3)
+    )
+    tm.assert_frame_equal(subset, expected)
+    if mutate_parent:
+        df_orig.iloc[1, 1] = 0
+    tm.assert_frame_equal(df, df_orig)
+
+
+@pytest.mark.parametrize(
+    "dtype", ["int64", "float64"], ids=["single-block", "mixed-block"]
+)
+@pytest.mark.parametrize(
+    "row_indexer",
+    [slice(1, 3), np.array([False, True, True]), np.array([1, 2])],
+    ids=["slice", "mask", "array"],
+)
+@pytest.mark.parametrize(
+    "column_indexer",
+    [slice(1, 3), np.array([False, True, True]), [1, 2]],
+    ids=["slice", "mask", "array"],
+)
+def test_subset_iloc_rows_columns(
+    backend,
+    dtype,
+    row_indexer,
+    column_indexer,
+    using_array_manager,
+    using_copy_on_write,
+    warn_copy_on_write,
+):
+    # Case: taking a subset of the rows+columns of a DataFrame using .iloc
+    # + afterwards modifying the subset
+    # Generic test for several combinations of row/column indexers, not all
+    # of those could actually return a view / need CoW (so this test is not
+    # checking memory sharing, only ensuring subsequent mutation doesn't
+    # affect the parent dataframe)
+    dtype_backend, DataFrame, _ = backend
+    df = DataFrame(
+        {"a": [1, 2, 3], "b": [4, 5, 6], "c": np.array([7, 8, 9], dtype=dtype)}
+    )
+    df_orig = df.copy()
+
+    subset = df.iloc[row_indexer, column_indexer]
+
+    # a few corner cases _do_ actually modify the parent (with both row and column
+    # slice, and in case of ArrayManager or BlockManager with single block)
+    mutate_parent = (
+        isinstance(row_indexer, slice)
+        and isinstance(column_indexer, slice)
+        and (
+            using_array_manager
+            or (
+                dtype == "int64"
+                and dtype_backend == "numpy"
+                and not using_copy_on_write
+            )
+        )
+    )
+
+    # modifying the subset never modifies the parent
+    with tm.assert_cow_warning(warn_copy_on_write and mutate_parent):
+        subset.iloc[0, 0] = 0
+
+    expected = DataFrame(
+        {"b": [0, 6], "c": np.array([8, 9], dtype=dtype)}, index=range(1, 3)
+    )
+    tm.assert_frame_equal(subset, expected)
+    if mutate_parent:
+        df_orig.iloc[1, 1] = 0
+    tm.assert_frame_equal(df, df_orig)
+
+
+@pytest.mark.parametrize(
+    "indexer",
+    [slice(0, 2), np.array([True, True, False]), np.array([0, 1])],
+    ids=["slice", "mask", "array"],
+)
+def test_subset_set_with_row_indexer(
+    backend, indexer_si, indexer, using_copy_on_write, warn_copy_on_write
+):
+    # Case: setting values with a row indexer on a viewing subset
+    # subset[indexer] = value and subset.iloc[indexer] = value
+    _, DataFrame, _ = backend
+    df = DataFrame({"a": [1, 2, 3, 4], "b": [4, 5, 6, 7], "c": [0.1, 0.2, 0.3, 0.4]})
+    df_orig = df.copy()
+    subset = df[1:4]
+
+    if (
+        indexer_si is tm.setitem
+        and isinstance(indexer, np.ndarray)
+        and indexer.dtype == "int"
+    ):
+        pytest.skip("setitem with labels selects on columns")
+
+    if using_copy_on_write:
+        indexer_si(subset)[indexer] = 0
+    elif warn_copy_on_write:
+        with tm.assert_cow_warning():
+            indexer_si(subset)[indexer] = 0
+    else:
+        # INFO iloc no longer raises warning since pandas 1.4
+        warn = SettingWithCopyWarning if indexer_si is tm.setitem else None
+        with pd.option_context("chained_assignment", "warn"):
+            with tm.assert_produces_warning(warn):
+                indexer_si(subset)[indexer] = 0
+
+    expected = DataFrame(
+        {"a": [0, 0, 4], "b": [0, 0, 7], "c": [0.0, 0.0, 0.4]}, index=range(1, 4)
+    )
+    tm.assert_frame_equal(subset, expected)
+    if using_copy_on_write:
+        # original parent dataframe is not modified (CoW)
+        tm.assert_frame_equal(df, df_orig)
+    else:
+        # original parent dataframe is actually updated
+        df_orig[1:3] = 0
+        tm.assert_frame_equal(df, df_orig)
+
+
+def test_subset_set_with_mask(backend, using_copy_on_write, warn_copy_on_write):
+    # Case: setting values with a mask on a viewing subset: subset[mask] = value
+    _, DataFrame, _ = backend
+    df = DataFrame({"a": [1, 2, 3, 4], "b": [4, 5, 6, 7], "c": [0.1, 0.2, 0.3, 0.4]})
+    df_orig = df.copy()
+    subset = df[1:4]
+
+    mask = subset > 3
+
+    if using_copy_on_write:
+        subset[mask] = 0
+    elif warn_copy_on_write:
+        with tm.assert_cow_warning():
+            subset[mask] = 0
+    else:
+        with pd.option_context("chained_assignment", "warn"):
+            with tm.assert_produces_warning(SettingWithCopyWarning):
+                subset[mask] = 0
+
+    expected = DataFrame(
+        {"a": [2, 3, 0], "b": [0, 0, 0], "c": [0.20, 0.3, 0.4]}, index=range(1, 4)
+    )
+    tm.assert_frame_equal(subset, expected)
+    if using_copy_on_write:
+        # original parent dataframe is not modified (CoW)
+        tm.assert_frame_equal(df, df_orig)
+    else:
+        # original parent dataframe is actually updated
+        df_orig.loc[3, "a"] = 0
+        df_orig.loc[1:3, "b"] = 0
+        tm.assert_frame_equal(df, df_orig)
+
+
+def test_subset_set_column(backend, using_copy_on_write, warn_copy_on_write):
+    # Case: setting a single column on a viewing subset -> subset[col] = value
+    dtype_backend, DataFrame, _ = backend
+    df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [0.1, 0.2, 0.3]})
+    df_orig = df.copy()
+    subset = df[1:3]
+
+    if dtype_backend == "numpy":
+        arr = np.array([10, 11], dtype="int64")
+    else:
+        arr = pd.array([10, 11], dtype="Int64")
+
+    if using_copy_on_write or warn_copy_on_write:
+        subset["a"] = arr
+    else:
+        with pd.option_context("chained_assignment", "warn"):
+            with tm.assert_produces_warning(SettingWithCopyWarning):
+                subset["a"] = arr
+
+    subset._mgr._verify_integrity()
+    expected = DataFrame(
+        {"a": [10, 11], "b": [5, 6], "c": [0.2, 0.3]}, index=range(1, 3)
+    )
+    tm.assert_frame_equal(subset, expected)
+    tm.assert_frame_equal(df, df_orig)
+
+
+@pytest.mark.parametrize(
+    "dtype", ["int64", "float64"], ids=["single-block", "mixed-block"]
+)
+def test_subset_set_column_with_loc(
+    backend, using_copy_on_write, warn_copy_on_write, using_array_manager, dtype
+):
+    # Case: setting a single column with loc on a viewing subset
+    # -> subset.loc[:, col] = value
+    _, DataFrame, _ = backend
+    df = DataFrame(
+        {"a": [1, 2, 3], "b": [4, 5, 6], "c": np.array([7, 8, 9], dtype=dtype)}
+    )
+    df_orig = df.copy()
+    subset = df[1:3]
+
+    if using_copy_on_write:
+        subset.loc[:, "a"] = np.array([10, 11], dtype="int64")
+    elif warn_copy_on_write:
+        with tm.assert_cow_warning():
+            subset.loc[:, "a"] = np.array([10, 11], dtype="int64")
+    else:
+        with pd.option_context("chained_assignment", "warn"):
+            with tm.assert_produces_warning(
+                None,
+                raise_on_extra_warnings=not using_array_manager,
+            ):
+                subset.loc[:, "a"] = np.array([10, 11], dtype="int64")
+
+    subset._mgr._verify_integrity()
+    expected = DataFrame(
+        {"a": [10, 11], "b": [5, 6], "c": np.array([8, 9], dtype=dtype)},
+        index=range(1, 3),
+    )
+    tm.assert_frame_equal(subset, expected)
+    if using_copy_on_write:
+        # original parent dataframe is not modified (CoW)
+        tm.assert_frame_equal(df, df_orig)
+    else:
+        # original parent dataframe is actually updated
+        df_orig.loc[1:3, "a"] = np.array([10, 11], dtype="int64")
+        tm.assert_frame_equal(df, df_orig)
+
+
+def test_subset_set_column_with_loc2(
+    backend, using_copy_on_write, warn_copy_on_write, using_array_manager
+):
+    # Case: setting a single column with loc on a viewing subset
+    # -> subset.loc[:, col] = value
+    # separate test for case of DataFrame of a single column -> takes a separate
+    # code path
+    _, DataFrame, _ = backend
+    df = DataFrame({"a": [1, 2, 3]})
+    df_orig = df.copy()
+    subset = df[1:3]
+
+    if using_copy_on_write:
+        subset.loc[:, "a"] = 0
+    elif warn_copy_on_write:
+        with tm.assert_cow_warning():
+            subset.loc[:, "a"] = 0
+    else:
+        with pd.option_context("chained_assignment", "warn"):
+            with tm.assert_produces_warning(
+                None,
+                raise_on_extra_warnings=not using_array_manager,
+            ):
+                subset.loc[:, "a"] = 0
+
+    subset._mgr._verify_integrity()
+    expected = DataFrame({"a": [0, 0]}, index=range(1, 3))
+    tm.assert_frame_equal(subset, expected)
+    if using_copy_on_write:
+        # original parent dataframe is not modified (CoW)
+        tm.assert_frame_equal(df, df_orig)
+    else:
+        # original parent dataframe is actually updated
+        df_orig.loc[1:3, "a"] = 0
+        tm.assert_frame_equal(df, df_orig)
+
+
+@pytest.mark.parametrize(
+    "dtype", ["int64", "float64"], ids=["single-block", "mixed-block"]
+)
+def test_subset_set_columns(backend, using_copy_on_write, warn_copy_on_write, dtype):
+    # Case: setting multiple columns on a viewing subset
+    # -> subset[[col1, col2]] = value
+    dtype_backend, DataFrame, _ = backend
+    df = DataFrame(
+        {"a": [1, 2, 3], "b": [4, 5, 6], "c": np.array([7, 8, 9], dtype=dtype)}
+    )
+    df_orig = df.copy()
+    subset = df[1:3]
+
+    if using_copy_on_write or warn_copy_on_write:
+        subset[["a", "c"]] = 0
+    else:
+        with pd.option_context("chained_assignment", "warn"):
+            with tm.assert_produces_warning(SettingWithCopyWarning):
+                subset[["a", "c"]] = 0
+
+    subset._mgr._verify_integrity()
+    if using_copy_on_write:
+        # first and third column should certainly have no references anymore
+        assert all(subset._mgr._has_no_reference(i) for i in [0, 2])
+    expected = DataFrame({"a": [0, 0], "b": [5, 6], "c": [0, 0]}, index=range(1, 3))
+    if dtype_backend == "nullable":
+        # there is not yet a global option, so overriding a column by setting a scalar
+        # defaults to numpy dtype even if original column was nullable
+        expected["a"] = expected["a"].astype("int64")
+        expected["c"] = expected["c"].astype("int64")
+
+    tm.assert_frame_equal(subset, expected)
+    tm.assert_frame_equal(df, df_orig)
+
+
+@pytest.mark.parametrize(
+    "indexer",
+    [slice("a", "b"), np.array([True, True, False]), ["a", "b"]],
+    ids=["slice", "mask", "array"],
+)
+def test_subset_set_with_column_indexer(
+    backend, indexer, using_copy_on_write, warn_copy_on_write
+):
+    # Case: setting multiple columns with a column indexer on a viewing subset
+    # -> subset.loc[:, [col1, col2]] = value
+    _, DataFrame, _ = backend
+    df = DataFrame({"a": [1, 2, 3], "b": [0.1, 0.2, 0.3], "c": [4, 5, 6]})
+    df_orig = df.copy()
+    subset = df[1:3]
+
+    if using_copy_on_write:
+        subset.loc[:, indexer] = 0
+    elif warn_copy_on_write:
+        with tm.assert_cow_warning():
+            subset.loc[:, indexer] = 0
+    else:
+        with pd.option_context("chained_assignment", "warn"):
+            # As of 2.0, this setitem attempts (successfully) to set values
+            #  inplace, so the assignment is not chained.
+            subset.loc[:, indexer] = 0
+
+    subset._mgr._verify_integrity()
+    expected = DataFrame({"a": [0, 0], "b": [0.0, 0.0], "c": [5, 6]}, index=range(1, 3))
+    tm.assert_frame_equal(subset, expected)
+    if using_copy_on_write:
+        tm.assert_frame_equal(df, df_orig)
+    else:
+        # pre-2.0, in the mixed case with BlockManager, only column "a"
+        #  would be mutated in the parent frame. this changed with the
+        #  enforcement of GH#45333
+        df_orig.loc[1:2, ["a", "b"]] = 0
+        tm.assert_frame_equal(df, df_orig)
+
+
+@pytest.mark.parametrize(
+    "method",
+    [
+        lambda df: df[["a", "b"]][0:2],
+        lambda df: df[0:2][["a", "b"]],
+        lambda df: df[["a", "b"]].iloc[0:2],
+        lambda df: df[["a", "b"]].loc[0:1],
+        lambda df: df[0:2].iloc[:, 0:2],
+        lambda df: df[0:2].loc[:, "a":"b"],  # type: ignore[misc]
+    ],
+    ids=[
+        "row-getitem-slice",
+        "column-getitem",
+        "row-iloc-slice",
+        "row-loc-slice",
+        "column-iloc-slice",
+        "column-loc-slice",
+    ],
+)
+@pytest.mark.parametrize(
+    "dtype", ["int64", "float64"], ids=["single-block", "mixed-block"]
+)
+def test_subset_chained_getitem(
+    request,
+    backend,
+    method,
+    dtype,
+    using_copy_on_write,
+    using_array_manager,
+    warn_copy_on_write,
+):
+    # Case: creating a subset using multiple, chained getitem calls using views
+    # still needs to guarantee proper CoW behaviour
+    _, DataFrame, _ = backend
+    df = DataFrame(
+        {"a": [1, 2, 3], "b": [4, 5, 6], "c": np.array([7, 8, 9], dtype=dtype)}
+    )
+    df_orig = df.copy()
+
+    # when not using CoW, it depends on whether we have a single block or not
+    # and whether we are slicing the columns -> in that case we have a view
+    test_callspec = request.node.callspec.id
+    if not using_array_manager:
+        subset_is_view = test_callspec in (
+            "numpy-single-block-column-iloc-slice",
+            "numpy-single-block-column-loc-slice",
+        )
+    else:
+        # with ArrayManager, it doesn't matter whether we have
+        # single vs mixed block or numpy vs nullable dtypes
+        subset_is_view = test_callspec.endswith(
+            ("column-iloc-slice", "column-loc-slice")
+        )
+
+    # modify subset -> don't modify parent
+    subset = method(df)
+
+    with tm.assert_cow_warning(warn_copy_on_write and subset_is_view):
+        subset.iloc[0, 0] = 0
+    if using_copy_on_write or (not subset_is_view):
+        tm.assert_frame_equal(df, df_orig)
+    else:
+        assert df.iloc[0, 0] == 0
+
+    # modify parent -> don't modify subset
+    subset = method(df)
+    with tm.assert_cow_warning(warn_copy_on_write and subset_is_view):
+        df.iloc[0, 0] = 0
+    expected = DataFrame({"a": [1, 2], "b": [4, 5]})
+    if using_copy_on_write or not subset_is_view:
+        tm.assert_frame_equal(subset, expected)
+    else:
+        assert subset.iloc[0, 0] == 0
+
+
+@pytest.mark.parametrize(
+    "dtype", ["int64", "float64"], ids=["single-block", "mixed-block"]
+)
+def test_subset_chained_getitem_column(
+    backend, dtype, using_copy_on_write, warn_copy_on_write
+):
+    # Case: creating a subset using multiple, chained getitem calls using views
+    # still needs to guarantee proper CoW behaviour
+    dtype_backend, DataFrame, Series = backend
+    df = DataFrame(
+        {"a": [1, 2, 3], "b": [4, 5, 6], "c": np.array([7, 8, 9], dtype=dtype)}
+    )
+    df_orig = df.copy()
+
+    # modify subset -> don't modify parent
+    subset = df[:]["a"][0:2]
+    df._clear_item_cache()
+    with tm.assert_cow_warning(warn_copy_on_write):
+        subset.iloc[0] = 0
+    if using_copy_on_write:
+        tm.assert_frame_equal(df, df_orig)
+    else:
+        assert df.iloc[0, 0] == 0
+
+    # modify parent -> don't modify subset
+    subset = df[:]["a"][0:2]
+    df._clear_item_cache()
+    with tm.assert_cow_warning(warn_copy_on_write):
+        df.iloc[0, 0] = 0
+    expected = Series([1, 2], name="a")
+    if using_copy_on_write:
+        tm.assert_series_equal(subset, expected)
+    else:
+        assert subset.iloc[0] == 0
+
+
+@pytest.mark.parametrize(
+    "method",
+    [
+        lambda s: s["a":"c"]["a":"b"],  # type: ignore[misc]
+        lambda s: s.iloc[0:3].iloc[0:2],
+        lambda s: s.loc["a":"c"].loc["a":"b"],  # type: ignore[misc]
+        lambda s: s.loc["a":"c"]  # type: ignore[misc]
+        .iloc[0:3]
+        .iloc[0:2]
+        .loc["a":"b"]  # type: ignore[misc]
+        .iloc[0:1],
+    ],
+    ids=["getitem", "iloc", "loc", "long-chain"],
+)
+def test_subset_chained_getitem_series(
+    backend, method, using_copy_on_write, warn_copy_on_write
+):
+    # Case: creating a subset using multiple, chained getitem calls using views
+    # still needs to guarantee proper CoW behaviour
+    _, _, Series = backend
+    s = Series([1, 2, 3], index=["a", "b", "c"])
+    s_orig = s.copy()
+
+    # modify subset -> don't modify parent
+    subset = method(s)
+    with tm.assert_cow_warning(warn_copy_on_write):
+        subset.iloc[0] = 0
+    if using_copy_on_write:
+        tm.assert_series_equal(s, s_orig)
+    else:
+        assert s.iloc[0] == 0
+
+    # modify parent -> don't modify subset
+    subset = s.iloc[0:3].iloc[0:2]
+    with tm.assert_cow_warning(warn_copy_on_write):
+        s.iloc[0] = 0
+    expected = Series([1, 2], index=["a", "b"])
+    if using_copy_on_write:
+        tm.assert_series_equal(subset, expected)
+    else:
+        assert subset.iloc[0] == 0
+
+
+def test_subset_chained_single_block_row(
+    using_copy_on_write, using_array_manager, warn_copy_on_write
+):
+    # not parametrizing this for dtype backend, since this explicitly tests single block
+    df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]})
+    df_orig = df.copy()
+
+    # modify subset -> don't modify parent
+    subset = df[:].iloc[0].iloc[0:2]
+    with tm.assert_cow_warning(warn_copy_on_write):
+        subset.iloc[0] = 0
+    if using_copy_on_write or using_array_manager:
+        tm.assert_frame_equal(df, df_orig)
+    else:
+        assert df.iloc[0, 0] == 0
+
+    # modify parent -> don't modify subset
+    subset = df[:].iloc[0].iloc[0:2]
+    with tm.assert_cow_warning(warn_copy_on_write):
+        df.iloc[0, 0] = 0
+    expected = Series([1, 4], index=["a", "b"], name=0)
+    if using_copy_on_write or using_array_manager:
+        tm.assert_series_equal(subset, expected)
+    else:
+        assert subset.iloc[0] == 0
+
+
+@pytest.mark.parametrize(
+    "method",
+    [
+        lambda df: df[:],
+        lambda df: df.loc[:, :],
+        lambda df: df.loc[:],
+        lambda df: df.iloc[:, :],
+        lambda df: df.iloc[:],
+    ],
+    ids=["getitem", "loc", "loc-rows", "iloc", "iloc-rows"],
+)
+def test_null_slice(backend, method, using_copy_on_write, warn_copy_on_write):
+    # Case: also all variants of indexing with a null slice (:) should return
+    # new objects to ensure we correctly use CoW for the results
+    dtype_backend, DataFrame, _ = backend
+    df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]})
+    df_orig = df.copy()
+
+    df2 = method(df)
+
+    # we always return new objects (shallow copy), regardless of CoW or not
+    assert df2 is not df
+
+    # and those trigger CoW when mutated
+    with tm.assert_cow_warning(warn_copy_on_write):
+        df2.iloc[0, 0] = 0
+    if using_copy_on_write:
+        tm.assert_frame_equal(df, df_orig)
+    else:
+        assert df.iloc[0, 0] == 0
+
+
+@pytest.mark.parametrize(
+    "method",
+    [
+        lambda s: s[:],
+        lambda s: s.loc[:],
+        lambda s: s.iloc[:],
+    ],
+    ids=["getitem", "loc", "iloc"],
+)
+def test_null_slice_series(backend, method, using_copy_on_write, warn_copy_on_write):
+    _, _, Series = backend
+    s = Series([1, 2, 3], index=["a", "b", "c"])
+    s_orig = s.copy()
+
+    s2 = method(s)
+
+    # we always return new objects, regardless of CoW or not
+    assert s2 is not s
+
+    # and those trigger CoW when mutated
+    with tm.assert_cow_warning(warn_copy_on_write):
+        s2.iloc[0] = 0
+    if using_copy_on_write:
+        tm.assert_series_equal(s, s_orig)
+    else:
+        assert s.iloc[0] == 0
+
+
+# TODO add more tests modifying the parent
+
+
+# -----------------------------------------------------------------------------
+# Series -- Indexing operations taking subset + modifying the subset/parent
+
+
+def test_series_getitem_slice(backend, using_copy_on_write, warn_copy_on_write):
+    # Case: taking a slice of a Series + afterwards modifying the subset
+    _, _, Series = backend
+    s = Series([1, 2, 3], index=["a", "b", "c"])
+    s_orig = s.copy()
+
+    subset = s[:]
+    assert np.shares_memory(get_array(subset), get_array(s))
+
+    with tm.assert_cow_warning(warn_copy_on_write):
+        subset.iloc[0] = 0
+
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(subset), get_array(s))
+
+    expected = Series([0, 2, 3], index=["a", "b", "c"])
+    tm.assert_series_equal(subset, expected)
+
+    if using_copy_on_write:
+        # original parent series is not modified (CoW)
+        tm.assert_series_equal(s, s_orig)
+    else:
+        # original parent series is actually updated
+        assert s.iloc[0] == 0
+
+
+def test_series_getitem_ellipsis(using_copy_on_write, warn_copy_on_write):
+    # Case: taking a view of a Series using Ellipsis + afterwards modifying the subset
+    s = Series([1, 2, 3])
+    s_orig = s.copy()
+
+    subset = s[...]
+    assert np.shares_memory(get_array(subset), get_array(s))
+
+    with tm.assert_cow_warning(warn_copy_on_write):
+        subset.iloc[0] = 0
+
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(subset), get_array(s))
+
+    expected = Series([0, 2, 3])
+    tm.assert_series_equal(subset, expected)
+
+    if using_copy_on_write:
+        # original parent series is not modified (CoW)
+        tm.assert_series_equal(s, s_orig)
+    else:
+        # original parent series is actually updated
+        assert s.iloc[0] == 0
+
+
+@pytest.mark.parametrize(
+    "indexer",
+    [slice(0, 2), np.array([True, True, False]), np.array([0, 1])],
+    ids=["slice", "mask", "array"],
+)
+def test_series_subset_set_with_indexer(
+    backend, indexer_si, indexer, using_copy_on_write, warn_copy_on_write
+):
+    # Case: setting values in a viewing Series with an indexer
+    _, _, Series = backend
+    s = Series([1, 2, 3], index=["a", "b", "c"])
+    s_orig = s.copy()
+    subset = s[:]
+
+    warn = None
+    msg = "Series.__setitem__ treating keys as positions is deprecated"
+    if (
+        indexer_si is tm.setitem
+        and isinstance(indexer, np.ndarray)
+        and indexer.dtype.kind == "i"
+    ):
+        warn = FutureWarning
+    if warn_copy_on_write:
+        with tm.assert_cow_warning(raise_on_extra_warnings=warn is not None):
+            indexer_si(subset)[indexer] = 0
+    else:
+        with tm.assert_produces_warning(warn, match=msg):
+            indexer_si(subset)[indexer] = 0
+    expected = Series([0, 0, 3], index=["a", "b", "c"])
+    tm.assert_series_equal(subset, expected)
+
+    if using_copy_on_write:
+        tm.assert_series_equal(s, s_orig)
+    else:
+        tm.assert_series_equal(s, expected)
+
+
+# -----------------------------------------------------------------------------
+# del operator
+
+
+def test_del_frame(backend, using_copy_on_write, warn_copy_on_write):
+    # Case: deleting a column with `del` on a viewing child dataframe should
+    # not modify parent + update the references
+    dtype_backend, DataFrame, _ = backend
+    df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [0.1, 0.2, 0.3]})
+    df_orig = df.copy()
+    df2 = df[:]
+
+    assert np.shares_memory(get_array(df, "a"), get_array(df2, "a"))
+
+    del df2["b"]
+
+    assert np.shares_memory(get_array(df, "a"), get_array(df2, "a"))
+    tm.assert_frame_equal(df, df_orig)
+    tm.assert_frame_equal(df2, df_orig[["a", "c"]])
+    df2._mgr._verify_integrity()
+
+    with tm.assert_cow_warning(warn_copy_on_write and dtype_backend == "numpy"):
+        df.loc[0, "b"] = 200
+    assert np.shares_memory(get_array(df, "a"), get_array(df2, "a"))
+    df_orig = df.copy()
+
+    with tm.assert_cow_warning(warn_copy_on_write):
+        df2.loc[0, "a"] = 100
+    if using_copy_on_write:
+        # modifying child after deleting a column still doesn't update parent
+        tm.assert_frame_equal(df, df_orig)
+    else:
+        assert df.loc[0, "a"] == 100
+
+
+def test_del_series(backend):
+    _, _, Series = backend
+    s = Series([1, 2, 3], index=["a", "b", "c"])
+    s_orig = s.copy()
+    s2 = s[:]
+
+    assert np.shares_memory(get_array(s), get_array(s2))
+
+    del s2["a"]
+
+    assert not np.shares_memory(get_array(s), get_array(s2))
+    tm.assert_series_equal(s, s_orig)
+    tm.assert_series_equal(s2, s_orig[["b", "c"]])
+
+    # modifying s2 doesn't need copy on write (due to `del`, s2 is backed by new array)
+    values = s2.values
+    s2.loc["b"] = 100
+    assert values[0] == 100
+
+
+# -----------------------------------------------------------------------------
+# Accessing column as Series
+
+
+def test_column_as_series(
+    backend, using_copy_on_write, warn_copy_on_write, using_array_manager
+):
+    # Case: selecting a single column now also uses Copy-on-Write
+    dtype_backend, DataFrame, Series = backend
+    df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [0.1, 0.2, 0.3]})
+    df_orig = df.copy()
+
+    s = df["a"]
+
+    assert np.shares_memory(get_array(s, "a"), get_array(df, "a"))
+
+    if using_copy_on_write or using_array_manager:
+        s[0] = 0
+    else:
+        if warn_copy_on_write:
+            with tm.assert_cow_warning():
+                s[0] = 0
+        else:
+            warn = SettingWithCopyWarning if dtype_backend == "numpy" else None
+            with pd.option_context("chained_assignment", "warn"):
+                with tm.assert_produces_warning(warn):
+                    s[0] = 0
+
+    expected = Series([0, 2, 3], name="a")
+    tm.assert_series_equal(s, expected)
+    if using_copy_on_write:
+        # assert not np.shares_memory(s.values, get_array(df, "a"))
+        tm.assert_frame_equal(df, df_orig)
+        # ensure cached series on getitem is not the changed series
+        tm.assert_series_equal(df["a"], df_orig["a"])
+    else:
+        df_orig.iloc[0, 0] = 0
+        tm.assert_frame_equal(df, df_orig)
+
+
+def test_column_as_series_set_with_upcast(
+    backend, using_copy_on_write, using_array_manager, warn_copy_on_write
+):
+    # Case: selecting a single column now also uses Copy-on-Write -> when
+    # setting a value causes an upcast, we don't need to update the parent
+    # DataFrame through the cache mechanism
+    dtype_backend, DataFrame, Series = backend
+    df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [0.1, 0.2, 0.3]})
+    df_orig = df.copy()
+
+    s = df["a"]
+    if dtype_backend == "nullable":
+        with tm.assert_cow_warning(warn_copy_on_write):
+            with pytest.raises(TypeError, match="Invalid value"):
+                s[0] = "foo"
+        expected = Series([1, 2, 3], name="a")
+    elif using_copy_on_write or warn_copy_on_write or using_array_manager:
+        # TODO(CoW-warn) assert the FutureWarning for CoW is also raised
+        with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"):
+            s[0] = "foo"
+        expected = Series(["foo", 2, 3], dtype=object, name="a")
+    else:
+        with pd.option_context("chained_assignment", "warn"):
+            msg = "|".join(
+                [
+                    "A value is trying to be set on a copy of a slice from a DataFrame",
+                    "Setting an item of incompatible dtype is deprecated",
+                ]
+            )
+            with tm.assert_produces_warning(
+                (SettingWithCopyWarning, FutureWarning), match=msg
+            ):
+                s[0] = "foo"
+        expected = Series(["foo", 2, 3], dtype=object, name="a")
+
+    tm.assert_series_equal(s, expected)
+    if using_copy_on_write:
+        tm.assert_frame_equal(df, df_orig)
+        # ensure cached series on getitem is not the changed series
+        tm.assert_series_equal(df["a"], df_orig["a"])
+    else:
+        df_orig["a"] = expected
+        tm.assert_frame_equal(df, df_orig)
+
+
+@pytest.mark.parametrize(
+    "method",
+    [
+        lambda df: df["a"],
+        lambda df: df.loc[:, "a"],
+        lambda df: df.iloc[:, 0],
+    ],
+    ids=["getitem", "loc", "iloc"],
+)
+def test_column_as_series_no_item_cache(
+    request,
+    backend,
+    method,
+    using_copy_on_write,
+    warn_copy_on_write,
+    using_array_manager,
+):
+    # Case: selecting a single column (which now also uses Copy-on-Write to protect
+    # the view) should always give a new object (i.e. not make use of a cache)
+    dtype_backend, DataFrame, _ = backend
+    df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [0.1, 0.2, 0.3]})
+    df_orig = df.copy()
+
+    s1 = method(df)
+    s2 = method(df)
+
+    is_iloc = "iloc" in request.node.name
+    if using_copy_on_write or warn_copy_on_write or is_iloc:
+        assert s1 is not s2
+    else:
+        assert s1 is s2
+
+    if using_copy_on_write or using_array_manager:
+        s1.iloc[0] = 0
+    elif warn_copy_on_write:
+        with tm.assert_cow_warning():
+            s1.iloc[0] = 0
+    else:
+        warn = SettingWithCopyWarning if dtype_backend == "numpy" else None
+        with pd.option_context("chained_assignment", "warn"):
+            with tm.assert_produces_warning(warn):
+                s1.iloc[0] = 0
+
+    if using_copy_on_write:
+        tm.assert_series_equal(s2, df_orig["a"])
+        tm.assert_frame_equal(df, df_orig)
+    else:
+        assert s2.iloc[0] == 0
+
+
+# TODO add tests for other indexing methods on the Series
+
+
+def test_dataframe_add_column_from_series(backend, using_copy_on_write):
+    # Case: adding a new column to a DataFrame from an existing column/series
+    # -> delays copy under CoW
+    _, DataFrame, Series = backend
+    df = DataFrame({"a": [1, 2, 3], "b": [0.1, 0.2, 0.3]})
+
+    s = Series([10, 11, 12])
+    df["new"] = s
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(df, "new"), get_array(s))
+    else:
+        assert not np.shares_memory(get_array(df, "new"), get_array(s))
+
+    # editing series -> doesn't modify column in frame
+    s[0] = 0
+    expected = DataFrame({"a": [1, 2, 3], "b": [0.1, 0.2, 0.3], "new": [10, 11, 12]})
+    tm.assert_frame_equal(df, expected)
+
+
+@pytest.mark.parametrize("val", [100, "a"])
+@pytest.mark.parametrize(
+    "indexer_func, indexer",
+    [
+        (tm.loc, (0, "a")),
+        (tm.iloc, (0, 0)),
+        (tm.loc, ([0], "a")),
+        (tm.iloc, ([0], 0)),
+        (tm.loc, (slice(None), "a")),
+        (tm.iloc, (slice(None), 0)),
+    ],
+)
+@pytest.mark.parametrize(
+    "col", [[0.1, 0.2, 0.3], [7, 8, 9]], ids=["mixed-block", "single-block"]
+)
+def test_set_value_copy_only_necessary_column(
+    using_copy_on_write, warn_copy_on_write, indexer_func, indexer, val, col
+):
+    # When setting inplace, only copy column that is modified instead of the whole
+    # block (by splitting the block)
+    df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": col})
+    df_orig = df.copy()
+    view = df[:]
+
+    if val == "a" and not warn_copy_on_write:
+        with tm.assert_produces_warning(
+            FutureWarning, match="Setting an item of incompatible dtype is deprecated"
+        ):
+            indexer_func(df)[indexer] = val
+    if val == "a" and warn_copy_on_write:
+        with tm.assert_produces_warning(
+            FutureWarning, match="incompatible dtype|Setting a value on a view"
+        ):
+            indexer_func(df)[indexer] = val
+    else:
+        with tm.assert_cow_warning(warn_copy_on_write and val == 100):
+            indexer_func(df)[indexer] = val
+
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(df, "b"), get_array(view, "b"))
+        assert not np.shares_memory(get_array(df, "a"), get_array(view, "a"))
+        tm.assert_frame_equal(view, df_orig)
+    else:
+        assert np.shares_memory(get_array(df, "c"), get_array(view, "c"))
+        if val == "a":
+            assert not np.shares_memory(get_array(df, "a"), get_array(view, "a"))
+        else:
+            assert np.shares_memory(get_array(df, "a"), get_array(view, "a"))
+
+
+def test_series_midx_slice(using_copy_on_write, warn_copy_on_write):
+    ser = Series([1, 2, 3], index=pd.MultiIndex.from_arrays([[1, 1, 2], [3, 4, 5]]))
+    ser_orig = ser.copy()
+    result = ser[1]
+    assert np.shares_memory(get_array(ser), get_array(result))
+    with tm.assert_cow_warning(warn_copy_on_write):
+        result.iloc[0] = 100
+    if using_copy_on_write:
+        tm.assert_series_equal(ser, ser_orig)
+    else:
+        expected = Series(
+            [100, 2, 3], index=pd.MultiIndex.from_arrays([[1, 1, 2], [3, 4, 5]])
+        )
+        tm.assert_series_equal(ser, expected)
+
+
+def test_getitem_midx_slice(
+    using_copy_on_write, warn_copy_on_write, using_array_manager
+):
+    df = DataFrame({("a", "x"): [1, 2], ("a", "y"): 1, ("b", "x"): 2})
+    df_orig = df.copy()
+    new_df = df[("a",)]
+
+    if using_copy_on_write:
+        assert not new_df._mgr._has_no_reference(0)
+
+    if not using_array_manager:
+        assert np.shares_memory(get_array(df, ("a", "x")), get_array(new_df, "x"))
+    if using_copy_on_write:
+        new_df.iloc[0, 0] = 100
+        tm.assert_frame_equal(df_orig, df)
+    else:
+        if warn_copy_on_write:
+            with tm.assert_cow_warning():
+                new_df.iloc[0, 0] = 100
+        else:
+            with pd.option_context("chained_assignment", "warn"):
+                with tm.assert_produces_warning(SettingWithCopyWarning):
+                    new_df.iloc[0, 0] = 100
+        assert df.iloc[0, 0] == 100
+
+
+def test_series_midx_tuples_slice(using_copy_on_write, warn_copy_on_write):
+    ser = Series(
+        [1, 2, 3],
+        index=pd.MultiIndex.from_tuples([((1, 2), 3), ((1, 2), 4), ((2, 3), 4)]),
+    )
+    result = ser[(1, 2)]
+    assert np.shares_memory(get_array(ser), get_array(result))
+    with tm.assert_cow_warning(warn_copy_on_write):
+        result.iloc[0] = 100
+    if using_copy_on_write:
+        expected = Series(
+            [1, 2, 3],
+            index=pd.MultiIndex.from_tuples([((1, 2), 3), ((1, 2), 4), ((2, 3), 4)]),
+        )
+        tm.assert_series_equal(ser, expected)
+
+
+def test_midx_read_only_bool_indexer():
+    # GH#56635
+    def mklbl(prefix, n):
+        return [f"{prefix}{i}" for i in range(n)]
+
+    idx = pd.MultiIndex.from_product(
+        [mklbl("A", 4), mklbl("B", 2), mklbl("C", 4), mklbl("D", 2)]
+    )
+    cols = pd.MultiIndex.from_tuples(
+        [("a", "foo"), ("a", "bar"), ("b", "foo"), ("b", "bah")], names=["lvl0", "lvl1"]
+    )
+    df = DataFrame(1, index=idx, columns=cols).sort_index().sort_index(axis=1)
+
+    mask = df[("a", "foo")] == 1
+    expected_mask = mask.copy()
+    result = df.loc[pd.IndexSlice[mask, :, ["C1", "C3"]], :]
+    expected = df.loc[pd.IndexSlice[:, :, ["C1", "C3"]], :]
+    tm.assert_frame_equal(result, expected)
+    tm.assert_series_equal(mask, expected_mask)
+
+
+def test_loc_enlarging_with_dataframe(using_copy_on_write):
+    df = DataFrame({"a": [1, 2, 3]})
+    rhs = DataFrame({"b": [1, 2, 3], "c": [4, 5, 6]})
+    rhs_orig = rhs.copy()
+    df.loc[:, ["b", "c"]] = rhs
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(df, "b"), get_array(rhs, "b"))
+        assert np.shares_memory(get_array(df, "c"), get_array(rhs, "c"))
+        assert not df._mgr._has_no_reference(1)
+    else:
+        assert not np.shares_memory(get_array(df, "b"), get_array(rhs, "b"))
+
+    df.iloc[0, 1] = 100
+    tm.assert_frame_equal(rhs, rhs_orig)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/copy_view/test_internals.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/copy_view/test_internals.py
new file mode 100644
index 0000000000000000000000000000000000000000..8526d385888974a8c369faec9c3d26d7f35d0d89
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/copy_view/test_internals.py
@@ -0,0 +1,154 @@
+import numpy as np
+import pytest
+
+import pandas.util._test_decorators as td
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Series,
+)
+import pandas._testing as tm
+from pandas.tests.copy_view.util import get_array
+
+
+@td.skip_array_manager_invalid_test
+def test_consolidate(using_copy_on_write):
+    # create unconsolidated DataFrame
+    df = DataFrame({"a": [1, 2, 3], "b": [0.1, 0.2, 0.3]})
+    df["c"] = [4, 5, 6]
+
+    # take a viewing subset
+    subset = df[:]
+
+    # each block of subset references a block of df
+    assert all(blk.refs.has_reference() for blk in subset._mgr.blocks)
+
+    # consolidate the two int64 blocks
+    subset._consolidate_inplace()
+
+    # the float64 block still references the parent one because it still a view
+    assert subset._mgr.blocks[0].refs.has_reference()
+    # equivalent of assert np.shares_memory(df["b"].values, subset["b"].values)
+    # but avoids caching df["b"]
+    assert np.shares_memory(get_array(df, "b"), get_array(subset, "b"))
+
+    # the new consolidated int64 block does not reference another
+    assert not subset._mgr.blocks[1].refs.has_reference()
+
+    # the parent dataframe now also only is linked for the float column
+    assert not df._mgr.blocks[0].refs.has_reference()
+    assert df._mgr.blocks[1].refs.has_reference()
+    assert not df._mgr.blocks[2].refs.has_reference()
+
+    # and modifying subset still doesn't modify parent
+    if using_copy_on_write:
+        subset.iloc[0, 1] = 0.0
+        assert not df._mgr.blocks[1].refs.has_reference()
+        assert df.loc[0, "b"] == 0.1
+
+
+@pytest.mark.single_cpu
+@td.skip_array_manager_invalid_test
+def test_switch_options():
+    # ensure we can switch the value of the option within one session
+    # (assuming data is constructed after switching)
+
+    # using the option_context to ensure we set back to global option value
+    # after running the test
+    with pd.option_context("mode.copy_on_write", False):
+        df = DataFrame({"a": [1, 2, 3], "b": [0.1, 0.2, 0.3]})
+        subset = df[:]
+        subset.iloc[0, 0] = 0
+        # df updated with CoW disabled
+        assert df.iloc[0, 0] == 0
+
+        pd.options.mode.copy_on_write = True
+        df = DataFrame({"a": [1, 2, 3], "b": [0.1, 0.2, 0.3]})
+        subset = df[:]
+        subset.iloc[0, 0] = 0
+        # df not updated with CoW enabled
+        assert df.iloc[0, 0] == 1
+
+        pd.options.mode.copy_on_write = False
+        df = DataFrame({"a": [1, 2, 3], "b": [0.1, 0.2, 0.3]})
+        subset = df[:]
+        subset.iloc[0, 0] = 0
+        # df updated with CoW disabled
+        assert df.iloc[0, 0] == 0
+
+
+@td.skip_array_manager_invalid_test
+@pytest.mark.parametrize("dtype", [np.intp, np.int8])
+@pytest.mark.parametrize(
+    "locs, arr",
+    [
+        ([0], np.array([-1, -2, -3])),
+        ([1], np.array([-1, -2, -3])),
+        ([5], np.array([-1, -2, -3])),
+        ([0, 1], np.array([[-1, -2, -3], [-4, -5, -6]]).T),
+        ([0, 2], np.array([[-1, -2, -3], [-4, -5, -6]]).T),
+        ([0, 1, 2], np.array([[-1, -2, -3], [-4, -5, -6], [-4, -5, -6]]).T),
+        ([1, 2], np.array([[-1, -2, -3], [-4, -5, -6]]).T),
+        ([1, 3], np.array([[-1, -2, -3], [-4, -5, -6]]).T),
+        ([1, 3], np.array([[-1, -2, -3], [-4, -5, -6]]).T),
+    ],
+)
+def test_iset_splits_blocks_inplace(using_copy_on_write, locs, arr, dtype):
+    # Nothing currently calls iset with
+    # more than 1 loc with inplace=True (only happens with inplace=False)
+    # but ensure that it works
+    df = DataFrame(
+        {
+            "a": [1, 2, 3],
+            "b": [4, 5, 6],
+            "c": [7, 8, 9],
+            "d": [10, 11, 12],
+            "e": [13, 14, 15],
+            "f": Series(["a", "b", "c"], dtype=object),
+        },
+    )
+    arr = arr.astype(dtype)
+    df_orig = df.copy()
+    df2 = df.copy(deep=None)  # Trigger a CoW (if enabled, otherwise makes copy)
+    df2._mgr.iset(locs, arr, inplace=True)
+
+    tm.assert_frame_equal(df, df_orig)
+
+    if using_copy_on_write:
+        for i, col in enumerate(df.columns):
+            if i not in locs:
+                assert np.shares_memory(get_array(df, col), get_array(df2, col))
+    else:
+        for col in df.columns:
+            assert not np.shares_memory(get_array(df, col), get_array(df2, col))
+
+
+def test_exponential_backoff():
+    # GH#55518
+    df = DataFrame({"a": [1, 2, 3]})
+    for i in range(490):
+        df.copy(deep=False)
+
+    assert len(df._mgr.blocks[0].refs.referenced_blocks) == 491
+
+    df = DataFrame({"a": [1, 2, 3]})
+    dfs = [df.copy(deep=False) for i in range(510)]
+
+    for i in range(20):
+        df.copy(deep=False)
+    assert len(df._mgr.blocks[0].refs.referenced_blocks) == 531
+    assert df._mgr.blocks[0].refs.clear_counter == 1000
+
+    for i in range(500):
+        df.copy(deep=False)
+
+    # Don't reduce since we still have over 500 objects alive
+    assert df._mgr.blocks[0].refs.clear_counter == 1000
+
+    dfs = dfs[:300]
+    for i in range(500):
+        df.copy(deep=False)
+
+    # Reduce since there are less than 500 objects alive
+    assert df._mgr.blocks[0].refs.clear_counter == 500
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/copy_view/test_interp_fillna.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/copy_view/test_interp_fillna.py
new file mode 100644
index 0000000000000000000000000000000000000000..0bcc968014242b0555a5eefb93e04e3dfa773f28
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/copy_view/test_interp_fillna.py
@@ -0,0 +1,441 @@
+import numpy as np
+import pytest
+
+from pandas.compat import WARNING_CHECK_DISABLED
+
+from pandas import (
+    NA,
+    ArrowDtype,
+    DataFrame,
+    Interval,
+    NaT,
+    Series,
+    Timestamp,
+    interval_range,
+    option_context,
+)
+import pandas._testing as tm
+from pandas.tests.copy_view.util import get_array
+
+
+@pytest.mark.parametrize("method", ["pad", "nearest", "linear"])
+def test_interpolate_no_op(using_copy_on_write, method):
+    df = DataFrame({"a": [1, 2]})
+    df_orig = df.copy()
+
+    warn = None
+    if method == "pad":
+        warn = FutureWarning
+    msg = "DataFrame.interpolate with method=pad is deprecated"
+    with tm.assert_produces_warning(warn, match=msg):
+        result = df.interpolate(method=method)
+
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(result, "a"), get_array(df, "a"))
+    else:
+        assert not np.shares_memory(get_array(result, "a"), get_array(df, "a"))
+
+    result.iloc[0, 0] = 100
+
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(result, "a"), get_array(df, "a"))
+    tm.assert_frame_equal(df, df_orig)
+
+
+@pytest.mark.parametrize("func", ["ffill", "bfill"])
+def test_interp_fill_functions(using_copy_on_write, func):
+    # Check that these takes the same code paths as interpolate
+    df = DataFrame({"a": [1, 2]})
+    df_orig = df.copy()
+
+    result = getattr(df, func)()
+
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(result, "a"), get_array(df, "a"))
+    else:
+        assert not np.shares_memory(get_array(result, "a"), get_array(df, "a"))
+
+    result.iloc[0, 0] = 100
+
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(result, "a"), get_array(df, "a"))
+    tm.assert_frame_equal(df, df_orig)
+
+
+@pytest.mark.parametrize("func", ["ffill", "bfill"])
+@pytest.mark.parametrize(
+    "vals", [[1, np.nan, 2], [Timestamp("2019-12-31"), NaT, Timestamp("2020-12-31")]]
+)
+def test_interpolate_triggers_copy(using_copy_on_write, vals, func):
+    df = DataFrame({"a": vals})
+    result = getattr(df, func)()
+
+    assert not np.shares_memory(get_array(result, "a"), get_array(df, "a"))
+    if using_copy_on_write:
+        # Check that we don't have references when triggering a copy
+        assert result._mgr._has_no_reference(0)
+
+
+@pytest.mark.parametrize(
+    "vals", [[1, np.nan, 2], [Timestamp("2019-12-31"), NaT, Timestamp("2020-12-31")]]
+)
+def test_interpolate_inplace_no_reference_no_copy(using_copy_on_write, vals):
+    df = DataFrame({"a": vals})
+    arr = get_array(df, "a")
+    df.interpolate(method="linear", inplace=True)
+
+    assert np.shares_memory(arr, get_array(df, "a"))
+    if using_copy_on_write:
+        # Check that we don't have references when triggering a copy
+        assert df._mgr._has_no_reference(0)
+
+
+@pytest.mark.parametrize(
+    "vals", [[1, np.nan, 2], [Timestamp("2019-12-31"), NaT, Timestamp("2020-12-31")]]
+)
+def test_interpolate_inplace_with_refs(using_copy_on_write, vals, warn_copy_on_write):
+    df = DataFrame({"a": [1, np.nan, 2]})
+    df_orig = df.copy()
+    arr = get_array(df, "a")
+    view = df[:]
+    with tm.assert_cow_warning(warn_copy_on_write):
+        df.interpolate(method="linear", inplace=True)
+
+    if using_copy_on_write:
+        # Check that copy was triggered in interpolate and that we don't
+        # have any references left
+        assert not np.shares_memory(arr, get_array(df, "a"))
+        tm.assert_frame_equal(df_orig, view)
+        assert df._mgr._has_no_reference(0)
+        assert view._mgr._has_no_reference(0)
+    else:
+        assert np.shares_memory(arr, get_array(df, "a"))
+
+
+@pytest.mark.parametrize("func", ["ffill", "bfill"])
+@pytest.mark.parametrize("dtype", ["float64", "Float64"])
+def test_interp_fill_functions_inplace(
+    using_copy_on_write, func, warn_copy_on_write, dtype
+):
+    # Check that these takes the same code paths as interpolate
+    df = DataFrame({"a": [1, np.nan, 2]}, dtype=dtype)
+    df_orig = df.copy()
+    arr = get_array(df, "a")
+    view = df[:]
+
+    with tm.assert_cow_warning(warn_copy_on_write and dtype == "float64"):
+        getattr(df, func)(inplace=True)
+
+    if using_copy_on_write:
+        # Check that copy was triggered in interpolate and that we don't
+        # have any references left
+        assert not np.shares_memory(arr, get_array(df, "a"))
+        tm.assert_frame_equal(df_orig, view)
+        assert df._mgr._has_no_reference(0)
+        assert view._mgr._has_no_reference(0)
+    else:
+        assert np.shares_memory(arr, get_array(df, "a")) is (dtype == "float64")
+
+
+def test_interpolate_cannot_with_object_dtype(using_copy_on_write):
+    df = DataFrame({"a": ["a", np.nan, "c"], "b": 1})
+    df["a"] = df["a"].astype(object)
+    df_orig = df.copy()
+
+    msg = "DataFrame.interpolate with object dtype"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        result = df.interpolate(method="linear")
+
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(result, "a"), get_array(df, "a"))
+    else:
+        assert not np.shares_memory(get_array(result, "a"), get_array(df, "a"))
+
+    result.iloc[0, 0] = Timestamp("2021-12-31")
+
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(result, "a"), get_array(df, "a"))
+    tm.assert_frame_equal(df, df_orig)
+
+
+def test_interpolate_object_convert_no_op(using_copy_on_write, using_infer_string):
+    df = DataFrame({"a": ["a", "b", "c"], "b": 1})
+    df["a"] = df["a"].astype(object)
+    arr_a = get_array(df, "a")
+    msg = "DataFrame.interpolate with method=pad is deprecated"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        df.interpolate(method="pad", inplace=True)
+
+    # Now CoW makes a copy, it should not!
+    if using_copy_on_write and not using_infer_string:
+        assert df._mgr._has_no_reference(0)
+        assert np.shares_memory(arr_a, get_array(df, "a"))
+
+
+def test_interpolate_object_convert_copies(using_copy_on_write):
+    df = DataFrame({"a": Series([1, 2], dtype=object), "b": 1})
+    arr_a = get_array(df, "a")
+    msg = "DataFrame.interpolate with method=pad is deprecated"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        df.interpolate(method="pad", inplace=True)
+
+    if using_copy_on_write:
+        assert df._mgr._has_no_reference(0)
+        assert not np.shares_memory(arr_a, get_array(df, "a"))
+
+
+def test_interpolate_downcast(using_copy_on_write):
+    df = DataFrame({"a": [1, np.nan, 2.5], "b": 1})
+    arr_a = get_array(df, "a")
+    msg = "DataFrame.interpolate with method=pad is deprecated"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        df.interpolate(method="pad", inplace=True, downcast="infer")
+
+    if using_copy_on_write:
+        assert df._mgr._has_no_reference(0)
+    assert np.shares_memory(arr_a, get_array(df, "a"))
+
+
+def test_interpolate_downcast_reference_triggers_copy(using_copy_on_write):
+    df = DataFrame({"a": [1, np.nan, 2.5], "b": 1})
+    df_orig = df.copy()
+    arr_a = get_array(df, "a")
+    view = df[:]
+    msg = "DataFrame.interpolate with method=pad is deprecated"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        df.interpolate(method="pad", inplace=True, downcast="infer")
+
+    if using_copy_on_write:
+        assert df._mgr._has_no_reference(0)
+        assert not np.shares_memory(arr_a, get_array(df, "a"))
+        tm.assert_frame_equal(df_orig, view)
+    else:
+        tm.assert_frame_equal(df, view)
+
+
+def test_fillna(using_copy_on_write):
+    df = DataFrame({"a": [1.5, np.nan], "b": 1})
+    df_orig = df.copy()
+
+    df2 = df.fillna(5.5)
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(df, "b"), get_array(df2, "b"))
+    else:
+        assert not np.shares_memory(get_array(df, "b"), get_array(df2, "b"))
+
+    df2.iloc[0, 1] = 100
+    tm.assert_frame_equal(df_orig, df)
+
+
+def test_fillna_dict(using_copy_on_write):
+    df = DataFrame({"a": [1.5, np.nan], "b": 1})
+    df_orig = df.copy()
+
+    df2 = df.fillna({"a": 100.5})
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(df, "b"), get_array(df2, "b"))
+        assert not np.shares_memory(get_array(df, "a"), get_array(df2, "a"))
+    else:
+        assert not np.shares_memory(get_array(df, "b"), get_array(df2, "b"))
+
+    df2.iloc[0, 1] = 100
+    tm.assert_frame_equal(df_orig, df)
+
+
+@pytest.mark.parametrize("downcast", [None, False])
+def test_fillna_inplace(using_copy_on_write, downcast):
+    df = DataFrame({"a": [1.5, np.nan], "b": 1})
+    arr_a = get_array(df, "a")
+    arr_b = get_array(df, "b")
+
+    msg = "The 'downcast' keyword in fillna is deprecated"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        df.fillna(5.5, inplace=True, downcast=downcast)
+    assert np.shares_memory(get_array(df, "a"), arr_a)
+    assert np.shares_memory(get_array(df, "b"), arr_b)
+    if using_copy_on_write:
+        assert df._mgr._has_no_reference(0)
+        assert df._mgr._has_no_reference(1)
+
+
+def test_fillna_inplace_reference(using_copy_on_write, warn_copy_on_write):
+    df = DataFrame({"a": [1.5, np.nan], "b": 1})
+    df_orig = df.copy()
+    arr_a = get_array(df, "a")
+    arr_b = get_array(df, "b")
+    view = df[:]
+
+    with tm.assert_cow_warning(warn_copy_on_write):
+        df.fillna(5.5, inplace=True)
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(df, "a"), arr_a)
+        assert np.shares_memory(get_array(df, "b"), arr_b)
+        assert view._mgr._has_no_reference(0)
+        assert df._mgr._has_no_reference(0)
+        tm.assert_frame_equal(view, df_orig)
+    else:
+        assert np.shares_memory(get_array(df, "a"), arr_a)
+        assert np.shares_memory(get_array(df, "b"), arr_b)
+    expected = DataFrame({"a": [1.5, 5.5], "b": 1})
+    tm.assert_frame_equal(df, expected)
+
+
+def test_fillna_interval_inplace_reference(using_copy_on_write, warn_copy_on_write):
+    # Set dtype explicitly to avoid implicit cast when setting nan
+    ser = Series(
+        interval_range(start=0, end=5), name="a", dtype="interval[float64, right]"
+    )
+    ser.iloc[1] = np.nan
+
+    ser_orig = ser.copy()
+    view = ser[:]
+    with tm.assert_cow_warning(warn_copy_on_write):
+        ser.fillna(value=Interval(left=0, right=5), inplace=True)
+
+    if using_copy_on_write:
+        assert not np.shares_memory(
+            get_array(ser, "a").left.values, get_array(view, "a").left.values
+        )
+        tm.assert_series_equal(view, ser_orig)
+    else:
+        assert np.shares_memory(
+            get_array(ser, "a").left.values, get_array(view, "a").left.values
+        )
+
+
+def test_fillna_series_empty_arg(using_copy_on_write):
+    ser = Series([1, np.nan, 2])
+    ser_orig = ser.copy()
+    result = ser.fillna({})
+
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(ser), get_array(result))
+    else:
+        assert not np.shares_memory(get_array(ser), get_array(result))
+
+    ser.iloc[0] = 100.5
+    tm.assert_series_equal(ser_orig, result)
+
+
+def test_fillna_series_empty_arg_inplace(using_copy_on_write):
+    ser = Series([1, np.nan, 2])
+    arr = get_array(ser)
+    ser.fillna({}, inplace=True)
+
+    assert np.shares_memory(get_array(ser), arr)
+    if using_copy_on_write:
+        assert ser._mgr._has_no_reference(0)
+
+
+def test_fillna_ea_noop_shares_memory(
+    using_copy_on_write, any_numeric_ea_and_arrow_dtype
+):
+    df = DataFrame({"a": [1, NA, 3], "b": 1}, dtype=any_numeric_ea_and_arrow_dtype)
+    df_orig = df.copy()
+    df2 = df.fillna(100)
+
+    assert not np.shares_memory(get_array(df, "a"), get_array(df2, "a"))
+
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(df, "b"), get_array(df2, "b"))
+        assert not df2._mgr._has_no_reference(1)
+    elif isinstance(df.dtypes.iloc[0], ArrowDtype):
+        # arrow is immutable, so no-ops do not need to copy underlying array
+        assert np.shares_memory(get_array(df, "b"), get_array(df2, "b"))
+    else:
+        assert not np.shares_memory(get_array(df, "b"), get_array(df2, "b"))
+
+    tm.assert_frame_equal(df_orig, df)
+
+    df2.iloc[0, 1] = 100
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(df, "b"), get_array(df2, "b"))
+        assert df2._mgr._has_no_reference(1)
+        assert df._mgr._has_no_reference(1)
+    tm.assert_frame_equal(df_orig, df)
+
+
+def test_fillna_inplace_ea_noop_shares_memory(
+    using_copy_on_write, warn_copy_on_write, any_numeric_ea_and_arrow_dtype
+):
+    df = DataFrame({"a": [1, NA, 3], "b": 1}, dtype=any_numeric_ea_and_arrow_dtype)
+    df_orig = df.copy()
+    view = df[:]
+    with tm.assert_cow_warning(warn_copy_on_write):
+        df.fillna(100, inplace=True)
+
+    if isinstance(df["a"].dtype, ArrowDtype) or using_copy_on_write:
+        assert not np.shares_memory(get_array(df, "a"), get_array(view, "a"))
+    else:
+        # MaskedArray can actually respect inplace=True
+        assert np.shares_memory(get_array(df, "a"), get_array(view, "a"))
+
+    assert np.shares_memory(get_array(df, "b"), get_array(view, "b"))
+    if using_copy_on_write:
+        assert not df._mgr._has_no_reference(1)
+        assert not view._mgr._has_no_reference(1)
+
+    with tm.assert_cow_warning(
+        warn_copy_on_write and "pyarrow" not in any_numeric_ea_and_arrow_dtype
+    ):
+        df.iloc[0, 1] = 100
+    if isinstance(df["a"].dtype, ArrowDtype) or using_copy_on_write:
+        tm.assert_frame_equal(df_orig, view)
+    else:
+        # we actually have a view
+        tm.assert_frame_equal(df, view)
+
+
+def test_fillna_chained_assignment(using_copy_on_write):
+    df = DataFrame({"a": [1, np.nan, 2], "b": 1})
+    df_orig = df.copy()
+    if using_copy_on_write:
+        with tm.raises_chained_assignment_error():
+            df["a"].fillna(100, inplace=True)
+        tm.assert_frame_equal(df, df_orig)
+
+        with tm.raises_chained_assignment_error():
+            df[["a"]].fillna(100, inplace=True)
+        tm.assert_frame_equal(df, df_orig)
+    else:
+        with tm.assert_produces_warning(None):
+            with option_context("mode.chained_assignment", None):
+                df[["a"]].fillna(100, inplace=True)
+
+        with tm.assert_produces_warning(None):
+            with option_context("mode.chained_assignment", None):
+                df[df.a > 5].fillna(100, inplace=True)
+
+        with tm.assert_produces_warning(
+            FutureWarning if not WARNING_CHECK_DISABLED else None,
+            match="inplace method",
+        ):
+            df["a"].fillna(100, inplace=True)
+
+
+@pytest.mark.parametrize("func", ["interpolate", "ffill", "bfill"])
+def test_interpolate_chained_assignment(using_copy_on_write, func):
+    df = DataFrame({"a": [1, np.nan, 2], "b": 1})
+    df_orig = df.copy()
+    if using_copy_on_write:
+        with tm.raises_chained_assignment_error():
+            getattr(df["a"], func)(inplace=True)
+        tm.assert_frame_equal(df, df_orig)
+
+        with tm.raises_chained_assignment_error():
+            getattr(df[["a"]], func)(inplace=True)
+        tm.assert_frame_equal(df, df_orig)
+    else:
+        with tm.assert_produces_warning(
+            FutureWarning if not WARNING_CHECK_DISABLED else None,
+            match="inplace method",
+        ):
+            getattr(df["a"], func)(inplace=True)
+
+        with tm.assert_produces_warning(None):
+            with option_context("mode.chained_assignment", None):
+                getattr(df[["a"]], func)(inplace=True)
+
+        with tm.assert_produces_warning(None):
+            with option_context("mode.chained_assignment", None):
+                getattr(df[df["a"] > 1], func)(inplace=True)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/copy_view/test_methods.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/copy_view/test_methods.py
new file mode 100644
index 0000000000000000000000000000000000000000..2df39a1ec702314c51d81a05cc966140d9e13561
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/copy_view/test_methods.py
@@ -0,0 +1,2079 @@
+import numpy as np
+import pytest
+
+from pandas.compat import (
+    HAS_PYARROW,
+    WARNING_CHECK_DISABLED,
+)
+from pandas.errors import SettingWithCopyWarning
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Index,
+    MultiIndex,
+    Period,
+    Series,
+    Timestamp,
+    date_range,
+    option_context,
+    period_range,
+)
+import pandas._testing as tm
+from pandas.tests.copy_view.util import get_array
+from pandas.util.version import Version
+
+
+def test_copy(using_copy_on_write):
+    df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [0.1, 0.2, 0.3]})
+    df_copy = df.copy()
+
+    # the deep copy by defaults takes a shallow copy of the Index
+    assert df_copy.index is not df.index
+    assert df_copy.columns is not df.columns
+    assert df_copy.index.is_(df.index)
+    assert df_copy.columns.is_(df.columns)
+
+    # the deep copy doesn't share memory
+    assert not np.shares_memory(get_array(df_copy, "a"), get_array(df, "a"))
+    if using_copy_on_write:
+        assert not df_copy._mgr.blocks[0].refs.has_reference()
+        assert not df_copy._mgr.blocks[1].refs.has_reference()
+
+    # mutating copy doesn't mutate original
+    df_copy.iloc[0, 0] = 0
+    assert df.iloc[0, 0] == 1
+
+
+def test_copy_shallow(using_copy_on_write, warn_copy_on_write):
+    df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [0.1, 0.2, 0.3]})
+    df_copy = df.copy(deep=False)
+
+    # the shallow copy also makes a shallow copy of the index
+    if using_copy_on_write:
+        assert df_copy.index is not df.index
+        assert df_copy.columns is not df.columns
+        assert df_copy.index.is_(df.index)
+        assert df_copy.columns.is_(df.columns)
+    else:
+        assert df_copy.index is df.index
+        assert df_copy.columns is df.columns
+
+    # the shallow copy still shares memory
+    assert np.shares_memory(get_array(df_copy, "a"), get_array(df, "a"))
+    if using_copy_on_write:
+        assert df_copy._mgr.blocks[0].refs.has_reference()
+        assert df_copy._mgr.blocks[1].refs.has_reference()
+
+    if using_copy_on_write:
+        # mutating shallow copy doesn't mutate original
+        df_copy.iloc[0, 0] = 0
+        assert df.iloc[0, 0] == 1
+        # mutating triggered a copy-on-write -> no longer shares memory
+        assert not np.shares_memory(get_array(df_copy, "a"), get_array(df, "a"))
+        # but still shares memory for the other columns/blocks
+        assert np.shares_memory(get_array(df_copy, "c"), get_array(df, "c"))
+    else:
+        # mutating shallow copy does mutate original
+        with tm.assert_cow_warning(warn_copy_on_write):
+            df_copy.iloc[0, 0] = 0
+        assert df.iloc[0, 0] == 0
+        # and still shares memory
+        assert np.shares_memory(get_array(df_copy, "a"), get_array(df, "a"))
+
+
+@pytest.mark.parametrize("copy", [True, None, False])
+@pytest.mark.parametrize(
+    "method",
+    [
+        lambda df, copy: df.rename(columns=str.lower, copy=copy),
+        lambda df, copy: df.reindex(columns=["a", "c"], copy=copy),
+        lambda df, copy: df.reindex_like(df, copy=copy),
+        lambda df, copy: df.align(df, copy=copy)[0],
+        lambda df, copy: df.set_axis(["a", "b", "c"], axis="index", copy=copy),
+        lambda df, copy: df.rename_axis(index="test", copy=copy),
+        lambda df, copy: df.rename_axis(columns="test", copy=copy),
+        lambda df, copy: df.astype({"b": "int64"}, copy=copy),
+        # lambda df, copy: df.swaplevel(0, 0, copy=copy),
+        lambda df, copy: df.swapaxes(0, 0, copy=copy),
+        lambda df, copy: df.truncate(0, 5, copy=copy),
+        lambda df, copy: df.infer_objects(copy=copy),
+        lambda df, copy: df.to_timestamp(copy=copy),
+        lambda df, copy: df.to_period(freq="D", copy=copy),
+        lambda df, copy: df.tz_localize("US/Central", copy=copy),
+        lambda df, copy: df.tz_convert("US/Central", copy=copy),
+        lambda df, copy: df.set_flags(allows_duplicate_labels=False, copy=copy),
+    ],
+    ids=[
+        "rename",
+        "reindex",
+        "reindex_like",
+        "align",
+        "set_axis",
+        "rename_axis0",
+        "rename_axis1",
+        "astype",
+        # "swaplevel",  # only series
+        "swapaxes",
+        "truncate",
+        "infer_objects",
+        "to_timestamp",
+        "to_period",
+        "tz_localize",
+        "tz_convert",
+        "set_flags",
+    ],
+)
+def test_methods_copy_keyword(
+    request, method, copy, using_copy_on_write, using_array_manager
+):
+    index = None
+    if "to_timestamp" in request.node.callspec.id:
+        index = period_range("2012-01-01", freq="D", periods=3)
+    elif "to_period" in request.node.callspec.id:
+        index = date_range("2012-01-01", freq="D", periods=3)
+    elif "tz_localize" in request.node.callspec.id:
+        index = date_range("2012-01-01", freq="D", periods=3)
+    elif "tz_convert" in request.node.callspec.id:
+        index = date_range("2012-01-01", freq="D", periods=3, tz="Europe/Brussels")
+
+    df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [0.1, 0.2, 0.3]}, index=index)
+
+    if "swapaxes" in request.node.callspec.id:
+        msg = "'DataFrame.swapaxes' is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            df2 = method(df, copy=copy)
+    else:
+        df2 = method(df, copy=copy)
+
+    share_memory = using_copy_on_write or copy is False
+
+    if request.node.callspec.id.startswith("reindex-"):
+        # TODO copy=False without CoW still returns a copy in this case
+        if not using_copy_on_write and not using_array_manager and copy is False:
+            share_memory = False
+
+    if share_memory:
+        assert np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+    else:
+        assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+
+
+@pytest.mark.parametrize("copy", [True, None, False])
+@pytest.mark.parametrize(
+    "method",
+    [
+        lambda ser, copy: ser.rename(index={0: 100}, copy=copy),
+        lambda ser, copy: ser.rename(None, copy=copy),
+        lambda ser, copy: ser.reindex(index=ser.index, copy=copy),
+        lambda ser, copy: ser.reindex_like(ser, copy=copy),
+        lambda ser, copy: ser.align(ser, copy=copy)[0],
+        lambda ser, copy: ser.set_axis(["a", "b", "c"], axis="index", copy=copy),
+        lambda ser, copy: ser.rename_axis(index="test", copy=copy),
+        lambda ser, copy: ser.astype("int64", copy=copy),
+        lambda ser, copy: ser.swaplevel(0, 1, copy=copy),
+        lambda ser, copy: ser.swapaxes(0, 0, copy=copy),
+        lambda ser, copy: ser.truncate(0, 5, copy=copy),
+        lambda ser, copy: ser.infer_objects(copy=copy),
+        lambda ser, copy: ser.to_timestamp(copy=copy),
+        lambda ser, copy: ser.to_period(freq="D", copy=copy),
+        lambda ser, copy: ser.tz_localize("US/Central", copy=copy),
+        lambda ser, copy: ser.tz_convert("US/Central", copy=copy),
+        lambda ser, copy: ser.set_flags(allows_duplicate_labels=False, copy=copy),
+    ],
+    ids=[
+        "rename (dict)",
+        "rename",
+        "reindex",
+        "reindex_like",
+        "align",
+        "set_axis",
+        "rename_axis0",
+        "astype",
+        "swaplevel",
+        "swapaxes",
+        "truncate",
+        "infer_objects",
+        "to_timestamp",
+        "to_period",
+        "tz_localize",
+        "tz_convert",
+        "set_flags",
+    ],
+)
+def test_methods_series_copy_keyword(request, method, copy, using_copy_on_write):
+    index = None
+    if "to_timestamp" in request.node.callspec.id:
+        index = period_range("2012-01-01", freq="D", periods=3)
+    elif "to_period" in request.node.callspec.id:
+        index = date_range("2012-01-01", freq="D", periods=3)
+    elif "tz_localize" in request.node.callspec.id:
+        index = date_range("2012-01-01", freq="D", periods=3)
+    elif "tz_convert" in request.node.callspec.id:
+        index = date_range("2012-01-01", freq="D", periods=3, tz="Europe/Brussels")
+    elif "swaplevel" in request.node.callspec.id:
+        index = MultiIndex.from_arrays([[1, 2, 3], [4, 5, 6]])
+
+    ser = Series([1, 2, 3], index=index)
+
+    if "swapaxes" in request.node.callspec.id:
+        msg = "'Series.swapaxes' is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            ser2 = method(ser, copy=copy)
+    else:
+        ser2 = method(ser, copy=copy)
+
+    share_memory = using_copy_on_write or copy is False
+
+    if share_memory:
+        assert np.shares_memory(get_array(ser2), get_array(ser))
+    else:
+        assert not np.shares_memory(get_array(ser2), get_array(ser))
+
+
+@pytest.mark.parametrize("copy", [True, None, False])
+def test_transpose_copy_keyword(using_copy_on_write, copy, using_array_manager):
+    df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
+    result = df.transpose(copy=copy)
+    share_memory = using_copy_on_write or copy is False or copy is None
+    share_memory = share_memory and not using_array_manager
+
+    if share_memory:
+        assert np.shares_memory(get_array(df, "a"), get_array(result, 0))
+    else:
+        assert not np.shares_memory(get_array(df, "a"), get_array(result, 0))
+
+
+# -----------------------------------------------------------------------------
+# DataFrame methods returning new DataFrame using shallow copy
+
+
+def test_reset_index(using_copy_on_write):
+    # Case: resetting the index (i.e. adding a new column) + mutating the
+    # resulting dataframe
+    df = DataFrame(
+        {"a": [1, 2, 3], "b": [4, 5, 6], "c": [0.1, 0.2, 0.3]}, index=[10, 11, 12]
+    )
+    df_orig = df.copy()
+    df2 = df.reset_index()
+    df2._mgr._verify_integrity()
+
+    if using_copy_on_write:
+        # still shares memory (df2 is a shallow copy)
+        assert np.shares_memory(get_array(df2, "b"), get_array(df, "b"))
+        assert np.shares_memory(get_array(df2, "c"), get_array(df, "c"))
+    # mutating df2 triggers a copy-on-write for that column / block
+    df2.iloc[0, 2] = 0
+    assert not np.shares_memory(get_array(df2, "b"), get_array(df, "b"))
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(df2, "c"), get_array(df, "c"))
+    tm.assert_frame_equal(df, df_orig)
+
+
+@pytest.mark.parametrize("index", [pd.RangeIndex(0, 2), Index([1, 2])])
+def test_reset_index_series_drop(using_copy_on_write, index):
+    ser = Series([1, 2], index=index)
+    ser_orig = ser.copy()
+    ser2 = ser.reset_index(drop=True)
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(ser), get_array(ser2))
+        assert not ser._mgr._has_no_reference(0)
+    else:
+        assert not np.shares_memory(get_array(ser), get_array(ser2))
+
+    ser2.iloc[0] = 100
+    tm.assert_series_equal(ser, ser_orig)
+
+
+def test_groupby_column_index_in_references():
+    df = DataFrame(
+        {"A": ["a", "b", "c", "d"], "B": [1, 2, 3, 4], "C": ["a", "a", "b", "b"]}
+    )
+    df = df.set_index("A")
+    key = df["C"]
+    result = df.groupby(key, observed=True).sum()
+    expected = df.groupby("C", observed=True).sum()
+    tm.assert_frame_equal(result, expected)
+
+
+def test_rename_columns(using_copy_on_write):
+    # Case: renaming columns returns a new dataframe
+    # + afterwards modifying the result
+    df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [0.1, 0.2, 0.3]})
+    df_orig = df.copy()
+    df2 = df.rename(columns=str.upper)
+
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(df2, "A"), get_array(df, "a"))
+    df2.iloc[0, 0] = 0
+    assert not np.shares_memory(get_array(df2, "A"), get_array(df, "a"))
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(df2, "C"), get_array(df, "c"))
+    expected = DataFrame({"A": [0, 2, 3], "B": [4, 5, 6], "C": [0.1, 0.2, 0.3]})
+    tm.assert_frame_equal(df2, expected)
+    tm.assert_frame_equal(df, df_orig)
+
+
+def test_rename_columns_modify_parent(using_copy_on_write):
+    # Case: renaming columns returns a new dataframe
+    # + afterwards modifying the original (parent) dataframe
+    df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [0.1, 0.2, 0.3]})
+    df2 = df.rename(columns=str.upper)
+    df2_orig = df2.copy()
+
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(df2, "A"), get_array(df, "a"))
+    else:
+        assert not np.shares_memory(get_array(df2, "A"), get_array(df, "a"))
+    df.iloc[0, 0] = 0
+    assert not np.shares_memory(get_array(df2, "A"), get_array(df, "a"))
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(df2, "C"), get_array(df, "c"))
+    expected = DataFrame({"a": [0, 2, 3], "b": [4, 5, 6], "c": [0.1, 0.2, 0.3]})
+    tm.assert_frame_equal(df, expected)
+    tm.assert_frame_equal(df2, df2_orig)
+
+
+def test_pipe(using_copy_on_write):
+    df = DataFrame({"a": [1, 2, 3], "b": 1.5})
+    df_orig = df.copy()
+
+    def testfunc(df):
+        return df
+
+    df2 = df.pipe(testfunc)
+
+    assert np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+
+    # mutating df2 triggers a copy-on-write for that column
+    df2.iloc[0, 0] = 0
+    if using_copy_on_write:
+        tm.assert_frame_equal(df, df_orig)
+        assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+    else:
+        expected = DataFrame({"a": [0, 2, 3], "b": 1.5})
+        tm.assert_frame_equal(df, expected)
+
+        assert np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+    assert np.shares_memory(get_array(df2, "b"), get_array(df, "b"))
+
+
+def test_pipe_modify_df(using_copy_on_write):
+    df = DataFrame({"a": [1, 2, 3], "b": 1.5})
+    df_orig = df.copy()
+
+    def testfunc(df):
+        df.iloc[0, 0] = 100
+        return df
+
+    df2 = df.pipe(testfunc)
+
+    assert np.shares_memory(get_array(df2, "b"), get_array(df, "b"))
+
+    if using_copy_on_write:
+        tm.assert_frame_equal(df, df_orig)
+        assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+    else:
+        expected = DataFrame({"a": [100, 2, 3], "b": 1.5})
+        tm.assert_frame_equal(df, expected)
+
+        assert np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+    assert np.shares_memory(get_array(df2, "b"), get_array(df, "b"))
+
+
+def test_reindex_columns(using_copy_on_write):
+    # Case: reindexing the column returns a new dataframe
+    # + afterwards modifying the result
+    df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [0.1, 0.2, 0.3]})
+    df_orig = df.copy()
+    df2 = df.reindex(columns=["a", "c"])
+
+    if using_copy_on_write:
+        # still shares memory (df2 is a shallow copy)
+        assert np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+    else:
+        assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+    # mutating df2 triggers a copy-on-write for that column
+    df2.iloc[0, 0] = 0
+    assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(df2, "c"), get_array(df, "c"))
+    tm.assert_frame_equal(df, df_orig)
+
+
+@pytest.mark.parametrize(
+    "index",
+    [
+        lambda idx: idx,
+        lambda idx: idx.view(),
+        lambda idx: idx.copy(),
+        lambda idx: list(idx),
+    ],
+    ids=["identical", "view", "copy", "values"],
+)
+def test_reindex_rows(index, using_copy_on_write):
+    # Case: reindexing the rows with an index that matches the current index
+    # can use a shallow copy
+    df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [0.1, 0.2, 0.3]})
+    df_orig = df.copy()
+    df2 = df.reindex(index=index(df.index))
+
+    if using_copy_on_write:
+        # still shares memory (df2 is a shallow copy)
+        assert np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+    else:
+        assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+    # mutating df2 triggers a copy-on-write for that column
+    df2.iloc[0, 0] = 0
+    assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(df2, "c"), get_array(df, "c"))
+    tm.assert_frame_equal(df, df_orig)
+
+
+def test_drop_on_column(using_copy_on_write):
+    df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [0.1, 0.2, 0.3]})
+    df_orig = df.copy()
+    df2 = df.drop(columns="a")
+    df2._mgr._verify_integrity()
+
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(df2, "b"), get_array(df, "b"))
+        assert np.shares_memory(get_array(df2, "c"), get_array(df, "c"))
+    else:
+        assert not np.shares_memory(get_array(df2, "b"), get_array(df, "b"))
+        assert not np.shares_memory(get_array(df2, "c"), get_array(df, "c"))
+    df2.iloc[0, 0] = 0
+    assert not np.shares_memory(get_array(df2, "b"), get_array(df, "b"))
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(df2, "c"), get_array(df, "c"))
+    tm.assert_frame_equal(df, df_orig)
+
+
+def test_select_dtypes(using_copy_on_write):
+    # Case: selecting columns using `select_dtypes()` returns a new dataframe
+    # + afterwards modifying the result
+    df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [0.1, 0.2, 0.3]})
+    df_orig = df.copy()
+    df2 = df.select_dtypes("int64")
+    df2._mgr._verify_integrity()
+
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+    else:
+        assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+
+    # mutating df2 triggers a copy-on-write for that column/block
+    df2.iloc[0, 0] = 0
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+    tm.assert_frame_equal(df, df_orig)
+
+
+@pytest.mark.parametrize(
+    "filter_kwargs", [{"items": ["a"]}, {"like": "a"}, {"regex": "a"}]
+)
+def test_filter(using_copy_on_write, filter_kwargs):
+    # Case: selecting columns using `filter()` returns a new dataframe
+    # + afterwards modifying the result
+    df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [0.1, 0.2, 0.3]})
+    df_orig = df.copy()
+    df2 = df.filter(**filter_kwargs)
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+    else:
+        assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+
+    # mutating df2 triggers a copy-on-write for that column/block
+    if using_copy_on_write:
+        df2.iloc[0, 0] = 0
+        assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+    tm.assert_frame_equal(df, df_orig)
+
+
+def test_shift_no_op(using_copy_on_write):
+    df = DataFrame(
+        [[1, 2], [3, 4], [5, 6]],
+        index=date_range("2020-01-01", "2020-01-03"),
+        columns=["a", "b"],
+    )
+    df_orig = df.copy()
+    df2 = df.shift(periods=0)
+
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+    else:
+        assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+
+    df.iloc[0, 0] = 0
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(df, "a"), get_array(df2, "a"))
+        assert np.shares_memory(get_array(df, "b"), get_array(df2, "b"))
+    tm.assert_frame_equal(df2, df_orig)
+
+
+def test_shift_index(using_copy_on_write):
+    df = DataFrame(
+        [[1, 2], [3, 4], [5, 6]],
+        index=date_range("2020-01-01", "2020-01-03"),
+        columns=["a", "b"],
+    )
+    df2 = df.shift(periods=1, axis=0)
+
+    assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+
+
+def test_shift_rows_freq(using_copy_on_write):
+    df = DataFrame(
+        [[1, 2], [3, 4], [5, 6]],
+        index=date_range("2020-01-01", "2020-01-03"),
+        columns=["a", "b"],
+    )
+    df_orig = df.copy()
+    df_orig.index = date_range("2020-01-02", "2020-01-04")
+    df2 = df.shift(periods=1, freq="1D")
+
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+    else:
+        assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+
+    df.iloc[0, 0] = 0
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(df, "a"), get_array(df2, "a"))
+    tm.assert_frame_equal(df2, df_orig)
+
+
+def test_shift_columns(using_copy_on_write, warn_copy_on_write):
+    df = DataFrame(
+        [[1, 2], [3, 4], [5, 6]], columns=date_range("2020-01-01", "2020-01-02")
+    )
+    df2 = df.shift(periods=1, axis=1)
+
+    assert np.shares_memory(get_array(df2, "2020-01-02"), get_array(df, "2020-01-01"))
+    with tm.assert_cow_warning(warn_copy_on_write):
+        df.iloc[0, 0] = 0
+    if using_copy_on_write:
+        assert not np.shares_memory(
+            get_array(df2, "2020-01-02"), get_array(df, "2020-01-01")
+        )
+        expected = DataFrame(
+            [[np.nan, 1], [np.nan, 3], [np.nan, 5]],
+            columns=date_range("2020-01-01", "2020-01-02"),
+        )
+        tm.assert_frame_equal(df2, expected)
+
+
+def test_pop(using_copy_on_write, warn_copy_on_write):
+    df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [0.1, 0.2, 0.3]})
+    df_orig = df.copy()
+    view_original = df[:]
+    result = df.pop("a")
+
+    assert np.shares_memory(result.values, get_array(view_original, "a"))
+    assert np.shares_memory(get_array(df, "b"), get_array(view_original, "b"))
+
+    if using_copy_on_write:
+        result.iloc[0] = 0
+        assert not np.shares_memory(result.values, get_array(view_original, "a"))
+    with tm.assert_cow_warning(warn_copy_on_write):
+        df.iloc[0, 0] = 0
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(df, "b"), get_array(view_original, "b"))
+        tm.assert_frame_equal(view_original, df_orig)
+    else:
+        expected = DataFrame({"a": [1, 2, 3], "b": [0, 5, 6], "c": [0.1, 0.2, 0.3]})
+        tm.assert_frame_equal(view_original, expected)
+
+
+@pytest.mark.parametrize(
+    "func",
+    [
+        lambda x, y: x.align(y),
+        lambda x, y: x.align(y.a, axis=0),
+        lambda x, y: x.align(y.a.iloc[slice(0, 1)], axis=1),
+    ],
+)
+def test_align_frame(using_copy_on_write, func):
+    df = DataFrame({"a": [1, 2, 3], "b": "a"})
+    df_orig = df.copy()
+    df_changed = df[["b", "a"]].copy()
+    df2, _ = func(df, df_changed)
+
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+    else:
+        assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+
+    df2.iloc[0, 0] = 0
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+    tm.assert_frame_equal(df, df_orig)
+
+
+def test_align_series(using_copy_on_write):
+    ser = Series([1, 2])
+    ser_orig = ser.copy()
+    ser_other = ser.copy()
+    ser2, ser_other_result = ser.align(ser_other)
+
+    if using_copy_on_write:
+        assert np.shares_memory(ser2.values, ser.values)
+        assert np.shares_memory(ser_other_result.values, ser_other.values)
+    else:
+        assert not np.shares_memory(ser2.values, ser.values)
+        assert not np.shares_memory(ser_other_result.values, ser_other.values)
+
+    ser2.iloc[0] = 0
+    ser_other_result.iloc[0] = 0
+    if using_copy_on_write:
+        assert not np.shares_memory(ser2.values, ser.values)
+        assert not np.shares_memory(ser_other_result.values, ser_other.values)
+    tm.assert_series_equal(ser, ser_orig)
+    tm.assert_series_equal(ser_other, ser_orig)
+
+
+def test_align_copy_false(using_copy_on_write):
+    df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
+    df_orig = df.copy()
+    df2, df3 = df.align(df, copy=False)
+
+    assert np.shares_memory(get_array(df, "b"), get_array(df2, "b"))
+    assert np.shares_memory(get_array(df, "a"), get_array(df2, "a"))
+
+    if using_copy_on_write:
+        df2.loc[0, "a"] = 0
+        tm.assert_frame_equal(df, df_orig)  # Original is unchanged
+
+        df3.loc[0, "a"] = 0
+        tm.assert_frame_equal(df, df_orig)  # Original is unchanged
+
+
+def test_align_with_series_copy_false(using_copy_on_write):
+    df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
+    ser = Series([1, 2, 3], name="x")
+    ser_orig = ser.copy()
+    df_orig = df.copy()
+    df2, ser2 = df.align(ser, copy=False, axis=0)
+
+    assert np.shares_memory(get_array(df, "b"), get_array(df2, "b"))
+    assert np.shares_memory(get_array(df, "a"), get_array(df2, "a"))
+    assert np.shares_memory(get_array(ser, "x"), get_array(ser2, "x"))
+
+    if using_copy_on_write:
+        df2.loc[0, "a"] = 0
+        tm.assert_frame_equal(df, df_orig)  # Original is unchanged
+
+        ser2.loc[0] = 0
+        tm.assert_series_equal(ser, ser_orig)  # Original is unchanged
+
+
+def test_to_frame(using_copy_on_write, warn_copy_on_write):
+    # Case: converting a Series to a DataFrame with to_frame
+    ser = Series([1, 2, 3])
+    ser_orig = ser.copy()
+
+    df = ser[:].to_frame()
+
+    # currently this always returns a "view"
+    assert np.shares_memory(ser.values, get_array(df, 0))
+
+    with tm.assert_cow_warning(warn_copy_on_write):
+        df.iloc[0, 0] = 0
+
+    if using_copy_on_write:
+        # mutating df triggers a copy-on-write for that column
+        assert not np.shares_memory(ser.values, get_array(df, 0))
+        tm.assert_series_equal(ser, ser_orig)
+    else:
+        # but currently select_dtypes() actually returns a view -> mutates parent
+        expected = ser_orig.copy()
+        expected.iloc[0] = 0
+        tm.assert_series_equal(ser, expected)
+
+    # modify original series -> don't modify dataframe
+    df = ser[:].to_frame()
+    with tm.assert_cow_warning(warn_copy_on_write):
+        ser.iloc[0] = 0
+
+    if using_copy_on_write:
+        tm.assert_frame_equal(df, ser_orig.to_frame())
+    else:
+        expected = ser_orig.copy().to_frame()
+        expected.iloc[0, 0] = 0
+        tm.assert_frame_equal(df, expected)
+
+
+@pytest.mark.parametrize("ax", ["index", "columns"])
+def test_swapaxes_noop(using_copy_on_write, ax):
+    df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
+    df_orig = df.copy()
+    msg = "'DataFrame.swapaxes' is deprecated"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        df2 = df.swapaxes(ax, ax)
+
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+    else:
+        assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+
+    # mutating df2 triggers a copy-on-write for that column/block
+    df2.iloc[0, 0] = 0
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+    tm.assert_frame_equal(df, df_orig)
+
+
+def test_swapaxes_single_block(using_copy_on_write):
+    df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}, index=["x", "y", "z"])
+    df_orig = df.copy()
+    msg = "'DataFrame.swapaxes' is deprecated"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        df2 = df.swapaxes("index", "columns")
+
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(df2, "x"), get_array(df, "a"))
+    else:
+        assert not np.shares_memory(get_array(df2, "x"), get_array(df, "a"))
+
+    # mutating df2 triggers a copy-on-write for that column/block
+    df2.iloc[0, 0] = 0
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(df2, "x"), get_array(df, "a"))
+    tm.assert_frame_equal(df, df_orig)
+
+
+def test_swapaxes_read_only_array():
+    df = DataFrame({"a": [1, 2], "b": 3})
+    msg = "'DataFrame.swapaxes' is deprecated"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        df = df.swapaxes(axis1="index", axis2="columns")
+    df.iloc[0, 0] = 100
+    expected = DataFrame({0: [100, 3], 1: [2, 3]}, index=["a", "b"])
+    tm.assert_frame_equal(df, expected)
+
+
+@pytest.mark.parametrize(
+    "method, idx",
+    [
+        (lambda df: df.copy(deep=False).copy(deep=False), 0),
+        (lambda df: df.reset_index().reset_index(), 2),
+        (lambda df: df.rename(columns=str.upper).rename(columns=str.lower), 0),
+        (lambda df: df.copy(deep=False).select_dtypes(include="number"), 0),
+    ],
+    ids=["shallow-copy", "reset_index", "rename", "select_dtypes"],
+)
+def test_chained_methods(request, method, idx, using_copy_on_write, warn_copy_on_write):
+    df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [0.1, 0.2, 0.3]})
+    df_orig = df.copy()
+
+    # when not using CoW, only the copy() variant actually gives a view
+    df2_is_view = not using_copy_on_write and request.node.callspec.id == "shallow-copy"
+
+    # modify df2 -> don't modify df
+    df2 = method(df)
+    with tm.assert_cow_warning(warn_copy_on_write and df2_is_view):
+        df2.iloc[0, idx] = 0
+    if not df2_is_view:
+        tm.assert_frame_equal(df, df_orig)
+
+    # modify df -> don't modify df2
+    df2 = method(df)
+    with tm.assert_cow_warning(warn_copy_on_write and df2_is_view):
+        df.iloc[0, 0] = 0
+    if not df2_is_view:
+        tm.assert_frame_equal(df2.iloc[:, idx:], df_orig)
+
+
+@pytest.mark.parametrize("obj", [Series([1, 2], name="a"), DataFrame({"a": [1, 2]})])
+def test_to_timestamp(using_copy_on_write, obj):
+    obj.index = Index([Period("2012-1-1", freq="D"), Period("2012-1-2", freq="D")])
+
+    obj_orig = obj.copy()
+    obj2 = obj.to_timestamp()
+
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(obj2, "a"), get_array(obj, "a"))
+    else:
+        assert not np.shares_memory(get_array(obj2, "a"), get_array(obj, "a"))
+
+    # mutating obj2 triggers a copy-on-write for that column / block
+    obj2.iloc[0] = 0
+    assert not np.shares_memory(get_array(obj2, "a"), get_array(obj, "a"))
+    tm.assert_equal(obj, obj_orig)
+
+
+@pytest.mark.parametrize("obj", [Series([1, 2], name="a"), DataFrame({"a": [1, 2]})])
+def test_to_period(using_copy_on_write, obj):
+    obj.index = Index([Timestamp("2019-12-31"), Timestamp("2020-12-31")])
+
+    obj_orig = obj.copy()
+    obj2 = obj.to_period(freq="Y")
+
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(obj2, "a"), get_array(obj, "a"))
+    else:
+        assert not np.shares_memory(get_array(obj2, "a"), get_array(obj, "a"))
+
+    # mutating obj2 triggers a copy-on-write for that column / block
+    obj2.iloc[0] = 0
+    assert not np.shares_memory(get_array(obj2, "a"), get_array(obj, "a"))
+    tm.assert_equal(obj, obj_orig)
+
+
+def test_set_index(using_copy_on_write):
+    # GH 49473
+    df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [0.1, 0.2, 0.3]})
+    df_orig = df.copy()
+    df2 = df.set_index("a")
+
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(df2, "b"), get_array(df, "b"))
+    else:
+        assert not np.shares_memory(get_array(df2, "b"), get_array(df, "b"))
+
+    # mutating df2 triggers a copy-on-write for that column / block
+    df2.iloc[0, 1] = 0
+    assert not np.shares_memory(get_array(df2, "c"), get_array(df, "c"))
+    tm.assert_frame_equal(df, df_orig)
+
+
+def test_set_index_mutating_parent_does_not_mutate_index():
+    df = DataFrame({"a": [1, 2, 3], "b": 1})
+    result = df.set_index("a")
+    expected = result.copy()
+
+    df.iloc[0, 0] = 100
+    tm.assert_frame_equal(result, expected)
+
+
+def test_add_prefix(using_copy_on_write):
+    # GH 49473
+    df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [0.1, 0.2, 0.3]})
+    df_orig = df.copy()
+    df2 = df.add_prefix("CoW_")
+
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(df2, "CoW_a"), get_array(df, "a"))
+    df2.iloc[0, 0] = 0
+
+    assert not np.shares_memory(get_array(df2, "CoW_a"), get_array(df, "a"))
+
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(df2, "CoW_c"), get_array(df, "c"))
+    expected = DataFrame(
+        {"CoW_a": [0, 2, 3], "CoW_b": [4, 5, 6], "CoW_c": [0.1, 0.2, 0.3]}
+    )
+    tm.assert_frame_equal(df2, expected)
+    tm.assert_frame_equal(df, df_orig)
+
+
+def test_add_suffix(using_copy_on_write):
+    # GH 49473
+    df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [0.1, 0.2, 0.3]})
+    df_orig = df.copy()
+    df2 = df.add_suffix("_CoW")
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(df2, "a_CoW"), get_array(df, "a"))
+    df2.iloc[0, 0] = 0
+    assert not np.shares_memory(get_array(df2, "a_CoW"), get_array(df, "a"))
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(df2, "c_CoW"), get_array(df, "c"))
+    expected = DataFrame(
+        {"a_CoW": [0, 2, 3], "b_CoW": [4, 5, 6], "c_CoW": [0.1, 0.2, 0.3]}
+    )
+    tm.assert_frame_equal(df2, expected)
+    tm.assert_frame_equal(df, df_orig)
+
+
+@pytest.mark.parametrize("axis, val", [(0, 5.5), (1, np.nan)])
+def test_dropna(using_copy_on_write, axis, val):
+    df = DataFrame({"a": [1, 2, 3], "b": [4, val, 6], "c": "d"})
+    df_orig = df.copy()
+    df2 = df.dropna(axis=axis)
+
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+    else:
+        assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+
+    df2.iloc[0, 0] = 0
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+    tm.assert_frame_equal(df, df_orig)
+
+
+@pytest.mark.parametrize("val", [5, 5.5])
+def test_dropna_series(using_copy_on_write, val):
+    ser = Series([1, val, 4])
+    ser_orig = ser.copy()
+    ser2 = ser.dropna()
+
+    if using_copy_on_write:
+        assert np.shares_memory(ser2.values, ser.values)
+    else:
+        assert not np.shares_memory(ser2.values, ser.values)
+
+    ser2.iloc[0] = 0
+    if using_copy_on_write:
+        assert not np.shares_memory(ser2.values, ser.values)
+    tm.assert_series_equal(ser, ser_orig)
+
+
+@pytest.mark.parametrize(
+    "method",
+    [
+        lambda df: df.head(),
+        lambda df: df.head(2),
+        lambda df: df.tail(),
+        lambda df: df.tail(3),
+    ],
+)
+def test_head_tail(method, using_copy_on_write, warn_copy_on_write):
+    df = DataFrame({"a": [1, 2, 3], "b": [0.1, 0.2, 0.3]})
+    df_orig = df.copy()
+    df2 = method(df)
+    df2._mgr._verify_integrity()
+
+    if using_copy_on_write:
+        # We are explicitly deviating for CoW here to make an eager copy (avoids
+        # tracking references for very cheap ops)
+        assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+        assert not np.shares_memory(get_array(df2, "b"), get_array(df, "b"))
+
+    # modify df2 to trigger CoW for that block
+    with tm.assert_cow_warning(warn_copy_on_write):
+        df2.iloc[0, 0] = 0
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(df2, "b"), get_array(df, "b"))
+        assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+    else:
+        # without CoW enabled, head and tail return views. Mutating df2 also mutates df.
+        assert np.shares_memory(get_array(df2, "b"), get_array(df, "b"))
+        with tm.assert_cow_warning(warn_copy_on_write):
+            df2.iloc[0, 0] = 1
+    tm.assert_frame_equal(df, df_orig)
+
+
+def test_infer_objects(using_copy_on_write, using_infer_string):
+    df = DataFrame(
+        {"a": [1, 2], "b": Series(["x", "y"], dtype=object), "c": 1, "d": "x"}
+    )
+    df_orig = df.copy()
+    df2 = df.infer_objects()
+
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+        if using_infer_string:
+            assert not tm.shares_memory(get_array(df2, "b"), get_array(df, "b"))
+        else:
+            assert np.shares_memory(get_array(df2, "b"), get_array(df, "b"))
+
+    else:
+        assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+        assert not np.shares_memory(get_array(df2, "b"), get_array(df, "b"))
+
+    df2.iloc[0, 0] = 0
+    df2.iloc[0, 1] = "d"
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+        assert not np.shares_memory(get_array(df2, "b"), get_array(df, "b"))
+    tm.assert_frame_equal(df, df_orig)
+
+
+def test_infer_objects_no_reference(using_copy_on_write, using_infer_string):
+    df = DataFrame(
+        {
+            "a": [1, 2],
+            "b": Series(["x", "y"], dtype=object),
+            "c": 1,
+            "d": Series(
+                [Timestamp("2019-12-31"), Timestamp("2020-12-31")], dtype="object"
+            ),
+            "e": Series(["z", "w"], dtype=object),
+        }
+    )
+    df = df.infer_objects()
+
+    arr_a = get_array(df, "a")
+    arr_b = get_array(df, "b")
+    arr_d = get_array(df, "d")
+
+    df.iloc[0, 0] = 0
+    df.iloc[0, 1] = "d"
+    df.iloc[0, 3] = Timestamp("2018-12-31")
+    if using_copy_on_write:
+        assert np.shares_memory(arr_a, get_array(df, "a"))
+        if using_infer_string:
+            # note that the underlying memory of arr_b has been copied anyway
+            # because of the assignment, but the EA is updated inplace so still
+            # appears the share memory
+            assert tm.shares_memory(arr_b, get_array(df, "b"))
+        else:
+            # TODO(CoW): Block splitting causes references here
+            assert not np.shares_memory(arr_b, get_array(df, "b"))
+        assert np.shares_memory(arr_d, get_array(df, "d"))
+
+
+def test_infer_objects_reference(using_copy_on_write, using_infer_string):
+    df = DataFrame(
+        {
+            "a": [1, 2],
+            "b": Series(["x", "y"], dtype=object),
+            "c": 1,
+            "d": Series(
+                [Timestamp("2019-12-31"), Timestamp("2020-12-31")], dtype="object"
+            ),
+        }
+    )
+    view = df[:]  # noqa: F841
+    df = df.infer_objects()
+
+    arr_a = get_array(df, "a")
+    arr_b = get_array(df, "b")
+    arr_d = get_array(df, "d")
+
+    df.iloc[0, 0] = 0
+    df.iloc[0, 1] = "d"
+    df.iloc[0, 3] = Timestamp("2018-12-31")
+    if using_copy_on_write:
+        assert not np.shares_memory(arr_a, get_array(df, "a"))
+        if not using_infer_string or HAS_PYARROW:
+            assert not np.shares_memory(arr_b, get_array(df, "b"))
+        assert np.shares_memory(arr_d, get_array(df, "d"))
+
+
+@pytest.mark.parametrize(
+    "kwargs",
+    [
+        {"before": "a", "after": "b", "axis": 1},
+        {"before": 0, "after": 1, "axis": 0},
+    ],
+)
+def test_truncate(using_copy_on_write, kwargs):
+    df = DataFrame({"a": [1, 2, 3], "b": 1, "c": 2})
+    df_orig = df.copy()
+    df2 = df.truncate(**kwargs)
+    df2._mgr._verify_integrity()
+
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+    else:
+        assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+
+    df2.iloc[0, 0] = 0
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+    tm.assert_frame_equal(df, df_orig)
+
+
+@pytest.mark.parametrize("method", ["assign", "drop_duplicates"])
+def test_assign_drop_duplicates(using_copy_on_write, method):
+    df = DataFrame({"a": [1, 2, 3]})
+    df_orig = df.copy()
+    df2 = getattr(df, method)()
+    df2._mgr._verify_integrity()
+
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+    else:
+        assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+
+    df2.iloc[0, 0] = 0
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+    tm.assert_frame_equal(df, df_orig)
+
+
+@pytest.mark.parametrize("obj", [Series([1, 2]), DataFrame({"a": [1, 2]})])
+def test_take(using_copy_on_write, obj):
+    # Check that no copy is made when we take all rows in original order
+    obj_orig = obj.copy()
+    obj2 = obj.take([0, 1])
+
+    if using_copy_on_write:
+        assert np.shares_memory(obj2.values, obj.values)
+    else:
+        assert not np.shares_memory(obj2.values, obj.values)
+
+    obj2.iloc[0] = 0
+    if using_copy_on_write:
+        assert not np.shares_memory(obj2.values, obj.values)
+    tm.assert_equal(obj, obj_orig)
+
+
+@pytest.mark.parametrize("obj", [Series([1, 2]), DataFrame({"a": [1, 2]})])
+def test_between_time(using_copy_on_write, obj):
+    obj.index = date_range("2018-04-09", periods=2, freq="1D20min")
+    obj_orig = obj.copy()
+    obj2 = obj.between_time("0:00", "1:00")
+
+    if using_copy_on_write:
+        assert np.shares_memory(obj2.values, obj.values)
+    else:
+        assert not np.shares_memory(obj2.values, obj.values)
+
+    obj2.iloc[0] = 0
+    if using_copy_on_write:
+        assert not np.shares_memory(obj2.values, obj.values)
+    tm.assert_equal(obj, obj_orig)
+
+
+def test_reindex_like(using_copy_on_write):
+    df = DataFrame({"a": [1, 2], "b": "a"})
+    other = DataFrame({"b": "a", "a": [1, 2]})
+
+    df_orig = df.copy()
+    df2 = df.reindex_like(other)
+
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+    else:
+        assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+
+    df2.iloc[0, 1] = 0
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+    tm.assert_frame_equal(df, df_orig)
+
+
+def test_sort_index(using_copy_on_write):
+    # GH 49473
+    ser = Series([1, 2, 3])
+    ser_orig = ser.copy()
+    ser2 = ser.sort_index()
+
+    if using_copy_on_write:
+        assert np.shares_memory(ser.values, ser2.values)
+    else:
+        assert not np.shares_memory(ser.values, ser2.values)
+
+    # mutating ser triggers a copy-on-write for the column / block
+    ser2.iloc[0] = 0
+    assert not np.shares_memory(ser2.values, ser.values)
+    tm.assert_series_equal(ser, ser_orig)
+
+
+@pytest.mark.parametrize(
+    "obj, kwargs",
+    [(Series([1, 2, 3], name="a"), {}), (DataFrame({"a": [1, 2, 3]}), {"by": "a"})],
+)
+def test_sort_values(using_copy_on_write, obj, kwargs):
+    obj_orig = obj.copy()
+    obj2 = obj.sort_values(**kwargs)
+
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(obj2, "a"), get_array(obj, "a"))
+    else:
+        assert not np.shares_memory(get_array(obj2, "a"), get_array(obj, "a"))
+
+    # mutating df triggers a copy-on-write for the column / block
+    obj2.iloc[0] = 0
+    assert not np.shares_memory(get_array(obj2, "a"), get_array(obj, "a"))
+    tm.assert_equal(obj, obj_orig)
+
+
+@pytest.mark.parametrize(
+    "obj, kwargs",
+    [(Series([1, 2, 3], name="a"), {}), (DataFrame({"a": [1, 2, 3]}), {"by": "a"})],
+)
+def test_sort_values_inplace(using_copy_on_write, obj, kwargs, warn_copy_on_write):
+    obj_orig = obj.copy()
+    view = obj[:]
+    obj.sort_values(inplace=True, **kwargs)
+
+    assert np.shares_memory(get_array(obj, "a"), get_array(view, "a"))
+
+    # mutating obj triggers a copy-on-write for the column / block
+    with tm.assert_cow_warning(warn_copy_on_write):
+        obj.iloc[0] = 0
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(obj, "a"), get_array(view, "a"))
+        tm.assert_equal(view, obj_orig)
+    else:
+        assert np.shares_memory(get_array(obj, "a"), get_array(view, "a"))
+
+
+@pytest.mark.parametrize("decimals", [-1, 0, 1])
+def test_round(using_copy_on_write, warn_copy_on_write, decimals):
+    df = DataFrame({"a": [1, 2], "b": "c"})
+    df_orig = df.copy()
+    df2 = df.round(decimals=decimals)
+
+    if using_copy_on_write:
+        assert tm.shares_memory(get_array(df2, "b"), get_array(df, "b"))
+        # TODO: Make inplace by using out parameter of ndarray.round?
+        if decimals >= 0 and Version(np.__version__) < Version("2.4.0.dev0"):
+            # Ensure lazy copy if no-op
+            # TODO: Cannot rely on Numpy returning view after version 2.3
+            assert np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+        else:
+            assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+    else:
+        assert not np.shares_memory(get_array(df2, "b"), get_array(df, "b"))
+        assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+
+    df2.iloc[0, 1] = "d"
+    df2.iloc[0, 0] = 4
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(df2, "b"), get_array(df, "b"))
+        assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+    tm.assert_frame_equal(df, df_orig)
+
+
+def test_reorder_levels(using_copy_on_write):
+    index = MultiIndex.from_tuples(
+        [(1, 1), (1, 2), (2, 1), (2, 2)], names=["one", "two"]
+    )
+    df = DataFrame({"a": [1, 2, 3, 4]}, index=index)
+    df_orig = df.copy()
+    df2 = df.reorder_levels(order=["two", "one"])
+
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+    else:
+        assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+
+    df2.iloc[0, 0] = 0
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+    tm.assert_frame_equal(df, df_orig)
+
+
+def test_series_reorder_levels(using_copy_on_write):
+    index = MultiIndex.from_tuples(
+        [(1, 1), (1, 2), (2, 1), (2, 2)], names=["one", "two"]
+    )
+    ser = Series([1, 2, 3, 4], index=index)
+    ser_orig = ser.copy()
+    ser2 = ser.reorder_levels(order=["two", "one"])
+
+    if using_copy_on_write:
+        assert np.shares_memory(ser2.values, ser.values)
+    else:
+        assert not np.shares_memory(ser2.values, ser.values)
+
+    ser2.iloc[0] = 0
+    if using_copy_on_write:
+        assert not np.shares_memory(ser2.values, ser.values)
+    tm.assert_series_equal(ser, ser_orig)
+
+
+@pytest.mark.parametrize("obj", [Series([1, 2, 3]), DataFrame({"a": [1, 2, 3]})])
+def test_swaplevel(using_copy_on_write, obj):
+    index = MultiIndex.from_tuples([(1, 1), (1, 2), (2, 1)], names=["one", "two"])
+    obj.index = index
+    obj_orig = obj.copy()
+    obj2 = obj.swaplevel()
+
+    if using_copy_on_write:
+        assert np.shares_memory(obj2.values, obj.values)
+    else:
+        assert not np.shares_memory(obj2.values, obj.values)
+
+    obj2.iloc[0] = 0
+    if using_copy_on_write:
+        assert not np.shares_memory(obj2.values, obj.values)
+    tm.assert_equal(obj, obj_orig)
+
+
+def test_frame_set_axis(using_copy_on_write):
+    # GH 49473
+    df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [0.1, 0.2, 0.3]})
+    df_orig = df.copy()
+    df2 = df.set_axis(["a", "b", "c"], axis="index")
+
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+    else:
+        assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+
+    # mutating df2 triggers a copy-on-write for that column / block
+    df2.iloc[0, 0] = 0
+    assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+    tm.assert_frame_equal(df, df_orig)
+
+
+def test_series_set_axis(using_copy_on_write):
+    # GH 49473
+    ser = Series([1, 2, 3])
+    ser_orig = ser.copy()
+    ser2 = ser.set_axis(["a", "b", "c"], axis="index")
+
+    if using_copy_on_write:
+        assert np.shares_memory(ser, ser2)
+    else:
+        assert not np.shares_memory(ser, ser2)
+
+    # mutating ser triggers a copy-on-write for the column / block
+    ser2.iloc[0] = 0
+    assert not np.shares_memory(ser2, ser)
+    tm.assert_series_equal(ser, ser_orig)
+
+
+def test_set_flags(using_copy_on_write, warn_copy_on_write):
+    ser = Series([1, 2, 3])
+    ser_orig = ser.copy()
+    ser2 = ser.set_flags(allows_duplicate_labels=False)
+
+    assert np.shares_memory(ser, ser2)
+
+    # mutating ser triggers a copy-on-write for the column / block
+    with tm.assert_cow_warning(warn_copy_on_write):
+        ser2.iloc[0] = 0
+    if using_copy_on_write:
+        assert not np.shares_memory(ser2, ser)
+        tm.assert_series_equal(ser, ser_orig)
+    else:
+        assert np.shares_memory(ser2, ser)
+        expected = Series([0, 2, 3])
+        tm.assert_series_equal(ser, expected)
+
+
+@pytest.mark.parametrize("kwargs", [{"mapper": "test"}, {"index": "test"}])
+def test_rename_axis(using_copy_on_write, kwargs):
+    df = DataFrame({"a": [1, 2, 3, 4]}, index=Index([1, 2, 3, 4], name="a"))
+    df_orig = df.copy()
+    df2 = df.rename_axis(**kwargs)
+
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+    else:
+        assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+
+    df2.iloc[0, 0] = 0
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+    tm.assert_frame_equal(df, df_orig)
+
+
+@pytest.mark.parametrize(
+    "func, tz", [("tz_convert", "Europe/Berlin"), ("tz_localize", None)]
+)
+def test_tz_convert_localize(using_copy_on_write, func, tz):
+    # GH 49473
+    ser = Series(
+        [1, 2], index=date_range(start="2014-08-01 09:00", freq="h", periods=2, tz=tz)
+    )
+    ser_orig = ser.copy()
+    ser2 = getattr(ser, func)("US/Central")
+
+    if using_copy_on_write:
+        assert np.shares_memory(ser.values, ser2.values)
+    else:
+        assert not np.shares_memory(ser.values, ser2.values)
+
+    # mutating ser triggers a copy-on-write for the column / block
+    ser2.iloc[0] = 0
+    assert not np.shares_memory(ser2.values, ser.values)
+    tm.assert_series_equal(ser, ser_orig)
+
+
+def test_droplevel(using_copy_on_write):
+    # GH 49473
+    index = MultiIndex.from_tuples([(1, 1), (1, 2), (2, 1)], names=["one", "two"])
+    df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]}, index=index)
+    df_orig = df.copy()
+    df2 = df.droplevel(0)
+
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(df2, "c"), get_array(df, "c"))
+        assert np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+    else:
+        assert not np.shares_memory(get_array(df2, "c"), get_array(df, "c"))
+        assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+
+    # mutating df2 triggers a copy-on-write for that column / block
+    df2.iloc[0, 0] = 0
+
+    assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(df2, "b"), get_array(df, "b"))
+
+    tm.assert_frame_equal(df, df_orig)
+
+
+def test_squeeze(using_copy_on_write, warn_copy_on_write):
+    df = DataFrame({"a": [1, 2, 3]})
+    df_orig = df.copy()
+    series = df.squeeze()
+
+    # Should share memory regardless of CoW since squeeze is just an iloc
+    assert np.shares_memory(series.values, get_array(df, "a"))
+
+    # mutating squeezed df triggers a copy-on-write for that column/block
+    with tm.assert_cow_warning(warn_copy_on_write):
+        series.iloc[0] = 0
+    if using_copy_on_write:
+        assert not np.shares_memory(series.values, get_array(df, "a"))
+        tm.assert_frame_equal(df, df_orig)
+    else:
+        # Without CoW the original will be modified
+        assert np.shares_memory(series.values, get_array(df, "a"))
+        assert df.loc[0, "a"] == 0
+
+
+def test_items(using_copy_on_write, warn_copy_on_write):
+    df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]})
+    df_orig = df.copy()
+
+    # Test this twice, since the second time, the item cache will be
+    # triggered, and we want to make sure it still works then.
+    for i in range(2):
+        for name, ser in df.items():
+            assert np.shares_memory(get_array(ser, name), get_array(df, name))
+
+            # mutating df triggers a copy-on-write for that column / block
+            with tm.assert_cow_warning(warn_copy_on_write):
+                ser.iloc[0] = 0
+
+            if using_copy_on_write:
+                assert not np.shares_memory(get_array(ser, name), get_array(df, name))
+                tm.assert_frame_equal(df, df_orig)
+            else:
+                # Original frame will be modified
+                assert df.loc[0, name] == 0
+
+
+@pytest.mark.parametrize("dtype", ["int64", "Int64"])
+def test_putmask(using_copy_on_write, dtype, warn_copy_on_write):
+    df = DataFrame({"a": [1, 2], "b": 1, "c": 2}, dtype=dtype)
+    view = df[:]
+    df_orig = df.copy()
+    with tm.assert_cow_warning(warn_copy_on_write):
+        df[df == df] = 5
+
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(view, "a"), get_array(df, "a"))
+        tm.assert_frame_equal(view, df_orig)
+    else:
+        # Without CoW the original will be modified
+        assert np.shares_memory(get_array(view, "a"), get_array(df, "a"))
+        assert view.iloc[0, 0] == 5
+
+
+@pytest.mark.parametrize("dtype", ["int64", "Int64"])
+def test_putmask_no_reference(using_copy_on_write, dtype):
+    df = DataFrame({"a": [1, 2], "b": 1, "c": 2}, dtype=dtype)
+    arr_a = get_array(df, "a")
+    df[df == df] = 5
+
+    if using_copy_on_write:
+        assert np.shares_memory(arr_a, get_array(df, "a"))
+
+
+@pytest.mark.parametrize("dtype", ["float64", "Float64"])
+def test_putmask_aligns_rhs_no_reference(using_copy_on_write, dtype):
+    df = DataFrame({"a": [1.5, 2], "b": 1.5}, dtype=dtype)
+    arr_a = get_array(df, "a")
+    df[df == df] = DataFrame({"a": [5.5, 5]})
+
+    if using_copy_on_write:
+        assert np.shares_memory(arr_a, get_array(df, "a"))
+
+
+@pytest.mark.parametrize(
+    "val, exp, warn", [(5.5, True, FutureWarning), (5, False, None)]
+)
+def test_putmask_dont_copy_some_blocks(
+    using_copy_on_write, val, exp, warn, warn_copy_on_write
+):
+    df = DataFrame({"a": [1, 2], "b": 1, "c": 1.5})
+    view = df[:]
+    df_orig = df.copy()
+    indexer = DataFrame(
+        [[True, False, False], [True, False, False]], columns=list("abc")
+    )
+    if warn_copy_on_write:
+        with tm.assert_cow_warning():
+            df[indexer] = val
+    else:
+        with tm.assert_produces_warning(warn, match="incompatible dtype"):
+            df[indexer] = val
+
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(view, "a"), get_array(df, "a"))
+        # TODO(CoW): Could split blocks to avoid copying the whole block
+        assert np.shares_memory(get_array(view, "b"), get_array(df, "b")) is exp
+        assert np.shares_memory(get_array(view, "c"), get_array(df, "c"))
+        assert df._mgr._has_no_reference(1) is not exp
+        assert not df._mgr._has_no_reference(2)
+        tm.assert_frame_equal(view, df_orig)
+    elif val == 5:
+        # Without CoW the original will be modified, the other case upcasts, e.g. copy
+        assert np.shares_memory(get_array(view, "a"), get_array(df, "a"))
+        assert np.shares_memory(get_array(view, "c"), get_array(df, "c"))
+        assert view.iloc[0, 0] == 5
+
+
+@pytest.mark.parametrize("dtype", ["int64", "Int64"])
+@pytest.mark.parametrize(
+    "func",
+    [
+        lambda ser: ser.where(ser > 0, 10),
+        lambda ser: ser.mask(ser <= 0, 10),
+    ],
+)
+def test_where_mask_noop(using_copy_on_write, dtype, func):
+    ser = Series([1, 2, 3], dtype=dtype)
+    ser_orig = ser.copy()
+
+    result = func(ser)
+
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(ser), get_array(result))
+    else:
+        assert not np.shares_memory(get_array(ser), get_array(result))
+
+    result.iloc[0] = 10
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(ser), get_array(result))
+    tm.assert_series_equal(ser, ser_orig)
+
+
+@pytest.mark.parametrize("dtype", ["int64", "Int64"])
+@pytest.mark.parametrize(
+    "func",
+    [
+        lambda ser: ser.where(ser < 0, 10),
+        lambda ser: ser.mask(ser >= 0, 10),
+    ],
+)
+def test_where_mask(using_copy_on_write, dtype, func):
+    ser = Series([1, 2, 3], dtype=dtype)
+    ser_orig = ser.copy()
+
+    result = func(ser)
+
+    assert not np.shares_memory(get_array(ser), get_array(result))
+    tm.assert_series_equal(ser, ser_orig)
+
+
+@pytest.mark.parametrize("dtype, val", [("int64", 10.5), ("Int64", 10)])
+@pytest.mark.parametrize(
+    "func",
+    [
+        lambda df, val: df.where(df < 0, val),
+        lambda df, val: df.mask(df >= 0, val),
+    ],
+)
+def test_where_mask_noop_on_single_column(using_copy_on_write, dtype, val, func):
+    df = DataFrame({"a": [1, 2, 3], "b": [-4, -5, -6]}, dtype=dtype)
+    df_orig = df.copy()
+
+    result = func(df, val)
+
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(df, "b"), get_array(result, "b"))
+        assert not np.shares_memory(get_array(df, "a"), get_array(result, "a"))
+    else:
+        assert not np.shares_memory(get_array(df, "b"), get_array(result, "b"))
+
+    result.iloc[0, 1] = 10
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(df, "b"), get_array(result, "b"))
+    tm.assert_frame_equal(df, df_orig)
+
+
+@pytest.mark.parametrize("func", ["mask", "where"])
+def test_chained_where_mask(using_copy_on_write, func):
+    df = DataFrame({"a": [1, 4, 2], "b": 1})
+    df_orig = df.copy()
+    if using_copy_on_write:
+        with tm.raises_chained_assignment_error():
+            getattr(df["a"], func)(df["a"] > 2, 5, inplace=True)
+        tm.assert_frame_equal(df, df_orig)
+
+        with tm.raises_chained_assignment_error():
+            getattr(df[["a"]], func)(df["a"] > 2, 5, inplace=True)
+        tm.assert_frame_equal(df, df_orig)
+    else:
+        with tm.assert_produces_warning(
+            FutureWarning if not WARNING_CHECK_DISABLED else None,
+            match="inplace method",
+        ):
+            getattr(df["a"], func)(df["a"] > 2, 5, inplace=True)
+
+        with tm.assert_produces_warning(None):
+            with option_context("mode.chained_assignment", None):
+                getattr(df[["a"]], func)(df["a"] > 2, 5, inplace=True)
+
+        with tm.assert_produces_warning(None):
+            with option_context("mode.chained_assignment", None):
+                getattr(df[df["a"] > 1], func)(df["a"] > 2, 5, inplace=True)
+
+
+def test_asfreq_noop(using_copy_on_write):
+    df = DataFrame(
+        {"a": [0.0, None, 2.0, 3.0]},
+        index=date_range("1/1/2000", periods=4, freq="min"),
+    )
+    df_orig = df.copy()
+    df2 = df.asfreq(freq="min")
+
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+    else:
+        assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+
+    # mutating df2 triggers a copy-on-write for that column / block
+    df2.iloc[0, 0] = 0
+
+    assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+    tm.assert_frame_equal(df, df_orig)
+
+
+def test_iterrows(using_copy_on_write):
+    df = DataFrame({"a": 0, "b": 1}, index=[1, 2, 3])
+    df_orig = df.copy()
+
+    for _, sub in df.iterrows():
+        sub.iloc[0] = 100
+    if using_copy_on_write:
+        tm.assert_frame_equal(df, df_orig)
+
+
+def test_interpolate_creates_copy(using_copy_on_write, warn_copy_on_write):
+    # GH#51126
+    df = DataFrame({"a": [1.5, np.nan, 3]})
+    view = df[:]
+    expected = df.copy()
+
+    with tm.assert_cow_warning(warn_copy_on_write):
+        df.ffill(inplace=True)
+    with tm.assert_cow_warning(warn_copy_on_write):
+        df.iloc[0, 0] = 100.5
+
+    if using_copy_on_write:
+        tm.assert_frame_equal(view, expected)
+    else:
+        expected = DataFrame({"a": [100.5, 1.5, 3]})
+        tm.assert_frame_equal(view, expected)
+
+
+def test_isetitem(using_copy_on_write):
+    df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]})
+    df_orig = df.copy()
+    df2 = df.copy(deep=None)  # Trigger a CoW
+    df2.isetitem(1, np.array([-1, -2, -3]))  # This is inplace
+
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(df, "c"), get_array(df2, "c"))
+        assert np.shares_memory(get_array(df, "a"), get_array(df2, "a"))
+    else:
+        assert not np.shares_memory(get_array(df, "c"), get_array(df2, "c"))
+        assert not np.shares_memory(get_array(df, "a"), get_array(df2, "a"))
+
+    df2.loc[0, "a"] = 0
+    tm.assert_frame_equal(df, df_orig)  # Original is unchanged
+
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(df, "c"), get_array(df2, "c"))
+    else:
+        assert not np.shares_memory(get_array(df, "c"), get_array(df2, "c"))
+
+
+@pytest.mark.parametrize(
+    "dtype", ["int64", "float64"], ids=["single-block", "mixed-block"]
+)
+def test_isetitem_series(using_copy_on_write, dtype):
+    df = DataFrame({"a": [1, 2, 3], "b": np.array([4, 5, 6], dtype=dtype)})
+    ser = Series([7, 8, 9])
+    ser_orig = ser.copy()
+    df.isetitem(0, ser)
+
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(df, "a"), get_array(ser))
+        assert not df._mgr._has_no_reference(0)
+
+    # mutating dataframe doesn't update series
+    df.loc[0, "a"] = 0
+    tm.assert_series_equal(ser, ser_orig)
+
+    # mutating series doesn't update dataframe
+    df = DataFrame({"a": [1, 2, 3], "b": np.array([4, 5, 6], dtype=dtype)})
+    ser = Series([7, 8, 9])
+    df.isetitem(0, ser)
+
+    ser.loc[0] = 0
+    expected = DataFrame({"a": [7, 8, 9], "b": np.array([4, 5, 6], dtype=dtype)})
+    tm.assert_frame_equal(df, expected)
+
+
+def test_isetitem_frame(using_copy_on_write):
+    df = DataFrame({"a": [1, 2, 3], "b": 1, "c": 2})
+    rhs = DataFrame({"a": [4, 5, 6], "b": 2})
+    df.isetitem([0, 1], rhs)
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(df, "a"), get_array(rhs, "a"))
+        assert np.shares_memory(get_array(df, "b"), get_array(rhs, "b"))
+        assert not df._mgr._has_no_reference(0)
+    else:
+        assert not np.shares_memory(get_array(df, "a"), get_array(rhs, "a"))
+        assert not np.shares_memory(get_array(df, "b"), get_array(rhs, "b"))
+    expected = df.copy()
+    rhs.iloc[0, 0] = 100
+    rhs.iloc[0, 1] = 100
+    tm.assert_frame_equal(df, expected)
+
+
+@pytest.mark.parametrize("key", ["a", ["a"]])
+def test_get(using_copy_on_write, warn_copy_on_write, key):
+    df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
+    df_orig = df.copy()
+
+    result = df.get(key)
+
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(result, "a"), get_array(df, "a"))
+        result.iloc[0] = 0
+        assert not np.shares_memory(get_array(result, "a"), get_array(df, "a"))
+        tm.assert_frame_equal(df, df_orig)
+    else:
+        # for non-CoW it depends on whether we got a Series or DataFrame if it
+        # is a view or copy or triggers a warning or not
+        if warn_copy_on_write:
+            warn = FutureWarning if isinstance(key, str) else None
+        else:
+            warn = SettingWithCopyWarning if isinstance(key, list) else None
+        with option_context("chained_assignment", "warn"):
+            with tm.assert_produces_warning(warn):
+                result.iloc[0] = 0
+
+        if isinstance(key, list):
+            tm.assert_frame_equal(df, df_orig)
+        else:
+            assert df.iloc[0, 0] == 0
+
+
+@pytest.mark.parametrize("axis, key", [(0, 0), (1, "a")])
+@pytest.mark.parametrize(
+    "dtype", ["int64", "float64"], ids=["single-block", "mixed-block"]
+)
+def test_xs(
+    using_copy_on_write, warn_copy_on_write, using_array_manager, axis, key, dtype
+):
+    single_block = (dtype == "int64") and not using_array_manager
+    is_view = single_block or (using_array_manager and axis == 1)
+    df = DataFrame(
+        {"a": [1, 2, 3], "b": [4, 5, 6], "c": np.array([7, 8, 9], dtype=dtype)}
+    )
+    df_orig = df.copy()
+
+    result = df.xs(key, axis=axis)
+
+    if axis == 1 or single_block:
+        assert np.shares_memory(get_array(df, "a"), get_array(result))
+    elif using_copy_on_write:
+        assert result._mgr._has_no_reference(0)
+
+    if using_copy_on_write or (is_view and not warn_copy_on_write):
+        result.iloc[0] = 0
+    elif warn_copy_on_write:
+        with tm.assert_cow_warning(single_block or axis == 1):
+            result.iloc[0] = 0
+    else:
+        with option_context("chained_assignment", "warn"):
+            with tm.assert_produces_warning(SettingWithCopyWarning):
+                result.iloc[0] = 0
+
+    if using_copy_on_write or (not single_block and axis == 0):
+        tm.assert_frame_equal(df, df_orig)
+    else:
+        assert df.iloc[0, 0] == 0
+
+
+@pytest.mark.parametrize("axis", [0, 1])
+@pytest.mark.parametrize("key, level", [("l1", 0), (2, 1)])
+def test_xs_multiindex(
+    using_copy_on_write, warn_copy_on_write, using_array_manager, key, level, axis
+):
+    arr = np.arange(18).reshape(6, 3)
+    index = MultiIndex.from_product([["l1", "l2"], [1, 2, 3]], names=["lev1", "lev2"])
+    df = DataFrame(arr, index=index, columns=list("abc"))
+    if axis == 1:
+        df = df.transpose().copy()
+    df_orig = df.copy()
+
+    result = df.xs(key, level=level, axis=axis)
+
+    if level == 0:
+        assert np.shares_memory(
+            get_array(df, df.columns[0]), get_array(result, result.columns[0])
+        )
+
+    if warn_copy_on_write:
+        warn = FutureWarning if level == 0 else None
+    elif not using_copy_on_write and not using_array_manager:
+        warn = SettingWithCopyWarning
+    else:
+        warn = None
+    with option_context("chained_assignment", "warn"):
+        with tm.assert_produces_warning(warn):
+            result.iloc[0, 0] = 0
+
+    tm.assert_frame_equal(df, df_orig)
+
+
+def test_update_frame(using_copy_on_write, warn_copy_on_write):
+    df1 = DataFrame({"a": [1.0, 2.0, 3.0], "b": [4.0, 5.0, 6.0]})
+    df2 = DataFrame({"b": [100.0]}, index=[1])
+    df1_orig = df1.copy()
+    view = df1[:]
+
+    # TODO(CoW) better warning message?
+    with tm.assert_cow_warning(warn_copy_on_write):
+        df1.update(df2)
+
+    expected = DataFrame({"a": [1.0, 2.0, 3.0], "b": [4.0, 100.0, 6.0]})
+    tm.assert_frame_equal(df1, expected)
+    if using_copy_on_write:
+        # df1 is updated, but its view not
+        tm.assert_frame_equal(view, df1_orig)
+        assert np.shares_memory(get_array(df1, "a"), get_array(view, "a"))
+        assert not np.shares_memory(get_array(df1, "b"), get_array(view, "b"))
+    else:
+        tm.assert_frame_equal(view, expected)
+
+
+def test_update_series(using_copy_on_write, warn_copy_on_write):
+    ser1 = Series([1.0, 2.0, 3.0])
+    ser2 = Series([100.0], index=[1])
+    ser1_orig = ser1.copy()
+    view = ser1[:]
+
+    if warn_copy_on_write:
+        with tm.assert_cow_warning():
+            ser1.update(ser2)
+    else:
+        ser1.update(ser2)
+
+    expected = Series([1.0, 100.0, 3.0])
+    tm.assert_series_equal(ser1, expected)
+    if using_copy_on_write:
+        # ser1 is updated, but its view not
+        tm.assert_series_equal(view, ser1_orig)
+    else:
+        tm.assert_series_equal(view, expected)
+
+
+def test_update_chained_assignment(using_copy_on_write):
+    df = DataFrame({"a": [1, 2, 3]})
+    ser2 = Series([100.0], index=[1])
+    df_orig = df.copy()
+    if using_copy_on_write:
+        with tm.raises_chained_assignment_error():
+            df["a"].update(ser2)
+        tm.assert_frame_equal(df, df_orig)
+
+        with tm.raises_chained_assignment_error():
+            df[["a"]].update(ser2.to_frame())
+        tm.assert_frame_equal(df, df_orig)
+    else:
+        with tm.assert_produces_warning(
+            FutureWarning if not WARNING_CHECK_DISABLED else None,
+            match="inplace method",
+        ):
+            df["a"].update(ser2)
+
+        with tm.assert_produces_warning(None):
+            with option_context("mode.chained_assignment", None):
+                df[["a"]].update(ser2.to_frame())
+
+        with tm.assert_produces_warning(None):
+            with option_context("mode.chained_assignment", None):
+                df[df["a"] > 1].update(ser2.to_frame())
+
+
+def test_inplace_arithmetic_series(using_copy_on_write):
+    ser = Series([1, 2, 3])
+    ser_orig = ser.copy()
+    data = get_array(ser)
+    ser *= 2
+    if using_copy_on_write:
+        # https://github.com/pandas-dev/pandas/pull/55745
+        # changed to NOT update inplace because there is no benefit (actual
+        # operation already done non-inplace). This was only for the optics
+        # of updating the backing array inplace, but we no longer want to make
+        # that guarantee
+        assert not np.shares_memory(get_array(ser), data)
+        tm.assert_numpy_array_equal(data, get_array(ser_orig))
+    else:
+        assert np.shares_memory(get_array(ser), data)
+        tm.assert_numpy_array_equal(data, get_array(ser))
+
+
+def test_inplace_arithmetic_series_with_reference(
+    using_copy_on_write, warn_copy_on_write
+):
+    ser = Series([1, 2, 3])
+    ser_orig = ser.copy()
+    view = ser[:]
+    with tm.assert_cow_warning(warn_copy_on_write):
+        ser *= 2
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(ser), get_array(view))
+        tm.assert_series_equal(ser_orig, view)
+    else:
+        assert np.shares_memory(get_array(ser), get_array(view))
+
+
+@pytest.mark.parametrize("copy", [True, False])
+def test_transpose(using_copy_on_write, copy, using_array_manager):
+    df = DataFrame({"a": [1, 2, 3], "b": 1})
+    df_orig = df.copy()
+    result = df.transpose(copy=copy)
+
+    if not copy and not using_array_manager or using_copy_on_write:
+        assert np.shares_memory(get_array(df, "a"), get_array(result, 0))
+    else:
+        assert not np.shares_memory(get_array(df, "a"), get_array(result, 0))
+
+    result.iloc[0, 0] = 100
+    if using_copy_on_write:
+        tm.assert_frame_equal(df, df_orig)
+
+
+def test_transpose_different_dtypes(using_copy_on_write):
+    df = DataFrame({"a": [1, 2, 3], "b": 1.5})
+    df_orig = df.copy()
+    result = df.T
+
+    assert not np.shares_memory(get_array(df, "a"), get_array(result, 0))
+    result.iloc[0, 0] = 100
+    if using_copy_on_write:
+        tm.assert_frame_equal(df, df_orig)
+
+
+def test_transpose_ea_single_column(using_copy_on_write):
+    df = DataFrame({"a": [1, 2, 3]}, dtype="Int64")
+    result = df.T
+
+    assert not np.shares_memory(get_array(df, "a"), get_array(result, 0))
+
+
+def test_transform_frame(using_copy_on_write, warn_copy_on_write):
+    df = DataFrame({"a": [1, 2, 3], "b": 1})
+    df_orig = df.copy()
+
+    def func(ser):
+        ser.iloc[0] = 100
+        return ser
+
+    with tm.assert_cow_warning(warn_copy_on_write):
+        df.transform(func)
+    if using_copy_on_write:
+        tm.assert_frame_equal(df, df_orig)
+
+
+def test_transform_series(using_copy_on_write, warn_copy_on_write):
+    ser = Series([1, 2, 3])
+    ser_orig = ser.copy()
+
+    def func(ser):
+        ser.iloc[0] = 100
+        return ser
+
+    with tm.assert_cow_warning(warn_copy_on_write):
+        ser.transform(func)
+    if using_copy_on_write:
+        tm.assert_series_equal(ser, ser_orig)
+
+
+def test_count_read_only_array():
+    df = DataFrame({"a": [1, 2], "b": 3})
+    result = df.count()
+    result.iloc[0] = 100
+    expected = Series([100, 2], index=["a", "b"])
+    tm.assert_series_equal(result, expected)
+
+
+def test_series_view(using_copy_on_write, warn_copy_on_write):
+    ser = Series([1, 2, 3])
+    ser_orig = ser.copy()
+
+    with tm.assert_produces_warning(FutureWarning, match="is deprecated"):
+        ser2 = ser.view()
+    assert np.shares_memory(get_array(ser), get_array(ser2))
+    if using_copy_on_write:
+        assert not ser2._mgr._has_no_reference(0)
+
+    with tm.assert_cow_warning(warn_copy_on_write):
+        ser2.iloc[0] = 100
+
+    if using_copy_on_write:
+        tm.assert_series_equal(ser_orig, ser)
+    else:
+        expected = Series([100, 2, 3])
+        tm.assert_series_equal(ser, expected)
+
+
+def test_insert_series(using_copy_on_write):
+    df = DataFrame({"a": [1, 2, 3]})
+    ser = Series([1, 2, 3])
+    ser_orig = ser.copy()
+    df.insert(loc=1, value=ser, column="b")
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(ser), get_array(df, "b"))
+        assert not df._mgr._has_no_reference(1)
+    else:
+        assert not np.shares_memory(get_array(ser), get_array(df, "b"))
+
+    df.iloc[0, 1] = 100
+    tm.assert_series_equal(ser, ser_orig)
+
+
+def test_eval(using_copy_on_write):
+    df = DataFrame({"a": [1, 2, 3], "b": 1})
+    df_orig = df.copy()
+
+    result = df.eval("c = a+b")
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(df, "a"), get_array(result, "a"))
+    else:
+        assert not np.shares_memory(get_array(df, "a"), get_array(result, "a"))
+
+    result.iloc[0, 0] = 100
+    tm.assert_frame_equal(df, df_orig)
+
+
+def test_eval_inplace(using_copy_on_write, warn_copy_on_write):
+    df = DataFrame({"a": [1, 2, 3], "b": 1})
+    df_orig = df.copy()
+    df_view = df[:]
+
+    df.eval("c = a+b", inplace=True)
+    assert np.shares_memory(get_array(df, "a"), get_array(df_view, "a"))
+
+    with tm.assert_cow_warning(warn_copy_on_write):
+        df.iloc[0, 0] = 100
+    if using_copy_on_write:
+        tm.assert_frame_equal(df_view, df_orig)
+
+
+def test_apply_modify_row(using_copy_on_write, warn_copy_on_write):
+    # Case: applying a function on each row as a Series object, where the
+    # function mutates the row object (which needs to trigger CoW if row is a view)
+    df = DataFrame({"A": [1, 2], "B": [3, 4]})
+    df_orig = df.copy()
+
+    def transform(row):
+        row["B"] = 100
+        return row
+
+    with tm.assert_cow_warning(warn_copy_on_write):
+        df.apply(transform, axis=1)
+
+    if using_copy_on_write:
+        tm.assert_frame_equal(df, df_orig)
+    else:
+        assert df.loc[0, "B"] == 100
+
+    # row Series is a copy
+    df = DataFrame({"A": [1, 2], "B": ["b", "c"]})
+    df_orig = df.copy()
+
+    with tm.assert_produces_warning(None):
+        df.apply(transform, axis=1)
+
+    tm.assert_frame_equal(df, df_orig)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/copy_view/test_replace.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/copy_view/test_replace.py
new file mode 100644
index 0000000000000000000000000000000000000000..70158141d0ceedbe39f29c2869a855647b4d1a1e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/copy_view/test_replace.py
@@ -0,0 +1,495 @@
+import numpy as np
+import pytest
+
+from pandas.compat import WARNING_CHECK_DISABLED
+
+from pandas import (
+    Categorical,
+    DataFrame,
+    option_context,
+)
+import pandas._testing as tm
+from pandas.tests.copy_view.util import get_array
+
+
+@pytest.mark.parametrize(
+    "replace_kwargs",
+    [
+        {"to_replace": {"a": 1, "b": 4}, "value": -1},
+        # Test CoW splits blocks to avoid copying unchanged columns
+        {"to_replace": {"a": 1}, "value": -1},
+        {"to_replace": {"b": 4}, "value": -1},
+        {"to_replace": {"b": {4: 1}}},
+        # TODO: Add these in a further optimization
+        # We would need to see which columns got replaced in the mask
+        # which could be expensive
+        # {"to_replace": {"b": 1}},
+        # 1
+    ],
+)
+def test_replace(using_copy_on_write, replace_kwargs):
+    df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [0.1, 0.2, 0.3]})
+    df_orig = df.copy()
+
+    df_replaced = df.replace(**replace_kwargs)
+
+    if using_copy_on_write:
+        if (df_replaced["b"] == df["b"]).all():
+            assert np.shares_memory(get_array(df_replaced, "b"), get_array(df, "b"))
+        assert tm.shares_memory(get_array(df_replaced, "c"), get_array(df, "c"))
+
+    # mutating squeezed df triggers a copy-on-write for that column/block
+    df_replaced.loc[0, "c"] = -1
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(df_replaced, "c"), get_array(df, "c"))
+
+    if "a" in replace_kwargs["to_replace"]:
+        arr = get_array(df_replaced, "a")
+        df_replaced.loc[0, "a"] = 100
+        assert np.shares_memory(get_array(df_replaced, "a"), arr)
+    tm.assert_frame_equal(df, df_orig)
+
+
+def test_replace_regex_inplace_refs(using_copy_on_write, warn_copy_on_write):
+    df = DataFrame({"a": ["aaa", "bbb"]})
+    df_orig = df.copy()
+    view = df[:]
+    arr = get_array(df, "a")
+    with tm.assert_cow_warning(warn_copy_on_write):
+        df.replace(to_replace=r"^a.*$", value="new", inplace=True, regex=True)
+    if using_copy_on_write:
+        assert not tm.shares_memory(arr, get_array(df, "a"))
+        assert df._mgr._has_no_reference(0)
+        tm.assert_frame_equal(view, df_orig)
+    else:
+        assert np.shares_memory(arr, get_array(df, "a"))
+
+
+def test_replace_regex_inplace(using_copy_on_write):
+    df = DataFrame({"a": ["aaa", "bbb"]})
+    arr = get_array(df, "a")
+    df.replace(to_replace=r"^a.*$", value="new", inplace=True, regex=True)
+    if using_copy_on_write:
+        assert df._mgr._has_no_reference(0)
+    assert tm.shares_memory(arr, get_array(df, "a"))
+
+    df_orig = df.copy()
+    df2 = df.replace(to_replace=r"^b.*$", value="new", regex=True)
+    tm.assert_frame_equal(df_orig, df)
+    assert not tm.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+
+
+def test_replace_regex_inplace_no_op(using_copy_on_write):
+    df = DataFrame({"a": [1, 2]})
+    arr = get_array(df, "a")
+    df.replace(to_replace=r"^a.$", value="new", inplace=True, regex=True)
+    if using_copy_on_write:
+        assert df._mgr._has_no_reference(0)
+    assert np.shares_memory(arr, get_array(df, "a"))
+
+    df_orig = df.copy()
+    df2 = df.replace(to_replace=r"^x.$", value="new", regex=True)
+    tm.assert_frame_equal(df_orig, df)
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+    else:
+        assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+
+
+def test_replace_mask_all_false_second_block(using_copy_on_write):
+    df = DataFrame({"a": [1.5, 2, 3], "b": 100.5, "c": 1, "d": 2})
+    df_orig = df.copy()
+
+    df2 = df.replace(to_replace=1.5, value=55.5)
+
+    if using_copy_on_write:
+        # TODO: Block splitting would allow us to avoid copying b
+        assert np.shares_memory(get_array(df, "c"), get_array(df2, "c"))
+        assert not np.shares_memory(get_array(df, "a"), get_array(df2, "a"))
+
+    else:
+        assert not np.shares_memory(get_array(df, "c"), get_array(df2, "c"))
+        assert not np.shares_memory(get_array(df, "a"), get_array(df2, "a"))
+
+    df2.loc[0, "c"] = 1
+    tm.assert_frame_equal(df, df_orig)  # Original is unchanged
+
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(df, "c"), get_array(df2, "c"))
+        # TODO: This should split and not copy the whole block
+        # assert np.shares_memory(get_array(df, "d"), get_array(df2, "d"))
+
+
+def test_replace_coerce_single_column(using_copy_on_write, using_array_manager):
+    df = DataFrame({"a": [1.5, 2, 3], "b": 100.5})
+    df_orig = df.copy()
+
+    df2 = df.replace(to_replace=1.5, value="a")
+
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(df, "b"), get_array(df2, "b"))
+        assert not np.shares_memory(get_array(df, "a"), get_array(df2, "a"))
+
+    elif not using_array_manager:
+        assert np.shares_memory(get_array(df, "b"), get_array(df2, "b"))
+        assert not np.shares_memory(get_array(df, "a"), get_array(df2, "a"))
+
+    if using_copy_on_write:
+        df2.loc[0, "b"] = 0.5
+        tm.assert_frame_equal(df, df_orig)  # Original is unchanged
+        assert not np.shares_memory(get_array(df, "b"), get_array(df2, "b"))
+
+
+def test_replace_to_replace_wrong_dtype(using_copy_on_write):
+    df = DataFrame({"a": [1.5, 2, 3], "b": 100.5})
+    df_orig = df.copy()
+
+    df2 = df.replace(to_replace="xxx", value=1.5)
+
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(df, "b"), get_array(df2, "b"))
+        assert np.shares_memory(get_array(df, "a"), get_array(df2, "a"))
+
+    else:
+        assert not np.shares_memory(get_array(df, "b"), get_array(df2, "b"))
+        assert not np.shares_memory(get_array(df, "a"), get_array(df2, "a"))
+
+    df2.loc[0, "b"] = 0.5
+    tm.assert_frame_equal(df, df_orig)  # Original is unchanged
+
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(df, "b"), get_array(df2, "b"))
+
+
+def test_replace_list_categorical(using_copy_on_write):
+    df = DataFrame({"a": ["a", "b", "c"]}, dtype="category")
+    arr = get_array(df, "a")
+    msg = (
+        r"The behavior of Series\.replace \(and DataFrame.replace\) "
+        "with CategoricalDtype"
+    )
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        df.replace(["c"], value="a", inplace=True)
+    assert np.shares_memory(arr.codes, get_array(df, "a").codes)
+    if using_copy_on_write:
+        assert df._mgr._has_no_reference(0)
+
+    df_orig = df.copy()
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        df2 = df.replace(["b"], value="a")
+    assert not np.shares_memory(arr.codes, get_array(df2, "a").codes)
+
+    tm.assert_frame_equal(df, df_orig)
+
+
+def test_replace_list_inplace_refs_categorical(using_copy_on_write):
+    df = DataFrame({"a": ["a", "b", "c"]}, dtype="category")
+    view = df[:]
+    df_orig = df.copy()
+    msg = (
+        r"The behavior of Series\.replace \(and DataFrame.replace\) "
+        "with CategoricalDtype"
+    )
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        df.replace(["c"], value="a", inplace=True)
+    if using_copy_on_write:
+        assert not np.shares_memory(
+            get_array(view, "a").codes, get_array(df, "a").codes
+        )
+        tm.assert_frame_equal(df_orig, view)
+    else:
+        # This could be inplace
+        assert not np.shares_memory(
+            get_array(view, "a").codes, get_array(df, "a").codes
+        )
+
+
+@pytest.mark.parametrize("to_replace", [1.5, [1.5], []])
+def test_replace_inplace(using_copy_on_write, to_replace):
+    df = DataFrame({"a": [1.5, 2, 3]})
+    arr_a = get_array(df, "a")
+    df.replace(to_replace=1.5, value=15.5, inplace=True)
+
+    assert np.shares_memory(get_array(df, "a"), arr_a)
+    if using_copy_on_write:
+        assert df._mgr._has_no_reference(0)
+
+
+@pytest.mark.parametrize("to_replace", [1.5, [1.5]])
+def test_replace_inplace_reference(using_copy_on_write, to_replace, warn_copy_on_write):
+    df = DataFrame({"a": [1.5, 2, 3]})
+    arr_a = get_array(df, "a")
+    view = df[:]
+    with tm.assert_cow_warning(warn_copy_on_write):
+        df.replace(to_replace=to_replace, value=15.5, inplace=True)
+
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(df, "a"), arr_a)
+        assert df._mgr._has_no_reference(0)
+        assert view._mgr._has_no_reference(0)
+    else:
+        assert np.shares_memory(get_array(df, "a"), arr_a)
+
+
+@pytest.mark.parametrize("to_replace", ["a", 100.5])
+def test_replace_inplace_reference_no_op(using_copy_on_write, to_replace):
+    df = DataFrame({"a": [1.5, 2, 3]})
+    arr_a = get_array(df, "a")
+    view = df[:]
+    df.replace(to_replace=to_replace, value=15.5, inplace=True)
+
+    assert np.shares_memory(get_array(df, "a"), arr_a)
+    if using_copy_on_write:
+        assert not df._mgr._has_no_reference(0)
+        assert not view._mgr._has_no_reference(0)
+
+
+@pytest.mark.parametrize("to_replace", [1, [1]])
+@pytest.mark.parametrize("val", [1, 1.5])
+def test_replace_categorical_inplace_reference(using_copy_on_write, val, to_replace):
+    df = DataFrame({"a": Categorical([1, 2, 3])})
+    df_orig = df.copy()
+    arr_a = get_array(df, "a")
+    view = df[:]
+    msg = (
+        r"The behavior of Series\.replace \(and DataFrame.replace\) "
+        "with CategoricalDtype"
+    )
+    warn = FutureWarning if val == 1.5 else None
+    with tm.assert_produces_warning(warn, match=msg):
+        df.replace(to_replace=to_replace, value=val, inplace=True)
+
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(df, "a").codes, arr_a.codes)
+        assert df._mgr._has_no_reference(0)
+        assert view._mgr._has_no_reference(0)
+        tm.assert_frame_equal(view, df_orig)
+    else:
+        assert np.shares_memory(get_array(df, "a").codes, arr_a.codes)
+
+
+@pytest.mark.parametrize("val", [1, 1.5])
+def test_replace_categorical_inplace(using_copy_on_write, val):
+    df = DataFrame({"a": Categorical([1, 2, 3])})
+    arr_a = get_array(df, "a")
+    msg = (
+        r"The behavior of Series\.replace \(and DataFrame.replace\) "
+        "with CategoricalDtype"
+    )
+    warn = FutureWarning if val == 1.5 else None
+    with tm.assert_produces_warning(warn, match=msg):
+        df.replace(to_replace=1, value=val, inplace=True)
+
+    assert np.shares_memory(get_array(df, "a").codes, arr_a.codes)
+    if using_copy_on_write:
+        assert df._mgr._has_no_reference(0)
+
+    expected = DataFrame({"a": Categorical([val, 2, 3])})
+    tm.assert_frame_equal(df, expected)
+
+
+@pytest.mark.parametrize("val", [1, 1.5])
+def test_replace_categorical(using_copy_on_write, val):
+    df = DataFrame({"a": Categorical([1, 2, 3])})
+    df_orig = df.copy()
+    msg = (
+        r"The behavior of Series\.replace \(and DataFrame.replace\) "
+        "with CategoricalDtype"
+    )
+    warn = FutureWarning if val == 1.5 else None
+    with tm.assert_produces_warning(warn, match=msg):
+        df2 = df.replace(to_replace=1, value=val)
+
+    if using_copy_on_write:
+        assert df._mgr._has_no_reference(0)
+        assert df2._mgr._has_no_reference(0)
+    assert not np.shares_memory(get_array(df, "a").codes, get_array(df2, "a").codes)
+    tm.assert_frame_equal(df, df_orig)
+
+    arr_a = get_array(df2, "a").codes
+    df2.iloc[0, 0] = 2.0
+    assert np.shares_memory(get_array(df2, "a").codes, arr_a)
+
+
+@pytest.mark.parametrize("method", ["where", "mask"])
+def test_masking_inplace(using_copy_on_write, method, warn_copy_on_write):
+    df = DataFrame({"a": [1.5, 2, 3]})
+    df_orig = df.copy()
+    arr_a = get_array(df, "a")
+    view = df[:]
+
+    method = getattr(df, method)
+    if warn_copy_on_write:
+        with tm.assert_cow_warning():
+            method(df["a"] > 1.6, -1, inplace=True)
+    else:
+        method(df["a"] > 1.6, -1, inplace=True)
+
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(df, "a"), arr_a)
+        assert df._mgr._has_no_reference(0)
+        assert view._mgr._has_no_reference(0)
+        tm.assert_frame_equal(view, df_orig)
+    else:
+        assert np.shares_memory(get_array(df, "a"), arr_a)
+
+
+def test_replace_empty_list(using_copy_on_write):
+    df = DataFrame({"a": [1, 2]})
+
+    df2 = df.replace([], [])
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+        assert not df._mgr._has_no_reference(0)
+    else:
+        assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+
+    arr_a = get_array(df, "a")
+    df.replace([], [])
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(df, "a"), arr_a)
+        assert not df._mgr._has_no_reference(0)
+        assert not df2._mgr._has_no_reference(0)
+
+
+@pytest.mark.parametrize("value", ["d", None])
+def test_replace_object_list_inplace(using_copy_on_write, value):
+    df = DataFrame({"a": ["a", "b", "c"]}, dtype=object)
+    arr = get_array(df, "a")
+    df.replace(["c"], value, inplace=True)
+    if using_copy_on_write or value is None:
+        assert tm.shares_memory(arr, get_array(df, "a"))
+    else:
+        # This could be inplace
+        assert not np.shares_memory(arr, get_array(df, "a"))
+    if using_copy_on_write:
+        assert df._mgr._has_no_reference(0)
+
+
+def test_replace_list_multiple_elements_inplace(using_copy_on_write):
+    df = DataFrame({"a": [1, 2, 3]})
+    arr = get_array(df, "a")
+    df.replace([1, 2], 4, inplace=True)
+    if using_copy_on_write:
+        assert np.shares_memory(arr, get_array(df, "a"))
+        assert df._mgr._has_no_reference(0)
+    else:
+        assert np.shares_memory(arr, get_array(df, "a"))
+
+
+def test_replace_list_none(using_copy_on_write):
+    df = DataFrame({"a": ["a", "b", "c"]})
+
+    df_orig = df.copy()
+    df2 = df.replace(["b"], value=None)
+    tm.assert_frame_equal(df, df_orig)
+
+    assert not np.shares_memory(get_array(df, "a"), get_array(df2, "a"))
+
+    # replace multiple values that don't actually replace anything with None
+    # https://github.com/pandas-dev/pandas/issues/59770
+    df3 = df.replace(["d", "e", "f"], value=None)
+    tm.assert_frame_equal(df3, df_orig)
+    if using_copy_on_write:
+        assert tm.shares_memory(get_array(df, "a"), get_array(df3, "a"))
+    else:
+        assert not tm.shares_memory(get_array(df, "a"), get_array(df3, "a"))
+
+
+def test_replace_list_none_inplace_refs(using_copy_on_write, warn_copy_on_write):
+    df = DataFrame({"a": ["a", "b", "c"]})
+    arr = get_array(df, "a")
+    df_orig = df.copy()
+    view = df[:]
+    with tm.assert_cow_warning(warn_copy_on_write):
+        df.replace(["a"], value=None, inplace=True)
+    if using_copy_on_write:
+        assert df._mgr._has_no_reference(0)
+        assert not np.shares_memory(arr, get_array(df, "a"))
+        tm.assert_frame_equal(df_orig, view)
+    else:
+        assert np.shares_memory(arr, get_array(df, "a"))
+
+
+def test_replace_columnwise_no_op_inplace(using_copy_on_write):
+    df = DataFrame({"a": [1, 2, 3], "b": [1, 2, 3]})
+    view = df[:]
+    df_orig = df.copy()
+    df.replace({"a": 10}, 100, inplace=True)
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(view, "a"), get_array(df, "a"))
+        df.iloc[0, 0] = 100
+        tm.assert_frame_equal(view, df_orig)
+
+
+def test_replace_columnwise_no_op(using_copy_on_write):
+    df = DataFrame({"a": [1, 2, 3], "b": [1, 2, 3]})
+    df_orig = df.copy()
+    df2 = df.replace({"a": 10}, 100)
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(df2, "a"), get_array(df, "a"))
+    df2.iloc[0, 0] = 100
+    tm.assert_frame_equal(df, df_orig)
+
+
+def test_replace_chained_assignment(using_copy_on_write):
+    df = DataFrame({"a": [1, np.nan, 2], "b": 1})
+    df_orig = df.copy()
+    if using_copy_on_write:
+        with tm.raises_chained_assignment_error():
+            df["a"].replace(1, 100, inplace=True)
+        tm.assert_frame_equal(df, df_orig)
+
+        with tm.raises_chained_assignment_error():
+            df[["a"]].replace(1, 100, inplace=True)
+        tm.assert_frame_equal(df, df_orig)
+    else:
+        with tm.assert_produces_warning(None):
+            with option_context("mode.chained_assignment", None):
+                df[["a"]].replace(1, 100, inplace=True)
+
+        with tm.assert_produces_warning(None):
+            with option_context("mode.chained_assignment", None):
+                df[df.a > 5].replace(1, 100, inplace=True)
+
+        with tm.assert_produces_warning(
+            FutureWarning if not WARNING_CHECK_DISABLED else None,
+            match="inplace method",
+        ):
+            df["a"].replace(1, 100, inplace=True)
+
+
+def test_replace_listlike(using_copy_on_write):
+    df = DataFrame({"a": [1, 2, 3], "b": [1, 2, 3]})
+    df_orig = df.copy()
+
+    result = df.replace([200, 201], [11, 11])
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(result, "a"), get_array(df, "a"))
+    else:
+        assert not np.shares_memory(get_array(result, "a"), get_array(df, "a"))
+
+    result.iloc[0, 0] = 100
+    tm.assert_frame_equal(df, df)
+
+    result = df.replace([200, 2], [10, 10])
+    assert not np.shares_memory(get_array(df, "a"), get_array(result, "a"))
+    tm.assert_frame_equal(df, df_orig)
+
+
+def test_replace_listlike_inplace(using_copy_on_write, warn_copy_on_write):
+    df = DataFrame({"a": [1, 2, 3], "b": [1, 2, 3]})
+    arr = get_array(df, "a")
+    df.replace([200, 2], [10, 11], inplace=True)
+    assert np.shares_memory(get_array(df, "a"), arr)
+
+    view = df[:]
+    df_orig = df.copy()
+    with tm.assert_cow_warning(warn_copy_on_write):
+        df.replace([200, 3], [10, 11], inplace=True)
+    if using_copy_on_write:
+        assert not np.shares_memory(get_array(df, "a"), arr)
+        tm.assert_frame_equal(view, df_orig)
+    else:
+        assert np.shares_memory(get_array(df, "a"), arr)
+        tm.assert_frame_equal(df, view)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/copy_view/test_setitem.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/copy_view/test_setitem.py
new file mode 100644
index 0000000000000000000000000000000000000000..bc3b939734534520f0cf7051dbc72989d0caf990
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/copy_view/test_setitem.py
@@ -0,0 +1,156 @@
+import numpy as np
+
+from pandas import (
+    DataFrame,
+    Index,
+    MultiIndex,
+    RangeIndex,
+    Series,
+)
+import pandas._testing as tm
+from pandas.tests.copy_view.util import get_array
+
+# -----------------------------------------------------------------------------
+# Copy/view behaviour for the values that are set in a DataFrame
+
+
+def test_set_column_with_array():
+    # Case: setting an array as a new column (df[col] = arr) copies that data
+    df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
+    arr = np.array([1, 2, 3], dtype="int64")
+
+    df["c"] = arr
+
+    # the array data is copied
+    assert not np.shares_memory(get_array(df, "c"), arr)
+    # and thus modifying the array does not modify the DataFrame
+    arr[0] = 0
+    tm.assert_series_equal(df["c"], Series([1, 2, 3], name="c"))
+
+
+def test_set_column_with_series(using_copy_on_write):
+    # Case: setting a series as a new column (df[col] = s) copies that data
+    # (with delayed copy with CoW)
+    df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
+    ser = Series([1, 2, 3])
+
+    df["c"] = ser
+
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(df, "c"), get_array(ser))
+    else:
+        # the series data is copied
+        assert not np.shares_memory(get_array(df, "c"), get_array(ser))
+
+    # and modifying the series does not modify the DataFrame
+    ser.iloc[0] = 0
+    assert ser.iloc[0] == 0
+    tm.assert_series_equal(df["c"], Series([1, 2, 3], name="c"))
+
+
+def test_set_column_with_index(using_copy_on_write):
+    # Case: setting an index as a new column (df[col] = idx) copies that data
+    df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
+    idx = Index([1, 2, 3])
+
+    df["c"] = idx
+
+    # the index data is copied
+    assert not np.shares_memory(get_array(df, "c"), idx.values)
+
+    idx = RangeIndex(1, 4)
+    arr = idx.values
+
+    df["d"] = idx
+
+    assert not np.shares_memory(get_array(df, "d"), arr)
+
+
+def test_set_columns_with_dataframe(using_copy_on_write):
+    # Case: setting a DataFrame as new columns copies that data
+    # (with delayed copy with CoW)
+    df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
+    df2 = DataFrame({"c": [7, 8, 9], "d": [10, 11, 12]})
+
+    df[["c", "d"]] = df2
+
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(df, "c"), get_array(df2, "c"))
+    else:
+        # the data is copied
+        assert not np.shares_memory(get_array(df, "c"), get_array(df2, "c"))
+
+    # and modifying the set DataFrame does not modify the original DataFrame
+    df2.iloc[0, 0] = 0
+    tm.assert_series_equal(df["c"], Series([7, 8, 9], name="c"))
+
+
+def test_setitem_series_no_copy(using_copy_on_write):
+    # Case: setting a Series as column into a DataFrame can delay copying that data
+    df = DataFrame({"a": [1, 2, 3]})
+    rhs = Series([4, 5, 6])
+    rhs_orig = rhs.copy()
+
+    # adding a new column
+    df["b"] = rhs
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(rhs), get_array(df, "b"))
+
+    df.iloc[0, 1] = 100
+    tm.assert_series_equal(rhs, rhs_orig)
+
+
+def test_setitem_series_no_copy_single_block(using_copy_on_write):
+    # Overwriting an existing column that is a single block
+    df = DataFrame({"a": [1, 2, 3], "b": [0.1, 0.2, 0.3]})
+    rhs = Series([4, 5, 6])
+    rhs_orig = rhs.copy()
+
+    df["a"] = rhs
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(rhs), get_array(df, "a"))
+
+    df.iloc[0, 0] = 100
+    tm.assert_series_equal(rhs, rhs_orig)
+
+
+def test_setitem_series_no_copy_split_block(using_copy_on_write):
+    # Overwriting an existing column that is part of a larger block
+    df = DataFrame({"a": [1, 2, 3], "b": 1})
+    rhs = Series([4, 5, 6])
+    rhs_orig = rhs.copy()
+
+    df["b"] = rhs
+    if using_copy_on_write:
+        assert np.shares_memory(get_array(rhs), get_array(df, "b"))
+
+    df.iloc[0, 1] = 100
+    tm.assert_series_equal(rhs, rhs_orig)
+
+
+def test_setitem_series_column_midx_broadcasting(using_copy_on_write):
+    # Setting a Series to multiple columns will repeat the data
+    # (currently copying the data eagerly)
+    df = DataFrame(
+        [[1, 2, 3], [3, 4, 5]],
+        columns=MultiIndex.from_arrays([["a", "a", "b"], [1, 2, 3]]),
+    )
+    rhs = Series([10, 11])
+    df["a"] = rhs
+    assert not np.shares_memory(get_array(rhs), df._get_column_array(0))
+    if using_copy_on_write:
+        assert df._mgr._has_no_reference(0)
+
+
+def test_set_column_with_inplace_operator(using_copy_on_write, warn_copy_on_write):
+    df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
+
+    # this should not raise any warning
+    with tm.assert_produces_warning(None):
+        df["a"] += 1
+
+    # when it is not in a chain, then it should produce a warning
+    df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
+    ser = df["a"]
+    with tm.assert_cow_warning(warn_copy_on_write):
+        ser += 1
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/copy_view/test_util.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/copy_view/test_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..ff55330d70b28c5459a4c0915dd93c8640a91add
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/copy_view/test_util.py
@@ -0,0 +1,14 @@
+import numpy as np
+
+from pandas import DataFrame
+from pandas.tests.copy_view.util import get_array
+
+
+def test_get_array_numpy():
+    df = DataFrame({"a": [1, 2, 3]})
+    assert np.shares_memory(get_array(df, "a"), get_array(df, "a"))
+
+
+def test_get_array_masked():
+    df = DataFrame({"a": [1, 2, 3]}, dtype="Int64")
+    assert np.shares_memory(get_array(df, "a"), get_array(df, "a"))
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/copy_view/util.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/copy_view/util.py
new file mode 100644
index 0000000000000000000000000000000000000000..969334424936559767b0bca87093acfec52f9763
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/copy_view/util.py
@@ -0,0 +1,30 @@
+from pandas import (
+    Categorical,
+    Index,
+    Series,
+)
+from pandas.core.arrays import BaseMaskedArray
+
+
+def get_array(obj, col=None):
+    """
+    Helper method to get array for a DataFrame column or a Series.
+
+    Equivalent of df[col].values, but without going through normal getitem,
+    which triggers tracking references / CoW (and we might be testing that
+    this is done by some other operation).
+    """
+    if isinstance(obj, Index):
+        arr = obj._values
+    elif isinstance(obj, Series) and (col is None or obj.name == col):
+        arr = obj._values
+    else:
+        assert col is not None
+        icol = obj.columns.get_loc(col)
+        assert isinstance(icol, int)
+        arr = obj._get_column_array(icol)
+    if isinstance(arr, BaseMaskedArray):
+        return arr._data
+    elif isinstance(arr, Categorical):
+        return arr
+    return getattr(arr, "_ndarray", arr)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/dtypes/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/dtypes/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/dtypes/cast/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/dtypes/cast/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_can_hold_element.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_can_hold_element.py
new file mode 100644
index 0000000000000000000000000000000000000000..3b7d76ead119a1bad784ca3fda3303c7a9e23244
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_can_hold_element.py
@@ -0,0 +1,79 @@
+import numpy as np
+
+from pandas.core.dtypes.cast import can_hold_element
+
+
+def test_can_hold_element_range(any_int_numpy_dtype):
+    # GH#44261
+    dtype = np.dtype(any_int_numpy_dtype)
+    arr = np.array([], dtype=dtype)
+
+    rng = range(2, 127)
+    assert can_hold_element(arr, rng)
+
+    # negatives -> can't be held by uint dtypes
+    rng = range(-2, 127)
+    if dtype.kind == "i":
+        assert can_hold_element(arr, rng)
+    else:
+        assert not can_hold_element(arr, rng)
+
+    rng = range(2, 255)
+    if dtype == "int8":
+        assert not can_hold_element(arr, rng)
+    else:
+        assert can_hold_element(arr, rng)
+
+    rng = range(-255, 65537)
+    if dtype.kind == "u":
+        assert not can_hold_element(arr, rng)
+    elif dtype.itemsize < 4:
+        assert not can_hold_element(arr, rng)
+    else:
+        assert can_hold_element(arr, rng)
+
+    # empty
+    rng = range(-(10**10), -(10**10))
+    assert len(rng) == 0
+    # assert can_hold_element(arr, rng)
+
+    rng = range(10**10, 10**10)
+    assert len(rng) == 0
+    assert can_hold_element(arr, rng)
+
+
+def test_can_hold_element_int_values_float_ndarray():
+    arr = np.array([], dtype=np.int64)
+
+    element = np.array([1.0, 2.0])
+    assert can_hold_element(arr, element)
+
+    assert not can_hold_element(arr, element + 0.5)
+
+    # integer but not losslessly castable to int64
+    element = np.array([3, 2**65], dtype=np.float64)
+    assert not can_hold_element(arr, element)
+
+
+def test_can_hold_element_int8_int():
+    arr = np.array([], dtype=np.int8)
+
+    element = 2
+    assert can_hold_element(arr, element)
+    assert can_hold_element(arr, np.int8(element))
+    assert can_hold_element(arr, np.uint8(element))
+    assert can_hold_element(arr, np.int16(element))
+    assert can_hold_element(arr, np.uint16(element))
+    assert can_hold_element(arr, np.int32(element))
+    assert can_hold_element(arr, np.uint32(element))
+    assert can_hold_element(arr, np.int64(element))
+    assert can_hold_element(arr, np.uint64(element))
+
+    element = 2**9
+    assert not can_hold_element(arr, element)
+    assert not can_hold_element(arr, np.int16(element))
+    assert not can_hold_element(arr, np.uint16(element))
+    assert not can_hold_element(arr, np.int32(element))
+    assert not can_hold_element(arr, np.uint32(element))
+    assert not can_hold_element(arr, np.int64(element))
+    assert not can_hold_element(arr, np.uint64(element))
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_construct_from_scalar.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_construct_from_scalar.py
new file mode 100644
index 0000000000000000000000000000000000000000..0ce04ce2e64cda1d3fc7c48390baa91ee2b06525
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_construct_from_scalar.py
@@ -0,0 +1,55 @@
+import numpy as np
+import pytest
+
+from pandas.core.dtypes.cast import construct_1d_arraylike_from_scalar
+from pandas.core.dtypes.dtypes import CategoricalDtype
+
+from pandas import (
+    Categorical,
+    Timedelta,
+)
+import pandas._testing as tm
+
+
+def test_cast_1d_array_like_from_scalar_categorical():
+    # see gh-19565
+    #
+    # Categorical result from scalar did not maintain
+    # categories and ordering of the passed dtype.
+    cats = ["a", "b", "c"]
+    cat_type = CategoricalDtype(categories=cats, ordered=False)
+    expected = Categorical(["a", "a"], categories=cats)
+
+    result = construct_1d_arraylike_from_scalar("a", len(expected), cat_type)
+    tm.assert_categorical_equal(result, expected)
+
+
+def test_cast_1d_array_like_from_timestamp(fixed_now_ts):
+    # check we dont lose nanoseconds
+    ts = fixed_now_ts + Timedelta(1)
+    res = construct_1d_arraylike_from_scalar(ts, 2, np.dtype("M8[ns]"))
+    assert res[0] == ts
+
+
+def test_cast_1d_array_like_from_timedelta():
+    # check we dont lose nanoseconds
+    td = Timedelta(1)
+    res = construct_1d_arraylike_from_scalar(td, 2, np.dtype("m8[ns]"))
+    assert res[0] == td
+
+
+def test_cast_1d_array_like_mismatched_datetimelike():
+    td = np.timedelta64("NaT", "ns")
+    dt = np.datetime64("NaT", "ns")
+
+    with pytest.raises(TypeError, match="Cannot cast"):
+        construct_1d_arraylike_from_scalar(td, 2, dt.dtype)
+
+    with pytest.raises(TypeError, match="Cannot cast"):
+        construct_1d_arraylike_from_scalar(np.timedelta64(4, "ns"), 2, dt.dtype)
+
+    with pytest.raises(TypeError, match="Cannot cast"):
+        construct_1d_arraylike_from_scalar(dt, 2, td.dtype)
+
+    with pytest.raises(TypeError, match="Cannot cast"):
+        construct_1d_arraylike_from_scalar(np.datetime64(4, "ns"), 2, td.dtype)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_construct_ndarray.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_construct_ndarray.py
new file mode 100644
index 0000000000000000000000000000000000000000..6b9b2dfda6e8b81a0f1f29d3ba97589b9d385600
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_construct_ndarray.py
@@ -0,0 +1,36 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.core.construction import sanitize_array
+
+
+@pytest.mark.parametrize(
+    "values, dtype, expected",
+    [
+        ([1, 2, 3], None, np.array([1, 2, 3], dtype=np.int64)),
+        (np.array([1, 2, 3]), None, np.array([1, 2, 3])),
+        (["1", "2", None], None, np.array(["1", "2", None])),
+        (["1", "2", None], np.dtype("str"), np.array(["1", "2", None])),
+        ([1, 2, None], np.dtype("str"), np.array(["1", "2", None])),
+    ],
+)
+def test_construct_1d_ndarray_preserving_na(
+    values, dtype, expected, using_infer_string
+):
+    result = sanitize_array(values, index=None, dtype=dtype)
+    if using_infer_string and expected.dtype == object and dtype is None:
+        tm.assert_extension_array_equal(result, pd.array(expected, dtype="str"))
+    else:
+        tm.assert_numpy_array_equal(result, expected)
+
+
+@pytest.mark.parametrize("dtype", ["m8[ns]", "M8[ns]"])
+def test_construct_1d_ndarray_preserving_na_datetimelike(dtype):
+    arr = np.arange(5, dtype=np.int64).view(dtype)
+    expected = np.array(list(arr), dtype=object)
+    assert all(isinstance(x, type(arr[0])) for x in expected)
+
+    result = sanitize_array(arr, index=None, dtype=np.dtype(object))
+    tm.assert_numpy_array_equal(result, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_construct_object_arr.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_construct_object_arr.py
new file mode 100644
index 0000000000000000000000000000000000000000..cb44f91f34dec80c090d3ce3fc9a2dbd4578bb57
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_construct_object_arr.py
@@ -0,0 +1,20 @@
+import pytest
+
+from pandas.core.dtypes.cast import construct_1d_object_array_from_listlike
+
+
+@pytest.mark.parametrize("datum1", [1, 2.0, "3", (4, 5), [6, 7], None])
+@pytest.mark.parametrize("datum2", [8, 9.0, "10", (11, 12), [13, 14], None])
+def test_cast_1d_array(datum1, datum2):
+    data = [datum1, datum2]
+    result = construct_1d_object_array_from_listlike(data)
+
+    # Direct comparison fails: https://github.com/numpy/numpy/issues/10218
+    assert result.dtype == "object"
+    assert list(result) == data
+
+
+@pytest.mark.parametrize("val", [1, 2.0, None])
+def test_cast_1d_array_invalid_scalar(val):
+    with pytest.raises(TypeError, match="has no len()"):
+        construct_1d_object_array_from_listlike(val)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_dict_compat.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_dict_compat.py
new file mode 100644
index 0000000000000000000000000000000000000000..13dc82d779f953fbea54323785bdcadc3e24dfd8
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_dict_compat.py
@@ -0,0 +1,14 @@
+import numpy as np
+
+from pandas.core.dtypes.cast import dict_compat
+
+from pandas import Timestamp
+
+
+def test_dict_compat():
+    data_datetime64 = {np.datetime64("1990-03-15"): 1, np.datetime64("2015-03-15"): 2}
+    data_unchanged = {1: 2, 3: 4, 5: 6}
+    expected = {Timestamp("1990-3-15"): 1, Timestamp("2015-03-15"): 2}
+    assert dict_compat(data_datetime64) == expected
+    assert dict_compat(expected) == expected
+    assert dict_compat(data_unchanged) == data_unchanged
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_downcast.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_downcast.py
new file mode 100644
index 0000000000000000000000000000000000000000..9430ba2c478ae40a4a21bcc6dc034783cdf9543c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_downcast.py
@@ -0,0 +1,97 @@
+import decimal
+
+import numpy as np
+import pytest
+
+from pandas.core.dtypes.cast import maybe_downcast_to_dtype
+
+from pandas import (
+    Series,
+    Timedelta,
+)
+import pandas._testing as tm
+
+
+@pytest.mark.parametrize(
+    "arr,dtype,expected",
+    [
+        (
+            np.array([8.5, 8.6, 8.7, 8.8, 8.9999999999995]),
+            "infer",
+            np.array([8.5, 8.6, 8.7, 8.8, 8.9999999999995]),
+        ),
+        (
+            np.array([8.0, 8.0, 8.0, 8.0, 8.9999999999995]),
+            "infer",
+            np.array([8, 8, 8, 8, 9], dtype=np.int64),
+        ),
+        (
+            np.array([8.0, 8.0, 8.0, 8.0, 9.0000000000005]),
+            "infer",
+            np.array([8, 8, 8, 8, 9], dtype=np.int64),
+        ),
+        (
+            # This is a judgement call, but we do _not_ downcast Decimal
+            #  objects
+            np.array([decimal.Decimal(0.0)]),
+            "int64",
+            np.array([decimal.Decimal(0.0)]),
+        ),
+        (
+            # GH#45837
+            np.array([Timedelta(days=1), Timedelta(days=2)], dtype=object),
+            "infer",
+            np.array([1, 2], dtype="m8[D]").astype("m8[ns]"),
+        ),
+        # TODO: similar for dt64, dt64tz, Period, Interval?
+    ],
+)
+def test_downcast(arr, expected, dtype):
+    result = maybe_downcast_to_dtype(arr, dtype)
+    tm.assert_numpy_array_equal(result, expected)
+
+
+def test_downcast_booleans():
+    # see gh-16875: coercing of booleans.
+    ser = Series([True, True, False])
+    result = maybe_downcast_to_dtype(ser, np.dtype(np.float64))
+
+    expected = ser.values
+    tm.assert_numpy_array_equal(result, expected)
+
+
+def test_downcast_conversion_no_nan(any_real_numpy_dtype):
+    dtype = any_real_numpy_dtype
+    expected = np.array([1, 2])
+    arr = np.array([1.0, 2.0], dtype=dtype)
+
+    result = maybe_downcast_to_dtype(arr, "infer")
+    tm.assert_almost_equal(result, expected, check_dtype=False)
+
+
+def test_downcast_conversion_nan(float_numpy_dtype):
+    dtype = float_numpy_dtype
+    data = [1.0, 2.0, np.nan]
+
+    expected = np.array(data, dtype=dtype)
+    arr = np.array(data, dtype=dtype)
+
+    result = maybe_downcast_to_dtype(arr, "infer")
+    tm.assert_almost_equal(result, expected)
+
+
+def test_downcast_conversion_empty(any_real_numpy_dtype):
+    dtype = any_real_numpy_dtype
+    arr = np.array([], dtype=dtype)
+    result = maybe_downcast_to_dtype(arr, np.dtype("int64"))
+    tm.assert_numpy_array_equal(result, np.array([], dtype=np.int64))
+
+
+@pytest.mark.parametrize("klass", [np.datetime64, np.timedelta64])
+def test_datetime_likes_nan(klass):
+    dtype = klass.__name__ + "[ns]"
+    arr = np.array([1, 2, np.nan])
+
+    exp = np.array([1, 2, klass("NaT")], dtype)
+    res = maybe_downcast_to_dtype(arr, dtype)
+    tm.assert_numpy_array_equal(res, exp)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_find_common_type.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_find_common_type.py
new file mode 100644
index 0000000000000000000000000000000000000000..83ef7382fbe8a27ad96511a3675c51b9eadc2331
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_find_common_type.py
@@ -0,0 +1,175 @@
+import numpy as np
+import pytest
+
+from pandas.core.dtypes.cast import find_common_type
+from pandas.core.dtypes.common import pandas_dtype
+from pandas.core.dtypes.dtypes import (
+    CategoricalDtype,
+    DatetimeTZDtype,
+    IntervalDtype,
+    PeriodDtype,
+)
+
+from pandas import (
+    Categorical,
+    Index,
+)
+
+
+@pytest.mark.parametrize(
+    "source_dtypes,expected_common_dtype",
+    [
+        ((np.int64,), np.int64),
+        ((np.uint64,), np.uint64),
+        ((np.float32,), np.float32),
+        ((object,), object),
+        # Into ints.
+        ((np.int16, np.int64), np.int64),
+        ((np.int32, np.uint32), np.int64),
+        ((np.uint16, np.uint64), np.uint64),
+        # Into floats.
+        ((np.float16, np.float32), np.float32),
+        ((np.float16, np.int16), np.float32),
+        ((np.float32, np.int16), np.float32),
+        ((np.uint64, np.int64), np.float64),
+        ((np.int16, np.float64), np.float64),
+        ((np.float16, np.int64), np.float64),
+        # Into others.
+        ((np.complex128, np.int32), np.complex128),
+        ((object, np.float32), object),
+        ((object, np.int16), object),
+        # Bool with int.
+        ((np.dtype("bool"), np.int64), object),
+        ((np.dtype("bool"), np.int32), object),
+        ((np.dtype("bool"), np.int16), object),
+        ((np.dtype("bool"), np.int8), object),
+        ((np.dtype("bool"), np.uint64), object),
+        ((np.dtype("bool"), np.uint32), object),
+        ((np.dtype("bool"), np.uint16), object),
+        ((np.dtype("bool"), np.uint8), object),
+        # Bool with float.
+        ((np.dtype("bool"), np.float64), object),
+        ((np.dtype("bool"), np.float32), object),
+        (
+            (np.dtype("datetime64[ns]"), np.dtype("datetime64[ns]")),
+            np.dtype("datetime64[ns]"),
+        ),
+        (
+            (np.dtype("timedelta64[ns]"), np.dtype("timedelta64[ns]")),
+            np.dtype("timedelta64[ns]"),
+        ),
+        (
+            (np.dtype("datetime64[ns]"), np.dtype("datetime64[ms]")),
+            np.dtype("datetime64[ns]"),
+        ),
+        (
+            (np.dtype("timedelta64[ms]"), np.dtype("timedelta64[ns]")),
+            np.dtype("timedelta64[ns]"),
+        ),
+        ((np.dtype("datetime64[ns]"), np.dtype("timedelta64[ns]")), object),
+        ((np.dtype("datetime64[ns]"), np.int64), object),
+    ],
+)
+def test_numpy_dtypes(source_dtypes, expected_common_dtype):
+    source_dtypes = [pandas_dtype(x) for x in source_dtypes]
+    assert find_common_type(source_dtypes) == expected_common_dtype
+
+
+def test_raises_empty_input():
+    with pytest.raises(ValueError, match="no types given"):
+        find_common_type([])
+
+
+@pytest.mark.parametrize(
+    "dtypes,exp_type",
+    [
+        ([CategoricalDtype()], "category"),
+        ([object, CategoricalDtype()], object),
+        ([CategoricalDtype(), CategoricalDtype()], "category"),
+    ],
+)
+def test_categorical_dtype(dtypes, exp_type):
+    assert find_common_type(dtypes) == exp_type
+
+
+def test_datetimetz_dtype_match():
+    dtype = DatetimeTZDtype(unit="ns", tz="US/Eastern")
+    assert find_common_type([dtype, dtype]) == "datetime64[ns, US/Eastern]"
+
+
+@pytest.mark.parametrize(
+    "dtype2",
+    [
+        DatetimeTZDtype(unit="ns", tz="Asia/Tokyo"),
+        np.dtype("datetime64[ns]"),
+        object,
+        np.int64,
+    ],
+)
+def test_datetimetz_dtype_mismatch(dtype2):
+    dtype = DatetimeTZDtype(unit="ns", tz="US/Eastern")
+    assert find_common_type([dtype, dtype2]) == object
+    assert find_common_type([dtype2, dtype]) == object
+
+
+def test_period_dtype_match():
+    dtype = PeriodDtype(freq="D")
+    assert find_common_type([dtype, dtype]) == "period[D]"
+
+
+@pytest.mark.parametrize(
+    "dtype2",
+    [
+        DatetimeTZDtype(unit="ns", tz="Asia/Tokyo"),
+        PeriodDtype(freq="2D"),
+        PeriodDtype(freq="h"),
+        np.dtype("datetime64[ns]"),
+        object,
+        np.int64,
+    ],
+)
+def test_period_dtype_mismatch(dtype2):
+    dtype = PeriodDtype(freq="D")
+    assert find_common_type([dtype, dtype2]) == object
+    assert find_common_type([dtype2, dtype]) == object
+
+
+interval_dtypes = [
+    IntervalDtype(np.int64, "right"),
+    IntervalDtype(np.float64, "right"),
+    IntervalDtype(np.uint64, "right"),
+    IntervalDtype(DatetimeTZDtype(unit="ns", tz="US/Eastern"), "right"),
+    IntervalDtype("M8[ns]", "right"),
+    IntervalDtype("m8[ns]", "right"),
+]
+
+
+@pytest.mark.parametrize("left", interval_dtypes)
+@pytest.mark.parametrize("right", interval_dtypes)
+def test_interval_dtype(left, right):
+    result = find_common_type([left, right])
+
+    if left is right:
+        assert result is left
+
+    elif left.subtype.kind in ["i", "u", "f"]:
+        # i.e. numeric
+        if right.subtype.kind in ["i", "u", "f"]:
+            # both numeric -> common numeric subtype
+            expected = IntervalDtype(np.float64, "right")
+            assert result == expected
+        else:
+            assert result == object
+
+    else:
+        assert result == object
+
+
+@pytest.mark.parametrize("dtype", interval_dtypes)
+def test_interval_dtype_with_categorical(dtype):
+    obj = Index([], dtype=dtype)
+
+    cat = Categorical([], categories=obj)
+
+    result = find_common_type([dtype, cat.dtype])
+    assert result == dtype
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_infer_datetimelike.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_infer_datetimelike.py
new file mode 100644
index 0000000000000000000000000000000000000000..3c3844e69586d2f49377e77910627ee42fef9bb2
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_infer_datetimelike.py
@@ -0,0 +1,28 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    DataFrame,
+    NaT,
+    Series,
+    Timestamp,
+)
+
+
+@pytest.mark.parametrize(
+    "data,exp_size",
+    [
+        # see gh-16362.
+        ([[NaT, "a", "b", 0], [NaT, "b", "c", 1]], 8),
+        ([[NaT, "a", 0], [NaT, "b", 1]], 6),
+    ],
+)
+def test_maybe_infer_to_datetimelike_df_construct(data, exp_size):
+    result = DataFrame(np.array(data))
+    assert result.size == exp_size
+
+
+def test_maybe_infer_to_datetimelike_ser_construct():
+    # see gh-19671.
+    result = Series(["M1701", Timestamp("20130101")])
+    assert result.dtype.kind == "O"
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_infer_dtype.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_infer_dtype.py
new file mode 100644
index 0000000000000000000000000000000000000000..679031a625c2da1386af78059b5e2986975a73ab
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_infer_dtype.py
@@ -0,0 +1,216 @@
+from datetime import (
+    date,
+    datetime,
+    timedelta,
+)
+
+import numpy as np
+import pytest
+
+from pandas.core.dtypes.cast import (
+    infer_dtype_from,
+    infer_dtype_from_array,
+    infer_dtype_from_scalar,
+)
+from pandas.core.dtypes.common import is_dtype_equal
+
+from pandas import (
+    Categorical,
+    Interval,
+    Period,
+    Series,
+    Timedelta,
+    Timestamp,
+    date_range,
+)
+
+
+def test_infer_dtype_from_int_scalar(any_int_numpy_dtype):
+    # Test that infer_dtype_from_scalar is
+    # returning correct dtype for int and float.
+    data = np.dtype(any_int_numpy_dtype).type(12)
+    dtype, val = infer_dtype_from_scalar(data)
+    assert dtype == type(data)
+
+
+def test_infer_dtype_from_float_scalar(float_numpy_dtype):
+    float_numpy_dtype = np.dtype(float_numpy_dtype).type
+    data = float_numpy_dtype(12)
+
+    dtype, val = infer_dtype_from_scalar(data)
+    assert dtype == float_numpy_dtype
+
+
+@pytest.mark.parametrize(
+    "data,exp_dtype", [(12, np.int64), (np.float64(12), np.float64)]
+)
+def test_infer_dtype_from_python_scalar(data, exp_dtype):
+    dtype, val = infer_dtype_from_scalar(data)
+    assert dtype == exp_dtype
+
+
+@pytest.mark.parametrize("bool_val", [True, False])
+def test_infer_dtype_from_boolean(bool_val):
+    dtype, val = infer_dtype_from_scalar(bool_val)
+    assert dtype == np.bool_
+
+
+def test_infer_dtype_from_complex(complex_dtype):
+    data = np.dtype(complex_dtype).type(1)
+    dtype, val = infer_dtype_from_scalar(data)
+    assert dtype == np.complex128
+
+
+def test_infer_dtype_from_datetime():
+    dt64 = np.datetime64(1, "ns")
+    dtype, val = infer_dtype_from_scalar(dt64)
+    assert dtype == "M8[ns]"
+
+    ts = Timestamp(1)
+    dtype, val = infer_dtype_from_scalar(ts)
+    assert dtype == "M8[ns]"
+
+    dt = datetime(2000, 1, 1, 0, 0)
+    dtype, val = infer_dtype_from_scalar(dt)
+    assert dtype == "M8[us]"
+
+
+def test_infer_dtype_from_timedelta():
+    td64 = np.timedelta64(1, "ns")
+    dtype, val = infer_dtype_from_scalar(td64)
+    assert dtype == "m8[ns]"
+
+    pytd = timedelta(1)
+    dtype, val = infer_dtype_from_scalar(pytd)
+    assert dtype == "m8[us]"
+
+    td = Timedelta(1)
+    dtype, val = infer_dtype_from_scalar(td)
+    assert dtype == "m8[ns]"
+
+
+@pytest.mark.parametrize("freq", ["M", "D"])
+def test_infer_dtype_from_period(freq):
+    p = Period("2011-01-01", freq=freq)
+    dtype, val = infer_dtype_from_scalar(p)
+
+    exp_dtype = f"period[{freq}]"
+
+    assert dtype == exp_dtype
+    assert val == p
+
+
+def test_infer_dtype_misc():
+    dt = date(2000, 1, 1)
+    dtype, val = infer_dtype_from_scalar(dt)
+    assert dtype == np.object_
+
+    ts = Timestamp(1, tz="US/Eastern")
+    dtype, val = infer_dtype_from_scalar(ts)
+    assert dtype == "datetime64[ns, US/Eastern]"
+
+
+@pytest.mark.parametrize("tz", ["UTC", "US/Eastern", "Asia/Tokyo"])
+def test_infer_from_scalar_tz(tz):
+    dt = Timestamp(1, tz=tz)
+    dtype, val = infer_dtype_from_scalar(dt)
+
+    exp_dtype = f"datetime64[ns, {tz}]"
+
+    assert dtype == exp_dtype
+    assert val == dt
+
+
+@pytest.mark.parametrize(
+    "left, right, subtype",
+    [
+        (0, 1, "int64"),
+        (0.0, 1.0, "float64"),
+        (Timestamp(0), Timestamp(1), "datetime64[ns]"),
+        (Timestamp(0, tz="UTC"), Timestamp(1, tz="UTC"), "datetime64[ns, UTC]"),
+        (Timedelta(0), Timedelta(1), "timedelta64[ns]"),
+    ],
+)
+def test_infer_from_interval(left, right, subtype, closed):
+    # GH 30337
+    interval = Interval(left, right, closed)
+    result_dtype, result_value = infer_dtype_from_scalar(interval)
+    expected_dtype = f"interval[{subtype}, {closed}]"
+    assert result_dtype == expected_dtype
+    assert result_value == interval
+
+
+def test_infer_dtype_from_scalar_errors():
+    msg = "invalid ndarray passed to infer_dtype_from_scalar"
+
+    with pytest.raises(ValueError, match=msg):
+        infer_dtype_from_scalar(np.array([1]))
+
+
+@pytest.mark.parametrize(
+    "value, expected",
+    [
+        ("foo", np.object_),
+        (b"foo", np.object_),
+        (1, np.int64),
+        (1.5, np.float64),
+        (np.datetime64("2016-01-01"), np.dtype("M8[s]")),
+        (Timestamp("20160101"), np.dtype("M8[s]")),
+        (Timestamp("20160101", tz="UTC"), "datetime64[s, UTC]"),
+    ],
+)
+def test_infer_dtype_from_scalar(value, expected, using_infer_string):
+    dtype, _ = infer_dtype_from_scalar(value)
+    if using_infer_string and value == "foo":
+        expected = "string"
+    assert is_dtype_equal(dtype, expected)
+
+    with pytest.raises(TypeError, match="must be list-like"):
+        infer_dtype_from_array(value)
+
+
+@pytest.mark.parametrize(
+    "arr, expected",
+    [
+        ([1], np.dtype(int)),
+        (np.array([1], dtype=np.int64), np.int64),
+        ([np.nan, 1, ""], np.object_),
+        (np.array([[1.0, 2.0]]), np.float64),
+        (Categorical(list("aabc")), "category"),
+        (Categorical([1, 2, 3]), "category"),
+        (date_range("20160101", periods=3), np.dtype("=M8[ns]")),
+        (
+            date_range("20160101", periods=3, tz="US/Eastern"),
+            "datetime64[ns, US/Eastern]",
+        ),
+        (Series([1.0, 2, 3]), np.float64),
+        (Series(list("abc")), np.object_),
+        (
+            Series(date_range("20160101", periods=3, tz="US/Eastern")),
+            "datetime64[ns, US/Eastern]",
+        ),
+    ],
+)
+def test_infer_dtype_from_array(arr, expected, using_infer_string):
+    dtype, _ = infer_dtype_from_array(arr)
+    if (
+        using_infer_string
+        and isinstance(arr, Series)
+        and arr.tolist() == ["a", "b", "c"]
+    ):
+        expected = "string"
+    assert is_dtype_equal(dtype, expected)
+
+
+@pytest.mark.parametrize("cls", [np.datetime64, np.timedelta64])
+def test_infer_dtype_from_scalar_zerodim_datetimelike(cls):
+    # ndarray.item() can incorrectly return int instead of td64/dt64
+    val = cls(1234, "ns")
+    arr = np.array(val)
+
+    dtype, res = infer_dtype_from_scalar(arr)
+    assert dtype.type is cls
+    assert isinstance(res, cls)
+
+    dtype, res = infer_dtype_from(arr)
+    assert dtype.type is cls
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_maybe_box_native.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_maybe_box_native.py
new file mode 100644
index 0000000000000000000000000000000000000000..3f62f31dac2191a15d7df8db028a9286262d0080
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_maybe_box_native.py
@@ -0,0 +1,40 @@
+from datetime import datetime
+
+import numpy as np
+import pytest
+
+from pandas.core.dtypes.cast import maybe_box_native
+
+from pandas import (
+    Interval,
+    Period,
+    Timedelta,
+    Timestamp,
+)
+
+
+@pytest.mark.parametrize(
+    "obj,expected_dtype",
+    [
+        (b"\x00\x10", bytes),
+        (int(4), int),
+        (np.uint(4), int),
+        (np.int32(-4), int),
+        (np.uint8(4), int),
+        (float(454.98), float),
+        (np.float16(0.4), float),
+        (np.float64(1.4), float),
+        (np.bool_(False), bool),
+        (datetime(2005, 2, 25), datetime),
+        (np.datetime64("2005-02-25"), Timestamp),
+        (Timestamp("2005-02-25"), Timestamp),
+        (np.timedelta64(1, "D"), Timedelta),
+        (Timedelta(1, "D"), Timedelta),
+        (Interval(0, 1), Interval),
+        (Period("4Q2005"), Period),
+    ],
+)
+def test_maybe_box_native(obj, expected_dtype):
+    boxed_obj = maybe_box_native(obj)
+    result_dtype = type(boxed_obj)
+    assert result_dtype is expected_dtype
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_promote.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_promote.py
new file mode 100644
index 0000000000000000000000000000000000000000..021107724bef73d998191d65b55fb29848fc8b9a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_promote.py
@@ -0,0 +1,530 @@
+"""
+These test the method maybe_promote from core/dtypes/cast.py
+"""
+
+import datetime
+from decimal import Decimal
+
+import numpy as np
+import pytest
+
+from pandas._libs.tslibs import NaT
+
+from pandas.core.dtypes.cast import maybe_promote
+from pandas.core.dtypes.common import is_scalar
+from pandas.core.dtypes.dtypes import DatetimeTZDtype
+from pandas.core.dtypes.missing import isna
+
+import pandas as pd
+
+
+def _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar=None):
+    """
+    Auxiliary function to unify testing of scalar/array promotion.
+
+    Parameters
+    ----------
+    dtype : dtype
+        The value to pass on as the first argument to maybe_promote.
+    fill_value : scalar
+        The value to pass on as the second argument to maybe_promote as
+        a scalar.
+    expected_dtype : dtype
+        The expected dtype returned by maybe_promote (by design this is the
+        same regardless of whether fill_value was passed as a scalar or in an
+        array!).
+    exp_val_for_scalar : scalar
+        The expected value for the (potentially upcast) fill_value returned by
+        maybe_promote.
+    """
+    assert is_scalar(fill_value)
+
+    # here, we pass on fill_value as a scalar directly; the expected value
+    # returned from maybe_promote is fill_value, potentially upcast to the
+    # returned dtype.
+    result_dtype, result_fill_value = maybe_promote(dtype, fill_value)
+    expected_fill_value = exp_val_for_scalar
+
+    assert result_dtype == expected_dtype
+    _assert_match(result_fill_value, expected_fill_value)
+
+
+def _assert_match(result_fill_value, expected_fill_value):
+    # GH#23982/25425 require the same type in addition to equality/NA-ness
+    res_type = type(result_fill_value)
+    ex_type = type(expected_fill_value)
+
+    if hasattr(result_fill_value, "dtype"):
+        # Compare types in a way that is robust to platform-specific
+        #  idiosyncrasies where e.g. sometimes we get "ulonglong" as an alias
+        #  for "uint64" or "intc" as an alias for "int32"
+        assert result_fill_value.dtype.kind == expected_fill_value.dtype.kind
+        assert result_fill_value.dtype.itemsize == expected_fill_value.dtype.itemsize
+    else:
+        # On some builds, type comparison fails, e.g. np.int32 != np.int32
+        assert res_type == ex_type or res_type.__name__ == ex_type.__name__
+
+    match_value = result_fill_value == expected_fill_value
+    if match_value is pd.NA:
+        match_value = False
+
+    # Note: type check above ensures that we have the _same_ NA value
+    # for missing values, None == None (which is checked
+    # through match_value above), but np.nan != np.nan and pd.NaT != pd.NaT
+    match_missing = isna(result_fill_value) and isna(expected_fill_value)
+
+    assert match_value or match_missing
+
+
+@pytest.mark.parametrize(
+    "dtype, fill_value, expected_dtype",
+    [
+        # size 8
+        ("int8", 1, "int8"),
+        ("int8", np.iinfo("int8").max + 1, "int16"),
+        ("int8", np.iinfo("int16").max + 1, "int32"),
+        ("int8", np.iinfo("int32").max + 1, "int64"),
+        ("int8", np.iinfo("int64").max + 1, "object"),
+        ("int8", -1, "int8"),
+        ("int8", np.iinfo("int8").min - 1, "int16"),
+        ("int8", np.iinfo("int16").min - 1, "int32"),
+        ("int8", np.iinfo("int32").min - 1, "int64"),
+        ("int8", np.iinfo("int64").min - 1, "object"),
+        # keep signed-ness as long as possible
+        ("uint8", 1, "uint8"),
+        ("uint8", np.iinfo("int8").max + 1, "uint8"),
+        ("uint8", np.iinfo("uint8").max + 1, "uint16"),
+        ("uint8", np.iinfo("int16").max + 1, "uint16"),
+        ("uint8", np.iinfo("uint16").max + 1, "uint32"),
+        ("uint8", np.iinfo("int32").max + 1, "uint32"),
+        ("uint8", np.iinfo("uint32").max + 1, "uint64"),
+        ("uint8", np.iinfo("int64").max + 1, "uint64"),
+        ("uint8", np.iinfo("uint64").max + 1, "object"),
+        # max of uint8 cannot be contained in int8
+        ("uint8", -1, "int16"),
+        ("uint8", np.iinfo("int8").min - 1, "int16"),
+        ("uint8", np.iinfo("int16").min - 1, "int32"),
+        ("uint8", np.iinfo("int32").min - 1, "int64"),
+        ("uint8", np.iinfo("int64").min - 1, "object"),
+        # size 16
+        ("int16", 1, "int16"),
+        ("int16", np.iinfo("int8").max + 1, "int16"),
+        ("int16", np.iinfo("int16").max + 1, "int32"),
+        ("int16", np.iinfo("int32").max + 1, "int64"),
+        ("int16", np.iinfo("int64").max + 1, "object"),
+        ("int16", -1, "int16"),
+        ("int16", np.iinfo("int8").min - 1, "int16"),
+        ("int16", np.iinfo("int16").min - 1, "int32"),
+        ("int16", np.iinfo("int32").min - 1, "int64"),
+        ("int16", np.iinfo("int64").min - 1, "object"),
+        ("uint16", 1, "uint16"),
+        ("uint16", np.iinfo("int8").max + 1, "uint16"),
+        ("uint16", np.iinfo("uint8").max + 1, "uint16"),
+        ("uint16", np.iinfo("int16").max + 1, "uint16"),
+        ("uint16", np.iinfo("uint16").max + 1, "uint32"),
+        ("uint16", np.iinfo("int32").max + 1, "uint32"),
+        ("uint16", np.iinfo("uint32").max + 1, "uint64"),
+        ("uint16", np.iinfo("int64").max + 1, "uint64"),
+        ("uint16", np.iinfo("uint64").max + 1, "object"),
+        ("uint16", -1, "int32"),
+        ("uint16", np.iinfo("int8").min - 1, "int32"),
+        ("uint16", np.iinfo("int16").min - 1, "int32"),
+        ("uint16", np.iinfo("int32").min - 1, "int64"),
+        ("uint16", np.iinfo("int64").min - 1, "object"),
+        # size 32
+        ("int32", 1, "int32"),
+        ("int32", np.iinfo("int8").max + 1, "int32"),
+        ("int32", np.iinfo("int16").max + 1, "int32"),
+        ("int32", np.iinfo("int32").max + 1, "int64"),
+        ("int32", np.iinfo("int64").max + 1, "object"),
+        ("int32", -1, "int32"),
+        ("int32", np.iinfo("int8").min - 1, "int32"),
+        ("int32", np.iinfo("int16").min - 1, "int32"),
+        ("int32", np.iinfo("int32").min - 1, "int64"),
+        ("int32", np.iinfo("int64").min - 1, "object"),
+        ("uint32", 1, "uint32"),
+        ("uint32", np.iinfo("int8").max + 1, "uint32"),
+        ("uint32", np.iinfo("uint8").max + 1, "uint32"),
+        ("uint32", np.iinfo("int16").max + 1, "uint32"),
+        ("uint32", np.iinfo("uint16").max + 1, "uint32"),
+        ("uint32", np.iinfo("int32").max + 1, "uint32"),
+        ("uint32", np.iinfo("uint32").max + 1, "uint64"),
+        ("uint32", np.iinfo("int64").max + 1, "uint64"),
+        ("uint32", np.iinfo("uint64").max + 1, "object"),
+        ("uint32", -1, "int64"),
+        ("uint32", np.iinfo("int8").min - 1, "int64"),
+        ("uint32", np.iinfo("int16").min - 1, "int64"),
+        ("uint32", np.iinfo("int32").min - 1, "int64"),
+        ("uint32", np.iinfo("int64").min - 1, "object"),
+        # size 64
+        ("int64", 1, "int64"),
+        ("int64", np.iinfo("int8").max + 1, "int64"),
+        ("int64", np.iinfo("int16").max + 1, "int64"),
+        ("int64", np.iinfo("int32").max + 1, "int64"),
+        ("int64", np.iinfo("int64").max + 1, "object"),
+        ("int64", -1, "int64"),
+        ("int64", np.iinfo("int8").min - 1, "int64"),
+        ("int64", np.iinfo("int16").min - 1, "int64"),
+        ("int64", np.iinfo("int32").min - 1, "int64"),
+        ("int64", np.iinfo("int64").min - 1, "object"),
+        ("uint64", 1, "uint64"),
+        ("uint64", np.iinfo("int8").max + 1, "uint64"),
+        ("uint64", np.iinfo("uint8").max + 1, "uint64"),
+        ("uint64", np.iinfo("int16").max + 1, "uint64"),
+        ("uint64", np.iinfo("uint16").max + 1, "uint64"),
+        ("uint64", np.iinfo("int32").max + 1, "uint64"),
+        ("uint64", np.iinfo("uint32").max + 1, "uint64"),
+        ("uint64", np.iinfo("int64").max + 1, "uint64"),
+        ("uint64", np.iinfo("uint64").max + 1, "object"),
+        ("uint64", -1, "object"),
+        ("uint64", np.iinfo("int8").min - 1, "object"),
+        ("uint64", np.iinfo("int16").min - 1, "object"),
+        ("uint64", np.iinfo("int32").min - 1, "object"),
+        ("uint64", np.iinfo("int64").min - 1, "object"),
+    ],
+)
+def test_maybe_promote_int_with_int(dtype, fill_value, expected_dtype):
+    dtype = np.dtype(dtype)
+    expected_dtype = np.dtype(expected_dtype)
+
+    # output is not a generic int, but corresponds to expected_dtype
+    exp_val_for_scalar = np.array([fill_value], dtype=expected_dtype)[0]
+
+    _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
+
+
+def test_maybe_promote_int_with_float(any_int_numpy_dtype, float_numpy_dtype):
+    dtype = np.dtype(any_int_numpy_dtype)
+    fill_dtype = np.dtype(float_numpy_dtype)
+
+    # create array of given dtype; casts "1" to correct dtype
+    fill_value = np.array([1], dtype=fill_dtype)[0]
+
+    # filling int with float always upcasts to float64
+    expected_dtype = np.float64
+    # fill_value can be different float type
+    exp_val_for_scalar = np.float64(fill_value)
+
+    _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
+
+
+def test_maybe_promote_float_with_int(float_numpy_dtype, any_int_numpy_dtype):
+    dtype = np.dtype(float_numpy_dtype)
+    fill_dtype = np.dtype(any_int_numpy_dtype)
+
+    # create array of given dtype; casts "1" to correct dtype
+    fill_value = np.array([1], dtype=fill_dtype)[0]
+
+    # filling float with int always keeps float dtype
+    # because: np.finfo('float32').max > np.iinfo('uint64').max
+    expected_dtype = dtype
+    # output is not a generic float, but corresponds to expected_dtype
+    exp_val_for_scalar = np.array([fill_value], dtype=expected_dtype)[0]
+
+    _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
+
+
+@pytest.mark.parametrize(
+    "dtype, fill_value, expected_dtype",
+    [
+        # float filled with float
+        ("float32", 1, "float32"),
+        ("float32", float(np.finfo("float32").max) * 1.1, "float64"),
+        ("float64", 1, "float64"),
+        ("float64", float(np.finfo("float32").max) * 1.1, "float64"),
+        # complex filled with float
+        ("complex64", 1, "complex64"),
+        ("complex64", float(np.finfo("float32").max) * 1.1, "complex128"),
+        ("complex128", 1, "complex128"),
+        ("complex128", float(np.finfo("float32").max) * 1.1, "complex128"),
+        # float filled with complex
+        ("float32", 1 + 1j, "complex64"),
+        ("float32", float(np.finfo("float32").max) * (1.1 + 1j), "complex128"),
+        ("float64", 1 + 1j, "complex128"),
+        ("float64", float(np.finfo("float32").max) * (1.1 + 1j), "complex128"),
+        # complex filled with complex
+        ("complex64", 1 + 1j, "complex64"),
+        ("complex64", float(np.finfo("float32").max) * (1.1 + 1j), "complex128"),
+        ("complex128", 1 + 1j, "complex128"),
+        ("complex128", float(np.finfo("float32").max) * (1.1 + 1j), "complex128"),
+    ],
+)
+def test_maybe_promote_float_with_float(dtype, fill_value, expected_dtype):
+    dtype = np.dtype(dtype)
+    expected_dtype = np.dtype(expected_dtype)
+
+    # output is not a generic float, but corresponds to expected_dtype
+    exp_val_for_scalar = np.array([fill_value], dtype=expected_dtype)[0]
+
+    _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
+
+
+def test_maybe_promote_bool_with_any(any_numpy_dtype):
+    dtype = np.dtype(bool)
+    fill_dtype = np.dtype(any_numpy_dtype)
+
+    # create array of given dtype; casts "1" to correct dtype
+    fill_value = np.array([1], dtype=fill_dtype)[0]
+
+    # filling bool with anything but bool casts to object
+    expected_dtype = np.dtype(object) if fill_dtype != bool else fill_dtype
+    exp_val_for_scalar = fill_value
+
+    _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
+
+
+def test_maybe_promote_any_with_bool(any_numpy_dtype):
+    dtype = np.dtype(any_numpy_dtype)
+    fill_value = True
+
+    # filling anything but bool with bool casts to object
+    expected_dtype = np.dtype(object) if dtype != bool else dtype
+    # output is not a generic bool, but corresponds to expected_dtype
+    exp_val_for_scalar = np.array([fill_value], dtype=expected_dtype)[0]
+
+    _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
+
+
+def test_maybe_promote_bytes_with_any(bytes_dtype, any_numpy_dtype):
+    dtype = np.dtype(bytes_dtype)
+    fill_dtype = np.dtype(any_numpy_dtype)
+
+    # create array of given dtype; casts "1" to correct dtype
+    fill_value = np.array([1], dtype=fill_dtype)[0]
+
+    # we never use bytes dtype internally, always promote to object
+    expected_dtype = np.dtype(np.object_)
+    exp_val_for_scalar = fill_value
+
+    _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
+
+
+def test_maybe_promote_any_with_bytes(any_numpy_dtype):
+    dtype = np.dtype(any_numpy_dtype)
+
+    # create array of given dtype
+    fill_value = b"abc"
+
+    # we never use bytes dtype internally, always promote to object
+    expected_dtype = np.dtype(np.object_)
+    # output is not a generic bytes, but corresponds to expected_dtype
+    exp_val_for_scalar = np.array([fill_value], dtype=expected_dtype)[0]
+
+    _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
+
+
+def test_maybe_promote_datetime64_with_any(datetime64_dtype, any_numpy_dtype):
+    dtype = np.dtype(datetime64_dtype)
+    fill_dtype = np.dtype(any_numpy_dtype)
+
+    # create array of given dtype; casts "1" to correct dtype
+    fill_value = np.array([1], dtype=fill_dtype)[0]
+
+    # filling datetime with anything but datetime casts to object
+    if fill_dtype.kind == "M":
+        expected_dtype = dtype
+        # for datetime dtypes, scalar values get cast to to_datetime64
+        exp_val_for_scalar = pd.Timestamp(fill_value).to_datetime64()
+    else:
+        expected_dtype = np.dtype(object)
+        exp_val_for_scalar = fill_value
+
+    _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
+
+
+@pytest.mark.parametrize(
+    "fill_value",
+    [
+        pd.Timestamp("now"),
+        np.datetime64("now"),
+        datetime.datetime.now(),
+        datetime.date.today(),
+    ],
+    ids=["pd.Timestamp", "np.datetime64", "datetime.datetime", "datetime.date"],
+)
+def test_maybe_promote_any_with_datetime64(any_numpy_dtype, fill_value):
+    dtype = np.dtype(any_numpy_dtype)
+
+    # filling datetime with anything but datetime casts to object
+    if dtype.kind == "M":
+        expected_dtype = dtype
+        # for datetime dtypes, scalar values get cast to pd.Timestamp.value
+        exp_val_for_scalar = pd.Timestamp(fill_value).to_datetime64()
+    else:
+        expected_dtype = np.dtype(object)
+        exp_val_for_scalar = fill_value
+
+    if type(fill_value) is datetime.date and dtype.kind == "M":
+        # Casting date to dt64 is deprecated, in 2.0 enforced to cast to object
+        expected_dtype = np.dtype(object)
+        exp_val_for_scalar = fill_value
+
+    _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
+
+
+@pytest.mark.parametrize(
+    "fill_value",
+    [
+        pd.Timestamp(2023, 1, 1),
+        np.datetime64("2023-01-01"),
+        datetime.datetime(2023, 1, 1),
+        datetime.date(2023, 1, 1),
+    ],
+    ids=["pd.Timestamp", "np.datetime64", "datetime.datetime", "datetime.date"],
+)
+def test_maybe_promote_any_numpy_dtype_with_datetimetz(
+    any_numpy_dtype, tz_aware_fixture, fill_value
+):
+    dtype = np.dtype(any_numpy_dtype)
+    fill_dtype = DatetimeTZDtype(tz=tz_aware_fixture)
+
+    fill_value = pd.Series([fill_value], dtype=fill_dtype)[0]
+
+    # filling any numpy dtype with datetimetz casts to object
+    expected_dtype = np.dtype(object)
+    exp_val_for_scalar = fill_value
+
+    _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
+
+
+def test_maybe_promote_timedelta64_with_any(timedelta64_dtype, any_numpy_dtype):
+    dtype = np.dtype(timedelta64_dtype)
+    fill_dtype = np.dtype(any_numpy_dtype)
+
+    # create array of given dtype; casts "1" to correct dtype
+    fill_value = np.array([1], dtype=fill_dtype)[0]
+
+    # filling timedelta with anything but timedelta casts to object
+    if fill_dtype.kind == "m":
+        expected_dtype = dtype
+        # for timedelta dtypes, scalar values get cast to pd.Timedelta.value
+        exp_val_for_scalar = pd.Timedelta(fill_value).to_timedelta64()
+    else:
+        expected_dtype = np.dtype(object)
+        exp_val_for_scalar = fill_value
+
+    _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
+
+
+@pytest.mark.parametrize(
+    "fill_value",
+    [pd.Timedelta(days=1), np.timedelta64(24, "h"), datetime.timedelta(1)],
+    ids=["pd.Timedelta", "np.timedelta64", "datetime.timedelta"],
+)
+def test_maybe_promote_any_with_timedelta64(any_numpy_dtype, fill_value):
+    dtype = np.dtype(any_numpy_dtype)
+
+    # filling anything but timedelta with timedelta casts to object
+    if dtype.kind == "m":
+        expected_dtype = dtype
+        # for timedelta dtypes, scalar values get cast to pd.Timedelta.value
+        exp_val_for_scalar = pd.Timedelta(fill_value).to_timedelta64()
+    else:
+        expected_dtype = np.dtype(object)
+        exp_val_for_scalar = fill_value
+
+    _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
+
+
+def test_maybe_promote_string_with_any(string_dtype, any_numpy_dtype):
+    dtype = np.dtype(string_dtype)
+    fill_dtype = np.dtype(any_numpy_dtype)
+
+    # create array of given dtype; casts "1" to correct dtype
+    fill_value = np.array([1], dtype=fill_dtype)[0]
+
+    # filling string with anything casts to object
+    expected_dtype = np.dtype(object)
+    exp_val_for_scalar = fill_value
+
+    _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
+
+
+def test_maybe_promote_any_with_string(any_numpy_dtype):
+    dtype = np.dtype(any_numpy_dtype)
+
+    # create array of given dtype
+    fill_value = "abc"
+
+    # filling anything with a string casts to object
+    expected_dtype = np.dtype(object)
+    exp_val_for_scalar = fill_value
+
+    _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
+
+
+def test_maybe_promote_object_with_any(object_dtype, any_numpy_dtype):
+    dtype = np.dtype(object_dtype)
+    fill_dtype = np.dtype(any_numpy_dtype)
+
+    # create array of given dtype; casts "1" to correct dtype
+    fill_value = np.array([1], dtype=fill_dtype)[0]
+
+    # filling object with anything stays object
+    expected_dtype = np.dtype(object)
+    exp_val_for_scalar = fill_value
+
+    _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
+
+
+def test_maybe_promote_any_with_object(any_numpy_dtype):
+    dtype = np.dtype(any_numpy_dtype)
+
+    # create array of object dtype from a scalar value (i.e. passing
+    # dtypes.common.is_scalar), which can however not be cast to int/float etc.
+    fill_value = pd.DateOffset(1)
+
+    # filling object with anything stays object
+    expected_dtype = np.dtype(object)
+    exp_val_for_scalar = fill_value
+
+    _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
+
+
+def test_maybe_promote_any_numpy_dtype_with_na(any_numpy_dtype, nulls_fixture):
+    fill_value = nulls_fixture
+    dtype = np.dtype(any_numpy_dtype)
+
+    if isinstance(fill_value, Decimal):
+        # Subject to change, but ATM (When Decimal(NAN) is being added to nulls_fixture)
+        #  this is the existing behavior in maybe_promote,
+        #  hinges on is_valid_na_for_dtype
+        if dtype.kind in "iufc":
+            if dtype.kind in "iu":
+                expected_dtype = np.dtype(np.float64)
+            else:
+                expected_dtype = dtype
+            exp_val_for_scalar = np.nan
+        else:
+            expected_dtype = np.dtype(object)
+            exp_val_for_scalar = fill_value
+    elif dtype.kind in "iu" and fill_value is not NaT:
+        # integer + other missing value (np.nan / None) casts to float
+        expected_dtype = np.float64
+        exp_val_for_scalar = np.nan
+    elif dtype == object and fill_value is NaT:
+        # inserting into object does not cast the value
+        # but *does* cast None to np.nan
+        expected_dtype = np.dtype(object)
+        exp_val_for_scalar = fill_value
+    elif dtype.kind in "mM":
+        # datetime / timedelta cast all missing values to dtyped-NaT
+        expected_dtype = dtype
+        exp_val_for_scalar = dtype.type("NaT", "ns")
+    elif fill_value is NaT:
+        # NaT upcasts everything that's not datetime/timedelta to object
+        expected_dtype = np.dtype(object)
+        exp_val_for_scalar = NaT
+    elif dtype.kind in "fc":
+        # float / complex + missing value (!= NaT) stays the same
+        expected_dtype = dtype
+        exp_val_for_scalar = np.nan
+    else:
+        # all other cases cast to object, and use np.nan as missing value
+        expected_dtype = np.dtype(object)
+        if fill_value is pd.NA:
+            exp_val_for_scalar = pd.NA
+        else:
+            exp_val_for_scalar = np.nan
+
+    _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/dtypes/test_common.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/dtypes/test_common.py
new file mode 100644
index 0000000000000000000000000000000000000000..579f5636922dc3fb4ed652e1fa374607ec57501a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/dtypes/test_common.py
@@ -0,0 +1,865 @@
+from __future__ import annotations
+
+import numpy as np
+import pytest
+
+from pandas.compat import HAS_PYARROW
+import pandas.util._test_decorators as td
+
+from pandas.core.dtypes.astype import astype_array
+import pandas.core.dtypes.common as com
+from pandas.core.dtypes.dtypes import (
+    CategoricalDtype,
+    CategoricalDtypeType,
+    DatetimeTZDtype,
+    ExtensionDtype,
+    IntervalDtype,
+    PeriodDtype,
+)
+from pandas.core.dtypes.missing import isna
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.api.types import pandas_dtype
+from pandas.arrays import SparseArray
+from pandas.util.version import Version
+
+
+# EA & Actual Dtypes
+def to_ea_dtypes(dtypes):
+    """convert list of string dtypes to EA dtype"""
+    return [getattr(pd, dt + "Dtype") for dt in dtypes]
+
+
+def to_numpy_dtypes(dtypes):
+    """convert list of string dtypes to numpy dtype"""
+    return [getattr(np, dt) for dt in dtypes if isinstance(dt, str)]
+
+
+class TestNumpyEADtype:
+    # Passing invalid dtype, both as a string or object, must raise TypeError
+    # Per issue GH15520
+    @pytest.mark.parametrize("box", [pd.Timestamp, "pd.Timestamp", list])
+    def test_invalid_dtype_error(self, box):
+        with pytest.raises(TypeError, match="not understood"):
+            com.pandas_dtype(box)
+
+    @pytest.mark.parametrize(
+        "dtype",
+        [
+            object,
+            "float64",
+            np.object_,
+            np.dtype("object"),
+            "O",
+            np.float64,
+            float,
+            np.dtype("float64"),
+            "object_",
+        ],
+    )
+    def test_pandas_dtype_valid(self, dtype):
+        assert com.pandas_dtype(dtype) == dtype
+
+    @pytest.mark.parametrize(
+        "dtype", ["M8[ns]", "m8[ns]", "object", "float64", "int64"]
+    )
+    def test_numpy_dtype(self, dtype):
+        assert com.pandas_dtype(dtype) == np.dtype(dtype)
+
+    def test_numpy_string_dtype(self):
+        # do not parse freq-like string as period dtype
+        assert com.pandas_dtype("U") == np.dtype("U")
+        assert com.pandas_dtype("S") == np.dtype("S")
+
+    @pytest.mark.parametrize(
+        "dtype",
+        [
+            "datetime64[ns, US/Eastern]",
+            "datetime64[ns, Asia/Tokyo]",
+            "datetime64[ns, UTC]",
+            # GH#33885 check that the M8 alias is understood
+            "M8[ns, US/Eastern]",
+            "M8[ns, Asia/Tokyo]",
+            "M8[ns, UTC]",
+        ],
+    )
+    def test_datetimetz_dtype(self, dtype):
+        assert com.pandas_dtype(dtype) == DatetimeTZDtype.construct_from_string(dtype)
+        assert com.pandas_dtype(dtype) == dtype
+
+    def test_categorical_dtype(self):
+        assert com.pandas_dtype("category") == CategoricalDtype()
+
+    @pytest.mark.parametrize(
+        "dtype",
+        [
+            "period[D]",
+            "period[3M]",
+            "period[us]",
+            "Period[D]",
+            "Period[3M]",
+            "Period[us]",
+        ],
+    )
+    def test_period_dtype(self, dtype):
+        assert com.pandas_dtype(dtype) is not PeriodDtype(dtype)
+        assert com.pandas_dtype(dtype) == PeriodDtype(dtype)
+        assert com.pandas_dtype(dtype) == dtype
+
+
+dtypes = {
+    "datetime_tz": com.pandas_dtype("datetime64[ns, US/Eastern]"),
+    "datetime": com.pandas_dtype("datetime64[ns]"),
+    "timedelta": com.pandas_dtype("timedelta64[ns]"),
+    "period": PeriodDtype("D"),
+    "integer": np.dtype(np.int64),
+    "float": np.dtype(np.float64),
+    "object": np.dtype(object),
+    "category": com.pandas_dtype("category"),
+    "string": pd.StringDtype(),
+}
+
+
+@pytest.mark.parametrize("name1,dtype1", list(dtypes.items()), ids=lambda x: str(x))
+@pytest.mark.parametrize("name2,dtype2", list(dtypes.items()), ids=lambda x: str(x))
+def test_dtype_equal(name1, dtype1, name2, dtype2):
+    # match equal to self, but not equal to other
+    assert com.is_dtype_equal(dtype1, dtype1)
+    if name1 != name2:
+        assert not com.is_dtype_equal(dtype1, dtype2)
+
+
+@pytest.mark.parametrize("name,dtype", list(dtypes.items()), ids=lambda x: str(x))
+def test_pyarrow_string_import_error(name, dtype):
+    # GH-44276
+    assert not com.is_dtype_equal(dtype, "string[pyarrow]")
+
+
+@pytest.mark.parametrize(
+    "dtype1,dtype2",
+    [
+        (np.int8, np.int64),
+        (np.int16, np.int64),
+        (np.int32, np.int64),
+        (np.float32, np.float64),
+        (PeriodDtype("D"), PeriodDtype("2D")),  # PeriodType
+        (
+            com.pandas_dtype("datetime64[ns, US/Eastern]"),
+            com.pandas_dtype("datetime64[ns, CET]"),
+        ),  # Datetime
+        (None, None),  # gh-15941: no exception should be raised.
+    ],
+)
+def test_dtype_equal_strict(dtype1, dtype2):
+    assert not com.is_dtype_equal(dtype1, dtype2)
+
+
+def get_is_dtype_funcs():
+    """
+    Get all functions in pandas.core.dtypes.common that
+    begin with 'is_' and end with 'dtype'
+
+    """
+    fnames = [f for f in dir(com) if (f.startswith("is_") and f.endswith("dtype"))]
+    fnames.remove("is_string_or_object_np_dtype")  # fastpath requires np.dtype obj
+    return [getattr(com, fname) for fname in fnames]
+
+
+@pytest.mark.filterwarnings(
+    "ignore:is_categorical_dtype is deprecated:DeprecationWarning"
+)
+@pytest.mark.parametrize("func", get_is_dtype_funcs(), ids=lambda x: x.__name__)
+def test_get_dtype_error_catch(func):
+    # see gh-15941
+    #
+    # No exception should be raised.
+
+    msg = f"{func.__name__} is deprecated"
+    warn = None
+    if (
+        func is com.is_int64_dtype
+        or func is com.is_interval_dtype
+        or func is com.is_datetime64tz_dtype
+        or func is com.is_categorical_dtype
+        or func is com.is_period_dtype
+    ):
+        warn = DeprecationWarning
+
+    with tm.assert_produces_warning(warn, match=msg):
+        assert not func(None)
+
+
+def test_is_object():
+    assert com.is_object_dtype(object)
+    assert com.is_object_dtype(np.array([], dtype=object))
+
+    assert not com.is_object_dtype(int)
+    assert not com.is_object_dtype(np.array([], dtype=int))
+    assert not com.is_object_dtype([1, 2, 3])
+
+
+@pytest.mark.parametrize(
+    "check_scipy", [False, pytest.param(True, marks=td.skip_if_no("scipy"))]
+)
+def test_is_sparse(check_scipy):
+    msg = "is_sparse is deprecated"
+    with tm.assert_produces_warning(DeprecationWarning, match=msg):
+        assert com.is_sparse(SparseArray([1, 2, 3]))
+
+        assert not com.is_sparse(np.array([1, 2, 3]))
+
+        if check_scipy:
+            import scipy.sparse
+
+            assert not com.is_sparse(scipy.sparse.bsr_matrix([1, 2, 3]))
+
+
+def test_is_scipy_sparse():
+    sp_sparse = pytest.importorskip("scipy.sparse")
+
+    assert com.is_scipy_sparse(sp_sparse.bsr_matrix([1, 2, 3]))
+
+    assert not com.is_scipy_sparse(SparseArray([1, 2, 3]))
+
+
+def test_is_datetime64_dtype():
+    assert not com.is_datetime64_dtype(object)
+    assert not com.is_datetime64_dtype([1, 2, 3])
+    assert not com.is_datetime64_dtype(np.array([], dtype=int))
+
+    assert com.is_datetime64_dtype(np.datetime64)
+    assert com.is_datetime64_dtype(np.array([], dtype=np.datetime64))
+
+
+def test_is_datetime64tz_dtype():
+    msg = "is_datetime64tz_dtype is deprecated"
+    with tm.assert_produces_warning(DeprecationWarning, match=msg):
+        assert not com.is_datetime64tz_dtype(object)
+        assert not com.is_datetime64tz_dtype([1, 2, 3])
+        assert not com.is_datetime64tz_dtype(pd.DatetimeIndex([1, 2, 3]))
+        assert com.is_datetime64tz_dtype(pd.DatetimeIndex(["2000"], tz="US/Eastern"))
+
+
+def test_custom_ea_kind_M_not_datetime64tz():
+    # GH 34986
+    class NotTZDtype(ExtensionDtype):
+        @property
+        def kind(self) -> str:
+            return "M"
+
+    not_tz_dtype = NotTZDtype()
+    msg = "is_datetime64tz_dtype is deprecated"
+    with tm.assert_produces_warning(DeprecationWarning, match=msg):
+        assert not com.is_datetime64tz_dtype(not_tz_dtype)
+        assert not com.needs_i8_conversion(not_tz_dtype)
+
+
+def test_is_timedelta64_dtype():
+    assert not com.is_timedelta64_dtype(object)
+    assert not com.is_timedelta64_dtype(None)
+    assert not com.is_timedelta64_dtype([1, 2, 3])
+    assert not com.is_timedelta64_dtype(np.array([], dtype=np.datetime64))
+    assert not com.is_timedelta64_dtype("0 days")
+    assert not com.is_timedelta64_dtype("0 days 00:00:00")
+    assert not com.is_timedelta64_dtype(["0 days 00:00:00"])
+    assert not com.is_timedelta64_dtype("NO DATE")
+
+    assert com.is_timedelta64_dtype(np.timedelta64)
+    assert com.is_timedelta64_dtype(pd.Series([], dtype="timedelta64[ns]"))
+    assert com.is_timedelta64_dtype(pd.to_timedelta(["0 days", "1 days"]))
+
+
+def test_is_period_dtype():
+    msg = "is_period_dtype is deprecated"
+    with tm.assert_produces_warning(DeprecationWarning, match=msg):
+        assert not com.is_period_dtype(object)
+        assert not com.is_period_dtype([1, 2, 3])
+        assert not com.is_period_dtype(pd.Period("2017-01-01"))
+
+        assert com.is_period_dtype(PeriodDtype(freq="D"))
+        assert com.is_period_dtype(pd.PeriodIndex([], freq="Y"))
+
+
+def test_is_interval_dtype():
+    msg = "is_interval_dtype is deprecated"
+    with tm.assert_produces_warning(DeprecationWarning, match=msg):
+        assert not com.is_interval_dtype(object)
+        assert not com.is_interval_dtype([1, 2, 3])
+
+        assert com.is_interval_dtype(IntervalDtype())
+
+        interval = pd.Interval(1, 2, closed="right")
+        assert not com.is_interval_dtype(interval)
+        assert com.is_interval_dtype(pd.IntervalIndex([interval]))
+
+
+def test_is_categorical_dtype():
+    msg = "is_categorical_dtype is deprecated"
+    with tm.assert_produces_warning(DeprecationWarning, match=msg):
+        assert not com.is_categorical_dtype(object)
+        assert not com.is_categorical_dtype([1, 2, 3])
+
+        assert com.is_categorical_dtype(CategoricalDtype())
+        assert com.is_categorical_dtype(pd.Categorical([1, 2, 3]))
+        assert com.is_categorical_dtype(pd.CategoricalIndex([1, 2, 3]))
+
+
+@pytest.mark.parametrize(
+    "dtype, expected",
+    [
+        (int, False),
+        (pd.Series([1, 2]), False),
+        (str, True),
+        (object, True),
+        (np.array(["a", "b"]), True),
+        (pd.StringDtype(), True),
+        (pd.Index([], dtype="O"), True),
+    ],
+)
+def test_is_string_dtype(dtype, expected):
+    # GH#54661
+
+    result = com.is_string_dtype(dtype)
+    assert result is expected
+
+
+@pytest.mark.parametrize(
+    "data",
+    [[(0, 1), (1, 1)], pd.Categorical([1, 2, 3]), np.array([1, 2], dtype=object)],
+)
+def test_is_string_dtype_arraylike_with_object_elements_not_strings(data):
+    # GH 15585
+    assert not com.is_string_dtype(pd.Series(data))
+
+
+def test_is_string_dtype_nullable(nullable_string_dtype):
+    assert com.is_string_dtype(pd.array(["a", "b"], dtype=nullable_string_dtype))
+
+
+integer_dtypes: list = []
+
+
+@pytest.mark.parametrize(
+    "dtype",
+    integer_dtypes
+    + [pd.Series([1, 2])]
+    + tm.ALL_INT_NUMPY_DTYPES
+    + to_numpy_dtypes(tm.ALL_INT_NUMPY_DTYPES)
+    + tm.ALL_INT_EA_DTYPES
+    + to_ea_dtypes(tm.ALL_INT_EA_DTYPES),
+)
+def test_is_integer_dtype(dtype):
+    assert com.is_integer_dtype(dtype)
+
+
+@pytest.mark.parametrize(
+    "dtype",
+    [
+        str,
+        float,
+        np.datetime64,
+        np.timedelta64,
+        pd.Index([1, 2.0]),
+        np.array(["a", "b"]),
+        np.array([], dtype=np.timedelta64),
+    ],
+)
+def test_is_not_integer_dtype(dtype):
+    assert not com.is_integer_dtype(dtype)
+
+
+signed_integer_dtypes: list = []
+
+
+@pytest.mark.parametrize(
+    "dtype",
+    signed_integer_dtypes
+    + [pd.Series([1, 2])]
+    + tm.SIGNED_INT_NUMPY_DTYPES
+    + to_numpy_dtypes(tm.SIGNED_INT_NUMPY_DTYPES)
+    + tm.SIGNED_INT_EA_DTYPES
+    + to_ea_dtypes(tm.SIGNED_INT_EA_DTYPES),
+)
+def test_is_signed_integer_dtype(dtype):
+    assert com.is_integer_dtype(dtype)
+
+
+@pytest.mark.parametrize(
+    "dtype",
+    [
+        str,
+        float,
+        np.datetime64,
+        np.timedelta64,
+        pd.Index([1, 2.0]),
+        np.array(["a", "b"]),
+        np.array([], dtype=np.timedelta64),
+    ]
+    + tm.UNSIGNED_INT_NUMPY_DTYPES
+    + to_numpy_dtypes(tm.UNSIGNED_INT_NUMPY_DTYPES)
+    + tm.UNSIGNED_INT_EA_DTYPES
+    + to_ea_dtypes(tm.UNSIGNED_INT_EA_DTYPES),
+)
+def test_is_not_signed_integer_dtype(dtype):
+    assert not com.is_signed_integer_dtype(dtype)
+
+
+unsigned_integer_dtypes: list = []
+
+
+@pytest.mark.parametrize(
+    "dtype",
+    unsigned_integer_dtypes
+    + [pd.Series([1, 2], dtype=np.uint32)]
+    + tm.UNSIGNED_INT_NUMPY_DTYPES
+    + to_numpy_dtypes(tm.UNSIGNED_INT_NUMPY_DTYPES)
+    + tm.UNSIGNED_INT_EA_DTYPES
+    + to_ea_dtypes(tm.UNSIGNED_INT_EA_DTYPES),
+)
+def test_is_unsigned_integer_dtype(dtype):
+    assert com.is_unsigned_integer_dtype(dtype)
+
+
+@pytest.mark.parametrize(
+    "dtype",
+    [
+        str,
+        float,
+        np.datetime64,
+        np.timedelta64,
+        pd.Index([1, 2.0]),
+        np.array(["a", "b"]),
+        np.array([], dtype=np.timedelta64),
+    ]
+    + tm.SIGNED_INT_NUMPY_DTYPES
+    + to_numpy_dtypes(tm.SIGNED_INT_NUMPY_DTYPES)
+    + tm.SIGNED_INT_EA_DTYPES
+    + to_ea_dtypes(tm.SIGNED_INT_EA_DTYPES),
+)
+def test_is_not_unsigned_integer_dtype(dtype):
+    assert not com.is_unsigned_integer_dtype(dtype)
+
+
+@pytest.mark.parametrize(
+    "dtype", [np.int64, np.array([1, 2], dtype=np.int64), "Int64", pd.Int64Dtype]
+)
+def test_is_int64_dtype(dtype):
+    msg = "is_int64_dtype is deprecated"
+    with tm.assert_produces_warning(DeprecationWarning, match=msg):
+        assert com.is_int64_dtype(dtype)
+
+
+def test_type_comparison_with_numeric_ea_dtype(any_numeric_ea_dtype):
+    # GH#43038
+    assert pandas_dtype(any_numeric_ea_dtype) == any_numeric_ea_dtype
+
+
+def test_type_comparison_with_real_numpy_dtype(any_real_numpy_dtype):
+    # GH#43038
+    assert pandas_dtype(any_real_numpy_dtype) == any_real_numpy_dtype
+
+
+def test_type_comparison_with_signed_int_ea_dtype_and_signed_int_numpy_dtype(
+    any_signed_int_ea_dtype, any_signed_int_numpy_dtype
+):
+    # GH#43038
+    assert not pandas_dtype(any_signed_int_ea_dtype) == any_signed_int_numpy_dtype
+
+
+@pytest.mark.parametrize(
+    "dtype",
+    [
+        str,
+        float,
+        np.int32,
+        np.uint64,
+        pd.Index([1, 2.0]),
+        np.array(["a", "b"]),
+        np.array([1, 2], dtype=np.uint32),
+        "int8",
+        "Int8",
+        pd.Int8Dtype,
+    ],
+)
+def test_is_not_int64_dtype(dtype):
+    msg = "is_int64_dtype is deprecated"
+    with tm.assert_produces_warning(DeprecationWarning, match=msg):
+        assert not com.is_int64_dtype(dtype)
+
+
+def test_is_datetime64_any_dtype():
+    assert not com.is_datetime64_any_dtype(int)
+    assert not com.is_datetime64_any_dtype(str)
+    assert not com.is_datetime64_any_dtype(np.array([1, 2]))
+    assert not com.is_datetime64_any_dtype(np.array(["a", "b"]))
+
+    assert com.is_datetime64_any_dtype(np.datetime64)
+    assert com.is_datetime64_any_dtype(np.array([], dtype=np.datetime64))
+    assert com.is_datetime64_any_dtype(DatetimeTZDtype("ns", "US/Eastern"))
+    assert com.is_datetime64_any_dtype(
+        pd.DatetimeIndex([1, 2, 3], dtype="datetime64[ns]")
+    )
+
+
+def test_is_datetime64_ns_dtype():
+    assert not com.is_datetime64_ns_dtype(int)
+    assert not com.is_datetime64_ns_dtype(str)
+    assert not com.is_datetime64_ns_dtype(np.datetime64)
+    assert not com.is_datetime64_ns_dtype(np.array([1, 2]))
+    assert not com.is_datetime64_ns_dtype(np.array(["a", "b"]))
+    assert not com.is_datetime64_ns_dtype(np.array([], dtype=np.datetime64))
+
+    # This datetime array has the wrong unit (ps instead of ns)
+    assert not com.is_datetime64_ns_dtype(np.array([], dtype="datetime64[ps]"))
+
+    assert com.is_datetime64_ns_dtype(DatetimeTZDtype("ns", "US/Eastern"))
+    assert com.is_datetime64_ns_dtype(
+        pd.DatetimeIndex([1, 2, 3], dtype=np.dtype("datetime64[ns]"))
+    )
+
+    # non-nano dt64tz
+    assert not com.is_datetime64_ns_dtype(DatetimeTZDtype("us", "US/Eastern"))
+
+
+def test_is_timedelta64_ns_dtype():
+    assert not com.is_timedelta64_ns_dtype(np.dtype("m8[ps]"))
+    assert not com.is_timedelta64_ns_dtype(np.array([1, 2], dtype=np.timedelta64))
+
+    assert com.is_timedelta64_ns_dtype(np.dtype("m8[ns]"))
+    assert com.is_timedelta64_ns_dtype(np.array([1, 2], dtype="m8[ns]"))
+
+
+def test_is_numeric_v_string_like():
+    assert not com.is_numeric_v_string_like(np.array([1]), 1)
+    assert not com.is_numeric_v_string_like(np.array([1]), np.array([2]))
+    assert not com.is_numeric_v_string_like(np.array(["foo"]), np.array(["foo"]))
+
+    assert com.is_numeric_v_string_like(np.array([1]), "foo")
+    assert com.is_numeric_v_string_like(np.array([1, 2]), np.array(["foo"]))
+    assert com.is_numeric_v_string_like(np.array(["foo"]), np.array([1, 2]))
+
+
+def test_needs_i8_conversion():
+    assert not com.needs_i8_conversion(str)
+    assert not com.needs_i8_conversion(np.int64)
+    assert not com.needs_i8_conversion(pd.Series([1, 2]))
+    assert not com.needs_i8_conversion(np.array(["a", "b"]))
+
+    assert not com.needs_i8_conversion(np.datetime64)
+    assert com.needs_i8_conversion(np.dtype(np.datetime64))
+    assert not com.needs_i8_conversion(pd.Series([], dtype="timedelta64[ns]"))
+    assert com.needs_i8_conversion(pd.Series([], dtype="timedelta64[ns]").dtype)
+    assert not com.needs_i8_conversion(pd.DatetimeIndex(["2000"], tz="US/Eastern"))
+    assert com.needs_i8_conversion(pd.DatetimeIndex(["2000"], tz="US/Eastern").dtype)
+
+
+def test_is_numeric_dtype():
+    assert not com.is_numeric_dtype(str)
+    assert not com.is_numeric_dtype(np.datetime64)
+    assert not com.is_numeric_dtype(np.timedelta64)
+    assert not com.is_numeric_dtype(np.array(["a", "b"]))
+    assert not com.is_numeric_dtype(np.array([], dtype=np.timedelta64))
+
+    assert com.is_numeric_dtype(int)
+    assert com.is_numeric_dtype(float)
+    assert com.is_numeric_dtype(np.uint64)
+    assert com.is_numeric_dtype(pd.Series([1, 2]))
+    assert com.is_numeric_dtype(pd.Index([1, 2.0]))
+
+    class MyNumericDType(ExtensionDtype):
+        @property
+        def type(self):
+            return str
+
+        @property
+        def name(self):
+            raise NotImplementedError
+
+        @classmethod
+        def construct_array_type(cls):
+            raise NotImplementedError
+
+        def _is_numeric(self) -> bool:
+            return True
+
+    assert com.is_numeric_dtype(MyNumericDType())
+
+
+def test_is_any_real_numeric_dtype():
+    assert not com.is_any_real_numeric_dtype(str)
+    assert not com.is_any_real_numeric_dtype(bool)
+    assert not com.is_any_real_numeric_dtype(complex)
+    assert not com.is_any_real_numeric_dtype(object)
+    assert not com.is_any_real_numeric_dtype(np.datetime64)
+    assert not com.is_any_real_numeric_dtype(np.array(["a", "b", complex(1, 2)]))
+    assert not com.is_any_real_numeric_dtype(pd.DataFrame([complex(1, 2), True]))
+
+    assert com.is_any_real_numeric_dtype(int)
+    assert com.is_any_real_numeric_dtype(float)
+    assert com.is_any_real_numeric_dtype(np.array([1, 2.5]))
+
+
+def test_is_float_dtype():
+    assert not com.is_float_dtype(str)
+    assert not com.is_float_dtype(int)
+    assert not com.is_float_dtype(pd.Series([1, 2]))
+    assert not com.is_float_dtype(np.array(["a", "b"]))
+
+    assert com.is_float_dtype(float)
+    assert com.is_float_dtype(pd.Index([1, 2.0]))
+
+
+def test_is_bool_dtype():
+    assert not com.is_bool_dtype(int)
+    assert not com.is_bool_dtype(str)
+    assert not com.is_bool_dtype(pd.Series([1, 2]))
+    assert not com.is_bool_dtype(pd.Series(["a", "b"], dtype="category"))
+    assert not com.is_bool_dtype(np.array(["a", "b"]))
+    assert not com.is_bool_dtype(pd.Index(["a", "b"]))
+    assert not com.is_bool_dtype("Int64")
+
+    assert com.is_bool_dtype(bool)
+    assert com.is_bool_dtype(np.bool_)
+    assert com.is_bool_dtype(pd.Series([True, False], dtype="category"))
+    assert com.is_bool_dtype(np.array([True, False]))
+    assert com.is_bool_dtype(pd.Index([True, False]))
+
+    assert com.is_bool_dtype(pd.BooleanDtype())
+    assert com.is_bool_dtype(pd.array([True, False, None], dtype="boolean"))
+    assert com.is_bool_dtype("boolean")
+
+
+def test_is_bool_dtype_numpy_error():
+    # GH39010
+    assert not com.is_bool_dtype("0 - Name")
+
+
+@pytest.mark.parametrize(
+    "check_scipy", [False, pytest.param(True, marks=td.skip_if_no("scipy"))]
+)
+def test_is_extension_array_dtype(check_scipy):
+    assert not com.is_extension_array_dtype([1, 2, 3])
+    assert not com.is_extension_array_dtype(np.array([1, 2, 3]))
+    assert not com.is_extension_array_dtype(pd.DatetimeIndex([1, 2, 3]))
+
+    cat = pd.Categorical([1, 2, 3])
+    assert com.is_extension_array_dtype(cat)
+    assert com.is_extension_array_dtype(pd.Series(cat))
+    assert com.is_extension_array_dtype(SparseArray([1, 2, 3]))
+    assert com.is_extension_array_dtype(pd.DatetimeIndex(["2000"], tz="US/Eastern"))
+
+    dtype = DatetimeTZDtype("ns", tz="US/Eastern")
+    s = pd.Series([], dtype=dtype)
+    assert com.is_extension_array_dtype(s)
+
+    if check_scipy:
+        import scipy.sparse
+
+        assert not com.is_extension_array_dtype(scipy.sparse.bsr_matrix([1, 2, 3]))
+
+
+def test_is_complex_dtype():
+    assert not com.is_complex_dtype(int)
+    assert not com.is_complex_dtype(str)
+    assert not com.is_complex_dtype(pd.Series([1, 2]))
+    assert not com.is_complex_dtype(np.array(["a", "b"]))
+
+    assert com.is_complex_dtype(np.complex128)
+    assert com.is_complex_dtype(complex)
+    assert com.is_complex_dtype(np.array([1 + 1j, 5]))
+
+
+@pytest.mark.parametrize(
+    "input_param,result",
+    [
+        (int, np.dtype(int)),
+        ("int32", np.dtype("int32")),
+        (float, np.dtype(float)),
+        ("float64", np.dtype("float64")),
+        (np.dtype("float64"), np.dtype("float64")),
+        (str, np.dtype(str)),
+        (pd.Series([1, 2], dtype=np.dtype("int16")), np.dtype("int16")),
+        (pd.Series(["a", "b"], dtype=object), np.dtype(object)),
+        (pd.Index([1, 2]), np.dtype("int64")),
+        (pd.Index(["a", "b"], dtype=object), np.dtype(object)),
+        ("category", "category"),
+        (pd.Categorical(["a", "b"]).dtype, CategoricalDtype(["a", "b"])),
+        (pd.Categorical(["a", "b"]), CategoricalDtype(["a", "b"])),
+        (pd.CategoricalIndex(["a", "b"]).dtype, CategoricalDtype(["a", "b"])),
+        (pd.CategoricalIndex(["a", "b"]), CategoricalDtype(["a", "b"])),
+        (CategoricalDtype(), CategoricalDtype()),
+        (pd.DatetimeIndex([1, 2]), np.dtype("=M8[ns]")),
+        (pd.DatetimeIndex([1, 2]).dtype, np.dtype("=M8[ns]")),
+        (" df.two.sum()
+
+    with tm.assert_produces_warning(None):
+        #  successfully modify column in place
+        #  this should not raise a warning
+        df.one += 1
+        assert df.one.iloc[0] == 2
+
+    with tm.assert_produces_warning(None):
+        #  successfully add an attribute to a series
+        #  this should not raise a warning
+        df.two.not_an_index = [1, 2]
+
+    with tm.assert_produces_warning(UserWarning):
+        #  warn when setting column to nonexistent name
+        df.four = df.two + 2
+        assert df.four.sum() > df.two.sum()
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/dtypes/test_inference.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/dtypes/test_inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..79b7e6ff092b6efc519d8b29ad134c8019c6602f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/dtypes/test_inference.py
@@ -0,0 +1,2072 @@
+"""
+These the test the public routines exposed in types/common.py
+related to inference and not otherwise tested in types/test_common.py
+
+"""
+import collections
+from collections import namedtuple
+from collections.abc import Iterator
+from datetime import (
+    date,
+    datetime,
+    time,
+    timedelta,
+)
+from decimal import Decimal
+from fractions import Fraction
+from io import StringIO
+import itertools
+from numbers import Number
+import re
+import sys
+from typing import (
+    Generic,
+    TypeVar,
+)
+
+import numpy as np
+import pytest
+import pytz
+
+from pandas._libs import (
+    lib,
+    missing as libmissing,
+    ops as libops,
+)
+from pandas.compat.numpy import np_version_gt2
+
+from pandas.core.dtypes import inference
+from pandas.core.dtypes.cast import find_result_type
+from pandas.core.dtypes.common import (
+    ensure_int32,
+    is_bool,
+    is_complex,
+    is_datetime64_any_dtype,
+    is_datetime64_dtype,
+    is_datetime64_ns_dtype,
+    is_datetime64tz_dtype,
+    is_float,
+    is_integer,
+    is_number,
+    is_scalar,
+    is_scipy_sparse,
+    is_timedelta64_dtype,
+    is_timedelta64_ns_dtype,
+)
+
+import pandas as pd
+from pandas import (
+    Categorical,
+    DataFrame,
+    DateOffset,
+    DatetimeIndex,
+    Index,
+    Interval,
+    Period,
+    PeriodIndex,
+    Series,
+    Timedelta,
+    TimedeltaIndex,
+    Timestamp,
+)
+import pandas._testing as tm
+from pandas.core.arrays import (
+    BooleanArray,
+    FloatingArray,
+    IntegerArray,
+)
+
+
+@pytest.fixture(params=[True, False], ids=str)
+def coerce(request):
+    return request.param
+
+
+class MockNumpyLikeArray:
+    """
+    A class which is numpy-like (e.g. Pint's Quantity) but not actually numpy
+
+    The key is that it is not actually a numpy array so
+    ``util.is_array(mock_numpy_like_array_instance)`` returns ``False``. Other
+    important properties are that the class defines a :meth:`__iter__` method
+    (so that ``isinstance(abc.Iterable)`` returns ``True``) and has a
+    :meth:`ndim` property, as pandas special-cases 0-dimensional arrays in some
+    cases.
+
+    We expect pandas to behave with respect to such duck arrays exactly as
+    with real numpy arrays. In particular, a 0-dimensional duck array is *NOT*
+    a scalar (`is_scalar(np.array(1)) == False`), but it is not list-like either.
+    """
+
+    def __init__(self, values) -> None:
+        self._values = values
+
+    def __iter__(self) -> Iterator:
+        iter_values = iter(self._values)
+
+        def it_outer():
+            yield from iter_values
+
+        return it_outer()
+
+    def __len__(self) -> int:
+        return len(self._values)
+
+    def __array__(self, dtype=None, copy=None):
+        return np.asarray(self._values, dtype=dtype)
+
+    @property
+    def ndim(self):
+        return self._values.ndim
+
+    @property
+    def dtype(self):
+        return self._values.dtype
+
+    @property
+    def size(self):
+        return self._values.size
+
+    @property
+    def shape(self):
+        return self._values.shape
+
+
+# collect all objects to be tested for list-like-ness; use tuples of objects,
+# whether they are list-like or not (special casing for sets), and their ID
+ll_params = [
+    ([1], True, "list"),
+    ([], True, "list-empty"),
+    ((1,), True, "tuple"),
+    ((), True, "tuple-empty"),
+    ({"a": 1}, True, "dict"),
+    ({}, True, "dict-empty"),
+    ({"a", 1}, "set", "set"),
+    (set(), "set", "set-empty"),
+    (frozenset({"a", 1}), "set", "frozenset"),
+    (frozenset(), "set", "frozenset-empty"),
+    (iter([1, 2]), True, "iterator"),
+    (iter([]), True, "iterator-empty"),
+    ((x for x in [1, 2]), True, "generator"),
+    ((_ for _ in []), True, "generator-empty"),
+    (Series([1]), True, "Series"),
+    (Series([], dtype=object), True, "Series-empty"),
+    # Series.str will still raise a TypeError if iterated
+    (Series(["a"]).str, True, "StringMethods"),
+    (Series([], dtype="O").str, True, "StringMethods-empty"),
+    (Index([1]), True, "Index"),
+    (Index([]), True, "Index-empty"),
+    (DataFrame([[1]]), True, "DataFrame"),
+    (DataFrame(), True, "DataFrame-empty"),
+    (np.ndarray((2,) * 1), True, "ndarray-1d"),
+    (np.array([]), True, "ndarray-1d-empty"),
+    (np.ndarray((2,) * 2), True, "ndarray-2d"),
+    (np.array([[]]), True, "ndarray-2d-empty"),
+    (np.ndarray((2,) * 3), True, "ndarray-3d"),
+    (np.array([[[]]]), True, "ndarray-3d-empty"),
+    (np.ndarray((2,) * 4), True, "ndarray-4d"),
+    (np.array([[[[]]]]), True, "ndarray-4d-empty"),
+    (np.array(2), False, "ndarray-0d"),
+    (MockNumpyLikeArray(np.ndarray((2,) * 1)), True, "duck-ndarray-1d"),
+    (MockNumpyLikeArray(np.array([])), True, "duck-ndarray-1d-empty"),
+    (MockNumpyLikeArray(np.ndarray((2,) * 2)), True, "duck-ndarray-2d"),
+    (MockNumpyLikeArray(np.array([[]])), True, "duck-ndarray-2d-empty"),
+    (MockNumpyLikeArray(np.ndarray((2,) * 3)), True, "duck-ndarray-3d"),
+    (MockNumpyLikeArray(np.array([[[]]])), True, "duck-ndarray-3d-empty"),
+    (MockNumpyLikeArray(np.ndarray((2,) * 4)), True, "duck-ndarray-4d"),
+    (MockNumpyLikeArray(np.array([[[[]]]])), True, "duck-ndarray-4d-empty"),
+    (MockNumpyLikeArray(np.array(2)), False, "duck-ndarray-0d"),
+    (1, False, "int"),
+    (b"123", False, "bytes"),
+    (b"", False, "bytes-empty"),
+    ("123", False, "string"),
+    ("", False, "string-empty"),
+    (str, False, "string-type"),
+    (object(), False, "object"),
+    (np.nan, False, "NaN"),
+    (None, False, "None"),
+]
+objs, expected, ids = zip(*ll_params)
+
+
+@pytest.fixture(params=zip(objs, expected), ids=ids)
+def maybe_list_like(request):
+    return request.param
+
+
+def test_is_list_like(maybe_list_like):
+    obj, expected = maybe_list_like
+    expected = True if expected == "set" else expected
+    assert inference.is_list_like(obj) == expected
+
+
+def test_is_list_like_disallow_sets(maybe_list_like):
+    obj, expected = maybe_list_like
+    expected = False if expected == "set" else expected
+    assert inference.is_list_like(obj, allow_sets=False) == expected
+
+
+def test_is_list_like_recursion():
+    # GH 33721
+    # interpreter would crash with SIGABRT
+    def list_like():
+        inference.is_list_like([])
+        list_like()
+
+    rec_limit = sys.getrecursionlimit()
+    try:
+        # Limit to avoid stack overflow on Windows CI
+        sys.setrecursionlimit(100)
+        with tm.external_error_raised(RecursionError):
+            list_like()
+    finally:
+        sys.setrecursionlimit(rec_limit)
+
+
+def test_is_list_like_iter_is_none():
+    # GH 43373
+    # is_list_like was yielding false positives with __iter__ == None
+    class NotListLike:
+        def __getitem__(self, item):
+            return self
+
+        __iter__ = None
+
+    assert not inference.is_list_like(NotListLike())
+
+
+def test_is_list_like_generic():
+    # GH 49649
+    # is_list_like was yielding false positives for Generic classes in python 3.11
+    T = TypeVar("T")
+
+    class MyDataFrame(DataFrame, Generic[T]):
+        ...
+
+    tstc = MyDataFrame[int]
+    tst = MyDataFrame[int]({"x": [1, 2, 3]})
+
+    assert not inference.is_list_like(tstc)
+    assert isinstance(tst, DataFrame)
+    assert inference.is_list_like(tst)
+
+
+def test_is_sequence():
+    is_seq = inference.is_sequence
+    assert is_seq((1, 2))
+    assert is_seq([1, 2])
+    assert not is_seq("abcd")
+    assert not is_seq(np.int64)
+
+    class A:
+        def __getitem__(self, item):
+            return 1
+
+    assert not is_seq(A())
+
+
+def test_is_array_like():
+    assert inference.is_array_like(Series([], dtype=object))
+    assert inference.is_array_like(Series([1, 2]))
+    assert inference.is_array_like(np.array(["a", "b"]))
+    assert inference.is_array_like(Index(["2016-01-01"]))
+    assert inference.is_array_like(np.array([2, 3]))
+    assert inference.is_array_like(MockNumpyLikeArray(np.array([2, 3])))
+
+    class DtypeList(list):
+        dtype = "special"
+
+    assert inference.is_array_like(DtypeList())
+
+    assert not inference.is_array_like([1, 2, 3])
+    assert not inference.is_array_like(())
+    assert not inference.is_array_like("foo")
+    assert not inference.is_array_like(123)
+
+
+@pytest.mark.parametrize(
+    "inner",
+    [
+        [],
+        [1],
+        (1,),
+        (1, 2),
+        {"a": 1},
+        {1, "a"},
+        Series([1]),
+        Series([], dtype=object),
+        Series(["a"]).str,
+        (x for x in range(5)),
+    ],
+)
+@pytest.mark.parametrize("outer", [list, Series, np.array, tuple])
+def test_is_nested_list_like_passes(inner, outer):
+    result = outer([inner for _ in range(5)])
+    assert inference.is_list_like(result)
+
+
+@pytest.mark.parametrize(
+    "obj",
+    [
+        "abc",
+        [],
+        [1],
+        (1,),
+        ["a"],
+        "a",
+        {"a"},
+        [1, 2, 3],
+        Series([1]),
+        DataFrame({"A": [1]}),
+        ([1, 2] for _ in range(5)),
+    ],
+)
+def test_is_nested_list_like_fails(obj):
+    assert not inference.is_nested_list_like(obj)
+
+
+@pytest.mark.parametrize("ll", [{}, {"A": 1}, Series([1]), collections.defaultdict()])
+def test_is_dict_like_passes(ll):
+    assert inference.is_dict_like(ll)
+
+
+@pytest.mark.parametrize(
+    "ll",
+    [
+        "1",
+        1,
+        [1, 2],
+        (1, 2),
+        range(2),
+        Index([1]),
+        dict,
+        collections.defaultdict,
+        Series,
+    ],
+)
+def test_is_dict_like_fails(ll):
+    assert not inference.is_dict_like(ll)
+
+
+@pytest.mark.parametrize("has_keys", [True, False])
+@pytest.mark.parametrize("has_getitem", [True, False])
+@pytest.mark.parametrize("has_contains", [True, False])
+def test_is_dict_like_duck_type(has_keys, has_getitem, has_contains):
+    class DictLike:
+        def __init__(self, d) -> None:
+            self.d = d
+
+        if has_keys:
+
+            def keys(self):
+                return self.d.keys()
+
+        if has_getitem:
+
+            def __getitem__(self, key):
+                return self.d.__getitem__(key)
+
+        if has_contains:
+
+            def __contains__(self, key) -> bool:
+                return self.d.__contains__(key)
+
+    d = DictLike({1: 2})
+    result = inference.is_dict_like(d)
+    expected = has_keys and has_getitem and has_contains
+
+    assert result is expected
+
+
+def test_is_file_like():
+    class MockFile:
+        pass
+
+    is_file = inference.is_file_like
+
+    data = StringIO("data")
+    assert is_file(data)
+
+    # No read / write attributes
+    # No iterator attributes
+    m = MockFile()
+    assert not is_file(m)
+
+    MockFile.write = lambda self: 0
+
+    # Write attribute but not an iterator
+    m = MockFile()
+    assert not is_file(m)
+
+    # gh-16530: Valid iterator just means we have the
+    # __iter__ attribute for our purposes.
+    MockFile.__iter__ = lambda self: self
+
+    # Valid write-only file
+    m = MockFile()
+    assert is_file(m)
+
+    del MockFile.write
+    MockFile.read = lambda self: 0
+
+    # Valid read-only file
+    m = MockFile()
+    assert is_file(m)
+
+    # Iterator but no read / write attributes
+    data = [1, 2, 3]
+    assert not is_file(data)
+
+
+test_tuple = collections.namedtuple("test_tuple", ["a", "b", "c"])
+
+
+@pytest.mark.parametrize("ll", [test_tuple(1, 2, 3)])
+def test_is_names_tuple_passes(ll):
+    assert inference.is_named_tuple(ll)
+
+
+@pytest.mark.parametrize("ll", [(1, 2, 3), "a", Series({"pi": 3.14})])
+def test_is_names_tuple_fails(ll):
+    assert not inference.is_named_tuple(ll)
+
+
+def test_is_hashable():
+    # all new-style classes are hashable by default
+    class HashableClass:
+        pass
+
+    class UnhashableClass1:
+        __hash__ = None
+
+    class UnhashableClass2:
+        def __hash__(self):
+            raise TypeError("Not hashable")
+
+    hashable = (1, 3.14, np.float64(3.14), "a", (), (1,), HashableClass())
+    not_hashable = ([], UnhashableClass1())
+    abc_hashable_not_really_hashable = (([],), UnhashableClass2())
+
+    for i in hashable:
+        assert inference.is_hashable(i)
+    for i in not_hashable:
+        assert not inference.is_hashable(i)
+    for i in abc_hashable_not_really_hashable:
+        assert not inference.is_hashable(i)
+
+    # numpy.array is no longer collections.abc.Hashable as of
+    # https://github.com/numpy/numpy/pull/5326, just test
+    # is_hashable()
+    assert not inference.is_hashable(np.array([]))
+
+
+@pytest.mark.parametrize("ll", [re.compile("ad")])
+def test_is_re_passes(ll):
+    assert inference.is_re(ll)
+
+
+@pytest.mark.parametrize("ll", ["x", 2, 3, object()])
+def test_is_re_fails(ll):
+    assert not inference.is_re(ll)
+
+
+@pytest.mark.parametrize(
+    "ll", [r"a", "x", r"asdf", re.compile("adsf"), r"\u2233\s*", re.compile(r"")]
+)
+def test_is_recompilable_passes(ll):
+    assert inference.is_re_compilable(ll)
+
+
+@pytest.mark.parametrize("ll", [1, [], object()])
+def test_is_recompilable_fails(ll):
+    assert not inference.is_re_compilable(ll)
+
+
+class TestInference:
+    @pytest.mark.parametrize(
+        "arr",
+        [
+            np.array(list("abc"), dtype="S1"),
+            np.array(list("abc"), dtype="S1").astype(object),
+            [b"a", np.nan, b"c"],
+        ],
+    )
+    def test_infer_dtype_bytes(self, arr):
+        result = lib.infer_dtype(arr, skipna=True)
+        assert result == "bytes"
+
+    @pytest.mark.parametrize(
+        "value, expected",
+        [
+            (float("inf"), True),
+            (np.inf, True),
+            (-np.inf, False),
+            (1, False),
+            ("a", False),
+        ],
+    )
+    def test_isposinf_scalar(self, value, expected):
+        # GH 11352
+        result = libmissing.isposinf_scalar(value)
+        assert result is expected
+
+    @pytest.mark.parametrize(
+        "value, expected",
+        [
+            (float("-inf"), True),
+            (-np.inf, True),
+            (np.inf, False),
+            (1, False),
+            ("a", False),
+        ],
+    )
+    def test_isneginf_scalar(self, value, expected):
+        result = libmissing.isneginf_scalar(value)
+        assert result is expected
+
+    @pytest.mark.parametrize(
+        "convert_to_masked_nullable, exp",
+        [
+            (
+                True,
+                BooleanArray(
+                    np.array([True, False], dtype="bool"), np.array([False, True])
+                ),
+            ),
+            (False, np.array([True, np.nan], dtype="object")),
+        ],
+    )
+    def test_maybe_convert_nullable_boolean(self, convert_to_masked_nullable, exp):
+        # GH 40687
+        arr = np.array([True, np.nan], dtype=object)
+        result = libops.maybe_convert_bool(
+            arr, set(), convert_to_masked_nullable=convert_to_masked_nullable
+        )
+        if convert_to_masked_nullable:
+            tm.assert_extension_array_equal(BooleanArray(*result), exp)
+        else:
+            result = result[0]
+            tm.assert_numpy_array_equal(result, exp)
+
+    @pytest.mark.parametrize("convert_to_masked_nullable", [True, False])
+    @pytest.mark.parametrize("coerce_numeric", [True, False])
+    @pytest.mark.parametrize(
+        "infinity", ["inf", "inF", "iNf", "Inf", "iNF", "InF", "INf", "INF"]
+    )
+    @pytest.mark.parametrize("prefix", ["", "-", "+"])
+    def test_maybe_convert_numeric_infinities(
+        self, coerce_numeric, infinity, prefix, convert_to_masked_nullable
+    ):
+        # see gh-13274
+        result, _ = lib.maybe_convert_numeric(
+            np.array([prefix + infinity], dtype=object),
+            na_values={"", "NULL", "nan"},
+            coerce_numeric=coerce_numeric,
+            convert_to_masked_nullable=convert_to_masked_nullable,
+        )
+        expected = np.array([np.inf if prefix in ["", "+"] else -np.inf])
+        tm.assert_numpy_array_equal(result, expected)
+
+    @pytest.mark.parametrize("convert_to_masked_nullable", [True, False])
+    def test_maybe_convert_numeric_infinities_raises(self, convert_to_masked_nullable):
+        msg = "Unable to parse string"
+        with pytest.raises(ValueError, match=msg):
+            lib.maybe_convert_numeric(
+                np.array(["foo_inf"], dtype=object),
+                na_values={"", "NULL", "nan"},
+                coerce_numeric=False,
+                convert_to_masked_nullable=convert_to_masked_nullable,
+            )
+
+    @pytest.mark.parametrize("convert_to_masked_nullable", [True, False])
+    def test_maybe_convert_numeric_post_floatify_nan(
+        self, coerce, convert_to_masked_nullable
+    ):
+        # see gh-13314
+        data = np.array(["1.200", "-999.000", "4.500"], dtype=object)
+        expected = np.array([1.2, np.nan, 4.5], dtype=np.float64)
+        nan_values = {-999, -999.0}
+
+        out = lib.maybe_convert_numeric(
+            data,
+            nan_values,
+            coerce,
+            convert_to_masked_nullable=convert_to_masked_nullable,
+        )
+        if convert_to_masked_nullable:
+            expected = FloatingArray(expected, np.isnan(expected))
+            tm.assert_extension_array_equal(expected, FloatingArray(*out))
+        else:
+            out = out[0]
+            tm.assert_numpy_array_equal(out, expected)
+
+    def test_convert_infs(self):
+        arr = np.array(["inf", "inf", "inf"], dtype="O")
+        result, _ = lib.maybe_convert_numeric(arr, set(), False)
+        assert result.dtype == np.float64
+
+        arr = np.array(["-inf", "-inf", "-inf"], dtype="O")
+        result, _ = lib.maybe_convert_numeric(arr, set(), False)
+        assert result.dtype == np.float64
+
+    def test_scientific_no_exponent(self):
+        # See PR 12215
+        arr = np.array(["42E", "2E", "99e", "6e"], dtype="O")
+        result, _ = lib.maybe_convert_numeric(arr, set(), False, True)
+        assert np.all(np.isnan(result))
+
+    def test_convert_non_hashable(self):
+        # GH13324
+        # make sure that we are handing non-hashables
+        arr = np.array([[10.0, 2], 1.0, "apple"], dtype=object)
+        result, _ = lib.maybe_convert_numeric(arr, set(), False, True)
+        tm.assert_numpy_array_equal(result, np.array([np.nan, 1.0, np.nan]))
+
+    def test_convert_numeric_uint64(self):
+        arr = np.array([2**63], dtype=object)
+        exp = np.array([2**63], dtype=np.uint64)
+        tm.assert_numpy_array_equal(lib.maybe_convert_numeric(arr, set())[0], exp)
+
+        arr = np.array([str(2**63)], dtype=object)
+        exp = np.array([2**63], dtype=np.uint64)
+        tm.assert_numpy_array_equal(lib.maybe_convert_numeric(arr, set())[0], exp)
+
+        arr = np.array([np.uint64(2**63)], dtype=object)
+        exp = np.array([2**63], dtype=np.uint64)
+        tm.assert_numpy_array_equal(lib.maybe_convert_numeric(arr, set())[0], exp)
+
+    @pytest.mark.parametrize(
+        "arr",
+        [
+            np.array([2**63, np.nan], dtype=object),
+            np.array([str(2**63), np.nan], dtype=object),
+            np.array([np.nan, 2**63], dtype=object),
+            np.array([np.nan, str(2**63)], dtype=object),
+        ],
+    )
+    def test_convert_numeric_uint64_nan(self, coerce, arr):
+        expected = arr.astype(float) if coerce else arr.copy()
+        result, _ = lib.maybe_convert_numeric(arr, set(), coerce_numeric=coerce)
+        tm.assert_almost_equal(result, expected)
+
+    @pytest.mark.parametrize("convert_to_masked_nullable", [True, False])
+    def test_convert_numeric_uint64_nan_values(
+        self, coerce, convert_to_masked_nullable
+    ):
+        arr = np.array([2**63, 2**63 + 1], dtype=object)
+        na_values = {2**63}
+
+        expected = (
+            np.array([np.nan, 2**63 + 1], dtype=float) if coerce else arr.copy()
+        )
+        result = lib.maybe_convert_numeric(
+            arr,
+            na_values,
+            coerce_numeric=coerce,
+            convert_to_masked_nullable=convert_to_masked_nullable,
+        )
+        if convert_to_masked_nullable and coerce:
+            expected = IntegerArray(
+                np.array([0, 2**63 + 1], dtype="u8"),
+                np.array([True, False], dtype="bool"),
+            )
+            result = IntegerArray(*result)
+        else:
+            result = result[0]  # discard mask
+        tm.assert_almost_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "case",
+        [
+            np.array([2**63, -1], dtype=object),
+            np.array([str(2**63), -1], dtype=object),
+            np.array([str(2**63), str(-1)], dtype=object),
+            np.array([-1, 2**63], dtype=object),
+            np.array([-1, str(2**63)], dtype=object),
+            np.array([str(-1), str(2**63)], dtype=object),
+        ],
+    )
+    @pytest.mark.parametrize("convert_to_masked_nullable", [True, False])
+    def test_convert_numeric_int64_uint64(
+        self, case, coerce, convert_to_masked_nullable
+    ):
+        expected = case.astype(float) if coerce else case.copy()
+        result, _ = lib.maybe_convert_numeric(
+            case,
+            set(),
+            coerce_numeric=coerce,
+            convert_to_masked_nullable=convert_to_masked_nullable,
+        )
+
+        tm.assert_almost_equal(result, expected)
+
+    @pytest.mark.parametrize("convert_to_masked_nullable", [True, False])
+    def test_convert_numeric_string_uint64(self, convert_to_masked_nullable):
+        # GH32394
+        result = lib.maybe_convert_numeric(
+            np.array(["uint64"], dtype=object),
+            set(),
+            coerce_numeric=True,
+            convert_to_masked_nullable=convert_to_masked_nullable,
+        )
+        if convert_to_masked_nullable:
+            result = FloatingArray(*result)
+        else:
+            result = result[0]
+        assert np.isnan(result)
+
+    @pytest.mark.parametrize("value", [-(2**63) - 1, 2**64])
+    def test_convert_int_overflow(self, value):
+        # see gh-18584
+        arr = np.array([value], dtype=object)
+        result = lib.maybe_convert_objects(arr)
+        tm.assert_numpy_array_equal(arr, result)
+
+    @pytest.mark.parametrize("val", [None, np.nan, float("nan")])
+    @pytest.mark.parametrize("dtype", ["M8[ns]", "m8[ns]"])
+    def test_maybe_convert_objects_nat_inference(self, val, dtype):
+        dtype = np.dtype(dtype)
+        vals = np.array([pd.NaT, val], dtype=object)
+        result = lib.maybe_convert_objects(
+            vals,
+            convert_non_numeric=True,
+            dtype_if_all_nat=dtype,
+        )
+        assert result.dtype == dtype
+        assert np.isnat(result).all()
+
+        result = lib.maybe_convert_objects(
+            vals[::-1],
+            convert_non_numeric=True,
+            dtype_if_all_nat=dtype,
+        )
+        assert result.dtype == dtype
+        assert np.isnat(result).all()
+
+    @pytest.mark.parametrize(
+        "value, expected_dtype",
+        [
+            # see gh-4471
+            ([2**63], np.uint64),
+            # NumPy bug: can't compare uint64 to int64, as that
+            # results in both casting to float64, so we should
+            # make sure that this function is robust against it
+            ([np.uint64(2**63)], np.uint64),
+            ([2, -1], np.int64),
+            ([2**63, -1], object),
+            # GH#47294
+            ([np.uint8(1)], np.uint8),
+            ([np.uint16(1)], np.uint16),
+            ([np.uint32(1)], np.uint32),
+            ([np.uint64(1)], np.uint64),
+            ([np.uint8(2), np.uint16(1)], np.uint16),
+            ([np.uint32(2), np.uint16(1)], np.uint32),
+            ([np.uint32(2), -1], object),
+            ([np.uint32(2), 1], np.uint64),
+            ([np.uint32(2), np.int32(1)], object),
+        ],
+    )
+    def test_maybe_convert_objects_uint(self, value, expected_dtype):
+        arr = np.array(value, dtype=object)
+        exp = np.array(value, dtype=expected_dtype)
+        tm.assert_numpy_array_equal(lib.maybe_convert_objects(arr), exp)
+
+    def test_maybe_convert_objects_datetime(self):
+        # GH27438
+        arr = np.array(
+            [np.datetime64("2000-01-01"), np.timedelta64(1, "s")], dtype=object
+        )
+        exp = arr.copy()
+        out = lib.maybe_convert_objects(arr, convert_non_numeric=True)
+        tm.assert_numpy_array_equal(out, exp)
+
+        arr = np.array([pd.NaT, np.timedelta64(1, "s")], dtype=object)
+        exp = np.array([np.timedelta64("NaT"), np.timedelta64(1, "s")], dtype="m8[ns]")
+        out = lib.maybe_convert_objects(arr, convert_non_numeric=True)
+        tm.assert_numpy_array_equal(out, exp)
+
+        # with convert_non_numeric=True, the nan is a valid NA value for td64
+        arr = np.array([np.timedelta64(1, "s"), np.nan], dtype=object)
+        exp = exp[::-1]
+        out = lib.maybe_convert_objects(arr, convert_non_numeric=True)
+        tm.assert_numpy_array_equal(out, exp)
+
+    def test_maybe_convert_objects_dtype_if_all_nat(self):
+        arr = np.array([pd.NaT, pd.NaT], dtype=object)
+        out = lib.maybe_convert_objects(arr, convert_non_numeric=True)
+        # no dtype_if_all_nat passed -> we dont guess
+        tm.assert_numpy_array_equal(out, arr)
+
+        out = lib.maybe_convert_objects(
+            arr,
+            convert_non_numeric=True,
+            dtype_if_all_nat=np.dtype("timedelta64[ns]"),
+        )
+        exp = np.array(["NaT", "NaT"], dtype="timedelta64[ns]")
+        tm.assert_numpy_array_equal(out, exp)
+
+        out = lib.maybe_convert_objects(
+            arr,
+            convert_non_numeric=True,
+            dtype_if_all_nat=np.dtype("datetime64[ns]"),
+        )
+        exp = np.array(["NaT", "NaT"], dtype="datetime64[ns]")
+        tm.assert_numpy_array_equal(out, exp)
+
+    def test_maybe_convert_objects_dtype_if_all_nat_invalid(self):
+        # we accept datetime64[ns], timedelta64[ns], and EADtype
+        arr = np.array([pd.NaT, pd.NaT], dtype=object)
+
+        with pytest.raises(ValueError, match="int64"):
+            lib.maybe_convert_objects(
+                arr,
+                convert_non_numeric=True,
+                dtype_if_all_nat=np.dtype("int64"),
+            )
+
+    @pytest.mark.parametrize("dtype", ["datetime64[ns]", "timedelta64[ns]"])
+    def test_maybe_convert_objects_datetime_overflow_safe(self, dtype):
+        stamp = datetime(2363, 10, 4)  # Enterprise-D launch date
+        if dtype == "timedelta64[ns]":
+            stamp = stamp - datetime(1970, 1, 1)
+        arr = np.array([stamp], dtype=object)
+
+        out = lib.maybe_convert_objects(arr, convert_non_numeric=True)
+        # no OutOfBoundsDatetime/OutOfBoundsTimedeltas
+        tm.assert_numpy_array_equal(out, arr)
+
+    def test_maybe_convert_objects_mixed_datetimes(self):
+        ts = Timestamp("now")
+        vals = [ts, ts.to_pydatetime(), ts.to_datetime64(), pd.NaT, np.nan, None]
+
+        for data in itertools.permutations(vals):
+            data = np.array(list(data), dtype=object)
+            expected = DatetimeIndex(data)._data._ndarray
+            result = lib.maybe_convert_objects(data, convert_non_numeric=True)
+            tm.assert_numpy_array_equal(result, expected)
+
+    def test_maybe_convert_objects_timedelta64_nat(self):
+        obj = np.timedelta64("NaT", "ns")
+        arr = np.array([obj], dtype=object)
+        assert arr[0] is obj
+
+        result = lib.maybe_convert_objects(arr, convert_non_numeric=True)
+
+        expected = np.array([obj], dtype="m8[ns]")
+        tm.assert_numpy_array_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "exp",
+        [
+            IntegerArray(np.array([2, 0], dtype="i8"), np.array([False, True])),
+            IntegerArray(np.array([2, 0], dtype="int64"), np.array([False, True])),
+        ],
+    )
+    def test_maybe_convert_objects_nullable_integer(self, exp):
+        # GH27335
+        arr = np.array([2, np.nan], dtype=object)
+        result = lib.maybe_convert_objects(arr, convert_to_nullable_dtype=True)
+
+        tm.assert_extension_array_equal(result, exp)
+
+    @pytest.mark.parametrize(
+        "dtype, val", [("int64", 1), ("uint64", np.iinfo(np.int64).max + 1)]
+    )
+    def test_maybe_convert_objects_nullable_none(self, dtype, val):
+        # GH#50043
+        arr = np.array([val, None, 3], dtype="object")
+        result = lib.maybe_convert_objects(arr, convert_to_nullable_dtype=True)
+        expected = IntegerArray(
+            np.array([val, 0, 3], dtype=dtype), np.array([False, True, False])
+        )
+        tm.assert_extension_array_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "convert_to_masked_nullable, exp",
+        [
+            (True, IntegerArray(np.array([2, 0], dtype="i8"), np.array([False, True]))),
+            (False, np.array([2, np.nan], dtype="float64")),
+        ],
+    )
+    def test_maybe_convert_numeric_nullable_integer(
+        self, convert_to_masked_nullable, exp
+    ):
+        # GH 40687
+        arr = np.array([2, np.nan], dtype=object)
+        result = lib.maybe_convert_numeric(
+            arr, set(), convert_to_masked_nullable=convert_to_masked_nullable
+        )
+        if convert_to_masked_nullable:
+            result = IntegerArray(*result)
+            tm.assert_extension_array_equal(result, exp)
+        else:
+            result = result[0]
+            tm.assert_numpy_array_equal(result, exp)
+
+    @pytest.mark.parametrize(
+        "convert_to_masked_nullable, exp",
+        [
+            (
+                True,
+                FloatingArray(
+                    np.array([2.0, 0.0], dtype="float64"), np.array([False, True])
+                ),
+            ),
+            (False, np.array([2.0, np.nan], dtype="float64")),
+        ],
+    )
+    def test_maybe_convert_numeric_floating_array(
+        self, convert_to_masked_nullable, exp
+    ):
+        # GH 40687
+        arr = np.array([2.0, np.nan], dtype=object)
+        result = lib.maybe_convert_numeric(
+            arr, set(), convert_to_masked_nullable=convert_to_masked_nullable
+        )
+        if convert_to_masked_nullable:
+            tm.assert_extension_array_equal(FloatingArray(*result), exp)
+        else:
+            result = result[0]
+            tm.assert_numpy_array_equal(result, exp)
+
+    def test_maybe_convert_objects_bool_nan(self):
+        # GH32146
+        ind = Index([True, False, np.nan], dtype=object)
+        exp = np.array([True, False, np.nan], dtype=object)
+        out = lib.maybe_convert_objects(ind.values, safe=1)
+        tm.assert_numpy_array_equal(out, exp)
+
+    def test_maybe_convert_objects_nullable_boolean(self):
+        # GH50047
+        arr = np.array([True, False], dtype=object)
+        exp = np.array([True, False])
+        out = lib.maybe_convert_objects(arr, convert_to_nullable_dtype=True)
+        tm.assert_numpy_array_equal(out, exp)
+
+        arr = np.array([True, False, pd.NaT], dtype=object)
+        exp = np.array([True, False, pd.NaT], dtype=object)
+        out = lib.maybe_convert_objects(arr, convert_to_nullable_dtype=True)
+        tm.assert_numpy_array_equal(out, exp)
+
+    @pytest.mark.parametrize("val", [None, np.nan])
+    def test_maybe_convert_objects_nullable_boolean_na(self, val):
+        # GH50047
+        arr = np.array([True, False, val], dtype=object)
+        exp = BooleanArray(
+            np.array([True, False, False]), np.array([False, False, True])
+        )
+        out = lib.maybe_convert_objects(arr, convert_to_nullable_dtype=True)
+        tm.assert_extension_array_equal(out, exp)
+
+    @pytest.mark.parametrize(
+        "data0",
+        [
+            True,
+            1,
+            1.0,
+            1.0 + 1.0j,
+            np.int8(1),
+            np.int16(1),
+            np.int32(1),
+            np.int64(1),
+            np.float16(1),
+            np.float32(1),
+            np.float64(1),
+            np.complex64(1),
+            np.complex128(1),
+        ],
+    )
+    @pytest.mark.parametrize(
+        "data1",
+        [
+            True,
+            1,
+            1.0,
+            1.0 + 1.0j,
+            np.int8(1),
+            np.int16(1),
+            np.int32(1),
+            np.int64(1),
+            np.float16(1),
+            np.float32(1),
+            np.float64(1),
+            np.complex64(1),
+            np.complex128(1),
+        ],
+    )
+    def test_maybe_convert_objects_itemsize(self, data0, data1):
+        # GH 40908
+        data = [data0, data1]
+        arr = np.array(data, dtype="object")
+
+        common_kind = np.result_type(type(data0), type(data1)).kind
+        kind0 = "python" if not hasattr(data0, "dtype") else data0.dtype.kind
+        kind1 = "python" if not hasattr(data1, "dtype") else data1.dtype.kind
+        if kind0 != "python" and kind1 != "python":
+            kind = common_kind
+            itemsize = max(data0.dtype.itemsize, data1.dtype.itemsize)
+        elif is_bool(data0) or is_bool(data1):
+            kind = "bool" if (is_bool(data0) and is_bool(data1)) else "object"
+            itemsize = ""
+        elif is_complex(data0) or is_complex(data1):
+            kind = common_kind
+            itemsize = 16
+        else:
+            kind = common_kind
+            itemsize = 8
+
+        expected = np.array(data, dtype=f"{kind}{itemsize}")
+        result = lib.maybe_convert_objects(arr)
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_mixed_dtypes_remain_object_array(self):
+        # GH14956
+        arr = np.array([datetime(2015, 1, 1, tzinfo=pytz.utc), 1], dtype=object)
+        result = lib.maybe_convert_objects(arr, convert_non_numeric=True)
+        tm.assert_numpy_array_equal(result, arr)
+
+    @pytest.mark.parametrize(
+        "idx",
+        [
+            pd.IntervalIndex.from_breaks(range(5), closed="both"),
+            pd.period_range("2016-01-01", periods=3, freq="D"),
+        ],
+    )
+    def test_maybe_convert_objects_ea(self, idx):
+        result = lib.maybe_convert_objects(
+            np.array(idx, dtype=object),
+            convert_non_numeric=True,
+        )
+        tm.assert_extension_array_equal(result, idx._data)
+
+
+class TestTypeInference:
+    # Dummy class used for testing with Python objects
+    class Dummy:
+        pass
+
+    def test_inferred_dtype_fixture(self, any_skipna_inferred_dtype):
+        # see pandas/conftest.py
+        inferred_dtype, values = any_skipna_inferred_dtype
+
+        # make sure the inferred dtype of the fixture is as requested
+        assert inferred_dtype == lib.infer_dtype(values, skipna=True)
+
+    @pytest.mark.parametrize("skipna", [True, False])
+    def test_length_zero(self, skipna):
+        result = lib.infer_dtype(np.array([], dtype="i4"), skipna=skipna)
+        assert result == "integer"
+
+        result = lib.infer_dtype([], skipna=skipna)
+        assert result == "empty"
+
+        # GH 18004
+        arr = np.array([np.array([], dtype=object), np.array([], dtype=object)])
+        result = lib.infer_dtype(arr, skipna=skipna)
+        assert result == "empty"
+
+    def test_integers(self):
+        arr = np.array([1, 2, 3, np.int64(4), np.int32(5)], dtype="O")
+        result = lib.infer_dtype(arr, skipna=True)
+        assert result == "integer"
+
+        arr = np.array([1, 2, 3, np.int64(4), np.int32(5), "foo"], dtype="O")
+        result = lib.infer_dtype(arr, skipna=True)
+        assert result == "mixed-integer"
+
+        arr = np.array([1, 2, 3, 4, 5], dtype="i4")
+        result = lib.infer_dtype(arr, skipna=True)
+        assert result == "integer"
+
+    @pytest.mark.parametrize(
+        "arr, skipna",
+        [
+            (np.array([1, 2, np.nan, np.nan, 3], dtype="O"), False),
+            (np.array([1, 2, np.nan, np.nan, 3], dtype="O"), True),
+            (np.array([1, 2, 3, np.int64(4), np.int32(5), np.nan], dtype="O"), False),
+            (np.array([1, 2, 3, np.int64(4), np.int32(5), np.nan], dtype="O"), True),
+        ],
+    )
+    def test_integer_na(self, arr, skipna):
+        # GH 27392
+        result = lib.infer_dtype(arr, skipna=skipna)
+        expected = "integer" if skipna else "integer-na"
+        assert result == expected
+
+    def test_infer_dtype_skipna_default(self):
+        # infer_dtype `skipna` default deprecated in GH#24050,
+        #  changed to True in GH#29876
+        arr = np.array([1, 2, 3, np.nan], dtype=object)
+
+        result = lib.infer_dtype(arr)
+        assert result == "integer"
+
+    def test_bools(self):
+        arr = np.array([True, False, True, True, True], dtype="O")
+        result = lib.infer_dtype(arr, skipna=True)
+        assert result == "boolean"
+
+        arr = np.array([np.bool_(True), np.bool_(False)], dtype="O")
+        result = lib.infer_dtype(arr, skipna=True)
+        assert result == "boolean"
+
+        arr = np.array([True, False, True, "foo"], dtype="O")
+        result = lib.infer_dtype(arr, skipna=True)
+        assert result == "mixed"
+
+        arr = np.array([True, False, True], dtype=bool)
+        result = lib.infer_dtype(arr, skipna=True)
+        assert result == "boolean"
+
+        arr = np.array([True, np.nan, False], dtype="O")
+        result = lib.infer_dtype(arr, skipna=True)
+        assert result == "boolean"
+
+        result = lib.infer_dtype(arr, skipna=False)
+        assert result == "mixed"
+
+    def test_floats(self):
+        arr = np.array([1.0, 2.0, 3.0, np.float64(4), np.float32(5)], dtype="O")
+        result = lib.infer_dtype(arr, skipna=True)
+        assert result == "floating"
+
+        arr = np.array([1, 2, 3, np.float64(4), np.float32(5), "foo"], dtype="O")
+        result = lib.infer_dtype(arr, skipna=True)
+        assert result == "mixed-integer"
+
+        arr = np.array([1, 2, 3, 4, 5], dtype="f4")
+        result = lib.infer_dtype(arr, skipna=True)
+        assert result == "floating"
+
+        arr = np.array([1, 2, 3, 4, 5], dtype="f8")
+        result = lib.infer_dtype(arr, skipna=True)
+        assert result == "floating"
+
+    def test_decimals(self):
+        # GH15690
+        arr = np.array([Decimal(1), Decimal(2), Decimal(3)])
+        result = lib.infer_dtype(arr, skipna=True)
+        assert result == "decimal"
+
+        arr = np.array([1.0, 2.0, Decimal(3)])
+        result = lib.infer_dtype(arr, skipna=True)
+        assert result == "mixed"
+
+        result = lib.infer_dtype(arr[::-1], skipna=True)
+        assert result == "mixed"
+
+        arr = np.array([Decimal(1), Decimal("NaN"), Decimal(3)])
+        result = lib.infer_dtype(arr, skipna=True)
+        assert result == "decimal"
+
+        arr = np.array([Decimal(1), np.nan, Decimal(3)], dtype="O")
+        result = lib.infer_dtype(arr, skipna=True)
+        assert result == "decimal"
+
+    # complex is compatible with nan, so skipna has no effect
+    @pytest.mark.parametrize("skipna", [True, False])
+    def test_complex(self, skipna):
+        # gets cast to complex on array construction
+        arr = np.array([1.0, 2.0, 1 + 1j])
+        result = lib.infer_dtype(arr, skipna=skipna)
+        assert result == "complex"
+
+        arr = np.array([1.0, 2.0, 1 + 1j], dtype="O")
+        result = lib.infer_dtype(arr, skipna=skipna)
+        assert result == "mixed"
+
+        result = lib.infer_dtype(arr[::-1], skipna=skipna)
+        assert result == "mixed"
+
+        # gets cast to complex on array construction
+        arr = np.array([1, np.nan, 1 + 1j])
+        result = lib.infer_dtype(arr, skipna=skipna)
+        assert result == "complex"
+
+        arr = np.array([1.0, np.nan, 1 + 1j], dtype="O")
+        result = lib.infer_dtype(arr, skipna=skipna)
+        assert result == "mixed"
+
+        # complex with nans stays complex
+        arr = np.array([1 + 1j, np.nan, 3 + 3j], dtype="O")
+        result = lib.infer_dtype(arr, skipna=skipna)
+        assert result == "complex"
+
+        # test smaller complex dtype; will pass through _try_infer_map fastpath
+        arr = np.array([1 + 1j, np.nan, 3 + 3j], dtype=np.complex64)
+        result = lib.infer_dtype(arr, skipna=skipna)
+        assert result == "complex"
+
+    def test_string(self):
+        pass
+
+    def test_unicode(self):
+        arr = ["a", np.nan, "c"]
+        result = lib.infer_dtype(arr, skipna=False)
+        # This currently returns "mixed", but it's not clear that's optimal.
+        # This could also return "string" or "mixed-string"
+        assert result == "mixed"
+
+        # even though we use skipna, we are only skipping those NAs that are
+        #  considered matching by is_string_array
+        arr = ["a", np.nan, "c"]
+        result = lib.infer_dtype(arr, skipna=True)
+        assert result == "string"
+
+        arr = ["a", pd.NA, "c"]
+        result = lib.infer_dtype(arr, skipna=True)
+        assert result == "string"
+
+        arr = ["a", pd.NaT, "c"]
+        result = lib.infer_dtype(arr, skipna=True)
+        assert result == "mixed"
+
+        arr = ["a", "c"]
+        result = lib.infer_dtype(arr, skipna=False)
+        assert result == "string"
+
+    @pytest.mark.parametrize(
+        "dtype, missing, skipna, expected",
+        [
+            (float, np.nan, False, "floating"),
+            (float, np.nan, True, "floating"),
+            (object, np.nan, False, "floating"),
+            (object, np.nan, True, "empty"),
+            (object, None, False, "mixed"),
+            (object, None, True, "empty"),
+        ],
+    )
+    @pytest.mark.parametrize("box", [Series, np.array])
+    def test_object_empty(self, box, missing, dtype, skipna, expected):
+        # GH 23421
+        arr = box([missing, missing], dtype=dtype)
+
+        result = lib.infer_dtype(arr, skipna=skipna)
+        assert result == expected
+
+    def test_datetime(self):
+        dates = [datetime(2012, 1, x) for x in range(1, 20)]
+        index = Index(dates)
+        assert index.inferred_type == "datetime64"
+
+    def test_infer_dtype_datetime64(self):
+        arr = np.array(
+            [np.datetime64("2011-01-01"), np.datetime64("2011-01-01")], dtype=object
+        )
+        assert lib.infer_dtype(arr, skipna=True) == "datetime64"
+
+    @pytest.mark.parametrize("na_value", [pd.NaT, np.nan])
+    def test_infer_dtype_datetime64_with_na(self, na_value):
+        # starts with nan
+        arr = np.array([na_value, np.datetime64("2011-01-02")])
+        assert lib.infer_dtype(arr, skipna=True) == "datetime64"
+
+        arr = np.array([na_value, np.datetime64("2011-01-02"), na_value])
+        assert lib.infer_dtype(arr, skipna=True) == "datetime64"
+
+    @pytest.mark.parametrize(
+        "arr",
+        [
+            np.array(
+                [np.timedelta64("nat"), np.datetime64("2011-01-02")], dtype=object
+            ),
+            np.array(
+                [np.datetime64("2011-01-02"), np.timedelta64("nat")], dtype=object
+            ),
+            np.array([np.datetime64("2011-01-01"), Timestamp("2011-01-02")]),
+            np.array([Timestamp("2011-01-02"), np.datetime64("2011-01-01")]),
+            np.array([np.nan, Timestamp("2011-01-02"), 1.1]),
+            np.array([np.nan, "2011-01-01", Timestamp("2011-01-02")], dtype=object),
+            np.array([np.datetime64("nat"), np.timedelta64(1, "D")], dtype=object),
+            np.array([np.timedelta64(1, "D"), np.datetime64("nat")], dtype=object),
+        ],
+    )
+    def test_infer_datetimelike_dtype_mixed(self, arr):
+        assert lib.infer_dtype(arr, skipna=False) == "mixed"
+
+    def test_infer_dtype_mixed_integer(self):
+        arr = np.array([np.nan, Timestamp("2011-01-02"), 1])
+        assert lib.infer_dtype(arr, skipna=True) == "mixed-integer"
+
+    @pytest.mark.parametrize(
+        "arr",
+        [
+            np.array([Timestamp("2011-01-01"), Timestamp("2011-01-02")]),
+            np.array([datetime(2011, 1, 1), datetime(2012, 2, 1)]),
+            np.array([datetime(2011, 1, 1), Timestamp("2011-01-02")]),
+        ],
+    )
+    def test_infer_dtype_datetime(self, arr):
+        assert lib.infer_dtype(arr, skipna=True) == "datetime"
+
+    @pytest.mark.parametrize("na_value", [pd.NaT, np.nan])
+    @pytest.mark.parametrize(
+        "time_stamp", [Timestamp("2011-01-01"), datetime(2011, 1, 1)]
+    )
+    def test_infer_dtype_datetime_with_na(self, na_value, time_stamp):
+        # starts with nan
+        arr = np.array([na_value, time_stamp])
+        assert lib.infer_dtype(arr, skipna=True) == "datetime"
+
+        arr = np.array([na_value, time_stamp, na_value])
+        assert lib.infer_dtype(arr, skipna=True) == "datetime"
+
+    @pytest.mark.parametrize(
+        "arr",
+        [
+            np.array([Timedelta("1 days"), Timedelta("2 days")]),
+            np.array([np.timedelta64(1, "D"), np.timedelta64(2, "D")], dtype=object),
+            np.array([timedelta(1), timedelta(2)]),
+        ],
+    )
+    def test_infer_dtype_timedelta(self, arr):
+        assert lib.infer_dtype(arr, skipna=True) == "timedelta"
+
+    @pytest.mark.parametrize("na_value", [pd.NaT, np.nan])
+    @pytest.mark.parametrize(
+        "delta", [Timedelta("1 days"), np.timedelta64(1, "D"), timedelta(1)]
+    )
+    def test_infer_dtype_timedelta_with_na(self, na_value, delta):
+        # starts with nan
+        arr = np.array([na_value, delta])
+        assert lib.infer_dtype(arr, skipna=True) == "timedelta"
+
+        arr = np.array([na_value, delta, na_value])
+        assert lib.infer_dtype(arr, skipna=True) == "timedelta"
+
+    def test_infer_dtype_period(self):
+        # GH 13664
+        arr = np.array([Period("2011-01", freq="D"), Period("2011-02", freq="D")])
+        assert lib.infer_dtype(arr, skipna=True) == "period"
+
+        # non-homogeneous freqs -> mixed
+        arr = np.array([Period("2011-01", freq="D"), Period("2011-02", freq="M")])
+        assert lib.infer_dtype(arr, skipna=True) == "mixed"
+
+    @pytest.mark.parametrize("klass", [pd.array, Series, Index])
+    @pytest.mark.parametrize("skipna", [True, False])
+    def test_infer_dtype_period_array(self, klass, skipna):
+        # https://github.com/pandas-dev/pandas/issues/23553
+        values = klass(
+            [
+                Period("2011-01-01", freq="D"),
+                Period("2011-01-02", freq="D"),
+                pd.NaT,
+            ]
+        )
+        assert lib.infer_dtype(values, skipna=skipna) == "period"
+
+        # periods but mixed freq
+        values = klass(
+            [
+                Period("2011-01-01", freq="D"),
+                Period("2011-01-02", freq="M"),
+                pd.NaT,
+            ]
+        )
+        # with pd.array this becomes NumpyExtensionArray which ends up
+        #  as "unknown-array"
+        exp = "unknown-array" if klass is pd.array else "mixed"
+        assert lib.infer_dtype(values, skipna=skipna) == exp
+
+    def test_infer_dtype_period_mixed(self):
+        arr = np.array(
+            [Period("2011-01", freq="M"), np.datetime64("nat")], dtype=object
+        )
+        assert lib.infer_dtype(arr, skipna=False) == "mixed"
+
+        arr = np.array(
+            [np.datetime64("nat"), Period("2011-01", freq="M")], dtype=object
+        )
+        assert lib.infer_dtype(arr, skipna=False) == "mixed"
+
+    @pytest.mark.parametrize("na_value", [pd.NaT, np.nan])
+    def test_infer_dtype_period_with_na(self, na_value):
+        # starts with nan
+        arr = np.array([na_value, Period("2011-01", freq="D")])
+        assert lib.infer_dtype(arr, skipna=True) == "period"
+
+        arr = np.array([na_value, Period("2011-01", freq="D"), na_value])
+        assert lib.infer_dtype(arr, skipna=True) == "period"
+
+    def test_infer_dtype_all_nan_nat_like(self):
+        arr = np.array([np.nan, np.nan])
+        assert lib.infer_dtype(arr, skipna=True) == "floating"
+
+        # nan and None mix are result in mixed
+        arr = np.array([np.nan, np.nan, None])
+        assert lib.infer_dtype(arr, skipna=True) == "empty"
+        assert lib.infer_dtype(arr, skipna=False) == "mixed"
+
+        arr = np.array([None, np.nan, np.nan])
+        assert lib.infer_dtype(arr, skipna=True) == "empty"
+        assert lib.infer_dtype(arr, skipna=False) == "mixed"
+
+        # pd.NaT
+        arr = np.array([pd.NaT])
+        assert lib.infer_dtype(arr, skipna=False) == "datetime"
+
+        arr = np.array([pd.NaT, np.nan])
+        assert lib.infer_dtype(arr, skipna=False) == "datetime"
+
+        arr = np.array([np.nan, pd.NaT])
+        assert lib.infer_dtype(arr, skipna=False) == "datetime"
+
+        arr = np.array([np.nan, pd.NaT, np.nan])
+        assert lib.infer_dtype(arr, skipna=False) == "datetime"
+
+        arr = np.array([None, pd.NaT, None])
+        assert lib.infer_dtype(arr, skipna=False) == "datetime"
+
+        # np.datetime64(nat)
+        arr = np.array([np.datetime64("nat")])
+        assert lib.infer_dtype(arr, skipna=False) == "datetime64"
+
+        for n in [np.nan, pd.NaT, None]:
+            arr = np.array([n, np.datetime64("nat"), n])
+            assert lib.infer_dtype(arr, skipna=False) == "datetime64"
+
+            arr = np.array([pd.NaT, n, np.datetime64("nat"), n])
+            assert lib.infer_dtype(arr, skipna=False) == "datetime64"
+
+        arr = np.array([np.timedelta64("nat")], dtype=object)
+        assert lib.infer_dtype(arr, skipna=False) == "timedelta"
+
+        for n in [np.nan, pd.NaT, None]:
+            arr = np.array([n, np.timedelta64("nat"), n])
+            assert lib.infer_dtype(arr, skipna=False) == "timedelta"
+
+            arr = np.array([pd.NaT, n, np.timedelta64("nat"), n])
+            assert lib.infer_dtype(arr, skipna=False) == "timedelta"
+
+        # datetime / timedelta mixed
+        arr = np.array([pd.NaT, np.datetime64("nat"), np.timedelta64("nat"), np.nan])
+        assert lib.infer_dtype(arr, skipna=False) == "mixed"
+
+        arr = np.array([np.timedelta64("nat"), np.datetime64("nat")], dtype=object)
+        assert lib.infer_dtype(arr, skipna=False) == "mixed"
+
+    def test_is_datetimelike_array_all_nan_nat_like(self):
+        arr = np.array([np.nan, pd.NaT, np.datetime64("nat")])
+        assert lib.is_datetime_array(arr)
+        assert lib.is_datetime64_array(arr)
+        assert not lib.is_timedelta_or_timedelta64_array(arr)
+
+        arr = np.array([np.nan, pd.NaT, np.timedelta64("nat")])
+        assert not lib.is_datetime_array(arr)
+        assert not lib.is_datetime64_array(arr)
+        assert lib.is_timedelta_or_timedelta64_array(arr)
+
+        arr = np.array([np.nan, pd.NaT, np.datetime64("nat"), np.timedelta64("nat")])
+        assert not lib.is_datetime_array(arr)
+        assert not lib.is_datetime64_array(arr)
+        assert not lib.is_timedelta_or_timedelta64_array(arr)
+
+        arr = np.array([np.nan, pd.NaT])
+        assert lib.is_datetime_array(arr)
+        assert lib.is_datetime64_array(arr)
+        assert lib.is_timedelta_or_timedelta64_array(arr)
+
+        arr = np.array([np.nan, np.nan], dtype=object)
+        assert not lib.is_datetime_array(arr)
+        assert not lib.is_datetime64_array(arr)
+        assert not lib.is_timedelta_or_timedelta64_array(arr)
+
+        assert lib.is_datetime_with_singletz_array(
+            np.array(
+                [
+                    Timestamp("20130101", tz="US/Eastern"),
+                    Timestamp("20130102", tz="US/Eastern"),
+                ],
+                dtype=object,
+            )
+        )
+        assert not lib.is_datetime_with_singletz_array(
+            np.array(
+                [
+                    Timestamp("20130101", tz="US/Eastern"),
+                    Timestamp("20130102", tz="CET"),
+                ],
+                dtype=object,
+            )
+        )
+
+    @pytest.mark.parametrize(
+        "func",
+        [
+            "is_datetime_array",
+            "is_datetime64_array",
+            "is_bool_array",
+            "is_timedelta_or_timedelta64_array",
+            "is_date_array",
+            "is_time_array",
+            "is_interval_array",
+        ],
+    )
+    def test_other_dtypes_for_array(self, func):
+        func = getattr(lib, func)
+        arr = np.array(["foo", "bar"])
+        assert not func(arr)
+        assert not func(arr.reshape(2, 1))
+
+        arr = np.array([1, 2])
+        assert not func(arr)
+        assert not func(arr.reshape(2, 1))
+
+    def test_date(self):
+        dates = [date(2012, 1, day) for day in range(1, 20)]
+        index = Index(dates)
+        assert index.inferred_type == "date"
+
+        dates = [date(2012, 1, day) for day in range(1, 20)] + [np.nan]
+        result = lib.infer_dtype(dates, skipna=False)
+        assert result == "mixed"
+
+        result = lib.infer_dtype(dates, skipna=True)
+        assert result == "date"
+
+    @pytest.mark.parametrize(
+        "values",
+        [
+            [date(2020, 1, 1), Timestamp("2020-01-01")],
+            [Timestamp("2020-01-01"), date(2020, 1, 1)],
+            [date(2020, 1, 1), pd.NaT],
+            [pd.NaT, date(2020, 1, 1)],
+        ],
+    )
+    @pytest.mark.parametrize("skipna", [True, False])
+    def test_infer_dtype_date_order_invariant(self, values, skipna):
+        # https://github.com/pandas-dev/pandas/issues/33741
+        result = lib.infer_dtype(values, skipna=skipna)
+        assert result == "date"
+
+    def test_is_numeric_array(self):
+        assert lib.is_float_array(np.array([1, 2.0]))
+        assert lib.is_float_array(np.array([1, 2.0, np.nan]))
+        assert not lib.is_float_array(np.array([1, 2]))
+
+        assert lib.is_integer_array(np.array([1, 2]))
+        assert not lib.is_integer_array(np.array([1, 2.0]))
+
+    def test_is_string_array(self):
+        # We should only be accepting pd.NA, np.nan,
+        # other floating point nans e.g. float('nan')]
+        # when skipna is True.
+        assert lib.is_string_array(np.array(["foo", "bar"]))
+        assert not lib.is_string_array(
+            np.array(["foo", "bar", pd.NA], dtype=object), skipna=False
+        )
+        assert lib.is_string_array(
+            np.array(["foo", "bar", pd.NA], dtype=object), skipna=True
+        )
+        # we allow NaN/None in the StringArray constructor, so its allowed here
+        assert lib.is_string_array(
+            np.array(["foo", "bar", None], dtype=object), skipna=True
+        )
+        assert lib.is_string_array(
+            np.array(["foo", "bar", np.nan], dtype=object), skipna=True
+        )
+        # But not e.g. datetimelike or Decimal NAs
+        assert not lib.is_string_array(
+            np.array(["foo", "bar", pd.NaT], dtype=object), skipna=True
+        )
+        assert not lib.is_string_array(
+            np.array(["foo", "bar", np.datetime64("NaT")], dtype=object), skipna=True
+        )
+        assert not lib.is_string_array(
+            np.array(["foo", "bar", Decimal("NaN")], dtype=object), skipna=True
+        )
+
+        assert not lib.is_string_array(
+            np.array(["foo", "bar", None], dtype=object), skipna=False
+        )
+        assert not lib.is_string_array(
+            np.array(["foo", "bar", np.nan], dtype=object), skipna=False
+        )
+        assert not lib.is_string_array(np.array([1, 2]))
+
+    @pytest.mark.parametrize(
+        "func",
+        [
+            "is_bool_array",
+            "is_date_array",
+            "is_datetime_array",
+            "is_datetime64_array",
+            "is_float_array",
+            "is_integer_array",
+            "is_interval_array",
+            "is_string_array",
+            "is_time_array",
+            "is_timedelta_or_timedelta64_array",
+        ],
+    )
+    def test_is_dtype_array_empty_obj(self, func):
+        # https://github.com/pandas-dev/pandas/pull/60796
+        func = getattr(lib, func)
+
+        arr = np.empty((2, 0), dtype=object)
+        assert not func(arr)
+
+        arr = np.empty((0, 2), dtype=object)
+        assert not func(arr)
+
+    def test_to_object_array_tuples(self):
+        r = (5, 6)
+        values = [r]
+        lib.to_object_array_tuples(values)
+
+        # make sure record array works
+        record = namedtuple("record", "x y")
+        r = record(5, 6)
+        values = [r]
+        lib.to_object_array_tuples(values)
+
+    def test_object(self):
+        # GH 7431
+        # cannot infer more than this as only a single element
+        arr = np.array([None], dtype="O")
+        result = lib.infer_dtype(arr, skipna=False)
+        assert result == "mixed"
+        result = lib.infer_dtype(arr, skipna=True)
+        assert result == "empty"
+
+    def test_to_object_array_width(self):
+        # see gh-13320
+        rows = [[1, 2, 3], [4, 5, 6]]
+
+        expected = np.array(rows, dtype=object)
+        out = lib.to_object_array(rows)
+        tm.assert_numpy_array_equal(out, expected)
+
+        expected = np.array(rows, dtype=object)
+        out = lib.to_object_array(rows, min_width=1)
+        tm.assert_numpy_array_equal(out, expected)
+
+        expected = np.array(
+            [[1, 2, 3, None, None], [4, 5, 6, None, None]], dtype=object
+        )
+        out = lib.to_object_array(rows, min_width=5)
+        tm.assert_numpy_array_equal(out, expected)
+
+    def test_is_period(self):
+        # GH#55264
+        msg = "is_period is deprecated and will be removed in a future version"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            assert lib.is_period(Period("2011-01", freq="M"))
+            assert not lib.is_period(PeriodIndex(["2011-01"], freq="M"))
+            assert not lib.is_period(Timestamp("2011-01"))
+            assert not lib.is_period(1)
+            assert not lib.is_period(np.nan)
+
+    def test_is_interval(self):
+        # GH#55264
+        msg = "is_interval is deprecated and will be removed in a future version"
+        item = Interval(1, 2)
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            assert lib.is_interval(item)
+            assert not lib.is_interval(pd.IntervalIndex([item]))
+            assert not lib.is_interval(pd.IntervalIndex([item])._engine)
+
+    def test_categorical(self):
+        # GH 8974
+        arr = Categorical(list("abc"))
+        result = lib.infer_dtype(arr, skipna=True)
+        assert result == "categorical"
+
+        result = lib.infer_dtype(Series(arr), skipna=True)
+        assert result == "categorical"
+
+        arr = Categorical(list("abc"), categories=["cegfab"], ordered=True)
+        result = lib.infer_dtype(arr, skipna=True)
+        assert result == "categorical"
+
+        result = lib.infer_dtype(Series(arr), skipna=True)
+        assert result == "categorical"
+
+    @pytest.mark.parametrize("asobject", [True, False])
+    def test_interval(self, asobject):
+        idx = pd.IntervalIndex.from_breaks(range(5), closed="both")
+        if asobject:
+            idx = idx.astype(object)
+
+        inferred = lib.infer_dtype(idx, skipna=False)
+        assert inferred == "interval"
+
+        inferred = lib.infer_dtype(idx._data, skipna=False)
+        assert inferred == "interval"
+
+        inferred = lib.infer_dtype(Series(idx, dtype=idx.dtype), skipna=False)
+        assert inferred == "interval"
+
+    @pytest.mark.parametrize("value", [Timestamp(0), Timedelta(0), 0, 0.0])
+    def test_interval_mismatched_closed(self, value):
+        first = Interval(value, value, closed="left")
+        second = Interval(value, value, closed="right")
+
+        # if closed match, we should infer "interval"
+        arr = np.array([first, first], dtype=object)
+        assert lib.infer_dtype(arr, skipna=False) == "interval"
+
+        # if closed dont match, we should _not_ get "interval"
+        arr2 = np.array([first, second], dtype=object)
+        assert lib.infer_dtype(arr2, skipna=False) == "mixed"
+
+    def test_interval_mismatched_subtype(self):
+        first = Interval(0, 1, closed="left")
+        second = Interval(Timestamp(0), Timestamp(1), closed="left")
+        third = Interval(Timedelta(0), Timedelta(1), closed="left")
+
+        arr = np.array([first, second])
+        assert lib.infer_dtype(arr, skipna=False) == "mixed"
+
+        arr = np.array([second, third])
+        assert lib.infer_dtype(arr, skipna=False) == "mixed"
+
+        arr = np.array([first, third])
+        assert lib.infer_dtype(arr, skipna=False) == "mixed"
+
+        # float vs int subdtype are compatible
+        flt_interval = Interval(1.5, 2.5, closed="left")
+        arr = np.array([first, flt_interval], dtype=object)
+        assert lib.infer_dtype(arr, skipna=False) == "interval"
+
+    @pytest.mark.parametrize("klass", [pd.array, Series])
+    @pytest.mark.parametrize("skipna", [True, False])
+    @pytest.mark.parametrize("data", [["a", "b", "c"], ["a", "b", pd.NA]])
+    def test_string_dtype(self, data, skipna, klass, nullable_string_dtype):
+        # StringArray
+        val = klass(data, dtype=nullable_string_dtype)
+        inferred = lib.infer_dtype(val, skipna=skipna)
+        assert inferred == "string"
+
+    @pytest.mark.parametrize("klass", [pd.array, Series])
+    @pytest.mark.parametrize("skipna", [True, False])
+    @pytest.mark.parametrize("data", [[True, False, True], [True, False, pd.NA]])
+    def test_boolean_dtype(self, data, skipna, klass):
+        # BooleanArray
+        val = klass(data, dtype="boolean")
+        inferred = lib.infer_dtype(val, skipna=skipna)
+        assert inferred == "boolean"
+
+
+class TestNumberScalar:
+    def test_is_number(self):
+        assert is_number(True)
+        assert is_number(1)
+        assert is_number(1.1)
+        assert is_number(1 + 3j)
+        assert is_number(np.int64(1))
+        assert is_number(np.float64(1.1))
+        assert is_number(np.complex128(1 + 3j))
+        assert is_number(np.nan)
+
+        assert not is_number(None)
+        assert not is_number("x")
+        assert not is_number(datetime(2011, 1, 1))
+        assert not is_number(np.datetime64("2011-01-01"))
+        assert not is_number(Timestamp("2011-01-01"))
+        assert not is_number(Timestamp("2011-01-01", tz="US/Eastern"))
+        assert not is_number(timedelta(1000))
+        assert not is_number(Timedelta("1 days"))
+
+        # questionable
+        assert not is_number(np.bool_(False))
+        assert is_number(np.timedelta64(1, "D"))
+
+    def test_is_bool(self):
+        assert is_bool(True)
+        assert is_bool(False)
+        assert is_bool(np.bool_(False))
+
+        assert not is_bool(1)
+        assert not is_bool(1.1)
+        assert not is_bool(1 + 3j)
+        assert not is_bool(np.int64(1))
+        assert not is_bool(np.float64(1.1))
+        assert not is_bool(np.complex128(1 + 3j))
+        assert not is_bool(np.nan)
+        assert not is_bool(None)
+        assert not is_bool("x")
+        assert not is_bool(datetime(2011, 1, 1))
+        assert not is_bool(np.datetime64("2011-01-01"))
+        assert not is_bool(Timestamp("2011-01-01"))
+        assert not is_bool(Timestamp("2011-01-01", tz="US/Eastern"))
+        assert not is_bool(timedelta(1000))
+        assert not is_bool(np.timedelta64(1, "D"))
+        assert not is_bool(Timedelta("1 days"))
+
+    def test_is_integer(self):
+        assert is_integer(1)
+        assert is_integer(np.int64(1))
+
+        assert not is_integer(True)
+        assert not is_integer(1.1)
+        assert not is_integer(1 + 3j)
+        assert not is_integer(False)
+        assert not is_integer(np.bool_(False))
+        assert not is_integer(np.float64(1.1))
+        assert not is_integer(np.complex128(1 + 3j))
+        assert not is_integer(np.nan)
+        assert not is_integer(None)
+        assert not is_integer("x")
+        assert not is_integer(datetime(2011, 1, 1))
+        assert not is_integer(np.datetime64("2011-01-01"))
+        assert not is_integer(Timestamp("2011-01-01"))
+        assert not is_integer(Timestamp("2011-01-01", tz="US/Eastern"))
+        assert not is_integer(timedelta(1000))
+        assert not is_integer(Timedelta("1 days"))
+        assert not is_integer(np.timedelta64(1, "D"))
+
+    def test_is_float(self):
+        assert is_float(1.1)
+        assert is_float(np.float64(1.1))
+        assert is_float(np.nan)
+
+        assert not is_float(True)
+        assert not is_float(1)
+        assert not is_float(1 + 3j)
+        assert not is_float(False)
+        assert not is_float(np.bool_(False))
+        assert not is_float(np.int64(1))
+        assert not is_float(np.complex128(1 + 3j))
+        assert not is_float(None)
+        assert not is_float("x")
+        assert not is_float(datetime(2011, 1, 1))
+        assert not is_float(np.datetime64("2011-01-01"))
+        assert not is_float(Timestamp("2011-01-01"))
+        assert not is_float(Timestamp("2011-01-01", tz="US/Eastern"))
+        assert not is_float(timedelta(1000))
+        assert not is_float(np.timedelta64(1, "D"))
+        assert not is_float(Timedelta("1 days"))
+
+    def test_is_datetime_dtypes(self):
+        ts = pd.date_range("20130101", periods=3)
+        tsa = pd.date_range("20130101", periods=3, tz="US/Eastern")
+
+        msg = "is_datetime64tz_dtype is deprecated"
+
+        assert is_datetime64_dtype("datetime64")
+        assert is_datetime64_dtype("datetime64[ns]")
+        assert is_datetime64_dtype(ts)
+        assert not is_datetime64_dtype(tsa)
+
+        assert not is_datetime64_ns_dtype("datetime64")
+        assert is_datetime64_ns_dtype("datetime64[ns]")
+        assert is_datetime64_ns_dtype(ts)
+        assert is_datetime64_ns_dtype(tsa)
+
+        assert is_datetime64_any_dtype("datetime64")
+        assert is_datetime64_any_dtype("datetime64[ns]")
+        assert is_datetime64_any_dtype(ts)
+        assert is_datetime64_any_dtype(tsa)
+
+        with tm.assert_produces_warning(DeprecationWarning, match=msg):
+            assert not is_datetime64tz_dtype("datetime64")
+            assert not is_datetime64tz_dtype("datetime64[ns]")
+            assert not is_datetime64tz_dtype(ts)
+            assert is_datetime64tz_dtype(tsa)
+
+    @pytest.mark.parametrize("tz", ["US/Eastern", "UTC"])
+    def test_is_datetime_dtypes_with_tz(self, tz):
+        dtype = f"datetime64[ns, {tz}]"
+        assert not is_datetime64_dtype(dtype)
+
+        msg = "is_datetime64tz_dtype is deprecated"
+        with tm.assert_produces_warning(DeprecationWarning, match=msg):
+            assert is_datetime64tz_dtype(dtype)
+        assert is_datetime64_ns_dtype(dtype)
+        assert is_datetime64_any_dtype(dtype)
+
+    def test_is_timedelta(self):
+        assert is_timedelta64_dtype("timedelta64")
+        assert is_timedelta64_dtype("timedelta64[ns]")
+        assert not is_timedelta64_ns_dtype("timedelta64")
+        assert is_timedelta64_ns_dtype("timedelta64[ns]")
+
+        tdi = TimedeltaIndex([1e14, 2e14], dtype="timedelta64[ns]")
+        assert is_timedelta64_dtype(tdi)
+        assert is_timedelta64_ns_dtype(tdi)
+        assert is_timedelta64_ns_dtype(tdi.astype("timedelta64[ns]"))
+
+        assert not is_timedelta64_ns_dtype(Index([], dtype=np.float64))
+        assert not is_timedelta64_ns_dtype(Index([], dtype=np.int64))
+
+
+class TestIsScalar:
+    def test_is_scalar_builtin_scalars(self):
+        assert is_scalar(None)
+        assert is_scalar(True)
+        assert is_scalar(False)
+        assert is_scalar(Fraction())
+        assert is_scalar(0.0)
+        assert is_scalar(1)
+        assert is_scalar(complex(2))
+        assert is_scalar(float("NaN"))
+        assert is_scalar(np.nan)
+        assert is_scalar("foobar")
+        assert is_scalar(b"foobar")
+        assert is_scalar(datetime(2014, 1, 1))
+        assert is_scalar(date(2014, 1, 1))
+        assert is_scalar(time(12, 0))
+        assert is_scalar(timedelta(hours=1))
+        assert is_scalar(pd.NaT)
+        assert is_scalar(pd.NA)
+
+    def test_is_scalar_builtin_nonscalars(self):
+        assert not is_scalar({})
+        assert not is_scalar([])
+        assert not is_scalar([1])
+        assert not is_scalar(())
+        assert not is_scalar((1,))
+        assert not is_scalar(slice(None))
+        assert not is_scalar(Ellipsis)
+
+    def test_is_scalar_numpy_array_scalars(self):
+        assert is_scalar(np.int64(1))
+        assert is_scalar(np.float64(1.0))
+        assert is_scalar(np.int32(1))
+        assert is_scalar(np.complex64(2))
+        assert is_scalar(np.object_("foobar"))
+        assert is_scalar(np.str_("foobar"))
+        assert is_scalar(np.bytes_(b"foobar"))
+        assert is_scalar(np.datetime64("2014-01-01"))
+        assert is_scalar(np.timedelta64(1, "h"))
+
+    @pytest.mark.parametrize(
+        "zerodim",
+        [
+            np.array(1),
+            np.array("foobar"),
+            np.array(np.datetime64("2014-01-01")),
+            np.array(np.timedelta64(1, "h")),
+            np.array(np.datetime64("NaT")),
+        ],
+    )
+    def test_is_scalar_numpy_zerodim_arrays(self, zerodim):
+        assert not is_scalar(zerodim)
+        assert is_scalar(lib.item_from_zerodim(zerodim))
+
+    @pytest.mark.parametrize("arr", [np.array([]), np.array([[]])])
+    def test_is_scalar_numpy_arrays(self, arr):
+        assert not is_scalar(arr)
+        assert not is_scalar(MockNumpyLikeArray(arr))
+
+    def test_is_scalar_pandas_scalars(self):
+        assert is_scalar(Timestamp("2014-01-01"))
+        assert is_scalar(Timedelta(hours=1))
+        assert is_scalar(Period("2014-01-01"))
+        assert is_scalar(Interval(left=0, right=1))
+        assert is_scalar(DateOffset(days=1))
+        assert is_scalar(pd.offsets.Minute(3))
+
+    def test_is_scalar_pandas_containers(self):
+        assert not is_scalar(Series(dtype=object))
+        assert not is_scalar(Series([1]))
+        assert not is_scalar(DataFrame())
+        assert not is_scalar(DataFrame([[1]]))
+        assert not is_scalar(Index([]))
+        assert not is_scalar(Index([1]))
+        assert not is_scalar(Categorical([]))
+        assert not is_scalar(DatetimeIndex([])._data)
+        assert not is_scalar(TimedeltaIndex([])._data)
+        assert not is_scalar(DatetimeIndex([])._data.to_period("D"))
+        assert not is_scalar(pd.array([1, 2, 3]))
+
+    def test_is_scalar_number(self):
+        # Number() is not recognied by PyNumber_Check, so by extension
+        #  is not recognized by is_scalar, but instances of non-abstract
+        #  subclasses are.
+
+        class Numeric(Number):
+            def __init__(self, value) -> None:
+                self.value = value
+
+            def __int__(self) -> int:
+                return self.value
+
+        num = Numeric(1)
+        assert is_scalar(num)
+
+
+@pytest.mark.parametrize("unit", ["ms", "us", "ns"])
+def test_datetimeindex_from_empty_datetime64_array(unit):
+    idx = DatetimeIndex(np.array([], dtype=f"datetime64[{unit}]"))
+    assert len(idx) == 0
+
+
+def test_nan_to_nat_conversions():
+    df = DataFrame(
+        {"A": np.asarray(range(10), dtype="float64"), "B": Timestamp("20010101")}
+    )
+    df.iloc[3:6, :] = np.nan
+    result = df.loc[4, "B"]
+    assert result is pd.NaT
+
+    s = df["B"].copy()
+    s[8:9] = np.nan
+    assert s[8] is pd.NaT
+
+
+@pytest.mark.filterwarnings("ignore::PendingDeprecationWarning")
+def test_is_scipy_sparse(spmatrix):
+    pytest.importorskip("scipy")
+    assert is_scipy_sparse(spmatrix([[0, 1]]))
+    assert not is_scipy_sparse(np.array([1]))
+
+
+def test_ensure_int32():
+    values = np.arange(10, dtype=np.int32)
+    result = ensure_int32(values)
+    assert result.dtype == np.int32
+
+    values = np.arange(10, dtype=np.int64)
+    result = ensure_int32(values)
+    assert result.dtype == np.int32
+
+
+@pytest.mark.parametrize(
+    "right,result",
+    [
+        (0, np.uint8),
+        (-1, np.int16),
+        (300, np.uint16),
+        # For floats, we just upcast directly to float64 instead of trying to
+        # find a smaller floating dtype
+        (300.0, np.uint16),  # for integer floats, we convert them to ints
+        (300.1, np.float64),
+        (np.int16(300), np.int16 if np_version_gt2 else np.uint16),
+    ],
+)
+def test_find_result_type_uint_int(right, result):
+    left_dtype = np.dtype("uint8")
+    assert find_result_type(left_dtype, right) == result
+
+
+@pytest.mark.parametrize(
+    "right,result",
+    [
+        (0, np.int8),
+        (-1, np.int8),
+        (300, np.int16),
+        # For floats, we just upcast directly to float64 instead of trying to
+        # find a smaller floating dtype
+        (300.0, np.int16),  # for integer floats, we convert them to ints
+        (300.1, np.float64),
+        (np.int16(300), np.int16),
+    ],
+)
+def test_find_result_type_int_int(right, result):
+    left_dtype = np.dtype("int8")
+    assert find_result_type(left_dtype, right) == result
+
+
+@pytest.mark.parametrize(
+    "right,result",
+    [
+        (300.0, np.float64),
+        (np.float32(300), np.float32),
+    ],
+)
+def test_find_result_type_floats(right, result):
+    left_dtype = np.dtype("float16")
+    assert find_result_type(left_dtype, right) == result
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/dtypes/test_missing.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/dtypes/test_missing.py
new file mode 100644
index 0000000000000000000000000000000000000000..e3d3e98ae2b93f0a182ea15a3a11d6af39fd2f05
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/dtypes/test_missing.py
@@ -0,0 +1,923 @@
+from contextlib import nullcontext
+from datetime import datetime
+from decimal import Decimal
+
+import numpy as np
+import pytest
+
+from pandas._config import config as cf
+
+from pandas._libs import missing as libmissing
+from pandas._libs.tslibs import iNaT
+from pandas.compat.numpy import np_version_gte1p25
+
+from pandas.core.dtypes.common import (
+    is_float,
+    is_scalar,
+    pandas_dtype,
+)
+from pandas.core.dtypes.dtypes import (
+    CategoricalDtype,
+    DatetimeTZDtype,
+    IntervalDtype,
+    PeriodDtype,
+)
+from pandas.core.dtypes.missing import (
+    array_equivalent,
+    is_valid_na_for_dtype,
+    isna,
+    isnull,
+    na_value_for_dtype,
+    notna,
+    notnull,
+)
+
+import pandas as pd
+from pandas import (
+    DatetimeIndex,
+    Index,
+    NaT,
+    Series,
+    TimedeltaIndex,
+    date_range,
+    period_range,
+)
+import pandas._testing as tm
+
+fix_now = pd.Timestamp("2021-01-01")
+fix_utcnow = pd.Timestamp("2021-01-01", tz="UTC")
+
+
+@pytest.mark.parametrize("notna_f", [notna, notnull])
+def test_notna_notnull(notna_f):
+    assert notna_f(1.0)
+    assert not notna_f(None)
+    assert not notna_f(np.nan)
+
+    msg = "use_inf_as_na option is deprecated"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        with cf.option_context("mode.use_inf_as_na", False):
+            assert notna_f(np.inf)
+            assert notna_f(-np.inf)
+
+            arr = np.array([1.5, np.inf, 3.5, -np.inf])
+            result = notna_f(arr)
+            assert result.all()
+
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        with cf.option_context("mode.use_inf_as_na", True):
+            assert not notna_f(np.inf)
+            assert not notna_f(-np.inf)
+
+            arr = np.array([1.5, np.inf, 3.5, -np.inf])
+            result = notna_f(arr)
+            assert result.sum() == 2
+
+
+@pytest.mark.parametrize("null_func", [notna, notnull, isna, isnull])
+@pytest.mark.parametrize(
+    "ser",
+    [
+        Series(
+            [str(i) for i in range(5)],
+            index=Index([str(i) for i in range(5)], dtype=object),
+            dtype=object,
+        ),
+        Series(range(5), date_range("2020-01-01", periods=5)),
+        Series(range(5), period_range("2020-01-01", periods=5)),
+    ],
+)
+def test_null_check_is_series(null_func, ser):
+    msg = "use_inf_as_na option is deprecated"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        with cf.option_context("mode.use_inf_as_na", False):
+            assert isinstance(null_func(ser), Series)
+
+
+class TestIsNA:
+    def test_0d_array(self):
+        assert isna(np.array(np.nan))
+        assert not isna(np.array(0.0))
+        assert not isna(np.array(0))
+        # test object dtype
+        assert isna(np.array(np.nan, dtype=object))
+        assert not isna(np.array(0.0, dtype=object))
+        assert not isna(np.array(0, dtype=object))
+
+    @pytest.mark.parametrize("shape", [(4, 0), (4,)])
+    def test_empty_object(self, shape):
+        arr = np.empty(shape=shape, dtype=object)
+        result = isna(arr)
+        expected = np.ones(shape=shape, dtype=bool)
+        tm.assert_numpy_array_equal(result, expected)
+
+    @pytest.mark.parametrize("isna_f", [isna, isnull])
+    def test_isna_isnull(self, isna_f):
+        assert not isna_f(1.0)
+        assert isna_f(None)
+        assert isna_f(np.nan)
+        assert float("nan")
+        assert not isna_f(np.inf)
+        assert not isna_f(-np.inf)
+
+        # type
+        assert not isna_f(type(Series(dtype=object)))
+        assert not isna_f(type(Series(dtype=np.float64)))
+        assert not isna_f(type(pd.DataFrame()))
+
+    @pytest.mark.parametrize("isna_f", [isna, isnull])
+    @pytest.mark.parametrize(
+        "data",
+        [
+            np.arange(4, dtype=float),
+            [0.0, 1.0, 0.0, 1.0],
+            Series(list("abcd")),
+            date_range("2020-01-01", periods=4),
+        ],
+    )
+    @pytest.mark.parametrize(
+        "index",
+        [
+            date_range("2020-01-01", periods=4),
+            range(4),
+            period_range("2020-01-01", periods=4),
+        ],
+    )
+    def test_isna_isnull_frame(self, isna_f, data, index):
+        # frame
+        df = pd.DataFrame(data, index=index)
+        result = isna_f(df)
+        expected = df.apply(isna_f)
+        tm.assert_frame_equal(result, expected)
+
+    def test_isna_lists(self):
+        result = isna([[False]])
+        exp = np.array([[False]])
+        tm.assert_numpy_array_equal(result, exp)
+
+        result = isna([[1], [2]])
+        exp = np.array([[False], [False]])
+        tm.assert_numpy_array_equal(result, exp)
+
+        # list of strings / unicode
+        result = isna(["foo", "bar"])
+        exp = np.array([False, False])
+        tm.assert_numpy_array_equal(result, exp)
+
+        result = isna(["foo", "bar"])
+        exp = np.array([False, False])
+        tm.assert_numpy_array_equal(result, exp)
+
+        # GH20675
+        result = isna([np.nan, "world"])
+        exp = np.array([True, False])
+        tm.assert_numpy_array_equal(result, exp)
+
+    def test_isna_nat(self):
+        result = isna([NaT])
+        exp = np.array([True])
+        tm.assert_numpy_array_equal(result, exp)
+
+        result = isna(np.array([NaT], dtype=object))
+        exp = np.array([True])
+        tm.assert_numpy_array_equal(result, exp)
+
+    def test_isna_numpy_nat(self):
+        arr = np.array(
+            [
+                NaT,
+                np.datetime64("NaT"),
+                np.timedelta64("NaT"),
+                np.datetime64("NaT", "s"),
+            ]
+        )
+        result = isna(arr)
+        expected = np.array([True] * 4)
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_isna_datetime(self):
+        assert not isna(datetime.now())
+        assert notna(datetime.now())
+
+        idx = date_range("1/1/1990", periods=20)
+        exp = np.ones(len(idx), dtype=bool)
+        tm.assert_numpy_array_equal(notna(idx), exp)
+
+        idx = np.asarray(idx)
+        idx[0] = iNaT
+        idx = DatetimeIndex(idx)
+        mask = isna(idx)
+        assert mask[0]
+        exp = np.array([True] + [False] * (len(idx) - 1), dtype=bool)
+        tm.assert_numpy_array_equal(mask, exp)
+
+        # GH 9129
+        pidx = idx.to_period(freq="M")
+        mask = isna(pidx)
+        assert mask[0]
+        exp = np.array([True] + [False] * (len(idx) - 1), dtype=bool)
+        tm.assert_numpy_array_equal(mask, exp)
+
+        mask = isna(pidx[1:])
+        exp = np.zeros(len(mask), dtype=bool)
+        tm.assert_numpy_array_equal(mask, exp)
+
+    def test_isna_old_datetimelike(self):
+        # isna_old should work for dt64tz, td64, and period, not just tznaive
+        dti = date_range("2016-01-01", periods=3)
+        dta = dti._data
+        dta[-1] = NaT
+        expected = np.array([False, False, True], dtype=bool)
+
+        objs = [dta, dta.tz_localize("US/Eastern"), dta - dta, dta.to_period("D")]
+
+        for obj in objs:
+            msg = "use_inf_as_na option is deprecated"
+            with tm.assert_produces_warning(FutureWarning, match=msg):
+                with cf.option_context("mode.use_inf_as_na", True):
+                    result = isna(obj)
+
+            tm.assert_numpy_array_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "value, expected",
+        [
+            (np.complex128(np.nan), True),
+            (np.float64(1), False),
+            (np.array([1, 1 + 0j, np.nan, 3]), np.array([False, False, True, False])),
+            (
+                np.array([1, 1 + 0j, np.nan, 3], dtype=object),
+                np.array([False, False, True, False]),
+            ),
+            (
+                np.array([1, 1 + 0j, np.nan, 3]).astype(object),
+                np.array([False, False, True, False]),
+            ),
+        ],
+    )
+    def test_complex(self, value, expected):
+        result = isna(value)
+        if is_scalar(result):
+            assert result is expected
+        else:
+            tm.assert_numpy_array_equal(result, expected)
+
+    def test_datetime_other_units(self):
+        idx = DatetimeIndex(["2011-01-01", "NaT", "2011-01-02"])
+        exp = np.array([False, True, False])
+        tm.assert_numpy_array_equal(isna(idx), exp)
+        tm.assert_numpy_array_equal(notna(idx), ~exp)
+        tm.assert_numpy_array_equal(isna(idx.values), exp)
+        tm.assert_numpy_array_equal(notna(idx.values), ~exp)
+
+    @pytest.mark.parametrize(
+        "dtype",
+        [
+            "datetime64[D]",
+            "datetime64[h]",
+            "datetime64[m]",
+            "datetime64[s]",
+            "datetime64[ms]",
+            "datetime64[us]",
+            "datetime64[ns]",
+        ],
+    )
+    def test_datetime_other_units_astype(self, dtype):
+        idx = DatetimeIndex(["2011-01-01", "NaT", "2011-01-02"])
+        values = idx.values.astype(dtype)
+
+        exp = np.array([False, True, False])
+        tm.assert_numpy_array_equal(isna(values), exp)
+        tm.assert_numpy_array_equal(notna(values), ~exp)
+
+        exp = Series([False, True, False])
+        s = Series(values)
+        tm.assert_series_equal(isna(s), exp)
+        tm.assert_series_equal(notna(s), ~exp)
+        s = Series(values, dtype=object)
+        tm.assert_series_equal(isna(s), exp)
+        tm.assert_series_equal(notna(s), ~exp)
+
+    def test_timedelta_other_units(self):
+        idx = TimedeltaIndex(["1 days", "NaT", "2 days"])
+        exp = np.array([False, True, False])
+        tm.assert_numpy_array_equal(isna(idx), exp)
+        tm.assert_numpy_array_equal(notna(idx), ~exp)
+        tm.assert_numpy_array_equal(isna(idx.values), exp)
+        tm.assert_numpy_array_equal(notna(idx.values), ~exp)
+
+    @pytest.mark.parametrize(
+        "dtype",
+        [
+            "timedelta64[D]",
+            "timedelta64[h]",
+            "timedelta64[m]",
+            "timedelta64[s]",
+            "timedelta64[ms]",
+            "timedelta64[us]",
+            "timedelta64[ns]",
+        ],
+    )
+    def test_timedelta_other_units_dtype(self, dtype):
+        idx = TimedeltaIndex(["1 days", "NaT", "2 days"])
+        values = idx.values.astype(dtype)
+
+        exp = np.array([False, True, False])
+        tm.assert_numpy_array_equal(isna(values), exp)
+        tm.assert_numpy_array_equal(notna(values), ~exp)
+
+        exp = Series([False, True, False])
+        s = Series(values)
+        tm.assert_series_equal(isna(s), exp)
+        tm.assert_series_equal(notna(s), ~exp)
+        s = Series(values, dtype=object)
+        tm.assert_series_equal(isna(s), exp)
+        tm.assert_series_equal(notna(s), ~exp)
+
+    def test_period(self):
+        idx = pd.PeriodIndex(["2011-01", "NaT", "2012-01"], freq="M")
+        exp = np.array([False, True, False])
+        tm.assert_numpy_array_equal(isna(idx), exp)
+        tm.assert_numpy_array_equal(notna(idx), ~exp)
+
+        exp = Series([False, True, False])
+        s = Series(idx)
+        tm.assert_series_equal(isna(s), exp)
+        tm.assert_series_equal(notna(s), ~exp)
+        s = Series(idx, dtype=object)
+        tm.assert_series_equal(isna(s), exp)
+        tm.assert_series_equal(notna(s), ~exp)
+
+    def test_decimal(self):
+        # scalars GH#23530
+        a = Decimal(1.0)
+        assert isna(a) is False
+        assert notna(a) is True
+
+        b = Decimal("NaN")
+        assert isna(b) is True
+        assert notna(b) is False
+
+        # array
+        arr = np.array([a, b])
+        expected = np.array([False, True])
+        result = isna(arr)
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = notna(arr)
+        tm.assert_numpy_array_equal(result, ~expected)
+
+        # series
+        ser = Series(arr)
+        expected = Series(expected)
+        result = isna(ser)
+        tm.assert_series_equal(result, expected)
+
+        result = notna(ser)
+        tm.assert_series_equal(result, ~expected)
+
+        # index
+        idx = Index(arr)
+        expected = np.array([False, True])
+        result = isna(idx)
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = notna(idx)
+        tm.assert_numpy_array_equal(result, ~expected)
+
+
+@pytest.mark.parametrize("dtype_equal", [True, False])
+def test_array_equivalent(dtype_equal):
+    assert array_equivalent(
+        np.array([np.nan, np.nan]), np.array([np.nan, np.nan]), dtype_equal=dtype_equal
+    )
+    assert array_equivalent(
+        np.array([np.nan, 1, np.nan]),
+        np.array([np.nan, 1, np.nan]),
+        dtype_equal=dtype_equal,
+    )
+    assert array_equivalent(
+        np.array([np.nan, None], dtype="object"),
+        np.array([np.nan, None], dtype="object"),
+        dtype_equal=dtype_equal,
+    )
+    # Check the handling of nested arrays in array_equivalent_object
+    assert array_equivalent(
+        np.array([np.array([np.nan, None], dtype="object"), None], dtype="object"),
+        np.array([np.array([np.nan, None], dtype="object"), None], dtype="object"),
+        dtype_equal=dtype_equal,
+    )
+    assert array_equivalent(
+        np.array([np.nan, 1 + 1j], dtype="complex"),
+        np.array([np.nan, 1 + 1j], dtype="complex"),
+        dtype_equal=dtype_equal,
+    )
+    assert not array_equivalent(
+        np.array([np.nan, 1 + 1j], dtype="complex"),
+        np.array([np.nan, 1 + 2j], dtype="complex"),
+        dtype_equal=dtype_equal,
+    )
+    assert not array_equivalent(
+        np.array([np.nan, 1, np.nan]),
+        np.array([np.nan, 2, np.nan]),
+        dtype_equal=dtype_equal,
+    )
+    assert not array_equivalent(
+        np.array(["a", "b", "c", "d"]), np.array(["e", "e"]), dtype_equal=dtype_equal
+    )
+    assert array_equivalent(
+        Index([0, np.nan]), Index([0, np.nan]), dtype_equal=dtype_equal
+    )
+    assert not array_equivalent(
+        Index([0, np.nan]), Index([1, np.nan]), dtype_equal=dtype_equal
+    )
+
+
+@pytest.mark.parametrize("dtype_equal", [True, False])
+def test_array_equivalent_tdi(dtype_equal):
+    assert array_equivalent(
+        TimedeltaIndex([0, np.nan]),
+        TimedeltaIndex([0, np.nan]),
+        dtype_equal=dtype_equal,
+    )
+    assert not array_equivalent(
+        TimedeltaIndex([0, np.nan]),
+        TimedeltaIndex([1, np.nan]),
+        dtype_equal=dtype_equal,
+    )
+
+
+@pytest.mark.parametrize("dtype_equal", [True, False])
+def test_array_equivalent_dti(dtype_equal):
+    assert array_equivalent(
+        DatetimeIndex([0, np.nan]), DatetimeIndex([0, np.nan]), dtype_equal=dtype_equal
+    )
+    assert not array_equivalent(
+        DatetimeIndex([0, np.nan]), DatetimeIndex([1, np.nan]), dtype_equal=dtype_equal
+    )
+
+    dti1 = DatetimeIndex([0, np.nan], tz="US/Eastern")
+    dti2 = DatetimeIndex([0, np.nan], tz="CET")
+    dti3 = DatetimeIndex([1, np.nan], tz="US/Eastern")
+
+    assert array_equivalent(
+        dti1,
+        dti1,
+        dtype_equal=dtype_equal,
+    )
+    assert not array_equivalent(
+        dti1,
+        dti3,
+        dtype_equal=dtype_equal,
+    )
+    # The rest are not dtype_equal
+    assert not array_equivalent(DatetimeIndex([0, np.nan]), dti1)
+    assert array_equivalent(
+        dti2,
+        dti1,
+    )
+
+    assert not array_equivalent(DatetimeIndex([0, np.nan]), TimedeltaIndex([0, np.nan]))
+
+
+@pytest.mark.parametrize(
+    "val", [1, 1.1, 1 + 1j, True, "abc", [1, 2], (1, 2), {1, 2}, {"a": 1}, None]
+)
+def test_array_equivalent_series(val):
+    arr = np.array([1, 2])
+    msg = "elementwise comparison failed"
+    cm = (
+        # stacklevel is chosen to make sense when called from .equals
+        tm.assert_produces_warning(FutureWarning, match=msg, check_stacklevel=False)
+        if isinstance(val, str) and not np_version_gte1p25
+        else nullcontext()
+    )
+    with cm:
+        assert not array_equivalent(Series([arr, arr]), Series([arr, val]))
+
+
+def test_array_equivalent_array_mismatched_shape():
+    # to trigger the motivating bug, the first N elements of the arrays need
+    #  to match
+    first = np.array([1, 2, 3])
+    second = np.array([1, 2])
+
+    left = Series([first, "a"], dtype=object)
+    right = Series([second, "a"], dtype=object)
+    assert not array_equivalent(left, right)
+
+
+def test_array_equivalent_array_mismatched_dtype():
+    # same shape, different dtype can still be equivalent
+    first = np.array([1, 2], dtype=np.float64)
+    second = np.array([1, 2])
+
+    left = Series([first, "a"], dtype=object)
+    right = Series([second, "a"], dtype=object)
+    assert array_equivalent(left, right)
+
+
+def test_array_equivalent_different_dtype_but_equal():
+    # Unclear if this is exposed anywhere in the public-facing API
+    assert array_equivalent(np.array([1, 2]), np.array([1.0, 2.0]))
+
+
+@pytest.mark.parametrize(
+    "lvalue, rvalue",
+    [
+        # There are 3 variants for each of lvalue and rvalue. We include all
+        #  three for the tz-naive `now` and exclude the datetim64 variant
+        #  for utcnow because it drops tzinfo.
+        (fix_now, fix_utcnow),
+        (fix_now.to_datetime64(), fix_utcnow),
+        (fix_now.to_pydatetime(), fix_utcnow),
+        (fix_now, fix_utcnow),
+        (fix_now.to_datetime64(), fix_utcnow.to_pydatetime()),
+        (fix_now.to_pydatetime(), fix_utcnow.to_pydatetime()),
+    ],
+)
+def test_array_equivalent_tzawareness(lvalue, rvalue):
+    # we shouldn't raise if comparing tzaware and tznaive datetimes
+    left = np.array([lvalue], dtype=object)
+    right = np.array([rvalue], dtype=object)
+
+    assert not array_equivalent(left, right, strict_nan=True)
+    assert not array_equivalent(left, right, strict_nan=False)
+
+
+def test_array_equivalent_compat():
+    # see gh-13388
+    m = np.array([(1, 2), (3, 4)], dtype=[("a", int), ("b", float)])
+    n = np.array([(1, 2), (3, 4)], dtype=[("a", int), ("b", float)])
+    assert array_equivalent(m, n, strict_nan=True)
+    assert array_equivalent(m, n, strict_nan=False)
+
+    m = np.array([(1, 2), (3, 4)], dtype=[("a", int), ("b", float)])
+    n = np.array([(1, 2), (4, 3)], dtype=[("a", int), ("b", float)])
+    assert not array_equivalent(m, n, strict_nan=True)
+    assert not array_equivalent(m, n, strict_nan=False)
+
+    m = np.array([(1, 2), (3, 4)], dtype=[("a", int), ("b", float)])
+    n = np.array([(1, 2), (3, 4)], dtype=[("b", int), ("a", float)])
+    assert not array_equivalent(m, n, strict_nan=True)
+    assert not array_equivalent(m, n, strict_nan=False)
+
+
+@pytest.mark.parametrize("dtype", ["O", "S", "U"])
+def test_array_equivalent_str(dtype):
+    assert array_equivalent(
+        np.array(["A", "B"], dtype=dtype), np.array(["A", "B"], dtype=dtype)
+    )
+    assert not array_equivalent(
+        np.array(["A", "B"], dtype=dtype), np.array(["A", "X"], dtype=dtype)
+    )
+
+
+@pytest.mark.parametrize("strict_nan", [True, False])
+def test_array_equivalent_nested(strict_nan):
+    # reached in groupby aggregations, make sure we use np.any when checking
+    #  if the comparison is truthy
+    left = np.array([np.array([50, 70, 90]), np.array([20, 30])], dtype=object)
+    right = np.array([np.array([50, 70, 90]), np.array([20, 30])], dtype=object)
+
+    assert array_equivalent(left, right, strict_nan=strict_nan)
+    assert not array_equivalent(left, right[::-1], strict_nan=strict_nan)
+
+    left = np.empty(2, dtype=object)
+    left[:] = [np.array([50, 70, 90]), np.array([20, 30, 40])]
+    right = np.empty(2, dtype=object)
+    right[:] = [np.array([50, 70, 90]), np.array([20, 30, 40])]
+    assert array_equivalent(left, right, strict_nan=strict_nan)
+    assert not array_equivalent(left, right[::-1], strict_nan=strict_nan)
+
+    left = np.array([np.array([50, 50, 50]), np.array([40, 40])], dtype=object)
+    right = np.array([50, 40])
+    assert not array_equivalent(left, right, strict_nan=strict_nan)
+
+
+@pytest.mark.filterwarnings("ignore:elementwise comparison failed:DeprecationWarning")
+@pytest.mark.parametrize("strict_nan", [True, False])
+def test_array_equivalent_nested2(strict_nan):
+    # more than one level of nesting
+    left = np.array(
+        [
+            np.array([np.array([50, 70]), np.array([90])], dtype=object),
+            np.array([np.array([20, 30])], dtype=object),
+        ],
+        dtype=object,
+    )
+    right = np.array(
+        [
+            np.array([np.array([50, 70]), np.array([90])], dtype=object),
+            np.array([np.array([20, 30])], dtype=object),
+        ],
+        dtype=object,
+    )
+    assert array_equivalent(left, right, strict_nan=strict_nan)
+    assert not array_equivalent(left, right[::-1], strict_nan=strict_nan)
+
+    left = np.array([np.array([np.array([50, 50, 50])], dtype=object)], dtype=object)
+    right = np.array([50])
+    assert not array_equivalent(left, right, strict_nan=strict_nan)
+
+
+@pytest.mark.parametrize("strict_nan", [True, False])
+def test_array_equivalent_nested_list(strict_nan):
+    left = np.array([[50, 70, 90], [20, 30]], dtype=object)
+    right = np.array([[50, 70, 90], [20, 30]], dtype=object)
+
+    assert array_equivalent(left, right, strict_nan=strict_nan)
+    assert not array_equivalent(left, right[::-1], strict_nan=strict_nan)
+
+    left = np.array([[50, 50, 50], [40, 40]], dtype=object)
+    right = np.array([50, 40])
+    assert not array_equivalent(left, right, strict_nan=strict_nan)
+
+
+@pytest.mark.filterwarnings("ignore:elementwise comparison failed:DeprecationWarning")
+@pytest.mark.xfail(reason="failing")
+@pytest.mark.parametrize("strict_nan", [True, False])
+def test_array_equivalent_nested_mixed_list(strict_nan):
+    # mixed arrays / lists in left and right
+    # https://github.com/pandas-dev/pandas/issues/50360
+    left = np.array([np.array([1, 2, 3]), np.array([4, 5])], dtype=object)
+    right = np.array([[1, 2, 3], [4, 5]], dtype=object)
+
+    assert array_equivalent(left, right, strict_nan=strict_nan)
+    assert not array_equivalent(left, right[::-1], strict_nan=strict_nan)
+
+    # multiple levels of nesting
+    left = np.array(
+        [
+            np.array([np.array([1, 2, 3]), np.array([4, 5])], dtype=object),
+            np.array([np.array([6]), np.array([7, 8]), np.array([9])], dtype=object),
+        ],
+        dtype=object,
+    )
+    right = np.array([[[1, 2, 3], [4, 5]], [[6], [7, 8], [9]]], dtype=object)
+    assert array_equivalent(left, right, strict_nan=strict_nan)
+    assert not array_equivalent(left, right[::-1], strict_nan=strict_nan)
+
+    # same-length lists
+    subarr = np.empty(2, dtype=object)
+    subarr[:] = [
+        np.array([None, "b"], dtype=object),
+        np.array(["c", "d"], dtype=object),
+    ]
+    left = np.array([subarr, None], dtype=object)
+    right = np.array([[[None, "b"], ["c", "d"]], None], dtype=object)
+    assert array_equivalent(left, right, strict_nan=strict_nan)
+    assert not array_equivalent(left, right[::-1], strict_nan=strict_nan)
+
+
+@pytest.mark.xfail(reason="failing")
+@pytest.mark.parametrize("strict_nan", [True, False])
+def test_array_equivalent_nested_dicts(strict_nan):
+    left = np.array([{"f1": 1, "f2": np.array(["a", "b"], dtype=object)}], dtype=object)
+    right = np.array(
+        [{"f1": 1, "f2": np.array(["a", "b"], dtype=object)}], dtype=object
+    )
+    assert array_equivalent(left, right, strict_nan=strict_nan)
+    assert not array_equivalent(left, right[::-1], strict_nan=strict_nan)
+
+    right2 = np.array([{"f1": 1, "f2": ["a", "b"]}], dtype=object)
+    assert array_equivalent(left, right2, strict_nan=strict_nan)
+    assert not array_equivalent(left, right2[::-1], strict_nan=strict_nan)
+
+
+def test_array_equivalent_index_with_tuples():
+    # GH#48446
+    idx1 = Index(np.array([(pd.NA, 4), (1, 1)], dtype="object"))
+    idx2 = Index(np.array([(1, 1), (pd.NA, 4)], dtype="object"))
+    assert not array_equivalent(idx1, idx2)
+    assert not idx1.equals(idx2)
+    assert not array_equivalent(idx2, idx1)
+    assert not idx2.equals(idx1)
+
+    idx1 = Index(np.array([(4, pd.NA), (1, 1)], dtype="object"))
+    idx2 = Index(np.array([(1, 1), (4, pd.NA)], dtype="object"))
+    assert not array_equivalent(idx1, idx2)
+    assert not idx1.equals(idx2)
+    assert not array_equivalent(idx2, idx1)
+    assert not idx2.equals(idx1)
+
+
+@pytest.mark.parametrize(
+    "dtype, na_value",
+    [
+        # Datetime-like
+        (np.dtype("M8[ns]"), np.datetime64("NaT", "ns")),
+        (np.dtype("m8[ns]"), np.timedelta64("NaT", "ns")),
+        (DatetimeTZDtype.construct_from_string("datetime64[ns, US/Eastern]"), NaT),
+        (PeriodDtype("M"), NaT),
+        # Integer
+        ("u1", 0),
+        ("u2", 0),
+        ("u4", 0),
+        ("u8", 0),
+        ("i1", 0),
+        ("i2", 0),
+        ("i4", 0),
+        ("i8", 0),
+        # Bool
+        ("bool", False),
+        # Float
+        ("f2", np.nan),
+        ("f4", np.nan),
+        ("f8", np.nan),
+        # Object
+        ("O", np.nan),
+        # Interval
+        (IntervalDtype(), np.nan),
+    ],
+)
+def test_na_value_for_dtype(dtype, na_value):
+    result = na_value_for_dtype(pandas_dtype(dtype))
+    # identify check doesn't work for datetime64/timedelta64("NaT") bc they
+    #  are not singletons
+    assert result is na_value or (
+        isna(result) and isna(na_value) and type(result) is type(na_value)
+    )
+
+
+class TestNAObj:
+    def _check_behavior(self, arr, expected):
+        result = libmissing.isnaobj(arr)
+        tm.assert_numpy_array_equal(result, expected)
+        result = libmissing.isnaobj(arr, inf_as_na=True)
+        tm.assert_numpy_array_equal(result, expected)
+
+        arr = np.atleast_2d(arr)
+        expected = np.atleast_2d(expected)
+
+        result = libmissing.isnaobj(arr)
+        tm.assert_numpy_array_equal(result, expected)
+        result = libmissing.isnaobj(arr, inf_as_na=True)
+        tm.assert_numpy_array_equal(result, expected)
+
+        # Test fortran order
+        arr = arr.copy(order="F")
+        result = libmissing.isnaobj(arr)
+        tm.assert_numpy_array_equal(result, expected)
+        result = libmissing.isnaobj(arr, inf_as_na=True)
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_basic(self):
+        arr = np.array([1, None, "foo", -5.1, NaT, np.nan])
+        expected = np.array([False, True, False, False, True, True])
+
+        self._check_behavior(arr, expected)
+
+    def test_non_obj_dtype(self):
+        arr = np.array([1, 3, np.nan, 5], dtype=float)
+        expected = np.array([False, False, True, False])
+
+        self._check_behavior(arr, expected)
+
+    def test_empty_arr(self):
+        arr = np.array([])
+        expected = np.array([], dtype=bool)
+
+        self._check_behavior(arr, expected)
+
+    def test_empty_str_inp(self):
+        arr = np.array([""])  # empty but not na
+        expected = np.array([False])
+
+        self._check_behavior(arr, expected)
+
+    def test_empty_like(self):
+        # see gh-13717: no segfaults!
+        arr = np.empty_like([None])
+        expected = np.array([True])
+
+        self._check_behavior(arr, expected)
+
+
+m8_units = ["as", "ps", "ns", "us", "ms", "s", "m", "h", "D", "W", "M", "Y"]
+
+na_vals = (
+    [
+        None,
+        NaT,
+        float("NaN"),
+        complex("NaN"),
+        np.nan,
+        np.float64("NaN"),
+        np.float32("NaN"),
+        np.complex64(np.nan),
+        np.complex128(np.nan),
+        np.datetime64("NaT"),
+        np.timedelta64("NaT"),
+    ]
+    + [np.datetime64("NaT", unit) for unit in m8_units]
+    + [np.timedelta64("NaT", unit) for unit in m8_units]
+)
+
+inf_vals = [
+    float("inf"),
+    float("-inf"),
+    complex("inf"),
+    complex("-inf"),
+    np.inf,
+    -np.inf,
+]
+
+int_na_vals = [
+    # Values that match iNaT, which we treat as null in specific cases
+    np.int64(NaT._value),
+    int(NaT._value),
+]
+
+sometimes_na_vals = [Decimal("NaN")]
+
+never_na_vals = [
+    # float/complex values that when viewed as int64 match iNaT
+    -0.0,
+    np.float64("-0.0"),
+    -0j,
+    np.complex64(-0j),
+]
+
+
+class TestLibMissing:
+    @pytest.mark.parametrize("func", [libmissing.checknull, isna])
+    @pytest.mark.parametrize(
+        "value", na_vals + sometimes_na_vals  # type: ignore[operator]
+    )
+    def test_checknull_na_vals(self, func, value):
+        assert func(value)
+
+    @pytest.mark.parametrize("func", [libmissing.checknull, isna])
+    @pytest.mark.parametrize("value", inf_vals)
+    def test_checknull_inf_vals(self, func, value):
+        assert not func(value)
+
+    @pytest.mark.parametrize("func", [libmissing.checknull, isna])
+    @pytest.mark.parametrize("value", int_na_vals)
+    def test_checknull_intna_vals(self, func, value):
+        assert not func(value)
+
+    @pytest.mark.parametrize("func", [libmissing.checknull, isna])
+    @pytest.mark.parametrize("value", never_na_vals)
+    def test_checknull_never_na_vals(self, func, value):
+        assert not func(value)
+
+    @pytest.mark.parametrize(
+        "value", na_vals + sometimes_na_vals  # type: ignore[operator]
+    )
+    def test_checknull_old_na_vals(self, value):
+        assert libmissing.checknull(value, inf_as_na=True)
+
+    @pytest.mark.parametrize("value", inf_vals)
+    def test_checknull_old_inf_vals(self, value):
+        assert libmissing.checknull(value, inf_as_na=True)
+
+    @pytest.mark.parametrize("value", int_na_vals)
+    def test_checknull_old_intna_vals(self, value):
+        assert not libmissing.checknull(value, inf_as_na=True)
+
+    @pytest.mark.parametrize("value", int_na_vals)
+    def test_checknull_old_never_na_vals(self, value):
+        assert not libmissing.checknull(value, inf_as_na=True)
+
+    def test_is_matching_na(self, nulls_fixture, nulls_fixture2):
+        left = nulls_fixture
+        right = nulls_fixture2
+
+        assert libmissing.is_matching_na(left, left)
+
+        if left is right:
+            assert libmissing.is_matching_na(left, right)
+        elif is_float(left) and is_float(right):
+            # np.nan vs float("NaN") we consider as matching
+            assert libmissing.is_matching_na(left, right)
+        elif type(left) is type(right):
+            # e.g. both Decimal("NaN")
+            assert libmissing.is_matching_na(left, right)
+        else:
+            assert not libmissing.is_matching_na(left, right)
+
+    def test_is_matching_na_nan_matches_none(self):
+        assert not libmissing.is_matching_na(None, np.nan)
+        assert not libmissing.is_matching_na(np.nan, None)
+
+        assert libmissing.is_matching_na(None, np.nan, nan_matches_none=True)
+        assert libmissing.is_matching_na(np.nan, None, nan_matches_none=True)
+
+
+class TestIsValidNAForDtype:
+    def test_is_valid_na_for_dtype_interval(self):
+        dtype = IntervalDtype("int64", "left")
+        assert not is_valid_na_for_dtype(NaT, dtype)
+
+        dtype = IntervalDtype("datetime64[ns]", "both")
+        assert not is_valid_na_for_dtype(NaT, dtype)
+
+    def test_is_valid_na_for_dtype_categorical(self):
+        dtype = CategoricalDtype(categories=[0, 1, 2])
+        assert is_valid_na_for_dtype(np.nan, dtype)
+
+        assert not is_valid_na_for_dtype(NaT, dtype)
+        assert not is_valid_na_for_dtype(np.datetime64("NaT", "ns"), dtype)
+        assert not is_valid_na_for_dtype(np.timedelta64("NaT", "ns"), dtype)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/array_with_attr/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/array_with_attr/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..49da6af024a31726743815ba1e36d66c03daafe5
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/array_with_attr/__init__.py
@@ -0,0 +1,6 @@
+from pandas.tests.extension.array_with_attr.array import (
+    FloatAttrArray,
+    FloatAttrDtype,
+)
+
+__all__ = ["FloatAttrArray", "FloatAttrDtype"]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/array_with_attr/array.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/array_with_attr/array.py
new file mode 100644
index 0000000000000000000000000000000000000000..2789d51ec2ce3096c64b41af90b5a416ceef9f5b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/array_with_attr/array.py
@@ -0,0 +1,89 @@
+"""
+Test extension array that has custom attribute information (not stored on the dtype).
+
+"""
+from __future__ import annotations
+
+import numbers
+from typing import TYPE_CHECKING
+
+import numpy as np
+
+from pandas.core.dtypes.base import ExtensionDtype
+
+import pandas as pd
+from pandas.core.arrays import ExtensionArray
+
+if TYPE_CHECKING:
+    from pandas._typing import type_t
+
+
+class FloatAttrDtype(ExtensionDtype):
+    type = float
+    name = "float_attr"
+    na_value = np.nan
+
+    @classmethod
+    def construct_array_type(cls) -> type_t[FloatAttrArray]:
+        """
+        Return the array type associated with this dtype.
+
+        Returns
+        -------
+        type
+        """
+        return FloatAttrArray
+
+
+class FloatAttrArray(ExtensionArray):
+    dtype = FloatAttrDtype()
+    __array_priority__ = 1000
+
+    def __init__(self, values, attr=None) -> None:
+        if not isinstance(values, np.ndarray):
+            raise TypeError("Need to pass a numpy array of float64 dtype as values")
+        if not values.dtype == "float64":
+            raise TypeError("Need to pass a numpy array of float64 dtype as values")
+        self.data = values
+        self.attr = attr
+
+    @classmethod
+    def _from_sequence(cls, scalars, *, dtype=None, copy=False):
+        if not copy:
+            data = np.asarray(scalars, dtype="float64")
+        else:
+            data = np.array(scalars, dtype="float64", copy=copy)
+        return cls(data)
+
+    def __getitem__(self, item):
+        if isinstance(item, numbers.Integral):
+            return self.data[item]
+        else:
+            # slice, list-like, mask
+            item = pd.api.indexers.check_array_indexer(self, item)
+            return type(self)(self.data[item], self.attr)
+
+    def __len__(self) -> int:
+        return len(self.data)
+
+    def isna(self):
+        return np.isnan(self.data)
+
+    def take(self, indexer, allow_fill=False, fill_value=None):
+        from pandas.api.extensions import take
+
+        data = self.data
+        if allow_fill and fill_value is None:
+            fill_value = self.dtype.na_value
+
+        result = take(data, indexer, fill_value=fill_value, allow_fill=allow_fill)
+        return type(self)(result, self.attr)
+
+    def copy(self):
+        return type(self)(self.data.copy(), self.attr)
+
+    @classmethod
+    def _concat_same_type(cls, to_concat):
+        data = np.concatenate([x.data for x in to_concat])
+        attr = to_concat[0].attr if len(to_concat) else None
+        return cls(data, attr)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/array_with_attr/test_array_with_attr.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/array_with_attr/test_array_with_attr.py
new file mode 100644
index 0000000000000000000000000000000000000000..3735fe40a0d67784b3603a177b6694e56e26d479
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/array_with_attr/test_array_with_attr.py
@@ -0,0 +1,33 @@
+import numpy as np
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.tests.extension.array_with_attr import FloatAttrArray
+
+
+def test_concat_with_all_na():
+    # https://github.com/pandas-dev/pandas/pull/47762
+    # ensure that attribute of the column array is preserved (when it gets
+    # preserved in reindexing the array) during merge/concat
+    arr = FloatAttrArray(np.array([np.nan, np.nan], dtype="float64"), attr="test")
+
+    df1 = pd.DataFrame({"col": arr, "key": [0, 1]})
+    df2 = pd.DataFrame({"key": [0, 1], "col2": [1, 2]})
+    result = pd.merge(df1, df2, on="key")
+    expected = pd.DataFrame({"col": arr, "key": [0, 1], "col2": [1, 2]})
+    tm.assert_frame_equal(result, expected)
+    assert result["col"].array.attr == "test"
+
+    df1 = pd.DataFrame({"col": arr, "key": [0, 1]})
+    df2 = pd.DataFrame({"key": [0, 2], "col2": [1, 2]})
+    result = pd.merge(df1, df2, on="key")
+    expected = pd.DataFrame({"col": arr.take([0]), "key": [0], "col2": [1]})
+    tm.assert_frame_equal(result, expected)
+    assert result["col"].array.attr == "test"
+
+    result = pd.concat([df1.set_index("key"), df2.set_index("key")], axis=1)
+    expected = pd.DataFrame(
+        {"col": arr.take([0, 1, -1]), "col2": [1, np.nan, 2], "key": [0, 1, 2]}
+    ).set_index("key")
+    tm.assert_frame_equal(result, expected)
+    assert result["col"].array.attr == "test"
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/base/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/base/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..6efaa95aef1b51c33df668db870eaa8741010a59
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/base/__init__.py
@@ -0,0 +1,131 @@
+"""
+Base test suite for extension arrays.
+
+These tests are intended for third-party libraries to subclass to validate
+that their extension arrays and dtypes satisfy the interface. Moving or
+renaming the tests should not be done lightly.
+
+Libraries are expected to implement a few pytest fixtures to provide data
+for the tests. The fixtures may be located in either
+
+* The same module as your test class.
+* A ``conftest.py`` in the same directory as your test class.
+
+The full list of fixtures may be found in the ``conftest.py`` next to this
+file.
+
+.. code-block:: python
+
+   import pytest
+   from pandas.tests.extension.base import BaseDtypeTests
+
+
+   @pytest.fixture
+   def dtype():
+       return MyDtype()
+
+
+   class TestMyDtype(BaseDtypeTests):
+       pass
+
+
+Your class ``TestDtype`` will inherit all the tests defined on
+``BaseDtypeTests``. pytest's fixture discover will supply your ``dtype``
+wherever the test requires it. You're free to implement additional tests.
+
+"""
+from pandas.tests.extension.base.accumulate import BaseAccumulateTests
+from pandas.tests.extension.base.casting import BaseCastingTests
+from pandas.tests.extension.base.constructors import BaseConstructorsTests
+from pandas.tests.extension.base.dim2 import (  # noqa: F401
+    Dim2CompatTests,
+    NDArrayBacked2DTests,
+)
+from pandas.tests.extension.base.dtype import BaseDtypeTests
+from pandas.tests.extension.base.getitem import BaseGetitemTests
+from pandas.tests.extension.base.groupby import BaseGroupbyTests
+from pandas.tests.extension.base.index import BaseIndexTests
+from pandas.tests.extension.base.interface import BaseInterfaceTests
+from pandas.tests.extension.base.io import BaseParsingTests
+from pandas.tests.extension.base.methods import BaseMethodsTests
+from pandas.tests.extension.base.missing import BaseMissingTests
+from pandas.tests.extension.base.ops import (  # noqa: F401
+    BaseArithmeticOpsTests,
+    BaseComparisonOpsTests,
+    BaseOpsUtil,
+    BaseUnaryOpsTests,
+)
+from pandas.tests.extension.base.printing import BasePrintingTests
+from pandas.tests.extension.base.reduce import BaseReduceTests
+from pandas.tests.extension.base.reshaping import BaseReshapingTests
+from pandas.tests.extension.base.setitem import BaseSetitemTests
+
+
+# One test class that you can inherit as an alternative to inheriting all the
+# test classes above.
+# Note 1) this excludes Dim2CompatTests and NDArrayBacked2DTests.
+# Note 2) this uses BaseReduceTests and and _not_ BaseBooleanReduceTests,
+#  BaseNoReduceTests, or BaseNumericReduceTests
+class ExtensionTests(
+    BaseAccumulateTests,
+    BaseCastingTests,
+    BaseConstructorsTests,
+    BaseDtypeTests,
+    BaseGetitemTests,
+    BaseGroupbyTests,
+    BaseIndexTests,
+    BaseInterfaceTests,
+    BaseParsingTests,
+    BaseMethodsTests,
+    BaseMissingTests,
+    BaseArithmeticOpsTests,
+    BaseComparisonOpsTests,
+    BaseUnaryOpsTests,
+    BasePrintingTests,
+    BaseReduceTests,
+    BaseReshapingTests,
+    BaseSetitemTests,
+    Dim2CompatTests,
+):
+    pass
+
+
+def __getattr__(name: str):
+    import warnings
+
+    if name == "BaseNoReduceTests":
+        warnings.warn(
+            "BaseNoReduceTests is deprecated and will be removed in a "
+            "future version. Use BaseReduceTests and override "
+            "`_supports_reduction` instead.",
+            FutureWarning,
+        )
+        from pandas.tests.extension.base.reduce import BaseNoReduceTests
+
+        return BaseNoReduceTests
+
+    elif name == "BaseNumericReduceTests":
+        warnings.warn(
+            "BaseNumericReduceTests is deprecated and will be removed in a "
+            "future version. Use BaseReduceTests and override "
+            "`_supports_reduction` instead.",
+            FutureWarning,
+        )
+        from pandas.tests.extension.base.reduce import BaseNumericReduceTests
+
+        return BaseNumericReduceTests
+
+    elif name == "BaseBooleanReduceTests":
+        warnings.warn(
+            "BaseBooleanReduceTests is deprecated and will be removed in a "
+            "future version. Use BaseReduceTests and override "
+            "`_supports_reduction` instead.",
+            FutureWarning,
+        )
+        from pandas.tests.extension.base.reduce import BaseBooleanReduceTests
+
+        return BaseBooleanReduceTests
+
+    raise AttributeError(
+        f"module 'pandas.tests.extension.base' has no attribute '{name}'"
+    )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/base/accumulate.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/base/accumulate.py
new file mode 100644
index 0000000000000000000000000000000000000000..9a2f186c2a00bf933cf3fdbcb7a07482930846e6
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/base/accumulate.py
@@ -0,0 +1,40 @@
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+
+
+class BaseAccumulateTests:
+    """
+    Accumulation specific tests. Generally these only
+    make sense for numeric/boolean operations.
+    """
+
+    def _supports_accumulation(self, ser: pd.Series, op_name: str) -> bool:
+        # Do we expect this accumulation to be supported for this dtype?
+        # We default to assuming "no"; subclass authors should override here.
+        return False
+
+    def check_accumulate(self, ser: pd.Series, op_name: str, skipna: bool):
+        try:
+            alt = ser.astype("float64")
+        except (TypeError, ValueError):
+            # e.g. Period can't be cast to float64 (TypeError)
+            #      String can't be cast to float64 (ValueError)
+            alt = ser.astype(object)
+
+        result = getattr(ser, op_name)(skipna=skipna)
+        expected = getattr(alt, op_name)(skipna=skipna)
+        tm.assert_series_equal(result, expected, check_dtype=False)
+
+    @pytest.mark.parametrize("skipna", [True, False])
+    def test_accumulate_series(self, data, all_numeric_accumulations, skipna):
+        op_name = all_numeric_accumulations
+        ser = pd.Series(data)
+
+        if self._supports_accumulation(ser, op_name):
+            self.check_accumulate(ser, op_name, skipna)
+        else:
+            with pytest.raises((NotImplementedError, TypeError)):
+                # TODO: require TypeError for things that will _never_ work?
+                getattr(ser, op_name)(skipna=skipna)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/base/base.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/base/base.py
new file mode 100644
index 0000000000000000000000000000000000000000..747ebee738c1ee5cf9bbcc858aedd55dea39b38c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/base/base.py
@@ -0,0 +1,2 @@
+class BaseExtensionTests:
+    pass
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/base/casting.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/base/casting.py
new file mode 100644
index 0000000000000000000000000000000000000000..56879129c3a28e69442ff4ba6bc1e1bdbed0183d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/base/casting.py
@@ -0,0 +1,87 @@
+import numpy as np
+import pytest
+
+import pandas.util._test_decorators as td
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.core.internals.blocks import NumpyBlock
+
+
+class BaseCastingTests:
+    """Casting to and from ExtensionDtypes"""
+
+    def test_astype_object_series(self, all_data):
+        ser = pd.Series(all_data, name="A")
+        result = ser.astype(object)
+        assert result.dtype == np.dtype(object)
+        if hasattr(result._mgr, "blocks"):
+            blk = result._mgr.blocks[0]
+            assert isinstance(blk, NumpyBlock)
+            assert blk.is_object
+        assert isinstance(result._mgr.array, np.ndarray)
+        assert result._mgr.array.dtype == np.dtype(object)
+
+    def test_astype_object_frame(self, all_data):
+        df = pd.DataFrame({"A": all_data})
+
+        result = df.astype(object)
+        if hasattr(result._mgr, "blocks"):
+            blk = result._mgr.blocks[0]
+            assert isinstance(blk, NumpyBlock), type(blk)
+            assert blk.is_object
+        assert isinstance(result._mgr.arrays[0], np.ndarray)
+        assert result._mgr.arrays[0].dtype == np.dtype(object)
+
+        # check that we can compare the dtypes
+        comp = result.dtypes == df.dtypes
+        assert not comp.any()
+
+    def test_tolist(self, data):
+        result = pd.Series(data).tolist()
+        expected = list(data)
+        assert result == expected
+
+    def test_astype_str(self, data):
+        result = pd.Series(data[:2]).astype(str)
+        expected = pd.Series([str(x) for x in data[:2]], dtype=str)
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "nullable_string_dtype",
+        [
+            "string[python]",
+            pytest.param("string[pyarrow]", marks=td.skip_if_no("pyarrow")),
+        ],
+    )
+    def test_astype_string(self, data, nullable_string_dtype):
+        # GH-33465, GH#45326 as of 2.0 we decode bytes instead of calling str(obj)
+        result = pd.Series(data[:5]).astype(nullable_string_dtype)
+        expected = pd.Series(
+            [str(x) if not isinstance(x, bytes) else x.decode() for x in data[:5]],
+            dtype=nullable_string_dtype,
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_to_numpy(self, data):
+        expected = np.asarray(data)
+
+        result = data.to_numpy()
+        tm.assert_equal(result, expected)
+
+        result = pd.Series(data).to_numpy()
+        tm.assert_equal(result, expected)
+
+    def test_astype_empty_dataframe(self, dtype):
+        # https://github.com/pandas-dev/pandas/issues/33113
+        df = pd.DataFrame()
+        result = df.astype(dtype)
+        tm.assert_frame_equal(result, df)
+
+    @pytest.mark.parametrize("copy", [True, False])
+    def test_astype_own_type(self, data, copy):
+        # ensure that astype returns the original object for equal dtype and copy=False
+        # https://github.com/pandas-dev/pandas/issues/28488
+        result = data.astype(data.dtype, copy=copy)
+        assert (result is data) is (not copy)
+        tm.assert_extension_array_equal(result, data)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/base/constructors.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/base/constructors.py
new file mode 100644
index 0000000000000000000000000000000000000000..c32a6a6a115ac992a9b85b6a35e77e4d446fbd07
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/base/constructors.py
@@ -0,0 +1,142 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.api.extensions import ExtensionArray
+from pandas.core.internals.blocks import EABackedBlock
+
+
+class BaseConstructorsTests:
+    def test_from_sequence_from_cls(self, data):
+        result = type(data)._from_sequence(data, dtype=data.dtype)
+        tm.assert_extension_array_equal(result, data)
+
+        data = data[:0]
+        result = type(data)._from_sequence(data, dtype=data.dtype)
+        tm.assert_extension_array_equal(result, data)
+
+    def test_array_from_scalars(self, data):
+        scalars = [data[0], data[1], data[2]]
+        result = data._from_sequence(scalars, dtype=data.dtype)
+        assert isinstance(result, type(data))
+
+    def test_series_constructor(self, data):
+        result = pd.Series(data, copy=False)
+        assert result.dtype == data.dtype
+        assert len(result) == len(data)
+        if hasattr(result._mgr, "blocks"):
+            assert isinstance(result._mgr.blocks[0], EABackedBlock)
+        assert result._mgr.array is data
+
+        # Series[EA] is unboxed / boxed correctly
+        result2 = pd.Series(result)
+        assert result2.dtype == data.dtype
+        if hasattr(result._mgr, "blocks"):
+            assert isinstance(result2._mgr.blocks[0], EABackedBlock)
+
+    def test_series_constructor_no_data_with_index(self, dtype, na_value):
+        result = pd.Series(index=[1, 2, 3], dtype=dtype)
+        expected = pd.Series([na_value] * 3, index=[1, 2, 3], dtype=dtype)
+        tm.assert_series_equal(result, expected)
+
+        # GH 33559 - empty index
+        result = pd.Series(index=[], dtype=dtype)
+        expected = pd.Series([], index=pd.Index([], dtype="object"), dtype=dtype)
+        tm.assert_series_equal(result, expected)
+
+    def test_series_constructor_scalar_na_with_index(self, dtype, na_value):
+        result = pd.Series(na_value, index=[1, 2, 3], dtype=dtype)
+        expected = pd.Series([na_value] * 3, index=[1, 2, 3], dtype=dtype)
+        tm.assert_series_equal(result, expected)
+
+    def test_series_constructor_scalar_with_index(self, data, dtype):
+        scalar = data[0]
+        result = pd.Series(scalar, index=[1, 2, 3], dtype=dtype)
+        expected = pd.Series([scalar] * 3, index=[1, 2, 3], dtype=dtype)
+        tm.assert_series_equal(result, expected)
+
+        result = pd.Series(scalar, index=["foo"], dtype=dtype)
+        expected = pd.Series([scalar], index=["foo"], dtype=dtype)
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("from_series", [True, False])
+    def test_dataframe_constructor_from_dict(self, data, from_series):
+        if from_series:
+            data = pd.Series(data)
+        result = pd.DataFrame({"A": data})
+        assert result.dtypes["A"] == data.dtype
+        assert result.shape == (len(data), 1)
+        if hasattr(result._mgr, "blocks"):
+            assert isinstance(result._mgr.blocks[0], EABackedBlock)
+        assert isinstance(result._mgr.arrays[0], ExtensionArray)
+
+    def test_dataframe_from_series(self, data):
+        result = pd.DataFrame(pd.Series(data))
+        assert result.dtypes[0] == data.dtype
+        assert result.shape == (len(data), 1)
+        if hasattr(result._mgr, "blocks"):
+            assert isinstance(result._mgr.blocks[0], EABackedBlock)
+        assert isinstance(result._mgr.arrays[0], ExtensionArray)
+
+    def test_series_given_mismatched_index_raises(self, data):
+        msg = r"Length of values \(3\) does not match length of index \(5\)"
+        with pytest.raises(ValueError, match=msg):
+            pd.Series(data[:3], index=[0, 1, 2, 3, 4])
+
+    def test_from_dtype(self, data):
+        # construct from our dtype & string dtype
+        dtype = data.dtype
+
+        expected = pd.Series(data)
+        result = pd.Series(list(data), dtype=dtype)
+        tm.assert_series_equal(result, expected)
+
+        result = pd.Series(list(data), dtype=str(dtype))
+        tm.assert_series_equal(result, expected)
+
+        # gh-30280
+
+        expected = pd.DataFrame(data).astype(dtype)
+        result = pd.DataFrame(list(data), dtype=dtype)
+        tm.assert_frame_equal(result, expected)
+
+        result = pd.DataFrame(list(data), dtype=str(dtype))
+        tm.assert_frame_equal(result, expected)
+
+    def test_pandas_array(self, data):
+        # pd.array(extension_array) should be idempotent...
+        result = pd.array(data)
+        tm.assert_extension_array_equal(result, data)
+
+    def test_pandas_array_dtype(self, data):
+        # ... but specifying dtype will override idempotency
+        result = pd.array(data, dtype=np.dtype(object))
+        expected = pd.arrays.NumpyExtensionArray(np.asarray(data, dtype=object))
+        tm.assert_equal(result, expected)
+
+    def test_construct_empty_dataframe(self, dtype):
+        # GH 33623
+        result = pd.DataFrame(columns=["a"], dtype=dtype)
+        expected = pd.DataFrame(
+            {"a": pd.array([], dtype=dtype)}, index=pd.RangeIndex(0)
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_empty(self, dtype):
+        cls = dtype.construct_array_type()
+        result = cls._empty((4,), dtype=dtype)
+        assert isinstance(result, cls)
+        assert result.dtype == dtype
+        assert result.shape == (4,)
+
+        # GH#19600 method on ExtensionDtype
+        result2 = dtype.empty((4,))
+        assert isinstance(result2, cls)
+        assert result2.dtype == dtype
+        assert result2.shape == (4,)
+
+        result2 = dtype.empty(4)
+        assert isinstance(result2, cls)
+        assert result2.dtype == dtype
+        assert result2.shape == (4,)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/base/dim2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/base/dim2.py
new file mode 100644
index 0000000000000000000000000000000000000000..132cda5a94ed00e74b7f36869bfe0c789dd5ccd7
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/base/dim2.py
@@ -0,0 +1,345 @@
+"""
+Tests for 2D compatibility.
+"""
+import numpy as np
+import pytest
+
+from pandas._libs.missing import is_matching_na
+
+from pandas.core.dtypes.common import (
+    is_bool_dtype,
+    is_integer_dtype,
+)
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.core.arrays.integer import NUMPY_INT_TO_DTYPE
+
+
+class Dim2CompatTests:
+    # Note: these are ONLY for ExtensionArray subclasses that support 2D arrays.
+    #  i.e. not for pyarrow-backed EAs.
+
+    @pytest.fixture(autouse=True)
+    def skip_if_doesnt_support_2d(self, dtype, request):
+        if not dtype._supports_2d:
+            node = request.node
+            # In cases where we are mixed in to ExtensionTests, we only want to
+            #  skip tests that are defined in Dim2CompatTests
+            test_func = node._obj
+            if test_func.__qualname__.startswith("Dim2CompatTests"):
+                # TODO: is there a less hacky way of checking this?
+                pytest.skip(f"{dtype} does not support 2D.")
+
+    def test_transpose(self, data):
+        arr2d = data.repeat(2).reshape(-1, 2)
+        shape = arr2d.shape
+        assert shape[0] != shape[-1]  # otherwise the rest of the test is useless
+
+        assert arr2d.T.shape == shape[::-1]
+
+    def test_frame_from_2d_array(self, data):
+        arr2d = data.repeat(2).reshape(-1, 2)
+
+        df = pd.DataFrame(arr2d)
+        expected = pd.DataFrame({0: arr2d[:, 0], 1: arr2d[:, 1]})
+        tm.assert_frame_equal(df, expected)
+
+    def test_swapaxes(self, data):
+        arr2d = data.repeat(2).reshape(-1, 2)
+
+        result = arr2d.swapaxes(0, 1)
+        expected = arr2d.T
+        tm.assert_extension_array_equal(result, expected)
+
+    def test_delete_2d(self, data):
+        arr2d = data.repeat(3).reshape(-1, 3)
+
+        # axis = 0
+        result = arr2d.delete(1, axis=0)
+        expected = data.delete(1).repeat(3).reshape(-1, 3)
+        tm.assert_extension_array_equal(result, expected)
+
+        # axis = 1
+        result = arr2d.delete(1, axis=1)
+        expected = data.repeat(2).reshape(-1, 2)
+        tm.assert_extension_array_equal(result, expected)
+
+    def test_take_2d(self, data):
+        arr2d = data.reshape(-1, 1)
+
+        result = arr2d.take([0, 0, -1], axis=0)
+
+        expected = data.take([0, 0, -1]).reshape(-1, 1)
+        tm.assert_extension_array_equal(result, expected)
+
+    def test_repr_2d(self, data):
+        # this could fail in a corner case where an element contained the name
+        res = repr(data.reshape(1, -1))
+        assert res.count(f"<{type(data).__name__}") == 1
+
+        res = repr(data.reshape(-1, 1))
+        assert res.count(f"<{type(data).__name__}") == 1
+
+    def test_reshape(self, data):
+        arr2d = data.reshape(-1, 1)
+        assert arr2d.shape == (data.size, 1)
+        assert len(arr2d) == len(data)
+
+        arr2d = data.reshape((-1, 1))
+        assert arr2d.shape == (data.size, 1)
+        assert len(arr2d) == len(data)
+
+        with pytest.raises(ValueError):
+            data.reshape((data.size, 2))
+        with pytest.raises(ValueError):
+            data.reshape(data.size, 2)
+
+    def test_getitem_2d(self, data):
+        arr2d = data.reshape(1, -1)
+
+        result = arr2d[0]
+        tm.assert_extension_array_equal(result, data)
+
+        with pytest.raises(IndexError):
+            arr2d[1]
+
+        with pytest.raises(IndexError):
+            arr2d[-2]
+
+        result = arr2d[:]
+        tm.assert_extension_array_equal(result, arr2d)
+
+        result = arr2d[:, :]
+        tm.assert_extension_array_equal(result, arr2d)
+
+        result = arr2d[:, 0]
+        expected = data[[0]]
+        tm.assert_extension_array_equal(result, expected)
+
+        # dimension-expanding getitem on 1D
+        result = data[:, np.newaxis]
+        tm.assert_extension_array_equal(result, arr2d.T)
+
+    def test_iter_2d(self, data):
+        arr2d = data.reshape(1, -1)
+
+        objs = list(iter(arr2d))
+        assert len(objs) == arr2d.shape[0]
+
+        for obj in objs:
+            assert isinstance(obj, type(data))
+            assert obj.dtype == data.dtype
+            assert obj.ndim == 1
+            assert len(obj) == arr2d.shape[1]
+
+    def test_tolist_2d(self, data):
+        arr2d = data.reshape(1, -1)
+
+        result = arr2d.tolist()
+        expected = [data.tolist()]
+
+        assert isinstance(result, list)
+        assert all(isinstance(x, list) for x in result)
+
+        assert result == expected
+
+    def test_concat_2d(self, data):
+        left = type(data)._concat_same_type([data, data]).reshape(-1, 2)
+        right = left.copy()
+
+        # axis=0
+        result = left._concat_same_type([left, right], axis=0)
+        expected = data._concat_same_type([data] * 4).reshape(-1, 2)
+        tm.assert_extension_array_equal(result, expected)
+
+        # axis=1
+        result = left._concat_same_type([left, right], axis=1)
+        assert result.shape == (len(data), 4)
+        tm.assert_extension_array_equal(result[:, :2], left)
+        tm.assert_extension_array_equal(result[:, 2:], right)
+
+        # axis > 1 -> invalid
+        msg = "axis 2 is out of bounds for array of dimension 2"
+        with pytest.raises(ValueError, match=msg):
+            left._concat_same_type([left, right], axis=2)
+
+    @pytest.mark.parametrize("method", ["backfill", "pad"])
+    def test_fillna_2d_method(self, data_missing, method):
+        # pad_or_backfill is always along axis=0
+        arr = data_missing.repeat(2).reshape(2, 2)
+        assert arr[0].isna().all()
+        assert not arr[1].isna().any()
+
+        result = arr._pad_or_backfill(method=method, limit=None)
+
+        expected = data_missing._pad_or_backfill(method=method).repeat(2).reshape(2, 2)
+        tm.assert_extension_array_equal(result, expected)
+
+        # Reverse so that backfill is not a no-op.
+        arr2 = arr[::-1]
+        assert not arr2[0].isna().any()
+        assert arr2[1].isna().all()
+
+        result2 = arr2._pad_or_backfill(method=method, limit=None)
+
+        expected2 = (
+            data_missing[::-1]._pad_or_backfill(method=method).repeat(2).reshape(2, 2)
+        )
+        tm.assert_extension_array_equal(result2, expected2)
+
+    @pytest.mark.parametrize("method", ["mean", "median", "var", "std", "sum", "prod"])
+    def test_reductions_2d_axis_none(self, data, method):
+        arr2d = data.reshape(1, -1)
+
+        err_expected = None
+        err_result = None
+        try:
+            expected = getattr(data, method)()
+        except Exception as err:
+            # if the 1D reduction is invalid, the 2D reduction should be as well
+            err_expected = err
+            try:
+                result = getattr(arr2d, method)(axis=None)
+            except Exception as err2:
+                err_result = err2
+
+        else:
+            result = getattr(arr2d, method)(axis=None)
+
+        if err_result is not None or err_expected is not None:
+            assert type(err_result) == type(err_expected)
+            return
+
+        assert is_matching_na(result, expected) or result == expected
+
+    @pytest.mark.parametrize("method", ["mean", "median", "var", "std", "sum", "prod"])
+    @pytest.mark.parametrize("min_count", [0, 1])
+    def test_reductions_2d_axis0(self, data, method, min_count):
+        if min_count == 1 and method not in ["sum", "prod"]:
+            pytest.skip(f"min_count not relevant for {method}")
+
+        arr2d = data.reshape(1, -1)
+
+        kwargs = {}
+        if method in ["std", "var"]:
+            # pass ddof=0 so we get all-zero std instead of all-NA std
+            kwargs["ddof"] = 0
+        elif method in ["prod", "sum"]:
+            kwargs["min_count"] = min_count
+
+        try:
+            result = getattr(arr2d, method)(axis=0, **kwargs)
+        except Exception as err:
+            try:
+                getattr(data, method)()
+            except Exception as err2:
+                assert type(err) == type(err2)
+                return
+            else:
+                raise AssertionError("Both reductions should raise or neither")
+
+        def get_reduction_result_dtype(dtype):
+            # windows and 32bit builds will in some cases have int32/uint32
+            #  where other builds will have int64/uint64.
+            if dtype.itemsize == 8:
+                return dtype
+            elif dtype.kind in "ib":
+                return NUMPY_INT_TO_DTYPE[np.dtype(int)]
+            else:
+                # i.e. dtype.kind == "u"
+                return NUMPY_INT_TO_DTYPE[np.dtype("uint")]
+
+        if method in ["sum", "prod"]:
+            # std and var are not dtype-preserving
+            expected = data
+            if data.dtype.kind in "iub":
+                dtype = get_reduction_result_dtype(data.dtype)
+                expected = data.astype(dtype)
+                assert dtype == expected.dtype
+
+            if min_count == 0:
+                fill_value = 1 if method == "prod" else 0
+                expected = expected.fillna(fill_value)
+
+            tm.assert_extension_array_equal(result, expected)
+        elif method == "median":
+            # std and var are not dtype-preserving
+            expected = data
+            tm.assert_extension_array_equal(result, expected)
+        elif method in ["mean", "std", "var"]:
+            if is_integer_dtype(data) or is_bool_dtype(data):
+                data = data.astype("Float64")
+            if method == "mean":
+                tm.assert_extension_array_equal(result, data)
+            else:
+                tm.assert_extension_array_equal(result, data - data)
+
+    @pytest.mark.parametrize("method", ["mean", "median", "var", "std", "sum", "prod"])
+    def test_reductions_2d_axis1(self, data, method):
+        arr2d = data.reshape(1, -1)
+
+        try:
+            result = getattr(arr2d, method)(axis=1)
+        except Exception as err:
+            try:
+                getattr(data, method)()
+            except Exception as err2:
+                assert type(err) == type(err2)
+                return
+            else:
+                raise AssertionError("Both reductions should raise or neither")
+
+        # not necessarily type/dtype-preserving, so weaker assertions
+        assert result.shape == (1,)
+        expected_scalar = getattr(data, method)()
+        res = result[0]
+        assert is_matching_na(res, expected_scalar) or res == expected_scalar
+
+
+class NDArrayBacked2DTests(Dim2CompatTests):
+    # More specific tests for NDArrayBackedExtensionArray subclasses
+
+    def test_copy_order(self, data):
+        # We should be matching numpy semantics for the "order" keyword in 'copy'
+        arr2d = data.repeat(2).reshape(-1, 2)
+        assert arr2d._ndarray.flags["C_CONTIGUOUS"]
+
+        res = arr2d.copy()
+        assert res._ndarray.flags["C_CONTIGUOUS"]
+
+        res = arr2d[::2, ::2].copy()
+        assert res._ndarray.flags["C_CONTIGUOUS"]
+
+        res = arr2d.copy("F")
+        assert not res._ndarray.flags["C_CONTIGUOUS"]
+        assert res._ndarray.flags["F_CONTIGUOUS"]
+
+        res = arr2d.copy("K")
+        assert res._ndarray.flags["C_CONTIGUOUS"]
+
+        res = arr2d.T.copy("K")
+        assert not res._ndarray.flags["C_CONTIGUOUS"]
+        assert res._ndarray.flags["F_CONTIGUOUS"]
+
+        # order not accepted by numpy
+        msg = r"order must be one of 'C', 'F', 'A', or 'K' \(got 'Q'\)"
+        with pytest.raises(ValueError, match=msg):
+            arr2d.copy("Q")
+
+        # neither contiguity
+        arr_nc = arr2d[::2]
+        assert not arr_nc._ndarray.flags["C_CONTIGUOUS"]
+        assert not arr_nc._ndarray.flags["F_CONTIGUOUS"]
+
+        assert arr_nc.copy()._ndarray.flags["C_CONTIGUOUS"]
+        assert not arr_nc.copy()._ndarray.flags["F_CONTIGUOUS"]
+
+        assert arr_nc.copy("C")._ndarray.flags["C_CONTIGUOUS"]
+        assert not arr_nc.copy("C")._ndarray.flags["F_CONTIGUOUS"]
+
+        assert not arr_nc.copy("F")._ndarray.flags["C_CONTIGUOUS"]
+        assert arr_nc.copy("F")._ndarray.flags["F_CONTIGUOUS"]
+
+        assert arr_nc.copy("K")._ndarray.flags["C_CONTIGUOUS"]
+        assert not arr_nc.copy("K")._ndarray.flags["F_CONTIGUOUS"]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/base/dtype.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/base/dtype.py
new file mode 100644
index 0000000000000000000000000000000000000000..c7b768f6e3c88f32a7f9f5c945642e4d69a17c66
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/base/dtype.py
@@ -0,0 +1,123 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.api.types import (
+    infer_dtype,
+    is_object_dtype,
+    is_string_dtype,
+)
+
+
+class BaseDtypeTests:
+    """Base class for ExtensionDtype classes"""
+
+    def test_name(self, dtype):
+        assert isinstance(dtype.name, str)
+
+    def test_kind(self, dtype):
+        valid = set("biufcmMOSUV")
+        assert dtype.kind in valid
+
+    def test_is_dtype_from_name(self, dtype):
+        result = type(dtype).is_dtype(dtype.name)
+        assert result is True
+
+    def test_is_dtype_unboxes_dtype(self, data, dtype):
+        assert dtype.is_dtype(data) is True
+
+    def test_is_dtype_from_self(self, dtype):
+        result = type(dtype).is_dtype(dtype)
+        assert result is True
+
+    def test_is_dtype_other_input(self, dtype):
+        assert dtype.is_dtype([1, 2, 3]) is False
+
+    def test_is_not_string_type(self, dtype):
+        assert not is_string_dtype(dtype)
+
+    def test_is_not_object_type(self, dtype):
+        assert not is_object_dtype(dtype)
+
+    def test_eq_with_str(self, dtype):
+        assert dtype == dtype.name
+        assert dtype != dtype.name + "-suffix"
+
+    def test_eq_with_numpy_object(self, dtype):
+        assert dtype != np.dtype("object")
+
+    def test_eq_with_self(self, dtype):
+        assert dtype == dtype
+        assert dtype != object()
+
+    def test_array_type(self, data, dtype):
+        assert dtype.construct_array_type() is type(data)
+
+    def test_check_dtype(self, data):
+        dtype = data.dtype
+
+        # check equivalency for using .dtypes
+        df = pd.DataFrame(
+            {
+                "A": pd.Series(data, dtype=dtype),
+                "B": data,
+                "C": pd.Series(["foo"] * len(data), dtype=object),
+                "D": 1,
+            }
+        )
+        result = df.dtypes == str(dtype)
+        assert np.dtype("int64") != "Int64"
+
+        expected = pd.Series([True, True, False, False], index=list("ABCD"))
+
+        tm.assert_series_equal(result, expected)
+
+        expected = pd.Series([True, True, False, False], index=list("ABCD"))
+        result = df.dtypes.apply(str) == str(dtype)
+        tm.assert_series_equal(result, expected)
+
+    def test_hashable(self, dtype):
+        hash(dtype)  # no error
+
+    def test_str(self, dtype):
+        assert str(dtype) == dtype.name
+
+    def test_eq(self, dtype):
+        assert dtype == dtype.name
+        assert dtype != "anonther_type"
+
+    def test_construct_from_string_own_name(self, dtype):
+        result = dtype.construct_from_string(dtype.name)
+        assert type(result) is type(dtype)
+
+        # check OK as classmethod
+        result = type(dtype).construct_from_string(dtype.name)
+        assert type(result) is type(dtype)
+
+    def test_construct_from_string_another_type_raises(self, dtype):
+        msg = f"Cannot construct a '{type(dtype).__name__}' from 'another_type'"
+        with pytest.raises(TypeError, match=msg):
+            type(dtype).construct_from_string("another_type")
+
+    def test_construct_from_string_wrong_type_raises(self, dtype):
+        with pytest.raises(
+            TypeError,
+            match="'construct_from_string' expects a string, got ",
+        ):
+            type(dtype).construct_from_string(0)
+
+    def test_get_common_dtype(self, dtype):
+        # in practice we will not typically call this with a 1-length list
+        # (we shortcut to just use that dtype as the common dtype), but
+        # still testing as good practice to have this working (and it is the
+        # only case we can test in general)
+        assert dtype._get_common_dtype([dtype]) == dtype
+
+    @pytest.mark.parametrize("skipna", [True, False])
+    def test_infer_dtype(self, data, data_missing, skipna):
+        # only testing that this works without raising an error
+        res = infer_dtype(data, skipna=skipna)
+        assert isinstance(res, str)
+        res = infer_dtype(data_missing, skipna=skipna)
+        assert isinstance(res, str)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/base/getitem.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/base/getitem.py
new file mode 100644
index 0000000000000000000000000000000000000000..5f0c1b960a4758e7cd5423188d5f190922b4eee4
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/base/getitem.py
@@ -0,0 +1,469 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+
+
+class BaseGetitemTests:
+    """Tests for ExtensionArray.__getitem__."""
+
+    def test_iloc_series(self, data):
+        ser = pd.Series(data)
+        result = ser.iloc[:4]
+        expected = pd.Series(data[:4])
+        tm.assert_series_equal(result, expected)
+
+        result = ser.iloc[[0, 1, 2, 3]]
+        tm.assert_series_equal(result, expected)
+
+    def test_iloc_frame(self, data):
+        df = pd.DataFrame({"A": data, "B": np.arange(len(data), dtype="int64")})
+        expected = pd.DataFrame({"A": data[:4]})
+
+        # slice -> frame
+        result = df.iloc[:4, [0]]
+        tm.assert_frame_equal(result, expected)
+
+        # sequence -> frame
+        result = df.iloc[[0, 1, 2, 3], [0]]
+        tm.assert_frame_equal(result, expected)
+
+        expected = pd.Series(data[:4], name="A")
+
+        # slice -> series
+        result = df.iloc[:4, 0]
+        tm.assert_series_equal(result, expected)
+
+        # sequence -> series
+        result = df.iloc[:4, 0]
+        tm.assert_series_equal(result, expected)
+
+        # GH#32959 slice columns with step
+        result = df.iloc[:, ::2]
+        tm.assert_frame_equal(result, df[["A"]])
+        result = df[["B", "A"]].iloc[:, ::2]
+        tm.assert_frame_equal(result, df[["B"]])
+
+    def test_iloc_frame_single_block(self, data):
+        # GH#32959 null slice along index, slice along columns with single-block
+        df = pd.DataFrame({"A": data})
+
+        result = df.iloc[:, :]
+        tm.assert_frame_equal(result, df)
+
+        result = df.iloc[:, :1]
+        tm.assert_frame_equal(result, df)
+
+        result = df.iloc[:, :2]
+        tm.assert_frame_equal(result, df)
+
+        result = df.iloc[:, ::2]
+        tm.assert_frame_equal(result, df)
+
+        result = df.iloc[:, 1:2]
+        tm.assert_frame_equal(result, df.iloc[:, :0])
+
+        result = df.iloc[:, -1:]
+        tm.assert_frame_equal(result, df)
+
+    def test_loc_series(self, data):
+        ser = pd.Series(data)
+        result = ser.loc[:3]
+        expected = pd.Series(data[:4])
+        tm.assert_series_equal(result, expected)
+
+        result = ser.loc[[0, 1, 2, 3]]
+        tm.assert_series_equal(result, expected)
+
+    def test_loc_frame(self, data):
+        df = pd.DataFrame({"A": data, "B": np.arange(len(data), dtype="int64")})
+        expected = pd.DataFrame({"A": data[:4]})
+
+        # slice -> frame
+        result = df.loc[:3, ["A"]]
+        tm.assert_frame_equal(result, expected)
+
+        # sequence -> frame
+        result = df.loc[[0, 1, 2, 3], ["A"]]
+        tm.assert_frame_equal(result, expected)
+
+        expected = pd.Series(data[:4], name="A")
+
+        # slice -> series
+        result = df.loc[:3, "A"]
+        tm.assert_series_equal(result, expected)
+
+        # sequence -> series
+        result = df.loc[:3, "A"]
+        tm.assert_series_equal(result, expected)
+
+    def test_loc_iloc_frame_single_dtype(self, data):
+        # GH#27110 bug in ExtensionBlock.iget caused df.iloc[n] to incorrectly
+        #  return a scalar
+        df = pd.DataFrame({"A": data})
+        expected = pd.Series([data[2]], index=["A"], name=2, dtype=data.dtype)
+
+        result = df.loc[2]
+        tm.assert_series_equal(result, expected)
+
+        expected = pd.Series(
+            [data[-1]], index=["A"], name=len(data) - 1, dtype=data.dtype
+        )
+        result = df.iloc[-1]
+        tm.assert_series_equal(result, expected)
+
+    def test_getitem_scalar(self, data):
+        result = data[0]
+        assert isinstance(result, data.dtype.type)
+
+        result = pd.Series(data)[0]
+        assert isinstance(result, data.dtype.type)
+
+    def test_getitem_invalid(self, data):
+        # TODO: box over scalar, [scalar], (scalar,)?
+
+        msg = (
+            r"only integers, slices \(`:`\), ellipsis \(`...`\), numpy.newaxis "
+            r"\(`None`\) and integer or boolean arrays are valid indices"
+        )
+        with pytest.raises(IndexError, match=msg):
+            data["foo"]
+        with pytest.raises(IndexError, match=msg):
+            data[2.5]
+
+        ub = len(data)
+        msg = "|".join(
+            [
+                "list index out of range",  # json
+                "index out of bounds",  # pyarrow
+                "Out of bounds access",  # Sparse
+                f"loc must be an integer between -{ub} and {ub}",  # Sparse
+                f"index {ub+1} is out of bounds for axis 0 with size {ub}",
+                f"index -{ub+1} is out of bounds for axis 0 with size {ub}",
+            ]
+        )
+        with pytest.raises(IndexError, match=msg):
+            data[ub + 1]
+        with pytest.raises(IndexError, match=msg):
+            data[-ub - 1]
+
+    def test_getitem_scalar_na(self, data_missing, na_cmp, na_value):
+        result = data_missing[0]
+        assert na_cmp(result, na_value)
+
+    def test_getitem_empty(self, data):
+        # Indexing with empty list
+        result = data[[]]
+        assert len(result) == 0
+        assert isinstance(result, type(data))
+
+        expected = data[np.array([], dtype="int64")]
+        tm.assert_extension_array_equal(result, expected)
+
+    def test_getitem_mask(self, data):
+        # Empty mask, raw array
+        mask = np.zeros(len(data), dtype=bool)
+        result = data[mask]
+        assert len(result) == 0
+        assert isinstance(result, type(data))
+
+        # Empty mask, in series
+        mask = np.zeros(len(data), dtype=bool)
+        result = pd.Series(data)[mask]
+        assert len(result) == 0
+        assert result.dtype == data.dtype
+
+        # non-empty mask, raw array
+        mask[0] = True
+        result = data[mask]
+        assert len(result) == 1
+        assert isinstance(result, type(data))
+
+        # non-empty mask, in series
+        result = pd.Series(data)[mask]
+        assert len(result) == 1
+        assert result.dtype == data.dtype
+
+    def test_getitem_mask_raises(self, data):
+        mask = np.array([True, False])
+        msg = f"Boolean index has wrong length: 2 instead of {len(data)}"
+        with pytest.raises(IndexError, match=msg):
+            data[mask]
+
+        mask = pd.array(mask, dtype="boolean")
+        with pytest.raises(IndexError, match=msg):
+            data[mask]
+
+    def test_getitem_boolean_array_mask(self, data):
+        mask = pd.array(np.zeros(data.shape, dtype="bool"), dtype="boolean")
+        result = data[mask]
+        assert len(result) == 0
+        assert isinstance(result, type(data))
+
+        result = pd.Series(data)[mask]
+        assert len(result) == 0
+        assert result.dtype == data.dtype
+
+        mask[:5] = True
+        expected = data.take([0, 1, 2, 3, 4])
+        result = data[mask]
+        tm.assert_extension_array_equal(result, expected)
+
+        expected = pd.Series(expected)
+        result = pd.Series(data)[mask]
+        tm.assert_series_equal(result, expected)
+
+    def test_getitem_boolean_na_treated_as_false(self, data):
+        # https://github.com/pandas-dev/pandas/issues/31503
+        mask = pd.array(np.zeros(data.shape, dtype="bool"), dtype="boolean")
+        mask[:2] = pd.NA
+        mask[2:4] = True
+
+        result = data[mask]
+        expected = data[mask.fillna(False)]
+
+        tm.assert_extension_array_equal(result, expected)
+
+        s = pd.Series(data)
+
+        result = s[mask]
+        expected = s[mask.fillna(False)]
+
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "idx",
+        [[0, 1, 2], pd.array([0, 1, 2], dtype="Int64"), np.array([0, 1, 2])],
+        ids=["list", "integer-array", "numpy-array"],
+    )
+    def test_getitem_integer_array(self, data, idx):
+        result = data[idx]
+        assert len(result) == 3
+        assert isinstance(result, type(data))
+        expected = data.take([0, 1, 2])
+        tm.assert_extension_array_equal(result, expected)
+
+        expected = pd.Series(expected)
+        result = pd.Series(data)[idx]
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "idx",
+        [[0, 1, 2, pd.NA], pd.array([0, 1, 2, pd.NA], dtype="Int64")],
+        ids=["list", "integer-array"],
+    )
+    def test_getitem_integer_with_missing_raises(self, data, idx):
+        msg = "Cannot index with an integer indexer containing NA values"
+        with pytest.raises(ValueError, match=msg):
+            data[idx]
+
+    @pytest.mark.xfail(
+        reason="Tries label-based and raises KeyError; "
+        "in some cases raises when calling np.asarray"
+    )
+    @pytest.mark.parametrize(
+        "idx",
+        [[0, 1, 2, pd.NA], pd.array([0, 1, 2, pd.NA], dtype="Int64")],
+        ids=["list", "integer-array"],
+    )
+    def test_getitem_series_integer_with_missing_raises(self, data, idx):
+        msg = "Cannot index with an integer indexer containing NA values"
+        # TODO: this raises KeyError about labels not found (it tries label-based)
+
+        ser = pd.Series(data, index=[chr(100 + i) for i in range(len(data))])
+        with pytest.raises(ValueError, match=msg):
+            ser[idx]
+
+    def test_getitem_slice(self, data):
+        # getitem[slice] should return an array
+        result = data[slice(0)]  # empty
+        assert isinstance(result, type(data))
+
+        result = data[slice(1)]  # scalar
+        assert isinstance(result, type(data))
+
+    def test_getitem_ellipsis_and_slice(self, data):
+        # GH#40353 this is called from slice_block_rows
+        result = data[..., :]
+        tm.assert_extension_array_equal(result, data)
+
+        result = data[:, ...]
+        tm.assert_extension_array_equal(result, data)
+
+        result = data[..., :3]
+        tm.assert_extension_array_equal(result, data[:3])
+
+        result = data[:3, ...]
+        tm.assert_extension_array_equal(result, data[:3])
+
+        result = data[..., ::2]
+        tm.assert_extension_array_equal(result, data[::2])
+
+        result = data[::2, ...]
+        tm.assert_extension_array_equal(result, data[::2])
+
+    def test_get(self, data):
+        # GH 20882
+        s = pd.Series(data, index=[2 * i for i in range(len(data))])
+        assert s.get(4) == s.iloc[2]
+
+        result = s.get([4, 6])
+        expected = s.iloc[[2, 3]]
+        tm.assert_series_equal(result, expected)
+
+        result = s.get(slice(2))
+        expected = s.iloc[[0, 1]]
+        tm.assert_series_equal(result, expected)
+
+        assert s.get(-1) is None
+        assert s.get(s.index.max() + 1) is None
+
+        s = pd.Series(data[:6], index=list("abcdef"))
+        assert s.get("c") == s.iloc[2]
+
+        result = s.get(slice("b", "d"))
+        expected = s.iloc[[1, 2, 3]]
+        tm.assert_series_equal(result, expected)
+
+        result = s.get("Z")
+        assert result is None
+
+        msg = "Series.__getitem__ treating keys as positions is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            assert s.get(4) == s.iloc[4]
+            assert s.get(-1) == s.iloc[-1]
+            assert s.get(len(s)) is None
+
+        # GH 21257
+        s = pd.Series(data)
+        with tm.assert_produces_warning(None):
+            # GH#45324 make sure we aren't giving a spurious FutureWarning
+            s2 = s[::2]
+        assert s2.get(1) is None
+
+    def test_take_sequence(self, data):
+        result = pd.Series(data)[[0, 1, 3]]
+        assert result.iloc[0] == data[0]
+        assert result.iloc[1] == data[1]
+        assert result.iloc[2] == data[3]
+
+    def test_take(self, data, na_value, na_cmp):
+        result = data.take([0, -1])
+        assert result.dtype == data.dtype
+        assert result[0] == data[0]
+        assert result[1] == data[-1]
+
+        result = data.take([0, -1], allow_fill=True, fill_value=na_value)
+        assert result[0] == data[0]
+        assert na_cmp(result[1], na_value)
+
+        with pytest.raises(IndexError, match="out of bounds"):
+            data.take([len(data) + 1])
+
+    def test_take_empty(self, data, na_value, na_cmp):
+        empty = data[:0]
+
+        result = empty.take([-1], allow_fill=True)
+        assert na_cmp(result[0], na_value)
+
+        msg = "cannot do a non-empty take from an empty axes|out of bounds"
+
+        with pytest.raises(IndexError, match=msg):
+            empty.take([-1])
+
+        with pytest.raises(IndexError, match="cannot do a non-empty take"):
+            empty.take([0, 1])
+
+    def test_take_negative(self, data):
+        # https://github.com/pandas-dev/pandas/issues/20640
+        n = len(data)
+        result = data.take([0, -n, n - 1, -1])
+        expected = data.take([0, 0, n - 1, n - 1])
+        tm.assert_extension_array_equal(result, expected)
+
+    def test_take_non_na_fill_value(self, data_missing):
+        fill_value = data_missing[1]  # valid
+        na = data_missing[0]
+
+        arr = data_missing._from_sequence(
+            [na, fill_value, na], dtype=data_missing.dtype
+        )
+        result = arr.take([-1, 1], fill_value=fill_value, allow_fill=True)
+        expected = arr.take([1, 1])
+        tm.assert_extension_array_equal(result, expected)
+
+    def test_take_pandas_style_negative_raises(self, data, na_value):
+        with pytest.raises(ValueError, match=""):
+            data.take([0, -2], fill_value=na_value, allow_fill=True)
+
+    @pytest.mark.parametrize("allow_fill", [True, False])
+    def test_take_out_of_bounds_raises(self, data, allow_fill):
+        arr = data[:3]
+
+        with pytest.raises(IndexError, match="out of bounds|out-of-bounds"):
+            arr.take(np.asarray([0, 3]), allow_fill=allow_fill)
+
+    def test_take_series(self, data):
+        s = pd.Series(data)
+        result = s.take([0, -1])
+        expected = pd.Series(
+            data._from_sequence([data[0], data[len(data) - 1]], dtype=s.dtype),
+            index=[0, len(data) - 1],
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_reindex(self, data, na_value):
+        s = pd.Series(data)
+        result = s.reindex([0, 1, 3])
+        expected = pd.Series(data.take([0, 1, 3]), index=[0, 1, 3])
+        tm.assert_series_equal(result, expected)
+
+        n = len(data)
+        result = s.reindex([-1, 0, n])
+        expected = pd.Series(
+            data._from_sequence([na_value, data[0], na_value], dtype=s.dtype),
+            index=[-1, 0, n],
+        )
+        tm.assert_series_equal(result, expected)
+
+        result = s.reindex([n, n + 1])
+        expected = pd.Series(
+            data._from_sequence([na_value, na_value], dtype=s.dtype), index=[n, n + 1]
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_reindex_non_na_fill_value(self, data_missing):
+        valid = data_missing[1]
+        na = data_missing[0]
+
+        arr = data_missing._from_sequence([na, valid], dtype=data_missing.dtype)
+        ser = pd.Series(arr)
+        result = ser.reindex([0, 1, 2], fill_value=valid)
+        expected = pd.Series(
+            data_missing._from_sequence([na, valid, valid], dtype=data_missing.dtype)
+        )
+
+        tm.assert_series_equal(result, expected)
+
+    def test_loc_len1(self, data):
+        # see GH-27785 take_nd with indexer of len 1 resulting in wrong ndim
+        df = pd.DataFrame({"A": data})
+        res = df.loc[[0], "A"]
+        assert res.ndim == 1
+        assert res._mgr.arrays[0].ndim == 1
+        if hasattr(res._mgr, "blocks"):
+            assert res._mgr._block.ndim == 1
+
+    def test_item(self, data):
+        # https://github.com/pandas-dev/pandas/pull/30175
+        s = pd.Series(data)
+        result = s[:1].item()
+        assert result == data[0]
+
+        msg = "can only convert an array of size 1 to a Python scalar"
+        with pytest.raises(ValueError, match=msg):
+            s[:0].item()
+
+        with pytest.raises(ValueError, match=msg):
+            s.item()
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/base/groupby.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/base/groupby.py
new file mode 100644
index 0000000000000000000000000000000000000000..6947e672f3d44f8c48dec6bb53cf5e6bd85182ab
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/base/groupby.py
@@ -0,0 +1,174 @@
+import re
+
+import pytest
+
+from pandas.core.dtypes.common import (
+    is_bool_dtype,
+    is_numeric_dtype,
+    is_object_dtype,
+    is_string_dtype,
+)
+
+import pandas as pd
+import pandas._testing as tm
+
+
+@pytest.mark.filterwarnings(
+    "ignore:The default of observed=False is deprecated:FutureWarning"
+)
+class BaseGroupbyTests:
+    """Groupby-specific tests."""
+
+    def test_grouping_grouper(self, data_for_grouping):
+        df = pd.DataFrame(
+            {
+                "A": pd.Series(
+                    ["B", "B", None, None, "A", "A", "B", "C"], dtype=object
+                ),
+                "B": data_for_grouping,
+            }
+        )
+        gr1 = df.groupby("A")._grouper.groupings[0]
+        gr2 = df.groupby("B")._grouper.groupings[0]
+
+        tm.assert_numpy_array_equal(gr1.grouping_vector, df.A.values)
+        tm.assert_extension_array_equal(gr2.grouping_vector, data_for_grouping)
+
+    @pytest.mark.parametrize("as_index", [True, False])
+    def test_groupby_extension_agg(self, as_index, data_for_grouping):
+        df = pd.DataFrame({"A": [1, 1, 2, 2, 3, 3, 1, 4], "B": data_for_grouping})
+
+        is_bool = data_for_grouping.dtype._is_boolean
+        if is_bool:
+            # only 2 unique values, and the final entry has c==b
+            #  (see data_for_grouping docstring)
+            df = df.iloc[:-1]
+
+        result = df.groupby("B", as_index=as_index).A.mean()
+        _, uniques = pd.factorize(data_for_grouping, sort=True)
+
+        exp_vals = [3.0, 1.0, 4.0]
+        if is_bool:
+            exp_vals = exp_vals[:-1]
+        if as_index:
+            index = pd.Index(uniques, name="B")
+            expected = pd.Series(exp_vals, index=index, name="A")
+            tm.assert_series_equal(result, expected)
+        else:
+            expected = pd.DataFrame({"B": uniques, "A": exp_vals})
+            tm.assert_frame_equal(result, expected)
+
+    def test_groupby_agg_extension(self, data_for_grouping):
+        # GH#38980 groupby agg on extension type fails for non-numeric types
+        df = pd.DataFrame({"A": [1, 1, 2, 2, 3, 3, 1, 4], "B": data_for_grouping})
+
+        expected = df.iloc[[0, 2, 4, 7]]
+        expected = expected.set_index("A")
+
+        result = df.groupby("A").agg({"B": "first"})
+        tm.assert_frame_equal(result, expected)
+
+        result = df.groupby("A").agg("first")
+        tm.assert_frame_equal(result, expected)
+
+        result = df.groupby("A").first()
+        tm.assert_frame_equal(result, expected)
+
+    def test_groupby_extension_no_sort(self, data_for_grouping):
+        df = pd.DataFrame({"A": [1, 1, 2, 2, 3, 3, 1, 4], "B": data_for_grouping})
+
+        is_bool = data_for_grouping.dtype._is_boolean
+        if is_bool:
+            # only 2 unique values, and the final entry has c==b
+            #  (see data_for_grouping docstring)
+            df = df.iloc[:-1]
+
+        result = df.groupby("B", sort=False).A.mean()
+        _, index = pd.factorize(data_for_grouping, sort=False)
+
+        index = pd.Index(index, name="B")
+        exp_vals = [1.0, 3.0, 4.0]
+        if is_bool:
+            exp_vals = exp_vals[:-1]
+        expected = pd.Series(exp_vals, index=index, name="A")
+        tm.assert_series_equal(result, expected)
+
+    def test_groupby_extension_transform(self, data_for_grouping):
+        is_bool = data_for_grouping.dtype._is_boolean
+
+        valid = data_for_grouping[~data_for_grouping.isna()]
+        df = pd.DataFrame({"A": [1, 1, 3, 3, 1, 4], "B": valid})
+        is_bool = data_for_grouping.dtype._is_boolean
+        if is_bool:
+            # only 2 unique values, and the final entry has c==b
+            #  (see data_for_grouping docstring)
+            df = df.iloc[:-1]
+
+        result = df.groupby("B").A.transform(len)
+        expected = pd.Series([3, 3, 2, 2, 3, 1], name="A")
+        if is_bool:
+            expected = expected[:-1]
+
+        tm.assert_series_equal(result, expected)
+
+    def test_groupby_extension_apply(self, data_for_grouping, groupby_apply_op):
+        df = pd.DataFrame({"A": [1, 1, 2, 2, 3, 3, 1, 4], "B": data_for_grouping})
+        msg = "DataFrameGroupBy.apply operated on the grouping columns"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            df.groupby("B", group_keys=False, observed=False).apply(groupby_apply_op)
+        df.groupby("B", group_keys=False, observed=False).A.apply(groupby_apply_op)
+        msg = "DataFrameGroupBy.apply operated on the grouping columns"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            df.groupby("A", group_keys=False, observed=False).apply(groupby_apply_op)
+        df.groupby("A", group_keys=False, observed=False).B.apply(groupby_apply_op)
+
+    def test_groupby_apply_identity(self, data_for_grouping):
+        df = pd.DataFrame({"A": [1, 1, 2, 2, 3, 3, 1, 4], "B": data_for_grouping})
+        result = df.groupby("A").B.apply(lambda x: x.array)
+        expected = pd.Series(
+            [
+                df.B.iloc[[0, 1, 6]].array,
+                df.B.iloc[[2, 3]].array,
+                df.B.iloc[[4, 5]].array,
+                df.B.iloc[[7]].array,
+            ],
+            index=pd.Index([1, 2, 3, 4], name="A"),
+            name="B",
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_in_numeric_groupby(self, data_for_grouping):
+        df = pd.DataFrame(
+            {
+                "A": [1, 1, 2, 2, 3, 3, 1, 4],
+                "B": data_for_grouping,
+                "C": [1, 1, 1, 1, 1, 1, 1, 1],
+            }
+        )
+
+        dtype = data_for_grouping.dtype
+        if (
+            is_numeric_dtype(dtype)
+            or is_bool_dtype(dtype)
+            or dtype.name == "decimal"
+            or is_string_dtype(dtype)
+            or is_object_dtype(dtype)
+            or dtype.kind == "m"  # in particular duration[*][pyarrow]
+        ):
+            expected = pd.Index(["B", "C"])
+            result = df.groupby("A").sum().columns
+        else:
+            expected = pd.Index(["C"])
+
+            msg = "|".join(
+                [
+                    # period/datetime
+                    "does not support sum operations",
+                    # all others
+                    re.escape(f"agg function failed [how->sum,dtype->{dtype}"),
+                ]
+            )
+            with pytest.raises(TypeError, match=msg):
+                df.groupby("A").sum()
+            result = df.groupby("A").sum(numeric_only=True).columns
+        tm.assert_index_equal(result, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/base/index.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/base/index.py
new file mode 100644
index 0000000000000000000000000000000000000000..72c4ebfb5d84ae82f74a4c814e72372a651ae6b8
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/base/index.py
@@ -0,0 +1,19 @@
+"""
+Tests for Indexes backed by arbitrary ExtensionArrays.
+"""
+import pandas as pd
+
+
+class BaseIndexTests:
+    """Tests for Index object backed by an ExtensionArray"""
+
+    def test_index_from_array(self, data):
+        idx = pd.Index(data)
+        assert data.dtype == idx.dtype
+
+    def test_index_from_listlike_with_dtype(self, data):
+        idx = pd.Index(data, dtype=data.dtype)
+        assert idx.dtype == data.dtype
+
+        idx = pd.Index(list(data), dtype=data.dtype)
+        assert idx.dtype == data.dtype
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/base/interface.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/base/interface.py
new file mode 100644
index 0000000000000000000000000000000000000000..38cece7da3308d6ec4b484238d702cf298ebcca0
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/base/interface.py
@@ -0,0 +1,172 @@
+import warnings
+
+import numpy as np
+import pytest
+
+from pandas.compat.numpy import np_version_gt2
+
+from pandas.core.dtypes.cast import construct_1d_object_array_from_listlike
+from pandas.core.dtypes.common import is_extension_array_dtype
+from pandas.core.dtypes.dtypes import ExtensionDtype
+
+import pandas as pd
+import pandas._testing as tm
+
+
+class BaseInterfaceTests:
+    """Tests that the basic interface is satisfied."""
+
+    # ------------------------------------------------------------------------
+    # Interface
+    # ------------------------------------------------------------------------
+
+    def test_len(self, data):
+        assert len(data) == 100
+
+    def test_size(self, data):
+        assert data.size == 100
+
+    def test_ndim(self, data):
+        assert data.ndim == 1
+
+    def test_can_hold_na_valid(self, data):
+        # GH-20761
+        assert data._can_hold_na is True
+
+    def test_contains(self, data, data_missing):
+        # GH-37867
+        # Tests for membership checks. Membership checks for nan-likes is tricky and
+        # the settled on rule is: `nan_like in arr` is True if nan_like is
+        # arr.dtype.na_value and arr.isna().any() is True. Else the check returns False.
+
+        na_value = data.dtype.na_value
+        # ensure data without missing values
+        data = data[~data.isna()]
+
+        # first elements are non-missing
+        assert data[0] in data
+        assert data_missing[0] in data_missing
+
+        # check the presence of na_value
+        assert na_value in data_missing
+        assert na_value not in data
+
+        # the data can never contain other nan-likes than na_value
+        for na_value_obj in tm.NULL_OBJECTS:
+            if na_value_obj is na_value or type(na_value_obj) == type(na_value):
+                # type check for e.g. two instances of Decimal("NAN")
+                continue
+            assert na_value_obj not in data
+            assert na_value_obj not in data_missing
+
+    def test_memory_usage(self, data):
+        s = pd.Series(data)
+        result = s.memory_usage(index=False)
+        assert result == s.nbytes
+
+    def test_array_interface(self, data):
+        result = np.array(data)
+        assert result[0] == data[0]
+
+        result = np.array(data, dtype=object)
+        expected = np.array(list(data), dtype=object)
+        if expected.ndim > 1:
+            # nested data, explicitly construct as 1D
+            expected = construct_1d_object_array_from_listlike(list(data))
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_array_interface_copy(self, data):
+        result_copy1 = np.array(data, copy=True)
+        result_copy2 = np.array(data, copy=True)
+        assert not np.may_share_memory(result_copy1, result_copy2)
+
+        if not np_version_gt2:
+            # copy=False semantics are only supported in NumPy>=2.
+            return
+
+        warning_raised = False
+        msg = "Starting with NumPy 2.0, the behavior of the 'copy' keyword has changed"
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter("always")
+            result_nocopy1 = np.array(data, copy=False)
+            assert len(w) <= 1
+            if len(w):
+                warning_raised = True
+                assert msg in str(w[0].message)
+
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter("always")
+            result_nocopy2 = np.array(data, copy=False)
+            assert len(w) <= 1
+            if len(w):
+                warning_raised = True
+                assert msg in str(w[0].message)
+
+        if not warning_raised:
+            # If copy=False was given and did not raise, these must share the same data
+            assert np.may_share_memory(result_nocopy1, result_nocopy2)
+
+    def test_is_extension_array_dtype(self, data):
+        assert is_extension_array_dtype(data)
+        assert is_extension_array_dtype(data.dtype)
+        assert is_extension_array_dtype(pd.Series(data))
+        assert isinstance(data.dtype, ExtensionDtype)
+
+    def test_no_values_attribute(self, data):
+        # GH-20735: EA's with .values attribute give problems with internal
+        # code, disallowing this for now until solved
+        assert not hasattr(data, "values")
+        assert not hasattr(data, "_values")
+
+    def test_is_numeric_honored(self, data):
+        result = pd.Series(data)
+        if hasattr(result._mgr, "blocks"):
+            assert result._mgr.blocks[0].is_numeric is data.dtype._is_numeric
+
+    def test_isna_extension_array(self, data_missing):
+        # If your `isna` returns an ExtensionArray, you must also implement
+        # _reduce. At the *very* least, you must implement any and all
+        na = data_missing.isna()
+        if is_extension_array_dtype(na):
+            assert na._reduce("any")
+            assert na.any()
+
+            assert not na._reduce("all")
+            assert not na.all()
+
+            assert na.dtype._is_boolean
+
+    def test_copy(self, data):
+        # GH#27083 removing deep keyword from EA.copy
+        assert data[0] != data[1]
+        result = data.copy()
+
+        if data.dtype._is_immutable:
+            pytest.skip(f"test_copy assumes mutability and {data.dtype} is immutable")
+
+        data[1] = data[0]
+        assert result[1] != result[0]
+
+    def test_view(self, data):
+        # view with no dtype should return a shallow copy, *not* the same
+        #  object
+        assert data[1] != data[0]
+
+        result = data.view()
+        assert result is not data
+        assert type(result) == type(data)
+
+        if data.dtype._is_immutable:
+            pytest.skip(f"test_view assumes mutability and {data.dtype} is immutable")
+
+        result[1] = result[0]
+        assert data[1] == data[0]
+
+        # check specifically that the `dtype` kwarg is accepted
+        data.view(dtype=None)
+
+    def test_tolist(self, data):
+        result = data.tolist()
+        expected = list(data)
+        assert isinstance(result, list)
+        assert result == expected
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/base/io.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/base/io.py
new file mode 100644
index 0000000000000000000000000000000000000000..3a6f2eb5ba8b1854ac48a22efa02e89672b2f2ac
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/base/io.py
@@ -0,0 +1,39 @@
+from io import StringIO
+
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.core.arrays import ExtensionArray
+
+
+class BaseParsingTests:
+    @pytest.mark.parametrize("engine", ["c", "python"])
+    def test_EA_types(self, engine, data, request):
+        if isinstance(data.dtype, pd.CategoricalDtype):
+            # in parsers.pyx _convert_with_dtype there is special-casing for
+            #  Categorical that pre-empts _from_sequence_of_strings
+            pass
+        elif isinstance(data.dtype, pd.core.dtypes.dtypes.NumpyEADtype):
+            # These get unwrapped internally so are treated as numpy dtypes
+            #  in the parsers.pyx code
+            pass
+        elif (
+            type(data)._from_sequence_of_strings.__func__
+            is ExtensionArray._from_sequence_of_strings.__func__
+        ):
+            # i.e. the EA hasn't overridden _from_sequence_of_strings
+            mark = pytest.mark.xfail(
+                reason="_from_sequence_of_strings not implemented",
+                raises=NotImplementedError,
+            )
+            request.node.add_marker(mark)
+
+        df = pd.DataFrame({"with_dtype": pd.Series(data, dtype=str(data.dtype))})
+        csv_output = df.to_csv(index=False, na_rep=np.nan)
+        result = pd.read_csv(
+            StringIO(csv_output), dtype={"with_dtype": str(data.dtype)}, engine=engine
+        )
+        expected = df
+        tm.assert_frame_equal(result, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/base/methods.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/base/methods.py
new file mode 100644
index 0000000000000000000000000000000000000000..5cb2c14e4c841c17453e3d2fe17bab69e8813cc1
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/base/methods.py
@@ -0,0 +1,720 @@
+import inspect
+import operator
+
+import numpy as np
+import pytest
+
+from pandas._typing import Dtype
+
+from pandas.core.dtypes.common import is_bool_dtype
+from pandas.core.dtypes.dtypes import NumpyEADtype
+from pandas.core.dtypes.missing import na_value_for_dtype
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.core.sorting import nargsort
+
+
+class BaseMethodsTests:
+    """Various Series and DataFrame methods."""
+
+    def test_hash_pandas_object(self, data):
+        # _hash_pandas_object should return a uint64 ndarray of the same length
+        # as the data
+        from pandas.core.util.hashing import _default_hash_key
+
+        res = data._hash_pandas_object(
+            encoding="utf-8", hash_key=_default_hash_key, categorize=False
+        )
+        assert res.dtype == np.uint64
+        assert res.shape == data.shape
+
+    def test_value_counts_default_dropna(self, data):
+        # make sure we have consistent default dropna kwarg
+        if not hasattr(data, "value_counts"):
+            pytest.skip(f"value_counts is not implemented for {type(data)}")
+        sig = inspect.signature(data.value_counts)
+        kwarg = sig.parameters["dropna"]
+        assert kwarg.default is True
+
+    @pytest.mark.parametrize("dropna", [True, False])
+    def test_value_counts(self, all_data, dropna):
+        all_data = all_data[:10]
+        if dropna:
+            other = all_data[~all_data.isna()]
+        else:
+            other = all_data
+
+        result = pd.Series(all_data).value_counts(dropna=dropna).sort_index()
+        expected = pd.Series(other).value_counts(dropna=dropna).sort_index()
+
+        tm.assert_series_equal(result, expected)
+
+    def test_value_counts_with_normalize(self, data):
+        # GH 33172
+        data = data[:10].unique()
+        values = np.array(data[~data.isna()])
+        ser = pd.Series(data, dtype=data.dtype)
+
+        result = ser.value_counts(normalize=True).sort_index()
+
+        if not isinstance(data, pd.Categorical):
+            expected = pd.Series(
+                [1 / len(values)] * len(values), index=result.index, name="proportion"
+            )
+        else:
+            expected = pd.Series(0.0, index=result.index, name="proportion")
+            expected[result > 0] = 1 / len(values)
+
+        if isinstance(data.dtype, pd.StringDtype) and data.dtype.na_value is np.nan:
+            # TODO: avoid special-casing
+            expected = expected.astype("float64")
+        elif getattr(data.dtype, "storage", "") == "pyarrow" or isinstance(
+            data.dtype, pd.ArrowDtype
+        ):
+            # TODO: avoid special-casing
+            expected = expected.astype("double[pyarrow]")
+        elif na_value_for_dtype(data.dtype) is pd.NA:
+            # TODO(GH#44692): avoid special-casing
+            expected = expected.astype("Float64")
+
+        tm.assert_series_equal(result, expected)
+
+    def test_count(self, data_missing):
+        df = pd.DataFrame({"A": data_missing})
+        result = df.count(axis="columns")
+        expected = pd.Series([0, 1])
+        tm.assert_series_equal(result, expected)
+
+    def test_series_count(self, data_missing):
+        # GH#26835
+        ser = pd.Series(data_missing)
+        result = ser.count()
+        expected = 1
+        assert result == expected
+
+    def test_apply_simple_series(self, data):
+        result = pd.Series(data).apply(id)
+        assert isinstance(result, pd.Series)
+
+    @pytest.mark.parametrize("na_action", [None, "ignore"])
+    def test_map(self, data_missing, na_action):
+        result = data_missing.map(lambda x: x, na_action=na_action)
+        expected = data_missing.to_numpy()
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_argsort(self, data_for_sorting):
+        result = pd.Series(data_for_sorting).argsort()
+        # argsort result gets passed to take, so should be np.intp
+        expected = pd.Series(np.array([2, 0, 1], dtype=np.intp))
+        tm.assert_series_equal(result, expected)
+
+    def test_argsort_missing_array(self, data_missing_for_sorting):
+        result = data_missing_for_sorting.argsort()
+        # argsort result gets passed to take, so should be np.intp
+        expected = np.array([2, 0, 1], dtype=np.intp)
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_argsort_missing(self, data_missing_for_sorting):
+        msg = "The behavior of Series.argsort in the presence of NA values"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = pd.Series(data_missing_for_sorting).argsort()
+        expected = pd.Series(np.array([1, -1, 0], dtype=np.intp))
+        tm.assert_series_equal(result, expected)
+
+    def test_argmin_argmax(self, data_for_sorting, data_missing_for_sorting, na_value):
+        # GH 24382
+        is_bool = data_for_sorting.dtype._is_boolean
+
+        exp_argmax = 1
+        exp_argmax_repeated = 3
+        if is_bool:
+            # See data_for_sorting docstring
+            exp_argmax = 0
+            exp_argmax_repeated = 1
+
+        # data_for_sorting -> [B, C, A] with A < B < C
+        assert data_for_sorting.argmax() == exp_argmax
+        assert data_for_sorting.argmin() == 2
+
+        # with repeated values -> first occurrence
+        data = data_for_sorting.take([2, 0, 0, 1, 1, 2])
+        assert data.argmax() == exp_argmax_repeated
+        assert data.argmin() == 0
+
+        # with missing values
+        # data_missing_for_sorting -> [B, NA, A] with A < B and NA missing.
+        assert data_missing_for_sorting.argmax() == 0
+        assert data_missing_for_sorting.argmin() == 2
+
+    @pytest.mark.parametrize("method", ["argmax", "argmin"])
+    def test_argmin_argmax_empty_array(self, method, data):
+        # GH 24382
+        err_msg = "attempt to get"
+        with pytest.raises(ValueError, match=err_msg):
+            getattr(data[:0], method)()
+
+    @pytest.mark.parametrize("method", ["argmax", "argmin"])
+    def test_argmin_argmax_all_na(self, method, data, na_value):
+        # all missing with skipna=True is the same as empty
+        err_msg = "attempt to get"
+        data_na = type(data)._from_sequence([na_value, na_value], dtype=data.dtype)
+        with pytest.raises(ValueError, match=err_msg):
+            getattr(data_na, method)()
+
+    @pytest.mark.parametrize(
+        "op_name, skipna, expected",
+        [
+            ("idxmax", True, 0),
+            ("idxmin", True, 2),
+            ("argmax", True, 0),
+            ("argmin", True, 2),
+            ("idxmax", False, np.nan),
+            ("idxmin", False, np.nan),
+            ("argmax", False, -1),
+            ("argmin", False, -1),
+        ],
+    )
+    def test_argreduce_series(
+        self, data_missing_for_sorting, op_name, skipna, expected
+    ):
+        # data_missing_for_sorting -> [B, NA, A] with A < B and NA missing.
+        warn = None
+        msg = "The behavior of Series.argmax/argmin"
+        if op_name.startswith("arg") and expected == -1:
+            warn = FutureWarning
+        if op_name.startswith("idx") and np.isnan(expected):
+            warn = FutureWarning
+            msg = f"The behavior of Series.{op_name}"
+        ser = pd.Series(data_missing_for_sorting)
+        with tm.assert_produces_warning(warn, match=msg):
+            result = getattr(ser, op_name)(skipna=skipna)
+        tm.assert_almost_equal(result, expected)
+
+    def test_argmax_argmin_no_skipna_notimplemented(self, data_missing_for_sorting):
+        # GH#38733
+        data = data_missing_for_sorting
+
+        with pytest.raises(NotImplementedError, match=""):
+            data.argmin(skipna=False)
+
+        with pytest.raises(NotImplementedError, match=""):
+            data.argmax(skipna=False)
+
+    @pytest.mark.parametrize(
+        "na_position, expected",
+        [
+            ("last", np.array([2, 0, 1], dtype=np.dtype("intp"))),
+            ("first", np.array([1, 2, 0], dtype=np.dtype("intp"))),
+        ],
+    )
+    def test_nargsort(self, data_missing_for_sorting, na_position, expected):
+        # GH 25439
+        result = nargsort(data_missing_for_sorting, na_position=na_position)
+        tm.assert_numpy_array_equal(result, expected)
+
+    @pytest.mark.parametrize("ascending", [True, False])
+    def test_sort_values(self, data_for_sorting, ascending, sort_by_key):
+        ser = pd.Series(data_for_sorting)
+        result = ser.sort_values(ascending=ascending, key=sort_by_key)
+        expected = ser.iloc[[2, 0, 1]]
+        if not ascending:
+            # GH 35922. Expect stable sort
+            if ser.nunique() == 2:
+                expected = ser.iloc[[0, 1, 2]]
+            else:
+                expected = ser.iloc[[1, 0, 2]]
+
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("ascending", [True, False])
+    def test_sort_values_missing(
+        self, data_missing_for_sorting, ascending, sort_by_key
+    ):
+        ser = pd.Series(data_missing_for_sorting)
+        result = ser.sort_values(ascending=ascending, key=sort_by_key)
+        if ascending:
+            expected = ser.iloc[[2, 0, 1]]
+        else:
+            expected = ser.iloc[[0, 2, 1]]
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("ascending", [True, False])
+    def test_sort_values_frame(self, data_for_sorting, ascending):
+        df = pd.DataFrame({"A": [1, 2, 1], "B": data_for_sorting})
+        result = df.sort_values(["A", "B"])
+        expected = pd.DataFrame(
+            {"A": [1, 1, 2], "B": data_for_sorting.take([2, 0, 1])}, index=[2, 0, 1]
+        )
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("keep", ["first", "last", False])
+    def test_duplicated(self, data, keep):
+        arr = data.take([0, 1, 0, 1])
+        result = arr.duplicated(keep=keep)
+        if keep == "first":
+            expected = np.array([False, False, True, True])
+        elif keep == "last":
+            expected = np.array([True, True, False, False])
+        else:
+            expected = np.array([True, True, True, True])
+        tm.assert_numpy_array_equal(result, expected)
+
+    @pytest.mark.parametrize("box", [pd.Series, lambda x: x])
+    @pytest.mark.parametrize("method", [lambda x: x.unique(), pd.unique])
+    def test_unique(self, data, box, method):
+        duplicated = box(data._from_sequence([data[0], data[0]], dtype=data.dtype))
+
+        result = method(duplicated)
+
+        assert len(result) == 1
+        assert isinstance(result, type(data))
+        assert result[0] == duplicated[0]
+
+    def test_factorize(self, data_for_grouping):
+        codes, uniques = pd.factorize(data_for_grouping, use_na_sentinel=True)
+
+        is_bool = data_for_grouping.dtype._is_boolean
+        if is_bool:
+            # only 2 unique values
+            expected_codes = np.array([0, 0, -1, -1, 1, 1, 0, 0], dtype=np.intp)
+            expected_uniques = data_for_grouping.take([0, 4])
+        else:
+            expected_codes = np.array([0, 0, -1, -1, 1, 1, 0, 2], dtype=np.intp)
+            expected_uniques = data_for_grouping.take([0, 4, 7])
+
+        tm.assert_numpy_array_equal(codes, expected_codes)
+        tm.assert_extension_array_equal(uniques, expected_uniques)
+
+    def test_factorize_equivalence(self, data_for_grouping):
+        codes_1, uniques_1 = pd.factorize(data_for_grouping, use_na_sentinel=True)
+        codes_2, uniques_2 = data_for_grouping.factorize(use_na_sentinel=True)
+
+        tm.assert_numpy_array_equal(codes_1, codes_2)
+        tm.assert_extension_array_equal(uniques_1, uniques_2)
+        assert len(uniques_1) == len(pd.unique(uniques_1))
+        assert uniques_1.dtype == data_for_grouping.dtype
+
+    def test_factorize_empty(self, data):
+        codes, uniques = pd.factorize(data[:0])
+        expected_codes = np.array([], dtype=np.intp)
+        expected_uniques = type(data)._from_sequence([], dtype=data[:0].dtype)
+
+        tm.assert_numpy_array_equal(codes, expected_codes)
+        tm.assert_extension_array_equal(uniques, expected_uniques)
+
+    def test_fillna_copy_frame(self, data_missing):
+        arr = data_missing.take([1, 1])
+        df = pd.DataFrame({"A": arr})
+        df_orig = df.copy()
+
+        filled_val = df.iloc[0, 0]
+        result = df.fillna(filled_val)
+
+        result.iloc[0, 0] = filled_val
+
+        tm.assert_frame_equal(df, df_orig)
+
+    def test_fillna_copy_series(self, data_missing):
+        arr = data_missing.take([1, 1])
+        ser = pd.Series(arr, copy=False)
+        ser_orig = ser.copy()
+
+        filled_val = ser[0]
+        result = ser.fillna(filled_val)
+        result.iloc[0] = filled_val
+
+        tm.assert_series_equal(ser, ser_orig)
+
+    def test_fillna_length_mismatch(self, data_missing):
+        msg = "Length of 'value' does not match."
+        with pytest.raises(ValueError, match=msg):
+            data_missing.fillna(data_missing.take([1]))
+
+    # Subclasses can override if we expect e.g Sparse[bool], boolean, pyarrow[bool]
+    _combine_le_expected_dtype: Dtype = NumpyEADtype("bool")
+
+    def test_combine_le(self, data_repeated):
+        # GH 20825
+        # Test that combine works when doing a <= (le) comparison
+        orig_data1, orig_data2 = data_repeated(2)
+        s1 = pd.Series(orig_data1)
+        s2 = pd.Series(orig_data2)
+        result = s1.combine(s2, lambda x1, x2: x1 <= x2)
+        expected = pd.Series(
+            pd.array(
+                [a <= b for (a, b) in zip(list(orig_data1), list(orig_data2))],
+                dtype=self._combine_le_expected_dtype,
+            )
+        )
+        tm.assert_series_equal(result, expected)
+
+        val = s1.iloc[0]
+        result = s1.combine(val, lambda x1, x2: x1 <= x2)
+        expected = pd.Series(
+            pd.array(
+                [a <= val for a in list(orig_data1)],
+                dtype=self._combine_le_expected_dtype,
+            )
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_combine_add(self, data_repeated):
+        # GH 20825
+        orig_data1, orig_data2 = data_repeated(2)
+        s1 = pd.Series(orig_data1)
+        s2 = pd.Series(orig_data2)
+
+        # Check if the operation is supported pointwise for our scalars. If not,
+        #  we will expect Series.combine to raise as well.
+        try:
+            with np.errstate(over="ignore"):
+                expected = pd.Series(
+                    orig_data1._from_sequence(
+                        [a + b for (a, b) in zip(list(orig_data1), list(orig_data2))]
+                    )
+                )
+        except TypeError:
+            # If the operation is not supported pointwise for our scalars,
+            #  then Series.combine should also raise
+            with pytest.raises(TypeError):
+                s1.combine(s2, lambda x1, x2: x1 + x2)
+            return
+
+        result = s1.combine(s2, lambda x1, x2: x1 + x2)
+        tm.assert_series_equal(result, expected)
+
+        val = s1.iloc[0]
+        result = s1.combine(val, lambda x1, x2: x1 + x2)
+        expected = pd.Series(
+            orig_data1._from_sequence([a + val for a in list(orig_data1)])
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_combine_first(self, data):
+        # https://github.com/pandas-dev/pandas/issues/24147
+        a = pd.Series(data[:3])
+        b = pd.Series(data[2:5], index=[2, 3, 4])
+        result = a.combine_first(b)
+        expected = pd.Series(data[:5])
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("frame", [True, False])
+    @pytest.mark.parametrize(
+        "periods, indices",
+        [(-2, [2, 3, 4, -1, -1]), (0, [0, 1, 2, 3, 4]), (2, [-1, -1, 0, 1, 2])],
+    )
+    def test_container_shift(self, data, frame, periods, indices):
+        # https://github.com/pandas-dev/pandas/issues/22386
+        subset = data[:5]
+        data = pd.Series(subset, name="A")
+        expected = pd.Series(subset.take(indices, allow_fill=True), name="A")
+
+        if frame:
+            result = data.to_frame(name="A").assign(B=1).shift(periods)
+            expected = pd.concat(
+                [expected, pd.Series([1] * 5, name="B").shift(periods)], axis=1
+            )
+            compare = tm.assert_frame_equal
+        else:
+            result = data.shift(periods)
+            compare = tm.assert_series_equal
+
+        compare(result, expected)
+
+    def test_shift_0_periods(self, data):
+        # GH#33856 shifting with periods=0 should return a copy, not same obj
+        result = data.shift(0)
+        assert data[0] != data[1]  # otherwise below is invalid
+        data[0] = data[1]
+        assert result[0] != result[1]  # i.e. not the same object/view
+
+    @pytest.mark.parametrize("periods", [1, -2])
+    def test_diff(self, data, periods):
+        data = data[:5]
+        if is_bool_dtype(data.dtype):
+            op = operator.xor
+        else:
+            op = operator.sub
+        try:
+            # does this array implement ops?
+            op(data, data)
+        except Exception:
+            pytest.skip(f"{type(data)} does not support diff")
+        s = pd.Series(data)
+        result = s.diff(periods)
+        expected = pd.Series(op(data, data.shift(periods)))
+        tm.assert_series_equal(result, expected)
+
+        df = pd.DataFrame({"A": data, "B": [1.0] * 5})
+        result = df.diff(periods)
+        if periods == 1:
+            b = [np.nan, 0, 0, 0, 0]
+        else:
+            b = [0, 0, 0, np.nan, np.nan]
+        expected = pd.DataFrame({"A": expected, "B": b})
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "periods, indices",
+        [[-4, [-1, -1]], [-1, [1, -1]], [0, [0, 1]], [1, [-1, 0]], [4, [-1, -1]]],
+    )
+    def test_shift_non_empty_array(self, data, periods, indices):
+        # https://github.com/pandas-dev/pandas/issues/23911
+        subset = data[:2]
+        result = subset.shift(periods)
+        expected = subset.take(indices, allow_fill=True)
+        tm.assert_extension_array_equal(result, expected)
+
+    @pytest.mark.parametrize("periods", [-4, -1, 0, 1, 4])
+    def test_shift_empty_array(self, data, periods):
+        # https://github.com/pandas-dev/pandas/issues/23911
+        empty = data[:0]
+        result = empty.shift(periods)
+        expected = empty
+        tm.assert_extension_array_equal(result, expected)
+
+    def test_shift_zero_copies(self, data):
+        # GH#31502
+        result = data.shift(0)
+        assert result is not data
+
+        result = data[:0].shift(2)
+        assert result is not data
+
+    def test_shift_fill_value(self, data):
+        arr = data[:4]
+        fill_value = data[0]
+        result = arr.shift(1, fill_value=fill_value)
+        expected = data.take([0, 0, 1, 2])
+        tm.assert_extension_array_equal(result, expected)
+
+        result = arr.shift(-2, fill_value=fill_value)
+        expected = data.take([2, 3, 0, 0])
+        tm.assert_extension_array_equal(result, expected)
+
+    def test_not_hashable(self, data):
+        # We are in general mutable, so not hashable
+        with pytest.raises(TypeError, match="unhashable type"):
+            hash(data)
+
+    def test_hash_pandas_object_works(self, data, as_frame):
+        # https://github.com/pandas-dev/pandas/issues/23066
+        data = pd.Series(data)
+        if as_frame:
+            data = data.to_frame()
+        a = pd.util.hash_pandas_object(data)
+        b = pd.util.hash_pandas_object(data)
+        tm.assert_equal(a, b)
+
+    def test_searchsorted(self, data_for_sorting, as_series):
+        if data_for_sorting.dtype._is_boolean:
+            return self._test_searchsorted_bool_dtypes(data_for_sorting, as_series)
+
+        b, c, a = data_for_sorting
+        arr = data_for_sorting.take([2, 0, 1])  # to get [a, b, c]
+
+        if as_series:
+            arr = pd.Series(arr)
+        assert arr.searchsorted(a) == 0
+        assert arr.searchsorted(a, side="right") == 1
+
+        assert arr.searchsorted(b) == 1
+        assert arr.searchsorted(b, side="right") == 2
+
+        assert arr.searchsorted(c) == 2
+        assert arr.searchsorted(c, side="right") == 3
+
+        result = arr.searchsorted(arr.take([0, 2]))
+        expected = np.array([0, 2], dtype=np.intp)
+
+        tm.assert_numpy_array_equal(result, expected)
+
+        # sorter
+        sorter = np.array([1, 2, 0])
+        assert data_for_sorting.searchsorted(a, sorter=sorter) == 0
+
+    def _test_searchsorted_bool_dtypes(self, data_for_sorting, as_series):
+        # We call this from test_searchsorted in cases where we have a
+        #  boolean-like dtype. The non-bool test assumes we have more than 2
+        #  unique values.
+        dtype = data_for_sorting.dtype
+        data_for_sorting = pd.array([True, False], dtype=dtype)
+        b, a = data_for_sorting
+        arr = type(data_for_sorting)._from_sequence([a, b])
+
+        if as_series:
+            arr = pd.Series(arr)
+        assert arr.searchsorted(a) == 0
+        assert arr.searchsorted(a, side="right") == 1
+
+        assert arr.searchsorted(b) == 1
+        assert arr.searchsorted(b, side="right") == 2
+
+        result = arr.searchsorted(arr.take([0, 1]))
+        expected = np.array([0, 1], dtype=np.intp)
+
+        tm.assert_numpy_array_equal(result, expected)
+
+        # sorter
+        sorter = np.array([1, 0])
+        assert data_for_sorting.searchsorted(a, sorter=sorter) == 0
+
+    def test_where_series(self, data, na_value, as_frame):
+        assert data[0] != data[1]
+        cls = type(data)
+        a, b = data[:2]
+
+        orig = pd.Series(cls._from_sequence([a, a, b, b], dtype=data.dtype))
+        ser = orig.copy()
+        cond = np.array([True, True, False, False])
+
+        if as_frame:
+            ser = ser.to_frame(name="a")
+            cond = cond.reshape(-1, 1)
+
+        result = ser.where(cond)
+        expected = pd.Series(
+            cls._from_sequence([a, a, na_value, na_value], dtype=data.dtype)
+        )
+
+        if as_frame:
+            expected = expected.to_frame(name="a")
+        tm.assert_equal(result, expected)
+
+        ser.mask(~cond, inplace=True)
+        tm.assert_equal(ser, expected)
+
+        # array other
+        ser = orig.copy()
+        if as_frame:
+            ser = ser.to_frame(name="a")
+        cond = np.array([True, False, True, True])
+        other = cls._from_sequence([a, b, a, b], dtype=data.dtype)
+        if as_frame:
+            other = pd.DataFrame({"a": other})
+            cond = pd.DataFrame({"a": cond})
+        result = ser.where(cond, other)
+        expected = pd.Series(cls._from_sequence([a, b, b, b], dtype=data.dtype))
+        if as_frame:
+            expected = expected.to_frame(name="a")
+        tm.assert_equal(result, expected)
+
+        ser.mask(~cond, other, inplace=True)
+        tm.assert_equal(ser, expected)
+
+    @pytest.mark.parametrize("repeats", [0, 1, 2, [1, 2, 3]])
+    def test_repeat(self, data, repeats, as_series, use_numpy):
+        arr = type(data)._from_sequence(data[:3], dtype=data.dtype)
+        if as_series:
+            arr = pd.Series(arr)
+
+        result = np.repeat(arr, repeats) if use_numpy else arr.repeat(repeats)
+
+        repeats = [repeats] * 3 if isinstance(repeats, int) else repeats
+        expected = [x for x, n in zip(arr, repeats) for _ in range(n)]
+        expected = type(data)._from_sequence(expected, dtype=data.dtype)
+        if as_series:
+            expected = pd.Series(expected, index=arr.index.repeat(repeats))
+
+        tm.assert_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "repeats, kwargs, error, msg",
+        [
+            (2, {"axis": 1}, ValueError, "axis"),
+            (-1, {}, ValueError, "negative"),
+            ([1, 2], {}, ValueError, "shape"),
+            (2, {"foo": "bar"}, TypeError, "'foo'"),
+        ],
+    )
+    def test_repeat_raises(self, data, repeats, kwargs, error, msg, use_numpy):
+        with pytest.raises(error, match=msg):
+            if use_numpy:
+                np.repeat(data, repeats, **kwargs)
+            else:
+                data.repeat(repeats, **kwargs)
+
+    def test_delete(self, data):
+        result = data.delete(0)
+        expected = data[1:]
+        tm.assert_extension_array_equal(result, expected)
+
+        result = data.delete([1, 3])
+        expected = data._concat_same_type([data[[0]], data[[2]], data[4:]])
+        tm.assert_extension_array_equal(result, expected)
+
+    def test_insert(self, data):
+        # insert at the beginning
+        result = data[1:].insert(0, data[0])
+        tm.assert_extension_array_equal(result, data)
+
+        result = data[1:].insert(-len(data[1:]), data[0])
+        tm.assert_extension_array_equal(result, data)
+
+        # insert at the middle
+        result = data[:-1].insert(4, data[-1])
+
+        taker = np.arange(len(data))
+        taker[5:] = taker[4:-1]
+        taker[4] = len(data) - 1
+        expected = data.take(taker)
+        tm.assert_extension_array_equal(result, expected)
+
+    def test_insert_invalid(self, data, invalid_scalar):
+        item = invalid_scalar
+
+        with pytest.raises((TypeError, ValueError)):
+            data.insert(0, item)
+
+        with pytest.raises((TypeError, ValueError)):
+            data.insert(4, item)
+
+        with pytest.raises((TypeError, ValueError)):
+            data.insert(len(data) - 1, item)
+
+    def test_insert_invalid_loc(self, data):
+        ub = len(data)
+
+        with pytest.raises(IndexError):
+            data.insert(ub + 1, data[0])
+
+        with pytest.raises(IndexError):
+            data.insert(-ub - 1, data[0])
+
+        with pytest.raises(TypeError):
+            # we expect TypeError here instead of IndexError to match np.insert
+            data.insert(1.5, data[0])
+
+    @pytest.mark.parametrize("box", [pd.array, pd.Series, pd.DataFrame])
+    def test_equals(self, data, na_value, as_series, box):
+        data2 = type(data)._from_sequence([data[0]] * len(data), dtype=data.dtype)
+        data_na = type(data)._from_sequence([na_value] * len(data), dtype=data.dtype)
+
+        data = tm.box_expected(data, box, transpose=False)
+        data2 = tm.box_expected(data2, box, transpose=False)
+        data_na = tm.box_expected(data_na, box, transpose=False)
+
+        # we are asserting with `is True/False` explicitly, to test that the
+        # result is an actual Python bool, and not something "truthy"
+
+        assert data.equals(data) is True
+        assert data.equals(data.copy()) is True
+
+        # unequal other data
+        assert data.equals(data2) is False
+        assert data.equals(data_na) is False
+
+        # different length
+        assert data[:2].equals(data[:3]) is False
+
+        # empty are equal
+        assert data[:0].equals(data[:0]) is True
+
+        # other types
+        assert data.equals(None) is False
+        assert data[[0]].equals(data[0]) is False
+
+    def test_equals_same_data_different_object(self, data):
+        # https://github.com/pandas-dev/pandas/issues/34660
+        assert pd.Series(data).equals(pd.Series(data))
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/base/missing.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/base/missing.py
new file mode 100644
index 0000000000000000000000000000000000000000..fb15b2dec869c7d311ddf0b52d13de4e66078dbc
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/base/missing.py
@@ -0,0 +1,190 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+
+
+class BaseMissingTests:
+    def test_isna(self, data_missing):
+        expected = np.array([True, False])
+
+        result = pd.isna(data_missing)
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = pd.Series(data_missing).isna()
+        expected = pd.Series(expected)
+        tm.assert_series_equal(result, expected)
+
+        # GH 21189
+        result = pd.Series(data_missing).drop([0, 1]).isna()
+        expected = pd.Series([], dtype=bool)
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("na_func", ["isna", "notna"])
+    def test_isna_returns_copy(self, data_missing, na_func):
+        result = pd.Series(data_missing)
+        expected = result.copy()
+        mask = getattr(result, na_func)()
+        if isinstance(mask.dtype, pd.SparseDtype):
+            # TODO: GH 57739
+            mask = np.array(mask)
+            mask.flags.writeable = True
+
+        mask[:] = True
+        tm.assert_series_equal(result, expected)
+
+    def test_dropna_array(self, data_missing):
+        result = data_missing.dropna()
+        expected = data_missing[[1]]
+        tm.assert_extension_array_equal(result, expected)
+
+    def test_dropna_series(self, data_missing):
+        ser = pd.Series(data_missing)
+        result = ser.dropna()
+        expected = ser.iloc[[1]]
+        tm.assert_series_equal(result, expected)
+
+    def test_dropna_frame(self, data_missing):
+        df = pd.DataFrame({"A": data_missing}, columns=pd.Index(["A"], dtype=object))
+
+        # defaults
+        result = df.dropna()
+        expected = df.iloc[[1]]
+        tm.assert_frame_equal(result, expected)
+
+        # axis = 1
+        result = df.dropna(axis="columns")
+        expected = pd.DataFrame(index=pd.RangeIndex(2), columns=pd.Index([]))
+        tm.assert_frame_equal(result, expected)
+
+        # multiple
+        df = pd.DataFrame({"A": data_missing, "B": [1, np.nan]})
+        result = df.dropna()
+        expected = df.iloc[:0]
+        tm.assert_frame_equal(result, expected)
+
+    def test_fillna_scalar(self, data_missing):
+        valid = data_missing[1]
+        result = data_missing.fillna(valid)
+        expected = data_missing.fillna(valid)
+        tm.assert_extension_array_equal(result, expected)
+
+    @pytest.mark.filterwarnings(
+        "ignore:Series.fillna with 'method' is deprecated:FutureWarning"
+    )
+    def test_fillna_limit_pad(self, data_missing):
+        arr = data_missing.take([1, 0, 0, 0, 1])
+        result = pd.Series(arr).ffill(limit=2)
+        expected = pd.Series(data_missing.take([1, 1, 1, 0, 1]))
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "limit_area, input_ilocs, expected_ilocs",
+        [
+            ("outside", [1, 0, 0, 0, 1], [1, 0, 0, 0, 1]),
+            ("outside", [1, 0, 1, 0, 1], [1, 0, 1, 0, 1]),
+            ("outside", [0, 1, 1, 1, 0], [0, 1, 1, 1, 1]),
+            ("outside", [0, 1, 0, 1, 0], [0, 1, 0, 1, 1]),
+            ("inside", [1, 0, 0, 0, 1], [1, 1, 1, 1, 1]),
+            ("inside", [1, 0, 1, 0, 1], [1, 1, 1, 1, 1]),
+            ("inside", [0, 1, 1, 1, 0], [0, 1, 1, 1, 0]),
+            ("inside", [0, 1, 0, 1, 0], [0, 1, 1, 1, 0]),
+        ],
+    )
+    def test_ffill_limit_area(
+        self, data_missing, limit_area, input_ilocs, expected_ilocs
+    ):
+        # GH#56616
+        arr = data_missing.take(input_ilocs)
+        result = pd.Series(arr).ffill(limit_area=limit_area)
+        expected = pd.Series(data_missing.take(expected_ilocs))
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.filterwarnings(
+        "ignore:Series.fillna with 'method' is deprecated:FutureWarning"
+    )
+    def test_fillna_limit_backfill(self, data_missing):
+        arr = data_missing.take([1, 0, 0, 0, 1])
+        result = pd.Series(arr).fillna(method="backfill", limit=2)
+        expected = pd.Series(data_missing.take([1, 0, 1, 1, 1]))
+        tm.assert_series_equal(result, expected)
+
+    def test_fillna_no_op_returns_copy(self, data):
+        data = data[~data.isna()]
+
+        valid = data[0]
+        result = data.fillna(valid)
+        assert result is not data
+        tm.assert_extension_array_equal(result, data)
+
+        result = data._pad_or_backfill(method="backfill")
+        assert result is not data
+        tm.assert_extension_array_equal(result, data)
+
+    def test_fillna_series(self, data_missing):
+        fill_value = data_missing[1]
+        ser = pd.Series(data_missing)
+
+        result = ser.fillna(fill_value)
+        expected = pd.Series(
+            data_missing._from_sequence(
+                [fill_value, fill_value], dtype=data_missing.dtype
+            )
+        )
+        tm.assert_series_equal(result, expected)
+
+        # Fill with a series
+        result = ser.fillna(expected)
+        tm.assert_series_equal(result, expected)
+
+        # Fill with a series not affecting the missing values
+        result = ser.fillna(ser)
+        tm.assert_series_equal(result, ser)
+
+    def test_fillna_series_method(self, data_missing, fillna_method):
+        fill_value = data_missing[1]
+
+        if fillna_method == "ffill":
+            data_missing = data_missing[::-1]
+
+        result = getattr(pd.Series(data_missing), fillna_method)()
+        expected = pd.Series(
+            data_missing._from_sequence(
+                [fill_value, fill_value], dtype=data_missing.dtype
+            )
+        )
+
+        tm.assert_series_equal(result, expected)
+
+    def test_fillna_frame(self, data_missing):
+        fill_value = data_missing[1]
+
+        result = pd.DataFrame({"A": data_missing, "B": [1, 2]}).fillna(fill_value)
+
+        expected = pd.DataFrame(
+            {
+                "A": data_missing._from_sequence(
+                    [fill_value, fill_value], dtype=data_missing.dtype
+                ),
+                "B": [1, 2],
+            }
+        )
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_fillna_fill_other(self, data):
+        result = pd.DataFrame({"A": data, "B": [np.nan] * len(data)}).fillna({"B": 0.0})
+
+        expected = pd.DataFrame({"A": data, "B": [0.0] * len(result)})
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_use_inf_as_na_no_effect(self, data_missing):
+        ser = pd.Series(data_missing)
+        expected = ser.isna()
+        msg = "use_inf_as_na option is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            with pd.option_context("mode.use_inf_as_na", True):
+                result = ser.isna()
+        tm.assert_series_equal(result, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/base/ops.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/base/ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..222ff42d4505260c751166d93e1590344787bb79
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/base/ops.py
@@ -0,0 +1,289 @@
+from __future__ import annotations
+
+from typing import final
+
+import numpy as np
+import pytest
+
+from pandas.core.dtypes.common import is_string_dtype
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.core import ops
+
+
+class BaseOpsUtil:
+    series_scalar_exc: type[Exception] | None = TypeError
+    frame_scalar_exc: type[Exception] | None = TypeError
+    series_array_exc: type[Exception] | None = TypeError
+    divmod_exc: type[Exception] | None = TypeError
+
+    def _get_expected_exception(
+        self, op_name: str, obj, other
+    ) -> type[Exception] | tuple[type[Exception], ...] | None:
+        # Find the Exception, if any we expect to raise calling
+        #  obj.__op_name__(other)
+
+        # The self.obj_bar_exc pattern isn't great in part because it can depend
+        #  on op_name or dtypes, but we use it here for backward-compatibility.
+        if op_name in ["__divmod__", "__rdivmod__"]:
+            result = self.divmod_exc
+        elif isinstance(obj, pd.Series) and isinstance(other, pd.Series):
+            result = self.series_array_exc
+        elif isinstance(obj, pd.Series):
+            result = self.series_scalar_exc
+        else:
+            result = self.frame_scalar_exc
+
+        return result
+
+    def _cast_pointwise_result(self, op_name: str, obj, other, pointwise_result):
+        # In _check_op we check that the result of a pointwise operation
+        #  (found via _combine) matches the result of the vectorized
+        #  operation obj.__op_name__(other).
+        #  In some cases pandas dtype inference on the scalar result may not
+        #  give a matching dtype even if both operations are behaving "correctly".
+        #  In these cases, do extra required casting here.
+        return pointwise_result
+
+    def get_op_from_name(self, op_name: str):
+        return tm.get_op_from_name(op_name)
+
+    # Subclasses are not expected to need to override check_opname, _check_op,
+    #  _check_divmod_op, or _combine.
+    #  Ideally any relevant overriding can be done in _cast_pointwise_result,
+    #  get_op_from_name, and the specification of `exc`. If you find a use
+    #  case that still requires overriding _check_op or _combine, please let
+    #  us know at github.com/pandas-dev/pandas/issues
+    @final
+    def check_opname(self, ser: pd.Series, op_name: str, other):
+        exc = self._get_expected_exception(op_name, ser, other)
+        op = self.get_op_from_name(op_name)
+
+        self._check_op(ser, op, other, op_name, exc)
+
+    # see comment on check_opname
+    @final
+    def _combine(self, obj, other, op):
+        if isinstance(obj, pd.DataFrame):
+            if len(obj.columns) != 1:
+                raise NotImplementedError
+            expected = obj.iloc[:, 0].combine(other, op).to_frame()
+        else:
+            expected = obj.combine(other, op)
+        return expected
+
+    # see comment on check_opname
+    @final
+    def _check_op(
+        self, ser: pd.Series, op, other, op_name: str, exc=NotImplementedError
+    ):
+        # Check that the Series/DataFrame arithmetic/comparison method matches
+        #  the pointwise result from _combine.
+
+        if exc is None:
+            result = op(ser, other)
+            expected = self._combine(ser, other, op)
+            expected = self._cast_pointwise_result(op_name, ser, other, expected)
+            assert isinstance(result, type(ser))
+            tm.assert_equal(result, expected)
+        else:
+            with pytest.raises(exc):
+                op(ser, other)
+
+    # see comment on check_opname
+    @final
+    def _check_divmod_op(self, ser: pd.Series, op, other):
+        # check that divmod behavior matches behavior of floordiv+mod
+        if op is divmod:
+            exc = self._get_expected_exception("__divmod__", ser, other)
+        else:
+            exc = self._get_expected_exception("__rdivmod__", ser, other)
+        if exc is None:
+            result_div, result_mod = op(ser, other)
+            if op is divmod:
+                expected_div, expected_mod = ser // other, ser % other
+            else:
+                expected_div, expected_mod = other // ser, other % ser
+            tm.assert_series_equal(result_div, expected_div)
+            tm.assert_series_equal(result_mod, expected_mod)
+        else:
+            with pytest.raises(exc):
+                divmod(ser, other)
+
+
+class BaseArithmeticOpsTests(BaseOpsUtil):
+    """
+    Various Series and DataFrame arithmetic ops methods.
+
+    Subclasses supporting various ops should set the class variables
+    to indicate that they support ops of that kind
+
+    * series_scalar_exc = TypeError
+    * frame_scalar_exc = TypeError
+    * series_array_exc = TypeError
+    * divmod_exc = TypeError
+    """
+
+    series_scalar_exc: type[Exception] | None = TypeError
+    frame_scalar_exc: type[Exception] | None = TypeError
+    series_array_exc: type[Exception] | None = TypeError
+    divmod_exc: type[Exception] | None = TypeError
+
+    def test_arith_series_with_scalar(self, data, all_arithmetic_operators):
+        # series & scalar
+        if all_arithmetic_operators == "__rmod__" and is_string_dtype(data.dtype):
+            pytest.skip("Skip testing Python string formatting")
+
+        op_name = all_arithmetic_operators
+        ser = pd.Series(data)
+        self.check_opname(ser, op_name, ser.iloc[0])
+
+    def test_arith_frame_with_scalar(self, data, all_arithmetic_operators):
+        # frame & scalar
+        if all_arithmetic_operators == "__rmod__" and is_string_dtype(data.dtype):
+            pytest.skip("Skip testing Python string formatting")
+
+        op_name = all_arithmetic_operators
+        df = pd.DataFrame({"A": data})
+        self.check_opname(df, op_name, data[0])
+
+    def test_arith_series_with_array(self, data, all_arithmetic_operators):
+        # ndarray & other series
+        op_name = all_arithmetic_operators
+        ser = pd.Series(data)
+        self.check_opname(ser, op_name, pd.Series([ser.iloc[0]] * len(ser)))
+
+    def test_divmod(self, data):
+        ser = pd.Series(data)
+        self._check_divmod_op(ser, divmod, 1)
+        self._check_divmod_op(1, ops.rdivmod, ser)
+
+    def test_divmod_series_array(self, data, data_for_twos):
+        ser = pd.Series(data)
+        self._check_divmod_op(ser, divmod, data)
+
+        other = data_for_twos
+        self._check_divmod_op(other, ops.rdivmod, ser)
+
+        other = pd.Series(other)
+        self._check_divmod_op(other, ops.rdivmod, ser)
+
+    def test_add_series_with_extension_array(self, data):
+        # Check adding an ExtensionArray to a Series of the same dtype matches
+        # the behavior of adding the arrays directly and then wrapping in a
+        # Series.
+
+        ser = pd.Series(data)
+
+        exc = self._get_expected_exception("__add__", ser, data)
+        if exc is not None:
+            with pytest.raises(exc):
+                ser + data
+            return
+
+        result = ser + data
+        expected = pd.Series(data + data)
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("box", [pd.Series, pd.DataFrame, pd.Index])
+    @pytest.mark.parametrize(
+        "op_name",
+        [
+            x
+            for x in tm.arithmetic_dunder_methods + tm.comparison_dunder_methods
+            if not x.startswith("__r")
+        ],
+    )
+    def test_direct_arith_with_ndframe_returns_not_implemented(
+        self, data, box, op_name
+    ):
+        # EAs should return NotImplemented for ops with Series/DataFrame/Index
+        # Pandas takes care of unboxing the series and calling the EA's op.
+        other = box(data)
+
+        if hasattr(data, op_name):
+            result = getattr(data, op_name)(other)
+            assert result is NotImplemented
+
+
+class BaseComparisonOpsTests(BaseOpsUtil):
+    """Various Series and DataFrame comparison ops methods."""
+
+    def _compare_other(self, ser: pd.Series, data, op, other):
+        if op.__name__ in ["eq", "ne"]:
+            # comparison should match point-wise comparisons
+            result = op(ser, other)
+            expected = ser.combine(other, op)
+            expected = self._cast_pointwise_result(op.__name__, ser, other, expected)
+            tm.assert_series_equal(result, expected)
+
+        else:
+            exc = None
+            try:
+                result = op(ser, other)
+            except Exception as err:
+                exc = err
+
+            if exc is None:
+                # Didn't error, then should match pointwise behavior
+                expected = ser.combine(other, op)
+                expected = self._cast_pointwise_result(
+                    op.__name__, ser, other, expected
+                )
+                tm.assert_series_equal(result, expected)
+            else:
+                with pytest.raises(type(exc)):
+                    ser.combine(other, op)
+
+    def test_compare_scalar(self, data, comparison_op):
+        ser = pd.Series(data)
+        self._compare_other(ser, data, comparison_op, 0)
+
+    def test_compare_array(self, data, comparison_op):
+        ser = pd.Series(data)
+        other = pd.Series([data[0]] * len(data), dtype=data.dtype)
+        self._compare_other(ser, data, comparison_op, other)
+
+
+class BaseUnaryOpsTests(BaseOpsUtil):
+    def test_invert(self, data):
+        ser = pd.Series(data, name="name")
+        try:
+            # 10 is an arbitrary choice here, just avoid iterating over
+            #  the whole array to trim test runtime
+            [~x for x in data[:10]]
+        except TypeError:
+            # scalars don't support invert -> we don't expect the vectorized
+            #  operation to succeed
+            with pytest.raises(TypeError):
+                ~ser
+            with pytest.raises(TypeError):
+                ~data
+        else:
+            # Note we do not reuse the pointwise result to construct expected
+            #  because python semantics for negating bools are weird see GH#54569
+            result = ~ser
+            expected = pd.Series(~data, name="name")
+            tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("ufunc", [np.positive, np.negative, np.abs])
+    def test_unary_ufunc_dunder_equivalence(self, data, ufunc):
+        # the dunder __pos__ works if and only if np.positive works,
+        #  same for __neg__/np.negative and __abs__/np.abs
+        attr = {np.positive: "__pos__", np.negative: "__neg__", np.abs: "__abs__"}[
+            ufunc
+        ]
+
+        exc = None
+        try:
+            result = getattr(data, attr)()
+        except Exception as err:
+            exc = err
+
+            # if __pos__ raised, then so should the ufunc
+            with pytest.raises((type(exc), TypeError)):
+                ufunc(data)
+        else:
+            alt = ufunc(data)
+            tm.assert_extension_array_equal(result, alt)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/base/printing.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/base/printing.py
new file mode 100644
index 0000000000000000000000000000000000000000..b20236ec107b04a09238c472a1d7172256334d3b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/base/printing.py
@@ -0,0 +1,41 @@
+import io
+
+import pytest
+
+import pandas as pd
+
+
+class BasePrintingTests:
+    """Tests checking the formatting of your EA when printed."""
+
+    @pytest.mark.parametrize("size", ["big", "small"])
+    def test_array_repr(self, data, size):
+        if size == "small":
+            data = data[:5]
+        else:
+            data = type(data)._concat_same_type([data] * 5)
+
+        result = repr(data)
+        assert type(data).__name__ in result
+        assert f"Length: {len(data)}" in result
+        assert str(data.dtype) in result
+        if size == "big":
+            assert "..." in result
+
+    def test_array_repr_unicode(self, data):
+        result = str(data)
+        assert isinstance(result, str)
+
+    def test_series_repr(self, data):
+        ser = pd.Series(data)
+        assert data.dtype.name in repr(ser)
+
+    def test_dataframe_repr(self, data):
+        df = pd.DataFrame({"A": data})
+        repr(df)
+
+    def test_dtype_name_in_info(self, data):
+        buf = io.StringIO()
+        pd.DataFrame({"A": data}).info(buf=buf)
+        result = buf.getvalue()
+        assert data.dtype.name in result
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/base/reduce.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/base/reduce.py
new file mode 100644
index 0000000000000000000000000000000000000000..6ea1b3a6fbe9da36e430c26b7ce7bcb706464108
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/base/reduce.py
@@ -0,0 +1,153 @@
+from typing import final
+
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.api.types import is_numeric_dtype
+
+
+class BaseReduceTests:
+    """
+    Reduction specific tests. Generally these only
+    make sense for numeric/boolean operations.
+    """
+
+    def _supports_reduction(self, ser: pd.Series, op_name: str) -> bool:
+        # Specify if we expect this reduction to succeed.
+        return False
+
+    def check_reduce(self, ser: pd.Series, op_name: str, skipna: bool):
+        # We perform the same operation on the np.float64 data and check
+        #  that the results match. Override if you need to cast to something
+        #  other than float64.
+        res_op = getattr(ser, op_name)
+
+        try:
+            alt = ser.astype("float64")
+        except (TypeError, ValueError):
+            # e.g. Interval can't cast (TypeError), StringArray can't cast
+            #  (ValueError), so let's cast to object and do
+            #  the reduction pointwise
+            alt = ser.astype(object)
+
+        exp_op = getattr(alt, op_name)
+        if op_name == "count":
+            result = res_op()
+            expected = exp_op()
+        else:
+            result = res_op(skipna=skipna)
+            expected = exp_op(skipna=skipna)
+        tm.assert_almost_equal(result, expected)
+
+    def _get_expected_reduction_dtype(self, arr, op_name: str, skipna: bool):
+        # Find the expected dtype when the given reduction is done on a DataFrame
+        # column with this array.  The default assumes float64-like behavior,
+        # i.e. retains the dtype.
+        return arr.dtype
+
+    # We anticipate that authors should not need to override check_reduce_frame,
+    #  but should be able to do any necessary overriding in
+    #  _get_expected_reduction_dtype. If you have a use case where this
+    #  does not hold, please let us know at github.com/pandas-dev/pandas/issues.
+    @final
+    def check_reduce_frame(self, ser: pd.Series, op_name: str, skipna: bool):
+        # Check that the 2D reduction done in a DataFrame reduction "looks like"
+        # a wrapped version of the 1D reduction done by Series.
+        arr = ser.array
+        df = pd.DataFrame({"a": arr})
+
+        kwargs = {"ddof": 1} if op_name in ["var", "std"] else {}
+
+        cmp_dtype = self._get_expected_reduction_dtype(arr, op_name, skipna)
+
+        # The DataFrame method just calls arr._reduce with keepdims=True,
+        #  so this first check is perfunctory.
+        result1 = arr._reduce(op_name, skipna=skipna, keepdims=True, **kwargs)
+        result2 = getattr(df, op_name)(skipna=skipna, **kwargs).array
+        tm.assert_extension_array_equal(result1, result2)
+
+        # Check that the 2D reduction looks like a wrapped version of the
+        #  1D reduction
+        if not skipna and ser.isna().any():
+            expected = pd.array([pd.NA], dtype=cmp_dtype)
+        else:
+            exp_value = getattr(ser.dropna(), op_name)()
+            expected = pd.array([exp_value], dtype=cmp_dtype)
+
+        tm.assert_extension_array_equal(result1, expected)
+
+    @pytest.mark.parametrize("skipna", [True, False])
+    def test_reduce_series_boolean(self, data, all_boolean_reductions, skipna):
+        op_name = all_boolean_reductions
+        ser = pd.Series(data)
+
+        if not self._supports_reduction(ser, op_name):
+            # TODO: the message being checked here isn't actually checking anything
+            msg = (
+                "[Cc]annot perform|Categorical is not ordered for operation|"
+                "does not support reduction|"
+            )
+
+            with pytest.raises(TypeError, match=msg):
+                getattr(ser, op_name)(skipna=skipna)
+
+        else:
+            self.check_reduce(ser, op_name, skipna)
+
+    @pytest.mark.filterwarnings("ignore::RuntimeWarning")
+    @pytest.mark.parametrize("skipna", [True, False])
+    def test_reduce_series_numeric(self, data, all_numeric_reductions, skipna):
+        op_name = all_numeric_reductions
+        ser = pd.Series(data)
+
+        if not self._supports_reduction(ser, op_name):
+            # TODO: the message being checked here isn't actually checking anything
+            msg = (
+                "[Cc]annot perform|Categorical is not ordered for operation|"
+                "does not support reduction|"
+            )
+
+            with pytest.raises(TypeError, match=msg):
+                getattr(ser, op_name)(skipna=skipna)
+
+        else:
+            # min/max with empty produce numpy warnings
+            self.check_reduce(ser, op_name, skipna)
+
+    @pytest.mark.parametrize("skipna", [True, False])
+    def test_reduce_frame(self, data, all_numeric_reductions, skipna):
+        op_name = all_numeric_reductions
+        ser = pd.Series(data)
+        if not is_numeric_dtype(ser.dtype):
+            pytest.skip(f"{ser.dtype} is not numeric dtype")
+
+        if op_name in ["count", "kurt", "sem"]:
+            pytest.skip(f"{op_name} not an array method")
+
+        if not self._supports_reduction(ser, op_name):
+            pytest.skip(f"Reduction {op_name} not supported for this dtype")
+
+        self.check_reduce_frame(ser, op_name, skipna)
+
+
+# TODO(3.0): remove BaseNoReduceTests, BaseNumericReduceTests,
+#  BaseBooleanReduceTests
+class BaseNoReduceTests(BaseReduceTests):
+    """we don't define any reductions"""
+
+
+class BaseNumericReduceTests(BaseReduceTests):
+    # For backward compatibility only, this only runs the numeric reductions
+    def _supports_reduction(self, ser: pd.Series, op_name: str) -> bool:
+        if op_name in ["any", "all"]:
+            pytest.skip("These are tested in BaseBooleanReduceTests")
+        return True
+
+
+class BaseBooleanReduceTests(BaseReduceTests):
+    # For backward compatibility only, this only runs the numeric reductions
+    def _supports_reduction(self, ser: pd.Series, op_name: str) -> bool:
+        if op_name not in ["any", "all"]:
+            pytest.skip("These are tested in BaseNumericReduceTests")
+        return True
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/base/reshaping.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/base/reshaping.py
new file mode 100644
index 0000000000000000000000000000000000000000..4550e3b055cfeaea60f9d0b44c97e099e8e4d47c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/base/reshaping.py
@@ -0,0 +1,379 @@
+import itertools
+
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.api.extensions import ExtensionArray
+from pandas.core.internals.blocks import EABackedBlock
+
+
+class BaseReshapingTests:
+    """Tests for reshaping and concatenation."""
+
+    @pytest.mark.parametrize("in_frame", [True, False])
+    def test_concat(self, data, in_frame):
+        wrapped = pd.Series(data)
+        if in_frame:
+            wrapped = pd.DataFrame(wrapped)
+        result = pd.concat([wrapped, wrapped], ignore_index=True)
+
+        assert len(result) == len(data) * 2
+
+        if in_frame:
+            dtype = result.dtypes[0]
+        else:
+            dtype = result.dtype
+
+        assert dtype == data.dtype
+        if hasattr(result._mgr, "blocks"):
+            assert isinstance(result._mgr.blocks[0], EABackedBlock)
+        assert isinstance(result._mgr.arrays[0], ExtensionArray)
+
+    @pytest.mark.parametrize("in_frame", [True, False])
+    def test_concat_all_na_block(self, data_missing, in_frame):
+        valid_block = pd.Series(data_missing.take([1, 1]), index=[0, 1])
+        na_block = pd.Series(data_missing.take([0, 0]), index=[2, 3])
+        if in_frame:
+            valid_block = pd.DataFrame({"a": valid_block})
+            na_block = pd.DataFrame({"a": na_block})
+        result = pd.concat([valid_block, na_block])
+        if in_frame:
+            expected = pd.DataFrame({"a": data_missing.take([1, 1, 0, 0])})
+            tm.assert_frame_equal(result, expected)
+        else:
+            expected = pd.Series(data_missing.take([1, 1, 0, 0]))
+            tm.assert_series_equal(result, expected)
+
+    def test_concat_mixed_dtypes(self, data):
+        # https://github.com/pandas-dev/pandas/issues/20762
+        df1 = pd.DataFrame({"A": data[:3]})
+        df2 = pd.DataFrame({"A": [1, 2, 3]})
+        df3 = pd.DataFrame({"A": ["a", "b", "c"]}).astype("category")
+        dfs = [df1, df2, df3]
+
+        # dataframes
+        result = pd.concat(dfs)
+        expected = pd.concat([x.astype(object) for x in dfs])
+        tm.assert_frame_equal(result, expected)
+
+        # series
+        result = pd.concat([x["A"] for x in dfs])
+        expected = pd.concat([x["A"].astype(object) for x in dfs])
+        tm.assert_series_equal(result, expected)
+
+        # simple test for just EA and one other
+        result = pd.concat([df1, df2.astype(object)])
+        expected = pd.concat([df1.astype("object"), df2.astype("object")])
+        tm.assert_frame_equal(result, expected)
+
+        result = pd.concat([df1["A"], df2["A"].astype(object)])
+        expected = pd.concat([df1["A"].astype("object"), df2["A"].astype("object")])
+        tm.assert_series_equal(result, expected)
+
+    def test_concat_columns(self, data, na_value):
+        df1 = pd.DataFrame({"A": data[:3]})
+        df2 = pd.DataFrame({"B": [1, 2, 3]})
+
+        expected = pd.DataFrame({"A": data[:3], "B": [1, 2, 3]})
+        result = pd.concat([df1, df2], axis=1)
+        tm.assert_frame_equal(result, expected)
+        result = pd.concat([df1["A"], df2["B"]], axis=1)
+        tm.assert_frame_equal(result, expected)
+
+        # non-aligned
+        df2 = pd.DataFrame({"B": [1, 2, 3]}, index=[1, 2, 3])
+        expected = pd.DataFrame(
+            {
+                "A": data._from_sequence(list(data[:3]) + [na_value], dtype=data.dtype),
+                "B": [np.nan, 1, 2, 3],
+            }
+        )
+
+        result = pd.concat([df1, df2], axis=1)
+        tm.assert_frame_equal(result, expected)
+        result = pd.concat([df1["A"], df2["B"]], axis=1)
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_extension_arrays_copy_false(self, data, na_value):
+        # GH 20756
+        df1 = pd.DataFrame({"A": data[:3]})
+        df2 = pd.DataFrame({"B": data[3:7]})
+        expected = pd.DataFrame(
+            {
+                "A": data._from_sequence(list(data[:3]) + [na_value], dtype=data.dtype),
+                "B": data[3:7],
+            }
+        )
+        result = pd.concat([df1, df2], axis=1, copy=False)
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_with_reindex(self, data):
+        # GH-33027
+        a = pd.DataFrame({"a": data[:5]})
+        b = pd.DataFrame({"b": data[:5]})
+        result = pd.concat([a, b], ignore_index=True)
+        expected = pd.DataFrame(
+            {
+                "a": data.take(list(range(5)) + ([-1] * 5), allow_fill=True),
+                "b": data.take(([-1] * 5) + list(range(5)), allow_fill=True),
+            }
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_align(self, data, na_value):
+        a = data[:3]
+        b = data[2:5]
+        r1, r2 = pd.Series(a).align(pd.Series(b, index=[1, 2, 3]))
+
+        # Assumes that the ctor can take a list of scalars of the type
+        e1 = pd.Series(data._from_sequence(list(a) + [na_value], dtype=data.dtype))
+        e2 = pd.Series(data._from_sequence([na_value] + list(b), dtype=data.dtype))
+        tm.assert_series_equal(r1, e1)
+        tm.assert_series_equal(r2, e2)
+
+    def test_align_frame(self, data, na_value):
+        a = data[:3]
+        b = data[2:5]
+        r1, r2 = pd.DataFrame({"A": a}).align(pd.DataFrame({"A": b}, index=[1, 2, 3]))
+
+        # Assumes that the ctor can take a list of scalars of the type
+        e1 = pd.DataFrame(
+            {"A": data._from_sequence(list(a) + [na_value], dtype=data.dtype)}
+        )
+        e2 = pd.DataFrame(
+            {"A": data._from_sequence([na_value] + list(b), dtype=data.dtype)}
+        )
+        tm.assert_frame_equal(r1, e1)
+        tm.assert_frame_equal(r2, e2)
+
+    def test_align_series_frame(self, data, na_value):
+        # https://github.com/pandas-dev/pandas/issues/20576
+        ser = pd.Series(data, name="a")
+        df = pd.DataFrame({"col": np.arange(len(ser) + 1)})
+        r1, r2 = ser.align(df)
+
+        e1 = pd.Series(
+            data._from_sequence(list(data) + [na_value], dtype=data.dtype),
+            name=ser.name,
+        )
+
+        tm.assert_series_equal(r1, e1)
+        tm.assert_frame_equal(r2, df)
+
+    def test_set_frame_expand_regular_with_extension(self, data):
+        df = pd.DataFrame({"A": [1] * len(data)})
+        df["B"] = data
+        expected = pd.DataFrame({"A": [1] * len(data), "B": data})
+        tm.assert_frame_equal(df, expected)
+
+    def test_set_frame_expand_extension_with_regular(self, data):
+        df = pd.DataFrame({"A": data})
+        df["B"] = [1] * len(data)
+        expected = pd.DataFrame({"A": data, "B": [1] * len(data)})
+        tm.assert_frame_equal(df, expected)
+
+    def test_set_frame_overwrite_object(self, data):
+        # https://github.com/pandas-dev/pandas/issues/20555
+        df = pd.DataFrame({"A": [1] * len(data)}, dtype=object)
+        df["A"] = data
+        assert df.dtypes["A"] == data.dtype
+
+    def test_merge(self, data, na_value):
+        # GH-20743
+        df1 = pd.DataFrame({"ext": data[:3], "int1": [1, 2, 3], "key": [0, 1, 2]})
+        df2 = pd.DataFrame({"int2": [1, 2, 3, 4], "key": [0, 0, 1, 3]})
+
+        res = pd.merge(df1, df2)
+        exp = pd.DataFrame(
+            {
+                "int1": [1, 1, 2],
+                "int2": [1, 2, 3],
+                "key": [0, 0, 1],
+                "ext": data._from_sequence(
+                    [data[0], data[0], data[1]], dtype=data.dtype
+                ),
+            }
+        )
+        tm.assert_frame_equal(res, exp[["ext", "int1", "key", "int2"]])
+
+        res = pd.merge(df1, df2, how="outer")
+        exp = pd.DataFrame(
+            {
+                "int1": [1, 1, 2, 3, np.nan],
+                "int2": [1, 2, 3, np.nan, 4],
+                "key": [0, 0, 1, 2, 3],
+                "ext": data._from_sequence(
+                    [data[0], data[0], data[1], data[2], na_value], dtype=data.dtype
+                ),
+            }
+        )
+        tm.assert_frame_equal(res, exp[["ext", "int1", "key", "int2"]])
+
+    def test_merge_on_extension_array(self, data):
+        # GH 23020
+        a, b = data[:2]
+        key = type(data)._from_sequence([a, b], dtype=data.dtype)
+
+        df = pd.DataFrame({"key": key, "val": [1, 2]})
+        result = pd.merge(df, df, on="key")
+        expected = pd.DataFrame({"key": key, "val_x": [1, 2], "val_y": [1, 2]})
+        tm.assert_frame_equal(result, expected)
+
+        # order
+        result = pd.merge(df.iloc[[1, 0]], df, on="key")
+        expected = expected.iloc[[1, 0]].reset_index(drop=True)
+        tm.assert_frame_equal(result, expected)
+
+    def test_merge_on_extension_array_duplicates(self, data):
+        # GH 23020
+        a, b = data[:2]
+        key = type(data)._from_sequence([a, b, a], dtype=data.dtype)
+        df1 = pd.DataFrame({"key": key, "val": [1, 2, 3]})
+        df2 = pd.DataFrame({"key": key, "val": [1, 2, 3]})
+
+        result = pd.merge(df1, df2, on="key")
+        expected = pd.DataFrame(
+            {
+                "key": key.take([0, 0, 1, 2, 2]),
+                "val_x": [1, 1, 2, 3, 3],
+                "val_y": [1, 3, 2, 1, 3],
+            }
+        )
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    @pytest.mark.parametrize(
+        "columns",
+        [
+            ["A", "B"],
+            pd.MultiIndex.from_tuples(
+                [("A", "a"), ("A", "b")], names=["outer", "inner"]
+            ),
+        ],
+    )
+    @pytest.mark.parametrize("future_stack", [True, False])
+    def test_stack(self, data, columns, future_stack):
+        df = pd.DataFrame({"A": data[:5], "B": data[:5]})
+        df.columns = columns
+        result = df.stack(future_stack=future_stack)
+        expected = df.astype(object).stack(future_stack=future_stack)
+        # we need a second astype(object), in case the constructor inferred
+        # object -> specialized, as is done for period.
+        expected = expected.astype(object)
+
+        if isinstance(expected, pd.Series):
+            assert result.dtype == df.iloc[:, 0].dtype
+        else:
+            assert all(result.dtypes == df.iloc[:, 0].dtype)
+
+        result = result.astype(object)
+        tm.assert_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "index",
+        [
+            # Two levels, uniform.
+            pd.MultiIndex.from_product(([["A", "B"], ["a", "b"]]), names=["a", "b"]),
+            # non-uniform
+            pd.MultiIndex.from_tuples([("A", "a"), ("A", "b"), ("B", "b")]),
+            # three levels, non-uniform
+            pd.MultiIndex.from_product([("A", "B"), ("a", "b", "c"), (0, 1, 2)]),
+            pd.MultiIndex.from_tuples(
+                [
+                    ("A", "a", 1),
+                    ("A", "b", 0),
+                    ("A", "a", 0),
+                    ("B", "a", 0),
+                    ("B", "c", 1),
+                ]
+            ),
+        ],
+    )
+    @pytest.mark.parametrize("obj", ["series", "frame"])
+    def test_unstack(self, data, index, obj):
+        data = data[: len(index)]
+        if obj == "series":
+            ser = pd.Series(data, index=index)
+        else:
+            ser = pd.DataFrame({"A": data, "B": data}, index=index)
+
+        n = index.nlevels
+        levels = list(range(n))
+        # [0, 1, 2]
+        # [(0,), (1,), (2,), (0, 1), (0, 2), (1, 0), (1, 2), (2, 0), (2, 1)]
+        combinations = itertools.chain.from_iterable(
+            itertools.permutations(levels, i) for i in range(1, n)
+        )
+
+        for level in combinations:
+            result = ser.unstack(level=level)
+            assert all(
+                isinstance(result[col].array, type(data)) for col in result.columns
+            )
+
+            if obj == "series":
+                # We should get the same result with to_frame+unstack+droplevel
+                df = ser.to_frame()
+
+                alt = df.unstack(level=level).droplevel(0, axis=1)
+                tm.assert_frame_equal(result, alt)
+
+            obj_ser = ser.astype(object)
+
+            expected = obj_ser.unstack(level=level, fill_value=data.dtype.na_value)
+            if obj == "series":
+                assert (expected.dtypes == object).all()
+
+            result = result.astype(object)
+            tm.assert_frame_equal(result, expected)
+
+    def test_ravel(self, data):
+        # as long as EA is 1D-only, ravel is a no-op
+        result = data.ravel()
+        assert type(result) == type(data)
+
+        if data.dtype._is_immutable:
+            pytest.skip(f"test_ravel assumes mutability and {data.dtype} is immutable")
+
+        # Check that we have a view, not a copy
+        result[0] = result[1]
+        assert data[0] == data[1]
+
+    def test_transpose(self, data):
+        result = data.transpose()
+        assert type(result) == type(data)
+
+        # check we get a new object
+        assert result is not data
+
+        # If we ever _did_ support 2D, shape should be reversed
+        assert result.shape == data.shape[::-1]
+
+        if data.dtype._is_immutable:
+            pytest.skip(
+                f"test_transpose assumes mutability and {data.dtype} is immutable"
+            )
+
+        # Check that we have a view, not a copy
+        result[0] = result[1]
+        assert data[0] == data[1]
+
+    def test_transpose_frame(self, data):
+        df = pd.DataFrame({"A": data[:4], "B": data[:4]}, index=["a", "b", "c", "d"])
+        result = df.T
+        expected = pd.DataFrame(
+            {
+                "a": type(data)._from_sequence([data[0]] * 2, dtype=data.dtype),
+                "b": type(data)._from_sequence([data[1]] * 2, dtype=data.dtype),
+                "c": type(data)._from_sequence([data[2]] * 2, dtype=data.dtype),
+                "d": type(data)._from_sequence([data[3]] * 2, dtype=data.dtype),
+            },
+            index=["A", "B"],
+        )
+        tm.assert_frame_equal(result, expected)
+        tm.assert_frame_equal(np.transpose(np.transpose(df)), df)
+        tm.assert_frame_equal(np.transpose(np.transpose(df[["A"]])), df[["A"]])
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/base/setitem.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/base/setitem.py
new file mode 100644
index 0000000000000000000000000000000000000000..ca19845041e231f141d480ad57f668e4d6fcd5fc
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/base/setitem.py
@@ -0,0 +1,451 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+
+
+class BaseSetitemTests:
+    @pytest.fixture(
+        params=[
+            lambda x: x.index,
+            lambda x: list(x.index),
+            lambda x: slice(None),
+            lambda x: slice(0, len(x)),
+            lambda x: range(len(x)),
+            lambda x: list(range(len(x))),
+            lambda x: np.ones(len(x), dtype=bool),
+        ],
+        ids=[
+            "index",
+            "list[index]",
+            "null_slice",
+            "full_slice",
+            "range",
+            "list(range)",
+            "mask",
+        ],
+    )
+    def full_indexer(self, request):
+        """
+        Fixture for an indexer to pass to obj.loc to get/set the full length of the
+        object.
+
+        In some cases, assumes that obj.index is the default RangeIndex.
+        """
+        return request.param
+
+    @pytest.fixture(autouse=True)
+    def skip_if_immutable(self, dtype, request):
+        if dtype._is_immutable:
+            node = request.node
+            if node.name.split("[")[0] == "test_is_immutable":
+                # This fixture is auto-used, but we want to not-skip
+                # test_is_immutable.
+                return
+
+            # When BaseSetitemTests is mixed into ExtensionTests, we only
+            #  want this fixture to operate on the tests defined in this
+            #  class/file.
+            defined_in = node.function.__qualname__.split(".")[0]
+            if defined_in == "BaseSetitemTests":
+                pytest.skip("__setitem__ test not applicable with immutable dtype")
+
+    def test_is_immutable(self, data):
+        if data.dtype._is_immutable:
+            with pytest.raises(TypeError):
+                data[0] = data[0]
+        else:
+            data[0] = data[1]
+            assert data[0] == data[1]
+
+    def test_setitem_scalar_series(self, data, box_in_series):
+        if box_in_series:
+            data = pd.Series(data)
+        data[0] = data[1]
+        assert data[0] == data[1]
+
+    def test_setitem_sequence(self, data, box_in_series):
+        if box_in_series:
+            data = pd.Series(data)
+        original = data.copy()
+
+        data[[0, 1]] = [data[1], data[0]]
+        assert data[0] == original[1]
+        assert data[1] == original[0]
+
+    def test_setitem_sequence_mismatched_length_raises(self, data, as_array):
+        ser = pd.Series(data)
+        original = ser.copy()
+        value = [data[0]]
+        if as_array:
+            value = data._from_sequence(value, dtype=data.dtype)
+
+        xpr = "cannot set using a {} indexer with a different length"
+        with pytest.raises(ValueError, match=xpr.format("list-like")):
+            ser[[0, 1]] = value
+        # Ensure no modifications made before the exception
+        tm.assert_series_equal(ser, original)
+
+        with pytest.raises(ValueError, match=xpr.format("slice")):
+            ser[slice(3)] = value
+        tm.assert_series_equal(ser, original)
+
+    def test_setitem_empty_indexer(self, data, box_in_series):
+        if box_in_series:
+            data = pd.Series(data)
+        original = data.copy()
+        data[np.array([], dtype=int)] = []
+        tm.assert_equal(data, original)
+
+    def test_setitem_sequence_broadcasts(self, data, box_in_series):
+        if box_in_series:
+            data = pd.Series(data)
+        data[[0, 1]] = data[2]
+        assert data[0] == data[2]
+        assert data[1] == data[2]
+
+    @pytest.mark.parametrize("setter", ["loc", "iloc"])
+    def test_setitem_scalar(self, data, setter):
+        arr = pd.Series(data)
+        setter = getattr(arr, setter)
+        setter[0] = data[1]
+        assert arr[0] == data[1]
+
+    def test_setitem_loc_scalar_mixed(self, data):
+        df = pd.DataFrame({"A": np.arange(len(data)), "B": data})
+        df.loc[0, "B"] = data[1]
+        assert df.loc[0, "B"] == data[1]
+
+    def test_setitem_loc_scalar_single(self, data):
+        df = pd.DataFrame({"B": data})
+        df.loc[10, "B"] = data[1]
+        assert df.loc[10, "B"] == data[1]
+
+    def test_setitem_loc_scalar_multiple_homogoneous(self, data):
+        df = pd.DataFrame({"A": data, "B": data})
+        df.loc[10, "B"] = data[1]
+        assert df.loc[10, "B"] == data[1]
+
+    def test_setitem_iloc_scalar_mixed(self, data):
+        df = pd.DataFrame({"A": np.arange(len(data)), "B": data})
+        df.iloc[0, 1] = data[1]
+        assert df.loc[0, "B"] == data[1]
+
+    def test_setitem_iloc_scalar_single(self, data):
+        df = pd.DataFrame({"B": data})
+        df.iloc[10, 0] = data[1]
+        assert df.loc[10, "B"] == data[1]
+
+    def test_setitem_iloc_scalar_multiple_homogoneous(self, data):
+        df = pd.DataFrame({"A": data, "B": data})
+        df.iloc[10, 1] = data[1]
+        assert df.loc[10, "B"] == data[1]
+
+    @pytest.mark.parametrize(
+        "mask",
+        [
+            np.array([True, True, True, False, False]),
+            pd.array([True, True, True, False, False], dtype="boolean"),
+            pd.array([True, True, True, pd.NA, pd.NA], dtype="boolean"),
+        ],
+        ids=["numpy-array", "boolean-array", "boolean-array-na"],
+    )
+    def test_setitem_mask(self, data, mask, box_in_series):
+        arr = data[:5].copy()
+        expected = arr.take([0, 0, 0, 3, 4])
+        if box_in_series:
+            arr = pd.Series(arr)
+            expected = pd.Series(expected)
+        arr[mask] = data[0]
+        tm.assert_equal(expected, arr)
+
+    def test_setitem_mask_raises(self, data, box_in_series):
+        # wrong length
+        mask = np.array([True, False])
+
+        if box_in_series:
+            data = pd.Series(data)
+
+        with pytest.raises(IndexError, match="wrong length"):
+            data[mask] = data[0]
+
+        mask = pd.array(mask, dtype="boolean")
+        with pytest.raises(IndexError, match="wrong length"):
+            data[mask] = data[0]
+
+    def test_setitem_mask_boolean_array_with_na(self, data, box_in_series):
+        mask = pd.array(np.zeros(data.shape, dtype="bool"), dtype="boolean")
+        mask[:3] = True
+        mask[3:5] = pd.NA
+
+        if box_in_series:
+            data = pd.Series(data)
+
+        data[mask] = data[0]
+
+        assert (data[:3] == data[0]).all()
+
+    @pytest.mark.parametrize(
+        "idx",
+        [[0, 1, 2], pd.array([0, 1, 2], dtype="Int64"), np.array([0, 1, 2])],
+        ids=["list", "integer-array", "numpy-array"],
+    )
+    def test_setitem_integer_array(self, data, idx, box_in_series):
+        arr = data[:5].copy()
+        expected = data.take([0, 0, 0, 3, 4])
+
+        if box_in_series:
+            arr = pd.Series(arr)
+            expected = pd.Series(expected)
+
+        arr[idx] = arr[0]
+        tm.assert_equal(arr, expected)
+
+    @pytest.mark.parametrize(
+        "idx, box_in_series",
+        [
+            ([0, 1, 2, pd.NA], False),
+            pytest.param(
+                [0, 1, 2, pd.NA], True, marks=pytest.mark.xfail(reason="GH-31948")
+            ),
+            (pd.array([0, 1, 2, pd.NA], dtype="Int64"), False),
+            (pd.array([0, 1, 2, pd.NA], dtype="Int64"), False),
+        ],
+        ids=["list-False", "list-True", "integer-array-False", "integer-array-True"],
+    )
+    def test_setitem_integer_with_missing_raises(self, data, idx, box_in_series):
+        arr = data.copy()
+
+        # TODO(xfail) this raises KeyError about labels not found (it tries label-based)
+        # for list of labels with Series
+        if box_in_series:
+            arr = pd.Series(data, index=[chr(100 + i) for i in range(len(data))])
+
+        msg = "Cannot index with an integer indexer containing NA values"
+        with pytest.raises(ValueError, match=msg):
+            arr[idx] = arr[0]
+
+    @pytest.mark.parametrize("as_callable", [True, False])
+    @pytest.mark.parametrize("setter", ["loc", None])
+    def test_setitem_mask_aligned(self, data, as_callable, setter):
+        ser = pd.Series(data)
+        mask = np.zeros(len(data), dtype=bool)
+        mask[:2] = True
+
+        if as_callable:
+            mask2 = lambda x: mask
+        else:
+            mask2 = mask
+
+        if setter:
+            # loc
+            target = getattr(ser, setter)
+        else:
+            # Series.__setitem__
+            target = ser
+
+        target[mask2] = data[5:7]
+
+        ser[mask2] = data[5:7]
+        assert ser[0] == data[5]
+        assert ser[1] == data[6]
+
+    @pytest.mark.parametrize("setter", ["loc", None])
+    def test_setitem_mask_broadcast(self, data, setter):
+        ser = pd.Series(data)
+        mask = np.zeros(len(data), dtype=bool)
+        mask[:2] = True
+
+        if setter:  # loc
+            target = getattr(ser, setter)
+        else:  # __setitem__
+            target = ser
+
+        target[mask] = data[10]
+        assert ser[0] == data[10]
+        assert ser[1] == data[10]
+
+    def test_setitem_expand_columns(self, data):
+        df = pd.DataFrame({"A": data})
+        result = df.copy()
+        result["B"] = 1
+        expected = pd.DataFrame({"A": data, "B": [1] * len(data)})
+        tm.assert_frame_equal(result, expected)
+
+        result = df.copy()
+        result.loc[:, "B"] = 1
+        tm.assert_frame_equal(result, expected)
+
+        # overwrite with new type
+        result["B"] = data
+        expected = pd.DataFrame({"A": data, "B": data})
+        tm.assert_frame_equal(result, expected)
+
+    def test_setitem_expand_with_extension(self, data):
+        df = pd.DataFrame({"A": [1] * len(data)})
+        result = df.copy()
+        result["B"] = data
+        expected = pd.DataFrame({"A": [1] * len(data), "B": data})
+        tm.assert_frame_equal(result, expected)
+
+        result = df.copy()
+        result.loc[:, "B"] = data
+        tm.assert_frame_equal(result, expected)
+
+    def test_setitem_frame_invalid_length(self, data):
+        df = pd.DataFrame({"A": [1] * len(data)})
+        xpr = (
+            rf"Length of values \({len(data[:5])}\) "
+            rf"does not match length of index \({len(df)}\)"
+        )
+        with pytest.raises(ValueError, match=xpr):
+            df["B"] = data[:5]
+
+    def test_setitem_tuple_index(self, data):
+        ser = pd.Series(data[:2], index=[(0, 0), (0, 1)])
+        expected = pd.Series(data.take([1, 1]), index=ser.index)
+        ser[(0, 0)] = data[1]
+        tm.assert_series_equal(ser, expected)
+
+    def test_setitem_slice(self, data, box_in_series):
+        arr = data[:5].copy()
+        expected = data.take([0, 0, 0, 3, 4])
+        if box_in_series:
+            arr = pd.Series(arr)
+            expected = pd.Series(expected)
+
+        arr[:3] = data[0]
+        tm.assert_equal(arr, expected)
+
+    def test_setitem_loc_iloc_slice(self, data):
+        arr = data[:5].copy()
+        s = pd.Series(arr, index=["a", "b", "c", "d", "e"])
+        expected = pd.Series(data.take([0, 0, 0, 3, 4]), index=s.index)
+
+        result = s.copy()
+        result.iloc[:3] = data[0]
+        tm.assert_equal(result, expected)
+
+        result = s.copy()
+        result.loc[:"c"] = data[0]
+        tm.assert_equal(result, expected)
+
+    def test_setitem_slice_mismatch_length_raises(self, data):
+        arr = data[:5]
+        with pytest.raises(ValueError):
+            arr[:1] = arr[:2]
+
+    def test_setitem_slice_array(self, data):
+        arr = data[:5].copy()
+        arr[:5] = data[-5:]
+        tm.assert_extension_array_equal(arr, data[-5:])
+
+    def test_setitem_scalar_key_sequence_raise(self, data):
+        arr = data[:5].copy()
+        with pytest.raises(ValueError):
+            arr[0] = arr[[0, 1]]
+
+    def test_setitem_preserves_views(self, data):
+        # GH#28150 setitem shouldn't swap the underlying data
+        view1 = data.view()
+        view2 = data[:]
+
+        data[0] = data[1]
+        assert view1[0] == data[1]
+        assert view2[0] == data[1]
+
+    def test_setitem_with_expansion_dataframe_column(self, data, full_indexer):
+        # https://github.com/pandas-dev/pandas/issues/32395
+        df = expected = pd.DataFrame({0: pd.Series(data)})
+        result = pd.DataFrame(index=df.index)
+
+        key = full_indexer(df)
+        result.loc[key, 0] = df[0]
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_setitem_with_expansion_row(self, data, na_value):
+        df = pd.DataFrame({"data": data[:1]})
+
+        df.loc[1, "data"] = data[1]
+        expected = pd.DataFrame({"data": data[:2]})
+        tm.assert_frame_equal(df, expected)
+
+        # https://github.com/pandas-dev/pandas/issues/47284
+        df.loc[2, "data"] = na_value
+        expected = pd.DataFrame(
+            {"data": pd.Series([data[0], data[1], na_value], dtype=data.dtype)}
+        )
+        tm.assert_frame_equal(df, expected)
+
+    def test_setitem_series(self, data, full_indexer):
+        # https://github.com/pandas-dev/pandas/issues/32395
+        ser = pd.Series(data, name="data")
+        result = pd.Series(index=ser.index, dtype=object, name="data")
+
+        # because result has object dtype, the attempt to do setting inplace
+        #  is successful, and object dtype is retained
+        key = full_indexer(ser)
+        result.loc[key] = ser
+
+        expected = pd.Series(
+            data.astype(object), index=ser.index, name="data", dtype=object
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_setitem_frame_2d_values(self, data):
+        # GH#44514
+        df = pd.DataFrame({"A": data})
+
+        # Avoiding using_array_manager fixture
+        #  https://github.com/pandas-dev/pandas/pull/44514#discussion_r754002410
+        using_array_manager = isinstance(df._mgr, pd.core.internals.ArrayManager)
+        using_copy_on_write = pd.options.mode.copy_on_write
+
+        blk_data = df._mgr.arrays[0]
+
+        orig = df.copy()
+
+        df.iloc[:] = df.copy()
+        tm.assert_frame_equal(df, orig)
+
+        df.iloc[:-1] = df.iloc[:-1].copy()
+        tm.assert_frame_equal(df, orig)
+
+        df.iloc[:] = df.values
+        tm.assert_frame_equal(df, orig)
+        if not using_array_manager and not using_copy_on_write:
+            # GH#33457 Check that this setting occurred in-place
+            # FIXME(ArrayManager): this should work there too
+            assert df._mgr.arrays[0] is blk_data
+
+        df.iloc[:-1] = df.values[:-1]
+        tm.assert_frame_equal(df, orig)
+
+    def test_delitem_series(self, data):
+        # GH#40763
+        ser = pd.Series(data, name="data")
+
+        taker = np.arange(len(ser))
+        taker = np.delete(taker, 1)
+
+        expected = ser[taker]
+        del ser[1]
+        tm.assert_series_equal(ser, expected)
+
+    def test_setitem_invalid(self, data, invalid_scalar):
+        msg = ""  # messages vary by subclass, so we do not test it
+        with pytest.raises((ValueError, TypeError), match=msg):
+            data[0] = invalid_scalar
+
+        with pytest.raises((ValueError, TypeError), match=msg):
+            data[:] = invalid_scalar
+
+    def test_setitem_2d_values(self, data):
+        # GH50085
+        original = data.copy()
+        df = pd.DataFrame({"a": data, "b": data})
+        df.loc[[0, 1], :] = df.loc[[1, 0], :].values
+        assert (df.loc[0, :] == original[1]).all()
+        assert (df.loc[1, :] == original[0]).all()
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/conftest.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/conftest.py
new file mode 100644
index 0000000000000000000000000000000000000000..c5b1295ee4a7d4ad8f4a76b58e4b40837cd46d4c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/conftest.py
@@ -0,0 +1,230 @@
+import operator
+
+import pytest
+
+from pandas._config.config import _get_option
+
+from pandas import (
+    Series,
+    options,
+)
+
+
+@pytest.fixture
+def dtype():
+    """A fixture providing the ExtensionDtype to validate."""
+    raise NotImplementedError
+
+
+@pytest.fixture
+def data():
+    """
+    Length-100 array for this type.
+
+    * data[0] and data[1] should both be non missing
+    * data[0] and data[1] should not be equal
+    """
+    raise NotImplementedError
+
+
+@pytest.fixture
+def data_for_twos(dtype):
+    """
+    Length-100 array in which all the elements are two.
+
+    Call pytest.skip in your fixture if the dtype does not support divmod.
+    """
+    if not (dtype._is_numeric or dtype.kind == "m"):
+        # Object-dtypes may want to allow this, but for the most part
+        #  only numeric and timedelta-like dtypes will need to implement this.
+        pytest.skip(f"{dtype} is not a numeric dtype")
+
+    raise NotImplementedError
+
+
+@pytest.fixture
+def data_missing():
+    """Length-2 array with [NA, Valid]"""
+    raise NotImplementedError
+
+
+@pytest.fixture(params=["data", "data_missing"])
+def all_data(request, data, data_missing):
+    """Parametrized fixture giving 'data' and 'data_missing'"""
+    if request.param == "data":
+        return data
+    elif request.param == "data_missing":
+        return data_missing
+
+
+@pytest.fixture
+def data_repeated(data):
+    """
+    Generate many datasets.
+
+    Parameters
+    ----------
+    data : fixture implementing `data`
+
+    Returns
+    -------
+    Callable[[int], Generator]:
+        A callable that takes a `count` argument and
+        returns a generator yielding `count` datasets.
+    """
+
+    def gen(count):
+        for _ in range(count):
+            yield data
+
+    return gen
+
+
+@pytest.fixture
+def data_for_sorting():
+    """
+    Length-3 array with a known sort order.
+
+    This should be three items [B, C, A] with
+    A < B < C
+
+    For boolean dtypes (for which there are only 2 values available),
+    set B=C=True
+    """
+    raise NotImplementedError
+
+
+@pytest.fixture
+def data_missing_for_sorting():
+    """
+    Length-3 array with a known sort order.
+
+    This should be three items [B, NA, A] with
+    A < B and NA missing.
+    """
+    raise NotImplementedError
+
+
+@pytest.fixture
+def na_cmp():
+    """
+    Binary operator for comparing NA values.
+
+    Should return a function of two arguments that returns
+    True if both arguments are (scalar) NA for your type.
+
+    By default, uses ``operator.is_``
+    """
+    return operator.is_
+
+
+@pytest.fixture
+def na_value(dtype):
+    """
+    The scalar missing value for this type. Default dtype.na_value.
+
+    TODO: can be removed in 3.x (see https://github.com/pandas-dev/pandas/pull/54930)
+    """
+    return dtype.na_value
+
+
+@pytest.fixture
+def data_for_grouping():
+    """
+    Data for factorization, grouping, and unique tests.
+
+    Expected to be like [B, B, NA, NA, A, A, B, C]
+
+    Where A < B < C and NA is missing.
+
+    If a dtype has _is_boolean = True, i.e. only 2 unique non-NA entries,
+    then set C=B.
+    """
+    raise NotImplementedError
+
+
+@pytest.fixture(params=[True, False])
+def box_in_series(request):
+    """Whether to box the data in a Series"""
+    return request.param
+
+
+@pytest.fixture(
+    params=[
+        lambda x: 1,
+        lambda x: [1] * len(x),
+        lambda x: Series([1] * len(x)),
+        lambda x: x,
+    ],
+    ids=["scalar", "list", "series", "object"],
+)
+def groupby_apply_op(request):
+    """
+    Functions to test groupby.apply().
+    """
+    return request.param
+
+
+@pytest.fixture(params=[True, False])
+def as_frame(request):
+    """
+    Boolean fixture to support Series and Series.to_frame() comparison testing.
+    """
+    return request.param
+
+
+@pytest.fixture(params=[True, False])
+def as_series(request):
+    """
+    Boolean fixture to support arr and Series(arr) comparison testing.
+    """
+    return request.param
+
+
+@pytest.fixture(params=[True, False])
+def use_numpy(request):
+    """
+    Boolean fixture to support comparison testing of ExtensionDtype array
+    and numpy array.
+    """
+    return request.param
+
+
+@pytest.fixture(params=["ffill", "bfill"])
+def fillna_method(request):
+    """
+    Parametrized fixture giving method parameters 'ffill' and 'bfill' for
+    Series.fillna(method=) testing.
+    """
+    return request.param
+
+
+@pytest.fixture(params=[True, False])
+def as_array(request):
+    """
+    Boolean fixture to support ExtensionDtype _from_sequence method testing.
+    """
+    return request.param
+
+
+@pytest.fixture
+def invalid_scalar(data):
+    """
+    A scalar that *cannot* be held by this ExtensionArray.
+
+    The default should work for most subclasses, but is not guaranteed.
+
+    If the array can hold any item (i.e. object dtype), then use pytest.skip.
+    """
+    return object.__new__(object)
+
+
+@pytest.fixture
+def using_copy_on_write() -> bool:
+    """
+    Fixture to check if Copy-on-Write is enabled.
+    """
+    return (
+        options.mode.copy_on_write is True
+        and _get_option("mode.data_manager", silent=True) == "block"
+    )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/date/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/date/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..2a8c7e9f57a5da982530b8db854edd37baf13b6b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/date/__init__.py
@@ -0,0 +1,6 @@
+from pandas.tests.extension.date.array import (
+    DateArray,
+    DateDtype,
+)
+
+__all__ = ["DateArray", "DateDtype"]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/date/array.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/date/array.py
new file mode 100644
index 0000000000000000000000000000000000000000..2306f5974ba186587dedb1159d64374601f55c86
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/date/array.py
@@ -0,0 +1,188 @@
+from __future__ import annotations
+
+import datetime as dt
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    cast,
+)
+
+import numpy as np
+
+from pandas.core.dtypes.dtypes import register_extension_dtype
+
+from pandas.api.extensions import (
+    ExtensionArray,
+    ExtensionDtype,
+)
+from pandas.api.types import pandas_dtype
+
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+
+    from pandas._typing import (
+        Dtype,
+        PositionalIndexer,
+    )
+
+
+@register_extension_dtype
+class DateDtype(ExtensionDtype):
+    @property
+    def type(self):
+        return dt.date
+
+    @property
+    def name(self):
+        return "DateDtype"
+
+    @classmethod
+    def construct_from_string(cls, string: str):
+        if not isinstance(string, str):
+            raise TypeError(
+                f"'construct_from_string' expects a string, got {type(string)}"
+            )
+
+        if string == cls.__name__:
+            return cls()
+        else:
+            raise TypeError(f"Cannot construct a '{cls.__name__}' from '{string}'")
+
+    @classmethod
+    def construct_array_type(cls):
+        return DateArray
+
+    @property
+    def na_value(self):
+        return dt.date.min
+
+    def __repr__(self) -> str:
+        return self.name
+
+
+class DateArray(ExtensionArray):
+    def __init__(
+        self,
+        dates: (
+            dt.date
+            | Sequence[dt.date]
+            | tuple[np.ndarray, np.ndarray, np.ndarray]
+            | np.ndarray
+        ),
+    ) -> None:
+        if isinstance(dates, dt.date):
+            self._year = np.array([dates.year])
+            self._month = np.array([dates.month])
+            self._day = np.array([dates.year])
+            return
+
+        ldates = len(dates)
+        if isinstance(dates, list):
+            # pre-allocate the arrays since we know the size before hand
+            self._year = np.zeros(ldates, dtype=np.uint16)  # 65535 (0, 9999)
+            self._month = np.zeros(ldates, dtype=np.uint8)  # 255 (1, 31)
+            self._day = np.zeros(ldates, dtype=np.uint8)  # 255 (1, 12)
+            # populate them
+            for i, (y, m, d) in enumerate(
+                (date.year, date.month, date.day) for date in dates
+            ):
+                self._year[i] = y
+                self._month[i] = m
+                self._day[i] = d
+
+        elif isinstance(dates, tuple):
+            # only support triples
+            if ldates != 3:
+                raise ValueError("only triples are valid")
+            # check if all elements have the same type
+            if any(not isinstance(x, np.ndarray) for x in dates):
+                raise TypeError("invalid type")
+            ly, lm, ld = (len(cast(np.ndarray, d)) for d in dates)
+            if not ly == lm == ld:
+                raise ValueError(
+                    f"tuple members must have the same length: {(ly, lm, ld)}"
+                )
+            self._year = dates[0].astype(np.uint16)
+            self._month = dates[1].astype(np.uint8)
+            self._day = dates[2].astype(np.uint8)
+
+        elif isinstance(dates, np.ndarray) and dates.dtype == "U10":
+            self._year = np.zeros(ldates, dtype=np.uint16)  # 65535 (0, 9999)
+            self._month = np.zeros(ldates, dtype=np.uint8)  # 255 (1, 31)
+            self._day = np.zeros(ldates, dtype=np.uint8)  # 255 (1, 12)
+
+            # error: "object_" object is not iterable
+            obj = np.char.split(dates, sep="-")
+            for (i,), (y, m, d) in np.ndenumerate(obj):  # type: ignore[misc]
+                self._year[i] = int(y)
+                self._month[i] = int(m)
+                self._day[i] = int(d)
+
+        else:
+            raise TypeError(f"{type(dates)} is not supported")
+
+    @property
+    def dtype(self) -> ExtensionDtype:
+        return DateDtype()
+
+    def astype(self, dtype, copy=True):
+        dtype = pandas_dtype(dtype)
+
+        if isinstance(dtype, DateDtype):
+            data = self.copy() if copy else self
+        else:
+            data = self.to_numpy(dtype=dtype, copy=copy, na_value=dt.date.min)
+
+        return data
+
+    @property
+    def nbytes(self) -> int:
+        return self._year.nbytes + self._month.nbytes + self._day.nbytes
+
+    def __len__(self) -> int:
+        return len(self._year)  # all 3 arrays are enforced to have the same length
+
+    def __getitem__(self, item: PositionalIndexer):
+        if isinstance(item, int):
+            return dt.date(self._year[item], self._month[item], self._day[item])
+        else:
+            raise NotImplementedError("only ints are supported as indexes")
+
+    def __setitem__(self, key: int | slice | np.ndarray, value: Any) -> None:
+        if not isinstance(key, int):
+            raise NotImplementedError("only ints are supported as indexes")
+
+        if not isinstance(value, dt.date):
+            raise TypeError("you can only set datetime.date types")
+
+        self._year[key] = value.year
+        self._month[key] = value.month
+        self._day[key] = value.day
+
+    def __repr__(self) -> str:
+        return f"DateArray{list(zip(self._year, self._month, self._day))}"
+
+    def copy(self) -> DateArray:
+        return DateArray((self._year.copy(), self._month.copy(), self._day.copy()))
+
+    def isna(self) -> np.ndarray:
+        return np.logical_and(
+            np.logical_and(
+                self._year == dt.date.min.year, self._month == dt.date.min.month
+            ),
+            self._day == dt.date.min.day,
+        )
+
+    @classmethod
+    def _from_sequence(cls, scalars, *, dtype: Dtype | None = None, copy=False):
+        if isinstance(scalars, dt.date):
+            raise TypeError
+        elif isinstance(scalars, DateArray):
+            if dtype is not None:
+                return scalars.astype(dtype, copy=copy)
+            if copy:
+                return scalars.copy()
+            return scalars[:]
+        elif isinstance(scalars, np.ndarray):
+            scalars = scalars.astype("U10")  # 10 chars for yyyy-mm-dd
+            return DateArray(scalars)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/decimal/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/decimal/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..34727b43a7b0fb325143dfedee4db25c4b56f5db
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/decimal/__init__.py
@@ -0,0 +1,8 @@
+from pandas.tests.extension.decimal.array import (
+    DecimalArray,
+    DecimalDtype,
+    make_data,
+    to_decimal,
+)
+
+__all__ = ["DecimalArray", "DecimalDtype", "to_decimal", "make_data"]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/decimal/array.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/decimal/array.py
new file mode 100644
index 0000000000000000000000000000000000000000..521c1ff0b96bc12672b64be0fa191e153692f6da
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/decimal/array.py
@@ -0,0 +1,311 @@
+from __future__ import annotations
+
+import decimal
+import numbers
+import sys
+from typing import TYPE_CHECKING
+
+import numpy as np
+
+from pandas.core.dtypes.base import ExtensionDtype
+from pandas.core.dtypes.common import (
+    is_dtype_equal,
+    is_float,
+    is_integer,
+    pandas_dtype,
+)
+
+import pandas as pd
+from pandas.api.extensions import (
+    no_default,
+    register_extension_dtype,
+)
+from pandas.api.types import (
+    is_list_like,
+    is_scalar,
+)
+from pandas.core import arraylike
+from pandas.core.algorithms import value_counts_internal as value_counts
+from pandas.core.arraylike import OpsMixin
+from pandas.core.arrays import (
+    ExtensionArray,
+    ExtensionScalarOpsMixin,
+)
+from pandas.core.indexers import check_array_indexer
+
+if TYPE_CHECKING:
+    from pandas._typing import type_t
+
+
+@register_extension_dtype
+class DecimalDtype(ExtensionDtype):
+    type = decimal.Decimal
+    name = "decimal"
+    na_value = decimal.Decimal("NaN")
+    _metadata = ("context",)
+
+    def __init__(self, context=None) -> None:
+        self.context = context or decimal.getcontext()
+
+    def __repr__(self) -> str:
+        return f"DecimalDtype(context={self.context})"
+
+    @classmethod
+    def construct_array_type(cls) -> type_t[DecimalArray]:
+        """
+        Return the array type associated with this dtype.
+
+        Returns
+        -------
+        type
+        """
+        return DecimalArray
+
+    @property
+    def _is_numeric(self) -> bool:
+        return True
+
+
+class DecimalArray(OpsMixin, ExtensionScalarOpsMixin, ExtensionArray):
+    __array_priority__ = 1000
+
+    def __init__(self, values, dtype=None, copy=False, context=None) -> None:
+        for i, val in enumerate(values):
+            if is_float(val) or is_integer(val):
+                if np.isnan(val):
+                    values[i] = DecimalDtype.na_value
+                else:
+                    # error: Argument 1 has incompatible type "float | int |
+                    # integer[Any]"; expected "Decimal | float | str | tuple[int,
+                    # Sequence[int], int]"
+                    values[i] = DecimalDtype.type(val)  # type: ignore[arg-type]
+            elif not isinstance(val, decimal.Decimal):
+                raise TypeError("All values must be of type " + str(decimal.Decimal))
+        values = np.asarray(values, dtype=object)
+
+        self._data = values
+        # Some aliases for common attribute names to ensure pandas supports
+        # these
+        self._items = self.data = self._data
+        # those aliases are currently not working due to assumptions
+        # in internal code (GH-20735)
+        # self._values = self.values = self.data
+        self._dtype = DecimalDtype(context)
+
+    @property
+    def dtype(self):
+        return self._dtype
+
+    @classmethod
+    def _from_sequence(cls, scalars, *, dtype=None, copy=False):
+        return cls(scalars)
+
+    @classmethod
+    def _from_sequence_of_strings(cls, strings, dtype=None, copy=False):
+        return cls._from_sequence(
+            [decimal.Decimal(x) for x in strings], dtype=dtype, copy=copy
+        )
+
+    @classmethod
+    def _from_factorized(cls, values, original):
+        return cls(values)
+
+    _HANDLED_TYPES = (decimal.Decimal, numbers.Number, np.ndarray)
+
+    def to_numpy(
+        self,
+        dtype=None,
+        copy: bool = False,
+        na_value: object = no_default,
+        decimals=None,
+    ) -> np.ndarray:
+        result = np.asarray(self, dtype=dtype)
+        if decimals is not None:
+            result = np.asarray([round(x, decimals) for x in result])
+        return result
+
+    def __array_ufunc__(self, ufunc: np.ufunc, method: str, *inputs, **kwargs):
+        #
+        if not all(
+            isinstance(t, self._HANDLED_TYPES + (DecimalArray,)) for t in inputs
+        ):
+            return NotImplemented
+
+        result = arraylike.maybe_dispatch_ufunc_to_dunder_op(
+            self, ufunc, method, *inputs, **kwargs
+        )
+        if result is not NotImplemented:
+            # e.g. test_array_ufunc_series_scalar_other
+            return result
+
+        if "out" in kwargs:
+            return arraylike.dispatch_ufunc_with_out(
+                self, ufunc, method, *inputs, **kwargs
+            )
+
+        inputs = tuple(x._data if isinstance(x, DecimalArray) else x for x in inputs)
+        result = getattr(ufunc, method)(*inputs, **kwargs)
+
+        if method == "reduce":
+            result = arraylike.dispatch_reduction_ufunc(
+                self, ufunc, method, *inputs, **kwargs
+            )
+            if result is not NotImplemented:
+                return result
+
+        def reconstruct(x):
+            if isinstance(x, (decimal.Decimal, numbers.Number)):
+                return x
+            else:
+                return type(self)._from_sequence(x, dtype=self.dtype)
+
+        if ufunc.nout > 1:
+            return tuple(reconstruct(x) for x in result)
+        else:
+            return reconstruct(result)
+
+    def __getitem__(self, item):
+        if isinstance(item, numbers.Integral):
+            return self._data[item]
+        else:
+            # array, slice.
+            item = pd.api.indexers.check_array_indexer(self, item)
+            return type(self)(self._data[item])
+
+    def take(self, indexer, allow_fill=False, fill_value=None):
+        from pandas.api.extensions import take
+
+        data = self._data
+        if allow_fill and fill_value is None:
+            fill_value = self.dtype.na_value
+
+        result = take(data, indexer, fill_value=fill_value, allow_fill=allow_fill)
+        return self._from_sequence(result, dtype=self.dtype)
+
+    def copy(self):
+        return type(self)(self._data.copy(), dtype=self.dtype)
+
+    def astype(self, dtype, copy=True):
+        if is_dtype_equal(dtype, self._dtype):
+            if not copy:
+                return self
+        dtype = pandas_dtype(dtype)
+        if isinstance(dtype, type(self.dtype)):
+            return type(self)(self._data, copy=copy, context=dtype.context)
+
+        return super().astype(dtype, copy=copy)
+
+    def __setitem__(self, key, value) -> None:
+        if is_list_like(value):
+            if is_scalar(key):
+                raise ValueError("setting an array element with a sequence.")
+            value = [decimal.Decimal(v) for v in value]
+        else:
+            value = decimal.Decimal(value)
+
+        key = check_array_indexer(self, key)
+        self._data[key] = value
+
+    def __len__(self) -> int:
+        return len(self._data)
+
+    def __contains__(self, item) -> bool | np.bool_:
+        if not isinstance(item, decimal.Decimal):
+            return False
+        elif item.is_nan():
+            return self.isna().any()
+        else:
+            return super().__contains__(item)
+
+    @property
+    def nbytes(self) -> int:
+        n = len(self)
+        if n:
+            return n * sys.getsizeof(self[0])
+        return 0
+
+    def isna(self):
+        return np.array([x.is_nan() for x in self._data], dtype=bool)
+
+    @property
+    def _na_value(self):
+        return decimal.Decimal("NaN")
+
+    def _formatter(self, boxed=False):
+        if boxed:
+            return "Decimal: {}".format
+        return repr
+
+    @classmethod
+    def _concat_same_type(cls, to_concat):
+        return cls(np.concatenate([x._data for x in to_concat]))
+
+    def _reduce(
+        self, name: str, *, skipna: bool = True, keepdims: bool = False, **kwargs
+    ):
+        if skipna and self.isna().any():
+            # If we don't have any NAs, we can ignore skipna
+            other = self[~self.isna()]
+            result = other._reduce(name, **kwargs)
+        elif name == "sum" and len(self) == 0:
+            # GH#29630 avoid returning int 0 or np.bool_(False) on old numpy
+            result = decimal.Decimal(0)
+        else:
+            try:
+                op = getattr(self.data, name)
+            except AttributeError as err:
+                raise NotImplementedError(
+                    f"decimal does not support the {name} operation"
+                ) from err
+            result = op(axis=0)
+
+        if keepdims:
+            return type(self)([result])
+        else:
+            return result
+
+    def _cmp_method(self, other, op):
+        # For use with OpsMixin
+        def convert_values(param):
+            if isinstance(param, ExtensionArray) or is_list_like(param):
+                ovalues = param
+            else:
+                # Assume it's an object
+                ovalues = [param] * len(self)
+            return ovalues
+
+        lvalues = self
+        rvalues = convert_values(other)
+
+        # If the operator is not defined for the underlying objects,
+        # a TypeError should be raised
+        res = [op(a, b) for (a, b) in zip(lvalues, rvalues)]
+
+        return np.asarray(res, dtype=bool)
+
+    def value_counts(self, dropna: bool = True):
+        return value_counts(self.to_numpy(), dropna=dropna)
+
+    # We override fillna here to simulate a 3rd party EA that has done so. This
+    #  lets us test the deprecation telling authors to implement _pad_or_backfill
+    # Simulate a 3rd-party EA that has not yet updated to include a "copy"
+    #  keyword in its fillna method.
+    # error: Signature of "fillna" incompatible with supertype "ExtensionArray"
+    def fillna(  # type: ignore[override]
+        self,
+        value=None,
+        method=None,
+        limit: int | None = None,
+    ):
+        return super().fillna(value=value, method=method, limit=limit, copy=True)
+
+
+def to_decimal(values, context=None):
+    return DecimalArray([decimal.Decimal(x) for x in values], context=context)
+
+
+def make_data():
+    return [decimal.Decimal(val) for val in np.random.default_rng(2).random(100)]
+
+
+DecimalArray._add_arithmetic_ops()
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/decimal/test_decimal.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/decimal/test_decimal.py
new file mode 100644
index 0000000000000000000000000000000000000000..8590cd7fdc23539b148509ea04b88538c9bc5b25
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/decimal/test_decimal.py
@@ -0,0 +1,587 @@
+from __future__ import annotations
+
+import decimal
+import operator
+
+import numpy as np
+import pytest
+
+from pandas.compat.numpy import np_version_gt2
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.tests.extension import base
+from pandas.tests.extension.decimal.array import (
+    DecimalArray,
+    DecimalDtype,
+    make_data,
+    to_decimal,
+)
+
+
+@pytest.fixture
+def dtype():
+    return DecimalDtype()
+
+
+@pytest.fixture
+def data():
+    return DecimalArray(make_data())
+
+
+@pytest.fixture
+def data_for_twos():
+    return DecimalArray([decimal.Decimal(2) for _ in range(100)])
+
+
+@pytest.fixture
+def data_missing():
+    return DecimalArray([decimal.Decimal("NaN"), decimal.Decimal(1)])
+
+
+@pytest.fixture
+def data_for_sorting():
+    return DecimalArray(
+        [decimal.Decimal("1"), decimal.Decimal("2"), decimal.Decimal("0")]
+    )
+
+
+@pytest.fixture
+def data_missing_for_sorting():
+    return DecimalArray(
+        [decimal.Decimal("1"), decimal.Decimal("NaN"), decimal.Decimal("0")]
+    )
+
+
+@pytest.fixture
+def na_cmp():
+    return lambda x, y: x.is_nan() and y.is_nan()
+
+
+@pytest.fixture
+def data_for_grouping():
+    b = decimal.Decimal("1.0")
+    a = decimal.Decimal("0.0")
+    c = decimal.Decimal("2.0")
+    na = decimal.Decimal("NaN")
+    return DecimalArray([b, b, na, na, a, a, b, c])
+
+
+class TestDecimalArray(base.ExtensionTests):
+    def _get_expected_exception(
+        self, op_name: str, obj, other
+    ) -> type[Exception] | tuple[type[Exception], ...] | None:
+        return None
+
+    def _supports_reduction(self, ser: pd.Series, op_name: str) -> bool:
+        return True
+
+    def check_reduce(self, ser: pd.Series, op_name: str, skipna: bool):
+        if op_name == "count":
+            return super().check_reduce(ser, op_name, skipna)
+        else:
+            result = getattr(ser, op_name)(skipna=skipna)
+            expected = getattr(np.asarray(ser), op_name)()
+            tm.assert_almost_equal(result, expected)
+
+    def test_reduce_series_numeric(self, data, all_numeric_reductions, skipna, request):
+        if all_numeric_reductions in ["kurt", "skew", "sem", "median"]:
+            mark = pytest.mark.xfail(raises=NotImplementedError)
+            request.applymarker(mark)
+        super().test_reduce_series_numeric(data, all_numeric_reductions, skipna)
+
+    def test_reduce_frame(self, data, all_numeric_reductions, skipna, request):
+        op_name = all_numeric_reductions
+        if op_name in ["skew", "median"]:
+            mark = pytest.mark.xfail(raises=NotImplementedError)
+            request.applymarker(mark)
+
+        return super().test_reduce_frame(data, all_numeric_reductions, skipna)
+
+    def test_compare_scalar(self, data, comparison_op):
+        ser = pd.Series(data)
+        self._compare_other(ser, data, comparison_op, 0.5)
+
+    def test_compare_array(self, data, comparison_op):
+        ser = pd.Series(data)
+
+        alter = np.random.default_rng(2).choice([-1, 0, 1], len(data))
+        # Randomly double, halve or keep same value
+        other = pd.Series(data) * [decimal.Decimal(pow(2.0, i)) for i in alter]
+        self._compare_other(ser, data, comparison_op, other)
+
+    def test_arith_series_with_array(self, data, all_arithmetic_operators):
+        op_name = all_arithmetic_operators
+        ser = pd.Series(data)
+
+        context = decimal.getcontext()
+        divbyzerotrap = context.traps[decimal.DivisionByZero]
+        invalidoptrap = context.traps[decimal.InvalidOperation]
+        context.traps[decimal.DivisionByZero] = 0
+        context.traps[decimal.InvalidOperation] = 0
+
+        # Decimal supports ops with int, but not float
+        other = pd.Series([int(d * 100) for d in data])
+        self.check_opname(ser, op_name, other)
+
+        if "mod" not in op_name:
+            self.check_opname(ser, op_name, ser * 2)
+
+        self.check_opname(ser, op_name, 0)
+        self.check_opname(ser, op_name, 5)
+        context.traps[decimal.DivisionByZero] = divbyzerotrap
+        context.traps[decimal.InvalidOperation] = invalidoptrap
+
+    def test_fillna_frame(self, data_missing):
+        msg = "ExtensionArray.fillna added a 'copy' keyword"
+        with tm.assert_produces_warning(
+            DeprecationWarning, match=msg, check_stacklevel=False
+        ):
+            super().test_fillna_frame(data_missing)
+
+    def test_fillna_limit_pad(self, data_missing):
+        msg = "ExtensionArray.fillna 'method' keyword is deprecated"
+        with tm.assert_produces_warning(
+            DeprecationWarning,
+            match=msg,
+            check_stacklevel=False,
+            raise_on_extra_warnings=False,
+        ):
+            super().test_fillna_limit_pad(data_missing)
+
+        msg = "The 'method' keyword in DecimalArray.fillna is deprecated"
+        with tm.assert_produces_warning(
+            FutureWarning,
+            match=msg,
+            check_stacklevel=False,
+            raise_on_extra_warnings=False,
+        ):
+            super().test_fillna_limit_pad(data_missing)
+
+    @pytest.mark.parametrize(
+        "limit_area, input_ilocs, expected_ilocs",
+        [
+            ("outside", [1, 0, 0, 0, 1], [1, 0, 0, 0, 1]),
+            ("outside", [1, 0, 1, 0, 1], [1, 0, 1, 0, 1]),
+            ("outside", [0, 1, 1, 1, 0], [0, 1, 1, 1, 1]),
+            ("outside", [0, 1, 0, 1, 0], [0, 1, 0, 1, 1]),
+            ("inside", [1, 0, 0, 0, 1], [1, 1, 1, 1, 1]),
+            ("inside", [1, 0, 1, 0, 1], [1, 1, 1, 1, 1]),
+            ("inside", [0, 1, 1, 1, 0], [0, 1, 1, 1, 0]),
+            ("inside", [0, 1, 0, 1, 0], [0, 1, 1, 1, 0]),
+        ],
+    )
+    def test_ffill_limit_area(
+        self, data_missing, limit_area, input_ilocs, expected_ilocs
+    ):
+        # GH#56616
+        msg = "ExtensionArray.fillna 'method' keyword is deprecated"
+        with tm.assert_produces_warning(
+            DeprecationWarning,
+            match=msg,
+            check_stacklevel=False,
+            raise_on_extra_warnings=False,
+        ):
+            msg = "DecimalArray does not implement limit_area"
+            with pytest.raises(NotImplementedError, match=msg):
+                super().test_ffill_limit_area(
+                    data_missing, limit_area, input_ilocs, expected_ilocs
+                )
+
+    def test_fillna_limit_backfill(self, data_missing):
+        msg = "Series.fillna with 'method' is deprecated"
+        with tm.assert_produces_warning(
+            FutureWarning,
+            match=msg,
+            check_stacklevel=False,
+            raise_on_extra_warnings=False,
+        ):
+            super().test_fillna_limit_backfill(data_missing)
+
+        msg = "ExtensionArray.fillna 'method' keyword is deprecated"
+        with tm.assert_produces_warning(
+            DeprecationWarning,
+            match=msg,
+            check_stacklevel=False,
+            raise_on_extra_warnings=False,
+        ):
+            super().test_fillna_limit_backfill(data_missing)
+
+        msg = "The 'method' keyword in DecimalArray.fillna is deprecated"
+        with tm.assert_produces_warning(
+            FutureWarning,
+            match=msg,
+            check_stacklevel=False,
+            raise_on_extra_warnings=False,
+        ):
+            super().test_fillna_limit_backfill(data_missing)
+
+    def test_fillna_no_op_returns_copy(self, data):
+        msg = "|".join(
+            [
+                "ExtensionArray.fillna 'method' keyword is deprecated",
+                "The 'method' keyword in DecimalArray.fillna is deprecated",
+            ]
+        )
+        with tm.assert_produces_warning(
+            (FutureWarning, DeprecationWarning), match=msg, check_stacklevel=False
+        ):
+            super().test_fillna_no_op_returns_copy(data)
+
+    def test_fillna_series(self, data_missing):
+        msg = "ExtensionArray.fillna added a 'copy' keyword"
+        with tm.assert_produces_warning(
+            DeprecationWarning, match=msg, check_stacklevel=False
+        ):
+            super().test_fillna_series(data_missing)
+
+    def test_fillna_series_method(self, data_missing, fillna_method):
+        msg = "|".join(
+            [
+                "ExtensionArray.fillna 'method' keyword is deprecated",
+                "The 'method' keyword in DecimalArray.fillna is deprecated",
+            ]
+        )
+        with tm.assert_produces_warning(
+            (FutureWarning, DeprecationWarning), match=msg, check_stacklevel=False
+        ):
+            super().test_fillna_series_method(data_missing, fillna_method)
+
+    def test_fillna_copy_frame(self, data_missing, using_copy_on_write):
+        warn = DeprecationWarning if not using_copy_on_write else None
+        msg = "ExtensionArray.fillna added a 'copy' keyword"
+        with tm.assert_produces_warning(warn, match=msg, check_stacklevel=False):
+            super().test_fillna_copy_frame(data_missing)
+
+    def test_fillna_copy_series(self, data_missing, using_copy_on_write):
+        warn = DeprecationWarning if not using_copy_on_write else None
+        msg = "ExtensionArray.fillna added a 'copy' keyword"
+        with tm.assert_produces_warning(warn, match=msg, check_stacklevel=False):
+            super().test_fillna_copy_series(data_missing)
+
+    @pytest.mark.parametrize("dropna", [True, False])
+    def test_value_counts(self, all_data, dropna, request):
+        all_data = all_data[:10]
+        if dropna:
+            other = np.array(all_data[~all_data.isna()])
+        else:
+            other = all_data
+
+        vcs = pd.Series(all_data).value_counts(dropna=dropna)
+        vcs_ex = pd.Series(other).value_counts(dropna=dropna)
+
+        with decimal.localcontext() as ctx:
+            # avoid raising when comparing Decimal("NAN") < Decimal(2)
+            ctx.traps[decimal.InvalidOperation] = False
+
+            result = vcs.sort_index()
+            expected = vcs_ex.sort_index()
+
+        tm.assert_series_equal(result, expected)
+
+    def test_series_repr(self, data):
+        # Overriding this base test to explicitly test that
+        # the custom _formatter is used
+        ser = pd.Series(data)
+        assert data.dtype.name in repr(ser)
+        assert "Decimal: " in repr(ser)
+
+    @pytest.mark.xfail(reason="Inconsistent array-vs-scalar behavior")
+    @pytest.mark.parametrize("ufunc", [np.positive, np.negative, np.abs])
+    def test_unary_ufunc_dunder_equivalence(self, data, ufunc):
+        super().test_unary_ufunc_dunder_equivalence(data, ufunc)
+
+    def test_array_interface_copy(self, data):
+        result_copy1 = np.array(data, copy=True)
+        result_copy2 = np.array(data, copy=True)
+        assert not np.may_share_memory(result_copy1, result_copy2)
+        if not np_version_gt2:
+            # copy=False semantics are only supported in NumPy>=2.
+            return
+
+        try:
+            result_nocopy1 = np.array(data, copy=False)
+        except ValueError:
+            # An error is always acceptable for `copy=False`
+            return
+
+        result_nocopy2 = np.array(data, copy=False)
+        # If copy=False was given and did not raise, these must share the same data
+        assert np.may_share_memory(result_nocopy1, result_nocopy2)
+
+
+def test_take_na_value_other_decimal():
+    arr = DecimalArray([decimal.Decimal("1.0"), decimal.Decimal("2.0")])
+    result = arr.take([0, -1], allow_fill=True, fill_value=decimal.Decimal("-1.0"))
+    expected = DecimalArray([decimal.Decimal("1.0"), decimal.Decimal("-1.0")])
+    tm.assert_extension_array_equal(result, expected)
+
+
+def test_series_constructor_coerce_data_to_extension_dtype():
+    dtype = DecimalDtype()
+    ser = pd.Series([0, 1, 2], dtype=dtype)
+
+    arr = DecimalArray(
+        [decimal.Decimal(0), decimal.Decimal(1), decimal.Decimal(2)],
+        dtype=dtype,
+    )
+    exp = pd.Series(arr)
+    tm.assert_series_equal(ser, exp)
+
+
+def test_series_constructor_with_dtype():
+    arr = DecimalArray([decimal.Decimal("10.0")])
+    result = pd.Series(arr, dtype=DecimalDtype())
+    expected = pd.Series(arr)
+    tm.assert_series_equal(result, expected)
+
+    result = pd.Series(arr, dtype="int64")
+    expected = pd.Series([10])
+    tm.assert_series_equal(result, expected)
+
+
+def test_dataframe_constructor_with_dtype():
+    arr = DecimalArray([decimal.Decimal("10.0")])
+
+    result = pd.DataFrame({"A": arr}, dtype=DecimalDtype())
+    expected = pd.DataFrame({"A": arr})
+    tm.assert_frame_equal(result, expected)
+
+    arr = DecimalArray([decimal.Decimal("10.0")])
+    result = pd.DataFrame({"A": arr}, dtype="int64")
+    expected = pd.DataFrame({"A": [10]})
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("frame", [True, False])
+def test_astype_dispatches(frame):
+    # This is a dtype-specific test that ensures Series[decimal].astype
+    # gets all the way through to ExtensionArray.astype
+    # Designing a reliable smoke test that works for arbitrary data types
+    # is difficult.
+    data = pd.Series(DecimalArray([decimal.Decimal(2)]), name="a")
+    ctx = decimal.Context()
+    ctx.prec = 5
+
+    if frame:
+        data = data.to_frame()
+
+    result = data.astype(DecimalDtype(ctx))
+
+    if frame:
+        result = result["a"]
+
+    assert result.dtype.context.prec == ctx.prec
+
+
+class DecimalArrayWithoutFromSequence(DecimalArray):
+    """Helper class for testing error handling in _from_sequence."""
+
+    @classmethod
+    def _from_sequence(cls, scalars, *, dtype=None, copy=False):
+        raise KeyError("For the test")
+
+
+class DecimalArrayWithoutCoercion(DecimalArrayWithoutFromSequence):
+    @classmethod
+    def _create_arithmetic_method(cls, op):
+        return cls._create_method(op, coerce_to_dtype=False)
+
+
+DecimalArrayWithoutCoercion._add_arithmetic_ops()
+
+
+def test_combine_from_sequence_raises(monkeypatch):
+    # https://github.com/pandas-dev/pandas/issues/22850
+    cls = DecimalArrayWithoutFromSequence
+
+    @classmethod
+    def construct_array_type(cls):
+        return DecimalArrayWithoutFromSequence
+
+    monkeypatch.setattr(DecimalDtype, "construct_array_type", construct_array_type)
+
+    arr = cls([decimal.Decimal("1.0"), decimal.Decimal("2.0")])
+    ser = pd.Series(arr)
+    result = ser.combine(ser, operator.add)
+
+    # note: object dtype
+    expected = pd.Series(
+        [decimal.Decimal("2.0"), decimal.Decimal("4.0")], dtype="object"
+    )
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "class_", [DecimalArrayWithoutFromSequence, DecimalArrayWithoutCoercion]
+)
+def test_scalar_ops_from_sequence_raises(class_):
+    # op(EA, EA) should return an EA, or an ndarray if it's not possible
+    # to return an EA with the return values.
+    arr = class_([decimal.Decimal("1.0"), decimal.Decimal("2.0")])
+    result = arr + arr
+    expected = np.array(
+        [decimal.Decimal("2.0"), decimal.Decimal("4.0")], dtype="object"
+    )
+    tm.assert_numpy_array_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "reverse, expected_div, expected_mod",
+    [(False, [0, 1, 1, 2], [1, 0, 1, 0]), (True, [2, 1, 0, 0], [0, 0, 2, 2])],
+)
+def test_divmod_array(reverse, expected_div, expected_mod):
+    # https://github.com/pandas-dev/pandas/issues/22930
+    arr = to_decimal([1, 2, 3, 4])
+    if reverse:
+        div, mod = divmod(2, arr)
+    else:
+        div, mod = divmod(arr, 2)
+    expected_div = to_decimal(expected_div)
+    expected_mod = to_decimal(expected_mod)
+
+    tm.assert_extension_array_equal(div, expected_div)
+    tm.assert_extension_array_equal(mod, expected_mod)
+
+
+def test_ufunc_fallback(data):
+    a = data[:5]
+    s = pd.Series(a, index=range(3, 8))
+    result = np.abs(s)
+    expected = pd.Series(np.abs(a), index=range(3, 8))
+    tm.assert_series_equal(result, expected)
+
+
+def test_array_ufunc():
+    a = to_decimal([1, 2, 3])
+    result = np.exp(a)
+    expected = to_decimal(np.exp(a._data))
+    tm.assert_extension_array_equal(result, expected)
+
+
+def test_array_ufunc_series():
+    a = to_decimal([1, 2, 3])
+    s = pd.Series(a)
+    result = np.exp(s)
+    expected = pd.Series(to_decimal(np.exp(a._data)))
+    tm.assert_series_equal(result, expected)
+
+
+def test_array_ufunc_series_scalar_other():
+    # check _HANDLED_TYPES
+    a = to_decimal([1, 2, 3])
+    s = pd.Series(a)
+    result = np.add(s, decimal.Decimal(1))
+    expected = pd.Series(np.add(a, decimal.Decimal(1)))
+    tm.assert_series_equal(result, expected)
+
+
+def test_array_ufunc_series_defer():
+    a = to_decimal([1, 2, 3])
+    s = pd.Series(a)
+
+    expected = pd.Series(to_decimal([2, 4, 6]))
+    r1 = np.add(s, a)
+    r2 = np.add(a, s)
+
+    tm.assert_series_equal(r1, expected)
+    tm.assert_series_equal(r2, expected)
+
+
+def test_groupby_agg():
+    # Ensure that the result of agg is inferred to be decimal dtype
+    # https://github.com/pandas-dev/pandas/issues/29141
+
+    data = make_data()[:5]
+    df = pd.DataFrame(
+        {"id1": [0, 0, 0, 1, 1], "id2": [0, 1, 0, 1, 1], "decimals": DecimalArray(data)}
+    )
+
+    # single key, selected column
+    expected = pd.Series(to_decimal([data[0], data[3]]))
+    result = df.groupby("id1")["decimals"].agg(lambda x: x.iloc[0])
+    tm.assert_series_equal(result, expected, check_names=False)
+    result = df["decimals"].groupby(df["id1"]).agg(lambda x: x.iloc[0])
+    tm.assert_series_equal(result, expected, check_names=False)
+
+    # multiple keys, selected column
+    expected = pd.Series(
+        to_decimal([data[0], data[1], data[3]]),
+        index=pd.MultiIndex.from_tuples([(0, 0), (0, 1), (1, 1)]),
+    )
+    result = df.groupby(["id1", "id2"])["decimals"].agg(lambda x: x.iloc[0])
+    tm.assert_series_equal(result, expected, check_names=False)
+    result = df["decimals"].groupby([df["id1"], df["id2"]]).agg(lambda x: x.iloc[0])
+    tm.assert_series_equal(result, expected, check_names=False)
+
+    # multiple columns
+    expected = pd.DataFrame({"id2": [0, 1], "decimals": to_decimal([data[0], data[3]])})
+    result = df.groupby("id1").agg(lambda x: x.iloc[0])
+    tm.assert_frame_equal(result, expected, check_names=False)
+
+
+def test_groupby_agg_ea_method(monkeypatch):
+    # Ensure that the result of agg is inferred to be decimal dtype
+    # https://github.com/pandas-dev/pandas/issues/29141
+
+    def DecimalArray__my_sum(self):
+        return np.sum(np.array(self))
+
+    monkeypatch.setattr(DecimalArray, "my_sum", DecimalArray__my_sum, raising=False)
+
+    data = make_data()[:5]
+    df = pd.DataFrame({"id": [0, 0, 0, 1, 1], "decimals": DecimalArray(data)})
+    expected = pd.Series(to_decimal([data[0] + data[1] + data[2], data[3] + data[4]]))
+
+    result = df.groupby("id")["decimals"].agg(lambda x: x.values.my_sum())
+    tm.assert_series_equal(result, expected, check_names=False)
+    s = pd.Series(DecimalArray(data))
+    grouper = np.array([0, 0, 0, 1, 1], dtype=np.int64)
+    result = s.groupby(grouper).agg(lambda x: x.values.my_sum())
+    tm.assert_series_equal(result, expected, check_names=False)
+
+
+def test_indexing_no_materialize(monkeypatch):
+    # See https://github.com/pandas-dev/pandas/issues/29708
+    # Ensure that indexing operations do not materialize (convert to a numpy
+    # array) the ExtensionArray unnecessary
+
+    def DecimalArray__array__(self, dtype=None):
+        raise Exception("tried to convert a DecimalArray to a numpy array")
+
+    monkeypatch.setattr(DecimalArray, "__array__", DecimalArray__array__, raising=False)
+
+    data = make_data()
+    s = pd.Series(DecimalArray(data))
+    df = pd.DataFrame({"a": s, "b": range(len(s))})
+
+    # ensure the following operations do not raise an error
+    s[s > 0.5]
+    df[s > 0.5]
+    s.at[0]
+    df.at[0, "a"]
+
+
+def test_to_numpy_keyword():
+    # test the extra keyword
+    values = [decimal.Decimal("1.1111"), decimal.Decimal("2.2222")]
+    expected = np.array(
+        [decimal.Decimal("1.11"), decimal.Decimal("2.22")], dtype="object"
+    )
+    a = pd.array(values, dtype="decimal")
+    result = a.to_numpy(decimals=2)
+    tm.assert_numpy_array_equal(result, expected)
+
+    result = pd.Series(a).to_numpy(decimals=2)
+    tm.assert_numpy_array_equal(result, expected)
+
+
+def test_array_copy_on_write(using_copy_on_write):
+    df = pd.DataFrame({"a": [decimal.Decimal(2), decimal.Decimal(3)]}, dtype="object")
+    df2 = df.astype(DecimalDtype())
+    df.iloc[0, 0] = 0
+    if using_copy_on_write:
+        expected = pd.DataFrame(
+            {"a": [decimal.Decimal(2), decimal.Decimal(3)]}, dtype=DecimalDtype()
+        )
+        tm.assert_equal(df2.values, expected.values)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/json/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/json/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..7ebfd54a5b0d6bf1ff2c4602ed72f5214e32608f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/json/__init__.py
@@ -0,0 +1,7 @@
+from pandas.tests.extension.json.array import (
+    JSONArray,
+    JSONDtype,
+    make_data,
+)
+
+__all__ = ["JSONArray", "JSONDtype", "make_data"]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/json/array.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/json/array.py
new file mode 100644
index 0000000000000000000000000000000000000000..5ff99589a19611922b6fb82aa106a7bf829fc2ce
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/json/array.py
@@ -0,0 +1,273 @@
+"""
+Test extension array for storing nested data in a pandas container.
+
+The JSONArray stores lists of dictionaries. The storage mechanism is a list,
+not an ndarray.
+
+Note
+----
+We currently store lists of UserDicts. Pandas has a few places
+internally that specifically check for dicts, and does non-scalar things
+in that case. We *want* the dictionaries to be treated as scalars, so we
+hack around pandas by using UserDicts.
+"""
+from __future__ import annotations
+
+from collections import (
+    UserDict,
+    abc,
+)
+import itertools
+import numbers
+import string
+import sys
+from typing import (
+    TYPE_CHECKING,
+    Any,
+)
+import warnings
+
+import numpy as np
+
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.cast import construct_1d_object_array_from_listlike
+from pandas.core.dtypes.common import (
+    is_bool_dtype,
+    is_list_like,
+    pandas_dtype,
+)
+
+import pandas as pd
+from pandas.api.extensions import (
+    ExtensionArray,
+    ExtensionDtype,
+)
+from pandas.core.indexers import unpack_tuple_and_ellipses
+
+if TYPE_CHECKING:
+    from collections.abc import Mapping
+
+    from pandas._typing import type_t
+
+
+class JSONDtype(ExtensionDtype):
+    type = abc.Mapping
+    name = "json"
+    na_value: Mapping[str, Any] = UserDict()
+
+    @classmethod
+    def construct_array_type(cls) -> type_t[JSONArray]:
+        """
+        Return the array type associated with this dtype.
+
+        Returns
+        -------
+        type
+        """
+        return JSONArray
+
+
+class JSONArray(ExtensionArray):
+    dtype = JSONDtype()
+    __array_priority__ = 1000
+
+    def __init__(self, values, dtype=None, copy=False) -> None:
+        for val in values:
+            if not isinstance(val, self.dtype.type):
+                raise TypeError("All values must be of type " + str(self.dtype.type))
+        self.data = values
+
+        # Some aliases for common attribute names to ensure pandas supports
+        # these
+        self._items = self._data = self.data
+        # those aliases are currently not working due to assumptions
+        # in internal code (GH-20735)
+        # self._values = self.values = self.data
+
+    @classmethod
+    def _from_sequence(cls, scalars, *, dtype=None, copy=False):
+        return cls(scalars)
+
+    @classmethod
+    def _from_factorized(cls, values, original):
+        return cls([UserDict(x) for x in values if x != ()])
+
+    def __getitem__(self, item):
+        if isinstance(item, tuple):
+            item = unpack_tuple_and_ellipses(item)
+
+        if isinstance(item, numbers.Integral):
+            return self.data[item]
+        elif isinstance(item, slice) and item == slice(None):
+            # Make sure we get a view
+            return type(self)(self.data)
+        elif isinstance(item, slice):
+            # slice
+            return type(self)(self.data[item])
+        elif not is_list_like(item):
+            # e.g. "foo" or 2.5
+            # exception message copied from numpy
+            raise IndexError(
+                r"only integers, slices (`:`), ellipsis (`...`), numpy.newaxis "
+                r"(`None`) and integer or boolean arrays are valid indices"
+            )
+        else:
+            item = pd.api.indexers.check_array_indexer(self, item)
+            if is_bool_dtype(item.dtype):
+                return type(self)._from_sequence(
+                    [x for x, m in zip(self, item) if m], dtype=self.dtype
+                )
+            # integer
+            return type(self)([self.data[i] for i in item])
+
+    def __setitem__(self, key, value) -> None:
+        if isinstance(key, numbers.Integral):
+            self.data[key] = value
+        else:
+            if not isinstance(value, (type(self), abc.Sequence)):
+                # broadcast value
+                value = itertools.cycle([value])
+
+            if isinstance(key, np.ndarray) and key.dtype == "bool":
+                # masking
+                for i, (k, v) in enumerate(zip(key, value)):
+                    if k:
+                        assert isinstance(v, self.dtype.type)
+                        self.data[i] = v
+            else:
+                for k, v in zip(key, value):
+                    assert isinstance(v, self.dtype.type)
+                    self.data[k] = v
+
+    def __len__(self) -> int:
+        return len(self.data)
+
+    def __eq__(self, other):
+        return NotImplemented
+
+    def __ne__(self, other):
+        return NotImplemented
+
+    def __array__(self, dtype=None, copy=None):
+        if copy is False:
+            warnings.warn(
+                "Starting with NumPy 2.0, the behavior of the 'copy' keyword has "
+                "changed and passing 'copy=False' raises an error when returning "
+                "a zero-copy NumPy array is not possible. pandas will follow "
+                "this behavior starting with pandas 3.0.\nThis conversion to "
+                "NumPy requires a copy, but 'copy=False' was passed. Consider "
+                "using 'np.asarray(..)' instead.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+
+        if dtype is None:
+            dtype = object
+        if dtype == object:
+            # on py38 builds it looks like numpy is inferring to a non-1D array
+            return construct_1d_object_array_from_listlike(list(self))
+        if copy is None:
+            # Note: branch avoids `copy=None` for NumPy 1.x support
+            return np.asarray(self.data, dtype=dtype)
+        return np.asarray(self.data, dtype=dtype, copy=copy)
+
+    @property
+    def nbytes(self) -> int:
+        return sys.getsizeof(self.data)
+
+    def isna(self):
+        return np.array([x == self.dtype.na_value for x in self.data], dtype=bool)
+
+    def take(self, indexer, allow_fill=False, fill_value=None):
+        # re-implement here, since NumPy has trouble setting
+        # sized objects like UserDicts into scalar slots of
+        # an ndarary.
+        indexer = np.asarray(indexer)
+        msg = (
+            "Index is out of bounds or cannot do a "
+            "non-empty take from an empty array."
+        )
+
+        if allow_fill:
+            if fill_value is None:
+                fill_value = self.dtype.na_value
+            # bounds check
+            if (indexer < -1).any():
+                raise ValueError
+            try:
+                output = [
+                    self.data[loc] if loc != -1 else fill_value for loc in indexer
+                ]
+            except IndexError as err:
+                raise IndexError(msg) from err
+        else:
+            try:
+                output = [self.data[loc] for loc in indexer]
+            except IndexError as err:
+                raise IndexError(msg) from err
+
+        return type(self)._from_sequence(output, dtype=self.dtype)
+
+    def copy(self):
+        return type(self)(self.data[:])
+
+    def astype(self, dtype, copy=True):
+        # NumPy has issues when all the dicts are the same length.
+        # np.array([UserDict(...), UserDict(...)]) fails,
+        # but np.array([{...}, {...}]) works, so cast.
+        from pandas.core.arrays.string_ import StringDtype
+
+        dtype = pandas_dtype(dtype)
+        # needed to add this check for the Series constructor
+        if isinstance(dtype, type(self.dtype)) and dtype == self.dtype:
+            if copy:
+                return self.copy()
+            return self
+        elif isinstance(dtype, StringDtype):
+            arr_cls = dtype.construct_array_type()
+            return arr_cls._from_sequence(self, dtype=dtype, copy=False)
+        elif not copy:
+            return np.asarray([dict(x) for x in self], dtype=dtype)
+        else:
+            return np.array([dict(x) for x in self], dtype=dtype, copy=copy)
+
+    def unique(self):
+        # Parent method doesn't work since np.array will try to infer
+        # a 2-dim object.
+        return type(self)([dict(x) for x in {tuple(d.items()) for d in self.data}])
+
+    @classmethod
+    def _concat_same_type(cls, to_concat):
+        data = list(itertools.chain.from_iterable(x.data for x in to_concat))
+        return cls(data)
+
+    def _values_for_factorize(self):
+        frozen = self._values_for_argsort()
+        if len(frozen) == 0:
+            # factorize_array expects 1-d array, this is a len-0 2-d array.
+            frozen = frozen.ravel()
+        return frozen, ()
+
+    def _values_for_argsort(self):
+        # Bypass NumPy's shape inference to get a (N,) array of tuples.
+        frozen = [tuple(x.items()) for x in self]
+        return construct_1d_object_array_from_listlike(frozen)
+
+    def _pad_or_backfill(self, *, method, limit=None, copy=True):
+        # GH#56616 - test EA method without limit_area argument
+        return super()._pad_or_backfill(method=method, limit=limit, copy=copy)
+
+
+def make_data():
+    # TODO: Use a regular dict. See _NDFrameIndexer._setitem_with_indexer
+    rng = np.random.default_rng(2)
+    return [
+        UserDict(
+            [
+                (rng.choice(list(string.ascii_letters)), rng.integers(0, 100))
+                for _ in range(rng.integers(0, 10))
+            ]
+        )
+        for _ in range(100)
+    ]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/json/test_json.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/json/test_json.py
new file mode 100644
index 0000000000000000000000000000000000000000..a18edac9aef93804bd02698dd0b44d5b31f6b887
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/json/test_json.py
@@ -0,0 +1,490 @@
+import collections
+import operator
+import sys
+
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.tests.extension import base
+from pandas.tests.extension.json.array import (
+    JSONArray,
+    JSONDtype,
+    make_data,
+)
+
+# We intentionally don't run base.BaseSetitemTests because pandas'
+# internals has trouble setting sequences of values into scalar positions.
+unhashable = pytest.mark.xfail(reason="Unhashable")
+
+
+@pytest.fixture
+def dtype():
+    return JSONDtype()
+
+
+@pytest.fixture
+def data():
+    """Length-100 PeriodArray for semantics test."""
+    data = make_data()
+
+    # Why the while loop? NumPy is unable to construct an ndarray from
+    # equal-length ndarrays. Many of our operations involve coercing the
+    # EA to an ndarray of objects. To avoid random test failures, we ensure
+    # that our data is coercible to an ndarray. Several tests deal with only
+    # the first two elements, so that's what we'll check.
+
+    while len(data[0]) == len(data[1]):
+        data = make_data()
+
+    return JSONArray(data)
+
+
+@pytest.fixture
+def data_missing():
+    """Length 2 array with [NA, Valid]"""
+    return JSONArray([{}, {"a": 10}])
+
+
+@pytest.fixture
+def data_for_sorting():
+    return JSONArray([{"b": 1}, {"c": 4}, {"a": 2, "c": 3}])
+
+
+@pytest.fixture
+def data_missing_for_sorting():
+    return JSONArray([{"b": 1}, {}, {"a": 4}])
+
+
+@pytest.fixture
+def na_cmp():
+    return operator.eq
+
+
+@pytest.fixture
+def data_for_grouping():
+    return JSONArray(
+        [
+            {"b": 1},
+            {"b": 1},
+            {},
+            {},
+            {"a": 0, "c": 2},
+            {"a": 0, "c": 2},
+            {"b": 1},
+            {"c": 2},
+        ]
+    )
+
+
+class TestJSONArray(base.ExtensionTests):
+    @pytest.mark.xfail(
+        reason="comparison method not implemented for JSONArray (GH-37867)"
+    )
+    def test_contains(self, data):
+        # GH-37867
+        super().test_contains(data)
+
+    @pytest.mark.xfail(reason="not implemented constructor from dtype")
+    def test_from_dtype(self, data):
+        # construct from our dtype & string dtype
+        super().test_from_dtype(data)
+
+    @pytest.mark.xfail(reason="RecursionError, GH-33900")
+    def test_series_constructor_no_data_with_index(self, dtype, na_value):
+        # RecursionError: maximum recursion depth exceeded in comparison
+        rec_limit = sys.getrecursionlimit()
+        try:
+            # Limit to avoid stack overflow on Windows CI
+            sys.setrecursionlimit(100)
+            super().test_series_constructor_no_data_with_index(dtype, na_value)
+        finally:
+            sys.setrecursionlimit(rec_limit)
+
+    @pytest.mark.xfail(reason="RecursionError, GH-33900")
+    def test_series_constructor_scalar_na_with_index(self, dtype, na_value):
+        # RecursionError: maximum recursion depth exceeded in comparison
+        rec_limit = sys.getrecursionlimit()
+        try:
+            # Limit to avoid stack overflow on Windows CI
+            sys.setrecursionlimit(100)
+            super().test_series_constructor_scalar_na_with_index(dtype, na_value)
+        finally:
+            sys.setrecursionlimit(rec_limit)
+
+    @pytest.mark.xfail(reason="collection as scalar, GH-33901")
+    def test_series_constructor_scalar_with_index(self, data, dtype):
+        # TypeError: All values must be of type 
+        rec_limit = sys.getrecursionlimit()
+        try:
+            # Limit to avoid stack overflow on Windows CI
+            sys.setrecursionlimit(100)
+            super().test_series_constructor_scalar_with_index(data, dtype)
+        finally:
+            sys.setrecursionlimit(rec_limit)
+
+    @pytest.mark.xfail(reason="Different definitions of NA")
+    def test_stack(self):
+        """
+        The test does .astype(object).stack(future_stack=True). If we happen to have
+        any missing values in `data`, then we'll end up with different
+        rows since we consider `{}` NA, but `.astype(object)` doesn't.
+        """
+        super().test_stack()
+
+    @pytest.mark.xfail(reason="dict for NA")
+    def test_unstack(self, data, index):
+        # The base test has NaN for the expected NA value.
+        # this matches otherwise
+        return super().test_unstack(data, index)
+
+    @pytest.mark.xfail(reason="Setting a dict as a scalar")
+    def test_fillna_series(self):
+        """We treat dictionaries as a mapping in fillna, not a scalar."""
+        super().test_fillna_series()
+
+    @pytest.mark.xfail(reason="Setting a dict as a scalar")
+    def test_fillna_frame(self):
+        """We treat dictionaries as a mapping in fillna, not a scalar."""
+        super().test_fillna_frame()
+
+    @pytest.mark.parametrize(
+        "limit_area, input_ilocs, expected_ilocs",
+        [
+            ("outside", [1, 0, 0, 0, 1], [1, 0, 0, 0, 1]),
+            ("outside", [1, 0, 1, 0, 1], [1, 0, 1, 0, 1]),
+            ("outside", [0, 1, 1, 1, 0], [0, 1, 1, 1, 1]),
+            ("outside", [0, 1, 0, 1, 0], [0, 1, 0, 1, 1]),
+            ("inside", [1, 0, 0, 0, 1], [1, 1, 1, 1, 1]),
+            ("inside", [1, 0, 1, 0, 1], [1, 1, 1, 1, 1]),
+            ("inside", [0, 1, 1, 1, 0], [0, 1, 1, 1, 0]),
+            ("inside", [0, 1, 0, 1, 0], [0, 1, 1, 1, 0]),
+        ],
+    )
+    def test_ffill_limit_area(
+        self, data_missing, limit_area, input_ilocs, expected_ilocs
+    ):
+        # GH#56616
+        msg = "JSONArray does not implement limit_area"
+        with pytest.raises(NotImplementedError, match=msg):
+            super().test_ffill_limit_area(
+                data_missing, limit_area, input_ilocs, expected_ilocs
+            )
+
+    @unhashable
+    def test_value_counts(self, all_data, dropna):
+        super().test_value_counts(all_data, dropna)
+
+    @unhashable
+    def test_value_counts_with_normalize(self, data):
+        super().test_value_counts_with_normalize(data)
+
+    @unhashable
+    def test_sort_values_frame(self):
+        # TODO (EA.factorize): see if _values_for_factorize allows this.
+        super().test_sort_values_frame()
+
+    @pytest.mark.parametrize("ascending", [True, False])
+    def test_sort_values(self, data_for_sorting, ascending, sort_by_key):
+        super().test_sort_values(data_for_sorting, ascending, sort_by_key)
+
+    @pytest.mark.parametrize("ascending", [True, False])
+    def test_sort_values_missing(
+        self, data_missing_for_sorting, ascending, sort_by_key
+    ):
+        super().test_sort_values_missing(
+            data_missing_for_sorting, ascending, sort_by_key
+        )
+
+    @pytest.mark.xfail(reason="combine for JSONArray not supported")
+    def test_combine_le(self, data_repeated):
+        super().test_combine_le(data_repeated)
+
+    @pytest.mark.xfail(
+        reason="combine for JSONArray not supported - "
+        "may pass depending on random data",
+        strict=False,
+        raises=AssertionError,
+    )
+    def test_combine_first(self, data):
+        super().test_combine_first(data)
+
+    @pytest.mark.xfail(reason="broadcasting error")
+    def test_where_series(self, data, na_value):
+        # Fails with
+        # *** ValueError: operands could not be broadcast together
+        # with shapes (4,) (4,) (0,)
+        super().test_where_series(data, na_value)
+
+    @pytest.mark.xfail(reason="Can't compare dicts.")
+    def test_searchsorted(self, data_for_sorting):
+        super().test_searchsorted(data_for_sorting)
+
+    @pytest.mark.xfail(reason="Can't compare dicts.")
+    def test_equals(self, data, na_value, as_series):
+        super().test_equals(data, na_value, as_series)
+
+    @pytest.mark.skip("fill-value is interpreted as a dict of values")
+    def test_fillna_copy_frame(self, data_missing):
+        super().test_fillna_copy_frame(data_missing)
+
+    def test_equals_same_data_different_object(
+        self, data, using_copy_on_write, request
+    ):
+        if using_copy_on_write:
+            mark = pytest.mark.xfail(reason="Fails with CoW")
+            request.applymarker(mark)
+        super().test_equals_same_data_different_object(data)
+
+    @pytest.mark.xfail(reason="failing on np.array(self, dtype=str)")
+    def test_astype_str(self):
+        """This currently fails in NumPy on np.array(self, dtype=str) with
+
+        *** ValueError: setting an array element with a sequence
+        """
+        super().test_astype_str()
+
+    @unhashable
+    def test_groupby_extension_transform(self):
+        """
+        This currently fails in Series.name.setter, since the
+        name must be hashable, but the value is a dictionary.
+        I think this is what we want, i.e. `.name` should be the original
+        values, and not the values for factorization.
+        """
+        super().test_groupby_extension_transform()
+
+    @unhashable
+    def test_groupby_extension_apply(self):
+        """
+        This fails in Index._do_unique_check with
+
+        >   hash(val)
+        E   TypeError: unhashable type: 'UserDict' with
+
+        I suspect that once we support Index[ExtensionArray],
+        we'll be able to dispatch unique.
+        """
+        super().test_groupby_extension_apply()
+
+    @unhashable
+    def test_groupby_extension_agg(self):
+        """
+        This fails when we get to tm.assert_series_equal when left.index
+        contains dictionaries, which are not hashable.
+        """
+        super().test_groupby_extension_agg()
+
+    @unhashable
+    def test_groupby_extension_no_sort(self):
+        """
+        This fails when we get to tm.assert_series_equal when left.index
+        contains dictionaries, which are not hashable.
+        """
+        super().test_groupby_extension_no_sort()
+
+    def test_arith_frame_with_scalar(self, data, all_arithmetic_operators, request):
+        if len(data[0]) != 1:
+            mark = pytest.mark.xfail(reason="raises in coercing to Series")
+            request.applymarker(mark)
+        super().test_arith_frame_with_scalar(data, all_arithmetic_operators)
+
+    def test_compare_array(self, data, comparison_op, request):
+        if comparison_op.__name__ in ["eq", "ne"]:
+            mark = pytest.mark.xfail(reason="Comparison methods not implemented")
+            request.applymarker(mark)
+        super().test_compare_array(data, comparison_op)
+
+    @pytest.mark.xfail(reason="ValueError: Must have equal len keys and value")
+    def test_setitem_loc_scalar_mixed(self, data):
+        super().test_setitem_loc_scalar_mixed(data)
+
+    @pytest.mark.xfail(reason="ValueError: Must have equal len keys and value")
+    def test_setitem_loc_scalar_multiple_homogoneous(self, data):
+        super().test_setitem_loc_scalar_multiple_homogoneous(data)
+
+    @pytest.mark.xfail(reason="ValueError: Must have equal len keys and value")
+    def test_setitem_iloc_scalar_mixed(self, data):
+        super().test_setitem_iloc_scalar_mixed(data)
+
+    @pytest.mark.xfail(reason="ValueError: Must have equal len keys and value")
+    def test_setitem_iloc_scalar_multiple_homogoneous(self, data):
+        super().test_setitem_iloc_scalar_multiple_homogoneous(data)
+
+    @pytest.mark.parametrize(
+        "mask",
+        [
+            np.array([True, True, True, False, False]),
+            pd.array([True, True, True, False, False], dtype="boolean"),
+            pd.array([True, True, True, pd.NA, pd.NA], dtype="boolean"),
+        ],
+        ids=["numpy-array", "boolean-array", "boolean-array-na"],
+    )
+    def test_setitem_mask(self, data, mask, box_in_series, request):
+        if box_in_series:
+            mark = pytest.mark.xfail(
+                reason="cannot set using a list-like indexer with a different length"
+            )
+            request.applymarker(mark)
+        elif not isinstance(mask, np.ndarray):
+            mark = pytest.mark.xfail(reason="Issues unwanted DeprecationWarning")
+            request.applymarker(mark)
+        super().test_setitem_mask(data, mask, box_in_series)
+
+    def test_setitem_mask_raises(self, data, box_in_series, request):
+        if not box_in_series:
+            mark = pytest.mark.xfail(reason="Fails to raise")
+            request.applymarker(mark)
+
+        super().test_setitem_mask_raises(data, box_in_series)
+
+    @pytest.mark.xfail(
+        reason="cannot set using a list-like indexer with a different length"
+    )
+    def test_setitem_mask_boolean_array_with_na(self, data, box_in_series):
+        super().test_setitem_mask_boolean_array_with_na(data, box_in_series)
+
+    @pytest.mark.parametrize(
+        "idx",
+        [[0, 1, 2], pd.array([0, 1, 2], dtype="Int64"), np.array([0, 1, 2])],
+        ids=["list", "integer-array", "numpy-array"],
+    )
+    def test_setitem_integer_array(self, data, idx, box_in_series, request):
+        if box_in_series:
+            mark = pytest.mark.xfail(
+                reason="cannot set using a list-like indexer with a different length"
+            )
+            request.applymarker(mark)
+        super().test_setitem_integer_array(data, idx, box_in_series)
+
+    @pytest.mark.xfail(reason="list indices must be integers or slices, not NAType")
+    @pytest.mark.parametrize(
+        "idx, box_in_series",
+        [
+            ([0, 1, 2, pd.NA], False),
+            pytest.param(
+                [0, 1, 2, pd.NA], True, marks=pytest.mark.xfail(reason="GH-31948")
+            ),
+            (pd.array([0, 1, 2, pd.NA], dtype="Int64"), False),
+            (pd.array([0, 1, 2, pd.NA], dtype="Int64"), False),
+        ],
+        ids=["list-False", "list-True", "integer-array-False", "integer-array-True"],
+    )
+    def test_setitem_integer_with_missing_raises(self, data, idx, box_in_series):
+        super().test_setitem_integer_with_missing_raises(data, idx, box_in_series)
+
+    @pytest.mark.xfail(reason="Fails to raise")
+    def test_setitem_scalar_key_sequence_raise(self, data):
+        super().test_setitem_scalar_key_sequence_raise(data)
+
+    def test_setitem_with_expansion_dataframe_column(self, data, full_indexer, request):
+        if "full_slice" in request.node.name:
+            mark = pytest.mark.xfail(reason="slice is not iterable")
+            request.applymarker(mark)
+        super().test_setitem_with_expansion_dataframe_column(data, full_indexer)
+
+    @pytest.mark.xfail(reason="slice is not iterable")
+    def test_setitem_frame_2d_values(self, data):
+        super().test_setitem_frame_2d_values(data)
+
+    @pytest.mark.xfail(
+        reason="cannot set using a list-like indexer with a different length"
+    )
+    @pytest.mark.parametrize("setter", ["loc", None])
+    def test_setitem_mask_broadcast(self, data, setter):
+        super().test_setitem_mask_broadcast(data, setter)
+
+    @pytest.mark.xfail(
+        reason="cannot set using a slice indexer with a different length"
+    )
+    def test_setitem_slice(self, data, box_in_series):
+        super().test_setitem_slice(data, box_in_series)
+
+    @pytest.mark.xfail(reason="slice object is not iterable")
+    def test_setitem_loc_iloc_slice(self, data):
+        super().test_setitem_loc_iloc_slice(data)
+
+    @pytest.mark.xfail(reason="slice object is not iterable")
+    def test_setitem_slice_mismatch_length_raises(self, data):
+        super().test_setitem_slice_mismatch_length_raises(data)
+
+    @pytest.mark.xfail(reason="slice object is not iterable")
+    def test_setitem_slice_array(self, data):
+        super().test_setitem_slice_array(data)
+
+    @pytest.mark.xfail(reason="Fail to raise")
+    def test_setitem_invalid(self, data, invalid_scalar):
+        super().test_setitem_invalid(data, invalid_scalar)
+
+    @pytest.mark.xfail(reason="only integer scalar arrays can be converted")
+    def test_setitem_2d_values(self, data):
+        super().test_setitem_2d_values(data)
+
+    @pytest.mark.xfail(reason="data type 'json' not understood")
+    @pytest.mark.parametrize("engine", ["c", "python"])
+    def test_EA_types(self, engine, data, request):
+        super().test_EA_types(engine, data, request)
+
+
+def custom_assert_series_equal(left, right, *args, **kwargs):
+    # NumPy doesn't handle an array of equal-length UserDicts.
+    # The default assert_series_equal eventually does a
+    # Series.values, which raises. We work around it by
+    # converting the UserDicts to dicts.
+    if left.dtype.name == "json":
+        assert left.dtype == right.dtype
+        left = pd.Series(
+            JSONArray(left.values.astype(object)), index=left.index, name=left.name
+        )
+        right = pd.Series(
+            JSONArray(right.values.astype(object)),
+            index=right.index,
+            name=right.name,
+        )
+    tm.assert_series_equal(left, right, *args, **kwargs)
+
+
+def custom_assert_frame_equal(left, right, *args, **kwargs):
+    obj_type = kwargs.get("obj", "DataFrame")
+    tm.assert_index_equal(
+        left.columns,
+        right.columns,
+        exact=kwargs.get("check_column_type", "equiv"),
+        check_names=kwargs.get("check_names", True),
+        check_exact=kwargs.get("check_exact", False),
+        check_categorical=kwargs.get("check_categorical", True),
+        obj=f"{obj_type}.columns",
+    )
+
+    jsons = (left.dtypes == "json").index
+
+    for col in jsons:
+        custom_assert_series_equal(left[col], right[col], *args, **kwargs)
+
+    left = left.drop(columns=jsons)
+    right = right.drop(columns=jsons)
+    tm.assert_frame_equal(left, right, *args, **kwargs)
+
+
+def test_custom_asserts():
+    # This would always trigger the KeyError from trying to put
+    # an array of equal-length UserDicts inside an ndarray.
+    data = JSONArray(
+        [
+            collections.UserDict({"a": 1}),
+            collections.UserDict({"b": 2}),
+            collections.UserDict({"c": 3}),
+        ]
+    )
+    a = pd.Series(data)
+    custom_assert_series_equal(a, a)
+    custom_assert_frame_equal(a.to_frame(), a.to_frame())
+
+    b = pd.Series(data.take([0, 0, 1]))
+    msg = r"Series are different"
+    with pytest.raises(AssertionError, match=msg):
+        custom_assert_series_equal(a, b)
+
+    with pytest.raises(AssertionError, match=msg):
+        custom_assert_frame_equal(a.to_frame(), b.to_frame())
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/list/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/list/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..0f3f2f35377882a0fae603edfc8edb46371429fe
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/list/__init__.py
@@ -0,0 +1,7 @@
+from pandas.tests.extension.list.array import (
+    ListArray,
+    ListDtype,
+    make_data,
+)
+
+__all__ = ["ListArray", "ListDtype", "make_data"]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/list/array.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/list/array.py
new file mode 100644
index 0000000000000000000000000000000000000000..b3bb35c9396f4d1748fff37b7334c68a0b055daf
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/list/array.py
@@ -0,0 +1,137 @@
+"""
+Test extension array for storing nested data in a pandas container.
+
+The ListArray stores an ndarray of lists.
+"""
+from __future__ import annotations
+
+import numbers
+import string
+from typing import TYPE_CHECKING
+
+import numpy as np
+
+from pandas.core.dtypes.base import ExtensionDtype
+
+import pandas as pd
+from pandas.api.types import (
+    is_object_dtype,
+    is_string_dtype,
+)
+from pandas.core.arrays import ExtensionArray
+
+if TYPE_CHECKING:
+    from pandas._typing import type_t
+
+
+class ListDtype(ExtensionDtype):
+    type = list
+    name = "list"
+    na_value = np.nan
+
+    @classmethod
+    def construct_array_type(cls) -> type_t[ListArray]:
+        """
+        Return the array type associated with this dtype.
+
+        Returns
+        -------
+        type
+        """
+        return ListArray
+
+
+class ListArray(ExtensionArray):
+    dtype = ListDtype()
+    __array_priority__ = 1000
+
+    def __init__(self, values, dtype=None, copy=False) -> None:
+        if not isinstance(values, np.ndarray):
+            raise TypeError("Need to pass a numpy array as values")
+        for val in values:
+            if not isinstance(val, self.dtype.type) and not pd.isna(val):
+                raise TypeError("All values must be of type " + str(self.dtype.type))
+        self.data = values
+
+    @classmethod
+    def _from_sequence(cls, scalars, *, dtype=None, copy=False):
+        data = np.empty(len(scalars), dtype=object)
+        data[:] = scalars
+        return cls(data)
+
+    def __getitem__(self, item):
+        if isinstance(item, numbers.Integral):
+            return self.data[item]
+        else:
+            # slice, list-like, mask
+            return type(self)(self.data[item])
+
+    def __len__(self) -> int:
+        return len(self.data)
+
+    def isna(self):
+        return np.array(
+            [not isinstance(x, list) and np.isnan(x) for x in self.data], dtype=bool
+        )
+
+    def take(self, indexer, allow_fill=False, fill_value=None):
+        # re-implement here, since NumPy has trouble setting
+        # sized objects like UserDicts into scalar slots of
+        # an ndarary.
+        indexer = np.asarray(indexer)
+        msg = (
+            "Index is out of bounds or cannot do a "
+            "non-empty take from an empty array."
+        )
+
+        if allow_fill:
+            if fill_value is None:
+                fill_value = self.dtype.na_value
+            # bounds check
+            if (indexer < -1).any():
+                raise ValueError
+            try:
+                output = [
+                    self.data[loc] if loc != -1 else fill_value for loc in indexer
+                ]
+            except IndexError as err:
+                raise IndexError(msg) from err
+        else:
+            try:
+                output = [self.data[loc] for loc in indexer]
+            except IndexError as err:
+                raise IndexError(msg) from err
+
+        return self._from_sequence(output)
+
+    def copy(self):
+        return type(self)(self.data[:])
+
+    def astype(self, dtype, copy=True):
+        if isinstance(dtype, type(self.dtype)) and dtype == self.dtype:
+            if copy:
+                return self.copy()
+            return self
+        elif is_string_dtype(dtype) and not is_object_dtype(dtype):
+            # numpy has problems with astype(str) for nested elements
+            return np.array([str(x) for x in self.data], dtype=dtype)
+        elif not copy:
+            return np.asarray(self.data, dtype=dtype)
+        else:
+            return np.array(self.data, dtype=dtype, copy=copy)
+
+    @classmethod
+    def _concat_same_type(cls, to_concat):
+        data = np.concatenate([x.data for x in to_concat])
+        return cls(data)
+
+
+def make_data():
+    # TODO: Use a regular dict. See _NDFrameIndexer._setitem_with_indexer
+    rng = np.random.default_rng(2)
+    data = np.empty(100, dtype=object)
+    data[:] = [
+        [rng.choice(list(string.ascii_letters)) for _ in range(rng.integers(0, 10))]
+        for _ in range(100)
+    ]
+    return data
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/list/test_list.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/list/test_list.py
new file mode 100644
index 0000000000000000000000000000000000000000..ac396cd3c60d435d34f95d5027d80d116d4560d5
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/list/test_list.py
@@ -0,0 +1,33 @@
+import pytest
+
+import pandas as pd
+from pandas.tests.extension.list.array import (
+    ListArray,
+    ListDtype,
+    make_data,
+)
+
+
+@pytest.fixture
+def dtype():
+    return ListDtype()
+
+
+@pytest.fixture
+def data():
+    """Length-100 ListArray for semantics test."""
+    data = make_data()
+
+    while len(data[0]) == len(data[1]):
+        data = make_data()
+
+    return ListArray(data)
+
+
+def test_to_csv(data):
+    # https://github.com/pandas-dev/pandas/issues/28840
+    # array with list-likes fail when doing astype(str) on the numpy array
+    # which was done in get_values_for_csv
+    df = pd.DataFrame({"a": data})
+    res = df.to_csv()
+    assert str(data[0]) in res
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/test_arrow.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/test_arrow.py
new file mode 100644
index 0000000000000000000000000000000000000000..479497497a2ea3cfb1434da98469ceeeaa3565d6
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/test_arrow.py
@@ -0,0 +1,3425 @@
+"""
+This file contains a minimal set of tests for compliance with the extension
+array interface test suite, and should contain no other tests.
+The test suite for the full functionality of the array is located in
+`pandas/tests/arrays/`.
+The tests in this file are inherited from the BaseExtensionTests, and only
+minimal tweaks should be applied to get the tests passing (by overwriting a
+parent method).
+Additional tests should either be added to one of the BaseExtensionTests
+classes (if they are relevant for the extension interface for all dtypes), or
+be added to the array-specific tests in `pandas/tests/arrays/`.
+"""
+from __future__ import annotations
+
+from datetime import (
+    date,
+    datetime,
+    time,
+    timedelta,
+)
+from decimal import Decimal
+from io import (
+    BytesIO,
+    StringIO,
+)
+import operator
+import pickle
+import re
+
+import numpy as np
+import pytest
+
+from pandas._libs import lib
+from pandas._libs.tslibs import timezones
+from pandas.compat import (
+    PY311,
+    PY312,
+    is_ci_environment,
+    is_platform_windows,
+    pa_version_under11p0,
+    pa_version_under13p0,
+    pa_version_under14p0,
+    pa_version_under20p0,
+    pa_version_under21p0,
+)
+
+from pandas.core.dtypes.dtypes import (
+    ArrowDtype,
+    CategoricalDtypeType,
+)
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.api.extensions import no_default
+from pandas.api.types import (
+    is_bool_dtype,
+    is_float_dtype,
+    is_integer_dtype,
+    is_numeric_dtype,
+    is_signed_integer_dtype,
+    is_string_dtype,
+    is_unsigned_integer_dtype,
+)
+from pandas.tests.extension import base
+
+pa = pytest.importorskip("pyarrow")
+
+from pandas.core.arrays.arrow.array import ArrowExtensionArray
+from pandas.core.arrays.arrow.extension_types import ArrowPeriodType
+
+
+def _require_timezone_database(request):
+    if is_platform_windows() and is_ci_environment():
+        mark = pytest.mark.xfail(
+            raises=pa.ArrowInvalid,
+            reason=(
+                "TODO: Set ARROW_TIMEZONE_DATABASE environment variable "
+                "on CI to path to the tzdata for pyarrow."
+            ),
+        )
+        request.applymarker(mark)
+
+
+@pytest.fixture(params=tm.ALL_PYARROW_DTYPES, ids=str)
+def dtype(request):
+    return ArrowDtype(pyarrow_dtype=request.param)
+
+
+@pytest.fixture
+def data(dtype):
+    pa_dtype = dtype.pyarrow_dtype
+    if pa.types.is_boolean(pa_dtype):
+        data = [True, False] * 4 + [None] + [True, False] * 44 + [None] + [True, False]
+    elif pa.types.is_floating(pa_dtype):
+        data = [1.0, 0.0] * 4 + [None] + [-2.0, -1.0] * 44 + [None] + [0.5, 99.5]
+    elif pa.types.is_signed_integer(pa_dtype):
+        data = [1, 0] * 4 + [None] + [-2, -1] * 44 + [None] + [1, 99]
+    elif pa.types.is_unsigned_integer(pa_dtype):
+        data = [1, 0] * 4 + [None] + [2, 1] * 44 + [None] + [1, 99]
+    elif pa.types.is_decimal(pa_dtype):
+        data = (
+            [Decimal("1"), Decimal("0.0")] * 4
+            + [None]
+            + [Decimal("-2.0"), Decimal("-1.0")] * 44
+            + [None]
+            + [Decimal("0.5"), Decimal("33.123")]
+        )
+    elif pa.types.is_date(pa_dtype):
+        data = (
+            [date(2022, 1, 1), date(1999, 12, 31)] * 4
+            + [None]
+            + [date(2022, 1, 1), date(2022, 1, 1)] * 44
+            + [None]
+            + [date(1999, 12, 31), date(1999, 12, 31)]
+        )
+    elif pa.types.is_timestamp(pa_dtype):
+        data = (
+            [datetime(2020, 1, 1, 1, 1, 1, 1), datetime(1999, 1, 1, 1, 1, 1, 1)] * 4
+            + [None]
+            + [datetime(2020, 1, 1, 1), datetime(1999, 1, 1, 1)] * 44
+            + [None]
+            + [datetime(2020, 1, 1), datetime(1999, 1, 1)]
+        )
+    elif pa.types.is_duration(pa_dtype):
+        data = (
+            [timedelta(1), timedelta(1, 1)] * 4
+            + [None]
+            + [timedelta(-1), timedelta(0)] * 44
+            + [None]
+            + [timedelta(-10), timedelta(10)]
+        )
+    elif pa.types.is_time(pa_dtype):
+        data = (
+            [time(12, 0), time(0, 12)] * 4
+            + [None]
+            + [time(0, 0), time(1, 1)] * 44
+            + [None]
+            + [time(0, 5), time(5, 0)]
+        )
+    elif pa.types.is_string(pa_dtype):
+        data = ["a", "b"] * 4 + [None] + ["1", "2"] * 44 + [None] + ["!", ">"]
+    elif pa.types.is_binary(pa_dtype):
+        data = [b"a", b"b"] * 4 + [None] + [b"1", b"2"] * 44 + [None] + [b"!", b">"]
+    else:
+        raise NotImplementedError
+    return pd.array(data, dtype=dtype)
+
+
+@pytest.fixture
+def data_missing(data):
+    """Length-2 array with [NA, Valid]"""
+    return type(data)._from_sequence([None, data[0]], dtype=data.dtype)
+
+
+@pytest.fixture(params=["data", "data_missing"])
+def all_data(request, data, data_missing):
+    """Parametrized fixture returning 'data' or 'data_missing' integer arrays.
+
+    Used to test dtype conversion with and without missing values.
+    """
+    if request.param == "data":
+        return data
+    elif request.param == "data_missing":
+        return data_missing
+
+
+@pytest.fixture
+def data_for_grouping(dtype):
+    """
+    Data for factorization, grouping, and unique tests.
+
+    Expected to be like [B, B, NA, NA, A, A, B, C]
+
+    Where A < B < C and NA is missing
+    """
+    pa_dtype = dtype.pyarrow_dtype
+    if pa.types.is_boolean(pa_dtype):
+        A = False
+        B = True
+        C = True
+    elif pa.types.is_floating(pa_dtype):
+        A = -1.1
+        B = 0.0
+        C = 1.1
+    elif pa.types.is_signed_integer(pa_dtype):
+        A = -1
+        B = 0
+        C = 1
+    elif pa.types.is_unsigned_integer(pa_dtype):
+        A = 0
+        B = 1
+        C = 10
+    elif pa.types.is_date(pa_dtype):
+        A = date(1999, 12, 31)
+        B = date(2010, 1, 1)
+        C = date(2022, 1, 1)
+    elif pa.types.is_timestamp(pa_dtype):
+        A = datetime(1999, 1, 1, 1, 1, 1, 1)
+        B = datetime(2020, 1, 1)
+        C = datetime(2020, 1, 1, 1)
+    elif pa.types.is_duration(pa_dtype):
+        A = timedelta(-1)
+        B = timedelta(0)
+        C = timedelta(1, 4)
+    elif pa.types.is_time(pa_dtype):
+        A = time(0, 0)
+        B = time(0, 12)
+        C = time(12, 12)
+    elif pa.types.is_string(pa_dtype):
+        A = "a"
+        B = "b"
+        C = "c"
+    elif pa.types.is_binary(pa_dtype):
+        A = b"a"
+        B = b"b"
+        C = b"c"
+    elif pa.types.is_decimal(pa_dtype):
+        A = Decimal("-1.1")
+        B = Decimal("0.0")
+        C = Decimal("1.1")
+    else:
+        raise NotImplementedError
+    return pd.array([B, B, None, None, A, A, B, C], dtype=dtype)
+
+
+@pytest.fixture
+def data_for_sorting(data_for_grouping):
+    """
+    Length-3 array with a known sort order.
+
+    This should be three items [B, C, A] with
+    A < B < C
+    """
+    return type(data_for_grouping)._from_sequence(
+        [data_for_grouping[0], data_for_grouping[7], data_for_grouping[4]],
+        dtype=data_for_grouping.dtype,
+    )
+
+
+@pytest.fixture
+def data_missing_for_sorting(data_for_grouping):
+    """
+    Length-3 array with a known sort order.
+
+    This should be three items [B, NA, A] with
+    A < B and NA missing.
+    """
+    return type(data_for_grouping)._from_sequence(
+        [data_for_grouping[0], data_for_grouping[2], data_for_grouping[4]],
+        dtype=data_for_grouping.dtype,
+    )
+
+
+@pytest.fixture
+def data_for_twos(data):
+    """Length-100 array in which all the elements are two."""
+    pa_dtype = data.dtype.pyarrow_dtype
+    if (
+        pa.types.is_integer(pa_dtype)
+        or pa.types.is_floating(pa_dtype)
+        or pa.types.is_decimal(pa_dtype)
+        or pa.types.is_duration(pa_dtype)
+    ):
+        return pd.array([2] * 100, dtype=data.dtype)
+    # tests will be xfailed where 2 is not a valid scalar for pa_dtype
+    return data
+    # TODO: skip otherwise?
+
+
+class TestArrowArray(base.ExtensionTests):
+    def test_compare_scalar(self, data, comparison_op):
+        ser = pd.Series(data)
+        self._compare_other(ser, data, comparison_op, data[0])
+
+    @pytest.mark.parametrize("na_action", [None, "ignore"])
+    def test_map(self, data_missing, na_action):
+        if data_missing.dtype.kind in "mM":
+            result = data_missing.map(lambda x: x, na_action=na_action)
+            expected = data_missing.to_numpy(dtype=object)
+            tm.assert_numpy_array_equal(result, expected)
+        else:
+            result = data_missing.map(lambda x: x, na_action=na_action)
+            if data_missing.dtype == "float32[pyarrow]":
+                # map roundtrips through objects, which converts to float64
+                expected = data_missing.to_numpy(dtype="float64", na_value=np.nan)
+            else:
+                expected = data_missing.to_numpy()
+            tm.assert_numpy_array_equal(result, expected)
+
+    def test_astype_str(self, data, request, using_infer_string):
+        pa_dtype = data.dtype.pyarrow_dtype
+        if pa.types.is_binary(pa_dtype):
+            request.applymarker(
+                pytest.mark.xfail(
+                    reason=f"For {pa_dtype} .astype(str) decodes.",
+                )
+            )
+        elif not using_infer_string and (
+            (pa.types.is_timestamp(pa_dtype) and pa_dtype.tz is None)
+            or pa.types.is_duration(pa_dtype)
+        ):
+            request.applymarker(
+                pytest.mark.xfail(
+                    reason="pd.Timestamp/pd.Timedelta repr different from numpy repr",
+                )
+            )
+        super().test_astype_str(data)
+
+    def test_from_dtype(self, data, request):
+        pa_dtype = data.dtype.pyarrow_dtype
+        if pa.types.is_string(pa_dtype) or pa.types.is_decimal(pa_dtype):
+            if pa.types.is_string(pa_dtype):
+                reason = "ArrowDtype(pa.string()) != StringDtype('pyarrow')"
+            else:
+                reason = f"pyarrow.type_for_alias cannot infer {pa_dtype}"
+
+            request.applymarker(
+                pytest.mark.xfail(
+                    reason=reason,
+                )
+            )
+        super().test_from_dtype(data)
+
+    def test_from_sequence_pa_array(self, data):
+        # https://github.com/pandas-dev/pandas/pull/47034#discussion_r955500784
+        # data._pa_array = pa.ChunkedArray
+        result = type(data)._from_sequence(data._pa_array, dtype=data.dtype)
+        tm.assert_extension_array_equal(result, data)
+        assert isinstance(result._pa_array, pa.ChunkedArray)
+
+        result = type(data)._from_sequence(
+            data._pa_array.combine_chunks(), dtype=data.dtype
+        )
+        tm.assert_extension_array_equal(result, data)
+        assert isinstance(result._pa_array, pa.ChunkedArray)
+
+    def test_from_sequence_pa_array_notimplemented(self, request):
+        with pytest.raises(NotImplementedError, match="Converting strings to"):
+            ArrowExtensionArray._from_sequence_of_strings(
+                ["12-1"], dtype=pa.month_day_nano_interval()
+            )
+
+    def test_from_sequence_of_strings_pa_array(self, data, request):
+        pa_dtype = data.dtype.pyarrow_dtype
+        if pa.types.is_time64(pa_dtype) and pa_dtype.equals("time64[ns]") and not PY311:
+            request.applymarker(
+                pytest.mark.xfail(
+                    reason="Nanosecond time parsing not supported.",
+                )
+            )
+        elif pa_version_under11p0 and (
+            pa.types.is_duration(pa_dtype) or pa.types.is_decimal(pa_dtype)
+        ):
+            request.applymarker(
+                pytest.mark.xfail(
+                    raises=pa.ArrowNotImplementedError,
+                    reason=f"pyarrow doesn't support parsing {pa_dtype}",
+                )
+            )
+        elif pa.types.is_timestamp(pa_dtype) and pa_dtype.tz is not None:
+            _require_timezone_database(request)
+
+        pa_array = data._pa_array.cast(pa.string())
+        result = type(data)._from_sequence_of_strings(pa_array, dtype=data.dtype)
+        tm.assert_extension_array_equal(result, data)
+
+        pa_array = pa_array.combine_chunks()
+        result = type(data)._from_sequence_of_strings(pa_array, dtype=data.dtype)
+        tm.assert_extension_array_equal(result, data)
+
+    def check_accumulate(self, ser, op_name, skipna):
+        result = getattr(ser, op_name)(skipna=skipna)
+
+        pa_type = ser.dtype.pyarrow_dtype
+        if pa.types.is_temporal(pa_type):
+            # Just check that we match the integer behavior.
+            if pa_type.bit_width == 32:
+                int_type = "int32[pyarrow]"
+            else:
+                int_type = "int64[pyarrow]"
+            ser = ser.astype(int_type)
+            result = result.astype(int_type)
+
+        result = result.astype("Float64")
+        expected = getattr(ser.astype("Float64"), op_name)(skipna=skipna)
+        tm.assert_series_equal(result, expected, check_dtype=False)
+
+    def _supports_accumulation(self, ser: pd.Series, op_name: str) -> bool:
+        # error: Item "dtype[Any]" of "dtype[Any] | ExtensionDtype" has no
+        # attribute "pyarrow_dtype"
+        pa_type = ser.dtype.pyarrow_dtype  # type: ignore[union-attr]
+
+        if pa.types.is_binary(pa_type) or pa.types.is_decimal(pa_type):
+            if op_name in ["cumsum", "cumprod", "cummax", "cummin"]:
+                return False
+        elif pa.types.is_string(pa_type):
+            if op_name == "cumprod":
+                return False
+        elif pa.types.is_boolean(pa_type):
+            if op_name in ["cumprod", "cummax", "cummin"]:
+                return False
+        elif pa.types.is_temporal(pa_type):
+            if op_name == "cumsum" and not pa.types.is_duration(pa_type):
+                return False
+            elif op_name == "cumprod":
+                return False
+        return True
+
+    @pytest.mark.parametrize("skipna", [True, False])
+    def test_accumulate_series(self, data, all_numeric_accumulations, skipna, request):
+        pa_type = data.dtype.pyarrow_dtype
+        op_name = all_numeric_accumulations
+
+        if pa.types.is_string(pa_type) and op_name in ["cumsum", "cummin", "cummax"]:
+            # https://github.com/pandas-dev/pandas/pull/60633
+            # Doesn't fit test structure, tested in series/test_cumulative.py instead.
+            return
+
+        ser = pd.Series(data)
+
+        if not self._supports_accumulation(ser, op_name):
+            # The base class test will check that we raise
+            return super().test_accumulate_series(
+                data, all_numeric_accumulations, skipna
+            )
+
+        if pa_version_under13p0 and all_numeric_accumulations != "cumsum":
+            # xfailing takes a long time to run because pytest
+            # renders the exception messages even when not showing them
+            opt = request.config.option
+            if opt.markexpr and "not slow" in opt.markexpr:
+                pytest.skip(
+                    f"{all_numeric_accumulations} not implemented for pyarrow < 9"
+                )
+            mark = pytest.mark.xfail(
+                reason=f"{all_numeric_accumulations} not implemented for pyarrow < 9"
+            )
+            request.applymarker(mark)
+
+        elif all_numeric_accumulations == "cumsum" and (
+            pa.types.is_boolean(pa_type) or pa.types.is_decimal(pa_type)
+        ):
+            request.applymarker(
+                pytest.mark.xfail(
+                    reason=f"{all_numeric_accumulations} not implemented for {pa_type}",
+                    raises=TypeError,
+                )
+            )
+
+        self.check_accumulate(ser, op_name, skipna)
+
+    def _supports_reduction(self, ser: pd.Series, op_name: str) -> bool:
+        if op_name == "kurt" or (pa_version_under20p0 and op_name == "skew"):
+            return False
+
+        dtype = ser.dtype
+        # error: Item "dtype[Any]" of "dtype[Any] | ExtensionDtype" has
+        # no attribute "pyarrow_dtype"
+        pa_dtype = dtype.pyarrow_dtype  # type: ignore[union-attr]
+        if pa.types.is_temporal(pa_dtype) and op_name in [
+            "sum",
+            "var",
+            "skew",
+            "kurt",
+            "prod",
+        ]:
+            if pa.types.is_duration(pa_dtype) and op_name in ["sum"]:
+                # summing timedeltas is one case that *is* well-defined
+                pass
+            else:
+                return False
+        elif pa.types.is_binary(pa_dtype) and op_name in ["sum", "skew"]:
+            return False
+        elif (
+            pa.types.is_string(pa_dtype) or pa.types.is_binary(pa_dtype)
+        ) and op_name in [
+            "mean",
+            "median",
+            "prod",
+            "std",
+            "sem",
+            "var",
+            "skew",
+            "kurt",
+        ]:
+            return False
+
+        if (
+            pa.types.is_temporal(pa_dtype)
+            and not pa.types.is_duration(pa_dtype)
+            and op_name in ["any", "all"]
+        ):
+            # xref GH#34479 we support this in our non-pyarrow datetime64 dtypes,
+            #  but it isn't obvious we _should_.  For now, we keep the pyarrow
+            #  behavior which does not support this.
+            return False
+
+        return True
+
+    def check_reduce(self, ser: pd.Series, op_name: str, skipna: bool):
+        # error: Item "dtype[Any]" of "dtype[Any] | ExtensionDtype" has no
+        # attribute "pyarrow_dtype"
+        pa_dtype = ser.dtype.pyarrow_dtype  # type: ignore[union-attr]
+        if pa.types.is_integer(pa_dtype) or pa.types.is_floating(pa_dtype):
+            alt = ser.astype("Float64")
+        else:
+            # TODO: in the opposite case, aren't we testing... nothing? For
+            # e.g. date/time dtypes trying to calculate 'expected' by converting
+            # to object will raise for mean, std etc
+            alt = ser
+
+        # TODO: in the opposite case, aren't we testing... nothing?
+        if op_name == "count":
+            result = getattr(ser, op_name)()
+            expected = getattr(alt, op_name)()
+        else:
+            result = getattr(ser, op_name)(skipna=skipna)
+            expected = getattr(alt, op_name)(skipna=skipna)
+        tm.assert_almost_equal(result, expected)
+
+    @pytest.mark.parametrize("skipna", [True, False])
+    def test_reduce_series_numeric(self, data, all_numeric_reductions, skipna, request):
+        dtype = data.dtype
+        pa_dtype = dtype.pyarrow_dtype
+
+        xfail_mark = pytest.mark.xfail(
+            raises=TypeError,
+            reason=(
+                f"{all_numeric_reductions} is not implemented in "
+                f"pyarrow={pa.__version__} for {pa_dtype}"
+            ),
+        )
+        if pa.types.is_boolean(pa_dtype) and all_numeric_reductions in {
+            "sem",
+            "std",
+            "var",
+            "median",
+        }:
+            request.applymarker(xfail_mark)
+        elif (
+            not pa_version_under20p0
+            and all_numeric_reductions == "skew"
+            and (
+                pa.types.is_boolean(pa_dtype)
+                or (
+                    skipna
+                    and (
+                        pa.types.is_integer(pa_dtype) or pa.types.is_floating(pa_dtype)
+                    )
+                )
+            )
+        ):
+            request.applymarker(
+                pytest.mark.xfail(
+                    reason="https://github.com/apache/arrow/issues/45733",
+                )
+            )
+        super().test_reduce_series_numeric(data, all_numeric_reductions, skipna)
+
+    @pytest.mark.parametrize("skipna", [True, False])
+    def test_reduce_series_boolean(
+        self, data, all_boolean_reductions, skipna, na_value, request
+    ):
+        pa_dtype = data.dtype.pyarrow_dtype
+        xfail_mark = pytest.mark.xfail(
+            raises=TypeError,
+            reason=(
+                f"{all_boolean_reductions} is not implemented in "
+                f"pyarrow={pa.__version__} for {pa_dtype}"
+            ),
+        )
+        if pa.types.is_string(pa_dtype) or pa.types.is_binary(pa_dtype):
+            # We *might* want to make this behave like the non-pyarrow cases,
+            #  but have not yet decided.
+            request.applymarker(xfail_mark)
+
+        return super().test_reduce_series_boolean(data, all_boolean_reductions, skipna)
+
+    def _get_expected_reduction_dtype(self, arr, op_name: str, skipna: bool):
+        pa_type = arr._pa_array.type
+        if op_name in ["max", "min"]:
+            cmp_dtype = arr.dtype
+        elif arr.dtype.name == "decimal128(7, 3)[pyarrow]":
+            if op_name == "sum" and not pa_version_under21p0:
+                # https://github.com/apache/arrow/pull/44184
+                cmp_dtype = ArrowDtype(pa.decimal128(38, 3))
+            elif op_name not in ["median", "var", "std", "skew"]:
+                cmp_dtype = arr.dtype
+            else:
+                cmp_dtype = "float64[pyarrow]"
+        elif op_name in ["median", "var", "std", "mean", "skew"]:
+            cmp_dtype = "float64[pyarrow]"
+        elif op_name == "sum" and pa.types.is_string(pa_type):
+            cmp_dtype = arr.dtype
+        else:
+            cmp_dtype = {
+                "i": "int64[pyarrow]",
+                "u": "uint64[pyarrow]",
+                "f": "float64[pyarrow]",
+            }[arr.dtype.kind]
+        return cmp_dtype
+
+    @pytest.mark.parametrize("skipna", [True, False])
+    def test_reduce_frame(self, data, all_numeric_reductions, skipna, request):
+        op_name = all_numeric_reductions
+        if op_name == "skew" and pa_version_under20p0:
+            if data.dtype._is_numeric:
+                mark = pytest.mark.xfail(reason="skew not implemented")
+                request.applymarker(mark)
+        return super().test_reduce_frame(data, all_numeric_reductions, skipna)
+
+    @pytest.mark.parametrize("typ", ["int64", "uint64", "float64"])
+    def test_median_not_approximate(self, typ):
+        # GH 52679
+        result = pd.Series([1, 2], dtype=f"{typ}[pyarrow]").median()
+        assert result == 1.5
+
+    def test_construct_from_string_own_name(self, dtype, request):
+        pa_dtype = dtype.pyarrow_dtype
+        if pa.types.is_decimal(pa_dtype):
+            request.applymarker(
+                pytest.mark.xfail(
+                    raises=NotImplementedError,
+                    reason=f"pyarrow.type_for_alias cannot infer {pa_dtype}",
+                )
+            )
+
+        if pa.types.is_string(pa_dtype):
+            # We still support StringDtype('pyarrow') over ArrowDtype(pa.string())
+            msg = r"string\[pyarrow\] should be constructed by StringDtype"
+            with pytest.raises(TypeError, match=msg):
+                dtype.construct_from_string(dtype.name)
+
+            return
+
+        super().test_construct_from_string_own_name(dtype)
+
+    def test_is_dtype_from_name(self, dtype, request):
+        pa_dtype = dtype.pyarrow_dtype
+        if pa.types.is_string(pa_dtype):
+            # We still support StringDtype('pyarrow') over ArrowDtype(pa.string())
+            assert not type(dtype).is_dtype(dtype.name)
+        else:
+            if pa.types.is_decimal(pa_dtype):
+                request.applymarker(
+                    pytest.mark.xfail(
+                        raises=NotImplementedError,
+                        reason=f"pyarrow.type_for_alias cannot infer {pa_dtype}",
+                    )
+                )
+            super().test_is_dtype_from_name(dtype)
+
+    def test_construct_from_string_another_type_raises(self, dtype):
+        msg = r"'another_type' must end with '\[pyarrow\]'"
+        with pytest.raises(TypeError, match=msg):
+            type(dtype).construct_from_string("another_type")
+
+    def test_get_common_dtype(self, dtype, request):
+        pa_dtype = dtype.pyarrow_dtype
+        if (
+            pa.types.is_date(pa_dtype)
+            or pa.types.is_time(pa_dtype)
+            or (pa.types.is_timestamp(pa_dtype) and pa_dtype.tz is not None)
+            or pa.types.is_binary(pa_dtype)
+            or pa.types.is_decimal(pa_dtype)
+        ):
+            request.applymarker(
+                pytest.mark.xfail(
+                    reason=(
+                        f"{pa_dtype} does not have associated numpy "
+                        f"dtype findable by find_common_type"
+                    )
+                )
+            )
+        super().test_get_common_dtype(dtype)
+
+    def test_is_not_string_type(self, dtype):
+        pa_dtype = dtype.pyarrow_dtype
+        if pa.types.is_string(pa_dtype):
+            assert is_string_dtype(dtype)
+        else:
+            super().test_is_not_string_type(dtype)
+
+    @pytest.mark.xfail(
+        reason="GH 45419: pyarrow.ChunkedArray does not support views.", run=False
+    )
+    def test_view(self, data):
+        super().test_view(data)
+
+    def test_fillna_no_op_returns_copy(self, data):
+        data = data[~data.isna()]
+
+        valid = data[0]
+        result = data.fillna(valid)
+        assert result is not data
+        tm.assert_extension_array_equal(result, data)
+
+        result = data.fillna(method="backfill")
+        assert result is not data
+        tm.assert_extension_array_equal(result, data)
+
+    @pytest.mark.xfail(
+        reason="GH 45419: pyarrow.ChunkedArray does not support views", run=False
+    )
+    def test_transpose(self, data):
+        super().test_transpose(data)
+
+    @pytest.mark.xfail(
+        reason="GH 45419: pyarrow.ChunkedArray does not support views", run=False
+    )
+    def test_setitem_preserves_views(self, data):
+        super().test_setitem_preserves_views(data)
+
+    @pytest.mark.parametrize("dtype_backend", ["pyarrow", no_default])
+    @pytest.mark.parametrize("engine", ["c", "python"])
+    def test_EA_types(self, engine, data, dtype_backend, request):
+        pa_dtype = data.dtype.pyarrow_dtype
+        if pa.types.is_decimal(pa_dtype):
+            request.applymarker(
+                pytest.mark.xfail(
+                    raises=NotImplementedError,
+                    reason=f"Parameterized types {pa_dtype} not supported.",
+                )
+            )
+        elif pa.types.is_timestamp(pa_dtype) and pa_dtype.unit in ("us", "ns"):
+            request.applymarker(
+                pytest.mark.xfail(
+                    raises=ValueError,
+                    reason="https://github.com/pandas-dev/pandas/issues/49767",
+                )
+            )
+        elif pa.types.is_binary(pa_dtype):
+            request.applymarker(
+                pytest.mark.xfail(reason="CSV parsers don't correctly handle binary")
+            )
+        df = pd.DataFrame({"with_dtype": pd.Series(data, dtype=str(data.dtype))})
+        csv_output = df.to_csv(index=False, na_rep=np.nan)
+        if pa.types.is_binary(pa_dtype):
+            csv_output = BytesIO(csv_output)
+        else:
+            csv_output = StringIO(csv_output)
+        result = pd.read_csv(
+            csv_output,
+            dtype={"with_dtype": str(data.dtype)},
+            engine=engine,
+            dtype_backend=dtype_backend,
+        )
+        expected = df
+        tm.assert_frame_equal(result, expected)
+
+    def test_invert(self, data, request):
+        pa_dtype = data.dtype.pyarrow_dtype
+        if not (
+            pa.types.is_boolean(pa_dtype)
+            or pa.types.is_integer(pa_dtype)
+            or pa.types.is_string(pa_dtype)
+        ):
+            request.applymarker(
+                pytest.mark.xfail(
+                    raises=pa.ArrowNotImplementedError,
+                    reason=f"pyarrow.compute.invert does support {pa_dtype}",
+                )
+            )
+        if PY312 and pa.types.is_boolean(pa_dtype):
+            with tm.assert_produces_warning(
+                DeprecationWarning, match="Bitwise inversion", check_stacklevel=False
+            ):
+                super().test_invert(data)
+        else:
+            super().test_invert(data)
+
+    @pytest.mark.parametrize("periods", [1, -2])
+    def test_diff(self, data, periods, request):
+        pa_dtype = data.dtype.pyarrow_dtype
+        if pa.types.is_unsigned_integer(pa_dtype) and periods == 1:
+            request.applymarker(
+                pytest.mark.xfail(
+                    raises=pa.ArrowInvalid,
+                    reason=(
+                        f"diff with {pa_dtype} and periods={periods} will overflow"
+                    ),
+                )
+            )
+        super().test_diff(data, periods)
+
+    def test_value_counts_returns_pyarrow_int64(self, data):
+        # GH 51462
+        data = data[:10]
+        result = data.value_counts()
+        assert result.dtype == ArrowDtype(pa.int64())
+
+    _combine_le_expected_dtype = "bool[pyarrow]"
+
+    def get_op_from_name(self, op_name):
+        short_opname = op_name.strip("_")
+        if short_opname == "rtruediv":
+            # use the numpy version that won't raise on division by zero
+
+            def rtruediv(x, y):
+                return np.divide(y, x)
+
+            return rtruediv
+        elif short_opname == "rfloordiv":
+            return lambda x, y: np.floor_divide(y, x)
+
+        return tm.get_op_from_name(op_name)
+
+    def _cast_pointwise_result(self, op_name: str, obj, other, pointwise_result):
+        # BaseOpsUtil._combine can upcast expected dtype
+        # (because it generates expected on python scalars)
+        # while ArrowExtensionArray maintains original type
+        expected = pointwise_result
+
+        if op_name in ["eq", "ne", "lt", "le", "gt", "ge"]:
+            return pointwise_result.astype("boolean[pyarrow]")
+
+        was_frame = False
+        if isinstance(expected, pd.DataFrame):
+            was_frame = True
+            expected_data = expected.iloc[:, 0]
+            original_dtype = obj.iloc[:, 0].dtype
+        else:
+            expected_data = expected
+            original_dtype = obj.dtype
+
+        orig_pa_type = original_dtype.pyarrow_dtype
+        if not was_frame and isinstance(other, pd.Series):
+            # i.e. test_arith_series_with_array
+            if not (
+                pa.types.is_floating(orig_pa_type)
+                or (
+                    pa.types.is_integer(orig_pa_type)
+                    and op_name not in ["__truediv__", "__rtruediv__"]
+                )
+                or pa.types.is_duration(orig_pa_type)
+                or pa.types.is_timestamp(orig_pa_type)
+                or pa.types.is_date(orig_pa_type)
+                or pa.types.is_decimal(orig_pa_type)
+            ):
+                # base class _combine always returns int64, while
+                #  ArrowExtensionArray does not upcast
+                return expected
+        elif not (
+            (op_name == "__floordiv__" and pa.types.is_integer(orig_pa_type))
+            or pa.types.is_duration(orig_pa_type)
+            or pa.types.is_timestamp(orig_pa_type)
+            or pa.types.is_date(orig_pa_type)
+            or pa.types.is_decimal(orig_pa_type)
+        ):
+            # base class _combine always returns int64, while
+            #  ArrowExtensionArray does not upcast
+            return expected
+
+        pa_expected = pa.array(expected_data._values)
+
+        if pa.types.is_duration(pa_expected.type):
+            if pa.types.is_date(orig_pa_type):
+                if pa.types.is_date64(orig_pa_type):
+                    # TODO: why is this different vs date32?
+                    unit = "ms"
+                else:
+                    unit = "s"
+            else:
+                # pyarrow sees sequence of datetime/timedelta objects and defaults
+                #  to "us" but the non-pointwise op retains unit
+                # timestamp or duration
+                unit = orig_pa_type.unit
+                if type(other) in [datetime, timedelta] and unit in ["s", "ms"]:
+                    # pydatetime/pytimedelta objects have microsecond reso, so we
+                    #  take the higher reso of the original and microsecond. Note
+                    #  this matches what we would do with DatetimeArray/TimedeltaArray
+                    unit = "us"
+
+            pa_expected = pa_expected.cast(f"duration[{unit}]")
+
+        elif pa.types.is_decimal(pa_expected.type) and pa.types.is_decimal(
+            orig_pa_type
+        ):
+            # decimal precision can resize in the result type depending on data
+            # just compare the float values
+            alt = getattr(obj, op_name)(other)
+            alt_dtype = tm.get_dtype(alt)
+            assert isinstance(alt_dtype, ArrowDtype)
+            if op_name == "__pow__" and isinstance(other, Decimal):
+                # TODO: would it make more sense to retain Decimal here?
+                alt_dtype = ArrowDtype(pa.float64())
+            elif (
+                op_name == "__pow__"
+                and isinstance(other, pd.Series)
+                and other.dtype == original_dtype
+            ):
+                # TODO: would it make more sense to retain Decimal here?
+                alt_dtype = ArrowDtype(pa.float64())
+            else:
+                assert pa.types.is_decimal(alt_dtype.pyarrow_dtype)
+            return expected.astype(alt_dtype)
+
+        else:
+            pa_expected = pa_expected.cast(orig_pa_type)
+
+        pd_expected = type(expected_data._values)(pa_expected)
+        if was_frame:
+            expected = pd.DataFrame(
+                pd_expected, index=expected.index, columns=expected.columns
+            )
+        else:
+            expected = pd.Series(pd_expected)
+        return expected
+
+    def _is_temporal_supported(self, opname, pa_dtype):
+        return (
+            (
+                opname in ("__add__", "__radd__")
+                or (
+                    opname
+                    in ("__truediv__", "__rtruediv__", "__floordiv__", "__rfloordiv__")
+                    and not pa_version_under14p0
+                )
+            )
+            and pa.types.is_duration(pa_dtype)
+            or opname in ("__sub__", "__rsub__")
+            and pa.types.is_temporal(pa_dtype)
+        )
+
+    def _get_expected_exception(
+        self, op_name: str, obj, other
+    ) -> type[Exception] | tuple[type[Exception], ...] | None:
+        if op_name in ("__divmod__", "__rdivmod__"):
+            return (NotImplementedError, TypeError)
+
+        exc: type[Exception] | tuple[type[Exception], ...] | None
+        dtype = tm.get_dtype(obj)
+        # error: Item "dtype[Any]" of "dtype[Any] | ExtensionDtype" has no
+        # attribute "pyarrow_dtype"
+        pa_dtype = dtype.pyarrow_dtype  # type: ignore[union-attr]
+
+        arrow_temporal_supported = self._is_temporal_supported(op_name, pa_dtype)
+        if op_name in {
+            "__mod__",
+            "__rmod__",
+        }:
+            exc = (NotImplementedError, TypeError)
+        elif arrow_temporal_supported:
+            exc = None
+        elif op_name in ["__add__", "__radd__"] and (
+            pa.types.is_string(pa_dtype) or pa.types.is_binary(pa_dtype)
+        ):
+            exc = None
+        elif not (
+            pa.types.is_floating(pa_dtype)
+            or pa.types.is_integer(pa_dtype)
+            or pa.types.is_decimal(pa_dtype)
+        ):
+            exc = TypeError
+        else:
+            exc = None
+        return exc
+
+    def _get_arith_xfail_marker(self, opname, pa_dtype):
+        mark = None
+
+        arrow_temporal_supported = self._is_temporal_supported(opname, pa_dtype)
+
+        if opname == "__rpow__" and (
+            pa.types.is_floating(pa_dtype)
+            or pa.types.is_integer(pa_dtype)
+            or pa.types.is_decimal(pa_dtype)
+        ):
+            mark = pytest.mark.xfail(
+                reason=(
+                    f"GH#29997: 1**pandas.NA == 1 while 1**pyarrow.NA == NULL "
+                    f"for {pa_dtype}"
+                )
+            )
+        elif arrow_temporal_supported and (
+            pa.types.is_time(pa_dtype)
+            or (
+                opname
+                in ("__truediv__", "__rtruediv__", "__floordiv__", "__rfloordiv__")
+                and pa.types.is_duration(pa_dtype)
+            )
+        ):
+            mark = pytest.mark.xfail(
+                raises=TypeError,
+                reason=(
+                    f"{opname} not supported between"
+                    f"pd.NA and {pa_dtype} Python scalar"
+                ),
+            )
+        elif opname == "__rfloordiv__" and (
+            pa.types.is_integer(pa_dtype) or pa.types.is_decimal(pa_dtype)
+        ):
+            mark = pytest.mark.xfail(
+                raises=pa.ArrowInvalid,
+                reason="divide by 0",
+            )
+        elif opname == "__rtruediv__" and pa.types.is_decimal(pa_dtype):
+            mark = pytest.mark.xfail(
+                raises=pa.ArrowInvalid,
+                reason="divide by 0",
+            )
+
+        return mark
+
+    def test_arith_series_with_scalar(self, data, all_arithmetic_operators, request):
+        pa_dtype = data.dtype.pyarrow_dtype
+
+        if all_arithmetic_operators == "__rmod__" and pa.types.is_binary(pa_dtype):
+            pytest.skip("Skip testing Python string formatting")
+
+        mark = self._get_arith_xfail_marker(all_arithmetic_operators, pa_dtype)
+        if mark is not None:
+            request.applymarker(mark)
+
+        super().test_arith_series_with_scalar(data, all_arithmetic_operators)
+
+    def test_arith_frame_with_scalar(self, data, all_arithmetic_operators, request):
+        pa_dtype = data.dtype.pyarrow_dtype
+
+        if all_arithmetic_operators == "__rmod__" and (
+            pa.types.is_string(pa_dtype) or pa.types.is_binary(pa_dtype)
+        ):
+            pytest.skip("Skip testing Python string formatting")
+
+        mark = self._get_arith_xfail_marker(all_arithmetic_operators, pa_dtype)
+        if mark is not None:
+            request.applymarker(mark)
+
+        super().test_arith_frame_with_scalar(data, all_arithmetic_operators)
+
+    def test_arith_series_with_array(self, data, all_arithmetic_operators, request):
+        pa_dtype = data.dtype.pyarrow_dtype
+
+        if all_arithmetic_operators in (
+            "__sub__",
+            "__rsub__",
+        ) and pa.types.is_unsigned_integer(pa_dtype):
+            request.applymarker(
+                pytest.mark.xfail(
+                    raises=pa.ArrowInvalid,
+                    reason=(
+                        f"Implemented pyarrow.compute.subtract_checked "
+                        f"which raises on overflow for {pa_dtype}"
+                    ),
+                )
+            )
+
+        mark = self._get_arith_xfail_marker(all_arithmetic_operators, pa_dtype)
+        if mark is not None:
+            request.applymarker(mark)
+
+        op_name = all_arithmetic_operators
+        ser = pd.Series(data)
+        # pd.Series([ser.iloc[0]] * len(ser)) may not return ArrowExtensionArray
+        # since ser.iloc[0] is a python scalar
+        other = pd.Series(pd.array([ser.iloc[0]] * len(ser), dtype=data.dtype))
+
+        self.check_opname(ser, op_name, other)
+
+    def test_add_series_with_extension_array(self, data, request):
+        pa_dtype = data.dtype.pyarrow_dtype
+
+        if pa_dtype.equals("int8"):
+            request.applymarker(
+                pytest.mark.xfail(
+                    raises=pa.ArrowInvalid,
+                    reason=f"raises on overflow for {pa_dtype}",
+                )
+            )
+        super().test_add_series_with_extension_array(data)
+
+    def test_invalid_other_comp(self, data, comparison_op):
+        # GH 48833
+        with pytest.raises(
+            NotImplementedError, match=".* not implemented for "
+        ):
+            comparison_op(data, object())
+
+    @pytest.mark.parametrize("masked_dtype", ["boolean", "Int64", "Float64"])
+    def test_comp_masked_numpy(self, masked_dtype, comparison_op):
+        # GH 52625
+        data = [1, 0, None]
+        ser_masked = pd.Series(data, dtype=masked_dtype)
+        ser_pa = pd.Series(data, dtype=f"{masked_dtype.lower()}[pyarrow]")
+        result = comparison_op(ser_pa, ser_masked)
+        if comparison_op in [operator.lt, operator.gt, operator.ne]:
+            exp = [False, False, None]
+        else:
+            exp = [True, True, None]
+        expected = pd.Series(exp, dtype=ArrowDtype(pa.bool_()))
+        tm.assert_series_equal(result, expected)
+
+
+class TestLogicalOps:
+    """Various Series and DataFrame logical ops methods."""
+
+    def test_kleene_or(self):
+        a = pd.Series([True] * 3 + [False] * 3 + [None] * 3, dtype="boolean[pyarrow]")
+        b = pd.Series([True, False, None] * 3, dtype="boolean[pyarrow]")
+        result = a | b
+        expected = pd.Series(
+            [True, True, True, True, False, None, True, None, None],
+            dtype="boolean[pyarrow]",
+        )
+        tm.assert_series_equal(result, expected)
+
+        result = b | a
+        tm.assert_series_equal(result, expected)
+
+        # ensure we haven't mutated anything inplace
+        tm.assert_series_equal(
+            a,
+            pd.Series([True] * 3 + [False] * 3 + [None] * 3, dtype="boolean[pyarrow]"),
+        )
+        tm.assert_series_equal(
+            b, pd.Series([True, False, None] * 3, dtype="boolean[pyarrow]")
+        )
+
+    @pytest.mark.parametrize(
+        "other, expected",
+        [
+            (None, [True, None, None]),
+            (pd.NA, [True, None, None]),
+            (True, [True, True, True]),
+            (np.bool_(True), [True, True, True]),
+            (False, [True, False, None]),
+            (np.bool_(False), [True, False, None]),
+        ],
+    )
+    def test_kleene_or_scalar(self, other, expected):
+        a = pd.Series([True, False, None], dtype="boolean[pyarrow]")
+        result = a | other
+        expected = pd.Series(expected, dtype="boolean[pyarrow]")
+        tm.assert_series_equal(result, expected)
+
+        result = other | a
+        tm.assert_series_equal(result, expected)
+
+        # ensure we haven't mutated anything inplace
+        tm.assert_series_equal(
+            a, pd.Series([True, False, None], dtype="boolean[pyarrow]")
+        )
+
+    def test_kleene_and(self):
+        a = pd.Series([True] * 3 + [False] * 3 + [None] * 3, dtype="boolean[pyarrow]")
+        b = pd.Series([True, False, None] * 3, dtype="boolean[pyarrow]")
+        result = a & b
+        expected = pd.Series(
+            [True, False, None, False, False, False, None, False, None],
+            dtype="boolean[pyarrow]",
+        )
+        tm.assert_series_equal(result, expected)
+
+        result = b & a
+        tm.assert_series_equal(result, expected)
+
+        # ensure we haven't mutated anything inplace
+        tm.assert_series_equal(
+            a,
+            pd.Series([True] * 3 + [False] * 3 + [None] * 3, dtype="boolean[pyarrow]"),
+        )
+        tm.assert_series_equal(
+            b, pd.Series([True, False, None] * 3, dtype="boolean[pyarrow]")
+        )
+
+    @pytest.mark.parametrize(
+        "other, expected",
+        [
+            (None, [None, False, None]),
+            (pd.NA, [None, False, None]),
+            (True, [True, False, None]),
+            (False, [False, False, False]),
+            (np.bool_(True), [True, False, None]),
+            (np.bool_(False), [False, False, False]),
+        ],
+    )
+    def test_kleene_and_scalar(self, other, expected):
+        a = pd.Series([True, False, None], dtype="boolean[pyarrow]")
+        result = a & other
+        expected = pd.Series(expected, dtype="boolean[pyarrow]")
+        tm.assert_series_equal(result, expected)
+
+        result = other & a
+        tm.assert_series_equal(result, expected)
+
+        # ensure we haven't mutated anything inplace
+        tm.assert_series_equal(
+            a, pd.Series([True, False, None], dtype="boolean[pyarrow]")
+        )
+
+    def test_kleene_xor(self):
+        a = pd.Series([True] * 3 + [False] * 3 + [None] * 3, dtype="boolean[pyarrow]")
+        b = pd.Series([True, False, None] * 3, dtype="boolean[pyarrow]")
+        result = a ^ b
+        expected = pd.Series(
+            [False, True, None, True, False, None, None, None, None],
+            dtype="boolean[pyarrow]",
+        )
+        tm.assert_series_equal(result, expected)
+
+        result = b ^ a
+        tm.assert_series_equal(result, expected)
+
+        # ensure we haven't mutated anything inplace
+        tm.assert_series_equal(
+            a,
+            pd.Series([True] * 3 + [False] * 3 + [None] * 3, dtype="boolean[pyarrow]"),
+        )
+        tm.assert_series_equal(
+            b, pd.Series([True, False, None] * 3, dtype="boolean[pyarrow]")
+        )
+
+    @pytest.mark.parametrize(
+        "other, expected",
+        [
+            (None, [None, None, None]),
+            (pd.NA, [None, None, None]),
+            (True, [False, True, None]),
+            (np.bool_(True), [False, True, None]),
+            (np.bool_(False), [True, False, None]),
+        ],
+    )
+    def test_kleene_xor_scalar(self, other, expected):
+        a = pd.Series([True, False, None], dtype="boolean[pyarrow]")
+        result = a ^ other
+        expected = pd.Series(expected, dtype="boolean[pyarrow]")
+        tm.assert_series_equal(result, expected)
+
+        result = other ^ a
+        tm.assert_series_equal(result, expected)
+
+        # ensure we haven't mutated anything inplace
+        tm.assert_series_equal(
+            a, pd.Series([True, False, None], dtype="boolean[pyarrow]")
+        )
+
+    @pytest.mark.parametrize(
+        "op, exp",
+        [
+            ["__and__", True],
+            ["__or__", True],
+            ["__xor__", False],
+        ],
+    )
+    def test_logical_masked_numpy(self, op, exp):
+        # GH 52625
+        data = [True, False, None]
+        ser_masked = pd.Series(data, dtype="boolean")
+        ser_pa = pd.Series(data, dtype="boolean[pyarrow]")
+        result = getattr(ser_pa, op)(ser_masked)
+        expected = pd.Series([exp, False, None], dtype=ArrowDtype(pa.bool_()))
+        tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("pa_type", tm.ALL_INT_PYARROW_DTYPES)
+def test_bitwise(pa_type):
+    # GH 54495
+    dtype = ArrowDtype(pa_type)
+    left = pd.Series([1, None, 3, 4], dtype=dtype)
+    right = pd.Series([None, 3, 5, 4], dtype=dtype)
+
+    result = left | right
+    expected = pd.Series([None, None, 3 | 5, 4 | 4], dtype=dtype)
+    tm.assert_series_equal(result, expected)
+
+    result = left & right
+    expected = pd.Series([None, None, 3 & 5, 4 & 4], dtype=dtype)
+    tm.assert_series_equal(result, expected)
+
+    result = left ^ right
+    expected = pd.Series([None, None, 3 ^ 5, 4 ^ 4], dtype=dtype)
+    tm.assert_series_equal(result, expected)
+
+    result = ~left
+    expected = ~(left.fillna(0).to_numpy())
+    expected = pd.Series(expected, dtype=dtype).mask(left.isnull())
+    tm.assert_series_equal(result, expected)
+
+
+def test_arrowdtype_construct_from_string_type_with_unsupported_parameters():
+    with pytest.raises(NotImplementedError, match="Passing pyarrow type"):
+        ArrowDtype.construct_from_string("not_a_real_dype[s, tz=UTC][pyarrow]")
+
+    with pytest.raises(NotImplementedError, match="Passing pyarrow type"):
+        ArrowDtype.construct_from_string("decimal(7, 2)[pyarrow]")
+
+
+def test_arrowdtype_construct_from_string_supports_dt64tz():
+    # as of GH#50689, timestamptz is supported
+    dtype = ArrowDtype.construct_from_string("timestamp[s, tz=UTC][pyarrow]")
+    expected = ArrowDtype(pa.timestamp("s", "UTC"))
+    assert dtype == expected
+
+
+def test_arrowdtype_construct_from_string_type_only_one_pyarrow():
+    # GH#51225
+    invalid = "int64[pyarrow]foobar[pyarrow]"
+    msg = (
+        r"Passing pyarrow type specific parameters \(\[pyarrow\]\) in the "
+        r"string is not supported\."
+    )
+    with pytest.raises(NotImplementedError, match=msg):
+        pd.Series(range(3), dtype=invalid)
+
+
+def test_arrow_string_multiplication():
+    # GH 56537
+    binary = pd.Series(["abc", "defg"], dtype=ArrowDtype(pa.string()))
+    repeat = pd.Series([2, -2], dtype="int64[pyarrow]")
+    result = binary * repeat
+    expected = pd.Series(["abcabc", ""], dtype=ArrowDtype(pa.string()))
+    tm.assert_series_equal(result, expected)
+    reflected_result = repeat * binary
+    tm.assert_series_equal(result, reflected_result)
+
+
+def test_arrow_string_multiplication_scalar_repeat():
+    binary = pd.Series(["abc", "defg"], dtype=ArrowDtype(pa.string()))
+    result = binary * 2
+    expected = pd.Series(["abcabc", "defgdefg"], dtype=ArrowDtype(pa.string()))
+    tm.assert_series_equal(result, expected)
+    reflected_result = 2 * binary
+    tm.assert_series_equal(reflected_result, expected)
+
+
+@pytest.mark.parametrize(
+    "interpolation", ["linear", "lower", "higher", "nearest", "midpoint"]
+)
+@pytest.mark.parametrize("quantile", [0.5, [0.5, 0.5]])
+def test_quantile(data, interpolation, quantile, request):
+    pa_dtype = data.dtype.pyarrow_dtype
+
+    data = data.take([0, 0, 0])
+    ser = pd.Series(data)
+
+    if (
+        pa.types.is_string(pa_dtype)
+        or pa.types.is_binary(pa_dtype)
+        or pa.types.is_boolean(pa_dtype)
+    ):
+        # For string, bytes, and bool, we don't *expect* to have quantile work
+        # Note this matches the non-pyarrow behavior
+        msg = r"Function 'quantile' has no kernel matching input types \(.*\)"
+        with pytest.raises(pa.ArrowNotImplementedError, match=msg):
+            ser.quantile(q=quantile, interpolation=interpolation)
+        return
+
+    if (
+        pa.types.is_integer(pa_dtype)
+        or pa.types.is_floating(pa_dtype)
+        or pa.types.is_decimal(pa_dtype)
+    ):
+        pass
+    elif pa.types.is_temporal(data._pa_array.type):
+        pass
+    else:
+        request.applymarker(
+            pytest.mark.xfail(
+                raises=pa.ArrowNotImplementedError,
+                reason=f"quantile not supported by pyarrow for {pa_dtype}",
+            )
+        )
+    data = data.take([0, 0, 0])
+    ser = pd.Series(data)
+    result = ser.quantile(q=quantile, interpolation=interpolation)
+
+    if pa.types.is_timestamp(pa_dtype) and interpolation not in ["lower", "higher"]:
+        # rounding error will make the check below fail
+        #  (e.g. '2020-01-01 01:01:01.000001' vs '2020-01-01 01:01:01.000001024'),
+        #  so we'll check for now that we match the numpy analogue
+        if pa_dtype.tz:
+            pd_dtype = f"M8[{pa_dtype.unit}, {pa_dtype.tz}]"
+        else:
+            pd_dtype = f"M8[{pa_dtype.unit}]"
+        ser_np = ser.astype(pd_dtype)
+
+        expected = ser_np.quantile(q=quantile, interpolation=interpolation)
+        if quantile == 0.5:
+            if pa_dtype.unit == "us":
+                expected = expected.to_pydatetime(warn=False)
+            assert result == expected
+        else:
+            if pa_dtype.unit == "us":
+                expected = expected.dt.floor("us")
+            tm.assert_series_equal(result, expected.astype(data.dtype))
+        return
+
+    if quantile == 0.5:
+        assert result == data[0]
+    else:
+        # Just check the values
+        expected = pd.Series(data.take([0, 0]), index=[0.5, 0.5])
+        if (
+            pa.types.is_integer(pa_dtype)
+            or pa.types.is_floating(pa_dtype)
+            or pa.types.is_decimal(pa_dtype)
+        ):
+            expected = expected.astype("float64[pyarrow]")
+            result = result.astype("float64[pyarrow]")
+        tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "take_idx, exp_idx",
+    [[[0, 0, 2, 2, 4, 4], [4, 0]], [[0, 0, 0, 2, 4, 4], [0]]],
+    ids=["multi_mode", "single_mode"],
+)
+def test_mode_dropna_true(data_for_grouping, take_idx, exp_idx):
+    data = data_for_grouping.take(take_idx)
+    ser = pd.Series(data)
+    result = ser.mode(dropna=True)
+    expected = pd.Series(data_for_grouping.take(exp_idx))
+    tm.assert_series_equal(result, expected)
+
+
+def test_mode_dropna_false_mode_na(data):
+    # GH 50982
+    more_nans = pd.Series([None, None, data[0]], dtype=data.dtype)
+    result = more_nans.mode(dropna=False)
+    expected = pd.Series([None], dtype=data.dtype)
+    tm.assert_series_equal(result, expected)
+
+    expected = pd.Series([data[0], None], dtype=data.dtype)
+    result = expected.mode(dropna=False)
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "arrow_dtype, expected_type",
+    [
+        [pa.binary(), bytes],
+        [pa.binary(16), bytes],
+        [pa.large_binary(), bytes],
+        [pa.large_string(), str],
+        [pa.list_(pa.int64()), list],
+        [pa.large_list(pa.int64()), list],
+        [pa.map_(pa.string(), pa.int64()), list],
+        [pa.struct([("f1", pa.int8()), ("f2", pa.string())]), dict],
+        [pa.dictionary(pa.int64(), pa.int64()), CategoricalDtypeType],
+    ],
+)
+def test_arrow_dtype_type(arrow_dtype, expected_type):
+    # GH 51845
+    # TODO: Redundant with test_getitem_scalar once arrow_dtype exists in data fixture
+    assert ArrowDtype(arrow_dtype).type == expected_type
+
+
+def test_is_bool_dtype():
+    # GH 22667
+    data = ArrowExtensionArray(pa.array([True, False, True]))
+    assert is_bool_dtype(data)
+    assert pd.core.common.is_bool_indexer(data)
+    s = pd.Series(range(len(data)))
+    result = s[data]
+    expected = s[np.asarray(data)]
+    tm.assert_series_equal(result, expected)
+
+
+def test_is_numeric_dtype(data):
+    # GH 50563
+    pa_type = data.dtype.pyarrow_dtype
+    if (
+        pa.types.is_floating(pa_type)
+        or pa.types.is_integer(pa_type)
+        or pa.types.is_decimal(pa_type)
+    ):
+        assert is_numeric_dtype(data)
+    else:
+        assert not is_numeric_dtype(data)
+
+
+def test_is_integer_dtype(data):
+    # GH 50667
+    pa_type = data.dtype.pyarrow_dtype
+    if pa.types.is_integer(pa_type):
+        assert is_integer_dtype(data)
+    else:
+        assert not is_integer_dtype(data)
+
+
+def test_is_signed_integer_dtype(data):
+    pa_type = data.dtype.pyarrow_dtype
+    if pa.types.is_signed_integer(pa_type):
+        assert is_signed_integer_dtype(data)
+    else:
+        assert not is_signed_integer_dtype(data)
+
+
+def test_is_unsigned_integer_dtype(data):
+    pa_type = data.dtype.pyarrow_dtype
+    if pa.types.is_unsigned_integer(pa_type):
+        assert is_unsigned_integer_dtype(data)
+    else:
+        assert not is_unsigned_integer_dtype(data)
+
+
+def test_is_float_dtype(data):
+    pa_type = data.dtype.pyarrow_dtype
+    if pa.types.is_floating(pa_type):
+        assert is_float_dtype(data)
+    else:
+        assert not is_float_dtype(data)
+
+
+def test_pickle_roundtrip(data):
+    # GH 42600
+    expected = pd.Series(data)
+    expected_sliced = expected.head(2)
+    full_pickled = pickle.dumps(expected)
+    sliced_pickled = pickle.dumps(expected_sliced)
+
+    assert len(full_pickled) > len(sliced_pickled)
+
+    result = pickle.loads(full_pickled)
+    tm.assert_series_equal(result, expected)
+
+    result_sliced = pickle.loads(sliced_pickled)
+    tm.assert_series_equal(result_sliced, expected_sliced)
+
+
+def test_astype_from_non_pyarrow(data):
+    # GH49795
+    pd_array = data._pa_array.to_pandas().array
+    result = pd_array.astype(data.dtype)
+    assert not isinstance(pd_array.dtype, ArrowDtype)
+    assert isinstance(result.dtype, ArrowDtype)
+    tm.assert_extension_array_equal(result, data)
+
+
+def test_astype_float_from_non_pyarrow_str():
+    # GH50430
+    ser = pd.Series(["1.0"])
+    result = ser.astype("float64[pyarrow]")
+    expected = pd.Series([1.0], dtype="float64[pyarrow]")
+    tm.assert_series_equal(result, expected)
+
+
+def test_astype_errors_ignore():
+    # GH 55399
+    expected = pd.DataFrame({"col": [17000000]}, dtype="int32[pyarrow]")
+    result = expected.astype("float[pyarrow]", errors="ignore")
+    tm.assert_frame_equal(result, expected)
+
+
+def test_to_numpy_with_defaults(data):
+    # GH49973
+    result = data.to_numpy()
+
+    pa_type = data._pa_array.type
+    if pa.types.is_duration(pa_type) or pa.types.is_timestamp(pa_type):
+        pytest.skip("Tested in test_to_numpy_temporal")
+    elif pa.types.is_date(pa_type):
+        expected = np.array(list(data))
+    else:
+        expected = np.array(data._pa_array)
+
+    if data._hasna and not is_numeric_dtype(data.dtype):
+        expected = expected.astype(object)
+        expected[pd.isna(data)] = pd.NA
+
+    tm.assert_numpy_array_equal(result, expected)
+
+
+def test_to_numpy_int_with_na():
+    # GH51227: ensure to_numpy does not convert int to float
+    data = [1, None]
+    arr = pd.array(data, dtype="int64[pyarrow]")
+    result = arr.to_numpy()
+    expected = np.array([1, np.nan])
+    assert isinstance(result[0], float)
+    tm.assert_numpy_array_equal(result, expected)
+
+
+@pytest.mark.parametrize("na_val, exp", [(lib.no_default, np.nan), (1, 1)])
+def test_to_numpy_null_array(na_val, exp):
+    # GH#52443
+    arr = pd.array([pd.NA, pd.NA], dtype="null[pyarrow]")
+    result = arr.to_numpy(dtype="float64", na_value=na_val)
+    expected = np.array([exp] * 2, dtype="float64")
+    tm.assert_numpy_array_equal(result, expected)
+
+
+def test_to_numpy_null_array_no_dtype():
+    # GH#52443
+    arr = pd.array([pd.NA, pd.NA], dtype="null[pyarrow]")
+    result = arr.to_numpy(dtype=None)
+    expected = np.array([pd.NA] * 2, dtype="object")
+    tm.assert_numpy_array_equal(result, expected)
+
+
+def test_to_numpy_without_dtype():
+    # GH 54808
+    arr = pd.array([True, pd.NA], dtype="boolean[pyarrow]")
+    result = arr.to_numpy(na_value=False)
+    expected = np.array([True, False], dtype=np.bool_)
+    tm.assert_numpy_array_equal(result, expected)
+
+    arr = pd.array([1.0, pd.NA], dtype="float32[pyarrow]")
+    result = arr.to_numpy(na_value=0.0)
+    expected = np.array([1.0, 0.0], dtype=np.float32)
+    tm.assert_numpy_array_equal(result, expected)
+
+
+def test_setitem_null_slice(data):
+    # GH50248
+    orig = data.copy()
+
+    result = orig.copy()
+    result[:] = data[0]
+    expected = ArrowExtensionArray._from_sequence(
+        [data[0]] * len(data),
+        dtype=data.dtype,
+    )
+    tm.assert_extension_array_equal(result, expected)
+
+    result = orig.copy()
+    result[:] = data[::-1]
+    expected = data[::-1]
+    tm.assert_extension_array_equal(result, expected)
+
+    result = orig.copy()
+    result[:] = data.tolist()
+    expected = data
+    tm.assert_extension_array_equal(result, expected)
+
+
+def test_setitem_invalid_dtype(data):
+    # GH50248
+    pa_type = data._pa_array.type
+    if pa.types.is_string(pa_type) or pa.types.is_binary(pa_type):
+        fill_value = 123
+        err = TypeError
+        msg = "Invalid value '123' for dtype"
+    elif (
+        pa.types.is_integer(pa_type)
+        or pa.types.is_floating(pa_type)
+        or pa.types.is_boolean(pa_type)
+    ):
+        fill_value = "foo"
+        err = pa.ArrowInvalid
+        msg = "Could not convert"
+    else:
+        fill_value = "foo"
+        err = TypeError
+        msg = "Invalid value 'foo' for dtype"
+    with pytest.raises(err, match=msg):
+        data[:] = fill_value
+
+
+def test_from_arrow_respecting_given_dtype():
+    date_array = pa.array(
+        [pd.Timestamp("2019-12-31"), pd.Timestamp("2019-12-31")], type=pa.date32()
+    )
+    result = date_array.to_pandas(
+        types_mapper={pa.date32(): ArrowDtype(pa.date64())}.get
+    )
+    expected = pd.Series(
+        [pd.Timestamp("2019-12-31"), pd.Timestamp("2019-12-31")],
+        dtype=ArrowDtype(pa.date64()),
+    )
+    tm.assert_series_equal(result, expected)
+
+
+def test_from_arrow_respecting_given_dtype_unsafe():
+    array = pa.array([1.5, 2.5], type=pa.float64())
+    with tm.external_error_raised(pa.ArrowInvalid):
+        array.to_pandas(types_mapper={pa.float64(): ArrowDtype(pa.int64())}.get)
+
+
+def test_round():
+    dtype = "float64[pyarrow]"
+
+    ser = pd.Series([0.0, 1.23, 2.56, pd.NA], dtype=dtype)
+    result = ser.round(1)
+    expected = pd.Series([0.0, 1.2, 2.6, pd.NA], dtype=dtype)
+    tm.assert_series_equal(result, expected)
+
+    ser = pd.Series([123.4, pd.NA, 56.78], dtype=dtype)
+    result = ser.round(-1)
+    expected = pd.Series([120.0, pd.NA, 60.0], dtype=dtype)
+    tm.assert_series_equal(result, expected)
+
+
+def test_searchsorted_with_na_raises(data_for_sorting, as_series):
+    # GH50447
+    b, c, a = data_for_sorting
+    arr = data_for_sorting.take([2, 0, 1])  # to get [a, b, c]
+    arr[-1] = pd.NA
+
+    if as_series:
+        arr = pd.Series(arr)
+
+    msg = (
+        "searchsorted requires array to be sorted, "
+        "which is impossible with NAs present."
+    )
+    with pytest.raises(ValueError, match=msg):
+        arr.searchsorted(b)
+
+
+def test_sort_values_dictionary():
+    df = pd.DataFrame(
+        {
+            "a": pd.Series(
+                ["x", "y"], dtype=ArrowDtype(pa.dictionary(pa.int32(), pa.string()))
+            ),
+            "b": [1, 2],
+        },
+    )
+    expected = df.copy()
+    result = df.sort_values(by=["a", "b"])
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("pat", ["abc", "a[a-z]{2}"])
+def test_str_count(pat):
+    ser = pd.Series(["abc", None], dtype=ArrowDtype(pa.string()))
+    result = ser.str.count(pat)
+    expected = pd.Series([1, None], dtype=ArrowDtype(pa.int32()))
+    tm.assert_series_equal(result, expected)
+
+
+def test_str_count_flags_unsupported():
+    ser = pd.Series(["abc", None], dtype=ArrowDtype(pa.string()))
+    with pytest.raises(NotImplementedError, match="count not"):
+        ser.str.count("abc", flags=1)
+
+
+@pytest.mark.parametrize(
+    "side, str_func", [["left", "rjust"], ["right", "ljust"], ["both", "center"]]
+)
+def test_str_pad(side, str_func):
+    ser = pd.Series(["a", None], dtype=ArrowDtype(pa.string()))
+    result = ser.str.pad(width=3, side=side, fillchar="x")
+    expected = pd.Series(
+        [getattr("a", str_func)(3, "x"), None], dtype=ArrowDtype(pa.string())
+    )
+    tm.assert_series_equal(result, expected)
+
+
+def test_str_pad_invalid_side():
+    ser = pd.Series(["a", None], dtype=ArrowDtype(pa.string()))
+    with pytest.raises(ValueError, match="Invalid side: foo"):
+        ser.str.pad(3, "foo", "x")
+
+
+@pytest.mark.parametrize(
+    "pat, case, na, regex, exp",
+    [
+        ["ab", False, None, False, [True, None]],
+        ["Ab", True, None, False, [False, None]],
+        ["ab", False, True, False, [True, True]],
+        ["a[a-z]{1}", False, None, True, [True, None]],
+        ["A[a-z]{1}", True, None, True, [False, None]],
+    ],
+)
+def test_str_contains(pat, case, na, regex, exp):
+    ser = pd.Series(["abc", None], dtype=ArrowDtype(pa.string()))
+    result = ser.str.contains(pat, case=case, na=na, regex=regex)
+    expected = pd.Series(exp, dtype=ArrowDtype(pa.bool_()))
+    tm.assert_series_equal(result, expected)
+
+
+def test_str_contains_flags_unsupported():
+    ser = pd.Series(["abc", None], dtype=ArrowDtype(pa.string()))
+    with pytest.raises(NotImplementedError, match="contains not"):
+        ser.str.contains("a", flags=1)
+
+
+@pytest.mark.parametrize(
+    "side, pat, na, exp",
+    [
+        ["startswith", "ab", None, [True, None, False]],
+        ["startswith", "b", False, [False, False, False]],
+        ["endswith", "b", True, [False, True, False]],
+        ["endswith", "bc", None, [True, None, False]],
+        ["startswith", ("a", "e", "g"), None, [True, None, True]],
+        ["endswith", ("a", "c", "g"), None, [True, None, True]],
+        ["startswith", (), None, [False, None, False]],
+        ["endswith", (), None, [False, None, False]],
+    ],
+)
+def test_str_start_ends_with(side, pat, na, exp):
+    ser = pd.Series(["abc", None, "efg"], dtype=ArrowDtype(pa.string()))
+    result = getattr(ser.str, side)(pat, na=na)
+    expected = pd.Series(exp, dtype=ArrowDtype(pa.bool_()))
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("side", ("startswith", "endswith"))
+def test_str_starts_ends_with_all_nulls_empty_tuple(side):
+    ser = pd.Series([None, None], dtype=ArrowDtype(pa.string()))
+    result = getattr(ser.str, side)(())
+
+    # bool datatype preserved for all nulls.
+    expected = pd.Series([None, None], dtype=ArrowDtype(pa.bool_()))
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "arg_name, arg",
+    [["pat", re.compile("b")], ["repl", str], ["case", False], ["flags", 1]],
+)
+def test_str_replace_unsupported(arg_name, arg):
+    ser = pd.Series(["abc", None], dtype=ArrowDtype(pa.string()))
+    kwargs = {"pat": "b", "repl": "x", "regex": True}
+    kwargs[arg_name] = arg
+    with pytest.raises(NotImplementedError, match="replace is not supported"):
+        ser.str.replace(**kwargs)
+
+
+@pytest.mark.parametrize(
+    "pat, repl, n, regex, exp",
+    [
+        ["a", "x", -1, False, ["xbxc", None]],
+        ["a", "x", 1, False, ["xbac", None]],
+        ["[a-b]", "x", -1, True, ["xxxc", None]],
+    ],
+)
+def test_str_replace(pat, repl, n, regex, exp):
+    ser = pd.Series(["abac", None], dtype=ArrowDtype(pa.string()))
+    result = ser.str.replace(pat, repl, n=n, regex=regex)
+    expected = pd.Series(exp, dtype=ArrowDtype(pa.string()))
+    tm.assert_series_equal(result, expected)
+
+
+def test_str_replace_negative_n():
+    # GH 56404
+    ser = pd.Series(["abc", "aaaaaa"], dtype=ArrowDtype(pa.string()))
+    actual = ser.str.replace("a", "", -3, True)
+    expected = pd.Series(["bc", ""], dtype=ArrowDtype(pa.string()))
+    tm.assert_series_equal(expected, actual)
+
+    # Same bug for pyarrow-backed StringArray GH#59628
+    ser2 = ser.astype(pd.StringDtype(storage="pyarrow"))
+    actual2 = ser2.str.replace("a", "", -3, True)
+    expected2 = expected.astype(ser2.dtype)
+    tm.assert_series_equal(expected2, actual2)
+
+    ser3 = ser.astype(pd.StringDtype(storage="pyarrow", na_value=np.nan))
+    actual3 = ser3.str.replace("a", "", -3, True)
+    expected3 = expected.astype(ser3.dtype)
+    tm.assert_series_equal(expected3, actual3)
+
+
+def test_str_repeat_unsupported():
+    ser = pd.Series(["abc", None], dtype=ArrowDtype(pa.string()))
+    with pytest.raises(NotImplementedError, match="repeat is not"):
+        ser.str.repeat([1, 2])
+
+
+def test_str_repeat():
+    ser = pd.Series(["abc", None], dtype=ArrowDtype(pa.string()))
+    result = ser.str.repeat(2)
+    expected = pd.Series(["abcabc", None], dtype=ArrowDtype(pa.string()))
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "pat, case, na, exp",
+    [
+        ["ab", False, None, [True, None]],
+        ["Ab", True, None, [False, None]],
+        ["bc", True, None, [False, None]],
+        ["ab", False, True, [True, True]],
+        ["a[a-z]{1}", False, None, [True, None]],
+        ["A[a-z]{1}", True, None, [False, None]],
+    ],
+)
+def test_str_match(pat, case, na, exp):
+    ser = pd.Series(["abc", None], dtype=ArrowDtype(pa.string()))
+    result = ser.str.match(pat, case=case, na=na)
+    expected = pd.Series(exp, dtype=ArrowDtype(pa.bool_()))
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "pat, case, na, exp",
+    # Note: keep cases in sync with
+    # pandas/tests/strings/test_find_replace.py::test_str_fullmatch_extra_cases
+    [
+        ["abc", False, None, [True, False, False, None]],
+        ["Abc", True, None, [False, False, False, None]],
+        ["bc", True, None, [False, False, False, None]],
+        ["ab", False, None, [False, False, False, None]],
+        ["a[a-z]{2}", False, None, [True, False, False, None]],
+        ["A[a-z]{1}", True, None, [False, False, False, None]],
+        # GH Issue: #56652
+        ["abc$", False, None, [True, False, False, None]],
+        ["abc\\$", False, None, [False, True, False, None]],
+        ["Abc$", True, None, [False, False, False, None]],
+        ["Abc\\$", True, None, [False, False, False, None]],
+        # https://github.com/pandas-dev/pandas/issues/61072
+        ["(abc)|(abx)", True, None, [True, False, False, None]],
+        ["((abc)|(abx))", True, None, [True, False, False, None]],
+    ],
+)
+def test_str_fullmatch(pat, case, na, exp):
+    ser = pd.Series(["abc", "abc$", "$abc", None], dtype=ArrowDtype(pa.string()))
+    result = ser.str.fullmatch(pat, case=case, na=na)
+    expected = pd.Series(exp, dtype=ArrowDtype(pa.bool_()))
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "sub, start, end, exp, exp_typ",
+    [["ab", 0, None, [0, None], pa.int32()], ["bc", 1, 3, [1, None], pa.int64()]],
+)
+def test_str_find(sub, start, end, exp, exp_typ):
+    ser = pd.Series(["abc", None], dtype=ArrowDtype(pa.string()))
+    result = ser.str.find(sub, start=start, end=end)
+    expected = pd.Series(exp, dtype=ArrowDtype(exp_typ))
+    tm.assert_series_equal(result, expected)
+
+
+def test_str_find_negative_start():
+    # GH 56411
+    ser = pd.Series(["abc", None], dtype=ArrowDtype(pa.string()))
+    result = ser.str.find(sub="b", start=-1000, end=3)
+    expected = pd.Series([1, None], dtype=ArrowDtype(pa.int64()))
+    tm.assert_series_equal(result, expected)
+
+
+def test_str_find_no_end():
+    ser = pd.Series(["abc", None], dtype=ArrowDtype(pa.string()))
+    result = ser.str.find("ab", start=1)
+    expected = pd.Series([-1, None], dtype="int64[pyarrow]")
+    tm.assert_series_equal(result, expected)
+
+
+def test_str_find_negative_start_negative_end():
+    # GH 56791
+    ser = pd.Series(["abcdefg", None], dtype=ArrowDtype(pa.string()))
+    result = ser.str.find(sub="d", start=-6, end=-3)
+    expected = pd.Series([3, None], dtype=ArrowDtype(pa.int64()))
+    tm.assert_series_equal(result, expected)
+
+
+def test_str_find_large_start():
+    # GH 56791
+    ser = pd.Series(["abcdefg", None], dtype=ArrowDtype(pa.string()))
+    result = ser.str.find(sub="d", start=16)
+    expected = pd.Series([-1, None], dtype=ArrowDtype(pa.int64()))
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.skipif(
+    pa_version_under13p0, reason="https://github.com/apache/arrow/issues/36311"
+)
+@pytest.mark.parametrize("start", [-15, -3, 0, 1, 15, None])
+@pytest.mark.parametrize("end", [-15, -1, 0, 3, 15, None])
+@pytest.mark.parametrize("sub", ["", "az", "abce", "a", "caa"])
+def test_str_find_e2e(start, end, sub):
+    s = pd.Series(
+        ["abcaadef", "abc", "abcdeddefgj8292", "ab", "a", ""],
+        dtype=ArrowDtype(pa.string()),
+    )
+    object_series = s.astype(pd.StringDtype(storage="python"))
+    result = s.str.find(sub, start, end)
+    expected = object_series.str.find(sub, start, end).astype(result.dtype)
+    tm.assert_series_equal(result, expected)
+
+    arrow_str_series = s.astype(pd.StringDtype(storage="pyarrow"))
+    result2 = arrow_str_series.str.find(sub, start, end).astype(result.dtype)
+    tm.assert_series_equal(result2, expected)
+
+
+def test_str_find_negative_start_negative_end_no_match():
+    # GH 56791
+    ser = pd.Series(["abcdefg", None], dtype=ArrowDtype(pa.string()))
+    result = ser.str.find(sub="d", start=-3, end=-6)
+    expected = pd.Series([-1, None], dtype=ArrowDtype(pa.int64()))
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "i, exp",
+    [
+        [1, ["b", "e", None]],
+        [-1, ["c", "e", None]],
+        [2, ["c", None, None]],
+        [-3, ["a", None, None]],
+        [4, [None, None, None]],
+    ],
+)
+def test_str_get(i, exp):
+    ser = pd.Series(["abc", "de", None], dtype=ArrowDtype(pa.string()))
+    result = ser.str.get(i)
+    expected = pd.Series(exp, dtype=ArrowDtype(pa.string()))
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.xfail(
+    reason="TODO: StringMethods._validate should support Arrow list types",
+    raises=AttributeError,
+)
+def test_str_join():
+    ser = pd.Series(ArrowExtensionArray(pa.array([list("abc"), list("123"), None])))
+    result = ser.str.join("=")
+    expected = pd.Series(["a=b=c", "1=2=3", None], dtype=ArrowDtype(pa.string()))
+    tm.assert_series_equal(result, expected)
+
+
+def test_str_join_string_type():
+    ser = pd.Series(ArrowExtensionArray(pa.array(["abc", "123", None])))
+    result = ser.str.join("=")
+    expected = pd.Series(["a=b=c", "1=2=3", None], dtype=ArrowDtype(pa.string()))
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "start, stop, step, exp",
+    [
+        [None, 2, None, ["ab", None]],
+        [None, 2, 1, ["ab", None]],
+        [1, 3, 1, ["bc", None]],
+        (None, None, -1, ["dcba", None]),
+    ],
+)
+def test_str_slice(start, stop, step, exp):
+    ser = pd.Series(["abcd", None], dtype=ArrowDtype(pa.string()))
+    result = ser.str.slice(start, stop, step)
+    expected = pd.Series(exp, dtype=ArrowDtype(pa.string()))
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "start, stop, repl, exp",
+    [
+        [1, 2, "x", ["axcd", None]],
+        [None, 2, "x", ["xcd", None]],
+        [None, 2, None, ["cd", None]],
+    ],
+)
+def test_str_slice_replace(start, stop, repl, exp):
+    ser = pd.Series(["abcd", None], dtype=ArrowDtype(pa.string()))
+    result = ser.str.slice_replace(start, stop, repl)
+    expected = pd.Series(exp, dtype=ArrowDtype(pa.string()))
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "value, method, exp",
+    [
+        ["a1c", "isalnum", True],
+        ["!|,", "isalnum", False],
+        ["aaa", "isalpha", True],
+        ["!!!", "isalpha", False],
+        ["٠", "isdecimal", True],  # noqa: RUF001
+        ["~!", "isdecimal", False],
+        ["2", "isdigit", True],
+        ["~", "isdigit", False],
+        ["aaa", "islower", True],
+        ["aaA", "islower", False],
+        ["123", "isnumeric", True],
+        ["11I", "isnumeric", False],
+        [" ", "isspace", True],
+        ["", "isspace", False],
+        ["The That", "istitle", True],
+        ["the That", "istitle", False],
+        ["AAA", "isupper", True],
+        ["AAc", "isupper", False],
+    ],
+)
+def test_str_is_functions(value, method, exp):
+    ser = pd.Series([value, None], dtype=ArrowDtype(pa.string()))
+    result = getattr(ser.str, method)()
+    expected = pd.Series([exp, None], dtype=ArrowDtype(pa.bool_()))
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "method, exp",
+    [
+        ["capitalize", "Abc def"],
+        ["title", "Abc Def"],
+        ["swapcase", "AbC Def"],
+        ["lower", "abc def"],
+        ["upper", "ABC DEF"],
+        ["casefold", "abc def"],
+    ],
+)
+def test_str_transform_functions(method, exp):
+    ser = pd.Series(["aBc dEF", None], dtype=ArrowDtype(pa.string()))
+    result = getattr(ser.str, method)()
+    expected = pd.Series([exp, None], dtype=ArrowDtype(pa.string()))
+    tm.assert_series_equal(result, expected)
+
+
+def test_str_len():
+    ser = pd.Series(["abcd", None], dtype=ArrowDtype(pa.string()))
+    result = ser.str.len()
+    expected = pd.Series([4, None], dtype=ArrowDtype(pa.int32()))
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "method, to_strip, val",
+    [
+        ["strip", None, " abc "],
+        ["strip", "x", "xabcx"],
+        ["lstrip", None, " abc"],
+        ["lstrip", "x", "xabc"],
+        ["rstrip", None, "abc "],
+        ["rstrip", "x", "abcx"],
+    ],
+)
+def test_str_strip(method, to_strip, val):
+    ser = pd.Series([val, None], dtype=ArrowDtype(pa.string()))
+    result = getattr(ser.str, method)(to_strip=to_strip)
+    expected = pd.Series(["abc", None], dtype=ArrowDtype(pa.string()))
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("val", ["abc123", "abc"])
+def test_str_removesuffix(val):
+    ser = pd.Series([val, None], dtype=ArrowDtype(pa.string()))
+    result = ser.str.removesuffix("123")
+    expected = pd.Series(["abc", None], dtype=ArrowDtype(pa.string()))
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("val", ["123abc", "abc"])
+def test_str_removeprefix(val):
+    ser = pd.Series([val, None], dtype=ArrowDtype(pa.string()))
+    result = ser.str.removeprefix("123")
+    expected = pd.Series(["abc", None], dtype=ArrowDtype(pa.string()))
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("errors", ["ignore", "strict"])
+@pytest.mark.parametrize(
+    "encoding, exp",
+    [
+        ["utf8", b"abc"],
+        ["utf32", b"\xff\xfe\x00\x00a\x00\x00\x00b\x00\x00\x00c\x00\x00\x00"],
+    ],
+)
+def test_str_encode(errors, encoding, exp):
+    ser = pd.Series(["abc", None], dtype=ArrowDtype(pa.string()))
+    result = ser.str.encode(encoding, errors)
+    expected = pd.Series([exp, None], dtype=ArrowDtype(pa.binary()))
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("flags", [0, 2])
+def test_str_findall(flags):
+    ser = pd.Series(["abc", "efg", None], dtype=ArrowDtype(pa.string()))
+    result = ser.str.findall("b", flags=flags)
+    expected = pd.Series([["b"], [], None], dtype=ArrowDtype(pa.list_(pa.string())))
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("method", ["index", "rindex"])
+@pytest.mark.parametrize(
+    "start, end",
+    [
+        [0, None],
+        [1, 4],
+    ],
+)
+def test_str_r_index(method, start, end):
+    ser = pd.Series(["abcba", None], dtype=ArrowDtype(pa.string()))
+    result = getattr(ser.str, method)("c", start, end)
+    expected = pd.Series([2, None], dtype=ArrowDtype(pa.int64()))
+    tm.assert_series_equal(result, expected)
+
+    with pytest.raises(ValueError, match="substring not found"):
+        getattr(ser.str, method)("foo", start, end)
+
+
+@pytest.mark.parametrize("form", ["NFC", "NFKC"])
+def test_str_normalize(form):
+    ser = pd.Series(["abc", None], dtype=ArrowDtype(pa.string()))
+    result = ser.str.normalize(form)
+    expected = ser.copy()
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "start, end",
+    [
+        [0, None],
+        [1, 4],
+    ],
+)
+def test_str_rfind(start, end):
+    ser = pd.Series(["abcba", "foo", None], dtype=ArrowDtype(pa.string()))
+    result = ser.str.rfind("c", start, end)
+    expected = pd.Series([2, -1, None], dtype=ArrowDtype(pa.int64()))
+    tm.assert_series_equal(result, expected)
+
+
+def test_str_translate():
+    ser = pd.Series(["abcba", None], dtype=ArrowDtype(pa.string()))
+    result = ser.str.translate({97: "b"})
+    expected = pd.Series(["bbcbb", None], dtype=ArrowDtype(pa.string()))
+    tm.assert_series_equal(result, expected)
+
+
+def test_str_wrap():
+    ser = pd.Series(["abcba", None], dtype=ArrowDtype(pa.string()))
+    result = ser.str.wrap(3)
+    expected = pd.Series(["abc\nba", None], dtype=ArrowDtype(pa.string()))
+    tm.assert_series_equal(result, expected)
+
+
+def test_get_dummies():
+    ser = pd.Series(["a|b", None, "a|c"], dtype=ArrowDtype(pa.string()))
+    result = ser.str.get_dummies()
+    expected = pd.DataFrame(
+        [[True, True, False], [False, False, False], [True, False, True]],
+        dtype=ArrowDtype(pa.bool_()),
+        columns=["a", "b", "c"],
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_str_partition():
+    ser = pd.Series(["abcba", None], dtype=ArrowDtype(pa.string()))
+    result = ser.str.partition("b")
+    expected = pd.DataFrame(
+        [["a", "b", "cba"], [None, None, None]], dtype=ArrowDtype(pa.string())
+    )
+    tm.assert_frame_equal(result, expected)
+
+    result = ser.str.partition("b", expand=False)
+    expected = pd.Series(ArrowExtensionArray(pa.array([["a", "b", "cba"], None])))
+    tm.assert_series_equal(result, expected)
+
+    result = ser.str.rpartition("b")
+    expected = pd.DataFrame(
+        [["abc", "b", "a"], [None, None, None]], dtype=ArrowDtype(pa.string())
+    )
+    tm.assert_frame_equal(result, expected)
+
+    result = ser.str.rpartition("b", expand=False)
+    expected = pd.Series(ArrowExtensionArray(pa.array([["abc", "b", "a"], None])))
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("method", ["rsplit", "split"])
+def test_str_split_pat_none(method):
+    # GH 56271
+    ser = pd.Series(["a1 cbc\nb", None], dtype=ArrowDtype(pa.string()))
+    result = getattr(ser.str, method)()
+    expected = pd.Series(ArrowExtensionArray(pa.array([["a1", "cbc", "b"], None])))
+    tm.assert_series_equal(result, expected)
+
+
+def test_str_split():
+    # GH 52401
+    ser = pd.Series(["a1cbcb", "a2cbcb", None], dtype=ArrowDtype(pa.string()))
+    result = ser.str.split("c")
+    expected = pd.Series(
+        ArrowExtensionArray(pa.array([["a1", "b", "b"], ["a2", "b", "b"], None]))
+    )
+    tm.assert_series_equal(result, expected)
+
+    result = ser.str.split("c", n=1)
+    expected = pd.Series(
+        ArrowExtensionArray(pa.array([["a1", "bcb"], ["a2", "bcb"], None]))
+    )
+    tm.assert_series_equal(result, expected)
+
+    result = ser.str.split("[1-2]", regex=True)
+    expected = pd.Series(
+        ArrowExtensionArray(pa.array([["a", "cbcb"], ["a", "cbcb"], None]))
+    )
+    tm.assert_series_equal(result, expected)
+
+    result = ser.str.split("[1-2]", regex=True, expand=True)
+    expected = pd.DataFrame(
+        {
+            0: ArrowExtensionArray(pa.array(["a", "a", None])),
+            1: ArrowExtensionArray(pa.array(["cbcb", "cbcb", None])),
+        }
+    )
+    tm.assert_frame_equal(result, expected)
+
+    result = ser.str.split("1", expand=True)
+    expected = pd.DataFrame(
+        {
+            0: ArrowExtensionArray(pa.array(["a", "a2cbcb", None])),
+            1: ArrowExtensionArray(pa.array(["cbcb", None, None])),
+        }
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_str_rsplit():
+    # GH 52401
+    ser = pd.Series(["a1cbcb", "a2cbcb", None], dtype=ArrowDtype(pa.string()))
+    result = ser.str.rsplit("c")
+    expected = pd.Series(
+        ArrowExtensionArray(pa.array([["a1", "b", "b"], ["a2", "b", "b"], None]))
+    )
+    tm.assert_series_equal(result, expected)
+
+    result = ser.str.rsplit("c", n=1)
+    expected = pd.Series(
+        ArrowExtensionArray(pa.array([["a1cb", "b"], ["a2cb", "b"], None]))
+    )
+    tm.assert_series_equal(result, expected)
+
+    result = ser.str.rsplit("c", n=1, expand=True)
+    expected = pd.DataFrame(
+        {
+            0: ArrowExtensionArray(pa.array(["a1cb", "a2cb", None])),
+            1: ArrowExtensionArray(pa.array(["b", "b", None])),
+        }
+    )
+    tm.assert_frame_equal(result, expected)
+
+    result = ser.str.rsplit("1", expand=True)
+    expected = pd.DataFrame(
+        {
+            0: ArrowExtensionArray(pa.array(["a", "a2cbcb", None])),
+            1: ArrowExtensionArray(pa.array(["cbcb", None, None])),
+        }
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_str_extract_non_symbolic():
+    ser = pd.Series(["a1", "b2", "c3"], dtype=ArrowDtype(pa.string()))
+    with pytest.raises(ValueError, match="pat=.* must contain a symbolic group name."):
+        ser.str.extract(r"[ab](\d)")
+
+
+@pytest.mark.parametrize("expand", [True, False])
+def test_str_extract(expand):
+    ser = pd.Series(["a1", "b2", "c3"], dtype=ArrowDtype(pa.string()))
+    result = ser.str.extract(r"(?P[ab])(?P\d)", expand=expand)
+    expected = pd.DataFrame(
+        {
+            "letter": ArrowExtensionArray(pa.array(["a", "b", None])),
+            "digit": ArrowExtensionArray(pa.array(["1", "2", None])),
+        }
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_str_extract_expand():
+    ser = pd.Series(["a1", "b2", "c3"], dtype=ArrowDtype(pa.string()))
+    result = ser.str.extract(r"[ab](?P\d)", expand=True)
+    expected = pd.DataFrame(
+        {
+            "digit": ArrowExtensionArray(pa.array(["1", "2", None])),
+        }
+    )
+    tm.assert_frame_equal(result, expected)
+
+    result = ser.str.extract(r"[ab](?P\d)", expand=False)
+    expected = pd.Series(ArrowExtensionArray(pa.array(["1", "2", None])), name="digit")
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("unit", ["ns", "us", "ms", "s"])
+def test_duration_from_strings_with_nat(unit):
+    # GH51175
+    strings = ["1000", "NaT"]
+    pa_type = pa.duration(unit)
+    result = ArrowExtensionArray._from_sequence_of_strings(strings, dtype=pa_type)
+    expected = ArrowExtensionArray(pa.array([1000, None], type=pa_type))
+    tm.assert_extension_array_equal(result, expected)
+
+
+def test_unsupported_dt(data):
+    pa_dtype = data.dtype.pyarrow_dtype
+    if not pa.types.is_temporal(pa_dtype):
+        with pytest.raises(
+            AttributeError, match="Can only use .dt accessor with datetimelike values"
+        ):
+            pd.Series(data).dt
+
+
+@pytest.mark.parametrize(
+    "prop, expected",
+    [
+        ["year", 2023],
+        ["day", 2],
+        ["day_of_week", 0],
+        ["dayofweek", 0],
+        ["weekday", 0],
+        ["day_of_year", 2],
+        ["dayofyear", 2],
+        ["hour", 3],
+        ["minute", 4],
+        ["is_leap_year", False],
+        ["microsecond", 5],
+        ["month", 1],
+        ["nanosecond", 6],
+        ["quarter", 1],
+        ["second", 7],
+        ["date", date(2023, 1, 2)],
+        ["time", time(3, 4, 7, 5)],
+    ],
+)
+def test_dt_properties(prop, expected):
+    ser = pd.Series(
+        [
+            pd.Timestamp(
+                year=2023,
+                month=1,
+                day=2,
+                hour=3,
+                minute=4,
+                second=7,
+                microsecond=5,
+                nanosecond=6,
+            ),
+            None,
+        ],
+        dtype=ArrowDtype(pa.timestamp("ns")),
+    )
+    result = getattr(ser.dt, prop)
+    exp_type = None
+    if isinstance(expected, date):
+        exp_type = pa.date32()
+    elif isinstance(expected, time):
+        exp_type = pa.time64("ns")
+    expected = pd.Series(ArrowExtensionArray(pa.array([expected, None], type=exp_type)))
+    tm.assert_series_equal(result, expected)
+
+
+def test_dt_is_month_start_end():
+    ser = pd.Series(
+        [
+            datetime(year=2023, month=12, day=2, hour=3),
+            datetime(year=2023, month=1, day=1, hour=3),
+            datetime(year=2023, month=3, day=31, hour=3),
+            None,
+        ],
+        dtype=ArrowDtype(pa.timestamp("us")),
+    )
+    result = ser.dt.is_month_start
+    expected = pd.Series([False, True, False, None], dtype=ArrowDtype(pa.bool_()))
+    tm.assert_series_equal(result, expected)
+
+    result = ser.dt.is_month_end
+    expected = pd.Series([False, False, True, None], dtype=ArrowDtype(pa.bool_()))
+    tm.assert_series_equal(result, expected)
+
+
+def test_dt_is_year_start_end():
+    ser = pd.Series(
+        [
+            datetime(year=2023, month=12, day=31, hour=3),
+            datetime(year=2023, month=1, day=1, hour=3),
+            datetime(year=2023, month=3, day=31, hour=3),
+            None,
+        ],
+        dtype=ArrowDtype(pa.timestamp("us")),
+    )
+    result = ser.dt.is_year_start
+    expected = pd.Series([False, True, False, None], dtype=ArrowDtype(pa.bool_()))
+    tm.assert_series_equal(result, expected)
+
+    result = ser.dt.is_year_end
+    expected = pd.Series([True, False, False, None], dtype=ArrowDtype(pa.bool_()))
+    tm.assert_series_equal(result, expected)
+
+
+def test_dt_is_quarter_start_end():
+    ser = pd.Series(
+        [
+            datetime(year=2023, month=11, day=30, hour=3),
+            datetime(year=2023, month=1, day=1, hour=3),
+            datetime(year=2023, month=3, day=31, hour=3),
+            None,
+        ],
+        dtype=ArrowDtype(pa.timestamp("us")),
+    )
+    result = ser.dt.is_quarter_start
+    expected = pd.Series([False, True, False, None], dtype=ArrowDtype(pa.bool_()))
+    tm.assert_series_equal(result, expected)
+
+    result = ser.dt.is_quarter_end
+    expected = pd.Series([False, False, True, None], dtype=ArrowDtype(pa.bool_()))
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("method", ["days_in_month", "daysinmonth"])
+def test_dt_days_in_month(method):
+    ser = pd.Series(
+        [
+            datetime(year=2023, month=3, day=30, hour=3),
+            datetime(year=2023, month=4, day=1, hour=3),
+            datetime(year=2023, month=2, day=3, hour=3),
+            None,
+        ],
+        dtype=ArrowDtype(pa.timestamp("us")),
+    )
+    result = getattr(ser.dt, method)
+    expected = pd.Series([31, 30, 28, None], dtype=ArrowDtype(pa.int64()))
+    tm.assert_series_equal(result, expected)
+
+
+def test_dt_normalize():
+    ser = pd.Series(
+        [
+            datetime(year=2023, month=3, day=30),
+            datetime(year=2023, month=4, day=1, hour=3),
+            datetime(year=2023, month=2, day=3, hour=23, minute=59, second=59),
+            None,
+        ],
+        dtype=ArrowDtype(pa.timestamp("us")),
+    )
+    result = ser.dt.normalize()
+    expected = pd.Series(
+        [
+            datetime(year=2023, month=3, day=30),
+            datetime(year=2023, month=4, day=1),
+            datetime(year=2023, month=2, day=3),
+            None,
+        ],
+        dtype=ArrowDtype(pa.timestamp("us")),
+    )
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("unit", ["us", "ns"])
+def test_dt_time_preserve_unit(unit):
+    ser = pd.Series(
+        [datetime(year=2023, month=1, day=2, hour=3), None],
+        dtype=ArrowDtype(pa.timestamp(unit)),
+    )
+    assert ser.dt.unit == unit
+
+    result = ser.dt.time
+    expected = pd.Series(
+        ArrowExtensionArray(pa.array([time(3, 0), None], type=pa.time64(unit)))
+    )
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("tz", [None, "UTC", "US/Pacific"])
+def test_dt_tz(tz):
+    ser = pd.Series(
+        [datetime(year=2023, month=1, day=2, hour=3), None],
+        dtype=ArrowDtype(pa.timestamp("ns", tz=tz)),
+    )
+    result = ser.dt.tz
+    assert result == timezones.maybe_get_tz(tz)
+
+
+def test_dt_isocalendar():
+    ser = pd.Series(
+        [datetime(year=2023, month=1, day=2, hour=3), None],
+        dtype=ArrowDtype(pa.timestamp("ns")),
+    )
+    result = ser.dt.isocalendar()
+    expected = pd.DataFrame(
+        [[2023, 1, 1], [0, 0, 0]],
+        columns=["year", "week", "day"],
+        dtype="int64[pyarrow]",
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "method, exp", [["day_name", "Sunday"], ["month_name", "January"]]
+)
+def test_dt_day_month_name(method, exp, request):
+    # GH 52388
+    _require_timezone_database(request)
+
+    ser = pd.Series([datetime(2023, 1, 1), None], dtype=ArrowDtype(pa.timestamp("ms")))
+    result = getattr(ser.dt, method)()
+    expected = pd.Series([exp, None], dtype=ArrowDtype(pa.string()))
+    tm.assert_series_equal(result, expected)
+
+
+def test_dt_strftime(request):
+    _require_timezone_database(request)
+
+    ser = pd.Series(
+        [datetime(year=2023, month=1, day=2, hour=3), None],
+        dtype=ArrowDtype(pa.timestamp("ns")),
+    )
+    result = ser.dt.strftime("%Y-%m-%dT%H:%M:%S")
+    expected = pd.Series(
+        ["2023-01-02T03:00:00.000000000", None], dtype=ArrowDtype(pa.string())
+    )
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("method", ["ceil", "floor", "round"])
+def test_dt_roundlike_tz_options_not_supported(method):
+    ser = pd.Series(
+        [datetime(year=2023, month=1, day=2, hour=3), None],
+        dtype=ArrowDtype(pa.timestamp("ns")),
+    )
+    with pytest.raises(NotImplementedError, match="ambiguous is not supported."):
+        getattr(ser.dt, method)("1h", ambiguous="NaT")
+
+    with pytest.raises(NotImplementedError, match="nonexistent is not supported."):
+        getattr(ser.dt, method)("1h", nonexistent="NaT")
+
+
+@pytest.mark.parametrize("method", ["ceil", "floor", "round"])
+def test_dt_roundlike_unsupported_freq(method):
+    ser = pd.Series(
+        [datetime(year=2023, month=1, day=2, hour=3), None],
+        dtype=ArrowDtype(pa.timestamp("ns")),
+    )
+    with pytest.raises(ValueError, match="freq='1B' is not supported"):
+        getattr(ser.dt, method)("1B")
+
+    with pytest.raises(ValueError, match="Must specify a valid frequency: None"):
+        getattr(ser.dt, method)(None)
+
+
+@pytest.mark.parametrize("freq", ["D", "h", "min", "s", "ms", "us", "ns"])
+@pytest.mark.parametrize("method", ["ceil", "floor", "round"])
+def test_dt_ceil_year_floor(freq, method):
+    ser = pd.Series(
+        [datetime(year=2023, month=1, day=1), None],
+    )
+    pa_dtype = ArrowDtype(pa.timestamp("ns"))
+    expected = getattr(ser.dt, method)(f"1{freq}").astype(pa_dtype)
+    result = getattr(ser.astype(pa_dtype).dt, method)(f"1{freq}")
+    tm.assert_series_equal(result, expected)
+
+
+def test_dt_to_pydatetime():
+    # GH 51859
+    data = [datetime(2022, 1, 1), datetime(2023, 1, 1)]
+    ser = pd.Series(data, dtype=ArrowDtype(pa.timestamp("ns")))
+
+    msg = "The behavior of ArrowTemporalProperties.to_pydatetime is deprecated"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        result = ser.dt.to_pydatetime()
+    expected = np.array(data, dtype=object)
+    tm.assert_numpy_array_equal(result, expected)
+    assert all(type(res) is datetime for res in result)
+
+    msg = "The behavior of DatetimeProperties.to_pydatetime is deprecated"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        expected = ser.astype("datetime64[ns]").dt.to_pydatetime()
+    tm.assert_numpy_array_equal(result, expected)
+
+
+@pytest.mark.parametrize("date_type", [32, 64])
+def test_dt_to_pydatetime_date_error(date_type):
+    # GH 52812
+    ser = pd.Series(
+        [date(2022, 12, 31)],
+        dtype=ArrowDtype(getattr(pa, f"date{date_type}")()),
+    )
+    msg = "The behavior of ArrowTemporalProperties.to_pydatetime is deprecated"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        with pytest.raises(ValueError, match="to_pydatetime cannot be called with"):
+            ser.dt.to_pydatetime()
+
+
+def test_dt_tz_localize_unsupported_tz_options():
+    ser = pd.Series(
+        [datetime(year=2023, month=1, day=2, hour=3), None],
+        dtype=ArrowDtype(pa.timestamp("ns")),
+    )
+    with pytest.raises(NotImplementedError, match="ambiguous='NaT' is not supported"):
+        ser.dt.tz_localize("UTC", ambiguous="NaT")
+
+    with pytest.raises(NotImplementedError, match="nonexistent='NaT' is not supported"):
+        ser.dt.tz_localize("UTC", nonexistent="NaT")
+
+
+def test_dt_tz_localize_none():
+    ser = pd.Series(
+        [datetime(year=2023, month=1, day=2, hour=3), None],
+        dtype=ArrowDtype(pa.timestamp("ns", tz="US/Pacific")),
+    )
+    result = ser.dt.tz_localize(None)
+    expected = pd.Series(
+        [datetime(year=2023, month=1, day=2, hour=3), None],
+        dtype=ArrowDtype(pa.timestamp("ns")),
+    )
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("unit", ["us", "ns"])
+def test_dt_tz_localize(unit, request):
+    _require_timezone_database(request)
+
+    ser = pd.Series(
+        [datetime(year=2023, month=1, day=2, hour=3), None],
+        dtype=ArrowDtype(pa.timestamp(unit)),
+    )
+    result = ser.dt.tz_localize("US/Pacific")
+    exp_data = pa.array(
+        [datetime(year=2023, month=1, day=2, hour=3), None], type=pa.timestamp(unit)
+    )
+    exp_data = pa.compute.assume_timezone(exp_data, "US/Pacific")
+    expected = pd.Series(ArrowExtensionArray(exp_data))
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "nonexistent, exp_date",
+    [
+        ["shift_forward", datetime(year=2023, month=3, day=12, hour=3)],
+        ["shift_backward", pd.Timestamp("2023-03-12 01:59:59.999999999")],
+    ],
+)
+def test_dt_tz_localize_nonexistent(nonexistent, exp_date, request):
+    _require_timezone_database(request)
+
+    ser = pd.Series(
+        [datetime(year=2023, month=3, day=12, hour=2, minute=30), None],
+        dtype=ArrowDtype(pa.timestamp("ns")),
+    )
+    result = ser.dt.tz_localize("US/Pacific", nonexistent=nonexistent)
+    exp_data = pa.array([exp_date, None], type=pa.timestamp("ns"))
+    exp_data = pa.compute.assume_timezone(exp_data, "US/Pacific")
+    expected = pd.Series(ArrowExtensionArray(exp_data))
+    tm.assert_series_equal(result, expected)
+
+
+def test_dt_tz_convert_not_tz_raises():
+    ser = pd.Series(
+        [datetime(year=2023, month=1, day=2, hour=3), None],
+        dtype=ArrowDtype(pa.timestamp("ns")),
+    )
+    with pytest.raises(TypeError, match="Cannot convert tz-naive timestamps"):
+        ser.dt.tz_convert("UTC")
+
+
+def test_dt_tz_convert_none():
+    ser = pd.Series(
+        [datetime(year=2023, month=1, day=2, hour=3), None],
+        dtype=ArrowDtype(pa.timestamp("ns", "US/Pacific")),
+    )
+    result = ser.dt.tz_convert(None)
+    expected = pd.Series(
+        [datetime(year=2023, month=1, day=2, hour=3), None],
+        dtype=ArrowDtype(pa.timestamp("ns")),
+    )
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("unit", ["us", "ns"])
+def test_dt_tz_convert(unit):
+    ser = pd.Series(
+        [datetime(year=2023, month=1, day=2, hour=3), None],
+        dtype=ArrowDtype(pa.timestamp(unit, "US/Pacific")),
+    )
+    result = ser.dt.tz_convert("US/Eastern")
+    expected = pd.Series(
+        [datetime(year=2023, month=1, day=2, hour=3), None],
+        dtype=ArrowDtype(pa.timestamp(unit, "US/Eastern")),
+    )
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("dtype", ["timestamp[ms][pyarrow]", "duration[ms][pyarrow]"])
+def test_as_unit(dtype):
+    # GH 52284
+    ser = pd.Series([1000, None], dtype=dtype)
+    result = ser.dt.as_unit("ns")
+    expected = ser.astype(dtype.replace("ms", "ns"))
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "prop, expected",
+    [
+        ["days", 1],
+        ["seconds", 2],
+        ["microseconds", 3],
+        ["nanoseconds", 4],
+    ],
+)
+def test_dt_timedelta_properties(prop, expected):
+    # GH 52284
+    ser = pd.Series(
+        [
+            pd.Timedelta(
+                days=1,
+                seconds=2,
+                microseconds=3,
+                nanoseconds=4,
+            ),
+            None,
+        ],
+        dtype=ArrowDtype(pa.duration("ns")),
+    )
+    result = getattr(ser.dt, prop)
+    expected = pd.Series(
+        ArrowExtensionArray(pa.array([expected, None], type=pa.int32()))
+    )
+    tm.assert_series_equal(result, expected)
+
+
+def test_dt_timedelta_total_seconds():
+    # GH 52284
+    ser = pd.Series(
+        [
+            pd.Timedelta(
+                days=1,
+                seconds=2,
+                microseconds=3,
+                nanoseconds=4,
+            ),
+            None,
+        ],
+        dtype=ArrowDtype(pa.duration("ns")),
+    )
+    result = ser.dt.total_seconds()
+    expected = pd.Series(
+        ArrowExtensionArray(pa.array([86402.000003, None], type=pa.float64()))
+    )
+    tm.assert_series_equal(result, expected)
+
+
+def test_dt_to_pytimedelta():
+    # GH 52284
+    data = [timedelta(1, 2, 3), timedelta(1, 2, 4)]
+    ser = pd.Series(data, dtype=ArrowDtype(pa.duration("ns")))
+
+    result = ser.dt.to_pytimedelta()
+    expected = np.array(data, dtype=object)
+    tm.assert_numpy_array_equal(result, expected)
+    assert all(type(res) is timedelta for res in result)
+
+    expected = ser.astype("timedelta64[ns]").dt.to_pytimedelta()
+    tm.assert_numpy_array_equal(result, expected)
+
+
+def test_dt_components():
+    # GH 52284
+    ser = pd.Series(
+        [
+            pd.Timedelta(
+                days=1,
+                seconds=2,
+                microseconds=3,
+                nanoseconds=4,
+            ),
+            None,
+        ],
+        dtype=ArrowDtype(pa.duration("ns")),
+    )
+    result = ser.dt.components
+    expected = pd.DataFrame(
+        [[1, 0, 0, 2, 0, 3, 4], [None, None, None, None, None, None, None]],
+        columns=[
+            "days",
+            "hours",
+            "minutes",
+            "seconds",
+            "milliseconds",
+            "microseconds",
+            "nanoseconds",
+        ],
+        dtype="int32[pyarrow]",
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("skipna", [True, False])
+def test_boolean_reduce_series_all_null(all_boolean_reductions, skipna):
+    # GH51624
+    ser = pd.Series([None], dtype="float64[pyarrow]")
+    result = getattr(ser, all_boolean_reductions)(skipna=skipna)
+    if skipna:
+        expected = all_boolean_reductions == "all"
+    else:
+        expected = pd.NA
+    assert result is expected
+
+
+def test_from_sequence_of_strings_boolean():
+    true_strings = ["true", "TRUE", "True", "1", "1.0"]
+    false_strings = ["false", "FALSE", "False", "0", "0.0"]
+    nulls = [None]
+    strings = true_strings + false_strings + nulls
+    bools = (
+        [True] * len(true_strings) + [False] * len(false_strings) + [None] * len(nulls)
+    )
+
+    result = ArrowExtensionArray._from_sequence_of_strings(strings, dtype=pa.bool_())
+    expected = pd.array(bools, dtype="boolean[pyarrow]")
+    tm.assert_extension_array_equal(result, expected)
+
+    strings = ["True", "foo"]
+    with pytest.raises(pa.ArrowInvalid, match="Failed to parse"):
+        ArrowExtensionArray._from_sequence_of_strings(strings, dtype=pa.bool_())
+
+
+def test_concat_empty_arrow_backed_series(dtype):
+    # GH#51734
+    ser = pd.Series([], dtype=dtype)
+    expected = ser.copy()
+    result = pd.concat([ser[np.array([], dtype=np.bool_)]])
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("dtype", ["string", "string[pyarrow]"])
+def test_series_from_string_array(dtype):
+    arr = pa.array("the quick brown fox".split())
+    ser = pd.Series(arr, dtype=dtype)
+    expected = pd.Series(ArrowExtensionArray(arr), dtype=dtype)
+    tm.assert_series_equal(ser, expected)
+
+
+# _data was renamed to _pa_data
+class OldArrowExtensionArray(ArrowExtensionArray):
+    def __getstate__(self):
+        state = super().__getstate__()
+        state["_data"] = state.pop("_pa_array")
+        return state
+
+
+def test_pickle_old_arrowextensionarray():
+    data = pa.array([1])
+    expected = OldArrowExtensionArray(data)
+    result = pickle.loads(pickle.dumps(expected))
+    tm.assert_extension_array_equal(result, expected)
+    assert result._pa_array == pa.chunked_array(data)
+    assert not hasattr(result, "_data")
+
+
+def test_setitem_boolean_replace_with_mask_segfault():
+    # GH#52059
+    N = 145_000
+    arr = ArrowExtensionArray(pa.chunked_array([np.ones((N,), dtype=np.bool_)]))
+    expected = arr.copy()
+    arr[np.zeros((N,), dtype=np.bool_)] = False
+    assert arr._pa_array == expected._pa_array
+
+
+@pytest.mark.parametrize(
+    "data, arrow_dtype",
+    [
+        ([b"a", b"b"], pa.large_binary()),
+        (["a", "b"], pa.large_string()),
+    ],
+)
+def test_conversion_large_dtypes_from_numpy_array(data, arrow_dtype):
+    dtype = ArrowDtype(arrow_dtype)
+    result = pd.array(np.array(data), dtype=dtype)
+    expected = pd.array(data, dtype=dtype)
+    tm.assert_extension_array_equal(result, expected)
+
+
+def test_concat_null_array():
+    df = pd.DataFrame({"a": [None, None]}, dtype=ArrowDtype(pa.null()))
+    df2 = pd.DataFrame({"a": [0, 1]}, dtype="int64[pyarrow]")
+
+    result = pd.concat([df, df2], ignore_index=True)
+    expected = pd.DataFrame({"a": [None, None, 0, 1]}, dtype="int64[pyarrow]")
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("pa_type", tm.ALL_INT_PYARROW_DTYPES + tm.FLOAT_PYARROW_DTYPES)
+def test_describe_numeric_data(pa_type):
+    # GH 52470
+    data = pd.Series([1, 2, 3], dtype=ArrowDtype(pa_type))
+    result = data.describe()
+    expected = pd.Series(
+        [3, 2, 1, 1, 1.5, 2.0, 2.5, 3],
+        dtype=ArrowDtype(pa.float64()),
+        index=["count", "mean", "std", "min", "25%", "50%", "75%", "max"],
+    )
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("pa_type", tm.TIMEDELTA_PYARROW_DTYPES)
+def test_describe_timedelta_data(pa_type):
+    # GH53001
+    data = pd.Series(range(1, 10), dtype=ArrowDtype(pa_type))
+    result = data.describe()
+    expected = pd.Series(
+        [9] + pd.to_timedelta([5, 2, 1, 3, 5, 7, 9], unit=pa_type.unit).tolist(),
+        dtype=object,
+        index=["count", "mean", "std", "min", "25%", "50%", "75%", "max"],
+    )
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("pa_type", tm.DATETIME_PYARROW_DTYPES)
+def test_describe_datetime_data(pa_type):
+    # GH53001
+    data = pd.Series(range(1, 10), dtype=ArrowDtype(pa_type))
+    result = data.describe()
+    expected = pd.Series(
+        [9]
+        + [
+            pd.Timestamp(v, tz=pa_type.tz, unit=pa_type.unit)
+            for v in [5, 1, 3, 5, 7, 9]
+        ],
+        dtype=object,
+        index=["count", "mean", "min", "25%", "50%", "75%", "max"],
+    )
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "pa_type", tm.DATETIME_PYARROW_DTYPES + tm.TIMEDELTA_PYARROW_DTYPES
+)
+def test_quantile_temporal(pa_type):
+    # GH52678
+    data = [1, 2, 3]
+    ser = pd.Series(data, dtype=ArrowDtype(pa_type))
+    result = ser.quantile(0.1)
+    expected = ser[0]
+    assert result == expected
+
+
+def test_date32_repr():
+    # GH48238
+    arrow_dt = pa.array([date.fromisoformat("2020-01-01")], type=pa.date32())
+    ser = pd.Series(arrow_dt, dtype=ArrowDtype(arrow_dt.type))
+    assert repr(ser) == "0    2020-01-01\ndtype: date32[day][pyarrow]"
+
+
+def test_duration_overflow_from_ndarray_containing_nat():
+    # GH52843
+    data_ts = pd.to_datetime([1, None])
+    data_td = pd.to_timedelta([1, None])
+    ser_ts = pd.Series(data_ts, dtype=ArrowDtype(pa.timestamp("ns")))
+    ser_td = pd.Series(data_td, dtype=ArrowDtype(pa.duration("ns")))
+    result = ser_ts + ser_td
+    expected = pd.Series([2, None], dtype=ArrowDtype(pa.timestamp("ns")))
+    tm.assert_series_equal(result, expected)
+
+
+def test_infer_dtype_pyarrow_dtype(data, request):
+    res = lib.infer_dtype(data)
+    assert res != "unknown-array"
+
+    if data._hasna and res in ["floating", "datetime64", "timedelta64"]:
+        mark = pytest.mark.xfail(
+            reason="in infer_dtype pd.NA is not ignored in these cases "
+            "even with skipna=True in the list(data) check below"
+        )
+        request.applymarker(mark)
+
+    assert res == lib.infer_dtype(list(data), skipna=True)
+
+
+@pytest.mark.parametrize(
+    "pa_type", tm.DATETIME_PYARROW_DTYPES + tm.TIMEDELTA_PYARROW_DTYPES
+)
+def test_from_sequence_temporal(pa_type):
+    # GH 53171
+    val = 3
+    unit = pa_type.unit
+    if pa.types.is_duration(pa_type):
+        seq = [pd.Timedelta(val, unit=unit).as_unit(unit)]
+    else:
+        seq = [pd.Timestamp(val, unit=unit, tz=pa_type.tz).as_unit(unit)]
+
+    result = ArrowExtensionArray._from_sequence(seq, dtype=pa_type)
+    expected = ArrowExtensionArray(pa.array([val], type=pa_type))
+    tm.assert_extension_array_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "pa_type", tm.DATETIME_PYARROW_DTYPES + tm.TIMEDELTA_PYARROW_DTYPES
+)
+def test_setitem_temporal(pa_type):
+    # GH 53171
+    unit = pa_type.unit
+    if pa.types.is_duration(pa_type):
+        val = pd.Timedelta(1, unit=unit).as_unit(unit)
+    else:
+        val = pd.Timestamp(1, unit=unit, tz=pa_type.tz).as_unit(unit)
+
+    arr = ArrowExtensionArray(pa.array([1, 2, 3], type=pa_type))
+
+    result = arr.copy()
+    result[:] = val
+    expected = ArrowExtensionArray(pa.array([1, 1, 1], type=pa_type))
+    tm.assert_extension_array_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "pa_type", tm.DATETIME_PYARROW_DTYPES + tm.TIMEDELTA_PYARROW_DTYPES
+)
+def test_arithmetic_temporal(pa_type, request):
+    # GH 53171
+    arr = ArrowExtensionArray(pa.array([1, 2, 3], type=pa_type))
+    unit = pa_type.unit
+    result = arr - pd.Timedelta(1, unit=unit).as_unit(unit)
+    expected = ArrowExtensionArray(pa.array([0, 1, 2], type=pa_type))
+    tm.assert_extension_array_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "pa_type", tm.DATETIME_PYARROW_DTYPES + tm.TIMEDELTA_PYARROW_DTYPES
+)
+def test_comparison_temporal(pa_type):
+    # GH 53171
+    unit = pa_type.unit
+    if pa.types.is_duration(pa_type):
+        val = pd.Timedelta(1, unit=unit).as_unit(unit)
+    else:
+        val = pd.Timestamp(1, unit=unit, tz=pa_type.tz).as_unit(unit)
+
+    arr = ArrowExtensionArray(pa.array([1, 2, 3], type=pa_type))
+
+    result = arr > val
+    expected = ArrowExtensionArray(pa.array([False, True, True], type=pa.bool_()))
+    tm.assert_extension_array_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "pa_type", tm.DATETIME_PYARROW_DTYPES + tm.TIMEDELTA_PYARROW_DTYPES
+)
+def test_getitem_temporal(pa_type):
+    # GH 53326
+    arr = ArrowExtensionArray(pa.array([1, 2, 3], type=pa_type))
+    result = arr[1]
+    if pa.types.is_duration(pa_type):
+        expected = pd.Timedelta(2, unit=pa_type.unit).as_unit(pa_type.unit)
+        assert isinstance(result, pd.Timedelta)
+    else:
+        expected = pd.Timestamp(2, unit=pa_type.unit, tz=pa_type.tz).as_unit(
+            pa_type.unit
+        )
+        assert isinstance(result, pd.Timestamp)
+    assert result.unit == expected.unit
+    assert result == expected
+
+
+@pytest.mark.parametrize(
+    "pa_type", tm.DATETIME_PYARROW_DTYPES + tm.TIMEDELTA_PYARROW_DTYPES
+)
+def test_iter_temporal(pa_type):
+    # GH 53326
+    arr = ArrowExtensionArray(pa.array([1, None], type=pa_type))
+    result = list(arr)
+    if pa.types.is_duration(pa_type):
+        expected = [
+            pd.Timedelta(1, unit=pa_type.unit).as_unit(pa_type.unit),
+            pd.NA,
+        ]
+        assert isinstance(result[0], pd.Timedelta)
+    else:
+        expected = [
+            pd.Timestamp(1, unit=pa_type.unit, tz=pa_type.tz).as_unit(pa_type.unit),
+            pd.NA,
+        ]
+        assert isinstance(result[0], pd.Timestamp)
+    assert result[0].unit == expected[0].unit
+    assert result == expected
+
+
+def test_groupby_series_size_returns_pa_int(data):
+    # GH 54132
+    ser = pd.Series(data[:3], index=["a", "a", "b"])
+    result = ser.groupby(level=0).size()
+    expected = pd.Series([2, 1], dtype="int64[pyarrow]", index=["a", "b"])
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "pa_type", tm.DATETIME_PYARROW_DTYPES + tm.TIMEDELTA_PYARROW_DTYPES, ids=repr
+)
+@pytest.mark.parametrize("dtype", [None, object])
+def test_to_numpy_temporal(pa_type, dtype):
+    # GH 53326
+    # GH 55997: Return datetime64/timedelta64 types with NaT if possible
+    arr = ArrowExtensionArray(pa.array([1, None], type=pa_type))
+    result = arr.to_numpy(dtype=dtype)
+    if pa.types.is_duration(pa_type):
+        value = pd.Timedelta(1, unit=pa_type.unit).as_unit(pa_type.unit)
+    else:
+        value = pd.Timestamp(1, unit=pa_type.unit, tz=pa_type.tz).as_unit(pa_type.unit)
+
+    if dtype == object or (pa.types.is_timestamp(pa_type) and pa_type.tz is not None):
+        if dtype == object:
+            na = pd.NA
+        else:
+            na = pd.NaT
+        expected = np.array([value, na], dtype=object)
+        assert result[0].unit == value.unit
+    else:
+        na = pa_type.to_pandas_dtype().type("nat", pa_type.unit)
+        value = value.to_numpy()
+        expected = np.array([value, na])
+        assert np.datetime_data(result[0])[0] == pa_type.unit
+    tm.assert_numpy_array_equal(result, expected)
+
+
+def test_groupby_count_return_arrow_dtype(data_missing):
+    df = pd.DataFrame({"A": [1, 1], "B": data_missing, "C": data_missing})
+    result = df.groupby("A").count()
+    expected = pd.DataFrame(
+        [[1, 1]],
+        index=pd.Index([1], name="A"),
+        columns=["B", "C"],
+        dtype="int64[pyarrow]",
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_fixed_size_list():
+    # GH#55000
+    ser = pd.Series(
+        [[1, 2], [3, 4]], dtype=ArrowDtype(pa.list_(pa.int64(), list_size=2))
+    )
+    result = ser.dtype.type
+    assert result == list
+
+
+def test_arrowextensiondtype_dataframe_repr():
+    # GH 54062
+    df = pd.DataFrame(
+        pd.period_range("2012", periods=3),
+        columns=["col"],
+        dtype=ArrowDtype(ArrowPeriodType("D")),
+    )
+    result = repr(df)
+    # TODO: repr value may not be expected; address how
+    # pyarrow.ExtensionType values are displayed
+    expected = "     col\n0  15340\n1  15341\n2  15342"
+    assert result == expected
+
+
+def test_pow_missing_operand():
+    # GH 55512
+    k = pd.Series([2, None], dtype="int64[pyarrow]")
+    result = k.pow(None, fill_value=3)
+    expected = pd.Series([8, None], dtype="int64[pyarrow]")
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("pa_type", tm.TIMEDELTA_PYARROW_DTYPES)
+def test_duration_fillna_numpy(pa_type):
+    # GH 54707
+    ser1 = pd.Series([None, 2], dtype=ArrowDtype(pa_type))
+    ser2 = pd.Series(np.array([1, 3], dtype=f"m8[{pa_type.unit}]"))
+    result = ser1.fillna(ser2)
+    expected = pd.Series([1, 2], dtype=ArrowDtype(pa_type))
+    tm.assert_series_equal(result, expected)
+
+
+def test_comparison_not_propagating_arrow_error():
+    # GH#54944
+    a = pd.Series([1 << 63], dtype="uint64[pyarrow]")
+    b = pd.Series([None], dtype="int64[pyarrow]")
+    with pytest.raises(pa.lib.ArrowInvalid, match="Integer value"):
+        a < b
+
+
+def test_factorize_chunked_dictionary():
+    # GH 54844
+    pa_array = pa.chunked_array(
+        [pa.array(["a"]).dictionary_encode(), pa.array(["b"]).dictionary_encode()]
+    )
+    ser = pd.Series(ArrowExtensionArray(pa_array))
+    res_indices, res_uniques = ser.factorize()
+    exp_indicies = np.array([0, 1], dtype=np.intp)
+    exp_uniques = pd.Index(ArrowExtensionArray(pa_array.combine_chunks()))
+    tm.assert_numpy_array_equal(res_indices, exp_indicies)
+    tm.assert_index_equal(res_uniques, exp_uniques)
+
+
+def test_dictionary_astype_categorical():
+    # GH#56672
+    arrs = [
+        pa.array(np.array(["a", "x", "c", "a"])).dictionary_encode(),
+        pa.array(np.array(["a", "d", "c"])).dictionary_encode(),
+    ]
+    ser = pd.Series(ArrowExtensionArray(pa.chunked_array(arrs)))
+    result = ser.astype("category")
+    categories = pd.Index(["a", "x", "c", "d"], dtype=ArrowDtype(pa.string()))
+    expected = pd.Series(
+        ["a", "x", "c", "a", "a", "d", "c"],
+        dtype=pd.CategoricalDtype(categories=categories),
+    )
+    tm.assert_series_equal(result, expected)
+
+
+def test_arrow_floordiv():
+    # GH 55561
+    a = pd.Series([-7], dtype="int64[pyarrow]")
+    b = pd.Series([4], dtype="int64[pyarrow]")
+    expected = pd.Series([-2], dtype="int64[pyarrow]")
+    result = a // b
+    tm.assert_series_equal(result, expected)
+
+
+def test_arrow_floordiv_large_values():
+    # GH 56645
+    a = pd.Series([1425801600000000000], dtype="int64[pyarrow]")
+    expected = pd.Series([1425801600000], dtype="int64[pyarrow]")
+    result = a // 1_000_000
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("dtype", ["int64[pyarrow]", "uint64[pyarrow]"])
+def test_arrow_floordiv_large_integral_result(dtype):
+    # GH 56676
+    a = pd.Series([18014398509481983], dtype=dtype)
+    result = a // 1
+    tm.assert_series_equal(result, a)
+
+
+@pytest.mark.parametrize("pa_type", tm.SIGNED_INT_PYARROW_DTYPES)
+def test_arrow_floordiv_larger_divisor(pa_type):
+    # GH 56676
+    dtype = ArrowDtype(pa_type)
+    a = pd.Series([-23], dtype=dtype)
+    result = a // 24
+    expected = pd.Series([-1], dtype=dtype)
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("pa_type", tm.SIGNED_INT_PYARROW_DTYPES)
+def test_arrow_floordiv_integral_invalid(pa_type):
+    # GH 56676
+    min_value = np.iinfo(pa_type.to_pandas_dtype()).min
+    a = pd.Series([min_value], dtype=ArrowDtype(pa_type))
+    with pytest.raises(pa.lib.ArrowInvalid, match="overflow|not in range"):
+        a // -1
+    with pytest.raises(pa.lib.ArrowInvalid, match="divide by zero"):
+        a // 0
+
+
+@pytest.mark.parametrize("dtype", tm.FLOAT_PYARROW_DTYPES_STR_REPR)
+def test_arrow_floordiv_floating_0_divisor(dtype):
+    # GH 56676
+    a = pd.Series([2], dtype=dtype)
+    result = a // 0
+    expected = pd.Series([float("inf")], dtype=dtype)
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("dtype", ["float64", "datetime64[ns]", "timedelta64[ns]"])
+def test_astype_int_with_null_to_numpy_dtype(dtype):
+    # GH 57093
+    ser = pd.Series([1, None], dtype="int64[pyarrow]")
+    result = ser.astype(dtype)
+    expected = pd.Series([1, None], dtype=dtype)
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("pa_type", tm.ALL_INT_PYARROW_DTYPES)
+def test_arrow_integral_floordiv_large_values(pa_type):
+    # GH 56676
+    max_value = np.iinfo(pa_type.to_pandas_dtype()).max
+    dtype = ArrowDtype(pa_type)
+    a = pd.Series([max_value], dtype=dtype)
+    b = pd.Series([1], dtype=dtype)
+    result = a // b
+    tm.assert_series_equal(result, a)
+
+
+@pytest.mark.parametrize("dtype", ["int64[pyarrow]", "uint64[pyarrow]"])
+def test_arrow_true_division_large_divisor(dtype):
+    # GH 56706
+    a = pd.Series([0], dtype=dtype)
+    b = pd.Series([18014398509481983], dtype=dtype)
+    expected = pd.Series([0], dtype="float64[pyarrow]")
+    result = a / b
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("dtype", ["int64[pyarrow]", "uint64[pyarrow]"])
+def test_arrow_floor_division_large_divisor(dtype):
+    # GH 56706
+    a = pd.Series([0], dtype=dtype)
+    b = pd.Series([18014398509481983], dtype=dtype)
+    expected = pd.Series([0], dtype=dtype)
+    result = a // b
+    tm.assert_series_equal(result, expected)
+
+
+def test_string_to_datetime_parsing_cast():
+    # GH 56266
+    string_dates = ["2020-01-01 04:30:00", "2020-01-02 00:00:00", "2020-01-03 00:00:00"]
+    result = pd.Series(string_dates, dtype="timestamp[ns][pyarrow]")
+    expected = pd.Series(
+        ArrowExtensionArray(pa.array(pd.to_datetime(string_dates), from_pandas=True))
+    )
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.skipif(
+    pa_version_under13p0, reason="pairwise_diff_checked not implemented in pyarrow"
+)
+def test_interpolate_not_numeric(data):
+    if not data.dtype._is_numeric:
+        ser = pd.Series(data)
+        msg = re.escape(f"Cannot interpolate with {ser.dtype} dtype")
+        with pytest.raises(TypeError, match=msg):
+            pd.Series(data).interpolate()
+
+
+def test_string_to_time_parsing_cast():
+    # GH 56463
+    string_times = ["11:41:43.076160"]
+    result = pd.Series(string_times, dtype="time64[us][pyarrow]")
+    expected = pd.Series(
+        ArrowExtensionArray(pa.array([time(11, 41, 43, 76160)], from_pandas=True))
+    )
+    tm.assert_series_equal(result, expected)
+
+
+def test_to_numpy_float():
+    # GH#56267
+    ser = pd.Series([32, 40, None], dtype="float[pyarrow]")
+    result = ser.astype("float64")
+    expected = pd.Series([32, 40, np.nan], dtype="float64")
+    tm.assert_series_equal(result, expected)
+
+
+def test_to_numpy_timestamp_to_int():
+    # GH 55997
+    ser = pd.Series(["2020-01-01 04:30:00"], dtype="timestamp[ns][pyarrow]")
+    result = ser.to_numpy(dtype=np.int64)
+    expected = np.array([1577853000000000000])
+    tm.assert_numpy_array_equal(result, expected)
+
+
+def test_map_numeric_na_action():
+    ser = pd.Series([32, 40, None], dtype="int64[pyarrow]")
+    result = ser.map(lambda x: 42, na_action="ignore")
+    expected = pd.Series([42.0, 42.0, np.nan], dtype="float64")
+    tm.assert_series_equal(result, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/test_categorical.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/test_categorical.py
new file mode 100644
index 0000000000000000000000000000000000000000..135ea67c924d0b04a28ad8a3eba4f39d43a0b44a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/test_categorical.py
@@ -0,0 +1,200 @@
+"""
+This file contains a minimal set of tests for compliance with the extension
+array interface test suite, and should contain no other tests.
+The test suite for the full functionality of the array is located in
+`pandas/tests/arrays/`.
+
+The tests in this file are inherited from the BaseExtensionTests, and only
+minimal tweaks should be applied to get the tests passing (by overwriting a
+parent method).
+
+Additional tests should either be added to one of the BaseExtensionTests
+classes (if they are relevant for the extension interface for all dtypes), or
+be added to the array-specific tests in `pandas/tests/arrays/`.
+
+"""
+import string
+
+import numpy as np
+import pytest
+
+from pandas._config import using_string_dtype
+
+import pandas as pd
+from pandas import Categorical
+import pandas._testing as tm
+from pandas.api.types import CategoricalDtype
+from pandas.tests.extension import base
+
+
+def make_data():
+    while True:
+        values = np.random.default_rng(2).choice(list(string.ascii_letters), size=100)
+        # ensure we meet the requirements
+        # 1. first two not null
+        # 2. first and second are different
+        if values[0] != values[1]:
+            break
+    return values
+
+
+@pytest.fixture
+def dtype():
+    return CategoricalDtype()
+
+
+@pytest.fixture
+def data():
+    """Length-100 array for this type.
+
+    * data[0] and data[1] should both be non missing
+    * data[0] and data[1] should not be equal
+    """
+    return Categorical(make_data())
+
+
+@pytest.fixture
+def data_missing():
+    """Length 2 array with [NA, Valid]"""
+    return Categorical([np.nan, "A"])
+
+
+@pytest.fixture
+def data_for_sorting():
+    return Categorical(["A", "B", "C"], categories=["C", "A", "B"], ordered=True)
+
+
+@pytest.fixture
+def data_missing_for_sorting():
+    return Categorical(["A", None, "B"], categories=["B", "A"], ordered=True)
+
+
+@pytest.fixture
+def data_for_grouping():
+    return Categorical(["a", "a", None, None, "b", "b", "a", "c"])
+
+
+class TestCategorical(base.ExtensionTests):
+    @pytest.mark.xfail(reason="Memory usage doesn't match")
+    def test_memory_usage(self, data):
+        # TODO: Is this deliberate?
+        super().test_memory_usage(data)
+
+    def test_contains(self, data, data_missing):
+        # GH-37867
+        # na value handling in Categorical.__contains__ is deprecated.
+        # See base.BaseInterFaceTests.test_contains for more details.
+
+        na_value = data.dtype.na_value
+        # ensure data without missing values
+        data = data[~data.isna()]
+
+        # first elements are non-missing
+        assert data[0] in data
+        assert data_missing[0] in data_missing
+
+        # check the presence of na_value
+        assert na_value in data_missing
+        assert na_value not in data
+
+        # Categoricals can contain other nan-likes than na_value
+        for na_value_obj in tm.NULL_OBJECTS:
+            if na_value_obj is na_value:
+                continue
+            assert na_value_obj not in data
+            # this section suffers from super method
+            if not using_string_dtype():
+                assert na_value_obj in data_missing
+
+    def test_empty(self, dtype):
+        cls = dtype.construct_array_type()
+        result = cls._empty((4,), dtype=dtype)
+
+        assert isinstance(result, cls)
+        # the dtype we passed is not initialized, so will not match the
+        #  dtype on our result.
+        assert result.dtype == CategoricalDtype([])
+
+    @pytest.mark.skip(reason="Backwards compatibility")
+    def test_getitem_scalar(self, data):
+        # CategoricalDtype.type isn't "correct" since it should
+        # be a parent of the elements (object). But don't want
+        # to break things by changing.
+        super().test_getitem_scalar(data)
+
+    @pytest.mark.xfail(reason="Unobserved categories included")
+    def test_value_counts(self, all_data, dropna):
+        return super().test_value_counts(all_data, dropna)
+
+    def test_combine_add(self, data_repeated):
+        # GH 20825
+        # When adding categoricals in combine, result is a string
+        orig_data1, orig_data2 = data_repeated(2)
+        s1 = pd.Series(orig_data1)
+        s2 = pd.Series(orig_data2)
+        result = s1.combine(s2, lambda x1, x2: x1 + x2)
+        expected = pd.Series(
+            [a + b for (a, b) in zip(list(orig_data1), list(orig_data2))]
+        )
+        tm.assert_series_equal(result, expected)
+
+        val = s1.iloc[0]
+        result = s1.combine(val, lambda x1, x2: x1 + x2)
+        expected = pd.Series([a + val for a in list(orig_data1)])
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("na_action", [None, "ignore"])
+    def test_map(self, data, na_action):
+        result = data.map(lambda x: x, na_action=na_action)
+        tm.assert_extension_array_equal(result, data)
+
+    def test_arith_frame_with_scalar(self, data, all_arithmetic_operators, request):
+        # frame & scalar
+        op_name = all_arithmetic_operators
+        if op_name == "__rmod__":
+            request.applymarker(
+                pytest.mark.xfail(
+                    reason="rmod never called when string is first argument"
+                )
+            )
+        super().test_arith_frame_with_scalar(data, op_name)
+
+    def test_arith_series_with_scalar(self, data, all_arithmetic_operators, request):
+        op_name = all_arithmetic_operators
+        if op_name == "__rmod__":
+            request.applymarker(
+                pytest.mark.xfail(
+                    reason="rmod never called when string is first argument"
+                )
+            )
+        super().test_arith_series_with_scalar(data, op_name)
+
+    def _compare_other(self, ser: pd.Series, data, op, other):
+        op_name = f"__{op.__name__}__"
+        if op_name not in ["__eq__", "__ne__"]:
+            msg = "Unordered Categoricals can only compare equality or not"
+            with pytest.raises(TypeError, match=msg):
+                op(data, other)
+        else:
+            return super()._compare_other(ser, data, op, other)
+
+    @pytest.mark.xfail(reason="Categorical overrides __repr__")
+    @pytest.mark.parametrize("size", ["big", "small"])
+    def test_array_repr(self, data, size):
+        super().test_array_repr(data, size)
+
+    @pytest.mark.xfail(reason="TBD")
+    @pytest.mark.parametrize("as_index", [True, False])
+    def test_groupby_extension_agg(self, as_index, data_for_grouping):
+        super().test_groupby_extension_agg(as_index, data_for_grouping)
+
+
+class Test2DCompat(base.NDArrayBacked2DTests):
+    def test_repr_2d(self, data):
+        # Categorical __repr__ doesn't include "Categorical", so we need
+        #  to special-case
+        res = repr(data.reshape(1, -1))
+        assert res.count("\nCategories") == 1
+
+        res = repr(data.reshape(-1, 1))
+        assert res.count("\nCategories") == 1
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/test_common.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/test_common.py
new file mode 100644
index 0000000000000000000000000000000000000000..5eda0f00f54cae1002b0e7e60e9de765870a9ad8
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/test_common.py
@@ -0,0 +1,105 @@
+import numpy as np
+import pytest
+
+from pandas.core.dtypes import dtypes
+from pandas.core.dtypes.common import is_extension_array_dtype
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.core.arrays import ExtensionArray
+
+
+class DummyDtype(dtypes.ExtensionDtype):
+    pass
+
+
+class DummyArray(ExtensionArray):
+    def __init__(self, data) -> None:
+        self.data = data
+
+    def __array__(self, dtype=None, copy=None):
+        return self.data
+
+    @property
+    def dtype(self):
+        return DummyDtype()
+
+    def astype(self, dtype, copy=True):
+        # we don't support anything but a single dtype
+        if isinstance(dtype, DummyDtype):
+            if copy:
+                return type(self)(self.data)
+            return self
+        elif not copy:
+            return np.asarray(self, dtype=dtype)
+        else:
+            return np.array(self, dtype=dtype, copy=copy)
+
+
+class TestExtensionArrayDtype:
+    @pytest.mark.parametrize(
+        "values",
+        [
+            pd.Categorical([]),
+            pd.Categorical([]).dtype,
+            pd.Series(pd.Categorical([])),
+            DummyDtype(),
+            DummyArray(np.array([1, 2])),
+        ],
+    )
+    def test_is_extension_array_dtype(self, values):
+        assert is_extension_array_dtype(values)
+
+    @pytest.mark.parametrize("values", [np.array([]), pd.Series(np.array([]))])
+    def test_is_not_extension_array_dtype(self, values):
+        assert not is_extension_array_dtype(values)
+
+
+def test_astype():
+    arr = DummyArray(np.array([1, 2, 3]))
+    expected = np.array([1, 2, 3], dtype=object)
+
+    result = arr.astype(object)
+    tm.assert_numpy_array_equal(result, expected)
+
+    result = arr.astype("object")
+    tm.assert_numpy_array_equal(result, expected)
+
+
+def test_astype_no_copy():
+    arr = DummyArray(np.array([1, 2, 3], dtype=np.int64))
+    result = arr.astype(arr.dtype, copy=False)
+
+    assert arr is result
+
+    result = arr.astype(arr.dtype)
+    assert arr is not result
+
+
+@pytest.mark.parametrize("dtype", [dtypes.CategoricalDtype(), dtypes.IntervalDtype()])
+def test_is_extension_array_dtype(dtype):
+    assert isinstance(dtype, dtypes.ExtensionDtype)
+    assert is_extension_array_dtype(dtype)
+
+
+class CapturingStringArray(pd.arrays.StringArray):
+    """Extend StringArray to capture arguments to __getitem__"""
+
+    def __getitem__(self, item):
+        self.last_item_arg = item
+        return super().__getitem__(item)
+
+
+def test_ellipsis_index():
+    # GH#42430 1D slices over extension types turn into N-dimensional slices
+    #  over ExtensionArrays
+    df = pd.DataFrame(
+        {"col1": CapturingStringArray(np.array(["hello", "world"], dtype=object))}
+    )
+    _ = df.iloc[:1]
+
+    # String comparison because there's no native way to compare slices.
+    # Before the fix for GH#42430, last_item_arg would get set to the 2D slice
+    # (Ellipsis, slice(None, 1, None))
+    out = df["col1"].array.last_item_arg
+    assert str(out) == "slice(None, 1, None)"
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/test_datetime.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/test_datetime.py
new file mode 100644
index 0000000000000000000000000000000000000000..7f70957007dad9cc589e6f589c48555fd90f527d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/test_datetime.py
@@ -0,0 +1,144 @@
+"""
+This file contains a minimal set of tests for compliance with the extension
+array interface test suite, and should contain no other tests.
+The test suite for the full functionality of the array is located in
+`pandas/tests/arrays/`.
+
+The tests in this file are inherited from the BaseExtensionTests, and only
+minimal tweaks should be applied to get the tests passing (by overwriting a
+parent method).
+
+Additional tests should either be added to one of the BaseExtensionTests
+classes (if they are relevant for the extension interface for all dtypes), or
+be added to the array-specific tests in `pandas/tests/arrays/`.
+
+"""
+import numpy as np
+import pytest
+
+from pandas.core.dtypes.dtypes import DatetimeTZDtype
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.core.arrays import DatetimeArray
+from pandas.tests.extension import base
+
+
+@pytest.fixture(params=["US/Central"])
+def dtype(request):
+    return DatetimeTZDtype(unit="ns", tz=request.param)
+
+
+@pytest.fixture
+def data(dtype):
+    data = DatetimeArray._from_sequence(
+        pd.date_range("2000", periods=100, tz=dtype.tz), dtype=dtype
+    )
+    return data
+
+
+@pytest.fixture
+def data_missing(dtype):
+    return DatetimeArray._from_sequence(
+        np.array(["NaT", "2000-01-01"], dtype="datetime64[ns]"), dtype=dtype
+    )
+
+
+@pytest.fixture
+def data_for_sorting(dtype):
+    a = pd.Timestamp("2000-01-01")
+    b = pd.Timestamp("2000-01-02")
+    c = pd.Timestamp("2000-01-03")
+    return DatetimeArray._from_sequence(
+        np.array([b, c, a], dtype="datetime64[ns]"), dtype=dtype
+    )
+
+
+@pytest.fixture
+def data_missing_for_sorting(dtype):
+    a = pd.Timestamp("2000-01-01")
+    b = pd.Timestamp("2000-01-02")
+    return DatetimeArray._from_sequence(
+        np.array([b, "NaT", a], dtype="datetime64[ns]"), dtype=dtype
+    )
+
+
+@pytest.fixture
+def data_for_grouping(dtype):
+    """
+    Expected to be like [B, B, NA, NA, A, A, B, C]
+
+    Where A < B < C and NA is missing
+    """
+    a = pd.Timestamp("2000-01-01")
+    b = pd.Timestamp("2000-01-02")
+    c = pd.Timestamp("2000-01-03")
+    na = "NaT"
+    return DatetimeArray._from_sequence(
+        np.array([b, b, na, na, a, a, b, c], dtype="datetime64[ns]"), dtype=dtype
+    )
+
+
+@pytest.fixture
+def na_cmp():
+    def cmp(a, b):
+        return a is pd.NaT and a is b
+
+    return cmp
+
+
+# ----------------------------------------------------------------------------
+class TestDatetimeArray(base.ExtensionTests):
+    def _get_expected_exception(self, op_name, obj, other):
+        if op_name in ["__sub__", "__rsub__"]:
+            return None
+        return super()._get_expected_exception(op_name, obj, other)
+
+    def _supports_accumulation(self, ser, op_name: str) -> bool:
+        return op_name in ["cummin", "cummax"]
+
+    def _supports_reduction(self, obj, op_name: str) -> bool:
+        return op_name in ["min", "max", "median", "mean", "std", "any", "all"]
+
+    @pytest.mark.parametrize("skipna", [True, False])
+    def test_reduce_series_boolean(self, data, all_boolean_reductions, skipna):
+        meth = all_boolean_reductions
+        msg = f"'{meth}' with datetime64 dtypes is deprecated and will raise in"
+        with tm.assert_produces_warning(
+            FutureWarning, match=msg, check_stacklevel=False
+        ):
+            super().test_reduce_series_boolean(data, all_boolean_reductions, skipna)
+
+    def test_series_constructor(self, data):
+        # Series construction drops any .freq attr
+        data = data._with_freq(None)
+        super().test_series_constructor(data)
+
+    @pytest.mark.parametrize("na_action", [None, "ignore"])
+    def test_map(self, data, na_action):
+        result = data.map(lambda x: x, na_action=na_action)
+        tm.assert_extension_array_equal(result, data)
+
+    def check_reduce(self, ser: pd.Series, op_name: str, skipna: bool):
+        if op_name in ["median", "mean", "std"]:
+            alt = ser.astype("int64")
+
+            res_op = getattr(ser, op_name)
+            exp_op = getattr(alt, op_name)
+            result = res_op(skipna=skipna)
+            expected = exp_op(skipna=skipna)
+            if op_name in ["mean", "median"]:
+                # error: Item "dtype[Any]" of "dtype[Any] | ExtensionDtype"
+                # has no attribute "tz"
+                tz = ser.dtype.tz  # type: ignore[union-attr]
+                expected = pd.Timestamp(expected, tz=tz)
+            else:
+                expected = pd.Timedelta(expected)
+            tm.assert_almost_equal(result, expected)
+
+        else:
+            return super().check_reduce(ser, op_name, skipna)
+
+
+class Test2DCompat(base.NDArrayBacked2DTests):
+    pass
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/test_extension.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/test_extension.py
new file mode 100644
index 0000000000000000000000000000000000000000..1ed626cd5108081eff7156275f439ececdf28241
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/test_extension.py
@@ -0,0 +1,26 @@
+"""
+Tests for behavior if an author does *not* implement EA methods.
+"""
+import numpy as np
+import pytest
+
+from pandas.core.arrays import ExtensionArray
+
+
+class MyEA(ExtensionArray):
+    def __init__(self, values) -> None:
+        self._values = values
+
+
+@pytest.fixture
+def data():
+    arr = np.arange(10)
+    return MyEA(arr)
+
+
+class TestExtensionArray:
+    def test_errors(self, data, all_arithmetic_operators):
+        # invalid ops
+        op_name = all_arithmetic_operators
+        with pytest.raises(AttributeError):
+            getattr(data, op_name)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/test_interval.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/test_interval.py
new file mode 100644
index 0000000000000000000000000000000000000000..6292e6051aa90e4a79420c4bc6ba8ef94d27940a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/test_interval.py
@@ -0,0 +1,123 @@
+"""
+This file contains a minimal set of tests for compliance with the extension
+array interface test suite, and should contain no other tests.
+The test suite for the full functionality of the array is located in
+`pandas/tests/arrays/`.
+
+The tests in this file are inherited from the BaseExtensionTests, and only
+minimal tweaks should be applied to get the tests passing (by overwriting a
+parent method).
+
+Additional tests should either be added to one of the BaseExtensionTests
+classes (if they are relevant for the extension interface for all dtypes), or
+be added to the array-specific tests in `pandas/tests/arrays/`.
+
+"""
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import numpy as np
+import pytest
+
+from pandas.core.dtypes.dtypes import IntervalDtype
+
+from pandas import Interval
+from pandas.core.arrays import IntervalArray
+from pandas.tests.extension import base
+
+if TYPE_CHECKING:
+    import pandas as pd
+
+
+def make_data():
+    N = 100
+    left_array = np.random.default_rng(2).uniform(size=N).cumsum()
+    right_array = left_array + np.random.default_rng(2).uniform(size=N)
+    return [Interval(left, right) for left, right in zip(left_array, right_array)]
+
+
+@pytest.fixture
+def dtype():
+    return IntervalDtype()
+
+
+@pytest.fixture
+def data():
+    """Length-100 PeriodArray for semantics test."""
+    return IntervalArray(make_data())
+
+
+@pytest.fixture
+def data_missing():
+    """Length 2 array with [NA, Valid]"""
+    return IntervalArray.from_tuples([None, (0, 1)])
+
+
+@pytest.fixture
+def data_for_twos():
+    pytest.skip("Interval is not a numeric dtype")
+
+
+@pytest.fixture
+def data_for_sorting():
+    return IntervalArray.from_tuples([(1, 2), (2, 3), (0, 1)])
+
+
+@pytest.fixture
+def data_missing_for_sorting():
+    return IntervalArray.from_tuples([(1, 2), None, (0, 1)])
+
+
+@pytest.fixture
+def data_for_grouping():
+    a = (0, 1)
+    b = (1, 2)
+    c = (2, 3)
+    return IntervalArray.from_tuples([b, b, None, None, a, a, b, c])
+
+
+class TestIntervalArray(base.ExtensionTests):
+    divmod_exc = TypeError
+
+    def _supports_reduction(self, ser: pd.Series, op_name: str) -> bool:
+        return op_name in ["min", "max"]
+
+    @pytest.mark.xfail(
+        reason="Raises with incorrect message bc it disallows *all* listlikes "
+        "instead of just wrong-length listlikes"
+    )
+    def test_fillna_length_mismatch(self, data_missing):
+        super().test_fillna_length_mismatch(data_missing)
+
+    @pytest.mark.filterwarnings(
+        "ignore:invalid value encountered in cast:RuntimeWarning"
+    )
+    def test_hash_pandas_object(self, data):
+        super().test_hash_pandas_object(data)
+
+    @pytest.mark.filterwarnings(
+        "ignore:invalid value encountered in cast:RuntimeWarning"
+    )
+    def test_hash_pandas_object_works(self, data, as_frame):
+        super().test_hash_pandas_object_works(data, as_frame)
+
+    @pytest.mark.filterwarnings(
+        "ignore:invalid value encountered in cast:RuntimeWarning"
+    )
+    @pytest.mark.parametrize("engine", ["c", "python"])
+    def test_EA_types(self, engine, data, request):
+        super().test_EA_types(engine, data, request)
+
+    @pytest.mark.filterwarnings(
+        "ignore:invalid value encountered in cast:RuntimeWarning"
+    )
+    def test_astype_str(self, data):
+        super().test_astype_str(data)
+
+
+# TODO: either belongs in tests.arrays.interval or move into base tests.
+def test_fillna_non_scalar_raises(data_missing):
+    msg = "can only insert Interval objects and NA into an IntervalArray"
+    with pytest.raises(TypeError, match=msg):
+        data_missing.fillna([1, 1])
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/test_masked.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/test_masked.py
new file mode 100644
index 0000000000000000000000000000000000000000..651f783b44d1f788b57df4fc1ff0c8b2b33bc0f3
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/test_masked.py
@@ -0,0 +1,417 @@
+"""
+This file contains a minimal set of tests for compliance with the extension
+array interface test suite, and should contain no other tests.
+The test suite for the full functionality of the array is located in
+`pandas/tests/arrays/`.
+
+The tests in this file are inherited from the BaseExtensionTests, and only
+minimal tweaks should be applied to get the tests passing (by overwriting a
+parent method).
+
+Additional tests should either be added to one of the BaseExtensionTests
+classes (if they are relevant for the extension interface for all dtypes), or
+be added to the array-specific tests in `pandas/tests/arrays/`.
+
+"""
+import warnings
+
+import numpy as np
+import pytest
+
+from pandas.compat import (
+    IS64,
+    is_platform_windows,
+)
+from pandas.compat.numpy import np_version_gt2
+
+from pandas.core.dtypes.common import (
+    is_float_dtype,
+    is_signed_integer_dtype,
+    is_unsigned_integer_dtype,
+)
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.core.arrays.boolean import BooleanDtype
+from pandas.core.arrays.floating import (
+    Float32Dtype,
+    Float64Dtype,
+)
+from pandas.core.arrays.integer import (
+    Int8Dtype,
+    Int16Dtype,
+    Int32Dtype,
+    Int64Dtype,
+    UInt8Dtype,
+    UInt16Dtype,
+    UInt32Dtype,
+    UInt64Dtype,
+)
+from pandas.tests.extension import base
+
+is_windows_or_32bit = (is_platform_windows() and not np_version_gt2) or not IS64
+
+pytestmark = [
+    pytest.mark.filterwarnings(
+        "ignore:invalid value encountered in divide:RuntimeWarning"
+    ),
+    pytest.mark.filterwarnings("ignore:Mean of empty slice:RuntimeWarning"),
+    # overflow only relevant for Floating dtype cases cases
+    pytest.mark.filterwarnings("ignore:overflow encountered in reduce:RuntimeWarning"),
+]
+
+
+def make_data():
+    return list(range(1, 9)) + [pd.NA] + list(range(10, 98)) + [pd.NA] + [99, 100]
+
+
+def make_float_data():
+    return (
+        list(np.arange(0.1, 0.9, 0.1))
+        + [pd.NA]
+        + list(np.arange(1, 9.8, 0.1))
+        + [pd.NA]
+        + [9.9, 10.0]
+    )
+
+
+def make_bool_data():
+    return [True, False] * 4 + [np.nan] + [True, False] * 44 + [np.nan] + [True, False]
+
+
+@pytest.fixture(
+    params=[
+        Int8Dtype,
+        Int16Dtype,
+        Int32Dtype,
+        Int64Dtype,
+        UInt8Dtype,
+        UInt16Dtype,
+        UInt32Dtype,
+        UInt64Dtype,
+        Float32Dtype,
+        Float64Dtype,
+        BooleanDtype,
+    ]
+)
+def dtype(request):
+    return request.param()
+
+
+@pytest.fixture
+def data(dtype):
+    if dtype.kind == "f":
+        data = make_float_data()
+    elif dtype.kind == "b":
+        data = make_bool_data()
+    else:
+        data = make_data()
+    return pd.array(data, dtype=dtype)
+
+
+@pytest.fixture
+def data_for_twos(dtype):
+    if dtype.kind == "b":
+        return pd.array(np.ones(100), dtype=dtype)
+    return pd.array(np.ones(100) * 2, dtype=dtype)
+
+
+@pytest.fixture
+def data_missing(dtype):
+    if dtype.kind == "f":
+        return pd.array([pd.NA, 0.1], dtype=dtype)
+    elif dtype.kind == "b":
+        return pd.array([np.nan, True], dtype=dtype)
+    return pd.array([pd.NA, 1], dtype=dtype)
+
+
+@pytest.fixture
+def data_for_sorting(dtype):
+    if dtype.kind == "f":
+        return pd.array([0.1, 0.2, 0.0], dtype=dtype)
+    elif dtype.kind == "b":
+        return pd.array([True, True, False], dtype=dtype)
+    return pd.array([1, 2, 0], dtype=dtype)
+
+
+@pytest.fixture
+def data_missing_for_sorting(dtype):
+    if dtype.kind == "f":
+        return pd.array([0.1, pd.NA, 0.0], dtype=dtype)
+    elif dtype.kind == "b":
+        return pd.array([True, np.nan, False], dtype=dtype)
+    return pd.array([1, pd.NA, 0], dtype=dtype)
+
+
+@pytest.fixture
+def na_cmp():
+    # we are pd.NA
+    return lambda x, y: x is pd.NA and y is pd.NA
+
+
+@pytest.fixture
+def data_for_grouping(dtype):
+    if dtype.kind == "f":
+        b = 0.1
+        a = 0.0
+        c = 0.2
+    elif dtype.kind == "b":
+        b = True
+        a = False
+        c = b
+    else:
+        b = 1
+        a = 0
+        c = 2
+
+    na = pd.NA
+    return pd.array([b, b, na, na, a, a, b, c], dtype=dtype)
+
+
+class TestMaskedArrays(base.ExtensionTests):
+    @pytest.mark.parametrize("na_action", [None, "ignore"])
+    def test_map(self, data_missing, na_action):
+        result = data_missing.map(lambda x: x, na_action=na_action)
+        if data_missing.dtype == Float32Dtype():
+            # map roundtrips through objects, which converts to float64
+            expected = data_missing.to_numpy(dtype="float64", na_value=np.nan)
+        else:
+            expected = data_missing.to_numpy()
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_map_na_action_ignore(self, data_missing_for_sorting):
+        zero = data_missing_for_sorting[2]
+        result = data_missing_for_sorting.map(lambda x: zero, na_action="ignore")
+        if data_missing_for_sorting.dtype.kind == "b":
+            expected = np.array([False, pd.NA, False], dtype=object)
+        else:
+            expected = np.array([zero, np.nan, zero])
+        tm.assert_numpy_array_equal(result, expected)
+
+    def _get_expected_exception(self, op_name, obj, other):
+        try:
+            dtype = tm.get_dtype(obj)
+        except AttributeError:
+            # passed arguments reversed
+            dtype = tm.get_dtype(other)
+
+        if dtype.kind == "b":
+            if op_name.strip("_").lstrip("r") in ["pow", "truediv", "floordiv"]:
+                # match behavior with non-masked bool dtype
+                return NotImplementedError
+            elif op_name in ["__sub__", "__rsub__"]:
+                # exception message would include "numpy boolean subtract""
+                return TypeError
+            return None
+        return None
+
+    def _cast_pointwise_result(self, op_name: str, obj, other, pointwise_result):
+        sdtype = tm.get_dtype(obj)
+        expected = pointwise_result
+
+        if op_name in ("eq", "ne", "le", "ge", "lt", "gt"):
+            return expected.astype("boolean")
+
+        if sdtype.kind in "iu":
+            if op_name in ("__rtruediv__", "__truediv__", "__div__"):
+                with warnings.catch_warnings():
+                    warnings.filterwarnings(
+                        "ignore",
+                        "Downcasting object dtype arrays",
+                        category=FutureWarning,
+                    )
+                    filled = expected.fillna(np.nan)
+                expected = filled.astype("Float64")
+            else:
+                # combine method result in 'biggest' (int64) dtype
+                expected = expected.astype(sdtype)
+        elif sdtype.kind == "b":
+            if op_name in (
+                "__floordiv__",
+                "__rfloordiv__",
+                "__pow__",
+                "__rpow__",
+                "__mod__",
+                "__rmod__",
+            ):
+                # combine keeps boolean type
+                expected = expected.astype("Int8")
+
+            elif op_name in ("__truediv__", "__rtruediv__"):
+                # combine with bools does not generate the correct result
+                #  (numpy behaviour for div is to regard the bools as numeric)
+                op = self.get_op_from_name(op_name)
+                expected = self._combine(obj.astype(float), other, op)
+                expected = expected.astype("Float64")
+
+            if op_name == "__rpow__":
+                # for rpow, combine does not propagate NaN
+                result = getattr(obj, op_name)(other)
+                expected[result.isna()] = np.nan
+        else:
+            # combine method result in 'biggest' (float64) dtype
+            expected = expected.astype(sdtype)
+        return expected
+
+    def test_divmod_series_array(self, data, data_for_twos, request):
+        if data.dtype.kind == "b":
+            mark = pytest.mark.xfail(
+                reason="Inconsistency between floordiv and divmod; we raise for "
+                "floordiv but not for divmod. This matches what we do for "
+                "non-masked bool dtype."
+            )
+            request.applymarker(mark)
+        super().test_divmod_series_array(data, data_for_twos)
+
+    def test_combine_le(self, data_repeated):
+        # TODO: patching self is a bad pattern here
+        orig_data1, orig_data2 = data_repeated(2)
+        if orig_data1.dtype.kind == "b":
+            self._combine_le_expected_dtype = "boolean"
+        else:
+            # TODO: can we make this boolean?
+            self._combine_le_expected_dtype = object
+        super().test_combine_le(data_repeated)
+
+    def _supports_reduction(self, ser: pd.Series, op_name: str) -> bool:
+        if op_name in ["any", "all"] and ser.dtype.kind != "b":
+            pytest.skip(reason="Tested in tests/reductions/test_reductions.py")
+        return True
+
+    def check_reduce(self, ser: pd.Series, op_name: str, skipna: bool):
+        # overwrite to ensure pd.NA is tested instead of np.nan
+        # https://github.com/pandas-dev/pandas/issues/30958
+
+        cmp_dtype = "int64"
+        if ser.dtype.kind == "f":
+            # Item "dtype[Any]" of "Union[dtype[Any], ExtensionDtype]" has
+            # no attribute "numpy_dtype"
+            cmp_dtype = ser.dtype.numpy_dtype  # type: ignore[union-attr]
+        elif ser.dtype.kind == "b":
+            if op_name in ["min", "max"]:
+                cmp_dtype = "bool"
+
+        # TODO: prod with integer dtypes does *not* match the result we would
+        #  get if we used object for cmp_dtype. In that cae the object result
+        #  is a large integer while the non-object case overflows and returns 0
+        alt = ser.dropna().astype(cmp_dtype)
+        if op_name == "count":
+            result = getattr(ser, op_name)()
+            expected = getattr(alt, op_name)()
+        else:
+            result = getattr(ser, op_name)(skipna=skipna)
+            expected = getattr(alt, op_name)(skipna=skipna)
+            if not skipna and ser.isna().any() and op_name not in ["any", "all"]:
+                expected = pd.NA
+        tm.assert_almost_equal(result, expected)
+
+    def _get_expected_reduction_dtype(self, arr, op_name: str, skipna: bool):
+        if is_float_dtype(arr.dtype):
+            cmp_dtype = arr.dtype.name
+        elif op_name in ["mean", "median", "var", "std", "skew"]:
+            cmp_dtype = "Float64"
+        elif op_name in ["max", "min"]:
+            cmp_dtype = arr.dtype.name
+        elif arr.dtype in ["Int64", "UInt64"]:
+            cmp_dtype = arr.dtype.name
+        elif is_signed_integer_dtype(arr.dtype):
+            # TODO: Why does Window Numpy 2.0 dtype depend on skipna?
+            cmp_dtype = (
+                "Int32"
+                if (is_platform_windows() and (not np_version_gt2 or not skipna))
+                or not IS64
+                else "Int64"
+            )
+        elif is_unsigned_integer_dtype(arr.dtype):
+            cmp_dtype = (
+                "UInt32"
+                if (is_platform_windows() and (not np_version_gt2 or not skipna))
+                or not IS64
+                else "UInt64"
+            )
+        elif arr.dtype.kind == "b":
+            if op_name in ["mean", "median", "var", "std", "skew"]:
+                cmp_dtype = "Float64"
+            elif op_name in ["min", "max"]:
+                cmp_dtype = "boolean"
+            elif op_name in ["sum", "prod"]:
+                cmp_dtype = (
+                    "Int32"
+                    if (is_platform_windows() and (not np_version_gt2 or not skipna))
+                    or not IS64
+                    else "Int64"
+                )
+            else:
+                raise TypeError("not supposed to reach this")
+        else:
+            raise TypeError("not supposed to reach this")
+        return cmp_dtype
+
+    def _supports_accumulation(self, ser: pd.Series, op_name: str) -> bool:
+        return True
+
+    def check_accumulate(self, ser: pd.Series, op_name: str, skipna: bool):
+        # overwrite to ensure pd.NA is tested instead of np.nan
+        # https://github.com/pandas-dev/pandas/issues/30958
+        length = 64
+        if is_windows_or_32bit:
+            # Item "ExtensionDtype" of "Union[dtype[Any], ExtensionDtype]" has
+            # no attribute "itemsize"
+            if not ser.dtype.itemsize == 8:  # type: ignore[union-attr]
+                length = 32
+
+        if ser.dtype.name.startswith("U"):
+            expected_dtype = f"UInt{length}"
+        elif ser.dtype.name.startswith("I"):
+            expected_dtype = f"Int{length}"
+        elif ser.dtype.name.startswith("F"):
+            # Incompatible types in assignment (expression has type
+            # "Union[dtype[Any], ExtensionDtype]", variable has type "str")
+            expected_dtype = ser.dtype  # type: ignore[assignment]
+        elif ser.dtype.kind == "b":
+            if op_name in ("cummin", "cummax"):
+                expected_dtype = "boolean"
+            else:
+                expected_dtype = f"Int{length}"
+
+        if expected_dtype == "Float32" and op_name == "cumprod" and skipna:
+            # TODO: xfail?
+            pytest.skip(
+                f"Float32 precision lead to large differences with op {op_name} "
+                f"and skipna={skipna}"
+            )
+
+        if op_name == "cumsum":
+            result = getattr(ser, op_name)(skipna=skipna)
+            expected = pd.Series(
+                pd.array(
+                    getattr(ser.astype("float64"), op_name)(skipna=skipna),
+                    dtype=expected_dtype,
+                )
+            )
+            tm.assert_series_equal(result, expected)
+        elif op_name in ["cummax", "cummin"]:
+            result = getattr(ser, op_name)(skipna=skipna)
+            expected = pd.Series(
+                pd.array(
+                    getattr(ser.astype("float64"), op_name)(skipna=skipna),
+                    dtype=ser.dtype,
+                )
+            )
+            tm.assert_series_equal(result, expected)
+        elif op_name == "cumprod":
+            result = getattr(ser[:12], op_name)(skipna=skipna)
+            expected = pd.Series(
+                pd.array(
+                    getattr(ser[:12].astype("float64"), op_name)(skipna=skipna),
+                    dtype=expected_dtype,
+                )
+            )
+            tm.assert_series_equal(result, expected)
+
+        else:
+            raise NotImplementedError(f"{op_name} not supported")
+
+
+class Test2DCompat(base.Dim2CompatTests):
+    pass
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/test_numpy.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/test_numpy.py
new file mode 100644
index 0000000000000000000000000000000000000000..e38144f4c615b22c864a5b385e3b73fd74374f83
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/test_numpy.py
@@ -0,0 +1,426 @@
+"""
+This file contains a minimal set of tests for compliance with the extension
+array interface test suite, and should contain no other tests.
+The test suite for the full functionality of the array is located in
+`pandas/tests/arrays/`.
+
+The tests in this file are inherited from the BaseExtensionTests, and only
+minimal tweaks should be applied to get the tests passing (by overwriting a
+parent method).
+
+Additional tests should either be added to one of the BaseExtensionTests
+classes (if they are relevant for the extension interface for all dtypes), or
+be added to the array-specific tests in `pandas/tests/arrays/`.
+
+Note: we do not bother with base.BaseIndexTests because NumpyExtensionArray
+will never be held in an Index.
+"""
+import numpy as np
+import pytest
+
+from pandas.core.dtypes.dtypes import NumpyEADtype
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.api.types import is_object_dtype
+from pandas.core.arrays.numpy_ import NumpyExtensionArray
+from pandas.tests.extension import base
+
+orig_assert_attr_equal = tm.assert_attr_equal
+
+
+def _assert_attr_equal(attr: str, left, right, obj: str = "Attributes"):
+    """
+    patch tm.assert_attr_equal so NumpyEADtype("object") is closed enough to
+    np.dtype("object")
+    """
+    if attr == "dtype":
+        lattr = getattr(left, "dtype", None)
+        rattr = getattr(right, "dtype", None)
+        if isinstance(lattr, NumpyEADtype) and not isinstance(rattr, NumpyEADtype):
+            left = left.astype(lattr.numpy_dtype)
+        elif isinstance(rattr, NumpyEADtype) and not isinstance(lattr, NumpyEADtype):
+            right = right.astype(rattr.numpy_dtype)
+
+    orig_assert_attr_equal(attr, left, right, obj)
+
+
+@pytest.fixture(params=["float", "object"])
+def dtype(request):
+    return NumpyEADtype(np.dtype(request.param))
+
+
+@pytest.fixture
+def allow_in_pandas(monkeypatch):
+    """
+    A monkeypatch to tells pandas to let us in.
+
+    By default, passing a NumpyExtensionArray to an index / series / frame
+    constructor will unbox that NumpyExtensionArray to an ndarray, and treat
+    it as a non-EA column. We don't want people using EAs without
+    reason.
+
+    The mechanism for this is a check against ABCNumpyExtensionArray
+    in each constructor.
+
+    But, for testing, we need to allow them in pandas. So we patch
+    the _typ of NumpyExtensionArray, so that we evade the ABCNumpyExtensionArray
+    check.
+    """
+    with monkeypatch.context() as m:
+        m.setattr(NumpyExtensionArray, "_typ", "extension")
+        m.setattr(tm.asserters, "assert_attr_equal", _assert_attr_equal)
+        yield
+
+
+@pytest.fixture
+def data(allow_in_pandas, dtype):
+    if dtype.numpy_dtype == "object":
+        return pd.Series([(i,) for i in range(100)]).array
+    return NumpyExtensionArray(np.arange(1, 101, dtype=dtype._dtype))
+
+
+@pytest.fixture
+def data_missing(allow_in_pandas, dtype):
+    if dtype.numpy_dtype == "object":
+        return NumpyExtensionArray(np.array([np.nan, (1,)], dtype=object))
+    return NumpyExtensionArray(np.array([np.nan, 1.0]))
+
+
+@pytest.fixture
+def na_cmp():
+    def cmp(a, b):
+        return np.isnan(a) and np.isnan(b)
+
+    return cmp
+
+
+@pytest.fixture
+def data_for_sorting(allow_in_pandas, dtype):
+    """Length-3 array with a known sort order.
+
+    This should be three items [B, C, A] with
+    A < B < C
+    """
+    if dtype.numpy_dtype == "object":
+        # Use an empty tuple for first element, then remove,
+        # to disable np.array's shape inference.
+        return NumpyExtensionArray(np.array([(), (2,), (3,), (1,)], dtype=object)[1:])
+    return NumpyExtensionArray(np.array([1, 2, 0]))
+
+
+@pytest.fixture
+def data_missing_for_sorting(allow_in_pandas, dtype):
+    """Length-3 array with a known sort order.
+
+    This should be three items [B, NA, A] with
+    A < B and NA missing.
+    """
+    if dtype.numpy_dtype == "object":
+        return NumpyExtensionArray(np.array([(1,), np.nan, (0,)], dtype=object))
+    return NumpyExtensionArray(np.array([1, np.nan, 0]))
+
+
+@pytest.fixture
+def data_for_grouping(allow_in_pandas, dtype):
+    """Data for factorization, grouping, and unique tests.
+
+    Expected to be like [B, B, NA, NA, A, A, B, C]
+
+    Where A < B < C and NA is missing
+    """
+    if dtype.numpy_dtype == "object":
+        a, b, c = (1,), (2,), (3,)
+    else:
+        a, b, c = np.arange(3)
+    return NumpyExtensionArray(
+        np.array([b, b, np.nan, np.nan, a, a, b, c], dtype=dtype.numpy_dtype)
+    )
+
+
+@pytest.fixture
+def data_for_twos(dtype):
+    if dtype.kind == "O":
+        pytest.skip(f"{dtype} is not a numeric dtype")
+    arr = np.ones(100) * 2
+    return NumpyExtensionArray._from_sequence(arr, dtype=dtype)
+
+
+@pytest.fixture
+def skip_numpy_object(dtype, request):
+    """
+    Tests for NumpyExtensionArray with nested data. Users typically won't create
+    these objects via `pd.array`, but they can show up through `.array`
+    on a Series with nested data. Many of the base tests fail, as they aren't
+    appropriate for nested data.
+
+    This fixture allows these tests to be skipped when used as a usefixtures
+    marker to either an individual test or a test class.
+    """
+    if dtype == "object":
+        mark = pytest.mark.xfail(reason="Fails for object dtype")
+        request.applymarker(mark)
+
+
+skip_nested = pytest.mark.usefixtures("skip_numpy_object")
+
+
+class TestNumpyExtensionArray(base.ExtensionTests):
+    @pytest.mark.skip(reason="We don't register our dtype")
+    # We don't want to register. This test should probably be split in two.
+    def test_from_dtype(self, data):
+        pass
+
+    @skip_nested
+    def test_series_constructor_scalar_with_index(self, data, dtype):
+        # ValueError: Length of passed values is 1, index implies 3.
+        super().test_series_constructor_scalar_with_index(data, dtype)
+
+    def test_check_dtype(self, data, request, using_infer_string):
+        if data.dtype.numpy_dtype == "object":
+            request.applymarker(
+                pytest.mark.xfail(
+                    reason=f"NumpyExtensionArray expectedly clashes with a "
+                    f"NumPy name: {data.dtype.numpy_dtype}"
+                )
+            )
+        super().test_check_dtype(data)
+
+    def test_is_not_object_type(self, dtype, request):
+        if dtype.numpy_dtype == "object":
+            # Different from BaseDtypeTests.test_is_not_object_type
+            # because NumpyEADtype(object) is an object type
+            assert is_object_dtype(dtype)
+        else:
+            super().test_is_not_object_type(dtype)
+
+    @skip_nested
+    def test_getitem_scalar(self, data):
+        # AssertionError
+        super().test_getitem_scalar(data)
+
+    @skip_nested
+    def test_shift_fill_value(self, data):
+        # np.array shape inference. Shift implementation fails.
+        super().test_shift_fill_value(data)
+
+    @skip_nested
+    def test_fillna_copy_frame(self, data_missing):
+        # The "scalar" for this array isn't a scalar.
+        super().test_fillna_copy_frame(data_missing)
+
+    @skip_nested
+    def test_fillna_copy_series(self, data_missing):
+        # The "scalar" for this array isn't a scalar.
+        super().test_fillna_copy_series(data_missing)
+
+    @skip_nested
+    def test_searchsorted(self, data_for_sorting, as_series):
+        # TODO: NumpyExtensionArray.searchsorted calls ndarray.searchsorted which
+        #  isn't quite what we want in nested data cases. Instead we need to
+        #  adapt something like libindex._bin_search.
+        super().test_searchsorted(data_for_sorting, as_series)
+
+    @pytest.mark.xfail(reason="NumpyExtensionArray.diff may fail on dtype")
+    def test_diff(self, data, periods):
+        return super().test_diff(data, periods)
+
+    def test_insert(self, data, request):
+        if data.dtype.numpy_dtype == object:
+            mark = pytest.mark.xfail(reason="Dimension mismatch in np.concatenate")
+            request.applymarker(mark)
+
+        super().test_insert(data)
+
+    @skip_nested
+    def test_insert_invalid(self, data, invalid_scalar):
+        # NumpyExtensionArray[object] can hold anything, so skip
+        super().test_insert_invalid(data, invalid_scalar)
+
+    divmod_exc = None
+    series_scalar_exc = None
+    frame_scalar_exc = None
+    series_array_exc = None
+
+    def test_divmod(self, data):
+        divmod_exc = None
+        if data.dtype.kind == "O":
+            divmod_exc = TypeError
+        self.divmod_exc = divmod_exc
+        super().test_divmod(data)
+
+    def test_divmod_series_array(self, data):
+        ser = pd.Series(data)
+        exc = None
+        if data.dtype.kind == "O":
+            exc = TypeError
+            self.divmod_exc = exc
+        self._check_divmod_op(ser, divmod, data)
+
+    def test_arith_series_with_scalar(self, data, all_arithmetic_operators, request):
+        opname = all_arithmetic_operators
+        series_scalar_exc = None
+        if data.dtype.numpy_dtype == object:
+            if opname in ["__mul__", "__rmul__"]:
+                mark = pytest.mark.xfail(
+                    reason="the Series.combine step raises but not the Series method."
+                )
+                request.node.add_marker(mark)
+            series_scalar_exc = TypeError
+        self.series_scalar_exc = series_scalar_exc
+        super().test_arith_series_with_scalar(data, all_arithmetic_operators)
+
+    def test_arith_series_with_array(self, data, all_arithmetic_operators):
+        opname = all_arithmetic_operators
+        series_array_exc = None
+        if data.dtype.numpy_dtype == object and opname not in ["__add__", "__radd__"]:
+            series_array_exc = TypeError
+        self.series_array_exc = series_array_exc
+        super().test_arith_series_with_array(data, all_arithmetic_operators)
+
+    def test_arith_frame_with_scalar(self, data, all_arithmetic_operators, request):
+        opname = all_arithmetic_operators
+        frame_scalar_exc = None
+        if data.dtype.numpy_dtype == object:
+            if opname in ["__mul__", "__rmul__"]:
+                mark = pytest.mark.xfail(
+                    reason="the Series.combine step raises but not the Series method."
+                )
+                request.node.add_marker(mark)
+            frame_scalar_exc = TypeError
+        self.frame_scalar_exc = frame_scalar_exc
+        super().test_arith_frame_with_scalar(data, all_arithmetic_operators)
+
+    def _supports_reduction(self, ser: pd.Series, op_name: str) -> bool:
+        if ser.dtype.kind == "O":
+            return op_name in ["sum", "min", "max", "any", "all"]
+        return True
+
+    def check_reduce(self, ser: pd.Series, op_name: str, skipna: bool):
+        res_op = getattr(ser, op_name)
+        # avoid coercing int -> float. Just cast to the actual numpy type.
+        # error: Item "ExtensionDtype" of "dtype[Any] | ExtensionDtype" has
+        # no attribute "numpy_dtype"
+        cmp_dtype = ser.dtype.numpy_dtype  # type: ignore[union-attr]
+        alt = ser.astype(cmp_dtype)
+        exp_op = getattr(alt, op_name)
+        if op_name == "count":
+            result = res_op()
+            expected = exp_op()
+        else:
+            result = res_op(skipna=skipna)
+            expected = exp_op(skipna=skipna)
+        tm.assert_almost_equal(result, expected)
+
+    @pytest.mark.skip("TODO: tests not written yet")
+    @pytest.mark.parametrize("skipna", [True, False])
+    def test_reduce_frame(self, data, all_numeric_reductions, skipna):
+        pass
+
+    @skip_nested
+    def test_fillna_series(self, data_missing):
+        # Non-scalar "scalar" values.
+        super().test_fillna_series(data_missing)
+
+    @skip_nested
+    def test_fillna_frame(self, data_missing):
+        # Non-scalar "scalar" values.
+        super().test_fillna_frame(data_missing)
+
+    @skip_nested
+    def test_setitem_invalid(self, data, invalid_scalar):
+        # object dtype can hold anything, so doesn't raise
+        super().test_setitem_invalid(data, invalid_scalar)
+
+    @skip_nested
+    def test_setitem_sequence_broadcasts(self, data, box_in_series):
+        # ValueError: cannot set using a list-like indexer with a different
+        # length than the value
+        super().test_setitem_sequence_broadcasts(data, box_in_series)
+
+    @skip_nested
+    @pytest.mark.parametrize("setter", ["loc", None])
+    def test_setitem_mask_broadcast(self, data, setter):
+        # ValueError: cannot set using a list-like indexer with a different
+        # length than the value
+        super().test_setitem_mask_broadcast(data, setter)
+
+    @skip_nested
+    def test_setitem_scalar_key_sequence_raise(self, data):
+        # Failed: DID NOT RAISE 
+        super().test_setitem_scalar_key_sequence_raise(data)
+
+    # TODO: there is some issue with NumpyExtensionArray, therefore,
+    #   skip the setitem test for now, and fix it later (GH 31446)
+
+    @skip_nested
+    @pytest.mark.parametrize(
+        "mask",
+        [
+            np.array([True, True, True, False, False]),
+            pd.array([True, True, True, False, False], dtype="boolean"),
+        ],
+        ids=["numpy-array", "boolean-array"],
+    )
+    def test_setitem_mask(self, data, mask, box_in_series):
+        super().test_setitem_mask(data, mask, box_in_series)
+
+    @skip_nested
+    @pytest.mark.parametrize(
+        "idx",
+        [[0, 1, 2], pd.array([0, 1, 2], dtype="Int64"), np.array([0, 1, 2])],
+        ids=["list", "integer-array", "numpy-array"],
+    )
+    def test_setitem_integer_array(self, data, idx, box_in_series):
+        super().test_setitem_integer_array(data, idx, box_in_series)
+
+    @pytest.mark.parametrize(
+        "idx, box_in_series",
+        [
+            ([0, 1, 2, pd.NA], False),
+            pytest.param([0, 1, 2, pd.NA], True, marks=pytest.mark.xfail),
+            (pd.array([0, 1, 2, pd.NA], dtype="Int64"), False),
+            (pd.array([0, 1, 2, pd.NA], dtype="Int64"), False),
+        ],
+        ids=["list-False", "list-True", "integer-array-False", "integer-array-True"],
+    )
+    def test_setitem_integer_with_missing_raises(self, data, idx, box_in_series):
+        super().test_setitem_integer_with_missing_raises(data, idx, box_in_series)
+
+    @skip_nested
+    def test_setitem_slice(self, data, box_in_series):
+        super().test_setitem_slice(data, box_in_series)
+
+    @skip_nested
+    def test_setitem_loc_iloc_slice(self, data):
+        super().test_setitem_loc_iloc_slice(data)
+
+    def test_setitem_with_expansion_dataframe_column(self, data, full_indexer):
+        # https://github.com/pandas-dev/pandas/issues/32395
+        df = expected = pd.DataFrame({"data": pd.Series(data)})
+        result = pd.DataFrame(index=df.index)
+
+        # because result has object dtype, the attempt to do setting inplace
+        #  is successful, and object dtype is retained
+        key = full_indexer(df)
+        result.loc[key, "data"] = df["data"]
+
+        # base class method has expected = df; NumpyExtensionArray behaves oddly because
+        #  we patch _typ for these tests.
+        if data.dtype.numpy_dtype != object:
+            if not isinstance(key, slice) or key != slice(None):
+                expected = pd.DataFrame({"data": data.to_numpy()})
+        tm.assert_frame_equal(result, expected, check_column_type=False)
+
+    @pytest.mark.xfail(reason="NumpyEADtype is unpacked")
+    def test_index_from_listlike_with_dtype(self, data):
+        super().test_index_from_listlike_with_dtype(data)
+
+    @skip_nested
+    @pytest.mark.parametrize("engine", ["c", "python"])
+    def test_EA_types(self, engine, data, request):
+        super().test_EA_types(engine, data, request)
+
+
+class Test2DCompat(base.NDArrayBacked2DTests):
+    pass
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/test_period.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/test_period.py
new file mode 100644
index 0000000000000000000000000000000000000000..2d1d213322bac02e65f710ac77943876425102a5
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/test_period.py
@@ -0,0 +1,119 @@
+"""
+This file contains a minimal set of tests for compliance with the extension
+array interface test suite, and should contain no other tests.
+The test suite for the full functionality of the array is located in
+`pandas/tests/arrays/`.
+
+The tests in this file are inherited from the BaseExtensionTests, and only
+minimal tweaks should be applied to get the tests passing (by overwriting a
+parent method).
+
+Additional tests should either be added to one of the BaseExtensionTests
+classes (if they are relevant for the extension interface for all dtypes), or
+be added to the array-specific tests in `pandas/tests/arrays/`.
+
+"""
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import numpy as np
+import pytest
+
+from pandas._libs import (
+    Period,
+    iNaT,
+)
+from pandas.compat import is_platform_windows
+from pandas.compat.numpy import np_version_gte1p24
+
+from pandas.core.dtypes.dtypes import PeriodDtype
+
+import pandas._testing as tm
+from pandas.core.arrays import PeriodArray
+from pandas.tests.extension import base
+
+if TYPE_CHECKING:
+    import pandas as pd
+
+
+@pytest.fixture(params=["D", "2D"])
+def dtype(request):
+    return PeriodDtype(freq=request.param)
+
+
+@pytest.fixture
+def data(dtype):
+    return PeriodArray(np.arange(1970, 2070), dtype=dtype)
+
+
+@pytest.fixture
+def data_for_sorting(dtype):
+    return PeriodArray([2018, 2019, 2017], dtype=dtype)
+
+
+@pytest.fixture
+def data_missing(dtype):
+    return PeriodArray([iNaT, 2017], dtype=dtype)
+
+
+@pytest.fixture
+def data_missing_for_sorting(dtype):
+    return PeriodArray([2018, iNaT, 2017], dtype=dtype)
+
+
+@pytest.fixture
+def data_for_grouping(dtype):
+    B = 2018
+    NA = iNaT
+    A = 2017
+    C = 2019
+    return PeriodArray([B, B, NA, NA, A, A, B, C], dtype=dtype)
+
+
+class TestPeriodArray(base.ExtensionTests):
+    def _get_expected_exception(self, op_name, obj, other):
+        if op_name in ("__sub__", "__rsub__"):
+            return None
+        return super()._get_expected_exception(op_name, obj, other)
+
+    def _supports_accumulation(self, ser, op_name: str) -> bool:
+        return op_name in ["cummin", "cummax"]
+
+    def _supports_reduction(self, obj, op_name: str) -> bool:
+        return op_name in ["min", "max", "median"]
+
+    def check_reduce(self, ser: pd.Series, op_name: str, skipna: bool):
+        if op_name == "median":
+            res_op = getattr(ser, op_name)
+
+            alt = ser.astype("int64")
+
+            exp_op = getattr(alt, op_name)
+            result = res_op(skipna=skipna)
+            expected = exp_op(skipna=skipna)
+            # error: Item "dtype[Any]" of "dtype[Any] | ExtensionDtype" has no
+            # attribute "freq"
+            freq = ser.dtype.freq  # type: ignore[union-attr]
+            expected = Period._from_ordinal(int(expected), freq=freq)
+            tm.assert_almost_equal(result, expected)
+
+        else:
+            return super().check_reduce(ser, op_name, skipna)
+
+    @pytest.mark.parametrize("periods", [1, -2])
+    def test_diff(self, data, periods):
+        if is_platform_windows() and np_version_gte1p24:
+            with tm.assert_produces_warning(RuntimeWarning, check_stacklevel=False):
+                super().test_diff(data, periods)
+        else:
+            super().test_diff(data, periods)
+
+    @pytest.mark.parametrize("na_action", [None, "ignore"])
+    def test_map(self, data, na_action):
+        result = data.map(lambda x: x, na_action=na_action)
+        tm.assert_extension_array_equal(result, data)
+
+
+class Test2DCompat(base.NDArrayBacked2DTests):
+    pass
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/test_sparse.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/test_sparse.py
new file mode 100644
index 0000000000000000000000000000000000000000..2d5989a5b4f1de3c8928504034ee938939b57878
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/test_sparse.py
@@ -0,0 +1,503 @@
+"""
+This file contains a minimal set of tests for compliance with the extension
+array interface test suite, and should contain no other tests.
+The test suite for the full functionality of the array is located in
+`pandas/tests/arrays/`.
+
+The tests in this file are inherited from the BaseExtensionTests, and only
+minimal tweaks should be applied to get the tests passing (by overwriting a
+parent method).
+
+Additional tests should either be added to one of the BaseExtensionTests
+classes (if they are relevant for the extension interface for all dtypes), or
+be added to the array-specific tests in `pandas/tests/arrays/`.
+
+"""
+
+import numpy as np
+import pytest
+
+from pandas.errors import PerformanceWarning
+
+import pandas as pd
+from pandas import SparseDtype
+import pandas._testing as tm
+from pandas.arrays import SparseArray
+from pandas.tests.extension import base
+
+
+def make_data(fill_value):
+    rng = np.random.default_rng(2)
+    if np.isnan(fill_value):
+        data = rng.uniform(size=100)
+    else:
+        data = rng.integers(1, 100, size=100, dtype=int)
+        if data[0] == data[1]:
+            data[0] += 1
+
+    data[2::3] = fill_value
+    return data
+
+
+@pytest.fixture
+def dtype():
+    return SparseDtype()
+
+
+@pytest.fixture(params=[0, np.nan])
+def data(request):
+    """Length-100 PeriodArray for semantics test."""
+    res = SparseArray(make_data(request.param), fill_value=request.param)
+    return res
+
+
+@pytest.fixture
+def data_for_twos():
+    return SparseArray(np.ones(100) * 2)
+
+
+@pytest.fixture(params=[0, np.nan])
+def data_missing(request):
+    """Length 2 array with [NA, Valid]"""
+    return SparseArray([np.nan, 1], fill_value=request.param)
+
+
+@pytest.fixture(params=[0, np.nan])
+def data_repeated(request):
+    """Return different versions of data for count times"""
+
+    def gen(count):
+        for _ in range(count):
+            yield SparseArray(make_data(request.param), fill_value=request.param)
+
+    yield gen
+
+
+@pytest.fixture(params=[0, np.nan])
+def data_for_sorting(request):
+    return SparseArray([2, 3, 1], fill_value=request.param)
+
+
+@pytest.fixture(params=[0, np.nan])
+def data_missing_for_sorting(request):
+    return SparseArray([2, np.nan, 1], fill_value=request.param)
+
+
+@pytest.fixture
+def na_cmp():
+    return lambda left, right: pd.isna(left) and pd.isna(right)
+
+
+@pytest.fixture(params=[0, np.nan])
+def data_for_grouping(request):
+    return SparseArray([1, 1, np.nan, np.nan, 2, 2, 1, 3], fill_value=request.param)
+
+
+@pytest.fixture(params=[0, np.nan])
+def data_for_compare(request):
+    return SparseArray([0, 0, np.nan, -2, -1, 4, 2, 3, 0, 0], fill_value=request.param)
+
+
+class TestSparseArray(base.ExtensionTests):
+    def _supports_reduction(self, obj, op_name: str) -> bool:
+        return True
+
+    @pytest.mark.parametrize("skipna", [True, False])
+    def test_reduce_series_numeric(self, data, all_numeric_reductions, skipna, request):
+        if all_numeric_reductions in [
+            "prod",
+            "median",
+            "var",
+            "std",
+            "sem",
+            "skew",
+            "kurt",
+        ]:
+            mark = pytest.mark.xfail(
+                reason="This should be viable but is not implemented"
+            )
+            request.node.add_marker(mark)
+        elif (
+            all_numeric_reductions in ["sum", "max", "min", "mean"]
+            and data.dtype.kind == "f"
+            and not skipna
+        ):
+            mark = pytest.mark.xfail(reason="getting a non-nan float")
+            request.node.add_marker(mark)
+
+        super().test_reduce_series_numeric(data, all_numeric_reductions, skipna)
+
+    @pytest.mark.parametrize("skipna", [True, False])
+    def test_reduce_frame(self, data, all_numeric_reductions, skipna, request):
+        if all_numeric_reductions in [
+            "prod",
+            "median",
+            "var",
+            "std",
+            "sem",
+            "skew",
+            "kurt",
+        ]:
+            mark = pytest.mark.xfail(
+                reason="This should be viable but is not implemented"
+            )
+            request.node.add_marker(mark)
+        elif (
+            all_numeric_reductions in ["sum", "max", "min", "mean"]
+            and data.dtype.kind == "f"
+            and not skipna
+        ):
+            mark = pytest.mark.xfail(reason="ExtensionArray NA mask are different")
+            request.node.add_marker(mark)
+
+        super().test_reduce_frame(data, all_numeric_reductions, skipna)
+
+    def _check_unsupported(self, data):
+        if data.dtype == SparseDtype(int, 0):
+            pytest.skip("Can't store nan in int array.")
+
+    def test_concat_mixed_dtypes(self, data):
+        # https://github.com/pandas-dev/pandas/issues/20762
+        # This should be the same, aside from concat([sparse, float])
+        df1 = pd.DataFrame({"A": data[:3]})
+        df2 = pd.DataFrame({"A": [1, 2, 3]})
+        df3 = pd.DataFrame({"A": ["a", "b", "c"]}).astype("category")
+        dfs = [df1, df2, df3]
+
+        # dataframes
+        result = pd.concat(dfs)
+        expected = pd.concat(
+            [x.apply(lambda s: np.asarray(s).astype(object)) for x in dfs]
+        )
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    @pytest.mark.parametrize(
+        "columns",
+        [
+            ["A", "B"],
+            pd.MultiIndex.from_tuples(
+                [("A", "a"), ("A", "b")], names=["outer", "inner"]
+            ),
+        ],
+    )
+    @pytest.mark.parametrize("future_stack", [True, False])
+    def test_stack(self, data, columns, future_stack):
+        super().test_stack(data, columns, future_stack)
+
+    def test_concat_columns(self, data, na_value):
+        self._check_unsupported(data)
+        super().test_concat_columns(data, na_value)
+
+    def test_concat_extension_arrays_copy_false(self, data, na_value):
+        self._check_unsupported(data)
+        super().test_concat_extension_arrays_copy_false(data, na_value)
+
+    def test_align(self, data, na_value):
+        self._check_unsupported(data)
+        super().test_align(data, na_value)
+
+    def test_align_frame(self, data, na_value):
+        self._check_unsupported(data)
+        super().test_align_frame(data, na_value)
+
+    def test_align_series_frame(self, data, na_value):
+        self._check_unsupported(data)
+        super().test_align_series_frame(data, na_value)
+
+    def test_merge(self, data, na_value):
+        self._check_unsupported(data)
+        super().test_merge(data, na_value)
+
+    def test_get(self, data):
+        ser = pd.Series(data, index=[2 * i for i in range(len(data))])
+        if np.isnan(ser.values.fill_value):
+            assert np.isnan(ser.get(4)) and np.isnan(ser.iloc[2])
+        else:
+            assert ser.get(4) == ser.iloc[2]
+        assert ser.get(2) == ser.iloc[1]
+
+    def test_reindex(self, data, na_value):
+        self._check_unsupported(data)
+        super().test_reindex(data, na_value)
+
+    def test_isna(self, data_missing):
+        sarr = SparseArray(data_missing)
+        expected_dtype = SparseDtype(bool, pd.isna(data_missing.dtype.fill_value))
+        expected = SparseArray([True, False], dtype=expected_dtype)
+        result = sarr.isna()
+        tm.assert_sp_array_equal(result, expected)
+
+        # test isna for arr without na
+        sarr = sarr.fillna(0)
+        expected_dtype = SparseDtype(bool, pd.isna(data_missing.dtype.fill_value))
+        expected = SparseArray([False, False], fill_value=False, dtype=expected_dtype)
+        tm.assert_equal(sarr.isna(), expected)
+
+    def test_fillna_limit_backfill(self, data_missing):
+        warns = (PerformanceWarning, FutureWarning)
+        with tm.assert_produces_warning(warns, check_stacklevel=False):
+            super().test_fillna_limit_backfill(data_missing)
+
+    def test_fillna_no_op_returns_copy(self, data, request):
+        if np.isnan(data.fill_value):
+            request.applymarker(
+                pytest.mark.xfail(reason="returns array with different fill value")
+            )
+        super().test_fillna_no_op_returns_copy(data)
+
+    @pytest.mark.xfail(reason="Unsupported")
+    def test_fillna_series(self, data_missing):
+        # this one looks doable.
+        # TODO: this fails bc we do not pass through data_missing. If we did,
+        #  the 0-fill case would xpass
+        super().test_fillna_series()
+
+    def test_fillna_frame(self, data_missing):
+        # Have to override to specify that fill_value will change.
+        fill_value = data_missing[1]
+
+        result = pd.DataFrame({"A": data_missing, "B": [1, 2]}).fillna(fill_value)
+
+        if pd.isna(data_missing.fill_value):
+            dtype = SparseDtype(data_missing.dtype, fill_value)
+        else:
+            dtype = data_missing.dtype
+
+        expected = pd.DataFrame(
+            {
+                "A": data_missing._from_sequence([fill_value, fill_value], dtype=dtype),
+                "B": [1, 2],
+            }
+        )
+
+        tm.assert_frame_equal(result, expected)
+
+    _combine_le_expected_dtype = "Sparse[bool]"
+
+    def test_fillna_copy_frame(self, data_missing, using_copy_on_write):
+        arr = data_missing.take([1, 1])
+        df = pd.DataFrame({"A": arr}, copy=False)
+
+        filled_val = df.iloc[0, 0]
+        result = df.fillna(filled_val)
+
+        if hasattr(df._mgr, "blocks"):
+            if using_copy_on_write:
+                assert df.values.base is result.values.base
+            else:
+                assert df.values.base is not result.values.base
+        assert df.A._values.to_dense() is arr.to_dense()
+
+    def test_fillna_copy_series(self, data_missing, using_copy_on_write):
+        arr = data_missing.take([1, 1])
+        ser = pd.Series(arr, copy=False)
+
+        filled_val = ser[0]
+        result = ser.fillna(filled_val)
+
+        if using_copy_on_write:
+            assert ser._values is result._values
+
+        else:
+            assert ser._values is not result._values
+        assert ser._values.to_dense() is arr.to_dense()
+
+    @pytest.mark.xfail(reason="Not Applicable")
+    def test_fillna_length_mismatch(self, data_missing):
+        super().test_fillna_length_mismatch(data_missing)
+
+    def test_where_series(self, data, na_value):
+        assert data[0] != data[1]
+        cls = type(data)
+        a, b = data[:2]
+
+        ser = pd.Series(cls._from_sequence([a, a, b, b], dtype=data.dtype))
+
+        cond = np.array([True, True, False, False])
+        result = ser.where(cond)
+
+        new_dtype = SparseDtype("float", 0.0)
+        expected = pd.Series(
+            cls._from_sequence([a, a, na_value, na_value], dtype=new_dtype)
+        )
+        tm.assert_series_equal(result, expected)
+
+        other = cls._from_sequence([a, b, a, b], dtype=data.dtype)
+        cond = np.array([True, False, True, True])
+        result = ser.where(cond, other)
+        expected = pd.Series(cls._from_sequence([a, b, b, b], dtype=data.dtype))
+        tm.assert_series_equal(result, expected)
+
+    def test_searchsorted(self, data_for_sorting, as_series):
+        with tm.assert_produces_warning(PerformanceWarning, check_stacklevel=False):
+            super().test_searchsorted(data_for_sorting, as_series)
+
+    def test_shift_0_periods(self, data):
+        # GH#33856 shifting with periods=0 should return a copy, not same obj
+        result = data.shift(0)
+
+        data._sparse_values[0] = data._sparse_values[1]
+        assert result._sparse_values[0] != result._sparse_values[1]
+
+    @pytest.mark.parametrize("method", ["argmax", "argmin"])
+    def test_argmin_argmax_all_na(self, method, data, na_value):
+        # overriding because Sparse[int64, 0] cannot handle na_value
+        self._check_unsupported(data)
+        super().test_argmin_argmax_all_na(method, data, na_value)
+
+    @pytest.mark.fails_arm_wheels
+    @pytest.mark.parametrize("box", [pd.array, pd.Series, pd.DataFrame])
+    def test_equals(self, data, na_value, as_series, box):
+        self._check_unsupported(data)
+        super().test_equals(data, na_value, as_series, box)
+
+    @pytest.mark.fails_arm_wheels
+    def test_equals_same_data_different_object(self, data):
+        super().test_equals_same_data_different_object(data)
+
+    @pytest.mark.parametrize(
+        "func, na_action, expected",
+        [
+            (lambda x: x, None, SparseArray([1.0, np.nan])),
+            (lambda x: x, "ignore", SparseArray([1.0, np.nan])),
+            (str, None, SparseArray(["1.0", "nan"], fill_value="nan")),
+            (str, "ignore", SparseArray(["1.0", np.nan])),
+        ],
+    )
+    def test_map(self, func, na_action, expected):
+        # GH52096
+        data = SparseArray([1, np.nan])
+        result = data.map(func, na_action=na_action)
+        tm.assert_extension_array_equal(result, expected)
+
+    @pytest.mark.parametrize("na_action", [None, "ignore"])
+    def test_map_raises(self, data, na_action):
+        # GH52096
+        msg = "fill value in the sparse values not supported"
+        with pytest.raises(ValueError, match=msg):
+            data.map(lambda x: np.nan, na_action=na_action)
+
+    @pytest.mark.xfail(raises=TypeError, reason="no sparse StringDtype")
+    def test_astype_string(self, data, nullable_string_dtype):
+        # TODO: this fails bc we do not pass through nullable_string_dtype;
+        #  If we did, the 0-cases would xpass
+        super().test_astype_string(data)
+
+    series_scalar_exc = None
+    frame_scalar_exc = None
+    divmod_exc = None
+    series_array_exc = None
+
+    def _skip_if_different_combine(self, data):
+        if data.fill_value == 0:
+            # arith ops call on dtype.fill_value so that the sparsity
+            # is maintained. Combine can't be called on a dtype in
+            # general, so we can't make the expected. This is tested elsewhere
+            pytest.skip("Incorrected expected from Series.combine and tested elsewhere")
+
+    def test_arith_series_with_scalar(self, data, all_arithmetic_operators):
+        self._skip_if_different_combine(data)
+        super().test_arith_series_with_scalar(data, all_arithmetic_operators)
+
+    def test_arith_series_with_array(self, data, all_arithmetic_operators):
+        self._skip_if_different_combine(data)
+        super().test_arith_series_with_array(data, all_arithmetic_operators)
+
+    def test_arith_frame_with_scalar(self, data, all_arithmetic_operators, request):
+        if data.dtype.fill_value != 0:
+            pass
+        elif all_arithmetic_operators.strip("_") not in [
+            "mul",
+            "rmul",
+            "floordiv",
+            "rfloordiv",
+            "pow",
+            "mod",
+            "rmod",
+        ]:
+            mark = pytest.mark.xfail(reason="result dtype.fill_value mismatch")
+            request.applymarker(mark)
+        super().test_arith_frame_with_scalar(data, all_arithmetic_operators)
+
+    def _compare_other(
+        self, ser: pd.Series, data_for_compare: SparseArray, comparison_op, other
+    ):
+        op = comparison_op
+
+        result = op(data_for_compare, other)
+        if isinstance(other, pd.Series):
+            assert isinstance(result, pd.Series)
+            assert isinstance(result.dtype, SparseDtype)
+        else:
+            assert isinstance(result, SparseArray)
+        assert result.dtype.subtype == np.bool_
+
+        if isinstance(other, pd.Series):
+            fill_value = op(data_for_compare.fill_value, other._values.fill_value)
+            expected = SparseArray(
+                op(data_for_compare.to_dense(), np.asarray(other)),
+                fill_value=fill_value,
+                dtype=np.bool_,
+            )
+
+        else:
+            fill_value = np.all(
+                op(np.asarray(data_for_compare.fill_value), np.asarray(other))
+            )
+
+            expected = SparseArray(
+                op(data_for_compare.to_dense(), np.asarray(other)),
+                fill_value=fill_value,
+                dtype=np.bool_,
+            )
+        if isinstance(other, pd.Series):
+            # error: Incompatible types in assignment
+            expected = pd.Series(expected)  # type: ignore[assignment]
+        tm.assert_equal(result, expected)
+
+    def test_scalar(self, data_for_compare: SparseArray, comparison_op):
+        ser = pd.Series(data_for_compare)
+        self._compare_other(ser, data_for_compare, comparison_op, 0)
+        self._compare_other(ser, data_for_compare, comparison_op, 1)
+        self._compare_other(ser, data_for_compare, comparison_op, -1)
+        self._compare_other(ser, data_for_compare, comparison_op, np.nan)
+
+    def test_array(self, data_for_compare: SparseArray, comparison_op, request):
+        if data_for_compare.dtype.fill_value == 0 and comparison_op.__name__ in [
+            "eq",
+            "ge",
+            "le",
+        ]:
+            mark = pytest.mark.xfail(reason="Wrong fill_value")
+            request.applymarker(mark)
+
+        arr = np.linspace(-4, 5, 10)
+        ser = pd.Series(data_for_compare)
+        self._compare_other(ser, data_for_compare, comparison_op, arr)
+
+    def test_sparse_array(self, data_for_compare: SparseArray, comparison_op, request):
+        if data_for_compare.dtype.fill_value == 0 and comparison_op.__name__ != "gt":
+            mark = pytest.mark.xfail(reason="Wrong fill_value")
+            request.applymarker(mark)
+
+        ser = pd.Series(data_for_compare)
+        arr = data_for_compare + 1
+        self._compare_other(ser, data_for_compare, comparison_op, arr)
+        arr = data_for_compare * 2
+        self._compare_other(ser, data_for_compare, comparison_op, arr)
+
+    @pytest.mark.xfail(reason="Different repr")
+    def test_array_repr(self, data, size):
+        super().test_array_repr(data, size)
+
+    @pytest.mark.xfail(reason="result does not match expected")
+    @pytest.mark.parametrize("as_index", [True, False])
+    def test_groupby_extension_agg(self, as_index, data_for_grouping):
+        super().test_groupby_extension_agg(as_index, data_for_grouping)
+
+
+def test_array_type_with_arg(dtype):
+    assert dtype.construct_array_type() is SparseArray
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/test_string.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/test_string.py
new file mode 100644
index 0000000000000000000000000000000000000000..71d4f7cc5c4bffcec673867f3675e4cfc1df2b3d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/extension/test_string.py
@@ -0,0 +1,293 @@
+"""
+This file contains a minimal set of tests for compliance with the extension
+array interface test suite, and should contain no other tests.
+The test suite for the full functionality of the array is located in
+`pandas/tests/arrays/`.
+
+The tests in this file are inherited from the BaseExtensionTests, and only
+minimal tweaks should be applied to get the tests passing (by overwriting a
+parent method).
+
+Additional tests should either be added to one of the BaseExtensionTests
+classes (if they are relevant for the extension interface for all dtypes), or
+be added to the array-specific tests in `pandas/tests/arrays/`.
+
+"""
+from __future__ import annotations
+
+import string
+from typing import cast
+
+import numpy as np
+import pytest
+
+from pandas.compat import HAS_PYARROW
+
+from pandas.core.dtypes.base import StorageExtensionDtype
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.api.types import is_string_dtype
+from pandas.core.arrays import ArrowStringArray
+from pandas.core.arrays.string_ import StringDtype
+from pandas.tests.arrays.string_.test_string import string_dtype_highest_priority
+from pandas.tests.extension import base
+
+
+def maybe_split_array(arr, chunked):
+    if not chunked:
+        return arr
+    elif arr.dtype.storage != "pyarrow":
+        return arr
+
+    pa = pytest.importorskip("pyarrow")
+
+    arrow_array = arr._pa_array
+    split = len(arrow_array) // 2
+    arrow_array = pa.chunked_array(
+        [*arrow_array[:split].chunks, *arrow_array[split:].chunks]
+    )
+    assert arrow_array.num_chunks == 2
+    return type(arr)(arrow_array)
+
+
+@pytest.fixture(params=[True, False])
+def chunked(request):
+    return request.param
+
+
+@pytest.fixture
+def dtype(string_dtype_arguments):
+    storage, na_value = string_dtype_arguments
+    return StringDtype(storage=storage, na_value=na_value)
+
+
+@pytest.fixture
+def data(dtype, chunked):
+    strings = np.random.default_rng(2).choice(list(string.ascii_letters), size=100)
+    while strings[0] == strings[1]:
+        strings = np.random.default_rng(2).choice(list(string.ascii_letters), size=100)
+
+    arr = dtype.construct_array_type()._from_sequence(strings, dtype=dtype)
+    return maybe_split_array(arr, chunked)
+
+
+@pytest.fixture
+def data_missing(dtype, chunked):
+    """Length 2 array with [NA, Valid]"""
+    arr = dtype.construct_array_type()._from_sequence([pd.NA, "A"], dtype=dtype)
+    return maybe_split_array(arr, chunked)
+
+
+@pytest.fixture
+def data_for_sorting(dtype, chunked):
+    arr = dtype.construct_array_type()._from_sequence(["B", "C", "A"], dtype=dtype)
+    return maybe_split_array(arr, chunked)
+
+
+@pytest.fixture
+def data_missing_for_sorting(dtype, chunked):
+    arr = dtype.construct_array_type()._from_sequence(["B", pd.NA, "A"], dtype=dtype)
+    return maybe_split_array(arr, chunked)
+
+
+@pytest.fixture
+def data_for_grouping(dtype, chunked):
+    arr = dtype.construct_array_type()._from_sequence(
+        ["B", "B", pd.NA, pd.NA, "A", "A", "B", "C"], dtype=dtype
+    )
+    return maybe_split_array(arr, chunked)
+
+
+class TestStringArray(base.ExtensionTests):
+    def test_eq_with_str(self, dtype):
+        super().test_eq_with_str(dtype)
+
+        if dtype.na_value is pd.NA:
+            # only the NA-variant supports parametrized string alias
+            assert dtype == f"string[{dtype.storage}]"
+        elif dtype.storage == "pyarrow":
+            with tm.assert_produces_warning(FutureWarning):
+                assert dtype == "string[pyarrow_numpy]"
+
+    def test_is_not_string_type(self, dtype):
+        # Different from BaseDtypeTests.test_is_not_string_type
+        # because StringDtype is a string type
+        assert is_string_dtype(dtype)
+
+    def test_is_dtype_from_name(self, dtype, using_infer_string):
+        if dtype.na_value is np.nan and not using_infer_string:
+            result = type(dtype).is_dtype(dtype.name)
+            assert result is False
+        else:
+            super().test_is_dtype_from_name(dtype)
+
+    def test_construct_from_string_own_name(self, dtype, using_infer_string):
+        if dtype.na_value is np.nan and not using_infer_string:
+            with pytest.raises(TypeError, match="Cannot construct a 'StringDtype'"):
+                dtype.construct_from_string(dtype.name)
+        else:
+            super().test_construct_from_string_own_name(dtype)
+
+    def test_view(self, data):
+        if data.dtype.storage == "pyarrow":
+            pytest.skip(reason="2D support not implemented for ArrowStringArray")
+        super().test_view(data)
+
+    def test_from_dtype(self, data):
+        # base test uses string representation of dtype
+        pass
+
+    def test_transpose(self, data):
+        if data.dtype.storage == "pyarrow":
+            pytest.skip(reason="2D support not implemented for ArrowStringArray")
+        super().test_transpose(data)
+
+    def test_setitem_preserves_views(self, data):
+        if data.dtype.storage == "pyarrow":
+            pytest.skip(reason="2D support not implemented for ArrowStringArray")
+        super().test_setitem_preserves_views(data)
+
+    def test_dropna_array(self, data_missing):
+        result = data_missing.dropna()
+        expected = data_missing[[1]]
+        tm.assert_extension_array_equal(result, expected)
+
+    def test_fillna_no_op_returns_copy(self, data):
+        data = data[~data.isna()]
+
+        valid = data[0]
+        result = data.fillna(valid)
+        assert result is not data
+        tm.assert_extension_array_equal(result, data)
+
+        result = data.fillna(method="backfill")
+        assert result is not data
+        tm.assert_extension_array_equal(result, data)
+
+    def _get_expected_exception(
+        self, op_name: str, obj, other
+    ) -> type[Exception] | tuple[type[Exception], ...] | None:
+        if op_name in [
+            "__mod__",
+            "__rmod__",
+            "__divmod__",
+            "__rdivmod__",
+            "__pow__",
+            "__rpow__",
+        ]:
+            return TypeError
+        elif op_name in ["__mul__", "__rmul__"]:
+            # Can only multiply strings by integers
+            return TypeError
+        elif op_name in [
+            "__truediv__",
+            "__rtruediv__",
+            "__floordiv__",
+            "__rfloordiv__",
+            "__sub__",
+            "__rsub__",
+        ]:
+            return TypeError
+
+        return None
+
+    def _supports_reduction(self, ser: pd.Series, op_name: str) -> bool:
+        return (
+            op_name in ["min", "max", "sum"]
+            or ser.dtype.na_value is np.nan  # type: ignore[union-attr]
+            and op_name in ("any", "all")
+        )
+
+    def _supports_accumulation(self, ser: pd.Series, op_name: str) -> bool:
+        assert isinstance(ser.dtype, StorageExtensionDtype)
+        return op_name in ["cummin", "cummax", "cumsum"]
+
+    def _cast_pointwise_result(self, op_name: str, obj, other, pointwise_result):
+        dtype = cast(StringDtype, tm.get_dtype(obj))
+        if op_name in ["__add__", "__radd__"]:
+            cast_to = dtype
+            dtype_other = tm.get_dtype(other) if not isinstance(other, str) else None
+            if isinstance(dtype_other, StringDtype):
+                cast_to = string_dtype_highest_priority(dtype, dtype_other)
+        elif dtype.na_value is np.nan:
+            cast_to = np.bool_  # type: ignore[assignment]
+        elif dtype.storage == "pyarrow":
+            cast_to = "bool[pyarrow]"  # type: ignore[assignment]
+        else:
+            cast_to = "boolean"  # type: ignore[assignment]
+        return pointwise_result.astype(cast_to)
+
+    def test_compare_scalar(self, data, comparison_op):
+        ser = pd.Series(data)
+        self._compare_other(ser, data, comparison_op, "abc")
+
+    def test_combine_add(self, data_repeated, using_infer_string, request):
+        dtype = next(data_repeated(1)).dtype
+        if using_infer_string and (
+            (dtype.na_value is pd.NA) and dtype.storage == "python"
+        ):
+            mark = pytest.mark.xfail(
+                reason="The pointwise operation result will be inferred to "
+                "string[nan, pyarrow], which does not match the input dtype"
+            )
+            request.applymarker(mark)
+        super().test_combine_add(data_repeated)
+
+    def test_arith_series_with_array(
+        self, data, all_arithmetic_operators, using_infer_string, request
+    ):
+        dtype = data.dtype
+        if (
+            using_infer_string
+            and all_arithmetic_operators == "__radd__"
+            and dtype.na_value is pd.NA
+            and (HAS_PYARROW or dtype.storage == "pyarrow")
+        ):
+            # TODO(infer_string)
+            mark = pytest.mark.xfail(
+                reason="The pointwise operation result will be inferred to "
+                "string[nan, pyarrow], which does not match the input dtype"
+            )
+            request.applymarker(mark)
+        super().test_arith_series_with_array(data, all_arithmetic_operators)
+
+
+class Test2DCompat(base.Dim2CompatTests):
+    @pytest.fixture(autouse=True)
+    def arrow_not_supported(self, data):
+        if isinstance(data, ArrowStringArray):
+            pytest.skip(reason="2D support not implemented for ArrowStringArray")
+
+
+def test_searchsorted_with_na_raises(data_for_sorting, as_series):
+    # GH50447
+    b, c, a = data_for_sorting
+    arr = data_for_sorting.take([2, 0, 1])  # to get [a, b, c]
+    arr[-1] = pd.NA
+
+    if as_series:
+        arr = pd.Series(arr)
+
+    msg = (
+        "searchsorted requires array to be sorted, "
+        "which is impossible with NAs present."
+    )
+    with pytest.raises(ValueError, match=msg):
+        arr.searchsorted(b)
+
+
+def test_mixed_object_comparison(dtype):
+    # GH#60228
+    ser = pd.Series(["a", "b"], dtype=dtype)
+
+    mixed = pd.Series([1, "b"], dtype=object)
+
+    result = ser == mixed
+    expected = pd.Series([False, True], dtype=bool)
+    if dtype.storage == "python" and dtype.na_value is pd.NA:
+        expected = expected.astype("boolean")
+    elif dtype.storage == "pyarrow" and dtype.na_value is pd.NA:
+        expected = expected.astype("bool[pyarrow]")
+
+    tm.assert_series_equal(result, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/common.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/common.py
new file mode 100644
index 0000000000000000000000000000000000000000..fc41d7907a240f0dd9dc19e0ae1296bee86be421
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/common.py
@@ -0,0 +1,63 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+from pandas import (
+    DataFrame,
+    concat,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import AxisInt
+
+
+def _check_mixed_float(df, dtype=None):
+    # float16 are most likely to be upcasted to float32
+    dtypes = {"A": "float32", "B": "float32", "C": "float16", "D": "float64"}
+    if isinstance(dtype, str):
+        dtypes = {k: dtype for k, v in dtypes.items()}
+    elif isinstance(dtype, dict):
+        dtypes.update(dtype)
+    if dtypes.get("A"):
+        assert df.dtypes["A"] == dtypes["A"]
+    if dtypes.get("B"):
+        assert df.dtypes["B"] == dtypes["B"]
+    if dtypes.get("C"):
+        assert df.dtypes["C"] == dtypes["C"]
+    if dtypes.get("D"):
+        assert df.dtypes["D"] == dtypes["D"]
+
+
+def _check_mixed_int(df, dtype=None):
+    dtypes = {"A": "int32", "B": "uint64", "C": "uint8", "D": "int64"}
+    if isinstance(dtype, str):
+        dtypes = {k: dtype for k, v in dtypes.items()}
+    elif isinstance(dtype, dict):
+        dtypes.update(dtype)
+    if dtypes.get("A"):
+        assert df.dtypes["A"] == dtypes["A"]
+    if dtypes.get("B"):
+        assert df.dtypes["B"] == dtypes["B"]
+    if dtypes.get("C"):
+        assert df.dtypes["C"] == dtypes["C"]
+    if dtypes.get("D"):
+        assert df.dtypes["D"] == dtypes["D"]
+
+
+def zip_frames(frames: list[DataFrame], axis: AxisInt = 1) -> DataFrame:
+    """
+    take a list of frames, zip them together under the
+    assumption that these all have the first frames' index/columns.
+
+    Returns
+    -------
+    new_frame : DataFrame
+    """
+    if axis == 1:
+        columns = frames[0].columns
+        zipped = [f.loc[:, c] for c in columns for f in frames]
+        return concat(zipped, axis=1)
+    else:
+        index = frames[0].index
+        zipped = [f.loc[i, :] for i in index for f in frames]
+        return DataFrame(zipped)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/conftest.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/conftest.py
new file mode 100644
index 0000000000000000000000000000000000000000..b7293946d38c9de9ef4b406d0d8fc933d33abfdd
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/conftest.py
@@ -0,0 +1,100 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    DataFrame,
+    Index,
+    NaT,
+    date_range,
+)
+
+
+@pytest.fixture
+def datetime_frame() -> DataFrame:
+    """
+    Fixture for DataFrame of floats with DatetimeIndex
+
+    Columns are ['A', 'B', 'C', 'D']
+    """
+    return DataFrame(
+        np.random.default_rng(2).standard_normal((100, 4)),
+        columns=Index(list("ABCD")),
+        index=date_range("2000-01-01", periods=100, freq="B"),
+    )
+
+
+@pytest.fixture
+def float_string_frame():
+    """
+    Fixture for DataFrame of floats and strings with index of unique strings
+
+    Columns are ['A', 'B', 'C', 'D', 'foo'].
+    """
+    df = DataFrame(
+        np.random.default_rng(2).standard_normal((30, 4)),
+        index=Index([f"foo_{i}" for i in range(30)], dtype=object),
+        columns=Index(list("ABCD")),
+    )
+    df["foo"] = "bar"
+    return df
+
+
+@pytest.fixture
+def mixed_float_frame():
+    """
+    Fixture for DataFrame of different float types with index of unique strings
+
+    Columns are ['A', 'B', 'C', 'D'].
+    """
+    df = DataFrame(
+        {
+            col: np.random.default_rng(2).random(30, dtype=dtype)
+            for col, dtype in zip(
+                list("ABCD"), ["float32", "float32", "float32", "float64"]
+            )
+        },
+        index=Index([f"foo_{i}" for i in range(30)], dtype=object),
+    )
+    # not supported by numpy random
+    df["C"] = df["C"].astype("float16")
+    return df
+
+
+@pytest.fixture
+def mixed_int_frame():
+    """
+    Fixture for DataFrame of different int types with index of unique strings
+
+    Columns are ['A', 'B', 'C', 'D'].
+    """
+    return DataFrame(
+        {
+            col: np.ones(30, dtype=dtype)
+            for col, dtype in zip(list("ABCD"), ["int32", "uint64", "uint8", "int64"])
+        },
+        index=Index([f"foo_{i}" for i in range(30)], dtype=object),
+    )
+
+
+@pytest.fixture
+def timezone_frame():
+    """
+    Fixture for DataFrame of date_range Series with different time zones
+
+    Columns are ['A', 'B', 'C']; some entries are missing
+
+               A                         B                         C
+    0 2013-01-01 2013-01-01 00:00:00-05:00 2013-01-01 00:00:00+01:00
+    1 2013-01-02                       NaT                       NaT
+    2 2013-01-03 2013-01-03 00:00:00-05:00 2013-01-03 00:00:00+01:00
+    """
+    df = DataFrame(
+        {
+            "A": date_range("20130101", periods=3),
+            "B": date_range("20130101", periods=3, tz="US/Eastern"),
+            "C": date_range("20130101", periods=3, tz="CET"),
+        }
+    )
+    df.iloc[1, 1] = NaT
+    df.iloc[1, 2] = NaT
+    return df
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/constructors/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/constructors/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/constructors/test_from_dict.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/constructors/test_from_dict.py
new file mode 100644
index 0000000000000000000000000000000000000000..845174bbf600e0211d4514ff6b713e0b3b40d756
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/constructors/test_from_dict.py
@@ -0,0 +1,223 @@
+from collections import OrderedDict
+
+import numpy as np
+import pytest
+
+from pandas import (
+    DataFrame,
+    Index,
+    MultiIndex,
+    RangeIndex,
+    Series,
+)
+import pandas._testing as tm
+
+
+class TestFromDict:
+    # Note: these tests are specific to the from_dict method, not for
+    #  passing dictionaries to DataFrame.__init__
+
+    def test_constructor_list_of_odicts(self):
+        data = [
+            OrderedDict([["a", 1.5], ["b", 3], ["c", 4], ["d", 6]]),
+            OrderedDict([["a", 1.5], ["b", 3], ["d", 6]]),
+            OrderedDict([["a", 1.5], ["d", 6]]),
+            OrderedDict(),
+            OrderedDict([["a", 1.5], ["b", 3], ["c", 4]]),
+            OrderedDict([["b", 3], ["c", 4], ["d", 6]]),
+        ]
+
+        result = DataFrame(data)
+        expected = DataFrame.from_dict(
+            dict(zip(range(len(data)), data)), orient="index"
+        )
+        tm.assert_frame_equal(result, expected.reindex(result.index))
+
+    def test_constructor_single_row(self):
+        data = [OrderedDict([["a", 1.5], ["b", 3], ["c", 4], ["d", 6]])]
+
+        result = DataFrame(data)
+        expected = DataFrame.from_dict(dict(zip([0], data)), orient="index").reindex(
+            result.index
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_constructor_list_of_series(self):
+        data = [
+            OrderedDict([["a", 1.5], ["b", 3.0], ["c", 4.0]]),
+            OrderedDict([["a", 1.5], ["b", 3.0], ["c", 6.0]]),
+        ]
+        sdict = OrderedDict(zip(["x", "y"], data))
+        idx = Index(["a", "b", "c"])
+
+        # all named
+        data2 = [
+            Series([1.5, 3, 4], idx, dtype="O", name="x"),
+            Series([1.5, 3, 6], idx, name="y"),
+        ]
+        result = DataFrame(data2)
+        expected = DataFrame.from_dict(sdict, orient="index")
+        tm.assert_frame_equal(result, expected)
+
+        # some unnamed
+        data2 = [
+            Series([1.5, 3, 4], idx, dtype="O", name="x"),
+            Series([1.5, 3, 6], idx),
+        ]
+        result = DataFrame(data2)
+
+        sdict = OrderedDict(zip(["x", "Unnamed 0"], data))
+        expected = DataFrame.from_dict(sdict, orient="index")
+        tm.assert_frame_equal(result, expected)
+
+        # none named
+        data = [
+            OrderedDict([["a", 1.5], ["b", 3], ["c", 4], ["d", 6]]),
+            OrderedDict([["a", 1.5], ["b", 3], ["d", 6]]),
+            OrderedDict([["a", 1.5], ["d", 6]]),
+            OrderedDict(),
+            OrderedDict([["a", 1.5], ["b", 3], ["c", 4]]),
+            OrderedDict([["b", 3], ["c", 4], ["d", 6]]),
+        ]
+        data = [Series(d) for d in data]
+
+        result = DataFrame(data)
+        sdict = OrderedDict(zip(range(len(data)), data))
+        expected = DataFrame.from_dict(sdict, orient="index")
+        tm.assert_frame_equal(result, expected.reindex(result.index))
+
+        result2 = DataFrame(data, index=np.arange(6, dtype=np.int64))
+        tm.assert_frame_equal(result, result2)
+
+        result = DataFrame([Series(dtype=object)])
+        expected = DataFrame(index=[0])
+        tm.assert_frame_equal(result, expected)
+
+        data = [
+            OrderedDict([["a", 1.5], ["b", 3.0], ["c", 4.0]]),
+            OrderedDict([["a", 1.5], ["b", 3.0], ["c", 6.0]]),
+        ]
+        sdict = OrderedDict(zip(range(len(data)), data))
+
+        idx = Index(["a", "b", "c"])
+        data2 = [Series([1.5, 3, 4], idx, dtype="O"), Series([1.5, 3, 6], idx)]
+        result = DataFrame(data2)
+        expected = DataFrame.from_dict(sdict, orient="index")
+        tm.assert_frame_equal(result, expected)
+
+    def test_constructor_orient(self, float_string_frame):
+        data_dict = float_string_frame.T._series
+        recons = DataFrame.from_dict(data_dict, orient="index")
+        expected = float_string_frame.reindex(index=recons.index)
+        tm.assert_frame_equal(recons, expected)
+
+        # dict of sequence
+        a = {"hi": [32, 3, 3], "there": [3, 5, 3]}
+        rs = DataFrame.from_dict(a, orient="index")
+        xp = DataFrame.from_dict(a).T.reindex(list(a.keys()))
+        tm.assert_frame_equal(rs, xp)
+
+    def test_constructor_from_ordered_dict(self):
+        # GH#8425
+        a = OrderedDict(
+            [
+                ("one", OrderedDict([("col_a", "foo1"), ("col_b", "bar1")])),
+                ("two", OrderedDict([("col_a", "foo2"), ("col_b", "bar2")])),
+                ("three", OrderedDict([("col_a", "foo3"), ("col_b", "bar3")])),
+            ]
+        )
+        expected = DataFrame.from_dict(a, orient="columns").T
+        result = DataFrame.from_dict(a, orient="index")
+        tm.assert_frame_equal(result, expected)
+
+    def test_from_dict_columns_parameter(self):
+        # GH#18529
+        # Test new columns parameter for from_dict that was added to make
+        # from_items(..., orient='index', columns=[...]) easier to replicate
+        result = DataFrame.from_dict(
+            OrderedDict([("A", [1, 2]), ("B", [4, 5])]),
+            orient="index",
+            columns=["one", "two"],
+        )
+        expected = DataFrame([[1, 2], [4, 5]], index=["A", "B"], columns=["one", "two"])
+        tm.assert_frame_equal(result, expected)
+
+        msg = "cannot use columns parameter with orient='columns'"
+        with pytest.raises(ValueError, match=msg):
+            DataFrame.from_dict(
+                {"A": [1, 2], "B": [4, 5]},
+                orient="columns",
+                columns=["one", "two"],
+            )
+        with pytest.raises(ValueError, match=msg):
+            DataFrame.from_dict({"A": [1, 2], "B": [4, 5]}, columns=["one", "two"])
+
+    @pytest.mark.parametrize(
+        "data_dict, orient, expected",
+        [
+            ({}, "index", RangeIndex(0)),
+            (
+                [{("a",): 1}, {("a",): 2}],
+                "columns",
+                Index([("a",)], tupleize_cols=False),
+            ),
+            (
+                [OrderedDict([(("a",), 1), (("b",), 2)])],
+                "columns",
+                Index([("a",), ("b",)], tupleize_cols=False),
+            ),
+            ([{("a", "b"): 1}], "columns", Index([("a", "b")], tupleize_cols=False)),
+        ],
+    )
+    def test_constructor_from_dict_tuples(self, data_dict, orient, expected):
+        # GH#16769
+        df = DataFrame.from_dict(data_dict, orient)
+        result = df.columns
+        tm.assert_index_equal(result, expected)
+
+    def test_frame_dict_constructor_empty_series(self):
+        s1 = Series(
+            [1, 2, 3, 4], index=MultiIndex.from_tuples([(1, 2), (1, 3), (2, 2), (2, 4)])
+        )
+        s2 = Series(
+            [1, 2, 3, 4], index=MultiIndex.from_tuples([(1, 2), (1, 3), (3, 2), (3, 4)])
+        )
+        s3 = Series(dtype=object)
+
+        # it works!
+        DataFrame({"foo": s1, "bar": s2, "baz": s3})
+        DataFrame.from_dict({"foo": s1, "baz": s3, "bar": s2})
+
+    def test_from_dict_scalars_requires_index(self):
+        msg = "If using all scalar values, you must pass an index"
+        with pytest.raises(ValueError, match=msg):
+            DataFrame.from_dict(OrderedDict([("b", 8), ("a", 5), ("a", 6)]))
+
+    def test_from_dict_orient_invalid(self):
+        msg = (
+            "Expected 'index', 'columns' or 'tight' for orient parameter. "
+            "Got 'abc' instead"
+        )
+        with pytest.raises(ValueError, match=msg):
+            DataFrame.from_dict({"foo": 1, "baz": 3, "bar": 2}, orient="abc")
+
+    def test_from_dict_order_with_single_column(self):
+        data = {
+            "alpha": {
+                "value2": 123,
+                "value1": 532,
+                "animal": 222,
+                "plant": False,
+                "name": "test",
+            }
+        }
+        result = DataFrame.from_dict(
+            data,
+            orient="columns",
+        )
+        expected = DataFrame(
+            [[123], [532], [222], [False], ["test"]],
+            index=["value2", "value1", "animal", "plant", "name"],
+            columns=["alpha"],
+        )
+        tm.assert_frame_equal(result, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/constructors/test_from_records.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/constructors/test_from_records.py
new file mode 100644
index 0000000000000000000000000000000000000000..58e47ba48f89445ee888c61055ad5a397a192f4a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/constructors/test_from_records.py
@@ -0,0 +1,503 @@
+from collections.abc import Iterator
+from datetime import datetime
+from decimal import Decimal
+
+import numpy as np
+import pytest
+import pytz
+
+from pandas._config import using_string_dtype
+
+from pandas.compat import is_platform_little_endian
+
+from pandas import (
+    CategoricalIndex,
+    DataFrame,
+    Index,
+    Interval,
+    RangeIndex,
+    Series,
+    date_range,
+)
+import pandas._testing as tm
+
+
+class TestFromRecords:
+    def test_from_records_dt64tz_frame(self):
+        # GH#51162 don't lose tz when calling from_records with DataFrame input
+        dti = date_range("2016-01-01", periods=10, tz="US/Pacific")
+        df = DataFrame({i: dti for i in range(4)})
+        with tm.assert_produces_warning(FutureWarning):
+            res = DataFrame.from_records(df)
+        tm.assert_frame_equal(res, df)
+
+    def test_from_records_with_datetimes(self):
+        # this may fail on certain platforms because of a numpy issue
+        # related GH#6140
+        if not is_platform_little_endian():
+            pytest.skip("known failure of test on non-little endian")
+
+        # construction with a null in a recarray
+        # GH#6140
+        expected = DataFrame({"EXPIRY": [datetime(2005, 3, 1, 0, 0), None]})
+
+        arrdata = [np.array([datetime(2005, 3, 1, 0, 0), None])]
+        dtypes = [("EXPIRY", " None:
+                self.args = args
+
+            def __getitem__(self, i):
+                return self.args[i]
+
+            def __iter__(self) -> Iterator:
+                return iter(self.args)
+
+        recs = [Record(1, 2, 3), Record(4, 5, 6), Record(7, 8, 9)]
+        tups = [tuple(rec) for rec in recs]
+
+        result = DataFrame.from_records(recs)
+        expected = DataFrame.from_records(tups)
+        tm.assert_frame_equal(result, expected)
+
+    def test_from_records_len0_with_columns(self):
+        # GH#2633
+        result = DataFrame.from_records([], index="foo", columns=["foo", "bar"])
+        expected = Index(["bar"])
+
+        assert len(result) == 0
+        assert result.index.name == "foo"
+        tm.assert_index_equal(result.columns, expected)
+
+    def test_from_records_series_list_dict(self):
+        # GH#27358
+        expected = DataFrame([[{"a": 1, "b": 2}, {"a": 3, "b": 4}]]).T
+        data = Series([[{"a": 1, "b": 2}], [{"a": 3, "b": 4}]])
+        result = DataFrame.from_records(data)
+        tm.assert_frame_equal(result, expected)
+
+    def test_from_records_series_categorical_index(self):
+        # GH#32805
+        index = CategoricalIndex(
+            [Interval(-20, -10), Interval(-10, 0), Interval(0, 10)]
+        )
+        series_of_dicts = Series([{"a": 1}, {"a": 2}, {"b": 3}], index=index)
+        frame = DataFrame.from_records(series_of_dicts, index=index)
+        expected = DataFrame(
+            {"a": [1, 2, np.nan], "b": [np.nan, np.nan, 3]}, index=index
+        )
+        tm.assert_frame_equal(frame, expected)
+
+    def test_frame_from_records_utc(self):
+        rec = {"datum": 1.5, "begin_time": datetime(2006, 4, 27, tzinfo=pytz.utc)}
+
+        # it works
+        DataFrame.from_records([rec], index="begin_time")
+
+    def test_from_records_to_records(self):
+        # from numpy documentation
+        arr = np.zeros((2,), dtype=("i4,f4,S10"))
+        arr[:] = [(1, 2.0, "Hello"), (2, 3.0, "World")]
+
+        DataFrame.from_records(arr)
+
+        index = Index(np.arange(len(arr))[::-1])
+        indexed_frame = DataFrame.from_records(arr, index=index)
+        tm.assert_index_equal(indexed_frame.index, index)
+
+        # without names, it should go to last ditch
+        arr2 = np.zeros((2, 3))
+        tm.assert_frame_equal(DataFrame.from_records(arr2), DataFrame(arr2))
+
+        # wrong length
+        msg = "|".join(
+            [
+                r"Length of values \(2\) does not match length of index \(1\)",
+            ]
+        )
+        with pytest.raises(ValueError, match=msg):
+            DataFrame.from_records(arr, index=index[:-1])
+
+        indexed_frame = DataFrame.from_records(arr, index="f1")
+
+        # what to do?
+        records = indexed_frame.to_records()
+        assert len(records.dtype.names) == 3
+
+        records = indexed_frame.to_records(index=False)
+        assert len(records.dtype.names) == 2
+        assert "index" not in records.dtype.names
+
+    def test_from_records_nones(self):
+        tuples = [(1, 2, None, 3), (1, 2, None, 3), (None, 2, 5, 3)]
+
+        df = DataFrame.from_records(tuples, columns=["a", "b", "c", "d"])
+        assert np.isnan(df["c"][0])
+
+    def test_from_records_iterator(self):
+        arr = np.array(
+            [(1.0, 1.0, 2, 2), (3.0, 3.0, 4, 4), (5.0, 5.0, 6, 6), (7.0, 7.0, 8, 8)],
+            dtype=[
+                ("x", np.float64),
+                ("u", np.float32),
+                ("y", np.int64),
+                ("z", np.int32),
+            ],
+        )
+        df = DataFrame.from_records(iter(arr), nrows=2)
+        xp = DataFrame(
+            {
+                "x": np.array([1.0, 3.0], dtype=np.float64),
+                "u": np.array([1.0, 3.0], dtype=np.float32),
+                "y": np.array([2, 4], dtype=np.int64),
+                "z": np.array([2, 4], dtype=np.int32),
+            }
+        )
+        tm.assert_frame_equal(df.reindex_like(xp), xp)
+
+        # no dtypes specified here, so just compare with the default
+        arr = [(1.0, 2), (3.0, 4), (5.0, 6), (7.0, 8)]
+        df = DataFrame.from_records(iter(arr), columns=["x", "y"], nrows=2)
+        tm.assert_frame_equal(df, xp.reindex(columns=["x", "y"]), check_dtype=False)
+
+    def test_from_records_tuples_generator(self):
+        def tuple_generator(length):
+            for i in range(length):
+                letters = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
+                yield (i, letters[i % len(letters)], i / length)
+
+        columns_names = ["Integer", "String", "Float"]
+        columns = [
+            [i[j] for i in tuple_generator(10)] for j in range(len(columns_names))
+        ]
+        data = {"Integer": columns[0], "String": columns[1], "Float": columns[2]}
+        expected = DataFrame(data, columns=columns_names)
+
+        generator = tuple_generator(10)
+        result = DataFrame.from_records(generator, columns=columns_names)
+        tm.assert_frame_equal(result, expected)
+
+    def test_from_records_lists_generator(self):
+        def list_generator(length):
+            for i in range(length):
+                letters = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
+                yield [i, letters[i % len(letters)], i / length]
+
+        columns_names = ["Integer", "String", "Float"]
+        columns = [
+            [i[j] for i in list_generator(10)] for j in range(len(columns_names))
+        ]
+        data = {"Integer": columns[0], "String": columns[1], "Float": columns[2]}
+        expected = DataFrame(data, columns=columns_names)
+
+        generator = list_generator(10)
+        result = DataFrame.from_records(generator, columns=columns_names)
+        tm.assert_frame_equal(result, expected)
+
+    def test_from_records_columns_not_modified(self):
+        tuples = [(1, 2, 3), (1, 2, 3), (2, 5, 3)]
+
+        columns = ["a", "b", "c"]
+        original_columns = list(columns)
+
+        DataFrame.from_records(tuples, columns=columns, index="a")
+
+        assert columns == original_columns
+
+    def test_from_records_decimal(self):
+        tuples = [(Decimal("1.5"),), (Decimal("2.5"),), (None,)]
+
+        df = DataFrame.from_records(tuples, columns=["a"])
+        assert df["a"].dtype == object
+
+        df = DataFrame.from_records(tuples, columns=["a"], coerce_float=True)
+        assert df["a"].dtype == np.float64
+        assert np.isnan(df["a"].values[-1])
+
+    def test_from_records_duplicates(self):
+        result = DataFrame.from_records([(1, 2, 3), (4, 5, 6)], columns=["a", "b", "a"])
+
+        expected = DataFrame([(1, 2, 3), (4, 5, 6)], columns=["a", "b", "a"])
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_from_records_set_index_name(self):
+        def create_dict(order_id):
+            return {
+                "order_id": order_id,
+                "quantity": np.random.default_rng(2).integers(1, 10),
+                "price": np.random.default_rng(2).integers(1, 10),
+            }
+
+        documents = [create_dict(i) for i in range(10)]
+        # demo missing data
+        documents.append({"order_id": 10, "quantity": 5})
+
+        result = DataFrame.from_records(documents, index="order_id")
+        assert result.index.name == "order_id"
+
+        # MultiIndex
+        result = DataFrame.from_records(documents, index=["order_id", "quantity"])
+        assert result.index.names == ("order_id", "quantity")
+
+    def test_from_records_misc_brokenness(self):
+        # GH#2179
+
+        data = {1: ["foo"], 2: ["bar"]}
+
+        result = DataFrame.from_records(data, columns=["a", "b"])
+        exp = DataFrame(data, columns=["a", "b"])
+        tm.assert_frame_equal(result, exp)
+
+        # overlap in index/index_names
+
+        data = {"a": [1, 2, 3], "b": [4, 5, 6]}
+
+        result = DataFrame.from_records(data, index=["a", "b", "c"])
+        exp = DataFrame(data, index=["a", "b", "c"])
+        tm.assert_frame_equal(result, exp)
+
+    def test_from_records_misc_brokenness2(self):
+        # GH#2623
+        rows = []
+        rows.append([datetime(2010, 1, 1), 1])
+        rows.append([datetime(2010, 1, 2), "hi"])  # test col upconverts to obj
+        result = DataFrame.from_records(rows, columns=["date", "test"])
+        expected = DataFrame(
+            {"date": [row[0] for row in rows], "test": [row[1] for row in rows]}
+        )
+        tm.assert_frame_equal(result, expected)
+        assert result.dtypes["test"] == np.dtype(object)
+
+    def test_from_records_misc_brokenness3(self):
+        rows = []
+        rows.append([datetime(2010, 1, 1), 1])
+        rows.append([datetime(2010, 1, 2), 1])
+        result = DataFrame.from_records(rows, columns=["date", "test"])
+        expected = DataFrame(
+            {"date": [row[0] for row in rows], "test": [row[1] for row in rows]}
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_from_records_empty(self):
+        # GH#3562
+        result = DataFrame.from_records([], columns=["a", "b", "c"])
+        expected = DataFrame(columns=["a", "b", "c"])
+        tm.assert_frame_equal(result, expected)
+
+        result = DataFrame.from_records([], columns=["a", "b", "b"])
+        expected = DataFrame(columns=["a", "b", "b"])
+        tm.assert_frame_equal(result, expected)
+
+    def test_from_records_empty_with_nonempty_fields_gh3682(self):
+        a = np.array([(1, 2)], dtype=[("id", np.int64), ("value", np.int64)])
+        df = DataFrame.from_records(a, index="id")
+
+        ex_index = Index([1], name="id")
+        expected = DataFrame({"value": [2]}, index=ex_index, columns=["value"])
+        tm.assert_frame_equal(df, expected)
+
+        b = a[:0]
+        df2 = DataFrame.from_records(b, index="id")
+        tm.assert_frame_equal(df2, df.iloc[:0])
+
+    def test_from_records_empty2(self):
+        # GH#42456
+        dtype = [("prop", int)]
+        shape = (0, len(dtype))
+        arr = np.empty(shape, dtype=dtype)
+
+        result = DataFrame.from_records(arr)
+        expected = DataFrame({"prop": np.array([], dtype=int)})
+        tm.assert_frame_equal(result, expected)
+
+        alt = DataFrame(arr)
+        tm.assert_frame_equal(alt, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/indexing/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/indexing/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/indexing/test_coercion.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/indexing/test_coercion.py
new file mode 100644
index 0000000000000000000000000000000000000000..6186dcd2868cfc6a3e1ca4f3afb1bc2151284963
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/indexing/test_coercion.py
@@ -0,0 +1,209 @@
+"""
+Tests for values coercion in setitem-like operations on DataFrame.
+
+For the most part, these should be multi-column DataFrames, otherwise
+we would share the tests with Series.
+"""
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    MultiIndex,
+    NaT,
+    Series,
+    Timestamp,
+    date_range,
+)
+import pandas._testing as tm
+
+
+class TestDataFrameSetitemCoercion:
+    @pytest.mark.parametrize("consolidate", [True, False])
+    def test_loc_setitem_multiindex_columns(self, consolidate):
+        # GH#18415 Setting values in a single column preserves dtype,
+        #  while setting them in multiple columns did unwanted cast.
+
+        # Note that A here has 2 blocks, below we do the same thing
+        #  with a consolidated frame.
+        A = DataFrame(np.zeros((6, 5), dtype=np.float32))
+        A = pd.concat([A, A], axis=1, keys=[1, 2])
+        if consolidate:
+            A = A._consolidate()
+
+        A.loc[2:3, (1, slice(2, 3))] = np.ones((2, 2), dtype=np.float32)
+        assert (A.dtypes == np.float32).all()
+
+        A.loc[0:5, (1, slice(2, 3))] = np.ones((6, 2), dtype=np.float32)
+
+        assert (A.dtypes == np.float32).all()
+
+        A.loc[:, (1, slice(2, 3))] = np.ones((6, 2), dtype=np.float32)
+        assert (A.dtypes == np.float32).all()
+
+        # TODO: i think this isn't about MultiIndex and could be done with iloc?
+
+
+def test_37477():
+    # fixed by GH#45121
+    orig = DataFrame({"A": [1, 2, 3], "B": [3, 4, 5]})
+    expected = DataFrame({"A": [1, 2, 3], "B": [3, 1.2, 5]})
+
+    df = orig.copy()
+    with tm.assert_produces_warning(
+        FutureWarning, match="Setting an item of incompatible dtype"
+    ):
+        df.at[1, "B"] = 1.2
+    tm.assert_frame_equal(df, expected)
+
+    df = orig.copy()
+    with tm.assert_produces_warning(
+        FutureWarning, match="Setting an item of incompatible dtype"
+    ):
+        df.loc[1, "B"] = 1.2
+    tm.assert_frame_equal(df, expected)
+
+    df = orig.copy()
+    with tm.assert_produces_warning(
+        FutureWarning, match="Setting an item of incompatible dtype"
+    ):
+        df.iat[1, 1] = 1.2
+    tm.assert_frame_equal(df, expected)
+
+    df = orig.copy()
+    with tm.assert_produces_warning(
+        FutureWarning, match="Setting an item of incompatible dtype"
+    ):
+        df.iloc[1, 1] = 1.2
+    tm.assert_frame_equal(df, expected)
+
+
+def test_6942(indexer_al):
+    # check that the .at __setitem__ after setting "Live" actually sets the data
+    start = Timestamp("2014-04-01")
+    t1 = Timestamp("2014-04-23 12:42:38.883082")
+    t2 = Timestamp("2014-04-24 01:33:30.040039")
+
+    dti = date_range(start, periods=1)
+    orig = DataFrame(index=dti, columns=["timenow", "Live"])
+
+    df = orig.copy()
+    indexer_al(df)[start, "timenow"] = t1
+
+    df["Live"] = True
+
+    df.at[start, "timenow"] = t2
+    assert df.iloc[0, 0] == t2
+
+
+def test_26395(indexer_al):
+    # .at case fixed by GH#45121 (best guess)
+    df = DataFrame(index=["A", "B", "C"])
+    df["D"] = 0
+
+    indexer_al(df)["C", "D"] = 2
+    expected = DataFrame({"D": [0, 0, 2]}, index=["A", "B", "C"], dtype=np.int64)
+    tm.assert_frame_equal(df, expected)
+
+    with tm.assert_produces_warning(
+        FutureWarning, match="Setting an item of incompatible dtype"
+    ):
+        indexer_al(df)["C", "D"] = 44.5
+    expected = DataFrame(
+        {"D": [0, 0, 44.5]},
+        index=["A", "B", "C"],
+        columns=["D"],
+        dtype=np.float64,
+    )
+    tm.assert_frame_equal(df, expected)
+
+    with tm.assert_produces_warning(
+        FutureWarning, match="Setting an item of incompatible dtype"
+    ):
+        indexer_al(df)["C", "D"] = "hello"
+    expected = DataFrame(
+        {"D": [0, 0, "hello"]},
+        index=["A", "B", "C"],
+        columns=["D"],
+        dtype=object,
+    )
+    tm.assert_frame_equal(df, expected)
+
+
+@pytest.mark.xfail(reason="unwanted upcast")
+def test_15231():
+    df = DataFrame([[1, 2], [3, 4]], columns=["a", "b"])
+    df.loc[2] = Series({"a": 5, "b": 6})
+    assert (df.dtypes == np.int64).all()
+
+    df.loc[3] = Series({"a": 7})
+
+    # df["a"] doesn't have any NaNs, should not have been cast
+    exp_dtypes = Series([np.int64, np.float64], dtype=object, index=["a", "b"])
+    tm.assert_series_equal(df.dtypes, exp_dtypes)
+
+
+def test_iloc_setitem_unnecesssary_float_upcasting():
+    # GH#12255
+    df = DataFrame(
+        {
+            0: np.array([1, 3], dtype=np.float32),
+            1: np.array([2, 4], dtype=np.float32),
+            2: ["a", "b"],
+        }
+    )
+    orig = df.copy()
+
+    values = df[0].values.reshape(2, 1)
+    df.iloc[:, 0:1] = values
+
+    tm.assert_frame_equal(df, orig)
+
+
+@pytest.mark.xfail(reason="unwanted casting to dt64")
+def test_12499():
+    # TODO: OP in GH#12499 used np.datetim64("NaT") instead of pd.NaT,
+    #  which has consequences for the expected df["two"] (though i think at
+    #  the time it might not have because of a separate bug). See if it makes
+    #  a difference which one we use here.
+    ts = Timestamp("2016-03-01 03:13:22.98986", tz="UTC")
+
+    data = [{"one": 0, "two": ts}]
+    orig = DataFrame(data)
+    df = orig.copy()
+    df.loc[1] = [np.nan, NaT]
+
+    expected = DataFrame(
+        {"one": [0, np.nan], "two": Series([ts, NaT], dtype="datetime64[ns, UTC]")}
+    )
+    tm.assert_frame_equal(df, expected)
+
+    data = [{"one": 0, "two": ts}]
+    df = orig.copy()
+    df.loc[1, :] = [np.nan, NaT]
+    tm.assert_frame_equal(df, expected)
+
+
+def test_20476():
+    mi = MultiIndex.from_product([["A", "B"], ["a", "b", "c"]])
+    df = DataFrame(-1, index=range(3), columns=mi)
+    filler = DataFrame([[1, 2, 3.0]] * 3, index=range(3), columns=["a", "b", "c"])
+    df["A"] = filler
+
+    expected = DataFrame(
+        {
+            0: [1, 1, 1],
+            1: [2, 2, 2],
+            2: [3.0, 3.0, 3.0],
+            3: [-1, -1, -1],
+            4: [-1, -1, -1],
+            5: [-1, -1, -1],
+        }
+    )
+    expected.columns = mi
+    exp_dtypes = Series(
+        [np.dtype(np.int64)] * 2 + [np.dtype(np.float64)] + [np.dtype(np.int64)] * 3,
+        index=mi,
+    )
+    tm.assert_series_equal(df.dtypes, exp_dtypes)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/indexing/test_delitem.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/indexing/test_delitem.py
new file mode 100644
index 0000000000000000000000000000000000000000..daec991b7a8dbf8de0221f041e767cb9ce58ae29
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/indexing/test_delitem.py
@@ -0,0 +1,60 @@
+import re
+
+import numpy as np
+import pytest
+
+from pandas import (
+    DataFrame,
+    MultiIndex,
+)
+
+
+class TestDataFrameDelItem:
+    def test_delitem(self, float_frame):
+        del float_frame["A"]
+        assert "A" not in float_frame
+
+    def test_delitem_multiindex(self):
+        midx = MultiIndex.from_product([["A", "B"], [1, 2]])
+        df = DataFrame(np.random.default_rng(2).standard_normal((4, 4)), columns=midx)
+        assert len(df.columns) == 4
+        assert ("A",) in df.columns
+        assert "A" in df.columns
+
+        result = df["A"]
+        assert isinstance(result, DataFrame)
+        del df["A"]
+
+        assert len(df.columns) == 2
+
+        # A still in the levels, BUT get a KeyError if trying
+        # to delete
+        assert ("A",) not in df.columns
+        with pytest.raises(KeyError, match=re.escape("('A',)")):
+            del df[("A",)]
+
+        # behavior of dropped/deleted MultiIndex levels changed from
+        # GH 2770 to GH 19027: MultiIndex no longer '.__contains__'
+        # levels which are dropped/deleted
+        assert "A" not in df.columns
+        with pytest.raises(KeyError, match=re.escape("('A',)")):
+            del df["A"]
+
+    def test_delitem_corner(self, float_frame):
+        f = float_frame.copy()
+        del f["D"]
+        assert len(f.columns) == 3
+        with pytest.raises(KeyError, match=r"^'D'$"):
+            del f["D"]
+        del f["B"]
+        assert len(f.columns) == 2
+
+    def test_delitem_col_still_multiindex(self):
+        arrays = [["a", "b", "c", "top"], ["", "", "", "OD"], ["", "", "", "wx"]]
+
+        tuples = sorted(zip(*arrays))
+        index = MultiIndex.from_tuples(tuples)
+
+        df = DataFrame(np.random.default_rng(2).standard_normal((3, 4)), columns=index)
+        del df[("a", "", "")]
+        assert isinstance(df.columns, MultiIndex)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/indexing/test_get.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/indexing/test_get.py
new file mode 100644
index 0000000000000000000000000000000000000000..5f2651eec683c10097fb623728048b64778c87e8
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/indexing/test_get.py
@@ -0,0 +1,27 @@
+import pytest
+
+from pandas import DataFrame
+import pandas._testing as tm
+
+
+class TestGet:
+    def test_get(self, float_frame):
+        b = float_frame.get("B")
+        tm.assert_series_equal(b, float_frame["B"])
+
+        assert float_frame.get("foo") is None
+        tm.assert_series_equal(
+            float_frame.get("foo", float_frame["B"]), float_frame["B"]
+        )
+
+    @pytest.mark.parametrize(
+        "df",
+        [
+            DataFrame(),
+            DataFrame(columns=list("AB")),
+            DataFrame(columns=list("AB"), index=range(3)),
+        ],
+    )
+    def test_get_none(self, df):
+        # see gh-5652
+        assert df.get(None) is None
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/indexing/test_get_value.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/indexing/test_get_value.py
new file mode 100644
index 0000000000000000000000000000000000000000..65a1c64a1578ad0cadd9ed6470ab60a2087ffec5
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/indexing/test_get_value.py
@@ -0,0 +1,22 @@
+import pytest
+
+from pandas import (
+    DataFrame,
+    MultiIndex,
+)
+
+
+class TestGetValue:
+    def test_get_set_value_no_partial_indexing(self):
+        # partial w/ MultiIndex raise exception
+        index = MultiIndex.from_tuples([(0, 1), (0, 2), (1, 1), (1, 2)])
+        df = DataFrame(index=index, columns=range(4))
+        with pytest.raises(KeyError, match=r"^0$"):
+            df._get_value(0, 1)
+
+    def test_get_value(self, float_frame):
+        for idx in float_frame.index:
+            for col in float_frame.columns:
+                result = float_frame._get_value(idx, col)
+                expected = float_frame[col][idx]
+                assert result == expected
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/indexing/test_getitem.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/indexing/test_getitem.py
new file mode 100644
index 0000000000000000000000000000000000000000..a36b0c0e850b3d0994349065cc5390c9f40cbf60
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/indexing/test_getitem.py
@@ -0,0 +1,472 @@
+import re
+
+import numpy as np
+import pytest
+
+from pandas import (
+    Categorical,
+    CategoricalDtype,
+    CategoricalIndex,
+    DataFrame,
+    DateOffset,
+    DatetimeIndex,
+    Index,
+    MultiIndex,
+    Series,
+    Timestamp,
+    concat,
+    date_range,
+    get_dummies,
+    period_range,
+)
+import pandas._testing as tm
+from pandas.core.arrays import SparseArray
+
+
+class TestGetitem:
+    def test_getitem_unused_level_raises(self):
+        # GH#20410
+        mi = MultiIndex(
+            levels=[["a_lot", "onlyone", "notevenone"], [1970, ""]],
+            codes=[[1, 0], [1, 0]],
+        )
+        df = DataFrame(-1, index=range(3), columns=mi)
+
+        with pytest.raises(KeyError, match="notevenone"):
+            df["notevenone"]
+
+    def test_getitem_periodindex(self):
+        rng = period_range("1/1/2000", periods=5)
+        df = DataFrame(np.random.default_rng(2).standard_normal((10, 5)), columns=rng)
+
+        ts = df[rng[0]]
+        tm.assert_series_equal(ts, df.iloc[:, 0])
+
+        ts = df["1/1/2000"]
+        tm.assert_series_equal(ts, df.iloc[:, 0])
+
+    def test_getitem_list_of_labels_categoricalindex_cols(self):
+        # GH#16115
+        cats = Categorical([Timestamp("12-31-1999"), Timestamp("12-31-2000")])
+
+        expected = DataFrame([[1, 0], [0, 1]], dtype="bool", index=[0, 1], columns=cats)
+        dummies = get_dummies(cats)
+        result = dummies[list(dummies.columns)]
+        tm.assert_frame_equal(result, expected)
+
+    def test_getitem_sparse_column_return_type_and_dtype(self):
+        # https://github.com/pandas-dev/pandas/issues/23559
+        data = SparseArray([0, 1])
+        df = DataFrame({"A": data})
+        expected = Series(data, name="A")
+        result = df["A"]
+        tm.assert_series_equal(result, expected)
+
+        # Also check iloc and loc while we're here
+        result = df.iloc[:, 0]
+        tm.assert_series_equal(result, expected)
+
+        result = df.loc[:, "A"]
+        tm.assert_series_equal(result, expected)
+
+    def test_getitem_string_columns(self):
+        # GH#46185
+        df = DataFrame([[1, 2]], columns=Index(["A", "B"], dtype="string"))
+        result = df.A
+        expected = df["A"]
+        tm.assert_series_equal(result, expected)
+
+
+class TestGetitemListLike:
+    def test_getitem_list_missing_key(self):
+        # GH#13822, incorrect error string with non-unique columns when missing
+        # column is accessed
+        df = DataFrame({"x": [1.0], "y": [2.0], "z": [3.0]})
+        df.columns = ["x", "x", "z"]
+
+        # Check that we get the correct value in the KeyError
+        with pytest.raises(KeyError, match=r"\['y'\] not in index"):
+            df[["x", "y", "z"]]
+
+    def test_getitem_list_duplicates(self):
+        # GH#1943
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((4, 4)), columns=list("AABC")
+        )
+        df.columns.name = "foo"
+
+        result = df[["B", "C"]]
+        assert result.columns.name == "foo"
+
+        expected = df.iloc[:, 2:]
+        tm.assert_frame_equal(result, expected)
+
+    def test_getitem_dupe_cols(self):
+        df = DataFrame([[1, 2, 3], [4, 5, 6]], columns=["a", "a", "b"])
+        msg = "\"None of [Index(['baf'], dtype="
+        with pytest.raises(KeyError, match=re.escape(msg)):
+            df[["baf"]]
+
+    @pytest.mark.parametrize(
+        "idx_type",
+        [
+            list,
+            iter,
+            Index,
+            set,
+            lambda keys: dict(zip(keys, range(len(keys)))),
+            lambda keys: dict(zip(keys, range(len(keys)))).keys(),
+        ],
+        ids=["list", "iter", "Index", "set", "dict", "dict_keys"],
+    )
+    @pytest.mark.parametrize("levels", [1, 2])
+    def test_getitem_listlike(self, idx_type, levels, float_frame):
+        # GH#21294
+
+        if levels == 1:
+            frame, missing = float_frame, "food"
+        else:
+            # MultiIndex columns
+            frame = DataFrame(
+                np.random.default_rng(2).standard_normal((8, 3)),
+                columns=Index(
+                    [("foo", "bar"), ("baz", "qux"), ("peek", "aboo")],
+                    name=("sth", "sth2"),
+                ),
+            )
+            missing = ("good", "food")
+
+        keys = [frame.columns[1], frame.columns[0]]
+        idx = idx_type(keys)
+        idx_check = list(idx_type(keys))
+
+        if isinstance(idx, (set, dict)):
+            with pytest.raises(TypeError, match="as an indexer is not supported"):
+                frame[idx]
+
+            return
+        else:
+            result = frame[idx]
+
+        expected = frame.loc[:, idx_check]
+        expected.columns.names = frame.columns.names
+
+        tm.assert_frame_equal(result, expected)
+
+        idx = idx_type(keys + [missing])
+        with pytest.raises(KeyError, match="not in index"):
+            frame[idx]
+
+    def test_getitem_iloc_generator(self):
+        # GH#39614
+        df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
+        indexer = (x for x in [1, 2])
+        result = df.iloc[indexer]
+        expected = DataFrame({"a": [2, 3], "b": [5, 6]}, index=[1, 2])
+        tm.assert_frame_equal(result, expected)
+
+    def test_getitem_iloc_two_dimensional_generator(self):
+        df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
+        indexer = (x for x in [1, 2])
+        result = df.iloc[indexer, 1]
+        expected = Series([5, 6], name="b", index=[1, 2])
+        tm.assert_series_equal(result, expected)
+
+    def test_getitem_iloc_dateoffset_days(self):
+        # GH 46671
+        df = DataFrame(
+            list(range(10)),
+            index=date_range("01-01-2022", periods=10, freq=DateOffset(days=1)),
+        )
+        result = df.loc["2022-01-01":"2022-01-03"]
+        expected = DataFrame(
+            [0, 1, 2],
+            index=DatetimeIndex(
+                ["2022-01-01", "2022-01-02", "2022-01-03"],
+                dtype="datetime64[ns]",
+                freq=DateOffset(days=1),
+            ),
+        )
+        tm.assert_frame_equal(result, expected)
+
+        df = DataFrame(
+            list(range(10)),
+            index=date_range(
+                "01-01-2022", periods=10, freq=DateOffset(days=1, hours=2)
+            ),
+        )
+        result = df.loc["2022-01-01":"2022-01-03"]
+        expected = DataFrame(
+            [0, 1, 2],
+            index=DatetimeIndex(
+                ["2022-01-01 00:00:00", "2022-01-02 02:00:00", "2022-01-03 04:00:00"],
+                dtype="datetime64[ns]",
+                freq=DateOffset(days=1, hours=2),
+            ),
+        )
+        tm.assert_frame_equal(result, expected)
+
+        df = DataFrame(
+            list(range(10)),
+            index=date_range("01-01-2022", periods=10, freq=DateOffset(minutes=3)),
+        )
+        result = df.loc["2022-01-01":"2022-01-03"]
+        tm.assert_frame_equal(result, df)
+
+
+class TestGetitemCallable:
+    def test_getitem_callable(self, float_frame):
+        # GH#12533
+        result = float_frame[lambda x: "A"]
+        expected = float_frame.loc[:, "A"]
+        tm.assert_series_equal(result, expected)
+
+        result = float_frame[lambda x: ["A", "B"]]
+        expected = float_frame.loc[:, ["A", "B"]]
+        tm.assert_frame_equal(result, float_frame.loc[:, ["A", "B"]])
+
+        df = float_frame[:3]
+        result = df[lambda x: [True, False, True]]
+        expected = float_frame.iloc[[0, 2], :]
+        tm.assert_frame_equal(result, expected)
+
+    def test_loc_multiindex_columns_one_level(self):
+        # GH#29749
+        df = DataFrame([[1, 2]], columns=[["a", "b"]])
+        expected = DataFrame([1], columns=[["a"]])
+
+        result = df["a"]
+        tm.assert_frame_equal(result, expected)
+
+        result = df.loc[:, "a"]
+        tm.assert_frame_equal(result, expected)
+
+
+class TestGetitemBooleanMask:
+    def test_getitem_bool_mask_categorical_index(self):
+        df3 = DataFrame(
+            {
+                "A": np.arange(6, dtype="int64"),
+            },
+            index=CategoricalIndex(
+                [1, 1, 2, 1, 3, 2],
+                dtype=CategoricalDtype([3, 2, 1], ordered=True),
+                name="B",
+            ),
+        )
+        df4 = DataFrame(
+            {
+                "A": np.arange(6, dtype="int64"),
+            },
+            index=CategoricalIndex(
+                [1, 1, 2, 1, 3, 2],
+                dtype=CategoricalDtype([3, 2, 1], ordered=False),
+                name="B",
+            ),
+        )
+
+        result = df3[df3.index == "a"]
+        expected = df3.iloc[[]]
+        tm.assert_frame_equal(result, expected)
+
+        result = df4[df4.index == "a"]
+        expected = df4.iloc[[]]
+        tm.assert_frame_equal(result, expected)
+
+        result = df3[df3.index == 1]
+        expected = df3.iloc[[0, 1, 3]]
+        tm.assert_frame_equal(result, expected)
+
+        result = df4[df4.index == 1]
+        expected = df4.iloc[[0, 1, 3]]
+        tm.assert_frame_equal(result, expected)
+
+        # since we have an ordered categorical
+
+        # CategoricalIndex([1, 1, 2, 1, 3, 2],
+        #         categories=[3, 2, 1],
+        #         ordered=True,
+        #         name='B')
+        result = df3[df3.index < 2]
+        expected = df3.iloc[[4]]
+        tm.assert_frame_equal(result, expected)
+
+        result = df3[df3.index > 1]
+        expected = df3.iloc[[]]
+        tm.assert_frame_equal(result, expected)
+
+        # unordered
+        # cannot be compared
+
+        # CategoricalIndex([1, 1, 2, 1, 3, 2],
+        #         categories=[3, 2, 1],
+        #         ordered=False,
+        #         name='B')
+        msg = "Unordered Categoricals can only compare equality or not"
+        with pytest.raises(TypeError, match=msg):
+            df4[df4.index < 2]
+        with pytest.raises(TypeError, match=msg):
+            df4[df4.index > 1]
+
+    @pytest.mark.parametrize(
+        "data1,data2,expected_data",
+        (
+            (
+                [[1, 2], [3, 4]],
+                [[0.5, 6], [7, 8]],
+                [[np.nan, 3.0], [np.nan, 4.0], [np.nan, 7.0], [6.0, 8.0]],
+            ),
+            (
+                [[1, 2], [3, 4]],
+                [[5, 6], [7, 8]],
+                [[np.nan, 3.0], [np.nan, 4.0], [5, 7], [6, 8]],
+            ),
+        ),
+    )
+    def test_getitem_bool_mask_duplicate_columns_mixed_dtypes(
+        self,
+        data1,
+        data2,
+        expected_data,
+    ):
+        # GH#31954
+
+        df1 = DataFrame(np.array(data1))
+        df2 = DataFrame(np.array(data2))
+        df = concat([df1, df2], axis=1)
+
+        result = df[df > 2]
+
+        exdict = {i: np.array(col) for i, col in enumerate(expected_data)}
+        expected = DataFrame(exdict).rename(columns={2: 0, 3: 1})
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.fixture
+    def df_dup_cols(self):
+        dups = ["A", "A", "C", "D"]
+        df = DataFrame(np.arange(12).reshape(3, 4), columns=dups, dtype="float64")
+        return df
+
+    def test_getitem_boolean_frame_unaligned_with_duplicate_columns(self, df_dup_cols):
+        # `df.A > 6` is a DataFrame with a different shape from df
+
+        # boolean with the duplicate raises
+        df = df_dup_cols
+        msg = "cannot reindex on an axis with duplicate labels"
+        with pytest.raises(ValueError, match=msg):
+            df[df.A > 6]
+
+    def test_getitem_boolean_series_with_duplicate_columns(self, df_dup_cols):
+        # boolean indexing
+        # GH#4879
+        df = DataFrame(
+            np.arange(12).reshape(3, 4), columns=["A", "B", "C", "D"], dtype="float64"
+        )
+        expected = df[df.C > 6]
+        expected.columns = df_dup_cols.columns
+
+        df = df_dup_cols
+        result = df[df.C > 6]
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_getitem_boolean_frame_with_duplicate_columns(self, df_dup_cols):
+        # where
+        df = DataFrame(
+            np.arange(12).reshape(3, 4), columns=["A", "B", "C", "D"], dtype="float64"
+        )
+        # `df > 6` is a DataFrame with the same shape+alignment as df
+        expected = df[df > 6]
+        expected.columns = df_dup_cols.columns
+
+        df = df_dup_cols
+        result = df[df > 6]
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_getitem_empty_frame_with_boolean(self):
+        # Test for issue GH#11859
+
+        df = DataFrame()
+        df2 = df[df > 0]
+        tm.assert_frame_equal(df, df2)
+
+    def test_getitem_returns_view_when_column_is_unique_in_df(
+        self, using_copy_on_write, warn_copy_on_write
+    ):
+        # GH#45316
+        df = DataFrame([[1, 2, 3], [4, 5, 6]], columns=["a", "a", "b"])
+        df_orig = df.copy()
+        view = df["b"]
+        with tm.assert_cow_warning(warn_copy_on_write):
+            view.loc[:] = 100
+        if using_copy_on_write:
+            expected = df_orig
+        else:
+            expected = DataFrame([[1, 2, 100], [4, 5, 100]], columns=["a", "a", "b"])
+        tm.assert_frame_equal(df, expected)
+
+    def test_getitem_frozenset_unique_in_column(self):
+        # GH#41062
+        df = DataFrame([[1, 2, 3, 4]], columns=[frozenset(["KEY"]), "B", "C", "C"])
+        result = df[frozenset(["KEY"])]
+        expected = Series([1], name=frozenset(["KEY"]))
+        tm.assert_series_equal(result, expected)
+
+
+class TestGetitemSlice:
+    def test_getitem_slice_float64(self, frame_or_series):
+        values = np.arange(10.0, 50.0, 2)
+        index = Index(values)
+
+        start, end = values[[5, 15]]
+
+        data = np.random.default_rng(2).standard_normal((20, 3))
+        if frame_or_series is not DataFrame:
+            data = data[:, 0]
+
+        obj = frame_or_series(data, index=index)
+
+        result = obj[start:end]
+        expected = obj.iloc[5:16]
+        tm.assert_equal(result, expected)
+
+        result = obj.loc[start:end]
+        tm.assert_equal(result, expected)
+
+    def test_getitem_datetime_slice(self):
+        # GH#43223
+        df = DataFrame(
+            {"a": 0},
+            index=DatetimeIndex(
+                [
+                    "11.01.2011 22:00",
+                    "11.01.2011 23:00",
+                    "12.01.2011 00:00",
+                    "2011-01-13 00:00",
+                ]
+            ),
+        )
+        with pytest.raises(
+            KeyError, match="Value based partial slicing on non-monotonic"
+        ):
+            df["2011-01-01":"2011-11-01"]
+
+    def test_getitem_slice_same_dim_only_one_axis(self):
+        # GH#54622
+        df = DataFrame(np.random.default_rng(2).standard_normal((10, 8)))
+        result = df.iloc[(slice(None, None, 2),)]
+        assert result.shape == (5, 8)
+        expected = df.iloc[slice(None, None, 2), slice(None)]
+        tm.assert_frame_equal(result, expected)
+
+
+class TestGetitemDeprecatedIndexers:
+    @pytest.mark.parametrize("key", [{"a", "b"}, {"a": "a"}])
+    def test_getitem_dict_and_set_deprecated(self, key):
+        # GH#42825 enforced in 2.0
+        df = DataFrame(
+            [[1, 2], [3, 4]], columns=MultiIndex.from_tuples([("a", 1), ("b", 2)])
+        )
+        with pytest.raises(TypeError, match="as an indexer is not supported"):
+            df[key]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/test_alter_axes.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/test_alter_axes.py
new file mode 100644
index 0000000000000000000000000000000000000000..c68171ab254c7c8582a206a8e9b44b3845c47efc
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/test_alter_axes.py
@@ -0,0 +1,30 @@
+from datetime import datetime
+
+import pytz
+
+from pandas import DataFrame
+import pandas._testing as tm
+
+
+class TestDataFrameAlterAxes:
+    # Tests for setting index/columns attributes directly (i.e. __setattr__)
+
+    def test_set_axis_setattr_index(self):
+        # GH 6785
+        # set the index manually
+
+        df = DataFrame([{"ts": datetime(2014, 4, 1, tzinfo=pytz.utc), "foo": 1}])
+        expected = df.set_index("ts")
+        df.index = df["ts"]
+        df.pop("ts")
+        tm.assert_frame_equal(df, expected)
+
+    # Renaming
+
+    def test_assign_columns(self, float_frame):
+        float_frame["hi"] = "there"
+
+        df = float_frame.copy()
+        df.columns = ["foo", "bar", "baz", "quux", "foo2"]
+        tm.assert_series_equal(float_frame["C"], df["baz"], check_names=False)
+        tm.assert_series_equal(float_frame["hi"], df["foo2"], check_names=False)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/test_api.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/test_api.py
new file mode 100644
index 0000000000000000000000000000000000000000..6c6944f806a2ae77b4a826684a9474769cd18e30
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/test_api.py
@@ -0,0 +1,395 @@
+from copy import deepcopy
+import inspect
+import pydoc
+
+import numpy as np
+import pytest
+
+from pandas._config import using_string_dtype
+from pandas._config.config import option_context
+
+from pandas.compat import HAS_PYARROW
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Series,
+    date_range,
+    timedelta_range,
+)
+import pandas._testing as tm
+
+
+class TestDataFrameMisc:
+    def test_getitem_pop_assign_name(self, float_frame):
+        s = float_frame["A"]
+        assert s.name == "A"
+
+        s = float_frame.pop("A")
+        assert s.name == "A"
+
+        s = float_frame.loc[:, "B"]
+        assert s.name == "B"
+
+        s2 = s.loc[:]
+        assert s2.name == "B"
+
+    def test_get_axis(self, float_frame):
+        f = float_frame
+        assert f._get_axis_number(0) == 0
+        assert f._get_axis_number(1) == 1
+        assert f._get_axis_number("index") == 0
+        assert f._get_axis_number("rows") == 0
+        assert f._get_axis_number("columns") == 1
+
+        assert f._get_axis_name(0) == "index"
+        assert f._get_axis_name(1) == "columns"
+        assert f._get_axis_name("index") == "index"
+        assert f._get_axis_name("rows") == "index"
+        assert f._get_axis_name("columns") == "columns"
+
+        assert f._get_axis(0) is f.index
+        assert f._get_axis(1) is f.columns
+
+        with pytest.raises(ValueError, match="No axis named"):
+            f._get_axis_number(2)
+
+        with pytest.raises(ValueError, match="No axis.*foo"):
+            f._get_axis_name("foo")
+
+        with pytest.raises(ValueError, match="No axis.*None"):
+            f._get_axis_name(None)
+
+        with pytest.raises(ValueError, match="No axis named"):
+            f._get_axis_number(None)
+
+    def test_column_contains_raises(self, float_frame):
+        with pytest.raises(TypeError, match="unhashable type: 'Index'"):
+            float_frame.columns in float_frame
+
+    def test_tab_completion(self):
+        # DataFrame whose columns are identifiers shall have them in __dir__.
+        df = DataFrame([list("abcd"), list("efgh")], columns=list("ABCD"))
+        for key in list("ABCD"):
+            assert key in dir(df)
+        assert isinstance(df.__getitem__("A"), Series)
+
+        # DataFrame whose first-level columns are identifiers shall have
+        # them in __dir__.
+        df = DataFrame(
+            [list("abcd"), list("efgh")],
+            columns=pd.MultiIndex.from_tuples(list(zip("ABCD", "EFGH"))),
+        )
+        for key in list("ABCD"):
+            assert key in dir(df)
+        for key in list("EFGH"):
+            assert key not in dir(df)
+        assert isinstance(df.__getitem__("A"), DataFrame)
+
+    def test_display_max_dir_items(self):
+        # display.max_dir_items increaes the number of columns that are in __dir__.
+        columns = ["a" + str(i) for i in range(420)]
+        values = [range(420), range(420)]
+        df = DataFrame(values, columns=columns)
+
+        # The default value for display.max_dir_items is 100
+        assert "a99" in dir(df)
+        assert "a100" not in dir(df)
+
+        with option_context("display.max_dir_items", 300):
+            df = DataFrame(values, columns=columns)
+            assert "a299" in dir(df)
+            assert "a300" not in dir(df)
+
+        with option_context("display.max_dir_items", None):
+            df = DataFrame(values, columns=columns)
+            assert "a419" in dir(df)
+
+    def test_not_hashable(self):
+        empty_frame = DataFrame()
+
+        df = DataFrame([1])
+        msg = "unhashable type: 'DataFrame'"
+        with pytest.raises(TypeError, match=msg):
+            hash(df)
+        with pytest.raises(TypeError, match=msg):
+            hash(empty_frame)
+
+    @pytest.mark.xfail(
+        using_string_dtype() and HAS_PYARROW, reason="surrogates not allowed"
+    )
+    def test_column_name_contains_unicode_surrogate(self):
+        # GH 25509
+        colname = "\ud83d"
+        df = DataFrame({colname: []})
+        # this should not crash
+        assert colname not in dir(df)
+        assert df.columns[0] == colname
+
+    def test_new_empty_index(self):
+        df1 = DataFrame(np.random.default_rng(2).standard_normal((0, 3)))
+        df2 = DataFrame(np.random.default_rng(2).standard_normal((0, 3)))
+        df1.index.name = "foo"
+        assert df2.index.name is None
+
+    def test_get_agg_axis(self, float_frame):
+        cols = float_frame._get_agg_axis(0)
+        assert cols is float_frame.columns
+
+        idx = float_frame._get_agg_axis(1)
+        assert idx is float_frame.index
+
+        msg = r"Axis must be 0 or 1 \(got 2\)"
+        with pytest.raises(ValueError, match=msg):
+            float_frame._get_agg_axis(2)
+
+    def test_empty(self, float_frame, float_string_frame):
+        empty_frame = DataFrame()
+        assert empty_frame.empty
+
+        assert not float_frame.empty
+        assert not float_string_frame.empty
+
+        # corner case
+        df = DataFrame({"A": [1.0, 2.0, 3.0], "B": ["a", "b", "c"]}, index=np.arange(3))
+        del df["A"]
+        assert not df.empty
+
+    def test_len(self, float_frame):
+        assert len(float_frame) == len(float_frame.index)
+
+        # single block corner case
+        arr = float_frame[["A", "B"]].values
+        expected = float_frame.reindex(columns=["A", "B"]).values
+        tm.assert_almost_equal(arr, expected)
+
+    def test_axis_aliases(self, float_frame):
+        f = float_frame
+
+        # reg name
+        expected = f.sum(axis=0)
+        result = f.sum(axis="index")
+        tm.assert_series_equal(result, expected)
+
+        expected = f.sum(axis=1)
+        result = f.sum(axis="columns")
+        tm.assert_series_equal(result, expected)
+
+    def test_class_axis(self):
+        # GH 18147
+        # no exception and no empty docstring
+        assert pydoc.getdoc(DataFrame.index)
+        assert pydoc.getdoc(DataFrame.columns)
+
+    def test_series_put_names(self, float_string_frame):
+        series = float_string_frame._series
+        for k, v in series.items():
+            assert v.name == k
+
+    def test_empty_nonzero(self):
+        df = DataFrame([1, 2, 3])
+        assert not df.empty
+        df = DataFrame(index=[1], columns=[1])
+        assert not df.empty
+        df = DataFrame(index=["a", "b"], columns=["c", "d"]).dropna()
+        assert df.empty
+        assert df.T.empty
+
+    @pytest.mark.parametrize(
+        "df",
+        [
+            DataFrame(),
+            DataFrame(index=[1]),
+            DataFrame(columns=[1]),
+            DataFrame({1: []}),
+        ],
+    )
+    def test_empty_like(self, df):
+        assert df.empty
+        assert df.T.empty
+
+    def test_with_datetimelikes(self):
+        df = DataFrame(
+            {
+                "A": date_range("20130101", periods=10),
+                "B": timedelta_range("1 day", periods=10),
+            }
+        )
+        t = df.T
+
+        result = t.dtypes.value_counts()
+        expected = Series({np.dtype("object"): 10}, name="count")
+        tm.assert_series_equal(result, expected)
+
+    def test_deepcopy(self, float_frame):
+        cp = deepcopy(float_frame)
+        cp.loc[0, "A"] = 10
+        assert not float_frame.equals(cp)
+
+    def test_inplace_return_self(self):
+        # GH 1893
+
+        data = DataFrame(
+            {"a": ["foo", "bar", "baz", "qux"], "b": [0, 0, 1, 1], "c": [1, 2, 3, 4]}
+        )
+
+        def _check_f(base, f):
+            result = f(base)
+            assert result is None
+
+        # -----DataFrame-----
+
+        # set_index
+        f = lambda x: x.set_index("a", inplace=True)
+        _check_f(data.copy(), f)
+
+        # reset_index
+        f = lambda x: x.reset_index(inplace=True)
+        _check_f(data.set_index("a"), f)
+
+        # drop_duplicates
+        f = lambda x: x.drop_duplicates(inplace=True)
+        _check_f(data.copy(), f)
+
+        # sort
+        f = lambda x: x.sort_values("b", inplace=True)
+        _check_f(data.copy(), f)
+
+        # sort_index
+        f = lambda x: x.sort_index(inplace=True)
+        _check_f(data.copy(), f)
+
+        # fillna
+        f = lambda x: x.fillna(0, inplace=True)
+        _check_f(data.copy(), f)
+
+        # replace
+        f = lambda x: x.replace(1, 0, inplace=True)
+        _check_f(data.copy(), f)
+
+        # rename
+        f = lambda x: x.rename({1: "foo"}, inplace=True)
+        _check_f(data.copy(), f)
+
+        # -----Series-----
+        d = data.copy()["c"]
+
+        # reset_index
+        f = lambda x: x.reset_index(inplace=True, drop=True)
+        _check_f(data.set_index("a")["c"], f)
+
+        # fillna
+        f = lambda x: x.fillna(0, inplace=True)
+        _check_f(d.copy(), f)
+
+        # replace
+        f = lambda x: x.replace(1, 0, inplace=True)
+        _check_f(d.copy(), f)
+
+        # rename
+        f = lambda x: x.rename({1: "foo"}, inplace=True)
+        _check_f(d.copy(), f)
+
+    def test_tab_complete_warning(self, ip, frame_or_series):
+        # GH 16409
+        pytest.importorskip("IPython", minversion="6.0.0")
+        from IPython.core.completer import provisionalcompleter
+
+        if frame_or_series is DataFrame:
+            code = "from pandas import DataFrame; obj = DataFrame()"
+        else:
+            code = "from pandas import Series; obj = Series(dtype=object)"
+
+        ip.run_cell(code)
+        # GH 31324 newer jedi version raises Deprecation warning;
+        #  appears resolved 2021-02-02
+        with tm.assert_produces_warning(None, raise_on_extra_warnings=False):
+            with provisionalcompleter("ignore"):
+                list(ip.Completer.completions("obj.", 1))
+
+    def test_attrs(self):
+        df = DataFrame({"A": [2, 3]})
+        assert df.attrs == {}
+        df.attrs["version"] = 1
+
+        result = df.rename(columns=str)
+        assert result.attrs == {"version": 1}
+
+    def test_attrs_deepcopy(self):
+        df = DataFrame({"A": [2, 3]})
+        assert df.attrs == {}
+        df.attrs["tags"] = {"spam", "ham"}
+
+        result = df.rename(columns=str)
+        assert result.attrs == df.attrs
+        assert result.attrs["tags"] is not df.attrs["tags"]
+
+    @pytest.mark.parametrize("allows_duplicate_labels", [True, False, None])
+    def test_set_flags(
+        self,
+        allows_duplicate_labels,
+        frame_or_series,
+        using_copy_on_write,
+        warn_copy_on_write,
+    ):
+        obj = DataFrame({"A": [1, 2]})
+        key = (0, 0)
+        if frame_or_series is Series:
+            obj = obj["A"]
+            key = 0
+
+        result = obj.set_flags(allows_duplicate_labels=allows_duplicate_labels)
+
+        if allows_duplicate_labels is None:
+            # We don't update when it's not provided
+            assert result.flags.allows_duplicate_labels is True
+        else:
+            assert result.flags.allows_duplicate_labels is allows_duplicate_labels
+
+        # We made a copy
+        assert obj is not result
+
+        # We didn't mutate obj
+        assert obj.flags.allows_duplicate_labels is True
+
+        # But we didn't copy data
+        if frame_or_series is Series:
+            assert np.may_share_memory(obj.values, result.values)
+        else:
+            assert np.may_share_memory(obj["A"].values, result["A"].values)
+
+        with tm.assert_cow_warning(warn_copy_on_write):
+            result.iloc[key] = 0
+        if using_copy_on_write:
+            assert obj.iloc[key] == 1
+        else:
+            assert obj.iloc[key] == 0
+            # set back to 1 for test below
+            with tm.assert_cow_warning(warn_copy_on_write):
+                result.iloc[key] = 1
+
+        # Now we do copy.
+        result = obj.set_flags(
+            copy=True, allows_duplicate_labels=allows_duplicate_labels
+        )
+        result.iloc[key] = 10
+        assert obj.iloc[key] == 1
+
+    def test_constructor_expanddim(self):
+        # GH#33628 accessing _constructor_expanddim should not raise NotImplementedError
+        # GH38782 pandas has no container higher than DataFrame (two-dim), so
+        # DataFrame._constructor_expand_dim, doesn't make sense, so is removed.
+        df = DataFrame()
+
+        msg = "'DataFrame' object has no attribute '_constructor_expanddim'"
+        with pytest.raises(AttributeError, match=msg):
+            df._constructor_expanddim(np.arange(27).reshape(3, 3, 3))
+
+    def test_inspect_getmembers(self):
+        # GH38740
+        df = DataFrame()
+        msg = "DataFrame._data is deprecated"
+        with tm.assert_produces_warning(
+            DeprecationWarning, match=msg, check_stacklevel=False
+        ):
+            inspect.getmembers(df)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/test_arithmetic.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/test_arithmetic.py
new file mode 100644
index 0000000000000000000000000000000000000000..195126f1c53822f103d2c558ebb2843feac45a30
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/test_arithmetic.py
@@ -0,0 +1,2145 @@
+from collections import deque
+from datetime import (
+    datetime,
+    timezone,
+)
+from enum import Enum
+import functools
+import operator
+import re
+
+import numpy as np
+import pytest
+
+from pandas.compat import HAS_PYARROW
+import pandas.util._test_decorators as td
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Index,
+    MultiIndex,
+    Series,
+)
+import pandas._testing as tm
+from pandas.core.computation import expressions as expr
+from pandas.tests.frame.common import (
+    _check_mixed_float,
+    _check_mixed_int,
+)
+
+
+@pytest.fixture
+def simple_frame():
+    """
+    Fixture for simple 3x3 DataFrame
+
+    Columns are ['one', 'two', 'three'], index is ['a', 'b', 'c'].
+
+       one  two  three
+    a  1.0  2.0    3.0
+    b  4.0  5.0    6.0
+    c  7.0  8.0    9.0
+    """
+    arr = np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]])
+
+    return DataFrame(arr, columns=["one", "two", "three"], index=["a", "b", "c"])
+
+
+@pytest.fixture(autouse=True, params=[0, 100], ids=["numexpr", "python"])
+def switch_numexpr_min_elements(request, monkeypatch):
+    with monkeypatch.context() as m:
+        m.setattr(expr, "_MIN_ELEMENTS", request.param)
+        yield request.param
+
+
+class DummyElement:
+    def __init__(self, value, dtype) -> None:
+        self.value = value
+        self.dtype = np.dtype(dtype)
+
+    def __array__(self, dtype=None, copy=None):
+        return np.array(self.value, dtype=self.dtype)
+
+    def __str__(self) -> str:
+        return f"DummyElement({self.value}, {self.dtype})"
+
+    def __repr__(self) -> str:
+        return str(self)
+
+    def astype(self, dtype, copy=False):
+        self.dtype = dtype
+        return self
+
+    def view(self, dtype):
+        return type(self)(self.value.view(dtype), dtype)
+
+    def any(self, axis=None):
+        return bool(self.value)
+
+
+# -------------------------------------------------------------------
+# Comparisons
+
+
+class TestFrameComparisons:
+    # Specifically _not_ flex-comparisons
+
+    def test_comparison_with_categorical_dtype(self):
+        # GH#12564
+
+        df = DataFrame({"A": ["foo", "bar", "baz"]})
+        exp = DataFrame({"A": [True, False, False]})
+
+        res = df == "foo"
+        tm.assert_frame_equal(res, exp)
+
+        # casting to categorical shouldn't affect the result
+        df["A"] = df["A"].astype("category")
+
+        res = df == "foo"
+        tm.assert_frame_equal(res, exp)
+
+    def test_frame_in_list(self):
+        # GH#12689 this should raise at the DataFrame level, not blocks
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((6, 4)), columns=list("ABCD")
+        )
+        msg = "The truth value of a DataFrame is ambiguous"
+        with pytest.raises(ValueError, match=msg):
+            df in [None]
+
+    @pytest.mark.parametrize(
+        "arg, arg2",
+        [
+            [
+                {
+                    "a": np.random.default_rng(2).integers(10, size=10),
+                    "b": pd.date_range("20010101", periods=10),
+                },
+                {
+                    "a": np.random.default_rng(2).integers(10, size=10),
+                    "b": np.random.default_rng(2).integers(10, size=10),
+                },
+            ],
+            [
+                {
+                    "a": np.random.default_rng(2).integers(10, size=10),
+                    "b": np.random.default_rng(2).integers(10, size=10),
+                },
+                {
+                    "a": np.random.default_rng(2).integers(10, size=10),
+                    "b": pd.date_range("20010101", periods=10),
+                },
+            ],
+            [
+                {
+                    "a": pd.date_range("20010101", periods=10),
+                    "b": pd.date_range("20010101", periods=10),
+                },
+                {
+                    "a": np.random.default_rng(2).integers(10, size=10),
+                    "b": np.random.default_rng(2).integers(10, size=10),
+                },
+            ],
+            [
+                {
+                    "a": np.random.default_rng(2).integers(10, size=10),
+                    "b": pd.date_range("20010101", periods=10),
+                },
+                {
+                    "a": pd.date_range("20010101", periods=10),
+                    "b": pd.date_range("20010101", periods=10),
+                },
+            ],
+        ],
+    )
+    def test_comparison_invalid(self, arg, arg2):
+        # GH4968
+        # invalid date/int comparisons
+        x = DataFrame(arg)
+        y = DataFrame(arg2)
+        # we expect the result to match Series comparisons for
+        # == and !=, inequalities should raise
+        result = x == y
+        expected = DataFrame(
+            {col: x[col] == y[col] for col in x.columns},
+            index=x.index,
+            columns=x.columns,
+        )
+        tm.assert_frame_equal(result, expected)
+
+        result = x != y
+        expected = DataFrame(
+            {col: x[col] != y[col] for col in x.columns},
+            index=x.index,
+            columns=x.columns,
+        )
+        tm.assert_frame_equal(result, expected)
+
+        msgs = [
+            r"Invalid comparison between dtype=datetime64\[ns\] and ndarray",
+            "invalid type promotion",
+            (
+                # npdev 1.20.0
+                r"The DTypes  and "
+                r" do not have a common DType."
+            ),
+        ]
+        msg = "|".join(msgs)
+        with pytest.raises(TypeError, match=msg):
+            x >= y
+        with pytest.raises(TypeError, match=msg):
+            x > y
+        with pytest.raises(TypeError, match=msg):
+            x < y
+        with pytest.raises(TypeError, match=msg):
+            x <= y
+
+    @pytest.mark.parametrize(
+        "left, right",
+        [
+            ("gt", "lt"),
+            ("lt", "gt"),
+            ("ge", "le"),
+            ("le", "ge"),
+            ("eq", "eq"),
+            ("ne", "ne"),
+        ],
+    )
+    def test_timestamp_compare(self, left, right):
+        # make sure we can compare Timestamps on the right AND left hand side
+        # GH#4982
+        df = DataFrame(
+            {
+                "dates1": pd.date_range("20010101", periods=10),
+                "dates2": pd.date_range("20010102", periods=10),
+                "intcol": np.random.default_rng(2).integers(1000000000, size=10),
+                "floatcol": np.random.default_rng(2).standard_normal(10),
+                "stringcol": [chr(100 + i) for i in range(10)],
+            }
+        )
+        df.loc[np.random.default_rng(2).random(len(df)) > 0.5, "dates2"] = pd.NaT
+        left_f = getattr(operator, left)
+        right_f = getattr(operator, right)
+
+        # no nats
+        if left in ["eq", "ne"]:
+            expected = left_f(df, pd.Timestamp("20010109"))
+            result = right_f(pd.Timestamp("20010109"), df)
+            tm.assert_frame_equal(result, expected)
+        else:
+            msg = (
+                "'(<|>)=?' not supported between "
+                "instances of 'numpy.ndarray' and 'Timestamp'"
+            )
+            with pytest.raises(TypeError, match=msg):
+                left_f(df, pd.Timestamp("20010109"))
+            with pytest.raises(TypeError, match=msg):
+                right_f(pd.Timestamp("20010109"), df)
+        # nats
+        if left in ["eq", "ne"]:
+            expected = left_f(df, pd.Timestamp("nat"))
+            result = right_f(pd.Timestamp("nat"), df)
+            tm.assert_frame_equal(result, expected)
+        else:
+            msg = (
+                "'(<|>)=?' not supported between "
+                "instances of 'numpy.ndarray' and 'NaTType'"
+            )
+            with pytest.raises(TypeError, match=msg):
+                left_f(df, pd.Timestamp("nat"))
+            with pytest.raises(TypeError, match=msg):
+                right_f(pd.Timestamp("nat"), df)
+
+    def test_mixed_comparison(self):
+        # GH#13128, GH#22163 != datetime64 vs non-dt64 should be False,
+        # not raise TypeError
+        # (this appears to be fixed before GH#22163, not sure when)
+        df = DataFrame([["1989-08-01", 1], ["1989-08-01", 2]])
+        other = DataFrame([["a", "b"], ["c", "d"]])
+
+        result = df == other
+        assert not result.any().any()
+
+        result = df != other
+        assert result.all().all()
+
+    def test_df_boolean_comparison_error(self):
+        # GH#4576, GH#22880
+        # comparing DataFrame against list/tuple with len(obj) matching
+        #  len(df.columns) is supported as of GH#22800
+        df = DataFrame(np.arange(6).reshape((3, 2)))
+
+        expected = DataFrame([[False, False], [True, False], [False, False]])
+
+        result = df == (2, 2)
+        tm.assert_frame_equal(result, expected)
+
+        result = df == [2, 2]
+        tm.assert_frame_equal(result, expected)
+
+    def test_df_float_none_comparison(self):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((8, 3)),
+            index=range(8),
+            columns=["A", "B", "C"],
+        )
+
+        result = df.__eq__(None)
+        assert not result.any().any()
+
+    def test_df_string_comparison(self):
+        df = DataFrame([{"a": 1, "b": "foo"}, {"a": 2, "b": "bar"}])
+        mask_a = df.a > 1
+        tm.assert_frame_equal(df[mask_a], df.loc[1:1, :])
+        tm.assert_frame_equal(df[-mask_a], df.loc[0:0, :])
+
+        mask_b = df.b == "foo"
+        tm.assert_frame_equal(df[mask_b], df.loc[0:0, :])
+        tm.assert_frame_equal(df[-mask_b], df.loc[1:1, :])
+
+
+class TestFrameFlexComparisons:
+    # TODO: test_bool_flex_frame needs a better name
+    @pytest.mark.parametrize("op", ["eq", "ne", "gt", "lt", "ge", "le"])
+    def test_bool_flex_frame(self, op):
+        data = np.random.default_rng(2).standard_normal((5, 3))
+        other_data = np.random.default_rng(2).standard_normal((5, 3))
+        df = DataFrame(data)
+        other = DataFrame(other_data)
+        ndim_5 = np.ones(df.shape + (1, 3))
+
+        # DataFrame
+        assert df.eq(df).values.all()
+        assert not df.ne(df).values.any()
+        f = getattr(df, op)
+        o = getattr(operator, op)
+        # No NAs
+        tm.assert_frame_equal(f(other), o(df, other))
+        # Unaligned
+        part_o = other.loc[3:, 1:].copy()
+        rs = f(part_o)
+        xp = o(df, part_o.reindex(index=df.index, columns=df.columns))
+        tm.assert_frame_equal(rs, xp)
+        # ndarray
+        tm.assert_frame_equal(f(other.values), o(df, other.values))
+        # scalar
+        tm.assert_frame_equal(f(0), o(df, 0))
+        # NAs
+        msg = "Unable to coerce to Series/DataFrame"
+        tm.assert_frame_equal(f(np.nan), o(df, np.nan))
+        with pytest.raises(ValueError, match=msg):
+            f(ndim_5)
+
+    @pytest.mark.parametrize("box", [np.array, Series])
+    def test_bool_flex_series(self, box):
+        # Series
+        # list/tuple
+        data = np.random.default_rng(2).standard_normal((5, 3))
+        df = DataFrame(data)
+        idx_ser = box(np.random.default_rng(2).standard_normal(5))
+        col_ser = box(np.random.default_rng(2).standard_normal(3))
+
+        idx_eq = df.eq(idx_ser, axis=0)
+        col_eq = df.eq(col_ser)
+        idx_ne = df.ne(idx_ser, axis=0)
+        col_ne = df.ne(col_ser)
+        tm.assert_frame_equal(col_eq, df == Series(col_ser))
+        tm.assert_frame_equal(col_eq, -col_ne)
+        tm.assert_frame_equal(idx_eq, -idx_ne)
+        tm.assert_frame_equal(idx_eq, df.T.eq(idx_ser).T)
+        tm.assert_frame_equal(col_eq, df.eq(list(col_ser)))
+        tm.assert_frame_equal(idx_eq, df.eq(Series(idx_ser), axis=0))
+        tm.assert_frame_equal(idx_eq, df.eq(list(idx_ser), axis=0))
+
+        idx_gt = df.gt(idx_ser, axis=0)
+        col_gt = df.gt(col_ser)
+        idx_le = df.le(idx_ser, axis=0)
+        col_le = df.le(col_ser)
+
+        tm.assert_frame_equal(col_gt, df > Series(col_ser))
+        tm.assert_frame_equal(col_gt, -col_le)
+        tm.assert_frame_equal(idx_gt, -idx_le)
+        tm.assert_frame_equal(idx_gt, df.T.gt(idx_ser).T)
+
+        idx_ge = df.ge(idx_ser, axis=0)
+        col_ge = df.ge(col_ser)
+        idx_lt = df.lt(idx_ser, axis=0)
+        col_lt = df.lt(col_ser)
+        tm.assert_frame_equal(col_ge, df >= Series(col_ser))
+        tm.assert_frame_equal(col_ge, -col_lt)
+        tm.assert_frame_equal(idx_ge, -idx_lt)
+        tm.assert_frame_equal(idx_ge, df.T.ge(idx_ser).T)
+
+        idx_ser = Series(np.random.default_rng(2).standard_normal(5))
+        col_ser = Series(np.random.default_rng(2).standard_normal(3))
+
+    def test_bool_flex_frame_na(self):
+        df = DataFrame(np.random.default_rng(2).standard_normal((5, 3)))
+        # NA
+        df.loc[0, 0] = np.nan
+        rs = df.eq(df)
+        assert not rs.loc[0, 0]
+        rs = df.ne(df)
+        assert rs.loc[0, 0]
+        rs = df.gt(df)
+        assert not rs.loc[0, 0]
+        rs = df.lt(df)
+        assert not rs.loc[0, 0]
+        rs = df.ge(df)
+        assert not rs.loc[0, 0]
+        rs = df.le(df)
+        assert not rs.loc[0, 0]
+
+    def test_bool_flex_frame_complex_dtype(self):
+        # complex
+        arr = np.array([np.nan, 1, 6, np.nan])
+        arr2 = np.array([2j, np.nan, 7, None])
+        df = DataFrame({"a": arr})
+        df2 = DataFrame({"a": arr2})
+
+        msg = "|".join(
+            [
+                "'>' not supported between instances of '.*' and 'complex'",
+                r"unorderable types: .*complex\(\)",  # PY35
+            ]
+        )
+        with pytest.raises(TypeError, match=msg):
+            # inequalities are not well-defined for complex numbers
+            df.gt(df2)
+        with pytest.raises(TypeError, match=msg):
+            # regression test that we get the same behavior for Series
+            df["a"].gt(df2["a"])
+        with pytest.raises(TypeError, match=msg):
+            # Check that we match numpy behavior here
+            df.values > df2.values
+
+        rs = df.ne(df2)
+        assert rs.values.all()
+
+        arr3 = np.array([2j, np.nan, None])
+        df3 = DataFrame({"a": arr3})
+
+        with pytest.raises(TypeError, match=msg):
+            # inequalities are not well-defined for complex numbers
+            df3.gt(2j)
+        with pytest.raises(TypeError, match=msg):
+            # regression test that we get the same behavior for Series
+            df3["a"].gt(2j)
+        with pytest.raises(TypeError, match=msg):
+            # Check that we match numpy behavior here
+            df3.values > 2j
+
+    def test_bool_flex_frame_object_dtype(self):
+        # corner, dtype=object
+        df1 = DataFrame({"col": ["foo", np.nan, "bar"]}, dtype=object)
+        df2 = DataFrame({"col": ["foo", datetime.now(), "bar"]}, dtype=object)
+        result = df1.ne(df2)
+        exp = DataFrame({"col": [False, True, False]})
+        tm.assert_frame_equal(result, exp)
+
+    def test_flex_comparison_nat(self):
+        # GH 15697, GH 22163 df.eq(pd.NaT) should behave like df == pd.NaT,
+        # and _definitely_ not be NaN
+        df = DataFrame([pd.NaT])
+
+        result = df == pd.NaT
+        # result.iloc[0, 0] is a np.bool_ object
+        assert result.iloc[0, 0].item() is False
+
+        result = df.eq(pd.NaT)
+        assert result.iloc[0, 0].item() is False
+
+        result = df != pd.NaT
+        assert result.iloc[0, 0].item() is True
+
+        result = df.ne(pd.NaT)
+        assert result.iloc[0, 0].item() is True
+
+    @pytest.mark.parametrize("opname", ["eq", "ne", "gt", "lt", "ge", "le"])
+    def test_df_flex_cmp_constant_return_types(self, opname):
+        # GH 15077, non-empty DataFrame
+        df = DataFrame({"x": [1, 2, 3], "y": [1.0, 2.0, 3.0]})
+        const = 2
+
+        result = getattr(df, opname)(const).dtypes.value_counts()
+        tm.assert_series_equal(
+            result, Series([2], index=[np.dtype(bool)], name="count")
+        )
+
+    @pytest.mark.parametrize("opname", ["eq", "ne", "gt", "lt", "ge", "le"])
+    def test_df_flex_cmp_constant_return_types_empty(self, opname):
+        # GH 15077 empty DataFrame
+        df = DataFrame({"x": [1, 2, 3], "y": [1.0, 2.0, 3.0]})
+        const = 2
+
+        empty = df.iloc[:0]
+        result = getattr(empty, opname)(const).dtypes.value_counts()
+        tm.assert_series_equal(
+            result, Series([2], index=[np.dtype(bool)], name="count")
+        )
+
+    def test_df_flex_cmp_ea_dtype_with_ndarray_series(self):
+        ii = pd.IntervalIndex.from_breaks([1, 2, 3])
+        df = DataFrame({"A": ii, "B": ii})
+
+        ser = Series([0, 0])
+        res = df.eq(ser, axis=0)
+
+        expected = DataFrame({"A": [False, False], "B": [False, False]})
+        tm.assert_frame_equal(res, expected)
+
+        ser2 = Series([1, 2], index=["A", "B"])
+        res2 = df.eq(ser2, axis=1)
+        tm.assert_frame_equal(res2, expected)
+
+
+# -------------------------------------------------------------------
+# Arithmetic
+
+
+class TestFrameFlexArithmetic:
+    def test_floordiv_axis0(self):
+        # make sure we df.floordiv(ser, axis=0) matches column-wise result
+        arr = np.arange(3)
+        ser = Series(arr)
+        df = DataFrame({"A": ser, "B": ser})
+
+        result = df.floordiv(ser, axis=0)
+
+        expected = DataFrame({col: df[col] // ser for col in df.columns})
+
+        tm.assert_frame_equal(result, expected)
+
+        result2 = df.floordiv(ser.values, axis=0)
+        tm.assert_frame_equal(result2, expected)
+
+    def test_df_add_td64_columnwise(self):
+        # GH 22534 Check that column-wise addition broadcasts correctly
+        dti = pd.date_range("2016-01-01", periods=10)
+        tdi = pd.timedelta_range("1", periods=10)
+        tser = Series(tdi)
+        df = DataFrame({0: dti, 1: tdi})
+
+        result = df.add(tser, axis=0)
+        expected = DataFrame({0: dti + tdi, 1: tdi + tdi})
+        tm.assert_frame_equal(result, expected)
+
+    def test_df_add_flex_filled_mixed_dtypes(self):
+        # GH 19611
+        dti = pd.date_range("2016-01-01", periods=3)
+        ser = Series(["1 Day", "NaT", "2 Days"], dtype="timedelta64[ns]")
+        df = DataFrame({"A": dti, "B": ser})
+        other = DataFrame({"A": ser, "B": ser})
+        fill = pd.Timedelta(days=1).to_timedelta64()
+        result = df.add(other, fill_value=fill)
+
+        expected = DataFrame(
+            {
+                "A": Series(
+                    ["2016-01-02", "2016-01-03", "2016-01-05"], dtype="datetime64[ns]"
+                ),
+                "B": ser * 2,
+            }
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_arith_flex_frame(
+        self, all_arithmetic_operators, float_frame, mixed_float_frame
+    ):
+        # one instance of parametrized fixture
+        op = all_arithmetic_operators
+
+        def f(x, y):
+            # r-versions not in operator-stdlib; get op without "r" and invert
+            if op.startswith("__r"):
+                return getattr(operator, op.replace("__r", "__"))(y, x)
+            return getattr(operator, op)(x, y)
+
+        result = getattr(float_frame, op)(2 * float_frame)
+        expected = f(float_frame, 2 * float_frame)
+        tm.assert_frame_equal(result, expected)
+
+        # vs mix float
+        result = getattr(mixed_float_frame, op)(2 * mixed_float_frame)
+        expected = f(mixed_float_frame, 2 * mixed_float_frame)
+        tm.assert_frame_equal(result, expected)
+        _check_mixed_float(result, dtype={"C": None})
+
+    @pytest.mark.parametrize("op", ["__add__", "__sub__", "__mul__"])
+    def test_arith_flex_frame_mixed(
+        self,
+        op,
+        int_frame,
+        mixed_int_frame,
+        mixed_float_frame,
+        switch_numexpr_min_elements,
+    ):
+        f = getattr(operator, op)
+
+        # vs mix int
+        result = getattr(mixed_int_frame, op)(2 + mixed_int_frame)
+        expected = f(mixed_int_frame, 2 + mixed_int_frame)
+
+        # no overflow in the uint
+        dtype = None
+        if op in ["__sub__"]:
+            dtype = {"B": "uint64", "C": None}
+        elif op in ["__add__", "__mul__"]:
+            dtype = {"C": None}
+        if expr.USE_NUMEXPR and switch_numexpr_min_elements == 0:
+            # when using numexpr, the casting rules are slightly different:
+            # in the `2 + mixed_int_frame` operation, int32 column becomes
+            # and int64 column (not preserving dtype in operation with Python
+            # scalar), and then the int32/int64 combo results in int64 result
+            dtype["A"] = (2 + mixed_int_frame)["A"].dtype
+        tm.assert_frame_equal(result, expected)
+        _check_mixed_int(result, dtype=dtype)
+
+        # vs mix float
+        result = getattr(mixed_float_frame, op)(2 * mixed_float_frame)
+        expected = f(mixed_float_frame, 2 * mixed_float_frame)
+        tm.assert_frame_equal(result, expected)
+        _check_mixed_float(result, dtype={"C": None})
+
+        # vs plain int
+        result = getattr(int_frame, op)(2 * int_frame)
+        expected = f(int_frame, 2 * int_frame)
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("dim", range(3, 6))
+    def test_arith_flex_frame_raise(self, all_arithmetic_operators, float_frame, dim):
+        # one instance of parametrized fixture
+        op = all_arithmetic_operators
+
+        # Check that arrays with dim >= 3 raise
+        arr = np.ones((1,) * dim)
+        msg = "Unable to coerce to Series/DataFrame"
+        with pytest.raises(ValueError, match=msg):
+            getattr(float_frame, op)(arr)
+
+    def test_arith_flex_frame_corner(self, float_frame):
+        const_add = float_frame.add(1)
+        tm.assert_frame_equal(const_add, float_frame + 1)
+
+        # corner cases
+        result = float_frame.add(float_frame[:0])
+        expected = float_frame.sort_index() * np.nan
+        tm.assert_frame_equal(result, expected)
+
+        result = float_frame[:0].add(float_frame)
+        expected = float_frame.sort_index() * np.nan
+        tm.assert_frame_equal(result, expected)
+
+        with pytest.raises(NotImplementedError, match="fill_value"):
+            float_frame.add(float_frame.iloc[0], fill_value=3)
+
+        with pytest.raises(NotImplementedError, match="fill_value"):
+            float_frame.add(float_frame.iloc[0], axis="index", fill_value=3)
+
+    @pytest.mark.parametrize("op", ["add", "sub", "mul", "mod"])
+    def test_arith_flex_series_ops(self, simple_frame, op):
+        # after arithmetic refactor, add truediv here
+        df = simple_frame
+
+        row = df.xs("a")
+        col = df["two"]
+        f = getattr(df, op)
+        op = getattr(operator, op)
+        tm.assert_frame_equal(f(row), op(df, row))
+        tm.assert_frame_equal(f(col, axis=0), op(df.T, col).T)
+
+    def test_arith_flex_series(self, simple_frame):
+        df = simple_frame
+
+        row = df.xs("a")
+        col = df["two"]
+        # special case for some reason
+        tm.assert_frame_equal(df.add(row, axis=None), df + row)
+
+        # cases which will be refactored after big arithmetic refactor
+        tm.assert_frame_equal(df.div(row), df / row)
+        tm.assert_frame_equal(df.div(col, axis=0), (df.T / col).T)
+
+    @pytest.mark.parametrize("dtype", ["int64", "float64"])
+    def test_arith_flex_series_broadcasting(self, dtype):
+        # broadcasting issue in GH 7325
+        df = DataFrame(np.arange(3 * 2).reshape((3, 2)), dtype=dtype)
+        expected = DataFrame([[np.nan, np.inf], [1.0, 1.5], [1.0, 1.25]])
+        result = df.div(df[0], axis="index")
+        tm.assert_frame_equal(result, expected)
+
+    def test_arith_flex_zero_len_raises(self):
+        # GH 19522 passing fill_value to frame flex arith methods should
+        # raise even in the zero-length special cases
+        ser_len0 = Series([], dtype=object)
+        df_len0 = DataFrame(columns=["A", "B"])
+        df = DataFrame([[1, 2], [3, 4]], columns=["A", "B"])
+
+        with pytest.raises(NotImplementedError, match="fill_value"):
+            df.add(ser_len0, fill_value="E")
+
+        with pytest.raises(NotImplementedError, match="fill_value"):
+            df_len0.sub(df["A"], axis=None, fill_value=3)
+
+    def test_flex_add_scalar_fill_value(self):
+        # GH#12723
+        dat = np.array([0, 1, np.nan, 3, 4, 5], dtype="float")
+        df = DataFrame({"foo": dat}, index=range(6))
+
+        exp = df.fillna(0).add(2)
+        res = df.add(2, fill_value=0)
+        tm.assert_frame_equal(res, exp)
+
+    def test_sub_alignment_with_duplicate_index(self):
+        # GH#5185 dup aligning operations should work
+        df1 = DataFrame([1, 2, 3, 4, 5], index=[1, 2, 1, 2, 3])
+        df2 = DataFrame([1, 2, 3], index=[1, 2, 3])
+        expected = DataFrame([0, 2, 0, 2, 2], index=[1, 1, 2, 2, 3])
+        result = df1.sub(df2)
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("op", ["__add__", "__mul__", "__sub__", "__truediv__"])
+    def test_arithmetic_with_duplicate_columns(self, op):
+        # operations
+        df = DataFrame({"A": np.arange(10), "B": np.random.default_rng(2).random(10)})
+        expected = getattr(df, op)(df)
+        expected.columns = ["A", "A"]
+        df.columns = ["A", "A"]
+        result = getattr(df, op)(df)
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("level", [0, None])
+    def test_broadcast_multiindex(self, level):
+        # GH34388
+        df1 = DataFrame({"A": [0, 1, 2], "B": [1, 2, 3]})
+        df1.columns = df1.columns.set_names("L1")
+
+        df2 = DataFrame({("A", "C"): [0, 0, 0], ("A", "D"): [0, 0, 0]})
+        df2.columns = df2.columns.set_names(["L1", "L2"])
+
+        result = df1.add(df2, level=level)
+        expected = DataFrame({("A", "C"): [0, 1, 2], ("A", "D"): [0, 1, 2]})
+        expected.columns = expected.columns.set_names(["L1", "L2"])
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_frame_multiindex_operations(self):
+        # GH 43321
+        df = DataFrame(
+            {2010: [1, 2, 3], 2020: [3, 4, 5]},
+            index=MultiIndex.from_product(
+                [["a"], ["b"], [0, 1, 2]], names=["scen", "mod", "id"]
+            ),
+        )
+
+        series = Series(
+            [0.4],
+            index=MultiIndex.from_product([["b"], ["a"]], names=["mod", "scen"]),
+        )
+
+        expected = DataFrame(
+            {2010: [1.4, 2.4, 3.4], 2020: [3.4, 4.4, 5.4]},
+            index=MultiIndex.from_product(
+                [["a"], ["b"], [0, 1, 2]], names=["scen", "mod", "id"]
+            ),
+        )
+        result = df.add(series, axis=0)
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_frame_multiindex_operations_series_index_to_frame_index(self):
+        # GH 43321
+        df = DataFrame(
+            {2010: [1], 2020: [3]},
+            index=MultiIndex.from_product([["a"], ["b"]], names=["scen", "mod"]),
+        )
+
+        series = Series(
+            [10.0, 20.0, 30.0],
+            index=MultiIndex.from_product(
+                [["a"], ["b"], [0, 1, 2]], names=["scen", "mod", "id"]
+            ),
+        )
+
+        expected = DataFrame(
+            {2010: [11.0, 21, 31.0], 2020: [13.0, 23.0, 33.0]},
+            index=MultiIndex.from_product(
+                [["a"], ["b"], [0, 1, 2]], names=["scen", "mod", "id"]
+            ),
+        )
+        result = df.add(series, axis=0)
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_frame_multiindex_operations_no_align(self):
+        df = DataFrame(
+            {2010: [1, 2, 3], 2020: [3, 4, 5]},
+            index=MultiIndex.from_product(
+                [["a"], ["b"], [0, 1, 2]], names=["scen", "mod", "id"]
+            ),
+        )
+
+        series = Series(
+            [0.4],
+            index=MultiIndex.from_product([["c"], ["a"]], names=["mod", "scen"]),
+        )
+
+        expected = DataFrame(
+            {2010: np.nan, 2020: np.nan},
+            index=MultiIndex.from_tuples(
+                [
+                    ("a", "b", 0),
+                    ("a", "b", 1),
+                    ("a", "b", 2),
+                    ("a", "c", np.nan),
+                ],
+                names=["scen", "mod", "id"],
+            ),
+        )
+        result = df.add(series, axis=0)
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_frame_multiindex_operations_part_align(self):
+        df = DataFrame(
+            {2010: [1, 2, 3], 2020: [3, 4, 5]},
+            index=MultiIndex.from_tuples(
+                [
+                    ("a", "b", 0),
+                    ("a", "b", 1),
+                    ("a", "c", 2),
+                ],
+                names=["scen", "mod", "id"],
+            ),
+        )
+
+        series = Series(
+            [0.4],
+            index=MultiIndex.from_product([["b"], ["a"]], names=["mod", "scen"]),
+        )
+
+        expected = DataFrame(
+            {2010: [1.4, 2.4, np.nan], 2020: [3.4, 4.4, np.nan]},
+            index=MultiIndex.from_tuples(
+                [
+                    ("a", "b", 0),
+                    ("a", "b", 1),
+                    ("a", "c", 2),
+                ],
+                names=["scen", "mod", "id"],
+            ),
+        )
+        result = df.add(series, axis=0)
+
+        tm.assert_frame_equal(result, expected)
+
+
+class TestFrameArithmetic:
+    def test_td64_op_nat_casting(self):
+        # Make sure we don't accidentally treat timedelta64(NaT) as datetime64
+        #  when calling dispatch_to_series in DataFrame arithmetic
+        ser = Series(["NaT", "NaT"], dtype="timedelta64[ns]")
+        df = DataFrame([[1, 2], [3, 4]])
+
+        result = df * ser
+        expected = DataFrame({0: ser, 1: ser})
+        tm.assert_frame_equal(result, expected)
+
+    def test_df_add_2d_array_rowlike_broadcasts(self):
+        # GH#23000
+        arr = np.arange(6).reshape(3, 2)
+        df = DataFrame(arr, columns=[True, False], index=["A", "B", "C"])
+
+        rowlike = arr[[1], :]  # shape --> (1, ncols)
+        assert rowlike.shape == (1, df.shape[1])
+
+        expected = DataFrame(
+            [[2, 4], [4, 6], [6, 8]],
+            columns=df.columns,
+            index=df.index,
+            # specify dtype explicitly to avoid failing
+            # on 32bit builds
+            dtype=arr.dtype,
+        )
+        result = df + rowlike
+        tm.assert_frame_equal(result, expected)
+        result = rowlike + df
+        tm.assert_frame_equal(result, expected)
+
+    def test_df_add_2d_array_collike_broadcasts(self):
+        # GH#23000
+        arr = np.arange(6).reshape(3, 2)
+        df = DataFrame(arr, columns=[True, False], index=["A", "B", "C"])
+
+        collike = arr[:, [1]]  # shape --> (nrows, 1)
+        assert collike.shape == (df.shape[0], 1)
+
+        expected = DataFrame(
+            [[1, 2], [5, 6], [9, 10]],
+            columns=df.columns,
+            index=df.index,
+            # specify dtype explicitly to avoid failing
+            # on 32bit builds
+            dtype=arr.dtype,
+        )
+        result = df + collike
+        tm.assert_frame_equal(result, expected)
+        result = collike + df
+        tm.assert_frame_equal(result, expected)
+
+    def test_df_arith_2d_array_rowlike_broadcasts(
+        self, request, all_arithmetic_operators, using_array_manager
+    ):
+        # GH#23000
+        opname = all_arithmetic_operators
+
+        if using_array_manager and opname in ("__rmod__", "__rfloordiv__"):
+            # TODO(ArrayManager) decide on dtypes
+            td.mark_array_manager_not_yet_implemented(request)
+
+        arr = np.arange(6).reshape(3, 2)
+        df = DataFrame(arr, columns=[True, False], index=["A", "B", "C"])
+
+        rowlike = arr[[1], :]  # shape --> (1, ncols)
+        assert rowlike.shape == (1, df.shape[1])
+
+        exvals = [
+            getattr(df.loc["A"], opname)(rowlike.squeeze()),
+            getattr(df.loc["B"], opname)(rowlike.squeeze()),
+            getattr(df.loc["C"], opname)(rowlike.squeeze()),
+        ]
+
+        expected = DataFrame(exvals, columns=df.columns, index=df.index)
+
+        result = getattr(df, opname)(rowlike)
+        tm.assert_frame_equal(result, expected)
+
+    def test_df_arith_2d_array_collike_broadcasts(
+        self, request, all_arithmetic_operators, using_array_manager
+    ):
+        # GH#23000
+        opname = all_arithmetic_operators
+
+        if using_array_manager and opname in ("__rmod__", "__rfloordiv__"):
+            # TODO(ArrayManager) decide on dtypes
+            td.mark_array_manager_not_yet_implemented(request)
+
+        arr = np.arange(6).reshape(3, 2)
+        df = DataFrame(arr, columns=[True, False], index=["A", "B", "C"])
+
+        collike = arr[:, [1]]  # shape --> (nrows, 1)
+        assert collike.shape == (df.shape[0], 1)
+
+        exvals = {
+            True: getattr(df[True], opname)(collike.squeeze()),
+            False: getattr(df[False], opname)(collike.squeeze()),
+        }
+
+        dtype = None
+        if opname in ["__rmod__", "__rfloordiv__"]:
+            # Series ops may return mixed int/float dtypes in cases where
+            #   DataFrame op will return all-float.  So we upcast `expected`
+            dtype = np.common_type(*(x.values for x in exvals.values()))
+
+        expected = DataFrame(exvals, columns=df.columns, index=df.index, dtype=dtype)
+
+        result = getattr(df, opname)(collike)
+        tm.assert_frame_equal(result, expected)
+
+    def test_df_bool_mul_int(self):
+        # GH 22047, GH 22163 multiplication by 1 should result in int dtype,
+        # not object dtype
+        df = DataFrame([[False, True], [False, False]])
+        result = df * 1
+
+        # On appveyor this comes back as np.int32 instead of np.int64,
+        # so we check dtype.kind instead of just dtype
+        kinds = result.dtypes.apply(lambda x: x.kind)
+        assert (kinds == "i").all()
+
+        result = 1 * df
+        kinds = result.dtypes.apply(lambda x: x.kind)
+        assert (kinds == "i").all()
+
+    def test_arith_mixed(self):
+        left = DataFrame({"A": ["a", "b", "c"], "B": [1, 2, 3]})
+
+        result = left + left
+        expected = DataFrame({"A": ["aa", "bb", "cc"], "B": [2, 4, 6]})
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("col", ["A", "B"])
+    def test_arith_getitem_commute(self, all_arithmetic_functions, col):
+        df = DataFrame({"A": [1.1, 3.3], "B": [2.5, -3.9]})
+        result = all_arithmetic_functions(df, 1)[col]
+        expected = all_arithmetic_functions(df[col], 1)
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "values", [[1, 2], (1, 2), np.array([1, 2]), range(1, 3), deque([1, 2])]
+    )
+    def test_arith_alignment_non_pandas_object(self, values):
+        # GH#17901
+        df = DataFrame({"A": [1, 1], "B": [1, 1]})
+        expected = DataFrame({"A": [2, 2], "B": [3, 3]})
+        result = df + values
+        tm.assert_frame_equal(result, expected)
+
+    def test_arith_non_pandas_object(self):
+        df = DataFrame(
+            np.arange(1, 10, dtype="f8").reshape(3, 3),
+            columns=["one", "two", "three"],
+            index=["a", "b", "c"],
+        )
+
+        val1 = df.xs("a").values
+        added = DataFrame(df.values + val1, index=df.index, columns=df.columns)
+        tm.assert_frame_equal(df + val1, added)
+
+        added = DataFrame((df.values.T + val1).T, index=df.index, columns=df.columns)
+        tm.assert_frame_equal(df.add(val1, axis=0), added)
+
+        val2 = list(df["two"])
+
+        added = DataFrame(df.values + val2, index=df.index, columns=df.columns)
+        tm.assert_frame_equal(df + val2, added)
+
+        added = DataFrame((df.values.T + val2).T, index=df.index, columns=df.columns)
+        tm.assert_frame_equal(df.add(val2, axis="index"), added)
+
+        val3 = np.random.default_rng(2).random(df.shape)
+        added = DataFrame(df.values + val3, index=df.index, columns=df.columns)
+        tm.assert_frame_equal(df.add(val3), added)
+
+    def test_operations_with_interval_categories_index(self, all_arithmetic_operators):
+        # GH#27415
+        op = all_arithmetic_operators
+        ind = pd.CategoricalIndex(pd.interval_range(start=0.0, end=2.0))
+        data = [1, 2]
+        df = DataFrame([data], columns=ind)
+        num = 10
+        result = getattr(df, op)(num)
+        expected = DataFrame([[getattr(n, op)(num) for n in data]], columns=ind)
+        tm.assert_frame_equal(result, expected)
+
+    def test_frame_with_frame_reindex(self):
+        # GH#31623
+        df = DataFrame(
+            {
+                "foo": [pd.Timestamp("2019"), pd.Timestamp("2020")],
+                "bar": [pd.Timestamp("2018"), pd.Timestamp("2021")],
+            },
+            columns=["foo", "bar"],
+            dtype="M8[ns]",
+        )
+        df2 = df[["foo"]]
+
+        result = df - df2
+
+        expected = DataFrame(
+            {"foo": [pd.Timedelta(0), pd.Timedelta(0)], "bar": [np.nan, np.nan]},
+            columns=["bar", "foo"],
+        )
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "value, dtype",
+        [
+            (1, "i8"),
+            (1.0, "f8"),
+            (2**63, "f8"),
+            (1j, "complex128"),
+            (2**63, "complex128"),
+            (True, "bool"),
+            (np.timedelta64(20, "ns"), "]=?' not supported between instances of 'str' and 'int'",
+                "Invalid comparison between dtype=str and int",
+            ]
+        )
+        with pytest.raises(TypeError, match=msg):
+            f(df, 0)
+
+    def test_comparison_protected_from_errstate(self):
+        missing_df = DataFrame(
+            np.ones((10, 4), dtype=np.float64),
+            columns=Index(list("ABCD"), dtype=object),
+        )
+        missing_df.loc[missing_df.index[0], "A"] = np.nan
+        with np.errstate(invalid="ignore"):
+            expected = missing_df.values < 0
+        with np.errstate(invalid="raise"):
+            result = (missing_df < 0).values
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_boolean_comparison(self):
+        # GH 4576
+        # boolean comparisons with a tuple/list give unexpected results
+        df = DataFrame(np.arange(6).reshape((3, 2)))
+        b = np.array([2, 2])
+        b_r = np.atleast_2d([2, 2])
+        b_c = b_r.T
+        lst = [2, 2, 2]
+        tup = tuple(lst)
+
+        # gt
+        expected = DataFrame([[False, False], [False, True], [True, True]])
+        result = df > b
+        tm.assert_frame_equal(result, expected)
+
+        result = df.values > b
+        tm.assert_numpy_array_equal(result, expected.values)
+
+        msg1d = "Unable to coerce to Series, length must be 2: given 3"
+        msg2d = "Unable to coerce to DataFrame, shape must be"
+        msg2db = "operands could not be broadcast together with shapes"
+        with pytest.raises(ValueError, match=msg1d):
+            # wrong shape
+            df > lst
+
+        with pytest.raises(ValueError, match=msg1d):
+            # wrong shape
+            df > tup
+
+        # broadcasts like ndarray (GH#23000)
+        result = df > b_r
+        tm.assert_frame_equal(result, expected)
+
+        result = df.values > b_r
+        tm.assert_numpy_array_equal(result, expected.values)
+
+        with pytest.raises(ValueError, match=msg2d):
+            df > b_c
+
+        with pytest.raises(ValueError, match=msg2db):
+            df.values > b_c
+
+        # ==
+        expected = DataFrame([[False, False], [True, False], [False, False]])
+        result = df == b
+        tm.assert_frame_equal(result, expected)
+
+        with pytest.raises(ValueError, match=msg1d):
+            df == lst
+
+        with pytest.raises(ValueError, match=msg1d):
+            df == tup
+
+        # broadcasts like ndarray (GH#23000)
+        result = df == b_r
+        tm.assert_frame_equal(result, expected)
+
+        result = df.values == b_r
+        tm.assert_numpy_array_equal(result, expected.values)
+
+        with pytest.raises(ValueError, match=msg2d):
+            df == b_c
+
+        assert df.values.shape != b_c.shape
+
+        # with alignment
+        df = DataFrame(
+            np.arange(6).reshape((3, 2)), columns=list("AB"), index=list("abc")
+        )
+        expected.index = df.index
+        expected.columns = df.columns
+
+        with pytest.raises(ValueError, match=msg1d):
+            df == lst
+
+        with pytest.raises(ValueError, match=msg1d):
+            df == tup
+
+    def test_inplace_ops_alignment(self):
+        # inplace ops / ops alignment
+        # GH 8511
+
+        columns = list("abcdefg")
+        X_orig = DataFrame(
+            np.arange(10 * len(columns)).reshape(-1, len(columns)),
+            columns=columns,
+            index=range(10),
+        )
+        Z = 100 * X_orig.iloc[:, 1:-1].copy()
+        block1 = list("bedcf")
+        subs = list("bcdef")
+
+        # add
+        X = X_orig.copy()
+        result1 = (X[block1] + Z).reindex(columns=subs)
+
+        X[block1] += Z
+        result2 = X.reindex(columns=subs)
+
+        X = X_orig.copy()
+        result3 = (X[block1] + Z[block1]).reindex(columns=subs)
+
+        X[block1] += Z[block1]
+        result4 = X.reindex(columns=subs)
+
+        tm.assert_frame_equal(result1, result2)
+        tm.assert_frame_equal(result1, result3)
+        tm.assert_frame_equal(result1, result4)
+
+        # sub
+        X = X_orig.copy()
+        result1 = (X[block1] - Z).reindex(columns=subs)
+
+        X[block1] -= Z
+        result2 = X.reindex(columns=subs)
+
+        X = X_orig.copy()
+        result3 = (X[block1] - Z[block1]).reindex(columns=subs)
+
+        X[block1] -= Z[block1]
+        result4 = X.reindex(columns=subs)
+
+        tm.assert_frame_equal(result1, result2)
+        tm.assert_frame_equal(result1, result3)
+        tm.assert_frame_equal(result1, result4)
+
+    def test_inplace_ops_identity(self):
+        # GH 5104
+        # make sure that we are actually changing the object
+        s_orig = Series([1, 2, 3])
+        df_orig = DataFrame(
+            np.random.default_rng(2).integers(0, 5, size=10).reshape(-1, 5)
+        )
+
+        # no dtype change
+        s = s_orig.copy()
+        s2 = s
+        s += 1
+        tm.assert_series_equal(s, s2)
+        tm.assert_series_equal(s_orig + 1, s)
+        assert s is s2
+        assert s._mgr is s2._mgr
+
+        df = df_orig.copy()
+        df2 = df
+        df += 1
+        tm.assert_frame_equal(df, df2)
+        tm.assert_frame_equal(df_orig + 1, df)
+        assert df is df2
+        assert df._mgr is df2._mgr
+
+        # dtype change
+        s = s_orig.copy()
+        s2 = s
+        s += 1.5
+        tm.assert_series_equal(s, s2)
+        tm.assert_series_equal(s_orig + 1.5, s)
+
+        df = df_orig.copy()
+        df2 = df
+        df += 1.5
+        tm.assert_frame_equal(df, df2)
+        tm.assert_frame_equal(df_orig + 1.5, df)
+        assert df is df2
+        assert df._mgr is df2._mgr
+
+        # mixed dtype
+        arr = np.random.default_rng(2).integers(0, 10, size=5)
+        df_orig = DataFrame({"A": arr.copy(), "B": "foo"})
+        df = df_orig.copy()
+        df2 = df
+        df["A"] += 1
+        expected = DataFrame({"A": arr.copy() + 1, "B": "foo"})
+        tm.assert_frame_equal(df, expected)
+        tm.assert_frame_equal(df2, expected)
+        assert df._mgr is df2._mgr
+
+        df = df_orig.copy()
+        df2 = df
+        df["A"] += 1.5
+        expected = DataFrame({"A": arr.copy() + 1.5, "B": "foo"})
+        tm.assert_frame_equal(df, expected)
+        tm.assert_frame_equal(df2, expected)
+        assert df._mgr is df2._mgr
+
+    @pytest.mark.parametrize(
+        "op",
+        [
+            "add",
+            "and",
+            pytest.param(
+                "div",
+                marks=pytest.mark.xfail(
+                    raises=AttributeError, reason="__idiv__ not implemented"
+                ),
+            ),
+            "floordiv",
+            "mod",
+            "mul",
+            "or",
+            "pow",
+            "sub",
+            "truediv",
+            "xor",
+        ],
+    )
+    def test_inplace_ops_identity2(self, op):
+        df = DataFrame({"a": [1.0, 2.0, 3.0], "b": [1, 2, 3]})
+
+        operand = 2
+        if op in ("and", "or", "xor"):
+            # cannot use floats for boolean ops
+            df["a"] = [True, False, True]
+
+        df_copy = df.copy()
+        iop = f"__i{op}__"
+        op = f"__{op}__"
+
+        # no id change and value is correct
+        getattr(df, iop)(operand)
+        expected = getattr(df_copy, op)(operand)
+        tm.assert_frame_equal(df, expected)
+        expected = id(df)
+        assert id(df) == expected
+
+    @pytest.mark.parametrize(
+        "val",
+        [
+            [1, 2, 3],
+            (1, 2, 3),
+            np.array([1, 2, 3], dtype=np.int64),
+            range(1, 4),
+        ],
+    )
+    def test_alignment_non_pandas(self, val):
+        index = ["A", "B", "C"]
+        columns = ["X", "Y", "Z"]
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((3, 3)),
+            index=index,
+            columns=columns,
+        )
+
+        align = DataFrame._align_for_op
+
+        expected = DataFrame({"X": val, "Y": val, "Z": val}, index=df.index)
+        tm.assert_frame_equal(align(df, val, axis=0)[1], expected)
+
+        expected = DataFrame(
+            {"X": [1, 1, 1], "Y": [2, 2, 2], "Z": [3, 3, 3]}, index=df.index
+        )
+        tm.assert_frame_equal(align(df, val, axis=1)[1], expected)
+
+    @pytest.mark.parametrize("val", [[1, 2], (1, 2), np.array([1, 2]), range(1, 3)])
+    def test_alignment_non_pandas_length_mismatch(self, val):
+        index = ["A", "B", "C"]
+        columns = ["X", "Y", "Z"]
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((3, 3)),
+            index=index,
+            columns=columns,
+        )
+
+        align = DataFrame._align_for_op
+        # length mismatch
+        msg = "Unable to coerce to Series, length must be 3: given 2"
+        with pytest.raises(ValueError, match=msg):
+            align(df, val, axis=0)
+
+        with pytest.raises(ValueError, match=msg):
+            align(df, val, axis=1)
+
+    def test_alignment_non_pandas_index_columns(self):
+        index = ["A", "B", "C"]
+        columns = ["X", "Y", "Z"]
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((3, 3)),
+            index=index,
+            columns=columns,
+        )
+
+        align = DataFrame._align_for_op
+        val = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
+        tm.assert_frame_equal(
+            align(df, val, axis=0)[1],
+            DataFrame(val, index=df.index, columns=df.columns),
+        )
+        tm.assert_frame_equal(
+            align(df, val, axis=1)[1],
+            DataFrame(val, index=df.index, columns=df.columns),
+        )
+
+        # shape mismatch
+        msg = "Unable to coerce to DataFrame, shape must be"
+        val = np.array([[1, 2, 3], [4, 5, 6]])
+        with pytest.raises(ValueError, match=msg):
+            align(df, val, axis=0)
+
+        with pytest.raises(ValueError, match=msg):
+            align(df, val, axis=1)
+
+        val = np.zeros((3, 3, 3))
+        msg = re.escape(
+            "Unable to coerce to Series/DataFrame, dimension must be <= 2: (3, 3, 3)"
+        )
+        with pytest.raises(ValueError, match=msg):
+            align(df, val, axis=0)
+        with pytest.raises(ValueError, match=msg):
+            align(df, val, axis=1)
+
+    def test_no_warning(self, all_arithmetic_operators):
+        df = DataFrame({"A": [0.0, 0.0], "B": [0.0, None]})
+        b = df["B"]
+        with tm.assert_produces_warning(None):
+            getattr(df, all_arithmetic_operators)(b)
+
+    def test_dunder_methods_binary(self, all_arithmetic_operators):
+        # GH#??? frame.__foo__ should only accept one argument
+        df = DataFrame({"A": [0.0, 0.0], "B": [0.0, None]})
+        b = df["B"]
+        with pytest.raises(TypeError, match="takes 2 positional arguments"):
+            getattr(df, all_arithmetic_operators)(b, 0)
+
+    def test_align_int_fill_bug(self):
+        # GH#910
+        X = np.arange(10 * 10, dtype="float64").reshape(10, 10)
+        Y = np.ones((10, 1), dtype=int)
+
+        df1 = DataFrame(X)
+        df1["0.X"] = Y.squeeze()
+
+        df2 = df1.astype(float)
+
+        result = df1 - df1.mean()
+        expected = df2 - df2.mean()
+        tm.assert_frame_equal(result, expected)
+
+
+def test_pow_with_realignment():
+    # GH#32685 pow has special semantics for operating with null values
+    left = DataFrame({"A": [0, 1, 2]})
+    right = DataFrame(index=[0, 1, 2])
+
+    result = left**right
+    expected = DataFrame({"A": [np.nan, 1.0, np.nan]})
+    tm.assert_frame_equal(result, expected)
+
+
+def test_dataframe_series_extension_dtypes():
+    # https://github.com/pandas-dev/pandas/issues/34311
+    df = DataFrame(
+        np.random.default_rng(2).integers(0, 100, (10, 3)), columns=["a", "b", "c"]
+    )
+    ser = Series([1, 2, 3], index=["a", "b", "c"])
+
+    expected = df.to_numpy("int64") + ser.to_numpy("int64").reshape(-1, 3)
+    expected = DataFrame(expected, columns=df.columns, dtype="Int64")
+
+    df_ea = df.astype("Int64")
+    result = df_ea + ser
+    tm.assert_frame_equal(result, expected)
+    result = df_ea + ser.astype("Int64")
+    tm.assert_frame_equal(result, expected)
+
+
+def test_dataframe_blockwise_slicelike():
+    # GH#34367
+    arr = np.random.default_rng(2).integers(0, 1000, (100, 10))
+    df1 = DataFrame(arr)
+    # Explicit cast to float to avoid implicit cast when setting nan
+    df2 = df1.copy().astype({1: "float", 3: "float", 7: "float"})
+    df2.iloc[0, [1, 3, 7]] = np.nan
+
+    # Explicit cast to float to avoid implicit cast when setting nan
+    df3 = df1.copy().astype({5: "float"})
+    df3.iloc[0, [5]] = np.nan
+
+    # Explicit cast to float to avoid implicit cast when setting nan
+    df4 = df1.copy().astype({2: "float", 3: "float", 4: "float"})
+    df4.iloc[0, np.arange(2, 5)] = np.nan
+    # Explicit cast to float to avoid implicit cast when setting nan
+    df5 = df1.copy().astype({4: "float", 5: "float", 6: "float"})
+    df5.iloc[0, np.arange(4, 7)] = np.nan
+
+    for left, right in [(df1, df2), (df2, df3), (df4, df5)]:
+        res = left + right
+
+        expected = DataFrame({i: left[i] + right[i] for i in left.columns})
+        tm.assert_frame_equal(res, expected)
+
+
+@pytest.mark.parametrize(
+    "df, col_dtype",
+    [
+        (DataFrame([[1.0, 2.0], [4.0, 5.0]], columns=list("ab")), "float64"),
+        (
+            DataFrame([[1.0, "b"], [4.0, "b"]], columns=list("ab")).astype(
+                {"b": object}
+            ),
+            "object",
+        ),
+    ],
+)
+def test_dataframe_operation_with_non_numeric_types(df, col_dtype):
+    # GH #22663
+    expected = DataFrame([[0.0, np.nan], [3.0, np.nan]], columns=list("ab"))
+    expected = expected.astype({"b": col_dtype})
+    result = df + Series([-1.0], index=list("a"))
+    tm.assert_frame_equal(result, expected)
+
+
+def test_arith_reindex_with_duplicates():
+    # https://github.com/pandas-dev/pandas/issues/35194
+    df1 = DataFrame(data=[[0]], columns=["second"])
+    df2 = DataFrame(data=[[0, 0, 0]], columns=["first", "second", "second"])
+    result = df1 + df2
+    expected = DataFrame([[np.nan, 0, 0]], columns=["first", "second", "second"])
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("to_add", [[Series([1, 1])], [Series([1, 1]), Series([1, 1])]])
+def test_arith_list_of_arraylike_raise(to_add):
+    # GH 36702. Raise when trying to add list of array-like to DataFrame
+    df = DataFrame({"x": [1, 2], "y": [1, 2]})
+
+    msg = f"Unable to coerce list of {type(to_add[0])} to Series/DataFrame"
+    with pytest.raises(ValueError, match=msg):
+        df + to_add
+    with pytest.raises(ValueError, match=msg):
+        to_add + df
+
+
+def test_inplace_arithmetic_series_update(using_copy_on_write, warn_copy_on_write):
+    # https://github.com/pandas-dev/pandas/issues/36373
+    df = DataFrame({"A": [1, 2, 3]})
+    df_orig = df.copy()
+    series = df["A"]
+    vals = series._values
+
+    with tm.assert_cow_warning(warn_copy_on_write):
+        series += 1
+    if using_copy_on_write:
+        assert series._values is not vals
+        tm.assert_frame_equal(df, df_orig)
+    else:
+        assert series._values is vals
+
+        expected = DataFrame({"A": [2, 3, 4]})
+        tm.assert_frame_equal(df, expected)
+
+
+def test_arithmetic_multiindex_align():
+    """
+    Regression test for: https://github.com/pandas-dev/pandas/issues/33765
+    """
+    df1 = DataFrame(
+        [[1]],
+        index=["a"],
+        columns=MultiIndex.from_product([[0], [1]], names=["a", "b"]),
+    )
+    df2 = DataFrame([[1]], index=["a"], columns=Index([0], name="a"))
+    expected = DataFrame(
+        [[0]],
+        index=["a"],
+        columns=MultiIndex.from_product([[0], [1]], names=["a", "b"]),
+    )
+    result = df1 - df2
+    tm.assert_frame_equal(result, expected)
+
+
+def test_bool_frame_mult_float():
+    # GH 18549
+    df = DataFrame(True, list("ab"), list("cd"))
+    result = df * 1.0
+    expected = DataFrame(np.ones((2, 2)), list("ab"), list("cd"))
+    tm.assert_frame_equal(result, expected)
+
+
+def test_frame_sub_nullable_int(any_int_ea_dtype):
+    # GH 32822
+    series1 = Series([1, 2, None], dtype=any_int_ea_dtype)
+    series2 = Series([1, 2, 3], dtype=any_int_ea_dtype)
+    expected = DataFrame([0, 0, None], dtype=any_int_ea_dtype)
+    result = series1.to_frame() - series2.to_frame()
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.filterwarnings(
+    "ignore:Passing a BlockManager|Passing a SingleBlockManager:DeprecationWarning"
+)
+def test_frame_op_subclass_nonclass_constructor():
+    # GH#43201 subclass._constructor is a function, not the subclass itself
+
+    class SubclassedSeries(Series):
+        @property
+        def _constructor(self):
+            return SubclassedSeries
+
+        @property
+        def _constructor_expanddim(self):
+            return SubclassedDataFrame
+
+    class SubclassedDataFrame(DataFrame):
+        _metadata = ["my_extra_data"]
+
+        def __init__(self, my_extra_data, *args, **kwargs) -> None:
+            self.my_extra_data = my_extra_data
+            super().__init__(*args, **kwargs)
+
+        @property
+        def _constructor(self):
+            return functools.partial(type(self), self.my_extra_data)
+
+        @property
+        def _constructor_sliced(self):
+            return SubclassedSeries
+
+    sdf = SubclassedDataFrame("some_data", {"A": [1, 2, 3], "B": [4, 5, 6]})
+    result = sdf * 2
+    expected = SubclassedDataFrame("some_data", {"A": [2, 4, 6], "B": [8, 10, 12]})
+    tm.assert_frame_equal(result, expected)
+
+    result = sdf + sdf
+    tm.assert_frame_equal(result, expected)
+
+
+def test_enum_column_equality():
+    Cols = Enum("Cols", "col1 col2")
+
+    q1 = DataFrame({Cols.col1: [1, 2, 3]})
+    q2 = DataFrame({Cols.col1: [1, 2, 3]})
+
+    result = q1[Cols.col1] == q2[Cols.col1]
+    expected = Series([True, True, True], name=Cols.col1)
+
+    tm.assert_series_equal(result, expected)
+
+
+def test_mixed_col_index_dtype(using_infer_string):
+    # GH 47382
+    df1 = DataFrame(columns=list("abc"), data=1.0, index=[0])
+    df2 = DataFrame(columns=list("abc"), data=0.0, index=[0])
+    df1.columns = df2.columns.astype("string")
+    result = df1 + df2
+    expected = DataFrame(columns=list("abc"), data=1.0, index=[0])
+    if using_infer_string:
+        # df2.columns.dtype will be "str" instead of object,
+        #  so the aligned result will be "string", not object
+        if HAS_PYARROW:
+            dtype = "string[pyarrow]"
+        else:
+            dtype = "string"
+        expected.columns = expected.columns.astype(dtype)
+    tm.assert_frame_equal(result, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/test_arrow_interface.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/test_arrow_interface.py
new file mode 100644
index 0000000000000000000000000000000000000000..b36b6b5ffe0cc3f843243a09cb08a2b74d6b728f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/test_arrow_interface.py
@@ -0,0 +1,47 @@
+import ctypes
+
+import pytest
+
+import pandas.util._test_decorators as td
+
+import pandas as pd
+
+pa = pytest.importorskip("pyarrow")
+
+
+@td.skip_if_no("pyarrow", min_version="14.0")
+def test_dataframe_arrow_interface(using_infer_string):
+    df = pd.DataFrame({"a": [1, 2, 3], "b": ["a", "b", "c"]})
+
+    capsule = df.__arrow_c_stream__()
+    assert (
+        ctypes.pythonapi.PyCapsule_IsValid(
+            ctypes.py_object(capsule), b"arrow_array_stream"
+        )
+        == 1
+    )
+
+    table = pa.table(df)
+    string_type = pa.large_string() if using_infer_string else pa.string()
+    expected = pa.table({"a": [1, 2, 3], "b": pa.array(["a", "b", "c"], string_type)})
+    assert table.equals(expected)
+
+    schema = pa.schema([("a", pa.int8()), ("b", pa.string())])
+    table = pa.table(df, schema=schema)
+    expected = expected.cast(schema)
+    assert table.equals(expected)
+
+
+@td.skip_if_no("pyarrow", min_version="15.0")
+def test_dataframe_to_arrow(using_infer_string):
+    df = pd.DataFrame({"a": [1, 2, 3], "b": ["a", "b", "c"]})
+
+    table = pa.RecordBatchReader.from_stream(df).read_all()
+    string_type = pa.large_string() if using_infer_string else pa.string()
+    expected = pa.table({"a": [1, 2, 3], "b": pa.array(["a", "b", "c"], string_type)})
+    assert table.equals(expected)
+
+    schema = pa.schema([("a", pa.int8()), ("b", pa.string())])
+    table = pa.RecordBatchReader.from_stream(df, schema=schema).read_all()
+    expected = expected.cast(schema)
+    assert table.equals(expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/test_block_internals.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/test_block_internals.py
new file mode 100644
index 0000000000000000000000000000000000000000..ac3e4f7c9224f9958317aa09080631604f02591b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/test_block_internals.py
@@ -0,0 +1,453 @@
+from datetime import (
+    datetime,
+    timedelta,
+)
+import itertools
+
+import numpy as np
+import pytest
+
+from pandas.compat import WARNING_CHECK_DISABLED
+from pandas.errors import PerformanceWarning
+import pandas.util._test_decorators as td
+
+import pandas as pd
+from pandas import (
+    Categorical,
+    DataFrame,
+    Series,
+    Timestamp,
+    date_range,
+    option_context,
+)
+import pandas._testing as tm
+from pandas.core.internals.blocks import NumpyBlock
+
+# Segregated collection of methods that require the BlockManager internal data
+# structure
+
+
+# TODO(ArrayManager) check which of those tests need to be rewritten to test the
+# equivalent for ArrayManager
+pytestmark = td.skip_array_manager_invalid_test
+
+
+class TestDataFrameBlockInternals:
+    def test_setitem_invalidates_datetime_index_freq(self):
+        # GH#24096 altering a datetime64tz column inplace invalidates the
+        #  `freq` attribute on the underlying DatetimeIndex
+
+        dti = date_range("20130101", periods=3, tz="US/Eastern")
+        ts = dti[1]
+
+        df = DataFrame({"B": dti})
+        assert df["B"]._values.freq is None
+
+        df.iloc[1, 0] = pd.NaT
+        assert df["B"]._values.freq is None
+
+        # check that the DatetimeIndex was not altered in place
+        assert dti.freq == "D"
+        assert dti[1] == ts
+
+    def test_cast_internals(self, float_frame):
+        msg = "Passing a BlockManager to DataFrame"
+        with tm.assert_produces_warning(
+            DeprecationWarning, match=msg, check_stacklevel=False
+        ):
+            casted = DataFrame(float_frame._mgr, dtype=int)
+        expected = DataFrame(float_frame._series, dtype=int)
+        tm.assert_frame_equal(casted, expected)
+
+        with tm.assert_produces_warning(
+            DeprecationWarning, match=msg, check_stacklevel=False
+        ):
+            casted = DataFrame(float_frame._mgr, dtype=np.int32)
+        expected = DataFrame(float_frame._series, dtype=np.int32)
+        tm.assert_frame_equal(casted, expected)
+
+    def test_consolidate(self, float_frame):
+        float_frame["E"] = 7.0
+        consolidated = float_frame._consolidate()
+        assert len(consolidated._mgr.blocks) == 1
+
+        # Ensure copy, do I want this?
+        recons = consolidated._consolidate()
+        assert recons is not consolidated
+        tm.assert_frame_equal(recons, consolidated)
+
+        float_frame["F"] = 8.0
+        assert len(float_frame._mgr.blocks) == 3
+
+        return_value = float_frame._consolidate_inplace()
+        assert return_value is None
+        assert len(float_frame._mgr.blocks) == 1
+
+    def test_consolidate_inplace(self, float_frame):
+        # triggers in-place consolidation
+        for letter in range(ord("A"), ord("Z")):
+            float_frame[chr(letter)] = chr(letter)
+
+    def test_modify_values(self, float_frame, using_copy_on_write):
+        if using_copy_on_write:
+            with pytest.raises(ValueError, match="read-only"):
+                float_frame.values[5] = 5
+            assert (float_frame.values[5] != 5).all()
+            return
+
+        float_frame.values[5] = 5
+        assert (float_frame.values[5] == 5).all()
+
+        # unconsolidated
+        float_frame["E"] = 7.0
+        col = float_frame["E"]
+        float_frame.values[6] = 6
+        # as of 2.0 .values does not consolidate, so subsequent calls to .values
+        #  does not share data
+        assert not (float_frame.values[6] == 6).all()
+
+        assert (col == 7).all()
+
+    def test_boolean_set_uncons(self, float_frame):
+        float_frame["E"] = 7.0
+
+        expected = float_frame.values.copy()
+        expected[expected > 1] = 2
+
+        float_frame[float_frame > 1] = 2
+        tm.assert_almost_equal(expected, float_frame.values)
+
+    def test_constructor_with_convert(self):
+        # this is actually mostly a test of lib.maybe_convert_objects
+        # #2845
+        df = DataFrame({"A": [2**63 - 1]})
+        result = df["A"]
+        expected = Series(np.asarray([2**63 - 1], np.int64), name="A")
+        tm.assert_series_equal(result, expected)
+
+        df = DataFrame({"A": [2**63]})
+        result = df["A"]
+        expected = Series(np.asarray([2**63], np.uint64), name="A")
+        tm.assert_series_equal(result, expected)
+
+        df = DataFrame({"A": [datetime(2005, 1, 1), True]})
+        result = df["A"]
+        expected = Series(
+            np.asarray([datetime(2005, 1, 1), True], np.object_), name="A"
+        )
+        tm.assert_series_equal(result, expected)
+
+        df = DataFrame({"A": [None, 1]})
+        result = df["A"]
+        expected = Series(np.asarray([np.nan, 1], np.float64), name="A")
+        tm.assert_series_equal(result, expected)
+
+        df = DataFrame({"A": [1.0, 2]})
+        result = df["A"]
+        expected = Series(np.asarray([1.0, 2], np.float64), name="A")
+        tm.assert_series_equal(result, expected)
+
+        df = DataFrame({"A": [1.0 + 2.0j, 3]})
+        result = df["A"]
+        expected = Series(np.asarray([1.0 + 2.0j, 3], np.complex128), name="A")
+        tm.assert_series_equal(result, expected)
+
+        df = DataFrame({"A": [1.0 + 2.0j, 3.0]})
+        result = df["A"]
+        expected = Series(np.asarray([1.0 + 2.0j, 3.0], np.complex128), name="A")
+        tm.assert_series_equal(result, expected)
+
+        df = DataFrame({"A": [1.0 + 2.0j, True]})
+        result = df["A"]
+        expected = Series(np.asarray([1.0 + 2.0j, True], np.object_), name="A")
+        tm.assert_series_equal(result, expected)
+
+        df = DataFrame({"A": [1.0, None]})
+        result = df["A"]
+        expected = Series(np.asarray([1.0, np.nan], np.float64), name="A")
+        tm.assert_series_equal(result, expected)
+
+        df = DataFrame({"A": [1.0 + 2.0j, None]})
+        result = df["A"]
+        expected = Series(np.asarray([1.0 + 2.0j, np.nan], np.complex128), name="A")
+        tm.assert_series_equal(result, expected)
+
+        df = DataFrame({"A": [2.0, 1, True, None]})
+        result = df["A"]
+        expected = Series(np.asarray([2.0, 1, True, None], np.object_), name="A")
+        tm.assert_series_equal(result, expected)
+
+        df = DataFrame({"A": [2.0, 1, datetime(2006, 1, 1), None]})
+        result = df["A"]
+        expected = Series(
+            np.asarray([2.0, 1, datetime(2006, 1, 1), None], np.object_), name="A"
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_construction_with_mixed(self, float_string_frame, using_infer_string):
+        # mixed-type frames
+        float_string_frame["datetime"] = datetime.now()
+        float_string_frame["timedelta"] = timedelta(days=1, seconds=1)
+        assert float_string_frame["datetime"].dtype == "M8[us]"
+        assert float_string_frame["timedelta"].dtype == "m8[us]"
+        result = float_string_frame.dtypes
+        expected = Series(
+            [np.dtype("float64")] * 4
+            + [
+                np.dtype("object")
+                if not using_infer_string
+                else pd.StringDtype(na_value=np.nan),
+                np.dtype("datetime64[us]"),
+                np.dtype("timedelta64[us]"),
+            ],
+            index=list("ABCD") + ["foo", "datetime", "timedelta"],
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_construction_with_conversions(self):
+        # convert from a numpy array of non-ns timedelta64; as of 2.0 this does
+        #  *not* convert
+        arr = np.array([1, 2, 3], dtype="timedelta64[s]")
+        df = DataFrame({"A": arr})
+        expected = DataFrame(
+            {"A": pd.timedelta_range("00:00:01", periods=3, freq="s")}, index=range(3)
+        )
+        tm.assert_numpy_array_equal(df["A"].to_numpy(), arr)
+
+        expected = DataFrame(
+            {
+                "dt1": Timestamp("20130101"),
+                "dt2": date_range("20130101", periods=3).astype("M8[s]"),
+                # 'dt3' : date_range('20130101 00:00:01',periods=3,freq='s'),
+                # FIXME: don't leave commented-out
+            },
+            index=range(3),
+        )
+        assert expected.dtypes["dt1"] == "M8[s]"
+        assert expected.dtypes["dt2"] == "M8[s]"
+
+        dt1 = np.datetime64("2013-01-01")
+        dt2 = np.array(
+            ["2013-01-01", "2013-01-02", "2013-01-03"], dtype="datetime64[D]"
+        )
+        df = DataFrame({"dt1": dt1, "dt2": dt2})
+
+        # df['dt3'] = np.array(['2013-01-01 00:00:01','2013-01-01
+        # 00:00:02','2013-01-01 00:00:03'],dtype='datetime64[s]')
+        # FIXME: don't leave commented-out
+
+        tm.assert_frame_equal(df, expected)
+
+    def test_constructor_compound_dtypes(self):
+        # GH 5191
+        # compound dtypes should raise not-implementederror
+
+        def f(dtype):
+            data = list(itertools.repeat((datetime(2001, 1, 1), "aa", 20), 9))
+            return DataFrame(data=data, columns=["A", "B", "C"], dtype=dtype)
+
+        msg = "compound dtypes are not implemented in the DataFrame constructor"
+        with pytest.raises(NotImplementedError, match=msg):
+            f([("A", "datetime64[h]"), ("B", "str"), ("C", "int32")])
+
+        # pre-2.0 these used to work (though results may be unexpected)
+        with pytest.raises(TypeError, match="argument must be"):
+            f("int64")
+        with pytest.raises(TypeError, match="argument must be"):
+            f("float64")
+
+        # 10822
+        msg = "^Unknown datetime string format, unable to parse: aa, at position 0$"
+        with pytest.raises(ValueError, match=msg):
+            f("M8[ns]")
+
+    def test_pickle(self, float_string_frame, timezone_frame):
+        empty_frame = DataFrame()
+
+        unpickled = tm.round_trip_pickle(float_string_frame)
+        tm.assert_frame_equal(float_string_frame, unpickled)
+
+        # buglet
+        float_string_frame._mgr.ndim
+
+        # empty
+        unpickled = tm.round_trip_pickle(empty_frame)
+        repr(unpickled)
+
+        # tz frame
+        unpickled = tm.round_trip_pickle(timezone_frame)
+        tm.assert_frame_equal(timezone_frame, unpickled)
+
+    def test_consolidate_datetime64(self):
+        # numpy vstack bug
+
+        df = DataFrame(
+            {
+                "starting": pd.to_datetime(
+                    [
+                        "2012-06-21 00:00",
+                        "2012-06-23 07:00",
+                        "2012-06-23 16:30",
+                        "2012-06-25 08:00",
+                        "2012-06-26 12:00",
+                    ]
+                ),
+                "ending": pd.to_datetime(
+                    [
+                        "2012-06-23 07:00",
+                        "2012-06-23 16:30",
+                        "2012-06-25 08:00",
+                        "2012-06-26 12:00",
+                        "2012-06-27 08:00",
+                    ]
+                ),
+                "measure": [77, 65, 77, 0, 77],
+            }
+        )
+
+        ser_starting = df.starting
+        ser_starting.index = ser_starting.values
+        ser_starting = ser_starting.tz_localize("US/Eastern")
+        ser_starting = ser_starting.tz_convert("UTC")
+        ser_starting.index.name = "starting"
+
+        ser_ending = df.ending
+        ser_ending.index = ser_ending.values
+        ser_ending = ser_ending.tz_localize("US/Eastern")
+        ser_ending = ser_ending.tz_convert("UTC")
+        ser_ending.index.name = "ending"
+
+        df.starting = ser_starting.index
+        df.ending = ser_ending.index
+
+        tm.assert_index_equal(pd.DatetimeIndex(df.starting), ser_starting.index)
+        tm.assert_index_equal(pd.DatetimeIndex(df.ending), ser_ending.index)
+
+    def test_is_mixed_type(self, float_frame, float_string_frame):
+        assert not float_frame._is_mixed_type
+        assert float_string_frame._is_mixed_type
+
+    def test_stale_cached_series_bug_473(self, using_copy_on_write, warn_copy_on_write):
+        # this is chained, but ok
+        with option_context("chained_assignment", None):
+            Y = DataFrame(
+                np.random.default_rng(2).random((4, 4)),
+                index=("a", "b", "c", "d"),
+                columns=("e", "f", "g", "h"),
+            )
+            repr(Y)
+            Y["e"] = Y["e"].astype("object")
+            with tm.raises_chained_assignment_error():
+                Y["g"]["c"] = np.nan
+            repr(Y)
+            Y.sum()
+            Y["g"].sum()
+            if using_copy_on_write:
+                assert not pd.isna(Y["g"]["c"])
+            else:
+                assert pd.isna(Y["g"]["c"])
+
+    @pytest.mark.filterwarnings("ignore:Setting a value on a view:FutureWarning")
+    def test_strange_column_corruption_issue(self, using_copy_on_write):
+        # TODO(wesm): Unclear how exactly this is related to internal matters
+        df = DataFrame(index=[0, 1])
+        df[0] = np.nan
+        wasCol = {}
+
+        with tm.assert_produces_warning(
+            PerformanceWarning, raise_on_extra_warnings=False
+        ):
+            for i, dt in enumerate(df.index):
+                for col in range(100, 200):
+                    if col not in wasCol:
+                        wasCol[col] = 1
+                        df[col] = np.nan
+                    if using_copy_on_write:
+                        df.loc[dt, col] = i
+                    else:
+                        df[col][dt] = i
+
+        myid = 100
+
+        first = len(df.loc[pd.isna(df[myid]), [myid]])
+        second = len(df.loc[pd.isna(df[myid]), [myid]])
+        assert first == second == 0
+
+    def test_constructor_no_pandas_array(self):
+        # Ensure that NumpyExtensionArray isn't allowed inside Series
+        # See https://github.com/pandas-dev/pandas/issues/23995 for more.
+        arr = Series([1, 2, 3]).array
+        result = DataFrame({"A": arr})
+        expected = DataFrame({"A": [1, 2, 3]})
+        tm.assert_frame_equal(result, expected)
+        assert isinstance(result._mgr.blocks[0], NumpyBlock)
+        assert result._mgr.blocks[0].is_numeric
+
+    def test_add_column_with_pandas_array(self):
+        # GH 26390
+        df = DataFrame({"a": [1, 2, 3, 4], "b": ["a", "b", "c", "d"]})
+        df["c"] = pd.arrays.NumpyExtensionArray(np.array([1, 2, None, 3], dtype=object))
+        df2 = DataFrame(
+            {
+                "a": [1, 2, 3, 4],
+                "b": ["a", "b", "c", "d"],
+                "c": pd.arrays.NumpyExtensionArray(
+                    np.array([1, 2, None, 3], dtype=object)
+                ),
+            }
+        )
+        assert type(df["c"]._mgr.blocks[0]) == NumpyBlock
+        assert df["c"]._mgr.blocks[0].is_object
+        assert type(df2["c"]._mgr.blocks[0]) == NumpyBlock
+        assert df2["c"]._mgr.blocks[0].is_object
+        tm.assert_frame_equal(df, df2)
+
+
+def test_update_inplace_sets_valid_block_values(using_copy_on_write):
+    # https://github.com/pandas-dev/pandas/issues/33457
+    df = DataFrame({"a": Series([1, 2, None], dtype="category")})
+
+    # inplace update of a single column
+    if using_copy_on_write:
+        with tm.raises_chained_assignment_error():
+            df["a"].fillna(1, inplace=True)
+    else:
+        with tm.assert_produces_warning(
+            FutureWarning if not WARNING_CHECK_DISABLED else None,
+            match="inplace method",
+        ):
+            df["a"].fillna(1, inplace=True)
+
+    # check we haven't put a Series into any block.values
+    assert isinstance(df._mgr.blocks[0].values, Categorical)
+
+    if not using_copy_on_write:
+        # smoketest for OP bug from GH#35731
+        assert df.isnull().sum().sum() == 0
+
+
+def test_nonconsolidated_item_cache_take():
+    # https://github.com/pandas-dev/pandas/issues/35521
+
+    # create non-consolidated dataframe with object dtype columns
+    df = DataFrame(
+        {
+            "col1": Series(["a"], dtype=object),
+        }
+    )
+    df["col2"] = Series([0], dtype=object)
+    assert not df._mgr.is_consolidated()
+
+    # access column (item cache)
+    df["col1"] == "A"
+    # take operation
+    # (regression was that this consolidated but didn't reset item cache,
+    # resulting in an invalid cache and the .at operation not working properly)
+    df[df["col2"] == 0]
+
+    # now setting value should update actual dataframe
+    df.at[0, "col1"] = "A"
+
+    expected = DataFrame({"col1": ["A"], "col2": [0]}, dtype=object)
+    tm.assert_frame_equal(df, expected)
+    assert df.at[0, "col1"] == "A"
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/test_constructors.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/test_constructors.py
new file mode 100644
index 0000000000000000000000000000000000000000..efc40536d56beb94b5a3f72f36267bd15cc72702
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/test_constructors.py
@@ -0,0 +1,3387 @@
+import array
+from collections import (
+    OrderedDict,
+    abc,
+    defaultdict,
+    namedtuple,
+)
+from collections.abc import Iterator
+from dataclasses import make_dataclass
+from datetime import (
+    date,
+    datetime,
+    timedelta,
+)
+import functools
+import re
+
+import numpy as np
+from numpy import ma
+from numpy.ma import mrecords
+import pytest
+import pytz
+
+from pandas._libs import lib
+from pandas.compat.numpy import np_version_gt2
+from pandas.errors import IntCastingNaNError
+import pandas.util._test_decorators as td
+
+from pandas.core.dtypes.common import is_integer_dtype
+from pandas.core.dtypes.dtypes import (
+    DatetimeTZDtype,
+    IntervalDtype,
+    NumpyEADtype,
+    PeriodDtype,
+)
+
+import pandas as pd
+from pandas import (
+    Categorical,
+    CategoricalIndex,
+    DataFrame,
+    DatetimeIndex,
+    Index,
+    Interval,
+    MultiIndex,
+    Period,
+    RangeIndex,
+    Series,
+    Timedelta,
+    Timestamp,
+    cut,
+    date_range,
+    isna,
+)
+import pandas._testing as tm
+from pandas.arrays import (
+    DatetimeArray,
+    IntervalArray,
+    PeriodArray,
+    SparseArray,
+    TimedeltaArray,
+)
+
+MIXED_FLOAT_DTYPES = ["float16", "float32", "float64"]
+MIXED_INT_DTYPES = [
+    "uint8",
+    "uint16",
+    "uint32",
+    "uint64",
+    "int8",
+    "int16",
+    "int32",
+    "int64",
+]
+
+
+class TestDataFrameConstructors:
+    def test_constructor_from_ndarray_with_str_dtype(self):
+        # If we don't ravel/reshape around ensure_str_array, we end up
+        #  with an array of strings each of which is e.g. "[0 1 2]"
+        arr = np.arange(12).reshape(4, 3)
+        df = DataFrame(arr, dtype=str)
+        expected = DataFrame(arr.astype(str), dtype="str")
+        tm.assert_frame_equal(df, expected)
+
+    def test_constructor_from_2d_datetimearray(self, using_array_manager):
+        dti = date_range("2016-01-01", periods=6, tz="US/Pacific")
+        dta = dti._data.reshape(3, 2)
+
+        df = DataFrame(dta)
+        expected = DataFrame({0: dta[:, 0], 1: dta[:, 1]})
+        tm.assert_frame_equal(df, expected)
+        if not using_array_manager:
+            # GH#44724 big performance hit if we de-consolidate
+            assert len(df._mgr.blocks) == 1
+
+    def test_constructor_dict_with_tzaware_scalar(self):
+        # GH#42505
+        dt = Timestamp("2019-11-03 01:00:00-0700").tz_convert("America/Los_Angeles")
+        dt = dt.as_unit("ns")
+
+        df = DataFrame({"dt": dt}, index=[0])
+        expected = DataFrame({"dt": [dt]})
+        tm.assert_frame_equal(df, expected)
+
+        # Non-homogeneous
+        df = DataFrame({"dt": dt, "value": [1]})
+        expected = DataFrame({"dt": [dt], "value": [1]})
+        tm.assert_frame_equal(df, expected)
+
+    def test_construct_ndarray_with_nas_and_int_dtype(self):
+        # GH#26919 match Series by not casting np.nan to meaningless int
+        arr = np.array([[1, np.nan], [2, 3]])
+        msg = r"Cannot convert non-finite values \(NA or inf\) to integer"
+        with pytest.raises(IntCastingNaNError, match=msg):
+            DataFrame(arr, dtype="i8")
+
+        # check this matches Series behavior
+        with pytest.raises(IntCastingNaNError, match=msg):
+            Series(arr[0], dtype="i8", name=0)
+
+    def test_construct_from_list_of_datetimes(self):
+        df = DataFrame([datetime.now(), datetime.now()])
+        assert df[0].dtype == np.dtype("M8[ns]")
+
+    def test_constructor_from_tzaware_datetimeindex(self):
+        # don't cast a DatetimeIndex WITH a tz, leave as object
+        # GH#6032
+        naive = DatetimeIndex(["2013-1-1 13:00", "2013-1-2 14:00"], name="B")
+        idx = naive.tz_localize("US/Pacific")
+
+        expected = Series(np.array(idx.tolist(), dtype="object"), name="B")
+        assert expected.dtype == idx.dtype
+
+        # convert index to series
+        result = Series(idx)
+        tm.assert_series_equal(result, expected)
+
+    def test_columns_with_leading_underscore_work_with_to_dict(self):
+        col_underscore = "_b"
+        df = DataFrame({"a": [1, 2], col_underscore: [3, 4]})
+        d = df.to_dict(orient="records")
+
+        ref_d = [{"a": 1, col_underscore: 3}, {"a": 2, col_underscore: 4}]
+
+        assert ref_d == d
+
+    def test_columns_with_leading_number_and_underscore_work_with_to_dict(self):
+        col_with_num = "1_b"
+        df = DataFrame({"a": [1, 2], col_with_num: [3, 4]})
+        d = df.to_dict(orient="records")
+
+        ref_d = [{"a": 1, col_with_num: 3}, {"a": 2, col_with_num: 4}]
+
+        assert ref_d == d
+
+    def test_array_of_dt64_nat_with_td64dtype_raises(self, frame_or_series):
+        # GH#39462
+        nat = np.datetime64("NaT", "ns")
+        arr = np.array([nat], dtype=object)
+        if frame_or_series is DataFrame:
+            arr = arr.reshape(1, 1)
+
+        msg = "Invalid type for timedelta scalar: "
+        with pytest.raises(TypeError, match=msg):
+            frame_or_series(arr, dtype="m8[ns]")
+
+    @pytest.mark.parametrize("kind", ["m", "M"])
+    def test_datetimelike_values_with_object_dtype(self, kind, frame_or_series):
+        # with dtype=object, we should cast dt64 values to Timestamps, not pydatetimes
+        if kind == "M":
+            dtype = "M8[ns]"
+            scalar_type = Timestamp
+        else:
+            dtype = "m8[ns]"
+            scalar_type = Timedelta
+
+        arr = np.arange(6, dtype="i8").view(dtype).reshape(3, 2)
+        if frame_or_series is Series:
+            arr = arr[:, 0]
+
+        obj = frame_or_series(arr, dtype=object)
+        assert obj._mgr.arrays[0].dtype == object
+        assert isinstance(obj._mgr.arrays[0].ravel()[0], scalar_type)
+
+        # go through a different path in internals.construction
+        obj = frame_or_series(frame_or_series(arr), dtype=object)
+        assert obj._mgr.arrays[0].dtype == object
+        assert isinstance(obj._mgr.arrays[0].ravel()[0], scalar_type)
+
+        obj = frame_or_series(frame_or_series(arr), dtype=NumpyEADtype(object))
+        assert obj._mgr.arrays[0].dtype == object
+        assert isinstance(obj._mgr.arrays[0].ravel()[0], scalar_type)
+
+        if frame_or_series is DataFrame:
+            # other paths through internals.construction
+            sers = [Series(x) for x in arr]
+            obj = frame_or_series(sers, dtype=object)
+            assert obj._mgr.arrays[0].dtype == object
+            assert isinstance(obj._mgr.arrays[0].ravel()[0], scalar_type)
+
+    def test_series_with_name_not_matching_column(self):
+        # GH#9232
+        x = Series(range(5), name=1)
+        y = Series(range(5), name=0)
+
+        result = DataFrame(x, columns=[0])
+        expected = DataFrame([], columns=[0])
+        tm.assert_frame_equal(result, expected)
+
+        result = DataFrame(y, columns=[1])
+        expected = DataFrame([], columns=[1])
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "constructor",
+        [
+            lambda: DataFrame(),
+            lambda: DataFrame(None),
+            lambda: DataFrame(()),
+            lambda: DataFrame([]),
+            lambda: DataFrame(_ for _ in []),
+            lambda: DataFrame(range(0)),
+            lambda: DataFrame(data=None),
+            lambda: DataFrame(data=()),
+            lambda: DataFrame(data=[]),
+            lambda: DataFrame(data=(_ for _ in [])),
+            lambda: DataFrame(data=range(0)),
+        ],
+    )
+    def test_empty_constructor(self, constructor):
+        expected = DataFrame()
+        result = constructor()
+        assert len(result.index) == 0
+        assert len(result.columns) == 0
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "constructor",
+        [
+            lambda: DataFrame({}),
+            lambda: DataFrame(data={}),
+        ],
+    )
+    def test_empty_constructor_object_index(self, constructor):
+        expected = DataFrame(index=RangeIndex(0), columns=RangeIndex(0))
+        result = constructor()
+        assert len(result.index) == 0
+        assert len(result.columns) == 0
+        tm.assert_frame_equal(result, expected, check_index_type=True)
+
+    @pytest.mark.parametrize(
+        "emptylike,expected_index,expected_columns",
+        [
+            ([[]], RangeIndex(1), RangeIndex(0)),
+            ([[], []], RangeIndex(2), RangeIndex(0)),
+            ([(_ for _ in [])], RangeIndex(1), RangeIndex(0)),
+        ],
+    )
+    def test_emptylike_constructor(self, emptylike, expected_index, expected_columns):
+        expected = DataFrame(index=expected_index, columns=expected_columns)
+        result = DataFrame(emptylike)
+        tm.assert_frame_equal(result, expected)
+
+    def test_constructor_mixed(self, float_string_frame, using_infer_string):
+        dtype = "str" if using_infer_string else np.object_
+        assert float_string_frame["foo"].dtype == dtype
+
+    def test_constructor_cast_failure(self):
+        # as of 2.0, we raise if we can't respect "dtype", previously we
+        #  silently ignored
+        msg = "could not convert string to float"
+        with pytest.raises(ValueError, match=msg):
+            DataFrame({"a": ["a", "b", "c"]}, dtype=np.float64)
+
+        # GH 3010, constructing with odd arrays
+        df = DataFrame(np.ones((4, 2)))
+
+        # this is ok
+        df["foo"] = np.ones((4, 2)).tolist()
+
+        # this is not ok
+        msg = "Expected a 1D array, got an array with shape \\(4, 2\\)"
+        with pytest.raises(ValueError, match=msg):
+            df["test"] = np.ones((4, 2))
+
+        # this is ok
+        df["foo2"] = np.ones((4, 2)).tolist()
+
+    def test_constructor_dtype_copy(self):
+        orig_df = DataFrame({"col1": [1.0], "col2": [2.0], "col3": [3.0]})
+
+        new_df = DataFrame(orig_df, dtype=float, copy=True)
+
+        new_df["col1"] = 200.0
+        assert orig_df["col1"][0] == 1.0
+
+    def test_constructor_dtype_nocast_view_dataframe(
+        self, using_copy_on_write, warn_copy_on_write
+    ):
+        df = DataFrame([[1, 2]])
+        should_be_view = DataFrame(df, dtype=df[0].dtype)
+        if using_copy_on_write:
+            should_be_view.iloc[0, 0] = 99
+            assert df.values[0, 0] == 1
+        else:
+            with tm.assert_cow_warning(warn_copy_on_write):
+                should_be_view.iloc[0, 0] = 99
+            assert df.values[0, 0] == 99
+
+    def test_constructor_dtype_nocast_view_2d_array(
+        self, using_array_manager, using_copy_on_write, warn_copy_on_write
+    ):
+        df = DataFrame([[1, 2], [3, 4]], dtype="int64")
+        if not using_array_manager and not using_copy_on_write:
+            should_be_view = DataFrame(df.values, dtype=df[0].dtype)
+            # TODO(CoW-warn) this should warn
+            # with tm.assert_cow_warning(warn_copy_on_write):
+            should_be_view.iloc[0, 0] = 97
+            assert df.values[0, 0] == 97
+        else:
+            # INFO(ArrayManager) DataFrame(ndarray) doesn't necessarily preserve
+            # a view on the array to ensure contiguous 1D arrays
+            df2 = DataFrame(df.values, dtype=df[0].dtype)
+            assert df2._mgr.arrays[0].flags.c_contiguous
+
+    @td.skip_array_manager_invalid_test
+    def test_1d_object_array_does_not_copy(self, using_infer_string):
+        # https://github.com/pandas-dev/pandas/issues/39272
+        arr = np.array(["a", "b"], dtype="object")
+        df = DataFrame(arr, copy=False)
+        if using_infer_string:
+            if df[0].dtype.storage == "pyarrow":
+                # object dtype strings are converted to arrow memory,
+                # no numpy arrays to compare
+                pass
+            else:
+                assert np.shares_memory(df[0].to_numpy(), arr)
+        else:
+            assert np.shares_memory(df.values, arr)
+
+        df = DataFrame(arr, dtype=object, copy=False)
+        assert np.shares_memory(df.values, arr)
+
+    @td.skip_array_manager_invalid_test
+    def test_2d_object_array_does_not_copy(self, using_infer_string):
+        # https://github.com/pandas-dev/pandas/issues/39272
+        arr = np.array([["a", "b"], ["c", "d"]], dtype="object")
+        df = DataFrame(arr, copy=False)
+        if using_infer_string:
+            if df[0].dtype.storage == "pyarrow":
+                # object dtype strings are converted to arrow memory,
+                # no numpy arrays to compare
+                pass
+            else:
+                assert np.shares_memory(df[0].to_numpy(), arr)
+        else:
+            assert np.shares_memory(df.values, arr)
+
+        df = DataFrame(arr, dtype=object, copy=False)
+        assert np.shares_memory(df.values, arr)
+
+    def test_constructor_dtype_list_data(self):
+        df = DataFrame([[1, "2"], [None, "a"]], dtype=object)
+        assert df.loc[1, 0] is None
+        assert df.loc[0, 1] == "2"
+
+    def test_constructor_list_of_2d_raises(self):
+        # https://github.com/pandas-dev/pandas/issues/32289
+        a = DataFrame()
+        b = np.empty((0, 0))
+        with pytest.raises(ValueError, match=r"shape=\(1, 0, 0\)"):
+            DataFrame([a])
+
+        with pytest.raises(ValueError, match=r"shape=\(1, 0, 0\)"):
+            DataFrame([b])
+
+        a = DataFrame({"A": [1, 2]})
+        with pytest.raises(ValueError, match=r"shape=\(2, 2, 1\)"):
+            DataFrame([a, a])
+
+    @pytest.mark.parametrize(
+        "typ, ad",
+        [
+            # mixed floating and integer coexist in the same frame
+            ["float", {}],
+            # add lots of types
+            ["float", {"A": 1, "B": "foo", "C": "bar"}],
+            # GH 622
+            ["int", {}],
+        ],
+    )
+    def test_constructor_mixed_dtypes(self, typ, ad):
+        if typ == "int":
+            dtypes = MIXED_INT_DTYPES
+            arrays = [
+                np.array(np.random.default_rng(2).random(10), dtype=d) for d in dtypes
+            ]
+        elif typ == "float":
+            dtypes = MIXED_FLOAT_DTYPES
+            arrays = [
+                np.array(np.random.default_rng(2).integers(10, size=10), dtype=d)
+                for d in dtypes
+            ]
+
+        for d, a in zip(dtypes, arrays):
+            assert a.dtype == d
+        ad.update(dict(zip(dtypes, arrays)))
+        df = DataFrame(ad)
+
+        dtypes = MIXED_FLOAT_DTYPES + MIXED_INT_DTYPES
+        for d in dtypes:
+            if d in df:
+                assert df.dtypes[d] == d
+
+    def test_constructor_complex_dtypes(self):
+        # GH10952
+        a = np.random.default_rng(2).random(10).astype(np.complex64)
+        b = np.random.default_rng(2).random(10).astype(np.complex128)
+
+        df = DataFrame({"a": a, "b": b})
+        assert a.dtype == df.a.dtype
+        assert b.dtype == df.b.dtype
+
+    def test_constructor_dtype_str_na_values(self, string_dtype):
+        # https://github.com/pandas-dev/pandas/issues/21083
+        df = DataFrame({"A": ["x", None]}, dtype=string_dtype)
+        result = df.isna()
+        expected = DataFrame({"A": [False, True]})
+        tm.assert_frame_equal(result, expected)
+        assert df.iloc[1, 0] is None
+
+        df = DataFrame({"A": ["x", np.nan]}, dtype=string_dtype)
+        assert np.isnan(df.iloc[1, 0])
+
+    def test_constructor_rec(self, float_frame):
+        rec = float_frame.to_records(index=False)
+        rec.dtype.names = list(rec.dtype.names)[::-1]
+
+        index = float_frame.index
+
+        df = DataFrame(rec)
+        tm.assert_index_equal(df.columns, Index(rec.dtype.names))
+
+        df2 = DataFrame(rec, index=index)
+        tm.assert_index_equal(df2.columns, Index(rec.dtype.names))
+        tm.assert_index_equal(df2.index, index)
+
+        # case with columns != the ones we would infer from the data
+        rng = np.arange(len(rec))[::-1]
+        df3 = DataFrame(rec, index=rng, columns=["C", "B"])
+        expected = DataFrame(rec, index=rng).reindex(columns=["C", "B"])
+        tm.assert_frame_equal(df3, expected)
+
+    def test_constructor_bool(self):
+        df = DataFrame({0: np.ones(10, dtype=bool), 1: np.zeros(10, dtype=bool)})
+        assert df.values.dtype == np.bool_
+
+    def test_constructor_overflow_int64(self):
+        # see gh-14881
+        values = np.array([2**64 - i for i in range(1, 10)], dtype=np.uint64)
+
+        result = DataFrame({"a": values})
+        assert result["a"].dtype == np.uint64
+
+        # see gh-2355
+        data_scores = [
+            (6311132704823138710, 273),
+            (2685045978526272070, 23),
+            (8921811264899370420, 45),
+            (17019687244989530680, 270),
+            (9930107427299601010, 273),
+        ]
+        dtype = [("uid", "u8"), ("score", "u8")]
+        data = np.zeros((len(data_scores),), dtype=dtype)
+        data[:] = data_scores
+        df_crawls = DataFrame(data)
+        assert df_crawls["uid"].dtype == np.uint64
+
+    @pytest.mark.parametrize(
+        "values",
+        [
+            np.array([2**64], dtype=object),
+            np.array([2**65]),
+            [2**64 + 1],
+            np.array([-(2**63) - 4], dtype=object),
+            np.array([-(2**64) - 1]),
+            [-(2**65) - 2],
+        ],
+    )
+    def test_constructor_int_overflow(self, values):
+        # see gh-18584
+        value = values[0]
+        result = DataFrame(values)
+
+        assert result[0].dtype == object
+        assert result[0][0] == value
+
+    @pytest.mark.parametrize(
+        "values",
+        [
+            np.array([1], dtype=np.uint16),
+            np.array([1], dtype=np.uint32),
+            np.array([1], dtype=np.uint64),
+            [np.uint16(1)],
+            [np.uint32(1)],
+            [np.uint64(1)],
+        ],
+    )
+    def test_constructor_numpy_uints(self, values):
+        # GH#47294
+        value = values[0]
+        result = DataFrame(values)
+
+        assert result[0].dtype == value.dtype
+        assert result[0][0] == value
+
+    def test_constructor_ordereddict(self):
+        nitems = 100
+        nums = list(range(nitems))
+        np.random.default_rng(2).shuffle(nums)
+        expected = [f"A{i:d}" for i in nums]
+        df = DataFrame(OrderedDict(zip(expected, [[0]] * nitems)))
+        assert expected == list(df.columns)
+
+    def test_constructor_dict(self):
+        datetime_series = Series(
+            np.arange(30, dtype=np.float64), index=date_range("2020-01-01", periods=30)
+        )
+        # test expects index shifted by 5
+        datetime_series_short = datetime_series[5:]
+
+        frame = DataFrame({"col1": datetime_series, "col2": datetime_series_short})
+
+        # col2 is padded with NaN
+        assert len(datetime_series) == 30
+        assert len(datetime_series_short) == 25
+
+        tm.assert_series_equal(frame["col1"], datetime_series.rename("col1"))
+
+        exp = Series(
+            np.concatenate([[np.nan] * 5, datetime_series_short.values]),
+            index=datetime_series.index,
+            name="col2",
+        )
+        tm.assert_series_equal(exp, frame["col2"])
+
+        frame = DataFrame(
+            {"col1": datetime_series, "col2": datetime_series_short},
+            columns=["col2", "col3", "col4"],
+        )
+
+        assert len(frame) == len(datetime_series_short)
+        assert "col1" not in frame
+        assert isna(frame["col3"]).all()
+
+        # Corner cases
+        assert len(DataFrame()) == 0
+
+        # mix dict and array, wrong size - no spec for which error should raise
+        # first
+        msg = "Mixing dicts with non-Series may lead to ambiguous ordering."
+        with pytest.raises(ValueError, match=msg):
+            DataFrame({"A": {"a": "a", "b": "b"}, "B": ["a", "b", "c"]})
+
+    def test_constructor_dict_length1(self):
+        # Length-one dict micro-optimization
+        frame = DataFrame({"A": {"1": 1, "2": 2}})
+        tm.assert_index_equal(frame.index, Index(["1", "2"]))
+
+    def test_constructor_dict_with_index(self):
+        # empty dict plus index
+        idx = Index([0, 1, 2])
+        frame = DataFrame({}, index=idx)
+        assert frame.index is idx
+
+    def test_constructor_dict_with_index_and_columns(self):
+        # empty dict with index and columns
+        idx = Index([0, 1, 2])
+        frame = DataFrame({}, index=idx, columns=idx)
+        assert frame.index is idx
+        assert frame.columns is idx
+        assert len(frame._series) == 3
+
+    def test_constructor_dict_of_empty_lists(self):
+        # with dict of empty list and Series
+        frame = DataFrame({"A": [], "B": []}, columns=["A", "B"])
+        tm.assert_index_equal(frame.index, RangeIndex(0), exact=True)
+
+    def test_constructor_dict_with_none(self):
+        # GH 14381
+        # Dict with None value
+        frame_none = DataFrame({"a": None}, index=[0])
+        frame_none_list = DataFrame({"a": [None]}, index=[0])
+        assert frame_none._get_value(0, "a") is None
+        assert frame_none_list._get_value(0, "a") is None
+        tm.assert_frame_equal(frame_none, frame_none_list)
+
+    def test_constructor_dict_errors(self):
+        # GH10856
+        # dict with scalar values should raise error, even if columns passed
+        msg = "If using all scalar values, you must pass an index"
+        with pytest.raises(ValueError, match=msg):
+            DataFrame({"a": 0.7})
+
+        with pytest.raises(ValueError, match=msg):
+            DataFrame({"a": 0.7}, columns=["a"])
+
+    @pytest.mark.parametrize("scalar", [2, np.nan, None, "D"])
+    def test_constructor_invalid_items_unused(self, scalar):
+        # No error if invalid (scalar) value is in fact not used:
+        result = DataFrame({"a": scalar}, columns=["b"])
+        expected = DataFrame(columns=["b"])
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("value", [2, np.nan, None, float("nan")])
+    def test_constructor_dict_nan_key(self, value):
+        # GH 18455
+        cols = [1, value, 3]
+        idx = ["a", value]
+        values = [[0, 3], [1, 4], [2, 5]]
+        data = {cols[c]: Series(values[c], index=idx) for c in range(3)}
+        result = DataFrame(data).sort_values(1).sort_values("a", axis=1)
+        expected = DataFrame(
+            np.arange(6, dtype="int64").reshape(2, 3), index=idx, columns=cols
+        )
+        tm.assert_frame_equal(result, expected)
+
+        result = DataFrame(data, index=idx).sort_values("a", axis=1)
+        tm.assert_frame_equal(result, expected)
+
+        result = DataFrame(data, index=idx, columns=cols)
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("value", [np.nan, None, float("nan")])
+    def test_constructor_dict_nan_tuple_key(self, value):
+        # GH 18455
+        cols = Index([(11, 21), (value, 22), (13, value)])
+        idx = Index([("a", value), (value, 2)])
+        values = [[0, 3], [1, 4], [2, 5]]
+        data = {cols[c]: Series(values[c], index=idx) for c in range(3)}
+        result = DataFrame(data).sort_values((11, 21)).sort_values(("a", value), axis=1)
+        expected = DataFrame(
+            np.arange(6, dtype="int64").reshape(2, 3), index=idx, columns=cols
+        )
+        tm.assert_frame_equal(result, expected)
+
+        result = DataFrame(data, index=idx).sort_values(("a", value), axis=1)
+        tm.assert_frame_equal(result, expected)
+
+        result = DataFrame(data, index=idx, columns=cols)
+        tm.assert_frame_equal(result, expected)
+
+    def test_constructor_dict_order_insertion(self):
+        datetime_series = Series(
+            np.arange(10, dtype=np.float64), index=date_range("2020-01-01", periods=10)
+        )
+        datetime_series_short = datetime_series[:5]
+
+        # GH19018
+        # initialization ordering: by insertion order if python>= 3.6
+        d = {"b": datetime_series_short, "a": datetime_series}
+        frame = DataFrame(data=d)
+        expected = DataFrame(data=d, columns=list("ba"))
+        tm.assert_frame_equal(frame, expected)
+
+    def test_constructor_dict_nan_key_and_columns(self):
+        # GH 16894
+        result = DataFrame({np.nan: [1, 2], 2: [2, 3]}, columns=[np.nan, 2])
+        expected = DataFrame([[1, 2], [2, 3]], columns=[np.nan, 2])
+        tm.assert_frame_equal(result, expected)
+
+    def test_constructor_multi_index(self):
+        # GH 4078
+        # construction error with mi and all-nan frame
+        tuples = [(2, 3), (3, 3), (3, 3)]
+        mi = MultiIndex.from_tuples(tuples)
+        df = DataFrame(index=mi, columns=mi)
+        assert isna(df).values.ravel().all()
+
+        tuples = [(3, 3), (2, 3), (3, 3)]
+        mi = MultiIndex.from_tuples(tuples)
+        df = DataFrame(index=mi, columns=mi)
+        assert isna(df).values.ravel().all()
+
+    def test_constructor_2d_index(self):
+        # GH 25416
+        # handling of 2d index in construction
+        df = DataFrame([[1]], columns=[[1]], index=[1, 2])
+        expected = DataFrame(
+            [1, 1],
+            index=Index([1, 2], dtype="int64"),
+            columns=MultiIndex(levels=[[1]], codes=[[0]]),
+        )
+        tm.assert_frame_equal(df, expected)
+
+        df = DataFrame([[1]], columns=[[1]], index=[[1, 2]])
+        expected = DataFrame(
+            [1, 1],
+            index=MultiIndex(levels=[[1, 2]], codes=[[0, 1]]),
+            columns=MultiIndex(levels=[[1]], codes=[[0]]),
+        )
+        tm.assert_frame_equal(df, expected)
+
+    def test_constructor_error_msgs(self):
+        msg = "Empty data passed with indices specified."
+        # passing an empty array with columns specified.
+        with pytest.raises(ValueError, match=msg):
+            DataFrame(np.empty(0), index=[1])
+
+        msg = "Mixing dicts with non-Series may lead to ambiguous ordering."
+        # mix dict and array, wrong size
+        with pytest.raises(ValueError, match=msg):
+            DataFrame({"A": {"a": "a", "b": "b"}, "B": ["a", "b", "c"]})
+
+        # wrong size ndarray, GH 3105
+        msg = r"Shape of passed values is \(4, 3\), indices imply \(3, 3\)"
+        with pytest.raises(ValueError, match=msg):
+            DataFrame(
+                np.arange(12).reshape((4, 3)),
+                columns=["foo", "bar", "baz"],
+                index=date_range("2000-01-01", periods=3),
+            )
+
+        arr = np.array([[4, 5, 6]])
+        msg = r"Shape of passed values is \(1, 3\), indices imply \(1, 4\)"
+        with pytest.raises(ValueError, match=msg):
+            DataFrame(index=[0], columns=range(4), data=arr)
+
+        arr = np.array([4, 5, 6])
+        msg = r"Shape of passed values is \(3, 1\), indices imply \(1, 4\)"
+        with pytest.raises(ValueError, match=msg):
+            DataFrame(index=[0], columns=range(4), data=arr)
+
+        # higher dim raise exception
+        with pytest.raises(ValueError, match="Must pass 2-d input"):
+            DataFrame(np.zeros((3, 3, 3)), columns=["A", "B", "C"], index=[1])
+
+        # wrong size axis labels
+        msg = r"Shape of passed values is \(2, 3\), indices imply \(1, 3\)"
+        with pytest.raises(ValueError, match=msg):
+            DataFrame(
+                np.random.default_rng(2).random((2, 3)),
+                columns=["A", "B", "C"],
+                index=[1],
+            )
+
+        msg = r"Shape of passed values is \(2, 3\), indices imply \(2, 2\)"
+        with pytest.raises(ValueError, match=msg):
+            DataFrame(
+                np.random.default_rng(2).random((2, 3)),
+                columns=["A", "B"],
+                index=[1, 2],
+            )
+
+        # gh-26429
+        msg = "2 columns passed, passed data had 10 columns"
+        with pytest.raises(ValueError, match=msg):
+            DataFrame((range(10), range(10, 20)), columns=("ones", "twos"))
+
+        msg = "If using all scalar values, you must pass an index"
+        with pytest.raises(ValueError, match=msg):
+            DataFrame({"a": False, "b": True})
+
+    def test_constructor_subclass_dict(self, dict_subclass):
+        # Test for passing dict subclass to constructor
+        data = {
+            "col1": dict_subclass((x, 10.0 * x) for x in range(10)),
+            "col2": dict_subclass((x, 20.0 * x) for x in range(10)),
+        }
+        df = DataFrame(data)
+        refdf = DataFrame({col: dict(val.items()) for col, val in data.items()})
+        tm.assert_frame_equal(refdf, df)
+
+        data = dict_subclass(data.items())
+        df = DataFrame(data)
+        tm.assert_frame_equal(refdf, df)
+
+    def test_constructor_defaultdict(self, float_frame):
+        # try with defaultdict
+        data = {}
+        float_frame.loc[: float_frame.index[10], "B"] = np.nan
+
+        for k, v in float_frame.items():
+            dct = defaultdict(dict)
+            dct.update(v.to_dict())
+            data[k] = dct
+        frame = DataFrame(data)
+        expected = frame.reindex(index=float_frame.index)
+        tm.assert_frame_equal(float_frame, expected)
+
+    def test_constructor_dict_block(self):
+        expected = np.array([[4.0, 3.0, 2.0, 1.0]])
+        df = DataFrame(
+            {"d": [4.0], "c": [3.0], "b": [2.0], "a": [1.0]},
+            columns=["d", "c", "b", "a"],
+        )
+        tm.assert_numpy_array_equal(df.values, expected)
+
+    def test_constructor_dict_cast(self, using_infer_string):
+        # cast float tests
+        test_data = {"A": {"1": 1, "2": 2}, "B": {"1": "1", "2": "2", "3": "3"}}
+        frame = DataFrame(test_data, dtype=float)
+        assert len(frame) == 3
+        assert frame["B"].dtype == np.float64
+        assert frame["A"].dtype == np.float64
+
+        frame = DataFrame(test_data)
+        assert len(frame) == 3
+        assert frame["B"].dtype == np.object_ if not using_infer_string else "str"
+        assert frame["A"].dtype == np.float64
+
+    def test_constructor_dict_cast2(self):
+        # can't cast to float
+        test_data = {
+            "A": dict(zip(range(20), [f"word_{i}" for i in range(20)])),
+            "B": dict(zip(range(15), np.random.default_rng(2).standard_normal(15))),
+        }
+        with pytest.raises(ValueError, match="could not convert string"):
+            DataFrame(test_data, dtype=float)
+
+    def test_constructor_dict_dont_upcast(self):
+        d = {"Col1": {"Row1": "A String", "Row2": np.nan}}
+        df = DataFrame(d)
+        assert isinstance(df["Col1"]["Row2"], float)
+
+    def test_constructor_dict_dont_upcast2(self):
+        dm = DataFrame([[1, 2], ["a", "b"]], index=[1, 2], columns=[1, 2])
+        assert isinstance(dm[1][1], int)
+
+    def test_constructor_dict_of_tuples(self):
+        # GH #1491
+        data = {"a": (1, 2, 3), "b": (4, 5, 6)}
+
+        result = DataFrame(data)
+        expected = DataFrame({k: list(v) for k, v in data.items()})
+        tm.assert_frame_equal(result, expected, check_dtype=False)
+
+    def test_constructor_dict_of_ranges(self):
+        # GH 26356
+        data = {"a": range(3), "b": range(3, 6)}
+
+        result = DataFrame(data)
+        expected = DataFrame({"a": [0, 1, 2], "b": [3, 4, 5]})
+        tm.assert_frame_equal(result, expected)
+
+    def test_constructor_dict_of_iterators(self):
+        # GH 26349
+        data = {"a": iter(range(3)), "b": reversed(range(3))}
+
+        result = DataFrame(data)
+        expected = DataFrame({"a": [0, 1, 2], "b": [2, 1, 0]})
+        tm.assert_frame_equal(result, expected)
+
+    def test_constructor_dict_of_generators(self):
+        # GH 26349
+        data = {"a": (i for i in (range(3))), "b": (i for i in reversed(range(3)))}
+        result = DataFrame(data)
+        expected = DataFrame({"a": [0, 1, 2], "b": [2, 1, 0]})
+        tm.assert_frame_equal(result, expected)
+
+    def test_constructor_dict_multiindex(self):
+        d = {
+            ("a", "a"): {("i", "i"): 0, ("i", "j"): 1, ("j", "i"): 2},
+            ("b", "a"): {("i", "i"): 6, ("i", "j"): 5, ("j", "i"): 4},
+            ("b", "c"): {("i", "i"): 7, ("i", "j"): 8, ("j", "i"): 9},
+        }
+        _d = sorted(d.items())
+        df = DataFrame(d)
+        expected = DataFrame(
+            [x[1] for x in _d], index=MultiIndex.from_tuples([x[0] for x in _d])
+        ).T
+        expected.index = MultiIndex.from_tuples(expected.index)
+        tm.assert_frame_equal(
+            df,
+            expected,
+        )
+
+        d["z"] = {"y": 123.0, ("i", "i"): 111, ("i", "j"): 111, ("j", "i"): 111}
+        _d.insert(0, ("z", d["z"]))
+        expected = DataFrame(
+            [x[1] for x in _d], index=Index([x[0] for x in _d], tupleize_cols=False)
+        ).T
+        expected.index = Index(expected.index, tupleize_cols=False)
+        df = DataFrame(d)
+        df = df.reindex(columns=expected.columns, index=expected.index)
+        tm.assert_frame_equal(df, expected)
+
+    def test_constructor_dict_datetime64_index(self):
+        # GH 10160
+        dates_as_str = ["1984-02-19", "1988-11-06", "1989-12-03", "1990-03-15"]
+
+        def create_data(constructor):
+            return {i: {constructor(s): 2 * i} for i, s in enumerate(dates_as_str)}
+
+        data_datetime64 = create_data(np.datetime64)
+        data_datetime = create_data(lambda x: datetime.strptime(x, "%Y-%m-%d"))
+        data_Timestamp = create_data(Timestamp)
+
+        expected = DataFrame(
+            [
+                {0: 0, 1: None, 2: None, 3: None},
+                {0: None, 1: 2, 2: None, 3: None},
+                {0: None, 1: None, 2: 4, 3: None},
+                {0: None, 1: None, 2: None, 3: 6},
+            ],
+            index=[Timestamp(dt) for dt in dates_as_str],
+        )
+
+        result_datetime64 = DataFrame(data_datetime64)
+        result_datetime = DataFrame(data_datetime)
+        result_Timestamp = DataFrame(data_Timestamp)
+        tm.assert_frame_equal(result_datetime64, expected)
+        tm.assert_frame_equal(result_datetime, expected)
+        tm.assert_frame_equal(result_Timestamp, expected)
+
+    @pytest.mark.parametrize(
+        "klass,name",
+        [
+            (lambda x: np.timedelta64(x, "D"), "timedelta64"),
+            (lambda x: timedelta(days=x), "pytimedelta"),
+            (lambda x: Timedelta(x, "D"), "Timedelta[ns]"),
+            (lambda x: Timedelta(x, "D").as_unit("s"), "Timedelta[s]"),
+        ],
+    )
+    def test_constructor_dict_timedelta64_index(self, klass, name):
+        # GH 10160
+        td_as_int = [1, 2, 3, 4]
+
+        data = {i: {klass(s): 2 * i} for i, s in enumerate(td_as_int)}
+
+        expected = DataFrame(
+            [
+                {0: 0, 1: None, 2: None, 3: None},
+                {0: None, 1: 2, 2: None, 3: None},
+                {0: None, 1: None, 2: 4, 3: None},
+                {0: None, 1: None, 2: None, 3: 6},
+            ],
+            index=[Timedelta(td, "D") for td in td_as_int],
+        )
+
+        result = DataFrame(data)
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_constructor_period_dict(self):
+        # PeriodIndex
+        a = pd.PeriodIndex(["2012-01", "NaT", "2012-04"], freq="M")
+        b = pd.PeriodIndex(["2012-02-01", "2012-03-01", "NaT"], freq="D")
+        df = DataFrame({"a": a, "b": b})
+        assert df["a"].dtype == a.dtype
+        assert df["b"].dtype == b.dtype
+
+        # list of periods
+        df = DataFrame({"a": a.astype(object).tolist(), "b": b.astype(object).tolist()})
+        assert df["a"].dtype == a.dtype
+        assert df["b"].dtype == b.dtype
+
+    def test_constructor_dict_extension_scalar(self, ea_scalar_and_dtype):
+        ea_scalar, ea_dtype = ea_scalar_and_dtype
+        df = DataFrame({"a": ea_scalar}, index=[0])
+        assert df["a"].dtype == ea_dtype
+
+        expected = DataFrame(index=[0], columns=["a"], data=ea_scalar)
+
+        tm.assert_frame_equal(df, expected)
+
+    @pytest.mark.parametrize(
+        "data,dtype",
+        [
+            (Period("2020-01"), PeriodDtype("M")),
+            (Interval(left=0, right=5), IntervalDtype("int64", "right")),
+            (
+                Timestamp("2011-01-01", tz="US/Eastern"),
+                DatetimeTZDtype(unit="s", tz="US/Eastern"),
+            ),
+        ],
+    )
+    def test_constructor_extension_scalar_data(self, data, dtype):
+        # GH 34832
+        df = DataFrame(index=[0, 1], columns=["a", "b"], data=data)
+
+        assert df["a"].dtype == dtype
+        assert df["b"].dtype == dtype
+
+        arr = pd.array([data] * 2, dtype=dtype)
+        expected = DataFrame({"a": arr, "b": arr})
+
+        tm.assert_frame_equal(df, expected)
+
+    def test_nested_dict_frame_constructor(self):
+        rng = pd.period_range("1/1/2000", periods=5)
+        df = DataFrame(np.random.default_rng(2).standard_normal((10, 5)), columns=rng)
+
+        data = {}
+        for col in df.columns:
+            for row in df.index:
+                data.setdefault(col, {})[row] = df._get_value(row, col)
+
+        result = DataFrame(data, columns=rng)
+        tm.assert_frame_equal(result, df)
+
+        data = {}
+        for col in df.columns:
+            for row in df.index:
+                data.setdefault(row, {})[col] = df._get_value(row, col)
+
+        result = DataFrame(data, index=rng).T
+        tm.assert_frame_equal(result, df)
+
+    def _check_basic_constructor(self, empty):
+        # mat: 2d matrix with shape (3, 2) to input. empty - makes sized
+        # objects
+        mat = empty((2, 3), dtype=float)
+        # 2-D input
+        frame = DataFrame(mat, columns=["A", "B", "C"], index=[1, 2])
+
+        assert len(frame.index) == 2
+        assert len(frame.columns) == 3
+
+        # 1-D input
+        frame = DataFrame(empty((3,)), columns=["A"], index=[1, 2, 3])
+        assert len(frame.index) == 3
+        assert len(frame.columns) == 1
+
+        if empty is not np.ones:
+            msg = r"Cannot convert non-finite values \(NA or inf\) to integer"
+            with pytest.raises(IntCastingNaNError, match=msg):
+                DataFrame(mat, columns=["A", "B", "C"], index=[1, 2], dtype=np.int64)
+            return
+        else:
+            frame = DataFrame(
+                mat, columns=["A", "B", "C"], index=[1, 2], dtype=np.int64
+            )
+            assert frame.values.dtype == np.int64
+
+        # wrong size axis labels
+        msg = r"Shape of passed values is \(2, 3\), indices imply \(1, 3\)"
+        with pytest.raises(ValueError, match=msg):
+            DataFrame(mat, columns=["A", "B", "C"], index=[1])
+        msg = r"Shape of passed values is \(2, 3\), indices imply \(2, 2\)"
+        with pytest.raises(ValueError, match=msg):
+            DataFrame(mat, columns=["A", "B"], index=[1, 2])
+
+        # higher dim raise exception
+        with pytest.raises(ValueError, match="Must pass 2-d input"):
+            DataFrame(empty((3, 3, 3)), columns=["A", "B", "C"], index=[1])
+
+        # automatic labeling
+        frame = DataFrame(mat)
+        tm.assert_index_equal(frame.index, Index(range(2)), exact=True)
+        tm.assert_index_equal(frame.columns, Index(range(3)), exact=True)
+
+        frame = DataFrame(mat, index=[1, 2])
+        tm.assert_index_equal(frame.columns, Index(range(3)), exact=True)
+
+        frame = DataFrame(mat, columns=["A", "B", "C"])
+        tm.assert_index_equal(frame.index, Index(range(2)), exact=True)
+
+        # 0-length axis
+        frame = DataFrame(empty((0, 3)))
+        assert len(frame.index) == 0
+
+        frame = DataFrame(empty((3, 0)))
+        assert len(frame.columns) == 0
+
+    def test_constructor_ndarray(self):
+        self._check_basic_constructor(np.ones)
+
+        frame = DataFrame(["foo", "bar"], index=[0, 1], columns=["A"])
+        assert len(frame) == 2
+
+    def test_constructor_maskedarray(self):
+        self._check_basic_constructor(ma.masked_all)
+
+        # Check non-masked values
+        mat = ma.masked_all((2, 3), dtype=float)
+        mat[0, 0] = 1.0
+        mat[1, 2] = 2.0
+        frame = DataFrame(mat, columns=["A", "B", "C"], index=[1, 2])
+        assert 1.0 == frame["A"][1]
+        assert 2.0 == frame["C"][2]
+
+        # what is this even checking??
+        mat = ma.masked_all((2, 3), dtype=float)
+        frame = DataFrame(mat, columns=["A", "B", "C"], index=[1, 2])
+        assert np.all(~np.asarray(frame == frame))
+
+    @pytest.mark.filterwarnings(
+        "ignore:elementwise comparison failed:DeprecationWarning"
+    )
+    def test_constructor_maskedarray_nonfloat(self):
+        # masked int promoted to float
+        mat = ma.masked_all((2, 3), dtype=int)
+        # 2-D input
+        frame = DataFrame(mat, columns=["A", "B", "C"], index=[1, 2])
+
+        assert len(frame.index) == 2
+        assert len(frame.columns) == 3
+        assert np.all(~np.asarray(frame == frame))
+
+        # cast type
+        frame = DataFrame(mat, columns=["A", "B", "C"], index=[1, 2], dtype=np.float64)
+        assert frame.values.dtype == np.float64
+
+        # Check non-masked values
+        mat2 = ma.copy(mat)
+        mat2[0, 0] = 1
+        mat2[1, 2] = 2
+        frame = DataFrame(mat2, columns=["A", "B", "C"], index=[1, 2])
+        assert 1 == frame["A"][1]
+        assert 2 == frame["C"][2]
+
+        # masked np.datetime64 stays (use NaT as null)
+        mat = ma.masked_all((2, 3), dtype="M8[ns]")
+        # 2-D input
+        frame = DataFrame(mat, columns=["A", "B", "C"], index=[1, 2])
+
+        assert len(frame.index) == 2
+        assert len(frame.columns) == 3
+        assert isna(frame).values.all()
+
+        # cast type
+        msg = r"datetime64\[ns\] values and dtype=int64 is not supported"
+        with pytest.raises(TypeError, match=msg):
+            DataFrame(mat, columns=["A", "B", "C"], index=[1, 2], dtype=np.int64)
+
+        # Check non-masked values
+        mat2 = ma.copy(mat)
+        mat2[0, 0] = 1
+        mat2[1, 2] = 2
+        frame = DataFrame(mat2, columns=["A", "B", "C"], index=[1, 2])
+        assert 1 == frame["A"].astype("i8")[1]
+        assert 2 == frame["C"].astype("i8")[2]
+
+        # masked bool promoted to object
+        mat = ma.masked_all((2, 3), dtype=bool)
+        # 2-D input
+        frame = DataFrame(mat, columns=["A", "B", "C"], index=[1, 2])
+
+        assert len(frame.index) == 2
+        assert len(frame.columns) == 3
+        assert np.all(~np.asarray(frame == frame))
+
+        # cast type
+        frame = DataFrame(mat, columns=["A", "B", "C"], index=[1, 2], dtype=object)
+        assert frame.values.dtype == object
+
+        # Check non-masked values
+        mat2 = ma.copy(mat)
+        mat2[0, 0] = True
+        mat2[1, 2] = False
+        frame = DataFrame(mat2, columns=["A", "B", "C"], index=[1, 2])
+        assert frame["A"][1] is True
+        assert frame["C"][2] is False
+
+    def test_constructor_maskedarray_hardened(self):
+        # Check numpy masked arrays with hard masks -- from GH24574
+        mat_hard = ma.masked_all((2, 2), dtype=float).harden_mask()
+        result = DataFrame(mat_hard, columns=["A", "B"], index=[1, 2])
+        expected = DataFrame(
+            {"A": [np.nan, np.nan], "B": [np.nan, np.nan]},
+            columns=["A", "B"],
+            index=[1, 2],
+            dtype=float,
+        )
+        tm.assert_frame_equal(result, expected)
+        # Check case where mask is hard but no data are masked
+        mat_hard = ma.ones((2, 2), dtype=float).harden_mask()
+        result = DataFrame(mat_hard, columns=["A", "B"], index=[1, 2])
+        expected = DataFrame(
+            {"A": [1.0, 1.0], "B": [1.0, 1.0]},
+            columns=["A", "B"],
+            index=[1, 2],
+            dtype=float,
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_constructor_maskedrecarray_dtype(self):
+        # Ensure constructor honors dtype
+        data = np.ma.array(
+            np.ma.zeros(5, dtype=[("date", " None:
+                self._lst = lst
+
+            def __getitem__(self, n):
+                return self._lst.__getitem__(n)
+
+            def __len__(self) -> int:
+                return self._lst.__len__()
+
+        lst_containers = [DummyContainer([1, "a"]), DummyContainer([2, "b"])]
+        columns = ["num", "str"]
+        result = DataFrame(lst_containers, columns=columns)
+        expected = DataFrame([[1, "a"], [2, "b"]], columns=columns)
+        tm.assert_frame_equal(result, expected, check_dtype=False)
+
+    def test_constructor_stdlib_array(self):
+        # GH 4297
+        # support Array
+        result = DataFrame({"A": array.array("i", range(10))})
+        expected = DataFrame({"A": list(range(10))})
+        tm.assert_frame_equal(result, expected, check_dtype=False)
+
+        expected = DataFrame([list(range(10)), list(range(10))])
+        result = DataFrame([array.array("i", range(10)), array.array("i", range(10))])
+        tm.assert_frame_equal(result, expected, check_dtype=False)
+
+    def test_constructor_range(self):
+        # GH26342
+        result = DataFrame(range(10))
+        expected = DataFrame(list(range(10)))
+        tm.assert_frame_equal(result, expected)
+
+    def test_constructor_list_of_ranges(self):
+        result = DataFrame([range(10), range(10)])
+        expected = DataFrame([list(range(10)), list(range(10))])
+        tm.assert_frame_equal(result, expected)
+
+    def test_constructor_iterable(self):
+        # GH 21987
+        class Iter:
+            def __iter__(self) -> Iterator:
+                for i in range(10):
+                    yield [1, 2, 3]
+
+        expected = DataFrame([[1, 2, 3]] * 10)
+        result = DataFrame(Iter())
+        tm.assert_frame_equal(result, expected)
+
+    def test_constructor_iterator(self):
+        result = DataFrame(iter(range(10)))
+        expected = DataFrame(list(range(10)))
+        tm.assert_frame_equal(result, expected)
+
+    def test_constructor_list_of_iterators(self):
+        result = DataFrame([iter(range(10)), iter(range(10))])
+        expected = DataFrame([list(range(10)), list(range(10))])
+        tm.assert_frame_equal(result, expected)
+
+    def test_constructor_generator(self):
+        # related #2305
+
+        gen1 = (i for i in range(10))
+        gen2 = (i for i in range(10))
+
+        expected = DataFrame([list(range(10)), list(range(10))])
+        result = DataFrame([gen1, gen2])
+        tm.assert_frame_equal(result, expected)
+
+        gen = ([i, "a"] for i in range(10))
+        result = DataFrame(gen)
+        expected = DataFrame({0: range(10), 1: "a"})
+        tm.assert_frame_equal(result, expected, check_dtype=False)
+
+    def test_constructor_list_of_dicts(self):
+        result = DataFrame([{}])
+        expected = DataFrame(index=RangeIndex(1), columns=[])
+        tm.assert_frame_equal(result, expected)
+
+    def test_constructor_ordered_dict_nested_preserve_order(self):
+        # see gh-18166
+        nested1 = OrderedDict([("b", 1), ("a", 2)])
+        nested2 = OrderedDict([("b", 2), ("a", 5)])
+        data = OrderedDict([("col2", nested1), ("col1", nested2)])
+        result = DataFrame(data)
+        data = {"col2": [1, 2], "col1": [2, 5]}
+        expected = DataFrame(data=data, index=["b", "a"])
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("dict_type", [dict, OrderedDict])
+    def test_constructor_ordered_dict_preserve_order(self, dict_type):
+        # see gh-13304
+        expected = DataFrame([[2, 1]], columns=["b", "a"])
+
+        data = dict_type()
+        data["b"] = [2]
+        data["a"] = [1]
+
+        result = DataFrame(data)
+        tm.assert_frame_equal(result, expected)
+
+        data = dict_type()
+        data["b"] = 2
+        data["a"] = 1
+
+        result = DataFrame([data])
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("dict_type", [dict, OrderedDict])
+    def test_constructor_ordered_dict_conflicting_orders(self, dict_type):
+        # the first dict element sets the ordering for the DataFrame,
+        # even if there are conflicting orders from subsequent ones
+        row_one = dict_type()
+        row_one["b"] = 2
+        row_one["a"] = 1
+
+        row_two = dict_type()
+        row_two["a"] = 1
+        row_two["b"] = 2
+
+        row_three = {"b": 2, "a": 1}
+
+        expected = DataFrame([[2, 1], [2, 1]], columns=["b", "a"])
+        result = DataFrame([row_one, row_two])
+        tm.assert_frame_equal(result, expected)
+
+        expected = DataFrame([[2, 1], [2, 1], [2, 1]], columns=["b", "a"])
+        result = DataFrame([row_one, row_two, row_three])
+        tm.assert_frame_equal(result, expected)
+
+    def test_constructor_list_of_series_aligned_index(self):
+        series = [Series(i, index=["b", "a", "c"], name=str(i)) for i in range(3)]
+        result = DataFrame(series)
+        expected = DataFrame(
+            {"b": [0, 1, 2], "a": [0, 1, 2], "c": [0, 1, 2]},
+            columns=["b", "a", "c"],
+            index=["0", "1", "2"],
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_constructor_list_of_derived_dicts(self):
+        class CustomDict(dict):
+            pass
+
+        d = {"a": 1.5, "b": 3}
+
+        data_custom = [CustomDict(d)]
+        data = [d]
+
+        result_custom = DataFrame(data_custom)
+        result = DataFrame(data)
+        tm.assert_frame_equal(result, result_custom)
+
+    def test_constructor_ragged(self):
+        data = {
+            "A": np.random.default_rng(2).standard_normal(10),
+            "B": np.random.default_rng(2).standard_normal(8),
+        }
+        with pytest.raises(ValueError, match="All arrays must be of the same length"):
+            DataFrame(data)
+
+    def test_constructor_scalar(self):
+        idx = Index(range(3))
+        df = DataFrame({"a": 0}, index=idx)
+        expected = DataFrame({"a": [0, 0, 0]}, index=idx)
+        tm.assert_frame_equal(df, expected, check_dtype=False)
+
+    def test_constructor_Series_copy_bug(self, float_frame):
+        df = DataFrame(float_frame["A"], index=float_frame.index, columns=["A"])
+        df.copy()
+
+    def test_constructor_mixed_dict_and_Series(self):
+        data = {}
+        data["A"] = {"foo": 1, "bar": 2, "baz": 3}
+        data["B"] = Series([4, 3, 2, 1], index=["bar", "qux", "baz", "foo"])
+
+        result = DataFrame(data)
+        assert result.index.is_monotonic_increasing
+
+        # ordering ambiguous, raise exception
+        with pytest.raises(ValueError, match="ambiguous ordering"):
+            DataFrame({"A": ["a", "b"], "B": {"a": "a", "b": "b"}})
+
+        # this is OK though
+        result = DataFrame({"A": ["a", "b"], "B": Series(["a", "b"], index=["a", "b"])})
+        expected = DataFrame({"A": ["a", "b"], "B": ["a", "b"]}, index=["a", "b"])
+        tm.assert_frame_equal(result, expected)
+
+    def test_constructor_mixed_type_rows(self):
+        # Issue 25075
+        data = [[1, 2], (3, 4)]
+        result = DataFrame(data)
+        expected = DataFrame([[1, 2], [3, 4]])
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "tuples,lists",
+        [
+            ((), []),
+            ((()), []),
+            (((), ()), [(), ()]),
+            (((), ()), [[], []]),
+            (([], []), [[], []]),
+            (([1], [2]), [[1], [2]]),  # GH 32776
+            (([1, 2, 3], [4, 5, 6]), [[1, 2, 3], [4, 5, 6]]),
+        ],
+    )
+    def test_constructor_tuple(self, tuples, lists):
+        # GH 25691
+        result = DataFrame(tuples)
+        expected = DataFrame(lists)
+        tm.assert_frame_equal(result, expected)
+
+    def test_constructor_list_of_tuples(self):
+        result = DataFrame({"A": [(1, 2), (3, 4)]})
+        expected = DataFrame({"A": Series([(1, 2), (3, 4)])})
+        tm.assert_frame_equal(result, expected)
+
+    def test_constructor_list_of_namedtuples(self):
+        # GH11181
+        named_tuple = namedtuple("Pandas", list("ab"))
+        tuples = [named_tuple(1, 3), named_tuple(2, 4)]
+        expected = DataFrame({"a": [1, 2], "b": [3, 4]})
+        result = DataFrame(tuples)
+        tm.assert_frame_equal(result, expected)
+
+        # with columns
+        expected = DataFrame({"y": [1, 2], "z": [3, 4]})
+        result = DataFrame(tuples, columns=["y", "z"])
+        tm.assert_frame_equal(result, expected)
+
+    def test_constructor_list_of_dataclasses(self):
+        # GH21910
+        Point = make_dataclass("Point", [("x", int), ("y", int)])
+
+        data = [Point(0, 3), Point(1, 3)]
+        expected = DataFrame({"x": [0, 1], "y": [3, 3]})
+        result = DataFrame(data)
+        tm.assert_frame_equal(result, expected)
+
+    def test_constructor_list_of_dataclasses_with_varying_types(self):
+        # GH21910
+        # varying types
+        Point = make_dataclass("Point", [("x", int), ("y", int)])
+        HLine = make_dataclass("HLine", [("x0", int), ("x1", int), ("y", int)])
+
+        data = [Point(0, 3), HLine(1, 3, 3)]
+
+        expected = DataFrame(
+            {"x": [0, np.nan], "y": [3, 3], "x0": [np.nan, 1], "x1": [np.nan, 3]}
+        )
+        result = DataFrame(data)
+        tm.assert_frame_equal(result, expected)
+
+    def test_constructor_list_of_dataclasses_error_thrown(self):
+        # GH21910
+        Point = make_dataclass("Point", [("x", int), ("y", int)])
+
+        # expect TypeError
+        msg = "asdict() should be called on dataclass instances"
+        with pytest.raises(TypeError, match=re.escape(msg)):
+            DataFrame([Point(0, 0), {"x": 1, "y": 0}])
+
+    def test_constructor_list_of_dict_order(self):
+        # GH10056
+        data = [
+            {"First": 1, "Second": 4, "Third": 7, "Fourth": 10},
+            {"Second": 5, "First": 2, "Fourth": 11, "Third": 8},
+            {"Second": 6, "First": 3, "Fourth": 12, "Third": 9, "YYY": 14, "XXX": 13},
+        ]
+        expected = DataFrame(
+            {
+                "First": [1, 2, 3],
+                "Second": [4, 5, 6],
+                "Third": [7, 8, 9],
+                "Fourth": [10, 11, 12],
+                "YYY": [None, None, 14],
+                "XXX": [None, None, 13],
+            }
+        )
+        result = DataFrame(data)
+        tm.assert_frame_equal(result, expected)
+
+    def test_constructor_Series_named(self):
+        a = Series([1, 2, 3], index=["a", "b", "c"], name="x")
+        df = DataFrame(a)
+        assert df.columns[0] == "x"
+        tm.assert_index_equal(df.index, a.index)
+
+        # ndarray like
+        arr = np.random.default_rng(2).standard_normal(10)
+        s = Series(arr, name="x")
+        df = DataFrame(s)
+        expected = DataFrame({"x": s})
+        tm.assert_frame_equal(df, expected)
+
+        s = Series(arr, index=range(3, 13))
+        df = DataFrame(s)
+        expected = DataFrame({0: s})
+        tm.assert_frame_equal(df, expected)
+
+        msg = r"Shape of passed values is \(10, 1\), indices imply \(10, 2\)"
+        with pytest.raises(ValueError, match=msg):
+            DataFrame(s, columns=[1, 2])
+
+        # #2234
+        a = Series([], name="x", dtype=object)
+        df = DataFrame(a)
+        assert df.columns[0] == "x"
+
+        # series with name and w/o
+        s1 = Series(arr, name="x")
+        df = DataFrame([s1, arr]).T
+        expected = DataFrame({"x": s1, "Unnamed 0": arr}, columns=["x", "Unnamed 0"])
+        tm.assert_frame_equal(df, expected)
+
+        # this is a bit non-intuitive here; the series collapse down to arrays
+        df = DataFrame([arr, s1]).T
+        expected = DataFrame({1: s1, 0: arr}, columns=[0, 1])
+        tm.assert_frame_equal(df, expected)
+
+    def test_constructor_Series_named_and_columns(self):
+        # GH 9232 validation
+
+        s0 = Series(range(5), name=0)
+        s1 = Series(range(5), name=1)
+
+        # matching name and column gives standard frame
+        tm.assert_frame_equal(DataFrame(s0, columns=[0]), s0.to_frame())
+        tm.assert_frame_equal(DataFrame(s1, columns=[1]), s1.to_frame())
+
+        # non-matching produces empty frame
+        assert DataFrame(s0, columns=[1]).empty
+        assert DataFrame(s1, columns=[0]).empty
+
+    def test_constructor_Series_differently_indexed(self):
+        # name
+        s1 = Series([1, 2, 3], index=["a", "b", "c"], name="x")
+
+        # no name
+        s2 = Series([1, 2, 3], index=["a", "b", "c"])
+
+        other_index = Index(["a", "b"])
+
+        df1 = DataFrame(s1, index=other_index)
+        exp1 = DataFrame(s1.reindex(other_index))
+        assert df1.columns[0] == "x"
+        tm.assert_frame_equal(df1, exp1)
+
+        df2 = DataFrame(s2, index=other_index)
+        exp2 = DataFrame(s2.reindex(other_index))
+        assert df2.columns[0] == 0
+        tm.assert_index_equal(df2.index, other_index)
+        tm.assert_frame_equal(df2, exp2)
+
+    @pytest.mark.parametrize(
+        "name_in1,name_in2,name_in3,name_out",
+        [
+            ("idx", "idx", "idx", "idx"),
+            ("idx", "idx", None, None),
+            ("idx", None, None, None),
+            ("idx1", "idx2", None, None),
+            ("idx1", "idx1", "idx2", None),
+            ("idx1", "idx2", "idx3", None),
+            (None, None, None, None),
+        ],
+    )
+    def test_constructor_index_names(self, name_in1, name_in2, name_in3, name_out):
+        # GH13475
+        indices = [
+            Index(["a", "b", "c"], name=name_in1),
+            Index(["b", "c", "d"], name=name_in2),
+            Index(["c", "d", "e"], name=name_in3),
+        ]
+        series = {
+            c: Series([0, 1, 2], index=i) for i, c in zip(indices, ["x", "y", "z"])
+        }
+        result = DataFrame(series)
+
+        exp_ind = Index(["a", "b", "c", "d", "e"], name=name_out)
+        expected = DataFrame(
+            {
+                "x": [0, 1, 2, np.nan, np.nan],
+                "y": [np.nan, 0, 1, 2, np.nan],
+                "z": [np.nan, np.nan, 0, 1, 2],
+            },
+            index=exp_ind,
+        )
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_constructor_manager_resize(self, float_frame):
+        index = list(float_frame.index[:5])
+        columns = list(float_frame.columns[:3])
+
+        msg = "Passing a BlockManager to DataFrame"
+        with tm.assert_produces_warning(
+            DeprecationWarning, match=msg, check_stacklevel=False
+        ):
+            result = DataFrame(float_frame._mgr, index=index, columns=columns)
+        tm.assert_index_equal(result.index, Index(index))
+        tm.assert_index_equal(result.columns, Index(columns))
+
+    def test_constructor_mix_series_nonseries(self, float_frame):
+        df = DataFrame(
+            {"A": float_frame["A"], "B": list(float_frame["B"])}, columns=["A", "B"]
+        )
+        tm.assert_frame_equal(df, float_frame.loc[:, ["A", "B"]])
+
+        msg = "does not match index length"
+        with pytest.raises(ValueError, match=msg):
+            DataFrame({"A": float_frame["A"], "B": list(float_frame["B"])[:-2]})
+
+    def test_constructor_miscast_na_int_dtype(self):
+        msg = r"Cannot convert non-finite values \(NA or inf\) to integer"
+
+        with pytest.raises(IntCastingNaNError, match=msg):
+            DataFrame([[np.nan, 1], [1, 0]], dtype=np.int64)
+
+    def test_constructor_column_duplicates(self):
+        # it works! #2079
+        df = DataFrame([[8, 5]], columns=["a", "a"])
+        edf = DataFrame([[8, 5]])
+        edf.columns = ["a", "a"]
+
+        tm.assert_frame_equal(df, edf)
+
+        idf = DataFrame.from_records([(8, 5)], columns=["a", "a"])
+
+        tm.assert_frame_equal(idf, edf)
+
+    def test_constructor_empty_with_string_dtype(self, using_infer_string):
+        # GH 9428
+        expected = DataFrame(index=[0, 1], columns=[0, 1], dtype=object)
+        expected_str = DataFrame(
+            index=[0, 1], columns=[0, 1], dtype=pd.StringDtype(na_value=np.nan)
+        )
+
+        df = DataFrame(index=[0, 1], columns=[0, 1], dtype=str)
+        if using_infer_string:
+            tm.assert_frame_equal(df, expected_str)
+        else:
+            tm.assert_frame_equal(df, expected)
+        df = DataFrame(index=[0, 1], columns=[0, 1], dtype=np.str_)
+        tm.assert_frame_equal(df, expected)
+        df = DataFrame(index=[0, 1], columns=[0, 1], dtype="U5")
+        tm.assert_frame_equal(df, expected)
+
+    def test_constructor_empty_with_string_extension(self, nullable_string_dtype):
+        # GH 34915
+        expected = DataFrame(columns=["c1"], dtype=nullable_string_dtype)
+        df = DataFrame(columns=["c1"], dtype=nullable_string_dtype)
+        tm.assert_frame_equal(df, expected)
+
+    def test_constructor_single_value(self):
+        # expecting single value upcasting here
+        df = DataFrame(0.0, index=[1, 2, 3], columns=["a", "b", "c"])
+        tm.assert_frame_equal(
+            df, DataFrame(np.zeros(df.shape).astype("float64"), df.index, df.columns)
+        )
+
+        df = DataFrame(0, index=[1, 2, 3], columns=["a", "b", "c"])
+        tm.assert_frame_equal(
+            df, DataFrame(np.zeros(df.shape).astype("int64"), df.index, df.columns)
+        )
+
+        df = DataFrame("a", index=[1, 2], columns=["a", "c"])
+        tm.assert_frame_equal(
+            df,
+            DataFrame(
+                np.array([["a", "a"], ["a", "a"]], dtype=object),
+                index=[1, 2],
+                columns=["a", "c"],
+            ),
+        )
+
+        msg = "DataFrame constructor not properly called!"
+        with pytest.raises(ValueError, match=msg):
+            DataFrame("a", [1, 2])
+        with pytest.raises(ValueError, match=msg):
+            DataFrame("a", columns=["a", "c"])
+
+        msg = "incompatible data and dtype"
+        with pytest.raises(TypeError, match=msg):
+            DataFrame("a", [1, 2], ["a", "c"], float)
+
+    def test_constructor_with_datetimes(self, using_infer_string):
+        intname = np.dtype(int).name
+        floatname = np.dtype(np.float64).name
+        objectname = np.dtype(np.object_).name
+
+        # single item
+        df = DataFrame(
+            {
+                "A": 1,
+                "B": "foo",
+                "C": "bar",
+                "D": Timestamp("20010101"),
+                "E": datetime(2001, 1, 2, 0, 0),
+            },
+            index=np.arange(10),
+        )
+        result = df.dtypes
+        expected = Series(
+            [np.dtype("int64")]
+            + [
+                np.dtype(objectname)
+                if not using_infer_string
+                else pd.StringDtype(na_value=np.nan)
+            ]
+            * 2
+            + [np.dtype("M8[s]"), np.dtype("M8[us]")],
+            index=list("ABCDE"),
+        )
+        tm.assert_series_equal(result, expected)
+
+        # check with ndarray construction ndim==0 (e.g. we are passing a ndim 0
+        # ndarray with a dtype specified)
+        df = DataFrame(
+            {
+                "a": 1.0,
+                "b": 2,
+                "c": "foo",
+                floatname: np.array(1.0, dtype=floatname),
+                intname: np.array(1, dtype=intname),
+            },
+            index=np.arange(10),
+        )
+        result = df.dtypes
+        expected = Series(
+            [np.dtype("float64")]
+            + [np.dtype("int64")]
+            + [
+                np.dtype("object")
+                if not using_infer_string
+                else pd.StringDtype(na_value=np.nan)
+            ]
+            + [np.dtype("float64")]
+            + [np.dtype(intname)],
+            index=["a", "b", "c", floatname, intname],
+        )
+        tm.assert_series_equal(result, expected)
+
+        # check with ndarray construction ndim>0
+        df = DataFrame(
+            {
+                "a": 1.0,
+                "b": 2,
+                "c": "foo",
+                floatname: np.array([1.0] * 10, dtype=floatname),
+                intname: np.array([1] * 10, dtype=intname),
+            },
+            index=np.arange(10),
+        )
+        result = df.dtypes
+        expected = Series(
+            [np.dtype("float64")]
+            + [np.dtype("int64")]
+            + [
+                np.dtype("object")
+                if not using_infer_string
+                else pd.StringDtype(na_value=np.nan)
+            ]
+            + [np.dtype("float64")]
+            + [np.dtype(intname)],
+            index=["a", "b", "c", floatname, intname],
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_constructor_with_datetimes1(self):
+        # GH 2809
+        ind = date_range(start="2000-01-01", freq="D", periods=10)
+        datetimes = [ts.to_pydatetime() for ts in ind]
+        datetime_s = Series(datetimes)
+        assert datetime_s.dtype == "M8[ns]"
+
+    def test_constructor_with_datetimes2(self):
+        # GH 2810
+        ind = date_range(start="2000-01-01", freq="D", periods=10)
+        datetimes = [ts.to_pydatetime() for ts in ind]
+        dates = [ts.date() for ts in ind]
+        df = DataFrame(datetimes, columns=["datetimes"])
+        df["dates"] = dates
+        result = df.dtypes
+        expected = Series(
+            [np.dtype("datetime64[ns]"), np.dtype("object")],
+            index=["datetimes", "dates"],
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_constructor_with_datetimes3(self):
+        # GH 7594
+        # don't coerce tz-aware
+        tz = pytz.timezone("US/Eastern")
+        dt = tz.localize(datetime(2012, 1, 1))
+
+        df = DataFrame({"End Date": dt}, index=[0])
+        assert df.iat[0, 0] == dt
+        tm.assert_series_equal(
+            df.dtypes, Series({"End Date": "datetime64[us, US/Eastern]"}, dtype=object)
+        )
+
+        df = DataFrame([{"End Date": dt}])
+        assert df.iat[0, 0] == dt
+        tm.assert_series_equal(
+            df.dtypes, Series({"End Date": "datetime64[ns, US/Eastern]"}, dtype=object)
+        )
+
+    def test_constructor_with_datetimes4(self):
+        # tz-aware (UTC and other tz's)
+        # GH 8411
+        dr = date_range("20130101", periods=3)
+        df = DataFrame({"value": dr})
+        assert df.iat[0, 0].tz is None
+        dr = date_range("20130101", periods=3, tz="UTC")
+        df = DataFrame({"value": dr})
+        assert str(df.iat[0, 0].tz) == "UTC"
+        dr = date_range("20130101", periods=3, tz="US/Eastern")
+        df = DataFrame({"value": dr})
+        assert str(df.iat[0, 0].tz) == "US/Eastern"
+
+    def test_constructor_with_datetimes5(self):
+        # GH 7822
+        # preserver an index with a tz on dict construction
+        i = date_range("1/1/2011", periods=5, freq="10s", tz="US/Eastern")
+
+        expected = DataFrame({"a": i.to_series().reset_index(drop=True)})
+        df = DataFrame()
+        df["a"] = i
+        tm.assert_frame_equal(df, expected)
+
+        df = DataFrame({"a": i})
+        tm.assert_frame_equal(df, expected)
+
+    def test_constructor_with_datetimes6(self):
+        # multiples
+        i = date_range("1/1/2011", periods=5, freq="10s", tz="US/Eastern")
+        i_no_tz = date_range("1/1/2011", periods=5, freq="10s")
+        df = DataFrame({"a": i, "b": i_no_tz})
+        expected = DataFrame({"a": i.to_series().reset_index(drop=True), "b": i_no_tz})
+        tm.assert_frame_equal(df, expected)
+
+    @pytest.mark.parametrize(
+        "arr",
+        [
+            np.array([None, None, None, None, datetime.now(), None]),
+            np.array([None, None, datetime.now(), None]),
+            [[np.datetime64("NaT")], [None]],
+            [[np.datetime64("NaT")], [pd.NaT]],
+            [[None], [np.datetime64("NaT")]],
+            [[None], [pd.NaT]],
+            [[pd.NaT], [np.datetime64("NaT")]],
+            [[pd.NaT], [None]],
+        ],
+    )
+    def test_constructor_datetimes_with_nulls(self, arr):
+        # gh-15869, GH#11220
+        result = DataFrame(arr).dtypes
+        expected = Series([np.dtype("datetime64[ns]")])
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("order", ["K", "A", "C", "F"])
+    @pytest.mark.parametrize(
+        "unit",
+        ["M", "D", "h", "m", "s", "ms", "us", "ns"],
+    )
+    def test_constructor_datetimes_non_ns(self, order, unit):
+        dtype = f"datetime64[{unit}]"
+        na = np.array(
+            [
+                ["2015-01-01", "2015-01-02", "2015-01-03"],
+                ["2017-01-01", "2017-01-02", "2017-02-03"],
+            ],
+            dtype=dtype,
+            order=order,
+        )
+        df = DataFrame(na)
+        expected = DataFrame(na.astype("M8[ns]"))
+        if unit in ["M", "D", "h", "m"]:
+            with pytest.raises(TypeError, match="Cannot cast"):
+                expected.astype(dtype)
+
+            # instead the constructor casts to the closest supported reso, i.e. "s"
+            expected = expected.astype("datetime64[s]")
+        else:
+            expected = expected.astype(dtype=dtype)
+
+        tm.assert_frame_equal(df, expected)
+
+    @pytest.mark.parametrize("order", ["K", "A", "C", "F"])
+    @pytest.mark.parametrize(
+        "unit",
+        [
+            "D",
+            "h",
+            "m",
+            "s",
+            "ms",
+            "us",
+            "ns",
+        ],
+    )
+    def test_constructor_timedelta_non_ns(self, order, unit):
+        dtype = f"timedelta64[{unit}]"
+        na = np.array(
+            [
+                [np.timedelta64(1, "D"), np.timedelta64(2, "D")],
+                [np.timedelta64(4, "D"), np.timedelta64(5, "D")],
+            ],
+            dtype=dtype,
+            order=order,
+        )
+        df = DataFrame(na)
+        if unit in ["D", "h", "m"]:
+            # we get the nearest supported unit, i.e. "s"
+            exp_unit = "s"
+        else:
+            exp_unit = unit
+        exp_dtype = np.dtype(f"m8[{exp_unit}]")
+        expected = DataFrame(
+            [
+                [Timedelta(1, "D"), Timedelta(2, "D")],
+                [Timedelta(4, "D"), Timedelta(5, "D")],
+            ],
+            dtype=exp_dtype,
+        )
+        # TODO(2.0): ideally we should get the same 'expected' without passing
+        #  dtype=exp_dtype.
+        tm.assert_frame_equal(df, expected)
+
+    def test_constructor_for_list_with_dtypes(self, using_infer_string):
+        # test list of lists/ndarrays
+        df = DataFrame([np.arange(5) for x in range(5)])
+        result = df.dtypes
+        expected = Series([np.dtype("int")] * 5)
+        tm.assert_series_equal(result, expected)
+
+        df = DataFrame([np.array(np.arange(5), dtype="int32") for x in range(5)])
+        result = df.dtypes
+        expected = Series([np.dtype("int32")] * 5)
+        tm.assert_series_equal(result, expected)
+
+        # overflow issue? (we always expected int64 upcasting here)
+        df = DataFrame({"a": [2**31, 2**31 + 1]})
+        assert df.dtypes.iloc[0] == np.dtype("int64")
+
+        # GH #2751 (construction with no index specified), make sure we cast to
+        # platform values
+        df = DataFrame([1, 2])
+        assert df.dtypes.iloc[0] == np.dtype("int64")
+
+        df = DataFrame([1.0, 2.0])
+        assert df.dtypes.iloc[0] == np.dtype("float64")
+
+        df = DataFrame({"a": [1, 2]})
+        assert df.dtypes.iloc[0] == np.dtype("int64")
+
+        df = DataFrame({"a": [1.0, 2.0]})
+        assert df.dtypes.iloc[0] == np.dtype("float64")
+
+        df = DataFrame({"a": 1}, index=range(3))
+        assert df.dtypes.iloc[0] == np.dtype("int64")
+
+        df = DataFrame({"a": 1.0}, index=range(3))
+        assert df.dtypes.iloc[0] == np.dtype("float64")
+
+        # with object list
+        df = DataFrame(
+            {
+                "a": [1, 2, 4, 7],
+                "b": [1.2, 2.3, 5.1, 6.3],
+                "c": list("abcd"),
+                "d": [datetime(2000, 1, 1) for i in range(4)],
+                "e": [1.0, 2, 4.0, 7],
+            }
+        )
+        result = df.dtypes
+        expected = Series(
+            [
+                np.dtype("int64"),
+                np.dtype("float64"),
+                np.dtype("object")
+                if not using_infer_string
+                else pd.StringDtype(na_value=np.nan),
+                np.dtype("datetime64[ns]"),
+                np.dtype("float64"),
+            ],
+            index=list("abcde"),
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_constructor_frame_copy(self, float_frame):
+        cop = DataFrame(float_frame, copy=True)
+        cop["A"] = 5
+        assert (cop["A"] == 5).all()
+        assert not (float_frame["A"] == 5).all()
+
+    def test_constructor_frame_shallow_copy(self, float_frame):
+        # constructing a DataFrame from DataFrame with copy=False should still
+        # give a "shallow" copy (share data, not attributes)
+        # https://github.com/pandas-dev/pandas/issues/49523
+        orig = float_frame.copy()
+        cop = DataFrame(float_frame)
+        assert cop._mgr is not float_frame._mgr
+        # Overwriting index of copy doesn't change original
+        cop.index = np.arange(len(cop))
+        tm.assert_frame_equal(float_frame, orig)
+
+    def test_constructor_ndarray_copy(
+        self, float_frame, using_array_manager, using_copy_on_write
+    ):
+        if not using_array_manager:
+            arr = float_frame.values.copy()
+            df = DataFrame(arr)
+
+            arr[5] = 5
+            if using_copy_on_write:
+                assert not (df.values[5] == 5).all()
+            else:
+                assert (df.values[5] == 5).all()
+
+            df = DataFrame(arr, copy=True)
+            arr[6] = 6
+            assert not (df.values[6] == 6).all()
+        else:
+            arr = float_frame.values.copy()
+            # default: copy to ensure contiguous arrays
+            df = DataFrame(arr)
+            assert df._mgr.arrays[0].flags.c_contiguous
+            arr[0, 0] = 100
+            assert df.iloc[0, 0] != 100
+
+            # manually specify copy=False
+            df = DataFrame(arr, copy=False)
+            assert not df._mgr.arrays[0].flags.c_contiguous
+            arr[0, 0] = 1000
+            assert df.iloc[0, 0] == 1000
+
+    def test_constructor_series_copy(self, float_frame):
+        series = float_frame._series
+
+        df = DataFrame({"A": series["A"]}, copy=True)
+        # TODO can be replaced with `df.loc[:, "A"] = 5` after deprecation about
+        # inplace mutation is enforced
+        df.loc[df.index[0] : df.index[-1], "A"] = 5
+
+        assert not (series["A"] == 5).all()
+
+    @pytest.mark.parametrize(
+        "df",
+        [
+            DataFrame([[1, 2, 3], [4, 5, 6]], index=[1, np.nan]),
+            DataFrame([[1, 2, 3], [4, 5, 6]], columns=[1.1, 2.2, np.nan]),
+            DataFrame([[0, 1, 2, 3], [4, 5, 6, 7]], columns=[np.nan, 1.1, 2.2, np.nan]),
+            DataFrame(
+                [[0.0, 1, 2, 3.0], [4, 5, 6, 7]], columns=[np.nan, 1.1, 2.2, np.nan]
+            ),
+            DataFrame([[0.0, 1, 2, 3.0], [4, 5, 6, 7]], columns=[np.nan, 1, 2, 2]),
+        ],
+    )
+    def test_constructor_with_nas(self, df):
+        # GH 5016
+        # na's in indices
+        # GH 21428 (non-unique columns)
+
+        for i in range(len(df.columns)):
+            df.iloc[:, i]
+
+        indexer = np.arange(len(df.columns))[isna(df.columns)]
+
+        # No NaN found -> error
+        if len(indexer) == 0:
+            with pytest.raises(KeyError, match="^nan$"):
+                df.loc[:, np.nan]
+        # single nan should result in Series
+        elif len(indexer) == 1:
+            tm.assert_series_equal(df.iloc[:, indexer[0]], df.loc[:, np.nan])
+        # multiple nans should result in DataFrame
+        else:
+            tm.assert_frame_equal(df.iloc[:, indexer], df.loc[:, np.nan])
+
+    def test_constructor_lists_to_object_dtype(self):
+        # from #1074
+        d = DataFrame({"a": [np.nan, False]})
+        assert d["a"].dtype == np.object_
+        assert not d["a"][1]
+
+    def test_constructor_ndarray_categorical_dtype(self):
+        cat = Categorical(["A", "B", "C"])
+        arr = np.array(cat).reshape(-1, 1)
+        arr = np.broadcast_to(arr, (3, 4))
+
+        result = DataFrame(arr, dtype=cat.dtype)
+
+        expected = DataFrame({0: cat, 1: cat, 2: cat, 3: cat})
+        tm.assert_frame_equal(result, expected)
+
+    def test_constructor_categorical(self):
+        # GH8626
+
+        # dict creation
+        df = DataFrame({"A": list("abc")}, dtype="category")
+        expected = Series(list("abc"), dtype="category", name="A")
+        tm.assert_series_equal(df["A"], expected)
+
+        # to_frame
+        s = Series(list("abc"), dtype="category")
+        result = s.to_frame()
+        expected = Series(list("abc"), dtype="category", name=0)
+        tm.assert_series_equal(result[0], expected)
+        result = s.to_frame(name="foo")
+        expected = Series(list("abc"), dtype="category", name="foo")
+        tm.assert_series_equal(result["foo"], expected)
+
+        # list-like creation
+        df = DataFrame(list("abc"), dtype="category")
+        expected = Series(list("abc"), dtype="category", name=0)
+        tm.assert_series_equal(df[0], expected)
+
+    def test_construct_from_1item_list_of_categorical(self):
+        # pre-2.0 this behaved as DataFrame({0: cat}), in 2.0 we remove
+        #  Categorical special case
+        # ndim != 1
+        cat = Categorical(list("abc"))
+        df = DataFrame([cat])
+        expected = DataFrame([cat.astype(object)])
+        tm.assert_frame_equal(df, expected)
+
+    def test_construct_from_list_of_categoricals(self):
+        # pre-2.0 this behaved as DataFrame({0: cat}), in 2.0 we remove
+        #  Categorical special case
+
+        df = DataFrame([Categorical(list("abc")), Categorical(list("abd"))])
+        expected = DataFrame([["a", "b", "c"], ["a", "b", "d"]])
+        tm.assert_frame_equal(df, expected)
+
+    def test_from_nested_listlike_mixed_types(self):
+        # pre-2.0 this behaved as DataFrame({0: cat}), in 2.0 we remove
+        #  Categorical special case
+        # mixed
+        df = DataFrame([Categorical(list("abc")), list("def")])
+        expected = DataFrame([["a", "b", "c"], ["d", "e", "f"]])
+        tm.assert_frame_equal(df, expected)
+
+    def test_construct_from_listlikes_mismatched_lengths(self):
+        df = DataFrame([Categorical(list("abc")), Categorical(list("abdefg"))])
+        expected = DataFrame([list("abc"), list("abdefg")])
+        tm.assert_frame_equal(df, expected)
+
+    def test_constructor_categorical_series(self):
+        items = [1, 2, 3, 1]
+        exp = Series(items).astype("category")
+        res = Series(items, dtype="category")
+        tm.assert_series_equal(res, exp)
+
+        items = ["a", "b", "c", "a"]
+        exp = Series(items).astype("category")
+        res = Series(items, dtype="category")
+        tm.assert_series_equal(res, exp)
+
+        # insert into frame with different index
+        # GH 8076
+        index = date_range("20000101", periods=3)
+        expected = Series(
+            Categorical(values=[np.nan, np.nan, np.nan], categories=["a", "b", "c"])
+        )
+        expected.index = index
+
+        expected = DataFrame({"x": expected})
+        df = DataFrame({"x": Series(["a", "b", "c"], dtype="category")}, index=index)
+        tm.assert_frame_equal(df, expected)
+
+    @pytest.mark.parametrize(
+        "dtype",
+        tm.ALL_NUMERIC_DTYPES
+        + tm.DATETIME64_DTYPES
+        + tm.TIMEDELTA64_DTYPES
+        + tm.BOOL_DTYPES,
+    )
+    def test_check_dtype_empty_numeric_column(self, dtype):
+        # GH24386: Ensure dtypes are set correctly for an empty DataFrame.
+        # Empty DataFrame is generated via dictionary data with non-overlapping columns.
+        data = DataFrame({"a": [1, 2]}, columns=["b"], dtype=dtype)
+
+        assert data.b.dtype == dtype
+
+    @pytest.mark.parametrize(
+        "dtype", tm.STRING_DTYPES + tm.BYTES_DTYPES + tm.OBJECT_DTYPES
+    )
+    def test_check_dtype_empty_string_column(self, request, dtype, using_array_manager):
+        # GH24386: Ensure dtypes are set correctly for an empty DataFrame.
+        # Empty DataFrame is generated via dictionary data with non-overlapping columns.
+        data = DataFrame({"a": [1, 2]}, columns=["b"], dtype=dtype)
+
+        if using_array_manager and dtype in tm.BYTES_DTYPES:
+            # TODO(ArrayManager) astype to bytes dtypes does not yet give object dtype
+            td.mark_array_manager_not_yet_implemented(request)
+
+        assert data.b.dtype.name == "object"
+
+    def test_to_frame_with_falsey_names(self):
+        # GH 16114
+        result = Series(name=0, dtype=object).to_frame().dtypes
+        expected = Series({0: object})
+        tm.assert_series_equal(result, expected)
+
+        result = DataFrame(Series(name=0, dtype=object)).dtypes
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.arm_slow
+    @pytest.mark.parametrize("dtype", [None, "uint8", "category"])
+    def test_constructor_range_dtype(self, dtype):
+        expected = DataFrame({"A": [0, 1, 2, 3, 4]}, dtype=dtype or "int64")
+
+        # GH 26342
+        result = DataFrame(range(5), columns=["A"], dtype=dtype)
+        tm.assert_frame_equal(result, expected)
+
+        # GH 16804
+        result = DataFrame({"A": range(5)}, dtype=dtype)
+        tm.assert_frame_equal(result, expected)
+
+    def test_frame_from_list_subclass(self):
+        # GH21226
+        class List(list):
+            pass
+
+        expected = DataFrame([[1, 2, 3], [4, 5, 6]])
+        result = DataFrame(List([List([1, 2, 3]), List([4, 5, 6])]))
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "extension_arr",
+        [
+            Categorical(list("aabbc")),
+            SparseArray([1, np.nan, np.nan, np.nan]),
+            IntervalArray([Interval(0, 1), Interval(1, 5)]),
+            PeriodArray(pd.period_range(start="1/1/2017", end="1/1/2018", freq="M")),
+        ],
+    )
+    def test_constructor_with_extension_array(self, extension_arr):
+        # GH11363
+        expected = DataFrame(Series(extension_arr))
+        result = DataFrame(extension_arr)
+        tm.assert_frame_equal(result, expected)
+
+    def test_datetime_date_tuple_columns_from_dict(self):
+        # GH 10863
+        v = date.today()
+        tup = v, v
+        result = DataFrame({tup: Series(range(3), index=range(3))}, columns=[tup])
+        expected = DataFrame([0, 1, 2], columns=Index(Series([tup])))
+        tm.assert_frame_equal(result, expected)
+
+    def test_construct_with_two_categoricalindex_series(self):
+        # GH 14600
+        s1 = Series([39, 6, 4], index=CategoricalIndex(["female", "male", "unknown"]))
+        s2 = Series(
+            [2, 152, 2, 242, 150],
+            index=CategoricalIndex(["f", "female", "m", "male", "unknown"]),
+        )
+        result = DataFrame([s1, s2])
+        expected = DataFrame(
+            np.array([[39, 6, 4, np.nan, np.nan], [152.0, 242.0, 150.0, 2.0, 2.0]]),
+            columns=["female", "male", "unknown", "f", "m"],
+        )
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.filterwarnings(
+        "ignore:invalid value encountered in cast:RuntimeWarning"
+    )
+    def test_constructor_series_nonexact_categoricalindex(self):
+        # GH 42424
+        ser = Series(range(100))
+        ser1 = cut(ser, 10).value_counts().head(5)
+        ser2 = cut(ser, 10).value_counts().tail(5)
+        result = DataFrame({"1": ser1, "2": ser2})
+        index = CategoricalIndex(
+            [
+                Interval(-0.099, 9.9, closed="right"),
+                Interval(9.9, 19.8, closed="right"),
+                Interval(19.8, 29.7, closed="right"),
+                Interval(29.7, 39.6, closed="right"),
+                Interval(39.6, 49.5, closed="right"),
+                Interval(49.5, 59.4, closed="right"),
+                Interval(59.4, 69.3, closed="right"),
+                Interval(69.3, 79.2, closed="right"),
+                Interval(79.2, 89.1, closed="right"),
+                Interval(89.1, 99, closed="right"),
+            ],
+            ordered=True,
+        )
+        expected = DataFrame(
+            {"1": [10] * 5 + [np.nan] * 5, "2": [np.nan] * 5 + [10] * 5}, index=index
+        )
+        tm.assert_frame_equal(expected, result)
+
+    def test_from_M8_structured(self):
+        dates = [(datetime(2012, 9, 9, 0, 0), datetime(2012, 9, 8, 15, 10))]
+        arr = np.array(dates, dtype=[("Date", "M8[us]"), ("Forecasting", "M8[us]")])
+        df = DataFrame(arr)
+
+        assert df["Date"][0] == dates[0][0]
+        assert df["Forecasting"][0] == dates[0][1]
+
+        s = Series(arr["Date"])
+        assert isinstance(s[0], Timestamp)
+        assert s[0] == dates[0][0]
+
+    def test_from_datetime_subclass(self):
+        # GH21142 Verify whether Datetime subclasses are also of dtype datetime
+        class DatetimeSubclass(datetime):
+            pass
+
+        data = DataFrame({"datetime": [DatetimeSubclass(2020, 1, 1, 1, 1)]})
+        assert data.datetime.dtype == "datetime64[ns]"
+
+    def test_with_mismatched_index_length_raises(self):
+        # GH#33437
+        dti = date_range("2016-01-01", periods=3, tz="US/Pacific")
+        msg = "Shape of passed values|Passed arrays should have the same length"
+        with pytest.raises(ValueError, match=msg):
+            DataFrame(dti, index=range(4))
+
+    def test_frame_ctor_datetime64_column(self):
+        rng = date_range("1/1/2000 00:00:00", "1/1/2000 1:59:50", freq="10s")
+        dates = np.asarray(rng)
+
+        df = DataFrame(
+            {"A": np.random.default_rng(2).standard_normal(len(rng)), "B": dates}
+        )
+        assert np.issubdtype(df["B"].dtype, np.dtype("M8[ns]"))
+
+    def test_dataframe_constructor_infer_multiindex(self):
+        index_lists = [["a", "a", "b", "b"], ["x", "y", "x", "y"]]
+
+        multi = DataFrame(
+            np.random.default_rng(2).standard_normal((4, 4)),
+            index=[np.array(x) for x in index_lists],
+        )
+        assert isinstance(multi.index, MultiIndex)
+        assert not isinstance(multi.columns, MultiIndex)
+
+        multi = DataFrame(
+            np.random.default_rng(2).standard_normal((4, 4)), columns=index_lists
+        )
+        assert isinstance(multi.columns, MultiIndex)
+
+    @pytest.mark.parametrize(
+        "input_vals",
+        [
+            ([1, 2]),
+            (["1", "2"]),
+            (list(date_range("1/1/2011", periods=2, freq="h"))),
+            (list(date_range("1/1/2011", periods=2, freq="h", tz="US/Eastern"))),
+            ([Interval(left=0, right=5)]),
+        ],
+    )
+    def test_constructor_list_str(self, input_vals, string_dtype):
+        # GH#16605
+        # Ensure that data elements are converted to strings when
+        # dtype is str, 'str', or 'U'
+
+        result = DataFrame({"A": input_vals}, dtype=string_dtype)
+        expected = DataFrame({"A": input_vals}).astype({"A": string_dtype})
+        tm.assert_frame_equal(result, expected)
+
+    def test_constructor_list_str_na(self, string_dtype):
+        result = DataFrame({"A": [1.0, 2.0, None]}, dtype=string_dtype)
+        expected = DataFrame({"A": ["1.0", "2.0", None]}, dtype=object)
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("copy", [False, True])
+    def test_dict_nocopy(
+        self,
+        request,
+        copy,
+        any_numeric_ea_dtype,
+        any_numpy_dtype,
+        using_array_manager,
+        using_copy_on_write,
+    ):
+        if (
+            using_array_manager
+            and not copy
+            and any_numpy_dtype not in tm.STRING_DTYPES + tm.BYTES_DTYPES
+        ):
+            # TODO(ArrayManager) properly honor copy keyword for dict input
+            td.mark_array_manager_not_yet_implemented(request)
+
+        a = np.array([1, 2], dtype=any_numpy_dtype)
+        b = np.array([3, 4], dtype=any_numpy_dtype)
+        if b.dtype.kind in ["S", "U"]:
+            # These get cast, making the checks below more cumbersome
+            pytest.skip(f"{b.dtype} get cast, making the checks below more cumbersome")
+
+        c = pd.array([1, 2], dtype=any_numeric_ea_dtype)
+        c_orig = c.copy()
+        df = DataFrame({"a": a, "b": b, "c": c}, copy=copy)
+
+        def get_base(obj):
+            if isinstance(obj, np.ndarray):
+                return obj.base
+            elif isinstance(obj.dtype, np.dtype):
+                # i.e. DatetimeArray, TimedeltaArray
+                return obj._ndarray.base
+            else:
+                raise TypeError
+
+        def check_views(c_only: bool = False):
+            # written to work for either BlockManager or ArrayManager
+
+            # Check that the underlying data behind df["c"] is still `c`
+            #  after setting with iloc.  Since we don't know which entry in
+            #  df._mgr.arrays corresponds to df["c"], we just check that exactly
+            #  one of these arrays is `c`.  GH#38939
+            assert sum(x is c for x in df._mgr.arrays) == 1
+            if c_only:
+                # If we ever stop consolidating in setitem_with_indexer,
+                #  this will become unnecessary.
+                return
+
+            assert (
+                sum(
+                    get_base(x) is a
+                    for x in df._mgr.arrays
+                    if isinstance(x.dtype, np.dtype)
+                )
+                == 1
+            )
+            assert (
+                sum(
+                    get_base(x) is b
+                    for x in df._mgr.arrays
+                    if isinstance(x.dtype, np.dtype)
+                )
+                == 1
+            )
+
+        if not copy:
+            # constructor preserves views
+            check_views()
+
+        # TODO: most of the rest of this test belongs in indexing tests
+        if lib.is_np_dtype(df.dtypes.iloc[0], "fciuO"):
+            warn = None
+        else:
+            warn = FutureWarning
+        with tm.assert_produces_warning(warn, match="incompatible dtype"):
+            df.iloc[0, 0] = 0
+            df.iloc[0, 1] = 0
+        if not copy:
+            check_views(True)
+
+        # FIXME(GH#35417): until GH#35417, iloc.setitem into EA values does not preserve
+        #  view, so we have to check in the other direction
+        df.iloc[:, 2] = pd.array([45, 46], dtype=c.dtype)
+        assert df.dtypes.iloc[2] == c.dtype
+        if not copy and not using_copy_on_write:
+            check_views(True)
+
+        if copy:
+            if a.dtype.kind == "M":
+                assert a[0] == a.dtype.type(1, "ns")
+                assert b[0] == b.dtype.type(3, "ns")
+            else:
+                assert a[0] == a.dtype.type(1)
+                assert b[0] == b.dtype.type(3)
+            # FIXME(GH#35417): enable after GH#35417
+            assert c[0] == c_orig[0]  # i.e. df.iloc[0, 2]=45 did *not* update c
+        elif not using_copy_on_write:
+            # TODO: we can call check_views if we stop consolidating
+            #  in setitem_with_indexer
+            assert c[0] == 45  # i.e. df.iloc[0, 2]=45 *did* update c
+            # TODO: we can check b[0] == 0 if we stop consolidating in
+            #  setitem_with_indexer (except for datetimelike?)
+
+    def test_construct_from_dict_ea_series(self):
+        # GH#53744 - default of copy=True should also apply for Series with
+        # extension dtype
+        ser = Series([1, 2, 3], dtype="Int64")
+        df = DataFrame({"a": ser})
+        assert not np.shares_memory(ser.values._data, df["a"].values._data)
+
+    def test_from_series_with_name_with_columns(self):
+        # GH 7893
+        result = DataFrame(Series(1, name="foo"), columns=["bar"])
+        expected = DataFrame(columns=["bar"])
+        tm.assert_frame_equal(result, expected)
+
+    def test_nested_list_columns(self):
+        # GH 14467
+        result = DataFrame(
+            [[1, 2, 3], [4, 5, 6]], columns=[["A", "A", "A"], ["a", "b", "c"]]
+        )
+        expected = DataFrame(
+            [[1, 2, 3], [4, 5, 6]],
+            columns=MultiIndex.from_tuples([("A", "a"), ("A", "b"), ("A", "c")]),
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_from_2d_object_array_of_periods_or_intervals(self):
+        # Period analogue to GH#26825
+        pi = pd.period_range("2016-04-05", periods=3)
+        data = pi._data.astype(object).reshape(1, -1)
+        df = DataFrame(data)
+        assert df.shape == (1, 3)
+        assert (df.dtypes == pi.dtype).all()
+        assert (df == pi).all().all()
+
+        ii = pd.IntervalIndex.from_breaks([3, 4, 5, 6])
+        data2 = ii._data.astype(object).reshape(1, -1)
+        df2 = DataFrame(data2)
+        assert df2.shape == (1, 3)
+        assert (df2.dtypes == ii.dtype).all()
+        assert (df2 == ii).all().all()
+
+        # mixed
+        data3 = np.r_[data, data2, data, data2].T
+        df3 = DataFrame(data3)
+        expected = DataFrame({0: pi, 1: ii, 2: pi, 3: ii})
+        tm.assert_frame_equal(df3, expected)
+
+    @pytest.mark.parametrize(
+        "col_a, col_b",
+        [
+            ([[1], [2]], np.array([[1], [2]])),
+            (np.array([[1], [2]]), [[1], [2]]),
+            (np.array([[1], [2]]), np.array([[1], [2]])),
+        ],
+    )
+    def test_error_from_2darray(self, col_a, col_b):
+        msg = "Per-column arrays must each be 1-dimensional"
+        with pytest.raises(ValueError, match=msg):
+            DataFrame({"a": col_a, "b": col_b})
+
+    def test_from_dict_with_missing_copy_false(self):
+        # GH#45369 filled columns should not be views of one another
+        df = DataFrame(index=[1, 2, 3], columns=["a", "b", "c"], copy=False)
+        assert not np.shares_memory(df["a"]._values, df["b"]._values)
+
+        df.iloc[0, 0] = 0
+        expected = DataFrame(
+            {
+                "a": [0, np.nan, np.nan],
+                "b": [np.nan, np.nan, np.nan],
+                "c": [np.nan, np.nan, np.nan],
+            },
+            index=[1, 2, 3],
+            dtype=object,
+        )
+        tm.assert_frame_equal(df, expected)
+
+    def test_construction_empty_array_multi_column_raises(self):
+        # GH#46822
+        msg = r"Shape of passed values is \(0, 1\), indices imply \(0, 2\)"
+        with pytest.raises(ValueError, match=msg):
+            DataFrame(data=np.array([]), columns=["a", "b"])
+
+    def test_construct_with_strings_and_none(self):
+        # GH#32218
+        df = DataFrame(["1", "2", None], columns=["a"], dtype="str")
+        expected = DataFrame({"a": ["1", "2", None]}, dtype="str")
+        tm.assert_frame_equal(df, expected)
+
+    def test_frame_string_inference(self):
+        # GH#54430
+        dtype = pd.StringDtype(na_value=np.nan)
+        expected = DataFrame(
+            {"a": ["a", "b"]}, dtype=dtype, columns=Index(["a"], dtype=dtype)
+        )
+        with pd.option_context("future.infer_string", True):
+            df = DataFrame({"a": ["a", "b"]})
+        tm.assert_frame_equal(df, expected)
+
+        expected = DataFrame(
+            {"a": ["a", "b"]},
+            dtype=dtype,
+            columns=Index(["a"], dtype=dtype),
+            index=Index(["x", "y"], dtype=dtype),
+        )
+        with pd.option_context("future.infer_string", True):
+            df = DataFrame({"a": ["a", "b"]}, index=["x", "y"])
+        tm.assert_frame_equal(df, expected)
+
+        expected = DataFrame(
+            {"a": ["a", 1]}, dtype="object", columns=Index(["a"], dtype=dtype)
+        )
+        with pd.option_context("future.infer_string", True):
+            df = DataFrame({"a": ["a", 1]})
+        tm.assert_frame_equal(df, expected)
+
+        expected = DataFrame(
+            {"a": ["a", "b"]}, dtype="object", columns=Index(["a"], dtype=dtype)
+        )
+        with pd.option_context("future.infer_string", True):
+            df = DataFrame({"a": ["a", "b"]}, dtype="object")
+        tm.assert_frame_equal(df, expected)
+
+    def test_frame_string_inference_array_string_dtype(self):
+        # GH#54496
+        dtype = pd.StringDtype(na_value=np.nan)
+        expected = DataFrame(
+            {"a": ["a", "b"]}, dtype=dtype, columns=Index(["a"], dtype=dtype)
+        )
+        with pd.option_context("future.infer_string", True):
+            df = DataFrame({"a": np.array(["a", "b"])})
+        tm.assert_frame_equal(df, expected)
+
+        expected = DataFrame({0: ["a", "b"], 1: ["c", "d"]}, dtype=dtype)
+        with pd.option_context("future.infer_string", True):
+            df = DataFrame(np.array([["a", "c"], ["b", "d"]]))
+        tm.assert_frame_equal(df, expected)
+
+        expected = DataFrame(
+            {"a": ["a", "b"], "b": ["c", "d"]},
+            dtype=dtype,
+            columns=Index(["a", "b"], dtype=dtype),
+        )
+        with pd.option_context("future.infer_string", True):
+            df = DataFrame(np.array([["a", "c"], ["b", "d"]]), columns=["a", "b"])
+        tm.assert_frame_equal(df, expected)
+
+    def test_frame_string_inference_block_dim(self):
+        # GH#55363
+        with pd.option_context("future.infer_string", True):
+            df = DataFrame(np.array([["hello", "goodbye"], ["hello", "Hello"]]))
+        assert df._mgr.blocks[0].ndim == 2
+
+    def test_inference_on_pandas_objects(self):
+        # GH#56012
+        idx = Index([Timestamp("2019-12-31")], dtype=object)
+        with tm.assert_produces_warning(FutureWarning, match="Dtype inference"):
+            result = DataFrame(idx, columns=["a"])
+        assert result.dtypes.iloc[0] != np.object_
+        result = DataFrame({"a": idx})
+        assert result.dtypes.iloc[0] == np.object_
+
+        ser = Series([Timestamp("2019-12-31")], dtype=object)
+
+        with tm.assert_produces_warning(FutureWarning, match="Dtype inference"):
+            result = DataFrame(ser, columns=["a"])
+        assert result.dtypes.iloc[0] != np.object_
+        result = DataFrame({"a": ser})
+        assert result.dtypes.iloc[0] == np.object_
+
+
+class TestDataFrameConstructorIndexInference:
+    def test_frame_from_dict_of_series_overlapping_monthly_period_indexes(self):
+        rng1 = pd.period_range("1/1/1999", "1/1/2012", freq="M")
+        s1 = Series(np.random.default_rng(2).standard_normal(len(rng1)), rng1)
+
+        rng2 = pd.period_range("1/1/1980", "12/1/2001", freq="M")
+        s2 = Series(np.random.default_rng(2).standard_normal(len(rng2)), rng2)
+        df = DataFrame({"s1": s1, "s2": s2})
+
+        exp = pd.period_range("1/1/1980", "1/1/2012", freq="M")
+        tm.assert_index_equal(df.index, exp)
+
+    def test_frame_from_dict_with_mixed_tzaware_indexes(self):
+        # GH#44091
+        dti = date_range("2016-01-01", periods=3)
+
+        ser1 = Series(range(3), index=dti)
+        ser2 = Series(range(3), index=dti.tz_localize("UTC"))
+        ser3 = Series(range(3), index=dti.tz_localize("US/Central"))
+        ser4 = Series(range(3))
+
+        # no tz-naive, but we do have mixed tzs and a non-DTI
+        df1 = DataFrame({"A": ser2, "B": ser3, "C": ser4})
+        exp_index = Index(
+            list(ser2.index) + list(ser3.index) + list(ser4.index), dtype=object
+        )
+        tm.assert_index_equal(df1.index, exp_index)
+
+        df2 = DataFrame({"A": ser2, "C": ser4, "B": ser3})
+        exp_index3 = Index(
+            list(ser2.index) + list(ser4.index) + list(ser3.index), dtype=object
+        )
+        tm.assert_index_equal(df2.index, exp_index3)
+
+        df3 = DataFrame({"B": ser3, "A": ser2, "C": ser4})
+        exp_index3 = Index(
+            list(ser3.index) + list(ser2.index) + list(ser4.index), dtype=object
+        )
+        tm.assert_index_equal(df3.index, exp_index3)
+
+        df4 = DataFrame({"C": ser4, "B": ser3, "A": ser2})
+        exp_index4 = Index(
+            list(ser4.index) + list(ser3.index) + list(ser2.index), dtype=object
+        )
+        tm.assert_index_equal(df4.index, exp_index4)
+
+        # TODO: not clear if these raising is desired (no extant tests),
+        #  but this is de facto behavior 2021-12-22
+        msg = "Cannot join tz-naive with tz-aware DatetimeIndex"
+        with pytest.raises(TypeError, match=msg):
+            DataFrame({"A": ser2, "B": ser3, "C": ser4, "D": ser1})
+        with pytest.raises(TypeError, match=msg):
+            DataFrame({"A": ser2, "B": ser3, "D": ser1})
+        with pytest.raises(TypeError, match=msg):
+            DataFrame({"D": ser1, "A": ser2, "B": ser3})
+
+    @pytest.mark.parametrize(
+        "key_val, col_vals, col_type",
+        [
+            ["3", ["3", "4"], "utf8"],
+            [3, [3, 4], "int8"],
+        ],
+    )
+    def test_dict_data_arrow_column_expansion(self, key_val, col_vals, col_type):
+        # GH 53617
+        pa = pytest.importorskip("pyarrow")
+        cols = pd.arrays.ArrowExtensionArray(
+            pa.array(col_vals, type=pa.dictionary(pa.int8(), getattr(pa, col_type)()))
+        )
+        result = DataFrame({key_val: [1, 2]}, columns=cols)
+        expected = DataFrame([[1, np.nan], [2, np.nan]], columns=cols)
+        expected.isetitem(1, expected.iloc[:, 1].astype(object))
+        tm.assert_frame_equal(result, expected)
+
+
+class TestDataFrameConstructorWithDtypeCoercion:
+    def test_floating_values_integer_dtype(self):
+        # GH#40110 make DataFrame behavior with arraylike floating data and
+        #  inty dtype match Series behavior
+
+        arr = np.random.default_rng(2).standard_normal((10, 5))
+
+        # GH#49599 in 2.0 we raise instead of either
+        #  a) silently ignoring dtype and returningfloat (the old Series behavior) or
+        #  b) rounding (the old DataFrame behavior)
+        msg = "Trying to coerce float values to integers"
+        with pytest.raises(ValueError, match=msg):
+            DataFrame(arr, dtype="i8")
+
+        df = DataFrame(arr.round(), dtype="i8")
+        assert (df.dtypes == "i8").all()
+
+        # with NaNs, we go through a different path with a different warning
+        arr[0, 0] = np.nan
+        msg = r"Cannot convert non-finite values \(NA or inf\) to integer"
+        with pytest.raises(IntCastingNaNError, match=msg):
+            DataFrame(arr, dtype="i8")
+        with pytest.raises(IntCastingNaNError, match=msg):
+            Series(arr[0], dtype="i8")
+        # The future (raising) behavior matches what we would get via astype:
+        msg = r"Cannot convert non-finite values \(NA or inf\) to integer"
+        with pytest.raises(IntCastingNaNError, match=msg):
+            DataFrame(arr).astype("i8")
+        with pytest.raises(IntCastingNaNError, match=msg):
+            Series(arr[0]).astype("i8")
+
+
+class TestDataFrameConstructorWithDatetimeTZ:
+    @pytest.mark.parametrize("tz", ["US/Eastern", "dateutil/US/Eastern"])
+    def test_construction_preserves_tzaware_dtypes(self, tz):
+        # after GH#7822
+        # these retain the timezones on dict construction
+        dr = date_range("2011/1/1", "2012/1/1", freq="W-FRI")
+        dr_tz = dr.tz_localize(tz)
+        df = DataFrame({"A": "foo", "B": dr_tz}, index=dr)
+        tz_expected = DatetimeTZDtype("ns", dr_tz.tzinfo)
+        assert df["B"].dtype == tz_expected
+
+        # GH#2810 (with timezones)
+        datetimes_naive = [ts.to_pydatetime() for ts in dr]
+        datetimes_with_tz = [ts.to_pydatetime() for ts in dr_tz]
+        df = DataFrame({"dr": dr})
+        df["dr_tz"] = dr_tz
+        df["datetimes_naive"] = datetimes_naive
+        df["datetimes_with_tz"] = datetimes_with_tz
+        result = df.dtypes
+        expected = Series(
+            [
+                np.dtype("datetime64[ns]"),
+                DatetimeTZDtype(tz=tz),
+                np.dtype("datetime64[ns]"),
+                DatetimeTZDtype(tz=tz),
+            ],
+            index=["dr", "dr_tz", "datetimes_naive", "datetimes_with_tz"],
+        )
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("pydt", [True, False])
+    def test_constructor_data_aware_dtype_naive(self, tz_aware_fixture, pydt):
+        # GH#25843, GH#41555, GH#33401
+        tz = tz_aware_fixture
+        ts = Timestamp("2019", tz=tz)
+        if pydt:
+            ts = ts.to_pydatetime()
+
+        msg = (
+            "Cannot convert timezone-aware data to timezone-naive dtype. "
+            r"Use pd.Series\(values\).dt.tz_localize\(None\) instead."
+        )
+        with pytest.raises(ValueError, match=msg):
+            DataFrame({0: [ts]}, dtype="datetime64[ns]")
+
+        msg2 = "Cannot unbox tzaware Timestamp to tznaive dtype"
+        with pytest.raises(TypeError, match=msg2):
+            DataFrame({0: ts}, index=[0], dtype="datetime64[ns]")
+
+        with pytest.raises(ValueError, match=msg):
+            DataFrame([ts], dtype="datetime64[ns]")
+
+        with pytest.raises(ValueError, match=msg):
+            DataFrame(np.array([ts], dtype=object), dtype="datetime64[ns]")
+
+        with pytest.raises(TypeError, match=msg2):
+            DataFrame(ts, index=[0], columns=[0], dtype="datetime64[ns]")
+
+        with pytest.raises(ValueError, match=msg):
+            DataFrame([Series([ts])], dtype="datetime64[ns]")
+
+        with pytest.raises(ValueError, match=msg):
+            DataFrame([[ts]], columns=[0], dtype="datetime64[ns]")
+
+    def test_from_dict(self):
+        # 8260
+        # support datetime64 with tz
+
+        idx = Index(date_range("20130101", periods=3, tz="US/Eastern"), name="foo")
+        dr = date_range("20130110", periods=3)
+
+        # construction
+        df = DataFrame({"A": idx, "B": dr})
+        assert df["A"].dtype, "M8[ns, US/Eastern"
+        assert df["A"].name == "A"
+        tm.assert_series_equal(df["A"], Series(idx, name="A"))
+        tm.assert_series_equal(df["B"], Series(dr, name="B"))
+
+    def test_from_index(self):
+        # from index
+        idx2 = date_range("20130101", periods=3, tz="US/Eastern", name="foo")
+        df2 = DataFrame(idx2)
+        tm.assert_series_equal(df2["foo"], Series(idx2, name="foo"))
+        df2 = DataFrame(Series(idx2))
+        tm.assert_series_equal(df2["foo"], Series(idx2, name="foo"))
+
+        idx2 = date_range("20130101", periods=3, tz="US/Eastern")
+        df2 = DataFrame(idx2)
+        tm.assert_series_equal(df2[0], Series(idx2, name=0))
+        df2 = DataFrame(Series(idx2))
+        tm.assert_series_equal(df2[0], Series(idx2, name=0))
+
+    def test_frame_dict_constructor_datetime64_1680(self):
+        dr = date_range("1/1/2012", periods=10)
+        s = Series(dr, index=dr)
+
+        # it works!
+        DataFrame({"a": "foo", "b": s}, index=dr)
+        DataFrame({"a": "foo", "b": s.values}, index=dr)
+
+    def test_frame_datetime64_mixed_index_ctor_1681(self):
+        dr = date_range("2011/1/1", "2012/1/1", freq="W-FRI")
+        ts = Series(dr)
+
+        # it works!
+        d = DataFrame({"A": "foo", "B": ts}, index=dr)
+        assert d["B"].isna().all()
+
+    def test_frame_timeseries_column(self):
+        # GH19157
+        dr = date_range(
+            start="20130101T10:00:00", periods=3, freq="min", tz="US/Eastern"
+        )
+        result = DataFrame(dr, columns=["timestamps"])
+        expected = DataFrame(
+            {
+                "timestamps": [
+                    Timestamp("20130101T10:00:00", tz="US/Eastern"),
+                    Timestamp("20130101T10:01:00", tz="US/Eastern"),
+                    Timestamp("20130101T10:02:00", tz="US/Eastern"),
+                ]
+            }
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_nested_dict_construction(self):
+        # GH22227
+        columns = ["Nevada", "Ohio"]
+        pop = {
+            "Nevada": {2001: 2.4, 2002: 2.9},
+            "Ohio": {2000: 1.5, 2001: 1.7, 2002: 3.6},
+        }
+        result = DataFrame(pop, index=[2001, 2002, 2003], columns=columns)
+        expected = DataFrame(
+            [(2.4, 1.7), (2.9, 3.6), (np.nan, np.nan)],
+            columns=columns,
+            index=Index([2001, 2002, 2003]),
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_from_tzaware_object_array(self):
+        # GH#26825 2D object array of tzaware timestamps should not raise
+        dti = date_range("2016-04-05 04:30", periods=3, tz="UTC")
+        data = dti._data.astype(object).reshape(1, -1)
+        df = DataFrame(data)
+        assert df.shape == (1, 3)
+        assert (df.dtypes == dti.dtype).all()
+        assert (df == dti).all().all()
+
+    def test_from_tzaware_mixed_object_array(self):
+        # GH#26825
+        arr = np.array(
+            [
+                [
+                    Timestamp("2013-01-01 00:00:00"),
+                    Timestamp("2013-01-02 00:00:00"),
+                    Timestamp("2013-01-03 00:00:00"),
+                ],
+                [
+                    Timestamp("2013-01-01 00:00:00-0500", tz="US/Eastern"),
+                    pd.NaT,
+                    Timestamp("2013-01-03 00:00:00-0500", tz="US/Eastern"),
+                ],
+                [
+                    Timestamp("2013-01-01 00:00:00+0100", tz="CET"),
+                    pd.NaT,
+                    Timestamp("2013-01-03 00:00:00+0100", tz="CET"),
+                ],
+            ],
+            dtype=object,
+        ).T
+        res = DataFrame(arr, columns=["A", "B", "C"])
+
+        expected_dtypes = [
+            "datetime64[ns]",
+            "datetime64[ns, US/Eastern]",
+            "datetime64[ns, CET]",
+        ]
+        assert (res.dtypes == expected_dtypes).all()
+
+    def test_from_2d_ndarray_with_dtype(self):
+        # GH#12513
+        array_dim2 = np.arange(10).reshape((5, 2))
+        df = DataFrame(array_dim2, dtype="datetime64[ns, UTC]")
+
+        expected = DataFrame(array_dim2).astype("datetime64[ns, UTC]")
+        tm.assert_frame_equal(df, expected)
+
+    @pytest.mark.parametrize("typ", [set, frozenset])
+    def test_construction_from_set_raises(self, typ):
+        # https://github.com/pandas-dev/pandas/issues/32582
+        values = typ({1, 2, 3})
+        msg = f"'{typ.__name__}' type is unordered"
+        with pytest.raises(TypeError, match=msg):
+            DataFrame({"a": values})
+
+        with pytest.raises(TypeError, match=msg):
+            Series(values)
+
+    def test_construction_from_ndarray_datetimelike(self):
+        # ensure the underlying arrays are properly wrapped as EA when
+        # constructed from 2D ndarray
+        arr = np.arange(0, 12, dtype="datetime64[ns]").reshape(4, 3)
+        df = DataFrame(arr)
+        assert all(isinstance(arr, DatetimeArray) for arr in df._mgr.arrays)
+
+    def test_construction_from_ndarray_with_eadtype_mismatched_columns(self):
+        arr = np.random.default_rng(2).standard_normal((10, 2))
+        dtype = pd.array([2.0]).dtype
+        msg = r"len\(arrays\) must match len\(columns\)"
+        with pytest.raises(ValueError, match=msg):
+            DataFrame(arr, columns=["foo"], dtype=dtype)
+
+        arr2 = pd.array([2.0, 3.0, 4.0])
+        with pytest.raises(ValueError, match=msg):
+            DataFrame(arr2, columns=["foo", "bar"])
+
+    def test_columns_indexes_raise_on_sets(self):
+        # GH 47215
+        data = [[1, 2, 3], [4, 5, 6]]
+        with pytest.raises(ValueError, match="index cannot be a set"):
+            DataFrame(data, index={"a", "b"})
+        with pytest.raises(ValueError, match="columns cannot be a set"):
+            DataFrame(data, columns={"a", "b", "c"})
+
+    # TODO: make this not cast to object in pandas 3.0
+    @pytest.mark.skipif(
+        not np_version_gt2, reason="StringDType only available in numpy 2 and above"
+    )
+    @pytest.mark.parametrize(
+        "data",
+        [
+            {"a": ["a", "b", "c"], "b": [1.0, 2.0, 3.0], "c": ["d", "e", "f"]},
+        ],
+    )
+    def test_np_string_array_object_cast(self, data):
+        from numpy.dtypes import StringDType
+
+        data["a"] = np.array(data["a"], dtype=StringDType())
+        res = DataFrame(data)
+        assert res["a"].dtype == np.object_
+        assert (res["a"] == data["a"]).all()
+
+
+def get1(obj):  # TODO: make a helper in tm?
+    if isinstance(obj, Series):
+        return obj.iloc[0]
+    else:
+        return obj.iloc[0, 0]
+
+
+class TestFromScalar:
+    @pytest.fixture(params=[list, dict, None])
+    def box(self, request):
+        return request.param
+
+    @pytest.fixture
+    def constructor(self, frame_or_series, box):
+        extra = {"index": range(2)}
+        if frame_or_series is DataFrame:
+            extra["columns"] = ["A"]
+
+        if box is None:
+            return functools.partial(frame_or_series, **extra)
+
+        elif box is dict:
+            if frame_or_series is Series:
+                return lambda x, **kwargs: frame_or_series(
+                    {0: x, 1: x}, **extra, **kwargs
+                )
+            else:
+                return lambda x, **kwargs: frame_or_series({"A": x}, **extra, **kwargs)
+        elif frame_or_series is Series:
+            return lambda x, **kwargs: frame_or_series([x, x], **extra, **kwargs)
+        else:
+            return lambda x, **kwargs: frame_or_series({"A": [x, x]}, **extra, **kwargs)
+
+    @pytest.mark.parametrize("dtype", ["M8[ns]", "m8[ns]"])
+    def test_from_nat_scalar(self, dtype, constructor):
+        obj = constructor(pd.NaT, dtype=dtype)
+        assert np.all(obj.dtypes == dtype)
+        assert np.all(obj.isna())
+
+    def test_from_timedelta_scalar_preserves_nanos(self, constructor):
+        td = Timedelta(1)
+
+        obj = constructor(td, dtype="m8[ns]")
+        assert get1(obj) == td
+
+    def test_from_timestamp_scalar_preserves_nanos(self, constructor, fixed_now_ts):
+        ts = fixed_now_ts + Timedelta(1)
+
+        obj = constructor(ts, dtype="M8[ns]")
+        assert get1(obj) == ts
+
+    def test_from_timedelta64_scalar_object(self, constructor):
+        td = Timedelta(1)
+        td64 = td.to_timedelta64()
+
+        obj = constructor(td64, dtype=object)
+        assert isinstance(get1(obj), np.timedelta64)
+
+    @pytest.mark.parametrize("cls", [np.datetime64, np.timedelta64])
+    def test_from_scalar_datetimelike_mismatched(self, constructor, cls):
+        scalar = cls("NaT", "ns")
+        dtype = {np.datetime64: "m8[ns]", np.timedelta64: "M8[ns]"}[cls]
+
+        if cls is np.datetime64:
+            msg1 = "Invalid type for timedelta scalar: "
+        else:
+            msg1 = " is not convertible to datetime"
+        msg = "|".join(["Cannot cast", msg1])
+
+        with pytest.raises(TypeError, match=msg):
+            constructor(scalar, dtype=dtype)
+
+        scalar = cls(4, "ns")
+        with pytest.raises(TypeError, match=msg):
+            constructor(scalar, dtype=dtype)
+
+    @pytest.mark.parametrize("cls", [datetime, np.datetime64])
+    def test_from_out_of_bounds_ns_datetime(
+        self, constructor, cls, request, box, frame_or_series
+    ):
+        # scalar that won't fit in nanosecond dt64, but will fit in microsecond
+        if box is list or (frame_or_series is Series and box is dict):
+            mark = pytest.mark.xfail(
+                reason="Timestamp constructor has been updated to cast dt64 to "
+                "non-nano, but DatetimeArray._from_sequence has not",
+                strict=True,
+            )
+            request.applymarker(mark)
+
+        scalar = datetime(9999, 1, 1)
+        exp_dtype = "M8[us]"  # pydatetime objects default to this reso
+
+        if cls is np.datetime64:
+            scalar = np.datetime64(scalar, "D")
+            exp_dtype = "M8[s]"  # closest reso to input
+        result = constructor(scalar)
+
+        item = get1(result)
+        dtype = tm.get_dtype(result)
+
+        assert type(item) is Timestamp
+        assert item.asm8.dtype == exp_dtype
+        assert dtype == exp_dtype
+
+    @pytest.mark.skip_ubsan
+    def test_out_of_s_bounds_datetime64(self, constructor):
+        scalar = np.datetime64(np.iinfo(np.int64).max, "D")
+        result = constructor(scalar)
+        item = get1(result)
+        assert type(item) is np.datetime64
+        dtype = tm.get_dtype(result)
+        assert dtype == object
+
+    @pytest.mark.parametrize("cls", [timedelta, np.timedelta64])
+    def test_from_out_of_bounds_ns_timedelta(
+        self, constructor, cls, request, box, frame_or_series
+    ):
+        # scalar that won't fit in nanosecond td64, but will fit in microsecond
+        if box is list or (frame_or_series is Series and box is dict):
+            mark = pytest.mark.xfail(
+                reason="TimedeltaArray constructor has been updated to cast td64 "
+                "to non-nano, but TimedeltaArray._from_sequence has not",
+                strict=True,
+            )
+            request.applymarker(mark)
+
+        scalar = datetime(9999, 1, 1) - datetime(1970, 1, 1)
+        exp_dtype = "m8[us]"  # smallest reso that fits
+        if cls is np.timedelta64:
+            scalar = np.timedelta64(scalar, "D")
+            exp_dtype = "m8[s]"  # closest reso to input
+        result = constructor(scalar)
+
+        item = get1(result)
+        dtype = tm.get_dtype(result)
+
+        assert type(item) is Timedelta
+        assert item.asm8.dtype == exp_dtype
+        assert dtype == exp_dtype
+
+    @pytest.mark.skip_ubsan
+    @pytest.mark.parametrize("cls", [np.datetime64, np.timedelta64])
+    def test_out_of_s_bounds_timedelta64(self, constructor, cls):
+        scalar = cls(np.iinfo(np.int64).max, "D")
+        result = constructor(scalar)
+        item = get1(result)
+        assert type(item) is cls
+        dtype = tm.get_dtype(result)
+        assert dtype == object
+
+    def test_tzaware_data_tznaive_dtype(self, constructor, box, frame_or_series):
+        tz = "US/Eastern"
+        ts = Timestamp("2019", tz=tz)
+
+        if box is None or (frame_or_series is DataFrame and box is dict):
+            msg = "Cannot unbox tzaware Timestamp to tznaive dtype"
+            err = TypeError
+        else:
+            msg = (
+                "Cannot convert timezone-aware data to timezone-naive dtype. "
+                r"Use pd.Series\(values\).dt.tz_localize\(None\) instead."
+            )
+            err = ValueError
+
+        with pytest.raises(err, match=msg):
+            constructor(ts, dtype="M8[ns]")
+
+
+# TODO: better location for this test?
+class TestAllowNonNano:
+    # Until 2.0, we do not preserve non-nano dt64/td64 when passed as ndarray,
+    #  but do preserve it when passed as DTA/TDA
+
+    @pytest.fixture(params=[True, False])
+    def as_td(self, request):
+        return request.param
+
+    @pytest.fixture
+    def arr(self, as_td):
+        values = np.arange(5).astype(np.int64).view("M8[s]")
+        if as_td:
+            values = values - values[0]
+            return TimedeltaArray._simple_new(values, dtype=values.dtype)
+        else:
+            return DatetimeArray._simple_new(values, dtype=values.dtype)
+
+    def test_index_allow_non_nano(self, arr):
+        idx = Index(arr)
+        assert idx.dtype == arr.dtype
+
+    def test_dti_tdi_allow_non_nano(self, arr, as_td):
+        if as_td:
+            idx = pd.TimedeltaIndex(arr)
+        else:
+            idx = DatetimeIndex(arr)
+        assert idx.dtype == arr.dtype
+
+    def test_series_allow_non_nano(self, arr):
+        ser = Series(arr)
+        assert ser.dtype == arr.dtype
+
+    def test_frame_allow_non_nano(self, arr):
+        df = DataFrame(arr)
+        assert df.dtypes[0] == arr.dtype
+
+    def test_frame_from_dict_allow_non_nano(self, arr):
+        df = DataFrame({0: arr})
+        assert df.dtypes[0] == arr.dtype
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/test_cumulative.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/test_cumulative.py
new file mode 100644
index 0000000000000000000000000000000000000000..5bd9c426123159fcfcf6bf5289fd08a60dfd91b2
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/test_cumulative.py
@@ -0,0 +1,81 @@
+"""
+Tests for DataFrame cumulative operations
+
+See also
+--------
+tests.series.test_cumulative
+"""
+
+import numpy as np
+import pytest
+
+from pandas import (
+    DataFrame,
+    Series,
+)
+import pandas._testing as tm
+
+
+class TestDataFrameCumulativeOps:
+    # ---------------------------------------------------------------------
+    # Cumulative Operations - cumsum, cummax, ...
+
+    def test_cumulative_ops_smoke(self):
+        # it works
+        df = DataFrame({"A": np.arange(20)}, index=np.arange(20))
+        df.cummax()
+        df.cummin()
+        df.cumsum()
+
+        dm = DataFrame(np.arange(20).reshape(4, 5), index=range(4), columns=range(5))
+        # TODO(wesm): do something with this?
+        dm.cumsum()
+
+    def test_cumprod_smoke(self, datetime_frame):
+        datetime_frame.iloc[5:10, 0] = np.nan
+        datetime_frame.iloc[10:15, 1] = np.nan
+        datetime_frame.iloc[15:, 2] = np.nan
+
+        # ints
+        df = datetime_frame.fillna(0).astype(int)
+        df.cumprod(0)
+        df.cumprod(1)
+
+        # ints32
+        df = datetime_frame.fillna(0).astype(np.int32)
+        df.cumprod(0)
+        df.cumprod(1)
+
+    @pytest.mark.parametrize("method", ["cumsum", "cumprod", "cummin", "cummax"])
+    def test_cumulative_ops_match_series_apply(self, datetime_frame, method):
+        datetime_frame.iloc[5:10, 0] = np.nan
+        datetime_frame.iloc[10:15, 1] = np.nan
+        datetime_frame.iloc[15:, 2] = np.nan
+
+        # axis = 0
+        result = getattr(datetime_frame, method)()
+        expected = datetime_frame.apply(getattr(Series, method))
+        tm.assert_frame_equal(result, expected)
+
+        # axis = 1
+        result = getattr(datetime_frame, method)(axis=1)
+        expected = datetime_frame.apply(getattr(Series, method), axis=1)
+        tm.assert_frame_equal(result, expected)
+
+        # fix issue TODO: GH ref?
+        assert np.shape(result) == np.shape(datetime_frame)
+
+    def test_cumsum_preserve_dtypes(self):
+        # GH#19296 dont incorrectly upcast to object
+        df = DataFrame({"A": [1, 2, 3], "B": [1, 2, 3.0], "C": [True, False, False]})
+
+        result = df.cumsum()
+
+        expected = DataFrame(
+            {
+                "A": Series([1, 3, 6], dtype=np.int64),
+                "B": Series([1, 3, 6], dtype=np.float64),
+                "C": df["C"].cumsum(),
+            }
+        )
+        tm.assert_frame_equal(result, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/test_iteration.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/test_iteration.py
new file mode 100644
index 0000000000000000000000000000000000000000..a1c23ff05f3e19aca490444216ec295453483e80
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/test_iteration.py
@@ -0,0 +1,160 @@
+import datetime
+
+import numpy as np
+import pytest
+
+from pandas.compat import (
+    IS64,
+    is_platform_windows,
+)
+
+from pandas import (
+    Categorical,
+    DataFrame,
+    Series,
+    date_range,
+)
+import pandas._testing as tm
+
+
+class TestIteration:
+    def test_keys(self, float_frame):
+        assert float_frame.keys() is float_frame.columns
+
+    def test_iteritems(self):
+        df = DataFrame([[1, 2, 3], [4, 5, 6]], columns=["a", "a", "b"])
+        for k, v in df.items():
+            assert isinstance(v, DataFrame._constructor_sliced)
+
+    def test_items(self):
+        # GH#17213, GH#13918
+        cols = ["a", "b", "c"]
+        df = DataFrame([[1, 2, 3], [4, 5, 6]], columns=cols)
+        for c, (k, v) in zip(cols, df.items()):
+            assert c == k
+            assert isinstance(v, Series)
+            assert (df[k] == v).all()
+
+    def test_items_names(self, float_string_frame):
+        for k, v in float_string_frame.items():
+            assert v.name == k
+
+    def test_iter(self, float_frame):
+        assert list(float_frame) == list(float_frame.columns)
+
+    def test_iterrows(self, float_frame, float_string_frame):
+        for k, v in float_frame.iterrows():
+            exp = float_frame.loc[k]
+            tm.assert_series_equal(v, exp)
+
+        for k, v in float_string_frame.iterrows():
+            exp = float_string_frame.loc[k]
+            tm.assert_series_equal(v, exp)
+
+    def test_iterrows_iso8601(self):
+        # GH#19671
+        s = DataFrame(
+            {
+                "non_iso8601": ["M1701", "M1802", "M1903", "M2004"],
+                "iso8601": date_range("2000-01-01", periods=4, freq="ME"),
+            }
+        )
+        for k, v in s.iterrows():
+            exp = s.loc[k]
+            tm.assert_series_equal(v, exp)
+
+    def test_iterrows_corner(self):
+        # GH#12222
+        df = DataFrame(
+            {
+                "a": [datetime.datetime(2015, 1, 1)],
+                "b": [None],
+                "c": [None],
+                "d": [""],
+                "e": [[]],
+                "f": [set()],
+                "g": [{}],
+            }
+        )
+        expected = Series(
+            [datetime.datetime(2015, 1, 1), None, None, "", [], set(), {}],
+            index=list("abcdefg"),
+            name=0,
+            dtype="object",
+        )
+        _, result = next(df.iterrows())
+        tm.assert_series_equal(result, expected)
+
+    def test_itertuples(self, float_frame):
+        for i, tup in enumerate(float_frame.itertuples()):
+            ser = DataFrame._constructor_sliced(tup[1:])
+            ser.name = tup[0]
+            expected = float_frame.iloc[i, :].reset_index(drop=True)
+            tm.assert_series_equal(ser, expected)
+
+    def test_itertuples_index_false(self):
+        df = DataFrame(
+            {"floats": np.random.default_rng(2).standard_normal(5), "ints": range(5)},
+            columns=["floats", "ints"],
+        )
+
+        for tup in df.itertuples(index=False):
+            assert isinstance(tup[1], int)
+
+    def test_itertuples_duplicate_cols(self):
+        df = DataFrame(data={"a": [1, 2, 3], "b": [4, 5, 6]})
+        dfaa = df[["a", "a"]]
+
+        assert list(dfaa.itertuples()) == [(0, 1, 1), (1, 2, 2), (2, 3, 3)]
+
+        # repr with int on 32-bit/windows
+        if not (is_platform_windows() or not IS64):
+            assert (
+                repr(list(df.itertuples(name=None)))
+                == "[(0, 1, 4), (1, 2, 5), (2, 3, 6)]"
+            )
+
+    def test_itertuples_tuple_name(self):
+        df = DataFrame(data={"a": [1, 2, 3], "b": [4, 5, 6]})
+        tup = next(df.itertuples(name="TestName"))
+        assert tup._fields == ("Index", "a", "b")
+        assert (tup.Index, tup.a, tup.b) == tup
+        assert type(tup).__name__ == "TestName"
+
+    def test_itertuples_disallowed_col_labels(self):
+        df = DataFrame(data={"def": [1, 2, 3], "return": [4, 5, 6]})
+        tup2 = next(df.itertuples(name="TestName"))
+        assert tup2 == (0, 1, 4)
+        assert tup2._fields == ("Index", "_1", "_2")
+
+    @pytest.mark.parametrize("limit", [254, 255, 1024])
+    @pytest.mark.parametrize("index", [True, False])
+    def test_itertuples_py2_3_field_limit_namedtuple(self, limit, index):
+        # GH#28282
+        df = DataFrame([{f"foo_{i}": f"bar_{i}" for i in range(limit)}])
+        result = next(df.itertuples(index=index))
+        assert isinstance(result, tuple)
+        assert hasattr(result, "_fields")
+
+    def test_sequence_like_with_categorical(self):
+        # GH#7839
+        # make sure can iterate
+        df = DataFrame(
+            {"id": [1, 2, 3, 4, 5, 6], "raw_grade": ["a", "b", "b", "a", "a", "e"]}
+        )
+        df["grade"] = Categorical(df["raw_grade"])
+
+        # basic sequencing testing
+        result = list(df.grade.values)
+        expected = np.array(df.grade.values).tolist()
+        tm.assert_almost_equal(result, expected)
+
+        # iteration
+        for t in df.itertuples(index=False):
+            str(t)
+
+        for row, s in df.iterrows():
+            str(s)
+
+        for c, col in df.items():
+            str(col)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/test_logical_ops.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/test_logical_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..f1163e994557f8469931c7b5c6833a84abac3a6d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/test_logical_ops.py
@@ -0,0 +1,215 @@
+import operator
+import re
+
+import numpy as np
+import pytest
+
+from pandas import (
+    CategoricalIndex,
+    DataFrame,
+    Interval,
+    Series,
+    isnull,
+)
+import pandas._testing as tm
+
+
+class TestDataFrameLogicalOperators:
+    # &, |, ^
+
+    @pytest.mark.parametrize(
+        "left, right, op, expected",
+        [
+            (
+                [True, False, np.nan],
+                [True, False, True],
+                operator.and_,
+                [True, False, False],
+            ),
+            (
+                [True, False, True],
+                [True, False, np.nan],
+                operator.and_,
+                [True, False, False],
+            ),
+            (
+                [True, False, np.nan],
+                [True, False, True],
+                operator.or_,
+                [True, False, False],
+            ),
+            (
+                [True, False, True],
+                [True, False, np.nan],
+                operator.or_,
+                [True, False, True],
+            ),
+        ],
+    )
+    def test_logical_operators_nans(self, left, right, op, expected, frame_or_series):
+        # GH#13896
+        result = op(frame_or_series(left), frame_or_series(right))
+        expected = frame_or_series(expected)
+
+        tm.assert_equal(result, expected)
+
+    def test_logical_ops_empty_frame(self):
+        # GH#5808
+        # empty frames, non-mixed dtype
+        df = DataFrame(index=[1])
+
+        result = df & df
+        tm.assert_frame_equal(result, df)
+
+        result = df | df
+        tm.assert_frame_equal(result, df)
+
+        df2 = DataFrame(index=[1, 2])
+        result = df & df2
+        tm.assert_frame_equal(result, df2)
+
+        dfa = DataFrame(index=[1], columns=["A"])
+
+        result = dfa & dfa
+        expected = DataFrame(False, index=[1], columns=["A"])
+        tm.assert_frame_equal(result, expected)
+
+    def test_logical_ops_bool_frame(self):
+        # GH#5808
+        df1a_bool = DataFrame(True, index=[1], columns=["A"])
+
+        result = df1a_bool & df1a_bool
+        tm.assert_frame_equal(result, df1a_bool)
+
+        result = df1a_bool | df1a_bool
+        tm.assert_frame_equal(result, df1a_bool)
+
+    def test_logical_ops_int_frame(self):
+        # GH#5808
+        df1a_int = DataFrame(1, index=[1], columns=["A"])
+        df1a_bool = DataFrame(True, index=[1], columns=["A"])
+
+        result = df1a_int | df1a_bool
+        tm.assert_frame_equal(result, df1a_bool)
+
+        # Check that this matches Series behavior
+        res_ser = df1a_int["A"] | df1a_bool["A"]
+        tm.assert_series_equal(res_ser, df1a_bool["A"])
+
+    def test_logical_ops_invalid(self, using_infer_string):
+        # GH#5808
+
+        df1 = DataFrame(1.0, index=[1], columns=["A"])
+        df2 = DataFrame(True, index=[1], columns=["A"])
+        msg = re.escape("unsupported operand type(s) for |: 'float' and 'bool'")
+        with pytest.raises(TypeError, match=msg):
+            df1 | df2
+
+        df1 = DataFrame("foo", index=[1], columns=["A"])
+        df2 = DataFrame(True, index=[1], columns=["A"])
+        if using_infer_string and df1["A"].dtype.storage == "pyarrow":
+            msg = "operation 'or_' not supported for dtype 'str'"
+        else:
+            msg = re.escape("unsupported operand type(s) for |: 'str' and 'bool'")
+        with pytest.raises(TypeError, match=msg):
+            df1 | df2
+
+    def test_logical_operators(self):
+        def _check_bin_op(op):
+            result = op(df1, df2)
+            expected = DataFrame(
+                op(df1.values, df2.values), index=df1.index, columns=df1.columns
+            )
+            assert result.values.dtype == np.bool_
+            tm.assert_frame_equal(result, expected)
+
+        def _check_unary_op(op):
+            result = op(df1)
+            expected = DataFrame(op(df1.values), index=df1.index, columns=df1.columns)
+            assert result.values.dtype == np.bool_
+            tm.assert_frame_equal(result, expected)
+
+        df1 = {
+            "a": {"a": True, "b": False, "c": False, "d": True, "e": True},
+            "b": {"a": False, "b": True, "c": False, "d": False, "e": False},
+            "c": {"a": False, "b": False, "c": True, "d": False, "e": False},
+            "d": {"a": True, "b": False, "c": False, "d": True, "e": True},
+            "e": {"a": True, "b": False, "c": False, "d": True, "e": True},
+        }
+
+        df2 = {
+            "a": {"a": True, "b": False, "c": True, "d": False, "e": False},
+            "b": {"a": False, "b": True, "c": False, "d": False, "e": False},
+            "c": {"a": True, "b": False, "c": True, "d": False, "e": False},
+            "d": {"a": False, "b": False, "c": False, "d": True, "e": False},
+            "e": {"a": False, "b": False, "c": False, "d": False, "e": True},
+        }
+
+        df1 = DataFrame(df1)
+        df2 = DataFrame(df2)
+
+        _check_bin_op(operator.and_)
+        _check_bin_op(operator.or_)
+        _check_bin_op(operator.xor)
+
+        _check_unary_op(operator.inv)  # TODO: belongs elsewhere
+
+    @pytest.mark.filterwarnings("ignore:Downcasting object dtype arrays:FutureWarning")
+    def test_logical_with_nas(self):
+        d = DataFrame({"a": [np.nan, False], "b": [True, True]})
+
+        # GH4947
+        # bool comparisons should return bool
+        result = d["a"] | d["b"]
+        expected = Series([False, True])
+        tm.assert_series_equal(result, expected)
+
+        # GH4604, automatic casting here
+        result = d["a"].fillna(False) | d["b"]
+        expected = Series([True, True])
+        tm.assert_series_equal(result, expected)
+
+        msg = "The 'downcast' keyword in fillna is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = d["a"].fillna(False, downcast=False) | d["b"]
+        expected = Series([True, True])
+        tm.assert_series_equal(result, expected)
+
+    def test_logical_ops_categorical_columns(self):
+        # GH#38367
+        intervals = [Interval(1, 2), Interval(3, 4)]
+        data = DataFrame(
+            [[1, np.nan], [2, np.nan]],
+            columns=CategoricalIndex(
+                intervals, categories=intervals + [Interval(5, 6)]
+            ),
+        )
+        mask = DataFrame(
+            [[False, False], [False, False]], columns=data.columns, dtype=bool
+        )
+        result = mask | isnull(data)
+        expected = DataFrame(
+            [[False, True], [False, True]],
+            columns=CategoricalIndex(
+                intervals, categories=intervals + [Interval(5, 6)]
+            ),
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_int_dtype_different_index_not_bool(self):
+        # GH 52500
+        df1 = DataFrame([1, 2, 3], index=[10, 11, 23], columns=["a"])
+        df2 = DataFrame([10, 20, 30], index=[11, 10, 23], columns=["a"])
+        result = np.bitwise_xor(df1, df2)
+        expected = DataFrame([21, 8, 29], index=[10, 11, 23], columns=["a"])
+        tm.assert_frame_equal(result, expected)
+
+        result = df1 ^ df2
+        tm.assert_frame_equal(result, expected)
+
+    def test_different_dtypes_different_index_raises(self):
+        # GH 52538
+        df1 = DataFrame([1, 2], index=["a", "b"])
+        df2 = DataFrame([3, 4], index=["b", "c"])
+        with pytest.raises(TypeError, match="unsupported operand type"):
+            df1 & df2
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/test_nonunique_indexes.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/test_nonunique_indexes.py
new file mode 100644
index 0000000000000000000000000000000000000000..34f172e900ab7e16d66afe32aed3d5b87be301c3
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/test_nonunique_indexes.py
@@ -0,0 +1,337 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Series,
+    date_range,
+)
+import pandas._testing as tm
+
+
+class TestDataFrameNonuniqueIndexes:
+    def test_setattr_columns_vs_construct_with_columns(self):
+        # assignment
+        # GH 3687
+        arr = np.random.default_rng(2).standard_normal((3, 2))
+        idx = list(range(2))
+        df = DataFrame(arr, columns=["A", "A"])
+        df.columns = idx
+        expected = DataFrame(arr, columns=idx)
+        tm.assert_frame_equal(df, expected)
+
+    def test_setattr_columns_vs_construct_with_columns_datetimeindx(self):
+        idx = date_range("20130101", periods=4, freq="QE-NOV")
+        df = DataFrame(
+            [[1, 1, 1, 5], [1, 1, 2, 5], [2, 1, 3, 5]], columns=["a", "a", "a", "a"]
+        )
+        df.columns = idx
+        expected = DataFrame([[1, 1, 1, 5], [1, 1, 2, 5], [2, 1, 3, 5]], columns=idx)
+        tm.assert_frame_equal(df, expected)
+
+    def test_insert_with_duplicate_columns(self):
+        # insert
+        df = DataFrame(
+            [[1, 1, 1, 5], [1, 1, 2, 5], [2, 1, 3, 5]],
+            columns=["foo", "bar", "foo", "hello"],
+        )
+        df["string"] = "bah"
+        expected = DataFrame(
+            [[1, 1, 1, 5, "bah"], [1, 1, 2, 5, "bah"], [2, 1, 3, 5, "bah"]],
+            columns=["foo", "bar", "foo", "hello", "string"],
+        )
+        tm.assert_frame_equal(df, expected)
+        with pytest.raises(ValueError, match="Length of value"):
+            df.insert(0, "AnotherColumn", range(len(df.index) - 1))
+
+        # insert same dtype
+        df["foo2"] = 3
+        expected = DataFrame(
+            [[1, 1, 1, 5, "bah", 3], [1, 1, 2, 5, "bah", 3], [2, 1, 3, 5, "bah", 3]],
+            columns=["foo", "bar", "foo", "hello", "string", "foo2"],
+        )
+        tm.assert_frame_equal(df, expected)
+
+        # set (non-dup)
+        df["foo2"] = 4
+        expected = DataFrame(
+            [[1, 1, 1, 5, "bah", 4], [1, 1, 2, 5, "bah", 4], [2, 1, 3, 5, "bah", 4]],
+            columns=["foo", "bar", "foo", "hello", "string", "foo2"],
+        )
+        tm.assert_frame_equal(df, expected)
+        df["foo2"] = 3
+
+        # delete (non dup)
+        del df["bar"]
+        expected = DataFrame(
+            [[1, 1, 5, "bah", 3], [1, 2, 5, "bah", 3], [2, 3, 5, "bah", 3]],
+            columns=["foo", "foo", "hello", "string", "foo2"],
+        )
+        tm.assert_frame_equal(df, expected)
+
+        # try to delete again (its not consolidated)
+        del df["hello"]
+        expected = DataFrame(
+            [[1, 1, "bah", 3], [1, 2, "bah", 3], [2, 3, "bah", 3]],
+            columns=["foo", "foo", "string", "foo2"],
+        )
+        tm.assert_frame_equal(df, expected)
+
+        # consolidate
+        df = df._consolidate()
+        expected = DataFrame(
+            [[1, 1, "bah", 3], [1, 2, "bah", 3], [2, 3, "bah", 3]],
+            columns=["foo", "foo", "string", "foo2"],
+        )
+        tm.assert_frame_equal(df, expected)
+
+        # insert
+        df.insert(2, "new_col", 5.0)
+        expected = DataFrame(
+            [[1, 1, 5.0, "bah", 3], [1, 2, 5.0, "bah", 3], [2, 3, 5.0, "bah", 3]],
+            columns=["foo", "foo", "new_col", "string", "foo2"],
+        )
+        tm.assert_frame_equal(df, expected)
+
+        # insert a dup
+        with pytest.raises(ValueError, match="cannot insert"):
+            df.insert(2, "new_col", 4.0)
+
+        df.insert(2, "new_col", 4.0, allow_duplicates=True)
+        expected = DataFrame(
+            [
+                [1, 1, 4.0, 5.0, "bah", 3],
+                [1, 2, 4.0, 5.0, "bah", 3],
+                [2, 3, 4.0, 5.0, "bah", 3],
+            ],
+            columns=["foo", "foo", "new_col", "new_col", "string", "foo2"],
+        )
+        tm.assert_frame_equal(df, expected)
+
+        # delete (dup)
+        del df["foo"]
+        expected = DataFrame(
+            [[4.0, 5.0, "bah", 3], [4.0, 5.0, "bah", 3], [4.0, 5.0, "bah", 3]],
+            columns=["new_col", "new_col", "string", "foo2"],
+        )
+        tm.assert_frame_equal(df, expected)
+
+    def test_dup_across_dtypes(self):
+        # dup across dtypes
+        df = DataFrame(
+            [[1, 1, 1.0, 5], [1, 1, 2.0, 5], [2, 1, 3.0, 5]],
+            columns=["foo", "bar", "foo", "hello"],
+        )
+
+        df["foo2"] = 7.0
+        expected = DataFrame(
+            [[1, 1, 1.0, 5, 7.0], [1, 1, 2.0, 5, 7.0], [2, 1, 3.0, 5, 7.0]],
+            columns=["foo", "bar", "foo", "hello", "foo2"],
+        )
+        tm.assert_frame_equal(df, expected)
+
+        result = df["foo"]
+        expected = DataFrame([[1, 1.0], [1, 2.0], [2, 3.0]], columns=["foo", "foo"])
+        tm.assert_frame_equal(result, expected)
+
+        # multiple replacements
+        df["foo"] = "string"
+        expected = DataFrame(
+            [
+                ["string", 1, "string", 5, 7.0],
+                ["string", 1, "string", 5, 7.0],
+                ["string", 1, "string", 5, 7.0],
+            ],
+            columns=["foo", "bar", "foo", "hello", "foo2"],
+        )
+        tm.assert_frame_equal(df, expected)
+
+        del df["foo"]
+        expected = DataFrame(
+            [[1, 5, 7.0], [1, 5, 7.0], [1, 5, 7.0]], columns=["bar", "hello", "foo2"]
+        )
+        tm.assert_frame_equal(df, expected)
+
+    def test_column_dups_indexes(self):
+        # check column dups with index equal and not equal to df's index
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((5, 3)),
+            index=["a", "b", "c", "d", "e"],
+            columns=["A", "B", "A"],
+        )
+        for index in [df.index, pd.Index(list("edcba"))]:
+            this_df = df.copy()
+            expected_ser = Series(index.values, index=this_df.index)
+            expected_df = DataFrame(
+                {"A": expected_ser, "B": this_df["B"]},
+                columns=["A", "B", "A"],
+            )
+            this_df["A"] = index
+            tm.assert_frame_equal(this_df, expected_df)
+
+    def test_changing_dtypes_with_duplicate_columns(self):
+        # multiple assignments that change dtypes
+        # the location indexer is a slice
+        # GH 6120
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((5, 2)), columns=["that", "that"]
+        )
+        expected = DataFrame(1.0, index=range(5), columns=["that", "that"])
+
+        df["that"] = 1.0
+        tm.assert_frame_equal(df, expected)
+
+        df = DataFrame(
+            np.random.default_rng(2).random((5, 2)), columns=["that", "that"]
+        )
+        expected = DataFrame(1, index=range(5), columns=["that", "that"])
+
+        df["that"] = 1
+        tm.assert_frame_equal(df, expected)
+
+    def test_dup_columns_comparisons(self):
+        # equality
+        df1 = DataFrame([[1, 2], [2, np.nan], [3, 4], [4, 4]], columns=["A", "B"])
+        df2 = DataFrame([[0, 1], [2, 4], [2, np.nan], [4, 5]], columns=["A", "A"])
+
+        # not-comparing like-labelled
+        msg = (
+            r"Can only compare identically-labeled \(both index and columns\) "
+            "DataFrame objects"
+        )
+        with pytest.raises(ValueError, match=msg):
+            df1 == df2
+
+        df1r = df1.reindex_like(df2)
+        result = df1r == df2
+        expected = DataFrame(
+            [[False, True], [True, False], [False, False], [True, False]],
+            columns=["A", "A"],
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_mixed_column_selection(self):
+        # mixed column selection
+        # GH 5639
+        dfbool = DataFrame(
+            {
+                "one": Series([True, True, False], index=["a", "b", "c"]),
+                "two": Series([False, False, True, False], index=["a", "b", "c", "d"]),
+                "three": Series([False, True, True, True], index=["a", "b", "c", "d"]),
+            }
+        )
+        expected = pd.concat([dfbool["one"], dfbool["three"], dfbool["one"]], axis=1)
+        result = dfbool[["one", "three", "one"]]
+        tm.assert_frame_equal(result, expected)
+
+    def test_multi_axis_dups(self):
+        # multi-axis dups
+        # GH 6121
+        df = DataFrame(
+            np.arange(25.0).reshape(5, 5),
+            index=["a", "b", "c", "d", "e"],
+            columns=["A", "B", "C", "D", "E"],
+        )
+        z = df[["A", "C", "A"]].copy()
+        expected = z.loc[["a", "c", "a"]]
+
+        df = DataFrame(
+            np.arange(25.0).reshape(5, 5),
+            index=["a", "b", "c", "d", "e"],
+            columns=["A", "B", "C", "D", "E"],
+        )
+        z = df[["A", "C", "A"]]
+        result = z.loc[["a", "c", "a"]]
+        tm.assert_frame_equal(result, expected)
+
+    def test_columns_with_dups(self):
+        # GH 3468 related
+
+        # basic
+        df = DataFrame([[1, 2]], columns=["a", "a"])
+        df.columns = ["a", "a.1"]
+        expected = DataFrame([[1, 2]], columns=["a", "a.1"])
+        tm.assert_frame_equal(df, expected)
+
+        df = DataFrame([[1, 2, 3]], columns=["b", "a", "a"])
+        df.columns = ["b", "a", "a.1"]
+        expected = DataFrame([[1, 2, 3]], columns=["b", "a", "a.1"])
+        tm.assert_frame_equal(df, expected)
+
+    def test_columns_with_dup_index(self):
+        # with a dup index
+        df = DataFrame([[1, 2]], columns=["a", "a"])
+        df.columns = ["b", "b"]
+        expected = DataFrame([[1, 2]], columns=["b", "b"])
+        tm.assert_frame_equal(df, expected)
+
+    def test_multi_dtype(self):
+        # multi-dtype
+        df = DataFrame(
+            [[1, 2, 1.0, 2.0, 3.0, "foo", "bar"]],
+            columns=["a", "a", "b", "b", "d", "c", "c"],
+        )
+        df.columns = list("ABCDEFG")
+        expected = DataFrame(
+            [[1, 2, 1.0, 2.0, 3.0, "foo", "bar"]], columns=list("ABCDEFG")
+        )
+        tm.assert_frame_equal(df, expected)
+
+    def test_multi_dtype2(self):
+        df = DataFrame([[1, 2, "foo", "bar"]], columns=["a", "a", "a", "a"])
+        df.columns = ["a", "a.1", "a.2", "a.3"]
+        expected = DataFrame([[1, 2, "foo", "bar"]], columns=["a", "a.1", "a.2", "a.3"])
+        tm.assert_frame_equal(df, expected)
+
+    def test_dups_across_blocks(self, using_array_manager):
+        # dups across blocks
+        df_float = DataFrame(
+            np.random.default_rng(2).standard_normal((10, 3)), dtype="float64"
+        )
+        df_int = DataFrame(
+            np.random.default_rng(2).standard_normal((10, 3)).astype("int64")
+        )
+        df_bool = DataFrame(True, index=df_float.index, columns=df_float.columns)
+        df_object = DataFrame("foo", index=df_float.index, columns=df_float.columns)
+        df_dt = DataFrame(
+            pd.Timestamp("20010101"), index=df_float.index, columns=df_float.columns
+        )
+        df = pd.concat([df_float, df_int, df_bool, df_object, df_dt], axis=1)
+
+        if not using_array_manager:
+            assert len(df._mgr.blknos) == len(df.columns)
+            assert len(df._mgr.blklocs) == len(df.columns)
+
+        # testing iloc
+        for i in range(len(df.columns)):
+            df.iloc[:, i]
+
+    def test_dup_columns_across_dtype(self):
+        # dup columns across dtype GH 2079/2194
+        vals = [[1, -1, 2.0], [2, -2, 3.0]]
+        rs = DataFrame(vals, columns=["A", "A", "B"])
+        xp = DataFrame(vals)
+        xp.columns = ["A", "A", "B"]
+        tm.assert_frame_equal(rs, xp)
+
+    def test_set_value_by_index(self):
+        # See gh-12344
+        warn = None
+        msg = "will attempt to set the values inplace"
+
+        df = DataFrame(np.arange(9).reshape(3, 3).T)
+        df.columns = list("AAA")
+        expected = df.iloc[:, 2].copy()
+
+        with tm.assert_produces_warning(warn, match=msg):
+            df.iloc[:, 0] = 3
+        tm.assert_series_equal(df.iloc[:, 2], expected)
+
+        df = DataFrame(np.arange(9).reshape(3, 3).T)
+        df.columns = [2, float(2), str(2)]
+        expected = df.iloc[:, 1].copy()
+
+        with tm.assert_produces_warning(warn, match=msg):
+            df.iloc[:, 0] = 3
+        tm.assert_series_equal(df.iloc[:, 1], expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/test_npfuncs.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/test_npfuncs.py
new file mode 100644
index 0000000000000000000000000000000000000000..afb53bf2de93aa591ca9d7b99af185bc0c4083ee
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/test_npfuncs.py
@@ -0,0 +1,89 @@
+"""
+Tests for np.foo applied to DataFrame, not necessarily ufuncs.
+"""
+import numpy as np
+
+from pandas import (
+    Categorical,
+    DataFrame,
+)
+import pandas._testing as tm
+
+
+class TestAsArray:
+    def test_asarray_homogeneous(self):
+        df = DataFrame({"A": Categorical([1, 2]), "B": Categorical([1, 2])})
+        result = np.asarray(df)
+        # may change from object in the future
+        expected = np.array([[1, 1], [2, 2]], dtype="object")
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_np_sqrt(self, float_frame):
+        with np.errstate(all="ignore"):
+            result = np.sqrt(float_frame)
+        assert isinstance(result, type(float_frame))
+        assert result.index.is_(float_frame.index)
+        assert result.columns.is_(float_frame.columns)
+
+        tm.assert_frame_equal(result, float_frame.apply(np.sqrt))
+
+    def test_sum_deprecated_axis_behavior(self):
+        # GH#52042 deprecated behavior of df.sum(axis=None), which gets
+        #  called when we do np.sum(df)
+
+        arr = np.random.default_rng(2).standard_normal((4, 3))
+        df = DataFrame(arr)
+
+        msg = "The behavior of DataFrame.sum with axis=None is deprecated"
+        with tm.assert_produces_warning(
+            FutureWarning, match=msg, check_stacklevel=False
+        ):
+            res = np.sum(df)
+
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            expected = df.sum(axis=None)
+        tm.assert_series_equal(res, expected)
+
+    def test_np_ravel(self):
+        # GH26247
+        arr = np.array(
+            [
+                [0.11197053, 0.44361564, -0.92589452],
+                [0.05883648, -0.00948922, -0.26469934],
+            ]
+        )
+
+        result = np.ravel([DataFrame(batch.reshape(1, 3)) for batch in arr])
+        expected = np.array(
+            [
+                0.11197053,
+                0.44361564,
+                -0.92589452,
+                0.05883648,
+                -0.00948922,
+                -0.26469934,
+            ]
+        )
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = np.ravel(DataFrame(arr[0].reshape(1, 3), columns=["x1", "x2", "x3"]))
+        expected = np.array([0.11197053, 0.44361564, -0.92589452])
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = np.ravel(
+            [
+                DataFrame(batch.reshape(1, 3), columns=["x1", "x2", "x3"])
+                for batch in arr
+            ]
+        )
+        expected = np.array(
+            [
+                0.11197053,
+                0.44361564,
+                -0.92589452,
+                0.05883648,
+                -0.00948922,
+                -0.26469934,
+            ]
+        )
+        tm.assert_numpy_array_equal(result, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/test_query_eval.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/test_query_eval.py
new file mode 100644
index 0000000000000000000000000000000000000000..ffabf238a4884d3b7436c890da77e2700dc8bde4
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/test_query_eval.py
@@ -0,0 +1,1437 @@
+import operator
+
+import numpy as np
+import pytest
+
+from pandas.errors import (
+    NumExprClobberingError,
+    UndefinedVariableError,
+)
+import pandas.util._test_decorators as td
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Index,
+    MultiIndex,
+    Series,
+    date_range,
+)
+import pandas._testing as tm
+from pandas.core.computation.check import NUMEXPR_INSTALLED
+
+
+@pytest.fixture(params=["python", "pandas"], ids=lambda x: x)
+def parser(request):
+    return request.param
+
+
+@pytest.fixture(
+    params=["python", pytest.param("numexpr", marks=td.skip_if_no("numexpr"))],
+    ids=lambda x: x,
+)
+def engine(request):
+    return request.param
+
+
+def skip_if_no_pandas_parser(parser):
+    if parser != "pandas":
+        pytest.skip(f"cannot evaluate with parser={parser}")
+
+
+class TestCompat:
+    @pytest.fixture
+    def df(self):
+        return DataFrame({"A": [1, 2, 3]})
+
+    @pytest.fixture
+    def expected1(self, df):
+        return df[df.A > 0]
+
+    @pytest.fixture
+    def expected2(self, df):
+        return df.A + 1
+
+    def test_query_default(self, df, expected1, expected2):
+        # GH 12749
+        # this should always work, whether NUMEXPR_INSTALLED or not
+        result = df.query("A>0")
+        tm.assert_frame_equal(result, expected1)
+        result = df.eval("A+1")
+        tm.assert_series_equal(result, expected2, check_names=False)
+
+    def test_query_None(self, df, expected1, expected2):
+        result = df.query("A>0", engine=None)
+        tm.assert_frame_equal(result, expected1)
+        result = df.eval("A+1", engine=None)
+        tm.assert_series_equal(result, expected2, check_names=False)
+
+    def test_query_python(self, df, expected1, expected2):
+        result = df.query("A>0", engine="python")
+        tm.assert_frame_equal(result, expected1)
+        result = df.eval("A+1", engine="python")
+        tm.assert_series_equal(result, expected2, check_names=False)
+
+    def test_query_numexpr(self, df, expected1, expected2):
+        if NUMEXPR_INSTALLED:
+            result = df.query("A>0", engine="numexpr")
+            tm.assert_frame_equal(result, expected1)
+            result = df.eval("A+1", engine="numexpr")
+            tm.assert_series_equal(result, expected2, check_names=False)
+        else:
+            msg = (
+                r"'numexpr' is not installed or an unsupported version. "
+                r"Cannot use engine='numexpr' for query/eval if 'numexpr' is "
+                r"not installed"
+            )
+            with pytest.raises(ImportError, match=msg):
+                df.query("A>0", engine="numexpr")
+            with pytest.raises(ImportError, match=msg):
+                df.eval("A+1", engine="numexpr")
+
+
+class TestDataFrameEval:
+    # smaller hits python, larger hits numexpr
+    @pytest.mark.parametrize("n", [4, 4000])
+    @pytest.mark.parametrize(
+        "op_str,op,rop",
+        [
+            ("+", "__add__", "__radd__"),
+            ("-", "__sub__", "__rsub__"),
+            ("*", "__mul__", "__rmul__"),
+            ("/", "__truediv__", "__rtruediv__"),
+        ],
+    )
+    def test_ops(self, op_str, op, rop, n):
+        # tst ops and reversed ops in evaluation
+        # GH7198
+
+        df = DataFrame(1, index=range(n), columns=list("abcd"))
+        df.iloc[0] = 2
+        m = df.mean()
+
+        base = DataFrame(  # noqa: F841
+            np.tile(m.values, n).reshape(n, -1), columns=list("abcd")
+        )
+
+        expected = eval(f"base {op_str} df")
+
+        # ops as strings
+        result = eval(f"m {op_str} df")
+        tm.assert_frame_equal(result, expected)
+
+        # these are commutative
+        if op in ["+", "*"]:
+            result = getattr(df, op)(m)
+            tm.assert_frame_equal(result, expected)
+
+        # these are not
+        elif op in ["-", "/"]:
+            result = getattr(df, rop)(m)
+            tm.assert_frame_equal(result, expected)
+
+    def test_dataframe_sub_numexpr_path(self):
+        # GH7192: Note we need a large number of rows to ensure this
+        #  goes through the numexpr path
+        df = DataFrame({"A": np.random.default_rng(2).standard_normal(25000)})
+        df.iloc[0:5] = np.nan
+        expected = 1 - np.isnan(df.iloc[0:25])
+        result = (1 - np.isnan(df)).iloc[0:25]
+        tm.assert_frame_equal(result, expected)
+
+    def test_query_non_str(self):
+        # GH 11485
+        df = DataFrame({"A": [1, 2, 3], "B": ["a", "b", "b"]})
+
+        msg = "expr must be a string to be evaluated"
+        with pytest.raises(ValueError, match=msg):
+            df.query(lambda x: x.B == "b")
+
+        with pytest.raises(ValueError, match=msg):
+            df.query(111)
+
+    def test_query_empty_string(self):
+        # GH 13139
+        df = DataFrame({"A": [1, 2, 3]})
+
+        msg = "expr cannot be an empty string"
+        with pytest.raises(ValueError, match=msg):
+            df.query("")
+
+    def test_eval_resolvers_as_list(self):
+        # GH 14095
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((10, 2)), columns=list("ab")
+        )
+        dict1 = {"a": 1}
+        dict2 = {"b": 2}
+        assert df.eval("a + b", resolvers=[dict1, dict2]) == dict1["a"] + dict2["b"]
+        assert pd.eval("a + b", resolvers=[dict1, dict2]) == dict1["a"] + dict2["b"]
+
+    def test_eval_resolvers_combined(self):
+        # GH 34966
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((10, 2)), columns=list("ab")
+        )
+        dict1 = {"c": 2}
+
+        # Both input and default index/column resolvers should be usable
+        result = df.eval("a + b * c", resolvers=[dict1])
+
+        expected = df["a"] + df["b"] * dict1["c"]
+        tm.assert_series_equal(result, expected)
+
+    def test_eval_object_dtype_binop(self):
+        # GH#24883
+        df = DataFrame({"a1": ["Y", "N"]})
+        res = df.eval("c = ((a1 == 'Y') & True)")
+        expected = DataFrame({"a1": ["Y", "N"], "c": [True, False]})
+        tm.assert_frame_equal(res, expected)
+
+    def test_extension_array_eval(self, engine, parser, request):
+        # GH#58748
+        if engine == "numexpr":
+            mark = pytest.mark.xfail(
+                reason="numexpr does not support extension array dtypes"
+            )
+            request.applymarker(mark)
+        df = DataFrame({"a": pd.array([1, 2, 3]), "b": pd.array([4, 5, 6])})
+        result = df.eval("a / b", engine=engine, parser=parser)
+        expected = Series(pd.array([0.25, 0.40, 0.50]))
+        tm.assert_series_equal(result, expected)
+
+    def test_complex_eval(self, engine, parser):
+        # GH#21374
+        df = DataFrame({"a": [1 + 2j], "b": [1 + 1j]})
+        result = df.eval("a/b", engine=engine, parser=parser)
+        expected = Series([1.5 + 0.5j])
+        tm.assert_series_equal(result, expected)
+
+
+class TestDataFrameQueryWithMultiIndex:
+    def test_query_with_named_multiindex(self, parser, engine):
+        skip_if_no_pandas_parser(parser)
+        a = np.random.default_rng(2).choice(["red", "green"], size=10)
+        b = np.random.default_rng(2).choice(["eggs", "ham"], size=10)
+        index = MultiIndex.from_arrays([a, b], names=["color", "food"])
+        df = DataFrame(np.random.default_rng(2).standard_normal((10, 2)), index=index)
+        ind = Series(
+            df.index.get_level_values("color").values, index=index, name="color"
+        )
+
+        # equality
+        res1 = df.query('color == "red"', parser=parser, engine=engine)
+        res2 = df.query('"red" == color', parser=parser, engine=engine)
+        exp = df[ind == "red"]
+        tm.assert_frame_equal(res1, exp)
+        tm.assert_frame_equal(res2, exp)
+
+        # inequality
+        res1 = df.query('color != "red"', parser=parser, engine=engine)
+        res2 = df.query('"red" != color', parser=parser, engine=engine)
+        exp = df[ind != "red"]
+        tm.assert_frame_equal(res1, exp)
+        tm.assert_frame_equal(res2, exp)
+
+        # list equality (really just set membership)
+        res1 = df.query('color == ["red"]', parser=parser, engine=engine)
+        res2 = df.query('["red"] == color', parser=parser, engine=engine)
+        exp = df[ind.isin(["red"])]
+        tm.assert_frame_equal(res1, exp)
+        tm.assert_frame_equal(res2, exp)
+
+        res1 = df.query('color != ["red"]', parser=parser, engine=engine)
+        res2 = df.query('["red"] != color', parser=parser, engine=engine)
+        exp = df[~ind.isin(["red"])]
+        tm.assert_frame_equal(res1, exp)
+        tm.assert_frame_equal(res2, exp)
+
+        # in/not in ops
+        res1 = df.query('["red"] in color', parser=parser, engine=engine)
+        res2 = df.query('"red" in color', parser=parser, engine=engine)
+        exp = df[ind.isin(["red"])]
+        tm.assert_frame_equal(res1, exp)
+        tm.assert_frame_equal(res2, exp)
+
+        res1 = df.query('["red"] not in color', parser=parser, engine=engine)
+        res2 = df.query('"red" not in color', parser=parser, engine=engine)
+        exp = df[~ind.isin(["red"])]
+        tm.assert_frame_equal(res1, exp)
+        tm.assert_frame_equal(res2, exp)
+
+    def test_query_with_unnamed_multiindex(self, parser, engine):
+        skip_if_no_pandas_parser(parser)
+        a = np.random.default_rng(2).choice(["red", "green"], size=10)
+        b = np.random.default_rng(2).choice(["eggs", "ham"], size=10)
+        index = MultiIndex.from_arrays([a, b])
+        df = DataFrame(np.random.default_rng(2).standard_normal((10, 2)), index=index)
+        ind = Series(df.index.get_level_values(0).values, index=index)
+
+        res1 = df.query('ilevel_0 == "red"', parser=parser, engine=engine)
+        res2 = df.query('"red" == ilevel_0', parser=parser, engine=engine)
+        exp = df[ind == "red"]
+        tm.assert_frame_equal(res1, exp)
+        tm.assert_frame_equal(res2, exp)
+
+        # inequality
+        res1 = df.query('ilevel_0 != "red"', parser=parser, engine=engine)
+        res2 = df.query('"red" != ilevel_0', parser=parser, engine=engine)
+        exp = df[ind != "red"]
+        tm.assert_frame_equal(res1, exp)
+        tm.assert_frame_equal(res2, exp)
+
+        # list equality (really just set membership)
+        res1 = df.query('ilevel_0 == ["red"]', parser=parser, engine=engine)
+        res2 = df.query('["red"] == ilevel_0', parser=parser, engine=engine)
+        exp = df[ind.isin(["red"])]
+        tm.assert_frame_equal(res1, exp)
+        tm.assert_frame_equal(res2, exp)
+
+        res1 = df.query('ilevel_0 != ["red"]', parser=parser, engine=engine)
+        res2 = df.query('["red"] != ilevel_0', parser=parser, engine=engine)
+        exp = df[~ind.isin(["red"])]
+        tm.assert_frame_equal(res1, exp)
+        tm.assert_frame_equal(res2, exp)
+
+        # in/not in ops
+        res1 = df.query('["red"] in ilevel_0', parser=parser, engine=engine)
+        res2 = df.query('"red" in ilevel_0', parser=parser, engine=engine)
+        exp = df[ind.isin(["red"])]
+        tm.assert_frame_equal(res1, exp)
+        tm.assert_frame_equal(res2, exp)
+
+        res1 = df.query('["red"] not in ilevel_0', parser=parser, engine=engine)
+        res2 = df.query('"red" not in ilevel_0', parser=parser, engine=engine)
+        exp = df[~ind.isin(["red"])]
+        tm.assert_frame_equal(res1, exp)
+        tm.assert_frame_equal(res2, exp)
+
+        # ## LEVEL 1
+        ind = Series(df.index.get_level_values(1).values, index=index)
+        res1 = df.query('ilevel_1 == "eggs"', parser=parser, engine=engine)
+        res2 = df.query('"eggs" == ilevel_1', parser=parser, engine=engine)
+        exp = df[ind == "eggs"]
+        tm.assert_frame_equal(res1, exp)
+        tm.assert_frame_equal(res2, exp)
+
+        # inequality
+        res1 = df.query('ilevel_1 != "eggs"', parser=parser, engine=engine)
+        res2 = df.query('"eggs" != ilevel_1', parser=parser, engine=engine)
+        exp = df[ind != "eggs"]
+        tm.assert_frame_equal(res1, exp)
+        tm.assert_frame_equal(res2, exp)
+
+        # list equality (really just set membership)
+        res1 = df.query('ilevel_1 == ["eggs"]', parser=parser, engine=engine)
+        res2 = df.query('["eggs"] == ilevel_1', parser=parser, engine=engine)
+        exp = df[ind.isin(["eggs"])]
+        tm.assert_frame_equal(res1, exp)
+        tm.assert_frame_equal(res2, exp)
+
+        res1 = df.query('ilevel_1 != ["eggs"]', parser=parser, engine=engine)
+        res2 = df.query('["eggs"] != ilevel_1', parser=parser, engine=engine)
+        exp = df[~ind.isin(["eggs"])]
+        tm.assert_frame_equal(res1, exp)
+        tm.assert_frame_equal(res2, exp)
+
+        # in/not in ops
+        res1 = df.query('["eggs"] in ilevel_1', parser=parser, engine=engine)
+        res2 = df.query('"eggs" in ilevel_1', parser=parser, engine=engine)
+        exp = df[ind.isin(["eggs"])]
+        tm.assert_frame_equal(res1, exp)
+        tm.assert_frame_equal(res2, exp)
+
+        res1 = df.query('["eggs"] not in ilevel_1', parser=parser, engine=engine)
+        res2 = df.query('"eggs" not in ilevel_1', parser=parser, engine=engine)
+        exp = df[~ind.isin(["eggs"])]
+        tm.assert_frame_equal(res1, exp)
+        tm.assert_frame_equal(res2, exp)
+
+    def test_query_with_partially_named_multiindex(self, parser, engine):
+        skip_if_no_pandas_parser(parser)
+        a = np.random.default_rng(2).choice(["red", "green"], size=10)
+        b = np.arange(10)
+        index = MultiIndex.from_arrays([a, b])
+        index.names = [None, "rating"]
+        df = DataFrame(np.random.default_rng(2).standard_normal((10, 2)), index=index)
+        res = df.query("rating == 1", parser=parser, engine=engine)
+        ind = Series(
+            df.index.get_level_values("rating").values, index=index, name="rating"
+        )
+        exp = df[ind == 1]
+        tm.assert_frame_equal(res, exp)
+
+        res = df.query("rating != 1", parser=parser, engine=engine)
+        ind = Series(
+            df.index.get_level_values("rating").values, index=index, name="rating"
+        )
+        exp = df[ind != 1]
+        tm.assert_frame_equal(res, exp)
+
+        res = df.query('ilevel_0 == "red"', parser=parser, engine=engine)
+        ind = Series(df.index.get_level_values(0).values, index=index)
+        exp = df[ind == "red"]
+        tm.assert_frame_equal(res, exp)
+
+        res = df.query('ilevel_0 != "red"', parser=parser, engine=engine)
+        ind = Series(df.index.get_level_values(0).values, index=index)
+        exp = df[ind != "red"]
+        tm.assert_frame_equal(res, exp)
+
+    def test_query_multiindex_get_index_resolvers(self):
+        df = DataFrame(
+            np.ones((10, 3)),
+            index=MultiIndex.from_arrays(
+                [range(10) for _ in range(2)], names=["spam", "eggs"]
+            ),
+        )
+        resolvers = df._get_index_resolvers()
+
+        def to_series(mi, level):
+            level_values = mi.get_level_values(level)
+            s = level_values.to_series()
+            s.index = mi
+            return s
+
+        col_series = df.columns.to_series()
+        expected = {
+            "index": df.index,
+            "columns": col_series,
+            "spam": to_series(df.index, "spam"),
+            "eggs": to_series(df.index, "eggs"),
+            "clevel_0": col_series,
+        }
+        for k, v in resolvers.items():
+            if isinstance(v, Index):
+                assert v.is_(expected[k])
+            elif isinstance(v, Series):
+                tm.assert_series_equal(v, expected[k])
+            else:
+                raise AssertionError("object must be a Series or Index")
+
+
+@td.skip_if_no("numexpr")
+class TestDataFrameQueryNumExprPandas:
+    @pytest.fixture
+    def engine(self):
+        return "numexpr"
+
+    @pytest.fixture
+    def parser(self):
+        return "pandas"
+
+    def test_date_query_with_attribute_access(self, engine, parser):
+        skip_if_no_pandas_parser(parser)
+        df = DataFrame(np.random.default_rng(2).standard_normal((5, 3)))
+        df["dates1"] = date_range("1/1/2012", periods=5)
+        df["dates2"] = date_range("1/1/2013", periods=5)
+        df["dates3"] = date_range("1/1/2014", periods=5)
+        res = df.query(
+            "@df.dates1 < 20130101 < @df.dates3", engine=engine, parser=parser
+        )
+        expec = df[(df.dates1 < "20130101") & ("20130101" < df.dates3)]
+        tm.assert_frame_equal(res, expec)
+
+    def test_date_query_no_attribute_access(self, engine, parser):
+        df = DataFrame(np.random.default_rng(2).standard_normal((5, 3)))
+        df["dates1"] = date_range("1/1/2012", periods=5)
+        df["dates2"] = date_range("1/1/2013", periods=5)
+        df["dates3"] = date_range("1/1/2014", periods=5)
+        res = df.query("dates1 < 20130101 < dates3", engine=engine, parser=parser)
+        expec = df[(df.dates1 < "20130101") & ("20130101" < df.dates3)]
+        tm.assert_frame_equal(res, expec)
+
+    def test_date_query_with_NaT(self, engine, parser):
+        n = 10
+        df = DataFrame(np.random.default_rng(2).standard_normal((n, 3)))
+        df["dates1"] = date_range("1/1/2012", periods=n)
+        df["dates2"] = date_range("1/1/2013", periods=n)
+        df["dates3"] = date_range("1/1/2014", periods=n)
+        df.loc[np.random.default_rng(2).random(n) > 0.5, "dates1"] = pd.NaT
+        df.loc[np.random.default_rng(2).random(n) > 0.5, "dates3"] = pd.NaT
+        res = df.query("dates1 < 20130101 < dates3", engine=engine, parser=parser)
+        expec = df[(df.dates1 < "20130101") & ("20130101" < df.dates3)]
+        tm.assert_frame_equal(res, expec)
+
+    def test_date_index_query(self, engine, parser):
+        n = 10
+        df = DataFrame(np.random.default_rng(2).standard_normal((n, 3)))
+        df["dates1"] = date_range("1/1/2012", periods=n)
+        df["dates3"] = date_range("1/1/2014", periods=n)
+        return_value = df.set_index("dates1", inplace=True, drop=True)
+        assert return_value is None
+        res = df.query("index < 20130101 < dates3", engine=engine, parser=parser)
+        expec = df[(df.index < "20130101") & ("20130101" < df.dates3)]
+        tm.assert_frame_equal(res, expec)
+
+    def test_date_index_query_with_NaT(self, engine, parser):
+        n = 10
+        # Cast to object to avoid implicit cast when setting entry to pd.NaT below
+        df = DataFrame(np.random.default_rng(2).standard_normal((n, 3))).astype(
+            {0: object}
+        )
+        df["dates1"] = date_range("1/1/2012", periods=n)
+        df["dates3"] = date_range("1/1/2014", periods=n)
+        df.iloc[0, 0] = pd.NaT
+        return_value = df.set_index("dates1", inplace=True, drop=True)
+        assert return_value is None
+        res = df.query("index < 20130101 < dates3", engine=engine, parser=parser)
+        expec = df[(df.index < "20130101") & ("20130101" < df.dates3)]
+        tm.assert_frame_equal(res, expec)
+
+    def test_date_index_query_with_NaT_duplicates(self, engine, parser):
+        n = 10
+        d = {}
+        d["dates1"] = date_range("1/1/2012", periods=n)
+        d["dates3"] = date_range("1/1/2014", periods=n)
+        df = DataFrame(d)
+        df.loc[np.random.default_rng(2).random(n) > 0.5, "dates1"] = pd.NaT
+        return_value = df.set_index("dates1", inplace=True, drop=True)
+        assert return_value is None
+        res = df.query("dates1 < 20130101 < dates3", engine=engine, parser=parser)
+        expec = df[(df.index.to_series() < "20130101") & ("20130101" < df.dates3)]
+        tm.assert_frame_equal(res, expec)
+
+    def test_date_query_with_non_date(self, engine, parser):
+        n = 10
+        df = DataFrame(
+            {"dates": date_range("1/1/2012", periods=n), "nondate": np.arange(n)}
+        )
+
+        result = df.query("dates == nondate", parser=parser, engine=engine)
+        assert len(result) == 0
+
+        result = df.query("dates != nondate", parser=parser, engine=engine)
+        tm.assert_frame_equal(result, df)
+
+        msg = r"Invalid comparison between dtype=datetime64\[ns\] and ndarray"
+        for op in ["<", ">", "<=", ">="]:
+            with pytest.raises(TypeError, match=msg):
+                df.query(f"dates {op} nondate", parser=parser, engine=engine)
+
+    def test_query_syntax_error(self, engine, parser):
+        df = DataFrame({"i": range(10), "+": range(3, 13), "r": range(4, 14)})
+        msg = "invalid syntax"
+        with pytest.raises(SyntaxError, match=msg):
+            df.query("i - +", engine=engine, parser=parser)
+
+    def test_query_scope(self, engine, parser):
+        skip_if_no_pandas_parser(parser)
+
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((20, 2)), columns=list("ab")
+        )
+
+        a, b = 1, 2  # noqa: F841
+        res = df.query("a > b", engine=engine, parser=parser)
+        expected = df[df.a > df.b]
+        tm.assert_frame_equal(res, expected)
+
+        res = df.query("@a > b", engine=engine, parser=parser)
+        expected = df[a > df.b]
+        tm.assert_frame_equal(res, expected)
+
+        # no local variable c
+        with pytest.raises(
+            UndefinedVariableError, match="local variable 'c' is not defined"
+        ):
+            df.query("@a > b > @c", engine=engine, parser=parser)
+
+        # no column named 'c'
+        with pytest.raises(UndefinedVariableError, match="name 'c' is not defined"):
+            df.query("@a > b > c", engine=engine, parser=parser)
+
+    def test_query_doesnt_pickup_local(self, engine, parser):
+        n = m = 10
+        df = DataFrame(
+            np.random.default_rng(2).integers(m, size=(n, 3)), columns=list("abc")
+        )
+
+        # we don't pick up the local 'sin'
+        with pytest.raises(UndefinedVariableError, match="name 'sin' is not defined"):
+            df.query("sin > 5", engine=engine, parser=parser)
+
+    def test_query_builtin(self, engine, parser):
+        n = m = 10
+        df = DataFrame(
+            np.random.default_rng(2).integers(m, size=(n, 3)), columns=list("abc")
+        )
+
+        df.index.name = "sin"
+        msg = "Variables in expression.+"
+        with pytest.raises(NumExprClobberingError, match=msg):
+            df.query("sin > 5", engine=engine, parser=parser)
+
+    def test_query(self, engine, parser):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((10, 3)), columns=["a", "b", "c"]
+        )
+
+        tm.assert_frame_equal(
+            df.query("a < b", engine=engine, parser=parser), df[df.a < df.b]
+        )
+        tm.assert_frame_equal(
+            df.query("a + b > b * c", engine=engine, parser=parser),
+            df[df.a + df.b > df.b * df.c],
+        )
+
+    def test_query_index_with_name(self, engine, parser):
+        df = DataFrame(
+            np.random.default_rng(2).integers(10, size=(10, 3)),
+            index=Index(range(10), name="blob"),
+            columns=["a", "b", "c"],
+        )
+        res = df.query("(blob < 5) & (a < b)", engine=engine, parser=parser)
+        expec = df[(df.index < 5) & (df.a < df.b)]
+        tm.assert_frame_equal(res, expec)
+
+        res = df.query("blob < b", engine=engine, parser=parser)
+        expec = df[df.index < df.b]
+
+        tm.assert_frame_equal(res, expec)
+
+    def test_query_index_without_name(self, engine, parser):
+        df = DataFrame(
+            np.random.default_rng(2).integers(10, size=(10, 3)),
+            index=range(10),
+            columns=["a", "b", "c"],
+        )
+
+        # "index" should refer to the index
+        res = df.query("index < b", engine=engine, parser=parser)
+        expec = df[df.index < df.b]
+        tm.assert_frame_equal(res, expec)
+
+        # test against a scalar
+        res = df.query("index < 5", engine=engine, parser=parser)
+        expec = df[df.index < 5]
+        tm.assert_frame_equal(res, expec)
+
+    def test_nested_scope(self, engine, parser):
+        skip_if_no_pandas_parser(parser)
+
+        df = DataFrame(np.random.default_rng(2).standard_normal((5, 3)))
+        df2 = DataFrame(np.random.default_rng(2).standard_normal((5, 3)))
+        expected = df[(df > 0) & (df2 > 0)]
+
+        result = df.query("(@df > 0) & (@df2 > 0)", engine=engine, parser=parser)
+        tm.assert_frame_equal(result, expected)
+
+        result = pd.eval("df[df > 0 and df2 > 0]", engine=engine, parser=parser)
+        tm.assert_frame_equal(result, expected)
+
+        result = pd.eval(
+            "df[df > 0 and df2 > 0 and df[df > 0] > 0]", engine=engine, parser=parser
+        )
+        expected = df[(df > 0) & (df2 > 0) & (df[df > 0] > 0)]
+        tm.assert_frame_equal(result, expected)
+
+        result = pd.eval("df[(df>0) & (df2>0)]", engine=engine, parser=parser)
+        expected = df.query("(@df>0) & (@df2>0)", engine=engine, parser=parser)
+        tm.assert_frame_equal(result, expected)
+
+    def test_nested_raises_on_local_self_reference(self, engine, parser):
+        df = DataFrame(np.random.default_rng(2).standard_normal((5, 3)))
+
+        # can't reference ourself b/c we're a local so @ is necessary
+        with pytest.raises(UndefinedVariableError, match="name 'df' is not defined"):
+            df.query("df > 0", engine=engine, parser=parser)
+
+    def test_local_syntax(self, engine, parser):
+        skip_if_no_pandas_parser(parser)
+
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((100, 10)),
+            columns=list("abcdefghij"),
+        )
+        b = 1
+        expect = df[df.a < b]
+        result = df.query("a < @b", engine=engine, parser=parser)
+        tm.assert_frame_equal(result, expect)
+
+        expect = df[df.a < df.b]
+        result = df.query("a < b", engine=engine, parser=parser)
+        tm.assert_frame_equal(result, expect)
+
+    def test_chained_cmp_and_in(self, engine, parser):
+        skip_if_no_pandas_parser(parser)
+        cols = list("abc")
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((100, len(cols))), columns=cols
+        )
+        res = df.query(
+            "a < b < c and a not in b not in c", engine=engine, parser=parser
+        )
+        ind = (df.a < df.b) & (df.b < df.c) & ~df.b.isin(df.a) & ~df.c.isin(df.b)
+        expec = df[ind]
+        tm.assert_frame_equal(res, expec)
+
+    def test_local_variable_with_in(self, engine, parser):
+        skip_if_no_pandas_parser(parser)
+        a = Series(np.random.default_rng(2).integers(3, size=15), name="a")
+        b = Series(np.random.default_rng(2).integers(10, size=15), name="b")
+        df = DataFrame({"a": a, "b": b})
+
+        expected = df.loc[(df.b - 1).isin(a)]
+        result = df.query("b - 1 in a", engine=engine, parser=parser)
+        tm.assert_frame_equal(expected, result)
+
+        b = Series(np.random.default_rng(2).integers(10, size=15), name="b")
+        expected = df.loc[(b - 1).isin(a)]
+        result = df.query("@b - 1 in a", engine=engine, parser=parser)
+        tm.assert_frame_equal(expected, result)
+
+    def test_at_inside_string(self, engine, parser):
+        skip_if_no_pandas_parser(parser)
+        c = 1  # noqa: F841
+        df = DataFrame({"a": ["a", "a", "b", "b", "@c", "@c"]})
+        result = df.query('a == "@c"', engine=engine, parser=parser)
+        expected = df[df.a == "@c"]
+        tm.assert_frame_equal(result, expected)
+
+    def test_query_undefined_local(self):
+        engine, parser = self.engine, self.parser
+        skip_if_no_pandas_parser(parser)
+
+        df = DataFrame(np.random.default_rng(2).random((10, 2)), columns=list("ab"))
+        with pytest.raises(
+            UndefinedVariableError, match="local variable 'c' is not defined"
+        ):
+            df.query("a == @c", engine=engine, parser=parser)
+
+    def test_index_resolvers_come_after_columns_with_the_same_name(
+        self, engine, parser
+    ):
+        n = 1  # noqa: F841
+        a = np.r_[20:101:20]
+
+        df = DataFrame(
+            {"index": a, "b": np.random.default_rng(2).standard_normal(a.size)}
+        )
+        df.index.name = "index"
+        result = df.query("index > 5", engine=engine, parser=parser)
+        expected = df[df["index"] > 5]
+        tm.assert_frame_equal(result, expected)
+
+        df = DataFrame(
+            {"index": a, "b": np.random.default_rng(2).standard_normal(a.size)}
+        )
+        result = df.query("ilevel_0 > 5", engine=engine, parser=parser)
+        expected = df.loc[df.index[df.index > 5]]
+        tm.assert_frame_equal(result, expected)
+
+        df = DataFrame({"a": a, "b": np.random.default_rng(2).standard_normal(a.size)})
+        df.index.name = "a"
+        result = df.query("a > 5", engine=engine, parser=parser)
+        expected = df[df.a > 5]
+        tm.assert_frame_equal(result, expected)
+
+        result = df.query("index > 5", engine=engine, parser=parser)
+        expected = df.loc[df.index[df.index > 5]]
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("op, f", [["==", operator.eq], ["!=", operator.ne]])
+    def test_inf(self, op, f, engine, parser):
+        n = 10
+        df = DataFrame(
+            {
+                "a": np.random.default_rng(2).random(n),
+                "b": np.random.default_rng(2).random(n),
+            }
+        )
+        df.loc[::2, 0] = np.inf
+        q = f"a {op} inf"
+        expected = df[f(df.a, np.inf)]
+        result = df.query(q, engine=engine, parser=parser)
+        tm.assert_frame_equal(result, expected)
+
+    def test_check_tz_aware_index_query(self, tz_aware_fixture):
+        # https://github.com/pandas-dev/pandas/issues/29463
+        tz = tz_aware_fixture
+        df_index = date_range(
+            start="2019-01-01", freq="1d", periods=10, tz=tz, name="time"
+        )
+        expected = DataFrame(index=df_index)
+        df = DataFrame(index=df_index)
+        result = df.query('"2018-01-03 00:00:00+00" < time')
+        tm.assert_frame_equal(result, expected)
+
+        expected = DataFrame(df_index)
+        result = df.reset_index().query('"2018-01-03 00:00:00+00" < time')
+        tm.assert_frame_equal(result, expected)
+
+    def test_method_calls_in_query(self, engine, parser):
+        # https://github.com/pandas-dev/pandas/issues/22435
+        n = 10
+        df = DataFrame(
+            {
+                "a": 2 * np.random.default_rng(2).random(n),
+                "b": np.random.default_rng(2).random(n),
+            }
+        )
+        expected = df[df["a"].astype("int") == 0]
+        result = df.query("a.astype('int') == 0", engine=engine, parser=parser)
+        tm.assert_frame_equal(result, expected)
+
+        df = DataFrame(
+            {
+                "a": np.where(
+                    np.random.default_rng(2).random(n) < 0.5,
+                    np.nan,
+                    np.random.default_rng(2).standard_normal(n),
+                ),
+                "b": np.random.default_rng(2).standard_normal(n),
+            }
+        )
+        expected = df[df["a"].notnull()]
+        result = df.query("a.notnull()", engine=engine, parser=parser)
+        tm.assert_frame_equal(result, expected)
+
+
+@td.skip_if_no("numexpr")
+class TestDataFrameQueryNumExprPython(TestDataFrameQueryNumExprPandas):
+    @pytest.fixture
+    def engine(self):
+        return "numexpr"
+
+    @pytest.fixture
+    def parser(self):
+        return "python"
+
+    def test_date_query_no_attribute_access(self, engine, parser):
+        df = DataFrame(np.random.default_rng(2).standard_normal((5, 3)))
+        df["dates1"] = date_range("1/1/2012", periods=5)
+        df["dates2"] = date_range("1/1/2013", periods=5)
+        df["dates3"] = date_range("1/1/2014", periods=5)
+        res = df.query(
+            "(dates1 < 20130101) & (20130101 < dates3)", engine=engine, parser=parser
+        )
+        expec = df[(df.dates1 < "20130101") & ("20130101" < df.dates3)]
+        tm.assert_frame_equal(res, expec)
+
+    def test_date_query_with_NaT(self, engine, parser):
+        n = 10
+        df = DataFrame(np.random.default_rng(2).standard_normal((n, 3)))
+        df["dates1"] = date_range("1/1/2012", periods=n)
+        df["dates2"] = date_range("1/1/2013", periods=n)
+        df["dates3"] = date_range("1/1/2014", periods=n)
+        df.loc[np.random.default_rng(2).random(n) > 0.5, "dates1"] = pd.NaT
+        df.loc[np.random.default_rng(2).random(n) > 0.5, "dates3"] = pd.NaT
+        res = df.query(
+            "(dates1 < 20130101) & (20130101 < dates3)", engine=engine, parser=parser
+        )
+        expec = df[(df.dates1 < "20130101") & ("20130101" < df.dates3)]
+        tm.assert_frame_equal(res, expec)
+
+    def test_date_index_query(self, engine, parser):
+        n = 10
+        df = DataFrame(np.random.default_rng(2).standard_normal((n, 3)))
+        df["dates1"] = date_range("1/1/2012", periods=n)
+        df["dates3"] = date_range("1/1/2014", periods=n)
+        return_value = df.set_index("dates1", inplace=True, drop=True)
+        assert return_value is None
+        res = df.query(
+            "(index < 20130101) & (20130101 < dates3)", engine=engine, parser=parser
+        )
+        expec = df[(df.index < "20130101") & ("20130101" < df.dates3)]
+        tm.assert_frame_equal(res, expec)
+
+    def test_date_index_query_with_NaT(self, engine, parser):
+        n = 10
+        # Cast to object to avoid implicit cast when setting entry to pd.NaT below
+        df = DataFrame(np.random.default_rng(2).standard_normal((n, 3))).astype(
+            {0: object}
+        )
+        df["dates1"] = date_range("1/1/2012", periods=n)
+        df["dates3"] = date_range("1/1/2014", periods=n)
+        df.iloc[0, 0] = pd.NaT
+        return_value = df.set_index("dates1", inplace=True, drop=True)
+        assert return_value is None
+        res = df.query(
+            "(index < 20130101) & (20130101 < dates3)", engine=engine, parser=parser
+        )
+        expec = df[(df.index < "20130101") & ("20130101" < df.dates3)]
+        tm.assert_frame_equal(res, expec)
+
+    def test_date_index_query_with_NaT_duplicates(self, engine, parser):
+        n = 10
+        df = DataFrame(np.random.default_rng(2).standard_normal((n, 3)))
+        df["dates1"] = date_range("1/1/2012", periods=n)
+        df["dates3"] = date_range("1/1/2014", periods=n)
+        df.loc[np.random.default_rng(2).random(n) > 0.5, "dates1"] = pd.NaT
+        return_value = df.set_index("dates1", inplace=True, drop=True)
+        assert return_value is None
+        msg = r"'BoolOp' nodes are not implemented"
+        with pytest.raises(NotImplementedError, match=msg):
+            df.query("index < 20130101 < dates3", engine=engine, parser=parser)
+
+    def test_nested_scope(self, engine, parser):
+        # smoke test
+        x = 1  # noqa: F841
+        result = pd.eval("x + 1", engine=engine, parser=parser)
+        assert result == 2
+
+        df = DataFrame(np.random.default_rng(2).standard_normal((5, 3)))
+        df2 = DataFrame(np.random.default_rng(2).standard_normal((5, 3)))
+
+        # don't have the pandas parser
+        msg = r"The '@' prefix is only supported by the pandas parser"
+        with pytest.raises(SyntaxError, match=msg):
+            df.query("(@df>0) & (@df2>0)", engine=engine, parser=parser)
+
+        with pytest.raises(UndefinedVariableError, match="name 'df' is not defined"):
+            df.query("(df>0) & (df2>0)", engine=engine, parser=parser)
+
+        expected = df[(df > 0) & (df2 > 0)]
+        result = pd.eval("df[(df > 0) & (df2 > 0)]", engine=engine, parser=parser)
+        tm.assert_frame_equal(expected, result)
+
+        expected = df[(df > 0) & (df2 > 0) & (df[df > 0] > 0)]
+        result = pd.eval(
+            "df[(df > 0) & (df2 > 0) & (df[df > 0] > 0)]", engine=engine, parser=parser
+        )
+        tm.assert_frame_equal(expected, result)
+
+    def test_query_numexpr_with_min_and_max_columns(self):
+        df = DataFrame({"min": [1, 2, 3], "max": [4, 5, 6]})
+        regex_to_match = (
+            r"Variables in expression \"\(min\) == \(1\)\" "
+            r"overlap with builtins: \('min'\)"
+        )
+        with pytest.raises(NumExprClobberingError, match=regex_to_match):
+            df.query("min == 1")
+
+        regex_to_match = (
+            r"Variables in expression \"\(max\) == \(1\)\" "
+            r"overlap with builtins: \('max'\)"
+        )
+        with pytest.raises(NumExprClobberingError, match=regex_to_match):
+            df.query("max == 1")
+
+
+class TestDataFrameQueryPythonPandas(TestDataFrameQueryNumExprPandas):
+    @pytest.fixture
+    def engine(self):
+        return "python"
+
+    @pytest.fixture
+    def parser(self):
+        return "pandas"
+
+    def test_query_builtin(self, engine, parser):
+        n = m = 10
+        df = DataFrame(
+            np.random.default_rng(2).integers(m, size=(n, 3)), columns=list("abc")
+        )
+
+        df.index.name = "sin"
+        expected = df[df.index > 5]
+        result = df.query("sin > 5", engine=engine, parser=parser)
+        tm.assert_frame_equal(expected, result)
+
+
+class TestDataFrameQueryPythonPython(TestDataFrameQueryNumExprPython):
+    @pytest.fixture
+    def engine(self):
+        return "python"
+
+    @pytest.fixture
+    def parser(self):
+        return "python"
+
+    def test_query_builtin(self, engine, parser):
+        n = m = 10
+        df = DataFrame(
+            np.random.default_rng(2).integers(m, size=(n, 3)), columns=list("abc")
+        )
+
+        df.index.name = "sin"
+        expected = df[df.index > 5]
+        result = df.query("sin > 5", engine=engine, parser=parser)
+        tm.assert_frame_equal(expected, result)
+
+
+class TestDataFrameQueryStrings:
+    def test_str_query_method(self, parser, engine):
+        df = DataFrame(np.random.default_rng(2).standard_normal((10, 1)), columns=["b"])
+        df["strings"] = Series(list("aabbccddee"))
+        expect = df[df.strings == "a"]
+
+        if parser != "pandas":
+            col = "strings"
+            lst = '"a"'
+
+            lhs = [col] * 2 + [lst] * 2
+            rhs = lhs[::-1]
+
+            eq, ne = "==", "!="
+            ops = 2 * ([eq] + [ne])
+            msg = r"'(Not)?In' nodes are not implemented"
+
+            for lhs, op, rhs in zip(lhs, ops, rhs):
+                ex = f"{lhs} {op} {rhs}"
+                with pytest.raises(NotImplementedError, match=msg):
+                    df.query(
+                        ex,
+                        engine=engine,
+                        parser=parser,
+                        local_dict={"strings": df.strings},
+                    )
+        else:
+            res = df.query('"a" == strings', engine=engine, parser=parser)
+            tm.assert_frame_equal(res, expect)
+
+            res = df.query('strings == "a"', engine=engine, parser=parser)
+            tm.assert_frame_equal(res, expect)
+            tm.assert_frame_equal(res, df[df.strings.isin(["a"])])
+
+            expect = df[df.strings != "a"]
+            res = df.query('strings != "a"', engine=engine, parser=parser)
+            tm.assert_frame_equal(res, expect)
+
+            res = df.query('"a" != strings', engine=engine, parser=parser)
+            tm.assert_frame_equal(res, expect)
+            tm.assert_frame_equal(res, df[~df.strings.isin(["a"])])
+
+    def test_str_list_query_method(self, parser, engine):
+        df = DataFrame(np.random.default_rng(2).standard_normal((10, 1)), columns=["b"])
+        df["strings"] = Series(list("aabbccddee"))
+        expect = df[df.strings.isin(["a", "b"])]
+
+        if parser != "pandas":
+            col = "strings"
+            lst = '["a", "b"]'
+
+            lhs = [col] * 2 + [lst] * 2
+            rhs = lhs[::-1]
+
+            eq, ne = "==", "!="
+            ops = 2 * ([eq] + [ne])
+            msg = r"'(Not)?In' nodes are not implemented"
+
+            for lhs, op, rhs in zip(lhs, ops, rhs):
+                ex = f"{lhs} {op} {rhs}"
+                with pytest.raises(NotImplementedError, match=msg):
+                    df.query(ex, engine=engine, parser=parser)
+        else:
+            res = df.query('strings == ["a", "b"]', engine=engine, parser=parser)
+            tm.assert_frame_equal(res, expect)
+
+            res = df.query('["a", "b"] == strings', engine=engine, parser=parser)
+            tm.assert_frame_equal(res, expect)
+
+            expect = df[~df.strings.isin(["a", "b"])]
+
+            res = df.query('strings != ["a", "b"]', engine=engine, parser=parser)
+            tm.assert_frame_equal(res, expect)
+
+            res = df.query('["a", "b"] != strings', engine=engine, parser=parser)
+            tm.assert_frame_equal(res, expect)
+
+    def test_query_with_string_columns(self, parser, engine):
+        df = DataFrame(
+            {
+                "a": list("aaaabbbbcccc"),
+                "b": list("aabbccddeeff"),
+                "c": np.random.default_rng(2).integers(5, size=12),
+                "d": np.random.default_rng(2).integers(9, size=12),
+            }
+        )
+        if parser == "pandas":
+            res = df.query("a in b", parser=parser, engine=engine)
+            expec = df[df.a.isin(df.b)]
+            tm.assert_frame_equal(res, expec)
+
+            res = df.query("a in b and c < d", parser=parser, engine=engine)
+            expec = df[df.a.isin(df.b) & (df.c < df.d)]
+            tm.assert_frame_equal(res, expec)
+        else:
+            msg = r"'(Not)?In' nodes are not implemented"
+            with pytest.raises(NotImplementedError, match=msg):
+                df.query("a in b", parser=parser, engine=engine)
+
+            msg = r"'BoolOp' nodes are not implemented"
+            with pytest.raises(NotImplementedError, match=msg):
+                df.query("a in b and c < d", parser=parser, engine=engine)
+
+    def test_object_array_eq_ne(self, parser, engine):
+        df = DataFrame(
+            {
+                "a": list("aaaabbbbcccc"),
+                "b": list("aabbccddeeff"),
+                "c": np.random.default_rng(2).integers(5, size=12),
+                "d": np.random.default_rng(2).integers(9, size=12),
+            }
+        )
+        res = df.query("a == b", parser=parser, engine=engine)
+        exp = df[df.a == df.b]
+        tm.assert_frame_equal(res, exp)
+
+        res = df.query("a != b", parser=parser, engine=engine)
+        exp = df[df.a != df.b]
+        tm.assert_frame_equal(res, exp)
+
+    def test_query_with_nested_strings(self, parser, engine):
+        skip_if_no_pandas_parser(parser)
+        events = [
+            f"page {n} {act}" for n in range(1, 4) for act in ["load", "exit"]
+        ] * 2
+        stamps1 = date_range("2014-01-01 0:00:01", freq="30s", periods=6)
+        stamps2 = date_range("2014-02-01 1:00:01", freq="30s", periods=6)
+        df = DataFrame(
+            {
+                "id": np.arange(1, 7).repeat(2),
+                "event": events,
+                "timestamp": stamps1.append(stamps2),
+            }
+        )
+
+        expected = df[df.event == '"page 1 load"']
+        res = df.query("""'"page 1 load"' in event""", parser=parser, engine=engine)
+        tm.assert_frame_equal(expected, res)
+
+    def test_query_with_nested_special_character(self, parser, engine):
+        skip_if_no_pandas_parser(parser)
+        df = DataFrame({"a": ["a", "b", "test & test"], "b": [1, 2, 3]})
+        res = df.query('a == "test & test"', parser=parser, engine=engine)
+        expec = df[df.a == "test & test"]
+        tm.assert_frame_equal(res, expec)
+
+    @pytest.mark.parametrize(
+        "op, func",
+        [
+            ["<", operator.lt],
+            [">", operator.gt],
+            ["<=", operator.le],
+            [">=", operator.ge],
+        ],
+    )
+    def test_query_lex_compare_strings(self, parser, engine, op, func):
+        a = Series(np.random.default_rng(2).choice(list("abcde"), 20))
+        b = Series(np.arange(a.size))
+        df = DataFrame({"X": a, "Y": b})
+
+        res = df.query(f'X {op} "d"', engine=engine, parser=parser)
+        expected = df[func(df.X, "d")]
+        tm.assert_frame_equal(res, expected)
+
+    def test_query_single_element_booleans(self, parser, engine):
+        columns = "bid", "bidsize", "ask", "asksize"
+        data = np.random.default_rng(2).integers(2, size=(1, len(columns))).astype(bool)
+        df = DataFrame(data, columns=columns)
+        res = df.query("bid & ask", engine=engine, parser=parser)
+        expected = df[df.bid & df.ask]
+        tm.assert_frame_equal(res, expected)
+
+    def test_query_string_scalar_variable(self, parser, engine):
+        skip_if_no_pandas_parser(parser)
+        df = DataFrame(
+            {
+                "Symbol": ["BUD US", "BUD US", "IBM US", "IBM US"],
+                "Price": [109.70, 109.72, 183.30, 183.35],
+            }
+        )
+        e = df[df.Symbol == "BUD US"]
+        symb = "BUD US"  # noqa: F841
+        r = df.query("Symbol == @symb", parser=parser, engine=engine)
+        tm.assert_frame_equal(e, r)
+
+    @pytest.mark.parametrize(
+        "in_list",
+        [
+            [None, "asdf", "ghjk"],
+            ["asdf", None, "ghjk"],
+            ["asdf", "ghjk", None],
+            [None, None, "asdf"],
+            ["asdf", None, None],
+            [None, None, None],
+        ],
+    )
+    def test_query_string_null_elements(self, in_list):
+        # GITHUB ISSUE #31516
+        parser = "pandas"
+        engine = "python"
+        expected = {i: value for i, value in enumerate(in_list) if value == "asdf"}
+
+        df_expected = DataFrame({"a": expected}, dtype="string")
+        df_expected.index = df_expected.index.astype("int64")
+        df = DataFrame({"a": in_list}, dtype="string")
+        res1 = df.query("a == 'asdf'", parser=parser, engine=engine)
+        res2 = df[df["a"] == "asdf"]
+        res3 = df.query("a <= 'asdf'", parser=parser, engine=engine)
+        tm.assert_frame_equal(res1, df_expected)
+        tm.assert_frame_equal(res1, res2)
+        tm.assert_frame_equal(res1, res3)
+        tm.assert_frame_equal(res2, res3)
+
+
+class TestDataFrameEvalWithFrame:
+    @pytest.fixture
+    def frame(self):
+        return DataFrame(
+            np.random.default_rng(2).standard_normal((10, 3)), columns=list("abc")
+        )
+
+    def test_simple_expr(self, frame, parser, engine):
+        res = frame.eval("a + b", engine=engine, parser=parser)
+        expect = frame.a + frame.b
+        tm.assert_series_equal(res, expect)
+
+    def test_bool_arith_expr(self, frame, parser, engine):
+        res = frame.eval("a[a < 1] + b", engine=engine, parser=parser)
+        expect = frame.a[frame.a < 1] + frame.b
+        tm.assert_series_equal(res, expect)
+
+    @pytest.mark.parametrize("op", ["+", "-", "*", "/"])
+    def test_invalid_type_for_operator_raises(self, parser, engine, op):
+        df = DataFrame({"a": [1, 2], "b": ["c", "d"]})
+        msg = r"unsupported operand type\(s\) for .+: '.+' and '.+'|Cannot"
+
+        with pytest.raises(TypeError, match=msg):
+            df.eval(f"a {op} b", engine=engine, parser=parser)
+
+
+class TestDataFrameQueryBacktickQuoting:
+    @pytest.fixture
+    def df(self):
+        """
+        Yields a dataframe with strings that may or may not need escaping
+        by backticks. The last two columns cannot be escaped by backticks
+        and should raise a ValueError.
+        """
+        yield DataFrame(
+            {
+                "A": [1, 2, 3],
+                "B B": [3, 2, 1],
+                "C C": [4, 5, 6],
+                "C  C": [7, 4, 3],
+                "C_C": [8, 9, 10],
+                "D_D D": [11, 1, 101],
+                "E.E": [6, 3, 5],
+                "F-F": [8, 1, 10],
+                "1e1": [2, 4, 8],
+                "def": [10, 11, 2],
+                "A (x)": [4, 1, 3],
+                "B(x)": [1, 1, 5],
+                "B (x)": [2, 7, 4],
+                "  &^ :!€$?(} >    <++*''  ": [2, 5, 6],
+                "": [10, 11, 1],
+                " A": [4, 7, 9],
+                "  ": [1, 2, 1],
+                "it's": [6, 3, 1],
+                "that's": [9, 1, 8],
+                "☺": [8, 7, 6],
+                "foo#bar": [2, 4, 5],
+                1: [5, 7, 9],
+            }
+        )
+
+    def test_single_backtick_variable_query(self, df):
+        res = df.query("1 < `B B`")
+        expect = df[1 < df["B B"]]
+        tm.assert_frame_equal(res, expect)
+
+    def test_two_backtick_variables_query(self, df):
+        res = df.query("1 < `B B` and 4 < `C C`")
+        expect = df[(1 < df["B B"]) & (4 < df["C C"])]
+        tm.assert_frame_equal(res, expect)
+
+    def test_single_backtick_variable_expr(self, df):
+        res = df.eval("A + `B B`")
+        expect = df["A"] + df["B B"]
+        tm.assert_series_equal(res, expect)
+
+    def test_two_backtick_variables_expr(self, df):
+        res = df.eval("`B B` + `C C`")
+        expect = df["B B"] + df["C C"]
+        tm.assert_series_equal(res, expect)
+
+    def test_already_underscore_variable(self, df):
+        res = df.eval("`C_C` + A")
+        expect = df["C_C"] + df["A"]
+        tm.assert_series_equal(res, expect)
+
+    def test_same_name_but_underscores(self, df):
+        res = df.eval("C_C + `C C`")
+        expect = df["C_C"] + df["C C"]
+        tm.assert_series_equal(res, expect)
+
+    def test_mixed_underscores_and_spaces(self, df):
+        res = df.eval("A + `D_D D`")
+        expect = df["A"] + df["D_D D"]
+        tm.assert_series_equal(res, expect)
+
+    def test_backtick_quote_name_with_no_spaces(self, df):
+        res = df.eval("A + `C_C`")
+        expect = df["A"] + df["C_C"]
+        tm.assert_series_equal(res, expect)
+
+    def test_special_characters(self, df):
+        res = df.eval("`E.E` + `F-F` - A")
+        expect = df["E.E"] + df["F-F"] - df["A"]
+        tm.assert_series_equal(res, expect)
+
+    def test_start_with_digit(self, df):
+        res = df.eval("A + `1e1`")
+        expect = df["A"] + df["1e1"]
+        tm.assert_series_equal(res, expect)
+
+    def test_keyword(self, df):
+        res = df.eval("A + `def`")
+        expect = df["A"] + df["def"]
+        tm.assert_series_equal(res, expect)
+
+    def test_unneeded_quoting(self, df):
+        res = df.query("`A` > 2")
+        expect = df[df["A"] > 2]
+        tm.assert_frame_equal(res, expect)
+
+    def test_parenthesis(self, df):
+        res = df.query("`A (x)` > 2")
+        expect = df[df["A (x)"] > 2]
+        tm.assert_frame_equal(res, expect)
+
+    def test_empty_string(self, df):
+        res = df.query("`` > 5")
+        expect = df[df[""] > 5]
+        tm.assert_frame_equal(res, expect)
+
+    def test_multiple_spaces(self, df):
+        res = df.query("`C  C` > 5")
+        expect = df[df["C  C"] > 5]
+        tm.assert_frame_equal(res, expect)
+
+    def test_start_with_spaces(self, df):
+        res = df.eval("` A` + `  `")
+        expect = df[" A"] + df["  "]
+        tm.assert_series_equal(res, expect)
+
+    def test_lots_of_operators_string(self, df):
+        res = df.query("`  &^ :!€$?(} >    <++*''  ` > 4")
+        expect = df[df["  &^ :!€$?(} >    <++*''  "] > 4]
+        tm.assert_frame_equal(res, expect)
+
+    def test_missing_attribute(self, df):
+        message = "module 'pandas' has no attribute 'thing'"
+        with pytest.raises(AttributeError, match=message):
+            df.eval("@pd.thing")
+
+    def test_failing_quote(self, df):
+        msg = r"(Could not convert ).*( to a valid Python identifier.)"
+        with pytest.raises(SyntaxError, match=msg):
+            df.query("`it's` > `that's`")
+
+    def test_failing_character_outside_range(self, df):
+        msg = r"(Could not convert ).*( to a valid Python identifier.)"
+        with pytest.raises(SyntaxError, match=msg):
+            df.query("`☺` > 4")
+
+    def test_failing_hashtag(self, df):
+        msg = "Failed to parse backticks"
+        with pytest.raises(SyntaxError, match=msg):
+            df.query("`foo#bar` > 4")
+
+    def test_call_non_named_expression(self, df):
+        """
+        Only attributes and variables ('named functions') can be called.
+        .__call__() is not an allowed attribute because that would allow
+        calling anything.
+        https://github.com/pandas-dev/pandas/pull/32460
+        """
+
+        def func(*_):
+            return 1
+
+        funcs = [func]  # noqa: F841
+
+        df.eval("@func()")
+
+        with pytest.raises(TypeError, match="Only named functions are supported"):
+            df.eval("@funcs[0]()")
+
+        with pytest.raises(TypeError, match="Only named functions are supported"):
+            df.eval("@funcs[0].__call__()")
+
+    def test_ea_dtypes(self, any_numeric_ea_and_arrow_dtype):
+        # GH#29618
+        df = DataFrame(
+            [[1, 2], [3, 4]], columns=["a", "b"], dtype=any_numeric_ea_and_arrow_dtype
+        )
+        warning = RuntimeWarning if NUMEXPR_INSTALLED else None
+        with tm.assert_produces_warning(warning):
+            result = df.eval("c = b - a")
+        expected = DataFrame(
+            [[1, 2, 1], [3, 4, 1]],
+            columns=["a", "b", "c"],
+            dtype=any_numeric_ea_and_arrow_dtype,
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_ea_dtypes_and_scalar(self):
+        # GH#29618
+        df = DataFrame([[1, 2], [3, 4]], columns=["a", "b"], dtype="Float64")
+        warning = RuntimeWarning if NUMEXPR_INSTALLED else None
+        with tm.assert_produces_warning(warning):
+            result = df.eval("c = b - 1")
+        expected = DataFrame(
+            [[1, 2, 1], [3, 4, 3]], columns=["a", "b", "c"], dtype="Float64"
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_ea_dtypes_and_scalar_operation(self, any_numeric_ea_and_arrow_dtype):
+        # GH#29618
+        df = DataFrame(
+            [[1, 2], [3, 4]], columns=["a", "b"], dtype=any_numeric_ea_and_arrow_dtype
+        )
+        result = df.eval("c = 2 - 1")
+        expected = DataFrame(
+            {
+                "a": Series([1, 3], dtype=any_numeric_ea_and_arrow_dtype),
+                "b": Series([2, 4], dtype=any_numeric_ea_and_arrow_dtype),
+                "c": Series([1, 1], dtype=result["c"].dtype),
+            }
+        )
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("dtype", ["int64", "Int64", "int64[pyarrow]"])
+    def test_query_ea_dtypes(self, dtype):
+        if dtype == "int64[pyarrow]":
+            pytest.importorskip("pyarrow")
+        # GH#50261
+        df = DataFrame({"a": Series([1, 2], dtype=dtype)})
+        ref = {2}  # noqa: F841
+        warning = RuntimeWarning if dtype == "Int64" and NUMEXPR_INSTALLED else None
+        with tm.assert_produces_warning(warning):
+            result = df.query("a in @ref")
+        expected = DataFrame({"a": Series([2], dtype=dtype, index=[1])})
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("engine", ["python", "numexpr"])
+    @pytest.mark.parametrize("dtype", ["int64", "Int64", "int64[pyarrow]"])
+    def test_query_ea_equality_comparison(self, dtype, engine):
+        # GH#50261
+        warning = RuntimeWarning if engine == "numexpr" else None
+        if engine == "numexpr" and not NUMEXPR_INSTALLED:
+            pytest.skip("numexpr not installed")
+        if dtype == "int64[pyarrow]":
+            pytest.importorskip("pyarrow")
+        df = DataFrame(
+            {"A": Series([1, 1, 2], dtype="Int64"), "B": Series([1, 2, 2], dtype=dtype)}
+        )
+        with tm.assert_produces_warning(warning):
+            result = df.query("A == B", engine=engine)
+        expected = DataFrame(
+            {
+                "A": Series([1, 2], dtype="Int64", index=[0, 2]),
+                "B": Series([1, 2], dtype=dtype, index=[0, 2]),
+            }
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_all_nat_in_object(self):
+        # GH#57068
+        now = pd.Timestamp.now("UTC")  # noqa: F841
+        df = DataFrame({"a": pd.to_datetime([None, None], utc=True)}, dtype=object)
+        result = df.query("a > @now")
+        expected = DataFrame({"a": []}, dtype=object)
+        tm.assert_frame_equal(result, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/test_reductions.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/test_reductions.py
new file mode 100644
index 0000000000000000000000000000000000000000..8b450cecfca00f0d82195d64a48a2f4e617c7660
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/test_reductions.py
@@ -0,0 +1,2133 @@
+from datetime import timedelta
+from decimal import Decimal
+import re
+
+from dateutil.tz import tzlocal
+import numpy as np
+import pytest
+
+from pandas.compat import (
+    IS64,
+    is_platform_windows,
+)
+from pandas.compat.numpy import np_version_gt2
+import pandas.util._test_decorators as td
+
+import pandas as pd
+from pandas import (
+    Categorical,
+    CategoricalDtype,
+    DataFrame,
+    DatetimeIndex,
+    Index,
+    PeriodIndex,
+    RangeIndex,
+    Series,
+    Timestamp,
+    date_range,
+    isna,
+    notna,
+    to_datetime,
+    to_timedelta,
+)
+import pandas._testing as tm
+from pandas.core import (
+    algorithms,
+    nanops,
+)
+
+is_windows_np2_or_is32 = (is_platform_windows() and not np_version_gt2) or not IS64
+is_windows_or_is32 = is_platform_windows() or not IS64
+
+
+def make_skipna_wrapper(alternative, skipna_alternative=None):
+    """
+    Create a function for calling on an array.
+
+    Parameters
+    ----------
+    alternative : function
+        The function to be called on the array with no NaNs.
+        Only used when 'skipna_alternative' is None.
+    skipna_alternative : function
+        The function to be called on the original array
+
+    Returns
+    -------
+    function
+    """
+    if skipna_alternative:
+
+        def skipna_wrapper(x):
+            return skipna_alternative(x.values)
+
+    else:
+
+        def skipna_wrapper(x):
+            nona = x.dropna()
+            if len(nona) == 0:
+                return np.nan
+            return alternative(nona)
+
+    return skipna_wrapper
+
+
+def assert_stat_op_calc(
+    opname,
+    alternative,
+    frame,
+    has_skipna=True,
+    check_dtype=True,
+    check_dates=False,
+    rtol=1e-5,
+    atol=1e-8,
+    skipna_alternative=None,
+):
+    """
+    Check that operator opname works as advertised on frame
+
+    Parameters
+    ----------
+    opname : str
+        Name of the operator to test on frame
+    alternative : function
+        Function that opname is tested against; i.e. "frame.opname()" should
+        equal "alternative(frame)".
+    frame : DataFrame
+        The object that the tests are executed on
+    has_skipna : bool, default True
+        Whether the method "opname" has the kwarg "skip_na"
+    check_dtype : bool, default True
+        Whether the dtypes of the result of "frame.opname()" and
+        "alternative(frame)" should be checked.
+    check_dates : bool, default false
+        Whether opname should be tested on a Datetime Series
+    rtol : float, default 1e-5
+        Relative tolerance.
+    atol : float, default 1e-8
+        Absolute tolerance.
+    skipna_alternative : function, default None
+        NaN-safe version of alternative
+    """
+    f = getattr(frame, opname)
+
+    if check_dates:
+        df = DataFrame({"b": date_range("1/1/2001", periods=2)})
+        with tm.assert_produces_warning(None):
+            result = getattr(df, opname)()
+        assert isinstance(result, Series)
+
+        df["a"] = range(len(df))
+        with tm.assert_produces_warning(None):
+            result = getattr(df, opname)()
+        assert isinstance(result, Series)
+        assert len(result)
+
+    if has_skipna:
+
+        def wrapper(x):
+            return alternative(x.values)
+
+        skipna_wrapper = make_skipna_wrapper(alternative, skipna_alternative)
+        result0 = f(axis=0, skipna=False)
+        result1 = f(axis=1, skipna=False)
+        tm.assert_series_equal(
+            result0, frame.apply(wrapper), check_dtype=check_dtype, rtol=rtol, atol=atol
+        )
+        tm.assert_series_equal(
+            result1,
+            frame.apply(wrapper, axis=1),
+            rtol=rtol,
+            atol=atol,
+        )
+    else:
+        skipna_wrapper = alternative
+
+    result0 = f(axis=0)
+    result1 = f(axis=1)
+    tm.assert_series_equal(
+        result0,
+        frame.apply(skipna_wrapper),
+        check_dtype=check_dtype,
+        rtol=rtol,
+        atol=atol,
+    )
+
+    if opname in ["sum", "prod"]:
+        expected = frame.apply(skipna_wrapper, axis=1)
+        tm.assert_series_equal(
+            result1, expected, check_dtype=False, rtol=rtol, atol=atol
+        )
+
+    # check dtypes
+    if check_dtype:
+        lcd_dtype = frame.values.dtype
+        assert lcd_dtype == result0.dtype
+        assert lcd_dtype == result1.dtype
+
+    # bad axis
+    with pytest.raises(ValueError, match="No axis named 2"):
+        f(axis=2)
+
+    # all NA case
+    if has_skipna:
+        all_na = frame * np.nan
+        r0 = getattr(all_na, opname)(axis=0)
+        r1 = getattr(all_na, opname)(axis=1)
+        if opname in ["sum", "prod"]:
+            unit = 1 if opname == "prod" else 0  # result for empty sum/prod
+            expected = Series(unit, index=r0.index, dtype=r0.dtype)
+            tm.assert_series_equal(r0, expected)
+            expected = Series(unit, index=r1.index, dtype=r1.dtype)
+            tm.assert_series_equal(r1, expected)
+
+
+@pytest.fixture
+def bool_frame_with_na():
+    """
+    Fixture for DataFrame of booleans with index of unique strings
+
+    Columns are ['A', 'B', 'C', 'D']; some entries are missing
+    """
+    df = DataFrame(
+        np.concatenate(
+            [np.ones((15, 4), dtype=bool), np.zeros((15, 4), dtype=bool)], axis=0
+        ),
+        index=Index([f"foo_{i}" for i in range(30)], dtype=object),
+        columns=Index(list("ABCD"), dtype=object),
+        dtype=object,
+    )
+    # set some NAs
+    df.iloc[5:10] = np.nan
+    df.iloc[15:20, -2:] = np.nan
+    return df
+
+
+@pytest.fixture
+def float_frame_with_na():
+    """
+    Fixture for DataFrame of floats with index of unique strings
+
+    Columns are ['A', 'B', 'C', 'D']; some entries are missing
+    """
+    df = DataFrame(
+        np.random.default_rng(2).standard_normal((30, 4)),
+        index=Index([f"foo_{i}" for i in range(30)], dtype=object),
+        columns=Index(list("ABCD"), dtype=object),
+    )
+    # set some NAs
+    df.iloc[5:10] = np.nan
+    df.iloc[15:20, -2:] = np.nan
+    return df
+
+
+class TestDataFrameAnalytics:
+    # ---------------------------------------------------------------------
+    # Reductions
+    @pytest.mark.parametrize("axis", [0, 1])
+    @pytest.mark.parametrize(
+        "opname",
+        [
+            "count",
+            "sum",
+            "mean",
+            "product",
+            "median",
+            "min",
+            "max",
+            "nunique",
+            "var",
+            "std",
+            "sem",
+            pytest.param("skew", marks=td.skip_if_no("scipy")),
+            pytest.param("kurt", marks=td.skip_if_no("scipy")),
+        ],
+    )
+    def test_stat_op_api_float_string_frame(self, float_string_frame, axis, opname):
+        if (opname in ("sum", "min", "max") and axis == 0) or opname in (
+            "count",
+            "nunique",
+        ):
+            getattr(float_string_frame, opname)(axis=axis)
+        else:
+            if opname in ["var", "std", "sem", "skew", "kurt"]:
+                msg = "could not convert string to float: 'bar'"
+            elif opname == "product":
+                if axis == 1:
+                    msg = "can't multiply sequence by non-int of type 'float'"
+                else:
+                    msg = "can't multiply sequence by non-int of type 'str'"
+            elif opname == "sum":
+                msg = r"unsupported operand type\(s\) for \+: 'float' and 'str'"
+            elif opname == "mean":
+                if axis == 0:
+                    # different message on different builds
+                    msg = "|".join(
+                        [
+                            r"Could not convert \['.*'\] to numeric",
+                            "Could not convert string '(bar){30}' to numeric",
+                        ]
+                    )
+                else:
+                    msg = r"unsupported operand type\(s\) for \+: 'float' and 'str'"
+            elif opname in ["min", "max"]:
+                msg = "'[><]=' not supported between instances of 'float' and 'str'"
+            elif opname == "median":
+                msg = re.compile(
+                    r"Cannot convert \[.*\] to numeric|does not support|Cannot perform",
+                    flags=re.S,
+                )
+            if not isinstance(msg, re.Pattern):
+                msg = msg + "|does not support|Cannot perform reduction"
+            with pytest.raises(TypeError, match=msg):
+                getattr(float_string_frame, opname)(axis=axis)
+        if opname != "nunique":
+            getattr(float_string_frame, opname)(axis=axis, numeric_only=True)
+
+    @pytest.mark.parametrize("axis", [0, 1])
+    @pytest.mark.parametrize(
+        "opname",
+        [
+            "count",
+            "sum",
+            "mean",
+            "product",
+            "median",
+            "min",
+            "max",
+            "var",
+            "std",
+            "sem",
+            pytest.param("skew", marks=td.skip_if_no("scipy")),
+            pytest.param("kurt", marks=td.skip_if_no("scipy")),
+        ],
+    )
+    def test_stat_op_api_float_frame(self, float_frame, axis, opname):
+        getattr(float_frame, opname)(axis=axis, numeric_only=False)
+
+    def test_stat_op_calc(self, float_frame_with_na, mixed_float_frame):
+        def count(s):
+            return notna(s).sum()
+
+        def nunique(s):
+            return len(algorithms.unique1d(s.dropna()))
+
+        def var(x):
+            return np.var(x, ddof=1)
+
+        def std(x):
+            return np.std(x, ddof=1)
+
+        def sem(x):
+            return np.std(x, ddof=1) / np.sqrt(len(x))
+
+        assert_stat_op_calc(
+            "nunique",
+            nunique,
+            float_frame_with_na,
+            has_skipna=False,
+            check_dtype=False,
+            check_dates=True,
+        )
+
+        # GH#32571: rol needed for flaky CI builds
+        # mixed types (with upcasting happening)
+        assert_stat_op_calc(
+            "sum",
+            np.sum,
+            mixed_float_frame.astype("float32"),
+            check_dtype=False,
+            rtol=1e-3,
+        )
+
+        assert_stat_op_calc(
+            "sum", np.sum, float_frame_with_na, skipna_alternative=np.nansum
+        )
+        assert_stat_op_calc("mean", np.mean, float_frame_with_na, check_dates=True)
+        assert_stat_op_calc(
+            "product", np.prod, float_frame_with_na, skipna_alternative=np.nanprod
+        )
+
+        assert_stat_op_calc("var", var, float_frame_with_na)
+        assert_stat_op_calc("std", std, float_frame_with_na)
+        assert_stat_op_calc("sem", sem, float_frame_with_na)
+
+        assert_stat_op_calc(
+            "count",
+            count,
+            float_frame_with_na,
+            has_skipna=False,
+            check_dtype=False,
+            check_dates=True,
+        )
+
+    def test_stat_op_calc_skew_kurtosis(self, float_frame_with_na):
+        sp_stats = pytest.importorskip("scipy.stats")
+
+        def skewness(x):
+            if len(x) < 3:
+                return np.nan
+            return sp_stats.skew(x, bias=False)
+
+        def kurt(x):
+            if len(x) < 4:
+                return np.nan
+            return sp_stats.kurtosis(x, bias=False)
+
+        assert_stat_op_calc("skew", skewness, float_frame_with_na)
+        assert_stat_op_calc("kurt", kurt, float_frame_with_na)
+
+    def test_median(self, float_frame_with_na, int_frame):
+        def wrapper(x):
+            if isna(x).any():
+                return np.nan
+            return np.median(x)
+
+        assert_stat_op_calc("median", wrapper, float_frame_with_na, check_dates=True)
+        assert_stat_op_calc(
+            "median", wrapper, int_frame, check_dtype=False, check_dates=True
+        )
+
+    @pytest.mark.parametrize(
+        "method", ["sum", "mean", "prod", "var", "std", "skew", "min", "max"]
+    )
+    @pytest.mark.parametrize(
+        "df",
+        [
+            DataFrame(
+                {
+                    "a": [
+                        -0.00049987540199591344,
+                        -0.0016467257772919831,
+                        0.00067695870775883013,
+                    ],
+                    "b": [-0, -0, 0.0],
+                    "c": [
+                        0.00031111847529610595,
+                        0.0014902627951905339,
+                        -0.00094099200035979691,
+                    ],
+                },
+                index=["foo", "bar", "baz"],
+                dtype="O",
+            ),
+            DataFrame({0: [np.nan, 2], 1: [np.nan, 3], 2: [np.nan, 4]}, dtype=object),
+        ],
+    )
+    @pytest.mark.filterwarnings("ignore:Mismatched null-like values:FutureWarning")
+    def test_stat_operators_attempt_obj_array(self, method, df, axis):
+        # GH#676
+        assert df.values.dtype == np.object_
+        result = getattr(df, method)(axis=axis)
+        expected = getattr(df.astype("f8"), method)(axis=axis).astype(object)
+        if axis in [1, "columns"] and method in ["min", "max"]:
+            expected[expected.isna()] = None
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("op", ["mean", "std", "var", "skew", "kurt", "sem"])
+    def test_mixed_ops(self, op):
+        # GH#16116
+        df = DataFrame(
+            {
+                "int": [1, 2, 3, 4],
+                "float": [1.0, 2.0, 3.0, 4.0],
+                "str": ["a", "b", "c", "d"],
+            }
+        )
+        msg = "|".join(
+            [
+                "Could not convert",
+                "could not convert",
+                "can't multiply sequence by non-int",
+                "does not support",
+                "Cannot perform",
+            ]
+        )
+        with pytest.raises(TypeError, match=msg):
+            getattr(df, op)()
+
+        with pd.option_context("use_bottleneck", False):
+            with pytest.raises(TypeError, match=msg):
+                getattr(df, op)()
+
+    def test_reduce_mixed_frame(self):
+        # GH 6806
+        df = DataFrame(
+            {
+                "bool_data": [True, True, False, False, False],
+                "int_data": [10, 20, 30, 40, 50],
+                "string_data": ["a", "b", "c", "d", "e"],
+            }
+        )
+        df.reindex(columns=["bool_data", "int_data", "string_data"])
+        test = df.sum(axis=0)
+        tm.assert_numpy_array_equal(
+            test.values, np.array([2, 150, "abcde"], dtype=object)
+        )
+        alt = df.T.sum(axis=1)
+        tm.assert_series_equal(test, alt)
+
+    def test_nunique(self):
+        df = DataFrame({"A": [1, 1, 1], "B": [1, 2, 3], "C": [1, np.nan, 3]})
+        tm.assert_series_equal(df.nunique(), Series({"A": 1, "B": 3, "C": 2}))
+        tm.assert_series_equal(
+            df.nunique(dropna=False), Series({"A": 1, "B": 3, "C": 3})
+        )
+        tm.assert_series_equal(df.nunique(axis=1), Series({0: 1, 1: 2, 2: 2}))
+        tm.assert_series_equal(
+            df.nunique(axis=1, dropna=False), Series({0: 1, 1: 3, 2: 2})
+        )
+
+    @pytest.mark.parametrize("tz", [None, "UTC"])
+    def test_mean_mixed_datetime_numeric(self, tz):
+        # https://github.com/pandas-dev/pandas/issues/24752
+        df = DataFrame({"A": [1, 1], "B": [Timestamp("2000", tz=tz)] * 2})
+        result = df.mean()
+        expected = Series([1.0, Timestamp("2000", tz=tz)], index=["A", "B"])
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("tz", [None, "UTC"])
+    def test_mean_includes_datetimes(self, tz):
+        # https://github.com/pandas-dev/pandas/issues/24752
+        # Behavior in 0.24.0rc1 was buggy.
+        # As of 2.0 with numeric_only=None we do *not* drop datetime columns
+        df = DataFrame({"A": [Timestamp("2000", tz=tz)] * 2})
+        result = df.mean()
+
+        expected = Series([Timestamp("2000", tz=tz)], index=["A"])
+        tm.assert_series_equal(result, expected)
+
+    def test_mean_mixed_string_decimal(self):
+        # GH 11670
+        # possible bug when calculating mean of DataFrame?
+
+        d = [
+            {"A": 2, "B": None, "C": Decimal("628.00")},
+            {"A": 1, "B": None, "C": Decimal("383.00")},
+            {"A": 3, "B": None, "C": Decimal("651.00")},
+            {"A": 2, "B": None, "C": Decimal("575.00")},
+            {"A": 4, "B": None, "C": Decimal("1114.00")},
+            {"A": 1, "B": "TEST", "C": Decimal("241.00")},
+            {"A": 2, "B": None, "C": Decimal("572.00")},
+            {"A": 4, "B": None, "C": Decimal("609.00")},
+            {"A": 3, "B": None, "C": Decimal("820.00")},
+            {"A": 5, "B": None, "C": Decimal("1223.00")},
+        ]
+
+        df = DataFrame(d)
+
+        with pytest.raises(
+            TypeError, match="unsupported operand type|does not support|Cannot perform"
+        ):
+            df.mean()
+        result = df[["A", "C"]].mean()
+        expected = Series([2.7, 681.6], index=["A", "C"], dtype=object)
+        tm.assert_series_equal(result, expected)
+
+    def test_var_std(self, datetime_frame):
+        result = datetime_frame.std(ddof=4)
+        expected = datetime_frame.apply(lambda x: x.std(ddof=4))
+        tm.assert_almost_equal(result, expected)
+
+        result = datetime_frame.var(ddof=4)
+        expected = datetime_frame.apply(lambda x: x.var(ddof=4))
+        tm.assert_almost_equal(result, expected)
+
+        arr = np.repeat(np.random.default_rng(2).random((1, 1000)), 1000, 0)
+        result = nanops.nanvar(arr, axis=0)
+        assert not (result < 0).any()
+
+        with pd.option_context("use_bottleneck", False):
+            result = nanops.nanvar(arr, axis=0)
+            assert not (result < 0).any()
+
+    @pytest.mark.parametrize("meth", ["sem", "var", "std"])
+    def test_numeric_only_flag(self, meth):
+        # GH 9201
+        df1 = DataFrame(
+            np.random.default_rng(2).standard_normal((5, 3)),
+            columns=["foo", "bar", "baz"],
+        )
+        # Cast to object to avoid implicit cast when setting entry to "100" below
+        df1 = df1.astype({"foo": object})
+        # set one entry to a number in str format
+        df1.loc[0, "foo"] = "100"
+
+        df2 = DataFrame(
+            np.random.default_rng(2).standard_normal((5, 3)),
+            columns=["foo", "bar", "baz"],
+        )
+        # Cast to object to avoid implicit cast when setting entry to "a" below
+        df2 = df2.astype({"foo": object})
+        # set one entry to a non-number str
+        df2.loc[0, "foo"] = "a"
+
+        result = getattr(df1, meth)(axis=1, numeric_only=True)
+        expected = getattr(df1[["bar", "baz"]], meth)(axis=1)
+        tm.assert_series_equal(expected, result)
+
+        result = getattr(df2, meth)(axis=1, numeric_only=True)
+        expected = getattr(df2[["bar", "baz"]], meth)(axis=1)
+        tm.assert_series_equal(expected, result)
+
+        # df1 has all numbers, df2 has a letter inside
+        msg = r"unsupported operand type\(s\) for -: 'float' and 'str'"
+        with pytest.raises(TypeError, match=msg):
+            getattr(df1, meth)(axis=1, numeric_only=False)
+        msg = "could not convert string to float: 'a'"
+        with pytest.raises(TypeError, match=msg):
+            getattr(df2, meth)(axis=1, numeric_only=False)
+
+    def test_sem(self, datetime_frame):
+        result = datetime_frame.sem(ddof=4)
+        expected = datetime_frame.apply(lambda x: x.std(ddof=4) / np.sqrt(len(x)))
+        tm.assert_almost_equal(result, expected)
+
+        arr = np.repeat(np.random.default_rng(2).random((1, 1000)), 1000, 0)
+        result = nanops.nansem(arr, axis=0)
+        assert not (result < 0).any()
+
+        with pd.option_context("use_bottleneck", False):
+            result = nanops.nansem(arr, axis=0)
+            assert not (result < 0).any()
+
+    @pytest.mark.parametrize(
+        "dropna, expected",
+        [
+            (
+                True,
+                {
+                    "A": [12],
+                    "B": [10.0],
+                    "C": [1.0],
+                    "D": ["a"],
+                    "E": Categorical(["a"], categories=["a"]),
+                    "F": DatetimeIndex(["2000-01-02"], dtype="M8[ns]"),
+                    "G": to_timedelta(["1 days"]),
+                },
+            ),
+            (
+                False,
+                {
+                    "A": [12],
+                    "B": [10.0],
+                    "C": [np.nan],
+                    "D": Series([np.nan], dtype="str"),
+                    "E": Categorical([np.nan], categories=["a"]),
+                    "F": DatetimeIndex([pd.NaT], dtype="M8[ns]"),
+                    "G": to_timedelta([pd.NaT]),
+                },
+            ),
+            (
+                True,
+                {
+                    "H": [8, 9, np.nan, np.nan],
+                    "I": [8, 9, np.nan, np.nan],
+                    "J": [1, np.nan, np.nan, np.nan],
+                    "K": Categorical(["a", np.nan, np.nan, np.nan], categories=["a"]),
+                    "L": DatetimeIndex(
+                        ["2000-01-02", "NaT", "NaT", "NaT"], dtype="M8[ns]"
+                    ),
+                    "M": to_timedelta(["1 days", "nan", "nan", "nan"]),
+                    "N": [0, 1, 2, 3],
+                },
+            ),
+            (
+                False,
+                {
+                    "H": [8, 9, np.nan, np.nan],
+                    "I": [8, 9, np.nan, np.nan],
+                    "J": [1, np.nan, np.nan, np.nan],
+                    "K": Categorical([np.nan, "a", np.nan, np.nan], categories=["a"]),
+                    "L": DatetimeIndex(
+                        ["NaT", "2000-01-02", "NaT", "NaT"], dtype="M8[ns]"
+                    ),
+                    "M": to_timedelta(["nan", "1 days", "nan", "nan"]),
+                    "N": [0, 1, 2, 3],
+                },
+            ),
+        ],
+    )
+    def test_mode_dropna(self, dropna, expected):
+        df = DataFrame(
+            {
+                "A": [12, 12, 19, 11],
+                "B": [10, 10, np.nan, 3],
+                "C": [1, np.nan, np.nan, np.nan],
+                "D": Series([np.nan, np.nan, "a", np.nan], dtype="str"),
+                "E": Categorical([np.nan, np.nan, "a", np.nan]),
+                "F": DatetimeIndex(["NaT", "2000-01-02", "NaT", "NaT"], dtype="M8[ns]"),
+                "G": to_timedelta(["1 days", "nan", "nan", "nan"]),
+                "H": [8, 8, 9, 9],
+                "I": [9, 9, 8, 8],
+                "J": [1, 1, np.nan, np.nan],
+                "K": Categorical(["a", np.nan, "a", np.nan]),
+                "L": DatetimeIndex(
+                    ["2000-01-02", "2000-01-02", "NaT", "NaT"], dtype="M8[ns]"
+                ),
+                "M": to_timedelta(["1 days", "nan", "1 days", "nan"]),
+                "N": np.arange(4, dtype="int64"),
+            }
+        )
+
+        result = df[sorted(expected.keys())].mode(dropna=dropna)
+        expected = DataFrame(expected)
+        tm.assert_frame_equal(result, expected)
+
+    def test_mode_sort_with_na(self, using_infer_string):
+        df = DataFrame({"A": [np.nan, np.nan, "a", "a"]})
+        expected = DataFrame({"A": ["a", np.nan]})
+        result = df.mode(dropna=False)
+        tm.assert_frame_equal(result, expected)
+
+    def test_mode_empty_df(self):
+        df = DataFrame([], columns=["a", "b"])
+        result = df.mode()
+        expected = DataFrame([], columns=["a", "b"], index=Index([], dtype=np.int64))
+        tm.assert_frame_equal(result, expected)
+
+    def test_operators_timedelta64(self):
+        df = DataFrame(
+            {
+                "A": date_range("2012-1-1", periods=3, freq="D"),
+                "B": date_range("2012-1-2", periods=3, freq="D"),
+                "C": Timestamp("20120101") - timedelta(minutes=5, seconds=5),
+            }
+        )
+
+        diffs = DataFrame({"A": df["A"] - df["C"], "B": df["A"] - df["B"]})
+
+        # min
+        result = diffs.min()
+        assert result.iloc[0] == diffs.loc[0, "A"]
+        assert result.iloc[1] == diffs.loc[0, "B"]
+
+        result = diffs.min(axis=1)
+        assert (result == diffs.loc[0, "B"]).all()
+
+        # max
+        result = diffs.max()
+        assert result.iloc[0] == diffs.loc[2, "A"]
+        assert result.iloc[1] == diffs.loc[2, "B"]
+
+        result = diffs.max(axis=1)
+        assert (result == diffs["A"]).all()
+
+        # abs
+        result = diffs.abs()
+        result2 = abs(diffs)
+        expected = DataFrame({"A": df["A"] - df["C"], "B": df["B"] - df["A"]})
+        tm.assert_frame_equal(result, expected)
+        tm.assert_frame_equal(result2, expected)
+
+        # mixed frame
+        mixed = diffs.copy()
+        mixed["C"] = "foo"
+        mixed["D"] = 1
+        mixed["E"] = 1.0
+        mixed["F"] = Timestamp("20130101")
+
+        # results in an object array
+        result = mixed.min()
+        expected = Series(
+            [
+                pd.Timedelta(timedelta(seconds=5 * 60 + 5)),
+                pd.Timedelta(timedelta(days=-1)),
+                "foo",
+                1,
+                1.0,
+                Timestamp("20130101"),
+            ],
+            index=mixed.columns,
+        )
+        tm.assert_series_equal(result, expected)
+
+        # excludes non-numeric
+        result = mixed.min(axis=1, numeric_only=True)
+        expected = Series([1, 1, 1.0], index=[0, 1, 2])
+        tm.assert_series_equal(result, expected)
+
+        # works when only those columns are selected
+        result = mixed[["A", "B"]].min(1)
+        expected = Series([timedelta(days=-1)] * 3)
+        tm.assert_series_equal(result, expected)
+
+        result = mixed[["A", "B"]].min()
+        expected = Series(
+            [timedelta(seconds=5 * 60 + 5), timedelta(days=-1)], index=["A", "B"]
+        )
+        tm.assert_series_equal(result, expected)
+
+        # GH 3106
+        df = DataFrame(
+            {
+                "time": date_range("20130102", periods=5),
+                "time2": date_range("20130105", periods=5),
+            }
+        )
+        df["off1"] = df["time2"] - df["time"]
+        assert df["off1"].dtype == "timedelta64[ns]"
+
+        df["off2"] = df["time"] - df["time2"]
+        df._consolidate_inplace()
+        assert df["off1"].dtype == "timedelta64[ns]"
+        assert df["off2"].dtype == "timedelta64[ns]"
+
+    def test_std_timedelta64_skipna_false(self):
+        # GH#37392
+        tdi = pd.timedelta_range("1 Day", periods=10)
+        df = DataFrame({"A": tdi, "B": tdi}, copy=True)
+        df.iloc[-2, -1] = pd.NaT
+
+        result = df.std(skipna=False)
+        expected = Series(
+            [df["A"].std(), pd.NaT], index=["A", "B"], dtype="timedelta64[ns]"
+        )
+        tm.assert_series_equal(result, expected)
+
+        result = df.std(axis=1, skipna=False)
+        expected = Series([pd.Timedelta(0)] * 8 + [pd.NaT, pd.Timedelta(0)])
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "values", [["2022-01-01", "2022-01-02", pd.NaT, "2022-01-03"], 4 * [pd.NaT]]
+    )
+    def test_std_datetime64_with_nat(
+        self, values, skipna, using_array_manager, request, unit
+    ):
+        # GH#51335
+        if using_array_manager and (
+            not skipna or all(value is pd.NaT for value in values)
+        ):
+            mark = pytest.mark.xfail(
+                reason="GH#51446: Incorrect type inference on NaT in reduction result"
+            )
+            request.applymarker(mark)
+        dti = to_datetime(values).as_unit(unit)
+        df = DataFrame({"a": dti})
+        result = df.std(skipna=skipna)
+        if not skipna or all(value is pd.NaT for value in values):
+            expected = Series({"a": pd.NaT}, dtype=f"timedelta64[{unit}]")
+        else:
+            # 86400000000000ns == 1 day
+            expected = Series({"a": 86400000000000}, dtype=f"timedelta64[{unit}]")
+        tm.assert_series_equal(result, expected)
+
+    def test_sum_corner(self):
+        empty_frame = DataFrame()
+
+        axis0 = empty_frame.sum(0)
+        axis1 = empty_frame.sum(1)
+        assert isinstance(axis0, Series)
+        assert isinstance(axis1, Series)
+        assert len(axis0) == 0
+        assert len(axis1) == 0
+
+    @pytest.mark.parametrize(
+        "index",
+        [
+            RangeIndex(0),
+            DatetimeIndex([]),
+            Index([], dtype=np.int64),
+            Index([], dtype=np.float64),
+            DatetimeIndex([], freq="ME"),
+            PeriodIndex([], freq="D"),
+        ],
+    )
+    def test_axis_1_empty(self, all_reductions, index):
+        df = DataFrame(columns=["a"], index=index)
+        result = getattr(df, all_reductions)(axis=1)
+        if all_reductions in ("any", "all"):
+            expected_dtype = "bool"
+        elif all_reductions == "count":
+            expected_dtype = "int64"
+        else:
+            expected_dtype = "object"
+        expected = Series([], index=index, dtype=expected_dtype)
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("min_count", [0, 1])
+    def test_axis_1_sum_na(self, string_dtype_no_object, skipna, min_count):
+        # https://github.com/pandas-dev/pandas/issues/60229
+        dtype = string_dtype_no_object
+        df = DataFrame({"a": [pd.NA]}, dtype=dtype)
+        result = df.sum(axis=1, skipna=skipna, min_count=min_count)
+        value = "" if skipna and min_count == 0 else pd.NA
+        expected = Series([value], dtype=dtype)
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("method, unit", [("sum", 0), ("prod", 1)])
+    @pytest.mark.parametrize("numeric_only", [None, True, False])
+    def test_sum_prod_nanops(self, method, unit, numeric_only):
+        idx = ["a", "b", "c"]
+        df = DataFrame({"a": [unit, unit], "b": [unit, np.nan], "c": [np.nan, np.nan]})
+        # The default
+        result = getattr(df, method)(numeric_only=numeric_only)
+        expected = Series([unit, unit, unit], index=idx, dtype="float64")
+        tm.assert_series_equal(result, expected)
+
+        # min_count=1
+        result = getattr(df, method)(numeric_only=numeric_only, min_count=1)
+        expected = Series([unit, unit, np.nan], index=idx)
+        tm.assert_series_equal(result, expected)
+
+        # min_count=0
+        result = getattr(df, method)(numeric_only=numeric_only, min_count=0)
+        expected = Series([unit, unit, unit], index=idx, dtype="float64")
+        tm.assert_series_equal(result, expected)
+
+        result = getattr(df.iloc[1:], method)(numeric_only=numeric_only, min_count=1)
+        expected = Series([unit, np.nan, np.nan], index=idx)
+        tm.assert_series_equal(result, expected)
+
+        # min_count > 1
+        df = DataFrame({"A": [unit] * 10, "B": [unit] * 5 + [np.nan] * 5})
+        result = getattr(df, method)(numeric_only=numeric_only, min_count=5)
+        expected = Series(result, index=["A", "B"])
+        tm.assert_series_equal(result, expected)
+
+        result = getattr(df, method)(numeric_only=numeric_only, min_count=6)
+        expected = Series(result, index=["A", "B"])
+        tm.assert_series_equal(result, expected)
+
+    def test_sum_nanops_timedelta(self):
+        # prod isn't defined on timedeltas
+        idx = ["a", "b", "c"]
+        df = DataFrame({"a": [0, 0], "b": [0, np.nan], "c": [np.nan, np.nan]})
+
+        df2 = df.apply(to_timedelta)
+
+        # 0 by default
+        result = df2.sum()
+        expected = Series([0, 0, 0], dtype="m8[ns]", index=idx)
+        tm.assert_series_equal(result, expected)
+
+        # min_count=0
+        result = df2.sum(min_count=0)
+        tm.assert_series_equal(result, expected)
+
+        # min_count=1
+        result = df2.sum(min_count=1)
+        expected = Series([0, 0, np.nan], dtype="m8[ns]", index=idx)
+        tm.assert_series_equal(result, expected)
+
+    def test_sum_nanops_min_count(self):
+        # https://github.com/pandas-dev/pandas/issues/39738
+        df = DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]})
+        result = df.sum(min_count=10)
+        expected = Series([np.nan, np.nan], index=["x", "y"])
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("float_type", ["float16", "float32", "float64"])
+    @pytest.mark.parametrize(
+        "kwargs, expected_result",
+        [
+            ({"axis": 1, "min_count": 2}, [3.2, 5.3, np.nan]),
+            ({"axis": 1, "min_count": 3}, [np.nan, np.nan, np.nan]),
+            ({"axis": 1, "skipna": False}, [3.2, 5.3, np.nan]),
+        ],
+    )
+    def test_sum_nanops_dtype_min_count(self, float_type, kwargs, expected_result):
+        # GH#46947
+        df = DataFrame({"a": [1.0, 2.3, 4.4], "b": [2.2, 3, np.nan]}, dtype=float_type)
+        result = df.sum(**kwargs)
+        expected = Series(expected_result).astype(float_type)
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("float_type", ["float16", "float32", "float64"])
+    @pytest.mark.parametrize(
+        "kwargs, expected_result",
+        [
+            ({"axis": 1, "min_count": 2}, [2.0, 4.0, np.nan]),
+            ({"axis": 1, "min_count": 3}, [np.nan, np.nan, np.nan]),
+            ({"axis": 1, "skipna": False}, [2.0, 4.0, np.nan]),
+        ],
+    )
+    def test_prod_nanops_dtype_min_count(self, float_type, kwargs, expected_result):
+        # GH#46947
+        df = DataFrame(
+            {"a": [1.0, 2.0, 4.4], "b": [2.0, 2.0, np.nan]}, dtype=float_type
+        )
+        result = df.prod(**kwargs)
+        expected = Series(expected_result).astype(float_type)
+        tm.assert_series_equal(result, expected)
+
+    def test_sum_object(self, float_frame):
+        values = float_frame.values.astype(int)
+        frame = DataFrame(values, index=float_frame.index, columns=float_frame.columns)
+        deltas = frame * timedelta(1)
+        deltas.sum()
+
+    def test_sum_bool(self, float_frame):
+        # ensure this works, bug report
+        bools = np.isnan(float_frame)
+        bools.sum(1)
+        bools.sum(0)
+
+    def test_sum_mixed_datetime(self):
+        # GH#30886
+        df = DataFrame({"A": date_range("2000", periods=4), "B": [1, 2, 3, 4]}).reindex(
+            [2, 3, 4]
+        )
+        with pytest.raises(TypeError, match="does not support reduction 'sum'"):
+            df.sum()
+
+    def test_mean_corner(self, float_frame, float_string_frame):
+        # unit test when have object data
+        msg = "Could not convert|does not support|Cannot perform"
+        with pytest.raises(TypeError, match=msg):
+            float_string_frame.mean(axis=0)
+
+        # xs sum mixed type, just want to know it works...
+        with pytest.raises(TypeError, match="unsupported operand type"):
+            float_string_frame.mean(axis=1)
+
+        # take mean of boolean column
+        float_frame["bool"] = float_frame["A"] > 0
+        means = float_frame.mean(0)
+        assert means["bool"] == float_frame["bool"].values.mean()
+
+    def test_mean_datetimelike(self):
+        # GH#24757 check that datetimelike are excluded by default, handled
+        #  correctly with numeric_only=True
+        #  As of 2.0, datetimelike are *not* excluded with numeric_only=None
+
+        df = DataFrame(
+            {
+                "A": np.arange(3),
+                "B": date_range("2016-01-01", periods=3),
+                "C": pd.timedelta_range("1D", periods=3),
+                "D": pd.period_range("2016", periods=3, freq="Y"),
+            }
+        )
+        result = df.mean(numeric_only=True)
+        expected = Series({"A": 1.0})
+        tm.assert_series_equal(result, expected)
+
+        with pytest.raises(TypeError, match="mean is not implemented for PeriodArray"):
+            df.mean()
+
+    def test_mean_datetimelike_numeric_only_false(self):
+        df = DataFrame(
+            {
+                "A": np.arange(3),
+                "B": date_range("2016-01-01", periods=3),
+                "C": pd.timedelta_range("1D", periods=3),
+            }
+        )
+
+        # datetime(tz) and timedelta work
+        result = df.mean(numeric_only=False)
+        expected = Series({"A": 1, "B": df.loc[1, "B"], "C": df.loc[1, "C"]})
+        tm.assert_series_equal(result, expected)
+
+        # mean of period is not allowed
+        df["D"] = pd.period_range("2016", periods=3, freq="Y")
+
+        with pytest.raises(TypeError, match="mean is not implemented for Period"):
+            df.mean(numeric_only=False)
+
+    def test_mean_extensionarray_numeric_only_true(self):
+        # https://github.com/pandas-dev/pandas/issues/33256
+        arr = np.random.default_rng(2).integers(1000, size=(10, 5))
+        df = DataFrame(arr, dtype="Int64")
+        result = df.mean(numeric_only=True)
+        expected = DataFrame(arr).mean().astype("Float64")
+        tm.assert_series_equal(result, expected)
+
+    def test_stats_mixed_type(self, float_string_frame):
+        with pytest.raises(TypeError, match="could not convert"):
+            float_string_frame.std(1)
+        with pytest.raises(TypeError, match="could not convert"):
+            float_string_frame.var(1)
+        with pytest.raises(TypeError, match="unsupported operand type"):
+            float_string_frame.mean(1)
+        with pytest.raises(TypeError, match="could not convert"):
+            float_string_frame.skew(1)
+
+    def test_sum_bools(self):
+        df = DataFrame(index=range(1), columns=range(10))
+        bools = isna(df)
+        assert bools.sum(axis=1)[0] == 10
+
+    # ----------------------------------------------------------------------
+    # Index of max / min
+
+    @pytest.mark.parametrize("skipna", [True, False])
+    @pytest.mark.parametrize("axis", [0, 1])
+    def test_idxmin(self, float_frame, int_frame, skipna, axis):
+        frame = float_frame
+        frame.iloc[5:10] = np.nan
+        frame.iloc[15:20, -2:] = np.nan
+        for df in [frame, int_frame]:
+            warn = None
+            if skipna is False or axis == 1:
+                warn = None if df is int_frame else FutureWarning
+            msg = "The behavior of DataFrame.idxmin with all-NA values"
+            with tm.assert_produces_warning(warn, match=msg):
+                result = df.idxmin(axis=axis, skipna=skipna)
+
+            msg2 = "The behavior of Series.idxmin"
+            with tm.assert_produces_warning(warn, match=msg2):
+                expected = df.apply(Series.idxmin, axis=axis, skipna=skipna)
+            expected = expected.astype(df.index.dtype)
+            tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("axis", [0, 1])
+    @pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning")
+    def test_idxmin_empty(self, index, skipna, axis):
+        # GH53265
+        if axis == 0:
+            frame = DataFrame(index=index)
+        else:
+            frame = DataFrame(columns=index)
+
+        result = frame.idxmin(axis=axis, skipna=skipna)
+        expected = Series(dtype=index.dtype)
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("numeric_only", [True, False])
+    def test_idxmin_numeric_only(self, numeric_only):
+        df = DataFrame({"a": [2, 3, 1], "b": [2, 1, 1], "c": list("xyx")})
+        result = df.idxmin(numeric_only=numeric_only)
+        if numeric_only:
+            expected = Series([2, 1], index=["a", "b"])
+        else:
+            expected = Series([2, 1, 0], index=["a", "b", "c"])
+        tm.assert_series_equal(result, expected)
+
+    def test_idxmin_axis_2(self, float_frame):
+        frame = float_frame
+        msg = "No axis named 2 for object type DataFrame"
+        with pytest.raises(ValueError, match=msg):
+            frame.idxmin(axis=2)
+
+    @pytest.mark.parametrize("axis", [0, 1])
+    def test_idxmax(self, float_frame, int_frame, skipna, axis):
+        frame = float_frame
+        frame.iloc[5:10] = np.nan
+        frame.iloc[15:20, -2:] = np.nan
+        for df in [frame, int_frame]:
+            warn = None
+            if skipna is False or axis == 1:
+                warn = None if df is int_frame else FutureWarning
+            msg = "The behavior of DataFrame.idxmax with all-NA values"
+            with tm.assert_produces_warning(warn, match=msg):
+                result = df.idxmax(axis=axis, skipna=skipna)
+
+            msg2 = "The behavior of Series.idxmax"
+            with tm.assert_produces_warning(warn, match=msg2):
+                expected = df.apply(Series.idxmax, axis=axis, skipna=skipna)
+            expected = expected.astype(df.index.dtype)
+            tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("axis", [0, 1])
+    @pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning")
+    def test_idxmax_empty(self, index, skipna, axis):
+        # GH53265
+        if axis == 0:
+            frame = DataFrame(index=index)
+        else:
+            frame = DataFrame(columns=index)
+
+        result = frame.idxmax(axis=axis, skipna=skipna)
+        expected = Series(dtype=index.dtype)
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("numeric_only", [True, False])
+    def test_idxmax_numeric_only(self, numeric_only):
+        df = DataFrame({"a": [2, 3, 1], "b": [2, 1, 1], "c": list("xyx")})
+        result = df.idxmax(numeric_only=numeric_only)
+        if numeric_only:
+            expected = Series([1, 0], index=["a", "b"])
+        else:
+            expected = Series([1, 0, 1], index=["a", "b", "c"])
+        tm.assert_series_equal(result, expected)
+
+    def test_idxmax_arrow_types(self):
+        # GH#55368
+        pytest.importorskip("pyarrow")
+
+        df = DataFrame({"a": [2, 3, 1], "b": [2, 1, 1]}, dtype="int64[pyarrow]")
+        result = df.idxmax()
+        expected = Series([1, 0], index=["a", "b"])
+        tm.assert_series_equal(result, expected)
+
+        result = df.idxmin()
+        expected = Series([2, 1], index=["a", "b"])
+        tm.assert_series_equal(result, expected)
+
+        df = DataFrame({"a": ["b", "c", "a"]}, dtype="string[pyarrow]")
+        result = df.idxmax(numeric_only=False)
+        expected = Series([1], index=["a"])
+        tm.assert_series_equal(result, expected)
+
+        result = df.idxmin(numeric_only=False)
+        expected = Series([2], index=["a"])
+        tm.assert_series_equal(result, expected)
+
+    def test_idxmax_axis_2(self, float_frame):
+        frame = float_frame
+        msg = "No axis named 2 for object type DataFrame"
+        with pytest.raises(ValueError, match=msg):
+            frame.idxmax(axis=2)
+
+    def test_idxmax_mixed_dtype(self):
+        # don't cast to object, which would raise in nanops
+        dti = date_range("2016-01-01", periods=3)
+
+        # Copying dti is needed for ArrayManager otherwise when we set
+        #  df.loc[0, 3] = pd.NaT below it edits dti
+        df = DataFrame({1: [0, 2, 1], 2: range(3)[::-1], 3: dti.copy(deep=True)})
+
+        result = df.idxmax()
+        expected = Series([1, 0, 2], index=[1, 2, 3])
+        tm.assert_series_equal(result, expected)
+
+        result = df.idxmin()
+        expected = Series([0, 2, 0], index=[1, 2, 3])
+        tm.assert_series_equal(result, expected)
+
+        # with NaTs
+        df.loc[0, 3] = pd.NaT
+        result = df.idxmax()
+        expected = Series([1, 0, 2], index=[1, 2, 3])
+        tm.assert_series_equal(result, expected)
+
+        result = df.idxmin()
+        expected = Series([0, 2, 1], index=[1, 2, 3])
+        tm.assert_series_equal(result, expected)
+
+        # with multi-column dt64 block
+        df[4] = dti[::-1]
+        df._consolidate_inplace()
+
+        result = df.idxmax()
+        expected = Series([1, 0, 2, 0], index=[1, 2, 3, 4])
+        tm.assert_series_equal(result, expected)
+
+        result = df.idxmin()
+        expected = Series([0, 2, 1, 2], index=[1, 2, 3, 4])
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "op, expected_value",
+        [("idxmax", [0, 4]), ("idxmin", [0, 5])],
+    )
+    def test_idxmax_idxmin_convert_dtypes(self, op, expected_value):
+        # GH 40346
+        df = DataFrame(
+            {
+                "ID": [100, 100, 100, 200, 200, 200],
+                "value": [0, 0, 0, 1, 2, 0],
+            },
+            dtype="Int64",
+        )
+        df = df.groupby("ID")
+
+        result = getattr(df, op)()
+        expected = DataFrame(
+            {"value": expected_value},
+            index=Index([100, 200], name="ID", dtype="Int64"),
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_idxmax_dt64_multicolumn_axis1(self):
+        dti = date_range("2016-01-01", periods=3)
+        df = DataFrame({3: dti, 4: dti[::-1]}, copy=True)
+        df.iloc[0, 0] = pd.NaT
+
+        df._consolidate_inplace()
+
+        result = df.idxmax(axis=1)
+        expected = Series([4, 3, 3])
+        tm.assert_series_equal(result, expected)
+
+        result = df.idxmin(axis=1)
+        expected = Series([4, 3, 4])
+        tm.assert_series_equal(result, expected)
+
+    # ----------------------------------------------------------------------
+    # Logical reductions
+
+    @pytest.mark.parametrize("opname", ["any", "all"])
+    @pytest.mark.parametrize("axis", [0, 1])
+    @pytest.mark.parametrize("bool_only", [False, True])
+    def test_any_all_mixed_float(self, opname, axis, bool_only, float_string_frame):
+        # make sure op works on mixed-type frame
+        mixed = float_string_frame
+        mixed["_bool_"] = np.random.default_rng(2).standard_normal(len(mixed)) > 0.5
+
+        getattr(mixed, opname)(axis=axis, bool_only=bool_only)
+
+    @pytest.mark.parametrize("opname", ["any", "all"])
+    @pytest.mark.parametrize("axis", [0, 1])
+    def test_any_all_bool_with_na(self, opname, axis, bool_frame_with_na):
+        getattr(bool_frame_with_na, opname)(axis=axis, bool_only=False)
+
+    @pytest.mark.filterwarnings("ignore:Downcasting object dtype arrays:FutureWarning")
+    @pytest.mark.parametrize("opname", ["any", "all"])
+    def test_any_all_bool_frame(self, opname, bool_frame_with_na):
+        # GH#12863: numpy gives back non-boolean data for object type
+        # so fill NaNs to compare with pandas behavior
+        frame = bool_frame_with_na.fillna(True)
+        alternative = getattr(np, opname)
+        f = getattr(frame, opname)
+
+        def skipna_wrapper(x):
+            nona = x.dropna().values
+            return alternative(nona)
+
+        def wrapper(x):
+            return alternative(x.values)
+
+        result0 = f(axis=0, skipna=False)
+        result1 = f(axis=1, skipna=False)
+
+        tm.assert_series_equal(result0, frame.apply(wrapper))
+        tm.assert_series_equal(result1, frame.apply(wrapper, axis=1))
+
+        result0 = f(axis=0)
+        result1 = f(axis=1)
+
+        tm.assert_series_equal(result0, frame.apply(skipna_wrapper))
+        tm.assert_series_equal(
+            result1, frame.apply(skipna_wrapper, axis=1), check_dtype=False
+        )
+
+        # bad axis
+        with pytest.raises(ValueError, match="No axis named 2"):
+            f(axis=2)
+
+        # all NA case
+        all_na = frame * np.nan
+        r0 = getattr(all_na, opname)(axis=0)
+        r1 = getattr(all_na, opname)(axis=1)
+        if opname == "any":
+            assert not r0.any()
+            assert not r1.any()
+        else:
+            assert r0.all()
+            assert r1.all()
+
+    def test_any_all_extra(self):
+        df = DataFrame(
+            {
+                "A": [True, False, False],
+                "B": [True, True, False],
+                "C": [True, True, True],
+            },
+            index=["a", "b", "c"],
+        )
+        result = df[["A", "B"]].any(axis=1)
+        expected = Series([True, True, False], index=["a", "b", "c"])
+        tm.assert_series_equal(result, expected)
+
+        result = df[["A", "B"]].any(axis=1, bool_only=True)
+        tm.assert_series_equal(result, expected)
+
+        result = df.all(1)
+        expected = Series([True, False, False], index=["a", "b", "c"])
+        tm.assert_series_equal(result, expected)
+
+        result = df.all(1, bool_only=True)
+        tm.assert_series_equal(result, expected)
+
+        # Axis is None
+        result = df.all(axis=None).item()
+        assert result is False
+
+        result = df.any(axis=None).item()
+        assert result is True
+
+        result = df[["C"]].all(axis=None).item()
+        assert result is True
+
+    @pytest.mark.parametrize("axis", [0, 1])
+    @pytest.mark.parametrize("bool_agg_func", ["any", "all"])
+    @pytest.mark.parametrize("skipna", [True, False])
+    def test_any_all_object_dtype(self, axis, bool_agg_func, skipna):
+        # GH#35450
+        df = DataFrame(
+            data=[
+                [1, np.nan, np.nan, True],
+                [np.nan, 2, np.nan, True],
+                [np.nan, np.nan, np.nan, True],
+                [np.nan, np.nan, "5", np.nan],
+            ]
+        )
+        result = getattr(df, bool_agg_func)(axis=axis, skipna=skipna)
+        expected = Series([True, True, True, True])
+        tm.assert_series_equal(result, expected)
+
+    # GH#50947 deprecates this but it is not emitting a warning in some builds.
+    @pytest.mark.filterwarnings(
+        "ignore:'any' with datetime64 dtypes is deprecated.*:FutureWarning"
+    )
+    def test_any_datetime(self):
+        # GH 23070
+        float_data = [1, np.nan, 3, np.nan]
+        datetime_data = [
+            Timestamp("1960-02-15"),
+            Timestamp("1960-02-16"),
+            pd.NaT,
+            pd.NaT,
+        ]
+        df = DataFrame({"A": float_data, "B": datetime_data})
+
+        result = df.any(axis=1)
+
+        expected = Series([True, True, True, False])
+        tm.assert_series_equal(result, expected)
+
+    def test_any_all_bool_only(self):
+        # GH 25101
+        df = DataFrame(
+            {"col1": [1, 2, 3], "col2": [4, 5, 6], "col3": [None, None, None]},
+            columns=Index(["col1", "col2", "col3"], dtype=object),
+        )
+
+        result = df.all(bool_only=True)
+        expected = Series(dtype=np.bool_, index=[])
+        tm.assert_series_equal(result, expected)
+
+        df = DataFrame(
+            {
+                "col1": [1, 2, 3],
+                "col2": [4, 5, 6],
+                "col3": [None, None, None],
+                "col4": [False, False, True],
+            }
+        )
+
+        result = df.all(bool_only=True)
+        expected = Series({"col4": False})
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "func, data, expected",
+        [
+            (np.any, {}, False),
+            (np.all, {}, True),
+            (np.any, {"A": []}, False),
+            (np.all, {"A": []}, True),
+            (np.any, {"A": [False, False]}, False),
+            (np.all, {"A": [False, False]}, False),
+            (np.any, {"A": [True, False]}, True),
+            (np.all, {"A": [True, False]}, False),
+            (np.any, {"A": [True, True]}, True),
+            (np.all, {"A": [True, True]}, True),
+            (np.any, {"A": [False], "B": [False]}, False),
+            (np.all, {"A": [False], "B": [False]}, False),
+            (np.any, {"A": [False, False], "B": [False, True]}, True),
+            (np.all, {"A": [False, False], "B": [False, True]}, False),
+            # other types
+            (np.all, {"A": Series([0.0, 1.0], dtype="float")}, False),
+            (np.any, {"A": Series([0.0, 1.0], dtype="float")}, True),
+            (np.all, {"A": Series([0, 1], dtype=int)}, False),
+            (np.any, {"A": Series([0, 1], dtype=int)}, True),
+            pytest.param(np.all, {"A": Series([0, 1], dtype="M8[ns]")}, False),
+            pytest.param(np.all, {"A": Series([0, 1], dtype="M8[ns, UTC]")}, False),
+            pytest.param(np.any, {"A": Series([0, 1], dtype="M8[ns]")}, True),
+            pytest.param(np.any, {"A": Series([0, 1], dtype="M8[ns, UTC]")}, True),
+            pytest.param(np.all, {"A": Series([1, 2], dtype="M8[ns]")}, True),
+            pytest.param(np.all, {"A": Series([1, 2], dtype="M8[ns, UTC]")}, True),
+            pytest.param(np.any, {"A": Series([1, 2], dtype="M8[ns]")}, True),
+            pytest.param(np.any, {"A": Series([1, 2], dtype="M8[ns, UTC]")}, True),
+            pytest.param(np.all, {"A": Series([0, 1], dtype="m8[ns]")}, False),
+            pytest.param(np.any, {"A": Series([0, 1], dtype="m8[ns]")}, True),
+            pytest.param(np.all, {"A": Series([1, 2], dtype="m8[ns]")}, True),
+            pytest.param(np.any, {"A": Series([1, 2], dtype="m8[ns]")}, True),
+            # np.all on Categorical raises, so the reduction drops the
+            #  column, so all is being done on an empty Series, so is True
+            (np.all, {"A": Series([0, 1], dtype="category")}, True),
+            (np.any, {"A": Series([0, 1], dtype="category")}, False),
+            (np.all, {"A": Series([1, 2], dtype="category")}, True),
+            (np.any, {"A": Series([1, 2], dtype="category")}, False),
+            # Mix GH#21484
+            pytest.param(
+                np.all,
+                {
+                    "A": Series([10, 20], dtype="M8[ns]"),
+                    "B": Series([10, 20], dtype="m8[ns]"),
+                },
+                True,
+            ),
+        ],
+    )
+    def test_any_all_np_func(self, func, data, expected):
+        # GH 19976
+        data = DataFrame(data)
+
+        if any(isinstance(x, CategoricalDtype) for x in data.dtypes):
+            with pytest.raises(
+                TypeError, match="dtype category does not support reduction"
+            ):
+                func(data)
+
+            # method version
+            with pytest.raises(
+                TypeError, match="dtype category does not support reduction"
+            ):
+                getattr(DataFrame(data), func.__name__)(axis=None)
+        else:
+            msg = "'(any|all)' with datetime64 dtypes is deprecated"
+            if data.dtypes.apply(lambda x: x.kind == "M").any():
+                warn = FutureWarning
+            else:
+                warn = None
+
+            with tm.assert_produces_warning(warn, match=msg, check_stacklevel=False):
+                # GH#34479
+                result = func(data)
+            assert isinstance(result, np.bool_)
+            assert result.item() is expected
+
+            # method version
+            with tm.assert_produces_warning(warn, match=msg):
+                # GH#34479
+                result = getattr(DataFrame(data), func.__name__)(axis=None)
+            assert isinstance(result, np.bool_)
+            assert result.item() is expected
+
+    def test_any_all_object(self):
+        # GH 19976
+        result = np.all(DataFrame(columns=["a", "b"])).item()
+        assert result is True
+
+        result = np.any(DataFrame(columns=["a", "b"])).item()
+        assert result is False
+
+    def test_any_all_object_bool_only(self):
+        df = DataFrame({"A": ["foo", 2], "B": [True, False]}).astype(object)
+        df._consolidate_inplace()
+        df["C"] = Series([True, True])
+
+        # Categorical of bools is _not_ considered booly
+        df["D"] = df["C"].astype("category")
+
+        # The underlying bug is in DataFrame._get_bool_data, so we check
+        #  that while we're here
+        res = df._get_bool_data()
+        expected = df[["C"]]
+        tm.assert_frame_equal(res, expected)
+
+        res = df.all(bool_only=True, axis=0)
+        expected = Series([True], index=["C"])
+        tm.assert_series_equal(res, expected)
+
+        # operating on a subset of columns should not produce a _larger_ Series
+        res = df[["B", "C"]].all(bool_only=True, axis=0)
+        tm.assert_series_equal(res, expected)
+
+        assert df.all(bool_only=True, axis=None)
+
+        res = df.any(bool_only=True, axis=0)
+        expected = Series([True], index=["C"])
+        tm.assert_series_equal(res, expected)
+
+        # operating on a subset of columns should not produce a _larger_ Series
+        res = df[["C"]].any(bool_only=True, axis=0)
+        tm.assert_series_equal(res, expected)
+
+        assert df.any(bool_only=True, axis=None)
+
+    # ---------------------------------------------------------------------
+    # Unsorted
+
+    def test_series_broadcasting(self):
+        # smoke test for numpy warnings
+        # GH 16378, GH 16306
+        df = DataFrame([1.0, 1.0, 1.0])
+        df_nan = DataFrame({"A": [np.nan, 2.0, np.nan]})
+        s = Series([1, 1, 1])
+        s_nan = Series([np.nan, np.nan, 1])
+
+        with tm.assert_produces_warning(None):
+            df_nan.clip(lower=s, axis=0)
+            for op in ["lt", "le", "gt", "ge", "eq", "ne"]:
+                getattr(df, op)(s_nan, axis=0)
+
+
+class TestDataFrameReductions:
+    def test_min_max_dt64_with_NaT(self):
+        # Both NaT and Timestamp are in DataFrame.
+        df = DataFrame({"foo": [pd.NaT, pd.NaT, Timestamp("2012-05-01")]})
+
+        res = df.min()
+        exp = Series([Timestamp("2012-05-01")], index=["foo"])
+        tm.assert_series_equal(res, exp)
+
+        res = df.max()
+        exp = Series([Timestamp("2012-05-01")], index=["foo"])
+        tm.assert_series_equal(res, exp)
+
+        # GH12941, only NaTs are in DataFrame.
+        df = DataFrame({"foo": [pd.NaT, pd.NaT]})
+
+        res = df.min()
+        exp = Series([pd.NaT], index=["foo"])
+        tm.assert_series_equal(res, exp)
+
+        res = df.max()
+        exp = Series([pd.NaT], index=["foo"])
+        tm.assert_series_equal(res, exp)
+
+    def test_min_max_dt64_with_NaT_skipna_false(self, request, tz_naive_fixture):
+        # GH#36907
+        tz = tz_naive_fixture
+        if isinstance(tz, tzlocal) and is_platform_windows():
+            pytest.skip(
+                "GH#37659 OSError raised within tzlocal bc Windows "
+                "chokes in times before 1970-01-01"
+            )
+
+        df = DataFrame(
+            {
+                "a": [
+                    Timestamp("2020-01-01 08:00:00", tz=tz),
+                    Timestamp("1920-02-01 09:00:00", tz=tz),
+                ],
+                "b": [Timestamp("2020-02-01 08:00:00", tz=tz), pd.NaT],
+            }
+        )
+        res = df.min(axis=1, skipna=False)
+        expected = Series([df.loc[0, "a"], pd.NaT])
+        assert expected.dtype == df["a"].dtype
+
+        tm.assert_series_equal(res, expected)
+
+        res = df.max(axis=1, skipna=False)
+        expected = Series([df.loc[0, "b"], pd.NaT])
+        assert expected.dtype == df["a"].dtype
+
+        tm.assert_series_equal(res, expected)
+
+    def test_min_max_dt64_api_consistency_with_NaT(self):
+        # Calling the following sum functions returned an error for dataframes but
+        # returned NaT for series. These tests check that the API is consistent in
+        # min/max calls on empty Series/DataFrames. See GH:33704 for more
+        # information
+        df = DataFrame({"x": to_datetime([])})
+        expected_dt_series = Series(to_datetime([]))
+        # check axis 0
+        assert (df.min(axis=0).x is pd.NaT) == (expected_dt_series.min() is pd.NaT)
+        assert (df.max(axis=0).x is pd.NaT) == (expected_dt_series.max() is pd.NaT)
+
+        # check axis 1
+        tm.assert_series_equal(df.min(axis=1), expected_dt_series)
+        tm.assert_series_equal(df.max(axis=1), expected_dt_series)
+
+    def test_min_max_dt64_api_consistency_empty_df(self):
+        # check DataFrame/Series api consistency when calling min/max on an empty
+        # DataFrame/Series.
+        df = DataFrame({"x": []})
+        expected_float_series = Series([], dtype=float)
+        # check axis 0
+        assert np.isnan(df.min(axis=0).x) == np.isnan(expected_float_series.min())
+        assert np.isnan(df.max(axis=0).x) == np.isnan(expected_float_series.max())
+        # check axis 1
+        tm.assert_series_equal(df.min(axis=1), expected_float_series)
+        tm.assert_series_equal(df.min(axis=1), expected_float_series)
+
+    @pytest.mark.parametrize(
+        "initial",
+        ["2018-10-08 13:36:45+00:00", "2018-10-08 13:36:45+03:00"],  # Non-UTC timezone
+    )
+    @pytest.mark.parametrize("method", ["min", "max"])
+    def test_preserve_timezone(self, initial: str, method):
+        # GH 28552
+        initial_dt = to_datetime(initial)
+        expected = Series([initial_dt])
+        df = DataFrame([expected])
+        result = getattr(df, method)(axis=1)
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("method", ["min", "max"])
+    def test_minmax_tzaware_skipna_axis_1(self, method, skipna):
+        # GH#51242
+        val = to_datetime("1900-01-01", utc=True)
+        df = DataFrame(
+            {"a": Series([pd.NaT, pd.NaT, val]), "b": Series([pd.NaT, val, val])}
+        )
+        op = getattr(df, method)
+        result = op(axis=1, skipna=skipna)
+        if skipna:
+            expected = Series([pd.NaT, val, val])
+        else:
+            expected = Series([pd.NaT, pd.NaT, val])
+        tm.assert_series_equal(result, expected)
+
+    def test_frame_any_with_timedelta(self):
+        # GH#17667
+        df = DataFrame(
+            {
+                "a": Series([0, 0]),
+                "t": Series([to_timedelta(0, "s"), to_timedelta(1, "ms")]),
+            }
+        )
+
+        result = df.any(axis=0)
+        expected = Series(data=[False, True], index=["a", "t"])
+        tm.assert_series_equal(result, expected)
+
+        result = df.any(axis=1)
+        expected = Series(data=[False, True])
+        tm.assert_series_equal(result, expected)
+
+    def test_reductions_skipna_none_raises(
+        self, request, frame_or_series, all_reductions
+    ):
+        if all_reductions == "count":
+            request.applymarker(
+                pytest.mark.xfail(reason="Count does not accept skipna")
+            )
+        obj = frame_or_series([1, 2, 3])
+        msg = 'For argument "skipna" expected type bool, received type NoneType.'
+        with pytest.raises(ValueError, match=msg):
+            getattr(obj, all_reductions)(skipna=None)
+
+    @td.skip_array_manager_invalid_test
+    def test_reduction_timestamp_smallest_unit(self):
+        # GH#52524
+        df = DataFrame(
+            {
+                "a": Series([Timestamp("2019-12-31")], dtype="datetime64[s]"),
+                "b": Series(
+                    [Timestamp("2019-12-31 00:00:00.123")], dtype="datetime64[ms]"
+                ),
+            }
+        )
+        result = df.max()
+        expected = Series(
+            [Timestamp("2019-12-31"), Timestamp("2019-12-31 00:00:00.123")],
+            dtype="datetime64[ms]",
+            index=["a", "b"],
+        )
+        tm.assert_series_equal(result, expected)
+
+    @td.skip_array_manager_not_yet_implemented
+    def test_reduction_timedelta_smallest_unit(self):
+        # GH#52524
+        df = DataFrame(
+            {
+                "a": Series([pd.Timedelta("1 days")], dtype="timedelta64[s]"),
+                "b": Series([pd.Timedelta("1 days")], dtype="timedelta64[ms]"),
+            }
+        )
+        result = df.max()
+        expected = Series(
+            [pd.Timedelta("1 days"), pd.Timedelta("1 days")],
+            dtype="timedelta64[ms]",
+            index=["a", "b"],
+        )
+        tm.assert_series_equal(result, expected)
+
+
+class TestNuisanceColumns:
+    @pytest.mark.parametrize("method", ["any", "all"])
+    def test_any_all_categorical_dtype_nuisance_column(self, method):
+        # GH#36076 DataFrame should match Series behavior
+        ser = Series([0, 1], dtype="category", name="A")
+        df = ser.to_frame()
+
+        # Double-check the Series behavior is to raise
+        with pytest.raises(TypeError, match="does not support reduction"):
+            getattr(ser, method)()
+
+        with pytest.raises(TypeError, match="does not support reduction"):
+            getattr(np, method)(ser)
+
+        with pytest.raises(TypeError, match="does not support reduction"):
+            getattr(df, method)(bool_only=False)
+
+        with pytest.raises(TypeError, match="does not support reduction"):
+            getattr(df, method)(bool_only=None)
+
+        with pytest.raises(TypeError, match="does not support reduction"):
+            getattr(np, method)(df, axis=0)
+
+    def test_median_categorical_dtype_nuisance_column(self):
+        # GH#21020 DataFrame.median should match Series.median
+        df = DataFrame({"A": Categorical([1, 2, 2, 2, 3])})
+        ser = df["A"]
+
+        # Double-check the Series behavior is to raise
+        with pytest.raises(TypeError, match="does not support reduction"):
+            ser.median()
+
+        with pytest.raises(TypeError, match="does not support reduction"):
+            df.median(numeric_only=False)
+
+        with pytest.raises(TypeError, match="does not support reduction"):
+            df.median()
+
+        # same thing, but with an additional non-categorical column
+        df["B"] = df["A"].astype(int)
+
+        with pytest.raises(TypeError, match="does not support reduction"):
+            df.median(numeric_only=False)
+
+        with pytest.raises(TypeError, match="does not support reduction"):
+            df.median()
+
+        # TODO: np.median(df, axis=0) gives np.array([2.0, 2.0]) instead
+        #  of expected.values
+
+    @pytest.mark.parametrize("method", ["min", "max"])
+    def test_min_max_categorical_dtype_non_ordered_nuisance_column(self, method):
+        # GH#28949 DataFrame.min should behave like Series.min
+        cat = Categorical(["a", "b", "c", "b"], ordered=False)
+        ser = Series(cat)
+        df = ser.to_frame("A")
+
+        # Double-check the Series behavior
+        with pytest.raises(TypeError, match="is not ordered for operation"):
+            getattr(ser, method)()
+
+        with pytest.raises(TypeError, match="is not ordered for operation"):
+            getattr(np, method)(ser)
+
+        with pytest.raises(TypeError, match="is not ordered for operation"):
+            getattr(df, method)(numeric_only=False)
+
+        with pytest.raises(TypeError, match="is not ordered for operation"):
+            getattr(df, method)()
+
+        with pytest.raises(TypeError, match="is not ordered for operation"):
+            getattr(np, method)(df, axis=0)
+
+        # same thing, but with an additional non-categorical column
+        df["B"] = df["A"].astype(object)
+        with pytest.raises(TypeError, match="is not ordered for operation"):
+            getattr(df, method)()
+
+        with pytest.raises(TypeError, match="is not ordered for operation"):
+            getattr(np, method)(df, axis=0)
+
+
+class TestEmptyDataFrameReductions:
+    @pytest.mark.parametrize(
+        "opname, dtype, exp_value, exp_dtype",
+        [
+            ("sum", np.int8, 0, np.int64),
+            ("prod", np.int8, 1, np.int_),
+            ("sum", np.int64, 0, np.int64),
+            ("prod", np.int64, 1, np.int64),
+            ("sum", np.uint8, 0, np.uint64),
+            ("prod", np.uint8, 1, np.uint),
+            ("sum", np.uint64, 0, np.uint64),
+            ("prod", np.uint64, 1, np.uint64),
+            ("sum", np.float32, 0, np.float32),
+            ("prod", np.float32, 1, np.float32),
+            ("sum", np.float64, 0, np.float64),
+        ],
+    )
+    def test_df_empty_min_count_0(self, opname, dtype, exp_value, exp_dtype):
+        df = DataFrame({0: [], 1: []}, dtype=dtype)
+        result = getattr(df, opname)(min_count=0)
+
+        expected = Series([exp_value, exp_value], dtype=exp_dtype)
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "opname, dtype, exp_dtype",
+        [
+            ("sum", np.int8, np.float64),
+            ("prod", np.int8, np.float64),
+            ("sum", np.int64, np.float64),
+            ("prod", np.int64, np.float64),
+            ("sum", np.uint8, np.float64),
+            ("prod", np.uint8, np.float64),
+            ("sum", np.uint64, np.float64),
+            ("prod", np.uint64, np.float64),
+            ("sum", np.float32, np.float32),
+            ("prod", np.float32, np.float32),
+            ("sum", np.float64, np.float64),
+        ],
+    )
+    def test_df_empty_min_count_1(self, opname, dtype, exp_dtype):
+        df = DataFrame({0: [], 1: []}, dtype=dtype)
+        result = getattr(df, opname)(min_count=1)
+
+        expected = Series([np.nan, np.nan], dtype=exp_dtype)
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "opname, dtype, exp_value, exp_dtype",
+        [
+            ("sum", "Int8", 0, ("Int32" if is_windows_np2_or_is32 else "Int64")),
+            ("prod", "Int8", 1, ("Int32" if is_windows_np2_or_is32 else "Int64")),
+            ("prod", "Int8", 1, ("Int32" if is_windows_np2_or_is32 else "Int64")),
+            ("sum", "Int64", 0, "Int64"),
+            ("prod", "Int64", 1, "Int64"),
+            ("sum", "UInt8", 0, ("UInt32" if is_windows_np2_or_is32 else "UInt64")),
+            ("prod", "UInt8", 1, ("UInt32" if is_windows_np2_or_is32 else "UInt64")),
+            ("sum", "UInt64", 0, "UInt64"),
+            ("prod", "UInt64", 1, "UInt64"),
+            ("sum", "Float32", 0, "Float32"),
+            ("prod", "Float32", 1, "Float32"),
+            ("sum", "Float64", 0, "Float64"),
+        ],
+    )
+    def test_df_empty_nullable_min_count_0(self, opname, dtype, exp_value, exp_dtype):
+        df = DataFrame({0: [], 1: []}, dtype=dtype)
+        result = getattr(df, opname)(min_count=0)
+
+        expected = Series([exp_value, exp_value], dtype=exp_dtype)
+        tm.assert_series_equal(result, expected)
+
+    # TODO: why does min_count=1 impact the resulting Windows dtype
+    # differently than min_count=0?
+    @pytest.mark.parametrize(
+        "opname, dtype, exp_dtype",
+        [
+            ("sum", "Int8", ("Int32" if is_windows_or_is32 else "Int64")),
+            ("prod", "Int8", ("Int32" if is_windows_or_is32 else "Int64")),
+            ("sum", "Int64", "Int64"),
+            ("prod", "Int64", "Int64"),
+            ("sum", "UInt8", ("UInt32" if is_windows_or_is32 else "UInt64")),
+            ("prod", "UInt8", ("UInt32" if is_windows_or_is32 else "UInt64")),
+            ("sum", "UInt64", "UInt64"),
+            ("prod", "UInt64", "UInt64"),
+            ("sum", "Float32", "Float32"),
+            ("prod", "Float32", "Float32"),
+            ("sum", "Float64", "Float64"),
+        ],
+    )
+    def test_df_empty_nullable_min_count_1(self, opname, dtype, exp_dtype):
+        df = DataFrame({0: [], 1: []}, dtype=dtype)
+        result = getattr(df, opname)(min_count=1)
+
+        expected = Series([pd.NA, pd.NA], dtype=exp_dtype)
+        tm.assert_series_equal(result, expected)
+
+
+def test_sum_timedelta64_skipna_false(using_array_manager, request):
+    # GH#17235
+    if using_array_manager:
+        mark = pytest.mark.xfail(
+            reason="Incorrect type inference on NaT in reduction result"
+        )
+        request.applymarker(mark)
+
+    arr = np.arange(8).astype(np.int64).view("m8[s]").reshape(4, 2)
+    arr[-1, -1] = "Nat"
+
+    df = DataFrame(arr)
+    assert (df.dtypes == arr.dtype).all()
+
+    result = df.sum(skipna=False)
+    expected = Series([pd.Timedelta(seconds=12), pd.NaT], dtype="m8[s]")
+    tm.assert_series_equal(result, expected)
+
+    result = df.sum(axis=0, skipna=False)
+    tm.assert_series_equal(result, expected)
+
+    result = df.sum(axis=1, skipna=False)
+    expected = Series(
+        [
+            pd.Timedelta(seconds=1),
+            pd.Timedelta(seconds=5),
+            pd.Timedelta(seconds=9),
+            pd.NaT,
+        ],
+        dtype="m8[s]",
+    )
+    tm.assert_series_equal(result, expected)
+
+
+def test_mixed_frame_with_integer_sum():
+    # https://github.com/pandas-dev/pandas/issues/34520
+    df = DataFrame([["a", 1]], columns=list("ab"))
+    df = df.astype({"b": "Int64"})
+    result = df.sum()
+    expected = Series(["a", 1], index=["a", "b"])
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("numeric_only", [True, False, None])
+@pytest.mark.parametrize("method", ["min", "max"])
+def test_minmax_extensionarray(method, numeric_only):
+    # https://github.com/pandas-dev/pandas/issues/32651
+    int64_info = np.iinfo("int64")
+    ser = Series([int64_info.max, None, int64_info.min], dtype=pd.Int64Dtype())
+    df = DataFrame({"Int64": ser})
+    result = getattr(df, method)(numeric_only=numeric_only)
+    expected = Series(
+        [getattr(int64_info, method)],
+        dtype="Int64",
+        index=Index(["Int64"]),
+    )
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("ts_value", [Timestamp("2000-01-01"), pd.NaT])
+def test_frame_mixed_numeric_object_with_timestamp(ts_value):
+    # GH 13912
+    df = DataFrame({"a": [1], "b": [1.1], "c": ["foo"], "d": [ts_value]})
+    with pytest.raises(
+        TypeError, match="does not support (operation|reduction)|Cannot perform"
+    ):
+        df.sum()
+
+
+def test_prod_sum_min_count_mixed_object():
+    # https://github.com/pandas-dev/pandas/issues/41074
+    df = DataFrame([1, "a", True])
+
+    result = df.prod(axis=0, min_count=1, numeric_only=False)
+    expected = Series(["a"], dtype=object)
+    tm.assert_series_equal(result, expected)
+
+    msg = re.escape("unsupported operand type(s) for +: 'int' and 'str'")
+    with pytest.raises(TypeError, match=msg):
+        df.sum(axis=0, min_count=1, numeric_only=False)
+
+
+@pytest.mark.parametrize("method", ["min", "max", "mean", "median", "skew", "kurt"])
+@pytest.mark.parametrize("numeric_only", [True, False])
+@pytest.mark.parametrize("dtype", ["float64", "Float64"])
+def test_reduction_axis_none_returns_scalar(method, numeric_only, dtype):
+    # GH#21597 As of 2.0, axis=None reduces over all axes.
+
+    df = DataFrame(np.random.default_rng(2).standard_normal((4, 4)), dtype=dtype)
+
+    result = getattr(df, method)(axis=None, numeric_only=numeric_only)
+    np_arr = df.to_numpy(dtype=np.float64)
+    if method in {"skew", "kurt"}:
+        comp_mod = pytest.importorskip("scipy.stats")
+        if method == "kurt":
+            method = "kurtosis"
+        expected = getattr(comp_mod, method)(np_arr, bias=False, axis=None)
+        tm.assert_almost_equal(result, expected)
+    else:
+        expected = getattr(np, method)(np_arr, axis=None)
+        assert result == expected
+
+
+@pytest.mark.parametrize(
+    "kernel",
+    [
+        "corr",
+        "corrwith",
+        "cov",
+        "idxmax",
+        "idxmin",
+        "kurt",
+        "max",
+        "mean",
+        "median",
+        "min",
+        "prod",
+        "quantile",
+        "sem",
+        "skew",
+        "std",
+        "sum",
+        "var",
+    ],
+)
+def test_fails_on_non_numeric(kernel):
+    # GH#46852
+    df = DataFrame({"a": [1, 2, 3], "b": object})
+    args = (df,) if kernel == "corrwith" else ()
+    msg = "|".join(
+        [
+            "not allowed for this dtype",
+            "argument must be a string or a number",
+            "not supported between instances of",
+            "unsupported operand type",
+            "argument must be a string or a real number",
+        ]
+    )
+    if kernel == "median":
+        # slightly different message on different builds
+        msg1 = (
+            r"Cannot convert \[\[  "
+            r"\]\] to numeric"
+        )
+        msg2 = (
+            r"Cannot convert \[  "
+            r"\] to numeric"
+        )
+        msg = "|".join([msg1, msg2])
+    with pytest.raises(TypeError, match=msg):
+        getattr(df, kernel)(*args)
+
+
+@pytest.mark.parametrize(
+    "method",
+    [
+        "all",
+        "any",
+        "count",
+        "idxmax",
+        "idxmin",
+        "kurt",
+        "kurtosis",
+        "max",
+        "mean",
+        "median",
+        "min",
+        "nunique",
+        "prod",
+        "product",
+        "sem",
+        "skew",
+        "std",
+        "sum",
+        "var",
+    ],
+)
+@pytest.mark.parametrize("min_count", [0, 2])
+def test_numeric_ea_axis_1(method, skipna, min_count, any_numeric_ea_dtype):
+    # GH 54341
+    df = DataFrame(
+        {
+            "a": Series([0, 1, 2, 3], dtype=any_numeric_ea_dtype),
+            "b": Series([0, 1, pd.NA, 3], dtype=any_numeric_ea_dtype),
+        },
+    )
+    expected_df = DataFrame(
+        {
+            "a": [0.0, 1.0, 2.0, 3.0],
+            "b": [0.0, 1.0, np.nan, 3.0],
+        },
+    )
+    if method in ("count", "nunique"):
+        expected_dtype = "int64"
+    elif method in ("all", "any"):
+        expected_dtype = "boolean"
+    elif method in (
+        "kurt",
+        "kurtosis",
+        "mean",
+        "median",
+        "sem",
+        "skew",
+        "std",
+        "var",
+    ) and not any_numeric_ea_dtype.startswith("Float"):
+        expected_dtype = "Float64"
+    else:
+        expected_dtype = any_numeric_ea_dtype
+
+    kwargs = {}
+    if method not in ("count", "nunique", "quantile"):
+        kwargs["skipna"] = skipna
+    if method in ("prod", "product", "sum"):
+        kwargs["min_count"] = min_count
+
+    warn = None
+    msg = None
+    if not skipna and method in ("idxmax", "idxmin"):
+        warn = FutureWarning
+        msg = f"The behavior of DataFrame.{method} with all-NA values"
+    with tm.assert_produces_warning(warn, match=msg):
+        result = getattr(df, method)(axis=1, **kwargs)
+    with tm.assert_produces_warning(warn, match=msg):
+        expected = getattr(expected_df, method)(axis=1, **kwargs)
+    if method not in ("idxmax", "idxmin"):
+        expected = expected.astype(expected_dtype)
+    tm.assert_series_equal(result, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/test_repr.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/test_repr.py
new file mode 100644
index 0000000000000000000000000000000000000000..6184e791cab5d00f8a1057d142e4c63c3690615a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/test_repr.py
@@ -0,0 +1,518 @@
+from datetime import (
+    datetime,
+    timedelta,
+)
+from io import StringIO
+
+import numpy as np
+import pytest
+
+from pandas import (
+    NA,
+    Categorical,
+    CategoricalIndex,
+    DataFrame,
+    IntervalIndex,
+    MultiIndex,
+    NaT,
+    PeriodIndex,
+    Series,
+    Timestamp,
+    date_range,
+    option_context,
+    period_range,
+)
+import pandas._testing as tm
+
+
+class TestDataFrameRepr:
+    def test_repr_should_return_str(self):
+        # https://docs.python.org/3/reference/datamodel.html#object.__repr__
+        # "...The return value must be a string object."
+
+        # (str on py2.x, str (unicode) on py3)
+
+        data = [8, 5, 3, 5]
+        index1 = ["\u03c3", "\u03c4", "\u03c5", "\u03c6"]
+        cols = ["\u03c8"]
+        df = DataFrame(data, columns=cols, index=index1)
+        assert type(df.__repr__()) is str  # noqa: E721
+
+        ser = df[cols[0]]
+        assert type(ser.__repr__()) is str  # noqa: E721
+
+    def test_repr_bytes_61_lines(self):
+        # GH#12857
+        lets = list("ACDEFGHIJKLMNOP")
+        words = np.random.default_rng(2).choice(lets, (1000, 50))
+        df = DataFrame(words).astype("U1")
+        assert (df.dtypes == object).all()
+
+        # smoke tests; at one point this raised with 61 but not 60
+        repr(df)
+        repr(df.iloc[:60, :])
+        repr(df.iloc[:61, :])
+
+    def test_repr_unicode_level_names(self, frame_or_series):
+        index = MultiIndex.from_tuples([(0, 0), (1, 1)], names=["\u0394", "i1"])
+
+        obj = DataFrame(np.random.default_rng(2).standard_normal((2, 4)), index=index)
+        obj = tm.get_obj(obj, frame_or_series)
+        repr(obj)
+
+    def test_assign_index_sequences(self):
+        # GH#2200
+        df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]}).set_index(
+            ["a", "b"]
+        )
+        index = list(df.index)
+        index[0] = ("faz", "boo")
+        df.index = index
+        repr(df)
+
+        # this travels an improper code path
+        index[0] = ["faz", "boo"]
+        df.index = index
+        repr(df)
+
+    def test_repr_with_mi_nat(self):
+        df = DataFrame({"X": [1, 2]}, index=[[NaT, Timestamp("20130101")], ["a", "b"]])
+        result = repr(df)
+        expected = "              X\nNaT        a  1\n2013-01-01 b  2"
+        assert result == expected
+
+    def test_repr_with_different_nulls(self):
+        # GH45263
+        df = DataFrame([1, 2, 3, 4], [True, None, np.nan, NaT])
+        result = repr(df)
+        expected = """      0
+True  1
+None  2
+NaN   3
+NaT   4"""
+        assert result == expected
+
+    def test_repr_with_different_nulls_cols(self):
+        # GH45263
+        d = {np.nan: [1, 2], None: [3, 4], NaT: [6, 7], True: [8, 9]}
+        df = DataFrame(data=d)
+        result = repr(df)
+        expected = """   NaN  None  NaT  True
+0    1     3    6     8
+1    2     4    7     9"""
+        assert result == expected
+
+    def test_multiindex_na_repr(self):
+        # only an issue with long columns
+        df3 = DataFrame(
+            {
+                "A" * 30: {("A", "A0006000", "nuit"): "A0006000"},
+                "B" * 30: {("A", "A0006000", "nuit"): np.nan},
+                "C" * 30: {("A", "A0006000", "nuit"): np.nan},
+                "D" * 30: {("A", "A0006000", "nuit"): np.nan},
+                "E" * 30: {("A", "A0006000", "nuit"): "A"},
+                "F" * 30: {("A", "A0006000", "nuit"): np.nan},
+            }
+        )
+
+        idf = df3.set_index(["A" * 30, "C" * 30])
+        repr(idf)
+
+    def test_repr_name_coincide(self):
+        index = MultiIndex.from_tuples(
+            [("a", 0, "foo"), ("b", 1, "bar")], names=["a", "b", "c"]
+        )
+
+        df = DataFrame({"value": [0, 1]}, index=index)
+
+        lines = repr(df).split("\n")
+        assert lines[2].startswith("a 0 foo")
+
+    def test_repr_to_string(
+        self,
+        multiindex_year_month_day_dataframe_random_data,
+        multiindex_dataframe_random_data,
+    ):
+        ymd = multiindex_year_month_day_dataframe_random_data
+        frame = multiindex_dataframe_random_data
+
+        repr(frame)
+        repr(ymd)
+        repr(frame.T)
+        repr(ymd.T)
+
+        buf = StringIO()
+        frame.to_string(buf=buf)
+        ymd.to_string(buf=buf)
+        frame.T.to_string(buf=buf)
+        ymd.T.to_string(buf=buf)
+
+    def test_repr_empty(self):
+        # empty
+        repr(DataFrame())
+
+        # empty with index
+        frame = DataFrame(index=np.arange(1000))
+        repr(frame)
+
+    def test_repr_mixed(self, float_string_frame):
+        # mixed
+        repr(float_string_frame)
+
+    @pytest.mark.slow
+    def test_repr_mixed_big(self):
+        # big mixed
+        biggie = DataFrame(
+            {
+                "A": np.random.default_rng(2).standard_normal(200),
+                "B": [str(i) for i in range(200)],
+            },
+            index=range(200),
+        )
+        biggie.loc[:20, "A"] = np.nan
+        biggie.loc[:20, "B"] = np.nan
+
+        repr(biggie)
+
+    def test_repr(self):
+        # columns but no index
+        no_index = DataFrame(columns=[0, 1, 3])
+        repr(no_index)
+
+        df = DataFrame(["a\n\r\tb"], columns=["a\n\r\td"], index=["a\n\r\tf"])
+        assert "\t" not in repr(df)
+        assert "\r" not in repr(df)
+        assert "a\n" not in repr(df)
+
+    def test_repr_dimensions(self):
+        df = DataFrame([[1, 2], [3, 4]])
+        with option_context("display.show_dimensions", True):
+            assert "2 rows x 2 columns" in repr(df)
+
+        with option_context("display.show_dimensions", False):
+            assert "2 rows x 2 columns" not in repr(df)
+
+        with option_context("display.show_dimensions", "truncate"):
+            assert "2 rows x 2 columns" not in repr(df)
+
+    @pytest.mark.slow
+    def test_repr_big(self):
+        # big one
+        biggie = DataFrame(np.zeros((200, 4)), columns=range(4), index=range(200))
+        repr(biggie)
+
+    def test_repr_unsortable(self):
+        # columns are not sortable
+
+        unsortable = DataFrame(
+            {
+                "foo": [1] * 50,
+                datetime.today(): [1] * 50,
+                "bar": ["bar"] * 50,
+                datetime.today() + timedelta(1): ["bar"] * 50,
+            },
+            index=np.arange(50),
+        )
+        repr(unsortable)
+
+    def test_repr_float_frame_options(self, float_frame):
+        repr(float_frame)
+
+        with option_context("display.precision", 3):
+            repr(float_frame)
+
+        with option_context("display.max_rows", 10, "display.max_columns", 2):
+            repr(float_frame)
+
+        with option_context("display.max_rows", 1000, "display.max_columns", 1000):
+            repr(float_frame)
+
+    def test_repr_unicode(self):
+        uval = "\u03c3\u03c3\u03c3\u03c3"
+
+        df = DataFrame({"A": [uval, uval]})
+
+        result = repr(df)
+        ex_top = "      A"
+        assert result.split("\n")[0].rstrip() == ex_top
+
+        df = DataFrame({"A": [uval, uval]})
+        result = repr(df)
+        assert result.split("\n")[0].rstrip() == ex_top
+
+    def test_unicode_string_with_unicode(self):
+        df = DataFrame({"A": ["\u05d0"]})
+        str(df)
+
+    def test_repr_unicode_columns(self):
+        df = DataFrame({"\u05d0": [1, 2, 3], "\u05d1": [4, 5, 6], "c": [7, 8, 9]})
+        repr(df.columns)  # should not raise UnicodeDecodeError
+
+    def test_str_to_bytes_raises(self):
+        # GH 26447
+        df = DataFrame({"A": ["abc"]})
+        msg = "^'str' object cannot be interpreted as an integer$"
+        with pytest.raises(TypeError, match=msg):
+            bytes(df)
+
+    def test_very_wide_repr(self):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((10, 20)),
+            columns=np.array(["a" * 10] * 20, dtype=object),
+        )
+        repr(df)
+
+    def test_repr_column_name_unicode_truncation_bug(self):
+        # #1906
+        df = DataFrame(
+            {
+                "Id": [7117434],
+                "StringCol": (
+                    "Is it possible to modify drop plot code"
+                    "so that the output graph is displayed "
+                    "in iphone simulator, Is it possible to "
+                    "modify drop plot code so that the "
+                    "output graph is \xe2\x80\xa8displayed "
+                    "in iphone simulator.Now we are adding "
+                    "the CSV file externally. I want to Call "
+                    "the File through the code.."
+                ),
+            }
+        )
+
+        with option_context("display.max_columns", 20):
+            assert "StringCol" in repr(df)
+
+    def test_latex_repr(self):
+        pytest.importorskip("jinja2")
+        expected = r"""\begin{tabular}{llll}
+\toprule
+ & 0 & 1 & 2 \\
+\midrule
+0 & $\alpha$ & b & c \\
+1 & 1 & 2 & 3 \\
+\bottomrule
+\end{tabular}
+"""
+        with option_context(
+            "styler.format.escape", None, "styler.render.repr", "latex"
+        ):
+            df = DataFrame([[r"$\alpha$", "b", "c"], [1, 2, 3]])
+            result = df._repr_latex_()
+            assert result == expected
+
+        # GH 12182
+        assert df._repr_latex_() is None
+
+    def test_repr_with_datetimeindex(self):
+        df = DataFrame({"A": [1, 2, 3]}, index=date_range("2000", periods=3))
+        result = repr(df)
+        expected = "            A\n2000-01-01  1\n2000-01-02  2\n2000-01-03  3"
+        assert result == expected
+
+    def test_repr_with_intervalindex(self):
+        # https://github.com/pandas-dev/pandas/pull/24134/files
+        df = DataFrame(
+            {"A": [1, 2, 3, 4]}, index=IntervalIndex.from_breaks([0, 1, 2, 3, 4])
+        )
+        result = repr(df)
+        expected = "        A\n(0, 1]  1\n(1, 2]  2\n(2, 3]  3\n(3, 4]  4"
+        assert result == expected
+
+    def test_repr_with_categorical_index(self):
+        df = DataFrame({"A": [1, 2, 3]}, index=CategoricalIndex(["a", "b", "c"]))
+        result = repr(df)
+        expected = "   A\na  1\nb  2\nc  3"
+        assert result == expected
+
+    def test_repr_categorical_dates_periods(self):
+        # normal DataFrame
+        dt = date_range("2011-01-01 09:00", freq="h", periods=5, tz="US/Eastern")
+        p = period_range("2011-01", freq="M", periods=5)
+        df = DataFrame({"dt": dt, "p": p})
+        exp = """                         dt        p
+0 2011-01-01 09:00:00-05:00  2011-01
+1 2011-01-01 10:00:00-05:00  2011-02
+2 2011-01-01 11:00:00-05:00  2011-03
+3 2011-01-01 12:00:00-05:00  2011-04
+4 2011-01-01 13:00:00-05:00  2011-05"""
+
+        assert repr(df) == exp
+
+        df2 = DataFrame({"dt": Categorical(dt), "p": Categorical(p)})
+        assert repr(df2) == exp
+
+    @pytest.mark.parametrize("arg", [np.datetime64, np.timedelta64])
+    @pytest.mark.parametrize(
+        "box, expected",
+        [[Series, "0    NaT\ndtype: object"], [DataFrame, "     0\n0  NaT"]],
+    )
+    def test_repr_np_nat_with_object(self, arg, box, expected):
+        # GH 25445
+        result = repr(box([arg("NaT")], dtype=object))
+        assert result == expected
+
+    def test_frame_datetime64_pre1900_repr(self):
+        df = DataFrame({"year": date_range("1/1/1700", periods=50, freq="YE-DEC")})
+        # it works!
+        repr(df)
+
+    def test_frame_to_string_with_periodindex(self):
+        index = PeriodIndex(["2011-1", "2011-2", "2011-3"], freq="M")
+        frame = DataFrame(np.random.default_rng(2).standard_normal((3, 4)), index=index)
+
+        # it works!
+        frame.to_string()
+
+    def test_to_string_ea_na_in_multiindex(self):
+        # GH#47986
+        df = DataFrame(
+            {"a": [1, 2]},
+            index=MultiIndex.from_arrays([Series([NA, 1], dtype="Int64")]),
+        )
+
+        result = df.to_string()
+        expected = """      a
+  1
+1     2"""
+        assert result == expected
+
+    def test_datetime64tz_slice_non_truncate(self):
+        # GH 30263
+        df = DataFrame({"x": date_range("2019", periods=10, tz="UTC")})
+        expected = repr(df)
+        df = df.iloc[:, :5]
+        result = repr(df)
+        assert result == expected
+
+    def test_to_records_no_typeerror_in_repr(self):
+        # GH 48526
+        df = DataFrame([["a", "b"], ["c", "d"], ["e", "f"]], columns=["left", "right"])
+        df["record"] = df[["left", "right"]].to_records()
+        expected = """  left right     record
+0    a     b  [0, a, b]
+1    c     d  [1, c, d]
+2    e     f  [2, e, f]"""
+        result = repr(df)
+        assert result == expected
+
+    def test_to_records_with_na_record_value(self):
+        # GH 48526
+        df = DataFrame(
+            [["a", np.nan], ["c", "d"], ["e", "f"]], columns=["left", "right"]
+        )
+        df["record"] = df[["left", "right"]].to_records()
+        expected = """  left right       record
+0    a   NaN  [0, a, nan]
+1    c     d    [1, c, d]
+2    e     f    [2, e, f]"""
+        result = repr(df)
+        assert result == expected
+
+    def test_to_records_with_na_record(self):
+        # GH 48526
+        df = DataFrame(
+            [["a", "b"], [np.nan, np.nan], ["e", "f"]], columns=[np.nan, "right"]
+        )
+        df["record"] = df[[np.nan, "right"]].to_records()
+        expected = """   NaN right         record
+0    a     b      [0, a, b]
+1  NaN   NaN  [1, nan, nan]
+2    e     f      [2, e, f]"""
+        result = repr(df)
+        assert result == expected
+
+    def test_to_records_with_inf_as_na_record(self):
+        # GH 48526
+        expected = """   NaN  inf         record
+0  inf    b    [0, inf, b]
+1  NaN  NaN  [1, nan, nan]
+2    e    f      [2, e, f]"""
+        msg = "use_inf_as_na option is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            with option_context("use_inf_as_na", True):
+                df = DataFrame(
+                    [[np.inf, "b"], [np.nan, np.nan], ["e", "f"]],
+                    columns=[np.nan, np.inf],
+                )
+                df["record"] = df[[np.nan, np.inf]].to_records()
+                result = repr(df)
+        assert result == expected
+
+    def test_to_records_with_inf_record(self):
+        # GH 48526
+        expected = """   NaN  inf         record
+0  inf    b    [0, inf, b]
+1  NaN  NaN  [1, nan, nan]
+2    e    f      [2, e, f]"""
+        msg = "use_inf_as_na option is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            with option_context("use_inf_as_na", False):
+                df = DataFrame(
+                    [[np.inf, "b"], [np.nan, np.nan], ["e", "f"]],
+                    columns=[np.nan, np.inf],
+                )
+                df["record"] = df[[np.nan, np.inf]].to_records()
+                result = repr(df)
+        assert result == expected
+
+    def test_masked_ea_with_formatter(self):
+        # GH#39336
+        df = DataFrame(
+            {
+                "a": Series([0.123456789, 1.123456789], dtype="Float64"),
+                "b": Series([1, 2], dtype="Int64"),
+            }
+        )
+        result = df.to_string(formatters=["{:.2f}".format, "{:.2f}".format])
+        expected = """      a     b
+0  0.12  1.00
+1  1.12  2.00"""
+        assert result == expected
+
+    def test_repr_ea_columns(self, any_string_dtype):
+        # GH#54797
+        pytest.importorskip("pyarrow")
+        df = DataFrame({"long_column_name": [1, 2, 3], "col2": [4, 5, 6]})
+        df.columns = df.columns.astype(any_string_dtype)
+        expected = """   long_column_name  col2
+0                 1     4
+1                 2     5
+2                 3     6"""
+        assert repr(df) == expected
+
+
+@pytest.mark.parametrize(
+    "data,output",
+    [
+        ([2, complex("nan"), 1], [" 2.0+0.0j", " NaN+0.0j", " 1.0+0.0j"]),
+        ([2, complex("nan"), -1], [" 2.0+0.0j", " NaN+0.0j", "-1.0+0.0j"]),
+        ([-2, complex("nan"), -1], ["-2.0+0.0j", " NaN+0.0j", "-1.0+0.0j"]),
+        ([-1.23j, complex("nan"), -1], ["-0.00-1.23j", "  NaN+0.00j", "-1.00+0.00j"]),
+        ([1.23j, complex("nan"), 1.23], [" 0.00+1.23j", "  NaN+0.00j", " 1.23+0.00j"]),
+        (
+            [-1.23j, complex(np.nan, np.nan), 1],
+            ["-0.00-1.23j", "  NaN+ NaNj", " 1.00+0.00j"],
+        ),
+        (
+            [-1.23j, complex(1.2, np.nan), 1],
+            ["-0.00-1.23j", " 1.20+ NaNj", " 1.00+0.00j"],
+        ),
+        (
+            [-1.23j, complex(np.nan, -1.2), 1],
+            ["-0.00-1.23j", "  NaN-1.20j", " 1.00+0.00j"],
+        ),
+    ],
+)
+@pytest.mark.parametrize("as_frame", [True, False])
+def test_repr_with_complex_nans(data, output, as_frame):
+    # GH#53762, GH#53841
+    obj = Series(np.array(data))
+    if as_frame:
+        obj = obj.to_frame(name="val")
+        reprs = [f"{i} {val}" for i, val in enumerate(output)]
+        expected = f"{'val': >{len(reprs[0])}}\n" + "\n".join(reprs)
+    else:
+        reprs = [f"{i}   {val}" for i, val in enumerate(output)]
+        expected = "\n".join(reprs) + "\ndtype: complex128"
+    assert str(obj) == expected, f"\n{str(obj)}\n\n{expected}"
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/test_stack_unstack.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/test_stack_unstack.py
new file mode 100644
index 0000000000000000000000000000000000000000..de470fcda18ed47417ff84b88c6b48bc888cec8f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/test_stack_unstack.py
@@ -0,0 +1,2684 @@
+from datetime import datetime
+import itertools
+import re
+
+import numpy as np
+import pytest
+
+from pandas._libs import lib
+from pandas.errors import PerformanceWarning
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Index,
+    MultiIndex,
+    Period,
+    Series,
+    Timedelta,
+    date_range,
+)
+import pandas._testing as tm
+from pandas.core.reshape import reshape as reshape_lib
+
+
+@pytest.fixture(params=[True, False])
+def future_stack(request):
+    return request.param
+
+
+class TestDataFrameReshape:
+    def test_stack_unstack(self, float_frame, future_stack):
+        df = float_frame.copy()
+        df[:] = np.arange(np.prod(df.shape)).reshape(df.shape)
+
+        stacked = df.stack(future_stack=future_stack)
+        stacked_df = DataFrame({"foo": stacked, "bar": stacked})
+
+        unstacked = stacked.unstack()
+        unstacked_df = stacked_df.unstack()
+
+        tm.assert_frame_equal(unstacked, df)
+        tm.assert_frame_equal(unstacked_df["bar"], df)
+
+        unstacked_cols = stacked.unstack(0)
+        unstacked_cols_df = stacked_df.unstack(0)
+        tm.assert_frame_equal(unstacked_cols.T, df)
+        tm.assert_frame_equal(unstacked_cols_df["bar"].T, df)
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    def test_stack_mixed_level(self, future_stack):
+        # GH 18310
+        levels = [range(3), [3, "a", "b"], [1, 2]]
+
+        # flat columns:
+        df = DataFrame(1, index=levels[0], columns=levels[1])
+        result = df.stack(future_stack=future_stack)
+        expected = Series(1, index=MultiIndex.from_product(levels[:2]))
+        tm.assert_series_equal(result, expected)
+
+        # MultiIndex columns:
+        df = DataFrame(1, index=levels[0], columns=MultiIndex.from_product(levels[1:]))
+        result = df.stack(1, future_stack=future_stack)
+        expected = DataFrame(
+            1, index=MultiIndex.from_product([levels[0], levels[2]]), columns=levels[1]
+        )
+        tm.assert_frame_equal(result, expected)
+
+        # as above, but used labels in level are actually of homogeneous type
+        result = df[["a", "b"]].stack(1, future_stack=future_stack)
+        expected = expected[["a", "b"]]
+        tm.assert_frame_equal(result, expected)
+
+    def test_unstack_not_consolidated(self, using_array_manager):
+        # Gh#34708
+        df = DataFrame({"x": [1, 2, np.nan], "y": [3.0, 4, np.nan]})
+        df2 = df[["x"]]
+        df2["y"] = df["y"]
+        if not using_array_manager:
+            assert len(df2._mgr.blocks) == 2
+
+        res = df2.unstack()
+        expected = df.unstack()
+        tm.assert_series_equal(res, expected)
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    def test_unstack_fill(self, future_stack):
+        # GH #9746: fill_value keyword argument for Series
+        # and DataFrame unstack
+
+        # From a series
+        data = Series([1, 2, 4, 5], dtype=np.int16)
+        data.index = MultiIndex.from_tuples(
+            [("x", "a"), ("x", "b"), ("y", "b"), ("z", "a")]
+        )
+
+        result = data.unstack(fill_value=-1)
+        expected = DataFrame(
+            {"a": [1, -1, 5], "b": [2, 4, -1]}, index=["x", "y", "z"], dtype=np.int16
+        )
+        tm.assert_frame_equal(result, expected)
+
+        # From a series with incorrect data type for fill_value
+        result = data.unstack(fill_value=0.5)
+        expected = DataFrame(
+            {"a": [1, 0.5, 5], "b": [2, 4, 0.5]}, index=["x", "y", "z"], dtype=float
+        )
+        tm.assert_frame_equal(result, expected)
+
+        # GH #13971: fill_value when unstacking multiple levels:
+        df = DataFrame(
+            {"x": ["a", "a", "b"], "y": ["j", "k", "j"], "z": [0, 1, 2], "w": [0, 1, 2]}
+        ).set_index(["x", "y", "z"])
+        unstacked = df.unstack(["x", "y"], fill_value=0)
+        key = ("w", "b", "j")
+        expected = unstacked[key]
+        result = Series([0, 0, 2], index=unstacked.index, name=key)
+        tm.assert_series_equal(result, expected)
+
+        stacked = unstacked.stack(["x", "y"], future_stack=future_stack)
+        stacked.index = stacked.index.reorder_levels(df.index.names)
+        # Workaround for GH #17886 (unnecessarily casts to float):
+        stacked = stacked.astype(np.int64)
+        result = stacked.loc[df.index]
+        tm.assert_frame_equal(result, df)
+
+        # From a series
+        s = df["w"]
+        result = s.unstack(["x", "y"], fill_value=0)
+        expected = unstacked["w"]
+        tm.assert_frame_equal(result, expected)
+
+    def test_unstack_fill_frame(self):
+        # From a dataframe
+        rows = [[1, 2], [3, 4], [5, 6], [7, 8]]
+        df = DataFrame(rows, columns=list("AB"), dtype=np.int32)
+        df.index = MultiIndex.from_tuples(
+            [("x", "a"), ("x", "b"), ("y", "b"), ("z", "a")]
+        )
+
+        result = df.unstack(fill_value=-1)
+
+        rows = [[1, 3, 2, 4], [-1, 5, -1, 6], [7, -1, 8, -1]]
+        expected = DataFrame(rows, index=list("xyz"), dtype=np.int32)
+        expected.columns = MultiIndex.from_tuples(
+            [("A", "a"), ("A", "b"), ("B", "a"), ("B", "b")]
+        )
+        tm.assert_frame_equal(result, expected)
+
+        # From a mixed type dataframe
+        df["A"] = df["A"].astype(np.int16)
+        df["B"] = df["B"].astype(np.float64)
+
+        result = df.unstack(fill_value=-1)
+        expected["A"] = expected["A"].astype(np.int16)
+        expected["B"] = expected["B"].astype(np.float64)
+        tm.assert_frame_equal(result, expected)
+
+        # From a dataframe with incorrect data type for fill_value
+        result = df.unstack(fill_value=0.5)
+
+        rows = [[1, 3, 2, 4], [0.5, 5, 0.5, 6], [7, 0.5, 8, 0.5]]
+        expected = DataFrame(rows, index=list("xyz"), dtype=float)
+        expected.columns = MultiIndex.from_tuples(
+            [("A", "a"), ("A", "b"), ("B", "a"), ("B", "b")]
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_unstack_fill_frame_datetime(self):
+        # Test unstacking with date times
+        dv = date_range("2012-01-01", periods=4).values
+        data = Series(dv)
+        data.index = MultiIndex.from_tuples(
+            [("x", "a"), ("x", "b"), ("y", "b"), ("z", "a")]
+        )
+
+        result = data.unstack()
+        expected = DataFrame(
+            {"a": [dv[0], pd.NaT, dv[3]], "b": [dv[1], dv[2], pd.NaT]},
+            index=["x", "y", "z"],
+        )
+        tm.assert_frame_equal(result, expected)
+
+        result = data.unstack(fill_value=dv[0])
+        expected = DataFrame(
+            {"a": [dv[0], dv[0], dv[3]], "b": [dv[1], dv[2], dv[0]]},
+            index=["x", "y", "z"],
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_unstack_fill_frame_timedelta(self):
+        # Test unstacking with time deltas
+        td = [Timedelta(days=i) for i in range(4)]
+        data = Series(td)
+        data.index = MultiIndex.from_tuples(
+            [("x", "a"), ("x", "b"), ("y", "b"), ("z", "a")]
+        )
+
+        result = data.unstack()
+        expected = DataFrame(
+            {"a": [td[0], pd.NaT, td[3]], "b": [td[1], td[2], pd.NaT]},
+            index=["x", "y", "z"],
+        )
+        tm.assert_frame_equal(result, expected)
+
+        result = data.unstack(fill_value=td[1])
+        expected = DataFrame(
+            {"a": [td[0], td[1], td[3]], "b": [td[1], td[2], td[1]]},
+            index=["x", "y", "z"],
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_unstack_fill_frame_period(self):
+        # Test unstacking with period
+        periods = [
+            Period("2012-01"),
+            Period("2012-02"),
+            Period("2012-03"),
+            Period("2012-04"),
+        ]
+        data = Series(periods)
+        data.index = MultiIndex.from_tuples(
+            [("x", "a"), ("x", "b"), ("y", "b"), ("z", "a")]
+        )
+
+        result = data.unstack()
+        expected = DataFrame(
+            {"a": [periods[0], None, periods[3]], "b": [periods[1], periods[2], None]},
+            index=["x", "y", "z"],
+        )
+        tm.assert_frame_equal(result, expected)
+
+        result = data.unstack(fill_value=periods[1])
+        expected = DataFrame(
+            {
+                "a": [periods[0], periods[1], periods[3]],
+                "b": [periods[1], periods[2], periods[1]],
+            },
+            index=["x", "y", "z"],
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_unstack_fill_frame_categorical(self):
+        # Test unstacking with categorical
+        data = Series(["a", "b", "c", "a"], dtype="category")
+        data.index = MultiIndex.from_tuples(
+            [("x", "a"), ("x", "b"), ("y", "b"), ("z", "a")]
+        )
+
+        # By default missing values will be NaN
+        result = data.unstack()
+        expected = DataFrame(
+            {
+                "a": pd.Categorical(list("axa"), categories=list("abc")),
+                "b": pd.Categorical(list("bcx"), categories=list("abc")),
+            },
+            index=list("xyz"),
+        )
+        tm.assert_frame_equal(result, expected)
+
+        # Fill with non-category results in a ValueError
+        msg = r"Cannot setitem on a Categorical with a new category \(d\)"
+        with pytest.raises(TypeError, match=msg):
+            data.unstack(fill_value="d")
+
+        # Fill with category value replaces missing values as expected
+        result = data.unstack(fill_value="c")
+        expected = DataFrame(
+            {
+                "a": pd.Categorical(list("aca"), categories=list("abc")),
+                "b": pd.Categorical(list("bcc"), categories=list("abc")),
+            },
+            index=list("xyz"),
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_unstack_tuplename_in_multiindex(self):
+        # GH 19966
+        idx = MultiIndex.from_product(
+            [["a", "b", "c"], [1, 2, 3]], names=[("A", "a"), ("B", "b")]
+        )
+        df = DataFrame({"d": [1] * 9, "e": [2] * 9}, index=idx)
+        result = df.unstack(("A", "a"))
+
+        expected = DataFrame(
+            [[1, 1, 1, 2, 2, 2], [1, 1, 1, 2, 2, 2], [1, 1, 1, 2, 2, 2]],
+            columns=MultiIndex.from_tuples(
+                [
+                    ("d", "a"),
+                    ("d", "b"),
+                    ("d", "c"),
+                    ("e", "a"),
+                    ("e", "b"),
+                    ("e", "c"),
+                ],
+                names=[None, ("A", "a")],
+            ),
+            index=Index([1, 2, 3], name=("B", "b")),
+        )
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "unstack_idx, expected_values, expected_index, expected_columns",
+        [
+            (
+                ("A", "a"),
+                [[1, 1, 2, 2], [1, 1, 2, 2], [1, 1, 2, 2], [1, 1, 2, 2]],
+                MultiIndex.from_tuples(
+                    [(1, 3), (1, 4), (2, 3), (2, 4)], names=["B", "C"]
+                ),
+                MultiIndex.from_tuples(
+                    [("d", "a"), ("d", "b"), ("e", "a"), ("e", "b")],
+                    names=[None, ("A", "a")],
+                ),
+            ),
+            (
+                (("A", "a"), "B"),
+                [[1, 1, 1, 1, 2, 2, 2, 2], [1, 1, 1, 1, 2, 2, 2, 2]],
+                Index([3, 4], name="C"),
+                MultiIndex.from_tuples(
+                    [
+                        ("d", "a", 1),
+                        ("d", "a", 2),
+                        ("d", "b", 1),
+                        ("d", "b", 2),
+                        ("e", "a", 1),
+                        ("e", "a", 2),
+                        ("e", "b", 1),
+                        ("e", "b", 2),
+                    ],
+                    names=[None, ("A", "a"), "B"],
+                ),
+            ),
+        ],
+    )
+    def test_unstack_mixed_type_name_in_multiindex(
+        self, unstack_idx, expected_values, expected_index, expected_columns
+    ):
+        # GH 19966
+        idx = MultiIndex.from_product(
+            [["a", "b"], [1, 2], [3, 4]], names=[("A", "a"), "B", "C"]
+        )
+        df = DataFrame({"d": [1] * 8, "e": [2] * 8}, index=idx)
+        result = df.unstack(unstack_idx)
+
+        expected = DataFrame(
+            expected_values, columns=expected_columns, index=expected_index
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_unstack_preserve_dtypes(self):
+        # Checks fix for #11847
+        df = DataFrame(
+            {
+                "state": ["IL", "MI", "NC"],
+                "index": ["a", "b", "c"],
+                "some_categories": Series(["a", "b", "c"]).astype("category"),
+                "A": np.random.default_rng(2).random(3),
+                "B": 1,
+                "C": "foo",
+                "D": pd.Timestamp("20010102"),
+                "E": Series([1.0, 50.0, 100.0]).astype("float32"),
+                "F": Series([3.0, 4.0, 5.0]).astype("float64"),
+                "G": False,
+                "H": Series([1, 200, 923442]).astype("int8"),
+            }
+        )
+
+        def unstack_and_compare(df, column_name):
+            unstacked1 = df.unstack([column_name])
+            unstacked2 = df.unstack(column_name)
+            tm.assert_frame_equal(unstacked1, unstacked2)
+
+        df1 = df.set_index(["state", "index"])
+        unstack_and_compare(df1, "index")
+
+        df1 = df.set_index(["state", "some_categories"])
+        unstack_and_compare(df1, "some_categories")
+
+        df1 = df.set_index(["F", "C"])
+        unstack_and_compare(df1, "F")
+
+        df1 = df.set_index(["G", "B", "state"])
+        unstack_and_compare(df1, "B")
+
+        df1 = df.set_index(["E", "A"])
+        unstack_and_compare(df1, "E")
+
+        df1 = df.set_index(["state", "index"])
+        s = df1["A"]
+        unstack_and_compare(s, "index")
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    def test_stack_ints(self, future_stack):
+        columns = MultiIndex.from_tuples(list(itertools.product(range(3), repeat=3)))
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((30, 27)), columns=columns
+        )
+
+        tm.assert_frame_equal(
+            df.stack(level=[1, 2], future_stack=future_stack),
+            df.stack(level=1, future_stack=future_stack).stack(
+                level=1, future_stack=future_stack
+            ),
+        )
+        tm.assert_frame_equal(
+            df.stack(level=[-2, -1], future_stack=future_stack),
+            df.stack(level=1, future_stack=future_stack).stack(
+                level=1, future_stack=future_stack
+            ),
+        )
+
+        df_named = df.copy()
+        return_value = df_named.columns.set_names(range(3), inplace=True)
+        assert return_value is None
+
+        tm.assert_frame_equal(
+            df_named.stack(level=[1, 2], future_stack=future_stack),
+            df_named.stack(level=1, future_stack=future_stack).stack(
+                level=1, future_stack=future_stack
+            ),
+        )
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    def test_stack_mixed_levels(self, future_stack):
+        columns = MultiIndex.from_tuples(
+            [
+                ("A", "cat", "long"),
+                ("B", "cat", "long"),
+                ("A", "dog", "short"),
+                ("B", "dog", "short"),
+            ],
+            names=["exp", "animal", "hair_length"],
+        )
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((4, 4)), columns=columns
+        )
+
+        animal_hair_stacked = df.stack(
+            level=["animal", "hair_length"], future_stack=future_stack
+        )
+        exp_hair_stacked = df.stack(
+            level=["exp", "hair_length"], future_stack=future_stack
+        )
+
+        # GH #8584: Need to check that stacking works when a number
+        # is passed that is both a level name and in the range of
+        # the level numbers
+        df2 = df.copy()
+        df2.columns.names = ["exp", "animal", 1]
+        tm.assert_frame_equal(
+            df2.stack(level=["animal", 1], future_stack=future_stack),
+            animal_hair_stacked,
+            check_names=False,
+        )
+        tm.assert_frame_equal(
+            df2.stack(level=["exp", 1], future_stack=future_stack),
+            exp_hair_stacked,
+            check_names=False,
+        )
+
+        # When mixed types are passed and the ints are not level
+        # names, raise
+        msg = (
+            "level should contain all level names or all level numbers, not "
+            "a mixture of the two"
+        )
+        with pytest.raises(ValueError, match=msg):
+            df2.stack(level=["animal", 0], future_stack=future_stack)
+
+        # GH #8584: Having 0 in the level names could raise a
+        # strange error about lexsort depth
+        df3 = df.copy()
+        df3.columns.names = ["exp", "animal", 0]
+        tm.assert_frame_equal(
+            df3.stack(level=["animal", 0], future_stack=future_stack),
+            animal_hair_stacked,
+            check_names=False,
+        )
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    def test_stack_int_level_names(self, future_stack):
+        columns = MultiIndex.from_tuples(
+            [
+                ("A", "cat", "long"),
+                ("B", "cat", "long"),
+                ("A", "dog", "short"),
+                ("B", "dog", "short"),
+            ],
+            names=["exp", "animal", "hair_length"],
+        )
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((4, 4)), columns=columns
+        )
+
+        exp_animal_stacked = df.stack(
+            level=["exp", "animal"], future_stack=future_stack
+        )
+        animal_hair_stacked = df.stack(
+            level=["animal", "hair_length"], future_stack=future_stack
+        )
+        exp_hair_stacked = df.stack(
+            level=["exp", "hair_length"], future_stack=future_stack
+        )
+
+        df2 = df.copy()
+        df2.columns.names = [0, 1, 2]
+        tm.assert_frame_equal(
+            df2.stack(level=[1, 2], future_stack=future_stack),
+            animal_hair_stacked,
+            check_names=False,
+        )
+        tm.assert_frame_equal(
+            df2.stack(level=[0, 1], future_stack=future_stack),
+            exp_animal_stacked,
+            check_names=False,
+        )
+        tm.assert_frame_equal(
+            df2.stack(level=[0, 2], future_stack=future_stack),
+            exp_hair_stacked,
+            check_names=False,
+        )
+
+        # Out-of-order int column names
+        df3 = df.copy()
+        df3.columns.names = [2, 0, 1]
+        tm.assert_frame_equal(
+            df3.stack(level=[0, 1], future_stack=future_stack),
+            animal_hair_stacked,
+            check_names=False,
+        )
+        tm.assert_frame_equal(
+            df3.stack(level=[2, 0], future_stack=future_stack),
+            exp_animal_stacked,
+            check_names=False,
+        )
+        tm.assert_frame_equal(
+            df3.stack(level=[2, 1], future_stack=future_stack),
+            exp_hair_stacked,
+            check_names=False,
+        )
+
+    def test_unstack_bool(self):
+        df = DataFrame(
+            [False, False],
+            index=MultiIndex.from_arrays([["a", "b"], ["c", "l"]]),
+            columns=["col"],
+        )
+        rs = df.unstack()
+        xp = DataFrame(
+            np.array([[False, np.nan], [np.nan, False]], dtype=object),
+            index=["a", "b"],
+            columns=MultiIndex.from_arrays([["col", "col"], ["c", "l"]]),
+        )
+        tm.assert_frame_equal(rs, xp)
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    def test_unstack_level_binding(self, future_stack):
+        # GH9856
+        mi = MultiIndex(
+            levels=[["foo", "bar"], ["one", "two"], ["a", "b"]],
+            codes=[[0, 0, 1, 1], [0, 1, 0, 1], [1, 0, 1, 0]],
+            names=["first", "second", "third"],
+        )
+        s = Series(0, index=mi)
+        result = s.unstack([1, 2]).stack(0, future_stack=future_stack)
+
+        expected_mi = MultiIndex(
+            levels=[["foo", "bar"], ["one", "two"]],
+            codes=[[0, 0, 1, 1], [0, 1, 0, 1]],
+            names=["first", "second"],
+        )
+
+        expected = DataFrame(
+            np.array(
+                [[0, np.nan], [np.nan, 0], [0, np.nan], [np.nan, 0]], dtype=np.float64
+            ),
+            index=expected_mi,
+            columns=Index(["b", "a"], name="third"),
+        )
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_unstack_to_series(self, float_frame):
+        # check reversibility
+        data = float_frame.unstack()
+
+        assert isinstance(data, Series)
+        undo = data.unstack().T
+        tm.assert_frame_equal(undo, float_frame)
+
+        # check NA handling
+        data = DataFrame({"x": [1, 2, np.nan], "y": [3.0, 4, np.nan]})
+        data.index = Index(["a", "b", "c"])
+        result = data.unstack()
+
+        midx = MultiIndex(
+            levels=[["x", "y"], ["a", "b", "c"]],
+            codes=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]],
+        )
+        expected = Series([1, 2, np.nan, 3, 4, np.nan], index=midx)
+
+        tm.assert_series_equal(result, expected)
+
+        # check composability of unstack
+        old_data = data.copy()
+        for _ in range(4):
+            data = data.unstack()
+        tm.assert_frame_equal(old_data, data)
+
+    def test_unstack_dtypes(self, using_infer_string):
+        # GH 2929
+        rows = [[1, 1, 3, 4], [1, 2, 3, 4], [2, 1, 3, 4], [2, 2, 3, 4]]
+
+        df = DataFrame(rows, columns=list("ABCD"))
+        result = df.dtypes
+        expected = Series([np.dtype("int64")] * 4, index=list("ABCD"))
+        tm.assert_series_equal(result, expected)
+
+        # single dtype
+        df2 = df.set_index(["A", "B"])
+        df3 = df2.unstack("B")
+        result = df3.dtypes
+        expected = Series(
+            [np.dtype("int64")] * 4,
+            index=MultiIndex.from_arrays(
+                [["C", "C", "D", "D"], [1, 2, 1, 2]], names=(None, "B")
+            ),
+        )
+        tm.assert_series_equal(result, expected)
+
+        # mixed
+        df2 = df.set_index(["A", "B"])
+        df2["C"] = 3.0
+        df3 = df2.unstack("B")
+        result = df3.dtypes
+        expected = Series(
+            [np.dtype("float64")] * 2 + [np.dtype("int64")] * 2,
+            index=MultiIndex.from_arrays(
+                [["C", "C", "D", "D"], [1, 2, 1, 2]], names=(None, "B")
+            ),
+        )
+        tm.assert_series_equal(result, expected)
+        df2["D"] = "foo"
+        df3 = df2.unstack("B")
+        result = df3.dtypes
+        dtype = (
+            pd.StringDtype(na_value=np.nan)
+            if using_infer_string
+            else np.dtype("object")
+        )
+        expected = Series(
+            [np.dtype("float64")] * 2 + [dtype] * 2,
+            index=MultiIndex.from_arrays(
+                [["C", "C", "D", "D"], [1, 2, 1, 2]], names=(None, "B")
+            ),
+        )
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "c, d",
+        (
+            (np.zeros(5), np.zeros(5)),
+            (np.arange(5, dtype="f8"), np.arange(5, 10, dtype="f8")),
+        ),
+    )
+    def test_unstack_dtypes_mixed_date(self, c, d):
+        # GH7405
+        df = DataFrame(
+            {
+                "A": ["a"] * 5,
+                "C": c,
+                "D": d,
+                "B": date_range("2012-01-01", periods=5),
+            }
+        )
+
+        right = df.iloc[:3].copy(deep=True)
+
+        df = df.set_index(["A", "B"])
+        df["D"] = df["D"].astype("int64")
+
+        left = df.iloc[:3].unstack(0)
+        right = right.set_index(["A", "B"]).unstack(0)
+        right[("D", "a")] = right[("D", "a")].astype("int64")
+
+        assert left.shape == (3, 2)
+        tm.assert_frame_equal(left, right)
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    def test_unstack_non_unique_index_names(self, future_stack):
+        idx = MultiIndex.from_tuples([("a", "b"), ("c", "d")], names=["c1", "c1"])
+        df = DataFrame([1, 2], index=idx)
+        msg = "The name c1 occurs multiple times, use a level number"
+        with pytest.raises(ValueError, match=msg):
+            df.unstack("c1")
+
+        with pytest.raises(ValueError, match=msg):
+            df.T.stack("c1", future_stack=future_stack)
+
+    def test_unstack_unused_levels(self):
+        # GH 17845: unused codes in index make unstack() cast int to float
+        idx = MultiIndex.from_product([["a"], ["A", "B", "C", "D"]])[:-1]
+        df = DataFrame([[1, 0]] * 3, index=idx)
+
+        result = df.unstack()
+        exp_col = MultiIndex.from_product([[0, 1], ["A", "B", "C"]])
+        expected = DataFrame([[1, 1, 1, 0, 0, 0]], index=["a"], columns=exp_col)
+        tm.assert_frame_equal(result, expected)
+        assert (result.columns.levels[1] == idx.levels[1]).all()
+
+        # Unused items on both levels
+        levels = [[0, 1, 7], [0, 1, 2, 3]]
+        codes = [[0, 0, 1, 1], [0, 2, 0, 2]]
+        idx = MultiIndex(levels, codes)
+        block = np.arange(4).reshape(2, 2)
+        df = DataFrame(np.concatenate([block, block + 4]), index=idx)
+        result = df.unstack()
+        expected = DataFrame(
+            np.concatenate([block * 2, block * 2 + 1], axis=1), columns=idx
+        )
+        tm.assert_frame_equal(result, expected)
+        assert (result.columns.levels[1] == idx.levels[1]).all()
+
+    @pytest.mark.parametrize(
+        "level, idces, col_level, idx_level",
+        (
+            (0, [13, 16, 6, 9, 2, 5, 8, 11], [np.nan, "a", 2], [np.nan, 5, 1]),
+            (1, [8, 11, 1, 4, 12, 15, 13, 16], [np.nan, 5, 1], [np.nan, "a", 2]),
+        ),
+    )
+    def test_unstack_unused_levels_mixed_with_nan(
+        self, level, idces, col_level, idx_level
+    ):
+        # With mixed dtype and NaN
+        levels = [["a", 2, "c"], [1, 3, 5, 7]]
+        codes = [[0, -1, 1, 1], [0, 2, -1, 2]]
+        idx = MultiIndex(levels, codes)
+        data = np.arange(8)
+        df = DataFrame(data.reshape(4, 2), index=idx)
+
+        result = df.unstack(level=level)
+        exp_data = np.zeros(18) * np.nan
+        exp_data[idces] = data
+        cols = MultiIndex.from_product([[0, 1], col_level])
+        expected = DataFrame(exp_data.reshape(3, 6), index=idx_level, columns=cols)
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("cols", [["A", "C"], slice(None)])
+    def test_unstack_unused_level(self, cols):
+        # GH 18562 : unused codes on the unstacked level
+        df = DataFrame([[2010, "a", "I"], [2011, "b", "II"]], columns=["A", "B", "C"])
+
+        ind = df.set_index(["A", "B", "C"], drop=False)
+        selection = ind.loc[(slice(None), slice(None), "I"), cols]
+        result = selection.unstack()
+
+        expected = ind.iloc[[0]][cols]
+        expected.columns = MultiIndex.from_product(
+            [expected.columns, ["I"]], names=[None, "C"]
+        )
+        expected.index = expected.index.droplevel("C")
+        tm.assert_frame_equal(result, expected)
+
+    def test_unstack_long_index(self):
+        # PH 32624: Error when using a lot of indices to unstack.
+        # The error occurred only, if a lot of indices are used.
+        df = DataFrame(
+            [[1]],
+            columns=MultiIndex.from_tuples([[0]], names=["c1"]),
+            index=MultiIndex.from_tuples(
+                [[0, 0, 1, 0, 0, 0, 1]],
+                names=["i1", "i2", "i3", "i4", "i5", "i6", "i7"],
+            ),
+        )
+        result = df.unstack(["i2", "i3", "i4", "i5", "i6", "i7"])
+        expected = DataFrame(
+            [[1]],
+            columns=MultiIndex.from_tuples(
+                [[0, 0, 1, 0, 0, 0, 1]],
+                names=["c1", "i2", "i3", "i4", "i5", "i6", "i7"],
+            ),
+            index=Index([0], name="i1"),
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_unstack_multi_level_cols(self):
+        # PH 24729: Unstack a df with multi level columns
+        df = DataFrame(
+            [[0.0, 0.0], [0.0, 0.0]],
+            columns=MultiIndex.from_tuples(
+                [["B", "C"], ["B", "D"]], names=["c1", "c2"]
+            ),
+            index=MultiIndex.from_tuples(
+                [[10, 20, 30], [10, 20, 40]], names=["i1", "i2", "i3"]
+            ),
+        )
+        assert df.unstack(["i2", "i1"]).columns.names[-2:] == ["i2", "i1"]
+
+    def test_unstack_multi_level_rows_and_cols(self):
+        # PH 28306: Unstack df with multi level cols and rows
+        df = DataFrame(
+            [[1, 2], [3, 4], [-1, -2], [-3, -4]],
+            columns=MultiIndex.from_tuples([["a", "b", "c"], ["d", "e", "f"]]),
+            index=MultiIndex.from_tuples(
+                [
+                    ["m1", "P3", 222],
+                    ["m1", "A5", 111],
+                    ["m2", "P3", 222],
+                    ["m2", "A5", 111],
+                ],
+                names=["i1", "i2", "i3"],
+            ),
+        )
+        result = df.unstack(["i3", "i2"])
+        expected = df.unstack(["i3"]).unstack(["i2"])
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("idx", [("jim", "joe"), ("joe", "jim")])
+    @pytest.mark.parametrize("lev", list(range(2)))
+    def test_unstack_nan_index1(self, idx, lev):
+        # GH7466
+        def cast(val):
+            val_str = "" if val != val else val
+            return f"{val_str:1}"
+
+        df = DataFrame(
+            {
+                "jim": ["a", "b", np.nan, "d"],
+                "joe": ["w", "x", "y", "z"],
+                "jolie": ["a.w", "b.x", " .y", "d.z"],
+            }
+        )
+
+        left = df.set_index(["jim", "joe"]).unstack()["jolie"]
+        right = df.set_index(["joe", "jim"]).unstack()["jolie"].T
+        tm.assert_frame_equal(left, right)
+
+        mi = df.set_index(list(idx))
+        udf = mi.unstack(level=lev)
+        assert udf.notna().values.sum() == len(df)
+        mk_list = lambda a: list(a) if isinstance(a, tuple) else [a]
+        rows, cols = udf["jolie"].notna().values.nonzero()
+        for i, j in zip(rows, cols):
+            left = sorted(udf["jolie"].iloc[i, j].split("."))
+            right = mk_list(udf["jolie"].index[i]) + mk_list(udf["jolie"].columns[j])
+            right = sorted(map(cast, right))
+            assert left == right
+
+    @pytest.mark.parametrize("idx", itertools.permutations(["1st", "2nd", "3rd"]))
+    @pytest.mark.parametrize("lev", list(range(3)))
+    @pytest.mark.parametrize("col", ["4th", "5th"])
+    def test_unstack_nan_index_repeats(self, idx, lev, col):
+        def cast(val):
+            val_str = "" if val != val else val
+            return f"{val_str:1}"
+
+        df = DataFrame(
+            {
+                "1st": ["d"] * 3
+                + [np.nan] * 5
+                + ["a"] * 2
+                + ["c"] * 3
+                + ["e"] * 2
+                + ["b"] * 5,
+                "2nd": ["y"] * 2
+                + ["w"] * 3
+                + [np.nan] * 3
+                + ["z"] * 4
+                + [np.nan] * 3
+                + ["x"] * 3
+                + [np.nan] * 2,
+                "3rd": [
+                    67,
+                    39,
+                    53,
+                    72,
+                    57,
+                    80,
+                    31,
+                    18,
+                    11,
+                    30,
+                    59,
+                    50,
+                    62,
+                    59,
+                    76,
+                    52,
+                    14,
+                    53,
+                    60,
+                    51,
+                ],
+            }
+        )
+
+        df["4th"], df["5th"] = (
+            df.apply(lambda r: ".".join(map(cast, r)), axis=1),
+            df.apply(lambda r: ".".join(map(cast, r.iloc[::-1])), axis=1),
+        )
+
+        mi = df.set_index(list(idx))
+        udf = mi.unstack(level=lev)
+        assert udf.notna().values.sum() == 2 * len(df)
+        mk_list = lambda a: list(a) if isinstance(a, tuple) else [a]
+        rows, cols = udf[col].notna().values.nonzero()
+        for i, j in zip(rows, cols):
+            left = sorted(udf[col].iloc[i, j].split("."))
+            right = mk_list(udf[col].index[i]) + mk_list(udf[col].columns[j])
+            right = sorted(map(cast, right))
+            assert left == right
+
+    def test_unstack_nan_index2(self):
+        # GH7403
+        df = DataFrame({"A": list("aaaabbbb"), "B": range(8), "C": range(8)})
+        # Explicit cast to avoid implicit cast when setting to np.nan
+        df = df.astype({"B": "float"})
+        df.iloc[3, 1] = np.nan
+        left = df.set_index(["A", "B"]).unstack(0)
+
+        vals = [
+            [3, 0, 1, 2, np.nan, np.nan, np.nan, np.nan],
+            [np.nan, np.nan, np.nan, np.nan, 4, 5, 6, 7],
+        ]
+        vals = list(map(list, zip(*vals)))
+        idx = Index([np.nan, 0, 1, 2, 4, 5, 6, 7], name="B")
+        cols = MultiIndex(
+            levels=[["C"], ["a", "b"]], codes=[[0, 0], [0, 1]], names=[None, "A"]
+        )
+
+        right = DataFrame(vals, columns=cols, index=idx)
+        tm.assert_frame_equal(left, right)
+
+        df = DataFrame({"A": list("aaaabbbb"), "B": list(range(4)) * 2, "C": range(8)})
+        # Explicit cast to avoid implicit cast when setting to np.nan
+        df = df.astype({"B": "float"})
+        df.iloc[2, 1] = np.nan
+        left = df.set_index(["A", "B"]).unstack(0)
+
+        vals = [[2, np.nan], [0, 4], [1, 5], [np.nan, 6], [3, 7]]
+        cols = MultiIndex(
+            levels=[["C"], ["a", "b"]], codes=[[0, 0], [0, 1]], names=[None, "A"]
+        )
+        idx = Index([np.nan, 0, 1, 2, 3], name="B")
+        right = DataFrame(vals, columns=cols, index=idx)
+        tm.assert_frame_equal(left, right)
+
+        df = DataFrame({"A": list("aaaabbbb"), "B": list(range(4)) * 2, "C": range(8)})
+        # Explicit cast to avoid implicit cast when setting to np.nan
+        df = df.astype({"B": "float"})
+        df.iloc[3, 1] = np.nan
+        left = df.set_index(["A", "B"]).unstack(0)
+
+        vals = [[3, np.nan], [0, 4], [1, 5], [2, 6], [np.nan, 7]]
+        cols = MultiIndex(
+            levels=[["C"], ["a", "b"]], codes=[[0, 0], [0, 1]], names=[None, "A"]
+        )
+        idx = Index([np.nan, 0, 1, 2, 3], name="B")
+        right = DataFrame(vals, columns=cols, index=idx)
+        tm.assert_frame_equal(left, right)
+
+    def test_unstack_nan_index3(self, using_array_manager):
+        # GH7401
+        df = DataFrame(
+            {
+                "A": list("aaaaabbbbb"),
+                "B": (date_range("2012-01-01", periods=5).tolist() * 2),
+                "C": np.arange(10),
+            }
+        )
+
+        df.iloc[3, 1] = np.nan
+        left = df.set_index(["A", "B"]).unstack()
+
+        vals = np.array([[3, 0, 1, 2, np.nan, 4], [np.nan, 5, 6, 7, 8, 9]])
+        idx = Index(["a", "b"], name="A")
+        cols = MultiIndex(
+            levels=[["C"], date_range("2012-01-01", periods=5)],
+            codes=[[0, 0, 0, 0, 0, 0], [-1, 0, 1, 2, 3, 4]],
+            names=[None, "B"],
+        )
+
+        right = DataFrame(vals, columns=cols, index=idx)
+        if using_array_manager:
+            # INFO(ArrayManager) with ArrayManager preserve dtype where possible
+            cols = right.columns[[1, 2, 3, 5]]
+            right[cols] = right[cols].astype(df["C"].dtype)
+        tm.assert_frame_equal(left, right)
+
+    def test_unstack_nan_index4(self):
+        # GH4862
+        vals = [
+            ["Hg", np.nan, np.nan, 680585148],
+            ["U", 0.0, np.nan, 680585148],
+            ["Pb", 7.07e-06, np.nan, 680585148],
+            ["Sn", 2.3614e-05, 0.0133, 680607017],
+            ["Ag", 0.0, 0.0133, 680607017],
+            ["Hg", -0.00015, 0.0133, 680607017],
+        ]
+        df = DataFrame(
+            vals,
+            columns=["agent", "change", "dosage", "s_id"],
+            index=[17263, 17264, 17265, 17266, 17267, 17268],
+        )
+
+        left = df.copy().set_index(["s_id", "dosage", "agent"]).unstack()
+
+        vals = [
+            [np.nan, np.nan, 7.07e-06, np.nan, 0.0],
+            [0.0, -0.00015, np.nan, 2.3614e-05, np.nan],
+        ]
+
+        idx = MultiIndex(
+            levels=[[680585148, 680607017], [0.0133]],
+            codes=[[0, 1], [-1, 0]],
+            names=["s_id", "dosage"],
+        )
+
+        cols = MultiIndex(
+            levels=[["change"], ["Ag", "Hg", "Pb", "Sn", "U"]],
+            codes=[[0, 0, 0, 0, 0], [0, 1, 2, 3, 4]],
+            names=[None, "agent"],
+        )
+
+        right = DataFrame(vals, columns=cols, index=idx)
+        tm.assert_frame_equal(left, right)
+
+        left = df.loc[17264:].copy().set_index(["s_id", "dosage", "agent"])
+        tm.assert_frame_equal(left.unstack(), right)
+
+    def test_unstack_nan_index5(self):
+        # GH9497 - multiple unstack with nulls
+        df = DataFrame(
+            {
+                "1st": [1, 2, 1, 2, 1, 2],
+                "2nd": date_range("2014-02-01", periods=6, freq="D"),
+                "jim": 100 + np.arange(6),
+                "joe": (np.random.default_rng(2).standard_normal(6) * 10).round(2),
+            }
+        )
+
+        df["3rd"] = df["2nd"] - pd.Timestamp("2014-02-02")
+        df.loc[1, "2nd"] = df.loc[3, "2nd"] = np.nan
+        df.loc[1, "3rd"] = df.loc[4, "3rd"] = np.nan
+
+        left = df.set_index(["1st", "2nd", "3rd"]).unstack(["2nd", "3rd"])
+        assert left.notna().values.sum() == 2 * len(df)
+
+        for col in ["jim", "joe"]:
+            for _, r in df.iterrows():
+                key = r["1st"], (col, r["2nd"], r["3rd"])
+                assert r[col] == left.loc[key]
+
+    def test_stack_datetime_column_multiIndex(self, future_stack):
+        # GH 8039
+        t = datetime(2014, 1, 1)
+        df = DataFrame([1, 2, 3, 4], columns=MultiIndex.from_tuples([(t, "A", "B")]))
+        warn = None if future_stack else FutureWarning
+        msg = "The previous implementation of stack is deprecated"
+        with tm.assert_produces_warning(warn, match=msg):
+            result = df.stack(future_stack=future_stack)
+
+        eidx = MultiIndex.from_product([(0, 1, 2, 3), ("B",)])
+        ecols = MultiIndex.from_tuples([(t, "A")])
+        expected = DataFrame([1, 2, 3, 4], index=eidx, columns=ecols)
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    @pytest.mark.parametrize(
+        "multiindex_columns",
+        [
+            [0, 1, 2, 3, 4],
+            [0, 1, 2, 3],
+            [0, 1, 2, 4],
+            [0, 1, 2],
+            [1, 2, 3],
+            [2, 3, 4],
+            [0, 1],
+            [0, 2],
+            [0, 3],
+            [0],
+            [2],
+            [4],
+            [4, 3, 2, 1, 0],
+            [3, 2, 1, 0],
+            [4, 2, 1, 0],
+            [2, 1, 0],
+            [3, 2, 1],
+            [4, 3, 2],
+            [1, 0],
+            [2, 0],
+            [3, 0],
+        ],
+    )
+    @pytest.mark.parametrize("level", (-1, 0, 1, [0, 1], [1, 0]))
+    def test_stack_partial_multiIndex(self, multiindex_columns, level, future_stack):
+        # GH 8844
+        dropna = False if not future_stack else lib.no_default
+        full_multiindex = MultiIndex.from_tuples(
+            [("B", "x"), ("B", "z"), ("A", "y"), ("C", "x"), ("C", "u")],
+            names=["Upper", "Lower"],
+        )
+        multiindex = full_multiindex[multiindex_columns]
+        df = DataFrame(
+            np.arange(3 * len(multiindex)).reshape(3, len(multiindex)),
+            columns=multiindex,
+        )
+        result = df.stack(level=level, dropna=dropna, future_stack=future_stack)
+
+        if isinstance(level, int) and not future_stack:
+            # Stacking a single level should not make any all-NaN rows,
+            # so df.stack(level=level, dropna=False) should be the same
+            # as df.stack(level=level, dropna=True).
+            expected = df.stack(level=level, dropna=True, future_stack=future_stack)
+            if isinstance(expected, Series):
+                tm.assert_series_equal(result, expected)
+            else:
+                tm.assert_frame_equal(result, expected)
+
+        df.columns = MultiIndex.from_tuples(
+            df.columns.to_numpy(), names=df.columns.names
+        )
+        expected = df.stack(level=level, dropna=dropna, future_stack=future_stack)
+        if isinstance(expected, Series):
+            tm.assert_series_equal(result, expected)
+        else:
+            tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    def test_stack_full_multiIndex(self, future_stack):
+        # GH 8844
+        full_multiindex = MultiIndex.from_tuples(
+            [("B", "x"), ("B", "z"), ("A", "y"), ("C", "x"), ("C", "u")],
+            names=["Upper", "Lower"],
+        )
+        df = DataFrame(np.arange(6).reshape(2, 3), columns=full_multiindex[[0, 1, 3]])
+        dropna = False if not future_stack else lib.no_default
+        result = df.stack(dropna=dropna, future_stack=future_stack)
+        expected = DataFrame(
+            [[0, 2], [1, np.nan], [3, 5], [4, np.nan]],
+            index=MultiIndex(
+                levels=[[0, 1], ["u", "x", "y", "z"]],
+                codes=[[0, 0, 1, 1], [1, 3, 1, 3]],
+                names=[None, "Lower"],
+            ),
+            columns=Index(["B", "C"], name="Upper"),
+        )
+        expected["B"] = expected["B"].astype(df.dtypes.iloc[0])
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("ordered", [False, True])
+    def test_stack_preserve_categorical_dtype(self, ordered, future_stack):
+        # GH13854
+        cidx = pd.CategoricalIndex(list("yxz"), categories=list("xyz"), ordered=ordered)
+        df = DataFrame([[10, 11, 12]], columns=cidx)
+        result = df.stack(future_stack=future_stack)
+
+        # `MultiIndex.from_product` preserves categorical dtype -
+        # it's tested elsewhere.
+        midx = MultiIndex.from_product([df.index, cidx])
+        expected = Series([10, 11, 12], index=midx)
+
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    @pytest.mark.parametrize("ordered", [False, True])
+    @pytest.mark.parametrize(
+        "labels,data",
+        [
+            (list("xyz"), [10, 11, 12, 13, 14, 15]),
+            (list("zyx"), [14, 15, 12, 13, 10, 11]),
+        ],
+    )
+    def test_stack_multi_preserve_categorical_dtype(
+        self, ordered, labels, data, future_stack
+    ):
+        # GH-36991
+        cidx = pd.CategoricalIndex(labels, categories=sorted(labels), ordered=ordered)
+        cidx2 = pd.CategoricalIndex(["u", "v"], ordered=ordered)
+        midx = MultiIndex.from_product([cidx, cidx2])
+        df = DataFrame([sorted(data)], columns=midx)
+        result = df.stack([0, 1], future_stack=future_stack)
+
+        labels = labels if future_stack else sorted(labels)
+        s_cidx = pd.CategoricalIndex(labels, ordered=ordered)
+        expected_data = sorted(data) if future_stack else data
+        expected = Series(
+            expected_data, index=MultiIndex.from_product([[0], s_cidx, cidx2])
+        )
+
+        tm.assert_series_equal(result, expected)
+
+    def test_stack_preserve_categorical_dtype_values(self, future_stack):
+        # GH-23077
+        cat = pd.Categorical(["a", "a", "b", "c"])
+        df = DataFrame({"A": cat, "B": cat})
+        result = df.stack(future_stack=future_stack)
+        index = MultiIndex.from_product([[0, 1, 2, 3], ["A", "B"]])
+        expected = Series(
+            pd.Categorical(["a", "a", "a", "a", "b", "b", "c", "c"]), index=index
+        )
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    @pytest.mark.filterwarnings("ignore:Downcasting object dtype arrays:FutureWarning")
+    @pytest.mark.parametrize(
+        "index, columns",
+        [
+            ([0, 0, 1, 1], MultiIndex.from_product([[1, 2], ["a", "b"]])),
+            ([0, 0, 2, 3], MultiIndex.from_product([[1, 2], ["a", "b"]])),
+            ([0, 1, 2, 3], MultiIndex.from_product([[1, 2], ["a", "b"]])),
+        ],
+    )
+    def test_stack_multi_columns_non_unique_index(self, index, columns, future_stack):
+        # GH-28301
+
+        df = DataFrame(index=index, columns=columns).fillna(1)
+        stacked = df.stack(future_stack=future_stack)
+        new_index = MultiIndex.from_tuples(stacked.index.to_numpy())
+        expected = DataFrame(
+            stacked.to_numpy(), index=new_index, columns=stacked.columns
+        )
+        tm.assert_frame_equal(stacked, expected)
+        stacked_codes = np.asarray(stacked.index.codes)
+        expected_codes = np.asarray(new_index.codes)
+        tm.assert_numpy_array_equal(stacked_codes, expected_codes)
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    @pytest.mark.parametrize(
+        "vals1, vals2, dtype1, dtype2, expected_dtype",
+        [
+            ([1, 2], [3.0, 4.0], "Int64", "Float64", "Float64"),
+            ([1, 2], ["foo", "bar"], "Int64", "string", "object"),
+        ],
+    )
+    def test_stack_multi_columns_mixed_extension_types(
+        self, vals1, vals2, dtype1, dtype2, expected_dtype, future_stack
+    ):
+        # GH45740
+        df = DataFrame(
+            {
+                ("A", 1): Series(vals1, dtype=dtype1),
+                ("A", 2): Series(vals2, dtype=dtype2),
+            }
+        )
+        result = df.stack(future_stack=future_stack)
+        expected = (
+            df.astype(object).stack(future_stack=future_stack).astype(expected_dtype)
+        )
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("level", [0, 1])
+    def test_unstack_mixed_extension_types(self, level):
+        index = MultiIndex.from_tuples([("A", 0), ("A", 1), ("B", 1)], names=["a", "b"])
+        df = DataFrame(
+            {
+                "A": pd.array([0, 1, None], dtype="Int64"),
+                "B": pd.Categorical(["a", "a", "b"]),
+            },
+            index=index,
+        )
+
+        result = df.unstack(level=level)
+        expected = df.astype(object).unstack(level=level)
+        if level == 0:
+            expected[("A", "B")] = expected[("A", "B")].fillna(pd.NA)
+        else:
+            expected[("A", 0)] = expected[("A", 0)].fillna(pd.NA)
+
+        expected_dtypes = Series(
+            [df.A.dtype] * 2 + [df.B.dtype] * 2, index=result.columns
+        )
+        tm.assert_series_equal(result.dtypes, expected_dtypes)
+        tm.assert_frame_equal(result.astype(object), expected)
+
+    @pytest.mark.parametrize("level", [0, "baz"])
+    def test_unstack_swaplevel_sortlevel(self, level):
+        # GH 20994
+        mi = MultiIndex.from_product([[0], ["d", "c"]], names=["bar", "baz"])
+        df = DataFrame([[0, 2], [1, 3]], index=mi, columns=["B", "A"])
+        df.columns.name = "foo"
+
+        expected = DataFrame(
+            [[3, 1, 2, 0]],
+            columns=MultiIndex.from_tuples(
+                [("c", "A"), ("c", "B"), ("d", "A"), ("d", "B")], names=["baz", "foo"]
+            ),
+        )
+        expected.index.name = "bar"
+
+        result = df.unstack().swaplevel(axis=1).sort_index(axis=1, level=level)
+        tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("dtype", ["float64", "Float64"])
+def test_unstack_sort_false(frame_or_series, dtype):
+    # GH 15105
+    index = MultiIndex.from_tuples(
+        [("two", "z", "b"), ("two", "y", "a"), ("one", "z", "b"), ("one", "y", "a")]
+    )
+    obj = frame_or_series(np.arange(1.0, 5.0), index=index, dtype=dtype)
+    result = obj.unstack(level=-1, sort=False)
+
+    if frame_or_series is DataFrame:
+        expected_columns = MultiIndex.from_tuples([(0, "b"), (0, "a")])
+    else:
+        expected_columns = ["b", "a"]
+    expected = DataFrame(
+        [[1.0, np.nan], [np.nan, 2.0], [3.0, np.nan], [np.nan, 4.0]],
+        columns=expected_columns,
+        index=MultiIndex.from_tuples(
+            [("two", "z"), ("two", "y"), ("one", "z"), ("one", "y")]
+        ),
+        dtype=dtype,
+    )
+    tm.assert_frame_equal(result, expected)
+
+    result = obj.unstack(level=[1, 2], sort=False)
+
+    if frame_or_series is DataFrame:
+        expected_columns = MultiIndex.from_tuples([(0, "z", "b"), (0, "y", "a")])
+    else:
+        expected_columns = MultiIndex.from_tuples([("z", "b"), ("y", "a")])
+    expected = DataFrame(
+        [[1.0, 2.0], [3.0, 4.0]],
+        index=["two", "one"],
+        columns=expected_columns,
+        dtype=dtype,
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_unstack_fill_frame_object():
+    # GH12815 Test unstacking with object.
+    data = Series(["a", "b", "c", "a"], dtype="object")
+    data.index = MultiIndex.from_tuples(
+        [("x", "a"), ("x", "b"), ("y", "b"), ("z", "a")]
+    )
+
+    # By default missing values will be NaN
+    result = data.unstack()
+    expected = DataFrame(
+        {"a": ["a", np.nan, "a"], "b": ["b", "c", np.nan]},
+        index=list("xyz"),
+        dtype=object,
+    )
+    tm.assert_frame_equal(result, expected)
+
+    # Fill with any value replaces missing values as expected
+    result = data.unstack(fill_value="d")
+    expected = DataFrame(
+        {"a": ["a", "d", "a"], "b": ["b", "c", "d"]}, index=list("xyz"), dtype=object
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_unstack_timezone_aware_values():
+    # GH 18338
+    df = DataFrame(
+        {
+            "timestamp": [pd.Timestamp("2017-08-27 01:00:00.709949+0000", tz="UTC")],
+            "a": ["a"],
+            "b": ["b"],
+            "c": ["c"],
+        },
+        columns=["timestamp", "a", "b", "c"],
+    )
+    result = df.set_index(["a", "b"]).unstack()
+    expected = DataFrame(
+        [[pd.Timestamp("2017-08-27 01:00:00.709949+0000", tz="UTC"), "c"]],
+        index=Index(["a"], name="a"),
+        columns=MultiIndex(
+            levels=[["timestamp", "c"], ["b"]],
+            codes=[[0, 1], [0, 0]],
+            names=[None, "b"],
+        ),
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_stack_timezone_aware_values(future_stack):
+    # GH 19420
+    ts = date_range(freq="D", start="20180101", end="20180103", tz="America/New_York")
+    df = DataFrame({"A": ts}, index=["a", "b", "c"])
+    result = df.stack(future_stack=future_stack)
+    expected = Series(
+        ts,
+        index=MultiIndex(levels=[["a", "b", "c"], ["A"]], codes=[[0, 1, 2], [0, 0, 0]]),
+    )
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.filterwarnings("ignore:The previous implementation of stack is deprecated")
+@pytest.mark.parametrize("dropna", [True, False, lib.no_default])
+def test_stack_empty_frame(dropna, future_stack):
+    # GH 36113
+    levels = [np.array([], dtype=np.int64), np.array([], dtype=np.int64)]
+    expected = Series(dtype=np.float64, index=MultiIndex(levels=levels, codes=[[], []]))
+    if future_stack and dropna is not lib.no_default:
+        with pytest.raises(ValueError, match="dropna must be unspecified"):
+            DataFrame(dtype=np.float64).stack(dropna=dropna, future_stack=future_stack)
+    else:
+        result = DataFrame(dtype=np.float64).stack(
+            dropna=dropna, future_stack=future_stack
+        )
+        tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.filterwarnings("ignore:The previous implementation of stack is deprecated")
+@pytest.mark.parametrize("dropna", [True, False, lib.no_default])
+@pytest.mark.parametrize("fill_value", [None, 0])
+def test_stack_unstack_empty_frame(dropna, fill_value, future_stack):
+    # GH 36113
+    if future_stack and dropna is not lib.no_default:
+        with pytest.raises(ValueError, match="dropna must be unspecified"):
+            DataFrame(dtype=np.int64).stack(
+                dropna=dropna, future_stack=future_stack
+            ).unstack(fill_value=fill_value)
+    else:
+        result = (
+            DataFrame(dtype=np.int64)
+            .stack(dropna=dropna, future_stack=future_stack)
+            .unstack(fill_value=fill_value)
+        )
+        expected = DataFrame(dtype=np.int64)
+        tm.assert_frame_equal(result, expected)
+
+
+def test_unstack_single_index_series():
+    # GH 36113
+    msg = r"index must be a MultiIndex to unstack.*"
+    with pytest.raises(ValueError, match=msg):
+        Series(dtype=np.int64).unstack()
+
+
+def test_unstacking_multi_index_df():
+    # see gh-30740
+    df = DataFrame(
+        {
+            "name": ["Alice", "Bob"],
+            "score": [9.5, 8],
+            "employed": [False, True],
+            "kids": [0, 0],
+            "gender": ["female", "male"],
+        }
+    )
+    df = df.set_index(["name", "employed", "kids", "gender"])
+    df = df.unstack(["gender"], fill_value=0)
+    expected = df.unstack("employed", fill_value=0).unstack("kids", fill_value=0)
+    result = df.unstack(["employed", "kids"], fill_value=0)
+    expected = DataFrame(
+        [[9.5, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 8.0]],
+        index=Index(["Alice", "Bob"], name="name"),
+        columns=MultiIndex.from_tuples(
+            [
+                ("score", "female", False, 0),
+                ("score", "female", True, 0),
+                ("score", "male", False, 0),
+                ("score", "male", True, 0),
+            ],
+            names=[None, "gender", "employed", "kids"],
+        ),
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.filterwarnings("ignore:The previous implementation of stack is deprecated")
+def test_stack_positional_level_duplicate_column_names(future_stack):
+    # https://github.com/pandas-dev/pandas/issues/36353
+    columns = MultiIndex.from_product([("x", "y"), ("y", "z")], names=["a", "a"])
+    df = DataFrame([[1, 1, 1, 1]], columns=columns)
+    result = df.stack(0, future_stack=future_stack)
+
+    new_columns = Index(["y", "z"], name="a")
+    new_index = MultiIndex.from_tuples([(0, "x"), (0, "y")], names=[None, "a"])
+    expected = DataFrame([[1, 1], [1, 1]], index=new_index, columns=new_columns)
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_unstack_non_slice_like_blocks(using_array_manager):
+    # Case where the mgr_locs of a DataFrame's underlying blocks are not slice-like
+
+    mi = MultiIndex.from_product([range(5), ["A", "B", "C"]])
+    df = DataFrame(
+        {
+            0: np.random.default_rng(2).standard_normal(15),
+            1: np.random.default_rng(2).standard_normal(15).astype(np.int64),
+            2: np.random.default_rng(2).standard_normal(15),
+            3: np.random.default_rng(2).standard_normal(15),
+        },
+        index=mi,
+    )
+    if not using_array_manager:
+        assert any(not x.mgr_locs.is_slice_like for x in df._mgr.blocks)
+
+    res = df.unstack()
+
+    expected = pd.concat([df[n].unstack() for n in range(4)], keys=range(4), axis=1)
+    tm.assert_frame_equal(res, expected)
+
+
+@pytest.mark.filterwarnings("ignore:The previous implementation of stack is deprecated")
+def test_stack_sort_false(future_stack):
+    # GH 15105
+    data = [[1, 2, 3.0, 4.0], [2, 3, 4.0, 5.0], [3, 4, np.nan, np.nan]]
+    df = DataFrame(
+        data,
+        columns=MultiIndex(
+            levels=[["B", "A"], ["x", "y"]], codes=[[0, 0, 1, 1], [0, 1, 0, 1]]
+        ),
+    )
+    kwargs = {} if future_stack else {"sort": False}
+    result = df.stack(level=0, future_stack=future_stack, **kwargs)
+    if future_stack:
+        expected = DataFrame(
+            {
+                "x": [1.0, 3.0, 2.0, 4.0, 3.0, np.nan],
+                "y": [2.0, 4.0, 3.0, 5.0, 4.0, np.nan],
+            },
+            index=MultiIndex.from_arrays(
+                [[0, 0, 1, 1, 2, 2], ["B", "A", "B", "A", "B", "A"]]
+            ),
+        )
+    else:
+        expected = DataFrame(
+            {"x": [1.0, 3.0, 2.0, 4.0, 3.0], "y": [2.0, 4.0, 3.0, 5.0, 4.0]},
+            index=MultiIndex.from_arrays([[0, 0, 1, 1, 2], ["B", "A", "B", "A", "B"]]),
+        )
+    tm.assert_frame_equal(result, expected)
+
+    # Codes sorted in this call
+    df = DataFrame(
+        data,
+        columns=MultiIndex.from_arrays([["B", "B", "A", "A"], ["x", "y", "x", "y"]]),
+    )
+    kwargs = {} if future_stack else {"sort": False}
+    result = df.stack(level=0, future_stack=future_stack, **kwargs)
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.filterwarnings("ignore:The previous implementation of stack is deprecated")
+def test_stack_sort_false_multi_level(future_stack):
+    # GH 15105
+    idx = MultiIndex.from_tuples([("weight", "kg"), ("height", "m")])
+    df = DataFrame([[1.0, 2.0], [3.0, 4.0]], index=["cat", "dog"], columns=idx)
+    kwargs = {} if future_stack else {"sort": False}
+    result = df.stack([0, 1], future_stack=future_stack, **kwargs)
+    expected_index = MultiIndex.from_tuples(
+        [
+            ("cat", "weight", "kg"),
+            ("cat", "height", "m"),
+            ("dog", "weight", "kg"),
+            ("dog", "height", "m"),
+        ]
+    )
+    expected = Series([1.0, 2.0, 3.0, 4.0], index=expected_index)
+    tm.assert_series_equal(result, expected)
+
+
+class TestStackUnstackMultiLevel:
+    def test_unstack(self, multiindex_year_month_day_dataframe_random_data):
+        # just check that it works for now
+        ymd = multiindex_year_month_day_dataframe_random_data
+
+        unstacked = ymd.unstack()
+        unstacked.unstack()
+
+        # test that ints work
+        ymd.astype(int).unstack()
+
+        # test that int32 work
+        ymd.astype(np.int32).unstack()
+
+    @pytest.mark.parametrize(
+        "result_rows,result_columns,index_product,expected_row",
+        [
+            (
+                [[1, 1, None, None, 30.0, None], [2, 2, None, None, 30.0, None]],
+                ["ix1", "ix2", "col1", "col2", "col3", "col4"],
+                2,
+                [None, None, 30.0, None],
+            ),
+            (
+                [[1, 1, None, None, 30.0], [2, 2, None, None, 30.0]],
+                ["ix1", "ix2", "col1", "col2", "col3"],
+                2,
+                [None, None, 30.0],
+            ),
+            (
+                [[1, 1, None, None, 30.0], [2, None, None, None, 30.0]],
+                ["ix1", "ix2", "col1", "col2", "col3"],
+                None,
+                [None, None, 30.0],
+            ),
+        ],
+    )
+    def test_unstack_partial(
+        self, result_rows, result_columns, index_product, expected_row
+    ):
+        # check for regressions on this issue:
+        # https://github.com/pandas-dev/pandas/issues/19351
+        # make sure DataFrame.unstack() works when its run on a subset of the DataFrame
+        # and the Index levels contain values that are not present in the subset
+        result = DataFrame(result_rows, columns=result_columns).set_index(
+            ["ix1", "ix2"]
+        )
+        result = result.iloc[1:2].unstack("ix2")
+        expected = DataFrame(
+            [expected_row],
+            columns=MultiIndex.from_product(
+                [result_columns[2:], [index_product]], names=[None, "ix2"]
+            ),
+            index=Index([2], name="ix1"),
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_unstack_multiple_no_empty_columns(self):
+        index = MultiIndex.from_tuples(
+            [(0, "foo", 0), (0, "bar", 0), (1, "baz", 1), (1, "qux", 1)]
+        )
+
+        s = Series(np.random.default_rng(2).standard_normal(4), index=index)
+
+        unstacked = s.unstack([1, 2])
+        expected = unstacked.dropna(axis=1, how="all")
+        tm.assert_frame_equal(unstacked, expected)
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    def test_stack(self, multiindex_year_month_day_dataframe_random_data, future_stack):
+        ymd = multiindex_year_month_day_dataframe_random_data
+
+        # regular roundtrip
+        unstacked = ymd.unstack()
+        restacked = unstacked.stack(future_stack=future_stack)
+        if future_stack:
+            # NA values in unstacked persist to restacked in version 3
+            restacked = restacked.dropna(how="all")
+        tm.assert_frame_equal(restacked, ymd)
+
+        unlexsorted = ymd.sort_index(level=2)
+
+        unstacked = unlexsorted.unstack(2)
+        restacked = unstacked.stack(future_stack=future_stack)
+        if future_stack:
+            # NA values in unstacked persist to restacked in version 3
+            restacked = restacked.dropna(how="all")
+        tm.assert_frame_equal(restacked.sort_index(level=0), ymd)
+
+        unlexsorted = unlexsorted[::-1]
+        unstacked = unlexsorted.unstack(1)
+        restacked = unstacked.stack(future_stack=future_stack).swaplevel(1, 2)
+        if future_stack:
+            # NA values in unstacked persist to restacked in version 3
+            restacked = restacked.dropna(how="all")
+        tm.assert_frame_equal(restacked.sort_index(level=0), ymd)
+
+        unlexsorted = unlexsorted.swaplevel(0, 1)
+        unstacked = unlexsorted.unstack(0).swaplevel(0, 1, axis=1)
+        restacked = unstacked.stack(0, future_stack=future_stack).swaplevel(1, 2)
+        if future_stack:
+            # NA values in unstacked persist to restacked in version 3
+            restacked = restacked.dropna(how="all")
+        tm.assert_frame_equal(restacked.sort_index(level=0), ymd)
+
+        # columns unsorted
+        unstacked = ymd.unstack()
+        restacked = unstacked.stack(future_stack=future_stack)
+        if future_stack:
+            # NA values in unstacked persist to restacked in version 3
+            restacked = restacked.dropna(how="all")
+        tm.assert_frame_equal(restacked, ymd)
+
+        # more than 2 levels in the columns
+        unstacked = ymd.unstack(1).unstack(1)
+
+        result = unstacked.stack(1, future_stack=future_stack)
+        expected = ymd.unstack()
+        tm.assert_frame_equal(result, expected)
+
+        result = unstacked.stack(2, future_stack=future_stack)
+        expected = ymd.unstack(1)
+        tm.assert_frame_equal(result, expected)
+
+        result = unstacked.stack(0, future_stack=future_stack)
+        expected = ymd.stack(future_stack=future_stack).unstack(1).unstack(1)
+        tm.assert_frame_equal(result, expected)
+
+        # not all levels present in each echelon
+        unstacked = ymd.unstack(2).loc[:, ::3]
+        stacked = unstacked.stack(future_stack=future_stack).stack(
+            future_stack=future_stack
+        )
+        ymd_stacked = ymd.stack(future_stack=future_stack)
+        if future_stack:
+            # NA values in unstacked persist to restacked in version 3
+            stacked = stacked.dropna(how="all")
+            ymd_stacked = ymd_stacked.dropna(how="all")
+        tm.assert_series_equal(stacked, ymd_stacked.reindex(stacked.index))
+
+        # stack with negative number
+        result = ymd.unstack(0).stack(-2, future_stack=future_stack)
+        expected = ymd.unstack(0).stack(0, future_stack=future_stack)
+        tm.assert_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "idx, columns, exp_idx",
+        [
+            [
+                list("abab"),
+                ["1st", "2nd", "1st"],
+                MultiIndex(
+                    levels=[["a", "b"], ["1st", "2nd"]],
+                    codes=[np.tile(np.arange(2).repeat(3), 2), np.tile([0, 1, 0], 4)],
+                ),
+            ],
+            [
+                MultiIndex.from_tuples((("a", 2), ("b", 1), ("a", 1), ("b", 2))),
+                ["1st", "2nd", "1st"],
+                MultiIndex(
+                    levels=[["a", "b"], [1, 2], ["1st", "2nd"]],
+                    codes=[
+                        np.tile(np.arange(2).repeat(3), 2),
+                        np.repeat([1, 0, 1], [3, 6, 3]),
+                        np.tile([0, 1, 0], 4),
+                    ],
+                ),
+            ],
+        ],
+    )
+    def test_stack_duplicate_index(self, idx, columns, exp_idx, future_stack):
+        # GH10417
+        df = DataFrame(
+            np.arange(12).reshape(4, 3),
+            index=idx,
+            columns=columns,
+        )
+        if future_stack:
+            msg = "Columns with duplicate values are not supported in stack"
+            with pytest.raises(ValueError, match=msg):
+                df.stack(future_stack=future_stack)
+        else:
+            result = df.stack(future_stack=future_stack)
+            expected = Series(np.arange(12), index=exp_idx)
+            tm.assert_series_equal(result, expected)
+            assert result.index.is_unique is False
+            li, ri = result.index, expected.index
+            tm.assert_index_equal(li, ri)
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    def test_unstack_odd_failure(self, future_stack):
+        mi = MultiIndex.from_arrays(
+            [
+                ["Fri"] * 4 + ["Sat"] * 2 + ["Sun"] * 2 + ["Thu"] * 3,
+                ["Dinner"] * 2 + ["Lunch"] * 2 + ["Dinner"] * 5 + ["Lunch"] * 2,
+                ["No", "Yes"] * 4 + ["No", "No", "Yes"],
+            ],
+            names=["day", "time", "smoker"],
+        )
+        df = DataFrame(
+            {
+                "sum": np.arange(11, dtype="float64"),
+                "len": np.arange(11, dtype="float64"),
+            },
+            index=mi,
+        )
+        # it works, #2100
+        result = df.unstack(2)
+
+        recons = result.stack(future_stack=future_stack)
+        if future_stack:
+            # NA values in unstacked persist to restacked in version 3
+            recons = recons.dropna(how="all")
+        tm.assert_frame_equal(recons, df)
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    def test_stack_mixed_dtype(self, multiindex_dataframe_random_data, future_stack):
+        frame = multiindex_dataframe_random_data
+
+        df = frame.T
+        df["foo", "four"] = "foo"
+        df = df.sort_index(level=1, axis=1)
+
+        stacked = df.stack(future_stack=future_stack)
+        result = df["foo"].stack(future_stack=future_stack).sort_index()
+        tm.assert_series_equal(stacked["foo"], result, check_names=False)
+        assert result.name is None
+        assert stacked["bar"].dtype == np.float64
+
+    def test_unstack_bug(self, future_stack):
+        df = DataFrame(
+            {
+                "state": ["naive", "naive", "naive", "active", "active", "active"],
+                "exp": ["a", "b", "b", "b", "a", "a"],
+                "barcode": [1, 2, 3, 4, 1, 3],
+                "v": ["hi", "hi", "bye", "bye", "bye", "peace"],
+                "extra": np.arange(6.0),
+            }
+        )
+
+        msg = "DataFrameGroupBy.apply operated on the grouping columns"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = df.groupby(["state", "exp", "barcode", "v"]).apply(len)
+
+        unstacked = result.unstack()
+        restacked = unstacked.stack(future_stack=future_stack)
+        tm.assert_series_equal(restacked, result.reindex(restacked.index).astype(float))
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    def test_stack_unstack_preserve_names(
+        self, multiindex_dataframe_random_data, future_stack
+    ):
+        frame = multiindex_dataframe_random_data
+
+        unstacked = frame.unstack()
+        assert unstacked.index.name == "first"
+        assert unstacked.columns.names == ["exp", "second"]
+
+        restacked = unstacked.stack(future_stack=future_stack)
+        assert restacked.index.names == frame.index.names
+
+    @pytest.mark.parametrize("method", ["stack", "unstack"])
+    def test_stack_unstack_wrong_level_name(
+        self, method, multiindex_dataframe_random_data, future_stack
+    ):
+        # GH 18303 - wrong level name should raise
+        frame = multiindex_dataframe_random_data
+
+        # A DataFrame with flat axes:
+        df = frame.loc["foo"]
+
+        kwargs = {"future_stack": future_stack} if method == "stack" else {}
+        with pytest.raises(KeyError, match="does not match index name"):
+            getattr(df, method)("mistake", **kwargs)
+
+        if method == "unstack":
+            # Same on a Series:
+            s = df.iloc[:, 0]
+            with pytest.raises(KeyError, match="does not match index name"):
+                getattr(s, method)("mistake", **kwargs)
+
+    def test_unstack_level_name(self, multiindex_dataframe_random_data):
+        frame = multiindex_dataframe_random_data
+
+        result = frame.unstack("second")
+        expected = frame.unstack(level=1)
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    def test_stack_level_name(self, multiindex_dataframe_random_data, future_stack):
+        frame = multiindex_dataframe_random_data
+
+        unstacked = frame.unstack("second")
+        result = unstacked.stack("exp", future_stack=future_stack)
+        expected = frame.unstack().stack(0, future_stack=future_stack)
+        tm.assert_frame_equal(result, expected)
+
+        result = frame.stack("exp", future_stack=future_stack)
+        expected = frame.stack(future_stack=future_stack)
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    def test_stack_unstack_multiple(
+        self, multiindex_year_month_day_dataframe_random_data, future_stack
+    ):
+        ymd = multiindex_year_month_day_dataframe_random_data
+
+        unstacked = ymd.unstack(["year", "month"])
+        expected = ymd.unstack("year").unstack("month")
+        tm.assert_frame_equal(unstacked, expected)
+        assert unstacked.columns.names == expected.columns.names
+
+        # series
+        s = ymd["A"]
+        s_unstacked = s.unstack(["year", "month"])
+        tm.assert_frame_equal(s_unstacked, expected["A"])
+
+        restacked = unstacked.stack(["year", "month"], future_stack=future_stack)
+        if future_stack:
+            # NA values in unstacked persist to restacked in version 3
+            restacked = restacked.dropna(how="all")
+        restacked = restacked.swaplevel(0, 1).swaplevel(1, 2)
+        restacked = restacked.sort_index(level=0)
+
+        tm.assert_frame_equal(restacked, ymd)
+        assert restacked.index.names == ymd.index.names
+
+        # GH #451
+        unstacked = ymd.unstack([1, 2])
+        expected = ymd.unstack(1).unstack(1).dropna(axis=1, how="all")
+        tm.assert_frame_equal(unstacked, expected)
+
+        unstacked = ymd.unstack([2, 1])
+        expected = ymd.unstack(2).unstack(1).dropna(axis=1, how="all")
+        tm.assert_frame_equal(unstacked, expected.loc[:, unstacked.columns])
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    def test_stack_names_and_numbers(
+        self, multiindex_year_month_day_dataframe_random_data, future_stack
+    ):
+        ymd = multiindex_year_month_day_dataframe_random_data
+
+        unstacked = ymd.unstack(["year", "month"])
+
+        # Can't use mixture of names and numbers to stack
+        with pytest.raises(ValueError, match="level should contain"):
+            unstacked.stack([0, "month"], future_stack=future_stack)
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    def test_stack_multiple_out_of_bounds(
+        self, multiindex_year_month_day_dataframe_random_data, future_stack
+    ):
+        # nlevels == 3
+        ymd = multiindex_year_month_day_dataframe_random_data
+
+        unstacked = ymd.unstack(["year", "month"])
+
+        with pytest.raises(IndexError, match="Too many levels"):
+            unstacked.stack([2, 3], future_stack=future_stack)
+        with pytest.raises(IndexError, match="not a valid level number"):
+            unstacked.stack([-4, -3], future_stack=future_stack)
+
+    def test_unstack_period_series(self):
+        # GH4342
+        idx1 = pd.PeriodIndex(
+            ["2013-01", "2013-01", "2013-02", "2013-02", "2013-03", "2013-03"],
+            freq="M",
+            name="period",
+        )
+        idx2 = Index(["A", "B"] * 3, name="str")
+        value = [1, 2, 3, 4, 5, 6]
+
+        idx = MultiIndex.from_arrays([idx1, idx2])
+        s = Series(value, index=idx)
+
+        result1 = s.unstack()
+        result2 = s.unstack(level=1)
+        result3 = s.unstack(level=0)
+
+        e_idx = pd.PeriodIndex(
+            ["2013-01", "2013-02", "2013-03"], freq="M", name="period"
+        )
+        expected = DataFrame(
+            {"A": [1, 3, 5], "B": [2, 4, 6]}, index=e_idx, columns=["A", "B"]
+        )
+        expected.columns.name = "str"
+
+        tm.assert_frame_equal(result1, expected)
+        tm.assert_frame_equal(result2, expected)
+        tm.assert_frame_equal(result3, expected.T)
+
+        idx1 = pd.PeriodIndex(
+            ["2013-01", "2013-01", "2013-02", "2013-02", "2013-03", "2013-03"],
+            freq="M",
+            name="period1",
+        )
+
+        idx2 = pd.PeriodIndex(
+            ["2013-12", "2013-11", "2013-10", "2013-09", "2013-08", "2013-07"],
+            freq="M",
+            name="period2",
+        )
+        idx = MultiIndex.from_arrays([idx1, idx2])
+        s = Series(value, index=idx)
+
+        result1 = s.unstack()
+        result2 = s.unstack(level=1)
+        result3 = s.unstack(level=0)
+
+        e_idx = pd.PeriodIndex(
+            ["2013-01", "2013-02", "2013-03"], freq="M", name="period1"
+        )
+        e_cols = pd.PeriodIndex(
+            ["2013-07", "2013-08", "2013-09", "2013-10", "2013-11", "2013-12"],
+            freq="M",
+            name="period2",
+        )
+        expected = DataFrame(
+            [
+                [np.nan, np.nan, np.nan, np.nan, 2, 1],
+                [np.nan, np.nan, 4, 3, np.nan, np.nan],
+                [6, 5, np.nan, np.nan, np.nan, np.nan],
+            ],
+            index=e_idx,
+            columns=e_cols,
+        )
+
+        tm.assert_frame_equal(result1, expected)
+        tm.assert_frame_equal(result2, expected)
+        tm.assert_frame_equal(result3, expected.T)
+
+    def test_unstack_period_frame(self):
+        # GH4342
+        idx1 = pd.PeriodIndex(
+            ["2014-01", "2014-02", "2014-02", "2014-02", "2014-01", "2014-01"],
+            freq="M",
+            name="period1",
+        )
+        idx2 = pd.PeriodIndex(
+            ["2013-12", "2013-12", "2014-02", "2013-10", "2013-10", "2014-02"],
+            freq="M",
+            name="period2",
+        )
+        value = {"A": [1, 2, 3, 4, 5, 6], "B": [6, 5, 4, 3, 2, 1]}
+        idx = MultiIndex.from_arrays([idx1, idx2])
+        df = DataFrame(value, index=idx)
+
+        result1 = df.unstack()
+        result2 = df.unstack(level=1)
+        result3 = df.unstack(level=0)
+
+        e_1 = pd.PeriodIndex(["2014-01", "2014-02"], freq="M", name="period1")
+        e_2 = pd.PeriodIndex(
+            ["2013-10", "2013-12", "2014-02", "2013-10", "2013-12", "2014-02"],
+            freq="M",
+            name="period2",
+        )
+        e_cols = MultiIndex.from_arrays(["A A A B B B".split(), e_2])
+        expected = DataFrame(
+            [[5, 1, 6, 2, 6, 1], [4, 2, 3, 3, 5, 4]], index=e_1, columns=e_cols
+        )
+
+        tm.assert_frame_equal(result1, expected)
+        tm.assert_frame_equal(result2, expected)
+
+        e_1 = pd.PeriodIndex(
+            ["2014-01", "2014-02", "2014-01", "2014-02"], freq="M", name="period1"
+        )
+        e_2 = pd.PeriodIndex(
+            ["2013-10", "2013-12", "2014-02"], freq="M", name="period2"
+        )
+        e_cols = MultiIndex.from_arrays(["A A B B".split(), e_1])
+        expected = DataFrame(
+            [[5, 4, 2, 3], [1, 2, 6, 5], [6, 3, 1, 4]], index=e_2, columns=e_cols
+        )
+
+        tm.assert_frame_equal(result3, expected)
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    def test_stack_multiple_bug(self, future_stack, using_infer_string):
+        # bug when some uniques are not present in the data GH#3170
+        id_col = ([1] * 3) + ([2] * 3)
+        name = (["a"] * 3) + (["b"] * 3)
+        date = pd.to_datetime(["2013-01-03", "2013-01-04", "2013-01-05"] * 2)
+        var1 = np.random.default_rng(2).integers(0, 100, 6)
+        df = DataFrame({"ID": id_col, "NAME": name, "DATE": date, "VAR1": var1})
+
+        multi = df.set_index(["DATE", "ID"])
+        multi.columns.name = "Params"
+        unst = multi.unstack("ID")
+        msg = re.escape("agg function failed [how->mean,dtype->")
+        if using_infer_string:
+            msg = "dtype 'str' does not support operation 'mean'"
+        with pytest.raises(TypeError, match=msg):
+            unst.resample("W-THU").mean()
+        down = unst.resample("W-THU").mean(numeric_only=True)
+        rs = down.stack("ID", future_stack=future_stack)
+        xp = (
+            unst.loc[:, ["VAR1"]]
+            .resample("W-THU")
+            .mean()
+            .stack("ID", future_stack=future_stack)
+        )
+        xp.columns.name = "Params"
+        tm.assert_frame_equal(rs, xp)
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    def test_stack_dropna(self, future_stack):
+        # GH#3997
+        df = DataFrame({"A": ["a1", "a2"], "B": ["b1", "b2"], "C": [1, 1]})
+        df = df.set_index(["A", "B"])
+
+        dropna = False if not future_stack else lib.no_default
+        stacked = df.unstack().stack(dropna=dropna, future_stack=future_stack)
+        assert len(stacked) > len(stacked.dropna())
+
+        if future_stack:
+            with pytest.raises(ValueError, match="dropna must be unspecified"):
+                df.unstack().stack(dropna=True, future_stack=future_stack)
+        else:
+            stacked = df.unstack().stack(dropna=True, future_stack=future_stack)
+            tm.assert_frame_equal(stacked, stacked.dropna())
+
+    def test_unstack_multiple_hierarchical(self, future_stack):
+        df = DataFrame(
+            index=[
+                [0, 0, 0, 0, 1, 1, 1, 1],
+                [0, 0, 1, 1, 0, 0, 1, 1],
+                [0, 1, 0, 1, 0, 1, 0, 1],
+            ],
+            columns=[[0, 0, 1, 1], [0, 1, 0, 1]],
+        )
+
+        df.index.names = ["a", "b", "c"]
+        df.columns.names = ["d", "e"]
+
+        # it works!
+        df.unstack(["b", "c"])
+
+    def test_unstack_sparse_keyspace(self):
+        # memory problems with naive impl GH#2278
+        # Generate Long File & Test Pivot
+        NUM_ROWS = 1000
+
+        df = DataFrame(
+            {
+                "A": np.random.default_rng(2).integers(100, size=NUM_ROWS),
+                "B": np.random.default_rng(3).integers(300, size=NUM_ROWS),
+                "C": np.random.default_rng(4).integers(-7, 7, size=NUM_ROWS),
+                "D": np.random.default_rng(5).integers(-19, 19, size=NUM_ROWS),
+                "E": np.random.default_rng(6).integers(3000, size=NUM_ROWS),
+                "F": np.random.default_rng(7).standard_normal(NUM_ROWS),
+            }
+        )
+
+        idf = df.set_index(["A", "B", "C", "D", "E"])
+
+        # it works! is sufficient
+        idf.unstack("E")
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    def test_unstack_unobserved_keys(self, future_stack):
+        # related to GH#2278 refactoring
+        levels = [[0, 1], [0, 1, 2, 3]]
+        codes = [[0, 0, 1, 1], [0, 2, 0, 2]]
+
+        index = MultiIndex(levels, codes)
+
+        df = DataFrame(np.random.default_rng(2).standard_normal((4, 2)), index=index)
+
+        result = df.unstack()
+        assert len(result.columns) == 4
+
+        recons = result.stack(future_stack=future_stack)
+        tm.assert_frame_equal(recons, df)
+
+    @pytest.mark.slow
+    def test_unstack_number_of_levels_larger_than_int32(self, monkeypatch):
+        # GH#20601
+        # GH 26314: Change ValueError to PerformanceWarning
+
+        class MockUnstacker(reshape_lib._Unstacker):
+            def __init__(self, *args, **kwargs) -> None:
+                # __init__ will raise the warning
+                super().__init__(*args, **kwargs)
+                raise Exception("Don't compute final result.")
+
+        with monkeypatch.context() as m:
+            m.setattr(reshape_lib, "_Unstacker", MockUnstacker)
+            df = DataFrame(
+                np.zeros((2**16, 2)),
+                index=[np.arange(2**16), np.arange(2**16)],
+            )
+            msg = "The following operation may generate"
+            with tm.assert_produces_warning(PerformanceWarning, match=msg):
+                with pytest.raises(Exception, match="Don't compute final result."):
+                    df.unstack()
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    @pytest.mark.parametrize(
+        "levels",
+        itertools.chain.from_iterable(
+            itertools.product(itertools.permutations([0, 1, 2], width), repeat=2)
+            for width in [2, 3]
+        ),
+    )
+    @pytest.mark.parametrize("stack_lev", range(2))
+    @pytest.mark.parametrize("sort", [True, False])
+    def test_stack_order_with_unsorted_levels(
+        self, levels, stack_lev, sort, future_stack
+    ):
+        # GH#16323
+        # deep check for 1-row case
+        columns = MultiIndex(levels=levels, codes=[[0, 0, 1, 1], [0, 1, 0, 1]])
+        df = DataFrame(columns=columns, data=[range(4)])
+        kwargs = {} if future_stack else {"sort": sort}
+        df_stacked = df.stack(stack_lev, future_stack=future_stack, **kwargs)
+        for row in df.index:
+            for col in df.columns:
+                expected = df.loc[row, col]
+                result_row = row, col[stack_lev]
+                result_col = col[1 - stack_lev]
+                result = df_stacked.loc[result_row, result_col]
+                assert result == expected
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    def test_stack_order_with_unsorted_levels_multi_row(self, future_stack):
+        # GH#16323
+
+        # check multi-row case
+        mi = MultiIndex(
+            levels=[["A", "C", "B"], ["B", "A", "C"]],
+            codes=[np.repeat(range(3), 3), np.tile(range(3), 3)],
+        )
+        df = DataFrame(
+            columns=mi, index=range(5), data=np.arange(5 * len(mi)).reshape(5, -1)
+        )
+        assert all(
+            df.loc[row, col]
+            == df.stack(0, future_stack=future_stack).loc[(row, col[0]), col[1]]
+            for row in df.index
+            for col in df.columns
+        )
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    def test_stack_order_with_unsorted_levels_multi_row_2(self, future_stack):
+        # GH#53636
+        levels = ((0, 1), (1, 0))
+        stack_lev = 1
+        columns = MultiIndex(levels=levels, codes=[[0, 0, 1, 1], [0, 1, 0, 1]])
+        df = DataFrame(columns=columns, data=[range(4)], index=[1, 0, 2, 3])
+        kwargs = {} if future_stack else {"sort": True}
+        result = df.stack(stack_lev, future_stack=future_stack, **kwargs)
+        expected_index = MultiIndex(
+            levels=[[0, 1, 2, 3], [0, 1]],
+            codes=[[1, 1, 0, 0, 2, 2, 3, 3], [1, 0, 1, 0, 1, 0, 1, 0]],
+        )
+        expected = DataFrame(
+            {
+                0: [0, 1, 0, 1, 0, 1, 0, 1],
+                1: [2, 3, 2, 3, 2, 3, 2, 3],
+            },
+            index=expected_index,
+        )
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    def test_stack_unstack_unordered_multiindex(self, future_stack):
+        # GH# 18265
+        values = np.arange(5)
+        data = np.vstack(
+            [
+                [f"b{x}" for x in values],  # b0, b1, ..
+                [f"a{x}" for x in values],  # a0, a1, ..
+            ]
+        )
+        df = DataFrame(data.T, columns=["b", "a"])
+        df.columns.name = "first"
+        second_level_dict = {"x": df}
+        multi_level_df = pd.concat(second_level_dict, axis=1)
+        multi_level_df.columns.names = ["second", "first"]
+        df = multi_level_df.reindex(sorted(multi_level_df.columns), axis=1)
+        result = df.stack(["first", "second"], future_stack=future_stack).unstack(
+            ["first", "second"]
+        )
+        expected = DataFrame(
+            [["a0", "b0"], ["a1", "b1"], ["a2", "b2"], ["a3", "b3"], ["a4", "b4"]],
+            index=[0, 1, 2, 3, 4],
+            columns=MultiIndex.from_tuples(
+                [("a", "x"), ("b", "x")], names=["first", "second"]
+            ),
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_unstack_preserve_types(
+        self, multiindex_year_month_day_dataframe_random_data, using_infer_string
+    ):
+        # GH#403
+        ymd = multiindex_year_month_day_dataframe_random_data
+        ymd["E"] = "foo"
+        ymd["F"] = 2
+
+        unstacked = ymd.unstack("month")
+        assert unstacked["A", 1].dtype == np.float64
+        assert (
+            unstacked["E", 1].dtype == np.object_
+            if not using_infer_string
+            else "string"
+        )
+        assert unstacked["F", 1].dtype == np.float64
+
+    def test_unstack_group_index_overflow(self, future_stack):
+        codes = np.tile(np.arange(500), 2)
+        level = np.arange(500)
+
+        index = MultiIndex(
+            levels=[level] * 8 + [[0, 1]],
+            codes=[codes] * 8 + [np.arange(2).repeat(500)],
+        )
+
+        s = Series(np.arange(1000), index=index)
+        result = s.unstack()
+        assert result.shape == (500, 2)
+
+        # test roundtrip
+        stacked = result.stack(future_stack=future_stack)
+        tm.assert_series_equal(s, stacked.reindex(s.index))
+
+        # put it at beginning
+        index = MultiIndex(
+            levels=[[0, 1]] + [level] * 8,
+            codes=[np.arange(2).repeat(500)] + [codes] * 8,
+        )
+
+        s = Series(np.arange(1000), index=index)
+        result = s.unstack(0)
+        assert result.shape == (500, 2)
+
+        # put it in middle
+        index = MultiIndex(
+            levels=[level] * 4 + [[0, 1]] + [level] * 4,
+            codes=([codes] * 4 + [np.arange(2).repeat(500)] + [codes] * 4),
+        )
+
+        s = Series(np.arange(1000), index=index)
+        result = s.unstack(4)
+        assert result.shape == (500, 2)
+
+    def test_unstack_with_missing_int_cast_to_float(self, using_array_manager):
+        # https://github.com/pandas-dev/pandas/issues/37115
+        df = DataFrame(
+            {
+                "a": ["A", "A", "B"],
+                "b": ["ca", "cb", "cb"],
+                "v": [10] * 3,
+            }
+        ).set_index(["a", "b"])
+
+        # add another int column to get 2 blocks
+        df["is_"] = 1
+        if not using_array_manager:
+            assert len(df._mgr.blocks) == 2
+
+        result = df.unstack("b")
+        result[("is_", "ca")] = result[("is_", "ca")].fillna(0)
+
+        expected = DataFrame(
+            [[10.0, 10.0, 1.0, 1.0], [np.nan, 10.0, 0.0, 1.0]],
+            index=Index(["A", "B"], name="a"),
+            columns=MultiIndex.from_tuples(
+                [("v", "ca"), ("v", "cb"), ("is_", "ca"), ("is_", "cb")],
+                names=[None, "b"],
+            ),
+        )
+        if using_array_manager:
+            # INFO(ArrayManager) with ArrayManager preserve dtype where possible
+            expected[("v", "cb")] = expected[("v", "cb")].astype("int64")
+            expected[("is_", "cb")] = expected[("is_", "cb")].astype("int64")
+        tm.assert_frame_equal(result, expected)
+
+    def test_unstack_with_level_has_nan(self):
+        # GH 37510
+        df1 = DataFrame(
+            {
+                "L1": [1, 2, 3, 4],
+                "L2": [3, 4, 1, 2],
+                "L3": [1, 1, 1, 1],
+                "x": [1, 2, 3, 4],
+            }
+        )
+        df1 = df1.set_index(["L1", "L2", "L3"])
+        new_levels = ["n1", "n2", "n3", None]
+        df1.index = df1.index.set_levels(levels=new_levels, level="L1")
+        df1.index = df1.index.set_levels(levels=new_levels, level="L2")
+
+        result = df1.unstack("L3")[("x", 1)].sort_index().index
+        expected = MultiIndex(
+            levels=[["n1", "n2", "n3", None], ["n1", "n2", "n3", None]],
+            codes=[[0, 1, 2, 3], [2, 3, 0, 1]],
+            names=["L1", "L2"],
+        )
+
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    def test_stack_nan_in_multiindex_columns(self, future_stack):
+        # GH#39481
+        df = DataFrame(
+            np.zeros([1, 5]),
+            columns=MultiIndex.from_tuples(
+                [
+                    (0, None, None),
+                    (0, 2, 0),
+                    (0, 2, 1),
+                    (0, 3, 0),
+                    (0, 3, 1),
+                ],
+            ),
+        )
+        result = df.stack(2, future_stack=future_stack)
+        if future_stack:
+            index = MultiIndex(levels=[[0], [0.0, 1.0]], codes=[[0, 0, 0], [-1, 0, 1]])
+            columns = MultiIndex(levels=[[0], [2, 3]], codes=[[0, 0, 0], [-1, 0, 1]])
+        else:
+            index = Index([(0, None), (0, 0), (0, 1)])
+            columns = Index([(0, None), (0, 2), (0, 3)])
+        expected = DataFrame(
+            [[0.0, np.nan, np.nan], [np.nan, 0.0, 0.0], [np.nan, 0.0, 0.0]],
+            index=index,
+            columns=columns,
+        )
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    def test_multi_level_stack_categorical(self, future_stack):
+        # GH 15239
+        midx = MultiIndex.from_arrays(
+            [
+                ["A"] * 2 + ["B"] * 2,
+                pd.Categorical(list("abab")),
+                pd.Categorical(list("ccdd")),
+            ]
+        )
+        df = DataFrame(np.arange(8).reshape(2, 4), columns=midx)
+        result = df.stack([1, 2], future_stack=future_stack)
+        if future_stack:
+            expected = DataFrame(
+                [
+                    [0, np.nan],
+                    [1, np.nan],
+                    [np.nan, 2],
+                    [np.nan, 3],
+                    [4, np.nan],
+                    [5, np.nan],
+                    [np.nan, 6],
+                    [np.nan, 7],
+                ],
+                columns=["A", "B"],
+                index=MultiIndex.from_arrays(
+                    [
+                        [0] * 4 + [1] * 4,
+                        pd.Categorical(list("abababab")),
+                        pd.Categorical(list("ccddccdd")),
+                    ]
+                ),
+            )
+        else:
+            expected = DataFrame(
+                [
+                    [0, np.nan],
+                    [np.nan, 2],
+                    [1, np.nan],
+                    [np.nan, 3],
+                    [4, np.nan],
+                    [np.nan, 6],
+                    [5, np.nan],
+                    [np.nan, 7],
+                ],
+                columns=["A", "B"],
+                index=MultiIndex.from_arrays(
+                    [
+                        [0] * 4 + [1] * 4,
+                        pd.Categorical(list("aabbaabb")),
+                        pd.Categorical(list("cdcdcdcd")),
+                    ]
+                ),
+            )
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    def test_stack_nan_level(self, future_stack):
+        # GH 9406
+        df_nan = DataFrame(
+            np.arange(4).reshape(2, 2),
+            columns=MultiIndex.from_tuples(
+                [("A", np.nan), ("B", "b")], names=["Upper", "Lower"]
+            ),
+            index=Index([0, 1], name="Num"),
+            dtype=np.float64,
+        )
+        result = df_nan.stack(future_stack=future_stack)
+        if future_stack:
+            index = MultiIndex(
+                levels=[[0, 1], [np.nan, "b"]],
+                codes=[[0, 0, 1, 1], [0, 1, 0, 1]],
+                names=["Num", "Lower"],
+            )
+        else:
+            index = MultiIndex.from_tuples(
+                [(0, np.nan), (0, "b"), (1, np.nan), (1, "b")], names=["Num", "Lower"]
+            )
+        expected = DataFrame(
+            [[0.0, np.nan], [np.nan, 1], [2.0, np.nan], [np.nan, 3.0]],
+            columns=Index(["A", "B"], name="Upper"),
+            index=index,
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_unstack_categorical_columns(self):
+        # GH 14018
+        idx = MultiIndex.from_product([["A"], [0, 1]])
+        df = DataFrame({"cat": pd.Categorical(["a", "b"])}, index=idx)
+        result = df.unstack()
+        expected = DataFrame(
+            {
+                0: pd.Categorical(["a"], categories=["a", "b"]),
+                1: pd.Categorical(["b"], categories=["a", "b"]),
+            },
+            index=["A"],
+        )
+        expected.columns = MultiIndex.from_tuples([("cat", 0), ("cat", 1)])
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    def test_stack_unsorted(self, future_stack):
+        # GH 16925
+        PAE = ["ITA", "FRA"]
+        VAR = ["A1", "A2"]
+        TYP = ["CRT", "DBT", "NET"]
+        MI = MultiIndex.from_product([PAE, VAR, TYP], names=["PAE", "VAR", "TYP"])
+
+        V = list(range(len(MI)))
+        DF = DataFrame(data=V, index=MI, columns=["VALUE"])
+
+        DF = DF.unstack(["VAR", "TYP"])
+        DF.columns = DF.columns.droplevel(0)
+        DF.loc[:, ("A0", "NET")] = 9999
+
+        result = DF.stack(["VAR", "TYP"], future_stack=future_stack).sort_index()
+        expected = (
+            DF.sort_index(axis=1)
+            .stack(["VAR", "TYP"], future_stack=future_stack)
+            .sort_index()
+        )
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
+    def test_stack_nullable_dtype(self, future_stack):
+        # GH#43561
+        columns = MultiIndex.from_product(
+            [["54511", "54515"], ["r", "t_mean"]], names=["station", "element"]
+        )
+        index = Index([1, 2, 3], name="time")
+
+        arr = np.array([[50, 226, 10, 215], [10, 215, 9, 220], [305, 232, 111, 220]])
+        df = DataFrame(arr, columns=columns, index=index, dtype=pd.Int64Dtype())
+
+        result = df.stack("station", future_stack=future_stack)
+
+        expected = (
+            df.astype(np.int64)
+            .stack("station", future_stack=future_stack)
+            .astype(pd.Int64Dtype())
+        )
+        tm.assert_frame_equal(result, expected)
+
+        # non-homogeneous case
+        df[df.columns[0]] = df[df.columns[0]].astype(pd.Float64Dtype())
+        result = df.stack("station", future_stack=future_stack)
+
+        expected = DataFrame(
+            {
+                "r": pd.array(
+                    [50.0, 10.0, 10.0, 9.0, 305.0, 111.0], dtype=pd.Float64Dtype()
+                ),
+                "t_mean": pd.array(
+                    [226, 215, 215, 220, 232, 220], dtype=pd.Int64Dtype()
+                ),
+            },
+            index=MultiIndex.from_product([index, columns.levels[0]]),
+        )
+        expected.columns.name = "element"
+        tm.assert_frame_equal(result, expected)
+
+    def test_unstack_mixed_level_names(self):
+        # GH#48763
+        arrays = [["a", "a"], [1, 2], ["red", "blue"]]
+        idx = MultiIndex.from_arrays(arrays, names=("x", 0, "y"))
+        df = DataFrame({"m": [1, 2]}, index=idx)
+        result = df.unstack("x")
+        expected = DataFrame(
+            [[1], [2]],
+            columns=MultiIndex.from_tuples([("m", "a")], names=[None, "x"]),
+            index=MultiIndex.from_tuples([(1, "red"), (2, "blue")], names=[0, "y"]),
+        )
+        tm.assert_frame_equal(result, expected)
+
+
+def test_stack_tuple_columns(future_stack):
+    # GH#54948 - test stack when the input has a non-MultiIndex with tuples
+    df = DataFrame(
+        [[1, 2, 3], [4, 5, 6], [7, 8, 9]], columns=[("a", 1), ("a", 2), ("b", 1)]
+    )
+    result = df.stack(future_stack=future_stack)
+    expected = Series(
+        [1, 2, 3, 4, 5, 6, 7, 8, 9],
+        index=MultiIndex(
+            levels=[[0, 1, 2], [("a", 1), ("a", 2), ("b", 1)]],
+            codes=[[0, 0, 0, 1, 1, 1, 2, 2, 2], [0, 1, 2, 0, 1, 2, 0, 1, 2]],
+        ),
+    )
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "dtype, na_value",
+    [
+        ("float64", np.nan),
+        ("Float64", np.nan),
+        ("Float64", pd.NA),
+        ("Int64", pd.NA),
+    ],
+)
+@pytest.mark.parametrize("test_multiindex", [True, False])
+def test_stack_preserves_na(dtype, na_value, test_multiindex):
+    # GH#56573
+    if test_multiindex:
+        index = MultiIndex.from_arrays(2 * [Index([na_value], dtype=dtype)])
+    else:
+        index = Index([na_value], dtype=dtype)
+    df = DataFrame({"a": [1]}, index=index)
+    result = df.stack(future_stack=True)
+
+    if test_multiindex:
+        expected_index = MultiIndex.from_arrays(
+            [
+                Index([na_value], dtype=dtype),
+                Index([na_value], dtype=dtype),
+                Index(["a"]),
+            ]
+        )
+    else:
+        expected_index = MultiIndex.from_arrays(
+            [
+                Index([na_value], dtype=dtype),
+                Index(["a"]),
+            ]
+        )
+    expected = Series(1, index=expected_index)
+    tm.assert_series_equal(result, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/test_subclass.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/test_subclass.py
new file mode 100644
index 0000000000000000000000000000000000000000..39a29406c6e9a0d2e7b3b188314f51d2d721d5b5
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/test_subclass.py
@@ -0,0 +1,832 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Index,
+    MultiIndex,
+    Series,
+)
+import pandas._testing as tm
+
+pytestmark = pytest.mark.filterwarnings(
+    "ignore:Passing a BlockManager|Passing a SingleBlockManager:DeprecationWarning"
+)
+
+
+@pytest.fixture()
+def gpd_style_subclass_df():
+    class SubclassedDataFrame(DataFrame):
+        @property
+        def _constructor(self):
+            return SubclassedDataFrame
+
+    return SubclassedDataFrame({"a": [1, 2, 3]})
+
+
+class TestDataFrameSubclassing:
+    def test_no_warning_on_mgr(self):
+        # GH#57032
+        df = tm.SubclassedDataFrame(
+            {"X": [1, 2, 3], "Y": [1, 2, 3]}, index=["a", "b", "c"]
+        )
+        with tm.assert_produces_warning(None):
+            # df.isna() goes through _constructor_from_mgr, which we want to
+            #  *not* pass a Manager do __init__
+            df.isna()
+            df["X"].isna()
+
+    def test_frame_subclassing_and_slicing(self):
+        # Subclass frame and ensure it returns the right class on slicing it
+        # In reference to PR 9632
+
+        class CustomSeries(Series):
+            @property
+            def _constructor(self):
+                return CustomSeries
+
+            def custom_series_function(self):
+                return "OK"
+
+        class CustomDataFrame(DataFrame):
+            """
+            Subclasses pandas DF, fills DF with simulation results, adds some
+            custom plotting functions.
+            """
+
+            def __init__(self, *args, **kw) -> None:
+                super().__init__(*args, **kw)
+
+            @property
+            def _constructor(self):
+                return CustomDataFrame
+
+            _constructor_sliced = CustomSeries
+
+            def custom_frame_function(self):
+                return "OK"
+
+        data = {"col1": range(10), "col2": range(10)}
+        cdf = CustomDataFrame(data)
+
+        # Did we get back our own DF class?
+        assert isinstance(cdf, CustomDataFrame)
+
+        # Do we get back our own Series class after selecting a column?
+        cdf_series = cdf.col1
+        assert isinstance(cdf_series, CustomSeries)
+        assert cdf_series.custom_series_function() == "OK"
+
+        # Do we get back our own DF class after slicing row-wise?
+        cdf_rows = cdf[1:5]
+        assert isinstance(cdf_rows, CustomDataFrame)
+        assert cdf_rows.custom_frame_function() == "OK"
+
+        # Make sure sliced part of multi-index frame is custom class
+        mcol = MultiIndex.from_tuples([("A", "A"), ("A", "B")])
+        cdf_multi = CustomDataFrame([[0, 1], [2, 3]], columns=mcol)
+        assert isinstance(cdf_multi["A"], CustomDataFrame)
+
+        mcol = MultiIndex.from_tuples([("A", ""), ("B", "")])
+        cdf_multi2 = CustomDataFrame([[0, 1], [2, 3]], columns=mcol)
+        assert isinstance(cdf_multi2["A"], CustomSeries)
+
+    def test_dataframe_metadata(self):
+        df = tm.SubclassedDataFrame(
+            {"X": [1, 2, 3], "Y": [1, 2, 3]}, index=["a", "b", "c"]
+        )
+        df.testattr = "XXX"
+
+        assert df.testattr == "XXX"
+        assert df[["X"]].testattr == "XXX"
+        assert df.loc[["a", "b"], :].testattr == "XXX"
+        assert df.iloc[[0, 1], :].testattr == "XXX"
+
+        # see gh-9776
+        assert df.iloc[0:1, :].testattr == "XXX"
+
+        # see gh-10553
+        unpickled = tm.round_trip_pickle(df)
+        tm.assert_frame_equal(df, unpickled)
+        assert df._metadata == unpickled._metadata
+        assert df.testattr == unpickled.testattr
+
+    def test_indexing_sliced(self):
+        # GH 11559
+        df = tm.SubclassedDataFrame(
+            {"X": [1, 2, 3], "Y": [4, 5, 6], "Z": [7, 8, 9]}, index=["a", "b", "c"]
+        )
+        res = df.loc[:, "X"]
+        exp = tm.SubclassedSeries([1, 2, 3], index=list("abc"), name="X")
+        tm.assert_series_equal(res, exp)
+        assert isinstance(res, tm.SubclassedSeries)
+
+        res = df.iloc[:, 1]
+        exp = tm.SubclassedSeries([4, 5, 6], index=list("abc"), name="Y")
+        tm.assert_series_equal(res, exp)
+        assert isinstance(res, tm.SubclassedSeries)
+
+        res = df.loc[:, "Z"]
+        exp = tm.SubclassedSeries([7, 8, 9], index=list("abc"), name="Z")
+        tm.assert_series_equal(res, exp)
+        assert isinstance(res, tm.SubclassedSeries)
+
+        res = df.loc["a", :]
+        exp = tm.SubclassedSeries([1, 4, 7], index=list("XYZ"), name="a")
+        tm.assert_series_equal(res, exp)
+        assert isinstance(res, tm.SubclassedSeries)
+
+        res = df.iloc[1, :]
+        exp = tm.SubclassedSeries([2, 5, 8], index=list("XYZ"), name="b")
+        tm.assert_series_equal(res, exp)
+        assert isinstance(res, tm.SubclassedSeries)
+
+        res = df.loc["c", :]
+        exp = tm.SubclassedSeries([3, 6, 9], index=list("XYZ"), name="c")
+        tm.assert_series_equal(res, exp)
+        assert isinstance(res, tm.SubclassedSeries)
+
+    def test_subclass_attr_err_propagation(self):
+        # GH 11808
+        class A(DataFrame):
+            @property
+            def nonexistence(self):
+                return self.i_dont_exist
+
+        with pytest.raises(AttributeError, match=".*i_dont_exist.*"):
+            A().nonexistence
+
+    def test_subclass_align(self):
+        # GH 12983
+        df1 = tm.SubclassedDataFrame(
+            {"a": [1, 3, 5], "b": [1, 3, 5]}, index=list("ACE")
+        )
+        df2 = tm.SubclassedDataFrame(
+            {"c": [1, 2, 4], "d": [1, 2, 4]}, index=list("ABD")
+        )
+
+        res1, res2 = df1.align(df2, axis=0)
+        exp1 = tm.SubclassedDataFrame(
+            {"a": [1, np.nan, 3, np.nan, 5], "b": [1, np.nan, 3, np.nan, 5]},
+            index=list("ABCDE"),
+        )
+        exp2 = tm.SubclassedDataFrame(
+            {"c": [1, 2, np.nan, 4, np.nan], "d": [1, 2, np.nan, 4, np.nan]},
+            index=list("ABCDE"),
+        )
+        assert isinstance(res1, tm.SubclassedDataFrame)
+        tm.assert_frame_equal(res1, exp1)
+        assert isinstance(res2, tm.SubclassedDataFrame)
+        tm.assert_frame_equal(res2, exp2)
+
+        res1, res2 = df1.a.align(df2.c)
+        assert isinstance(res1, tm.SubclassedSeries)
+        tm.assert_series_equal(res1, exp1.a)
+        assert isinstance(res2, tm.SubclassedSeries)
+        tm.assert_series_equal(res2, exp2.c)
+
+    def test_subclass_align_combinations(self):
+        # GH 12983
+        df = tm.SubclassedDataFrame({"a": [1, 3, 5], "b": [1, 3, 5]}, index=list("ACE"))
+        s = tm.SubclassedSeries([1, 2, 4], index=list("ABD"), name="x")
+
+        # frame + series
+        res1, res2 = df.align(s, axis=0)
+        exp1 = tm.SubclassedDataFrame(
+            {"a": [1, np.nan, 3, np.nan, 5], "b": [1, np.nan, 3, np.nan, 5]},
+            index=list("ABCDE"),
+        )
+        # name is lost when
+        exp2 = tm.SubclassedSeries(
+            [1, 2, np.nan, 4, np.nan], index=list("ABCDE"), name="x"
+        )
+
+        assert isinstance(res1, tm.SubclassedDataFrame)
+        tm.assert_frame_equal(res1, exp1)
+        assert isinstance(res2, tm.SubclassedSeries)
+        tm.assert_series_equal(res2, exp2)
+
+        # series + frame
+        res1, res2 = s.align(df)
+        assert isinstance(res1, tm.SubclassedSeries)
+        tm.assert_series_equal(res1, exp2)
+        assert isinstance(res2, tm.SubclassedDataFrame)
+        tm.assert_frame_equal(res2, exp1)
+
+    def test_subclass_iterrows(self):
+        # GH 13977
+        df = tm.SubclassedDataFrame({"a": [1]})
+        for i, row in df.iterrows():
+            assert isinstance(row, tm.SubclassedSeries)
+            tm.assert_series_equal(row, df.loc[i])
+
+    def test_subclass_stack(self):
+        # GH 15564
+        df = tm.SubclassedDataFrame(
+            [[1, 2, 3], [4, 5, 6], [7, 8, 9]],
+            index=["a", "b", "c"],
+            columns=["X", "Y", "Z"],
+        )
+
+        res = df.stack(future_stack=True)
+        exp = tm.SubclassedSeries(
+            [1, 2, 3, 4, 5, 6, 7, 8, 9], index=[list("aaabbbccc"), list("XYZXYZXYZ")]
+        )
+
+        tm.assert_series_equal(res, exp)
+
+    def test_subclass_stack_multi(self):
+        # GH 15564
+        df = tm.SubclassedDataFrame(
+            [[10, 11, 12, 13], [20, 21, 22, 23], [30, 31, 32, 33], [40, 41, 42, 43]],
+            index=MultiIndex.from_tuples(
+                list(zip(list("AABB"), list("cdcd"))), names=["aaa", "ccc"]
+            ),
+            columns=MultiIndex.from_tuples(
+                list(zip(list("WWXX"), list("yzyz"))), names=["www", "yyy"]
+            ),
+        )
+
+        exp = tm.SubclassedDataFrame(
+            [
+                [10, 12],
+                [11, 13],
+                [20, 22],
+                [21, 23],
+                [30, 32],
+                [31, 33],
+                [40, 42],
+                [41, 43],
+            ],
+            index=MultiIndex.from_tuples(
+                list(zip(list("AAAABBBB"), list("ccddccdd"), list("yzyzyzyz"))),
+                names=["aaa", "ccc", "yyy"],
+            ),
+            columns=Index(["W", "X"], name="www"),
+        )
+
+        res = df.stack(future_stack=True)
+        tm.assert_frame_equal(res, exp)
+
+        res = df.stack("yyy", future_stack=True)
+        tm.assert_frame_equal(res, exp)
+
+        exp = tm.SubclassedDataFrame(
+            [
+                [10, 11],
+                [12, 13],
+                [20, 21],
+                [22, 23],
+                [30, 31],
+                [32, 33],
+                [40, 41],
+                [42, 43],
+            ],
+            index=MultiIndex.from_tuples(
+                list(zip(list("AAAABBBB"), list("ccddccdd"), list("WXWXWXWX"))),
+                names=["aaa", "ccc", "www"],
+            ),
+            columns=Index(["y", "z"], name="yyy"),
+        )
+
+        res = df.stack("www", future_stack=True)
+        tm.assert_frame_equal(res, exp)
+
+    def test_subclass_stack_multi_mixed(self):
+        # GH 15564
+        df = tm.SubclassedDataFrame(
+            [
+                [10, 11, 12.0, 13.0],
+                [20, 21, 22.0, 23.0],
+                [30, 31, 32.0, 33.0],
+                [40, 41, 42.0, 43.0],
+            ],
+            index=MultiIndex.from_tuples(
+                list(zip(list("AABB"), list("cdcd"))), names=["aaa", "ccc"]
+            ),
+            columns=MultiIndex.from_tuples(
+                list(zip(list("WWXX"), list("yzyz"))), names=["www", "yyy"]
+            ),
+        )
+
+        exp = tm.SubclassedDataFrame(
+            [
+                [10, 12.0],
+                [11, 13.0],
+                [20, 22.0],
+                [21, 23.0],
+                [30, 32.0],
+                [31, 33.0],
+                [40, 42.0],
+                [41, 43.0],
+            ],
+            index=MultiIndex.from_tuples(
+                list(zip(list("AAAABBBB"), list("ccddccdd"), list("yzyzyzyz"))),
+                names=["aaa", "ccc", "yyy"],
+            ),
+            columns=Index(["W", "X"], name="www"),
+        )
+
+        res = df.stack(future_stack=True)
+        tm.assert_frame_equal(res, exp)
+
+        res = df.stack("yyy", future_stack=True)
+        tm.assert_frame_equal(res, exp)
+
+        exp = tm.SubclassedDataFrame(
+            [
+                [10.0, 11.0],
+                [12.0, 13.0],
+                [20.0, 21.0],
+                [22.0, 23.0],
+                [30.0, 31.0],
+                [32.0, 33.0],
+                [40.0, 41.0],
+                [42.0, 43.0],
+            ],
+            index=MultiIndex.from_tuples(
+                list(zip(list("AAAABBBB"), list("ccddccdd"), list("WXWXWXWX"))),
+                names=["aaa", "ccc", "www"],
+            ),
+            columns=Index(["y", "z"], name="yyy"),
+        )
+
+        res = df.stack("www", future_stack=True)
+        tm.assert_frame_equal(res, exp)
+
+    def test_subclass_unstack(self):
+        # GH 15564
+        df = tm.SubclassedDataFrame(
+            [[1, 2, 3], [4, 5, 6], [7, 8, 9]],
+            index=["a", "b", "c"],
+            columns=["X", "Y", "Z"],
+        )
+
+        res = df.unstack()
+        exp = tm.SubclassedSeries(
+            [1, 4, 7, 2, 5, 8, 3, 6, 9], index=[list("XXXYYYZZZ"), list("abcabcabc")]
+        )
+
+        tm.assert_series_equal(res, exp)
+
+    def test_subclass_unstack_multi(self):
+        # GH 15564
+        df = tm.SubclassedDataFrame(
+            [[10, 11, 12, 13], [20, 21, 22, 23], [30, 31, 32, 33], [40, 41, 42, 43]],
+            index=MultiIndex.from_tuples(
+                list(zip(list("AABB"), list("cdcd"))), names=["aaa", "ccc"]
+            ),
+            columns=MultiIndex.from_tuples(
+                list(zip(list("WWXX"), list("yzyz"))), names=["www", "yyy"]
+            ),
+        )
+
+        exp = tm.SubclassedDataFrame(
+            [[10, 20, 11, 21, 12, 22, 13, 23], [30, 40, 31, 41, 32, 42, 33, 43]],
+            index=Index(["A", "B"], name="aaa"),
+            columns=MultiIndex.from_tuples(
+                list(zip(list("WWWWXXXX"), list("yyzzyyzz"), list("cdcdcdcd"))),
+                names=["www", "yyy", "ccc"],
+            ),
+        )
+
+        res = df.unstack()
+        tm.assert_frame_equal(res, exp)
+
+        res = df.unstack("ccc")
+        tm.assert_frame_equal(res, exp)
+
+        exp = tm.SubclassedDataFrame(
+            [[10, 30, 11, 31, 12, 32, 13, 33], [20, 40, 21, 41, 22, 42, 23, 43]],
+            index=Index(["c", "d"], name="ccc"),
+            columns=MultiIndex.from_tuples(
+                list(zip(list("WWWWXXXX"), list("yyzzyyzz"), list("ABABABAB"))),
+                names=["www", "yyy", "aaa"],
+            ),
+        )
+
+        res = df.unstack("aaa")
+        tm.assert_frame_equal(res, exp)
+
+    def test_subclass_unstack_multi_mixed(self):
+        # GH 15564
+        df = tm.SubclassedDataFrame(
+            [
+                [10, 11, 12.0, 13.0],
+                [20, 21, 22.0, 23.0],
+                [30, 31, 32.0, 33.0],
+                [40, 41, 42.0, 43.0],
+            ],
+            index=MultiIndex.from_tuples(
+                list(zip(list("AABB"), list("cdcd"))), names=["aaa", "ccc"]
+            ),
+            columns=MultiIndex.from_tuples(
+                list(zip(list("WWXX"), list("yzyz"))), names=["www", "yyy"]
+            ),
+        )
+
+        exp = tm.SubclassedDataFrame(
+            [
+                [10, 20, 11, 21, 12.0, 22.0, 13.0, 23.0],
+                [30, 40, 31, 41, 32.0, 42.0, 33.0, 43.0],
+            ],
+            index=Index(["A", "B"], name="aaa"),
+            columns=MultiIndex.from_tuples(
+                list(zip(list("WWWWXXXX"), list("yyzzyyzz"), list("cdcdcdcd"))),
+                names=["www", "yyy", "ccc"],
+            ),
+        )
+
+        res = df.unstack()
+        tm.assert_frame_equal(res, exp)
+
+        res = df.unstack("ccc")
+        tm.assert_frame_equal(res, exp)
+
+        exp = tm.SubclassedDataFrame(
+            [
+                [10, 30, 11, 31, 12.0, 32.0, 13.0, 33.0],
+                [20, 40, 21, 41, 22.0, 42.0, 23.0, 43.0],
+            ],
+            index=Index(["c", "d"], name="ccc"),
+            columns=MultiIndex.from_tuples(
+                list(zip(list("WWWWXXXX"), list("yyzzyyzz"), list("ABABABAB"))),
+                names=["www", "yyy", "aaa"],
+            ),
+        )
+
+        res = df.unstack("aaa")
+        tm.assert_frame_equal(res, exp)
+
+    def test_subclass_pivot(self):
+        # GH 15564
+        df = tm.SubclassedDataFrame(
+            {
+                "index": ["A", "B", "C", "C", "B", "A"],
+                "columns": ["One", "One", "One", "Two", "Two", "Two"],
+                "values": [1.0, 2.0, 3.0, 3.0, 2.0, 1.0],
+            }
+        )
+
+        pivoted = df.pivot(index="index", columns="columns", values="values")
+
+        expected = tm.SubclassedDataFrame(
+            {
+                "One": {"A": 1.0, "B": 2.0, "C": 3.0},
+                "Two": {"A": 1.0, "B": 2.0, "C": 3.0},
+            }
+        )
+
+        expected.index.name, expected.columns.name = "index", "columns"
+
+        tm.assert_frame_equal(pivoted, expected)
+
+    def test_subclassed_melt(self):
+        # GH 15564
+        cheese = tm.SubclassedDataFrame(
+            {
+                "first": ["John", "Mary"],
+                "last": ["Doe", "Bo"],
+                "height": [5.5, 6.0],
+                "weight": [130, 150],
+            }
+        )
+
+        melted = pd.melt(cheese, id_vars=["first", "last"])
+
+        expected = tm.SubclassedDataFrame(
+            [
+                ["John", "Doe", "height", 5.5],
+                ["Mary", "Bo", "height", 6.0],
+                ["John", "Doe", "weight", 130],
+                ["Mary", "Bo", "weight", 150],
+            ],
+            columns=["first", "last", "variable", "value"],
+        )
+
+        tm.assert_frame_equal(melted, expected)
+
+    def test_subclassed_wide_to_long(self):
+        # GH 9762
+
+        x = np.random.default_rng(2).standard_normal(3)
+        df = tm.SubclassedDataFrame(
+            {
+                "A1970": {0: "a", 1: "b", 2: "c"},
+                "A1980": {0: "d", 1: "e", 2: "f"},
+                "B1970": {0: 2.5, 1: 1.2, 2: 0.7},
+                "B1980": {0: 3.2, 1: 1.3, 2: 0.1},
+                "X": dict(zip(range(3), x)),
+            }
+        )
+
+        df["id"] = df.index
+        exp_data = {
+            "X": x.tolist() + x.tolist(),
+            "A": ["a", "b", "c", "d", "e", "f"],
+            "B": [2.5, 1.2, 0.7, 3.2, 1.3, 0.1],
+            "year": [1970, 1970, 1970, 1980, 1980, 1980],
+            "id": [0, 1, 2, 0, 1, 2],
+        }
+        expected = tm.SubclassedDataFrame(exp_data)
+        expected = expected.set_index(["id", "year"])[["X", "A", "B"]]
+        long_frame = pd.wide_to_long(df, ["A", "B"], i="id", j="year")
+
+        tm.assert_frame_equal(long_frame, expected)
+
+    def test_subclassed_apply(self):
+        # GH 19822
+
+        def check_row_subclass(row):
+            assert isinstance(row, tm.SubclassedSeries)
+
+        def stretch(row):
+            if row["variable"] == "height":
+                row["value"] += 0.5
+            return row
+
+        df = tm.SubclassedDataFrame(
+            [
+                ["John", "Doe", "height", 5.5],
+                ["Mary", "Bo", "height", 6.0],
+                ["John", "Doe", "weight", 130],
+                ["Mary", "Bo", "weight", 150],
+            ],
+            columns=["first", "last", "variable", "value"],
+        )
+
+        df.apply(lambda x: check_row_subclass(x))
+        df.apply(lambda x: check_row_subclass(x), axis=1)
+
+        expected = tm.SubclassedDataFrame(
+            [
+                ["John", "Doe", "height", 6.0],
+                ["Mary", "Bo", "height", 6.5],
+                ["John", "Doe", "weight", 130],
+                ["Mary", "Bo", "weight", 150],
+            ],
+            columns=["first", "last", "variable", "value"],
+        )
+
+        result = df.apply(lambda x: stretch(x), axis=1)
+        assert isinstance(result, tm.SubclassedDataFrame)
+        tm.assert_frame_equal(result, expected)
+
+        expected = tm.SubclassedDataFrame([[1, 2, 3], [1, 2, 3], [1, 2, 3], [1, 2, 3]])
+
+        result = df.apply(lambda x: tm.SubclassedSeries([1, 2, 3]), axis=1)
+        assert isinstance(result, tm.SubclassedDataFrame)
+        tm.assert_frame_equal(result, expected)
+
+        result = df.apply(lambda x: [1, 2, 3], axis=1, result_type="expand")
+        assert isinstance(result, tm.SubclassedDataFrame)
+        tm.assert_frame_equal(result, expected)
+
+        expected = tm.SubclassedSeries([[1, 2, 3], [1, 2, 3], [1, 2, 3], [1, 2, 3]])
+
+        result = df.apply(lambda x: [1, 2, 3], axis=1)
+        assert not isinstance(result, tm.SubclassedDataFrame)
+        tm.assert_series_equal(result, expected)
+
+    def test_subclassed_reductions(self, all_reductions):
+        # GH 25596
+
+        df = tm.SubclassedDataFrame({"A": [1, 2, 3], "B": [4, 5, 6], "C": [7, 8, 9]})
+        result = getattr(df, all_reductions)()
+        assert isinstance(result, tm.SubclassedSeries)
+
+    def test_subclassed_count(self):
+        df = tm.SubclassedDataFrame(
+            {
+                "Person": ["John", "Myla", "Lewis", "John", "Myla"],
+                "Age": [24.0, np.nan, 21.0, 33, 26],
+                "Single": [False, True, True, True, False],
+            }
+        )
+        result = df.count()
+        assert isinstance(result, tm.SubclassedSeries)
+
+        df = tm.SubclassedDataFrame({"A": [1, 0, 3], "B": [0, 5, 6], "C": [7, 8, 0]})
+        result = df.count()
+        assert isinstance(result, tm.SubclassedSeries)
+
+        df = tm.SubclassedDataFrame(
+            [[10, 11, 12, 13], [20, 21, 22, 23], [30, 31, 32, 33], [40, 41, 42, 43]],
+            index=MultiIndex.from_tuples(
+                list(zip(list("AABB"), list("cdcd"))), names=["aaa", "ccc"]
+            ),
+            columns=MultiIndex.from_tuples(
+                list(zip(list("WWXX"), list("yzyz"))), names=["www", "yyy"]
+            ),
+        )
+        result = df.count()
+        assert isinstance(result, tm.SubclassedSeries)
+
+        df = tm.SubclassedDataFrame()
+        result = df.count()
+        assert isinstance(result, tm.SubclassedSeries)
+
+    def test_isin(self):
+        df = tm.SubclassedDataFrame(
+            {"num_legs": [2, 4], "num_wings": [2, 0]}, index=["falcon", "dog"]
+        )
+        result = df.isin([0, 2])
+        assert isinstance(result, tm.SubclassedDataFrame)
+
+    def test_duplicated(self):
+        df = tm.SubclassedDataFrame({"A": [1, 2, 3], "B": [4, 5, 6], "C": [7, 8, 9]})
+        result = df.duplicated()
+        assert isinstance(result, tm.SubclassedSeries)
+
+        df = tm.SubclassedDataFrame()
+        result = df.duplicated()
+        assert isinstance(result, tm.SubclassedSeries)
+
+    @pytest.mark.parametrize("idx_method", ["idxmax", "idxmin"])
+    def test_idx(self, idx_method):
+        df = tm.SubclassedDataFrame({"A": [1, 2, 3], "B": [4, 5, 6], "C": [7, 8, 9]})
+        result = getattr(df, idx_method)()
+        assert isinstance(result, tm.SubclassedSeries)
+
+    def test_dot(self):
+        df = tm.SubclassedDataFrame([[0, 1, -2, -1], [1, 1, 1, 1]])
+        s = tm.SubclassedSeries([1, 1, 2, 1])
+        result = df.dot(s)
+        assert isinstance(result, tm.SubclassedSeries)
+
+        df = tm.SubclassedDataFrame([[0, 1, -2, -1], [1, 1, 1, 1]])
+        s = tm.SubclassedDataFrame([1, 1, 2, 1])
+        result = df.dot(s)
+        assert isinstance(result, tm.SubclassedDataFrame)
+
+    def test_memory_usage(self):
+        df = tm.SubclassedDataFrame({"A": [1, 2, 3], "B": [4, 5, 6], "C": [7, 8, 9]})
+        result = df.memory_usage()
+        assert isinstance(result, tm.SubclassedSeries)
+
+        result = df.memory_usage(index=False)
+        assert isinstance(result, tm.SubclassedSeries)
+
+    def test_corrwith(self):
+        pytest.importorskip("scipy")
+        index = ["a", "b", "c", "d", "e"]
+        columns = ["one", "two", "three", "four"]
+        df1 = tm.SubclassedDataFrame(
+            np.random.default_rng(2).standard_normal((5, 4)),
+            index=index,
+            columns=columns,
+        )
+        df2 = tm.SubclassedDataFrame(
+            np.random.default_rng(2).standard_normal((4, 4)),
+            index=index[:4],
+            columns=columns,
+        )
+        correls = df1.corrwith(df2, axis=1, drop=True, method="kendall")
+
+        assert isinstance(correls, (tm.SubclassedSeries))
+
+    def test_asof(self):
+        N = 3
+        rng = pd.date_range("1/1/1990", periods=N, freq="53s")
+        df = tm.SubclassedDataFrame(
+            {
+                "A": [np.nan, np.nan, np.nan],
+                "B": [np.nan, np.nan, np.nan],
+                "C": [np.nan, np.nan, np.nan],
+            },
+            index=rng,
+        )
+
+        result = df.asof(rng[-2:])
+        assert isinstance(result, tm.SubclassedDataFrame)
+
+        result = df.asof(rng[-2])
+        assert isinstance(result, tm.SubclassedSeries)
+
+        result = df.asof("1989-12-31")
+        assert isinstance(result, tm.SubclassedSeries)
+
+    def test_idxmin_preserves_subclass(self):
+        # GH 28330
+
+        df = tm.SubclassedDataFrame({"A": [1, 2, 3], "B": [4, 5, 6], "C": [7, 8, 9]})
+        result = df.idxmin()
+        assert isinstance(result, tm.SubclassedSeries)
+
+    def test_idxmax_preserves_subclass(self):
+        # GH 28330
+
+        df = tm.SubclassedDataFrame({"A": [1, 2, 3], "B": [4, 5, 6], "C": [7, 8, 9]})
+        result = df.idxmax()
+        assert isinstance(result, tm.SubclassedSeries)
+
+    def test_convert_dtypes_preserves_subclass(self, gpd_style_subclass_df):
+        # GH 43668
+        df = tm.SubclassedDataFrame({"A": [1, 2, 3], "B": [4, 5, 6], "C": [7, 8, 9]})
+        result = df.convert_dtypes()
+        assert isinstance(result, tm.SubclassedDataFrame)
+
+        result = gpd_style_subclass_df.convert_dtypes()
+        assert isinstance(result, type(gpd_style_subclass_df))
+
+    def test_astype_preserves_subclass(self):
+        # GH#40810
+        df = tm.SubclassedDataFrame({"A": [1, 2, 3], "B": [4, 5, 6], "C": [7, 8, 9]})
+
+        result = df.astype({"A": np.int64, "B": np.int32, "C": np.float64})
+        assert isinstance(result, tm.SubclassedDataFrame)
+
+    def test_equals_subclass(self):
+        # https://github.com/pandas-dev/pandas/pull/34402
+        # allow subclass in both directions
+        df1 = DataFrame({"a": [1, 2, 3]})
+        df2 = tm.SubclassedDataFrame({"a": [1, 2, 3]})
+        assert df1.equals(df2)
+        assert df2.equals(df1)
+
+    def test_replace_list_method(self):
+        # https://github.com/pandas-dev/pandas/pull/46018
+        df = tm.SubclassedDataFrame({"A": [0, 1, 2]})
+        msg = "The 'method' keyword in SubclassedDataFrame.replace is deprecated"
+        with tm.assert_produces_warning(
+            FutureWarning, match=msg, raise_on_extra_warnings=False
+        ):
+            result = df.replace([1, 2], method="ffill")
+        expected = tm.SubclassedDataFrame({"A": [0, 0, 0]})
+        assert isinstance(result, tm.SubclassedDataFrame)
+        tm.assert_frame_equal(result, expected)
+
+
+class MySubclassWithMetadata(DataFrame):
+    _metadata = ["my_metadata"]
+
+    def __init__(self, *args, **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+
+        my_metadata = kwargs.pop("my_metadata", None)
+        if args and isinstance(args[0], MySubclassWithMetadata):
+            my_metadata = args[0].my_metadata  # type: ignore[has-type]
+        self.my_metadata = my_metadata
+
+    @property
+    def _constructor(self):
+        return MySubclassWithMetadata
+
+
+def test_constructor_with_metadata():
+    # https://github.com/pandas-dev/pandas/pull/54922
+    # https://github.com/pandas-dev/pandas/issues/55120
+    df = MySubclassWithMetadata(
+        np.random.default_rng(2).random((5, 3)), columns=["A", "B", "C"]
+    )
+    subset = df[["A", "B"]]
+    assert isinstance(subset, MySubclassWithMetadata)
+
+
+def test_constructor_with_metadata_from_records():
+    # GH#57008
+    df = MySubclassWithMetadata.from_records([{"a": 1, "b": 2}])
+    assert df.my_metadata is None
+    assert type(df) is MySubclassWithMetadata
+
+
+class SimpleDataFrameSubClass(DataFrame):
+    """A subclass of DataFrame that does not define a constructor."""
+
+
+class SimpleSeriesSubClass(Series):
+    """A subclass of Series that does not define a constructor."""
+
+
+class TestSubclassWithoutConstructor:
+    def test_copy_df(self):
+        expected = DataFrame({"a": [1, 2, 3]})
+        result = SimpleDataFrameSubClass(expected).copy()
+
+        assert (
+            type(result) is DataFrame
+        )  # assert_frame_equal only checks isinstance(lhs, type(rhs))
+        tm.assert_frame_equal(result, expected)
+
+    def test_copy_series(self):
+        expected = Series([1, 2, 3])
+        result = SimpleSeriesSubClass(expected).copy()
+
+        tm.assert_series_equal(result, expected)
+
+    def test_series_to_frame(self):
+        orig = Series([1, 2, 3])
+        expected = orig.to_frame()
+        result = SimpleSeriesSubClass(orig).to_frame()
+
+        assert (
+            type(result) is DataFrame
+        )  # assert_frame_equal only checks isinstance(lhs, type(rhs))
+        tm.assert_frame_equal(result, expected)
+
+    def test_groupby(self):
+        df = SimpleDataFrameSubClass(DataFrame({"a": [1, 2, 3]}))
+
+        for _, v in df.groupby("a"):
+            assert type(v) is DataFrame
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/test_ufunc.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/test_ufunc.py
new file mode 100644
index 0000000000000000000000000000000000000000..88c62da2b0a735b103f7a6b03634aa185fc46d2c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/test_ufunc.py
@@ -0,0 +1,311 @@
+from functools import partial
+import re
+
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.api.types import is_extension_array_dtype
+
+dtypes = [
+    "int64",
+    "Int64",
+    {"A": "int64", "B": "Int64"},
+]
+
+
+@pytest.mark.parametrize("dtype", dtypes)
+def test_unary_unary(dtype):
+    # unary input, unary output
+    values = np.array([[-1, -1], [1, 1]], dtype="int64")
+    df = pd.DataFrame(values, columns=["A", "B"], index=["a", "b"]).astype(dtype=dtype)
+    result = np.positive(df)
+    expected = pd.DataFrame(
+        np.positive(values), index=df.index, columns=df.columns
+    ).astype(dtype)
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("dtype", dtypes)
+def test_unary_binary(request, dtype):
+    # unary input, binary output
+    if is_extension_array_dtype(dtype) or isinstance(dtype, dict):
+        request.applymarker(
+            pytest.mark.xfail(
+                reason="Extension / mixed with multiple outputs not implemented."
+            )
+        )
+
+    values = np.array([[-1, -1], [1, 1]], dtype="int64")
+    df = pd.DataFrame(values, columns=["A", "B"], index=["a", "b"]).astype(dtype=dtype)
+    result_pandas = np.modf(df)
+    assert isinstance(result_pandas, tuple)
+    assert len(result_pandas) == 2
+    expected_numpy = np.modf(values)
+
+    for result, b in zip(result_pandas, expected_numpy):
+        expected = pd.DataFrame(b, index=df.index, columns=df.columns)
+        tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("dtype", dtypes)
+def test_binary_input_dispatch_binop(dtype):
+    # binop ufuncs are dispatched to our dunder methods.
+    values = np.array([[-1, -1], [1, 1]], dtype="int64")
+    df = pd.DataFrame(values, columns=["A", "B"], index=["a", "b"]).astype(dtype=dtype)
+    result = np.add(df, df)
+    expected = pd.DataFrame(
+        np.add(values, values), index=df.index, columns=df.columns
+    ).astype(dtype)
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "func,arg,expected",
+    [
+        (np.add, 1, [2, 3, 4, 5]),
+        (
+            partial(np.add, where=[[False, True], [True, False]]),
+            np.array([[1, 1], [1, 1]]),
+            [0, 3, 4, 0],
+        ),
+        (np.power, np.array([[1, 1], [2, 2]]), [1, 2, 9, 16]),
+        (np.subtract, 2, [-1, 0, 1, 2]),
+        (
+            partial(np.negative, where=np.array([[False, True], [True, False]])),
+            None,
+            [0, -2, -3, 0],
+        ),
+    ],
+)
+def test_ufunc_passes_args(func, arg, expected):
+    # GH#40662
+    arr = np.array([[1, 2], [3, 4]])
+    df = pd.DataFrame(arr)
+    result_inplace = np.zeros_like(arr)
+    # 1-argument ufunc
+    if arg is None:
+        result = func(df, out=result_inplace)
+    else:
+        result = func(df, arg, out=result_inplace)
+
+    expected = np.array(expected).reshape(2, 2)
+    tm.assert_numpy_array_equal(result_inplace, expected)
+
+    expected = pd.DataFrame(expected)
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("dtype_a", dtypes)
+@pytest.mark.parametrize("dtype_b", dtypes)
+def test_binary_input_aligns_columns(request, dtype_a, dtype_b):
+    if (
+        is_extension_array_dtype(dtype_a)
+        or isinstance(dtype_a, dict)
+        or is_extension_array_dtype(dtype_b)
+        or isinstance(dtype_b, dict)
+    ):
+        request.applymarker(
+            pytest.mark.xfail(
+                reason="Extension / mixed with multiple inputs not implemented."
+            )
+        )
+
+    df1 = pd.DataFrame({"A": [1, 2], "B": [3, 4]}).astype(dtype_a)
+
+    if isinstance(dtype_a, dict) and isinstance(dtype_b, dict):
+        dtype_b = dtype_b.copy()
+        dtype_b["C"] = dtype_b.pop("B")
+    df2 = pd.DataFrame({"A": [1, 2], "C": [3, 4]}).astype(dtype_b)
+    # As of 2.0, align first before applying the ufunc
+    result = np.heaviside(df1, df2)
+    expected = np.heaviside(
+        np.array([[1, 3, np.nan], [2, 4, np.nan]]),
+        np.array([[1, np.nan, 3], [2, np.nan, 4]]),
+    )
+    expected = pd.DataFrame(expected, index=[0, 1], columns=["A", "B", "C"])
+    tm.assert_frame_equal(result, expected)
+
+    result = np.heaviside(df1, df2.values)
+    expected = pd.DataFrame([[1.0, 1.0], [1.0, 1.0]], columns=["A", "B"])
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("dtype", dtypes)
+def test_binary_input_aligns_index(request, dtype):
+    if is_extension_array_dtype(dtype) or isinstance(dtype, dict):
+        request.applymarker(
+            pytest.mark.xfail(
+                reason="Extension / mixed with multiple inputs not implemented."
+            )
+        )
+    df1 = pd.DataFrame({"A": [1, 2], "B": [3, 4]}, index=["a", "b"]).astype(dtype)
+    df2 = pd.DataFrame({"A": [1, 2], "B": [3, 4]}, index=["a", "c"]).astype(dtype)
+    result = np.heaviside(df1, df2)
+    expected = np.heaviside(
+        np.array([[1, 3], [3, 4], [np.nan, np.nan]]),
+        np.array([[1, 3], [np.nan, np.nan], [3, 4]]),
+    )
+    # TODO(FloatArray): this will be Float64Dtype.
+    expected = pd.DataFrame(expected, index=["a", "b", "c"], columns=["A", "B"])
+    tm.assert_frame_equal(result, expected)
+
+    result = np.heaviside(df1, df2.values)
+    expected = pd.DataFrame(
+        [[1.0, 1.0], [1.0, 1.0]], columns=["A", "B"], index=["a", "b"]
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_binary_frame_series_raises():
+    # We don't currently implement
+    df = pd.DataFrame({"A": [1, 2]})
+    with pytest.raises(NotImplementedError, match="logaddexp"):
+        np.logaddexp(df, df["A"])
+
+    with pytest.raises(NotImplementedError, match="logaddexp"):
+        np.logaddexp(df["A"], df)
+
+
+def test_unary_accumulate_axis():
+    # https://github.com/pandas-dev/pandas/issues/39259
+    df = pd.DataFrame({"a": [1, 3, 2, 4]})
+    result = np.maximum.accumulate(df)
+    expected = pd.DataFrame({"a": [1, 3, 3, 4]})
+    tm.assert_frame_equal(result, expected)
+
+    df = pd.DataFrame({"a": [1, 3, 2, 4], "b": [0.1, 4.0, 3.0, 2.0]})
+    result = np.maximum.accumulate(df)
+    # in theory could preserve int dtype for default axis=0
+    expected = pd.DataFrame({"a": [1.0, 3.0, 3.0, 4.0], "b": [0.1, 4.0, 4.0, 4.0]})
+    tm.assert_frame_equal(result, expected)
+
+    result = np.maximum.accumulate(df, axis=0)
+    tm.assert_frame_equal(result, expected)
+
+    result = np.maximum.accumulate(df, axis=1)
+    expected = pd.DataFrame({"a": [1.0, 3.0, 2.0, 4.0], "b": [1.0, 4.0, 3.0, 4.0]})
+    tm.assert_frame_equal(result, expected)
+
+
+def test_frame_outer_disallowed():
+    df = pd.DataFrame({"A": [1, 2]})
+    with pytest.raises(NotImplementedError, match=""):
+        # deprecation enforced in 2.0
+        np.subtract.outer(df, df)
+
+
+def test_alignment_deprecation_enforced():
+    # Enforced in 2.0
+    # https://github.com/pandas-dev/pandas/issues/39184
+    df1 = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
+    df2 = pd.DataFrame({"b": [1, 2, 3], "c": [4, 5, 6]})
+    s1 = pd.Series([1, 2], index=["a", "b"])
+    s2 = pd.Series([1, 2], index=["b", "c"])
+
+    # binary dataframe / dataframe
+    expected = pd.DataFrame({"a": [2, 4, 6], "b": [8, 10, 12]})
+
+    with tm.assert_produces_warning(None):
+        # aligned -> no warning!
+        result = np.add(df1, df1)
+    tm.assert_frame_equal(result, expected)
+
+    result = np.add(df1, df2.values)
+    tm.assert_frame_equal(result, expected)
+
+    result = np.add(df1, df2)
+    expected = pd.DataFrame({"a": [np.nan] * 3, "b": [5, 7, 9], "c": [np.nan] * 3})
+    tm.assert_frame_equal(result, expected)
+
+    result = np.add(df1.values, df2)
+    expected = pd.DataFrame({"b": [2, 4, 6], "c": [8, 10, 12]})
+    tm.assert_frame_equal(result, expected)
+
+    # binary dataframe / series
+    expected = pd.DataFrame({"a": [2, 3, 4], "b": [6, 7, 8]})
+
+    with tm.assert_produces_warning(None):
+        # aligned -> no warning!
+        result = np.add(df1, s1)
+    tm.assert_frame_equal(result, expected)
+
+    result = np.add(df1, s2.values)
+    tm.assert_frame_equal(result, expected)
+
+    expected = pd.DataFrame(
+        {"a": [np.nan] * 3, "b": [5.0, 6.0, 7.0], "c": [np.nan] * 3}
+    )
+    result = np.add(df1, s2)
+    tm.assert_frame_equal(result, expected)
+
+    msg = "Cannot apply ufunc  to mixed DataFrame and Series inputs."
+    with pytest.raises(NotImplementedError, match=msg):
+        np.add(s2, df1)
+
+
+def test_alignment_deprecation_many_inputs_enforced():
+    # Enforced in 2.0
+    # https://github.com/pandas-dev/pandas/issues/39184
+    # test that the deprecation also works with > 2 inputs -> using a numba
+    # written ufunc for this because numpy itself doesn't have such ufuncs
+    numba = pytest.importorskip("numba")
+
+    @numba.vectorize([numba.float64(numba.float64, numba.float64, numba.float64)])
+    def my_ufunc(x, y, z):
+        return x + y + z
+
+    df1 = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
+    df2 = pd.DataFrame({"b": [1, 2, 3], "c": [4, 5, 6]})
+    df3 = pd.DataFrame({"a": [1, 2, 3], "c": [4, 5, 6]})
+
+    result = my_ufunc(df1, df2, df3)
+    expected = pd.DataFrame(np.full((3, 3), np.nan), columns=["a", "b", "c"])
+    tm.assert_frame_equal(result, expected)
+
+    # all aligned -> no warning
+    with tm.assert_produces_warning(None):
+        result = my_ufunc(df1, df1, df1)
+    expected = pd.DataFrame([[3.0, 12.0], [6.0, 15.0], [9.0, 18.0]], columns=["a", "b"])
+    tm.assert_frame_equal(result, expected)
+
+    # mixed frame / arrays
+    msg = (
+        r"operands could not be broadcast together with shapes \(3,3\) \(3,3\) \(3,2\)"
+    )
+    with pytest.raises(ValueError, match=msg):
+        my_ufunc(df1, df2, df3.values)
+
+    # single frame -> no warning
+    with tm.assert_produces_warning(None):
+        result = my_ufunc(df1, df2.values, df3.values)
+    tm.assert_frame_equal(result, expected)
+
+    # takes indices of first frame
+    msg = (
+        r"operands could not be broadcast together with shapes \(3,2\) \(3,3\) \(3,3\)"
+    )
+    with pytest.raises(ValueError, match=msg):
+        my_ufunc(df1.values, df2, df3)
+
+
+def test_array_ufuncs_for_many_arguments():
+    # GH39853
+    def add3(x, y, z):
+        return x + y + z
+
+    ufunc = np.frompyfunc(add3, 3, 1)
+    df = pd.DataFrame([[1, 2], [3, 4]])
+
+    result = ufunc(df, df, 1)
+    expected = pd.DataFrame([[3, 5], [7, 9]], dtype=object)
+    tm.assert_frame_equal(result, expected)
+
+    ser = pd.Series([1, 2])
+    msg = (
+        "Cannot apply ufunc  "
+        "to mixed DataFrame and Series inputs."
+    )
+    with pytest.raises(NotImplementedError, match=re.escape(msg)):
+        ufunc(df, df, ser)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/test_unary.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/test_unary.py
new file mode 100644
index 0000000000000000000000000000000000000000..a48b5c51f9ca73495cfab4d9ace73b0cded221b4
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/test_unary.py
@@ -0,0 +1,195 @@
+from decimal import Decimal
+
+import numpy as np
+import pytest
+
+from pandas.compat.numpy import np_version_gte1p25
+
+import pandas as pd
+import pandas._testing as tm
+
+
+class TestDataFrameUnaryOperators:
+    # __pos__, __neg__, __invert__
+
+    @pytest.mark.parametrize(
+        "df,expected",
+        [
+            (pd.DataFrame({"a": [-1, 1]}), pd.DataFrame({"a": [1, -1]})),
+            (pd.DataFrame({"a": [False, True]}), pd.DataFrame({"a": [True, False]})),
+            (
+                pd.DataFrame({"a": pd.Series(pd.to_timedelta([-1, 1]))}),
+                pd.DataFrame({"a": pd.Series(pd.to_timedelta([1, -1]))}),
+            ),
+        ],
+    )
+    def test_neg_numeric(self, df, expected):
+        tm.assert_frame_equal(-df, expected)
+        tm.assert_series_equal(-df["a"], expected["a"])
+
+    @pytest.mark.parametrize(
+        "df, expected",
+        [
+            (np.array([1, 2], dtype=object), np.array([-1, -2], dtype=object)),
+            ([Decimal("1.0"), Decimal("2.0")], [Decimal("-1.0"), Decimal("-2.0")]),
+        ],
+    )
+    def test_neg_object(self, df, expected):
+        # GH#21380
+        df = pd.DataFrame({"a": df})
+        expected = pd.DataFrame({"a": expected})
+        tm.assert_frame_equal(-df, expected)
+        tm.assert_series_equal(-df["a"], expected["a"])
+
+    @pytest.mark.parametrize(
+        "df",
+        [
+            pd.DataFrame({"a": ["a", "b"]}),
+            pd.DataFrame({"a": pd.to_datetime(["2017-01-22", "1970-01-01"])}),
+        ],
+    )
+    def test_neg_raises(self, df, using_infer_string):
+        msg = (
+            "bad operand type for unary -: 'str'|"
+            r"bad operand type for unary -: 'DatetimeArray'|"
+            "unary '-' not supported for dtype"
+        )
+        with pytest.raises(TypeError, match=msg):
+            (-df)
+        with pytest.raises(TypeError, match=msg):
+            (-df["a"])
+
+    def test_invert(self, float_frame):
+        df = float_frame
+
+        tm.assert_frame_equal(-(df < 0), ~(df < 0))
+
+    def test_invert_mixed(self):
+        shape = (10, 5)
+        df = pd.concat(
+            [
+                pd.DataFrame(np.zeros(shape, dtype="bool")),
+                pd.DataFrame(np.zeros(shape, dtype=int)),
+            ],
+            axis=1,
+            ignore_index=True,
+        )
+        result = ~df
+        expected = pd.concat(
+            [
+                pd.DataFrame(np.ones(shape, dtype="bool")),
+                pd.DataFrame(-np.ones(shape, dtype=int)),
+            ],
+            axis=1,
+            ignore_index=True,
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_invert_empty_not_input(self):
+        # GH#51032
+        df = pd.DataFrame()
+        result = ~df
+        tm.assert_frame_equal(df, result)
+        assert df is not result
+
+    @pytest.mark.parametrize(
+        "df",
+        [
+            pd.DataFrame({"a": [-1, 1]}),
+            pd.DataFrame({"a": [False, True]}),
+            pd.DataFrame({"a": pd.Series(pd.to_timedelta([-1, 1]))}),
+        ],
+    )
+    def test_pos_numeric(self, df):
+        # GH#16073
+        tm.assert_frame_equal(+df, df)
+        tm.assert_series_equal(+df["a"], df["a"])
+
+    @pytest.mark.parametrize(
+        "df",
+        [
+            pd.DataFrame({"a": np.array([-1, 2], dtype=object)}),
+            pd.DataFrame({"a": [Decimal("-1.0"), Decimal("2.0")]}),
+        ],
+    )
+    def test_pos_object(self, df):
+        # GH#21380
+        tm.assert_frame_equal(+df, df)
+        tm.assert_series_equal(+df["a"], df["a"])
+
+    @pytest.mark.parametrize(
+        "df",
+        [
+            pytest.param(
+                pd.DataFrame({"a": ["a", "b"]}),
+                # filterwarnings removable once min numpy version is 1.25
+                marks=[
+                    pytest.mark.filterwarnings("ignore:Applying:DeprecationWarning")
+                ],
+            ),
+        ],
+    )
+    def test_pos_object_raises(self, df):
+        # GH#21380
+        if np_version_gte1p25:
+            with pytest.raises(
+                TypeError, match=r"^bad operand type for unary \+: \'str\'$"
+            ):
+                tm.assert_frame_equal(+df, df)
+        else:
+            tm.assert_series_equal(+df["a"], df["a"])
+
+    @pytest.mark.parametrize(
+        "df", [pd.DataFrame({"a": pd.to_datetime(["2017-01-22", "1970-01-01"])})]
+    )
+    def test_pos_raises(self, df):
+        msg = r"bad operand type for unary \+: 'DatetimeArray'"
+        with pytest.raises(TypeError, match=msg):
+            (+df)
+        with pytest.raises(TypeError, match=msg):
+            (+df["a"])
+
+    def test_unary_nullable(self):
+        df = pd.DataFrame(
+            {
+                "a": pd.array([1, -2, 3, pd.NA], dtype="Int64"),
+                "b": pd.array([4.0, -5.0, 6.0, pd.NA], dtype="Float32"),
+                "c": pd.array([True, False, False, pd.NA], dtype="boolean"),
+                # include numpy bool to make sure bool-vs-boolean behavior
+                #  is consistent in non-NA locations
+                "d": np.array([True, False, False, True]),
+            }
+        )
+
+        result = +df
+        res_ufunc = np.positive(df)
+        expected = df
+        # TODO: assert that we have copies?
+        tm.assert_frame_equal(result, expected)
+        tm.assert_frame_equal(res_ufunc, expected)
+
+        result = -df
+        res_ufunc = np.negative(df)
+        expected = pd.DataFrame(
+            {
+                "a": pd.array([-1, 2, -3, pd.NA], dtype="Int64"),
+                "b": pd.array([-4.0, 5.0, -6.0, pd.NA], dtype="Float32"),
+                "c": pd.array([False, True, True, pd.NA], dtype="boolean"),
+                "d": np.array([False, True, True, False]),
+            }
+        )
+        tm.assert_frame_equal(result, expected)
+        tm.assert_frame_equal(res_ufunc, expected)
+
+        result = abs(df)
+        res_ufunc = np.abs(df)
+        expected = pd.DataFrame(
+            {
+                "a": pd.array([1, 2, 3, pd.NA], dtype="Int64"),
+                "b": pd.array([4.0, 5.0, 6.0, pd.NA], dtype="Float32"),
+                "c": pd.array([True, False, False, pd.NA], dtype="boolean"),
+                "d": np.array([True, False, False, True]),
+            }
+        )
+        tm.assert_frame_equal(result, expected)
+        tm.assert_frame_equal(res_ufunc, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/test_validate.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/test_validate.py
new file mode 100644
index 0000000000000000000000000000000000000000..e99e0a686384883d570feef949597d08da7e8ff9
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/frame/test_validate.py
@@ -0,0 +1,41 @@
+import pytest
+
+from pandas.core.frame import DataFrame
+
+
+@pytest.fixture
+def dataframe():
+    return DataFrame({"a": [1, 2], "b": [3, 4]})
+
+
+class TestDataFrameValidate:
+    """Tests for error handling related to data types of method arguments."""
+
+    @pytest.mark.parametrize(
+        "func",
+        [
+            "query",
+            "eval",
+            "set_index",
+            "reset_index",
+            "dropna",
+            "drop_duplicates",
+            "sort_values",
+        ],
+    )
+    @pytest.mark.parametrize("inplace", [1, "True", [1, 2, 3], 5.0])
+    def test_validate_bool_args(self, dataframe, func, inplace):
+        msg = 'For argument "inplace" expected type bool'
+        kwargs = {"inplace": inplace}
+
+        if func == "query":
+            kwargs["expr"] = "a > b"
+        elif func == "eval":
+            kwargs["expr"] = "a + b"
+        elif func == "set_index":
+            kwargs["keys"] = ["a"]
+        elif func == "sort_values":
+            kwargs["by"] = ["a"]
+
+        with pytest.raises(ValueError, match=msg):
+            getattr(dataframe, func)(**kwargs)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/test_aggregation.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/test_aggregation.py
new file mode 100644
index 0000000000000000000000000000000000000000..7695c953712ed9925e4e804d0db1e8cf606a97eb
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/test_aggregation.py
@@ -0,0 +1,93 @@
+import numpy as np
+import pytest
+
+from pandas.core.apply import (
+    _make_unique_kwarg_list,
+    maybe_mangle_lambdas,
+)
+
+
+def test_maybe_mangle_lambdas_passthrough():
+    assert maybe_mangle_lambdas("mean") == "mean"
+    assert maybe_mangle_lambdas(lambda x: x).__name__ == ""
+    # don't mangel single lambda.
+    assert maybe_mangle_lambdas([lambda x: x])[0].__name__ == ""
+
+
+def test_maybe_mangle_lambdas_listlike():
+    aggfuncs = [lambda x: 1, lambda x: 2]
+    result = maybe_mangle_lambdas(aggfuncs)
+    assert result[0].__name__ == ""
+    assert result[1].__name__ == ""
+    assert aggfuncs[0](None) == result[0](None)
+    assert aggfuncs[1](None) == result[1](None)
+
+
+def test_maybe_mangle_lambdas():
+    func = {"A": [lambda x: 0, lambda x: 1]}
+    result = maybe_mangle_lambdas(func)
+    assert result["A"][0].__name__ == ""
+    assert result["A"][1].__name__ == ""
+
+
+def test_maybe_mangle_lambdas_args():
+    func = {"A": [lambda x, a, b=1: (0, a, b), lambda x: 1]}
+    result = maybe_mangle_lambdas(func)
+    assert result["A"][0].__name__ == ""
+    assert result["A"][1].__name__ == ""
+
+    assert func["A"][0](0, 1) == (0, 1, 1)
+    assert func["A"][0](0, 1, 2) == (0, 1, 2)
+    assert func["A"][0](0, 2, b=3) == (0, 2, 3)
+
+
+def test_maybe_mangle_lambdas_named():
+    func = {"C": np.mean, "D": {"foo": np.mean, "bar": np.mean}}
+    result = maybe_mangle_lambdas(func)
+    assert result == func
+
+
+@pytest.mark.parametrize(
+    "order, expected_reorder",
+    [
+        (
+            [
+                ("height", ""),
+                ("height", "max"),
+                ("weight", "max"),
+                ("height", ""),
+                ("weight", ""),
+            ],
+            [
+                ("height", "_0"),
+                ("height", "max"),
+                ("weight", "max"),
+                ("height", "_1"),
+                ("weight", ""),
+            ],
+        ),
+        (
+            [
+                ("col2", "min"),
+                ("col1", ""),
+                ("col1", ""),
+                ("col1", ""),
+            ],
+            [
+                ("col2", "min"),
+                ("col1", "_0"),
+                ("col1", "_1"),
+                ("col1", "_2"),
+            ],
+        ),
+        (
+            [("col", ""), ("col", ""), ("col", "")],
+            [("col", "_0"), ("col", "_1"), ("col", "_2")],
+        ),
+    ],
+)
+def test_make_unique(order, expected_reorder):
+    # GH 27519, test if make_unique function reorders correctly
+    result = _make_unique_kwarg_list(order)
+
+    assert result == expected_reorder
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/test_algos.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/test_algos.py
new file mode 100644
index 0000000000000000000000000000000000000000..80ee0f6e067f97e1177db1f0663d4cfd8e0d3324
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/test_algos.py
@@ -0,0 +1,2059 @@
+from datetime import datetime
+import struct
+
+import numpy as np
+import pytest
+
+from pandas._config import using_string_dtype
+
+from pandas._libs import (
+    algos as libalgos,
+    hashtable as ht,
+)
+
+from pandas.core.dtypes.common import (
+    is_bool_dtype,
+    is_complex_dtype,
+    is_float_dtype,
+    is_integer_dtype,
+    is_object_dtype,
+)
+from pandas.core.dtypes.dtypes import CategoricalDtype
+
+import pandas as pd
+from pandas import (
+    Categorical,
+    CategoricalIndex,
+    DataFrame,
+    DatetimeIndex,
+    Index,
+    IntervalIndex,
+    MultiIndex,
+    NaT,
+    Period,
+    PeriodIndex,
+    Series,
+    Timedelta,
+    Timestamp,
+    cut,
+    date_range,
+    timedelta_range,
+    to_datetime,
+    to_timedelta,
+)
+import pandas._testing as tm
+import pandas.core.algorithms as algos
+from pandas.core.arrays import (
+    DatetimeArray,
+    TimedeltaArray,
+)
+import pandas.core.common as com
+
+
+class TestFactorize:
+    def test_factorize_complex(self):
+        # GH#17927
+        array = [1, 2, 2 + 1j]
+        msg = "factorize with argument that is not not a Series"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            labels, uniques = algos.factorize(array)
+
+        expected_labels = np.array([0, 1, 2], dtype=np.intp)
+        tm.assert_numpy_array_equal(labels, expected_labels)
+
+        # Should return a complex dtype in the future
+        expected_uniques = np.array([(1 + 0j), (2 + 0j), (2 + 1j)], dtype=object)
+        tm.assert_numpy_array_equal(uniques, expected_uniques)
+
+    @pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)", strict=False)
+    @pytest.mark.parametrize("sort", [True, False])
+    def test_factorize(self, index_or_series_obj, sort):
+        obj = index_or_series_obj
+        result_codes, result_uniques = obj.factorize(sort=sort)
+
+        constructor = Index
+        if isinstance(obj, MultiIndex):
+            constructor = MultiIndex.from_tuples
+        expected_arr = obj.unique()
+        if expected_arr.dtype == np.float16:
+            expected_arr = expected_arr.astype(np.float32)
+        expected_uniques = constructor(expected_arr)
+        if (
+            isinstance(obj, Index)
+            and expected_uniques.dtype == bool
+            and obj.dtype == object
+        ):
+            expected_uniques = expected_uniques.astype(object)
+
+        if sort:
+            expected_uniques = expected_uniques.sort_values()
+
+        # construct an integer ndarray so that
+        # `expected_uniques.take(expected_codes)` is equal to `obj`
+        expected_uniques_list = list(expected_uniques)
+        expected_codes = [expected_uniques_list.index(val) for val in obj]
+        expected_codes = np.asarray(expected_codes, dtype=np.intp)
+
+        tm.assert_numpy_array_equal(result_codes, expected_codes)
+        tm.assert_index_equal(result_uniques, expected_uniques, exact=True)
+
+    def test_series_factorize_use_na_sentinel_false(self):
+        # GH#35667
+        values = np.array([1, 2, 1, np.nan])
+        ser = Series(values)
+        codes, uniques = ser.factorize(use_na_sentinel=False)
+
+        expected_codes = np.array([0, 1, 0, 2], dtype=np.intp)
+        expected_uniques = Index([1.0, 2.0, np.nan])
+
+        tm.assert_numpy_array_equal(codes, expected_codes)
+        tm.assert_index_equal(uniques, expected_uniques)
+
+    def test_basic(self):
+        items = np.array(["a", "b", "b", "a", "a", "c", "c", "c"], dtype=object)
+        codes, uniques = algos.factorize(items)
+        tm.assert_numpy_array_equal(uniques, np.array(["a", "b", "c"], dtype=object))
+
+        codes, uniques = algos.factorize(items, sort=True)
+        exp = np.array([0, 1, 1, 0, 0, 2, 2, 2], dtype=np.intp)
+        tm.assert_numpy_array_equal(codes, exp)
+        exp = np.array(["a", "b", "c"], dtype=object)
+        tm.assert_numpy_array_equal(uniques, exp)
+
+        arr = np.arange(5, dtype=np.intp)[::-1]
+
+        codes, uniques = algos.factorize(arr)
+        exp = np.array([0, 1, 2, 3, 4], dtype=np.intp)
+        tm.assert_numpy_array_equal(codes, exp)
+        exp = np.array([4, 3, 2, 1, 0], dtype=arr.dtype)
+        tm.assert_numpy_array_equal(uniques, exp)
+
+        codes, uniques = algos.factorize(arr, sort=True)
+        exp = np.array([4, 3, 2, 1, 0], dtype=np.intp)
+        tm.assert_numpy_array_equal(codes, exp)
+        exp = np.array([0, 1, 2, 3, 4], dtype=arr.dtype)
+        tm.assert_numpy_array_equal(uniques, exp)
+
+        arr = np.arange(5.0)[::-1]
+
+        codes, uniques = algos.factorize(arr)
+        exp = np.array([0, 1, 2, 3, 4], dtype=np.intp)
+        tm.assert_numpy_array_equal(codes, exp)
+        exp = np.array([4.0, 3.0, 2.0, 1.0, 0.0], dtype=arr.dtype)
+        tm.assert_numpy_array_equal(uniques, exp)
+
+        codes, uniques = algos.factorize(arr, sort=True)
+        exp = np.array([4, 3, 2, 1, 0], dtype=np.intp)
+        tm.assert_numpy_array_equal(codes, exp)
+        exp = np.array([0.0, 1.0, 2.0, 3.0, 4.0], dtype=arr.dtype)
+        tm.assert_numpy_array_equal(uniques, exp)
+
+    def test_mixed(self):
+        # doc example reshaping.rst
+        x = Series(["A", "A", np.nan, "B", 3.14, np.inf])
+        codes, uniques = algos.factorize(x)
+
+        exp = np.array([0, 0, -1, 1, 2, 3], dtype=np.intp)
+        tm.assert_numpy_array_equal(codes, exp)
+        exp = Index(["A", "B", 3.14, np.inf])
+        tm.assert_index_equal(uniques, exp)
+
+        codes, uniques = algos.factorize(x, sort=True)
+        exp = np.array([2, 2, -1, 3, 0, 1], dtype=np.intp)
+        tm.assert_numpy_array_equal(codes, exp)
+        exp = Index([3.14, np.inf, "A", "B"])
+        tm.assert_index_equal(uniques, exp)
+
+    def test_factorize_datetime64(self):
+        # M8
+        v1 = Timestamp("20130101 09:00:00.00004")
+        v2 = Timestamp("20130101")
+        x = Series([v1, v1, v1, v2, v2, v1])
+        codes, uniques = algos.factorize(x)
+
+        exp = np.array([0, 0, 0, 1, 1, 0], dtype=np.intp)
+        tm.assert_numpy_array_equal(codes, exp)
+        exp = DatetimeIndex([v1, v2])
+        tm.assert_index_equal(uniques, exp)
+
+        codes, uniques = algos.factorize(x, sort=True)
+        exp = np.array([1, 1, 1, 0, 0, 1], dtype=np.intp)
+        tm.assert_numpy_array_equal(codes, exp)
+        exp = DatetimeIndex([v2, v1])
+        tm.assert_index_equal(uniques, exp)
+
+    def test_factorize_period(self):
+        # period
+        v1 = Period("201302", freq="M")
+        v2 = Period("201303", freq="M")
+        x = Series([v1, v1, v1, v2, v2, v1])
+
+        # periods are not 'sorted' as they are converted back into an index
+        codes, uniques = algos.factorize(x)
+        exp = np.array([0, 0, 0, 1, 1, 0], dtype=np.intp)
+        tm.assert_numpy_array_equal(codes, exp)
+        tm.assert_index_equal(uniques, PeriodIndex([v1, v2]))
+
+        codes, uniques = algos.factorize(x, sort=True)
+        exp = np.array([0, 0, 0, 1, 1, 0], dtype=np.intp)
+        tm.assert_numpy_array_equal(codes, exp)
+        tm.assert_index_equal(uniques, PeriodIndex([v1, v2]))
+
+    def test_factorize_timedelta(self):
+        # GH 5986
+        v1 = to_timedelta("1 day 1 min")
+        v2 = to_timedelta("1 day")
+        x = Series([v1, v2, v1, v1, v2, v2, v1])
+        codes, uniques = algos.factorize(x)
+        exp = np.array([0, 1, 0, 0, 1, 1, 0], dtype=np.intp)
+        tm.assert_numpy_array_equal(codes, exp)
+        tm.assert_index_equal(uniques, to_timedelta([v1, v2]))
+
+        codes, uniques = algos.factorize(x, sort=True)
+        exp = np.array([1, 0, 1, 1, 0, 0, 1], dtype=np.intp)
+        tm.assert_numpy_array_equal(codes, exp)
+        tm.assert_index_equal(uniques, to_timedelta([v2, v1]))
+
+    def test_factorize_nan(self):
+        # nan should map to na_sentinel, not reverse_indexer[na_sentinel]
+        # rizer.factorize should not raise an exception if na_sentinel indexes
+        # outside of reverse_indexer
+        key = np.array([1, 2, 1, np.nan], dtype="O")
+        rizer = ht.ObjectFactorizer(len(key))
+        for na_sentinel in (-1, 20):
+            ids = rizer.factorize(key, na_sentinel=na_sentinel)
+            expected = np.array([0, 1, 0, na_sentinel], dtype=np.intp)
+            assert len(set(key)) == len(set(expected))
+            tm.assert_numpy_array_equal(pd.isna(key), expected == na_sentinel)
+            tm.assert_numpy_array_equal(ids, expected)
+
+    def test_factorizer_with_mask(self):
+        # GH#49549
+        data = np.array([1, 2, 3, 1, 1, 0], dtype="int64")
+        mask = np.array([False, False, False, False, False, True])
+        rizer = ht.Int64Factorizer(len(data))
+        result = rizer.factorize(data, mask=mask)
+        expected = np.array([0, 1, 2, 0, 0, -1], dtype=np.intp)
+        tm.assert_numpy_array_equal(result, expected)
+        expected_uniques = np.array([1, 2, 3], dtype="int64")
+        tm.assert_numpy_array_equal(rizer.uniques.to_array(), expected_uniques)
+
+    def test_factorizer_object_with_nan(self):
+        # GH#49549
+        data = np.array([1, 2, 3, 1, np.nan])
+        rizer = ht.ObjectFactorizer(len(data))
+        result = rizer.factorize(data.astype(object))
+        expected = np.array([0, 1, 2, 0, -1], dtype=np.intp)
+        tm.assert_numpy_array_equal(result, expected)
+        expected_uniques = np.array([1, 2, 3], dtype=object)
+        tm.assert_numpy_array_equal(rizer.uniques.to_array(), expected_uniques)
+
+    @pytest.mark.parametrize(
+        "data, expected_codes, expected_uniques",
+        [
+            (
+                [(1, 1), (1, 2), (0, 0), (1, 2), "nonsense"],
+                [0, 1, 2, 1, 3],
+                [(1, 1), (1, 2), (0, 0), "nonsense"],
+            ),
+            (
+                [(1, 1), (1, 2), (0, 0), (1, 2), (1, 2, 3)],
+                [0, 1, 2, 1, 3],
+                [(1, 1), (1, 2), (0, 0), (1, 2, 3)],
+            ),
+            ([(1, 1), (1, 2), (0, 0), (1, 2)], [0, 1, 2, 1], [(1, 1), (1, 2), (0, 0)]),
+        ],
+    )
+    def test_factorize_tuple_list(self, data, expected_codes, expected_uniques):
+        # GH9454
+        msg = "factorize with argument that is not not a Series"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            codes, uniques = pd.factorize(data)
+
+        tm.assert_numpy_array_equal(codes, np.array(expected_codes, dtype=np.intp))
+
+        expected_uniques_array = com.asarray_tuplesafe(expected_uniques, dtype=object)
+        tm.assert_numpy_array_equal(uniques, expected_uniques_array)
+
+    def test_complex_sorting(self):
+        # gh 12666 - check no segfault
+        x17 = np.array([complex(i) for i in range(17)], dtype=object)
+
+        msg = "'[<>]' not supported between instances of .*"
+        with pytest.raises(TypeError, match=msg):
+            algos.factorize(x17[::-1], sort=True)
+
+    def test_numeric_dtype_factorize(self, any_real_numpy_dtype):
+        # GH41132
+        dtype = any_real_numpy_dtype
+        data = np.array([1, 2, 2, 1], dtype=dtype)
+        expected_codes = np.array([0, 1, 1, 0], dtype=np.intp)
+        expected_uniques = np.array([1, 2], dtype=dtype)
+
+        codes, uniques = algos.factorize(data)
+        tm.assert_numpy_array_equal(codes, expected_codes)
+        tm.assert_numpy_array_equal(uniques, expected_uniques)
+
+    def test_float64_factorize(self, writable):
+        data = np.array([1.0, 1e8, 1.0, 1e-8, 1e8, 1.0], dtype=np.float64)
+        data.setflags(write=writable)
+        expected_codes = np.array([0, 1, 0, 2, 1, 0], dtype=np.intp)
+        expected_uniques = np.array([1.0, 1e8, 1e-8], dtype=np.float64)
+
+        codes, uniques = algos.factorize(data)
+        tm.assert_numpy_array_equal(codes, expected_codes)
+        tm.assert_numpy_array_equal(uniques, expected_uniques)
+
+    def test_uint64_factorize(self, writable):
+        data = np.array([2**64 - 1, 1, 2**64 - 1], dtype=np.uint64)
+        data.setflags(write=writable)
+        expected_codes = np.array([0, 1, 0], dtype=np.intp)
+        expected_uniques = np.array([2**64 - 1, 1], dtype=np.uint64)
+
+        codes, uniques = algos.factorize(data)
+        tm.assert_numpy_array_equal(codes, expected_codes)
+        tm.assert_numpy_array_equal(uniques, expected_uniques)
+
+    def test_int64_factorize(self, writable):
+        data = np.array([2**63 - 1, -(2**63), 2**63 - 1], dtype=np.int64)
+        data.setflags(write=writable)
+        expected_codes = np.array([0, 1, 0], dtype=np.intp)
+        expected_uniques = np.array([2**63 - 1, -(2**63)], dtype=np.int64)
+
+        codes, uniques = algos.factorize(data)
+        tm.assert_numpy_array_equal(codes, expected_codes)
+        tm.assert_numpy_array_equal(uniques, expected_uniques)
+
+    def test_string_factorize(self, writable):
+        data = np.array(["a", "c", "a", "b", "c"], dtype=object)
+        data.setflags(write=writable)
+        expected_codes = np.array([0, 1, 0, 2, 1], dtype=np.intp)
+        expected_uniques = np.array(["a", "c", "b"], dtype=object)
+
+        codes, uniques = algos.factorize(data)
+        tm.assert_numpy_array_equal(codes, expected_codes)
+        tm.assert_numpy_array_equal(uniques, expected_uniques)
+
+    def test_object_factorize(self, writable):
+        data = np.array(["a", "c", None, np.nan, "a", "b", NaT, "c"], dtype=object)
+        data.setflags(write=writable)
+        expected_codes = np.array([0, 1, -1, -1, 0, 2, -1, 1], dtype=np.intp)
+        expected_uniques = np.array(["a", "c", "b"], dtype=object)
+
+        codes, uniques = algos.factorize(data)
+        tm.assert_numpy_array_equal(codes, expected_codes)
+        tm.assert_numpy_array_equal(uniques, expected_uniques)
+
+    def test_datetime64_factorize(self, writable):
+        # GH35650 Verify whether read-only datetime64 array can be factorized
+        data = np.array([np.datetime64("2020-01-01T00:00:00.000")], dtype="M8[ns]")
+        data.setflags(write=writable)
+        expected_codes = np.array([0], dtype=np.intp)
+        expected_uniques = np.array(
+            ["2020-01-01T00:00:00.000000000"], dtype="datetime64[ns]"
+        )
+
+        codes, uniques = pd.factorize(data)
+        tm.assert_numpy_array_equal(codes, expected_codes)
+        tm.assert_numpy_array_equal(uniques, expected_uniques)
+
+    @pytest.mark.parametrize("sort", [True, False])
+    def test_factorize_rangeindex(self, sort):
+        # increasing -> sort doesn't matter
+        ri = pd.RangeIndex.from_range(range(10))
+        expected = np.arange(10, dtype=np.intp), ri
+
+        result = algos.factorize(ri, sort=sort)
+        tm.assert_numpy_array_equal(result[0], expected[0])
+        tm.assert_index_equal(result[1], expected[1], exact=True)
+
+        result = ri.factorize(sort=sort)
+        tm.assert_numpy_array_equal(result[0], expected[0])
+        tm.assert_index_equal(result[1], expected[1], exact=True)
+
+    @pytest.mark.parametrize("sort", [True, False])
+    def test_factorize_rangeindex_decreasing(self, sort):
+        # decreasing -> sort matters
+        ri = pd.RangeIndex.from_range(range(10))
+        expected = np.arange(10, dtype=np.intp), ri
+
+        ri2 = ri[::-1]
+        expected = expected[0], ri2
+        if sort:
+            expected = expected[0][::-1], expected[1][::-1]
+
+        result = algos.factorize(ri2, sort=sort)
+        tm.assert_numpy_array_equal(result[0], expected[0])
+        tm.assert_index_equal(result[1], expected[1], exact=True)
+
+        result = ri2.factorize(sort=sort)
+        tm.assert_numpy_array_equal(result[0], expected[0])
+        tm.assert_index_equal(result[1], expected[1], exact=True)
+
+    def test_deprecate_order(self):
+        # gh 19727 - check warning is raised for deprecated keyword, order.
+        # Test not valid once order keyword is removed.
+        data = np.array([2**63, 1, 2**63], dtype=np.uint64)
+        with pytest.raises(TypeError, match="got an unexpected keyword"):
+            algos.factorize(data, order=True)
+        with tm.assert_produces_warning(False):
+            algos.factorize(data)
+
+    @pytest.mark.parametrize(
+        "data",
+        [
+            np.array([0, 1, 0], dtype="u8"),
+            np.array([-(2**63), 1, -(2**63)], dtype="i8"),
+            np.array(["__nan__", "foo", "__nan__"], dtype="object"),
+        ],
+    )
+    def test_parametrized_factorize_na_value_default(self, data):
+        # arrays that include the NA default for that type, but isn't used.
+        codes, uniques = algos.factorize(data)
+        expected_uniques = data[[0, 1]]
+        expected_codes = np.array([0, 1, 0], dtype=np.intp)
+        tm.assert_numpy_array_equal(codes, expected_codes)
+        tm.assert_numpy_array_equal(uniques, expected_uniques)
+
+    @pytest.mark.parametrize(
+        "data, na_value",
+        [
+            (np.array([0, 1, 0, 2], dtype="u8"), 0),
+            (np.array([1, 0, 1, 2], dtype="u8"), 1),
+            (np.array([-(2**63), 1, -(2**63), 0], dtype="i8"), -(2**63)),
+            (np.array([1, -(2**63), 1, 0], dtype="i8"), 1),
+            (np.array(["a", "", "a", "b"], dtype=object), "a"),
+            (np.array([(), ("a", 1), (), ("a", 2)], dtype=object), ()),
+            (np.array([("a", 1), (), ("a", 1), ("a", 2)], dtype=object), ("a", 1)),
+        ],
+    )
+    def test_parametrized_factorize_na_value(self, data, na_value):
+        codes, uniques = algos.factorize_array(data, na_value=na_value)
+        expected_uniques = data[[1, 3]]
+        expected_codes = np.array([-1, 0, -1, 1], dtype=np.intp)
+        tm.assert_numpy_array_equal(codes, expected_codes)
+        tm.assert_numpy_array_equal(uniques, expected_uniques)
+
+    @pytest.mark.parametrize("sort", [True, False])
+    @pytest.mark.parametrize(
+        "data, uniques",
+        [
+            (
+                np.array(["b", "a", None, "b"], dtype=object),
+                np.array(["b", "a"], dtype=object),
+            ),
+            (
+                pd.array([2, 1, np.nan, 2], dtype="Int64"),
+                pd.array([2, 1], dtype="Int64"),
+            ),
+        ],
+        ids=["numpy_array", "extension_array"],
+    )
+    def test_factorize_use_na_sentinel(self, sort, data, uniques):
+        codes, uniques = algos.factorize(data, sort=sort, use_na_sentinel=True)
+        if sort:
+            expected_codes = np.array([1, 0, -1, 1], dtype=np.intp)
+            expected_uniques = algos.safe_sort(uniques)
+        else:
+            expected_codes = np.array([0, 1, -1, 0], dtype=np.intp)
+            expected_uniques = uniques
+        tm.assert_numpy_array_equal(codes, expected_codes)
+        if isinstance(data, np.ndarray):
+            tm.assert_numpy_array_equal(uniques, expected_uniques)
+        else:
+            tm.assert_extension_array_equal(uniques, expected_uniques)
+
+    @pytest.mark.parametrize(
+        "data, expected_codes, expected_uniques",
+        [
+            (
+                ["a", None, "b", "a"],
+                np.array([0, 1, 2, 0], dtype=np.dtype("intp")),
+                np.array(["a", np.nan, "b"], dtype=object),
+            ),
+            (
+                ["a", np.nan, "b", "a"],
+                np.array([0, 1, 2, 0], dtype=np.dtype("intp")),
+                np.array(["a", np.nan, "b"], dtype=object),
+            ),
+        ],
+    )
+    def test_object_factorize_use_na_sentinel_false(
+        self, data, expected_codes, expected_uniques
+    ):
+        codes, uniques = algos.factorize(
+            np.array(data, dtype=object), use_na_sentinel=False
+        )
+
+        tm.assert_numpy_array_equal(uniques, expected_uniques, strict_nan=True)
+        tm.assert_numpy_array_equal(codes, expected_codes, strict_nan=True)
+
+    @pytest.mark.parametrize(
+        "data, expected_codes, expected_uniques",
+        [
+            (
+                [1, None, 1, 2],
+                np.array([0, 1, 0, 2], dtype=np.dtype("intp")),
+                np.array([1, np.nan, 2], dtype="O"),
+            ),
+            (
+                [1, np.nan, 1, 2],
+                np.array([0, 1, 0, 2], dtype=np.dtype("intp")),
+                np.array([1, np.nan, 2], dtype=np.float64),
+            ),
+        ],
+    )
+    def test_int_factorize_use_na_sentinel_false(
+        self, data, expected_codes, expected_uniques
+    ):
+        msg = "factorize with argument that is not not a Series"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            codes, uniques = algos.factorize(data, use_na_sentinel=False)
+
+        tm.assert_numpy_array_equal(uniques, expected_uniques, strict_nan=True)
+        tm.assert_numpy_array_equal(codes, expected_codes, strict_nan=True)
+
+    @pytest.mark.parametrize(
+        "data, expected_codes, expected_uniques",
+        [
+            (
+                Index(Categorical(["a", "a", "b"])),
+                np.array([0, 0, 1], dtype=np.intp),
+                CategoricalIndex(["a", "b"], categories=["a", "b"], dtype="category"),
+            ),
+            (
+                Series(Categorical(["a", "a", "b"])),
+                np.array([0, 0, 1], dtype=np.intp),
+                CategoricalIndex(["a", "b"], categories=["a", "b"], dtype="category"),
+            ),
+            (
+                Series(DatetimeIndex(["2017", "2017"], tz="US/Eastern")),
+                np.array([0, 0], dtype=np.intp),
+                DatetimeIndex(["2017"], tz="US/Eastern"),
+            ),
+        ],
+    )
+    def test_factorize_mixed_values(self, data, expected_codes, expected_uniques):
+        # GH 19721
+        codes, uniques = algos.factorize(data)
+        tm.assert_numpy_array_equal(codes, expected_codes)
+        tm.assert_index_equal(uniques, expected_uniques)
+
+    def test_factorize_interval_non_nano(self, unit):
+        # GH#56099
+        left = DatetimeIndex(["2016-01-01", np.nan, "2015-10-11"]).as_unit(unit)
+        right = DatetimeIndex(["2016-01-02", np.nan, "2015-10-15"]).as_unit(unit)
+        idx = IntervalIndex.from_arrays(left, right)
+        codes, cats = idx.factorize()
+        assert cats.dtype == f"interval[datetime64[{unit}], right]"
+
+        ts = Timestamp(0).as_unit(unit)
+        idx2 = IntervalIndex.from_arrays(left - ts, right - ts)
+        codes2, cats2 = idx2.factorize()
+        assert cats2.dtype == f"interval[timedelta64[{unit}], right]"
+
+        idx3 = IntervalIndex.from_arrays(
+            left.tz_localize("US/Pacific"), right.tz_localize("US/Pacific")
+        )
+        codes3, cats3 = idx3.factorize()
+        assert cats3.dtype == f"interval[datetime64[{unit}, US/Pacific], right]"
+
+
+class TestUnique:
+    def test_ints(self):
+        arr = np.random.default_rng(2).integers(0, 100, size=50)
+
+        result = algos.unique(arr)
+        assert isinstance(result, np.ndarray)
+
+    def test_objects(self):
+        arr = np.random.default_rng(2).integers(0, 100, size=50).astype("O")
+
+        result = algos.unique(arr)
+        assert isinstance(result, np.ndarray)
+
+    def test_object_refcount_bug(self):
+        lst = np.array(["A", "B", "C", "D", "E"], dtype=object)
+        for i in range(1000):
+            len(algos.unique(lst))
+
+    def test_on_index_object(self):
+        mindex = MultiIndex.from_arrays(
+            [np.arange(5).repeat(5), np.tile(np.arange(5), 5)]
+        )
+        expected = mindex.values
+        expected.sort()
+
+        mindex = mindex.repeat(2)
+
+        result = pd.unique(mindex)
+        result.sort()
+
+        tm.assert_almost_equal(result, expected)
+
+    def test_dtype_preservation(self, any_numpy_dtype):
+        # GH 15442
+        if any_numpy_dtype in (tm.BYTES_DTYPES + tm.STRING_DTYPES):
+            data = [1, 2, 2]
+            uniques = [1, 2]
+        elif is_integer_dtype(any_numpy_dtype):
+            data = [1, 2, 2]
+            uniques = [1, 2]
+        elif is_float_dtype(any_numpy_dtype):
+            data = [1, 2, 2]
+            uniques = [1.0, 2.0]
+        elif is_complex_dtype(any_numpy_dtype):
+            data = [complex(1, 0), complex(2, 0), complex(2, 0)]
+            uniques = [complex(1, 0), complex(2, 0)]
+        elif is_bool_dtype(any_numpy_dtype):
+            data = [True, True, False]
+            uniques = [True, False]
+        elif is_object_dtype(any_numpy_dtype):
+            data = ["A", "B", "B"]
+            uniques = ["A", "B"]
+        else:
+            # datetime64[ns]/M8[ns]/timedelta64[ns]/m8[ns] tested elsewhere
+            data = [1, 2, 2]
+            uniques = [1, 2]
+
+        result = Series(data, dtype=any_numpy_dtype).unique()
+        expected = np.array(uniques, dtype=any_numpy_dtype)
+
+        if any_numpy_dtype in tm.STRING_DTYPES:
+            expected = expected.astype(object)
+
+        if expected.dtype.kind in ["m", "M"]:
+            # We get TimedeltaArray/DatetimeArray
+            assert isinstance(result, (DatetimeArray, TimedeltaArray))
+            result = np.array(result)
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_datetime64_dtype_array_returned(self):
+        # GH 9431
+        expected = np.array(
+            [
+                "2015-01-03T00:00:00.000000000",
+                "2015-01-01T00:00:00.000000000",
+            ],
+            dtype="M8[ns]",
+        )
+
+        dt_index = to_datetime(
+            [
+                "2015-01-03T00:00:00.000000000",
+                "2015-01-01T00:00:00.000000000",
+                "2015-01-01T00:00:00.000000000",
+            ]
+        )
+        result = algos.unique(dt_index)
+        tm.assert_numpy_array_equal(result, expected)
+        assert result.dtype == expected.dtype
+
+        s = Series(dt_index)
+        result = algos.unique(s)
+        tm.assert_numpy_array_equal(result, expected)
+        assert result.dtype == expected.dtype
+
+        arr = s.values
+        result = algos.unique(arr)
+        tm.assert_numpy_array_equal(result, expected)
+        assert result.dtype == expected.dtype
+
+    def test_datetime_non_ns(self):
+        a = np.array(["2000", "2000", "2001"], dtype="datetime64[s]")
+        result = pd.unique(a)
+        expected = np.array(["2000", "2001"], dtype="datetime64[s]")
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_timedelta_non_ns(self):
+        a = np.array(["2000", "2000", "2001"], dtype="timedelta64[s]")
+        result = pd.unique(a)
+        expected = np.array([2000, 2001], dtype="timedelta64[s]")
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_timedelta64_dtype_array_returned(self):
+        # GH 9431
+        expected = np.array([31200, 45678, 10000], dtype="m8[ns]")
+
+        td_index = to_timedelta([31200, 45678, 31200, 10000, 45678])
+        result = algos.unique(td_index)
+        tm.assert_numpy_array_equal(result, expected)
+        assert result.dtype == expected.dtype
+
+        s = Series(td_index)
+        result = algos.unique(s)
+        tm.assert_numpy_array_equal(result, expected)
+        assert result.dtype == expected.dtype
+
+        arr = s.values
+        result = algos.unique(arr)
+        tm.assert_numpy_array_equal(result, expected)
+        assert result.dtype == expected.dtype
+
+    def test_uint64_overflow(self):
+        s = Series([1, 2, 2**63, 2**63], dtype=np.uint64)
+        exp = np.array([1, 2, 2**63], dtype=np.uint64)
+        tm.assert_numpy_array_equal(algos.unique(s), exp)
+
+    def test_nan_in_object_array(self):
+        duplicated_items = ["a", np.nan, "c", "c"]
+        result = pd.unique(np.array(duplicated_items, dtype=object))
+        expected = np.array(["a", np.nan, "c"], dtype=object)
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_categorical(self):
+        # we are expecting to return in the order
+        # of appearance
+        expected = Categorical(list("bac"))
+
+        # we are expecting to return in the order
+        # of the categories
+        expected_o = Categorical(list("bac"), categories=list("abc"), ordered=True)
+
+        # GH 15939
+        c = Categorical(list("baabc"))
+        result = c.unique()
+        tm.assert_categorical_equal(result, expected)
+
+        result = algos.unique(c)
+        tm.assert_categorical_equal(result, expected)
+
+        c = Categorical(list("baabc"), ordered=True)
+        result = c.unique()
+        tm.assert_categorical_equal(result, expected_o)
+
+        result = algos.unique(c)
+        tm.assert_categorical_equal(result, expected_o)
+
+        # Series of categorical dtype
+        s = Series(Categorical(list("baabc")), name="foo")
+        result = s.unique()
+        tm.assert_categorical_equal(result, expected)
+
+        result = pd.unique(s)
+        tm.assert_categorical_equal(result, expected)
+
+        # CI -> return CI
+        ci = CategoricalIndex(Categorical(list("baabc"), categories=list("abc")))
+        expected = CategoricalIndex(expected)
+        result = ci.unique()
+        tm.assert_index_equal(result, expected)
+
+        result = pd.unique(ci)
+        tm.assert_index_equal(result, expected)
+
+    def test_datetime64tz_aware(self, unit):
+        # GH 15939
+
+        dti = Index(
+            [
+                Timestamp("20160101", tz="US/Eastern"),
+                Timestamp("20160101", tz="US/Eastern"),
+            ]
+        ).as_unit(unit)
+        ser = Series(dti)
+
+        result = ser.unique()
+        expected = dti[:1]._data
+        tm.assert_extension_array_equal(result, expected)
+
+        result = dti.unique()
+        expected = dti[:1]
+        tm.assert_index_equal(result, expected)
+
+        result = pd.unique(ser)
+        expected = dti[:1]._data
+        tm.assert_extension_array_equal(result, expected)
+
+        result = pd.unique(dti)
+        expected = dti[:1]
+        tm.assert_index_equal(result, expected)
+
+    def test_order_of_appearance(self):
+        # 9346
+        # light testing of guarantee of order of appearance
+        # these also are the doc-examples
+        result = pd.unique(Series([2, 1, 3, 3]))
+        tm.assert_numpy_array_equal(result, np.array([2, 1, 3], dtype="int64"))
+
+        result = pd.unique(Series([2] + [1] * 5))
+        tm.assert_numpy_array_equal(result, np.array([2, 1], dtype="int64"))
+
+        msg = "unique with argument that is not not a Series, Index,"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = pd.unique(list("aabc"))
+        expected = np.array(["a", "b", "c"], dtype=object)
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = pd.unique(Series(Categorical(list("aabc"))))
+        expected = Categorical(list("abc"))
+        tm.assert_categorical_equal(result, expected)
+
+    def test_order_of_appearance_dt64(self, unit):
+        ser = Series([Timestamp("20160101"), Timestamp("20160101")]).dt.as_unit(unit)
+        result = pd.unique(ser)
+        expected = np.array(["2016-01-01T00:00:00.000000000"], dtype=f"M8[{unit}]")
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_order_of_appearance_dt64tz(self, unit):
+        dti = DatetimeIndex(
+            [
+                Timestamp("20160101", tz="US/Eastern"),
+                Timestamp("20160101", tz="US/Eastern"),
+            ]
+        ).as_unit(unit)
+        result = pd.unique(dti)
+        expected = DatetimeIndex(
+            ["2016-01-01 00:00:00"], dtype=f"datetime64[{unit}, US/Eastern]", freq=None
+        )
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "arg ,expected",
+        [
+            (("1", "1", "2"), np.array(["1", "2"], dtype=object)),
+            (("foo",), np.array(["foo"], dtype=object)),
+        ],
+    )
+    def test_tuple_with_strings(self, arg, expected):
+        # see GH 17108
+        msg = "unique with argument that is not not a Series"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = pd.unique(arg)
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_obj_none_preservation(self):
+        # GH 20866
+        arr = np.array(["foo", None], dtype=object)
+        result = pd.unique(arr)
+        expected = np.array(["foo", None], dtype=object)
+
+        tm.assert_numpy_array_equal(result, expected, strict_nan=True)
+
+    def test_signed_zero(self):
+        # GH 21866
+        a = np.array([-0.0, 0.0])
+        result = pd.unique(a)
+        expected = np.array([-0.0])  # 0.0 and -0.0 are equivalent
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_different_nans(self):
+        # GH 21866
+        # create different nans from bit-patterns:
+        NAN1 = struct.unpack("d", struct.pack("=Q", 0x7FF8000000000000))[0]
+        NAN2 = struct.unpack("d", struct.pack("=Q", 0x7FF8000000000001))[0]
+        assert NAN1 != NAN1
+        assert NAN2 != NAN2
+        a = np.array([NAN1, NAN2])  # NAN1 and NAN2 are equivalent
+        result = pd.unique(a)
+        expected = np.array([np.nan])
+        tm.assert_numpy_array_equal(result, expected)
+
+    @pytest.mark.parametrize("el_type", [np.float64, object])
+    def test_first_nan_kept(self, el_type):
+        # GH 22295
+        # create different nans from bit-patterns:
+        bits_for_nan1 = 0xFFF8000000000001
+        bits_for_nan2 = 0x7FF8000000000001
+        NAN1 = struct.unpack("d", struct.pack("=Q", bits_for_nan1))[0]
+        NAN2 = struct.unpack("d", struct.pack("=Q", bits_for_nan2))[0]
+        assert NAN1 != NAN1
+        assert NAN2 != NAN2
+        a = np.array([NAN1, NAN2], dtype=el_type)
+        result = pd.unique(a)
+        assert result.size == 1
+        # use bit patterns to identify which nan was kept:
+        result_nan_bits = struct.unpack("=Q", struct.pack("d", result[0]))[0]
+        assert result_nan_bits == bits_for_nan1
+
+    def test_do_not_mangle_na_values(self, unique_nulls_fixture, unique_nulls_fixture2):
+        # GH 22295
+        if unique_nulls_fixture is unique_nulls_fixture2:
+            return  # skip it, values not unique
+        a = np.array([unique_nulls_fixture, unique_nulls_fixture2], dtype=object)
+        result = pd.unique(a)
+        assert result.size == 2
+        assert a[0] is unique_nulls_fixture
+        assert a[1] is unique_nulls_fixture2
+
+    def test_unique_masked(self, any_numeric_ea_dtype):
+        # GH#48019
+        ser = Series([1, pd.NA, 2] * 3, dtype=any_numeric_ea_dtype)
+        result = pd.unique(ser)
+        expected = pd.array([1, pd.NA, 2], dtype=any_numeric_ea_dtype)
+        tm.assert_extension_array_equal(result, expected)
+
+
+def test_nunique_ints(index_or_series_or_array):
+    # GH#36327
+    values = index_or_series_or_array(np.random.default_rng(2).integers(0, 20, 30))
+    result = algos.nunique_ints(values)
+    expected = len(algos.unique(values))
+    assert result == expected
+
+
+class TestIsin:
+    def test_invalid(self):
+        msg = (
+            r"only list-like objects are allowed to be passed to isin\(\), "
+            r"you passed a `int`"
+        )
+        with pytest.raises(TypeError, match=msg):
+            algos.isin(1, 1)
+        with pytest.raises(TypeError, match=msg):
+            algos.isin(1, [1])
+        with pytest.raises(TypeError, match=msg):
+            algos.isin([1], 1)
+
+    def test_basic(self):
+        msg = "isin with argument that is not not a Series"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = algos.isin([1, 2], [1])
+        expected = np.array([True, False])
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = algos.isin(np.array([1, 2]), [1])
+        expected = np.array([True, False])
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = algos.isin(Series([1, 2]), [1])
+        expected = np.array([True, False])
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = algos.isin(Series([1, 2]), Series([1]))
+        expected = np.array([True, False])
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = algos.isin(Series([1, 2]), {1})
+        expected = np.array([True, False])
+        tm.assert_numpy_array_equal(result, expected)
+
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = algos.isin(["a", "b"], ["a"])
+        expected = np.array([True, False])
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = algos.isin(Series(["a", "b"]), Series(["a"]))
+        expected = np.array([True, False])
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = algos.isin(Series(["a", "b"]), {"a"})
+        expected = np.array([True, False])
+        tm.assert_numpy_array_equal(result, expected)
+
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = algos.isin(["a", "b"], [1])
+        expected = np.array([False, False])
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_i8(self):
+        arr = date_range("20130101", periods=3).values
+        result = algos.isin(arr, [arr[0]])
+        expected = np.array([True, False, False])
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = algos.isin(arr, arr[0:2])
+        expected = np.array([True, True, False])
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = algos.isin(arr, set(arr[0:2]))
+        expected = np.array([True, True, False])
+        tm.assert_numpy_array_equal(result, expected)
+
+        arr = timedelta_range("1 day", periods=3).values
+        result = algos.isin(arr, [arr[0]])
+        expected = np.array([True, False, False])
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = algos.isin(arr, arr[0:2])
+        expected = np.array([True, True, False])
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = algos.isin(arr, set(arr[0:2]))
+        expected = np.array([True, True, False])
+        tm.assert_numpy_array_equal(result, expected)
+
+    @pytest.mark.parametrize("dtype1", ["m8[ns]", "M8[ns]", "M8[ns, UTC]", "period[D]"])
+    @pytest.mark.parametrize("dtype", ["i8", "f8", "u8"])
+    def test_isin_datetimelike_values_numeric_comps(self, dtype, dtype1):
+        # Anything but object and we get all-False shortcut
+
+        dta = date_range("2013-01-01", periods=3)._values
+        arr = Series(dta.view("i8")).array.view(dtype1)
+
+        comps = arr.view("i8").astype(dtype)
+
+        result = algos.isin(comps, arr)
+        expected = np.zeros(comps.shape, dtype=bool)
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_large(self):
+        s = date_range("20000101", periods=2000000, freq="s").values
+        result = algos.isin(s, s[0:2])
+        expected = np.zeros(len(s), dtype=bool)
+        expected[0] = True
+        expected[1] = True
+        tm.assert_numpy_array_equal(result, expected)
+
+    @pytest.mark.parametrize("dtype", ["m8[ns]", "M8[ns]", "M8[ns, UTC]", "period[D]"])
+    def test_isin_datetimelike_all_nat(self, dtype):
+        # GH#56427
+        dta = date_range("2013-01-01", periods=3)._values
+        arr = Series(dta.view("i8")).array.view(dtype)
+
+        arr[0] = NaT
+        result = algos.isin(arr, [NaT])
+        expected = np.array([True, False, False], dtype=bool)
+        tm.assert_numpy_array_equal(result, expected)
+
+    @pytest.mark.parametrize("dtype", ["m8[ns]", "M8[ns]", "M8[ns, UTC]"])
+    def test_isin_datetimelike_strings_deprecated(self, dtype):
+        # GH#53111
+        dta = date_range("2013-01-01", periods=3)._values
+        arr = Series(dta.view("i8")).array.view(dtype)
+
+        vals = [str(x) for x in arr]
+        msg = "The behavior of 'isin' with dtype=.* is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            res = algos.isin(arr, vals)
+        assert res.all()
+
+        vals2 = np.array(vals, dtype=str)
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            res2 = algos.isin(arr, vals2)
+        assert res2.all()
+
+    def test_isin_dt64tz_with_nat(self):
+        # the all-NaT values used to get inferred to tznaive, which was evaluated
+        #  as non-matching GH#56427
+        dti = date_range("2016-01-01", periods=3, tz="UTC")
+        ser = Series(dti)
+        ser[0] = NaT
+
+        res = algos.isin(ser._values, [NaT])
+        exp = np.array([True, False, False], dtype=bool)
+        tm.assert_numpy_array_equal(res, exp)
+
+    def test_categorical_from_codes(self):
+        # GH 16639
+        vals = np.array([0, 1, 2, 0])
+        cats = ["a", "b", "c"]
+        Sd = Series(Categorical([1]).from_codes(vals, cats))
+        St = Series(Categorical([1]).from_codes(np.array([0, 1]), cats))
+        expected = np.array([True, True, False, True])
+        result = algos.isin(Sd, St)
+        tm.assert_numpy_array_equal(expected, result)
+
+    def test_categorical_isin(self):
+        vals = np.array([0, 1, 2, 0])
+        cats = ["a", "b", "c"]
+        cat = Categorical([1]).from_codes(vals, cats)
+        other = Categorical([1]).from_codes(np.array([0, 1]), cats)
+
+        expected = np.array([True, True, False, True])
+        result = algos.isin(cat, other)
+        tm.assert_numpy_array_equal(expected, result)
+
+    def test_same_nan_is_in(self):
+        # GH 22160
+        # nan is special, because from " a is b" doesn't follow "a == b"
+        # at least, isin() should follow python's "np.nan in [nan] == True"
+        # casting to -> np.float64 -> another float-object somewhere on
+        # the way could lead jeopardize this behavior
+        comps = [np.nan]  # could be casted to float64
+        values = [np.nan]
+        expected = np.array([True])
+        msg = "isin with argument that is not not a Series"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = algos.isin(comps, values)
+        tm.assert_numpy_array_equal(expected, result)
+
+    def test_same_nan_is_in_large(self):
+        # https://github.com/pandas-dev/pandas/issues/22205
+        s = np.tile(1.0, 1_000_001)
+        s[0] = np.nan
+        result = algos.isin(s, np.array([np.nan, 1]))
+        expected = np.ones(len(s), dtype=bool)
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_same_nan_is_in_large_series(self):
+        # https://github.com/pandas-dev/pandas/issues/22205
+        s = np.tile(1.0, 1_000_001)
+        series = Series(s)
+        s[0] = np.nan
+        result = series.isin(np.array([np.nan, 1]))
+        expected = Series(np.ones(len(s), dtype=bool))
+        tm.assert_series_equal(result, expected)
+
+    def test_same_object_is_in(self):
+        # GH 22160
+        # there could be special treatment for nans
+        # the user however could define a custom class
+        # with similar behavior, then we at least should
+        # fall back to usual python's behavior: "a in [a] == True"
+        class LikeNan:
+            def __eq__(self, other) -> bool:
+                return False
+
+            def __hash__(self):
+                return 0
+
+        a, b = LikeNan(), LikeNan()
+
+        msg = "isin with argument that is not not a Series"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            # same object -> True
+            tm.assert_numpy_array_equal(algos.isin([a], [a]), np.array([True]))
+            # different objects -> False
+            tm.assert_numpy_array_equal(algos.isin([a], [b]), np.array([False]))
+
+    def test_different_nans(self):
+        # GH 22160
+        # all nans are handled as equivalent
+
+        comps = [float("nan")]
+        values = [float("nan")]
+        assert comps[0] is not values[0]  # different nan-objects
+
+        # as list of python-objects:
+        result = algos.isin(np.array(comps), values)
+        tm.assert_numpy_array_equal(np.array([True]), result)
+
+        # as object-array:
+        result = algos.isin(
+            np.asarray(comps, dtype=object), np.asarray(values, dtype=object)
+        )
+        tm.assert_numpy_array_equal(np.array([True]), result)
+
+        # as float64-array:
+        result = algos.isin(
+            np.asarray(comps, dtype=np.float64), np.asarray(values, dtype=np.float64)
+        )
+        tm.assert_numpy_array_equal(np.array([True]), result)
+
+    def test_no_cast(self):
+        # GH 22160
+        # ensure 42 is not casted to a string
+        comps = ["ss", 42]
+        values = ["42"]
+        expected = np.array([False, False])
+        msg = "isin with argument that is not not a Series, Index"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = algos.isin(comps, values)
+        tm.assert_numpy_array_equal(expected, result)
+
+    @pytest.mark.parametrize("empty", [[], Series(dtype=object), np.array([])])
+    def test_empty(self, empty):
+        # see gh-16991
+        vals = Index(["a", "b"])
+        expected = np.array([False, False])
+
+        result = algos.isin(vals, empty)
+        tm.assert_numpy_array_equal(expected, result)
+
+    def test_different_nan_objects(self):
+        # GH 22119
+        comps = np.array(["nan", np.nan * 1j, float("nan")], dtype=object)
+        vals = np.array([float("nan")], dtype=object)
+        expected = np.array([False, False, True])
+        result = algos.isin(comps, vals)
+        tm.assert_numpy_array_equal(expected, result)
+
+    def test_different_nans_as_float64(self):
+        # GH 21866
+        # create different nans from bit-patterns,
+        # these nans will land in different buckets in the hash-table
+        # if no special care is taken
+        NAN1 = struct.unpack("d", struct.pack("=Q", 0x7FF8000000000000))[0]
+        NAN2 = struct.unpack("d", struct.pack("=Q", 0x7FF8000000000001))[0]
+        assert NAN1 != NAN1
+        assert NAN2 != NAN2
+
+        # check that NAN1 and NAN2 are equivalent:
+        arr = np.array([NAN1, NAN2], dtype=np.float64)
+        lookup1 = np.array([NAN1], dtype=np.float64)
+        result = algos.isin(arr, lookup1)
+        expected = np.array([True, True])
+        tm.assert_numpy_array_equal(result, expected)
+
+        lookup2 = np.array([NAN2], dtype=np.float64)
+        result = algos.isin(arr, lookup2)
+        expected = np.array([True, True])
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_isin_int_df_string_search(self):
+        """Comparing df with int`s (1,2) with a string at isin() ("1")
+        -> should not match values because int 1 is not equal str 1"""
+        df = DataFrame({"values": [1, 2]})
+        result = df.isin(["1"])
+        expected_false = DataFrame({"values": [False, False]})
+        tm.assert_frame_equal(result, expected_false)
+
+    def test_isin_nan_df_string_search(self):
+        """Comparing df with nan value (np.nan,2) with a string at isin() ("NaN")
+        -> should not match values because np.nan is not equal str NaN"""
+        df = DataFrame({"values": [np.nan, 2]})
+        result = df.isin(np.array(["NaN"], dtype=object))
+        expected_false = DataFrame({"values": [False, False]})
+        tm.assert_frame_equal(result, expected_false)
+
+    def test_isin_float_df_string_search(self):
+        """Comparing df with floats (1.4245,2.32441) with a string at isin() ("1.4245")
+        -> should not match values because float 1.4245 is not equal str 1.4245"""
+        df = DataFrame({"values": [1.4245, 2.32441]})
+        result = df.isin(np.array(["1.4245"], dtype=object))
+        expected_false = DataFrame({"values": [False, False]})
+        tm.assert_frame_equal(result, expected_false)
+
+    def test_isin_unsigned_dtype(self):
+        # GH#46485
+        ser = Series([1378774140726870442], dtype=np.uint64)
+        result = ser.isin([1378774140726870528])
+        expected = Series(False)
+        tm.assert_series_equal(result, expected)
+
+
+class TestValueCounts:
+    def test_value_counts(self):
+        arr = np.random.default_rng(1234).standard_normal(4)
+        factor = cut(arr, 4)
+
+        # assert isinstance(factor, n)
+        msg = "pandas.value_counts is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = algos.value_counts(factor)
+        breaks = [-1.606, -1.018, -0.431, 0.155, 0.741]
+        index = IntervalIndex.from_breaks(breaks).astype(CategoricalDtype(ordered=True))
+        expected = Series([1, 0, 2, 1], index=index, name="count")
+        tm.assert_series_equal(result.sort_index(), expected.sort_index())
+
+    def test_value_counts_bins(self):
+        s = [1, 2, 3, 4]
+        msg = "pandas.value_counts is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = algos.value_counts(s, bins=1)
+        expected = Series(
+            [4], index=IntervalIndex.from_tuples([(0.996, 4.0)]), name="count"
+        )
+        tm.assert_series_equal(result, expected)
+
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = algos.value_counts(s, bins=2, sort=False)
+        expected = Series(
+            [2, 2],
+            index=IntervalIndex.from_tuples([(0.996, 2.5), (2.5, 4.0)]),
+            name="count",
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_value_counts_dtypes(self):
+        msg2 = "pandas.value_counts is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg2):
+            result = algos.value_counts(np.array([1, 1.0]))
+        assert len(result) == 1
+
+        with tm.assert_produces_warning(FutureWarning, match=msg2):
+            result = algos.value_counts(np.array([1, 1.0]), bins=1)
+        assert len(result) == 1
+
+        with tm.assert_produces_warning(FutureWarning, match=msg2):
+            result = algos.value_counts(Series([1, 1.0, "1"]))  # object
+        assert len(result) == 2
+
+        msg = "bins argument only works with numeric data"
+        with pytest.raises(TypeError, match=msg):
+            with tm.assert_produces_warning(FutureWarning, match=msg2):
+                algos.value_counts(np.array(["1", 1], dtype=object), bins=1)
+
+    def test_value_counts_nat(self):
+        td = Series([np.timedelta64(10000), NaT], dtype="timedelta64[ns]")
+        dt = to_datetime(["NaT", "2014-01-01"])
+
+        msg = "pandas.value_counts is deprecated"
+
+        for ser in [td, dt]:
+            with tm.assert_produces_warning(FutureWarning, match=msg):
+                vc = algos.value_counts(ser)
+                vc_with_na = algos.value_counts(ser, dropna=False)
+            assert len(vc) == 1
+            assert len(vc_with_na) == 2
+
+        exp_dt = Series({Timestamp("2014-01-01 00:00:00"): 1}, name="count")
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result_dt = algos.value_counts(dt)
+        tm.assert_series_equal(result_dt, exp_dt)
+
+        exp_td = Series([1], index=[np.timedelta64(10000)], name="count")
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result_td = algos.value_counts(td)
+        tm.assert_series_equal(result_td, exp_td)
+
+    @pytest.mark.parametrize("dtype", [object, "M8[us]"])
+    def test_value_counts_datetime_outofbounds(self, dtype):
+        # GH 13663
+        ser = Series(
+            [
+                datetime(3000, 1, 1),
+                datetime(5000, 1, 1),
+                datetime(5000, 1, 1),
+                datetime(6000, 1, 1),
+                datetime(3000, 1, 1),
+                datetime(3000, 1, 1),
+            ],
+            dtype=dtype,
+        )
+        res = ser.value_counts()
+
+        exp_index = Index(
+            [datetime(3000, 1, 1), datetime(5000, 1, 1), datetime(6000, 1, 1)],
+            dtype=dtype,
+        )
+        exp = Series([3, 2, 1], index=exp_index, name="count")
+        tm.assert_series_equal(res, exp)
+
+    def test_categorical(self):
+        s = Series(Categorical(list("aaabbc")))
+        result = s.value_counts()
+        expected = Series(
+            [3, 2, 1], index=CategoricalIndex(["a", "b", "c"]), name="count"
+        )
+
+        tm.assert_series_equal(result, expected, check_index_type=True)
+
+        # preserve order?
+        s = s.cat.as_ordered()
+        result = s.value_counts()
+        expected.index = expected.index.as_ordered()
+        tm.assert_series_equal(result, expected, check_index_type=True)
+
+    def test_categorical_nans(self):
+        s = Series(Categorical(list("aaaaabbbcc")))  # 4,3,2,1 (nan)
+        s.iloc[1] = np.nan
+        result = s.value_counts()
+        expected = Series(
+            [4, 3, 2],
+            index=CategoricalIndex(["a", "b", "c"], categories=["a", "b", "c"]),
+            name="count",
+        )
+        tm.assert_series_equal(result, expected, check_index_type=True)
+        result = s.value_counts(dropna=False)
+        expected = Series(
+            [4, 3, 2, 1], index=CategoricalIndex(["a", "b", "c", np.nan]), name="count"
+        )
+        tm.assert_series_equal(result, expected, check_index_type=True)
+
+        # out of order
+        s = Series(
+            Categorical(list("aaaaabbbcc"), ordered=True, categories=["b", "a", "c"])
+        )
+        s.iloc[1] = np.nan
+        result = s.value_counts()
+        expected = Series(
+            [4, 3, 2],
+            index=CategoricalIndex(
+                ["a", "b", "c"],
+                categories=["b", "a", "c"],
+                ordered=True,
+            ),
+            name="count",
+        )
+        tm.assert_series_equal(result, expected, check_index_type=True)
+
+        result = s.value_counts(dropna=False)
+        expected = Series(
+            [4, 3, 2, 1],
+            index=CategoricalIndex(
+                ["a", "b", "c", np.nan], categories=["b", "a", "c"], ordered=True
+            ),
+            name="count",
+        )
+        tm.assert_series_equal(result, expected, check_index_type=True)
+
+    def test_categorical_zeroes(self):
+        # keep the `d` category with 0
+        s = Series(Categorical(list("bbbaac"), categories=list("abcd"), ordered=True))
+        result = s.value_counts()
+        expected = Series(
+            [3, 2, 1, 0],
+            index=Categorical(
+                ["b", "a", "c", "d"], categories=list("abcd"), ordered=True
+            ),
+            name="count",
+        )
+        tm.assert_series_equal(result, expected, check_index_type=True)
+
+    def test_value_counts_dropna(self):
+        # https://github.com/pandas-dev/pandas/issues/9443#issuecomment-73719328
+
+        tm.assert_series_equal(
+            Series([True, True, False]).value_counts(dropna=True),
+            Series([2, 1], index=[True, False], name="count"),
+        )
+        tm.assert_series_equal(
+            Series([True, True, False]).value_counts(dropna=False),
+            Series([2, 1], index=[True, False], name="count"),
+        )
+
+        tm.assert_series_equal(
+            Series([True] * 3 + [False] * 2 + [None] * 5).value_counts(dropna=True),
+            Series([3, 2], index=Index([True, False], dtype=object), name="count"),
+        )
+        tm.assert_series_equal(
+            Series([True] * 5 + [False] * 3 + [None] * 2).value_counts(dropna=False),
+            Series([5, 3, 2], index=[True, False, None], name="count"),
+        )
+        tm.assert_series_equal(
+            Series([10.3, 5.0, 5.0]).value_counts(dropna=True),
+            Series([2, 1], index=[5.0, 10.3], name="count"),
+        )
+        tm.assert_series_equal(
+            Series([10.3, 5.0, 5.0]).value_counts(dropna=False),
+            Series([2, 1], index=[5.0, 10.3], name="count"),
+        )
+
+        tm.assert_series_equal(
+            Series([10.3, 5.0, 5.0, None]).value_counts(dropna=True),
+            Series([2, 1], index=[5.0, 10.3], name="count"),
+        )
+
+        result = Series([10.3, 10.3, 5.0, 5.0, 5.0, None]).value_counts(dropna=False)
+        expected = Series([3, 2, 1], index=[5.0, 10.3, None], name="count")
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("dtype", (np.float64, object, "M8[ns]"))
+    def test_value_counts_normalized(self, dtype):
+        # GH12558
+        s = Series([1] * 2 + [2] * 3 + [np.nan] * 5)
+        s_typed = s.astype(dtype)
+        result = s_typed.value_counts(normalize=True, dropna=False)
+        expected = Series(
+            [0.5, 0.3, 0.2],
+            index=Series([np.nan, 2.0, 1.0], dtype=dtype),
+            name="proportion",
+        )
+        tm.assert_series_equal(result, expected)
+
+        result = s_typed.value_counts(normalize=True, dropna=True)
+        expected = Series(
+            [0.6, 0.4], index=Series([2.0, 1.0], dtype=dtype), name="proportion"
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_value_counts_uint64(self):
+        arr = np.array([2**63], dtype=np.uint64)
+        expected = Series([1], index=[2**63], name="count")
+        msg = "pandas.value_counts is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = algos.value_counts(arr)
+
+        tm.assert_series_equal(result, expected)
+
+        arr = np.array([-1, 2**63], dtype=object)
+        expected = Series([1, 1], index=[-1, 2**63], name="count")
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = algos.value_counts(arr)
+
+        tm.assert_series_equal(result, expected)
+
+    def test_value_counts_series(self):
+        # GH#54857
+        values = np.array([3, 1, 2, 3, 4, np.nan])
+        result = Series(values).value_counts(bins=3)
+        expected = Series(
+            [2, 2, 1],
+            index=IntervalIndex.from_tuples(
+                [(0.996, 2.0), (2.0, 3.0), (3.0, 4.0)], dtype="interval[float64, right]"
+            ),
+            name="count",
+        )
+        tm.assert_series_equal(result, expected)
+
+
+class TestDuplicated:
+    def test_duplicated_with_nas(self):
+        keys = np.array([0, 1, np.nan, 0, 2, np.nan], dtype=object)
+
+        result = algos.duplicated(keys)
+        expected = np.array([False, False, False, True, False, True])
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = algos.duplicated(keys, keep="first")
+        expected = np.array([False, False, False, True, False, True])
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = algos.duplicated(keys, keep="last")
+        expected = np.array([True, False, True, False, False, False])
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = algos.duplicated(keys, keep=False)
+        expected = np.array([True, False, True, True, False, True])
+        tm.assert_numpy_array_equal(result, expected)
+
+        keys = np.empty(8, dtype=object)
+        for i, t in enumerate(
+            zip([0, 0, np.nan, np.nan] * 2, [0, np.nan, 0, np.nan] * 2)
+        ):
+            keys[i] = t
+
+        result = algos.duplicated(keys)
+        falses = [False] * 4
+        trues = [True] * 4
+        expected = np.array(falses + trues)
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = algos.duplicated(keys, keep="last")
+        expected = np.array(trues + falses)
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = algos.duplicated(keys, keep=False)
+        expected = np.array(trues + trues)
+        tm.assert_numpy_array_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "case",
+        [
+            np.array([1, 2, 1, 5, 3, 2, 4, 1, 5, 6]),
+            np.array([1.1, 2.2, 1.1, np.nan, 3.3, 2.2, 4.4, 1.1, np.nan, 6.6]),
+            np.array(
+                [
+                    1 + 1j,
+                    2 + 2j,
+                    1 + 1j,
+                    5 + 5j,
+                    3 + 3j,
+                    2 + 2j,
+                    4 + 4j,
+                    1 + 1j,
+                    5 + 5j,
+                    6 + 6j,
+                ]
+            ),
+            np.array(["a", "b", "a", "e", "c", "b", "d", "a", "e", "f"], dtype=object),
+            np.array(
+                [1, 2**63, 1, 3**5, 10, 2**63, 39, 1, 3**5, 7], dtype=np.uint64
+            ),
+        ],
+    )
+    def test_numeric_object_likes(self, case):
+        exp_first = np.array(
+            [False, False, True, False, False, True, False, True, True, False]
+        )
+        exp_last = np.array(
+            [True, True, True, True, False, False, False, False, False, False]
+        )
+        exp_false = exp_first | exp_last
+
+        res_first = algos.duplicated(case, keep="first")
+        tm.assert_numpy_array_equal(res_first, exp_first)
+
+        res_last = algos.duplicated(case, keep="last")
+        tm.assert_numpy_array_equal(res_last, exp_last)
+
+        res_false = algos.duplicated(case, keep=False)
+        tm.assert_numpy_array_equal(res_false, exp_false)
+
+        # index
+        for idx in [Index(case), Index(case, dtype="category")]:
+            res_first = idx.duplicated(keep="first")
+            tm.assert_numpy_array_equal(res_first, exp_first)
+
+            res_last = idx.duplicated(keep="last")
+            tm.assert_numpy_array_equal(res_last, exp_last)
+
+            res_false = idx.duplicated(keep=False)
+            tm.assert_numpy_array_equal(res_false, exp_false)
+
+        # series
+        for s in [Series(case), Series(case, dtype="category")]:
+            res_first = s.duplicated(keep="first")
+            tm.assert_series_equal(res_first, Series(exp_first))
+
+            res_last = s.duplicated(keep="last")
+            tm.assert_series_equal(res_last, Series(exp_last))
+
+            res_false = s.duplicated(keep=False)
+            tm.assert_series_equal(res_false, Series(exp_false))
+
+    def test_datetime_likes(self):
+        dt = [
+            "2011-01-01",
+            "2011-01-02",
+            "2011-01-01",
+            "NaT",
+            "2011-01-03",
+            "2011-01-02",
+            "2011-01-04",
+            "2011-01-01",
+            "NaT",
+            "2011-01-06",
+        ]
+        td = [
+            "1 days",
+            "2 days",
+            "1 days",
+            "NaT",
+            "3 days",
+            "2 days",
+            "4 days",
+            "1 days",
+            "NaT",
+            "6 days",
+        ]
+
+        cases = [
+            np.array([Timestamp(d) for d in dt]),
+            np.array([Timestamp(d, tz="US/Eastern") for d in dt]),
+            np.array([Period(d, freq="D") for d in dt]),
+            np.array([np.datetime64(d) for d in dt]),
+            np.array([Timedelta(d) for d in td]),
+        ]
+
+        exp_first = np.array(
+            [False, False, True, False, False, True, False, True, True, False]
+        )
+        exp_last = np.array(
+            [True, True, True, True, False, False, False, False, False, False]
+        )
+        exp_false = exp_first | exp_last
+
+        for case in cases:
+            res_first = algos.duplicated(case, keep="first")
+            tm.assert_numpy_array_equal(res_first, exp_first)
+
+            res_last = algos.duplicated(case, keep="last")
+            tm.assert_numpy_array_equal(res_last, exp_last)
+
+            res_false = algos.duplicated(case, keep=False)
+            tm.assert_numpy_array_equal(res_false, exp_false)
+
+            # index
+            for idx in [
+                Index(case),
+                Index(case, dtype="category"),
+                Index(case, dtype=object),
+            ]:
+                res_first = idx.duplicated(keep="first")
+                tm.assert_numpy_array_equal(res_first, exp_first)
+
+                res_last = idx.duplicated(keep="last")
+                tm.assert_numpy_array_equal(res_last, exp_last)
+
+                res_false = idx.duplicated(keep=False)
+                tm.assert_numpy_array_equal(res_false, exp_false)
+
+            # series
+            for s in [
+                Series(case),
+                Series(case, dtype="category"),
+                Series(case, dtype=object),
+            ]:
+                res_first = s.duplicated(keep="first")
+                tm.assert_series_equal(res_first, Series(exp_first))
+
+                res_last = s.duplicated(keep="last")
+                tm.assert_series_equal(res_last, Series(exp_last))
+
+                res_false = s.duplicated(keep=False)
+                tm.assert_series_equal(res_false, Series(exp_false))
+
+    @pytest.mark.parametrize("case", [Index([1, 2, 3]), pd.RangeIndex(0, 3)])
+    def test_unique_index(self, case):
+        assert case.is_unique is True
+        tm.assert_numpy_array_equal(case.duplicated(), np.array([False, False, False]))
+
+    @pytest.mark.parametrize(
+        "arr, uniques",
+        [
+            (
+                [(0, 0), (0, 1), (1, 0), (1, 1), (0, 0), (0, 1), (1, 0), (1, 1)],
+                [(0, 0), (0, 1), (1, 0), (1, 1)],
+            ),
+            (
+                [("b", "c"), ("a", "b"), ("a", "b"), ("b", "c")],
+                [("b", "c"), ("a", "b")],
+            ),
+            ([("a", 1), ("b", 2), ("a", 3), ("a", 1)], [("a", 1), ("b", 2), ("a", 3)]),
+        ],
+    )
+    def test_unique_tuples(self, arr, uniques):
+        # https://github.com/pandas-dev/pandas/issues/16519
+        expected = np.empty(len(uniques), dtype=object)
+        expected[:] = uniques
+
+        msg = "unique with argument that is not not a Series"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = pd.unique(arr)
+        tm.assert_numpy_array_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "array,expected",
+        [
+            (
+                [1 + 1j, 0, 1, 1j, 1 + 2j, 1 + 2j],
+                # Should return a complex dtype in the future
+                np.array([(1 + 1j), 0j, (1 + 0j), 1j, (1 + 2j)], dtype=object),
+            )
+        ],
+    )
+    def test_unique_complex_numbers(self, array, expected):
+        # GH 17927
+        msg = "unique with argument that is not not a Series"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = pd.unique(array)
+        tm.assert_numpy_array_equal(result, expected)
+
+
+class TestHashTable:
+    @pytest.mark.parametrize(
+        "htable, data",
+        [
+            (
+                ht.PyObjectHashTable,
+                np.array([f"foo_{i}" for i in range(1000)], dtype=object),
+            ),
+            (
+                ht.StringHashTable,
+                np.array([f"foo_{i}" for i in range(1000)], dtype=object),
+            ),
+            (ht.Float64HashTable, np.arange(1000, dtype=np.float64)),
+            (ht.Int64HashTable, np.arange(1000, dtype=np.int64)),
+            (ht.UInt64HashTable, np.arange(1000, dtype=np.uint64)),
+        ],
+    )
+    def test_hashtable_unique(self, htable, data, writable):
+        # output of maker has guaranteed unique elements
+        s = Series(data, dtype=data.dtype)
+        if htable == ht.Float64HashTable:
+            # add NaN for float column
+            s.loc[500] = np.nan
+        elif htable == ht.PyObjectHashTable:
+            # use different NaN types for object column
+            s.loc[500:502] = [np.nan, None, NaT]
+
+        # create duplicated selection
+        s_duplicated = s.sample(frac=3, replace=True).reset_index(drop=True)
+        s_duplicated.values.setflags(write=writable)
+
+        # drop_duplicates has own cython code (hash_table_func_helper.pxi)
+        # and is tested separately; keeps first occurrence like ht.unique()
+        expected_unique = s_duplicated.drop_duplicates(keep="first").values
+        result_unique = htable().unique(s_duplicated.values)
+        tm.assert_numpy_array_equal(result_unique, expected_unique)
+
+        # test return_inverse=True
+        # reconstruction can only succeed if the inverse is correct
+        result_unique, result_inverse = htable().unique(
+            s_duplicated.values, return_inverse=True
+        )
+        tm.assert_numpy_array_equal(result_unique, expected_unique)
+        reconstr = result_unique[result_inverse]
+        tm.assert_numpy_array_equal(reconstr, s_duplicated.values)
+
+    @pytest.mark.parametrize(
+        "htable, data",
+        [
+            (
+                ht.PyObjectHashTable,
+                np.array([f"foo_{i}" for i in range(1000)], dtype=object),
+            ),
+            (
+                ht.StringHashTable,
+                np.array([f"foo_{i}" for i in range(1000)], dtype=object),
+            ),
+            (ht.Float64HashTable, np.arange(1000, dtype=np.float64)),
+            (ht.Int64HashTable, np.arange(1000, dtype=np.int64)),
+            (ht.UInt64HashTable, np.arange(1000, dtype=np.uint64)),
+        ],
+    )
+    def test_hashtable_factorize(self, htable, writable, data):
+        # output of maker has guaranteed unique elements
+        s = Series(data, dtype=data.dtype)
+        if htable == ht.Float64HashTable:
+            # add NaN for float column
+            s.loc[500] = np.nan
+        elif htable == ht.PyObjectHashTable:
+            # use different NaN types for object column
+            s.loc[500:502] = [np.nan, None, NaT]
+
+        # create duplicated selection
+        s_duplicated = s.sample(frac=3, replace=True).reset_index(drop=True)
+        s_duplicated.values.setflags(write=writable)
+        na_mask = s_duplicated.isna().values
+
+        result_unique, result_inverse = htable().factorize(s_duplicated.values)
+
+        # drop_duplicates has own cython code (hash_table_func_helper.pxi)
+        # and is tested separately; keeps first occurrence like ht.factorize()
+        # since factorize removes all NaNs, we do the same here
+        expected_unique = s_duplicated.dropna().drop_duplicates().values
+        tm.assert_numpy_array_equal(result_unique, expected_unique)
+
+        # reconstruction can only succeed if the inverse is correct. Since
+        # factorize removes the NaNs, those have to be excluded here as well
+        result_reconstruct = result_unique[result_inverse[~na_mask]]
+        expected_reconstruct = s_duplicated.dropna().values
+        tm.assert_numpy_array_equal(result_reconstruct, expected_reconstruct)
+
+
+class TestRank:
+    @pytest.mark.parametrize(
+        "arr",
+        [
+            [np.nan, np.nan, 5.0, 5.0, 5.0, np.nan, 1, 2, 3, np.nan],
+            [4.0, np.nan, 5.0, 5.0, 5.0, np.nan, 1, 2, 4.0, np.nan],
+        ],
+    )
+    def test_scipy_compat(self, arr):
+        sp_stats = pytest.importorskip("scipy.stats")
+
+        arr = np.array(arr)
+
+        mask = ~np.isfinite(arr)
+        arr = arr.copy()
+        result = libalgos.rank_1d(arr)
+        arr[mask] = np.inf
+        exp = sp_stats.rankdata(arr)
+        exp[mask] = np.nan
+        tm.assert_almost_equal(result, exp)
+
+    @pytest.mark.parametrize("dtype", np.typecodes["AllInteger"])
+    def test_basic(self, writable, dtype):
+        exp = np.array([1, 2], dtype=np.float64)
+
+        data = np.array([1, 100], dtype=dtype)
+        data.setflags(write=writable)
+        ser = Series(data)
+        result = algos.rank(ser)
+        tm.assert_numpy_array_equal(result, exp)
+
+    @pytest.mark.parametrize("dtype", [np.float64, np.uint64])
+    def test_uint64_overflow(self, dtype):
+        exp = np.array([1, 2], dtype=np.float64)
+
+        s = Series([1, 2**63], dtype=dtype)
+        tm.assert_numpy_array_equal(algos.rank(s), exp)
+
+    def test_too_many_ndims(self):
+        arr = np.array([[[1, 2, 3], [4, 5, 6], [7, 8, 9]]])
+        msg = "Array with ndim > 2 are not supported"
+
+        with pytest.raises(TypeError, match=msg):
+            algos.rank(arr)
+
+    @pytest.mark.single_cpu
+    def test_pct_max_many_rows(self):
+        # GH 18271
+        values = np.arange(2**24 + 1)
+        result = algos.rank(values, pct=True).max()
+        assert result == 1
+
+        values = np.arange(2**25 + 2).reshape(2**24 + 1, 2)
+        result = algos.rank(values, pct=True).max()
+        assert result == 1
+
+
+class TestMode:
+    def test_no_mode(self):
+        exp = Series([], dtype=np.float64, index=Index([], dtype=int))
+        tm.assert_numpy_array_equal(algos.mode(np.array([])), exp.values)
+
+    @pytest.mark.parametrize("dt", np.typecodes["AllInteger"] + np.typecodes["Float"])
+    def test_mode_single(self, dt):
+        # GH 15714
+        exp_single = [1]
+        data_single = [1]
+
+        exp_multi = [1]
+        data_multi = [1, 1]
+
+        ser = Series(data_single, dtype=dt)
+        exp = Series(exp_single, dtype=dt)
+        tm.assert_numpy_array_equal(algos.mode(ser.values), exp.values)
+        tm.assert_series_equal(ser.mode(), exp)
+
+        ser = Series(data_multi, dtype=dt)
+        exp = Series(exp_multi, dtype=dt)
+        tm.assert_numpy_array_equal(algos.mode(ser.values), exp.values)
+        tm.assert_series_equal(ser.mode(), exp)
+
+    def test_mode_obj_int(self):
+        exp = Series([1], dtype=int)
+        tm.assert_numpy_array_equal(algos.mode(exp.values), exp.values)
+
+        exp = Series(["a", "b", "c"], dtype=object)
+        tm.assert_numpy_array_equal(algos.mode(exp.values), exp.values)
+
+    @pytest.mark.parametrize("dt", np.typecodes["AllInteger"] + np.typecodes["Float"])
+    def test_number_mode(self, dt):
+        exp_single = [1]
+        data_single = [1] * 5 + [2] * 3
+
+        exp_multi = [1, 3]
+        data_multi = [1] * 5 + [2] * 3 + [3] * 5
+
+        ser = Series(data_single, dtype=dt)
+        exp = Series(exp_single, dtype=dt)
+        tm.assert_numpy_array_equal(algos.mode(ser.values), exp.values)
+        tm.assert_series_equal(ser.mode(), exp)
+
+        ser = Series(data_multi, dtype=dt)
+        exp = Series(exp_multi, dtype=dt)
+        tm.assert_numpy_array_equal(algos.mode(ser.values), exp.values)
+        tm.assert_series_equal(ser.mode(), exp)
+
+    def test_strobj_mode(self):
+        exp = ["b"]
+        data = ["a"] * 2 + ["b"] * 3
+
+        ser = Series(data, dtype="c")
+        exp = Series(exp, dtype="c")
+        tm.assert_numpy_array_equal(algos.mode(ser.values), exp.values)
+        tm.assert_series_equal(ser.mode(), exp)
+
+    @pytest.mark.parametrize("dt", [str, object])
+    def test_strobj_multi_char(self, dt, using_infer_string):
+        exp = ["bar"]
+        data = ["foo"] * 2 + ["bar"] * 3
+
+        ser = Series(data, dtype=dt)
+        exp = Series(exp, dtype=dt)
+        if using_infer_string and dt is str:
+            tm.assert_extension_array_equal(algos.mode(ser.values), exp.values)
+        else:
+            tm.assert_numpy_array_equal(algos.mode(ser.values), exp.values)
+        tm.assert_series_equal(ser.mode(), exp)
+
+    def test_datelike_mode(self):
+        exp = Series(["1900-05-03", "2011-01-03", "2013-01-02"], dtype="M8[ns]")
+        ser = Series(["2011-01-03", "2013-01-02", "1900-05-03"], dtype="M8[ns]")
+        tm.assert_extension_array_equal(algos.mode(ser.values), exp._values)
+        tm.assert_series_equal(ser.mode(), exp)
+
+        exp = Series(["2011-01-03", "2013-01-02"], dtype="M8[ns]")
+        ser = Series(
+            ["2011-01-03", "2013-01-02", "1900-05-03", "2011-01-03", "2013-01-02"],
+            dtype="M8[ns]",
+        )
+        tm.assert_extension_array_equal(algos.mode(ser.values), exp._values)
+        tm.assert_series_equal(ser.mode(), exp)
+
+    def test_timedelta_mode(self):
+        exp = Series(["-1 days", "0 days", "1 days"], dtype="timedelta64[ns]")
+        ser = Series(["1 days", "-1 days", "0 days"], dtype="timedelta64[ns]")
+        tm.assert_extension_array_equal(algos.mode(ser.values), exp._values)
+        tm.assert_series_equal(ser.mode(), exp)
+
+        exp = Series(["2 min", "1 day"], dtype="timedelta64[ns]")
+        ser = Series(
+            ["1 day", "1 day", "-1 day", "-1 day 2 min", "2 min", "2 min"],
+            dtype="timedelta64[ns]",
+        )
+        tm.assert_extension_array_equal(algos.mode(ser.values), exp._values)
+        tm.assert_series_equal(ser.mode(), exp)
+
+    def test_mixed_dtype(self):
+        exp = Series(["foo"], dtype=object)
+        ser = Series([1, "foo", "foo"])
+        tm.assert_numpy_array_equal(algos.mode(ser.values), exp.values)
+        tm.assert_series_equal(ser.mode(), exp)
+
+    def test_uint64_overflow(self):
+        exp = Series([2**63], dtype=np.uint64)
+        ser = Series([1, 2**63, 2**63], dtype=np.uint64)
+        tm.assert_numpy_array_equal(algos.mode(ser.values), exp.values)
+        tm.assert_series_equal(ser.mode(), exp)
+
+        exp = Series([1, 2**63], dtype=np.uint64)
+        ser = Series([1, 2**63], dtype=np.uint64)
+        tm.assert_numpy_array_equal(algos.mode(ser.values), exp.values)
+        tm.assert_series_equal(ser.mode(), exp)
+
+    def test_categorical(self):
+        c = Categorical([1, 2])
+        exp = c
+        res = Series(c).mode()._values
+        tm.assert_categorical_equal(res, exp)
+
+        c = Categorical([1, "a", "a"])
+        exp = Categorical(["a"], categories=[1, "a"])
+        res = Series(c).mode()._values
+        tm.assert_categorical_equal(res, exp)
+
+        c = Categorical([1, 1, 2, 3, 3])
+        exp = Categorical([1, 3], categories=[1, 2, 3])
+        res = Series(c).mode()._values
+        tm.assert_categorical_equal(res, exp)
+
+    def test_index(self):
+        idx = Index([1, 2, 3])
+        exp = Series([1, 2, 3], dtype=np.int64)
+        tm.assert_numpy_array_equal(algos.mode(idx), exp.values)
+
+        idx = Index([1, "a", "a"])
+        exp = Series(["a"], dtype=object)
+        tm.assert_numpy_array_equal(algos.mode(idx), exp.values)
+
+        idx = Index([1, 1, 2, 3, 3])
+        exp = Series([1, 3], dtype=np.int64)
+        tm.assert_numpy_array_equal(algos.mode(idx), exp.values)
+
+        idx = Index(
+            ["1 day", "1 day", "-1 day", "-1 day 2 min", "2 min", "2 min"],
+            dtype="timedelta64[ns]",
+        )
+        with pytest.raises(AttributeError, match="TimedeltaIndex"):
+            # algos.mode expects Arraylike, does *not* unwrap TimedeltaIndex
+            algos.mode(idx)
+
+    def test_ser_mode_with_name(self):
+        # GH 46737
+        ser = Series([1, 1, 3], name="foo")
+        result = ser.mode()
+        expected = Series([1], name="foo")
+        tm.assert_series_equal(result, expected)
+
+
+class TestDiff:
+    @pytest.mark.parametrize("dtype", ["M8[ns]", "m8[ns]"])
+    def test_diff_datetimelike_nat(self, dtype):
+        # NaT - NaT is NaT, not 0
+        arr = np.arange(12).astype(np.int64).view(dtype).reshape(3, 4)
+        arr[:, 2] = arr.dtype.type("NaT", "ns")
+        result = algos.diff(arr, 1, axis=0)
+
+        expected = np.ones(arr.shape, dtype="timedelta64[ns]") * 4
+        expected[:, 2] = np.timedelta64("NaT", "ns")
+        expected[0, :] = np.timedelta64("NaT", "ns")
+
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = algos.diff(arr.T, 1, axis=1)
+        tm.assert_numpy_array_equal(result, expected.T)
+
+    def test_diff_ea_axis(self):
+        dta = date_range("2016-01-01", periods=3, tz="US/Pacific")._data
+
+        msg = "cannot diff DatetimeArray on axis=1"
+        with pytest.raises(ValueError, match=msg):
+            algos.diff(dta, 1, axis=1)
+
+    @pytest.mark.parametrize("dtype", ["int8", "int16"])
+    def test_diff_low_precision_int(self, dtype):
+        arr = np.array([0, 1, 1, 0, 0], dtype=dtype)
+        result = algos.diff(arr, 1)
+        expected = np.array([np.nan, 1, 0, -1, 0], dtype="float32")
+        tm.assert_numpy_array_equal(result, expected)
+
+
+@pytest.mark.parametrize("op", [np.array, pd.array])
+def test_union_with_duplicates(op):
+    # GH#36289
+    lvals = op([3, 1, 3, 4])
+    rvals = op([2, 3, 1, 1])
+    expected = op([3, 3, 1, 1, 4, 2])
+    if isinstance(expected, np.ndarray):
+        result = algos.union_with_duplicates(lvals, rvals)
+        tm.assert_numpy_array_equal(result, expected)
+    else:
+        result = algos.union_with_duplicates(lvals, rvals)
+        tm.assert_extension_array_equal(result, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/test_common.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/test_common.py
new file mode 100644
index 0000000000000000000000000000000000000000..e8a1c961c8cb6e5b1014f6baa193d4593d85d981
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/test_common.py
@@ -0,0 +1,267 @@
+import collections
+from functools import partial
+import string
+import subprocess
+import sys
+import textwrap
+
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import Series
+import pandas._testing as tm
+from pandas.core import ops
+import pandas.core.common as com
+from pandas.util.version import Version
+
+
+def test_get_callable_name():
+    getname = com.get_callable_name
+
+    def fn(x):
+        return x
+
+    lambda_ = lambda x: x
+    part1 = partial(fn)
+    part2 = partial(part1)
+
+    class somecall:
+        def __call__(self):
+            # This shouldn't actually get called below; somecall.__init__
+            #  should.
+            raise NotImplementedError
+
+    assert getname(fn) == "fn"
+    assert getname(lambda_)
+    assert getname(part1) == "fn"
+    assert getname(part2) == "fn"
+    assert getname(somecall()) == "somecall"
+    assert getname(1) is None
+
+
+def test_any_none():
+    assert com.any_none(1, 2, 3, None)
+    assert not com.any_none(1, 2, 3, 4)
+
+
+def test_all_not_none():
+    assert com.all_not_none(1, 2, 3, 4)
+    assert not com.all_not_none(1, 2, 3, None)
+    assert not com.all_not_none(None, None, None, None)
+
+
+def test_random_state():
+    # Check with seed
+    state = com.random_state(5)
+    assert state.uniform() == np.random.RandomState(5).uniform()
+
+    # Check with random state object
+    state2 = np.random.RandomState(10)
+    assert com.random_state(state2).uniform() == np.random.RandomState(10).uniform()
+
+    # check with no arg random state
+    assert com.random_state() is np.random
+
+    # check array-like
+    # GH32503
+    state_arr_like = np.random.default_rng(None).integers(
+        0, 2**31, size=624, dtype="uint32"
+    )
+    assert (
+        com.random_state(state_arr_like).uniform()
+        == np.random.RandomState(state_arr_like).uniform()
+    )
+
+    # Check BitGenerators
+    # GH32503
+    assert (
+        com.random_state(np.random.MT19937(3)).uniform()
+        == np.random.RandomState(np.random.MT19937(3)).uniform()
+    )
+    assert (
+        com.random_state(np.random.PCG64(11)).uniform()
+        == np.random.RandomState(np.random.PCG64(11)).uniform()
+    )
+
+    # Error for floats or strings
+    msg = (
+        "random_state must be an integer, array-like, a BitGenerator, Generator, "
+        "a numpy RandomState, or None"
+    )
+    with pytest.raises(ValueError, match=msg):
+        com.random_state("test")
+
+    with pytest.raises(ValueError, match=msg):
+        com.random_state(5.5)
+
+
+@pytest.mark.parametrize(
+    "left, right, expected",
+    [
+        (Series([1], name="x"), Series([2], name="x"), "x"),
+        (Series([1], name="x"), Series([2], name="y"), None),
+        (Series([1]), Series([2], name="x"), None),
+        (Series([1], name="x"), Series([2]), None),
+        (Series([1], name="x"), [2], "x"),
+        ([1], Series([2], name="y"), "y"),
+        # matching NAs
+        (Series([1], name=np.nan), pd.Index([], name=np.nan), np.nan),
+        (Series([1], name=np.nan), pd.Index([], name=pd.NaT), None),
+        (Series([1], name=pd.NA), pd.Index([], name=pd.NA), pd.NA),
+        # tuple name GH#39757
+        (
+            Series([1], name=np.int64(1)),
+            pd.Index([], name=(np.int64(1), np.int64(2))),
+            None,
+        ),
+        (
+            Series([1], name=(np.int64(1), np.int64(2))),
+            pd.Index([], name=(np.int64(1), np.int64(2))),
+            (np.int64(1), np.int64(2)),
+        ),
+        pytest.param(
+            Series([1], name=(np.float64("nan"), np.int64(2))),
+            pd.Index([], name=(np.float64("nan"), np.int64(2))),
+            (np.float64("nan"), np.int64(2)),
+            marks=pytest.mark.xfail(
+                reason="Not checking for matching NAs inside tuples."
+            ),
+        ),
+    ],
+)
+def test_maybe_match_name(left, right, expected):
+    res = ops.common._maybe_match_name(left, right)
+    assert res is expected or res == expected
+
+
+def test_standardize_mapping():
+    # No uninitialized defaultdicts
+    msg = r"to_dict\(\) only accepts initialized defaultdicts"
+    with pytest.raises(TypeError, match=msg):
+        com.standardize_mapping(collections.defaultdict)
+
+    # No non-mapping subtypes, instance
+    msg = "unsupported type: "
+    with pytest.raises(TypeError, match=msg):
+        com.standardize_mapping([])
+
+    # No non-mapping subtypes, class
+    with pytest.raises(TypeError, match=msg):
+        com.standardize_mapping(list)
+
+    fill = {"bad": "data"}
+    assert com.standardize_mapping(fill) == dict
+
+    # Convert instance to type
+    assert com.standardize_mapping({}) == dict
+
+    dd = collections.defaultdict(list)
+    assert isinstance(com.standardize_mapping(dd), partial)
+
+
+def test_git_version():
+    # GH 21295
+    git_version = pd.__git_version__
+    assert len(git_version) == 40
+    assert all(c in string.hexdigits for c in git_version)
+
+
+def test_version_tag():
+    version = Version(pd.__version__)
+    try:
+        version > Version("0.0.1")
+    except TypeError:
+        raise ValueError(
+            "No git tags exist, please sync tags between upstream and your repo"
+        )
+
+
+@pytest.mark.parametrize(
+    "obj", [(obj,) for obj in pd.__dict__.values() if callable(obj)]
+)
+def test_serializable(obj):
+    # GH 35611
+    unpickled = tm.round_trip_pickle(obj)
+    assert type(obj) == type(unpickled)
+
+
+class TestIsBoolIndexer:
+    def test_non_bool_array_with_na(self):
+        # in particular, this should not raise
+        arr = np.array(["A", "B", np.nan], dtype=object)
+        assert not com.is_bool_indexer(arr)
+
+    def test_list_subclass(self):
+        # GH#42433
+
+        class MyList(list):
+            pass
+
+        val = MyList(["a"])
+
+        assert not com.is_bool_indexer(val)
+
+        val = MyList([True])
+        assert com.is_bool_indexer(val)
+
+    def test_frozenlist(self):
+        # GH#42461
+        data = {"col1": [1, 2], "col2": [3, 4]}
+        df = pd.DataFrame(data=data)
+
+        frozen = df.index.names[1:]
+        assert not com.is_bool_indexer(frozen)
+
+        result = df[frozen]
+        expected = df[[]]
+        tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("with_exception", [True, False])
+def test_temp_setattr(with_exception):
+    # GH#45954
+    ser = Series(dtype=object)
+    ser.name = "first"
+    # Raise a ValueError in either case to satisfy pytest.raises
+    match = "Inside exception raised" if with_exception else "Outside exception raised"
+    with pytest.raises(ValueError, match=match):
+        with com.temp_setattr(ser, "name", "second"):
+            assert ser.name == "second"
+            if with_exception:
+                raise ValueError("Inside exception raised")
+        raise ValueError("Outside exception raised")
+    assert ser.name == "first"
+
+
+@pytest.mark.single_cpu
+def test_str_size():
+    # GH#21758
+    a = "a"
+    expected = sys.getsizeof(a)
+    pyexe = sys.executable.replace("\\", "/")
+    call = [
+        pyexe,
+        "-c",
+        "a='a';import sys;sys.getsizeof(a);import pandas;print(sys.getsizeof(a));",
+    ]
+    result = subprocess.check_output(call).decode()[-4:-1].strip("\n")
+    assert int(result) == int(expected)
+
+
+@pytest.mark.single_cpu
+def test_bz2_missing_import():
+    # Check whether bz2 missing import is handled correctly (issue #53857)
+    code = """
+        import sys
+        sys.modules['bz2'] = None
+        import pytest
+        import pandas as pd
+        from pandas.compat import get_bz2_file
+        msg = 'bz2 module not available.'
+        with pytest.raises(RuntimeError, match=msg):
+            get_bz2_file()
+    """
+    code = textwrap.dedent(code)
+    call = [sys.executable, "-c", code]
+    subprocess.check_output(call)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/test_downstream.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/test_downstream.py
new file mode 100644
index 0000000000000000000000000000000000000000..d448773c3bd4a94a8007f55cab157113e4deb85e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/test_downstream.py
@@ -0,0 +1,370 @@
+"""
+Testing that we work in the downstream packages
+"""
+import array
+import subprocess
+import sys
+
+import numpy as np
+import pytest
+
+from pandas.errors import IntCastingNaNError
+import pandas.util._test_decorators as td
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    DatetimeIndex,
+    Series,
+    TimedeltaIndex,
+)
+import pandas._testing as tm
+from pandas.core.arrays import (
+    DatetimeArray,
+    TimedeltaArray,
+)
+from pandas.util.version import Version
+
+
+@pytest.fixture
+def df():
+    return DataFrame({"A": [1, 2, 3]})
+
+
+def test_dask(df):
+    # dask sets "compute.use_numexpr" to False, so catch the current value
+    # and ensure to reset it afterwards to avoid impacting other tests
+    olduse = pd.get_option("compute.use_numexpr")
+
+    try:
+        pytest.importorskip("toolz")
+        dd = pytest.importorskip("dask.dataframe")
+
+        ddf = dd.from_pandas(df, npartitions=3)
+        assert ddf.A is not None
+        assert ddf.compute() is not None
+    finally:
+        pd.set_option("compute.use_numexpr", olduse)
+
+
+def test_dask_ufunc():
+    # dask sets "compute.use_numexpr" to False, so catch the current value
+    # and ensure to reset it afterwards to avoid impacting other tests
+    olduse = pd.get_option("compute.use_numexpr")
+
+    try:
+        da = pytest.importorskip("dask.array")
+        dd = pytest.importorskip("dask.dataframe")
+
+        s = Series([1.5, 2.3, 3.7, 4.0])
+        ds = dd.from_pandas(s, npartitions=2)
+
+        result = da.fix(ds).compute()
+        expected = np.fix(s)
+        tm.assert_series_equal(result, expected)
+    finally:
+        pd.set_option("compute.use_numexpr", olduse)
+
+
+def test_construct_dask_float_array_int_dtype_match_ndarray():
+    # GH#40110 make sure we treat a float-dtype dask array with the same
+    #  rules we would for an ndarray
+    dd = pytest.importorskip("dask.dataframe")
+
+    arr = np.array([1, 2.5, 3])
+    darr = dd.from_array(arr)
+
+    res = Series(darr)
+    expected = Series(arr)
+    tm.assert_series_equal(res, expected)
+
+    # GH#49599 in 2.0 we raise instead of silently ignoring the dtype
+    msg = "Trying to coerce float values to integers"
+    with pytest.raises(ValueError, match=msg):
+        Series(darr, dtype="i8")
+
+    msg = r"Cannot convert non-finite values \(NA or inf\) to integer"
+    arr[2] = np.nan
+    with pytest.raises(IntCastingNaNError, match=msg):
+        Series(darr, dtype="i8")
+    # which is the same as we get with a numpy input
+    with pytest.raises(IntCastingNaNError, match=msg):
+        Series(arr, dtype="i8")
+
+
+def test_xarray(df):
+    pytest.importorskip("xarray")
+
+    assert df.to_xarray() is not None
+
+
+def test_xarray_cftimeindex_nearest():
+    # https://github.com/pydata/xarray/issues/3751
+    cftime = pytest.importorskip("cftime")
+    xarray = pytest.importorskip("xarray")
+
+    times = xarray.cftime_range("0001", periods=2)
+    key = cftime.DatetimeGregorian(2000, 1, 1)
+    result = times.get_indexer([key], method="nearest")
+    expected = 1
+    assert result == expected
+
+
+@pytest.mark.single_cpu
+def test_oo_optimizable():
+    # GH 21071
+    subprocess.check_call([sys.executable, "-OO", "-c", "import pandas"])
+
+
+@pytest.mark.single_cpu
+def test_oo_optimized_datetime_index_unpickle():
+    # GH 42866
+    subprocess.check_call(
+        [
+            sys.executable,
+            "-OO",
+            "-c",
+            (
+                "import pandas as pd, pickle; "
+                "pickle.loads(pickle.dumps(pd.date_range('2021-01-01', periods=1)))"
+            ),
+        ]
+    )
+
+
+def test_statsmodels():
+    smf = pytest.importorskip("statsmodels.formula.api")
+
+    df = DataFrame(
+        {"Lottery": range(5), "Literacy": range(5), "Pop1831": range(100, 105)}
+    )
+    smf.ols("Lottery ~ Literacy + np.log(Pop1831)", data=df).fit()
+
+
+def test_scikit_learn():
+    pytest.importorskip("sklearn")
+    from sklearn import (
+        datasets,
+        svm,
+    )
+
+    digits = datasets.load_digits()
+    clf = svm.SVC(gamma=0.001, C=100.0)
+    clf.fit(digits.data[:-1], digits.target[:-1])
+    clf.predict(digits.data[-1:])
+
+
+def test_seaborn():
+    seaborn = pytest.importorskip("seaborn")
+    tips = DataFrame(
+        {"day": pd.date_range("2023", freq="D", periods=5), "total_bill": range(5)}
+    )
+    seaborn.stripplot(x="day", y="total_bill", data=tips)
+
+
+def test_pandas_datareader():
+    pytest.importorskip("pandas_datareader")
+
+
+@pytest.mark.filterwarnings("ignore:Passing a BlockManager:DeprecationWarning")
+def test_pyarrow(df):
+    pyarrow = pytest.importorskip("pyarrow")
+    table = pyarrow.Table.from_pandas(df)
+    result = table.to_pandas()
+    tm.assert_frame_equal(result, df)
+
+
+def test_yaml_dump(df):
+    # GH#42748
+    yaml = pytest.importorskip("yaml")
+
+    dumped = yaml.dump(df)
+
+    loaded = yaml.load(dumped, Loader=yaml.Loader)
+    tm.assert_frame_equal(df, loaded)
+
+    loaded2 = yaml.load(dumped, Loader=yaml.UnsafeLoader)
+    tm.assert_frame_equal(df, loaded2)
+
+
+@pytest.mark.single_cpu
+def test_missing_required_dependency():
+    # GH 23868
+    # To ensure proper isolation, we pass these flags
+    # -S : disable site-packages
+    # -s : disable user site-packages
+    # -E : disable PYTHON* env vars, especially PYTHONPATH
+    # https://github.com/MacPython/pandas-wheels/pull/50
+
+    pyexe = sys.executable.replace("\\", "/")
+
+    # We skip this test if pandas is installed as a site package. We first
+    # import the package normally and check the path to the module before
+    # executing the test which imports pandas with site packages disabled.
+    call = [pyexe, "-c", "import pandas;print(pandas.__file__)"]
+    output = subprocess.check_output(call).decode()
+    if "site-packages" in output:
+        pytest.skip("pandas installed as site package")
+
+    # This test will fail if pandas is installed as a site package. The flags
+    # prevent pandas being imported and the test will report Failed: DID NOT
+    # RAISE 
+    call = [pyexe, "-sSE", "-c", "import pandas"]
+
+    msg = (
+        rf"Command '\['{pyexe}', '-sSE', '-c', 'import pandas'\]' "
+        "returned non-zero exit status 1."
+    )
+
+    with pytest.raises(subprocess.CalledProcessError, match=msg) as exc:
+        subprocess.check_output(call, stderr=subprocess.STDOUT)
+
+    output = exc.value.stdout.decode()
+    for name in ["numpy", "pytz", "dateutil"]:
+        assert name in output
+
+
+def test_frame_setitem_dask_array_into_new_col(request):
+    # GH#47128
+
+    # dask sets "compute.use_numexpr" to False, so catch the current value
+    # and ensure to reset it afterwards to avoid impacting other tests
+    olduse = pd.get_option("compute.use_numexpr")
+
+    try:
+        dask = pytest.importorskip("dask")
+        da = pytest.importorskip("dask.array")
+        if Version(dask.__version__) <= Version("2025.1.0") and Version(
+            np.__version__
+        ) >= Version("2.1"):
+            request.applymarker(
+                pytest.mark.xfail(reason="loc.__setitem__ incorrectly mutated column c")
+            )
+
+        dda = da.array([1, 2])
+        df = DataFrame({"a": ["a", "b"]})
+        df["b"] = dda
+        df["c"] = dda
+        df.loc[[False, True], "b"] = 100
+        result = df.loc[[1], :]
+        expected = DataFrame({"a": ["b"], "b": [100], "c": [2]}, index=[1])
+        tm.assert_frame_equal(result, expected)
+    finally:
+        pd.set_option("compute.use_numexpr", olduse)
+
+
+def test_pandas_priority():
+    # GH#48347
+
+    class MyClass:
+        __pandas_priority__ = 5000
+
+        def __radd__(self, other):
+            return self
+
+    left = MyClass()
+    right = Series(range(3))
+
+    assert right.__add__(left) is NotImplemented
+    assert right + left is left
+
+
+@pytest.fixture(
+    params=[
+        "memoryview",
+        "array",
+        pytest.param("dask", marks=td.skip_if_no("dask.array")),
+        pytest.param("xarray", marks=td.skip_if_no("xarray")),
+    ]
+)
+def array_likes(request):
+    """
+    Fixture giving a numpy array and a parametrized 'data' object, which can
+    be a memoryview, array, dask or xarray object created from the numpy array.
+    """
+    # GH#24539 recognize e.g xarray, dask, ...
+    arr = np.array([1, 2, 3], dtype=np.int64)
+
+    name = request.param
+    if name == "memoryview":
+        data = memoryview(arr)
+    elif name == "array":
+        data = array.array("i", arr)
+    elif name == "dask":
+        import dask.array
+
+        data = dask.array.array(arr)
+    elif name == "xarray":
+        import xarray as xr
+
+        data = xr.DataArray(arr)
+
+    return arr, data
+
+
+@pytest.mark.parametrize("dtype", ["M8[ns]", "m8[ns]"])
+def test_from_obscure_array(dtype, array_likes):
+    # GH#24539 recognize e.g xarray, dask, ...
+    # Note: we dont do this for PeriodArray bc _from_sequence won't accept
+    #  an array of integers
+    # TODO: could check with arraylike of Period objects
+    arr, data = array_likes
+
+    cls = {"M8[ns]": DatetimeArray, "m8[ns]": TimedeltaArray}[dtype]
+
+    depr_msg = f"{cls.__name__}.__init__ is deprecated"
+    with tm.assert_produces_warning(FutureWarning, match=depr_msg):
+        expected = cls(arr)
+    result = cls._from_sequence(data, dtype=dtype)
+    tm.assert_extension_array_equal(result, expected)
+
+    if not isinstance(data, memoryview):
+        # FIXME(GH#44431) these raise on memoryview and attempted fix
+        #  fails on py3.10
+        func = {"M8[ns]": pd.to_datetime, "m8[ns]": pd.to_timedelta}[dtype]
+        result = func(arr).array
+        expected = func(data).array
+        tm.assert_equal(result, expected)
+
+    # Let's check the Indexes while we're here
+    idx_cls = {"M8[ns]": DatetimeIndex, "m8[ns]": TimedeltaIndex}[dtype]
+    result = idx_cls(arr)
+    expected = idx_cls(data)
+    tm.assert_index_equal(result, expected)
+
+
+def test_dataframe_consortium() -> None:
+    """
+    Test some basic methods of the dataframe consortium standard.
+
+    Full testing is done at https://github.com/data-apis/dataframe-api-compat,
+    this is just to check that the entry point works as expected.
+    """
+    pytest.importorskip("dataframe_api_compat")
+    df_pd = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
+    df = df_pd.__dataframe_consortium_standard__()
+    result_1 = df.get_column_names()
+    expected_1 = ["a", "b"]
+    assert result_1 == expected_1
+
+    ser = Series([1, 2, 3], name="a")
+    col = ser.__column_consortium_standard__()
+    assert col.name == "a"
+
+
+def test_xarray_coerce_unit():
+    # GH44053
+    xr = pytest.importorskip("xarray")
+
+    arr = xr.DataArray([1, 2, 3])
+    result = pd.to_datetime(arr, unit="ns")
+    expected = DatetimeIndex(
+        [
+            "1970-01-01 00:00:00.000000001",
+            "1970-01-01 00:00:00.000000002",
+            "1970-01-01 00:00:00.000000003",
+        ],
+        dtype="datetime64[ns]",
+        freq=None,
+    )
+    tm.assert_index_equal(result, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/test_errors.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/test_errors.py
new file mode 100644
index 0000000000000000000000000000000000000000..aeddc08e4b888c0937a3095a46003613e0115876
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/test_errors.py
@@ -0,0 +1,112 @@
+import pytest
+
+from pandas.errors import (
+    AbstractMethodError,
+    UndefinedVariableError,
+)
+
+import pandas as pd
+
+
+@pytest.mark.parametrize(
+    "exc",
+    [
+        "AttributeConflictWarning",
+        "CSSWarning",
+        "CategoricalConversionWarning",
+        "ClosedFileError",
+        "DataError",
+        "DatabaseError",
+        "DtypeWarning",
+        "EmptyDataError",
+        "IncompatibilityWarning",
+        "IndexingError",
+        "InvalidColumnName",
+        "InvalidComparison",
+        "InvalidVersion",
+        "LossySetitemError",
+        "MergeError",
+        "NoBufferPresent",
+        "NumExprClobberingError",
+        "NumbaUtilError",
+        "OptionError",
+        "OutOfBoundsDatetime",
+        "ParserError",
+        "ParserWarning",
+        "PerformanceWarning",
+        "PossibleDataLossError",
+        "PossiblePrecisionLoss",
+        "PyperclipException",
+        "SettingWithCopyError",
+        "SettingWithCopyWarning",
+        "SpecificationError",
+        "UnsortedIndexError",
+        "UnsupportedFunctionCall",
+        "ValueLabelTypeMismatch",
+    ],
+)
+def test_exception_importable(exc):
+    from pandas import errors
+
+    err = getattr(errors, exc)
+    assert err is not None
+
+    # check that we can raise on them
+
+    msg = "^$"
+
+    with pytest.raises(err, match=msg):
+        raise err()
+
+
+def test_catch_oob():
+    from pandas import errors
+
+    msg = "Cannot cast 1500-01-01 00:00:00 to unit='ns' without overflow"
+    with pytest.raises(errors.OutOfBoundsDatetime, match=msg):
+        pd.Timestamp("15000101").as_unit("ns")
+
+
+@pytest.mark.parametrize(
+    "is_local",
+    [
+        True,
+        False,
+    ],
+)
+def test_catch_undefined_variable_error(is_local):
+    variable_name = "x"
+    if is_local:
+        msg = f"local variable '{variable_name}' is not defined"
+    else:
+        msg = f"name '{variable_name}' is not defined"
+
+    with pytest.raises(UndefinedVariableError, match=msg):
+        raise UndefinedVariableError(variable_name, is_local)
+
+
+class Foo:
+    @classmethod
+    def classmethod(cls):
+        raise AbstractMethodError(cls, methodtype="classmethod")
+
+    @property
+    def property(self):
+        raise AbstractMethodError(self, methodtype="property")
+
+    def method(self):
+        raise AbstractMethodError(self)
+
+
+def test_AbstractMethodError_classmethod():
+    xpr = "This classmethod must be defined in the concrete class Foo"
+    with pytest.raises(AbstractMethodError, match=xpr):
+        Foo.classmethod()
+
+    xpr = "This property must be defined in the concrete class Foo"
+    with pytest.raises(AbstractMethodError, match=xpr):
+        Foo().property
+
+    xpr = "This method must be defined in the concrete class Foo"
+    with pytest.raises(AbstractMethodError, match=xpr):
+        Foo().method()
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/test_expressions.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/test_expressions.py
new file mode 100644
index 0000000000000000000000000000000000000000..dfec99f0786ebf11a44dedfad8aa8e1015356fab
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/test_expressions.py
@@ -0,0 +1,466 @@
+import operator
+import re
+
+import numpy as np
+import pytest
+
+from pandas import option_context
+import pandas._testing as tm
+from pandas.core.api import (
+    DataFrame,
+    Index,
+    Series,
+)
+from pandas.core.computation import expressions as expr
+
+
+@pytest.fixture
+def _frame():
+    return DataFrame(
+        np.random.default_rng(2).standard_normal((10001, 4)),
+        columns=list("ABCD"),
+        dtype="float64",
+    )
+
+
+@pytest.fixture
+def _frame2():
+    return DataFrame(
+        np.random.default_rng(2).standard_normal((100, 4)),
+        columns=list("ABCD"),
+        dtype="float64",
+    )
+
+
+@pytest.fixture
+def _mixed(_frame):
+    return DataFrame(
+        {
+            "A": _frame["A"].copy(),
+            "B": _frame["B"].astype("float32"),
+            "C": _frame["C"].astype("int64"),
+            "D": _frame["D"].astype("int32"),
+        }
+    )
+
+
+@pytest.fixture
+def _mixed2(_frame2):
+    return DataFrame(
+        {
+            "A": _frame2["A"].copy(),
+            "B": _frame2["B"].astype("float32"),
+            "C": _frame2["C"].astype("int64"),
+            "D": _frame2["D"].astype("int32"),
+        }
+    )
+
+
+@pytest.fixture
+def _integer():
+    return DataFrame(
+        np.random.default_rng(2).integers(1, 100, size=(10001, 4)),
+        columns=list("ABCD"),
+        dtype="int64",
+    )
+
+
+@pytest.fixture
+def _integer_integers(_integer):
+    # integers to get a case with zeros
+    return _integer * np.random.default_rng(2).integers(0, 2, size=np.shape(_integer))
+
+
+@pytest.fixture
+def _integer2():
+    return DataFrame(
+        np.random.default_rng(2).integers(1, 100, size=(101, 4)),
+        columns=list("ABCD"),
+        dtype="int64",
+    )
+
+
+@pytest.fixture
+def _array(_frame):
+    return _frame["A"].values.copy()
+
+
+@pytest.fixture
+def _array2(_frame2):
+    return _frame2["A"].values.copy()
+
+
+@pytest.fixture
+def _array_mixed(_mixed):
+    return _mixed["D"].values.copy()
+
+
+@pytest.fixture
+def _array_mixed2(_mixed2):
+    return _mixed2["D"].values.copy()
+
+
+@pytest.mark.skipif(not expr.USE_NUMEXPR, reason="not using numexpr")
+class TestExpressions:
+    @staticmethod
+    def call_op(df, other, flex: bool, opname: str):
+        if flex:
+            op = lambda x, y: getattr(x, opname)(y)
+            op.__name__ = opname
+        else:
+            op = getattr(operator, opname)
+
+        with option_context("compute.use_numexpr", False):
+            expected = op(df, other)
+
+        expr.get_test_result()
+
+        result = op(df, other)
+        return result, expected
+
+    @pytest.mark.parametrize(
+        "fixture",
+        [
+            "_integer",
+            "_integer2",
+            "_integer_integers",
+            "_frame",
+            "_frame2",
+            "_mixed",
+            "_mixed2",
+        ],
+    )
+    @pytest.mark.parametrize("flex", [True, False])
+    @pytest.mark.parametrize(
+        "arith", ["add", "sub", "mul", "mod", "truediv", "floordiv"]
+    )
+    def test_run_arithmetic(self, request, fixture, flex, arith, monkeypatch):
+        df = request.getfixturevalue(fixture)
+        with monkeypatch.context() as m:
+            m.setattr(expr, "_MIN_ELEMENTS", 0)
+            result, expected = self.call_op(df, df, flex, arith)
+
+            if arith == "truediv":
+                assert all(x.kind == "f" for x in expected.dtypes.values)
+            tm.assert_equal(expected, result)
+
+            for i in range(len(df.columns)):
+                result, expected = self.call_op(
+                    df.iloc[:, i], df.iloc[:, i], flex, arith
+                )
+                if arith == "truediv":
+                    assert expected.dtype.kind == "f"
+                tm.assert_equal(expected, result)
+
+    @pytest.mark.parametrize(
+        "fixture",
+        [
+            "_integer",
+            "_integer2",
+            "_integer_integers",
+            "_frame",
+            "_frame2",
+            "_mixed",
+            "_mixed2",
+        ],
+    )
+    @pytest.mark.parametrize("flex", [True, False])
+    def test_run_binary(self, request, fixture, flex, comparison_op, monkeypatch):
+        """
+        tests solely that the result is the same whether or not numexpr is
+        enabled.  Need to test whether the function does the correct thing
+        elsewhere.
+        """
+        df = request.getfixturevalue(fixture)
+        arith = comparison_op.__name__
+        with option_context("compute.use_numexpr", False):
+            other = df.copy() + 1
+
+        with monkeypatch.context() as m:
+            m.setattr(expr, "_MIN_ELEMENTS", 0)
+            expr.set_test_mode(True)
+
+            result, expected = self.call_op(df, other, flex, arith)
+
+            used_numexpr = expr.get_test_result()
+            assert used_numexpr, "Did not use numexpr as expected."
+            tm.assert_equal(expected, result)
+
+            for i in range(len(df.columns)):
+                binary_comp = other.iloc[:, i] + 1
+                self.call_op(df.iloc[:, i], binary_comp, flex, "add")
+
+    def test_invalid(self):
+        array = np.random.default_rng(2).standard_normal(1_000_001)
+        array2 = np.random.default_rng(2).standard_normal(100)
+
+        # no op
+        result = expr._can_use_numexpr(operator.add, None, array, array, "evaluate")
+        assert not result
+
+        # min elements
+        result = expr._can_use_numexpr(operator.add, "+", array2, array2, "evaluate")
+        assert not result
+
+        # ok, we only check on first part of expression
+        result = expr._can_use_numexpr(operator.add, "+", array, array2, "evaluate")
+        assert result
+
+    @pytest.mark.filterwarnings("ignore:invalid value encountered in:RuntimeWarning")
+    @pytest.mark.parametrize(
+        "opname,op_str",
+        [("add", "+"), ("sub", "-"), ("mul", "*"), ("truediv", "/"), ("pow", "**")],
+    )
+    @pytest.mark.parametrize(
+        "left_fix,right_fix", [("_array", "_array2"), ("_array_mixed", "_array_mixed2")]
+    )
+    def test_binary_ops(self, request, opname, op_str, left_fix, right_fix):
+        left = request.getfixturevalue(left_fix)
+        right = request.getfixturevalue(right_fix)
+
+        def testit(left, right, opname, op_str):
+            if opname == "pow":
+                left = np.abs(left)
+
+            op = getattr(operator, opname)
+
+            # array has 0s
+            result = expr.evaluate(op, left, left, use_numexpr=True)
+            expected = expr.evaluate(op, left, left, use_numexpr=False)
+            tm.assert_numpy_array_equal(result, expected)
+
+            result = expr._can_use_numexpr(op, op_str, right, right, "evaluate")
+            assert not result
+
+        with option_context("compute.use_numexpr", False):
+            testit(left, right, opname, op_str)
+
+        expr.set_numexpr_threads(1)
+        testit(left, right, opname, op_str)
+        expr.set_numexpr_threads()
+        testit(left, right, opname, op_str)
+
+    @pytest.mark.parametrize(
+        "left_fix,right_fix", [("_array", "_array2"), ("_array_mixed", "_array_mixed2")]
+    )
+    def test_comparison_ops(self, request, comparison_op, left_fix, right_fix):
+        left = request.getfixturevalue(left_fix)
+        right = request.getfixturevalue(right_fix)
+
+        def testit():
+            f12 = left + 1
+            f22 = right + 1
+
+            op = comparison_op
+
+            result = expr.evaluate(op, left, f12, use_numexpr=True)
+            expected = expr.evaluate(op, left, f12, use_numexpr=False)
+            tm.assert_numpy_array_equal(result, expected)
+
+            result = expr._can_use_numexpr(op, op, right, f22, "evaluate")
+            assert not result
+
+        with option_context("compute.use_numexpr", False):
+            testit()
+
+        expr.set_numexpr_threads(1)
+        testit()
+        expr.set_numexpr_threads()
+        testit()
+
+    @pytest.mark.parametrize("cond", [True, False])
+    @pytest.mark.parametrize("fixture", ["_frame", "_frame2", "_mixed", "_mixed2"])
+    def test_where(self, request, cond, fixture):
+        df = request.getfixturevalue(fixture)
+
+        def testit():
+            c = np.empty(df.shape, dtype=np.bool_)
+            c.fill(cond)
+            result = expr.where(c, df.values, df.values + 1)
+            expected = np.where(c, df.values, df.values + 1)
+            tm.assert_numpy_array_equal(result, expected)
+
+        with option_context("compute.use_numexpr", False):
+            testit()
+
+        expr.set_numexpr_threads(1)
+        testit()
+        expr.set_numexpr_threads()
+        testit()
+
+    @pytest.mark.parametrize(
+        "op_str,opname", [("/", "truediv"), ("//", "floordiv"), ("**", "pow")]
+    )
+    def test_bool_ops_raise_on_arithmetic(self, op_str, opname):
+        df = DataFrame(
+            {
+                "a": np.random.default_rng(2).random(10) > 0.5,
+                "b": np.random.default_rng(2).random(10) > 0.5,
+            }
+        )
+
+        msg = f"operator '{opname}' not implemented for bool dtypes"
+        f = getattr(operator, opname)
+        err_msg = re.escape(msg)
+
+        with pytest.raises(NotImplementedError, match=err_msg):
+            f(df, df)
+
+        with pytest.raises(NotImplementedError, match=err_msg):
+            f(df.a, df.b)
+
+        with pytest.raises(NotImplementedError, match=err_msg):
+            f(df.a, True)
+
+        with pytest.raises(NotImplementedError, match=err_msg):
+            f(False, df.a)
+
+        with pytest.raises(NotImplementedError, match=err_msg):
+            f(False, df)
+
+        with pytest.raises(NotImplementedError, match=err_msg):
+            f(df, True)
+
+    @pytest.mark.parametrize(
+        "op_str,opname", [("+", "add"), ("*", "mul"), ("-", "sub")]
+    )
+    def test_bool_ops_warn_on_arithmetic(self, op_str, opname):
+        n = 10
+        df = DataFrame(
+            {
+                "a": np.random.default_rng(2).random(n) > 0.5,
+                "b": np.random.default_rng(2).random(n) > 0.5,
+            }
+        )
+
+        subs = {"+": "|", "*": "&", "-": "^"}
+        sub_funcs = {"|": "or_", "&": "and_", "^": "xor"}
+
+        f = getattr(operator, opname)
+        fe = getattr(operator, sub_funcs[subs[op_str]])
+
+        if op_str == "-":
+            # raises TypeError
+            return
+
+        with tm.use_numexpr(True, min_elements=5):
+            with tm.assert_produces_warning():
+                r = f(df, df)
+                e = fe(df, df)
+                tm.assert_frame_equal(r, e)
+
+            with tm.assert_produces_warning():
+                r = f(df.a, df.b)
+                e = fe(df.a, df.b)
+                tm.assert_series_equal(r, e)
+
+            with tm.assert_produces_warning():
+                r = f(df.a, True)
+                e = fe(df.a, True)
+                tm.assert_series_equal(r, e)
+
+            with tm.assert_produces_warning():
+                r = f(False, df.a)
+                e = fe(False, df.a)
+                tm.assert_series_equal(r, e)
+
+            with tm.assert_produces_warning():
+                r = f(False, df)
+                e = fe(False, df)
+                tm.assert_frame_equal(r, e)
+
+            with tm.assert_produces_warning():
+                r = f(df, True)
+                e = fe(df, True)
+                tm.assert_frame_equal(r, e)
+
+    @pytest.mark.parametrize(
+        "test_input,expected",
+        [
+            (
+                DataFrame(
+                    [[0, 1, 2, "aa"], [0, 1, 2, "aa"]], columns=["a", "b", "c", "dtype"]
+                ),
+                DataFrame([[False, False], [False, False]], columns=["a", "dtype"]),
+            ),
+            (
+                DataFrame(
+                    [[0, 3, 2, "aa"], [0, 4, 2, "aa"], [0, 1, 1, "bb"]],
+                    columns=["a", "b", "c", "dtype"],
+                ),
+                DataFrame(
+                    [[False, False], [False, False], [False, False]],
+                    columns=["a", "dtype"],
+                ),
+            ),
+        ],
+    )
+    def test_bool_ops_column_name_dtype(self, test_input, expected):
+        # GH 22383 - .ne fails if columns containing column name 'dtype'
+        result = test_input.loc[:, ["a", "dtype"]].ne(test_input.loc[:, ["a", "dtype"]])
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "arith", ("add", "sub", "mul", "mod", "truediv", "floordiv")
+    )
+    @pytest.mark.parametrize("axis", (0, 1))
+    def test_frame_series_axis(self, axis, arith, _frame, monkeypatch):
+        # GH#26736 Dataframe.floordiv(Series, axis=1) fails
+
+        df = _frame
+        if axis == 1:
+            other = df.iloc[0, :]
+        else:
+            other = df.iloc[:, 0]
+
+        with monkeypatch.context() as m:
+            m.setattr(expr, "_MIN_ELEMENTS", 0)
+
+            op_func = getattr(df, arith)
+
+            with option_context("compute.use_numexpr", False):
+                expected = op_func(other, axis=axis)
+
+            result = op_func(other, axis=axis)
+            tm.assert_frame_equal(expected, result)
+
+    @pytest.mark.parametrize(
+        "op",
+        [
+            "__mod__",
+            "__rmod__",
+            "__floordiv__",
+            "__rfloordiv__",
+        ],
+    )
+    @pytest.mark.parametrize("box", [DataFrame, Series, Index])
+    @pytest.mark.parametrize("scalar", [-5, 5])
+    def test_python_semantics_with_numexpr_installed(
+        self, op, box, scalar, monkeypatch
+    ):
+        # https://github.com/pandas-dev/pandas/issues/36047
+        with monkeypatch.context() as m:
+            m.setattr(expr, "_MIN_ELEMENTS", 0)
+            data = np.arange(-50, 50)
+            obj = box(data)
+            method = getattr(obj, op)
+            result = method(scalar)
+
+            # compare result with numpy
+            with option_context("compute.use_numexpr", False):
+                expected = method(scalar)
+
+            tm.assert_equal(result, expected)
+
+            # compare result element-wise with Python
+            for i, elem in enumerate(data):
+                if box == DataFrame:
+                    scalar_result = result.iloc[i, 0]
+                else:
+                    scalar_result = result[i]
+                try:
+                    expected = getattr(int(elem), op)(scalar)
+                except ZeroDivisionError:
+                    pass
+                else:
+                    assert scalar_result == expected
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/test_flags.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/test_flags.py
new file mode 100644
index 0000000000000000000000000000000000000000..9294b3fc3319b78b59d5637acdf3fd75737cd836
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/test_flags.py
@@ -0,0 +1,48 @@
+import pytest
+
+import pandas as pd
+
+
+class TestFlags:
+    def test_equality(self):
+        a = pd.DataFrame().set_flags(allows_duplicate_labels=True).flags
+        b = pd.DataFrame().set_flags(allows_duplicate_labels=False).flags
+
+        assert a == a
+        assert b == b
+        assert a != b
+        assert a != 2
+
+    def test_set(self):
+        df = pd.DataFrame().set_flags(allows_duplicate_labels=True)
+        a = df.flags
+        a.allows_duplicate_labels = False
+        assert a.allows_duplicate_labels is False
+        a["allows_duplicate_labels"] = True
+        assert a.allows_duplicate_labels is True
+
+    def test_repr(self):
+        a = repr(pd.DataFrame({"A"}).set_flags(allows_duplicate_labels=True).flags)
+        assert a == ""
+        a = repr(pd.DataFrame({"A"}).set_flags(allows_duplicate_labels=False).flags)
+        assert a == ""
+
+    def test_obj_ref(self):
+        df = pd.DataFrame()
+        flags = df.flags
+        del df
+        with pytest.raises(ValueError, match="object has been deleted"):
+            flags.allows_duplicate_labels = True
+
+    def test_getitem(self):
+        df = pd.DataFrame()
+        flags = df.flags
+        assert flags["allows_duplicate_labels"] is True
+        flags["allows_duplicate_labels"] = False
+        assert flags["allows_duplicate_labels"] is False
+
+        with pytest.raises(KeyError, match="a"):
+            flags["a"]
+
+        with pytest.raises(ValueError, match="a"):
+            flags["a"] = 10
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/test_multilevel.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/test_multilevel.py
new file mode 100644
index 0000000000000000000000000000000000000000..6644ec82fab17ac9e1c1744b595c38fda17114f5
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/test_multilevel.py
@@ -0,0 +1,355 @@
+import datetime
+
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    MultiIndex,
+    Series,
+)
+import pandas._testing as tm
+
+
+class TestMultiLevel:
+    def test_reindex_level(self, multiindex_year_month_day_dataframe_random_data):
+        # axis=0
+        ymd = multiindex_year_month_day_dataframe_random_data
+
+        month_sums = ymd.groupby("month").sum()
+        result = month_sums.reindex(ymd.index, level=1)
+        expected = ymd.groupby(level="month").transform("sum")
+
+        tm.assert_frame_equal(result, expected)
+
+        # Series
+        result = month_sums["A"].reindex(ymd.index, level=1)
+        expected = ymd["A"].groupby(level="month").transform("sum")
+        tm.assert_series_equal(result, expected, check_names=False)
+
+        # axis=1
+        msg = "DataFrame.groupby with axis=1 is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            gb = ymd.T.groupby("month", axis=1)
+
+        month_sums = gb.sum()
+        result = month_sums.reindex(columns=ymd.index, level=1)
+        expected = ymd.groupby(level="month").transform("sum").T
+        tm.assert_frame_equal(result, expected)
+
+    def test_reindex(self, multiindex_dataframe_random_data):
+        frame = multiindex_dataframe_random_data
+
+        expected = frame.iloc[[0, 3]]
+        reindexed = frame.loc[[("foo", "one"), ("bar", "one")]]
+        tm.assert_frame_equal(reindexed, expected)
+
+    def test_reindex_preserve_levels(
+        self, multiindex_year_month_day_dataframe_random_data, using_copy_on_write
+    ):
+        ymd = multiindex_year_month_day_dataframe_random_data
+
+        new_index = ymd.index[::10]
+        chunk = ymd.reindex(new_index)
+        if using_copy_on_write:
+            assert chunk.index.is_(new_index)
+        else:
+            assert chunk.index is new_index
+
+        chunk = ymd.loc[new_index]
+        assert chunk.index.equals(new_index)
+
+        ymdT = ymd.T
+        chunk = ymdT.reindex(columns=new_index)
+        if using_copy_on_write:
+            assert chunk.columns.is_(new_index)
+        else:
+            assert chunk.columns is new_index
+
+        chunk = ymdT.loc[:, new_index]
+        assert chunk.columns.equals(new_index)
+
+    def test_groupby_transform(self, multiindex_dataframe_random_data):
+        frame = multiindex_dataframe_random_data
+
+        s = frame["A"]
+        grouper = s.index.get_level_values(0)
+
+        grouped = s.groupby(grouper, group_keys=False)
+
+        applied = grouped.apply(lambda x: x * 2)
+        expected = grouped.transform(lambda x: x * 2)
+        result = applied.reindex(expected.index)
+        tm.assert_series_equal(result, expected, check_names=False)
+
+    def test_groupby_corner(self):
+        midx = MultiIndex(
+            levels=[["foo"], ["bar"], ["baz"]],
+            codes=[[0], [0], [0]],
+            names=["one", "two", "three"],
+        )
+        df = DataFrame(
+            [np.random.default_rng(2).random(4)],
+            columns=["a", "b", "c", "d"],
+            index=midx,
+        )
+        # should work
+        df.groupby(level="three")
+
+    def test_groupby_level_no_obs(self):
+        # #1697
+        midx = MultiIndex.from_tuples(
+            [
+                ("f1", "s1"),
+                ("f1", "s2"),
+                ("f2", "s1"),
+                ("f2", "s2"),
+                ("f3", "s1"),
+                ("f3", "s2"),
+            ]
+        )
+        df = DataFrame([[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12]], columns=midx)
+        df1 = df.loc(axis=1)[df.columns.map(lambda u: u[0] in ["f2", "f3"])]
+
+        msg = "DataFrame.groupby with axis=1 is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            grouped = df1.groupby(axis=1, level=0)
+        result = grouped.sum()
+        assert (result.columns == ["f2", "f3"]).all()
+
+    def test_setitem_with_expansion_multiindex_columns(
+        self, multiindex_year_month_day_dataframe_random_data
+    ):
+        ymd = multiindex_year_month_day_dataframe_random_data
+
+        df = ymd[:5].T
+        df[2000, 1, 10] = df[2000, 1, 7]
+        assert isinstance(df.columns, MultiIndex)
+        assert (df[2000, 1, 10] == df[2000, 1, 7]).all()
+
+    def test_alignment(self):
+        x = Series(
+            data=[1, 2, 3], index=MultiIndex.from_tuples([("A", 1), ("A", 2), ("B", 3)])
+        )
+
+        y = Series(
+            data=[4, 5, 6], index=MultiIndex.from_tuples([("Z", 1), ("Z", 2), ("B", 3)])
+        )
+
+        res = x - y
+        exp_index = x.index.union(y.index)
+        exp = x.reindex(exp_index) - y.reindex(exp_index)
+        tm.assert_series_equal(res, exp)
+
+        # hit non-monotonic code path
+        res = x[::-1] - y[::-1]
+        exp_index = x.index.union(y.index)
+        exp = x.reindex(exp_index) - y.reindex(exp_index)
+        tm.assert_series_equal(res, exp)
+
+    def test_groupby_multilevel(self, multiindex_year_month_day_dataframe_random_data):
+        ymd = multiindex_year_month_day_dataframe_random_data
+
+        result = ymd.groupby(level=[0, 1]).mean()
+
+        k1 = ymd.index.get_level_values(0)
+        k2 = ymd.index.get_level_values(1)
+
+        expected = ymd.groupby([k1, k2]).mean()
+
+        # TODO groupby with level_values drops names
+        tm.assert_frame_equal(result, expected, check_names=False)
+        assert result.index.names == ymd.index.names[:2]
+
+        result2 = ymd.groupby(level=ymd.index.names[:2]).mean()
+        tm.assert_frame_equal(result, result2)
+
+    def test_multilevel_consolidate(self):
+        index = MultiIndex.from_tuples(
+            [("foo", "one"), ("foo", "two"), ("bar", "one"), ("bar", "two")]
+        )
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((4, 4)), index=index, columns=index
+        )
+        df["Totals", ""] = df.sum(1)
+        df = df._consolidate()
+
+    def test_level_with_tuples(self):
+        index = MultiIndex(
+            levels=[[("foo", "bar", 0), ("foo", "baz", 0), ("foo", "qux", 0)], [0, 1]],
+            codes=[[0, 0, 1, 1, 2, 2], [0, 1, 0, 1, 0, 1]],
+        )
+
+        series = Series(np.random.default_rng(2).standard_normal(6), index=index)
+        frame = DataFrame(np.random.default_rng(2).standard_normal((6, 4)), index=index)
+
+        result = series[("foo", "bar", 0)]
+        result2 = series.loc[("foo", "bar", 0)]
+        expected = series[:2]
+        expected.index = expected.index.droplevel(0)
+        tm.assert_series_equal(result, expected)
+        tm.assert_series_equal(result2, expected)
+
+        with pytest.raises(KeyError, match=r"^\(\('foo', 'bar', 0\), 2\)$"):
+            series[("foo", "bar", 0), 2]
+
+        result = frame.loc[("foo", "bar", 0)]
+        result2 = frame.xs(("foo", "bar", 0))
+        expected = frame[:2]
+        expected.index = expected.index.droplevel(0)
+        tm.assert_frame_equal(result, expected)
+        tm.assert_frame_equal(result2, expected)
+
+        index = MultiIndex(
+            levels=[[("foo", "bar"), ("foo", "baz"), ("foo", "qux")], [0, 1]],
+            codes=[[0, 0, 1, 1, 2, 2], [0, 1, 0, 1, 0, 1]],
+        )
+
+        series = Series(np.random.default_rng(2).standard_normal(6), index=index)
+        frame = DataFrame(np.random.default_rng(2).standard_normal((6, 4)), index=index)
+
+        result = series[("foo", "bar")]
+        result2 = series.loc[("foo", "bar")]
+        expected = series[:2]
+        expected.index = expected.index.droplevel(0)
+        tm.assert_series_equal(result, expected)
+        tm.assert_series_equal(result2, expected)
+
+        result = frame.loc[("foo", "bar")]
+        result2 = frame.xs(("foo", "bar"))
+        expected = frame[:2]
+        expected.index = expected.index.droplevel(0)
+        tm.assert_frame_equal(result, expected)
+        tm.assert_frame_equal(result2, expected)
+
+    def test_reindex_level_partial_selection(self, multiindex_dataframe_random_data):
+        frame = multiindex_dataframe_random_data
+
+        result = frame.reindex(["foo", "qux"], level=0)
+        expected = frame.iloc[[0, 1, 2, 7, 8, 9]]
+        tm.assert_frame_equal(result, expected)
+
+        result = frame.T.reindex(["foo", "qux"], axis=1, level=0)
+        tm.assert_frame_equal(result, expected.T)
+
+        result = frame.loc[["foo", "qux"]]
+        tm.assert_frame_equal(result, expected)
+
+        result = frame["A"].loc[["foo", "qux"]]
+        tm.assert_series_equal(result, expected["A"])
+
+        result = frame.T.loc[:, ["foo", "qux"]]
+        tm.assert_frame_equal(result, expected.T)
+
+    @pytest.mark.parametrize("d", [4, "d"])
+    def test_empty_frame_groupby_dtypes_consistency(self, d):
+        # GH 20888
+        group_keys = ["a", "b", "c"]
+        df = DataFrame({"a": [1], "b": [2], "c": [3], "d": [d]})
+
+        g = df[df.a == 2].groupby(group_keys)
+        result = g.first().index
+        expected = MultiIndex(
+            levels=[[1], [2], [3]], codes=[[], [], []], names=["a", "b", "c"]
+        )
+
+        tm.assert_index_equal(result, expected)
+
+    def test_duplicate_groupby_issues(self):
+        idx_tp = [
+            ("600809", "20061231"),
+            ("600809", "20070331"),
+            ("600809", "20070630"),
+            ("600809", "20070331"),
+        ]
+        dt = ["demo", "demo", "demo", "demo"]
+
+        idx = MultiIndex.from_tuples(idx_tp, names=["STK_ID", "RPT_Date"])
+        s = Series(dt, index=idx)
+
+        result = s.groupby(s.index).first()
+        assert len(result) == 3
+
+    def test_subsets_multiindex_dtype(self):
+        # GH 20757
+        data = [["x", 1]]
+        columns = [("a", "b", np.nan), ("a", "c", 0.0)]
+        df = DataFrame(data, columns=MultiIndex.from_tuples(columns))
+        expected = df.dtypes.a.b
+        result = df.a.b.dtypes
+        tm.assert_series_equal(result, expected)
+
+    def test_datetime_object_multiindex(self):
+        data_dic = {
+            (0, datetime.date(2018, 3, 3)): {"A": 1, "B": 10},
+            (0, datetime.date(2018, 3, 4)): {"A": 2, "B": 11},
+            (1, datetime.date(2018, 3, 3)): {"A": 3, "B": 12},
+            (1, datetime.date(2018, 3, 4)): {"A": 4, "B": 13},
+        }
+        result = DataFrame.from_dict(data_dic, orient="index")
+        data = {"A": [1, 2, 3, 4], "B": [10, 11, 12, 13]}
+        index = [
+            [0, 0, 1, 1],
+            [
+                datetime.date(2018, 3, 3),
+                datetime.date(2018, 3, 4),
+                datetime.date(2018, 3, 3),
+                datetime.date(2018, 3, 4),
+            ],
+        ]
+        expected = DataFrame(data=data, index=index)
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_multiindex_with_na(self):
+        df = DataFrame(
+            [
+                ["A", np.nan, 1.23, 4.56],
+                ["A", "G", 1.23, 4.56],
+                ["A", "D", 9.87, 10.54],
+            ],
+            columns=["pivot_0", "pivot_1", "col_1", "col_2"],
+        ).set_index(["pivot_0", "pivot_1"])
+
+        df.at[("A", "F"), "col_2"] = 0.0
+
+        expected = DataFrame(
+            [
+                ["A", np.nan, 1.23, 4.56],
+                ["A", "G", 1.23, 4.56],
+                ["A", "D", 9.87, 10.54],
+                ["A", "F", np.nan, 0.0],
+            ],
+            columns=["pivot_0", "pivot_1", "col_1", "col_2"],
+        ).set_index(["pivot_0", "pivot_1"])
+
+        tm.assert_frame_equal(df, expected)
+
+
+class TestSorted:
+    """everything you wanted to test about sorting"""
+
+    def test_sort_non_lexsorted(self):
+        # degenerate case where we sort but don't
+        # have a satisfying result :<
+        # GH 15797
+        idx = MultiIndex(
+            [["A", "B", "C"], ["c", "b", "a"]], [[0, 1, 2, 0, 1, 2], [0, 2, 1, 1, 0, 2]]
+        )
+
+        df = DataFrame({"col": range(len(idx))}, index=idx, dtype="int64")
+        assert df.index.is_monotonic_increasing is False
+
+        sorted = df.sort_index()
+        assert sorted.index.is_monotonic_increasing is True
+
+        expected = DataFrame(
+            {"col": [1, 4, 5, 2]},
+            index=MultiIndex.from_tuples(
+                [("B", "a"), ("B", "c"), ("C", "a"), ("C", "b")]
+            ),
+            dtype="int64",
+        )
+        result = sorted.loc[pd.IndexSlice["B":"C", "a":"c"], :]
+        tm.assert_frame_equal(result, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/test_nanops.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/test_nanops.py
new file mode 100644
index 0000000000000000000000000000000000000000..a50054f33f382ed913261e0cafd944c2fd86aaa3
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/test_nanops.py
@@ -0,0 +1,1274 @@
+from functools import partial
+
+import numpy as np
+import pytest
+
+import pandas.util._test_decorators as td
+
+from pandas.core.dtypes.common import is_integer_dtype
+
+import pandas as pd
+from pandas import (
+    Series,
+    isna,
+)
+import pandas._testing as tm
+from pandas.core import nanops
+
+use_bn = nanops._USE_BOTTLENECK
+
+
+@pytest.fixture
+def disable_bottleneck(monkeypatch):
+    with monkeypatch.context() as m:
+        m.setattr(nanops, "_USE_BOTTLENECK", False)
+        yield
+
+
+@pytest.fixture
+def arr_shape():
+    return 11, 7
+
+
+@pytest.fixture
+def arr_float(arr_shape):
+    return np.random.default_rng(2).standard_normal(arr_shape)
+
+
+@pytest.fixture
+def arr_complex(arr_float):
+    return arr_float + arr_float * 1j
+
+
+@pytest.fixture
+def arr_int(arr_shape):
+    return np.random.default_rng(2).integers(-10, 10, arr_shape)
+
+
+@pytest.fixture
+def arr_bool(arr_shape):
+    return np.random.default_rng(2).integers(0, 2, arr_shape) == 0
+
+
+@pytest.fixture
+def arr_str(arr_float):
+    return np.abs(arr_float).astype("S")
+
+
+@pytest.fixture
+def arr_utf(arr_float):
+    return np.abs(arr_float).astype("U")
+
+
+@pytest.fixture
+def arr_date(arr_shape):
+    return np.random.default_rng(2).integers(0, 20000, arr_shape).astype("M8[ns]")
+
+
+@pytest.fixture
+def arr_tdelta(arr_shape):
+    return np.random.default_rng(2).integers(0, 20000, arr_shape).astype("m8[ns]")
+
+
+@pytest.fixture
+def arr_nan(arr_shape):
+    return np.tile(np.nan, arr_shape)
+
+
+@pytest.fixture
+def arr_float_nan(arr_float, arr_nan):
+    return np.vstack([arr_float, arr_nan])
+
+
+@pytest.fixture
+def arr_nan_float1(arr_nan, arr_float):
+    return np.vstack([arr_nan, arr_float])
+
+
+@pytest.fixture
+def arr_nan_nan(arr_nan):
+    return np.vstack([arr_nan, arr_nan])
+
+
+@pytest.fixture
+def arr_inf(arr_float):
+    return arr_float * np.inf
+
+
+@pytest.fixture
+def arr_float_inf(arr_float, arr_inf):
+    return np.vstack([arr_float, arr_inf])
+
+
+@pytest.fixture
+def arr_nan_inf(arr_nan, arr_inf):
+    return np.vstack([arr_nan, arr_inf])
+
+
+@pytest.fixture
+def arr_float_nan_inf(arr_float, arr_nan, arr_inf):
+    return np.vstack([arr_float, arr_nan, arr_inf])
+
+
+@pytest.fixture
+def arr_nan_nan_inf(arr_nan, arr_inf):
+    return np.vstack([arr_nan, arr_nan, arr_inf])
+
+
+@pytest.fixture
+def arr_obj(
+    arr_float, arr_int, arr_bool, arr_complex, arr_str, arr_utf, arr_date, arr_tdelta
+):
+    return np.vstack(
+        [
+            arr_float.astype("O"),
+            arr_int.astype("O"),
+            arr_bool.astype("O"),
+            arr_complex.astype("O"),
+            arr_str.astype("O"),
+            arr_utf.astype("O"),
+            arr_date.astype("O"),
+            arr_tdelta.astype("O"),
+        ]
+    )
+
+
+@pytest.fixture
+def arr_nan_nanj(arr_nan):
+    with np.errstate(invalid="ignore"):
+        return arr_nan + arr_nan * 1j
+
+
+@pytest.fixture
+def arr_complex_nan(arr_complex, arr_nan_nanj):
+    with np.errstate(invalid="ignore"):
+        return np.vstack([arr_complex, arr_nan_nanj])
+
+
+@pytest.fixture
+def arr_nan_infj(arr_inf):
+    with np.errstate(invalid="ignore"):
+        return arr_inf * 1j
+
+
+@pytest.fixture
+def arr_complex_nan_infj(arr_complex, arr_nan_infj):
+    with np.errstate(invalid="ignore"):
+        return np.vstack([arr_complex, arr_nan_infj])
+
+
+@pytest.fixture
+def arr_float_1d(arr_float):
+    return arr_float[:, 0]
+
+
+@pytest.fixture
+def arr_nan_1d(arr_nan):
+    return arr_nan[:, 0]
+
+
+@pytest.fixture
+def arr_float_nan_1d(arr_float_nan):
+    return arr_float_nan[:, 0]
+
+
+@pytest.fixture
+def arr_float1_nan_1d(arr_float1_nan):
+    return arr_float1_nan[:, 0]
+
+
+@pytest.fixture
+def arr_nan_float1_1d(arr_nan_float1):
+    return arr_nan_float1[:, 0]
+
+
+class TestnanopsDataFrame:
+    def setup_method(self):
+        nanops._USE_BOTTLENECK = False
+
+        arr_shape = (11, 7)
+
+        self.arr_float = np.random.default_rng(2).standard_normal(arr_shape)
+        self.arr_float1 = np.random.default_rng(2).standard_normal(arr_shape)
+        self.arr_complex = self.arr_float + self.arr_float1 * 1j
+        self.arr_int = np.random.default_rng(2).integers(-10, 10, arr_shape)
+        self.arr_bool = np.random.default_rng(2).integers(0, 2, arr_shape) == 0
+        self.arr_str = np.abs(self.arr_float).astype("S")
+        self.arr_utf = np.abs(self.arr_float).astype("U")
+        self.arr_date = (
+            np.random.default_rng(2).integers(0, 20000, arr_shape).astype("M8[ns]")
+        )
+        self.arr_tdelta = (
+            np.random.default_rng(2).integers(0, 20000, arr_shape).astype("m8[ns]")
+        )
+
+        self.arr_nan = np.tile(np.nan, arr_shape)
+        self.arr_float_nan = np.vstack([self.arr_float, self.arr_nan])
+        self.arr_float1_nan = np.vstack([self.arr_float1, self.arr_nan])
+        self.arr_nan_float1 = np.vstack([self.arr_nan, self.arr_float1])
+        self.arr_nan_nan = np.vstack([self.arr_nan, self.arr_nan])
+
+        self.arr_inf = self.arr_float * np.inf
+        self.arr_float_inf = np.vstack([self.arr_float, self.arr_inf])
+
+        self.arr_nan_inf = np.vstack([self.arr_nan, self.arr_inf])
+        self.arr_float_nan_inf = np.vstack([self.arr_float, self.arr_nan, self.arr_inf])
+        self.arr_nan_nan_inf = np.vstack([self.arr_nan, self.arr_nan, self.arr_inf])
+        self.arr_obj = np.vstack(
+            [
+                self.arr_float.astype("O"),
+                self.arr_int.astype("O"),
+                self.arr_bool.astype("O"),
+                self.arr_complex.astype("O"),
+                self.arr_str.astype("O"),
+                self.arr_utf.astype("O"),
+                self.arr_date.astype("O"),
+                self.arr_tdelta.astype("O"),
+            ]
+        )
+
+        with np.errstate(invalid="ignore"):
+            self.arr_nan_nanj = self.arr_nan + self.arr_nan * 1j
+            self.arr_complex_nan = np.vstack([self.arr_complex, self.arr_nan_nanj])
+
+            self.arr_nan_infj = self.arr_inf * 1j
+            self.arr_complex_nan_infj = np.vstack([self.arr_complex, self.arr_nan_infj])
+
+        self.arr_float_2d = self.arr_float
+        self.arr_float1_2d = self.arr_float1
+
+        self.arr_nan_2d = self.arr_nan
+        self.arr_float_nan_2d = self.arr_float_nan
+        self.arr_float1_nan_2d = self.arr_float1_nan
+        self.arr_nan_float1_2d = self.arr_nan_float1
+
+        self.arr_float_1d = self.arr_float[:, 0]
+        self.arr_float1_1d = self.arr_float1[:, 0]
+
+        self.arr_nan_1d = self.arr_nan[:, 0]
+        self.arr_float_nan_1d = self.arr_float_nan[:, 0]
+        self.arr_float1_nan_1d = self.arr_float1_nan[:, 0]
+        self.arr_nan_float1_1d = self.arr_nan_float1[:, 0]
+
+    def teardown_method(self):
+        nanops._USE_BOTTLENECK = use_bn
+
+    def check_results(self, targ, res, axis, check_dtype=True):
+        res = getattr(res, "asm8", res)
+
+        if (
+            axis != 0
+            and hasattr(targ, "shape")
+            and targ.ndim
+            and targ.shape != res.shape
+        ):
+            res = np.split(res, [targ.shape[0]], axis=0)[0]
+
+        try:
+            tm.assert_almost_equal(targ, res, check_dtype=check_dtype)
+        except AssertionError:
+            # handle timedelta dtypes
+            if hasattr(targ, "dtype") and targ.dtype == "m8[ns]":
+                raise
+
+            # There are sometimes rounding errors with
+            # complex and object dtypes.
+            # If it isn't one of those, re-raise the error.
+            if not hasattr(res, "dtype") or res.dtype.kind not in ["c", "O"]:
+                raise
+            # convert object dtypes to something that can be split into
+            # real and imaginary parts
+            if res.dtype.kind == "O":
+                if targ.dtype.kind != "O":
+                    res = res.astype(targ.dtype)
+                else:
+                    cast_dtype = "c16" if hasattr(np, "complex128") else "f8"
+                    res = res.astype(cast_dtype)
+                    targ = targ.astype(cast_dtype)
+            # there should never be a case where numpy returns an object
+            # but nanops doesn't, so make that an exception
+            elif targ.dtype.kind == "O":
+                raise
+            tm.assert_almost_equal(np.real(targ), np.real(res), check_dtype=check_dtype)
+            tm.assert_almost_equal(np.imag(targ), np.imag(res), check_dtype=check_dtype)
+
+    def check_fun_data(
+        self,
+        testfunc,
+        targfunc,
+        testarval,
+        targarval,
+        skipna,
+        check_dtype=True,
+        empty_targfunc=None,
+        **kwargs,
+    ):
+        for axis in list(range(targarval.ndim)) + [None]:
+            targartempval = targarval if skipna else testarval
+            if skipna and empty_targfunc and isna(targartempval).all():
+                targ = empty_targfunc(targartempval, axis=axis, **kwargs)
+            else:
+                targ = targfunc(targartempval, axis=axis, **kwargs)
+
+            if targartempval.dtype == object and (
+                targfunc is np.any or targfunc is np.all
+            ):
+                # GH#12863 the numpy functions will retain e.g. floatiness
+                if isinstance(targ, np.ndarray):
+                    targ = targ.astype(bool)
+                else:
+                    targ = bool(targ)
+
+            res = testfunc(testarval, axis=axis, skipna=skipna, **kwargs)
+
+            if (
+                isinstance(targ, np.complex128)
+                and isinstance(res, float)
+                and np.isnan(targ)
+                and np.isnan(res)
+            ):
+                # GH#18463
+                targ = res
+
+            self.check_results(targ, res, axis, check_dtype=check_dtype)
+            if skipna:
+                res = testfunc(testarval, axis=axis, **kwargs)
+                self.check_results(targ, res, axis, check_dtype=check_dtype)
+            if axis is None:
+                res = testfunc(testarval, skipna=skipna, **kwargs)
+                self.check_results(targ, res, axis, check_dtype=check_dtype)
+            if skipna and axis is None:
+                res = testfunc(testarval, **kwargs)
+                self.check_results(targ, res, axis, check_dtype=check_dtype)
+
+        if testarval.ndim <= 1:
+            return
+
+        # Recurse on lower-dimension
+        testarval2 = np.take(testarval, 0, axis=-1)
+        targarval2 = np.take(targarval, 0, axis=-1)
+        self.check_fun_data(
+            testfunc,
+            targfunc,
+            testarval2,
+            targarval2,
+            skipna=skipna,
+            check_dtype=check_dtype,
+            empty_targfunc=empty_targfunc,
+            **kwargs,
+        )
+
+    def check_fun(
+        self, testfunc, targfunc, testar, skipna, empty_targfunc=None, **kwargs
+    ):
+        targar = testar
+        if testar.endswith("_nan") and hasattr(self, testar[:-4]):
+            targar = testar[:-4]
+
+        testarval = getattr(self, testar)
+        targarval = getattr(self, targar)
+        self.check_fun_data(
+            testfunc,
+            targfunc,
+            testarval,
+            targarval,
+            skipna=skipna,
+            empty_targfunc=empty_targfunc,
+            **kwargs,
+        )
+
+    def check_funs(
+        self,
+        testfunc,
+        targfunc,
+        skipna,
+        allow_complex=True,
+        allow_all_nan=True,
+        allow_date=True,
+        allow_tdelta=True,
+        allow_obj=True,
+        **kwargs,
+    ):
+        self.check_fun(testfunc, targfunc, "arr_float", skipna, **kwargs)
+        self.check_fun(testfunc, targfunc, "arr_float_nan", skipna, **kwargs)
+        self.check_fun(testfunc, targfunc, "arr_int", skipna, **kwargs)
+        self.check_fun(testfunc, targfunc, "arr_bool", skipna, **kwargs)
+        objs = [
+            self.arr_float.astype("O"),
+            self.arr_int.astype("O"),
+            self.arr_bool.astype("O"),
+        ]
+
+        if allow_all_nan:
+            self.check_fun(testfunc, targfunc, "arr_nan", skipna, **kwargs)
+
+        if allow_complex:
+            self.check_fun(testfunc, targfunc, "arr_complex", skipna, **kwargs)
+            self.check_fun(testfunc, targfunc, "arr_complex_nan", skipna, **kwargs)
+            if allow_all_nan:
+                self.check_fun(testfunc, targfunc, "arr_nan_nanj", skipna, **kwargs)
+            objs += [self.arr_complex.astype("O")]
+
+        if allow_date:
+            targfunc(self.arr_date)
+            self.check_fun(testfunc, targfunc, "arr_date", skipna, **kwargs)
+            objs += [self.arr_date.astype("O")]
+
+        if allow_tdelta:
+            try:
+                targfunc(self.arr_tdelta)
+            except TypeError:
+                pass
+            else:
+                self.check_fun(testfunc, targfunc, "arr_tdelta", skipna, **kwargs)
+                objs += [self.arr_tdelta.astype("O")]
+
+        if allow_obj:
+            self.arr_obj = np.vstack(objs)
+            # some nanops handle object dtypes better than their numpy
+            # counterparts, so the numpy functions need to be given something
+            # else
+            if allow_obj == "convert":
+                targfunc = partial(
+                    self._badobj_wrap, func=targfunc, allow_complex=allow_complex
+                )
+            self.check_fun(testfunc, targfunc, "arr_obj", skipna, **kwargs)
+
+    def _badobj_wrap(self, value, func, allow_complex=True, **kwargs):
+        if value.dtype.kind == "O":
+            if allow_complex:
+                value = value.astype("c16")
+            else:
+                value = value.astype("f8")
+        return func(value, **kwargs)
+
+    @pytest.mark.parametrize(
+        "nan_op,np_op", [(nanops.nanany, np.any), (nanops.nanall, np.all)]
+    )
+    def test_nan_funcs(self, nan_op, np_op, skipna):
+        self.check_funs(nan_op, np_op, skipna, allow_all_nan=False, allow_date=False)
+
+    def test_nansum(self, skipna):
+        self.check_funs(
+            nanops.nansum,
+            np.sum,
+            skipna,
+            allow_date=False,
+            check_dtype=False,
+            empty_targfunc=np.nansum,
+        )
+
+    def test_nanmean(self, skipna):
+        self.check_funs(
+            nanops.nanmean, np.mean, skipna, allow_obj=False, allow_date=False
+        )
+
+    @pytest.mark.filterwarnings("ignore::RuntimeWarning")
+    def test_nanmedian(self, skipna):
+        self.check_funs(
+            nanops.nanmedian,
+            np.median,
+            skipna,
+            allow_complex=False,
+            allow_date=False,
+            allow_obj="convert",
+        )
+
+    @pytest.mark.parametrize("ddof", range(3))
+    def test_nanvar(self, ddof, skipna):
+        self.check_funs(
+            nanops.nanvar,
+            np.var,
+            skipna,
+            allow_complex=False,
+            allow_date=False,
+            allow_obj="convert",
+            ddof=ddof,
+        )
+
+    @pytest.mark.parametrize("ddof", range(3))
+    def test_nanstd(self, ddof, skipna):
+        self.check_funs(
+            nanops.nanstd,
+            np.std,
+            skipna,
+            allow_complex=False,
+            allow_date=False,
+            allow_obj="convert",
+            ddof=ddof,
+        )
+
+    @pytest.mark.parametrize("ddof", range(3))
+    def test_nansem(self, ddof, skipna):
+        sp_stats = pytest.importorskip("scipy.stats")
+
+        with np.errstate(invalid="ignore"):
+            self.check_funs(
+                nanops.nansem,
+                sp_stats.sem,
+                skipna,
+                allow_complex=False,
+                allow_date=False,
+                allow_tdelta=False,
+                allow_obj="convert",
+                ddof=ddof,
+            )
+
+    @pytest.mark.filterwarnings("ignore::RuntimeWarning")
+    @pytest.mark.parametrize(
+        "nan_op,np_op", [(nanops.nanmin, np.min), (nanops.nanmax, np.max)]
+    )
+    def test_nanops_with_warnings(self, nan_op, np_op, skipna):
+        self.check_funs(nan_op, np_op, skipna, allow_obj=False)
+
+    def _argminmax_wrap(self, value, axis=None, func=None):
+        res = func(value, axis)
+        nans = np.min(value, axis)
+        nullnan = isna(nans)
+        if res.ndim:
+            res[nullnan] = -1
+        elif (
+            hasattr(nullnan, "all")
+            and nullnan.all()
+            or not hasattr(nullnan, "all")
+            and nullnan
+        ):
+            res = -1
+        return res
+
+    @pytest.mark.filterwarnings("ignore::RuntimeWarning")
+    def test_nanargmax(self, skipna):
+        func = partial(self._argminmax_wrap, func=np.argmax)
+        self.check_funs(nanops.nanargmax, func, skipna, allow_obj=False)
+
+    @pytest.mark.filterwarnings("ignore::RuntimeWarning")
+    def test_nanargmin(self, skipna):
+        func = partial(self._argminmax_wrap, func=np.argmin)
+        self.check_funs(nanops.nanargmin, func, skipna, allow_obj=False)
+
+    def _skew_kurt_wrap(self, values, axis=None, func=None):
+        if not isinstance(values.dtype.type, np.floating):
+            values = values.astype("f8")
+        result = func(values, axis=axis, bias=False)
+        # fix for handling cases where all elements in an axis are the same
+        if isinstance(result, np.ndarray):
+            result[np.max(values, axis=axis) == np.min(values, axis=axis)] = 0
+            return result
+        elif np.max(values) == np.min(values):
+            return 0.0
+        return result
+
+    def test_nanskew(self, skipna):
+        sp_stats = pytest.importorskip("scipy.stats")
+
+        func = partial(self._skew_kurt_wrap, func=sp_stats.skew)
+        with np.errstate(invalid="ignore"):
+            self.check_funs(
+                nanops.nanskew,
+                func,
+                skipna,
+                allow_complex=False,
+                allow_date=False,
+                allow_tdelta=False,
+            )
+
+    def test_nankurt(self, skipna):
+        sp_stats = pytest.importorskip("scipy.stats")
+
+        func1 = partial(sp_stats.kurtosis, fisher=True)
+        func = partial(self._skew_kurt_wrap, func=func1)
+        with np.errstate(invalid="ignore"):
+            self.check_funs(
+                nanops.nankurt,
+                func,
+                skipna,
+                allow_complex=False,
+                allow_date=False,
+                allow_tdelta=False,
+            )
+
+    def test_nanprod(self, skipna):
+        self.check_funs(
+            nanops.nanprod,
+            np.prod,
+            skipna,
+            allow_date=False,
+            allow_tdelta=False,
+            empty_targfunc=np.nanprod,
+        )
+
+    def check_nancorr_nancov_2d(self, checkfun, targ0, targ1, **kwargs):
+        res00 = checkfun(self.arr_float_2d, self.arr_float1_2d, **kwargs)
+        res01 = checkfun(
+            self.arr_float_2d,
+            self.arr_float1_2d,
+            min_periods=len(self.arr_float_2d) - 1,
+            **kwargs,
+        )
+        tm.assert_almost_equal(targ0, res00)
+        tm.assert_almost_equal(targ0, res01)
+
+        res10 = checkfun(self.arr_float_nan_2d, self.arr_float1_nan_2d, **kwargs)
+        res11 = checkfun(
+            self.arr_float_nan_2d,
+            self.arr_float1_nan_2d,
+            min_periods=len(self.arr_float_2d) - 1,
+            **kwargs,
+        )
+        tm.assert_almost_equal(targ1, res10)
+        tm.assert_almost_equal(targ1, res11)
+
+        targ2 = np.nan
+        res20 = checkfun(self.arr_nan_2d, self.arr_float1_2d, **kwargs)
+        res21 = checkfun(self.arr_float_2d, self.arr_nan_2d, **kwargs)
+        res22 = checkfun(self.arr_nan_2d, self.arr_nan_2d, **kwargs)
+        res23 = checkfun(self.arr_float_nan_2d, self.arr_nan_float1_2d, **kwargs)
+        res24 = checkfun(
+            self.arr_float_nan_2d,
+            self.arr_nan_float1_2d,
+            min_periods=len(self.arr_float_2d) - 1,
+            **kwargs,
+        )
+        res25 = checkfun(
+            self.arr_float_2d,
+            self.arr_float1_2d,
+            min_periods=len(self.arr_float_2d) + 1,
+            **kwargs,
+        )
+        tm.assert_almost_equal(targ2, res20)
+        tm.assert_almost_equal(targ2, res21)
+        tm.assert_almost_equal(targ2, res22)
+        tm.assert_almost_equal(targ2, res23)
+        tm.assert_almost_equal(targ2, res24)
+        tm.assert_almost_equal(targ2, res25)
+
+    def check_nancorr_nancov_1d(self, checkfun, targ0, targ1, **kwargs):
+        res00 = checkfun(self.arr_float_1d, self.arr_float1_1d, **kwargs)
+        res01 = checkfun(
+            self.arr_float_1d,
+            self.arr_float1_1d,
+            min_periods=len(self.arr_float_1d) - 1,
+            **kwargs,
+        )
+        tm.assert_almost_equal(targ0, res00)
+        tm.assert_almost_equal(targ0, res01)
+
+        res10 = checkfun(self.arr_float_nan_1d, self.arr_float1_nan_1d, **kwargs)
+        res11 = checkfun(
+            self.arr_float_nan_1d,
+            self.arr_float1_nan_1d,
+            min_periods=len(self.arr_float_1d) - 1,
+            **kwargs,
+        )
+        tm.assert_almost_equal(targ1, res10)
+        tm.assert_almost_equal(targ1, res11)
+
+        targ2 = np.nan
+        res20 = checkfun(self.arr_nan_1d, self.arr_float1_1d, **kwargs)
+        res21 = checkfun(self.arr_float_1d, self.arr_nan_1d, **kwargs)
+        res22 = checkfun(self.arr_nan_1d, self.arr_nan_1d, **kwargs)
+        res23 = checkfun(self.arr_float_nan_1d, self.arr_nan_float1_1d, **kwargs)
+        res24 = checkfun(
+            self.arr_float_nan_1d,
+            self.arr_nan_float1_1d,
+            min_periods=len(self.arr_float_1d) - 1,
+            **kwargs,
+        )
+        res25 = checkfun(
+            self.arr_float_1d,
+            self.arr_float1_1d,
+            min_periods=len(self.arr_float_1d) + 1,
+            **kwargs,
+        )
+        tm.assert_almost_equal(targ2, res20)
+        tm.assert_almost_equal(targ2, res21)
+        tm.assert_almost_equal(targ2, res22)
+        tm.assert_almost_equal(targ2, res23)
+        tm.assert_almost_equal(targ2, res24)
+        tm.assert_almost_equal(targ2, res25)
+
+    def test_nancorr(self):
+        targ0 = np.corrcoef(self.arr_float_2d, self.arr_float1_2d)[0, 1]
+        targ1 = np.corrcoef(self.arr_float_2d.flat, self.arr_float1_2d.flat)[0, 1]
+        self.check_nancorr_nancov_2d(nanops.nancorr, targ0, targ1)
+        targ0 = np.corrcoef(self.arr_float_1d, self.arr_float1_1d)[0, 1]
+        targ1 = np.corrcoef(self.arr_float_1d.flat, self.arr_float1_1d.flat)[0, 1]
+        self.check_nancorr_nancov_1d(nanops.nancorr, targ0, targ1, method="pearson")
+
+    def test_nancorr_pearson(self):
+        targ0 = np.corrcoef(self.arr_float_2d, self.arr_float1_2d)[0, 1]
+        targ1 = np.corrcoef(self.arr_float_2d.flat, self.arr_float1_2d.flat)[0, 1]
+        self.check_nancorr_nancov_2d(nanops.nancorr, targ0, targ1, method="pearson")
+        targ0 = np.corrcoef(self.arr_float_1d, self.arr_float1_1d)[0, 1]
+        targ1 = np.corrcoef(self.arr_float_1d.flat, self.arr_float1_1d.flat)[0, 1]
+        self.check_nancorr_nancov_1d(nanops.nancorr, targ0, targ1, method="pearson")
+
+    def test_nancorr_kendall(self):
+        sp_stats = pytest.importorskip("scipy.stats")
+
+        targ0 = sp_stats.kendalltau(self.arr_float_2d, self.arr_float1_2d)[0]
+        targ1 = sp_stats.kendalltau(self.arr_float_2d.flat, self.arr_float1_2d.flat)[0]
+        self.check_nancorr_nancov_2d(nanops.nancorr, targ0, targ1, method="kendall")
+        targ0 = sp_stats.kendalltau(self.arr_float_1d, self.arr_float1_1d)[0]
+        targ1 = sp_stats.kendalltau(self.arr_float_1d.flat, self.arr_float1_1d.flat)[0]
+        self.check_nancorr_nancov_1d(nanops.nancorr, targ0, targ1, method="kendall")
+
+    def test_nancorr_spearman(self):
+        sp_stats = pytest.importorskip("scipy.stats")
+
+        targ0 = sp_stats.spearmanr(self.arr_float_2d, self.arr_float1_2d)[0]
+        targ1 = sp_stats.spearmanr(self.arr_float_2d.flat, self.arr_float1_2d.flat)[0]
+        self.check_nancorr_nancov_2d(nanops.nancorr, targ0, targ1, method="spearman")
+        targ0 = sp_stats.spearmanr(self.arr_float_1d, self.arr_float1_1d)[0]
+        targ1 = sp_stats.spearmanr(self.arr_float_1d.flat, self.arr_float1_1d.flat)[0]
+        self.check_nancorr_nancov_1d(nanops.nancorr, targ0, targ1, method="spearman")
+
+    def test_invalid_method(self):
+        pytest.importorskip("scipy")
+        targ0 = np.corrcoef(self.arr_float_2d, self.arr_float1_2d)[0, 1]
+        targ1 = np.corrcoef(self.arr_float_2d.flat, self.arr_float1_2d.flat)[0, 1]
+        msg = "Unknown method 'foo', expected one of 'kendall', 'spearman'"
+        with pytest.raises(ValueError, match=msg):
+            self.check_nancorr_nancov_1d(nanops.nancorr, targ0, targ1, method="foo")
+
+    def test_nancov(self):
+        targ0 = np.cov(self.arr_float_2d, self.arr_float1_2d)[0, 1]
+        targ1 = np.cov(self.arr_float_2d.flat, self.arr_float1_2d.flat)[0, 1]
+        self.check_nancorr_nancov_2d(nanops.nancov, targ0, targ1)
+        targ0 = np.cov(self.arr_float_1d, self.arr_float1_1d)[0, 1]
+        targ1 = np.cov(self.arr_float_1d.flat, self.arr_float1_1d.flat)[0, 1]
+        self.check_nancorr_nancov_1d(nanops.nancov, targ0, targ1)
+
+
+@pytest.mark.parametrize(
+    "arr, correct",
+    [
+        ("arr_complex", False),
+        ("arr_int", False),
+        ("arr_bool", False),
+        ("arr_str", False),
+        ("arr_utf", False),
+        ("arr_complex", False),
+        ("arr_complex_nan", False),
+        ("arr_nan_nanj", False),
+        ("arr_nan_infj", True),
+        ("arr_complex_nan_infj", True),
+    ],
+)
+def test_has_infs_non_float(request, arr, correct, disable_bottleneck):
+    val = request.getfixturevalue(arr)
+    while getattr(val, "ndim", True):
+        res0 = nanops._has_infs(val)
+        if correct:
+            assert res0
+        else:
+            assert not res0
+
+        if not hasattr(val, "ndim"):
+            break
+
+        # Reduce dimension for next step in the loop
+        val = np.take(val, 0, axis=-1)
+
+
+@pytest.mark.parametrize(
+    "arr, correct",
+    [
+        ("arr_float", False),
+        ("arr_nan", False),
+        ("arr_float_nan", False),
+        ("arr_nan_nan", False),
+        ("arr_float_inf", True),
+        ("arr_inf", True),
+        ("arr_nan_inf", True),
+        ("arr_float_nan_inf", True),
+        ("arr_nan_nan_inf", True),
+    ],
+)
+@pytest.mark.parametrize("astype", [None, "f4", "f2"])
+def test_has_infs_floats(request, arr, correct, astype, disable_bottleneck):
+    val = request.getfixturevalue(arr)
+    if astype is not None:
+        val = val.astype(astype)
+    while getattr(val, "ndim", True):
+        res0 = nanops._has_infs(val)
+        if correct:
+            assert res0
+        else:
+            assert not res0
+
+        if not hasattr(val, "ndim"):
+            break
+
+        # Reduce dimension for next step in the loop
+        val = np.take(val, 0, axis=-1)
+
+
+@pytest.mark.parametrize(
+    "fixture", ["arr_float", "arr_complex", "arr_int", "arr_bool", "arr_str", "arr_utf"]
+)
+def test_bn_ok_dtype(fixture, request, disable_bottleneck):
+    obj = request.getfixturevalue(fixture)
+    assert nanops._bn_ok_dtype(obj.dtype, "test")
+
+
+@pytest.mark.parametrize(
+    "fixture",
+    [
+        "arr_date",
+        "arr_tdelta",
+        "arr_obj",
+    ],
+)
+def test_bn_not_ok_dtype(fixture, request, disable_bottleneck):
+    obj = request.getfixturevalue(fixture)
+    assert not nanops._bn_ok_dtype(obj.dtype, "test")
+
+
+class TestEnsureNumeric:
+    def test_numeric_values(self):
+        # Test integer
+        assert nanops._ensure_numeric(1) == 1
+
+        # Test float
+        assert nanops._ensure_numeric(1.1) == 1.1
+
+        # Test complex
+        assert nanops._ensure_numeric(1 + 2j) == 1 + 2j
+
+    def test_ndarray(self):
+        # Test numeric ndarray
+        values = np.array([1, 2, 3])
+        assert np.allclose(nanops._ensure_numeric(values), values)
+
+        # Test object ndarray
+        o_values = values.astype(object)
+        assert np.allclose(nanops._ensure_numeric(o_values), values)
+
+        # Test convertible string ndarray
+        s_values = np.array(["1", "2", "3"], dtype=object)
+        msg = r"Could not convert \['1' '2' '3'\] to numeric"
+        with pytest.raises(TypeError, match=msg):
+            nanops._ensure_numeric(s_values)
+
+        # Test non-convertible string ndarray
+        s_values = np.array(["foo", "bar", "baz"], dtype=object)
+        msg = r"Could not convert .* to numeric"
+        with pytest.raises(TypeError, match=msg):
+            nanops._ensure_numeric(s_values)
+
+    def test_convertable_values(self):
+        with pytest.raises(TypeError, match="Could not convert string '1' to numeric"):
+            nanops._ensure_numeric("1")
+        with pytest.raises(
+            TypeError, match="Could not convert string '1.1' to numeric"
+        ):
+            nanops._ensure_numeric("1.1")
+        with pytest.raises(
+            TypeError, match=r"Could not convert string '1\+1j' to numeric"
+        ):
+            nanops._ensure_numeric("1+1j")
+
+    def test_non_convertable_values(self):
+        msg = "Could not convert string 'foo' to numeric"
+        with pytest.raises(TypeError, match=msg):
+            nanops._ensure_numeric("foo")
+
+        # with the wrong type, python raises TypeError for us
+        msg = "argument must be a string or a number"
+        with pytest.raises(TypeError, match=msg):
+            nanops._ensure_numeric({})
+        with pytest.raises(TypeError, match=msg):
+            nanops._ensure_numeric([])
+
+
+class TestNanvarFixedValues:
+    # xref GH10242
+    # Samples from a normal distribution.
+    @pytest.fixture
+    def variance(self):
+        return 3.0
+
+    @pytest.fixture
+    def samples(self, variance):
+        return self.prng.normal(scale=variance**0.5, size=100000)
+
+    def test_nanvar_all_finite(self, samples, variance):
+        actual_variance = nanops.nanvar(samples)
+        tm.assert_almost_equal(actual_variance, variance, rtol=1e-2)
+
+    def test_nanvar_nans(self, samples, variance):
+        samples_test = np.nan * np.ones(2 * samples.shape[0])
+        samples_test[::2] = samples
+
+        actual_variance = nanops.nanvar(samples_test, skipna=True)
+        tm.assert_almost_equal(actual_variance, variance, rtol=1e-2)
+
+        actual_variance = nanops.nanvar(samples_test, skipna=False)
+        tm.assert_almost_equal(actual_variance, np.nan, rtol=1e-2)
+
+    def test_nanstd_nans(self, samples, variance):
+        samples_test = np.nan * np.ones(2 * samples.shape[0])
+        samples_test[::2] = samples
+
+        actual_std = nanops.nanstd(samples_test, skipna=True)
+        tm.assert_almost_equal(actual_std, variance**0.5, rtol=1e-2)
+
+        actual_std = nanops.nanvar(samples_test, skipna=False)
+        tm.assert_almost_equal(actual_std, np.nan, rtol=1e-2)
+
+    def test_nanvar_axis(self, samples, variance):
+        # Generate some sample data.
+        samples_unif = self.prng.uniform(size=samples.shape[0])
+        samples = np.vstack([samples, samples_unif])
+
+        actual_variance = nanops.nanvar(samples, axis=1)
+        tm.assert_almost_equal(
+            actual_variance, np.array([variance, 1.0 / 12]), rtol=1e-2
+        )
+
+    def test_nanvar_ddof(self):
+        n = 5
+        samples = self.prng.uniform(size=(10000, n + 1))
+        samples[:, -1] = np.nan  # Force use of our own algorithm.
+
+        variance_0 = nanops.nanvar(samples, axis=1, skipna=True, ddof=0).mean()
+        variance_1 = nanops.nanvar(samples, axis=1, skipna=True, ddof=1).mean()
+        variance_2 = nanops.nanvar(samples, axis=1, skipna=True, ddof=2).mean()
+
+        # The unbiased estimate.
+        var = 1.0 / 12
+        tm.assert_almost_equal(variance_1, var, rtol=1e-2)
+
+        # The underestimated variance.
+        tm.assert_almost_equal(variance_0, (n - 1.0) / n * var, rtol=1e-2)
+
+        # The overestimated variance.
+        tm.assert_almost_equal(variance_2, (n - 1.0) / (n - 2.0) * var, rtol=1e-2)
+
+    @pytest.mark.parametrize("axis", range(2))
+    @pytest.mark.parametrize("ddof", range(3))
+    def test_ground_truth(self, axis, ddof):
+        # Test against values that were precomputed with Numpy.
+        samples = np.empty((4, 4))
+        samples[:3, :3] = np.array(
+            [
+                [0.97303362, 0.21869576, 0.55560287],
+                [0.72980153, 0.03109364, 0.99155171],
+                [0.09317602, 0.60078248, 0.15871292],
+            ]
+        )
+        samples[3] = samples[:, 3] = np.nan
+
+        # Actual variances along axis=0, 1 for ddof=0, 1, 2
+        variance = np.array(
+            [
+                [
+                    [0.13762259, 0.05619224, 0.11568816],
+                    [0.20643388, 0.08428837, 0.17353224],
+                    [0.41286776, 0.16857673, 0.34706449],
+                ],
+                [
+                    [0.09519783, 0.16435395, 0.05082054],
+                    [0.14279674, 0.24653093, 0.07623082],
+                    [0.28559348, 0.49306186, 0.15246163],
+                ],
+            ]
+        )
+
+        # Test nanvar.
+        var = nanops.nanvar(samples, skipna=True, axis=axis, ddof=ddof)
+        tm.assert_almost_equal(var[:3], variance[axis, ddof])
+        assert np.isnan(var[3])
+
+        # Test nanstd.
+        std = nanops.nanstd(samples, skipna=True, axis=axis, ddof=ddof)
+        tm.assert_almost_equal(std[:3], variance[axis, ddof] ** 0.5)
+        assert np.isnan(std[3])
+
+    @pytest.mark.parametrize("ddof", range(3))
+    def test_nanstd_roundoff(self, ddof):
+        # Regression test for GH 10242 (test data taken from GH 10489). Ensure
+        # that variance is stable.
+        data = Series(766897346 * np.ones(10))
+        result = data.std(ddof=ddof)
+        assert result == 0.0
+
+    @property
+    def prng(self):
+        return np.random.default_rng(2)
+
+
+class TestNanskewFixedValues:
+    # xref GH 11974
+    # Test data + skewness value (computed with scipy.stats.skew)
+    @pytest.fixture
+    def samples(self):
+        return np.sin(np.linspace(0, 1, 200))
+
+    @pytest.fixture
+    def actual_skew(self):
+        return -0.1875895205961754
+
+    @pytest.mark.parametrize("val", [3075.2, 3075.3, 3075.5])
+    def test_constant_series(self, val):
+        # xref GH 11974
+        data = val * np.ones(300)
+        skew = nanops.nanskew(data)
+        assert skew == 0.0
+
+    def test_all_finite(self):
+        alpha, beta = 0.3, 0.1
+        left_tailed = self.prng.beta(alpha, beta, size=100)
+        assert nanops.nanskew(left_tailed) < 0
+
+        alpha, beta = 0.1, 0.3
+        right_tailed = self.prng.beta(alpha, beta, size=100)
+        assert nanops.nanskew(right_tailed) > 0
+
+    def test_ground_truth(self, samples, actual_skew):
+        skew = nanops.nanskew(samples)
+        tm.assert_almost_equal(skew, actual_skew)
+
+    def test_axis(self, samples, actual_skew):
+        samples = np.vstack([samples, np.nan * np.ones(len(samples))])
+        skew = nanops.nanskew(samples, axis=1)
+        tm.assert_almost_equal(skew, np.array([actual_skew, np.nan]))
+
+    def test_nans(self, samples):
+        samples = np.hstack([samples, np.nan])
+        skew = nanops.nanskew(samples, skipna=False)
+        assert np.isnan(skew)
+
+    def test_nans_skipna(self, samples, actual_skew):
+        samples = np.hstack([samples, np.nan])
+        skew = nanops.nanskew(samples, skipna=True)
+        tm.assert_almost_equal(skew, actual_skew)
+
+    @property
+    def prng(self):
+        return np.random.default_rng(2)
+
+
+class TestNankurtFixedValues:
+    # xref GH 11974
+    # Test data + kurtosis value (computed with scipy.stats.kurtosis)
+    @pytest.fixture
+    def samples(self):
+        return np.sin(np.linspace(0, 1, 200))
+
+    @pytest.fixture
+    def actual_kurt(self):
+        return -1.2058303433799713
+
+    @pytest.mark.parametrize("val", [3075.2, 3075.3, 3075.5])
+    def test_constant_series(self, val):
+        # xref GH 11974
+        data = val * np.ones(300)
+        kurt = nanops.nankurt(data)
+        assert kurt == 0.0
+
+    def test_all_finite(self):
+        alpha, beta = 0.3, 0.1
+        left_tailed = self.prng.beta(alpha, beta, size=100)
+        assert nanops.nankurt(left_tailed) < 2
+
+        alpha, beta = 0.1, 0.3
+        right_tailed = self.prng.beta(alpha, beta, size=100)
+        assert nanops.nankurt(right_tailed) < 0
+
+    def test_ground_truth(self, samples, actual_kurt):
+        kurt = nanops.nankurt(samples)
+        tm.assert_almost_equal(kurt, actual_kurt)
+
+    def test_axis(self, samples, actual_kurt):
+        samples = np.vstack([samples, np.nan * np.ones(len(samples))])
+        kurt = nanops.nankurt(samples, axis=1)
+        tm.assert_almost_equal(kurt, np.array([actual_kurt, np.nan]))
+
+    def test_nans(self, samples):
+        samples = np.hstack([samples, np.nan])
+        kurt = nanops.nankurt(samples, skipna=False)
+        assert np.isnan(kurt)
+
+    def test_nans_skipna(self, samples, actual_kurt):
+        samples = np.hstack([samples, np.nan])
+        kurt = nanops.nankurt(samples, skipna=True)
+        tm.assert_almost_equal(kurt, actual_kurt)
+
+    @property
+    def prng(self):
+        return np.random.default_rng(2)
+
+
+class TestDatetime64NaNOps:
+    @pytest.fixture(params=["s", "ms", "us", "ns"])
+    def unit(self, request):
+        return request.param
+
+    # Enabling mean changes the behavior of DataFrame.mean
+    # See https://github.com/pandas-dev/pandas/issues/24752
+    def test_nanmean(self, unit):
+        dti = pd.date_range("2016-01-01", periods=3).as_unit(unit)
+        expected = dti[1]
+
+        for obj in [dti, dti._data]:
+            result = nanops.nanmean(obj)
+            assert result == expected
+
+        dti2 = dti.insert(1, pd.NaT)
+
+        for obj in [dti2, dti2._data]:
+            result = nanops.nanmean(obj)
+            assert result == expected
+
+    @pytest.mark.parametrize("constructor", ["M8", "m8"])
+    def test_nanmean_skipna_false(self, constructor, unit):
+        dtype = f"{constructor}[{unit}]"
+        arr = np.arange(12).astype(np.int64).view(dtype).reshape(4, 3)
+
+        arr[-1, -1] = "NaT"
+
+        result = nanops.nanmean(arr, skipna=False)
+        assert np.isnat(result)
+        assert result.dtype == dtype
+
+        result = nanops.nanmean(arr, axis=0, skipna=False)
+        expected = np.array([4, 5, "NaT"], dtype=arr.dtype)
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = nanops.nanmean(arr, axis=1, skipna=False)
+        expected = np.array([arr[0, 1], arr[1, 1], arr[2, 1], arr[-1, -1]])
+        tm.assert_numpy_array_equal(result, expected)
+
+
+def test_use_bottleneck():
+    if nanops._BOTTLENECK_INSTALLED:
+        with pd.option_context("use_bottleneck", True):
+            assert pd.get_option("use_bottleneck")
+
+        with pd.option_context("use_bottleneck", False):
+            assert not pd.get_option("use_bottleneck")
+
+
+@pytest.mark.parametrize(
+    "numpy_op, expected",
+    [
+        (np.sum, 10),
+        (np.nansum, 10),
+        (np.mean, 2.5),
+        (np.nanmean, 2.5),
+        (np.median, 2.5),
+        (np.nanmedian, 2.5),
+        (np.min, 1),
+        (np.max, 4),
+        (np.nanmin, 1),
+        (np.nanmax, 4),
+    ],
+)
+def test_numpy_ops(numpy_op, expected):
+    # GH8383
+    result = numpy_op(Series([1, 2, 3, 4]))
+    assert result == expected
+
+
+@pytest.mark.parametrize(
+    "operation",
+    [
+        nanops.nanany,
+        nanops.nanall,
+        nanops.nansum,
+        nanops.nanmean,
+        nanops.nanmedian,
+        nanops.nanstd,
+        nanops.nanvar,
+        nanops.nansem,
+        nanops.nanargmax,
+        nanops.nanargmin,
+        nanops.nanmax,
+        nanops.nanmin,
+        nanops.nanskew,
+        nanops.nankurt,
+        nanops.nanprod,
+    ],
+)
+def test_nanops_independent_of_mask_param(operation):
+    # GH22764
+    ser = Series([1, 2, np.nan, 3, np.nan, 4])
+    mask = ser.isna()
+    median_expected = operation(ser._values)
+    median_result = operation(ser._values, mask=mask)
+    assert median_expected == median_result
+
+
+@pytest.mark.parametrize("min_count", [-1, 0])
+def test_check_below_min_count_negative_or_zero_min_count(min_count):
+    # GH35227
+    result = nanops.check_below_min_count((21, 37), None, min_count)
+    expected_result = False
+    assert result == expected_result
+
+
+@pytest.mark.parametrize(
+    "mask", [None, np.array([False, False, True]), np.array([True] + 9 * [False])]
+)
+@pytest.mark.parametrize("min_count, expected_result", [(1, False), (101, True)])
+def test_check_below_min_count_positive_min_count(mask, min_count, expected_result):
+    # GH35227
+    shape = (10, 10)
+    result = nanops.check_below_min_count(shape, mask, min_count)
+    assert result == expected_result
+
+
+@td.skip_if_windows
+@td.skip_if_32bit
+@pytest.mark.parametrize("min_count, expected_result", [(1, False), (2812191852, True)])
+def test_check_below_min_count_large_shape(min_count, expected_result):
+    # GH35227 large shape used to show that the issue is fixed
+    shape = (2244367, 1253)
+    result = nanops.check_below_min_count(shape, mask=None, min_count=min_count)
+    assert result == expected_result
+
+
+@pytest.mark.parametrize("func", ["nanmean", "nansum"])
+def test_check_bottleneck_disallow(any_real_numpy_dtype, func):
+    # GH 42878 bottleneck sometimes produces unreliable results for mean and sum
+    assert not nanops._bn_ok_dtype(np.dtype(any_real_numpy_dtype).type, func)
+
+
+@pytest.mark.parametrize("val", [2**55, -(2**55), 20150515061816532])
+def test_nanmean_overflow(disable_bottleneck, val):
+    # GH 10155
+    # In the previous implementation mean can overflow for int dtypes, it
+    # is now consistent with numpy
+
+    ser = Series(val, index=range(500), dtype=np.int64)
+    result = ser.mean()
+    np_result = ser.values.mean()
+    assert result == val
+    assert result == np_result
+    assert result.dtype == np.float64
+
+
+@pytest.mark.parametrize(
+    "dtype",
+    [
+        np.int16,
+        np.int32,
+        np.int64,
+        np.float32,
+        np.float64,
+        getattr(np, "float128", None),
+    ],
+)
+@pytest.mark.parametrize("method", ["mean", "std", "var", "skew", "kurt", "min", "max"])
+def test_returned_dtype(disable_bottleneck, dtype, method):
+    if dtype is None:
+        pytest.skip("np.float128 not available")
+
+    ser = Series(range(10), dtype=dtype)
+    result = getattr(ser, method)()
+    if is_integer_dtype(dtype) and method not in ["min", "max"]:
+        assert result.dtype == np.float64
+    else:
+        assert result.dtype == dtype
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/test_optional_dependency.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/test_optional_dependency.py
new file mode 100644
index 0000000000000000000000000000000000000000..52b5f636b1254ceddf869704a6378f4ea5012b8c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/test_optional_dependency.py
@@ -0,0 +1,100 @@
+import sys
+import types
+
+import pytest
+
+from pandas.compat._optional import (
+    VERSIONS,
+    import_optional_dependency,
+)
+
+import pandas._testing as tm
+
+
+def test_import_optional():
+    match = "Missing .*notapackage.* pip .* conda .* notapackage"
+    with pytest.raises(ImportError, match=match) as exc_info:
+        import_optional_dependency("notapackage")
+    # The original exception should be there as context:
+    assert isinstance(exc_info.value.__context__, ImportError)
+
+    result = import_optional_dependency("notapackage", errors="ignore")
+    assert result is None
+
+
+def test_xlrd_version_fallback():
+    pytest.importorskip("xlrd")
+    import_optional_dependency("xlrd")
+
+
+def test_bad_version(monkeypatch):
+    name = "fakemodule"
+    module = types.ModuleType(name)
+    module.__version__ = "0.9.0"
+    sys.modules[name] = module
+    monkeypatch.setitem(VERSIONS, name, "1.0.0")
+
+    match = "Pandas requires .*1.0.0.* of .fakemodule.*'0.9.0'"
+    with pytest.raises(ImportError, match=match):
+        import_optional_dependency("fakemodule")
+
+    # Test min_version parameter
+    result = import_optional_dependency("fakemodule", min_version="0.8")
+    assert result is module
+
+    with tm.assert_produces_warning(UserWarning):
+        result = import_optional_dependency("fakemodule", errors="warn")
+    assert result is None
+
+    module.__version__ = "1.0.0"  # exact match is OK
+    result = import_optional_dependency("fakemodule")
+    assert result is module
+
+    with pytest.raises(ImportError, match="Pandas requires version '1.1.0'"):
+        import_optional_dependency("fakemodule", min_version="1.1.0")
+
+    with tm.assert_produces_warning(UserWarning):
+        result = import_optional_dependency(
+            "fakemodule", errors="warn", min_version="1.1.0"
+        )
+    assert result is None
+
+    result = import_optional_dependency(
+        "fakemodule", errors="ignore", min_version="1.1.0"
+    )
+    assert result is None
+
+
+def test_submodule(monkeypatch):
+    # Create a fake module with a submodule
+    name = "fakemodule"
+    module = types.ModuleType(name)
+    module.__version__ = "0.9.0"
+    sys.modules[name] = module
+    sub_name = "submodule"
+    submodule = types.ModuleType(sub_name)
+    setattr(module, sub_name, submodule)
+    sys.modules[f"{name}.{sub_name}"] = submodule
+    monkeypatch.setitem(VERSIONS, name, "1.0.0")
+
+    match = "Pandas requires .*1.0.0.* of .fakemodule.*'0.9.0'"
+    with pytest.raises(ImportError, match=match):
+        import_optional_dependency("fakemodule.submodule")
+
+    with tm.assert_produces_warning(UserWarning):
+        result = import_optional_dependency("fakemodule.submodule", errors="warn")
+    assert result is None
+
+    module.__version__ = "1.0.0"  # exact match is OK
+    result = import_optional_dependency("fakemodule.submodule")
+    assert result is submodule
+
+
+def test_no_version_raises(monkeypatch):
+    name = "fakemodule"
+    module = types.ModuleType(name)
+    sys.modules[name] = module
+    monkeypatch.setitem(VERSIONS, name, "1.0.0")
+
+    with pytest.raises(ImportError, match="Can't determine .* fakemodule"):
+        import_optional_dependency(name)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/test_register_accessor.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/test_register_accessor.py
new file mode 100644
index 0000000000000000000000000000000000000000..4e569dc40005d5883870b82f46859d5bc36578f9
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/test_register_accessor.py
@@ -0,0 +1,103 @@
+from collections.abc import Generator
+import contextlib
+
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.core import accessor
+
+
+def test_dirname_mixin() -> None:
+    # GH37173
+
+    class X(accessor.DirNamesMixin):
+        x = 1
+        y: int
+
+        def __init__(self) -> None:
+            self.z = 3
+
+    result = [attr_name for attr_name in dir(X()) if not attr_name.startswith("_")]
+
+    assert result == ["x", "z"]
+
+
+@contextlib.contextmanager
+def ensure_removed(obj, attr) -> Generator[None, None, None]:
+    """Ensure that an attribute added to 'obj' during the test is
+    removed when we're done
+    """
+    try:
+        yield
+    finally:
+        try:
+            delattr(obj, attr)
+        except AttributeError:
+            pass
+        obj._accessors.discard(attr)
+
+
+class MyAccessor:
+    def __init__(self, obj) -> None:
+        self.obj = obj
+        self.item = "item"
+
+    @property
+    def prop(self):
+        return self.item
+
+    def method(self):
+        return self.item
+
+
+@pytest.mark.parametrize(
+    "obj, registrar",
+    [
+        (pd.Series, pd.api.extensions.register_series_accessor),
+        (pd.DataFrame, pd.api.extensions.register_dataframe_accessor),
+        (pd.Index, pd.api.extensions.register_index_accessor),
+    ],
+)
+def test_register(obj, registrar):
+    with ensure_removed(obj, "mine"):
+        before = set(dir(obj))
+        registrar("mine")(MyAccessor)
+        o = obj([]) if obj is not pd.Series else obj([], dtype=object)
+        assert o.mine.prop == "item"
+        after = set(dir(obj))
+        assert (before ^ after) == {"mine"}
+        assert "mine" in obj._accessors
+
+
+def test_accessor_works():
+    with ensure_removed(pd.Series, "mine"):
+        pd.api.extensions.register_series_accessor("mine")(MyAccessor)
+
+        s = pd.Series([1, 2])
+        assert s.mine.obj is s
+
+        assert s.mine.prop == "item"
+        assert s.mine.method() == "item"
+
+
+def test_overwrite_warns():
+    match = r".*MyAccessor.*fake.*Series.*"
+    with tm.assert_produces_warning(UserWarning, match=match):
+        with ensure_removed(pd.Series, "fake"):
+            setattr(pd.Series, "fake", 123)
+            pd.api.extensions.register_series_accessor("fake")(MyAccessor)
+            s = pd.Series([1, 2])
+            assert s.fake.prop == "item"
+
+
+def test_raises_attribute_error():
+    with ensure_removed(pd.Series, "bad"):
+
+        @pd.api.extensions.register_series_accessor("bad")
+        class Bad:
+            def __init__(self, data) -> None:
+                raise AttributeError("whoops")
+
+        with pytest.raises(AttributeError, match="whoops"):
+            pd.Series([], dtype=object).bad
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/test_sorting.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/test_sorting.py
new file mode 100644
index 0000000000000000000000000000000000000000..285f240028152072ed52b3657113d30a7fd63fea
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/test_sorting.py
@@ -0,0 +1,487 @@
+from collections import defaultdict
+from datetime import datetime
+from itertools import product
+
+import numpy as np
+import pytest
+
+from pandas import (
+    NA,
+    DataFrame,
+    MultiIndex,
+    Series,
+    array,
+    concat,
+    merge,
+)
+import pandas._testing as tm
+from pandas.core.algorithms import safe_sort
+import pandas.core.common as com
+from pandas.core.sorting import (
+    _decons_group_index,
+    get_group_index,
+    is_int64_overflow_possible,
+    lexsort_indexer,
+    nargsort,
+)
+
+
+@pytest.fixture
+def left_right():
+    low, high, n = -1 << 10, 1 << 10, 1 << 20
+    left = DataFrame(
+        np.random.default_rng(2).integers(low, high, (n, 7)), columns=list("ABCDEFG")
+    )
+    left["left"] = left.sum(axis=1)
+
+    # one-2-one match
+    i = np.random.default_rng(2).permutation(len(left))
+    right = left.iloc[i].copy()
+    right.columns = right.columns[:-1].tolist() + ["right"]
+    right.index = np.arange(len(right))
+    right["right"] *= -1
+    return left, right
+
+
+class TestSorting:
+    @pytest.mark.slow
+    def test_int64_overflow(self):
+        B = np.concatenate((np.arange(1000), np.arange(1000), np.arange(500)))
+        A = np.arange(2500)
+        df = DataFrame(
+            {
+                "A": A,
+                "B": B,
+                "C": A,
+                "D": B,
+                "E": A,
+                "F": B,
+                "G": A,
+                "H": B,
+                "values": np.random.default_rng(2).standard_normal(2500),
+            }
+        )
+
+        lg = df.groupby(["A", "B", "C", "D", "E", "F", "G", "H"])
+        rg = df.groupby(["H", "G", "F", "E", "D", "C", "B", "A"])
+
+        left = lg.sum()["values"]
+        right = rg.sum()["values"]
+
+        exp_index, _ = left.index.sortlevel()
+        tm.assert_index_equal(left.index, exp_index)
+
+        exp_index, _ = right.index.sortlevel(0)
+        tm.assert_index_equal(right.index, exp_index)
+
+        tups = list(map(tuple, df[["A", "B", "C", "D", "E", "F", "G", "H"]].values))
+        tups = com.asarray_tuplesafe(tups)
+
+        expected = df.groupby(tups).sum()["values"]
+
+        for k, v in expected.items():
+            assert left[k] == right[k[::-1]]
+            assert left[k] == v
+        assert len(left) == len(right)
+
+    def test_int64_overflow_groupby_large_range(self):
+        # GH9096
+        values = range(55109)
+        data = DataFrame.from_dict({"a": values, "b": values, "c": values, "d": values})
+        grouped = data.groupby(["a", "b", "c", "d"])
+        assert len(grouped) == len(values)
+
+    @pytest.mark.parametrize("agg", ["mean", "median"])
+    def test_int64_overflow_groupby_large_df_shuffled(self, agg):
+        rs = np.random.default_rng(2)
+        arr = rs.integers(-1 << 12, 1 << 12, (1 << 15, 5))
+        i = rs.choice(len(arr), len(arr) * 4)
+        arr = np.vstack((arr, arr[i]))  # add some duplicate rows
+
+        i = rs.permutation(len(arr))
+        arr = arr[i]  # shuffle rows
+
+        df = DataFrame(arr, columns=list("abcde"))
+        df["jim"], df["joe"] = np.zeros((2, len(df)))
+        gr = df.groupby(list("abcde"))
+
+        # verify this is testing what it is supposed to test!
+        assert is_int64_overflow_possible(gr._grouper.shape)
+
+        mi = MultiIndex.from_arrays(
+            [ar.ravel() for ar in np.array_split(np.unique(arr, axis=0), 5, axis=1)],
+            names=list("abcde"),
+        )
+
+        res = DataFrame(
+            np.zeros((len(mi), 2)), columns=["jim", "joe"], index=mi
+        ).sort_index()
+
+        tm.assert_frame_equal(getattr(gr, agg)(), res)
+
+    @pytest.mark.parametrize(
+        "order, na_position, exp",
+        [
+            [
+                True,
+                "last",
+                list(range(5, 105)) + list(range(5)) + list(range(105, 110)),
+            ],
+            [
+                True,
+                "first",
+                list(range(5)) + list(range(105, 110)) + list(range(5, 105)),
+            ],
+            [
+                False,
+                "last",
+                list(range(104, 4, -1)) + list(range(5)) + list(range(105, 110)),
+            ],
+            [
+                False,
+                "first",
+                list(range(5)) + list(range(105, 110)) + list(range(104, 4, -1)),
+            ],
+        ],
+    )
+    def test_lexsort_indexer(self, order, na_position, exp):
+        keys = [[np.nan] * 5 + list(range(100)) + [np.nan] * 5]
+        result = lexsort_indexer(keys, orders=order, na_position=na_position)
+        tm.assert_numpy_array_equal(result, np.array(exp, dtype=np.intp))
+
+    @pytest.mark.parametrize(
+        "ascending, na_position, exp",
+        [
+            [
+                True,
+                "last",
+                list(range(5, 105)) + list(range(5)) + list(range(105, 110)),
+            ],
+            [
+                True,
+                "first",
+                list(range(5)) + list(range(105, 110)) + list(range(5, 105)),
+            ],
+            [
+                False,
+                "last",
+                list(range(104, 4, -1)) + list(range(5)) + list(range(105, 110)),
+            ],
+            [
+                False,
+                "first",
+                list(range(5)) + list(range(105, 110)) + list(range(104, 4, -1)),
+            ],
+        ],
+    )
+    def test_nargsort(self, ascending, na_position, exp):
+        # list places NaNs last, np.array(..., dtype="O") may not place NaNs first
+        items = np.array([np.nan] * 5 + list(range(100)) + [np.nan] * 5, dtype="O")
+
+        # mergesort is the most difficult to get right because we want it to be
+        # stable.
+
+        # According to numpy/core/tests/test_multiarray, """The number of
+        # sorted items must be greater than ~50 to check the actual algorithm
+        # because quick and merge sort fall over to insertion sort for small
+        # arrays."""
+
+        result = nargsort(
+            items, kind="mergesort", ascending=ascending, na_position=na_position
+        )
+        tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False)
+
+
+class TestMerge:
+    def test_int64_overflow_outer_merge(self):
+        # #2690, combinatorial explosion
+        df1 = DataFrame(
+            np.random.default_rng(2).standard_normal((1000, 7)),
+            columns=list("ABCDEF") + ["G1"],
+        )
+        df2 = DataFrame(
+            np.random.default_rng(3).standard_normal((1000, 7)),
+            columns=list("ABCDEF") + ["G2"],
+        )
+        result = merge(df1, df2, how="outer")
+        assert len(result) == 2000
+
+    @pytest.mark.slow
+    def test_int64_overflow_check_sum_col(self, left_right):
+        left, right = left_right
+
+        out = merge(left, right, how="outer")
+        assert len(out) == len(left)
+        tm.assert_series_equal(out["left"], -out["right"], check_names=False)
+        result = out.iloc[:, :-2].sum(axis=1)
+        tm.assert_series_equal(out["left"], result, check_names=False)
+        assert result.name is None
+
+    @pytest.mark.slow
+    @pytest.mark.parametrize("how", ["left", "right", "outer", "inner"])
+    def test_int64_overflow_how_merge(self, left_right, how):
+        left, right = left_right
+
+        out = merge(left, right, how="outer")
+        out.sort_values(out.columns.tolist(), inplace=True)
+        out.index = np.arange(len(out))
+        tm.assert_frame_equal(out, merge(left, right, how=how, sort=True))
+
+    @pytest.mark.slow
+    def test_int64_overflow_sort_false_order(self, left_right):
+        left, right = left_right
+
+        # check that left merge w/ sort=False maintains left frame order
+        out = merge(left, right, how="left", sort=False)
+        tm.assert_frame_equal(left, out[left.columns.tolist()])
+
+        out = merge(right, left, how="left", sort=False)
+        tm.assert_frame_equal(right, out[right.columns.tolist()])
+
+    @pytest.mark.slow
+    @pytest.mark.parametrize("how", ["left", "right", "outer", "inner"])
+    @pytest.mark.parametrize("sort", [True, False])
+    def test_int64_overflow_one_to_many_none_match(self, how, sort):
+        # one-2-many/none match
+        low, high, n = -1 << 10, 1 << 10, 1 << 11
+        left = DataFrame(
+            np.random.default_rng(2).integers(low, high, (n, 7)).astype("int64"),
+            columns=list("ABCDEFG"),
+        )
+
+        # confirm that this is checking what it is supposed to check
+        shape = left.apply(Series.nunique).values
+        assert is_int64_overflow_possible(shape)
+
+        # add duplicates to left frame
+        left = concat([left, left], ignore_index=True)
+
+        right = DataFrame(
+            np.random.default_rng(3).integers(low, high, (n // 2, 7)).astype("int64"),
+            columns=list("ABCDEFG"),
+        )
+
+        # add duplicates & overlap with left to the right frame
+        i = np.random.default_rng(4).choice(len(left), n)
+        right = concat([right, right, left.iloc[i]], ignore_index=True)
+
+        left["left"] = np.random.default_rng(2).standard_normal(len(left))
+        right["right"] = np.random.default_rng(2).standard_normal(len(right))
+
+        # shuffle left & right frames
+        i = np.random.default_rng(5).permutation(len(left))
+        left = left.iloc[i].copy()
+        left.index = np.arange(len(left))
+
+        i = np.random.default_rng(6).permutation(len(right))
+        right = right.iloc[i].copy()
+        right.index = np.arange(len(right))
+
+        # manually compute outer merge
+        ldict, rdict = defaultdict(list), defaultdict(list)
+
+        for idx, row in left.set_index(list("ABCDEFG")).iterrows():
+            ldict[idx].append(row["left"])
+
+        for idx, row in right.set_index(list("ABCDEFG")).iterrows():
+            rdict[idx].append(row["right"])
+
+        vals = []
+        for k, lval in ldict.items():
+            rval = rdict.get(k, [np.nan])
+            for lv, rv in product(lval, rval):
+                vals.append(
+                    k
+                    + (
+                        lv,
+                        rv,
+                    )
+                )
+
+        for k, rval in rdict.items():
+            if k not in ldict:
+                vals.extend(
+                    k
+                    + (
+                        np.nan,
+                        rv,
+                    )
+                    for rv in rval
+                )
+
+        def align(df):
+            df = df.sort_values(df.columns.tolist())
+            df.index = np.arange(len(df))
+            return df
+
+        out = DataFrame(vals, columns=list("ABCDEFG") + ["left", "right"])
+        out = align(out)
+
+        jmask = {
+            "left": out["left"].notna(),
+            "right": out["right"].notna(),
+            "inner": out["left"].notna() & out["right"].notna(),
+            "outer": np.ones(len(out), dtype="bool"),
+        }
+
+        mask = jmask[how]
+        frame = align(out[mask].copy())
+        assert mask.all() ^ mask.any() or how == "outer"
+
+        res = merge(left, right, how=how, sort=sort)
+        if sort:
+            kcols = list("ABCDEFG")
+            tm.assert_frame_equal(
+                res[kcols].copy(), res[kcols].sort_values(kcols, kind="mergesort")
+            )
+
+        # as in GH9092 dtypes break with outer/right join
+        # 2021-12-18: dtype does not break anymore
+        tm.assert_frame_equal(frame, align(res))
+
+
+@pytest.mark.parametrize(
+    "codes_list, shape",
+    [
+        [
+            [
+                np.tile([0, 1, 2, 3, 0, 1, 2, 3], 100).astype(np.int64),
+                np.tile([0, 2, 4, 3, 0, 1, 2, 3], 100).astype(np.int64),
+                np.tile([5, 1, 0, 2, 3, 0, 5, 4], 100).astype(np.int64),
+            ],
+            (4, 5, 6),
+        ],
+        [
+            [
+                np.tile(np.arange(10000, dtype=np.int64), 5),
+                np.tile(np.arange(10000, dtype=np.int64), 5),
+            ],
+            (10000, 10000),
+        ],
+    ],
+)
+def test_decons(codes_list, shape):
+    group_index = get_group_index(codes_list, shape, sort=True, xnull=True)
+    codes_list2 = _decons_group_index(group_index, shape)
+
+    for a, b in zip(codes_list, codes_list2):
+        tm.assert_numpy_array_equal(a, b)
+
+
+class TestSafeSort:
+    @pytest.mark.parametrize(
+        "arg, exp",
+        [
+            [[3, 1, 2, 0, 4], [0, 1, 2, 3, 4]],
+            [
+                np.array(list("baaacb"), dtype=object),
+                np.array(list("aaabbc"), dtype=object),
+            ],
+            [[], []],
+        ],
+    )
+    def test_basic_sort(self, arg, exp):
+        result = safe_sort(np.array(arg))
+        expected = np.array(exp)
+        tm.assert_numpy_array_equal(result, expected)
+
+    @pytest.mark.parametrize("verify", [True, False])
+    @pytest.mark.parametrize(
+        "codes, exp_codes",
+        [
+            [[0, 1, 1, 2, 3, 0, -1, 4], [3, 1, 1, 2, 0, 3, -1, 4]],
+            [[], []],
+        ],
+    )
+    def test_codes(self, verify, codes, exp_codes):
+        values = np.array([3, 1, 2, 0, 4])
+        expected = np.array([0, 1, 2, 3, 4])
+
+        result, result_codes = safe_sort(
+            values, codes, use_na_sentinel=True, verify=verify
+        )
+        expected_codes = np.array(exp_codes, dtype=np.intp)
+        tm.assert_numpy_array_equal(result, expected)
+        tm.assert_numpy_array_equal(result_codes, expected_codes)
+
+    def test_codes_out_of_bound(self):
+        values = np.array([3, 1, 2, 0, 4])
+        expected = np.array([0, 1, 2, 3, 4])
+
+        # out of bound indices
+        codes = [0, 101, 102, 2, 3, 0, 99, 4]
+        result, result_codes = safe_sort(values, codes, use_na_sentinel=True)
+        expected_codes = np.array([3, -1, -1, 2, 0, 3, -1, 4], dtype=np.intp)
+        tm.assert_numpy_array_equal(result, expected)
+        tm.assert_numpy_array_equal(result_codes, expected_codes)
+
+    def test_mixed_integer(self):
+        values = np.array(["b", 1, 0, "a", 0, "b"], dtype=object)
+        result = safe_sort(values)
+        expected = np.array([0, 0, 1, "a", "b", "b"], dtype=object)
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_mixed_integer_with_codes(self):
+        values = np.array(["b", 1, 0, "a"], dtype=object)
+        codes = [0, 1, 2, 3, 0, -1, 1]
+        result, result_codes = safe_sort(values, codes)
+        expected = np.array([0, 1, "a", "b"], dtype=object)
+        expected_codes = np.array([3, 1, 0, 2, 3, -1, 1], dtype=np.intp)
+        tm.assert_numpy_array_equal(result, expected)
+        tm.assert_numpy_array_equal(result_codes, expected_codes)
+
+    def test_unsortable(self):
+        # GH 13714
+        arr = np.array([1, 2, datetime.now(), 0, 3], dtype=object)
+        msg = "'[<>]' not supported between instances of .*"
+        with pytest.raises(TypeError, match=msg):
+            safe_sort(arr)
+
+    @pytest.mark.parametrize(
+        "arg, codes, err, msg",
+        [
+            [1, None, TypeError, "Only np.ndarray, ExtensionArray, and Index"],
+            [np.array([0, 1, 2]), 1, TypeError, "Only list-like objects or None"],
+            [np.array([0, 1, 2, 1]), [0, 1], ValueError, "values should be unique"],
+        ],
+    )
+    def test_exceptions(self, arg, codes, err, msg):
+        with pytest.raises(err, match=msg):
+            safe_sort(values=arg, codes=codes)
+
+    @pytest.mark.parametrize(
+        "arg, exp", [[[1, 3, 2], [1, 2, 3]], [[1, 3, np.nan, 2], [1, 2, 3, np.nan]]]
+    )
+    def test_extension_array(self, arg, exp):
+        a = array(arg, dtype="Int64")
+        result = safe_sort(a)
+        expected = array(exp, dtype="Int64")
+        tm.assert_extension_array_equal(result, expected)
+
+    @pytest.mark.parametrize("verify", [True, False])
+    def test_extension_array_codes(self, verify):
+        a = array([1, 3, 2], dtype="Int64")
+        result, codes = safe_sort(a, [0, 1, -1, 2], use_na_sentinel=True, verify=verify)
+        expected_values = array([1, 2, 3], dtype="Int64")
+        expected_codes = np.array([0, 2, -1, 1], dtype=np.intp)
+        tm.assert_extension_array_equal(result, expected_values)
+        tm.assert_numpy_array_equal(codes, expected_codes)
+
+
+def test_mixed_str_null(nulls_fixture):
+    values = np.array(["b", nulls_fixture, "a", "b"], dtype=object)
+    result = safe_sort(values)
+    expected = np.array(["a", "b", "b", nulls_fixture], dtype=object)
+    tm.assert_numpy_array_equal(result, expected)
+
+
+def test_safe_sort_multiindex():
+    # GH#48412
+    arr1 = Series([2, 1, NA, NA], dtype="Int64")
+    arr2 = [2, 1, 3, 3]
+    midx = MultiIndex.from_arrays([arr1, arr2])
+    result = safe_sort(midx)
+    expected = MultiIndex.from_arrays(
+        [Series([1, 2, NA, NA], dtype="Int64"), [1, 2, 3, 3]]
+    )
+    tm.assert_index_equal(result, expected)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/test_take.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/test_take.py
new file mode 100644
index 0000000000000000000000000000000000000000..4f34ab34c35f0c2446597001f59526d4c8b0900d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/pandas/tests/test_take.py
@@ -0,0 +1,307 @@
+from datetime import datetime
+
+import numpy as np
+import pytest
+
+from pandas._libs import iNaT
+
+import pandas._testing as tm
+import pandas.core.algorithms as algos
+
+
+@pytest.fixture(
+    params=[
+        (np.int8, np.int16(127), np.int8),
+        (np.int8, np.int16(128), np.int16),
+        (np.int32, 1, np.int32),
+        (np.int32, 2.0, np.float64),
+        (np.int32, 3.0 + 4.0j, np.complex128),
+        (np.int32, True, np.object_),
+        (np.int32, "", np.object_),
+        (np.float64, 1, np.float64),
+        (np.float64, 2.0, np.float64),
+        (np.float64, 3.0 + 4.0j, np.complex128),
+        (np.float64, True, np.object_),
+        (np.float64, "", np.object_),
+        (np.complex128, 1, np.complex128),
+        (np.complex128, 2.0, np.complex128),
+        (np.complex128, 3.0 + 4.0j, np.complex128),
+        (np.complex128, True, np.object_),
+        (np.complex128, "", np.object_),
+        (np.bool_, 1, np.object_),
+        (np.bool_, 2.0, np.object_),
+        (np.bool_, 3.0 + 4.0j, np.object_),
+        (np.bool_, True, np.bool_),
+        (np.bool_, "", np.object_),
+    ]
+)
+def dtype_fill_out_dtype(request):
+    return request.param
+
+
+class TestTake:
+    def test_1d_fill_nonna(self, dtype_fill_out_dtype):
+        dtype, fill_value, out_dtype = dtype_fill_out_dtype
+        data = np.random.default_rng(2).integers(0, 2, 4).astype(dtype)
+        indexer = [2, 1, 0, -1]
+
+        result = algos.take_nd(data, indexer, fill_value=fill_value)
+        assert (result[[0, 1, 2]] == data[[2, 1, 0]]).all()
+        assert result[3] == fill_value
+        assert result.dtype == out_dtype
+
+        indexer = [2, 1, 0, 1]
+
+        result = algos.take_nd(data, indexer, fill_value=fill_value)
+        assert (result[[0, 1, 2, 3]] == data[indexer]).all()
+        assert result.dtype == dtype
+
+    def test_2d_fill_nonna(self, dtype_fill_out_dtype):
+        dtype, fill_value, out_dtype = dtype_fill_out_dtype
+        data = np.random.default_rng(2).integers(0, 2, (5, 3)).astype(dtype)
+        indexer = [2, 1, 0, -1]
+
+        result = algos.take_nd(data, indexer, axis=0, fill_value=fill_value)
+        assert (result[[0, 1, 2], :] == data[[2, 1, 0], :]).all()
+        assert (result[3, :] == fill_value).all()
+        assert result.dtype == out_dtype
+
+        result = algos.take_nd(data, indexer, axis=1, fill_value=fill_value)
+        assert (result[:, [0, 1, 2]] == data[:, [2, 1, 0]]).all()
+        assert (result[:, 3] == fill_value).all()
+        assert result.dtype == out_dtype
+
+        indexer = [2, 1, 0, 1]
+        result = algos.take_nd(data, indexer, axis=0, fill_value=fill_value)
+        assert (result[[0, 1, 2, 3], :] == data[indexer, :]).all()
+        assert result.dtype == dtype
+
+        result = algos.take_nd(data, indexer, axis=1, fill_value=fill_value)
+        assert (result[:, [0, 1, 2, 3]] == data[:, indexer]).all()
+        assert result.dtype == dtype
+
+    def test_3d_fill_nonna(self, dtype_fill_out_dtype):
+        dtype, fill_value, out_dtype = dtype_fill_out_dtype
+
+        data = np.random.default_rng(2).integers(0, 2, (5, 4, 3)).astype(dtype)
+        indexer = [2, 1, 0, -1]
+
+        result = algos.take_nd(data, indexer, axis=0, fill_value=fill_value)
+        assert (result[[0, 1, 2], :, :] == data[[2, 1, 0], :, :]).all()
+        assert (result[3, :, :] == fill_value).all()
+        assert result.dtype == out_dtype
+
+        result = algos.take_nd(data, indexer, axis=1, fill_value=fill_value)
+        assert (result[:, [0, 1, 2], :] == data[:, [2, 1, 0], :]).all()
+        assert (result[:, 3, :] == fill_value).all()
+        assert result.dtype == out_dtype
+
+        result = algos.take_nd(data, indexer, axis=2, fill_value=fill_value)
+        assert (result[:, :, [0, 1, 2]] == data[:, :, [2, 1, 0]]).all()
+        assert (result[:, :, 3] == fill_value).all()
+        assert result.dtype == out_dtype
+
+        indexer = [2, 1, 0, 1]
+        result = algos.take_nd(data, indexer, axis=0, fill_value=fill_value)
+        assert (result[[0, 1, 2, 3], :, :] == data[indexer, :, :]).all()
+        assert result.dtype == dtype
+
+        result = algos.take_nd(data, indexer, axis=1, fill_value=fill_value)
+        assert (result[:, [0, 1, 2, 3], :] == data[:, indexer, :]).all()
+        assert result.dtype == dtype
+
+        result = algos.take_nd(data, indexer, axis=2, fill_value=fill_value)
+        assert (result[:, :, [0, 1, 2, 3]] == data[:, :, indexer]).all()
+        assert result.dtype == dtype
+
+    def test_1d_other_dtypes(self):
+        arr = np.random.default_rng(2).standard_normal(10).astype(np.float32)
+
+        indexer = [1, 2, 3, -1]
+        result = algos.take_nd(arr, indexer)
+        expected = arr.take(indexer)
+        expected[-1] = np.nan
+        tm.assert_almost_equal(result, expected)
+
+    def test_2d_other_dtypes(self):
+        arr = np.random.default_rng(2).standard_normal((10, 5)).astype(np.float32)
+
+        indexer = [1, 2, 3, -1]
+
+        # axis=0
+        result = algos.take_nd(arr, indexer, axis=0)
+        expected = arr.take(indexer, axis=0)
+        expected[-1] = np.nan
+        tm.assert_almost_equal(result, expected)
+
+        # axis=1
+        result = algos.take_nd(arr, indexer, axis=1)
+        expected = arr.take(indexer, axis=1)
+        expected[:, -1] = np.nan
+        tm.assert_almost_equal(result, expected)
+
+    def test_1d_bool(self):
+        arr = np.array([0, 1, 0], dtype=bool)
+
+        result = algos.take_nd(arr, [0, 2, 2, 1])
+        expected = arr.take([0, 2, 2, 1])
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = algos.take_nd(arr, [0, 2, -1])
+        assert result.dtype == np.object_
+
+    def test_2d_bool(self):
+        arr = np.array([[0, 1, 0], [1, 0, 1], [0, 1, 1]], dtype=bool)
+
+        result = algos.take_nd(arr, [0, 2, 2, 1])
+        expected = arr.take([0, 2, 2, 1], axis=0)
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = algos.take_nd(arr, [0, 2, 2, 1], axis=1)
+        expected = arr.take([0, 2, 2, 1], axis=1)
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = algos.take_nd(arr, [0, 2, -1])
+        assert result.dtype == np.object_
+
+    def test_2d_float32(self):
+        arr = np.random.default_rng(2).standard_normal((4, 3)).astype(np.float32)
+        indexer = [0, 2, -1, 1, -1]
+
+        # axis=0
+        result = algos.take_nd(arr, indexer, axis=0)
+
+        expected = arr.take(indexer, axis=0)
+        expected[[2, 4], :] = np.nan
+        tm.assert_almost_equal(result, expected)
+
+        # axis=1
+        result = algos.take_nd(arr, indexer, axis=1)
+        expected = arr.take(indexer, axis=1)
+        expected[:, [2, 4]] = np.nan
+        tm.assert_almost_equal(result, expected)
+
+    def test_2d_datetime64(self):
+        # 2005/01/01 - 2006/01/01
+        arr = (
+            np.random.default_rng(2).integers(11_045_376, 11_360_736, (5, 3))
+            * 100_000_000_000
+        )
+        arr = arr.view(dtype="datetime64[ns]")
+        indexer = [0, 2, -1, 1, -1]
+
+        # axis=0
+        result = algos.take_nd(arr, indexer, axis=0)
+        expected = arr.take(indexer, axis=0)
+        expected.view(np.int64)[[2, 4], :] = iNaT
+        tm.assert_almost_equal(result, expected)
+
+        result = algos.take_nd(arr, indexer, axis=0, fill_value=datetime(2007, 1, 1))
+        expected = arr.take(indexer, axis=0)
+        expected[[2, 4], :] = datetime(2007, 1, 1)
+        tm.assert_almost_equal(result, expected)
+
+        # axis=1
+        result = algos.take_nd(arr, indexer, axis=1)
+        expected = arr.take(indexer, axis=1)
+        expected.view(np.int64)[:, [2, 4]] = iNaT
+        tm.assert_almost_equal(result, expected)
+
+        result = algos.take_nd(arr, indexer, axis=1, fill_value=datetime(2007, 1, 1))
+        expected = arr.take(indexer, axis=1)
+        expected[:, [2, 4]] = datetime(2007, 1, 1)
+        tm.assert_almost_equal(result, expected)
+
+    def test_take_axis_0(self):
+        arr = np.arange(12).reshape(4, 3)
+        result = algos.take(arr, [0, -1])
+        expected = np.array([[0, 1, 2], [9, 10, 11]])
+        tm.assert_numpy_array_equal(result, expected)
+
+        # allow_fill=True
+        result = algos.take(arr, [0, -1], allow_fill=True, fill_value=0)
+        expected = np.array([[0, 1, 2], [0, 0, 0]])
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_take_axis_1(self):
+        arr = np.arange(12).reshape(4, 3)
+        result = algos.take(arr, [0, -1], axis=1)
+        expected = np.array([[0, 2], [3, 5], [6, 8], [9, 11]])
+        tm.assert_numpy_array_equal(result, expected)
+
+        # allow_fill=True
+        result = algos.take(arr, [0, -1], axis=1, allow_fill=True, fill_value=0)
+        expected = np.array([[0, 0], [3, 0], [6, 0], [9, 0]])
+        tm.assert_numpy_array_equal(result, expected)
+
+        # GH#26976 make sure we validate along the correct axis
+        with pytest.raises(IndexError, match="indices are out-of-bounds"):
+            algos.take(arr, [0, 3], axis=1, allow_fill=True, fill_value=0)
+
+    def test_take_non_hashable_fill_value(self):
+        arr = np.array([1, 2, 3])
+        indexer = np.array([1, -1])
+        with pytest.raises(ValueError, match="fill_value must be a scalar"):
+            algos.take(arr, indexer, allow_fill=True, fill_value=[1])
+
+        # with object dtype it is allowed
+        arr = np.array([1, 2, 3], dtype=object)
+        result = algos.take(arr, indexer, allow_fill=True, fill_value=[1])
+        expected = np.array([2, [1]], dtype=object)
+        tm.assert_numpy_array_equal(result, expected)
+
+
+class TestExtensionTake:
+    # The take method found in pd.api.extensions
+
+    def test_bounds_check_large(self):
+        arr = np.array([1, 2])
+
+        msg = "indices are out-of-bounds"
+        with pytest.raises(IndexError, match=msg):
+            algos.take(arr, [2, 3], allow_fill=True)
+
+        msg = "index 2 is out of bounds for( axis 0 with)? size 2"
+        with pytest.raises(IndexError, match=msg):
+            algos.take(arr, [2, 3], allow_fill=False)
+
+    def test_bounds_check_small(self):
+        arr = np.array([1, 2, 3], dtype=np.int64)
+        indexer = [0, -1, -2]
+
+        msg = r"'indices' contains values less than allowed \(-2 < -1\)"
+        with pytest.raises(ValueError, match=msg):
+            algos.take(arr, indexer, allow_fill=True)
+
+        result = algos.take(arr, indexer)
+        expected = np.array([1, 3, 2], dtype=np.int64)
+        tm.assert_numpy_array_equal(result, expected)
+
+    @pytest.mark.parametrize("allow_fill", [True, False])
+    def test_take_empty(self, allow_fill):
+        arr = np.array([], dtype=np.int64)
+        # empty take is ok
+        result = algos.take(arr, [], allow_fill=allow_fill)
+        tm.assert_numpy_array_equal(arr, result)
+
+        msg = "|".join(
+            [
+                "cannot do a non-empty take from an empty axes.",
+                "indices are out-of-bounds",
+            ]
+        )
+        with pytest.raises(IndexError, match=msg):
+            algos.take(arr, [0], allow_fill=allow_fill)
+
+    def test_take_na_empty(self):
+        result = algos.take(np.array([]), [-1, -1], allow_fill=True, fill_value=0.0)
+        expected = np.array([0.0, 0.0])
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_take_coerces_list(self):
+        arr = [1, 2, 3]
+        msg = "take accepting non-standard inputs is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = algos.take(arr, [0, 0])
+        expected = np.array([1, 1])
+        tm.assert_numpy_array_equal(result, expected)